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MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used for tasks such as 3D shape classification or segmentation. This framework includes convolution, pooling and unpooling layers which are applied directly on the mesh edges.
Volumetric CNN (Convolutional Neural Networks) for Object Classification on 3D Data, with Torch implementation.
Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification
Created by Itai Lang, Asaf Manor, and Shai Avidan from Tel Aviv University.
The main features of itailang/samplenet are: 3D Object Classification, 3D Shape Analysis.
Open-source alternatives to itailang/samplenet include: ranahanocka/meshcnn — MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used for tasks such as 3D… 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… ajbrock/generative-and-discriminative-voxel-modeling — Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification.
