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.

DPM-MVS		97.86 8	97.25 18	99.68 1	98.25 107	99.10 1	99.76 12	97.78 63	96.61 4	98.15 34	99.53 7	93.62 16	100.00 1	91.79 145	99.80 27	99.94 18

MSC_two_6792asdad				99.51 2	99.61 27	98.60 2		97.69 81					99.98 10	99.55 9	99.83 15	99.96 10

No_MVS				99.51 2	99.61 27	98.60 2		97.69 81					99.98 10	99.55 9	99.83 15	99.96 10

OPU-MVS				99.49 4	99.64 20	98.51 4	99.77 9				99.19 34	95.12 7	99.97 23	99.90 1	99.92 3	99.99 1

MCST-MVS		98.18 2	97.95 8	98.86 5	99.85 3	96.60 9	99.70 17	97.98 44	97.18 2	95.96 90	99.33 23	92.62 25	100.00 1	98.99 17	99.93 1	99.98 6

MVS		93.92 111	92.28 135	98.83 6	95.69 188	96.82 7	96.22 281	98.17 31	84.89 247	84.34 226	98.61 101	79.32 205	99.83 61	93.88 120	99.43 68	99.86 32

test_0728_SECOND				98.77 7	99.66 15	96.37 13	99.72 14	97.68 83					99.98 10	99.64 6	99.82 19	99.96 10

CNVR-MVS		98.46 1	98.38 1	98.72 8	99.80 4	96.19 14	99.80 7	97.99 43	97.05 3	99.41 2	99.59 2	92.89 24	100.00 1	98.99 17	99.90 7	99.96 10

ETH3 D test6400		97.67 11	97.33 17	98.69 9	99.69 9	96.43 11	99.63 25	97.73 72	91.05 98	98.66 22	99.53 7	90.59 41	99.71 77	99.32 11	99.80 27	99.91 22

DELS-MVS		97.12 25	96.60 36	98.68 10	98.03 115	96.57 10	99.84 3	97.84 53	96.36 8	95.20 107	98.24 121	88.17 76	99.83 61	96.11 77	99.60 56	99.64 71
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
CANet		97.00 28	96.49 38	98.55 11	98.86 93	96.10 15	99.83 4	97.52 121	95.90 9	97.21 60	98.90 79	82.66 174	99.93 39	98.71 20	98.80 98	99.63 73

WTY-MVS		95.97 60	95.11 79	98.54 12	97.62 124	96.65 8	99.44 52	98.74 13	92.25 72	95.21 106	98.46 113	86.56 114	99.46 115	95.00 102	92.69 177	99.50 86

HY-MVS	88.56 7	95.29 78	94.23 92	98.48 13	97.72 120	96.41 12	94.03 312	98.74 13	92.42 68	95.65 100	94.76 212	86.52 115	99.49 108	95.29 96	92.97 173	99.53 82

MG-MVS		97.24 19	96.83 30	98.47 14	99.79 5	95.71 17	99.07 96	99.06 9	94.45 23	96.42 83	98.70 95	88.81 66	99.74 74	95.35 94	99.86 12	99.97 7

DVP-MVS++.		98.18 2	98.09 5	98.44 15	99.61 27	95.38 21	99.55 34	97.68 83	93.01 51	99.23 7	99.45 16	95.12 7	99.98 10	99.25 14	99.92 3	99.97 7

DPE-MVS		98.11 6	98.00 6	98.44 15	99.50 47	95.39 20	99.29 71	97.72 74	94.50 21	98.64 23	99.54 3	93.32 18	99.97 23	99.58 8	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		98.18 2	98.10 4	98.41 17	99.63 21	95.24 24	99.77 9	97.72 74	94.17 25	99.30 5	99.54 3	93.32 18	99.98 10	99.70 3	99.81 23	99.99 1

DVP-MVS		98.07 7	98.00 6	98.29 18	99.66 15	95.20 29	99.72 14	97.47 132	93.95 30	99.07 10	99.46 11	93.18 21	99.97 23	99.64 6	99.82 19	99.69 64
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
PS-MVSNAJ		96.87 34	96.40 40	98.29 18	97.35 133	97.29 5	99.03 101	97.11 171	95.83 10	98.97 13	99.14 45	82.48 177	99.60 95	98.60 23	99.08 84	98.00 182

canonicalmvs		95.02 86	93.96 103	98.20 20	97.53 130	95.92 16	98.71 131	96.19 226	91.78 81	95.86 95	98.49 109	79.53 203	99.03 144	96.12 76	91.42 200	99.66 69

3Dnovator+	87.72 8	93.43 126	91.84 147	98.17 21	95.73 187	95.08 32	98.92 113	97.04 178	91.42 92	81.48 269	97.60 142	74.60 230	99.79 69	90.84 155	98.97 89	99.64 71

HPM-MVS++		97.72 10	97.59 10	98.14 22	99.53 45	94.76 43	99.19 75	97.75 66	95.66 13	98.21 33	99.29 24	91.10 31	99.99 5	97.68 46	99.87 9	99.68 65

NCCC		98.12 5	98.11 3	98.13 23	99.76 6	94.46 50	99.81 5	97.88 49	96.54 5	98.84 17	99.46 11	92.55 26	99.98 10	98.25 38	99.93 1	99.94 18

DeepC-MVS_fast	93.52 2	97.16 24	96.84 29	98.13 23	99.61 27	94.45 51	98.85 119	97.64 92	96.51 7	95.88 93	99.39 21	87.35 96	99.99 5	96.61 65	99.69 41	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		97.22 22	96.92 25	98.12 25	99.11 78	94.88 36	99.44 52	97.45 135	89.60 137	98.70 20	99.42 19	90.42 45	99.72 75	98.47 29	99.65 44	99.77 48

ETH3D-3000-0.1		97.29 17	97.01 23	98.12 25	99.18 74	94.97 33	99.47 44	97.52 121	89.85 128	98.79 19	99.46 11	90.41 47	99.69 79	98.78 19	99.67 42	99.70 61

xiu_mvs_v2_base		96.66 38	96.17 48	98.11 27	97.11 143	96.96 6	99.01 104	97.04 178	95.51 16	98.86 16	99.11 53	82.19 183	99.36 125	98.59 25	98.14 113	98.00 182

alignmvs		95.77 70	95.00 81	98.06 28	97.35 133	95.68 18	99.71 16	97.50 127	91.50 87	96.16 86	98.61 101	86.28 121	99.00 145	96.19 75	91.74 194	99.51 85

ETH3D cwj APD-0.16		96.94 32	96.58 37	98.01 29	98.62 100	94.73 45	99.13 92	97.38 146	88.44 174	98.53 27	99.39 21	89.66 58	99.69 79	98.43 31	99.61 55	99.61 76

SMA-MVS		97.24 19	96.99 24	98.00 30	99.30 65	94.20 57	99.16 80	97.65 91	89.55 141	99.22 9	99.52 9	90.34 49	99.99 5	98.32 36	99.83 15	99.82 34
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
DP-MVS Recon		95.85 66	95.15 78	97.95 31	99.87 2	94.38 54	99.60 28	97.48 130	86.58 220	94.42 118	99.13 47	87.36 95	99.98 10	93.64 125	98.33 111	99.48 89

PAPR		96.35 48	95.82 60	97.94 32	99.63 21	94.19 58	99.42 57	97.55 114	92.43 65	93.82 131	99.12 48	87.30 97	99.91 42	94.02 117	99.06 85	99.74 55

1314		93.44 125	91.98 144	97.84 33	95.24 203	94.38 54	96.22 281	97.92 47	90.18 119	82.28 251	97.71 137	77.63 217	99.80 68	91.94 144	98.67 103	99.34 97

test12				97.83 34	99.33 64	94.45 51		97.55 114		97.56 51		88.60 68	99.50 107		99.71 38	99.55 81

ACMMP_NAP		96.59 40	96.18 46	97.81 35	98.82 94	93.55 68	98.88 118	97.59 106	90.66 104	97.98 44	99.14 45	86.59 112	100.00 1	96.47 69	99.46 64	99.89 27

SD-MVS		97.51 13	97.40 15	97.81 35	99.01 84	93.79 65	99.33 69	97.38 146	93.73 41	98.83 18	99.02 60	90.87 36	99.88 48	98.69 21	99.74 32	99.77 48
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
xxxxxxxxxxxxxcwj		97.51 13	97.42 14	97.78 37	99.34 58	93.85 63	99.65 23	95.45 279	95.69 11	98.70 20	99.42 19	90.42 45	99.72 75	98.47 29	99.65 44	99.77 48

testtj		97.23 21	97.05 21	97.75 38	99.75 7	93.34 73	99.16 80	97.74 68	91.28 95	98.40 29	99.29 24	89.95 52	99.98 10	98.20 39	99.70 39	99.94 18

APDe-MVS		97.53 12	97.47 11	97.70 39	99.58 33	93.63 66	99.56 33	97.52 121	93.59 44	98.01 43	99.12 48	90.80 38	99.55 98	99.26 13	99.79 29	99.93 21

CDPH-MVS		96.56 41	96.18 46	97.70 39	99.59 31	93.92 61	99.13 92	97.44 139	89.02 153	97.90 48	99.22 31	88.90 65	99.49 108	94.63 110	99.79 29	99.68 65

MSLP-MVS++		97.50 15	97.45 13	97.63 41	99.65 19	93.21 75	99.70 17	98.13 36	94.61 19	97.78 50	99.46 11	89.85 53	99.81 66	97.97 42	99.91 6	99.88 28

APD-MVS		96.95 30	96.72 33	97.63 41	99.51 46	93.58 67	99.16 80	97.44 139	90.08 124	98.59 25	99.07 54	89.06 62	99.42 119	97.92 43	99.66 43	99.88 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
sss		94.85 89	93.94 105	97.58 43	96.43 163	94.09 60	98.93 111	99.16 8	89.50 142	95.27 105	97.85 128	81.50 190	99.65 88	92.79 139	94.02 165	98.99 123

PAPM		96.35 48	95.94 56	97.58 43	94.10 241	95.25 23	98.93 111	98.17 31	94.26 24	93.94 127	98.72 92	89.68 57	97.88 185	96.36 72	99.29 78	99.62 75

train_agg		97.20 23	97.08 20	97.57 45	99.57 37	93.17 76	99.38 61	97.66 86	90.18 119	98.39 30	99.18 37	90.94 33	99.66 84	98.58 26	99.85 13	99.88 28

VNet		95.08 85	94.26 91	97.55 46	98.07 114	93.88 62	98.68 138	98.73 15	90.33 115	97.16 62	97.43 149	79.19 206	99.53 101	96.91 61	91.85 192	99.24 107

Regformer-1		96.97 29	96.80 31	97.47 47	99.46 52	93.11 78	98.89 116	97.94 45	92.89 57	96.90 65	99.02 60	89.78 54	99.53 101	97.06 54	99.26 80	99.75 52

lupinMVS		96.32 50	95.94 56	97.44 48	95.05 219	94.87 37	99.86 2	96.50 205	93.82 39	98.04 41	98.77 86	85.52 129	98.09 172	96.98 59	98.97 89	99.37 94

