CSPN implemented in Pytorch 0.4.1
Introduction
This is a PyTorch(0.4.1) implementation of Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network. At present, we can provide train script in NYU Depth V2 dataset for depth completion and monocular depth estimation. KITTI will be available soon!
Note: we fix some bugs in original code.
Reselt
We use 2 Titan X to train CSPN for depth completion and monocular depth estimation.
Monocular Depth Estimation
Method | rml | rmse | log10 | Delta1 | Delta2 | Delta3 |
---|---|---|---|---|---|---|
CSPN_ours | 0.151 | 0.546 | 0.064 | 0.793 | 0.949 | 0.985 |
Depth Completion
We test CSPN for depth completion in NYU Depth dataset and use 500 sparse samples.
Method | rml | rmse | log10 | Delta1 | Delta2 | Delta3 |
---|---|---|---|---|---|---|
CSPN | 0.016 | 0.117 | - | 0.992 | 0.999 | 1.000 |
CSPN_ours | 0.023 | 0.152 | 0.010 | 0.988 | 0.997 | 0.999 |