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
Manuel Dahnert - Panoptic 3D Scene Reconstruction
In this episode of the Talking Papers Podcast, I hosted Manuel Dahnert to chat about his paper “Panoptic 3D Scene Reconstruction From a Single RGB Image”, published in NeurIPS 2021. In this paper, they unify the task of reconstruction, semantic segmentation and instance segmentation in 3D from a single RGB image. They propose a holistic approach to lift the 2D features into a 3D grid. Manuel is a good friend and colleague. We first met in my research visit at TUM during my PhD, we spent some long evenings together at the office. We have both come a long way since then and I am really looking forward to seeing what he will cook up next. I have a feeling it is not his last visit in the podcast.
PAPER TITLE
"Panoptic 3D Scene Reconstruction From a Single RGB Image" : https://bit.ly/3phnLGp
AUTHORS
Manuel Dahnert, Ji Hou, Matthias Niessner, Angela Dai
ABSTRACT
In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit representations results in Richly segmented 3D scene reconstructions are an integral basis for many high-level scene understanding tasks, such as for robotics, motion planning, or augmented reality. Existing works in 3D perception from a single RGB image tend to focus on geometric reconstruction only, or geometric reconstruction with semantic segmentation or instance segmentation. Inspired by 2D panoptic segmentation, we propose to unify the tasks of geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into the task of panoptic 3D scene reconstruction -- from a single RGB image, predicting the complete geometric reconstruction of the scene in the camera frustum of the image, along with semantic and instance segmentations. We propose a new approach for holistic 3D scene understanding from a single RGB image which learns to lift and propagate 2D features from an input image to a 3D volumetric scene representation. Our panoptic 3D reconstruction metric evaluates both geometric reconstruction quality as well as panoptic segmentation. Our experiments demonstrate that our approach for panoptic 3D scene reconstruction outperforms alternative approaches for this task
RELATED PAPERS
📚 Panoptic Segmentation: https://bit.ly/3vd1FZd
📚MeshCNN: https://bit.ly/3M2lWH6
📚Total3DUnderstanding: https://bit.ly/36yH9bf
LINKS AND RESOURCES
💻 Project Page: https://bit.ly/3JT2Dy1
💻 CODE: https://github.com/xheon/panoptic-reconstruction
🤐Paper's peer review: https://bit.ly/3Cij44t
To stay up to date with Manuel's latest research, check out his personal page and follow him on:
👨🎓Google Scholar: https://scholar.google.com/citations?user=eNypkO0AAAAJ
🐦Twitter: https://twitter.com/manuel_dahnert
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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