NerfAcc: A General NeRF Acceleration Toolbox
read the original abstract
We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields. We build on the techniques proposed in Instant-NGP, and extend these techniques to not only support bounded static scenes, but also for dynamic scenes and unbounded scenes. NerfAcc comes with a user-friendly Python API, and is ready for plug-and-play acceleration of most NeRFs. Various examples are provided to show how to use this toolbox. Code can be found here: https://github.com/KAIR-BAIR/nerfacc. Note this write-up matches with NerfAcc v0.3.5. For the latest features in NerfAcc, please check out our more recent write-up at arXiv:2305.04966
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
Mind the Gap: Standard 3DGS Evaluation Primarily Measures Near-Trajectory Interpolation
Standard every-Nth-frame holdouts in 3D scene reconstruction primarily measure near-trajectory interpolation, with a consistent 3-12 dB gap to matched-count contiguous spatial holdouts that persists across Gaussian, n...
-
ART: Articulated Reconstruction Transformer
ART is a category-agnostic transformer that maps sparse multi-state RGB images to per-part 3D geometry, texture, and articulation parameters via learnable part slots.
-
Learning a Delighting Prior for Facial Appearance Capture in the Wild
A delighting network trained via Dataset Latent Modulation on heterogeneous OLAT and Light Stage data enables high-quality in-the-wild facial reflectance capture from video and produces the NeRSemble-Scan dataset.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.