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.

fmvsm_s_conf0.5_n_8		94.52 30	95.04 24	92.96 108	95.15 159	81.14 187	99.09 20	96.66 90	95.53 3	97.84 10	98.71 22	76.33 159	99.81 28	99.24 1	96.85 108	97.92 111

MM		95.85 6	95.74 11	96.15 9	96.34 110	89.50 10	99.18 9	98.10 8	95.68 1	96.64 35	97.92 80	80.72 76	99.80 32	99.16 2	97.96 62	99.15 28

MGCNet		95.58 9	95.44 17	96.01 11	97.63 77	89.26 13	99.27 5	96.59 101	94.71 9	97.08 25	97.99 74	78.69 109	99.86 14	99.15 3	97.85 66	98.91 40

fmvsm_s_conf0.5_n_9		94.52 30	95.22 21	92.41 144	95.79 133	78.61 286	98.73 38	96.00 168	94.91 8	97.73 13	98.73 21	79.09 101	99.79 36	99.14 4	96.86 106	98.83 43

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 16				98.54 30	92.06 3	99.84 18	99.11 5	99.37 1	99.74 1

PC_three_1452									91.12 50	98.33 5	98.42 44	92.51 2	99.81 28	98.96 6	99.37 1	99.70 4

fmvsm_l_conf0.5_n_9		94.91 17	95.60 12	92.84 116	95.20 154	80.55 215	99.45 1	96.36 138	95.17 4	98.48 4	98.55 28	80.53 79	99.78 39	98.87 7	97.79 69	98.19 85

fmvsm_l_conf0.5_n_a		94.91 17	95.30 19	93.72 68	94.50 184	84.30 95	99.14 14	96.00 168	91.94 42	97.91 8	98.60 26	84.78 42	99.77 43	98.84 8	96.03 128	97.08 198

fmvsm_s_conf0.5_n_11		94.41 33	95.19 22	92.09 168	95.65 137	80.91 202	99.23 7	94.85 246	94.92 7	97.68 16	98.82 12	79.31 95	99.78 39	98.83 9	97.38 83	95.60 256

fmvsm_l_conf0.5_n		94.89 19	95.24 20	93.86 59	94.42 187	84.61 89	99.13 15	96.15 156	92.06 39	97.92 6	98.52 34	84.52 45	99.74 53	98.76 10	95.67 135	97.22 181

fmvsm_s_conf0.5_n_10		94.36 34	94.73 29	93.23 94	95.19 155	82.87 128	99.18 9	96.39 131	93.97 18	97.91 8	98.53 32	75.88 172	99.82 24	98.58 11	96.95 101	97.00 201

test_fmvsm_n_1920		94.81 23	95.60 12	92.45 139	95.29 150	80.96 199	99.29 4	97.21 26	94.50 13	97.29 23	98.44 41	82.15 68	99.78 39	98.56 12	97.68 72	96.61 223

fmvsm_s_conf0.5_n_2		92.97 63	93.38 61	91.73 194	94.10 200	80.64 210	98.96 30	95.89 182	94.09 16	97.05 26	98.40 45	68.92 277	99.80 32	98.53 13	94.50 149	94.74 283

fmvsm_s_conf0.5_n		93.69 48	94.13 45	92.34 148	94.56 176	82.01 155	99.07 22	97.13 34	92.09 37	96.25 40	98.53 32	76.47 154	99.80 32	98.39 14	94.71 145	95.22 270

fmvsm_s_conf0.5_n_5		93.57 52	93.75 48	93.01 105	92.87 251	82.73 131	98.93 32	95.90 181	90.96 55	95.61 49	98.39 46	76.57 152	99.63 73	98.32 15	96.24 120	96.68 222

fmvsm_s_conf0.5_n_7		92.88 67	93.82 47	90.08 260	92.79 255	76.45 346	98.54 48	96.74 77	92.28 34	95.22 54	98.49 36	74.91 196	98.15 176	98.28 16	97.13 93	95.63 254

fmvsm_l_conf0.5_n_3		94.61 26	94.92 27	93.68 72	94.52 179	82.80 130	99.33 2	96.37 136	95.08 6	97.59 20	98.48 38	77.40 132	99.79 36	98.28 16	97.21 89	98.44 68

fmvsm_s_conf0.5_n_3		93.95 45	94.53 33	92.20 162	94.41 188	80.04 238	98.90 33	95.96 173	94.53 12	97.63 19	98.58 27	75.95 169	99.79 36	98.25 18	96.60 114	96.77 216

patch_mono-2		95.14 15	96.08 7	92.33 150	98.44 48	77.84 315	98.43 52	97.21 26	92.58 29	97.68 16	97.65 98	86.88 30	99.83 22	98.25 18	97.60 74	99.33 19

test_fmvsmconf_n		93.99 44	94.36 39	92.86 113	92.82 252	81.12 188	99.26 6	96.37 136	93.47 22	95.16 55	98.21 56	79.00 102	99.64 71	98.21 20	96.73 112	97.83 120

fmvsm_s_conf0.1_n_2		92.26 96	92.48 82	91.60 202	92.29 278	80.55 215	98.73 38	94.33 295	93.80 20	96.18 42	98.11 65	66.93 295	99.75 50	98.19 21	93.74 163	94.50 290

SED-MVS		95.88 5	96.22 4	94.87 27	99.03 20	85.03 81	99.12 16	96.78 66	88.72 84	97.79 11	98.91 3	88.48 20	99.82 24	98.15 22	98.97 17	99.74 1

IU-MVS					99.03 20	85.34 66		96.86 61	92.05 41	98.74 2				98.15 22	98.97 17	99.42 14

test_241102_TWO								96.78 66	88.72 84	97.70 14	98.91 3	87.86 25	99.82 24	98.15 22	99.00 15	99.47 10

fmvsm_s_conf0.5_n_4		93.59 50	94.32 40	91.41 211	93.89 206	79.24 260	98.89 34	96.53 112	92.82 27	97.37 22	98.47 39	77.21 140	99.78 39	98.11 25	95.59 137	95.21 271

fmvsm_s_conf0.5_n_6		94.17 39	94.70 30	92.58 133	93.50 222	81.20 185	99.08 21	96.48 120	92.24 35	98.62 3	98.39 46	78.58 111	99.72 58	98.08 26	97.36 84	96.81 213

MSC_two_6792asdad				97.14 4	99.05 14	92.19 4		96.83 63					99.81 28	98.08 26	98.81 24	99.43 12

No_MVS				97.14 4	99.05 14	92.19 4		96.83 63					99.81 28	98.08 26	98.81 24	99.43 12

test_fmvsmconf0.1_n		93.08 61	93.22 64	92.65 126	88.45 381	80.81 205	99.00 28	95.11 231	93.21 24	94.00 76	97.91 82	76.84 146	99.59 77	97.91 29	96.55 116	97.54 149

DVP-MVS		95.58 9	95.91 9	94.57 37	99.05 14	85.18 72	99.06 23	96.46 121	88.75 82	96.69 32	98.76 18	87.69 26	99.76 45	97.90 30	98.85 21	98.77 46
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
test_0728_SECOND				95.14 22	99.04 19	86.14 44	99.06 23	96.77 72					99.84 18	97.90 30	98.85 21	99.45 11

fmvsm_s_conf0.5_n_a		93.34 56	93.71 50	92.22 159	93.38 225	81.71 173	98.86 35	96.98 47	91.64 43	96.85 30	98.55 28	75.58 178	99.77 43	97.88 32	93.68 164	95.18 272

fmvsm_s_conf0.1_n		92.93 65	93.16 65	92.24 156	90.52 337	81.92 161	98.42 54	96.24 148	91.17 49	96.02 45	98.35 51	75.34 189	99.74 53	97.84 33	94.58 147	95.05 275

DeepPCF-MVS	89.82 1	94.61 26	96.17 5	89.91 269	97.09 101	70.21 418	98.99 29	96.69 85	95.57 2	95.08 59	99.23 2	86.40 34	99.87 12	97.84 33	98.66 32	99.65 7

MED-MVS test				94.20 49	99.06 11	83.70 107	98.35 57	97.14 31	87.45 120	97.03 27	98.90 6		99.96 4	97.78 35	98.60 34	98.94 36

MED-MVS		95.43 12	95.84 10	94.20 49	99.06 11	83.70 107	98.35 57	97.14 31	85.79 174	97.03 27	98.90 6	89.87 13	99.96 4	97.78 35	98.60 34	98.94 36

ME-MVS		94.82 22	95.04 24	94.17 51	99.17 9	83.70 107	97.66 106	97.22 25	85.79 174	95.34 52	98.90 6	84.89 40	99.86 14	97.78 35	98.60 34	98.94 36

DPE-MVS		95.32 13	95.55 14	94.64 35	98.79 28	84.87 86	97.77 97	96.74 77	86.11 161	96.54 38	98.89 11	88.39 22	99.74 53	97.67 38	99.05 12	99.31 21
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNVR-MVS		96.30 1	96.54 1	95.55 17	99.31 6	87.69 26	99.06 23	97.12 36	94.66 10	96.79 31	98.78 15	86.42 33	99.95 7	97.59 39	99.18 7	99.00 33

DVP-MVS++		96.05 4	96.41 3	94.96 26	99.05 14	85.34 66	98.13 71	96.77 72	88.38 92	97.70 14	98.77 16	92.06 3	99.84 18	97.47 40	99.37 1	99.70 4

test_0728_THIRD									88.38 92	96.69 32	98.76 18	89.64 15	99.76 45	97.47 40	98.84 23	99.38 15

balanced_ft_v1		92.00 101	91.12 116	94.64 35	96.35 109	86.78 35	94.96 316	94.70 254	87.65 116	90.20 138	93.01 272	69.71 268	98.02 181	97.40 42	96.13 124	99.11 29

APDe-MVS		94.56 29	94.75 28	93.96 57	98.84 27	83.40 116	98.04 79	96.41 127	85.79 174	95.00 61	98.28 54	84.32 50	99.18 115	97.35 43	98.77 28	99.28 22
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
balanced_conf03		94.60 28	94.30 41	95.48 18	96.45 107	88.82 15	96.33 227	95.58 200	91.12 50	95.84 47	93.87 254	83.47 59	98.37 165	97.26 44	98.81 24	99.24 24

fmvsm_s_conf0.1_n_a		92.38 92	92.49 81	92.06 171	88.08 386	81.62 178	97.97 83	96.01 167	90.62 58	96.58 36	98.33 52	74.09 209	99.71 61	97.23 45	93.46 169	94.86 279

MCST-MVS		96.17 3	96.12 6	96.32 8	99.42 3	89.36 11	98.94 31	97.10 38	95.17 4	92.11 107	98.46 40	87.33 28	99.97 3	97.21 46	99.31 4	99.63 8

test_fmvsmconf0.01_n		91.08 128	90.68 124	92.29 153	82.43 445	80.12 235	97.94 84	93.93 323	92.07 38	91.97 109	97.60 101	67.56 287	99.53 85	97.09 47	95.56 138	97.21 184

CANet		94.89 19	94.64 32	95.63 15	97.55 83	88.12 20	99.06 23	96.39 131	94.07 17	95.34 52	97.80 89	76.83 148	99.87 12	97.08 48	97.64 73	98.89 41

test_fmvsmvis_n_1920		92.12 98	92.10 95	92.17 164	90.87 329	81.04 191	98.34 61	93.90 327	92.71 28	87.24 192	97.90 83	74.83 197	99.72 58	96.96 49	96.20 121	95.76 252

dcpmvs_2		93.10 60	93.46 59	92.02 174	97.77 72	79.73 248	94.82 321	93.86 330	86.91 141	91.33 120	96.76 143	85.20 38	98.06 178	96.90 50	97.60 74	98.27 80

PS-MVSNAJ		94.17 39	93.52 56	96.10 10	95.65 137	92.35 2	98.21 66	95.79 189	92.42 31	96.24 41	98.18 58	71.04 254	99.17 116	96.77 51	97.39 82	96.79 214

test_vis1_n_1920		89.95 159	90.59 125	88.03 319	92.36 268	68.98 427	99.12 16	94.34 292	93.86 19	93.64 81	97.01 133	51.54 411	99.59 77	96.76 52	96.71 113	95.53 260

xiu_mvs_v2_base		93.92 46	93.26 62	95.91 12	95.07 162	92.02 6	98.19 67	95.68 195	92.06 39	96.01 46	98.14 63	70.83 259	98.96 130	96.74 53	96.57 115	96.76 218

TSAR-MVS + GP.		94.35 35	94.50 34	93.89 58	97.38 95	83.04 124	98.10 73	95.29 225	91.57 44	93.81 78	97.45 107	86.64 31	99.43 93	96.28 54	94.01 155	99.20 26

SD-MVS		94.84 21	95.02 26	94.29 43	97.87 69	84.61 89	97.76 99	96.19 154	89.59 74	96.66 34	98.17 61	84.33 47	99.60 76	96.09 55	98.50 42	98.66 55
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
MSP-MVS		95.62 8	96.54 1	92.86 113	98.31 53	80.10 236	97.42 130	96.78 66	92.20 36	97.11 24	98.29 53	93.46 1	99.10 122	96.01 56	99.30 5	99.38 15
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
test9_res														96.00 57	99.03 13	98.31 76

9.14			94.26 43		98.10 62		98.14 68	96.52 113	84.74 208	94.83 65	98.80 13	82.80 66	99.37 97	95.95 58	98.42 46

train_agg		94.28 36	94.45 36	93.74 65	98.64 36	83.71 105	97.82 92	96.65 91	84.50 218	95.16 55	98.09 67	84.33 47	99.36 98	95.91 59	98.96 19	98.16 88

SMA-MVS		94.70 25	94.68 31	94.76 31	98.02 64	85.94 49	97.47 123	96.77 72	85.32 188	97.92 6	98.70 23	83.09 63	99.84 18	95.79 60	99.08 10	98.49 64
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
ETV-MVS		92.72 75	92.87 71	92.28 154	94.54 178	81.89 164	97.98 81	95.21 229	89.77 72	93.11 88	96.83 139	77.23 138	97.50 225	95.74 61	95.38 139	97.44 166

test_vis1_n		85.60 270	85.70 244	85.33 375	84.79 426	64.98 446	96.83 182	91.61 411	87.36 125	91.00 127	94.84 220	36.14 465	97.18 257	95.66 62	93.03 174	93.82 302

NCCC		95.63 7	95.94 8	94.69 34	99.21 7	85.15 77	99.16 11	96.96 51	94.11 15	95.59 50	98.64 25	85.07 39	99.91 8	95.61 63	99.10 9	99.00 33

test_cas_vis1_n_1920		89.90 160	90.02 147	89.54 279	90.14 349	74.63 371	98.71 40	94.43 284	93.04 26	92.40 99	96.35 153	53.41 407	99.08 124	95.59 64	96.16 122	94.90 277

TSAR-MVS + MP.		94.79 24	95.17 23	93.64 74	97.66 76	84.10 98	95.85 270	96.42 126	91.26 48	97.49 21	96.80 142	86.50 32	98.49 155	95.54 65	99.03 13	98.33 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZD-MVS					99.09 10	83.22 120		96.60 100	82.88 270	93.61 82	98.06 72	82.93 64	99.14 118	95.51 66	98.49 43

