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.

CNVR-MVS		96.30 1	96.54 1	95.55 15	99.31 5	87.69 24	99.06 18	97.12 30	94.66 6	96.79 20	98.78 9	86.42 30	99.95 3	97.59 27	99.18 7	99.00 31

DPM-MVS		96.21 2	95.53 13	98.26 1	96.26 105	95.09 1	99.15 9	96.98 41	93.39 18	96.45 28	98.79 8	90.17 9	99.99 1	89.33 144	99.25 6	99.70 3

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 26	97.10 32	95.17 3	92.11 90	98.46 28	87.33 25	99.97 2	97.21 33	99.31 4	99.63 7

DVP-MVS++		96.05 4	96.41 3	94.96 24	99.05 9	85.34 61	98.13 54	96.77 65	88.38 79	97.70 8	98.77 10	92.06 3	99.84 13	97.47 28	99.37 1	99.70 3

SED-MVS		95.88 5	96.22 4	94.87 25	99.03 15	85.03 74	99.12 13	96.78 59	88.72 71	97.79 6	98.91 2	88.48 17	99.82 19	98.15 15	98.97 17	99.74 1

MM		95.85 6	95.74 10	96.15 8	96.34 102	89.50 9	99.18 7	98.10 8	95.68 1	96.64 24	97.92 65	80.72 72	99.80 25	99.16 1	97.96 58	99.15 27

NCCC		95.63 7	95.94 8	94.69 32	99.21 6	85.15 71	99.16 8	96.96 44	94.11 11	95.59 38	98.64 17	85.07 36	99.91 4	95.61 50	99.10 9	99.00 31

MSP-MVS		95.62 8	96.54 1	92.86 101	98.31 48	80.10 188	97.42 110	96.78 59	92.20 26	97.11 16	98.29 39	93.46 1	99.10 108	96.01 43	99.30 5	99.38 14
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
DVP-MVS		95.58 9	95.91 9	94.57 34	99.05 9	85.18 66	99.06 18	96.46 107	88.75 69	96.69 21	98.76 12	87.69 23	99.76 34	97.90 21	98.85 21	98.77 40
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
MVS_0304		95.58 9	95.44 15	96.01 10	97.63 70	89.26 12	99.27 4	96.59 92	94.71 5	97.08 17	97.99 59	78.69 103	99.86 10	99.15 2	97.85 62	98.91 35

DPE-MVS		95.32 11	95.55 12	94.64 33	98.79 23	84.87 79	97.77 78	96.74 70	86.11 132	96.54 27	98.89 6	88.39 19	99.74 42	97.67 26	99.05 12	99.31 20
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		95.32 11	95.48 14	94.85 26	98.62 34	86.04 40	97.81 75	96.93 47	92.45 24	95.69 37	98.50 25	85.38 34	99.85 11	94.75 63	99.18 7	98.65 50

patch_mono-2		95.14 13	96.08 7	92.33 126	98.44 43	77.84 255	98.43 41	97.21 23	92.58 23	97.68 10	97.65 83	86.88 27	99.83 17	98.25 11	97.60 69	99.33 18

DELS-MVS		94.98 14	94.49 26	96.44 6	96.42 101	90.59 7	99.21 6	97.02 38	94.40 10	91.46 99	97.08 114	83.32 56	99.69 53	92.83 92	98.70 31	99.04 29
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
fmvsm_l_conf0.5_n_a		94.91 15	95.30 16	93.72 62	94.50 170	84.30 87	99.14 11	96.00 151	91.94 32	97.91 5	98.60 18	84.78 38	99.77 32	98.84 5	96.03 112	97.08 163

fmvsm_l_conf0.5_n		94.89 16	95.24 17	93.86 53	94.42 173	84.61 82	99.13 12	96.15 140	92.06 29	97.92 3	98.52 24	84.52 41	99.74 42	98.76 6	95.67 119	97.22 154

CANet		94.89 16	94.64 23	95.63 13	97.55 76	88.12 18	99.06 18	96.39 117	94.07 13	95.34 40	97.80 74	76.83 136	99.87 8	97.08 35	97.64 68	98.89 36

SD-MVS		94.84 18	95.02 19	94.29 40	97.87 64	84.61 82	97.76 80	96.19 138	89.59 61	96.66 23	98.17 47	84.33 43	99.60 63	96.09 42	98.50 38	98.66 49
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
test_fmvsm_n_1920		94.81 19	95.60 11	92.45 118	95.29 140	80.96 159	99.29 3	97.21 23	94.50 9	97.29 15	98.44 29	82.15 64	99.78 31	98.56 7	97.68 67	96.61 182

TSAR-MVS + MP.		94.79 20	95.17 18	93.64 68	97.66 69	84.10 90	95.85 225	96.42 112	91.26 38	97.49 14	96.80 127	86.50 29	98.49 139	95.54 52	99.03 13	98.33 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SMA-MVS		94.70 21	94.68 22	94.76 29	98.02 59	85.94 44	97.47 103	96.77 65	85.32 150	97.92 3	98.70 15	83.09 59	99.84 13	95.79 47	99.08 10	98.49 57
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_l_conf0.5_n_3		94.61 22	94.92 20	93.68 66	94.52 165	82.80 115	99.33 1	96.37 121	95.08 4	97.59 13	98.48 27	77.40 124	99.79 29	98.28 10	97.21 82	98.44 61

DeepPCF-MVS	89.82 1	94.61 22	96.17 5	89.91 217	97.09 94	70.21 351	98.99 24	96.69 77	95.57 2	95.08 45	99.23 1	86.40 31	99.87 8	97.84 24	98.66 32	99.65 6

balanced_conf03		94.60 24	94.30 31	95.48 16	96.45 100	88.82 14	96.33 196	95.58 180	91.12 40	95.84 36	93.87 208	83.47 55	98.37 148	97.26 31	98.81 24	99.24 23

APDe-MVS		94.56 25	94.75 21	93.96 51	98.84 22	83.40 105	98.04 62	96.41 113	85.79 141	95.00 47	98.28 40	84.32 46	99.18 101	97.35 30	98.77 28	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS_fast	89.06 2	94.48 26	94.30 31	95.02 22	98.86 21	85.68 51	98.06 60	96.64 85	93.64 16	91.74 97	98.54 21	80.17 81	99.90 5	92.28 98	98.75 29	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		94.35 27	94.50 25	93.89 52	97.38 88	83.04 112	98.10 56	95.29 203	91.57 34	93.81 63	97.45 92	86.64 28	99.43 80	96.28 41	94.01 138	99.20 25

train_agg		94.28 28	94.45 27	93.74 59	98.64 31	83.71 97	97.82 73	96.65 82	84.50 174	95.16 41	98.09 52	84.33 43	99.36 85	95.91 46	98.96 19	98.16 80

MSLP-MVS++		94.28 28	94.39 29	93.97 50	98.30 49	84.06 91	98.64 36	96.93 47	90.71 46	93.08 74	98.70 15	79.98 85	99.21 94	94.12 72	99.07 11	98.63 51

MG-MVS		94.25 30	93.72 37	95.85 12	99.38 3	89.35 11	97.98 64	98.09 9	89.99 57	92.34 86	96.97 119	81.30 70	98.99 114	88.54 151	98.88 20	99.20 25

SF-MVS		94.17 31	94.05 36	94.55 35	97.56 75	85.95 42	97.73 82	96.43 111	84.02 190	95.07 46	98.74 14	82.93 60	99.38 82	95.42 54	98.51 36	98.32 67

PS-MVSNAJ		94.17 31	93.52 43	96.10 9	95.65 128	92.35 2	98.21 49	95.79 170	92.42 25	96.24 30	98.18 44	71.04 227	99.17 102	96.77 38	97.39 77	96.79 174

SteuartSystems-ACMMP		94.13 33	94.44 28	93.20 87	95.41 135	81.35 149	99.02 22	96.59 92	89.50 63	94.18 59	98.36 36	83.68 54	99.45 79	94.77 62	98.45 41	98.81 39
Skip Steuart: Steuart Systems R&D Blog.
EPNet		94.06 34	94.15 34	93.76 57	97.27 91	84.35 85	98.29 46	97.64 14	94.57 7	95.36 39	96.88 122	79.96 86	99.12 107	91.30 110	96.11 109	97.82 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		93.99 35	94.36 30	92.86 101	92.82 226	81.12 152	99.26 5	96.37 121	93.47 17	95.16 41	98.21 42	79.00 96	99.64 59	98.21 13	96.73 99	97.83 107

fmvsm_s_conf0.5_n_3		93.95 36	94.53 24	92.20 135	94.41 174	80.04 189	98.90 27	95.96 155	94.53 8	97.63 12	98.58 19	75.95 152	99.79 29	98.25 11	96.60 101	96.77 176

xiu_mvs_v2_base		93.92 37	93.26 49	95.91 11	95.07 148	92.02 6	98.19 50	95.68 176	92.06 29	96.01 35	98.14 48	70.83 231	98.96 116	96.74 40	96.57 102	96.76 178

lupinMVS		93.87 38	93.58 42	94.75 30	93.00 219	88.08 19	99.15 9	95.50 187	91.03 43	94.90 48	97.66 79	78.84 99	97.56 189	94.64 66	97.46 72	98.62 52

fmvsm_s_conf0.5_n		93.69 39	94.13 35	92.34 124	94.56 162	82.01 127	99.07 17	97.13 28	92.09 27	96.25 29	98.53 23	76.47 141	99.80 25	98.39 9	94.71 128	95.22 221

APD-MVS		93.61 40	93.59 41	93.69 65	98.76 24	83.26 108	97.21 122	96.09 144	82.41 233	94.65 53	98.21 42	81.96 67	98.81 126	94.65 65	98.36 47	99.01 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PHI-MVS		93.59 41	93.63 40	93.48 79	98.05 58	81.76 139	98.64 36	97.13 28	82.60 229	94.09 60	98.49 26	80.35 76	99.85 11	94.74 64	98.62 33	98.83 38

BP-MVS1		93.55 42	93.50 44	93.71 63	92.64 233	85.39 60	97.78 77	96.84 55	89.52 62	92.00 91	97.06 116	88.21 20	98.03 162	91.45 109	96.00 114	97.70 118

ACMMP_NAP		93.46 43	93.23 50	94.17 46	97.16 92	84.28 88	96.82 163	96.65 82	86.24 130	94.27 57	97.99 59	77.94 113	99.83 17	93.39 79	98.57 34	98.39 64

MVS_111021_HR		93.41 44	93.39 47	93.47 81	97.34 89	82.83 114	97.56 95	98.27 6	89.16 67	89.71 124	97.14 109	79.77 87	99.56 70	93.65 77	97.94 59	98.02 89

fmvsm_s_conf0.5_n_a		93.34 45	93.71 38	92.22 133	93.38 208	81.71 142	98.86 28	96.98 41	91.64 33	96.85 19	98.55 20	75.58 160	99.77 32	97.88 23	93.68 146	95.18 222

PVSNet_Blended		93.13 46	92.98 55	93.57 73	97.47 77	83.86 93	99.32 2	96.73 71	91.02 44	89.53 129	96.21 138	76.42 143	99.57 68	94.29 69	95.81 118	97.29 152

CDPH-MVS		93.12 47	92.91 56	93.74 59	98.65 30	83.88 92	97.67 86	96.26 130	83.00 219	93.22 71	98.24 41	81.31 69	99.21 94	89.12 145	98.74 30	98.14 82

dcpmvs_2		93.10 48	93.46 46	92.02 145	97.77 65	79.73 199	94.82 267	93.86 287	86.91 120	91.33 103	96.76 128	85.20 35	98.06 160	96.90 37	97.60 69	98.27 73

test_fmvsmconf0.1_n		93.08 49	93.22 51	92.65 111	88.45 327	80.81 164	99.00 23	95.11 209	93.21 19	94.00 61	97.91 67	76.84 134	99.59 64	97.91 20	96.55 103	97.54 129

SPE-MVS-test		92.98 50	93.67 39	90.90 186	96.52 99	76.87 278	98.68 33	94.73 230	90.36 54	94.84 50	97.89 69	77.94 113	97.15 220	94.28 71	97.80 64	98.70 48

fmvsm_s_conf0.5_n_2		92.97 51	93.38 48	91.73 158	94.10 186	80.64 169	98.96 25	95.89 163	94.09 12	97.05 18	98.40 33	68.92 240	99.80 25	98.53 8	94.50 132	94.74 231

alignmvs		92.97 51	92.26 74	95.12 21	95.54 132	87.77 22	98.67 34	96.38 118	88.04 90	93.01 75	97.45 92	79.20 94	98.60 132	93.25 85	88.76 195	98.99 33

fmvsm_s_conf0.1_n		92.93 53	93.16 52	92.24 131	90.52 292	81.92 131	98.42 42	96.24 132	91.17 39	96.02 34	98.35 37	75.34 171	99.74 42	97.84 24	94.58 130	95.05 223

HFP-MVS		92.89 54	92.86 59	92.98 96	98.71 25	81.12 152	97.58 93	96.70 75	85.20 155	91.75 96	97.97 64	78.47 105	99.71 49	90.95 113	98.41 43	98.12 85

