  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.
