# PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement

arXiv: https://arxiv.org/abs/1911.12236 · HTML: https://mlsanity.com/paper/1911.12236/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [3D Object Detection for Autonomous Driving: A Survey](https://mlsanity.com/paper/2106.10823/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences](https://mlsanity.com/paper/2103.06028/) (2021)
- [SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation](https://mlsanity.com/paper/2105.04447/) (2021)
- [SketchLattice: Latticed Representation for Sketch Manipulation](https://mlsanity.com/paper/2108.11636/) (2021)
- [Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency](https://mlsanity.com/paper/2303.08686/) (2023)
- [PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection](https://mlsanity.com/paper/2208.13414/) (2022)
- [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://mlsanity.com/paper/2311.06043/) (2023)
- [TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System](https://mlsanity.com/paper/2405.13989/) (2024)
- [A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2605.17131/) (2026)
- [Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection](https://mlsanity.com/paper/2106.10013/) (2021)
- [ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection](https://mlsanity.com/paper/2211.09518/) (2022)

## Abstract (arXiv)

In autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount importance for a safe driving as it identifies the position of other agents sharing the road. In our work, we propose PointRGCN: a graph-based 3D object detection pipeline based on graph convolutional networks (GCNs) which operates exclusively on 3D LiDAR point clouds. To perform more accurate 3D object detection, we leverage a graph representation that performs proposal feature and context aggregation. We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation. In particular, R-GCN is a residual GCN that classifies and regresses 3D proposals, and C-GCN is a contextual GCN that further refines proposals by sharing contextual information between multiple proposals. We integrate our refinement modules into a novel 3D detection pipeline, PointRGCN, and achieve state-of-the-art performance on the easy difficulty for the bird eye view detection task.