mlsanityData to 7 Oct 2026

From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder

arXiv 2108.03648 ↗First posted 8 Aug 2021cs.CVcited by 23

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Abstract (arXiv)

In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder. To preserve the necessary information from all raw points and maintain the high box recall in voxel based Region Proposal Network (RPN), we propose a residual voxel-to-point decoder to extract the point features in addition to the map-view features from the voxel based RPN. We use a 3D Region of Interest (RoI) alignment to crop and align the features with the proposal boxes for accurately perceiving the object position. The RoI-Aligned features are finally aggregated with the corner geometry embeddings that can provide the potentially missing corner information in the box refinement stage. We propose a simple and efficient method to align the estimated IoUs to the refined proposal boxes as a more relevant localization confidence. The comprehensive experiments on KITTI and Waymo Open Dataset demonstrate that our method achieves significant improvements with novel architectures against the existing methods. The code is available on Github URLfootnoteurlhttps://github.com/jialeli1/From-Voxel-to-Point.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 40 of its 51 references that are in the map. First posted on arXiv 8 Aug 2021.

Builds on

40 of 51 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
  5. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  6. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  8. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  9. 2017Decoupled Weight Decay Regularizationcited by 1,722
  10. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  11. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  12. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
28 more
  1. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  2. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  3. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  4. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  5. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  6. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  7. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  8. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  9. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  10. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  11. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  12. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  13. 2017Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Ratescited by 196
  14. 2018Acquisition of Localization Confidence for Accurate Object Detectioncited by 174
  15. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  16. 2019Stereo R-CNN based 3D Object Detection for Autonomous Drivingcited by 163
  17. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  18. 2019Fast Point R-CNNcited by 119
  19. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  20. 2019Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspotscited by 84
  21. 2015Sparse 3D convolutional neural networkscited by 71
  22. 2019PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Modulecited by 56
  23. 2021M3DSSD: Monocular 3D Single Stage Object Detectorcited by 39
  24. 2019L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attentioncited by 32
  25. 2020Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detectioncited by 31
  26. 2020BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye Viewcited by 19
  27. 20203D IoU-Net: IoU Guided 3D Object Detector for Point Cloudscited by 15
  28. 2020SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentationcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 4; 2024: 4; 2025: 1; 2026: 2.

23 papers.

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  1. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  2. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  3. 2022Fully Sparse 3D Object Detectioncited by 84
  4. 2023MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Drivingcited by 68
  5. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  6. 2022Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detectioncited by 48
  7. 2023Super Sparse 3D Object Detectioncited by 32
  8. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  9. 2022Paint and Distill: Boosting 3D Object Detection with Semantic Passing Networkcited by 7
  10. 2024DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detectioncited by 5
  11. 2021Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Networkcited by 4
  12. 2023Multi-Modal 3D Object Detection by Box Matchingcited by 4
11 more
  1. 2022Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detectioncited by 3
  2. 2024Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformercited by 3
  3. 2025CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Predictioncited by 2
  4. 20213D Object Detection Combining Semantic and Geometric Features from Point Cloudscited by 1
  5. 2022SEFormer: Structure Embedding Transformer for 3D Object Detectioncited by 1
  6. 2022Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud Classificationcited by 1
  7. 2024Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Drivingcited by 1
  8. 2021Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detectioncited by 0
  9. 2024SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanismcited by 0
  10. 2026Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detectioncited by 0
  11. 2026Multi-Modal Decouple and Recouple Network for Robust 3D Object Detectioncited by 0