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_l_conf0.5_n_9		97.59 13	97.79 6	96.97 87	98.28 96	91.49 146	97.61 141	98.71 13	97.10 5	99.70 1	98.93 24	90.95 77	99.77 53	99.35 6	99.53 33	99.65 21

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 102	98.43 84	90.32 207	97.80 105	98.53 29	97.24 4	99.62 2	99.14 2	88.65 110	99.80 41	99.54 1	99.15 94	99.74 10

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 87	98.24 102	91.96 128	97.89 89	98.72 12	96.77 7	99.46 3	99.06 12	87.78 128	99.84 27	99.40 4	99.27 75	99.12 94

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 120	98.42 85	91.37 153	98.04 64	98.00 118	97.30 3	99.45 4	99.21 1	89.28 98	99.80 41	99.27 10	99.35 69	98.12 231

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 35	98.14 115	93.94 58	97.93 84	98.65 23	96.70 8	99.38 5	99.07 11	89.92 92	99.81 36	99.16 14	99.43 53	99.61 30

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 155	97.98 127	90.43 199	97.50 157	98.59 26	96.59 10	99.31 6	99.08 8	84.47 212	99.75 59	99.37 5	98.45 134	97.88 252

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 62	98.21 108	92.75 94	97.83 99	98.73 10	95.04 47	99.30 7	98.84 38	93.34 26	99.78 50	99.32 7	99.13 97	99.50 52

test_fmvsm_n_1920		97.55 16	97.89 4	96.53 106	98.41 87	91.73 132	98.01 67	99.02 1	96.37 13	99.30 7	98.92 25	92.39 45	99.79 47	99.16 14	99.46 46	98.08 239

fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 108	97.98 127	91.19 163	97.84 96	98.65 23	97.08 6	99.25 9	99.10 6	87.88 126	99.79 47	99.32 7	99.18 90	98.59 180

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 69	98.25 101	92.59 102	97.81 104	98.68 18	94.93 50	99.24 10	98.87 33	93.52 23	99.79 47	99.32 7	99.21 83	99.40 66

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 90	97.28 171	91.73 132	97.75 111	98.50 30	94.86 54	99.22 11	98.78 42	89.75 95	99.76 55	99.10 17	99.29 73	98.94 125

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 89	98.28 96	91.07 171	97.76 109	98.62 25	97.53 2	99.20 12	99.12 5	88.24 118	99.81 36	99.41 3	99.17 91	99.67 16

SED-MVS		98.05 3	97.99 2	98.24 12	99.42 10	95.30 19	98.25 40	98.27 55	95.13 42	99.19 13	98.89 30	95.54 5	99.85 22	97.52 42	99.66 10	99.56 40

test_241102_ONE					99.42 10	95.30 19		98.27 55	95.09 45	99.19 13	98.81 39	95.54 5	99.65 80

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 159	97.30 169	90.37 205	97.53 153	97.92 128	96.52 11	99.14 15	99.08 8	83.21 236	99.74 60	99.22 11	98.06 152	97.88 252

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 178	97.76 143	89.57 239	97.66 130	98.66 21	95.36 32	99.03 16	98.90 27	88.39 115	99.73 62	99.17 13	98.66 122	98.08 239

SD-MVS		97.41 23	97.53 18	97.06 83	98.57 79	94.46 40	97.92 85	98.14 84	94.82 59	99.01 17	98.55 51	94.18 15	97.41 416	96.94 59	99.64 14	99.32 74
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
test0726					99.45 6	95.36 15	98.31 32	98.29 50	94.92 52	98.99 18	98.92 25	95.08 9

TestfortrainingZip a		97.79 7	97.62 12	98.28 10	99.56 1	95.15 25	98.69 11	98.35 41	95.63 25	98.95 19	98.95 20	93.45 24	99.88 4	96.63 71	98.41 137	99.82 1

IU-MVS					99.42 10	95.39 13		97.94 125	90.40 274	98.94 20				97.41 49	99.66 10	99.74 10

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 143	95.48 329	90.69 190	97.91 86	98.33 45	94.07 94	98.93 21	99.14 2	87.44 142	99.61 92	98.63 26	98.32 140	98.18 224

DVP-MVS++		98.06 2	97.99 2	98.28 10	98.67 68	95.39 13	99.29 1	98.28 52	94.78 63	98.93 21	98.87 33	96.04 2	99.86 11	97.45 46	99.58 25	99.59 32

test_241102_TWO								98.27 55	95.13 42	98.93 21	98.89 30	94.99 12	99.85 22	97.52 42	99.65 13	99.74 10

test_fmvsmconf_n		97.49 21	97.56 16	97.29 65	97.44 166	92.37 109	97.91 86	98.88 4	95.83 19	98.92 24	99.05 14	91.45 62	99.80 41	99.12 16	99.46 46	99.69 15

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 141	97.64 152	90.72 189	98.00 68	98.73 10	94.55 75	98.91 25	99.08 8	88.22 119	99.63 89	98.91 21	98.37 138	98.25 219

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 80	97.58 162	92.56 103	97.68 126	98.47 34	94.02 96	98.90 26	98.89 30	88.94 104	99.78 50	99.18 12	99.03 106	98.93 129

PC_three_1452									90.77 250	98.89 27	98.28 86	96.24 1	98.35 294	95.76 108	99.58 25	99.59 32

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 45	95.42 12	97.94 82	98.18 77	90.57 267	98.85 28	98.94 23	93.33 27	99.83 32	96.72 67	99.68 4	99.63 26
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
fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 158	96.67 234	90.25 209	97.91 86	98.38 37	94.48 79	98.84 29	99.14 2	88.06 121	99.62 91	98.82 23	98.60 126	98.15 228

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 152	98.07 122	90.28 208	97.97 78	98.76 9	94.93 50	98.84 29	99.06 12	88.80 107	99.65 80	99.06 18	98.63 124	98.18 224

aaatest				98.00 25	99.56 1	94.50 37	98.69 11	98.70 16	93.45 124	98.73 31	98.53 53		99.86 11	97.40 50	99.58 25	99.65 21

MED-MVS		98.08 1	98.08 1	98.06 21	99.56 1	94.50 37	98.69 11	98.70 16	95.63 25	98.73 31	98.95 20	95.46 7	99.86 11	97.40 50	99.63 16	99.82 1

DVP-MVS		97.91 4	97.81 5	98.22 15	99.45 6	95.36 15	98.21 48	97.85 138	94.92 52	98.73 31	98.87 33	95.08 9	99.84 27	97.52 42	99.67 6	99.48 56
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_THIRD									94.78 63	98.73 31	98.87 33	95.87 4	99.84 27	97.45 46	99.72 2	99.77 4

DPE-MVS		97.86 5	97.65 10	98.47 5	99.17 39	95.78 8	97.21 202	98.35 41	95.16 40	98.71 35	98.80 40	95.05 11	99.89 3	96.70 69	99.73 1	99.73 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
lecture		97.58 15	97.63 11	97.43 59	99.37 19	92.93 88	98.86 7	98.85 5	95.27 36	98.65 36	98.90 27	91.97 53	99.80 41	97.63 38	99.21 83	99.57 36

TSAR-MVS + MP.		97.42 22	97.33 29	97.69 47	99.25 33	94.24 47	98.07 61	97.85 138	93.72 107	98.57 37	98.35 72	93.69 20	99.40 135	97.06 57	99.46 46	99.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSP-MVS		97.59 13	97.54 17	97.73 43	99.40 14	93.77 63	98.53 19	98.29 50	95.55 29	98.56 38	97.81 140	93.90 17	99.65 80	96.62 72	99.21 83	99.77 4
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
FOURS1					99.55 4	93.34 73	99.29 1	98.35 41	94.98 48	98.49 39

test_one_0601					99.32 27	95.20 22		98.25 61	95.13 42	98.48 40	98.87 33	95.16 8

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 214	97.29 170	88.38 296	97.23 199	98.47 34	95.14 41	98.43 41	99.09 7	87.58 134	99.72 66	98.80 25	99.21 83	98.02 243

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 74	95.67 320	92.21 116	97.95 81	98.27 55	95.78 23	98.40 42	99.00 16	89.99 90	99.78 50	99.06 18	99.41 59	99.59 32

APDe-MVS		97.82 6	97.73 9	98.08 20	99.15 40	94.82 31	98.81 8	98.30 48	94.76 66	98.30 43	98.90 27	93.77 19	99.68 76	97.93 29	99.69 3	99.75 8
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SF-MVS		97.39 24	97.13 31	98.17 17	99.02 49	95.28 21	98.23 44	98.27 55	92.37 178	98.27 44	98.65 47	93.33 27	99.72 66	96.49 77	99.52 35	99.51 49

BridgeMVS		96.84 56	96.89 48	96.68 94	97.63 154	92.22 115	98.17 54	97.82 145	94.44 81	98.23 45	97.36 187	90.97 76	99.22 154	97.74 32	99.66 10	98.61 178

TestfortrainingZip				98.34 8	98.54 80	96.25 4	98.69 11	97.85 138	94.15 91	98.17 46	97.94 113	94.00 16	99.63 89		97.45 175	99.15 88

aaEdge-Enhanced		97.54 17	97.39 27	98.00 25	99.21 37	94.50 37	97.75 111	98.34 44	94.23 89	98.15 47	98.53 53	93.32 29	99.84 27	97.40 50	99.58 25	99.65 21

SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 29	98.39 90	94.25 46	98.43 27	98.27 55	95.34 34	98.11 48	98.56 49	94.53 13	99.71 68	96.57 75	99.62 19	99.65 21
Skip Steuart: Steuart Systems R&D Blog.
test_vis1_n_1920		94.17 175	94.58 149	92.91 366	97.42 167	82.02 438	97.83 99	97.85 138	94.68 69	98.10 49	98.49 58	70.15 424	99.32 143	97.91 30	98.82 114	97.40 282

test_part2					99.28 31	95.74 9				98.10 49

APD-MVS		96.95 47	96.60 66	98.01 23	99.03 48	94.93 30	97.72 119	98.10 92	91.50 215	98.01 51	98.32 80	92.33 46	99.58 100	94.85 144	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test-260524					99.31 29	95.74 9		98.19 74		97.99 52		93.53 22	99.87 8	98.08 28	99.63 16

reproduce_model		97.51 20	97.51 20	97.50 55	98.99 53	93.01 84	97.79 107	98.21 67	95.73 24	97.99 52	99.03 15	92.63 40	99.82 34	97.80 31	99.42 56	99.67 16

patch_mono-2		96.83 57	97.44 24	95.01 235	99.05 46	85.39 390	96.98 222	98.77 8	94.70 68	97.99 52	98.66 45	93.61 21	99.91 1	97.67 37	99.50 40	99.72 14

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 226	98.09 118	86.63 356	96.00 327	98.15 82	95.43 30	97.95 55	98.56 49	93.40 25	99.36 139	96.77 64	99.48 44	99.45 59

ACMMP_NAP		97.20 33	96.86 49	98.23 13	99.09 41	95.16 24	97.60 142	98.19 74	92.82 160	97.93 56	98.74 44	91.60 60	99.86 11	96.26 82	99.52 35	99.67 16

reproduce-ours		97.53 18	97.51 20	97.60 52	98.97 54	93.31 75	97.71 122	98.20 69	95.80 21	97.88 57	98.98 18	92.91 32	99.81 36	97.68 33	99.43 53	99.67 16

our_new_method		97.53 18	97.51 20	97.60 52	98.97 54	93.31 75	97.71 122	98.20 69	95.80 21	97.88 57	98.98 18	92.91 32	99.81 36	97.68 33	99.43 53	99.67 16

9.14			96.75 61		98.93 57		97.73 116	98.23 66	91.28 227	97.88 57	98.44 64	93.00 31	99.65 80	95.76 108	99.47 45

CNVR-MVS		97.68 8	97.44 24	98.37 7	98.90 60	95.86 7	97.27 193	98.08 94	95.81 20	97.87 60	98.31 81	94.26 14	99.68 76	97.02 58	99.49 43	99.57 36

test_vis1_n		92.37 259	92.26 243	92.72 374	94.75 379	82.64 428	98.02 66	96.80 306	91.18 234	97.77 61	97.93 114	58.02 483	98.29 301	97.63 38	98.21 145	97.23 291

test_cas_vis1_n_1920		94.48 167	94.55 153	94.28 289	96.78 225	86.45 362	97.63 137	97.64 165	93.32 130	97.68 62	98.36 71	73.75 392	99.08 180	96.73 66	99.05 103	97.31 287

test_fmvsmconf0.01_n		96.15 88	95.85 91	97.03 84	92.66 446	91.83 131	97.97 78	97.84 143	95.57 28	97.53 63	99.00 16	84.20 219	99.76 55	98.82 23	99.08 101	99.48 56

MM		97.29 31	96.98 42	98.23 13	98.01 125	95.03 29	98.07 61	95.76 367	97.78 1	97.52 64	98.80 40	88.09 120	99.86 11	99.44 2	99.37 67	99.80 3

VNet		95.89 98	95.45 101	97.21 72	98.07 122	92.94 87	97.50 157	98.15 82	93.87 102	97.52 64	97.61 167	85.29 195	99.53 114	95.81 107	95.27 259	99.16 86

SR-MVS		97.01 44	96.86 49	97.47 57	99.09 41	93.27 77	97.98 72	98.07 99	93.75 106	97.45 66	98.48 61	91.43 64	99.59 97	96.22 85	99.27 75	99.54 45

APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 77	99.01 52	92.31 112	97.98 72	98.06 102	93.11 141	97.44 67	98.55 51	90.93 78	99.55 110	96.06 95	99.25 80	99.51 49

TSAR-MVS + GP.		96.69 67	96.49 71	97.27 68	98.31 94	93.39 69	96.79 247	96.72 309	94.17 90	97.44 67	97.66 159	92.76 35	99.33 141	96.86 63	97.76 164	99.08 100

SR-MVS-dyc-post		96.88 51	96.80 57	97.11 79	99.02 49	92.34 110	97.98 72	98.03 111	93.52 121	97.43 69	98.51 56	91.40 65	99.56 108	96.05 96	99.26 78	99.43 63

RE-MVS-def			96.72 62		99.02 49	92.34 110	97.98 72	98.03 111	93.52 121	97.43 69	98.51 56	90.71 82		96.05 96	99.26 78	99.43 63

dcpmvs_2		96.37 81	97.05 38	94.31 287	98.96 56	84.11 411	97.56 147	97.51 195	93.92 100	97.43 69	98.52 55	92.75 36	99.32 143	97.32 55	99.50 40	99.51 49

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 103	98.07 122	91.97 126	98.14 55	97.79 147	90.43 272	97.34 72	97.52 177	91.29 68	99.19 157	98.12 27	99.64 14	98.60 179

旧先验2							95.94 330		81.66 463	97.34 72			98.82 212	92.26 212

MSLP-MVS++		96.94 48	97.06 35	96.59 103	98.72 65	91.86 130	97.67 127	98.49 31	94.66 71	97.24 74	98.41 67	92.31 48	98.94 198	96.61 73	99.46 46	98.96 118

HFP-MVS		97.14 37	96.92 47	97.83 31	99.42 10	94.12 52	98.52 20	98.32 46	93.21 132	97.18 75	98.29 84	92.08 50	99.83 32	95.63 116	99.59 21	99.54 45

MGCNet		96.74 64	96.31 81	98.02 22	96.87 207	94.65 33	97.58 143	94.39 438	96.47 12	97.16 76	98.39 68	87.53 137	99.87 8	98.97 20	99.41 59	99.55 43

ACMMPR		97.07 41	96.84 51	97.79 35	99.44 9	93.88 59	98.52 20	98.31 47	93.21 132	97.15 77	98.33 78	91.35 66	99.86 11	95.63 116	99.59 21	99.62 27

region2R		97.07 41	96.84 51	97.77 39	99.46 5	93.79 61	98.52 20	98.24 63	93.19 135	97.14 78	98.34 75	91.59 61	99.87 8	95.46 124	99.59 21	99.64 25

PGM-MVS		96.81 58	96.53 69	97.65 48	99.35 25	93.53 67	97.65 131	98.98 2	92.22 185	97.14 78	98.44 64	91.17 72	99.85 22	94.35 171	99.46 46	99.57 36

PHI-MVS		96.77 60	96.46 76	97.71 46	98.40 88	94.07 54	98.21 48	98.45 36	89.86 284	97.11 80	98.01 106	92.52 43	99.69 74	96.03 99	99.53 33	99.36 72

