Datasets › PhoCAL
PhoCAL
Object pose estimation is crucial for robotic applications and augmented reality. 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.
Benchmarks archive 2025-07-28
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Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 6 papers for it but never published that list.
Dataset loaders archive 2025-07-28
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Tasks archive 2025-07-28
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License archive 2025-07-28
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Modalities archive 2025-07-28
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Languages archive 2025-07-28
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Variants archive 2025-07-28
- PhoCAL
1 variant name, as the archive lists them.
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