Fusing oblique imagery with augmented aerial LiDAR

We present a scalable out-of-core technique for mapping colors from aerial oblique imagery to large scale aerial LiDAR (Light Detection and Ranging) point cloud. Our method does not require meshing or intensive processing of points, only fast and effective augmentation is applied to fill occluded points on building walls and under tree canopies. The presented system applies a modified visibility pass of GPU splatting to map colors, where occluded points are filtered out by projecting all points as oriented surface splats into images. A weighting scheme is utilized to accumulate colors from all contributing images while leveraging image resolution and surface orientation. The effectiveness of color mapping is demonstrated through visualizations of colored points by a GPU splatting algorithm.

[1]  Quan Wang,et al.  A vision-based 2D-3D registration system , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[2]  Michael Moser,et al.  Combination of Different Surveying Methods for Archaeological Documentation: the Case Study of the Bronze Age Wooden Chest from Mitterberg , 2013 .

[3]  Christian Früh,et al.  Automated texture mapping of 3D city models with oblique aerial imagery , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[4]  Avideh Zakhor,et al.  Automated texture mapping of 3D city models with oblique aerial imagery , 2004 .

[5]  C. Armenakis,et al.  FUSION OF OPTICAL AND TERRESTRIAL LASER SCANNER DATA , 2010 .

[6]  Paolo Cignoni,et al.  Improved color acquisition and mapping on 3D models via flash-based photography , 2010, JOCCH.

[7]  Borut Zalik,et al.  Visualization of LIDAR datasets using point-based rendering technique , 2010, Comput. Geosci..

[8]  Michael Wimmer,et al.  Interactive Domitilla catacomb exploration , 2009, VAST'09.

[9]  John W. Fisher,et al.  Automatic registration of LIDAR and optical images of urban scenes , 2009, CVPR.

[10]  Ulrich Neumann,et al.  Visually-complete aerial LiDAR point cloud rendering , 2012, SIGSPATIAL/GIS.

[11]  Lu Wang,et al.  A robust approach for automatic registration of aerial images with untextured aerial LiDAR data , 2009, CVPR.

[12]  R. Pintus,et al.  A Streaming Framework for Seamless Detailed Photo Blending on Massive Point Clouds , 2011, Eurographics.

[13]  William R. Mark,et al.  Cg: a system for programming graphics hardware in a C-like language , 2003, ACM Trans. Graph..