Compatible Intrinsic Triangulations – Supplementary Material
KENSHI TAKAYAMA, National Institute of Informatics / CyberAgent, Japan
1

EVALUATION WITH SCHMIDT ET AL. [2020]’SDATASET

Fig. 1 and Table 1 show the result of our method applied to Schmidt
et al. [2020]’s dataset.
Table 1. Statistics of our evaluation with Schmidt et al. [2020]’s dataset.
#F refers to the total number of triangles of the input triangle mesh pair,#N refers to the number of optimization steps taken, T refers to the total
time taken for the optimization, and Ebefore and Eafter refer to the mapping
distortion before and after optimization, respectively.

Case name

#F

#N

T

EbeforeEafter

Cow-Horse
Hands
Genus3Genus5
PlanesDonut-Duck
PretzelVase
Pig-Armadillo
Ant-Octopus

10k
32k
3k
8k
25k
24k
12k
10k
22k
8k

225
366
440
767
231
585
532
542
370
4

3.8h
37.6h
2.9h
15.0h
15.0h
78.9h
11.2h
9.1h
19.9h
1m

11.6
13.2
4.72
5.44
13.1
11.5
6.55
10.4
11.3
189.4

4.73
4.35
4.21
4.49
4.35
4.68
5.25
4.60
8.10
98.3

2

EVALUATION WITH PRINCETON SEGMENTATIONDATASET
2.1 Method for generating random pairs
To generate random pairs of models, our basic idea is to shuffle
the models by sorting them by SHA-256 hash values. The input toSHA-256 is the model ID as an ASCII string. For example, the firsttwo bytes of SHA-256 of the model ID “1” is 6b86 (we only show
the first two bytes hereafter). We use this value as a key to sort themodels, and generate pairs of consecutive models after sorting.
For example, for the Airplane category, the top ten models in the
SHA-256 sorted list are:• 108c (“65”)• 349c (“78”)• 3ada (“66”)• 4844 (“80”)• 49d1 (“67”)• 7f22 (“71”)• 81b8 (“62”)• 8722 (“72”)• 9606 (“73”)• 98a3 (“79”)where the number in each parenthesis denotes the model ID. Ourpairs for this category are thus 65-78, 66-80, 67-71, 62-72, and 73-79.
Author’s address: Kenshi Takayama, kenshi84@gmail.com, National Institute of Informatics / CyberAgent, Japan.

Note that there are some categories where we exclude somemodels and do additional grouping based on the characteristic of
the models. See below for individual explanations.

2.2

Human category

This category is rather exceptional and needed special treatment tocreate pairs in a meaningful way. First, we excluded scanned models
where the arms are glued to the torso or the legs (16 and 18). We
then excluded severely noise-laden scanned models (5, 13, 17, 19,
and 20) where geometries are not sufficiently resolved, especiallynear hands and feet. We also excluded two artist-created models (9and 12) because their resolutions were too low.
Four artist-created models (1, 7, 8, and 11) had too few triangles
near hands and feet, so we replaced those parts with round blobs
with regular remeshing. We created two pairs in this group: 1-7 and
8-11.
There are two scanned models (2 and 10) that are sufficiently
cleanly captured, so we paired them: 2-10.
The rest are five artist-created models (3, 4, 6, 14, and 15) with
their fingers cleanly separated and triangulated. We created twopairs 3-4 and 6-14 in this group.
Fig. 2 and Table 2 show the result of our method applied to thesepairs. Hereafter, the notation in the figure and in the table is thesame as in Fig. 1 and in Table 1).
Table 2. Statistics of our evaluation with the Human category in the Princeton Segmentation dataset.

2.3

Case name

#F

#N

T

EbeforeEafter

1-7
2-10
3-4
6-14
8-11

27k
39k
22k
27k
28k

36
162
120
21
254

3.6h
55.2h
11.1h
4.4h
16.6h

6.78
5.03
4.36
10.8
7.76

5.92
4.22
4.15
9.39
4.81

Cup category

In this category, there are four genus 0 models (25, 30, 35 and 40).
25 and 40 look more like cylinders while 30 and 35 look more likewine glasses, so we paired them: 25-40 and 35-30.
For the rest of the models, the top six in the SHA-256 sorted list
are:
• 0b91 (“39”)• 3513 (“29”)• 535f (“23”)• 59e1 (“28”)• 5f9c (“26”)• 6706 (“27”)resulting in three pairs: 39-29, 23-28, and 26-27.
Fig. 3 and Table 3 show the result of our method applied to thesepairs.

