{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/3d-object-detection/papers/3","list_of":"/task/3d-object-detection","task":"3D Object Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":16,"rows_per_page":100,"rows":[201,300],"of":1576,"counts":{"archive_papers_tagged":1576,"with_a_code_link":764,"where_syntology_ran_a_sample":277,"not_listed_spam_title":0,"listed":1576,"listed_where_code_ran":277,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":251,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":251,"listed_every_run_a_failure_of_syntologys_instrument":26,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/3d-object-detection","prev":"/task/3d-object-detection/papers/2","next":"/task/3d-object-detection/papers/4","papers":[{"url":"/paper/cross-modal-self-supervised-learning-with","slug":"cross-modal-self-supervised-learning-with","title":"Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds","date":"2024-09-10","arxiv_id":"2409.06827","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-discrepancy-and-feature","slug":"distribution-discrepancy-and-feature","title":"Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection","date":"2024-09-09","arxiv_id":"2409.05425","repositories_listed":1,"syntology":null},{"url":"/paper/lerojd-lidar-extended-radar-only-object","slug":"lerojd-lidar-extended-radar-only-object","title":"LEROjD: Lidar Extended Radar-Only Object Detection","date":"2024-09-09","arxiv_id":"2409.05564","repositories_listed":1,"syntology":null},{"url":"/paper/multi-v2x-a-large-scale-multi-modal-multi","slug":"multi-v2x-a-large-scale-multi-modal-multi","title":"Multi-V2X: A Large Scale Multi-modal Multi-penetration-rate Dataset for Cooperative Perception","date":"2024-09-08","arxiv_id":"2409.04980","repositories_listed":1,"syntology":null},{"url":"/paper/unidet3d-multi-dataset-indoor-3d-object","slug":"unidet3d-multi-dataset-indoor-3d-object","title":"UniDet3D: Multi-dataset Indoor 3D Object Detection","date":"2024-09-06","arxiv_id":"2409.04234","repositories_listed":1,"syntology":null},{"url":"/paper/geobev-learning-geometric-bev-representation","slug":"geobev-learning-geometric-bev-representation","title":"GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection","date":"2024-09-03","arxiv_id":"2409.01816","repositories_listed":1,"syntology":null},{"url":"/paper/entropy-loss-an-interpretability-amplifier-of","slug":"entropy-loss-an-interpretability-amplifier-of","title":"Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving","date":"2024-09-01","arxiv_id":"2409.00839","repositories_listed":1,"syntology":null},{"url":"/paper/polarbevdet-exploring-polar-representation","slug":"polarbevdet-exploring-polar-representation","title":"PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View","date":"2024-08-29","arxiv_id":"2408.16200","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-review-of-3d-object-detection","slug":"a-comprehensive-review-of-3d-object-detection","title":"A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions","date":"2024-08-28","arxiv_id":"2408.16530","repositories_listed":1,"syntology":null},{"url":"/paper/opennav-efficient-open-vocabulary-3d-object","slug":"opennav-efficient-open-vocabulary-3d-object","title":"OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation","date":"2024-08-25","arxiv_id":"2408.13936","repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-intracranial-hemorrhage-for","slug":"detection-of-intracranial-hemorrhage-for","title":"Detection of Intracranial Hemorrhage for Trauma Patients","date":"2024-08-20","arxiv_id":"2408.10768","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-inverse-contextual-vision","slug":"quantum-inverse-contextual-vision","title":"Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs","date":"2024-08-20","arxiv_id":"2408.11207","repositories_listed":1,"syntology":null},{"url":"/paper/co-fix3d-enhancing-3d-object-detection-with","slug":"co-fix3d-enhancing-3d-object-detection-with","title":"Co-Fix3D: Enhancing 3D Object Detection with Collaborative Refinement","date":"2024-08-15","arxiv_id":"2408.07999","repositories_listed":1,"syntology":null},{"url":"/paper/panacea-panoramic-and-controllable-video-1","slug":"panacea-panoramic-and-controllable-video-1","title":"Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving","date":"2024-08-14","arxiv_id":"2408.07605","repositories_listed":1,"syntology":null},{"url":"/paper/mr3d-net-dynamic-multi-resolution-3d-sparse","slug":"mr3d-net-dynamic-multi-resolution-3d-sparse","title":"MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception","date":"2024-08-12","arxiv_id":"2408.06137","repositories_listed":1,"syntology":null},{"url":"/paper/deepinteraction-multi-modality-interaction","slug":"deepinteraction-multi-modality-interaction","title":"DeepInteraction++: Multi-Modality Interaction for Autonomous Driving","date":"2024-08-09","arxiv_id":"2408.05075","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deepinteraction-multi-modality-interaction#ran","syntology_url":"https://syntology.ai/paper/2408.05075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.05075"}},"official":{"repos":["fudan-zvg/deepinteraction"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/l4dr-lidar-4dradar-fusion-for-weather-robust","slug":"l4dr-lidar-4dradar-fusion-for-weather-robust","title":"L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection","date":"2024-08-07","arxiv_id":"2408.03677","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/l4dr-lidar-4dradar-fusion-for-weather-robust#ran","syntology_url":"https://syntology.ai/paper/2408.03677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.03677"}},"official":{"repos":["ylwhxht/l4dr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/vision-language-guidance-for-lidar-based","slug":"vision-language-guidance-for-lidar-based","title":"Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection","date":"2024-08-07","arxiv_id":"2408.03790","repositories_listed":1,"syntology":null},{"url":"/paper/do-you-remember-the-future-weak-to-strong","slug":"do-you-remember-the-future-weak-to-strong","title":"Do You Remember . . . the Future? Weak-to-Strong generalization in 3D Object Detection","date":"2024-08-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-uncertainty-aware-bounding-boxes","slug":"harnessing-uncertainty-aware-bounding-boxes","title":"Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection","date":"2024-08-01","arxiv_id":"2408.00619","repositories_listed":1,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/harnessing-uncertainty-aware-bounding-boxes#ran","syntology_url":"https://syntology.ai/paper/2408.00619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.00619"}},"official":{"repos":["Ruiyang-061X/UA3D"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/inscope-a-new-real-world-3d-infrastructure","slug":"inscope-a-new-real-world-3d-infrastructure","title":"InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios","date":"2024-07-31","arxiv_id":"2407.21581","repositories_listed":1,"syntology":null},{"url":"/paper/warm-3d-a-weakly-supervised-sim2real-domain","slug":"warm-3d-a-weakly-supervised-sim2real-domain","title":"WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection","date":"2024-07-30","arxiv_id":"2407.20818","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multimodal-3d-object-detection-via","slug":"robust-multimodal-3d-object-detection-via","title":"Robust Multimodal 3D Object Detection via Modality-Agnostic Decoding and Proximity-based Modality Ensemble","date":"2024-07-27","arxiv_id":"2407.19156","repositories_listed":1,"syntology":null},{"url":"/paper/lion-linear-group-rnn-for-3d-object-detection","slug":"lion-linear-group-rnn-for-3d-object-detection","title":"LION: Linear Group RNN for 3D Object Detection in Point Clouds","date":"2024-07-25","arxiv_id":"2407.18232","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lion-linear-group-rnn-for-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2407.18232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.18232"}},"official":{"repos":["happinesslz/LION"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/alpi-auto-labeller-with-proxy-injection-for","slug":"alpi-auto-labeller-with-proxy-injection-for","title":"ALPI: Auto-Labeller with Proxy Injection for 3D Object Detection using 2D Labels Only","date":"2024-07-24","arxiv_id":"2407.17197","repositories_listed":1,"syntology":null},{"url":"/paper/dvpe-divided-view-position-embedding-for","slug":"dvpe-divided-view-position-embedding-for","title":"DVPE: Divided View Position Embedding for Multi-View 3D Object