Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting
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1 Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting R. Maier 1,2, K. Kim 1, D. Cremers 2, J. Kautz 1, M. Nießner 2,3 Fusion Ours 1 2 NVIDIA TU Munich 3 Stanford University International Conference on Computer Vision 2017
2 Overview Motivation & State-of-the-art Approach Results Conclusion 2
3 Overview Motivation & State-of-the-art Approach Results Conclusion 3
4 Motivation Recent progress in Augmented Reality (AR) / Virtual Reality (VR) Microsoft HoloLens HTC Vive 4
5 Motivation Recent progress in Augmented Reality (AR) / Virtual Reality (VR) Requirement of high-quality 3D content for AR, VR, Gaming Microsoft HoloLens HTC Vive NVIDIA VR Funhouse 5
6 Motivation Recent progress in Augmented Reality (AR) / Virtual Reality (VR) Requirement of high-quality 3D content for AR, VR, Gaming Usually: manual modelling (e.g. Maya) Microsoft HoloLens HTC Vive NVIDIA VR Funhouse 6
7 Motivation Recent progress in Augmented Reality (AR) / Virtual Reality (VR) Requirement of high-quality 3D content for AR, VR, Gaming Usually: manual modelling (e.g. Maya) Wide availability of commodity RGB-D sensors: efficient methods for 3D reconstruction Microsoft HoloLens HTC Vive NVIDIA VR Funhouse Asus Xtion 7
8 Motivation Recent progress in Augmented Reality (AR) / Virtual Reality (VR) Requirement of high-quality 3D content for AR, VR, Gaming Usually: manual modelling (e.g. Maya) Wide availability of commodity RGB-D sensors: efficient methods for 3D reconstruction Microsoft HoloLens HTC Vive Challenge: how to reconstruct high-quality 3D models with best-possible geometry and color from low-cost depth sensors? NVIDIA VR Funhouse Asus Xtion 8
9 State-of-the-art RGB-D based 3D Reconstruction Goal: stream of RGB-D frames of a scene 3D shape that maximizes the geometric consistency 9
10 State-of-the-art RGB-D based 3D Reconstruction Goal: stream of RGB-D frames of a scene 3D shape that maximizes the geometric consistency Real-time, robust, fairly accurate geometric reconstructions KinectFusion, 2011 KinectFusion: Real-time Dense Surface Mapping and Tracking, Newcombe et al., ISMAR
11 State-of-the-art RGB-D based 3D Reconstruction Goal: stream of RGB-D frames of a scene 3D shape that maximizes the geometric consistency Real-time, robust, fairly accurate geometric reconstructions KinectFusion, 2011 KinectFusion: Real-time Dense Surface Mapping and Tracking, Newcombe et al., ISMAR DynamicFusion, 2015 DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-time, Newcombe et al., CVPR
12 State-of-the-art RGB-D based 3D Reconstruction Goal: stream of RGB-D frames of a scene 3D shape that maximizes the geometric consistency Real-time, robust, fairly accurate geometric reconstructions KinectFusion, 2011 KinectFusion: Real-time Dense Surface Mapping and Tracking, Newcombe et al., ISMAR DynamicFusion, 2015 DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-time, Newcombe et al., CVPR BundleFusion, 2017 BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration, Dai et al., ToG
13 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
14 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Challenges: (Slightly) inaccurate and over-smoothed geometry Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
15 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Challenges: (Slightly) inaccurate and over-smoothed geometry Bad colors Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
16 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Challenges: (Slightly) inaccurate and over-smoothed geometry Bad colors Inaccurate camera pose estimation Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
17 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Challenges: (Slightly) inaccurate and over-smoothed geometry Bad colors Inaccurate camera pose estimation Input data quality (e.g. motion blur, sensor noise) Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
18 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Challenges: (Slightly) inaccurate and over-smoothed geometry Bad colors Inaccurate camera pose estimation Input data quality (e.g. motion blur, sensor noise) Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
19 State-of-the-art Voxel Hashing Baseline RGB-D based 3D reconstruction framework initial camera poses sparse SDF reconstruction Challenges: (Slightly) inaccurate and over-smoothed geometry Bad colors Inaccurate camera pose estimation Input data quality (e.g. motion blur, sensor noise) Goal: High-Quality Reconstruction of Geometry and Color Real-time 3D Reconstruction at Scale using Voxel Hashing, Nießner et al., ToG
20 State-of-the-art 20
21 State-of-the-art High-Quality Colors [Zhou2014] Optimize camera poses and image deformations to optimally fit initial (maybe wrong) reconstruction But: HQ images required, no geometry refinement involved Color Map Optimization for 3D Reconstruction with Consumer Depth Cameras, Zhou and Koltun, ToG
22 State-of-the-art High-Quality Colors [Zhou2014] High-Quality Geometry [Zollhöfer2015] Optimize camera poses and image deformations to optimally fit initial (maybe wrong) reconstruction But: HQ images required, no geometry refinement involved Color Map Optimization for 3D Reconstruction with Consumer Depth Cameras, Zhou and Koltun, ToG 2014 Adjust camera poses in advance (bundle adjustment) to improve color Use shading cues (RGB) to refine geometry (shading based refinement of surface & albedo) But: RGB is fixed (no color refinement based on refined geometry) Shading-based Refinement on Volumetric Signed Distance Functions, Zollhöfer et al., ToG
![State-of-the-art High-Quality Colors [Zhou2014] High-Quality](/docs-images/75/72681473/images/23-0.jpg "Geometry [Zollhöfer2015] Optimize camera poses and image")
23 State-of-the-art High-Quality Colors [Zhou2014] High-Quality Geometry [Zollhöfer2015] Optimize camera poses and image deformations to optimally fit initial (maybe wrong) reconstruction But: HQ images required, no geometry refinement involved Color Map Optimization for 3D Reconstruction with Consumer Depth Cameras, Zhou and Koltun, ToG 2014 Adjust camera poses in advance (bundle adjustment) to improve color Use shading cues (RGB) to refine geometry (shading based refinement of surface & albedo) But: RGB is fixed (no color refinement based on refined geometry) Shading-based Refinement on Volumetric Signed Distance Functions, Zollhöfer et al., ToG 2015 Idea: jointly optimize for geometry, albedo and image formation model to simultaneously obtain high-quality geometry and appearance! 23
24 Our Method 24
25 Our Method Temporal view sampling & filtering techniques (input frames) 25
26 Our Method Temporal view sampling & filtering techniques (input frames) Joint optimization of surface & albedo (Signed Distance Field) image formation model 26
27 Our Method Temporal view sampling & filtering techniques (input frames) Joint optimization of surface & albedo (Signed Distance Field) image formation model 27
28 Our Method Temporal view sampling & filtering techniques (input frames) Joint optimization of surface & albedo (Signed Distance Field) image formation model Lighting estimation using Spatially- Varying Spherical Harmonics (SVSH) 28
29 Our Method Temporal view sampling & filtering techniques (input frames) Joint optimization of surface & albedo (Signed Distance Field) image formation model Lighting estimation using Spatially- Varying Spherical Harmonics (SVSH) Optimized colors (by-product) 29
30 Overview Motivation & State-of-the-art Approach Results Conclusion 30
31 Approach Overview RGB-D 31
32 Approach Overview RGB-D SDF Fusion 32
33 Approach Overview RGB-D SDF Fusion Shading-based Refinement (Shape-from-Shading) 33
34 Approach Overview RGB-D SDF Fusion Shading-based Refinement (Shape-from-Shading) Temporal view sampling / filtering 34
35 Approach Overview RGB-D SDF Fusion Shading-based Refinement (Shape-from-Shading) Spatially-Varying Lighting Estimation Temporal view sampling / filtering 35
36 Approach Overview RGB-D SDF Fusion Shading-based Refinement (Shape-from-Shading) Spatially-Varying Lighting Estimation Joint Appearance and Geometry Optimization surface albedo image formation model Temporal view sampling / filtering 36
37 Approach Overview RGB-D SDF Fusion Shading-based Refinement (Shape-from-Shading) High-Quality 3D Reconstruction Spatially-Varying Lighting Estimation Joint Appearance and Geometry Optimization surface albedo image formation model Temporal view sampling / filtering 37
38 Approach Overview RGB-D 38
