New models like VGGT achieve excellent 3D reconstruction results using only neural network components in a feed-forward manner, entirely eschewing optimization. As such, they are credible building blocks for the future foundations of computer vision. However, most 3D reconstruction methods remain limited to static data, even though reconstructing dynamic scenes in 3D is, generally speaking, much more useful. In this talk, I will present Dynamic Point Maps, a principled dynamic extension of the point maps popularized by DuST3R, which encode both 3D geometry and motion, restoring certain invariants. I will also address the problem of data scarcity in 4D vision and demonstrate, through Geo4D, how one can build high-quality 4D reconstruction networks starting from video generators pre-trained on millions of videos. Finally, I will explore the challenge of modeling 4D object categories rather than entire scenes. To this end, I will introduce Dual Point Maps as an alternative to traditional deformable models for capturing the 3D shape and motion of dynamic objects.

As vision has changed from a discipline with potential to one with impact, one great new opportunity is empowering researchers in other areas of inquiry with better data. Over the past five years, I've been doing so with solar physics and evolutionary ecology, transferring what I've learned from 3D vision. Although solar physics and ecology study objects of radically different scales, they are unified by a need for data that is higher volume, higher quality, and is easier to obtain. In this talk, I'll show efforts to this end done in collaboration with domain experts. In solar physics, our work includes systems to estimate the Sun's 3D vector magnetic field from multiple signals, which inform our understanding of a key driver of space weather. In evolutionary ecology, our collaborations have built one of the largest repositories of bird morphology, which has helped understand drivers of evolution. Throughout, I'll talk about some of our applications and lessons I've learned.