Skip to yearly menu bar Skip to main content


Poster

A Probability-guided Sampler for Neural Implicit Surface Rendering

Gonçalo José Dias Pais · Valter André Piedade · Moitreya Chatterjee · Marcus Greiff · Pedro Miraldo

# 323
Strong blind review: This paper was not made available on public preprint services during the review process Strong Double Blind
[ ] [ Paper PDF ]
Thu 3 Oct 1:30 a.m. PDT — 3:30 a.m. PDT

Abstract:

Several variants of Neural Radiance Fields (NeRFs) have significantly improved the accuracy of synthesized images and surface reconstruction of 3D scenes/objects. In all of these methods, a key characteristic is that none can train the neural network with every possible input data, specifically, every pixel and potential 3D point along the projection rays due to scalability issues. While vanilla NeRFs uniformly sample both the image pixels and 3D points along the projection rays, some variants focus only on guiding the sampling of the 3D points along the projection rays. In this paper, we leverage the implicit surface representation of the foreground scene and model a probability density function in a 3D image projection space to achieve a more targeted sampling of the rays toward regions of interest, resulting in improved rendering. Additionally, a new surface reconstruction loss is proposed for improved performance. This new loss fully explores the proposed 3D image projection space model and incorporates near-to-surface and empty space components. By integrating our novel sampling strategy and novel loss into any current state-of-the-art neural implicit surface renderer, we achieve more accurate and detailed 3D reconstructions and improved image rendering, especially for the regions of interest in any given scene. The code will be made available.

Live content is unavailable. Log in and register to view live content