Current approaches for 3D reconstruction from feature points of images are classed as sparse and dense techniques. However, the sparse approaches are insufficient for surface reconstruction since only sparsely distributed feature points are presented. Further, existing dense reconstruction approaches require pre-calibrated camera orientation, which limits the applicability and flexibility. This paper proposes a one-stop 3D reconstruction solution that reconstructs a highly dense surface from an uncalibrated video sequence, the camera orientations and surface reconstruction are simultaneously computed from new dense point features using an approach motivated by Structure from Motion (SfM) techniques. Further, this paper presents a flexible automatic method with the simple interface of 'videos to 3D model'. These improvements are essential to practical applications in 3D modeling and visualization. The reliability of the proposed algorithm has been tested on various data sets and the accuracy and performance are compared with both sparse and dense reconstruction benchmark algorithms.
['Li Ling', 'Ian S. Burrent', 'Eva Cheng']
A Dense 3D Reconstruction Approach from Uncalibrated Video Sequences