Complete point clouds with very few points using Wasserstein GAN and Transformers with Wu

Complete point clouds with very few points using Wasserstein GAN and Transformers with Wu

Completing point cloud from few points by Wasserstein GAN and Transformers
arXiv paper abstract https://arxiv.org/abs/2211.12746
arXiv PDF paper https://arxiv.org/ftp/arxiv/papers/2211/2211.12746.pdf
GitHub https://github.com/WxfQjh/Stability-point-recovery

In many vision and robotics applications, it is common that the captured objects are represented by very few points.

Most of the existing completion methods are designed for partial point clouds with many points, and they perform poorly or even fail completely in the case of few points.

However, due to the lack of detail information, completing objects from few points faces a huge challenge.

… introduce GAN and Transformer techniques to address the above problem.

Firstly, the end-to-end encoder-decoder network with Transformers and the Wasserstein GAN with Transformer are pre-trained, and then the overall network is fine-tuned.

… show that … method can not only improve the completion performance for many input points, but also keep stable for few input points …

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.