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. -- Get Oxen 🐂 https://oxen.ai/ Oxen.ai makes versioning your datasets as easy as versioning your code! Even is millions of unstructured images, we quickly handle any type of data so you can build cutting-edge AI. -- 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   -- 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