Summary of Metricgold: Leveraging Text-to-image Latent Diffusion Models For Metric Depth Estimation, by Ansh Shah et al.
MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth Estimation
by Ansh Shah, K Madhava Krishna
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Robotics (cs.RO)
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 The paper introduces MetricGold, a novel approach for recovering metric depth from a single image using generative diffusion models. The method builds upon recent advances in MariGold, DDVM, and Depth Anything V2, combining latent diffusion, log-scaled metric depth representation, and synthetic data training. MetricGold achieves efficient training on a single RTX 3090 within two days using photo-realistic synthetic data from HyperSIM, VirtualKitti, and TartanAir. The experiments demonstrate robust generalization across diverse datasets, producing sharper and higher quality metric depth estimates compared to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MetricGold is a new way for computers to figure out how far objects are from the camera based on just one photo. This is useful because it’s hard for computers to do this accurately without seeing multiple photos or using special equipment. The new approach uses something called generative diffusion models, which are good at guessing what might be in an image even if they’ve never seen anything like it before. It also uses fake images that look real to train the computer. This means the computer can learn how to do this task without needing a lot of special training data. The results show that MetricGold is better than other approaches at getting accurate distances. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Synthetic data
