Planarity constrained multi-view depth map reconstruction for urban scenes

Abstract

Multi-view depth map reconstruction is regarded as a suitable approach for 3D generation of large-scale scenes due to its flexibility and scalability. However, there are challenges when this technique is applied to urban scenes where apparent man-made regular shapes may present. To address this need, this paper proposes a planarity constrained multi-view depth (PMVD) map reconstruction method. Starting with image segmentation and feature matching for each input image, the main procedure is iterative optimization under the constraints of planar geometry and smoothness. A set of candidate local planes are first generated by an extended PatchMatch method. The image matching costs are then computed and aggregated by an adaptive-manifold filter (AMF), whereby the smoothness constraint is applied to adjacent pixels through belief propagation. Finally, multiple criteria are used to eliminate image matching outliers. (Vertical) aerial images, oblique (aerial) images and ground images are used for qualitative and quantitative evaluations. The experiments demonstrated that the PMVD outperforms the popular multi-view depth map reconstruction with an accuracy two times better for the aerial datasets and achieves an outcome comparable to the state-of-the-art for ground images. As expected, PMVD is able to preserve the planarity for piecewise flat structures in urban scenes and restore the edges in depth discontinuous areas.