We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
computer-vision
deep-learning
rendering
computer-graphics
voxel
point-cloud
pytorch
mesh
gan
neural-networks
shapenet
3d-reconstruction
loss-functions
shapenet-dataset
cub-dataset
pascal3d
pose-prediction
3d-computer-graphics
single-view-reconstruction
kaolin
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Updated
Mar 4, 2024 - Python