Irregular boundaries stereo images dataset creating using depth estimation model

Wahsh, Muntasser A. and Hussain, Zainab M. (2024) Irregular boundaries stereo images dataset creating using depth estimation model. POLLACK PERIODICA : AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES, 19 (1). pp. 143-150. ISSN 1788-1994 (print); 1788-3911 (online)

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Abstract

This paper introduces a stereoscopic image and depth dataset created using a deep learning model. It addresses the challenge of obtaining accurate and annotated stereo image pairs with irregular boundaries for deep learning model training. Stereoscopic image and depth dataset provides a unique resource for training deep learning models to handle irregular boundary stereoscopic images, which are valuable for real-world scenarios with complex shapes or occlusions. The dataset is created using monocular depth estimation, a state-of-the-art depth estimation model, and it can be used in applications like rectifying images, estimating depth, detecting objects, and autonomous driving. Overall, this paper presents a novel dataset that demonstrates its effectiveness and potential for advancing stereo vision and developing deep learning models for computer vision applications.

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