SF-MVS		94.17 39	94.05 46	94.55 38	97.56 82	85.95 47	97.73 101	96.43 125	84.02 235	95.07 60	98.74 20	82.93 64	99.38 95	95.42 67	98.51 40	98.32 74

reproduce-ours		92.70 78	93.02 66	91.75 191	97.45 86	77.77 319	96.16 242	95.94 177	84.12 231	92.45 96	98.43 42	80.06 87	99.24 104	95.35 68	97.18 90	98.24 82

our_new_method		92.70 78	93.02 66	91.75 191	97.45 86	77.77 319	96.16 242	95.94 177	84.12 231	92.45 96	98.43 42	80.06 87	99.24 104	95.35 68	97.18 90	98.24 82

test_fmvs1		87.79 224	88.52 184	85.62 370	92.98 243	64.31 448	97.88 89	92.42 393	87.95 105	92.24 102	95.82 163	47.94 429	98.44 162	95.31 70	94.09 152	94.09 297

reproduce_model		92.53 87	92.87 71	91.50 207	97.41 90	77.14 336	96.02 249	95.91 180	83.65 253	92.45 96	98.39 46	79.75 92	99.21 108	95.27 71	96.98 99	98.14 90

test_prior2							98.37 56		86.08 163	94.57 69	98.02 73	83.14 61		95.05 72	98.79 27

test_fmvs1_n		86.34 254	86.72 229	85.17 378	87.54 393	63.64 453	96.91 178	92.37 395	87.49 119	91.33 120	95.58 177	40.81 458	98.46 158	95.00 73	93.49 167	93.41 311

mvsmamba		90.53 147	90.08 143	91.88 181	94.81 170	80.93 200	93.94 347	94.45 281	88.24 98	87.02 198	92.35 281	68.04 280	95.80 330	94.86 74	97.03 98	98.92 39

SteuartSystems-ACMMP		94.13 42	94.44 37	93.20 96	95.41 145	81.35 183	99.02 27	96.59 101	89.50 76	94.18 74	98.36 50	83.68 58	99.45 92	94.77 75	98.45 45	98.81 45
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.32 13	95.48 16	94.85 28	98.62 39	86.04 45	97.81 94	96.93 54	92.45 30	95.69 48	98.50 35	85.38 37	99.85 16	94.75 76	99.18 7	98.65 56

PHI-MVS		93.59 50	93.63 52	93.48 85	98.05 63	81.76 170	98.64 44	97.13 34	82.60 277	94.09 75	98.49 36	80.35 80	99.85 16	94.74 77	98.62 33	98.83 43

APD-MVS		93.61 49	93.59 53	93.69 71	98.76 29	83.26 119	97.21 142	96.09 160	82.41 281	94.65 68	98.21 56	81.96 71	98.81 140	94.65 78	98.36 51	99.01 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
lupinMVS		93.87 47	93.58 54	94.75 32	93.00 239	88.08 21	99.15 12	95.50 207	91.03 53	94.90 62	97.66 94	78.84 105	97.56 212	94.64 79	97.46 77	98.62 58

MVSMamba_PlusPlus		92.37 93	91.55 105	94.83 29	95.37 147	87.69 26	95.60 284	95.42 216	74.65 398	93.95 77	92.81 274	83.11 62	97.70 200	94.49 80	98.53 39	99.11 29

agg_prior2														94.30 81	99.00 15	98.57 60

PVSNet_BlendedMVS		90.05 156	89.96 149	90.33 253	97.47 84	83.86 101	98.02 80	96.73 79	87.98 104	89.53 148	89.61 328	76.42 156	99.57 81	94.29 82	79.59 323	87.57 408

PVSNet_Blended		93.13 58	92.98 68	93.57 79	97.47 84	83.86 101	99.32 3	96.73 79	91.02 54	89.53 148	96.21 155	76.42 156	99.57 81	94.29 82	95.81 134	97.29 179

SPE-MVS-test		92.98 62	93.67 51	90.90 233	96.52 106	76.87 338	98.68 41	94.73 253	90.36 65	94.84 64	97.89 84	77.94 121	97.15 262	94.28 84	97.80 68	98.70 54

MSLP-MVS++		94.28 36	94.39 38	93.97 56	98.30 54	84.06 99	98.64 44	96.93 54	90.71 57	93.08 89	98.70 23	79.98 89	99.21 108	94.12 85	99.07 11	98.63 57

CHOSEN 280x420		91.71 111	91.85 98	91.29 216	94.94 166	82.69 132	87.89 433	96.17 155	85.94 171	87.27 191	94.31 236	90.27 9	95.65 342	94.04 86	95.86 132	95.53 260

lecture		93.17 57	93.57 55	91.96 176	97.80 70	78.79 281	98.50 50	96.98 47	86.61 152	94.75 67	98.16 62	78.36 115	99.35 100	93.89 87	97.12 94	97.75 127

CS-MVS		92.73 73	93.48 58	90.48 246	96.27 112	75.93 359	98.55 47	94.93 239	89.32 77	94.54 70	97.67 93	78.91 104	97.02 267	93.80 88	97.32 86	98.49 64

EC-MVSNet		91.73 108	92.11 94	90.58 242	93.54 216	77.77 319	98.07 76	94.40 287	87.44 122	92.99 91	97.11 127	74.59 203	96.87 284	93.75 89	97.08 96	97.11 191

SR-MVS		92.16 97	92.27 88	91.83 189	98.37 50	78.41 292	96.67 199	95.76 190	82.19 285	91.97 109	98.07 71	76.44 155	98.64 144	93.71 90	97.27 87	98.45 67

MVS_111021_HR		93.41 55	93.39 60	93.47 87	97.34 96	82.83 129	97.56 115	98.27 6	89.16 80	89.71 143	97.14 124	79.77 91	99.56 83	93.65 91	97.94 63	98.02 98

diffmvs		91.17 125	90.74 123	92.44 141	93.11 237	82.50 140	96.25 233	93.62 358	87.79 110	90.40 136	95.93 160	73.44 219	97.42 235	93.62 92	92.55 179	97.41 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMMP_NAP		93.46 54	93.23 63	94.17 51	97.16 99	84.28 96	96.82 184	96.65 91	86.24 158	94.27 72	97.99 74	77.94 121	99.83 22	93.39 93	98.57 38	98.39 71

VNet		92.11 99	91.22 111	94.79 30	96.91 102	86.98 33	97.91 87	97.96 10	86.38 155	93.65 80	95.74 165	70.16 265	98.95 132	93.39 93	88.87 233	98.43 69

sasdasda		92.27 94	91.22 111	95.41 19	95.80 131	88.31 17	97.09 160	94.64 265	88.49 89	92.99 91	97.31 114	72.68 227	98.57 148	93.38 95	88.58 241	99.36 17

canonicalmvs		92.27 94	91.22 111	95.41 19	95.80 131	88.31 17	97.09 160	94.64 265	88.49 89	92.99 91	97.31 114	72.68 227	98.57 148	93.38 95	88.58 241	99.36 17

jason		92.73 73	92.23 90	94.21 47	90.50 338	87.30 32	98.65 43	95.09 232	90.61 59	92.76 95	97.13 125	75.28 190	97.30 248	93.32 97	96.75 111	98.02 98
jason: jason.
MP-MVS-pluss		92.58 85	92.35 84	93.29 91	97.30 97	82.53 135	96.44 215	96.04 166	84.68 211	89.12 155	98.37 49	77.48 131	99.74 53	93.31 98	98.38 49	97.59 145
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
alignmvs		92.97 63	92.26 89	95.12 23	95.54 142	87.77 24	98.67 42	96.38 133	88.04 103	93.01 90	97.45 107	79.20 99	98.60 146	93.25 99	88.76 234	98.99 35

RRT-MVS		89.67 167	88.67 176	92.67 124	94.44 186	81.08 190	94.34 333	94.45 281	86.05 164	85.79 217	92.39 280	63.39 324	98.16 175	93.22 100	93.95 159	98.76 47

diffmvs_AUTHOR		90.86 137	90.41 131	92.24 156	92.01 298	82.22 149	96.18 241	93.64 356	87.28 127	90.46 135	95.64 172	72.82 225	97.39 240	93.17 101	92.46 182	97.11 191

h-mvs33		89.30 179	88.95 172	90.36 252	95.07 162	76.04 353	96.96 173	97.11 37	90.39 63	92.22 103	95.10 203	74.70 199	98.86 137	93.14 102	65.89 426	96.16 236

hse-mvs2		88.22 212	88.21 190	88.25 309	93.54 216	73.41 380	95.41 292	95.89 182	90.39 63	92.22 103	94.22 240	74.70 199	96.66 296	93.14 102	64.37 431	94.69 288

MGCFI-Net		91.95 102	91.03 118	94.72 33	95.68 136	86.38 39	96.93 176	94.48 275	88.25 97	92.78 94	97.24 120	72.34 232	98.46 158	93.13 104	88.43 248	99.32 20

MVS_111021_LR		91.60 114	91.64 104	91.47 209	95.74 134	78.79 281	96.15 244	96.77 72	88.49 89	88.64 166	97.07 130	72.33 233	99.19 114	93.13 104	96.48 118	96.43 228

VDD-MVS		88.28 210	87.02 222	92.06 171	95.09 160	80.18 233	97.55 117	94.45 281	83.09 262	89.10 156	95.92 162	47.97 428	98.49 155	93.08 106	86.91 265	97.52 155

DELS-MVS		94.98 16	94.49 35	96.44 7	96.42 108	90.59 8	99.21 8	97.02 44	94.40 14	91.46 116	97.08 129	83.32 60	99.69 65	92.83 107	98.70 31	99.04 31
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
xiu_mvs_v1_base_debu		90.54 144	89.54 157	93.55 80	92.31 270	87.58 28	96.99 166	94.87 243	87.23 130	93.27 83	97.56 103	57.43 378	98.32 167	92.72 108	93.46 169	94.74 283

xiu_mvs_v1_base		90.54 144	89.54 157	93.55 80	92.31 270	87.58 28	96.99 166	94.87 243	87.23 130	93.27 83	97.56 103	57.43 378	98.32 167	92.72 108	93.46 169	94.74 283

xiu_mvs_v1_base_debi		90.54 144	89.54 157	93.55 80	92.31 270	87.58 28	96.99 166	94.87 243	87.23 130	93.27 83	97.56 103	57.43 378	98.32 167	92.72 108	93.46 169	94.74 283

MTAPA		92.45 89	92.31 87	92.86 113	97.90 66	80.85 204	92.88 376	96.33 140	87.92 106	90.20 138	98.18 58	76.71 151	99.76 45	92.57 111	98.09 57	97.96 110

AstraMVS		88.99 186	88.35 187	90.92 231	90.81 333	78.29 295	96.73 192	94.24 301	89.96 69	86.13 214	95.04 205	62.12 335	97.41 236	92.54 112	87.57 261	97.06 200

myMVS_eth3d28		92.72 75	92.23 90	94.21 47	96.16 116	87.46 31	97.37 134	96.99 46	88.13 101	88.18 176	95.47 181	84.12 52	98.04 179	92.46 113	91.17 202	97.14 190

DeepC-MVS_fast	89.06 2	94.48 32	94.30 41	95.02 24	98.86 26	85.68 56	98.06 77	96.64 94	93.64 21	91.74 114	98.54 30	80.17 85	99.90 9	92.28 114	98.75 29	99.49 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UBG		92.68 82	92.35 84	93.70 70	95.61 139	85.65 59	97.25 140	97.06 41	87.92 106	89.28 152	95.03 206	86.06 36	98.07 177	92.24 115	90.69 209	97.37 172

mvsany_test1		87.58 232	88.22 189	85.67 368	89.78 355	67.18 435	95.25 300	87.93 449	83.96 238	88.79 162	97.06 131	72.52 229	94.53 399	92.21 116	86.45 269	95.30 267

MP-MVS		92.61 84	92.67 76	92.42 143	98.13 61	79.73 248	97.33 137	96.20 152	85.63 178	90.53 132	97.66 94	78.14 119	99.70 64	92.12 117	98.30 54	97.85 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS_3200maxsize		91.23 124	91.35 108	90.89 234	97.89 67	76.35 349	96.30 230	95.52 205	79.82 335	91.03 126	97.88 85	74.70 199	98.54 152	92.11 118	96.89 103	97.77 125

SR-MVS-dyc-post		91.29 122	91.45 107	90.80 236	97.76 74	76.03 354	96.20 239	95.44 212	80.56 313	90.72 130	97.84 86	75.76 174	98.61 145	91.99 119	96.79 109	97.75 127

RE-MVS-def			91.18 115		97.76 74	76.03 354	96.20 239	95.44 212	80.56 313	90.72 130	97.84 86	73.36 220		91.99 119	96.79 109	97.75 127

TestfortrainingZip a		95.44 11	95.38 18	95.64 14	99.06 11	88.36 16	98.35 57	97.14 31	87.45 120	97.03 27	98.90 6	89.87 13	99.96 4	91.98 121	98.60 34	98.61 59

guyue		89.85 162	89.33 162	91.40 212	92.53 266	80.15 234	96.82 184	95.68 195	89.66 73	86.43 209	94.23 239	67.00 293	97.16 258	91.96 122	89.65 218	96.89 208

testing11		92.48 88	92.04 97	93.78 62	95.94 125	86.00 46	97.56 115	97.08 39	87.52 118	89.32 151	95.40 183	84.60 43	98.02 181	91.93 123	89.04 230	97.32 175

casdiffmvs_mvg		91.13 126	90.45 130	93.17 98	92.99 242	83.58 112	97.46 125	94.56 271	87.69 113	87.19 194	94.98 211	74.50 204	97.60 206	91.88 124	92.79 176	98.34 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS	86.58 3	91.53 115	91.06 117	92.94 110	94.52 179	81.89 164	95.95 253	95.98 171	90.76 56	83.76 251	96.76 143	73.24 221	99.71 61	91.67 125	96.96 100	97.22 181
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
BP-MVS1		93.55 53	93.50 57	93.71 69	92.64 261	85.39 65	97.78 96	96.84 62	89.52 75	92.00 108	97.06 131	88.21 23	98.03 180	91.45 126	96.00 130	97.70 133

EPNet		94.06 43	94.15 44	93.76 63	97.27 98	84.35 93	98.29 63	97.64 14	94.57 11	95.36 51	96.88 137	79.96 90	99.12 121	91.30 127	96.11 125	97.82 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		90.70 140	89.90 152	93.09 102	93.61 213	83.48 114	95.20 303	92.79 387	83.22 259	91.82 112	95.70 167	71.82 244	97.48 227	91.25 128	93.67 165	98.32 74

E3new		90.90 135	90.35 135	92.55 134	93.63 212	82.40 143	96.79 187	94.49 274	87.07 137	88.54 167	95.70 167	73.85 212	97.60 206	91.23 129	91.86 193	97.64 138

CP-MVS		92.54 86	92.60 78	92.34 148	98.50 45	79.90 241	98.40 55	96.40 129	84.75 207	90.48 134	98.09 67	77.40 132	99.21 108	91.15 130	98.23 56	97.92 111

