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.

MSC_two_6792asdad				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

No_MVS				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 48	90.85 3	97.13 19				97.08 70	92.59 2	98.94 91	92.25 92	98.99 14	98.84 19

HPM-MVS++		95.14 13	94.91 26	95.83 4	98.25 35	89.65 4	95.92 87	96.96 68	91.75 13	94.02 71	96.83 82	88.12 27	99.55 20	93.41 66	98.94 16	98.28 61

MM		95.10 14	94.91 26	95.68 5	96.09 115	88.34 9	96.68 38	94.37 293	95.08 1	94.68 57	97.72 39	82.94 100	99.64 4	97.85 5	98.76 33	99.06 7

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 8	96.26 54	97.28 40	85.90 201	97.67 4	98.10 14	88.41 23	99.56 16	94.66 49	99.19 1	98.71 25
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
3Dnovator+	87.14 4	92.42 103	91.37 122	95.55 7	95.63 143	88.73 6	97.07 23	96.77 91	90.84 26	84.02 327	96.62 95	75.95 210	99.34 42	87.77 178	97.68 97	98.59 29

TestfortrainingZip a		95.70 4	95.76 5	95.51 8	98.88 1	87.98 10	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44	92.42 4	99.67 1	93.66 60	98.85 20	98.89 15

CNVR-MVS		95.40 9	95.37 11	95.50 9	98.11 42	88.51 7	95.29 130	96.96 68	92.09 10	95.32 49	97.08 70	89.49 17	99.33 45	95.10 44	98.85 20	98.66 26

MGCNet		94.18 50	93.80 64	95.34 10	94.91 182	87.62 15	95.97 82	93.01 337	92.58 6	94.22 62	97.20 64	80.56 139	99.59 11	97.04 20	98.68 41	98.81 22

ACMMP_NAP		94.74 25	94.56 33	95.28 11	98.02 47	87.70 12	95.68 106	97.34 30	88.28 120	95.30 50	97.67 41	85.90 54	99.54 24	93.91 57	98.95 15	98.60 28

DPE-MVS		95.57 6	95.67 6	95.25 12	98.36 31	87.28 19	95.56 118	97.51 11	89.13 87	97.14 18	97.91 32	91.64 9	99.62 5	94.61 50	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 17	94.90 28	95.20 13	97.84 56	87.76 11	96.65 39	97.48 16	87.76 146	95.71 44	97.70 40	88.28 26	99.35 41	93.89 58	98.78 30	98.48 35

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 22	87.50 17	95.00 153	97.12 55	87.13 167	92.51 112	96.30 104	89.24 19	99.34 42	93.46 63	98.62 50	98.73 23

NCCC		94.81 22	94.69 32	95.17 15	97.83 57	87.46 18	95.66 109	96.93 72	92.34 7	93.94 72	96.58 97	87.74 30	99.44 33	92.83 75	98.40 58	98.62 27

DPM-MVS		92.58 99	91.74 109	95.08 16	96.19 106	89.31 5	92.66 308	96.56 112	83.44 275	91.68 137	95.04 182	86.60 46	98.99 81	85.60 212	97.92 85	96.93 181

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 121	93.15 87	97.04 73	86.17 51	99.62 5	92.40 86	98.81 27	98.52 31

test_0728_SECOND				95.01 18	98.79 5	86.43 40	97.09 21	97.49 12					99.61 7	95.62 34	99.08 7	98.99 9

MTAPA		94.42 39	94.22 48	95.00 19	98.42 24	86.95 22	94.36 204	96.97 65	91.07 22	93.14 88	97.56 43	84.30 81	99.56 16	93.43 64	98.75 34	98.47 38

MSP-MVS		95.42 8	95.56 8	94.98 20	98.49 20	86.52 37	96.91 30	97.47 17	91.73 14	96.10 36	96.69 87	89.90 14	99.30 48	94.70 48	98.04 80	99.13 2
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
region2R		94.43 36	94.27 47	94.92 21	98.65 11	86.67 31	96.92 29	97.23 43	88.60 111	93.58 79	97.27 58	85.22 64	99.54 24	92.21 94	98.74 35	98.56 30

APDe-MVS		95.46 7	95.64 7	94.91 22	98.26 34	86.29 47	97.46 7	97.40 26	89.03 92	96.20 35	98.10 14	89.39 18	99.34 42	95.88 29	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		94.43 36	94.28 45	94.91 22	98.63 12	86.69 29	96.94 25	97.32 34	88.63 108	93.53 82	97.26 60	85.04 68	99.54 24	92.35 89	98.78 30	98.50 32

GST-MVS		94.21 45	93.97 60	94.90 24	98.41 25	86.82 25	96.54 41	97.19 44	88.24 121	93.26 84	96.83 82	85.48 60	99.59 11	91.43 120	98.40 58	98.30 55

HFP-MVS		94.52 31	94.40 38	94.86 25	98.61 13	86.81 26	96.94 25	97.34 30	88.63 108	93.65 77	97.21 62	86.10 52	99.49 30	92.35 89	98.77 32	98.30 55

sasdasda		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 206	82.33 109	98.62 131	92.40 86	92.86 226	98.27 63

MP-MVS-pluss		94.21 45	94.00 59	94.85 26	98.17 39	86.65 32	94.82 166	97.17 49	86.26 193	92.83 97	97.87 34	85.57 59	99.56 16	94.37 53	98.92 17	98.34 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 206	82.33 109	98.62 131	92.40 86	92.86 226	98.27 63

XVS		94.45 34	94.32 41	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	97.16 68	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

X-MVStestdata		88.31 230	86.13 279	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 481	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 26	96.99 81	86.33 43	97.33 8	97.30 37	91.38 19	95.39 48	97.46 50	88.98 22	99.40 34	94.12 54	98.89 18	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
MED-MVS test				94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44		99.67 1	95.27 40	98.85 20	98.95 11

MED-MVS		95.74 3	96.04 3	94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	89.01 94	97.21 14	97.54 44	92.42 4	99.67 1	95.27 40	98.85 20	98.95 11

DVP-MVS++		95.98 1	96.36 1	94.82 34	97.78 60	86.00 53	98.29 1	97.49 12	90.75 29	97.62 8	98.06 22	92.59 2	99.61 7	95.64 32	99.02 12	98.86 16

ME-MVS		95.17 12	95.29 14	94.81 35	98.39 28	85.89 64	95.91 88	97.55 9	89.01 94	95.86 42	97.54 44	89.24 19	99.59 11	95.27 40	98.85 20	98.95 11

alignmvs		93.08 90	92.50 98	94.81 35	95.62 144	87.61 16	95.99 79	96.07 164	89.77 64	94.12 66	94.87 190	80.56 139	98.66 123	92.42 85	93.10 222	98.15 75

SED-MVS		95.91 2	96.28 2	94.80 37	98.77 8	85.99 55	97.13 19	97.44 21	90.31 41	97.71 2	98.07 20	92.31 6	99.58 14	95.66 30	99.13 3	98.84 19

DeepC-MVS_fast	89.43 2	94.04 53	93.79 65	94.80 37	97.48 70	86.78 27	95.65 111	96.89 77	89.40 75	92.81 98	96.97 75	85.37 62	99.24 51	90.87 129	98.69 39	98.38 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		94.25 42	94.07 56	94.77 39	98.47 21	86.31 45	96.71 36	96.98 64	89.04 90	91.98 123	97.19 65	85.43 61	99.56 16	92.06 103	98.79 28	98.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 43	94.07 56	94.75 40	98.06 45	86.90 24	95.88 90	96.94 71	85.68 208	95.05 55	97.18 66	87.31 38	99.07 64	91.90 111	98.61 52	98.28 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 40	94.21 50	94.74 41	98.39 28	86.64 33	97.60 5	97.24 41	88.53 113	92.73 103	97.23 61	85.20 65	99.32 46	92.15 97	98.83 26	98.25 68

PGM-MVS		93.96 58	93.72 70	94.68 42	98.43 23	86.22 48	95.30 128	97.78 4	87.45 157	93.26 84	97.33 56	84.62 78	99.51 28	90.75 131	98.57 53	98.32 54

DVP-MVS		95.67 5	96.02 4	94.64 43	98.78 6	85.93 58	97.09 21	96.73 97	90.27 45	97.04 22	98.05 25	91.47 10	99.55 20	95.62 34	99.08 7	98.45 41
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
mPP-MVS		93.99 56	93.78 66	94.63 44	98.50 19	85.90 63	96.87 31	96.91 75	88.70 106	91.83 132	97.17 67	83.96 85	99.55 20	91.44 119	98.64 49	98.43 43

PHI-MVS		93.89 60	93.65 74	94.62 45	96.84 84	86.43 40	96.69 37	97.49 12	85.15 232	93.56 81	96.28 105	85.60 58	99.31 47	92.45 83	98.79 28	98.12 80

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 46	98.25 35	86.33 43	96.11 67	96.62 107	88.14 126	96.10 36	96.96 76	89.09 21	98.94 91	94.48 51	98.68 41	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		93.54 69	93.20 83	94.55 47	95.65 141	85.73 71	94.94 156	96.69 103	91.89 12	90.69 160	95.88 133	81.99 121	99.54 24	93.14 70	97.95 84	98.39 45

train_agg		93.44 75	93.08 85	94.52 48	97.53 67	86.49 38	94.07 224	96.78 89	81.86 319	92.77 100	96.20 108	87.63 32	99.12 62	92.14 98	98.69 39	97.94 96

CDPH-MVS		92.83 94	92.30 101	94.44 49	97.79 58	86.11 52	94.06 226	96.66 104	80.09 350	92.77 100	96.63 94	86.62 44	99.04 68	87.40 185	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 120	90.82 137	94.44 49	94.59 208	86.37 42	97.18 17	97.02 62	89.20 84	84.31 322	96.66 90	73.74 250	99.17 56	86.74 195	97.96 83	97.79 115

SR-MVS		94.23 44	94.17 54	94.43 51	98.21 38	85.78 69	96.40 43	96.90 76	88.20 124	94.33 61	97.40 53	84.75 77	99.03 69	93.35 67	97.99 82	98.48 35

HPM-MVS		94.02 54	93.88 61	94.43 51	98.39 28	85.78 69	97.25 15	97.07 60	86.90 177	92.62 109	96.80 86	84.85 75	99.17 56	92.43 84	98.65 48	98.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 67	93.41 78	94.41 53	96.59 91	86.78 27	94.40 196	93.93 311	89.77 64	94.21 63	95.59 153	87.35 37	98.61 133	92.72 78	96.15 135	97.83 112

reproduce-ours		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

our_new_method		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

NormalMVS		93.46 72	93.16 84	94.37 56	98.40 26	86.20 49	96.30 47	96.27 135	91.65 17	92.68 105	96.13 115	77.97 180	98.84 105	90.75 131	98.26 63	98.07 82

test12				94.34 57	97.13 79	86.15 51		96.29 131		91.04 156		85.08 67	99.01 74		98.13 75	97.86 107

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 289	91.65 17	92.68 105	96.13 115	77.97 180	98.84 105	90.75 131	94.72 167	97.92 101

ACMMP		93.24 84	92.88 90	94.30 59	98.09 44	85.33 78	96.86 32	97.45 20	88.33 117	90.15 175	97.03 74	81.44 129	99.51 28	90.85 130	95.74 142	98.04 89
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
reproduce_model		94.76 24	94.92 25	94.29 60	97.92 49	85.18 80	95.95 85	97.19 44	89.67 67	95.27 51	98.16 6	86.53 47	99.36 40	95.42 37	98.15 73	98.33 50

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 61	96.07 117	85.83 67	94.89 159	96.99 63	89.02 93	89.56 184	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		93.03 91	92.63 95	94.23 62	95.62 144	85.92 60	96.08 69	96.33 129	89.86 55	93.89 74	94.66 203	82.11 116	98.50 139	92.33 91	92.82 229	98.27 63

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 63	95.64 142	85.08 81	96.09 68	97.36 28	90.98 24	97.09 20	98.12 10	84.98 73	98.94 91	97.07 17	97.80 92	98.43 43

EPNet		91.79 111	91.02 131	94.10 64	90.10 405	85.25 79	96.03 76	92.05 364	92.83 5	87.39 233	95.78 144	79.39 162	99.01 74	88.13 172	97.48 100	98.05 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 14	95.46 10	94.01 65	98.40 26	84.36 106	97.70 3	97.78 4	91.19 20	96.22 34	98.08 19	86.64 43	99.37 37	94.91 46	98.26 63	98.29 60

test_fmvsmconf_n		94.60 28	94.81 30	93.98 66	94.62 204	84.96 84	96.15 62	97.35 29	89.37 76	96.03 39	98.11 11	86.36 48	99.01 74	97.45 10	97.83 90	97.96 95

DELS-MVS		93.43 79	93.25 81	93.97 67	95.42 152	85.04 82	93.06 290	97.13 54	90.74 31	91.84 130	95.09 181	86.32 49	99.21 54	91.22 121	98.45 56	97.65 124
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
DP-MVS Recon		91.95 108	91.28 125	93.96 68	98.33 33	85.92 60	94.66 178	96.66 104	82.69 296	90.03 177	95.82 140	82.30 111	99.03 69	84.57 230	96.48 128	96.91 183

HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 205	92.47 113	97.13 69	82.38 107	99.07 64	90.51 136	98.40 58	97.92 101

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 320	84.80 87	96.18 59	96.82 85	89.29 81	95.68 45	98.11 11	85.10 66	98.99 81	97.38 11	97.75 96	97.86 107

SD-MVS		94.96 18	95.33 12	93.88 70	97.25 78	86.69 29	96.19 57	97.11 58	90.42 37	96.95 24	97.27 58	89.53 16	96.91 308	94.38 52	98.85 20	98.03 90
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
MVS_111021_HR		93.45 74	93.31 79	93.84 72	96.99 81	84.84 85	93.24 281	97.24 41	88.76 103	91.60 138	95.85 137	86.07 53	98.66 123	91.91 109	98.16 71	98.03 90

