VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations
It provides a dataset to enable 3D perception for AI in construction, potentially reducing costs and advancing computer vision applications, but is incremental as it focuses on data creation rather than novel methods.
This study created and released VCVW-3D, a virtual dataset with 3D annotations for construction object detection, covering 15 scenes and 10 categories, to address the lack of 3D data in the field, and benchmarked it with typical 2D and 3D detection models.
Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligence (AI) applications only applicable to some scenarios that only require 2D information. However, most advanced applications usually require AI agents to perceive 3D spatial information, which limits the further development of the current computer vision (CV) in construction. The lack of 3D annotated datasets for construction object detection worsens the situation. Therefore, this study creates and releases a virtual dataset with 3D annotations named VCVW-3D, which covers 15 construction scenes and involves ten categories of construction vehicles and workers. The VCVW-3D dataset is characterized by multi-scene, multi-category, multi-randomness, multi-viewpoint, multi-annotation, and binocular vision. Several typical 2D and monocular 3D object detection models are then trained and evaluated on the VCVW-3D dataset to provide a benchmark for subsequent research. The VCVW-3D is expected to bring considerable economic benefits and practical significance by reducing the costs of data construction, prototype development, and exploration of space-awareness applications, thus promoting the development of CV in construction, especially those of 3D applications.