NCCC		97.30 29	97.03 40	98.11 19	98.77 63	95.06 28	97.34 182	98.04 109	95.96 15	97.09 81	97.88 127	93.18 30	99.71 68	95.84 106	99.17 91	99.56 40

CS-MVS		96.86 52	97.06 35	96.26 136	98.16 114	91.16 168	99.09 3	97.87 133	95.30 35	97.06 82	98.03 103	91.72 55	98.71 246	97.10 56	99.17 91	98.90 134

ZD-MVS					99.05 46	94.59 35		98.08 94	89.22 309	97.03 83	98.10 95	92.52 43	99.65 80	94.58 164	99.31 72

testdata				95.46 211	98.18 113	88.90 275		97.66 161	82.73 453	97.03 83	98.07 98	90.06 88	98.85 208	89.67 280	98.98 109	98.64 176

SPE-MVS-test		96.89 50	97.04 39	96.45 119	98.29 95	91.66 139	99.03 4	97.85 138	95.84 18	96.90 85	97.97 111	91.24 69	98.75 235	96.92 60	99.33 70	98.94 125

mvsany_test1		93.93 194	93.98 172	93.78 322	94.94 369	86.80 349	94.62 398	92.55 473	88.77 331	96.85 86	98.49 58	88.98 102	98.08 326	95.03 134	95.62 249	96.46 317

GDP-MVS		95.62 106	95.13 117	97.09 80	96.79 220	93.26 78	97.89 89	97.83 144	93.58 113	96.80 87	97.82 138	83.06 243	99.16 164	94.40 168	97.95 158	98.87 145

test_fmvs1		93.21 222	93.53 187	92.25 390	96.55 252	81.20 445	97.40 176	96.96 288	90.68 255	96.80 87	98.04 101	69.25 433	98.40 286	97.58 41	98.50 129	97.16 294

test_fmvs1_n		92.73 248	92.88 216	92.29 387	96.08 303	81.05 446	97.98 72	97.08 268	90.72 253	96.79 89	98.18 91	63.07 472	98.45 283	97.62 40	98.42 136	97.36 283

HPM-MVS_fast		96.51 74	96.27 83	97.22 71	99.32 27	92.74 95	98.74 10	98.06 102	90.57 267	96.77 90	98.35 72	90.21 87	99.53 114	94.80 151	99.63 16	99.38 70

h-mvs33		94.15 178	93.52 189	96.04 152	97.81 140	90.22 210	97.62 140	97.58 176	95.19 38	96.74 91	97.45 180	83.67 227	99.61 92	95.85 104	79.73 452	98.29 216

hse-mvs2		93.45 215	92.99 209	94.81 249	97.02 194	88.59 286	96.69 261	96.47 327	95.19 38	96.74 91	96.16 268	83.67 227	98.48 281	95.85 104	79.13 456	97.35 285

GST-MVS		96.85 54	96.52 70	97.82 32	99.36 23	94.14 51	98.29 34	98.13 85	92.72 163	96.70 93	98.06 99	91.35 66	99.86 11	94.83 147	99.28 74	99.47 58

xiu_mvs_v1_base_debu		95.01 140	94.76 139	95.75 184	96.58 245	91.71 135	96.25 306	97.35 233	92.99 145	96.70 93	96.63 241	82.67 254	99.44 131	96.22 85	97.46 171	96.11 329

xiu_mvs_v1_base		95.01 140	94.76 139	95.75 184	96.58 245	91.71 135	96.25 306	97.35 233	92.99 145	96.70 93	96.63 241	82.67 254	99.44 131	96.22 85	97.46 171	96.11 329

xiu_mvs_v1_base_debi		95.01 140	94.76 139	95.75 184	96.58 245	91.71 135	96.25 306	97.35 233	92.99 145	96.70 93	96.63 241	82.67 254	99.44 131	96.22 85	97.46 171	96.11 329

CDPH-MVS		95.97 94	95.38 107	97.77 39	98.93 57	94.44 41	96.35 294	97.88 131	86.98 382	96.65 97	97.89 122	91.99 52	99.47 127	92.26 212	99.46 46	99.39 68

EC-MVSNet		96.42 78	96.47 73	96.26 136	97.01 195	91.52 145	98.89 5	97.75 150	94.42 82	96.64 98	97.68 156	89.32 97	98.60 268	97.45 46	99.11 100	98.67 175

UA-Net		95.95 95	95.53 97	97.20 73	97.67 148	92.98 86	97.65 131	98.13 85	94.81 61	96.61 99	98.35 72	88.87 105	99.51 119	90.36 266	97.35 179	99.11 96

HPM-MVS++		97.34 26	96.97 43	98.47 5	99.08 43	96.16 5	97.55 152	97.97 122	95.59 27	96.61 99	97.89 122	92.57 42	99.84 27	95.95 101	99.51 38	99.40 66

XVS		97.18 34	96.96 45	97.81 33	99.38 17	94.03 56	98.59 17	98.20 69	94.85 55	96.59 101	98.29 84	91.70 57	99.80 41	95.66 111	99.40 61	99.62 27

X-MVStestdata		91.71 286	89.67 356	97.81 33	99.38 17	94.03 56	98.59 17	98.20 69	94.85 55	96.59 101	32.69 552	91.70 57	99.80 41	95.66 111	99.40 61	99.62 27

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 37	98.64 74	94.30 43	97.41 172	98.04 109	94.81 61	96.59 101	98.37 70	91.24 69	99.64 88	95.16 131	99.52 35	99.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NormalMVS		96.36 82	96.11 86	97.12 77	99.37 19	92.90 89	97.99 69	97.63 167	95.92 16	96.57 104	97.93 114	85.34 193	99.50 122	94.99 136	99.21 83	98.97 115

SymmetryMVS		95.94 96	95.54 96	97.15 75	97.85 137	92.90 89	97.99 69	96.91 296	95.92 16	96.57 104	97.93 114	85.34 193	99.50 122	94.99 136	96.39 231	99.05 105

diffmvs_AUTHOR		95.33 116	95.27 112	95.50 206	96.37 273	89.08 266	96.08 320	97.38 228	93.09 143	96.53 106	97.74 149	86.45 162	98.68 250	96.32 80	97.48 170	98.75 165

PS-MVSNAJ		95.37 114	95.33 109	95.49 207	97.35 168	90.66 192	95.31 370	97.48 201	93.85 103	96.51 107	95.70 295	88.65 110	99.65 80	94.80 151	98.27 143	96.17 323

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 99	98.24 102	91.20 162	96.89 233	97.73 153	94.74 67	96.49 108	98.49 58	90.88 80	99.58 100	96.44 78	98.32 140	99.13 91

ETV-MVS		96.02 91	95.89 90	96.40 123	97.16 178	92.44 107	97.47 166	97.77 149	94.55 75	96.48 109	94.51 352	91.23 71	98.92 201	95.65 114	98.19 146	97.82 260

alignmvs		95.87 100	95.23 113	97.78 37	97.56 164	95.19 23	97.86 92	97.17 257	94.39 85	96.47 110	96.40 255	85.89 174	99.20 156	96.21 89	95.11 264	98.95 122

KinetiMVS		95.26 120	94.75 142	96.79 91	96.99 197	92.05 122	97.82 101	97.78 148	94.77 65	96.46 111	97.70 153	80.62 300	99.34 140	92.37 211	98.28 142	98.97 115

xiu_mvs_v2_base		95.32 117	95.29 110	95.40 213	97.22 173	90.50 195	95.44 363	97.44 216	93.70 109	96.46 111	96.18 265	88.59 114	99.53 114	94.79 154	97.81 161	96.17 323

CP-MVS		97.02 43	96.81 56	97.64 50	99.33 26	93.54 66	98.80 9	98.28 52	92.99 145	96.45 113	98.30 83	91.90 54	99.85 22	95.61 118	99.68 4	99.54 45

HPM-MVS		96.69 67	96.45 77	97.40 60	99.36 23	93.11 82	98.87 6	98.06 102	91.17 235	96.40 114	97.99 109	90.99 75	99.58 100	95.61 118	99.61 20	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ZNCC-MVS		96.96 46	96.67 64	97.85 30	99.37 19	94.12 52	98.49 24	98.18 77	92.64 168	96.39 115	98.18 91	91.61 59	99.88 4	95.59 121	99.55 30	99.57 36

balanced_ft_v1		95.56 110	95.40 105	96.07 149	97.16 178	90.36 206	98.23 44	97.31 238	92.89 157	96.36 116	97.11 206	83.28 234	99.26 150	97.40 50	98.80 116	98.58 181

BP-MVS1		95.89 98	95.49 98	97.08 82	96.67 234	93.20 79	98.08 59	96.32 335	94.56 74	96.32 117	97.84 134	84.07 222	99.15 166	96.75 65	98.78 117	98.90 134

diffmvs		95.25 122	95.13 117	95.63 192	96.43 267	89.34 253	95.99 328	97.35 233	92.83 159	96.31 118	97.37 186	86.44 163	98.67 253	96.26 82	97.19 189	98.87 145
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LFMVS		93.60 205	92.63 228	96.52 108	98.13 117	91.27 157	97.94 82	93.39 461	90.57 267	96.29 119	98.31 81	69.00 435	99.16 164	94.18 173	95.87 241	99.12 94

sasdasda		96.02 91	95.45 101	97.75 41	97.59 158	95.15 25	98.28 35	97.60 172	94.52 77	96.27 120	96.12 270	87.65 131	99.18 160	96.20 90	94.82 268	98.91 131

canonicalmvs		96.02 91	95.45 101	97.75 41	97.59 158	95.15 25	98.28 35	97.60 172	94.52 77	96.27 120	96.12 270	87.65 131	99.18 160	96.20 90	94.82 268	98.91 131

MVSFormer		95.37 114	95.16 115	95.99 160	96.34 275	91.21 160	98.22 46	97.57 179	91.42 219	96.22 122	97.32 188	86.20 169	97.92 358	94.07 174	99.05 103	98.85 147

lupinMVS		94.99 144	94.56 150	96.29 134	96.34 275	91.21 160	95.83 337	96.27 342	88.93 322	96.22 122	96.88 223	86.20 169	98.85 208	95.27 127	99.05 103	98.82 153

MGCFI-Net		95.94 96	95.40 105	97.56 54	97.59 158	94.62 34	98.21 48	97.57 179	94.41 83	96.17 124	96.16 268	87.54 136	99.17 162	96.19 92	94.73 273	98.91 131

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 116	98.20 109	90.93 178	96.86 236	97.72 155	94.67 70	96.16 125	98.46 62	90.43 85	99.58 100	96.23 84	97.96 157	98.90 134

MTAPA		97.08 39	96.78 59	97.97 28	99.37 19	94.42 42	97.24 195	98.08 94	95.07 46	96.11 126	98.59 48	90.88 80	99.90 2	96.18 94	99.50 40	99.58 35

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 130	96.62 236	91.73 132	97.98 72	98.30 48	96.19 14	96.10 127	98.95 20	89.42 96	99.76 55	98.90 22	99.08 101	97.43 280

MCST-MVS		97.18 34	96.84 51	98.20 16	99.30 30	95.35 17	97.12 209	98.07 99	93.54 118	96.08 128	97.69 155	93.86 18	99.71 68	96.50 76	99.39 63	99.55 43

TEST9					98.70 66	94.19 48	96.41 285	98.02 114	88.17 347	96.03 129	97.56 174	92.74 37	99.59 97

train_agg		96.30 85	95.83 92	97.72 44	98.70 66	94.19 48	96.41 285	98.02 114	88.58 334	96.03 129	97.56 174	92.73 38	99.59 97	95.04 133	99.37 67	99.39 68

test_prior2							96.35 294		92.80 161	96.03 129	97.59 170	92.01 51		95.01 135	99.38 64

jason		94.84 152	94.39 160	96.18 142	95.52 327	90.93 178	96.09 319	96.52 324	89.28 307	96.01 132	97.32 188	84.70 208	98.77 223	95.15 132	98.91 113	98.85 147
jason: jason.
onestephybrid01		95.12 132	95.01 124	95.46 211	96.39 272	88.92 273	96.28 304	97.27 244	92.67 164	96.00 133	97.73 152	86.28 165	98.66 256	95.58 122	96.85 202	98.79 156

test_8					98.67 68	94.06 55	96.37 293	98.01 117	88.58 334	95.98 134	97.55 176	92.73 38	99.58 100

mPP-MVS		96.86 52	96.60 66	97.64 50	99.40 14	93.44 68	98.50 23	98.09 93	93.27 131	95.95 135	98.33 78	91.04 74	99.88 4	95.20 129	99.57 29	99.60 31

LuminaMVS		94.89 148	94.35 162	96.53 106	95.48 329	92.80 93	96.88 235	96.18 352	92.85 158	95.92 136	96.87 225	81.44 282	98.83 211	96.43 79	97.10 192	97.94 248

DELS-MVS		96.61 71	96.38 80	97.30 64	97.79 141	93.19 80	95.96 329	98.18 77	95.23 37	95.87 137	97.65 160	91.45 62	99.70 73	95.87 102	99.44 52	99.00 112
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
VDD-MVS		93.82 198	93.08 207	96.02 155	97.88 136	89.96 223	97.72 119	95.85 363	92.43 176	95.86 138	98.44 64	68.42 442	99.39 136	96.31 81	94.85 266	98.71 171

MVS_111021_HR		96.68 69	96.58 68	96.99 85	98.46 81	92.31 112	96.20 312	98.90 3	94.30 88	95.86 138	97.74 149	92.33 46	99.38 138	96.04 98	99.42 56	99.28 77

MVS_111021_LR		96.24 87	96.19 85	96.39 125	98.23 107	91.35 155	96.24 309	98.79 7	93.99 98	95.80 140	97.65 160	89.92 92	99.24 152	95.87 102	99.20 88	98.58 181

VDDNet		93.05 231	92.07 246	96.02 155	96.84 211	90.39 201	98.08 59	95.85 363	86.22 398	95.79 141	98.46 62	67.59 445	99.19 157	94.92 139	94.85 266	98.47 195

新几何1				97.32 63	98.60 75	93.59 65		97.75 150	81.58 464	95.75 142	97.85 132	90.04 89	99.67 78	86.50 362	99.13 97	98.69 173

guyue		95.17 131	94.96 127	95.82 173	96.97 199	89.65 234	97.56 147	95.58 379	94.82 59	95.72 143	97.42 183	82.90 248	98.84 210	96.71 68	96.93 197	98.96 118

test_yl		94.78 156	94.23 165	96.43 120	97.74 144	91.22 158	96.85 237	97.10 265	91.23 232	95.71 144	96.93 218	84.30 216	99.31 145	93.10 198	95.12 262	98.75 165

DCV-MVSNet		94.78 156	94.23 165	96.43 120	97.74 144	91.22 158	96.85 237	97.10 265	91.23 232	95.71 144	96.93 218	84.30 216	99.31 145	93.10 198	95.12 262	98.75 165

AstraMVS		94.82 154	94.64 145	95.34 217	96.36 274	88.09 313	97.58 143	94.56 430	94.98 48	95.70 146	97.92 117	81.93 274	98.93 199	96.87 62	95.88 240	98.99 114

agg_prior					98.67 68	93.79 61		98.00 118		95.68 147			99.57 107

MG-MVS		95.61 107	95.38 107	96.31 130	98.42 85	90.53 194	96.04 323	97.48 201	93.47 123	95.67 148	98.10 95	89.17 100	99.25 151	91.27 241	98.77 118	99.13 91

viewmamba		95.18 130	95.15 116	95.26 221	96.31 277	88.25 303	96.29 302	97.27 244	93.61 112	95.65 149	97.91 119	86.79 154	98.64 260	95.69 110	96.82 204	98.88 142

baseline		95.58 108	95.42 104	96.08 147	96.78 225	90.41 200	97.16 206	97.45 212	93.69 110	95.65 149	97.85 132	87.29 146	98.68 250	95.66 111	97.25 186	99.13 91

MVS_Test		94.89 148	94.62 146	95.68 190	96.83 214	89.55 242	96.70 259	97.17 257	91.17 235	95.60 151	96.11 274	87.87 127	98.76 229	93.01 205	97.17 190	98.72 169

hybridnocas07		94.93 145	94.78 138	95.37 214	96.27 279	88.62 284	96.10 318	97.26 246	92.35 179	95.58 152	97.48 178	85.60 188	98.65 258	95.47 123	96.90 200	98.85 147

DPM-MVS		95.69 102	94.92 129	98.01 23	98.08 121	95.71 11	95.27 373	97.62 171	90.43 272	95.55 153	97.07 209	91.72 55	99.50 122	89.62 282	98.94 111	98.82 153

