Maeda, RyotaTakayama, KenshiTaketomi, TakafumiChaine, RaphaëlleDeng, ZhigangKim, Min H.2023-10-092023-10-0920231467-8659https://doi.org/10.1111/cgf.14970https://diglib.eg.org:443/handle/10.1111/cgf14970Reconstructing 3D hair is challenging due to its complex micro-scale geometry, and is of essential importance for the efficient creation of high-fidelity virtual humans. Existing hair capture methods based on multi-view stereo tend to generate results that are noisy and inaccurate. In this study, we propose a refinement method for hair geometry by incorporating the gradient of strands into the computation of their position. We formulate a gradient integration strategy for hair strands. We evaluate the performance of our method using a synthetic multi-view dataset containing four hairstyles, and show that our refinement produces more accurate hair geometry. Furthermore, we tested our method with a real image input. Our method produces a plausible result. Our source code is publicly available at https://github.com/elerac/strand_integration.CCS Concepts: Computing methodologies -> Reconstruction; Shape modelingComputing methodologiesReconstructionShape modeling10.1111/cgf.149709 pages