Mirror3D --- init_depth_generator BTS

We've updated the dataloader and the main train/test script to support our input format (coco format).

BTS

From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
arXiv
Supplementary material

Video Demo 1

Video Demo 2

Note

This repository contains TensorFlow and PyTorch implementations of BTS.

Preparation for all implementations

$ cd ~
$ mkdir workspace
$ cd workspace
### Make a folder for datasets
$ mkdir dataset
### Clone this repo
$ git clone https://github.com/cogaplex-bts/bts

Prepare NYU Depth V2 test set

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

Follow instructions from one of the below implementations with your choice.

TensorFlow Implementation

[./tensorflow/]

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 both of TensorFlow and Pytorch.
For best performance, get correct intrinsic values for your webcam and put them in bts_live_3d.py.
Sample usage for PyTorch:

$ cd ~/workspace/bts/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

Citation

If you find this work useful for your research, please consider citing our paper:

@article{lee2019big,
  title={From big to small: Multi-scale local planar guidance for monocular depth estimation},
  author={Lee, Jin Han and Han, Myung-Kyu and Ko, Dong Wook and Suh, Il Hong},
  journal={arXiv preprint arXiv:1907.10326},
  year={2019}
}

License

Copyright (C) 2019 Jin Han Lee, Myung-Kyu Han, Dong Wook Ko and Il Hong Suh
This Software is licensed under GPL-3.0-or-later.