MP-MVS-pluss		96.70 65	96.27 83	97.98 27	99.23 36	94.71 32	96.96 224	98.06 102	90.67 256	95.55 153	98.78 42	91.07 73	99.86 11	96.58 74	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		96.77 60	96.45 77	97.72 44	99.39 16	93.80 60	98.41 28	98.06 102	93.37 127	95.54 155	98.34 75	90.59 84	99.88 4	94.83 147	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test12				97.65 48	98.46 81	94.26 45		97.66 161		95.52 156		90.89 79	99.46 128		99.25 80	99.22 82

viewmanbaseed2359cas		95.24 123	95.02 123	95.91 164	96.87 207	89.98 220	96.82 242	97.49 198	92.26 183	95.47 157	97.82 138	86.47 161	98.69 248	94.80 151	97.20 188	99.06 104

casdiffmvs		95.64 105	95.49 98	96.08 147	96.76 231	90.45 197	97.29 188	97.44 216	94.00 97	95.46 158	97.98 110	87.52 139	98.73 239	95.64 115	97.33 180	99.08 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
hybrid		94.76 158	94.60 147	95.27 219	96.24 281	88.36 297	96.05 322	97.25 249	91.40 221	95.40 159	97.59 170	85.48 191	98.63 263	95.23 128	96.71 212	98.83 152

E3new		95.28 118	95.11 120	95.80 175	97.03 192	89.76 229	96.78 251	97.54 192	92.06 196	95.40 159	97.75 146	87.49 140	98.76 229	94.85 144	97.10 192	98.88 142

viewcassd2359sk11		95.26 120	95.09 121	95.80 175	96.95 201	89.72 231	96.80 246	97.56 187	92.21 187	95.37 161	97.80 142	87.17 149	98.77 223	94.82 149	97.10 192	98.90 134

viewmacassd2359aftdt		95.07 135	94.80 137	95.87 167	96.53 255	89.84 226	96.90 231	97.48 201	92.44 175	95.36 162	97.89 122	85.23 196	98.68 250	94.40 168	97.00 196	99.09 98

E2		95.20 126	95.00 125	95.79 178	96.79 220	89.66 232	96.82 242	97.58 176	92.35 179	95.28 163	97.83 136	86.68 156	98.76 229	94.79 154	96.92 198	98.95 122

E3		95.20 126	95.00 125	95.79 178	96.77 227	89.66 232	96.82 242	97.58 176	92.35 179	95.28 163	97.83 136	86.69 155	98.76 229	94.79 154	96.92 198	98.95 122

test222					98.24 102	92.21 116	95.33 368	97.60 172	79.22 477	95.25 165	97.84 134	88.80 107			99.15 94	98.72 169

Casviewmamba		95.67 104	95.55 95	96.03 154	96.95 201	90.12 212	97.72 119	97.55 191	94.10 93	95.23 166	98.18 91	87.32 145	98.80 217	95.40 125	97.52 169	99.19 83

test2506		91.60 295	90.78 300	94.04 302	97.66 150	83.81 414	98.27 37	75.53 519	93.43 125	95.23 166	98.21 88	67.21 448	99.07 184	93.01 205	98.49 130	99.25 80

原ACMM1				96.38 126	98.59 76	91.09 170		97.89 129	87.41 374	95.22 168	97.68 156	90.25 86	99.54 112	87.95 319	99.12 99	98.49 192

CPTT-MVS		95.57 109	95.19 114	96.70 93	99.27 32	91.48 148	98.33 31	98.11 90	87.79 362	95.17 169	98.03 103	87.09 150	99.61 92	93.51 189	99.42 56	99.02 106

casdiffmvs_mvg		95.81 101	95.57 94	96.51 112	96.87 207	91.49 146	97.50 157	97.56 187	93.99 98	95.13 170	97.92 117	87.89 125	98.78 219	95.97 100	97.33 180	99.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DP-MVS Recon		95.68 103	95.12 119	97.37 61	99.19 38	94.19 48	97.03 213	98.08 94	88.35 343	95.09 171	97.65 160	89.97 91	99.48 126	92.08 223	98.59 127	98.44 200

E5new		95.04 136	94.88 131	95.52 200	96.62 236	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.89 122	85.65 183	98.77 223	94.92 139	96.44 226	98.78 157

E6new		95.04 136	94.88 131	95.52 200	96.60 241	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.90 120	85.66 181	98.77 223	94.92 139	96.44 226	98.78 157

E6		95.04 136	94.88 131	95.52 200	96.60 241	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.90 120	85.66 181	98.77 223	94.92 139	96.44 226	98.78 157

E5		95.04 136	94.88 131	95.52 200	96.62 236	89.02 268	97.29 188	97.57 179	92.54 169	95.04 172	97.89 122	85.65 183	98.77 223	94.92 139	96.44 226	98.78 157

E4		95.09 133	94.86 135	95.77 181	96.58 245	89.56 240	96.85 237	97.56 187	92.50 173	95.03 176	97.86 130	86.03 172	98.78 219	94.71 157	96.65 216	98.96 118

dtuplus		94.16 177	93.98 172	94.70 258	96.18 290	86.85 348	96.04 323	97.07 271	89.75 291	95.02 177	97.79 144	84.94 205	98.62 266	92.62 210	96.43 230	98.62 177

viewmambaseed2359dif		94.28 171	94.14 167	94.71 257	96.21 282	86.97 345	95.93 331	97.11 264	89.00 317	95.00 178	97.70 153	86.02 173	98.59 272	93.71 185	96.59 218	98.57 183

RRT-MVS		94.51 165	94.35 162	94.98 239	96.40 268	86.55 359	97.56 147	97.41 222	93.19 135	94.93 179	97.04 211	79.12 329	99.30 147	96.19 92	97.32 182	99.09 98

Vis-MVSNet		95.23 124	94.81 136	96.51 112	97.18 177	91.58 143	98.26 39	98.12 87	94.38 86	94.90 180	98.15 94	82.28 264	98.92 201	91.45 238	98.58 128	99.01 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CANet		96.39 80	96.02 87	97.50 55	97.62 155	93.38 70	97.02 215	97.96 123	95.42 31	94.86 181	97.81 140	87.38 144	99.82 34	96.88 61	99.20 88	99.29 75

Elysia		94.00 188	93.12 205	96.64 95	96.08 303	92.72 97	97.50 157	97.63 167	91.15 237	94.82 182	97.12 204	74.98 379	99.06 186	90.78 251	98.02 153	98.12 231

StellarMVS		94.00 188	93.12 205	96.64 95	96.08 303	92.72 97	97.50 157	97.63 167	91.15 237	94.82 182	97.12 204	74.98 379	99.06 186	90.78 251	98.02 153	98.12 231

API-MVS		94.84 152	94.49 156	95.90 165	97.90 135	92.00 125	97.80 105	97.48 201	89.19 310	94.81 184	96.71 230	88.84 106	99.17 162	88.91 304	98.76 119	96.53 312

hybridcas		95.46 112	95.29 110	95.96 162	96.83 214	90.08 214	97.63 137	97.49 198	93.76 105	94.79 185	98.04 101	86.87 152	98.72 244	94.71 157	97.53 168	99.08 100

mvsmamba		94.57 162	94.14 167	95.87 167	97.03 192	89.93 224	97.84 96	95.85 363	91.34 223	94.79 185	96.80 226	80.67 298	98.81 214	94.85 144	98.12 150	98.85 147

OMC-MVS		95.09 133	94.70 143	96.25 139	98.46 81	91.28 156	96.43 281	97.57 179	92.04 197	94.77 187	97.96 112	87.01 151	99.09 178	91.31 240	96.77 206	98.36 207

ECVR-MVS		93.19 224	92.73 224	94.57 269	97.66 150	85.41 388	98.21 48	88.23 499	93.43 125	94.70 188	98.21 88	72.57 401	99.07 184	93.05 202	98.49 130	99.25 80

viewdifsd2359ckpt13		94.87 150	94.52 154	95.90 165	96.88 206	90.19 211	96.92 228	97.36 231	91.26 228	94.65 189	97.46 179	85.79 178	98.64 260	93.64 186	96.76 207	98.88 142

WTY-MVS		94.71 161	94.02 170	96.79 91	97.71 146	92.05 122	96.59 274	97.35 233	90.61 262	94.64 190	96.93 218	86.41 164	99.39 136	91.20 243	94.71 274	98.94 125

test1111		93.19 224	92.82 218	94.30 288	97.58 162	84.56 405	98.21 48	89.02 497	93.53 119	94.58 191	98.21 88	72.69 400	99.05 189	93.06 201	98.48 132	99.28 77

ACMMP		96.27 86	95.93 88	97.28 67	99.24 34	92.62 100	98.25 40	98.81 6	92.99 145	94.56 192	98.39 68	88.96 103	99.85 22	94.57 165	97.63 165	99.36 72
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
viewdifsd2359ckpt07		94.76 158	94.68 144	95.01 235	96.76 231	87.41 331	96.38 291	97.43 219	92.65 166	94.52 193	97.75 146	85.55 189	98.81 214	94.36 170	96.69 213	98.82 153

Effi-MVS+		94.93 145	94.45 158	96.36 128	96.61 239	91.47 149	96.41 285	97.41 222	91.02 243	94.50 194	95.92 279	87.53 137	98.78 219	93.89 180	96.81 205	98.84 151

sss		94.51 165	93.80 176	96.64 95	97.07 184	91.97 126	96.32 299	98.06 102	88.94 321	94.50 194	96.78 227	84.60 209	99.27 149	91.90 224	96.02 235	98.68 174

mmtdpeth		89.70 375	88.96 373	91.90 399	95.84 315	84.42 406	97.46 168	95.53 386	90.27 275	94.46 196	90.50 457	69.74 430	98.95 196	97.39 54	69.48 495	92.34 469

PVSNet_BlendedMVS		94.06 184	93.92 174	94.47 275	98.27 98	89.46 248	96.73 255	98.36 38	90.17 277	94.36 197	95.24 318	88.02 122	99.58 100	93.44 191	90.72 345	94.36 431

PVSNet_Blended		94.87 150	94.56 150	95.81 174	98.27 98	89.46 248	95.47 361	98.36 38	88.84 325	94.36 197	96.09 275	88.02 122	99.58 100	93.44 191	98.18 147	98.40 203

PRO-TEST		94.38 168	94.94 128	92.69 376	97.21 175	80.23 460	97.52 155	97.02 284	93.62 111	94.32 199	97.21 198	81.92 275	99.15 166	96.65 70	99.00 108	98.70 172

viewdifsd2359ckpt09		94.81 155	94.37 161	96.12 146	96.91 203	90.75 188	96.94 225	97.31 238	90.51 270	94.31 200	97.38 185	85.70 180	98.71 246	93.54 187	96.75 208	98.90 134

PMMVS		92.86 242	92.34 240	94.42 279	94.92 370	86.73 352	94.53 402	96.38 333	84.78 421	94.27 201	95.12 323	83.13 240	98.40 286	91.47 237	96.49 223	98.12 231

EPP-MVSNet		95.22 125	95.04 122	95.76 182	97.49 165	89.56 240	98.67 15	97.00 286	90.69 254	94.24 202	97.62 166	89.79 94	98.81 214	93.39 194	96.49 223	98.92 130

viewmsd2359difaftdt		93.46 212	93.23 202	94.17 293	96.12 298	85.42 386	96.43 281	97.08 268	92.91 153	94.21 203	98.00 107	80.82 296	98.74 237	94.41 167	89.05 363	98.34 213

viewdifsd2359ckpt11		93.46 212	93.22 203	94.17 293	96.11 300	85.42 386	96.43 281	97.07 271	92.91 153	94.20 204	98.00 107	80.82 296	98.73 239	94.42 166	89.04 365	98.34 213

FA-MVS(test-final)		93.52 210	92.92 214	95.31 218	96.77 227	88.54 289	94.82 394	96.21 349	89.61 296	94.20 204	95.25 317	83.24 235	99.14 170	90.01 270	96.16 234	98.25 219

PVSNet_Blended_VisFu		95.27 119	94.91 130	96.38 126	98.20 109	90.86 181	97.27 193	98.25 61	90.21 276	94.18 206	97.27 194	87.48 141	99.73 62	93.53 188	97.77 163	98.55 184

SSM_0404		94.73 160	94.31 164	95.98 161	97.05 189	90.90 180	97.01 218	97.29 240	91.24 229	94.17 207	97.60 168	85.03 200	98.76 229	92.14 217	97.30 183	98.29 216

FE-MVS		92.05 275	91.05 288	95.08 230	96.83 214	87.93 317	93.91 431	95.70 370	86.30 395	94.15 208	94.97 326	76.59 363	99.21 155	84.10 397	96.86 201	98.09 238

thisisatest0530		93.03 232	92.21 244	95.49 207	97.07 184	89.11 265	97.49 165	92.19 478	90.16 278	94.09 209	96.41 254	76.43 367	99.05 189	90.38 265	95.68 247	98.31 215

XVG-OURS-SEG-HR		93.86 197	93.55 185	94.81 249	97.06 187	88.53 291	95.28 371	97.45 212	91.68 207	94.08 210	97.68 156	82.41 262	98.90 204	93.84 182	92.47 314	96.98 297

XVG-OURS		93.72 202	93.35 198	94.80 252	97.07 184	88.61 285	94.79 395	97.46 207	91.97 200	93.99 211	97.86 130	81.74 278	98.88 205	92.64 209	92.67 313	96.92 302

IS-MVSNet		94.90 147	94.52 154	96.05 151	97.67 148	90.56 193	98.44 26	96.22 347	93.21 132	93.99 211	97.74 149	85.55 189	98.45 283	89.98 271	97.86 159	99.14 90

CSCG		96.05 90	95.91 89	96.46 118	99.24 34	90.47 196	98.30 33	98.57 28	89.01 316	93.97 213	97.57 172	92.62 41	99.76 55	94.66 159	99.27 75	99.15 88

casdiffseed414692147		94.55 163	94.02 170	96.15 144	96.61 239	90.79 184	97.42 170	97.39 224	92.18 192	93.95 214	97.64 163	84.37 215	98.66 256	90.68 256	95.91 239	99.00 112

EIA-MVS		95.53 111	95.47 100	95.71 189	97.06 187	89.63 235	97.82 101	97.87 133	93.57 114	93.92 215	95.04 324	90.61 83	98.95 196	94.62 161	98.68 121	98.54 185

tttt0517		92.96 235	92.33 241	94.87 246	97.11 182	87.16 341	97.97 78	92.09 479	90.63 260	93.88 216	97.01 217	76.50 364	99.06 186	90.29 268	95.45 256	98.38 205

HyFIR lowres test		93.66 204	92.92 214	95.87 167	98.24 102	89.88 225	94.58 400	98.49 31	85.06 416	93.78 217	95.78 290	82.86 249	98.67 253	91.77 229	95.71 246	99.07 103

CHOSEN 1792x2688		94.15 178	93.51 190	96.06 150	98.27 98	89.38 251	95.18 382	98.48 33	85.60 406	93.76 218	97.11 206	83.15 239	99.61 92	91.33 239	98.72 120	99.19 83

mamba_0408		93.70 203	92.99 209	95.83 172	96.79 220	90.38 202	88.69 495	97.07 271	90.96 245	93.68 219	97.31 190	84.97 203	98.76 229	90.95 247	96.51 219	98.35 209

SSM_04072		93.51 211	92.99 209	95.05 231	96.79 220	90.38 202	88.69 495	97.07 271	90.96 245	93.68 219	97.31 190	84.97 203	96.42 448	90.95 247	96.51 219	98.35 209

SSM_0407		94.54 164	94.12 169	95.80 175	96.79 220	90.38 202	96.79 247	97.29 240	91.24 229	93.68 219	97.60 168	85.03 200	98.67 253	92.14 217	96.51 219	98.35 209

Anonymous202405211		92.07 274	90.83 299	95.76 182	98.19 111	88.75 279	97.58 143	95.00 409	86.00 401	93.64 222	97.45 180	66.24 457	99.53 114	90.68 256	92.71 311	99.01 109

IMVS_0403		93.98 190	93.79 177	94.55 270	96.19 286	86.16 371	96.35 294	97.24 251	91.54 210	93.59 223	97.04 211	85.86 175	98.73 239	90.68 256	95.59 250	98.76 161

CDS-MVSNet		94.14 181	93.54 186	95.93 163	96.18 290	91.46 150	96.33 298	97.04 280	88.97 320	93.56 224	96.51 249	87.55 135	97.89 362	89.80 276	95.95 237	98.44 200
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view						70.35 499	93.10 455		83.88 433	93.55 225		82.47 261		86.25 365		98.38 205

