Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Official implementation of the paper

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

CVPR 2022 [oral]

Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla

[arXiv] [openaccess] [oral presentation]

We present MaGNet (Monocular and Geometric Network), a novel framework for fusing single-view depth probability with multi-view geometry, to improve the accuracy, robustness and efficiency of multi-view depth estimation. For each frame, MaGNet estimates a single-view depth probability distribution, parameterized as a pixel-wise Gaussian. The distribution estimated for the reference frame is then used to sample per-pixel depth candidates. Such probabilistic sampling enables the network to achieve higher accuracy while evaluating fewer depth candidates. We also propose depth consistency weighting for the multi-view matching score, to ensure that the multi-view depth is consistent with the single-view predictions. The proposed method achieves state-of-the-art performance on ScanNet, 7-Scenes and KITTI. Qualitative evaluation demonstrates that our method is more robust against challenging artifacts such as texture-less/reflective surfaces and moving objects.

Datasets

We evaluated MaGNet on ScanNet, 7-Scenes and KITTI

ScanNet

• In order to download ScanNet, you should submit an agreement to the Terms of Use. Please follow the instructions in this link.
• The folder should be organized as

/path/to/ScanNet
/path/to/ScanNet/scans
/path/to/ScanNet/scans/scene0000_00 ...
/path/to/ScanNet/scans_test
/path/to/ScanNet/scans_test/scene0707_00 ...

7-Scenes

• Download all seven scenes (Chess, Fire, Heads, Office, Pumpkin, RedKitchen, Stairs) from this link.
• The folder should be organized as:

/path/to/SevenScenes
/path/to/SevenScenes/chess ...

KITTI

• Download raw data from this link.
• Download depth maps from this link
• The folder should be organized as:

/path/to/KITTI
/path/to/KITTI/rawdata
/path/to/KITTI/rawdata/2011_09_26 ...
/path/to/KITTI/train
/path/to/KITTI/train/2011_09_26_drive_0001_sync ...
/path/to/KITTI/val
/path/to/KITTI/val/2011_09_26_drive_0002_sync ...

Download model weights

Download model weights by

python ckpts/download.py

If some files are not downloaded properly, download them manually from this link and place the files under ./ckpts.

Install dependencies

We recommend using a virtual environment.

python3.6 -m venv --system-site-packages ./venv
source ./venv/bin/activate

Install the necessary dependencies by

python3.6 -m pip install -r requirements.txt

Test scripts

If you wish to evaluate the accuracy of our D-Net (single-view), run

python test_DNet.py ./test_scripts/dnet/scannet.txt
python test_DNet.py ./test_scripts/dnet/7scenes.txt
python test_DNet.py ./test_scripts/dnet/kitti_eigen.txt
python test_DNet.py ./test_scripts/dnet/kitti_official.txt

You should get the following results:

Dataset	abs_rel	abs_diff	sq_rel	rmse	rmse_log	irmse	log_10	silog	a1	a2	a3	NLL
ScanNet	0.1186	0.2070	0.0493	0.2708	0.1461	0.1086	0.0515	10.0098	0.8546	0.9703	0.9928	2.2352
7-Scenes	0.1339	0.2209	0.0549	0.2932	0.1677	0.1165	0.0566	12.8807	0.8308	0.9716	0.9948	2.7941
KITTI (eigen)	0.0605	1.1331	0.2086	2.4215	0.0921	0.0075	0.0261	8.4312	0.9602	0.9946	0.9989	2.6443
KITTI (official)	0.0629	1.1682	0.2541	2.4708	0.1021	0.0080	0.0270	9.5752	0.9581	0.9905	0.9971	1.7810

In order to evaluate the accuracy of the full pipeline (multi-view), run

python test_MaGNet.py ./test_scripts/magnet/scannet.txt
python test_MaGNet.py ./test_scripts/magnet/7scenes.txt
python test_MaGNet.py ./test_scripts/magnet/kitti_eigen.txt
python test_MaGNet.py ./test_scripts/magnet/kitti_official.txt

You should get the following results:

Dataset	abs_rel	abs_diff	sq_rel	rmse	rmse_log	irmse	log_10	silog	a1	a2	a3	NLL
ScanNet	0.0810	0.1466	0.0302	0.2098	0.1101	0.1055	0.0351	8.7686	0.9298	0.9835	0.9946	0.1454
7-Scenes	0.1257	0.2133	0.0552	0.2957	0.1639	0.1782	0.0527	13.6210	0.8552	0.9715	0.9935	1.5605
KITTI (eigen)	0.0535	0.9995	0.1623	2.1584	0.0826	0.0566	0.0235	7.4645	0.9714	0.9958	0.9990	1.8053
KITTI (official)	0.0503	0.9135	0.1667	1.9707	0.0848	0.2423	0.0219	7.9451	0.9769	0.9941	0.9979	1.4750

Training scripts

If you wish to train the models, run

python train_DNet.py ./test_scripts/dnet/{scannet, kitti_eigen, kitti_official}.txt
python train_DNet.py ./train_scripts/dnet/scannet.txt
python train_FNet.py ./test_scripts/dnet/{scannet, kitti_eigen, kitti_official}.txt
python train_MaGNet.py ./test_scripts/dnet/{scannet, kitti_eigen, kitti_official}.txt

Note that the dataset_path argument in the script .txt files should be modified

Citation

If you find our work useful in your research please consider citing our paper:

@InProceedings{Bae2022,
  title = {Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry}
  author = {Gwangbin Bae and Ignas Budvytis and Roberto Cipolla},
  booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022}                         
}