Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment (CVPR 2023)

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This repository contains the code to reproduce the results from the paper. Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment.

You can find detailed usage instructions for training your own models and using pretrained models below.

If you find our code or paper useful, please consider citing

@inproceedings{BaoruiTowards,
    title = {Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment},
    author = {Baorui Ma and Junsheng Zhou and Yu-Shen Liu and Zhizhong Han},
    booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2023}
}

Coming Soon

Related work

Pytorch 
https://github.com/mabaorui/NeuralPull-Pytorch
https://github.com/junshengzhou/CAP-UDF
Tensorflow
https://github.com/mabaorui/NeuralPull
https://github.com/mabaorui/OnSurfacePrior
https://github.com/mabaorui/PredictableContextPrior

Demo

Installation

First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda.

You can create an anaconda environment called tf using

conda env create -f tf.yaml
conda activate tf

ToDo

In different datasets or your own data, because of the variation in point cloud density, this '0.25' parameter has a very strong influence on the final result, which controls the distance between the query points and the point cloud. So if you want to get better results, you should adjust this parameter. We give '0.25' here as a reference value, and this value can be used for most object-level reconstructions. For the scene dataset, we will later publish the reference values for the hyperparameter settings for the scene dataset.