Apr 11, 2024 · In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function.

Apr 11, 2024 · In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function.

In this study, we propose a simplified and adaptable approach to improve depth estimation accu- racy using transfer learning and an optimized loss function. The ...

1 INTRODUCTION. In the context of computer vision, depth estimation. is the task of finding the distance of different ob-. jects from the camera in an image. · 2 ...

Apr 11, 2024 · This paper introduces a new depth estimation technique that combines weighted-loss and transfer learning to improve the accuracy of depth prediction.

In this study, we first show the domain shift immunity of different deep homography estimation models. We then use a shallow network with a specially designed ...

Aug 26, 2023 · A turbocharged convolutional neural network (CNN) armed with a streamlined architecture and underpinned by the knowledge of transfer learning.

In this paper, a novel monocular depth estimation method was proposed that primarily utilizes a lighter-weight Convolutional Neural Network (CNN) structure for ...

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 ...

Oct 5, 2022 · Monocular depth estimation aims to recover the depth information in three-dimensional (3D) space from a single image efficiently, ...