Morton Code-Based Geometry-Adaptive Surface Reconstruction

Neural implicit surface representations have yielded impressive results in 3D reconstruction, yet existing methods tend to introduce noise in smooth regions or fail to capture fine details in complex areas, primarily due to a lack of explicit spatial structure modeling. To address these limitations, we propose a geometry-adaptive surface reconstruction method based on Morton codes. By mapping 3D space onto octree traversal paths, this approach provides a natural spatial structural prior for the reconstruction process. For each query point, an implicit octree generates a unique root-to-leaf trajectory, yielding spatially adaptive weights that modulate multi-resolution geometric features. Specifically, low-frequency coarse features dominate in flat regions to suppress noise, whereas high-frequency fine features are activated in edge-rich areas to recover intricate geometry. Experimental results demonstrate competitive performance across multiple datasets, particularly in reconstructing sharp features and fine-grained geometric details.