17 citations · 24 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
InterNeRF: Scaling Radiance Fields via Parameter Interpolation
Clinton Wang, Peter Hedman, Polina Golland +2
Neural Radiance Fields (NeRFs) have unmatched fidelity on large, real-world scenes. A common approach for scaling NeRFs is to partition the scene into regions, each of which is ass…
Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
Christian Reiser, Stephan Garbin, Pratul P. Srinivasan +6
While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensi…
MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
Christian Reiser, Richard Szeliski, Dor Verbin +5
Neural radiance fields enable state-of-the-art photorealistic view synthesis. However, existing radiance field representations are either too compute-intensive for real-time render…
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
Lior Yariv, Peter Hedman, Christian Reiser +5
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural…
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Dor Verbin, Peter Hedman, Ben Mildenhall +3
Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provid…