Paper
28 July 2023 Part-level single-view 3D shape reconstruction with multiple types of primitives
Author Affiliations +
Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274902 (2023) https://doi.org/10.1117/12.2688290
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France
Abstract
In recent years, various methods have been proposed for reconstructing the 3D shape of an object from a single view image. While methods that reconstruct the object as a single model show promising results, they often lack part-level details. On the other hand, part-level reconstruction methods provide recognition of parts but struggle to represent detailed shapes due to the use of a single primitive. To address this issue, this paper proposes a Compositionally Generalizable 3D Structure Prediction Network using Multiple Types of Primitives (CompNet-MTP). CompNet-MTP first estimates the parameters of each type of primitive for every part and then selects the appropriate primitive type to construct the 3D shape of the object. In the experiments, we used cylinders in addition to cuboids, which are commonly used as primitive shapes. Experimental results confirm the effectiveness of the proposed network in handling multiple types of primitives.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mami Kikuchi, Seiya Ito, Naoshi Kaneko, and Kazuhiko Sumi "Part-level single-view 3D shape reconstruction with multiple types of primitives", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274902 (28 July 2023); https://doi.org/10.1117/12.2688290
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KEYWORDS
3D modeling

Education and training

Image segmentation

Network architectures

3D mask effects

3D image processing

3D image reconstruction

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