Regformer-2		96.94 32	96.78 32	97.42 49	99.46 52	92.97 85	98.89 116	97.93 46	92.86 59	96.88 66	99.02 60	89.74 56	99.53 101	97.03 55	99.26 80	99.75 52

1121		95.19 82	94.45 88	97.42 49	98.88 91	92.58 93	96.22 281	97.75 66	85.50 235	96.86 69	99.01 64	88.59 70	99.90 44	87.64 191	99.60 56	99.79 38

新几何1				97.40 51	98.92 89	92.51 95		97.77 65	85.52 233	96.69 78	99.06 56	88.08 79	99.89 47	84.88 219	99.62 51	99.79 38

TSAR-MVS + MP.		97.44 16	97.46 12	97.39 52	99.12 77	93.49 71	98.52 158	97.50 127	94.46 22	98.99 12	98.64 98	91.58 28	99.08 143	98.49 28	99.83 15	99.60 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
agg_prior1		97.12 25	97.03 22	97.38 53	99.54 40	92.66 88	99.35 66	97.64 92	90.38 113	97.98 44	99.17 39	90.84 37	99.61 93	98.57 27	99.78 31	99.87 31

3Dnovator	87.35 11	93.17 136	91.77 149	97.37 54	95.41 200	93.07 80	98.82 122	97.85 52	91.53 85	82.56 245	97.58 144	71.97 257	99.82 64	91.01 152	99.23 82	99.22 110

MP-MVS-pluss		95.80 68	95.30 72	97.29 55	98.95 88	92.66 88	98.59 153	97.14 167	88.95 156	93.12 137	99.25 27	85.62 128	99.94 37	96.56 67	99.48 63	99.28 103
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_yl		95.27 79	94.60 85	97.28 56	98.53 103	92.98 83	99.05 99	98.70 16	86.76 217	94.65 116	97.74 135	87.78 82	99.44 116	95.57 90	92.61 178	99.44 91

DCV-MVSNet		95.27 79	94.60 85	97.28 56	98.53 103	92.98 83	99.05 99	98.70 16	86.76 217	94.65 116	97.74 135	87.78 82	99.44 116	95.57 90	92.61 178	99.44 91

EPNet		96.82 35	96.68 35	97.25 58	98.65 98	93.10 79	99.48 42	98.76 12	96.54 5	97.84 49	98.22 122	87.49 89	99.66 84	95.35 94	97.78 119	99.00 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		96.65 39	96.46 39	97.21 59	99.34 58	91.77 99	99.70 17	98.05 39	86.48 223	98.05 40	99.20 33	89.33 60	99.96 30	98.38 32	99.62 51	99.90 24

CANet_DTU		94.31 105	93.35 114	97.20 60	97.03 147	94.71 46	98.62 147	95.54 274	95.61 14	97.21 60	98.47 111	71.88 258	99.84 59	88.38 182	97.46 126	97.04 206

QAPM		91.41 166	89.49 183	97.17 61	95.66 191	93.42 72	98.60 151	97.51 124	80.92 305	81.39 270	97.41 150	72.89 250	99.87 51	82.33 249	98.68 102	98.21 177

TSAR-MVS + GP.		96.95 30	96.91 26	97.07 62	98.88 91	91.62 104	99.58 30	96.54 203	95.09 18	96.84 72	98.63 100	91.16 29	99.77 71	99.04 16	96.42 137	99.81 35

114514_t		94.06 107	93.05 121	97.06 63	99.08 81	92.26 97	98.97 108	97.01 182	82.58 283	92.57 143	98.22 122	80.68 196	99.30 132	89.34 172	99.02 87	99.63 73

jason		95.40 77	94.86 82	97.03 64	92.91 272	94.23 56	99.70 17	96.30 216	93.56 45	96.73 77	98.52 105	81.46 192	97.91 182	96.08 78	98.47 109	98.96 126
jason: jason.
test_prior3		97.07 27	97.09 19	97.01 65	99.58 33	91.77 99	99.57 31	97.57 111	91.43 90	98.12 37	98.97 66	90.43 43	99.49 108	98.33 34	99.81 23	99.79 38

test_prior				97.01 65	99.58 33	91.77 99		97.57 111					99.49 108			99.79 38

SteuartSystems-ACMMP		97.25 18	97.34 16	97.01 65	97.38 132	91.46 108	99.75 13	97.66 86	94.14 29	98.13 35	99.26 26	92.16 27	99.66 84	97.91 44	99.64 47	99.90 24
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v1_base_debu		94.73 92	93.98 100	96.99 68	95.19 206	95.24 24	98.62 147	96.50 205	92.99 53	97.52 52	98.83 83	72.37 253	99.15 137	97.03 55	96.74 132	96.58 212

xiu_mvs_v1_base		94.73 92	93.98 100	96.99 68	95.19 206	95.24 24	98.62 147	96.50 205	92.99 53	97.52 52	98.83 83	72.37 253	99.15 137	97.03 55	96.74 132	96.58 212

xiu_mvs_v1_base_debi		94.73 92	93.98 100	96.99 68	95.19 206	95.24 24	98.62 147	96.50 205	92.99 53	97.52 52	98.83 83	72.37 253	99.15 137	97.03 55	96.74 132	96.58 212

GG-mvs-BLEND				96.98 71	96.53 160	94.81 42	87.20 342	97.74 68		93.91 128	96.40 186	96.56 2	96.94 234	95.08 99	98.95 92	99.20 111

thres200		93.69 118	92.59 131	96.97 72	97.76 119	94.74 44	99.35 66	99.36 2	89.23 146	91.21 162	96.97 168	83.42 157	98.77 150	85.08 215	90.96 203	97.39 195

zzz-MVS		96.21 54	95.96 55	96.96 73	99.29 66	91.19 114	98.69 136	97.45 135	92.58 60	94.39 119	99.24 29	86.43 118	99.99 5	96.22 73	99.40 72	99.71 59

MTAPA		96.09 56	95.80 64	96.96 73	99.29 66	91.19 114	97.23 244	97.45 135	92.58 60	94.39 119	99.24 29	86.43 118	99.99 5	96.22 73	99.40 72	99.71 59

ZNCC-MVS		96.09 56	95.81 62	96.95 75	99.42 54	91.19 114	99.55 34	97.53 118	89.72 132	95.86 95	98.94 78	86.59 112	99.97 23	95.13 98	99.56 59	99.68 65

Regformer-3		96.50 43	96.36 42	96.91 76	99.34 58	91.72 102	98.71 131	97.90 48	92.48 64	96.00 87	98.95 73	88.60 68	99.52 104	96.44 70	98.83 95	99.49 87

GST-MVS		95.97 60	95.66 67	96.90 77	99.49 50	91.22 112	99.45 51	97.48 130	89.69 133	95.89 92	98.72 92	86.37 120	99.95 34	94.62 111	99.22 83	99.52 83

thres100view900		93.34 130	92.15 140	96.90 77	97.62 124	94.84 39	99.06 98	99.36 2	87.96 189	90.47 173	96.78 176	83.29 160	98.75 152	84.11 230	90.69 205	97.12 201

tfpn200view9		93.43 126	92.27 136	96.90 77	97.68 122	94.84 39	99.18 77	99.36 2	88.45 171	90.79 165	96.90 171	83.31 158	98.75 152	84.11 230	90.69 205	97.12 201

HFP-MVS		96.42 47	96.26 45	96.90 77	99.69 9	90.96 126	99.47 44	97.81 58	90.54 109	96.88 66	99.05 57	87.57 86	99.96 30	95.65 85	99.72 34	99.78 42

#test#		96.48 44	96.34 43	96.90 77	99.69 9	90.96 126	99.53 39	97.81 58	90.94 102	96.88 66	99.05 57	87.57 86	99.96 30	95.87 81	99.72 34	99.78 42

gg-mvs-nofinetune		90.00 193	87.71 214	96.89 82	96.15 176	94.69 47	85.15 348	97.74 68	68.32 351	92.97 141	60.16 360	96.10 3	96.84 236	93.89 119	98.87 93	99.14 114

Regformer-4		96.45 46	96.33 44	96.81 83	99.34 58	91.44 109	98.71 131	97.88 49	92.43 65	95.97 89	98.95 73	88.42 72	99.51 105	96.40 71	98.83 95	99.49 87

XVS		96.47 45	96.37 41	96.77 84	99.62 25	90.66 134	99.43 55	97.58 108	92.41 69	96.86 69	98.96 71	87.37 92	99.87 51	95.65 85	99.43 68	99.78 42

X-MVStestdata		90.69 180	88.66 200	96.77 84	99.62 25	90.66 134	99.43 55	97.58 108	92.41 69	96.86 69	29.59 371	87.37 92	99.87 51	95.65 85	99.43 68	99.78 42

thres600view7		93.18 135	92.00 143	96.75 86	97.62 124	94.92 34	99.07 96	99.36 2	87.96 189	90.47 173	96.78 176	83.29 160	98.71 156	82.93 244	90.47 209	96.61 210

PVSNet_Blended		95.94 62	95.66 67	96.75 86	98.77 95	91.61 105	99.88 1	98.04 40	93.64 43	94.21 122	97.76 133	83.50 154	99.87 51	97.41 49	97.75 120	98.79 145

ACMMPR		96.28 52	96.14 52	96.73 88	99.68 12	90.47 137	99.47 44	97.80 60	90.54 109	96.83 74	99.03 59	86.51 116	99.95 34	95.65 85	99.72 34	99.75 52

thres400		93.39 128	92.27 136	96.73 88	97.68 122	94.84 39	99.18 77	99.36 2	88.45 171	90.79 165	96.90 171	83.31 158	98.75 152	84.11 230	90.69 205	96.61 210

MVS_111021_HR		96.69 37	96.69 34	96.72 90	98.58 102	91.00 125	99.14 89	99.45 1	93.86 36	95.15 108	98.73 90	88.48 71	99.76 72	97.23 53	99.56 59	99.40 93

region2R		96.30 51	96.17 48	96.70 91	99.70 8	90.31 139	99.46 49	97.66 86	90.55 108	97.07 63	99.07 54	86.85 104	99.97 23	95.43 92	99.74 32	99.81 35

MVS_Test		93.67 121	92.67 129	96.69 92	96.72 155	92.66 88	97.22 245	96.03 233	87.69 200	95.12 109	94.03 220	81.55 189	98.28 165	89.17 176	96.46 135	99.14 114

ab-mvs		91.05 172	89.17 189	96.69 92	95.96 181	91.72 102	92.62 325	97.23 157	85.61 232	89.74 183	93.89 226	68.55 277	99.42 119	91.09 150	87.84 217	98.92 133

CHOSEN 280x420		96.80 36	96.85 28	96.66 94	97.85 118	94.42 53	94.76 304	98.36 23	92.50 63	95.62 101	97.52 145	97.92 1	97.38 220	98.31 37	98.80 98	98.20 178

MVSFormer		94.71 95	94.08 98	96.61 95	95.05 219	94.87 37	97.77 221	96.17 227	86.84 214	98.04 41	98.52 105	85.52 129	95.99 283	89.83 163	98.97 89	98.96 126

API-MVS		94.78 90	94.18 95	96.59 96	99.21 73	90.06 150	98.80 124	97.78 63	83.59 266	93.85 129	99.21 32	83.79 151	99.97 23	92.37 141	99.00 88	99.74 55

baseline1		92.61 145	91.28 156	96.58 97	97.05 146	94.63 48	97.72 225	96.20 224	89.82 129	88.56 192	96.85 174	86.85 104	97.82 189	88.42 181	80.10 265	97.30 197

