High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MG-MVS		87.11 44	86.27 62	89.62 8	97.79 1	76.27 4	94.96 47	94.49 53	78.74 121	83.87 93	92.94 144	64.34 102	96.94 121	75.19 208	94.09 42	95.66 59

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 11	97.13 2	95.58 11	89.33 1	85.77 71	96.26 46	72.84 32	99.38 1	92.64 33	95.93 9	97.08 11

OPU-MVS				89.97 3	97.52 3	73.15 15	96.89 6				97.00 15	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 44	93.96 89	94.37 62	72.48 241	92.07 11	96.85 27	83.82 2	99.15 2	91.53 47	97.42 4	97.55 4

MSC_two_6792asdad				89.60 9	97.31 4	73.22 13		95.05 29					99.07 13	92.01 39	94.77 26	96.51 24

No_MVS				89.60 9	97.31 4	73.22 13		95.05 29					99.07 13	92.01 39	94.77 26	96.51 24

DP-MVS Recon		82.73 156	81.65 164	85.98 109	97.31 4	67.06 138	95.15 37	91.99 166	69.08 321	76.50 198	93.89 126	54.48 255	98.20 41	70.76 255	85.66 165	92.69 217

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 31	96.47 14	94.83 36	84.83 17	89.07 43	96.80 30	70.86 45	99.06 15	92.64 33	95.71 11	96.12 41

ZD-MVS					96.63 9	65.50 189		93.50 94	70.74 296	85.26 80	95.19 83	64.92 94	97.29 89	87.51 74	93.01 60

NCCC		89.07 16	89.46 15	87.91 29	96.60 10	69.05 76	96.38 15	94.64 46	84.42 21	86.74 61	96.20 47	66.56 74	98.76 27	89.03 64	94.56 34	95.92 51

IU-MVS					96.46 11	69.91 44		95.18 23	80.75 66	95.28 1				92.34 36	95.36 14	96.47 28

SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 60	96.89 6	94.44 55	71.65 271	92.11 9	97.21 9	76.79 9	99.11 6	92.34 36	95.36 14	97.62 2

test_241102_ONE					96.45 12	69.38 60		94.44 55	71.65 271	92.11 9	97.05 12	76.79 9	99.11 6

test_0728_SECOND				88.70 18	96.45 12	70.43 35	96.64 10	94.37 62					99.15 2	91.91 42	94.90 22	96.51 24

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 40	96.64 10	94.52 51	71.92 257	90.55 30	96.93 21	73.77 25	99.08 11	91.91 42	94.90 22	96.29 36
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					96.40 15	69.99 40	96.76 8	94.33 64	71.92 257	91.89 14	97.11 11	73.77 25

AdaColmap		78.94 237	77.00 254	84.76 167	96.34 17	65.86 179	92.66 160	87.97 370	62.18 390	70.56 280	92.37 159	43.53 369	97.35 85	64.50 329	82.86 202	91.05 267

ME-MVS		88.25 20	88.55 27	87.33 51	96.33 18	67.28 128	93.93 91	94.81 37	70.09 303	88.91 44	96.95 17	70.12 49	98.73 28	91.55 44	94.28 37	95.99 47

test_one_0601					96.32 19	69.74 52		94.18 67	71.42 282	90.67 29	96.85 27	74.45 22

test_part2					96.29 20	68.16 105				90.78 27

DPE-MVS		88.77 18	89.21 19	87.45 45	96.26 21	67.56 121	94.17 75	94.15 69	68.77 324	90.74 28	97.27 6	76.09 14	98.49 33	90.58 55	94.91 21	96.30 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MAR-MVS		84.18 117	83.43 120	86.44 94	96.25 22	65.93 178	94.28 73	94.27 66	74.41 197	79.16 162	95.61 62	53.99 262	98.88 25	69.62 264	93.26 58	94.50 141
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
API-MVS		82.28 165	80.53 186	87.54 43	96.13 23	70.59 32	93.63 111	91.04 230	65.72 359	75.45 209	92.83 149	56.11 234	98.89 24	64.10 331	89.75 116	93.15 201

APDe-MVS		87.54 35	87.84 37	86.65 76	96.07 24	66.30 166	94.84 52	93.78 76	69.35 313	88.39 47	96.34 42	67.74 64	97.66 64	90.62 54	93.44 55	96.01 45
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
patch_mono-2		89.71 11	90.99 6	85.85 115	96.04 25	63.70 255	95.04 43	95.19 22	86.74 8	91.53 20	95.15 84	73.86 24	97.58 69	93.38 27	92.00 75	96.28 38

PAPR		85.15 89	84.47 97	87.18 54	96.02 26	68.29 98	91.85 207	93.00 118	76.59 169	79.03 163	95.00 86	61.59 150	97.61 68	78.16 189	89.00 122	95.63 60

APD-MVS		85.93 72	85.99 71	85.76 119	95.98 27	65.21 195	93.59 113	92.58 140	66.54 347	86.17 67	95.88 56	63.83 110	97.00 111	86.39 89	92.94 61	95.06 94
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 32	95.86 28	68.32 97	95.74 21	94.11 70	83.82 26	83.49 97	96.19 48	64.53 101	98.44 35	83.42 128	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS		69.90 366	66.48 373	80.14 325	95.36 29	62.93 279	89.56 305	76.11 446	50.27 450	57.69 414	85.23 309	39.68 385	95.73 191	33.35 462	71.05 318	81.78 419

114514_t		79.17 231	77.67 236	83.68 218	95.32 30	65.53 188	92.85 148	91.60 190	63.49 376	67.92 319	90.63 209	46.65 348	95.72 196	67.01 298	83.54 197	89.79 285

HPM-MVS++		89.37 14	89.95 13	87.64 36	95.10 31	68.23 103	95.24 34	94.49 53	82.43 42	88.90 45	96.35 41	71.89 42	98.63 30	88.76 65	96.40 6	96.06 42

CSCG		86.87 47	86.26 63	88.72 17	95.05 32	70.79 30	93.83 102	95.33 18	68.48 328	77.63 181	94.35 109	73.04 30	98.45 34	84.92 104	93.71 51	96.92 14

dcpmvs_2		87.37 41	87.55 42	86.85 63	95.04 33	68.20 104	90.36 283	90.66 252	79.37 105	81.20 121	93.67 130	74.73 18	96.55 140	90.88 52	92.00 75	95.82 54

MED-MVS test				87.42 46	94.76 34	67.28 128	94.47 62	94.87 32	73.09 228	91.27 23	96.95 17		98.98 16	91.55 44	94.28 37	95.99 47

MED-MVS		88.94 17	89.45 16	87.42 46	94.76 34	67.28 128	94.47 62	94.87 32	70.09 303	91.27 23	96.95 17	76.77 11	98.98 16	91.55 44	94.28 37	95.99 47

TestfortrainingZip a		88.66 19	88.99 21	87.70 34	94.76 34	68.73 86	94.47 62	94.87 32	73.09 228	91.27 23	96.95 17	76.77 11	98.98 16	84.41 112	94.28 37	95.37 71

LFMVS		84.34 111	82.73 143	89.18 13	94.76 34	73.25 12	94.99 46	91.89 172	71.90 259	82.16 111	93.49 135	47.98 329	97.05 106	82.55 138	84.82 176	97.25 8

CDPH-MVS		85.71 77	85.46 81	86.46 92	94.75 38	67.19 133	93.89 95	92.83 125	70.90 291	83.09 102	95.28 75	63.62 116	97.36 84	80.63 163	94.18 41	94.84 107

test_prior				86.42 95	94.71 39	67.35 127		93.10 113					96.84 129			95.05 95

test12				87.09 57	94.60 40	68.86 80		92.91 122		82.67 109		65.44 86	97.55 72		93.69 52	94.84 107

test_yl		84.28 112	83.16 132	87.64 36	94.52 41	69.24 69	95.78 18	95.09 26	69.19 316	81.09 123	92.88 147	57.00 219	97.44 78	81.11 160	81.76 220	96.23 39

DCV-MVSNet		84.28 112	83.16 132	87.64 36	94.52 41	69.24 69	95.78 18	95.09 26	69.19 316	81.09 123	92.88 147	57.00 219	97.44 78	81.11 160	81.76 220	96.23 39

CANet		89.61 12	89.99 12	88.46 24	94.39 43	69.71 53	96.53 13	93.78 76	86.89 7	89.68 40	95.78 57	65.94 80	99.10 9	92.99 30	93.91 46	96.58 21

test_8					94.19 44	67.19 133	94.15 78	93.42 99	71.87 262	85.38 78	95.35 70	68.19 59	96.95 120

TEST9					94.18 45	67.28 128	94.16 76	93.51 92	71.75 268	85.52 75	95.33 71	68.01 61	97.27 93

train_agg		87.21 43	87.42 44	86.60 79	94.18 45	67.28 128	94.16 76	93.51 92	71.87 262	85.52 75	95.33 71	68.19 59	97.27 93	89.09 62	94.90 22	95.25 87

agg_prior					94.16 47	66.97 148		93.31 102		84.49 86			96.75 132

PAPM_NR		82.97 152	81.84 162	86.37 97	94.10 48	66.76 154	87.66 350	92.84 124	69.96 306	74.07 233	93.57 133	63.10 130	97.50 75	70.66 257	90.58 100	94.85 104

MGCNet		90.32 6	90.90 7	88.55 23	94.05 49	70.23 38	97.00 5	93.73 83	87.30 4	92.15 8	96.15 50	66.38 75	98.94 20	96.71 3	94.67 33	96.47 28

FOURS1					93.95 50	61.77 309	93.96 89	91.92 169	62.14 392	86.57 62

VNet		86.20 66	85.65 78	87.84 31	93.92 51	69.99 40	95.73 23	95.94 7	78.43 126	86.00 69	93.07 141	58.22 202	97.00 111	85.22 98	84.33 183	96.52 23

9.14			87.63 39		93.86 52		94.41 67	94.18 67	72.76 236	86.21 65	96.51 36	66.64 72	97.88 52	90.08 56	94.04 43

save fliter					93.84 53	67.89 112	95.05 41	92.66 134	78.19 129

PVSNet_BlendedMVS		83.38 143	83.43 120	83.22 237	93.76 54	67.53 123	94.06 81	93.61 87	79.13 111	81.00 127	85.14 310	63.19 125	97.29 89	87.08 83	73.91 297	84.83 380

PVSNet_Blended		86.73 54	86.86 53	86.31 101	93.76 54	67.53 123	96.33 16	93.61 87	82.34 44	81.00 127	93.08 140	63.19 125	97.29 89	87.08 83	91.38 88	94.13 162

HFP-MVS		84.73 102	84.40 99	85.72 121	93.75 56	65.01 201	93.50 118	93.19 108	72.19 251	79.22 161	94.93 89	59.04 191	97.67 61	81.55 152	92.21 69	94.49 142

Anonymous202405211		77.96 259	75.33 281	85.87 113	93.73 57	64.52 214	94.85 51	85.36 407	62.52 388	76.11 199	90.18 220	29.43 442	97.29 89	68.51 277	77.24 274	95.81 55

balanced_conf03		89.08 15	88.84 23	89.81 6	93.66 58	75.15 5	90.61 276	93.43 98	84.06 24	86.20 66	90.17 226	72.42 37	96.98 115	93.09 29	95.92 10	97.29 7

testing99		86.01 70	85.47 80	87.63 40	93.62 59	71.25 24	93.47 121	95.23 21	80.42 73	80.60 134	91.95 176	71.73 43	96.50 144	80.02 169	82.22 212	95.13 90

SD-MVS		87.49 38	87.49 43	87.50 44	93.60 60	68.82 83	93.90 94	92.63 138	76.86 158	87.90 50	95.76 58	66.17 77	97.63 66	89.06 63	91.48 85	96.05 43
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
testing91		85.93 72	85.31 84	87.78 33	93.59 61	71.47 20	93.50 118	95.08 28	80.26 78	80.53 137	91.93 177	70.43 47	96.51 143	80.32 167	82.13 214	95.37 71

myMVS_eth3d28		86.31 64	86.15 67	86.78 69	93.56 62	70.49 34	92.94 142	95.28 19	82.47 41	78.70 171	92.07 169	72.45 36	95.41 213	82.11 142	85.78 163	94.44 145

ACMMPR		84.37 109	84.06 104	85.28 141	93.56 62	64.37 224	93.50 118	93.15 110	72.19 251	78.85 169	94.86 92	56.69 226	97.45 77	81.55 152	92.20 70	94.02 169

testing11		86.71 55	86.44 60	87.55 42	93.54 64	71.35 22	93.65 109	95.58 11	81.36 59	80.69 132	92.21 165	72.30 38	96.46 146	85.18 100	83.43 198	94.82 111

region2R		84.36 110	84.03 105	85.36 137	93.54 64	64.31 227	93.43 123	92.95 121	72.16 254	78.86 168	94.84 93	56.97 221	97.53 73	81.38 156	92.11 72	94.24 155

TSAR-MVS + GP.		87.96 27	88.37 30	86.70 73	93.51 66	65.32 192	95.15 37	93.84 75	78.17 130	85.93 70	94.80 94	75.80 15	98.21 40	89.38 58	88.78 124	96.59 19

PHI-MVS		86.83 50	86.85 54	86.78 69	93.47 67	65.55 187	95.39 31	95.10 25	71.77 267	85.69 73	96.52 35	62.07 144	98.77 26	86.06 92	95.60 12	96.03 44

SR-MVS		82.81 155	82.58 149	83.50 226	93.35 68	61.16 326	92.23 183	91.28 206	64.48 366	81.27 120	95.28 75	53.71 266	95.86 177	82.87 134	88.77 125	93.49 191

balanced_ft_v1		84.95 95	83.81 107	88.38 26	93.31 69	73.59 10	85.95 369	92.51 142	77.25 152	73.97 235	89.14 247	59.30 185	95.25 225	92.50 35	90.34 106	96.31 34

EPNet		87.84 32	88.38 29	86.23 102	93.30 70	66.05 171	95.26 33	94.84 35	87.09 5	88.06 48	94.53 100	66.79 71	97.34 86	83.89 119	91.68 81	95.29 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVS		83.87 127	83.47 118	85.05 150	93.22 71	63.78 247	92.92 143	92.66 134	73.99 205	78.18 175	94.31 112	55.25 241	97.41 81	79.16 178	91.58 83	93.95 171

X-MVStestdata		76.86 279	74.13 301	85.05 150	93.22 71	63.78 247	92.92 143	92.66 134	73.99 205	78.18 175	10.19 495	55.25 241	97.41 81	79.16 178	91.58 83	93.95 171

