Seamless Reconstruction of Part-Based High-Relief Models from Hand-Drawn Images

We present a new approach to reconstruction of high-relief surface models from hand-made drawings. Our method is tailored to an interactive modeling scenario where the input drawing can be separated into a set of semantically meaningful parts of which relative depth order is known beforehand. For this kind of input, our technique allows inflating individual components to have a semi-elliptical profile, positioning them to satisfy prescribed depth order, and providing their seamless interconnection. Compared to previous methods, our approach is the first that formulates this reconstruction process as a single non-linear optimization problem. Because its direct optimization is computationally challenging, we propose an approximate solution which delivers comparable results orders of magnitude faster enabling an interactive user workflow. We evaluate our approach on various hand-made drawings and demonstrate that it provides state-of-the-art quality in comparison with previous methods which require comparable user intervention.

Marek Dvoroznak, Saman Sepehri Nejad, Ondrej Jamriska, Alec Jacobson, Ladislav Kavan, Daniel Sykora. Seamless Reconstruction of Part-Based High-Relief Models from Hand-Drawn Images. Expressive, 2018.  

We would like to thank all anonymous reviewers for their fruitful comments and suggestions.We gratefully acknowledge the support of Activision, Adobe, and hardware donation from NVIDIA Corp. This research has been supported by the Technology Agency of the Czech Republic under research program TE01020415 (V3C – Visual Computing Competence Center), the Grant Agency of the Czech Technical University in Prague, grant No. SGS16/237/OHK3/3T/13 (Research of Modern Computer Graphics Methods), Research Center for Informatics No. CZ.02.1.01/0.0/0.0/16_019/0000765, the Fulbright Commission in the Czech Republic, the NSERC Discovery Grants (RGPIN-2017-05235 and RGPAS-2017-507938), a Canada Research Chair award, the Connaught Fund, and the National Science Foundation under Grant Numbers IIS-1617172 and IIS-1622360. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.