NormalMVS		92.88 67	92.97 69	92.59 132	97.80 70	82.02 153	97.94 84	94.70 254	92.34 32	92.15 105	96.53 150	77.03 141	98.57 148	91.13 131	97.12 94	97.19 187

SymmetryMVS		92.45 89	92.33 86	92.82 117	95.19 155	82.02 153	97.94 84	97.43 17	92.34 32	92.15 105	96.53 150	77.03 141	98.57 148	91.13 131	91.19 200	97.87 115

HFP-MVS		92.89 66	92.86 73	92.98 107	98.71 30	81.12 188	97.58 113	96.70 83	85.20 193	91.75 113	97.97 79	78.47 112	99.71 61	90.95 133	98.41 47	98.12 93

ACMMPR		92.69 80	92.67 76	92.75 120	98.66 33	80.57 214	97.58 113	96.69 85	85.20 193	91.57 115	97.92 80	77.01 143	99.67 69	90.95 133	98.41 47	98.00 104

HPM-MVS		91.62 113	91.53 106	91.89 180	97.88 68	79.22 262	96.99 166	95.73 193	82.07 287	89.50 150	97.19 123	75.59 177	98.93 135	90.91 135	97.94 63	97.54 149
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
casdiffmvs		90.95 133	90.39 132	92.63 129	92.82 252	82.53 135	96.83 182	94.47 278	87.69 113	88.47 168	95.56 178	74.04 210	97.54 219	90.90 136	92.74 177	97.83 120
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		90.66 141	90.06 145	92.47 137	93.22 229	82.21 150	96.70 197	94.47 278	86.94 140	88.22 175	95.50 180	73.15 222	97.59 208	90.86 137	91.48 197	97.60 144

viewmanbaseed2359cas		90.74 139	90.07 144	92.76 119	92.98 243	82.93 127	96.53 207	94.28 298	87.08 136	88.96 158	95.64 172	72.03 242	97.58 210	90.85 138	92.26 187	97.76 126

mmtdpeth		78.04 381	76.76 375	81.86 420	89.60 364	66.12 443	92.34 384	87.18 453	76.83 381	85.55 220	76.49 464	46.77 434	97.02 267	90.85 138	45.24 481	82.43 461

region2R		92.72 75	92.70 75	92.79 118	98.68 31	80.53 220	97.53 118	96.51 114	85.22 191	91.94 111	97.98 77	77.26 134	99.67 69	90.83 140	98.37 50	98.18 86

EIA-MVS		91.73 108	92.05 96	90.78 238	94.52 179	76.40 348	98.06 77	95.34 221	89.19 79	88.90 160	97.28 119	77.56 129	97.73 199	90.77 141	96.86 106	98.20 84

CSCG		92.02 100	91.65 103	93.12 100	98.53 41	80.59 211	97.47 123	97.18 29	77.06 377	84.64 235	97.98 77	83.98 54	99.52 86	90.72 142	97.33 85	99.23 25

MonoMVSNet		85.68 266	84.22 274	90.03 262	88.43 382	77.83 316	92.95 375	91.46 412	87.28 127	78.11 317	85.96 391	66.31 302	94.81 389	90.71 143	76.81 343	97.46 161

ET-MVSNet_ETH3D		90.01 157	89.03 166	92.95 109	94.38 189	86.77 36	98.14 68	96.31 143	89.30 78	63.33 442	96.72 146	90.09 11	93.63 417	90.70 144	82.29 310	98.46 66

CLD-MVS		87.97 219	87.48 210	89.44 280	92.16 288	80.54 219	98.14 68	94.92 240	91.41 46	79.43 305	95.40 183	62.34 329	97.27 251	90.60 145	82.90 302	90.50 329
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ZNCC-MVS		92.75 71	92.60 78	93.23 94	98.24 56	81.82 168	97.63 107	96.50 116	85.00 203	91.05 125	97.74 91	78.38 113	99.80 32	90.48 146	98.34 52	98.07 95

reproduce_monomvs		87.80 223	87.60 206	88.40 301	96.56 105	80.26 228	95.80 273	96.32 142	91.56 45	73.60 369	88.36 347	88.53 19	96.25 310	90.47 147	67.23 415	88.67 383

XVS		92.69 80	92.71 74	92.63 129	98.52 42	80.29 225	97.37 134	96.44 123	87.04 138	91.38 117	97.83 88	77.24 136	99.59 77	90.46 148	98.07 58	98.02 98

X-MVStestdata		86.26 256	84.14 277	92.63 129	98.52 42	80.29 225	97.37 134	96.44 123	87.04 138	91.38 117	20.73 499	77.24 136	99.59 77	90.46 148	98.07 58	98.02 98

baseline		90.76 138	90.10 142	92.74 121	92.90 250	82.56 134	94.60 326	94.56 271	87.69 113	89.06 157	95.67 170	73.76 214	97.51 224	90.43 150	92.23 189	98.16 88

test_yl		91.46 116	90.53 127	94.24 45	97.41 90	85.18 72	98.08 74	97.72 11	80.94 302	89.85 140	96.14 156	75.61 175	98.81 140	90.42 151	88.56 243	98.74 48

DCV-MVSNet		91.46 116	90.53 127	94.24 45	97.41 90	85.18 72	98.08 74	97.72 11	80.94 302	89.85 140	96.14 156	75.61 175	98.81 140	90.42 151	88.56 243	98.74 48

E2		90.33 151	89.65 155	92.37 146	92.66 257	81.99 156	96.58 202	94.39 288	86.71 150	87.88 181	95.25 188	72.18 236	97.56 212	90.37 153	90.88 206	97.57 146

E3		90.33 151	89.65 155	92.37 146	92.64 261	81.99 156	96.58 202	94.39 288	86.71 150	87.87 182	95.27 187	72.17 237	97.56 212	90.37 153	90.88 206	97.57 146

EI-MVSNet-Vis-set		91.84 107	91.77 101	92.04 173	97.60 79	81.17 186	96.61 200	96.87 59	88.20 99	89.19 153	97.55 106	78.69 109	99.14 118	90.29 155	90.94 205	95.80 246

HY-MVS	84.06 6	91.63 112	90.37 134	95.39 21	96.12 118	88.25 19	90.22 409	97.58 15	88.33 95	90.50 133	91.96 291	79.26 97	99.06 125	90.29 155	89.07 229	98.88 42

SDMVSNet		87.02 240	85.61 246	91.24 219	94.14 197	83.30 118	93.88 349	95.98 171	84.30 226	79.63 303	92.01 287	58.23 363	97.68 202	90.28 157	82.02 311	92.75 312

PAPM		92.87 69	92.40 83	94.30 42	92.25 282	87.85 23	96.40 220	96.38 133	91.07 52	88.72 165	96.90 135	82.11 69	97.37 245	90.05 158	97.70 71	97.67 135

mPP-MVS		91.88 106	91.82 99	92.07 170	98.38 49	78.63 285	97.29 139	96.09 160	85.12 199	88.45 169	97.66 94	75.53 179	99.68 67	89.83 159	98.02 61	97.88 113

VDDNet		86.44 250	84.51 266	92.22 159	91.56 311	81.83 167	97.10 159	94.64 265	69.50 438	87.84 183	95.19 196	48.01 427	97.92 191	89.82 160	86.92 264	96.89 208

LuminaMVS		88.02 217	86.89 226	91.43 210	88.65 379	83.16 121	94.84 320	94.41 286	83.67 252	86.56 208	91.95 293	62.04 336	96.88 283	89.78 161	90.06 213	94.24 292

viewmambaseed2359dif		89.52 170	89.02 167	91.03 227	92.24 283	78.83 273	95.89 265	93.77 344	83.04 264	88.28 174	95.80 164	72.08 240	97.40 238	89.76 162	90.32 211	96.87 211

GST-MVS		92.43 91	92.22 92	93.04 104	98.17 59	81.64 176	97.40 132	96.38 133	84.71 210	90.90 128	97.40 112	77.55 130	99.76 45	89.75 163	97.74 70	97.72 130

testing3-2		91.37 119	91.01 119	92.44 141	95.93 126	83.77 104	98.83 36	97.45 16	86.88 142	86.63 206	94.69 226	84.57 44	97.75 198	89.65 164	84.44 288	95.80 246

MVS		90.60 143	88.64 177	96.50 6	94.25 192	90.53 9	93.33 364	97.21 26	77.59 368	78.88 309	97.31 114	71.52 249	99.69 65	89.60 165	98.03 60	99.27 23

E4		89.85 162	89.06 165	92.22 159	91.88 303	81.63 177	96.43 217	94.27 299	86.32 157	87.29 190	94.97 212	70.81 260	97.52 222	89.57 166	90.00 214	97.51 156

WTY-MVS		92.65 83	91.68 102	95.56 16	96.00 121	88.90 14	98.23 65	97.65 13	88.57 87	89.82 142	97.22 122	79.29 96	99.06 125	89.57 166	88.73 235	98.73 52

CPTT-MVS		89.72 166	89.87 153	89.29 282	98.33 52	73.30 383	97.70 103	95.35 220	75.68 389	87.40 187	97.44 110	70.43 262	98.25 170	89.56 168	96.90 102	96.33 233

LFMVS		89.27 180	87.64 202	94.16 54	97.16 99	85.52 63	97.18 146	94.66 262	79.17 349	89.63 146	96.57 148	55.35 396	98.22 171	89.52 169	89.54 219	98.74 48

EI-MVSNet-UG-set		91.35 121	91.22 111	91.73 194	97.39 93	80.68 208	96.47 212	96.83 63	87.92 106	88.30 173	97.36 113	77.84 124	99.13 120	89.43 170	89.45 220	95.37 264

DPM-MVS		96.21 2	95.53 15	98.26 1	96.26 113	95.09 1	99.15 12	96.98 47	93.39 23	96.45 39	98.79 14	90.17 10	99.99 1	89.33 171	99.25 6	99.70 4

CDPH-MVS		93.12 59	92.91 70	93.74 65	98.65 35	83.88 100	97.67 105	96.26 146	83.00 267	93.22 86	98.24 55	81.31 73	99.21 108	89.12 172	98.74 30	98.14 90

viewdifsd2359ckpt13		90.08 155	89.36 160	92.26 155	93.03 238	81.90 163	96.37 221	94.34 292	86.16 159	87.44 186	95.30 186	70.93 258	97.55 216	89.05 173	91.59 196	97.35 174

viewdifsd2359ckpt09		90.00 158	89.28 163	92.15 166	93.31 227	81.38 181	96.37 221	93.64 356	86.34 156	86.62 207	95.64 172	71.58 248	97.52 222	88.93 174	91.06 203	97.54 149

testing91		91.90 105	91.31 110	93.66 73	95.99 122	85.68 56	97.39 133	96.89 57	86.75 148	88.85 161	95.23 192	83.93 55	97.90 192	88.91 175	87.89 255	97.41 168

E5new		89.38 173	88.55 180	91.85 184	91.77 307	80.97 194	95.90 261	94.22 304	86.03 166	86.88 200	94.90 215	69.05 273	97.47 228	88.86 176	89.35 221	97.10 193

E6new		89.37 175	88.55 180	91.85 184	91.75 309	80.97 194	95.90 261	94.22 304	86.03 166	86.88 200	94.91 213	69.05 273	97.47 228	88.86 176	89.34 223	97.10 193

E6		89.37 175	88.55 180	91.85 184	91.75 309	80.97 194	95.90 261	94.22 304	86.03 166	86.88 200	94.91 213	69.05 273	97.47 228	88.86 176	89.34 223	97.10 193

E5		89.38 173	88.55 180	91.85 184	91.77 307	80.97 194	95.90 261	94.22 304	86.03 166	86.88 200	94.90 215	69.05 273	97.47 228	88.86 176	89.35 221	97.10 193

CHOSEN 1792x2688		91.07 129	90.21 139	93.64 74	95.18 157	83.53 113	96.26 232	96.13 157	88.92 81	84.90 228	93.10 270	72.86 224	99.62 75	88.86 176	95.67 135	97.79 124

PGM-MVS		91.93 103	91.80 100	92.32 152	98.27 55	79.74 247	95.28 295	97.27 22	83.83 245	90.89 129	97.78 90	76.12 166	99.56 83	88.82 181	97.93 65	97.66 136

testing99		91.91 104	91.35 108	93.60 77	95.98 123	85.70 54	97.31 138	96.92 56	86.82 144	88.91 159	95.25 188	84.26 51	97.89 193	88.80 182	87.94 254	97.21 184

PVSNet_Blended_VisFu		91.24 123	90.77 122	92.66 125	95.09 160	82.40 143	97.77 97	95.87 186	88.26 96	86.39 210	93.94 252	76.77 149	99.27 102	88.80 182	94.00 156	96.31 234

viewdifsd2359ckpt07		89.04 184	88.30 188	91.27 217	92.32 269	78.90 271	95.89 265	93.77 344	84.48 220	85.18 223	95.16 198	69.83 266	97.70 200	88.75 184	89.29 226	97.22 181

viewmacassd2359aftdt		89.89 161	89.01 169	92.52 136	91.56 311	82.46 141	96.32 228	94.06 318	86.41 154	88.11 178	95.01 208	69.68 269	97.47 228	88.73 185	91.19 200	97.63 140

PMMVS		89.46 172	89.92 151	88.06 317	94.64 173	69.57 424	96.22 237	94.95 238	87.27 129	91.37 119	96.54 149	65.88 303	97.39 240	88.54 186	93.89 160	97.23 180

MG-MVS		94.25 38	93.72 49	95.85 13	99.38 4	89.35 12	97.98 81	98.09 9	89.99 68	92.34 101	96.97 134	81.30 74	98.99 128	88.54 186	98.88 20	99.20 26

GG-mvs-BLEND				93.49 84	94.94 166	86.26 40	81.62 465	97.00 45		88.32 172	94.30 237	91.23 6	96.21 312	88.49 188	97.43 80	98.00 104

nrg030		86.79 246	85.43 249	90.87 235	88.76 372	85.34 66	97.06 163	94.33 295	84.31 224	80.45 293	91.98 290	72.36 231	96.36 305	88.48 189	71.13 376	90.93 325

旧先验2							96.97 171		74.06 403	96.10 43			97.76 197	88.38 190

ACMMP		90.39 148	89.97 148	91.64 199	97.58 81	78.21 302	96.78 189	96.72 81	84.73 209	84.72 232	97.23 121	71.22 251	99.63 73	88.37 191	92.41 185	97.08 198
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
CostFormer		89.08 183	88.39 186	91.15 223	93.13 235	79.15 265	88.61 425	96.11 159	83.14 261	89.58 147	86.93 372	83.83 57	96.87 284	88.22 192	85.92 277	97.42 167

PAPR		92.74 72	92.17 93	94.45 39	98.89 25	84.87 86	97.20 144	96.20 152	87.73 112	88.40 170	98.12 64	78.71 108	99.76 45	87.99 193	96.28 119	98.74 48

