CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

Vladislav Ishimtsev1Alexey Bokhovkin1Alexey Artemov1Savva Ignatyev1Matthias Nie├čner2Denis Zorin3, 1Evgeny Burnaev1

1Skolkovo Institute of Science and Technology2Technical University of Munich3New York University

European Conference on Computer Vision 2020

CAD-Deform takes as input a set of 3D CAD models aligned on a RGB-D scan (left). In order to achieve tight fits (middle), we propose a novel part-based deformation formulation that maintains the desired CAD properties such as sharp features.


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, 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.




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