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How Neural Nets estimate depth from 2D images? Monocular Depth Estimation Explained!
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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.
Members will get access the full write-up, all the animations, powerpoint slides, and annotated PDFs from this video for channel supporters and Patrons!
Visit the Patreon link to see what is available:
#science #machinelearning #deeplearning
Relevant videos:
Links:
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
Members will get access the full write-up, all the animations, powerpoint slides, and annotated PDFs from this video for channel supporters and Patrons!
Visit the Patreon link to see what is available:
#science #machinelearning #deeplearning
Relevant videos:
Links:
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
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