PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects

Wang P, Jung H, Li Y, Shen S, Srikanth RP, Garattoni L, Meier S, Navab N, Busam B (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

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

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 21190-21199

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

Event location: New Orleans, LA, USA

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.02054

Abstract

Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape has become a promising trend. As such, a new research field needs to be supported by well-designed datasets. To provide a benchmark with high-quality ground truth annotations to the community, we introduce a multimodal dataset for category-level object pose estimation with photometrically challenging objects termed PhoCaL. PhoCaL comprises 60 high quality 3D models of household objects over 8 categories including highly reflective, transparent and symmetric objects. We developed a novel robot-supported multi-modal (RGB, depth, polarisation) data acquisition and annotation process. It ensures sub-millimeter accuracy of the pose for opaque textured, shiny and transparent objects, no motion blur and perfect camera synchronisation. To set a benchmark for our dataset, state-of-the-art RGB-D and monocular RGB methods are evaluated on the challenging scenes of PhoCaL.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Toyota US United States (USA) (US)

How to cite

APA:

Wang, P., Jung, H., Li, Y., Shen, S., Srikanth, R.P., Garattoni, L.,... Busam, B. (2022). PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 21190-21199). New Orleans, LA, USA: IEEE Computer Society.

MLA:

Wang, Pengyuan, et al. "PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 21190-21199.

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