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—Unprecedented amounts of 3D data can be acquired in urban environments, but their use for scene understanding is challenging due to varying data resolution and variability of objects in the same class. An additional challenge is due to the nature of the point clouds themselves, since they lack detailed geometric or semantic information that would aid scene(More)
Modern 3D cameras allow for the acquisition of large 3D scenes containing objects of interest that can be detected and classified for scene understanding. This survey focuses on the task of object classification in urban range scans and indoor RGB-D images. Each approach will be compared based on several factors including classification accuracy,(More)
We examine the task of point-level object segmentation in outdoor urban LIDAR scans. A key challenge in this area is the problem of missing points in the scans due to technical limitations of the LIDAR sensors. Our core contributions are demonstrating the benefit of reframing the segmenta-tion task over the scan acquisition grid as opposed to considering(More)
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