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Volumetric CNN (Convolutional Neural Networks) for Object Classification on 3D Data, with Torch implementation.
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
Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification
This repository contains the implementation of our papers related with O-CNN. The code is released under the MIT license.
The main features of microsoft/o-cnn are: 3D Object Classification.
Open-source alternatives to microsoft/o-cnn include: amir32002/feedback-networks — Paper: Feedback Networks, CVPR 2017. charlesq34/3dcnn.torch — Volumetric CNN (Convolutional Neural Networks) for Object Classification on 3D Data, with Torch implementation. charlesq34/pointnet — PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without… charlesq34/pointnet2 — PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a… dimatura/voxnet — 3D/Volumetric Convolutional Neural Networks with Theano+Lasagne. ajbrock/generative-and-discriminative-voxel-modeling — Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification.
