Accurate building facade understanding supports urban analytics, hazard modeling, and robotic infrastructure inspection, but suburban facades are frequently obstructed by vegetation, parked vehicles, and fences. Street-level imagery (SLI), including Google Street View (GSV), offers broad coverage, yet existing single-view inpainting cannot exploit observations of the same facade from other viewpoints and may generate plausible but unsupported structure. This raises the research question addressed in this work: can multi-view facade reconstruction be extended from sensor-rich mobile mapping to RGB-only street-view imagery while preserving geometrically consistent facade evidence and controlling unsupported generative completion? We present a sensor-light extension of a previously introduced sensor-rich multi-view reconstruction pipeline. The RGB pathway removes the LiDAR requirement by combining perspective views rendered from street-view panoramas, explicitly derived pinhole intrinsics, monocular metric depth (UniDepthV2), cross-view depth-scale correction, conditional local pose refinement, and evidence-gated multi-view fusion before diffusion inpainting. Quantitative evaluation on LiDAR-paired mobile sequences with LiDAR withheld from the sensor-light input shows that scale correction reduces depth AbsRel from 0.142 to 0.091 and median reprojection error from 3.8 to 2.4 pixels, while increasing downstream door/stoop macro-F1 from 0.82 to 0.86, compared with 0.88 for the LiDAR reference. The scale-corrected sensor-light configuration reaches 28.4 dB PSNR compared with 29.0 dB for the sensor-rich reference. These results show that RGB-only geometry can recover much of the sensor-rich performance while producing one compact facade representation and explicitly flagging cases with insufficient cross-view support. The controlled experiments do not constitute an end-to-end city-scale GSV benchmark, but they establish the practical capability and trade-offs of sensor-light multi-view facade reconstruction.
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Hasan et al. (2026) studied this question.
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