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Ashwath shetty

shettyashwath2010@gmail.com
PhD - Université de Montréal
Supervisor
Noam Aigerman
Research Topics
Geometry Processing
GitHub

Publications

Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes
Ashwath shetty
Zihan Zhu
Soeren Pirk
Noam Aigerman
This work proposes an adaptation of the attention mechanism for triangle meshes. The core observation is that endowing the attention mechani… (see more)sm with critical properties for learning over meshes -- intrinsicality and triangulation-agnosticism -- enables it to attain state-of-the-art results over several learning-based tasks in geometry-processing. The above is achieved by modifying the attention mechanism from the bottom up based on simple principles from geometry-processing. Namely, the quantities used within attention -- queries, keys and values -- are created by an intrinsic, triangulation-agnostic network, and treated as discretizations of continuous functions. From that, we devise an appropriate attention mechanism that operates over triangle meshes through standard FEM discretization of the resulting integrals of the above functions. Surprisingly, as far as we know, this straightforward approach has not been utilized for learning over meshes. Experiments show our method exceeds current state of the art, including both mesh-based architectures as well as point cloud transformers. Namely, we show significant improvements on several common benchmarks and tasks -- predicting canonical high-frequency signals; predicting deformations; computing dense correspondences, both between full shapes and partial ones; and predicting feature descriptors.
2026-07-26
arXiv (preprint)
doi.org
arxiv.org