PAPM		92.87 55	92.40 69	94.30 39	92.25 247	87.85 21	96.40 191	96.38 118	91.07 42	88.72 145	96.90 120	82.11 65	97.37 206	90.05 134	97.70 66	97.67 120

GDP-MVS		92.85 56	92.55 66	93.75 58	92.82 226	85.76 47	97.63 87	95.05 213	88.34 81	93.15 72	97.10 113	86.92 26	98.01 164	87.95 159	94.00 139	97.47 138

ZNCC-MVS		92.75 57	92.60 64	93.23 86	98.24 51	81.82 137	97.63 87	96.50 103	85.00 161	91.05 108	97.74 76	78.38 106	99.80 25	90.48 124	98.34 48	98.07 87

PAPR		92.74 58	92.17 78	94.45 36	98.89 20	84.87 79	97.20 124	96.20 136	87.73 99	88.40 149	98.12 49	78.71 102	99.76 34	87.99 158	96.28 105	98.74 42

CS-MVS		92.73 59	93.48 45	90.48 199	96.27 104	75.93 298	98.55 39	94.93 217	89.32 64	94.54 55	97.67 78	78.91 98	97.02 224	93.80 74	97.32 79	98.49 57

jason		92.73 59	92.23 75	94.21 44	90.50 293	87.30 30	98.65 35	95.09 210	90.61 48	92.76 80	97.13 110	75.28 172	97.30 209	93.32 83	96.75 98	98.02 89
jason: jason.
myMVS_eth3d28		92.72 61	92.23 75	94.21 44	96.16 108	87.46 29	97.37 114	96.99 40	88.13 88	88.18 153	95.47 158	84.12 48	98.04 161	92.46 97	91.17 175	97.14 160

ETV-MVS		92.72 61	92.87 57	92.28 130	94.54 164	81.89 133	97.98 64	95.21 207	89.77 60	93.11 73	96.83 124	77.23 130	97.50 197	95.74 48	95.38 122	97.44 140

region2R		92.72 61	92.70 61	92.79 104	98.68 26	80.53 176	97.53 98	96.51 101	85.22 153	91.94 94	97.98 62	77.26 126	99.67 57	90.83 118	98.37 46	98.18 78

reproduce-ours		92.70 64	93.02 53	91.75 156	97.45 79	77.77 259	96.16 206	95.94 159	84.12 186	92.45 81	98.43 30	80.06 83	99.24 90	95.35 55	97.18 83	98.24 75

our_new_method		92.70 64	93.02 53	91.75 156	97.45 79	77.77 259	96.16 206	95.94 159	84.12 186	92.45 81	98.43 30	80.06 83	99.24 90	95.35 55	97.18 83	98.24 75

XVS		92.69 66	92.71 60	92.63 113	98.52 37	80.29 179	97.37 114	96.44 109	87.04 118	91.38 100	97.83 73	77.24 128	99.59 64	90.46 126	98.07 54	98.02 89

ACMMPR		92.69 66	92.67 62	92.75 105	98.66 28	80.57 171	97.58 93	96.69 77	85.20 155	91.57 98	97.92 65	77.01 131	99.67 57	90.95 113	98.41 43	98.00 94

UBG		92.68 68	92.35 70	93.70 64	95.61 129	85.65 54	97.25 120	97.06 35	87.92 93	89.28 133	95.03 178	86.06 33	98.07 159	92.24 99	90.69 180	97.37 146

WTY-MVS		92.65 69	91.68 87	95.56 14	96.00 113	88.90 13	98.23 48	97.65 13	88.57 74	89.82 123	97.22 107	79.29 91	99.06 111	89.57 140	88.73 196	98.73 46

MP-MVS		92.61 70	92.67 62	92.42 122	98.13 56	79.73 199	97.33 117	96.20 136	85.63 143	90.53 115	97.66 79	78.14 111	99.70 52	92.12 101	98.30 50	97.85 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 71	92.35 70	93.29 83	97.30 90	82.53 119	96.44 187	96.04 149	84.68 169	89.12 136	98.37 35	77.48 123	99.74 42	93.31 84	98.38 45	97.59 127
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 72	92.60 64	92.34 124	98.50 40	79.90 192	98.40 43	96.40 115	84.75 165	90.48 117	98.09 52	77.40 124	99.21 94	91.15 112	98.23 52	97.92 100

reproduce_model		92.53 73	92.87 57	91.50 168	97.41 83	77.14 276	96.02 213	95.91 162	83.65 206	92.45 81	98.39 34	79.75 88	99.21 94	95.27 58	96.98 89	98.14 82

testing11		92.48 74	92.04 82	93.78 56	95.94 117	86.00 41	97.56 95	97.08 33	87.52 104	89.32 132	95.40 160	84.60 39	98.02 163	91.93 106	89.04 191	97.32 148

MTAPA		92.45 75	92.31 72	92.86 101	97.90 61	80.85 163	92.88 318	96.33 124	87.92 93	90.20 120	98.18 44	76.71 139	99.76 34	92.57 96	98.09 53	97.96 99

GST-MVS		92.43 76	92.22 77	93.04 94	98.17 54	81.64 144	97.40 112	96.38 118	84.71 168	90.90 111	97.40 97	77.55 122	99.76 34	89.75 137	97.74 65	97.72 115

fmvsm_s_conf0.1_n_a		92.38 77	92.49 67	92.06 142	88.08 332	81.62 145	97.97 66	96.01 150	90.62 47	96.58 25	98.33 38	74.09 190	99.71 49	97.23 32	93.46 151	94.86 227

MVSMamba_PlusPlus		92.37 78	91.55 90	94.83 27	95.37 137	87.69 24	95.60 237	95.42 196	74.65 340	93.95 62	92.81 226	83.11 58	97.70 181	94.49 67	98.53 35	99.11 28

sasdasda		92.27 79	91.22 96	95.41 17	95.80 123	88.31 15	97.09 140	94.64 238	88.49 76	92.99 76	97.31 99	72.68 205	98.57 134	93.38 81	88.58 198	99.36 16

canonicalmvs		92.27 79	91.22 96	95.41 17	95.80 123	88.31 15	97.09 140	94.64 238	88.49 76	92.99 76	97.31 99	72.68 205	98.57 134	93.38 81	88.58 198	99.36 16

fmvsm_s_conf0.1_n_2		92.26 81	92.48 68	91.60 165	92.29 243	80.55 172	98.73 31	94.33 261	93.80 15	96.18 31	98.11 50	66.93 253	99.75 39	98.19 14	93.74 145	94.50 238

SR-MVS		92.16 82	92.27 73	91.83 154	98.37 45	78.41 233	96.67 174	95.76 171	82.19 237	91.97 92	98.07 56	76.44 142	98.64 130	93.71 76	97.27 80	98.45 60

test_fmvsmvis_n_1920		92.12 83	92.10 80	92.17 137	90.87 285	81.04 155	98.34 45	93.90 284	92.71 22	87.24 163	97.90 68	74.83 178	99.72 47	96.96 36	96.20 106	95.76 206

VNet		92.11 84	91.22 96	94.79 28	96.91 95	86.98 31	97.91 68	97.96 10	86.38 129	93.65 65	95.74 147	70.16 236	98.95 118	93.39 79	88.87 194	98.43 62

CSCG		92.02 85	91.65 88	93.12 90	98.53 36	80.59 170	97.47 103	97.18 26	77.06 322	84.64 193	97.98 62	83.98 50	99.52 73	90.72 120	97.33 78	99.23 24

MGCFI-Net		91.95 86	91.03 102	94.72 31	95.68 127	86.38 36	96.93 155	94.48 247	88.25 84	92.78 79	97.24 105	72.34 210	98.46 142	93.13 89	88.43 202	99.32 19

PGM-MVS		91.93 87	91.80 85	92.32 128	98.27 50	79.74 198	95.28 247	97.27 21	83.83 199	90.89 112	97.78 75	76.12 149	99.56 70	88.82 148	97.93 61	97.66 121

testing99		91.91 88	91.35 93	93.60 71	95.98 115	85.70 49	97.31 118	96.92 49	86.82 123	88.91 139	95.25 163	84.26 47	97.89 174	88.80 149	87.94 208	97.21 156

testing91		91.90 89	91.31 95	93.66 67	95.99 114	85.68 51	97.39 113	96.89 50	86.75 127	88.85 141	95.23 166	83.93 51	97.90 173	88.91 146	87.89 209	97.41 142

mPP-MVS		91.88 90	91.82 84	92.07 141	98.38 44	78.63 227	97.29 119	96.09 144	85.12 157	88.45 148	97.66 79	75.53 161	99.68 55	89.83 135	98.02 57	97.88 101

EI-MVSNet-Vis-set		91.84 91	91.77 86	92.04 144	97.60 72	81.17 151	96.61 175	96.87 52	88.20 86	89.19 134	97.55 91	78.69 103	99.14 104	90.29 131	90.94 177	95.80 203

EIA-MVS		91.73 92	92.05 81	90.78 191	94.52 165	76.40 287	98.06 60	95.34 201	89.19 66	88.90 140	97.28 104	77.56 121	97.73 180	90.77 119	96.86 95	98.20 77

EC-MVSNet		91.73 92	92.11 79	90.58 195	93.54 200	77.77 259	98.07 59	94.40 257	87.44 106	92.99 76	97.11 112	74.59 184	96.87 235	93.75 75	97.08 86	97.11 161

DP-MVS Recon		91.72 94	90.85 104	94.34 38	99.50 1	85.00 76	98.51 40	95.96 155	80.57 261	88.08 155	97.63 85	76.84 134	99.89 7	85.67 176	94.88 125	98.13 84

CHOSEN 280x420		91.71 95	91.85 83	91.29 174	94.94 152	82.69 116	87.89 366	96.17 139	85.94 138	87.27 162	94.31 195	90.27 8	95.65 292	94.04 73	95.86 116	95.53 212

HY-MVS	84.06 6	91.63 96	90.37 117	95.39 19	96.12 110	88.25 17	90.22 346	97.58 15	88.33 82	90.50 116	91.96 242	79.26 92	99.06 111	90.29 131	89.07 190	98.88 37

HPM-MVS		91.62 97	91.53 91	91.89 149	97.88 63	79.22 211	96.99 145	95.73 174	82.07 239	89.50 131	97.19 108	75.59 159	98.93 121	90.91 115	97.94 59	97.54 129
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 98	91.64 89	91.47 170	95.74 125	78.79 224	96.15 208	96.77 65	88.49 76	88.64 146	97.07 115	72.33 211	99.19 100	93.13 89	96.48 104	96.43 187

DeepC-MVS	86.58 3	91.53 99	91.06 101	92.94 98	94.52 165	81.89 133	95.95 217	95.98 153	90.76 45	83.76 204	96.76 128	73.24 201	99.71 49	91.67 108	96.96 90	97.22 154
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_yl		91.46 100	90.53 111	94.24 42	97.41 83	85.18 66	98.08 57	97.72 11	80.94 252	89.85 121	96.14 139	75.61 157	98.81 126	90.42 129	88.56 200	98.74 42

DCV-MVSNet		91.46 100	90.53 111	94.24 42	97.41 83	85.18 66	98.08 57	97.72 11	80.94 252	89.85 121	96.14 139	75.61 157	98.81 126	90.42 129	88.56 200	98.74 42

PAPM_NR		91.46 100	90.82 105	93.37 82	98.50 40	81.81 138	95.03 263	96.13 141	84.65 170	86.10 175	97.65 83	79.24 93	99.75 39	83.20 204	96.88 93	98.56 54

testing3-2		91.37 103	91.01 103	92.44 120	95.93 118	83.77 96	98.83 29	97.45 16	86.88 121	86.63 170	94.69 189	84.57 40	97.75 179	89.65 138	84.44 240	95.80 203

MVSFormer		91.36 104	90.57 110	93.73 61	93.00 219	88.08 19	94.80 269	94.48 247	80.74 257	94.90 48	97.13 110	78.84 99	95.10 320	83.77 193	97.46 72	98.02 89

EI-MVSNet-UG-set		91.35 105	91.22 96	91.73 158	97.39 86	80.68 167	96.47 184	96.83 56	87.92 93	88.30 152	97.36 98	77.84 116	99.13 106	89.43 143	89.45 186	95.37 216

SR-MVS-dyc-post		91.29 106	91.45 92	90.80 189	97.76 67	76.03 293	96.20 204	95.44 192	80.56 262	90.72 113	97.84 71	75.76 156	98.61 131	91.99 104	96.79 96	97.75 113

PVSNet_Blended_VisFu		91.24 107	90.77 106	92.66 110	95.09 146	82.40 123	97.77 78	95.87 167	88.26 83	86.39 171	93.94 206	76.77 137	99.27 88	88.80 149	94.00 139	96.31 193

