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	94.37 288	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	85.90 198	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	91.37 119	95.55 7	95.63 143	88.73 6	97.07 23	96.77 91	90.84 26	84.02 324	96.62 95	75.95 207	99.34 42	87.77 175	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	93.01 332	92.58 6	94.22 62	97.20 64	80.56 137	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	87.76 144	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	87.13 164	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	92.66 304	96.56 112	83.44 272	91.68 137	95.04 179	86.60 46	98.99 81	85.60 209	97.92 85	96.93 178

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	94.60 203	82.33 109	98.62 131	92.40 86	92.86 223	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	86.26 190	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	94.60 203	82.33 109	98.62 131	92.40 86	92.86 223	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		88.31 227	86.13 276	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 476	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	96.07 161	89.77 64	94.12 66	94.87 187	80.56 137	98.66 123	92.42 85	93.10 219	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	85.68 205	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	87.45 155	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	85.15 229	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	90.69 157	95.88 131	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	81.86 314	92.77 100	96.20 108	87.63 32	99.12 62	92.14 98	98.69 39	97.94 95

CDPH-MVS		92.83 94	92.30 101	94.44 49	97.79 58	86.11 52	94.06 225	96.66 104	80.09 345	92.77 100	96.63 94	86.62 44	99.04 68	87.40 182	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 118	90.82 134	94.44 49	94.59 207	86.37 42	97.18 17	97.02 62	89.20 84	84.31 319	96.66 90	73.74 247	99.17 56	86.74 192	97.96 83	97.79 112

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	86.90 174	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	93.93 306	89.77 64	94.21 63	95.59 150	87.35 37	98.61 133	92.72 78	96.15 135	97.83 109

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	77.97 177	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		91.04 153		85.08 67	99.01 74		98.13 75	97.86 104

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 284	91.65 17	92.68 105	96.13 114	77.97 177	98.84 105	90.75 131	94.72 167	97.92 99

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	90.15 172	97.03 74	81.44 128	99.51 28	90.85 130	95.74 142	98.04 88
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	89.56 181	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 127
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	94.66 200	82.11 116	98.50 139	92.33 91	92.82 226	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	91.02 128	94.10 64	90.10 401	85.25 79	96.03 76	92.05 359	92.83 5	87.39 230	95.78 142	79.39 159	99.01 74	88.13 169	97.48 100	98.05 87
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	94.62 203	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 94

DELS-MVS		93.43 79	93.25 81	93.97 67	95.42 152	85.04 82	93.06 287	97.13 54	90.74 31	91.84 130	95.09 178	86.32 49	99.21 54	91.22 121	98.45 56	97.65 121
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	91.28 122	93.96 68	98.33 33	85.92 60	94.66 178	96.66 104	82.69 292	90.03 174	95.82 138	82.30 111	99.03 69	84.57 227	96.48 128	96.91 180

HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 202	92.47 113	97.13 69	82.38 107	99.07 64	90.51 136	98.40 58	97.92 99

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 317	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	97.86 104

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	96.91 305	94.38 52	98.85 20	98.03 89
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	93.24 278	97.24 41	88.76 103	91.60 138	95.85 135	86.07 53	98.66 123	91.91 109	98.16 71	98.03 89

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	87.46 153	93.75 75	97.43 51	84.24 82	99.01 74	92.73 76	97.80 92	97.88 102

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	86.96 170	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	86.56 182	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 118	97.16 159

CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 253	90.05 173	95.66 147	87.77 29	99.15 60	89.91 141	98.27 62	98.07 82

GDP-MVS		92.04 106	91.46 116	93.75 78	94.55 213	84.69 90	95.60 117	96.56 112	87.83 141	93.07 91	95.89 130	73.44 251	98.65 125	90.22 139	96.03 137	97.91 101

BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 216	84.39 105	95.90 89	94.30 291	90.39 38	92.67 107	95.94 126	74.46 230	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	89.25 414	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 161	98.98 85	97.22 13	97.24 106	97.74 115

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	84.47 229	95.47 149	97.45 133

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	86.31 188	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 164

QAPM		89.51 184	88.15 211	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 372	83.41 340	96.19 111	73.18 256	99.30 48	77.11 340	96.54 125	96.89 181

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	97.17 152

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	91.30 120	93.39 86	94.72 196	83.36 137	95.45 121	96.37 126	90.33 40	92.17 118	96.03 120	72.32 268	98.75 115	87.94 172	96.34 130	98.07 82

casdiffmvs_mvg		92.96 93	92.83 91	93.35 87	94.59 207	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	97.25 145

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 187	95.77 189	90.87 25	92.52 111	96.67 89	84.50 79	99.00 79	91.99 105	94.44 180	97.36 136

Vis-MVSNet		91.75 117	91.23 123	93.29 89	95.32 155	83.78 122	96.14 64	95.98 168	89.89 53	90.45 161	96.58 97	75.09 219	98.31 166	84.75 221	96.90 114	97.78 113
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	95.51 153	84.50 79	98.79 112	94.83 47	98.86 19	97.72 117

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	95.77 143	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	95.90 129	77.85 183	98.17 173	88.90 159	93.38 208	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	97.20 150

VDD-MVS		90.74 142	89.92 156	93.20 94	96.27 104	83.02 155	95.73 103	93.86 310	88.42 116	92.53 110	96.84 81	62.09 374	98.64 128	90.95 127	92.62 233	97.93 98

Elysia		90.12 161	89.10 179	93.18 96	93.16 287	84.05 114	95.22 137	96.27 135	85.16 227	90.59 158	94.68 196	64.64 357	98.37 156	86.38 198	95.77 140	97.12 161

StellarMVS		90.12 161	89.10 179	93.18 96	93.16 287	84.05 114	95.22 137	96.27 135	85.16 227	90.59 158	94.68 196	64.64 357	98.37 156	86.38 198	95.77 140	97.12 161

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		91.08 136	90.39 140	93.17 98	93.07 294	86.91 23	96.41 42	96.26 139	88.30 119	88.37 206	94.85 190	82.19 115	97.64 231	91.09 122	82.95 357	94.96 266

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 250	94.09 67	95.56 152	85.01 72	98.69 122	94.96 45	98.66 45	97.67 120

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 195	95.74 192	90.71 33	92.05 121	96.60 96	84.00 84	98.99 81	91.55 117	93.63 198	97.17 152

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 273	84.26 108	95.83 95	96.14 152	89.00 96	92.43 114	97.50 48	83.37 92	98.72 119	96.61 24	97.44 101	96.32 206

新几何1				93.10 102	97.30 75	84.35 107		95.56 209	71.09 439	91.26 147	96.24 106	82.87 102	98.86 101	79.19 319	98.10 76	96.07 222

OMC-MVS		91.23 128	90.62 139	93.08 104	96.27 104	84.07 112	93.52 260	95.93 174	86.95 171	89.51 182	96.13 114	78.50 171	98.35 160	85.84 207	92.90 222	96.83 188

OpenMVS	83.78 11	88.74 214	87.29 233	93.08 104	92.70 312	85.39 77	96.57 40	96.43 120	78.74 367	80.85 372	96.07 117	69.64 303	99.01 74	78.01 331	96.65 123	94.83 274

MAR-MVS		90.30 157	89.37 172	93.07 106	96.61 90	84.48 98	95.68 106	95.67 200	82.36 297	87.85 217	92.85 270	76.63 196	98.80 110	80.01 307	96.68 122	95.91 228
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		90.92 137	90.21 144	93.03 107	93.86 258	83.88 119	92.81 299	93.86 310	79.84 348	91.76 134	94.29 217	77.92 180	98.04 194	90.48 137	97.11 107	97.17 152

Effi-MVS+		91.59 122	91.11 125	93.01 108	94.35 231	83.39 136	94.60 180	95.10 244	87.10 165	90.57 160	93.10 265	81.43 129	98.07 189	89.29 151	94.48 178	97.59 126

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 158	87.27 159	95.98 40	98.05 25	83.07 99	98.45 149	96.68 23	95.51 146	96.88 182

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 284	96.09 159	88.20 124	91.12 152	95.72 146	81.33 130	97.76 220	91.74 113	97.37 103	96.75 190

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 315	83.62 128	96.02 77	95.72 196	86.78 176	96.04 38	98.19 4	82.30 111	98.43 153	96.38 25	95.42 152	96.86 183

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	91.19 331	84.06 83	98.34 161	91.72 114	96.54 125	96.54 201

LFMVS		90.08 164	89.13 178	92.95 113	96.71 86	82.32 182	96.08 69	89.91 416	86.79 175	92.15 120	96.81 84	62.60 372	98.34 161	87.18 186	93.90 192	98.19 71

UGNet		89.95 171	88.95 187	92.95 113	94.51 215	83.31 138	95.70 105	95.23 237	89.37 76	87.58 224	93.94 233	64.00 362	98.78 113	83.92 236	96.31 131	96.74 191
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		90.80 140	90.10 148	92.90 115	93.04 297	83.53 131	93.08 284	94.15 299	80.22 342	91.41 144	94.91 184	76.87 190	97.93 210	90.28 138	96.90 114	97.24 146
jason: jason.
DP-MVS		87.25 268	85.36 305	92.90 115	97.65 64	83.24 140	94.81 167	92.00 361	74.99 407	81.92 361	95.00 180	72.66 261	99.05 66	66.92 420	92.33 238	96.40 203

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 117	95.96 127	81.32 209	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	96.12 156	88.13 127	95.82 43	98.04 28	83.43 89	98.48 141	96.97 21	96.23 132	96.92 179

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 265	83.13 146	96.02 77	95.74 192	87.68 147	95.89 41	98.17 5	82.78 103	98.46 145	96.71 22	96.17 134	96.98 173

CANet_DTU		90.26 159	89.41 171	92.81 120	93.46 280	83.01 156	93.48 261	94.47 283	89.43 74	87.76 222	94.23 222	70.54 291	99.03 69	84.97 216	96.39 129	96.38 204

MVSFormer		91.68 120	91.30 120	92.80 121	93.86 258	83.88 119	95.96 83	95.90 178	84.66 246	91.76 134	94.91 184	77.92 180	97.30 272	89.64 147	97.11 107	97.24 146

PVSNet_Blended_VisFu		91.38 125	90.91 131	92.80 121	96.39 101	83.17 144	94.87 161	96.66 104	83.29 277	89.27 188	94.46 212	80.29 140	99.17 56	87.57 179	95.37 153	96.05 225

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	97.83 109

LuminaMVS		90.55 153	89.81 158	92.77 123	92.78 310	84.21 109	94.09 221	94.17 298	85.82 199	91.54 139	94.14 224	69.93 297	97.92 211	91.62 116	94.21 187	96.18 214

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	97.43 135

VDDNet		89.56 183	88.49 202	92.76 125	95.07 169	82.09 186	96.30 47	93.19 327	81.05 336	91.88 128	96.86 80	61.16 390	98.33 163	88.43 166	92.49 237	97.84 108

viewdifsd2359ckpt09		91.18 131	90.65 138	92.75 127	94.61 206	82.36 181	94.32 204	95.74 192	84.72 243	89.66 180	95.15 176	79.69 154	98.04 194	87.70 176	94.27 186	97.85 107

h-mvs33		90.80 140	90.15 147	92.75 127	96.01 120	82.66 169	95.43 122	95.53 213	89.80 60	93.08 89	95.64 148	75.77 208	99.00 79	92.07 100	78.05 414	96.60 196

casdiffmvs		92.51 100	92.43 99	92.74 129	94.41 226	81.98 190	94.54 184	96.23 143	89.57 70	91.96 125	96.17 112	82.58 105	98.01 197	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		90.69 145	90.02 154	92.71 130	95.72 136	82.41 179	94.11 217	95.12 242	85.63 206	91.49 141	94.70 194	74.75 223	98.42 154	86.13 202	92.53 235	97.31 137

