Summary of Marigold-dc: Zero-shot Monocular Depth Completion with Guided Diffusion, by Massimiliano Viola et al.
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
by Massimiliano Viola, Kevin Qu, Nando Metzger, Bingxin Ke, Alexander Becker, Konrad Schindler, Anton Obukhov
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to depth completion is proposed in this paper, which upgrades sparse depth measurements into dense depth maps guided by a conventional image. The method, Marigold-DC, builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance via an optimization scheme that runs in tandem with the iterative inference of denoising diffusion. This technique exhibits excellent zero-shot generalization across diverse environments and effectively handles extremely sparse guidance. The results suggest that contemporary monocular depth priors greatly robustify depth completion, making it more suitable to recover dense depth from image pixels guided by sparse depth rather than inpainting sparse depth guided by an image. The method’s performance is evaluated on various benchmarks, demonstrating its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make dense pictures of the world from just a little bit of information about how far away things are. It works by taking a normal picture and using some hints about distances to make a more detailed map of the scene. The new method, called Marigold-DC, is very good at making maps even when it only has a few hints or when those hints are spread out unevenly. This makes it useful for real-world applications like self-driving cars or virtual reality. |
Keywords
» Artificial intelligence » Depth estimation » Diffusion » Diffusion model » Generalization » Inference » Optimization » Zero shot
