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 29	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 40	93.39 18	96.45 28	98.79 8	90.17 9	99.99 1	89.33 143	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 31	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 53	96.77 64	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 58	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 71	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 43	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	80.10 187	97.42 109	96.78 58	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	96.46 106	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 91	94.71 5	97.08 17	97.99 59	78.69 102	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 77	96.74 69	86.11 131	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 74	96.93 46	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	92.33 125	98.44 43	77.84 254	98.43 40	97.21 22	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 37	94.40 10	91.46 99	97.08 114	83.32 55	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	94.50 169	84.30 87	99.14 11	96.00 150	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	94.42 172	84.61 82	99.13 12	96.15 139	92.06 29	97.92 3	98.52 24	84.52 40	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	96.39 116	94.07 13	95.34 40	97.80 74	76.83 135	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 79	96.19 137	89.59 61	96.66 23	98.17 47	84.33 42	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	95.29 139	80.96 158	99.29 3	97.21 22	94.50 9	97.29 15	98.44 29	82.15 63	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	95.85 224	96.42 111	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	97.47 102	96.77 64	85.32 149	97.92 3	98.70 15	83.09 58	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	94.52 164	82.80 114	99.33 1	96.37 120	95.08 4	97.59 13	98.48 27	77.40 123	99.79 29	98.28 10	97.21 82	98.44 61

DeepPCF-MVS	89.82 1	94.61 22	96.17 5	89.91 216	97.09 94	70.21 349	98.99 24	96.69 76	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	96.33 195	95.58 179	91.12 40	95.84 36	93.87 207	83.47 54	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	83.40 104	98.04 61	96.41 112	85.79 140	95.00 47	98.28 40	84.32 45	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 59	96.64 84	93.64 16	91.74 97	98.54 21	80.17 80	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	83.04 111	98.10 55	95.29 202	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 96	97.82 72	96.65 81	84.50 173	95.16 41	98.09 52	84.33 42	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 35	96.93 46	90.71 46	93.08 74	98.70 15	79.98 84	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 63	98.09 9	89.99 57	92.34 86	96.97 119	81.30 69	98.99 114	88.54 150	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 81	96.43 110	84.02 189	95.07 46	98.74 14	82.93 59	99.38 82	95.42 54	98.51 36	98.32 67

PS-MVSNAJ		94.17 31	93.52 43	96.10 9	95.65 127	92.35 2	98.21 48	95.79 169	92.42 25	96.24 30	98.18 44	71.04 226	99.17 102	96.77 38	97.39 77	96.79 174

SteuartSystems-ACMMP		94.13 33	94.44 28	93.20 87	95.41 134	81.35 148	99.02 22	96.59 91	89.50 63	94.18 59	98.36 36	83.68 53	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 45	97.64 14	94.57 7	95.36 39	96.88 122	79.96 85	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	92.82 225	81.12 151	99.26 5	96.37 120	93.47 17	95.16 41	98.21 42	79.00 95	99.64 59	98.21 13	96.73 99	97.83 107

fmvsm_s_conf0.5_n_3		93.95 36	94.53 24	92.20 134	94.41 173	80.04 188	98.90 27	95.96 154	94.53 8	97.63 12	98.58 19	75.95 151	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	95.07 147	92.02 6	98.19 49	95.68 175	92.06 29	96.01 35	98.14 48	70.83 230	98.96 116	96.74 40	96.57 102	96.76 178

lupinMVS		93.87 38	93.58 42	94.75 30	93.00 218	88.08 19	99.15 9	95.50 186	91.03 43	94.90 48	97.66 79	78.84 98	97.56 188	94.64 66	97.46 72	98.62 52

fmvsm_s_conf0.5_n		93.69 39	94.13 35	92.34 123	94.56 161	82.01 126	99.07 17	97.13 27	92.09 27	96.25 29	98.53 23	76.47 140	99.80 25	98.39 9	94.71 128	95.22 220

APD-MVS		93.61 40	93.59 41	93.69 65	98.76 24	83.26 107	97.21 121	96.09 143	82.41 231	94.65 53	98.21 42	81.96 66	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	81.76 138	98.64 35	97.13 27	82.60 227	94.09 60	98.49 26	80.35 75	99.85 11	94.74 64	98.62 33	98.83 38

BP-MVS1		93.55 42	93.50 44	93.71 63	92.64 232	85.39 60	97.78 76	96.84 54	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	96.82 162	96.65 81	86.24 129	94.27 57	97.99 59	77.94 112	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	82.83 113	97.56 94	98.27 6	89.16 67	89.71 124	97.14 109	79.77 86	99.56 70	93.65 77	97.94 59	98.02 89

fmvsm_s_conf0.5_n_a		93.34 45	93.71 38	92.22 132	93.38 207	81.71 141	98.86 28	96.98 40	91.64 33	96.85 19	98.55 20	75.58 159	99.77 32	97.88 23	93.68 146	95.18 221

PVSNet_Blended		93.13 46	92.98 55	93.57 73	97.47 77	83.86 93	99.32 2	96.73 70	91.02 44	89.53 129	96.21 138	76.42 142	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 85	96.26 129	83.00 217	93.22 71	98.24 41	81.31 68	99.21 94	89.12 144	98.74 30	98.14 82

dcpmvs_2		93.10 48	93.46 46	92.02 144	97.77 65	79.73 198	94.82 266	93.86 285	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	88.45 325	80.81 163	99.00 23	95.11 207	93.21 19	94.00 61	97.91 67	76.84 133	99.59 64	97.91 20	96.55 103	97.54 129

SPE-MVS-test		92.98 50	93.67 39	90.90 185	96.52 99	76.87 277	98.68 32	94.73 228	90.36 54	94.84 50	97.89 69	77.94 112	97.15 219	94.28 71	97.80 64	98.70 48

fmvsm_s_conf0.5_n_2		92.97 51	93.38 48	91.73 157	94.10 185	80.64 168	98.96 25	95.89 162	94.09 12	97.05 18	98.40 33	68.92 239	99.80 25	98.53 8	94.50 132	94.74 230

alignmvs		92.97 51	92.26 74	95.12 21	95.54 131	87.77 22	98.67 33	96.38 117	88.04 90	93.01 75	97.45 92	79.20 93	98.60 132	93.25 85	88.76 195	98.99 33

fmvsm_s_conf0.1_n		92.93 53	93.16 52	92.24 130	90.52 291	81.92 130	98.42 41	96.24 131	91.17 39	96.02 34	98.35 37	75.34 170	99.74 42	97.84 24	94.58 130	95.05 222

HFP-MVS		92.89 54	92.86 59	92.98 96	98.71 25	81.12 151	97.58 92	96.70 74	85.20 154	91.75 96	97.97 64	78.47 104	99.71 49	90.95 113	98.41 43	98.12 85

PAPM		92.87 55	92.40 69	94.30 39	92.25 246	87.85 21	96.40 190	96.38 117	91.07 42	88.72 145	96.90 120	82.11 64	97.37 205	90.05 134	97.70 66	97.67 120

GDP-MVS		92.85 56	92.55 66	93.75 58	92.82 225	85.76 47	97.63 86	95.05 211	88.34 81	93.15 72	97.10 113	86.92 26	98.01 164	87.95 158	94.00 139	97.47 138

ZNCC-MVS		92.75 57	92.60 64	93.23 86	98.24 51	81.82 136	97.63 86	96.50 102	85.00 160	91.05 108	97.74 76	78.38 105	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	97.20 123	96.20 135	87.73 99	88.40 149	98.12 49	78.71 101	99.76 34	87.99 157	96.28 105	98.74 42

CS-MVS		92.73 59	93.48 45	90.48 198	96.27 104	75.93 297	98.55 38	94.93 215	89.32 64	94.54 55	97.67 78	78.91 97	97.02 223	93.80 74	97.32 79	98.49 57

jason		92.73 59	92.23 75	94.21 44	90.50 292	87.30 30	98.65 34	95.09 208	90.61 48	92.76 80	97.13 110	75.28 171	97.30 208	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	97.37 113	96.99 39	88.13 88	88.18 153	95.47 158	84.12 47	98.04 161	92.46 97	91.17 175	97.14 160

ETV-MVS		92.72 61	92.87 57	92.28 129	94.54 163	81.89 132	97.98 63	95.21 205	89.77 60	93.11 73	96.83 124	77.23 129	97.50 196	95.74 48	95.38 122	97.44 140

region2R		92.72 61	92.70 61	92.79 104	98.68 26	80.53 175	97.53 97	96.51 100	85.22 152	91.94 94	97.98 62	77.26 125	99.67 57	90.83 118	98.37 46	98.18 78

reproduce-ours		92.70 64	93.02 53	91.75 155	97.45 79	77.77 258	96.16 205	95.94 158	84.12 185	92.45 81	98.43 30	80.06 82	99.24 90	95.35 55	97.18 83	98.24 75

our_new_method		92.70 64	93.02 53	91.75 155	97.45 79	77.77 258	96.16 205	95.94 158	84.12 185	92.45 81	98.43 30	80.06 82	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	80.29 178	97.37 113	96.44 108	87.04 118	91.38 100	97.83 73	77.24 127	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	80.57 170	97.58 92	96.69 76	85.20 154	91.57 98	97.92 65	77.01 130	99.67 57	90.95 113	98.41 43	98.00 94

UBG		92.68 68	92.35 70	93.70 64	95.61 128	85.65 54	97.25 119	97.06 34	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 47	97.65 13	88.57 74	89.82 123	97.22 107	79.29 90	99.06 111	89.57 139	88.73 196	98.73 46

MP-MVS		92.61 70	92.67 62	92.42 121	98.13 56	79.73 198	97.33 116	96.20 135	85.63 142	90.53 115	97.66 79	78.14 110	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	82.53 118	96.44 186	96.04 148	84.68 168	89.12 136	98.37 35	77.48 122	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	92.34 123	98.50 40	79.90 191	98.40 42	96.40 114	84.75 164	90.48 117	98.09 52	77.40 123	99.21 94	91.15 112	98.23 52	97.92 100

reproduce_model		92.53 73	92.87 57	91.50 167	97.41 83	77.14 275	96.02 212	95.91 161	83.65 204	92.45 81	98.39 34	79.75 87	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 94	97.08 32	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	80.85 162	92.88 316	96.33 123	87.92 93	90.20 120	98.18 44	76.71 138	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	81.64 143	97.40 111	96.38 117	84.71 167	90.90 111	97.40 97	77.55 121	99.76 34	89.75 137	97.74 65	97.72 115

fmvsm_s_conf0.1_n_a		92.38 77	92.49 67	92.06 141	88.08 330	81.62 144	97.97 65	96.01 149	90.62 47	96.58 25	98.33 38	74.09 189	99.71 49	97.23 32	93.46 151	94.86 226

MVSMamba_PlusPlus		92.37 78	91.55 90	94.83 27	95.37 136	87.69 24	95.60 236	95.42 195	74.65 338	93.95 62	92.81 225	83.11 57	97.70 180	94.49 67	98.53 35	99.11 28

sasdasda		92.27 79	91.22 96	95.41 17	95.80 122	88.31 15	97.09 139	94.64 236	88.49 76	92.99 76	97.31 99	72.68 204	98.57 134	93.38 81	88.58 198	99.36 16

canonicalmvs		92.27 79	91.22 96	95.41 17	95.80 122	88.31 15	97.09 139	94.64 236	88.49 76	92.99 76	97.31 99	72.68 204	98.57 134	93.38 81	88.58 198	99.36 16

fmvsm_s_conf0.1_n_2		92.26 81	92.48 68	91.60 164	92.29 242	80.55 171	98.73 30	94.33 259	93.80 15	96.18 31	98.11 50	66.93 252	99.75 39	98.19 14	93.74 145	94.50 237