SMA-MVS		88.14 22	88.29 31	87.67 35	93.21 73	68.72 88	93.85 97	94.03 72	74.18 202	91.74 15	96.67 33	65.61 85	98.42 37	89.24 61	96.08 7	95.88 53
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
原ACMM1				84.42 186	93.21 73	64.27 229		93.40 101	65.39 360	79.51 156	92.50 153	58.11 204	96.69 134	65.27 321	93.96 44	92.32 232

MVS_111021_HR		86.19 67	85.80 75	87.37 48	93.17 75	69.79 49	93.99 88	93.76 79	79.08 113	78.88 167	93.99 124	62.25 142	98.15 42	85.93 93	91.15 92	94.15 161

CP-MVS		83.71 132	83.40 123	84.65 176	93.14 76	63.84 245	94.59 59	92.28 148	71.03 289	77.41 185	94.92 90	55.21 244	96.19 158	81.32 157	90.70 98	93.91 176

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 76	73.88 9	97.01 4	94.40 60	88.32 3	85.71 72	94.91 91	74.11 23	98.91 21	87.26 79	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
ZNCC-MVS		85.33 85	85.08 88	86.06 107	93.09 78	65.65 183	93.89 95	93.41 100	73.75 213	79.94 145	94.68 97	60.61 163	98.03 45	82.63 137	93.72 50	94.52 135

WBMVS		81.67 175	80.98 176	83.72 216	93.07 79	69.40 58	94.33 71	93.05 114	76.84 159	72.05 264	84.14 323	74.49 21	93.88 292	72.76 232	68.09 338	87.88 311

UBG		86.83 50	86.70 55	87.20 53	93.07 79	69.81 48	93.43 123	95.56 13	81.52 52	81.50 116	92.12 167	73.58 28	96.28 153	84.37 113	85.20 170	95.51 65

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 170	93.00 81	58.16 380	96.72 9	94.41 58	86.50 9	90.25 34	97.83 1	75.46 16	98.67 29	92.78 32	95.49 13	97.32 6

PLC	68.80 14	75.23 310	73.68 308	79.86 336	92.93 82	58.68 375	90.64 273	88.30 359	60.90 403	64.43 359	90.53 210	42.38 374	94.57 253	56.52 372	76.54 279	86.33 347
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
reproduce_monomvs		79.49 223	79.11 217	80.64 314	92.91 83	61.47 320	91.17 253	93.28 103	83.09 33	64.04 361	82.38 343	66.19 76	94.57 253	81.19 159	57.71 418	85.88 363

testing222		85.18 88	84.69 96	86.63 78	92.91 83	69.91 44	92.61 162	95.80 9	80.31 77	80.38 139	92.27 161	68.73 55	95.19 227	75.94 202	83.27 200	94.81 113

MSP-MVS		90.38 5	91.87 1	85.88 112	92.83 85	64.03 238	93.06 134	94.33 64	82.19 45	93.65 3	96.15 50	85.89 1	97.19 98	91.02 51	97.75 1	96.43 31
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
mPP-MVS		82.96 153	82.44 153	84.52 183	92.83 85	62.92 281	92.76 150	91.85 176	71.52 279	75.61 206	94.24 115	53.48 270	96.99 114	78.97 181	90.73 97	93.64 187

GST-MVS		84.63 104	84.29 101	85.66 124	92.82 87	65.27 193	93.04 136	93.13 111	73.20 222	78.89 164	94.18 117	59.41 183	97.85 53	81.45 154	92.48 68	93.86 179

WTY-MVS		86.32 62	85.81 74	87.85 30	92.82 87	69.37 62	95.20 35	95.25 20	82.71 38	81.91 112	94.73 95	67.93 63	97.63 66	79.55 172	82.25 211	96.54 22

PGM-MVS		83.25 145	82.70 144	84.92 154	92.81 89	64.07 237	90.44 278	92.20 154	71.28 283	77.23 189	94.43 103	55.17 245	97.31 88	79.33 177	91.38 88	93.37 193

EI-MVSNet-Vis-set		83.77 130	83.67 111	84.06 199	92.79 90	63.56 261	91.76 214	94.81 37	79.65 93	77.87 178	94.09 121	63.35 123	97.90 50	79.35 176	79.36 249	90.74 272

SF-MVS		87.03 45	87.09 47	86.84 64	92.70 91	67.45 126	93.64 110	93.76 79	70.78 295	86.25 64	96.44 38	66.98 69	97.79 55	88.68 66	94.56 34	95.28 82

MVSTER		82.47 162	82.05 156	83.74 212	92.68 92	69.01 77	91.90 204	93.21 105	79.83 87	72.14 262	85.71 305	74.72 19	94.72 244	75.72 204	72.49 307	87.50 316

SPE-MVS-test		86.14 68	87.01 48	83.52 223	92.63 93	59.36 368	95.49 28	91.92 169	80.09 82	85.46 77	95.53 66	61.82 149	95.77 189	86.77 87	93.37 56	95.41 68

MP-MVS		85.02 91	84.97 90	85.17 146	92.60 94	64.27 229	93.24 128	92.27 149	73.13 224	79.63 155	94.43 103	61.90 145	97.17 99	85.00 102	92.56 66	94.06 167
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETVMVS		84.22 116	83.71 110	85.76 119	92.58 95	68.25 102	92.45 174	95.53 15	79.54 100	79.46 157	91.64 187	70.29 48	94.18 273	69.16 270	82.76 206	94.84 107

thres200		79.66 219	78.33 224	83.66 220	92.54 96	65.82 181	93.06 134	96.31 3	74.90 192	73.30 242	88.66 253	59.67 177	95.61 203	47.84 412	78.67 258	89.56 290

APD-MVS_3200maxsize		81.64 177	81.32 167	82.59 254	92.36 97	58.74 374	91.39 234	91.01 232	63.35 378	79.72 153	94.62 99	51.82 282	96.14 160	79.71 170	87.93 133	92.89 213

新几何1				84.73 169	92.32 98	64.28 228		91.46 196	59.56 413	79.77 151	92.90 145	56.95 222	96.57 138	63.40 335	92.91 62	93.34 194

EI-MVSNet-UG-set		83.14 148	82.96 136	83.67 219	92.28 99	63.19 273	91.38 236	94.68 44	79.22 108	76.60 196	93.75 127	62.64 135	97.76 56	78.07 190	78.01 262	90.05 281

HPM-MVS		83.25 145	82.95 138	84.17 197	92.25 100	62.88 283	90.91 258	91.86 174	70.30 300	77.12 191	93.96 125	56.75 224	96.28 153	82.04 144	91.34 90	93.34 194
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	76.49 5	84.28 112	83.36 125	87.02 60	92.22 101	67.74 116	84.65 376	94.50 52	79.15 110	82.23 110	87.93 270	66.88 70	96.94 121	80.53 164	82.20 213	96.39 33

tfpn200view9		78.79 242	77.43 243	82.88 244	92.21 102	64.49 215	92.05 193	96.28 4	73.48 219	71.75 268	88.26 262	60.07 171	95.32 220	45.16 425	77.58 267	88.83 296

thres400		78.68 244	77.43 243	82.43 256	92.21 102	64.49 215	92.05 193	96.28 4	73.48 219	71.75 268	88.26 262	60.07 171	95.32 220	45.16 425	77.58 267	87.48 317

reproduce-ours		83.51 140	83.33 126	84.06 199	92.18 104	60.49 344	90.74 268	92.04 162	64.35 367	83.24 98	95.59 64	59.05 189	97.27 93	83.61 124	89.17 120	94.41 150

our_new_method		83.51 140	83.33 126	84.06 199	92.18 104	60.49 344	90.74 268	92.04 162	64.35 367	83.24 98	95.59 64	59.05 189	97.27 93	83.61 124	89.17 120	94.41 150

NormalMVS		86.39 59	86.66 58	85.60 127	92.12 106	65.95 176	94.88 48	90.83 240	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 96	91.15 92	93.93 173

lecture		84.77 99	84.81 94	84.65 176	92.12 106	62.27 297	94.74 54	92.64 137	68.35 329	85.53 74	95.30 73	59.77 175	97.91 49	83.73 123	91.15 92	93.77 182

MM		90.87 2	91.52 2	88.92 15	92.12 106	71.10 28	97.02 3	96.04 6	88.70 2	91.57 19	96.19 48	70.12 49	98.91 21	96.83 2	95.06 17	96.76 15

PS-MVSNAJ		88.14 22	87.61 41	89.71 7	92.06 109	76.72 1	95.75 20	93.26 104	83.86 25	89.55 41	96.06 52	53.55 267	97.89 51	91.10 49	93.31 57	94.54 133

reproduce_model		83.15 147	82.96 136	83.73 214	92.02 110	59.74 360	90.37 282	92.08 160	63.70 374	82.86 103	95.48 67	58.62 195	97.17 99	83.06 130	88.42 128	94.26 153

SR-MVS-dyc-post		81.06 191	80.70 180	82.15 270	92.02 110	58.56 377	90.90 259	90.45 257	62.76 385	78.89 164	94.46 101	51.26 294	95.61 203	78.77 185	86.77 149	92.28 234

RE-MVS-def			80.48 187		92.02 110	58.56 377	90.90 259	90.45 257	62.76 385	78.89 164	94.46 101	49.30 316		78.77 185	86.77 149	92.28 234

MSLP-MVS++		86.27 65	85.91 73	87.35 49	92.01 113	68.97 79	95.04 43	92.70 129	79.04 116	81.50 116	96.50 37	58.98 192	96.78 131	83.49 127	93.93 45	96.29 36

CS-MVS		85.80 75	86.65 59	83.27 235	92.00 114	58.92 372	95.31 32	91.86 174	79.97 83	84.82 83	95.40 69	62.26 141	95.51 212	86.11 91	92.08 73	95.37 71

旧先验1					91.94 115	60.74 336		91.50 194			94.36 105	65.23 89			91.84 78	94.55 131

thres600view7		78.00 257	76.66 258	82.03 277	91.93 116	63.69 256	91.30 244	96.33 1	72.43 244	70.46 282	87.89 271	60.31 166	94.92 237	42.64 437	76.64 278	87.48 317

testing3-2		83.11 149	83.15 134	82.98 242	91.92 117	64.01 240	94.39 70	95.37 16	78.32 127	75.53 208	90.06 232	73.18 29	93.18 315	74.34 218	75.27 286	91.77 250

LS3D		69.17 371	66.40 375	77.50 368	91.92 117	56.12 402	85.12 373	80.37 438	46.96 457	56.50 418	87.51 278	37.25 404	93.71 297	32.52 470	79.40 248	82.68 409

GG-mvs-BLEND				86.53 91	91.91 119	69.67 55	75.02 449	94.75 40		78.67 172	90.85 206	77.91 7	94.56 256	72.25 238	93.74 49	95.36 74

thres100view900		78.37 250	77.01 253	82.46 255	91.89 120	63.21 272	91.19 252	96.33 1	72.28 249	70.45 283	87.89 271	60.31 166	95.32 220	45.16 425	77.58 267	88.83 296

MTAPA		83.91 126	83.38 124	85.50 129	91.89 120	65.16 197	81.75 408	92.23 150	75.32 186	80.53 137	95.21 82	56.06 235	97.16 102	84.86 105	92.55 67	94.18 158

sasdasda		86.85 48	86.25 64	88.66 20	91.80 122	71.92 17	93.54 115	91.71 183	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 67	84.34 181	97.11 9

canonicalmvs		86.85 48	86.25 64	88.66 20	91.80 122	71.92 17	93.54 115	91.71 183	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 67	84.34 181	97.11 9

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 90	91.73 124	68.04 107	90.36 283	93.55 90	82.89 35	91.29 22	92.89 146	72.27 39	96.03 169	87.99 69	94.77 26	95.54 64
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		81.49 179	80.67 181	83.93 205	91.71 125	62.90 282	92.13 187	92.22 153	71.79 266	71.68 270	93.49 135	50.32 302	96.96 119	78.47 187	84.22 187	91.93 248
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
BH-RMVSNet		79.46 225	77.65 237	84.89 157	91.68 126	65.66 182	93.55 114	88.09 366	72.93 231	73.37 241	91.12 203	46.20 355	96.12 161	56.28 374	85.61 166	92.91 211

baseline1		81.84 173	81.03 174	84.28 193	91.60 127	66.62 158	91.08 255	91.66 188	81.87 48	74.86 219	91.67 186	69.98 51	94.92 237	71.76 244	64.75 369	91.29 263

ACMMP_NAP		86.05 69	85.80 75	86.80 68	91.58 128	67.53 123	91.79 209	93.49 95	74.93 191	84.61 84	95.30 73	59.42 182	97.92 48	86.13 90	94.92 20	94.94 101

MVS_Test		84.16 118	83.20 129	87.05 59	91.56 129	69.82 47	89.99 297	92.05 161	77.77 138	82.84 104	86.57 292	63.93 109	96.09 163	74.91 213	89.18 119	95.25 87

HPM-MVS_fast		80.25 209	79.55 205	82.33 262	91.55 130	59.95 357	91.32 243	89.16 320	65.23 363	74.71 223	93.07 141	47.81 334	95.74 190	74.87 215	88.23 129	91.31 262

CPTT-MVS		79.59 220	79.16 215	80.89 312	91.54 131	59.80 359	92.10 189	88.54 353	60.42 406	72.96 244	93.28 137	48.27 325	92.80 330	78.89 184	86.50 156	90.06 280

CNLPA		74.31 321	72.30 330	80.32 319	91.49 132	61.66 313	90.85 262	80.72 436	56.67 430	63.85 364	90.64 207	46.75 347	90.84 380	53.79 384	75.99 283	88.47 305

MP-MVS-pluss		85.24 86	85.13 87	85.56 128	91.42 133	65.59 185	91.54 228	92.51 142	74.56 194	80.62 133	95.64 61	59.15 188	97.00 111	86.94 85	93.80 47	94.07 166
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 273	74.31 295	85.80 117	91.42 133	68.36 96	71.78 454	94.72 41	49.61 451	77.12 191	45.92 480	77.41 8	93.98 287	67.62 289	93.16 59	95.05 95

mvsmamba		81.55 178	80.72 179	84.03 203	91.42 133	66.93 149	83.08 396	89.13 323	78.55 125	67.50 327	87.02 287	51.79 284	90.07 394	87.48 75	90.49 102	95.10 92

MGCFI-Net		85.59 81	85.73 77	85.17 146	91.41 136	62.44 290	92.87 147	91.31 200	79.65 93	86.99 60	95.14 85	62.90 133	96.12 161	87.13 82	84.13 189	96.96 13

xiu_mvs_v2_base		87.92 31	87.38 45	89.55 12	91.41 136	76.43 3	95.74 21	93.12 112	83.53 29	89.55 41	95.95 55	53.45 271	97.68 59	91.07 50	92.62 65	94.54 133

