Intro
Preface
Organization
Contents
Automatic Threshold RanSaC Algorithms for Pose Estimation Tasks
1 Introduction
2 RanSaC Methods
2.1 Notation
2.2 History of RanSaC Algorithms
3 Adaptative RanSaC Algorithms
4 Data Generation Methodology
4.1 Models and Estimators
4.2 Semi-artificial Data Generation Method
5 Benchmark and Results
5.1 Performance Measures
5.2 Parameters
5.3 Results
5.4 Analysis and Comparison
6 Conclusion
References
Semi-automated Generation of Accurate Ground-Truth for 3D Object Detection
1 Introduction

2 Related Work on 3D Object Detection
2.1 Techniques for Early Object Detection
2.2 CNN-Based 3D Object Detection
2.3 Conclusions on Related Work
3 Semi-automated 3D Dataset Generation
3.1 Orientation Estimation
3.2 3D Box Estimation
4 Experiments
4.1 Experimental Setup and Configuration
4.2 Evaluation 1: Annotation-Processing Chain
4.3 Evaluation 2: 3D Object Detector Trained on the Annotation-Processing Configurations
4.4 Cross-Validation on KITTI Dataset
4.5 Unsupervised Approach
5 Conclusion
References

A Quantitative and Qualitative Analysis on a GAN-Based Face Mask Removal on Masked Images and Videos
1 Introduction
2 Related Works
2.1 Inpainting
2.2 Face Completion
3 Method
3.1 Pix2pix-Based Inpainting
3.2 Custom Loss Function
3.3 System Overview
3.4 Predicting Feature Points on a Face
4 Experiment
4.1 Image Evaluation
4.2 Video Evaluation
5 Discussion
5.1 Quality of Generated Images
5.2 Discriminating Facial Expressions
5.3 Generating Smooth Videos
5.4 Additional Quantitative Analyses
6 Limitations
7 Conclusion
References

Dense Material Segmentation with Context-Aware Network
1 Introduction
2 Related Works
2.1 Material Segmentation Datasets
2.2 Fully Convolutional Network
2.3 Material Segmentation with FCN
2.4 Global and Local Training
2.5 Boundary Refinement
2.6 Self-training
3 CAM-SegNet Architecture
3.1 Feature Sharing Connection
3.2 Context-Aware Dense Material Segmentation
3.3 Self-training Approach
4 CAM-SegNet Experiment Configurations
4.1 Dataset
4.2 Evaluation Metrics
4.3 Implementation Details
5 CAM-SegNet Performance Analysis

5.1 Quantitative Analysis
5.2 Qualitative Analysis
5.3 Ablation Study
6 Conclusions
References
Partial Alignment of Time Series for Action and Activity Prediction
1 Introduction
2 Related Work
3 Temporal Alignment of Action/Activity Sequences
3.1 Alignment Methods
Segmented Sequences
3.2 Alignment Methods
Unsegmented Sequences
3.3 Action and Activity Prediction
4 Experimental Results
4.1 Datasets
4.2 Alignment-Based Prediction in Segmented Sequences
4.3 Alignment-Based Action Prediction in Unsegmented Sequences