{"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/16","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":16,"pages_in_order":16,"rows_per_page":100,"rows":[1501,1576],"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/15","next":null,"papers":[{"url":"/paper/pointrgcn-graph-convolution-networks-for-3d","slug":"pointrgcn-graph-convolution-networks-for-3d","title":"PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement","date":"2019-11-27","arxiv_id":"1911.12236","repositories_listed":0,"syntology":null},{"url":null,"slug":"refinedmpl-refined-monocular-pseudolidar-for","title":"RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving","date":"2019-11-21","arxiv_id":"1911.09712","repositories_listed":0,"syntology":null},{"url":null,"slug":"pi-rcnn-an-efficient-multi-sensor-3d-object","title":"PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module","date":"2019-11-14","arxiv_id":"1911.06084","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-multi-view-fusion-for-3d-object","title":"End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds","date":"2019-10-15","arxiv_id":"1910.06528","repositories_listed":0,"syntology":null},{"url":"/paper/frustum-voxnet-for-3d-object-detection-from","slug":"frustum-voxnet-for-3d-object-detection-from","title":"Frustum VoxNet for 3D object detection from RGB-D or Depth images","date":"2019-10-12","arxiv_id":"1910.05483","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-and-azimuth-aware-fusion-network-of","title":"Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection","date":"2019-10-10","arxiv_id":"1910.04392","repositories_listed":0,"syntology":null},{"url":"/paper/patch-refinement-localized-3d-object","slug":"patch-refinement-localized-3d-object","title":"Patch Refinement -- Localized 3D Object Detection","date":"2019-10-09","arxiv_id":"1910.04093","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-monocular-3d-object-detection-via-1","title":"Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-a-quality-of-3d-object-detection-by","title":"epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module","date":"2019-09-27","arxiv_id":"1910.04853","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-trust-you-on-calibration-of-a","title":"Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving","date":"2019-09-26","arxiv_id":"1909.12358","repositories_listed":0,"syntology":null},{"url":null,"slug":"range-adaptation-for-3d-object-detection-in","title":"Range Adaptation for 3D Object Detection in LiDAR","date":"2019-09-26","arxiv_id":"1909.12249","repositories_listed":0,"syntology":null},{"url":"/paper/object-centric-stereo-matching-for-3d-object","slug":"object-centric-stereo-matching-for-3d-object","title":"Object-Centric Stereo Matching for 3D Object Detection","date":"2019-09-17","arxiv_id":"1909.07566","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-sensor-3d-object-box-refinement-for","title":"Multi-Sensor 3D Object Box Refinement for Autonomous Driving","date":"2019-09-11","arxiv_id":"1909.04942","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlod-a-multi-view-3d-object-detection-based","title":"MLOD: A multi-view 3D object detection based on robust feature fusion method","date":"2019-09-09","arxiv_id":"1909.04163","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-viewpoint-classification-based-3d","title":"Object Viewpoint Classification Based 3D Bounding Box Estimation for Autonomous Vehicles","date":"2019-09-03","arxiv_id":"1909.01025","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-bounding-box-estimation-for-autonomous","title":"3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results","date":"2019-09-01","arxiv_id":"1909.01867","repositories_listed":0,"syntology":null},{"url":null,"slug":"starnet-targeted-computation-for-object","title":"StarNet: Targeted Computation for Object Detection in Point Clouds","date":"2019-08-29","arxiv_id":"1908.11069","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-point-r-cnn","title":"Fast Point R-CNN","date":"2019-08-08","arxiv_id":"1908.02990","repositories_listed":0,"syntology":null},{"url":"/paper/std-sparse-to-dense-3d-object-detector-for","slug":"std-sparse-to-dense-3d-object-detector-for","title":"STD: Sparse-to-Dense 3D Object Detector for Point Cloud","date":"2019-07-22","arxiv_id":"1907.10471","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-3d-object-detection-for-pedestrians","title":"Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation","date":"2019-07-15","arxiv_id":"1907.06777","repositories_listed":0,"syntology":null},{"url":null,"slug":"voxel-fpn-multi-scale-voxel-feature","title":"Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds","date":"2019-06-28","arxiv_id":"1907.05286","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-3d-object-detection-and-box-fitting","title":"Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss","date":"2019-06-19","arxiv_id":"1906.08070","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-diverse-lung-nodules-wherever","title":"Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection","date":"2019-06-12","arxiv_id":"1906.04962","repositories_listed":0,"syntology":null},{"url":null,"slug":"volmap-a-real-time-model-for-semantic","title":"VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view","date":"2019-06-12","arxiv_id":"1906.11873","repositories_listed":0,"syntology":null},{"url":"/paper/clouds-of-oriented-gradients-for-3d-detection","slug":"clouds-of-oriented-gradients-for-3d-detection","title":"Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts","date":"2019-06-11","arxiv_id":"1906.04725","repositories_listed":0,"syntology":null},{"url":"/paper/disentangling-monocular-3d-object-detection","slug":"disentangling-monocular-3d-object-detection","title":"Disentangling