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		98.46 1	98.38 1	98.72 10	99.80 4	96.19 15	99.80 20	97.99 54	97.05 9	99.41 5	99.59 2	92.89 26	100.00 1	98.99 31	99.90 7	99.96 10

DVP-MVS++		98.18 2	98.09 5	98.44 16	99.61 24	95.38 24	99.55 51	97.68 98	93.01 80	99.23 13	99.45 14	95.12 8	99.98 9	99.25 20	99.92 3	99.97 7

SED-MVS		98.18 2	98.10 4	98.41 18	99.63 18	95.24 27	99.77 23	97.72 87	94.17 50	99.30 11	99.54 3	93.32 20	99.98 9	99.70 5	99.81 23	99.99 1

MCST-MVS		98.18 2	97.95 9	98.86 5	99.85 3	96.60 10	99.70 33	97.98 55	97.18 7	95.96 107	99.33 22	92.62 27	100.00 1	98.99 31	99.93 1	99.98 6

NCCC		98.12 5	98.11 3	98.13 25	99.76 6	94.46 53	99.81 15	97.88 60	96.54 17	98.84 28	99.46 10	92.55 28	99.98 9	98.25 56	99.93 1	99.94 18

DPE-MVS		98.11 6	98.00 6	98.44 16	99.50 42	95.39 23	99.29 89	97.72 87	94.50 43	98.64 36	99.54 3	93.32 20	99.97 21	99.58 11	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.07 7	98.00 6	98.29 19	99.66 12	95.20 32	99.72 30	97.47 150	93.95 55	99.07 19	99.46 10	93.18 23	99.97 21	99.64 8	99.82 19	99.69 58
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
DPM-MVS		97.86 8	97.25 22	99.68 1	98.25 98	99.10 1	99.76 26	97.78 79	96.61 16	98.15 49	99.53 7	93.62 17	100.00 1	91.79 181	99.80 26	99.94 18

MVS_0304		97.81 9	97.51 15	98.74 9	98.97 73	96.57 11	99.91 2	98.17 37	97.45 3	98.76 31	98.97 70	86.69 118	99.96 28	99.72 3	98.92 91	99.69 58

MSP-MVS		97.77 10	98.18 2	96.53 103	99.54 36	90.14 155	99.41 76	97.70 92	95.46 33	98.60 37	99.19 35	95.71 5	99.49 121	98.15 58	99.85 13	99.95 15
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MM		97.76 11	97.39 20	98.86 5	98.30 97	96.83 7	99.81 15	99.13 9	97.66 2	98.29 47	98.96 75	85.84 139	99.90 52	99.72 3	98.80 97	99.85 30

HPM-MVS++		97.72 12	97.59 13	98.14 24	99.53 40	94.76 45	99.19 100	97.75 82	95.66 29	98.21 48	99.29 23	91.10 36	99.99 5	97.68 66	99.87 9	99.68 60

fmvsm_l_conf0.5_n_a		97.70 13	97.80 11	97.42 50	97.59 123	92.91 92	99.86 6	98.04 50	96.70 14	99.58 2	99.26 24	90.90 41	99.94 35	99.57 12	98.66 104	99.40 94

fmvsm_l_conf0.5_n		97.65 14	97.72 12	97.41 51	97.51 128	92.78 95	99.85 9	98.05 48	96.78 12	99.60 1	99.23 29	90.42 52	99.92 43	99.55 13	98.50 110	99.55 78

APDe-MVS		97.53 15	97.47 16	97.70 40	99.58 30	93.63 70	99.56 50	97.52 140	93.59 70	98.01 58	99.12 53	90.80 45	99.55 115	99.26 18	99.79 27	99.93 20
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SD-MVS		97.51 16	97.40 19	97.81 36	99.01 72	93.79 69	99.33 87	97.38 165	93.73 66	98.83 29	99.02 66	90.87 44	99.88 59	98.69 36	99.74 29	99.77 46
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
MSLP-MVS++		97.50 17	97.45 18	97.63 42	99.65 16	93.21 81	99.70 33	98.13 43	94.61 41	97.78 64	99.46 10	89.85 61	99.81 85	97.97 60	99.91 6	99.88 26

TSAR-MVS + MP.		97.44 18	97.46 17	97.39 53	99.12 65	93.49 76	98.52 187	97.50 145	94.46 45	98.99 21	98.64 108	91.58 33	99.08 157	98.49 46	99.83 15	99.60 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		97.25 19	97.34 21	97.01 68	97.38 134	91.46 120	99.75 28	97.66 103	94.14 54	98.13 50	99.26 24	92.16 32	99.66 103	97.91 62	99.64 42	99.90 22
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		97.24 20	96.99 24	98.00 31	99.30 54	94.20 61	99.16 106	97.65 110	89.55 174	99.22 15	99.52 8	90.34 55	99.99 5	98.32 53	99.83 15	99.82 32
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
MG-MVS		97.24 20	96.83 32	98.47 15	99.79 5	95.71 19	99.07 123	99.06 10	94.45 47	96.42 100	98.70 104	88.81 75	99.74 97	95.35 122	99.86 12	99.97 7

SF-MVS		97.22 22	96.92 25	98.12 27	99.11 66	94.88 38	99.44 69	97.45 153	89.60 170	98.70 33	99.42 17	90.42 52	99.72 98	98.47 47	99.65 40	99.77 46

train_agg		97.20 23	97.08 23	97.57 46	99.57 33	93.17 82	99.38 79	97.66 103	90.18 152	98.39 43	99.18 38	90.94 39	99.66 103	98.58 42	99.85 13	99.88 26

DeepC-MVS_fast	93.52 2	97.16 24	96.84 30	98.13 25	99.61 24	94.45 54	98.85 145	97.64 112	96.51 20	95.88 110	99.39 18	87.35 103	99.99 5	96.61 92	99.69 38	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_l_conf0.5_n_3		97.12 25	96.89 27	97.79 39	97.39 133	93.84 68	99.87 5	97.70 92	97.34 5	99.39 7	99.20 33	82.86 184	99.94 35	99.21 23	99.07 80	99.58 77

DELS-MVS		97.12 25	96.60 39	98.68 11	98.03 108	96.57 11	99.84 11	97.84 64	96.36 22	95.20 127	98.24 134	88.17 84	99.83 79	96.11 104	99.60 50	99.64 68
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
patch_mono-2		97.10 27	97.97 8	94.49 193	99.21 61	83.73 309	99.62 45	98.25 32	95.28 35	99.38 8	98.91 83	92.28 31	99.94 35	99.61 10	99.22 74	99.78 41

test_fmvsm_n_1920		97.08 28	97.55 14	95.67 148	97.94 110	89.61 174	99.93 1	98.48 23	97.08 8	99.08 18	99.13 50	88.17 84	99.93 40	99.11 27	99.06 81	97.47 216

CANet		97.00 29	96.49 42	98.55 12	98.86 84	96.10 16	99.83 12	97.52 140	95.90 24	97.21 75	98.90 85	82.66 192	99.93 40	98.71 35	98.80 97	99.63 70

TSAR-MVS + GP.		96.95 30	96.91 26	97.07 65	98.88 83	91.62 116	99.58 48	96.54 234	95.09 37	96.84 86	98.63 110	91.16 34	99.77 94	99.04 29	96.42 159	99.81 35

APD-MVS		96.95 30	96.72 36	97.63 42	99.51 41	93.58 71	99.16 106	97.44 157	90.08 157	98.59 38	99.07 58	89.06 69	99.42 132	97.92 61	99.66 39	99.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-MVSNAJ		96.87 32	96.40 46	98.29 19	97.35 136	97.29 5	99.03 129	97.11 193	95.83 25	98.97 23	99.14 48	82.48 196	99.60 112	98.60 39	99.08 78	98.00 202

balanced_conf03		96.83 33	96.51 41	97.81 36	97.60 122	95.15 34	98.40 205	96.77 217	93.00 82	98.69 34	96.19 225	89.75 63	98.76 172	98.45 48	99.72 32	99.51 83

EPNet		96.82 34	96.68 38	97.25 60	98.65 90	93.10 84	99.48 60	98.76 14	96.54 17	97.84 62	98.22 135	87.49 96	99.66 103	95.35 122	97.78 129	99.00 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		96.80 35	96.85 29	96.66 94	97.85 113	94.42 56	94.76 352	98.36 29	92.50 93	95.62 120	97.52 161	97.92 1	97.38 258	98.31 54	98.80 97	98.20 196

test_fmvsmconf_n		96.78 36	96.84 30	96.61 96	95.99 207	90.25 150	99.90 3	98.13 43	96.68 15	98.42 42	98.92 82	85.34 149	99.88 59	99.12 26	99.08 78	99.70 55

MVS_111021_HR		96.69 37	96.69 37	96.72 89	98.58 92	91.00 134	99.14 114	99.45 1	93.86 61	95.15 128	98.73 98	88.48 79	99.76 95	97.23 76	99.56 52	99.40 94

reproduce-ours		96.66 38	96.80 33	96.22 118	98.95 77	89.03 186	98.62 173	97.38 165	93.42 72	96.80 91	99.36 19	88.92 72	99.80 87	98.51 44	99.26 71	99.82 32

our_new_method		96.66 38	96.80 33	96.22 118	98.95 77	89.03 186	98.62 173	97.38 165	93.42 72	96.80 91	99.36 19	88.92 72	99.80 87	98.51 44	99.26 71	99.82 32

xiu_mvs_v2_base		96.66 38	96.17 57	98.11 28	97.11 155	96.96 6	99.01 132	97.04 200	95.51 32	98.86 27	99.11 57	82.19 204	99.36 139	98.59 41	98.14 121	98.00 202

PHI-MVS		96.65 41	96.46 45	97.21 61	99.34 50	91.77 112	99.70 33	98.05 48	86.48 266	98.05 55	99.20 33	89.33 67	99.96 28	98.38 49	99.62 46	99.90 22

BP-MVS1		96.59 42	96.36 48	97.29 56	95.05 252	94.72 47	99.44 69	97.45 153	92.71 89	96.41 101	98.50 118	94.11 16	98.50 185	95.61 117	97.97 123	98.66 168

ACMMP_NAP		96.59 42	96.18 54	97.81 36	98.82 85	93.55 73	98.88 144	97.59 125	90.66 134	97.98 59	99.14 48	86.59 121	100.00 1	96.47 96	99.46 57	99.89 25

fmvsm_s_conf0.5_n_3		96.58 44	96.55 40	96.66 94	97.23 143	92.59 100	99.81 15	97.82 68	97.35 4	99.42 4	99.16 41	80.27 224	99.93 40	99.26 18	98.60 106	97.45 217

reproduce_model		96.57 45	96.75 35	96.02 131	98.93 80	88.46 208	98.56 184	97.34 171	93.18 78	96.96 82	99.35 21	88.69 77	99.80 87	98.53 43	99.21 77	99.79 38

CDPH-MVS		96.56 46	96.18 54	97.70 40	99.59 28	93.92 65	99.13 117	97.44 157	89.02 187	97.90 61	99.22 30	88.90 74	99.49 121	94.63 141	99.79 27	99.68 60

DeepPCF-MVS	93.56 1	96.55 47	97.84 10	92.68 246	98.71 89	78.11 369	99.70 33	97.71 91	98.18 1	97.36 71	99.76 1	90.37 54	99.94 35	99.27 17	99.54 54	99.99 1

XVS		96.47 48	96.37 47	96.77 83	99.62 22	90.66 143	99.43 73	97.58 127	92.41 97	96.86 84	98.96 75	87.37 99	99.87 63	95.65 112	99.43 61	99.78 41

fmvsm_s_conf0.5_n_5		96.46 49	96.23 51	97.15 64	96.42 183	92.80 94	99.83 12	97.39 164	94.50 43	98.71 32	99.13 50	82.52 193	99.90 52	99.24 22	98.38 115	98.74 160

HFP-MVS		96.42 50	96.26 50	96.90 78	99.69 8	90.96 135	99.47 62	97.81 72	90.54 143	96.88 83	99.05 62	87.57 94	99.96 28	95.65 112	99.72 32	99.78 41

PAPR		96.35 51	95.82 68	97.94 33	99.63 18	94.19 62	99.42 75	97.55 132	92.43 94	93.82 156	99.12 53	87.30 104	99.91 48	94.02 149	99.06 81	99.74 50

PAPM		96.35 51	95.94 63	97.58 44	94.10 280	95.25 26	98.93 139	98.17 37	94.26 49	93.94 151	98.72 100	89.68 64	97.88 222	96.36 97	99.29 69	99.62 72

lupinMVS		96.32 53	95.94 63	97.44 48	95.05 252	94.87 39	99.86 6	96.50 236	93.82 64	98.04 56	98.77 94	85.52 141	98.09 209	96.98 81	98.97 87	99.37 97

region2R		96.30 54	96.17 57	96.70 90	99.70 7	90.31 149	99.46 66	97.66 103	90.55 142	97.07 79	99.07 58	86.85 113	99.97 21	95.43 120	99.74 29	99.81 35

ACMMPR		96.28 55	96.14 61	96.73 87	99.68 9	90.47 147	99.47 62	97.80 74	90.54 143	96.83 88	99.03 64	86.51 126	99.95 32	95.65 112	99.72 32	99.75 49