Detection","date":"2024-07-24","arxiv_id":"2407.16955","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dvpe-divided-view-position-embedding-for#ran","syntology_url":"https://syntology.ai/paper/2407.16955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16955"}},"official":{"repos":["dop0/dvpe"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monowad-weather-adaptive-diffusion-model-for","slug":"monowad-weather-adaptive-diffusion-model-for","title":"MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection","date":"2024-07-23","arxiv_id":"2407.16448","repositories_listed":1,"syntology":{"n":26,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/monowad-weather-adaptive-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2407.16448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16448"}},"official":{"repos":["visualaikhu/monowad"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/what-matters-in-range-view-3d-object","slug":"what-matters-in-range-view-3d-object","title":"What Matters in Range View 3D Object Detection","date":"2024-07-23","arxiv_id":"2407.16789","repositories_listed":1,"syntology":null},{"url":"/paper/explore-the-lidar-camera-dynamic-adjustment","slug":"explore-the-lidar-camera-dynamic-adjustment","title":"Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object Detection","date":"2024-07-22","arxiv_id":"2407.15334","repositories_listed":1,"syntology":null},{"url":"/paper/learning-high-resolution-vector","slug":"learning-high-resolution-vector","title":"Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection","date":"2024-07-22","arxiv_id":"2407.15354","repositories_listed":1,"syntology":null},{"url":"/paper/general-geometry-aware-weakly-supervised-3d","slug":"general-geometry-aware-weakly-supervised-3d","title":"General Geometry-aware Weakly Supervised 3D Object Detection","date":"2024-07-18","arxiv_id":"2407.13748","repositories_listed":1,"syntology":null},{"url":"/paper/open-object-wise-position-embedding-for-multi","slug":"open-object-wise-position-embedding-for-multi","title":"OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection","date":"2024-07-15","arxiv_id":"2407.10753","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/open-object-wise-position-embedding-for-multi#ran","syntology_url":"https://syntology.ai/paper/2407.10753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10753"}},"official":{"repos":["AlmoonYsl/OPEN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/repvf-a-unified-vector-fields-representation","slug":"repvf-a-unified-vector-fields-representation","title":"RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception","date":"2024-07-15","arxiv_id":"2407.10876","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/repvf-a-unified-vector-fields-representation#ran","syntology_url":"https://syntology.ai/paper/2407.10876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10876"}},"official":{"repos":["jbji/repvf"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fsd-bev-foreground-self-distillation-for","slug":"fsd-bev-foreground-self-distillation-for","title":"FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection","date":"2024-07-14","arxiv_id":"2407.10135","repositories_listed":1,"syntology":null},{"url":"/paper/labeldistill-label-guided-cross-modal","slug":"labeldistill-label-guided-cross-modal","title":"LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection","date":"2024-07-14","arxiv_id":"2407.10164","repositories_listed":1,"syntology":null},{"url":"/paper/shape2scene-3d-scene-representation-learning","slug":"shape2scene-3d-scene-representation-learning","title":"Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data","date":"2024-07-14","arxiv_id":"2407.10200","repositories_listed":1,"syntology":null},{"url":"/paper/when-pedestrian-detection-meets-multi-modal","slug":"when-pedestrian-detection-meets-multi-modal","title":"When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset","date":"2024-07-14","arxiv_id":"2407.10125","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-3d-object-detection-with-1","slug":"semi-supervised-3d-object-detection-with-1","title":"Semi-supervised 3D Object Detection with PatchTeacher and PillarMix","date":"2024-07-13","arxiv_id":"2407.09787","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semi-supervised-3d-object-detection-with-1#ran","syntology_url":"https://syntology.ai/paper/2407.09787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09787"}},"official":{"repos":["littlepey/ptpm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/approaching-outside-scaling-unsupervised-3d","slug":"approaching-outside-scaling-unsupervised-3d","title":"Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene","date":"2024-07-11","arxiv_id":"2407.08569","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/approaching-outside-scaling-unsupervised-3d#ran","syntology_url":"https://syntology.ai/paper/2407.08569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.08569"}},"official":{"repos":["ruiyang-061x/lise"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/detect-closer-surfaces-that-can-be-seen-new","slug":"detect-closer-surfaces-that-can-be-seen-new","title":"Detect