SR-MVS-dyc-post		93.82 62	93.82 63	93.82 73	97.92 49	84.57 93	96.28 51	96.76 92	87.46 155	93.75 75	97.43 51	84.24 82	99.01 74	92.73 76	97.80 92	97.88 105

test_prior				93.82 73	97.29 76	84.49 97		96.88 78					98.87 99			98.11 81

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 75	97.92 49	84.19 110	96.30 47	96.87 79	86.96 173	93.92 73	97.47 49	83.88 86	98.96 88	92.71 79	97.87 88	98.26 67

fmvsm_l_conf0.5_n		94.29 41	94.46 36	93.79 76	95.28 157	85.43 76	95.68 106	96.43 120	86.56 185	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 118	97.16 162

CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 256	90.05 176	95.66 150	87.77 29	99.15 60	89.91 142	98.27 62	98.07 82

GDP-MVS		92.04 106	91.46 119	93.75 78	94.55 214	84.69 90	95.60 117	96.56 112	87.83 143	93.07 91	95.89 132	73.44 254	98.65 125	90.22 139	96.03 137	97.91 103

BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 217	84.39 105	95.90 89	94.30 296	90.39 38	92.67 107	95.94 128	74.46 233	98.65 125	93.14 70	97.35 104	98.13 77

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 80	89.25 418	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 164	98.98 85	97.22 13	97.24 106	97.74 118

UA-Net		92.83 94	92.54 97	93.68 81	96.10 114	84.71 89	95.66 109	96.39 124	91.92 11	93.22 86	96.49 100	83.16 95	98.87 99	84.47 232	95.47 149	97.45 136

fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 82	95.29 156	84.98 83	95.61 114	96.28 134	86.31 191	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 167

QAPM		89.51 187	88.15 214	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 377	83.41 344	96.19 111	73.18 259	99.30 48	77.11 344	96.54 125	96.89 184

test_fmvsm_n_1920		94.71 26	95.11 19	93.50 84	95.79 132	84.62 91	96.15 62	97.64 6	89.85 56	97.19 17	97.89 33	86.28 50	98.71 121	97.11 16	98.08 79	97.17 155

fmvsm_s_conf0.5_n_9		94.99 16	95.50 9	93.44 85	96.51 99	82.25 183	95.76 101	96.92 73	93.37 3	97.63 7	98.43 1	84.82 76	99.16 59	98.15 1	97.92 85	98.90 14

KinetiMVS		91.82 110	91.30 123	93.39 86	94.72 196	83.36 137	95.45 121	96.37 126	90.33 40	92.17 118	96.03 122	72.32 271	98.75 115	87.94 175	96.34 130	98.07 82

casdiffmvs_mvg		92.96 93	92.83 91	93.35 87	94.59 208	83.40 135	95.00 153	96.34 128	90.30 43	92.05 121	96.05 119	83.43 89	98.15 175	92.07 100	95.67 143	98.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		93.96 58	94.18 53	93.30 88	94.79 189	83.81 121	95.77 99	96.74 96	88.02 132	96.23 33	97.84 36	83.36 93	98.83 108	97.49 8	97.34 105	97.25 148

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 187	95.77 192	90.87 25	92.52 111	96.67 89	84.50 79	99.00 79	91.99 105	94.44 180	97.36 139

Vis-MVSNet		91.75 118	91.23 126	93.29 89	95.32 155	83.78 122	96.14 64	95.98 171	89.89 53	90.45 164	96.58 97	75.09 222	98.31 166	84.75 224	96.90 114	97.78 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 57	94.22 48	93.26 91	96.13 109	83.29 139	96.27 53	96.52 115	89.82 57	95.56 47	95.51 156	84.50 79	98.79 112	94.83 47	98.86 19	97.72 120

SPE-MVS-test		94.02 54	94.29 44	93.24 92	96.69 87	83.24 140	97.49 6	96.92 73	92.14 9	92.90 93	95.77 145	85.02 69	98.33 163	93.03 72	98.62 50	98.13 77

VNet		92.24 105	91.91 106	93.24 92	96.59 91	83.43 133	94.84 165	96.44 119	89.19 85	94.08 70	95.90 131	77.85 186	98.17 173	88.90 162	93.38 211	98.13 77

fmvsm_s_conf0.5_n_10		94.43 36	94.84 29	93.20 94	95.73 135	83.19 143	95.99 79	97.31 36	91.08 21	97.67 4	98.11 11	81.87 123	99.22 52	97.86 4	97.91 87	97.20 153

VDD-MVS		90.74 145	89.92 159	93.20 94	96.27 104	83.02 155	95.73 103	93.86 315	88.42 116	92.53 110	96.84 81	62.09 378	98.64 128	90.95 127	92.62 236	97.93 100

Elysia		90.12 164	89.10 182	93.18 96	93.16 290	84.05 114	95.22 137	96.27 135	85.16 230	90.59 161	94.68 199	64.64 361	98.37 156	86.38 201	95.77 140	97.12 164

StellarMVS		90.12 164	89.10 182	93.18 96	93.16 290	84.05 114	95.22 137	96.27 135	85.16 230	90.59 161	94.68 199	64.64 361	98.37 156	86.38 201	95.77 140	97.12 164

CS-MVS		94.12 51	94.44 37	93.17 98	96.55 94	83.08 152	97.63 4	96.95 70	91.71 15	93.50 83	96.21 107	85.61 57	98.24 168	93.64 61	98.17 70	98.19 71

nrg030		91.08 139	90.39 143	93.17 98	93.07 297	86.91 23	96.41 42	96.26 139	88.30 119	88.37 209	94.85 193	82.19 115	97.64 234	91.09 122	82.95 361	94.96 269

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 253	94.09 67	95.56 155	85.01 72	98.69 122	94.96 45	98.66 45	97.67 123

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 196	95.74 195	90.71 33	92.05 121	96.60 96	84.00 84	98.99 81	91.55 117	93.63 201	97.17 155

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 276	84.26 108	95.83 95	96.14 155	89.00 96	92.43 114	97.50 48	83.37 92	98.72 119	96.61 24	97.44 101	96.32 209

新几何1				93.10 102	97.30 75	84.35 107		95.56 212	71.09 444	91.26 147	96.24 106	82.87 102	98.86 101	79.19 323	98.10 76	96.07 225

OMC-MVS		91.23 131	90.62 142	93.08 104	96.27 104	84.07 112	93.52 263	95.93 177	86.95 174	89.51 185	96.13 115	78.50 174	98.35 160	85.84 210	92.90 225	96.83 191

OpenMVS	83.78 11	88.74 217	87.29 236	93.08 104	92.70 315	85.39 77	96.57 40	96.43 120	78.74 372	80.85 376	96.07 118	69.64 307	99.01 74	78.01 335	96.65 123	94.83 277

MAR-MVS		90.30 160	89.37 175	93.07 106	96.61 90	84.48 98	95.68 106	95.67 203	82.36 301	87.85 220	92.85 273	76.63 199	98.80 110	80.01 310	96.68 122	95.91 231
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
lupinMVS		90.92 140	90.21 147	93.03 107	93.86 261	83.88 119	92.81 302	93.86 315	79.84 353	91.76 134	94.29 220	77.92 183	98.04 196	90.48 137	97.11 107	97.17 155

Effi-MVS+		91.59 125	91.11 128	93.01 108	94.35 232	83.39 136	94.60 180	95.10 247	87.10 168	90.57 163	93.10 268	81.43 130	98.07 190	89.29 154	94.48 178	97.59 129

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 161	87.27 161	95.98 40	98.05 25	83.07 99	98.45 149	96.68 23	95.51 146	96.88 185

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 287	96.09 162	88.20 124	91.12 152	95.72 148	81.33 131	97.76 223	91.74 113	97.37 103	96.75 193

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 318	83.62 128	96.02 77	95.72 199	86.78 179	96.04 38	98.19 4	82.30 111	98.43 153	96.38 25	95.42 152	96.86 186

ETV-MVS		92.74 97	92.66 94	92.97 111	95.20 163	84.04 116	95.07 149	96.51 116	90.73 32	92.96 92	91.19 334	84.06 83	98.34 161	91.72 114	96.54 125	96.54 204

LFMVS		90.08 167	89.13 181	92.95 113	96.71 86	82.32 182	96.08 69	89.91 421	86.79 178	92.15 120	96.81 84	62.60 376	98.34 161	87.18 189	93.90 194	98.19 71

UGNet		89.95 174	88.95 190	92.95 113	94.51 216	83.31 138	95.70 105	95.23 240	89.37 76	87.58 227	93.94 236	64.00 366	98.78 113	83.92 239	96.31 131	96.74 194
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
jason		90.80 143	90.10 151	92.90 115	93.04 300	83.53 131	93.08 287	94.15 304	80.22 347	91.41 144	94.91 187	76.87 193	97.93 213	90.28 138	96.90 114	97.24 149
jason: jason.
DP-MVS		87.25 271	85.36 309	92.90 115	97.65 64	83.24 140	94.81 167	92.00 366	74.99 412	81.92 365	95.00 183	72.66 264	99.05 66	66.92 425	92.33 241	96.40 206

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 117	95.96 127	81.32 210	95.76 101	97.57 8	93.48 2	97.53 10	98.32 3	81.78 126	99.13 61	97.91 2	97.81 91	98.16 74

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 118	95.62 144	83.17 144	96.14 64	96.12 159	88.13 127	95.82 43	98.04 28	83.43 89	98.48 141	96.97 21	96.23 132	96.92 182

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 268	83.13 146	96.02 77	95.74 195	87.68 149	95.89 41	98.17 5	82.78 103	98.46 145	96.71 22	96.17 134	96.98 176

CANet_DTU		90.26 162	89.41 174	92.81 120	93.46 283	83.01 156	93.48 264	94.47 288	89.43 74	87.76 225	94.23 225	70.54 295	99.03 69	84.97 219	96.39 129	96.38 207

MVSFormer		91.68 123	91.30 123	92.80 121	93.86 261	83.88 119	95.96 83	95.90 181	84.66 249	91.76 134	94.91 187	77.92 183	97.30 275	89.64 150	97.11 107	97.24 149

PVSNet_Blended_VisFu		91.38 128	90.91 134	92.80 121	96.39 101	83.17 144	94.87 161	96.66 104	83.29 280	89.27 191	94.46 215	80.29 142	99.17 56	87.57 182	95.37 153	96.05 228

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 123	95.95 128	81.83 194	95.53 119	97.12 55	91.68 16	97.89 1	98.06 22	85.71 56	98.65 125	97.32 12	98.26 63	97.83 112

LuminaMVS		90.55 156	89.81 161	92.77 123	92.78 313	84.21 109	94.09 222	94.17 303	85.82 202	91.54 139	94.14 227	69.93 301	97.92 214	91.62 116	94.21 188	96.18 217

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 125	94.98 175	81.96 192	95.79 97	97.29 39	89.31 79	97.52 11	97.61 42	83.25 94	98.88 98	97.05 19	98.22 69	97.43 138

VDDNet		89.56 186	88.49 205	92.76 125	95.07 169	82.09 186	96.30 47	93.19 332	81.05 341	91.88 128	96.86 80	61.16 394	98.33 163	88.43 169	92.49 240	97.84 111

viewdifsd2359ckpt09		91.18 134	90.65 141	92.75 127	94.61 207	82.36 181	94.32 205	95.74 195	84.72 246	89.66 183	95.15 179	79.69 157	98.04 196	87.70 179	94.27 187	97.85 110

h-mvs33		90.80 143	90.15 150	92.75 127	96.01 120	82.66 169	95.43 122	95.53 216	89.80 60	93.08 89	95.64 151	75.77 211	99.00 79	92.07 100	78.05 418	96.60 199

casdiffmvs		92.51 100	92.43 99	92.74 129	94.41 227	81.98 190	94.54 184	96.23 143	89.57 70	91.96 125	96.17 112	82.58 105	98.01 200	90.95 127	95.45 151	98.23 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl		90.69 148	90.02 157	92.71 130	95.72 136	82.41 179	94.11 218	95.12 245	85.63 209	91.49 141	94.70 197	74.75 226	98.42 154	86.13 205	92.53 238	97.31 140

DCV-MVSNet		90.69 148	90.02 157	92.71 130	95.72 136	82.41 179	94.11 218	95.12 245	85.63 209	91.49 141	94.70 197	74.75 226	98.42 154	86.13 205	92.53 238	97.31 140

PCF-MVS	84.11 10	87.74 245	86.08 283	92.70 132	94.02 250	84.43 102	89.27 399	95.87 186	73.62 426	84.43 314	94.33 217	78.48 176	98.86 101	70.27 399	94.45 179	94.81 278
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_11		94.60 28	95.23 16	92.69 133	96.05 119	82.00 188	96.31 46	96.71 100	92.27 8	96.68 30	98.39 2	85.32 63	98.92 94	97.20 14	98.16 71	97.17 155

SSM_0404		90.73 146	90.08 152	92.69 133	95.00 174	83.13 146	94.32 205	95.00 255	85.41 220	89.84 178	95.35 164	76.13 202	97.98 205	85.46 215	94.18 189	96.95 178

baseline		92.39 104	92.29 102	92.69 133	94.46 222	81.77 197	94.14 215	96.27 135	89.22 83	91.88 128	96.00 123	82.35 108	97.99 202	91.05 123	95.27 157	98.30 55

MSLP-MVS++		93.72 66	94.08 55	92.65 136	97.31 74	83.43 133	95.79 97	97.33 32	90.03 50	93.58 79	96.96 76	84.87 74	97.76 223	92.19 96	98.66 45	96.76 192

EC-MVSNet		93.44 75	93.71 71	92.63 137	95.21 162	82.43 176	97.27 14	96.71 100	90.57 36	92.88 94	95.80 141	83.16 95	98.16 174	93.68 59	98.14 74	97.31 140

ab-mvs		89.41 194	88.35 207	92.60 138	95.15 167	82.65 173	92.20 327	95.60 210	83.97 260	88.55 205	93.70 250	74.16 241	98.21 172	82.46 263	89.37 285	96.94 180

