Recognizing Objects in Range Data Using Regional Point Descriptors 1st edition by Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bulow, Jitendra Malik ISBN 3540219828 9783540219828

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Product details:

ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:   Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bülow, Jitendra Malik      
 

Recognizing Objects in Range Data Using Regional Point Descriptors 1st  Table of contents:

1. Introduction

   • 1.1 Motivation for Object Recognition in Range Data
   • 1.2 Challenges in 3D Object Recognition
   • 1.3 Regional Point Descriptors: Concept and Importance
   • 1.4 Contributions of the Paper
   • 1.5 Structure of the Paper

2. Related Work

   • 2.1 Object Recognition in 3D Data: Approaches and Techniques
   • 2.2 Feature Descriptors for 3D Point Clouds
   • 2.3 Regional Point Descriptors in 3D Object Recognition
   • 2.4 Matching and Alignment Methods in Range Data
   • 2.5 Limitations of Current Approaches

3. Problem Definition

   • 3.1 Defining the Object Recognition Task in Range Data
   • 3.2 The Role of Regional Point Descriptors in Object Recognition
   • 3.3 Assumptions and Constraints in the Recognition Process
   • 3.4 Challenges in Recognizing Objects from Range Data

4. Regional Point Descriptors

   • 4.1 Overview of Point Descriptors for 3D Data
   • 4.2 Regional vs Global Descriptors: A Comparative Analysis
   • 4.3 Local Features: Curvature, Normal Vectors, and Surface Geometry
   • 4.4 Construction of Regional Point Descriptors
   • 4.5 Stability and Robustness of Regional Descriptors to Noise and Occlusions

5. Feature Extraction from Range Data

   • 5.1 Preprocessing of Range Data: Noise Removal and Surface Smoothing
   • 5.2 Detection of Key Points in Range Data
   • 5.3 Computation of Regional Descriptors: Methodology
   • 5.4 Transforming 3D Data into Descriptive Feature Sets
   • 5.5 Dimensionality Reduction and Descriptor Optimization

6. Object Recognition Framework

   • 6.1 Overview of the Recognition Pipeline
   • 6.2 Descriptor Matching and Correspondence
   • 6.3 Geometric Alignment and Transformation
   • 6.4 Object Classification Based on Regional Descriptors
   • 6.5 Handling Partial Views and Occlusions in Object Recognition
   • 6.6 Post-Processing and Refining Recognition Results

7. Experimental Setup and Evaluation

   • 7.1 Datasets and Test Scenarios for Object Recognition
   • 7.2 Performance Metrics for Object Recognition (e.g., Precision, Recall, F-Score)
   • 7.3 Comparison with Other Descriptor-Based Recognition Methods
   • 7.4 Impact of Noise and Sensor Errors on Recognition Accuracy
   • 7.5 Experimental Setup: Hardware and Software Platforms

8. Results and Discussion

   • 8.1 Quantitative Results of Object Recognition Performance
   • 8.2 Visual Comparison of Recognition Results
   • 8.3 Effectiveness of Regional Descriptors in Different Scenarios
   • 8.4 Robustness to Noise, Occlusions, and Partial Scans
   • 8.5 Comparison with State-of-the-Art Methods
   • 8.6 Limitations and Areas for Improvement

9. Applications of Object Recognition in Range Data

   • 9.1 3D Object Recognition in Robotics and Autonomous Systems
   • 9.2 Recognition for Augmented Reality and Virtual Reality
   • 9.3 Medical Imaging and Surgical Planning
   • 9.4 Environmental Monitoring and Mapping
   • 9.5 Industrial Inspection and Quality Control

10. Challenges and Future Directions

• 10.1 Scalability to Large-Scale Range Data
• 10.2 Improving Descriptor Robustness in Cluttered Environments
• 10.3 Real-Time Object Recognition in Dynamic Scenarios
• 10.4 Integration with Deep Learning for Feature Learning
• 10.5 Future Work in Multi-Object Recognition
• 10.6 Addressing Real-World Constraints in Recognition Systems

11. Conclusion

• 11.1 Summary of Key Findings
• 11.2 Contributions to 3D Object Recognition Using Regional Descriptors
• 11.3 Final Remarks and Outlook for Future Research

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