APD-MVS_3200maxsize		91.23 108	91.35 93	90.89 187	97.89 62	76.35 288	96.30 198	95.52 185	79.82 280	91.03 109	97.88 70	74.70 180	98.54 136	92.11 102	96.89 92	97.77 112

diffmvs		91.17 109	90.74 107	92.44 120	93.11 218	82.50 121	96.25 201	93.62 302	87.79 97	90.40 118	95.93 143	73.44 199	97.42 201	93.62 78	92.55 161	97.41 142
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvg		91.13 110	90.45 114	93.17 89	92.99 222	83.58 101	97.46 105	94.56 244	87.69 100	87.19 164	94.98 182	74.50 185	97.60 186	91.88 107	92.79 158	98.34 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		91.09 111	90.49 113	92.87 100	95.82 121	85.04 73	96.51 182	97.28 20	86.05 135	89.13 135	95.34 162	80.16 82	96.62 247	85.82 174	88.31 204	96.96 166

test_fmvsmconf0.01_n		91.08 112	90.68 108	92.29 129	82.43 386	80.12 187	97.94 67	93.93 280	92.07 28	91.97 92	97.60 86	67.56 246	99.53 72	97.09 34	95.56 121	97.21 156

CHOSEN 1792x2688		91.07 113	90.21 121	93.64 68	95.18 144	83.53 102	96.26 200	96.13 141	88.92 68	84.90 187	93.10 224	72.86 203	99.62 62	88.86 147	95.67 119	97.79 111

ETVMVS		90.99 114	90.26 118	93.19 88	95.81 122	85.64 55	96.97 150	97.18 26	85.43 147	88.77 144	94.86 184	82.00 66	96.37 254	82.70 209	88.60 197	97.57 128

CANet_DTU		90.98 115	90.04 126	93.83 54	94.76 158	86.23 38	96.32 197	93.12 326	93.11 20	93.71 64	96.82 126	63.08 278	99.48 77	84.29 186	95.12 124	95.77 205

test2506		90.96 116	90.39 115	92.65 111	93.54 200	82.46 122	96.37 192	97.35 18	86.78 125	87.55 158	95.25 163	77.83 117	97.50 197	84.07 188	94.80 126	97.98 96

thisisatest0515		90.95 117	90.26 118	93.01 95	94.03 191	84.27 89	97.91 68	96.67 79	83.18 213	86.87 168	95.51 157	88.66 15	97.85 175	80.46 222	89.01 192	96.92 170

casdiffmvs		90.95 117	90.39 115	92.63 113	92.82 226	82.53 119	96.83 161	94.47 250	87.69 100	88.47 147	95.56 156	74.04 191	97.54 193	90.90 116	92.74 159	97.83 107
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sss		90.87 119	89.96 129	93.60 71	94.15 182	83.84 95	97.14 133	98.13 7	85.93 139	89.68 125	96.09 141	71.67 219	99.30 87	87.69 162	89.16 189	97.66 121

baseline		90.76 120	90.10 124	92.74 106	92.90 225	82.56 118	94.60 271	94.56 244	87.69 100	89.06 138	95.67 151	73.76 194	97.51 196	90.43 128	92.23 167	98.16 80

Effi-MVS+		90.70 121	89.90 132	93.09 92	93.61 197	83.48 103	95.20 253	92.79 332	83.22 212	91.82 95	95.70 149	71.82 218	97.48 199	91.25 111	93.67 147	98.32 67

MAR-MVS		90.63 122	90.22 120	91.86 151	98.47 42	78.20 243	97.18 126	96.61 88	83.87 197	88.18 153	98.18 44	68.71 241	99.75 39	83.66 198	97.15 85	97.63 124
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
MVS		90.60 123	88.64 149	96.50 5	94.25 178	90.53 8	93.33 306	97.21 23	77.59 313	78.88 258	97.31 99	71.52 222	99.69 53	89.60 139	98.03 56	99.27 22

xiu_mvs_v1_base_debu		90.54 124	89.54 135	93.55 74	92.31 239	87.58 26	96.99 145	94.87 221	87.23 113	93.27 68	97.56 88	57.43 320	98.32 150	92.72 93	93.46 151	94.74 231

xiu_mvs_v1_base		90.54 124	89.54 135	93.55 74	92.31 239	87.58 26	96.99 145	94.87 221	87.23 113	93.27 68	97.56 88	57.43 320	98.32 150	92.72 93	93.46 151	94.74 231

xiu_mvs_v1_base_debi		90.54 124	89.54 135	93.55 74	92.31 239	87.58 26	96.99 145	94.87 221	87.23 113	93.27 68	97.56 88	57.43 320	98.32 150	92.72 93	93.46 151	94.74 231

mvsmamba		90.53 127	90.08 125	91.88 150	94.81 156	80.93 160	93.94 291	94.45 252	88.24 85	87.02 167	92.35 233	68.04 243	95.80 280	94.86 61	97.03 88	98.92 34

baseline2		90.39 128	90.21 121	90.93 184	90.86 286	80.99 157	95.20 253	97.41 17	86.03 137	80.07 248	94.61 190	90.58 6	97.47 200	87.29 166	89.86 184	94.35 239

ACMMP		90.39 128	89.97 128	91.64 162	97.58 74	78.21 242	96.78 166	96.72 73	84.73 167	84.72 191	97.23 106	71.22 224	99.63 61	88.37 156	92.41 164	97.08 163
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
HPM-MVS_fast		90.38 130	90.17 123	91.03 182	97.61 71	77.35 270	97.15 132	95.48 188	79.51 286	88.79 142	96.90 120	71.64 221	98.81 126	87.01 170	97.44 74	96.94 167

MVS_Test		90.29 131	89.18 139	93.62 70	95.23 141	84.93 77	94.41 274	94.66 235	84.31 179	90.37 119	91.02 255	75.13 174	97.82 176	83.11 206	94.42 133	98.12 85

API-MVS		90.18 132	88.97 142	93.80 55	98.66 28	82.95 113	97.50 102	95.63 179	75.16 335	86.31 172	97.69 77	72.49 208	99.90 5	81.26 218	96.07 110	98.56 54

PVSNet_BlendedMVS		90.05 133	89.96 129	90.33 203	97.47 77	83.86 93	98.02 63	96.73 71	87.98 91	89.53 129	89.61 276	76.42 143	99.57 68	94.29 69	79.59 275	87.57 344

ET-MVSNet_ETH3D		90.01 134	89.03 140	92.95 97	94.38 175	86.77 33	98.14 51	96.31 127	89.30 65	63.33 378	96.72 131	90.09 10	93.63 355	90.70 122	82.29 262	98.46 59

test_vis1_n_1920		89.95 135	90.59 109	88.03 257	92.36 238	68.98 360	99.12 13	94.34 260	93.86 14	93.64 66	97.01 118	51.54 351	99.59 64	96.76 39	96.71 100	95.53 212

test_cas_vis1_n_1920		89.90 136	90.02 127	89.54 225	90.14 301	74.63 308	98.71 32	94.43 255	93.04 21	92.40 84	96.35 136	53.41 347	99.08 110	95.59 51	96.16 107	94.90 225

TESTMET0.1,1		89.83 137	89.34 138	91.31 172	92.54 236	80.19 185	97.11 136	96.57 95	86.15 131	86.85 169	91.83 246	79.32 90	96.95 229	81.30 217	92.35 165	96.77 176

EPP-MVSNet		89.76 138	89.72 134	89.87 218	93.78 193	76.02 295	97.22 121	96.51 101	79.35 288	85.11 183	95.01 180	84.82 37	97.10 222	87.46 165	88.21 206	96.50 185

CPTT-MVS		89.72 139	89.87 133	89.29 228	98.33 47	73.30 319	97.70 84	95.35 200	75.68 331	87.40 159	97.44 95	70.43 233	98.25 153	89.56 141	96.90 91	96.33 192

RRT-MVS		89.67 140	88.67 148	92.67 109	94.44 172	81.08 154	94.34 278	94.45 252	86.05 135	85.79 177	92.39 232	63.39 276	98.16 158	93.22 86	93.95 141	98.76 41

thisisatest0530		89.65 141	89.02 141	91.53 167	93.46 206	80.78 165	96.52 180	96.67 79	81.69 245	83.79 203	94.90 183	88.85 14	97.68 182	77.80 246	87.49 215	96.14 196

3Dnovator+	82.88 8	89.63 142	87.85 162	94.99 23	94.49 171	86.76 34	97.84 72	95.74 173	86.10 133	75.47 304	96.02 142	65.00 268	99.51 75	82.91 208	97.07 87	98.72 47

CDS-MVSNet		89.50 143	88.96 143	91.14 180	91.94 264	80.93 160	97.09 140	95.81 169	84.26 184	84.72 191	94.20 200	80.31 77	95.64 293	83.37 203	88.96 193	96.85 173
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 144	89.92 131	88.06 255	94.64 159	69.57 357	96.22 202	94.95 216	87.27 112	91.37 102	96.54 134	65.88 260	97.39 204	88.54 151	93.89 142	97.23 153

HyFIR lowres test		89.36 145	88.60 150	91.63 164	94.91 154	80.76 166	95.60 237	95.53 183	82.56 230	84.03 197	91.24 252	78.03 112	96.81 239	87.07 169	88.41 203	97.32 148

3Dnovator	82.32 10	89.33 146	87.64 167	94.42 37	93.73 196	85.70 49	97.73 82	96.75 69	86.73 128	76.21 293	95.93 143	62.17 282	99.68 55	81.67 216	97.81 63	97.88 101

h-mvs33		89.30 147	88.95 144	90.36 202	95.07 148	76.04 292	96.96 152	97.11 31	90.39 52	92.22 88	95.10 176	74.70 180	98.86 123	93.14 87	65.89 369	96.16 195

LFMVS		89.27 148	87.64 167	94.16 48	97.16 92	85.52 58	97.18 126	94.66 235	79.17 294	89.63 127	96.57 133	55.35 337	98.22 154	89.52 142	89.54 185	98.74 42

MVSTER		89.25 149	88.92 145	90.24 205	95.98 115	84.66 81	96.79 165	95.36 198	87.19 116	80.33 243	90.61 262	90.02 11	95.97 269	85.38 179	78.64 284	90.09 285

CostFormer		89.08 150	88.39 154	91.15 179	93.13 216	79.15 214	88.61 358	96.11 143	83.14 214	89.58 128	86.93 316	83.83 53	96.87 235	88.22 157	85.92 229	97.42 141

PVSNet	82.34 9	89.02 151	87.79 164	92.71 108	95.49 133	81.50 147	97.70 84	97.29 19	87.76 98	85.47 181	95.12 175	56.90 326	98.90 122	80.33 223	94.02 137	97.71 117

test-mter		88.95 152	88.60 150	89.98 213	92.26 245	77.23 272	97.11 136	95.96 155	85.32 150	86.30 173	91.38 249	76.37 145	96.78 241	80.82 219	91.92 169	95.94 200

1314		88.94 153	87.20 181	94.17 46	93.21 211	85.73 48	93.33 306	96.64 85	82.89 221	75.98 296	96.36 135	66.83 255	99.39 81	83.52 202	96.02 113	97.39 145

UA-Net		88.92 154	88.48 153	90.24 205	94.06 188	77.18 274	93.04 314	94.66 235	87.39 108	91.09 107	93.89 207	74.92 177	98.18 157	75.83 273	91.43 173	95.35 217

thres200		88.92 154	87.65 166	92.73 107	96.30 103	85.62 56	97.85 71	98.86 1	84.38 178	84.82 188	93.99 205	75.12 175	98.01 164	70.86 314	86.67 219	94.56 237

Vis-MVSNet (Re-imp)		88.88 156	88.87 147	88.91 235	93.89 192	74.43 311	96.93 155	94.19 269	84.39 177	83.22 209	95.67 151	78.24 108	94.70 332	78.88 241	94.40 134	97.61 126

baseline1		88.85 157	87.49 174	92.93 99	95.21 143	86.85 32	95.47 242	94.61 241	87.29 110	83.11 211	94.99 181	80.70 73	96.89 233	82.28 212	73.72 308	95.05 223

AdaColmap		88.81 158	87.61 170	92.39 123	99.33 4	79.95 190	96.70 173	95.58 180	77.51 314	83.05 212	96.69 132	61.90 288	99.72 47	84.29 186	93.47 150	97.50 135

OMC-MVS		88.80 159	88.16 158	90.72 192	95.30 139	77.92 252	94.81 268	94.51 246	86.80 124	84.97 186	96.85 123	67.53 247	98.60 132	85.08 180	87.62 212	95.63 208

114514_t		88.79 160	87.57 172	92.45 118	98.21 53	81.74 140	96.99 145	95.45 191	75.16 335	82.48 215	95.69 150	68.59 242	98.50 138	80.33 223	95.18 123	97.10 162

mvs_anonymous		88.68 161	87.62 169	91.86 151	94.80 157	81.69 143	93.53 302	94.92 218	82.03 240	78.87 259	90.43 265	75.77 155	95.34 306	85.04 181	93.16 155	98.55 56

