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

Abstract

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.