EIA-MVS		84.84 98	84.88 91	84.69 173	91.30 138	62.36 293	93.85 97	92.04 162	79.45 101	79.33 160	94.28 114	62.42 138	96.35 151	80.05 168	91.25 91	95.38 70

alignmvs		87.28 42	86.97 49	88.24 28	91.30 138	71.14 27	95.61 26	93.56 89	79.30 106	87.07 58	95.25 79	68.43 56	96.93 123	87.87 70	84.33 183	96.65 17

EPMVS		78.49 249	75.98 272	86.02 108	91.21 140	69.68 54	80.23 423	91.20 208	75.25 187	72.48 257	78.11 398	54.65 251	93.69 300	57.66 369	83.04 201	94.69 120

FMVSNet3		77.73 265	76.04 271	82.80 245	91.20 141	68.99 78	91.87 205	91.99 166	73.35 221	67.04 334	83.19 335	56.62 227	92.14 355	59.80 360	69.34 326	87.28 323

RRT-MVS		82.61 160	81.16 168	86.96 62	91.10 142	68.75 85	87.70 349	92.20 154	76.97 156	72.68 248	87.10 286	51.30 293	96.41 148	83.56 126	87.84 134	95.74 57

Anonymous20240529		76.84 281	74.15 300	84.88 158	91.02 143	64.95 203	93.84 100	91.09 220	53.57 439	73.00 243	87.42 279	35.91 413	97.32 87	69.14 271	72.41 309	92.36 229

tpmvs		72.88 338	69.76 354	82.22 267	90.98 144	67.05 139	78.22 436	88.30 359	63.10 383	64.35 360	74.98 427	55.09 246	94.27 269	43.25 431	69.57 325	85.34 375

MVS		84.66 103	82.86 141	90.06 2	90.93 145	74.56 7	87.91 344	95.54 14	68.55 326	72.35 261	94.71 96	59.78 174	98.90 23	81.29 158	94.69 32	96.74 16

PVSNet	73.49 8	80.05 213	78.63 221	84.31 191	90.92 146	64.97 202	92.47 173	91.05 229	79.18 109	72.43 259	90.51 211	37.05 409	94.06 280	68.06 283	86.00 159	93.90 178

3Dnovator+	73.60 7	82.10 170	80.60 184	86.60 79	90.89 147	66.80 153	95.20 35	93.44 97	74.05 204	67.42 329	92.49 155	49.46 314	97.65 65	70.80 254	91.68 81	95.33 76

VDD-MVS		83.06 150	81.81 163	86.81 67	90.86 148	67.70 117	95.40 30	91.50 194	75.46 181	81.78 113	92.34 160	40.09 384	97.13 104	86.85 86	82.04 215	95.60 61

BH-w/o		80.49 203	79.30 212	84.05 202	90.83 149	64.36 226	93.60 112	89.42 309	74.35 199	69.09 298	90.15 228	55.23 243	95.61 203	64.61 326	86.43 158	92.17 240

ET-MVSNet_ETH3D		84.01 122	83.15 134	86.58 82	90.78 150	70.89 29	94.74 54	94.62 47	81.44 56	58.19 408	93.64 131	73.64 27	92.35 350	82.66 136	78.66 259	96.50 27

Anonymous20231211		73.08 332	70.39 348	81.13 300	90.62 151	63.33 267	91.40 232	90.06 283	51.84 444	64.46 358	80.67 373	36.49 411	94.07 279	63.83 333	64.17 374	85.98 358

FA-MVS(test-final)		79.12 232	77.23 249	84.81 164	90.54 152	63.98 242	81.35 414	91.71 183	71.09 288	74.85 220	82.94 336	52.85 274	97.05 106	67.97 284	81.73 222	93.41 192

SymmetryMVS		86.32 62	86.39 61	86.12 106	90.52 153	65.95 176	94.88 48	94.58 50	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 96	86.59 153	95.51 65

TR-MVS		78.77 243	77.37 248	82.95 243	90.49 154	60.88 330	93.67 108	90.07 281	70.08 305	74.51 224	91.37 193	45.69 358	95.70 197	60.12 358	80.32 238	92.29 233

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 82	90.42 155	66.38 163	96.09 17	93.87 74	77.73 139	84.01 92	95.66 60	63.39 121	97.94 47	87.40 77	93.55 54	95.42 67
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 315	73.53 309	79.17 351	90.40 156	52.07 422	89.19 320	89.61 303	62.69 387	70.07 288	92.67 151	48.89 323	94.32 265	38.26 452	79.97 240	91.12 266
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 182	79.99 194	85.46 130	90.39 157	68.40 95	86.88 361	90.61 254	74.41 197	70.31 286	84.67 315	63.79 111	92.32 352	73.13 226	85.70 164	95.67 58

CANet_DTU		84.09 119	83.52 113	85.81 116	90.30 158	66.82 151	91.87 205	89.01 332	85.27 13	86.09 68	93.74 128	47.71 335	96.98 115	77.90 191	89.78 115	93.65 186

Fast-Effi-MVS+		81.14 188	80.01 193	84.51 184	90.24 159	65.86 179	94.12 80	89.15 321	73.81 212	75.37 211	88.26 262	57.26 214	94.53 258	66.97 299	84.92 175	93.15 201

ETV-MVS		86.01 70	86.11 68	85.70 123	90.21 160	67.02 142	93.43 123	91.92 169	81.21 61	84.13 91	94.07 123	60.93 158	95.63 200	89.28 60	89.81 113	94.46 144

MVSMamba_PlusPlus		84.97 94	83.65 112	88.93 14	90.17 161	74.04 8	87.84 346	92.69 132	62.18 390	81.47 118	87.64 275	71.47 44	96.28 153	84.69 106	94.74 31	96.47 28

tpmrst		80.57 200	79.14 216	84.84 160	90.10 162	68.28 99	81.70 409	89.72 299	77.63 143	75.96 200	79.54 389	64.94 93	92.71 333	75.43 206	77.28 273	93.55 188

PVSNet_Blended_VisFu		83.97 124	83.50 115	85.39 133	90.02 163	66.59 160	93.77 104	91.73 181	77.43 148	77.08 193	89.81 236	63.77 112	96.97 118	79.67 171	88.21 130	92.60 221

UGNet		79.87 217	78.68 220	83.45 228	89.96 164	61.51 317	92.13 187	90.79 247	76.83 160	78.85 169	86.33 296	38.16 395	96.17 159	67.93 286	87.17 142	92.67 218
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CHOSEN 1792x2688		84.98 93	83.45 119	89.57 11	89.94 165	75.14 6	92.07 192	92.32 147	81.87 48	75.68 203	88.27 261	60.18 168	98.60 31	80.46 165	90.27 107	94.96 99

BH-untuned		78.68 244	77.08 251	83.48 227	89.84 166	63.74 249	92.70 154	88.59 350	71.57 277	66.83 338	88.65 254	51.75 285	95.39 215	59.03 363	84.77 177	91.32 261

FE-MVS		75.97 299	73.02 318	84.82 161	89.78 167	65.56 186	77.44 439	91.07 225	64.55 365	72.66 249	79.85 385	46.05 356	96.69 134	54.97 378	80.82 233	92.21 239

test222					89.77 168	61.60 315	89.55 306	89.42 309	56.83 429	77.28 188	92.43 157	52.76 275			91.14 95	93.09 204

PMMVS		81.98 172	82.04 157	81.78 279	89.76 169	56.17 401	91.13 254	90.69 249	77.96 132	80.09 144	93.57 133	46.33 353	94.99 233	81.41 155	87.46 139	94.17 159

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 170	76.68 2	97.29 1	95.35 17	82.87 37	91.58 18	97.22 8	79.93 5	99.10 9	83.12 129	97.64 2	97.94 1

QAPM		79.95 216	77.39 247	87.64 36	89.63 171	71.41 21	93.30 127	93.70 84	65.34 362	67.39 331	91.75 181	47.83 333	98.96 19	57.71 368	89.81 113	92.54 224

3Dnovator	73.91 6	82.69 159	80.82 177	88.31 27	89.57 172	71.26 23	92.60 164	94.39 61	78.84 118	67.89 322	92.48 156	48.42 324	98.52 32	68.80 275	94.40 36	95.15 89

Effi-MVS+		83.82 128	82.76 142	86.99 61	89.56 173	69.40 58	91.35 241	86.12 398	72.59 238	83.22 101	92.81 150	59.60 178	96.01 171	81.76 151	87.80 135	95.56 63

PatchmatchNet		77.46 269	74.63 288	85.96 110	89.55 174	70.35 36	79.97 428	89.55 304	72.23 250	70.94 276	76.91 411	57.03 217	92.79 331	54.27 381	81.17 225	94.74 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 350	69.98 349	78.28 360	89.51 175	55.70 406	83.49 388	83.39 428	61.24 401	63.72 365	82.76 338	34.77 417	93.03 318	53.37 387	77.59 266	86.12 355

thisisatest0515		83.41 142	82.49 152	86.16 104	89.46 176	68.26 100	93.54 115	94.70 43	74.31 200	75.75 201	90.92 204	72.62 34	96.52 142	69.64 262	81.50 223	93.71 183

h-mvs33		83.01 151	82.56 151	84.35 190	89.34 177	62.02 301	92.72 152	93.76 79	81.45 54	82.73 107	92.25 163	60.11 169	97.13 104	87.69 72	62.96 383	93.91 176

EC-MVSNet		84.53 105	85.04 89	83.01 241	89.34 177	61.37 323	94.42 66	91.09 220	77.91 134	83.24 98	94.20 116	58.37 200	95.40 214	85.35 95	91.41 86	92.27 237

UWE-MVS		80.81 196	81.01 175	80.20 324	89.33 179	57.05 395	91.91 203	94.71 42	75.67 178	75.01 215	89.37 241	63.13 129	91.44 377	67.19 296	82.80 205	92.12 242

UA-Net		80.02 214	79.65 201	81.11 302	89.33 179	57.72 384	86.33 367	89.00 336	77.44 147	81.01 126	89.15 246	59.33 184	95.90 174	61.01 352	84.28 185	89.73 287

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 154	89.29 181	61.41 322	92.97 139	88.36 356	86.96 6	91.49 21	97.49 3	69.48 54	97.46 76	97.00 1	89.88 112	95.89 52

dp		75.01 314	72.09 332	83.76 211	89.28 182	66.22 169	79.96 429	89.75 294	71.16 285	67.80 324	77.19 408	51.81 283	92.54 341	50.39 395	71.44 316	92.51 226

SDMVSNet		80.26 208	78.88 219	84.40 187	89.25 183	67.63 120	85.35 372	93.02 115	76.77 162	70.84 278	87.12 284	47.95 332	96.09 163	85.04 101	74.55 288	89.48 291

sd_testset		77.08 276	75.37 279	82.20 268	89.25 183	62.11 300	82.06 406	89.09 326	76.77 162	70.84 278	87.12 284	41.43 378	95.01 232	67.23 295	74.55 288	89.48 291

sss		82.71 158	82.38 154	83.73 214	89.25 183	59.58 363	92.24 182	94.89 31	77.96 132	79.86 146	92.38 158	56.70 225	97.05 106	77.26 194	80.86 232	94.55 131

MVSFormer		83.75 131	82.88 140	86.37 97	89.24 186	71.18 25	89.07 322	90.69 249	65.80 357	87.13 56	94.34 110	64.99 91	92.67 336	72.83 229	91.80 79	95.27 83

lupinMVS		87.74 33	87.77 38	87.63 40	89.24 186	71.18 25	96.57 12	92.90 123	82.70 39	87.13 56	95.27 77	64.99 91	95.80 185	89.34 59	91.80 79	95.93 50

IB-MVS	77.80 4	82.18 166	80.46 188	87.35 49	89.14 188	70.28 37	95.59 27	95.17 24	78.85 117	70.19 287	85.82 303	70.66 46	97.67 61	72.19 241	66.52 352	94.09 164
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 196	89.07 189	61.60 315	94.87 50	89.06 329	85.65 11	91.09 26	97.41 4	68.26 58	97.43 80	95.07 13	92.74 64	93.66 185

E3new		84.94 96	84.36 100	86.69 75	89.06 190	69.31 64	92.68 159	91.29 205	80.72 67	81.03 125	92.14 166	61.89 146	95.91 173	84.59 108	85.85 162	94.86 103

MDTV_nov1_ep13			72.61 326		89.06 190	68.48 92	80.33 421	90.11 280	71.84 264	71.81 267	75.92 424	53.01 273	93.92 290	48.04 409	73.38 299

testdata				81.34 293	89.02 192	57.72 384		89.84 291	58.65 418	85.32 79	94.09 121	57.03 217	93.28 311	69.34 267	90.56 101	93.03 207

CostFormer		82.33 164	81.15 169	85.86 114	89.01 193	68.46 94	82.39 405	93.01 116	75.59 179	80.25 141	81.57 357	72.03 41	94.96 234	79.06 180	77.48 270	94.16 160

GeoE		78.90 238	77.43 243	83.29 233	88.95 194	62.02 301	92.31 178	86.23 394	70.24 301	71.34 275	89.27 244	54.43 256	94.04 283	63.31 337	80.81 234	93.81 181

GBi-Net		75.65 304	73.83 305	81.10 303	88.85 195	65.11 198	90.01 294	90.32 266	70.84 292	67.04 334	80.25 380	48.03 326	91.54 372	59.80 360	69.34 326	86.64 335

test1		75.65 304	73.83 305	81.10 303	88.85 195	65.11 198	90.01 294	90.32 266	70.84 292	67.04 334	80.25 380	48.03 326	91.54 372	59.80 360	69.34 326	86.64 335

FMVSNet2		76.07 293	74.01 303	82.26 266	88.85 195	67.66 118	91.33 242	91.61 189	70.84 292	65.98 343	82.25 345	48.03 326	92.00 360	58.46 365	68.73 334	87.10 326

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 97	88.80 198	66.64 157	92.15 186	93.68 85	81.07 63	76.91 194	93.64 131	62.59 136	98.44 35	85.50 94	92.84 63	94.03 168
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 174	81.52 165	82.61 252	88.77 199	60.21 352	93.02 138	93.66 86	68.52 327	72.90 246	90.39 214	72.19 40	94.96 234	74.93 212	79.29 252	92.67 218

fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 122	88.69 200	63.71 253	94.56 60	90.22 277	85.04 15	92.27 6	97.05 12	63.67 114	98.15 42	95.09 12	91.39 87	95.27 83

1112_ss		80.56 201	79.83 198	82.77 246	88.65 201	60.78 332	92.29 179	88.36 356	72.58 239	72.46 258	94.95 87	65.09 90	93.42 310	66.38 305	77.71 264	94.10 163

