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 54	90.86 1		96.85 67					99.61 4	96.03 17	99.06 9	99.07 5

No_MVS				96.52 1	97.78 54	90.86 1		96.85 67					99.61 4	96.03 17	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 55	92.59 2	98.94 83	92.25 76	98.99 14	98.84 14

HPM-MVS++		95.14 10	94.91 17	95.83 4	98.25 29	89.65 4	95.92 77	96.96 56	91.75 10	94.02 56	96.83 67	88.12 24	99.55 16	93.41 51	98.94 16	98.28 54

MM		95.10 11	94.91 17	95.68 5	96.09 106	88.34 9	96.68 33	94.37 247	95.08 1	94.68 43	97.72 29	82.94 89	99.64 1	97.85 2	98.76 29	99.06 7

SMA-MVS		95.20 8	95.07 12	95.59 6	98.14 35	88.48 8	96.26 46	97.28 31	85.90 165	97.67 3	98.10 9	88.41 20	99.56 12	94.66 35	99.19 1	98.71 20
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 87	91.37 97	95.55 7	95.63 129	88.73 6	97.07 18	96.77 78	90.84 17	84.02 279	96.62 80	75.95 174	99.34 37	87.77 143	97.68 83	98.59 24

CNVR-MVS		95.40 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 112	96.96 56	92.09 7	95.32 35	97.08 55	89.49 15	99.33 40	95.10 31	98.85 20	98.66 21

MVS_0304		94.18 40	93.80 51	95.34 9	94.91 165	87.62 14	95.97 72	93.01 287	92.58 4	94.22 48	97.20 49	80.56 119	99.59 8	97.04 11	98.68 37	98.81 17

ACMMP_NAP		94.74 19	94.56 23	95.28 10	98.02 41	87.70 11	95.68 91	97.34 23	88.28 102	95.30 36	97.67 31	85.90 50	99.54 20	93.91 43	98.95 15	98.60 23

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 25	87.28 18	95.56 102	97.51 5	89.13 73	97.14 10	97.91 24	91.64 7	99.62 2	94.61 36	99.17 2	98.86 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 12	94.90 19	95.20 12	97.84 50	87.76 10	96.65 34	97.48 10	87.76 123	95.71 30	97.70 30	88.28 23	99.35 36	93.89 44	98.78 26	98.48 30

MCST-MVS		94.45 25	94.20 39	95.19 13	98.46 19	87.50 16	95.00 133	97.12 45	87.13 134	92.51 95	96.30 89	89.24 17	99.34 37	93.46 48	98.62 46	98.73 18

NCCC		94.81 17	94.69 22	95.17 14	97.83 51	87.46 17	95.66 94	96.93 60	92.34 5	93.94 57	96.58 82	87.74 27	99.44 29	92.83 60	98.40 54	98.62 22

DPM-MVS		92.58 83	91.74 93	95.08 15	96.19 99	89.31 5	92.66 261	96.56 97	83.44 225	91.68 118	95.04 145	86.60 42	98.99 73	85.60 173	97.92 75	96.93 139

ZNCC-MVS		94.47 24	94.28 33	95.03 16	98.52 15	86.96 20	96.85 28	97.32 27	88.24 103	93.15 72	97.04 58	86.17 47	99.62 2	92.40 70	98.81 23	98.52 26

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 16	97.49 6					99.61 4	95.62 24	99.08 7	98.99 9

MTAPA		94.42 29	94.22 36	95.00 18	98.42 21	86.95 21	94.36 180	96.97 54	91.07 14	93.14 73	97.56 32	84.30 73	99.56 12	93.43 49	98.75 30	98.47 33

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 25	97.47 11	91.73 11	96.10 23	96.69 72	89.90 12	99.30 43	94.70 34	98.04 71	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 27	94.27 35	94.92 20	98.65 8	86.67 30	96.92 24	97.23 34	88.60 93	93.58 64	97.27 43	85.22 58	99.54 20	92.21 77	98.74 31	98.56 25

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 28	86.29 46	97.46 6	97.40 20	89.03 78	96.20 22	98.10 9	89.39 16	99.34 37	95.88 19	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 27	94.28 33	94.91 21	98.63 9	86.69 28	96.94 20	97.32 27	88.63 90	93.53 67	97.26 45	85.04 62	99.54 20	92.35 73	98.78 26	98.50 27

GST-MVS		94.21 35	93.97 47	94.90 23	98.41 22	86.82 24	96.54 36	97.19 35	88.24 103	93.26 69	96.83 67	85.48 55	99.59 8	91.43 101	98.40 54	98.30 49

HFP-MVS		94.52 22	94.40 27	94.86 24	98.61 10	86.81 25	96.94 20	97.34 23	88.63 90	93.65 62	97.21 47	86.10 48	99.49 26	92.35 73	98.77 28	98.30 49

sasdasda		93.27 68	92.75 77	94.85 25	95.70 125	87.66 12	96.33 39	96.41 107	90.00 41	94.09 52	94.60 165	82.33 98	98.62 114	92.40 70	92.86 186	98.27 56

MP-MVS-pluss		94.21 35	94.00 46	94.85 25	98.17 33	86.65 31	94.82 145	97.17 40	86.26 157	92.83 82	97.87 26	85.57 54	99.56 12	94.37 39	98.92 17	98.34 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 68	92.75 77	94.85 25	95.70 125	87.66 12	96.33 39	96.41 107	90.00 41	94.09 52	94.60 165	82.33 98	98.62 114	92.40 70	92.86 186	98.27 56

XVS		94.45 25	94.32 29	94.85 25	98.54 13	86.60 34	96.93 22	97.19 35	90.66 25	92.85 80	97.16 53	85.02 63	99.49 26	91.99 87	98.56 50	98.47 33

X-MVStestdata		88.31 185	86.13 232	94.85 25	98.54 13	86.60 34	96.93 22	97.19 35	90.66 25	92.85 80	23.41 423	85.02 63	99.49 26	91.99 87	98.56 50	98.47 33

SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 25	96.99 75	86.33 42	97.33 7	97.30 29	91.38 13	95.39 34	97.46 35	88.98 19	99.40 30	94.12 40	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 54	86.00 50	98.29 1	97.49 6	90.75 20	97.62 5	98.06 15	92.59 2	99.61 4	95.64 22	99.02 12	98.86 11

alignmvs		93.08 75	92.50 83	94.81 32	95.62 130	87.61 15	95.99 70	96.07 139	89.77 54	94.12 51	94.87 151	80.56 119	98.66 107	92.42 69	93.10 182	98.15 67

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 52	97.13 14	97.44 15	90.31 31	97.71 1	98.07 13	92.31 4	99.58 10	95.66 20	99.13 3	98.84 14

DeepC-MVS_fast	89.43 2	94.04 42	93.79 52	94.80 33	97.48 64	86.78 26	95.65 96	96.89 64	89.40 63	92.81 83	96.97 60	85.37 57	99.24 46	90.87 110	98.69 35	98.38 41
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 32	94.07 43	94.77 35	98.47 18	86.31 44	96.71 31	96.98 53	89.04 76	91.98 105	97.19 50	85.43 56	99.56 12	92.06 86	98.79 24	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 33	94.07 43	94.75 36	98.06 39	86.90 23	95.88 79	96.94 59	85.68 171	95.05 41	97.18 51	87.31 35	99.07 56	91.90 93	98.61 48	98.28 54
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 30	94.21 38	94.74 37	98.39 23	86.64 32	97.60 4	97.24 32	88.53 95	92.73 88	97.23 46	85.20 59	99.32 41	92.15 80	98.83 22	98.25 61

PGM-MVS		93.96 47	93.72 56	94.68 38	98.43 20	86.22 47	95.30 110	97.78 1	87.45 130	93.26 69	97.33 41	84.62 70	99.51 24	90.75 112	98.57 49	98.32 48

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 55	97.09 16	96.73 83	90.27 35	97.04 13	98.05 17	91.47 8	99.55 16	95.62 24	99.08 7	98.45 36
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 45	93.78 53	94.63 40	98.50 16	85.90 60	96.87 26	96.91 62	88.70 88	91.83 114	97.17 52	83.96 77	99.55 16	91.44 100	98.64 45	98.43 38

PHI-MVS		93.89 48	93.65 60	94.62 41	96.84 78	86.43 39	96.69 32	97.49 6	85.15 184	93.56 66	96.28 90	85.60 53	99.31 42	92.45 67	98.79 24	98.12 71

TSAR-MVS + MP.		94.85 14	94.94 15	94.58 42	98.25 29	86.33 42	96.11 59	96.62 92	88.14 108	96.10 23	96.96 61	89.09 18	98.94 83	94.48 37	98.68 37	98.48 30
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 56	93.20 69	94.55 43	95.65 128	85.73 65	94.94 136	96.69 88	91.89 9	90.69 130	95.88 110	81.99 110	99.54 20	93.14 55	97.95 74	98.39 39

train_agg		93.44 61	93.08 70	94.52 44	97.53 61	86.49 37	94.07 197	96.78 76	81.86 266	92.77 85	96.20 93	87.63 29	99.12 54	92.14 81	98.69 35	97.94 81

CDPH-MVS		92.83 79	92.30 85	94.44 45	97.79 52	86.11 49	94.06 199	96.66 89	80.09 297	92.77 85	96.63 79	86.62 40	99.04 60	87.40 148	98.66 41	98.17 66

3Dnovator	86.66 5	91.73 97	90.82 109	94.44 45	94.59 182	86.37 41	97.18 12	97.02 51	89.20 70	84.31 274	96.66 75	73.74 211	99.17 50	86.74 158	97.96 73	97.79 93

SR-MVS		94.23 34	94.17 41	94.43 47	98.21 32	85.78 63	96.40 38	96.90 63	88.20 106	94.33 47	97.40 38	84.75 69	99.03 61	93.35 52	97.99 72	98.48 30

HPM-MVS		94.02 43	93.88 48	94.43 47	98.39 23	85.78 63	97.25 10	97.07 49	86.90 142	92.62 92	96.80 71	84.85 68	99.17 50	92.43 68	98.65 44	98.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 54	93.41 64	94.41 49	96.59 85	86.78 26	94.40 172	93.93 264	89.77 54	94.21 49	95.59 123	87.35 34	98.61 116	92.72 63	96.15 118	97.83 91

reproduce-ours		94.82 15	94.97 13	94.38 50	97.91 47	85.46 68	95.86 80	97.15 42	89.82 47	95.23 38	98.10 9	87.09 37	99.37 33	95.30 28	98.25 60	98.30 49

our_new_method		94.82 15	94.97 13	94.38 50	97.91 47	85.46 68	95.86 80	97.15 42	89.82 47	95.23 38	98.10 9	87.09 37	99.37 33	95.30 28	98.25 60	98.30 49

test12				94.34 52	97.13 73	86.15 48		96.29 115		91.04 127		85.08 61	99.01 66		98.13 66	97.86 88

ACMMP		93.24 70	92.88 75	94.30 53	98.09 38	85.33 72	96.86 27	97.45 14	88.33 99	90.15 140	97.03 59	81.44 113	99.51 24	90.85 111	95.74 123	98.04 76
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 18	94.92 16	94.29 54	97.92 43	85.18 74	95.95 75	97.19 35	89.67 57	95.27 37	98.16 3	86.53 43	99.36 35	95.42 27	98.15 64	98.33 44