Vis-MVSNet		88.67 162	87.82 163	91.24 176	92.68 229	78.82 221	96.95 153	93.85 288	87.55 103	87.07 166	95.13 174	63.43 275	97.21 214	77.58 253	96.15 108	97.70 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 162	88.16 158	90.20 207	93.61 197	76.86 279	96.77 168	93.07 327	84.02 190	83.62 205	95.60 154	74.69 183	96.24 261	78.43 245	93.66 148	97.49 136

IB-MVS	85.34 4	88.67 162	87.14 184	93.26 84	93.12 217	84.32 86	98.76 30	97.27 21	87.19 116	79.36 254	90.45 264	83.92 52	98.53 137	84.41 185	69.79 336	96.93 168
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
1112_ss		88.60 165	87.47 176	92.00 146	93.21 211	80.97 158	96.47 184	92.46 335	83.64 207	80.86 236	97.30 102	80.24 79	97.62 185	77.60 252	85.49 234	97.40 144

tttt0517		88.57 166	88.19 157	89.71 224	93.00 219	75.99 296	95.67 232	96.67 79	80.78 256	81.82 228	94.40 194	88.97 13	97.58 188	76.05 271	86.31 223	95.57 210

UWE-MVS		88.56 167	88.91 146	87.50 271	94.17 181	72.19 331	95.82 227	97.05 36	84.96 162	84.78 189	93.51 218	81.33 68	94.75 330	79.43 234	89.17 188	95.57 210

tfpn200view9		88.48 168	87.15 182	92.47 117	96.21 106	85.30 64	97.44 106	98.85 2	83.37 210	83.99 198	93.82 210	75.36 168	97.93 167	69.04 322	86.24 226	94.17 240

test-LLR		88.48 168	87.98 160	89.98 213	92.26 245	77.23 272	97.11 136	95.96 155	83.76 202	86.30 173	91.38 249	72.30 212	96.78 241	80.82 219	91.92 169	95.94 200

TAMVS		88.48 168	87.79 164	90.56 196	91.09 280	79.18 212	96.45 186	95.88 165	83.64 207	83.12 210	93.33 219	75.94 153	95.74 288	82.40 211	88.27 205	96.75 179

thres400		88.42 171	87.15 182	92.23 132	96.21 106	85.30 64	97.44 106	98.85 2	83.37 210	83.99 198	93.82 210	75.36 168	97.93 167	69.04 322	86.24 226	93.45 256

tpmrst		88.36 172	87.38 178	91.31 172	94.36 176	79.92 191	87.32 370	95.26 205	85.32 150	88.34 150	86.13 333	80.60 75	96.70 243	83.78 192	85.34 237	97.30 151

ECVR-MVS		88.35 173	87.25 180	91.65 161	93.54 200	79.40 206	96.56 179	90.78 364	86.78 125	85.57 179	95.25 163	57.25 324	97.56 189	84.73 184	94.80 126	97.98 96

thres100view900		88.30 174	86.95 188	92.33 126	96.10 111	84.90 78	97.14 133	98.85 2	82.69 227	83.41 206	93.66 214	75.43 165	97.93 167	69.04 322	86.24 226	94.17 240

VDD-MVS		88.28 175	87.02 187	92.06 142	95.09 146	80.18 186	97.55 97	94.45 252	83.09 215	89.10 137	95.92 145	47.97 366	98.49 139	93.08 91	86.91 218	97.52 134

BH-w/o		88.24 176	87.47 176	90.54 198	95.03 151	78.54 228	97.41 111	93.82 289	84.08 188	78.23 265	94.51 193	69.34 239	97.21 214	80.21 227	94.58 130	95.87 202

hse-mvs2		88.22 177	88.21 156	88.25 251	93.54 200	73.41 316	95.41 245	95.89 163	90.39 52	92.22 88	94.22 198	74.70 180	96.66 246	93.14 87	64.37 374	94.69 236

test1111		88.11 178	87.04 186	91.35 171	93.15 214	78.79 224	96.57 177	90.78 364	86.88 121	85.04 184	95.20 169	57.23 325	97.39 204	83.88 190	94.59 129	97.87 103

thres600view7		88.06 179	86.70 194	92.15 139	96.10 111	85.17 70	97.14 133	98.85 2	82.70 226	83.41 206	93.66 214	75.43 165	97.82 176	67.13 331	85.88 230	93.45 256

Test_1112_low_res		88.03 180	86.73 192	91.94 148	93.15 214	80.88 162	96.44 187	92.41 337	83.59 209	80.74 238	91.16 253	80.18 80	97.59 187	77.48 255	85.40 235	97.36 147

PLC	83.97 7	88.00 181	87.38 178	89.83 220	98.02 59	76.46 285	97.16 130	94.43 255	79.26 293	81.98 225	96.28 137	69.36 238	99.27 88	77.71 250	92.25 166	93.77 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 182	87.48 175	89.44 226	92.16 252	80.54 175	98.14 51	94.92 218	91.41 36	79.43 253	95.40 160	62.34 281	97.27 212	90.60 123	82.90 254	90.50 275
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Fast-Effi-MVS+		87.93 183	86.94 189	90.92 185	94.04 189	79.16 213	98.26 47	93.72 298	81.29 248	83.94 201	92.90 225	69.83 237	96.68 244	76.70 263	91.74 171	96.93 168

HQP-MVS		87.91 184	87.55 173	88.98 234	92.08 256	78.48 229	97.63 87	94.80 226	90.52 49	82.30 218	94.56 191	65.40 264	97.32 207	87.67 163	83.01 251	91.13 268

reproduce_monomvs		87.80 185	87.60 171	88.40 245	96.56 98	80.26 182	95.80 228	96.32 126	91.56 35	73.60 315	88.36 292	88.53 16	96.25 260	90.47 125	67.23 362	88.67 319

test_fmvs1		87.79 186	88.52 152	85.62 307	92.98 223	64.31 380	97.88 70	92.42 336	87.95 92	92.24 87	95.82 146	47.94 367	98.44 146	95.31 57	94.09 135	94.09 244

WBMVS		87.73 187	86.79 190	90.56 196	95.61 129	85.68 51	97.63 87	95.52 185	83.77 201	78.30 264	88.44 291	86.14 32	95.78 282	82.54 210	73.15 314	90.21 280

UGNet		87.73 187	86.55 195	91.27 175	95.16 145	79.11 215	96.35 194	96.23 133	88.14 87	87.83 157	90.48 263	50.65 354	99.09 109	80.13 228	94.03 136	95.60 209
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
FA-MVS(test-final)		87.71 189	86.23 198	92.17 137	94.19 180	80.55 172	87.16 372	96.07 147	82.12 238	85.98 176	88.35 293	72.04 216	98.49 139	80.26 225	89.87 183	97.48 137

EPNet_dtu		87.65 190	87.89 161	86.93 284	94.57 161	71.37 345	96.72 169	96.50 103	88.56 75	87.12 165	95.02 179	75.91 154	94.01 347	66.62 335	90.00 182	95.42 215
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 191	88.22 155	85.67 305	89.78 305	67.18 367	95.25 250	87.93 385	83.96 193	88.79 142	97.06 116	72.52 207	94.53 337	92.21 100	86.45 222	95.30 219

HQP_MVS		87.50 192	87.09 185	88.74 239	91.86 265	77.96 249	97.18 126	94.69 231	89.89 58	81.33 231	94.15 201	64.77 269	97.30 209	87.08 167	82.82 255	90.96 270

EPMVS		87.47 193	85.90 201	92.18 136	95.41 135	82.26 126	87.00 373	96.28 128	85.88 140	84.23 195	85.57 340	75.07 176	96.26 258	71.14 312	92.50 162	98.03 88

tpm2		87.35 194	86.26 197	90.62 194	92.93 224	78.67 226	88.06 365	95.99 152	79.33 289	87.40 159	86.43 327	80.28 78	96.40 252	80.23 226	85.73 233	96.79 174

ab-mvs		87.08 195	84.94 217	93.48 79	93.34 209	83.67 99	88.82 355	95.70 175	81.18 249	84.55 194	90.14 271	62.72 279	98.94 120	85.49 178	82.54 259	97.85 105

SDMVSNet		87.02 196	85.61 203	91.24 176	94.14 183	83.30 107	93.88 293	95.98 153	84.30 181	79.63 251	92.01 238	58.23 310	97.68 182	90.28 133	82.02 263	92.75 259

CNLPA		86.96 197	85.37 208	91.72 160	97.59 73	79.34 209	97.21 122	91.05 359	74.22 342	78.90 257	96.75 130	67.21 251	98.95 118	74.68 283	90.77 178	96.88 172

BH-untuned		86.95 198	85.94 200	89.99 212	94.52 165	77.46 267	96.78 166	93.37 315	81.80 242	76.62 283	93.81 212	66.64 256	97.02 224	76.06 270	93.88 143	95.48 214

QAPM		86.88 199	84.51 221	93.98 49	94.04 189	85.89 45	97.19 125	96.05 148	73.62 347	75.12 307	95.62 153	62.02 285	99.74 42	70.88 313	96.06 111	96.30 194

BH-RMVSNet		86.84 200	85.28 209	91.49 169	95.35 138	80.26 182	96.95 153	92.21 339	82.86 223	81.77 230	95.46 159	59.34 302	97.64 184	69.79 320	93.81 144	96.57 184

PatchmatchNet		86.83 201	85.12 214	91.95 147	94.12 185	82.27 125	86.55 377	95.64 178	84.59 172	82.98 213	84.99 352	77.26 126	95.96 272	68.61 325	91.34 174	97.64 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 202	85.43 206	90.87 188	88.76 321	85.34 61	97.06 143	94.33 261	84.31 179	80.45 241	91.98 241	72.36 209	96.36 255	88.48 154	71.13 323	90.93 272

PCF-MVS	84.09 5	86.77 203	85.00 216	92.08 140	92.06 259	83.07 111	92.14 327	94.47 250	79.63 284	76.90 279	94.78 186	71.15 225	99.20 99	72.87 298	91.05 176	93.98 246
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 204	86.10 199	88.61 241	90.05 302	80.21 184	96.14 209	96.95 45	85.56 146	78.37 263	92.30 234	76.73 138	95.28 310	79.51 232	79.27 278	90.35 277

cascas		86.50 205	84.48 223	92.55 116	92.64 233	85.95 42	97.04 144	95.07 212	75.32 333	80.50 239	91.02 255	54.33 344	97.98 166	86.79 171	87.62 212	93.71 251

VDDNet		86.44 206	84.51 221	92.22 133	91.56 268	81.83 136	97.10 139	94.64 238	69.50 374	87.84 156	95.19 170	48.01 365	97.92 172	89.82 136	86.92 217	96.89 171

GeoE		86.36 207	85.20 210	89.83 220	93.17 213	76.13 290	97.53 98	92.11 340	79.58 285	80.99 234	94.01 204	66.60 257	96.17 264	73.48 295	89.30 187	97.20 158

test_fmvs1_n		86.34 208	86.72 193	85.17 314	87.54 339	63.64 385	96.91 157	92.37 338	87.49 105	91.33 103	95.58 155	40.81 394	98.46 142	95.00 60	93.49 149	93.41 258

TR-MVS		86.30 209	84.93 218	90.42 200	94.63 160	77.58 265	96.57 177	93.82 289	80.30 270	82.42 217	95.16 172	58.74 306	97.55 191	74.88 281	87.82 210	96.13 197

X-MVStestdata		86.26 210	84.14 231	92.63 113	98.52 37	80.29 179	97.37 114	96.44 109	87.04 118	91.38 100	20.73 430	77.24 128	99.59 64	90.46 126	98.07 54	98.02 89

AUN-MVS		86.25 211	85.57 204	88.26 250	93.57 199	73.38 317	95.45 243	95.88 165	83.94 194	85.47 181	94.21 199	73.70 197	96.67 245	83.54 200	64.41 373	94.73 235

OpenMVS	79.58 14	86.09 212	83.62 238	93.50 77	90.95 282	86.71 35	97.44 106	95.83 168	75.35 332	72.64 329	95.72 148	57.42 323	99.64 59	71.41 307	95.85 117	94.13 243

FE-MVS		86.06 213	84.15 230	91.78 155	94.33 177	79.81 193	84.58 390	96.61 88	76.69 325	85.00 185	87.38 307	70.71 232	98.37 148	70.39 317	91.70 172	97.17 159

FC-MVSNet-test		85.96 214	85.39 207	87.66 264	89.38 318	78.02 246	95.65 234	96.87 52	85.12 157	77.34 272	91.94 244	76.28 147	94.74 331	77.09 258	78.82 282	90.21 280

miper_enhance_ethall		85.95 215	85.20 210	88.19 254	94.85 155	79.76 195	96.00 214	94.06 277	82.98 220	77.74 270	88.76 284	79.42 89	95.46 302	80.58 221	72.42 316	89.36 299

OPM-MVS		85.84 216	85.10 215	88.06 255	88.34 329	77.83 256	95.72 230	94.20 268	87.89 96	80.45 241	94.05 203	58.57 307	97.26 213	83.88 190	82.76 257	89.09 306
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		85.80 217	85.20 210	87.59 267	91.55 269	77.41 268	95.13 257	95.36 198	80.43 267	80.33 243	94.71 187	73.72 195	95.97 269	76.96 261	78.64 284	89.39 293

