دانلود کتاب 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods

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Preface
Contents
Author Biographies
1 Introduction
1.1 Introduction
1.2 3D Point Clouds
1.2.1 Point Cloud Formation
1.2.2 Comparison with Other Visual Data Forms
1.3 Point Cloud Processing
1.3.1 Registration
1.3.2 Classification
1.3.3 Semantic Segmentation
1.3.4 Odometry
1.4 Applications
1.5 Datasets
1.5.1 ModelNet40
1.5.2 ShapeNet
1.5.3 S3DIS
1.5.4 3D Match
1.5.5 KITTI
1.6 Summary
References
2 Traditional Point Cloud Analysis
2.1 Filtering
2.1.1 Downsampling
2.1.2 Noise Removal
2.2 Nearest Neighbor Search
2.2.1 Binary Search Tree
2.2.2 k-Dimensional Tree
2.2.3 Octree
2.3 Model Fitting
2.3.1 Least Squares Fitting
2.3.2 Hough Transform
2.3.3 Random Sample Consensus
2.4 Point Cloud Features
2.4.1 Feature Detectors
2.4.1.1 Harris 3D/6D
2.4.1.2 Intrinsic Shape Signatures
2.4.2 Feature Descriptors
2.4.2.1 Point Feature Histogram
2.4.2.2 Fast Point Feature Histogram
2.4.2.3 Signature of Histograms of Orientations
2.5 Classification and Segmentation
2.6 Registration
2.6.1 Iterative Closest Point (ICP)
2.6.2 Point-to-Plane ICP
2.6.3 Generalized ICP
2.6.4 Global Registration
References
3 Deep Learning-Based Point Cloud Analysis
3.1 Introduction
3.2 Classification and Segmentation
3.2.1 PointNet
3.2.2 PointNet++
3.2.3 Dynamic Graph CNN
3.2.4 PointCNN
3.2.5 PointSIFT
3.2.6 Point Transformer
3.2.7 RandLA-Net
3.3 Registration
3.3.1 PointNetLK
3.3.2 Deep Closest Point
3.3.3 PRNet
3.3.4 3D Match
3.3.5 PPFNet
3.3.6 Deep Global Registration
References
4 Explainable Machine Learning Methods for Point Cloud Analysis
4.1 Successive Subspace Learning on 2D Images
4.1.1 Data-Driven Saak Transform
4.1.2 Handwritten Digit Recognition by Saak Transform
4.1.3 Interpretable Convolutional Neural Networks via Feedforward Design
4.1.4 PixelHop
4.1.5 PixelHop++
4.2 Classification and Part Segmentation
4.2.1 PointHop
4.2.2 PointHop++
4.2.3 Unsupervised Feedforward Feature (UFF)
4.3 Registration
4.3.1 Salient Points Analysis (SPA)
4.3.2 R-PointHop
4.4 Other Applications of Successive Subspace Learning
4.4.1 FaceHop
4.4.2 DefakeHop
4.4.3 AnomalyHop
References
5 Conclusion and Future Work
5.1 Conclusion
5.2 Future Work
Index