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	94.37 292	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	85.90 200	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	91.37 121	95.55 7	95.63 143	88.73 6	97.07 23	96.77 91	90.84 26	84.02 326	96.62 95	75.95 209	99.34 42	87.77 177	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	93.01 336	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	87.76 145	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	87.13 166	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	92.66 307	96.56 112	83.44 274	91.68 137	95.04 181	86.60 46	98.99 81	85.60 211	97.92 85	96.93 180

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	94.36 203	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	94.60 205	82.33 109	98.62 131	92.40 86	92.86 225	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	86.26 192	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	94.60 205	82.33 109	98.62 131	92.40 86	92.86 225	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		88.31 229	86.13 278	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 480	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	96.07 163	89.77 64	94.12 66	94.87 189	80.56 139	98.66 123	92.42 85	93.10 221	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	85.68 207	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	87.45 156	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	85.15 231	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	90.69 159	95.88 132	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	94.07 223	96.78 89	81.86 318	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	94.06 225	96.66 104	80.09 349	92.77 100	96.63 94	86.62 44	99.04 68	87.40 184	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 120	90.82 136	94.44 49	94.59 208	86.37 42	97.18 17	97.02 62	89.20 84	84.31 321	96.66 90	73.74 249	99.17 56	86.74 194	97.96 83	97.79 114

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	86.90 176	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	94.40 195	93.93 310	89.77 64	94.21 63	95.59 152	87.35 37	98.61 133	92.72 78	96.15 135	97.83 111

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	96.13 114	77.97 179	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		91.04 155		85.08 67	99.01 74		98.13 75	97.86 106

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 288	91.65 17	92.68 105	96.13 114	77.97 179	98.84 105	90.75 131	94.72 167	97.92 100

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	90.15 174	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	89.56 183	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 129
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	94.66 202	82.11 116	98.50 139	92.33 91	92.82 228	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	91.02 130	94.10 64	90.10 404	85.25 79	96.03 76	92.05 363	92.83 5	87.39 232	95.78 143	79.39 161	99.01 74	88.13 171	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	93.06 289	97.13 54	90.74 31	91.84 130	95.09 180	86.32 49	99.21 54	91.22 121	98.45 56	97.65 123
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	91.28 124	93.96 68	98.33 33	85.92 60	94.66 178	96.66 104	82.69 295	90.03 176	95.82 139	82.30 111	99.03 69	84.57 229	96.48 128	96.91 182

HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 204	92.47 113	97.13 69	82.38 107	99.07 64	90.51 136	98.40 58	97.92 100

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 319	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	97.86 106

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	96.91 307	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	93.24 280	97.24 41	88.76 103	91.60 138	95.85 136	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	87.46 154	93.75 75	97.43 51	84.24 82	99.01 74	92.73 76	97.80 92	97.88 104

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	86.96 172	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	86.56 184	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 118	97.16 161

CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 255	90.05 175	95.66 149	87.77 29	99.15 60	89.91 141	98.27 62	98.07 82

GDP-MVS		92.04 106	91.46 118	93.75 78	94.55 214	84.69 90	95.60 117	96.56 112	87.83 142	93.07 91	95.89 131	73.44 253	98.65 125	90.22 139	96.03 137	97.91 102

BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 217	84.39 105	95.90 89	94.30 295	90.39 38	92.67 107	95.94 127	74.46 232	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	89.25 417	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 163	98.98 85	97.22 13	97.24 106	97.74 117

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	84.47 231	95.47 149	97.45 135

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	86.31 190	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 166

QAPM		89.51 186	88.15 213	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 376	83.41 343	96.19 111	73.18 258	99.30 48	77.11 343	96.54 125	96.89 183

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	97.17 154

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	91.30 122	93.39 86	94.72 196	83.36 137	95.45 121	96.37 126	90.33 40	92.17 118	96.03 121	72.32 270	98.75 115	87.94 174	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	96.05 118	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	88.02 131	96.23 33	97.84 36	83.36 93	98.83 108	97.49 8	97.34 105	97.25 147

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 187	95.77 191	90.87 25	92.52 111	96.67 89	84.50 79	99.00 79	91.99 105	94.44 180	97.36 138

Vis-MVSNet		91.75 118	91.23 125	93.29 89	95.32 155	83.78 122	96.14 64	95.98 170	89.89 53	90.45 163	96.58 97	75.09 221	98.31 166	84.75 223	96.90 114	97.78 115
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	95.51 155	84.50 79	98.79 112	94.83 47	98.86 19	97.72 119

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	95.77 144	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	95.90 130	77.85 185	98.17 173	88.90 161	93.38 210	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	97.20 152

VDD-MVS		90.74 144	89.92 158	93.20 94	96.27 104	83.02 155	95.73 103	93.86 314	88.42 116	92.53 110	96.84 81	62.09 377	98.64 128	90.95 127	92.62 235	97.93 99

Elysia		90.12 163	89.10 181	93.18 96	93.16 289	84.05 114	95.22 137	96.27 135	85.16 229	90.59 160	94.68 198	64.64 360	98.37 156	86.38 200	95.77 140	97.12 163

StellarMVS		90.12 163	89.10 181	93.18 96	93.16 289	84.05 114	95.22 137	96.27 135	85.16 229	90.59 160	94.68 198	64.64 360	98.37 156	86.38 200	95.77 140	97.12 163

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		91.08 138	90.39 142	93.17 98	93.07 296	86.91 23	96.41 42	96.26 139	88.30 119	88.37 208	94.85 192	82.19 115	97.64 233	91.09 122	82.95 360	94.96 268

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 252	94.09 67	95.56 154	85.01 72	98.69 122	94.96 45	98.66 45	97.67 122

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 195	95.74 194	90.71 33	92.05 121	96.60 96	84.00 84	98.99 81	91.55 117	93.63 200	97.17 154

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 275	84.26 108	95.83 95	96.14 154	89.00 96	92.43 114	97.50 48	83.37 92	98.72 119	96.61 24	97.44 101	96.32 208

新几何1				93.10 102	97.30 75	84.35 107		95.56 211	71.09 443	91.26 147	96.24 106	82.87 102	98.86 101	79.19 322	98.10 76	96.07 224

OMC-MVS		91.23 130	90.62 141	93.08 104	96.27 104	84.07 112	93.52 262	95.93 176	86.95 173	89.51 184	96.13 114	78.50 173	98.35 160	85.84 209	92.90 224	96.83 190

OpenMVS	83.78 11	88.74 216	87.29 235	93.08 104	92.70 314	85.39 77	96.57 40	96.43 120	78.74 371	80.85 375	96.07 117	69.64 306	99.01 74	78.01 334	96.65 123	94.83 276

MAR-MVS		90.30 159	89.37 174	93.07 106	96.61 90	84.48 98	95.68 106	95.67 202	82.36 300	87.85 219	92.85 272	76.63 198	98.80 110	80.01 309	96.68 122	95.91 230
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		90.92 139	90.21 146	93.03 107	93.86 260	83.88 119	92.81 301	93.86 314	79.84 352	91.76 134	94.29 219	77.92 182	98.04 196	90.48 137	97.11 107	97.17 154

Effi-MVS+		91.59 124	91.11 127	93.01 108	94.35 232	83.39 136	94.60 180	95.10 246	87.10 167	90.57 162	93.10 267	81.43 130	98.07 190	89.29 153	94.48 178	97.59 128

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 160	87.27 160	95.98 40	98.05 25	83.07 99	98.45 149	96.68 23	95.51 146	96.88 184

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 286	96.09 161	88.20 124	91.12 152	95.72 147	81.33 131	97.76 222	91.74 113	97.37 103	96.75 192

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 317	83.62 128	96.02 77	95.72 198	86.78 178	96.04 38	98.19 4	82.30 111	98.43 153	96.38 25	95.42 152	96.86 185

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	91.19 333	84.06 83	98.34 161	91.72 114	96.54 125	96.54 203

LFMVS		90.08 166	89.13 180	92.95 113	96.71 86	82.32 182	96.08 69	89.91 420	86.79 177	92.15 120	96.81 84	62.60 375	98.34 161	87.18 188	93.90 194	98.19 71

UGNet		89.95 173	88.95 189	92.95 113	94.51 216	83.31 138	95.70 105	95.23 239	89.37 76	87.58 226	93.94 235	64.00 365	98.78 113	83.92 238	96.31 131	96.74 193
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		90.80 142	90.10 150	92.90 115	93.04 299	83.53 131	93.08 286	94.15 303	80.22 346	91.41 144	94.91 186	76.87 192	97.93 212	90.28 138	96.90 114	97.24 148
jason: jason.
DP-MVS		87.25 270	85.36 308	92.90 115	97.65 64	83.24 140	94.81 167	92.00 365	74.99 411	81.92 364	95.00 182	72.66 263	99.05 66	66.92 424	92.33 240	96.40 205

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	96.12 158	88.13 127	95.82 43	98.04 28	83.43 89	98.48 141	96.97 21	96.23 132	96.92 181

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 267	83.13 146	96.02 77	95.74 194	87.68 148	95.89 41	98.17 5	82.78 103	98.46 145	96.71 22	96.17 134	96.98 175

CANet_DTU		90.26 161	89.41 173	92.81 120	93.46 282	83.01 156	93.48 263	94.47 287	89.43 74	87.76 224	94.23 224	70.54 294	99.03 69	84.97 218	96.39 129	96.38 206

MVSFormer		91.68 122	91.30 122	92.80 121	93.86 260	83.88 119	95.96 83	95.90 180	84.66 248	91.76 134	94.91 186	77.92 182	97.30 274	89.64 149	97.11 107	97.24 148

PVSNet_Blended_VisFu		91.38 127	90.91 133	92.80 121	96.39 101	83.17 144	94.87 161	96.66 104	83.29 279	89.27 190	94.46 214	80.29 142	99.17 56	87.57 181	95.37 153	96.05 227

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	97.83 111

LuminaMVS		90.55 155	89.81 160	92.77 123	92.78 312	84.21 109	94.09 221	94.17 302	85.82 201	91.54 139	94.14 226	69.93 300	97.92 213	91.62 116	94.21 188	96.18 216

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	97.43 137

VDDNet		89.56 185	88.49 204	92.76 125	95.07 169	82.09 186	96.30 47	93.19 331	81.05 340	91.88 128	96.86 80	61.16 393	98.33 163	88.43 168	92.49 239	97.84 110

viewdifsd2359ckpt09		91.18 133	90.65 140	92.75 127	94.61 207	82.36 181	94.32 204	95.74 194	84.72 245	89.66 182	95.15 178	79.69 156	98.04 196	87.70 178	94.27 187	97.85 109

h-mvs33		90.80 142	90.15 149	92.75 127	96.01 120	82.66 169	95.43 122	95.53 215	89.80 60	93.08 89	95.64 150	75.77 210	99.00 79	92.07 100	78.05 417	96.60 198

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	98.01 199	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		90.69 147	90.02 156	92.71 130	95.72 136	82.41 179	94.11 217	95.12 244	85.63 208	91.49 141	94.70 196	74.75 225	98.42 154	86.13 204	92.53 237	97.31 139

DCV-MVSNet		90.69 147	90.02 156	92.71 130	95.72 136	82.41 179	94.11 217	95.12 244	85.63 208	91.49 141	94.70 196	74.75 225	98.42 154	86.13 204	92.53 237	97.31 139

PCF-MVS	84.11 10	87.74 244	86.08 282	92.70 132	94.02 249	84.43 102	89.27 398	95.87 185	73.62 425	84.43 313	94.33 216	78.48 175	98.86 101	70.27 398	94.45 179	94.81 277
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	97.17 154

