  In the wake of many new ML-inspired approaches for reconstructing and
representing high-quality 3D content, recent hybrid and explicitly learned
representations exhibit promising performance and quality characteristics.
However, their scaling to higher dimensions is challenging, e.g. when
accounting for dynamic content with respect to additional parameters such as
material properties, illumination, or time. In this paper, we tackle these
challenges for an explicit representations based on Gaussian mixture models.
With our solutions, we arrive at efficient fitting of compact N-dimensional
Gaussian mixtures and enable efficient evaluation at render time: For fast
fitting and evaluation, we introduce a high-dimensional culling scheme that
efficiently bounds N-D Gaussians, inspired by Locality Sensitive Hashing. For
adaptive refinement yet compact representation, we introduce a loss-adaptive
density control scheme that incrementally guides the use of additional capacity
towards missing details. With these tools we can for the first time represent
complex appearance that depends on many input dimensions beyond position or
viewing angle within a compact, explicit representation optimized in minutes
and rendered in milliseconds.
