Evidence Alignment and Transfer Boundaries in 3D Point-Cloud Learning And Road-Scene Geometry: Hierarchical Spatial Mamba Framework and WaveSamba Wavelet Transform SSM

Abstract

Progress in 3D point-cloud learning and road-scene geometry depends on more than accumulating favorable results. This critical synthesis connects hierarchical spatial state-space aggregation for point-cloud classification with a wavelet-transform state-space decoder for zero-shot depth estimation and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. Two target papers are triangulated against 12 locally validated publications. The comparison follows neighborhood construction, hierarchy, state-space mixing, sampling robustness, efficiency and deliberately separates mechanistic interpretation from performance ranking, because the latter can conceal incompatible experimental or operational conditions. Across the evidence base, the decisive issue is alignment: neighborhood construction shapes what is observed, hierarchy shapes how it is compared, and efficiency governs whether the conclusion can be transferred. Uncertainty is most informative when reported as part of the result rather than treated as a postscript. The contribution is a decision-oriented synthesis that connects method selection to failure cost and treats reproducibility, provenance, and bounded generalization as first-order design requirements.