Summary of Sm4depth: Seamless Monocular Metric Depth Estimation Across Multiple Cameras and Scenes by One Model, By Yihao Liu and Feng Xue and Anlong Ming and Mingshuai Zhao and Huadong Ma and Nicu Sebe
SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model
by Yihao Liu, Feng Xue, Anlong Ming, Mingshuai Zhao, Huadong Ma, Nicu Sebe
First submitted to arxiv on: 13 Mar 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 This research presents SM^4Depth, a novel approach to universal monocular metric depth estimation that addresses the limitations of current methods. The proposed model utilizes variation-based unnormalized depth bins to reduce ambiguity and improve adaptation to diverse scenes. Additionally, a “divide and conquer” solution is introduced to minimize reliance on large training datasets. The SM^4Depth model achieves outstanding performance on various indoor and outdoor scenes, maintaining consistent accuracy without the need for extensive pre-training or GPU clusters. Evaluation is performed using the BUPT Depth dataset, with the code available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to estimate depth in images that works well indoors and outdoors. The current methods are not very good because they require lots of training data and specific settings for each scene. The new model, called SM^4Depth, uses a different approach to calculate depth that is more accurate and consistent across various scenes. This is achieved by breaking down the problem into smaller parts and solving them separately. The results show that SM^4Depth performs well on unseen data, especially when it comes to indoor and outdoor scenes. |
Keywords
» Artificial intelligence » Depth estimation
