lkhphuc/pytorch-3d-point-cloud-generation

Pytorch code to construct a 3D point cloud model from single RGB image.

Pytorch: Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

A Pytorch implementation of the paper: Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

• Article: https://medium.com/@lkhphuc/create-3d-model-from-a-single-2d-image-in-pytorch-917aca00bb07
• Original Website: https://chenhsuanlin.bitbucket.io/3D-point-cloud-generation
• Original TF implementation: https://github.com/chenhsuanlin/3D-point-cloud-generation

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Training/evaluating the network

Prerequisites

This code is developed with Python3 (python3). Pytorch 0.4+ is required.

Dataset

(Provided in TF's repo) The dataset (8.8GB) can be downloaded by running the command

wget https://cmu.box.com/shared/static/s4lkm5ej7sh4px72vesr17b1gxam4hgy.gz

This file includes:

• Train/test split files (from Perspective Transformer Nets)
• Input RGB images (from Perspective Transformer Nets)
• Pre-rendered depth images for training
• Ground-truth point clouds of the test split (densified to 100K points)

After downloading, run tar -zxf s4lkm5ej7sh4px72vesr17b1gxam4hgy.gz under the main directory. The files will be extracted to the data directory. (Please also cite the relevant papers if you plan to use this dataset package.)

Running the code

The following scripts gives examples for running the code.

• Pretraining the network: scripts/train-stg1.sh
• Fine-tuning with joint 2D optimization: scripts/train-stg2.sh
• Evaluating on the test set: scripts/evaluate.sh
• Computing the error metrics: scripts/evaluate_dist.sh

Checkpoints are stored in models/${EXPERIMENTATION}, summaries are stored in runs/_${EXPERIMENTATION}, and evaluated point clouds are stored in results_${GROUP}. The list of optional arguments can be found by executing python3 train-stg1.py --help.

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Rendering ground-truth depth images

(Provided in TF's repo) We provide the code to render depth images for supervision.

Prerequisites

This code requires the following:

• Blender as the rendering engine. This code was developed with Blender 2.78. After installation, please make sure the command blender is callable (use which blender to check installation).
• The OpenEXR Python binding for .exr to .mat file conversion.

Dataset

The raw ShapeNet dataset can be downloaded here. This rendering code was developed to use ShapeNetCore v2. (The provided depth images were rendered from ShapeNetCore v1.)

Running the code

Under render, run ./run.sh 03001627 8 to render depth images for fixed and arbitrary viewpoints, and convert them to .mat files. This will convert all objects in the ShapeNet chair category (03001627) with 8 fixed viewpoints. The rendered files will be stored in the output directory.

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Creating densified point clouds of CAD models for evaluation

(Provided in TF's repo) We also provide the code to densify the vertices of CAD models to a specified number. This code can be run independently; only the ShapeNet dataset is required. It repeats the process of adding a vertex to the center of the longest edge of the triangular mesh and subsequently re-triangulating the mesh. This will create (generally) uniformly densified CAD models.

Running the code

Under densify, run ./run.sh 03001627 to run densification. The densified CAD models will be stored in the output directory.