Anonymous20240529		91.98 277	90.73 305	95.73 187	98.14 115	89.40 250	97.99 69	97.72 155	79.63 475	93.54 226	97.41 184	69.94 426	99.56 108	91.04 246	91.11 338	98.22 221

CANet_DTU		94.37 169	93.65 182	96.55 105	96.46 265	92.13 120	96.21 310	96.67 316	94.38 86	93.53 227	97.03 216	79.34 325	99.71 68	90.76 253	98.45 134	97.82 260

icg_test_0407_2		93.58 206	93.46 192	93.94 312	96.19 286	86.16 371	93.73 437	97.24 251	91.54 210	93.50 228	97.04 211	85.64 186	96.91 437	90.68 256	95.59 250	98.76 161

IMVS_0407		93.94 192	93.75 178	94.49 274	96.19 286	86.16 371	96.35 294	97.24 251	91.54 210	93.50 228	97.04 211	85.64 186	98.54 275	90.68 256	95.59 250	98.76 161

tpmrst		91.44 307	91.32 275	91.79 405	95.15 358	79.20 473	93.42 448	95.37 390	88.55 337	93.49 230	93.67 399	82.49 260	98.27 304	90.41 264	89.34 359	97.90 250

TAMVS		94.01 187	93.46 192	95.64 191	96.16 293	90.45 197	96.71 258	96.89 299	89.27 308	93.46 231	96.92 221	87.29 146	97.94 355	88.70 310	95.74 244	98.53 186

thisisatest0515		92.29 264	91.30 277	95.25 222	96.60 241	88.90 275	94.36 413	92.32 476	87.92 354	93.43 232	94.57 348	77.28 358	99.00 193	89.42 287	95.86 242	97.86 256

DeepC-MVS	93.07 3	96.06 89	95.66 93	97.29 65	97.96 129	93.17 81	97.30 187	98.06 102	93.92 100	93.38 233	98.66 45	86.83 153	99.73 62	95.60 120	99.22 82	98.96 118
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thres600view7		92.49 253	91.60 265	95.18 225	97.91 134	89.47 246	97.65 131	94.66 425	92.18 192	93.33 234	94.91 330	78.06 351	99.10 175	81.61 423	94.06 293	96.98 297

thres100view900		92.43 255	91.58 266	94.98 239	97.92 133	89.37 252	97.71 122	94.66 425	92.20 188	93.31 235	94.90 331	78.06 351	99.08 180	81.40 427	94.08 289	96.48 315

thres200		92.23 268	91.39 272	94.75 256	97.61 156	89.03 267	96.60 273	95.09 406	92.08 195	93.28 236	94.00 385	78.39 345	99.04 192	81.26 433	94.18 285	96.19 322

tfpn200view9		92.38 258	91.52 269	94.95 243	97.85 137	89.29 256	97.41 172	94.88 417	92.19 190	93.27 237	94.46 357	78.17 347	99.08 180	81.40 427	94.08 289	96.48 315

thres400		92.42 256	91.52 269	95.12 229	97.85 137	89.29 256	97.41 172	94.88 417	92.19 190	93.27 237	94.46 357	78.17 347	99.08 180	81.40 427	94.08 289	96.98 297

testing3-2		92.10 273	92.05 247	92.27 388	97.71 146	79.56 467	97.42 170	94.41 437	93.53 119	93.22 239	95.49 306	69.16 434	99.11 173	93.25 195	94.22 282	98.13 229

ab-mvs		93.57 208	92.55 232	96.64 95	97.28 171	91.96 128	95.40 364	97.45 212	89.81 288	93.22 239	96.28 261	79.62 322	99.46 128	90.74 254	93.11 305	98.50 190

Vis-MVSNet (Re-imp)		94.15 178	93.88 175	94.95 243	97.61 156	87.92 318	98.10 57	95.80 366	92.22 185	93.02 241	97.45 180	84.53 211	97.91 361	88.24 314	97.97 156	99.02 106

114514_t		93.95 191	93.06 208	96.63 99	99.07 44	91.61 140	97.46 168	97.96 123	77.99 483	93.00 242	97.57 172	86.14 171	99.33 141	89.22 294	99.15 94	98.94 125

UGNet		94.04 186	93.28 200	96.31 130	96.85 210	91.19 163	97.88 91	97.68 160	94.40 84	93.00 242	96.18 265	73.39 396	99.61 92	91.72 230	98.46 133	98.13 229
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
HY-MVS	89.66 9	93.87 196	92.95 213	96.63 99	97.10 183	92.49 106	95.64 352	96.64 317	89.05 315	93.00 242	95.79 289	85.77 179	99.45 130	89.16 298	94.35 277	97.96 246

PVSNet	86.66 18	92.24 267	91.74 262	93.73 323	97.77 142	83.69 418	92.88 458	96.72 309	87.91 355	93.00 242	94.86 333	78.51 342	99.05 189	86.53 360	97.45 175	98.47 195

MAR-MVS		94.22 173	93.46 192	96.51 112	98.00 126	92.19 119	97.67 127	97.47 205	88.13 351	93.00 242	95.84 283	84.86 207	99.51 119	87.99 318	98.17 148	97.83 259
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
PAPM_NR		95.01 140	94.59 148	96.26 136	98.89 61	90.68 191	97.24 195	97.73 153	91.80 202	92.93 247	96.62 244	89.13 101	99.14 170	89.21 295	97.78 162	98.97 115

MDTV_nov1_ep13			90.76 301		95.22 352	80.33 455	93.03 456	95.28 396	88.14 350	92.84 248	93.83 389	81.34 283	98.08 326	82.86 409	94.34 278

CostFormer		91.18 325	90.70 307	92.62 379	94.84 375	81.76 440	94.09 424	94.43 435	84.15 428	92.72 249	93.77 393	79.43 324	98.20 309	90.70 255	92.18 320	97.90 250

FBQ-MVS		91.77 284	90.62 311	95.21 223	96.84 211	88.89 277	96.90 231	95.31 395	90.60 264	92.64 250	92.29 439	69.43 431	98.48 281	87.33 348	94.21 283	98.27 218

EPNet		95.20 126	94.56 150	97.14 76	92.80 443	92.68 99	97.85 95	94.87 420	96.64 9	92.46 251	97.80 142	86.23 166	99.65 80	93.72 184	98.62 125	99.10 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		90.82 339	89.77 352	93.95 310	94.45 392	87.19 339	90.23 486	95.68 374	86.89 384	92.40 252	92.36 435	80.91 292	97.05 430	81.09 434	93.95 294	97.60 273

RPMNet		88.98 381	87.05 395	94.77 254	94.45 392	87.19 339	90.23 486	98.03 111	77.87 485	92.40 252	87.55 486	80.17 310	99.51 119	68.84 493	93.95 294	97.60 273

EPMVS		90.70 344	89.81 350	93.37 349	94.73 381	84.21 409	93.67 441	88.02 500	89.50 300	92.38 254	93.49 407	77.82 355	97.78 373	86.03 372	92.68 312	98.11 237

baseline1		92.82 245	91.90 255	95.55 198	97.20 176	90.77 186	97.19 203	94.58 429	92.20 188	92.36 255	96.34 258	84.16 220	98.21 308	89.20 296	83.90 431	97.68 267

PatchT		88.87 385	87.42 389	93.22 355	94.08 403	85.10 396	89.51 491	94.64 427	81.92 460	92.36 255	88.15 479	80.05 312	97.01 433	72.43 482	93.65 300	97.54 276

UWE-MVS		89.91 366	89.48 362	91.21 419	95.88 309	78.23 479	94.91 391	90.26 493	89.11 312	92.35 257	94.52 351	68.76 437	97.96 349	83.95 401	95.59 250	97.42 281

ETVMVS		90.52 350	89.14 371	94.67 260	96.81 219	87.85 323	95.91 333	93.97 452	89.71 292	92.34 258	92.48 430	65.41 463	97.96 349	81.37 430	94.27 281	98.21 222

PAPR		94.18 174	93.42 197	96.48 115	97.64 152	91.42 152	95.55 356	97.71 159	88.99 318	92.34 258	95.82 285	89.19 99	99.11 173	86.14 368	97.38 177	98.90 134

SCA		91.84 282	91.18 284	93.83 318	95.59 323	84.95 401	94.72 396	95.58 379	90.82 248	92.25 260	93.69 396	75.80 371	98.10 321	86.20 366	95.98 236	98.45 197

CVMVSNet		91.23 320	91.75 260	89.67 446	95.77 316	74.69 490	96.44 279	94.88 417	85.81 403	92.18 261	97.64 163	79.07 330	95.58 465	88.06 317	95.86 242	98.74 168

AUN-MVS		91.76 285	90.75 303	94.81 249	97.00 196	88.57 287	96.65 265	96.49 326	89.63 295	92.15 262	96.12 270	78.66 340	98.50 278	90.83 249	79.18 455	97.36 283

AdaColmap		94.34 170	93.68 181	96.31 130	98.59 76	91.68 138	96.59 274	97.81 146	89.87 283	92.15 262	97.06 210	83.62 229	99.54 112	89.34 289	98.07 151	97.70 266

GeoE		93.89 195	93.28 200	95.72 188	96.96 200	89.75 230	98.24 43	96.92 295	89.47 301	92.12 264	97.21 198	84.42 213	98.39 291	87.71 328	96.50 222	99.01 109

PatchmatchNet		91.91 279	91.35 273	93.59 337	95.38 336	84.11 411	93.15 453	95.39 388	89.54 298	92.10 265	93.68 398	82.82 251	98.13 316	84.81 388	95.32 258	98.52 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		93.24 221	92.48 237	95.51 204	95.70 318	92.39 108	97.86 92	98.66 21	92.30 182	92.09 266	95.37 310	80.49 303	98.40 286	93.95 177	85.86 397	95.75 346

tpm		90.25 357	89.74 355	91.76 408	93.92 406	79.73 465	93.98 425	93.54 459	88.28 344	91.99 267	93.25 417	77.51 357	97.44 413	87.30 350	87.94 376	98.12 231

myMVS_eth3d28		91.52 303	90.97 291	93.17 357	96.91 203	83.24 422	95.61 353	94.96 413	92.24 184	91.98 268	93.28 416	69.31 432	98.40 286	88.71 309	95.68 247	97.88 252

UBG		91.55 300	90.76 301	93.94 312	96.52 258	85.06 397	95.22 377	94.54 431	90.47 271	91.98 268	92.71 423	72.02 405	98.74 237	88.10 316	95.26 260	98.01 244

CNLPA		94.28 171	93.53 187	96.52 108	98.38 91	92.55 104	96.59 274	96.88 300	90.13 280	91.91 270	97.24 196	85.21 197	99.09 178	87.64 337	97.83 160	97.92 249

testing91		91.90 280	91.02 289	94.53 272	96.54 253	86.55 359	95.86 335	95.64 376	91.77 204	91.89 271	93.47 409	69.94 426	98.86 206	90.23 269	93.86 296	98.18 224

BH-RMVSNet		92.72 249	91.97 252	94.97 241	97.16 178	87.99 316	96.15 316	95.60 377	90.62 261	91.87 272	97.15 203	78.41 344	98.57 273	83.16 406	97.60 166	98.36 207

PatchMatch-RL		92.90 239	92.02 250	95.56 196	98.19 111	90.80 183	95.27 373	97.18 255	87.96 353	91.86 273	95.68 296	80.44 304	98.99 194	84.01 399	97.54 167	96.89 303

SDMVSNet		94.17 175	93.61 183	95.86 170	98.09 118	91.37 153	97.35 181	98.20 69	93.18 137	91.79 274	97.28 192	79.13 328	98.93 199	94.61 162	92.84 308	97.28 288

sd_testset		93.10 228	92.45 238	95.05 231	98.09 118	89.21 260	96.89 233	97.64 165	93.18 137	91.79 274	97.28 192	75.35 376	98.65 258	88.99 301	92.84 308	97.28 288

testing99		91.62 294	90.72 306	94.32 285	96.48 262	86.11 376	95.81 339	94.76 422	91.55 209	91.75 276	93.44 411	68.55 440	98.82 212	90.43 263	93.69 298	98.04 242

testing222		90.31 354	88.96 373	94.35 281	96.54 253	87.29 333	95.50 359	93.84 456	90.97 244	91.75 276	92.96 420	62.18 478	98.00 340	82.86 409	94.08 289	97.76 263

OPM-MVS		93.28 220	92.76 220	94.82 247	94.63 385	90.77 186	96.65 265	97.18 255	93.72 107	91.68 278	97.26 195	79.33 326	98.63 263	92.13 220	92.28 316	95.07 387
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpm2		89.96 365	89.21 368	92.23 391	94.91 372	81.25 443	93.78 435	94.42 436	80.62 471	91.56 279	93.44 411	76.44 366	97.94 355	85.60 378	92.08 324	97.49 277

TAPA-MVS	90.10 7	92.30 263	91.22 282	95.56 196	98.33 93	89.60 237	96.79 247	97.65 163	81.83 461	91.52 280	97.23 197	87.94 124	98.91 203	71.31 486	98.37 138	98.17 227
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_fmvs2		89.77 373	89.93 345	89.31 453	93.68 415	76.37 485	97.64 135	95.90 360	89.84 287	91.49 281	96.26 263	58.77 481	97.10 427	94.65 160	91.13 337	94.46 427

TR-MVS		91.48 306	90.59 315	94.16 296	96.40 268	87.33 332	95.67 347	95.34 394	87.68 368	91.46 282	95.52 305	76.77 362	98.35 294	82.85 411	93.61 302	96.79 306

RPSCF		90.75 341	90.86 295	90.42 436	96.84 211	76.29 486	95.61 353	96.34 334	83.89 432	91.38 283	97.87 128	76.45 365	98.78 219	87.16 354	92.23 317	96.20 321

dtuonly		90.88 337	91.13 285	90.13 440	92.98 438	75.01 489	92.74 464	95.54 382	87.69 367	91.37 284	96.61 246	79.65 321	98.15 314	87.44 345	96.21 233	97.23 291

PLC	91.00 6	94.11 182	93.43 195	96.13 145	98.58 78	91.15 169	96.69 261	97.39 224	87.29 377	91.37 284	96.71 230	88.39 115	99.52 118	87.33 348	97.13 191	97.73 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		93.12 227	92.72 225	94.34 283	96.71 233	87.27 335	90.29 485	97.72 155	86.61 390	91.34 286	95.29 312	84.29 218	98.41 285	93.25 195	98.94 111	97.35 285

HQP_MVS		93.78 200	93.43 195	94.82 247	96.21 282	89.99 218	97.74 114	97.51 195	94.85 55	91.34 286	96.64 237	81.32 284	98.60 268	93.02 203	92.23 317	95.86 334

plane_prior3						90.00 216			94.46 80	91.34 286

Fast-Effi-MVS+		93.46 212	92.75 222	95.59 195	96.77 227	90.03 215	96.81 245	97.13 259	88.19 346	91.30 289	94.27 370	86.21 168	98.63 263	87.66 336	96.46 225	98.12 231

EI-MVSNet		93.03 232	92.88 216	93.48 345	95.77 316	86.98 344	96.44 279	97.12 260	90.66 258	91.30 289	97.64 163	86.56 158	98.05 333	89.91 273	90.55 347	95.41 361

MVSTER		93.20 223	92.81 219	94.37 280	96.56 250	89.59 238	97.06 212	97.12 260	91.24 229	91.30 289	95.96 277	82.02 270	98.05 333	93.48 190	90.55 347	95.47 356

ADS-MVSNet2		89.45 377	88.59 379	92.03 395	95.86 310	82.26 436	90.93 481	94.32 443	83.23 446	91.28 292	91.81 447	79.01 335	95.99 454	79.52 444	91.39 333	97.84 257

ADS-MVSNet		89.89 368	88.68 378	93.53 341	95.86 310	84.89 402	90.93 481	95.07 407	83.23 446	91.28 292	91.81 447	79.01 335	97.85 364	79.52 444	91.39 333	97.84 257

testing11		91.68 289	90.75 303	94.47 275	96.53 255	86.56 358	95.76 343	94.51 433	91.10 241	91.24 294	93.59 404	68.59 439	98.86 206	91.10 244	94.29 280	98.00 245

nrg030		94.05 185	93.31 199	96.27 135	95.22 352	94.59 35	98.34 30	97.46 207	92.93 152	91.21 295	96.64 237	87.23 148	98.22 307	94.99 136	85.80 398	95.98 333