DCV-MVSNet		90.69 145	90.02 154	92.71 130	95.72 136	82.41 179	94.11 217	95.12 242	85.63 206	91.49 141	94.70 194	74.75 223	98.42 154	86.13 202	92.53 235	97.31 137

PCF-MVS	84.11 10	87.74 242	86.08 280	92.70 132	94.02 247	84.43 102	89.27 394	95.87 183	73.62 421	84.43 311	94.33 214	78.48 173	98.86 101	70.27 394	94.45 179	94.81 275
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	97.17 152

SSM_0404		90.73 143	90.08 149	92.69 133	95.00 174	83.13 146	94.32 204	95.00 252	85.41 217	89.84 175	95.35 161	76.13 199	97.98 202	85.46 212	94.18 188	96.95 175

baseline		92.39 104	92.29 102	92.69 133	94.46 221	81.77 196	94.14 214	96.27 135	89.22 83	91.88 128	96.00 121	82.35 108	97.99 199	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	97.76 220	92.19 96	98.66 45	96.76 189

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	95.80 139	83.16 95	98.16 174	93.68 59	98.14 74	97.31 137

ab-mvs		89.41 191	88.35 204	92.60 138	95.15 167	82.65 173	92.20 323	95.60 207	83.97 257	88.55 202	93.70 247	74.16 238	98.21 172	82.46 260	89.37 282	96.94 177

LS3D		87.89 237	86.32 269	92.59 139	96.07 117	82.92 159	95.23 135	94.92 259	75.66 399	82.89 347	95.98 123	72.48 265	99.21 54	68.43 408	95.23 158	95.64 242

Anonymous20240529		88.09 233	86.59 258	92.58 140	96.53 96	81.92 193	95.99 79	95.84 185	74.11 416	89.06 192	95.21 171	61.44 382	98.81 109	83.67 243	87.47 313	97.01 171

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 141	95.49 150	81.10 219	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	97.78 113

CPTT-MVS		91.99 107	91.80 107	92.55 142	98.24 37	81.98 190	96.76 35	96.49 118	81.89 313	90.24 165	96.44 102	78.59 169	98.61 133	89.68 146	97.85 89	97.06 165

viewdifsd2359ckpt13		91.20 130	90.75 136	92.54 143	94.30 233	82.13 185	94.03 227	95.89 180	85.60 208	90.20 167	95.36 160	79.69 154	97.90 214	87.85 174	93.86 193	97.61 123

114514_t		89.51 184	88.50 200	92.54 143	98.11 42	81.99 189	95.16 145	96.36 127	70.19 443	85.81 264	95.25 167	76.70 194	98.63 130	82.07 270	96.86 117	97.00 172

PAPM_NR		91.22 129	90.78 135	92.52 145	97.60 65	81.46 205	94.37 201	96.24 142	86.39 187	87.41 227	94.80 192	82.06 119	98.48 141	82.80 255	95.37 153	97.61 123

mamba_0408		89.06 204	87.92 218	92.50 146	94.76 190	82.66 169	79.84 463	94.64 277	85.18 222	88.96 194	95.00 180	76.00 204	97.98 202	83.74 240	93.15 216	96.85 184

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 147	96.52 97	80.00 261	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		90.47 155	89.80 159	92.46 148	94.76 190	82.66 169	93.98 234	95.00 252	85.41 217	88.96 194	95.35 161	76.13 199	97.88 215	85.46 212	93.15 216	96.85 184

IS-MVSNet		91.43 124	91.09 127	92.46 148	95.87 131	81.38 208	96.95 24	93.69 318	89.72 66	89.50 184	95.98 123	78.57 170	97.77 219	83.02 249	96.50 127	98.22 70

API-MVS		90.66 148	90.07 150	92.45 150	96.36 102	84.57 93	96.06 73	95.22 239	82.39 295	89.13 189	94.27 220	80.32 139	98.46 145	80.16 306	96.71 121	94.33 298

xiu_mvs_v1_base_debu		90.64 149	90.05 151	92.40 151	93.97 253	84.46 99	93.32 269	95.46 217	85.17 224	92.25 115	94.03 225	70.59 287	98.57 136	90.97 124	94.67 169	94.18 301

xiu_mvs_v1_base		90.64 149	90.05 151	92.40 151	93.97 253	84.46 99	93.32 269	95.46 217	85.17 224	92.25 115	94.03 225	70.59 287	98.57 136	90.97 124	94.67 169	94.18 301

xiu_mvs_v1_base_debi		90.64 149	90.05 151	92.40 151	93.97 253	84.46 99	93.32 269	95.46 217	85.17 224	92.25 115	94.03 225	70.59 287	98.57 136	90.97 124	94.67 169	94.18 301

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 154	95.36 153	81.19 215	95.20 142	96.56 112	90.37 39	97.13 19	98.03 29	77.47 186	98.96 88	97.79 6	96.58 124	97.03 168

viewmacassd2359aftdt		91.67 121	91.43 118	92.37 155	93.95 256	81.00 223	93.90 242	95.97 171	87.75 145	91.45 143	96.04 119	79.92 146	97.97 204	89.26 152	94.67 169	98.14 76

viewmanbaseed2359cas		91.78 114	91.58 114	92.37 155	94.32 232	81.07 220	93.76 247	95.96 172	87.26 160	91.50 140	95.88 131	80.92 136	97.97 204	89.70 145	94.92 163	98.07 82

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 155	94.62 203	81.13 217	95.23 135	95.89 180	90.30 43	96.74 29	98.02 30	76.14 198	98.95 90	97.64 7	96.21 133	97.03 168

AdaColmap		89.89 174	89.07 181	92.37 155	97.41 71	83.03 154	94.42 194	95.92 175	82.81 289	86.34 253	94.65 201	73.89 243	99.02 72	80.69 297	95.51 146	95.05 261

CNLPA		89.07 203	87.98 215	92.34 159	96.87 83	84.78 88	94.08 222	93.24 324	81.41 327	84.46 309	95.13 177	75.57 215	96.62 318	77.21 338	93.84 195	95.61 245

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 160	95.13 168	80.95 226	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	97.13 160

ET-MVSNet_ETH3D		87.51 256	85.91 288	92.32 161	93.70 272	83.93 117	92.33 317	90.94 393	84.16 252	72.09 441	92.52 283	69.90 298	95.85 365	89.20 153	88.36 300	97.17 152

E2		91.79 111	91.61 112	92.31 162	94.49 217	80.86 231	93.74 249	96.19 148	87.63 150	91.16 148	95.94 126	81.31 131	98.06 190	89.76 142	94.29 184	97.99 91

Anonymous202405211		87.68 243	86.13 276	92.31 162	96.66 88	80.74 236	94.87 161	91.49 378	80.47 341	89.46 185	95.44 156	54.72 427	98.23 169	82.19 266	89.89 272	97.97 93

E3		91.78 114	91.61 112	92.30 164	94.48 218	80.86 231	93.73 250	96.19 148	87.63 150	91.16 148	95.95 125	81.30 132	98.06 190	89.76 142	94.29 184	97.99 91

CHOSEN 1792x2688		88.84 210	87.69 223	92.30 164	96.14 108	81.42 207	90.01 381	95.86 184	74.52 412	87.41 227	93.94 233	75.46 216	98.36 158	80.36 302	95.53 145	97.12 161

viewcassd2359sk11		91.79 111	91.62 111	92.29 166	94.62 203	80.88 229	93.70 254	96.18 150	87.38 157	91.13 151	95.85 135	81.62 127	98.06 190	89.71 144	94.40 181	97.94 95

HY-MVS	83.01 12	89.03 206	87.94 217	92.29 166	94.86 185	82.77 161	92.08 328	94.49 282	81.52 326	86.93 234	92.79 276	78.32 175	98.23 169	79.93 308	90.55 259	95.88 231

CDS-MVSNet		89.45 187	88.51 199	92.29 166	93.62 275	83.61 130	93.01 288	94.68 275	81.95 307	87.82 220	93.24 259	78.69 167	96.99 299	80.34 303	93.23 213	96.28 209
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 167	89.27 177	92.29 166	95.78 133	80.95 226	92.68 303	96.22 144	81.91 309	86.66 244	93.75 245	82.23 113	98.44 151	79.40 318	94.79 166	97.48 131

mvsmamba		90.33 156	89.69 162	92.25 170	95.17 164	81.64 198	95.27 133	93.36 323	84.88 236	89.51 182	94.27 220	69.29 312	97.42 257	89.34 150	96.12 136	97.68 119

PLC	84.53 7	89.06 204	88.03 213	92.15 171	97.27 77	82.69 168	94.29 206	95.44 222	79.71 350	84.01 325	94.18 223	76.68 195	98.75 115	77.28 337	93.41 207	95.02 262
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 119	91.56 115	92.13 172	95.88 129	80.50 243	97.33 8	95.25 236	86.15 193	89.76 179	95.60 149	83.42 91	98.32 165	87.37 184	93.25 212	97.56 128

patch_mono-2		93.74 65	94.32 41	92.01 173	97.54 66	78.37 303	93.40 265	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				92.01 173	97.34 73	81.05 221		96.81 87	78.89 361	90.45 161	95.92 128	82.65 104	98.84 105	80.68 298	98.26 63	96.14 216

UniMVSNet (Re)		89.80 177	89.07 181	92.01 173	93.60 276	84.52 96	94.78 169	97.47 17	89.26 82	86.44 250	92.32 289	82.10 117	97.39 268	84.81 220	80.84 391	94.12 305

MG-MVS		91.77 116	91.70 110	92.00 176	97.08 80	80.03 259	93.60 258	95.18 240	87.85 140	90.89 155	96.47 101	82.06 119	98.36 158	85.07 215	97.04 110	97.62 122

EIA-MVS		91.95 108	91.94 105	91.98 177	95.16 165	80.01 260	95.36 123	96.73 97	88.44 114	89.34 186	92.16 294	83.82 87	98.45 149	89.35 149	97.06 109	97.48 131

PVSNet_Blended		90.73 143	90.32 142	91.98 177	96.12 110	81.25 211	92.55 308	96.83 83	82.04 305	89.10 190	92.56 282	81.04 134	98.85 103	86.72 194	95.91 138	95.84 233

guyue		91.12 134	90.84 133	91.96 179	94.59 207	80.57 241	94.87 161	93.71 317	88.96 97	91.14 150	95.22 168	73.22 255	97.76 220	92.01 104	93.81 196	97.54 130

PS-MVSNAJ		91.18 131	90.92 130	91.96 179	95.26 160	82.60 175	92.09 327	95.70 198	86.27 189	91.84 130	92.46 284	79.70 151	98.99 81	89.08 154	95.86 139	94.29 299

TAMVS		89.21 197	88.29 208	91.96 179	93.71 270	82.62 174	93.30 273	94.19 296	82.22 300	87.78 221	93.94 233	78.83 164	96.95 302	77.70 333	92.98 221	96.32 206

SDMVSNet		90.19 160	89.61 165	91.93 182	96.00 121	83.09 151	92.89 296	95.98 168	88.73 104	86.85 240	95.20 172	72.09 270	97.08 291	88.90 159	89.85 274	95.63 243

FA-MVS(test-final)		89.66 179	88.91 189	91.93 182	94.57 211	80.27 247	91.36 344	94.74 272	84.87 237	89.82 176	92.61 281	74.72 226	98.47 144	83.97 235	93.53 202	97.04 167

