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Network cameras observe physical spaces but lack an inherent understanding of the scenes they see. This thesis presents a pipeline for floor and wall instance segmentation from a single uncalibrated RGB image, requiring no scene-specific prior information such as camera intrinsics, depth sensors, floor plans, or labelled floor and wall data for training. The detected instances are intended to supp