SSM_0404		90.73 145	90.08 151	92.69 133	95.00 174	83.13 146	94.32 204	95.00 254	85.41 219	89.84 177	95.35 163	76.13 201	97.98 204	85.46 214	94.18 189	96.95 177

baseline		92.39 104	92.29 102	92.69 133	94.46 222	81.77 197	94.14 214	96.27 135	89.22 83	91.88 128	96.00 122	82.35 108	97.99 201	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	97.76 222	92.19 96	98.66 45	96.76 191

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	95.80 140	83.16 95	98.16 174	93.68 59	98.14 74	97.31 139

ab-mvs		89.41 193	88.35 206	92.60 138	95.15 167	82.65 173	92.20 326	95.60 209	83.97 259	88.55 204	93.70 249	74.16 240	98.21 172	82.46 262	89.37 284	96.94 179

LS3D		87.89 239	86.32 271	92.59 139	96.07 117	82.92 159	95.23 135	94.92 262	75.66 403	82.89 350	95.98 124	72.48 267	99.21 54	68.43 412	95.23 158	95.64 244

Anonymous20240529		88.09 235	86.59 260	92.58 140	96.53 96	81.92 193	95.99 79	95.84 187	74.11 420	89.06 194	95.21 173	61.44 385	98.81 109	83.67 245	87.47 315	97.01 173

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	97.78 115

CPTT-MVS		91.99 107	91.80 107	92.55 142	98.24 37	81.98 190	96.76 35	96.49 118	81.89 317	90.24 167	96.44 102	78.59 171	98.61 133	89.68 147	97.85 89	97.06 167

viewdifsd2359ckpt13		91.20 132	90.75 138	92.54 143	94.30 234	82.13 185	94.03 227	95.89 182	85.60 210	90.20 169	95.36 162	79.69 156	97.90 216	87.85 176	93.86 195	97.61 125

114514_t		89.51 186	88.50 202	92.54 143	98.11 42	81.99 189	95.16 145	96.36 127	70.19 447	85.81 266	95.25 169	76.70 196	98.63 130	82.07 272	96.86 117	97.00 174

PAPM_NR		91.22 131	90.78 137	92.52 145	97.60 65	81.46 206	94.37 201	96.24 142	86.39 189	87.41 229	94.80 194	82.06 119	98.48 141	82.80 257	95.37 153	97.61 125

mamba_0408		89.06 206	87.92 220	92.50 146	94.76 190	82.66 169	79.84 467	94.64 280	85.18 224	88.96 196	95.00 182	76.00 206	97.98 204	83.74 242	93.15 218	96.85 186

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 147	96.52 97	80.00 264	94.00 232	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		90.47 157	89.80 161	92.46 148	94.76 190	82.66 169	93.98 234	95.00 254	85.41 219	88.96 196	95.35 163	76.13 201	97.88 217	85.46 214	93.15 218	96.85 186

IS-MVSNet		91.43 126	91.09 129	92.46 148	95.87 131	81.38 209	96.95 24	93.69 322	89.72 66	89.50 186	95.98 124	78.57 172	97.77 221	83.02 251	96.50 127	98.22 70

API-MVS		90.66 150	90.07 152	92.45 150	96.36 102	84.57 93	96.06 73	95.22 241	82.39 298	89.13 191	94.27 222	80.32 141	98.46 145	80.16 308	96.71 121	94.33 300

xiu_mvs_v1_base_debu		90.64 151	90.05 153	92.40 151	93.97 255	84.46 99	93.32 271	95.46 219	85.17 226	92.25 115	94.03 227	70.59 290	98.57 136	90.97 124	94.67 169	94.18 303

xiu_mvs_v1_base		90.64 151	90.05 153	92.40 151	93.97 255	84.46 99	93.32 271	95.46 219	85.17 226	92.25 115	94.03 227	70.59 290	98.57 136	90.97 124	94.67 169	94.18 303

xiu_mvs_v1_base_debi		90.64 151	90.05 153	92.40 151	93.97 255	84.46 99	93.32 271	95.46 219	85.17 226	92.25 115	94.03 227	70.59 290	98.57 136	90.97 124	94.67 169	94.18 303

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	77.47 188	98.96 88	97.79 6	96.58 124	97.03 170

viewmacassd2359aftdt		91.67 123	91.43 120	92.37 155	93.95 258	81.00 224	93.90 242	95.97 173	87.75 146	91.45 143	96.04 120	79.92 148	97.97 206	89.26 154	94.67 169	98.14 76

viewmanbaseed2359cas		91.78 114	91.58 114	92.37 155	94.32 233	81.07 221	93.76 248	95.96 174	87.26 161	91.50 140	95.88 132	80.92 137	97.97 206	89.70 146	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	95.89 182	90.30 43	96.74 29	98.02 30	76.14 200	98.95 90	97.64 7	96.21 133	97.03 170

AdaColmap		89.89 176	89.07 183	92.37 155	97.41 71	83.03 154	94.42 194	95.92 177	82.81 292	86.34 255	94.65 203	73.89 245	99.02 72	80.69 299	95.51 146	95.05 263

CNLPA		89.07 205	87.98 217	92.34 159	96.87 83	84.78 88	94.08 222	93.24 328	81.41 331	84.46 311	95.13 179	75.57 217	96.62 321	77.21 341	93.84 197	95.61 247

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	97.13 162

ET-MVSNet_ETH3D		87.51 258	85.91 290	92.32 161	93.70 274	83.93 117	92.33 320	90.94 397	84.16 254	72.09 445	92.52 285	69.90 301	95.85 368	89.20 155	88.36 302	97.17 154

E4		91.74 119	91.55 117	92.31 162	94.27 236	80.80 237	93.81 245	96.17 151	87.97 133	91.11 153	96.05 118	80.75 138	98.08 188	89.78 142	94.02 191	98.06 87

E2		91.79 111	91.61 112	92.31 162	94.49 218	80.86 233	93.74 250	96.19 148	87.63 151	91.16 148	95.94 127	81.31 132	98.06 191	89.76 143	94.29 185	97.99 92

Anonymous202405211		87.68 245	86.13 278	92.31 162	96.66 88	80.74 239	94.87 161	91.49 382	80.47 345	89.46 187	95.44 158	54.72 431	98.23 169	82.19 268	89.89 274	97.97 94

E3		91.78 114	91.61 112	92.30 165	94.48 219	80.86 233	93.73 251	96.19 148	87.63 151	91.16 148	95.95 126	81.30 133	98.06 191	89.76 143	94.29 185	97.99 92

CHOSEN 1792x2688		88.84 212	87.69 225	92.30 165	96.14 108	81.42 208	90.01 385	95.86 186	74.52 416	87.41 229	93.94 235	75.46 218	98.36 158	80.36 304	95.53 145	97.12 163

viewcassd2359sk11		91.79 111	91.62 111	92.29 167	94.62 204	80.88 231	93.70 255	96.18 150	87.38 158	91.13 151	95.85 136	81.62 128	98.06 191	89.71 145	94.40 181	97.94 96

HY-MVS	83.01 12	89.03 208	87.94 219	92.29 167	94.86 185	82.77 161	92.08 331	94.49 286	81.52 330	86.93 236	92.79 278	78.32 177	98.23 169	79.93 310	90.55 261	95.88 233

CDS-MVSNet		89.45 189	88.51 201	92.29 167	93.62 277	83.61 130	93.01 290	94.68 278	81.95 311	87.82 222	93.24 261	78.69 169	96.99 301	80.34 305	93.23 215	96.28 211
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 169	89.27 179	92.29 167	95.78 133	80.95 227	92.68 306	96.22 144	81.91 313	86.66 246	93.75 247	82.23 113	98.44 151	79.40 321	94.79 166	97.48 133

E3new		91.76 117	91.58 114	92.28 171	94.69 201	80.90 230	93.68 258	96.17 151	87.15 164	91.09 154	95.70 148	81.75 127	98.05 195	89.67 148	94.35 182	97.90 103

mvsmamba		90.33 158	89.69 164	92.25 172	95.17 164	81.64 199	95.27 133	93.36 327	84.88 238	89.51 184	94.27 222	69.29 315	97.42 259	89.34 152	96.12 136	97.68 121

PLC	84.53 7	89.06 206	88.03 215	92.15 173	97.27 77	82.69 168	94.29 206	95.44 224	79.71 354	84.01 327	94.18 225	76.68 197	98.75 115	77.28 340	93.41 209	95.02 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 121	91.56 116	92.13 174	95.88 129	80.50 246	97.33 8	95.25 238	86.15 195	89.76 181	95.60 151	83.42 91	98.32 165	87.37 186	93.25 214	97.56 130

patch_mono-2		93.74 65	94.32 41	92.01 175	97.54 66	78.37 306	93.40 267	97.19 44	88.02 131	94.99 56	97.21 62	88.35 24	98.44 151	94.07 55	98.09 77	99.23 1

原ACMM1				92.01 175	97.34 73	81.05 222		96.81 87	78.89 365	90.45 163	95.92 129	82.65 104	98.84 105	80.68 300	98.26 63	96.14 218

UniMVSNet (Re)		89.80 179	89.07 183	92.01 175	93.60 278	84.52 96	94.78 169	97.47 17	89.26 82	86.44 252	92.32 291	82.10 117	97.39 270	84.81 222	80.84 394	94.12 307

MG-MVS		91.77 116	91.70 110	92.00 178	97.08 80	80.03 262	93.60 260	95.18 242	87.85 141	90.89 157	96.47 101	82.06 119	98.36 158	85.07 217	97.04 110	97.62 124

EIA-MVS		91.95 108	91.94 105	91.98 179	95.16 165	80.01 263	95.36 123	96.73 97	88.44 114	89.34 188	92.16 296	83.82 87	98.45 149	89.35 151	97.06 109	97.48 133

PVSNet_Blended		90.73 145	90.32 144	91.98 179	96.12 110	81.25 212	92.55 311	96.83 83	82.04 309	89.10 192	92.56 284	81.04 135	98.85 103	86.72 196	95.91 138	95.84 235

guyue		91.12 136	90.84 135	91.96 181	94.59 208	80.57 244	94.87 161	93.71 321	88.96 97	91.14 150	95.22 170	73.22 257	97.76 222	92.01 104	93.81 198	97.54 132

PS-MVSNAJ		91.18 133	90.92 132	91.96 181	95.26 160	82.60 175	92.09 330	95.70 200	86.27 191	91.84 130	92.46 286	79.70 153	98.99 81	89.08 156	95.86 139	94.29 301

TAMVS		89.21 199	88.29 210	91.96 181	93.71 272	82.62 174	93.30 275	94.19 300	82.22 303	87.78 223	93.94 235	78.83 166	96.95 304	77.70 336	92.98 223	96.32 208

SDMVSNet		90.19 162	89.61 167	91.93 184	96.00 121	83.09 151	92.89 298	95.98 170	88.73 104	86.85 242	95.20 174	72.09 273	97.08 293	88.90 161	89.85 276	95.63 245

FA-MVS(test-final)		89.66 181	88.91 191	91.93 184	94.57 212	80.27 250	91.36 347	94.74 275	84.87 239	89.82 178	92.61 283	74.72 228	98.47 144	83.97 237	93.53 204	97.04 169

MVS_Test		91.31 129	91.11 127	91.93 184	94.37 228	80.14 255	93.46 265	95.80 189	86.46 187	91.35 146	93.77 245	82.21 114	98.09 186	87.57 181	94.95 162	97.55 131

NR-MVSNet		88.58 222	87.47 231	91.93 184	93.04 299	84.16 111	94.77 170	96.25 141	89.05 89	80.04 389	93.29 259	79.02 165	97.05 298	81.71 283	80.05 404	94.59 284