SR-MVS		92.16 82	92.27 73	91.83 153	98.37 45	78.41 232	96.67 173	95.76 170	82.19 235	91.97 92	98.07 56	76.44 141	98.64 130	93.71 76	97.27 80	98.45 60

test_fmvsmvis_n_1920		92.12 83	92.10 80	92.17 136	90.87 284	81.04 154	98.34 44	93.90 282	92.71 22	87.24 163	97.90 68	74.83 177	99.72 47	96.96 36	96.20 106	95.76 205

VNet		92.11 84	91.22 96	94.79 28	96.91 95	86.98 31	97.91 67	97.96 10	86.38 128	93.65 65	95.74 147	70.16 235	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	80.59 169	97.47 102	97.18 25	77.06 320	84.64 192	97.98 62	83.98 49	99.52 73	90.72 120	97.33 78	99.23 24

MGCFI-Net		91.95 86	91.03 102	94.72 31	95.68 126	86.38 36	96.93 154	94.48 245	88.25 84	92.78 79	97.24 105	72.34 209	98.46 142	93.13 89	88.43 202	99.32 19

PGM-MVS		91.93 87	91.80 85	92.32 127	98.27 50	79.74 197	95.28 246	97.27 20	83.83 198	90.89 112	97.78 75	76.12 148	99.56 70	88.82 147	97.93 61	97.66 121

testing99		91.91 88	91.35 93	93.60 71	95.98 115	85.70 49	97.31 117	96.92 48	86.82 122	88.91 139	95.25 163	84.26 46	97.89 174	88.80 148	87.94 208	97.21 156

testing91		91.90 89	91.31 95	93.66 67	95.99 114	85.68 51	97.39 112	96.89 49	86.75 126	88.85 141	95.23 166	83.93 50	97.90 173	88.91 145	87.89 209	97.41 142

mPP-MVS		91.88 90	91.82 84	92.07 140	98.38 44	78.63 226	97.29 118	96.09 143	85.12 156	88.45 148	97.66 79	75.53 160	99.68 55	89.83 135	98.02 57	97.88 101

EI-MVSNet-Vis-set		91.84 91	91.77 86	92.04 143	97.60 72	81.17 150	96.61 174	96.87 51	88.20 86	89.19 134	97.55 91	78.69 102	99.14 104	90.29 131	90.94 177	95.80 203

EIA-MVS		91.73 92	92.05 81	90.78 190	94.52 164	76.40 286	98.06 59	95.34 200	89.19 66	88.90 140	97.28 104	77.56 120	97.73 179	90.77 119	96.86 95	98.20 77

EC-MVSNet		91.73 92	92.11 79	90.58 194	93.54 199	77.77 258	98.07 58	94.40 255	87.44 106	92.99 76	97.11 112	74.59 183	96.87 234	93.75 75	97.08 86	97.11 161

DP-MVS Recon		91.72 94	90.85 103	94.34 38	99.50 1	85.00 76	98.51 39	95.96 154	80.57 259	88.08 155	97.63 85	76.84 133	99.89 7	85.67 175	94.88 125	98.13 84

CHOSEN 280x420		91.71 95	91.85 83	91.29 173	94.94 151	82.69 115	87.89 364	96.17 138	85.94 137	87.27 162	94.31 194	90.27 8	95.65 291	94.04 73	95.86 116	95.53 211

HY-MVS	84.06 6	91.63 96	90.37 116	95.39 19	96.12 110	88.25 17	90.22 344	97.58 15	88.33 82	90.50 116	91.96 241	79.26 91	99.06 111	90.29 131	89.07 190	98.88 37

HPM-MVS		91.62 97	91.53 91	91.89 148	97.88 63	79.22 210	96.99 144	95.73 173	82.07 237	89.50 131	97.19 108	75.59 158	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	91.47 169	95.74 124	78.79 223	96.15 207	96.77 64	88.49 76	88.64 146	97.07 115	72.33 210	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	94.52 164	81.89 132	95.95 216	95.98 152	90.76 45	83.76 203	96.76 128	73.24 200	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	90.53 110	94.24 42	97.41 83	85.18 66	98.08 56	97.72 11	80.94 250	89.85 121	96.14 139	75.61 156	98.81 126	90.42 129	88.56 200	98.74 42

DCV-MVSNet		91.46 100	90.53 110	94.24 42	97.41 83	85.18 66	98.08 56	97.72 11	80.94 250	89.85 121	96.14 139	75.61 156	98.81 126	90.42 129	88.56 200	98.74 42

PAPM_NR		91.46 100	90.82 104	93.37 82	98.50 40	81.81 137	95.03 262	96.13 140	84.65 169	86.10 174	97.65 83	79.24 92	99.75 39	83.20 203	96.88 93	98.56 54

MVSFormer		91.36 103	90.57 109	93.73 61	93.00 218	88.08 19	94.80 268	94.48 245	80.74 255	94.90 48	97.13 110	78.84 98	95.10 319	83.77 192	97.46 72	98.02 89

EI-MVSNet-UG-set		91.35 104	91.22 96	91.73 157	97.39 86	80.68 166	96.47 183	96.83 55	87.92 93	88.30 152	97.36 98	77.84 115	99.13 106	89.43 142	89.45 186	95.37 215

SR-MVS-dyc-post		91.29 105	91.45 92	90.80 188	97.76 67	76.03 292	96.20 203	95.44 191	80.56 260	90.72 113	97.84 71	75.76 155	98.61 131	91.99 104	96.79 96	97.75 113

PVSNet_Blended_VisFu		91.24 106	90.77 105	92.66 110	95.09 145	82.40 122	97.77 77	95.87 166	88.26 83	86.39 170	93.94 205	76.77 136	99.27 88	88.80 148	94.00 139	96.31 193

APD-MVS_3200maxsize		91.23 107	91.35 93	90.89 186	97.89 62	76.35 287	96.30 197	95.52 184	79.82 278	91.03 109	97.88 70	74.70 179	98.54 136	92.11 102	96.89 92	97.77 112

diffmvs		91.17 108	90.74 106	92.44 120	93.11 217	82.50 120	96.25 200	93.62 300	87.79 97	90.40 118	95.93 143	73.44 198	97.42 200	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		91.13 109	90.45 113	93.17 89	92.99 221	83.58 100	97.46 104	94.56 242	87.69 100	87.19 164	94.98 182	74.50 184	97.60 185	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		91.09 110	90.49 112	92.87 100	95.82 120	85.04 73	96.51 181	97.28 19	86.05 134	89.13 135	95.34 162	80.16 81	96.62 246	85.82 173	88.31 204	96.96 166

test_fmvsmconf0.01_n		91.08 111	90.68 107	92.29 128	82.43 384	80.12 186	97.94 66	93.93 278	92.07 28	91.97 92	97.60 86	67.56 245	99.53 72	97.09 34	95.56 121	97.21 156

CHOSEN 1792x2688		91.07 112	90.21 120	93.64 68	95.18 143	83.53 101	96.26 199	96.13 140	88.92 68	84.90 186	93.10 223	72.86 202	99.62 62	88.86 146	95.67 119	97.79 111

ETVMVS		90.99 113	90.26 117	93.19 88	95.81 121	85.64 55	96.97 149	97.18 25	85.43 146	88.77 144	94.86 184	82.00 65	96.37 253	82.70 208	88.60 197	97.57 128

CANet_DTU		90.98 114	90.04 125	93.83 54	94.76 157	86.23 38	96.32 196	93.12 324	93.11 20	93.71 64	96.82 126	63.08 277	99.48 77	84.29 185	95.12 124	95.77 204

test2506		90.96 115	90.39 114	92.65 111	93.54 199	82.46 121	96.37 191	97.35 17	86.78 124	87.55 158	95.25 163	77.83 116	97.50 196	84.07 187	94.80 126	97.98 96

thisisatest0515		90.95 116	90.26 117	93.01 95	94.03 190	84.27 89	97.91 67	96.67 78	83.18 211	86.87 168	95.51 157	88.66 15	97.85 175	80.46 221	89.01 192	96.92 170

casdiffmvs		90.95 116	90.39 114	92.63 113	92.82 225	82.53 118	96.83 160	94.47 248	87.69 100	88.47 147	95.56 156	74.04 190	97.54 192	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		90.87 118	89.96 128	93.60 71	94.15 181	83.84 95	97.14 132	98.13 7	85.93 138	89.68 125	96.09 141	71.67 218	99.30 87	87.69 161	89.16 189	97.66 121

baseline		90.76 119	90.10 123	92.74 106	92.90 224	82.56 117	94.60 270	94.56 242	87.69 100	89.06 138	95.67 151	73.76 193	97.51 195	90.43 128	92.23 167	98.16 80

Effi-MVS+		90.70 120	89.90 131	93.09 92	93.61 196	83.48 102	95.20 252	92.79 330	83.22 210	91.82 95	95.70 149	71.82 217	97.48 198	91.25 111	93.67 147	98.32 67

MAR-MVS		90.63 121	90.22 119	91.86 150	98.47 42	78.20 242	97.18 125	96.61 87	83.87 196	88.18 153	98.18 44	68.71 240	99.75 39	83.66 197	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		90.60 122	88.64 148	96.50 5	94.25 177	90.53 8	93.33 304	97.21 22	77.59 311	78.88 257	97.31 99	71.52 221	99.69 53	89.60 138	98.03 56	99.27 22

xiu_mvs_v1_base_debu		90.54 123	89.54 134	93.55 74	92.31 238	87.58 26	96.99 144	94.87 219	87.23 113	93.27 68	97.56 88	57.43 319	98.32 150	92.72 93	93.46 151	94.74 230

xiu_mvs_v1_base		90.54 123	89.54 134	93.55 74	92.31 238	87.58 26	96.99 144	94.87 219	87.23 113	93.27 68	97.56 88	57.43 319	98.32 150	92.72 93	93.46 151	94.74 230

xiu_mvs_v1_base_debi		90.54 123	89.54 134	93.55 74	92.31 238	87.58 26	96.99 144	94.87 219	87.23 113	93.27 68	97.56 88	57.43 319	98.32 150	92.72 93	93.46 151	94.74 230

mvsmamba		90.53 126	90.08 124	91.88 149	94.81 155	80.93 159	93.94 289	94.45 250	88.24 85	87.02 167	92.35 232	68.04 242	95.80 279	94.86 61	97.03 88	98.92 34

baseline2		90.39 127	90.21 120	90.93 183	90.86 285	80.99 156	95.20 252	97.41 16	86.03 136	80.07 247	94.61 189	90.58 6	97.47 199	87.29 165	89.86 184	94.35 238

ACMMP		90.39 127	89.97 127	91.64 161	97.58 74	78.21 241	96.78 165	96.72 72	84.73 166	84.72 190	97.23 106	71.22 223	99.63 61	88.37 155	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		90.38 129	90.17 122	91.03 181	97.61 71	77.35 269	97.15 131	95.48 187	79.51 284	88.79 142	96.90 120	71.64 220	98.81 126	87.01 169	97.44 74	96.94 167

MVS_Test		90.29 130	89.18 138	93.62 70	95.23 140	84.93 77	94.41 273	94.66 233	84.31 178	90.37 119	91.02 254	75.13 173	97.82 176	83.11 205	94.42 133	98.12 85

API-MVS		90.18 131	88.97 141	93.80 55	98.66 28	82.95 112	97.50 101	95.63 178	75.16 333	86.31 171	97.69 77	72.49 207	99.90 5	81.26 217	96.07 110	98.56 54

PVSNet_BlendedMVS		90.05 132	89.96 128	90.33 202	97.47 77	83.86 93	98.02 62	96.73 70	87.98 91	89.53 129	89.61 275	76.42 142	99.57 68	94.29 69	79.59 274	87.57 342

