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We propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv is differentiable and can be plugged into existing architectures.
The main features of wangyueft/dgcnn are: 3D Computer Vision, Deep Learning Architectures, Deep Learning Frameworks.
Open-source alternatives to wangyueft/dgcnn 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. griegler/octnet — OctNet uses efficient space partitioning structures (i.e. octrees) to reduce memory and compute requirements of 3D…
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