{"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/2","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":2,"pages_in_order":16,"rows_per_page":100,"rows":[101,200],"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","next":"/task/3d-object-detection/papers/3","papers":[{"url":"/paper/learning-auxiliary-monocular-contexts-helps","slug":"learning-auxiliary-monocular-contexts-helps","title":"Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection","date":"2021-12-09","arxiv_id":"2112.04628","repositories_listed":2,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":9,"n_pointer_only":12,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-auxiliary-monocular-contexts-helps#ran","syntology_url":"https://syntology.ai/paper/2112.04628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.04628"}},"official":{"repos":["Xianpeng919/MonoCon"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/behind-the-curtain-learning-occluded-shapes","slug":"behind-the-curtain-learning-occluded-shapes","title":"Behind the Curtain: Learning Occluded Shapes for 3D Object Detection","date":"2021-12-04","arxiv_id":"2112.02205","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/behind-the-curtain-learning-occluded-shapes#ran","syntology_url":"https://syntology.ai/paper/2112.02205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02205"}},"official":{"repos":["xharlie/btcdet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fcaf3d-fully-convolutional-anchor-free-3d","slug":"fcaf3d-fully-convolutional-anchor-free-3d","title":"FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection","date":"2021-12-01","arxiv_id":"2112.00322","repositories_listed":2,"syntology":null},{"url":"/paper/learning-distilled-collaboration-graph-for","slug":"learning-distilled-collaboration-graph-for","title":"Learning Distilled Collaboration Graph for Multi-Agent Perception","date":"2021-11-01","arxiv_id":"2111.00643","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":4,"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 4 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; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-distilled-collaboration-graph-for#ran","syntology_url":"https://syntology.ai/paper/2111.00643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.00643"}},"official":{"repos":["ai4ce/DiscoNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/frustum-pointpillars-a-multi-stage-approach","slug":"frustum-pointpillars-a-multi-stage-approach","title":"Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR","date":"2021-10-11","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/advancing-self-supervised-monocular-depth","slug":"advancing-self-supervised-monocular-depth","title":"Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR","date":"2021-09-20","arxiv_id":"2109.09628","repositories_listed":2,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/advancing-self-supervised-monocular-depth#ran","syntology_url":"https://syntology.ai/paper/2109.09628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.09628"}},"official":{"repos":["AutoAILab/FusionDepth","fengziyue/FusionDepth"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/opv2v-an-open-benchmark-dataset-and-fusion","slug":"opv2v-an-open-benchmark-dataset-and-fusion","title":"OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication","date":"2021-09-16","arxiv_id":"2109.07644","repositories_listed":2,"syntology":null},{"url":"/paper/randomrooms-unsupervised-pre-training-from","slug":"randomrooms-unsupervised-pre-training-from","title":"RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection","date":"2021-08-17","arxiv_id":"2108.07794","repositories_listed":2,"syntology":null},{"url":"/paper/is-pseudo-lidar-needed-for-monocular-3d","slug":"is-pseudo-lidar-needed-for-monocular-3d","title":"Is Pseudo-Lidar needed for Monocular 3D Object detection?","date":"2021-08-13","arxiv_id":"2108.06417","repositories_listed":2,"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/is-pseudo-lidar-needed-for-monocular-3d#ran","syntology_url":"https://syntology.ai/paper/2108.06417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06417"}},"official":{"repos":["tri-ml/dd3d"],"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/point-voxel-transformer-an-efficient-approach","slug":"point-voxel-transformer-an-efficient-approach","title":"PVT: Point-Voxel Transformer for Point Cloud