depth/README.md

Depth Estimation

Getting Started

1. Install the mmcv-full library and some required packages.

pip install openmim
mim install mmcv-full
pip install -r requirements.txt

2. Prepare NYUDepthV2 datasets following GLPDepth and BTS.

mkdir nyu_depth_v2
wget http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat
python extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ./nyu_depth_v2/official_splits/

Download sync.zip provided by the authors of BTS from this url and unzip in ./nyu_depth_v2 folder.

Your dataset directory should be:

│nyu_depth_v2/
├──official_splits/
│  ├── test
│  ├── train
├──sync/

Results and Fine-tuned Models

EVP obtains 0.224 RMSE on NYUv2 depth estimation benchmark, establishing the new state-of-the-art.

	RMSE	d1	d2	d3	REL	log_10
EVP	0.224	0.976	0.997	0.999	0.061	0.027

EVP obtains 0.048 REL and 0.136 SqREL on KITTI depth estimation benchmark, establishing the new state-of-the-art.

	REL	SqREL	RMSE	RMSE log	d1	d2	d3
EVP	0.048	0.136	2.015	0.073	0.980	0.998	1.000

Training

Run the following instuction to train the EVP-Depth model.

bash train.sh <LOG_DIR>

Evaluation

Command format:

bash test.sh <CHECKPOINT_PATH>

Custom inference

PYTHONPATH="../":$PYTHONPATH python inference.py --img_path test_img.jpg --ckpt_dir nyu.ckpt