Among the most important prerequisites for creating and evaluating 6D object
pose detectors are datasets with labeled 6D poses. With the advent of deep
learning, demand for such datasets is growing continuously. Despite the fact
that some of exist, they are scarce and typically have restricted setups, such
as a single object per sequence, or they focus on specific object types, such
as textureless industrial parts. Besides, two significant components are often
ignored: training using only available 3D models instead of real data and
scalability, i.e. training one method to detect all objects rather than
training one detector per object. Other challenges, such as occlusions,
changing light conditions and changes in object appearance, as well precisely
defined benchmarks are either not present or are scattered among different
datasets. In this paper we present a dataset for 6D pose estimation that covers
the above-mentioned challenges, mainly targeting training from 3D models (both
textured and textureless), scalability, occlusions, and changes in light
conditions and object appearance. The dataset features 33 objects (17 toy, 8
household and 8 industry-relevant objects) over 13 scenes of various
difficulty. We also present a set of benchmarks to test various desired
detector properties, particularly focusing on scalability with respect to the
number of objects and resistance to changing light conditions, occlusions and
clutter. We also set a baseline for the presented benchmarks using a
state-of-the-art DPOD detector. Considering the difficulty of making such
datasets, we plan to release the code allowing other researchers to extend this
dataset or make their own datasets in the future.

本文介绍了一个新的数据集和基准测试标准，主要针对从 3D 模型（有纹理和无纹理）训练，可扩展性，遮挡，光照和物体外观的变化等方面测试 6D 姿态估计算法的性能，并使用最先进的 DPOD 检测器来设置基线。