Summary of Ecodepth: Effective Conditioning Of Diffusion Models For Monocular Depth Estimation, by Suraj Patni et al.
ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
by Suraj Patni, Aradhye Agarwal, Chetan Arora
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A new learning-based single image depth estimation (SIDE) model is proposed, which leverages global image priors generated from a pre-trained Vision Transformer (ViT) model to improve zero-shot transfer in various applications. The model conditions a diffusion backbone on ViT embeddings and achieves state-of-the-art performance on the NYUv2 and KITTI datasets, outperforming current state-of-the-art methods by 14% and 2%, respectively. Zero-shot transfer experiments also demonstrate significant improvements over existing methods on several benchmark datasets. This work showcases the effectiveness of using pre-trained ViT embeddings for SIDE tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a camera trying to figure out how far away things are from just one picture. It’s hard! But what if we used a special trick called “global image priors” to help it guess better? These priors come from training a machine learning model on lots of pictures and then using that knowledge to help the camera guess distances in new, unseen pictures. In this paper, scientists test this idea and find that it works really well! They show that their method is better than others at guessing distances in certain situations, which can be useful for things like self-driving cars or robots. |
Keywords
* Artificial intelligence * Depth estimation * Diffusion * Machine learning * Vision transformer * Vit * Zero shot
