11.2.2 Surface Patches, Planes, Descriptions from Range

Sederberg, T.W., and Anderson, D.C.,
Steiner Surface Patches,
IEEE_CGA(5), No. 5, May 1985, pp. 23-36. BibRef 8505

Brady, J.M.[J. Michael], Ponce, J.[Jean], Yuille, A.L.[Alan L.], Asada, H.[Haruo],
Describing Surfaces,
CVGIP(32), No. 1, October 1985, pp. 1-28.
Elsevier DOI BibRef 8510
Earlier: MIT AI Memo-822, January 1985.
WWW Link. A variety of techniques using range data for obtaining surface descriptions. A good and large set of references.
See also Toward a Surface Primal Sketch. BibRef

Fan, T.J., Medioni, G.G., and Nevatia, R.,
Segmented Description of 3-D Surfaces,
RA(3), No. 6, December 1987, pp. 527-538. BibRef 8712 USC Computer Vision BibRef
Earlier:
Surface Segmentation and Description from Curvature Features,
DARPA87(351-359). BibRef
And:
Description of Surfaces from Range Data using Curvature Properties,
CVPR86(86-91). BibRef
And:
Description of Surfaces from Range Data,
DARPA85(232-244). Use 1-D curvature of the surface in 4 directions to get important features at multiple resolutions, and use descriptions to reconstruct the objects.
See also Recognizing 3-D Objects Using Surface Descriptions. BibRef

Medioni, G.G., and Nevatia, R.,
Description of 3-D Surfaces Using Curvature Properties,
DARPA84(291-299). BibRef 8400 USC Computer Vision BibRef

Soucy, M.[Marc], Laurendeau, D.[Denis],
A General Surface Approach to the Integration of a Set of Range Views,
PAMI(17), No. 4, April 1995, pp. 344-358.
IEEE DOI BibRef 9504
And:
A Dynamic Integration Algorithm to Model Surfaces from Multiple Range Views,
MVA(8), No. 1, 1995, pp. 53-62.
Springer DOI BibRef
And:
Surface Modeling from Dynamic Integration of Multiple Range Views,
ICPR92(I:449-452).
IEEE DOI BibRef
Earlier:
Generating Non-redundant Surface Representations of 3-D Objects Using Multiple Range Views,
ICPR90(I: 198-200).
IEEE DOI 9006
Integrate range data from several views and model the surface. Compute a connected surface model from registered range images. General set of views allowed. Builds complex free-form models.
See also Towards a General Multiview Registration Technique. BibRef

Soucy, M.[Marc],
Modeling the Surface of 3D Objects Using Multiple Range Views,
Ph.D.Thesis, Laval University, Quebec, Canada, 1992. BibRef 9200

Soucy, M., Laurendeau, D.,
Multiresolution Surface Modeling Based on Hierarchical Triangulation,
CVIU(63), No. 1, January 1996, pp. 1-14.
DOI Link BibRef 9601
Earlier:
Multi-Resolution Surface Modeling from Multiple Range Views,
CVPR92(348-353).
IEEE DOI BibRef

Soucy, M.[Marc], Godin, G.[Guy], Baribeau, R.[Rejean], Blais, F.[Francois], Rioux, M.[Marc],
Sensors and Algorithms for the Construction of Digital 3-D Colour Models of Real Objects,
ICIP96(II: 409-412).
IEEE DOI BibRef 9600

Li, S.Z.,
Invariant Surface Segmentation through Energy Minimization with Discontinuities,
IJCV(5), No. 2, November 1990, pp. 161-194.
Springer DOI BibRef 9011
And:
Reconstruction without Discontinuities,
ICCV90(709-712).
IEEE DOI BibRef

Lu, H.Q., Todhunter, J.S., and Sze, T.W.,
Congruence Conditions for Nonplanar Developable Surfaces and Their Application to Surface Recognition,
CVGIP(58), No. 3, November 1993, pp. 265-285.
DOI Link BibRef 9311

