Affiliation:
1. University of Verona, Italy
2. ISTI-CNR, Italy
3. NTNU, Norway
4. CRS4, Italy
Abstract
We introduce an innovative multiresolution framework for encoding and interactively visualizing large relightable images using a neural reflectance model derived from a state-of-the-art technique. The framework is seamlessly integrated into a scalable multi-platform framework that supports adaptive streaming and exploration of multi-layered relightable models in web settings. To enhance efficiency, we optimized the neural model, simplified decoding, and implemented a custom WebGL shader specific to the task, eliminating the need for deep-learning library integration in the code. Additionally, we introduce an efficient level-of-detail management system supporting fine-grained adaptive rendering through on-the-fly resampling in latent feature space. The resulting viewer facilitates interactive neural relighting of large images. Its modular design allows the incorporation of functionalities for Cultural Heritage analysis, such as loading and simultaneous visualization of multiple relightable layers with arbitrary rotations.
Publisher
Association for Computing Machinery (ACM)
Reference58 articles.
1. Jonathan T Barron. 2019. A general and adaptive robust loss function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4331–4339.
2. X3DOM
3. A novel approach for exploring annotated data with interactive lenses
4. Homomorphic Latent Space Interpolation for Unpaired Image-To-Image Translation