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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Since being proposed, Neural Radiance Fields (NeRF) have achieved greatsuccess in related tasks, mainly adopting the hierarchical volume sampling(HVS) strategy for volume rendering. However, the HVS of NeRF approximatesdistributions using piecewise constant functions, which provides a relativelyrough estimation. Based on the observation that a well-trained weight function$w(t)$ and the $L_0$ distance between points and the surface have very highsimilarity, we propose $L_0$-Sampler by incorporating the $L_0$ model into$w(t)$ to guide the sampling process. Specifically, we propose to use piecewiseexponential functions rather than piecewise constant functions forinterpolation, which can not only approximate quasi-$L_0$ weight distributionsalong rays quite well but also can be easily implemented with few lines of codewithout additional computational burden. Stable performance improvements can beachieved by applying $L_0$-Sampler to NeRF and its related tasks like 3Dreconstruction. Code is available at https://ustc3dv.github.io/L0-Sampler/ .

$L_0$-Sampler: An $L_{0}$ Model Guided Volume Sampling for NeRF