CP-MVS		96.22 56	96.15 60	96.42 108	99.67 10	89.62 173	99.70 33	97.61 119	90.07 158	96.00 106	99.16 41	87.43 97	99.92 43	96.03 106	99.72 32	99.70 55

fmvsm_s_conf0.5_n		96.19 57	96.49 42	95.30 163	97.37 135	89.16 180	99.86 6	98.47 24	95.68 28	98.87 26	99.15 45	82.44 200	99.92 43	99.14 25	97.43 139	96.83 237

fmvsm_s_conf0.5_n_4		96.17 58	96.49 42	95.21 166	97.06 158	89.26 178	99.76 26	98.07 46	95.99 23	99.35 9	99.22 30	82.19 204	99.89 57	99.06 28	97.68 131	96.49 247

SR-MVS		96.13 59	96.16 59	96.07 128	99.42 47	89.04 184	98.59 181	97.33 172	90.44 146	96.84 86	99.12 53	86.75 115	99.41 135	97.47 69	99.44 60	99.76 48

ZNCC-MVS		96.09 60	95.81 70	96.95 76	99.42 47	91.19 124	99.55 51	97.53 136	89.72 165	95.86 112	98.94 81	86.59 121	99.97 21	95.13 128	99.56 52	99.68 60

MTAPA		96.09 60	95.80 71	96.96 75	99.29 55	91.19 124	97.23 285	97.45 153	92.58 91	94.39 142	99.24 28	86.43 128	99.99 5	96.22 99	99.40 64	99.71 54

GDP-MVS		96.05 62	95.63 80	97.31 55	95.37 231	94.65 50	99.36 83	96.42 241	92.14 104	97.07 79	98.53 114	93.33 19	98.50 185	91.76 182	96.66 156	98.78 156

ETV-MVS		96.00 63	96.00 62	96.00 133	96.56 175	91.05 132	99.63 44	96.61 226	93.26 77	97.39 70	98.30 132	86.62 120	98.13 206	98.07 59	97.57 133	98.82 151

MP-MVS		96.00 63	95.82 68	96.54 102	99.47 46	90.13 157	99.36 83	97.41 161	90.64 137	95.49 122	98.95 78	85.51 143	99.98 9	96.00 107	99.59 51	99.52 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SPE-MVS-test		95.98 65	96.34 49	94.90 178	98.06 107	87.66 223	99.69 40	96.10 265	93.66 67	98.35 46	99.05 62	86.28 130	97.66 240	96.96 82	98.90 93	99.37 97

fmvsm_s_conf0.5_n_a		95.97 66	96.19 52	95.31 162	96.51 179	89.01 188	99.81 15	98.39 27	95.46 33	99.19 17	99.16 41	81.44 216	99.91 48	98.83 34	96.97 149	97.01 233

GST-MVS		95.97 66	95.66 76	96.90 78	99.49 45	91.22 122	99.45 68	97.48 148	89.69 166	95.89 109	98.72 100	86.37 129	99.95 32	94.62 142	99.22 74	99.52 81

WTY-MVS		95.97 66	95.11 93	98.54 13	97.62 119	96.65 9	99.44 69	98.74 15	92.25 100	95.21 126	98.46 127	86.56 123	99.46 127	95.00 133	92.69 205	99.50 85

test_fmvsmconf0.1_n		95.94 69	95.79 72	96.40 110	92.42 318	89.92 166	99.79 21	96.85 212	96.53 19	97.22 74	98.67 106	82.71 191	99.84 75	98.92 33	98.98 86	99.43 93

PVSNet_Blended		95.94 69	95.66 76	96.75 85	98.77 87	91.61 117	99.88 4	98.04 50	93.64 69	94.21 145	97.76 148	83.50 169	99.87 63	97.41 70	97.75 130	98.79 154

mPP-MVS		95.90 71	95.75 73	96.38 111	99.58 30	89.41 177	99.26 95	97.41 161	90.66 134	94.82 132	98.95 78	86.15 134	99.98 9	95.24 127	99.64 42	99.74 50

fmvsm_s_conf0.5_n_2		95.85 72	95.83 67	95.91 138	97.19 147	91.79 111	99.78 22	97.65 110	97.23 6	99.22 15	99.06 60	75.93 254	99.90 52	99.30 16	97.09 148	96.02 257

PGM-MVS		95.85 72	95.65 78	96.45 106	99.50 42	89.77 170	98.22 223	98.90 13	89.19 182	96.74 93	98.95 78	85.91 138	99.92 43	93.94 150	99.46 57	99.66 64

DP-MVS Recon		95.85 72	95.15 90	97.95 32	99.87 2	94.38 57	99.60 46	97.48 148	86.58 261	94.42 140	99.13 50	87.36 102	99.98 9	93.64 157	98.33 117	99.48 87

MP-MVS-pluss		95.80 75	95.30 85	97.29 56	98.95 77	92.66 96	98.59 181	97.14 189	88.95 190	93.12 165	99.25 26	85.62 140	99.94 35	96.56 94	99.48 56	99.28 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVS_111021_LR		95.78 76	95.94 63	95.28 164	98.19 103	87.69 220	98.80 151	99.26 7	93.39 74	95.04 130	98.69 105	84.09 163	99.76 95	96.96 82	99.06 81	98.38 181

alignmvs		95.77 77	95.00 97	98.06 29	97.35 136	95.68 20	99.71 32	97.50 145	91.50 115	96.16 105	98.61 112	86.28 130	99.00 160	96.19 100	91.74 225	99.51 83

EI-MVSNet-Vis-set		95.76 78	95.63 80	96.17 124	99.14 64	90.33 148	98.49 193	97.82 68	91.92 106	94.75 134	98.88 89	87.06 109	99.48 125	95.40 121	97.17 146	98.70 163

SR-MVS-dyc-post		95.75 79	95.86 66	95.41 157	99.22 59	87.26 239	98.40 205	97.21 181	89.63 168	96.67 96	98.97 70	86.73 117	99.36 139	96.62 90	99.31 67	99.60 73

CS-MVS		95.75 79	96.19 52	94.40 197	97.88 112	86.22 259	99.66 41	96.12 264	92.69 90	98.07 54	98.89 87	87.09 107	97.59 246	96.71 87	98.62 105	99.39 96

myMVS_eth3d28		95.74 81	95.34 84	96.92 77	97.41 131	93.58 71	99.28 92	97.70 92	90.97 128	93.91 152	97.25 174	90.59 48	98.75 173	96.85 86	94.14 189	98.44 176

MVSMamba_PlusPlus		95.73 82	95.15 90	97.44 48	97.28 142	94.35 59	98.26 220	96.75 218	83.09 320	97.84 62	95.97 233	89.59 65	98.48 190	97.86 63	99.73 31	99.49 86

UBG		95.73 82	95.41 82	96.69 91	96.97 162	93.23 80	99.13 117	97.79 76	91.28 122	94.38 143	96.78 205	92.37 30	98.56 184	96.17 101	93.84 193	98.26 189

dcpmvs_2		95.67 84	96.18 54	94.12 209	98.82 85	84.22 302	97.37 278	95.45 319	90.70 133	95.77 115	98.63 110	90.47 50	98.68 179	99.20 24	99.22 74	99.45 90

APD-MVS_3200maxsize		95.64 85	95.65 78	95.62 151	99.24 58	87.80 219	98.42 200	97.22 180	88.93 192	96.64 98	98.98 69	85.49 144	99.36 139	96.68 89	99.27 70	99.70 55

fmvsm_s_conf0.1_n		95.56 86	95.68 75	95.20 167	94.35 272	89.10 182	99.50 58	97.67 102	94.76 40	98.68 35	99.03 64	81.13 219	99.86 69	98.63 38	97.36 141	96.63 240

test_fmvsmvis_n_1920		95.47 87	95.40 83	95.70 146	94.33 273	90.22 153	99.70 33	96.98 207	96.80 11	92.75 170	98.89 87	82.46 199	99.92 43	98.36 50	98.33 117	96.97 234

EI-MVSNet-UG-set		95.43 88	95.29 86	95.86 140	99.07 70	89.87 167	98.43 199	97.80 74	91.78 108	94.11 147	98.77 94	86.25 132	99.48 125	94.95 135	96.45 158	98.22 194

PAPM_NR		95.43 88	95.05 95	96.57 101	99.42 47	90.14 155	98.58 183	97.51 142	90.65 136	92.44 175	98.90 85	87.77 93	99.90 52	90.88 190	99.32 66	99.68 60

HPM-MVS		95.41 90	95.22 88	95.99 134	99.29 55	89.14 181	99.17 105	97.09 197	87.28 245	95.40 123	98.48 124	84.93 153	99.38 137	95.64 116	99.65 40	99.47 89
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
jason		95.40 91	94.86 99	97.03 67	92.91 312	94.23 60	99.70 33	96.30 248	93.56 71	96.73 94	98.52 116	81.46 215	97.91 219	96.08 105	98.47 113	98.96 134
jason: jason.
testing11		95.33 92	94.98 98	96.37 112	97.20 145	92.31 104	99.29 89	97.68 98	90.59 139	94.43 139	97.20 178	90.79 46	98.60 182	95.25 126	92.38 210	98.18 197

HY-MVS	88.56 7	95.29 93	94.23 110	98.48 14	97.72 115	96.41 13	94.03 361	98.74 15	92.42 96	95.65 119	94.76 255	86.52 125	99.49 121	95.29 125	92.97 201	99.53 80

test_yl		95.27 94	94.60 103	97.28 58	98.53 93	92.98 88	99.05 127	98.70 18	86.76 258	94.65 137	97.74 150	87.78 91	99.44 128	95.57 118	92.61 206	99.44 91

DCV-MVSNet		95.27 94	94.60 103	97.28 58	98.53 93	92.98 88	99.05 127	98.70 18	86.76 258	94.65 137	97.74 150	87.78 91	99.44 128	95.57 118	92.61 206	99.44 91

fmvsm_s_conf0.1_n_2		95.24 96	95.04 96	95.83 141	95.60 220	91.71 115	99.65 42	96.18 259	96.99 10	98.79 30	98.91 83	73.91 272	99.87 63	99.00 30	96.30 163	95.91 259

testing3-2		95.17 97	94.78 100	96.33 115	97.35 136	92.35 103	99.85 9	98.43 26	90.60 138	92.84 169	97.00 191	90.89 42	98.89 165	95.95 108	90.12 250	97.76 206

fmvsm_s_conf0.1_n_a		95.16 98	95.15 90	95.18 168	92.06 324	88.94 192	99.29 89	97.53 136	94.46 45	98.98 22	98.99 68	79.99 226	99.85 73	98.24 57	96.86 152	96.73 238

EIA-MVS		95.11 99	95.27 87	94.64 190	96.34 189	86.51 248	99.59 47	96.62 225	92.51 92	94.08 148	98.64 108	86.05 135	98.24 201	95.07 130	98.50 110	99.18 115

EC-MVSNet		95.09 100	95.17 89	94.84 181	95.42 227	88.17 211	99.48 60	95.92 284	91.47 116	97.34 72	98.36 129	82.77 187	97.41 257	97.24 75	98.58 107	98.94 139

VNet		95.08 101	94.26 109	97.55 47	98.07 106	93.88 66	98.68 164	98.73 17	90.33 149	97.16 78	97.43 166	79.19 236	99.53 118	96.91 84	91.85 223	99.24 110

sasdasda		95.02 102	93.96 123	98.20 21	97.53 126	95.92 17	98.71 159	96.19 257	91.78 108	95.86 112	98.49 121	79.53 231	99.03 158	96.12 102	91.42 237	99.66 64

canonicalmvs		95.02 102	93.96 123	98.20 21	97.53 126	95.92 17	98.71 159	96.19 257	91.78 108	95.86 112	98.49 121	79.53 231	99.03 158	96.12 102	91.42 237	99.66 64

MGCFI-Net		94.89 104	93.84 130	98.06 29	97.49 129	95.55 21	98.64 170	96.10 265	91.60 113	95.75 116	98.46 127	79.31 235	98.98 162	95.95 108	91.24 241	99.65 67

HPM-MVS_fast		94.89 104	94.62 102	95.70 146	99.11 66	88.44 209	99.14 114	97.11 193	85.82 274	95.69 118	98.47 125	83.46 171	99.32 144	93.16 167	99.63 45	99.35 100

testing91		94.88 106	94.44 106	96.21 120	97.19 147	91.90 110	99.23 97	97.66 103	89.91 161	93.66 158	97.05 189	90.21 57	98.50 185	93.52 159	91.53 234	98.25 190

testing99		94.88 106	94.45 105	96.17 124	97.20 145	91.91 109	99.20 99	97.66 103	89.95 160	93.68 157	97.06 187	90.28 56	98.50 185	93.52 159	91.54 231	98.12 199

