Interactive Point-based Modeling of Complex Objects from Images

1. Interactive Point-based Modeling of Complex Objects from Images Pierre Poulin (a,b) Marc Stamminger (a,c) François Duranleau (b) Marie-Claude Frasson (a) George Drettakis (a) (a) REVES, INRIA Sophia Antipolis (b) DIRO, Université de Montréal (c) University of Erlangen

2. Modeling Complex Objects

3. Modeling Complex Objects • High visual complexity • Time consuming • Algorithms for specialized objects • e.g., plants, mountains, etc. • Adaptive rendering • Many applications need such objects

4. Key Observations • Extracting complex models from photos is a very powerful approach • Point-based representation is very effective for complex models • Efficient display and storage • User interaction is beneficial when extracting quality models • Specify where details are needed • Resolve some ambiguities

5. Image-based Point Modeling • Images are very flexible • Reality-based (photos) • Acquisition is easy

6. Image-based Point Modeling • Points are very flexible • Fast rendering (hardware support) • Adaptive rendering for interactive display Stamminger

7. Image-based Point Modeling • Points are very flexible • Hierarchical organization and levels of detail Q-splat

8. Image-based Point Modeling • Points are very flexible • Visual quality • Many recent advances Deussen

9. Images Constraints Reconstruction Process 3D Model Image Automatic Reconstruction

10. Images Constraints Reconstruction Process 3D Model Image Interactive Reconstruction new images requantize recalibrate User

11. Images Constraints Reconstruction Process 3D Model Image Interactive Reconstruction color comparisons plausibility threshold new depth maps zone of interest User

12. Images Constraints Reconstruction Process 3D Model Image Interactive Reconstruction User revalidate the points request more points decimate the points jitter the points sample with patterns hole filling

13. Images Constraints Reconstruction Process 3D Model Image Interactive Reconstruction User undo changes remove points add polygons

14. Interactive Reconstruction • Interactive display • 6 M points/sec. on a PIII 1GHz with GeForce3 • Efficient reconstruction algorithm • Test more than 1K points/sec. • Simple and intuitive controls • Direct interaction with the points

15. Computer Vision Contributions • 3D scanners • Structured light • Stereo – N-views • Shape-from-X • Volumetric

16. Volumetric Reconstruction • Voxel coloring and Space carving • If a voxel is impossible, carved out of object • Silhouettes, transparency, shading • Photo-consistency Kutulakos Seitz

17. Image-based Polygon Modeling • Academic: Façade, Rekon, Reality • Industry: RealViz, Canoma, Photomodeler Façade

18. Image-based Polygon Modeling • Small polygonal scene (30-100 polygons) • Extracted textures and illumination Boivin

20. Input Images (4/14)

21. Input Images • Digital camera: Canon EOS-DS30 • 1080x720 and 2166x1440 • Fixed aperture and shutter speed • Try not to change zoom • OpenGL and ray traced test scenes

22. Camera Calibration

23. Camera Calibration • ImageModeler from RealViz • Fiduciary marks placed around the object • Interactive system • Intrinsic and extrinsic camera parameters

24. 3D Zone of Interest

25. Initial Random Points

26. Initial Random Points • Generated randomly within the envelope • More specific patterns discussed later • Projection of a point in each photo • Gather colors

27. Color Comparison • Euclidean distance • RGB, CIE xy, CIE Luv, CIE Lab • Speed vs. accuracy • Color quantized images • Precomputed (ppmquantall or more sophisticated) • Quantization only on projected zone of interest • 32 to 128 colors • Reduce shading variations • Efficient test for color equality

28. Dominant Color Plausibility with visibility A: 100% 100% B: 50% 33% C: 25%

29. Random Points with Depth Maps

30. Depth Maps • Computed from the current set of points • Updated on user demand • With depth maps, can raise the plausibility threshold • Generate more points within the object • Re-evaluation of previously generated points

31. Clean-up Points

32. Clean-up Points • In general • Increase color threshold and re-evaluate • With good depth maps • Project in each image • Reject if point visible and color too different

33. Generate More Points

34. Generate More Points • Randomly • Stratified sampling based on voxels • Point decimation based on voxels

35. Guide the Points

36. Guide the Points • Smaller 3D sphere of interest • Generate more points • Eliminate all points • 3D flood fill for branching patterns • Patterns for planar surfaces • Patterns for boundary surfaces

37. Filling with no Leaves

38. Filling with Leaves

39. Jitter the Points

40. Reprojection

41. Stepping through it again

42. Results

43. Synthetic Fruit Bowl color points reprojection ray tracing

44. Toy Soldier color points reprojection color points

45. Snack

46. Snack

47. Ficcus

48. Conclusions • Point-based reconstruction of complex objects from images • Tight integration • 3D color point representation • User-driven and/or automatic reconstructions • Interactive display • Flexible to integrate most advances in computer vision

49. Findings • First steps are encouraging, but objects are still of limited realism • Information in photos is inspiring, but also difficult to analyse correctly • How many things in a pixel? • How many pixels and colors for an object?

50. Future Work • Video sequences • High dynamic range photos • Shadows and shading in color comparison • Extraction of limited BRDFs • 3D texture synthesis of materials