mlsanityData to 14 Oct 2026

Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

arXiv 2404.16493 ↗First posted 25 Apr 2024cs.CVcited by 13

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Abstract (arXiv)

The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes. However, the challenge arises due to the sparsity of LiDAR scans, which leads to pseudo-labels with erroneous size and position, resulting in subpar detection performance. To tackle this problem, this paper introduces a Commonsense Prototype-based Detector, termed CPD, for unsupervised 3D object detection. CPD first constructs Commonsense Prototype (CProto) characterized by high-quality bounding box and dense points, based on commonsense intuition. Subsequently, CPD refines the low-quality pseudo-labels by leveraging the size prior from CProto. Furthermore, CPD enhances the detection accuracy of sparsely scanned objects by the geometric knowledge from CProto. CPD outperforms state-of-the-art unsupervised 3D detectors on Waymo Open Dataset (WOD), PandaSet, and KITTI datasets by a large margin. Besides, by training CPD on WOD and testing on KITTI, CPD attains 90.85% and 81.01% 3D Average Precision on easy and moderate car classes, respectively. These achievements position CPD in close proximity to fully supervised detectors, highlighting the significance of our method. The code will be available at https://github.com/hailanyi/CPD.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 28 of its 46 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

28 of 46 references.

References · 28

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  4. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  5. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  6. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  7. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  8. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  9. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
  10. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 368
  11. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  12. 2021PandaSet: Advanced Sensor Suite Dataset for Autonomous Drivingcited by 190
16 more
  1. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 151
  2. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  3. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  4. 2019SESS: Self-Ensembling Semi-Supervised 3D Object Detectioncited by 89
  5. 20203DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detectioncited by 89
  6. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  7. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  8. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  9. 2022Learning to Detect Mobile Objects from LiDAR Scans Without Labelscited by 44
  10. 2021Universal-Prototype Enhancing for Few-Shot Object Detectioncited by 43
  11. 2022CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detectioncited by 27
  12. 2022Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detectioncited by 14
  13. 2022Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detectioncited by 12
  14. 2023GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Cloudscited by 8
  15. 2016Robust Moving Objects Detection in Lidar Data Exploiting Visual Cuescited by 7
  16. 20223D Cascade RCNN: High Quality Object Detection in Point Cloudscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 4.

13 papers.

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  1. 2024UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classescited by 22
  2. 2024V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detectioncited by 18
  3. 2024Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detectioncited by 9
  4. 2025Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labelscited by 7
  5. 2025SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Promptscited by 6
  6. 2025OpenBox: Annotate Any Bounding Boxes in 3Dcited by 6
  7. 2025VESPA: Towards un(Human)supervised Open-World Pointcloud Labeling for Autonomous Drivingcited by 2
  8. 2024Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptationcited by 1
  9. 2026AW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detectioncited by 1
  10. 2025AutoExpert: Automating 3D LiDAR Annotation from Expert-Crafted Guidelinescited by 0
  11. 2026Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learningcited by 0
  12. 2026When the City Teaches the Car: Label-Free 3D Perception from Infrastructurecited by 0
1 more
  1. 2026Unsupervised Multi-agent and Single-agent Perception from Cooperative Viewscited by 0