CSCG		94.87 108	94.71 101	95.36 158	99.54 36	86.49 249	99.34 86	98.15 41	82.71 330	90.15 214	99.25 26	89.48 66	99.86 69	94.97 134	98.82 96	99.72 53

sss		94.85 109	93.94 125	97.58 44	96.43 182	94.09 64	98.93 139	99.16 8	89.50 175	95.27 125	97.85 142	81.50 213	99.65 107	92.79 173	94.02 191	98.99 131

test2506		94.80 110	94.21 111	96.58 99	96.41 185	92.18 107	98.01 243	98.96 11	90.82 131	93.46 161	97.28 170	85.92 136	98.45 191	89.82 203	97.19 144	99.12 121

API-MVS		94.78 111	94.18 114	96.59 98	99.21 61	90.06 162	98.80 151	97.78 79	83.59 312	93.85 154	99.21 32	83.79 166	99.97 21	92.37 176	99.00 85	99.74 50

thisisatest0515		94.75 112	94.19 112	96.43 107	96.13 204	92.64 99	99.47 62	97.60 121	87.55 240	93.17 164	97.59 158	94.71 12	98.42 192	88.28 222	93.20 198	98.24 193

xiu_mvs_v1_base_debu		94.73 113	93.98 120	96.99 70	95.19 237	95.24 27	98.62 173	96.50 236	92.99 83	97.52 66	98.83 91	72.37 286	99.15 150	97.03 78	96.74 153	96.58 243

xiu_mvs_v1_base		94.73 113	93.98 120	96.99 70	95.19 237	95.24 27	98.62 173	96.50 236	92.99 83	97.52 66	98.83 91	72.37 286	99.15 150	97.03 78	96.74 153	96.58 243

xiu_mvs_v1_base_debi		94.73 113	93.98 120	96.99 70	95.19 237	95.24 27	98.62 173	96.50 236	92.99 83	97.52 66	98.83 91	72.37 286	99.15 150	97.03 78	96.74 153	96.58 243

MVSFormer		94.71 116	94.08 117	96.61 96	95.05 252	94.87 39	97.77 257	96.17 261	86.84 254	98.04 56	98.52 116	85.52 141	95.99 327	89.83 201	98.97 87	98.96 134

PVSNet_Blended_VisFu		94.67 117	94.11 115	96.34 114	97.14 152	91.10 129	99.32 88	97.43 159	92.10 105	91.53 191	96.38 221	83.29 175	99.68 101	93.42 164	96.37 160	98.25 190

ACMMP		94.67 117	94.30 108	95.79 143	99.25 57	88.13 213	98.41 202	98.67 21	90.38 148	91.43 192	98.72 100	82.22 203	99.95 32	93.83 154	95.76 173	99.29 106
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
CPTT-MVS		94.60 119	94.43 107	95.09 171	99.66 12	86.85 244	99.44 69	97.47 150	83.22 317	94.34 144	98.96 75	82.50 194	99.55 115	94.81 136	99.50 55	98.88 144

diffmvs		94.59 120	94.19 112	95.81 142	95.54 223	90.69 141	98.70 162	95.68 306	91.61 111	95.96 107	97.81 144	80.11 225	98.06 211	96.52 95	95.76 173	98.67 165
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mvsany_test1		94.57 121	95.09 94	92.98 236	95.84 212	82.07 331	98.76 157	95.24 332	92.87 88	96.45 99	98.71 103	84.81 156	99.15 150	97.68 66	95.49 178	97.73 208

DeepC-MVS	91.02 4	94.56 122	93.92 126	96.46 105	97.16 151	90.76 139	98.39 209	97.11 193	93.92 57	88.66 229	98.33 130	78.14 246	99.85 73	95.02 131	98.57 108	98.78 156
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETVMVS		94.50 123	93.90 128	96.31 116	97.48 130	92.98 88	99.07 123	97.86 62	88.09 221	94.40 141	96.90 197	88.35 81	97.28 262	90.72 195	92.25 216	98.66 168

testing222		94.48 124	94.00 119	95.95 136	97.30 139	92.27 105	98.82 148	97.92 58	89.20 181	94.82 132	97.26 172	87.13 106	97.32 261	91.95 179	91.56 229	98.25 190

MAR-MVS		94.43 125	94.09 116	95.45 155	99.10 68	87.47 229	98.39 209	97.79 76	88.37 210	94.02 150	99.17 40	78.64 242	99.91 48	92.48 175	98.85 95	98.96 134
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
CHOSEN 1792x2688		94.35 126	93.82 131	95.95 136	97.40 132	88.74 201	98.41 202	98.27 31	92.18 102	91.43 192	96.40 218	78.88 237	99.81 85	93.59 158	97.81 126	99.30 105

CANet_DTU		94.31 127	93.35 142	97.20 62	97.03 161	94.71 48	98.62 173	95.54 314	95.61 30	97.21 75	98.47 125	71.88 291	99.84 75	88.38 221	97.46 138	97.04 231

mvsmamba		94.27 128	93.91 127	95.35 159	96.42 183	88.61 203	97.77 257	96.38 243	91.17 125	94.05 149	95.27 247	78.41 244	97.96 218	97.36 72	98.40 114	99.48 87

PLC	91.07 3	94.23 129	94.01 118	94.87 179	99.17 63	87.49 228	99.25 96	96.55 233	88.43 208	91.26 196	98.21 137	85.92 136	99.86 69	89.77 205	97.57 133	97.24 224
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_fmvsmconf0.01_n		94.14 130	93.51 138	96.04 129	86.79 391	89.19 179	99.28 92	95.94 279	95.70 26	95.50 121	98.49 121	73.27 278	99.79 91	98.28 55	98.32 119	99.15 117

114514_t		94.06 131	93.05 150	97.06 66	99.08 69	92.26 106	98.97 137	97.01 205	82.58 332	92.57 173	98.22 135	80.68 222	99.30 145	89.34 211	99.02 84	99.63 70

baseline2		94.04 132	93.80 132	94.74 185	93.07 311	90.25 150	98.12 233	98.16 40	89.86 162	86.53 250	96.95 194	95.56 6	98.05 213	91.44 184	94.53 185	95.93 258

thisisatest0530		94.00 133	93.52 137	95.43 156	95.76 215	90.02 164	98.99 134	97.60 121	86.58 261	91.74 183	97.36 169	94.78 11	98.34 194	86.37 243	92.48 209	97.94 204

casdiffmvs_mvg		94.00 133	93.33 143	96.03 130	95.22 235	90.90 137	99.09 121	95.99 272	90.58 140	91.55 190	97.37 168	79.91 227	98.06 211	95.01 132	95.22 180	99.13 120
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		93.98 135	93.43 139	95.61 152	95.07 251	89.86 168	98.80 151	95.84 297	90.98 127	92.74 171	97.66 155	79.71 228	98.10 208	94.72 139	95.37 179	98.87 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS		93.92 136	92.28 166	98.83 7	95.69 217	96.82 8	96.22 324	98.17 37	84.89 292	84.34 268	98.61 112	79.32 234	99.83 79	93.88 152	99.43 61	99.86 29

baseline		93.91 137	93.30 144	95.72 145	95.10 249	90.07 159	97.48 273	95.91 289	91.03 126	93.54 160	97.68 153	79.58 229	98.02 215	94.27 146	95.14 181	99.08 126

OMC-MVS		93.90 138	93.62 135	94.73 186	98.63 91	87.00 242	98.04 242	96.56 232	92.19 101	92.46 174	98.73 98	79.49 233	99.14 154	92.16 178	94.34 188	98.03 201

Effi-MVS+		93.87 139	93.15 148	96.02 131	95.79 213	90.76 139	96.70 308	95.78 298	86.98 251	95.71 117	97.17 182	79.58 229	98.01 216	94.57 143	96.09 168	99.31 104

test_cas_vis1_n_1920		93.86 140	93.74 133	94.22 205	95.39 230	86.08 265	99.73 29	96.07 269	96.38 21	97.19 77	97.78 147	65.46 342	99.86 69	96.71 87	98.92 91	96.73 238

TESTMET0.1,1		93.82 141	93.26 146	95.49 154	95.21 236	90.25 150	99.15 111	97.54 135	89.18 183	91.79 182	94.87 253	89.13 68	97.63 243	86.21 245	96.29 165	98.60 170

AdaColmap		93.82 141	93.06 149	96.10 127	99.88 1	89.07 183	98.33 214	97.55 132	86.81 256	90.39 211	98.65 107	75.09 259	99.98 9	93.32 165	97.53 136	99.26 109

EPP-MVSNet		93.75 143	93.67 134	94.01 215	95.86 211	85.70 277	98.67 166	97.66 103	84.46 297	91.36 195	97.18 181	91.16 34	97.79 228	92.93 170	93.75 194	98.53 172

thres200		93.69 144	92.59 162	96.97 74	97.76 114	94.74 46	99.35 85	99.36 2	89.23 180	91.21 198	96.97 193	83.42 172	98.77 170	85.08 257	90.96 242	97.39 219

PVSNet	87.13 12	93.69 144	92.83 156	96.28 117	97.99 109	90.22 153	99.38 79	98.93 12	91.42 119	93.66 158	97.68 153	71.29 298	99.64 109	87.94 227	97.20 143	98.98 132

HyFIR lowres test		93.68 146	93.29 145	94.87 179	97.57 125	88.04 215	98.18 227	98.47 24	87.57 239	91.24 197	95.05 251	85.49 144	97.46 253	93.22 166	92.82 202	99.10 124

MVS_Test		93.67 147	92.67 159	96.69 91	96.72 172	92.66 96	97.22 286	96.03 271	87.69 237	95.12 129	94.03 263	81.55 211	98.28 198	89.17 215	96.46 157	99.14 118

CNLPA		93.64 148	92.74 157	96.36 113	98.96 76	90.01 165	99.19 100	95.89 292	86.22 269	89.40 223	98.85 90	80.66 223	99.84 75	88.57 219	96.92 151	99.24 110

PMMVS		93.62 149	93.90 128	92.79 241	96.79 170	81.40 337	98.85 145	96.81 213	91.25 123	96.82 89	98.15 139	77.02 252	98.13 206	93.15 168	96.30 163	98.83 150

CDS-MVSNet		93.47 150	93.04 151	94.76 183	94.75 264	89.45 176	98.82 148	97.03 202	87.91 228	90.97 199	96.48 216	89.06 69	96.36 303	89.50 207	92.81 204	98.49 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		93.44 151	91.98 174	97.84 34	95.24 233	94.38 57	96.22 324	97.92 58	90.18 152	82.28 296	97.71 152	77.63 249	99.80 87	91.94 180	98.67 103	99.34 102

tfpn200view9		93.43 152	92.27 167	96.90 78	97.68 117	94.84 41	99.18 102	99.36 2	88.45 205	90.79 201	96.90 197	83.31 173	98.75 173	84.11 274	90.69 244	97.12 226

3Dnovator+	87.72 8	93.43 152	91.84 178	98.17 23	95.73 216	95.08 35	98.92 141	97.04 200	91.42 119	81.48 314	97.60 157	74.60 262	99.79 91	90.84 191	98.97 87	99.64 68

RRT-MVS		93.39 154	92.64 160	95.64 149	96.11 205	88.75 200	97.40 274	95.77 300	89.46 177	92.70 172	95.42 244	72.98 280	98.81 168	96.91 84	96.97 149	99.37 97

thres400		93.39 154	92.27 167	96.73 87	97.68 117	94.84 41	99.18 102	99.36 2	88.45 205	90.79 201	96.90 197	83.31 173	98.75 173	84.11 274	90.69 244	96.61 241

PVSNet_BlendedMVS		93.36 156	93.20 147	93.84 221	98.77 87	91.61 117	99.47 62	98.04 50	91.44 117	94.21 145	92.63 297	83.50 169	99.87 63	97.41 70	83.37 294	90.05 360

thres100view900		93.34 157	92.15 170	96.90 78	97.62 119	94.84 41	99.06 126	99.36 2	87.96 226	90.47 209	96.78 205	83.29 175	98.75 173	84.11 274	90.69 244	97.12 226

tttt0517		93.30 158	93.01 152	94.17 207	95.57 221	86.47 250	98.51 190	97.60 121	85.99 272	90.55 206	97.19 180	94.80 10	98.31 195	85.06 258	91.86 222	97.74 207

UA-Net		93.30 158	92.62 161	95.34 160	96.27 192	88.53 207	95.88 335	96.97 208	90.90 129	95.37 124	97.07 186	82.38 201	99.10 156	83.91 278	94.86 184	98.38 181

test-mter		93.27 160	92.89 155	94.40 197	94.94 258	87.27 237	99.15 111	97.25 175	88.95 190	91.57 187	94.04 261	88.03 89	97.58 247	85.94 249	96.13 166	98.36 185

Vis-MVSNet (Re-imp)		93.26 161	93.00 153	94.06 212	96.14 201	86.71 247	98.68 164	96.70 220	88.30 214	89.71 222	97.64 156	85.43 147	96.39 301	88.06 226	96.32 161	99.08 126

UWE-MVS		93.18 162	93.40 141	92.50 249	96.56 175	83.55 311	98.09 239	97.84 64	89.50 175	91.72 184	96.23 224	91.08 37	96.70 284	86.28 244	93.33 197	97.26 223

thres600view7		93.18 162	92.00 173	96.75 85	97.62 119	94.92 36	99.07 123	99.36 2	87.96 226	90.47 209	96.78 205	83.29 175	98.71 178	82.93 288	90.47 248	96.61 241