GA-MVS		85.79 218	84.04 232	91.02 183	89.47 316	80.27 181	96.90 158	94.84 224	85.57 144	80.88 235	89.08 279	56.56 330	96.47 251	77.72 249	85.35 236	96.34 190

XVG-OURS-SEG-HR		85.74 219	85.16 213	87.49 273	90.22 297	71.45 343	91.29 338	94.09 275	81.37 247	83.90 202	95.22 167	60.30 295	97.53 195	85.58 177	84.42 242	93.50 254

MonoMVSNet		85.68 220	84.22 228	90.03 210	88.43 328	77.83 256	92.95 317	91.46 350	87.28 111	78.11 266	85.96 335	66.31 259	94.81 329	90.71 121	76.81 295	97.46 139

SCA		85.63 221	83.64 237	91.60 165	92.30 242	81.86 135	92.88 318	95.56 182	84.85 163	82.52 214	85.12 350	58.04 313	95.39 303	73.89 291	87.58 214	97.54 129

test_vis1_n		85.60 222	85.70 202	85.33 311	84.79 370	64.98 378	96.83 161	91.61 349	87.36 109	91.00 110	94.84 185	36.14 401	97.18 216	95.66 49	93.03 156	93.82 249

tpm		85.55 223	84.47 224	88.80 238	90.19 298	75.39 303	88.79 356	94.69 231	84.83 164	83.96 200	85.21 346	78.22 109	94.68 334	76.32 269	78.02 292	96.34 190

mamv4		85.50 224	86.76 191	81.72 354	93.23 210	54.93 411	89.95 348	92.94 329	69.96 371	79.00 256	92.20 236	80.69 74	94.22 343	92.06 103	90.77 178	96.01 198

UniMVSNet_NR-MVSNet		85.49 225	84.59 220	88.21 253	89.44 317	79.36 207	96.71 171	96.41 113	85.22 153	78.11 266	90.98 257	76.97 133	95.14 317	79.14 238	68.30 350	90.12 283

gg-mvs-nofinetune		85.48 226	82.90 249	93.24 85	94.51 169	85.82 46	79.22 403	96.97 43	61.19 400	87.33 161	53.01 419	90.58 6	96.07 265	86.07 173	97.23 81	97.81 110

UWE-MVS-28		85.41 227	86.36 196	82.59 347	91.12 279	66.81 372	93.88 293	97.03 37	83.86 198	78.55 260	93.84 209	77.76 119	88.55 393	73.47 296	87.69 211	92.41 263

VPA-MVSNet		85.32 228	83.83 233	89.77 223	90.25 296	82.63 117	96.36 193	97.07 34	83.03 218	81.21 233	89.02 281	61.58 289	96.31 257	85.02 182	70.95 325	90.36 276

UniMVSNet (Re)		85.31 229	84.23 227	88.55 242	89.75 307	80.55 172	96.72 169	96.89 50	85.42 148	78.40 262	88.93 282	75.38 167	95.52 300	78.58 243	68.02 353	89.57 292

XVG-OURS		85.18 230	84.38 225	87.59 267	90.42 295	71.73 340	91.06 341	94.07 276	82.00 241	83.29 208	95.08 177	56.42 331	97.55 191	83.70 197	83.42 247	93.49 255

cl22		85.11 231	84.17 229	87.92 258	95.06 150	78.82 221	95.51 240	94.22 267	79.74 282	76.77 280	87.92 300	75.96 151	95.68 289	79.93 230	72.42 316	89.27 301

TAPA-MVS	81.61 12	85.02 232	83.67 235	89.06 231	96.79 96	73.27 322	95.92 219	94.79 228	74.81 338	80.47 240	96.83 124	71.07 226	98.19 156	49.82 399	92.57 160	95.71 207
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 233	83.66 236	89.02 233	95.86 120	74.55 310	92.49 322	93.60 303	79.30 291	79.29 255	91.47 247	58.53 308	98.45 144	70.22 318	92.17 168	94.07 245

PS-MVSNAJss		84.91 234	84.30 226	86.74 285	85.89 358	74.40 312	94.95 264	94.16 271	83.93 195	76.45 286	90.11 272	71.04 227	95.77 283	83.16 205	79.02 281	90.06 287

CVMVSNet		84.83 235	85.57 204	82.63 346	91.55 269	60.38 397	95.13 257	95.03 214	80.60 260	82.10 224	94.71 187	66.40 258	90.19 388	74.30 288	90.32 181	97.31 150

FMVSNet3		84.71 236	82.71 253	90.70 193	94.55 163	87.71 23	95.92 219	94.67 234	81.73 244	75.82 299	88.08 298	66.99 252	94.47 338	71.23 309	75.38 301	89.91 289

VPNet		84.69 237	82.92 248	90.01 211	89.01 320	83.45 104	96.71 171	95.46 190	85.71 142	79.65 250	92.18 237	56.66 329	96.01 268	83.05 207	67.84 356	90.56 274

sd_testset		84.62 238	83.11 246	89.17 229	94.14 183	77.78 258	91.54 337	94.38 258	84.30 181	79.63 251	92.01 238	52.28 349	96.98 227	77.67 251	82.02 263	92.75 259

Effi-MVS+-dtu		84.61 239	84.90 219	83.72 336	91.96 262	63.14 388	94.95 264	93.34 316	85.57 144	79.79 249	87.12 313	61.99 286	95.61 296	83.55 199	85.83 231	92.41 263

miper_ehance_all_eth		84.57 240	83.60 239	87.50 271	92.64 233	78.25 238	95.40 246	93.47 307	79.28 292	76.41 287	87.64 304	76.53 140	95.24 312	78.58 243	72.42 316	89.01 311

DU-MVS		84.57 240	83.33 244	88.28 249	88.76 321	79.36 207	96.43 189	95.41 197	85.42 148	78.11 266	90.82 258	67.61 244	95.14 317	79.14 238	68.30 350	90.33 278

F-COLMAP		84.50 242	83.44 243	87.67 263	95.22 142	72.22 329	95.95 217	93.78 294	75.74 330	76.30 290	95.18 171	59.50 300	98.45 144	72.67 300	86.59 221	92.35 265

Anonymous202405211		84.41 243	81.93 264	91.85 153	96.78 97	78.41 233	97.44 106	91.34 354	70.29 369	84.06 196	94.26 197	41.09 391	98.96 116	79.46 233	82.65 258	98.17 79

WR-MVS		84.32 244	82.96 247	88.41 244	89.38 318	80.32 178	96.59 176	96.25 131	83.97 192	76.63 282	90.36 266	67.53 247	94.86 327	75.82 274	70.09 334	90.06 287

dp		84.30 245	82.31 258	90.28 204	94.24 179	77.97 248	86.57 376	95.53 183	79.94 279	80.75 237	85.16 348	71.49 223	96.39 253	63.73 350	83.36 248	96.48 186

LPG-MVS_test		84.20 246	83.49 242	86.33 291	90.88 283	73.06 323	95.28 247	94.13 272	82.20 235	76.31 288	93.20 220	54.83 342	96.95 229	83.72 195	80.83 268	88.98 312

dmvs_re		84.10 247	82.90 249	87.70 262	91.41 273	73.28 320	90.59 344	93.19 320	85.02 159	77.96 269	93.68 213	57.92 318	96.18 263	75.50 276	80.87 267	93.63 252

WB-MVSnew		84.08 248	83.51 241	85.80 300	91.34 274	76.69 283	95.62 236	96.27 129	81.77 243	81.81 229	92.81 226	58.23 310	94.70 332	66.66 334	87.06 216	85.99 368

ACMP	81.66 11	84.00 249	83.22 245	86.33 291	91.53 271	72.95 327	95.91 221	93.79 293	83.70 205	73.79 314	92.22 235	54.31 345	96.89 233	83.98 189	79.74 273	89.16 304
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 250	82.80 252	87.31 277	91.46 272	77.39 269	95.66 233	93.43 310	80.44 265	75.51 303	87.26 310	73.72 195	95.16 316	76.99 259	70.72 327	89.39 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 251	82.00 263	89.35 227	87.13 341	81.38 148	95.72 230	94.26 264	80.15 274	75.92 298	90.63 261	61.96 287	96.52 249	78.98 240	73.28 313	90.14 282

c3_l		83.80 252	82.65 254	87.25 279	92.10 255	77.74 263	95.25 250	93.04 328	78.58 303	76.01 295	87.21 312	75.25 173	95.11 319	77.54 254	68.89 344	88.91 317

LCM-MVSNet-Re		83.75 253	83.54 240	84.39 329	93.54 200	64.14 382	92.51 321	84.03 405	83.90 196	66.14 366	86.59 321	67.36 249	92.68 362	84.89 183	92.87 157	96.35 189

ACMM	80.70 13	83.72 254	82.85 251	86.31 294	91.19 276	72.12 333	95.88 222	94.29 263	80.44 265	77.02 277	91.96 242	55.24 338	97.14 221	79.30 236	80.38 270	89.67 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 255	81.38 272	90.39 201	93.53 205	78.19 244	85.56 384	95.09 210	70.78 367	78.51 261	83.28 367	74.80 179	97.03 223	66.77 333	84.05 243	95.95 199

CR-MVSNet		83.53 256	81.36 273	90.06 209	90.16 299	79.75 196	79.02 405	91.12 356	84.24 185	82.27 222	80.35 382	75.45 163	93.67 354	63.37 353	86.25 224	96.75 179

v2v482		83.46 257	81.86 265	88.25 251	86.19 352	79.65 201	96.34 195	94.02 278	81.56 246	77.32 273	88.23 295	65.62 261	96.03 266	77.77 247	69.72 338	89.09 306

NR-MVSNet		83.35 258	81.52 271	88.84 236	88.76 321	81.31 150	94.45 273	95.16 208	84.65 170	67.81 355	90.82 258	70.36 234	94.87 326	74.75 282	66.89 366	90.33 278

Fast-Effi-MVS+-dtu		83.33 259	82.60 255	85.50 309	89.55 314	69.38 358	96.09 212	91.38 351	82.30 234	75.96 297	91.41 248	56.71 327	95.58 298	75.13 280	84.90 239	91.54 266

cl____		83.27 260	82.12 260	86.74 285	92.20 248	75.95 297	95.11 259	93.27 318	78.44 306	74.82 309	87.02 315	74.19 188	95.19 314	74.67 284	69.32 340	89.09 306

DIV-MVS_self_test		83.27 260	82.12 260	86.74 285	92.19 249	75.92 299	95.11 259	93.26 319	78.44 306	74.81 310	87.08 314	74.19 188	95.19 314	74.66 285	69.30 341	89.11 305

TranMVSNet+NR-MVSNet		83.24 262	81.71 267	87.83 259	87.71 336	78.81 223	96.13 211	94.82 225	84.52 173	76.18 294	90.78 260	64.07 272	94.60 335	74.60 286	66.59 368	90.09 285

Anonymous20240529		83.15 263	80.60 283	90.80 189	95.74 125	78.27 237	96.81 164	94.92 218	60.10 405	81.89 227	92.54 230	45.82 375	98.82 125	79.25 237	78.32 290	95.31 218

eth_miper_zixun_eth		83.12 264	82.01 262	86.47 290	91.85 267	74.80 306	94.33 279	93.18 322	79.11 295	75.74 302	87.25 311	72.71 204	95.32 308	76.78 262	67.13 363	89.27 301

MS-PatchMatch		83.05 265	81.82 266	86.72 289	89.64 311	79.10 216	94.88 266	94.59 243	79.70 283	70.67 343	89.65 275	50.43 356	96.82 238	70.82 316	95.99 115	84.25 381

V42		83.04 266	81.53 270	87.57 269	86.27 351	79.09 217	95.87 223	94.11 274	80.35 269	77.22 275	86.79 319	65.32 266	96.02 267	77.74 248	70.14 330	87.61 343

tpmvs		83.04 266	80.77 279	89.84 219	95.43 134	77.96 249	85.59 383	95.32 202	75.31 334	76.27 291	83.70 363	73.89 192	97.41 202	59.53 365	81.93 265	94.14 242

test_djsdf		83.00 268	82.45 257	84.64 322	84.07 378	69.78 354	94.80 269	94.48 247	80.74 257	75.41 305	87.70 303	61.32 292	95.10 320	83.77 193	79.76 271	89.04 309

v1144		82.90 269	81.27 274	87.78 261	86.29 350	79.07 218	96.14 209	93.93 280	80.05 276	77.38 271	86.80 318	65.50 262	95.93 274	75.21 279	70.13 331	88.33 330

test0.0.03 1		82.79 270	82.48 256	83.74 335	86.81 344	72.22 329	96.52 180	95.03 214	83.76 202	73.00 325	93.20 220	72.30 212	88.88 391	64.15 348	77.52 293	90.12 283

