3D Surface Reconstruction with Enhanced High-Frequency Details

TL;DR

This paper introduces FreNeuS, a neural implicit 3D reconstruction method that leverages high-frequency information to improve surface detail accuracy, overcoming the smoothness limitations of existing approaches.

Contribution

FreNeuS uses pixel gradient changes to identify high-frequency regions and guides dynamic sampling and weighting to enhance surface detail reconstruction.

Findings

Reconstructs finer surface details than previous methods.

Achieves higher quantitative surface reconstruction quality.

Generalizes well to other NeuS-based frameworks.

Abstract

Neural implicit 3D reconstruction can reproduce shapes without 3D supervision, and it learns the 3D scene through volume rendering methods and neural implicit representations. Current neural surface reconstruction methods tend to randomly sample the entire image, making it difficult to learn high-frequency details on the surface, and thus the reconstruction results tend to be too smooth. We designed a method (FreNeuS) based on high-frequency information to solve the problem of insufficient surface detail. Specifically, FreNeuS uses pixel gradient changes to easily acquire high-frequency regions in an image and uses the obtained high-frequency information to guide surface detail reconstruction. High-frequency information is first used to guide the dynamic sampling of rays, applying different sampling strategies according to variations in high-frequency regions. To further enhance the focus on surface details, we have designed a high-frequency weighting method that constrains the representation of high-frequency details during the reconstruction process. Qualitative and quantitative results show that our method can reconstruct fine surface details and obtain better surface reconstruction quality compared to existing methods. In addition, our method is more applicable and can be generalized to any NeuS-based work.