Awesome
TriDepth
The official Pytorch implementation of the paper "Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem" (WACV 2023)
We introduce TriDepth, a high-performance depth estimator with a highly generalizable redesigned triplet loss.
📈 KITTI Results
State-of-the-Art Performance:
Also Highly Generalizable:
✏️ Citation
If you find our work useful or interesting, please consider citing our paper:
@inproceedings{chen2023self,
title={{Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem}},
author={Chen, Xingyu and Zhang, Ruonan and Jiang, Ji and Wang, Yan and Li, Ge and Li, Thomas H},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={5776--5786},
year={2023}
}
👀 Training
Our code is build upon Manydepth.
KITTI-RAW dataset and pre-computed segmentation images provided by FSRE-Depth are needed for training.
🐷 Note: The pre-computed segmentation is NOT needed for evaluating.
KITTI-RAW/
├── 2011_09_26/
├── 2011_09_28/
├── 2011_09_29/
├── 2011_09_30/
├── 2011_10_03/
└── segmentation/ # download and unzip "segmentation.zip"
Training command goes like:
python -m manydepth.train --data_path {YOUR_KITTI_DATASET_PATH} --batch_size 8 --model_name {MODEL_NAME_YOU_LIKE}
To evaluate, run:
python -m manydepth.evaluate_depth --data_path {YOUR_KITTI_DATASET_PATH} --eval_mono --load_weights_folder {YOUR_MODEL_PATH}
🐷 Note: Make sure you have run export_gt_depth.py
to generate ground truth depth before evaluating.
🌟 Pretrained Model
Pretrained model (640x192) is now available!!