3Dnovator	87.35 11	93.17 164	91.77 181	97.37 54	95.41 228	93.07 85	98.82 148	97.85 63	91.53 114	82.56 289	97.58 159	71.97 290	99.82 82	91.01 188	99.23 73	99.22 113

test-LLR		93.11 165	92.68 158	94.40 197	94.94 258	87.27 237	99.15 111	97.25 175	90.21 150	91.57 187	94.04 261	84.89 154	97.58 247	85.94 249	96.13 166	98.36 185

test_vis1_n_1920		93.08 166	93.42 140	92.04 259	96.31 190	79.36 356	99.83 12	96.06 270	96.72 13	98.53 40	98.10 140	58.57 367	99.91 48	97.86 63	98.79 100	96.85 236

IS-MVSNet		93.00 167	92.51 163	94.49 193	96.14 201	87.36 233	98.31 217	95.70 304	88.58 201	90.17 213	97.50 162	83.02 182	97.22 263	87.06 232	96.07 170	98.90 143

CostFormer		92.89 168	92.48 164	94.12 209	94.99 255	85.89 272	92.89 371	97.00 206	86.98 251	95.00 131	90.78 333	90.05 60	97.51 251	92.92 171	91.73 226	98.96 134

tpmrst		92.78 169	92.16 169	94.65 188	96.27 192	87.45 230	91.83 381	97.10 196	89.10 186	94.68 136	90.69 337	88.22 83	97.73 238	89.78 204	91.80 224	98.77 158

MVSTER		92.71 170	92.32 165	93.86 220	97.29 140	92.95 91	99.01 132	96.59 228	90.09 156	85.51 258	94.00 265	94.61 15	96.56 290	90.77 194	83.03 296	92.08 298

1112_ss		92.71 170	91.55 185	96.20 121	95.56 222	91.12 127	98.48 195	94.69 350	88.29 215	86.89 247	98.50 118	87.02 110	98.66 180	84.75 262	89.77 253	98.81 152

Vis-MVSNet		92.64 172	91.85 177	95.03 175	95.12 244	88.23 210	98.48 195	96.81 213	91.61 111	92.16 180	97.22 177	71.58 296	98.00 217	85.85 252	97.81 126	98.88 144
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		92.62 173	92.09 172	94.20 206	94.10 280	87.68 221	98.41 202	96.97 208	87.53 241	89.74 220	96.04 231	84.77 158	96.49 296	88.97 217	92.31 213	98.42 177

baseline1		92.61 174	91.28 190	96.58 99	97.05 160	94.63 51	97.72 262	96.20 255	89.82 163	88.56 230	96.85 201	86.85 113	97.82 226	88.42 220	80.10 311	97.30 221

EPMVS		92.59 175	91.59 184	95.59 153	97.22 144	90.03 163	91.78 382	98.04 50	90.42 147	91.66 186	90.65 340	86.49 127	97.46 253	81.78 299	96.31 162	99.28 107

ET-MVSNet_ETH3D		92.56 176	91.45 187	95.88 139	96.39 187	94.13 63	99.46 66	96.97 208	92.18 102	66.94 402	98.29 133	94.65 14	94.28 371	94.34 145	83.82 289	99.24 110

mvs_anonymous		92.50 177	91.65 183	95.06 172	96.60 174	89.64 172	97.06 292	96.44 240	86.64 260	84.14 269	93.93 268	82.49 195	96.17 320	91.47 183	96.08 169	99.35 100

h-mvs33		92.47 178	91.95 175	94.05 213	97.13 153	85.01 291	98.36 212	98.08 45	93.85 62	96.27 103	96.73 208	83.19 178	99.43 131	95.81 110	68.09 384	97.70 209

test_fmvs1		92.35 179	92.94 154	90.57 291	97.19 147	75.43 382	99.55 51	94.97 339	95.20 36	96.82 89	97.57 160	59.59 365	99.84 75	97.30 73	98.29 120	96.46 249

BH-w/o		92.32 180	91.79 180	93.91 219	96.85 165	86.18 261	99.11 120	95.74 302	88.13 219	84.81 262	97.00 191	77.26 251	97.91 219	89.16 216	98.03 122	97.64 210

ECVR-MVS		92.29 181	91.33 189	95.15 169	96.41 185	87.84 218	98.10 236	94.84 343	90.82 131	91.42 194	97.28 170	65.61 339	98.49 189	90.33 197	97.19 144	99.12 121

EPNet_dtu		92.28 182	92.15 170	92.70 245	97.29 140	84.84 294	98.64 170	97.82 68	92.91 86	93.02 167	97.02 190	85.48 146	95.70 342	72.25 367	94.89 183	97.55 215
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		92.27 183	90.97 196	96.18 122	95.53 224	91.10 129	98.47 197	94.66 351	88.28 216	86.83 248	93.50 281	87.00 111	98.65 181	84.69 263	89.74 254	98.80 153

LFMVS		92.23 184	90.84 200	96.42 108	98.24 100	91.08 131	98.24 222	96.22 254	83.39 315	94.74 135	98.31 131	61.12 360	98.85 166	94.45 144	92.82 202	99.32 103

FA-MVS(test-final)		92.22 185	91.08 194	95.64 149	96.05 206	88.98 189	91.60 385	97.25 175	86.99 248	91.84 181	92.12 301	83.03 181	99.00 160	86.91 237	93.91 192	98.93 140

test1111		92.12 186	91.19 192	94.94 177	96.15 199	87.36 233	98.12 233	94.84 343	90.85 130	90.97 199	97.26 172	65.60 340	98.37 193	89.74 206	97.14 147	99.07 128

IB-MVS	89.43 6	92.12 186	90.83 202	95.98 135	95.40 229	90.78 138	99.81 15	98.06 47	91.23 124	85.63 257	93.66 276	90.63 47	98.78 169	91.22 185	71.85 373	98.36 185
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
reproduce_monomvs		92.11 188	91.82 179	92.98 236	98.25 98	90.55 145	98.38 211	97.93 57	94.81 38	80.46 323	92.37 299	96.46 3	97.17 264	94.06 148	73.61 355	91.23 328

F-COLMAP		92.07 189	91.75 182	93.02 235	98.16 104	82.89 321	98.79 155	95.97 274	86.54 263	87.92 234	97.80 145	78.69 241	99.65 107	85.97 247	95.93 172	96.53 246

PatchmatchNet		92.05 190	91.04 195	95.06 172	96.17 198	89.04 184	91.26 390	97.26 174	89.56 173	90.64 205	90.56 346	88.35 81	97.11 267	79.53 312	96.07 170	99.03 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UGNet		91.91 191	90.85 199	95.10 170	97.06 158	88.69 202	98.01 243	98.24 34	92.41 97	92.39 177	93.61 277	60.52 362	99.68 101	88.14 224	97.25 142	96.92 235
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
tpm2		91.77 192	91.09 193	93.82 222	94.83 262	85.56 280	92.51 376	97.16 188	84.00 303	93.83 155	90.66 339	87.54 95	97.17 264	87.73 229	91.55 230	98.72 161

Fast-Effi-MVS+		91.72 193	90.79 203	94.49 193	95.89 209	87.40 232	99.54 56	95.70 304	85.01 290	89.28 225	95.68 239	77.75 248	97.57 250	83.22 283	95.06 182	98.51 173

hse-mvs2		91.67 194	91.51 186	92.15 256	96.22 194	82.61 327	97.74 261	97.53 136	93.85 62	96.27 103	96.15 226	83.19 178	97.44 255	95.81 110	66.86 391	96.40 251

HQP-MVS		91.50 195	91.23 191	92.29 251	93.95 285	86.39 253	99.16 106	96.37 244	93.92 57	87.57 237	96.67 211	73.34 275	97.77 230	93.82 155	86.29 266	92.72 278

PatchMatch-RL		91.47 196	90.54 207	94.26 203	98.20 101	86.36 255	96.94 296	97.14 189	87.75 233	88.98 226	95.75 237	71.80 293	99.40 136	80.92 304	97.39 140	97.02 232

BH-untuned		91.46 197	90.84 200	93.33 230	96.51 179	84.83 295	98.84 147	95.50 316	86.44 268	83.50 273	96.70 209	75.49 258	97.77 230	86.78 240	97.81 126	97.40 218

mamv4		91.41 198	93.57 136	84.91 370	97.11 155	58.11 417	95.68 343	95.93 282	82.09 342	89.78 219	95.71 238	90.09 59	98.24 201	97.26 74	98.50 110	98.38 181

QAPM		91.41 198	89.49 222	97.17 63	95.66 219	93.42 77	98.60 179	97.51 142	80.92 356	81.39 315	97.41 167	72.89 283	99.87 63	82.33 293	98.68 102	98.21 195

FE-MVS		91.38 200	90.16 213	95.05 174	96.46 181	87.53 227	89.69 399	97.84 64	82.97 323	92.18 179	92.00 307	84.07 164	98.93 164	80.71 306	95.52 177	98.68 164

WBMVS		91.35 201	90.49 208	93.94 217	96.97 162	93.40 78	99.27 94	96.71 219	87.40 243	83.10 281	91.76 313	92.38 29	96.23 317	88.95 218	77.89 320	92.17 294

HQP_MVS		91.26 202	90.95 197	92.16 255	93.84 292	86.07 267	99.02 130	96.30 248	93.38 75	86.99 244	96.52 213	72.92 281	97.75 236	93.46 162	86.17 269	92.67 280

PCF-MVS	89.78 5	91.26 202	89.63 219	96.16 126	95.44 226	91.58 119	95.29 347	96.10 265	85.07 287	82.75 283	97.45 165	78.28 245	99.78 93	80.60 308	95.65 176	97.12 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet		91.25 204	89.99 214	95.03 175	96.75 171	88.55 205	98.65 168	94.95 340	87.74 234	87.74 236	97.80 145	68.27 316	98.14 205	80.53 309	97.49 137	98.41 178

VDD-MVS		91.24 205	90.18 212	94.45 196	97.08 157	85.84 275	98.40 205	96.10 265	86.99 248	93.36 162	98.16 138	54.27 386	99.20 147	96.59 93	90.63 247	98.31 188

SDMVSNet		91.09 206	89.91 215	94.65 188	96.80 168	90.54 146	97.78 255	97.81 72	88.34 212	85.73 254	95.26 248	66.44 334	98.26 199	94.25 147	86.75 263	95.14 263

test_fmvs1_n		91.07 207	91.41 188	90.06 305	94.10 280	74.31 386	99.18 102	94.84 343	94.81 38	96.37 102	97.46 164	50.86 399	99.82 82	97.14 77	97.90 124	96.04 256

CLD-MVS		91.06 208	90.71 204	92.10 257	94.05 284	86.10 264	99.55 51	96.29 251	94.16 52	84.70 263	97.17 182	69.62 307	97.82 226	94.74 138	86.08 271	92.39 283
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ab-mvs		91.05 209	89.17 228	96.69 91	95.96 208	91.72 114	92.62 375	97.23 179	85.61 278	89.74 220	93.89 270	68.55 313	99.42 132	91.09 186	87.84 258	98.92 142

UWE-MVS-28		90.99 210	91.93 176	88.15 340	95.12 244	77.87 372	97.18 289	97.79 76	88.72 197	88.69 228	96.52 213	86.54 124	90.75 400	84.64 265	92.16 220	95.83 260

XVG-OURS-SEG-HR		90.95 211	90.66 206	91.83 262	95.18 240	81.14 344	95.92 332	95.92 284	88.40 209	90.33 212	97.85 142	70.66 301	99.38 137	92.83 172	88.83 255	94.98 266

cascas		90.93 212	89.33 226	95.76 144	95.69 217	93.03 87	98.99 134	96.59 228	80.49 358	86.79 249	94.45 258	65.23 343	98.60 182	93.52 159	92.18 217	95.66 262

XVG-OURS		90.83 213	90.49 208	91.86 261	95.23 234	81.25 341	95.79 340	95.92 284	88.96 189	90.02 216	98.03 141	71.60 295	99.35 142	91.06 187	87.78 259	94.98 266

TR-MVS		90.77 214	89.44 223	94.76 183	96.31 190	88.02 216	97.92 247	95.96 276	85.52 279	88.22 233	97.23 176	66.80 330	98.09 209	84.58 266	92.38 210	98.17 198

OpenMVS	85.28 14	90.75 215	88.84 235	96.48 104	93.58 299	93.51 75	98.80 151	97.41 161	82.59 331	78.62 344	97.49 163	68.00 320	99.82 82	84.52 268	98.55 109	96.11 255

FIs		90.70 216	89.87 216	93.18 232	92.29 319	91.12 127	98.17 229	98.25 32	89.11 185	83.44 274	94.82 254	82.26 202	96.17 320	87.76 228	82.76 298	92.25 288

MonoMVSNet		90.69 217	89.78 217	93.45 227	91.78 332	84.97 293	96.51 312	94.44 355	90.56 141	85.96 253	90.97 329	78.61 243	96.27 316	95.35 122	83.79 290	99.11 123

X-MVStestdata		90.69 217	88.66 240	96.77 83	99.62 22	90.66 143	99.43 73	97.58 127	92.41 97	96.86 84	29.59 434	87.37 99	99.87 63	95.65 112	99.43 61	99.78 41

