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Loss functions: To train monocular depth estimators based on deep learning, many loss functions have been proposed. Depth losses directly measure the dif- ferences between ground-truth depths and their estimates.
Apr 11, 2024 · Loss functions help to quantify the errors between the ground truth and the predicted images, hence enabling a model to optimize and improve its ...
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Aug 30, 2021 · The goal in monocular depth estimation is to predict the depth value ... depth estimation model with a convnet and simple loss functions.
Loss Functions. We categorize the proposed loss functions for depth esti- mation into direct supervision losses which require ground- truth depth and self ...
Mar 14, 2020 · Inferring depth information from a single image (monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural ...
Mar 15, 2022 · The loss function is effectively used for training a model. In our work, We implement various loss functions based on edge detectors to train a ...
Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image.
In this paper, based on the existing methods, we have innovated the loss function and introduced perceptual loss, i.e., we use feedforward neural network to ...
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 ...
Dec 24, 2020 · This paper proposes a new deep convolutional neural network for monocular depth estimation. The network applies joint attention feature ...