ET-MVSNet_ETH3D		90.01 133	89.03 139	92.95 97	94.38 174	86.77 33	98.14 50	96.31 126	89.30 65	63.33 376	96.72 131	90.09 10	93.63 353	90.70 122	82.29 261	98.46 59

test_vis1_n_1920		89.95 134	90.59 108	88.03 256	92.36 237	68.98 358	99.12 13	94.34 258	93.86 14	93.64 66	97.01 118	51.54 350	99.59 64	96.76 39	96.71 100	95.53 211

test_cas_vis1_n_1920		89.90 135	90.02 126	89.54 224	90.14 300	74.63 307	98.71 31	94.43 253	93.04 21	92.40 84	96.35 136	53.41 346	99.08 110	95.59 51	96.16 107	94.90 224

TESTMET0.1,1		89.83 136	89.34 137	91.31 171	92.54 235	80.19 184	97.11 135	96.57 94	86.15 130	86.85 169	91.83 245	79.32 89	96.95 228	81.30 216	92.35 165	96.77 176

EPP-MVSNet		89.76 137	89.72 133	89.87 217	93.78 192	76.02 294	97.22 120	96.51 100	79.35 286	85.11 182	95.01 180	84.82 37	97.10 221	87.46 164	88.21 206	96.50 185

CPTT-MVS		89.72 138	89.87 132	89.29 227	98.33 47	73.30 318	97.70 83	95.35 199	75.68 329	87.40 159	97.44 95	70.43 232	98.25 153	89.56 140	96.90 91	96.33 192

RRT-MVS		89.67 139	88.67 147	92.67 109	94.44 171	81.08 153	94.34 276	94.45 250	86.05 134	85.79 176	92.39 231	63.39 275	98.16 158	93.22 86	93.95 141	98.76 41

thisisatest0530		89.65 140	89.02 140	91.53 166	93.46 205	80.78 164	96.52 179	96.67 78	81.69 243	83.79 202	94.90 183	88.85 14	97.68 181	77.80 245	87.49 215	96.14 196

3Dnovator+	82.88 8	89.63 141	87.85 161	94.99 23	94.49 170	86.76 34	97.84 71	95.74 172	86.10 132	75.47 302	96.02 142	65.00 267	99.51 75	82.91 207	97.07 87	98.72 47

CDS-MVSNet		89.50 142	88.96 142	91.14 179	91.94 263	80.93 159	97.09 139	95.81 168	84.26 183	84.72 190	94.20 199	80.31 76	95.64 292	83.37 202	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		89.46 143	89.92 130	88.06 254	94.64 158	69.57 355	96.22 201	94.95 214	87.27 112	91.37 102	96.54 134	65.88 259	97.39 203	88.54 150	93.89 142	97.23 153

HyFIR lowres test		89.36 144	88.60 149	91.63 163	94.91 153	80.76 165	95.60 236	95.53 182	82.56 228	84.03 196	91.24 251	78.03 111	96.81 238	87.07 168	88.41 203	97.32 148

3Dnovator	82.32 10	89.33 145	87.64 166	94.42 37	93.73 195	85.70 49	97.73 81	96.75 68	86.73 127	76.21 291	95.93 143	62.17 281	99.68 55	81.67 215	97.81 63	97.88 101

h-mvs33		89.30 146	88.95 143	90.36 201	95.07 147	76.04 291	96.96 151	97.11 30	90.39 52	92.22 88	95.10 176	74.70 179	98.86 123	93.14 87	65.89 367	96.16 195

LFMVS		89.27 147	87.64 166	94.16 48	97.16 92	85.52 58	97.18 125	94.66 233	79.17 292	89.63 127	96.57 133	55.35 336	98.22 154	89.52 141	89.54 185	98.74 42

MVSTER		89.25 148	88.92 144	90.24 204	95.98 115	84.66 81	96.79 164	95.36 197	87.19 116	80.33 242	90.61 261	90.02 11	95.97 268	85.38 178	78.64 283	90.09 284

CostFormer		89.08 149	88.39 153	91.15 178	93.13 215	79.15 213	88.61 356	96.11 142	83.14 212	89.58 128	86.93 314	83.83 52	96.87 234	88.22 156	85.92 229	97.42 141

PVSNet	82.34 9	89.02 150	87.79 163	92.71 108	95.49 132	81.50 146	97.70 83	97.29 18	87.76 98	85.47 180	95.12 175	56.90 325	98.90 122	80.33 222	94.02 137	97.71 117

test-mter		88.95 151	88.60 149	89.98 212	92.26 244	77.23 271	97.11 135	95.96 154	85.32 149	86.30 172	91.38 248	76.37 144	96.78 240	80.82 218	91.92 169	95.94 200

1314		88.94 152	87.20 180	94.17 46	93.21 210	85.73 48	93.33 304	96.64 84	82.89 219	75.98 294	96.36 135	66.83 254	99.39 81	83.52 201	96.02 113	97.39 145

UA-Net		88.92 153	88.48 152	90.24 204	94.06 187	77.18 273	93.04 312	94.66 233	87.39 108	91.09 107	93.89 206	74.92 176	98.18 157	75.83 272	91.43 173	95.35 216

thres200		88.92 153	87.65 165	92.73 107	96.30 103	85.62 56	97.85 70	98.86 1	84.38 177	84.82 187	93.99 204	75.12 174	98.01 164	70.86 313	86.67 219	94.56 236

Vis-MVSNet (Re-imp)		88.88 155	88.87 146	88.91 234	93.89 191	74.43 310	96.93 154	94.19 267	84.39 176	83.22 208	95.67 151	78.24 107	94.70 330	78.88 240	94.40 134	97.61 126

baseline1		88.85 156	87.49 173	92.93 99	95.21 142	86.85 32	95.47 241	94.61 239	87.29 110	83.11 210	94.99 181	80.70 72	96.89 232	82.28 211	73.72 307	95.05 222

AdaColmap		88.81 157	87.61 169	92.39 122	99.33 4	79.95 189	96.70 172	95.58 179	77.51 312	83.05 211	96.69 132	61.90 287	99.72 47	84.29 185	93.47 150	97.50 135

OMC-MVS		88.80 158	88.16 157	90.72 191	95.30 138	77.92 251	94.81 267	94.51 244	86.80 123	84.97 185	96.85 123	67.53 246	98.60 132	85.08 179	87.62 212	95.63 207

114514_t		88.79 159	87.57 171	92.45 118	98.21 53	81.74 139	96.99 144	95.45 190	75.16 333	82.48 214	95.69 150	68.59 241	98.50 138	80.33 222	95.18 123	97.10 162

mvs_anonymous		88.68 160	87.62 168	91.86 150	94.80 156	81.69 142	93.53 300	94.92 216	82.03 238	78.87 258	90.43 264	75.77 154	95.34 305	85.04 180	93.16 155	98.55 56

Vis-MVSNet		88.67 161	87.82 162	91.24 175	92.68 228	78.82 220	96.95 152	93.85 286	87.55 103	87.07 166	95.13 174	63.43 274	97.21 213	77.58 252	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		88.67 161	88.16 157	90.20 206	93.61 196	76.86 278	96.77 167	93.07 325	84.02 189	83.62 204	95.60 154	74.69 182	96.24 260	78.43 244	93.66 148	97.49 136

IB-MVS	85.34 4	88.67 161	87.14 183	93.26 84	93.12 216	84.32 86	98.76 29	97.27 20	87.19 116	79.36 253	90.45 263	83.92 51	98.53 137	84.41 184	69.79 334	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		88.60 164	87.47 175	92.00 145	93.21 210	80.97 157	96.47 183	92.46 333	83.64 205	80.86 235	97.30 102	80.24 78	97.62 184	77.60 251	85.49 234	97.40 144

tttt0517		88.57 165	88.19 156	89.71 223	93.00 218	75.99 295	95.67 231	96.67 78	80.78 254	81.82 227	94.40 193	88.97 13	97.58 187	76.05 270	86.31 223	95.57 209

UWE-MVS		88.56 166	88.91 145	87.50 270	94.17 180	72.19 329	95.82 226	97.05 35	84.96 161	84.78 188	93.51 217	81.33 67	94.75 328	79.43 233	89.17 188	95.57 209

tfpn200view9		88.48 167	87.15 181	92.47 117	96.21 106	85.30 64	97.44 105	98.85 2	83.37 208	83.99 197	93.82 209	75.36 167	97.93 167	69.04 321	86.24 226	94.17 239

test-LLR		88.48 167	87.98 159	89.98 212	92.26 244	77.23 271	97.11 135	95.96 154	83.76 201	86.30 172	91.38 248	72.30 211	96.78 240	80.82 218	91.92 169	95.94 200

TAMVS		88.48 167	87.79 163	90.56 195	91.09 279	79.18 211	96.45 185	95.88 164	83.64 205	83.12 209	93.33 218	75.94 152	95.74 287	82.40 210	88.27 205	96.75 179

thres400		88.42 170	87.15 181	92.23 131	96.21 106	85.30 64	97.44 105	98.85 2	83.37 208	83.99 197	93.82 209	75.36 167	97.93 167	69.04 321	86.24 226	93.45 255

tpmrst		88.36 171	87.38 177	91.31 171	94.36 175	79.92 190	87.32 368	95.26 204	85.32 149	88.34 150	86.13 331	80.60 74	96.70 242	83.78 191	85.34 237	97.30 151

ECVR-MVS		88.35 172	87.25 179	91.65 160	93.54 199	79.40 205	96.56 178	90.78 362	86.78 124	85.57 178	95.25 163	57.25 323	97.56 188	84.73 183	94.80 126	97.98 96

thres100view900		88.30 173	86.95 187	92.33 125	96.10 111	84.90 78	97.14 132	98.85 2	82.69 225	83.41 205	93.66 213	75.43 164	97.93 167	69.04 321	86.24 226	94.17 239

VDD-MVS		88.28 174	87.02 186	92.06 141	95.09 145	80.18 185	97.55 96	94.45 250	83.09 213	89.10 137	95.92 145	47.97 364	98.49 139	93.08 91	86.91 218	97.52 134

BH-w/o		88.24 175	87.47 175	90.54 197	95.03 150	78.54 227	97.41 110	93.82 287	84.08 187	78.23 264	94.51 192	69.34 238	97.21 213	80.21 226	94.58 130	95.87 202

hse-mvs2		88.22 176	88.21 155	88.25 250	93.54 199	73.41 315	95.41 244	95.89 162	90.39 52	92.22 88	94.22 197	74.70 179	96.66 245	93.14 87	64.37 372	94.69 235

test1111		88.11 177	87.04 185	91.35 170	93.15 213	78.79 223	96.57 176	90.78 362	86.88 121	85.04 183	95.20 169	57.23 324	97.39 203	83.88 189	94.59 129	97.87 103

thres600view7		88.06 178	86.70 193	92.15 138	96.10 111	85.17 70	97.14 132	98.85 2	82.70 224	83.41 205	93.66 213	75.43 164	97.82 176	67.13 330	85.88 230	93.45 255

Test_1112_low_res		88.03 179	86.73 191	91.94 147	93.15 213	80.88 161	96.44 186	92.41 335	83.59 207	80.74 237	91.16 252	80.18 79	97.59 186	77.48 254	85.40 235	97.36 147

PLC	83.97 7	88.00 180	87.38 177	89.83 219	98.02 59	76.46 284	97.16 129	94.43 253	79.26 291	81.98 224	96.28 137	69.36 237	99.27 88	77.71 249	92.25 166	93.77 249
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 181	87.48 174	89.44 225	92.16 251	80.54 174	98.14 50	94.92 216	91.41 36	79.43 252	95.40 160	62.34 280	97.27 211	90.60 123	82.90 253	90.50 274
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+		87.93 182	86.94 188	90.92 184	94.04 188	79.16 212	98.26 46	93.72 296	81.29 246	83.94 200	92.90 224	69.83 236	96.68 243	76.70 262	91.74 171	96.93 168