SCA		90.64 219	89.25 227	94.83 182	94.95 257	88.83 196	96.26 321	97.21 181	90.06 159	90.03 215	90.62 342	66.61 331	96.81 280	83.16 284	94.36 187	98.84 147

GeoE		90.60 220	89.56 220	93.72 225	95.10 249	85.43 281	99.41 76	94.94 341	83.96 305	87.21 243	96.83 204	74.37 266	97.05 271	80.50 310	93.73 195	98.67 165

test_vis1_n		90.40 221	90.27 211	90.79 286	91.55 336	76.48 376	99.12 119	94.44 355	94.31 48	97.34 72	96.95 194	43.60 410	99.42 132	97.57 68	97.60 132	96.47 248

TAPA-MVS	87.50 9	90.35 222	89.05 231	94.25 204	98.48 95	85.17 288	98.42 200	96.58 231	82.44 337	87.24 242	98.53 114	82.77 187	98.84 167	59.09 408	97.88 125	98.72 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
miper_enhance_ethall		90.33 223	89.70 218	92.22 252	97.12 154	88.93 194	98.35 213	95.96 276	88.60 200	83.14 280	92.33 300	87.38 98	96.18 319	86.49 242	77.89 320	91.55 314

CVMVSNet		90.30 224	90.91 198	88.46 339	94.32 274	73.58 390	97.61 270	97.59 125	90.16 155	88.43 232	97.10 184	76.83 253	92.86 382	82.64 290	93.54 196	98.93 140

nrg030		90.23 225	88.87 234	94.32 201	91.53 337	93.54 74	98.79 155	95.89 292	88.12 220	84.55 265	94.61 257	78.80 240	96.88 277	92.35 177	75.21 337	92.53 282

FC-MVSNet-test		90.22 226	89.40 224	92.67 247	91.78 332	89.86 168	97.89 248	98.22 35	88.81 195	82.96 282	94.66 256	81.90 209	95.96 329	85.89 251	82.52 301	92.20 293

LS3D		90.19 227	88.72 238	94.59 192	98.97 73	86.33 256	96.90 298	96.60 227	74.96 386	84.06 271	98.74 97	75.78 256	99.83 79	74.93 346	97.57 133	97.62 213

AUN-MVS		90.17 228	89.50 221	92.19 254	96.21 195	82.67 325	97.76 260	97.53 136	88.05 222	91.67 185	96.15 226	83.10 180	97.47 252	88.11 225	66.91 390	96.43 250

dp		90.16 229	88.83 236	94.14 208	96.38 188	86.42 251	91.57 386	97.06 199	84.76 294	88.81 227	90.19 358	84.29 161	97.43 256	75.05 345	91.35 240	98.56 171

GA-MVS		90.10 230	88.69 239	94.33 200	92.44 317	87.97 217	99.08 122	96.26 252	89.65 167	86.92 246	93.11 289	68.09 318	96.96 273	82.54 292	90.15 249	98.05 200

VDDNet		90.08 231	88.54 245	94.69 187	94.41 271	87.68 221	98.21 225	96.40 242	76.21 380	93.33 163	97.75 149	54.93 384	98.77 170	94.71 140	90.96 242	97.61 214

gg-mvs-nofinetune		90.00 232	87.71 257	96.89 82	96.15 199	94.69 49	85.15 409	97.74 83	68.32 408	92.97 168	60.16 422	96.10 4	96.84 278	93.89 151	98.87 94	99.14 118

Effi-MVS+-dtu		89.97 233	90.68 205	87.81 344	95.15 241	71.98 397	97.87 251	95.40 323	91.92 106	87.57 237	91.44 319	74.27 268	96.84 278	89.45 208	93.10 200	94.60 268

EI-MVSNet		89.87 234	89.38 225	91.36 273	94.32 274	85.87 273	97.61 270	96.59 228	85.10 285	85.51 258	97.10 184	81.30 218	96.56 290	83.85 280	83.03 296	91.64 306

OPM-MVS		89.76 235	89.15 229	91.57 270	90.53 349	85.58 279	98.11 235	95.93 282	92.88 87	86.05 251	96.47 217	67.06 329	97.87 223	89.29 214	86.08 271	91.26 327
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpm		89.67 236	88.95 233	91.82 263	92.54 316	81.43 336	92.95 370	95.92 284	87.81 230	90.50 208	89.44 366	84.99 152	95.65 343	83.67 281	82.71 299	98.38 181

UniMVSNet_NR-MVSNet		89.60 237	88.55 244	92.75 243	92.17 322	90.07 159	98.74 158	98.15 41	88.37 210	83.21 276	93.98 266	82.86 184	95.93 331	86.95 235	72.47 367	92.25 288

cl22		89.57 238	88.79 237	91.91 260	97.94 110	87.62 224	97.98 245	96.51 235	85.03 288	82.37 295	91.79 310	83.65 167	96.50 294	85.96 248	77.89 320	91.61 311

PS-MVSNAJss		89.54 239	89.05 231	91.00 279	88.77 371	84.36 300	97.39 275	95.97 274	88.47 202	81.88 307	93.80 272	82.48 196	96.50 294	89.34 211	83.34 295	92.15 295

UniMVSNet (Re)		89.50 240	88.32 248	93.03 234	92.21 321	90.96 135	98.90 143	98.39 27	89.13 184	83.22 275	92.03 303	81.69 210	96.34 309	86.79 239	72.53 366	91.81 303

sd_testset		89.23 241	88.05 254	92.74 244	96.80 168	85.33 284	95.85 338	97.03 202	88.34 212	85.73 254	95.26 248	61.12 360	97.76 235	85.61 253	86.75 263	95.14 263

tpmvs		89.16 242	87.76 255	93.35 229	97.19 147	84.75 296	90.58 397	97.36 169	81.99 343	84.56 264	89.31 369	83.98 165	98.17 204	74.85 348	90.00 252	97.12 226

VPA-MVSNet		89.10 243	87.66 258	93.45 227	92.56 315	91.02 133	97.97 246	98.32 30	86.92 253	86.03 252	92.01 305	68.84 312	97.10 269	90.92 189	75.34 336	92.23 290

ADS-MVSNet		88.99 244	87.30 263	94.07 211	96.21 195	87.56 226	87.15 403	96.78 216	83.01 321	89.91 217	87.27 383	78.87 238	97.01 272	74.20 353	92.27 214	97.64 210

test0.0.03 1		88.96 245	88.61 241	90.03 309	91.09 343	84.43 299	98.97 137	97.02 204	90.21 150	80.29 325	96.31 223	84.89 154	91.93 396	72.98 362	85.70 274	93.73 270

miper_ehance_all_eth		88.94 246	88.12 252	91.40 271	95.32 232	86.93 243	97.85 252	95.55 313	84.19 300	81.97 305	91.50 318	84.16 162	95.91 334	84.69 263	77.89 320	91.36 322

tpm cat1		88.89 247	87.27 264	93.76 223	95.79 213	85.32 285	90.76 395	97.09 197	76.14 381	85.72 256	88.59 372	82.92 183	98.04 214	76.96 331	91.43 236	97.90 205

LPG-MVS_test		88.86 248	88.47 246	90.06 305	93.35 306	80.95 346	98.22 223	95.94 279	87.73 235	83.17 278	96.11 228	66.28 335	97.77 230	90.19 199	85.19 276	91.46 317

Anonymous202405211		88.84 249	87.03 268	94.27 202	98.14 105	84.18 303	98.44 198	95.58 312	76.79 378	89.34 224	96.88 200	53.42 390	99.54 117	87.53 231	87.12 262	99.09 125

Fast-Effi-MVS+-dtu		88.84 249	88.59 243	89.58 320	93.44 304	78.18 367	98.65 168	94.62 352	88.46 204	84.12 270	95.37 246	68.91 310	96.52 293	82.06 296	91.70 227	94.06 269

DU-MVS		88.83 251	87.51 259	92.79 241	91.46 338	90.07 159	98.71 159	97.62 118	88.87 194	83.21 276	93.68 274	74.63 260	95.93 331	86.95 235	72.47 367	92.36 284

CR-MVSNet		88.83 251	87.38 262	93.16 233	93.47 301	86.24 257	84.97 411	94.20 364	88.92 193	90.76 203	86.88 387	84.43 159	94.82 363	70.64 371	92.17 218	98.41 178

FMVSNet3		88.81 253	87.08 267	93.99 216	96.52 178	94.59 52	98.08 240	96.20 255	85.85 273	82.12 299	91.60 316	74.05 270	95.40 351	79.04 316	80.24 308	91.99 301

ACMM	86.95 13	88.77 254	88.22 250	90.43 296	93.61 298	81.34 339	98.50 191	95.92 284	87.88 229	83.85 272	95.20 250	67.20 327	97.89 221	86.90 238	84.90 278	92.06 299
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		88.75 255	86.56 274	95.34 160	98.92 81	87.45 230	97.64 269	93.52 375	70.55 399	81.49 313	97.25 174	74.43 265	99.88 59	71.14 370	94.09 190	98.67 165

ACMP	87.39 10	88.71 256	88.24 249	90.12 304	93.91 290	81.06 345	98.50 191	95.67 307	89.43 178	80.37 324	95.55 240	65.67 337	97.83 225	90.55 196	84.51 280	91.47 316
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
WB-MVSnew		88.69 257	88.34 247	89.77 315	94.30 278	85.99 270	98.14 230	97.31 173	87.15 247	87.85 235	96.07 230	69.91 302	95.52 346	72.83 364	91.47 235	87.80 384

dmvs_re		88.69 257	88.06 253	90.59 290	93.83 294	78.68 363	95.75 341	96.18 259	87.99 225	84.48 267	96.32 222	67.52 324	96.94 275	84.98 260	85.49 275	96.14 254

myMVS_eth3d		88.68 259	89.07 230	87.50 348	95.14 242	79.74 354	97.68 265	96.66 222	86.52 264	82.63 286	96.84 202	85.22 151	89.89 405	69.43 376	91.54 231	92.87 276

LCM-MVSNet-Re		88.59 260	88.61 241	88.51 338	95.53 224	72.68 395	96.85 300	88.43 415	88.45 205	73.14 378	90.63 341	75.82 255	94.38 370	92.95 169	95.71 175	98.48 175

WR-MVS		88.54 261	87.22 266	92.52 248	91.93 329	89.50 175	98.56 184	97.84 64	86.99 248	81.87 308	93.81 271	74.25 269	95.92 333	85.29 255	74.43 346	92.12 296

IterMVS-LS		88.34 262	87.44 260	91.04 278	94.10 280	85.85 274	98.10 236	95.48 317	85.12 284	82.03 303	91.21 325	81.35 217	95.63 344	83.86 279	75.73 334	91.63 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPNet		88.30 263	86.57 273	93.49 226	91.95 327	91.35 121	98.18 227	97.20 185	88.61 199	84.52 266	94.89 252	62.21 355	96.76 283	89.34 211	72.26 370	92.36 284

MSDG		88.29 264	86.37 276	94.04 214	96.90 164	86.15 263	96.52 311	94.36 361	77.89 373	79.22 339	96.95 194	69.72 305	99.59 113	73.20 361	92.58 208	96.37 252

test_djsdf		88.26 265	87.73 256	89.84 312	88.05 380	82.21 329	97.77 257	96.17 261	86.84 254	82.41 294	91.95 309	72.07 289	95.99 327	89.83 201	84.50 281	91.32 324

c3_l		88.19 266	87.23 265	91.06 277	94.97 256	86.17 262	97.72 262	95.38 324	83.43 314	81.68 312	91.37 320	82.81 186	95.72 341	84.04 277	73.70 354	91.29 326

D2MVS		87.96 267	87.39 261	89.70 317	91.84 331	83.40 313	98.31 217	98.49 22	88.04 223	78.23 350	90.26 352	73.57 273	96.79 282	84.21 271	83.53 292	88.90 376

cl____		87.82 268	86.79 272	90.89 283	94.88 260	85.43 281	97.81 253	95.24 332	82.91 328	80.71 320	91.22 324	81.97 208	95.84 336	81.34 301	75.06 338	91.40 321

DIV-MVS_self_test		87.82 268	86.81 271	90.87 284	94.87 261	85.39 283	97.81 253	95.22 337	82.92 327	80.76 319	91.31 323	81.99 206	95.81 338	81.36 300	75.04 339	91.42 320

eth_miper_zixun_eth		87.76 270	87.00 269	90.06 305	94.67 266	82.65 326	97.02 295	95.37 325	84.19 300	81.86 310	91.58 317	81.47 214	95.90 335	83.24 282	73.61 355	91.61 311

testing3		87.75 271	88.22 250	86.36 357	94.66 267	77.41 373	99.52 57	97.95 56	86.05 271	81.12 316	96.69 210	86.18 133	89.31 409	61.65 402	90.12 250	92.35 287

TranMVSNet+NR-MVSNet		87.75 271	86.31 277	92.07 258	90.81 346	88.56 204	98.33 214	97.18 186	87.76 232	81.87 308	93.90 269	72.45 285	95.43 349	83.13 286	71.30 377	92.23 290