Monocular 3D Object Detection","date":"2019-05-29","arxiv_id":"1905.12365","repositories_listed":0,"syntology":null},{"url":null,"slug":"shift-r-cnn-deep-monocular-3d-object","title":"Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints","date":"2019-05-23","arxiv_id":"1905.09970","repositories_listed":0,"syntology":null},{"url":null,"slug":"riu-net-embarrassingly-simple-semantic","title":"RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud","date":"2019-05-21","arxiv_id":"1905.08748","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-plan-view-networks-for-autonomous","title":"Monocular Plan View Networks for Autonomous Driving","date":"2019-05-16","arxiv_id":"1905.06937","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-3d-object-detection-via-geometric","title":"Monocular 3D Object Detection via Geometric Reasoning on Keypoints","date":"2019-05-14","arxiv_id":"1905.05618","repositories_listed":0,"syntology":null},{"url":"/paper/deep-fitting-degree-scoring-network-for","slug":"deep-fitting-degree-scoring-network-for","title":"Deep Fitting Degree Scoring Network for Monocular 3D Object Detection","date":"2019-04-26","arxiv_id":"1904.12681","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensor-fusion-for-joint-3d-object-detection","title":"Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation","date":"2019-04-25","arxiv_id":"1904.11466","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferable-semi-supervised-3d-object","title":"Transferable Semi-supervised 3D Object Detection from RGB-D Data","date":"2019-04-23","arxiv_id":"1904.10300","repositories_listed":0,"syntology":null},{"url":null,"slug":"directshape-photometric-alignment-of-shape","title":"DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation","date":"2019-04-22","arxiv_id":"1904.10097","repositories_listed":0,"syntology":null},{"url":"/paper/deep-optics-for-monocular-depth-estimation","slug":"deep-optics-for-monocular-depth-estimation","title":"Deep Optics for Monocular Depth Estimation and 3D Object Detection","date":"2019-04-18","arxiv_id":"1904.08601","repositories_listed":0,"syntology":null},{"url":null,"slug":"complexer-yolo-real-time-3d-object-detection","title":"Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds","date":"2019-04-16","arxiv_id":"1904.07537","repositories_listed":0,"syntology":null},{"url":null,"slug":"190408494","title":"Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles","date":"2019-03-27","arxiv_id":"1904.08494","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-monocular-3d-object-detection-via","title":"Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving","date":"2019-03-27","arxiv_id":"1903.11444","repositories_listed":0,"syntology":null},{"url":null,"slug":"fvnet-3d-front-view-proposal-generation-for","title":"FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds","date":"2019-03-26","arxiv_id":"1903.10750","repositories_listed":0,"syntology":null},{"url":"/paper/gs3d-an-efficient-3d-object-detection","slug":"gs3d-an-efficient-3d-object-detection","title":"GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving","date":"2019-03-26","arxiv_id":"1903.10955","repositories_listed":0,"syntology":null},{"url":null,"slug":"lasernet-an-efficient-probabilistic-3d-object","title":"LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving","date":"2019-03-20","arxiv_id":"1903.08701","repositories_listed":0,"syntology":null},{"url":"/paper/the-h3d-dataset-for-full-surround-3d-multi","slug":"the-h3d-dataset-for-full-surround-3d-multi","title":"The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes","date":"2019-03-04","arxiv_id":"1903.01568","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-detection-using-scale-invariant-and","title":"3D Object Detection Using Scale Invariant and Feature Reweighting Networks","date":"2019-01-08","arxiv_id":"1901.02237","repositories_listed":0,"syntology":null},{"url":"/paper/ipod-intensive-point-based-object-detector","slug":"ipod-intensive-point-based-object-detector","title":"IPOD: Intensive Point-based Object Detector for Point Cloud","date":"2018-12-13","arxiv_id":"1812.05276","repositories_listed":0,"syntology":null},{"url":null,"slug":"roi-10d-monocular-lifting-of-2d-detection-to","title":"ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape","date":"2018-12-06","arxiv_id":"1812.02781","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-the-ambiguity-of-object-detection","title":"Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data","date":"2018-12-01","arxiv_id":"1812.00287","repositories_listed":0,"syntology":null},{"url":"/paper/roarnet-a-robust-3d-object-detection-based-on","slug":"roarnet-a-robust-3d-object-detection-based-on","title":"RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement","date":"2018-11-09","arxiv_id":"1811.03818","repositories_listed":0,"syntology":null},{"url":null,"slug":"eliminating-the-blind-spot-adapting-3d-object","title":"Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-pre-trained-3d-object-detection","title":"Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation","date":"2018-07-16","arxiv_id":"1807.06072","repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetry-aware-evaluation-of-3d-object","title":"Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk","date":"2018-06-21","arxiv_id":"1806.08129","repositories_listed":0,"syntology":null},{"url":"/paper/3d-object-detection-with-latent-support","slug":"3d-object-detection-with-latent-support","title":"3D