Benchmarking of Bootstrap Temporal Stereo using Statistical and Physical Scene Modelling

Temporal stereo vision algorithms can offer improved robustness, however, this can only be delivered after several frames of a stereo image sequence have been processed. We present a new method of bootstrapping temporal stereo which can overcome such start-up problems by applying additional coarse-to-fine pre-processing to the first few images in a stereo sequence. To gauge the performance of temporal bootstrapping, we have employed a new algorithmic evaluation technique that uses statistical and physical scene modelling to produce accurate result errors data. The performance of the bootstrap temporal stereo algorithm, as determined by the automatic evaluation technique, as well the results from real stereo image sequences, are presented.