VortexMVS		77.62 266	76.44 261	81.13 300	88.58 202	63.73 251	91.24 247	91.30 204	77.81 136	65.76 344	81.97 349	49.69 312	93.72 296	76.40 200	65.26 362	85.94 361

viewcassd2359sk11		84.74 101	84.11 103	86.64 77	88.57 203	69.20 71	92.61 162	91.23 207	80.58 68	80.85 129	91.96 174	61.39 152	95.89 175	84.28 114	85.49 167	94.82 111

icg_test_0407_2		80.38 205	79.22 214	83.88 206	88.54 204	64.75 206	86.79 362	90.80 243	76.73 164	73.95 236	90.18 220	51.55 289	92.45 345	73.47 221	80.95 227	94.43 146

IMVS_0407		80.80 197	79.39 210	85.00 153	88.54 204	64.75 206	88.40 335	90.80 243	76.73 164	73.95 236	90.18 220	51.55 289	95.81 184	73.47 221	80.95 227	94.43 146

IMVS_0404		78.11 256	76.29 267	83.59 221	88.54 204	64.75 206	84.63 377	90.80 243	76.73 164	61.16 384	90.18 220	40.17 383	91.58 370	73.47 221	80.95 227	94.43 146

IMVS_0403		81.19 186	79.88 196	85.13 148	88.54 204	64.75 206	88.84 327	90.80 243	76.73 164	75.21 212	90.18 220	54.22 260	96.21 157	73.47 221	80.95 227	94.43 146

tpm cat1		75.30 309	72.21 331	84.58 181	88.52 208	67.77 115	78.16 437	88.02 367	61.88 396	68.45 313	76.37 420	60.65 161	94.03 285	53.77 385	74.11 294	91.93 248

mamba_0408		76.22 290	73.37 312	84.77 165	88.50 209	66.98 145	58.80 480	86.18 396	69.12 319	74.12 230	89.01 250	47.50 336	95.35 217	67.57 290	79.52 244	91.98 245

SSM_04072		74.86 317	73.37 312	79.35 348	88.50 209	66.98 145	58.80 480	86.18 396	69.12 319	74.12 230	89.01 250	47.50 336	79.09 465	67.57 290	79.52 244	91.98 245

SSM_0407		79.09 233	77.21 250	84.75 168	88.50 209	66.98 145	89.21 318	87.03 382	67.99 332	74.12 230	89.32 242	47.98 329	95.29 224	71.23 249	79.52 244	91.98 245

viewmanbaseed2359cas		84.89 97	84.26 102	86.78 69	88.50 209	69.77 51	92.69 158	91.13 216	81.11 62	81.54 115	91.98 173	60.35 165	95.73 191	84.47 110	86.56 154	94.84 107

viewdifsd2359ckpt13		84.08 120	83.21 128	86.70 73	88.49 213	69.55 56	92.25 180	91.14 214	79.71 91	79.73 152	91.72 183	58.83 193	95.89 175	82.06 143	84.99 172	94.66 125

LCM-MVSNet-Re		72.93 336	71.84 335	76.18 385	88.49 213	48.02 445	80.07 426	70.17 467	73.96 208	52.25 434	80.09 383	49.98 307	88.24 410	67.35 292	84.23 186	92.28 234

Vis-MVSNet		80.92 194	79.98 195	83.74 212	88.48 215	61.80 307	93.44 122	88.26 363	73.96 208	77.73 179	91.76 180	49.94 308	94.76 241	65.84 311	90.37 105	94.65 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 230	79.57 202	78.24 362	88.46 216	52.29 421	90.41 280	89.12 324	74.24 201	69.13 297	91.91 178	65.77 83	90.09 393	59.00 364	88.09 131	92.33 231

ab-mvs		80.18 210	78.31 225	85.80 117	88.44 217	65.49 190	83.00 399	92.67 133	71.82 265	77.36 186	85.01 311	54.50 252	96.59 136	76.35 201	75.63 284	95.32 78

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 149	88.43 218	61.78 308	94.73 57	91.74 180	85.87 10	91.66 17	97.50 2	64.03 106	98.33 38	96.28 4	90.08 108	95.10 92

gm-plane-assit					88.42 219	67.04 140			78.62 123		91.83 179		97.37 83	76.57 198

MVS_111021_LR		82.02 171	81.52 165	83.51 225	88.42 219	62.88 283	89.77 300	88.93 337	76.78 161	75.55 207	93.10 138	50.31 303	95.38 216	83.82 120	87.02 143	92.26 238

test2506		83.29 144	82.92 139	84.37 189	88.39 221	63.18 274	92.01 195	91.35 199	77.66 141	78.49 174	91.42 190	64.58 100	95.09 229	73.19 225	89.23 117	94.85 104

ECVR-MVS		81.29 183	80.38 189	84.01 204	88.39 221	61.96 303	92.56 169	86.79 387	77.66 141	76.63 195	91.42 190	46.34 352	95.24 226	74.36 217	89.23 117	94.85 104

SSM_0404		79.46 225	77.65 237	84.91 156	88.37 223	67.04 140	89.59 302	87.03 382	67.99 332	75.45 209	89.32 242	47.98 329	95.34 219	71.23 249	81.90 219	92.34 230

baseline		85.01 92	84.44 98	86.71 72	88.33 224	68.73 86	90.24 288	91.82 178	81.05 64	81.18 122	92.50 153	63.69 113	96.08 166	84.45 111	86.71 151	95.32 78

tpm2		79.80 218	77.95 233	85.34 138	88.28 225	68.26 100	81.56 411	91.42 197	70.11 302	77.59 183	80.50 375	67.40 67	94.26 271	67.34 293	77.35 271	93.51 190

thisisatest0530		81.15 187	80.07 191	84.39 188	88.26 226	65.63 184	91.40 232	94.62 47	71.27 284	70.93 277	89.18 245	72.47 35	96.04 168	65.62 316	76.89 277	91.49 254

casdiffmvs		85.37 84	84.87 92	86.84 64	88.25 227	69.07 73	93.04 136	91.76 179	81.27 60	80.84 130	92.07 169	64.23 104	96.06 167	84.98 103	87.43 140	95.39 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Test_1112_low_res		79.56 221	78.60 222	82.43 256	88.24 228	60.39 348	92.09 190	87.99 368	72.10 255	71.84 266	87.42 279	64.62 98	93.04 317	65.80 312	77.30 272	93.85 180

casdiffmvs_mvg		85.66 79	85.18 86	87.09 57	88.22 229	69.35 63	93.74 106	91.89 172	81.47 53	80.10 143	91.45 189	64.80 96	96.35 151	87.23 80	87.69 136	95.58 62
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPM		85.89 74	85.46 81	87.18 54	88.20 230	72.42 16	92.41 176	92.77 127	82.11 46	80.34 140	93.07 141	68.27 57	95.02 230	78.39 188	93.59 53	94.09 164

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 159	88.15 231	61.94 305	95.65 25	89.70 301	85.54 12	92.07 11	97.33 5	67.51 66	97.27 93	96.23 5	92.07 74	95.35 75

TESTMET0.1,1		82.41 163	81.98 160	83.72 216	88.08 232	63.74 249	92.70 154	93.77 78	79.30 106	77.61 182	87.57 277	58.19 203	94.08 278	73.91 220	86.68 152	93.33 196

ADS-MVSNet2		66.90 391	63.44 399	77.26 374	88.06 233	60.70 339	68.01 464	75.56 450	57.57 421	64.48 356	69.87 449	38.68 387	84.10 440	40.87 443	67.89 343	86.97 327

ADS-MVSNet		68.54 378	64.38 394	81.03 307	88.06 233	66.90 150	68.01 464	84.02 419	57.57 421	64.48 356	69.87 449	38.68 387	89.21 401	40.87 443	67.89 343	86.97 327

EPNet_dtu		78.80 241	79.26 213	77.43 370	88.06 233	49.71 438	91.96 200	91.95 168	77.67 140	76.56 197	91.28 196	58.51 198	90.20 391	56.37 373	80.95 227	92.39 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt09		83.52 139	82.57 150	86.37 97	88.02 236	68.47 93	91.78 211	89.63 302	79.61 95	78.56 173	92.00 172	59.28 186	95.96 172	81.94 145	82.35 207	94.69 120

miper_enhance_ethall		78.86 239	77.97 231	81.54 287	88.00 237	65.17 196	91.41 230	89.15 321	75.19 188	68.79 307	83.98 326	67.17 68	92.82 328	72.73 233	65.30 359	86.62 339

IS-MVSNet		80.14 211	79.41 208	82.33 262	87.91 238	60.08 355	91.97 199	88.27 361	72.90 234	71.44 274	91.73 182	61.44 151	93.66 301	62.47 345	86.53 155	93.24 197

E2		84.45 106	83.74 108	86.56 84	87.90 239	69.06 74	92.53 170	91.13 216	80.35 75	80.58 135	91.69 184	60.70 159	95.84 178	83.80 121	84.99 172	94.79 114

E3		84.45 106	83.74 108	86.56 84	87.90 239	69.06 74	92.53 170	91.13 216	80.35 75	80.58 135	91.69 184	60.70 159	95.84 178	83.80 121	84.99 172	94.79 114

CLD-MVS		82.73 156	82.35 155	83.86 207	87.90 239	67.65 119	95.45 29	92.18 157	85.06 14	72.58 252	92.27 161	52.46 279	95.78 187	84.18 115	79.06 254	88.16 309
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Syy-MVS		69.65 368	69.52 355	70.03 431	87.87 242	43.21 466	88.07 340	89.01 332	72.91 232	63.11 370	88.10 266	45.28 362	85.54 432	22.07 481	69.23 329	81.32 421

myMVS_eth3d		72.58 345	72.74 323	72.10 422	87.87 242	49.45 440	88.07 340	89.01 332	72.91 232	63.11 370	88.10 266	63.63 115	85.54 432	32.73 468	69.23 329	81.32 421

test1111		80.84 195	80.02 192	83.33 230	87.87 242	60.76 334	92.62 161	86.86 386	77.86 135	75.73 202	91.39 192	46.35 351	94.70 249	72.79 231	88.68 126	94.52 135

HyFIR lowres test		81.03 192	79.56 203	85.43 131	87.81 245	68.11 106	90.18 289	90.01 286	70.65 297	72.95 245	86.06 299	63.61 117	94.50 260	75.01 211	79.75 243	93.67 184

BP-MVS1		86.54 57	86.68 57	86.13 105	87.80 246	67.18 135	92.97 139	95.62 10	79.92 86	82.84 104	94.14 118	74.95 17	96.46 146	82.91 133	88.96 123	94.74 116

dmvs_re		76.93 278	75.36 280	81.61 285	87.78 247	60.71 338	80.00 427	87.99 368	79.42 102	69.02 301	89.47 239	46.77 346	94.32 265	63.38 336	74.45 291	89.81 284

1314		80.70 198	78.95 218	85.94 111	87.77 248	67.56 121	87.91 344	92.55 141	72.17 253	67.44 328	93.09 139	50.27 304	97.04 109	71.68 246	87.64 137	93.23 198

GDP-MVS		85.54 82	85.32 83	86.18 103	87.64 249	67.95 111	92.91 145	92.36 146	77.81 136	83.69 94	94.31 112	72.84 32	96.41 148	80.39 166	85.95 160	94.19 157

cl22		77.94 260	76.78 256	81.42 289	87.57 250	64.93 204	90.67 271	88.86 340	72.45 243	67.63 326	82.68 340	64.07 105	92.91 326	71.79 242	65.30 359	86.44 341

HQP-NCC					87.54 251		94.06 81		79.80 88	74.18 226

ACMP_Plane					87.54 251		94.06 81		79.80 88	74.18 226

HQP-MVS		81.14 188	80.64 182	82.64 251	87.54 251	63.66 258	94.06 81	91.70 186	79.80 88	74.18 226	90.30 217	51.63 287	95.61 203	77.63 192	78.90 255	88.63 300

NP-MVS					87.41 254	63.04 275					90.30 217

diffmvs		84.28 112	83.83 106	85.61 126	87.40 255	68.02 108	90.88 261	89.24 315	80.54 69	81.64 114	92.52 152	59.83 173	94.52 259	87.32 78	85.11 171	94.29 152
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		83.68 134	83.42 122	84.48 185	87.37 256	66.00 173	90.06 292	95.93 8	79.71 91	69.08 299	90.39 214	77.92 6	96.28 153	78.91 183	81.38 224	91.16 265

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 161	87.36 257	63.54 263	94.74 54	90.02 285	82.52 40	90.14 37	96.92 23	62.93 132	97.84 54	95.28 11	82.26 209	93.07 206

fmvsm_s_conf0.5_n_3		86.88 46	87.99 36	83.58 222	87.26 258	60.74 336	93.21 131	87.94 371	84.22 22	91.70 16	97.27 6	65.91 82	95.02 230	93.95 24	90.42 103	94.99 98

plane_prior6					87.23 259	62.32 295						50.66 299

tttt0517		79.50 222	78.53 223	82.41 259	87.22 260	61.43 321	89.75 301	94.76 39	69.29 314	67.91 320	88.06 269	72.92 31	95.63 200	62.91 341	73.90 298	90.16 279

viewdifsd2359ckpt07		82.95 154	82.04 157	85.66 124	87.19 261	66.73 155	91.56 227	90.39 264	77.58 144	77.58 184	91.19 201	58.57 196	95.65 199	82.32 139	82.01 216	94.60 129

plane_prior1					87.15 262

cascas		78.18 253	75.77 275	85.41 132	87.14 263	69.11 72	92.96 141	91.15 213	66.71 346	70.47 281	86.07 298	37.49 403	96.48 145	70.15 260	79.80 242	90.65 273

fmvsm_l_conf0.5_n_a		87.44 40	88.15 34	85.30 139	87.10 264	64.19 233	94.41 67	88.14 364	80.24 81	92.54 5	96.97 16	69.52 53	97.17 99	95.89 6	88.51 127	94.56 130

CHOSEN 280x420		77.35 271	76.95 255	78.55 357	87.07 265	62.68 287	69.71 460	82.95 430	68.80 323	71.48 273	87.27 283	66.03 79	84.00 443	76.47 199	82.81 204	88.95 295

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 142	87.05 266	63.55 262	93.69 107	91.08 224	84.18 23	90.17 36	97.04 14	67.58 65	97.99 46	95.72 8	90.03 109	94.26 153

E4		84.00 123	83.19 130	86.46 92	86.99 267	68.85 81	92.39 177	90.99 233	79.94 84	80.17 142	91.36 194	59.73 176	95.79 186	82.87 134	84.22 187	94.74 116

E5new		83.62 135	82.65 145	86.55 86	86.98 268	69.28 67	91.69 218	90.96 234	79.61 95	79.80 147	91.25 197	58.04 205	95.84 178	81.83 149	83.66 195	94.52 135

