3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey

By Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, Min Lu, Jianwei Wan. 2014

606 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology, 3D Vision & Pose

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Citations in the corpus

NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “This empowers the computer vision solutions to move important steps towards 3D vision, such as 3D object recognition [2], 3D scene understanding [3], and 3D activity analysis [4], etc .”
Dynamic Graph CNN for Learning on Point Clouds (2018, background): “iptors such as global point signatures [37], the heat and wave kernel signatures [48, 2], and variants [8]. Most recently, several approaches wrap machine learning schemes around standard descriptors [15, 42]. Learned Features. In computer vision, approaches relying on ‘hand-crafted’ features have r”
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration (2020, background): “Accurate matching of partial 3D surfaces is critical for point cloud registration [17, 38, 6, 21, 29], segmentation [58, 28, 27], and recognition [24, 12, 47].”
State of research in automatic as-built modelling (2015, methodology): “For non-parametric shapes, descriptors with more discriminant power are available: spin images, fast point feature histograms, shape contexts, to mention only a few; we refer the reader to [55] for a more in-depth general study, and to [10] for an evaluation of their discriminatory power when applie”
TEASER: Fast and Certifiable Point Cloud Registration (2020, methodology): “Correspondence-based methods first detect and match 3D keypoints between point clouds using local [17], [34], [18], [1] or global [7], [35] descriptors, and then use an estimator to infer the transformation from these putative correspondences.”
Deep Learning for 3D Point Clouds: A Survey (2019, background): “3D data acquired by these sensors can provide rich geometric, shape and scale information [2], [3].”
Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution (2018, background): “For a more complete review on traditional feature descriptors, we refer to (Guo et al. 2014).”
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space (2019, background): “Traditionally, systems for such tasks have relied on the approximate computation of geometric features such as faces, edges or corners [31, 11] and hand-crafted features encoding statistical properties [3, 27].”
Fast Global Registration (2016, background): “This difficult form of the problem is encountered in scene reconstruction [7, 39], 3D object retrieval [15, 29], camera relocalization [13], and other applications.”
GDRNPP: A Geometry-Guided and Fully Learning-Based Object Pose Estimator (2021, neutral): “The Rotations and translations are uniformly sampled in 3D space, and within an interval of [4 , 8] , respectively.”
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors (2015, methodology): “Finally, the feature descriptors of one surface are matched against the feature descriptors of other surfaces of interest to yield point-to-point feature correspondences (Tombari et al. 2010b, 2013; Guo et al. 2014a).”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Based on the type of descriptors used in the pipeline, recognition methods are often characterized as global or local [Guo et al. 2014; Aldoma et al. 2012b].”
PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet (2019, background): “While interest points have the potential to improve the computationally speed of the registration approaches, they do not generalize to all applications [12].”
Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features (2014, methodology): “The UWA dataset is currently regarded as the most popular benchmark for 3D object recognition [4,10,11,13,19,27,32].”