MVS_Test		91.31 127	91.11 125	91.93 182	94.37 227	80.14 252	93.46 263	95.80 187	86.46 185	91.35 146	93.77 243	82.21 114	98.09 186	87.57 179	94.95 162	97.55 129

NR-MVSNet		88.58 220	87.47 229	91.93 182	93.04 297	84.16 111	94.77 170	96.25 141	89.05 89	80.04 386	93.29 257	79.02 163	97.05 296	81.71 281	80.05 401	94.59 282

HyFIR lowres test		88.09 233	86.81 246	91.93 182	96.00 121	80.63 238	90.01 381	95.79 188	73.42 423	87.68 223	92.10 300	73.86 244	97.96 206	80.75 296	91.70 242	97.19 151

GeoE		90.05 165	89.43 170	91.90 187	95.16 165	80.37 246	95.80 96	94.65 276	83.90 258	87.55 226	94.75 193	78.18 176	97.62 233	81.28 286	93.63 198	97.71 118

thisisatest0530		88.67 215	87.61 225	91.86 188	94.87 184	80.07 255	94.63 179	89.90 417	84.00 256	88.46 204	93.78 242	66.88 336	98.46 145	83.30 245	92.65 228	97.06 165

xiu_mvs_v2_base		91.13 133	90.89 132	91.86 188	94.97 176	82.42 177	92.24 320	95.64 205	86.11 197	91.74 136	93.14 263	79.67 156	98.89 97	89.06 155	95.46 150	94.28 300

DU-MVS		89.34 196	88.50 200	91.85 190	93.04 297	83.72 123	94.47 190	96.59 109	89.50 71	86.46 247	93.29 257	77.25 188	97.23 281	84.92 217	81.02 387	94.59 282

AstraMVS		90.69 145	90.30 143	91.84 191	93.81 261	79.85 266	94.76 171	92.39 347	88.96 97	91.01 154	95.87 134	70.69 285	97.94 209	92.49 82	92.70 227	97.73 116

OPM-MVS		90.12 161	89.56 166	91.82 192	93.14 289	83.90 118	94.16 213	95.74 192	88.96 97	87.86 216	95.43 158	72.48 265	97.91 212	88.10 171	90.18 266	93.65 336
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 152	90.19 145	91.82 192	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 236	94.86 188	74.23 234	98.12 176	88.15 167	89.99 268	94.63 279

UniMVSNet_NR-MVSNet		89.92 173	89.29 175	91.81 194	93.39 282	83.72 123	94.43 193	97.12 55	89.80 60	86.46 247	93.32 254	83.16 95	97.23 281	84.92 217	81.02 387	94.49 292

diffmvs		91.37 126	91.23 123	91.77 195	93.09 292	80.27 247	92.36 314	95.52 214	87.03 167	91.40 145	94.93 183	80.08 143	97.44 255	92.13 99	94.56 175	97.61 123
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		91.51 123	91.44 117	91.73 196	93.09 292	80.27 247	92.51 309	95.58 208	87.22 161	91.80 133	95.57 151	79.96 145	97.48 247	92.23 93	94.97 161	97.45 133

1112_ss		88.42 222	87.33 232	91.72 197	94.92 180	80.98 224	92.97 292	94.54 279	78.16 378	83.82 328	93.88 238	78.78 166	97.91 212	79.45 314	89.41 281	96.26 210

Fast-Effi-MVS+		89.41 191	88.64 195	91.71 198	94.74 193	80.81 234	93.54 259	95.10 244	83.11 281	86.82 242	90.67 354	79.74 150	97.75 224	80.51 301	93.55 200	96.57 199

WTY-MVS		89.60 181	88.92 188	91.67 199	95.47 151	81.15 216	92.38 313	94.78 270	83.11 281	89.06 192	94.32 215	78.67 168	96.61 321	81.57 282	90.89 255	97.24 146

TAPA-MVS	84.62 6	88.16 231	87.01 241	91.62 200	96.64 89	80.65 237	94.39 197	96.21 147	76.38 392	86.19 257	95.44 156	79.75 149	98.08 188	62.75 438	95.29 155	96.13 217
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 180	88.96 186	91.60 201	93.86 258	82.89 160	95.46 120	97.33 32	87.91 135	88.43 205	93.31 255	74.17 237	97.40 265	87.32 185	82.86 362	94.52 287

FE-MVS		87.40 261	86.02 282	91.57 202	94.56 212	79.69 270	90.27 368	93.72 316	80.57 339	88.80 198	91.62 320	65.32 352	98.59 135	74.97 363	94.33 183	96.44 202

XVG-OURS		89.40 193	88.70 194	91.52 203	94.06 245	81.46 205	91.27 348	96.07 161	86.14 194	88.89 197	95.77 143	68.73 321	97.26 278	87.39 183	89.96 270	95.83 234

hse-mvs2		89.88 175	89.34 173	91.51 204	94.83 187	81.12 218	93.94 236	93.91 309	89.80 60	93.08 89	93.60 248	75.77 208	97.66 228	92.07 100	77.07 421	95.74 238

TranMVSNet+NR-MVSNet		88.84 210	87.95 216	91.49 205	92.68 313	83.01 156	94.92 158	96.31 130	89.88 54	85.53 273	93.85 240	76.63 196	96.96 301	81.91 274	79.87 404	94.50 290

AUN-MVS		87.78 241	86.54 261	91.48 206	94.82 188	81.05 221	93.91 240	93.93 306	83.00 284	86.93 234	93.53 249	69.50 306	97.67 226	86.14 200	77.12 420	95.73 240

XVG-OURS-SEG-HR		89.95 171	89.45 168	91.47 207	94.00 251	81.21 214	91.87 332	96.06 163	85.78 201	88.55 202	95.73 145	74.67 227	97.27 276	88.71 163	89.64 279	95.91 228

MVS		87.44 259	86.10 279	91.44 208	92.61 314	83.62 128	92.63 305	95.66 202	67.26 449	81.47 364	92.15 295	77.95 179	98.22 171	79.71 310	95.48 148	92.47 379

viewdifsd2359ckpt07		91.11 135	91.02 128	91.41 209	94.21 238	78.37 303	92.91 295	95.71 197	87.50 152	90.32 164	95.88 131	80.27 141	97.99 199	88.78 162	93.55 200	97.86 104

F-COLMAP		87.95 236	86.80 247	91.40 210	96.35 103	80.88 229	94.73 173	95.45 220	79.65 351	82.04 359	94.61 202	71.13 277	98.50 139	76.24 350	91.05 253	94.80 276

dcpmvs_2		93.49 70	94.19 52	91.38 211	97.69 63	76.78 345	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		87.33 264	85.99 283	91.37 212	93.49 278	79.55 271	90.63 362	89.56 425	80.17 343	87.56 225	90.86 344	67.07 333	98.28 167	81.50 283	93.02 220	96.29 208

HQP-MVS		89.80 177	89.28 176	91.34 213	94.17 240	81.56 199	94.39 197	96.04 164	88.81 100	85.43 282	93.97 232	73.83 245	97.96 206	87.11 189	89.77 277	94.50 290

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 214	94.42 225	79.48 273	94.52 185	97.14 53	89.33 78	94.17 65	98.09 18	81.83 124	97.49 246	96.33 26	98.02 81	96.95 175

RRT-MVS		90.85 139	90.70 137	91.30 215	94.25 235	76.83 344	94.85 164	96.13 155	89.04 90	90.23 166	94.88 186	70.15 296	98.72 119	91.86 112	94.88 164	98.34 48

FMVSNet3		87.40 261	86.11 278	91.30 215	93.79 264	83.64 127	94.20 212	94.81 268	83.89 259	84.37 312	91.87 311	68.45 324	96.56 326	78.23 328	85.36 330	93.70 335

FMVSNet2		87.19 274	85.82 291	91.30 215	94.01 248	83.67 125	94.79 168	94.94 254	83.57 267	83.88 327	92.05 304	66.59 341	96.51 330	77.56 335	85.01 333	93.73 333

RPMNet		83.95 351	81.53 362	91.21 218	90.58 391	79.34 279	85.24 441	96.76 92	71.44 437	85.55 271	82.97 449	70.87 282	98.91 96	61.01 442	89.36 283	95.40 249

IB-MVS	80.51 15	85.24 328	83.26 346	91.19 219	92.13 326	79.86 265	91.75 335	91.29 383	83.28 278	80.66 376	88.49 401	61.28 384	98.46 145	80.99 292	79.46 408	95.25 255
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		89.47 186	88.90 190	91.18 220	94.22 237	82.07 187	92.13 325	96.09 159	87.90 136	85.37 288	92.45 285	74.38 232	97.56 238	87.15 187	90.43 261	93.93 314
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		89.45 187	88.90 190	91.12 221	94.47 219	81.49 203	95.30 128	96.14 152	86.73 178	85.45 279	95.16 174	69.89 299	98.10 178	87.70 176	89.23 286	93.77 329

LGP-MVS_train				91.12 221	94.47 219	81.49 203		96.14 152	86.73 178	85.45 279	95.16 174	69.89 299	98.10 178	87.70 176	89.23 286	93.77 329

ACMM	84.12 9	89.14 199	88.48 203	91.12 221	94.65 202	81.22 213	95.31 126	96.12 156	85.31 221	85.92 262	94.34 213	70.19 295	98.06 190	85.65 208	88.86 291	94.08 309
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 217	87.78 222	91.11 224	94.96 177	77.81 321	95.35 124	89.69 420	85.09 231	88.05 214	94.59 205	66.93 334	98.48 141	83.27 246	92.13 240	97.03 168

GBi-Net		87.26 266	85.98 284	91.08 225	94.01 248	83.10 148	95.14 146	94.94 254	83.57 267	84.37 312	91.64 316	66.59 341	96.34 343	78.23 328	85.36 330	93.79 324

test1		87.26 266	85.98 284	91.08 225	94.01 248	83.10 148	95.14 146	94.94 254	83.57 267	84.37 312	91.64 316	66.59 341	96.34 343	78.23 328	85.36 330	93.79 324

FMVSNet1		85.85 313	84.11 333	91.08 225	92.81 308	83.10 148	95.14 146	94.94 254	81.64 321	82.68 349	91.64 316	59.01 406	96.34 343	75.37 357	83.78 346	93.79 324

Test_1112_low_res		87.65 245	86.51 262	91.08 225	94.94 179	79.28 283	91.77 334	94.30 291	76.04 397	83.51 338	92.37 287	77.86 182	97.73 225	78.69 323	89.13 288	96.22 211

PS-MVSNAJss		89.97 169	89.62 164	91.02 229	91.90 335	80.85 233	95.26 134	95.98 168	86.26 190	86.21 256	94.29 217	79.70 151	97.65 229	88.87 161	88.10 302	94.57 284

BH-RMVSNet		88.37 225	87.48 228	91.02 229	95.28 157	79.45 275	92.89 296	93.07 330	85.45 216	86.91 236	94.84 191	70.35 292	97.76 220	73.97 371	94.59 174	95.85 232

UniMVSNet_ETH3D		87.53 255	86.37 266	91.00 231	92.44 318	78.96 288	94.74 172	95.61 206	84.07 255	85.36 289	94.52 207	59.78 398	97.34 270	82.93 250	87.88 307	96.71 192

FIs		90.51 154	90.35 141	90.99 232	93.99 252	80.98 224	95.73 103	97.54 10	89.15 86	86.72 243	94.68 196	81.83 124	97.24 280	85.18 214	88.31 301	94.76 277

ACMP	84.23 8	89.01 208	88.35 204	90.99 232	94.73 194	81.27 210	95.07 149	95.89 180	86.48 183	83.67 333	94.30 216	69.33 308	97.99 199	87.10 191	88.55 293	93.72 334
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 296	85.13 311	90.98 234	96.52 97	81.50 201	96.14 64	96.16 151	73.78 419	83.65 334	92.15 295	63.26 368	97.37 269	82.82 254	81.74 376	94.06 310