E6new		83.62 135	82.65 145	86.55 86	86.98 268	69.29 65	91.69 218	90.95 236	79.60 98	79.80 147	91.25 197	58.04 205	95.84 178	81.84 147	83.67 193	94.52 135

E6		83.62 135	82.65 145	86.55 86	86.98 268	69.29 65	91.69 218	90.95 236	79.60 98	79.80 147	91.25 197	58.04 205	95.84 178	81.84 147	83.67 193	94.52 135

E5		83.62 135	82.65 145	86.55 86	86.98 268	69.28 67	91.69 218	90.96 234	79.61 95	79.80 147	91.25 197	58.04 205	95.84 178	81.83 149	83.66 195	94.52 135

fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 133	86.95 272	64.37 224	94.30 72	88.45 354	80.51 70	92.70 4	96.86 25	69.98 51	97.15 103	95.83 7	88.08 132	94.65 126

HQP_MVS		80.34 207	79.75 200	82.12 272	86.94 273	62.42 291	93.13 132	91.31 200	78.81 119	72.53 253	89.14 247	50.66 299	95.55 209	76.74 195	78.53 260	88.39 306

plane_prior7					86.94 273	61.51 317

test-LLR		80.10 212	79.56 203	81.72 281	86.93 275	61.17 324	92.70 154	91.54 191	71.51 280	75.62 204	86.94 288	53.83 263	92.38 347	72.21 239	84.76 178	91.60 252

test-mter		79.96 215	79.38 211	81.72 281	86.93 275	61.17 324	92.70 154	91.54 191	73.85 210	75.62 204	86.94 288	49.84 310	92.38 347	72.21 239	84.76 178	91.60 252

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 139	86.92 277	62.63 288	95.02 45	90.28 272	84.95 16	90.27 33	96.86 25	65.36 87	97.52 74	94.93 15	90.03 109	95.76 56

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 229	86.92 277	60.53 343	94.41 67	87.31 379	83.30 32	88.72 46	96.72 32	54.28 259	97.75 57	94.07 22	84.68 180	92.04 243

fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 208	86.89 279	60.04 356	95.05 41	92.17 159	84.80 18	92.27 6	96.37 39	64.62 98	96.54 141	94.43 19	91.86 77	94.94 101

viewmacassd2359aftdt		84.03 121	83.18 131	86.59 81	86.76 280	69.44 57	92.44 175	90.85 239	80.38 74	80.78 131	91.33 195	58.54 197	95.62 202	82.15 141	85.41 168	94.72 119

guyue		81.23 185	80.57 185	83.21 239	86.64 281	61.85 306	92.52 172	92.78 126	78.69 122	74.92 218	89.42 240	50.07 306	95.35 217	80.79 162	79.31 251	92.42 227

SCA		75.82 302	72.76 322	85.01 152	86.63 282	70.08 39	81.06 416	89.19 318	71.60 276	70.01 289	77.09 409	45.53 359	90.25 386	60.43 355	73.27 300	94.68 122

KinetiMVS		81.43 180	80.11 190	85.38 136	86.60 283	65.47 191	92.90 146	93.54 91	75.33 185	77.31 187	90.39 214	46.81 344	96.75 132	71.65 247	86.46 157	93.93 173

AUN-MVS		78.37 250	77.43 243	81.17 298	86.60 283	57.45 390	89.46 312	91.16 210	74.11 203	74.40 225	90.49 212	55.52 240	94.57 253	74.73 216	60.43 409	91.48 255

SSC-MVS3.2		74.92 316	73.32 315	79.74 340	86.53 285	60.31 349	89.03 325	92.70 129	78.61 124	68.98 303	83.34 333	41.93 376	92.23 354	52.77 389	65.97 355	86.69 334

hse-mvs2		81.12 190	81.11 173	81.16 299	86.52 286	57.48 389	89.40 313	91.16 210	81.45 54	82.73 107	90.49 212	60.11 169	94.58 251	87.69 72	60.41 410	91.41 257

xiu_mvs_v1_base_debu		82.16 167	81.12 170	85.26 143	86.42 287	68.72 88	92.59 166	90.44 261	73.12 225	84.20 88	94.36 105	38.04 397	95.73 191	84.12 116	86.81 146	91.33 258

xiu_mvs_v1_base		82.16 167	81.12 170	85.26 143	86.42 287	68.72 88	92.59 166	90.44 261	73.12 225	84.20 88	94.36 105	38.04 397	95.73 191	84.12 116	86.81 146	91.33 258

xiu_mvs_v1_base_debi		82.16 167	81.12 170	85.26 143	86.42 287	68.72 88	92.59 166	90.44 261	73.12 225	84.20 88	94.36 105	38.04 397	95.73 191	84.12 116	86.81 146	91.33 258

F-COLMAP		70.66 358	68.44 365	77.32 372	86.37 290	55.91 404	88.00 342	86.32 391	56.94 428	57.28 416	88.07 268	33.58 424	92.49 343	51.02 392	68.37 336	83.55 391

CDS-MVSNet		81.43 180	80.74 178	83.52 223	86.26 291	64.45 218	92.09 190	90.65 253	75.83 177	73.95 236	89.81 236	63.97 108	92.91 326	71.27 248	82.82 203	93.20 200
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 202	78.26 226	87.21 52	86.19 292	69.79 49	94.48 61	91.31 200	60.42 406	79.34 159	90.91 205	38.48 392	96.56 139	82.16 140	81.05 226	95.27 83

WB-MVSnew		77.14 274	76.18 270	80.01 330	86.18 293	63.24 270	91.26 245	94.11 70	71.72 269	73.52 240	87.29 282	45.14 363	93.00 319	56.98 371	79.42 247	83.80 389

jason		86.40 58	86.17 66	87.11 56	86.16 294	70.54 33	95.71 24	92.19 156	82.00 47	84.58 85	94.34 110	61.86 147	95.53 211	87.76 71	90.89 96	95.27 83
jason: jason.
fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 194	86.15 295	61.48 319	94.69 58	91.16 210	83.79 28	90.51 32	96.28 44	64.24 103	98.22 39	95.00 14	86.88 144	93.11 203

diffmvs_AUTHOR		83.97 124	83.49 116	85.39 133	86.09 296	67.83 113	90.76 266	89.05 330	79.94 84	81.43 119	92.23 164	59.53 179	94.42 262	87.18 81	85.22 169	93.92 175

PCF-MVS	73.15 9	79.29 229	77.63 239	84.29 192	86.06 297	65.96 175	87.03 357	91.10 219	69.86 308	69.79 294	90.64 207	57.54 213	96.59 136	64.37 330	82.29 208	90.32 277
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 262	76.50 260	82.12 272	85.99 298	69.95 43	91.75 216	92.70 129	73.97 207	62.58 378	84.44 319	41.11 380	95.78 187	63.76 334	92.17 71	80.62 429

FIs		79.47 224	79.41 208	79.67 341	85.95 299	59.40 365	91.68 222	93.94 73	78.06 131	68.96 304	88.28 260	66.61 73	91.77 364	66.20 308	74.99 287	87.82 312

VPA-MVSNet		79.03 234	78.00 230	82.11 275	85.95 299	64.48 217	93.22 130	94.66 45	75.05 190	74.04 234	84.95 312	52.17 281	93.52 303	74.90 214	67.04 348	88.32 308

tpm		78.58 247	77.03 252	83.22 237	85.94 301	64.56 213	83.21 395	91.14 214	78.31 128	73.67 239	79.68 387	64.01 107	92.09 358	66.07 309	71.26 317	93.03 207

OpenMVS	70.45 11	78.54 248	75.92 273	86.41 96	85.93 302	71.68 19	92.74 151	92.51 142	66.49 348	64.56 355	91.96 174	43.88 368	98.10 44	54.61 379	90.65 99	89.44 293

viewmambaseed2359dif		82.60 161	81.91 161	84.67 175	85.83 303	66.09 170	90.50 277	89.01 332	75.46 181	79.64 154	92.01 171	59.51 180	94.38 264	82.99 132	82.26 209	93.54 189

testing3		70.38 362	70.83 342	69.03 436	85.82 304	43.93 465	90.72 270	90.56 256	68.06 331	60.24 395	86.82 290	64.83 95	84.12 439	26.33 476	64.10 375	79.04 443

0.4-1-1-0.2		81.28 184	79.42 207	86.84 64	85.80 305	68.82 83	95.10 39	94.43 57	74.45 196	77.18 190	85.54 306	62.27 140	95.70 197	76.72 197	63.30 382	96.01 45

OMC-MVS		78.67 246	77.91 235	80.95 309	85.76 306	57.40 391	88.49 333	88.67 347	73.85 210	72.43 259	92.10 168	49.29 317	94.55 257	72.73 233	77.89 263	90.91 271

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 170	85.73 307	63.58 260	93.79 103	89.32 312	81.42 57	90.21 35	96.91 24	62.41 139	97.67 61	94.48 18	80.56 237	92.90 212

miper_ehance_all_eth		77.60 267	76.44 261	81.09 306	85.70 308	64.41 222	90.65 272	88.64 349	72.31 247	67.37 332	82.52 341	64.77 97	92.64 339	70.67 256	65.30 359	86.24 350

KD-MVS_2432*1600		69.03 373	66.37 376	77.01 377	85.56 309	61.06 327	81.44 412	90.25 273	67.27 341	58.00 411	76.53 418	54.49 253	87.63 418	48.04 409	35.77 474	82.34 412

miper_refine_blended		69.03 373	66.37 376	77.01 377	85.56 309	61.06 327	81.44 412	90.25 273	67.27 341	58.00 411	76.53 418	54.49 253	87.63 418	48.04 409	35.77 474	82.34 412

SD_0403		73.79 328	73.48 311	74.69 397	85.33 311	45.56 460	83.80 384	85.57 405	76.55 171	62.96 373	88.45 256	50.62 301	87.59 420	48.80 405	79.28 253	90.92 270

EI-MVSNet		78.97 236	78.22 227	81.25 296	85.33 311	62.73 286	89.53 310	93.21 105	72.39 246	72.14 262	90.13 229	60.99 155	94.72 244	67.73 288	72.49 307	86.29 348

CVMVSNet		74.04 324	74.27 296	73.33 410	85.33 311	43.94 464	89.53 310	88.39 355	54.33 438	70.37 284	90.13 229	49.17 319	84.05 441	61.83 349	79.36 249	91.99 244

test_fmvsmconf_n		86.58 56	87.17 46	84.82 161	85.28 314	62.55 289	94.26 74	89.78 292	83.81 27	87.78 52	96.33 43	65.33 88	96.98 115	94.40 20	87.55 138	94.95 100

fmvsm_s_conf0.1_n_2		84.40 108	84.78 95	83.27 235	85.25 315	60.41 346	94.13 79	85.69 404	83.05 34	87.99 49	96.37 39	52.75 276	97.68 59	93.75 26	84.05 190	91.71 251

ACMH	63.93 17	68.62 376	64.81 387	80.03 329	85.22 316	63.25 269	87.72 348	84.66 413	60.83 404	51.57 438	79.43 390	27.29 448	94.96 234	41.76 439	64.84 367	81.88 417
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl____		76.07 293	74.67 286	80.28 321	85.15 317	61.76 310	90.12 290	88.73 344	71.16 285	65.43 347	81.57 357	61.15 153	92.95 321	66.54 302	62.17 391	86.13 354

DIV-MVS_self_test		76.07 293	74.67 286	80.28 321	85.14 318	61.75 311	90.12 290	88.73 344	71.16 285	65.42 348	81.60 356	61.15 153	92.94 325	66.54 302	62.16 393	86.14 352

TAMVS		80.37 206	79.45 206	83.13 240	85.14 318	63.37 266	91.23 248	90.76 248	74.81 193	72.65 250	88.49 255	60.63 162	92.95 321	69.41 266	81.95 218	93.08 205

MSDG		69.54 369	65.73 380	80.96 308	85.11 320	63.71 253	84.19 381	83.28 429	56.95 427	54.50 423	84.03 324	31.50 432	96.03 169	42.87 435	69.13 331	83.14 401

AstraMVS		80.66 199	79.79 199	83.28 234	85.07 321	61.64 314	92.19 184	90.58 255	79.40 103	74.77 221	90.18 220	45.93 357	95.61 203	83.04 131	76.96 276	92.60 221

c3_l		76.83 282	75.47 278	80.93 310	85.02 322	64.18 234	90.39 281	88.11 365	71.66 270	66.65 341	81.64 355	63.58 120	92.56 340	69.31 268	62.86 384	86.04 356

ACMP	71.68 10	75.58 307	74.23 297	79.62 343	84.97 323	59.64 361	90.80 264	89.07 328	70.39 299	62.95 374	87.30 281	38.28 393	93.87 293	72.89 228	71.45 315	85.36 374
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 258	78.08 229	77.70 365	84.89 324	55.51 407	90.27 286	93.75 82	76.87 157	66.80 339	87.59 276	65.71 84	90.23 390	62.89 342	73.94 296	87.37 320

PVSNet_0	68.08 15	71.81 351	68.32 367	82.27 264	84.68 325	62.31 296	88.68 330	90.31 269	75.84 176	57.93 413	80.65 374	37.85 400	94.19 272	69.94 261	29.05 483	90.31 278

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 172	84.67 326	63.29 268	94.04 85	89.99 287	82.88 36	87.85 51	96.03 53	62.89 134	96.36 150	94.15 21	89.95 111	94.48 143

eth_miper_zixun_eth		75.96 300	74.40 294	80.66 313	84.66 327	63.02 276	89.28 316	88.27 361	71.88 261	65.73 345	81.65 354	59.45 181	92.81 329	68.13 280	60.53 407	86.14 352

WR-MVS		76.76 284	75.74 276	79.82 337	84.60 328	62.27 297	92.60 164	92.51 142	76.06 174	67.87 323	85.34 308	56.76 223	90.24 389	62.20 346	63.69 380	86.94 329

ACMH+	65.35 16	67.65 386	64.55 390	76.96 379	84.59 329	57.10 394	88.08 339	80.79 435	58.59 419	53.00 431	81.09 369	26.63 450	92.95 321	46.51 418	61.69 400	80.82 426

UWE-MVS-28		76.83 282	77.60 240	74.51 400	84.58 330	50.34 434	88.22 338	94.60 49	74.46 195	66.66 340	88.98 252	62.53 137	85.50 435	57.55 370	80.80 235	87.69 314

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 311	84.52 331	60.10 354	93.35 126	90.35 265	83.41 31	86.54 63	96.27 45	60.50 164	90.02 395	94.84 16	90.38 104	92.61 220

