FADNet is a model used for depth estimation. It is a fast and accurate network for disparity estimation. It has three main features:
- It exploits efficient 2D-based correlation layers with stacked blocks to preserve fast computation.
- It combines the residual structures to make the deeper model easier to learn.
- It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy.
The following images show the result of depth estimation. The first image is the left camera image input, the second image is the right camera image input and the third image is the running result of the FADNet model.
Figure 1. FADNet Depth Estimation Example
The following table lists the depth estimation models supported by the Vitis AI library.
| No | Model Name | Framework |
|---|---|---|
| 1 | FADNet_0_pt | PyTorch |
| 2 | FADNet_1_pt | PyTorch |
| 3 | FADNet_2_pt | PyTorch |