GDP-MVS		92.85 70	92.55 80	93.75 64	92.82 252	85.76 52	97.63 107	95.05 235	88.34 94	93.15 87	97.10 128	86.92 29	98.01 183	87.95 194	94.00 156	97.47 160

test_fmvs2		79.59 364	79.90 349	78.67 439	82.86 444	55.82 478	95.20 303	89.55 436	81.09 300	80.12 299	89.80 323	34.31 470	93.51 419	87.82 195	78.36 337	86.69 421

viewdifsd2359ckpt11		86.38 251	85.29 252	89.66 278	90.42 340	75.65 363	95.27 298	92.45 391	85.54 183	84.27 239	94.73 222	62.16 331	97.39 240	87.78 196	74.97 354	95.96 239

viewmsd2359difaftdt		86.38 251	85.29 252	89.67 277	90.42 340	75.65 363	95.27 298	92.45 391	85.54 183	84.28 238	94.73 222	62.16 331	97.39 240	87.78 196	74.97 354	95.96 239

test_vis1_rt		73.96 407	72.40 410	78.64 440	83.91 437	61.16 464	95.63 282	68.18 494	76.32 384	60.09 459	74.77 467	29.01 481	97.54 219	87.74 198	75.94 347	77.22 477

sss		90.87 136	89.96 149	93.60 77	94.15 196	83.84 103	97.14 153	98.13 7	85.93 172	89.68 144	96.09 158	71.67 245	99.30 101	87.69 199	89.16 228	97.66 136

BP-MVS														87.67 200

HQP-MVS		87.91 221	87.55 208	88.98 289	92.08 293	78.48 288	97.63 107	94.80 249	90.52 60	82.30 270	94.56 228	65.40 307	97.32 246	87.67 200	83.01 299	91.13 321

EPP-MVSNet		89.76 165	89.72 154	89.87 270	93.78 208	76.02 356	97.22 141	96.51 114	79.35 343	85.11 224	95.01 208	84.82 41	97.10 265	87.46 202	88.21 252	96.50 226

baseline2		90.39 148	90.21 139	90.93 230	90.86 330	80.99 193	95.20 303	97.41 18	86.03 166	80.07 300	94.61 227	90.58 7	97.47 228	87.29 203	89.86 217	94.35 291

HQP_MVS		87.50 235	87.09 220	88.74 294	91.86 304	77.96 309	97.18 146	94.69 258	89.89 70	81.33 283	94.15 245	64.77 314	97.30 248	87.08 204	82.82 303	90.96 323

plane_prior5								94.69 258					97.30 248	87.08 204	82.82 303	90.96 323

HyFIR lowres test		89.36 177	88.60 178	91.63 201	94.91 168	80.76 207	95.60 284	95.53 203	82.56 278	84.03 244	91.24 302	78.03 120	96.81 288	87.07 206	88.41 249	97.32 175

HPM-MVS_fast		90.38 150	90.17 141	91.03 227	97.61 78	77.35 330	97.15 152	95.48 208	79.51 341	88.79 162	96.90 135	71.64 247	98.81 140	87.01 207	97.44 79	96.94 204

VortexMVS		85.45 274	84.40 270	88.63 296	93.25 228	81.66 175	95.39 294	94.34 292	87.15 135	75.10 361	87.65 359	66.58 300	95.19 366	86.89 208	73.21 366	89.03 371

0.3-1-1-0.015		87.79 224	85.93 240	93.38 89	89.87 353	85.09 79	98.43 52	96.55 107	81.13 299	87.21 193	89.75 324	77.23 138	97.02 267	86.87 209	66.38 423	98.02 98

0.4-1-1-0.2		87.73 226	85.82 243	93.46 88	89.97 352	85.31 69	98.49 51	96.55 107	81.24 297	87.14 195	89.63 327	76.16 164	97.02 267	86.84 210	66.38 423	98.05 96

cascas		86.50 249	84.48 268	92.55 134	92.64 261	85.95 47	97.04 164	95.07 234	75.32 391	80.50 291	91.02 305	54.33 404	97.98 185	86.79 211	87.62 258	93.71 304

PVSNet_0	77.72 15	81.70 340	78.95 359	89.94 268	90.77 334	76.72 342	95.96 252	96.95 52	85.01 202	70.24 409	88.53 342	52.32 408	98.20 172	86.68 212	44.08 484	94.89 278

0.4-1-1-0.1		87.53 234	85.67 245	93.13 99	89.70 360	84.41 92	98.30 62	96.55 107	80.85 304	86.94 199	89.53 329	76.18 162	96.99 272	86.62 213	66.36 425	97.98 106

gg-mvs-nofinetune		85.48 273	82.90 302	93.24 93	94.51 183	85.82 51	79.22 470	96.97 50	61.19 465	87.33 189	53.01 488	90.58 7	96.07 315	86.07 214	97.23 88	97.81 123

testing222		91.09 127	90.49 129	92.87 112	95.82 129	85.04 80	96.51 210	97.28 21	86.05 164	89.13 154	95.34 185	80.16 86	96.62 297	85.82 215	88.31 250	96.96 203

gm-plane-assit					92.27 279	79.64 251			84.47 221		95.15 200		97.93 186	85.81 216

DP-MVS Recon		91.72 110	90.85 120	94.34 41	99.50 1	85.00 83	98.51 49	95.96 173	80.57 312	88.08 179	97.63 100	76.84 146	99.89 11	85.67 217	94.88 142	98.13 92

XVG-OURS-SEG-HR		85.74 265	85.16 258	87.49 336	90.22 344	71.45 408	91.29 398	94.09 316	81.37 295	83.90 249	95.22 193	60.30 348	97.53 221	85.58 218	84.42 290	93.50 307

ab-mvs		87.08 239	84.94 262	93.48 85	93.34 226	83.67 110	88.82 422	95.70 194	81.18 298	84.55 236	90.14 321	62.72 327	98.94 134	85.49 219	82.54 307	97.85 118

MVSTER		89.25 181	88.92 173	90.24 256	95.98 123	84.66 88	96.79 187	95.36 218	87.19 133	80.33 295	90.61 312	90.02 12	95.97 319	85.38 220	78.64 332	90.09 339

OMC-MVS		88.80 194	88.16 192	90.72 239	95.30 149	77.92 312	94.81 322	94.51 273	86.80 145	84.97 227	96.85 138	67.53 288	98.60 146	85.08 221	87.62 258	95.63 254

mvs_anonymous		88.68 196	87.62 204	91.86 182	94.80 171	81.69 174	93.53 359	94.92 240	82.03 288	78.87 310	90.43 315	75.77 173	95.34 356	85.04 222	93.16 173	98.55 63

VPA-MVSNet		85.32 277	83.83 279	89.77 275	90.25 343	82.63 133	96.36 224	97.07 40	83.03 266	81.21 285	89.02 334	61.58 341	96.31 307	85.02 223	70.95 378	90.36 330

LCM-MVSNet-Re		83.75 306	83.54 289	84.39 393	93.54 216	64.14 450	92.51 379	84.03 472	83.90 241	66.14 430	86.59 377	67.36 290	92.68 424	84.89 224	92.87 175	96.35 230

ECVR-MVS		88.35 208	87.25 215	91.65 198	93.54 216	79.40 256	96.56 206	90.78 427	86.78 146	85.57 219	95.25 188	57.25 382	97.56 212	84.73 225	94.80 143	97.98 106

IB-MVS	85.34 4	88.67 197	87.14 219	93.26 92	93.12 236	84.32 94	98.76 37	97.27 22	87.19 133	79.36 306	90.45 314	83.92 56	98.53 153	84.41 226	69.79 389	96.93 205
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
CANet_DTU		90.98 131	90.04 146	93.83 60	94.76 172	86.23 43	96.32 228	93.12 382	93.11 25	93.71 79	96.82 141	63.08 326	99.48 90	84.29 227	95.12 141	95.77 251

AdaColmap		88.81 193	87.61 205	92.39 145	99.33 5	79.95 239	96.70 197	95.58 200	77.51 369	83.05 264	96.69 147	61.90 340	99.72 58	84.29 227	93.47 168	97.50 157

test2506		90.96 132	90.39 132	92.65 126	93.54 216	82.46 141	96.37 221	97.35 19	86.78 146	87.55 185	95.25 188	77.83 125	97.50 225	84.07 229	94.80 143	97.98 106

ACMP	81.66 11	84.00 302	83.22 296	86.33 354	91.53 315	72.95 391	95.91 260	93.79 340	83.70 251	73.79 368	92.22 283	54.31 405	96.89 281	83.98 230	79.74 321	89.16 361
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1111		88.11 213	87.04 221	91.35 213	93.15 233	78.79 281	96.57 204	90.78 427	86.88 142	85.04 225	95.20 195	57.23 383	97.39 240	83.88 231	94.59 146	97.87 115

OPM-MVS		85.84 262	85.10 260	88.06 317	88.34 383	77.83 316	95.72 275	94.20 309	87.89 109	80.45 293	94.05 247	58.57 360	97.26 252	83.88 231	82.76 305	89.09 363
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpmrst		88.36 207	87.38 213	91.31 214	94.36 190	79.92 240	87.32 437	95.26 227	85.32 188	88.34 171	86.13 389	80.60 78	96.70 293	83.78 233	85.34 285	97.30 178

MVSFormer		91.36 120	90.57 126	93.73 67	93.00 239	88.08 21	94.80 323	94.48 275	80.74 308	94.90 62	97.13 125	78.84 105	95.10 375	83.77 234	97.46 77	98.02 98

test_djsdf		83.00 321	82.45 310	84.64 386	84.07 435	69.78 421	94.80 323	94.48 275	80.74 308	75.41 358	87.70 358	61.32 345	95.10 375	83.77 234	79.76 319	89.04 369

LPG-MVS_test		84.20 299	83.49 291	86.33 354	90.88 327	73.06 387	95.28 295	94.13 313	82.20 283	76.31 341	93.20 266	54.83 401	96.95 275	83.72 236	80.83 316	88.98 376

LGP-MVS_train				86.33 354	90.88 327	73.06 387		94.13 313	82.20 283	76.31 341	93.20 266	54.83 401	96.95 275	83.72 236	80.83 316	88.98 376

XVG-OURS		85.18 280	84.38 271	87.59 330	90.42 340	71.73 405	91.06 402	94.07 317	82.00 289	83.29 260	95.08 204	56.42 389	97.55 216	83.70 238	83.42 295	93.49 308

MAR-MVS		90.63 142	90.22 138	91.86 182	98.47 47	78.20 303	97.18 146	96.61 97	83.87 242	88.18 176	98.18 58	68.71 278	99.75 50	83.66 239	97.15 92	97.63 140
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
Effi-MVS+-dtu		84.61 292	84.90 264	83.72 400	91.96 300	63.14 456	94.95 317	93.34 372	85.57 180	79.79 301	87.12 369	61.99 338	95.61 346	83.55 240	85.83 279	92.41 316

AUN-MVS		86.25 257	85.57 247	88.26 307	93.57 215	73.38 381	95.45 290	95.88 184	83.94 239	85.47 221	94.21 241	73.70 217	96.67 295	83.54 241	64.41 430	94.73 287

testdata				90.13 259	95.92 127	74.17 376		96.49 119	73.49 408	94.82 66	97.99 74	78.80 107	97.93 186	83.53 242	97.52 76	98.29 78

1314		88.94 188	87.20 216	94.17 51	93.21 230	85.73 53	93.33 364	96.64 94	82.89 269	75.98 349	96.36 152	66.83 297	99.39 94	83.52 243	96.02 129	97.39 171

CDS-MVSNet		89.50 171	88.96 171	91.14 224	91.94 302	80.93 200	97.09 160	95.81 188	84.26 229	84.72 232	94.20 242	80.31 81	95.64 343	83.37 244	88.96 232	96.85 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM_NR		91.46 116	90.82 121	93.37 90	98.50 45	81.81 169	95.03 315	96.13 157	84.65 212	86.10 215	97.65 98	79.24 98	99.75 50	83.20 245	96.88 104	98.56 61

PS-MVSNAJss		84.91 286	84.30 272	86.74 348	85.89 414	74.40 375	94.95 317	94.16 312	83.93 240	76.45 339	90.11 322	71.04 254	95.77 333	83.16 246	79.02 329	90.06 341

MVS_Test		90.29 153	89.18 164	93.62 76	95.23 151	84.93 84	94.41 329	94.66 262	84.31 224	90.37 137	91.02 305	75.13 192	97.82 195	83.11 247	94.42 150	98.12 93

VPNet		84.69 289	82.92 301	90.01 263	89.01 371	83.45 115	96.71 195	95.46 210	85.71 177	79.65 302	92.18 286	56.66 387	96.01 318	83.05 248	67.84 409	90.56 328

3Dnovator+	82.88 8	89.63 169	87.85 197	94.99 25	94.49 185	86.76 37	97.84 91	95.74 192	86.10 162	75.47 357	96.02 159	65.00 311	99.51 88	82.91 249	97.07 97	98.72 53

ETVMVS		90.99 130	90.26 136	93.19 97	95.81 130	85.64 60	96.97 171	97.18 29	85.43 185	88.77 164	94.86 218	82.00 70	96.37 304	82.70 250	88.60 240	97.57 146

WBMVS		87.73 226	86.79 227	90.56 243	95.61 139	85.68 56	97.63 107	95.52 205	83.77 247	78.30 315	88.44 346	86.14 35	95.78 332	82.54 251	73.15 367	90.21 334

TAMVS		88.48 203	87.79 199	90.56 243	91.09 324	79.18 263	96.45 214	95.88 184	83.64 254	83.12 262	93.33 265	75.94 170	95.74 338	82.40 252	88.27 251	96.75 219

SSM_0407		87.33 238	85.87 242	91.71 197	92.94 245	82.53 135	94.30 336	92.33 396	80.11 328	83.50 255	94.18 243	64.68 316	96.80 290	82.34 253	88.51 245	95.79 248

SSM_0404		87.69 230	86.26 235	91.95 177	92.94 245	83.02 125	94.69 325	92.33 396	80.11 328	84.65 234	94.18 243	64.68 316	96.90 279	82.34 253	90.44 210	95.94 242

baseline1		88.85 192	87.49 209	92.93 111	95.21 153	86.85 34	95.47 289	94.61 268	87.29 126	83.11 263	94.99 210	80.70 77	96.89 281	82.28 255	73.72 360	95.05 275

jajsoiax		82.12 334	81.15 329	85.03 380	84.19 433	70.70 413	94.22 341	93.95 322	83.07 263	73.48 371	89.75 324	49.66 422	95.37 355	82.24 256	79.76 319	89.02 373

mvs_tets		81.74 339	80.71 335	84.84 381	84.22 432	70.29 417	93.91 348	93.78 341	82.77 273	73.37 374	89.46 330	47.36 433	95.31 359	81.99 257	79.55 325	88.92 380

test_fmvs3		69.56 431	69.19 426	70.67 459	69.01 484	47.05 485	90.87 403	86.81 456	71.31 430	66.79 426	77.15 460	16.40 489	83.17 481	81.84 258	62.51 438	81.79 467

3Dnovator	82.32 10	89.33 178	87.64 202	94.42 40	93.73 211	85.70 54	97.73 101	96.75 76	86.73 149	76.21 346	95.93 160	62.17 330	99.68 67	81.67 259	97.81 67	97.88 113

icg_test_0407_2		87.55 233	86.59 232	90.43 247	92.30 273	78.81 276	92.17 385	93.84 332	85.14 195	83.68 252	94.49 231	67.75 283	95.02 383	81.33 260	88.61 236	97.46 161