VPNet		78.82 240	77.53 242	82.70 249	84.52 331	66.44 162	93.93 91	92.23 150	80.46 71	72.60 251	88.38 259	49.18 318	93.13 316	72.47 237	63.97 378	88.55 303

IterMVS-LS		76.49 286	75.18 283	80.43 318	84.49 333	62.74 285	90.64 273	88.80 342	72.40 245	65.16 350	81.72 353	60.98 156	92.27 353	67.74 287	64.65 371	86.29 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 254	77.55 241	79.98 331	84.46 334	60.26 350	92.25 180	93.20 107	77.50 146	68.88 305	86.61 291	66.10 78	92.13 356	66.38 305	62.55 387	87.54 315

FMVSNet5		68.04 383	65.66 382	75.18 392	84.43 335	57.89 381	83.54 386	86.26 393	61.83 397	53.64 429	73.30 432	37.15 407	85.08 436	48.99 403	61.77 396	82.56 411

MVS-HIRNet		60.25 425	55.55 432	74.35 402	84.37 336	56.57 400	71.64 455	74.11 454	34.44 477	45.54 461	42.24 485	31.11 436	89.81 396	40.36 446	76.10 282	76.67 458

LPG-MVS_test		75.82 302	74.58 290	79.56 345	84.31 337	59.37 366	90.44 278	89.73 297	69.49 311	64.86 351	88.42 257	38.65 389	94.30 267	72.56 235	72.76 304	85.01 378

LGP-MVS_train				79.56 345	84.31 337	59.37 366		89.73 297	69.49 311	64.86 351	88.42 257	38.65 389	94.30 267	72.56 235	72.76 304	85.01 378

ACMM	69.62 13	74.34 320	72.73 324	79.17 351	84.25 339	57.87 382	90.36 283	89.93 288	63.17 382	65.64 346	86.04 300	37.79 401	94.10 276	65.89 310	71.52 314	85.55 370
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 268	76.78 256	79.98 331	84.11 340	60.80 331	91.76 214	93.17 109	76.56 170	69.93 293	84.78 314	63.32 124	92.36 349	64.89 323	62.51 389	86.78 333

test_0402		64.54 404	61.09 410	74.92 396	84.10 341	60.75 335	87.95 343	79.71 440	52.03 442	52.41 433	77.20 407	32.21 430	91.64 367	23.14 479	61.03 403	72.36 467

LTVRE_ROB	59.60 19	66.27 395	63.54 398	74.45 401	84.00 342	51.55 425	67.08 468	83.53 425	58.78 417	54.94 422	80.31 378	34.54 418	93.23 314	40.64 445	68.03 339	78.58 449
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
viewmsd2359difaftdt		79.42 227	77.96 232	83.81 209	83.88 343	63.85 243	89.54 307	87.38 375	77.39 150	74.94 216	89.95 233	51.11 295	94.72 244	79.52 173	67.90 341	92.88 214

viewdifsd2359ckpt11		79.42 227	77.95 233	83.81 209	83.87 344	63.85 243	89.54 307	87.38 375	77.39 150	74.94 216	89.95 233	51.11 295	94.72 244	79.52 173	67.90 341	92.88 214

miper_lstm_enhance		73.05 334	71.73 337	77.03 376	83.80 345	58.32 379	81.76 407	88.88 338	69.80 309	61.01 385	78.23 397	57.19 215	87.51 422	65.34 320	59.53 412	85.27 377

Patchmatch-test		65.86 397	60.94 411	80.62 316	83.75 346	58.83 373	58.91 479	75.26 452	44.50 466	50.95 443	77.09 409	58.81 194	87.90 412	35.13 458	64.03 376	95.12 91

nrg030		80.93 193	79.86 197	84.13 198	83.69 347	68.83 82	93.23 129	91.20 208	75.55 180	75.06 214	88.22 265	63.04 131	94.74 243	81.88 146	66.88 349	88.82 298

GA-MVS		78.33 252	76.23 268	84.65 176	83.65 348	66.30 166	91.44 229	90.14 279	76.01 175	70.32 285	84.02 325	42.50 373	94.72 244	70.98 252	77.00 275	92.94 210

FMVSNet1		72.71 341	69.91 352	81.10 303	83.60 349	65.11 198	90.01 294	90.32 266	63.92 371	63.56 366	80.25 380	36.35 412	91.54 372	54.46 380	66.75 350	86.64 335

OPM-MVS		79.00 235	78.09 228	81.73 280	83.52 350	63.83 246	91.64 224	90.30 270	76.36 173	71.97 265	89.93 235	46.30 354	95.17 228	75.10 209	77.70 265	86.19 351
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		70.10 363	67.36 371	78.32 359	83.45 351	60.97 329	88.85 326	92.77 127	64.85 364	60.83 387	78.53 394	43.52 370	93.48 304	31.73 471	61.70 399	80.52 430

MonoMVSNet		76.99 277	75.08 284	82.73 247	83.32 352	63.24 270	86.47 366	86.37 390	79.08 113	66.31 342	79.30 391	49.80 311	91.72 365	79.37 175	65.70 357	93.23 198

Effi-MVS+-dtu		76.14 292	75.28 282	78.72 356	83.22 353	55.17 409	89.87 298	87.78 372	75.42 183	67.98 318	81.43 359	45.08 364	92.52 342	75.08 210	71.63 312	88.48 304

CR-MVSNet		73.79 328	70.82 344	82.70 249	83.15 354	67.96 109	70.25 457	84.00 420	73.67 217	69.97 291	72.41 437	57.82 210	89.48 399	52.99 388	73.13 301	90.64 274

RPMNet		70.42 361	65.68 381	84.63 179	83.15 354	67.96 109	70.25 457	90.45 257	46.83 459	69.97 291	65.10 462	56.48 231	95.30 223	35.79 457	73.13 301	90.64 274

DU-MVS		76.86 279	75.84 274	79.91 334	82.96 356	60.26 350	91.26 245	91.54 191	76.46 172	68.88 305	86.35 294	56.16 232	92.13 356	66.38 305	62.55 387	87.35 321

NR-MVSNet		76.05 296	74.59 289	80.44 317	82.96 356	62.18 299	90.83 263	91.73 181	77.12 153	60.96 386	86.35 294	59.28 186	91.80 363	60.74 353	61.34 402	87.35 321

fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 174	82.95 358	63.48 265	94.03 87	89.46 306	81.69 50	89.86 38	96.74 31	61.85 148	97.75 57	94.74 17	82.01 216	92.81 216

mmtdpeth		68.33 380	66.37 376	74.21 405	82.81 359	51.73 423	84.34 379	80.42 437	67.01 345	71.56 271	68.58 453	30.52 439	92.35 350	75.89 203	36.21 472	78.56 450

XXY-MVS		77.94 260	76.44 261	82.43 256	82.60 360	64.44 219	92.01 195	91.83 177	73.59 218	70.00 290	85.82 303	54.43 256	94.76 241	69.63 263	68.02 340	88.10 310

test_fmvsmvis_n_1920		83.80 129	83.48 117	84.77 165	82.51 361	63.72 252	91.37 237	83.99 422	81.42 57	77.68 180	95.74 59	58.37 200	97.58 69	93.38 27	86.87 145	93.00 209

TranMVSNet+NR-MVSNet		75.86 301	74.52 292	79.89 335	82.44 362	60.64 341	91.37 237	91.37 198	76.63 168	67.65 325	86.21 297	52.37 280	91.55 371	61.84 348	60.81 405	87.48 317

test_vis1_n_1920		81.66 176	82.01 159	80.64 314	82.24 363	55.09 410	94.76 53	86.87 385	81.67 51	84.40 87	94.63 98	38.17 394	94.67 250	91.98 41	83.34 199	92.16 241

IterMVS		72.65 344	70.83 342	78.09 363	82.17 364	62.96 278	87.64 351	86.28 392	71.56 278	60.44 392	78.85 393	45.42 361	86.66 426	63.30 338	61.83 395	84.65 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 388	63.93 396	78.34 358	82.12 365	64.38 223	68.72 461	84.00 420	48.23 456	59.24 400	72.41 437	57.82 210	89.27 400	46.10 421	56.68 423	81.36 420

PatchT		69.11 372	65.37 385	80.32 319	82.07 366	63.68 257	67.96 466	87.62 373	50.86 448	69.37 295	65.18 461	57.09 216	88.53 406	41.59 441	66.60 351	88.74 299

MIMVSNet		71.64 352	68.44 365	81.23 297	81.97 367	64.44 219	73.05 451	88.80 342	69.67 310	64.59 354	74.79 429	32.79 426	87.82 414	53.99 382	76.35 280	91.42 256

usedtu_dtu_shiyan1		77.89 263	76.39 264	82.40 260	81.92 368	67.01 143	91.94 201	93.00 118	77.01 154	68.44 314	84.15 321	54.78 249	93.25 312	65.76 313	70.53 320	86.94 329

FE-MVSNET3		77.89 263	76.39 264	82.40 260	81.92 368	67.01 143	91.94 201	93.00 118	77.01 154	68.44 314	84.15 321	54.78 249	93.25 312	65.76 313	70.53 320	86.94 329

MVP-Stereo		77.12 275	76.23 268	79.79 338	81.72 370	66.34 165	89.29 315	90.88 238	70.56 298	62.01 381	82.88 337	49.34 315	94.13 275	65.55 318	93.80 47	78.88 445
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
kuosan		60.86 422	60.24 412	62.71 451	81.57 371	46.43 456	75.70 447	85.88 400	57.98 420	48.95 450	69.53 451	58.42 199	76.53 467	28.25 475	35.87 473	65.15 474

IterMVS-SCA-FT		71.55 354	69.97 350	76.32 383	81.48 372	60.67 340	87.64 351	85.99 399	66.17 352	59.50 399	78.88 392	45.53 359	83.65 445	62.58 344	61.93 394	84.63 384

COLMAP_ROB	57.96 20	62.98 413	59.65 415	72.98 413	81.44 373	53.00 419	83.75 385	75.53 451	48.34 455	48.81 451	81.40 361	24.14 454	90.30 385	32.95 465	60.52 408	75.65 460
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 396	62.45 405	76.88 380	81.42 374	54.45 414	57.49 482	88.67 347	49.36 452	63.86 363	46.86 479	56.06 235	90.25 386	49.53 400	68.83 332	85.95 359

WR-MVS_H		70.59 359	69.94 351	72.53 416	81.03 375	51.43 426	87.35 354	92.03 165	67.38 340	60.23 396	80.70 371	55.84 238	83.45 448	46.33 420	58.58 417	82.72 406

Fast-Effi-MVS+-dtu		75.04 313	73.37 312	80.07 327	80.86 376	59.52 364	91.20 251	85.38 406	71.90 259	65.20 349	84.84 313	41.46 377	92.97 320	66.50 304	72.96 303	87.73 313

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 180	80.83 377	62.33 294	93.84 100	88.81 341	83.50 30	87.00 59	96.01 54	63.36 122	96.93 123	94.04 23	87.29 141	94.61 128

LuminaMVS		78.14 255	76.66 258	82.60 253	80.82 378	64.64 212	89.33 314	90.45 257	68.25 330	74.73 222	85.51 307	41.15 379	94.14 274	78.96 182	80.69 236	89.04 294

Baseline_NR-MVSNet		73.99 325	72.83 321	77.48 369	80.78 379	59.29 369	91.79 209	84.55 415	68.85 322	68.99 302	80.70 371	56.16 232	92.04 359	62.67 343	60.98 404	81.11 423

CP-MVSNet		70.50 360	69.91 352	72.26 419	80.71 380	51.00 430	87.23 356	90.30 270	67.84 335	59.64 398	82.69 339	50.23 305	82.30 456	51.28 391	59.28 413	83.46 395

v8		75.35 308	73.26 316	81.61 285	80.67 381	66.82 151	89.54 307	89.27 314	71.65 271	63.30 369	80.30 379	54.99 247	94.06 280	67.33 294	62.33 390	83.94 387

PS-MVSNAJss		77.26 272	76.31 266	80.13 326	80.64 382	59.16 370	90.63 275	91.06 226	72.80 235	68.58 311	84.57 317	53.55 267	93.96 288	72.97 227	71.96 311	87.27 324

TransMVSNet (Re)		70.07 364	67.66 369	77.31 373	80.62 383	59.13 371	91.78 211	84.94 411	65.97 355	60.08 397	80.44 376	50.78 298	91.87 361	48.84 404	45.46 456	80.94 425

Elysia		76.45 288	74.17 298	83.30 231	80.43 384	64.12 235	89.58 303	90.83 240	61.78 398	72.53 253	85.92 301	34.30 420	94.81 239	68.10 281	84.01 191	90.97 268

StellarMVS		76.45 288	74.17 298	83.30 231	80.43 384	64.12 235	89.58 303	90.83 240	61.78 398	72.53 253	85.92 301	34.30 420	94.81 239	68.10 281	84.01 191	90.97 268

v2v482		77.42 270	75.65 277	82.73 247	80.38 386	67.13 137	91.85 207	90.23 275	75.09 189	69.37 295	83.39 332	53.79 265	94.44 261	71.77 243	65.00 366	86.63 338

PS-CasMVS		69.86 367	69.13 361	72.07 423	80.35 387	50.57 433	87.02 358	89.75 294	67.27 341	59.19 402	82.28 344	46.58 349	82.24 457	50.69 394	59.02 414	83.39 397

v10		74.77 318	72.54 328	81.46 288	80.33 388	66.71 156	89.15 321	89.08 327	70.94 290	63.08 372	79.86 384	52.52 278	94.04 283	65.70 315	62.17 391	83.64 390

test0.0.03 1		72.76 339	72.71 325	72.88 414	80.25 389	47.99 446	91.22 249	89.45 307	71.51 280	62.51 379	87.66 274	53.83 263	85.06 437	50.16 397	67.84 345	85.58 368

fmvsm_s_conf0.1_n_a		84.76 100	84.84 93	84.53 182	80.23 390	63.50 264	92.79 149	88.73 344	80.46 71	89.84 39	96.65 34	60.96 157	97.57 71	93.80 25	80.14 239	92.53 225

v1144		76.73 285	74.88 285	82.27 264	80.23 390	66.60 159	91.68 222	90.21 278	73.69 215	69.06 300	81.89 350	52.73 277	94.40 263	69.21 269	65.23 363	85.80 364

v148		76.19 291	74.47 293	81.36 292	80.05 392	64.44 219	91.75 216	90.23 275	73.68 216	67.13 333	80.84 370	55.92 237	93.86 295	68.95 273	61.73 398	85.76 367

dmvs_testset		65.55 400	66.45 374	62.86 450	79.87 393	22.35 495	76.55 441	71.74 463	77.42 149	55.85 419	87.77 273	51.39 291	80.69 462	31.51 474	65.92 356	85.55 370

v1192		75.98 298	73.92 304	82.15 270	79.73 394	66.24 168	91.22 249	89.75 294	72.67 237	68.49 312	81.42 360	49.86 309	94.27 269	67.08 297	65.02 365	85.95 359

