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Accepted to CVPR2023. 🔥
The main features of blvlab/pimae are: 3D Detection and Segmentation, 3D Object Detection.
Projects with overlapping indexed features include: open-mmlab/openpcdet — OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud… tusimple/sst — This repo contains official implementations of our series of work in LiDAR-based 3D object detection:. zeliu98/group-free-3d — Group-Free 3D Object Detection via Transformers. yanx27/pointnet_pointnet2_pytorch — This project is a PyTorch-based framework of deep learning models designed for the classification and semantic… open-mmlab/mmdetection3d — MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting… manycore-research/spatiallm — SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data…
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 c
This repo contains official implementations of our series of work in LiDAR-based 3D object detection:
This project is a PyTorch-based framework of deep learning models designed for the classification and semantic segmentation of 3D point cloud data. It provides implementations of the PointNet architecture to perform global category labeling of entire objects and detailed partitioning of large-scale 3D environments. The system handles semantic segmentation across multiple scales, ranging from identifying individual components within a single object to labeling distinct category types within large-scale scenes. The framework includes structural components for processing unordered point sets, s