IMVS_0403		89.97 169	89.64 163	90.96 235	93.72 266	77.75 326	93.00 289	95.34 231	85.53 212	88.77 199	94.49 208	78.49 172	97.84 216	84.75 221	92.65 228	97.28 140

sss		88.93 209	88.26 210	90.94 236	94.05 246	80.78 235	91.71 336	95.38 226	81.55 325	88.63 201	93.91 237	75.04 220	95.47 384	82.47 259	91.61 243	96.57 199

IMVS_0407		89.85 176	89.51 167	90.88 237	93.72 266	77.75 326	93.07 286	95.34 231	85.53 212	88.34 207	94.49 208	77.69 184	97.60 234	84.75 221	92.65 228	97.28 140

viewmambaseed2359dif		90.04 166	89.78 160	90.83 238	92.85 307	77.92 315	92.23 321	95.01 248	81.90 310	90.20 167	95.45 155	79.64 158	97.34 270	87.52 181	93.17 214	97.23 149

sd_testset		88.59 219	87.85 221	90.83 238	96.00 121	80.42 245	92.35 315	94.71 273	88.73 104	86.85 240	95.20 172	67.31 328	96.43 337	79.64 312	89.85 274	95.63 243

PVSNet_BlendedMVS		89.98 168	89.70 161	90.82 240	96.12 110	81.25 211	93.92 238	96.83 83	83.49 271	89.10 190	92.26 292	81.04 134	98.85 103	86.72 194	87.86 308	92.35 385

cascas		86.43 304	84.98 314	90.80 241	92.10 328	80.92 228	90.24 372	95.91 177	73.10 426	83.57 337	88.39 402	65.15 354	97.46 251	84.90 219	91.43 245	94.03 312

ECVR-MVS		89.09 202	88.53 198	90.77 242	95.62 144	75.89 358	96.16 60	84.22 451	87.89 138	90.20 167	96.65 91	63.19 369	98.10 178	85.90 205	96.94 112	98.33 50

GA-MVS		86.61 294	85.27 308	90.66 243	91.33 358	78.71 292	90.40 367	93.81 313	85.34 220	85.12 292	89.57 383	61.25 385	97.11 290	80.99 292	89.59 280	96.15 215

thres600view7		87.65 245	86.67 253	90.59 244	96.08 116	78.72 290	94.88 160	91.58 374	87.06 166	88.08 212	92.30 290	68.91 318	98.10 178	70.05 401	91.10 248	94.96 266

thres400		87.62 250	86.64 254	90.57 245	95.99 124	78.64 293	94.58 181	91.98 363	86.94 172	88.09 210	91.77 312	69.18 314	98.10 178	70.13 398	91.10 248	94.96 266

baseline1		88.10 232	87.28 234	90.57 245	94.96 177	80.07 255	94.27 207	91.29 383	86.74 177	87.41 227	94.00 230	76.77 193	96.20 348	80.77 295	79.31 410	95.44 247

viewdifsd2359ckpt11		89.43 189	89.05 183	90.56 247	92.89 305	77.00 340	92.81 299	94.52 280	87.03 167	89.77 177	95.79 140	74.67 227	97.51 242	88.97 157	84.98 334	97.17 152

viewmsd2359difaftdt		89.43 189	89.05 183	90.56 247	92.89 305	77.00 340	92.81 299	94.52 280	87.03 167	89.77 177	95.79 140	74.67 227	97.51 242	88.97 157	84.98 334	97.17 152

FC-MVSNet-test		90.27 158	90.18 146	90.53 249	93.71 270	79.85 266	95.77 99	97.59 7	89.31 79	86.27 254	94.67 199	81.93 122	97.01 298	84.26 231	88.09 304	94.71 278

PAPM		86.68 293	85.39 303	90.53 249	93.05 296	79.33 282	89.79 384	94.77 271	78.82 364	81.95 360	93.24 259	76.81 191	97.30 272	66.94 418	93.16 215	94.95 270

WR-MVS		88.38 224	87.67 224	90.52 251	93.30 284	80.18 250	93.26 276	95.96 172	88.57 112	85.47 278	92.81 274	76.12 201	96.91 305	81.24 287	82.29 367	94.47 295

SSM_04072		88.57 221	87.92 218	90.51 252	94.76 190	82.66 169	79.84 463	94.64 277	85.18 222	88.96 194	95.00 180	76.00 204	92.03 432	83.74 240	93.15 216	96.85 184

MVSTER		88.84 210	88.29 208	90.51 252	92.95 302	80.44 244	93.73 250	95.01 248	84.66 246	87.15 231	93.12 264	72.79 260	97.21 283	87.86 173	87.36 316	93.87 319

testdata				90.49 254	96.40 100	77.89 318		95.37 228	72.51 431	93.63 78	96.69 87	82.08 118	97.65 229	83.08 247	97.39 102	95.94 227

test1111		89.10 200	88.64 195	90.48 255	95.53 149	74.97 368	96.08 69	84.89 449	88.13 127	90.16 171	96.65 91	63.29 367	98.10 178	86.14 200	96.90 114	98.39 45

tt0805		86.92 282	85.74 297	90.48 255	92.22 322	79.98 262	95.63 113	94.88 262	83.83 261	84.74 301	92.80 275	57.61 412	97.67 226	85.48 211	84.42 339	93.79 324

jajsoiax		88.24 229	87.50 227	90.48 255	90.89 379	80.14 252	95.31 126	95.65 204	84.97 234	84.24 320	94.02 228	65.31 353	97.42 257	88.56 164	88.52 295	93.89 315

PatchMatch-RL		86.77 290	85.54 299	90.47 258	95.88 129	82.71 167	90.54 365	92.31 351	79.82 349	84.32 317	91.57 324	68.77 320	96.39 339	73.16 377	93.48 206	92.32 386

tfpn200view9		87.58 253	86.64 254	90.41 259	95.99 124	78.64 293	94.58 181	91.98 363	86.94 172	88.09 210	91.77 312	69.18 314	98.10 178	70.13 398	91.10 248	94.48 293

VPNet		88.20 230	87.47 229	90.39 260	93.56 277	79.46 274	94.04 226	95.54 212	88.67 107	86.96 233	94.58 206	69.33 308	97.15 285	84.05 234	80.53 396	94.56 285

ACMH	80.38 17	85.36 323	83.68 340	90.39 260	94.45 222	80.63 238	94.73 173	94.85 264	82.09 302	77.24 411	92.65 279	60.01 396	97.58 236	72.25 382	84.87 336	92.96 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 248	86.71 250	90.38 262	96.12 110	78.55 296	95.03 152	91.58 374	87.15 163	88.06 213	92.29 291	68.91 318	98.10 178	70.13 398	91.10 248	94.48 293

mvs_tets		88.06 235	87.28 234	90.38 262	90.94 375	79.88 264	95.22 137	95.66 202	85.10 230	84.21 321	93.94 233	63.53 365	97.40 265	88.50 165	88.40 299	93.87 319

1314		87.51 256	86.57 259	90.34 264	92.42 319	79.74 269	92.63 305	95.35 230	78.35 373	80.14 383	91.62 320	74.05 239	97.15 285	81.05 288	93.53 202	94.12 305

LTVRE_ROB	82.13 13	86.26 307	84.90 317	90.34 264	94.44 223	81.50 201	92.31 319	94.89 260	83.03 283	79.63 393	92.67 278	69.69 302	97.79 218	71.20 387	86.26 325	91.72 396
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		89.03 206	88.64 195	90.21 266	90.74 386	79.28 283	95.96 83	95.90 178	84.66 246	85.33 290	92.94 269	74.02 240	97.30 272	89.64 147	88.53 294	94.05 311

v2v482		87.84 238	87.06 238	90.17 267	90.99 371	79.23 286	94.00 232	95.13 241	84.87 237	85.53 273	92.07 303	74.45 231	97.45 252	84.71 226	81.75 375	93.85 322

pmmvs4		85.43 321	83.86 338	90.16 268	90.02 404	82.97 158	90.27 368	92.67 342	75.93 398	80.73 374	91.74 314	71.05 278	95.73 373	78.85 322	83.46 353	91.78 395

V42		87.68 243	86.86 243	90.15 269	90.58 391	80.14 252	94.24 210	95.28 235	83.66 265	85.67 268	91.33 326	74.73 225	97.41 263	84.43 230	81.83 373	92.89 367

MSDG		84.86 336	83.09 349	90.14 270	93.80 262	80.05 257	89.18 397	93.09 329	78.89 361	78.19 403	91.91 309	65.86 351	97.27 276	68.47 407	88.45 297	93.11 359

sc_t1		81.53 375	78.67 396	90.12 271	90.78 383	78.64 293	93.91 240	90.20 406	68.42 446	80.82 373	89.88 376	46.48 450	96.76 310	76.03 353	71.47 435	94.96 266

anonymousdsp		87.84 238	87.09 237	90.12 271	89.13 415	80.54 242	94.67 177	95.55 210	82.05 303	83.82 328	92.12 297	71.47 275	97.15 285	87.15 187	87.80 311	92.67 373

thres200		87.21 272	86.24 273	90.12 271	95.36 153	78.53 297	93.26 276	92.10 357	86.42 186	88.00 215	91.11 337	69.24 313	98.00 198	69.58 402	91.04 254	93.83 323

CR-MVSNet		85.35 324	83.76 339	90.12 271	90.58 391	79.34 279	85.24 441	91.96 365	78.27 375	85.55 271	87.87 412	71.03 279	95.61 376	73.96 372	89.36 283	95.40 249

v1144		87.61 251	86.79 248	90.06 275	91.01 370	79.34 279	93.95 235	95.42 225	83.36 276	85.66 269	91.31 329	74.98 221	97.42 257	83.37 244	82.06 369	93.42 345

XXY-MVS		87.65 245	86.85 244	90.03 276	92.14 325	80.60 240	93.76 247	95.23 237	82.94 286	84.60 303	94.02 228	74.27 233	95.49 383	81.04 289	83.68 349	94.01 313

Vis-MVSNet (Re-imp)		89.59 182	89.44 169	90.03 276	95.74 134	75.85 359	95.61 114	90.80 397	87.66 149	87.83 219	95.40 159	76.79 192	96.46 335	78.37 324	96.73 120	97.80 111

test2506		87.21 272	86.28 271	90.02 278	95.62 144	73.64 384	96.25 55	71.38 474	87.89 138	90.45 161	96.65 91	55.29 424	98.09 186	86.03 204	96.94 112	98.33 50

BH-untuned		88.60 218	88.13 212	90.01 279	95.24 161	78.50 299	93.29 274	94.15 299	84.75 242	84.46 309	93.40 251	75.76 210	97.40 265	77.59 334	94.52 177	94.12 305

v1192		87.25 268	86.33 268	90.00 280	90.76 385	79.04 287	93.80 245	95.48 215	82.57 293	85.48 277	91.18 333	73.38 254	97.42 257	82.30 263	82.06 369	93.53 339

v7n		86.81 285	85.76 295	89.95 281	90.72 387	79.25 285	95.07 149	95.92 175	84.45 249	82.29 353	90.86 344	72.60 264	97.53 240	79.42 317	80.52 397	93.08 361

testing91		87.11 277	86.18 274	89.92 282	94.43 224	75.38 367	91.53 341	92.27 353	86.48 183	86.50 245	90.24 362	61.19 388	97.53 240	82.10 268	90.88 256	96.84 187

