Séminaire LIRIS de James Tompkin, "Scene Reconstruction across the Differentiable Rendering Spectrum"

Nous aurons le plaisir de recevoir James Tompkin (Brown University) pour notre séminaire LIRIS autours de thématiques de reconstruction 3D et rendu differentiable, pour un talk intitulé "Scene Reconstruction across the Differentiable Rendering Spectrum" le Mardi 14 Décembre à 10h en salle C5 du Nautibus.

From 14/12/2021 at 10:00 to 11:00. Nautibus, C5
Informations contact : Nicolas Bonneel. nicolas.bonneel@liris.cnrs.fr.

Scene reconstruction enables applications across visual computing, including media creation and editing, and capturing the real-world for virtual tourism and telecommunication. Advances in differentiable rendering for optimization- and learning-based reconstruction have increased quality but, as in 'forward' rendering, different methods have varying capabilities and computational costs that must be traded off against application needs. I will discuss our recent view reconstruction projects across the differentiable rendering spectrum, covering work on 6DoF video via image-based rendering for VR (visual.cs.brown.edu/matryodshka), depth reconstruction from sparse 3D points using differentiable splatting and diffusion (visual.cs.brown.edu/diffdiffdepth), and integrating time-of-flight imaging for monocular dynamic scene reconstruction (imaging.cs.cmu.edu/torf/). Finally, I will discuss how these trade-offs might inform how we can make differentiable rendering practical.

Short bio:
James Tompkin (www.jamestompkin.com) is an assistant professor of Computer Science at Brown University. His research at the intersection of computer vision, computer graphics, and human-computer interaction helps develop new visual computing tools and experiences. His doctoral work at University College London on large-scale video processing and exploration techniques led to creative exhibition work in the Museum of the Moving Image in New York City. Postdoctoral work at Max-Planck-Institute for Informatics and Harvard University helped create new methods to edit content within images and videos. Recent research has developed new multi-camera reconstruction techniques for light field, 360, and time-of-flight imagery, and has developed new image editing and generation methods through learning explicit structured representations.