HQP-MVS		87.91 183	87.55 172	88.98 233	92.08 255	78.48 228	97.63 86	94.80 224	90.52 49	82.30 217	94.56 190	65.40 263	97.32 206	87.67 162	83.01 250	91.13 267

reproduce_monomvs		87.80 184	87.60 170	88.40 244	96.56 98	80.26 181	95.80 227	96.32 125	91.56 35	73.60 313	88.36 291	88.53 16	96.25 259	90.47 125	67.23 360	88.67 317

test_fmvs1		87.79 185	88.52 151	85.62 305	92.98 222	64.31 378	97.88 69	92.42 334	87.95 92	92.24 87	95.82 146	47.94 365	98.44 146	95.31 57	94.09 135	94.09 243

WBMVS		87.73 186	86.79 189	90.56 195	95.61 128	85.68 51	97.63 86	95.52 184	83.77 200	78.30 263	88.44 290	86.14 32	95.78 281	82.54 209	73.15 313	90.21 279

UGNet		87.73 186	86.55 194	91.27 174	95.16 144	79.11 214	96.35 193	96.23 132	88.14 87	87.83 157	90.48 262	50.65 353	99.09 109	80.13 227	94.03 136	95.60 208
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
FA-MVS(test-final)		87.71 188	86.23 197	92.17 136	94.19 179	80.55 171	87.16 370	96.07 146	82.12 236	85.98 175	88.35 292	72.04 215	98.49 139	80.26 224	89.87 183	97.48 137

EPNet_dtu		87.65 189	87.89 160	86.93 283	94.57 160	71.37 343	96.72 168	96.50 102	88.56 75	87.12 165	95.02 179	75.91 153	94.01 345	66.62 333	90.00 182	95.42 214
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 190	88.22 154	85.67 303	89.78 304	67.18 365	95.25 249	87.93 383	83.96 192	88.79 142	97.06 116	72.52 206	94.53 335	92.21 100	86.45 222	95.30 218

HQP_MVS		87.50 191	87.09 184	88.74 238	91.86 264	77.96 248	97.18 125	94.69 229	89.89 58	81.33 230	94.15 200	64.77 268	97.30 208	87.08 166	82.82 254	90.96 269

EPMVS		87.47 192	85.90 200	92.18 135	95.41 134	82.26 125	87.00 371	96.28 127	85.88 139	84.23 194	85.57 338	75.07 175	96.26 257	71.14 311	92.50 162	98.03 88

tpm2		87.35 193	86.26 196	90.62 193	92.93 223	78.67 225	88.06 363	95.99 151	79.33 287	87.40 159	86.43 325	80.28 77	96.40 251	80.23 225	85.73 233	96.79 174

ab-mvs		87.08 194	84.94 216	93.48 79	93.34 208	83.67 98	88.82 353	95.70 174	81.18 247	84.55 193	90.14 270	62.72 278	98.94 120	85.49 177	82.54 258	97.85 105

SDMVSNet		87.02 195	85.61 202	91.24 175	94.14 182	83.30 106	93.88 291	95.98 152	84.30 180	79.63 250	92.01 237	58.23 309	97.68 181	90.28 133	82.02 262	92.75 258

CNLPA		86.96 196	85.37 207	91.72 159	97.59 73	79.34 208	97.21 121	91.05 357	74.22 340	78.90 256	96.75 130	67.21 250	98.95 118	74.68 282	90.77 178	96.88 172

BH-untuned		86.95 197	85.94 199	89.99 211	94.52 164	77.46 266	96.78 165	93.37 313	81.80 240	76.62 282	93.81 211	66.64 255	97.02 223	76.06 269	93.88 143	95.48 213

QAPM		86.88 198	84.51 220	93.98 49	94.04 188	85.89 45	97.19 124	96.05 147	73.62 345	75.12 305	95.62 153	62.02 284	99.74 42	70.88 312	96.06 111	96.30 194

BH-RMVSNet		86.84 199	85.28 208	91.49 168	95.35 137	80.26 181	96.95 152	92.21 337	82.86 221	81.77 229	95.46 159	59.34 301	97.64 183	69.79 319	93.81 144	96.57 184

PatchmatchNet		86.83 200	85.12 213	91.95 146	94.12 184	82.27 124	86.55 375	95.64 177	84.59 171	82.98 212	84.99 350	77.26 125	95.96 271	68.61 324	91.34 174	97.64 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 201	85.43 205	90.87 187	88.76 319	85.34 61	97.06 142	94.33 259	84.31 178	80.45 240	91.98 240	72.36 208	96.36 254	88.48 153	71.13 321	90.93 271

PCF-MVS	84.09 5	86.77 202	85.00 215	92.08 139	92.06 258	83.07 110	92.14 325	94.47 248	79.63 282	76.90 278	94.78 186	71.15 224	99.20 99	72.87 297	91.05 176	93.98 245
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 203	86.10 198	88.61 240	90.05 301	80.21 183	96.14 208	96.95 44	85.56 145	78.37 262	92.30 233	76.73 137	95.28 309	79.51 231	79.27 277	90.35 276

cascas		86.50 204	84.48 222	92.55 116	92.64 232	85.95 42	97.04 143	95.07 210	75.32 331	80.50 238	91.02 254	54.33 343	97.98 166	86.79 170	87.62 212	93.71 250

VDDNet		86.44 205	84.51 220	92.22 132	91.56 267	81.83 135	97.10 138	94.64 236	69.50 372	87.84 156	95.19 170	48.01 363	97.92 172	89.82 136	86.92 217	96.89 171

GeoE		86.36 206	85.20 209	89.83 219	93.17 212	76.13 289	97.53 97	92.11 338	79.58 283	80.99 233	94.01 203	66.60 256	96.17 263	73.48 294	89.30 187	97.20 158

test_fmvs1_n		86.34 207	86.72 192	85.17 312	87.54 337	63.64 383	96.91 156	92.37 336	87.49 105	91.33 103	95.58 155	40.81 392	98.46 142	95.00 60	93.49 149	93.41 257

TR-MVS		86.30 208	84.93 217	90.42 199	94.63 159	77.58 264	96.57 176	93.82 287	80.30 268	82.42 216	95.16 172	58.74 305	97.55 190	74.88 280	87.82 210	96.13 197

X-MVStestdata		86.26 209	84.14 230	92.63 113	98.52 37	80.29 178	97.37 113	96.44 108	87.04 118	91.38 100	20.73 428	77.24 127	99.59 64	90.46 126	98.07 54	98.02 89

AUN-MVS		86.25 210	85.57 203	88.26 249	93.57 198	73.38 316	95.45 242	95.88 164	83.94 193	85.47 180	94.21 198	73.70 196	96.67 244	83.54 199	64.41 371	94.73 234

OpenMVS	79.58 14	86.09 211	83.62 237	93.50 77	90.95 281	86.71 35	97.44 105	95.83 167	75.35 330	72.64 327	95.72 148	57.42 322	99.64 59	71.41 306	95.85 117	94.13 242

FE-MVS		86.06 212	84.15 229	91.78 154	94.33 176	79.81 192	84.58 388	96.61 87	76.69 323	85.00 184	87.38 305	70.71 231	98.37 148	70.39 316	91.70 172	97.17 159

FC-MVSNet-test		85.96 213	85.39 206	87.66 263	89.38 316	78.02 245	95.65 233	96.87 51	85.12 156	77.34 271	91.94 243	76.28 146	94.74 329	77.09 257	78.82 281	90.21 279

miper_enhance_ethall		85.95 214	85.20 209	88.19 253	94.85 154	79.76 194	96.00 213	94.06 275	82.98 218	77.74 269	88.76 283	79.42 88	95.46 301	80.58 220	72.42 315	89.36 297

OPM-MVS		85.84 215	85.10 214	88.06 254	88.34 327	77.83 255	95.72 229	94.20 266	87.89 96	80.45 240	94.05 202	58.57 306	97.26 212	83.88 189	82.76 256	89.09 304
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		85.80 216	85.20 209	87.59 266	91.55 268	77.41 267	95.13 256	95.36 197	80.43 265	80.33 242	94.71 187	73.72 194	95.97 268	76.96 260	78.64 283	89.39 292

GA-MVS		85.79 217	84.04 231	91.02 182	89.47 314	80.27 180	96.90 157	94.84 222	85.57 143	80.88 234	89.08 278	56.56 329	96.47 250	77.72 248	85.35 236	96.34 190

XVG-OURS-SEG-HR		85.74 218	85.16 212	87.49 272	90.22 296	71.45 341	91.29 336	94.09 273	81.37 245	83.90 201	95.22 167	60.30 294	97.53 194	85.58 176	84.42 241	93.50 253

MonoMVSNet		85.68 219	84.22 227	90.03 209	88.43 326	77.83 255	92.95 315	91.46 348	87.28 111	78.11 265	85.96 333	66.31 258	94.81 327	90.71 121	76.81 294	97.46 139

SCA		85.63 220	83.64 236	91.60 164	92.30 241	81.86 134	92.88 316	95.56 181	84.85 162	82.52 213	85.12 348	58.04 312	95.39 302	73.89 290	87.58 214	97.54 129

test_vis1_n		85.60 221	85.70 201	85.33 309	84.79 368	64.98 376	96.83 160	91.61 347	87.36 109	91.00 110	94.84 185	36.14 399	97.18 215	95.66 49	93.03 156	93.82 248

tpm		85.55 222	84.47 223	88.80 237	90.19 297	75.39 302	88.79 354	94.69 229	84.83 163	83.96 199	85.21 344	78.22 108	94.68 332	76.32 268	78.02 291	96.34 190

mamv4		85.50 223	86.76 190	81.72 352	93.23 209	54.93 409	89.95 346	92.94 327	69.96 369	79.00 255	92.20 235	80.69 73	94.22 341	92.06 103	90.77 178	96.01 198

UniMVSNet_NR-MVSNet		85.49 224	84.59 219	88.21 252	89.44 315	79.36 206	96.71 170	96.41 112	85.22 152	78.11 265	90.98 256	76.97 132	95.14 316	79.14 237	68.30 348	90.12 282

gg-mvs-nofinetune		85.48 225	82.90 248	93.24 85	94.51 168	85.82 46	79.22 401	96.97 42	61.19 398	87.33 161	53.01 417	90.58 6	96.07 264	86.07 172	97.23 81	97.81 110

UWE-MVS-28		85.41 226	86.36 195	82.59 345	91.12 278	66.81 370	93.88 291	97.03 36	83.86 197	78.55 259	93.84 208	77.76 118	88.55 391	73.47 295	87.69 211	92.41 262

VPA-MVSNet		85.32 227	83.83 232	89.77 222	90.25 295	82.63 116	96.36 192	97.07 33	83.03 216	81.21 232	89.02 280	61.58 288	96.31 256	85.02 181	70.95 323	90.36 275

UniMVSNet (Re)		85.31 228	84.23 226	88.55 241	89.75 305	80.55 171	96.72 168	96.89 49	85.42 147	78.40 261	88.93 281	75.38 166	95.52 299	78.58 242	68.02 351	89.57 291

XVG-OURS		85.18 229	84.38 224	87.59 266	90.42 294	71.73 338	91.06 339	94.07 274	82.00 239	83.29 207	95.08 177	56.42 330	97.55 190	83.70 196	83.42 246	93.49 254

cl22		85.11 230	84.17 228	87.92 257	95.06 149	78.82 220	95.51 239	94.22 265	79.74 280	76.77 279	87.92 299	75.96 150	95.68 288	79.93 229	72.42 315	89.27 299