PAPM_NR		95.43 74	95.05 80	96.57 98	99.42 54	90.14 143	98.58 155	97.51 124	90.65 106	92.44 145	98.90 79	87.77 84	99.90 44	90.88 154	99.32 75	99.68 65

MP-MVS		96.00 58	95.82 60	96.54 99	99.47 51	90.13 145	99.36 65	97.41 143	90.64 107	95.49 102	98.95 73	85.51 131	99.98 10	96.00 80	99.59 58	99.52 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSP-MVS		97.77 9	98.18 2	96.53 100	99.54 40	90.14 143	99.41 58	97.70 79	95.46 17	98.60 24	99.19 34	95.71 4	99.49 108	98.15 40	99.85 13	99.95 15
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
OpenMVS	85.28 14	90.75 178	88.84 195	96.48 101	93.58 258	93.51 70	98.80 124	97.41 143	82.59 282	78.62 298	97.49 147	68.00 283	99.82 64	84.52 224	98.55 107	96.11 220

DeepC-MVS	91.02 4	94.56 101	93.92 106	96.46 102	97.16 139	90.76 130	98.39 179	97.11 171	93.92 32	88.66 191	98.33 116	78.14 214	99.85 58	95.02 101	98.57 106	98.78 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		95.85 66	95.65 69	96.45 103	99.50 47	89.77 157	98.22 191	98.90 11	89.19 147	96.74 76	98.95 73	85.91 126	99.92 40	93.94 118	99.46 64	99.66 69

thisisatest0515		94.75 91	94.19 93	96.43 104	96.13 180	92.64 92	99.47 44	97.60 102	87.55 203	93.17 136	97.59 143	94.71 11	98.42 160	88.28 183	93.20 170	98.24 175

LFMVS		92.23 153	90.84 165	96.42 105	98.24 108	91.08 122	98.24 190	96.22 223	83.39 269	94.74 114	98.31 117	61.12 316	98.85 147	94.45 114	92.82 174	99.32 98

CP-MVS		96.22 53	96.15 51	96.42 105	99.67 13	89.62 161	99.70 17	97.61 100	90.07 125	96.00 87	99.16 41	87.43 90	99.92 40	96.03 79	99.72 34	99.70 61

mPP-MVS		95.90 64	95.75 65	96.38 107	99.58 33	89.41 165	99.26 72	97.41 143	90.66 104	94.82 112	98.95 73	86.15 123	99.98 10	95.24 97	99.64 47	99.74 55

CNLPA		93.64 122	92.74 127	96.36 108	98.96 87	90.01 153	99.19 75	95.89 251	86.22 226	89.40 186	98.85 82	80.66 197	99.84 59	88.57 180	96.92 131	99.24 107

PVSNet_Blended_VisFu		94.67 96	94.11 96	96.34 109	97.14 140	91.10 120	99.32 70	97.43 141	92.10 77	91.53 156	96.38 189	83.29 160	99.68 82	93.42 130	96.37 138	98.25 174

PVSNet	87.13 12	93.69 118	92.83 126	96.28 110	97.99 116	90.22 142	99.38 61	98.93 10	91.42 92	93.66 132	97.68 138	71.29 265	99.64 90	87.94 188	97.20 129	98.98 124

1112_ss		92.71 141	91.55 153	96.20 111	95.56 194	91.12 118	98.48 166	94.69 306	88.29 180	86.89 208	98.50 107	87.02 101	98.66 157	84.75 220	89.77 212	98.81 143

原ACMM1				96.18 112	99.03 83	90.08 146		97.63 97	88.98 154	97.00 64	98.97 66	88.14 78	99.71 77	88.23 184	99.62 51	98.76 149

Test_1112_low_res		92.27 152	90.97 161	96.18 112	95.53 196	91.10 120	98.47 168	94.66 307	88.28 181	86.83 210	93.50 238	87.00 102	98.65 158	84.69 221	89.74 213	98.80 144

EI-MVSNet-Vis-set		95.76 71	95.63 71	96.17 114	99.14 76	90.33 138	98.49 165	97.82 55	91.92 78	94.75 113	98.88 81	87.06 100	99.48 113	95.40 93	97.17 130	98.70 152

PCF-MVS	89.78 5	91.26 167	89.63 180	96.16 115	95.44 198	91.58 107	95.29 300	96.10 231	85.07 242	82.75 241	97.45 148	78.28 213	99.78 70	80.60 263	95.65 154	97.12 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
AdaColmap		93.82 115	93.06 120	96.10 116	99.88 1	89.07 167	98.33 183	97.55 114	86.81 216	90.39 175	98.65 97	75.09 226	99.98 10	93.32 131	97.53 124	99.26 105

SR-MVS		96.13 55	96.16 50	96.07 117	99.42 54	89.04 168	98.59 153	97.33 151	90.44 111	96.84 72	99.12 48	86.75 106	99.41 121	97.47 48	99.44 67	99.76 51

Effi-MVS+		93.87 114	93.15 119	96.02 118	95.79 184	90.76 130	96.70 266	95.78 258	86.98 211	95.71 98	97.17 160	79.58 201	98.01 180	94.57 112	96.09 145	99.31 99

ETV-MVS		96.00 58	96.00 54	96.00 119	96.56 159	91.05 123	99.63 25	96.61 194	93.26 49	97.39 57	98.30 118	86.62 111	98.13 169	98.07 41	97.57 121	98.82 142

HPM-MVS		95.41 76	95.22 76	95.99 120	99.29 66	89.14 166	99.17 79	97.09 175	87.28 207	95.40 103	98.48 110	84.93 140	99.38 123	95.64 89	99.65 44	99.47 90
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		95.86 65	95.81 62	95.98 121	95.62 192	91.26 111	99.80 7	96.12 230	92.15 76	97.93 47	98.45 114	85.88 127	97.55 211	97.56 47	98.80 98	99.14 114

IB-MVS	89.43 6	92.12 154	90.83 167	95.98 121	95.40 201	90.78 129	99.81 5	98.06 38	91.23 97	85.63 216	93.66 232	90.63 40	98.78 149	91.22 149	71.85 324	98.36 170
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
CHOSEN 1792x2688		94.35 104	93.82 108	95.95 123	97.40 131	88.74 179	98.41 173	98.27 25	92.18 74	91.43 157	96.40 186	78.88 207	99.81 66	93.59 126	97.81 116	99.30 100

ET-MVSNet_ETH3D		92.56 147	91.45 155	95.88 124	96.39 164	94.13 59	99.46 49	96.97 184	92.18 74	66.94 348	98.29 120	94.65 13	94.28 326	94.34 115	83.82 244	99.24 107

EI-MVSNet-UG-set		95.43 74	95.29 73	95.86 125	99.07 82	89.87 154	98.43 170	97.80 60	91.78 81	94.11 124	98.77 86	86.25 122	99.48 113	94.95 104	96.45 136	98.22 176

diffmvs		94.59 100	94.19 93	95.81 126	95.54 195	90.69 132	98.70 135	95.68 265	91.61 83	95.96 90	97.81 130	80.11 198	98.06 176	96.52 68	95.76 151	98.67 153

ACMMP		94.67 96	94.30 90	95.79 127	99.25 69	88.13 190	98.41 173	98.67 19	90.38 113	91.43 157	98.72 92	82.22 182	99.95 34	93.83 122	95.76 151	99.29 101
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
cascas		90.93 175	89.33 187	95.76 128	95.69 188	93.03 82	98.99 106	96.59 196	80.49 307	86.79 211	94.45 215	65.23 301	98.60 159	93.52 127	92.18 187	95.66 223

baseline		93.91 112	93.30 115	95.72 129	95.10 216	90.07 147	97.48 233	95.91 248	91.03 99	93.54 133	97.68 138	79.58 201	98.02 179	94.27 116	95.14 156	99.08 119

HPM-MVS_fast		94.89 87	94.62 84	95.70 130	99.11 78	88.44 185	99.14 89	97.11 171	85.82 230	95.69 99	98.47 111	83.46 156	99.32 131	93.16 133	99.63 50	99.35 95

APD-MVS_3200maxsize		95.64 73	95.65 69	95.62 131	99.24 70	87.80 195	98.42 171	97.22 158	88.93 158	96.64 81	98.98 65	85.49 132	99.36 125	96.68 62	99.27 79	99.70 61

DWT-MVSNet_test		94.36 103	93.95 104	95.62 131	96.99 148	89.47 163	96.62 268	97.38 146	90.96 101	93.07 139	97.27 152	93.73 15	98.09 172	85.86 211	93.65 168	99.29 101

casdiffmvs		93.98 110	93.43 113	95.61 133	95.07 218	89.86 155	98.80 124	95.84 256	90.98 100	92.74 142	97.66 140	79.71 200	98.10 171	94.72 108	95.37 155	98.87 137

EPMVS		92.59 146	91.59 152	95.59 134	97.22 137	90.03 151	91.78 330	98.04 40	90.42 112	91.66 152	90.65 290	86.49 117	97.46 215	81.78 255	96.31 140	99.28 103

TESTMET0.1,1		93.82 115	93.26 117	95.49 135	95.21 205	90.25 140	99.15 86	97.54 117	89.18 148	91.79 149	94.87 210	89.13 61	97.63 204	86.21 204	96.29 142	98.60 157

test1172		95.92 63	96.07 53	95.46 136	99.42 54	87.24 214	98.51 161	97.24 155	90.29 116	96.56 82	99.12 48	86.73 108	99.36 125	97.33 51	99.42 71	99.78 42

MAR-MVS		94.43 102	94.09 97	95.45 137	99.10 80	87.47 203	98.39 179	97.79 62	88.37 177	94.02 126	99.17 39	78.64 212	99.91 42	92.48 140	98.85 94	98.96 126
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
thisisatest0530		94.00 109	93.52 112	95.43 138	95.76 186	90.02 152	98.99 106	97.60 102	86.58 220	91.74 150	97.36 151	94.78 10	98.34 161	86.37 203	92.48 181	97.94 184

SR-MVS-dyc-post		95.75 72	95.86 59	95.41 139	99.22 71	87.26 212	98.40 176	97.21 159	89.63 135	96.67 79	98.97 66	86.73 108	99.36 125	96.62 63	99.31 76	99.60 77

CSCG		94.87 88	94.71 83	95.36 140	99.54 40	86.49 224	99.34 68	98.15 34	82.71 281	90.15 178	99.25 27	89.48 59	99.86 56	94.97 103	98.82 97	99.72 58

UA-Net		93.30 131	92.62 130	95.34 141	96.27 168	88.53 184	95.88 291	96.97 184	90.90 103	95.37 104	97.07 164	82.38 180	99.10 142	83.91 234	94.86 159	98.38 167

DP-MVS		88.75 216	86.56 232	95.34 141	98.92 89	87.45 204	97.64 229	93.52 329	70.55 343	81.49 268	97.25 153	74.43 234	99.88 48	71.14 321	94.09 164	98.67 153

