# SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

arXiv: https://arxiv.org/abs/2004.02774 · HTML: https://mlsanity.com/paper/2004.02774/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 30 of 74)

- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [An End-to-End Transformer Model for 3D Object Detection](https://mlsanity.com/paper/2109.08141/) (2021)
- [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://mlsanity.com/paper/2107.14160/) (2021)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://mlsanity.com/paper/2103.15297/) (2021)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving](https://mlsanity.com/paper/2303.11040/) (2023)
- [CenterFormer: Center-based Transformer for 3D Object Detection](https://mlsanity.com/paper/2209.05588/) (2022)
- [Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning](https://mlsanity.com/paper/2205.00272/) (2022)
- [Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency](https://mlsanity.com/paper/2107.11355/) (2021)
- [Object DGCNN: 3D Object Detection using Dynamic Graphs](https://mlsanity.com/paper/2110.06923/) (2021)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception](https://mlsanity.com/paper/2109.05441/) (2021)
- [Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images](https://mlsanity.com/paper/2205.13764/) (2022)
- [LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors](https://mlsanity.com/paper/2205.15410/) (2022)
- [FastPillars: A Deployment-friendly Pillar-based 3D Detector](https://mlsanity.com/paper/2302.02367/) (2023)
- [RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization](https://mlsanity.com/paper/2203.12870/) (2022)
- [Monocular 3D Object Detection with Depth from Motion](https://mlsanity.com/paper/2207.12988/) (2022)
- [STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes](https://mlsanity.com/paper/2204.01026/) (2022)
- [Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection](https://mlsanity.com/paper/2306.01438/) (2023)
- [CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection](https://mlsanity.com/paper/2212.00244/) (2022)
- [MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training](https://mlsanity.com/paper/2303.13510/) (2023)
- [Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR](https://mlsanity.com/paper/2211.16951/) (2022)
- [Boosting 3D Object Detection by Simulating Multimodality on Point Clouds](https://mlsanity.com/paper/2206.14971/) (2022)
- [RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM](https://mlsanity.com/paper/2401.03907/) (2024)
- [LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment](https://mlsanity.com/paper/2402.17171/) (2024)
- [3D Object Detection with a Self-supervised Lidar Scene Flow Backbone](https://mlsanity.com/paper/2205.00705/) (2022)
- [Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds](https://mlsanity.com/paper/2207.12659/) (2022)

## Abstract (arXiv)

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalability