Fast Point Feature Histograms (FPFH) for 3D registration

By Radu Bogdan Rusu, Nico Blodow, Michael Beetz. 2009

4,175 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Computer Vision and Pattern Recognition, Geology

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Citations in the corpus

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2016, background): “Point features often encode certain statistical properties of points and are designed to be invariant to certain transformations, which are typically classified as intrinsic [2, 21, 3] or extrinsic [18, 17, 13, 10, 5].”
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017, methodology): “Some of the earlier methods for feature representation are [41, 8, 7, 19, 42, 35, 6, 27, 1, 36, 2, 25, 26].”
Dynamic Graph CNN for Learning on Point Clouds (2018, methodology): “nd segmentation, two model tasks in point cloud processing. 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]. More recently, the success of deep neural networks for image processing ha”
Deep learning for detecting robotic grasps (2013, neutral): “We compare our recognition results in the Cornell grasping dataset with the features from [28], as well as the combination of these features and Fast Point Feature Histogram (FPFH) features [51].”
Orientation-boosted Voxel Nets for 3D Object Recognition (2016, methodology): “results using our orientation-boosted networks in most of the experiments. 2 Related Work Most works on 3D object recognition rely on handcrafted feature descriptors, such as Point Feature Histograms [3,4], 3D Shape Context [5], or Spin Images [6]. Descriptors based on surface normals have been arou”
Data-Driven Grasp Synthesis—A Survey (2013, neutral): “They are either heuristics that map structures in this data to grasp conﬁgurations directly [26, 117] or they try to detect and recognize objects and estimate their pose [106, 108].”
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration (2020, background): “Second, although a number of recent approaches [14, 13, 22, 37] introduce rotation-invariant descriptors, they simply integrate the handcrafted features such as point-pairs [48, 16] and point density [50, 62, 51], or external local reference frames (LRFs) [37, 33, 66] into”
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions (2016, methodology): “For Johnson et al. (Spin-Images) [19] and Rusu et al. (Fast Point Feature Histograms) [28], we use the implementation provided in the Point Cloud Library (PCL).”
PREDATOR: Registration of 3D Point Clouds with Low Overlap (2020, background): “Local 3D feature descriptors: Early local descriptors for point clouds [19, 31, 30, 37, 36] aimed to characterise the local geometry by using hand-crafted features.”
DeepIM: Deep Iterative Matching for 6D Pose Estimation (2018, background): “These include point pairs (Mian et al. 2006; Hinterstoisser et al. 2016), spin-images (Johnson and Hebert 1999), and point-pair histograms (Rusu et al. 2009; Tombari et al. 2010).”
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration (2016, methodology): “Note the short-hand notation used here: j ∗ varies as a function of ( R , t ) and also depends on x i .”
Parsing the Hand in Depth Images (2014, background): “, the fast point feature histograms [27], the geodesic distance map [33], and the depth kernel descriptors [34].”
SoftGroup for 3D Instance Segmentation on Point Clouds (2022, methodology): “To process point clouds, early methods [2,3,36,37] extract hand-crafted features based on statistical properties of points.”
Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes (2011, background): “We then present quantitative evaluations for challenging scenes that show that our multimodal templates outperform state-of-the-art approaches.”
Depth kernel descriptors for object recognition (2011, background): “There has been a lot of work in robotics on 3D features from point clouds: Spin Images [12] is a classical example of local 3D features analogous to SIFT [19]; the Fast Point Feature Histogram [21] is another example of an efficient 3D feature.”
A review of multimodal image matching: Methods and applications (2021, background): “Typical points descriptors include shape context (SC) [131], spin images [304], integral volume [305], and point feature histograms [306].”
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection (2020, background): “One of the most popular local descriptors is fast point feature histogram (FPFH) [2] which explores the local surface normal of each neighbour point.”
3D point cloud segmentation: A survey (2013, methodology): “Like Nurunnabi [1], this method based on surface segmentation, it extracted feature descriptor called Fast Point Feature Histograms (FPFH) [6] to encode the local surface geometry around a point.”
Voting-based pose estimation for robotic assembly using a 3D sensor (2012, background): “Several feature descriptors and matching algorithms have been proposed [12], [13], [14], [15], [16].”