Monocular Vision–Enabled 3D Truck Reconstruction: A Novel Optimization Approach Based on Parametric Modeling and Graphics Rendering

卡车 渲染(计算机图形) 计算机科学 挖掘机 摄影测量学 计算机视觉 方向(向量空间) 参数统计 绘图 人工智能 工程类 模拟 计算机图形学(图像) 汽车工程 数学 机械工程 统计 几何学
作者
Junjie Chen,Weisheng Lu,Zhiming Dong
出处
期刊:Journal of Computing in Civil Engineering [American Society of Civil Engineers]
卷期号:36 (5) 被引量:3
标识
DOI:10.1061/(asce)cp.1943-5487.0001041
摘要

Three-dimensional (3D) truck information, e.g., geometry, orientation, and position, can enable various smart construction applications such as monitoring earthwork, enhancing construction safety, and promoting productivity. Whereas stereo cameras have been explored extensively, the use of monocular vision (MV) for object 3D reconstruction still lacks substantial documentation. This study advances the field of MV-enabled 3D truck reconstruction by formulating it as an optimization problem. First, the general geometry of trucks was conceptualized and used to form a truck parametric model (TPM). Then the TPM was rendered by a computer graphics engine to generate synthetic views of the truck. Finally, an optimization algorithm is proposed to calibrate variables of the TPM progressively to maximize the alignment of the synthetic views with a target truck image. The proposed approach, called Mono-Truck, was evaluated by both lab tests and field experiments. The lab tests demonstrated an average error of 10.1%, 6.7 mm, and 0.7° in estimating the truck’s dimensions, position, and orientation, respectively. In the field experiments, Mono-Truck performed well compared with the baseline. This study contributes to the knowledge body by opening a new avenue to the monocular 3D truck reconstruction problem from an optimization perspective. The proposed approach can be generalized further to other types of construction machinery (e.g., excavators, cranes, and bulldozers) for their 3D reconstruction and smart applications.
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