Summary of Sgformer: Spherical Geometry Transformer For 360 Depth Estimation, by Junsong Zhang et al.
SGFormer: Spherical Geometry Transformer for 360 Depth Estimation
by Junsong Zhang, Zisong Chen, Chunyu Lin, Lang Nie, Zhijie Shen, Kang Liao, Junda Huang, Yao Zhao
First submitted to arxiv on: 23 Apr 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 SGFormer model addresses panoramic distortion in 360-degree depth estimation by integrating spherical geometric priors into vision transformers. The approach uses a spherical prior decoder (SPDecoder) to preserve equidistortion and continuity, leveraging techniques like bipolar re-projection, circular rotation, and curve local embedding. Additionally, the query-based global conditional position embedding compensates for spatial structure at varying resolutions, enhancing global perception and depth structure. Extensive experiments on popular benchmarks demonstrate superiority over state-of-the-art solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to estimate depth in 360-degree images. Currently, this task is challenging because the image distorts as you move towards the top or bottom. The authors develop a new model called SGFormer that uses information about the shape of the Earth (spherical geometry) to improve depth estimation. They create a special part of the model called SPDecoder that helps preserve the correct shapes and distances in the image. This improves both global perception (understanding of the whole scene) and local details. The authors test their approach on various datasets and show it performs better than existing methods. |
Keywords
» Artificial intelligence » Decoder » Depth estimation » Embedding