TAPA-MVS	81.61 12	85.02 231	83.67 234	89.06 230	96.79 96	73.27 321	95.92 218	94.79 226	74.81 336	80.47 239	96.83 124	71.07 225	98.19 156	49.82 397	92.57 160	95.71 206
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 232	83.66 235	89.02 232	95.86 119	74.55 309	92.49 320	93.60 301	79.30 289	79.29 254	91.47 246	58.53 307	98.45 144	70.22 317	92.17 168	94.07 244

PS-MVSNAJss		84.91 233	84.30 225	86.74 284	85.89 356	74.40 311	94.95 263	94.16 269	83.93 194	76.45 284	90.11 271	71.04 226	95.77 282	83.16 204	79.02 280	90.06 286

CVMVSNet		84.83 234	85.57 203	82.63 344	91.55 268	60.38 395	95.13 256	95.03 212	80.60 258	82.10 223	94.71 187	66.40 257	90.19 386	74.30 287	90.32 181	97.31 150

FMVSNet3		84.71 235	82.71 252	90.70 192	94.55 162	87.71 23	95.92 218	94.67 232	81.73 242	75.82 297	88.08 297	66.99 251	94.47 336	71.23 308	75.38 300	89.91 288

VPNet		84.69 236	82.92 247	90.01 210	89.01 318	83.45 103	96.71 170	95.46 189	85.71 141	79.65 249	92.18 236	56.66 328	96.01 267	83.05 206	67.84 354	90.56 273

sd_testset		84.62 237	83.11 245	89.17 228	94.14 182	77.78 257	91.54 335	94.38 256	84.30 180	79.63 250	92.01 237	52.28 348	96.98 226	77.67 250	82.02 262	92.75 258

Effi-MVS+-dtu		84.61 238	84.90 218	83.72 334	91.96 261	63.14 386	94.95 263	93.34 314	85.57 143	79.79 248	87.12 311	61.99 285	95.61 295	83.55 198	85.83 231	92.41 262

miper_ehance_all_eth		84.57 239	83.60 238	87.50 270	92.64 232	78.25 237	95.40 245	93.47 305	79.28 290	76.41 285	87.64 302	76.53 139	95.24 311	78.58 242	72.42 315	89.01 309

DU-MVS		84.57 239	83.33 243	88.28 248	88.76 319	79.36 206	96.43 188	95.41 196	85.42 147	78.11 265	90.82 257	67.61 243	95.14 316	79.14 237	68.30 348	90.33 277

F-COLMAP		84.50 241	83.44 242	87.67 262	95.22 141	72.22 327	95.95 216	93.78 292	75.74 328	76.30 288	95.18 171	59.50 299	98.45 144	72.67 299	86.59 221	92.35 264

Anonymous202405211		84.41 242	81.93 263	91.85 152	96.78 97	78.41 232	97.44 105	91.34 352	70.29 367	84.06 195	94.26 196	41.09 389	98.96 116	79.46 232	82.65 257	98.17 79

WR-MVS		84.32 243	82.96 246	88.41 243	89.38 316	80.32 177	96.59 175	96.25 130	83.97 191	76.63 281	90.36 265	67.53 246	94.86 325	75.82 273	70.09 332	90.06 286

dp		84.30 244	82.31 257	90.28 203	94.24 178	77.97 247	86.57 374	95.53 182	79.94 277	80.75 236	85.16 346	71.49 222	96.39 252	63.73 348	83.36 247	96.48 186

LPG-MVS_test		84.20 245	83.49 241	86.33 290	90.88 282	73.06 322	95.28 246	94.13 270	82.20 233	76.31 286	93.20 219	54.83 341	96.95 228	83.72 194	80.83 267	88.98 310

dmvs_re		84.10 246	82.90 248	87.70 261	91.41 272	73.28 319	90.59 342	93.19 318	85.02 158	77.96 268	93.68 212	57.92 317	96.18 262	75.50 275	80.87 266	93.63 251

WB-MVSnew		84.08 247	83.51 240	85.80 299	91.34 273	76.69 282	95.62 235	96.27 128	81.77 241	81.81 228	92.81 225	58.23 309	94.70 330	66.66 332	87.06 216	85.99 366

ACMP	81.66 11	84.00 248	83.22 244	86.33 290	91.53 270	72.95 325	95.91 220	93.79 291	83.70 203	73.79 312	92.22 234	54.31 344	96.89 232	83.98 188	79.74 272	89.16 302
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 249	82.80 251	87.31 276	91.46 271	77.39 268	95.66 232	93.43 308	80.44 263	75.51 301	87.26 308	73.72 194	95.16 315	76.99 258	70.72 325	89.39 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 250	82.00 262	89.35 226	87.13 339	81.38 147	95.72 229	94.26 262	80.15 272	75.92 296	90.63 260	61.96 286	96.52 248	78.98 239	73.28 312	90.14 281

c3_l		83.80 251	82.65 253	87.25 278	92.10 254	77.74 262	95.25 249	93.04 326	78.58 301	76.01 293	87.21 310	75.25 172	95.11 318	77.54 253	68.89 342	88.91 315

LCM-MVSNet-Re		83.75 252	83.54 239	84.39 327	93.54 199	64.14 380	92.51 319	84.03 403	83.90 195	66.14 364	86.59 319	67.36 248	92.68 360	84.89 182	92.87 157	96.35 189

ACMM	80.70 13	83.72 253	82.85 250	86.31 293	91.19 275	72.12 331	95.88 221	94.29 261	80.44 263	77.02 276	91.96 241	55.24 337	97.14 220	79.30 235	80.38 269	89.67 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 254	81.38 271	90.39 200	93.53 204	78.19 243	85.56 382	95.09 208	70.78 365	78.51 260	83.28 365	74.80 178	97.03 222	66.77 331	84.05 242	95.95 199

CR-MVSNet		83.53 255	81.36 272	90.06 208	90.16 298	79.75 195	79.02 403	91.12 354	84.24 184	82.27 221	80.35 380	75.45 162	93.67 352	63.37 351	86.25 224	96.75 179

v2v482		83.46 256	81.86 264	88.25 250	86.19 350	79.65 200	96.34 194	94.02 276	81.56 244	77.32 272	88.23 294	65.62 260	96.03 265	77.77 246	69.72 336	89.09 304

NR-MVSNet		83.35 257	81.52 270	88.84 235	88.76 319	81.31 149	94.45 272	95.16 206	84.65 169	67.81 353	90.82 257	70.36 233	94.87 324	74.75 281	66.89 364	90.33 277

Fast-Effi-MVS+-dtu		83.33 258	82.60 254	85.50 307	89.55 312	69.38 356	96.09 211	91.38 349	82.30 232	75.96 295	91.41 247	56.71 326	95.58 297	75.13 279	84.90 239	91.54 265

cl____		83.27 259	82.12 259	86.74 284	92.20 247	75.95 296	95.11 258	93.27 316	78.44 304	74.82 307	87.02 313	74.19 187	95.19 313	74.67 283	69.32 338	89.09 304

DIV-MVS_self_test		83.27 259	82.12 259	86.74 284	92.19 248	75.92 298	95.11 258	93.26 317	78.44 304	74.81 308	87.08 312	74.19 187	95.19 313	74.66 284	69.30 339	89.11 303

TranMVSNet+NR-MVSNet		83.24 261	81.71 266	87.83 258	87.71 334	78.81 222	96.13 210	94.82 223	84.52 172	76.18 292	90.78 259	64.07 271	94.60 333	74.60 285	66.59 366	90.09 284

Anonymous20240529		83.15 262	80.60 282	90.80 188	95.74 124	78.27 236	96.81 163	94.92 216	60.10 403	81.89 226	92.54 229	45.82 373	98.82 125	79.25 236	78.32 289	95.31 217

eth_miper_zixun_eth		83.12 263	82.01 261	86.47 289	91.85 266	74.80 305	94.33 277	93.18 320	79.11 293	75.74 300	87.25 309	72.71 203	95.32 307	76.78 261	67.13 361	89.27 299

MS-PatchMatch		83.05 264	81.82 265	86.72 288	89.64 309	79.10 215	94.88 265	94.59 241	79.70 281	70.67 341	89.65 274	50.43 355	96.82 237	70.82 315	95.99 115	84.25 379

V42		83.04 265	81.53 269	87.57 268	86.27 349	79.09 216	95.87 222	94.11 272	80.35 267	77.22 274	86.79 317	65.32 265	96.02 266	77.74 247	70.14 328	87.61 341

tpmvs		83.04 265	80.77 278	89.84 218	95.43 133	77.96 248	85.59 381	95.32 201	75.31 332	76.27 289	83.70 361	73.89 191	97.41 201	59.53 363	81.93 264	94.14 241

test_djsdf		83.00 267	82.45 256	84.64 320	84.07 376	69.78 352	94.80 268	94.48 245	80.74 255	75.41 303	87.70 301	61.32 291	95.10 319	83.77 192	79.76 270	89.04 307

v1144		82.90 268	81.27 273	87.78 260	86.29 348	79.07 217	96.14 208	93.93 278	80.05 274	77.38 270	86.80 316	65.50 261	95.93 273	75.21 278	70.13 329	88.33 328

test0.0.03 1		82.79 269	82.48 255	83.74 333	86.81 342	72.22 327	96.52 179	95.03 212	83.76 201	73.00 323	93.20 219	72.30 211	88.88 389	64.15 346	77.52 292	90.12 282

FMVSNet2		82.79 269	80.44 284	89.83 219	92.66 229	85.43 59	95.42 243	94.35 257	79.06 295	74.46 309	87.28 306	56.38 331	94.31 339	69.72 320	74.68 304	89.76 289

D2MVS		82.67 271	81.55 268	86.04 297	87.77 333	76.47 283	95.21 251	96.58 93	82.66 226	70.26 344	85.46 341	60.39 293	95.80 279	76.40 266	79.18 278	85.83 369

MVP-Stereo		82.65 272	81.67 267	85.59 306	86.10 353	78.29 235	93.33 304	92.82 329	77.75 309	69.17 351	87.98 298	59.28 302	95.76 283	71.77 303	96.88 93	82.73 387
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 273	80.79 277	87.79 259	86.11 352	80.49 176	93.55 299	93.18 320	77.29 315	73.35 319	89.40 277	65.26 266	95.05 322	75.32 277	73.61 308	87.83 336

v144192		82.43 274	80.73 279	87.54 269	85.81 357	78.22 238	95.98 214	93.78 292	79.09 294	77.11 275	86.49 321	64.66 270	95.91 274	74.20 288	69.42 337	88.49 322

GBi-Net		82.42 275	80.43 285	88.39 245	92.66 229	81.95 127	94.30 279	93.38 310	79.06 295	75.82 297	85.66 334	56.38 331	93.84 348	71.23 308	75.38 300	89.38 294

test1		82.42 275	80.43 285	88.39 245	92.66 229	81.95 127	94.30 279	93.38 310	79.06 295	75.82 297	85.66 334	56.38 331	93.84 348	71.23 308	75.38 300	89.38 294

v148		82.41 277	80.89 276	86.99 282	86.18 351	76.81 279	96.27 198	93.82 287	80.49 262	75.28 304	86.11 332	67.32 249	95.75 284	75.48 276	67.03 363	88.42 326

v1192		82.31 278	80.55 283	87.60 265	85.94 354	78.47 231	95.85 224	93.80 290	79.33 287	76.97 277	86.51 320	63.33 276	95.87 275	73.11 296	70.13 329	88.46 324

