IIT CS 695 - LECTURE NOTES (42 pages)

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LECTURE NOTES



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LECTURE NOTES

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Pages:
42
School:
Illinois Institute of Technology
Course:
Cs 695 - Doctoral Seminar
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Techniques for analysis and visualiza4on of large scale LIDAR scan data of urban environments Dr Patrick Flynn and Alexandri Zavodny ynn nd edu and azavodny nd edu University of Notre Dame This research sponsored by a gi1 from the NAVTEQ Corpora on Introduc4on What is LIDAR Acquisi4on System Typically aMach mul4ple scanners to cover a larger range Track vehicle s loca4on with GPS and IMUs Obtain color with mul4ple mounted cameras Data Fusion Raw Scanner Data Vehicle GPS Trace Vehicle IMU Log Fusion Points as la4tude longitude eleva4on color Color Camera Imagery Data Acquisi4on Di erent scanners provide very di erent data Acquisi4on Details SICK scanners High precision 10mm Low density 27 000 points sec max per scanner Total campus 95 809 671 points from all 3 Velodyne scanner Lower precision 2cm Very high density 1 5 million points sec Total campus 3 157 484 707 points Region Extrac4on Want to iden4fy underlying surface models from point sampling Look for sets of points that lie in the same plane Region Extrac4on Related Work Popular research topic for digital archaeology 1 3 aerial land surveys 4 6 urban planning applica4ons and more 7 8 However most papers rely on structured scan data Sta4onary plalorms 3 4 7 and single laser mobile plalorms 9 can exploit structure Planar Region Extrac4on We u4lize a region growing approach Our approach consists of 4 main steps Computa4on of planarity measures Region growing Region combina4on Region pruning Planar Region Extrac4on Planarity Measures For speed and accuracy only grow regions from points with planar neighborhoods Establish planarity measures for each point in two passes Pass 1 Compute surface normal approxima4on each point Ni for Planar Region Extrac4on Planarity Measures Pass 2 Compute the variance of normals within the neighborhood N PM P s i N avg N i N 2 Planar Region Extrac4on Region Growing Given a seed point Ps t a plane to the point s neighborhood Itera4vely add points near the edge of the region that t this planar equa4on Re t plane and re evaluate membership but only allow plane normal to change slowly 1 3 N N old N new 4 4 Planar Region Extrac4on Region Growing Globally Points are sorted based on planarity measure Do A region is grown from the most planar point Points found in this region are removed Un4l the next best planarity measure is above a threshold Planar Region Extrac4on Region Combina4on Region growing will nd all globally planar regions ideally Locally planar regions will be fragmented Planar Region Extrac4on Region Combina4on Combine regions that Are close in space Have similar planar normals Planar Region Extrac4on Region Pruning Noise regions ooen are iden4 ed Prune regions based on average planarity measure of cons4tuent points Paralleliza4on Region combina4on also works for globally planar regions if fragmented This observa4on allows us to split the dataset into spa ally coherent pieces and process these subsets separately Results We established a hard accuracy metric based on a hand created ground truth Per region metrics Ground truth regions correctly iden4 ed match Ground truth regions not iden4 ed miss Iden4 ed regions that are not in ground truth bogus For correctly matched regions the percentage of points shared between the two region accuracy Per point metrics True false posi4ves nega4ves for region membership Results Conducted experiments over a variety of subsets with varying thresholds and neighborhood de ni4ons for SICK data Best parameters gave on average Matched 88 62 True Posi ves 99 63 Missed 7 23 True Nega ves 87 44 Bogus 25 05 False Posi ves 12 56 Region Accuracy 90 36 False Nega ves 0 37 Region Extrac4on Ques4ons Visualiza4on Mesh Genera4on Points are just samples what is the underlying model Mesh Genera4on Related Work Solid representa4ons are preferable in many applica4ons including visualiza4on 1 object modeling 7 8 urban planning 10 and more Most work in the area exploits structure from scan data which is lost on scalable plalorms Mesh Genera4on Our approach uses extracted regions as input And each region can be triangulated in 2D Mesh Genera4on In 2D outline extrac4on is done via a modi ed convex hull approach Mesh Genera4on Once we have an ordered outline for a region we triangulate using an ear cuung method TODO Mesh Genera4on Once we have an ordered outline for a region we triangulate using an ear cuung method TODO Mesh Genera4on Once we have an ordered outline for a region we triangulate using an ear cuung method TODO Mesh Genera4on Once we have an ordered outline for a region we triangulate using an ear cuung method TODO Mesh Genera4on Once we have an ordered outline for a region we triangulate using an ear cuung method TODO Mesh Genera4on Single Region Points detected as belonging to a single region Mesh Genera4on Single Region Simpli ed outline aoer outline extrac4on Mesh Genera4on Single Region Triangulated region with texture generated from nearby points Mesh Genera4on Single Region Textured triangula4on with wireframe overlaid Mesh Genera4on Triangula4ng regions independently causes boundary issues Mesh Genera4on Global uni ca4on step iden4 es shared borders and corners among all regions Uses these guidelines to snap outlines Mesh Genera4on Perform the stages in this order Global edge uni ca4on Outline extrac4on and simpli ca4on Surface triangula4on Texture genera4on Mesh Genera4on Future Work Improved mesh and texture genera4on using camera images instead of point colors Improved region segmenta4on using domain speci c knowledge Cross 4me change detec4on using extracted regions Thank you Ques4ons Special thanks to the NAVTEQ Corpora on for their sponsorship of this research Publica4ons A Zavodny P Flynn and X Chen Region Extrac on in Large Scale Urban LIDAR Data 3D Digital Imaging and Modeling 2009 3DIM 09 A Zavodny P Flynn and X Chen Textured Mesh Genera on of Extracted Regions from Urban Range Scanned LIDAR Data submiMed 3DIMPVT 11 References 1 P Allen S Feiner A Troccoli H Benko E Ishak and B Smith Seeing into the past Crea0ng a 3d modeling pipeline for archaeological visualiza0on Proceedings of the Interna4onal Symposium on 3D Data Processing Visualiza4on and Transmission 3DPVT04 0 751 758 2004 2 W Boehler and A Marbs 3d scanning and photogrammetry for heritage recording A comparison In Procedings of the Tweloh Interna4onal Conference on Geoinforma4cs 2004 3 G Guidi F Remondino M Russo F Menna and A Rizzi 3d modeling of large and complex sites using mul0 sensor


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