Reconstruction of dense point cloud from uncalibrated widebaseline images

This paper presents a new approach to reconstruct 3D dense point cloud from uncalibrated wide-baseline images. It includes three steps: acquiring quasi-dense point correspondences, recovering structure from motion, and reconstructing the 3D dense point cloud. We present a two level propagation algorithm. The first level is implemented in the 2D image space and the second level is in the 3D scene space. We use affine iterative model to acquire accurate quasi-dense correspondences, and iterative optimization to recover the camera parameters and the 3D scene structure. It increases the robust and accuracy of self-calibration. In the second level propagation, the strategy of view selection and local photometric consistency are used to minimize the effects of occlusions and highlights etc. We demonstrate our algorithm with some high-quality reconstruction examples.