Liang, P., and Todhunter, J.S.,
Representation and Recognition of Surface Shapes in Range Images: A Differential Geometry Approach,
CVGIP(52), No. 1, October 1990, pp. 78-109.
Elsevier DOI Formal discussion of some parameters: lines of curvature. BibRef 9010

Berkmann, J.[Jens], and Caelli, T.M.[Terry M.],
Computation of Surface Geometry and Segmentation Using Covariance Techniques,
PAMI(16), No. 11, November 1994, pp. 1114-1116.
IEEE DOI BibRef 9411
Earlier:
On the Relationship Between Surface Covariance and Differential Geometry,
MDSG94(343) Segmentation of range images. BibRef

Kehtarnavaz, N., and de Figueiredo, R.J.P.,
A Framework for Surface Reconstruction for 3D Contours,
CVGIP(42), No. 1, April 1988, pp. 32-47.
Elsevier DOI Given 3-D contours, generate the parametric surface description.
See also Syntactic/Semantic Technique for Surface Reconstruction from Cross-Sectional Contours, A. BibRef 8804

Vemuri, B.C., and Aggarwal, J.K.,
Representation and Recognition of Objects from Dense Range Maps,
CirSys(34), No. 11, November 1987, pp. 1351-1363. BibRef 8711
Earlier:
3-D Model Construction from Multiple Views Using Range and Intensity Data,
CVPR86(435-437). Data Fusion. BibRef
Earlier:
3-D Reconstruction of Objects from Range Data,
ICPR84(752-754). BibRef
And:
Representation and Recognition of Objects from Depth Maps,
Univ. of Texas-TR-87-8-40, June 1987. Recognize Range Data. This is Vemuri's thesis, and thus it includes several different things such as object representation (surface patches), integration from multiple views (register with common coordinate system), and matching object descriptions to models. It appears to require precise models and good camera (view-point) information. BibRef

Vemuri, B.C., Aggarwal, J.K.,
Localization of Objects from Range Data,
CVPR88(893-898).
IEEE DOI BibRef 8800
Earlier:
Resolving the Orientation and Identity of an Object from Range Data,
SRMSF87(178-187). BibRef

Vemuri, B.C., Mitiche, A., and Aggarwal, J.K.,
3-D Object Representation from Range Data Using Intrinsic Surface Properties,
3DMV87(241-266). BibRef 8700
And:
Curvature-Based Representation of Objects From Range Data,
IVC(4), No. 2, May 1986, pp. 107-114.
Elsevier DOI Patches are found that are smooth in some intrinsic property, then classified using curvature etc. BibRef

Vemuri, B.C.[Baba C.], Diller, K.R., Davis, L.S., Aggarwal, J.K.,
Image analysis of solid-liquid interface morphology in freezing solutions,
PR(16), No. 1, 1983, pp. 51-61.
Elsevier DOI 0309
during freezing. BibRef

Vemuri, B.C., Diller, K.R., Aggarwal, J.K.,
A Model for Characterizing the Motion of the Solid-Liquid Interface in Freezing Solutions,
PR(17), No. 3, 1984, pp. 313-319.
Elsevier DOI 9611
BibRef

Yu, X.M., Bui, T.D., Krzyzak, A.,
Robust Estimation for Range Image Segmentation and Reconstruction,
PAMI(16), No. 5, May 1994, pp. 530-538.
IEEE DOI The RESC algorithm to eliminate outliers. BibRef 9405

Yu, X.M., Bui, T.D., Krzyzak, A.,
Range image segmentation and fitting by residual consensus,
CVPR92(657-660).
IEEE DOI 0403
BibRef

Koh, J., Suk, M.S., Bhandarkar, S.M.,
A Multilayer Self-Organizing Feature Map for Range Image Segmentation,
NeurNet(8), No. 1, 1995, pp. 67-86. BibRef 9500

Ghosal, S.[Sugata], Mehrotra, R.[Rajiv],
Segmentation of Range Images: An Orthogonal Moment-Based Integrated Approach,
RA(9), No. 4, 1993, pp. 385-399. BibRef 9300

