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Table of contents
1 Introduction
1.1 Depth perception and stereovision
1.2 Stereo matching problem
1.3 Epipolar geometry
1.4 3D reconstruction
1.5 The importance of time in stereo correspondence
1.6 Bio-inspired event-based vision
1.7 Stereo-correspondence in neuromorphic engineering
1.8 Motivation and contribution
2 Asynchronous Event-Based N-Ocular Stereomatching
2.1 Introduction
2.2 Asynchronous N-Ocular Stereo Vision
2.2.1 Trinocular geometry
2.2.2 Trinocular spatio-temporal match
2.2.3 Stereo match selection using bayesian inference
2.2.3.1 Prior
2.2.3.2 Likelihood
2.2.3.3 Posterior
2.2.4 Synchronization
2.2.5 N-ocular stereo matching
2.3 Experimental results
2.3.1 Experimental Setup
2.3.2 Reconstruction Evaluation
2.3.3 Processing time
2.4 Conclusion and Discussion
3 Scene flow from 3D point clouds
3.1 Introduction
3.2 Scene flow parametrization
3.2.1 Plane approximation
3.2.2 Rank of M
3.3 Velocity estimation
3.3.1 Error cost function
3.3.2 Optimal spatio-temporal neighbourhood
3.4 Results
3.4.1 Simulated scene
3.4.2 Natural scene
3.5 Discussion
3.6 Conclusions
4 It’s (all) about time
4.1 Introduction
4.2 Intensity and motion based stereo matching
4.3 Time encoded imaging
4.4 Event-based stereo matching
4.4.1 Geometrical error
4.4.2 Temporal error
4.4.3 Time-coded intensity matching
4.4.4 Motion matching
4.4.5 Error minimization
4.5 Results
4.5.1 Experimental setup
4.5.2 Method evaluation
4.5.3 Binocular matching
4.5.4 Trinocular matching
4.5.5 Performance evaluation
4.6 Discussion
4.6.1 3D Structure refinement using point cloud prediction
4.7 Conclusion
5 Discussion
References
