Monodepth2.jl
Implementation of the "Digging Into Self-Supervised Monocular Depth Estimation" paper.
Monodepth
Monocular depth estimation. Using single image to predict disparity map.
Training parameters:
- resolution
416x128
; - ResNet 18 model;
- no automasking & using pose prediction network.
Supported datasets
Install
Install model, image-augmentation library and the package itself:
]add https://github.com/pxl-th/ResNet.jl.git
]add https://github.com/pxl-th/Augmentations.jl.git
]add https://github.com/pxl-th/Monodepth2.jl.git
Simple disparity estimation
Simple disparity estimation using gradient descent with parameters:
- disparity map;
- rotation vector (so3);
- translation vector.
Visualization of the disparity map learning dynamics for the triplet above.
Important
-
Norm function is computed using
sqrt.(sum(abs2, ...))
. However,sqrt
function hasNaN
gradient at0
. This can be mitigated by defining subgradient or even better,norm
function that can act on the given axis, similar to PyTorch. -
For poses, struct
Pose
is used instead of arrays or tuple because of this issue.