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Enhancing Feature Tracking Performance with Eight-Point Algorithm Implementation

Our team has developed a profiler to analyze feature tracking performance, aiding in making GPU implementation decisions. We've begun implementing the eight-point algorithm and explored SVD-based techniques for improving rotation and translation accuracy. Observations show room for improvement to reach 35Hz capability. Our roadmap includes full eight-point algorithm integration, structure from motion, mesh generation, and texture mapping.

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Enhancing Feature Tracking Performance with Eight-Point Algorithm Implementation

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  1. REMINGTON GONG BENJAMIN HARRIS IURI PRILEPOV Guess the Depth

  2. WHAT WE HAVE DONE... • Created a profiler to sample current performance of feature tracking • Useful for deciding which of next stages are worth implementing on GPU • Started the eight-point algorithm implementation • Researched SVD-based methods for recovering rotation and translation between correspondence pairs

  3. FEATURE TRACKING PERFORMANCE • Setup: • Video - 640x320 @ 24Hz • Laptop - Intel Core 2 T7500, NVIDIA 8600M GT Note: Average times do not sum to total average time since some phases (i.e. new feature selection) are not always computed.

  4. Performance Observations • Feature tracking on GPU is fast, but GPU memory copy-back during feature selection hurts (5 ms) • Convolution is expensive on GPU due to numerous texture fetches despite trivial parallelism • Lots of room for improvement, but currently capable of 35Hz • Several stages still to be implemented

  5. Eight-Point Algorithm • Recover 3D coordinates from a set of point correspondences between two frames • Least-squares approximation of the Essential Matrix (camera movement) • SVD – Jama linear algebra package • Normalization of input coordinates

  6. OUR ROAD MAP... • Structure From Motion • Complete eight-point algorithm implementation with the normalization of input coordinates • Mesh Generation • Delaunay refinement mesh generation • Texture Mapping

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