TY - GEN
T1 - CAD-Deform
T2 - 16th European Conference on Computer Vision, ECCV 2020
AU - Ishimtsev, Vladislav
AU - Bokhovkin, Alexey
AU - Artemov, Alexey
AU - Ignatyev, Savva
AU - Niessner, Matthias
AU - Zorin, Denis
AU - Burnaev, Evgeny
N1 - Funding Information:
Acknowledgments. The authors acknowledge the usage of the Skoltech CDISE HPC cluster Zhores for obtaining the results presented in this paper. The work was partially supported by the Russian Science Foundation under Grant 19-41-04109.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios. Unfortunately, CAD model retrieval is limited by the availability of models in standard 3D shape collections (e.g., ShapeNet). In this work, we address this shortcoming by introducing CAD-Deform (The code for the project: https://github.com/alexeybokhovkin/CAD-Deform), a method which obtains more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models. Our key contribution is a new non-rigid deformation model incorporating smooth transformations and preservation of sharp features, that simultaneously achieves very tight fits from CAD models to the 3D scan and maintains the clean, high-quality surface properties of hand-modeled CAD objects. A series of thorough experiments demonstrate that our method achieves significantly tighter scan-to-CAD fits, allowing a more accurate digital replica of the scanned real-world environment while preserving important geometric features present in synthetic CAD environments.
AB - Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios. Unfortunately, CAD model retrieval is limited by the availability of models in standard 3D shape collections (e.g., ShapeNet). In this work, we address this shortcoming by introducing CAD-Deform (The code for the project: https://github.com/alexeybokhovkin/CAD-Deform), a method which obtains more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models. Our key contribution is a new non-rigid deformation model incorporating smooth transformations and preservation of sharp features, that simultaneously achieves very tight fits from CAD models to the 3D scan and maintains the clean, high-quality surface properties of hand-modeled CAD objects. A series of thorough experiments demonstrate that our method achieves significantly tighter scan-to-CAD fits, allowing a more accurate digital replica of the scanned real-world environment while preserving important geometric features present in synthetic CAD environments.
KW - Mesh deformation
KW - Scene reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85097627541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097627541&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58601-0_36
DO - 10.1007/978-3-030-58601-0_36
M3 - Conference contribution
AN - SCOPUS:85097627541
SN - 9783030586003
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 599
EP - 628
BT - Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
A2 - Vedaldi, Andrea
A2 - Bischof, Horst
A2 - Brox, Thomas
A2 - Frahm, Jan-Michael
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 August 2020 through 28 August 2020
ER -