A dense map optimization method based on common-view geometry

This paper focuses on noisy points filtering in dynamic reconstruction. RGBD cameras can acquire depth information directly, which is an effective way to obtain a global point cloud. However, the inevitable error leads to erroneous rebuilding, which is mainly caused by wrong depth information and dynamic disturbance. Different from algebraic methods such as median filter, a geometric method is proposed based on common-view geometry. Noisy points generate more intersections with other projection lights, which are used to distinguish incorrect points. Considering the huge amount of computation, a sampling method is adopted to avoid unnecessary calculations and a cuboid method to simplify the calculation. We also employ a hybrid strategy with two different standards to increase the robustness when filtering. By calculating the geometry relationship effectively and robustly, we get good results in removing map points with incorrectly measured depth and caused by dynamic disturbance. Compared with the algebraic methods, the proposed method can also remove dynamic objects. The performance is verified in three popular public datasets.