Going further with point pair features

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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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Hinterstoisser, S., Lepetit, V., Rajkumar, N., & Konolige, K. (2016). Going further with point pair features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9907 LNCS, pp. 834–848). Springer Verlag. https://doi.org/10.1007/978-3-319-46487-9_51

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