XXY-MVS		87.75 271	86.02 281	92.95 239	90.46 350	89.70 171	97.71 264	95.90 290	84.02 302	80.95 317	94.05 260	67.51 325	97.10 269	85.16 256	78.41 317	92.04 300

NR-MVSNet		87.74 274	86.00 282	92.96 238	91.46 338	90.68 142	96.65 309	97.42 160	88.02 224	73.42 375	93.68 274	77.31 250	95.83 337	84.26 270	71.82 374	92.36 284

Anonymous20240529		87.66 275	85.58 288	93.92 218	97.59 123	85.01 291	98.13 231	97.13 191	66.69 413	88.47 231	96.01 232	55.09 382	99.51 119	87.00 234	84.12 285	97.23 225

ADS-MVSNet2		87.62 276	86.88 270	89.86 311	96.21 195	79.14 359	87.15 403	92.99 378	83.01 321	89.91 217	87.27 383	78.87 238	92.80 385	74.20 353	92.27 214	97.64 210

pmmvs4		87.58 277	86.17 280	91.80 264	89.58 361	88.92 195	97.25 283	95.28 328	82.54 333	80.49 322	93.17 288	75.62 257	96.05 325	82.75 289	78.90 315	90.42 351

jajsoiax		87.35 278	86.51 275	89.87 310	87.75 385	81.74 333	97.03 293	95.98 273	88.47 202	80.15 327	93.80 272	61.47 357	96.36 303	89.44 209	84.47 282	91.50 315

PVSNet_0	83.28 16	87.31 279	85.16 294	93.74 224	94.78 263	84.59 297	98.91 142	98.69 20	89.81 164	78.59 346	93.23 286	61.95 356	99.34 143	94.75 137	55.72 413	97.30 221

v2v482		87.27 280	85.76 285	91.78 268	89.59 360	87.58 225	98.56 184	95.54 314	84.53 296	82.51 290	91.78 311	73.11 279	96.47 297	82.07 295	74.14 352	91.30 325

mvs_tets		87.09 281	86.22 278	89.71 316	87.87 381	81.39 338	96.73 307	95.90 290	88.19 218	79.99 329	93.61 277	59.96 364	96.31 311	89.40 210	84.34 283	91.43 319

V42		87.00 282	85.68 287	90.98 280	89.91 354	86.08 265	98.32 216	95.61 310	83.67 311	82.72 284	90.67 338	74.00 271	96.53 292	81.94 298	74.28 349	90.32 353

miper_lstm_enhance		86.90 283	86.20 279	89.00 333	94.53 269	81.19 342	96.74 306	95.24 332	82.33 338	80.15 327	90.51 349	81.99 206	94.68 367	80.71 306	73.58 357	91.12 331

FMVSNet2		86.90 283	84.79 302	93.24 231	95.11 246	92.54 101	97.67 267	95.86 296	82.94 324	80.55 321	91.17 326	62.89 352	95.29 353	77.23 328	79.71 314	91.90 302

v1144		86.83 285	85.31 293	91.40 271	89.75 358	87.21 241	98.31 217	95.45 319	83.22 317	82.70 285	90.78 333	73.36 274	96.36 303	79.49 313	74.69 343	90.63 348

MS-PatchMatch		86.75 286	85.92 283	89.22 327	91.97 325	82.47 328	96.91 297	96.14 263	83.74 308	77.73 352	93.53 280	58.19 369	97.37 260	76.75 334	98.35 116	87.84 382

anonymousdsp		86.69 287	85.75 286	89.53 321	86.46 393	82.94 318	96.39 315	95.71 303	83.97 304	79.63 334	90.70 336	68.85 311	95.94 330	86.01 246	84.02 286	89.72 366

GBi-Net		86.67 288	84.96 296	91.80 264	95.11 246	88.81 197	96.77 302	95.25 329	82.94 324	82.12 299	90.25 353	62.89 352	94.97 358	79.04 316	80.24 308	91.62 308

test1		86.67 288	84.96 296	91.80 264	95.11 246	88.81 197	96.77 302	95.25 329	82.94 324	82.12 299	90.25 353	62.89 352	94.97 358	79.04 316	80.24 308	91.62 308

MVP-Stereo		86.61 290	85.83 284	88.93 335	88.70 373	83.85 308	96.07 329	94.41 360	82.15 341	75.64 363	91.96 308	67.65 323	96.45 299	77.20 330	98.72 101	86.51 394
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CP-MVSNet		86.54 291	85.45 291	89.79 314	91.02 345	82.78 324	97.38 277	97.56 131	85.37 281	79.53 336	93.03 290	71.86 292	95.25 354	79.92 311	73.43 361	91.34 323

WR-MVS_H		86.53 292	85.49 290	89.66 319	91.04 344	83.31 315	97.53 272	98.20 36	84.95 291	79.64 333	90.90 331	78.01 247	95.33 352	76.29 338	72.81 363	90.35 352

tt0805		86.50 293	84.79 302	91.63 269	91.97 325	81.49 335	96.49 313	97.38 165	82.24 339	82.44 291	95.82 236	51.22 396	98.25 200	84.55 267	80.96 307	95.13 265

v144192		86.40 294	84.89 299	90.91 281	89.48 364	85.59 278	98.21 225	95.43 322	82.45 336	82.62 288	90.58 345	72.79 284	96.36 303	78.45 323	74.04 353	90.79 340

v148		86.38 295	85.06 295	90.37 300	89.47 365	84.10 304	98.52 187	95.48 317	83.80 307	80.93 318	90.22 356	74.60 262	96.31 311	80.92 304	71.55 375	90.69 346

v1192		86.32 296	84.71 304	91.17 275	89.53 363	86.40 252	98.13 231	95.44 321	82.52 334	82.42 293	90.62 342	71.58 296	96.33 310	77.23 328	74.88 340	90.79 340

Patchmatch-test		86.25 297	84.06 314	92.82 240	94.42 270	82.88 322	82.88 418	94.23 363	71.58 395	79.39 337	90.62 342	89.00 71	96.42 300	63.03 398	91.37 239	99.16 116

v8		86.11 298	84.45 309	91.10 276	89.99 353	86.85 244	97.24 284	95.36 326	81.99 343	79.89 331	89.86 362	74.53 264	96.39 301	78.83 320	72.32 369	90.05 360

v1921920		86.02 299	84.44 310	90.77 287	89.32 366	85.20 286	98.10 236	95.35 327	82.19 340	82.25 297	90.71 335	70.73 299	96.30 314	76.85 333	74.49 345	90.80 339

JIA-IIPM		85.97 300	84.85 300	89.33 326	93.23 308	73.68 389	85.05 410	97.13 191	69.62 404	91.56 189	68.03 420	88.03 89	96.96 273	77.89 326	93.12 199	97.34 220

pmmvs5		85.87 301	84.40 312	90.30 301	88.53 375	84.23 301	98.60 179	93.71 371	81.53 348	80.29 325	92.02 304	64.51 345	95.52 346	82.04 297	78.34 318	91.15 330

XVG-ACMP-BASELINE		85.86 302	84.95 298	88.57 337	89.90 355	77.12 374	94.30 356	95.60 311	87.40 243	82.12 299	92.99 292	53.42 390	97.66 240	85.02 259	83.83 287	90.92 336

Baseline_NR-MVSNet		85.83 303	84.82 301	88.87 336	88.73 372	83.34 314	98.63 172	91.66 396	80.41 361	82.44 291	91.35 321	74.63 260	95.42 350	84.13 273	71.39 376	87.84 382

PS-CasMVS		85.81 304	84.58 307	89.49 324	90.77 347	82.11 330	97.20 287	97.36 169	84.83 293	79.12 341	92.84 293	67.42 326	95.16 356	78.39 324	73.25 362	91.21 329

IterMVS		85.81 304	84.67 305	89.22 327	93.51 300	83.67 310	96.32 318	94.80 346	85.09 286	78.69 342	90.17 359	66.57 333	93.17 381	79.48 314	77.42 327	90.81 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		85.77 306	84.11 313	90.73 288	89.26 367	85.15 289	97.88 250	95.23 336	81.89 346	82.16 298	90.55 347	69.60 308	96.31 311	75.59 343	74.87 341	90.72 345

IterMVS-SCA-FT		85.73 307	84.64 306	89.00 333	93.46 303	82.90 320	96.27 319	94.70 349	85.02 289	78.62 344	90.35 351	66.61 331	93.33 378	79.38 315	77.36 328	90.76 342

v10		85.73 307	84.01 315	90.87 284	90.03 352	86.73 246	97.20 287	95.22 337	81.25 351	79.85 332	89.75 363	73.30 277	96.28 315	76.87 332	72.64 365	89.61 368

UniMVSNet_ETH3D		85.65 309	83.79 318	91.21 274	90.41 351	80.75 349	95.36 345	95.78 298	78.76 367	81.83 311	94.33 259	49.86 401	96.66 285	84.30 269	83.52 293	96.22 253

PatchT		85.44 310	83.19 321	92.22 252	93.13 310	83.00 317	83.80 417	96.37 244	70.62 398	90.55 206	79.63 412	84.81 156	94.87 361	58.18 410	91.59 228	98.79 154

RPSCF		85.33 311	85.55 289	84.67 373	94.63 268	62.28 412	93.73 363	93.76 369	74.38 389	85.23 261	97.06 187	64.09 346	98.31 195	80.98 302	86.08 271	93.41 274

SSC-MVS3.2		85.22 312	83.90 317	89.17 329	91.87 330	79.84 353	97.66 268	96.63 224	86.81 256	81.99 304	91.35 321	55.80 375	96.00 326	76.52 337	76.53 331	91.67 305

PEN-MVS		85.21 313	83.93 316	89.07 332	89.89 356	81.31 340	97.09 291	97.24 178	84.45 298	78.66 343	92.68 296	68.44 315	94.87 361	75.98 340	70.92 378	91.04 333

test_fmvs2		85.10 314	85.45 291	84.02 376	89.85 357	65.63 410	98.49 193	92.59 383	90.45 145	85.43 260	93.32 282	43.94 408	96.59 288	90.81 192	84.19 284	89.85 364

RPMNet		85.07 315	81.88 334	94.64 190	93.47 301	86.24 257	84.97 411	97.21 181	64.85 415	90.76 203	78.80 413	80.95 221	99.27 146	53.76 414	92.17 218	98.41 178

AllTest		84.97 316	83.12 322	90.52 294	96.82 166	78.84 361	95.89 333	92.17 388	77.96 371	75.94 359	95.50 241	55.48 378	99.18 148	71.15 368	87.14 260	93.55 272

USDC		84.74 317	82.93 323	90.16 303	91.73 334	83.54 312	95.00 350	93.30 377	88.77 196	73.19 377	93.30 284	53.62 389	97.65 242	75.88 341	81.54 305	89.30 371

Anonymous20231211		84.72 318	82.65 330	90.91 281	97.71 116	84.55 298	97.28 281	96.67 221	66.88 412	79.18 340	90.87 332	58.47 368	96.60 287	82.61 291	74.20 350	91.59 313

pm-mvs1		84.68 319	82.78 327	90.40 297	89.58 361	85.18 287	97.31 279	94.73 348	81.93 345	76.05 358	92.01 305	65.48 341	96.11 323	78.75 321	69.14 381	89.91 363

ACMH	83.09 17	84.60 320	82.61 331	90.57 291	93.18 309	82.94 318	96.27 319	94.92 342	81.01 354	72.61 384	93.61 277	56.54 373	97.79 228	74.31 351	81.07 306	90.99 334
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	81.71 19	84.59 321	82.72 329	90.18 302	92.89 313	83.18 316	93.15 368	94.74 347	78.99 364	75.14 366	92.69 295	65.64 338	97.63 243	69.46 375	81.82 304	89.74 365
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
COLMAP_ROB	82.69 18	84.54 322	82.82 324	89.70 317	96.72 172	78.85 360	95.89 333	92.83 381	71.55 396	77.54 354	95.89 235	59.40 366	99.14 154	67.26 385	88.26 256	91.11 332
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		84.48 323	81.83 335	92.42 250	91.73 334	87.36 233	85.52 406	94.42 359	81.40 349	81.91 306	87.58 377	51.92 393	92.81 384	73.84 356	88.15 257	97.08 230

our_test_3		84.47 324	82.80 325	89.50 322	89.01 368	83.90 307	97.03 293	94.56 353	81.33 350	75.36 365	90.52 348	71.69 294	94.54 369	68.81 379	76.84 329	90.07 358

v7n		84.42 325	82.75 328	89.43 325	88.15 378	81.86 332	96.75 305	95.67 307	80.53 357	78.38 348	89.43 367	69.89 303	96.35 308	73.83 357	72.13 371	90.07 358

kuosan		84.40 326	83.34 320	87.60 346	95.87 210	79.21 357	92.39 377	96.87 211	76.12 382	73.79 372	93.98 266	81.51 212	90.63 401	64.13 394	75.42 335	92.95 275

ACMH+	83.78 15	84.21 327	82.56 333	89.15 330	93.73 297	79.16 358	96.43 314	94.28 362	81.09 353	74.00 371	94.03 263	54.58 385	97.67 239	76.10 339	78.81 316	90.63 348

