In this episode of the Talking Papers Podcast, I hosted Itai Lang to chat about his paper "SampleNet: Differentiable Point Cloud Sampling”, published in CVPR 2020. In this paper, they propose a point soft-projection to allow differentiating through the sampling operation and enable learning task-specific point sampling. Combined with their regularization and task-specific losses, they can reduce the number of points to 3% of the original samples with a very low impact on task performance. I met Itai for the first time at CVPR 2019. Being a point-cloud guy myself, I have been following his research work ever since. It is amazing how much progress he has made and I can't wait to see what he comes up with next. It was a pleasure hosting him in the podcast.
PAPER TITLE
"SampleNet: Differentiable Point Cloud Sampling" https://bit.ly/3wMFwll
AUTHORS
Itai Lang, Asaf Manor, Shai Avidan
ABSTRACT
and offered a workaround instead. We introduce a novel differentiable relaxation for point cloud sampling that approximates sampled points as a mixture of points in the primary input cloud. Our approximation scheme leads to consistently good results on classification and geometry reconstruction applications. We also show that the proposed sampling method can be used as a front to a point cloud registration network. This is a challenging task since sampling must be consistent across two different point clouds for a shared downstream task. In all cases, our approach outperforms existing non-learned and learned sampling alternatives. Our code is publicly available.
RELATED PAPERS
📚 Learning to Sample https://bit.ly/3vd1FZd
📚 Farthest Point Sampling (FPS) https://bit.ly/3Lkcyx9
LINKS AND RESOURCES
💻 Code https://bit.ly/3NoS0pb
To stay up to date with Itai's latest research, follow him on:
🎓 Google Scholar: https://bit.ly/3wCMY2u
🐦 Twitter: https://twitter.com/ItaiLang
Recorded on February 15th 2022.
CONTACT
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This episode was recorded on February 11 2022.
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