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Submanifold sparse convolutional networks
The main features of facebookresearch/sparseconvnet are: Computer Vision, Deep Learning Architectures, Deep Learning Frameworks.
Open-source alternatives to facebookresearch/sparseconvnet include: charlesq34/pointnet2 — PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a… fxia22/kdnet.pytorch. albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… charlesq34/pointnet — PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without… alankbi/detecto — Build fully-functioning computer vision models with PyTorch. fxia22/pointnet.pytorch — This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in…
Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates
PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes. The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric
PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da