Learning","date":"2021-08-13","arxiv_id":"2108.06076","repositories_listed":2,"syntology":null},{"url":"/paper/anchor-free-3d-single-stage-detector-with","slug":"anchor-free-3d-single-stage-detector-with","title":"Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud","date":"2021-08-08","arxiv_id":"2108.03634","repositories_listed":2,"syntology":null},{"url":"/paper/representation-based-regression-for-object","slug":"representation-based-regression-for-object","title":"Representation Based Regression for Object Distance Estimation","date":"2021-06-27","arxiv_id":"2106.14208","repositories_listed":2,"syntology":null},{"url":"/paper/imvoxelnet-image-to-voxels-projection-for","slug":"imvoxelnet-image-to-voxels-projection-for","title":"ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection","date":"2021-06-02","arxiv_id":"2106.01178","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/imvoxelnet-image-to-voxels-projection-for#ran","syntology_url":"https://syntology.ai/paper/2106.01178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01178"}},"official":null}},{"url":"/paper/yolostereo3d-a-step-back-to-2d-for-efficient","slug":"yolostereo3d-a-step-back-to-2d-for-efficient","title":"YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection","date":"2021-03-17","arxiv_id":"2103.09422","repositories_listed":2,"syntology":null},{"url":"/paper/s-at-gcn-spatial-attention-graph-convolution","slug":"s-at-gcn-spatial-attention-graph-convolution","title":"S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection","date":"2021-03-15","arxiv_id":"2103.08439","repositories_listed":2,"syntology":null},{"url":"/paper/categorical-depth-distribution-network-for","slug":"categorical-depth-distribution-network-for","title":"Categorical Depth Distribution Network for Monocular 3D Object Detection","date":"2021-03-01","arxiv_id":"2103.01100","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/categorical-depth-distribution-network-for#ran","syntology_url":"https://syntology.ai/paper/2103.01100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01100"}},"official":{"repos":["TRAILab/CaDDN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/3dioumatch-leveraging-iou-prediction-for-semi","slug":"3dioumatch-leveraging-iou-prediction-for-semi","title":"3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection","date":"2020-12-08","arxiv_id":"2012.04355","repositories_listed":2,"syntology":null},{"url":"/paper/mlod-awareness-of-extrinsic-perturbation-in","slug":"mlod-awareness-of-extrinsic-perturbation-in","title":"MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving","date":"2020-09-29","arxiv_id":"2010.11702","repositories_listed":2,"syntology":null},{"url":"/paper/deformable-pv-rcnn-improving-3d-object","slug":"deformable-pv-rcnn-improving-3d-object","title":"Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations","date":"2020-08-20","arxiv_id":"2008.08766","repositories_listed":2,"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":1,"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/deformable-pv-rcnn-improving-3d-object#ran","syntology_url":"https://syntology.ai/paper/2008.08766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.08766"}},"official":{"repos":["AutoVision-cloud/Deformable-PV-RCNN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/kinematic-3d-object-detection-in-monocular","slug":"kinematic-3d-object-detection-in-monocular","title":"Kinematic 3D Object Detection in Monocular Video","date":"2020-07-19","arxiv_id":"2007.09548","repositories_listed":2,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":16,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":2,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/kinematic-3d-object-detection-in-monocular#ran","syntology_url":"https://syntology.ai/paper/2007.09548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09548"}},"official":null}},{"url":"/paper/centernet3d-an-anchor-free-object-detector","slug":"centernet3d-an-anchor-free-object-detector","title":"CenterNet3D: An Anchor Free Object Detector for Point Cloud","date":"2020-07-13","arxiv_id":"2007.07214","repositories_listed":2,"syntology":null},{"url":"/paper/h3dnet-3d-object-detection-using-hybrid","slug":"h3dnet-3d-object-detection-using-hybrid","title":"H3DNet: 3D Object Detection Using Hybrid Geometric