LS3D		82.22 279	79.94 293	89.06 230	97.43 82	74.06 314	93.20 310	92.05 339	61.90 393	73.33 320	95.21 168	59.35 300	99.21 94	54.54 384	92.48 163	93.90 247

jajsoiax		82.12 280	81.15 275	85.03 314	84.19 374	70.70 345	94.22 283	93.95 277	83.07 214	73.48 315	89.75 273	49.66 359	95.37 304	82.24 212	79.76 270	89.02 308

v1921920		82.02 281	80.23 287	87.41 273	85.62 358	77.92 251	95.79 228	93.69 297	78.86 298	76.67 280	86.44 323	62.50 279	95.83 277	72.69 298	69.77 335	88.47 323

myMVS_eth3d		81.93 282	82.18 258	81.18 355	92.13 252	67.18 365	93.97 287	94.23 263	82.43 229	73.39 316	93.57 215	76.98 131	87.86 395	50.53 395	82.34 259	88.51 320

v8		81.88 283	80.06 291	87.32 275	86.63 343	79.04 218	94.41 273	93.65 299	78.77 299	73.19 322	85.57 338	66.87 253	95.81 278	73.84 292	67.61 356	87.11 350

mvs_tets		81.74 284	80.71 280	84.84 315	84.22 373	70.29 348	93.91 290	93.78 292	82.77 223	73.37 318	89.46 276	47.36 369	95.31 308	81.99 213	79.55 276	88.92 314

v1240		81.70 285	79.83 295	87.30 277	85.50 359	77.70 263	95.48 240	93.44 306	78.46 303	76.53 283	86.44 323	60.85 292	95.84 276	71.59 305	70.17 327	88.35 327

PVSNet_0	77.72 15	81.70 285	78.95 302	89.94 215	90.77 288	76.72 281	95.96 215	96.95 44	85.01 159	70.24 345	88.53 288	52.32 347	98.20 155	86.68 171	44.08 413	94.89 225

miper_lstm_enhance		81.66 287	80.66 281	84.67 319	91.19 275	71.97 334	91.94 327	93.19 318	77.86 308	72.27 330	85.26 342	73.46 197	93.42 356	73.71 293	67.05 362	88.61 318

DP-MVS		81.47 288	78.28 305	91.04 180	98.14 55	78.48 228	95.09 261	86.97 387	61.14 399	71.12 338	92.78 228	59.59 297	99.38 82	53.11 388	86.61 220	95.27 219

v10		81.43 289	79.53 297	87.11 280	86.38 345	78.87 219	94.31 278	93.43 308	77.88 307	73.24 321	85.26 342	65.44 262	95.75 284	72.14 302	67.71 355	86.72 354

pmmvs5		81.34 290	79.54 296	86.73 287	85.02 366	76.91 276	96.22 201	91.65 345	77.65 310	73.55 314	88.61 285	55.70 334	94.43 337	74.12 289	73.35 311	88.86 316

ADS-MVSNet		81.26 291	78.36 304	89.96 214	93.78 192	79.78 193	79.48 399	93.60 301	73.09 351	80.14 244	79.99 383	62.15 282	95.24 311	59.49 364	83.52 244	94.85 227

Baseline_NR-MVSNet		81.22 292	80.07 290	84.68 318	85.32 364	75.12 304	96.48 182	88.80 378	76.24 327	77.28 273	86.40 326	67.61 243	94.39 338	75.73 274	66.73 365	84.54 376

tt0805		81.20 293	79.06 301	87.61 264	86.50 344	72.97 324	93.66 295	95.48 187	74.11 341	76.23 290	91.99 239	41.36 388	97.40 202	77.44 255	74.78 303	92.45 261

WR-MVS_H		81.02 294	80.09 288	83.79 331	88.08 330	71.26 344	94.46 271	96.54 97	80.08 273	72.81 326	86.82 315	70.36 233	92.65 361	64.18 345	67.50 357	87.46 347

CP-MVSNet		81.01 295	80.08 289	83.79 331	87.91 332	70.51 346	94.29 282	95.65 176	80.83 252	72.54 329	88.84 282	63.71 272	92.32 364	68.58 325	68.36 347	88.55 319

anonymousdsp		80.98 296	79.97 292	84.01 328	81.73 386	70.44 347	92.49 320	93.58 303	77.10 319	72.98 324	86.31 327	57.58 318	94.90 323	79.32 234	78.63 285	86.69 355

UniMVSNet_ETH3D		80.86 297	78.75 303	87.22 279	86.31 347	72.02 332	91.95 326	93.76 295	73.51 346	75.06 306	90.16 269	43.04 382	95.66 289	76.37 267	78.55 286	93.98 245

testing3		80.74 298	81.17 274	79.44 365	91.15 277	63.48 384	97.16 129	95.76 170	80.83 252	71.36 335	93.15 222	78.22 108	87.30 400	43.19 408	79.67 273	87.55 345

IterMVS		80.67 299	79.16 299	85.20 311	89.79 303	76.08 290	92.97 314	91.86 341	80.28 269	71.20 337	85.14 347	57.93 316	91.34 376	72.52 300	70.74 324	88.18 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 300	77.77 310	89.14 229	93.43 206	77.24 270	91.89 328	90.18 366	69.86 371	68.02 352	91.94 243	52.21 349	98.84 124	59.32 366	83.12 248	91.35 266

IterMVS-SCA-FT		80.51 301	79.10 300	84.73 317	89.63 310	74.66 306	92.98 313	91.81 343	80.05 274	71.06 339	85.18 345	58.04 312	91.40 375	72.48 301	70.70 326	88.12 332

PS-CasMVS		80.27 302	79.18 298	83.52 337	87.56 336	69.88 351	94.08 285	95.29 202	80.27 270	72.08 331	88.51 289	59.22 303	92.23 366	67.49 327	68.15 350	88.45 325

pm-mvs1		80.05 303	78.02 308	86.15 295	85.42 360	75.81 299	95.11 258	92.69 332	77.13 317	70.36 343	87.43 304	58.44 308	95.27 310	71.36 307	64.25 373	87.36 348

RPMNet		79.85 304	75.92 324	91.64 161	90.16 298	79.75 195	79.02 403	95.44 191	58.43 408	82.27 221	72.55 406	73.03 201	98.41 147	46.10 404	86.25 224	96.75 179

PatchT		79.75 305	76.85 317	88.42 242	89.55 312	75.49 301	77.37 407	94.61 239	63.07 388	82.46 215	73.32 403	75.52 161	93.41 357	51.36 391	84.43 240	96.36 188

Anonymous20231211		79.72 306	77.19 314	87.33 274	95.59 130	77.16 274	95.18 255	94.18 268	59.31 406	72.57 328	86.20 330	47.89 366	95.66 289	74.53 286	69.24 340	89.18 301

test_fmvs2		79.59 307	79.90 294	78.67 369	82.86 383	55.82 406	95.20 252	89.55 370	81.09 248	80.12 246	89.80 272	34.31 404	93.51 355	87.82 159	78.36 288	86.69 355

ADS-MVSNet2		79.57 308	77.53 311	85.71 302	93.78 192	72.13 330	79.48 399	86.11 394	73.09 351	80.14 244	79.99 383	62.15 282	90.14 387	59.49 364	83.52 244	94.85 227

FMVSNet1		79.50 309	76.54 320	88.39 245	88.47 324	81.95 127	94.30 279	93.38 310	73.14 350	72.04 332	85.66 334	43.86 376	93.84 348	65.48 340	72.53 314	89.38 294

PEN-MVS		79.47 310	78.26 306	83.08 340	86.36 346	68.58 359	93.85 293	94.77 227	79.76 279	71.37 334	88.55 286	59.79 295	92.46 362	64.50 344	65.40 368	88.19 330

XVG-ACMP-BASELINE		79.38 311	77.90 309	83.81 330	84.98 367	67.14 369	89.03 352	93.18 320	80.26 271	72.87 325	88.15 296	38.55 394	96.26 257	76.05 270	78.05 290	88.02 333

v7n		79.32 312	77.34 312	85.28 310	84.05 377	72.89 326	93.38 302	93.87 284	75.02 335	70.68 340	84.37 354	59.58 298	95.62 294	67.60 326	67.50 357	87.32 349

MIMVSNet		79.18 313	75.99 323	88.72 239	87.37 338	80.66 167	79.96 397	91.82 342	77.38 314	74.33 310	81.87 371	41.78 385	90.74 382	66.36 338	83.10 249	94.76 229

JIA-IIPM		79.00 314	77.20 313	84.40 326	89.74 307	64.06 381	75.30 411	95.44 191	62.15 392	81.90 225	59.08 415	78.92 96	95.59 296	66.51 336	85.78 232	93.54 252

USDC		78.65 315	76.25 321	85.85 298	87.58 335	74.60 308	89.58 348	90.58 365	84.05 188	63.13 377	88.23 294	40.69 393	96.86 236	66.57 335	75.81 298	86.09 364

DTE-MVSNet		78.37 316	77.06 315	82.32 348	85.22 365	67.17 368	93.40 301	93.66 298	78.71 300	70.53 342	88.29 293	59.06 304	92.23 366	61.38 358	63.28 377	87.56 343

Patchmatch-test		78.25 317	74.72 332	88.83 236	91.20 274	74.10 313	73.91 414	88.70 381	59.89 404	66.82 359	85.12 348	78.38 105	94.54 334	48.84 400	79.58 275	97.86 104

tfpnnormal		78.14 318	75.42 326	86.31 293	88.33 328	79.24 209	94.41 273	96.22 133	73.51 346	69.81 347	85.52 340	55.43 335	95.75 284	47.65 402	67.86 353	83.95 382

mmtdpeth		78.04 319	76.76 318	81.86 351	89.60 311	66.12 373	92.34 324	87.18 386	76.83 322	85.55 179	76.49 394	46.77 370	97.02 223	90.85 117	45.24 410	82.43 391

ACMH	75.40 17	77.99 320	74.96 328	87.10 281	90.67 289	76.41 285	93.19 311	91.64 346	72.47 357	63.44 375	87.61 303	43.34 379	97.16 216	58.34 368	73.94 306	87.72 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 320	75.74 325	84.74 316	90.45 293	72.02 332	86.41 376	91.12 354	72.57 356	66.63 361	87.27 307	54.95 340	96.98 226	56.29 378	75.98 295	85.21 373
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		77.97 322	78.05 307	77.74 373	92.13 252	56.85 402	93.97 287	94.23 263	82.43 229	73.39 316	93.57 215	57.95 315	87.86 395	32.40 416	82.34 259	88.51 320

our_test_3		77.90 323	75.37 327	85.48 308	85.39 361	76.74 280	93.63 296	91.67 344	73.39 349	65.72 366	84.65 353	58.20 311	93.13 359	57.82 370	67.87 352	86.57 357

RPSCF		77.73 324	76.63 319	81.06 356	88.66 323	55.76 407	87.77 365	87.88 384	64.82 386	74.14 311	92.79 227	49.22 360	96.81 238	67.47 328	76.88 293	90.62 272

KD-MVS_2432*1600		77.63 325	74.92 330	85.77 300	90.86 285	79.44 203	88.08 361	93.92 280	76.26 325	67.05 357	82.78 367	72.15 213	91.92 369	61.53 355	41.62 416	85.94 367

miper_refine_blended		77.63 325	74.92 330	85.77 300	90.86 285	79.44 203	88.08 361	93.92 280	76.26 325	67.05 357	82.78 367	72.15 213	91.92 369	61.53 355	41.62 416	85.94 367

ACMH+	76.62 16	77.47 327	74.94 329	85.05 313	91.07 280	71.58 340	93.26 308	90.01 367	71.80 360	64.76 370	88.55 286	41.62 386	96.48 249	62.35 354	71.00 322	87.09 351

Patchmtry		77.36 328	74.59 333	85.67 303	89.75 305	75.75 300	77.85 406	91.12 354	60.28 401	71.23 336	80.35 380	75.45 162	93.56 354	57.94 369	67.34 359	87.68 339

ppachtmachnet_test		77.19 329	74.22 337	86.13 296	85.39 361	78.22 238	93.98 286	91.36 351	71.74 361	67.11 356	84.87 351	56.67 327	93.37 358	52.21 389	64.59 370	86.80 353