DeepC-MVS	88.79 3	93.31 67	92.99 73	94.26 55	96.07 108	85.83 61	94.89 139	96.99 52	89.02 79	89.56 145	97.37 40	82.51 95	99.38 31	92.20 78	98.30 57	97.57 106
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 76	92.63 80	94.23 56	95.62 130	85.92 57	96.08 60	96.33 113	89.86 45	93.89 59	94.66 162	82.11 105	98.50 122	92.33 75	92.82 189	98.27 56

EPNet		91.79 94	91.02 105	94.10 57	90.10 350	85.25 73	96.03 67	92.05 313	92.83 3	87.39 187	95.78 115	79.39 135	99.01 66	88.13 139	97.48 86	98.05 75
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		94.60 21	94.81 20	93.98 58	94.62 180	84.96 77	96.15 54	97.35 22	89.37 64	96.03 26	98.11 7	86.36 44	99.01 66	97.45 5	97.83 78	97.96 80

DELS-MVS		93.43 65	93.25 67	93.97 59	95.42 138	85.04 75	93.06 249	97.13 44	90.74 22	91.84 112	95.09 144	86.32 45	99.21 48	91.22 102	98.45 52	97.65 101
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 92	91.28 99	93.96 60	98.33 27	85.92 57	94.66 156	96.66 89	82.69 245	90.03 142	95.82 113	82.30 100	99.03 61	84.57 185	96.48 112	96.91 141

HPM-MVS_fast		93.40 66	93.22 68	93.94 61	98.36 25	84.83 79	97.15 13	96.80 75	85.77 168	92.47 96	97.13 54	82.38 96	99.07 56	90.51 115	98.40 54	97.92 84

test_fmvsmconf0.1_n		94.20 37	94.31 31	93.88 62	92.46 269	84.80 80	96.18 51	96.82 72	89.29 67	95.68 31	98.11 7	85.10 60	98.99 73	97.38 6	97.75 82	97.86 88

SD-MVS		94.96 13	95.33 8	93.88 62	97.25 72	86.69 28	96.19 49	97.11 47	90.42 28	96.95 15	97.27 43	89.53 14	96.91 264	94.38 38	98.85 20	98.03 77
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 60	93.31 65	93.84 64	96.99 75	84.84 78	93.24 242	97.24 32	88.76 85	91.60 119	95.85 111	86.07 49	98.66 107	91.91 91	98.16 63	98.03 77

SR-MVS-dyc-post		93.82 49	93.82 50	93.82 65	97.92 43	84.57 86	96.28 43	96.76 79	87.46 128	93.75 60	97.43 36	84.24 74	99.01 66	92.73 61	97.80 79	97.88 86

test_prior				93.82 65	97.29 70	84.49 90		96.88 65					98.87 87			98.11 72

APD-MVS_3200maxsize		93.78 50	93.77 54	93.80 67	97.92 43	84.19 101	96.30 41	96.87 66	86.96 138	93.92 58	97.47 34	83.88 78	98.96 80	92.71 64	97.87 76	98.26 60

fmvsm_l_conf0.5_n		94.29 31	94.46 25	93.79 68	95.28 143	85.43 70	95.68 91	96.43 105	86.56 149	96.84 16	97.81 28	87.56 32	98.77 99	97.14 8	96.82 103	97.16 125

CSCG		93.23 71	93.05 71	93.76 69	98.04 40	84.07 103	96.22 48	97.37 21	84.15 207	90.05 141	95.66 120	87.77 26	99.15 53	89.91 120	98.27 58	98.07 73

GDP-MVS		92.04 90	91.46 96	93.75 70	94.55 187	84.69 83	95.60 101	96.56 97	87.83 120	93.07 76	95.89 109	73.44 215	98.65 109	90.22 118	96.03 120	97.91 85

BP-MVS1		92.48 85	92.07 88	93.72 71	94.50 190	84.39 98	95.90 78	94.30 250	90.39 29	92.67 90	95.94 106	74.46 195	98.65 109	93.14 55	97.35 90	98.13 68

test_fmvsmconf0.01_n		93.19 72	93.02 72	93.71 72	89.25 363	84.42 97	96.06 64	96.29 115	89.06 74	94.68 43	98.13 4	79.22 137	98.98 77	97.22 7	97.24 91	97.74 96

UA-Net		92.83 79	92.54 82	93.68 73	96.10 105	84.71 82	95.66 94	96.39 109	91.92 8	93.22 71	96.49 85	83.16 85	98.87 87	84.47 187	95.47 130	97.45 111

fmvsm_l_conf0.5_n_a		94.20 37	94.40 27	93.60 74	95.29 142	84.98 76	95.61 98	96.28 118	86.31 155	96.75 18	97.86 27	87.40 33	98.74 102	97.07 10	97.02 96	97.07 127

QAPM		89.51 148	88.15 172	93.59 75	94.92 163	84.58 85	96.82 29	96.70 87	78.43 323	83.41 294	96.19 96	73.18 219	99.30 43	77.11 295	96.54 109	96.89 142

test_fmvsm_n_1920		94.71 20	95.11 11	93.50 76	95.79 120	84.62 84	96.15 54	97.64 2	89.85 46	97.19 9	97.89 25	86.28 46	98.71 105	97.11 9	98.08 70	97.17 121

casdiffmvs_mvg		92.96 78	92.83 76	93.35 77	94.59 182	83.40 123	95.00 133	96.34 112	90.30 33	92.05 103	96.05 101	83.43 81	98.15 156	92.07 83	95.67 124	98.49 29
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EI-MVSNet-Vis-set		93.01 77	92.92 74	93.29 78	95.01 156	83.51 120	94.48 164	95.77 164	90.87 16	92.52 94	96.67 74	84.50 71	99.00 71	91.99 87	94.44 157	97.36 112

Vis-MVSNet		91.75 96	91.23 100	93.29 78	95.32 141	83.78 110	96.14 56	95.98 146	89.89 43	90.45 132	96.58 82	75.09 186	98.31 147	84.75 183	96.90 99	97.78 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 46	94.22 36	93.26 80	96.13 101	83.29 126	96.27 45	96.52 100	89.82 47	95.56 33	95.51 125	84.50 71	98.79 97	94.83 33	98.86 19	97.72 97

SPE-MVS-test		94.02 43	94.29 32	93.24 81	96.69 81	83.24 127	97.49 5	96.92 61	92.14 6	92.90 78	95.77 116	85.02 63	98.33 144	93.03 57	98.62 46	98.13 68

VNet		92.24 89	91.91 90	93.24 81	96.59 85	83.43 121	94.84 144	96.44 104	89.19 71	94.08 55	95.90 108	77.85 156	98.17 154	88.90 130	93.38 176	98.13 68

VDD-MVS		90.74 115	89.92 127	93.20 83	96.27 97	83.02 140	95.73 88	93.86 268	88.42 98	92.53 93	96.84 66	62.09 329	98.64 111	90.95 108	92.62 191	97.93 83

CS-MVS		94.12 41	94.44 26	93.17 84	96.55 88	83.08 137	97.63 3	96.95 58	91.71 12	93.50 68	96.21 92	85.61 52	98.24 149	93.64 46	98.17 62	98.19 64

nrg030		91.08 109	90.39 113	93.17 84	93.07 252	86.91 22	96.41 37	96.26 120	88.30 101	88.37 166	94.85 154	82.19 104	97.64 197	91.09 103	82.95 308	94.96 220

MVSMamba_PlusPlus		93.44 61	93.54 62	93.14 86	96.58 87	83.05 138	96.06 64	96.50 102	84.42 204	94.09 52	95.56 124	85.01 66	98.69 106	94.96 32	98.66 41	97.67 100

EI-MVSNet-UG-set		92.74 81	92.62 81	93.12 87	94.86 168	83.20 129	94.40 172	95.74 167	90.71 24	92.05 103	96.60 81	84.00 76	98.99 73	91.55 98	93.63 167	97.17 121

test_fmvsmvis_n_1920		93.44 61	93.55 61	93.10 88	93.67 234	84.26 100	95.83 84	96.14 130	89.00 80	92.43 97	97.50 33	83.37 84	98.72 103	96.61 15	97.44 87	96.32 162

新几何1				93.10 88	97.30 69	84.35 99		95.56 181	71.09 389	91.26 125	96.24 91	82.87 91	98.86 89	79.19 274	98.10 67	96.07 177

OMC-MVS		91.23 105	90.62 112	93.08 90	96.27 97	84.07 103	93.52 225	95.93 150	86.95 139	89.51 146	96.13 99	78.50 147	98.35 141	85.84 171	92.90 185	96.83 146

OpenMVS	83.78 11	88.74 174	87.29 191	93.08 90	92.70 264	85.39 71	96.57 35	96.43 105	78.74 318	80.85 326	96.07 100	69.64 262	99.01 66	78.01 286	96.65 107	94.83 227

MAR-MVS		90.30 126	89.37 138	93.07 92	96.61 84	84.48 91	95.68 91	95.67 173	82.36 250	87.85 175	92.85 226	76.63 167	98.80 95	80.01 262	96.68 106	95.91 183
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		90.92 110	90.21 116	93.03 93	93.86 224	83.88 108	92.81 258	93.86 268	79.84 300	91.76 115	94.29 175	77.92 153	98.04 172	90.48 116	97.11 92	97.17 121

Effi-MVS+		91.59 100	91.11 102	93.01 94	94.35 202	83.39 124	94.60 158	95.10 211	87.10 135	90.57 131	93.10 221	81.43 114	98.07 170	89.29 126	94.48 155	97.59 105

fmvsm_s_conf0.5_n_a		93.57 55	93.76 55	93.00 95	95.02 155	83.67 113	96.19 49	96.10 136	87.27 132	95.98 27	98.05 17	83.07 88	98.45 132	96.68 14	95.51 127	96.88 143

MVS_111021_LR		92.47 86	92.29 86	92.98 96	95.99 114	84.43 95	93.08 247	96.09 137	88.20 106	91.12 126	95.72 119	81.33 115	97.76 187	91.74 95	97.37 89	96.75 148

fmvsm_s_conf0.1_n_a		93.19 72	93.26 66	92.97 97	92.49 267	83.62 116	96.02 68	95.72 170	86.78 144	96.04 25	98.19 1	82.30 100	98.43 136	96.38 16	95.42 133	96.86 144

ETV-MVS		92.74 81	92.66 79	92.97 97	95.20 149	84.04 105	95.07 129	96.51 101	90.73 23	92.96 77	91.19 285	84.06 75	98.34 142	91.72 96	96.54 109	96.54 158

LFMVS		90.08 131	89.13 144	92.95 99	96.71 80	82.32 163	96.08 60	89.91 366	86.79 143	92.15 102	96.81 69	62.60 327	98.34 142	87.18 152	93.90 163	98.19 64

UGNet		89.95 136	88.95 148	92.95 99	94.51 189	83.31 125	95.70 90	95.23 204	89.37 64	87.58 181	93.94 190	64.00 318	98.78 98	83.92 194	96.31 114	96.74 149
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		90.80 113	90.10 120	92.90 101	93.04 255	83.53 119	93.08 247	94.15 257	80.22 294	91.41 122	94.91 148	76.87 161	97.93 181	90.28 117	96.90 99	97.24 117
jason: jason.
DP-MVS		87.25 225	85.36 261	92.90 101	97.65 58	83.24 127	94.81 146	92.00 315	74.99 357	81.92 315	95.00 146	72.66 224	99.05 58	66.92 370	92.33 196	96.40 160

fmvsm_s_conf0.5_n		93.76 51	94.06 45	92.86 103	95.62 130	83.17 130	96.14 56	96.12 134	88.13 109	95.82 29	98.04 20	83.43 81	98.48 124	96.97 12	96.23 115	96.92 140

fmvsm_s_conf0.1_n		93.46 59	93.66 59	92.85 104	93.75 230	83.13 132	96.02 68	95.74 167	87.68 125	95.89 28	98.17 2	82.78 92	98.46 128	96.71 13	96.17 117	96.98 136

CANet_DTU		90.26 128	89.41 137	92.81 105	93.46 241	83.01 141	93.48 226	94.47 243	89.43 62	87.76 179	94.23 180	70.54 251	99.03 61	84.97 178	96.39 113	96.38 161

MVSFormer		91.68 99	91.30 98	92.80 106	93.86 224	83.88 108	95.96 73	95.90 154	84.66 200	91.76 115	94.91 148	77.92 153	97.30 231	89.64 122	97.11 92	97.24 117

PVSNet_Blended_VisFu		91.38 102	90.91 107	92.80 106	96.39 94	83.17 130	94.87 141	96.66 89	83.29 230	89.27 152	94.46 170	80.29 122	99.17 50	87.57 146	95.37 134	96.05 180

VDDNet		89.56 147	88.49 163	92.76 108	95.07 154	82.09 165	96.30 41	93.19 282	81.05 288	91.88 110	96.86 65	61.16 345	98.33 144	88.43 136	92.49 195	97.84 90

h-mvs33		90.80 113	90.15 119	92.75 109	96.01 110	82.66 154	95.43 104	95.53 185	89.80 50	93.08 74	95.64 121	75.77 175	99.00 71	92.07 83	78.05 365	96.60 153

casdiffmvs		92.51 84	92.43 84	92.74 110	94.41 197	81.98 168	94.54 162	96.23 124	89.57 59	91.96 107	96.17 97	82.58 94	98.01 174	90.95 108	95.45 132	98.23 62
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		90.69 117	90.02 125	92.71 111	95.72 123	82.41 161	94.11 192	95.12 209	85.63 172	91.49 120	94.70 158	74.75 190	98.42 137	86.13 166	92.53 193	97.31 113

DCV-MVSNet		90.69 117	90.02 125	92.71 111	95.72 123	82.41 161	94.11 192	95.12 209	85.63 172	91.49 120	94.70 158	74.75 190	98.42 137	86.13 166	92.53 193	97.31 113

PCF-MVS	84.11 10	87.74 200	86.08 236	92.70 113	94.02 215	84.43 95	89.27 345	95.87 158	73.62 371	84.43 266	94.33 172	78.48 148	98.86 89	70.27 344	94.45 156	94.81 228
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline		92.39 88	92.29 86	92.69 114	94.46 193	81.77 172	94.14 189	96.27 119	89.22 69	91.88 110	96.00 102	82.35 97	97.99 176	91.05 104	95.27 138	98.30 49

MSLP-MVS++		93.72 53	94.08 42	92.65 115	97.31 68	83.43 121	95.79 86	97.33 25	90.03 40	93.58 64	96.96 61	84.87 67	97.76 187	92.19 79	98.66 41	96.76 147

EC-MVSNet		93.44 61	93.71 57	92.63 116	95.21 148	82.43 158	97.27 9	96.71 86	90.57 27	92.88 79	95.80 114	83.16 85	98.16 155	93.68 45	98.14 65	97.31 113

ab-mvs		89.41 153	88.35 165	92.60 117	95.15 153	82.65 155	92.20 278	95.60 180	83.97 211	88.55 162	93.70 203	74.16 203	98.21 153	82.46 216	89.37 237	96.94 138

LS3D		87.89 195	86.32 225	92.59 118	96.07 108	82.92 144	95.23 117	94.92 223	75.66 349	82.89 301	95.98 104	72.48 227	99.21 48	68.43 358	95.23 139	95.64 196

Anonymous20240529		88.09 191	86.59 214	92.58 119	96.53 90	81.92 170	95.99 70	95.84 160	74.11 366	89.06 156	95.21 138	61.44 337	98.81 94	83.67 199	87.47 268	97.01 134

fmvsm_s_conf0.5_n_3		94.49 23	95.13 10	92.56 120	95.49 136	81.10 194	95.93 76	97.16 41	92.96 2	97.39 7	98.13 4	83.63 80	98.80 95	97.89 1	97.61 85	97.78 94

CPTT-MVS		91.99 91	91.80 91	92.55 121	98.24 31	81.98 168	96.76 30	96.49 103	81.89 265	90.24 135	96.44 87	78.59 145	98.61 116	89.68 121	97.85 77	97.06 128

114514_t		89.51 148	88.50 161	92.54 122	98.11 36	81.99 167	95.16 125	96.36 111	70.19 393	85.81 220	95.25 135	76.70 165	98.63 113	82.07 226	96.86 102	97.00 135

PAPM_NR		91.22 106	90.78 110	92.52 123	97.60 59	81.46 181	94.37 178	96.24 123	86.39 154	87.41 184	94.80 156	82.06 108	98.48 124	82.80 211	95.37 134	97.61 103

DeepPCF-MVS	89.96 1	94.20 37	94.77 21	92.49 124	96.52 91	80.00 228	94.00 205	97.08 48	90.05 39	95.65 32	97.29 42	89.66 13	98.97 78	93.95 42	98.71 32	98.50 27

IS-MVSNet		91.43 101	91.09 104	92.46 125	95.87 119	81.38 184	96.95 19	93.69 274	89.72 56	89.50 148	95.98 104	78.57 146	97.77 186	83.02 205	96.50 111	98.22 63

API-MVS		90.66 119	90.07 121	92.45 126	96.36 95	84.57 86	96.06 64	95.22 206	82.39 248	89.13 153	94.27 178	80.32 121	98.46 128	80.16 261	96.71 105	94.33 251

xiu_mvs_v1_base_debu		90.64 120	90.05 122	92.40 127	93.97 221	84.46 92	93.32 233	95.46 188	85.17 181	92.25 98	94.03 182	70.59 247	98.57 119	90.97 105	94.67 147	94.18 254

xiu_mvs_v1_base		90.64 120	90.05 122	92.40 127	93.97 221	84.46 92	93.32 233	95.46 188	85.17 181	92.25 98	94.03 182	70.59 247	98.57 119	90.97 105	94.67 147	94.18 254

xiu_mvs_v1_base_debi		90.64 120	90.05 122	92.40 127	93.97 221	84.46 92	93.32 233	95.46 188	85.17 181	92.25 98	94.03 182	70.59 247	98.57 119	90.97 105	94.67 147	94.18 254

fmvsm_s_conf0.5_n_2		93.47 58	93.83 49	92.39 130	95.36 139	81.19 190	95.20 122	96.56 97	90.37 30	97.13 11	98.03 21	77.47 157	98.96 80	97.79 3	96.58 108	97.03 131

fmvsm_s_conf0.1_n_2		93.16 74	93.42 63	92.37 131	94.62 180	81.13 192	95.23 117	95.89 156	90.30 33	96.74 19	98.02 22	76.14 169	98.95 82	97.64 4	96.21 116	97.03 131

AdaColmap		89.89 139	89.07 145	92.37 131	97.41 65	83.03 139	94.42 171	95.92 151	82.81 242	86.34 210	94.65 163	73.89 207	99.02 64	80.69 252	95.51 127	95.05 215

CNLPA		89.07 164	87.98 175	92.34 133	96.87 77	84.78 81	94.08 196	93.24 280	81.41 279	84.46 264	95.13 143	75.57 182	96.62 275	77.21 293	93.84 165	95.61 199

ET-MVSNet_ETH3D		87.51 213	85.91 244	92.32 134	93.70 233	83.93 106	92.33 273	90.94 346	84.16 206	72.09 389	92.52 238	69.90 257	95.85 321	89.20 127	88.36 255	97.17 121

Anonymous202405211		87.68 201	86.13 232	92.31 135	96.66 82	80.74 205	94.87 141	91.49 332	80.47 293	89.46 149	95.44 127	54.72 378	98.23 150	82.19 222	89.89 227	97.97 79

CHOSEN 1792x2688		88.84 170	87.69 181	92.30 136	96.14 100	81.42 183	90.01 332	95.86 159	74.52 362	87.41 184	93.94 190	75.46 183	98.36 139	80.36 257	95.53 126	97.12 126

HY-MVS	83.01 12	89.03 166	87.94 177	92.29 137	94.86 168	82.77 146	92.08 283	94.49 242	81.52 278	86.93 191	92.79 232	78.32 150	98.23 150	79.93 263	90.55 216	95.88 185

CDS-MVSNet		89.45 151	88.51 160	92.29 137	93.62 236	83.61 118	93.01 250	94.68 239	81.95 260	87.82 177	93.24 215	78.69 143	96.99 258	80.34 258	93.23 180	96.28 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 133	89.27 143	92.29 137	95.78 121	80.95 199	92.68 260	96.22 125	81.91 262	86.66 201	93.75 202	82.23 102	98.44 134	79.40 273	94.79 145	97.48 109

mvsmamba		90.33 125	89.69 130	92.25 140	95.17 150	81.64 174	95.27 115	93.36 279	84.88 191	89.51 146	94.27 178	69.29 271	97.42 217	89.34 125	96.12 119	97.68 99

PLC	84.53 7	89.06 165	88.03 174	92.15 141	97.27 71	82.69 153	94.29 181	95.44 193	79.71 302	84.01 280	94.18 181	76.68 166	98.75 100	77.28 292	93.41 175	95.02 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 98	91.56 95	92.13 142	95.88 117	80.50 211	97.33 7	95.25 203	86.15 160	89.76 144	95.60 122	83.42 83	98.32 146	87.37 150	93.25 179	97.56 107

patch_mono-2		93.74 52	94.32 29	92.01 143	97.54 60	78.37 265	93.40 230	97.19 35	88.02 111	94.99 42	97.21 47	88.35 21	98.44 134	94.07 41	98.09 68	99.23 1

原ACMM1				92.01 143	97.34 67	81.05 195		96.81 74	78.89 313	90.45 132	95.92 107	82.65 93	98.84 93	80.68 253	98.26 59	96.14 171

UniMVSNet (Re)		89.80 141	89.07 145	92.01 143	93.60 237	84.52 89	94.78 148	97.47 11	89.26 68	86.44 207	92.32 244	82.10 106	97.39 228	84.81 182	80.84 342	94.12 258

MG-MVS		91.77 95	91.70 94	92.00 146	97.08 74	80.03 226	93.60 223	95.18 207	87.85 119	90.89 128	96.47 86	82.06 108	98.36 139	85.07 177	97.04 95	97.62 102

