  Recently, various methods for 6D pose and shape estimation of objects have
been proposed. Typically, these methods evaluate their pose estimation in terms
of average precision, and reconstruction quality with chamfer distance. In this
work we take a critical look at this predominant evaluation protocol including
metrics and datasets. We propose a new set of metrics, contribute new
annotations for the Redwood dataset and evaluate state-of-the-art methods in a
fair comparison. We find that existing methods do not generalize well to
unconstrained orientations, and are actually heavily biased towards objects
being upright. We contribute an easy-to-use evaluation toolbox with
well-defined metrics, method and dataset interfaces, which readily allows
evaluation and comparison with various state-of-the-art approaches (see
https://github.com/roym899/pose_and_shape_evaluation ).