Closer Surfaces that can be Seen: New Modeling and Evaluation in Cross-domain 3D Object Detection","date":"2024-07-04","arxiv_id":"2407.04061","repositories_listed":1,"syntology":null},{"url":"/paper/mdha-multi-scale-deformable-transformer-with","slug":"mdha-multi-scale-deformable-transformer-with","title":"MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object Detection","date":"2024-06-25","arxiv_id":"2406.17654","repositories_listed":1,"syntology":null},{"url":"/paper/mos-model-synergy-for-test-time-adaptation-on","slug":"mos-model-synergy-for-test-time-adaptation-on","title":"MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection","date":"2024-06-21","arxiv_id":"2406.14878","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mos-model-synergy-for-test-time-adaptation-on#ran","syntology_url":"https://syntology.ai/paper/2406.14878","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14878"}},"official":{"repos":["zhuoxiao-chen/mos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dpo-dual-perturbation-optimization-for-test","slug":"dpo-dual-perturbation-optimization-for-test","title":"DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection","date":"2024-06-19","arxiv_id":"2406.13891","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/dpo-dual-perturbation-optimization-for-test#ran","syntology_url":"https://syntology.ai/paper/2406.13891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13891"}},"official":{"repos":["jo-wang/dpo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-domain-adaptation-using","slug":"semi-supervised-domain-adaptation-using","title":"Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection","date":"2024-06-17","arxiv_id":"2406.11313","repositories_listed":1,"syntology":null},{"url":"/paper/syn-to-real-unsupervised-domain-adaptation","slug":"syn-to-real-unsupervised-domain-adaptation","title":"Syn-to-Real Unsupervised Domain Adaptation for Indoor 3D Object Detection","date":"2024-06-17","arxiv_id":"2406.11311","repositories_listed":1,"syntology":null},{"url":"/paper/voxel-mamba-group-free-state-space-models-for","slug":"voxel-mamba-group-free-state-space-models-for","title":"Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection","date":"2024-06-15","arxiv_id":"2406.10700","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/voxel-mamba-group-free-state-space-models-for#ran","syntology_url":"https://syntology.ai/paper/2406.10700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10700"}},"official":{"repos":["gwenzhang/voxel-mamba"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/efm3d-a-benchmark-for-measuring-progress","slug":"efm3d-a-benchmark-for-measuring-progress","title":"EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models","date":"2024-06-14","arxiv_id":"2406.10224","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efm3d-a-benchmark-for-measuring-progress#ran","syntology_url":"https://syntology.ai/paper/2406.10224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10224"}},"official":{"repos":["facebookresearch/efm3d"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/shelf-supervised-multi-modal-pre-training-for","slug":"shelf-supervised-multi-modal-pre-training-for","title":"Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection","date":"2024-06-14","arxiv_id":"2406.10115","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/shelf-supervised-multi-modal-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/2406.10115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10115"}},"official":{"repos":["meharkhurana03/cm3d"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/bevspread-spread-voxel-pooling-for-bird-s-eye-1","slug":"bevspread-spread-voxel-pooling-for-bird-s-eye-1","title":"BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection","date":"2024-06-13","arxiv_id":"2406.08785","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bevspread-spread-voxel-pooling-for-bird-s-eye-1#ran","syntology_url":"https://syntology.ai/paper/2406.08785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08785"}},"official":{"repos":["datongjie/bevspread"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ct3d-improving-3d-object-detection-with","slug":"ct3d-improving-3d-object-detection-with","title":"CT3D++: Improving 3D Object Detection with Keypoint-induced Channel-wise