Effi-MVS+-dtu		93.08 229	93.21 204	92.68 378	96.02 307	83.25 421	97.14 208	96.72 309	93.85 103	91.20 296	93.44 411	83.08 241	98.30 300	91.69 233	95.73 245	96.50 314

VPNet		92.23 268	91.31 276	94.99 237	95.56 325	90.96 174	97.22 201	97.86 137	92.96 151	90.96 297	96.62 244	75.06 377	98.20 309	91.90 224	83.65 433	95.80 340

JIA-IIPM		88.26 392	87.04 396	91.91 398	93.52 422	81.42 442	89.38 492	94.38 439	80.84 468	90.93 298	80.74 510	79.22 327	97.92 358	82.76 413	91.62 328	96.38 318

MonoMVSNet		91.92 278	91.77 258	92.37 382	92.94 439	83.11 424	97.09 211	95.55 381	92.91 153	90.85 299	94.55 349	81.27 286	96.52 446	93.01 205	87.76 378	97.47 279

WB-MVSnew		89.88 369	89.56 359	90.82 428	94.57 389	83.06 425	95.65 351	92.85 468	87.86 358	90.83 300	94.10 379	79.66 320	96.88 438	76.34 462	94.19 284	92.54 465

test-LLR		91.42 308	91.19 283	92.12 393	94.59 386	80.66 449	94.29 418	92.98 466	91.11 239	90.76 301	92.37 432	79.02 333	98.07 330	88.81 306	96.74 209	97.63 268

test-mter		90.19 361	89.54 360	92.12 393	94.59 386	80.66 449	94.29 418	92.98 466	87.68 368	90.76 301	92.37 432	67.67 444	98.07 330	88.81 306	96.74 209	97.63 268

ACMM	89.79 8	92.96 235	92.50 236	94.35 281	96.30 278	88.71 280	97.58 143	97.36 231	91.40 221	90.53 303	96.65 236	79.77 317	98.75 235	91.24 242	91.64 327	95.59 352
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
F-COLMAP		93.58 206	92.98 212	95.37 214	98.40 88	88.98 272	97.18 204	97.29 240	87.75 365	90.49 304	97.10 208	85.21 197	99.50 122	86.70 359	96.72 211	97.63 268

TESTMET0.1,1		90.06 363	89.42 363	91.97 396	94.41 394	80.62 451	94.29 418	91.97 481	87.28 378	90.44 305	92.47 431	68.79 436	97.67 383	88.50 313	96.60 217	97.61 272

FIs		94.09 183	93.70 180	95.27 219	95.70 318	92.03 124	98.10 57	98.68 18	93.36 129	90.39 306	96.70 232	87.63 133	97.94 355	92.25 214	90.50 349	95.84 337

GA-MVS		91.38 310	90.31 324	94.59 264	94.65 384	87.62 328	94.34 414	96.19 351	90.73 252	90.35 307	93.83 389	71.84 407	97.96 349	87.22 351	93.61 302	98.21 222

LS3D		93.57 208	92.61 230	96.47 116	97.59 158	91.61 140	97.67 127	97.72 155	85.17 414	90.29 308	98.34 75	84.60 209	99.73 62	83.85 404	98.27 143	98.06 241

FC-MVSNet-test		93.94 192	93.57 184	95.04 233	95.48 329	91.45 151	98.12 56	98.71 13	93.37 127	90.23 309	96.70 232	87.66 130	97.85 364	91.49 236	90.39 350	95.83 338

HQP-NCC					95.86 310		96.65 265		93.55 115	90.14 310

ACMP_Plane					95.86 310		96.65 265		93.55 115	90.14 310

HQP4-MVS										90.14 310			98.50 278			95.78 342

HQP-MVS		93.19 224	92.74 223	94.54 271	95.86 310	89.33 254	96.65 265	97.39 224	93.55 115	90.14 310	95.87 281	80.95 290	98.50 278	92.13 220	92.10 322	95.78 342

UniMVSNet_NR-MVSNet		93.37 217	92.67 226	95.47 210	95.34 341	92.83 91	97.17 205	98.58 27	92.98 150	90.13 314	95.80 286	88.37 117	97.85 364	91.71 231	83.93 428	95.73 348

DU-MVS		92.90 239	92.04 248	95.49 207	94.95 367	92.83 91	97.16 206	98.24 63	93.02 144	90.13 314	95.71 293	83.47 230	97.85 364	91.71 231	83.93 428	95.78 342

LPG-MVS_test		92.94 237	92.56 231	94.10 298	96.16 293	88.26 301	97.65 131	97.46 207	91.29 224	90.12 316	97.16 201	79.05 331	98.73 239	92.25 214	91.89 325	95.31 371

LGP-MVS_train				94.10 298	96.16 293	88.26 301		97.46 207	91.29 224	90.12 316	97.16 201	79.05 331	98.73 239	92.25 214	91.89 325	95.31 371

UniMVSNet (Re)		93.31 219	92.55 232	95.61 194	95.39 335	93.34 73	97.39 177	98.71 13	93.14 140	90.10 318	94.83 335	87.71 129	98.03 337	91.67 234	83.99 427	95.46 357

mvs_anonymous		93.82 198	93.74 179	94.06 300	96.44 266	85.41 388	95.81 339	97.05 278	89.85 286	90.09 319	96.36 257	87.44 142	97.75 378	93.97 176	96.69 213	99.02 106

test_djsdf		93.07 230	92.76 220	94.00 304	93.49 424	88.70 281	98.22 46	97.57 179	91.42 219	90.08 320	95.55 303	82.85 250	97.92 358	94.07 174	91.58 329	95.40 364

dp		88.90 384	88.26 384	90.81 429	94.58 388	76.62 484	92.85 460	94.93 414	85.12 415	90.07 321	93.07 418	75.81 370	98.12 319	80.53 438	87.42 383	97.71 265

PS-MVSNAJss		93.74 201	93.51 190	94.44 277	93.91 407	89.28 258	97.75 111	97.56 187	92.50 173	89.94 322	96.54 248	88.65 110	98.18 312	93.83 183	90.90 343	95.86 334

UniMVSNet_ETH3D		91.34 315	90.22 332	94.68 259	94.86 374	87.86 321	97.23 199	97.46 207	87.99 352	89.90 323	96.92 221	66.35 455	98.23 306	90.30 267	90.99 341	97.96 246

CLD-MVS		92.98 234	92.53 234	94.32 285	96.12 298	89.20 261	95.28 371	97.47 205	92.66 165	89.90 323	95.62 299	80.58 301	98.40 286	92.73 208	92.40 315	95.38 366
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
gg-mvs-nofinetune		87.82 395	85.61 409	94.44 277	94.46 391	89.27 259	91.21 479	84.61 510	80.88 467	89.89 325	74.98 516	71.50 410	97.53 405	85.75 377	97.21 187	96.51 313

1112_ss		93.37 217	92.42 239	96.21 140	97.05 189	90.99 172	96.31 300	96.72 309	86.87 385	89.83 326	96.69 234	86.51 160	99.14 170	88.12 315	93.67 299	98.50 190

BH-untuned		92.94 237	92.62 229	93.92 316	97.22 173	86.16 371	96.40 289	96.25 346	90.06 281	89.79 327	96.17 267	83.19 237	98.35 294	87.19 352	97.27 185	97.24 290

VortexMVS		92.88 241	92.64 227	93.58 338	96.58 245	87.53 330	96.93 227	97.28 243	92.78 162	89.75 328	94.99 325	82.73 253	97.76 376	94.60 163	88.16 374	95.46 357

V42		91.58 298	90.87 294	93.73 323	94.05 404	88.50 292	97.32 185	96.97 287	88.80 330	89.71 329	94.33 365	82.54 258	98.05 333	89.01 300	85.07 410	94.64 424

Baseline_NR-MVSNet		91.20 322	90.62 311	92.95 365	93.83 410	88.03 314	97.01 218	95.12 405	88.42 341	89.70 330	95.13 322	83.47 230	97.44 413	89.66 281	83.24 436	93.37 452

v144192		91.06 328	90.28 326	93.39 348	93.66 416	87.23 338	96.83 241	97.07 271	87.43 373	89.69 331	94.28 369	81.48 281	98.00 340	87.18 353	84.92 414	94.93 395

v1144		91.37 312	90.60 314	93.68 330	93.89 408	88.23 304	96.84 240	97.03 282	88.37 342	89.69 331	94.39 359	82.04 269	97.98 342	87.80 323	85.37 403	94.84 404

Test_1112_low_res		92.84 244	91.84 257	95.85 171	97.04 191	89.97 222	95.53 358	96.64 317	85.38 409	89.65 333	95.18 319	85.86 175	99.10 175	87.70 329	93.58 304	98.49 192

nomal-1		91.63 292	90.62 311	94.66 261	96.07 306	87.86 321	95.58 355	94.63 428	89.80 289	89.61 334	92.66 424	72.05 404	98.29 301	90.61 262	94.55 276	97.82 260

v1192		91.07 327	90.23 330	93.58 338	93.70 413	87.82 324	96.73 255	97.07 271	87.77 363	89.58 335	94.32 367	80.90 294	97.97 345	86.52 361	85.48 401	94.95 391

v1240		90.70 344	89.85 348	93.23 354	93.51 423	86.80 349	96.61 271	97.02 284	87.16 380	89.58 335	94.31 368	79.55 323	97.98 342	85.52 379	85.44 402	94.90 398

TranMVSNet+NR-MVSNet		92.50 251	91.63 264	95.14 227	94.76 378	92.07 121	97.53 153	98.11 90	92.90 156	89.56 337	96.12 270	83.16 238	97.60 393	89.30 290	83.20 437	95.75 346

v2v482		91.59 296	90.85 297	93.80 320	93.87 409	88.17 310	96.94 225	96.88 300	89.54 298	89.53 338	94.90 331	81.70 279	98.02 338	89.25 293	85.04 412	95.20 379

v1921920		90.85 338	90.03 341	93.29 352	93.55 420	86.96 347	96.74 254	97.04 280	87.36 375	89.52 339	94.34 364	80.23 309	97.97 345	86.27 364	85.21 407	94.94 393

IterMVS-LS		92.29 264	91.94 253	93.34 350	96.25 280	86.97 345	96.57 277	97.05 278	90.67 256	89.50 340	94.80 337	86.59 157	97.64 388	89.91 273	86.11 396	95.40 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cascas		91.20 322	90.08 336	94.58 268	94.97 365	89.16 264	93.65 443	97.59 175	79.90 474	89.40 341	92.92 421	75.36 375	98.36 293	92.14 217	94.75 271	96.23 319

XVG-ACMP-BASELINE		90.93 335	90.21 333	93.09 360	94.31 398	85.89 377	95.33 368	97.26 246	91.06 242	89.38 342	95.44 309	68.61 438	98.60 268	89.46 285	91.05 339	94.79 415

GBi-Net		91.35 313	90.27 327	94.59 264	96.51 259	91.18 165	97.50 157	96.93 291	88.82 327	89.35 343	94.51 352	73.87 388	97.29 423	86.12 369	88.82 366	95.31 371

test1		91.35 313	90.27 327	94.59 264	96.51 259	91.18 165	97.50 157	96.93 291	88.82 327	89.35 343	94.51 352	73.87 388	97.29 423	86.12 369	88.82 366	95.31 371

FMVSNet3		91.78 283	90.69 308	95.03 234	96.53 255	92.27 114	97.02 215	96.93 291	89.79 290	89.35 343	94.65 345	77.01 359	97.47 410	86.12 369	88.82 366	95.35 368

WR-MVS		92.34 260	91.53 268	94.77 254	95.13 360	90.83 182	96.40 289	97.98 121	91.88 201	89.29 346	95.54 304	82.50 259	97.80 371	89.79 277	85.27 406	95.69 349

DP-MVS		92.76 247	91.51 271	96.52 108	98.77 63	90.99 172	97.38 179	96.08 355	82.38 457	89.29 346	97.87 128	83.77 225	99.69 74	81.37 430	96.69 213	98.89 140

BH-w/o		92.14 272	91.75 260	93.31 351	96.99 197	85.73 381	95.67 347	95.69 372	88.73 332	89.26 348	94.82 336	82.97 246	98.07 330	85.26 384	96.32 232	96.13 328

3Dnovator	91.36 5	95.19 129	94.44 159	97.44 58	96.56 250	93.36 72	98.65 16	98.36 38	94.12 92	89.25 349	98.06 99	82.20 266	99.77 53	93.41 193	99.32 71	99.18 85

usedtu_dtu_shiyan1		91.65 290	90.67 309	94.60 262	93.65 418	90.95 175	94.86 392	97.12 260	89.69 293	89.21 350	93.62 401	81.17 287	97.67 383	87.54 340	89.14 361	95.17 384

FE-MVSNET3		91.65 290	90.67 309	94.60 262	93.65 418	90.95 175	94.86 392	97.12 260	89.69 293	89.21 350	93.62 401	81.17 287	97.67 383	87.54 340	89.14 361	95.17 384

tt0805		91.09 326	90.07 339	94.16 296	95.61 322	88.31 298	97.56 147	96.51 325	89.56 297	89.17 352	95.64 298	67.08 452	98.38 292	91.07 245	88.44 372	95.80 340

miper_enhance_ethall		91.54 302	91.01 290	93.15 358	95.35 340	87.07 343	93.97 426	96.90 297	86.79 386	89.17 352	93.43 414	86.55 159	97.64 388	89.97 272	86.93 387	94.74 420

Fast-Effi-MVS+-dtu		92.29 264	91.99 251	93.21 356	95.27 348	85.52 384	97.03 213	96.63 320	92.09 194	89.11 354	95.14 321	80.33 307	98.08 326	87.54 340	94.74 272	96.03 332

WBMVS		90.69 346	89.99 343	92.81 371	96.48 262	85.00 398	95.21 379	96.30 337	89.46 302	89.04 355	94.05 383	72.45 403	97.82 368	89.46 285	87.41 384	95.61 351

XXY-MVS		92.16 270	91.23 281	94.95 243	94.75 379	90.94 177	97.47 166	97.43 219	89.14 311	88.90 356	96.43 253	79.71 318	98.24 305	89.56 283	87.68 379	95.67 350

PCF-MVS	89.48 11	91.56 299	89.95 344	96.36 128	96.60 241	92.52 105	92.51 468	97.26 246	79.41 476	88.90 356	96.56 247	84.04 223	99.55 110	77.01 461	97.30 183	97.01 296
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_ehance_all_eth		91.59 296	91.13 285	92.97 364	95.55 326	86.57 357	94.47 407	96.88 300	87.77 363	88.88 358	94.01 384	86.22 167	97.54 403	89.49 284	86.93 387	94.79 415

SSC-MVS3.2		89.74 374	89.26 367	91.19 422	95.16 355	80.29 457	94.53 402	97.03 282	91.79 203	88.86 359	94.10 379	69.94 426	97.82 368	85.29 382	86.66 392	95.45 359

jajsoiax		92.42 256	91.89 256	94.03 303	93.33 432	88.50 292	97.73 116	97.53 193	92.00 199	88.85 360	96.50 250	75.62 374	98.11 320	93.88 181	91.56 330	95.48 354

eth_miper_zixun_eth		91.02 330	90.59 315	92.34 385	95.33 344	84.35 407	94.10 423	96.90 297	88.56 336	88.84 361	94.33 365	84.08 221	97.60 393	88.77 308	84.37 424	95.06 388

c3_l		91.38 310	90.89 293	92.88 368	95.58 324	86.30 365	94.68 397	96.84 304	88.17 347	88.83 362	94.23 373	85.65 183	97.47 410	89.36 288	84.63 416	94.89 399

mvs_tets		92.31 262	91.76 259	93.94 312	93.41 429	88.29 299	97.63 137	97.53 193	92.04 197	88.76 363	96.45 252	74.62 384	98.09 325	93.91 179	91.48 331	95.45 359

v148		90.99 331	90.38 321	92.81 371	93.83 410	85.80 378	96.78 251	96.68 314	89.45 303	88.75 364	93.93 388	82.96 247	97.82 368	87.83 321	83.25 435	94.80 413

FMVSNet2		91.31 316	90.08 336	94.99 237	96.51 259	92.21 116	97.41 172	96.95 289	88.82 327	88.62 365	94.75 339	73.87 388	97.42 415	85.20 385	88.55 371	95.35 368

PAPM		91.52 303	90.30 325	95.20 224	95.30 347	89.83 227	93.38 449	96.85 303	86.26 397	88.59 366	95.80 286	84.88 206	98.15 314	75.67 467	95.93 238	97.63 268

cl22		91.21 321	90.56 317	93.14 359	96.09 302	86.80 349	94.41 411	96.58 323	87.80 361	88.58 367	93.99 386	80.85 295	97.62 391	89.87 275	86.93 387	94.99 390