IMVS_0407		87.82 222	86.72 229	91.14 224	92.30 273	78.81 276	93.34 363	93.84 332	85.14 195	83.68 252	94.49 231	67.75 283	97.14 263	81.33 260	88.61 236	97.46 161

IMVS_0404		85.34 276	83.69 280	90.29 254	92.30 273	78.81 276	90.62 406	93.84 332	85.14 195	72.51 386	94.49 231	54.36 403	94.61 396	81.33 260	88.61 236	97.46 161

IMVS_0403		88.07 214	87.02 222	91.24 219	92.30 273	78.81 276	93.62 355	93.84 332	85.14 195	84.36 237	94.49 231	69.49 270	97.46 234	81.33 260	88.61 236	97.46 161

TESTMET0.1,1		89.83 164	89.34 161	91.31 214	92.54 265	80.19 232	97.11 156	96.57 104	86.15 160	86.85 205	91.83 296	79.32 94	96.95 275	81.30 264	92.35 186	96.77 216

KinetiMVS		89.13 182	87.95 195	92.65 126	92.16 288	82.39 145	97.04 164	96.05 164	86.59 153	88.08 179	94.85 219	61.54 342	98.38 164	81.28 265	93.99 158	97.19 187

API-MVS		90.18 154	88.97 170	93.80 61	98.66 33	82.95 126	97.50 122	95.63 199	75.16 393	86.31 211	97.69 92	72.49 230	99.90 9	81.26 266	96.07 126	98.56 61

test-LLR		88.48 203	87.98 194	89.98 265	92.26 280	77.23 332	97.11 156	95.96 173	83.76 248	86.30 212	91.38 299	72.30 234	96.78 291	80.82 267	91.92 191	95.94 242

test-mter		88.95 187	88.60 178	89.98 265	92.26 280	77.23 332	97.11 156	95.96 173	85.32 188	86.30 212	91.38 299	76.37 158	96.78 291	80.82 267	91.92 191	95.94 242

miper_enhance_ethall		85.95 261	85.20 255	88.19 314	94.85 169	79.76 244	96.00 250	94.06 318	82.98 268	77.74 321	88.76 337	79.42 93	95.46 352	80.58 269	72.42 369	89.36 355

thisisatest0515		90.95 133	90.26 136	93.01 105	94.03 205	84.27 97	97.91 87	96.67 87	83.18 260	86.87 204	95.51 179	88.66 18	97.85 194	80.46 270	89.01 231	96.92 207

114514_t		88.79 195	87.57 207	92.45 139	98.21 58	81.74 171	96.99 166	95.45 211	75.16 393	82.48 267	95.69 169	68.59 279	98.50 154	80.33 271	95.18 140	97.10 193

PVSNet	82.34 9	89.02 185	87.79 199	92.71 123	95.49 143	81.50 180	97.70 103	97.29 20	87.76 111	85.47 221	95.12 202	56.90 384	98.90 136	80.33 271	94.02 154	97.71 132

FA-MVS(test-final)		87.71 229	86.23 237	92.17 164	94.19 194	80.55 215	87.16 439	96.07 163	82.12 286	85.98 216	88.35 348	72.04 241	98.49 155	80.26 273	89.87 216	97.48 159

tpm2		87.35 237	86.26 235	90.62 241	92.93 249	78.67 284	88.06 432	95.99 170	79.33 344	87.40 187	86.43 383	80.28 82	96.40 302	80.23 274	85.73 281	96.79 214

BH-w/o		88.24 211	87.47 211	90.54 245	95.03 165	78.54 287	97.41 131	93.82 336	84.08 233	78.23 316	94.51 230	69.34 272	97.21 255	80.21 275	94.58 147	95.87 245

UGNet		87.73 226	86.55 233	91.27 217	95.16 158	79.11 266	96.35 225	96.23 149	88.14 100	87.83 184	90.48 313	50.65 416	99.09 123	80.13 276	94.03 153	95.60 256
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
无先验							96.87 180	96.78 66	77.39 370				99.52 86	79.95 277		98.43 69

cl22		85.11 281	84.17 275	87.92 320	95.06 164	78.82 274	95.51 287	94.22 304	79.74 337	76.77 333	87.92 355	75.96 168	95.68 339	79.93 278	72.42 369	89.27 357

原ACMM1				91.22 222	97.77 72	78.10 305		96.61 97	81.05 301	91.28 122	97.42 111	77.92 123	98.98 129	79.85 279	98.51 40	96.59 224

FIs		86.73 248	86.10 238	88.61 297	90.05 350	80.21 230	96.14 245	96.95 52	85.56 182	78.37 314	92.30 282	76.73 150	95.28 360	79.51 280	79.27 326	90.35 331

Anonymous202405211		84.41 296	81.93 317	91.85 184	96.78 104	78.41 292	97.44 126	91.34 416	70.29 433	84.06 243	94.26 238	41.09 455	98.96 130	79.46 281	82.65 306	98.17 87

UWE-MVS		88.56 202	88.91 174	87.50 334	94.17 195	72.19 395	95.82 272	97.05 42	84.96 204	84.78 230	93.51 264	81.33 72	94.75 391	79.43 282	89.17 227	95.57 258

mamba_0408		85.26 279	83.10 298	91.74 193	92.94 245	82.53 135	72.52 485	91.77 405	80.36 320	83.50 255	94.01 248	64.97 312	96.90 279	79.37 283	88.51 245	95.79 248

SSM_04072		84.64 290	83.10 298	89.25 283	92.94 245	82.53 135	72.52 485	91.77 405	80.36 320	83.50 255	94.01 248	64.97 312	89.41 456	79.37 283	88.51 245	95.79 248

anonymousdsp		80.98 353	79.97 347	84.01 394	81.73 447	70.44 416	92.49 380	93.58 361	77.10 376	72.98 380	86.31 385	57.58 377	94.90 384	79.32 285	78.63 334	86.69 421

ACMM	80.70 13	83.72 307	82.85 304	86.31 357	91.19 320	72.12 397	95.88 267	94.29 297	80.44 316	77.02 330	91.96 291	55.24 397	97.14 263	79.30 286	80.38 318	89.67 345
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		83.15 316	80.60 337	90.80 236	95.74 134	78.27 297	96.81 186	94.92 240	60.10 470	81.89 279	92.54 278	45.82 437	98.82 139	79.25 287	78.32 338	95.31 266

UniMVSNet_NR-MVSNet		85.49 272	84.59 265	88.21 313	89.44 368	79.36 257	96.71 195	96.41 127	85.22 191	78.11 317	90.98 307	76.97 145	95.14 372	79.14 288	68.30 403	90.12 337

DU-MVS		84.57 293	83.33 293	88.28 306	88.76 372	79.36 257	96.43 217	95.41 217	85.42 186	78.11 317	90.82 308	67.61 285	95.14 372	79.14 288	68.30 403	90.33 332

XXY-MVS		83.84 304	82.00 316	89.35 281	87.13 395	81.38 181	95.72 275	94.26 300	80.15 327	75.92 351	90.63 311	61.96 339	96.52 299	78.98 290	73.28 365	90.14 336

Vis-MVSNet (Re-imp)		88.88 191	88.87 175	88.91 290	93.89 206	74.43 374	96.93 176	94.19 310	84.39 222	83.22 261	95.67 170	78.24 116	94.70 393	78.88 291	94.40 151	97.61 143

mvsany_test3		67.19 439	65.34 440	72.72 457	63.08 491	48.57 484	83.12 462	78.09 485	72.07 424	61.21 454	77.11 461	22.94 484	87.78 467	78.59 292	51.88 468	81.80 466

miper_ehance_all_eth		84.57 293	83.60 288	87.50 334	92.64 261	78.25 298	95.40 293	93.47 363	79.28 347	76.41 340	87.64 360	76.53 153	95.24 364	78.58 293	72.42 369	89.01 375

UniMVSNet (Re)		85.31 278	84.23 273	88.55 298	89.75 357	80.55 215	96.72 193	96.89 57	85.42 186	78.40 313	88.93 335	75.38 185	95.52 350	78.58 293	68.02 406	89.57 348

IS-MVSNet		88.67 197	88.16 192	90.20 258	93.61 213	76.86 339	96.77 191	93.07 383	84.02 235	83.62 254	95.60 176	74.69 202	96.24 311	78.43 295	93.66 166	97.49 158

usedtu_blend_shiyan5		77.51 389	73.93 403	88.26 307	79.74 455	80.59 211	90.76 405	89.69 434	63.21 454	70.34 404	82.14 429	57.91 372	95.15 370	77.83 296	53.77 458	89.05 366

blend_shiyan4		81.76 338	79.58 351	88.31 305	80.00 454	80.59 211	95.95 253	93.73 349	72.26 423	71.14 397	82.52 428	76.13 165	95.15 370	77.83 296	66.62 421	89.19 359

Elysia		85.62 268	83.66 283	91.51 205	88.76 372	82.21 150	95.15 307	94.70 254	76.96 379	84.13 241	92.20 284	50.81 414	97.26 252	77.81 298	92.42 183	95.06 273

StellarMVS		85.62 268	83.66 283	91.51 205	88.76 372	82.21 150	95.15 307	94.70 254	76.96 379	84.13 241	92.20 284	50.81 414	97.26 252	77.81 298	92.42 183	95.06 273

thisisatest0530		89.65 168	89.02 167	91.53 204	93.46 223	80.78 206	96.52 208	96.67 87	81.69 293	83.79 250	94.90 215	88.85 17	97.68 202	77.80 300	87.49 262	96.14 237

v2v482		83.46 310	81.86 318	88.25 309	86.19 408	79.65 250	96.34 226	94.02 321	81.56 294	77.32 324	88.23 350	65.62 304	96.03 316	77.77 301	69.72 391	89.09 363

V42		83.04 319	81.53 323	87.57 332	86.27 407	79.09 268	95.87 268	94.11 315	80.35 322	77.22 326	86.79 375	65.32 309	96.02 317	77.74 302	70.14 383	87.61 407

GA-MVS		85.79 264	84.04 278	91.02 229	89.47 367	80.27 227	96.90 179	94.84 247	85.57 180	80.88 287	89.08 332	56.56 388	96.47 301	77.72 303	85.35 284	96.34 231

PLC	83.97 7	88.00 218	87.38 213	89.83 272	98.02 64	76.46 345	97.16 150	94.43 284	79.26 348	81.98 277	96.28 154	69.36 271	99.27 102	77.71 304	92.25 188	93.77 303
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
sd_testset		84.62 291	83.11 297	89.17 284	94.14 197	77.78 318	91.54 397	94.38 290	84.30 226	79.63 303	92.01 287	52.28 409	96.98 273	77.67 305	82.02 311	92.75 312

1112_ss		88.60 200	87.47 211	92.00 175	93.21 230	80.97 194	96.47 212	92.46 390	83.64 254	80.86 288	97.30 117	80.24 83	97.62 205	77.60 306	85.49 282	97.40 170

Vis-MVSNet		88.67 197	87.82 198	91.24 219	92.68 256	78.82 274	96.95 174	93.85 331	87.55 117	87.07 197	95.13 201	63.43 323	97.21 255	77.58 307	96.15 123	97.70 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
c3_l		83.80 305	82.65 307	87.25 342	92.10 292	77.74 323	95.25 300	93.04 384	78.58 358	76.01 348	87.21 368	75.25 191	95.11 374	77.54 308	68.89 397	88.91 381

Test_1112_low_res		88.03 216	86.73 228	91.94 179	93.15 233	80.88 203	96.44 215	92.41 394	83.59 256	80.74 290	91.16 303	80.18 84	97.59 208	77.48 309	85.40 283	97.36 173

tt0805		81.20 349	79.06 358	87.61 328	86.50 402	72.97 390	93.66 353	95.48 208	74.11 401	76.23 345	91.99 289	41.36 454	97.40 238	77.44 310	74.78 356	92.45 315

新几何1				93.12 100	97.44 88	81.60 179		96.71 82	74.54 399	91.22 123	97.57 102	79.13 100	99.51 88	77.40 311	98.46 44	98.26 81

FC-MVSNet-test		85.96 260	85.39 250	87.66 327	89.38 369	78.02 306	95.65 281	96.87 59	85.12 199	77.34 323	91.94 294	76.28 161	94.74 392	77.09 312	78.82 330	90.21 334

Patchmatch-RL test		76.65 396	74.01 402	84.55 388	77.37 469	64.23 449	78.49 474	82.84 477	78.48 359	64.63 437	73.40 472	76.05 167	91.70 440	76.99 313	57.84 445	97.72 130

IterMVS-LS		83.93 303	82.80 305	87.31 340	91.46 316	77.39 329	95.66 280	93.43 366	80.44 316	75.51 356	87.26 366	73.72 215	95.16 369	76.99 313	70.72 380	89.39 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		85.80 263	85.20 255	87.59 330	91.55 313	77.41 328	95.13 309	95.36 218	80.43 318	80.33 295	94.71 224	73.72 215	95.97 319	76.96 315	78.64 332	89.39 349

eth_miper_zixun_eth		83.12 317	82.01 315	86.47 353	91.85 306	74.80 369	94.33 334	93.18 378	79.11 350	75.74 355	87.25 367	72.71 226	95.32 358	76.78 316	67.13 416	89.27 357

Fast-Effi-MVS+		87.93 220	86.94 225	90.92 231	94.04 203	79.16 264	98.26 64	93.72 351	81.29 296	83.94 248	92.90 273	69.83 266	96.68 294	76.70 317	91.74 194	96.93 205

usedtu_dtu_shiyan1		85.03 282	83.24 294	90.37 250	86.62 400	86.24 41	96.23 235	95.30 223	84.55 215	77.22 326	88.47 344	67.85 281	95.27 361	76.59 318	76.35 344	89.61 346

FE-MVSNET3		85.03 282	83.24 294	90.37 250	86.62 400	86.24 41	96.23 235	95.30 223	84.55 215	77.22 326	88.47 344	67.85 281	95.27 361	76.59 318	76.35 344	89.61 346

CMPMVS	54.94 21	75.71 402	74.56 396	79.17 436	79.69 458	55.98 476	89.59 414	93.30 373	60.28 468	53.85 475	89.07 333	47.68 432	96.33 306	76.55 320	81.02 314	85.22 439
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view						81.74 171	86.80 441		80.65 310	85.65 218		74.26 206		76.52 321		96.98 202

testdata2													99.48 90	76.45 322

D2MVS		82.67 325	81.55 322	86.04 361	87.77 389	76.47 344	95.21 302	96.58 103	82.66 276	70.26 407	85.46 399	60.39 347	95.80 330	76.40 323	79.18 327	85.83 436

UniMVSNet_ETH3D		80.86 354	78.75 360	87.22 343	86.31 405	72.02 398	91.95 387	93.76 346	73.51 406	75.06 362	90.16 320	43.04 446	95.66 340	76.37 324	78.55 335	93.98 299

tpm		85.55 271	84.47 269	88.80 293	90.19 346	75.39 366	88.79 423	94.69 258	84.83 206	83.96 247	85.21 402	78.22 117	94.68 395	76.32 325	78.02 340	96.34 231

