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MIT AI Memo-822, January 1985.
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And:
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And:
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Earlier:
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ICPR90(I: 198-200).
IEEE DOI
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On the Relationship Between Surface Covariance and
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MDSG94(343)
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3-D Model Construction from Multiple Views Using
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Data Fusion.
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ICPR84(752-754).
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Recognize Range Data. This is Vemuri's thesis, and thus it includes several different
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IEEE DOI
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8800
Earlier:
Resolving the Orientation and Identity of an Object from Range Data,
SRMSF87(178-187).
BibRef
Vemuri, B.C.,
Mitiche, A., and
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3-D Object Representation from Range Data Using
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3DMV87(241-266).
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during freezing.
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0403
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BibRef
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Modeling 3D Objects with Patches of Quadratic Surfaces:
Application to the Recognition and Locating of Anatomic Structures,
CVRMed95(XX-YY)
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Minimize Adaptive Least Kth Order Squares. Better able to handle
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RESC (
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Reed, M.K.[Michael K.],
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Solid Model Construction Using Meshes and Volumes,
DARPA97(921-926).
merging mesh surfaces.
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A Robotic System for 3-D Model Acquisition from Multiple Range Images,
CRA97(xx-yy).
PS File.
Registration. Uses range sensors for planning. Uses information about the model and
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Reed, M.K.[Michael K.],
Allen, P.K.[Peter K.], and
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9702
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9704
Accurate CAD model from range views: tag imaged/unimaged surfaces
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Blaer, P.,
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IEEE DOI
0106
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GM(63), No. 5, September 2001, pp. 304-332.
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0203
BibRef
Earlier:
A2, A1, Add A3:
Chen, P.F.,
3DIM99(348-357).
IEEE DOI
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Whitaker, R.T.[Ross T.],
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BibRef
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IEEE DOI
0106
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IEEE DOI
0206
BibRef
Earlier: A3, A2, A1:
Inference of Segmented Overlapping Surfaces from Binocular and
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PercOrg01(xx-yy).
0106
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CVPR98(346-352).
IEEE DOI
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Range image reconstruction based on robust multiresolution estimation
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Pixelwise selection of resolution.
See also Segmentation based on fusion of range and intensity images using robust trimmed methods.
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Multiresolution Surface Parameter Estimation for Range Images,
ICIP96(I: 37-40).
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Improved range image segmentation by analyzing surface fit patterns,
CVIU(97), No. 2, February 2005, pp. 242-258.
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0412
Use Jiang and Bunke
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laser scanning; segmentation; clustering; neighborhood system
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Automatic Glacier Surface Analysis from Airborne Laser Scanning,
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PDF File.
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Geist, T.,
Rutzinger, M.,
Pfeifer, N.,
Glacier Surface Segmentation Using Airborne Laser Scanning Point Cloud
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PDF File.
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Elsevier DOI
1410
BibRef
Earlier: A1, A2, A3:
Diagnostic-Robust Statistical Analysis for Local Surface Fitting In 3d
Point Cloud Data,
AnnalsPRS(I-3), No. 2012, pp. 269-274.
DOI Link
1209
BibRef
And: A1, A2, A3:
Robust Segmentation in Laser Scanning 3D Point Cloud Data,
DICTA12(1-8).
IEEE DOI
1303
BibRef
And: A1, A2, A3:
Robust segmentation for multiple planar surface extraction in laser
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1302
3D modelling
Estimation
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Lindenbergh, R.,
Robust Cylinder Fitting In Three-dimensional Point Cloud Data,
Hannover17(63-70).
DOI Link
1805
BibRef
Nurunnabi, A.[Abdul],
West, G.A.W.[Geoff A.W.],
Belton, D.[David],
Outlier detection and robust normal-curvature estimation in mobile
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PR(48), No. 4, 2015, pp. 1404-1419.
Elsevier DOI
1502
BibRef
Earlier:
Robust Outlier Detection and Saliency Features Estimation in Point
Cloud Data,
CRV13(98-105)
IEEE DOI
1308
Feature extraction
BibRef
Nurunnabi, A.[Abdul],
West, G.A.W.[Geoff A.W.],
Belton, D.[David],
Robust Locally Weighted Regression Techniques for Ground Surface
Points Filtering in Mobile Laser Scanning Three Dimensional Point
Cloud Data,
GeoRS(54), No. 4, April 2016, pp. 2181-2193.
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Interpolation
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Nurunnabi, A.[Abdul],
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Robust Segmentation for Large Volumes of Laser Scanning
Three-Dimensional Point Cloud Data,
GeoRS(54), No. 8, August 2016, pp. 4790-4805.