EU-MVSNet		84.19 328	84.42 311	83.52 380	88.64 374	67.37 408	96.04 330	95.76 301	85.29 282	78.44 347	93.18 287	70.67 300	91.48 398	75.79 342	75.98 332	91.70 304

DTE-MVSNet		84.14 329	82.80 325	88.14 341	88.95 370	79.87 352	96.81 301	96.24 253	83.50 313	77.60 353	92.52 298	67.89 322	94.24 372	72.64 365	69.05 382	90.32 353

OurMVSNet-221017-0		84.13 330	83.59 319	85.77 364	87.81 382	70.24 402	94.89 351	93.65 373	86.08 270	76.53 355	93.28 285	61.41 358	96.14 322	80.95 303	77.69 326	90.93 335

Syy-MVS		84.10 331	84.53 308	82.83 382	95.14 242	65.71 409	97.68 265	96.66 222	86.52 264	82.63 286	96.84 202	68.15 317	89.89 405	45.62 420	91.54 231	92.87 276

FMVSNet1		83.94 332	81.32 341	91.80 264	91.94 328	88.81 197	96.77 302	95.25 329	77.98 369	78.25 349	90.25 353	50.37 400	94.97 358	73.27 360	77.81 325	91.62 308

mmtdpeth		83.69 333	82.59 332	86.99 353	92.82 314	76.98 375	96.16 327	91.63 397	82.89 329	92.41 176	82.90 398	54.95 383	98.19 203	96.27 98	53.27 416	85.81 398

tfpnnormal		83.65 334	81.35 340	90.56 293	91.37 340	88.06 214	97.29 280	97.87 61	78.51 368	76.20 356	90.91 330	64.78 344	96.47 297	61.71 401	73.50 358	87.13 391

ppachtmachnet_test		83.63 335	81.57 338	89.80 313	89.01 368	85.09 290	97.13 290	94.50 354	78.84 365	76.14 357	91.00 328	69.78 304	94.61 368	63.40 396	74.36 347	89.71 367

Patchmtry		83.61 336	81.64 336	89.50 322	93.36 305	82.84 323	84.10 414	94.20 364	69.47 405	79.57 335	86.88 387	84.43 159	94.78 364	68.48 381	74.30 348	90.88 337

KD-MVS_2432*1600		82.98 337	80.52 346	90.38 298	94.32 274	88.98 189	92.87 372	95.87 294	80.46 359	73.79 372	87.49 380	82.76 189	93.29 379	70.56 372	46.53 424	88.87 377

miper_refine_blended		82.98 337	80.52 346	90.38 298	94.32 274	88.98 189	92.87 372	95.87 294	80.46 359	73.79 372	87.49 380	82.76 189	93.29 379	70.56 372	46.53 424	88.87 377

SixPastTwentyTwo		82.63 339	81.58 337	85.79 363	88.12 379	71.01 400	95.17 348	92.54 384	84.33 299	72.93 382	92.08 302	60.41 363	95.61 345	74.47 350	74.15 351	90.75 343

testgi		82.29 340	81.00 343	86.17 359	87.24 388	74.84 385	97.39 275	91.62 398	88.63 198	75.85 362	95.42 244	46.07 407	91.55 397	66.87 388	79.94 312	92.12 296

FMVSNet5		82.29 340	80.54 345	87.52 347	93.79 296	84.01 305	93.73 363	92.47 385	76.92 376	74.27 369	86.15 391	63.69 350	89.24 410	69.07 378	74.79 342	89.29 372

TransMVSNet (Re)		81.97 342	79.61 352	89.08 331	89.70 359	84.01 305	97.26 282	91.85 394	78.84 365	73.07 381	91.62 315	67.17 328	95.21 355	67.50 384	59.46 407	88.02 381

LF4IMVS		81.94 343	81.17 342	84.25 375	87.23 389	68.87 407	93.35 367	91.93 393	83.35 316	75.40 364	93.00 291	49.25 404	96.65 286	78.88 319	78.11 319	87.22 390

Patchmatch-RL test		81.90 344	80.13 348	87.23 351	80.71 411	70.12 404	84.07 415	88.19 416	83.16 319	70.57 386	82.18 403	87.18 105	92.59 387	82.28 294	62.78 398	98.98 132

DSMNet-mixed		81.60 345	81.43 339	82.10 385	84.36 400	60.79 413	93.63 365	86.74 418	79.00 363	79.32 338	87.15 385	63.87 348	89.78 407	66.89 387	91.92 221	95.73 261

dongtai		81.36 346	80.61 344	83.62 379	94.25 279	73.32 391	95.15 349	96.81 213	73.56 392	69.79 389	92.81 294	81.00 220	86.80 416	52.08 417	70.06 380	90.75 343

test_vis1_rt		81.31 347	80.05 350	85.11 367	91.29 341	70.66 401	98.98 136	77.39 430	85.76 276	68.80 393	82.40 401	36.56 417	99.44 128	92.67 174	86.55 265	85.24 405

K. test v3		81.04 348	79.77 351	84.83 371	87.41 386	70.23 403	95.60 344	93.93 368	83.70 310	67.51 400	89.35 368	55.76 376	93.58 377	76.67 335	68.03 385	90.67 347

Anonymous20231206		80.76 349	79.42 353	84.79 372	84.78 399	72.98 392	96.53 310	92.97 379	79.56 362	74.33 368	88.83 370	61.27 359	92.15 393	60.59 404	75.92 333	89.24 373

CMPMVS	58.40 21	80.48 350	80.11 349	81.59 388	85.10 398	59.56 415	94.14 360	95.95 278	68.54 407	60.71 411	93.31 283	55.35 381	97.87 223	83.06 287	84.85 279	87.33 388
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		80.42 351	77.94 356	87.85 343	92.09 323	78.58 364	93.74 362	89.94 408	74.99 385	69.77 390	91.78 311	46.09 406	97.58 247	65.17 393	77.89 320	87.38 386

EG-PatchMatch MVS		79.92 352	77.59 358	86.90 354	87.06 390	77.90 371	96.20 326	94.06 366	74.61 387	66.53 404	88.76 371	40.40 415	96.20 318	67.02 386	83.66 291	86.61 392

pmmvs6		79.90 353	77.31 360	87.67 345	84.17 401	78.13 368	95.86 337	93.68 372	67.94 409	72.67 383	89.62 365	50.98 398	95.75 339	74.80 349	66.04 392	89.14 374

CL-MVSNet_self_test		79.89 354	78.34 355	84.54 374	81.56 409	75.01 383	96.88 299	95.62 309	81.10 352	75.86 361	85.81 392	68.49 314	90.26 403	63.21 397	56.51 411	88.35 379

ttmdpeth		79.80 355	77.91 357	85.47 366	83.34 404	75.75 379	95.32 346	91.45 401	76.84 377	74.81 367	91.71 314	53.98 388	94.13 373	72.42 366	61.29 402	86.51 394

MDA-MVSNet_test_wron		79.65 356	77.05 361	87.45 349	87.79 384	80.13 350	96.25 322	94.44 355	73.87 390	51.80 418	87.47 382	68.04 319	92.12 394	66.02 389	67.79 387	90.09 356

YYNet1		79.64 357	77.04 362	87.43 350	87.80 383	79.98 351	96.23 323	94.44 355	73.83 391	51.83 417	87.53 378	67.96 321	92.07 395	66.00 390	67.75 388	90.23 355

MVS-HIRNet		79.01 358	75.13 371	90.66 289	93.82 295	81.69 334	85.16 408	93.75 370	54.54 418	74.17 370	59.15 424	57.46 371	96.58 289	63.74 395	94.38 186	93.72 271

UnsupCasMVSNet_eth		78.90 359	76.67 364	85.58 365	82.81 407	74.94 384	91.98 380	96.31 247	84.64 295	65.84 406	87.71 376	51.33 395	92.23 392	72.89 363	56.50 412	89.56 369

test_0402		78.81 360	76.33 365	86.26 358	91.18 342	78.44 366	95.88 335	91.34 402	68.55 406	70.51 388	89.91 361	52.65 392	94.99 357	47.14 419	79.78 313	85.34 404

pmmvs-eth3d		78.71 361	76.16 366	86.38 356	80.25 414	81.19 342	94.17 359	92.13 390	77.97 370	66.90 403	82.31 402	55.76 376	92.56 388	73.63 359	62.31 401	85.38 402

Anonymous20240521		78.63 362	76.90 363	83.82 377	82.82 406	72.86 393	95.72 342	93.57 374	73.55 393	72.17 385	84.79 394	49.69 402	92.51 389	65.29 392	74.50 344	86.09 397

test20.03		78.51 363	77.48 359	81.62 387	83.07 405	71.03 399	96.11 328	92.83 381	81.66 347	69.31 392	89.68 364	57.53 370	87.29 415	58.65 409	68.47 383	86.53 393

mvs5depth		78.17 364	75.56 368	85.97 361	80.43 413	76.44 377	85.46 407	89.24 413	76.39 379	78.17 351	88.26 373	51.73 394	95.73 340	69.31 377	61.09 403	85.73 399

TDRefinement		78.01 365	75.31 369	86.10 360	70.06 425	73.84 388	93.59 366	91.58 399	74.51 388	73.08 380	91.04 327	49.63 403	97.12 266	74.88 347	59.47 406	87.33 388

OpenMVS_ROB	73.86 20	77.99 366	75.06 372	86.77 355	83.81 403	77.94 370	96.38 316	91.53 400	67.54 410	68.38 395	87.13 386	43.94 408	96.08 324	55.03 413	81.83 303	86.29 396

MDA-MVSNet-bldmvs		77.82 367	74.75 373	87.03 352	88.33 376	78.52 365	96.34 317	92.85 380	75.57 383	48.87 420	87.89 375	57.32 372	92.49 390	60.79 403	64.80 396	90.08 357

KD-MVS_self_test		77.47 368	75.88 367	82.24 383	81.59 408	68.93 406	92.83 374	94.02 367	77.03 375	73.14 378	83.39 397	55.44 380	90.42 402	67.95 382	57.53 410	87.38 386

dmvs_testset		77.17 369	78.99 354	71.71 398	87.25 387	38.55 435	91.44 387	81.76 426	85.77 275	69.49 391	95.94 234	69.71 306	84.37 418	52.71 416	76.82 330	92.21 292

MVStest1		76.56 370	73.43 376	85.96 362	86.30 395	80.88 348	94.26 357	91.74 395	61.98 417	58.53 413	89.96 360	69.30 309	91.47 399	59.26 407	49.56 422	85.52 401

new_pmnet		76.02 371	73.71 375	82.95 381	83.88 402	72.85 394	91.26 390	92.26 387	70.44 400	62.60 409	81.37 405	47.64 405	92.32 391	61.85 400	72.10 372	83.68 410

MIMVSNet1		75.92 372	73.30 377	83.81 378	81.29 410	75.57 381	92.26 378	92.05 391	73.09 394	67.48 401	86.18 390	40.87 414	87.64 414	55.78 412	70.68 379	88.21 380

mvsany_test3		75.85 373	74.52 374	79.83 390	73.53 422	60.64 414	91.73 383	87.87 417	83.91 306	70.55 387	82.52 400	31.12 419	93.66 375	86.66 241	62.83 397	85.19 406

test_fmvs3		75.09 374	75.19 370	74.81 395	77.45 418	54.08 421	95.93 331	90.64 405	82.51 335	73.29 376	81.19 406	22.29 424	86.29 417	85.50 254	67.89 386	84.06 408

PM-MVS		74.88 375	72.85 378	80.98 389	78.98 416	64.75 411	90.81 394	85.77 419	80.95 355	68.23 397	82.81 399	29.08 421	92.84 383	76.54 336	62.46 400	85.36 403

new-patchmatchnet		74.80 376	72.40 379	81.99 386	78.36 417	72.20 396	94.44 354	92.36 386	77.06 374	63.47 408	79.98 411	51.04 397	88.85 411	60.53 405	54.35 414	84.92 407

UnsupCasMVSNet_bld		73.85 377	70.14 381	84.99 369	79.44 415	75.73 380	88.53 400	95.24 332	70.12 402	61.94 410	74.81 417	41.41 413	93.62 376	68.65 380	51.13 420	85.62 400

pmmvs3		72.86 378	69.76 383	82.17 384	73.86 421	74.19 387	94.20 358	89.01 414	64.23 416	67.72 398	80.91 409	41.48 412	88.65 412	62.40 399	54.02 415	83.68 410

test_f		71.94 379	70.82 380	75.30 394	72.77 423	53.28 422	91.62 384	89.66 411	75.44 384	64.47 407	78.31 414	20.48 425	89.56 408	78.63 322	66.02 393	83.05 413

N_pmnet		70.19 380	69.87 382	71.12 400	88.24 377	30.63 439	95.85 338	28.70 438	70.18 401	68.73 394	86.55 389	64.04 347	93.81 374	53.12 415	73.46 359	88.94 375

test_method		70.10 381	68.66 384	74.41 397	86.30 395	55.84 419	94.47 353	89.82 409	35.18 426	66.15 405	84.75 395	30.54 420	77.96 427	70.40 374	60.33 405	89.44 370