MVS_111021_LR		95.78 69	95.94 56	95.28 143	98.19 111	87.69 196	98.80 124	99.26 7	93.39 46	95.04 110	98.69 96	84.09 149	99.76 72	96.96 60	99.06 85	98.38 167

testdata				95.26 144	98.20 109	87.28 209		97.60 102	85.21 238	98.48 28	99.15 43	88.15 77	98.72 155	90.29 160	99.45 66	99.78 42

UGNet		91.91 158	90.85 164	95.10 145	97.06 145	88.69 180	98.01 209	98.24 27	92.41 69	92.39 146	93.61 233	60.52 317	99.68 82	88.14 185	97.25 128	96.92 208
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
abl_6		94.63 98	94.48 87	95.09 146	98.61 101	86.96 217	98.06 207	96.97 184	89.31 145	95.86 95	98.56 103	79.82 199	99.64 90	94.53 113	98.65 104	98.66 156

CPTT-MVS		94.60 99	94.43 89	95.09 146	99.66 15	86.85 219	99.44 52	97.47 132	83.22 271	94.34 121	98.96 71	82.50 175	99.55 98	94.81 105	99.50 62	98.88 135

mvs_anonymous		92.50 148	91.65 151	95.06 148	96.60 158	89.64 160	97.06 250	96.44 209	86.64 219	84.14 227	93.93 224	82.49 176	96.17 277	91.47 146	96.08 146	99.35 95

PatchmatchNet		92.05 156	91.04 160	95.06 148	96.17 175	89.04 168	91.26 334	97.26 152	89.56 140	90.64 169	90.56 296	88.35 74	97.11 226	79.53 267	96.07 147	99.03 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
BH-RMVSNet		91.25 169	89.99 177	95.03 150	96.75 154	88.55 182	98.65 142	94.95 299	87.74 197	87.74 197	97.80 131	68.27 280	98.14 168	80.53 264	97.49 125	98.41 164

Vis-MVSNet		92.64 143	91.85 146	95.03 150	95.12 212	88.23 186	98.48 166	96.81 188	91.61 83	92.16 148	97.22 156	71.58 263	98.00 181	85.85 212	97.81 116	98.88 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS-test		95.20 81	95.27 74	94.98 152	95.67 190	88.17 187	99.62 27	95.84 256	91.52 86	97.42 55	98.30 118	85.07 138	97.51 212	95.81 82	98.20 112	99.26 105

HyFIR lowres test		93.68 120	93.29 116	94.87 153	97.57 128	88.04 192	98.18 195	98.47 21	87.57 202	91.24 161	95.05 208	85.49 132	97.46 215	93.22 132	92.82 174	99.10 117

PLC	91.07 3	94.23 106	94.01 99	94.87 153	99.17 75	87.49 202	99.25 73	96.55 201	88.43 175	91.26 160	98.21 124	85.92 125	99.86 56	89.77 166	97.57 121	97.24 199
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DROMVSNet		95.09 84	95.17 77	94.84 155	95.42 199	88.17 187	99.48 42	95.92 243	91.47 88	97.34 59	98.36 115	82.77 170	97.41 219	97.24 52	98.58 105	98.94 131

SCA		90.64 181	89.25 188	94.83 156	94.95 223	88.83 175	96.26 278	97.21 159	90.06 126	90.03 179	90.62 292	66.61 293	96.81 238	83.16 240	94.36 162	98.84 138

TR-MVS		90.77 177	89.44 184	94.76 157	96.31 167	88.02 193	97.92 212	95.96 237	85.52 233	88.22 195	97.23 155	66.80 292	98.09 172	84.58 223	92.38 182	98.17 179

CDS-MVSNet		93.47 124	93.04 122	94.76 157	94.75 230	89.45 164	98.82 122	97.03 180	87.91 191	90.97 164	96.48 184	89.06 62	96.36 262	89.50 167	92.81 176	98.49 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline2		94.04 108	93.80 109	94.74 159	93.07 270	90.25 140	98.12 200	98.16 33	89.86 127	86.53 212	96.95 169	95.56 5	98.05 177	91.44 147	94.53 160	95.93 221

OMC-MVS		93.90 113	93.62 111	94.73 160	98.63 99	87.00 216	98.04 208	96.56 200	92.19 73	92.46 144	98.73 90	79.49 204	99.14 140	92.16 143	94.34 163	98.03 181

VDDNet		90.08 192	88.54 205	94.69 161	94.41 236	87.68 197	98.21 193	96.40 210	76.21 328	93.33 135	97.75 134	54.93 335	98.77 150	94.71 109	90.96 203	97.61 192

tpmrst		92.78 140	92.16 139	94.65 162	96.27 168	87.45 204	91.83 329	97.10 174	89.10 151	94.68 115	90.69 287	88.22 75	97.73 200	89.78 165	91.80 193	98.77 148

EIA-MVS		95.11 83	95.27 74	94.64 163	96.34 166	86.51 223	99.59 29	96.62 193	92.51 62	94.08 125	98.64 98	86.05 124	98.24 166	95.07 100	98.50 108	99.18 112

RPMNet		85.07 270	81.88 287	94.64 163	93.47 260	86.24 232	84.97 350	97.21 159	64.85 357	90.76 167	78.80 354	80.95 195	99.27 133	53.76 358	92.17 188	98.41 164

LS3D		90.19 188	88.72 198	94.59 165	98.97 85	86.33 231	96.90 256	96.60 195	74.96 332	84.06 229	98.74 89	75.78 223	99.83 61	74.93 300	97.57 121	97.62 191

RRT_MVS		91.95 157	91.09 158	94.53 166	96.71 157	95.12 31	98.64 144	96.23 222	89.04 152	85.24 219	95.06 207	87.71 85	96.43 258	89.10 178	82.06 257	92.05 253

Fast-Effi-MVS+		91.72 160	90.79 168	94.49 167	95.89 182	87.40 206	99.54 38	95.70 263	85.01 245	89.28 188	95.68 199	77.75 216	97.57 210	83.22 239	95.06 157	98.51 160

IS-MVSNet		93.00 138	92.51 132	94.49 167	96.14 177	87.36 207	98.31 186	95.70 263	88.58 166	90.17 177	97.50 146	83.02 166	97.22 223	87.06 194	96.07 147	98.90 134

VDD-MVS		91.24 170	90.18 176	94.45 169	97.08 144	85.84 247	98.40 176	96.10 231	86.99 209	93.36 134	98.16 125	54.27 337	99.20 134	96.59 66	90.63 208	98.31 173

test-LLR		93.11 137	92.68 128	94.40 170	94.94 224	87.27 210	99.15 86	97.25 153	90.21 117	91.57 153	94.04 218	84.89 141	97.58 207	85.94 208	96.13 143	98.36 170

test-mter		93.27 133	92.89 125	94.40 170	94.94 224	87.27 210	99.15 86	97.25 153	88.95 156	91.57 153	94.04 218	88.03 80	97.58 207	85.94 208	96.13 143	98.36 170

GA-MVS		90.10 191	88.69 199	94.33 172	92.44 276	87.97 194	99.08 95	96.26 220	89.65 134	86.92 207	93.11 247	68.09 281	96.96 232	82.54 248	90.15 210	98.05 180

nrg030		90.23 186	88.87 194	94.32 173	91.53 290	93.54 69	98.79 128	95.89 251	88.12 185	84.55 224	94.61 214	78.80 210	96.88 235	92.35 142	75.21 289	92.53 237

Anonymous202405211		88.84 210	87.03 225	94.27 174	98.14 113	84.18 272	98.44 169	95.58 272	76.79 327	89.34 187	96.88 173	53.42 340	99.54 100	87.53 193	87.12 221	99.09 118

PatchMatch-RL		91.47 164	90.54 172	94.26 175	98.20 109	86.36 230	96.94 254	97.14 167	87.75 196	88.98 189	95.75 198	71.80 260	99.40 122	80.92 260	97.39 127	97.02 207

TAPA-MVS	87.50 9	90.35 183	89.05 191	94.25 176	98.48 105	85.17 259	98.42 171	96.58 199	82.44 287	87.24 203	98.53 104	82.77 170	98.84 148	59.09 353	97.88 115	98.72 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TAMVS		92.62 144	92.09 142	94.20 177	94.10 241	87.68 197	98.41 173	96.97 184	87.53 204	89.74 183	96.04 195	84.77 144	96.49 254	88.97 179	92.31 184	98.42 163

tttt0517		93.30 131	93.01 123	94.17 178	95.57 193	86.47 225	98.51 161	97.60 102	85.99 228	90.55 170	97.19 158	94.80 9	98.31 162	85.06 216	91.86 191	97.74 186

dp		90.16 190	88.83 196	94.14 179	96.38 165	86.42 226	91.57 331	97.06 177	84.76 249	88.81 190	90.19 308	84.29 147	97.43 218	75.05 299	91.35 202	98.56 158

CostFormer		92.89 139	92.48 133	94.12 180	94.99 221	85.89 244	92.89 321	97.00 183	86.98 211	95.00 111	90.78 283	90.05 51	97.51 212	92.92 137	91.73 195	98.96 126

ADS-MVSNet		88.99 205	87.30 220	94.07 181	96.21 171	87.56 201	87.15 343	96.78 190	83.01 274	89.91 181	87.27 330	78.87 208	97.01 231	74.20 306	92.27 185	97.64 188

Vis-MVSNet (Re-imp)		93.26 134	93.00 124	94.06 182	96.14 177	86.71 222	98.68 138	96.70 191	88.30 179	89.71 185	97.64 141	85.43 135	96.39 260	88.06 187	96.32 139	99.08 119

hse-mvs3		92.47 149	91.95 145	94.05 183	97.13 141	85.01 262	98.36 181	98.08 37	93.85 37	96.27 84	96.73 178	83.19 163	99.43 118	95.81 82	68.09 334	97.70 187

MSDG		88.29 223	86.37 234	94.04 184	96.90 149	86.15 237	96.52 270	94.36 315	77.89 323	79.22 293	96.95 169	69.72 271	99.59 96	73.20 314	92.58 180	96.37 218

EPP-MVSNet		93.75 117	93.67 110	94.01 185	95.86 183	85.70 249	98.67 140	97.66 86	84.46 252	91.36 159	97.18 159	91.16 29	97.79 191	92.93 136	93.75 166	98.53 159

FMVSNet3		88.81 214	87.08 224	93.99 186	96.52 161	94.59 49	98.08 205	96.20 224	85.85 229	82.12 254	91.60 268	74.05 240	95.40 306	79.04 271	80.24 262	91.99 255

Anonymous20240529		87.66 233	85.58 246	93.92 187	97.59 127	85.01 262	98.13 198	97.13 169	66.69 355	88.47 193	96.01 196	55.09 334	99.51 105	87.00 196	84.12 240	97.23 200

BH-w/o		92.32 150	91.79 148	93.91 188	96.85 150	86.18 235	99.11 94	95.74 261	88.13 184	84.81 221	97.00 167	77.26 219	97.91 182	89.16 177	98.03 114	97.64 188

MVSTER		92.71 141	92.32 134	93.86 189	97.29 135	92.95 86	99.01 104	96.59 196	90.09 123	85.51 217	94.00 222	94.61 14	96.56 248	90.77 157	83.03 250	92.08 251

