Conference item
Depth-SIMS: semi-parametric image and depth synthesis
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 5.9MB, Terms of use)
-
- Publisher copy:
- 10.1109/ICRA46639.2022.9811569
Authors
- Publisher:
- IEEE
- Host title:
- 2022 International Conference on Robotics and Automation (ICRA)
- Pages:
- 2388-2394
- Publication date:
- 2022-07-12
- Acceptance date:
- 2022-01-31
- Event title:
- International Conference on Robotics and Automation (ICRA 2022)
- Event location:
- Philadelphia, USA
- Event website:
- https://www.icra2022.org/
- Event start date:
- 2022-05-23
- Event end date:
- 2022-05-27
- DOI:
- EISBN:
- 9781728196817
- ISBN:
- 9781728196824
- Language:
-
English
- Keywords:
- Pubs id:
-
1242881
- Local pid:
-
pubs:1242881
- Deposit date:
-
2022-03-09
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2022
- Rights statement:
- © 2022 IEEE.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/ICRA46639.2022.9811569
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