SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation
This addresses the time-consuming and expensive data annotation problem for instance segmentation in computer vision, particularly for indoor and some outdoor scenes, though it appears incremental as it combines existing techniques.
The authors tackled the problem of costly instance segmentation annotation by introducing SRDA, a pipeline combining 3D scanning, reasoning, and GAN-based domain adaptation to generate large training samples with minimal effort, achieving decent performance in experiments on built scenes and a new dataset.
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.