39 RGB-D Data Example: Fountain dataset 1086 RGB-D frames Sensor: Depth 640x480px Color 1280x1024px ~10 Hz Primesense Poses estimated using Voxel Hashing Source: 39
40 Approach Overview SDF Fusion 40
41 Signed Distance Fields Volumetric 3D model representation Voxel grid: dense (e.g. KinectFusion) or sparse (e.g. Voxel Hashing) A volumetric method for building complex models from range images, Curless and Levoy, SIGGRAPH
42 Signed Distance Fields Volumetric 3D model representation Voxel grid: dense (e.g. KinectFusion) or sparse (e.g. Voxel Hashing) Each voxel stores: Signed Distance Function (SDF): signed distance to closest surface Color values Weights A volumetric method for building complex models from range images, Curless and Levoy, SIGGRAPH
43 Signed Distance Fields Fusion of depth maps Integrate depth maps into SDF with their estimated camera poses 43
44 Signed Distance Fields Fusion of depth maps Integrate depth maps into SDF with their estimated camera poses Voxel updates using weighted average 44
45 Signed Distance Fields Fusion of depth maps Integrate depth maps into SDF with their estimated camera poses Voxel updates using weighted average 45
46 Signed Distance Fields Fusion of depth maps Integrate depth maps into SDF with their estimated camera poses Voxel updates using weighted average 46
47 Signed Distance Fields Fusion of depth maps Integrate depth maps into SDF with their estimated camera poses Voxel updates using weighted average 47
48 Signed Distance Fields Fusion of depth maps Integrate depth maps into SDF with their estimated camera poses Voxel updates using weighted average Extract ISO-surface with Marching Cubes (triangle mesh) Marching cubes: A high resolution 3D surface construction algorithm, Lorensen and Cline, SIGGRAPH
49 Approach Overview Temporal view sampling / filtering 49
50 Keyframe Selection Compute per-frame blur score (for color image) Frame 81 Frame 84 Select frame with best score within a fixed size window as keyframe The blur effect: perception and estimation with a new no-reference perceptual blur metric, Crete et al., SPIE
51 Sampling / Filtering Sampling of voxel observations Sample from selected keyframes only Input keyframes Collect observations for voxel in input views: Reconstruction 51
52 Sampling / Filtering Sampling of voxel observations Sample from selected keyframes only Input keyframes Collect observations for voxel in input views: Voxel center transformed and projected into input view Reconstruction 52
53 Sampling / Filtering Sampling of voxel observations Sample from selected keyframes only Input keyframes Collect observations for voxel in input views: Voxel center transformed and projected into input view Observation weights: view-dependent on normal and depth Filter observations: keep only best 5 observations by weight Reconstruction 53
54 Approach Overview Double-hierarchical (coarse-to-fine: SDF Volume / RGB-D) Shading-based Refinement (Shape-from-Shading) Spatially-Varying Lighting Estimation Joint Appearance and Geometry Optimization surface albedo image formation model 54
55 Shape-from-Shading Shading equation: 55
56 Shape-from-Shading Shading equation: surface normal 56
57 Shape-from-Shading Shading equation: lighting surface normal 57
58 Shape-from-Shading Shading equation: albedo lighting surface normal 58
59 Shape-from-Shading Shading equation: Shading albedo lighting surface normal 59
60 Shape-from-Shading Shading equation: Shading albedo lighting surface normal Shading-based refinement: Intuition: high-frequency changes in surface geometry shading cues in input images 60
61 Shape-from-Shading Shading equation: Shading albedo lighting surface normal Shading-based refinement: Intuition: high-frequency changes in surface geometry shading cues in input images Estimate lighting given surface and albedo (intrinsic material properties) 61
62 Shape-from-Shading Shading equation: Shading albedo lighting surface normal - Shading-based refinement: Intuition: high-frequency changes in surface geometry shading cues in input images Estimate lighting given surface and albedo (intrinsic material properties) Estimate surface and albedo given the lighting: minimize difference between estimated shading and input luminance 62
63 Approach Overview Spatially-Varying Lighting Estimation 63
64 Lighting Estimation Spherical Harmonics (SH) Encode incident lighting for a given surface point Smooth for Lambertian surfaces 64
