PanoDepth
Getting Started
Requirements
- Python (tested on 3.7.4)
- PyTorch (tested on 1.4.0)
- Other dependencies
Datasets
We train and evaluate on Stanford2D3D, 360D, and 360 stereo dataset.
Usage
Train one-stage monocular depth estimation, run:
python main_mono.py
Train two-stage with the first coarse stage fixed, run:
python main_fullpipeline_pretrain.py
Train two-stage end-to-end, run:
python main_fullpipeline.py
Train 360 stereo matching only, if sample on disparity, use:
python main_stereo_disp.py
Train 360 stereo matching only, if sample on depth, use:
python main_stereo_depth.py
You can change the two-stage configurations in the args.
--baseline
defines the novel view synthesis baseline from the input view. --nlabels
defines the number of hypothesis planes for cascade levels. --interval
defines the depth interval for the second cascade level.
Here are some result comparisons.
If you find our code/models useful, please consider citing our paper:
@inproceedings{li2021panodepth,
title={PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation},
author={Li, Yuyan and Yan, Zhixin and Duan, Ye and Ren, Liu},
booktitle={2021 International Conference on 3D Vision (3DV)},
pages={648--658},
year={2021},
organization={IEEE}
}