13 citations · 25 across the 12 of their papers we have counts for
14 papers
Geometric Data Science
Olga D Anosova, Vitaliy A Kurlin
This book introduces the new research area of Geometric Data Science, where data can represent any real objects through geometric measurements. The first part of the book focuses o…
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…
SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds
Chiara Romanengo, Andrea Raffo, Silvia Biasotti +17
This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives w…
SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data
Elia Moscoso Thompson, Andrea Ranieri, Silvia Biasotti +6
This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic…
A Convolutional Architecture for 3D Model Embedding
Arniel Labrada, Benjamin Bustos, Ivan Sipiran
During the last years, many advances have been made in tasks like3D model retrieval, 3D model classification, and 3D model segmentation.The typical 3D representations such as point…
A Comprehensive Review of the Video-to-Text Problem
Jesus Perez-Martin, Benjamin Bustos, Silvio Jamil F. Guimarães +3
Research in the Vision and Language area encompasses challenging topics that seek to connect visual and textual information. When the visual information is related to videos, this…
Refinement of Predicted Missing Parts Enhance Point Cloud Completion
Alexis Mendoza, Alexander Apaza, Ivan Sipiran +1
Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural net…
3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network
Renato Hermoza, Ivan Sipiran
We introduce a data-driven approach to aid the repairing and conservation of archaeological objects: ORGAN, an object reconstruction generative adversarial network (GAN). By using…
SHREC 2011: robust feature detection and description benchmark
E. Boyer, A. M. Bronstein, M. M. Bronstein +15
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 r…