PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

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6.2. Piece-wise planar scene reconstruction 105 plane pixel [%] 30 25 20 15 10 Top FN [%] Top FP [%] Front FN [%] Front FP [%] 5 plane pixel [%] 0 30 25 20 15 10 5 10 15 20 25 30 35 40 45 50 55 60 view angle [°] (a) Top FN [%] Top FP [%] Front FN [%] Front FP [%] 5 0 5 10 15 20 25 30 35 40 45 50 55 60 view angle [°] (b) Figure 6.7: Comparison of ’Cube’ plane reconstructions to ground truth for different view angles. Seed regions have been matched automatically. (a) Calibrated method. (b) Uncalibrated method. shows the ’safe’ reconstruction of the scene while the 3D model in Figure 6.8(g) has been created using the homogeneity assumption. The ’Oberkapfenberg castle’ data-set is an outdoor scene recorded with the same calibrated camera with a resolution of 2160 × 1440 pixels. The images were oriented and the described method was applied for reconstruction. As seed regions the MSER regions [70] were used. The eight major planes have been recovered. The results are shown in Figure 6.9. In this particular case holes in the reconstruction are not exclusively due to homogenous image parts. Some walls we intended to reconstruct were not built completely planar. But even in this complex, partially cluttered scene a reconstruction of the overall structure has been possible. The results of the experiments can be summarized as follows. The experiments with synthetic data showed that the method allows an accurate reconstruction of the scene planes. The experiment also demonstrates that the method can cope with large baseline changes with almost constant error rates. The experiments on real scenes show the application to practically relevant reconstruction tasks. In both of the scenes the major planes could be reconstructed. The experiments revealed that in real world scenes difficulties with non-textured regions and with not completely planar structures occur. However, we showed that it is possible to overcome this
6.2. Piece-wise planar scene reconstruction 106 viewing ground manual seed regions automatic seed regions angle truth false neg. false pos. false neg. false pos. [ ◦ ] [pixel] [pixel (%)] [pixel (%)] [pixel (%)] [pixel (%)] 5 123432 6592 ( 5.34 ) 1082 ( 0.88 ) 7684 ( 6.23 ) 812 ( 0.66 ) 10 123491 6131 ( 4.96 ) 1217 ( 0.99 ) 6255 ( 5.07 ) 1203 ( 0.97 ) 15 123505 6267 ( 5.07 ) 1505 ( 1.22 ) 6615 ( 5.36 ) 1471 ( 1.19 ) 20 123518 6685 ( 5.41 ) 1430 ( 1.16 ) 6239 ( 5.05 ) 1907 ( 1.54 ) 25 123558 6405 ( 5.18 ) 1354 ( 1.10 ) 5970 ( 4.83 ) 1763 ( 1.43 ) 30 123540 6264 ( 5.07 ) 1389 ( 1.12 ) 6147 ( 4.98 ) 1465 ( 1.19 ) 35 123597 6264 ( 5.07 ) 1246 ( 1.01 ) 6175 ( 5.00 ) 1363 ( 1.10 ) 40 123540 6227 ( 5.04 ) 1219 ( 0.99 ) 6351 ( 5.14 ) 1185 ( 0.96 ) 45 123564 6193 ( 5.01 ) 980 ( 0.79 ) 6135 ( 4.97 ) 1012 ( 0.82 ) 50 123559 6355 ( 5.14 ) 824 ( 0.67 ) 6291 ( 5.09 ) 863 ( 0.70 ) 55 123594 6511 ( 5.27 ) 612 ( 0.50 ) 6339 ( 5.13 ) 739 ( 0.60 ) 60 123544 6700 ( 5.42 ) 519 ( 0.42 ) 5948 ( 4.81 ) 911 ( 0.74 ) 65 123571 7043 ( 5.70 ) 394 ( 0.32 ) — — 70 123551 7114 ( 5.76 ) 380 ( 0.31 ) — — 75 123507 7393 ( 5.99 ) 373 ( 0.30 ) — — Table 6.3: Comparison of ’Cube’ top plane reconstruction to ground truth for different view angles (calibrated method). viewing ground manual seed regions automatic seed regions angle truth false neg. false pos. false neg. false pos. [ ◦ ] [pixel] [pixel (%)] [pixel (%)] [pixel (%)] [pixel (%)] 5 200935 11396 ( 5.67 ) 1157 ( 0.58 ) 11120 ( 5.53 ) 2226 ( 1.11 ) 10 196926 11070 ( 5.62 ) 1166 ( 0.59 ) 11046 ( 5.61 ) 1291 ( 0.66 ) 15 190304 12596 ( 6.62 ) 1257 ( 0.66 ) 12284 ( 6.45 ) 1664 ( 0.87 ) 20 181260 12373 ( 6.83 ) 1308 ( 0.72 ) 12805 ( 7.06 ) 864 ( 0.48 ) 25 169992 11247 ( 6.62 ) 1251 ( 0.74 ) 11536 ( 6.79 ) 886 ( 0.52 ) 30 156766 9953 ( 6.35 ) 1235 ( 0.79 ) 9974 ( 6.36 ) 1092 ( 0.70 ) 35 141781 8615 ( 6.08 ) 1167 ( 0.82 ) 8655 ( 6.10 ) 1051 ( 0.74 ) 40 125572 6865 ( 5.47 ) 1061 ( 0.84 ) 6753 ( 5.38 ) 1169 ( 0.93 ) 45 108269 4878 ( 4.51 ) 1003 ( 0.93 ) 4861 ( 4.49 ) 949 ( 0.88 ) 50 90277 3450 ( 3.82 ) 954 ( 1.06 ) 3405 ( 3.77 ) 980 ( 1.09 ) 55 72018 2352 ( 3.27 ) 902 ( 1.25 ) 2305 ( 3.20 ) 905 ( 1.26 ) 60 53702 1788 ( 3.33 ) 914 ( 1.70 ) — — 65 35725 2127 ( 5.95 ) 879 ( 2.46 ) — — Table 6.4: Comparison of ’Cube’ front plane reconstruction to ground-truth for different view angles (calibrated method). problems by using some heuristic assumptions.