57:2 •

Kenshi Takayama

Hands

Cow-Horse

Genus5Genus3

Donut-DuckPlanes

Vase
Pretzel

Ant-Octopus

Pig-ArmadilloFig. 1. Visualization of surface mappings before and after optimization for the models in Schmidt et al. [2020]’s dataset. In each case, on the left is one modelwith predefined UV coordinates (generated via projection to 2D from a certain viewpoint), which gets mapped to the other model as shown on the center(before optimization) and on the right (after optimization).
Table 3. Statistics of our evaluation with the Cup category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter

23-28
25-40
26-27
30-35
39-29

30k
31k
30k
25k
36k

450
540
359
662
303

36.7h
82.8h
78.3h
89.3h
64.8h

4.28
6.28
4.63
6.91
8.97

4.08
4.09
4.11
4.26
4.34

2.4

Glasses category

The model 49 has a small handle, making its genus one, so we
excluded it. For the rest of the models, the top ten in the SHA-256
sorted list are:
• 02d2 (“55”)• 031b (“51”)• 1a65 (“50”)• 25fc (“46”)• 2858 (“53”)• 2fca (“54”)Compatible Intrinsic Triangulations – Supplementary Material

1-7

2-10

•

57:3

3-4

6-14

8-11
Fig. 2. Result of our method applied to the Human category in the Princeton Segmentation dataset.

23-28

26-27

25-40

30-35

39-29

Fig. 3. Result of our method applied to the Cup category in the Princeton Segmentation dataset.

47-60

Table 4. Statistics of our evaluation with the Glasses category in the Princeton Segmentation dataset.

Fig. 4. Result of our method applied to the Glasses category in the Princeton
Segmentation dataset.

• 3148 (“47”)• 39fa (“60”)• 3d91 (“41”)• 3e1e (“59”)resulting in five pairs: 55-51, 50-46, 53-54, 47-60, and 41-59. As notedin the main text, all of these pairs except 47-60 caused HOT to fail.
Fig. 4 and Table 4 show the result of our method applied to the pair47-60.

2.5

Case name

#F

#N

T

EbeforeEafter

47-60

8.8k

233

3.4h

4.67

4.12

Airplane category

This category needs no exclusion nor further grouping, and theway pairs are formed is already described in Section 2.1. As notedin the main text, our CIT generation algorithm failed for the pair
62-72. Fig. 5 and Table 5 show the result of our method applied to
the remaining four pairs.

57:4 •

Kenshi Takayama

66-80

65-78

67-71

73-79

Fig. 5. Result of our method applied to the Airplane category in the Princeton Segmentation dataset.
Table 5. Statistics of our evaluation with the Airplane category in the Princeton Segmentation dataset.

Table 6. Statistics of our evaluation with the Ant category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafterCase name

#F

#N

T

EbeforeEafter

65-78
66-80
67-71
73-79

28k
30k
27k
24k

235
262
246
82

41.8h
56.8h
43.7h
7.55h

7.27
7.08
5.18
6.12

4.69
5.35
4.22
5.71

81-90
86-84
88-99
91-98
96-92

28k
28k
30k
30k
29k

216
445
225
208
390

21.9h
75.9h
51.0h
38.5h
66.5h

6.17
8.36
6.11
8.04
11.76

4.27
4.31
4.60
4.67
5.46

2.7
2.6 Ant category
The model 93 has a small handle, making its genus one, so we
excluded it. For the rest of the models, the top ten in the SHA-256
sorted list are:• 1da5 (“91”)• 29db (“98”)• 434c (“86”)• 44c8 (“84”)• 5316 (“81”)• 69f5 (“90”)• 7b1a (“96”)• 8241 (“92”)• 8b94 (“88”)• 8c1f (“99”)resulting in five pairs: 91-98, 86-84, 81-90, 96-92, and 88-99. Fig. 6
and Table 6 show the result of our method applied to these pairs.

Chair category

We excluded 113 and 107 because their genus (2 and 4, respectively)
are different from any other models in the category. We also excluded
109 and 114 because they have too thin parts which would be too
challenging for SSM to handle. There are four models of genus 3,
and their SHA-256 sorted list is:• 1253 (“105”)• 3783 (“102”)• 9537 (“108”)• 9bdb (“110”)
resulting in two pairs: 105-102 and 108-110. For the rest of themodels, the top six in the SHA-256 sorted list are:• 16dc (“101”)• 28da (“115”)• 2aba (“120”)• 2ac8 (“117”)• 3038 (“119”)• 454f (“103”)
resulting in three pairs: 101-115, 120-117, and 119-103. Fig. 7 andTable 7 show the result of our method applied to these pairs.