Transformer","date":"2024-06-12","arxiv_id":"2406.08152","repositories_listed":1,"syntology":null},{"url":"/paper/sense-less-generate-more-pre-training-lidar","slug":"sense-less-generate-more-pre-training-lidar","title":"Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing","date":"2024-06-12","arxiv_id":"2406.07833","repositories_listed":1,"syntology":null},{"url":"/paper/effocc-a-minimal-baseline-for-efficient","slug":"effocc-a-minimal-baseline-for-efficient","title":"EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network","date":"2024-06-11","arxiv_id":"2406.07042","repositories_listed":1,"syntology":null},{"url":"/paper/geminifusion-efficient-pixel-wise-multimodal","slug":"geminifusion-efficient-pixel-wise-multimodal","title":"GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer","date":"2024-06-03","arxiv_id":"2406.01210","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/geminifusion-efficient-pixel-wise-multimodal#ran","syntology_url":"https://syntology.ai/paper/2406.01210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01210"}},"official":{"repos":["jiadingcn/geminifusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-novel-object-discovery-and-box","slug":"collaborative-novel-object-discovery-and-box","title":"Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection","date":"2024-06-02","arxiv_id":"2406.00830","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/collaborative-novel-object-discovery-and-box#ran","syntology_url":"https://syntology.ai/paper/2406.00830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00830"}},"official":{"repos":["yangcaoai/CoDA_NeurIPS2023"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fully-test-time-adaptation-for-monocular-3d","slug":"fully-test-time-adaptation-for-monocular-3d","title":"Fully Test-Time Adaptation for Monocular 3D Object Detection","date":"2024-05-30","arxiv_id":"2405.19682","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-pre-training-for-transferable","slug":"self-supervised-pre-training-for-transferable","title":"Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D Perception","date":"2024-05-28","arxiv_id":"2405.17942","repositories_listed":1,"syntology":null},{"url":"/paper/hardness-aware-scene-synthesis-for-semi","slug":"hardness-aware-scene-synthesis-for-semi","title":"Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection","date":"2024-05-27","arxiv_id":"2405.17422","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hardness-aware-scene-synthesis-for-semi#ran","syntology_url":"https://syntology.ai/paper/2405.17422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17422"}},"official":{"repos":["wzzheng/hass"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffubox-refining-3d-object-detection-with","slug":"diffubox-refining-3d-object-detection-with","title":"DiffuBox: Refining 3D Object Detection with Point Diffusion","date":"2024-05-25","arxiv_id":"2405.16034","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffubox-refining-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2405.16034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16034"}},"official":{"repos":["cxy1997/DiffuBox"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/union-unsupervised-3d-object-detection-using","slug":"union-unsupervised-3d-object-detection-using","title":"UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes","date":"2024-05-24","arxiv_id":"2405.15688","repositories_listed":1,"syntology":null},{"url":"/paper/drones-help-drones-a-collaborative-framework","slug":"drones-help-drones-a-collaborative-framework","title":"Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond","date":"2024-05-23","arxiv_id":"2405.14674","repositories_listed":1,"syntology":null},{"url":"/paper/se3d-a-framework-for-saliency-method","slug":"se3d-a-framework-for-saliency-method","title":"SE3D: A Framework For Saliency Method Evaluation In 3D Imaging","date":"2024-05-23","arxiv_id":"2405.14584","repositories_listed":1,"syntology":null},{"url":"/paper/ffam-feature-factorization-activation-map-for","slug":"ffam-feature-factorization-activation-map-for","title":"FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors","date":"2024-05-21","arxiv_id":"2405.12601","repositories_listed":1,"syntology":null},{"url":"/paper/fadet-a-multi-sensor-3d-object-detection","slug":"fadet-a-multi-sensor-3d-object-detection","title":"FADet: A Multi-sensor 3D Object Detection Network based on Local Featured Attention","date":"2024-05-19","arxiv_id":"2405.11682","repositories_listed":1,"syntology":null},{"url":"/paper/viewformer-exploring-spatiotemporal-modeling","slug":"viewformer-exploring-spatiotemporal-modeling","title":"ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided