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
MobileBrick - Kejie Li
In this episode of the Talking Papers Podcast, I hosted Kejie Li to chat about his CVPR 2023 paper "MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices".
All links are available in the blog post.
In this paper, they proposed a new dataset and paradigm for evaluating 3D object reconstruction. It is very difficult to create a digital twin of 3D objects, even with expensive sensors. They introduce a new RGBD dataset, captured from a mobile device. The nice trick to obtaining the ground truth is that they used LEGO bricks that have an exact CAD model.
Kejie is currently a research scientist at ByteDance/ TikTok. When writing this paper he was a postdoc at Oxford. Prior to this, he successfully obtained his PhD from the University of Adelaide. Although we hadn't crossed paths until this episode, we both have some common ground in our CVs, having been affiliated with different nodes of the ACRV (Adelaide for him and ANU for me). I'm excited to see what he comes up with next, and eagerly await his future endeavours.
AUTHORS
Kejie Li, Jia-Wang Bian, Robert Castle, Philip H.S. Torr, Victor Adrian Prisacariu
ABSTRACT
High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation. However, it is difficult to create a replica of an object in reality, and even 3D reconstructions generated by 3D scanners have artefacts that cause biases in evaluation. To address this issue, we introduce a novel multi-view RGBD dataset captured using a mobile device, which includes highly precise 3D ground-truth annotations for 153 object models featuring a diverse set of 3D structures. We obtain precise 3D ground-truth shape without relying on high-end 3D scanners by utilising LEGO models with known geometry as the 3D structures for image capture. The distinct data modality offered by high-resolution RGB images and low-resolution depth maps captured on a mobile device, when combined with precise 3D geometry annotations, presents a unique opportunity for future research on high-fidelity 3D reconstruction. Furthermore, we evaluate a range of 3D reconstruction algorithms on the proposed dataset.
RELATED PAPERS
📚COLMAP
📚NeRF
📚NeuS
📚CO3D
LINKS AND RESOURCES
📚 Paper
💻Project page
💻Code
SPONSOR
This episode was sponsored by YOOM. YOOM is an Israeli startup dedicated to volumetric video creation. They were voted as the 2022 best start-up to work for by Dun’s 100.
Join their team that works on geometric deep learning research, implicit representations of 3D humans, NeRFs, and 3D/4D generative models.
Visit YOOM
For job opportunities with YOOM visit https://www.yoom.com/careers/
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
This episode was recorded on May 8th, 2023.
#talkingpapers #CVPR2023 #NeRF #Dataset #mobilebrick #ComputerVision #AI #NeuS #DeepLearning #MachineLearning #research #artificialintelligence #podcasts
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