Primitives","date":"2020-06-10","arxiv_id":"2006.05682","repositories_listed":2,"syntology":null},{"url":"/paper/motionnet-joint-perception-and-motion","slug":"motionnet-joint-perception-and-motion","title":"MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps","date":"2020-03-15","arxiv_id":"2003.06754","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/motionnet-joint-perception-and-motion#ran","syntology_url":"https://syntology.ai/paper/2003.06754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06754"}},"official":{"repos":["pxiangwu/MotionNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/birdnet-end-to-end-3d-object-detection-in","slug":"birdnet-end-to-end-3d-object-detection-in","title":"BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View","date":"2020-03-09","arxiv_id":"2003.04188","repositories_listed":2,"syntology":null},{"url":"/paper/tanet-robust-3d-object-detection-from-point","slug":"tanet-robust-3d-object-detection-from-point","title":"TANet: Robust 3D Object Detection from Point Clouds with Triple Attention","date":"2019-12-11","arxiv_id":"1912.05163","repositories_listed":2,"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/tanet-robust-3d-object-detection-from-point#ran","syntology_url":"https://syntology.ai/paper/1912.05163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.05163"}},"official":null}},{"url":"/paper/learning-depth-guided-convolutions-for","slug":"learning-depth-guided-convolutions-for","title":"Learning Depth-Guided Convolutions for Monocular 3D Object Detection","date":"2019-12-10","arxiv_id":"1912.04799","repositories_listed":2,"syntology":{"n":16,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 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; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/learning-depth-guided-convolutions-for#ran","syntology_url":"https://syntology.ai/paper/1912.04799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.04799"}},"official":{"repos":["dingmyu/D4LCN"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/star-convex-polyhedra-for-3d-object-detection","slug":"star-convex-polyhedra-for-3d-object-detection","title":"Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy","date":"2019-08-09","arxiv_id":"1908.03636","repositories_listed":2,"syntology":null},{"url":"/paper/multimodal-3d-object-detection-from-simulated","slug":"multimodal-3d-object-detection-from-simulated","title":"Multimodal 3D Object Detection from Simulated Pretraining","date":"2019-05-19","arxiv_id":"1905.07754","repositories_listed":2,"syntology":null},{"url":"/paper/frustum-convnet-sliding-frustums-to-aggregate","slug":"frustum-convnet-sliding-frustums-to-aggregate","title":"Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection","date":"2019-03-05","arxiv_id":"1903.01864","repositories_listed":2,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/frustum-convnet-sliding-frustums-to-aggregate#ran","syntology_url":"https://syntology.ai/paper/1903.01864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.01864"}},"official":{"repos":["zhixinwang/frustum-convnet"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dpod-dense-6d-pose-object-detector-in-rgb","slug":"dpod-dense-6d-pose-object-detector-in-rgb","title":"DPOD: 6D Pose Object Detector and Refiner","date":"2019-02-28","arxiv_id":"1902.11020","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"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) · 2 unverified","sample_list":"/paper/dpod-dense-6d-pose-object-detector-in-rgb#ran","syntology_url":"https://syntology.ai/paper/1902.11020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.11020"}},"official":{"repos":["zakharos/DPOD"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/pixor-real-time-3d-object-detection-from","slug":"pixor-real-time-3d-object-detection-from","title":"PIXOR: Real-time 3D Object Detection from Point Clouds","date":"2019-02-17","arxiv_id":"1902.06326","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pixor-real-time-3d-object-detection-from#ran","syntology_url":"https://syntology.ai/paper/1902.06326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06326"}},"official":null}},{"url":"/paper/real-time-3d-traffic-cone-detection-for","slug":"real-time-3d-traffic-cone-detection-for","title":"Real-time 3D Traffic Cone Detection for Autonomous