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PanoDepth

image

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.

image

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}
}

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