Compatible Intrinsic Triangulations – Supplementary Material

81-90

57:5

88-99

86-84

91-98

•

96-92

Fig. 6. Result of our method applied to the Ant category in the Princeton Segmentation dataset.

101-115105-102119-103108-110120-117

Fig. 7. Result of our method applied to the Chair category in the Princeton Segmentation dataset.
Table 7. Statistics of our evaluation with the Chair category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter101-115105-102108-110119-103120-117

37k
50k
38k
44k
49k

344
260
11
253
227

90.4h
117h
2.1h
87.4h
127h

6.55
11.2
5.41
7.40
6.77

4.53
4.67
5.33
4.37
4.36

2.8 Octopus category
The model 121 has a small handle, making its genus one, so we
excluded it. We also excluded the model 125 because it has only
three tentacles. For the rest of the models, the top ten in the SHA-256
sorted list are:• 1367 (“135”)• 1be0 (“122”)• 2747 (“128”)• 36eb (“136”)

135-122

Fig. 8. Result of our method applied to the Octopus category in the Princeton
Segmentation dataset.

• 38d6 (“130”)• 5d38 (“134”)• 6566 (“129”)• 65a6 (“126”)• 6aff (“124”)• 8d27 (“139”)
resulting in five pairs: 135-122, 128-136, 130-134, 129-126, and 124139. As noted in the main text, all of these pairs except 135-122
caused our CIT generation algorithm to fail. Fig. 8 and Table 8 show
the result of our method applied to the pair 135-122.

57:6 •

Kenshi Takayama

Table 8. Statistics of our evaluation with the Octopus category in the Princeton Segmentation dataset.

Table 10. Statistics of our evaluation with the Teddy category in the Princeton Segmentation dataset.

Case name

#F

#N

T

EbeforeEafterCase name

#F

#N

T

EbeforeEafter135-122

30k

17

12.7h

113.2

22.1

167-162
172-170
174-173
178-171
179-164

22k
26k
31k
31k
32k

702
497
293
365
227

63.1h
57.7h
62.7h
69.0h
44.5h

5.40
7.81
5.30
7.08
174.4

4.32
4.71
4.08
4.42
4.24

2.9 Table category
We excluded 150 because it is the only model with three legs. There
are two models with eight legs, so we paired them: 142-148. For the
rest of the models, the top eight in the SHA-256 sorted list are:
• 0430 (“152”)• 05ad (“149”)• 0a5b (“146”)• 0fec (“156”)• 1d0e (“154”)• 1d28 (“147”)• 210e (“155”)• 2c7d (“141”)
resulting in four pairs: 152-149, 146-156, 154-147, and 155-141. As
noted in the main text, HOT failed for the pair 146-156 while our CIT
generation algorithm failed for the pair 152-149. Fig. 9 and Table 9
show the result of our method applied to the remaining three pairs.

Table 9. Statistics of our evaluation with the Table category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter142-148154-147
155-141

23k
28k
28k

278
223
381

20.1h
38.5h
67.9h

6.03
5.77
6.11

4.70
4.72
4.49

2.10 Teddy category
The model 163 has a small handle, making its genus one, so we
excluded it. For the rest of the models, the top ten in the SHA-256
sorted list are:
• 01d5 (“178”)• 284d (“171”)• 3068 (“179”)• 3f98 (“164”)• 41e5 (“174”)• 4a85 (“173”)• 6851 (“172”)• 734d (“170”)• 73d3 (“167”)• 79d6 (“162”)
resulting in five pairs: 178-171, 179-164, 174-173, 172-170, and 167162. Fig. 10 and Table 10 show the result of our method applied tothese pairs.

