ABSTRACT
We present a novel self-supervised framework for learning the discretization-agnostic surface parameterization of arbitrary 3D objects with both bounded and unbounded surfaces. Our framework leverages diffusion-enabled global-to-local shape context for each vertex first to partition the unbounded surface into multiple patches using the proposed self-supervised PatchNet and subsequently perform independent UV parameterization of these patches by learning forward and backward UV mapping for individual patches. Thus, our framework enables learning a discretization-agnostic parameterization at a lower resolution and then directly inferring the parameterization for a higher-resolution mesh without retraining. We evaluate our framework on multiple 3D objects from the publicly available SHREC [Lian et al. 2011] dataset and report superior/faster UV parameterization over conventional methods.
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- Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, and Thibault Groueix. 2022. Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes.Google Scholar
- Bruno Lévy, Sylvain Petitjean, Nicolas Ray, and Jérome Maillot. 2002. Least Squares Conformal Maps for Automatic Texture Atlas Generation. ACM Trans. Graph. (2002).Google Scholar
- Minchen Li, Danny M. Kaufman, Vladimir G. Kim, Justin Solomon, and Alla Sheffer. 2018. OptCuts: Joint Optimization of Surface Cuts and Parameterization. ACM Transactions on Graphics 37, 6 (2018).Google ScholarDigital Library
- Zhouhui Lian, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans, Shun Kawamura, Yukinori Kurita, Guillaume Lavoué, Hien Nguyen, Ryutarou Ohbuchi, Yuki Ohkita, Yuya Ohishi, Fatih Porikli, Martin Reuter, Ivan Sipiran, Dirk Smeets, Paul Suetens, Hedi Tabia, and Dirk Vandermeulen. 2011. SHREC ’11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes.Eurographics Workshop on 3D Object Retrieval (01 2011), 79–88.Google Scholar
- Pedro V. Sander, John Snyder, Steven J. Gortler, and Hugues Hoppe. 2001. Texture Mapping Progressive Meshes. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques(SIGGRAPH ’01). Association for Computing Machinery, New York, NY, USA, 409–416.Google ScholarDigital Library
- Rohan Sawhney and Keenan Crane. 2017. Boundary First Flattening. ACM Trans. Graph. (2017).Google Scholar
- Nicholas Sharp, Souhaib Attaiki, Keenan Crane, and Maks Ovsjanikov. 2020. DiffusionNet: Discretization Agnostic Learning on Surfaces. (2020).Google Scholar
- He Wang, Kirill A. Sidorov, Peter Sandilands, and Taku Komura. 2013. Harmonic Parameterization by Electrostatics. ACM Trans. Graph. (2013).Google Scholar
Index Terms
- Discretization-Agnostic Deep Self-Supervised 3D Surface Parameterization
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