EIA-MVS		91.95 92	91.94 89	91.98 147	95.16 151	80.01 227	95.36 105	96.73 83	88.44 96	89.34 150	92.16 249	83.82 79	98.45 132	89.35 124	97.06 94	97.48 109

PVSNet_Blended		90.73 116	90.32 115	91.98 147	96.12 102	81.25 186	92.55 265	96.83 70	82.04 258	89.10 154	92.56 237	81.04 117	98.85 91	86.72 160	95.91 121	95.84 187

PS-MVSNAJ		91.18 107	90.92 106	91.96 149	95.26 146	82.60 157	92.09 282	95.70 171	86.27 156	91.84 112	92.46 239	79.70 130	98.99 73	89.08 128	95.86 122	94.29 252

TAMVS		89.21 159	88.29 169	91.96 149	93.71 231	82.62 156	93.30 237	94.19 255	82.22 253	87.78 178	93.94 190	78.83 140	96.95 261	77.70 288	92.98 184	96.32 162

SDMVSNet		90.19 129	89.61 132	91.93 151	96.00 111	83.09 136	92.89 255	95.98 146	88.73 86	86.85 197	95.20 139	72.09 231	97.08 250	88.90 130	89.85 229	95.63 197

FA-MVS(test-final)		89.66 143	88.91 150	91.93 151	94.57 185	80.27 215	91.36 298	94.74 236	84.87 192	89.82 143	92.61 236	74.72 193	98.47 127	83.97 193	93.53 170	97.04 130

MVS_Test		91.31 104	91.11 102	91.93 151	94.37 198	80.14 219	93.46 228	95.80 162	86.46 152	91.35 124	93.77 200	82.21 103	98.09 167	87.57 146	94.95 142	97.55 108

NR-MVSNet		88.58 180	87.47 187	91.93 151	93.04 255	84.16 102	94.77 149	96.25 122	89.05 75	80.04 339	93.29 213	79.02 139	97.05 255	81.71 237	80.05 352	94.59 235

HyFIR lowres test		88.09 191	86.81 203	91.93 151	96.00 111	80.63 207	90.01 332	95.79 163	73.42 373	87.68 180	92.10 255	73.86 208	97.96 178	80.75 251	91.70 200	97.19 120

GeoE		90.05 132	89.43 136	91.90 156	95.16 151	80.37 214	95.80 85	94.65 240	83.90 212	87.55 183	94.75 157	78.18 151	97.62 199	81.28 241	93.63 167	97.71 98

thisisatest0530		88.67 175	87.61 183	91.86 157	94.87 167	80.07 222	94.63 157	89.90 367	84.00 210	88.46 164	93.78 199	66.88 295	98.46 128	83.30 201	92.65 190	97.06 128

xiu_mvs_v2_base		91.13 108	90.89 108	91.86 157	94.97 159	82.42 159	92.24 276	95.64 178	86.11 164	91.74 117	93.14 219	79.67 133	98.89 86	89.06 129	95.46 131	94.28 253

DU-MVS		89.34 158	88.50 161	91.85 159	93.04 255	83.72 111	94.47 167	96.59 94	89.50 60	86.46 204	93.29 213	77.25 159	97.23 240	84.92 179	81.02 338	94.59 235

OPM-MVS		90.12 130	89.56 133	91.82 160	93.14 248	83.90 107	94.16 188	95.74 167	88.96 81	87.86 174	95.43 129	72.48 227	97.91 182	88.10 141	90.18 222	93.65 288
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 123	90.19 117	91.82 160	94.70 176	82.73 150	95.85 82	96.22 125	90.81 18	86.91 193	94.86 152	74.23 199	98.12 157	88.15 137	89.99 223	94.63 232

UniMVSNet_NR-MVSNet		89.92 138	89.29 141	91.81 162	93.39 243	83.72 111	94.43 170	97.12 45	89.80 50	86.46 204	93.32 210	83.16 85	97.23 240	84.92 179	81.02 338	94.49 245

diffmvs		91.37 103	91.23 100	91.77 163	93.09 251	80.27 215	92.36 270	95.52 186	87.03 137	91.40 123	94.93 147	80.08 124	97.44 215	92.13 82	94.56 152	97.61 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
1112_ss		88.42 181	87.33 190	91.72 164	94.92 163	80.98 197	92.97 252	94.54 241	78.16 329	83.82 283	93.88 195	78.78 142	97.91 182	79.45 269	89.41 236	96.26 166

Fast-Effi-MVS+		89.41 153	88.64 156	91.71 165	94.74 172	80.81 203	93.54 224	95.10 211	83.11 234	86.82 199	90.67 306	79.74 129	97.75 190	80.51 256	93.55 169	96.57 156

WTY-MVS		89.60 145	88.92 149	91.67 166	95.47 137	81.15 191	92.38 269	94.78 234	83.11 234	89.06 156	94.32 173	78.67 144	96.61 278	81.57 238	90.89 213	97.24 117

TAPA-MVS	84.62 6	88.16 189	87.01 199	91.62 167	96.64 83	80.65 206	94.39 174	96.21 128	76.38 342	86.19 214	95.44 127	79.75 128	98.08 169	62.75 387	95.29 136	96.13 172
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 144	88.96 147	91.60 168	93.86 224	82.89 145	95.46 103	97.33 25	87.91 114	88.43 165	93.31 211	74.17 202	97.40 225	87.32 151	82.86 313	94.52 240

FE-MVS		87.40 218	86.02 238	91.57 169	94.56 186	79.69 236	90.27 319	93.72 273	80.57 291	88.80 159	91.62 274	65.32 310	98.59 118	74.97 317	94.33 159	96.44 159

XVG-OURS		89.40 155	88.70 155	91.52 170	94.06 213	81.46 181	91.27 302	96.07 139	86.14 161	88.89 158	95.77 116	68.73 280	97.26 237	87.39 149	89.96 225	95.83 188

hse-mvs2		89.88 140	89.34 139	91.51 171	94.83 170	81.12 193	93.94 208	93.91 267	89.80 50	93.08 74	93.60 204	75.77 175	97.66 194	92.07 83	77.07 372	95.74 192

TranMVSNet+NR-MVSNet		88.84 170	87.95 176	91.49 172	92.68 265	83.01 141	94.92 138	96.31 114	89.88 44	85.53 229	93.85 197	76.63 167	96.96 260	81.91 230	79.87 355	94.50 243

AUN-MVS		87.78 199	86.54 217	91.48 173	94.82 171	81.05 195	93.91 212	93.93 264	83.00 237	86.93 191	93.53 205	69.50 265	97.67 192	86.14 164	77.12 371	95.73 194

XVG-OURS-SEG-HR		89.95 136	89.45 134	91.47 174	94.00 219	81.21 189	91.87 286	96.06 141	85.78 167	88.55 162	95.73 118	74.67 194	97.27 235	88.71 133	89.64 234	95.91 183

MVS		87.44 216	86.10 235	91.44 175	92.61 266	83.62 116	92.63 262	95.66 175	67.26 398	81.47 318	92.15 250	77.95 152	98.22 152	79.71 265	95.48 129	92.47 329

F-COLMAP		87.95 194	86.80 204	91.40 176	96.35 96	80.88 201	94.73 151	95.45 191	79.65 303	82.04 313	94.61 164	71.13 238	98.50 122	76.24 305	91.05 211	94.80 229

dcpmvs_2		93.49 57	94.19 40	91.38 177	97.69 57	76.78 298	94.25 183	96.29 115	88.33 99	94.46 45	96.88 64	88.07 25	98.64 111	93.62 47	98.09 68	98.73 18

thisisatest0515		87.33 221	85.99 239	91.37 178	93.49 239	79.55 237	90.63 315	89.56 374	80.17 295	87.56 182	90.86 296	67.07 292	98.28 148	81.50 239	93.02 183	96.29 164

HQP-MVS		89.80 141	89.28 142	91.34 179	94.17 208	81.56 175	94.39 174	96.04 142	88.81 82	85.43 238	93.97 189	73.83 209	97.96 178	87.11 155	89.77 232	94.50 243

RRT-MVS		90.85 112	90.70 111	91.30 180	94.25 204	76.83 297	94.85 143	96.13 133	89.04 76	90.23 136	94.88 150	70.15 256	98.72 103	91.86 94	94.88 143	98.34 42

FMVSNet3		87.40 218	86.11 234	91.30 180	93.79 229	83.64 115	94.20 187	94.81 232	83.89 213	84.37 267	91.87 265	68.45 283	96.56 283	78.23 283	85.36 285	93.70 287

FMVSNet2		87.19 231	85.82 247	91.30 180	94.01 216	83.67 113	94.79 147	94.94 218	83.57 220	83.88 282	92.05 259	66.59 300	96.51 287	77.56 290	85.01 288	93.73 285

RPMNet		83.95 304	81.53 315	91.21 183	90.58 341	79.34 244	85.24 389	96.76 79	71.44 387	85.55 227	82.97 396	70.87 243	98.91 85	61.01 391	89.36 238	95.40 203

IB-MVS	80.51 15	85.24 283	83.26 299	91.19 184	92.13 278	79.86 232	91.75 289	91.29 337	83.28 231	80.66 329	88.49 351	61.28 339	98.46 128	80.99 247	79.46 359	95.25 209
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		89.47 150	88.90 151	91.18 185	94.22 206	82.07 166	92.13 280	96.09 137	87.90 115	85.37 244	92.45 240	74.38 197	97.56 202	87.15 153	90.43 218	93.93 267
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		89.45 151	88.90 151	91.12 186	94.47 191	81.49 179	95.30 110	96.14 130	86.73 146	85.45 235	95.16 141	69.89 258	98.10 159	87.70 144	89.23 241	93.77 282

LGP-MVS_train				91.12 186	94.47 191	81.49 179		96.14 130	86.73 146	85.45 235	95.16 141	69.89 258	98.10 159	87.70 144	89.23 241	93.77 282

ACMM	84.12 9	89.14 160	88.48 164	91.12 186	94.65 179	81.22 188	95.31 108	96.12 134	85.31 180	85.92 218	94.34 171	70.19 255	98.06 171	85.65 172	88.86 246	94.08 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 177	87.78 180	91.11 189	94.96 160	77.81 280	95.35 106	89.69 370	85.09 186	88.05 172	94.59 167	66.93 293	98.48 124	83.27 202	92.13 198	97.03 131

GBi-Net		87.26 223	85.98 240	91.08 190	94.01 216	83.10 133	95.14 126	94.94 218	83.57 220	84.37 267	91.64 270	66.59 300	96.34 300	78.23 283	85.36 285	93.79 277

test1		87.26 223	85.98 240	91.08 190	94.01 216	83.10 133	95.14 126	94.94 218	83.57 220	84.37 267	91.64 270	66.59 300	96.34 300	78.23 283	85.36 285	93.79 277

FMVSNet1		85.85 269	84.11 286	91.08 190	92.81 262	83.10 133	95.14 126	94.94 218	81.64 273	82.68 303	91.64 270	59.01 358	96.34 300	75.37 311	83.78 298	93.79 277