IMVS_0404		87.60 252	86.84 245	89.89 283	93.72 266	77.75 326	88.56 406	95.34 231	85.53 212	79.98 387	94.49 208	66.54 344	94.64 397	84.75 221	92.65 228	97.28 140

v8		87.50 258	86.71 250	89.89 283	91.37 355	79.40 276	94.50 186	95.38 226	84.81 240	83.60 336	91.33 326	76.05 202	97.42 257	82.84 253	80.51 398	92.84 369

v10		87.25 268	86.38 265	89.85 285	91.19 361	79.50 272	94.48 187	95.45 220	83.79 263	83.62 335	91.19 331	75.13 218	97.42 257	81.94 273	80.60 393	92.63 375

baseline2		86.50 300	85.39 303	89.84 286	91.12 366	76.70 347	91.88 331	88.58 429	82.35 298	79.95 388	90.95 342	73.42 252	97.63 232	80.27 305	89.95 271	95.19 256

pm-mvs1		86.61 294	85.54 299	89.82 287	91.44 350	80.18 250	95.28 132	94.85 264	83.84 260	81.66 362	92.62 280	72.45 267	96.48 332	79.67 311	78.06 413	92.82 370

TR-MVS		86.78 287	85.76 295	89.82 287	94.37 227	78.41 301	92.47 310	92.83 336	81.11 335	86.36 251	92.40 286	68.73 321	97.48 247	73.75 375	89.85 274	93.57 338

ACMH+	81.04 14	85.05 331	83.46 343	89.82 287	94.66 201	79.37 277	94.44 192	94.12 302	82.19 301	78.04 405	92.82 273	58.23 409	97.54 239	73.77 374	82.90 361	92.54 376

EI-MVSNet		89.10 200	88.86 192	89.80 290	91.84 337	78.30 306	93.70 254	95.01 248	85.73 203	87.15 231	95.28 165	79.87 148	97.21 283	83.81 238	87.36 316	93.88 318

v144192		87.19 274	86.35 267	89.74 291	90.64 389	78.24 308	93.92 238	95.43 223	81.93 308	85.51 275	91.05 340	74.21 236	97.45 252	82.86 252	81.56 377	93.53 339

COLMAP_ROB	80.39 16	83.96 350	82.04 359	89.74 291	95.28 157	79.75 268	94.25 208	92.28 352	75.17 405	78.02 406	93.77 243	58.60 408	97.84 216	65.06 429	85.92 326	91.63 398
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 306	85.18 310	89.73 293	92.15 324	76.60 348	91.12 352	91.69 370	83.53 270	85.50 276	88.81 395	66.79 337	96.48 332	76.65 343	90.35 263	96.12 218

IterMVS-LS		88.36 226	87.91 220	89.70 294	93.80 262	78.29 307	93.73 250	95.08 246	85.73 203	84.75 300	91.90 310	79.88 147	96.92 304	83.83 237	82.51 363	93.89 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 303	85.35 306	89.69 295	94.29 234	75.40 366	91.30 346	90.53 401	84.76 241	85.06 294	90.13 368	58.95 407	97.45 252	82.08 269	91.09 252	96.21 213

testing99		86.72 291	85.73 298	89.69 295	94.23 236	74.91 370	91.35 345	90.97 391	86.14 194	86.36 251	90.22 363	59.41 401	97.48 247	82.24 265	90.66 258	96.69 194

v1921920		86.97 281	86.06 281	89.69 295	90.53 394	78.11 311	93.80 245	95.43 223	81.90 310	85.33 290	91.05 340	72.66 261	97.41 263	82.05 271	81.80 374	93.53 339

icg_test_0407_2		89.15 198	88.97 185	89.68 298	93.72 266	77.75 326	88.26 411	95.34 231	85.53 212	88.34 207	94.49 208	77.69 184	93.99 408	84.75 221	92.65 228	97.28 140

VortexMVS		88.42 222	88.01 214	89.63 299	93.89 257	78.82 289	93.82 244	95.47 216	86.67 180	84.53 307	91.99 306	72.62 263	96.65 316	89.02 156	84.09 343	93.41 346

Fast-Effi-MVS+-dtu		87.44 259	86.72 249	89.63 299	92.04 329	77.68 331	94.03 227	93.94 305	85.81 200	82.42 352	91.32 328	70.33 293	97.06 294	80.33 304	90.23 265	94.14 304

v1240		86.78 287	85.85 290	89.56 301	90.45 396	77.79 323	93.61 257	95.37 228	81.65 320	85.43 282	91.15 335	71.50 274	97.43 256	81.47 284	82.05 371	93.47 343

Effi-MVS+-dtu		88.65 216	88.35 204	89.54 302	93.33 283	76.39 352	94.47 190	94.36 289	87.70 146	85.43 282	89.56 384	73.45 250	97.26 278	85.57 210	91.28 247	94.97 263

AllTest		83.42 357	81.39 363	89.52 303	95.01 171	77.79 323	93.12 280	90.89 395	77.41 382	76.12 420	93.34 252	54.08 430	97.51 242	68.31 409	84.27 341	93.26 349

TestCases				89.52 303	95.01 171	77.79 323		90.89 395	77.41 382	76.12 420	93.34 252	54.08 430	97.51 242	68.31 409	84.27 341	93.26 349

mvs_anonymous		89.37 195	89.32 174	89.51 305	93.47 279	74.22 377	91.65 339	94.83 266	82.91 287	85.45 279	93.79 241	81.23 133	96.36 342	86.47 196	94.09 189	97.94 95

XVG-ACMP-BASELINE		86.00 309	84.84 319	89.45 306	91.20 360	78.00 313	91.70 337	95.55 210	85.05 232	82.97 346	92.25 293	54.49 428	97.48 247	82.93 250	87.45 315	92.89 367

testing222		84.84 337	83.32 344	89.43 307	94.15 243	75.94 357	91.09 353	89.41 427	84.90 235	85.78 265	89.44 385	52.70 435	96.28 346	70.80 393	91.57 244	96.07 222

MVP-Stereo		85.97 310	84.86 318	89.32 308	90.92 377	82.19 184	92.11 326	94.19 296	78.76 366	78.77 402	91.63 319	68.38 325	96.56 326	75.01 362	93.95 191	89.20 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 313	84.70 321	89.29 309	91.76 341	75.54 363	88.49 407	91.30 382	81.63 322	85.05 295	88.70 399	71.71 271	96.24 347	74.61 367	89.05 289	96.08 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 279	86.32 269	89.21 310	90.94 375	77.26 336	93.71 253	94.43 284	84.84 239	84.36 315	90.80 348	76.04 203	97.05 296	82.12 267	79.60 407	93.31 348

tfpnnormal		84.72 339	83.23 347	89.20 311	92.79 309	80.05 257	94.48 187	95.81 186	82.38 296	81.08 370	91.21 330	69.01 317	96.95 302	61.69 440	80.59 394	90.58 423

cl22		86.78 287	85.98 284	89.18 312	92.34 320	77.62 332	90.84 358	94.13 301	81.33 329	83.97 326	90.15 367	73.96 241	96.60 323	84.19 232	82.94 358	93.33 347

BH-w/o		87.57 254	87.05 239	89.12 313	94.90 183	77.90 317	92.41 311	93.51 320	82.89 288	83.70 332	91.34 325	75.75 211	97.07 293	75.49 355	93.49 204	92.39 383

WR-MVS_H		87.80 240	87.37 231	89.10 314	93.23 285	78.12 310	95.61 114	97.30 37	87.90 136	83.72 331	92.01 305	79.65 157	96.01 357	76.36 347	80.54 395	93.16 357

miper_enhance_ethall		86.90 283	86.18 274	89.06 315	91.66 346	77.58 333	90.22 374	94.82 267	79.16 357	84.48 308	89.10 389	79.19 162	96.66 315	84.06 233	82.94 358	92.94 365

c3_l		87.14 276	86.50 263	89.04 316	92.20 323	77.26 336	91.22 351	94.70 274	82.01 306	84.34 316	90.43 359	78.81 165	96.61 321	83.70 242	81.09 384	93.25 351

miper_ehance_all_eth		87.22 271	86.62 257	89.02 317	92.13 326	77.40 335	90.91 357	94.81 268	81.28 330	84.32 317	90.08 370	79.26 160	96.62 318	83.81 238	82.94 358	93.04 362

gg-mvs-nofinetune		81.77 369	79.37 384	88.99 318	90.85 381	77.73 330	86.29 433	79.63 462	74.88 410	83.19 345	69.05 465	60.34 393	96.11 352	75.46 356	94.64 173	93.11 359

ETVMVS		84.43 344	82.92 353	88.97 319	94.37 227	74.67 371	91.23 350	88.35 431	83.37 275	86.06 260	89.04 390	55.38 422	95.67 375	67.12 416	91.34 246	96.58 198

pmmvs6		83.42 357	81.60 361	88.87 320	88.01 430	77.87 319	94.96 155	94.24 295	74.67 411	78.80 401	91.09 338	60.17 395	96.49 331	77.06 342	75.40 427	92.23 388

test_cas_vis1_n_1920		88.83 213	88.85 193	88.78 321	91.15 365	76.72 346	93.85 243	94.93 258	83.23 280	92.81 98	96.00 121	61.17 389	94.45 398	91.67 115	94.84 165	95.17 257

MIMVSNet		82.59 363	80.53 368	88.76 322	91.51 348	78.32 305	86.57 432	90.13 409	79.32 353	80.70 375	88.69 400	52.98 434	93.07 424	66.03 424	88.86 291	94.90 271

cl____		86.52 299	85.78 292	88.75 323	92.03 330	76.46 350	90.74 359	94.30 291	81.83 316	83.34 342	90.78 349	75.74 213	96.57 324	81.74 279	81.54 378	93.22 353

DIV-MVS_self_test		86.53 298	85.78 292	88.75 323	92.02 331	76.45 351	90.74 359	94.30 291	81.83 316	83.34 342	90.82 347	75.75 211	96.57 324	81.73 280	81.52 379	93.24 352

CP-MVSNet		87.63 248	87.26 236	88.74 325	93.12 290	76.59 349	95.29 130	96.58 110	88.43 115	83.49 339	92.98 268	75.28 217	95.83 366	78.97 320	81.15 383	93.79 324

eth_miper_zixun_eth		86.50 300	85.77 294	88.68 326	91.94 332	75.81 360	90.47 366	94.89 260	82.05 303	84.05 323	90.46 358	75.96 206	96.77 309	82.76 256	79.36 409	93.46 344

CHOSEN 280x420		85.15 329	83.99 336	88.65 327	92.47 316	78.40 302	79.68 465	92.76 339	74.90 409	81.41 366	89.59 382	69.85 301	95.51 380	79.92 309	95.29 155	92.03 391

PS-CasMVS		87.32 265	86.88 242	88.63 328	92.99 300	76.33 354	95.33 125	96.61 108	88.22 123	83.30 344	93.07 266	73.03 258	95.79 370	78.36 325	81.00 389	93.75 331

TransMVSNet (Re)		84.43 344	83.06 351	88.54 329	91.72 342	78.44 300	95.18 143	92.82 338	82.73 291	79.67 392	92.12 297	73.49 249	95.96 359	71.10 391	68.73 446	91.21 410

tt0320-xc		79.63 398	76.66 407	88.52 330	91.03 369	78.72 290	93.00 289	89.53 426	66.37 450	76.11 422	87.11 423	46.36 452	95.32 388	72.78 379	67.67 447	91.51 402

EG-PatchMatch MVS		82.37 365	80.34 371	88.46 331	90.27 398	79.35 278	92.80 302	94.33 290	77.14 386	73.26 438	90.18 366	47.47 447	96.72 311	70.25 395	87.32 318	89.30 433

