StrataMesh: Adaptive Allocation for Compact Meshes at Scale

Reconstructing explicit meshes from images at scale requires balancing rendering fidelity, triangle count, and geometric quality across regions with widely varying observation support. We introduce StrataMesh, which alternates adaptive capacity allocation with quality-aware fitting to address this challenge. Its dynamic Core–Shell representation assigns geometrically well-constrained regions to a Core and retains weakly constrained content in an appearance-oriented Shell; these roles evolve as multiview evidence accumulates. To decide where faces are useful, StrataMesh estimates the conditional fitting gain of a refinement beyond what existing geometry and appearance parameters can explain. Screened splits and collapses then redistribute capacity between the two strata. After each topology change, compatible learned state is transferred to the edited mesh. Joint supervision of the indexed output and a bounded Core proxy supports fitting, while octree-organized local, hierarchical geometry optimization and validity checks make quality-aware updates practical in large scenes. Experiments on Tanks and Temples, Mip-NeRF 360, and DTU demonstrate strong reconstruction and rendering fidelity with compact, well-shaped meshes at scale. On six Tanks and Temples scenes, StrataMesh achieves the best F1-score, rendering metrics, and triangle-shape results while using 67.3% fewer faces than MILo-base.