open-mmlab/OpenPCDet
OpenPCDet
OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud processing framework designed to develop, train, and evaluate machine learning models that identify and locate objects in three dimensional space.
The project includes a GPU-accelerated geometry engine for high-performance implementation of 3D intersection over union and rotated non-maximum suppression. It also provides a distributed model training tool to scale the training and testing of detection models across multiple GPUs and computing nodes.
The framework covers point cloud data processing through the standardization of 3D scene representations and the management of variable point counts across diverse LiDAR datasets.
Features
- Point Cloud Detections - Provides a comprehensive framework for training and evaluating 3D object detectors using raw LiDAR point clouds.
- Data-Parallel Training - Provides distributed training capabilities to synchronize gradients and parameters across multiple GPU nodes.
- GPU Accelerated Computer Vision - Leverages GPU acceleration for high-performance geometric computations like 3D intersection and non-maximum suppression.
- Distributed Training - Enables scaling of model training and testing workloads across multiple GPUs and computing nodes.
- PyTorch Spatial Detection Libraries - Provides a set of PyTorch-based tools for building and training neural networks for spatial LiDAR data.
- 3D Object Detection - Implements algorithms for identifying and localizing objects in 3D space using LiDAR data.
- Distributed Training Tools - Includes tools for scaling the training and communication of 3D detection models across multiple nodes.
- Point Cloud and 3D Processing - Acts as a processing framework for analyzing 3D point clouds and standardizing scene representations.
- Coordinate System Mappings - Standardizes diverse LiDAR datasets into a unified 3D coordinate system to maintain consistency across detection models.
- 3D Intersection Over Union Calculators - Calculates 3D intersection over union and rotated non-maximum suppression to refine detected object precision.
- Detection Pipelines - Implements a two-stage detection pipeline combining region proposals with a refined bounding box regression head.
- 3D Geometry Engines - Includes a high-performance computational engine for processing 3D geometric data and transformations.
- Config-Driven Instantiation - Implements a system where model architectures and hyperparameters are defined in external YAML files for flexible instantiation.
- Data Model Decoupling - Decouples raw data storage from scene representations to efficiently handle variable point counts across datasets.
- 3D Detection and Segmentation - Point-voxel feature abstraction for 3D object detection.
- Point Cloud Processing - Toolbox for LiDAR-based 3D object detection.