PEN-MVS		86.80 286	86.27 272	88.40 332	92.32 321	75.71 362	95.18 143	96.38 125	87.97 133	82.82 348	93.15 262	73.39 253	95.92 361	76.15 351	79.03 412	93.59 337

Baseline_NR-MVSNet		87.07 278	86.63 256	88.40 332	91.44 350	77.87 319	94.23 211	92.57 344	84.12 254	85.74 267	92.08 301	77.25 188	96.04 353	82.29 264	79.94 402	91.30 408

UBG		85.51 319	84.57 326	88.35 334	94.21 238	71.78 409	90.07 379	89.66 422	82.28 299	85.91 263	89.01 391	61.30 383	97.06 294	76.58 346	92.06 241	96.22 211

D2MVS		85.90 311	85.09 312	88.35 334	90.79 382	77.42 334	91.83 333	95.70 198	80.77 338	80.08 385	90.02 372	66.74 339	96.37 340	81.88 275	87.97 306	91.26 409

pmmvs5		84.21 346	82.84 356	88.34 336	88.95 417	76.94 342	92.41 311	91.91 367	75.63 400	80.28 380	91.18 333	64.59 359	95.57 377	77.09 341	83.47 352	92.53 377

mamv4		90.92 137	91.78 108	88.33 337	95.67 140	70.75 422	92.92 294	96.02 167	81.90 310	88.11 209	95.34 163	85.88 55	96.97 300	95.22 43	95.01 160	97.26 144

tt0320		80.13 391	77.41 400	88.29 338	90.50 395	78.02 312	93.10 283	90.71 399	66.06 453	76.75 415	86.97 424	49.56 442	95.40 385	71.65 383	71.41 436	91.46 405

LCM-MVSNet-Re		88.30 228	88.32 207	88.27 339	94.71 198	72.41 404	93.15 279	90.98 390	87.77 143	79.25 396	91.96 307	78.35 174	95.75 371	83.04 248	95.62 144	96.65 195

CostFormer		85.77 316	84.94 316	88.26 340	91.16 364	72.58 402	89.47 392	91.04 389	76.26 395	86.45 249	89.97 374	70.74 284	96.86 308	82.35 262	87.07 321	95.34 253

ITE_SJBPF				88.24 341	91.88 336	77.05 339		92.92 333	85.54 210	80.13 384	93.30 256	57.29 413	96.20 348	72.46 381	84.71 337	91.49 403

PVSNet	78.82 18	85.55 318	84.65 322	88.23 342	94.72 196	71.93 405	87.12 428	92.75 340	78.80 365	84.95 297	90.53 356	64.43 360	96.71 313	74.74 365	93.86 193	96.06 224

IterMVS-SCA-FT		85.45 320	84.53 327	88.18 343	91.71 343	76.87 343	90.19 376	92.65 343	85.40 219	81.44 365	90.54 355	66.79 337	95.00 394	81.04 289	81.05 385	92.66 374

EPNet_dtu		86.49 302	85.94 287	88.14 344	90.24 399	72.82 394	94.11 217	92.20 355	86.66 181	79.42 395	92.36 288	73.52 248	95.81 368	71.26 386	93.66 197	95.80 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 361	80.93 367	88.06 345	90.05 403	76.37 353	84.74 446	91.96 365	72.28 434	81.32 368	87.87 412	71.03 279	95.50 382	68.97 404	80.15 400	92.32 386

test_vis1_n_1920		89.39 194	89.84 157	88.04 346	92.97 301	72.64 399	94.71 175	96.03 166	86.18 192	91.94 127	96.56 99	61.63 378	95.74 372	93.42 65	95.11 159	95.74 238

DTE-MVSNet		86.11 308	85.48 301	87.98 347	91.65 347	74.92 369	94.93 157	95.75 191	87.36 158	82.26 354	93.04 267	72.85 259	95.82 367	74.04 370	77.46 418	93.20 355

PMMVS		85.71 317	84.96 315	87.95 348	88.90 418	77.09 338	88.68 404	90.06 411	72.32 433	86.47 246	90.76 350	72.15 269	94.40 400	81.78 278	93.49 204	92.36 384

GG-mvs-BLEND				87.94 349	89.73 410	77.91 316	87.80 417	78.23 467		80.58 377	83.86 442	59.88 397	95.33 387	71.20 387	92.22 239	90.60 422

MonoMVSNet		86.89 284	86.55 260	87.92 350	89.46 413	73.75 381	94.12 215	93.10 328	87.82 142	85.10 293	90.76 350	69.59 304	94.94 395	86.47 196	82.50 364	95.07 260

reproduce_monomvs		86.37 305	85.87 289	87.87 351	93.66 274	73.71 382	93.44 264	95.02 247	88.61 110	82.64 351	91.94 308	57.88 411	96.68 314	89.96 140	79.71 406	93.22 353

pmmvs-eth3d		80.97 384	78.72 395	87.74 352	84.99 448	79.97 263	90.11 378	91.65 372	75.36 402	73.51 436	86.03 431	59.45 400	93.96 411	75.17 359	72.21 432	89.29 435

MS-PatchMatch		85.05 331	84.16 331	87.73 353	91.42 353	78.51 298	91.25 349	93.53 319	77.50 381	80.15 382	91.58 322	61.99 375	95.51 380	75.69 354	94.35 182	89.16 437

mmtdpeth		85.04 333	84.15 332	87.72 354	93.11 291	75.74 361	94.37 201	92.83 336	84.98 233	89.31 187	86.41 428	61.61 380	97.14 288	92.63 81	62.11 457	90.29 424

test_0402		81.30 380	79.17 389	87.67 355	93.19 286	78.17 309	92.98 291	91.71 368	75.25 404	76.02 423	90.31 361	59.23 402	96.37 340	50.22 460	83.63 350	88.47 445

IterMVS		84.88 335	83.98 337	87.60 356	91.44 350	76.03 356	90.18 377	92.41 346	83.24 279	81.06 371	90.42 360	66.60 340	94.28 404	79.46 313	80.98 390	92.48 378
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 378	79.30 385	87.58 357	90.92 377	74.16 379	80.99 458	87.68 436	70.52 441	76.63 417	88.81 395	71.21 276	92.76 427	60.01 446	86.93 322	95.83 234

EPMVS		83.90 353	82.70 357	87.51 358	90.23 400	72.67 397	88.62 405	81.96 457	81.37 328	85.01 296	88.34 403	66.31 345	94.45 398	75.30 358	87.12 319	95.43 248

ADS-MVSNet2		81.66 372	79.71 381	87.50 359	91.35 356	74.19 378	83.33 451	88.48 430	72.90 428	82.24 355	85.77 434	64.98 355	93.20 422	64.57 431	83.74 347	95.12 258

OurMVSNet-221017-0		85.35 324	84.64 324	87.49 360	90.77 384	72.59 401	94.01 230	94.40 287	84.72 243	79.62 394	93.17 261	61.91 376	96.72 311	81.99 272	81.16 381	93.16 357

tpm2		84.08 348	82.94 352	87.48 361	91.39 354	71.27 414	89.23 396	90.37 403	71.95 435	84.64 302	89.33 386	67.30 329	96.55 328	75.17 359	87.09 320	94.63 279

RPSCF		85.07 330	84.27 328	87.48 361	92.91 304	70.62 424	91.69 338	92.46 345	76.20 396	82.67 350	95.22 168	63.94 363	97.29 275	77.51 336	85.80 327	94.53 286

myMVS_eth3d28		85.80 315	85.26 309	87.42 363	94.73 194	69.92 429	90.60 363	90.95 392	87.21 162	86.06 260	90.04 371	59.47 399	96.02 355	74.89 364	93.35 211	96.33 205

WBMVS		84.97 334	84.18 330	87.34 364	94.14 244	71.62 413	90.20 375	92.35 348	81.61 323	84.06 322	90.76 350	61.82 377	96.52 329	78.93 321	83.81 345	93.89 315

miper_lstm_enhance		85.27 327	84.59 325	87.31 365	91.28 359	74.63 372	87.69 422	94.09 303	81.20 334	81.36 367	89.85 378	74.97 222	94.30 403	81.03 291	79.84 405	93.01 363

FMVSNet5		81.52 376	79.60 382	87.27 366	91.17 362	77.95 314	91.49 342	92.26 354	76.87 388	76.16 419	87.91 411	51.67 436	92.34 430	67.74 413	81.16 381	91.52 401

USDC		82.76 360	81.26 365	87.26 367	91.17 362	74.55 373	89.27 394	93.39 322	78.26 376	75.30 427	92.08 301	54.43 429	96.63 317	71.64 384	85.79 328	90.61 420

test-LLR		85.87 312	85.41 302	87.25 368	90.95 373	71.67 411	89.55 388	89.88 418	83.41 273	84.54 305	87.95 409	67.25 330	95.11 391	81.82 276	93.37 209	94.97 263

test-mter		84.54 343	83.64 341	87.25 368	90.95 373	71.67 411	89.55 388	89.88 418	79.17 356	84.54 305	87.95 409	55.56 419	95.11 391	81.82 276	93.37 209	94.97 263

JIA-IIPM		81.04 381	78.98 393	87.25 368	88.64 419	73.48 386	81.75 457	89.61 424	73.19 425	82.05 358	73.71 461	66.07 350	95.87 364	71.18 389	84.60 338	92.41 382

TDRefinement		79.81 395	77.34 401	87.22 371	79.24 464	75.48 364	93.12 280	92.03 360	76.45 391	75.01 428	91.58 322	49.19 443	96.44 336	70.22 397	69.18 443	89.75 429

tpmvs		83.35 359	82.07 358	87.20 372	91.07 368	71.00 420	88.31 410	91.70 369	78.91 359	80.49 379	87.18 421	69.30 311	97.08 291	68.12 412	83.56 351	93.51 342

ppachtmachnet_test		81.84 368	80.07 376	87.15 373	88.46 423	74.43 376	89.04 400	92.16 356	75.33 403	77.75 408	88.99 392	66.20 347	95.37 386	65.12 428	77.60 416	91.65 397

dmvs_re		84.20 347	83.22 348	87.14 374	91.83 339	77.81 321	90.04 380	90.19 407	84.70 245	81.49 363	89.17 388	64.37 361	91.13 443	71.58 385	85.65 329	92.46 380

tpm cat1		81.96 366	80.27 372	87.01 375	91.09 367	71.02 419	87.38 426	91.53 377	66.25 451	80.17 381	86.35 430	68.22 326	96.15 351	69.16 403	82.29 367	93.86 321

test_fmvs1_n		87.03 280	87.04 240	86.97 376	89.74 409	71.86 406	94.55 183	94.43 284	78.47 370	91.95 126	95.50 154	51.16 438	93.81 412	93.02 73	94.56 175	95.26 254

OpenMVS_ROB	74.94 19	79.51 399	77.03 406	86.93 377	87.00 436	76.23 355	92.33 317	90.74 398	68.93 445	74.52 432	88.23 406	49.58 441	96.62 318	57.64 452	84.29 340	87.94 448

SixPastTwentyTwo		83.91 352	82.90 354	86.92 378	90.99 371	70.67 423	93.48 261	91.99 362	85.54 210	77.62 410	92.11 299	60.59 392	96.87 307	76.05 352	77.75 415	93.20 355

ADS-MVSNet		81.56 374	79.78 378	86.90 379	91.35 356	71.82 407	83.33 451	89.16 428	72.90 428	82.24 355	85.77 434	64.98 355	93.76 413	64.57 431	83.74 347	95.12 258