Transformers","date":"2024-05-07","arxiv_id":"2405.04299","repositories_listed":1,"syntology":null},{"url":"/paper/reliable-student-addressing-noise-in-semi-1","slug":"reliable-student-addressing-noise-in-semi-1","title":"Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection","date":"2024-04-27","arxiv_id":"2404.17910","repositories_listed":1,"syntology":null},{"url":"/paper/commonsense-prototype-for-outdoor","slug":"commonsense-prototype-for-outdoor","title":"Commonsense Prototype for Outdoor Unsupervised 3D Object Detection","date":"2024-04-25","arxiv_id":"2404.16493","repositories_listed":1,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":20,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/commonsense-prototype-for-outdoor#ran","syntology_url":"https://syntology.ai/paper/2404.16493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16493"}},"official":{"repos":["hailanyi/cpd"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-out-of-distribution-detection-in","slug":"revisiting-out-of-distribution-detection-in","title":"Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection","date":"2024-04-24","arxiv_id":"2404.15879","repositories_listed":1,"syntology":null},{"url":"/paper/language-driven-active-learning-for-diverse","slug":"language-driven-active-learning-for-diverse","title":"Language-Driven Active Learning for Diverse Open-Set 3D Object Detection","date":"2024-04-19","arxiv_id":"2404.12856","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-multi-camera-3d-object-detection","slug":"scaling-multi-camera-3d-object-detection","title":"Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting","date":"2024-04-10","arxiv_id":"2404.06700","repositories_listed":1,"syntology":null},{"url":"/paper/better-monocular-3d-detectors-with-lidar-from","slug":"better-monocular-3d-detectors-with-lidar-from","title":"Better Monocular 3D Detectors with LiDAR from the Past","date":"2024-04-08","arxiv_id":"2404.05139","repositories_listed":1,"syntology":null},{"url":"/paper/monotakd-teaching-assistant-knowledge","slug":"monotakd-teaching-assistant-knowledge","title":"MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection","date":"2024-04-07","arxiv_id":"2404.04910","repositories_listed":1,"syntology":null},{"url":"/paper/monocd-monocular-3d-object-detection-with","slug":"monocd-monocular-3d-object-detection-with","title":"MonoCD: Monocular 3D Object Detection with Complementary Depths","date":"2024-04-04","arxiv_id":"2404.03181","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/monocd-monocular-3d-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2404.03181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03181"}},"official":{"repos":["elvintanhust/monocd"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/henet-hybrid-encoding-for-end-to-end-multi","slug":"henet-hybrid-encoding-for-end-to-end-multi","title":"HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras","date":"2024-04-03","arxiv_id":"2404.02517","repositories_listed":1,"syntology":null},{"url":"/paper/nerf-mae-masked-autoencoders-for-self","slug":"nerf-mae-masked-autoencoders-for-self","title":"NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields","date":"2024-04-01","arxiv_id":"2404.01300","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nerf-mae-masked-autoencoders-for-self#ran","syntology_url":"https://syntology.ai/paper/2404.01300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01300"}},"official":{"repos":["zubair-irshad/NeRF-MAE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weak-to-strong-3d-object-detection-with-x-ray","slug":"weak-to-strong-3d-object-detection-with-x-ray","title":"Weak-to-Strong 3D Object Detection with X-Ray Distillation","date":"2024-03-31","arxiv_id":"2404.00679","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weak-to-strong-3d-object-detection-with-x-ray#ran","syntology_url":"https://syntology.ai/paper/2404.00679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00679"}},"official":{"repos":["sakharok13/x-ray-teacher-patching-tools"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/seabird-segmentation-in-bird-s-view-with-dice","slug":"seabird-segmentation-in-bird-s-view-with-dice","title":"SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects","date":"2024-03-29","arxiv_id":"2403.20318","repositories_listed":1,"syntology":null},{"url":"/paper/vsrd-instance-aware-volumetric-silhouette-1","slug":"vsrd-instance-aware-volumetric-silhouette-1","title":"VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection","date":"2024-03-29","arxiv_id":"2404.00149","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/vsrd-instance-aware-volumetric-silhouette-1#ran","syntology_url":"https://syntology.ai/paper/2404.00149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00149"}},"official":{"repos":["skmhrk1209/VSRD"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/ov-uni3detr-towards-unified-open-vocabulary","slug":"ov-uni3detr-towards-unified-open-vocabulary","title":"OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation","date":"2024-03-28","arxiv_id":"2403.19580","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ov-uni3detr-towards-unified-open-vocabulary#ran","syntology_url":"https://syntology.ai/paper/2403.19580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19580"}},"official":{"repos":["zhenyuw16/uni3detr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/uada3d-unsupervised-adversarial-domain","slug":"uada3d-unsupervised-adversarial-domain","title":"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps","date":"2024-03-26","arxiv_id":"2403.17633","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-lidar-placements-for-robust","slug":"optimizing-lidar-placements-for-robust","title":"Is Your LiDAR Placement Optimized for 3D Scene Understanding?","date":"2024-03-25","arxiv_id":"2403.17009","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimizing-lidar-placements-for-robust#ran","syntology_url":"https://syntology.ai/paper/2403.17009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17009"}},"official":{"repos":["ywyeli/place3d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rcbevdet-radar-camera-fusion-in-bird-s-eye","slug":"rcbevdet-radar-camera-fusion-in-bird-s-eye","title":"RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection","date":"2024-03-25","arxiv_id":"2403.16440","repositories_listed":1,"syntology":null},{"url":"/paper/cr3dt-camera-radar-fusion-for-3d-detection","slug":"cr3dt-camera-radar-fusion-for-3d-detection","title":"CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking","date":"2024-03-22","arxiv_id":"2403.15313","repositories_listed":1,"syntology":null},{"url":"/paper/is-fusion-instance-scene-collaborative-fusion","slug":"is-fusion-instance-scene-collaborative-fusion","title":"IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection","date":"2024-03-22","arxiv_id":"2403.15241","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/is-fusion-instance-scene-collaborative-fusion#ran","syntology_url":"https://syntology.ai/paper/2403.15241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15241"}},"official":{"repos":["yinjunbo/is-fusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/find-n-propagate-open-vocabulary-3d-object","slug":"find-n-propagate-open-vocabulary-3d-object","title":"Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments","date":"2024-03-20","arxiv_id":"2403.13556","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/find-n-propagate-open-vocabulary-3d-object#ran","syntology_url":"https://syntology.ai/paper/2403.13556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.13556"}},"official":{"repos":["djamahl99/findnpropagate"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rcooper-a-real-world-large-scale-dataset-for","slug":"rcooper-a-real-world-large-scale-dataset-for","title":"RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception","date":"2024-03-15","arxiv_id":"2403.10145","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rcooper-a-real-world-large-scale-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2403.10145","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10145"}},"official":{"repos":["air-thu/dair-rcooper"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/simpb-a-single-model-for-2d-and-3d-object","slug":"simpb-a-single-model-for-2d-and-3d-object","title":"SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras","date":"2024-03-15","arxiv_id":"2403.10353","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/simpb-a-single-model-for-2d-and-3d-object#ran","syntology_url":"https://syntology.ai/paper/2403.10353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10353"}},"official":{"repos":["nullmax-vision/simpb"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mim4d-masked-modeling-with-multi-view-video","slug":"mim4d-masked-modeling-with-multi-view-video","title":"MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning","date":"2024-03-13