How Neural Nets estimate depth from 2D images? Monocular Depth Estimation Explained!

In this video, we will be discussing the MiDAS paper, Depth Anything V1, and the latest Depth Anything V2 paper! We are going to learn the basics of Monocular Depth Estimation and some of the modern tricks, datasets, networks, and loss functions used to train these models.

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#science #machinelearning #deeplearning

Relevant videos:
History of CNNs:    • The entire history of Computer Vision...  
Segment Anything Paper:    • Explaining the Segment Anything Model...  

Links:
MiDAS: https://arxiv.org/abs/1907.01341
Depth Anything: https://depth-anything.github.io/
Depth Anything V2: https://depth-anything-v2.github.io/

Timestamps:
0:00 - Intro
1:20 - MiDAS
3:05 - Depth Anything V1
4:28 - Disparity Space
5:13 - Scale and Shift Invariant Loss
5:56 - Gradient Matching Loss
8:13 - Cut Mix Augmentation
8:30 - Semantic Assisted Perception
9:46 - Synthetic Datasets
11:20 - Depth Anything V2