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.

本研究旨在解决3D物体检测与姿态估计的评估问题，通过自动技术生产全注释RGBD数据的多个对象，并提出一致的评估方法和数据集，以获得更高的性能表现。

多零件场景下对称感知的三维物体检测和位姿估计评估