Source code for 2021 ICCV paper "In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces"

Related tags

Deep Learning Reconstrution_Through_Moving_Water
Overview

In-the-Wild Single Camera 3D Reconstruction
Through Moving Water Surfaces

This is the PyTorch implementation for 2021 ICCV paper "In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces"

Project Page | Paper | Supplemental Material

In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces
Jinhui Xiong, Wolfgang Heidrich
KAUST
ICCV 2021 (Oral)

We propose a differentiable framework to estimate underwater scene geometry along with the time-varying water surface. The inputs to our model are a video sequence captured by a fixed camera. Dense correspondence from each frame to a world reference frame (selected from the input sequences) is pre-computed, ensuring the reconstruction is performed in a unified coordinate system. We feed the flow fields, together with initialized water surfaces and scene geometry (all are initialized as planar surfaces), into the framework, which incorporates ray casting, Snell’s law and multi-view triangulation. The gradients of the specially designed losses with respect to water surfaces and scene geometry are back-propagated, and all parameters are simultaneously optimized. The final result is a quality reconstruction of the underwater scene, along with an estimate of the time-varying water-air interface. The data shown here was captured in a public fountain environment.

Prerequisite

The code was tested with python>=3.7 & PyTorch>=1.3 & cuda>=10.0 on Nvidia RTX 2080 Ti
Minor change on the code if there is compatibility issue. It needs around 10 GB GPU memory.

Setup

conda create -n moving_water python=3.7
conda activate moving_water

conda install pytorch torchvision -c pytorch
conda install -c conda-forge opencv scikit-image
conda install -c anaconda scipy

Run the code

Please go to example folder, download the cached coefficient matrices (there are three matrices for each example) and execute:

python3 run.py

Citation

@inproceedings{xiong2021inthewild,
  title={In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces},
  author={Jinhui Xiong and Wolfgang Heidrich},
  year={2021},
  booktitle={ICCV}
}

Contact

Please contact Jinhui Xiong [email protected] if you have any question or comment.

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Comments
  • How to get fine optical flow

    How to get fine optical flow

    I tried generating optical flow of example 1 using RAFT and its pretrained weights but the result was not good. Did you use pretrained model directly for optical flow or finetuned on specific date set?

    opened by JamesYang-7 1
  • How to generate cubic B-spline coefficients and confidence masks

    How to generate cubic B-spline coefficients and confidence masks

    Hi Jinhui,

    Thanks for sharing such a good paper, I have some problems after reading the paper when I checked the code:

    1. The paper has mentioned that 'cubic B-spline coefficients and confidence masks were pre-computed', I want to ask if could you give me some suggestions about how to generate them? or maybe part of them generated by random.
    2. I have checked the 'uv_com_100.npy' file, and I have found that it's 240 * 128, while in the paper it has mentioned that 1080p video will be downsampled by 8 to be 240 * 135. I want to ask why this 128 is different from the 135, maybe some extra steps I lost.

    Thanks a lot for your help Best regards, Tiancheng

    opened by GaryGuTC 1
  • Degree of the B-spline surface

    Degree of the B-spline surface

    Hi Jinhui,

    I find that you provide us with the coefficient matrices. Could you please tell us the degree of the B-spline surface (two degrees in u and v direction)? As you know, the degree of the B-spline surface is important for us to calculate the coefficient matrices.

    Cheers, Jiahao

    opened by Jiahao-Ma 1
  • Formula derivation and understanding.

    Formula derivation and understanding.

    Hi Jinhui,

    Thanks for sharing such great work! When reading your paper, there are two points confusing me.

    For the first one, if we get the surface normal, the ray of incidence, how can we compute the refracted ray based on Snell's law? In the paper you only give the results and omit the intermediate process, can you please provide more information about the derivation process? Or relevant information would be fine.

    image

    What's more, for the distance loss, why do we need to multiply refracted light twice? Is the refracted ray at this point a unit vector?

    image

    Your answers are very important to me and I look forward to hearing from you!

    Cheers, Jiahao

    opened by Jiahao-Ma 1
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