English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Neural Radiance Field (NeRF) is a popular method in data-driven 3Dreconstruction. Given its simplicity and high quality rendering, many NeRFapplications are being developed. However, NeRF's big limitation is its slowspeed. Many attempts are made to speeding up NeRF training and inference,including intricate code-level optimization and caching, use of sophisticateddata structures, and amortization through multi-task and meta learning. In thiswork, we revisit the basic building blocks of NeRF through the lens of classictechniques before NeRF. We propose Voxel-Accelearated NeRF (VaxNeRF),integrating NeRF with visual hull, a classic 3D reconstruction technique onlyrequiring binary foreground-background pixel labels per image. Visual hull,which can be optimized in about 10 seconds, can provide coarse in-out fieldseparation to omit substantial amounts of network evaluations in NeRF. Weprovide a clean fully-pythonic, JAX-based implementation on the popular JaxNeRFcodebase, consisting of only about 30 lines of code changes and a modularvisual hull subroutine, and achieve about 2-8x faster learning on top of thehighly-performative JaxNeRF baseline with zero degradation in renderingquality. With sufficient compute, this effectively brings down full NeRFtraining from hours to 30 minutes. We hope VaxNeRF -- a careful combination ofa classic technique with a deep method (that arguably replaced it) -- canempower and accelerate new NeRF extensions and applications, with itssimplicity, portability, and reliable performance gains. Codes are available athttps://github.com/naruya/VaxNeRF .

VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field