Abstract
The previous implementations of our Epipolar-Plane Image Analysis mapping technique demonstrated the feasibility and benefits of the approach, but were carried out for restricted camera geometries. The question of more general geometries made the technique's utility for autonomous navigation uncertain. We have developed a generalization of our analysis that (a) enables varying view direction, including variation over time (b) provides three-dimensional connectivity information for building coherent spatial descriptions of observed objects; and (c) operates sequentially, allowing initiation and refinement of scene feature estimates while the sensor is in motion. To implement this generalization it was necessary to develop an explicit description of the evolution of images over time. We have achieved this by building a process that creates a set of two-dimensional manifolds defined at the zeros of a three-dimensional spatiotemporal Laplacian. These manifolds represent explicitly both the spatial and temporal structure of the temporally evolving imagery, and we term them spatiotemporal surfaces. The surfaces are constructed incrementally, as the images are acquired. We describe a tracking mechanism that operates locally on these evolving surfaces in carrying out three-dimensional scene reconstruction.
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Baker, H.H., Bolles, R.C. Generalizing Epipolar-Plane Image Analysis on the spatiotemporal surface. Int J Comput Vision 3, 33–49 (1989). https://doi.org/10.1007/BF00054837
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DOI: https://doi.org/10.1007/BF00054837