Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation

By Aitor Aldoma, Zoltán-Csaba Márton, Federico Tombari, Walter Wohlkinger, C. Potthast, Bernhard Zeisl, Radu Bogdan Rusu, Suat Gedikli, Markus Vincze. IEEE robotics & automation magazine, 2012

385 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/53f85aa476673f50c7a4c255c46cde461d3ba949

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Citations in the corpus

Learning hierarchical sparse features for RGB-(D) object recognition (2014, methodology): “We compare HMP with the baseline (Lai et al., 2011a), fast point feature histograms (Aldoma et al., 2012), kernel descriptors (Bo et al., 2011b), and convolutional k-means descriptors (CKM Desc) (Blum et al., 2012) in Table 1.”
Learning to Detect Good 3D Keypoints (2017, methodology): “13, the relative ordering of hand-crafted detectors does change: on the one hand, the baseline uniform sampling approach performs better than KPQ, ISS, and Harris3D; on the other hand, NARF, whose performance was quite unsatisfactory on previous datasets, turns out the best hand-crafted detector onK”
A review of algorithms for filtering the 3D point cloud (2017, neutral): “The 3D point cloud [1–3], a new primitive representation for objects, has became increasingly prevalent in many research fields [2], such as object recognition [4] and reconstruction [5,6], due to its simplicity, flexibility and powerful representation capability.”
Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair Descriptor (2018, methodology): “For this, we use the mesh sampling approach from the Point Cloud Library [21] with a resolution of 1 cm.”
SHOT: Unique signatures of histograms for surface and texture description (2014, background): “In the evaluation proposed in [49], focused on object recognition in clutter and occlusion from CAD models,”
Analysis and Observations From the First Amazon Picking Challenge (2016, background): “exclusively on matching 3D perception against 3D models stored in a database [16].”
Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" (2016, background): “Fast point feature histogram (fpfh) shape features [Rusu et al., 2009] as implemented in the Point Cloud Library [Aldoma et al., 2012]; and 3) deep visual features from the 16-layer VGG network [Simonyan and Zisserman, 2014].”
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors (2015, methodology): “They are available in the Point Cloud Library (PCL) Version 1.7.1 (Rusu and Cousins 2011; Aldoma et al. 2012a), while the others were implemented in Matlab 2011b.”
PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks (2014, background): “Successful examples of such robot object perception systems are described in [9] using point features of 3D opaque objects, or MOPED , which uses visual keypoint descriptors for the learned textured objects [10] and specialized perception systems for translucent objects [11].”
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline (2021, background): “Processing 3D point cloud data accurately is crucial in many applications including autonomous driving (Navarro-Serment et al., 2010; Kidono et al., 2011), robotics (Rusu et al., 2009; Correll et al., 2016; Mousavian et al., 2019) and scene understanding (Aldoma et al., 2012).”