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Traditional indoor 3D structural environment modeling algorithms employ schemes such as clustering of dense point clouds for parameterization and identification of the 3D surfaces. RANSAC based plane fitting is one common approach in this regard. Alternatively, extensions to feature based stereo have also been used, mainly focusing on 3D line descriptions, along with techniques such as half-plane detection, real-plane or facade reconstruction, plane sweeping etc. Noise in the range data, especially in low texture regions, accidental line/plane grouping under lack of cues for visibility tests, presence of depth edges or discontinuities that are not visible in the 2D image and difficulties in adaptively estimating metrics for clustering can hamper efficiency of practical systems. In order to counter these issues, we propose a novel framework fusing 2D local and global features such as edges, texture and regions, with geometry information obtained from range data for reliable 3D indoor scene representation. The strength of the approach is derived from the novel depth diffusion and segmentation algorithms resulting in superior surface characterization as opposed to traditional feature based stereo or RANSAC based plane fitting approaches. These algorithms have also been heavily optimized to enable real-time deployments on personal, domestic and rehabilitation robots.