Driving","date":"2019-02-06","arxiv_id":"1902.02394","repositories_listed":2,"syntology":null},{"url":"/paper/3d-backbone-network-for-3d-object-detection","slug":"3d-backbone-network-for-3d-object-detection","title":"Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes","date":"2019-01-24","arxiv_id":"1901.08373","repositories_listed":2,"syntology":null},{"url":"/paper/pseudo-lidar-from-visual-depth-estimation","slug":"pseudo-lidar-from-visual-depth-estimation","title":"Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving","date":"2018-12-18","arxiv_id":"1812.07179","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pseudo-lidar-from-visual-depth-estimation#ran","syntology_url":"https://syntology.ai/paper/1812.07179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.07179"}},"official":{"repos":["mileyan/pseudo_lidar"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/birdnet-a-3d-object-detection-framework-from","slug":"birdnet-a-3d-object-detection-framework-from","title":"BirdNet: a 3D Object Detection Framework from LiDAR information","date":"2018-05-03","arxiv_id":"1805.01195","repositories_listed":2,"syntology":null},{"url":"/paper/sbnet-sparse-blocks-network-for-fast","slug":"sbnet-sparse-blocks-network-for-fast","title":"SBNet: Sparse Blocks Network for Fast Inference","date":"2018-01-07","arxiv_id":"1801.02108","repositories_listed":2,"syntology":null},{"url":"/paper/accurate-single-stage-detector-using","slug":"accurate-single-stage-detector-using","title":"Accurate Single Stage Detector Using Recurrent Rolling Convolution","date":"2017-04-19","arxiv_id":"1704.05776","repositories_listed":2,"syntology":null},{"url":"/paper/bb8-a-scalable-accurate-robust-to-partial","slug":"bb8-a-scalable-accurate-robust-to-partial","title":"BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth","date":"2017-03-31","arxiv_id":"1703.10896","repositories_listed":2,"syntology":null},{"url":"/paper/multispectral-deep-neural-networks-for","slug":"multispectral-deep-neural-networks-for","title":"Multispectral Deep Neural Networks for Pedestrian Detection","date":"2016-11-08","arxiv_id":"1611.02644","repositories_listed":2,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":1,"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; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/multispectral-deep-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/1611.02644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.02644"}},"official":null}},{"url":"/paper/beyond-one-shot-beyond-one-perspective-cross","slug":"beyond-one-shot-beyond-one-perspective-cross","title":"Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations","date":"2025-07-07","arxiv_id":"2507.05260","repositories_listed":1,"syntology":null},{"url":"/paper/mambafusion-height-fidelity-dense-global","slug":"mambafusion-height-fidelity-dense-global","title":"MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection","date":"2025-07-06","arxiv_id":"2507.04369","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/mambafusion-height-fidelity-dense-global#ran","syntology_url":"https://syntology.ai/paper/2507.04369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.04369"}},"official":{"repos":["AutoLab-SAI-SJTU/MambaFusion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/cosmos-drive-dreams-scalable-synthetic","slug":"cosmos-drive-dreams-scalable-synthetic","title":"Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models","date":"2025-06-10","arxiv_id":"2506.09042","repositories_listed":1,"syntology":null},{"url":"/paper/simulate-any-radar-attribute-controllable","slug":"simulate-any-radar-attribute-controllable","title":"Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding","date":"2025-06-03","arxiv_id":"2506.03134","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/simulate-any-radar-attribute-controllable#ran","syntology_url":"https://syntology.ai/paper/2506.03134","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.03134"}},"official":{"repos":["zhuxing0/sa-radar"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dualdiff-dual-branch-diffusion-model-for","slug":"dualdiff-dual-branch-diffusion-model-for","title":"DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic Fusion","date":"2025-05-03","arxiv_id":"2505.01857","repositories_listed":1,"syntology":null},{"url":"/paper/a-multimodal-hybrid-late-cascade-fusion","slug":"a-multimodal-hybrid-late-cascade-fusion","title":"A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection","date":"2025-04-25","arxiv_id":"2504.18419","repositories_listed":1,"syntology":null},{"url":"/paper/detect-anything-3d-in-the-wild","slug":"detect-anything-3d-in-the-wild","title":"Detect Anything 3D in the