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Superpixels of RGB-D Images for Indoor Scenes Based on Weighted Geodesic Driven Metric

Serving as a key step for applications of image processing, superpixel generation has been attracting increasing attention. RGB-D images are used pervasively in scenes reconstruction and representation, benefiting from their contained depth data. In this paper, we present a novel framework for generating superpixels focus on RGB-D images of indoor scenes, based on a weighted geodesic driven metric that combines both color and geometric information. In particular, taking into account the unique structures of indoor scenarios, we first denoise the given RGB-D image, and construct the corresponding triangular mesh. A new weighted geodesic driven metric is defined by introducing a weight function constrained with normal vectors and colors. Under this metric, an energy function is defined to measure our over-segmentation of the triangular mesh, by optimizing which, we can acquire an optimal over-segmentation of the triangular mesh with object boundaries respected, such that vertices in each sub-region have similar geometric structures and color intensities. Re-mapping the over-segmentation of the triangular mesh to the RGB-D image results in desired superpixels. We perform extensive experiments on a large-scale database of RGB-D images to verify the efficacy of our algorithm. The results show that our algorithm has considerable advantages over the existing state-of-the-art methods.