Abstract Robust next‐event estimation (NEE) remains a challenge in scenes characterized by sparse or small‐scale geometry where indirect illumination is the primary transport mechanism. In these scenes, traditional path construction, which relies on local directional sampling, often fails to find intersections with the sparse geometry, and standard NEE also struggles as it typically connects vertices directly to emitters, failing when those connections are occluded or require intermediate bounces. We propose a novel approach that constructs paths via direct geometry sampling. Instead of relying on stochastic ray casting, we repurpose the scene's bounding volume hierarchy (BVH) as a hierarchical sampling structure. By performing a stochastic top‐down traversal, we transform the selection of the next path vertex into a hierarchical problem. To prioritize high‐throughput connections, the traversal is guided by a proxy contribution function evaluated at each internal node. This function leverages aggregated statistics of the geometry contained in the BVH nodes to efficiently estimate contribution during traversal. We demonstrate orders of magnitude improvements in complex scenarios such as indirect illumination from sparse geometry or rendering discrete scattering media.

Abstract The light field in an underwater environment is characterized by complex multiple scattering interactions and wavelength‐dependent attenuation, requiring significant computational resources for the simulation of underwater scenes. We present a novel approach that makes it possible to simulate multi‐spectral underwater scenes, in a physically‐based manner, in real time. Our key observation is the following: In the vertical direction, the steady decay in irradiance as a function of depth is characterized by the diffuse downwelling attenuation coefficient, which oceanographers routinely measure for different types of waters. We rely on a database of such real‐world measurements to obtain an analytical approximation to the Radiative Transfer Equation, allowing for real‐time spectral rendering with results comparable to Monte Carlo ground‐truth references, in a fraction of the time. We show results simulating underwater appearance for the different optical water types, including volumetric shadows and dynamic, spatially varying lighting near the water surface.

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.