OurMVSNet-221017-0		77.18 330	76.06 322	80.55 359	83.78 380	60.00 397	90.35 343	91.05 357	77.01 321	66.62 362	87.92 299	47.73 367	94.03 344	71.63 304	68.44 346	87.62 340

TransMVSNet (Re)		76.94 331	74.38 335	84.62 321	85.92 355	75.25 303	95.28 246	89.18 375	73.88 344	67.22 354	86.46 322	59.64 296	94.10 343	59.24 367	52.57 398	84.50 377

EU-MVSNet		76.92 332	76.95 316	76.83 378	84.10 375	54.73 410	91.77 330	92.71 331	72.74 354	69.57 348	88.69 284	58.03 314	87.43 399	64.91 343	70.00 333	88.33 328

Patchmatch-RL test		76.65 333	74.01 340	84.55 322	77.37 401	64.23 379	78.49 405	82.84 407	78.48 302	64.63 371	73.40 402	76.05 149	91.70 374	76.99 258	57.84 386	97.72 115

FMVSNet5		76.46 334	74.16 338	83.35 339	90.05 301	76.17 288	89.58 348	89.85 368	71.39 363	65.29 369	80.42 379	50.61 354	87.70 398	61.05 360	69.24 340	86.18 362

SixPastTwentyTwo		76.04 335	74.32 336	81.22 354	84.54 370	61.43 393	91.16 337	89.30 374	77.89 306	64.04 372	86.31 327	48.23 361	94.29 340	63.54 350	63.84 375	87.93 335

AllTest		75.92 336	73.06 344	84.47 323	92.18 249	67.29 363	91.07 338	84.43 400	67.63 377	63.48 373	90.18 267	38.20 395	97.16 216	57.04 374	73.37 309	88.97 312

CL-MVSNet_self_test		75.81 337	74.14 339	80.83 358	78.33 397	67.79 362	94.22 283	93.52 304	77.28 316	69.82 346	81.54 374	61.47 290	89.22 388	57.59 372	53.51 394	85.48 371

COLMAP_ROB	73.24 19	75.74 338	73.00 345	83.94 329	92.38 236	69.08 357	91.85 329	86.93 388	61.48 396	65.32 368	90.27 266	42.27 384	96.93 231	50.91 393	75.63 299	85.80 370
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	75.71 339	74.56 334	79.17 367	79.69 392	55.98 404	89.59 347	93.30 315	60.28 401	53.85 405	89.07 279	47.68 368	96.33 255	76.55 263	81.02 265	85.22 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 340	73.64 341	80.22 361	80.75 387	63.38 385	93.36 303	90.71 364	73.09 351	67.12 355	83.70 361	50.33 356	90.85 381	53.63 387	70.10 331	86.44 358

EG-PatchMatch MVS		74.92 341	72.02 349	83.62 335	83.76 381	73.28 319	93.62 297	92.04 340	68.57 375	58.88 394	83.80 360	31.87 408	95.57 298	56.97 376	78.67 282	82.00 395

testgi		74.88 342	73.40 342	79.32 366	80.13 391	61.75 390	93.21 309	86.64 392	79.49 285	66.56 363	91.06 253	35.51 402	88.67 390	56.79 377	71.25 320	87.56 343

pmmvs6		74.65 343	71.67 350	83.60 336	79.13 394	69.94 350	93.31 307	90.88 361	61.05 400	65.83 365	84.15 357	43.43 378	94.83 326	66.62 333	60.63 382	86.02 365

test_vis1_rt		73.96 344	72.40 347	78.64 370	83.91 378	61.16 394	95.63 234	68.18 423	76.32 324	60.09 391	74.77 397	29.01 412	97.54 192	87.74 160	75.94 296	77.22 406

K. test v3		73.62 345	71.59 351	79.69 363	82.98 382	59.85 398	90.85 341	88.83 377	77.13 317	58.90 393	82.11 369	43.62 377	91.72 373	65.83 339	54.10 393	87.50 346

pmmvs-eth3d		73.59 346	70.66 354	82.38 346	76.40 405	73.38 316	89.39 351	89.43 372	72.69 355	60.34 390	77.79 389	46.43 372	91.26 378	66.42 337	57.06 387	82.51 388

kuosan		73.55 347	72.39 348	77.01 376	89.68 308	66.72 371	85.24 385	93.44 306	67.76 376	60.04 392	83.40 364	71.90 216	84.25 407	45.34 405	54.75 389	80.06 402

MDA-MVSNet_test_wron		73.54 348	70.43 356	82.86 341	84.55 369	71.85 335	91.74 331	91.32 353	67.63 377	46.73 410	81.09 377	55.11 338	90.42 385	55.91 380	59.76 383	86.31 360

YYNet1		73.53 349	70.43 356	82.85 342	84.52 371	71.73 338	91.69 332	91.37 350	67.63 377	46.79 409	81.21 376	55.04 339	90.43 384	55.93 379	59.70 384	86.38 359

UnsupCasMVSNet_eth		73.25 350	70.57 355	81.30 353	77.53 399	66.33 372	87.24 369	93.89 283	80.38 266	57.90 398	81.59 372	42.91 383	90.56 383	65.18 342	48.51 404	87.01 352

DSMNet-mixed		73.13 351	72.45 346	75.19 384	77.51 400	46.82 415	85.09 386	82.01 408	67.61 381	69.27 350	81.33 375	50.89 352	86.28 402	54.54 384	83.80 243	92.46 260

OpenMVS_ROB	68.52 20	73.02 352	69.57 359	83.37 338	80.54 390	71.82 336	93.60 298	88.22 382	62.37 391	61.98 383	83.15 366	35.31 403	95.47 300	45.08 406	75.88 297	82.82 385

test_0402		72.68 353	69.54 360	82.09 349	88.67 322	71.81 337	92.72 318	86.77 391	61.52 395	62.21 382	83.91 359	43.22 380	93.76 351	34.60 414	72.23 318	80.72 401

TinyColmap		72.41 354	68.99 363	82.68 343	88.11 329	69.59 354	88.41 357	85.20 396	65.55 383	57.91 397	84.82 352	30.80 410	95.94 272	51.38 390	68.70 343	82.49 390

test20.03		72.36 355	71.15 352	75.98 382	77.79 398	59.16 399	92.40 322	89.35 373	74.09 342	61.50 385	84.32 355	48.09 362	85.54 405	50.63 394	62.15 380	83.24 383

LF4IMVS		72.36 355	70.82 353	76.95 377	79.18 393	56.33 403	86.12 378	86.11 394	69.30 373	63.06 378	86.66 318	33.03 406	92.25 365	65.33 341	68.64 344	82.28 392

Anonymous20240521		72.06 357	69.91 358	78.50 371	77.11 402	61.67 392	91.62 334	90.97 359	65.52 384	62.37 381	79.05 386	36.32 398	90.96 380	57.75 371	68.52 345	82.87 384

dmvs_testset		72.00 358	73.36 343	67.91 390	83.83 379	31.90 430	85.30 384	77.12 415	82.80 222	63.05 379	92.46 230	61.54 289	82.55 412	42.22 411	71.89 319	89.29 298

MDA-MVSNet-bldmvs		71.45 359	67.94 366	81.98 350	85.33 363	68.50 360	92.35 323	88.76 379	70.40 366	42.99 413	81.96 370	46.57 371	91.31 377	48.75 401	54.39 392	86.11 363

mvs5depth		71.40 360	68.36 365	80.54 360	75.31 409	65.56 375	79.94 398	85.14 397	69.11 374	71.75 333	81.59 372	41.02 390	93.94 346	60.90 361	50.46 400	82.10 393

MVS-HIRNet		71.36 361	67.00 367	84.46 325	90.58 290	69.74 353	79.15 402	87.74 385	46.09 414	61.96 384	50.50 418	45.14 374	95.64 292	53.74 386	88.11 207	88.00 334

KD-MVS_self_test		70.97 362	69.31 361	75.95 383	76.24 407	55.39 408	87.45 366	90.94 360	70.20 368	62.96 380	77.48 390	44.01 375	88.09 393	61.25 359	53.26 395	84.37 378

ttmdpeth		69.58 363	66.92 369	77.54 375	75.95 408	62.40 388	88.09 360	84.32 402	62.87 390	65.70 367	86.25 329	36.53 397	88.53 392	55.65 382	46.96 409	81.70 398

test_fmvs3		69.56 364	69.19 362	70.67 388	69.01 414	47.05 414	90.87 340	86.81 389	71.31 364	66.79 360	77.15 391	16.40 419	83.17 410	81.84 214	62.51 379	81.79 397

dongtai		69.47 365	68.98 364	70.93 387	86.87 341	58.45 400	88.19 359	93.18 320	63.98 387	56.04 401	80.17 382	70.97 229	79.24 414	33.46 415	47.94 406	75.09 408

MIMVSNet1		69.44 366	66.65 370	77.84 372	76.48 404	62.84 387	87.42 367	88.97 376	66.96 382	57.75 399	79.72 385	32.77 407	85.83 404	46.32 403	63.42 376	84.85 375

PM-MVS		69.32 367	66.93 368	76.49 379	73.60 411	55.84 405	85.91 379	79.32 413	74.72 337	61.09 387	78.18 388	21.76 415	91.10 379	70.86 313	56.90 388	82.51 388

TDRefinement		69.20 368	65.78 372	79.48 364	66.04 419	62.21 389	88.21 358	86.12 393	62.92 389	61.03 388	85.61 337	33.23 405	94.16 342	55.82 381	53.02 396	82.08 394

new-patchmatchnet		68.85 369	65.93 371	77.61 374	73.57 412	63.94 382	90.11 345	88.73 380	71.62 362	55.08 403	73.60 401	40.84 391	87.22 401	51.35 392	48.49 405	81.67 399

UnsupCasMVSNet_bld		68.60 370	64.50 374	80.92 357	74.63 410	67.80 361	83.97 390	92.94 327	65.12 385	54.63 404	68.23 411	35.97 400	92.17 368	60.13 362	44.83 411	82.78 386

mvsany_test3		67.19 371	65.34 373	72.72 386	63.08 420	48.57 413	83.12 393	78.09 414	72.07 358	61.21 386	77.11 392	22.94 414	87.78 397	78.59 241	51.88 399	81.80 396

MVStest1		66.93 372	63.01 376	78.69 368	78.56 395	71.43 342	85.51 383	86.81 389	49.79 413	48.57 408	84.15 357	53.46 345	83.31 408	43.14 409	37.15 419	81.34 400

new_pmnet		66.18 373	63.18 375	75.18 385	76.27 406	61.74 391	83.79 391	84.66 399	56.64 410	51.57 406	71.85 409	31.29 409	87.93 394	49.98 396	62.55 378	75.86 407

pmmvs3		65.75 374	62.18 377	76.45 380	67.12 418	64.54 377	88.68 355	85.05 398	54.77 412	57.54 400	73.79 400	29.40 411	86.21 403	55.49 383	47.77 407	78.62 404

test_f		64.01 375	62.13 378	69.65 389	63.00 421	45.30 420	83.66 392	80.68 410	61.30 397	55.70 402	72.62 405	14.23 421	84.64 406	69.84 318	58.11 385	79.00 403

N_pmnet		61.30 376	60.20 379	64.60 395	84.32 372	17.00 436	91.67 333	10.98 434	61.77 394	58.45 396	78.55 387	49.89 358	91.83 372	42.27 410	63.94 374	84.97 374

WB-MVS		57.26 377	56.22 380	60.39 401	69.29 413	35.91 428	86.39 377	70.06 421	59.84 405	46.46 411	72.71 404	51.18 351	78.11 415	15.19 425	34.89 420	67.14 414

test_method		56.77 378	54.53 382	63.49 397	76.49 403	40.70 423	75.68 410	74.24 417	19.47 425	48.73 407	71.89 408	19.31 416	65.80 425	57.46 373	47.51 408	83.97 381

