Render lighting dataset: A collection of rendered images with varied lighting conditions using blender render engines

Khandoker Ashik Uz Zaman; Ashraful Islam; Md Abu Sayed

doi:10.1016/j.dib.2024.110331

Render lighting dataset: A collection of rendered images with varied lighting conditions using blender render engines

Data Brief. 2024 Mar 16:54:110331. doi: 10.1016/j.dib.2024.110331. eCollection 2024 Jun.

Authors

Khandoker Ashik Uz Zaman¹, Ashraful Islam^{1

2}, Md Abu Sayed¹

Affiliations

¹ Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka 1229, Bangladesh.
² Center for Computational & Data Sciences, Independent University Bangladesh, Dhaka 1229, Bangladesh.

Abstract

The quality of datasets is crucial in computer graphics and machine learning research and development. This paper presents the Render Lighting Dataset, featuring 63,648 rendered images of Blender's primitive shapes with various lighting conditions and engines. The images were created using Blender 4.0's Cycles and Eevee Render Engines, with careful attention to detail in texture mapping and UV unwrapping. The dataset covers six different lighting conditions, including Area Light, Spotlight, Point Light, Tri-Light, HDRI (Sunlight), and HDRI (Overcast), each adjusted using Blender's different options in the Color Management panel. With thirteen unique materials, ranging from Coastal Sand to Glossy Plastic, the dataset provides visual diversity for researchers to explore material properties under different lighting conditions using different render engines. This dataset serves as a valuable resource for researchers looking to enhance 3D rendering engines. Its diverse set of rendered images under varied lighting conditions and material properties allows researchers to benchmark and evaluate the performance of different rendering engines, develop new rendering algorithms and techniques, optimize rendering parameters, and understand rendering challenges. By enabling more realistic and efficient rendering, advancing research in lighting simulation, and facilitating the development of AI-driven rendering techniques, this dataset has the potential to shape the future of computer graphics and rendering technology.

Keywords: 3D lighting conditions; 3D materials; 3D render engines; 3D rendering lighting, Blender; Computer graphics; Image dataset; Machine Learning.