Peicheng Shi^1,*, Yueyue Tang¹, Yi Li¹, Xinlong Dong¹, Yu Sun², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3321-3343, 2025, DOI:10.32604/cmc.2025.065297 - 03 July 2025

Abstract In the dynamic scene of autonomous vehicles, the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation. To solve this problem, we propose an unsupervised monocular depth estimation model based on edge enhancement, which is specifically aimed at the depth perception challenge in dynamic scenes. The model consists of two core networks: a deep prediction network and a motion estimation network, both of which adopt an encoder-decoder architecture. The depth prediction network is based on the U-Net structure of ResNet18, which is responsible for generating the depth map of the… More >

Jing He¹, Haonan Zhu², Chenhao Zhao¹, Minrui Zhao^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4551-4573, 2025, DOI:10.32604/cmc.2025.062437 - 19 May 2025

Abstract Self-supervised monocular depth estimation has emerged as a major research focus in recent years, primarily due to the elimination of ground-truth depth dependence. However, the prevailing architectures in this domain suffer from inherent limitations: existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions. These assumptions are often violated in real-world scenarios due to dynamic objects, non-Lambertian reflectance, and unstructured background elements, leading to pervasive artifacts such as depth discontinuities (“holes”), structural collapse, and ambiguous reconstruction. To address these challenges, we propose a novel framework that integrates scene dynamic pose estimation into… More >

Xin Yang^1,2, Qingling Chang^1,2, Shiting Xu³, Xinlin Liu^1,2, Yan Cui^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 573-592, 2023, DOI:10.32604/cmes.2023.023424 - 05 January 2023

Abstract Monocular depth estimation is the basic task in computer vision. Its accuracy has tremendous improvement in the decade with the development of deep learning. However, the blurry boundary in the depth map is a serious problem. Researchers find that the blurry boundary is mainly caused by two factors. First, the low-level features, containing boundary and structure information, may be lost in deep networks during the convolution process. Second, the model ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area, during the backpropagation. Focusing More >