LS3D		87.89 240	86.32 272	92.59 139	96.07 117	82.92 159	95.23 135	94.92 263	75.66 404	82.89 351	95.98 125	72.48 268	99.21 54	68.43 413	95.23 158	95.64 245

Anonymous20240529		88.09 236	86.59 261	92.58 140	96.53 96	81.92 193	95.99 79	95.84 188	74.11 421	89.06 195	95.21 174	61.44 386	98.81 109	83.67 246	87.47 316	97.01 174

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 141	95.49 150	81.10 220	95.93 86	97.16 50	92.96 4	97.39 12	98.13 7	83.63 88	98.80 110	97.89 3	97.61 99	97.78 116

CPTT-MVS		91.99 107	91.80 107	92.55 142	98.24 37	81.98 190	96.76 35	96.49 118	81.89 318	90.24 168	96.44 102	78.59 172	98.61 133	89.68 148	97.85 89	97.06 168

viewdifsd2359ckpt13		91.20 133	90.75 139	92.54 143	94.30 235	82.13 185	94.03 228	95.89 183	85.60 211	90.20 170	95.36 163	79.69 157	97.90 217	87.85 177	93.86 195	97.61 126

114514_t		89.51 187	88.50 203	92.54 143	98.11 42	81.99 189	95.16 145	96.36 127	70.19 448	85.81 267	95.25 170	76.70 197	98.63 130	82.07 273	96.86 117	97.00 175

PAPM_NR		91.22 132	90.78 138	92.52 145	97.60 65	81.46 206	94.37 202	96.24 142	86.39 190	87.41 230	94.80 195	82.06 119	98.48 141	82.80 258	95.37 153	97.61 126

mamba_0408		89.06 207	87.92 221	92.50 146	94.76 190	82.66 169	79.84 468	94.64 281	85.18 225	88.96 197	95.00 183	76.00 207	97.98 205	83.74 243	93.15 219	96.85 187

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 147	96.52 97	80.00 265	94.00 233	97.08 59	90.05 49	95.65 46	97.29 57	89.66 15	98.97 86	93.95 56	98.71 36	98.50 32

SSM_0407		90.47 158	89.80 162	92.46 148	94.76 190	82.66 169	93.98 235	95.00 255	85.41 220	88.96 197	95.35 164	76.13 202	97.88 218	85.46 215	93.15 219	96.85 187

IS-MVSNet		91.43 127	91.09 130	92.46 148	95.87 131	81.38 209	96.95 24	93.69 323	89.72 66	89.50 187	95.98 125	78.57 173	97.77 222	83.02 252	96.50 127	98.22 70

API-MVS		90.66 151	90.07 153	92.45 150	96.36 102	84.57 93	96.06 73	95.22 242	82.39 299	89.13 192	94.27 223	80.32 141	98.46 145	80.16 309	96.71 121	94.33 301

xiu_mvs_v1_base_debu		90.64 152	90.05 154	92.40 151	93.97 256	84.46 99	93.32 272	95.46 220	85.17 227	92.25 115	94.03 228	70.59 291	98.57 136	90.97 124	94.67 169	94.18 304

xiu_mvs_v1_base		90.64 152	90.05 154	92.40 151	93.97 256	84.46 99	93.32 272	95.46 220	85.17 227	92.25 115	94.03 228	70.59 291	98.57 136	90.97 124	94.67 169	94.18 304

xiu_mvs_v1_base_debi		90.64 152	90.05 154	92.40 151	93.97 256	84.46 99	93.32 272	95.46 220	85.17 227	92.25 115	94.03 228	70.59 291	98.57 136	90.97 124	94.67 169	94.18 304

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 154	95.36 153	81.19 216	95.20 142	96.56 112	90.37 39	97.13 19	98.03 29	77.47 189	98.96 88	97.79 6	96.58 124	97.03 171

viewmacassd2359aftdt		91.67 124	91.43 121	92.37 155	93.95 259	81.00 224	93.90 243	95.97 174	87.75 147	91.45 143	96.04 121	79.92 148	97.97 207	89.26 155	94.67 169	98.14 76

viewmanbaseed2359cas		91.78 114	91.58 114	92.37 155	94.32 233	81.07 221	93.76 249	95.96 175	87.26 162	91.50 140	95.88 133	80.92 137	97.97 207	89.70 147	94.92 163	98.07 82

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 155	94.62 204	81.13 218	95.23 135	95.89 183	90.30 43	96.74 29	98.02 30	76.14 201	98.95 90	97.64 7	96.21 133	97.03 171

AdaColmap		89.89 177	89.07 184	92.37 155	97.41 71	83.03 154	94.42 194	95.92 178	82.81 293	86.34 256	94.65 204	73.89 246	99.02 72	80.69 300	95.51 146	95.05 264

CNLPA		89.07 206	87.98 218	92.34 159	96.87 83	84.78 88	94.08 223	93.24 329	81.41 332	84.46 312	95.13 180	75.57 218	96.62 322	77.21 342	93.84 197	95.61 248

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 160	95.13 168	80.95 227	95.64 112	96.97 65	89.60 69	96.85 25	97.77 38	83.08 98	98.92 94	97.49 8	96.78 119	97.13 163

ET-MVSNet_ETH3D		87.51 259	85.91 291	92.32 161	93.70 275	83.93 117	92.33 321	90.94 398	84.16 255	72.09 446	92.52 286	69.90 302	95.85 369	89.20 156	88.36 303	97.17 155

E4		91.74 119	91.55 117	92.31 162	94.27 237	80.80 237	93.81 246	96.17 152	87.97 134	91.11 153	96.05 119	80.75 138	98.08 188	89.78 143	94.02 191	98.06 87

E2		91.79 111	91.61 112	92.31 162	94.49 218	80.86 233	93.74 251	96.19 148	87.63 152	91.16 148	95.94 128	81.31 132	98.06 191	89.76 144	94.29 185	97.99 92

Anonymous202405211		87.68 246	86.13 279	92.31 162	96.66 88	80.74 239	94.87 161	91.49 383	80.47 346	89.46 188	95.44 159	54.72 432	98.23 169	82.19 269	89.89 275	97.97 94

E3		91.78 114	91.61 112	92.30 165	94.48 219	80.86 233	93.73 252	96.19 148	87.63 152	91.16 148	95.95 127	81.30 133	98.06 191	89.76 144	94.29 185	97.99 92

CHOSEN 1792x2688		88.84 213	87.69 226	92.30 165	96.14 108	81.42 208	90.01 386	95.86 187	74.52 417	87.41 230	93.94 236	75.46 219	98.36 158	80.36 305	95.53 145	97.12 164

viewcassd2359sk11		91.79 111	91.62 111	92.29 167	94.62 204	80.88 231	93.70 256	96.18 151	87.38 159	91.13 151	95.85 137	81.62 128	98.06 191	89.71 146	94.40 181	97.94 96

HY-MVS	83.01 12	89.03 209	87.94 220	92.29 167	94.86 185	82.77 161	92.08 332	94.49 287	81.52 331	86.93 237	92.79 279	78.32 178	98.23 169	79.93 311	90.55 262	95.88 234

CDS-MVSNet		89.45 190	88.51 202	92.29 167	93.62 278	83.61 130	93.01 291	94.68 279	81.95 312	87.82 223	93.24 262	78.69 170	96.99 302	80.34 306	93.23 216	96.28 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 170	89.27 180	92.29 167	95.78 133	80.95 227	92.68 307	96.22 144	81.91 314	86.66 247	93.75 248	82.23 113	98.44 151	79.40 322	94.79 166	97.48 134

E3new		91.76 117	91.58 114	92.28 171	94.69 201	80.90 230	93.68 259	96.17 152	87.15 165	91.09 155	95.70 149	81.75 127	98.05 195	89.67 149	94.35 182	97.90 104

mvsmamba		90.33 159	89.69 165	92.25 172	95.17 164	81.64 199	95.27 133	93.36 328	84.88 239	89.51 185	94.27 223	69.29 316	97.42 260	89.34 153	96.12 136	97.68 122

E6		91.71 121	91.55 117	92.20 173	94.32 233	80.62 243	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

PLC	84.53 7	89.06 207	88.03 216	92.15 174	97.27 77	82.69 168	94.29 207	95.44 225	79.71 355	84.01 328	94.18 226	76.68 198	98.75 115	77.28 341	93.41 210	95.02 265
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 122	91.56 116	92.13 175	95.88 129	80.50 247	97.33 8	95.25 239	86.15 196	89.76 182	95.60 152	83.42 91	98.32 165	87.37 187	93.25 215	97.56 131

patch_mono-2		93.74 65	94.32 41	92.01 176	97.54 66	78.37 307	93.40 268	97.19 44	88.02 132	94.99 56	97.21 62	88.35 24	98.44 151	94.07 55	98.09 77	99.23 1

原ACMM1				92.01 176	97.34 73	81.05 222		96.81 87	78.89 366	90.45 164	95.92 130	82.65 104	98.84 105	80.68 301	98.26 63	96.14 219

UniMVSNet (Re)		89.80 180	89.07 184	92.01 176	93.60 279	84.52 96	94.78 169	97.47 17	89.26 82	86.44 253	92.32 292	82.10 117	97.39 271	84.81 223	80.84 395	94.12 308

MG-MVS		91.77 116	91.70 110	92.00 179	97.08 80	80.03 263	93.60 261	95.18 243	87.85 142	90.89 158	96.47 101	82.06 119	98.36 158	85.07 218	97.04 110	97.62 125

EIA-MVS		91.95 108	91.94 105	91.98 180	95.16 165	80.01 264	95.36 123	96.73 97	88.44 114	89.34 189	92.16 297	83.82 87	98.45 149	89.35 152	97.06 109	97.48 134

PVSNet_Blended		90.73 146	90.32 145	91.98 180	96.12 110	81.25 212	92.55 312	96.83 83	82.04 310	89.10 193	92.56 285	81.04 135	98.85 103	86.72 197	95.91 138	95.84 236

guyue		91.12 137	90.84 136	91.96 182	94.59 208	80.57 245	94.87 161	93.71 322	88.96 97	91.14 150	95.22 171	73.22 258	97.76 223	92.01 104	93.81 198	97.54 133

PS-MVSNAJ		91.18 134	90.92 133	91.96 182	95.26 160	82.60 175	92.09 331	95.70 201	86.27 192	91.84 130	92.46 287	79.70 154	98.99 81	89.08 157	95.86 139	94.29 302

TAMVS		89.21 200	88.29 211	91.96 182	93.71 273	82.62 174	93.30 276	94.19 301	82.22 304	87.78 224	93.94 236	78.83 167	96.95 305	77.70 337	92.98 224	96.32 209

SDMVSNet		90.19 163	89.61 168	91.93 185	96.00 121	83.09 151	92.89 299	95.98 171	88.73 104	86.85 243	95.20 175	72.09 274	97.08 294	88.90 162	89.85 277	95.63 246

FA-MVS(test-final)		89.66 182	88.91 192	91.93 185	94.57 212	80.27 251	91.36 348	94.74 276	84.87 240	89.82 179	92.61 284	74.72 229	98.47 144	83.97 238	93.53 205	97.04 170

MVS_Test		91.31 130	91.11 128	91.93 185	94.37 228	80.14 256	93.46 266	95.80 190	86.46 188	91.35 146	93.77 246	82.21 114	98.09 186	87.57 182	94.95 162	97.55 132

NR-MVSNet		88.58 223	87.47 232	91.93 185	93.04 300	84.16 111	94.77 170	96.25 141	89.05 89	80.04 390	93.29 260	79.02 166	97.05 299	81.71 284	80.05 405	94.59 285

HyFIR lowres test		88.09 236	86.81 249	91.93 185	96.00 121	80.63 241	90.01 386	95.79 191	73.42 428	87.68 226	92.10 303	73.86 247	97.96 209	80.75 299	91.70 245	97.19 154

GeoE		90.05 168	89.43 173	91.90 190	95.16 165	80.37 250	95.80 96	94.65 280	83.90 261	87.55 229	94.75 196	78.18 179	97.62 236	81.28 289	93.63 201	97.71 121

thisisatest0530		88.67 218	87.61 228	91.86 191	94.87 184	80.07 259	94.63 179	89.90 422	84.00 259	88.46 207	93.78 245	66.88 340	98.46 145	83.30 248	92.65 231	97.06 168

xiu_mvs_v2_base		91.13 136	90.89 135	91.86 191	94.97 176	82.42 177	92.24 324	95.64 208	86.11 200	91.74 136	93.14 266	79.67 159	98.89 97	89.06 158	95.46 150	94.28 303

DU-MVS		89.34 199	88.50 203	91.85 193	93.04 300	83.72 123	94.47 190	96.59 109	89.50 71	86.46 250	93.29 260	77.25 191	97.23 284	84.92 220	81.02 391	94.59 285

AstraMVS		90.69 148	90.30 146	91.84 194	93.81 264	79.85 270	94.76 171	92.39 352	88.96 97	91.01 157	95.87 136	70.69 289	97.94 212	92.49 82	92.70 230	97.73 119

OPM-MVS		90.12 164	89.56 169	91.82 195	93.14 292	83.90 118	94.16 214	95.74 195	88.96 97	87.86 219	95.43 161	72.48 268	97.91 215	88.10 174	90.18 269	93.65 339
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 155	90.19 148	91.82 195	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 239	94.86 191	74.23 237	98.12 176	88.15 170	89.99 271	94.63 282

UniMVSNet_NR-MVSNet		89.92 176	89.29 178	91.81 197	93.39 285	83.72 123	94.43 193	97.12 55	89.80 60	86.46 250	93.32 257	83.16 95	97.23 284	84.92 220	81.02 391	94.49 295

diffmvs		91.37 129	91.23 126	91.77 198	93.09 295	80.27 251	92.36 318	95.52 217	87.03 170	91.40 145	94.93 186	80.08 145	97.44 258	92.13 99	94.56 175	97.61 126
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.51 126	91.44 120	91.73 199	93.09 295	80.27 251	92.51 313	95.58 211	87.22 163	91.80 133	95.57 154	79.96 147	97.48 250	92.23 93	94.97 161	97.45 136