Ghosal, S.[Sugata], Mehrotra, R.[Rajiv],
Range Surface Characterization and Segmentation Using Neural Networks,
PR(28), No. 5, May 1995, pp. 711-727.
Elsevier DOI BibRef 9505

Ghosal, S.[Sugata], Mehrotra, R.[Rajiv],
A Moment-Based Unified Approach to Image Feature Detection,
IP(6), No. 6, June 1997, pp. 781-793.
IEEE DOI 9705
BibRef

Bricault, I., Monga, O.,
From Volume Medical Images to Quadratic Surface Patches,
CVIU(67), No. 1, July 1997, pp. 24-38.
DOI Link 9707
BibRef
Earlier:
Modeling 3D Objects with Patches of Quadratic Surfaces: Application to the Recognition and Locating of Anatomic Structures,
CVRMed95(XX-YY) BibRef

Lee, K.M., Meer, P., Park, R.H.,
Robust Adaptive Segmentation of Range Images,
PAMI(20), No. 2, February 1998, pp. 200-205.
IEEE DOI 9803
Minimize Adaptive Least Kth Order Squares. Better able to handle outliers than MINPRAN (
See also MINPRAN: A New Robust Estimator for Computer Vision. ) or RESC (
See also Robust Estimation for Range Image Segmentation and Reconstruction. ).
See also Multiresolution Adaptive Image Smoothing. BibRef

Cho, W.[Wonjoon], Maekawa, T.[Takashi], Patrikalakis, N.M.[Nicholas M.], Peraire, J.[Jaime],
Topologically Reliable Approximation of Trimmed Polynomial Surface Patches,
GMIP(61), No. 2, March 1999, pp. 84-109. BibRef 9903

Maekawa, T., Ko, K.H.,
Surface construction by fitting unorganized curves,
GM(64), No. 5, September 2002, pp. Graphical Models, Volume 64, Issue 5, September 2002, 316-332.
Elsevier DOI 0309
From arbitrary stripes. BibRef

Ye, X., and Maekawa, T.,
Differential Geometry of Intersection Curves of Two Surfaces,
CAGD(16), No. 8, September 1999, pp. 767-788. BibRef 9909

Reed, M.K.[Michael K.], Allen, P.K.[Peter K.],
3-D Modeling from Range Imagery: An Incremental Method with a Planning Component,
IVC(17), No. 2, February 1999, pp. 99-111.
Elsevier DOI Sensor Planning. BibRef 9902
Earlier:
Solid Model Construction Using Meshes and Volumes,
DARPA97(921-926). merging mesh surfaces. BibRef

Reed, M.K.[Michael K.], Allen, P.K.[Peter K.],
System and Method for Generation of a Three-Dimensional Solid Model,
US_Patent6,249,600, June 19, 2001.
PDF File.
WWW Link. Laser scanner generation of 3-D models. BibRef 0106

Reed, M.K.[Michael K.],
Model Acquisition from Range Imagery,
Ph.D.Thesis, Dept. of Computer Science, Columbia University, 1998.
PDF File. BibRef 9800

Reed, M.K.[Michael K.], Allen, P.K.[Peter K.],
Constraint-Based Sensor Planning for Scene Modeling,
PAMI(22), No. 12, December 2000, pp. 1460-1467.
IEEE DOI
PDF File. 0012
Matching, Surfaces. BibRef
Earlier:
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 the unmodeled parts. BibRef

Reed, M.K.[Michael K.], Allen, P.K.[Peter K.], and Stamos, I.[Ioannis],
3D Modelling from Range Imagery: An Incremental Method with a Planning Component,
3DIM97(3 - View Planning)
PS File. 9702
BibRef
And:
Automatic Model Acquisition from Range Images with View Planning,
CVPR97(72-77).
IEEE DOI
PS File. 9704
Accurate CAD model from range views: tag imaged/unimaged surfaces BibRef

