Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes

By Andrew E. Johnson, Martial Hebert. 1999

Subfields: Computer Vision and Pattern Recognition

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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].”
A Joint Model of Language and Perception for Grounded Attribute Learning (2012, background): “Vision Current state-of-the-art object recognition systems (Felzenszwalb et al., 2009; Yang et al., 2009) are based on local image descriptors, for example SIFT over images (Lowe, 2004) and Spin Images over 3D point clouds (Johnson & Hebert, 1999).”
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].”
3D fully convolutional network for vehicle detection in point cloud (2016, background): “[16] also suggest it possible to predict 3D object localization by 2D CNN network on range scans.”
Vehicle Detection from 3D Lidar Using Fully Convolutional Network (2016, methodology): “For example, Johnson and Hebert [13] randomly samples points from the point cloud as correspondences.”
Character Recognition in Natural Images (2009, background): “, 2005), (Johnson and Herbert, 1999) is a two-dimensional histogram encoding the distribution of image brightness values in the neighborhood of a particular reference point.”
FPNN: Field Probing Neural Networks for 3D Data (2016, background): “3D shapes can be converted into 2D images and represented by descriptors of the converted images [13, 4].”
Dense Human Body Correspondences Using Convolutional Networks (2015, background): “Local geometric descriptors, such as spin images [18] or curvature [32] have proven to be insufficient for establishing reliable correspondences as they are extrinsic and fragile under deformations.”
Review: deep learning on 3D point clouds (2020, background): “In the past few years, processing of point cloud for visual intelligence has been based on handcrafted features [1, 2, 3, 4, 5, 6].”
Supervised learning of hidden and non-hidden 0-order affordances and detection in real scenes (2012, background): “Spin Images [18] —”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Some widely employed local descriptors are Spin Image [Johnson and Hebert 1999], 3D Shape Context (3DSC) [Frome et al. 2004], Point Feature Histogram (PFH) [Rusu et al. 2008], Signature of Histogram of Orientations (SHOT) [Tombari et al. 2010], and Rotational Projection Statistics (RoPS) [Guo et…”
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study (2006, unknown): “The SPIN descriptor, based on spin images used for matching range data [ 26 ], is a rotation-invariant two-dimensional histogram of intensities within an image region.”
Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly (2019, background): “correspondences is a fundamental task in vision and graphics, studied extensively in prior work via descriptors [13], [14], [15], [16], [17], [18].”
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.”
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, methodology): “Johnson and Hebert [64] used the normal n n of a keypoint p p as the local reference axis and expressed each neighboring point q q with two parameters: the radial distance a and the signed distance b.”