2.11 Hand category
The model 187 has a small handle, making its genus one, so we
excluded it. We also excluded the models 182, 191, and 198 because
their middle and ring fingers are severely fused. For the rest of themodels, the top ten in the SHA-256 sorted list are:
• 1dfa (“195”)• 2397 (“190”)• 27ba (“200”)• 2811 (“186”)• 52f1 (“184”)• 5808 (“181”)• 5a39 (“199”)• 61a2 (“185”)• 684f (“193”)• 7045 (“189”)
resulting in five pairs: 195-190, 200-186, 184-181, 199-185, and 193189. Fig. 11 and Table 11 show the result of our method applied tothese pairs.
Table 11. Statistics of our evaluation with the Hand category in the Princeton Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter184-181193-189
195-190
199-185
200-186

24k
22k
32k
22k
16k

466
327
296
951
788

33.7h
28.1h
66.3h
55.5h
30.0h

5.24
5.43
5.35
12.8
5.11

4.28
4.37
4.10
4.35
4.15

2.12 Plier category
This category needs no exclusion nor further grouping, and the top
ten in the SHA-256 sorted list are:
• 0934 (“211”)• 0f41 (“216”)• 16ba (“217”)• 314f (“219”)• 3679 (“220”)• 4397 (“201”)• 4621 (“203”)• 5966 (“218”)• 5cf4 (“206”)• 802b (“214”)Compatible Intrinsic Triangulations – Supplementary Material

142-148154-147•

57:7

155-141

Fig. 9. Result of our method applied to the Table category in the Princeton Segmentation dataset.

172-170

167-162

178-171

174-173

179-164

Fig. 10. Result of our method applied to the Teddy category in the Princeton Segmentation dataset.

195-190193-189184-181199-185

200-186

Fig. 11. Result of our method applied to the Hand category in the Princeton Segmentation dataset.

resulting in five pairs: 211-216, 217-219, 220-201, 203-218, and 206214. As noted in the main text, our CIT generation algorithm failed
for the pair 220-201. Fig. 12 and Table 12 show the result of our
method applied to the remaining four pairs.

Table 12. Statistics of our evaluation with the Plier category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter203-218
206-214211-216217-219

20k
17k
18k
18k

174
537
175
122

13.1h
33.0h
9.5h
6.4h

4.53
5.44
4.83
40.34

4.14
4.43
4.10
9.50

57:8 •

Kenshi Takayama

206-214203-218

211-216217-219

Fig. 12. Result of our method applied to the Plier category in the Princeton Segmentation dataset.

2.13 Fish category
This category needs no exclusion nor further grouping, and the top
ten in the SHA-256 sorted list are:• 0849 (“229”)• 0a2d (“235”)• 0e65 (“225”)• 114b (“234”)• 138d (“231”)• 56f4 (“223”)• 67e9 (“221”)• 6af1 (“240”)• 79bf (“239”)• 835d (“232”)
resulting in five pairs: 229-235, 225-234, 231-223, 221-240, and 239232. Fig. 13 and Table 13 show the result of our method applied tothese pairs.
Table 13. Statistics of our evaluation with the Fish category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter221-240225-234229-235
231-223239-232

28k
27k
21k
26k
21k

252
114
429
301
41

19.9h
12.2h
27.7h
40.0h
2.5h

6.35
6.51
5.66
5.50
8.07

4.42
5.11
4.47
4.44
7.01

2.14 Bird category
The models 257 and 258 contain extremely thin wings which would
be difficult for HOT to handle, so we excluded them. For the rest of
the models, the top ten in the SHA-256 sorted list are:
• 011a (“245”)• 1406 (“242”)• 1e47 (“250”)• 37c2 (“246”)• 396f (“247”)• 39bb (“260”)• 51e8 (“256”)• 7244 (“243”)• 749f (“241”)• 766c (“248”)
resulting in five pairs: 245-242, 250-246, 247-260, 256-243, and 241248. Fig. 14 and Table 14 show the result of our method applied tothese pairs.
Table 14. Statistics of our evaluation with the Bird category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter241-248245-242247-260
250-246
256-243

31k
29k
24k
17k
18k

11
249
328
84
382

1.96h
46.1h
42.9h
3.5h
12.0h

9.77
4.34
12.39
9.01
12.15

9.52
4.05
5.04
6.56
5.45

2.15 Armadillo category
We excluded the models 292, 294, 295, 296, 297, and 299 becausesome of their body parts are amputated in a unique way, making
it difficult to define a meaningful mapping with other models. We
grouped the models 293, 298, and 300 because they have in common
that their ears are missing; their SHA-256 sorted list is:• 76eb (“298”)• 7cb6 (“293”)• 983b (“300”) resulting in a pair 298-293.
For the rest of the models, the top eight in the SHA-256 sorted listare:• 0032 (“286”)• 0989 (“290”)• 1e68 (“284”)• 23c6 (“288”)Compatible Intrinsic Triangulations – Supplementary Material

221-240•

57:9229-235

225-234

231-223239-232
Fig. 13. Result of our method applied to the Fish category in the Princeton Segmentation dataset.

