A voxel-based approach for imaging voids in three-dimensional point clouds

Abstract

With recent advances in technologies, reconstructions of three-dimensional (3D) point clouds from multi-view aerial imagery are readily obtainable. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids in the point cloud are present in texturally flat areas that failed to generate features during the initial stages of reconstruction, as well as areas where multiple views were not obtained during collection or a constant occlusion existed due to collection angles or overlapping scene. A method is presented for identifying the type of void present using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. A similar line-of-sight analysis can then be used to pinpoint locations at aircraft altitude at which the voids in the point clouds could theoretically be imaged, such that the new images can be included in the 3D reconstruction, with the goal of reducing the voids in the point cloud that are a result of lack of coverage. This method has been tested on high-frame-rate oblique aerial imagery captured over Rochester, NY.