PVSNet_BlendedMVS		93.36 129	93.20 118	93.84 190	98.77 95	91.61 105	99.47 44	98.04 40	91.44 89	94.21 122	92.63 254	83.50 154	99.87 51	97.41 49	83.37 248	90.05 314

tpm2		91.77 159	91.09 158	93.82 191	94.83 228	85.56 252	92.51 326	97.16 166	84.00 258	93.83 130	90.66 289	87.54 88	97.17 224	87.73 190	91.55 198	98.72 150

tpm cat1		88.89 208	87.27 221	93.76 192	95.79 184	85.32 256	90.76 338	97.09 175	76.14 329	85.72 215	88.59 321	82.92 167	98.04 178	76.96 285	91.43 199	97.90 185

PVSNet_0	83.28 16	87.31 237	85.16 251	93.74 193	94.78 229	84.59 267	98.91 114	98.69 18	89.81 130	78.59 300	93.23 242	61.95 312	99.34 130	94.75 106	55.72 356	97.30 197

GeoE		90.60 182	89.56 181	93.72 194	95.10 216	85.43 253	99.41 58	94.94 300	83.96 260	87.21 204	96.83 175	74.37 235	97.05 230	80.50 265	93.73 167	98.67 153

test_part1		88.43 220	86.68 230	93.67 195	97.56 129	92.40 96	98.12 200	96.55 201	82.26 289	80.31 278	93.16 245	74.59 232	96.62 245	85.00 218	72.61 316	91.99 255

VPNet		88.30 222	86.57 231	93.49 196	91.95 283	91.35 110	98.18 195	97.20 163	88.61 164	84.52 225	94.89 209	62.21 311	96.76 241	89.34 172	72.26 321	92.36 240

VPA-MVSNet		89.10 203	87.66 215	93.45 197	92.56 274	91.02 124	97.97 211	98.32 24	86.92 213	86.03 214	92.01 260	68.84 276	97.10 228	90.92 153	75.34 288	92.23 245

tpmvs		89.16 202	87.76 212	93.35 198	97.19 138	84.75 266	90.58 340	97.36 149	81.99 292	84.56 223	89.31 318	83.98 150	98.17 167	74.85 302	90.00 211	97.12 201

BH-untuned		91.46 165	90.84 165	93.33 199	96.51 162	84.83 265	98.84 121	95.50 276	86.44 225	83.50 232	96.70 179	75.49 225	97.77 193	86.78 201	97.81 116	97.40 194

FMVSNet2		86.90 241	84.79 259	93.24 200	95.11 213	92.54 94	97.67 228	95.86 255	82.94 276	80.55 275	91.17 277	62.89 308	95.29 308	77.23 282	79.71 268	91.90 257

FIs		90.70 179	89.87 178	93.18 201	92.29 277	91.12 118	98.17 197	98.25 26	89.11 150	83.44 233	94.82 211	82.26 181	96.17 277	87.76 189	82.76 252	92.25 243

CR-MVSNet		88.83 212	87.38 219	93.16 202	93.47 260	86.24 232	84.97 350	94.20 318	88.92 159	90.76 167	86.88 334	84.43 145	94.82 318	70.64 322	92.17 188	98.41 164

UniMVSNet (Re)		89.50 201	88.32 207	93.03 203	92.21 279	90.96 126	98.90 115	98.39 22	89.13 149	83.22 234	92.03 258	81.69 188	96.34 268	86.79 200	72.53 317	91.81 258

F-COLMAP		92.07 155	91.75 150	93.02 204	98.16 112	82.89 288	98.79 128	95.97 235	86.54 222	87.92 196	97.80 131	78.69 211	99.65 88	85.97 206	95.93 149	96.53 215

NR-MVSNet		87.74 232	86.00 240	92.96 205	91.46 291	90.68 133	96.65 267	97.42 142	88.02 188	73.42 326	93.68 230	77.31 218	95.83 294	84.26 226	71.82 325	92.36 240

RRT_test8_iter05		91.04 173	90.40 175	92.95 206	96.20 174	89.75 158	98.97 108	96.38 211	88.52 167	82.00 259	93.51 237	90.69 39	96.73 242	90.43 159	76.91 283	92.38 239

XXY-MVS		87.75 230	86.02 239	92.95 206	90.46 302	89.70 159	97.71 227	95.90 249	84.02 257	80.95 271	94.05 217	67.51 287	97.10 228	85.16 214	78.41 272	92.04 254

Patchmatch-test		86.25 254	84.06 269	92.82 208	94.42 235	82.88 289	82.88 357	94.23 317	71.58 340	79.39 291	90.62 292	89.00 64	96.42 259	63.03 345	91.37 201	99.16 113

DU-MVS		88.83 212	87.51 216	92.79 209	91.46 291	90.07 147	98.71 131	97.62 99	88.87 160	83.21 235	93.68 230	74.63 228	95.93 287	86.95 197	72.47 318	92.36 240

PMMVS		93.62 123	93.90 107	92.79 209	96.79 153	81.40 302	98.85 119	96.81 188	91.25 96	96.82 75	98.15 126	77.02 220	98.13 169	93.15 134	96.30 141	98.83 141

bset_n11_16_dypcd		89.07 204	87.85 211	92.76 211	86.16 344	90.66 134	97.30 238	95.62 268	89.78 131	83.94 230	93.15 246	74.85 227	95.89 292	91.34 148	78.48 271	91.74 259

UniMVSNet_NR-MVSNet		89.60 198	88.55 204	92.75 212	92.17 280	90.07 147	98.74 130	98.15 34	88.37 177	83.21 235	93.98 223	82.86 168	95.93 287	86.95 197	72.47 318	92.25 243

EPNet_dtu		92.28 151	92.15 140	92.70 213	97.29 135	84.84 264	98.64 144	97.82 55	92.91 56	93.02 140	97.02 166	85.48 134	95.70 298	72.25 318	94.89 158	97.55 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	93.56 1	96.55 42	97.84 9	92.68 214	98.71 97	78.11 328	99.70 17	97.71 78	98.18 1	97.36 58	99.76 1	90.37 48	99.94 37	99.27 12	99.54 61	99.99 1

FC-MVSNet-test		90.22 187	89.40 185	92.67 215	91.78 287	89.86 155	97.89 213	98.22 28	88.81 161	82.96 240	94.66 213	81.90 187	95.96 285	85.89 210	82.52 255	92.20 247

WR-MVS		88.54 219	87.22 223	92.52 216	91.93 285	89.50 162	98.56 156	97.84 53	86.99 209	81.87 263	93.81 227	74.25 239	95.92 289	85.29 213	74.43 298	92.12 249

MIMVSNet		84.48 278	81.83 288	92.42 217	91.73 288	87.36 207	85.52 346	94.42 313	81.40 298	81.91 261	87.58 325	51.92 343	92.81 337	73.84 309	88.15 216	97.08 205

HQP-MVS		91.50 163	91.23 157	92.29 218	93.95 245	86.39 228	99.16 80	96.37 212	93.92 32	87.57 198	96.67 180	73.34 244	97.77 193	93.82 123	86.29 223	92.72 233

miper_enhance_ethall		90.33 184	89.70 179	92.22 219	97.12 142	88.93 173	98.35 182	95.96 237	88.60 165	83.14 239	92.33 256	87.38 91	96.18 276	86.49 202	77.89 275	91.55 269

PatchT		85.44 267	83.19 274	92.22 219	93.13 269	83.00 284	83.80 356	96.37 212	70.62 342	90.55 170	79.63 353	84.81 143	94.87 316	58.18 355	91.59 197	98.79 145

AUN-MVS		90.17 189	89.50 182	92.19 221	96.21 171	82.67 292	97.76 223	97.53 118	88.05 186	91.67 151	96.15 191	83.10 165	97.47 214	88.11 186	66.91 338	96.43 216

HQP_MVS		91.26 167	90.95 162	92.16 222	93.84 252	86.07 240	99.02 102	96.30 216	93.38 47	86.99 205	96.52 182	72.92 248	97.75 198	93.46 128	86.17 226	92.67 235

hse-mvs2		91.67 161	91.51 154	92.15 223	96.22 170	82.61 294	97.74 224	97.53 118	93.85 37	96.27 84	96.15 191	83.19 163	97.44 217	95.81 82	66.86 339	96.40 217

CLD-MVS		91.06 171	90.71 169	92.10 224	94.05 244	86.10 238	99.55 34	96.29 219	94.16 27	84.70 222	97.17 160	69.62 272	97.82 189	94.74 107	86.08 228	92.39 238
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		87.75 230	86.31 235	92.07 225	90.81 298	88.56 181	98.33 183	97.18 164	87.76 195	81.87 263	93.90 225	72.45 252	95.43 304	83.13 242	71.30 328	92.23 245

cl-mvsnet2		89.57 199	88.79 197	91.91 226	97.94 117	87.62 199	97.98 210	96.51 204	85.03 243	82.37 250	91.79 264	83.65 152	96.50 252	85.96 207	77.89 275	91.61 266

XVG-OURS		90.83 176	90.49 173	91.86 227	95.23 204	81.25 306	95.79 296	95.92 243	88.96 155	90.02 180	98.03 127	71.60 262	99.35 129	91.06 151	87.78 218	94.98 224

XVG-OURS-SEG-HR		90.95 174	90.66 171	91.83 228	95.18 209	81.14 309	95.92 288	95.92 243	88.40 176	90.33 176	97.85 128	70.66 268	99.38 123	92.83 138	88.83 214	94.98 224

tpm		89.67 197	88.95 193	91.82 229	92.54 275	81.43 301	92.95 320	95.92 243	87.81 193	90.50 172	89.44 315	84.99 139	95.65 299	83.67 237	82.71 253	98.38 167

pmmvs4		87.58 235	86.17 238	91.80 230	89.58 312	88.92 174	97.25 242	95.28 289	82.54 284	80.49 276	93.17 244	75.62 224	96.05 282	82.75 245	78.90 269	90.42 305

GBi-Net		86.67 246	84.96 253	91.80 230	95.11 213	88.81 176	96.77 260	95.25 290	82.94 276	82.12 254	90.25 303	62.89 308	94.97 313	79.04 271	80.24 262	91.62 263

test1		86.67 246	84.96 253	91.80 230	95.11 213	88.81 176	96.77 260	95.25 290	82.94 276	82.12 254	90.25 303	62.89 308	94.97 313	79.04 271	80.24 262	91.62 263

FMVSNet1		83.94 286	81.32 294	91.80 230	91.94 284	88.81 176	96.77 260	95.25 290	77.98 319	78.25 303	90.25 303	50.37 347	94.97 313	73.27 313	77.81 279	91.62 263

v2v482		87.27 238	85.76 243	91.78 234	89.59 311	87.58 200	98.56 156	95.54 274	84.53 251	82.51 246	91.78 265	73.11 247	96.47 255	82.07 251	74.14 304	91.30 280

OPM-MVS		89.76 196	89.15 190	91.57 235	90.53 301	85.58 251	98.11 202	95.93 242	92.88 58	86.05 213	96.47 185	67.06 291	97.87 186	89.29 175	86.08 228	91.26 282
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
miper_ehance_all_eth		88.94 207	88.12 210	91.40 236	95.32 202	86.93 218	97.85 217	95.55 273	84.19 255	81.97 260	91.50 270	84.16 148	95.91 290	84.69 221	77.89 275	91.36 277

v1144		86.83 243	85.31 250	91.40 236	89.75 309	87.21 215	98.31 186	95.45 279	83.22 271	82.70 243	90.78 283	73.36 243	96.36 262	79.49 268	74.69 295	90.63 302

