Applications of Geometry Processing: Grammar-based 3D facade segmentation and reconstruction
Computers and Graphics
Interactive Coherence-Based Façade Modeling
Computer Graphics Forum
Structure recovery by part assembly
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Learning domain knowledge for façade labelling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
International Journal of Computer Vision
Semantic decomposition and reconstruction of residential scenes from LiDAR data
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Projective analysis for 3D shape segmentation
ACM Transactions on Graphics (TOG)
Coupled structure-from-motion and 3D symmetry detection for urban facades
ACM Transactions on Graphics (TOG)
Structure-aware shape processing
SIGGRAPH Asia 2013 Courses
Consolidation of low-quality point clouds from outdoor scenes
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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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 enhance the 3D scan. Finally, the algorithm produces an enhanced, layered, textured model. We evaluate our algorithm on complex multi-planar building facades, where direct autocorrelation methods for repetition detection fail. We demonstrate how 2D photographs help improve the 3D scans by exploiting data redundancy, and transferring high level structural information to (plausibly) complete large missing regions.