Allen, P.K.[Peter K.], Reed, M.K.[Michael K.], Stamos, I.[Ioannis],
View Planning for Site Modeling,
DARPA98(1181-1192).
PDF File. BibRef 9800

Allen, P.K., Stamos, I., Gueorguiev, A., Gold, E., Blaer, P.,
AVENUE: Automated site modeling in urban environments,
3DIM01(357-364).
IEEE DOI 0106
BibRef

Gueorguiev, A., Allen, P.K., Gold, E., and Blaer, P.,
Design, Architecture, and Control of a Mobile Site Modeling Robot,
CRA00(xx-yy).
PDF File. BibRef 0001

Khachan, M.[Mohammed], Chenin, P.[Patrick], Deddi, H.[Hafsa],
Polyhedral Representation and Adjacency Graph in n-dimensional Digital Images,
CVIU(79), No. 3, September 2000, pp. 428-441.
DOI Link 0008
BibRef

Gregor, J., Whitaker, R.T.,
Indoor Scene Reconstruction from Sets of Noisy Range Images,
GM(63), No. 5, September 2001, pp. 304-332.
DOI Link 0203
BibRef
Earlier: A2, A1, Add A3: Chen, P.F., 3DIM99(348-357).
IEEE DOI 9910
BibRef

Whitaker, R.T.[Ross T.], Gregor, J.[Jens],
A Maximum-Likelihood Surface Estimator for Dense Range Data,
PAMI(24), No. 10, October 2002, pp. 1372-1387.
IEEE Abstract. 0210
Surface models from multiple range maps. BibRef

Dell'Acqua, F.[Fabio], Fisher, R.B.[Robert B.],
Reconstruction of Planar Surfaces Behind Occlusions in Range Images,
PAMI(24), No. 4, April 2002, pp. 569-575.
IEEE DOI 0204
BibRef Edinburgh BibRef

Stulp, F., Dell'Acqua, F., Fisher, R.B.,
Reconstruction of surfaces behind occlusions in range images,
3DIM01(232-239).
IEEE DOI 0106
BibRef

Lee, M.S.[Mi-Suen], Medioni, G.[Gérard], Mordohai, P.[Philippos],
Inference of Segmented Overlapping Surfaces from Binocular Stereo,
PAMI(24), No. 6, June 2002, pp. 824-837.
IEEE DOI 0206
BibRef
Earlier: A3, A2, A1:
Inference of Segmented Overlapping Surfaces from Binocular and Multiple-View Stereo,
PercOrg01(xx-yy). 0106
BibRef

Lee, M.S.[Mi-Suen], Medioni, G.[Gerard],
Inferring Segmented Surface Description from Stereo Data,
CVPR98(346-352).
IEEE DOI
PDF File. BibRef 9800

Wang, G.Y.[Guo-Yu], Houkes, Z.[Zweitze], Ji, G.[Guangrong], Zheng, B.[Bing], Li, X.[Xin],
An Estimation-Based Approach for Range Image Segmentation: On the Reliability of Primitive Extraction,
PR(36), No. 1, January 2003, pp. 157-169.
Elsevier DOI 0210
BibRef

Chang, I.S.[In Su], Park, R.H.[Rae-Hong],
Range image reconstruction based on robust multiresolution estimation of surface parameters,
PRL(24), No. 9-10, June 2003, pp. 1123-1131.
Elsevier DOI 0304
Pixelwise selection of resolution.
See also Segmentation based on fusion of range and intensity images using robust trimmed methods. BibRef

Chang, I.S.[In S.], Sim, D.G.[Dong-G.], Park, R.H.[Rae-Hong],
Multiresolution Surface Parameter Estimation for Range Images,
ICIP96(I: 37-40).
IEEE DOI BibRef 9600