Wild","date":"2025-04-10","arxiv_id":"2504.07958","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-fusion-and-vision-language-models","slug":"multimodal-fusion-and-vision-language-models","title":"Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision","date":"2025-04-03","arxiv_id":"2504.02477","repositories_listed":1,"syntology":null},{"url":"/paper/learning-class-prototypes-for-unified-sparse","slug":"learning-class-prototypes-for-unified-sparse","title":"Learning Class Prototypes for Unified Sparse Supervised 3D Object Detection","date":"2025-03-27","arxiv_id":"2503.21099","repositories_listed":1,"syntology":null},{"url":"/paper/savid-spectravista-aesthetic-vision","slug":"savid-spectravista-aesthetic-vision","title":"SaViD: Spectravista Aesthetic Vision Integration for Robust and Discerning 3D Object Detection in Challenging Environments","date":"2025-03-26","arxiv_id":"2503.20614","repositories_listed":1,"syntology":null},{"url":"/paper/dynopets-a-versatile-benchmark-for-dynamic","slug":"dynopets-a-versatile-benchmark-for-dynamic","title":"DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios","date":"2025-03-25","arxiv_id":"2503.19625","repositories_listed":1,"syntology":null},{"url":"/paper/state-space-model-meets-transformer-a-new-1","slug":"state-space-model-meets-transformer-a-new-1","title":"State Space Model Meets Transformer: A New Paradigm for 3D Object Detection","date":"2025-03-18","arxiv_id":"2503.14493","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/state-space-model-meets-transformer-a-new-1#ran","syntology_url":"https://syntology.ai/paper/2503.14493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.14493"}},"official":null}},{"url":"/paper/roco-sim-enhancing-roadside-collaborative","slug":"roco-sim-enhancing-roadside-collaborative","title":"RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation","date":"2025-03-13","arxiv_id":"2503.10410","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"1 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; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/roco-sim-enhancing-roadside-collaborative#ran","syntology_url":"https://syntology.ai/paper/2503.10410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.10410"}},"official":{"repos":["duyuwen-duen/roco-sim"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/accelerate-3d-object-detection-models-via","slug":"accelerate-3d-object-detection-models-via","title":"Accelerate 3D Object Detection Models via Zero-Shot Attention Key Pruning","date":"2025-03-11","arxiv_id":"2503.08101","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/accelerate-3d-object-detection-models-via#ran","syntology_url":"https://syntology.ai/paper/2503.08101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.08101"}},"official":{"repos":["iseri27/tg_gbc"],"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/learning-to-detect-objects-from-multi-agent","slug":"learning-to-detect-objects-from-multi-agent","title":"Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels","date":"2025-03-11","arxiv_id":"2503.08421","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"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) · 1 unverified","sample_list":"/paper/learning-to-detect-objects-from-multi-agent#ran","syntology_url":"https://syntology.ai/paper/2503.08421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.08421"}},"official":{"repos":["xmuqimingxia/dota"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sp3d-boosting-sparsely-supervised-3d-object","slug":"sp3d-boosting-sparsely-supervised-3d-object","title":"SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts","date":"2025-03-09","arxiv_id":"2503.06467","repositories_listed":1,"syntology":null},{"url":"/paper/dualdiff-dual-branch-diffusion-for-high","slug":"dualdiff-dual-branch-diffusion-for-high","title":"DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward Guidance","date":"2025-03-05","arxiv_id":"2503.03689","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-based-performance-evaluation-of-3d","slug":"simulation-based-performance-evaluation-of-3d","title":"Simulation-Based Performance Evaluation of 3D Object Detection Methods with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use Case","date":"2025-03-05","arxiv_id":"2503.03548","repositories_listed":1,"syntology":null},{"url":"/paper/faster-focal-token-acquiring-and-scaling","slug":"faster-focal-token-acquiring-and-scaling","title":"FASTer: Focal Token Acquiring-and-Scaling