Structural Indexing: Efficient 3-D Object Recognition

By F. Stein, Gérard G. Medioni. 1992

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Citations in the corpus

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].”
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes (2006, background): “The splash representation [39] makes assumptions about the shape of the objects and is also sensitive to noise and occlusions.”
SHOT: Unique signatures of histograms for surface and texture description (2014, methodology): “Method Category Unique LRF Color StInd [20] Sign.”
Model globally, match locally: Efficient and robust 3D object recognition (2010, background): “Splashs [20] describe the distribution of normal orientations around a point.”
MVX-Net: Multimodal VoxelNet for 3D Object Detection (2019, background): “Earlier approaches ([6], [36], [1], [8], [37]) employ hand-crafted representations and achieve satisfactory results in the presence of rich and detailed 3D information.”
COSMOS - A Representation Scheme for 3D Free-Form Objects (1997, background): “It is also not guaranteed to (cid:12)nd the best transformation if the scene contains occluded objects, or if the point densities in the model and scene representations are di(cid:11)erent, and if there are numerous spurious points from di(cid:11)erent objects.”
Learning to Rank 3D Features (2014, background): “Many of them are based on selecting salient points in the 3D point cloud and using feature representations that can invariantly describe regions around the points [29,15,27,8,2,1,26].”
Fully automatic registration of multiple 3D data sets (2003, background): “Proposed encodings include spin-images [1], splashes [ 3 ], point signatures [4], harmonic shape images [5], spherical attribute images [6], and the tripod-operator [7] .A n alternative approach is to explicitly detect extended features, such as curves of maximal curvature [8], intersections of plan”
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].”
Unique Signatures of Histograms for Local Surface Description (2010, methodology): “As highlighted in the third column, the majority of proposals are based on RFs that are not unique [6] [7] [8] [12] [2], i.”
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes (1999, background): “Stein and Medioni [24] recognize 3D objects by matching points using structural indexing and their " splash " representation.”
M2DP: A novel 3D point cloud descriptor and its application in loop closure detection (2016, methodology): “One of the first algorithms among signature methods is Structural Indexing (SI) [18].”
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition (2013, methodology): “Stein and Medioni (1992) proposed a splash feature by recording the relationship between the normals of the geodesic neighboring points and the feature point.”
Recognizing Objects in Range Data Using Regional Point Descriptors (2004, background): “Methods which use regional point descriptors have proven successful in the context of image-based recognition [17][15][2] as well as 3D recognition and surface matching [22][13][5][21].”