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Part semantic segmentation based on deep learning provides a new insight for accurate vision understanding of noncooperative satellite as well as for further on-orbit servicing tasks like inspection, repair, and close-proximity robotic manipulation. However, carrying out such researches requires a tremendous amount of data, which is extremely hard and expensive in space. Moreover, the manual annotation for fine-grained tasks like segmentation will cost a lot of labor. Thus, in this paper, we present an efficient method of automated synthetic datasets construction for part-level segmentation of non-cooperative satellite, which is capable of generating thousands of multi-source data (RGB image and point cloud) and the corresponding high-quality annotation. Specifically, the Fibonacci lattice is used for multiple viewpoints sampling of the virtual camera to capture RGB-D images. A trick of segmentation of the customized image in HSV color space is applied to get labels automatically. Furthermore, we employ several data augmentation techniques to expand and diversify the datasets, which improves the generalization of the algorithm. Finally, we carry out the case study using the pointnet++ network based on our generated point cloud data, to validate the feasibility and effectiveness of our method.
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