Jamie Watson, Michael Firman, Gabriel J. Brostow and Daniyar Turmukhambetov โ ICCV 2019
[Paper coming soon]
We introduce Depth Hints, which improve monocular depth estimation algorithms trained from stereo pairs.
We find that photometric reprojection losses used with self-supervised learning typically have multiple local minima.
This can restrict what a regression network learns, for example causing artifacts around thin structures.
Depth Hints are complementary depth suggestions obtained from simple off-the-shelf stereo algorithms, e.g. Semi-Global Matching. These hints are used during training to guide the network to learn better weights. They require no additional data, and are assumed to be right only sometimes.
Combined with other good practices, Depth Hints gives state-of-the-art depth predictions on the KITTI benchmark (see images above and results table below). We show additional monocular depth estimation results on the sceneflow dataset:
If you find our work useful or interesting, please consider citing our paper:
@inproceedings{watson-2019-depth-hints,
title = {Self-Supervised Monocular Depth Hints},
author = {Jamie Watson and
Michael Firman and
Gabriel J. Brostow and
Daniyar Turmukhambetov},
booktitle = {ICCV},
year = {2019}
}
| Model Name | Training Modality | ImageNet Pretrained | Resolution | Abs Rel | ๐ฟ < 1.25 |
|---|---|---|---|---|---|
| Ours Resnet50 | Stereo | yes | 640 x 192 | 0.102 | 0.880 |
| Ours Resnet50 no pt | Stereo | no | 640 x 192 | 0.118 | 0.850 |
| Ours HR Resnet50 | Stereo | yes | 1024 x 320 | 0.096 | 0.890 |
| Ours HR Resnet50 no pt | Stereo | no | 1024 x 320 | 0.112 | 0.861 |
| Ours HR | Mono + Stereo | yes | 1024 x 320 | 0.098 | 0.887 |
Coming soon!
In the meantime check out our monodepth2 repository for depth estimation from monocular and stereo training data.