Comparative analysis of surface reconstruction from gradient data

Abstract

Gradient-based surface reconstruction has emerged as a research field in computer vision, finding applications across various domains. This thesis aims to delve into the current state-of-the-art techniques utilized in gradient-based surface reconstruction. It has been observed that subtle variations in surface gradient can produce significant effects on the output of these reconstruction algorithms. Therefore, this study explores the influence of different surface features and parameters on various surface reconstruction techniques. Nonetheless, comparative studies to comprehensively understand the performance of different gradient-based surface reconstruction methods under similar scenarios are limited. To bridge this gap, this thesis analyzes the state-of-the-art techniques used in gradient-based surface reconstruction. We selected five representative methods and designed experiments that simulate diverse surface conditions, with the goal of measuring their robustness and efficacy. This review thesis will aid researchers in selecting the most suitable gradient-based surface reconstruction method for their specific applications, since we present comprehensive analyses highlighting the impact of various surface conditions and parameters on the output of the selected reconstruction techniques.