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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
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ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Frame Decimation for Structure and Motion
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
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The paper proposes a statistical framework that enables 3D structure and motion to be computed optimally from an image sequence, on the assumption that feature measurement errors are independent and Gaussian distributed. The analysis and results demonstrate that computing both camera/scene motion and 3D structure is essential to computing either with any accuracy. Having computed optimal estimates of structure and motion over a small number of initial images, a recursive version of the algorithm (previously reported) recomputes sub optimal estimates given new image data. The algorithm is designed explicitly for real time implementation, and the complexity is proportional to the number of tracked features. 3D projective, affine and Euclidean models of structure and motion recovery have been implemented, incorporating both point and line features into the computation. The framework can handle any feature type and camera model that may be encapsulated as a projection equation from scene to image.