APD_test1		56.56 379	53.58 383	65.50 392	67.93 417	46.51 417	77.24 409	72.95 418	38.09 416	42.75 414	75.17 396	13.38 422	82.78 411	40.19 412	54.53 391	67.23 413

SSC-MVS		56.01 380	54.96 381	59.17 402	68.42 415	34.13 429	84.98 387	69.23 422	58.08 409	45.36 412	71.67 410	50.30 357	77.46 416	14.28 426	32.33 421	65.91 415

FPMVS		55.09 381	52.93 384	61.57 399	55.98 423	40.51 424	83.11 394	83.41 406	37.61 417	34.95 418	71.95 407	14.40 420	76.95 417	29.81 417	65.16 369	67.25 412

test_vis3_rt		54.10 382	51.04 385	63.27 398	58.16 422	46.08 419	84.17 389	49.32 433	56.48 411	36.56 417	49.48 420	8.03 429	91.91 371	67.29 329	49.87 401	51.82 419

LCM-MVSNet		52.52 383	48.24 386	65.35 393	47.63 430	41.45 422	72.55 415	83.62 405	31.75 418	37.66 416	57.92 416	9.19 428	76.76 418	49.26 398	44.60 412	77.84 405

EGC-MVSNET		52.46 384	47.56 387	67.15 391	81.98 385	60.11 396	82.54 395	72.44 419	0.11 431	0.70 432	74.59 398	25.11 413	83.26 409	29.04 418	61.51 381	58.09 416

PMMVS2		50.90 385	46.31 388	64.67 394	55.53 424	46.67 416	77.30 408	71.02 420	40.89 415	34.16 419	59.32 414	9.83 427	76.14 420	40.09 413	28.63 422	71.21 409

ANet_high		46.22 386	41.28 393	61.04 400	39.91 432	46.25 418	70.59 416	76.18 416	58.87 407	23.09 424	48.00 421	12.58 424	66.54 424	28.65 419	13.62 425	70.35 410

testf1		45.70 387	42.41 389	55.58 403	53.29 427	40.02 425	68.96 417	62.67 427	27.45 420	29.85 420	61.58 412	5.98 430	73.83 422	28.49 420	43.46 414	52.90 417

APD_test2		45.70 387	42.41 389	55.58 403	53.29 427	40.02 425	68.96 417	62.67 427	27.45 420	29.85 420	61.58 412	5.98 430	73.83 422	28.49 420	43.46 414	52.90 417

Gipuma		45.11 389	42.05 391	54.30 405	80.69 388	51.30 412	35.80 423	83.81 404	28.13 419	27.94 423	34.53 423	11.41 426	76.70 419	21.45 422	54.65 390	34.90 423
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 390	41.93 392	40.38 408	20.10 434	26.84 432	61.93 420	59.09 429	14.81 427	28.51 422	80.58 378	35.53 401	48.33 429	63.70 349	13.11 426	45.96 422

PMVS	34.80 23	39.19 391	35.53 394	50.18 406	29.72 433	30.30 431	59.60 421	66.20 426	26.06 422	17.91 426	49.53 419	3.12 432	74.09 421	18.19 424	49.40 402	46.14 420
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 392	29.49 397	46.92 407	41.86 431	36.28 427	50.45 422	56.52 430	18.75 426	18.28 425	37.84 422	2.41 433	58.41 426	18.71 423	20.62 423	46.06 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 393	32.39 395	33.65 409	53.35 426	25.70 433	74.07 413	53.33 431	21.08 423	17.17 427	33.63 425	11.85 425	54.84 427	12.98 427	14.04 424	20.42 424

EMVS		31.70 394	31.45 396	32.48 410	50.72 429	23.95 434	74.78 412	52.30 432	20.36 424	16.08 428	31.48 426	12.80 423	53.60 428	11.39 428	13.10 427	19.88 425

cdsmvs_eth3d_5k		21.43 395	28.57 398	0.00 414	0.00 437	0.00 439	0.00 425	95.93 160	0.00 432	0.00 433	97.66 79	63.57 273	0.00 433	0.00 432	0.00 431	0.00 429

wuyk23d		14.10 396	13.89 399	14.72 411	55.23 425	22.91 435	33.83 424	3.56 435	4.94 428	4.11 429	2.28 431	2.06 434	19.66 430	10.23 429	8.74 428	1.59 428

testmvs		9.92 397	12.94 400	0.84 413	0.65 435	0.29 438	93.78 294	0.39 436	0.42 429	2.85 430	15.84 429	0.17 436	0.30 432	2.18 430	0.21 429	1.91 427

test123		9.07 398	11.73 401	1.11 412	0.50 436	0.77 437	89.44 350	0.20 437	0.34 430	2.15 431	10.72 430	0.34 435	0.32 431	1.79 431	0.08 430	2.23 426

ab-mvs-re		8.11 399	10.81 402	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	97.30 102	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

pcd_1.5k_mvsjas		5.92 400	7.89 403	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	71.04 226	0.00 433	0.00 432	0.00 431	0.00 429

mmdepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

monomultidepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

test_blank		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet_test		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

DCPMVS		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet-low-res		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uncertanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

Regformer		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

WAC-MVS						67.18 365								49.00 399

FOURS1					98.51 39	78.01 246	98.13 53	96.21 134	83.04 215	94.39 56

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 55					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 55					99.81 22	98.08 18	98.81 24	99.43 11

test_one_0601					98.91 18	84.56 84		96.70 74	88.06 89	96.57 26	98.77 10	88.04 21

eth-test2					0.00 437

eth-test					0.00 437

ZD-MVS					99.09 8	83.22 108		96.60 90	82.88 220	93.61 67	98.06 57	82.93 59	99.14 104	95.51 53	98.49 39

RE-MVS-def			91.18 100		97.76 67	76.03 292	96.20 203	95.44 191	80.56 260	90.72 113	97.84 71	73.36 199		91.99 104	96.79 96	97.75 113

IU-MVS					99.03 15	85.34 61		96.86 53	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 58	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 58	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 50	96.52 99	84.74 165	94.83 51	98.80 7	82.80 61	99.37 84	95.95 45	98.42 42

save fliter					98.24 51	83.34 105	98.61 37	96.57 94	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 64					99.84 13	97.90 21	98.85 21	99.45 10

test0726					99.05 9	85.18 66	99.11 16	96.78 58	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												77.59 119				97.54 129

sam_mvs												75.35 169

ambc				76.02 381	68.11 416	51.43 411	64.97 419	89.59 369		60.49 389	74.49 399	17.17 418	92.46 362	61.50 357	52.85 397	84.17 380

MTGPA								96.33 123

test_post1							85.88 380				30.24 427	73.77 192	95.07 321	73.89 290

test_post											33.80 424	76.17 147	95.97 268

patchmatchnet-post											77.09 393	77.78 117	95.39 302

GG-mvs-BLEND				93.49 78	94.94 151	86.26 37	81.62 396	97.00 38		88.32 151	94.30 195	91.23 5	96.21 261	88.49 152	97.43 75	98.00 94

MTMP							97.53 97	68.16 424

gm-plane-assit					92.27 243	79.64 201			84.47 175		95.15 173		97.93 167	85.81 174

test9_res														96.00 44	99.03 13	98.31 69

TEST9					98.64 31	83.71 96	97.82 72	96.65 81	84.29 182	95.16 41	98.09 52	84.39 41	99.36 85

test_8					98.63 33	83.64 99	97.81 74	96.63 86	84.50 173	95.10 44	98.11 50	84.33 42	99.23 92

agg_prior2														94.30 68	99.00 15	98.57 53

agg_prior					98.59 35	83.13 109		96.56 96		94.19 58			99.16 103

TestCases				84.47 323	92.18 249	67.29 363		84.43 400	67.63 377	63.48 373	90.18 267	38.20 395	97.16 216	57.04 374	73.37 309	88.97 312

test_prior4						82.34 123	97.75 80

test_prior2							98.37 43		86.08 133	94.57 54	98.02 58	83.14 56		95.05 59	98.79 27

test_prior				93.09 92	98.68 26	81.91 131		96.40 114					99.06 111			98.29 71

旧先验2							96.97 149		74.06 343	96.10 32			97.76 178	88.38 154

新几何2							96.42 189

新几何1				93.12 90	97.44 81	81.60 145		96.71 73	74.54 339	91.22 106	97.57 87	79.13 94	99.51 75	77.40 256	98.46 40	98.26 74

旧先验1					97.39 86	79.58 202		96.54 97			98.08 55	84.00 48			97.42 76	97.62 125

无先验							96.87 158	96.78 58	77.39 313				99.52 73	79.95 228		98.43 62

原ACMM2							96.84 159

原ACMM1				91.22 177	97.77 65	78.10 244		96.61 87	81.05 249	91.28 105	97.42 96	77.92 114	98.98 115	79.85 230	98.51 36	96.59 183

test222					96.15 109	78.41 232	95.87 222	96.46 106	71.97 359	89.66 126	97.45 92	76.33 145			98.24 51	98.30 70

testdata2													99.48 77	76.45 265

segment_acmp												82.69 62

testdata				90.13 207	95.92 118	74.17 312		96.49 105	73.49 348	94.82 52	97.99 59	78.80 100	97.93 167	83.53 200	97.52 71	98.29 71

testdata1							95.57 238		87.44 106

test12				94.25 41	98.34 46	85.55 57		96.35 122		92.36 85		80.84 70	99.22 93		98.31 49	97.98 96

plane_prior7					91.86 264	77.55 265

plane_prior6					91.98 260	77.92 251						64.77 268

plane_prior5								94.69 229					97.30 208	87.08 166	82.82 254	90.96 269

plane_prior4											94.15 200

plane_prior3						77.75 261			90.17 56	81.33 230

plane_prior2							97.18 125		89.89 58

plane_prior1					91.95 262

plane_prior						77.96 248	97.52 100		90.36 54						82.96 252

n2								0.00 438

nn								0.00 438

door-mid								79.75 412

lessismore_v0				79.98 362	80.59 389	58.34 401		80.87 409		58.49 395	83.46 363	43.10 381	93.89 347	63.11 352	48.68 403	87.72 337

LGP-MVS_train				86.33 290	90.88 282	73.06 322		94.13 270	82.20 233	76.31 286	93.20 219	54.83 341	96.95 228	83.72 194	80.83 267	88.98 310

test11								96.50 102

door								80.13 411

HQP5-MVS						78.48 228

HQP-NCC					92.08 255		97.63 86		90.52 49	82.30 217

ACMP_Plane					92.08 255		97.63 86		90.52 49	82.30 217

BP-MVS														87.67 162

HQP4-MVS										82.30 217			97.32 206			91.13 267

HQP3-MVS								94.80 224							83.01 250

HQP2-MVS												65.40 263

NP-MVS					92.04 259	78.22 238					94.56 190

MDTV_nov1_ep13_2view						81.74 139	86.80 372		80.65 257	85.65 177		74.26 186		76.52 264		96.98 165

MDTV_nov1_ep13			83.69 233		94.09 186	81.01 155	86.78 373	96.09 143	83.81 199	84.75 189	84.32 355	74.44 185	96.54 247	63.88 347	85.07 238

ACMMP++_ref															78.45 287

ACMMP++															79.05 279

Test By Simon												71.65 219

ITE_SJBPF				82.38 346	87.00 340	65.59 374		89.55 370	79.99 276	69.37 349	91.30 250	41.60 387	95.33 306	62.86 353	74.63 305	86.24 361

DeepMVS_CX				64.06 396	78.53 396	43.26 421		68.11 425	69.94 370	38.55 415	76.14 395	18.53 417	79.34 413	43.72 407	41.62 416	69.57 411

High-resolution multi-view benchmark