Test_1112_low_res		87.65 203	86.51 218	91.08 190	94.94 162	79.28 248	91.77 288	94.30 250	76.04 347	83.51 292	92.37 242	77.86 155	97.73 191	78.69 278	89.13 243	96.22 167

PS-MVSNAJss		89.97 135	89.62 131	91.02 194	91.90 287	80.85 202	95.26 116	95.98 146	86.26 157	86.21 213	94.29 175	79.70 130	97.65 195	88.87 132	88.10 257	94.57 237

BH-RMVSNet		88.37 183	87.48 186	91.02 194	95.28 143	79.45 240	92.89 255	93.07 285	85.45 177	86.91 193	94.84 155	70.35 252	97.76 187	73.97 323	94.59 151	95.85 186

UniMVSNet_ETH3D		87.53 212	86.37 222	91.00 196	92.44 270	78.96 253	94.74 150	95.61 179	84.07 209	85.36 245	94.52 169	59.78 353	97.34 230	82.93 206	87.88 262	96.71 150

FIs		90.51 124	90.35 114	90.99 197	93.99 220	80.98 197	95.73 88	97.54 4	89.15 72	86.72 200	94.68 160	81.83 112	97.24 239	85.18 176	88.31 256	94.76 230

ACMP	84.23 8	89.01 168	88.35 165	90.99 197	94.73 173	81.27 185	95.07 129	95.89 156	86.48 150	83.67 287	94.30 174	69.33 267	97.99 176	87.10 157	88.55 248	93.72 286
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 252	85.13 266	90.98 199	96.52 91	81.50 177	96.14 56	96.16 129	73.78 369	83.65 288	92.15 250	63.26 324	97.37 229	82.82 210	81.74 327	94.06 263

sss		88.93 169	88.26 171	90.94 200	94.05 214	80.78 204	91.71 290	95.38 197	81.55 277	88.63 161	93.91 194	75.04 187	95.47 339	82.47 215	91.61 201	96.57 156

sd_testset		88.59 179	87.85 179	90.83 201	96.00 111	80.42 213	92.35 271	94.71 237	88.73 86	86.85 197	95.20 139	67.31 287	96.43 294	79.64 267	89.85 229	95.63 197

PVSNet_BlendedMVS		89.98 134	89.70 129	90.82 202	96.12 102	81.25 186	93.92 210	96.83 70	83.49 224	89.10 154	92.26 247	81.04 117	98.85 91	86.72 160	87.86 263	92.35 335

cascas		86.43 260	84.98 269	90.80 203	92.10 280	80.92 200	90.24 323	95.91 153	73.10 376	83.57 291	88.39 352	65.15 312	97.46 211	84.90 181	91.43 203	94.03 265

ECVR-MVS		89.09 163	88.53 159	90.77 204	95.62 130	75.89 311	96.16 52	84.22 399	87.89 117	90.20 137	96.65 76	63.19 325	98.10 159	85.90 169	96.94 97	98.33 44

GA-MVS		86.61 250	85.27 264	90.66 205	91.33 310	78.71 255	90.40 318	93.81 271	85.34 179	85.12 248	89.57 333	61.25 340	97.11 249	80.99 247	89.59 235	96.15 170

thres600view7		87.65 203	86.67 209	90.59 206	96.08 107	78.72 254	94.88 140	91.58 328	87.06 136	88.08 170	92.30 245	68.91 277	98.10 159	70.05 351	91.10 206	94.96 220

thres400		87.62 208	86.64 210	90.57 207	95.99 114	78.64 256	94.58 159	91.98 317	86.94 140	88.09 168	91.77 266	69.18 273	98.10 159	70.13 348	91.10 206	94.96 220

baseline1		88.10 190	87.28 192	90.57 207	94.96 160	80.07 222	94.27 182	91.29 337	86.74 145	87.41 184	94.00 187	76.77 164	96.20 305	80.77 250	79.31 361	95.44 201

FC-MVSNet-test		90.27 127	90.18 118	90.53 209	93.71 231	79.85 233	95.77 87	97.59 3	89.31 66	86.27 211	94.67 161	81.93 111	97.01 257	84.26 189	88.09 259	94.71 231

PAPM		86.68 249	85.39 259	90.53 209	93.05 254	79.33 247	89.79 335	94.77 235	78.82 315	81.95 314	93.24 215	76.81 162	97.30 231	66.94 368	93.16 181	94.95 223

WR-MVS		88.38 182	87.67 182	90.52 211	93.30 245	80.18 217	93.26 240	95.96 149	88.57 94	85.47 234	92.81 230	76.12 170	96.91 264	81.24 242	82.29 318	94.47 248

MVSTER		88.84 170	88.29 169	90.51 212	92.95 260	80.44 212	93.73 217	95.01 215	84.66 200	87.15 188	93.12 220	72.79 223	97.21 242	87.86 142	87.36 271	93.87 272

testdata				90.49 213	96.40 93	77.89 277		95.37 199	72.51 381	93.63 63	96.69 72	82.08 107	97.65 195	83.08 203	97.39 88	95.94 182

test1111		89.10 161	88.64 156	90.48 214	95.53 135	74.97 321	96.08 60	84.89 397	88.13 109	90.16 139	96.65 76	63.29 323	98.10 159	86.14 164	96.90 99	98.39 39

tt0805		86.92 239	85.74 253	90.48 214	92.22 274	79.98 229	95.63 97	94.88 226	83.83 215	84.74 257	92.80 231	57.61 364	97.67 192	85.48 175	84.42 292	93.79 277

jajsoiax		88.24 187	87.50 185	90.48 214	90.89 330	80.14 219	95.31 108	95.65 177	84.97 189	84.24 275	94.02 185	65.31 311	97.42 217	88.56 134	88.52 250	93.89 268

PatchMatch-RL		86.77 247	85.54 255	90.47 217	95.88 117	82.71 152	90.54 316	92.31 305	79.82 301	84.32 272	91.57 278	68.77 279	96.39 296	73.16 329	93.48 174	92.32 336

tfpn200view9		87.58 210	86.64 210	90.41 218	95.99 114	78.64 256	94.58 159	91.98 317	86.94 140	88.09 168	91.77 266	69.18 273	98.10 159	70.13 348	91.10 206	94.48 246

VPNet		88.20 188	87.47 187	90.39 219	93.56 238	79.46 239	94.04 200	95.54 184	88.67 89	86.96 190	94.58 168	69.33 267	97.15 244	84.05 192	80.53 347	94.56 238

ACMH	80.38 17	85.36 278	83.68 293	90.39 219	94.45 194	80.63 207	94.73 151	94.85 228	82.09 255	77.24 361	92.65 234	60.01 351	97.58 200	72.25 333	84.87 289	92.96 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 206	86.71 207	90.38 221	96.12 102	78.55 258	95.03 132	91.58 328	87.15 133	88.06 171	92.29 246	68.91 277	98.10 159	70.13 348	91.10 206	94.48 246

mvs_tets		88.06 193	87.28 192	90.38 221	90.94 326	79.88 231	95.22 119	95.66 175	85.10 185	84.21 276	93.94 190	63.53 321	97.40 225	88.50 135	88.40 254	93.87 272

1314		87.51 213	86.57 215	90.34 223	92.42 271	79.74 235	92.63 262	95.35 201	78.35 324	80.14 336	91.62 274	74.05 204	97.15 244	81.05 243	93.53 170	94.12 258

LTVRE_ROB	82.13 13	86.26 263	84.90 272	90.34 223	94.44 195	81.50 177	92.31 275	94.89 224	83.03 236	79.63 345	92.67 233	69.69 261	97.79 185	71.20 337	86.26 280	91.72 346
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		89.03 166	88.64 156	90.21 225	90.74 336	79.28 248	95.96 73	95.90 154	84.66 200	85.33 246	92.94 225	74.02 205	97.30 231	89.64 122	88.53 249	94.05 264

v2v482		87.84 196	87.06 196	90.17 226	90.99 322	79.23 251	94.00 205	95.13 208	84.87 192	85.53 229	92.07 258	74.45 196	97.45 212	84.71 184	81.75 326	93.85 275

pmmvs4		85.43 276	83.86 291	90.16 227	90.02 353	82.97 143	90.27 319	92.67 297	75.93 348	80.73 327	91.74 268	71.05 239	95.73 329	78.85 277	83.46 305	91.78 345

V42		87.68 201	86.86 201	90.15 228	90.58 341	80.14 219	94.24 185	95.28 202	83.66 218	85.67 224	91.33 280	74.73 192	97.41 223	84.43 188	81.83 324	92.89 318

MSDG		84.86 291	83.09 302	90.14 229	93.80 227	80.05 224	89.18 348	93.09 284	78.89 313	78.19 354	91.91 263	65.86 309	97.27 235	68.47 357	88.45 252	93.11 310

anonymousdsp		87.84 196	87.09 195	90.12 230	89.13 364	80.54 210	94.67 155	95.55 182	82.05 256	83.82 283	92.12 252	71.47 236	97.15 244	87.15 153	87.80 266	92.67 323

thres200		87.21 229	86.24 229	90.12 230	95.36 139	78.53 259	93.26 240	92.10 311	86.42 153	88.00 173	91.11 291	69.24 272	98.00 175	69.58 352	91.04 212	93.83 276

CR-MVSNet		85.35 279	83.76 292	90.12 230	90.58 341	79.34 244	85.24 389	91.96 319	78.27 326	85.55 227	87.87 362	71.03 240	95.61 331	73.96 324	89.36 238	95.40 203

v1144		87.61 209	86.79 205	90.06 233	91.01 321	79.34 244	93.95 207	95.42 196	83.36 229	85.66 225	91.31 283	74.98 188	97.42 217	83.37 200	82.06 320	93.42 297

XXY-MVS		87.65 203	86.85 202	90.03 234	92.14 277	80.60 209	93.76 216	95.23 204	82.94 239	84.60 259	94.02 185	74.27 198	95.49 338	81.04 244	83.68 301	94.01 266

Vis-MVSNet (Re-imp)		89.59 146	89.44 135	90.03 234	95.74 122	75.85 312	95.61 98	90.80 350	87.66 127	87.83 176	95.40 130	76.79 163	96.46 292	78.37 279	96.73 104	97.80 92

test2506		87.21 229	86.28 227	90.02 236	95.62 130	73.64 337	96.25 47	71.38 421	87.89 117	90.45 132	96.65 76	55.29 375	98.09 167	86.03 168	96.94 97	98.33 44

