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

arXiv: https://arxiv.org/abs/1801.08839 · HTML: https://mlsanity.com/paper/1801.08839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation](https://mlsanity.com/paper/2508.13975/) (2025)

## Abstract (arXiv)

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.