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
-
Updated
Mar 4, 2024 - Python