SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation

TL;DR

This paper introduces SRDA, a pipeline combining 3D scanning, reasoning, and domain adaptation to generate large-scale instance segmentation annotations with minimal human effort, especially suited for indoor and outdoor scenes.

Contribution

The novel SRDA pipeline automates instance segmentation annotation creation by integrating 3D scanning, reasoning, and GAN-based domain adaptation techniques.

Findings

Achieves decent segmentation performance with minimal human labeling.

Builds a new dataset with 3D models and annotated images.

Effective for indoor and outdoor scene annotation.

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.