FMVSNet2		82.79 270	80.44 285	89.83 220	92.66 230	85.43 59	95.42 244	94.35 259	79.06 297	74.46 311	87.28 308	56.38 332	94.31 341	69.72 321	74.68 305	89.76 290

D2MVS		82.67 272	81.55 269	86.04 298	87.77 335	76.47 284	95.21 252	96.58 94	82.66 228	70.26 346	85.46 343	60.39 294	95.80 280	76.40 267	79.18 279	85.83 371

MVP-Stereo		82.65 273	81.67 268	85.59 308	86.10 355	78.29 236	93.33 306	92.82 331	77.75 311	69.17 353	87.98 299	59.28 303	95.76 284	71.77 304	96.88 93	82.73 389
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 274	80.79 278	87.79 260	86.11 354	80.49 177	93.55 301	93.18 322	77.29 317	73.35 321	89.40 278	65.26 267	95.05 323	75.32 278	73.61 309	87.83 338

v144192		82.43 275	80.73 280	87.54 270	85.81 359	78.22 239	95.98 215	93.78 294	79.09 296	77.11 276	86.49 323	64.66 271	95.91 275	74.20 289	69.42 339	88.49 324

GBi-Net		82.42 276	80.43 286	88.39 246	92.66 230	81.95 128	94.30 281	93.38 312	79.06 297	75.82 299	85.66 336	56.38 332	93.84 350	71.23 309	75.38 301	89.38 295

test1		82.42 276	80.43 286	88.39 246	92.66 230	81.95 128	94.30 281	93.38 312	79.06 297	75.82 299	85.66 336	56.38 332	93.84 350	71.23 309	75.38 301	89.38 295

v148		82.41 278	80.89 277	86.99 283	86.18 353	76.81 280	96.27 199	93.82 289	80.49 264	75.28 306	86.11 334	67.32 250	95.75 285	75.48 277	67.03 365	88.42 328

v1192		82.31 279	80.55 284	87.60 266	85.94 356	78.47 232	95.85 225	93.80 292	79.33 289	76.97 278	86.51 322	63.33 277	95.87 276	73.11 297	70.13 331	88.46 326

LS3D		82.22 280	79.94 294	89.06 231	97.43 82	74.06 315	93.20 312	92.05 341	61.90 395	73.33 322	95.21 168	59.35 301	99.21 94	54.54 386	92.48 163	93.90 248

jajsoiax		82.12 281	81.15 276	85.03 316	84.19 376	70.70 347	94.22 285	93.95 279	83.07 216	73.48 317	89.75 274	49.66 360	95.37 305	82.24 213	79.76 271	89.02 310

v1921920		82.02 282	80.23 288	87.41 274	85.62 360	77.92 252	95.79 229	93.69 299	78.86 300	76.67 281	86.44 325	62.50 280	95.83 278	72.69 299	69.77 337	88.47 325

myMVS_eth3d		81.93 283	82.18 259	81.18 357	92.13 253	67.18 367	93.97 289	94.23 265	82.43 231	73.39 318	93.57 216	76.98 132	87.86 397	50.53 397	82.34 260	88.51 322

v8		81.88 284	80.06 292	87.32 276	86.63 345	79.04 219	94.41 274	93.65 301	78.77 301	73.19 324	85.57 340	66.87 254	95.81 279	73.84 293	67.61 358	87.11 352

mvs_tets		81.74 285	80.71 281	84.84 317	84.22 375	70.29 350	93.91 292	93.78 294	82.77 225	73.37 320	89.46 277	47.36 371	95.31 309	81.99 214	79.55 277	88.92 316

v1240		81.70 286	79.83 296	87.30 278	85.50 361	77.70 264	95.48 241	93.44 308	78.46 305	76.53 285	86.44 325	60.85 293	95.84 277	71.59 306	70.17 329	88.35 329

PVSNet_0	77.72 15	81.70 286	78.95 304	89.94 216	90.77 289	76.72 282	95.96 216	96.95 45	85.01 160	70.24 347	88.53 289	52.32 348	98.20 155	86.68 172	44.08 415	94.89 226

miper_lstm_enhance		81.66 288	80.66 282	84.67 321	91.19 276	71.97 336	91.94 329	93.19 320	77.86 310	72.27 332	85.26 344	73.46 198	93.42 358	73.71 294	67.05 364	88.61 320

DP-MVS		81.47 289	78.28 307	91.04 181	98.14 55	78.48 229	95.09 262	86.97 389	61.14 401	71.12 340	92.78 229	59.59 298	99.38 82	53.11 390	86.61 220	95.27 220

v10		81.43 290	79.53 298	87.11 281	86.38 347	78.87 220	94.31 280	93.43 310	77.88 309	73.24 323	85.26 344	65.44 263	95.75 285	72.14 303	67.71 357	86.72 356

pmmvs5		81.34 291	79.54 297	86.73 288	85.02 368	76.91 277	96.22 202	91.65 347	77.65 312	73.55 316	88.61 286	55.70 335	94.43 339	74.12 290	73.35 312	88.86 318

ADS-MVSNet		81.26 292	78.36 306	89.96 215	93.78 193	79.78 194	79.48 401	93.60 303	73.09 353	80.14 245	79.99 385	62.15 283	95.24 312	59.49 366	83.52 245	94.85 228

Baseline_NR-MVSNet		81.22 293	80.07 291	84.68 320	85.32 366	75.12 305	96.48 183	88.80 380	76.24 329	77.28 274	86.40 328	67.61 244	94.39 340	75.73 275	66.73 367	84.54 378

tt0805		81.20 294	79.06 303	87.61 265	86.50 346	72.97 326	93.66 297	95.48 188	74.11 343	76.23 292	91.99 240	41.36 390	97.40 203	77.44 256	74.78 304	92.45 262

SSC-MVS3.2		81.06 295	79.49 299	85.75 303	89.78 305	73.00 325	94.40 277	95.23 206	83.76 202	76.61 284	87.82 302	49.48 361	94.88 325	66.80 332	71.56 321	89.38 295

WR-MVS_H		81.02 296	80.09 289	83.79 333	88.08 332	71.26 346	94.46 272	96.54 98	80.08 275	72.81 328	86.82 317	70.36 234	92.65 363	64.18 347	67.50 359	87.46 349

CP-MVSNet		81.01 297	80.08 290	83.79 333	87.91 334	70.51 348	94.29 284	95.65 177	80.83 254	72.54 331	88.84 283	63.71 273	92.32 366	68.58 326	68.36 349	88.55 321

anonymousdsp		80.98 298	79.97 293	84.01 330	81.73 388	70.44 349	92.49 322	93.58 305	77.10 321	72.98 326	86.31 329	57.58 319	94.90 324	79.32 235	78.63 286	86.69 357

UniMVSNet_ETH3D		80.86 299	78.75 305	87.22 280	86.31 349	72.02 334	91.95 328	93.76 297	73.51 348	75.06 308	90.16 270	43.04 384	95.66 290	76.37 268	78.55 287	93.98 246

testing3		80.74 300	81.17 275	79.44 367	91.15 278	63.48 386	97.16 130	95.76 171	80.83 254	71.36 337	93.15 223	78.22 109	87.30 402	43.19 410	79.67 274	87.55 347

IterMVS		80.67 301	79.16 301	85.20 313	89.79 304	76.08 291	92.97 316	91.86 343	80.28 271	71.20 339	85.14 349	57.93 317	91.34 378	72.52 301	70.74 326	88.18 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 302	77.77 312	89.14 230	93.43 207	77.24 271	91.89 330	90.18 368	69.86 373	68.02 354	91.94 244	52.21 350	98.84 124	59.32 368	83.12 249	91.35 267

IterMVS-SCA-FT		80.51 303	79.10 302	84.73 319	89.63 312	74.66 307	92.98 315	91.81 345	80.05 276	71.06 341	85.18 347	58.04 313	91.40 377	72.48 302	70.70 328	88.12 334

PS-CasMVS		80.27 304	79.18 300	83.52 339	87.56 338	69.88 353	94.08 287	95.29 203	80.27 272	72.08 333	88.51 290	59.22 304	92.23 368	67.49 328	68.15 352	88.45 327

pm-mvs1		80.05 305	78.02 310	86.15 296	85.42 362	75.81 300	95.11 259	92.69 334	77.13 319	70.36 345	87.43 306	58.44 309	95.27 311	71.36 308	64.25 375	87.36 350

RPMNet		79.85 306	75.92 326	91.64 162	90.16 299	79.75 196	79.02 405	95.44 192	58.43 410	82.27 222	72.55 408	73.03 202	98.41 147	46.10 406	86.25 224	96.75 179

PatchT		79.75 307	76.85 319	88.42 243	89.55 314	75.49 302	77.37 409	94.61 241	63.07 390	82.46 216	73.32 405	75.52 162	93.41 359	51.36 393	84.43 241	96.36 188

Anonymous20231211		79.72 308	77.19 316	87.33 275	95.59 131	77.16 275	95.18 256	94.18 270	59.31 408	72.57 330	86.20 332	47.89 368	95.66 290	74.53 287	69.24 342	89.18 303

test_fmvs2		79.59 309	79.90 295	78.67 371	82.86 385	55.82 408	95.20 253	89.55 372	81.09 250	80.12 247	89.80 273	34.31 406	93.51 357	87.82 160	78.36 289	86.69 357

ADS-MVSNet2		79.57 310	77.53 313	85.71 304	93.78 193	72.13 332	79.48 401	86.11 396	73.09 353	80.14 245	79.99 385	62.15 283	90.14 389	59.49 366	83.52 245	94.85 228

FMVSNet1		79.50 311	76.54 322	88.39 246	88.47 326	81.95 128	94.30 281	93.38 312	73.14 352	72.04 334	85.66 336	43.86 378	93.84 350	65.48 342	72.53 315	89.38 295

PEN-MVS		79.47 312	78.26 308	83.08 342	86.36 348	68.58 361	93.85 295	94.77 229	79.76 281	71.37 336	88.55 287	59.79 296	92.46 364	64.50 346	65.40 370	88.19 332

XVG-ACMP-BASELINE		79.38 313	77.90 311	83.81 332	84.98 369	67.14 371	89.03 354	93.18 322	80.26 273	72.87 327	88.15 297	38.55 396	96.26 258	76.05 271	78.05 291	88.02 335

v7n		79.32 314	77.34 314	85.28 312	84.05 379	72.89 328	93.38 304	93.87 286	75.02 337	70.68 342	84.37 356	59.58 299	95.62 295	67.60 327	67.50 359	87.32 351

MIMVSNet		79.18 315	75.99 325	88.72 240	87.37 340	80.66 168	79.96 399	91.82 344	77.38 316	74.33 312	81.87 373	41.78 387	90.74 384	66.36 340	83.10 250	94.76 230

JIA-IIPM		79.00 316	77.20 315	84.40 328	89.74 309	64.06 383	75.30 413	95.44 192	62.15 394	81.90 226	59.08 417	78.92 97	95.59 297	66.51 338	85.78 232	93.54 253

USDC		78.65 317	76.25 323	85.85 299	87.58 337	74.60 309	89.58 350	90.58 367	84.05 189	63.13 379	88.23 295	40.69 395	96.86 237	66.57 337	75.81 299	86.09 366

DTE-MVSNet		78.37 318	77.06 317	82.32 350	85.22 367	67.17 370	93.40 303	93.66 300	78.71 302	70.53 344	88.29 294	59.06 305	92.23 368	61.38 360	63.28 379	87.56 345

Patchmatch-test		78.25 319	74.72 334	88.83 237	91.20 275	74.10 314	73.91 416	88.70 383	59.89 406	66.82 361	85.12 350	78.38 106	94.54 336	48.84 402	79.58 276	97.86 104

tfpnnormal		78.14 320	75.42 328	86.31 294	88.33 330	79.24 210	94.41 274	96.22 134	73.51 348	69.81 349	85.52 342	55.43 336	95.75 285	47.65 404	67.86 355	83.95 384

mmtdpeth		78.04 321	76.76 320	81.86 353	89.60 313	66.12 375	92.34 326	87.18 388	76.83 324	85.55 180	76.49 396	46.77 372	97.02 224	90.85 117	45.24 412	82.43 393

ACMH	75.40 17	77.99 322	74.96 330	87.10 282	90.67 290	76.41 286	93.19 313	91.64 348	72.47 359	63.44 377	87.61 305	43.34 381	97.16 217	58.34 370	73.94 307	87.72 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 322	75.74 327	84.74 318	90.45 294	72.02 334	86.41 378	91.12 356	72.57 358	66.63 363	87.27 309	54.95 341	96.98 227	56.29 380	75.98 296	85.21 375
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
Syy-MVS		77.97 324	78.05 309	77.74 375	92.13 253	56.85 404	93.97 289	94.23 265	82.43 231	73.39 318	93.57 216	57.95 316	87.86 397	32.40 418	82.34 260	88.51 322

our_test_3		77.90 325	75.37 329	85.48 310	85.39 363	76.74 281	93.63 298	91.67 346	73.39 351	65.72 368	84.65 355	58.20 312	93.13 361	57.82 372	67.87 354	86.57 359