Object Detection With Latent Support Surfaces","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/multi-level-fusion-based-3d-object-detection","slug":"multi-level-fusion-based-3d-object-detection","title":"Multi-Level Fusion Based 3D Object Detection From Monocular Images","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-understanding-networks-for-autonomous","title":"Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System","date":"2018-05-18","arxiv_id":"1805.07029","repositories_listed":0,"syntology":null},{"url":"/paper/falling-things-a-synthetic-dataset-for-3d","slug":"falling-things-a-synthetic-dataset-for-3d","title":"Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation","date":"2018-04-18","arxiv_id":"1804.06534","repositories_listed":0,"syntology":null},{"url":"/paper/a-general-pipeline-for-3d-detection-of","slug":"a-general-pipeline-for-3d-detection-of","title":"A General Pipeline for 3D Detection of Vehicles","date":"2018-02-12","arxiv_id":"1803.00387","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusing-bird-view-lidar-point-cloud-and-front","title":"Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection","date":"2017-11-17","arxiv_id":"1711.06703","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-ssd-learning-hierarchical-features-from","title":"3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection","date":"2017-11-01","arxiv_id":"1711.00238","repositories_listed":0,"syntology":null},{"url":"/paper/2d-driven-3d-object-detection-in-rgb-d-images","slug":"2d-driven-3d-object-detection-in-rgb-d-images","title":"2D-Driven 3D Object Detection in RGB-D Images","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"in-search-of-inliers-3d-correspondence-by","title":"In search of inliers: 3d correspondence by local and global voting","date":"2017-08-23","arxiv_id":"1708.06966","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-inertial-semantic-scene-representation-1","title":"Visual-Inertial-Semantic Scene Representation for 3D Object Detection","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-3d-object-detection-and-pose-estimation","title":"A 3D Object Detection and Pose Estimation Pipeline Using RGB-D Images","date":"2017-03-11","arxiv_id":"1703.03940","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-pair-feature-based-object-detection-for","title":"Point Pair Feature based Object Detection for Random Bin Picking","date":"2016-12-05","arxiv_id":"1612.01288","repositories_listed":0,"syntology":null},{"url":"/paper/vote3deep-fast-object-detection-in-3d-point","slug":"vote3deep-fast-object-detection-in-3d-point","title":"Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks","date":"2016-09-21","arxiv_id":"1609.06666","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-proposals-using-stereo-imagery-for","title":"3D Object Proposals using Stereo Imagery for Accurate Object Class Detection","date":"2016-08-27","arxiv_id":"1608.07711","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-of-local-rgb-d-patches-for-3d","title":"Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation","date":"2016-07-20","arxiv_id":"1607.06038","repositories_listed":0,"syntology":null},{"url":null,"slug":"hashmod-a-hashing-method-for-scalable-3d","title":"Hashmod: A Hashing Method for Scalable 3D Object Detection","date":"2016-07-20","arxiv_id":"1607.06062","repositories_listed":0,"syntology":null},{"url":"/paper/monocular-3d-object-detection-for-autonomous","slug":"monocular-3d-object-detection-for-autonomous","title":"Monocular 3D Object Detection for Autonomous Driving","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/three-dimensional-object-detection-and-layout","slug":"three-dimensional-object-detection-and-layout","title":"Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-unsupervised-learning-for-3d-object","title":"Weighted Unsupervised Learning for 3D Object Detection","date":"2016-02-18","arxiv_id":"1602.05920","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-3d-object-detection-of-proteins-in","title":"Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis","date":"2016-01-06","arxiv_id":"1601.01216","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-representation-of-parts-for-accurate","title":"A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/deep-sliding-shapes-for-amodal-3d-object","slug":"deep-sliding-shapes-for-amodal-3d-object","title":"Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images","date":"2015-11-07","arxiv_id":"1511.02300","repositories_listed":0,"syntology":null},{"url":"/paper/voting-for-voting-in-online-point-cloud","slug":"voting-for-voting-in-online-point-cloud","title":"Voting for Voting in Online Point Cloud Object Detection","date":"2015-07-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"holistic-3d-scene-understanding-from-a-single","title":"Holistic 3D Scene Understanding From a Single Geo-Tagged Image","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-3d-object-detection-with-bimodal-deep","title":"Towards 3D Object Detection With Bimodal Deep Boltzmann Machines Over RGBD Imagery","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-object-detection-and-viewpoint-estimation","title":"3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"eba679ccb4cfb8ff53a652f75c4ec00e87171335437301a4129c71894a0efc75","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}