Fast and robust techniques for 3D/2D registration and photo blending on massive point clouds

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1 vcg.isti.cnr.it/ Fast and robust techniques for 3D/2D registration and photo blending on massive point clouds R. Pintus, E. Gobbetti, M.Agus, R. Combet CRS4 Visual Computing M. Callieri Visual Computing Group ISTI-CNR

2 Goal Fast and robust techniques for creating accurate colored models Acquisition 3D laser scanners Color digital cameras Point-based rendering pipeline Mapping photo-to-geometry Fast and Robust Semi-Automatic Registration of Photographs to 3D Geometry Photo blending A Streaming Framework for Seamless Detailed Photo Blending on Massive Point Clouds

3 vcg.isti.cnr.it/ Point-based Rendering Ruggero Pintus, Enrico Gobbetti, and Marco Agus. Real-time Rendering of Massive Unstructured Raw Point Clouds using Screen-space Operators. In The 12th International Symposium on Virtual Reality, Archaeology and Cultural Heritage, October 2011.

4 Problem Statement R. Pintus CRS4/ViC, LC3D, March 2012 NORMALS RADII

5 Problem Statement R. Pintus CRS4/ViC, LC3D, March 2012 NORMALS RADII

6 Related work Point based rendering (PBR) techniques Continuous surface, solving visibility Object space vs Screen space Point attributes (normals and radii) needed Require only radii [Grottel et al. 2010] No attributes [Wimmer et al. 2006] Visibility not solved Direct visibility algorithms [Katz et al. 2007] and [Mehra et al. 2010] Compute point set convex hull not feasible for a real-time application

7 Our method Point based rendering pipeline Unstructured raw point clouds (no attributes) Screen-space operators Direct visibility, Anisotropic filling, Shading Real-time with massive models Multi-resolution out-of-core structure Minimizing pre-computation Suitable for quick on-site visualizations

8 Pipeline - Overview Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering

9 Input data Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering Unstructured raw point cloud Position (Color?) No normals No influence radii Multi-resolution out-ofcore structure Fast pre-processing

10 Point projection R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

11 Point projection R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering

12 Visibility Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

13 Visibility Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

14 Visibility Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

15 Visibility Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

16 Visibility Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

17 Visibility Unstructured Raw Point Cloud Point projection Near Plane Visibility Filling Shape depiction Final rendering Far Plane

18 Visibility Unstructured Raw Point Cloud Point projection Screen-space implementation Visibility Filling Shape depiction Final rendering

19 Visibility Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering

20 Anisotropic filling R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection 3D surface reconstruction 2D infilling Multi-pass GPU shader Visibility Filling Shape depiction Neighbor distance from Kernel center (pixel) w i 1 r i 2 1 min 1, Depth value of neighbor i z i z 0 Depth filter dimension Depth value of current pixel Final rendering

21 Anisotropic filling R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering

22 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection Visibility Deferred shading Multi-level shading term Similar to [Rusinkiewicz et al. 2006] We start from (3x3 kernel) d i 0 min D Filling d i 0 max 0 0 Shape depiction Final rendering i 0 clamp d i 0 d max 0 i d d i 0 i 0 min min

23 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering

24 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud For a level D k d i k 1 k d 2 i k min Point projection Visibility Filling Shape depiction Final rendering

25 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud For a level D k d i k 1 k d 2 i k min Point projection Visibility Filling Shape depiction k 0 Final rendering

26 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud For a level D k d i k 1 k d 2 i k min Point projection Visibility Filling Shape depiction k 10 Final rendering

27 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud For a level D k d i k 1 k d 2 i k min Point projection Visibility Filling Shape depiction k 20 Final rendering

28 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud For a level D k d i k 1 k d 2 i k min Point projection Visibility Filling Shape depiction k 30 Final rendering

29 Shape depiction R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Merging levels i K k 1 k i 0 k 1 Point projection Visibility Filling 3 2 Shape depiction Final rendering

