Aerial 3D reconstruction with line-constrained dynamic programming

Abstract

Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-imagecolumn constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.

Real-Time 3d Reconstruction From Images Taken From an Uav

“…However, often, for obtaining better accuracies, the aspect of computational time is neglected. For example, that is the case of reconstruction from multiple images (Mayer, 2008), or by using computationally costly algorithms (Huei-Hung, 2011), which can take minutes for generating the desired 3D model. In order to achieve good precision without sacrificing the computational time, many techniques rely on the help of auxiliary instrumentation (Wefelscheid, 2011), like inertial navigation systems (INSs) and global positioning systems (GPSs).…”