Tensor-Based Unified Geometry and Reflectance Optimization for 3-D Reconstruction in Large-Scale Urban Scenes

Urban-scale 3D reconstruction presents a significant challenge due to its complex geometry and diverse material properties. Existing methods struggle to handle this complexity: neural radiance fields often produce blurry results, while hybrid approaches based on scene tiling not only impose high computational costs but also lead to spatial inconsistencies. We introduce UniUrban3D, a scalable framework that unifies geometry and appearance modeling within a holistic hierarchical hash-encoded tensor representation combined with dual-path rendering strategies. Our approach leverages an adaptive spatial decomposition and feature aggregation to handle scene complexity, a multi-resolution hash-encoded tensor representation that efficiently captures fine geometric details and appearance attributes, and a dual-path rendering pipeline to fuse implicit and explicit representations for enhanced photorealism. Experiments on large-scale datasets demonstrate that UniUrban3D significantly outperforms state-of-the-art methods in reconstruction accuracy and visual fidelity. Our method produces sharper and artifact-free renderings, while requiring substantially less memory. Our work enables realistic and scalable city modeling, opening new avenues for applications in virtual reality, digital twins, and urban simulation.