A Comparative Study of Neural Surface Reconstruction for Scientific Visualization

TL;DR

This study compares neural surface reconstruction methods for scientific visualization, highlighting the advantages of distance functions and benchmarking NeuS2 and NeUDF for different surface types.

Contribution

It provides a comprehensive benchmark of ten neural surface reconstruction methods, emphasizing the role of distance functions and sharing a dataset for future research.

Findings

NeuS2 excels in reconstructing closed surfaces.

NeUDF shows promise for open surface reconstruction.

Benchmark dataset facilitates method comparison.

Abstract

This comparative study evaluates various neural surface reconstruction methods, particularly focusing on their implications for scientific visualization through reconstructing 3D surfaces via multi-view rendering images. We categorize ten methods into neural radiance fields and neural implicit surfaces, uncovering the benefits of leveraging distance functions (i.e., SDFs and UDFs) to enhance the accuracy and smoothness of the reconstructed surfaces. Our findings highlight the efficiency and quality of NeuS2 for reconstructing closed surfaces and identify NeUDF as a promising candidate for reconstructing open surfaces despite some limitations. By sharing our benchmark dataset, we invite researchers to test the performance of their methods, contributing to the advancement of surface reconstruction solutions for scientific visualization.