HyFIR lowres test		88.09 235	86.81 248	91.93 184	96.00 121	80.63 241	90.01 385	95.79 190	73.42 427	87.68 225	92.10 302	73.86 246	97.96 208	80.75 298	91.70 244	97.19 153

GeoE		90.05 167	89.43 172	91.90 189	95.16 165	80.37 249	95.80 96	94.65 279	83.90 260	87.55 228	94.75 195	78.18 178	97.62 235	81.28 288	93.63 200	97.71 120

thisisatest0530		88.67 217	87.61 227	91.86 190	94.87 184	80.07 258	94.63 179	89.90 421	84.00 258	88.46 206	93.78 244	66.88 339	98.46 145	83.30 247	92.65 230	97.06 167

xiu_mvs_v2_base		91.13 135	90.89 134	91.86 190	94.97 176	82.42 177	92.24 323	95.64 207	86.11 199	91.74 136	93.14 265	79.67 158	98.89 97	89.06 157	95.46 150	94.28 302

DU-MVS		89.34 198	88.50 202	91.85 192	93.04 299	83.72 123	94.47 190	96.59 109	89.50 71	86.46 249	93.29 259	77.25 190	97.23 283	84.92 219	81.02 390	94.59 284

AstraMVS		90.69 147	90.30 145	91.84 193	93.81 263	79.85 269	94.76 171	92.39 351	88.96 97	91.01 156	95.87 135	70.69 288	97.94 211	92.49 82	92.70 229	97.73 118

OPM-MVS		90.12 163	89.56 168	91.82 194	93.14 291	83.90 118	94.16 213	95.74 194	88.96 97	87.86 218	95.43 160	72.48 267	97.91 214	88.10 173	90.18 268	93.65 338
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 154	90.19 147	91.82 194	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 238	94.86 190	74.23 236	98.12 176	88.15 169	89.99 270	94.63 281

UniMVSNet_NR-MVSNet		89.92 175	89.29 177	91.81 196	93.39 284	83.72 123	94.43 193	97.12 55	89.80 60	86.46 249	93.32 256	83.16 95	97.23 283	84.92 219	81.02 390	94.49 294

diffmvs		91.37 128	91.23 125	91.77 197	93.09 294	80.27 250	92.36 317	95.52 216	87.03 169	91.40 145	94.93 185	80.08 145	97.44 257	92.13 99	94.56 175	97.61 125
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		91.51 125	91.44 119	91.73 198	93.09 294	80.27 250	92.51 312	95.58 210	87.22 162	91.80 133	95.57 153	79.96 147	97.48 249	92.23 93	94.97 161	97.45 135

1112_ss		88.42 224	87.33 234	91.72 199	94.92 180	80.98 225	92.97 294	94.54 283	78.16 382	83.82 330	93.88 240	78.78 168	97.91 214	79.45 317	89.41 283	96.26 212

Fast-Effi-MVS+		89.41 193	88.64 197	91.71 200	94.74 193	80.81 236	93.54 261	95.10 246	83.11 283	86.82 244	90.67 356	79.74 152	97.75 226	80.51 303	93.55 202	96.57 201

WTY-MVS		89.60 183	88.92 190	91.67 201	95.47 151	81.15 217	92.38 316	94.78 273	83.11 283	89.06 194	94.32 217	78.67 170	96.61 324	81.57 284	90.89 257	97.24 148

TAPA-MVS	84.62 6	88.16 233	87.01 243	91.62 202	96.64 89	80.65 240	94.39 197	96.21 147	76.38 396	86.19 259	95.44 158	79.75 151	98.08 188	62.75 442	95.29 155	96.13 219
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 182	88.96 188	91.60 203	93.86 260	82.89 160	95.46 120	97.33 32	87.91 136	88.43 207	93.31 257	74.17 239	97.40 267	87.32 187	82.86 365	94.52 289

FE-MVS		87.40 263	86.02 284	91.57 204	94.56 213	79.69 273	90.27 372	93.72 320	80.57 343	88.80 200	91.62 322	65.32 355	98.59 135	74.97 366	94.33 184	96.44 204

XVG-OURS		89.40 195	88.70 196	91.52 205	94.06 247	81.46 206	91.27 351	96.07 163	86.14 196	88.89 199	95.77 144	68.73 324	97.26 280	87.39 185	89.96 272	95.83 236

hse-mvs2		89.88 177	89.34 175	91.51 206	94.83 187	81.12 219	93.94 236	93.91 313	89.80 60	93.08 89	93.60 250	75.77 210	97.66 230	92.07 100	77.07 424	95.74 240

TranMVSNet+NR-MVSNet		88.84 212	87.95 218	91.49 207	92.68 315	83.01 156	94.92 158	96.31 130	89.88 54	85.53 275	93.85 242	76.63 198	96.96 303	81.91 276	79.87 407	94.50 292

AUN-MVS		87.78 243	86.54 263	91.48 208	94.82 188	81.05 222	93.91 240	93.93 310	83.00 287	86.93 236	93.53 251	69.50 309	97.67 228	86.14 202	77.12 423	95.73 242

XVG-OURS-SEG-HR		89.95 173	89.45 170	91.47 209	94.00 253	81.21 215	91.87 335	96.06 165	85.78 203	88.55 204	95.73 146	74.67 229	97.27 278	88.71 165	89.64 281	95.91 230

MVS		87.44 261	86.10 281	91.44 210	92.61 316	83.62 128	92.63 308	95.66 204	67.26 453	81.47 367	92.15 297	77.95 181	98.22 171	79.71 312	95.48 148	92.47 382

viewdifsd2359ckpt07		91.11 137	91.02 130	91.41 211	94.21 240	78.37 306	92.91 297	95.71 199	87.50 153	90.32 166	95.88 132	80.27 143	97.99 201	88.78 164	93.55 202	97.86 106

F-COLMAP		87.95 238	86.80 249	91.40 212	96.35 103	80.88 231	94.73 173	95.45 222	79.65 355	82.04 362	94.61 204	71.13 280	98.50 139	76.24 353	91.05 255	94.80 278

dcpmvs_2		93.49 70	94.19 52	91.38 213	97.69 63	76.78 349	94.25 208	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		87.33 266	85.99 285	91.37 214	93.49 280	79.55 274	90.63 366	89.56 429	80.17 347	87.56 227	90.86 346	67.07 336	98.28 167	81.50 285	93.02 222	96.29 210

HQP-MVS		89.80 179	89.28 178	91.34 215	94.17 242	81.56 200	94.39 197	96.04 166	88.81 100	85.43 284	93.97 234	73.83 247	97.96 208	87.11 191	89.77 279	94.50 292

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 216	94.42 226	79.48 276	94.52 185	97.14 53	89.33 78	94.17 65	98.09 18	81.83 124	97.49 248	96.33 26	98.02 81	96.95 177

RRT-MVS		90.85 141	90.70 139	91.30 217	94.25 237	76.83 348	94.85 164	96.13 157	89.04 90	90.23 168	94.88 188	70.15 299	98.72 119	91.86 112	94.88 164	98.34 48

FMVSNet3		87.40 263	86.11 280	91.30 217	93.79 266	83.64 127	94.20 212	94.81 271	83.89 261	84.37 314	91.87 313	68.45 327	96.56 329	78.23 331	85.36 333	93.70 337

FMVSNet2		87.19 276	85.82 293	91.30 217	94.01 250	83.67 125	94.79 168	94.94 257	83.57 269	83.88 329	92.05 306	66.59 344	96.51 333	77.56 338	85.01 336	93.73 335

RPMNet		83.95 354	81.53 365	91.21 220	90.58 393	79.34 282	85.24 445	96.76 92	71.44 441	85.55 273	82.97 453	70.87 285	98.91 96	61.01 446	89.36 285	95.40 251

IB-MVS	80.51 15	85.24 331	83.26 349	91.19 221	92.13 328	79.86 268	91.75 338	91.29 387	83.28 280	80.66 379	88.49 404	61.28 387	98.46 145	80.99 294	79.46 411	95.25 257
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		89.47 188	88.90 192	91.18 222	94.22 239	82.07 187	92.13 328	96.09 161	87.90 137	85.37 290	92.45 287	74.38 234	97.56 240	87.15 189	90.43 263	93.93 316
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		89.45 189	88.90 192	91.12 223	94.47 220	81.49 204	95.30 128	96.14 154	86.73 180	85.45 281	95.16 176	69.89 302	98.10 178	87.70 178	89.23 288	93.77 331

LGP-MVS_train				91.12 223	94.47 220	81.49 204		96.14 154	86.73 180	85.45 281	95.16 176	69.89 302	98.10 178	87.70 178	89.23 288	93.77 331

ACMM	84.12 9	89.14 201	88.48 205	91.12 223	94.65 203	81.22 214	95.31 126	96.12 158	85.31 223	85.92 264	94.34 215	70.19 298	98.06 191	85.65 210	88.86 293	94.08 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 219	87.78 224	91.11 226	94.96 177	77.81 324	95.35 124	89.69 424	85.09 233	88.05 216	94.59 207	66.93 337	98.48 141	83.27 248	92.13 242	97.03 170

GBi-Net		87.26 268	85.98 286	91.08 227	94.01 250	83.10 148	95.14 146	94.94 257	83.57 269	84.37 314	91.64 318	66.59 344	96.34 346	78.23 331	85.36 333	93.79 326

test1		87.26 268	85.98 286	91.08 227	94.01 250	83.10 148	95.14 146	94.94 257	83.57 269	84.37 314	91.64 318	66.59 344	96.34 346	78.23 331	85.36 333	93.79 326

FMVSNet1		85.85 316	84.11 336	91.08 227	92.81 310	83.10 148	95.14 146	94.94 257	81.64 325	82.68 352	91.64 318	59.01 409	96.34 346	75.37 360	83.78 349	93.79 326

Test_1112_low_res		87.65 247	86.51 264	91.08 227	94.94 179	79.28 286	91.77 337	94.30 295	76.04 401	83.51 340	92.37 289	77.86 184	97.73 227	78.69 326	89.13 290	96.22 213

PS-MVSNAJss		89.97 171	89.62 166	91.02 231	91.90 337	80.85 235	95.26 134	95.98 170	86.26 192	86.21 258	94.29 219	79.70 153	97.65 231	88.87 163	88.10 304	94.57 286

BH-RMVSNet		88.37 227	87.48 230	91.02 231	95.28 157	79.45 278	92.89 298	93.07 334	85.45 218	86.91 238	94.84 193	70.35 295	97.76 222	73.97 375	94.59 174	95.85 234

UniMVSNet_ETH3D		87.53 257	86.37 268	91.00 233	92.44 320	78.96 291	94.74 172	95.61 208	84.07 257	85.36 291	94.52 209	59.78 401	97.34 272	82.93 252	87.88 309	96.71 194

FIs		90.51 156	90.35 143	90.99 234	93.99 254	80.98 225	95.73 103	97.54 10	89.15 86	86.72 245	94.68 198	81.83 124	97.24 282	85.18 216	88.31 303	94.76 279

ACMP	84.23 8	89.01 210	88.35 206	90.99 234	94.73 194	81.27 211	95.07 149	95.89 182	86.48 185	83.67 335	94.30 218	69.33 311	97.99 201	87.10 193	88.55 295	93.72 336
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 299	85.13 314	90.98 236	96.52 97	81.50 202	96.14 64	96.16 153	73.78 423	83.65 336	92.15 297	63.26 371	97.37 271	82.82 256	81.74 379	94.06 312

