| yjxiong/tsn-pytorch |
751 |
|
0 |
0 |
almost 7 years ago |
0 |
|
26 |
bsd-2-clause |
Python |
| Temporal Segment Networks (TSN) in PyTorch |
| loicland/superpoint_graph |
691 |
|
0 |
0 |
over 2 years ago |
0 |
|
14 |
mit |
Python |
| Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs |
| xwying/torchshow |
527 |
|
0 |
0 |
over 2 years ago |
9 |
November 07, 2022 |
4 |
mit |
Python |
| Visualize PyTorch tensors with a single line of code. |
| Ha0Tang/SelectionGAN |
427 |
|
0 |
0 |
about 3 years ago |
0 |
|
4 |
other |
Python |
| [CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation |
| piergiaj/pytorch-i3d |
393 |
|
0 |
0 |
over 6 years ago |
0 |
|
39 |
apache-2.0 |
Python |
| hassony2/kinetics_i3d_pytorch |
377 |
|
0 |
0 |
over 5 years ago |
0 |
|
9 |
mit |
Python |
| Inflated i3d network with inception backbone, weights transfered from tensorflow |
| wvangansbeke/Sparse-Depth-Completion |
372 |
|
0 |
0 |
almost 4 years ago |
0 |
|
1 |
other |
Python |
| Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) |
| fab-jul/L3C-PyTorch |
304 |
|
0 |
0 |
over 4 years ago |
0 |
|
8 |
gpl-3.0 |
Python |
| PyTorch Implementation of the CVPR'19 Paper "Practical Full Resolution Learned Lossless Image Compression" |
| art-programmer/PlaneNet |
295 |
|
0 |
0 |
almost 7 years ago |
0 |
|
13 |
mit |
Python |
| PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image |
| fangchangma/sparse-to-dense.pytorch |
283 |
|
0 |
0 |
about 7 years ago |
0 |
|
5 |
|
Python |
| ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (PyTorch Implementation) |
