sshaoshuai/PCDet
PCDet
PCDet is a LiDAR 3D object detection toolbox and point cloud processing library built on the PyTorch deep learning framework. It provides a system for identifying and locating three-dimensional objects within point cloud data.
The project utilizes a data-model separation pattern to decouple dataset loading logic from the core detection pipeline. It features a multi-sensor fusion pipeline that combines data from multiple sensors into a shared spatial view and a distributed GPU training system to scale workloads across multiple graphics processors.
The toolkit covers several capability areas, including voxel-based feature extraction and temporal point cloud analysis through multi-frame fusion. It also incorporates performance optimizations for GPU-accelerated 3D intersection-over-union and rotated non-maximum suppression.
Features
- Lidar Object Identifications - Provides a comprehensive system for identifying and locating three-dimensional objects within LiDAR point cloud data.
- Distributed Deep Learning - Scales the training and testing of 3D detection models across multiple GPUs.
- Distributed GPU Training - Distributes large-scale 3D object detection workloads across multiple graphics processors to accelerate development.
- Data-Parallel Training - Distributes model training workloads across multiple GPUs using synchronized gradient updates.
- Geometric Deep Learning Frameworks - Utilizes geometric deep learning frameworks to process non-Euclidean 3D point cloud data.
- Sensor Fusion - Combines data from multiple sensors into a shared coordinate system for joint feature extraction and localization.
- Sensor Fusion Frameworks - Implements a modular architecture for integrating multi-modal sensor data into unified environmental representations.
- Point Cloud Detections - Implements 3D object detection specifically processing raw LiDAR point clouds to predict oriented bounding boxes.
- Point-to-Voxel Conversion - Converts raw point clouds into a structured grid of voxels for processing with 3D convolutional networks.
- GPU-Accelerated Inference - Accelerates the inference phase by calculating 3D IoU and Non-Maximum Suppression on the GPU.
- GPU-Accelerated Computations - Computes 3D Intersection-over-Union and rotated Non-Maximum Suppression directly on the GPU to reduce inference latency.
- Custom Point Cloud Dataset Training - Supports the integration and training of custom point cloud datasets with specific annotations.
- Temporal Analysis - Analyzes sequences of point cloud frames over time to improve the accuracy of 3D object detection.
- Point Cloud Temporal Fusion - Aggregates sequential point cloud frames into a single spatial representation to improve object detection stability over time.
- Temporal Point Cloud Processing - Analyzes sequences of multiple point cloud frames over time to improve detection accuracy.
- Dataset Loading Templates - Provides a decoupled architecture for integrating various point cloud datasets through standardized templates.
- Point Cloud Processing - PyTorch-based codebase for 3D object detection.
Star history
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