IMVS_0403		89.97 171	89.64 165	90.96 237	93.72 268	77.75 329	93.00 291	95.34 233	85.53 214	88.77 201	94.49 210	78.49 174	97.84 218	84.75 223	92.65 230	97.28 142

sss		88.93 211	88.26 212	90.94 238	94.05 248	80.78 238	91.71 339	95.38 228	81.55 329	88.63 203	93.91 239	75.04 222	95.47 387	82.47 261	91.61 245	96.57 201

IMVS_0407		89.85 178	89.51 169	90.88 239	93.72 268	77.75 329	93.07 288	95.34 233	85.53 214	88.34 209	94.49 210	77.69 186	97.60 236	84.75 223	92.65 230	97.28 142

viewmambaseed2359dif		90.04 168	89.78 162	90.83 240	92.85 309	77.92 318	92.23 324	95.01 250	81.90 314	90.20 169	95.45 157	79.64 160	97.34 272	87.52 183	93.17 216	97.23 151

sd_testset		88.59 221	87.85 223	90.83 240	96.00 121	80.42 248	92.35 318	94.71 276	88.73 104	86.85 242	95.20 174	67.31 331	96.43 340	79.64 314	89.85 276	95.63 245

PVSNet_BlendedMVS		89.98 170	89.70 163	90.82 242	96.12 110	81.25 212	93.92 238	96.83 83	83.49 273	89.10 192	92.26 294	81.04 135	98.85 103	86.72 196	87.86 310	92.35 388

cascas		86.43 307	84.98 317	90.80 243	92.10 330	80.92 229	90.24 376	95.91 179	73.10 430	83.57 339	88.39 405	65.15 357	97.46 253	84.90 221	91.43 247	94.03 314

ECVR-MVS		89.09 204	88.53 200	90.77 244	95.62 144	75.89 362	96.16 60	84.22 455	87.89 139	90.20 169	96.65 91	63.19 372	98.10 178	85.90 207	96.94 112	98.33 50

GA-MVS		86.61 297	85.27 311	90.66 245	91.33 360	78.71 295	90.40 371	93.81 317	85.34 222	85.12 294	89.57 386	61.25 388	97.11 292	80.99 294	89.59 282	96.15 217

thres600view7		87.65 247	86.67 255	90.59 246	96.08 116	78.72 293	94.88 160	91.58 378	87.06 168	88.08 214	92.30 292	68.91 321	98.10 178	70.05 405	91.10 250	94.96 268

thres400		87.62 252	86.64 256	90.57 247	95.99 124	78.64 296	94.58 181	91.98 367	86.94 174	88.09 212	91.77 314	69.18 317	98.10 178	70.13 402	91.10 250	94.96 268

baseline1		88.10 234	87.28 236	90.57 247	94.96 177	80.07 258	94.27 207	91.29 387	86.74 179	87.41 229	94.00 232	76.77 195	96.20 351	80.77 297	79.31 413	95.44 249

viewdifsd2359ckpt11		89.43 191	89.05 185	90.56 249	92.89 307	77.00 344	92.81 301	94.52 284	87.03 169	89.77 179	95.79 141	74.67 229	97.51 244	88.97 159	84.98 337	97.17 154

viewmsd2359difaftdt		89.43 191	89.05 185	90.56 249	92.89 307	77.00 344	92.81 301	94.52 284	87.03 169	89.77 179	95.79 141	74.67 229	97.51 244	88.97 159	84.98 337	97.17 154

FE-MVSNET3		86.84 287	85.61 301	90.53 251	90.50 397	81.80 196	90.97 359	94.96 256	83.05 285	83.50 341	90.32 363	72.15 271	96.65 318	79.49 315	85.55 332	93.15 361

FC-MVSNet-test		90.27 160	90.18 148	90.53 251	93.71 272	79.85 269	95.77 99	97.59 7	89.31 79	86.27 256	94.67 201	81.93 122	97.01 300	84.26 233	88.09 306	94.71 280

PAPM		86.68 296	85.39 306	90.53 251	93.05 298	79.33 285	89.79 388	94.77 274	78.82 368	81.95 363	93.24 261	76.81 193	97.30 274	66.94 422	93.16 217	94.95 272

WR-MVS		88.38 226	87.67 226	90.52 254	93.30 286	80.18 253	93.26 278	95.96 174	88.57 112	85.47 280	92.81 276	76.12 203	96.91 307	81.24 289	82.29 370	94.47 297

SSM_04072		88.57 223	87.92 220	90.51 255	94.76 190	82.66 169	79.84 467	94.64 280	85.18 224	88.96 196	95.00 182	76.00 206	92.03 436	83.74 242	93.15 218	96.85 186

MVSTER		88.84 212	88.29 210	90.51 255	92.95 304	80.44 247	93.73 251	95.01 250	84.66 248	87.15 233	93.12 266	72.79 262	97.21 285	87.86 175	87.36 318	93.87 321

testdata				90.49 257	96.40 100	77.89 321		95.37 230	72.51 435	93.63 78	96.69 87	82.08 118	97.65 231	83.08 249	97.39 102	95.94 229

test1111		89.10 202	88.64 197	90.48 258	95.53 149	74.97 372	96.08 69	84.89 453	88.13 127	90.16 173	96.65 91	63.29 370	98.10 178	86.14 202	96.90 114	98.39 45

tt0805		86.92 284	85.74 299	90.48 258	92.22 324	79.98 265	95.63 113	94.88 265	83.83 263	84.74 303	92.80 277	57.61 416	97.67 228	85.48 213	84.42 342	93.79 326

jajsoiax		88.24 231	87.50 229	90.48 258	90.89 381	80.14 255	95.31 126	95.65 206	84.97 236	84.24 322	94.02 230	65.31 356	97.42 259	88.56 166	88.52 297	93.89 317

PatchMatch-RL		86.77 293	85.54 302	90.47 261	95.88 129	82.71 167	90.54 369	92.31 355	79.82 353	84.32 319	91.57 326	68.77 323	96.39 342	73.16 381	93.48 208	92.32 389

tfpn200view9		87.58 255	86.64 256	90.41 262	95.99 124	78.64 296	94.58 181	91.98 367	86.94 174	88.09 212	91.77 314	69.18 317	98.10 178	70.13 402	91.10 250	94.48 295

VPNet		88.20 232	87.47 231	90.39 263	93.56 279	79.46 277	94.04 226	95.54 214	88.67 107	86.96 235	94.58 208	69.33 311	97.15 287	84.05 236	80.53 399	94.56 287

ACMH	80.38 17	85.36 326	83.68 343	90.39 263	94.45 223	80.63 241	94.73 173	94.85 267	82.09 305	77.24 414	92.65 281	60.01 399	97.58 238	72.25 386	84.87 339	92.96 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 250	86.71 252	90.38 265	96.12 110	78.55 299	95.03 152	91.58 378	87.15 164	88.06 215	92.29 293	68.91 321	98.10 178	70.13 402	91.10 250	94.48 295

mvs_tets		88.06 237	87.28 236	90.38 265	90.94 377	79.88 267	95.22 137	95.66 204	85.10 232	84.21 323	93.94 235	63.53 368	97.40 267	88.50 167	88.40 301	93.87 321

1314		87.51 258	86.57 261	90.34 267	92.42 321	79.74 272	92.63 308	95.35 232	78.35 377	80.14 386	91.62 322	74.05 241	97.15 287	81.05 290	93.53 204	94.12 307

LTVRE_ROB	82.13 13	86.26 310	84.90 320	90.34 267	94.44 224	81.50 202	92.31 322	94.89 263	83.03 286	79.63 396	92.67 280	69.69 305	97.79 220	71.20 391	86.26 327	91.72 399
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		89.03 208	88.64 197	90.21 269	90.74 388	79.28 286	95.96 83	95.90 180	84.66 248	85.33 292	92.94 271	74.02 242	97.30 274	89.64 149	88.53 296	94.05 313

v2v482		87.84 240	87.06 240	90.17 270	90.99 373	79.23 289	94.00 232	95.13 243	84.87 239	85.53 275	92.07 305	74.45 233	97.45 254	84.71 228	81.75 378	93.85 324

pmmvs4		85.43 324	83.86 341	90.16 271	90.02 407	82.97 158	90.27 372	92.67 346	75.93 402	80.73 377	91.74 316	71.05 281	95.73 376	78.85 325	83.46 356	91.78 398

V42		87.68 245	86.86 245	90.15 272	90.58 393	80.14 255	94.24 210	95.28 237	83.66 267	85.67 270	91.33 328	74.73 227	97.41 265	84.43 232	81.83 376	92.89 370

MSDG		84.86 339	83.09 352	90.14 273	93.80 264	80.05 260	89.18 401	93.09 333	78.89 365	78.19 406	91.91 311	65.86 354	97.27 278	68.47 411	88.45 299	93.11 362

sc_t1		81.53 379	78.67 400	90.12 274	90.78 385	78.64 296	93.91 240	90.20 410	68.42 450	80.82 376	89.88 379	46.48 454	96.76 312	76.03 356	71.47 439	94.96 268

anonymousdsp		87.84 240	87.09 239	90.12 274	89.13 418	80.54 245	94.67 177	95.55 212	82.05 307	83.82 330	92.12 299	71.47 278	97.15 287	87.15 189	87.80 313	92.67 376

thres200		87.21 274	86.24 275	90.12 274	95.36 153	78.53 300	93.26 278	92.10 361	86.42 188	88.00 217	91.11 339	69.24 316	98.00 200	69.58 406	91.04 256	93.83 325

CR-MVSNet		85.35 327	83.76 342	90.12 274	90.58 393	79.34 282	85.24 445	91.96 369	78.27 379	85.55 273	87.87 415	71.03 282	95.61 379	73.96 376	89.36 285	95.40 251

v1144		87.61 253	86.79 250	90.06 278	91.01 372	79.34 282	93.95 235	95.42 227	83.36 278	85.66 271	91.31 331	74.98 223	97.42 259	83.37 246	82.06 372	93.42 347

XXY-MVS		87.65 247	86.85 246	90.03 279	92.14 327	80.60 243	93.76 248	95.23 239	82.94 289	84.60 305	94.02 230	74.27 235	95.49 386	81.04 291	83.68 352	94.01 315

Vis-MVSNet (Re-imp)		89.59 184	89.44 171	90.03 279	95.74 134	75.85 363	95.61 114	90.80 401	87.66 150	87.83 221	95.40 161	76.79 194	96.46 338	78.37 327	96.73 120	97.80 113

test2506		87.21 274	86.28 273	90.02 281	95.62 144	73.64 388	96.25 55	71.38 478	87.89 139	90.45 163	96.65 91	55.29 428	98.09 186	86.03 206	96.94 112	98.33 50

BH-untuned		88.60 220	88.13 214	90.01 282	95.24 161	78.50 302	93.29 276	94.15 303	84.75 244	84.46 311	93.40 253	75.76 212	97.40 267	77.59 337	94.52 177	94.12 307

v1192		87.25 270	86.33 270	90.00 283	90.76 387	79.04 290	93.80 246	95.48 217	82.57 296	85.48 279	91.18 335	73.38 256	97.42 259	82.30 265	82.06 372	93.53 341

v7n		86.81 288	85.76 297	89.95 284	90.72 389	79.25 288	95.07 149	95.92 177	84.45 251	82.29 356	90.86 346	72.60 266	97.53 242	79.42 320	80.52 400	93.08 364

testing91		87.11 279	86.18 276	89.92 285	94.43 225	75.38 371	91.53 344	92.27 357	86.48 185	86.50 247	90.24 365	61.19 391	97.53 242	82.10 270	90.88 258	96.84 189