30 Final rendering R. Pintus CRS4/ViC, LC3D, March 2012 Unstructured Raw Point Cloud Point projection Visibility Filling Shape depiction Final rendering

31 Results Massive unstructured raw point clouds Integrated in a pipeline for real-time out-ofcore rendering Datasets Sirmione 100Mpts Toronto 260Mpts Loggia (Brescia) 110Mpts

32 Results Video R. Pintus CRS4/ViC, LC3D, March 2012

33 Sirmione 100Mpts Point projection

34 Sirmione 100Mpts Pull-push method

35 Sirmione 100Mpts Our method

36 Sirmione 100Mpts Pull-push method

37 Sirmione 100Mpts Our method

38 Sirmione 100Mpts Our method with shape depiction

39 Toronto 260Mpts Point Splat Noisy dataset

40 Toronto 260Mpts Our Rendering Noisy dataset

41 Loggia (Brescia) 110Mpts Point Splat Noisy dataset

42 Loggia (Brescia) 110Mpts Our Rendering Noisy dataset

43 Conclusion PBR pipeline comparable to state-of-the-art techniques Unstructured raw point clouds Screen-space operators Real-time with massive models Multi-resolution out-of-core structure Minimizing pre-computation Robust to noise Suitable for quick on-site visualizations

44 vcg.isti.cnr.it/ Photo Mapping Ruggero Pintus, Enrico Gobbetti, and Roberto Combet. Fast and Robust Semi-Automatic Registration of Photographs to 3D Geometry. In The 12th International Symposium on Virtual Reality, Archaeology and Cultural Heritage, October 2011.

45 Problem Statement R. Pintus CRS4/ViC, LC3D, March D Geometry Unordered Set Of n Uncalibrated Photos n Camera Poses (2D/3D Registration)

46 Related work Manual selection of 2D-3D matches Massive user intervention Tiring and time-consuming Automatic feature matching Not robust enough for a generic dataset Semi-automatic statistical correlation Point cloud attributes not always provided Geometric multi-view reconstruction 2D-3D problem 3D-3D registration task dense and ordered frame sequence Our contribution Minimize user intervention / Large datasets / Semiautomatic / Multi-view based approach / No Attributes

47 Input Data User Dense 3D n Photos SfM Reconstruction Coarse Registration Refinement Output Data Dense Geometry Point cloud, triangle mesh, etc. No attributes No particular features n photos Naïve constraints: Blur, Noise, Under- or over-exposured Sufficient overlap

48 Multi-view User Dense 3D n Photos SfM Reconstruction Coarse Registration Refinement Bundler [Snavely et al. 2006] SfM system for unordered image collections n.edu/bundler/ Output A sparse point cloud n camera poses SIFT keypoints (projections of sparse 3D points) Output Data

49 Coarse registration User Dense 3D n Photos SfM Reconstruction Coarse Registration Refinement Output Data Register two point clouds with different: scales reference frames resolutions Automatic methods are not robust and efficient enough User aligns few images (one or more) to the dense geometry Affine transformation is applied to all cameras and sparse points

50 Refinement User Dense 3D n Photos P j C 1 Q C, 2 p j s 1, j SfM Reconstruction p j s 2, j NN F p j Coarse Registration Refinement Q C, 2 NN F p j C 2 Output Data E C, P N N P C j 1 i 1 v ij Q C i, NN F p j s i, j 2

51 Refinement User Dense 3D n Photos SfM Reconstruction Coarse Registration Refinement Sparse Bundle Adjustment (SBA) Constants SIFT keypoints, dense 3D points Variables Camera poses, sparse 3D points SBA A Generic SBA C/C++ Package Based on the Levenberg- Marquardt Algorithm kis/sba/ Output Data

52 Output data User Dense 3D n Photos n camera poses SfM Reconstruction Coarse Registration Refinement Input of photo blending n photos n camera poses Dense 3D geometry Output Data

53 Results Photo mapping R. Pintus CRS4/ViC, LC3D, March 2012

54 vcg.isti.cnr.it/ Photo Blending Ruggero Pintus, Enrico Gobbetti, and Marco Callieri. A Streaming Framework for Seamless Detailed Photo Blending on Massive Point Clouds. In Proc. Eurographics Area Papers. Pages 25-32, 2011.