65 Lighting Estimation Spherical Harmonics (SH) Encode incident lighting for a given surface point Smooth for Lambertian surfaces SH Basis functions H m parametrized by unit normal n 65
66 Lighting Estimation Spherical Harmonics (SH) Encode incident lighting for a given surface point Smooth for Lambertian surfaces SH Basis functions H m parametrized by unit normal n Good approx. using only 9 SH basis functions (2nd order) 66
67 Lighting Estimation Spherical Harmonics (SH) Encode incident lighting for a given surface point Smooth for Lambertian surfaces SH Basis functions H m parametrized by unit normal n Good approx. using only 9 SH basis functions (2nd order) Estimate SH coefficients: 67
68 Lighting Estimation Spherical Harmonics (SH) Encode incident lighting for a given surface point Smooth for Lambertian surfaces SH Basis functions H m parametrized by unit normal n Good approx. using only 9 SH basis functions (2nd order) Estimate SH coefficients: Shortcoming: purely directional cannot represent scene lighting for all surface points simultaneously 68
69 Spatially-Varying Lighting Subvolume Partitioning 69
70 Spatially-Varying Lighting Subvolume Partitioning Partition SDF volume into subvolumes 70
71 Spatially-Varying Lighting Subvolume Partitioning Partition SDF volume into subvolumes Estimate independent SH coefficients for each subvolume 71
72 Spatially-Varying Lighting Subvolume Partitioning Partition SDF volume into subvolumes Estimate independent SH coefficients for each subvolume Obtain per-voxel SH coefficients through tri-linear interpolation 72
73 Spatially-Varying Lighting Joint Optimization 73
74 Spatially-Varying Lighting Joint Optimization Estimate SVSH coefficients for all subvolumes jointly: 74
75 Spatially-Varying Lighting Joint Optimization Estimate SVSH coefficients for all subvolumes jointly: Data term: Similarity between estimated shading and input luminance 75
76 Spatially-Varying Lighting Joint Optimization Estimate SVSH coefficients for all subvolumes jointly: Data term: Similarity between estimated shading and input luminance Laplacian regularizer: Smooth illumination changes 76
77 Approach Overview Joint Appearance and Geometry Optimization surface albedo image formation model 77
78 Joint Optimization Shading-based SDF optimization Joint optimization of geometry, albedo and image formation model (camera poses and camera intrinsics): with 78
79 Joint Optimization Shading-based SDF optimization Joint optimization of geometry, albedo and image formation model (camera poses and camera intrinsics): with Gradient-based shading constraint (data term) 79
80 Joint Optimization Shading-based SDF optimization Joint optimization of geometry, albedo and image formation model (camera poses and camera intrinsics): with Gradient-based shading constraint (data term) Volumetric regularizer: smoothness in distance values (Laplacian) 80
81 Joint Optimization Shading-based SDF optimization Joint optimization of geometry, albedo and image formation model (camera poses and camera intrinsics): with Gradient-based shading constraint (data term) Volumetric regularizer: smoothness in distance values (Laplacian) Surface Stabilization constraint: stay close to initial distance values 81
82 Joint Optimization Shading-based SDF optimization Joint optimization of geometry, albedo and image formation model (camera poses and camera intrinsics): with Gradient-based shading constraint (data term) Volumetric regularizer: smoothness in distance values (Laplacian) Surface Stabilization constraint: stay close to initial distance values Albedo regularizer: constrain albedo changes based on chromaticity (Laplacian) 82
83 Joint Optimization Shading Constraint (data term) Idea: maximize consistency between estimated voxel shading and sampled intensities in input luminance images (gradient for robustness) 83
84 Joint Optimization Shading Constraint (data term) Idea: maximize consistency between estimated voxel shading and sampled intensities in input luminance images (gradient for robustness) Best views for voxel and respective view-dependent weights 84
85 Joint Optimization Shading Constraint (data term) Idea: maximize consistency between estimated voxel shading and sampled intensities in input luminance images (gradient for robustness) Best views for voxel and respective view-dependent weights Shading: allows for optimization of surface (through normal) and albedo 85