1112_ss		88.42 225	87.33 235	91.72 200	94.92 180	80.98 225	92.97 295	94.54 284	78.16 383	83.82 331	93.88 241	78.78 169	97.91 215	79.45 318	89.41 284	96.26 213

Fast-Effi-MVS+		89.41 194	88.64 198	91.71 201	94.74 193	80.81 236	93.54 262	95.10 247	83.11 284	86.82 245	90.67 357	79.74 153	97.75 227	80.51 304	93.55 203	96.57 202

WTY-MVS		89.60 184	88.92 191	91.67 202	95.47 151	81.15 217	92.38 317	94.78 274	83.11 284	89.06 195	94.32 218	78.67 171	96.61 325	81.57 285	90.89 258	97.24 149

TAPA-MVS	84.62 6	88.16 234	87.01 244	91.62 203	96.64 89	80.65 240	94.39 198	96.21 147	76.38 397	86.19 260	95.44 159	79.75 152	98.08 188	62.75 443	95.29 155	96.13 220
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 183	88.96 189	91.60 204	93.86 261	82.89 160	95.46 120	97.33 32	87.91 137	88.43 208	93.31 258	74.17 240	97.40 268	87.32 188	82.86 366	94.52 290

FE-MVS		87.40 264	86.02 285	91.57 205	94.56 213	79.69 274	90.27 373	93.72 321	80.57 344	88.80 201	91.62 323	65.32 356	98.59 135	74.97 367	94.33 184	96.44 205

XVG-OURS		89.40 196	88.70 197	91.52 206	94.06 248	81.46 206	91.27 352	96.07 164	86.14 197	88.89 200	95.77 145	68.73 325	97.26 281	87.39 186	89.96 273	95.83 237

hse-mvs2		89.88 178	89.34 176	91.51 207	94.83 187	81.12 219	93.94 237	93.91 314	89.80 60	93.08 89	93.60 251	75.77 211	97.66 231	92.07 100	77.07 425	95.74 241

TranMVSNet+NR-MVSNet		88.84 213	87.95 219	91.49 208	92.68 316	83.01 156	94.92 158	96.31 130	89.88 54	85.53 276	93.85 243	76.63 199	96.96 304	81.91 277	79.87 408	94.50 293

AUN-MVS		87.78 244	86.54 264	91.48 209	94.82 188	81.05 222	93.91 241	93.93 311	83.00 288	86.93 237	93.53 252	69.50 310	97.67 229	86.14 203	77.12 424	95.73 243

XVG-OURS-SEG-HR		89.95 174	89.45 171	91.47 210	94.00 254	81.21 215	91.87 336	96.06 166	85.78 204	88.55 205	95.73 147	74.67 230	97.27 279	88.71 166	89.64 282	95.91 231

MVS		87.44 262	86.10 282	91.44 211	92.61 317	83.62 128	92.63 309	95.66 205	67.26 454	81.47 368	92.15 298	77.95 182	98.22 171	79.71 313	95.48 148	92.47 383

viewdifsd2359ckpt07		91.11 138	91.02 131	91.41 212	94.21 241	78.37 307	92.91 298	95.71 200	87.50 154	90.32 167	95.88 133	80.27 143	97.99 202	88.78 165	93.55 203	97.86 107

F-COLMAP		87.95 239	86.80 250	91.40 213	96.35 103	80.88 231	94.73 173	95.45 223	79.65 356	82.04 363	94.61 205	71.13 281	98.50 139	76.24 354	91.05 256	94.80 279

dcpmvs_2		93.49 70	94.19 52	91.38 214	97.69 63	76.78 350	94.25 209	96.29 131	88.33 117	94.46 59	96.88 79	88.07 28	98.64 128	93.62 62	98.09 77	98.73 23

thisisatest0515		87.33 267	85.99 286	91.37 215	93.49 281	79.55 275	90.63 367	89.56 430	80.17 348	87.56 228	90.86 347	67.07 337	98.28 167	81.50 286	93.02 223	96.29 211

HQP-MVS		89.80 180	89.28 179	91.34 216	94.17 243	81.56 200	94.39 198	96.04 167	88.81 100	85.43 285	93.97 235	73.83 248	97.96 209	87.11 192	89.77 280	94.50 293

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 217	94.42 226	79.48 277	94.52 185	97.14 53	89.33 78	94.17 65	98.09 18	81.83 124	97.49 249	96.33 26	98.02 81	96.95 178

RRT-MVS		90.85 142	90.70 140	91.30 218	94.25 238	76.83 349	94.85 164	96.13 158	89.04 90	90.23 169	94.88 189	70.15 300	98.72 119	91.86 112	94.88 164	98.34 48

FMVSNet3		87.40 264	86.11 281	91.30 218	93.79 267	83.64 127	94.20 213	94.81 272	83.89 262	84.37 315	91.87 314	68.45 328	96.56 330	78.23 332	85.36 334	93.70 338

FMVSNet2		87.19 277	85.82 294	91.30 218	94.01 251	83.67 125	94.79 168	94.94 258	83.57 270	83.88 330	92.05 307	66.59 345	96.51 334	77.56 339	85.01 337	93.73 336

RPMNet		83.95 355	81.53 366	91.21 221	90.58 394	79.34 283	85.24 446	96.76 92	71.44 442	85.55 274	82.97 454	70.87 286	98.91 96	61.01 447	89.36 286	95.40 252

IB-MVS	80.51 15	85.24 332	83.26 350	91.19 222	92.13 329	79.86 269	91.75 339	91.29 388	83.28 281	80.66 380	88.49 405	61.28 388	98.46 145	80.99 295	79.46 412	95.25 258
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
CLD-MVS		89.47 189	88.90 193	91.18 223	94.22 240	82.07 187	92.13 329	96.09 162	87.90 138	85.37 291	92.45 288	74.38 235	97.56 241	87.15 190	90.43 264	93.93 317
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 190	88.90 193	91.12 224	94.47 220	81.49 204	95.30 128	96.14 155	86.73 181	85.45 282	95.16 177	69.89 303	98.10 178	87.70 179	89.23 289	93.77 332

LGP-MVS_train				91.12 224	94.47 220	81.49 204		96.14 155	86.73 181	85.45 282	95.16 177	69.89 303	98.10 178	87.70 179	89.23 289	93.77 332

ACMM	84.12 9	89.14 202	88.48 206	91.12 224	94.65 203	81.22 214	95.31 126	96.12 159	85.31 224	85.92 265	94.34 216	70.19 299	98.06 191	85.65 211	88.86 294	94.08 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 220	87.78 225	91.11 227	94.96 177	77.81 325	95.35 124	89.69 425	85.09 234	88.05 217	94.59 208	66.93 338	98.48 141	83.27 249	92.13 243	97.03 171

GBi-Net		87.26 269	85.98 287	91.08 228	94.01 251	83.10 148	95.14 146	94.94 258	83.57 270	84.37 315	91.64 319	66.59 345	96.34 347	78.23 332	85.36 334	93.79 327

test1		87.26 269	85.98 287	91.08 228	94.01 251	83.10 148	95.14 146	94.94 258	83.57 270	84.37 315	91.64 319	66.59 345	96.34 347	78.23 332	85.36 334	93.79 327

FMVSNet1		85.85 317	84.11 337	91.08 228	92.81 311	83.10 148	95.14 146	94.94 258	81.64 326	82.68 353	91.64 319	59.01 410	96.34 347	75.37 361	83.78 350	93.79 327

Test_1112_low_res		87.65 248	86.51 265	91.08 228	94.94 179	79.28 287	91.77 338	94.30 296	76.04 402	83.51 341	92.37 290	77.86 185	97.73 228	78.69 327	89.13 291	96.22 214

PS-MVSNAJss		89.97 172	89.62 167	91.02 232	91.90 338	80.85 235	95.26 134	95.98 171	86.26 193	86.21 259	94.29 220	79.70 154	97.65 232	88.87 164	88.10 305	94.57 287

BH-RMVSNet		88.37 228	87.48 231	91.02 232	95.28 157	79.45 279	92.89 299	93.07 335	85.45 219	86.91 239	94.84 194	70.35 296	97.76 223	73.97 376	94.59 174	95.85 235

UniMVSNet_ETH3D		87.53 258	86.37 269	91.00 234	92.44 321	78.96 292	94.74 172	95.61 209	84.07 258	85.36 292	94.52 210	59.78 402	97.34 273	82.93 253	87.88 310	96.71 195

FIs		90.51 157	90.35 144	90.99 235	93.99 255	80.98 225	95.73 103	97.54 10	89.15 86	86.72 246	94.68 199	81.83 124	97.24 283	85.18 217	88.31 304	94.76 280

ACMP	84.23 8	89.01 211	88.35 207	90.99 235	94.73 194	81.27 211	95.07 149	95.89 183	86.48 186	83.67 336	94.30 219	69.33 312	97.99 202	87.10 194	88.55 296	93.72 337
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 300	85.13 315	90.98 237	96.52 97	81.50 202	96.14 64	96.16 154	73.78 424	83.65 337	92.15 298	63.26 372	97.37 272	82.82 257	81.74 380	94.06 313

IMVS_0403		89.97 172	89.64 166	90.96 238	93.72 269	77.75 330	93.00 292	95.34 234	85.53 215	88.77 202	94.49 211	78.49 175	97.84 219	84.75 224	92.65 231	97.28 143

sss		88.93 212	88.26 213	90.94 239	94.05 249	80.78 238	91.71 340	95.38 229	81.55 330	88.63 204	93.91 240	75.04 223	95.47 388	82.47 262	91.61 246	96.57 202

IMVS_0407		89.85 179	89.51 170	90.88 240	93.72 269	77.75 330	93.07 289	95.34 234	85.53 215	88.34 210	94.49 211	77.69 187	97.60 237	84.75 224	92.65 231	97.28 143

viewmambaseed2359dif		90.04 169	89.78 163	90.83 241	92.85 310	77.92 319	92.23 325	95.01 251	81.90 315	90.20 170	95.45 158	79.64 161	97.34 273	87.52 184	93.17 217	97.23 152

sd_testset		88.59 222	87.85 224	90.83 241	96.00 121	80.42 249	92.35 319	94.71 277	88.73 104	86.85 243	95.20 175	67.31 332	96.43 341	79.64 315	89.85 277	95.63 246

PVSNet_BlendedMVS		89.98 171	89.70 164	90.82 243	96.12 110	81.25 212	93.92 239	96.83 83	83.49 274	89.10 193	92.26 295	81.04 135	98.85 103	86.72 197	87.86 311	92.35 389

cascas		86.43 308	84.98 318	90.80 244	92.10 331	80.92 229	90.24 377	95.91 180	73.10 431	83.57 340	88.39 406	65.15 358	97.46 254	84.90 222	91.43 248	94.03 315

ECVR-MVS		89.09 205	88.53 201	90.77 245	95.62 144	75.89 363	96.16 60	84.22 456	87.89 140	90.20 170	96.65 91	63.19 373	98.10 178	85.90 208	96.94 112	98.33 50

GA-MVS		86.61 298	85.27 312	90.66 246	91.33 361	78.71 296	90.40 372	93.81 318	85.34 223	85.12 295	89.57 387	61.25 389	97.11 293	80.99 295	89.59 283	96.15 218

thres600view7		87.65 248	86.67 256	90.59 247	96.08 116	78.72 294	94.88 160	91.58 379	87.06 169	88.08 215	92.30 293	68.91 322	98.10 178	70.05 406	91.10 251	94.96 269

thres400		87.62 253	86.64 257	90.57 248	95.99 124	78.64 297	94.58 181	91.98 368	86.94 175	88.09 213	91.77 315	69.18 318	98.10 178	70.13 403	91.10 251	94.96 269

baseline1		88.10 235	87.28 237	90.57 248	94.96 177	80.07 259	94.27 208	91.29 388	86.74 180	87.41 230	94.00 233	76.77 196	96.20 352	80.77 298	79.31 414	95.44 250

viewdifsd2359ckpt11		89.43 192	89.05 186	90.56 250	92.89 308	77.00 345	92.81 302	94.52 285	87.03 170	89.77 180	95.79 142	74.67 230	97.51 245	88.97 160	84.98 338	97.17 155

viewmsd2359difaftdt		89.43 192	89.05 186	90.56 250	92.89 308	77.00 345	92.81 302	94.52 285	87.03 170	89.77 180	95.79 142	74.67 230	97.51 245	88.97 160	84.98 338	97.17 155

FE-MVSNET3		86.84 288	85.61 302	90.53 252	90.50 398	81.80 196	90.97 360	94.96 257	83.05 286	83.50 342	90.32 364	72.15 272	96.65 319	79.49 316	85.55 333	93.15 362

FC-MVSNet-test		90.27 161	90.18 149	90.53 252	93.71 273	79.85 270	95.77 99	97.59 7	89.31 79	86.27 257	94.67 202	81.93 122	97.01 301	84.26 234	88.09 307	94.71 281

PAPM		86.68 297	85.39 307	90.53 252	93.05 299	79.33 286	89.79 389	94.77 275	78.82 369	81.95 364	93.24 262	76.81 194	97.30 275	66.94 423	93.16 218	94.95 273

WR-MVS		88.38 227	87.67 227	90.52 255	93.30 287	80.18 254	93.26 279	95.96 175	88.57 112	85.47 281	92.81 277	76.12 204	96.91 308	81.24 290	82.29 371	94.47 298

SSM_04072		88.57 224	87.92 221	90.51 256	94.76 190	82.66 169	79.84 468	94.64 281	85.18 225	88.96 197	95.00 183	76.00 207	92.03 437	83.74 243	93.15 219	96.85 187

