Summary of Depth-guided Texture Diffusion For Image Semantic Segmentation, by Wei Sun et al.
Depth-guided Texture Diffusion for Image Semantic Segmentation
by Wei Sun, Yuan Li, Qixiang Ye, Jianbin Jiao, Yanzhao Zhou
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Depth-guided Texture Diffusion approach tackles the modality gap between depth and vision information to enhance semantic segmentation tasks. The method extracts low-level features from edges and textures to create a texture image, which is selectively diffused across the depth map to enrich structural information. This enriched depth map is then combined with the original RGB image for joint feature embedding, bridging the disparity between depth and image. The approach is evaluated on various datasets, including COD, SOD, and indoor semantic segmentation, achieving new state-of-the-art results when using source-free estimated depth or depth captured by depth cameras. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a method to improve semantic segmentation tasks by combining depth and vision information. It uses texture diffusion to enhance the structure of objects in 3D space, making it easier to segment out objects from images. This approach is tested on different types of datasets and performs better than other methods when using depth information. |
Keywords
» Artificial intelligence » Diffusion » Embedding » Semantic segmentation