241-248245-242250-246247-260

256-243

Fig. 14. Result of our method applied to the Bird category in the Princeton Segmentation dataset.

• 27e1 (“282”)• 3351 (“291”)• 71a1 (“281”)• a0d1 (“285”)
resulting in four pairs: 286-290, 284-288, 282-291, and 281-285. Fig. 15
and Table 15 show the result of our method applied to these pairs.
Table 15. Statistics of our evaluation with the Armadillo category in the
Princeton Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter281-285282-291284-288286-290298-293

39k
49k
38k
26k
29k

323
46
319
263
51

95.4h
26.9h
70.9h
14.5h
8.2h

5.50
5.97
4.97
6.52
6.28

4.30
5.12
4.16
4.70
5.24

2.16 Bust category
This category contains quite a variety of geometries that make it
non-trivial to form meaningful pairs. First, we excluded five models(302, 305, 311, 319, and 320) containing unique parts (e.g. hats, scarfs,base) that make it difficult to define a meaningful mapping withother models. We then further grouped the remaining 15 models
into five sub-categories and created a pair in each.
The first sub-category is for models depicting a man’s head whoseback side is cut off by a plane (315, 316, and 318). Because 315 and316 seem to represent the same person, we chose to pair 315 and318.
The second sub-category is for models whose bottom part below
the neck is carved out (301, 304, and 317). Their SHA-256 sorted listis:
• 8d1e (“317”)• c3ea (“301”)• d874 (“304”)
resulting in a pair 317-301.
The third sub-category is for models whose part below the shoulder is cut off by a plane or a curved surface (303, 312, 313, and 314).
Their SHA-256 sorted list is:• 7480 (“314”)• 8657 (“312”)• 8bd9 (“303”)• 8efb (“313”)
resulting in a pair 314-312.
The fourth sub-category is for models depicting a child’s headand shoulder (306, 307, and 308). Their SHA-256 sorted list is:57:10 •Kenshi Takayama

281-285284-288282-291286-290298-293

Fig. 15. Result of our method applied to the Armadillo category in the Princeton Segmentation dataset.

• 38b8 (“306”)• 48a1 (“308”)• 6d97 (“307”)

resulting in five pairs: 327-334, 329-331, 324-328, 323-330, and 333338. Fig. 17 and Table 17 show the result of our method applied tothese pairs.

resulting in a pair 306-308.
The last sub-category is for models depicting a human head infull (309 and 310). Fig. 16 and Table 16 show the result of our method
applied to these pairs.
Table 16. Statistics of our evaluation with the Bust category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter306-308
310-309314-312315-318317-301

25k
27k
27k
30k
27k

796
183
364
1069
1570

52.6h
30.9h
37.0h
139.0h188.4h

6.66
8.91
8.74
6.90
10.86

4.47
5.49
4.83
4.24
4.78

Table 17. Statistics of our evaluation with the Mech category in the Princeton Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter323-330
324-328327-334
329-331333-338

30k
30k
15k
16k
27k

35
417
510
383
482

7.7h
116.3h
22.4h
23.5h
93.3h

4.19
6.80
4.46
4.55
5.41

4.12
4.29
4.04
4.10
4.19

2.18 Bearing category
2.17 Mech category
We excluded the models 326 and 337 because it was unclear how tomake correspondence with the other models which have in common
that the geometry consists of a cube-like base and a small part sitting
on top of it. The top ten in the SHA-256 sorted list of the remaining
eighteen models are:
• 02cc (“327”)• 058d (“334”)• 0791 (“329”)• 0bba (“331”)• 1038 (“324”)• 2452 (“328”)• 3949 (“323”)• 5426 (“330”)• 556d (“333”)• 5d8f (“338”)

We excluded the model 357 because it is the only one with genus
six. The models 358 and 359 are the only ones of genus four. Even
though they seem to represent an identical geometry, their meshes
are not identical with different number of triangles, so we included
the pair in our evaluation as a robustness test. For the rest of themodels, the top eight in the SHA-256 sorted list are:
• 0238 (“342”)• 02e6 (“344”)• 03a3 (“356”)• 04a8 (“351”)• 06b2 (“348”)• 09a1 (“354”)• 2289 (“347”)• 355d (“355”)
resulting in four pairs: 342-344, 356-351, 348-354, and 347-355. Fig. 18
and Table 18 show the result of our method applied to these pairs.

