Graphical Abstract
![](https://wqketang.oss-cn-beijing.aliyuncs.com/zip-unzip/zip-460e934a-89cc-4d6f-aa74-7feadd4ee94d/101320TP-2024-3-505/files/101320TP-2024-3-505-gab.jpg?Expires=1725756577&OSSAccessKeyId=STS.NSssK71aLjDMY9SbRgGRGskMT&Signature=tOEYWs1iOoyyydNpzpdhYutg%2FyY%3D&security-token=CAISywJ1q6Ft5B2yfSjIr5DGOPGD3L5t3YamWx%2FihlIyS91rnK7mtjz2IHtKenhsBOsbtfk1mG5W5%2FgZlqJ9SptIAEfJa9d99Myjb%2F9FltCT1fau5Jko1beHewHKeTOZsebWZ%2BLmNqC%2FHt6md1HDkAJq3LL%2Bbk%2FMdle5MJqP%2B%2FUFB5ZtKWveVzddA8pMLQZPsdITMWCrVcygKRn3mGHdfiEK00he8TouufTinpHMskGA1Aell7Mvyt6vcsT%2BXa5FJ4xiVtq55utye5fa3TRYgxowr%2Fwo0v0YpGya5YzHXwcPskvdKZbo78UqLQlla6w%2BGqFJqvPxr%2Fp8t%2Fx5fWJKAezhVgs8cVM8JOjIqKOscIsixo262SmqFV55c8Fdm%2BgUooJVgIMhTnduUfAPJAGOxzJitP%2BUVGGphr60TEnBL4rB5MUctfzRpwT64j3UTzDnGoABhFRSspTLrn9GrEN9pQYzC6eepGdfWDQh3fr5hA1VnwVUJMCz%2BNQEDWKceF%2FnEjWVMtfbvqYxuyhSCmIMMyh5bEREEKEiD%2BFVv4ZfsqnV7hQy5w6HhIkN8kZzP6L0zPvPgkBLOtdMDeDxhf8dVPrCSG4etXHjxcwUaRVb3ZR7Yu8gAA%3D%3D)
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Existing GAN-based generative methods are typically used for semantic image synthesis. We pose the question of whether GAN-based architectures can generate plausible depth maps and find that existing methods have difficulty in generating depth maps which reasonably represent 3D scene structure due to the lack of global geometric correlations. Thus, we propose DepthGAN, a novel method of generating a depth map using a semantic layout as input to aid construction, and manipulation of well-structured 3D scene point clouds. Specifically, we first build a feature generation model with a cascade of semantically-aware transformer blocks to obtain depth features with global structural information. For our semantically aware transformer block, we propose a mixed attention module and a semantically aware layer normalization module to better exploit semantic consistency for depth features generation. Moreover, we present a novel semantically weighted depth synthesis module, which generates adaptive depth intervals for the current scene. We generate the final depth map by using a weighted combination of semantically aware depth weights for different depth ranges. In this manner, we obtain a more accurate depth map. Extensive experiments on indoor and outdoor datasets demonstrate that DepthGAN achieves superior results both quantitatively and visually for the depth generation task.
249
Views
11
Downloads
0
Crossref
0
Web of Science
0
Scopus
0
CSCD
Altmetrics
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.