RPSCF		77.73 326	76.63 321	81.06 358	88.66 325	55.76 409	87.77 367	87.88 386	64.82 388	74.14 313	92.79 228	49.22 362	96.81 239	67.47 329	76.88 294	90.62 273

KD-MVS_2432*1600		77.63 327	74.92 332	85.77 301	90.86 286	79.44 204	88.08 363	93.92 282	76.26 327	67.05 359	82.78 369	72.15 214	91.92 371	61.53 357	41.62 418	85.94 369

miper_refine_blended		77.63 327	74.92 332	85.77 301	90.86 286	79.44 204	88.08 363	93.92 282	76.26 327	67.05 359	82.78 369	72.15 214	91.92 371	61.53 357	41.62 418	85.94 369

ACMH+	76.62 16	77.47 329	74.94 331	85.05 315	91.07 281	71.58 342	93.26 310	90.01 369	71.80 362	64.76 372	88.55 287	41.62 388	96.48 250	62.35 356	71.00 324	87.09 353

Patchmtry		77.36 330	74.59 335	85.67 305	89.75 307	75.75 301	77.85 408	91.12 356	60.28 403	71.23 338	80.35 382	75.45 163	93.56 356	57.94 371	67.34 361	87.68 341

ppachtmachnet_test		77.19 331	74.22 339	86.13 297	85.39 363	78.22 239	93.98 288	91.36 353	71.74 363	67.11 358	84.87 353	56.67 328	93.37 360	52.21 391	64.59 372	86.80 355

OurMVSNet-221017-0		77.18 332	76.06 324	80.55 361	83.78 382	60.00 399	90.35 345	91.05 359	77.01 323	66.62 364	87.92 300	47.73 369	94.03 346	71.63 305	68.44 348	87.62 342

TransMVSNet (Re)		76.94 333	74.38 337	84.62 323	85.92 357	75.25 304	95.28 247	89.18 377	73.88 346	67.22 356	86.46 324	59.64 297	94.10 345	59.24 369	52.57 400	84.50 379

EU-MVSNet		76.92 334	76.95 318	76.83 380	84.10 377	54.73 412	91.77 332	92.71 333	72.74 356	69.57 350	88.69 285	58.03 315	87.43 401	64.91 345	70.00 335	88.33 330

Patchmatch-RL test		76.65 335	74.01 342	84.55 324	77.37 403	64.23 381	78.49 407	82.84 409	78.48 304	64.63 373	73.40 404	76.05 150	91.70 376	76.99 259	57.84 388	97.72 115

FMVSNet5		76.46 336	74.16 340	83.35 341	90.05 302	76.17 289	89.58 350	89.85 370	71.39 365	65.29 371	80.42 381	50.61 355	87.70 400	61.05 362	69.24 342	86.18 364

SixPastTwentyTwo		76.04 337	74.32 338	81.22 356	84.54 372	61.43 395	91.16 339	89.30 376	77.89 308	64.04 374	86.31 329	48.23 363	94.29 342	63.54 352	63.84 377	87.93 337

AllTest		75.92 338	73.06 346	84.47 325	92.18 250	67.29 365	91.07 340	84.43 402	67.63 379	63.48 375	90.18 268	38.20 397	97.16 217	57.04 376	73.37 310	88.97 314

CL-MVSNet_self_test		75.81 339	74.14 341	80.83 360	78.33 399	67.79 364	94.22 285	93.52 306	77.28 318	69.82 348	81.54 376	61.47 291	89.22 390	57.59 374	53.51 396	85.48 373

COLMAP_ROB	73.24 19	75.74 340	73.00 347	83.94 331	92.38 237	69.08 359	91.85 331	86.93 390	61.48 398	65.32 370	90.27 267	42.27 386	96.93 232	50.91 395	75.63 300	85.80 372
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CMPMVS	54.94 21	75.71 341	74.56 336	79.17 369	79.69 394	55.98 406	89.59 349	93.30 317	60.28 403	53.85 407	89.07 280	47.68 370	96.33 256	76.55 264	81.02 266	85.22 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 342	73.64 343	80.22 363	80.75 389	63.38 387	93.36 305	90.71 366	73.09 353	67.12 357	83.70 363	50.33 357	90.85 383	53.63 389	70.10 333	86.44 360

EG-PatchMatch MVS		74.92 343	72.02 351	83.62 337	83.76 383	73.28 320	93.62 299	92.04 342	68.57 377	58.88 396	83.80 362	31.87 410	95.57 299	56.97 378	78.67 283	82.00 397

testgi		74.88 344	73.40 344	79.32 368	80.13 393	61.75 392	93.21 311	86.64 394	79.49 287	66.56 365	91.06 254	35.51 404	88.67 392	56.79 379	71.25 322	87.56 345

pmmvs6		74.65 345	71.67 352	83.60 338	79.13 396	69.94 352	93.31 309	90.88 363	61.05 402	65.83 367	84.15 359	43.43 380	94.83 328	66.62 335	60.63 384	86.02 367

test_vis1_rt		73.96 346	72.40 349	78.64 372	83.91 380	61.16 396	95.63 235	68.18 425	76.32 326	60.09 393	74.77 399	29.01 414	97.54 193	87.74 161	75.94 297	77.22 408

K. test v3		73.62 347	71.59 353	79.69 365	82.98 384	59.85 400	90.85 343	88.83 379	77.13 319	58.90 395	82.11 371	43.62 379	91.72 375	65.83 341	54.10 395	87.50 348

pmmvs-eth3d		73.59 348	70.66 356	82.38 348	76.40 407	73.38 317	89.39 353	89.43 374	72.69 357	60.34 392	77.79 391	46.43 374	91.26 380	66.42 339	57.06 389	82.51 390

kuosan		73.55 349	72.39 350	77.01 378	89.68 310	66.72 373	85.24 387	93.44 308	67.76 378	60.04 394	83.40 366	71.90 217	84.25 409	45.34 407	54.75 391	80.06 404

MDA-MVSNet_test_wron		73.54 350	70.43 358	82.86 343	84.55 371	71.85 337	91.74 333	91.32 355	67.63 379	46.73 412	81.09 379	55.11 339	90.42 387	55.91 382	59.76 385	86.31 362

YYNet1		73.53 351	70.43 358	82.85 344	84.52 373	71.73 340	91.69 334	91.37 352	67.63 379	46.79 411	81.21 378	55.04 340	90.43 386	55.93 381	59.70 386	86.38 361

UnsupCasMVSNet_eth		73.25 352	70.57 357	81.30 355	77.53 401	66.33 374	87.24 371	93.89 285	80.38 268	57.90 400	81.59 374	42.91 385	90.56 385	65.18 344	48.51 406	87.01 354

DSMNet-mixed		73.13 353	72.45 348	75.19 386	77.51 402	46.82 417	85.09 388	82.01 410	67.61 383	69.27 352	81.33 377	50.89 353	86.28 404	54.54 386	83.80 244	92.46 261

OpenMVS_ROB	68.52 20	73.02 354	69.57 361	83.37 340	80.54 392	71.82 338	93.60 300	88.22 384	62.37 393	61.98 385	83.15 368	35.31 405	95.47 301	45.08 408	75.88 298	82.82 387

test_0402		72.68 355	69.54 362	82.09 351	88.67 324	71.81 339	92.72 320	86.77 393	61.52 397	62.21 384	83.91 361	43.22 382	93.76 353	34.60 416	72.23 319	80.72 403

TinyColmap		72.41 356	68.99 365	82.68 345	88.11 331	69.59 356	88.41 359	85.20 398	65.55 385	57.91 399	84.82 354	30.80 412	95.94 273	51.38 392	68.70 345	82.49 392

test20.03		72.36 357	71.15 354	75.98 384	77.79 400	59.16 401	92.40 324	89.35 375	74.09 344	61.50 387	84.32 357	48.09 364	85.54 407	50.63 396	62.15 382	83.24 385

LF4IMVS		72.36 357	70.82 355	76.95 379	79.18 395	56.33 405	86.12 380	86.11 396	69.30 375	63.06 380	86.66 320	33.03 408	92.25 367	65.33 343	68.64 346	82.28 394

Anonymous20240521		72.06 359	69.91 360	78.50 373	77.11 404	61.67 394	91.62 336	90.97 361	65.52 386	62.37 383	79.05 388	36.32 400	90.96 382	57.75 373	68.52 347	82.87 386

dmvs_testset		72.00 360	73.36 345	67.91 392	83.83 381	31.90 432	85.30 386	77.12 417	82.80 224	63.05 381	92.46 231	61.54 290	82.55 414	42.22 413	71.89 320	89.29 300

MDA-MVSNet-bldmvs		71.45 361	67.94 368	81.98 352	85.33 365	68.50 362	92.35 325	88.76 381	70.40 368	42.99 415	81.96 372	46.57 373	91.31 379	48.75 403	54.39 394	86.11 365

mvs5depth		71.40 362	68.36 367	80.54 362	75.31 411	65.56 377	79.94 400	85.14 399	69.11 376	71.75 335	81.59 374	41.02 392	93.94 348	60.90 363	50.46 402	82.10 395

MVS-HIRNet		71.36 363	67.00 369	84.46 327	90.58 291	69.74 355	79.15 404	87.74 387	46.09 416	61.96 386	50.50 420	45.14 376	95.64 293	53.74 388	88.11 207	88.00 336

KD-MVS_self_test		70.97 364	69.31 363	75.95 385	76.24 409	55.39 410	87.45 368	90.94 362	70.20 370	62.96 382	77.48 392	44.01 377	88.09 395	61.25 361	53.26 397	84.37 380

ttmdpeth		69.58 365	66.92 371	77.54 377	75.95 410	62.40 390	88.09 362	84.32 404	62.87 392	65.70 369	86.25 331	36.53 399	88.53 394	55.65 384	46.96 411	81.70 400

test_fmvs3		69.56 366	69.19 364	70.67 390	69.01 416	47.05 416	90.87 342	86.81 391	71.31 366	66.79 362	77.15 393	16.40 421	83.17 412	81.84 215	62.51 381	81.79 399

dongtai		69.47 367	68.98 366	70.93 389	86.87 343	58.45 402	88.19 361	93.18 322	63.98 389	56.04 403	80.17 384	70.97 230	79.24 416	33.46 417	47.94 408	75.09 410

MIMVSNet1		69.44 368	66.65 372	77.84 374	76.48 406	62.84 389	87.42 369	88.97 378	66.96 384	57.75 401	79.72 387	32.77 409	85.83 406	46.32 405	63.42 378	84.85 377

PM-MVS		69.32 369	66.93 370	76.49 381	73.60 413	55.84 407	85.91 381	79.32 415	74.72 339	61.09 389	78.18 390	21.76 417	91.10 381	70.86 314	56.90 390	82.51 390

TDRefinement		69.20 370	65.78 374	79.48 366	66.04 421	62.21 391	88.21 360	86.12 395	62.92 391	61.03 390	85.61 339	33.23 407	94.16 344	55.82 383	53.02 398	82.08 396

new-patchmatchnet		68.85 371	65.93 373	77.61 376	73.57 414	63.94 384	90.11 347	88.73 382	71.62 364	55.08 405	73.60 403	40.84 393	87.22 403	51.35 394	48.49 407	81.67 401

UnsupCasMVSNet_bld		68.60 372	64.50 376	80.92 359	74.63 412	67.80 363	83.97 392	92.94 329	65.12 387	54.63 406	68.23 413	35.97 402	92.17 370	60.13 364	44.83 413	82.78 388

mvsany_test3		67.19 373	65.34 375	72.72 388	63.08 422	48.57 415	83.12 395	78.09 416	72.07 360	61.21 388	77.11 394	22.94 416	87.78 399	78.59 242	51.88 401	81.80 398

MVStest1		66.93 374	63.01 378	78.69 370	78.56 397	71.43 344	85.51 385	86.81 391	49.79 415	48.57 410	84.15 359	53.46 346	83.31 410	43.14 411	37.15 421	81.34 402

new_pmnet		66.18 375	63.18 377	75.18 387	76.27 408	61.74 393	83.79 393	84.66 401	56.64 412	51.57 408	71.85 411	31.29 411	87.93 396	49.98 398	62.55 380	75.86 409

pmmvs3		65.75 376	62.18 379	76.45 382	67.12 420	64.54 379	88.68 357	85.05 400	54.77 414	57.54 402	73.79 402	29.40 413	86.21 405	55.49 385	47.77 409	78.62 406

test_f		64.01 377	62.13 380	69.65 391	63.00 423	45.30 422	83.66 394	80.68 412	61.30 399	55.70 404	72.62 407	14.23 423	84.64 408	69.84 319	58.11 387	79.00 405

N_pmnet		61.30 378	60.20 381	64.60 397	84.32 374	17.00 438	91.67 335	10.98 436	61.77 396	58.45 398	78.55 389	49.89 359	91.83 374	42.27 412	63.94 376	84.97 376

