Summary of Depth Prompting For Sensor-agnostic Depth Estimation, by Jin-hwi Park et al.
Depth Prompting for Sensor-Agnostic Depth Estimation
by Jin-Hwi Park, Chanhwi Jeong, Junoh Lee, Hae-Gon Jeon
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 Prompt module is designed to mitigate systematic measurement biases in dense depth maps, allowing for more accurate and generalizable monocular depth estimation. The approach disentangles input modalities (e.g., images and depth) to create a joint representation that is robust to biases. By embedding the depth prompt into foundation models, the method enables absolute scale depth maps without being restrained by depth scan range limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a way to make dense depth maps better by reducing errors caused by how we take measurements. They created a new module called Depth Prompt that helps machines understand what they’re seeing in different situations. This makes it easier for computers to estimate distances and create accurate 3D models. The team tested their method and found it works well. |
Keywords
» Artificial intelligence » Depth estimation » Embedding » Prompt