BH-untuned		86.95 242	85.94 239	89.99 264	94.52 179	77.46 327	96.78 189	93.37 371	81.80 290	76.62 336	93.81 258	66.64 298	97.02 267	76.06 326	93.88 161	95.48 262

tttt0517		88.57 201	88.19 191	89.71 276	93.00 239	75.99 357	95.67 279	96.67 87	80.78 307	81.82 280	94.40 235	88.97 16	97.58 210	76.05 327	86.31 270	95.57 258

XVG-ACMP-BASELINE		79.38 368	77.90 366	83.81 396	84.98 425	67.14 439	89.03 421	93.18 378	80.26 326	72.87 381	88.15 352	38.55 460	96.26 308	76.05 327	78.05 339	88.02 399

UA-Net		88.92 189	88.48 185	90.24 256	94.06 202	77.18 334	93.04 372	94.66 262	87.39 124	91.09 124	93.89 253	74.92 195	98.18 174	75.83 329	91.43 198	95.35 265

WR-MVS		84.32 297	82.96 300	88.41 300	89.38 369	80.32 224	96.59 201	96.25 147	83.97 237	76.63 335	90.36 316	67.53 288	94.86 387	75.82 330	70.09 387	90.06 341

Baseline_NR-MVSNet		81.22 348	80.07 345	84.68 384	85.32 422	75.12 368	96.48 211	88.80 444	76.24 387	77.28 325	86.40 384	67.61 285	94.39 403	75.73 331	66.73 420	84.54 445

wanda-best-256-512		78.87 372	75.75 382	88.22 311	79.74 455	80.51 221	95.92 256	93.75 347	72.60 416	70.34 404	82.14 429	57.91 372	95.09 377	75.61 332	53.77 458	89.05 366

FE-blended-shiyan7		78.87 372	75.75 382	88.22 311	79.74 455	80.51 221	95.92 256	93.75 347	72.60 416	70.34 404	82.14 429	57.91 372	95.09 377	75.61 332	53.77 458	89.05 366

blended_shiyan8		78.76 374	75.65 386	88.10 315	79.58 460	80.20 231	95.70 278	93.71 352	72.43 421	70.26 407	82.12 432	57.66 376	95.08 379	75.57 334	53.80 457	89.02 373

dmvs_re		84.10 300	82.90 302	87.70 324	91.41 317	73.28 384	90.59 407	93.19 376	85.02 201	77.96 320	93.68 259	57.92 371	96.18 313	75.50 335	80.87 315	93.63 305

v148		82.41 331	80.89 331	86.99 346	86.18 409	76.81 340	96.27 231	93.82 336	80.49 315	75.28 359	86.11 390	67.32 291	95.75 335	75.48 336	67.03 418	88.42 392

blended_shiyan6		78.74 375	75.63 387	88.07 316	79.63 459	80.10 236	95.72 275	93.73 349	72.43 421	70.17 410	82.09 434	57.69 375	95.07 380	75.47 337	53.77 458	89.03 371

pmmvs4		82.54 327	80.79 332	87.79 322	86.11 410	80.49 223	93.55 358	93.18 378	77.29 372	73.35 375	89.40 331	65.26 310	95.05 382	75.32 338	73.61 361	87.83 402

v1144		82.90 322	81.27 327	87.78 323	86.29 406	79.07 269	96.14 245	93.93 323	80.05 331	77.38 322	86.80 374	65.50 305	95.93 324	75.21 339	70.13 384	88.33 394

Fast-Effi-MVS+-dtu		83.33 312	82.60 308	85.50 372	89.55 365	69.38 425	96.09 248	91.38 413	82.30 282	75.96 350	91.41 298	56.71 385	95.58 348	75.13 340	84.90 287	91.54 319

TR-MVS		86.30 255	84.93 263	90.42 248	94.63 174	77.58 325	96.57 204	93.82 336	80.30 323	82.42 269	95.16 198	58.74 359	97.55 216	74.88 341	87.82 256	96.13 238

NR-MVSNet		83.35 311	81.52 324	88.84 291	88.76 372	81.31 184	94.45 328	95.16 230	84.65 212	67.81 419	90.82 308	70.36 263	94.87 386	74.75 342	66.89 419	90.33 332

CNLPA		86.96 241	85.37 251	91.72 196	97.59 80	79.34 259	97.21 142	91.05 422	74.22 400	78.90 308	96.75 145	67.21 292	98.95 132	74.68 343	90.77 208	96.88 210

cl____		83.27 313	82.12 313	86.74 348	92.20 284	75.95 358	95.11 311	93.27 374	78.44 361	74.82 363	87.02 371	74.19 207	95.19 366	74.67 344	69.32 393	89.09 363

DIV-MVS_self_test		83.27 313	82.12 313	86.74 348	92.19 285	75.92 360	95.11 311	93.26 375	78.44 361	74.81 364	87.08 370	74.19 207	95.19 366	74.66 345	69.30 394	89.11 362

TranMVSNet+NR-MVSNet		83.24 315	81.71 320	87.83 321	87.71 390	78.81 276	96.13 247	94.82 248	84.52 217	76.18 347	90.78 310	64.07 319	94.60 397	74.60 346	66.59 422	90.09 339

Anonymous20231211		79.72 363	77.19 371	87.33 338	95.59 141	77.16 335	95.18 306	94.18 311	59.31 473	72.57 384	86.20 388	47.89 430	95.66 340	74.53 347	69.24 395	89.18 360

CVMVSNet		84.83 287	85.57 247	82.63 412	91.55 313	60.38 466	95.13 309	95.03 236	80.60 311	82.10 276	94.71 224	66.40 301	90.19 453	74.30 348	90.32 211	97.31 177

v144192		82.43 328	80.73 334	87.54 333	85.81 415	78.22 299	95.98 251	93.78 341	79.09 351	77.11 329	86.49 379	64.66 318	95.91 325	74.20 349	69.42 392	88.49 388

pmmvs5		81.34 345	79.54 352	86.73 351	85.02 424	76.91 337	96.22 237	91.65 409	77.65 367	73.55 370	88.61 339	55.70 394	94.43 402	74.12 350	73.35 364	88.86 382

test_post1							85.88 449				30.24 498	73.77 213	95.07 380	73.89 351

SCA		85.63 267	83.64 286	91.60 202	92.30 273	81.86 166	92.88 376	95.56 202	84.85 205	82.52 266	85.12 406	58.04 366	95.39 353	73.89 351	87.58 260	97.54 149

v8		81.88 337	80.06 346	87.32 339	86.63 399	79.04 270	94.41 329	93.65 355	78.77 356	73.19 378	85.57 396	66.87 296	95.81 329	73.84 353	67.61 411	87.11 416

miper_lstm_enhance		81.66 342	80.66 336	84.67 385	91.19 320	71.97 400	91.94 388	93.19 376	77.86 365	72.27 387	85.26 400	73.46 218	93.42 420	73.71 354	67.05 417	88.61 384

GeoE		86.36 253	85.20 255	89.83 272	93.17 232	76.13 351	97.53 118	92.11 399	79.58 340	80.99 286	94.01 248	66.60 299	96.17 314	73.48 355	89.30 225	97.20 186

UWE-MVS-28		85.41 275	86.36 234	82.59 413	91.12 323	66.81 440	93.88 349	97.03 43	83.86 244	78.55 311	93.84 255	77.76 127	88.55 460	73.47 356	87.69 257	92.41 316

gbinet_0.2-2-1-0.02		78.67 376	75.67 385	87.70 324	80.38 452	79.60 252	96.25 233	94.03 320	72.51 419	71.41 392	83.33 423	55.97 393	94.45 401	73.37 357	53.73 462	89.04 369

v1192		82.31 332	80.55 338	87.60 329	85.94 412	78.47 291	95.85 270	93.80 339	79.33 344	76.97 331	86.51 378	63.33 325	95.87 326	73.11 358	70.13 384	88.46 390

PCF-MVS	84.09 5	86.77 247	85.00 261	92.08 169	92.06 296	83.07 123	92.14 386	94.47 278	79.63 339	76.90 332	94.78 221	71.15 252	99.20 113	72.87 359	91.05 204	93.98 299
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1921920		82.02 335	80.23 342	87.41 337	85.62 416	77.92 312	95.79 274	93.69 353	78.86 355	76.67 334	86.44 381	62.50 328	95.83 328	72.69 360	69.77 390	88.47 389

F-COLMAP		84.50 295	83.44 292	87.67 326	95.22 152	72.22 393	95.95 253	93.78 341	75.74 388	76.30 343	95.18 197	59.50 353	98.45 160	72.67 361	86.59 268	92.35 318

IterMVS		80.67 356	79.16 356	85.20 377	89.79 354	76.08 352	92.97 374	91.86 402	80.28 324	71.20 396	85.14 405	57.93 370	91.34 442	72.52 362	70.74 379	88.18 397
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		80.51 358	79.10 357	84.73 383	89.63 363	74.66 370	92.98 373	91.81 404	80.05 331	71.06 399	85.18 403	58.04 366	91.40 441	72.48 363	70.70 381	88.12 398

v10		81.43 344	79.53 353	87.11 344	86.38 403	78.87 272	94.31 335	93.43 366	77.88 364	73.24 377	85.26 400	65.44 306	95.75 335	72.14 364	67.71 410	86.72 420

MVP-Stereo		82.65 326	81.67 321	85.59 371	86.10 411	78.29 295	93.33 364	92.82 386	77.75 366	69.17 416	87.98 354	59.28 356	95.76 334	71.77 365	96.88 104	82.73 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OurMVSNet-221017-0		77.18 393	76.06 379	80.55 428	83.78 439	60.00 468	90.35 408	91.05 422	77.01 378	66.62 428	87.92 355	47.73 431	94.03 408	71.63 366	68.44 401	87.62 406

v1240		81.70 340	79.83 350	87.30 341	85.50 417	77.70 324	95.48 288	93.44 364	78.46 360	76.53 338	86.44 381	60.85 346	95.84 327	71.59 367	70.17 382	88.35 393

OpenMVS	79.58 14	86.09 258	83.62 287	93.50 83	90.95 326	86.71 38	97.44 126	95.83 187	75.35 390	72.64 383	95.72 166	57.42 381	99.64 71	71.41 368	95.85 133	94.13 296

pm-mvs1		80.05 360	78.02 365	86.15 359	85.42 418	75.81 361	95.11 311	92.69 389	77.13 374	70.36 403	87.43 362	58.44 362	95.27 361	71.36 369	64.25 432	87.36 414

GBi-Net		82.42 329	80.43 340	88.39 302	92.66 257	81.95 158	94.30 336	93.38 368	79.06 352	75.82 352	85.66 392	56.38 390	93.84 412	71.23 370	75.38 351	89.38 351

test1		82.42 329	80.43 340	88.39 302	92.66 257	81.95 158	94.30 336	93.38 368	79.06 352	75.82 352	85.66 392	56.38 390	93.84 412	71.23 370	75.38 351	89.38 351

FMVSNet3		84.71 288	82.71 306	90.70 240	94.55 177	87.71 25	95.92 256	94.67 261	81.73 292	75.82 352	88.08 353	66.99 294	94.47 400	71.23 370	75.38 351	89.91 343

EPMVS		87.47 236	85.90 241	92.18 163	95.41 145	82.26 148	87.00 440	96.28 144	85.88 173	84.23 240	85.57 396	75.07 194	96.26 308	71.14 373	92.50 180	98.03 97

QAPM		86.88 243	84.51 266	93.98 55	94.04 203	85.89 50	97.19 145	96.05 164	73.62 405	75.12 360	95.62 175	62.02 337	99.74 53	70.88 374	96.06 127	96.30 235

thres200		88.92 189	87.65 201	92.73 122	96.30 111	85.62 61	97.85 90	98.86 1	84.38 223	84.82 229	93.99 251	75.12 193	98.01 183	70.86 375	86.67 266	94.56 289

PM-MVS		69.32 434	66.93 433	76.49 450	73.60 481	55.84 477	85.91 448	79.32 484	74.72 397	61.09 455	78.18 454	21.76 485	91.10 445	70.86 375	56.90 448	82.51 458

MS-PatchMatch		83.05 318	81.82 319	86.72 352	89.64 362	79.10 267	94.88 319	94.59 270	79.70 338	70.67 401	89.65 326	50.43 418	96.82 287	70.82 377	95.99 131	84.25 448

FE-MVS		86.06 259	84.15 276	91.78 190	94.33 191	79.81 242	84.58 457	96.61 97	76.69 383	85.00 226	87.38 363	70.71 261	98.37 165	70.39 378	91.70 195	97.17 189

PatchMatch-RL		85.00 285	83.66 283	89.02 288	95.86 128	74.55 373	92.49 380	93.60 359	79.30 346	79.29 307	91.47 297	58.53 361	98.45 160	70.22 379	92.17 190	94.07 298

test_f		64.01 444	62.13 446	69.65 460	63.00 492	45.30 491	83.66 461	80.68 481	61.30 464	55.70 472	72.62 475	14.23 491	84.64 477	69.84 380	58.11 444	79.00 474

BH-RMVSNet		86.84 244	85.28 254	91.49 208	95.35 148	80.26 228	96.95 174	92.21 398	82.86 271	81.77 282	95.46 182	59.34 355	97.64 204	69.79 381	93.81 162	96.57 225

FMVSNet2		82.79 323	80.44 339	89.83 272	92.66 257	85.43 64	95.42 291	94.35 291	79.06 352	74.46 365	87.28 364	56.38 390	94.31 404	69.72 382	74.68 357	89.76 344

thres100view900		88.30 209	86.95 224	92.33 150	96.10 119	84.90 85	97.14 153	98.85 2	82.69 275	83.41 258	93.66 260	75.43 183	97.93 186	69.04 383	86.24 273	94.17 293

tfpn200view9		88.48 203	87.15 217	92.47 137	96.21 114	85.30 70	97.44 126	98.85 2	83.37 257	83.99 245	93.82 256	75.36 186	97.93 186	69.04 383	86.24 273	94.17 293

thres400		88.42 206	87.15 217	92.23 158	96.21 114	85.30 70	97.44 126	98.85 2	83.37 257	83.99 245	93.82 256	75.36 186	97.93 186	69.04 383	86.24 273	93.45 309

PatchmatchNet		86.83 245	85.12 259	91.95 177	94.12 199	82.27 147	86.55 444	95.64 198	84.59 214	82.98 265	84.99 408	77.26 134	95.96 322	68.61 386	91.34 199	97.64 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		81.01 352	80.08 344	83.79 397	87.91 388	70.51 414	94.29 340	95.65 197	80.83 305	72.54 385	88.84 336	63.71 321	92.32 430	68.58 387	68.36 402	88.55 385

v7n		79.32 369	77.34 369	85.28 376	84.05 436	72.89 392	93.38 361	93.87 329	75.02 395	70.68 400	84.37 412	59.58 352	95.62 345	67.60 388	67.50 412	87.32 415

PS-CasMVS		80.27 359	79.18 355	83.52 403	87.56 392	69.88 420	94.08 343	95.29 225	80.27 325	72.08 388	88.51 343	59.22 357	92.23 432	67.49 389	68.15 405	88.45 391