MVSTER		88.84 213	88.29 211	90.51 256	92.95 305	80.44 248	93.73 252	95.01 251	84.66 249	87.15 234	93.12 267	72.79 263	97.21 286	87.86 176	87.36 319	93.87 322

testdata				90.49 258	96.40 100	77.89 322		95.37 231	72.51 436	93.63 78	96.69 87	82.08 118	97.65 232	83.08 250	97.39 102	95.94 230

test1111		89.10 203	88.64 198	90.48 259	95.53 149	74.97 373	96.08 69	84.89 454	88.13 127	90.16 174	96.65 91	63.29 371	98.10 178	86.14 203	96.90 114	98.39 45

tt0805		86.92 285	85.74 300	90.48 259	92.22 325	79.98 266	95.63 113	94.88 266	83.83 264	84.74 304	92.80 278	57.61 417	97.67 229	85.48 214	84.42 343	93.79 327

jajsoiax		88.24 232	87.50 230	90.48 259	90.89 382	80.14 256	95.31 126	95.65 207	84.97 237	84.24 323	94.02 231	65.31 357	97.42 260	88.56 167	88.52 298	93.89 318

PatchMatch-RL		86.77 294	85.54 303	90.47 262	95.88 129	82.71 167	90.54 370	92.31 356	79.82 354	84.32 320	91.57 327	68.77 324	96.39 343	73.16 382	93.48 209	92.32 390

tfpn200view9		87.58 256	86.64 257	90.41 263	95.99 124	78.64 297	94.58 181	91.98 368	86.94 175	88.09 213	91.77 315	69.18 318	98.10 178	70.13 403	91.10 251	94.48 296

VPNet		88.20 233	87.47 232	90.39 264	93.56 280	79.46 278	94.04 227	95.54 215	88.67 107	86.96 236	94.58 209	69.33 312	97.15 288	84.05 237	80.53 400	94.56 288

ACMH	80.38 17	85.36 327	83.68 344	90.39 264	94.45 223	80.63 241	94.73 173	94.85 268	82.09 306	77.24 415	92.65 282	60.01 400	97.58 239	72.25 387	84.87 340	92.96 368
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 251	86.71 253	90.38 266	96.12 110	78.55 300	95.03 152	91.58 379	87.15 165	88.06 216	92.29 294	68.91 322	98.10 178	70.13 403	91.10 251	94.48 296

mvs_tets		88.06 238	87.28 237	90.38 266	90.94 378	79.88 268	95.22 137	95.66 205	85.10 233	84.21 324	93.94 236	63.53 369	97.40 268	88.50 168	88.40 302	93.87 322

1314		87.51 259	86.57 262	90.34 268	92.42 322	79.74 273	92.63 309	95.35 233	78.35 378	80.14 387	91.62 323	74.05 242	97.15 288	81.05 291	93.53 205	94.12 308

LTVRE_ROB	82.13 13	86.26 311	84.90 321	90.34 268	94.44 224	81.50 202	92.31 323	94.89 264	83.03 287	79.63 397	92.67 281	69.69 306	97.79 221	71.20 392	86.26 328	91.72 400
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
test_djsdf		89.03 209	88.64 198	90.21 270	90.74 389	79.28 287	95.96 83	95.90 181	84.66 249	85.33 293	92.94 272	74.02 243	97.30 275	89.64 150	88.53 297	94.05 314

v2v482		87.84 241	87.06 241	90.17 271	90.99 374	79.23 290	94.00 233	95.13 244	84.87 240	85.53 276	92.07 306	74.45 234	97.45 255	84.71 229	81.75 379	93.85 325

pmmvs4		85.43 325	83.86 342	90.16 272	90.02 408	82.97 158	90.27 373	92.67 347	75.93 403	80.73 378	91.74 317	71.05 282	95.73 377	78.85 326	83.46 357	91.78 399

V42		87.68 246	86.86 246	90.15 273	90.58 394	80.14 256	94.24 211	95.28 238	83.66 268	85.67 271	91.33 329	74.73 228	97.41 266	84.43 233	81.83 377	92.89 371

MSDG		84.86 340	83.09 353	90.14 274	93.80 265	80.05 261	89.18 402	93.09 334	78.89 366	78.19 407	91.91 312	65.86 355	97.27 279	68.47 412	88.45 300	93.11 363

sc_t1		81.53 380	78.67 401	90.12 275	90.78 386	78.64 297	93.91 241	90.20 411	68.42 451	80.82 377	89.88 380	46.48 455	96.76 313	76.03 357	71.47 440	94.96 269

anonymousdsp		87.84 241	87.09 240	90.12 275	89.13 419	80.54 246	94.67 177	95.55 213	82.05 308	83.82 331	92.12 300	71.47 279	97.15 288	87.15 190	87.80 314	92.67 377

thres200		87.21 275	86.24 276	90.12 275	95.36 153	78.53 301	93.26 279	92.10 362	86.42 189	88.00 218	91.11 340	69.24 317	98.00 201	69.58 407	91.04 257	93.83 326

CR-MVSNet		85.35 328	83.76 343	90.12 275	90.58 394	79.34 283	85.24 446	91.96 370	78.27 380	85.55 274	87.87 416	71.03 283	95.61 380	73.96 377	89.36 286	95.40 252

v1144		87.61 254	86.79 251	90.06 279	91.01 373	79.34 283	93.95 236	95.42 228	83.36 279	85.66 272	91.31 332	74.98 224	97.42 260	83.37 247	82.06 373	93.42 348

XXY-MVS		87.65 248	86.85 247	90.03 280	92.14 328	80.60 244	93.76 249	95.23 240	82.94 290	84.60 306	94.02 231	74.27 236	95.49 387	81.04 292	83.68 353	94.01 316

Vis-MVSNet (Re-imp)		89.59 185	89.44 172	90.03 280	95.74 134	75.85 364	95.61 114	90.80 402	87.66 151	87.83 222	95.40 162	76.79 195	96.46 339	78.37 328	96.73 120	97.80 114

test2506		87.21 275	86.28 274	90.02 282	95.62 144	73.64 389	96.25 55	71.38 479	87.89 140	90.45 164	96.65 91	55.29 429	98.09 186	86.03 207	96.94 112	98.33 50

BH-untuned		88.60 221	88.13 215	90.01 283	95.24 161	78.50 303	93.29 277	94.15 304	84.75 245	84.46 312	93.40 254	75.76 213	97.40 268	77.59 338	94.52 177	94.12 308

v1192		87.25 271	86.33 271	90.00 284	90.76 388	79.04 291	93.80 247	95.48 218	82.57 297	85.48 280	91.18 336	73.38 257	97.42 260	82.30 266	82.06 373	93.53 342

v7n		86.81 289	85.76 298	89.95 285	90.72 390	79.25 289	95.07 149	95.92 178	84.45 252	82.29 357	90.86 347	72.60 267	97.53 243	79.42 321	80.52 401	93.08 365

testing91		87.11 280	86.18 277	89.92 286	94.43 225	75.38 372	91.53 345	92.27 358	86.48 186	86.50 248	90.24 366	61.19 392	97.53 243	82.10 271	90.88 259	96.84 190

IMVS_0404		87.60 255	86.84 248	89.89 287	93.72 269	77.75 330	88.56 411	95.34 234	85.53 215	79.98 391	94.49 211	66.54 348	94.64 401	84.75 224	92.65 231	97.28 143

v8		87.50 261	86.71 253	89.89 287	91.37 358	79.40 280	94.50 186	95.38 229	84.81 243	83.60 339	91.33 329	76.05 205	97.42 260	82.84 256	80.51 402	92.84 373

v10		87.25 271	86.38 268	89.85 289	91.19 364	79.50 276	94.48 187	95.45 223	83.79 266	83.62 338	91.19 334	75.13 221	97.42 260	81.94 276	80.60 397	92.63 379

baseline2		86.50 304	85.39 307	89.84 290	91.12 369	76.70 352	91.88 335	88.58 434	82.35 302	79.95 392	90.95 345	73.42 255	97.63 235	80.27 308	89.95 274	95.19 259

pm-mvs1		86.61 298	85.54 303	89.82 291	91.44 353	80.18 254	95.28 132	94.85 268	83.84 263	81.66 366	92.62 283	72.45 270	96.48 336	79.67 314	78.06 417	92.82 374

TR-MVS		86.78 291	85.76 298	89.82 291	94.37 228	78.41 305	92.47 314	92.83 341	81.11 340	86.36 254	92.40 289	68.73 325	97.48 250	73.75 380	89.85 277	93.57 341

ACMH+	81.04 14	85.05 335	83.46 347	89.82 291	94.66 202	79.37 281	94.44 192	94.12 307	82.19 305	78.04 409	92.82 276	58.23 413	97.54 242	73.77 379	82.90 365	92.54 380

EI-MVSNet		89.10 203	88.86 195	89.80 294	91.84 340	78.30 310	93.70 256	95.01 251	85.73 206	87.15 234	95.28 168	79.87 151	97.21 286	83.81 241	87.36 319	93.88 321

v144192		87.19 277	86.35 270	89.74 295	90.64 392	78.24 312	93.92 239	95.43 226	81.93 313	85.51 278	91.05 343	74.21 239	97.45 255	82.86 255	81.56 381	93.53 342

COLMAP_ROB	80.39 16	83.96 354	82.04 363	89.74 295	95.28 157	79.75 272	94.25 209	92.28 357	75.17 410	78.02 410	93.77 246	58.60 412	97.84 219	65.06 434	85.92 329	91.63 402
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 310	85.18 314	89.73 297	92.15 327	76.60 353	91.12 356	91.69 375	83.53 273	85.50 279	88.81 399	66.79 341	96.48 336	76.65 347	90.35 266	96.12 221

IterMVS-LS		88.36 229	87.91 223	89.70 298	93.80 265	78.29 311	93.73 252	95.08 249	85.73 206	84.75 303	91.90 313	79.88 150	96.92 307	83.83 240	82.51 367	93.89 318
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 307	85.35 310	89.69 299	94.29 236	75.40 371	91.30 350	90.53 406	84.76 244	85.06 297	90.13 372	58.95 411	97.45 255	82.08 272	91.09 255	96.21 216

testing99		86.72 295	85.73 301	89.69 299	94.23 239	74.91 375	91.35 349	90.97 396	86.14 197	86.36 254	90.22 367	59.41 405	97.48 250	82.24 268	90.66 261	96.69 197

v1921920		86.97 284	86.06 284	89.69 299	90.53 397	78.11 315	93.80 247	95.43 226	81.90 315	85.33 293	91.05 343	72.66 264	97.41 266	82.05 274	81.80 378	93.53 342

icg_test_0407_2		89.15 201	88.97 188	89.68 302	93.72 269	77.75 330	88.26 416	95.34 234	85.53 215	88.34 210	94.49 211	77.69 187	93.99 413	84.75 224	92.65 231	97.28 143

VortexMVS		88.42 225	88.01 217	89.63 303	93.89 260	78.82 293	93.82 245	95.47 219	86.67 183	84.53 310	91.99 309	72.62 266	96.65 319	89.02 159	84.09 347	93.41 349

Fast-Effi-MVS+-dtu		87.44 262	86.72 252	89.63 303	92.04 332	77.68 335	94.03 228	93.94 310	85.81 203	82.42 356	91.32 331	70.33 297	97.06 297	80.33 307	90.23 268	94.14 307

v1240		86.78 291	85.85 293	89.56 305	90.45 400	77.79 327	93.61 260	95.37 231	81.65 325	85.43 285	91.15 338	71.50 278	97.43 259	81.47 287	82.05 375	93.47 346

Effi-MVS+-dtu		88.65 219	88.35 207	89.54 306	93.33 286	76.39 357	94.47 190	94.36 294	87.70 148	85.43 285	89.56 388	73.45 253	97.26 281	85.57 213	91.28 250	94.97 266

AllTest		83.42 361	81.39 367	89.52 307	95.01 171	77.79 327	93.12 283	90.89 400	77.41 387	76.12 424	93.34 255	54.08 435	97.51 245	68.31 414	84.27 345	93.26 352

TestCases				89.52 307	95.01 171	77.79 327		90.89 400	77.41 387	76.12 424	93.34 255	54.08 435	97.51 245	68.31 414	84.27 345	93.26 352

mvs_anonymous		89.37 198	89.32 177	89.51 309	93.47 282	74.22 382	91.65 343	94.83 270	82.91 291	85.45 282	93.79 244	81.23 134	96.36 346	86.47 199	94.09 190	97.94 96

XVG-ACMP-BASELINE		86.00 313	84.84 323	89.45 310	91.20 363	78.00 317	91.70 341	95.55 213	85.05 235	82.97 350	92.25 296	54.49 433	97.48 250	82.93 253	87.45 318	92.89 371

testing222		84.84 341	83.32 348	89.43 311	94.15 246	75.94 362	91.09 357	89.41 432	84.90 238	85.78 268	89.44 389	52.70 440	96.28 350	70.80 398	91.57 247	96.07 225

MVP-Stereo		85.97 314	84.86 322	89.32 312	90.92 380	82.19 184	92.11 330	94.19 301	78.76 371	78.77 406	91.63 322	68.38 329	96.56 330	75.01 366	93.95 193	89.20 441
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 317	84.70 325	89.29 313	91.76 344	75.54 368	88.49 412	91.30 387	81.63 327	85.05 298	88.70 403	71.71 275	96.24 351	74.61 372	89.05 292	96.08 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 282	86.32 272	89.21 314	90.94 378	77.26 341	93.71 255	94.43 289	84.84 242	84.36 318	90.80 351	76.04 206	97.05 299	82.12 270	79.60 411	93.31 351

tfpnnormal		84.72 343	83.23 351	89.20 315	92.79 312	80.05 261	94.48 187	95.81 189	82.38 300	81.08 374	91.21 333	69.01 321	96.95 305	61.69 445	80.59 398	90.58 427

cl22		86.78 291	85.98 287	89.18 316	92.34 323	77.62 336	90.84 363	94.13 306	81.33 334	83.97 329	90.15 371	73.96 244	96.60 327	84.19 235	82.94 362	93.33 350