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1608
image segmentation
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Nurunnabi, A.[Abdul],
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Robust statistical approaches for circle fitting in laser scanning
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3D modeling, Feature extraction, Object detection,
Point cloud processing, Remote sensing, Robust statistics, Surface fitting
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Fabijanska, A.[Anna],
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The Segmentation of 3D Images Using the Random Walking Technique on a
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IEEE DOI
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graph theory
See also Airway Tree Segmentation from CT Scans Using Gradient-Guided 3D Region Growing.
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Wang, W.M.[Wei-Min],
Sakurada, K.[Ken],
Kawaguchi, N.[Nobuo],
Incremental and Enhanced Scanline-Based Segmentation Method for
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Pagnutti, G.[Giampaolo],
Zanuttigh, P.[Pietro],
Joint segmentation of color and depth data based on splitting and
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IVC(70), 2018, pp. 21 - 31.
Elsevier DOI
1804
BibRef
Earlier:
Scene segmentation from depth and color data driven by surface
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ICIP14(4407-4411)
IEEE DOI
1502
Segmentation, Depth, Spectral clustering, Kinect, NURBS.
Image color analysis
BibRef
Fan, Y.L.[Yu-Ling],
Wang, M.L.[Mei-Li],
Geng, N.[Nan],
He, D.J.[Dong-Jian],
Chang, J.[Jian],
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A self-adaptive segmentation method for a point cloud,
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Springer DOI
1804
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Wang, Y.[Yuan],
Wang, J.J.[Jia-Jing],
Chen, X.[Xiuwan],
Chu, T.X.[Tian-Xing],
Liu, M.L.[Mao-Lin],
Yang, T.[Ting],
Feature Surface Extraction and Reconstruction from Industrial
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Chen, S.,
Duan, C.,
Yang, Y.,
Li, D.,
Feng, C.,
Tian, D.,
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology
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IEEE DOI
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3D point cloud, deep autoencoder, graph filtering, graph topology inference
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Wang, L.[Lei],
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Kirk, T.F.,
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Toblerone: Surface-Based Partial Volume Estimation,
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IEEE DOI
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Image segmentation, Surface treatment, Neuroimaging, Estimation,
Volume measurement, Imaging, Spatial resolution,
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BibRef
Zhang, Z.Y.[Zhi-Yuan],
Hua, B.S.[Binh-Son],
Yeung, S.K.[Sai-Kit],
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds
Deep Learning,
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Springer DOI
2205
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Zhang, Z.Y.[Zhi-Yuan],
Hua, B.S.[Binh-Son],
Rosen, D.W.[David W.],
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3DV19(204-213)
IEEE DOI
1911
Feature extraction, Deep learning, Task analysis, Neural networks,
Training, Semantics, Deep Learning, 3D Point Clouds
BibRef
Suzuki, T.,
Ozawa, K.,
Sekikawa, Y.,
Rethinking PointNet Embedding for Faster and Compact Model,
3DV20(791-800)
IEEE DOI
2102
Kernel, Convolution,
Computational efficiency, Sensors, Task analysis, Neural networks,
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See also PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.
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Ben-Shabat, Y.[Yizhak],
Koneputugodage, C.H.[Chamin Hewa],
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DiGS: Divergence guided shape implicit neural representation for
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CVPR22(19301-19310)
IEEE DOI
2210
Point cloud compression, Representation learning,
Surface reconstruction, Solid modeling, Design automation, Vision+graphics
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Ben-Shabat, Y.[Yizhak],
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Springer DOI
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Ngo, P.[Phuc],
Digital Hyperplane Fitting,
IWCIA20(164-180).
Springer DOI
2009
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Li, R.,
Li, X.,
Heng, P.,
Fu, C.,
PointAugment: An Auto-Augmentation Framework for Point Cloud
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CVPR20(6377-6386)
IEEE DOI
2008
Training, Shape, Feature extraction,
Solid modeling, Training data
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Yuan, S.,
Fang, Y.,
ROSS: Robust Learning of One-Shot 3D Shape Segmentation,
WACV20(1950-1958)
IEEE DOI
2006
Shape, Task analysis, Convolution,
Learning systems, Robustness, Training
BibRef
Prokudin, S.[Sergey],
Lassner, C.[Christoph],
Romero, J.[Javier],
Efficient Learning on Point Clouds With Basis Point Sets,
ICCV19(4331-4340)
IEEE DOI
2004
BibRef
And:
CEFRL19(3072-3081)
IEEE DOI
2004
Compare to PointNet.
See also PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. image classification,
learning (artificial intelligence), neural nets, set theory,
Surface reconstruction.
