TopGun666 / Temporary

Temporary

Preamble

This codebase was developed and tested with python 3.6.5, Pytorch 1.5.0, and CUDA 10.0 on Ubuntu 18.04. It is based on Han Lee's BTS implementation.

Prepare NYU Depth V2 test set

$ cd ~/paper/Temporary/utils
### Get official NYU Depth V2 split file
$ wget http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat
### Convert mat file to image files
$ python extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ../../dataset/nyu_depth_v2/official_splits/

Prepare KITTI official ground truth depth maps

Download the ground truth depthmaps from this link KITTI.
Then,

$ cd ~/paper/dataset
$ mkdir kitti_dataset && cd kitti_dataset
$ mv ~/Downloads/data_depth_annotated.zip .
$ unzip data_depth_annotated.zip

PyTorch Implementation

[./pytorch/]

Model Zoo

KITTI Eigen Split

Base Network	cap	d1	d2	d3	AbsRel	SqRel	RMSE	RMSElog	SILog	log10	#Params	Model Download
ResNet50	0-80m	0.954	0.992	0.998	0.061	0.250	2.803	0.098	9.030	0.027	49.5M	bts_eigen_v2_pytorch_resnet50
ResNet101	0-80m	0.954	0.992	0.998	0.061	0.261	2.834	0.099	9.075	0.027	68.5M	bts_eigen_v2_pytorch_resnet101
ResNext50	0-80m	0.954	0.993	0.998	0.061	0.245	2.774	0.098	9.014	0.027	49.0M	bts_eigen_v2_pytorch_resnext50
ResNext101	0-80m	0.956	0.993	0.998	0.059	0.241	2.756	0.096	8.781	0.026	112.8M	bts_eigen_v2_pytorch_resnext101
DenseNet121	0-80m	0.951	0.993	0.998	0.063	0.256	2.850	0.100	9.221	0.028	21.2M	bts_eigen_v2_pytorch_densenet121
DenseNet161	0-80m	0.955	0.993	0.998	0.060	0.249	2.798	0.096	8.933	0.027	47.0M	bts_eigen_v2_pytorch_densenet161

NYU Depth V2

Base Network	d1	d2	d3	AbsRel	SqRel	RMSE	RMSElog	SILog	log10	#Params	Model Download
ResNet50	0.865	0.975	0.993	0.119	0.075	0.419	0.152	12.368	0.051	49.5M	bts_nyu_v2_pytorch_resnet50
ResNet101	0.871	0.977	0.995	0.113	0.068	0.407	0.148	11.886	0.049	68.5M	bts_nyu_v2_pytorch_resnet101
ResNext50	0.867	0.977	0.995	0.116	0.070	0.414	0.150	12.186	0.050	49.0M	bts_nyu_v2_pytorch_resnext50
ResNext101	0.880	0.977	0.994	0.111	0.069	0.399	0.145	11.680	0.048	112.8M	bts_nyu_v2_pytorch_resnext101
DenseNet121	0.871	0.977	0.993	0.118	0.072	0.410	0.149	12.028	0.050	21.2M	bts_nyu_v2_pytorch_densenet121
DenseNet161	0.885	0.978	0.994	0.110	0.066	0.392	0.142	11.533	0.047	47.0M	bts_nyu_v2_pytorch_densenet161

Note: Modify arguments '--encoder', '--model_name', '--checkpoint_path' and '--pred_path' accordingly.

Live Demo

Finally, we attach live 3d demo implementations for Pytorch.
For best performance, get correct intrinsic values for your webcam and put them in bts_live_3d.py.
Sample usage for PyTorch:

$ cd ~/paper/Temporary/pytorch
$ python bts_live_3d.py --model_name bts_nyu_v2_pytorch_densenet161 \
--encoder densenet161_bts \
--checkpoint_path ./models/bts_nyu_v2_pytorch_densenet161/model \
--max_depth 10 \
--input_height 480 \
--input_width 640

License

This Software is licensed under GPL-3.0-or-later.