smooth, interactive rendering and on-line modification of large-scale, geospatial data in flood...
TRANSCRIPT
1 Challenge the future
Smooth, Interactive Rendering and On-line Modification of Large-Scale, Geospatial Data in Flood Visualisations
2 Challenge the future
Introduction
• 3D Geospatial Data very large and heterogeneous
• applicable in e.g. hydrology and climatology
Cloud Vis of KNMI data Water Vis of 3Di subgrid data
3 Challenge the future
Introduction
• topographic data nowadays captured via LiDAR
• AHN-2 (Actueel Hoogtebestand Nederland), coloured: ~14 TB
• data too big for rendering
• interactive, on-line modification not possible without quality loss
4 Challenge the future
Approaches and Challenges
• Rendering Large-Scale data: Out-of-Core LoD structure (see
Kehl et al. ICT Open 2012 for starting point)
• Issue: How do modify streamed data, not being constantly
available ?
• Modification algorithm needs to handle detail-varying data
• idea: modify what you see on-chip modification
LoD’s
0 1 2 3
5 Challenge the future
• traditional LoD: visual jumps when loading new buckets [1]
• solution: Rendering-on-budget for LiDAR point sets
• combined importance-based streaming (similar to Sequential
Point Trees [2]) with PID controller for load balancing
Rendering-on-Budget
Methods
[1] Christian Kehl and Gerwin de Haan. Interactive simulation and visualisation of realistic flooding scenarios. In Intelligent Systems for Crisis Management, 2012. [2] Carsten Dachsbacher, Christian Vogelsang, and Marc Stamminger. Sequential point trees. In ACM Transaction on Graphics, pages 657-662, 2003.
6 Challenge the future
• Interface to Geo-Information: GoogleMaps KML polygons
• conversion from polygons to triangular mesh via constrained
DT
• exclusion from exterior triangles via polygonal restriction
• storage of triangular mesh and attributes in Quadtree
• storage of Quadtree in GPU Texture
• on-the-fly evaluation of Quadtree per vertex on GPU during
rendering
• application of attribute modification based on triangle data
On-line Modification of Large-Scale, Geospatial LiDAR point sets
Methods
7 Challenge the future
On-Line Modification of Large-Scale, Geospatial LiDAR point sets
Methods
8 Challenge the future
• Attribute modification possibilities:
• colour via pre-defined polygon colour (RGBA)
• vertex rendering discard via polygonal area
• colour via painting on texture for polygon
• displace vertices via painting on displacement map
• Also possible to adapt paths (line segments along streets)
given via GoogleMaps
On-Line Modification of Large-Scale, Geospatial LiDAR point sets
Methods
9 Challenge the future
proof of concept – colour via polygon
Results
10 Challenge the future
proof of concept – colour via texture
Results
11 Challenge the future
proof of concept – displace via texture
Results
12 Challenge the future
proof of concept – real-world scenarios
Results
today
1953
dyke adaptation
13 Challenge the future
performance measurements
Results
Comparison of rendering behaviour of initial approach (left) and Rendering-on-Budget (right)
14 Challenge the future
performance measurements
Results