103 citations · 113 across the 6 of their papers we have counts for
6 papers
Meshtryoshka: Differentiable Rendering of Real-World Scenes via Mesh Rasterization
David Charatan, Daniel Xu, Richard Szeliski +2
Differentiable rendering has emerged as a powerful approach for 3D reconstruction and novel view synthesis. State-of-the-art differentiable rendering methods combine a variety of c…
True Self-Supervised Novel View Synthesis is Transferable
Thomas W. Mitchel, Hyunwoo Ryu, Vincent Sitzmann
In this paper, we identify that the key criterion for determining whether a model is truly capable of novel view synthesis (NVS) is transferability: Whether any pose representation…
Neural Isometries: Taming Transformations for Equivariant ML
Thomas W. Mitchel, Michael Taylor, Vincent Sitzmann
Real-world geometry and 3D vision tasks are replete with challenging symmetries that defy tractable analytical expression. In this paper, we introduce Neural Isometries, an autoenc…
pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction
David Charatan, Sizhe Li, Andrea Tagliasacchi +1
We introduce pixelSplat, a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-ti…
Decomposing NeRF for Editing via Feature Field Distillation
Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann
Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image obser…
Deep Medial Fields
Daniel Rebain, Ke Li, Vincent Sitzmann +3
Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form.…