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OctNet uses efficient space partitioning structures (i.e. octrees) to reduce memory and compute requirements of 3D convolutional neural networks, thereby enabling deep learning at high resolutions.
The main features of griegler/octnet are: 3D Object Classification, Deep Learning Architectures, Deep Learning Frameworks, Object Reconstruction.
Open-source alternatives to griegler/octnet include: charlesq34/pointnet2 — PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a… charlesq34/pointnet — PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without… fxia22/pointnet.pytorch — This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in… facebookresearch/sparseconvnet — Submanifold sparse convolutional networks. fxia22/kdnet.pytorch. huguesthomas/kpconv.
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
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
Submanifold sparse convolutional networks