PatchT		82.68 362	81.27 364	86.89 380	90.09 402	70.94 421	84.06 448	90.15 408	74.91 408	85.63 270	83.57 444	69.37 307	94.87 396	65.19 426	88.50 296	94.84 273

tpm		84.73 338	84.02 335	86.87 381	90.33 397	68.90 432	89.06 399	89.94 415	80.85 337	85.75 266	89.86 377	68.54 323	95.97 358	77.76 332	84.05 344	95.75 237

Patchmatch-RL test		81.67 371	79.96 377	86.81 382	85.42 446	71.23 415	82.17 456	87.50 437	78.47 370	77.19 412	82.50 451	70.81 283	93.48 417	82.66 257	72.89 431	95.71 241

test_vis1_n		86.56 297	86.49 264	86.78 383	88.51 420	72.69 396	94.68 176	93.78 315	79.55 352	90.70 156	95.31 164	48.75 444	93.28 420	93.15 69	93.99 190	94.38 297

testing3-2		86.72 291	86.71 250	86.74 384	96.11 113	65.92 444	93.39 266	89.65 423	89.46 72	87.84 218	92.79 276	59.17 404	97.60 234	81.31 285	90.72 257	96.70 193

test_fmvs1		87.34 263	87.56 226	86.68 385	90.59 390	71.80 408	94.01 230	94.04 304	78.30 374	91.97 124	95.22 168	56.28 417	93.71 414	92.89 74	94.71 168	94.52 287

MDA-MVSNet-bldmvs		78.85 404	76.31 409	86.46 386	89.76 408	73.88 380	88.79 402	90.42 402	79.16 357	59.18 461	88.33 404	60.20 394	94.04 406	62.00 439	68.96 444	91.48 404

mvs5depth		80.98 383	79.15 390	86.45 387	84.57 449	73.29 389	87.79 418	91.67 371	80.52 340	82.20 357	89.72 380	55.14 425	95.93 360	73.93 373	66.83 449	90.12 426

tpmrst		85.35 324	84.99 313	86.43 388	90.88 380	67.88 437	88.71 403	91.43 380	80.13 344	86.08 259	88.80 397	73.05 257	96.02 355	82.48 258	83.40 355	95.40 249

TESTMET0.1,1		83.74 355	82.85 355	86.42 389	89.96 405	71.21 416	89.55 388	87.88 433	77.41 382	83.37 341	87.31 417	56.71 415	93.65 416	80.62 299	92.85 225	94.40 296

our_test_3		81.93 367	80.46 370	86.33 390	88.46 423	73.48 386	88.46 408	91.11 385	76.46 390	76.69 416	88.25 405	66.89 335	94.36 401	68.75 405	79.08 411	91.14 412

lessismore_v0				86.04 391	88.46 423	68.78 433		80.59 460		73.01 439	90.11 369	55.39 421	96.43 337	75.06 361	65.06 452	92.90 366

TinyColmap		79.76 396	77.69 399	85.97 392	91.71 343	73.12 390	89.55 388	90.36 404	75.03 406	72.03 442	90.19 365	46.22 453	96.19 350	63.11 435	81.03 386	88.59 444

KD-MVS_2432*1600		78.50 405	76.02 413	85.93 393	86.22 439	74.47 374	84.80 444	92.33 349	79.29 354	76.98 413	85.92 432	53.81 432	93.97 409	67.39 414	57.42 462	89.36 431

miper_refine_blended		78.50 405	76.02 413	85.93 393	86.22 439	74.47 374	84.80 444	92.33 349	79.29 354	76.98 413	85.92 432	53.81 432	93.97 409	67.39 414	57.42 462	89.36 431

K. test v3		81.59 373	80.15 375	85.91 395	89.89 407	69.42 431	92.57 307	87.71 435	85.56 209	73.44 437	89.71 381	55.58 418	95.52 379	77.17 339	69.76 440	92.78 371

SSC-MVS3.2		84.60 342	84.19 329	85.85 396	92.74 311	68.07 434	88.15 413	93.81 313	87.42 156	83.76 330	91.07 339	62.91 370	95.73 373	74.56 368	83.24 356	93.75 331

mvsany_test1		85.42 322	85.30 307	85.77 397	87.95 432	75.41 365	87.61 425	80.97 459	76.82 389	88.68 200	95.83 137	77.44 187	90.82 445	85.90 205	86.51 323	91.08 416

MIMVSNet1		79.38 400	77.28 402	85.69 398	86.35 438	73.67 383	91.61 340	92.75 340	78.11 379	72.64 440	88.12 407	48.16 445	91.97 436	60.32 443	77.49 417	91.43 406

UWE-MVS		83.69 356	83.09 349	85.48 399	93.06 295	65.27 449	90.92 356	86.14 441	79.90 347	86.26 255	90.72 353	57.17 414	95.81 368	71.03 392	92.62 233	95.35 252

UnsupCasMVSNet_eth		80.07 392	78.27 398	85.46 400	85.24 447	72.63 400	88.45 409	94.87 263	82.99 285	71.64 445	88.07 408	56.34 416	91.75 438	73.48 376	63.36 455	92.01 392

CL-MVSNet_self_test		81.74 370	80.53 368	85.36 401	85.96 441	72.45 403	90.25 370	93.07 330	81.24 332	79.85 391	87.29 418	70.93 281	92.52 428	66.95 417	69.23 442	91.11 414

MDA-MVSNet_test_wron		79.21 402	77.19 404	85.29 402	88.22 427	72.77 395	85.87 435	90.06 411	74.34 413	62.62 458	87.56 415	66.14 348	91.99 435	66.90 421	73.01 429	91.10 415

YYNet1		79.22 401	77.20 403	85.28 403	88.20 428	72.66 398	85.87 435	90.05 413	74.33 414	62.70 456	87.61 414	66.09 349	92.03 432	66.94 418	72.97 430	91.15 411

WB-MVSnew		83.77 354	83.28 345	85.26 404	91.48 349	71.03 418	91.89 330	87.98 432	78.91 359	84.78 299	90.22 363	69.11 316	94.02 407	64.70 430	90.44 260	90.71 418

dp		81.47 377	80.23 373	85.17 405	89.92 406	65.49 447	86.74 430	90.10 410	76.30 394	81.10 369	87.12 422	62.81 371	95.92 361	68.13 411	79.88 403	94.09 308

UnsupCasMVSNet_bld		76.23 415	73.27 419	85.09 406	83.79 451	72.92 392	85.65 438	93.47 321	71.52 436	68.84 451	79.08 456	49.77 440	93.21 421	66.81 422	60.52 459	89.13 439

SD_0403		84.71 340	84.65 322	84.92 407	92.95 302	65.95 443	92.07 329	93.23 325	83.82 262	79.03 397	93.73 246	73.90 242	92.91 426	63.02 437	90.05 267	95.89 230

Anonymous20231206		81.03 382	79.77 380	84.82 408	87.85 433	70.26 426	91.42 343	92.08 358	73.67 420	77.75 408	89.25 387	62.43 373	93.08 423	61.50 441	82.00 372	91.12 413

FE-MVSNET		78.19 407	76.03 412	84.69 409	83.70 452	73.31 388	90.58 364	90.00 414	77.11 387	71.91 443	85.47 436	55.53 420	91.94 437	59.69 447	70.24 438	88.83 441

test0.0.03 1		82.41 364	81.69 360	84.59 410	88.23 426	72.89 393	90.24 372	87.83 434	83.41 273	79.86 390	89.78 379	67.25 330	88.99 455	65.18 427	83.42 354	91.90 394

CMPMVS	59.16 21	80.52 386	79.20 388	84.48 411	83.98 450	67.63 440	89.95 383	93.84 312	64.79 455	66.81 453	91.14 336	57.93 410	95.17 389	76.25 349	88.10 302	90.65 419
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 341	84.79 320	84.37 412	91.84 337	64.92 450	93.70 254	91.47 379	66.19 452	86.16 258	95.28 165	67.18 332	93.33 419	80.89 294	90.42 262	94.88 272

PVSNet_0	73.20 20	77.22 411	74.83 417	84.37 412	90.70 388	71.10 417	83.09 453	89.67 421	72.81 430	73.93 435	83.13 446	60.79 391	93.70 415	68.54 406	50.84 467	88.30 446

LF4IMVS		80.37 389	79.07 392	84.27 414	86.64 437	69.87 430	89.39 393	91.05 388	76.38 392	74.97 429	90.00 373	47.85 446	94.25 405	74.55 369	80.82 392	88.69 443

Anonymous20240521		80.44 388	79.21 387	84.11 415	85.75 444	67.89 436	92.86 298	93.23 325	75.61 401	75.59 426	87.47 416	50.03 439	94.33 402	71.14 390	81.21 380	90.12 426

PM-MVS		78.11 408	76.12 411	84.09 416	83.54 453	70.08 427	88.97 401	85.27 448	79.93 346	74.73 431	86.43 427	34.70 464	93.48 417	79.43 316	72.06 433	88.72 442

test_fmvs2		83.98 349	84.03 334	83.83 417	87.16 435	67.53 441	93.93 237	92.89 334	77.62 380	86.89 239	93.53 249	47.18 448	92.02 434	90.54 134	86.51 323	91.93 393

testgi		80.94 385	80.20 374	83.18 418	87.96 431	66.29 442	91.28 347	90.70 400	83.70 264	78.12 404	92.84 271	51.37 437	90.82 445	63.34 434	82.46 365	92.43 381

KD-MVS_self_test		80.20 390	79.24 386	83.07 419	85.64 445	65.29 448	91.01 355	93.93 306	78.71 368	76.32 418	86.40 429	59.20 403	92.93 425	72.59 380	69.35 441	91.00 417

testing3		80.46 387	79.59 383	83.06 420	93.44 281	64.64 451	93.33 268	85.47 446	84.34 251	79.93 389	90.84 346	44.35 456	92.39 429	57.06 454	87.56 312	92.16 390

ambc				83.06 420	79.99 462	63.51 455	77.47 466	92.86 335		74.34 434	84.45 441	28.74 465	95.06 393	73.06 378	68.89 445	90.61 420

test20.03		79.95 394	79.08 391	82.55 422	85.79 443	67.74 439	91.09 353	91.08 386	81.23 333	74.48 433	89.96 375	61.63 378	90.15 447	60.08 444	76.38 423	89.76 428

MVStest1		72.91 419	69.70 424	82.54 423	78.14 465	73.05 391	88.21 412	86.21 440	60.69 459	64.70 454	90.53 356	46.44 451	85.70 462	58.78 450	53.62 464	88.87 440

test_vis1_rt		77.96 409	76.46 408	82.48 424	85.89 442	71.74 410	90.25 370	78.89 463	71.03 440	71.30 446	81.35 453	42.49 458	91.05 444	84.55 228	82.37 366	84.65 451

EU-MVSNet		81.32 379	80.95 366	82.42 425	88.50 422	63.67 454	93.32 269	91.33 381	64.02 456	80.57 378	92.83 272	61.21 387	92.27 431	76.34 348	80.38 399	91.32 407

myMVS_eth3d		79.67 397	78.79 394	82.32 426	91.92 333	64.08 452	89.75 386	87.40 438	81.72 318	78.82 399	87.20 419	45.33 454	91.29 441	59.09 449	87.84 309	91.60 399

ttmdpeth		76.55 413	74.64 418	82.29 427	82.25 458	67.81 438	89.76 385	85.69 444	70.35 442	75.76 424	91.69 315	46.88 449	89.77 449	66.16 423	63.23 456	89.30 433

pmmvs3		71.81 422	68.71 425	81.11 428	75.86 467	70.42 425	86.74 430	83.66 452	58.95 462	68.64 452	80.89 454	36.93 462	89.52 451	63.10 436	63.59 454	83.39 452

Syy-MVS		80.07 392	79.78 378	80.94 429	91.92 333	59.93 461	89.75 386	87.40 438	81.72 318	78.82 399	87.20 419	66.29 346	91.29 441	47.06 462	87.84 309	91.60 399