86 Joint Optimization Shading Constraint (data term) Idea: maximize consistency between estimated voxel shading and sampled intensities in input luminance images (gradient for robustness) Best views for voxel and respective view-dependent weights Shading: allows for optimization of surface (through normal) and albedo Voxel center transformed and projected into input view 86
87 Joint Optimization Shading Constraint (data term) Idea: maximize consistency between estimated voxel shading and sampled intensities in input luminance images (gradient for robustness) Best views for voxel and respective view-dependent weights Shading: allows for optimization of surface (through normal) and albedo Voxel center transformed and projected into input view Sampling: allows for optimization of camera poses and camera intrinsics 87
88 Recolorization Optimal colors Recompute voxel colors after optimization at each level 88
89 Recolorization Optimal colors Recompute voxel colors after optimization at each level Sampling Sample from keyframes only Collect, weight and filter observations 89
90 Recolorization Optimal colors Recompute voxel colors after optimization at each level Sampling Sample from keyframes only Collect, weight and filter observations Weighted average of observations: 90
91 Overview Motivation & State-of-the-art Approach Results Conclusion 91
92 Ground Truth: Geometry Frog (synthetic) Ours Fusion Ground truth 92
93 Ground Truth: Geometry Frog (synthetic) Ours Fusion Ground truth Zollhöfer et al
94 Ground Truth: Geometry Frog (synthetic) Ours Fusion Ground truth Zollhöfer et al. 15 Ours 94
95 Ground Truth: Quantitative Results Frog (synthetic) Zollhöfer et al. 15 Generated synthetic RGB-D dataset (noise on depth and camera poses) Quantitative surface accuracy evaluation Color coding: absolute distances (ground truth) 95
96 Ground Truth: Quantitative Results Frog (synthetic) Generated synthetic RGB-D dataset (noise on depth and camera poses) Quantitative surface accuracy evaluation Color coding: absolute distances (ground truth) Ours Zollhöfer et al
97 Ground Truth: Quantitative Results Frog (synthetic) Generated synthetic RGB-D dataset (noise on depth and camera poses) Quantitative surface accuracy evaluation Color coding: absolute distances (ground truth) Ours Zollhöfer et al. 15 Mean absolute deviation: Ours: 0.222mm (std.dev mm) Zollhöfer et al: 0.278mm (std.dev mm) 20.14% more accurate 97
98 Qualitative Results Relief (geometry) Fusion Input Color Ours Zollhöfer et al. 15 Ours 98
99 Qualitative Results Fountain (appearance) Fusion Input Color Ours Zollhöfer et al. 15 Ours 99
100 Qualitative Results Lion Input Color Geometry (ours) Appearance (ours) Fusion Ours Fusion Ours 100
101 Qualitative Results Tomb Statuary Input Color Geometry (ours) Appearance (ours) Fusion Ours Fusion Ours 101
102 Qualitative Results Gate Input Color Geometry (ours) Appearance (ours) Fusion Ours Fusion Ours 102
103 Qualitative Results Hieroglyphics Input Color Geometry (ours) Appearance (ours) Fusion Ours Fusion Ours 103
104 Qualitative Results Bricks Input Color Geometry (ours) Appearance (ours) Ours Fusion 104
105 Shading: Global SH vs. SVSH Fountain Luminance 105
106 Shading: Global SH vs. SVSH Fountain Luminance Albedo 106
107 Shading: Global SH vs. SVSH Fountain Global SH Luminance Shading Difference Albedo 107
108 Shading: Global SH vs. SVSH Fountain Global SH Luminance Shading Difference SVSH Albedo Shading Difference 108
109 Overview Motivation & State-of-the-art Approach Results Conclusion 109
110 Conclusion High-Quality 3D Reconstruction of Geometry and Appearance Temporal view sampling & filtering techniques Spatially-Varying Lighting estimation Joint optimization of surface & albedo (SDF) and image formation model Optimized texture as by-product 110
111 Conclusion High-Quality 3D Reconstruction of Geometry and Appearance Temporal view sampling & filtering techniques Spatially-Varying Lighting estimation Joint optimization of surface & albedo (SDF) and image formation model Optimized texture as by-product Thank you! Questions? Robert Maier Technical University of Munich Computer Vision Group 111
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