BH-untuned		88.60 178	88.13 173	90.01 237	95.24 147	78.50 261	93.29 238	94.15 257	84.75 197	84.46 264	93.40 207	75.76 177	97.40 225	77.59 289	94.52 154	94.12 258

v1192		87.25 225	86.33 224	90.00 238	90.76 335	79.04 252	93.80 214	95.48 187	82.57 246	85.48 233	91.18 287	73.38 218	97.42 217	82.30 219	82.06 320	93.53 291

v7n		86.81 242	85.76 251	89.95 239	90.72 337	79.25 250	95.07 129	95.92 151	84.45 203	82.29 307	90.86 296	72.60 226	97.53 204	79.42 272	80.52 348	93.08 312

testing91		87.11 234	86.18 230	89.92 240	94.43 196	75.38 320	91.53 295	92.27 307	86.48 150	86.50 202	90.24 314	61.19 343	97.53 204	82.10 224	90.88 214	96.84 145

v8		87.50 215	86.71 207	89.89 241	91.37 307	79.40 241	94.50 163	95.38 197	84.81 195	83.60 290	91.33 280	76.05 171	97.42 217	82.84 209	80.51 349	92.84 320

v10		87.25 225	86.38 221	89.85 242	91.19 313	79.50 238	94.48 164	95.45 191	83.79 216	83.62 289	91.19 285	75.13 185	97.42 217	81.94 229	80.60 344	92.63 325

baseline2		86.50 256	85.39 259	89.84 243	91.12 318	76.70 300	91.88 285	88.58 377	82.35 251	79.95 340	90.95 295	73.42 216	97.63 198	80.27 260	89.95 226	95.19 210

pm-mvs1		86.61 250	85.54 255	89.82 244	91.44 302	80.18 217	95.28 114	94.85 228	83.84 214	81.66 316	92.62 235	72.45 229	96.48 289	79.67 266	78.06 364	92.82 321

TR-MVS		86.78 244	85.76 251	89.82 244	94.37 198	78.41 263	92.47 266	92.83 291	81.11 287	86.36 208	92.40 241	68.73 280	97.48 208	73.75 327	89.85 229	93.57 290

ACMH+	81.04 14	85.05 286	83.46 296	89.82 244	94.66 178	79.37 242	94.44 169	94.12 260	82.19 254	78.04 356	92.82 229	58.23 361	97.54 203	73.77 326	82.90 312	92.54 326

EI-MVSNet		89.10 161	88.86 153	89.80 247	91.84 289	78.30 267	93.70 220	95.01 215	85.73 169	87.15 188	95.28 133	79.87 127	97.21 242	83.81 196	87.36 271	93.88 271

v144192		87.19 231	86.35 223	89.74 248	90.64 339	78.24 269	93.92 210	95.43 194	81.93 261	85.51 231	91.05 293	74.21 201	97.45 212	82.86 208	81.56 328	93.53 291

COLMAP_ROB	80.39 16	83.96 303	82.04 312	89.74 248	95.28 143	79.75 234	94.25 183	92.28 306	75.17 355	78.02 357	93.77 200	58.60 360	97.84 184	65.06 379	85.92 281	91.63 348
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 262	85.18 265	89.73 250	92.15 276	76.60 301	91.12 306	91.69 324	83.53 223	85.50 232	88.81 345	66.79 296	96.48 289	76.65 298	90.35 220	96.12 173

IterMVS-LS		88.36 184	87.91 178	89.70 251	93.80 227	78.29 268	93.73 217	95.08 213	85.73 169	84.75 256	91.90 264	79.88 126	96.92 263	83.83 195	82.51 314	93.89 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 259	85.35 262	89.69 252	94.29 203	75.40 319	91.30 300	90.53 353	84.76 196	85.06 250	90.13 320	58.95 359	97.45 212	82.08 225	91.09 210	96.21 169

testing99		86.72 248	85.73 254	89.69 252	94.23 205	74.91 323	91.35 299	90.97 345	86.14 161	86.36 208	90.22 315	59.41 355	97.48 208	82.24 221	90.66 215	96.69 151

v1921920		86.97 238	86.06 237	89.69 252	90.53 344	78.11 272	93.80 214	95.43 194	81.90 263	85.33 246	91.05 293	72.66 224	97.41 223	82.05 227	81.80 325	93.53 291

Fast-Effi-MVS+-dtu		87.44 216	86.72 206	89.63 255	92.04 281	77.68 286	94.03 201	93.94 263	85.81 166	82.42 306	91.32 282	70.33 253	97.06 253	80.33 259	90.23 221	94.14 257

v1240		86.78 244	85.85 246	89.56 256	90.45 345	77.79 282	93.61 222	95.37 199	81.65 272	85.43 238	91.15 289	71.50 235	97.43 216	81.47 240	82.05 322	93.47 295

Effi-MVS+-dtu		88.65 176	88.35 165	89.54 257	93.33 244	76.39 305	94.47 167	94.36 248	87.70 124	85.43 238	89.56 334	73.45 214	97.26 237	85.57 174	91.28 205	94.97 217

AllTest		83.42 310	81.39 316	89.52 258	95.01 156	77.79 282	93.12 244	90.89 348	77.41 333	76.12 369	93.34 208	54.08 381	97.51 206	68.31 359	84.27 294	93.26 300

TestCases				89.52 258	95.01 156	77.79 282		90.89 348	77.41 333	76.12 369	93.34 208	54.08 381	97.51 206	68.31 359	84.27 294	93.26 300

mvs_anonymous		89.37 157	89.32 140	89.51 260	93.47 240	74.22 330	91.65 293	94.83 230	82.91 240	85.45 235	93.79 198	81.23 116	96.36 299	86.47 162	94.09 160	97.94 81

XVG-ACMP-BASELINE		86.00 265	84.84 274	89.45 261	91.20 312	78.00 273	91.70 291	95.55 182	85.05 187	82.97 300	92.25 248	54.49 379	97.48 208	82.93 206	87.45 270	92.89 318

testing222		84.84 292	83.32 297	89.43 262	94.15 211	75.94 310	91.09 307	89.41 375	84.90 190	85.78 221	89.44 335	52.70 386	96.28 303	70.80 343	91.57 202	96.07 177

MVP-Stereo		85.97 266	84.86 273	89.32 263	90.92 328	82.19 164	92.11 281	94.19 255	78.76 317	78.77 353	91.63 273	68.38 284	96.56 283	75.01 316	93.95 162	89.20 384
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 269	84.70 276	89.29 264	91.76 293	75.54 316	88.49 357	91.30 336	81.63 274	85.05 251	88.70 349	71.71 232	96.24 304	74.61 320	89.05 244	96.08 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 236	86.32 225	89.21 265	90.94 326	77.26 291	93.71 219	94.43 244	84.84 194	84.36 270	90.80 300	76.04 172	97.05 255	82.12 223	79.60 358	93.31 299

tfpnnormal		84.72 294	83.23 300	89.20 266	92.79 263	80.05 224	94.48 164	95.81 161	82.38 249	81.08 324	91.21 284	69.01 276	96.95 261	61.69 389	80.59 345	90.58 371

cl22		86.78 244	85.98 240	89.18 267	92.34 272	77.62 287	90.84 312	94.13 259	81.33 281	83.97 281	90.15 319	73.96 206	96.60 280	84.19 190	82.94 309	93.33 298

BH-w/o		87.57 211	87.05 197	89.12 268	94.90 166	77.90 276	92.41 267	93.51 276	82.89 241	83.70 286	91.34 279	75.75 178	97.07 252	75.49 309	93.49 172	92.39 333

WR-MVS_H		87.80 198	87.37 189	89.10 269	93.23 246	78.12 271	95.61 98	97.30 29	87.90 115	83.72 285	92.01 260	79.65 134	96.01 313	76.36 302	80.54 346	93.16 308

miper_enhance_ethall		86.90 240	86.18 230	89.06 270	91.66 298	77.58 288	90.22 325	94.82 231	79.16 309	84.48 263	89.10 339	79.19 138	96.66 273	84.06 191	82.94 309	92.94 316

c3_l		87.14 233	86.50 219	89.04 271	92.20 275	77.26 291	91.22 305	94.70 238	82.01 259	84.34 271	90.43 311	78.81 141	96.61 278	83.70 198	81.09 335	93.25 302

miper_ehance_all_eth		87.22 228	86.62 213	89.02 272	92.13 278	77.40 290	90.91 311	94.81 232	81.28 282	84.32 272	90.08 322	79.26 136	96.62 275	83.81 196	82.94 309	93.04 313

gg-mvs-nofinetune		81.77 322	79.37 337	88.99 273	90.85 332	77.73 285	86.29 381	79.63 410	74.88 360	83.19 299	69.05 412	60.34 348	96.11 309	75.46 310	94.64 150	93.11 310

ETVMVS		84.43 297	82.92 306	88.97 274	94.37 198	74.67 324	91.23 304	88.35 379	83.37 228	86.06 217	89.04 340	55.38 373	95.67 330	67.12 366	91.34 204	96.58 155

pmmvs6		83.42 310	81.60 314	88.87 275	88.01 378	77.87 278	94.96 135	94.24 254	74.67 361	78.80 352	91.09 292	60.17 350	96.49 288	77.06 297	75.40 378	92.23 338

test_cas_vis1_n_1920		88.83 173	88.85 154	88.78 276	91.15 317	76.72 299	93.85 213	94.93 222	83.23 233	92.81 83	96.00 102	61.17 344	94.45 350	91.67 97	94.84 144	95.17 211

MIMVSNet		82.59 316	80.53 321	88.76 277	91.51 300	78.32 266	86.57 380	90.13 360	79.32 305	80.70 328	88.69 350	52.98 385	93.07 375	66.03 374	88.86 246	94.90 224

cl____		86.52 255	85.78 248	88.75 278	92.03 282	76.46 303	90.74 313	94.30 250	81.83 268	83.34 296	90.78 301	75.74 180	96.57 281	81.74 235	81.54 329	93.22 304

DIV-MVS_self_test		86.53 254	85.78 248	88.75 278	92.02 283	76.45 304	90.74 313	94.30 250	81.83 268	83.34 296	90.82 299	75.75 178	96.57 281	81.73 236	81.52 330	93.24 303

CP-MVSNet		87.63 206	87.26 194	88.74 280	93.12 249	76.59 302	95.29 112	96.58 95	88.43 97	83.49 293	92.98 224	75.28 184	95.83 322	78.97 275	81.15 334	93.79 277

eth_miper_zixun_eth		86.50 256	85.77 250	88.68 281	91.94 284	75.81 313	90.47 317	94.89 224	82.05 256	84.05 278	90.46 310	75.96 173	96.77 268	82.76 212	79.36 360	93.46 296

CHOSEN 280x420		85.15 284	83.99 289	88.65 282	92.47 268	78.40 264	79.68 411	92.76 294	74.90 359	81.41 320	89.59 332	69.85 260	95.51 335	79.92 264	95.29 136	92.03 341

PS-CasMVS		87.32 222	86.88 200	88.63 283	92.99 258	76.33 307	95.33 107	96.61 93	88.22 105	83.30 298	93.07 222	73.03 221	95.79 326	78.36 280	81.00 340	93.75 284

TransMVSNet (Re)		84.43 297	83.06 304	88.54 284	91.72 294	78.44 262	95.18 123	92.82 293	82.73 244	79.67 344	92.12 252	73.49 213	95.96 315	71.10 341	68.73 394	91.21 358

EG-PatchMatch MVS		82.37 318	80.34 324	88.46 285	90.27 347	79.35 243	92.80 259	94.33 249	77.14 337	73.26 386	90.18 318	47.47 397	96.72 269	70.25 345	87.32 273	89.30 381

PEN-MVS		86.80 243	86.27 228	88.40 286	92.32 273	75.71 315	95.18 123	96.38 110	87.97 112	82.82 302	93.15 218	73.39 217	95.92 317	76.15 306	79.03 363	93.59 289

