Feature-based laser data simplification for low polygon visualization

Slide 1: Fe ature -base d lase r data simplificatio n fo r lo w po lygo n visualizatio n Pamela Fox, pfox@usc.edu Shirish Doshi, shirishd@usc.edu Suya You, suyay@usc.edu Ulrich Neumann, uneumann@imsc.usc.edu Integrated Media Systems Center University of Southern California

Slide 2: Overview • Objective • Related Work • Our Approach – Pre-processing: Polygon Reduction – Simplification into Primitives • Results • Extension to outdoor/mixed scenes • Future Work • Conclusion

Slide 3: Objective • Simplified representation of scanned laser (range) data from indoor scenes • Problems: Too many triangles, too many holes, not enough memory! • Original application for RFID tracking

Slide 4: Applications • urban planning • virtual reality based geographics information system (VRGIS) • environment monitoring • military missions • Video game mods

Slide 5: Related Work – Polygon Reduction • Tim Garthwaite, Jason Reposa. “Mesh Decimation.” • Mathieu Desbruin. “Variational Shape Approximation.” University of Southern California

Slide 6: Related Work – Indoor Scenes • Rui Wang, David Luebke. “Efficient Reconstruction of Indoor Scenes with Color” Department of Computer Science, University of Virginia • R.T. Whitaker, J. Gregor, P.F. Chen . “Indoor Scene Reconstruction from Sets of Noisy Range Images” University of Tennessee

Slide 7: Related Work – Laser Data Simplification • “Urban Site Modeling from LiDAR” – Suya You, Jinhui Hu, Ulrich Neumann, Pamela Fox • Designed for buildings on land, laser data taken from aerial view, less dimensions

Slide 8: Our Approach – 2 Steps • Polygon Reduction for Corner/Edge (Feature) retention • Further simplification of pre-processed scenes into geometric primitives based on indoor constraints and user-selected features

Slide 9: 1) Pre-processing: Polygon Reduction • Standard Region planar area Growing Algorithm • Retain neighbors near corners & edges so we can use corner- finding algorithm in next step non-planar area and neighbors

Slide 10: 2) Simplification into Primitives • Indoor Constraints: • Standard components of indoor scene: – floor, ceiling, walls • User firsts selects ground, ceiling, walls – Least squares plane fitting algorithm, points •When creating other objects in room, user can project to ground, ceiling, walls, or any pre-created object (e.g. a table) •This means you often need only to select points on the frontal view of objects.

Slide 11: 2) Simplification into Primitives • Primitive Creation: – User selects minimally necessary points – Depends on type of primitive and available data • Corner locating: – If user can’t determine exact corner, they can select a nearby corner and let program use K-means clustering algorithm to determine most likely corner

Slide 12: Results • 99.9% lossy “compression” – 520,000 to 1,200 triangles • Texturing, lighting

Slide 13: Extension to Outdoor/Mixed Scenes • The user is not forced to tell the program a ground or ceiling or walls – so they can bypass those steps and simply use the primitive creation tools. So you can use the program to make simplified representations of any scenes that can be reduced to geometric primitives or customized preset objects. • For example, if you’re reducing a scene with trees, there could be two preset tree primitives – evergreen, deciduous. Then when you see a tree in the laser data you’d select the height, the end of the greenery, radius, and project it to the ground.

Slide 14: Future Work • add on increased automation – like advanced primitive fitting where the user would have to select less points and the program would search the surrounding points to fit it. • Other extensions could use intensity data as well to help with user or program segmentation – currently all triangles colored the same.

Slide 15: Conclusion • Fast loading and visualization • Semiautomated method • Built off existing graphics software