BH-w/o		87.57 257	87.05 242	89.12 317	94.90 183	77.90 321	92.41 315	93.51 325	82.89 292	83.70 335	91.34 328	75.75 214	97.07 296	75.49 359	93.49 207	92.39 387

WR-MVS_H		87.80 243	87.37 234	89.10 318	93.23 288	78.12 314	95.61 114	97.30 37	87.90 138	83.72 334	92.01 308	79.65 160	96.01 361	76.36 351	80.54 399	93.16 360

miper_enhance_ethall		86.90 286	86.18 277	89.06 319	91.66 349	77.58 337	90.22 379	94.82 271	79.16 362	84.48 311	89.10 393	79.19 165	96.66 318	84.06 236	82.94 362	92.94 369

c3_l		87.14 279	86.50 266	89.04 320	92.20 326	77.26 341	91.22 355	94.70 278	82.01 311	84.34 319	90.43 362	78.81 168	96.61 325	83.70 245	81.09 388	93.25 354

miper_ehance_all_eth		87.22 274	86.62 260	89.02 321	92.13 329	77.40 339	90.91 362	94.81 272	81.28 335	84.32 320	90.08 374	79.26 163	96.62 322	83.81 241	82.94 362	93.04 366

gg-mvs-nofinetune		81.77 374	79.37 389	88.99 322	90.85 384	77.73 334	86.29 438	79.63 467	74.88 415	83.19 349	69.05 470	60.34 397	96.11 356	75.46 360	94.64 173	93.11 363

ETVMVS		84.43 348	82.92 357	88.97 323	94.37 228	74.67 376	91.23 354	88.35 436	83.37 278	86.06 263	89.04 394	55.38 427	95.67 379	67.12 421	91.34 249	96.58 201

pmmvs6		83.42 361	81.60 365	88.87 324	88.01 434	77.87 323	94.96 155	94.24 300	74.67 416	78.80 405	91.09 341	60.17 399	96.49 335	77.06 346	75.40 431	92.23 392

test_cas_vis1_n_1920		88.83 216	88.85 196	88.78 325	91.15 368	76.72 351	93.85 244	94.93 262	83.23 283	92.81 98	96.00 123	61.17 393	94.45 402	91.67 115	94.84 165	95.17 260

MIMVSNet		82.59 367	80.53 372	88.76 326	91.51 351	78.32 309	86.57 437	90.13 414	79.32 358	80.70 379	88.69 404	52.98 439	93.07 429	66.03 429	88.86 294	94.90 274

cl____		86.52 303	85.78 295	88.75 327	92.03 333	76.46 355	90.74 364	94.30 296	81.83 321	83.34 346	90.78 352	75.74 216	96.57 328	81.74 282	81.54 382	93.22 356

DIV-MVS_self_test		86.53 302	85.78 295	88.75 327	92.02 334	76.45 356	90.74 364	94.30 296	81.83 321	83.34 346	90.82 350	75.75 214	96.57 328	81.73 283	81.52 383	93.24 355

CP-MVSNet		87.63 251	87.26 239	88.74 329	93.12 293	76.59 354	95.29 130	96.58 110	88.43 115	83.49 343	92.98 271	75.28 220	95.83 370	78.97 324	81.15 387	93.79 327

eth_miper_zixun_eth		86.50 304	85.77 297	88.68 330	91.94 335	75.81 365	90.47 371	94.89 264	82.05 308	84.05 326	90.46 361	75.96 209	96.77 312	82.76 259	79.36 413	93.46 347

CHOSEN 280x420		85.15 333	83.99 340	88.65 331	92.47 319	78.40 306	79.68 470	92.76 344	74.90 414	81.41 370	89.59 386	69.85 305	95.51 384	79.92 312	95.29 155	92.03 395

PS-CasMVS		87.32 268	86.88 245	88.63 332	92.99 303	76.33 359	95.33 125	96.61 108	88.22 123	83.30 348	93.07 269	73.03 261	95.79 374	78.36 329	81.00 393	93.75 334

TransMVSNet (Re)		84.43 348	83.06 355	88.54 333	91.72 345	78.44 304	95.18 143	92.82 343	82.73 295	79.67 396	92.12 300	73.49 252	95.96 363	71.10 396	68.73 451	91.21 414

tt0320-xc		79.63 403	76.66 412	88.52 334	91.03 372	78.72 294	93.00 292	89.53 431	66.37 455	76.11 426	87.11 427	46.36 457	95.32 392	72.78 384	67.67 452	91.51 406

EG-PatchMatch MVS		82.37 369	80.34 375	88.46 335	90.27 402	79.35 282	92.80 305	94.33 295	77.14 391	73.26 443	90.18 370	47.47 452	96.72 314	70.25 400	87.32 321	89.30 438

PEN-MVS		86.80 290	86.27 275	88.40 336	92.32 324	75.71 367	95.18 143	96.38 125	87.97 134	82.82 352	93.15 265	73.39 256	95.92 365	76.15 355	79.03 416	93.59 340

Baseline_NR-MVSNet		87.07 281	86.63 259	88.40 336	91.44 353	77.87 323	94.23 212	92.57 349	84.12 257	85.74 270	92.08 304	77.25 191	96.04 357	82.29 267	79.94 406	91.30 412

UBG		85.51 323	84.57 330	88.35 338	94.21 241	71.78 414	90.07 384	89.66 427	82.28 303	85.91 266	89.01 395	61.30 387	97.06 297	76.58 350	92.06 244	96.22 214

D2MVS		85.90 315	85.09 316	88.35 338	90.79 385	77.42 338	91.83 337	95.70 201	80.77 343	80.08 389	90.02 376	66.74 343	96.37 344	81.88 278	87.97 309	91.26 413

pmmvs5		84.21 350	82.84 360	88.34 340	88.95 421	76.94 347	92.41 315	91.91 372	75.63 405	80.28 384	91.18 336	64.59 363	95.57 381	77.09 345	83.47 356	92.53 381

mamv4		90.92 140	91.78 108	88.33 341	95.67 140	70.75 427	92.92 297	96.02 170	81.90 315	88.11 212	95.34 166	85.88 55	96.97 303	95.22 43	95.01 160	97.26 147

tt0320		80.13 396	77.41 405	88.29 342	90.50 398	78.02 316	93.10 286	90.71 404	66.06 458	76.75 419	86.97 428	49.56 447	95.40 389	71.65 388	71.41 441	91.46 409

LCM-MVSNet-Re		88.30 231	88.32 210	88.27 343	94.71 198	72.41 409	93.15 282	90.98 395	87.77 145	79.25 400	91.96 310	78.35 177	95.75 375	83.04 251	95.62 144	96.65 198

CostFormer		85.77 320	84.94 320	88.26 344	91.16 367	72.58 407	89.47 397	91.04 394	76.26 400	86.45 252	89.97 378	70.74 288	96.86 311	82.35 265	87.07 324	95.34 256

ITE_SJBPF				88.24 345	91.88 339	77.05 344		92.92 338	85.54 213	80.13 388	93.30 259	57.29 418	96.20 352	72.46 386	84.71 341	91.49 407

PVSNet	78.82 18	85.55 322	84.65 326	88.23 346	94.72 196	71.93 410	87.12 433	92.75 345	78.80 370	84.95 300	90.53 359	64.43 364	96.71 316	74.74 369	93.86 195	96.06 227

IterMVS-SCA-FT		85.45 324	84.53 331	88.18 347	91.71 346	76.87 348	90.19 381	92.65 348	85.40 222	81.44 369	90.54 358	66.79 341	95.00 398	81.04 292	81.05 389	92.66 378

EPNet_dtu		86.49 306	85.94 290	88.14 348	90.24 403	72.82 399	94.11 218	92.20 360	86.66 184	79.42 399	92.36 291	73.52 251	95.81 372	71.26 391	93.66 200	95.80 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 365	80.93 371	88.06 349	90.05 407	76.37 358	84.74 451	91.96 370	72.28 439	81.32 372	87.87 416	71.03 283	95.50 386	68.97 409	80.15 404	92.32 390

test_vis1_n_1920		89.39 197	89.84 160	88.04 350	92.97 304	72.64 404	94.71 175	96.03 169	86.18 195	91.94 127	96.56 99	61.63 382	95.74 376	93.42 65	95.11 159	95.74 241

DTE-MVSNet		86.11 312	85.48 305	87.98 351	91.65 350	74.92 374	94.93 157	95.75 194	87.36 160	82.26 358	93.04 270	72.85 262	95.82 371	74.04 375	77.46 422	93.20 358

PMMVS		85.71 321	84.96 319	87.95 352	88.90 422	77.09 343	88.68 409	90.06 416	72.32 438	86.47 249	90.76 353	72.15 272	94.40 405	81.78 281	93.49 207	92.36 388

GG-mvs-BLEND				87.94 353	89.73 414	77.91 320	87.80 422	78.23 472		80.58 381	83.86 447	59.88 401	95.33 391	71.20 392	92.22 242	90.60 426

MonoMVSNet		86.89 287	86.55 263	87.92 354	89.46 417	73.75 386	94.12 216	93.10 333	87.82 144	85.10 296	90.76 353	69.59 308	94.94 399	86.47 199	82.50 368	95.07 263

reproduce_monomvs		86.37 309	85.87 292	87.87 355	93.66 277	73.71 387	93.44 267	95.02 250	88.61 110	82.64 355	91.94 311	57.88 415	96.68 317	89.96 141	79.71 410	93.22 356

pmmvs-eth3d		80.97 389	78.72 400	87.74 356	84.99 452	79.97 267	90.11 383	91.65 377	75.36 407	73.51 441	86.03 436	59.45 404	93.96 416	75.17 363	72.21 437	89.29 440

MS-PatchMatch		85.05 335	84.16 335	87.73 357	91.42 356	78.51 302	91.25 353	93.53 324	77.50 386	80.15 386	91.58 325	61.99 379	95.51 384	75.69 358	94.35 182	89.16 442

mmtdpeth		85.04 337	84.15 336	87.72 358	93.11 294	75.74 366	94.37 202	92.83 341	84.98 236	89.31 190	86.41 433	61.61 384	97.14 291	92.63 81	62.11 462	90.29 428

test_0402		81.30 385	79.17 394	87.67 359	93.19 289	78.17 313	92.98 294	91.71 373	75.25 409	76.02 427	90.31 365	59.23 406	96.37 344	50.22 465	83.63 354	88.47 450

IterMVS		84.88 339	83.98 341	87.60 360	91.44 353	76.03 361	90.18 382	92.41 351	83.24 282	81.06 375	90.42 363	66.60 344	94.28 409	79.46 317	80.98 394	92.48 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 383	79.30 390	87.58 361	90.92 380	74.16 384	80.99 463	87.68 441	70.52 446	76.63 421	88.81 399	71.21 280	92.76 432	60.01 451	86.93 325	95.83 237

EPMVS		83.90 357	82.70 361	87.51 362	90.23 404	72.67 402	88.62 410	81.96 462	81.37 333	85.01 299	88.34 407	66.31 349	94.45 402	75.30 362	87.12 322	95.43 251

ADS-MVSNet2		81.66 377	79.71 386	87.50 363	91.35 359	74.19 383	83.33 456	88.48 435	72.90 433	82.24 359	85.77 439	64.98 359	93.20 427	64.57 436	83.74 351	95.12 261

OurMVSNet-221017-0		85.35 328	84.64 328	87.49 364	90.77 387	72.59 406	94.01 231	94.40 292	84.72 246	79.62 398	93.17 264	61.91 380	96.72 314	81.99 275	81.16 385	93.16 360

tpm2		84.08 352	82.94 356	87.48 365	91.39 357	71.27 419	89.23 401	90.37 408	71.95 440	84.64 305	89.33 390	67.30 333	96.55 332	75.17 363	87.09 323	94.63 282

RPSCF		85.07 334	84.27 332	87.48 365	92.91 307	70.62 429	91.69 342	92.46 350	76.20 401	82.67 354	95.22 171	63.94 367	97.29 278	77.51 340	85.80 330	94.53 289

myMVS_eth3d28		85.80 319	85.26 313	87.42 367	94.73 194	69.92 434	90.60 368	90.95 397	87.21 164	86.06 263	90.04 375	59.47 403	96.02 359	74.89 368	93.35 214	96.33 208

FE-MVSNET2		81.82 373	79.99 381	87.34 368	84.74 453	77.36 340	92.72 306	94.55 283	82.09 306	73.79 440	86.46 430	57.80 416	94.45 402	74.65 370	73.10 433	90.20 429

WBMVS		84.97 338	84.18 334	87.34 368	94.14 247	71.62 418	90.20 380	92.35 353	81.61 328	84.06 325	90.76 353	61.82 381	96.52 333	78.93 325	83.81 349	93.89 318

miper_lstm_enhance		85.27 331	84.59 329	87.31 370	91.28 362	74.63 377	87.69 427	94.09 308	81.20 339	81.36 371	89.85 382	74.97 225	94.30 408	81.03 294	79.84 409	93.01 367

FMVSNet5		81.52 381	79.60 387	87.27 371	91.17 365	77.95 318	91.49 346	92.26 359	76.87 393	76.16 423	87.91 415	51.67 441	92.34 435	67.74 418	81.16 385	91.52 405

USDC		82.76 364	81.26 369	87.26 372	91.17 365	74.55 378	89.27 399	93.39 327	78.26 381	75.30 431	92.08 304	54.43 434	96.63 321	71.64 389	85.79 331	90.61 424

test-LLR		85.87 316	85.41 306	87.25 373	90.95 376	71.67 416	89.55 393	89.88 423	83.41 276	84.54 308	87.95 413	67.25 334	95.11 395	81.82 279	93.37 212	94.97 266

test-mter		84.54 347	83.64 345	87.25 373	90.95 376	71.67 416	89.55 393	89.88 423	79.17 361	84.54 308	87.95 413	55.56 424	95.11 395	81.82 279	93.37 212	94.97 266