APD_test1		68.93 382	66.98 385	74.77 396	80.62 412	53.15 423	87.97 401	85.01 421	53.76 419	59.26 412	87.52 379	25.19 422	89.95 404	56.20 411	67.33 389	81.19 414

WB-MVS		66.44 383	66.29 386	66.89 403	74.84 419	44.93 430	93.00 369	84.09 424	71.15 397	55.82 415	81.63 404	63.79 349	80.31 425	21.85 429	50.47 421	75.43 416

SSC-MVS		65.42 384	65.20 387	66.06 404	73.96 420	43.83 431	92.08 379	83.54 425	69.77 403	54.73 416	80.92 408	63.30 351	79.92 426	20.48 430	48.02 423	74.44 417

FPMVS		61.57 385	60.32 388	65.34 405	60.14 432	42.44 433	91.02 393	89.72 410	44.15 421	42.63 424	80.93 407	19.02 426	80.59 424	42.50 421	72.76 364	73.00 418

test_vis3_rt		61.29 386	58.75 389	68.92 402	67.41 426	52.84 424	91.18 392	59.23 437	66.96 411	41.96 425	58.44 425	11.37 433	94.72 366	74.25 352	57.97 409	59.20 424

EGC-MVSNET		60.70 387	55.37 391	76.72 392	86.35 394	71.08 398	89.96 398	84.44 423	0.38 435	1.50 436	84.09 396	37.30 416	88.10 413	40.85 424	73.44 360	70.97 420

LCM-MVSNet		60.07 388	56.37 390	71.18 399	54.81 434	48.67 427	82.17 419	89.48 412	37.95 424	49.13 419	69.12 418	13.75 432	81.76 419	59.28 406	51.63 419	83.10 412

PMMVS2		58.97 389	55.07 392	70.69 401	62.72 429	55.37 420	85.97 405	80.52 427	49.48 420	45.94 421	68.31 419	15.73 430	80.78 423	49.79 418	37.12 426	75.91 415

testf1		56.38 390	53.73 393	64.31 407	64.84 427	45.11 428	80.50 420	75.94 432	38.87 422	42.74 422	75.07 415	11.26 434	81.19 421	41.11 422	53.27 416	66.63 421

APD_test2		56.38 390	53.73 393	64.31 407	64.84 427	45.11 428	80.50 420	75.94 432	38.87 422	42.74 422	75.07 415	11.26 434	81.19 421	41.11 422	53.27 416	66.63 421

Gipuma		54.77 392	52.22 396	62.40 409	86.50 392	59.37 416	50.20 427	90.35 407	36.52 425	41.20 426	49.49 427	18.33 428	81.29 420	32.10 426	65.34 394	46.54 427
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		53.66 393	52.86 395	56.05 410	32.75 438	41.97 434	73.42 424	76.12 431	21.91 431	39.68 427	96.39 220	42.59 411	65.10 430	78.00 325	14.92 431	61.08 423

ANet_high		50.71 394	46.17 397	64.33 406	44.27 436	52.30 425	76.13 423	78.73 428	64.95 414	27.37 429	55.23 426	14.61 431	67.74 429	36.01 425	18.23 429	72.95 419

PMVS	41.42 23	45.67 395	42.50 398	55.17 411	34.28 437	32.37 437	66.24 425	78.71 429	30.72 427	22.04 432	59.59 423	4.59 436	77.85 428	27.49 427	58.84 408	55.29 425
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 396	37.64 401	53.90 412	49.46 435	43.37 432	65.09 426	66.66 434	26.19 430	25.77 431	48.53 428	3.58 438	63.35 431	26.15 428	27.28 427	54.97 426
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 397	40.93 399	41.29 413	61.97 430	33.83 436	84.00 416	65.17 435	27.17 428	27.56 428	46.72 429	17.63 429	60.41 432	19.32 431	18.82 428	29.61 428

EMVS		39.96 398	39.88 400	40.18 414	59.57 433	32.12 438	84.79 413	64.57 436	26.27 429	26.14 430	44.18 432	18.73 427	59.29 433	17.03 432	17.67 430	29.12 429

cdsmvs_eth3d_5k		22.52 399	30.03 402	0.00 418	0.00 441	0.00 443	0.00 429	97.17 187	0.00 436	0.00 437	98.77 94	74.35 267	0.00 437	0.00 436	0.00 435	0.00 433

testmvs		18.81 400	23.05 403	6.10 417	4.48 439	2.29 442	97.78 255	3.00 440	3.27 433	18.60 433	62.71 421	1.53 440	2.49 436	14.26 434	1.80 433	13.50 431

wuyk23d		16.71 401	16.73 405	16.65 415	60.15 431	25.22 440	41.24 428	5.17 439	6.56 432	5.48 435	3.61 435	3.64 437	22.72 434	15.20 433	9.52 432	1.99 432

test123		16.58 402	19.47 404	7.91 416	3.59 440	5.37 441	94.32 355	1.39 441	2.49 434	13.98 434	44.60 431	2.91 439	2.65 435	11.35 435	0.57 434	15.70 430

ab-mvs-re		8.21 403	10.94 406	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	98.50 118	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

pcd_1.5k_mvsjas		6.87 404	9.16 407	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	82.48 196	0.00 437	0.00 436	0.00 435	0.00 433

mmdepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

monomultidepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

test_blank		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet_test		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

DCPMVS		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

sosnet-low-res		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

sosnet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uncertanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

Regformer		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

WAC-MVS						79.74 354								67.75 383

FOURS1					99.50 42	88.94 192	99.55 51	97.47 150	91.32 121	98.12 52

MSC_two_6792asdad				99.51 2	99.61 24	98.60 2		97.69 96					99.98 9	99.55 13	99.83 15	99.96 10

PC_three_1452									94.60 42	99.41 5	99.12 53	95.50 7	99.96 28	99.84 2	99.92 3	99.97 7

No_MVS				99.51 2	99.61 24	98.60 2		97.69 96					99.98 9	99.55 13	99.83 15	99.96 10

test_one_0601					99.59 28	94.89 37		97.64 112	93.14 79	98.93 25	99.45 14	93.45 18

eth-test2					0.00 441

eth-test					0.00 441

ZD-MVS					99.67 10	93.28 79		97.61 119	87.78 231	97.41 69	99.16 41	90.15 58	99.56 114	98.35 51	99.70 37

RE-MVS-def			95.70 74		99.22 59	87.26 239	98.40 205	97.21 181	89.63 168	96.67 96	98.97 70	85.24 150		96.62 90	99.31 67	99.60 73

IU-MVS					99.63 18	95.38 24		97.73 86	95.54 31	99.54 3				99.69 7	99.81 23	99.99 1

OPU-MVS				99.49 4	99.64 17	98.51 4	99.77 23				99.19 35	95.12 8	99.97 21	99.90 1	99.92 3	99.99 1

test_241102_TWO								97.72 87	94.17 50	99.23 13	99.54 3	93.14 25	99.98 9	99.70 5	99.82 19	99.99 1

test_241102_ONE					99.63 18	95.24 27		97.72 87	94.16 52	99.30 11	99.49 9	93.32 20	99.98 9

9.14			96.87 28		99.34 50		99.50 58	97.49 147	89.41 179	98.59 38	99.43 16	89.78 62	99.69 100	98.69 36	99.62 46

save fliter					99.34 50	93.85 67	99.65 42	97.63 116	95.69 27

test_0728_THIRD									93.01 80	99.07 19	99.46 10	94.66 13	99.97 21	99.25 20	99.82 19	99.95 15

test_0728_SECOND				98.77 8	99.66 12	96.37 14	99.72 30	97.68 98					99.98 9	99.64 8	99.82 19	99.96 10

test0726					99.66 12	95.20 32	99.77 23	97.70 92	93.95 55	99.35 9	99.54 3	93.18 23

GSMVS																98.84 147

test_part2					99.54 36	95.42 22				98.13 50

sam_mvs1												88.39 80				98.84 147

sam_mvs												87.08 108

ambc				79.60 391	72.76 424	56.61 418	76.20 422	92.01 392		68.25 396	80.23 410	23.34 423	94.73 365	73.78 358	60.81 404	87.48 385

MTGPA								97.45 153

test_post1							90.74 396				41.37 433	85.38 148	96.36 303	83.16 284

test_post											46.00 430	87.37 99	97.11 267

patchmatchnet-post											84.86 393	88.73 76	96.81 280

GG-mvs-BLEND				96.98 73	96.53 177	94.81 44	87.20 402	97.74 83		93.91 152	96.40 218	96.56 2	96.94 275	95.08 129	98.95 90	99.20 114

MTMP							99.21 98	91.09 403

gm-plane-assit					94.69 265	88.14 212			88.22 217		97.20 178		98.29 197	90.79 193

test9_res														98.60 39	99.87 9	99.90 22

TEST9					99.57 33	93.17 82	99.38 79	97.66 103	89.57 172	98.39 43	99.18 38	90.88 43	99.66 103

test_8					99.55 35	93.07 85	99.37 82	97.64 112	90.18 152	98.36 45	99.19 35	90.94 39	99.64 109

agg_prior2														97.84 65	99.87 9	99.91 21

agg_prior					99.54 36	92.66 96		97.64 112		97.98 59			99.61 111

TestCases				90.52 294	96.82 166	78.84 361		92.17 388	77.96 371	75.94 359	95.50 241	55.48 378	99.18 148	71.15 368	87.14 260	93.55 272

test_prior4						92.00 108	99.41 76

test_prior2							99.57 49		91.43 118	98.12 52	98.97 70	90.43 51		98.33 52	99.81 23

test_prior				97.01 68	99.58 30	91.77 112		97.57 130					99.49 121			99.79 38

旧先验2							98.67 166		85.75 277	98.96 24			98.97 163	93.84 153

新几何2							98.26 220

新几何1				97.40 52	98.92 81	92.51 102		97.77 81	85.52 279	96.69 95	99.06 60	88.08 88	99.89 57	84.88 261	99.62 46	99.79 38

旧先验1					98.97 73	92.90 93		97.74 83			99.15 45	91.05 38			99.33 65	99.60 73

无先验							98.52 187	97.82 68	87.20 246				99.90 52	87.64 230		99.85 30

原ACMM2							98.69 163

原ACMM1				96.18 122	99.03 71	90.08 158		97.63 116	88.98 188	97.00 81	98.97 70	88.14 87	99.71 99	88.23 223	99.62 46	98.76 159

test222					98.32 96	91.21 123	98.08 240	97.58 127	83.74 308	95.87 111	99.02 66	86.74 116			99.64 42	99.81 35

testdata2													99.88 59	84.16 272

segment_acmp												90.56 49

testdata				95.26 165	98.20 101	87.28 236		97.60 121	85.21 283	98.48 41	99.15 45	88.15 86	98.72 177	90.29 198	99.45 59	99.78 41

testdata1							97.89 248		92.43 94

test12				97.83 35	99.33 53	94.45 54		97.55 132		97.56 65		88.60 78	99.50 120		99.71 36	99.55 78

plane_prior7					93.84 292	85.73 276

plane_prior6					93.92 289	86.02 269						72.92 281

plane_prior5								96.30 248					97.75 236	93.46 162	86.17 269	92.67 280

plane_prior4											96.52 213

plane_prior3						85.91 271			93.65 68	86.99 244

plane_prior2							99.02 130		93.38 75

plane_prior1					93.90 291

plane_prior						86.07 267	99.14 114		93.81 65						86.26 268

n2								0.00 442

nn								0.00 442

door-mid								84.90 422

lessismore_v0				85.08 368	85.59 397	69.28 405		90.56 406		67.68 399	90.21 357	54.21 387	95.46 348	73.88 355	62.64 399	90.50 350

LGP-MVS_train				90.06 305	93.35 306	80.95 346		95.94 279	87.73 235	83.17 278	96.11 228	66.28 335	97.77 230	90.19 199	85.19 276	91.46 317

test11								97.68 98

door								85.30 420

HQP5-MVS						86.39 253

HQP-NCC					93.95 285		99.16 106		93.92 57	87.57 237

ACMP_Plane					93.95 285		99.16 106		93.92 57	87.57 237

BP-MVS														93.82 155

HQP4-MVS										87.57 237			97.77 230			92.72 278

HQP3-MVS								96.37 244							86.29 266

HQP2-MVS												73.34 275

NP-MVS					93.94 288	86.22 259					96.67 211

MDTV_nov1_ep13_2view						91.17 126	91.38 388		87.45 242	93.08 166		86.67 119		87.02 233		98.95 138

MDTV_nov1_ep13			90.47 210		96.14 201	88.55 205	91.34 389	97.51 142	89.58 171	92.24 178	90.50 350	86.99 112	97.61 245	77.64 327	92.34 212

ACMMP++_ref															82.64 300

ACMMP++															83.83 287

Test By Simon												83.62 168

ITE_SJBPF				87.93 342	92.26 320	76.44 377		93.47 376	87.67 238	79.95 330	95.49 243	56.50 374	97.38 258	75.24 344	82.33 302	89.98 362

DeepMVS_CX				76.08 393	90.74 348	51.65 426		90.84 404	86.47 267	57.89 414	87.98 374	35.88 418	92.60 386	65.77 391	65.06 395	83.97 409

High-resolution multi-view benchmark