LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling

Understand

Compared with traditional task-irrelevant downsampling methods, task-oriented neural networks have shown improved performance in point cloud downsampling range.

• Recently, Transformer family of networks has shown a more powerful learning capacity in visual tasks.
• However, Transformer-based architectures potentially consume too many resources which are usually worthless for low overhead task networks in downsampling range.
• This paper proposes a novel light-weight Transformer network (LighTN) for task-oriented point cloud downsampling, as an end-to-end and plug-and-play solution.