Volumetric Model

Volumetric models represent three-dimensional objects or scenes as a volume of data, enabling detailed and realistic representations, particularly for complex or fuzzy geometries. Current research focuses on improving the efficiency and accuracy of these models, employing techniques like neural implicit surfaces, multi-step probabilistic learning, and frequency-domain learning within various architectures including convolutional neural networks and neural radiance fields. These advancements are impacting diverse fields, from climate analysis (through improved cloud modeling) to robotics (via enhanced scene understanding and mapping) and medical imaging (facilitating improved visualization of complex organs like the placenta).