WB-MVS		57.26 379	56.22 382	60.39 403	69.29 415	35.91 430	86.39 379	70.06 423	59.84 407	46.46 413	72.71 406	51.18 352	78.11 417	15.19 427	34.89 422	67.14 416

test_method		56.77 380	54.53 384	63.49 399	76.49 405	40.70 425	75.68 412	74.24 419	19.47 427	48.73 409	71.89 410	19.31 418	65.80 427	57.46 375	47.51 410	83.97 383

APD_test1		56.56 381	53.58 385	65.50 394	67.93 419	46.51 419	77.24 411	72.95 420	38.09 418	42.75 416	75.17 398	13.38 424	82.78 413	40.19 414	54.53 393	67.23 415

SSC-MVS		56.01 382	54.96 383	59.17 404	68.42 417	34.13 431	84.98 389	69.23 424	58.08 411	45.36 414	71.67 412	50.30 358	77.46 418	14.28 428	32.33 423	65.91 417

FPMVS		55.09 383	52.93 386	61.57 401	55.98 425	40.51 426	83.11 396	83.41 408	37.61 419	34.95 420	71.95 409	14.40 422	76.95 419	29.81 419	65.16 371	67.25 414

test_vis3_rt		54.10 384	51.04 387	63.27 400	58.16 424	46.08 421	84.17 391	49.32 435	56.48 413	36.56 419	49.48 422	8.03 431	91.91 373	67.29 330	49.87 403	51.82 421

LCM-MVSNet		52.52 385	48.24 388	65.35 395	47.63 432	41.45 424	72.55 417	83.62 407	31.75 420	37.66 418	57.92 418	9.19 430	76.76 420	49.26 400	44.60 414	77.84 407

EGC-MVSNET		52.46 386	47.56 389	67.15 393	81.98 387	60.11 398	82.54 397	72.44 421	0.11 433	0.70 434	74.59 400	25.11 415	83.26 411	29.04 420	61.51 383	58.09 418

PMMVS2		50.90 387	46.31 390	64.67 396	55.53 426	46.67 418	77.30 410	71.02 422	40.89 417	34.16 421	59.32 416	9.83 429	76.14 422	40.09 415	28.63 424	71.21 411

ANet_high		46.22 388	41.28 395	61.04 402	39.91 434	46.25 420	70.59 418	76.18 418	58.87 409	23.09 426	48.00 423	12.58 426	66.54 426	28.65 421	13.62 427	70.35 412

testf1		45.70 389	42.41 391	55.58 405	53.29 429	40.02 427	68.96 419	62.67 429	27.45 422	29.85 422	61.58 414	5.98 432	73.83 424	28.49 422	43.46 416	52.90 419

APD_test2		45.70 389	42.41 391	55.58 405	53.29 429	40.02 427	68.96 419	62.67 429	27.45 422	29.85 422	61.58 414	5.98 432	73.83 424	28.49 422	43.46 416	52.90 419

Gipuma		45.11 391	42.05 393	54.30 407	80.69 390	51.30 414	35.80 425	83.81 406	28.13 421	27.94 425	34.53 425	11.41 428	76.70 421	21.45 424	54.65 392	34.90 425
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 392	41.93 394	40.38 410	20.10 436	26.84 434	61.93 422	59.09 431	14.81 429	28.51 424	80.58 380	35.53 403	48.33 431	63.70 351	13.11 428	45.96 424

PMVS	34.80 23	39.19 393	35.53 396	50.18 408	29.72 435	30.30 433	59.60 423	66.20 428	26.06 424	17.91 428	49.53 421	3.12 434	74.09 423	18.19 426	49.40 404	46.14 422
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 394	29.49 399	46.92 409	41.86 433	36.28 429	50.45 424	56.52 432	18.75 428	18.28 427	37.84 424	2.41 435	58.41 428	18.71 425	20.62 425	46.06 423
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 395	32.39 397	33.65 411	53.35 428	25.70 435	74.07 415	53.33 433	21.08 425	17.17 429	33.63 427	11.85 427	54.84 429	12.98 429	14.04 426	20.42 426

EMVS		31.70 396	31.45 398	32.48 412	50.72 431	23.95 436	74.78 414	52.30 434	20.36 426	16.08 430	31.48 428	12.80 425	53.60 430	11.39 430	13.10 429	19.88 427

cdsmvs_eth3d_5k		21.43 397	28.57 400	0.00 416	0.00 439	0.00 441	0.00 427	95.93 161	0.00 434	0.00 435	97.66 79	63.57 274	0.00 435	0.00 434	0.00 433	0.00 431

wuyk23d		14.10 398	13.89 401	14.72 413	55.23 427	22.91 437	33.83 426	3.56 437	4.94 430	4.11 431	2.28 433	2.06 436	19.66 432	10.23 431	8.74 430	1.59 430

testmvs		9.92 399	12.94 402	0.84 415	0.65 437	0.29 440	93.78 296	0.39 438	0.42 431	2.85 432	15.84 431	0.17 438	0.30 434	2.18 432	0.21 431	1.91 429

test123		9.07 400	11.73 403	1.11 414	0.50 438	0.77 439	89.44 352	0.20 439	0.34 432	2.15 433	10.72 432	0.34 437	0.32 433	1.79 433	0.08 432	2.23 428

ab-mvs-re		8.11 401	10.81 404	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	97.30 102	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

pcd_1.5k_mvsjas		5.92 402	7.89 405	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	71.04 227	0.00 435	0.00 434	0.00 433	0.00 431

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

WAC-MVS						67.18 367								49.00 401

FOURS1					98.51 39	78.01 247	98.13 54	96.21 135	83.04 217	94.39 56

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 56					99.81 22	98.08 18	98.81 24	99.43 11

PC_three_1452									91.12 40	98.33 2	98.42 32	92.51 2	99.81 22	98.96 4	99.37 1	99.70 3

No_MVS				97.14 3	99.05 9	92.19 4		96.83 56					99.81 22	98.08 18	98.81 24	99.43 11

test_one_0601					98.91 18	84.56 84		96.70 75	88.06 89	96.57 26	98.77 10	88.04 21

eth-test2					0.00 439

eth-test					0.00 439

ZD-MVS					99.09 8	83.22 109		96.60 91	82.88 222	93.61 67	98.06 57	82.93 60	99.14 104	95.51 53	98.49 39

RE-MVS-def			91.18 100		97.76 67	76.03 293	96.20 204	95.44 192	80.56 262	90.72 113	97.84 71	73.36 200		91.99 104	96.79 96	97.75 113

IU-MVS					99.03 15	85.34 61		96.86 54	92.05 31	98.74 1				98.15 15	98.97 17	99.42 13

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 13				98.54 21	92.06 3	99.84 13	99.11 3	99.37 1	99.74 1

test_241102_TWO								96.78 59	88.72 71	97.70 8	98.91 2	87.86 22	99.82 19	98.15 15	99.00 15	99.47 9

test_241102_ONE					99.03 15	85.03 74		96.78 59	88.72 71	97.79 6	98.90 5	88.48 17	99.82 19

9.14			94.26 33		98.10 57		98.14 51	96.52 100	84.74 166	94.83 51	98.80 7	82.80 62	99.37 84	95.95 45	98.42 42

save fliter					98.24 51	83.34 106	98.61 38	96.57 95	91.32 37

test_0728_THIRD									88.38 79	96.69 21	98.76 12	89.64 12	99.76 34	97.47 28	98.84 23	99.38 14

test_0728_SECOND				95.14 20	99.04 14	86.14 39	99.06 18	96.77 65					99.84 13	97.90 21	98.85 21	99.45 10

test0726					99.05 9	85.18 66	99.11 16	96.78 59	88.75 69	97.65 11	98.91 2	87.69 23

GSMVS																97.54 129

test_part2					98.90 19	85.14 72				96.07 33

sam_mvs1												77.59 120				97.54 129

sam_mvs												75.35 170

ambc				76.02 383	68.11 418	51.43 413	64.97 421	89.59 371		60.49 391	74.49 401	17.17 420	92.46 364	61.50 359	52.85 399	84.17 382

MTGPA								96.33 124

test_post1							85.88 382				30.24 429	73.77 193	95.07 322	73.89 291

test_post											33.80 426	76.17 148	95.97 269

patchmatchnet-post											77.09 395	77.78 118	95.39 303

GG-mvs-BLEND				93.49 78	94.94 152	86.26 37	81.62 398	97.00 39		88.32 151	94.30 196	91.23 5	96.21 262	88.49 153	97.43 75	98.00 94

MTMP							97.53 98	68.16 426

gm-plane-assit					92.27 244	79.64 202			84.47 176		95.15 173		97.93 167	85.81 175

test9_res														96.00 44	99.03 13	98.31 69

TEST9					98.64 31	83.71 97	97.82 73	96.65 82	84.29 183	95.16 41	98.09 52	84.39 42	99.36 85

test_8					98.63 33	83.64 100	97.81 75	96.63 87	84.50 174	95.10 44	98.11 50	84.33 43	99.23 92

agg_prior2														94.30 68	99.00 15	98.57 53

agg_prior					98.59 35	83.13 110		96.56 97		94.19 58			99.16 103

TestCases				84.47 325	92.18 250	67.29 365		84.43 402	67.63 379	63.48 375	90.18 268	38.20 397	97.16 217	57.04 376	73.37 310	88.97 314

test_prior4						82.34 124	97.75 81

test_prior2							98.37 44		86.08 134	94.57 54	98.02 58	83.14 57		95.05 59	98.79 27

test_prior				93.09 92	98.68 26	81.91 132		96.40 115					99.06 111			98.29 71

旧先验2							96.97 150		74.06 345	96.10 32			97.76 178	88.38 155

新几何2							96.42 190

新几何1				93.12 90	97.44 81	81.60 146		96.71 74	74.54 341	91.22 106	97.57 87	79.13 95	99.51 75	77.40 257	98.46 40	98.26 74

旧先验1					97.39 86	79.58 203		96.54 98			98.08 55	84.00 49			97.42 76	97.62 125

无先验							96.87 159	96.78 59	77.39 315				99.52 73	79.95 229		98.43 62

原ACMM2							96.84 160

原ACMM1				91.22 178	97.77 65	78.10 245		96.61 88	81.05 251	91.28 105	97.42 96	77.92 115	98.98 115	79.85 231	98.51 36	96.59 183

test222					96.15 109	78.41 233	95.87 223	96.46 107	71.97 361	89.66 126	97.45 92	76.33 146			98.24 51	98.30 70

testdata2													99.48 77	76.45 266

segment_acmp												82.69 63

testdata				90.13 208	95.92 119	74.17 313		96.49 106	73.49 350	94.82 52	97.99 59	78.80 101	97.93 167	83.53 201	97.52 71	98.29 71

testdata1							95.57 239		87.44 106

test12				94.25 41	98.34 46	85.55 57		96.35 123		92.36 85		80.84 71	99.22 93		98.31 49	97.98 96

plane_prior7					91.86 265	77.55 266

plane_prior6					91.98 261	77.92 252						64.77 269

plane_prior5								94.69 231					97.30 209	87.08 167	82.82 255	90.96 270

plane_prior4											94.15 201

plane_prior3						77.75 262			90.17 56	81.33 231

plane_prior2							97.18 126		89.89 58

plane_prior1					91.95 263

plane_prior						77.96 249	97.52 101		90.36 54						82.96 253

n2								0.00 440

nn								0.00 440

door-mid								79.75 414

lessismore_v0				79.98 364	80.59 391	58.34 403		80.87 411		58.49 397	83.46 365	43.10 383	93.89 349	63.11 354	48.68 405	87.72 339

LGP-MVS_train				86.33 291	90.88 283	73.06 323		94.13 272	82.20 235	76.31 288	93.20 220	54.83 342	96.95 229	83.72 195	80.83 268	88.98 312

test11								96.50 103

door								80.13 413

HQP5-MVS						78.48 229

HQP-NCC					92.08 256		97.63 87		90.52 49	82.30 218

ACMP_Plane					92.08 256		97.63 87		90.52 49	82.30 218

BP-MVS														87.67 163

HQP4-MVS										82.30 218			97.32 207			91.13 268

HQP3-MVS								94.80 226							83.01 251

HQP2-MVS												65.40 264

NP-MVS					92.04 260	78.22 239					94.56 191

MDTV_nov1_ep13_2view						81.74 140	86.80 374		80.65 259	85.65 178		74.26 187		76.52 265		96.98 165

MDTV_nov1_ep13			83.69 234		94.09 187	81.01 156	86.78 375	96.09 144	83.81 200	84.75 190	84.32 357	74.44 186	96.54 248	63.88 349	85.07 238

ACMMP++_ref															78.45 288

ACMMP++															79.05 280

Test By Simon												71.65 220

ITE_SJBPF				82.38 348	87.00 342	65.59 376		89.55 372	79.99 278	69.37 351	91.30 251	41.60 389	95.33 307	62.86 355	74.63 306	86.24 363

DeepMVS_CX				64.06 398	78.53 398	43.26 423		68.11 427	69.94 372	38.55 417	76.14 397	18.53 419	79.34 415	43.72 409	41.62 418	69.57 413

High-resolution multi-view benchmark