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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Talking Papers Podcast
Dejan Azinović - Neural RGBD Surface Reconstruction
In this episode of the Talking Papers Podcast, I hosted Dejan Azinović to chat about his paper "Neural RGB-D Surface Reconstruction”, published in CVPR 2022.
In this paper, they take on the task of RGBD surface reconstruction by using novel view synthesis. They incorporate depth measurements into the radiance field formulation by learning a neural network that stores a truncated signed distance field. This formulation is particularly useful in regions where depth is missing and the color information can help fill in the gaps.
PAPER TITLE
"Neural RGB-D Surface Reconstruction"
AUTHORS
Dejan Azinović Ricardo Martin-Brualla Dan B Goldman Matthias Nießner Justus Thies
ABSTRACT
In this work, we explore how to leverage the success of implicit novel view synthesis methods for surface reconstruction. Methods which learn a neural radiance field have shown amazing image synthesis results, but the underlying geometry representation is only a coarse approximation of the real geometry. We demonstrate how depth measurements can be incorporated into the radiance field formulation to produce more detailed and complete reconstruction results than using methods based on either color or depth data alone. In contrast to a density field as the underlying geometry representation, we propose to learn a deep neural network which stores a truncated signed distance field. Using this representation, we show that one can still leverage differentiable volume rendering to estimate color values of the observed images during training to compute a reconstruction loss. This is beneficial for learning the signed distance field in regions with missing depth measurements. Furthermore, we correct for misalignment errors of the camera, improving the overall reconstruction quality. In several experiments, we show-cast our method and compare to existing works on classical RGB-D fusion and learned representations.
RELATED PAPERS
📚 NeRF
📚 BundleFusion
LINKS AND RESOURCES
💻 Project Page
💻 Code
To stay up to date with Dejan's latest research, follow him on:
👨🏻🎓 Dejan's personal page
🎓 Google Scholar
🐦 Twitter
👨🏻🎓LinkedIn:
Recorded on April 4th 2022.
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