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Apply random jitter to point positions as a data augmentation for point cloud segmentation.
Distinct from Point Cloud Processing: Distinct from Point Cloud Processing: focuses on a specific jitter-based augmentation technique, not general processing.
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MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate
Applies random positional jitter as a data augmentation technique for point cloud segmentation models.