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Depth-SIMS: Semi-Parametric Image and Depth Synthesis

arXiv 2203.03405 ↗First posted 7 Mar 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 7 Mar 2022.

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27 of 33 references.

References · 27

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15 more
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