55 Problem Statement R. Pintus CRS4/ViC, LC3D, March 2012 Point Cloud Calibrated Photos

56 Problem Statement R. Pintus CRS4/ViC, LC3D, March 2012 Point Cloud P Calibrated Photos

57 Problem Statement R. Pintus CRS4/ViC, LC3D, March 2012 Point Cloud P Calibrated Photos Colored Point Cloud

58 Problem Statement R. Pintus CRS4/ViC, LC3D, March 2012 Point Cloud P Calibrated Photos Colored Point Cloud Problem Unlimited size of 3D model (Gpoints) and unlimited number of images

59 Related work State-of-the-art techniques Image quality estimation Stitching or blending Data representation Triangle meshes exploit connectivity Meshless approaches Both triangle meshes and point clouds Memory settings All in-core no massive geometry/images 3D in-core and images out-of-core no massive geometry All out-of-core Low performances Our contribution Blending function / Streaming framework / Massive point cloud / Adaptive geometry refinement

60 Pipeline Photo Stencil Per-pixel Weight Masked Per-pixel Weight

61 Simple blending R. Pintus CRS4/ViC, LC3D, March 2012

62 Edge extraction and Distance Transform

63 Smooth weight R. Pintus CRS4/ViC, LC3D, March 2012

64 Smooth weight R. Pintus CRS4/ViC, LC3D, March 2012

65 Single band blending R. Pintus CRS4/ViC, LC3D, March 2012

66 Multi band blending R. Pintus CRS4/ViC, LC3D, March 2012

67 Adaptive point refinement R. Pintus CRS4/ViC, LC3D, March 2012

68 Adaptive point refinement R. Pintus CRS4/ViC, LC3D, March 2012

69 Adaptive point refinement R. Pintus CRS4/ViC, LC3D, March 2012

70 Adaptive point refinement R. Pintus CRS4/ViC, LC3D, March 2012

71 Results David 470Mpoints Callieri et. al 2008 David 28M Disk space occupancy 6.2GB Computation time 15.5 hours

72 Results Church s Apse 14 Mpoint Geometry 40 photos

73 Results Church s Apse R. Pintus CRS4/ViC, LC3D, March 2012

74 Results Grave 8 Mpoint Geometry 21 photos

75 Results Grave R. Pintus CRS4/ViC, LC3D, March 2012

76 Results Church s detail Outliers

77 Results Church s detail R. Pintus CRS4/ViC, LC3D, March 2012

78 Results R. Pintus CRS4/ViC, LC3D, March 2012

79 Results R. Pintus CRS4/ViC, LC3D, March 2012

80 Results David 470Mpoints Image size 19456x Gpixel

81 Results R. Pintus CRS4/ViC, LC3D, March 2012

82 Conclusion Point-based rendering pipeline Image-to-geometry registration approach Minimum user intervention No constraints on geometry, attributes and features Specific robust cost function and SBA Out-of-core photo blending approach (Point clouds of unlimited size) Incremental color accumulation (Unlimited number of images) Smooth weight function (Seamless color blending) Streaming framework (Performance improvement) Adaptive point refinement Future work Automatic sparse-to-dense geometry registration Interactive blending - adding and removing images in an interactive tool Fast visual check of previous alignment step

83 Conclusion Low cost Personal computer Digital camera Decreased manual intervention Open Source / Free Software Bundler SfM reconstruction Sparse Bundle Adjustment SBA Minimization Opengl / GLSL shaders Rendering Qt Interface Opencv Manual registration Spaceland Library Geometric computation IIPImage Web-based Viewer

84 Questions & Contacts CRS4 VIC Ruggero Pintus

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