Baseline_NR-MVSNet		87.07 235	86.63 212	88.40 286	91.44 302	77.87 278	94.23 186	92.57 299	84.12 208	85.74 223	92.08 256	77.25 159	96.04 310	82.29 220	79.94 353	91.30 356

UBG		85.51 274	84.57 280	88.35 288	94.21 207	71.78 361	90.07 330	89.66 372	82.28 252	85.91 219	89.01 341	61.30 338	97.06 253	76.58 301	92.06 199	96.22 167

D2MVS		85.90 267	85.09 267	88.35 288	90.79 333	77.42 289	91.83 287	95.70 171	80.77 290	80.08 338	90.02 323	66.74 298	96.37 297	81.88 231	87.97 261	91.26 357

pmmvs5		84.21 299	82.84 309	88.34 290	88.95 366	76.94 295	92.41 267	91.91 321	75.63 350	80.28 333	91.18 287	64.59 315	95.57 332	77.09 296	83.47 304	92.53 327

mamv4		90.92 110	91.78 92	88.33 291	95.67 127	70.75 374	92.92 254	96.02 145	81.90 263	88.11 167	95.34 131	85.88 51	96.97 259	95.22 30	95.01 141	97.26 116

LCM-MVSNet-Re		88.30 186	88.32 168	88.27 292	94.71 175	72.41 356	93.15 243	90.98 344	87.77 122	79.25 348	91.96 261	78.35 149	95.75 327	83.04 204	95.62 125	96.65 152

CostFormer		85.77 271	84.94 271	88.26 293	91.16 316	72.58 354	89.47 343	91.04 343	76.26 345	86.45 206	89.97 325	70.74 245	96.86 267	82.35 218	87.07 276	95.34 207

ITE_SJBPF				88.24 294	91.88 288	77.05 294		92.92 288	85.54 175	80.13 337	93.30 212	57.29 365	96.20 305	72.46 332	84.71 290	91.49 352

PVSNet	78.82 18	85.55 273	84.65 277	88.23 295	94.72 174	71.93 357	87.12 376	92.75 295	78.80 316	84.95 253	90.53 308	64.43 316	96.71 271	74.74 318	93.86 164	96.06 179

IterMVS-SCA-FT		85.45 275	84.53 281	88.18 296	91.71 295	76.87 296	90.19 327	92.65 298	85.40 178	81.44 319	90.54 307	66.79 296	95.00 347	81.04 244	81.05 336	92.66 324

EPNet_dtu		86.49 258	85.94 243	88.14 297	90.24 348	72.82 346	94.11 192	92.20 309	86.66 148	79.42 347	92.36 243	73.52 212	95.81 324	71.26 336	93.66 166	95.80 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 314	80.93 320	88.06 298	90.05 352	76.37 306	84.74 394	91.96 319	72.28 384	81.32 322	87.87 362	71.03 240	95.50 337	68.97 354	80.15 351	92.32 336

test_vis1_n_1920		89.39 156	89.84 128	88.04 299	92.97 259	72.64 351	94.71 153	96.03 144	86.18 159	91.94 109	96.56 84	61.63 333	95.74 328	93.42 50	95.11 140	95.74 192

DTE-MVSNet		86.11 264	85.48 257	87.98 300	91.65 299	74.92 322	94.93 137	95.75 166	87.36 131	82.26 308	93.04 223	72.85 222	95.82 323	74.04 322	77.46 369	93.20 306

PMMVS		85.71 272	84.96 270	87.95 301	88.90 367	77.09 293	88.68 355	90.06 362	72.32 383	86.47 203	90.76 302	72.15 230	94.40 352	81.78 234	93.49 172	92.36 334

GG-mvs-BLEND				87.94 302	89.73 359	77.91 275	87.80 365	78.23 414		80.58 330	83.86 389	59.88 352	95.33 341	71.20 337	92.22 197	90.60 370

MonoMVSNet		86.89 241	86.55 216	87.92 303	89.46 362	73.75 334	94.12 190	93.10 283	87.82 121	85.10 249	90.76 302	69.59 263	94.94 348	86.47 162	82.50 315	95.07 214

reproduce_monomvs		86.37 261	85.87 245	87.87 304	93.66 235	73.71 335	93.44 229	95.02 214	88.61 92	82.64 305	91.94 262	57.88 363	96.68 272	89.96 119	79.71 357	93.22 304

pmmvs-eth3d		80.97 336	78.72 348	87.74 305	84.99 396	79.97 230	90.11 329	91.65 326	75.36 352	73.51 384	86.03 379	59.45 354	93.96 362	75.17 313	72.21 383	89.29 383

MS-PatchMatch		85.05 286	84.16 284	87.73 306	91.42 305	78.51 260	91.25 303	93.53 275	77.50 332	80.15 335	91.58 276	61.99 330	95.51 335	75.69 308	94.35 158	89.16 385

mmtdpeth		85.04 288	84.15 285	87.72 307	93.11 250	75.74 314	94.37 178	92.83 291	84.98 188	89.31 151	86.41 376	61.61 335	97.14 247	92.63 66	62.11 404	90.29 372

test_0402		81.30 332	79.17 342	87.67 308	93.19 247	78.17 270	92.98 251	91.71 322	75.25 354	76.02 371	90.31 313	59.23 356	96.37 297	50.22 407	83.63 302	88.47 392

IterMVS		84.88 290	83.98 290	87.60 309	91.44 302	76.03 309	90.18 328	92.41 301	83.24 232	81.06 325	90.42 312	66.60 299	94.28 356	79.46 268	80.98 341	92.48 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 330	79.30 338	87.58 310	90.92 328	74.16 332	80.99 406	87.68 384	70.52 391	76.63 366	88.81 345	71.21 237	92.76 377	60.01 395	86.93 277	95.83 188

EPMVS		83.90 306	82.70 310	87.51 311	90.23 349	72.67 349	88.62 356	81.96 405	81.37 280	85.01 252	88.34 353	66.31 303	94.45 350	75.30 312	87.12 274	95.43 202

ADS-MVSNet2		81.66 325	79.71 334	87.50 312	91.35 308	74.19 331	83.33 399	88.48 378	72.90 378	82.24 309	85.77 382	64.98 313	93.20 373	64.57 381	83.74 299	95.12 212

OurMVSNet-221017-0		85.35 279	84.64 278	87.49 313	90.77 334	72.59 353	94.01 203	94.40 246	84.72 198	79.62 346	93.17 217	61.91 331	96.72 269	81.99 228	81.16 332	93.16 308

tpm2		84.08 301	82.94 305	87.48 314	91.39 306	71.27 366	89.23 347	90.37 355	71.95 385	84.64 258	89.33 336	67.30 288	96.55 285	75.17 313	87.09 275	94.63 232

RPSCF		85.07 285	84.27 282	87.48 314	92.91 261	70.62 376	91.69 292	92.46 300	76.20 346	82.67 304	95.22 136	63.94 319	97.29 234	77.51 291	85.80 282	94.53 239

WBMVS		84.97 289	84.18 283	87.34 316	94.14 212	71.62 365	90.20 326	92.35 302	81.61 275	84.06 277	90.76 302	61.82 332	96.52 286	78.93 276	83.81 297	93.89 268

miper_lstm_enhance		85.27 282	84.59 279	87.31 317	91.28 311	74.63 325	87.69 370	94.09 261	81.20 286	81.36 321	89.85 328	74.97 189	94.30 355	81.03 246	79.84 356	93.01 314

FMVSNet5		81.52 328	79.60 335	87.27 318	91.17 314	77.95 274	91.49 296	92.26 308	76.87 338	76.16 368	87.91 361	51.67 387	92.34 380	67.74 363	81.16 332	91.52 351

USDC		82.76 313	81.26 318	87.26 319	91.17 314	74.55 326	89.27 345	93.39 278	78.26 327	75.30 375	92.08 256	54.43 380	96.63 274	71.64 334	85.79 283	90.61 368

test-LLR		85.87 268	85.41 258	87.25 320	90.95 324	71.67 363	89.55 339	89.88 368	83.41 226	84.54 261	87.95 359	67.25 289	95.11 344	81.82 232	93.37 177	94.97 217

test-mter		84.54 296	83.64 294	87.25 320	90.95 324	71.67 363	89.55 339	89.88 368	79.17 308	84.54 261	87.95 359	55.56 371	95.11 344	81.82 232	93.37 177	94.97 217

JIA-IIPM		81.04 333	78.98 346	87.25 320	88.64 368	73.48 339	81.75 405	89.61 373	73.19 375	82.05 312	73.71 408	66.07 308	95.87 320	71.18 339	84.60 291	92.41 332

TDRefinement		79.81 346	77.34 351	87.22 323	79.24 411	75.48 317	93.12 244	92.03 314	76.45 341	75.01 376	91.58 276	49.19 393	96.44 293	70.22 347	69.18 391	89.75 377

tpmvs		83.35 312	82.07 311	87.20 324	91.07 320	71.00 372	88.31 360	91.70 323	78.91 311	80.49 332	87.18 371	69.30 270	97.08 250	68.12 362	83.56 303	93.51 294

ppachtmachnet_test		81.84 321	80.07 329	87.15 325	88.46 372	74.43 329	89.04 351	92.16 310	75.33 353	77.75 358	88.99 342	66.20 305	95.37 340	65.12 378	77.60 367	91.65 347

dmvs_re		84.20 300	83.22 301	87.14 326	91.83 291	77.81 280	90.04 331	90.19 358	84.70 199	81.49 317	89.17 338	64.37 317	91.13 391	71.58 335	85.65 284	92.46 330

tpm cat1		81.96 319	80.27 325	87.01 327	91.09 319	71.02 371	87.38 374	91.53 331	66.25 399	80.17 334	86.35 378	68.22 285	96.15 308	69.16 353	82.29 318	93.86 274

test_fmvs1_n		87.03 237	87.04 198	86.97 328	89.74 358	71.86 358	94.55 161	94.43 244	78.47 321	91.95 108	95.50 126	51.16 389	93.81 363	93.02 58	94.56 152	95.26 208

OpenMVS_ROB	74.94 19	79.51 349	77.03 356	86.93 329	87.00 384	76.23 308	92.33 273	90.74 351	68.93 395	74.52 380	88.23 356	49.58 392	96.62 275	57.64 400	84.29 293	87.94 395

SixPastTwentyTwo		83.91 305	82.90 307	86.92 330	90.99 322	70.67 375	93.48 226	91.99 316	85.54 175	77.62 360	92.11 254	60.59 347	96.87 266	76.05 307	77.75 366	93.20 306

ADS-MVSNet		81.56 327	79.78 331	86.90 331	91.35 308	71.82 359	83.33 399	89.16 376	72.90 378	82.24 309	85.77 382	64.98 313	93.76 364	64.57 381	83.74 299	95.12 212

PatchT		82.68 315	81.27 317	86.89 332	90.09 351	70.94 373	84.06 396	90.15 359	74.91 358	85.63 226	83.57 391	69.37 266	94.87 349	65.19 376	88.50 251	94.84 226

tpm		84.73 293	84.02 288	86.87 333	90.33 346	68.90 383	89.06 350	89.94 365	80.85 289	85.75 222	89.86 327	68.54 282	95.97 314	77.76 287	84.05 296	95.75 191