EI-MVSNet		89.87 195	89.38 186	91.36 238	94.32 237	85.87 245	97.61 230	96.59 196	85.10 240	85.51 217	97.10 162	81.30 194	96.56 248	83.85 236	83.03 250	91.64 261

UniMVSNet_ETH3D		85.65 266	83.79 272	91.21 239	90.41 303	80.75 313	95.36 299	95.78 258	78.76 317	81.83 266	94.33 216	49.86 348	96.66 243	84.30 225	83.52 247	96.22 219

v1192		86.32 253	84.71 260	91.17 240	89.53 314	86.40 227	98.13 198	95.44 281	82.52 285	82.42 248	90.62 292	71.58 263	96.33 269	77.23 282	74.88 292	90.79 294

v8		86.11 255	84.45 264	91.10 241	89.99 305	86.85 219	97.24 243	95.36 287	81.99 292	79.89 285	89.86 311	74.53 233	96.39 260	78.83 275	72.32 320	90.05 314

cl_fuxian		88.19 225	87.23 222	91.06 242	94.97 222	86.17 236	97.72 225	95.38 285	83.43 268	81.68 267	91.37 272	82.81 169	95.72 297	84.04 233	73.70 306	91.29 281

IterMVS-LS		88.34 221	87.44 217	91.04 243	94.10 241	85.85 246	98.10 203	95.48 277	85.12 239	82.03 258	91.21 276	81.35 193	95.63 300	83.86 235	75.73 287	91.63 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-MVSNAJss		89.54 200	89.05 191	91.00 244	88.77 322	84.36 270	97.39 234	95.97 235	88.47 168	81.88 262	93.80 228	82.48 177	96.50 252	89.34 172	83.34 249	92.15 248

V42		87.00 240	85.68 245	90.98 245	89.91 306	86.08 239	98.32 185	95.61 270	83.67 265	82.72 242	90.67 288	74.00 241	96.53 250	81.94 254	74.28 301	90.32 307

Anonymous20231211		84.72 273	82.65 284	90.91 246	97.71 121	84.55 268	97.28 240	96.67 192	66.88 354	79.18 294	90.87 282	58.47 321	96.60 246	82.61 247	74.20 302	91.59 268

v144192		86.40 251	84.89 256	90.91 246	89.48 315	85.59 250	98.21 193	95.43 282	82.45 286	82.62 244	90.58 295	72.79 251	96.36 262	78.45 277	74.04 305	90.79 294

cl-mvsnet____		87.82 227	86.79 229	90.89 248	94.88 226	85.43 253	97.81 218	95.24 293	82.91 280	80.71 274	91.22 275	81.97 186	95.84 293	81.34 257	75.06 290	91.40 276

cl-mvsnet1		87.82 227	86.81 228	90.87 249	94.87 227	85.39 255	97.81 218	95.22 297	82.92 279	80.76 273	91.31 274	81.99 184	95.81 295	81.36 256	75.04 291	91.42 275

v10		85.73 264	84.01 270	90.87 249	90.03 304	86.73 221	97.20 246	95.22 297	81.25 300	79.85 286	89.75 312	73.30 246	96.28 274	76.87 286	72.64 315	89.61 321

v1921920		86.02 256	84.44 265	90.77 251	89.32 317	85.20 257	98.10 203	95.35 288	82.19 290	82.25 252	90.71 285	70.73 266	96.30 273	76.85 287	74.49 297	90.80 293

v1240		85.77 263	84.11 268	90.73 252	89.26 318	85.15 260	97.88 215	95.23 296	81.89 295	82.16 253	90.55 297	69.60 273	96.31 270	75.59 297	74.87 293	90.72 299

MVS-HIRNet		79.01 308	75.13 318	90.66 253	93.82 254	81.69 300	85.16 347	93.75 324	54.54 359	74.17 322	59.15 362	57.46 324	96.58 247	63.74 342	94.38 161	93.72 229

ACMH	83.09 17	84.60 275	82.61 285	90.57 254	93.18 268	82.94 285	96.27 276	94.92 301	81.01 303	72.61 334	93.61 233	56.54 326	97.79 191	74.31 305	81.07 261	90.99 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		83.65 287	81.35 293	90.56 255	91.37 293	88.06 191	97.29 239	97.87 51	78.51 318	76.20 309	90.91 280	64.78 302	96.47 255	61.71 348	73.50 309	87.13 343

AllTest		84.97 271	83.12 275	90.52 256	96.82 151	78.84 321	95.89 289	92.17 341	77.96 321	75.94 312	95.50 201	55.48 330	99.18 135	71.15 319	87.14 219	93.55 230

TestCases				90.52 256	96.82 151	78.84 321		92.17 341	77.96 321	75.94 312	95.50 201	55.48 330	99.18 135	71.15 319	87.14 219	93.55 230

ACMM	86.95 13	88.77 215	88.22 209	90.43 258	93.61 257	81.34 304	98.50 163	95.92 243	87.88 192	83.85 231	95.20 206	67.20 289	97.89 184	86.90 199	84.90 234	92.06 252
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pm-mvs1		84.68 274	82.78 280	90.40 259	89.58 312	85.18 258	97.31 237	94.73 304	81.93 294	76.05 311	92.01 260	65.48 300	96.11 280	78.75 276	69.14 331	89.91 317

KD-MVS_2432*1600		82.98 290	80.52 298	90.38 260	94.32 237	88.98 170	92.87 322	95.87 253	80.46 308	73.79 324	87.49 327	82.76 172	93.29 332	70.56 323	46.53 361	88.87 330

miper_refine_blended		82.98 290	80.52 298	90.38 260	94.32 237	88.98 170	92.87 322	95.87 253	80.46 308	73.79 324	87.49 327	82.76 172	93.29 332	70.56 323	46.53 361	88.87 330

v148		86.38 252	85.06 252	90.37 262	89.47 316	84.10 273	98.52 158	95.48 277	83.80 261	80.93 272	90.22 306	74.60 230	96.31 270	80.92 260	71.55 326	90.69 300

pmmvs5		85.87 258	84.40 267	90.30 263	88.53 326	84.23 271	98.60 151	93.71 325	81.53 297	80.29 279	92.02 259	64.51 303	95.52 302	82.04 253	78.34 273	91.15 284

LTVRE_ROB	81.71 19	84.59 276	82.72 282	90.18 264	92.89 273	83.18 283	93.15 319	94.74 303	78.99 314	75.14 319	92.69 252	65.64 299	97.63 204	69.46 326	81.82 259	89.74 318
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
USDC		84.74 272	82.93 276	90.16 265	91.73 288	83.54 279	95.00 302	93.30 331	88.77 162	73.19 327	93.30 240	53.62 339	97.65 203	75.88 295	81.54 260	89.30 324

ACMP	87.39 10	88.71 217	88.24 208	90.12 266	93.91 250	81.06 310	98.50 163	95.67 266	89.43 143	80.37 277	95.55 200	65.67 298	97.83 188	90.55 158	84.51 236	91.47 271
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		87.76 229	87.00 226	90.06 267	94.67 232	82.65 293	97.02 253	95.37 286	84.19 255	81.86 265	91.58 269	81.47 191	95.90 291	83.24 238	73.61 307	91.61 266

LPG-MVS_test		88.86 209	88.47 206	90.06 267	93.35 265	80.95 311	98.22 191	95.94 240	87.73 198	83.17 237	96.11 193	66.28 296	97.77 193	90.19 161	85.19 232	91.46 272

LGP-MVS_train				90.06 267	93.35 265	80.95 311		95.94 240	87.73 198	83.17 237	96.11 193	66.28 296	97.77 193	90.19 161	85.19 232	91.46 272

test0.0.03 1		88.96 206	88.61 201	90.03 270	91.09 295	84.43 269	98.97 108	97.02 181	90.21 117	80.29 279	96.31 190	84.89 141	91.93 349	72.98 315	85.70 231	93.73 228

jajsoiax		87.35 236	86.51 233	89.87 271	87.75 336	81.74 299	97.03 251	95.98 234	88.47 168	80.15 281	93.80 228	61.47 313	96.36 262	89.44 170	84.47 238	91.50 270

ADS-MVSNet2		87.62 234	86.88 227	89.86 272	96.21 171	79.14 319	87.15 343	92.99 332	83.01 274	89.91 181	87.27 330	78.87 208	92.80 338	74.20 306	92.27 185	97.64 188

test_djsdf		88.26 224	87.73 213	89.84 273	88.05 331	82.21 296	97.77 221	96.17 227	86.84 214	82.41 249	91.95 263	72.07 256	95.99 283	89.83 163	84.50 237	91.32 279

ppachtmachnet_test		83.63 288	81.57 291	89.80 274	89.01 319	85.09 261	97.13 248	94.50 310	78.84 315	76.14 310	91.00 279	69.78 270	94.61 323	63.40 343	74.36 299	89.71 320

CP-MVSNet		86.54 249	85.45 249	89.79 275	91.02 297	82.78 291	97.38 236	97.56 113	85.37 236	79.53 290	93.03 248	71.86 259	95.25 309	79.92 266	73.43 311	91.34 278

mvs_tets		87.09 239	86.22 236	89.71 276	87.87 332	81.39 303	96.73 265	95.90 249	88.19 183	79.99 283	93.61 233	59.96 319	96.31 270	89.40 171	84.34 239	91.43 274

D2MVS		87.96 226	87.39 218	89.70 277	91.84 286	83.40 280	98.31 186	98.49 20	88.04 187	78.23 304	90.26 302	73.57 242	96.79 240	84.21 227	83.53 246	88.90 329

mvs-test1		91.57 162	92.20 138	89.70 277	95.15 210	74.34 338	99.51 40	95.40 283	91.92 78	91.02 163	97.25 153	74.27 237	98.08 175	89.45 168	95.83 150	96.67 209

COLMAP_ROB	82.69 18	84.54 277	82.82 277	89.70 277	96.72 155	78.85 320	95.89 289	92.83 335	71.55 341	77.54 307	95.89 197	59.40 320	99.14 140	67.26 333	88.26 215	91.11 286
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		86.53 250	85.49 248	89.66 280	91.04 296	83.31 282	97.53 232	98.20 30	84.95 246	79.64 287	90.90 281	78.01 215	95.33 307	76.29 292	72.81 313	90.35 306

Fast-Effi-MVS+-dtu		88.84 210	88.59 203	89.58 281	93.44 263	78.18 326	98.65 142	94.62 308	88.46 170	84.12 228	95.37 205	68.91 274	96.52 251	82.06 252	91.70 196	94.06 227

anonymousdsp		86.69 245	85.75 244	89.53 282	86.46 342	82.94 285	96.39 272	95.71 262	83.97 259	79.63 288	90.70 286	68.85 275	95.94 286	86.01 205	84.02 241	89.72 319

our_test_3		84.47 279	82.80 278	89.50 283	89.01 319	83.90 276	97.03 251	94.56 309	81.33 299	75.36 318	90.52 298	71.69 261	94.54 324	68.81 328	76.84 284	90.07 312