Min, J.[Jaesik], Bowyer, K.W.[Kevin W.],
Improved range image segmentation by analyzing surface fit patterns,
CVIU(97), No. 2, February 2005, pp. 242-258.
Elsevier DOI 0412
Use Jiang and Bunke
See also Fast Segmentation of Range Images by Scan Line Grouping. as baseline algorithm, analyze errors and show how to improve results. BibRef

Filin, S.[Sagi], Pfeifer, N.[Norbert],
Segmentation of airborne laser scanning data using a slope adaptive neighborhood,
PandRS(60), No. 2, April 2006, pp. 71-80.
Elsevier DOI 0610
laser scanning; segmentation; clustering; neighborhood system BibRef

Pfeifer, N.[Norbert], Höfle, B.[Bernhard], Briese, C.[Christian], Rutzinger, M.[Martin], Haring, A.[Alexander],
Analysis of the Backscattered Energy in Terrestrial Laser Scanning Data,
ISPRS08(B5: 1045 ff).
PDF File. 0807
BibRef

Kodde, M., Pfeifer, N., Gorte, B., Geist, T., Höfle, B.,
Automatic Glacier Surface Analysis from Airborne Laser Scanning,
Laser07(221).
PDF File. 0709
BibRef

Höfle, B., Geist, T., Rutzinger, M., Pfeifer, N.,
Glacier Surface Segmentation Using Airborne Laser Scanning Point Cloud and Intensity Data,
Laser07(195).
PDF File. 0709
BibRef

Tävári, D., Pfeifer, N.[Norbert],
Segmentation based robust interpolation: A new approach to laser data filtering,
Laser05(xx-yy).
PDF File. 0509
BibRef

Akel, N.A.[Nizar Abo], Filin, S.[Sagi], Doytsher, Y.[Yerach],
Orthogonal Polynomials Supported by Regional Growing Segmentation for the Extraction of Terrain from Lidar Data,
PhEngRS(73), No. 11, November 2007, pp. 1253-1266.
WWW Link. 0709
BibRef
Earlier: A2, A1, A3:
Detection and Reconstruction of Free Form Surfaces from Airborne Laser Scanning Data,
Laser07(119).
PDF File. 0709
Orthogonal polynomials are utilized for terrain detection from lidar data; results are supplemented by local analysis of smoothness for refining the results.
See also Reconstruction of Complex Shape Buildings from Lidar Data Using Free Form Surfaces.
See also Recovery of Systematic Biases in Laser Altimetry Data Using Natural Surfaces. BibRef

Baruch, A.[Amit], Filin, S.[Sagi],
Detection of Subtle Ridgelines from Laser Scanning Data,
ISPRS08(B3a: 111 ff).
PDF File. 0807
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Mor, S., Filin, S.,
Image-based Topography Reconstruction by Minimizing the Reprojection-Error over a Surface,
AnnalsPRS(I-3), No. 2012, pp. 251-256.
DOI Link 1209
BibRef

Schmitt, A.[Andreas], Vögtle, T.[Thomas],
An Advanced Approach for Automatic Extraction of Planar Surfaces and their Topology from Point Clouds,
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WWW Link. 1211
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Nurunnabi, A.[Abdul], Belton, D.[David], West, G.A.W.[Geoff A.W.],
Robust statistical approaches for local planar surface fitting in 3D laser scanning data,
PandRS(96), No. 1, 2014, pp. 106-122.
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 scanning 3D point cloud data,
ICPR12(1367-1370).
WWW Link. 1302
3D modelling Estimation BibRef

Nurunnabi, A., Sadahiro, Y., 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 laser scanning 3D point cloud data,
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.
IEEE DOI 1604
Interpolation BibRef

Nurunnabi, A.[Abdul], Belton, D.[David], West, G.A.W.[Geoff A.W.],
Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data,
GeoRS(54), No. 8, August 2016, pp. 4790-4805.
IEEE DOI 1608
image segmentation BibRef

Nurunnabi, A.[Abdul], Sadahiro, Y.[Yukio], Laefer, D.F.[Debra F.],
Robust statistical approaches for circle fitting in laser scanning three-dimensional point cloud data,
PR(81), 2018, pp. 417-431.
Elsevier DOI 1806
3D modeling, Feature extraction, Object detection, Point cloud processing, Remote sensing, Robust statistics, Surface fitting BibRef

