Real-time Monocular Dense Mapping for Augmented Reality

Monocular simultaneous localization and mapping (SLAM) is a key enabling technique for many augmented reality (AR) applications. However, conventional methods for monocular SLAM can obtain only sparse or semi-dense maps in highly-textured image areas. Poorly-textured regions which widely exist in indoor and man-made urban environments can be hardly reconstructed, impeding interactions between virtual objects and real scenes in AR apps. In this paper, we present a novel method for real-time monocular dense mapping based on the piecewise planarity assumption for poorly textured regions. Specifically, a semi-dense map for highly-textured regions is first calculated by pixel matching and triangulation [6, 7]. Large textureless regions extracted by Maximally Stable Color Regions (MSCR) [11], which is a homogeneous-color region detector, are approximated using piecewise planar models which are estimated by the corresponding semi-dense 3D points and the proposed multi-plane segmentation algorithm. Plane models associated with the same 3D area across multiple overlapping views are linked and fused to ensure a consistent and accurate 3D reconstruction. Experimental results on two public datasets [15, 23] demonstrate that our method is 2.3X similar to 2.9X faster than the state-of-the-art method DPPTAM [2], and meanwhile achieves better reconstruction accuracy and completeness. We also apply our method to a real AR application and live experiments with a hand-held camera demonstrate the effectiveness and efficiency of our method in practical scenario.(1)