Patchmtry		83.61 289	81.64 289	89.50 283	93.36 264	82.84 290	84.10 353	94.20 318	69.47 348	79.57 289	86.88 334	84.43 145	94.78 319	68.48 330	74.30 300	90.88 291

PS-CasMVS		85.81 261	84.58 263	89.49 285	90.77 299	82.11 297	97.20 246	97.36 149	84.83 248	79.12 295	92.84 251	67.42 288	95.16 311	78.39 278	73.25 312	91.21 283

v7n		84.42 280	82.75 281	89.43 286	88.15 329	81.86 298	96.75 263	95.67 266	80.53 306	78.38 302	89.43 316	69.89 269	96.35 267	73.83 310	72.13 322	90.07 312

JIA-IIPM		85.97 257	84.85 257	89.33 287	93.23 267	73.68 341	85.05 349	97.13 169	69.62 347	91.56 155	68.03 358	88.03 80	96.96 232	77.89 280	93.12 171	97.34 196

MS-PatchMatch		86.75 244	85.92 241	89.22 288	91.97 282	82.47 295	96.91 255	96.14 229	83.74 262	77.73 305	93.53 236	58.19 322	97.37 222	76.75 288	98.35 110	87.84 335

IterMVS		85.81 261	84.67 261	89.22 288	93.51 259	83.67 278	96.32 275	94.80 302	85.09 241	78.69 296	90.17 309	66.57 295	93.17 334	79.48 269	77.42 281	90.81 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	83.78 15	84.21 281	82.56 286	89.15 290	93.73 256	79.16 318	96.43 271	94.28 316	81.09 302	74.00 323	94.03 220	54.58 336	97.67 201	76.10 293	78.81 270	90.63 302

TransMVSNet (Re)		81.97 295	79.61 303	89.08 291	89.70 310	84.01 274	97.26 241	91.85 347	78.84 315	73.07 331	91.62 267	67.17 290	95.21 310	67.50 332	59.46 351	88.02 334

PEN-MVS		85.21 269	83.93 271	89.07 292	89.89 308	81.31 305	97.09 249	97.24 155	84.45 253	78.66 297	92.68 253	68.44 279	94.87 316	75.98 294	70.92 329	91.04 287

miper_lstm_enhance		86.90 241	86.20 237	89.00 293	94.53 234	81.19 307	96.74 264	95.24 293	82.33 288	80.15 281	90.51 299	81.99 184	94.68 322	80.71 262	73.58 308	91.12 285

IterMVS-SCA-FT		85.73 264	84.64 262	89.00 293	93.46 262	82.90 287	96.27 276	94.70 305	85.02 244	78.62 298	90.35 301	66.61 293	93.33 331	79.38 270	77.36 282	90.76 296

MVP-Stereo		86.61 248	85.83 242	88.93 295	88.70 324	83.85 277	96.07 286	94.41 314	82.15 291	75.64 316	91.96 262	67.65 286	96.45 257	77.20 284	98.72 101	86.51 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Baseline_NR-MVSNet		85.83 260	84.82 258	88.87 296	88.73 323	83.34 281	98.63 146	91.66 348	80.41 310	82.44 247	91.35 273	74.63 228	95.42 305	84.13 229	71.39 327	87.84 335

XVG-ACMP-BASELINE		85.86 259	84.95 255	88.57 297	89.90 307	77.12 331	94.30 308	95.60 271	87.40 206	82.12 254	92.99 250	53.42 340	97.66 202	85.02 217	83.83 242	90.92 290

MVS_0304		84.13 284	82.66 283	88.52 298	93.07 270	80.15 314	95.81 295	98.21 29	79.27 312	86.85 209	86.40 337	41.33 359	94.69 321	76.36 291	86.69 222	90.73 298

LCM-MVSNet-Re		88.59 218	88.61 201	88.51 299	95.53 196	72.68 346	96.85 258	88.43 362	88.45 171	73.14 328	90.63 291	75.82 222	94.38 325	92.95 135	95.71 153	98.48 162

CVMVSNet		90.30 185	90.91 163	88.46 300	94.32 237	73.58 342	97.61 230	97.59 106	90.16 122	88.43 194	97.10 162	76.83 221	92.86 335	82.64 246	93.54 169	98.93 132

DTE-MVSNet		84.14 283	82.80 278	88.14 301	88.95 321	79.87 317	96.81 259	96.24 221	83.50 267	77.60 306	92.52 255	67.89 285	94.24 327	72.64 317	69.05 332	90.32 307

ITE_SJBPF				87.93 302	92.26 278	76.44 332		93.47 330	87.67 201	79.95 284	95.49 203	56.50 327	97.38 220	75.24 298	82.33 256	89.98 316

TinyColmap		80.42 302	77.94 306	87.85 303	92.09 281	78.58 323	93.74 313	89.94 357	74.99 331	69.77 338	91.78 265	46.09 353	97.58 207	65.17 341	77.89 275	87.38 338

Effi-MVS+-dtu		89.97 194	90.68 170	87.81 304	95.15 210	71.98 348	97.87 216	95.40 283	91.92 78	87.57 198	91.44 271	74.27 237	96.84 236	89.45 168	93.10 172	94.60 226

pmmvs6		79.90 304	77.31 309	87.67 305	84.17 349	78.13 327	95.86 293	93.68 326	67.94 352	72.67 333	89.62 314	50.98 346	95.75 296	74.80 303	66.04 340	89.14 327

FMVSNet5		82.29 293	80.54 297	87.52 306	93.79 255	84.01 274	93.73 314	92.47 338	76.92 326	74.27 321	86.15 339	63.69 307	89.24 355	69.07 327	74.79 294	89.29 325

MDA-MVSNet_test_wron		79.65 306	77.05 310	87.45 307	87.79 335	80.13 315	96.25 279	94.44 311	73.87 336	51.80 359	87.47 329	68.04 282	92.12 347	66.02 337	67.79 336	90.09 310

YYNet1		79.64 307	77.04 311	87.43 308	87.80 334	79.98 316	96.23 280	94.44 311	73.83 337	51.83 358	87.53 326	67.96 284	92.07 348	66.00 338	67.75 337	90.23 309

Patchmatch-RL test		81.90 297	80.13 300	87.23 309	80.71 358	70.12 353	84.07 354	88.19 363	83.16 273	70.57 336	82.18 347	87.18 98	92.59 340	82.28 250	62.78 344	98.98 124

MDA-MVSNet-bldmvs		77.82 316	74.75 320	87.03 310	88.33 327	78.52 324	96.34 274	92.85 334	75.57 330	48.87 361	87.89 323	57.32 325	92.49 343	60.79 349	64.80 343	90.08 311

EG-PatchMatch MVS		79.92 303	77.59 307	86.90 311	87.06 340	77.90 330	96.20 284	94.06 320	74.61 333	66.53 350	88.76 320	40.40 361	96.20 275	67.02 334	83.66 245	86.61 344

OpenMVS_ROB	73.86 20	77.99 315	75.06 319	86.77 312	83.81 351	77.94 329	96.38 273	91.53 351	67.54 353	68.38 341	87.13 333	43.94 355	96.08 281	55.03 357	81.83 258	86.29 347

pmmvs-eth3d		78.71 311	76.16 315	86.38 313	80.25 359	81.19 307	94.17 310	92.13 343	77.97 320	66.90 349	82.31 346	55.76 328	92.56 341	73.63 312	62.31 347	85.38 350

test_0402		78.81 310	76.33 314	86.26 314	91.18 294	78.44 325	95.88 291	91.34 352	68.55 349	70.51 337	89.91 310	52.65 342	94.99 312	47.14 361	79.78 267	85.34 352

testgi		82.29 293	81.00 296	86.17 315	87.24 338	74.84 337	97.39 234	91.62 349	88.63 163	75.85 315	95.42 204	46.07 354	91.55 350	66.87 336	79.94 266	92.12 249

TDRefinement		78.01 314	75.31 317	86.10 316	70.06 365	73.84 340	93.59 317	91.58 350	74.51 334	73.08 330	91.04 278	49.63 350	97.12 225	74.88 301	59.47 350	87.33 340

SixPastTwentyTwo		82.63 292	81.58 290	85.79 317	88.12 330	71.01 350	95.17 301	92.54 337	84.33 254	72.93 332	92.08 257	60.41 318	95.61 301	74.47 304	74.15 303	90.75 297

OurMVSNet-221017-0		84.13 284	83.59 273	85.77 318	87.81 333	70.24 351	94.89 303	93.65 327	86.08 227	76.53 308	93.28 241	61.41 314	96.14 279	80.95 259	77.69 280	90.93 289

UnsupCasMVSNet_eth		78.90 309	76.67 313	85.58 319	82.81 354	74.94 336	91.98 328	96.31 215	84.64 250	65.84 352	87.71 324	51.33 344	92.23 345	72.89 316	56.50 355	89.56 322

lessismore_v0				85.08 320	85.59 345	69.28 354		90.56 355		67.68 345	90.21 307	54.21 338	95.46 303	73.88 308	62.64 345	90.50 304

UnsupCasMVSNet_bld		73.85 322	70.14 325	84.99 321	79.44 360	75.73 333	88.53 341	95.24 293	70.12 346	61.94 355	74.81 355	41.41 358	93.62 329	68.65 329	51.13 360	85.62 349

K. test v3		81.04 299	79.77 302	84.83 322	87.41 337	70.23 352	95.60 298	93.93 322	83.70 264	67.51 346	89.35 317	55.76 328	93.58 330	76.67 289	68.03 335	90.67 301

Anonymous20231206		80.76 300	79.42 304	84.79 323	84.78 347	72.98 343	96.53 269	92.97 333	79.56 311	74.33 320	88.83 319	61.27 315	92.15 346	60.59 350	75.92 286	89.24 326

RPSCF		85.33 268	85.55 247	84.67 324	94.63 233	62.28 359	93.73 314	93.76 323	74.38 335	85.23 220	97.06 165	64.09 304	98.31 162	80.98 258	86.08 228	93.41 232

CL-MVSNet_2432*1600		79.89 305	78.34 305	84.54 325	81.56 356	75.01 335	96.88 257	95.62 268	81.10 301	75.86 314	85.81 340	68.49 278	90.26 353	63.21 344	56.51 354	88.35 332

LF4IMVS		81.94 296	81.17 295	84.25 326	87.23 339	68.87 356	93.35 318	91.93 346	83.35 270	75.40 317	93.00 249	49.25 351	96.65 244	78.88 274	78.11 274	87.22 342

Anonymous20240521		78.63 312	76.90 312	83.82 327	82.82 353	72.86 344	95.72 297	93.57 328	73.55 338	72.17 335	84.79 342	49.69 349	92.51 342	65.29 340	74.50 296	86.09 348

MIMVSNet1		75.92 319	73.30 322	83.81 328	81.29 357	75.57 334	92.26 327	92.05 344	73.09 339	67.48 347	86.18 338	40.87 360	87.64 358	55.78 356	70.68 330	88.21 333

