In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation.
First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose
normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To
obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along
the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension
distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if
yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them
together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the
standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor
achieves the best performance on feature matching experiments.
Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.