Persistent Point Feature Histograms for 3D Point Clouds

By Radu Bogdan Rusu, Zoltán-Csaba Márton, Nico Blodow, Michael Beetz. 2008

290 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Dynamic Graph CNN for Learning on Point Clouds (2018, methodology): “Traditional methods for solving these problems employ handcrafted features to capture geometric properties of point clouds [Lu et al. 2014; Rusu et al. 2009, 2008a].”
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration (2021, methodology): “From early-stage hand-crafted methods [1, 2, 3, 4, 5] to recently emerged deep learning-based approaches [6, 7, 8, 9, 10, 11, 12], many works contributed to improving the reliability of correspondences.”
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching (2018, methodology): “This shifts the attention to LRF-free methods such as the rotation invariant point pair features (PPF) to be used as basis for creating powerful descriptors like PPFH [36] and FPFH [35].”
Fast Point Feature Histograms (FPFH) for 3D registration (2009, methodology): “In our previous work [2], [15], besides the three features mentioned above, we used a fourth one characterizing the Euclidean distance from pi to pj .”
Point feature extraction on 3D range scans taking into account object boundaries (2011, methodology): “In [12] we presented a method for selecting interest points based on their persistence in growing point neighborhoods.”
Performance of global descriptors for velodyne-based urban object recognition (2014, background): “Generally, these descriptors can be roughly divided into two groups: the local descriptors for every point of the object [7], [8], [9], [10], [11], [12], [13] and the global descriptors for the whole object [3], [5], [14], [15], [16].”
Towards 3D Point cloud based object maps for household environments (2008, methodology): “We propose the use of a better system that combines several aspects of the geometry of a point’s neighborhood for feature estimation [39], which already showed promising results in our previous work [29,32,30], as a multi-value feature set for selecting point correspondences.”
Aligning point cloud views using persistent feature histograms (2008, methodology): “We propose the use of a better system that combines several aspects of the geometry of a point’s neighborhood [20], [21] for estimating a multi-value feature set (see Figure 1) and make an in-depth analysis of the points’ histogram signatures for different geometric primitives (i.”