IMVS_0404		87.60 254	86.84 247	89.89 286	93.72 268	77.75 329	88.56 410	95.34 233	85.53 214	79.98 390	94.49 210	66.54 347	94.64 400	84.75 223	92.65 230	97.28 142

v8		87.50 260	86.71 252	89.89 286	91.37 357	79.40 279	94.50 186	95.38 228	84.81 242	83.60 338	91.33 328	76.05 204	97.42 259	82.84 255	80.51 401	92.84 372

v10		87.25 270	86.38 267	89.85 288	91.19 363	79.50 275	94.48 187	95.45 222	83.79 265	83.62 337	91.19 333	75.13 220	97.42 259	81.94 275	80.60 396	92.63 378

baseline2		86.50 303	85.39 306	89.84 289	91.12 368	76.70 351	91.88 334	88.58 433	82.35 301	79.95 391	90.95 344	73.42 254	97.63 234	80.27 307	89.95 273	95.19 258

pm-mvs1		86.61 297	85.54 302	89.82 290	91.44 352	80.18 253	95.28 132	94.85 267	83.84 262	81.66 365	92.62 282	72.45 269	96.48 335	79.67 313	78.06 416	92.82 373

TR-MVS		86.78 290	85.76 297	89.82 290	94.37 228	78.41 304	92.47 313	92.83 340	81.11 339	86.36 253	92.40 288	68.73 324	97.48 249	73.75 379	89.85 276	93.57 340

ACMH+	81.04 14	85.05 334	83.46 346	89.82 290	94.66 202	79.37 280	94.44 192	94.12 306	82.19 304	78.04 408	92.82 275	58.23 412	97.54 241	73.77 378	82.90 364	92.54 379

EI-MVSNet		89.10 202	88.86 194	89.80 293	91.84 339	78.30 309	93.70 255	95.01 250	85.73 205	87.15 233	95.28 167	79.87 150	97.21 285	83.81 240	87.36 318	93.88 320

v144192		87.19 276	86.35 269	89.74 294	90.64 391	78.24 311	93.92 238	95.43 225	81.93 312	85.51 277	91.05 342	74.21 238	97.45 254	82.86 254	81.56 380	93.53 341

COLMAP_ROB	80.39 16	83.96 353	82.04 362	89.74 294	95.28 157	79.75 271	94.25 208	92.28 356	75.17 409	78.02 409	93.77 245	58.60 411	97.84 218	65.06 433	85.92 328	91.63 401
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 309	85.18 313	89.73 296	92.15 326	76.60 352	91.12 355	91.69 374	83.53 272	85.50 278	88.81 398	66.79 340	96.48 335	76.65 346	90.35 265	96.12 220

IterMVS-LS		88.36 228	87.91 222	89.70 297	93.80 264	78.29 310	93.73 251	95.08 248	85.73 205	84.75 302	91.90 312	79.88 149	96.92 306	83.83 239	82.51 366	93.89 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 306	85.35 309	89.69 298	94.29 235	75.40 370	91.30 349	90.53 405	84.76 243	85.06 296	90.13 371	58.95 410	97.45 254	82.08 271	91.09 254	96.21 215

testing99		86.72 294	85.73 300	89.69 298	94.23 238	74.91 374	91.35 348	90.97 395	86.14 196	86.36 253	90.22 366	59.41 404	97.48 249	82.24 267	90.66 260	96.69 196

v1921920		86.97 283	86.06 283	89.69 298	90.53 396	78.11 314	93.80 246	95.43 225	81.90 314	85.33 292	91.05 342	72.66 263	97.41 265	82.05 273	81.80 377	93.53 341

icg_test_0407_2		89.15 200	88.97 187	89.68 301	93.72 268	77.75 329	88.26 415	95.34 233	85.53 214	88.34 209	94.49 210	77.69 186	93.99 412	84.75 223	92.65 230	97.28 142

VortexMVS		88.42 224	88.01 216	89.63 302	93.89 259	78.82 292	93.82 244	95.47 218	86.67 182	84.53 309	91.99 308	72.62 265	96.65 318	89.02 158	84.09 346	93.41 348

Fast-Effi-MVS+-dtu		87.44 261	86.72 251	89.63 302	92.04 331	77.68 334	94.03 227	93.94 309	85.81 202	82.42 355	91.32 330	70.33 296	97.06 296	80.33 306	90.23 267	94.14 306

v1240		86.78 290	85.85 292	89.56 304	90.45 399	77.79 326	93.61 259	95.37 230	81.65 324	85.43 284	91.15 337	71.50 277	97.43 258	81.47 286	82.05 374	93.47 345

Effi-MVS+-dtu		88.65 218	88.35 206	89.54 305	93.33 285	76.39 356	94.47 190	94.36 293	87.70 147	85.43 284	89.56 387	73.45 252	97.26 280	85.57 212	91.28 249	94.97 265

AllTest		83.42 360	81.39 366	89.52 306	95.01 171	77.79 326	93.12 282	90.89 399	77.41 386	76.12 423	93.34 254	54.08 434	97.51 244	68.31 413	84.27 344	93.26 351

TestCases				89.52 306	95.01 171	77.79 326		90.89 399	77.41 386	76.12 423	93.34 254	54.08 434	97.51 244	68.31 413	84.27 344	93.26 351

mvs_anonymous		89.37 197	89.32 176	89.51 308	93.47 281	74.22 381	91.65 342	94.83 269	82.91 290	85.45 281	93.79 243	81.23 134	96.36 345	86.47 198	94.09 190	97.94 96

XVG-ACMP-BASELINE		86.00 312	84.84 322	89.45 309	91.20 362	78.00 316	91.70 340	95.55 212	85.05 234	82.97 349	92.25 295	54.49 432	97.48 249	82.93 252	87.45 317	92.89 370

testing222		84.84 340	83.32 347	89.43 310	94.15 245	75.94 361	91.09 356	89.41 431	84.90 237	85.78 267	89.44 388	52.70 439	96.28 349	70.80 397	91.57 246	96.07 224

MVP-Stereo		85.97 313	84.86 321	89.32 311	90.92 379	82.19 184	92.11 329	94.19 300	78.76 370	78.77 405	91.63 321	68.38 328	96.56 329	75.01 365	93.95 193	89.20 440
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 316	84.70 324	89.29 312	91.76 343	75.54 367	88.49 411	91.30 386	81.63 326	85.05 297	88.70 402	71.71 274	96.24 350	74.61 371	89.05 291	96.08 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 281	86.32 271	89.21 313	90.94 377	77.26 340	93.71 254	94.43 288	84.84 241	84.36 317	90.80 350	76.04 205	97.05 298	82.12 269	79.60 410	93.31 350

tfpnnormal		84.72 342	83.23 350	89.20 314	92.79 311	80.05 260	94.48 187	95.81 188	82.38 299	81.08 373	91.21 332	69.01 320	96.95 304	61.69 444	80.59 397	90.58 426

cl22		86.78 290	85.98 286	89.18 315	92.34 322	77.62 335	90.84 362	94.13 305	81.33 333	83.97 328	90.15 370	73.96 243	96.60 326	84.19 234	82.94 361	93.33 349

BH-w/o		87.57 256	87.05 241	89.12 316	94.90 183	77.90 320	92.41 314	93.51 324	82.89 291	83.70 334	91.34 327	75.75 213	97.07 295	75.49 358	93.49 206	92.39 386

WR-MVS_H		87.80 242	87.37 233	89.10 317	93.23 287	78.12 313	95.61 114	97.30 37	87.90 137	83.72 333	92.01 307	79.65 159	96.01 360	76.36 350	80.54 398	93.16 359

miper_enhance_ethall		86.90 285	86.18 276	89.06 318	91.66 348	77.58 336	90.22 378	94.82 270	79.16 361	84.48 310	89.10 392	79.19 164	96.66 317	84.06 235	82.94 361	92.94 368

c3_l		87.14 278	86.50 265	89.04 319	92.20 325	77.26 340	91.22 354	94.70 277	82.01 310	84.34 318	90.43 361	78.81 167	96.61 324	83.70 244	81.09 387	93.25 353

miper_ehance_all_eth		87.22 273	86.62 259	89.02 320	92.13 328	77.40 338	90.91 361	94.81 271	81.28 334	84.32 319	90.08 373	79.26 162	96.62 321	83.81 240	82.94 361	93.04 365

gg-mvs-nofinetune		81.77 373	79.37 388	88.99 321	90.85 383	77.73 333	86.29 437	79.63 466	74.88 414	83.19 348	69.05 469	60.34 396	96.11 355	75.46 359	94.64 173	93.11 362

ETVMVS		84.43 347	82.92 356	88.97 322	94.37 228	74.67 375	91.23 353	88.35 435	83.37 277	86.06 262	89.04 393	55.38 426	95.67 378	67.12 420	91.34 248	96.58 200

pmmvs6		83.42 360	81.60 364	88.87 323	88.01 433	77.87 322	94.96 155	94.24 299	74.67 415	78.80 404	91.09 340	60.17 398	96.49 334	77.06 345	75.40 430	92.23 391

test_cas_vis1_n_1920		88.83 215	88.85 195	88.78 324	91.15 367	76.72 350	93.85 243	94.93 261	83.23 282	92.81 98	96.00 122	61.17 392	94.45 401	91.67 115	94.84 165	95.17 259

MIMVSNet		82.59 366	80.53 371	88.76 325	91.51 350	78.32 308	86.57 436	90.13 413	79.32 357	80.70 378	88.69 403	52.98 438	93.07 428	66.03 428	88.86 293	94.90 273

cl____		86.52 302	85.78 294	88.75 326	92.03 332	76.46 354	90.74 363	94.30 295	81.83 320	83.34 345	90.78 351	75.74 215	96.57 327	81.74 281	81.54 381	93.22 355

DIV-MVS_self_test		86.53 301	85.78 294	88.75 326	92.02 333	76.45 355	90.74 363	94.30 295	81.83 320	83.34 345	90.82 349	75.75 213	96.57 327	81.73 282	81.52 382	93.24 354

CP-MVSNet		87.63 250	87.26 238	88.74 328	93.12 292	76.59 353	95.29 130	96.58 110	88.43 115	83.49 342	92.98 270	75.28 219	95.83 369	78.97 323	81.15 386	93.79 326

eth_miper_zixun_eth		86.50 303	85.77 296	88.68 329	91.94 334	75.81 364	90.47 370	94.89 263	82.05 307	84.05 325	90.46 360	75.96 208	96.77 311	82.76 258	79.36 412	93.46 346

CHOSEN 280x420		85.15 332	83.99 339	88.65 330	92.47 318	78.40 305	79.68 469	92.76 343	74.90 413	81.41 369	89.59 385	69.85 304	95.51 383	79.92 311	95.29 155	92.03 394

PS-CasMVS		87.32 267	86.88 244	88.63 331	92.99 302	76.33 358	95.33 125	96.61 108	88.22 123	83.30 347	93.07 268	73.03 260	95.79 373	78.36 328	81.00 392	93.75 333

TransMVSNet (Re)		84.43 347	83.06 354	88.54 332	91.72 344	78.44 303	95.18 143	92.82 342	82.73 294	79.67 395	92.12 299	73.49 251	95.96 362	71.10 395	68.73 450	91.21 413

tt0320-xc		79.63 402	76.66 411	88.52 333	91.03 371	78.72 293	93.00 291	89.53 430	66.37 454	76.11 425	87.11 426	46.36 456	95.32 391	72.78 383	67.67 451	91.51 405

EG-PatchMatch MVS		82.37 368	80.34 374	88.46 334	90.27 401	79.35 281	92.80 304	94.33 294	77.14 390	73.26 442	90.18 369	47.47 451	96.72 313	70.25 399	87.32 320	89.30 437

PEN-MVS		86.80 289	86.27 274	88.40 335	92.32 323	75.71 366	95.18 143	96.38 125	87.97 133	82.82 351	93.15 264	73.39 255	95.92 364	76.15 354	79.03 415	93.59 339

