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 m...

Yuliang Xiu - ICON

Talking Papers Podcast: deep dives into research papers in computer vision, 3D, machine learning, and AI, with the authors who wrote them. Where research meets conversation. By researchers, for researchers.Each episode is structured like the paper itself: a TL;DR / abstract to set the stage, then related work, approach, results, conclusions, and future work. We close with a bonus segment called "What did Reviewer 2 say?", where the authors share the candid peer-review story behind the publication.Hosted by Itzik Ben-Shabat. Guests are PhD students, postdocs, and faculty from leading labs across academia and industry. Aimed at fellow researchers and graduate students who want the candid version of the work, not a polished press release.