Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of
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Google, Mountain View, USA [email protected], [email protected], [email protected] 2 TU-Graz, Graz, Austria [email protected]
Abstract. Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.
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Introduction
Object instance recognition and 3D pose estimation have received a lot of attention recently, probably because of their importance in robotics applications [2,4,7,8,11,15,20,25]. For grasping and manipulation tasks, efficiency, reliability, and accuracy are all desirable properties that are still very challenging in general environments. While many approaches have been developed over the last years, we focus here on the approach from Drost et al. [8], which relies on a depth camera. Since its publication, it has been improved and extended by many authors [2,5,6,14,22]. However, we believe it has not delivered its full potential yet: Drost’s technique and its variants are based on votes from pairs of 3D points, but the sampling of these pairs has been overlooked so far. As a result, these techniques are very inefficient, and it usually still takes several seconds to run them. Moreover, this approach is also very sensitive to sensor noise and 3D background clutter—especially if it is close to the target object: Sensor noise can disturb the quantization on which the approach relies heavily for fast accesses. Background clutter casts spurious votes that can mask the effects of correct ones. As a result, several other approaches [4,11,15,20] have shown significantly better performance on recent datasets [11,15]. Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46487-9 51) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part III, LNCS 9907, pp. 834–848, 2016. DOI: 10.1007/978-3-319-46487-9 51
Going Further with Point Pair Features
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Fig. 1. Several 3D objects are simultaneously detected with our method under different poses on cluttered background with partial occlusion and illumination changes. Each detected object is augmented with its 3D model, its 3D bounding box and its coordinate systems. For better visibility, the background is kept in gray and only detected objects are in color. (Color figure online)
In this paper, we propose a much better and efficient sampling strategy that, together with small modifications to the pre- and post-processing steps, makes our approach competitive against state-of-the-art methods: It beats them on several objects on recent challenging datasets, at a low computational cost. In the remainder of thi
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