Transformer for Long-term 3D Object Detection","date":"2025-02-28","arxiv_id":"2503.01899","repositories_listed":1,"syntology":null},{"url":"/paper/ev-3dod-pushing-the-temporal-boundaries-of-3d","slug":"ev-3dod-pushing-the-temporal-boundaries-of-3d","title":"Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras","date":"2025-02-26","arxiv_id":"2502.19630","repositories_listed":1,"syntology":null},{"url":"/paper/synth-it-like-kitti-synthetic-data-generation","slug":"synth-it-like-kitti-synthetic-data-generation","title":"Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios","date":"2025-02-20","arxiv_id":"2502.15076","repositories_listed":1,"syntology":null},{"url":"/paper/codiff-conditional-diffusion-model-for","slug":"codiff-conditional-diffusion-model-for","title":"CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection","date":"2025-02-17","arxiv_id":"2502.14891","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/codiff-conditional-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2502.14891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.14891"}},"official":{"repos":["huangzhe885/codiff"],"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","unlocated"]}}},{"url":"/paper/text-guided-sparse-voxel-pruning-for","slug":"text-guided-sparse-voxel-pruning-for","title":"Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding","date":"2025-02-14","arxiv_id":"2502.10392","repositories_listed":1,"syntology":null},{"url":"/paper/improving-generalization-ability-for-3d","slug":"improving-generalization-ability-for-3d","title":"Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant Features","date":"2025-02-04","arxiv_id":"2502.02322","repositories_listed":1,"syntology":null},{"url":"/paper/simbev-a-synthetic-multi-task-multi-sensor","slug":"simbev-a-synthetic-multi-task-multi-sensor","title":"SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset","date":"2025-02-04","arxiv_id":"2502.01894","repositories_listed":1,"syntology":null},{"url":"/paper/spikingrtnh-spiking-neural-network-for-4d","slug":"spikingrtnh-spiking-neural-network-for-4d","title":"SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection","date":"2025-01-31","arxiv_id":"2502.00074","repositories_listed":1,"syntology":null},{"url":"/paper/lift-lightweight-fpga-tailored-3d-object","slug":"lift-lightweight-fpga-tailored-3d-object","title":"LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR data","date":"2025-01-19","arxiv_id":"2501.11159","repositories_listed":1,"syntology":null},{"url":"/paper/corenet-conflict-resolution-network-for-point","slug":"corenet-conflict-resolution-network-for-point","title":"CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object Detection","date":"2025-01-11","arxiv_id":"2501.06550","repositories_listed":1,"syntology":null},{"url":"/paper/ad-l-jepa-self-supervised-spatial-world","slug":"ad-l-jepa-self-supervised-spatial-world","title":"AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data","date":"2025-01-09","arxiv_id":"2501.04969","repositories_listed":1,"syntology":null},{"url":"/paper/radarnext-real-time-and-reliable-3d-object","slug":"radarnext-real-time-and-reliable-3d-object","title":"RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radar","date":"2025-01-04","arxiv_id":"2501.02314","repositories_listed":1,"syntology":null},{"url":"/paper/v2x-dgpe-addressing-domain-gaps-and-pose","slug":"v2x-dgpe-addressing-domain-gaps-and-pose","title":"V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detection","date":"2025-01-04","arxiv_id":"2501.02363","repositories_listed":1,"syntology":null},{"url":"/paper/go-n3rdet-geometry-optimized-nerf-enhanced-3d","slug":"go-n3rdet-geometry-optimized-nerf-enhanced-3d","title":"GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tigdistill-bev-multi-view-bev-3d-object","slug":"tigdistill-bev-multi-view-bev-3d-object","title":"TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation","date":"2024-12-30","arxiv_id":"2412.20911","repositories_listed":1,"syntology":null},{"url":"/paper/hv-bev-decoupling-horizontal-and-vertical","slug":"hv-bev-decoupling-horizontal-and-vertical","title":"HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection","date":"2024-12-25","arxiv_id":"2412.18884","repositories_listed":1,"syntology":null},{"url":"/paper/tscenejal-joint-active-learning-of-traffic","slug":"tscenejal-joint-active-learning-of-traffic","title":"TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection","date":"2024-12-25","arxiv_id":"2412.18870","repositories_listed":1,"syntology":null},{"url":"/paper/rctrans-radar-camera-transformer-via-radar","slug":"rctrans-radar-camera-transformer-via-radar","title":"RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection","date":"2024-12-17","arxiv_id":"2412.12799","repositories_listed":1,"syntology":null},{"url":"/paper/hgsfusion-radar-camera-fusion-with-hybrid","slug":"hgsfusion-radar-camera-fusion-with-hybrid","title":"HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection","date":"2024-12-16","arxiv_id":"2412.11489","repositories_listed":1,"syntology":null},{"url":"/paper/dsrc-learning-density-insensitive-and","slug":"dsrc-learning-density-insensitive-and","title":"DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions","date":"2024-12-14","arxiv_id":"2412.10739","repositories_listed":1,"syntology":null},{"url":"/paper/pointcformer-a-relation-based-progressive","slug":"pointcformer-a-relation-based-progressive","title":"PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion","date":"2024-12-11","arxiv_id":"2412.08421","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-3d-object-detection-using","slug":"real-time-3d-object-detection-using","title":"Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI Accelerator","date":"2024-12-07","arxiv_id":"2412.05594","repositories_listed":1,"syntology":null},{"url":"/paper/towards-flexible-3d-perception-object-centric","slug":"towards-flexible-3d-perception-object-centric","title":"Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection","date":"2024-12-06","arxiv_id":"2412.05154","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-flexible-3d-perception-object-centric#ran","syntology_url":"https://syntology.ai/paper/2412.05154","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05154"}},"official":{"repos":["ghostish/objectcentricocccompletion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cubify-anything-scaling-indoor-3d-object","slug":"cubify-anything-scaling-indoor-3d-object","title":"Cubify Anything: Scaling Indoor 3D Object Detection","date":"2024-12-05","arxiv_id":"2412.04458","repositories_listed":1,"syntology":null},{"url":"/paper/bootstraping-clustering-of-gaussians-for-view","slug":"bootstraping-clustering-of-gaussians-for-view","title":"Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding","date":"2024-11-29","arxiv_id":"2411.19551","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bootstraping-clustering-of-gaussians-for-view#ran","syntology_url":"https://syntology.ai/paper/2411.19551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.19551"}},"official":{"repos":["wb014/FreeGS"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/openad-open-world-autonomous-driving","slug":"openad-open-world-autonomous-driving","title":"OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection","date":"2024-11-26","arxiv_id":"2411.17761","repositories_listed":1,"syntology":null},{"url":"/paper/open-vocabulary-monocular-3d-object-detection","slug":"open-vocabulary-monocular-3d-object-detection","title":"Open Vocabulary Monocular 3D Object Detection","date":"2024-11-25","arxiv_id":"2411.16833","repositories_listed":1,"syntology":null},{"url":"/paper/gaussianpretrain-a-simple-unified-3d-gaussian","slug":"gaussianpretrain-a-simple-unified-3d-gaussian","title":"GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous Driving","date":"2024-11-19","arxiv_id":"2411.12452","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":3,"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/gaussianpretrain-a-simple-unified-3d-gaussian#ran","syntology_url":"https://syntology.ai/paper/2411.12452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.12452"}},"official":{"repos":["public-bots/gaussianpretrain"],"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/v2x-r-cooperative-lidar-4d-radar-fusion-for","slug":"v2x-r-cooperative-lidar-4d-radar-fusion-for","title":"V2X-R: Cooperative LiDAR-4D Radar Fusion for 3D Object Detection with Denoising