Illumination for computer generated pictures
Communications of the ACM
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Approaches to Large-Scale Urban Modeling
IEEE Computer Graphics and Applications
3D Building Detection and Modeling from Aerial LIDAR Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Point-Based Graphics
Immersive Visualization and Analysis of LiDAR Data
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
A survey of point-based techniques in computer graphics
Computers and Graphics
Visualization of LIDAR datasets using point-based rendering technique
Computers & Geosciences
2.5D dual contouring: a robust approach to creating building models from Aerial LiDAR point clouds
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Web-Based LiDAR Visualization with Point-Based Rendering
SITIS '11 Proceedings of the 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems
2.5D building modeling with topology control
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
2.5D building modeling by discovering global regularities
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Building large urban environments from unstructured point data
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
High-quality surface splatting on today's GPUs
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
Fusing oblique imagery with augmented aerial LiDAR
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Aerial LiDAR (Light Detection and Ranging) point clouds are gathered by a downward scanning laser on a low-flying aircraft. Due to the imaging process, vertical surface features such as building walls, and ground areas under tree canopies are totally or partially occluded, resulting in gaps and sparsely sampled areas. These gaps produce unwanted holes and uneven point distributions that often produce artifacts when visualized using point-based rendering (PBR) techniques. We show how to extend PBR by inferring the physical nature of LiDAR points for visual realism and added comprehension. More specifically, the class of object a point is related to augments the point cloud in pre-processing and/or adapts the online rendering, to produce visualizations that are more complete and realistic. We provide examples of point cloud augmentation for building walls and ground areas under tree canopies. We show how different types of procedurally generated geometry can be used to recover building walls. These methods are generic and can be applied to any aerial LiDAR data set with buildings and trees. Our work also incorporates an out-of-core strategy for hierarchical data management and GPU-accelerated PBR with extended deferred shading. The combined system provides interactive visually-complete rendering of virtually unlimited-size LiDAR point clouds. Experimental results show that our rendering approach adds only a slight overhead to PBR and provides comparable visual cues to visualizations generated by off-line pre-computation of 3D polygonal urban models.