3D Shape representation has substantial effects on 3D shape reconstruction. Primitive-based representations approximate 3D shape mainly by a set of simple implicit primitives, but the low flexibility of the primitives limit their outputs resolutions. Moreover, setting a sufficient number of primitives for an arbitrary shape is challenging. To overcome these issues, we propose ...
@InProceedings{Yavartanoo_2021_ICCV,
author = {Yavartanoo, Mohsen and Chung, Jaeyoung and Neshatavar, Reyhaneh and Lee, Kyoung Mu},
title = {3DIAS: 3D Shape Reconstruction With Implicit Algebraic Surfaces},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {12446-12455}}
We propose a constrained implicit algebraic surface as the primitive with few learnable coefficients and higher geometrical complexities and a deep neural network to produce these primitives.
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Qualitative comparison on single RGB image 3D shape reconstruction. SIF, AtlasNet, OccNet, CvxNet, and our 3DIAS output reconstructed 3D shape from the given RGB image. Comparison with other methods for the samples shown in CvxNet.
Qualitative results on unsupervised semantic segmentation. We visualize the results of 3DIAS for some samples in the category of airplane.
The complexity of our primitives. The first and the second rows show the reconstructed shapes and their corresponding primitives for two samples. The proposed primitive can effectively present curved and torus shapes.
Ph.D. Candidate
Department of Electrical and Computer Engineering College of Engineering, Seoul National University.
Ph.D. Student
Department of Electrical and Computer Engineering College of Engineering, Seoul National University.
Ph.D. Student
Department of Electrical and Computer Engineering College of Engineering, Seoul National University.
Professor, Ph.D.
Department of Electrical and Computer Engineering College of Engineering, Seoul National University.