Baseline_NR-MVSNet		87.07 280	86.63 258	88.40 335	91.44 352	77.87 322	94.23 211	92.57 348	84.12 256	85.74 269	92.08 303	77.25 190	96.04 356	82.29 266	79.94 405	91.30 411

UBG		85.51 322	84.57 329	88.35 337	94.21 240	71.78 413	90.07 383	89.66 426	82.28 302	85.91 265	89.01 394	61.30 386	97.06 296	76.58 349	92.06 243	96.22 213

D2MVS		85.90 314	85.09 315	88.35 337	90.79 384	77.42 337	91.83 336	95.70 200	80.77 342	80.08 388	90.02 375	66.74 342	96.37 343	81.88 277	87.97 308	91.26 412

pmmvs5		84.21 349	82.84 359	88.34 339	88.95 420	76.94 346	92.41 314	91.91 371	75.63 404	80.28 383	91.18 335	64.59 362	95.57 380	77.09 344	83.47 355	92.53 380

mamv4		90.92 139	91.78 108	88.33 340	95.67 140	70.75 426	92.92 296	96.02 169	81.90 314	88.11 211	95.34 165	85.88 55	96.97 302	95.22 43	95.01 160	97.26 146

tt0320		80.13 395	77.41 404	88.29 341	90.50 397	78.02 315	93.10 285	90.71 403	66.06 457	76.75 418	86.97 427	49.56 446	95.40 388	71.65 387	71.41 440	91.46 408

LCM-MVSNet-Re		88.30 230	88.32 209	88.27 342	94.71 198	72.41 408	93.15 281	90.98 394	87.77 144	79.25 399	91.96 309	78.35 176	95.75 374	83.04 250	95.62 144	96.65 197

CostFormer		85.77 319	84.94 319	88.26 343	91.16 366	72.58 406	89.47 396	91.04 393	76.26 399	86.45 251	89.97 377	70.74 287	96.86 310	82.35 264	87.07 323	95.34 255

ITE_SJBPF				88.24 344	91.88 338	77.05 343		92.92 337	85.54 212	80.13 387	93.30 258	57.29 417	96.20 351	72.46 385	84.71 340	91.49 406

PVSNet	78.82 18	85.55 321	84.65 325	88.23 345	94.72 196	71.93 409	87.12 432	92.75 344	78.80 369	84.95 299	90.53 358	64.43 363	96.71 315	74.74 368	93.86 195	96.06 226

IterMVS-SCA-FT		85.45 323	84.53 330	88.18 346	91.71 345	76.87 347	90.19 380	92.65 347	85.40 221	81.44 368	90.54 357	66.79 340	95.00 397	81.04 291	81.05 388	92.66 377

EPNet_dtu		86.49 305	85.94 289	88.14 347	90.24 402	72.82 398	94.11 217	92.20 359	86.66 183	79.42 398	92.36 290	73.52 250	95.81 371	71.26 390	93.66 199	95.80 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 364	80.93 370	88.06 348	90.05 406	76.37 357	84.74 450	91.96 369	72.28 438	81.32 371	87.87 415	71.03 282	95.50 385	68.97 408	80.15 403	92.32 389

test_vis1_n_1920		89.39 196	89.84 159	88.04 349	92.97 303	72.64 403	94.71 175	96.03 168	86.18 194	91.94 127	96.56 99	61.63 381	95.74 375	93.42 65	95.11 159	95.74 240

DTE-MVSNet		86.11 311	85.48 304	87.98 350	91.65 349	74.92 373	94.93 157	95.75 193	87.36 159	82.26 357	93.04 269	72.85 261	95.82 370	74.04 374	77.46 421	93.20 357

PMMVS		85.71 320	84.96 318	87.95 351	88.90 421	77.09 342	88.68 408	90.06 415	72.32 437	86.47 248	90.76 352	72.15 271	94.40 404	81.78 280	93.49 206	92.36 387

GG-mvs-BLEND				87.94 352	89.73 413	77.91 319	87.80 421	78.23 471		80.58 380	83.86 446	59.88 400	95.33 390	71.20 391	92.22 241	90.60 425

MonoMVSNet		86.89 286	86.55 262	87.92 353	89.46 416	73.75 385	94.12 215	93.10 332	87.82 143	85.10 295	90.76 352	69.59 307	94.94 398	86.47 198	82.50 367	95.07 262

reproduce_monomvs		86.37 308	85.87 291	87.87 354	93.66 276	73.71 386	93.44 266	95.02 249	88.61 110	82.64 354	91.94 310	57.88 414	96.68 316	89.96 140	79.71 409	93.22 355

pmmvs-eth3d		80.97 388	78.72 399	87.74 355	84.99 451	79.97 266	90.11 382	91.65 376	75.36 406	73.51 440	86.03 435	59.45 403	93.96 415	75.17 362	72.21 436	89.29 439

MS-PatchMatch		85.05 334	84.16 334	87.73 356	91.42 355	78.51 301	91.25 352	93.53 323	77.50 385	80.15 385	91.58 324	61.99 378	95.51 383	75.69 357	94.35 182	89.16 441

mmtdpeth		85.04 336	84.15 335	87.72 357	93.11 293	75.74 365	94.37 201	92.83 340	84.98 235	89.31 189	86.41 432	61.61 383	97.14 290	92.63 81	62.11 461	90.29 427

test_0402		81.30 384	79.17 393	87.67 358	93.19 288	78.17 312	92.98 293	91.71 372	75.25 408	76.02 426	90.31 364	59.23 405	96.37 343	50.22 464	83.63 353	88.47 449

IterMVS		84.88 338	83.98 340	87.60 359	91.44 352	76.03 360	90.18 381	92.41 350	83.24 281	81.06 374	90.42 362	66.60 343	94.28 408	79.46 316	80.98 393	92.48 381
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 382	79.30 389	87.58 360	90.92 379	74.16 383	80.99 462	87.68 440	70.52 445	76.63 420	88.81 398	71.21 279	92.76 431	60.01 450	86.93 324	95.83 236

EPMVS		83.90 356	82.70 360	87.51 361	90.23 403	72.67 401	88.62 409	81.96 461	81.37 332	85.01 298	88.34 406	66.31 348	94.45 401	75.30 361	87.12 321	95.43 250

ADS-MVSNet2		81.66 376	79.71 385	87.50 362	91.35 358	74.19 382	83.33 455	88.48 434	72.90 432	82.24 358	85.77 438	64.98 358	93.20 426	64.57 435	83.74 350	95.12 260

OurMVSNet-221017-0		85.35 327	84.64 327	87.49 363	90.77 386	72.59 405	94.01 230	94.40 291	84.72 245	79.62 397	93.17 263	61.91 379	96.72 313	81.99 274	81.16 384	93.16 359

tpm2		84.08 351	82.94 355	87.48 364	91.39 356	71.27 418	89.23 400	90.37 407	71.95 439	84.64 304	89.33 389	67.30 332	96.55 331	75.17 362	87.09 322	94.63 281

RPSCF		85.07 333	84.27 331	87.48 364	92.91 306	70.62 428	91.69 341	92.46 349	76.20 400	82.67 353	95.22 170	63.94 366	97.29 277	77.51 339	85.80 329	94.53 288

myMVS_eth3d28		85.80 318	85.26 312	87.42 366	94.73 194	69.92 433	90.60 367	90.95 396	87.21 163	86.06 262	90.04 374	59.47 402	96.02 358	74.89 367	93.35 213	96.33 207

FE-MVSNET2		81.82 372	79.99 380	87.34 367	84.74 452	77.36 339	92.72 305	94.55 282	82.09 305	73.79 439	86.46 429	57.80 415	94.45 401	74.65 369	73.10 432	90.20 428

WBMVS		84.97 337	84.18 333	87.34 367	94.14 246	71.62 417	90.20 379	92.35 352	81.61 327	84.06 324	90.76 352	61.82 380	96.52 332	78.93 324	83.81 348	93.89 317

miper_lstm_enhance		85.27 330	84.59 328	87.31 369	91.28 361	74.63 376	87.69 426	94.09 307	81.20 338	81.36 370	89.85 381	74.97 224	94.30 407	81.03 293	79.84 408	93.01 366

FMVSNet5		81.52 380	79.60 386	87.27 370	91.17 364	77.95 317	91.49 345	92.26 358	76.87 392	76.16 422	87.91 414	51.67 440	92.34 434	67.74 417	81.16 384	91.52 404

USDC		82.76 363	81.26 368	87.26 371	91.17 364	74.55 377	89.27 398	93.39 326	78.26 380	75.30 430	92.08 303	54.43 433	96.63 320	71.64 388	85.79 330	90.61 423

test-LLR		85.87 315	85.41 305	87.25 372	90.95 375	71.67 415	89.55 392	89.88 422	83.41 275	84.54 307	87.95 412	67.25 333	95.11 394	81.82 278	93.37 211	94.97 265

test-mter		84.54 346	83.64 344	87.25 372	90.95 375	71.67 415	89.55 392	89.88 422	79.17 360	84.54 307	87.95 412	55.56 423	95.11 394	81.82 278	93.37 211	94.97 265

JIA-IIPM		81.04 385	78.98 397	87.25 372	88.64 422	73.48 390	81.75 461	89.61 428	73.19 429	82.05 361	73.71 465	66.07 353	95.87 367	71.18 393	84.60 341	92.41 385

TDRefinement		79.81 399	77.34 405	87.22 375	79.24 468	75.48 368	93.12 282	92.03 364	76.45 395	75.01 431	91.58 324	49.19 447	96.44 339	70.22 401	69.18 447	89.75 433

tpmvs		83.35 362	82.07 361	87.20 376	91.07 370	71.00 424	88.31 414	91.70 373	78.91 363	80.49 382	87.18 424	69.30 314	97.08 293	68.12 416	83.56 354	93.51 344

ppachtmachnet_test		81.84 371	80.07 379	87.15 377	88.46 426	74.43 380	89.04 404	92.16 360	75.33 407	77.75 411	88.99 395	66.20 350	95.37 389	65.12 432	77.60 419	91.65 400

dmvs_re		84.20 350	83.22 351	87.14 378	91.83 341	77.81 324	90.04 384	90.19 411	84.70 247	81.49 366	89.17 391	64.37 364	91.13 447	71.58 389	85.65 331	92.46 383

tpm cat1		81.96 369	80.27 375	87.01 379	91.09 369	71.02 423	87.38 430	91.53 381	66.25 455	80.17 384	86.35 434	68.22 329	96.15 354	69.16 407	82.29 370	93.86 323

test_fmvs1_n		87.03 282	87.04 242	86.97 380	89.74 412	71.86 410	94.55 183	94.43 288	78.47 374	91.95 126	95.50 156	51.16 442	93.81 416	93.02 73	94.56 175	95.26 256

OpenMVS_ROB	74.94 19	79.51 403	77.03 410	86.93 381	87.00 439	76.23 359	92.33 320	90.74 402	68.93 449	74.52 435	88.23 409	49.58 445	96.62 321	57.64 456	84.29 343	87.94 452

SixPastTwentyTwo		83.91 355	82.90 357	86.92 382	90.99 373	70.67 427	93.48 263	91.99 366	85.54 212	77.62 413	92.11 301	60.59 395	96.87 309	76.05 355	77.75 418	93.20 357

ADS-MVSNet		81.56 378	79.78 382	86.90 383	91.35 358	71.82 411	83.33 455	89.16 432	72.90 432	82.24 358	85.77 438	64.98 358	93.76 417	64.57 435	83.74 350	95.12 260