3Dnovator+	91.43 4	95.40 113	94.48 157	98.16 18	96.90 205	95.34 18	98.48 25	97.87 133	94.65 72	88.53 368	98.02 105	83.69 226	99.71 68	93.18 197	98.96 110	99.44 61

dmvs_re		90.21 359	89.50 361	92.35 383	95.47 333	85.15 394	95.70 346	94.37 440	90.94 247	88.42 369	93.57 405	74.63 383	95.67 462	82.80 412	89.57 357	96.22 320

anonymousdsp		92.16 270	91.55 267	93.97 308	92.58 448	89.55 242	97.51 156	97.42 221	89.42 304	88.40 370	94.84 334	80.66 299	97.88 363	91.87 226	91.28 335	94.48 426

reproduce_monomvs		91.30 317	91.10 287	91.92 397	96.82 217	82.48 432	97.01 218	97.49 198	94.64 73	88.35 371	95.27 315	70.53 419	98.10 321	95.20 129	84.60 418	95.19 382

WR-MVS_H		92.00 276	91.35 273	93.95 310	95.09 362	89.47 246	98.04 64	98.68 18	91.46 217	88.34 372	94.68 342	85.86 175	97.56 396	85.77 376	84.24 425	94.82 410

v8		91.29 319	90.53 318	93.57 340	94.15 400	88.12 312	97.34 182	97.06 277	88.99 318	88.32 373	94.26 372	83.08 241	98.01 339	87.62 338	83.92 430	94.57 425

ACMP	89.59 10	92.62 250	92.14 245	94.05 301	96.40 268	88.20 308	97.36 180	97.25 249	91.52 214	88.30 374	96.64 237	78.46 343	98.72 244	91.86 227	91.48 331	95.23 378
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		91.04 329	90.23 330	93.49 344	94.12 401	88.16 311	97.32 185	97.08 268	88.26 345	88.29 375	94.22 375	82.17 267	97.97 345	86.45 363	84.12 426	94.33 432

QAPM		93.45 215	92.27 242	96.98 86	96.77 227	92.62 100	98.39 29	98.12 87	84.50 424	88.27 376	97.77 145	82.39 263	99.81 36	85.40 381	98.81 115	98.51 189

Anonymous20231211		90.63 347	89.42 363	94.27 290	98.24 102	89.19 263	98.05 63	97.89 129	79.95 473	88.25 377	94.96 327	72.56 402	98.13 316	89.70 279	85.14 408	95.49 353

CP-MVSNet		91.89 281	91.24 280	93.82 319	95.05 363	88.57 287	97.82 101	98.19 74	91.70 206	88.21 378	95.76 291	81.96 271	97.52 407	87.86 320	84.65 415	95.37 367

DIV-MVS_self_test		90.97 333	90.33 322	92.88 368	95.36 339	86.19 370	94.46 409	96.63 320	87.82 359	88.18 379	94.23 373	82.99 244	97.53 405	87.72 326	85.57 400	94.93 395

IMVS_0404		92.44 254	91.92 254	94.00 304	96.19 286	86.16 371	93.84 434	97.24 251	91.54 210	88.17 380	97.04 211	76.96 361	97.09 428	90.68 256	95.59 250	98.76 161

cl____		90.96 334	90.32 323	92.89 367	95.37 338	86.21 368	94.46 409	96.64 317	87.82 359	88.15 381	94.18 376	82.98 245	97.54 403	87.70 329	85.59 399	94.92 397

tpmvs		89.83 372	89.15 370	91.89 400	94.92 370	80.30 456	93.11 454	95.46 387	86.28 396	88.08 382	92.65 425	80.44 304	98.52 277	81.47 426	89.92 353	96.84 304

PS-CasMVS		91.55 300	90.84 298	93.69 327	94.96 366	88.28 300	97.84 96	98.24 63	91.46 217	88.04 383	95.80 286	79.67 319	97.48 409	87.02 356	84.54 421	95.31 371

MIMVSNet		88.50 389	86.76 399	93.72 325	94.84 375	87.77 325	91.39 475	94.05 449	86.41 393	87.99 384	92.59 428	63.27 471	95.82 459	77.44 455	92.84 308	97.57 275

GG-mvs-BLEND				93.62 335	93.69 414	89.20 261	92.39 470	83.33 513		87.98 385	89.84 465	71.00 415	96.87 439	82.08 420	95.40 257	94.80 413

miper_lstm_enhance		90.50 352	90.06 340	91.83 402	95.33 344	83.74 415	93.86 432	96.70 313	87.56 371	87.79 386	93.81 392	83.45 232	96.92 436	87.39 346	84.62 417	94.82 410

PEN-MVS		91.20 322	90.44 319	93.48 345	94.49 390	87.91 320	97.76 109	98.18 77	91.29 224	87.78 387	95.74 292	80.35 306	97.33 421	85.46 380	82.96 438	95.19 382

ITE_SJBPF				92.43 381	95.34 341	85.37 391		95.92 358	91.47 216	87.75 388	96.39 256	71.00 415	97.96 349	82.36 418	89.86 354	93.97 442

v7n		90.76 340	89.86 347	93.45 347	93.54 421	87.60 329	97.70 125	97.37 229	88.85 324	87.65 389	94.08 382	81.08 289	98.10 321	84.68 390	83.79 432	94.66 423

Patchmtry		88.64 388	87.25 391	92.78 373	94.09 402	86.64 353	89.82 490	95.68 374	80.81 469	87.63 390	92.36 435	80.91 292	97.03 431	78.86 450	85.12 409	94.67 422

testing3		87.67 397	86.88 398	90.05 441	96.14 296	80.71 448	97.10 210	92.85 468	90.15 279	87.54 391	94.55 349	55.70 488	94.10 482	73.77 477	94.10 288	95.35 368

pmmvs4		90.93 335	89.85 348	94.17 293	93.34 431	90.79 184	94.60 399	96.02 356	84.62 422	87.45 392	95.15 320	81.88 276	97.45 412	87.70 329	87.87 377	94.27 436

tpm cat1		88.36 390	87.21 393	91.81 404	95.13 360	80.55 452	92.58 467	95.70 370	74.97 489	87.45 392	91.96 445	78.01 353	98.17 313	80.39 439	88.74 369	96.72 308

FMVSNet1		89.88 369	88.31 382	94.59 264	95.41 334	91.18 165	97.50 157	96.93 291	86.62 389	87.41 394	94.51 352	65.94 460	97.29 423	83.04 408	87.43 382	95.31 371

IterMVS-SCA-FT		90.31 354	89.81 350	91.82 403	95.52 327	84.20 410	94.30 417	96.15 353	90.61 262	87.39 395	94.27 370	75.80 371	96.44 447	87.34 347	86.88 391	94.82 410

MVS		91.71 286	90.44 319	95.51 204	95.20 354	91.59 142	96.04 323	97.45 212	73.44 493	87.36 396	95.60 300	85.42 192	99.10 175	85.97 373	97.46 171	95.83 338

EU-MVSNet		88.72 387	88.90 375	88.20 458	93.15 435	74.21 492	96.63 270	94.22 445	85.18 413	87.32 397	95.97 276	76.16 368	94.98 473	85.27 383	86.17 394	95.41 361

IterMVS		90.15 362	89.67 356	91.61 410	95.48 329	83.72 416	94.33 415	96.12 354	89.99 282	87.31 398	94.15 378	75.78 373	96.27 452	86.97 357	86.89 390	94.83 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS-28		86.81 414	86.41 401	88.02 460	92.87 440	74.60 491	95.38 366	86.70 506	88.17 347	87.28 399	94.67 344	70.83 417	93.30 492	67.45 494	94.31 279	96.17 323

pmmvs5		89.86 371	88.87 376	92.82 370	92.86 441	86.23 367	96.26 305	95.39 388	84.24 427	87.12 400	94.51 352	74.27 386	97.36 420	87.61 339	87.57 380	94.86 400

DTE-MVSNet		90.56 348	89.75 354	93.01 362	93.95 405	87.25 336	97.64 135	97.65 163	90.74 251	87.12 400	95.68 296	79.97 314	97.00 434	83.33 405	81.66 444	94.78 417

mvs5depth		86.53 415	85.08 420	90.87 426	88.74 485	82.52 431	91.91 472	94.23 444	86.35 394	87.11 402	93.70 395	66.52 453	97.76 376	81.37 430	75.80 468	92.31 471

Patchmatch-test		89.42 378	87.99 385	93.70 326	95.27 348	85.11 395	88.98 493	94.37 440	81.11 465	87.10 403	93.69 396	82.28 264	97.50 408	74.37 473	94.76 270	98.48 194

IB-MVS	87.33 17	89.91 366	88.28 383	94.79 253	95.26 351	87.70 326	95.12 386	93.95 453	89.35 306	87.03 404	92.49 429	70.74 418	99.19 157	89.18 297	81.37 445	97.49 277
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
EPNet_dtu		91.71 286	91.28 278	92.99 363	93.76 412	83.71 417	96.69 261	95.28 396	93.15 139	87.02 405	95.95 278	83.37 233	97.38 419	79.46 447	96.84 203	97.88 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Syy-MVS		87.13 407	87.02 397	87.47 462	95.16 355	73.21 495	95.00 388	93.93 454	88.55 337	86.96 406	91.99 443	75.90 369	94.00 484	61.59 505	94.11 286	95.20 379

myMVS_eth3d		87.18 406	86.38 402	89.58 447	95.16 355	79.53 468	95.00 388	93.93 454	88.55 337	86.96 406	91.99 443	56.23 487	94.00 484	75.47 469	94.11 286	95.20 379

baseline2		91.63 292	90.86 295	93.94 312	94.33 396	86.32 364	95.92 332	91.64 483	89.37 305	86.94 408	94.69 341	81.62 280	98.69 248	88.64 311	94.57 275	96.81 305

MSDG		91.42 308	90.24 329	94.96 242	97.15 181	88.91 274	93.69 440	96.32 335	85.72 405	86.93 409	96.47 251	80.24 308	98.98 195	80.57 437	95.05 265	96.98 297

test0.0.03 1		89.37 379	88.70 377	91.41 415	92.47 450	85.63 382	95.22 377	92.70 471	91.11 239	86.91 410	93.65 400	79.02 333	93.19 495	78.00 454	89.18 360	95.41 361

COLMAP_ROB	87.81 15	90.40 353	89.28 366	93.79 321	97.95 130	87.13 342	96.92 228	95.89 362	82.83 449	86.88 411	97.18 200	73.77 391	99.29 148	78.44 452	93.62 301	94.95 391
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
D2MVS		91.30 317	90.95 292	92.35 383	94.71 382	85.52 384	96.18 314	98.21 67	88.89 323	86.60 412	93.82 391	79.92 315	97.95 353	89.29 291	90.95 342	93.56 447

SD_0403		90.01 364	90.02 342	89.96 443	95.65 321	76.76 482	95.76 343	96.46 328	90.58 266	86.59 413	96.29 260	82.12 268	94.78 475	73.00 481	93.76 297	98.35 209

OurMVSNet-221017-0		90.51 351	90.19 334	91.44 414	93.41 429	81.25 443	96.98 222	96.28 341	91.68 207	86.55 414	96.30 259	74.20 387	97.98 342	88.96 303	87.40 385	95.09 386

sc_t1		86.48 417	84.10 435	93.63 334	93.45 427	85.76 380	96.79 247	94.71 423	73.06 494	86.45 415	94.35 362	55.13 489	97.95 353	84.38 395	78.55 459	97.18 293

MS-PatchMatch		90.27 356	89.77 352	91.78 406	94.33 396	84.72 404	95.55 356	96.73 308	86.17 399	86.36 416	95.28 314	71.28 412	97.80 371	84.09 398	98.14 149	92.81 458

blended_shiyan8		87.58 399	85.55 410	93.66 332	88.76 484	88.54 289	95.21 379	96.29 340	82.81 450	86.25 417	87.73 483	73.70 393	97.58 395	87.81 322	71.42 487	94.85 403

1314		92.81 246	92.03 249	95.14 227	95.33 344	89.52 245	96.04 323	97.44 216	87.72 366	86.25 417	95.33 311	83.84 224	98.79 218	89.26 292	97.05 195	97.11 295

blend_shiyan4		86.87 411	84.61 429	93.67 331	88.87 480	88.70 281	95.17 383	96.30 337	82.80 451	86.16 419	87.11 489	65.12 468	97.55 398	87.73 324	72.21 484	94.75 419

tfpnnormal		89.70 375	88.40 381	93.60 336	95.15 358	90.10 213	97.56 147	98.16 81	87.28 378	86.16 419	94.63 346	77.57 356	98.05 333	74.48 471	84.59 419	92.65 462

gbinet_0.2-2-1-0.02		87.30 402	85.16 418	93.69 327	88.70 487	88.81 278	95.14 384	96.20 350	83.03 448	86.14 421	87.06 490	71.26 413	97.40 417	87.46 344	71.49 486	94.86 400

pm-mvs1		90.72 343	89.65 358	93.96 309	94.29 399	89.63 235	97.79 107	96.82 305	89.07 313	86.12 422	95.48 308	78.61 341	97.78 373	86.97 357	81.67 443	94.46 427

blended_shiyan6		87.55 400	85.52 411	93.64 333	88.78 482	88.50 292	95.23 376	96.30 337	82.80 451	86.09 423	87.70 484	73.69 394	97.56 396	87.70 329	71.36 488	94.86 400

0.4-1-1-0.1		86.83 412	84.27 432	94.50 273	91.39 461	88.23 304	92.62 466	92.27 477	84.04 430	86.01 424	83.30 503	65.29 465	98.31 298	89.08 299	74.45 474	96.96 301

wanda-best-256-512		87.29 403	85.21 416	93.53 341	88.54 488	88.21 306	94.51 405	96.27 342	82.69 454	85.92 425	86.89 492	73.04 397	97.55 398	87.68 333	71.36 488	94.83 405

FE-blended-shiyan7		87.29 403	85.21 416	93.53 341	88.54 488	88.21 306	94.51 405	96.27 342	82.69 454	85.92 425	86.89 492	73.03 398	97.55 398	87.68 333	71.36 488	94.83 405

usedtu_blend_shiyan5		87.06 409	84.84 424	93.69 327	88.54 488	88.70 281	95.83 337	95.54 382	78.74 479	85.92 425	86.89 492	73.03 398	97.55 398	87.73 324	71.36 488	94.83 405

OpenMVS	89.19 12	92.86 242	91.68 263	96.40 123	95.34 341	92.73 96	98.27 37	98.12 87	84.86 419	85.78 428	97.75 146	78.89 338	99.74 60	87.50 343	98.65 123	96.73 307

LTVRE_ROB	88.41 13	90.99 331	89.92 346	94.19 292	96.18 290	89.55 242	96.31 300	97.09 267	87.88 356	85.67 429	95.91 280	78.79 339	98.57 273	81.50 424	89.98 352	94.44 429
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
testgi		87.97 393	87.21 393	90.24 438	92.86 441	80.76 447	96.67 264	94.97 411	91.74 205	85.52 430	95.83 284	62.66 476	94.47 478	76.25 463	88.36 373	95.48 354

AllTest		90.23 358	88.98 372	93.98 306	97.94 131	86.64 353	96.51 278	95.54 382	85.38 409	85.49 431	96.77 228	70.28 421	99.15 166	80.02 441	92.87 306	96.15 326

TestCases				93.98 306	97.94 131	86.64 353		95.54 382	85.38 409	85.49 431	96.77 228	70.28 421	99.15 166	80.02 441	92.87 306	96.15 326

DSMNet-mixed		86.34 421	86.12 406	87.00 468	89.88 473	70.43 498	94.93 390	90.08 494	77.97 484	85.42 433	92.78 422	74.44 385	93.96 486	74.43 472	95.14 261	96.62 311

ppachtmachnet_test		88.35 391	87.29 390	91.53 411	92.45 451	83.57 419	93.75 436	95.97 357	84.28 425	85.32 434	94.18 376	79.00 337	96.93 435	75.71 466	84.99 413	94.10 437

0.4-1-1-0.2		86.27 423	83.62 437	94.20 291	90.38 468	87.69 327	91.04 480	92.52 474	83.43 444	85.22 435	81.49 508	65.31 464	98.29 301	88.90 305	74.30 476	96.64 310

