mlsanityData to 7 Oct 2026

Center-based 3D Object Detection and Tracking

arXiv 2006.11275 ↗First posted 19 Jun 2020cs.CVcited by 1,143

Not on the map. Why?

Cited by 55 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, CenterPoint outperforms all previous single model method by a large margin and ranks first among all Lidar-only submissions. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint.

Key facts. Cited by 1,143 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 70; 2022: 206; 2023: 284; 2024: 282; 2025: 184; 2026: 117.

1,143 papers.

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  1. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
  2. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  3. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 711
  4. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  5. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 655
  6. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  7. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 568
  8. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 550
  9. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 485
  10. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  11. 2022PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imagescited by 351
  12. 2022BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detectioncited by 340
38 more
  1. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  2. 2023Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Drivingcited by 325
  3. 2023Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detectioncited by 313
  4. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  5. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
  6. 2023OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Predictioncited by 287
  7. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 262
  8. 2023NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenariocited by 258
  9. 2021Voxel Transformer for 3D Object Detectioncited by 247
  10. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  11. 2023Uni3D: Exploring Unified 3D Representation at Scalecited by 231
  12. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  13. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  14. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  15. 2023OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perceptioncited by 218
  16. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  17. 2023ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understandingcited by 199
  18. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  19. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  20. 2022BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Drivingcited by 191
  21. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 190
  22. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Drivingcited by 188
  23. 2022PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learningcited by 186
  24. 2022Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detectioncited by 177
  25. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  26. 2021Multimodal Virtual Point 3D Detectioncited by 166
  27. 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 165
  28. 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 159
  29. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 158
  30. 2022M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representationcited by 155
  31. 2023SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videoscited by 149
  32. 2022PolarFormer: Multi-camera 3D Object Detection with Polar Transformercited by 147
  33. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  34. 2023LLM4Drive: A Survey of Large Language Models for Autonomous Drivingcited by 136
  35. 2022Learning from All Vehiclescited by 135
  36. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  37. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  38. 2022Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?cited by 128

50 most cited of 1,143 All 1,143 (JSON)