Remington gong benjamin harris iuri prilepov
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REMINGTON GONG BENJAMIN HARRIS IURI PRILEPOV. Guess the Depth. 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

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Guess the Depth

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Remington gong benjamin harris iuri prilepov

REMINGTON GONG

BENJAMIN HARRIS

IURI PRILEPOV

Guess the Depth


What we have done

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


Feature tracking performance

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.


Performance observations

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


Eight point algorithm

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


Our road map

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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