JIA-IIPM		81.04 386	78.98 398	87.25 373	88.64 423	73.48 391	81.75 462	89.61 429	73.19 430	82.05 362	73.71 466	66.07 354	95.87 368	71.18 394	84.60 342	92.41 386

TDRefinement		79.81 400	77.34 406	87.22 376	79.24 469	75.48 369	93.12 283	92.03 365	76.45 396	75.01 432	91.58 325	49.19 448	96.44 340	70.22 402	69.18 448	89.75 434

tpmvs		83.35 363	82.07 362	87.20 377	91.07 371	71.00 425	88.31 415	91.70 374	78.91 364	80.49 383	87.18 425	69.30 315	97.08 294	68.12 417	83.56 355	93.51 345

ppachtmachnet_test		81.84 372	80.07 380	87.15 378	88.46 427	74.43 381	89.04 405	92.16 361	75.33 408	77.75 412	88.99 396	66.20 351	95.37 390	65.12 433	77.60 420	91.65 401

dmvs_re		84.20 351	83.22 352	87.14 379	91.83 342	77.81 325	90.04 385	90.19 412	84.70 248	81.49 367	89.17 392	64.37 365	91.13 448	71.58 390	85.65 332	92.46 384

tpm cat1		81.96 370	80.27 376	87.01 380	91.09 370	71.02 424	87.38 431	91.53 382	66.25 456	80.17 385	86.35 435	68.22 330	96.15 355	69.16 408	82.29 371	93.86 324

test_fmvs1_n		87.03 283	87.04 243	86.97 381	89.74 413	71.86 411	94.55 183	94.43 289	78.47 375	91.95 126	95.50 157	51.16 443	93.81 417	93.02 73	94.56 175	95.26 257

OpenMVS_ROB	74.94 19	79.51 404	77.03 411	86.93 382	87.00 440	76.23 360	92.33 321	90.74 403	68.93 450	74.52 436	88.23 410	49.58 446	96.62 322	57.64 457	84.29 344	87.94 453

SixPastTwentyTwo		83.91 356	82.90 358	86.92 383	90.99 374	70.67 428	93.48 264	91.99 367	85.54 213	77.62 414	92.11 302	60.59 396	96.87 310	76.05 356	77.75 419	93.20 358

ADS-MVSNet		81.56 379	79.78 383	86.90 384	91.35 359	71.82 412	83.33 456	89.16 433	72.90 433	82.24 359	85.77 439	64.98 359	93.76 418	64.57 436	83.74 351	95.12 261

PatchT		82.68 366	81.27 368	86.89 385	90.09 406	70.94 426	84.06 453	90.15 413	74.91 413	85.63 273	83.57 449	69.37 311	94.87 400	65.19 431	88.50 299	94.84 276

tpm		84.73 342	84.02 339	86.87 386	90.33 401	68.90 437	89.06 404	89.94 420	80.85 342	85.75 269	89.86 381	68.54 327	95.97 362	77.76 336	84.05 348	95.75 240

Patchmatch-RL test		81.67 376	79.96 382	86.81 387	85.42 450	71.23 420	82.17 461	87.50 442	78.47 375	77.19 416	82.50 456	70.81 287	93.48 422	82.66 260	72.89 436	95.71 244

test_vis1_n		86.56 301	86.49 267	86.78 388	88.51 424	72.69 401	94.68 176	93.78 320	79.55 357	90.70 159	95.31 167	48.75 449	93.28 425	93.15 69	93.99 192	94.38 300

testing3-2		86.72 295	86.71 253	86.74 389	96.11 113	65.92 449	93.39 269	89.65 428	89.46 72	87.84 221	92.79 279	59.17 408	97.60 237	81.31 288	90.72 260	96.70 196

test_fmvs1		87.34 266	87.56 229	86.68 390	90.59 393	71.80 413	94.01 231	94.04 309	78.30 379	91.97 124	95.22 171	56.28 422	93.71 419	92.89 74	94.71 168	94.52 290

MDA-MVSNet-bldmvs		78.85 409	76.31 414	86.46 391	89.76 412	73.88 385	88.79 407	90.42 407	79.16 362	59.18 466	88.33 408	60.20 398	94.04 411	62.00 444	68.96 449	91.48 408

mvs5depth		80.98 388	79.15 395	86.45 392	84.57 454	73.29 394	87.79 423	91.67 376	80.52 345	82.20 361	89.72 384	55.14 430	95.93 364	73.93 378	66.83 454	90.12 431

tpmrst		85.35 328	84.99 317	86.43 393	90.88 383	67.88 442	88.71 408	91.43 385	80.13 349	86.08 262	88.80 401	73.05 260	96.02 359	82.48 261	83.40 359	95.40 252

TESTMET0.1,1		83.74 359	82.85 359	86.42 394	89.96 409	71.21 421	89.55 393	87.88 438	77.41 387	83.37 345	87.31 421	56.71 420	93.65 421	80.62 302	92.85 228	94.40 299

our_test_3		81.93 371	80.46 374	86.33 395	88.46 427	73.48 391	88.46 413	91.11 390	76.46 395	76.69 420	88.25 409	66.89 339	94.36 406	68.75 410	79.08 415	91.14 416

lessismore_v0				86.04 396	88.46 427	68.78 438		80.59 465		73.01 444	90.11 373	55.39 426	96.43 341	75.06 365	65.06 457	92.90 370

TinyColmap		79.76 401	77.69 404	85.97 397	91.71 346	73.12 395	89.55 393	90.36 409	75.03 411	72.03 447	90.19 369	46.22 458	96.19 354	63.11 440	81.03 390	88.59 449

KD-MVS_2432*1600		78.50 410	76.02 418	85.93 398	86.22 443	74.47 379	84.80 449	92.33 354	79.29 359	76.98 417	85.92 437	53.81 437	93.97 414	67.39 419	57.42 467	89.36 436

miper_refine_blended		78.50 410	76.02 418	85.93 398	86.22 443	74.47 379	84.80 449	92.33 354	79.29 359	76.98 417	85.92 437	53.81 437	93.97 414	67.39 419	57.42 467	89.36 436

K. test v3		81.59 378	80.15 379	85.91 400	89.89 411	69.42 436	92.57 311	87.71 440	85.56 212	73.44 442	89.71 385	55.58 423	95.52 383	77.17 343	69.76 445	92.78 375

SSC-MVS3.2		84.60 346	84.19 333	85.85 401	92.74 314	68.07 439	88.15 418	93.81 318	87.42 158	83.76 333	91.07 342	62.91 374	95.73 377	74.56 373	83.24 360	93.75 334

mvsany_test1		85.42 326	85.30 311	85.77 402	87.95 436	75.41 370	87.61 430	80.97 464	76.82 394	88.68 203	95.83 139	77.44 190	90.82 450	85.90 208	86.51 326	91.08 420

MIMVSNet1		79.38 405	77.28 407	85.69 403	86.35 442	73.67 388	91.61 344	92.75 345	78.11 384	72.64 445	88.12 411	48.16 450	91.97 441	60.32 448	77.49 421	91.43 410

UWE-MVS		83.69 360	83.09 353	85.48 404	93.06 298	65.27 454	90.92 361	86.14 446	79.90 352	86.26 258	90.72 356	57.17 419	95.81 372	71.03 397	92.62 236	95.35 255

UnsupCasMVSNet_eth		80.07 397	78.27 403	85.46 405	85.24 451	72.63 405	88.45 414	94.87 267	82.99 289	71.64 450	88.07 412	56.34 421	91.75 443	73.48 381	63.36 460	92.01 396

CL-MVSNet_self_test		81.74 375	80.53 372	85.36 406	85.96 445	72.45 408	90.25 375	93.07 335	81.24 337	79.85 395	87.29 422	70.93 285	92.52 433	66.95 422	69.23 447	91.11 418

MDA-MVSNet_test_wron		79.21 407	77.19 409	85.29 407	88.22 431	72.77 400	85.87 440	90.06 416	74.34 418	62.62 463	87.56 419	66.14 352	91.99 440	66.90 426	73.01 434	91.10 419

YYNet1		79.22 406	77.20 408	85.28 408	88.20 432	72.66 403	85.87 440	90.05 418	74.33 419	62.70 461	87.61 418	66.09 353	92.03 437	66.94 423	72.97 435	91.15 415

WB-MVSnew		83.77 358	83.28 349	85.26 409	91.48 352	71.03 423	91.89 334	87.98 437	78.91 364	84.78 302	90.22 367	69.11 320	94.02 412	64.70 435	90.44 263	90.71 422

dp		81.47 382	80.23 377	85.17 410	89.92 410	65.49 452	86.74 435	90.10 415	76.30 399	81.10 373	87.12 426	62.81 375	95.92 365	68.13 416	79.88 407	94.09 311

UnsupCasMVSNet_bld		76.23 420	73.27 424	85.09 411	83.79 456	72.92 397	85.65 443	93.47 326	71.52 441	68.84 456	79.08 461	49.77 445	93.21 426	66.81 427	60.52 464	89.13 444

SD_0403		84.71 344	84.65 326	84.92 412	92.95 305	65.95 448	92.07 333	93.23 330	83.82 265	79.03 401	93.73 249	73.90 245	92.91 431	63.02 442	90.05 270	95.89 233

Anonymous20231206		81.03 387	79.77 385	84.82 413	87.85 437	70.26 431	91.42 347	92.08 363	73.67 425	77.75 412	89.25 391	62.43 377	93.08 428	61.50 446	82.00 376	91.12 417

FE-MVSNET		78.19 412	76.03 417	84.69 414	83.70 457	73.31 393	90.58 369	90.00 419	77.11 392	71.91 448	85.47 441	55.53 425	91.94 442	59.69 452	70.24 443	88.83 446

test0.0.03 1		82.41 368	81.69 364	84.59 415	88.23 430	72.89 398	90.24 377	87.83 439	83.41 276	79.86 394	89.78 383	67.25 334	88.99 460	65.18 432	83.42 358	91.90 398

CMPMVS	59.16 21	80.52 391	79.20 393	84.48 416	83.98 455	67.63 445	89.95 388	93.84 317	64.79 460	66.81 458	91.14 339	57.93 414	95.17 393	76.25 353	88.10 305	90.65 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 345	84.79 324	84.37 417	91.84 340	64.92 455	93.70 256	91.47 384	66.19 457	86.16 261	95.28 168	67.18 336	93.33 424	80.89 297	90.42 265	94.88 275

PVSNet_0	73.20 20	77.22 416	74.83 422	84.37 417	90.70 391	71.10 422	83.09 458	89.67 426	72.81 435	73.93 439	83.13 451	60.79 395	93.70 420	68.54 411	50.84 472	88.30 451

LF4IMVS		80.37 394	79.07 397	84.27 419	86.64 441	69.87 435	89.39 398	91.05 393	76.38 397	74.97 433	90.00 377	47.85 451	94.25 410	74.55 374	80.82 396	88.69 448

Anonymous20240521		80.44 393	79.21 392	84.11 420	85.75 448	67.89 441	92.86 301	93.23 330	75.61 406	75.59 430	87.47 420	50.03 444	94.33 407	71.14 395	81.21 384	90.12 431

PM-MVS		78.11 413	76.12 416	84.09 421	83.54 458	70.08 432	88.97 406	85.27 453	79.93 351	74.73 435	86.43 432	34.70 469	93.48 422	79.43 320	72.06 438	88.72 447

test_fmvs2		83.98 353	84.03 338	83.83 422	87.16 439	67.53 446	93.93 238	92.89 339	77.62 385	86.89 242	93.53 252	47.18 453	92.02 439	90.54 134	86.51 326	91.93 397

testgi		80.94 390	80.20 378	83.18 423	87.96 435	66.29 447	91.28 351	90.70 405	83.70 267	78.12 408	92.84 274	51.37 442	90.82 450	63.34 439	82.46 369	92.43 385

KD-MVS_self_test		80.20 395	79.24 391	83.07 424	85.64 449	65.29 453	91.01 359	93.93 311	78.71 373	76.32 422	86.40 434	59.20 407	92.93 430	72.59 385	69.35 446	91.00 421

testing3		80.46 392	79.59 388	83.06 425	93.44 284	64.64 456	93.33 271	85.47 451	84.34 254	79.93 393	90.84 349	44.35 461	92.39 434	57.06 459	87.56 315	92.16 394

ambc				83.06 425	79.99 467	63.51 460	77.47 471	92.86 340		74.34 438	84.45 446	28.74 470	95.06 397	73.06 383	68.89 450	90.61 424

test20.03		79.95 399	79.08 396	82.55 427	85.79 447	67.74 444	91.09 357	91.08 391	81.23 338	74.48 437	89.96 379	61.63 382	90.15 452	60.08 449	76.38 427	89.76 433

MVStest1		72.91 424	69.70 429	82.54 428	78.14 470	73.05 396	88.21 417	86.21 445	60.69 464	64.70 459	90.53 359	46.44 456	85.70 467	58.78 455	53.62 469	88.87 445

test_vis1_rt		77.96 414	76.46 413	82.48 429	85.89 446	71.74 415	90.25 375	78.89 468	71.03 445	71.30 451	81.35 458	42.49 463	91.05 449	84.55 231	82.37 370	84.65 456

EU-MVSNet		81.32 384	80.95 370	82.42 430	88.50 426	63.67 459	93.32 272	91.33 386	64.02 461	80.57 382	92.83 275	61.21 391	92.27 436	76.34 352	80.38 403	91.32 411

myMVS_eth3d		79.67 402	78.79 399	82.32 431	91.92 336	64.08 457	89.75 391	87.40 443	81.72 323	78.82 403	87.20 423	45.33 459	91.29 446	59.09 454	87.84 312	91.60 403

ttmdpeth		76.55 418	74.64 423	82.29 432	82.25 463	67.81 443	89.76 390	85.69 449	70.35 447	75.76 428	91.69 318	46.88 454	89.77 454	66.16 428	63.23 461	89.30 438

pmmvs3		71.81 427	68.71 430	81.11 433	75.86 472	70.42 430	86.74 435	83.66 457	58.95 467	68.64 457	80.89 459	36.93 467	89.52 456	63.10 441	63.59 459	83.39 457