AllTest		61.66 416	58.06 420	72.46 417	79.57 395	51.42 427	80.17 424	68.61 470	51.25 446	45.88 457	81.23 363	19.86 468	86.58 427	38.98 449	57.01 421	79.39 439

TestCases				72.46 417	79.57 395	51.42 427		68.61 470	51.25 446	45.88 457	81.23 363	19.86 468	86.58 427	38.98 449	57.01 421	79.39 439

MDA-MVSNet-bldmvs		61.54 418	57.70 422	73.05 412	79.53 397	57.00 398	83.08 396	81.23 433	57.57 421	34.91 478	72.45 436	32.79 426	86.26 429	35.81 456	41.95 462	75.89 459

v144192		76.05 296	74.03 302	82.12 272	79.50 398	66.55 161	91.39 234	89.71 300	72.30 248	68.17 316	81.33 362	51.75 285	94.03 285	67.94 285	64.19 373	85.77 365

v1921920		75.63 306	73.49 310	82.06 276	79.38 399	66.35 164	91.07 257	89.48 305	71.98 256	67.99 317	81.22 365	49.16 320	93.90 291	66.56 301	64.56 372	85.92 362

PEN-MVS		69.46 370	68.56 363	72.17 421	79.27 400	49.71 438	86.90 360	89.24 315	67.24 344	59.08 403	82.51 342	47.23 339	83.54 447	48.42 407	57.12 419	83.25 398

v1240		75.21 311	72.98 320	81.88 278	79.20 401	66.00 173	90.75 267	89.11 325	71.63 275	67.41 330	81.22 365	47.36 338	93.87 293	65.46 319	64.72 370	85.77 365

pmmvs4		73.92 326	71.81 336	80.25 323	79.17 402	65.24 194	87.43 353	87.26 380	67.64 339	63.46 367	83.91 327	48.96 322	91.53 375	62.94 340	65.49 358	83.96 386

D2MVS		73.80 327	72.02 333	79.15 353	79.15 403	62.97 277	88.58 332	90.07 281	72.94 230	59.22 401	78.30 395	42.31 375	92.70 335	65.59 317	72.00 310	81.79 418

V42		76.46 287	74.55 291	82.19 269	79.14 404	67.82 114	90.26 287	89.42 309	73.75 213	68.63 310	81.89 350	51.31 292	94.09 277	71.69 245	64.84 367	84.66 381

pm-mvs1		72.89 337	71.09 341	78.26 361	79.10 405	57.62 386	90.80 264	89.30 313	67.66 337	62.91 375	81.78 352	49.11 321	92.95 321	60.29 357	58.89 415	84.22 385

our_test_3		68.29 381	64.69 389	79.11 354	78.92 406	64.85 205	88.40 335	85.06 409	60.32 408	52.68 432	76.12 422	40.81 381	89.80 398	44.25 430	55.65 424	82.67 410

ppachtmachnet_test		67.72 385	63.70 397	79.77 339	78.92 406	66.04 172	88.68 330	82.90 431	60.11 410	55.45 420	75.96 423	39.19 386	90.55 382	39.53 447	52.55 435	82.71 407

test_fmvs1		74.07 323	73.69 307	75.22 390	78.91 408	47.34 450	89.06 324	74.69 453	63.68 375	79.41 158	91.59 188	24.36 453	87.77 416	85.22 98	76.26 281	90.55 276

TinyColmap		60.32 424	56.42 431	72.00 424	78.78 409	53.18 418	78.36 435	75.64 449	52.30 441	41.59 472	75.82 425	14.76 476	88.35 409	35.84 455	54.71 429	74.46 461

SixPastTwentyTwo		64.92 402	61.78 409	74.34 403	78.74 410	49.76 437	83.42 391	79.51 441	62.86 384	50.27 444	77.35 404	30.92 437	90.49 384	45.89 422	47.06 450	82.78 403

EG-PatchMatch MVS		68.55 377	65.41 384	77.96 364	78.69 411	62.93 279	89.86 299	89.17 319	60.55 405	50.27 444	77.73 402	22.60 461	94.06 280	47.18 416	72.65 306	76.88 457

pmmvs5		73.35 331	71.52 338	78.86 355	78.64 412	60.61 342	91.08 255	86.90 384	67.69 336	63.32 368	83.64 328	44.33 367	90.53 383	62.04 347	66.02 354	85.46 372

UniMVSNet_ETH3D		72.74 340	70.53 347	79.36 347	78.62 413	56.64 399	85.01 374	89.20 317	63.77 373	64.84 353	84.44 319	34.05 422	91.86 362	63.94 332	70.89 319	89.57 289

tt0320-xc		61.51 419	56.89 428	75.37 389	78.50 414	58.61 376	82.61 403	71.27 466	44.31 467	53.17 430	68.03 457	23.38 457	88.46 407	47.77 413	43.00 461	79.03 444

XVG-OURS		74.25 322	72.46 329	79.63 342	78.45 415	57.59 388	80.33 421	87.39 374	63.86 372	68.76 308	89.62 238	40.50 382	91.72 365	69.00 272	74.25 293	89.58 288

tt0805		73.07 333	70.73 345	80.07 327	78.37 416	57.05 395	87.78 347	92.18 157	61.23 402	67.04 334	86.49 293	31.35 434	94.58 251	65.06 322	67.12 347	88.57 302

test_cas_vis1_n_1920		80.45 204	80.61 183	79.97 333	78.25 417	57.01 397	94.04 85	88.33 358	79.06 115	82.81 106	93.70 129	38.65 389	91.63 368	90.82 53	79.81 241	91.27 264

XVG-OURS-SEG-HR		74.70 319	73.08 317	79.57 344	78.25 417	57.33 392	80.49 419	87.32 377	63.22 380	68.76 308	90.12 231	44.89 365	91.59 369	70.55 258	74.09 295	89.79 285

MDA-MVSNet_test_wron		63.78 410	60.16 413	74.64 398	78.15 419	60.41 346	83.49 388	84.03 418	56.17 434	39.17 474	71.59 444	37.22 405	83.24 451	42.87 435	48.73 446	80.26 434

YYNet1		63.76 411	60.14 414	74.62 399	78.06 420	60.19 353	83.46 390	83.99 422	56.18 433	39.25 473	71.56 445	37.18 406	83.34 449	42.90 434	48.70 447	80.32 433

DTE-MVSNet		68.46 379	67.33 372	71.87 425	77.94 421	49.00 443	86.16 368	88.58 351	66.36 349	58.19 408	82.21 346	46.36 350	83.87 444	44.97 428	55.17 426	82.73 405

USDC		67.43 390	64.51 391	76.19 384	77.94 421	55.29 408	78.38 434	85.00 410	73.17 223	48.36 452	80.37 377	21.23 463	92.48 344	52.15 390	64.02 377	80.81 427

sc_t1		63.81 409	59.39 417	77.10 375	77.62 423	56.03 403	84.32 380	73.56 457	46.66 460	58.22 407	73.06 433	23.28 459	90.62 381	50.93 393	46.84 451	84.64 383

tt0320		61.85 415	57.45 424	75.03 393	77.49 424	57.60 387	82.74 401	73.65 456	43.65 470	53.65 428	68.18 455	25.47 452	88.66 402	45.56 424	46.68 452	78.81 447

jajsoiax		73.05 334	71.51 339	77.67 366	77.46 425	54.83 411	88.81 328	90.04 284	69.13 318	62.85 376	83.51 330	31.16 435	92.75 332	70.83 253	69.80 322	85.43 373

mvs_tets		72.71 341	71.11 340	77.52 367	77.41 426	54.52 413	88.45 334	89.76 293	68.76 325	62.70 377	83.26 334	29.49 441	92.71 333	70.51 259	69.62 324	85.34 375

N_pmnet		50.55 439	49.11 441	54.88 459	77.17 427	4.02 503	84.36 378	2.00 501	48.59 453	45.86 459	68.82 452	32.22 429	82.80 453	31.58 472	51.38 437	77.81 454

test_djsdf		73.76 330	72.56 327	77.39 371	77.00 428	53.93 415	89.07 322	90.69 249	65.80 357	63.92 362	82.03 348	43.14 372	92.67 336	72.83 229	68.53 335	85.57 369

OpenMVS_ROB	61.12 18	66.39 394	62.92 402	76.80 381	76.51 429	57.77 383	89.22 317	83.41 427	55.48 435	53.86 427	77.84 400	26.28 451	93.95 289	34.90 459	68.76 333	78.68 448

v7n		71.31 355	68.65 362	79.28 349	76.40 430	60.77 333	86.71 363	89.45 307	64.17 370	58.77 406	78.24 396	44.59 366	93.54 302	57.76 367	61.75 397	83.52 393

K. test v3		63.09 412	59.61 416	73.53 409	76.26 431	49.38 442	83.27 392	77.15 444	64.35 367	47.77 454	72.32 439	28.73 443	87.79 415	49.93 399	36.69 471	83.41 396

RPSCF		64.24 406	61.98 408	71.01 428	76.10 432	45.00 461	75.83 446	75.94 447	46.94 458	58.96 404	84.59 316	31.40 433	82.00 458	47.76 414	60.33 411	86.04 356

OurMVSNet-221017-0		64.68 403	62.17 407	72.21 420	76.08 433	47.35 449	80.67 418	81.02 434	56.19 432	51.60 437	79.66 388	27.05 449	88.56 405	53.60 386	53.63 431	80.71 428

dongtai		55.18 435	55.46 433	54.34 461	76.03 434	36.88 479	76.07 444	84.61 414	51.28 445	43.41 469	64.61 464	56.56 229	67.81 480	18.09 484	28.50 484	58.32 477

blend_shiyan4		75.18 312	73.00 319	81.69 283	75.62 435	64.75 206	91.78 211	91.06 226	65.89 356	61.35 383	77.39 403	62.16 143	93.71 297	68.18 278	63.60 381	86.61 340

wanda-best-256-512		72.42 346	69.43 356	81.37 290	75.39 436	64.24 231	91.58 225	91.09 220	66.36 349	60.64 388	76.86 412	47.20 340	93.47 305	64.80 324	50.98 439	86.40 342

FE-blended-shiyan7		72.42 346	69.43 356	81.37 290	75.39 436	64.24 231	91.58 225	91.09 220	66.36 349	60.64 388	76.86 412	47.20 340	93.47 305	64.80 324	50.98 439	86.40 342

usedtu_blend_shiyan5		71.06 357	67.54 370	81.62 284	75.39 436	64.75 206	85.67 370	86.47 389	56.48 431	60.64 388	76.85 414	47.20 340	93.71 297	68.18 278	50.98 439	86.40 342

test_fmvsmconf0.01_n		83.70 133	83.52 113	84.25 195	75.26 439	61.72 312	92.17 185	87.24 381	82.36 43	84.91 82	95.41 68	55.60 239	96.83 130	92.85 31	85.87 161	94.21 156

blended_shiyan6		72.26 348	69.26 359	81.27 295	75.24 440	64.00 241	91.37 237	91.06 226	66.12 353	60.34 394	76.75 415	46.82 343	93.45 308	64.61 326	50.98 439	86.37 345

blended_shiyan8		72.26 348	69.25 360	81.29 294	75.23 441	64.03 238	91.36 240	91.04 230	66.11 354	60.42 393	76.73 416	46.79 345	93.45 308	64.58 328	51.00 438	86.37 345

Anonymous20231206		67.53 388	65.78 379	72.79 415	74.95 442	47.59 448	88.23 337	87.32 377	61.75 400	58.07 410	77.29 406	37.79 401	87.29 424	42.91 433	63.71 379	83.48 394

EGC-MVSNET		42.35 446	38.09 449	55.11 458	74.57 443	46.62 455	71.63 456	55.77 482	0.04 496	0.24 497	62.70 468	14.24 477	74.91 471	17.59 485	46.06 455	43.80 482

ITE_SJBPF				70.43 430	74.44 444	47.06 453		77.32 443	60.16 409	54.04 426	83.53 329	23.30 458	84.01 442	43.07 432	61.58 401	80.21 436

EU-MVSNet		64.01 407	63.01 401	67.02 444	74.40 445	38.86 478	83.27 392	86.19 395	45.11 464	54.27 424	81.15 368	36.91 410	80.01 464	48.79 406	57.02 420	82.19 415

XVG-ACMP-BASELINE		68.04 383	65.53 383	75.56 387	74.06 446	52.37 420	78.43 433	85.88 400	62.03 393	58.91 405	81.21 367	20.38 466	91.15 379	60.69 354	68.18 337	83.16 400

mvsany_test1		68.77 375	68.56 363	69.39 434	73.57 447	45.88 459	80.93 417	60.88 481	59.65 412	71.56 271	90.26 219	43.22 371	75.05 469	74.26 219	62.70 386	87.25 325

CL-MVSNet_self_test		69.92 365	68.09 368	75.41 388	73.25 448	55.90 405	90.05 293	89.90 289	69.96 306	61.96 382	76.54 417	51.05 297	87.64 417	49.51 401	50.59 444	82.70 408

anonymousdsp		71.14 356	69.37 358	76.45 382	72.95 449	54.71 412	84.19 381	88.88 338	61.92 395	62.15 380	79.77 386	38.14 396	91.44 377	68.90 274	67.45 346	83.21 399

lessismore_v0				73.72 408	72.93 450	47.83 447		61.72 480		45.86 459	73.76 431	28.63 445	89.81 396	47.75 415	31.37 479	83.53 392

pmmvs6		67.57 387	64.76 388	76.00 386	72.82 451	53.37 417	88.71 329	86.78 388	53.19 440	57.58 415	78.03 399	35.33 416	92.41 346	55.56 376	54.88 428	82.21 414

testgi		64.48 405	62.87 403	69.31 435	71.24 452	40.62 472	85.49 371	79.92 439	65.36 361	54.18 425	83.49 331	23.74 456	84.55 438	41.60 440	60.79 406	82.77 404

Patchmatch-RL test		68.17 382	64.49 392	79.19 350	71.22 453	53.93 415	70.07 459	71.54 465	69.22 315	56.79 417	62.89 466	56.58 228	88.61 403	69.53 265	52.61 434	95.03 97

test_fmvs1_n		72.69 343	71.92 334	74.99 395	71.15 454	47.08 452	87.34 355	75.67 448	63.48 377	78.08 177	91.17 202	20.16 467	87.87 413	84.65 107	75.57 285	90.01 282