Fabijanska, A.[Anna], Goclawski, J.[Jaroslaw],
The Segmentation of 3D Images Using the Random Walking Technique on a Randomly Created Image Adjacency Graph,
IP(24), No. 2, February 2015, pp. 524-537.
IEEE DOI 1502
graph theory
See also Airway Tree Segmentation from CT Scans Using Gradient-Guided 3D Region Growing. BibRef

Wang, W.M.[Wei-Min], Sakurada, K.[Ken], Kawaguchi, N.[Nobuo],
Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data,
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DOI Link 1612
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Pagnutti, G.[Giampaolo], Zanuttigh, P.[Pietro],
Joint segmentation of color and depth data based on splitting and merging driven by surface fitting,
IVC(70), 2018, pp. 21 - 31.
Elsevier DOI 1804
BibRef
Earlier:
Scene segmentation from depth and color data driven by surface fitting,
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], Zhang, J.J.[Jian J.],
A self-adaptive segmentation method for a point cloud,
VC(34), No. 5, May 2018, pp. 659-673.
Springer DOI 1804
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Wang, Y.[Yuan], Wang, J.J.[Jia-Jing], Chen, X.W.[Xiu-Wan], Chu, T.X.[Tian-Xing], Liu, M.L.[Mao-Lin], Yang, T.[Ting],
Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization,
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DOI Link 1808
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Chen, S.H.[Si-Heng], Duan, C.J.[Chao-Jing], Yang, Y.Q.[Yao-Qing], Li, D.S.[Duan-Shun], Feng, C.[Chen], Tian, D.[Dong],
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering,
IP(29), 2020, pp. 3183-3198.
IEEE DOI 2002
3D point cloud, deep autoencoder, graph filtering, graph topology inference BibRef

Wang, L.[Lei], Liu, Y.X.[Yu-Xuan], Zhang, S.[Shenman], Yan, J.X.[Ji-Xing], Tao, P.J.[Peng-Jie],
Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Kirk, T.F., Coalson, T.S., Craig, M.S., Chappell, M.A.,
Toblerone: Surface-Based Partial Volume Estimation,
MedImg(39), No. 5, May 2020, pp. 1501-1510.
IEEE DOI 2005
Image segmentation, Surface treatment, Neuroimaging, Estimation, Volume measurement, Imaging, Spatial resolution, surface 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,
IJCV(130), No. 5, May 2022, pp. 1228-1243.
Springer DOI 2205
BibRef

Zhang, Z.Y.[Zhi-Yuan], Hua, B.S.[Binh-Son], Rosen, D.W.[David W.], Yeung, S.K.[Sai-Kit],
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning,
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, PointNet
See also PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. BibRef

Ben-Shabat, Y.[Yizhak], Koneputugodage, C.H.[Chamin Hewa], Gould, S.[Stephen],
DiGS: Divergence guided shape implicit neural representation for unoriented point clouds,
CVPR22(19301-19310)
IEEE DOI 2210
Point cloud compression, Representation learning, Surface reconstruction, Solid modeling, Design automation, Vision+graphics BibRef

Ben-Shabat, Y.[Yizhak], Gould, S.[Stephen],
Deepfit: 3d Surface Fitting via Neural Network Weighted Least Squares,
ECCV20(I:20-34).
Springer DOI 2011
BibRef

Ngo, P.[Phuc],
Digital Hyperplane Fitting,
IWCIA20(164-180).
Springer DOI 2009
BibRef

Li, R., Li, X., Heng, P., Fu, C.,
PointAugment: An Auto-Augmentation Framework for Point Cloud Classification,
CVPR20(6377-6386)
IEEE DOI 2008
Training, Shape, Feature extraction, Solid modeling, Training data BibRef

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
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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
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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
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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
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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 .