SRDA: Generating Instance Segmentation
Annotation Via Scanning, Reasoning And
Domain Adaptation
Abstract. Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D
scanning, reasoning, and GAN-based domain adaptation techniques, we
introduce a novel pipeline named SRDA to obtain large quantities of
training samples with very minor effort. Our pipeline is well-suited to
scenes that can be scanned, i.e. most indoor and some outdoor scenarios. To evaluate our performance, we build three representative scenes
and a new dataset, with 3D models of various common objects categories
and annotated real-world scene images. Extensive experiments show that
our pipeline can achieve decent instance segmentation performance given
very low human labor cost