Talking Papers Podcast
🎙️ Welcome to the Talking Papers Podcast: Where Research Meets Conversation 🌟
Are you ready to explore the fascinating world of cutting-edge research in computer vision, machine learning, artificial intelligence, graphics, and beyond? Join us on this podcast by researchers, for researchers, as we venture into the heart of groundbreaking academic papers.
At Talking Papers, we've reimagined the way research is shared. In each episode, we engage in insightful discussions with the main authors of academic papers, offering you a unique opportunity to dive deep into the minds behind the innovation.
📚 Structure That Resembles a Paper 📝
Just like a well-structured research paper, each episode takes you on a journey through the academic landscape. We provide a concise TL;DR (abstract) to set the stage, followed by a thorough exploration of related work, approach, results, conclusions, and a peek into future work.
🔍 Peer Review Unveiled: "What Did Reviewer 2 Say?" 📢
But that's not all! We bring you an exclusive bonus section where authors candidly share their experiences in the peer review process. Discover the insights, challenges, and triumphs behind the scenes of academic publishing.
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Whether you're a seasoned researcher or an enthusiast eager to explore the frontiers of knowledge, Talking Papers Podcast is your gateway to in-depth, engaging discussions with the experts shaping the future of technology and science.
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Talking Papers Podcast
Itai Lang - SampleNet
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
If you would like to be a guest, sponsor or just share your thoughts, feel free to reach out via email: talking.papers.podcast@gmail.com
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This episode was recorded on February 11 2022.
#talkingpapers #SampleNet #LearnToSample #CVPR2020 #3DVision #ComputerVision #AI #DeepLearning #MachineLearning #deeplearning #AI #neuralnetworks #research #artificialintelligence
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