mlsanityData to 7 Oct 2026

PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection

arXiv 2012.10412 ↗First posted 18 Dec 2020cs.CVcited by 35

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Abstract (arXiv)

LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions. On the one hand, we introduce a point cloud completion module to recover high-quality proposals of dense points and entire views with original structures preserved. On the other hand, a graph neural network module is designed, which comprehensively captures relations among points through a local-global attention mechanism as well as multi-scale graph based context aggregation, substantially strengthening encoded features. Extensive experiments on the KITTI benchmark show that the proposed approach outperforms the previous state-of-the-art baselines by remarkable margins, highlighting its effectiveness.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 9; 2023: 8; 2024: 12; 2025: 4; 2026: 1.

35 papers.

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  1. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  2. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
  3. 2022CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detectioncited by 32
  4. 2023OcTr: Octree-based Transformer for 3D Object Detectioncited by 32
  5. 2023SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detectioncited by 30
  6. 2024Vision-based 3D occupancy prediction in autonomous driving: a review and outlookcited by 29
  7. 2025CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridizationcited by 27
  8. 2023Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detectioncited by 24
  9. 2022Sparse2Dense: Learning to Densify 3D Features for 3D Object Detectioncited by 14
  10. 2023PG-RCNN: Semantic Surface Point Generation for 3D Object Detectioncited by 12
  11. 2024ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attentioncited by 11
  12. 2021KTNet: Knowledge Transfer for Unpaired 3D Shape Completioncited by 9
23 more
  1. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
  2. 2023Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detectioncited by 8
  3. 20223D Cascade RCNN: High Quality Object Detection in Point Cloudscited by 7
  4. 2024SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detectioncited by 7
  5. 2022Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Cloudscited by 6
  6. 2024STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learningcited by 6
  7. 2024FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detectioncited by 6
  8. 2026SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radarcited by 4
  9. 2022PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detectioncited by 3
  10. 2024Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?cited by 2
  11. 2024PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detectioncited by 2
  12. 2022EDeNN: Event Decay Neural Networks for low latency visioncited by 1
  13. 2023What You See Is What You Detect: Towards better Object Densification in 3D detectioncited by 1
  14. 2023PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysiscited by 1
  15. 2024PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detectioncited by 1
  16. 2024FSMDet: Vision-guided feature diffusion for fully sparse 3D detectorcited by 1
  17. 2022ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detectioncited by 0
  18. 2023SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networkscited by 0
  19. 2024Terrain Point Cloud Inpainting via Signal Decompositioncited by 0
  20. 2024Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detectioncited by 0
  21. 2025Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detectioncited by 0
  22. 2025Project-and-Fuse: Improving RGB-D Semantic Segmentation via Graph Convolution Networkscited by 0
  23. 2025TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decodercited by 0