Adaptive Risk Cultural Heritage Assessment In Conservation

Objective

The current project, ARCHAIC (Adaptive Risk Cultural Heritage Assessment In Conservation), aims to provide a new intelligent method for the risk management of heritages. In ARCHAIC, I plan to recognize physical deterioration and degradation evolution in monuments by computer vision methods (using a combination of Neural Radiance Fields (NeRF) and social media imagery) and use them as inputs to assess the degree of vulnerability via the neuro-fuzzy method. To compare social media photos captured at various times for detecting changes, deterioration, and achieving deterioration growth, I encounter the challenge that the images vary in camera. By reconstructing 3D models of monuments at various times via NeRF and comparing images, we can effectively identify changes and deterioration over time. Camera pose estimation is performed using Inerf, a deep neural network-based method specifically designed for accurate camera pose estimation of social media images. The estimated poses are utilized and given to the NeRF models to generate pair comparison viewpoints for image analysis. Finally, I consider detected deterioration’s type, boundary, amount, and growth for the fuzzy rules. ARCHAIC leads to determining the amount of intervention to manage the risks of a query case of a monument.