A Framework for Pencil of Points Structure from Motion 1st edition by Adrien Bartoli, Mathieu Coquerelle, Peter Sturm ISBN 3540219835 9783540219835
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Authors:Adrien Bartoli, Mathieu Coquerelle; Peter Sturm , Tags:Computer Vision – ECCV 2004 , Author sort:Adrien Bartoli, Mathieu Coquerelle & Sturm, Peter , Languages:Languages:eng , Published:Published:Mar 2004
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Product details:
ISBN 10: 3540219835
ISBN 13: 978-3540219835
Author: Adrien Bartoli, Mathieu Coquerelle, Peter Sturm
Our goal is to match contour lines between images and to recover structure and motion from those. The main difficulty is that pairs of lines from two images do not induce direct geometric constraint on camera motion. Previous work uses geometric attributes | orientation, length, etc. | for single or groups of lines. Our approach is based on using Pencil-of-Points (points on line) or pops for short. There are many advantages to using pops for structure-from-motion. The most important one is that, contrarily to pairs of lines, pairs of pops may constrain camera motion. We give a complete theoretical and practical framework for automatic structure-from-motion using pops | detection, matching, robust motion estimation, triangulation and bundle adjustment. For wide baseline matching, it has been shown that cross-correlation scores computed on neighbouring patches to the lines gives reliable results, given 2D homographic transformations to compensate for the pose of the patches. When cameras are known, this transformation has a 1-dimensional ambiguity. We show that when cameras are unknown, using pops lead to a 3-dimensional ambiguity, from which it is still possible to reliably compute cross-correlation. We propose linear and non-linear algorithms for estimating the fundamental matrix and for the multiple-view triangulation of pops. Experimental results are provided for simulated and real data.
A Framework for Pencil-of-Points Structure-from-Motion 1st Table of contents:
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Introduction
- 1.1 Background and Motivation
- 1.2 Structure-from-Motion (SfM) Overview
- 1.3 Pencil-of-Points Problem: Definition and Challenges
- 1.4 Objectives and Contributions of the Paper
- 1.5 Structure of the Paper
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Related Work
- 2.1 Classical Structure-from-Motion Approaches
- 2.2 Pencil-of-Points in 3D Reconstruction
- 2.3 Sparse Point Correspondence Methods
- 2.4 Optimization Techniques in SfM
- 2.5 Limitations and Gaps in Existing SfM Models
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The Pencil-of-Points Framework
- 3.1 Definition of Pencil-of-Points and its Application to SfM
- 3.2 Geometric and Mathematical Foundations
- 3.3 Point Correspondence and Scene Representation
- 3.4 Structure and Motion Estimation with Sparse Points
- 3.5 Relationship Between 2D Projections and 3D Structure
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Formulation of the Problem
- 4.1 Mathematical Model for Pencil-of-Points SfM
- 4.2 Camera Models and Projection Equations
- 4.3 Constraints from Sparse Correspondences
- 4.4 Challenges in Estimating Camera Pose and 3D Points
- 4.5 Nonlinear Optimization for Structure and Motion Estimation
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Proposed Framework for Pencil-of-Points SfM
- 5.1 High-Level Overview of the Framework
- 5.2 Key Steps: Feature Extraction, Matching, and Camera Pose Estimation
- 5.3 Sparse Correspondence Refinement
- 5.4 Optimization for 3D Point Cloud Reconstruction
- 5.5 Handling Ambiguities and Outliers in Sparse Data
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Algorithm Implementation
- 6.1 Preprocessing and Initial Estimation
- 6.2 Point Matching and Correspondence Establishment
- 6.3 Optimization Techniques: Bundle Adjustment and Other Solvers
- 6.4 Handling Missing or Noisy Data in Pencil-of-Points
- 6.5 Computational Considerations and Efficiency
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Experimental Setup and Evaluation
- 7.1 Datasets and Test Cases
- 7.2 Evaluation Metrics: Accuracy, Reconstruction Quality, and Robustness
- 7.3 Comparison with Other SfM Methods (Dense and Sparse)
- 7.4 Results from Experiments in Various Scenarios
- 7.5 Performance in Low-Feature or Sparse-Data Environments
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Results and Discussion
- 8.1 Visual Results: 3D Reconstructions from Sparse Data
- 8.2 Error Analysis: Evaluation of 3D Point Accuracy and Camera Pose
- 8.3 Impact of Sparse Point Correspondences on the Accuracy of Results
- 8.4 Robustness to Outliers and Noise in Sparse Correspondence Data
- 8.5 Comparison with State-of-the-Art SfM Methods
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Applications
- 9.1 3D Reconstruction from Sparse Views
- 9.2 Robotics: Navigation and Mapping
- 9.3 Augmented and Virtual Reality Systems
- 9.4 Computer Vision for Surveillance and Tracking
- 9.5 Cultural Heritage Preservation and Digital Archiving
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Challenges and Future Directions
- 10.1 Handling Extreme Sparsity in Point Correspondences
- 10.2 Real-Time Implementation for Large-Scale SfM
- 10.3 Incorporating Depth and Temporal Information
- 10.4 Scaling to Large-Scale 3D Scene Reconstruction
- 10.5 Integrating Machine Learning Techniques into SfM Frameworks
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Conclusion
- 11.1 Summary of Contributions and Key Findings
- 11.2 Practical Implications of the Pencil-of-Points SfM Approach
- 11.3 Closing Remarks and Future Work
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