Efficient Multi-Cue Scene Segmentation

Abstract. This paper presents a novel multi-cue framework for scene segmentation, involving a combination of appearance (grayscale images) and depth cues (dense stereo vision). An efficient 3D environment model is utilized to create a small set of meaningful free-form region hypothe-ses for object location and extent. Those regions are subsequently cat-egorized into several object classes using an extended multi-cue bag-of-features pipeline. For that, we augment grayscale bag-of-features by bag-of-depth-features operating on dense disparity maps, as well as height pooling to incorporate a 3D geometric ordering into our region descriptor. In experiments on a large real-world stereo vision data set, we obtain state-of-the-art segmentation results at significantly reduced computa-tional costs. Our dataset is made public for benchmarking purposes.