Gipuma		34.91 453	31.44 456	45.30 469	70.99 455	39.64 477	19.85 492	72.56 460	20.10 487	16.16 491	21.47 492	5.08 491	71.16 475	13.07 489	43.70 459	25.08 489
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 398	63.10 400	73.88 406	70.71 456	50.29 436	81.09 415	89.88 290	72.58 239	49.25 449	74.77 430	32.57 428	87.43 423	55.96 375	41.04 464	83.90 388

CMPMVS	48.56 21	66.77 393	64.41 393	73.84 407	70.65 457	50.31 435	77.79 438	85.73 403	45.54 462	44.76 463	82.14 347	35.40 415	90.14 392	63.18 339	74.54 290	81.07 424
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 408	62.65 404	67.38 443	70.58 458	39.94 474	86.57 364	84.17 417	63.29 379	51.86 436	77.30 405	37.09 408	82.47 454	38.87 451	54.13 430	79.73 437

FE-MVSNET2		66.80 392	64.06 395	75.03 393	69.84 459	57.11 393	86.57 364	88.57 352	67.94 334	50.97 442	72.16 441	33.79 423	87.55 421	53.94 383	52.74 432	80.45 431

MIMVSNet1		60.16 426	57.33 425	68.67 437	69.71 460	44.13 463	78.92 431	84.21 416	55.05 436	44.63 464	71.85 442	23.91 455	81.54 460	32.63 469	55.03 427	80.35 432

test_vis1_n		71.63 353	70.73 345	74.31 404	69.63 461	47.29 451	86.91 359	72.11 461	63.21 381	75.18 213	90.17 226	20.40 465	85.76 431	84.59 108	74.42 292	89.87 283

pmmvs-eth3d		65.53 401	62.32 406	75.19 391	69.39 462	59.59 362	82.80 400	83.43 426	62.52 388	51.30 440	72.49 435	32.86 425	87.16 425	55.32 377	50.73 443	78.83 446

UnsupCasMVSNet_bld		61.60 417	57.71 421	73.29 411	68.73 463	51.64 424	78.61 432	89.05 330	57.20 426	46.11 456	61.96 470	28.70 444	88.60 404	50.08 398	38.90 469	79.63 438

test_vis1_rt		59.09 429	57.31 426	64.43 447	68.44 464	46.02 458	83.05 398	48.63 490	51.96 443	49.57 447	63.86 465	16.30 471	80.20 463	71.21 251	62.79 385	67.07 473

FE-MVSNET		60.52 423	57.18 427	70.53 429	67.53 465	50.68 432	82.62 402	76.28 445	59.33 415	46.71 455	71.10 448	30.54 438	83.61 446	33.15 464	47.37 449	77.29 456

Anonymous20240521		62.09 414	59.08 418	71.10 427	67.19 466	48.72 444	83.91 383	85.23 408	50.38 449	47.84 453	71.22 447	20.74 464	85.51 434	46.47 419	58.75 416	79.06 442

mvs5depth		61.03 420	57.65 423	71.18 426	67.16 467	47.04 454	72.74 452	77.49 442	57.47 424	60.52 391	72.53 434	22.84 460	88.38 408	49.15 402	38.94 468	78.11 453

test_fmvs2		65.78 399	64.84 386	68.60 438	66.54 468	41.71 469	83.27 392	69.81 468	54.38 437	67.91 320	84.54 318	15.35 473	81.22 461	75.65 205	66.16 353	82.88 402

KD-MVS_self_test		60.87 421	58.60 419	67.68 441	66.13 469	39.93 475	75.63 448	84.70 412	57.32 425	49.57 447	68.45 454	29.55 440	82.87 452	48.09 408	47.94 448	80.25 435

new-patchmatchnet		59.30 428	56.48 430	67.79 440	65.86 470	44.19 462	82.47 404	81.77 432	59.94 411	43.65 468	66.20 460	27.67 447	81.68 459	39.34 448	41.40 463	77.50 455

MVStest1		51.35 438	46.89 442	64.74 446	65.06 471	51.10 429	67.33 467	72.58 459	30.20 481	35.30 476	74.82 428	27.70 446	69.89 477	24.44 478	24.57 485	73.22 463

PM-MVS		59.40 427	56.59 429	67.84 439	63.63 472	41.86 467	76.76 440	63.22 478	59.01 416	51.07 441	72.27 440	11.72 480	83.25 450	61.34 350	50.28 445	78.39 451

DSMNet-mixed		56.78 432	54.44 435	63.79 448	63.21 473	29.44 490	64.43 471	64.10 477	42.12 474	51.32 439	71.60 443	31.76 431	75.04 470	36.23 454	65.20 364	86.87 332

new_pmnet		49.31 440	46.44 443	57.93 454	62.84 474	40.74 471	68.47 463	62.96 479	36.48 476	35.09 477	57.81 474	14.97 475	72.18 474	32.86 467	46.44 453	60.88 476

LF4IMVS		54.01 436	52.12 437	59.69 453	62.41 475	39.91 476	68.59 462	68.28 472	42.96 472	44.55 465	75.18 426	14.09 478	68.39 479	41.36 442	51.68 436	70.78 468

WB-MVS		46.23 443	44.94 445	50.11 464	62.13 476	21.23 497	76.48 442	55.49 483	45.89 461	35.78 475	61.44 472	35.54 414	72.83 473	9.96 491	21.75 486	56.27 479

ttmdpeth		53.34 437	49.96 440	63.45 449	62.07 477	40.04 473	72.06 453	65.64 475	42.54 473	51.88 435	77.79 401	13.94 479	76.48 468	32.93 466	30.82 482	73.84 462

ambc				69.61 433	61.38 478	41.35 470	49.07 487	85.86 402		50.18 446	66.40 459	10.16 482	88.14 411	45.73 423	44.20 457	79.32 441

SSC-MVS		44.51 445	43.35 447	47.99 468	61.01 479	18.90 499	74.12 450	54.36 484	43.42 471	34.10 479	60.02 473	34.42 419	70.39 476	9.14 493	19.57 487	54.68 480

TDRefinement		55.28 434	51.58 438	66.39 445	59.53 480	46.15 457	76.23 443	72.80 458	44.60 465	42.49 470	76.28 421	15.29 474	82.39 455	33.20 463	43.75 458	70.62 469

pmmvs3		55.51 433	51.50 439	67.53 442	57.90 481	50.93 431	80.37 420	73.66 455	40.63 475	44.15 466	64.75 463	16.30 471	78.97 466	44.77 429	40.98 466	72.69 465

usedtu_dtu_shiyan2		57.76 430	53.69 436	69.95 432	57.60 482	41.80 468	83.50 387	83.67 424	45.26 463	43.79 467	62.82 467	17.63 470	85.93 430	42.56 438	46.40 454	82.12 416

test_method		38.59 451	35.16 454	48.89 466	54.33 483	21.35 496	45.32 488	53.71 485	7.41 493	28.74 481	51.62 477	8.70 485	52.87 491	33.73 460	32.89 478	72.47 466

test_fmvs3		56.82 431	54.86 434	62.69 452	53.59 484	35.47 481	75.87 445	65.64 475	43.91 468	55.10 421	71.43 446	6.91 488	74.40 472	68.64 276	52.63 433	78.20 452

APD_test1		40.50 448	37.31 451	50.09 465	51.88 485	35.27 482	59.45 478	52.59 486	21.64 485	26.12 483	57.80 475	4.56 492	66.56 482	22.64 480	39.09 467	48.43 481

DeepMVS_CX				34.71 474	51.45 486	24.73 494		28.48 500	31.46 480	17.49 490	52.75 476	5.80 490	42.60 495	18.18 483	19.42 488	36.81 487

FPMVS		45.64 444	43.10 448	53.23 462	51.42 487	36.46 480	64.97 470	71.91 462	29.13 482	27.53 482	61.55 471	9.83 483	65.01 486	16.00 488	55.58 425	58.22 478

wuyk23d		11.30 462	10.95 465	12.33 478	48.05 488	19.89 498	25.89 491	1.92 502	3.58 494	3.12 496	1.37 496	0.64 500	15.77 497	6.23 496	7.77 495	1.35 493

PMMVS2		37.93 452	33.61 455	50.92 463	46.31 489	24.76 493	60.55 477	50.05 487	28.94 483	20.93 485	47.59 478	4.41 494	65.13 485	25.14 477	18.55 489	62.87 475

mvsany_test3		48.86 441	46.35 444	56.41 455	46.00 490	31.67 486	62.26 473	47.25 491	43.71 469	45.54 461	68.15 456	10.84 481	64.44 488	57.95 366	35.44 476	73.13 464

test_f		46.58 442	43.45 446	55.96 456	45.18 491	32.05 485	61.18 474	49.49 489	33.39 478	42.05 471	62.48 469	7.00 487	65.56 484	47.08 417	43.21 460	70.27 470

test_vis3_rt		40.46 449	37.79 450	48.47 467	44.49 492	33.35 484	66.56 469	32.84 498	32.39 479	29.65 480	39.13 488	3.91 495	68.65 478	50.17 396	40.99 465	43.40 483

E-PMN		24.61 457	24.00 461	26.45 475	43.74 493	18.44 500	60.86 475	39.66 494	15.11 490	9.53 494	22.10 491	6.52 489	46.94 493	8.31 494	10.14 491	13.98 491

testf1		32.77 454	29.47 457	42.67 471	41.89 494	30.81 487	52.07 483	43.45 492	15.45 488	18.52 488	44.82 482	2.12 496	58.38 489	16.05 486	30.87 480	38.83 484

APD_test2		32.77 454	29.47 457	42.67 471	41.89 494	30.81 487	52.07 483	43.45 492	15.45 488	18.52 488	44.82 482	2.12 496	58.38 489	16.05 486	30.87 480	38.83 484

EMVS		23.76 459	23.20 463	25.46 476	41.52 496	16.90 501	60.56 476	38.79 497	14.62 491	8.99 495	20.24 494	7.35 486	45.82 494	7.25 495	9.46 492	13.64 492

LCM-MVSNet		40.54 447	35.79 452	54.76 460	36.92 497	30.81 487	51.41 485	69.02 469	22.07 484	24.63 484	45.37 481	4.56 492	65.81 483	33.67 461	34.50 477	67.67 471

ANet_high		40.27 450	35.20 453	55.47 457	34.74 498	34.47 483	63.84 472	71.56 464	48.42 454	18.80 487	41.08 486	9.52 484	64.45 487	20.18 482	8.66 494	67.49 472

MVE	24.84 23	24.35 458	19.77 464	38.09 473	34.56 499	26.92 492	26.57 490	38.87 496	11.73 492	11.37 493	27.44 489	1.37 499	50.42 492	11.41 490	14.60 490	36.93 486
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 456	28.16 459	42.89 470	25.87 500	27.58 491	50.92 486	49.78 488	21.37 486	14.17 492	40.81 487	2.01 498	66.62 481	9.61 492	38.88 470	34.49 488
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 460	23.75 462	17.80 477	5.23 501	12.06 502	35.26 489	39.48 495	2.82 495	18.94 486	44.20 484	22.23 462	24.64 496	36.30 453	9.31 493	16.69 490

testmvs		7.23 464	9.62 467	0.06 480	0.04 502	0.02 505	84.98 375	0.02 503	0.03 497	0.18 498	1.21 497	0.01 502	0.02 498	0.14 497	0.01 496	0.13 495

test123		6.92 465	9.21 468	0.08 479	0.03 503	0.05 504	81.65 410	0.01 504	0.02 498	0.14 499	0.85 498	0.03 501	0.02 498	0.12 498	0.00 497	0.16 494

mmdepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

monomultidepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

test_blank		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

eth-test2					0.00 504

eth-test					0.00 504

uanet_test		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

DCPMVS		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

cdsmvs_eth3d_5k		19.86 461	26.47 460	0.00 481	0.00 504	0.00 506	0.00 493	93.45 96	0.00 499	0.00 500	95.27 77	49.56 313	0.00 500	0.00 499	0.00 497	0.00 496

pcd_1.5k_mvsjas		4.46 466	5.95 469	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	53.55 267	0.00 500	0.00 499	0.00 497	0.00 496

sosnet-low-res		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

sosnet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

uncertanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

Regformer		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

ab-mvs-re		7.91 463	10.55 466	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	94.95 87	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

uanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 497	0.00 496

TestfortrainingZip							94.47 62

WAC-MVS						49.45 440								31.56 473

PC_three_1452									80.91 65	94.07 2	96.83 29	83.57 4	99.12 5	95.70 10	97.42 4	97.55 4

test_241102_TWO								94.41 58	71.65 271	92.07 11	97.21 9	74.58 20	99.11 6	92.34 36	95.36 14	96.59 19

test_0728_THIRD									72.48 241	90.55 30	96.93 21	76.24 13	99.08 11	91.53 47	94.99 18	96.43 31

GSMVS																94.68 122

sam_mvs1												57.85 209				94.68 122

sam_mvs												54.91 248

MTGPA								92.23 150

test_post1							78.95 430				20.70 493	53.05 272	91.50 376	60.43 355

test_post											23.01 490	56.49 230	92.67 336

patchmatchnet-post											67.62 458	57.62 212	90.25 386

MTMP							93.77 104	32.52 499

test9_res														89.41 57	94.96 19	95.29 80

agg_prior2														86.41 88	94.75 30	95.33 76

test_prior4						67.18 135	93.92 93

test_prior2							95.10 39		75.40 184	85.25 81	95.61 62	67.94 62		87.47 76	94.77 26

旧先验2							92.00 198		59.37 414	87.54 55			93.47 305	75.39 207

新几何2							91.41 230

无先验							92.71 153	92.61 139	62.03 393				97.01 110	66.63 300		93.97 170

原ACMM2							92.01 195

testdata2													96.09 163	61.26 351

segment_acmp												65.94 80

testdata1							89.21 318		77.55 145

plane_prior5								91.31 200					95.55 209	76.74 195	78.53 260	88.39 306

plane_prior4											89.14 247

plane_prior3						61.95 304			79.09 112	72.53 253

plane_prior2							93.13 132		78.81 119

plane_prior						62.42 291	93.85 97		79.38 104						78.80 257

n2								0.00 505

nn								0.00 505

door-mid								66.01 474

test11								93.01 116

door								66.57 473

HQP5-MVS						63.66 258

BP-MVS														77.63 192

HQP4-MVS										74.18 226			95.61 203			88.63 300

HQP3-MVS								91.70 186							78.90 255

HQP2-MVS												51.63 287

MDTV_nov1_ep13_2view						59.90 358	80.13 425		67.65 338	72.79 247		54.33 258		59.83 359		92.58 223

ACMMP++_ref															71.63 312

ACMMP++															69.72 323

Test By Simon												54.21 261

High-resolution multi-view benchmark