  While 3D object detection and pose estimation has been studied for a long
time, its evaluation is not yet completely satisfactory. Indeed, existing
datasets typically consist in numerous acquisitions of only a few scenes
because of the tediousness of pose annotation, and existing evaluation
protocols cannot handle properly objects with symmetries. This work aims at
addressing those two points. We first present automatic techniques to produce
fully annotated RGBD data of many object instances in arbitrary poses, with
which we produce a dataset of thousands of independent scenes of bulk parts
composed of both real and synthetic images. We then propose a consistent
evaluation methodology suitable for any rigid object, regardless of its
symmetries. We illustrate it with two reference object detection and pose
estimation methods on different objects, and show that incorporating symmetry
considerations into pose estimation methods themselves can lead to significant
performance gains. The proposed dataset is available at
http://rbregier.github.io/dataset2017.
