Shadow, a natural phenomenon resulting from the absence of direct lighting, finds diverse real-world applications beyond computer vision, such as studying its effect on photosynthesis in plants and on the reduction of solar energy harvesting through photovoltaic panels. This article presents a dataset comprising 50,000 pairs of photorealistic computer-rendered images along with their corresponding physics-based shadow masks, primarily focused on agricultural settings with human activity in the field. The images are generated by simulating a scene in 3D modeling software to produce a pair of top-down images, consisting of a regular image and an overexposed image achieved by adjusting lighting parameters. Specifically, the strength of the light source representing the sun is increased, and all indirect lighting, including global illumination and light bouncing, is disabled. The resulting overexposed image is later converted into a physically accurate shadow mask with minimal annotation errors through post-processing techniques. This dataset holds promise for future research, serving as a basis for transfer learning or as a benchmark for model evaluation in the realm of shadow-related applications such as shadow detection and removal.
Keywords: Agrovoltaic systems; Blender; Computer vision; Deep learning; Human activity recognition; Rendered shadow images; Shadow detection.
© 2024 The Authors.