2D-3D Fusion for Layer Decomposition of Urban Facades

We present a method for fusing two acquisition modes, 2D photographs and 3D LiDAR scans, for depth-layer decomposition of urban facades. The two modes have complementary characteristics: point cloud scans are coherent and inherently 3D, but are often sparse, noisy, and incomplete; photographs, on the other hand, are of high resolution, easy to acquire, and dense, but view-dependent and inherently 2D, lacking critical depth information. In this paper we use photographs to enhance the acquired LiDAR data. Our key observation is that with an initial registration of the 2D and 3D datasets we can decompose the input photographs into rectified depth layers. We decompose the input photographs into rectangular planar fragments and diffuse depth information from the corresponding 3D scan onto the fragments by solving a multi-label assignment problem. Our layer decomposition enables accurate repetition detection in each planar layer, using which we propagate geometry, remove outliers and enhan...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Yangyan Li, Qian Zheng, Andrei Sharf, Daniel Cohen-Or, Baoquan Chen, Niloy J. Mitra
• Keywords: Building Facades, Computer Vision, Data Redundancy, ICCV 2011, LiDAR Data