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Spectral-GAN

Tensorflow implementation of Spectral-GANs for High-Resolution 3D Point-cloud Generation (IROS 2020)

Introduction

Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners. This demands the ability to synthesize and reconstruct high-quality point-clouds. Current deep generative models for 3D data generally work on simplified representations (e.g., voxelized objects) and cannot deal with the inherent redundancy and irregularity in point-clouds. A few recent efforts on 3D point-cloud generation offer limited resolution and their complexity grows with the increase in output resolution. In this work, we develop a principled approach to synthesize 3D point-clouds using a spectral-domain Generative Adversarial Network (GAN). This is the Tensorflow code of our paper.

Usage

  1. Set the input path to the ground truth spherical harmonics in main.py
  2. Give the ground truth point cloud path in spatial_train.py as a single numpy array (num_clouds, num_points, 3)
  3. Train:

python main.py --mode=train

  1. Evaluate:

python main.py --mode=evaluate

Citation

@article{ramasinghe2020spectral, title={Spectral-GANs for High-Resolution 3D Point-cloud Generation}, author={Ramasinghe, Sameera and Khan, Salman and Barnes, Nick and Gould, Stephen}, journal={IEEE/RSJ Intenrational Conference on Robots and Systems (IROS)}, year={2020} }

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