Jun 2, 2018 · In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo ...
First, instead of manually labelling pixels with relative relationships, we acquire relative depths using existing stereo matching algorithm from stereo movie ...
We introduce a spacing-increasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem.
Missing: Augmented | Show results with:Augmented
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Oct 3, 2024 · This paper presents a comprehensive survey of the existing deep learning-based methods, the challenges they address, and how they have evolved in their ...
We propose TinyDepth, a lightweight self-supervised monocular depth estimation method based on Transformer that employs hierarchical representation learning.
The present study introduces a monocular depth estimation approach based on an end-to-end convolutional neural network.
In "Monocular Depth Estimation with Augmented Ordinal Depth Relationships" [101] , the metric depth estimation performance is suggested to be improved with the ...
Depth prediction from a monocular RGB image is very useful in applications such as augmented reality, image re- focusing, face parsing, etc., where coarse depth ...
Oct 9, 2024 · Depth maps generated from RGB images provide information about the distance of objects from the camera. Thus, depth estimation plays a ...
Dec 8, 2023 · In this paper, we take two data augmentation techniques, namely Resizing-Cropping and Splitting-Permuting, to fully exploit the potential of training datasets.