  Recently, various methods for 6D pose and shape estimation of objects at a
per-category level have been proposed. This work provides an overview of the
field in terms of methods, datasets, and evaluation protocols. First, an
overview of existing works and their commonalities and differences is provided.
Second, we take a critical look at the predominant evaluation protocol,
including metrics and datasets. Based on the findings, 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. The results indicate that
existing methods do not generalize well to unconstrained orientations and are
actually heavily biased towards objects being upright. We provide an
easy-to-use evaluation toolbox with well-defined metrics, methods, and dataset
interfaces, which allows evaluation and comparison with various
state-of-the-art approaches
(https://github.com/roym899/pose_and_shape_evaluation).
