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Currently, object detection applications in construction are almost based onpure 2D data (both image and annotation are 2D-based), resulting in thedeveloped artificial intelligence (AI) applications only applicable to somescenarios that only require 2D information. However, most advanced applicationsusually require AI agents to perceive 3D spatial information, which limits thefurther development of the current computer vision (CV) in construction. Thelack of 3D annotated datasets for construction object detection worsens thesituation. Therefore, this study creates and releases a virtual dataset with 3Dannotations named VCVW-3D, which covers 15 construction scenes and involves tencategories of construction vehicles and workers. The VCVW-3D dataset ischaracterized by multi-scene, multi-category, multi-randomness,multi-viewpoint, multi-annotation, and binocular vision. Several typical 2D andmonocular 3D object detection models are then trained and evaluated on theVCVW-3D dataset to provide a benchmark for subsequent research. The VCVW-3D isexpected to bring considerable economic benefits and practical significance byreducing the costs of data construction, prototype development, and explorationof space-awareness applications, thus promoting the development of CV inconstruction, especially those of 3D applications.

VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations