  In robotic bin-picking applications, the perception of texture-less, highly
reflective parts is a valuable but challenging task. The high glossiness can
introduce fake edges in RGB images and inaccurate depth measurements especially
in heavily cluttered bin scenario. In this paper, we present the ROBI
(Reflective Objects in BIns) dataset, a public dataset for 6D object pose
estimation and multi-view depth fusion in robotic bin-picking scenarios. The
ROBI dataset includes a total of 63 bin-picking scenes captured with two active
stereo camera: a high-cost Ensenso sensor and a low-cost RealSense sensor. For
each scene, the monochrome/RGB images and depth maps are captured from sampled
view spheres around the scene, and are annotated with accurate 6D poses of
visible objects and an associated visibility score. For evaluating the
performance of depth fusion, we captured the ground truth depth maps by
high-cost Ensenso camera with objects coated in anti-reflective scanning spray.
To show the utility of the dataset, we evaluated the representative algorithms
of 6D object pose estimation and multi-view depth fusion on the full dataset.
Evaluation results demonstrate the difficulty of highly reflective objects,
especially in difficult cases due to the degradation of depth data quality,
severe occlusions and cluttered scene. The ROBI dataset is available online at
https://www.trailab.utias.utoronto.ca/robi.