Encoding, Shape,
Computational modeling, Neural networks, deep learning
BibRef
Zhang, Z.Y.[Zhi-Yuan],
Hua, B.S.[Binh-Son],
Chen, W.,
Tian, Y.,
Yeung, S.K.[Sai-Kit],
Global Context Aware Convolutions for 3D Point Cloud Understanding,
3DV20(210-219)
IEEE DOI
2102
Deep learning, Task analysis, Training,
Shape, Neural networks, Feature extraction, Deep Learning, Point Cloud
BibRef
Chiang, H.,
Lin, Y.,
Liu, Y.,
Hsu, W.H.,
A Unified Point-Based Framework for 3D Segmentation,
3DV19(155-163)
IEEE DOI
1911
Feature extraction, Cameras, Semantics, Image segmentation, Geometry,
3D point cloud processing
BibRef
Li, X.,
Wang, L.,
Fang, Y.,
PC-Net: Unsupervised Point Correspondence Learning with Neural
Networks,
3DV19(145-154)
IEEE DOI
1911
Shape, Neural networks, Pipelines,
Optimization, Topology, Silicon, Unsupervised learning, point cloud,
landmark
BibRef
Xu, K.,
Yao, Y.,
Murasaki, K.,
Ando, S.,
Sagata, A.,
Semantic Segmentation of Sparsely Annotated 3D Point Clouds by
Pseudo-Labelling,
3DV19(463-471)
IEEE DOI
1911
Training, Labeling, Semantics,
Task analysis, Training data, Neural networks, PointNet,
Sparse Annotation
BibRef
Zhao, C.,
Zhou, W.,
Lu, L.,
Zhao, Q.,
Pooling Scores of Neighboring Points for Improved 3D Point Cloud
Segmentation,
ICIP19(1475-1479)
IEEE DOI
1910
Point Cloud, Segmentation, Attention, Score Refinement
BibRef
Blanc-Beyne, T.,
Carlier, A.,
Charvillat, V.,
Iterative Dataset Filtering for Weakly Supervised Segmentation of
Depth Images,
ICIP19(1515-1519)
IEEE DOI
1910
Depth image segmentation, Weakly supervised learning
BibRef
Soleimani, H.[Hossein],
Jacob, G.P.[George Poothicottu],
Michailovich, O.V.[Oleg V.],
Fitting Smooth Manifolds to Point Clouds in a Level Set Formulation,
ICIAR19(I:139-149).
Springer DOI
1909
BibRef
Cromwell, E.[Erol],
Flynn, D.[Donna],
Lidar Cloud Detection With Fully Convolutional Networks,
WACV19(619-627)
IEEE DOI
1904
atmospheric techniques, clouds, image classification,
image segmentation, learning (artificial intelligence),
Training
BibRef
Shui, P.,
Wang, P.,
Yu, F.,
Hu, B.,
Gan, Y.,
Liu, K.,
Zhang, Y.,
3D Shape Segmentation Based on Viewpoint Entropy and Projective Fully
Convolutional Networks Fusing Multi-view Features,
ICPR18(1056-1061)
IEEE DOI
1812
Shape, Face, Entropy, Image segmentation,
Surface treatment, Labeling, 3D shape segmentation, graph cuts
BibRef
Halimi, O.[Oshri],
Kimmel, R.[Ron],
Self Functional Maps,
3DV18(710-718)
IEEE DOI
1812
computational geometry, eigenvalues and eigenfunctions,
Laplace equations, matrix algebra, algebraic form,
Algebraic representation of surfaces
BibRef
Estellers, V.,
Schmidt, F.,
Cremers, D.,
Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis,
3DV18(277-285)
IEEE DOI
1812
computational geometry, differential geometry,
eigenvalues and eigenfunctions, image matching,
shape analysis
BibRef
Skaria, A.S.,
Yap, K.H.,
Integrated 3D feature augmentation and view selection in commercial
product search,
ICIP17(615-619)
IEEE DOI
1803
Databases, Feature extraction, Libraries,
Optical imaging, Search problems,
view selection
BibRef
Kalogerakis, E.[Evangelos],
Averkiou, M.,
Maji, S.,
Chaudhuri, S.,
3D Shape Segmentation with Projective Convolutional Networks,
CVPR17(6630-6639)
IEEE DOI
1711
Cognition, Image segmentation, Labeling, Shape, Surface treatment
BibRef
Charles, R.Q.[R. Qi],
Su, H.[Hao],
Kaichun, M.[Mo],
Guibas, L.J.[Leonidas J.],
PointNet:
Deep Learning on Point Sets for 3D Classification and Segmentation,
CVPR17(77-85)
IEEE DOI
1711
Feature extraction, Machine learning,
Semantics, Shape,
BibRef
Gunji, N.,
Niigaki, H.,
Tsutsuguchi, K.,
Kurozumi, T.,
Kinebuchi, T.,
3D object recognition from large-scale point clouds with global
descriptor and sliding window,
ICPR16(721-726)
IEEE DOI
1705
Computational modeling, Shape, Solid modeling,
Training, Training data
BibRef
Wang, C.L.[Cui-Lan],
Lai, S.H.[Shu-Hua],
Adaptive Isosurface Reconstruction Using a Volumetric-Divergence-Based
Metric,
ISVC16(I: 367-378).