EU-MVSNet		84.19 282	84.42 266	83.52 329	88.64 325	67.37 357	96.04 287	95.76 260	85.29 237	78.44 301	93.18 243	70.67 267	91.48 351	75.79 296	75.98 285	91.70 260

new_pmnet		76.02 318	73.71 321	82.95 330	83.88 350	72.85 345	91.26 334	92.26 340	70.44 344	62.60 354	81.37 348	47.64 352	92.32 344	61.85 347	72.10 323	83.68 355

DIV-MVS_2432*1600		77.47 317	75.88 316	82.24 331	81.59 355	68.93 355	92.83 324	94.02 321	77.03 325	73.14 328	83.39 344	55.44 332	90.42 352	67.95 331	57.53 353	87.38 338

pmmvs3		72.86 323	69.76 327	82.17 332	73.86 363	74.19 339	94.20 309	89.01 361	64.23 358	67.72 344	80.91 350	41.48 357	88.65 357	62.40 346	54.02 358	83.68 355

DSMNet-mixed		81.60 298	81.43 292	82.10 333	84.36 348	60.79 360	93.63 316	86.74 364	79.00 313	79.32 292	87.15 332	63.87 306	89.78 354	66.89 335	91.92 190	95.73 222

new-patchmatchnet		74.80 321	72.40 324	81.99 334	78.36 362	72.20 347	94.44 306	92.36 339	77.06 324	63.47 353	79.98 352	51.04 345	88.85 356	60.53 351	54.35 357	84.92 353

test20.03		78.51 313	77.48 308	81.62 335	83.07 352	71.03 349	96.11 285	92.83 335	81.66 296	69.31 339	89.68 313	57.53 323	87.29 359	58.65 354	68.47 333	86.53 345

CMPMVS	58.40 21	80.48 301	80.11 301	81.59 336	85.10 346	59.56 361	94.14 311	95.95 239	68.54 350	60.71 356	93.31 239	55.35 333	97.87 186	83.06 243	84.85 235	87.33 340
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		74.88 320	72.85 323	80.98 337	78.98 361	64.75 358	90.81 337	85.77 365	80.95 304	68.23 343	82.81 345	29.08 364	92.84 336	76.54 290	62.46 346	85.36 351

ambc				79.60 338	72.76 364	56.61 363	76.20 359	92.01 345		68.25 342	80.23 351	23.34 365	94.73 320	73.78 311	60.81 348	87.48 337

DeepMVS_CX				76.08 339	90.74 300	51.65 367		90.84 354	86.47 224	57.89 357	87.98 322	35.88 362	92.60 339	65.77 339	65.06 342	83.97 354

test_method		70.10 325	68.66 328	74.41 340	86.30 343	55.84 364	94.47 305	89.82 358	35.18 364	66.15 351	84.75 343	30.54 363	77.96 364	70.40 325	60.33 349	89.44 323

LCM-MVSNet		60.07 327	56.37 330	71.18 341	54.81 371	48.67 368	82.17 358	89.48 360	37.95 362	49.13 360	69.12 356	13.75 372	81.76 360	59.28 352	51.63 359	83.10 357

N_pmnet		70.19 324	69.87 326	71.12 342	88.24 328	30.63 375	95.85 294	28.70 375	70.18 345	68.73 340	86.55 336	64.04 305	93.81 328	53.12 359	73.46 310	88.94 328

PMMVS2		58.97 328	55.07 331	70.69 343	62.72 366	55.37 365	85.97 345	80.52 368	49.48 360	45.94 362	68.31 357	15.73 370	80.78 362	49.79 360	37.12 363	75.91 358

FPMVS		61.57 326	60.32 329	65.34 344	60.14 369	42.44 370	91.02 336	89.72 359	44.15 361	42.63 363	80.93 349	19.02 366	80.59 363	42.50 362	72.76 314	73.00 359

ANet_high		50.71 331	46.17 334	64.33 345	44.27 373	52.30 366	76.13 360	78.73 369	64.95 356	27.37 367	55.23 363	14.61 371	67.74 366	36.01 363	18.23 366	72.95 360

Gipuma		54.77 329	52.22 333	62.40 346	86.50 341	59.37 362	50.20 364	90.35 356	36.52 363	41.20 364	49.49 364	18.33 368	81.29 361	32.10 364	65.34 341	46.54 364
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 330	52.86 332	56.05 347	32.75 375	41.97 371	73.42 361	76.12 371	21.91 369	39.68 365	96.39 188	42.59 356	65.10 367	78.00 279	14.92 368	61.08 361

PMVS	41.42 23	45.67 332	42.50 335	55.17 348	34.28 374	32.37 373	66.24 362	78.71 370	30.72 365	22.04 370	59.59 361	4.59 373	77.85 365	27.49 365	58.84 352	55.29 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 333	37.64 338	53.90 349	49.46 372	43.37 369	65.09 363	66.66 372	26.19 368	25.77 369	48.53 365	3.58 375	63.35 368	26.15 366	27.28 364	54.97 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 334	40.93 336	41.29 350	61.97 367	33.83 372	84.00 355	65.17 373	27.17 366	27.56 366	46.72 366	17.63 369	60.41 369	19.32 367	18.82 365	29.61 365

EMVS		39.96 335	39.88 337	40.18 351	59.57 370	32.12 374	84.79 352	64.57 374	26.27 367	26.14 368	44.18 369	18.73 367	59.29 370	17.03 368	17.67 367	29.12 366

wuyk23d		16.71 338	16.73 342	16.65 352	60.15 368	25.22 376	41.24 365	5.17 376	6.56 370	5.48 373	3.61 372	3.64 374	22.72 371	15.20 369	9.52 369	1.99 369

test123		16.58 339	19.47 341	7.91 353	3.59 377	5.37 377	94.32 307	1.39 378	2.49 372	13.98 372	44.60 368	2.91 376	2.65 372	11.35 371	0.57 371	15.70 367

testmvs		18.81 337	23.05 340	6.10 354	4.48 376	2.29 378	97.78 220	3.00 377	3.27 371	18.60 371	62.71 359	1.53 377	2.49 373	14.26 370	1.80 370	13.50 368

test_blank		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		22.52 336	30.03 339	0.00 355	0.00 378	0.00 379	0.00 366	97.17 165	0.00 373	0.00 374	98.77 86	74.35 236	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		6.87 341	9.16 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	82.48 177	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		8.21 340	10.94 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	98.50 107	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

FOURS1					99.50 47	88.94 172	99.55 34	97.47 132	91.32 94	98.12 37

PC_three_1452									94.60 20	99.41 2	99.12 48	95.50 6	99.96 30	99.84 2	99.92 3	99.97 7

test_one_0601					99.59 31	94.89 35		97.64 92	93.14 50	98.93 15	99.45 16	93.45 17

eth-test2					0.00 378

eth-test					0.00 378

ZD-MVS					99.67 13	93.28 74		97.61 100	87.78 194	97.41 56	99.16 41	90.15 50	99.56 97	98.35 33	99.70 39

RE-MVS-def			95.70 66		99.22 71	87.26 212	98.40 176	97.21 159	89.63 135	96.67 79	98.97 66	85.24 137		96.62 63	99.31 76	99.60 77

IU-MVS					99.63 21	95.38 21		97.73 72	95.54 15	99.54 1				99.69 5	99.81 23	99.99 1

test_241102_TWO								97.72 74	94.17 25	99.23 7	99.54 3	93.14 23	99.98 10	99.70 3	99.82 19	99.99 1

test_241102_ONE					99.63 21	95.24 24		97.72 74	94.16 27	99.30 5	99.49 10	93.32 18	99.98 10

9.14			96.87 27		99.34 58		99.50 41	97.49 129	89.41 144	98.59 25	99.43 18	89.78 54	99.69 79	98.69 21	99.62 51

save fliter					99.34 58	93.85 63	99.65 23	97.63 97	95.69 11

test_0728_THIRD									93.01 51	99.07 10	99.46 11	94.66 12	99.97 23	99.25 14	99.82 19	99.95 15

test0726					99.66 15	95.20 29	99.77 9	97.70 79	93.95 30	99.35 4	99.54 3	93.18 21

GSMVS																98.84 138

test_part2					99.54 40	95.42 19				98.13 35

sam_mvs1												88.39 73				98.84 138

sam_mvs												87.08 99

MTGPA								97.45 135

test_post1							90.74 339				41.37 370	85.38 136	96.36 262	83.16 240

test_post											46.00 367	87.37 92	97.11 226

patchmatchnet-post											84.86 341	88.73 67	96.81 238

MTMP							99.21 74	91.09 353

gm-plane-assit					94.69 231	88.14 189			88.22 182		97.20 157		98.29 164	90.79 156

test9_res														98.60 23	99.87 9	99.90 24

TEST9					99.57 37	93.17 76	99.38 61	97.66 86	89.57 139	98.39 30	99.18 37	90.88 35	99.66 84

test_8					99.55 39	93.07 80	99.37 64	97.64 92	90.18 119	98.36 32	99.19 34	90.94 33	99.64 90

agg_prior2														97.84 45	99.87 9	99.91 22

agg_prior					99.54 40	92.66 88		97.64 92		97.98 44			99.61 93

test_prior4						92.00 98	99.41 58

test_prior2							99.57 31		91.43 90	98.12 37	98.97 66	90.43 43		98.33 34	99.81 23

旧先验2							98.67 140		85.75 231	98.96 14			98.97 146	93.84 121

新几何2							98.26 189

旧先验1					98.97 85	92.90 87		97.74 68			99.15 43	91.05 32			99.33 74	99.60 77

无先验							98.52 158	97.82 55	87.20 208				99.90 44	87.64 191		99.85 33

原ACMM2							98.69 136

test222					98.32 106	91.21 113	98.08 205	97.58 108	83.74 262	95.87 94	99.02 60	86.74 107			99.64 47	99.81 35

testdata2													99.88 48	84.16 228

segment_acmp												90.56 42

testdata1							97.89 213		92.43 65

plane_prior7					93.84 252	85.73 248

plane_prior6					93.92 249	86.02 242						72.92 248

plane_prior5								96.30 216					97.75 198	93.46 128	86.17 226	92.67 235

plane_prior4											96.52 182

plane_prior3						85.91 243			93.65 42	86.99 205

plane_prior2							99.02 102		93.38 47

plane_prior1					93.90 251

plane_prior						86.07 240	99.14 89		93.81 40						86.26 225

n2								0.00 379

nn								0.00 379

door-mid								84.90 367

test11								97.68 83

door								85.30 366

HQP5-MVS						86.39 228

HQP-NCC					93.95 245		99.16 80		93.92 32	87.57 198

ACMP_Plane					93.95 245		99.16 80		93.92 32	87.57 198

BP-MVS														93.82 123

HQP4-MVS										87.57 198			97.77 193			92.72 233

HQP3-MVS								96.37 212							86.29 223

HQP2-MVS												73.34 244

NP-MVS					93.94 248	86.22 234					96.67 180

MDTV_nov1_ep13_2view						91.17 117	91.38 332		87.45 205	93.08 138		86.67 110		87.02 195		98.95 130

MDTV_nov1_ep13			90.47 174		96.14 177	88.55 182	91.34 333	97.51 124	89.58 138	92.24 147	90.50 300	86.99 103	97.61 206	77.64 281	92.34 183

ACMMP++_ref															82.64 254

ACMMP++															83.83 242

Test By Simon												83.62 153

High-resolution multi-view benchmark