Bas-relief modelling from enriched detail and geometry with deep normal transfer

Authors: Wang, M., Wang, L., Jiang, T., Xiang, N., Lin, J., Wei, M., Yang, X., Komura, T. and Zhang, J.

Journal: Neurocomputing

Volume: 453

Pages: 825-838

eISSN: 1872-8286

ISSN: 0925-2312

DOI: 10.1016/j.neucom.2020.06.130

Abstract:

Detail-and-geometry richness is essential to bas-relief modelling. However, existing image-based and model-based bas-relief modelling techniques commonly suffer from detail monotony or geometry loss. In this paper, we introduce a new bas-relief modelling framework for detail abundance with visual attention based mask generation and geometry preservation, which benefits from our two key contributions. For detail richness, we propose a novel semantic neural network of normal transfer to enrich the texture styles on bas-reliefs. For geometry preservation, we introduce a normal decomposition scheme based on Domain Transfer Recursive Filter (DTRF). Experimental results demonstrate that our approach is advantageous on producing bas-relief modellings with both fine details and geometry preservation.

https://eprints.bournemouth.ac.uk/34845/

Source: Scopus