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Graz University of Technology Insti
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Abstract Visual map building and lo
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Contents 1 Introduction to mobile r
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CONTENTS vi 7.1.5 Sub-map linking .
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1.1. Localization and map building
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1.3. What has already been achieved
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1.5. How can it get solved? 6 fully
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1.6. Contribution of this thesis 8
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1.7. Structure of the thesis 10 com
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2.2. Localization from point featur
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2.2. Localization from point featur
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2.4. Localization from plane featur
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2.5. Summary 18 or not. Clearly thi
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Chapter 3 Local detectors Research
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3.1. Interest point detectors 22 fu
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3.2. Scale invariant detectors 24 r
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3.2. Scale invariant detectors 26 (
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3.2. Scale invariant detectors 28 3
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3.3. Affine invariant detectors 30
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3.4. Comparison of the described me
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3.4. Comparison of the described me
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44 But using a plane to plane homog
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4.2. Representation of the detectio
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4.3. Detection correspondence 48 th
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4.4. Point transfer using the trifo
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4.5. Ground truth generation 52 usi
Page 61 and 62: 4.6. Experimental evaluation 54 Fig
Page 63 and 64: 4.6. Experimental evaluation 56 rep
Page 65 and 66: 4.6. Experimental evaluation 58 MSE
Page 67 and 68: 4.6. Experimental evaluation 60 mat
Page 69 and 70: 4.6. Experimental evaluation 62 vie
Page 71 and 72: 4.6. Experimental evaluation 64 vie
Page 73 and 74: 4.6. Experimental evaluation 66 rel
Page 75 and 76: Chapter 5 Maximally Stable Corner C
Page 77 and 78: 5.1. The MSCC detector 70 (a) (b) (
Page 79 and 80: 5.2. Region representation 72 400 (
Page 81 and 82: 5.3. Computational complexity 74 6.
Page 83 and 84: 5.5. Detection examples 76 paramete
Page 85 and 86: 5.5. Detection examples 78 Figure 5
Page 87 and 88: 5.6. Detector evaluation: Repeatabi
Page 89 and 90: 5.7. Combining MSCC with other loca
Page 99 and 100: 6.1. Wide-baseline region matching
Page 101 and 102: 6.1. Wide-baseline region matching
Page 103 and 104: 6.2. Piece-wise planar scene recons
Page 111: 6.2. Piece-wise planar scene recons
Page 117 and 118: Chapter 7 Living in a piecewise pla
Page 119 and 120: 7.1. Map building 112 s,R,t sub-map
Page 121 and 122: 7.1. Map building 114 x = (x 1 ...x
Page 123 and 124: 7.1. Map building 116 distance is u
Page 125 and 126: 7.1. Map building 118 normalization
Page 127 and 128: 7.2. Localization 120 where N = |D
Page 129 and 130: 7.2. Localization 122 registration
Page 131 and 132: 7.2. Localization 124 (a) (b) (c) F
Page 133 and 134: 7.2. Localization 126 3D structure
Page 135 and 136: 7.2. Localization 128 other landmar
Page 137 and 138: Chapter 8 Map building and localiza
Page 139 and 140: 8.2. Map building experiments 132 8
Page 141 and 142: 8.2. Map building experiments 134 D
Page 143 and 144: 8.2. Map building experiments 136 (
Page 145 and 146: 8.2. Map building experiments 138 F
Page 147 and 148: 8.2. Map building experiments 140 F
Page 149 and 150: 8.3. Localization experiments 142 8
Page 151 and 152: 8.3. Localization experiments 144 F
Page 153 and 154: 8.3. Localization experiments 146 F
Page 155 and 156: 8.3. Localization experiments 148 8
Page 157 and 158: 8.3. Localization experiments 150 (
Page 159 and 160: Chapter 9 Conclusion More than 25 y
Page 161 and 162: 9.1. Future work 154 Map building a
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9.1. Future work 156 information ca
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A.1. Projective ellipse transfer 15
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A.1. Projective ellipse transfer 16
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A.2. Affine approximation of ellips
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Appendix B The trifocal tensor and
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Bibliography [1] S. Atiya and G. Ha
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168 [31] F. Fraundorfer and H. Bisc
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170 [61] U. Köthe. Edge and juncti
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172 [92] F. Schaffalitzky and A. Zi
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