PatchT		82.68 365	81.27 367	86.89 384	90.09 405	70.94 425	84.06 452	90.15 412	74.91 412	85.63 272	83.57 448	69.37 310	94.87 399	65.19 430	88.50 298	94.84 275

tpm		84.73 341	84.02 338	86.87 385	90.33 400	68.90 436	89.06 403	89.94 419	80.85 341	85.75 268	89.86 380	68.54 326	95.97 361	77.76 335	84.05 347	95.75 239

Patchmatch-RL test		81.67 375	79.96 381	86.81 386	85.42 449	71.23 419	82.17 460	87.50 441	78.47 374	77.19 415	82.50 455	70.81 286	93.48 421	82.66 259	72.89 435	95.71 243

test_vis1_n		86.56 300	86.49 266	86.78 387	88.51 423	72.69 400	94.68 176	93.78 319	79.55 356	90.70 158	95.31 166	48.75 448	93.28 424	93.15 69	93.99 192	94.38 299

testing3-2		86.72 294	86.71 252	86.74 388	96.11 113	65.92 448	93.39 268	89.65 427	89.46 72	87.84 220	92.79 278	59.17 407	97.60 236	81.31 287	90.72 259	96.70 195

test_fmvs1		87.34 265	87.56 228	86.68 389	90.59 392	71.80 412	94.01 230	94.04 308	78.30 378	91.97 124	95.22 170	56.28 421	93.71 418	92.89 74	94.71 168	94.52 289

MDA-MVSNet-bldmvs		78.85 408	76.31 413	86.46 390	89.76 411	73.88 384	88.79 406	90.42 406	79.16 361	59.18 465	88.33 407	60.20 397	94.04 410	62.00 443	68.96 448	91.48 407

mvs5depth		80.98 387	79.15 394	86.45 391	84.57 453	73.29 393	87.79 422	91.67 375	80.52 344	82.20 360	89.72 383	55.14 429	95.93 363	73.93 377	66.83 453	90.12 430

tpmrst		85.35 327	84.99 316	86.43 392	90.88 382	67.88 441	88.71 407	91.43 384	80.13 348	86.08 261	88.80 400	73.05 259	96.02 358	82.48 260	83.40 358	95.40 251

TESTMET0.1,1		83.74 358	82.85 358	86.42 393	89.96 408	71.21 420	89.55 392	87.88 437	77.41 386	83.37 344	87.31 420	56.71 419	93.65 420	80.62 301	92.85 227	94.40 298

our_test_3		81.93 370	80.46 373	86.33 394	88.46 426	73.48 390	88.46 412	91.11 389	76.46 394	76.69 419	88.25 408	66.89 338	94.36 405	68.75 409	79.08 414	91.14 415

lessismore_v0				86.04 395	88.46 426	68.78 437		80.59 464		73.01 443	90.11 372	55.39 425	96.43 340	75.06 364	65.06 456	92.90 369

TinyColmap		79.76 400	77.69 403	85.97 396	91.71 345	73.12 394	89.55 392	90.36 408	75.03 410	72.03 446	90.19 368	46.22 457	96.19 353	63.11 439	81.03 389	88.59 448

KD-MVS_2432*1600		78.50 409	76.02 417	85.93 397	86.22 442	74.47 378	84.80 448	92.33 353	79.29 358	76.98 416	85.92 436	53.81 436	93.97 413	67.39 418	57.42 466	89.36 435

miper_refine_blended		78.50 409	76.02 417	85.93 397	86.22 442	74.47 378	84.80 448	92.33 353	79.29 358	76.98 416	85.92 436	53.81 436	93.97 413	67.39 418	57.42 466	89.36 435

K. test v3		81.59 377	80.15 378	85.91 399	89.89 410	69.42 435	92.57 310	87.71 439	85.56 211	73.44 441	89.71 384	55.58 422	95.52 382	77.17 342	69.76 444	92.78 374

SSC-MVS3.2		84.60 345	84.19 332	85.85 400	92.74 313	68.07 438	88.15 417	93.81 317	87.42 157	83.76 332	91.07 341	62.91 373	95.73 376	74.56 372	83.24 359	93.75 333

mvsany_test1		85.42 325	85.30 310	85.77 401	87.95 435	75.41 369	87.61 429	80.97 463	76.82 393	88.68 202	95.83 138	77.44 189	90.82 449	85.90 207	86.51 325	91.08 419

MIMVSNet1		79.38 404	77.28 406	85.69 402	86.35 441	73.67 387	91.61 343	92.75 344	78.11 383	72.64 444	88.12 410	48.16 449	91.97 440	60.32 447	77.49 420	91.43 409

UWE-MVS		83.69 359	83.09 352	85.48 403	93.06 297	65.27 453	90.92 360	86.14 445	79.90 351	86.26 257	90.72 355	57.17 418	95.81 371	71.03 396	92.62 235	95.35 254

UnsupCasMVSNet_eth		80.07 396	78.27 402	85.46 404	85.24 450	72.63 404	88.45 413	94.87 266	82.99 288	71.64 449	88.07 411	56.34 420	91.75 442	73.48 380	63.36 459	92.01 395

CL-MVSNet_self_test		81.74 374	80.53 371	85.36 405	85.96 444	72.45 407	90.25 374	93.07 334	81.24 336	79.85 394	87.29 421	70.93 284	92.52 432	66.95 421	69.23 446	91.11 417

MDA-MVSNet_test_wron		79.21 406	77.19 408	85.29 406	88.22 430	72.77 399	85.87 439	90.06 415	74.34 417	62.62 462	87.56 418	66.14 351	91.99 439	66.90 425	73.01 433	91.10 418

YYNet1		79.22 405	77.20 407	85.28 407	88.20 431	72.66 402	85.87 439	90.05 417	74.33 418	62.70 460	87.61 417	66.09 352	92.03 436	66.94 422	72.97 434	91.15 414

WB-MVSnew		83.77 357	83.28 348	85.26 408	91.48 351	71.03 422	91.89 333	87.98 436	78.91 363	84.78 301	90.22 366	69.11 319	94.02 411	64.70 434	90.44 262	90.71 421

dp		81.47 381	80.23 376	85.17 409	89.92 409	65.49 451	86.74 434	90.10 414	76.30 398	81.10 372	87.12 425	62.81 374	95.92 364	68.13 415	79.88 406	94.09 310

UnsupCasMVSNet_bld		76.23 419	73.27 423	85.09 410	83.79 455	72.92 396	85.65 442	93.47 325	71.52 440	68.84 455	79.08 460	49.77 444	93.21 425	66.81 426	60.52 463	89.13 443

SD_0403		84.71 343	84.65 325	84.92 411	92.95 304	65.95 447	92.07 332	93.23 329	83.82 264	79.03 400	93.73 248	73.90 244	92.91 430	63.02 441	90.05 269	95.89 232

Anonymous20231206		81.03 386	79.77 384	84.82 412	87.85 436	70.26 430	91.42 346	92.08 362	73.67 424	77.75 411	89.25 390	62.43 376	93.08 427	61.50 445	82.00 375	91.12 416

FE-MVSNET		78.19 411	76.03 416	84.69 413	83.70 456	73.31 392	90.58 368	90.00 418	77.11 391	71.91 447	85.47 440	55.53 424	91.94 441	59.69 451	70.24 442	88.83 445

test0.0.03 1		82.41 367	81.69 363	84.59 414	88.23 429	72.89 397	90.24 376	87.83 438	83.41 275	79.86 393	89.78 382	67.25 333	88.99 459	65.18 431	83.42 357	91.90 397

CMPMVS	59.16 21	80.52 390	79.20 392	84.48 415	83.98 454	67.63 444	89.95 387	93.84 316	64.79 459	66.81 457	91.14 338	57.93 413	95.17 392	76.25 352	88.10 304	90.65 422
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 344	84.79 323	84.37 416	91.84 339	64.92 454	93.70 255	91.47 383	66.19 456	86.16 260	95.28 167	67.18 335	93.33 423	80.89 296	90.42 264	94.88 274

PVSNet_0	73.20 20	77.22 415	74.83 421	84.37 416	90.70 390	71.10 421	83.09 457	89.67 425	72.81 434	73.93 438	83.13 450	60.79 394	93.70 419	68.54 410	50.84 471	88.30 450

LF4IMVS		80.37 393	79.07 396	84.27 418	86.64 440	69.87 434	89.39 397	91.05 392	76.38 396	74.97 432	90.00 376	47.85 450	94.25 409	74.55 373	80.82 395	88.69 447

Anonymous20240521		80.44 392	79.21 391	84.11 419	85.75 447	67.89 440	92.86 300	93.23 329	75.61 405	75.59 429	87.47 419	50.03 443	94.33 406	71.14 394	81.21 383	90.12 430

PM-MVS		78.11 412	76.12 415	84.09 420	83.54 457	70.08 431	88.97 405	85.27 452	79.93 350	74.73 434	86.43 431	34.70 468	93.48 421	79.43 319	72.06 437	88.72 446

test_fmvs2		83.98 352	84.03 337	83.83 421	87.16 438	67.53 445	93.93 237	92.89 338	77.62 384	86.89 241	93.53 251	47.18 452	92.02 438	90.54 134	86.51 325	91.93 396

testgi		80.94 389	80.20 377	83.18 422	87.96 434	66.29 446	91.28 350	90.70 404	83.70 266	78.12 407	92.84 273	51.37 441	90.82 449	63.34 438	82.46 368	92.43 384

KD-MVS_self_test		80.20 394	79.24 390	83.07 423	85.64 448	65.29 452	91.01 358	93.93 310	78.71 372	76.32 421	86.40 433	59.20 406	92.93 429	72.59 384	69.35 445	91.00 420

testing3		80.46 391	79.59 387	83.06 424	93.44 283	64.64 455	93.33 270	85.47 450	84.34 253	79.93 392	90.84 348	44.35 460	92.39 433	57.06 458	87.56 314	92.16 393

ambc				83.06 424	79.99 466	63.51 459	77.47 470	92.86 339		74.34 437	84.45 445	28.74 469	95.06 396	73.06 382	68.89 449	90.61 423

test20.03		79.95 398	79.08 395	82.55 426	85.79 446	67.74 443	91.09 356	91.08 390	81.23 337	74.48 436	89.96 378	61.63 381	90.15 451	60.08 448	76.38 426	89.76 432

MVStest1		72.91 423	69.70 428	82.54 427	78.14 469	73.05 395	88.21 416	86.21 444	60.69 463	64.70 458	90.53 358	46.44 455	85.70 466	58.78 454	53.62 468	88.87 444

test_vis1_rt		77.96 413	76.46 412	82.48 428	85.89 445	71.74 414	90.25 374	78.89 467	71.03 444	71.30 450	81.35 457	42.49 462	91.05 448	84.55 230	82.37 369	84.65 455

EU-MVSNet		81.32 383	80.95 369	82.42 429	88.50 425	63.67 458	93.32 271	91.33 385	64.02 460	80.57 381	92.83 274	61.21 390	92.27 435	76.34 351	80.38 402	91.32 410

myMVS_eth3d		79.67 401	78.79 398	82.32 430	91.92 335	64.08 456	89.75 390	87.40 442	81.72 322	78.82 402	87.20 422	45.33 458	91.29 445	59.09 453	87.84 311	91.60 402

ttmdpeth		76.55 417	74.64 422	82.29 431	82.25 462	67.81 442	89.76 389	85.69 448	70.35 446	75.76 427	91.69 317	46.88 453	89.77 453	66.16 427	63.23 460	89.30 437

pmmvs3		71.81 426	68.71 429	81.11 432	75.86 471	70.42 429	86.74 434	83.66 456	58.95 466	68.64 456	80.89 458	36.93 466	89.52 455	63.10 440	63.59 458	83.39 456

