Abstract

In this paper we consider the problem of recovering 3D Euclidean structure from multi-frame point correspondence data in image sequences un- der perspective projection. Existing approaches rely either only on geometrical constraints reflecting the rigid nature of the object, or exploit temporal informa- tion by recasting the problem into a nonlinear filtering form. In contrast, here we introduce a new constraint that implicitly exploits the temporal ordering of the frames, leading to a provably correct algorithm to find Euclidean structure (up to a single scaling factor) without the need to alternate between projective depth and motion estimation, estimate the Fundamental matrices or assume a camera motion model. Finally, the proposed approach does not require an accurate cali- bration of the camera. The accuracy of the algorithm is illustrated using several examples involving both synthetic and real data.