A large-scale multi-pose 3D-RGB object database

We present a new RGB-D database for multi-pose object recognition tasks. With the help of a multi-axis rotation framework, we are capable of capturing depth and color data of arbitrary small objects from virtually any viewpoint. In addition, recording is performed in a nearly lossless fashion, avoiding typical bleeding artifacts present in related reference data bases. This contribution presents the main advantages of our setup and contrasts it against other reference data bases. Furthermore, it outlines possible use cases and application scenarios of our data set and is complemented by experiments with standard machine learning techniques used in, e.g., object recognition tasks within the robotics domain. The experiments demonstrate the validity of our data base as they corroborate that viewpoint variance is indeed an important factor to take into account for object detection, which, from our perspective, is sometimes not considered at the required level. Detection accuracy is high if samples can be trained on data taking into account as many viewpoints as possible.