GitHub repository
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
What is GitHub repository?
Superpoint Graphs (SPG) is a PyTorch-based framework for semantic segmentation of large 3D point clouds. It solves the problem of efficiently labeling each point in massive point clouds (e.g., from LiDAR scans) by first grouping points into superpoints based on geometric features, then using graph neural networks to classify these superpoints. This approach reduces computational complexity while capturing contextual relationships between regions.
Researchers and developers working on 3D computer vision, autonomous driving, robotics, or geospatial analysis who need to segment large-scale point cloud data like urban scenes or indoor environments.
SPG offers a structured graph-based approach that combines efficient geometric partitioning with deep learning, enabling accurate segmentation of massive point clouds where raw point-wise methods are computationally prohibitive. Its flexibility with datasets and optional PyTorch Geometric integration makes it adaptable for various applications.
Overview
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
Use Cases
Best For
- Semantic segmentation of urban LiDAR point clouds for autonomous driving
- Indoor scene understanding from 3D scans like S3DIS or ScanNet
- Research on graph-based deep learning for 3D point clouds
- Processing large-scale geospatial point cloud datasets
- Extending segmentation methods to custom point cloud formats
- Comparing handcrafted vs. learned geometric feature extraction
Not Ideal For
- Real-time applications requiring low-latency point cloud processing, as SPG involves computationally intensive partitioning and graph construction
- Production environments needing actively maintained and supported code, given the repo is deprecated in favor of SuperPoint Transformer
- Teams without expertise in C++ compilation and complex dependency management, due to manual setup of Boost, Eigen, and custom libraries
- Projects requiring out-of-the-box support for all modern datasets like ScanNet, as support is incomplete or pending
Pros & Cons
Pros
Groups points into geometrically homogeneous superpoints to reduce complexity, enabling segmentation of massive urban LiDAR scans as demonstrated in the CVPR2018 paper.
Supports both handcrafted geometric features and deep metric learning for superpoint generation, allowing users to choose based on data characteristics and research goals.
Optional use provides stable and fast graph convolutions, enhancing reliability and performance in graph-based learning, as noted in the README.
Includes utilities to visualize input, ground truth, partitions, results, and superedge structures with configurable output types, aiding in debugging and analysis.
Cons
The repository is explicitly marked as no longer maintained, with authors recommending SuperPoint Transformer for better performance, reducing long-term viability.
Installation requires compiling C++ libraries, managing specific versions of Boost and Eigen, and handling submodules, leading to a error-prone and time-consuming process.
Features for datasets like Semantic3D and ScanNet are listed as 'to come soon' or unavailable, limiting immediate use without custom adaptations.
The partition method is inherently stochastic, causing slight variations in results even with trained weights, as admitted in the disclaimer.
Frequently Asked Questions
Related Projects
GitHub repositoryOpen3D: A Modern Library for 3D Data Processing
DracoDraco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.
mmdetection3dOpenMMLab's next-generation platform for general 3D object detection.
PCDetOpenPCDet Toolbox for LiDAR-based 3D Object Detection.
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.