论文标题
深度SIMS：半参数图像和深度合成
Depth-SIMS: Semi-Parametric Image and Depth Synthesis
论文作者
论文摘要
在本文中，我们提出了一种合成图像合成方法，该方法将生成带有良好比较的分割图和稀疏深度图的RGB画布，并与镶嵌网络相结合，将RGB帆布转换为高质量的RGB图像，并将稀疏深度图转换为像素范围的密度深度映射。我们根据结构对齐和图像质量进行基准测试方法，显示MIOU对SOTA的增加增加了3.7个百分点，并且具有竞争力的FID。此外，我们将生成数据的质量分析为语义细分和深度完成的培训数据，并证明我们的方法比其他方法更适合于此。
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.