RPSCF		77.73 386	76.63 376	81.06 425	88.66 378	55.76 479	87.77 434	87.88 450	64.82 452	74.14 367	92.79 276	49.22 424	96.81 288	67.47 390	76.88 342	90.62 327

test_vis3_rt		54.10 451	51.04 454	63.27 469	58.16 493	46.08 490	84.17 458	49.32 504	56.48 478	36.56 488	49.48 491	8.03 499	91.91 437	67.29 391	49.87 471	51.82 490

thres600view7		88.06 215	86.70 231	92.15 166	96.10 119	85.17 76	97.14 153	98.85 2	82.70 274	83.41 258	93.66 260	75.43 183	97.82 195	67.13 392	85.88 278	93.45 309

SSC-MVS3.2		81.06 350	79.49 354	85.75 366	89.78 355	73.00 389	94.40 332	95.23 228	83.76 248	76.61 337	87.82 357	49.48 423	94.88 385	66.80 393	71.56 374	89.38 351

tpm cat1		83.63 308	81.38 325	90.39 249	93.53 221	78.19 304	85.56 451	95.09 232	70.78 431	78.51 312	83.28 424	74.80 198	97.03 266	66.77 394	84.05 291	95.95 241

WB-MVSnew		84.08 301	83.51 290	85.80 363	91.34 318	76.69 343	95.62 283	96.27 145	81.77 291	81.81 281	92.81 274	58.23 363	94.70 393	66.66 395	87.06 263	85.99 433

pmmvs6		74.65 406	71.67 413	83.60 402	79.13 462	69.94 419	93.31 367	90.88 426	61.05 467	65.83 431	84.15 415	43.43 442	94.83 388	66.62 396	60.63 441	86.02 432

EPNet_dtu		87.65 231	87.89 196	86.93 347	94.57 175	71.37 410	96.72 193	96.50 116	88.56 88	87.12 196	95.02 207	75.91 171	94.01 409	66.62 396	90.00 214	95.42 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
USDC		78.65 377	76.25 378	85.85 362	87.58 391	74.60 372	89.58 415	90.58 430	84.05 234	63.13 443	88.23 350	40.69 459	96.86 286	66.57 398	75.81 349	86.09 430

JIA-IIPM		79.00 371	77.20 370	84.40 392	89.74 359	64.06 451	75.30 480	95.44 212	62.15 459	81.90 278	59.08 486	78.92 103	95.59 347	66.51 399	85.78 280	93.54 306

pmmvs-eth3d		73.59 410	70.66 418	82.38 415	76.40 473	73.38 381	89.39 419	89.43 438	72.69 415	60.34 458	77.79 455	46.43 436	91.26 444	66.42 400	57.06 447	82.51 458

MIMVSNet		79.18 370	75.99 380	88.72 295	87.37 394	80.66 209	79.96 466	91.82 403	77.38 371	74.33 366	81.87 437	41.78 450	90.74 448	66.36 401	83.10 298	94.76 282

K. test v3		73.62 409	71.59 414	79.69 432	82.98 443	59.85 469	90.85 404	88.83 443	77.13 374	58.90 462	82.11 433	43.62 441	91.72 439	65.83 402	54.10 456	87.50 412

FMVSNet1		79.50 366	76.54 377	88.39 302	88.47 380	81.95 158	94.30 336	93.38 368	73.14 410	72.04 389	85.66 392	43.86 440	93.84 412	65.48 403	72.53 368	89.38 351

LF4IMVS		72.36 420	70.82 416	76.95 448	79.18 461	56.33 475	86.12 447	86.11 461	69.30 439	63.06 444	86.66 376	33.03 473	92.25 431	65.33 404	68.64 399	82.28 462

UnsupCasMVSNet_eth		73.25 414	70.57 419	81.30 422	77.53 467	66.33 442	87.24 438	93.89 328	80.38 319	57.90 467	81.59 438	42.91 447	90.56 449	65.18 405	48.51 475	87.01 418

EU-MVSNet		76.92 395	76.95 373	76.83 449	84.10 434	54.73 481	91.77 392	92.71 388	72.74 414	69.57 413	88.69 338	58.03 368	87.43 469	64.91 406	70.00 388	88.33 394

FE-MVSNET2		73.72 408	70.80 417	82.46 414	74.97 478	73.81 379	91.88 390	91.73 407	76.70 382	59.74 461	77.41 458	42.26 449	90.52 450	64.75 407	57.79 446	83.06 453

sc_t1		72.37 419	68.03 430	85.39 374	83.78 439	70.51 414	91.27 399	83.70 474	52.46 481	68.29 417	82.02 435	30.58 478	94.81 389	64.50 408	55.69 449	90.85 326

PEN-MVS		79.47 367	78.26 363	83.08 406	86.36 404	68.58 428	93.85 351	94.77 252	79.76 336	71.37 393	88.55 340	59.79 349	92.46 426	64.50 408	65.40 427	88.19 396

WR-MVS_H		81.02 351	80.09 343	83.79 397	88.08 386	71.26 411	94.46 327	96.54 110	80.08 330	72.81 382	86.82 373	70.36 263	92.65 425	64.18 410	67.50 412	87.46 413

test0.0.03 1		82.79 323	82.48 309	83.74 399	86.81 398	72.22 393	96.52 208	95.03 236	83.76 248	73.00 379	93.20 266	72.30 234	88.88 458	64.15 411	77.52 341	90.12 337

MDTV_nov1_ep13			83.69 280		94.09 201	81.01 192	86.78 442	96.09 160	83.81 246	84.75 231	84.32 413	74.44 205	96.54 298	63.88 412	85.07 286

dp		84.30 298	82.31 311	90.28 255	94.24 193	77.97 308	86.57 443	95.53 203	79.94 334	80.75 289	85.16 404	71.49 250	96.39 303	63.73 413	83.36 296	96.48 227

tmp_tt		41.54 459	41.93 461	40.38 479	20.10 505	26.84 503	61.93 491	59.09 500	14.81 498	28.51 493	80.58 444	35.53 467	48.33 500	63.70 414	13.11 497	45.96 493

SixPastTwentyTwo		76.04 398	74.32 398	81.22 423	84.54 428	61.43 463	91.16 400	89.30 440	77.89 363	64.04 438	86.31 385	48.23 425	94.29 405	63.54 415	63.84 434	87.93 401

CR-MVSNet		83.53 309	81.36 326	90.06 261	90.16 347	79.75 245	79.02 472	91.12 419	84.24 230	82.27 274	80.35 446	75.45 181	93.67 416	63.37 416	86.25 271	96.75 219

lessismore_v0				79.98 431	80.59 450	58.34 472		80.87 480		58.49 464	83.46 421	43.10 445	93.89 411	63.11 417	48.68 474	87.72 403

ITE_SJBPF				82.38 415	87.00 396	65.59 444		89.55 436	79.99 333	69.37 414	91.30 301	41.60 452	95.33 357	62.86 418	74.63 358	86.24 427

ACMH+	76.62 16	77.47 390	74.94 391	85.05 379	91.07 325	71.58 407	93.26 368	90.01 432	71.80 426	64.76 436	88.55 340	41.62 451	96.48 300	62.35 419	71.00 377	87.09 417

KD-MVS_2432*1600		77.63 387	74.92 392	85.77 364	90.86 330	79.44 254	88.08 430	93.92 325	76.26 385	67.05 423	82.78 426	72.15 238	91.92 435	61.53 420	41.62 487	85.94 434

miper_refine_blended		77.63 387	74.92 392	85.77 364	90.86 330	79.44 254	88.08 430	93.92 325	76.26 385	67.05 423	82.78 426	72.15 238	91.92 435	61.53 420	41.62 487	85.94 434

ambc				76.02 452	68.11 486	51.43 482	64.97 490	89.59 435		60.49 457	74.49 469	17.17 488	92.46 426	61.50 422	52.85 466	84.17 449

DTE-MVSNet		78.37 378	77.06 372	82.32 417	85.22 423	67.17 438	93.40 360	93.66 354	78.71 357	70.53 402	88.29 349	59.06 358	92.23 432	61.38 423	63.28 436	87.56 409

KD-MVS_self_test		70.97 427	69.31 425	75.95 454	76.24 475	55.39 480	87.45 435	90.94 425	70.20 435	62.96 446	77.48 457	44.01 439	88.09 463	61.25 424	53.26 464	84.37 447

FMVSNet5		76.46 397	74.16 400	83.35 405	90.05 350	76.17 350	89.58 415	89.85 433	71.39 429	65.29 435	80.42 445	50.61 417	87.70 468	61.05 425	69.24 395	86.18 428

mvs5depth		71.40 425	68.36 429	80.54 429	75.31 477	65.56 445	79.94 467	85.14 464	69.11 440	71.75 391	81.59 438	41.02 456	93.94 410	60.90 426	50.46 470	82.10 463

tt0320-xc		69.70 429	65.27 441	82.99 407	84.33 430	71.92 401	89.56 417	82.08 478	50.11 482	61.87 452	77.50 456	30.48 479	92.34 429	60.30 427	51.20 469	84.71 443

UnsupCasMVSNet_bld		68.60 438	64.50 442	80.92 426	74.63 479	67.80 431	83.97 459	92.94 385	65.12 451	54.63 474	68.23 482	35.97 466	92.17 434	60.13 428	44.83 482	82.78 456

SD_0403		81.29 346	81.13 330	81.78 421	90.20 345	60.43 465	89.97 411	91.31 418	83.87 242	71.78 390	93.08 271	63.86 320	89.61 455	60.00 429	86.07 276	95.30 267

tpmvs		83.04 319	80.77 333	89.84 271	95.43 144	77.96 309	85.59 450	95.32 222	75.31 392	76.27 344	83.70 419	73.89 211	97.41 236	59.53 430	81.93 313	94.14 295

ADS-MVSNet2		79.57 365	77.53 368	85.71 367	93.78 208	72.13 396	79.48 468	86.11 461	73.09 411	80.14 297	79.99 449	62.15 333	90.14 454	59.49 431	83.52 293	94.85 280

ADS-MVSNet		81.26 347	78.36 361	89.96 267	93.78 208	79.78 243	79.48 468	93.60 359	73.09 411	80.14 297	79.99 449	62.15 333	95.24 364	59.49 431	83.52 293	94.85 280

MSDG		80.62 357	77.77 367	89.14 285	93.43 224	77.24 331	91.89 389	90.18 431	69.86 437	68.02 418	91.94 294	52.21 410	98.84 138	59.32 433	83.12 297	91.35 320

TransMVSNet (Re)		76.94 394	74.38 397	84.62 387	85.92 413	75.25 367	95.28 295	89.18 441	73.88 404	67.22 420	86.46 380	59.64 350	94.10 407	59.24 434	52.57 467	84.50 446

tt0320		70.21 428	66.07 436	82.64 411	83.42 442	70.82 412	89.63 413	84.10 470	49.75 484	62.71 447	77.28 459	33.35 471	92.45 428	58.78 435	55.62 450	84.64 444

ACMH	75.40 17	77.99 382	74.96 390	87.10 345	90.67 335	76.41 347	93.19 371	91.64 410	72.47 420	63.44 441	87.61 361	43.34 443	97.16 258	58.34 436	73.94 359	87.72 403
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmtry		77.36 391	74.59 395	85.67 368	89.75 357	75.75 362	77.85 475	91.12 419	60.28 468	71.23 395	80.35 446	75.45 181	93.56 418	57.94 437	67.34 414	87.68 405

our_test_3		77.90 385	75.37 389	85.48 373	85.39 419	76.74 341	93.63 354	91.67 408	73.39 409	65.72 432	84.65 411	58.20 365	93.13 423	57.82 438	67.87 407	86.57 423

Anonymous20240521		72.06 422	69.91 422	78.50 441	77.11 470	61.67 462	91.62 396	90.97 424	65.52 450	62.37 448	79.05 452	36.32 464	90.96 446	57.75 439	68.52 400	82.87 454

CL-MVSNet_self_test		75.81 400	74.14 401	80.83 427	78.33 465	67.79 432	94.22 341	93.52 362	77.28 373	69.82 411	81.54 440	61.47 344	89.22 457	57.59 440	53.51 463	85.48 438

test_method		56.77 447	54.53 451	63.49 468	76.49 471	40.70 494	75.68 479	74.24 488	19.47 496	48.73 478	71.89 478	19.31 486	65.80 496	57.46 441	47.51 479	83.97 450

AllTest		75.92 399	73.06 407	84.47 389	92.18 286	67.29 433	91.07 401	84.43 467	67.63 443	63.48 439	90.18 318	38.20 461	97.16 258	57.04 442	73.37 362	88.97 378

TestCases				84.47 389	92.18 286	67.29 433		84.43 467	67.63 443	63.48 439	90.18 318	38.20 461	97.16 258	57.04 442	73.37 362	88.97 378

EG-PatchMatch MVS		74.92 404	72.02 412	83.62 401	83.76 441	73.28 384	93.62 355	92.04 401	68.57 441	58.88 463	83.80 418	31.87 475	95.57 349	56.97 444	78.67 331	82.00 465

testgi		74.88 405	73.40 405	79.32 435	80.13 453	61.75 460	93.21 369	86.64 459	79.49 342	66.56 429	91.06 304	35.51 468	88.67 459	56.79 445	71.25 375	87.56 409

LTVRE_ROB	73.68 18	77.99 382	75.74 384	84.74 382	90.45 339	72.02 398	86.41 445	91.12 419	72.57 418	66.63 427	87.27 365	54.95 400	96.98 273	56.29 446	75.98 346	85.21 440
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
YYNet1		73.53 413	70.43 420	82.85 409	84.52 429	71.73 405	91.69 394	91.37 414	67.63 443	46.79 480	81.21 442	55.04 399	90.43 451	55.93 447	59.70 443	86.38 425

MDA-MVSNet_test_wron		73.54 412	70.43 420	82.86 408	84.55 427	71.85 402	91.74 393	91.32 417	67.63 443	46.73 481	81.09 443	55.11 398	90.42 452	55.91 448	59.76 442	86.31 426

TDRefinement		69.20 436	65.78 439	79.48 433	66.04 489	62.21 459	88.21 427	86.12 460	62.92 456	61.03 456	85.61 395	33.23 472	94.16 406	55.82 449	53.02 465	82.08 464

ttmdpeth		69.58 430	66.92 434	77.54 445	75.95 476	62.40 458	88.09 429	84.32 469	62.87 457	65.70 433	86.25 387	36.53 463	88.53 461	55.65 450	46.96 480	81.70 468

pmmvs3		65.75 442	62.18 445	76.45 451	67.12 488	64.54 447	88.68 424	85.05 465	54.77 479	57.54 470	73.79 470	29.40 480	86.21 474	55.49 451	47.77 478	78.62 475

DSMNet-mixed		73.13 415	72.45 409	75.19 455	77.51 468	46.82 486	85.09 455	82.01 479	67.61 447	69.27 415	81.33 441	50.89 413	86.28 473	54.54 452	83.80 292	92.46 314

