Depth-SIMS：セグメンテーションと深度を備えた合成データ生成
Depth-SIMS: Semi-Parametric Image and Depth Synthesis
RGB画像・セグメンテーションマップ・疎深度マップを整合させて合成し、インペインティングで高品質画像と密深度に変換する手法を提案。生成データはセマンティックセグメンテーションと深度補完の学習に有効であることを示した。
著者: Valentina Musat, Daniele De Martini, Matthew Gadd, Paul Newman
分類: cs.CV, cs.RO
原文アブストラクト
In this paper we present a compositing image synthesis method that generates RGB canvases with well aligned segmentation maps and sparse depth maps, coupled with an in-painting network that transforms the RGB canvases into high quality RGB images and the sparse depth maps into pixel-wise dense depth maps. We benchmark our method in terms of structural alignment and image quality, showing an increase in mIoU over SOTA by 3.7 percentage points and a highly competitive FID. Furthermore, we analyse the quality of the generated data as training data for semantic segmentation and depth completion, and show that our approach is more suited for this purpose than other methods.
