Depth Anything - Generating Depth Maps from a Single Image with Neural Networks

This week we cover the "Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data" paper from TikTok, The University of Hong Kong, Zhejiang Lab, and Zhejiang University. In this paper, they create a large dataset of labeled and unlabeled imagery to train a neural network for depth estimation from a single image, without any extra hardware or algorithmic complexity.

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Depth Anything 📜 https://arxiv.org/abs/2401.10891

The Dataset 🔢 https://www.oxen.ai/datasets/HRWSI

Depth Anything Notes 📜 https://www.oxen.ai/blog/arxiv-dives-...

MiDas 📜 https://arxiv.org/abs/1907.01341v3

Demo Depth Anything 🤗 huggingface.co/spaces/LiheYoung/Depth-Anything

Join Arxiv Dives 🤿 https://oxen.ai/community

Discord 🗿   / discord  

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Chapters
0:00 Intro to Depth Anything
2:00 Use Cases
3:10 Real World Example
5:12 What is a Depth Map?
7:00 Crash Course in Traditional Techniques
9:42 Enter Depth Anything
16:00 Learning from the Teacher Model
18:35 DINOv2 Model
19:18 Depth Anything Architecture
21:29 Evaluation
25:55 Ablation Studies
28:22 Data, Perturbations, Feature Loss
31:15 Qualitative Results
33:00 Limitations