Our guest speaker is Vincent Sitzmann from the MIT CSAIL and you are all cordially invited to the CVG Seminar on July 22nd at 4:00 p.m. CET on Zoom (passcode is 566141), where‪ Vincent will give a talk titled “Light Field Networks“.

Abstract

Given only a single picture, people are capable of inferring a mental representation that encodes rich information about the underlying 3D scene. We acquire this skill not through massive labeled datasets of 3D scenes, but through self-supervised observation and interaction. Building machines that can infer similarly rich neural scene representations is critical if they are to one day parallel people’s ability to understand, navigate, and interact with their surroundings. This poses a unique set of challenges that sets neural scene representations apart from conventional representations of 3D scenes: Rendering and processing operations need to be differentiable, and the type of information they encode is unknown a priori, requiring them to be extraordinarily flexible. At the same time, training them without ground-truth 3D supervision is a highly underdetermined problem, highlighting the need for structure and inductive biases without which models converge to spurious explanations. 
Focusing on 3D structure, a fundamental feature of natural scenes, I will demonstrate how we can equip neural networks with inductive biases that enables them to learn 3D geometry, appearance, and even semantic information, self-supervised only from posed images. I will then discuss our recent work overcoming a key limitation of existing 3D-structured neural scene representations, the differentiable ray-marching, by directly parameterizing the 360 degree, 4D light field of 3D scenes.

Bio

Vincent Sitzmann is a Postdoc with Joshua Tenenbaum, William Freeman, and Fredo Durand at MIT CSAIL, and an incoming Assistant Professor, also at MIT. Previously, he finished his PhD at Stanford University. His primary research interests lie in the self-supervised learning of neural representations of 3D scenes, and their applications in computer graphics, computer vision, and robotics.