CL-MVSNet_self_test		86.31 422	85.15 419	89.80 445	88.83 481	81.74 441	93.93 429	96.22 347	86.67 388	85.03 436	90.80 456	78.09 350	94.50 476	74.92 470	71.86 485	93.15 454

our_test_3		88.78 386	87.98 386	91.20 421	92.45 451	82.53 430	93.61 445	95.69 372	85.77 404	84.88 437	93.71 394	79.99 313	96.78 443	79.47 446	86.24 393	94.28 435

MVP-Stereo		90.74 342	90.08 336	92.71 375	93.19 434	88.20 308	95.86 335	96.27 342	86.07 400	84.86 438	94.76 338	77.84 354	97.75 378	83.88 403	98.01 155	92.17 475
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	87.92 14	90.20 360	89.18 369	93.25 353	96.48 262	86.45 362	96.99 221	96.68 314	88.83 326	84.79 439	96.22 264	70.16 423	98.53 276	84.42 394	88.04 375	94.77 418

0.3-1-1-0.015		86.11 427	83.37 438	94.34 283	90.58 467	88.02 315	91.64 474	92.45 475	83.56 441	84.46 440	81.84 506	62.73 475	98.31 298	88.98 302	74.09 477	96.70 309

NR-MVSNet		92.34 260	91.27 279	95.53 199	94.95 367	93.05 83	97.39 177	98.07 99	92.65 166	84.46 440	95.71 293	85.00 202	97.77 375	89.71 278	83.52 434	95.78 342

LF4IMVS		87.94 394	87.25 391	89.98 442	92.38 454	80.05 463	94.38 412	95.25 399	87.59 370	84.34 442	94.74 340	64.31 469	97.66 387	84.83 387	87.45 381	92.23 472

LCM-MVSNet-Re		92.50 251	92.52 235	92.44 380	96.82 217	81.89 439	96.92 228	93.71 458	92.41 177	84.30 443	94.60 347	85.08 199	97.03 431	91.51 235	97.36 178	98.40 203

TransMVSNet (Re)		88.94 382	87.56 388	93.08 361	94.35 395	88.45 295	97.73 116	95.23 400	87.47 372	84.26 444	95.29 312	79.86 316	97.33 421	79.44 448	74.44 475	93.45 451

Anonymous20231206		87.09 408	86.14 405	89.93 444	91.22 463	80.35 454	96.11 317	95.35 391	83.57 440	84.16 445	93.02 419	73.54 395	95.61 463	72.16 483	86.14 395	93.84 444

SixPastTwentyTwo		89.15 380	88.54 380	90.98 424	93.49 424	80.28 458	96.70 259	94.70 424	90.78 249	84.15 446	95.57 301	71.78 408	97.71 381	84.63 391	85.07 410	94.94 393

test_fmvs3		83.21 446	83.02 441	83.78 474	86.77 499	68.34 503	96.76 253	94.91 415	86.49 391	84.14 447	89.48 468	36.04 506	91.73 500	91.86 227	80.77 448	91.26 486

TDRefinement		86.53 415	84.76 426	91.85 401	82.23 511	84.25 408	96.38 291	95.35 391	84.97 418	84.09 448	94.94 328	65.76 461	98.34 297	84.60 392	74.52 473	92.97 455

KD-MVS_self_test		85.95 429	84.95 422	88.96 455	89.55 476	79.11 474	95.13 385	96.42 330	85.91 402	84.07 449	90.48 458	70.03 425	94.82 474	80.04 440	72.94 481	92.94 456

pmmvs6		87.81 396	86.19 404	92.69 376	91.32 462	86.30 365	97.34 182	96.41 331	80.59 472	84.05 450	94.37 361	67.37 447	97.67 383	84.75 389	79.51 454	94.09 439

ACMH	87.59 16	90.53 349	89.42 363	93.87 317	96.21 282	87.92 318	97.24 195	96.94 290	88.45 340	83.91 451	96.27 262	71.92 406	98.62 266	84.43 393	89.43 358	95.05 389
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet5		87.29 403	85.79 407	91.78 406	94.80 377	87.28 334	95.49 360	95.28 396	84.09 429	83.85 452	91.82 446	62.95 473	94.17 481	78.48 451	85.34 405	93.91 443

USDC		88.94 382	87.83 387	92.27 388	94.66 383	84.96 400	93.86 432	95.90 360	87.34 376	83.40 453	95.56 302	67.43 446	98.19 311	82.64 416	89.67 356	93.66 446

ttmdpeth		85.91 430	84.76 426	89.36 451	89.14 477	80.25 459	95.66 350	93.16 465	83.77 435	83.39 454	95.26 316	66.24 457	95.26 472	80.65 436	75.57 469	92.57 463

Anonymous20240521		86.42 419	85.44 412	89.34 452	90.33 469	79.79 464	96.73 255	95.92 358	83.71 437	83.25 455	91.36 453	63.92 470	96.01 453	78.39 453	85.36 404	92.22 473

KD-MVS_2432*1600		84.81 440	82.64 443	91.31 417	91.07 464	85.34 392	91.22 477	95.75 368	85.56 407	83.09 456	90.21 461	67.21 448	95.89 455	77.18 459	62.48 507	92.69 460

miper_refine_blended		84.81 440	82.64 443	91.31 417	91.07 464	85.34 392	91.22 477	95.75 368	85.56 407	83.09 456	90.21 461	67.21 448	95.89 455	77.18 459	62.48 507	92.69 460

PVSNet_0	82.17 19	85.46 435	83.64 436	90.92 425	95.27 348	79.49 470	90.55 484	95.60 377	83.76 436	83.00 458	89.95 463	71.09 414	97.97 345	82.75 414	60.79 509	95.31 371

tt0320		85.39 436	83.12 439	92.19 392	93.44 428	85.79 379	96.19 313	94.87 420	71.19 497	82.92 459	91.76 449	58.43 482	96.81 441	81.03 435	78.26 460	93.98 441

mvsany_test3		83.59 444	82.44 446	87.03 467	83.80 504	73.82 493	93.70 438	90.92 491	86.42 392	82.51 460	90.26 460	46.76 498	95.71 460	90.82 250	76.76 465	91.57 480

ArgMatch-Sym		83.08 449	81.73 452	87.11 465	91.53 459	76.72 483	92.86 459	91.54 484	83.66 438	82.34 461	93.45 410	44.99 500	92.15 498	81.78 422	73.46 480	92.47 468

test_0402		86.46 418	84.79 425	91.45 413	95.02 364	85.55 383	96.29 302	94.89 416	80.90 466	82.21 462	93.97 387	68.21 443	97.29 423	62.98 503	88.68 370	91.51 481

Patchmatch-RL test		87.38 401	86.24 403	90.81 429	88.74 485	78.40 478	88.12 502	93.17 463	87.11 381	82.17 463	89.29 469	81.95 272	95.60 464	88.64 311	77.02 463	98.41 202

tt0320-xc		84.83 439	82.33 447	92.31 386	93.66 416	86.20 369	96.17 315	94.06 448	71.26 496	82.04 464	92.22 440	55.07 490	96.72 444	81.49 425	75.04 472	94.02 440

ArgMatch-SfM		83.09 448	81.67 453	87.34 464	91.48 460	76.29 486	92.76 462	91.31 487	84.26 426	81.99 465	93.35 415	45.52 499	92.98 496	81.83 421	72.49 483	92.76 459

TinyColmap		86.82 413	85.35 415	91.21 419	94.91 372	82.99 426	93.94 428	94.02 451	83.58 439	81.56 466	94.68 342	62.34 477	98.13 316	75.78 465	87.35 386	92.52 466

test20.03		86.14 426	85.40 414	88.35 456	90.12 470	80.06 462	95.90 334	95.20 401	88.59 333	81.29 467	93.62 401	71.43 411	92.65 497	71.26 487	81.17 446	92.34 469

dtuonlycased		85.91 430	85.69 408	86.60 469	92.42 453	76.96 481	93.66 442	94.49 434	86.68 387	80.87 468	92.00 442	71.52 409	93.23 494	79.58 443	79.97 450	89.60 493

N_pmnet		78.73 459	78.71 459	78.79 484	92.80 443	46.50 534	94.14 422	43.71 536	78.61 480	80.83 469	91.66 450	74.94 381	96.36 449	67.24 495	84.45 422	93.50 449

MVS-HIRNet		82.47 451	81.21 454	86.26 471	95.38 336	69.21 501	88.96 494	89.49 495	66.28 502	80.79 470	74.08 518	68.48 441	97.39 418	71.93 484	95.47 255	92.18 474

PM-MVS		83.48 445	81.86 451	88.31 457	87.83 493	77.59 480	93.43 447	91.75 482	86.91 383	80.63 471	89.91 464	44.42 502	95.84 458	85.17 386	76.73 466	91.50 483

ambc				86.56 470	83.60 506	70.00 500	85.69 507	94.97 411		80.60 472	88.45 475	37.42 505	96.84 440	82.69 415	75.44 471	92.86 457

MIMVSNet1		84.93 438	83.05 440	90.56 434	89.56 475	84.84 403	95.40 364	95.35 391	83.91 431	80.38 473	92.21 441	57.23 484	93.34 491	70.69 489	82.75 441	93.50 449

lessismore_v0				90.45 435	91.96 457	79.09 475		87.19 504		80.32 474	94.39 359	66.31 456	97.55 398	84.00 400	76.84 464	94.70 421

K. test v3		87.64 398	86.75 400	90.32 437	93.02 437	79.48 471	96.61 271	92.08 480	90.66 258	80.25 475	94.09 381	67.21 448	96.65 445	85.96 374	80.83 447	94.83 405

OpenMVS_ROB	81.14 20	84.42 442	82.28 448	90.83 427	90.06 471	84.05 413	95.73 345	94.04 450	73.89 492	80.17 476	91.53 451	59.15 480	97.64 388	66.92 497	89.05 363	90.80 488

EG-PatchMatch MVS		87.02 410	85.44 412	91.76 408	92.67 445	85.00 398	96.08 320	96.45 329	83.41 445	79.52 477	93.49 407	57.10 485	97.72 380	79.34 449	90.87 344	92.56 464

pmmvs-eth3d		86.22 424	84.45 430	91.53 411	88.34 491	87.25 336	94.47 407	95.01 408	83.47 442	79.51 478	89.61 467	69.75 429	95.71 460	83.13 407	76.73 466	91.64 478

FE-MVSNET2		86.36 420	84.68 428	91.39 416	87.67 494	86.47 361	96.21 310	96.41 331	87.87 357	79.31 479	89.64 466	65.29 465	95.58 465	82.42 417	77.28 462	92.14 476

test_vis1_rt		86.16 425	85.06 421	89.46 449	93.47 426	80.46 453	96.41 285	86.61 507	85.22 412	79.15 480	88.64 474	52.41 493	97.06 429	93.08 200	90.57 346	90.87 487

FE-MVSNET		83.85 443	81.97 449	89.51 448	87.19 497	83.19 423	95.21 379	93.17 463	83.45 443	78.90 481	89.05 471	65.46 462	93.84 488	69.71 492	75.56 470	91.51 481

pmmvs3		79.97 457	77.50 461	87.39 463	82.80 510	79.38 472	92.70 465	90.75 492	70.69 498	78.66 482	87.47 487	51.34 494	93.40 490	73.39 479	69.65 494	89.38 494

UnsupCasMVSNet_eth		85.99 428	84.45 430	90.62 433	89.97 472	82.40 435	93.62 444	97.37 229	89.86 284	78.59 483	92.37 432	65.25 467	95.35 471	82.27 419	70.75 492	94.10 437

dmvs_testset		81.38 454	82.60 445	77.73 485	91.74 458	51.49 525	93.03 456	84.21 512	89.07 313	78.28 484	91.25 454	76.97 360	88.53 507	56.57 513	82.24 442	93.16 453

test_f		80.57 455	79.62 457	83.41 476	83.38 508	67.80 505	93.57 446	93.72 457	80.80 470	77.91 485	87.63 485	33.40 507	92.08 499	87.14 355	79.04 457	90.34 490

new-patchmatchnet		83.18 447	81.87 450	87.11 465	86.88 498	75.99 488	93.70 438	95.18 402	85.02 417	77.30 486	88.40 476	65.99 459	93.88 487	74.19 475	70.18 493	91.47 484

usedtu_dtu_shiyan2		80.00 456	76.91 462	89.27 454	82.13 512	79.69 466	95.45 362	94.20 446	72.95 495	75.80 487	87.75 482	44.44 501	94.30 480	70.64 490	68.81 498	93.84 444

UnsupCasMVSNet_bld		82.13 453	79.46 458	90.14 439	88.00 492	82.47 433	90.89 483	96.62 322	78.94 478	75.61 488	84.40 501	56.63 486	96.31 451	77.30 458	66.77 501	91.63 479

ET-MVSNet_ETH3D		91.49 305	90.11 335	95.63 192	96.40 268	91.57 144	95.34 367	93.48 460	90.60 264	75.58 489	95.49 306	80.08 311	96.79 442	94.25 172	89.76 355	98.52 187

new_pmnet		82.89 450	81.12 455	88.18 459	89.63 474	80.18 461	91.77 473	92.57 472	76.79 487	75.56 490	88.23 478	61.22 479	94.48 477	71.43 485	82.92 439	89.87 491

dongtai		69.99 470	69.33 469	71.98 498	88.78 482	61.64 515	89.86 489	59.93 528	75.67 488	74.96 491	85.45 498	50.19 495	81.66 519	43.86 521	55.27 513	72.63 518

APD_test1		79.31 458	77.70 460	84.14 473	89.11 479	69.07 502	92.36 471	91.50 485	69.07 499	73.87 492	92.63 427	39.93 504	94.32 479	70.54 491	80.25 449	89.02 495

CMPMVS	62.92 21	85.62 434	84.92 423	87.74 461	89.14 477	73.12 496	94.17 421	96.80 306	73.98 490	73.65 493	94.93 329	66.36 454	97.61 392	83.95 401	91.28 335	92.48 467
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVStest1		82.38 452	80.04 456	89.37 450	87.63 495	82.83 427	95.03 387	93.37 462	73.90 491	73.50 494	94.35 362	62.89 474	93.25 493	73.80 476	65.92 503	92.04 477

MASt3R-SfM		71.17 467	70.37 466	73.55 496	74.50 524	51.20 526	82.17 513	80.88 517	64.49 507	72.54 495	91.37 452	25.17 515	81.85 518	75.86 464	66.37 502	87.59 497

WB-MVS		76.77 460	76.63 463	77.18 486	85.32 501	56.82 522	94.53 402	89.39 496	82.66 456	71.35 496	89.18 470	75.03 378	88.88 505	35.42 526	66.79 500	85.84 501

SSC-MVS		76.05 461	75.83 464	76.72 490	84.77 502	56.22 523	94.32 416	88.96 498	81.82 462	70.52 497	88.91 472	74.79 382	88.71 506	33.69 528	64.71 504	85.23 504

YYNet1		85.87 432	84.23 433	90.78 432	92.38 454	82.46 434	93.17 451	95.14 404	82.12 459	67.69 498	92.36 435	78.16 349	95.50 469	77.31 457	79.73 452	94.39 430

kuosan		65.27 478	64.66 478	67.11 504	83.80 504	61.32 516	88.53 498	60.77 527	68.22 500	67.67 499	80.52 511	49.12 496	70.76 529	29.67 530	53.64 515	69.26 520

MDA-MVSNet_test_wron		85.87 432	84.23 433	90.80 431	92.38 454	82.57 429	93.17 451	95.15 403	82.15 458	67.65 500	92.33 438	78.20 346	95.51 468	77.33 456	79.74 451	94.31 434

DeepMVS_CX				74.68 495	90.84 466	64.34 512		81.61 515	65.34 504	67.47 501	88.01 481	48.60 497	80.13 522	62.33 504	73.68 479	79.58 512

LCM-MVSNet		72.55 463	69.39 468	82.03 478	70.81 533	65.42 510	90.12 488	94.36 442	55.02 515	65.88 502	81.72 507	24.16 516	89.96 501	74.32 474	68.10 499	90.71 489

test_method		66.11 477	64.89 477	69.79 500	72.62 531	35.23 540	65.19 529	92.83 470	20.35 535	65.20 503	88.08 480	43.14 503	82.70 517	73.12 480	63.46 505	91.45 485

MDA-MVSNet-bldmvs		85.00 437	82.95 442	91.17 423	93.13 436	83.33 420	94.56 401	95.00 409	84.57 423	65.13 504	92.65 425	70.45 420	95.85 457	73.57 478	77.49 461	94.33 432

RoMa-SfM		70.64 468	67.48 472	80.09 479	84.70 503	66.61 506	88.62 497	73.09 523	65.10 505	64.98 505	88.91 472	22.38 519	87.00 510	63.51 502	56.06 512	86.67 499

