HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios

Jung HJ, Wu SC, Ruhkamp P, Zhai G, Schieber H, Rizzoli G, Wang P, Zhao H, Garattoni L, Roth D, Meier S, Navab N, Busam B (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Pages Range: 22498-22508

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: Seattle, WA, USA

ISBN: 9798350353006

DOI: 10.1109/CVPR52733.2024.02123

Abstract

Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation for practical applications. Current category-level datasets, however, fall short in annotation quality and pose variety. Addressing this, we introduce HouseCat6D, a new category-level 6D pose dataset. It features 1) multi-modality with Polarimetric RGB and Depth (RGBD+P), 2) encompasses 194 diverse objects across 10 household cat-egories, including two photometrically challenging ones, and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive view-point and occlusion coverage,5) a checkerboard-free en-vironment, and 6) dense 6D parallel-jaw robotic grasp annotations. Additionally, we present benchmark results for leading category-level pose estimation networks.

Authors with CRIS profile

Hannah Schieber Professur für Image Data Exploration and Analysis

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) University of Padua / Universita degli Studi di Padova IT Italy (IT) Toyota Motor Corporation / トヨタ自動車株式会社 JP Japan (JP)

How to cite

APA:

Jung, H.J., Wu, S.C., Ruhkamp, P., Zhai, G., Schieber, H., Rizzoli, G.,... Busam, B. (2024). HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 22498-22508). Seattle, WA, USA: IEEE Computer Society.

MLA:

Jung, Hyun Jun, et al. "HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios." Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA IEEE Computer Society, 2024. 22498-22508.

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