Reframing Road-Scene Geometry: Measurement Chains and Validation Design

Abstract

Two distinct lines of inquiry—attention-based fusion of LiDAR and camera cues for curb detection and a wavelet-transform state-space decoder for zero-shot depth estimation—converge on a practical question for road-scene geometry: what evidence is needed before a reported advantage becomes a defensible basis for explanation, comparison, or deployment? The analysis combines two focal publications with 12 previously verified sources and organizes the evidence around sensor fusion, depth priors, attention efficiency, domain transfer, and road safety. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. The combined literature indicates that methodological gains become actionable only when sensor fusion and depth priors are evaluated together and when limits associated with road safety are explicit. This shifts the emphasis from isolated scores toward traceable chains of evidence and decision relevance. The resulting framework supports reproducible comparison while preserving differences between study designs, and it identifies concrete points at which transfer claims should be narrowed or retested.