LightDepthMagic: An Advanced Deep Learning and Computer Vision Framework for Realistic 3D Object Embedding in RGB Images

Incorporating 3D objects into RGB images presents a multifaceted challenge, necessitating accurate estimations of various parameters such as depth, light color, direction, albedo, normals, and environment maps. In response to these complexities, this project introduces a framework that combines several advanced techniques: ‘Depth Anything’ for depth estimation, ‘OneNet’ for light color determination, ‘DiffusionLight’ for environment map estimation, and an inverse rendering model to gauge light intensity. This integrated approach leverages deep learning models to extract necessary environmental and object-specific data. Subsequently, the object is rendered and seamlessly inserted into images using post-processing. We experimented with different objects and different material properties. While the framework achieves satisfactory results with smaller objects, challenges remain when dealing with objects close to the camera or larger in scale. This highlights the need for further refinement to enhance realism and accuracy in complex imaging scenarios.