A Pytorch implementation of the paper: Pixel2point: 3D Object Reconstruction From a Single Image Using CNN and Initial Sphere
- A. J. Afifi, J. Magnusson, T. A. Soomro, and O. Hellwich, “Pixel2point: 3D Object Reconstruction From a Single Image Using CNN and Initial Sphere, ” IEEE Access, vol. 9, pp. 110–121, 2021, doi: 10.1109/ACCESS.2020.3046951.
| Input 2D Image | Ground Truth Point Cloud | Output Point Cloud |
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conda env create -f ./environment.yml
conda activate pixel2pointThe code has been tested on Ubuntu 20.04, Python 3.9.13, Pytorch 1.12.1, Pytorch3D 0.7.0, CUDA 11.7
The Earth Mover's Distance of point clouds is from: Colin97/MSN-Point-Cloud-Completion: Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)
- Shapenet Point cloud (shapenetcorev2_hdf5_2048.zip, 0.98G): antao97/PointCloudDatasets
- Shapenet renderer (image.tar, 30G): Xharlie/ShapenetRender_more_variation
required to modify the dataset path in the
./lib/settings.py
python main.pypython test.pyrequired to modify the model path in
./lib/settings.py
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2022.09.18
- Dataset: Not tested on Pix3D
- Model: Incomplete Fully Connected Layer because CUDA out of memory
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2022.09.23
- The "pytorch3d" chamfer distance on the CNN layer is non-deterministic at backward propagation on the GPU.
- This repo uses the "pytorch" function to calculate the chamfer distance to make the loss function deterministic on the CNN layers (Increase execution time by 50%).