11 citations · 23 across the 8 of their papers we have counts for
8 papers
Dark3R: Learning Structure from Motion in the Dark
Andrew Y Guo, Anagh Malik, SaiKiran Tedla +7
We introduce Dark3R, a framework for structure from motion in the dark that operates directly on raw images with signal-to-noise ratios (SNRs) below dB -- a regime where conve…
Neural Inverse Rendering from Propagating Light
Anagh Malik, Benjamin Attal, Andrew Xie +2
We present the first system for physically based, neural inverse rendering from multi-viewpoint videos of propagating light. Our approach relies on a time-resolved extension of neu…
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering
Benjamin Attal, Dor Verbin, Ben Mildenhall +4
State-of-the-art techniques for 3D reconstruction are largely based on volumetric scene representations, which require sampling multiple points to compute the color arriving along…
NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
Dor Verbin, Pratul P. Srinivasan, Peter Hedman +4
Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have im…
HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling
Benjamin Attal, Jia-Bin Huang, Christian Richardt +4
Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering…
Learning Neural Light Fields with Ray-Space Embedding Networks
Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer +2
Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results. However, they are slow to render, requiring hundreds of network evaluations per pixel to approximate…
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan +4
Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular vid…
MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
Benjamin Attal, Selena Ling, Aaron Gokaslan +2
We introduce a method to convert stereo 360° (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 3…