CSc Topics in Computer Graphics 3D Photography
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1 CSc Topics in Computer Graphics 3D Photography Tuesdays 11:45-1:45 1:45 Room 3305 Ioannis Stamos Office: 1090F, Hunter North (Entrance at 69 th bw/ / Park and Lexington Avenues) Class website: Computer Vision Illumination Sensors: Digital Cameras (2D) Range Scanners (3D) Physical 3D World Vision System Scene Description Geometry Reflectance
2 3D Photography & Graphics Scene Description Geometry Reflectance Captured from Vision System Model of Illumination Model of the Physical World Modeling [Representation of 3D objects] Rendering [Construction of 2D images from 3D models] Animation [Simulating changes over time] Representations of 3D Scenes Geometry and Material Geometry and Images Images with Depth Panorama with Depth Complete description Traditional texture mapping Global Geometry Local Geometry Light-Field/Lumigraph Facade Panorama Colored Voxels Scene as a light source No/Approx. Geometry False Geometry
3 New trends: Image-Based Rendering Chen and Williams (1993) - view interpolation McMillan and Bishop (1995) - plenoptic modeling Levoy and Hanrahan (1996) - light field rendering Slide by Ravi Ramamoorthi, Columbia University The graphics pipeline the traditional pipeline modeling animation rendering the new pipeline? 3D scanning motion capture image-based rendering Slide courtesy Marc Levoy
4 CSc Syllabus Capturing 3D data Laser scanning/stereo basics 3D modeling pipeline Segmentation/Registration Mesh/Volume Construction Rendering Image-based rendering Texture mapping Student projects (to be determined) 3D Photography - Applications Virtual worlds (Film Industry/Graphics) Urban Planning (Civil Applications) Historical Preservation and Archaeology Reverse Engineering (Manufacturing) E-commerce (Web-based models) Remote presence (Google Earth)
5 3D Photography System Cyrax Range Scanner Spot laser scanner Time of flight. Max Range: 100m. Scanning time: 20 minutes for 1000 x1000 points. Accuracy: 6mm.
6 Beauvais Cathedral, France.
7 Range Scanning Outdoor Structures
8 Technical Challenges Global and coherent geometry Remove redundancy Handle holes Handle all types of geometries Registration of point sets Reducing data complexity Range and intensity image fusion Brightness Images System Overview Range Images 2D line extraction Planar Segmentation 3D line extraction Solid Sweeps Range-Intensity Registration Range-Range Registration Volume Intersection FINAL PHOTOREALISTIC MODEL FINAL SOLID MODEL
9 Brightness Images System Overview Range Images 2D line extraction Planar Segmentation 3D line extraction Solid Sweeps Range-Intensity Registration Range-Range Registration Volume Intersection FINAL PHOTOREALISTIC MODEL FINAL SOLID MODEL Planar Segmentation (1)
10 Segmentation algorithm Fit local plane to neighborhood of range points. Classify range points: planar, non-planar, uknown. Sequentially label range image Connected clusters of co-planar points. Fit one plane to all points of cluster. Identify boundaries of planes. Local Planarity Comparison N N 1 2 P 1 P 2 R 12 Patches fit around points P1 and P2 P1 and P2 are coplanar if: -1 a=cos (N1. N2) < angle threshold d=max( R12 N1, R12 N2 ) < distance threshold
11 Italian House (view 1) Planar Segmentation (view 2)
12 Planar Segmentation (view 3)
13 Image Flat Iron Building Scan 1 Segmented Scan 1 Scan 2 Segmented Scan 2
14 Guggenheim (view 1) Segmentation (view 1)
15 Brightness Images System Overview Range Images 2D line extraction Planar Segmentation 3D line extraction Solid Sweeps Range-Intensity Registration Range-Range Registration Volume Intersection FINAL PHOTOREALISTIC MODEL FINAL SOLID MODEL 3D Line Detection Intersect neighboring planes. Discard lines produced by far away planes. Keep parts of lines verified by the range-set.