","arxiv_id":"2403.08760","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-hydra-hybrid-fusion-depth","slug":"unleashing-hydra-hybrid-fusion-depth","title":"Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception","date":"2024-03-12","arxiv_id":"2403.07746","repositories_listed":1,"syntology":null},{"url":"/paper/3d-semantic-segmentation-driven","slug":"3d-semantic-segmentation-driven","title":"SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics","date":"2024-03-11","arxiv_id":"2403.06501","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-pillar-feature-encoding-via","slug":"fine-grained-pillar-feature-encoding-via","title":"Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection","date":"2024-03-11","arxiv_id":"2403.06433","repositories_listed":1,"syntology":null},{"url":"/paper/liso-lidar-only-self-supervised-3d-object","slug":"liso-lidar-only-self-supervised-3d-object","title":"LISO: Lidar-only Self-Supervised 3D Object Detection","date":"2024-03-11","arxiv_id":"2403.07071","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/liso-lidar-only-self-supervised-3d-object#ran","syntology_url":"https://syntology.ai/paper/2403.07071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07071"}},"official":{"repos":["baurst/liso"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-3d-object-detection-with-2d","slug":"enhancing-3d-object-detection-with-2d","title":"Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors","date":"2024-03-10","arxiv_id":"2403.06093","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-3d-object-detection-with-2d#ran","syntology_url":"https://syntology.ai/paper/2403.06093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06093"}},"official":{"repos":["nullmax-vision/qaf2d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/safdnet-a-simple-and-effective-network-for","slug":"safdnet-a-simple-and-effective-network-for","title":"SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection","date":"2024-03-09","arxiv_id":"2403.05817","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/safdnet-a-simple-and-effective-network-for#ran","syntology_url":"https://syntology.ai/paper/2403.05817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05817"}},"official":{"repos":["zhanggang001/hednet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/radardistill-boosting-radar-based-object","slug":"radardistill-boosting-radar-based-object","title":"RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features","date":"2024-03-08","arxiv_id":"2403.05061","repositories_listed":1,"syntology":null},{"url":"/paper/cn-rma-combined-network-with-ray-marching","slug":"cn-rma-combined-network-with-ray-marching","title":"CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view Images","date":"2024-03-07","arxiv_id":"2403.04198","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cn-rma-combined-network-with-ray-marching#ran","syntology_url":"https://syntology.ai/paper/2403.04198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04198"}},"official":{"repos":["sercharles/cn-rma"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-anchor-based-lidar-3d-object","slug":"leveraging-anchor-based-lidar-3d-object","title":"Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection","date":"2024-03-04","arxiv_id":"2403.01978","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-vision-based-3d-object-detection-and","slug":"scalable-vision-based-3d-object-detection-and","title":"Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving","date":"2024-03-04","arxiv_id":"2403.02037","repositories_listed":1,"syntology":null},{"url":"/paper/sunshine-to-rainstorm-cross-weather-knowledge","slug":"sunshine-to-rainstorm-cross-weather-knowledge","title":"Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection","date":"2024-02-28","arxiv_id":"2402.18493","repositories_listed":1,"syntology":null},{"url":"/paper/avs-net-point-sampling-with-adaptive-voxel","slug":"avs-net-point-sampling-with-adaptive-voxel","title":"AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding","date":"2024-02-27","arxiv_id":"2402.17521","repositories_listed":1,"syntology":null},{"url":"/paper/lirafusion-deep-adaptive-lidar-radar-fusion","slug":"lirafusion-deep-adaptive-lidar-radar-fusion","title":"LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection","date":"2024-02-18","arxiv_id":"2402.11735","repositories_listed":1,"syntology":null}],"record_sha256":"6dd35aedce1cfd214a1eab84ce629502526558bf37cb62aa71da280bfe43c2f6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}