Apr 11, 2024 · The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge ...
People also ask
How does monocular depth estimation work?
Monocular depth estimation is a computer vision task that involves predicting the depth information of a scene from a single image. In other words, it is the process of estimating the distance of objects in a scene from a single camera viewpoint.
What is monocular depth estimation using neural networks?
Deep learning methods for monocular depth estimation often utilize gradient descent to train deep neural networks, and obtain a local minimum finally. The best local minimum depends on initialization and specific parameter settings.
What is the difference between monocular and stereo depth estimation?
Here, we focus on the task of monocular (single-view) depth estimation: we only have a single image available at test time, and no assumptions about the scene contents are made. In contrast, stereo (multi-view) depth estimation methods perform inference with multiple images.
What is relative depth estimation?
To address this issue, in this work, we define and estimate a scale-invariant quantity, called relative depth, which is the ratio between the depths of two regions in an image. If we know the relative depths of all pixel pairs in an. image, we can reconstruct the depth map with a normalized. scale.
An algorithm to combine multiple loss terms adaptively for training a monocular depth estimator is proposed in this work. We con- struct a loss function space ...
We employed three parts in the loss function in our model. The loss is a weighted sum of 3 parts: the depth loss, the gradient loss and the surface normal loss.
Godard et al. [9] made improvements to the depth estimation by introducing a symmetric left- right consistency criterion and better stereo loss function.
Abstract: The performance of deep learning-based depth estimation depends on encoding layers, decoding layers, and loss function. In this paper, we propose ...
Introduced novel loss for monocular depth estimation by taking advantage of dissimilarities on the left and right stereo pairs to learn depth. 3. Achieved ...
We propose a novel algorithm for monocular depth esti- mation using relative depth maps. First, using a convolu- tional neural network, we estimate relative ...
A standard loss function for depth regression problems considers the difference between the ground truth depth map y and the prediction of the depth regression ...
Mar 13, 2024 · To improve the overall estimation performances, we focus on three fundamental components: a specific loss function, a novel data augmentation ...
Dense monocular depth estimation for stereoscopic vision based on ...
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Mar 25, 2024 · The loss function must quantify the “incorrectness”, because the depth map is more “continuous” than “discrete”. Mean Squared Error (MSE) is ...