DenseAffine		72.53 464	69.17 470	82.59 477	87.49 496	70.91 497	88.38 499	81.13 516	67.58 501	64.27 506	87.44 488	23.61 518	88.47 509	66.10 498	56.56 511	88.38 496

PMMVS2		70.19 469	66.92 473	80.01 480	76.35 521	65.67 508	86.22 506	87.58 502	64.83 506	62.38 507	80.29 512	26.78 512	88.49 508	63.79 501	54.07 514	85.88 500

testf1		69.31 471	66.76 474	76.94 488	78.61 519	61.93 513	88.27 500	86.11 508	55.62 513	59.69 508	85.31 499	20.19 522	89.32 502	57.62 510	69.44 496	79.58 512

APD_test2		69.31 471	66.76 474	76.94 488	78.61 519	61.93 513	88.27 500	86.11 508	55.62 513	59.69 508	85.31 499	20.19 522	89.32 502	57.62 510	69.44 496	79.58 512

DKM		67.96 474	64.19 479	79.27 482	83.41 507	64.35 511	86.88 505	68.11 525	63.15 508	59.36 510	86.08 496	16.45 531	86.15 512	64.54 500	49.73 516	87.32 498

RoMa-HiRes		64.40 479	60.91 482	74.89 494	78.66 518	58.85 520	85.22 509	58.46 530	58.65 511	59.29 511	86.60 495	16.97 528	83.91 515	59.14 508	45.20 521	81.91 511

test_vis3_rt		72.73 462	70.55 465	79.27 482	80.02 516	68.13 504	93.92 430	74.30 522	76.90 486	58.99 512	73.58 519	20.29 521	95.37 470	84.16 396	72.80 482	74.31 515

FPMVS		71.27 466	69.85 467	75.50 492	74.64 523	59.03 519	91.30 476	91.50 485	58.80 510	57.92 513	88.28 477	29.98 510	85.53 513	53.43 516	82.84 440	81.95 510

Gipuma		67.86 475	65.41 476	75.18 493	92.66 446	73.45 494	66.50 528	94.52 432	53.33 518	57.80 514	66.07 524	30.81 508	89.20 504	48.15 519	78.88 458	62.90 527
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_clip		37.19 501	40.69 504	26.70 526	52.35 550	23.34 558	43.13 541	10.51 561	12.50 550	56.71 515	80.13 513	19.51 524	16.50 557	43.87 520	47.47 517	40.26 535

DKM-HiRes		64.02 480	59.97 483	76.17 491	79.46 517	59.20 518	84.48 510	58.37 531	58.52 512	56.03 516	83.71 502	13.19 539	83.72 516	60.49 507	45.50 520	85.59 502

LoFTR		72.43 465	68.71 471	83.60 475	85.67 500	65.61 509	88.04 503	87.40 503	66.11 503	55.94 517	85.54 497	25.43 513	95.55 467	60.87 506	63.38 506	89.63 492

MatchFormer		67.84 476	63.81 480	79.93 481	83.26 509	60.99 517	87.61 504	84.49 511	54.89 516	51.76 518	81.06 509	22.08 520	94.10 482	50.36 518	58.82 510	84.72 505

PDCNetPlus		61.05 482	58.26 485	69.44 501	75.52 522	55.68 524	81.49 514	51.76 533	62.45 509	51.54 519	82.02 505	23.69 517	78.90 523	65.91 499	29.91 538	73.74 516

tmp_tt		51.94 490	53.82 488	46.29 512	33.73 560	45.30 536	78.32 516	67.24 526	18.02 537	50.93 520	87.05 491	52.99 492	53.11 533	70.76 488	25.29 544	40.46 534

VLMVS_CLIP		39.93 500	41.64 500	34.80 519	33.81 559	19.16 560	46.81 536	59.30 529	16.50 538	47.57 521	67.74 523	14.11 536	49.88 534	42.98 522	45.94 519	35.36 537

PMatch-SfM		57.38 485	52.53 490	71.95 499	68.62 534	49.38 527	77.61 517	45.82 534	52.41 519	46.59 522	82.04 504	4.86 556	81.03 520	58.34 509	36.49 531	85.43 503

ELoFTR		60.03 483	55.86 486	72.52 497	67.65 535	48.49 529	76.21 518	75.14 521	53.94 517	45.93 523	79.98 514	9.14 541	85.06 514	55.39 514	39.36 529	84.02 507

PMatch-Up-SfM		52.53 488	47.58 493	67.36 503	63.24 538	43.29 537	72.10 520	34.71 546	47.03 520	43.51 524	79.07 515	3.90 559	75.83 524	54.68 515	30.02 537	82.95 508

ANet_high		63.94 481	59.58 484	77.02 487	61.24 540	66.06 507	85.66 508	87.93 501	78.53 481	42.94 525	71.04 520	25.42 514	80.71 521	52.60 517	30.83 535	84.28 506

E-PMN		53.28 486	52.56 489	55.43 507	74.43 525	47.13 533	83.63 512	76.30 518	42.23 522	42.59 526	62.22 528	28.57 511	74.40 526	31.53 529	31.51 533	44.78 531

SP-DiffGlue		43.94 495	43.32 496	45.79 515	47.79 556	33.03 541	63.37 530	42.65 539	25.71 529	41.26 527	69.27 521	18.83 526	38.88 541	34.96 527	46.05 518	65.47 526

EMVS		52.08 489	51.31 491	54.39 509	72.62 531	45.39 535	83.84 511	75.51 520	41.13 523	40.77 528	59.65 530	30.08 509	73.60 527	28.31 531	29.90 539	44.18 532

MVE	50.73 23	53.25 487	48.81 492	66.58 505	65.34 536	57.50 521	72.49 519	70.94 524	40.15 524	39.28 529	63.51 525	6.89 545	73.48 528	38.29 524	42.38 526	68.76 521
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ALIKED-LG		47.63 491	45.22 494	54.88 508	81.48 513	48.47 530	71.83 521	45.44 535	32.66 526	37.07 530	63.26 527	19.21 525	63.71 530	15.49 540	40.53 527	52.46 528

ALIKED-NN		46.19 493	43.87 495	53.16 511	80.39 515	47.77 531	69.82 527	43.65 537	27.89 527	36.60 531	63.35 526	17.30 527	61.29 532	15.84 539	39.98 528	50.41 530

PMVS	53.92 22	58.58 484	55.40 487	68.12 502	51.00 554	48.64 528	78.86 515	87.10 505	46.77 521	35.84 532	74.28 517	8.76 542	86.34 511	42.07 523	73.91 478	69.38 519
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GLUNet-SfM		46.44 492	41.21 502	62.14 506	51.92 551	38.44 539	58.72 531	57.51 532	34.08 525	34.61 533	67.84 522	11.40 540	74.90 525	35.48 525	19.30 550	73.08 517

ALIKED-MNN		45.42 494	42.62 497	53.80 510	80.52 514	47.58 532	70.83 524	43.05 538	27.21 528	34.32 534	61.10 529	14.85 535	62.94 531	14.90 541	36.82 530	50.89 529

SP-SuperGlue		43.33 497	42.50 498	45.81 514	73.95 528	31.24 544	71.34 522	41.17 540	23.96 530	33.42 535	56.47 532	16.72 530	39.64 539	21.11 535	44.32 523	66.57 523

SP-LightGlue		43.37 496	42.49 499	46.03 513	74.26 526	31.37 543	71.24 523	40.98 541	23.86 531	33.18 536	56.34 534	16.78 529	39.73 538	21.09 536	44.68 522	66.97 522

SP-NN		42.37 498	41.40 501	45.29 517	72.86 530	30.45 546	70.32 526	39.16 544	22.21 532	31.32 537	56.73 531	15.45 533	39.53 540	20.27 537	44.25 524	65.88 525

XFeat-NN		33.93 503	33.70 506	34.60 520	41.69 558	24.48 556	51.85 534	36.02 545	19.55 536	31.20 538	56.38 533	13.46 538	40.91 536	22.51 534	30.65 536	38.42 536

VLMVS		20.83 517	22.16 520	16.83 537	23.35 561	13.77 564	21.05 551	12.13 560	1.76 557	31.04 539	45.78 538	15.59 532	13.56 558	13.60 542	35.16 532	23.18 538

SP-MNN		42.11 499	40.98 503	45.49 516	72.87 529	30.19 548	70.72 525	39.96 542	20.98 533	30.21 540	55.72 536	15.26 534	40.07 537	19.70 538	43.42 525	66.21 524

XFeat-MNN		35.01 502	34.34 505	37.02 518	42.54 557	25.71 555	54.01 533	39.41 543	20.70 534	30.13 541	55.85 535	14.08 537	44.62 535	22.90 533	29.45 542	40.75 533

MVS_baseline		12.31 523	14.46 526	5.86 538	16.09 562	0.78 567	6.53 552	1.85 565	0.36 559	23.99 542	49.92 537	2.55 562	0.00 561	8.94 543	19.86 548	16.82 551

SIFT-NN		28.47 504	28.54 508	28.27 521	64.38 537	31.62 542	48.50 535	24.78 547	14.32 539	19.55 543	40.46 539	7.22 543	31.96 543	6.20 546	31.47 534	21.24 539

SIFT-MNN		27.50 505	27.40 509	27.80 522	61.71 539	30.57 545	46.59 537	24.66 548	14.04 540	17.35 544	39.90 540	6.52 546	31.80 544	6.13 547	29.65 540	21.04 540

SIFT-NN-NCMNet		27.16 506	27.05 510	27.51 523	59.97 542	30.42 547	46.49 538	24.52 549	13.94 542	17.23 545	39.47 541	6.39 547	31.40 545	5.94 548	29.49 541	20.72 542

SIFT-NN-CMatch		25.59 508	25.23 512	26.67 527	56.47 546	28.89 551	42.75 542	22.52 552	13.89 543	16.98 546	39.39 543	6.26 549	30.38 547	5.77 550	22.99 546	20.75 541

SIFT-NN-PointCN		23.81 513	23.84 516	23.73 532	52.41 549	22.80 559	42.30 544	20.98 554	13.02 549	15.14 547	37.74 548	6.20 550	28.40 552	5.52 552	21.24 547	19.98 544

SIFT-ConvMatch		24.62 511	24.14 515	26.03 528	58.66 543	29.15 550	40.80 545	21.31 553	13.69 544	13.51 548	38.52 544	5.65 552	30.22 549	5.51 553	19.65 549	18.73 547

SIFT-NN-UMatch		25.24 509	25.01 513	25.92 529	54.55 548	27.33 552	44.97 539	22.85 550	13.97 541	13.40 549	39.41 542	6.28 548	30.23 548	5.83 549	23.82 545	20.21 543

SIFT-CM-Cal		23.18 515	22.70 518	24.60 531	57.42 544	26.79 553	37.63 547	18.36 556	13.35 547	12.57 550	37.37 549	5.54 553	28.79 551	5.17 556	16.92 554	18.23 548

SIFT-UMatch		24.03 512	23.67 517	25.10 530	57.10 545	26.49 554	42.43 543	20.05 555	13.49 546	12.40 551	38.51 545	5.45 554	30.07 550	5.56 551	18.08 551	18.74 546

SIFT-NCM-Cal		25.87 507	25.57 511	26.75 524	60.60 541	29.37 549	44.96 540	22.64 551	13.57 545	11.67 552	37.90 546	5.81 551	31.26 546	5.32 554	27.70 543	19.63 545

SIFT-UM-Cal		22.52 516	22.27 519	23.27 533	56.41 547	23.87 557	39.94 546	16.81 558	13.33 548	10.54 553	37.90 546	5.16 555	28.36 553	5.23 555	15.12 555	17.57 549

wuyk23d		25.11 510	24.57 514	26.74 525	73.98 527	39.89 538	57.88 532	9.80 563	12.27 551	10.39 554	6.97 558	7.03 544	36.44 542	25.43 532	17.39 552	3.89 556

SIFT-PCN-Cal		20.26 519	20.34 522	20.01 535	51.70 552	17.74 562	35.64 549	16.15 559	11.90 553	10.28 555	33.69 550	4.55 557	25.68 554	4.57 557	14.59 556	16.60 552

SIFT-PointCN		20.70 518	20.89 521	20.14 534	51.62 553	18.11 561	37.52 548	17.71 557	12.03 552	10.05 556	33.23 551	4.33 558	25.40 555	4.55 558	16.94 553	16.90 550

SIFT-NCMNet		17.70 520	17.74 523	17.60 536	49.47 555	16.50 563	30.22 550	10.39 562	11.77 554	8.79 557	29.74 553	3.61 561	22.42 556	3.97 559	11.69 557	13.89 553

testmvs		13.36 521	16.33 524	4.48 540	5.04 563	2.26 566	93.18 450	3.28 564	2.70 555	8.24 558	21.66 554	2.29 563	2.19 559	7.58 544	2.96 558	9.00 555

test123		13.04 522	15.66 525	5.18 539	4.51 564	3.45 565	92.50 469	1.81 566	2.50 556	7.58 559	20.15 555	3.67 560	2.18 560	7.13 545	1.07 559	9.90 554

EGC-MVSNET		68.77 473	63.01 481	86.07 472	92.49 449	82.24 437	93.96 427	90.96 490	0.71 558	2.62 560	90.89 455	53.66 491	93.46 489	57.25 512	84.55 420	82.51 509

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		23.24 514	30.99 507	0.00 541	0.00 565	0.00 568	0.00 553	97.63 167	0.00 560	0.00 561	96.88 223	84.38 214	0.00 561	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		7.39 525	9.85 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	88.65 110	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		8.06 524	10.74 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	96.69 234	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	79.04 476	92.75 463	94.19 447	78.18 482
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														67.11 496	84.43 423	93.53 448
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													96.32 450
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						79.53 468								75.56 468

MSC_two_6792asdad				98.86 1	98.67 68	96.94 1		97.93 126					99.86 11	97.68 33	99.67 6	99.77 4

No_MVS				98.86 1	98.67 68	96.94 1		97.93 126					99.86 11	97.68 33	99.67 6	99.77 4

eth-test2					0.00 565

eth-test					0.00 565

OPU-MVS				98.55 3	98.82 62	96.86 3	98.25 40				98.26 87	96.04 2	99.24 152	95.36 126	99.59 21	99.56 40

save fliter					98.91 59	94.28 44	97.02 215	98.02 114	95.35 33

test_0728_SECOND				98.51 4	99.45 6	95.93 6	98.21 48	98.28 52					99.86 11	97.52 42	99.67 6	99.75 8

GSMVS																98.45 197

sam_mvs1												82.76 252				98.45 197

sam_mvs												81.94 273

MTGPA								98.08 94

test_post1							92.81 461				16.58 557	80.53 302	97.68 382	86.20 366

test_post											17.58 556	81.76 277	98.08 326

patchmatchnet-post											90.45 459	82.65 257	98.10 321

MTMP							97.86 92	82.03 514

gm-plane-assit					93.22 433	78.89 477			84.82 420		93.52 406		98.64 260	87.72 326

test9_res														94.81 150	99.38 64	99.45 59

agg_prior2														93.94 178	99.38 64	99.50 52

test_prior4						93.66 64	96.42 284

test_prior				97.23 70	98.67 68	92.99 85		98.00 118					99.41 134			99.29 75

新几何2							95.79 341

旧先验1					98.38 91	93.38 70		97.75 150			98.09 97	92.30 49			99.01 107	99.16 86

无先验							95.79 341	97.87 133	83.87 434				99.65 80	87.68 333		98.89 140

原ACMM2							95.67 347

testdata2													99.67 78	85.96 374

segment_acmp												92.89 34

testdata1							95.26 375		93.10 142

plane_prior7					96.21 282	89.98 220

plane_prior6					96.10 301	90.00 216						81.32 284

plane_prior5								97.51 195					98.60 268	93.02 203	92.23 317	95.86 334

plane_prior4											96.64 237

plane_prior2							97.74 114		94.85 55

plane_prior1					96.14 296

plane_prior						89.99 218	97.24 195		94.06 95						92.16 321

n2								0.00 567

nn								0.00 567

door-mid								91.06 489

test11								97.88 131

door								91.13 488

HQP5-MVS						89.33 254

BP-MVS														92.13 220

HQP3-MVS								97.39 224							92.10 322

HQP2-MVS												80.95 290

NP-MVS					95.99 308	89.81 228					95.87 281

ACMMP++_ref															90.30 351

ACMMP++															91.02 340

Test By Simon												88.73 109

High-resolution multi-view benchmark