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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.

Python 100.00%
3d-computer-graphics 3d-reconstruction pose-prediction single-view-reconstruction computer-vision computer-graphics kaolin pytorch deep-learning neural-networks loss-functions rendering gan shapenet shapenet-dataset cub-dataset pascal3d mesh voxel point-cloud

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