Diffusion","date":"2024-11-13","arxiv_id":"2411.08402","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":3,"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/v2x-r-cooperative-lidar-4d-radar-fusion-for#ran","syntology_url":"https://syntology.ai/paper/2411.08402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.08402"}},"official":{"repos":["ylwhxht/v2x-r"],"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/lssinst-improving-geometric-modeling-in-lss","slug":"lssinst-improving-geometric-modeling-in-lss","title":"LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation","date":"2024-11-09","arxiv_id":"2411.06173","repositories_listed":1,"syntology":null},{"url":"/paper/crt-fusion-camera-radar-temporal-fusion-using","slug":"crt-fusion-camera-radar-temporal-fusion-using","title":"CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection","date":"2024-11-05","arxiv_id":"2411.03013","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-feature-aggregation-and-scale-aware","slug":"efficient-feature-aggregation-and-scale-aware","title":"Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection","date":"2024-11-05","arxiv_id":"2411.02747","repositories_listed":1,"syntology":null},{"url":"/paper/imov3d-learning-open-vocabulary-point-clouds","slug":"imov3d-learning-open-vocabulary-point-clouds","title":"ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images","date":"2024-10-31","arxiv_id":"2410.24001","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/imov3d-learning-open-vocabulary-point-clouds#ran","syntology_url":"https://syntology.ai/paper/2410.24001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.24001"}},"official":{"repos":["yangtiming/imov3d"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mvsdet-multi-view-indoor-3d-object-detection","slug":"mvsdet-multi-view-indoor-3d-object-detection","title":"MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps","date":"2024-10-28","arxiv_id":"2410.21566","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/mvsdet-multi-view-indoor-3d-object-detection#ran","syntology_url":"https://syntology.ai/paper/2410.21566","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21566"}},"official":{"repos":["pixie8888/mvsdet"],"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/monodgp-monocular-3d-object-detection-with","slug":"monodgp-monocular-3d-object-detection-with","title":"MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors","date":"2024-10-25","arxiv_id":"2410.19590","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-stereo-based-3d-object-detection","slug":"real-time-stereo-based-3d-object-detection","title":"Real-time Stereo-based 3D Object Detection for Streaming Perception","date":"2024-10-16","arxiv_id":"2410.12394","repositories_listed":1,"syntology":null},{"url":"/paper/cvcp-fusion-on-implicit-depth-estimation-for","slug":"cvcp-fusion-on-implicit-depth-estimation-for","title":"CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction","date":"2024-10-15","arxiv_id":"2410.11211","repositories_listed":1,"syntology":null},{"url":"/paper/teocc-radar-camera-multi-modal-occupancy","slug":"teocc-radar-camera-multi-modal-occupancy","title":"TEOcc: Radar-camera Multi-modal Occupancy Prediction via Temporal Enhancement","date":"2024-10-15","arxiv_id":"2410.11228","repositories_listed":1,"syntology":null},{"url":"/paper/stone-a-submodular-optimization-framework-for","slug":"stone-a-submodular-optimization-framework-for","title":"STONE: A Submodular Optimization Framework for Active 3D Object Detection","date":"2024-10-04","arxiv_id":"2410.03918","repositories_listed":1,"syntology":null},{"url":"/paper/3dgs-det-empower-3d-gaussian-splatting-with","slug":"3dgs-det-empower-3d-gaussian-splatting-with","title":"3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection","date":"2024-10-02","arxiv_id":"2410.01647","repositories_listed":1,"syntology":null},{"url":"/paper/daocc-3d-object-detection-assisted-multi","slug":"daocc-3d-object-detection-assisted-multi","title":"DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction","date":"2024-09-30","arxiv_id":"2409.19972","repositories_listed":1,"syntology":null},{"url":"/paper/rocktrack-a-3d-robust-multi-camera-ken-multi","slug":"rocktrack-a-3d-robust-multi-camera-ken-multi","title":"RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework","date":"2024-09-18","arxiv_id":"2409.11749","repositories_listed":1,"syntology":null},{"url":"/paper/ultimatedo-an-efficient-framework-to-marry","slug":"ultimatedo-an-efficient-framework-to-marry","title":"UltimateDO: An Efficient Framework to Marry Occupancy Prediction with 3D Object Detection via Channel2height","date":"2024-09-17","arxiv_id":"2409.11160","repositories_listed":1,"syntology":null}],"record_sha256":"99cc83cb688e6bbe71ebe40f5a92cee3c030b1dd7e44832633b6804bb43a7d6c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}