Robust Volumetric Wave Rendering for Aerial Spatial Audio in Dynamic Media via Cumulant LBM

Abstract Spatial audio rendering in large‐scale urban scenes is crucial for immersive VR/AR, digital twins, and mobility‐centered simulations. Yet, accurately modeling wave propagation through dynamic media remains a significant challenge in visual computing, particularly when complex urban wind fields, non‐uniform shear flows, and voxelized geometries interact. While geometric acoustics inherently struggles to explicitly resolve low‐frequency diffraction and interference, conventional wave‐based solvers—including standard Lattice Boltzmann Methods (LBM)—often suffer from numerical dispersion or severe instability under strong convection. In this paper, we present the V‐LLBM framework, a robust volumetric wave‐rendering pipeline tailored for dynamic media. By recasting the D3Q27‐Cumulant collision operator into a graphics‐oriented acoustic context, our framework helps suppress spurious non‐physical modes through central‐moment relaxation in a co‐moving reference frame. We evaluate the solver hierarchically, progressing from canonical propagation and rigid‐boundary scattering to demanding aerodynamic stress tests involving high‐subsonic shear and broadband pulses in strong crosswinds. Across these scenarios, the cumulant formulation maintains the baseline acoustic fidelity of established reference models while providing improved robustness where standard collision models fail to maintain coherent wavefields. Furthermore, we scale our method to a 140‐million‐voxel real‐world urban canyon topology. By employing a two‐pass volumetric baking strategy, we successfully isolate delicate acoustic perturbations from orders‐of‐magnitude larger aerodynamic background pressures. This enables the extraction of wind‐aware receiver‐side temporal responses, quantitatively capturing critical physical wavefront distortions such as convective Doppler shifts and significant amplification in peak acoustic pressure. Ultimately, our approach establishes a reliable offline pre‐computation pipeline, providing physically grounded spatial audio assets and high‐fidelity data priors for downstream data‐driven representations, such as Neural Acoustic Fields (NAF).