Syy-MVS		80.07 397	79.78 383	80.94 434	91.92 336	59.93 466	89.75 391	87.40 443	81.72 323	78.82 403	87.20 423	66.29 350	91.29 446	47.06 467	87.84 312	91.60 403

UWE-MVS-28		78.98 408	78.38 402	80.80 435	88.18 433	60.66 465	90.65 366	78.51 469	78.84 368	77.93 411	90.93 346	59.08 409	89.02 459	50.96 464	90.33 267	92.72 376

new-patchmatchnet		76.41 419	75.17 421	80.13 436	82.65 462	59.61 467	87.66 428	91.08 391	78.23 382	69.85 454	83.22 450	54.76 431	91.63 445	64.14 438	64.89 458	89.16 442

mvsany_test3		74.95 421	73.26 425	80.02 437	74.61 473	63.16 461	85.53 444	78.42 470	74.16 420	74.89 434	86.46 430	36.02 468	89.09 458	82.39 264	66.91 453	87.82 454

test_fmvs3		77.67 415	77.16 410	79.22 438	79.52 468	61.14 463	92.34 320	91.64 378	73.98 422	78.86 402	86.59 429	27.38 473	87.03 462	88.12 173	75.97 429	89.50 435

DSMNet-mixed		76.94 417	76.29 415	78.89 439	83.10 460	56.11 475	87.78 424	79.77 466	60.65 465	75.64 429	88.71 402	61.56 385	88.34 461	60.07 450	89.29 288	92.21 393

EGC-MVSNET		61.97 435	56.37 440	78.77 440	89.63 415	73.50 390	89.12 403	82.79 459	0.21 486	1.24 487	84.80 444	39.48 464	90.04 453	44.13 469	75.94 430	72.79 468

new_pmnet		72.15 425	70.13 428	78.20 441	82.95 461	65.68 450	83.91 454	82.40 461	62.94 463	64.47 460	79.82 460	42.85 462	86.26 466	57.41 458	74.44 432	82.65 461

MVS-HIRNet		73.70 423	72.20 426	78.18 442	91.81 343	56.42 474	82.94 459	82.58 460	55.24 468	68.88 455	66.48 471	55.32 428	95.13 394	58.12 456	88.42 301	83.01 459

LCM-MVSNet		66.00 432	62.16 437	77.51 443	64.51 483	58.29 469	83.87 455	90.90 399	48.17 472	54.69 469	73.31 467	16.83 482	86.75 463	65.47 430	61.67 463	87.48 455

APD_test1		69.04 428	66.26 434	77.36 444	80.51 466	62.79 462	85.46 445	83.51 458	54.11 470	59.14 467	84.79 445	23.40 476	89.61 455	55.22 460	70.24 443	79.68 465

test_f		71.95 426	70.87 427	75.21 445	74.21 475	59.37 468	85.07 448	85.82 448	65.25 459	70.42 453	83.13 451	23.62 474	82.93 473	78.32 330	71.94 439	83.33 458

ANet_high		58.88 439	54.22 444	72.86 446	56.50 486	56.67 471	80.75 464	86.00 447	73.09 432	37.39 478	64.63 474	22.17 477	79.49 476	43.51 470	23.96 480	82.43 462

test_vis3_rt		65.12 433	62.60 435	72.69 447	71.44 476	60.71 464	87.17 432	65.55 480	63.80 462	53.22 470	65.65 473	14.54 483	89.44 457	76.65 347	65.38 456	67.91 471

FPMVS		64.63 434	62.55 436	70.88 448	70.80 477	56.71 470	84.42 452	84.42 455	51.78 471	49.57 471	81.61 457	23.49 475	81.48 474	40.61 474	76.25 428	74.46 467

dmvs_testset		74.57 422	75.81 420	70.86 449	87.72 438	40.47 484	87.05 434	77.90 474	82.75 294	71.15 452	85.47 441	67.98 331	84.12 471	45.26 468	76.98 426	88.00 452

N_pmnet		68.89 429	68.44 431	70.23 450	89.07 420	28.79 489	88.06 419	19.50 489	69.47 449	71.86 449	84.93 443	61.24 390	91.75 443	54.70 461	77.15 423	90.15 430

testf1		59.54 437	56.11 441	69.85 451	69.28 478	56.61 472	80.37 465	76.55 477	42.58 475	45.68 474	75.61 462	11.26 484	84.18 469	43.20 471	60.44 465	68.75 469

APD_test2		59.54 437	56.11 441	69.85 451	69.28 478	56.61 472	80.37 465	76.55 477	42.58 475	45.68 474	75.61 462	11.26 484	84.18 469	43.20 471	60.44 465	68.75 469

WB-MVS		67.92 430	67.49 432	69.21 453	81.09 464	41.17 483	88.03 420	78.00 473	73.50 427	62.63 462	83.11 453	63.94 367	86.52 464	25.66 479	51.45 471	79.94 464

PMMVS2		59.60 436	56.40 439	69.21 453	68.83 480	46.58 479	73.02 475	77.48 475	55.07 469	49.21 472	72.95 468	17.43 481	80.04 475	49.32 466	44.33 475	80.99 463

SSC-MVS		67.06 431	66.56 433	68.56 455	80.54 465	40.06 485	87.77 425	77.37 476	72.38 437	61.75 464	82.66 455	63.37 370	86.45 465	24.48 480	48.69 474	79.16 466

Gipuma		57.99 441	54.91 443	67.24 456	88.51 424	65.59 451	52.21 478	90.33 410	43.58 474	42.84 477	51.18 478	20.29 479	85.07 468	34.77 475	70.45 442	51.05 477
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 443	48.46 447	63.48 457	45.72 488	46.20 480	73.41 474	78.31 471	41.03 477	30.06 480	65.68 472	6.05 486	83.43 472	30.04 477	65.86 455	60.80 472
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 440	58.24 438	60.56 458	83.13 459	45.09 482	82.32 460	48.22 488	67.61 453	61.70 465	69.15 469	38.75 465	76.05 477	32.01 476	41.31 476	60.55 473

MVE	39.65 23	43.39 445	38.59 451	57.77 459	56.52 485	48.77 478	55.38 477	58.64 484	29.33 480	28.96 481	52.65 477	4.68 487	64.62 481	28.11 478	33.07 478	59.93 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 444	48.47 446	56.66 460	52.26 487	18.98 491	41.51 480	81.40 463	10.10 481	44.59 476	75.01 465	28.51 471	68.16 478	53.54 462	49.31 473	82.83 460

DeepMVS_CX				56.31 461	74.23 474	51.81 477		56.67 485	44.85 473	48.54 473	75.16 464	27.87 472	58.74 483	40.92 473	52.22 470	58.39 475

kuosan		53.51 442	53.30 445	54.13 462	76.06 471	45.36 481	80.11 467	48.36 487	59.63 466	54.84 468	63.43 475	37.41 466	62.07 482	20.73 482	39.10 477	54.96 476

E-PMN		43.23 446	42.29 448	46.03 463	65.58 482	37.41 486	73.51 473	64.62 481	33.99 478	28.47 482	47.87 479	19.90 480	67.91 479	22.23 481	24.45 479	32.77 478

EMVS		42.07 447	41.12 449	44.92 464	63.45 484	35.56 488	73.65 472	63.48 482	33.05 479	26.88 483	45.45 480	21.27 478	67.14 480	19.80 483	23.02 481	32.06 479

tmp_tt		35.64 448	39.24 450	24.84 465	14.87 489	23.90 490	62.71 476	51.51 486	6.58 483	36.66 479	62.08 476	44.37 460	30.34 485	52.40 463	22.00 482	20.27 480

wuyk23d		21.27 450	20.48 453	23.63 466	68.59 481	36.41 487	49.57 479	6.85 490	9.37 482	7.89 484	4.46 486	4.03 488	31.37 484	17.47 484	16.07 483	3.12 481

test123		8.76 452	11.22 455	1.39 467	0.85 491	0.97 492	85.76 442	0.35 492	0.54 485	2.45 486	8.14 485	0.60 489	0.48 486	2.16 486	0.17 485	2.71 482

testmvs		8.92 451	11.52 454	1.12 468	1.06 490	0.46 493	86.02 439	0.65 491	0.62 484	2.74 485	9.52 484	0.31 490	0.45 487	2.38 485	0.39 484	2.46 483

mmdepth		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

monomultidepth		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

test_blank		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uanet_test		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

DCPMVS		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

cdsmvs_eth3d_5k		22.14 449	29.52 452	0.00 469	0.00 492	0.00 494	0.00 481	95.76 193	0.00 487	0.00 488	94.29 220	75.66 217	0.00 488	0.00 487	0.00 486	0.00 484

pcd_1.5k_mvsjas		6.64 454	8.86 457	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	79.70 154	0.00 488	0.00 487	0.00 486	0.00 484

sosnet-low-res		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

sosnet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uncertanet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

Regformer		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

ab-mvs-re		7.82 453	10.43 456	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	93.88 241	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

uanet		0.00 455	0.00 458	0.00 469	0.00 492	0.00 494	0.00 481	0.00 493	0.00 487	0.00 488	0.00 487	0.00 491	0.00 488	0.00 487	0.00 486	0.00 484

TestfortrainingZip							97.32 10

WAC-MVS						64.08 457								59.14 453

FOURS1					98.86 4	85.54 73	98.29 1	97.49 12	89.79 63	96.29 32

PC_three_1452									82.47 298	97.09 20	97.07 72	92.72 1	98.04 196	92.70 80	99.02 12	98.86 16

test_one_0601					98.58 14	85.83 67		97.44 21	91.05 23	96.78 27	98.06 22	91.45 13

eth-test2					0.00 492

eth-test					0.00 492

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 269	94.19 64	96.91 78	87.57 34	99.26 50	91.99 105	98.44 57

RE-MVS-def			93.68 72		97.92 49	84.57 93	96.28 51	96.76 92	87.46 155	93.75 75	97.43 51	82.94 100		92.73 76	97.80 92	97.88 105

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 336	97.26 13				95.50 36	99.13 3	99.03 8

test_241102_TWO								97.44 21	90.31 41	97.62 8	98.07 20	91.46 12	99.58 14	95.66 30	99.12 6	98.98 10

test_241102_ONE					98.77 8	85.99 55		97.44 21	90.26 47	97.71 2	97.96 31	92.31 6	99.38 35

9.14			94.47 35		97.79 58		96.08 69	97.44 21	86.13 199	95.10 54	97.40 53	88.34 25	99.22 52	93.25 68	98.70 38

save fliter					97.85 55	85.63 72	95.21 140	96.82 85	89.44 73

test_0728_THIRD									90.75 29	97.04 22	98.05 25	92.09 8	99.55 20	95.64 32	99.13 3	99.13 2

test0726					98.78 6	85.93 58	97.19 16	97.47 17	90.27 45	97.64 6	98.13 7	91.47 10

GSMVS																96.12 221

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 276				96.12 221

sam_mvs												70.60 290

MTGPA								96.97 65

test_post1							88.00 421				9.81 483	69.31 314	95.53 382	76.65 347

test_post											10.29 482	70.57 294	95.91 367

patchmatchnet-post											83.76 448	71.53 277	96.48 336

MTMP							96.16 60	60.64 483

gm-plane-assit					89.60 416	68.00 440			77.28 390		88.99 396		97.57 240	79.44 319

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 224	96.78 89	81.61 328	92.77 100	96.20 108	87.71 31	99.12 62

test_8					97.49 69	86.30 46	94.02 230	96.76 92	81.86 319	92.70 104	96.20 108	87.63 32	99.02 72

agg_prior2														90.54 134	98.68 41	98.27 63

agg_prior					97.38 72	85.92 60		96.72 99		92.16 119			98.97 86

test_prior4						85.96 57	94.11 218

test_prior2							94.12 216		87.67 150	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

旧先验2							93.36 270		71.25 443	94.37 60			97.13 292	86.74 195

新几何2							93.11 285

旧先验1					96.79 85	81.81 195		95.67 203			96.81 84	86.69 42			97.66 98	96.97 177

无先验							93.28 278	96.26 139	73.95 423				99.05 66	80.56 303		96.59 200

原ACMM2							92.94 296

test222					96.55 94	81.70 198	92.22 326	95.01 251	68.36 452	90.20 170	96.14 114	80.26 144			97.80 92	96.05 228

testdata2													98.75 115	78.30 331

segment_acmp												87.16 39

testdata1							92.15 328		87.94 136

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 215	82.75 162						74.23 237

plane_prior5								96.22 144					98.12 176	88.15 170	89.99 271	94.63 282

plane_prior4											94.86 191

plane_prior3						82.75 162			90.26 47	86.91 239

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 208

plane_prior						82.73 165	95.21 140		89.66 68						89.88 276

n2								0.00 493

nn								0.00 493

door-mid								85.49 450

test11								96.57 111

door								85.33 452

HQP5-MVS						81.56 200

HQP-NCC					94.17 243		94.39 198		88.81 100	85.43 285

ACMP_Plane					94.17 243		94.39 198		88.81 100	85.43 285

BP-MVS														87.11 192

HQP4-MVS										85.43 285			97.96 209			94.51 292

HQP3-MVS								96.04 167							89.77 280

HQP2-MVS												73.83 248

NP-MVS					94.37 228	82.42 177					93.98 234

MDTV_nov1_ep13_2view						55.91 476	87.62 429		73.32 429	84.59 307		70.33 297		74.65 370		95.50 249

MDTV_nov1_ep13			83.56 346		91.69 348	69.93 433	87.75 426	91.54 381	78.60 374	84.86 301	88.90 398	69.54 309	96.03 358	70.25 400	88.93 293

ACMMP++_ref															87.47 316

ACMMP++															88.01 308

Test By Simon												80.02 146

High-resolution multi-view benchmark