Syy-MVS		80.07 396	79.78 382	80.94 433	91.92 335	59.93 465	89.75 390	87.40 442	81.72 322	78.82 402	87.20 422	66.29 349	91.29 445	47.06 466	87.84 311	91.60 402

UWE-MVS-28		78.98 407	78.38 401	80.80 434	88.18 432	60.66 464	90.65 365	78.51 468	78.84 367	77.93 410	90.93 345	59.08 408	89.02 458	50.96 463	90.33 266	92.72 375

new-patchmatchnet		76.41 418	75.17 420	80.13 435	82.65 461	59.61 466	87.66 427	91.08 390	78.23 381	69.85 453	83.22 449	54.76 430	91.63 444	64.14 437	64.89 457	89.16 441

mvsany_test3		74.95 420	73.26 424	80.02 436	74.61 472	63.16 460	85.53 443	78.42 469	74.16 419	74.89 433	86.46 429	36.02 467	89.09 457	82.39 263	66.91 452	87.82 453

test_fmvs3		77.67 414	77.16 409	79.22 437	79.52 467	61.14 462	92.34 319	91.64 377	73.98 421	78.86 401	86.59 428	27.38 472	87.03 461	88.12 172	75.97 428	89.50 434

DSMNet-mixed		76.94 416	76.29 414	78.89 438	83.10 459	56.11 474	87.78 423	79.77 465	60.65 464	75.64 428	88.71 401	61.56 384	88.34 460	60.07 449	89.29 287	92.21 392

EGC-MVSNET		61.97 434	56.37 439	78.77 439	89.63 414	73.50 389	89.12 402	82.79 458	0.21 485	1.24 486	84.80 443	39.48 463	90.04 452	44.13 468	75.94 429	72.79 467

new_pmnet		72.15 424	70.13 427	78.20 440	82.95 460	65.68 449	83.91 453	82.40 460	62.94 462	64.47 459	79.82 459	42.85 461	86.26 465	57.41 457	74.44 431	82.65 460

MVS-HIRNet		73.70 422	72.20 425	78.18 441	91.81 342	56.42 473	82.94 458	82.58 459	55.24 467	68.88 454	66.48 470	55.32 427	95.13 393	58.12 455	88.42 300	83.01 458

LCM-MVSNet		66.00 431	62.16 436	77.51 442	64.51 482	58.29 468	83.87 454	90.90 398	48.17 471	54.69 468	73.31 466	16.83 481	86.75 462	65.47 429	61.67 462	87.48 454

APD_test1		69.04 427	66.26 433	77.36 443	80.51 465	62.79 461	85.46 444	83.51 457	54.11 469	59.14 466	84.79 444	23.40 475	89.61 454	55.22 459	70.24 442	79.68 464

test_f		71.95 425	70.87 426	75.21 444	74.21 474	59.37 467	85.07 447	85.82 447	65.25 458	70.42 452	83.13 450	23.62 473	82.93 472	78.32 329	71.94 438	83.33 457

ANet_high		58.88 438	54.22 443	72.86 445	56.50 485	56.67 470	80.75 463	86.00 446	73.09 431	37.39 477	64.63 473	22.17 476	79.49 475	43.51 469	23.96 479	82.43 461

test_vis3_rt		65.12 432	62.60 434	72.69 446	71.44 475	60.71 463	87.17 431	65.55 479	63.80 461	53.22 469	65.65 472	14.54 482	89.44 456	76.65 346	65.38 455	67.91 470

FPMVS		64.63 433	62.55 435	70.88 447	70.80 476	56.71 469	84.42 451	84.42 454	51.78 470	49.57 470	81.61 456	23.49 474	81.48 473	40.61 473	76.25 427	74.46 466

dmvs_testset		74.57 421	75.81 419	70.86 448	87.72 437	40.47 483	87.05 433	77.90 473	82.75 293	71.15 451	85.47 440	67.98 330	84.12 470	45.26 467	76.98 425	88.00 451

N_pmnet		68.89 428	68.44 430	70.23 449	89.07 419	28.79 488	88.06 418	19.50 488	69.47 448	71.86 448	84.93 442	61.24 389	91.75 442	54.70 460	77.15 422	90.15 429

testf1		59.54 436	56.11 440	69.85 450	69.28 477	56.61 471	80.37 464	76.55 476	42.58 474	45.68 473	75.61 461	11.26 483	84.18 468	43.20 470	60.44 464	68.75 468

APD_test2		59.54 436	56.11 440	69.85 450	69.28 477	56.61 471	80.37 464	76.55 476	42.58 474	45.68 473	75.61 461	11.26 483	84.18 468	43.20 470	60.44 464	68.75 468

WB-MVS		67.92 429	67.49 431	69.21 452	81.09 463	41.17 482	88.03 419	78.00 472	73.50 426	62.63 461	83.11 452	63.94 366	86.52 463	25.66 478	51.45 470	79.94 463

PMMVS2		59.60 435	56.40 438	69.21 452	68.83 479	46.58 478	73.02 474	77.48 474	55.07 468	49.21 471	72.95 467	17.43 480	80.04 474	49.32 465	44.33 474	80.99 462

SSC-MVS		67.06 430	66.56 432	68.56 454	80.54 464	40.06 484	87.77 424	77.37 475	72.38 436	61.75 463	82.66 454	63.37 369	86.45 464	24.48 479	48.69 473	79.16 465

Gipuma		57.99 440	54.91 442	67.24 455	88.51 423	65.59 450	52.21 477	90.33 409	43.58 473	42.84 476	51.18 477	20.29 478	85.07 467	34.77 474	70.45 441	51.05 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 442	48.46 446	63.48 456	45.72 487	46.20 479	73.41 473	78.31 470	41.03 476	30.06 479	65.68 471	6.05 485	83.43 471	30.04 476	65.86 454	60.80 471
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 439	58.24 437	60.56 457	83.13 458	45.09 481	82.32 459	48.22 487	67.61 452	61.70 464	69.15 468	38.75 464	76.05 476	32.01 475	41.31 475	60.55 472

MVE	39.65 23	43.39 444	38.59 450	57.77 458	56.52 484	48.77 477	55.38 476	58.64 483	29.33 479	28.96 480	52.65 476	4.68 486	64.62 480	28.11 477	33.07 477	59.93 473
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 443	48.47 445	56.66 459	52.26 486	18.98 490	41.51 479	81.40 462	10.10 480	44.59 475	75.01 464	28.51 470	68.16 477	53.54 461	49.31 472	82.83 459

DeepMVS_CX				56.31 460	74.23 473	51.81 476		56.67 484	44.85 472	48.54 472	75.16 463	27.87 471	58.74 482	40.92 472	52.22 469	58.39 474

kuosan		53.51 441	53.30 444	54.13 461	76.06 470	45.36 480	80.11 466	48.36 486	59.63 465	54.84 467	63.43 474	37.41 465	62.07 481	20.73 481	39.10 476	54.96 475

E-PMN		43.23 445	42.29 447	46.03 462	65.58 481	37.41 485	73.51 472	64.62 480	33.99 477	28.47 481	47.87 478	19.90 479	67.91 478	22.23 480	24.45 478	32.77 477

EMVS		42.07 446	41.12 448	44.92 463	63.45 483	35.56 487	73.65 471	63.48 481	33.05 478	26.88 482	45.45 479	21.27 477	67.14 479	19.80 482	23.02 480	32.06 478

tmp_tt		35.64 447	39.24 449	24.84 464	14.87 488	23.90 489	62.71 475	51.51 485	6.58 482	36.66 478	62.08 475	44.37 459	30.34 484	52.40 462	22.00 481	20.27 479

wuyk23d		21.27 449	20.48 452	23.63 465	68.59 480	36.41 486	49.57 478	6.85 489	9.37 481	7.89 483	4.46 485	4.03 487	31.37 483	17.47 483	16.07 482	3.12 480

test123		8.76 451	11.22 454	1.39 466	0.85 490	0.97 491	85.76 441	0.35 491	0.54 484	2.45 485	8.14 484	0.60 488	0.48 485	2.16 485	0.17 484	2.71 481

testmvs		8.92 450	11.52 453	1.12 467	1.06 489	0.46 492	86.02 438	0.65 490	0.62 483	2.74 484	9.52 483	0.31 489	0.45 486	2.38 484	0.39 483	2.46 482

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

cdsmvs_eth3d_5k		22.14 448	29.52 451	0.00 468	0.00 491	0.00 493	0.00 480	95.76 192	0.00 486	0.00 487	94.29 219	75.66 216	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		6.64 453	8.86 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	79.70 153	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

ab-mvs-re		7.82 452	10.43 455	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	93.88 240	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

TestfortrainingZip							97.32 10

WAC-MVS						64.08 456								59.14 452

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

PC_three_1452									82.47 297	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					0.00 491

eth-test					0.00 491

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 268	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	87.46 154	93.75 75	97.43 51	82.94 100		92.73 76	97.80 92	97.88 104

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 335	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	86.13 198	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																96.12 220

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 275				96.12 220

sam_mvs												70.60 289

MTGPA								96.97 65

test_post1							88.00 420				9.81 482	69.31 313	95.53 381	76.65 346

test_post											10.29 481	70.57 293	95.91 366

patchmatchnet-post											83.76 447	71.53 276	96.48 335

MTMP							96.16 60	60.64 482

gm-plane-assit					89.60 415	68.00 439			77.28 389		88.99 395		97.57 239	79.44 318

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 223	96.78 89	81.61 327	92.77 100	96.20 108	87.71 31	99.12 62

test_8					97.49 69	86.30 46	94.02 229	96.76 92	81.86 318	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	94.11 217

test_prior2							94.12 215		87.67 149	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

旧先验2							93.36 269		71.25 442	94.37 60			97.13 291	86.74 194

新几何2							93.11 284

旧先验1					96.79 85	81.81 195		95.67 202			96.81 84	86.69 42			97.66 98	96.97 176

无先验							93.28 277	96.26 139	73.95 422				99.05 66	80.56 302		96.59 199

原ACMM2							92.94 295

test222					96.55 94	81.70 198	92.22 325	95.01 250	68.36 451	90.20 169	96.14 113	80.26 144			97.80 92	96.05 227

testdata2													98.75 115	78.30 330

segment_acmp												87.16 39

testdata1							92.15 327		87.94 135

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 215	82.75 162						74.23 236

plane_prior5								96.22 144					98.12 176	88.15 169	89.99 270	94.63 281

plane_prior4											94.86 190

plane_prior3						82.75 162			90.26 47	86.91 238

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 208

plane_prior						82.73 165	95.21 140		89.66 68						89.88 275

n2								0.00 492

nn								0.00 492

door-mid								85.49 449

test11								96.57 111

door								85.33 451

HQP5-MVS						81.56 200

HQP-NCC					94.17 242		94.39 197		88.81 100	85.43 284

ACMP_Plane					94.17 242		94.39 197		88.81 100	85.43 284

BP-MVS														87.11 191

HQP4-MVS										85.43 284			97.96 208			94.51 291

HQP3-MVS								96.04 166							89.77 279

HQP2-MVS												73.83 247

NP-MVS					94.37 228	82.42 177					93.98 233

MDTV_nov1_ep13_2view						55.91 475	87.62 428		73.32 428	84.59 306		70.33 296		74.65 369		95.50 248

MDTV_nov1_ep13			83.56 345		91.69 347	69.93 432	87.75 425	91.54 380	78.60 373	84.86 300	88.90 397	69.54 308	96.03 357	70.25 399	88.93 292

ACMMP++_ref															87.47 315

ACMMP++															88.01 307

Test By Simon												80.02 146

High-resolution multi-view benchmark