Compatible Intrinsic Triangulations – Supplementary Material306-308

310-309315-318314-312317-301

Fig. 16. Result of our method applied to the Bust category in the Princeton Segmentation dataset.

323-330

324-328329-331327-334

333-338

Fig. 17. Result of our method applied to the Mech category in the Princeton Segmentation dataset.

342-344347-355356-351348-354358-359

Fig. 18. Result of our method applied to the Bearing category in the Princeton Segmentation dataset.

•

57:11

57:12 •Kenshi Takayama

Table 18. Statistics of our evaluation with the Bearing category in the Princeton Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter342-344347-355348-354356-351358-359

15k
13k
13k
13k
30k

61
321
386
558
7

3.6h
12.7h
15.4h
32.5h
8.4h

6.68
40.67
6.42
6.84
4.07

5.74
5.64
4.55
5.02
4.06

2.19 Vase category
This category contains quite a variety of geometries that make it
non-trivial to form meaningful pairs. First, we excluded the model
361 because it is the only one with genus five. Two models 364
and 377 are the only ones with genus two, but their geometries are
extremely different, making it difficult to construct a meaningfulcorrespondence, so we excluded them as well.
For the nine genus-one models, three models 367, 372, and 378
are like a teapot with a handle and a spout, and 372 is a slightly
deformed version of 378, so we chose to pair 367 and 378.
The genus-one model 365 has a very different geometry from anyother genus-one models, so we excluded it. For the remaining fivegenus-one models, their SHA-256 sorted list is:• 0129 (“374”)• 3963 (“362”)• 5f19 (“369”)• 600b (“366”)• e52d (“373”)
We created three pairs out of this list (by additionally pairing thefirst and the last): 374-362, 369-366, and 373-374.
There are eight genus-zero models, but five of them (368, 375,
376, 379, and 380) have extremely different geometries, making it
difficult to construct a meaningful correspondence, so we excluded
them. For the remaining three models, their SHA-256 sorted list is:• 9b15 (“371”)• a432 (“363”)• f160 (“370”)
resulting in a pair 371-363. Fig. 19 and Table 19 show the result ofour method applied to these pairs.
Table 19. Statistics of our evaluation with the Vase category in the Princeton
Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter367-378369-366
371-363373-374374-362

21k
48k
11k
42k
59k

336
428
264
388
310

18.3h
243.8h6.7h118.9h
14.3h

8.21
5.59
7.12
5.36
4.37

4.52
4.37
4.54
4.22
4.10

2.20 Fourleg category
In this category, we further classified the models based on whether
the tail is separated from the body and whether the mouth is open

(i.e. the upper and lower jaws separated). There are four models(381, 382, 387, and 391) whose tail is missing (or merged to the body).
Among them, the model 387 is the only one whose mouth is open,
so we excluded it. The SHA-256 sorted list for the remaining threemodels is:• 392a (“381”)• a934 (“391”)• f65c (“382”)
so we formed a pair: 381-391.
For the remaining 16 models whose tail is separated from themain body, five models have their mouth open, and their SHA-256
sorted list is:• 04d1 (“394”)• 188c (“398”)• 37b7 (“384”)• 48b3 (“383”)• 99a0 (“393”)
resulting in two pairs: 394-398 and 384-383.
For the remaining 11 models, the top four in the SHA-256 sorted
list are:• 0f78 (“399”)• 131b (“385”)• 15a2 (“386”)• 1d20 (“397”)
resulting in two pairs: 399-385 and 386-397. As noted in the main
text, HOT failed for the pair 399-385 while our CIT generation
algorithm failed for the pair 386-397. Fig. 20 and Table 20 show theresult of our method applied to these pairs.
Table 20. Statistics of our evaluation with the Fourleg category in the Princeton Segmentation dataset.

Case name

#F

#N

T

EbeforeEafter381-391384-383394-398

23k
25k
29k

130
54
13

16.3h
8.5h
2.4h

40479.50
10.69
244.82

5.85
9.67
76.93

Compatible Intrinsic Triangulations – Supplementary Material

367-378369-366

373-374371-363374-362

Fig. 19. Result of our method applied to the Vase category in the Princeton Segmentation dataset.

381-391384-383394-398

Fig. 20. Result of our method applied to the Fourleg category in the Princeton Segmentation dataset.

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