16 3D Line Detection, Plane Intersections.d1: Distance from face 1.d2:Distance from face 2 Border of face 1 Border of face 2 3D line Infinite Plane 1 Infinite Plane 2 Max(d1, d2) must be less than threshold Tpoly 3D lines
17 3D lines (Italian House) Brightness Images System Overview Range Images 2D line extraction Planar Segmentation 3D line extraction Solid Sweeps Range-Intensity Registration Range-Range Registration Volume Intersection FINAL PHOTOREALISTIC MODEL FINAL SOLID MODEL
18 Italian House: 3 registered views Common Coordinate System Flat Iron: 2 registered views
19 Brightness Images System Overview Range Images 2D line extraction Planar Segmentation 3D line extraction Solid Sweeps Range-Intensity Registration Range-Range Registration Volume Intersection FINAL PHOTOREALISTIC MODEL FINAL SOLID MODEL
20 Range Scan and Extruded Solid Model White Facets: Imaged Object Surfaces Red Facets: Unimaged, Occluded Object Surfaces Surface Classifiers: 1) Angle between facet surface normal and scan direction. 2) geometry of triangular facet.
21 Example: Hip Replacement Example: Video Game Controller
22 Example: Propeller Blade Italian House: 2 sweeps
23 Italian House (cont). Third Sweep Final Solid Range-Intensity Registration
24 Automated Range-Image Registration 1. Find internal camera calibration: use vanishing points. 2. Find camera rotation: Use vanishing points and orthogonality constraints. 3. Find camera translation: Match rectangular features. 4. Refinement of Rotation, Translation: backprojection of 3D lines on 2D image. Vanishing Points
25 Matching higher order features Results: Extracted Rectangles (1) 2D rectangles 3D rectangles
26 Results (cont) Second View 2D rectangles 3D rectangles Matched Rectangles (1) Set of 6 matched rectangles found by RANSAC. 2D rectangles: GREEN. 3D rectangles projected on image (after registration): RED.
27 Matching: Refinement Matched Rectangles (2) 2D rectangles: GREEN. 3D rectangles projected on image (after registration): RED. Set of 8 matched Rectangles found by RANSAC. All rectangles.
28 First view:texture map Second view:texture map
29 Flat Iron Building 3 novel views Guggenheim Museum, NYC
30 Guggenheim: 3-D model with imagery A Site Modeling Robot Build accurate models of large structures Given a 2-D 2 D map, build 3-D 3 D models Integrates 2-D 2 D imagery with range data Use planning to navigate to new views Register sensor data with the environment
31 Mobile Robot for Site Modeling AVENUE Autonomous Vehicle Exploring & Navigating Urban Environments The Digital Michelangelo Project Marc Levoy Computer Science Department Stanford University
32 Marc Levoy (Stanford) calibrated motions pitch (yellow) pan (blue) horizontal translation (orange) uncalibrated motions vertical translation remounting the scan head moving the entire gantry Scanner design 4 motorized axes truss extensions for tall statues laser, range camera, white light, and color camera
33 Scanning the David height of gantry: weight of gantry: 7.5 meters 800 kilograms Statistics about the scan 480 individually aimed scans 2 billion polygons 7,000 color images 32 gigabytes 30 nights of scanning 22 people
34 Head of Michelangelo s David photograph 1.0 mm computer model David s s left eye 0.25 mm model photograph holes from Michelangelo s drill artifacts from space carving noise from laser scatter
35 Dynamic Scenes Image sequence (CMU, Virtualized Reality Project) Dynamic Scenes Dynamic 3D model.
36 Dynamic Scenes Dynamic texture-mapped model. Lumigraph Example acquisition stage volumetric model novel view Recent work: Unstructured Lumigraph (SIGGRAPH 2001)
37 Acquiring the light field (IBR) natural eye level artificial illumination 7 light slabs, each 70cm x 70cm
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