UWE-MVS-28		78.98 403	78.38 397	80.80 430	88.18 429	60.66 460	90.65 361	78.51 464	78.84 363	77.93 407	90.93 343	59.08 405	89.02 454	50.96 459	90.33 264	92.72 372

new-patchmatchnet		76.41 414	75.17 416	80.13 431	82.65 457	59.61 462	87.66 423	91.08 386	78.23 377	69.85 449	83.22 445	54.76 426	91.63 440	64.14 433	64.89 453	89.16 437

mvsany_test3		74.95 416	73.26 420	80.02 432	74.61 468	63.16 456	85.53 439	78.42 465	74.16 415	74.89 430	86.46 426	36.02 463	89.09 453	82.39 261	66.91 448	87.82 449

test_fmvs3		77.67 410	77.16 405	79.22 433	79.52 463	61.14 458	92.34 316	91.64 373	73.98 417	78.86 398	86.59 425	27.38 468	87.03 457	88.12 170	75.97 425	89.50 430

DSMNet-mixed		76.94 412	76.29 410	78.89 434	83.10 455	56.11 470	87.78 419	79.77 461	60.65 460	75.64 425	88.71 398	61.56 381	88.34 456	60.07 445	89.29 285	92.21 389

EGC-MVSNET		61.97 430	56.37 435	78.77 435	89.63 411	73.50 385	89.12 398	82.79 454	0.21 481	1.24 482	84.80 439	39.48 459	90.04 448	44.13 464	75.94 426	72.79 463

new_pmnet		72.15 420	70.13 423	78.20 436	82.95 456	65.68 445	83.91 449	82.40 456	62.94 458	64.47 455	79.82 455	42.85 457	86.26 461	57.41 453	74.44 428	82.65 456

MVS-HIRNet		73.70 418	72.20 421	78.18 437	91.81 340	56.42 469	82.94 454	82.58 455	55.24 463	68.88 450	66.48 466	55.32 423	95.13 390	58.12 451	88.42 298	83.01 454

LCM-MVSNet		66.00 427	62.16 432	77.51 438	64.51 478	58.29 464	83.87 450	90.90 394	48.17 467	54.69 464	73.31 462	16.83 477	86.75 458	65.47 425	61.67 458	87.48 450

APD_test1		69.04 423	66.26 429	77.36 439	80.51 461	62.79 457	85.46 440	83.51 453	54.11 465	59.14 462	84.79 440	23.40 471	89.61 450	55.22 455	70.24 438	79.68 460

test_f		71.95 421	70.87 422	75.21 440	74.21 470	59.37 463	85.07 443	85.82 443	65.25 454	70.42 448	83.13 446	23.62 469	82.93 468	78.32 326	71.94 434	83.33 453

ANet_high		58.88 434	54.22 439	72.86 441	56.50 481	56.67 466	80.75 459	86.00 442	73.09 427	37.39 473	64.63 469	22.17 472	79.49 471	43.51 465	23.96 475	82.43 457

test_vis3_rt		65.12 428	62.60 430	72.69 442	71.44 471	60.71 459	87.17 427	65.55 475	63.80 457	53.22 465	65.65 468	14.54 478	89.44 452	76.65 343	65.38 451	67.91 466

FPMVS		64.63 429	62.55 431	70.88 443	70.80 472	56.71 465	84.42 447	84.42 450	51.78 466	49.57 466	81.61 452	23.49 470	81.48 469	40.61 469	76.25 424	74.46 462

dmvs_testset		74.57 417	75.81 415	70.86 444	87.72 434	40.47 479	87.05 429	77.90 469	82.75 290	71.15 447	85.47 436	67.98 327	84.12 466	45.26 463	76.98 422	88.00 447

N_pmnet		68.89 424	68.44 426	70.23 445	89.07 416	28.79 484	88.06 414	19.50 484	69.47 444	71.86 444	84.93 438	61.24 386	91.75 438	54.70 456	77.15 419	90.15 425

testf1		59.54 432	56.11 436	69.85 446	69.28 473	56.61 467	80.37 460	76.55 472	42.58 470	45.68 469	75.61 457	11.26 479	84.18 464	43.20 466	60.44 460	68.75 464

APD_test2		59.54 432	56.11 436	69.85 446	69.28 473	56.61 467	80.37 460	76.55 472	42.58 470	45.68 469	75.61 457	11.26 479	84.18 464	43.20 466	60.44 460	68.75 464

WB-MVS		67.92 425	67.49 427	69.21 448	81.09 459	41.17 478	88.03 415	78.00 468	73.50 422	62.63 457	83.11 448	63.94 363	86.52 459	25.66 474	51.45 466	79.94 459

PMMVS2		59.60 431	56.40 434	69.21 448	68.83 475	46.58 474	73.02 470	77.48 470	55.07 464	49.21 467	72.95 463	17.43 476	80.04 470	49.32 461	44.33 470	80.99 458

SSC-MVS		67.06 426	66.56 428	68.56 450	80.54 460	40.06 480	87.77 420	77.37 471	72.38 432	61.75 459	82.66 450	63.37 366	86.45 460	24.48 475	48.69 469	79.16 461

Gipuma		57.99 436	54.91 438	67.24 451	88.51 420	65.59 446	52.21 473	90.33 405	43.58 469	42.84 472	51.18 473	20.29 474	85.07 463	34.77 470	70.45 437	51.05 472
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 438	48.46 442	63.48 452	45.72 483	46.20 475	73.41 469	78.31 466	41.03 472	30.06 475	65.68 467	6.05 481	83.43 467	30.04 472	65.86 450	60.80 467
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 435	58.24 433	60.56 453	83.13 454	45.09 477	82.32 455	48.22 483	67.61 448	61.70 460	69.15 464	38.75 460	76.05 472	32.01 471	41.31 471	60.55 468

MVE	39.65 23	43.39 440	38.59 446	57.77 454	56.52 480	48.77 473	55.38 472	58.64 479	29.33 475	28.96 476	52.65 472	4.68 482	64.62 476	28.11 473	33.07 473	59.93 469
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 439	48.47 441	56.66 455	52.26 482	18.98 486	41.51 475	81.40 458	10.10 476	44.59 471	75.01 460	28.51 466	68.16 473	53.54 457	49.31 468	82.83 455

DeepMVS_CX				56.31 456	74.23 469	51.81 472		56.67 480	44.85 468	48.54 468	75.16 459	27.87 467	58.74 478	40.92 468	52.22 465	58.39 470

kuosan		53.51 437	53.30 440	54.13 457	76.06 466	45.36 476	80.11 462	48.36 482	59.63 461	54.84 463	63.43 470	37.41 461	62.07 477	20.73 477	39.10 472	54.96 471

E-PMN		43.23 441	42.29 443	46.03 458	65.58 477	37.41 481	73.51 468	64.62 476	33.99 473	28.47 477	47.87 474	19.90 475	67.91 474	22.23 476	24.45 474	32.77 473

EMVS		42.07 442	41.12 444	44.92 459	63.45 479	35.56 483	73.65 467	63.48 477	33.05 474	26.88 478	45.45 475	21.27 473	67.14 475	19.80 478	23.02 476	32.06 474

tmp_tt		35.64 443	39.24 445	24.84 460	14.87 484	23.90 485	62.71 471	51.51 481	6.58 478	36.66 474	62.08 471	44.37 455	30.34 480	52.40 458	22.00 477	20.27 475

wuyk23d		21.27 445	20.48 448	23.63 461	68.59 476	36.41 482	49.57 474	6.85 485	9.37 477	7.89 479	4.46 481	4.03 483	31.37 479	17.47 479	16.07 478	3.12 476

test123		8.76 447	11.22 450	1.39 462	0.85 486	0.97 487	85.76 437	0.35 487	0.54 480	2.45 481	8.14 480	0.60 484	0.48 481	2.16 481	0.17 480	2.71 477

testmvs		8.92 446	11.52 449	1.12 463	1.06 485	0.46 488	86.02 434	0.65 486	0.62 479	2.74 480	9.52 479	0.31 485	0.45 482	2.38 480	0.39 479	2.46 478

mmdepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

monomultidepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

test_blank		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet_test		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

DCPMVS		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

cdsmvs_eth3d_5k		22.14 444	29.52 447	0.00 464	0.00 487	0.00 489	0.00 476	95.76 190	0.00 482	0.00 483	94.29 217	75.66 214	0.00 483	0.00 482	0.00 481	0.00 479

pcd_1.5k_mvsjas		6.64 449	8.86 452	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	79.70 151	0.00 483	0.00 482	0.00 481	0.00 479

sosnet-low-res		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uncertanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

Regformer		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

ab-mvs-re		7.82 448	10.43 451	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	93.88 238	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

TestfortrainingZip							97.32 10

WAC-MVS						64.08 452								59.14 448

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

PC_three_1452									82.47 294	97.09 20	97.07 72	92.72 1	98.04 194	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					0.00 487

eth-test					0.00 487

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 266	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	87.46 153	93.75 75	97.43 51	82.94 100		92.73 76	97.80 92	97.88 102

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 331	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	86.13 196	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																96.12 218

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 272				96.12 218

sam_mvs												70.60 286

MTGPA								96.97 65

test_post1							88.00 416				9.81 478	69.31 310	95.53 378	76.65 343

test_post											10.29 477	70.57 290	95.91 363

patchmatchnet-post											83.76 443	71.53 273	96.48 332

MTMP							96.16 60	60.64 478

gm-plane-assit					89.60 412	68.00 435			77.28 385		88.99 392		97.57 237	79.44 315

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 223	96.78 89	81.61 323	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	81.86 314	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		87.67 148	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

旧先验2							93.36 267		71.25 438	94.37 60			97.13 289	86.74 192

新几何2							93.11 282

旧先验1					96.79 85	81.81 195		95.67 200			96.81 84	86.69 42			97.66 98	96.97 174

无先验							93.28 275	96.26 139	73.95 418				99.05 66	80.56 300		96.59 197

原ACMM2							92.94 293

test222					96.55 94	81.70 197	92.22 322	95.01 248	68.36 447	90.20 167	96.14 113	80.26 142			97.80 92	96.05 225

testdata2													98.75 115	78.30 327

segment_acmp												87.16 39

testdata1							92.15 324		87.94 134

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 214	82.75 162						74.23 234

plane_prior5								96.22 144					98.12 176	88.15 167	89.99 268	94.63 279

plane_prior4											94.86 188

plane_prior3						82.75 162			90.26 47	86.91 236

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 207

plane_prior						82.73 165	95.21 140		89.66 68						89.88 273

n2								0.00 488

nn								0.00 488

door-mid								85.49 445

test11								96.57 111

door								85.33 447

HQP5-MVS						81.56 199

HQP-NCC					94.17 240		94.39 197		88.81 100	85.43 282

ACMP_Plane					94.17 240		94.39 197		88.81 100	85.43 282

BP-MVS														87.11 189

HQP4-MVS										85.43 282			97.96 206			94.51 289

HQP3-MVS								96.04 164							89.77 277

HQP2-MVS												73.83 245

NP-MVS					94.37 227	82.42 177					93.98 231

MDTV_nov1_ep13_2view						55.91 471	87.62 424		73.32 424	84.59 304		70.33 293		74.65 366		95.50 246

MDTV_nov1_ep13			83.56 342		91.69 345	69.93 428	87.75 421	91.54 376	78.60 369	84.86 298	88.90 394	69.54 305	96.03 354	70.25 395	88.93 290

ACMMP++_ref															87.47 313

ACMMP++															88.01 305

Test By Simon												80.02 144

High-resolution multi-view benchmark