Xu Cao, Boxin Shi, Fumio Okura and Yasuyuki Matsushita
CVPR 2021
2022.07 UPDATE: We have developed a new method substituting this method while allowing discontinuity preservation. See this repository. We believe there is no reason to use this repository any more in practice given the newly developed method.
This repository contains the official python implementation of the CVPR'21 normal integration paper, along with our python implementations based on the following papers:
- "Variational Methods for Normal Integration", Quéau et al., Journal of Mathematical Imaging and Vision 60(4), pp 609--632, 2018.
- "Normal Integration: a Survey", Quéau et al., Journal of Mathematical Imaging and Vision 60(4), pp 576--593, 2018. Official Matlab Code
- "Least Squares Surface Reconstruction on Arbitrary Domains", Zhu et al., ECCV, 2020. Official Matlab Code
- "Surface-from-Gradients: An Approach Based on Discrete Geometry Processing", Xie et al., CVPR, 2014.
cd to this repository's root folder and reproduce our anaconda environment by running
conda env create -f=environment.yml
conda activate ni
run
python comparison_on_analytically_computed_orthographic_normal_maps.py
This script compares 5 methods on 3 orthographic normal maps: sphere, vase, and anisotropic Gaussian.
The results will be saved in results/#TIME.
You can optionally add Gaussian noise and/or outliers to the input normal maps by running
python comparison_on_analytically_computed_orthographic_normal_maps.py --noise 0.1
python comparison_on_analytically_computed_orthographic_normal_maps.py --outlier 0.1
python comparison_on_analytically_computed_orthographic_normal_maps.py --outlier 0.1 --noise 0.1
The number after --noise is the standard deviation of Gaussian noise added to all normal vectors; the number after --outlier is the percentage (0~1) of outliers in the normal map.
-
Download the perspective normal maps from here and extract them under the
datafolder. These normal maps are picked out from DiLiGenT dataset. -
run
python comparison_on_perspective_diligent_normal_maps.py
This script compares 6 perspective normal integration methods on 9 DiLiGenT objects.
You might want to quickly check the results from a specific method on a specific object.
To this end, comment out the object names defined in surface_name list at line 19 and methods defined in results list at line 56.
As DiLiGenT contains normal maps estimated by different photometric stereo methods,
you can check the normal integration results on these normal maps by modifying the method_type list defined in line 31.
For example, add "ECCV12Shi", "CVPR12Shi", etc. to the list.
To visualize the estimated mesh surfaces, run
python plot_surface.py --path #YOUR_FOLDER_CONTAINING_PLY_FILES
A plot window of one surface will pop up, you can adjust the viewpoint that you would like to save as images. Then close the window, the images of all meshes viewed from the adjusted viewpoint will be saved in your input folder.
Choose the method you would like to use from methods folder and provide a .mat or a .npy file path.
For example:
python methods/perspective_five_point_plane_fitting.py --path data/sample_data/sample.npy
We recommend five point plane fitting in terms of the balance between robustness and computation time.
The .mat or .npy file should contain following key-value pairs:
"normal_map": (H, W, 3) input normal map. This normal map should be defined in such a camera coordinate system: x-axis upwards, y-axis rightwards, and z-axis (the camera's principle axis) towards the scene. Check figure 1 in our supplementary for a visualization. One correction for figure 1: u-axis and v-axis in the pixel coordinates should be swapped."mask": (H, W) boolean mask indicating the region of interest to be integrated. Foreground should be 1; background should be 0."K"(optional): the (3, 3) camera intrinsic matrix. You should prepare this matrix if you choose perspective normal integration methods. If you are not aware of the camera matrix, you can treat a perspective normal map as an orthographic one, and call orthographic normal integration methods. There will be slight global distortion in the estimated surface.
If you find our work useful in your research, please consider citing:
@inproceedings{cao2021normal,
title={Normal Integration via Inverse Plane Fitting With Minimum Point-to-Plane Distance},
author={Cao, Xu and Shi, Boxin and Okura, Fumio and Matsushita, Yasuyuki},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2382--2391},
year={2021}
}
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