LS3D		82.22 333	79.94 348	89.06 286	97.43 89	74.06 378	93.20 370	92.05 400	61.90 460	73.33 376	95.21 194	59.35 354	99.21 108	54.54 452	92.48 181	93.90 301

MVS-HIRNet		71.36 426	67.00 432	84.46 391	90.58 336	69.74 422	79.15 471	87.74 451	46.09 485	61.96 451	50.50 489	45.14 438	95.64 343	53.74 454	88.11 253	88.00 400

Anonymous20231206		75.29 403	73.64 404	80.22 430	80.75 448	63.38 455	93.36 362	90.71 429	73.09 411	67.12 421	83.70 419	50.33 419	90.85 447	53.63 455	70.10 386	86.44 424

DP-MVS		81.47 343	78.28 362	91.04 226	98.14 60	78.48 288	95.09 314	86.97 454	61.14 466	71.12 398	92.78 277	59.59 351	99.38 95	53.11 456	86.61 267	95.27 269

ppachtmachnet_test		77.19 392	74.22 399	86.13 360	85.39 419	78.22 299	93.98 344	91.36 415	71.74 427	67.11 422	84.87 409	56.67 386	93.37 422	52.21 457	64.59 429	86.80 419

usedtu_dtu_shiyan2		64.65 443	60.40 447	77.38 446	64.24 490	57.84 473	89.16 420	87.60 452	52.95 480	53.43 476	71.31 481	23.41 483	88.27 462	51.95 458	49.58 472	86.03 431

TinyColmap		72.41 418	68.99 427	82.68 410	88.11 385	69.59 423	88.41 426	85.20 463	65.55 449	57.91 466	84.82 410	30.80 477	95.94 323	51.38 459	68.70 398	82.49 460

PatchT		79.75 362	76.85 374	88.42 299	89.55 365	75.49 365	77.37 476	94.61 268	63.07 455	82.46 268	73.32 473	75.52 180	93.41 421	51.36 460	84.43 289	96.36 229

new-patchmatchnet		68.85 437	65.93 438	77.61 444	73.57 482	63.94 452	90.11 410	88.73 446	71.62 428	55.08 473	73.60 471	40.84 457	87.22 471	51.35 461	48.49 476	81.67 469

COLMAP_ROB	73.24 19	75.74 401	73.00 408	83.94 395	92.38 267	69.08 426	91.85 391	86.93 455	61.48 463	65.32 434	90.27 317	42.27 448	96.93 278	50.91 462	75.63 350	85.80 437
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test20.03		72.36 420	71.15 415	75.98 453	77.79 466	59.16 470	92.40 382	89.35 439	74.09 402	61.50 453	84.32 413	48.09 426	85.54 476	50.63 463	62.15 439	83.24 452

myMVS_eth3d		81.93 336	82.18 312	81.18 424	92.13 290	67.18 435	93.97 345	94.23 302	82.43 279	73.39 372	93.57 262	76.98 144	87.86 465	50.53 464	82.34 308	88.51 386

new_pmnet		66.18 441	63.18 443	75.18 456	76.27 474	61.74 461	83.79 460	84.66 466	56.64 477	51.57 477	71.85 479	31.29 476	87.93 464	49.98 465	62.55 437	75.86 478

TAPA-MVS	81.61 12	85.02 284	83.67 282	89.06 286	96.79 103	73.27 386	95.92 256	94.79 251	74.81 396	80.47 292	96.83 139	71.07 253	98.19 173	49.82 466	92.57 178	95.71 253
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet		52.52 452	48.24 455	65.35 464	47.63 501	41.45 493	72.55 484	83.62 475	31.75 489	37.66 487	57.92 487	9.19 498	76.76 489	49.26 467	44.60 483	77.84 476

WAC-MVS						67.18 435								49.00 468

Patchmatch-test		78.25 379	74.72 394	88.83 292	91.20 319	74.10 377	73.91 483	88.70 447	59.89 471	66.82 425	85.12 406	78.38 113	94.54 398	48.84 469	79.58 324	97.86 117

MDA-MVSNet-bldmvs		71.45 424	67.94 431	81.98 419	85.33 421	68.50 429	92.35 383	88.76 445	70.40 432	42.99 484	81.96 436	46.57 435	91.31 443	48.75 470	54.39 455	86.11 429

tfpnnormal		78.14 380	75.42 388	86.31 357	88.33 384	79.24 260	94.41 329	96.22 150	73.51 406	69.81 412	85.52 398	55.43 395	95.75 335	47.65 471	67.86 408	83.95 451

MIMVSNet1		69.44 433	66.65 435	77.84 442	76.48 472	62.84 457	87.42 436	88.97 442	66.96 448	57.75 469	79.72 451	32.77 474	85.83 475	46.32 472	63.42 435	84.85 442

RPMNet		79.85 361	75.92 381	91.64 199	90.16 347	79.75 245	79.02 472	95.44 212	58.43 475	82.27 274	72.55 476	73.03 223	98.41 163	46.10 473	86.25 271	96.75 219

FE-MVSNET		69.26 435	66.03 437	78.93 437	73.82 480	68.33 430	89.65 412	84.06 471	70.21 434	57.79 468	76.94 463	41.48 453	86.98 472	45.85 474	54.51 454	81.48 470

kuosan		73.55 411	72.39 411	77.01 447	89.68 361	66.72 441	85.24 454	93.44 364	67.76 442	60.04 460	83.40 422	71.90 243	84.25 478	45.34 475	54.75 451	80.06 473

OpenMVS_ROB	68.52 20	73.02 416	69.57 423	83.37 404	80.54 451	71.82 403	93.60 357	88.22 448	62.37 458	61.98 450	83.15 425	35.31 469	95.47 351	45.08 476	75.88 348	82.82 455

DeepMVS_CX				64.06 467	78.53 464	43.26 492		68.11 496	69.94 436	38.55 486	76.14 465	18.53 487	79.34 484	43.72 477	41.62 487	69.57 482

testing3		80.74 355	81.17 328	79.44 434	91.15 322	63.48 454	97.16 150	95.76 190	80.83 305	71.36 394	93.15 269	78.22 117	87.30 470	43.19 478	79.67 322	87.55 411

MVStest1		66.93 440	63.01 444	78.69 438	78.56 463	71.43 409	85.51 452	86.81 456	49.79 483	48.57 479	84.15 415	53.46 406	83.31 479	43.14 479	37.15 490	81.34 471

N_pmnet		61.30 445	60.20 448	64.60 466	84.32 431	17.00 507	91.67 395	10.98 505	61.77 461	58.45 465	78.55 453	49.89 421	91.83 438	42.27 480	63.94 433	84.97 441

dmvs_testset		72.00 423	73.36 406	67.91 461	83.83 438	31.90 501	85.30 453	77.12 486	82.80 272	63.05 445	92.46 279	61.54 342	82.55 483	42.22 481	71.89 373	89.29 356

APD_test1		56.56 448	53.58 452	65.50 463	67.93 487	46.51 488	77.24 478	72.95 489	38.09 487	42.75 485	75.17 466	13.38 492	82.78 482	40.19 482	54.53 453	67.23 484

PMMVS2		50.90 454	46.31 457	64.67 465	55.53 495	46.67 487	77.30 477	71.02 491	40.89 486	34.16 490	59.32 485	9.83 497	76.14 491	40.09 483	28.63 493	71.21 480

test_0402		72.68 417	69.54 424	82.09 418	88.67 377	71.81 404	92.72 378	86.77 458	61.52 462	62.21 449	83.91 417	43.22 444	93.76 415	34.60 484	72.23 372	80.72 472

dongtai		69.47 432	68.98 428	70.93 458	86.87 397	58.45 471	88.19 428	93.18 378	63.98 453	56.04 471	80.17 448	70.97 257	79.24 485	33.46 485	47.94 477	75.09 479

Syy-MVS		77.97 384	78.05 364	77.74 443	92.13 290	56.85 474	93.97 345	94.23 302	82.43 279	73.39 372	93.57 262	57.95 369	87.86 465	32.40 486	82.34 308	88.51 386

FPMVS		55.09 450	52.93 453	61.57 470	55.98 494	40.51 495	83.11 463	83.41 476	37.61 488	34.95 489	71.95 477	14.40 490	76.95 488	29.81 487	65.16 428	67.25 483

EGC-MVSNET		52.46 453	47.56 456	67.15 462	81.98 446	60.11 467	82.54 464	72.44 490	0.11 502	0.70 503	74.59 468	25.11 482	83.26 480	29.04 488	61.51 440	58.09 487

ANet_high		46.22 455	41.28 462	61.04 471	39.91 503	46.25 489	70.59 487	76.18 487	58.87 474	23.09 495	48.00 492	12.58 494	66.54 495	28.65 489	13.62 496	70.35 481

testf1		45.70 456	42.41 458	55.58 474	53.29 498	40.02 496	68.96 488	62.67 498	27.45 491	29.85 491	61.58 483	5.98 500	73.83 493	28.49 490	43.46 485	52.90 488

APD_test2		45.70 456	42.41 458	55.58 474	53.29 498	40.02 496	68.96 488	62.67 498	27.45 491	29.85 491	61.58 483	5.98 500	73.83 493	28.49 490	43.46 485	52.90 488

Gipuma		45.11 458	42.05 460	54.30 476	80.69 449	51.30 483	35.80 494	83.81 473	28.13 490	27.94 494	34.53 494	11.41 496	76.70 490	21.45 492	54.65 452	34.90 494
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	35.65 22	33.85 461	29.49 466	46.92 478	41.86 502	36.28 498	50.45 493	56.52 501	18.75 497	18.28 496	37.84 493	2.41 503	58.41 497	18.71 493	20.62 494	46.06 492
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	34.80 23	39.19 460	35.53 463	50.18 477	29.72 504	30.30 502	59.60 492	66.20 497	26.06 493	17.91 497	49.53 490	3.12 502	74.09 492	18.19 494	49.40 473	46.14 491
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		57.26 446	56.22 449	60.39 472	69.29 483	35.91 499	86.39 446	70.06 492	59.84 472	46.46 482	72.71 474	51.18 412	78.11 486	15.19 495	34.89 491	67.14 485

SSC-MVS		56.01 449	54.96 450	59.17 473	68.42 485	34.13 500	84.98 456	69.23 493	58.08 476	45.36 483	71.67 480	50.30 420	77.46 487	14.28 496	32.33 492	65.91 486

E-PMN		32.70 462	32.39 464	33.65 480	53.35 497	25.70 504	74.07 482	53.33 502	21.08 494	17.17 498	33.63 496	11.85 495	54.84 498	12.98 497	14.04 495	20.42 495

EMVS		31.70 463	31.45 465	32.48 481	50.72 500	23.95 505	74.78 481	52.30 503	20.36 495	16.08 499	31.48 497	12.80 493	53.60 499	11.39 498	13.10 498	19.88 496

wuyk23d		14.10 465	13.89 468	14.72 482	55.23 496	22.91 506	33.83 495	3.56 506	4.94 499	4.11 500	2.28 502	2.06 504	19.66 501	10.23 499	8.74 499	1.59 499

testmvs		9.92 466	12.94 469	0.84 484	0.65 506	0.29 509	93.78 352	0.39 507	0.42 500	2.85 501	15.84 500	0.17 506	0.30 503	2.18 500	0.21 500	1.91 498

test123		9.07 467	11.73 470	1.11 483	0.50 507	0.77 508	89.44 418	0.20 508	0.34 501	2.15 502	10.72 501	0.34 505	0.32 502	1.79 501	0.08 501	2.23 497

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

cdsmvs_eth3d_5k		21.43 464	28.57 467	0.00 485	0.00 508	0.00 510	0.00 496	95.93 179	0.00 503	0.00 504	97.66 94	63.57 322	0.00 504	0.00 502	0.00 502	0.00 500

pcd_1.5k_mvsjas		5.92 469	7.89 472	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	71.04 254	0.00 504	0.00 502	0.00 502	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

ab-mvs-re		8.11 468	10.81 471	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	97.30 117	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

TestfortrainingZip				97.22 3	99.48 2	91.93 7	98.35 57	97.26 24	85.61 179	99.54 1	99.26 1	91.36 5	99.98 2		96.55 116	99.73 3

FOURS1					98.51 44	78.01 307	98.13 71	96.21 151	83.04 264	94.39 71

test_one_0601					98.91 23	84.56 91		96.70 83	88.06 102	96.57 37	98.77 16	88.04 24

eth-test2					0.00 508

eth-test					0.00 508

test_241102_ONE					99.03 20	85.03 81		96.78 66	88.72 84	97.79 11	98.90 6	88.48 20	99.82 24

save fliter					98.24 56	83.34 117	98.61 46	96.57 104	91.32 47

test0726					99.05 14	85.18 72	99.11 19	96.78 66	88.75 82	97.65 18	98.91 3	87.69 26

GSMVS																97.54 149

test_part2					98.90 24	85.14 78				96.07 44

sam_mvs1												77.59 128				97.54 149

sam_mvs												75.35 188

MTGPA								96.33 140

test_post											33.80 495	76.17 163	95.97 319

patchmatchnet-post											77.09 462	77.78 126	95.39 353

MTMP							97.53 118	68.16 495

TEST9					98.64 36	83.71 105	97.82 92	96.65 91	84.29 228	95.16 55	98.09 67	84.39 46	99.36 98

test_8					98.63 38	83.64 111	97.81 94	96.63 96	84.50 218	95.10 58	98.11 65	84.33 47	99.23 106

agg_prior					98.59 40	83.13 122		96.56 106		94.19 73			99.16 117

test_prior4						82.34 146	97.75 100

test_prior				93.09 102	98.68 31	81.91 162		96.40 129					99.06 125			98.29 78

新几何2							96.42 219

旧先验1					97.39 93	79.58 253		96.54 110			98.08 70	84.00 53			97.42 81	97.62 142

原ACMM2							96.84 181

test222					96.15 117	78.41 292	95.87 268	96.46 121	71.97 425	89.66 145	97.45 107	76.33 159			98.24 55	98.30 77

segment_acmp												82.69 67

testdata1							95.57 286		87.44 122

test12				94.25 44	98.34 51	85.55 62		96.35 139		92.36 100		80.84 75	99.22 107		98.31 53	97.98 106

plane_prior7					91.86 304	77.55 326

plane_prior6					91.98 299	77.92 312						64.77 314

plane_prior4											94.15 245

plane_prior3						77.75 322			90.17 67	81.33 283

plane_prior2							97.18 146		89.89 70

plane_prior1					91.95 301

plane_prior						77.96 309	97.52 121		90.36 65						82.96 301

n2								0.00 509

nn								0.00 509

door-mid								79.75 483

test11								96.50 116

door								80.13 482

HQP5-MVS						78.48 288

HQP-NCC					92.08 293		97.63 107		90.52 60	82.30 270

ACMP_Plane					92.08 293		97.63 107		90.52 60	82.30 270

HQP4-MVS										82.30 270			97.32 246			91.13 321

HQP3-MVS								94.80 249							83.01 299

HQP2-MVS												65.40 307

NP-MVS					92.04 297	78.22 299					94.56 228

ACMMP++_ref															78.45 336

ACMMP++															79.05 328

Test By Simon												71.65 246

High-resolution multi-view benchmark