A Benchmarking Dataset for Performance Evaluation of Automatic Surface Reconstruction Algorithms

Anke Bellmann, Olaf Hellwich, Volker Rodehorst, Ulas Yilmaz

Abstract: Numerous techniques were invented in computer vision and photogrammetry to obtain spatial information from digital images. We intend to describe and improve the performance of these vision techniques by providing test objectives, data, metrics and test protocols. In this paper we propose a comprehensive benchmarking dataset for evaluating a variety of automatic surface reconstruction algorithms (shape-from-X) and a methodology for comparing their results.