# Relation Graph Network for 3D Object Detection in Point Clouds

arXiv: https://arxiv.org/abs/1912.00202 · HTML: https://mlsanity.com/paper/1912.00202/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 27 of 27)

- [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)
- [Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud](https://mlsanity.com/paper/2103.16381/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation](https://mlsanity.com/paper/2308.02874/) (2023)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions](https://mlsanity.com/paper/2406.05785/) (2024)
- [CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity](https://mlsanity.com/paper/2310.02815/) (2023)
- [HyperDet3D: Learning a Scene-conditioned 3D Object Detector](https://mlsanity.com/paper/2204.05599/) (2022)
- [Attribute Artifacts Removal for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2112.00560/) (2021)
- [PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition](https://mlsanity.com/paper/2209.07788/) (2022)
- [Dual Discriminator Adversarial Distillation for Data-free Model Compression](https://mlsanity.com/paper/2104.05382/) (2021)
- [CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning](https://mlsanity.com/paper/2201.08215/) (2022)
- [Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes](https://mlsanity.com/paper/2208.08621/) (2022)
- [Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos](https://mlsanity.com/paper/2202.06503/) (2022)
- [3D Cascade RCNN: High Quality Object Detection in Point Clouds](https://mlsanity.com/paper/2211.08248/) (2022)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [DisARM: Displacement Aware Relation Module for 3D Detection](https://mlsanity.com/paper/2203.01152/) (2022)
- [Anyview: Generalizable Indoor 3D Object Detection with Variable Frames](https://mlsanity.com/paper/2310.05346/) (2023)
- [3D Object Detection from Point Cloud via Voting Step Diffusion](https://mlsanity.com/paper/2403.14133/) (2024)
- [Reflectance Prediction-based Knowledge Distillation for Robust 3D Object Detection in Compressed Point Clouds](https://mlsanity.com/paper/2505.17442/) (2025)
- [3DRM:Pair-wise relation module for 3D object detection](https://mlsanity.com/paper/2202.09721/) (2022)
- [Large receptive field strategy and important feature extraction strategy in 3D object detection](https://mlsanity.com/paper/2401.11913/) (2024)
- [Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection](https://mlsanity.com/paper/2106.10013/) (2021)
- [3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning](https://mlsanity.com/paper/2208.14796/) (2022)
- [CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection](https://mlsanity.com/paper/2209.06641/) (2022)
- [EdgeFormer: local patch-based edge detection transformer on point clouds](https://mlsanity.com/paper/2604.21387/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images. However, their power has not been fully realised for detecting 3D objects in point clouds directly without converting them to regular grids. Existing state-of-art 3D object detection methods aim to recognize 3D objects individually without exploiting their relationships during learning or inference. In this paper, we first propose a strategy that associates the predictions of direction vectors and pseudo geometric centers together leading to a win-win solution for 3D bounding box candidates regression. Secondly, we propose point attention pooling to extract uniform appearance features for each 3D object proposal, benefiting from the learned direction features, semantic features and spatial coordinates of the object points. Finally, the appearance features are used together with the position features to build 3D object-object relationship graphs for all proposals to model their co-existence. We explore the effect of relation graphs on proposals' appearance features enhancement under supervised and unsupervised settings. The proposed relation graph network consists of a 3D object proposal generation module and a 3D relation module, makes it an end-to-end trainable network for detecting 3D object in point clouds. Experiments on challenging benchmarks ( SunRGB-Dand ScanNet datasets ) of 3D point clouds show that our algorithm can perform better than the existing state-of-the-art methods.