1 citations · 2 across the 8 of their papers we have counts for
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Symmetry Matters: Auditing and Symmetrizing 3D Generative Models
Nicolas Caytuiro, Ivan Sipiran
Symmetry is a strong prior present in many object categories, yet standard benchmarks for 3D generative models rarely report whether this prior is preserved. We study symmetry pres…
Symmetria: A Synthetic Dataset for Learning in Point Clouds
Ivan Sipiran, Gustavo Santelices, Lucas Oyarzún +4
Unlike image or text domains that benefit from an abundance of large-scale datasets, point cloud learning techniques frequently encounter limitations due to the scarcity of extensi…
3D Shape Generation: A Survey
Nicolas Caytuiro, Ivan Sipiran
Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects…
Training-free zero-shot 3D symmetry detection with visual features back-projected to geometry
Isaac Aguirre, Ivan Sipiran
We present a simple yet effective training-free approach for zero-shot 3D symmetry detection that leverages visual features from foundation vision models such as DINOv2. Our method…
A dataset-free approach for self-supervised learning of 3D reflectional symmetries
Isaac Aguirre, Ivan Sipiran, Gabriel Montañana
In this paper, we explore a self-supervised model that learns to detect the symmetry of a single object without requiring a dataset-relying solely on the input object itself. We hy…
Cultural Heritage 3D Reconstruction with Diffusion Networks
Pablo Jaramillo, Ivan Sipiran
This article explores the use of recent generative AI algorithms for repairing cultural heritage objects, leveraging a conditional diffusion model designed to reconstruct 3D point…