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Lecture Notes in Computer Science
We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our maindoi:10.1007/978-3-540-88682-2_5 fatcat:27qraxd5j5frdc2htm62re56ji