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

Pengyuan Wang, HyunJun Jung, Yitong Li, Siyuan Shen, Rahul Parthasarathy Srikanth, Lorenzo Garattoni, Sven Meier, Nassir Navab, Benjamin Busam

CVPR 2022 pp. 21222-21231

doi:10.1109/CVPR52688.2022.02054 /cvpr/2022/wang2022cvpr-phocal/

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.

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Cite

Text

Wang et al. "PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.02054

Markdown

[Wang et al. "PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/wang2022cvpr-phocal/) doi:10.1109/CVPR52688.2022.02054

BibTeX

@inproceedings{wang2022cvpr-phocal,
  title     = {{PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects}},
  author    = {Wang, Pengyuan and Jung, HyunJun and Li, Yitong and Shen, Siyuan and Srikanth, Rahul Parthasarathy and Garattoni, Lorenzo and Meier, Sven and Navab, Nassir and Busam, Benjamin},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2022},
  pages     = {21222-21231},
  doi       = {10.1109/CVPR52688.2022.02054},
  url       = {https://mlanthology.org/cvpr/2022/wang2022cvpr-phocal/}
}