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CDISE ADASE group researchers were granted the Geometry Processing Dataset Award

CDISE ADASE group researchers Albert Matveev (PhD student), Alexey Artemov (research scientist), and Prof. Evgeny Burnaev were granted the Geometry Processing Dataset Award at the Eurographics Symposium on Geometry Processing (SGP) for their research on ABC Dataset for Geometric Machine Learning, performed in collaboration with their colleagues from Technical University of Berlin and New York University. The SGP took place in Milan, Italy, on July 8-10 and is the premier venue for advancements in Geometry Processing.

SGP Dataset Award distinguishes authors of top-quality datasets and benchmarks provided to the community of Geometry Processing as testbeds for present and future algorithms.

The paper published at the IEEE CVPR 2019 conference introduces ABC Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for the dataset, the authors performed a large-scale benchmark for the estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.

This research is a result of the collaboration of CDISE researchers with their colleagues from TU Berlin (Sebastian Koch, Marc Alexa) and NYU (Zhongshi Jiang, Francis Williams, Denis Zorin, and Daniele Panozzo).

sgp2019_dataset_award

References:

  1. ABC: A Big CAD Model Dataset for Geometric Deep Learning  Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9601-9611

http://openaccess.thecvf.com/content_CVPR_2019/html/Koch_ABC_A_Big_CAD_Model_Dataset_for_Geometric_Deep_Learning_CVPR_2019_paper.html

 

  1. ABC Dataset A Big CAD Model Dataset For Geometric Deep Learning 

https://deep-geometry.github.io/abc-dataset/

 

  1. Symposium on Geometry Processing 2019 

https://sgp2019.di.unimi.it

 

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