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Implementation of Deep Level Sets paper
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Implemetation of the paper: Implicit Surface Representations as Layers in Neural Networks

Michalkiewicz M, Pontes K, Jack D, Baktashmotlagh M, Eriksson A. In ICCV 2019.

[link] (


To run the code, install the following packages in conda environment:

conda create  -n dls python=3.7
source activate dls
conda install scipy pillow Pillow trimesh numpy
conda install -c conda-forge scikit-fmm 
conda install  pytorch torchvision -c pytorch

General notes

The code is largely based on Matryoshka [1] repository [2] and was modified accordingly.

The 2D encoder used is based on Matryoshka paper [1], however using any other encoder should give similar results.

The very simple 3D decoder used is based on TL paper [3], however using any other 3D decoder should give similar (most likely better) results.


We have used 3D models from ShapeNetCore.v1

2D input images are expected to be have a shape of 128x128.

To process standard 3D-R2N2 [4] views, use

3D ground truth should be signed distance functions of watertight manifolds of shape 32x32x32. Watertight manifolds can be obtained with the Manifold code [5]

Datasets are loaded using and






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