Springer DOI
1701
BibRef
Fernandes, O.[Oliver],
Frey, S.[Steffen],
Ertl, T.[Thomas],
Interpolation-Based Extraction of Representative Isosurfaces,
ISVC16(I: 403-413).
Springer DOI
1701
BibRef
Minto, L.[Ludovico],
Pagnutti, G.[Giampaolo],
Zanuttigh, P.[Pietro],
Scene Segmentation Driven by Deep Learning and Surface Fitting,
DeepLearn16(III: 118-132).
Springer DOI
1611
BibRef
Nguatem, W.[William],
Mayer, H.[Helmut],
Contiguous Patch Segmentation in Pointclouds,
GCPR16(131-142).
Springer DOI
1611
BibRef
Nguyen, H.L.[Hoang Long],
Belton, D.[David],
Helmholz, P.[Petra],
Scan Profiles Based Method For Segmentation And Extraction Of Planar
Objects In Mobile Laser Scanning Point Clouds,
ISPRS16(B3: 351-358).
DOI Link
1610
BibRef
Käshammer, P.F.,
Nüchter, A.,
Mirror Identification and Correction of 3D Point Clouds,
3D-Arch15(109-114).
DOI Link
1504
Mirrors do not appear in laser data.
BibRef
Alis, C.[Christian],
Boehm, J.[Jan],
Liu, K.[Kun],
Parallel Processing Of Big Point Clouds Using Z-order-based
Partitioning,
ISPRS16(B2: 71-77).
DOI Link
1610
BibRef
Liu, K.[Kun],
Boehm, J.[Jan],
Alis, C.[Christian],
Change Detection Of Mobile Lidar Data Using Cloud Computing,
ISPRS16(B3: 309-313).
DOI Link
1610
BibRef
And: A1, A2, Only:
Classification of Big Point Cloud Data Using Cloud Computing,
GeoBigData15(553-557).
DOI Link
1602
BibRef
And:
A New Framework For Interactive Segmentation of Point Clouds,
CloseRange14(357-362).
DOI Link
1411
BibRef
López-Franco, C.[Carlos],
Hernández-Barragán, J.[Jesús],
López-Franco, M.[Michel],
Arana-Daniel, N.[Nancy],
Alanís, A.Y.[Alma Y.],
Plane Detection Using Particle Swarm Optimization and Conformal
Geometric Algebra,
CIARP14(852-859).
Springer DOI
1411
BibRef
Sui, W.[Wei],
Wang, L.F.[Ling-Feng],
Wu, H.Y.[Huai-Yu],
Pan, C.H.[Chun-Hong],
Planar Segmentation from Point Clouds via Graph Laplacian Regularized
K-Planes,
ACPR13(64-68)
IEEE DOI
1408
computer graphics
BibRef
Choi, O.[Ouk],
Kang, B.M.[Byong-Min],
Denoising of Time-of-Flight depth data via iteratively reweighted
least squares minimization,
ICIP13(1075-1079)
IEEE DOI
1402
Cameras
BibRef
Biddle, H.[Harry],
von Glehn, I.[Ingrid],
Macdonald, C.B.[Colin B.],
Marz, T.[Thomas],
A volume-based method for denoising on curved surfaces,
ICIP13(529-533)
IEEE DOI
1402
Equations
BibRef
Castaldo, F.[Francesco],
Lippiello, V.[Vincenzo],
Palmieri, F.A.N.[Francesco A.N.],
Siciliano, B.[Bruno],
Real-Time Estimation of Planar Surfaces in Arbitrary Environments Using
Microsoft Kinect Sensor,
CIAP13(II:552-561).
Springer DOI
1309
Planes from point cloud data
BibRef
Baker, C.L.,
Hoff, W.,
DIRSAC: A directed sampling and consensus approach to quasi-degenerate
data fitting,
WACV13(154-159).
IEEE DOI
1303
Similar to RANSAC
BibRef
Lee, S.M.[Sang-Mook],
Abbott, A.L.,
Schmoldt, D.L.,
Wavelet-based hierarchical surface approximation from height fields,
CVPR04(I: 299-305).
IEEE DOI
0408
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Calibration -- LiDAR, Laser Scanner, Depth Sensor, Scanner Error Analysis .