Patchmatch-RL test		81.67 324	79.96 330	86.81 334	85.42 394	71.23 367	82.17 404	87.50 385	78.47 321	77.19 362	82.50 398	70.81 244	93.48 368	82.66 213	72.89 382	95.71 195

test_vis1_n		86.56 253	86.49 220	86.78 335	88.51 369	72.69 348	94.68 154	93.78 272	79.55 304	90.70 129	95.31 132	48.75 394	93.28 371	93.15 54	93.99 161	94.38 250

test_fmvs1		87.34 220	87.56 184	86.68 336	90.59 340	71.80 360	94.01 203	94.04 262	78.30 325	91.97 106	95.22 136	56.28 369	93.71 365	92.89 59	94.71 146	94.52 240

MDA-MVSNet-bldmvs		78.85 353	76.31 358	86.46 337	89.76 357	73.88 333	88.79 353	90.42 354	79.16 309	59.18 408	88.33 354	60.20 349	94.04 358	62.00 388	68.96 392	91.48 353

mvs5depth		80.98 335	79.15 343	86.45 338	84.57 397	73.29 341	87.79 366	91.67 325	80.52 292	82.20 311	89.72 330	55.14 376	95.93 316	73.93 325	66.83 396	90.12 374

tpmrst		85.35 279	84.99 268	86.43 339	90.88 331	67.88 387	88.71 354	91.43 334	80.13 296	86.08 216	88.80 347	73.05 220	96.02 312	82.48 214	83.40 307	95.40 203

TESTMET0.1,1		83.74 308	82.85 308	86.42 340	89.96 354	71.21 368	89.55 339	87.88 381	77.41 333	83.37 295	87.31 367	56.71 367	93.65 367	80.62 254	92.85 188	94.40 249

our_test_3		81.93 320	80.46 323	86.33 341	88.46 372	73.48 339	88.46 358	91.11 339	76.46 340	76.69 365	88.25 355	66.89 294	94.36 353	68.75 355	79.08 362	91.14 360

lessismore_v0				86.04 342	88.46 372	68.78 384		80.59 408		73.01 387	90.11 321	55.39 372	96.43 294	75.06 315	65.06 399	92.90 317

TinyColmap		79.76 347	77.69 350	85.97 343	91.71 295	73.12 342	89.55 339	90.36 356	75.03 356	72.03 390	90.19 317	46.22 401	96.19 307	63.11 385	81.03 337	88.59 391

KD-MVS_2432*1600		78.50 354	76.02 361	85.93 344	86.22 387	74.47 327	84.80 392	92.33 303	79.29 306	76.98 363	85.92 380	53.81 383	93.97 360	67.39 364	57.42 409	89.36 379

miper_refine_blended		78.50 354	76.02 361	85.93 344	86.22 387	74.47 327	84.80 392	92.33 303	79.29 306	76.98 363	85.92 380	53.81 383	93.97 360	67.39 364	57.42 409	89.36 379

K. test v3		81.59 326	80.15 328	85.91 346	89.89 356	69.42 382	92.57 264	87.71 383	85.56 174	73.44 385	89.71 331	55.58 370	95.52 334	77.17 294	69.76 388	92.78 322

mvsany_test1		85.42 277	85.30 263	85.77 347	87.95 380	75.41 318	87.61 373	80.97 407	76.82 339	88.68 160	95.83 112	77.44 158	90.82 393	85.90 169	86.51 278	91.08 364

MIMVSNet1		79.38 350	77.28 352	85.69 348	86.35 386	73.67 336	91.61 294	92.75 295	78.11 330	72.64 388	88.12 357	48.16 395	91.97 385	60.32 392	77.49 368	91.43 354

UWE-MVS		83.69 309	83.09 302	85.48 349	93.06 253	65.27 397	90.92 310	86.14 389	79.90 299	86.26 212	90.72 305	57.17 366	95.81 324	71.03 342	92.62 191	95.35 206

UnsupCasMVSNet_eth		80.07 343	78.27 349	85.46 350	85.24 395	72.63 352	88.45 359	94.87 227	82.99 238	71.64 392	88.07 358	56.34 368	91.75 386	73.48 328	63.36 402	92.01 342

CL-MVSNet_self_test		81.74 323	80.53 321	85.36 351	85.96 389	72.45 355	90.25 321	93.07 285	81.24 284	79.85 343	87.29 368	70.93 242	92.52 378	66.95 367	69.23 390	91.11 362

MDA-MVSNet_test_wron		79.21 352	77.19 354	85.29 352	88.22 376	72.77 347	85.87 383	90.06 362	74.34 363	62.62 405	87.56 365	66.14 306	91.99 384	66.90 371	73.01 380	91.10 363

YYNet1		79.22 351	77.20 353	85.28 353	88.20 377	72.66 350	85.87 383	90.05 364	74.33 364	62.70 403	87.61 364	66.09 307	92.03 382	66.94 368	72.97 381	91.15 359

WB-MVSnew		83.77 307	83.28 298	85.26 354	91.48 301	71.03 370	91.89 284	87.98 380	78.91 311	84.78 255	90.22 315	69.11 275	94.02 359	64.70 380	90.44 217	90.71 366

dp		81.47 329	80.23 326	85.17 355	89.92 355	65.49 395	86.74 378	90.10 361	76.30 344	81.10 323	87.12 372	62.81 326	95.92 317	68.13 361	79.88 354	94.09 261

UnsupCasMVSNet_bld		76.23 363	73.27 367	85.09 356	83.79 399	72.92 344	85.65 386	93.47 277	71.52 386	68.84 398	79.08 403	49.77 391	93.21 372	66.81 372	60.52 406	89.13 387

Anonymous20231206		81.03 334	79.77 333	84.82 357	87.85 381	70.26 378	91.42 297	92.08 312	73.67 370	77.75 358	89.25 337	62.43 328	93.08 374	61.50 390	82.00 323	91.12 361

test0.0.03 1		82.41 317	81.69 313	84.59 358	88.23 375	72.89 345	90.24 323	87.83 382	83.41 226	79.86 342	89.78 329	67.25 289	88.99 402	65.18 377	83.42 306	91.90 344

CMPMVS	59.16 21	80.52 338	79.20 341	84.48 359	83.98 398	67.63 390	89.95 334	93.84 270	64.79 402	66.81 400	91.14 290	57.93 362	95.17 342	76.25 304	88.10 257	90.65 367
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 295	84.79 275	84.37 360	91.84 289	64.92 398	93.70 220	91.47 333	66.19 400	86.16 215	95.28 133	67.18 291	93.33 370	80.89 249	90.42 219	94.88 225

PVSNet_0	73.20 20	77.22 359	74.83 365	84.37 360	90.70 338	71.10 369	83.09 401	89.67 371	72.81 380	73.93 383	83.13 393	60.79 346	93.70 366	68.54 356	50.84 414	88.30 393

LF4IMVS		80.37 341	79.07 345	84.27 362	86.64 385	69.87 381	89.39 344	91.05 342	76.38 342	74.97 377	90.00 324	47.85 396	94.25 357	74.55 321	80.82 343	88.69 390

Anonymous20240521		80.44 340	79.21 340	84.11 363	85.75 392	67.89 386	92.86 257	93.23 281	75.61 351	75.59 374	87.47 366	50.03 390	94.33 354	71.14 340	81.21 331	90.12 374

PM-MVS		78.11 356	76.12 360	84.09 364	83.54 400	70.08 379	88.97 352	85.27 396	79.93 298	74.73 379	86.43 375	34.70 412	93.48 368	79.43 271	72.06 384	88.72 389

test_fmvs2		83.98 302	84.03 287	83.83 365	87.16 383	67.53 391	93.93 209	92.89 289	77.62 331	86.89 196	93.53 205	47.18 398	92.02 383	90.54 113	86.51 278	91.93 343

testgi		80.94 337	80.20 327	83.18 366	87.96 379	66.29 392	91.28 301	90.70 352	83.70 217	78.12 355	92.84 227	51.37 388	90.82 393	63.34 384	82.46 316	92.43 331

KD-MVS_self_test		80.20 342	79.24 339	83.07 367	85.64 393	65.29 396	91.01 309	93.93 264	78.71 319	76.32 367	86.40 377	59.20 357	92.93 376	72.59 331	69.35 389	91.00 365

testing3		80.46 339	79.59 336	83.06 368	93.44 242	64.64 399	93.33 232	85.47 394	84.34 205	79.93 341	90.84 298	44.35 404	92.39 379	57.06 402	87.56 267	92.16 340

ambc				83.06 368	79.99 409	63.51 403	77.47 412	92.86 290		74.34 382	84.45 388	28.74 413	95.06 346	73.06 330	68.89 393	90.61 368

test20.03		79.95 345	79.08 344	82.55 370	85.79 391	67.74 389	91.09 307	91.08 340	81.23 285	74.48 381	89.96 326	61.63 333	90.15 395	60.08 393	76.38 374	89.76 376

MVStest1		72.91 367	69.70 372	82.54 371	78.14 412	73.05 343	88.21 361	86.21 388	60.69 406	64.70 401	90.53 308	46.44 400	85.70 409	58.78 398	53.62 411	88.87 388

test_vis1_rt		77.96 357	76.46 357	82.48 372	85.89 390	71.74 362	90.25 321	78.89 411	71.03 390	71.30 393	81.35 400	42.49 406	91.05 392	84.55 186	82.37 317	84.65 398

EU-MVSNet		81.32 331	80.95 319	82.42 373	88.50 371	63.67 402	93.32 233	91.33 335	64.02 403	80.57 331	92.83 228	61.21 342	92.27 381	76.34 303	80.38 350	91.32 355

myMVS_eth3d		79.67 348	78.79 347	82.32 374	91.92 285	64.08 400	89.75 337	87.40 386	81.72 270	78.82 350	87.20 369	45.33 402	91.29 389	59.09 397	87.84 264	91.60 349

ttmdpeth		76.55 361	74.64 366	82.29 375	82.25 405	67.81 388	89.76 336	85.69 392	70.35 392	75.76 372	91.69 269	46.88 399	89.77 397	66.16 373	63.23 403	89.30 381

pmmvs3		71.81 370	68.71 373	81.11 376	75.86 414	70.42 377	86.74 378	83.66 400	58.95 409	68.64 399	80.89 401	36.93 410	89.52 399	63.10 386	63.59 401	83.39 399

Syy-MVS		80.07 343	79.78 331	80.94 377	91.92 285	59.93 408	89.75 337	87.40 386	81.72 270	78.82 350	87.20 369	66.29 304	91.29 389	47.06 409	87.84 264	91.60 349

new-patchmatchnet		76.41 362	75.17 364	80.13 378	82.65 404	59.61 409	87.66 371	91.08 340	78.23 328	69.85 396	83.22 392	54.76 377	91.63 388	64.14 383	64.89 400	89.16 385

mvsany_test3		74.95 364	73.26 368	80.02 379	74.61 415	63.16 404	85.53 387	78.42 412	74.16 365	74.89 378	86.46 374	36.02 411	89.09 401	82.39 217	66.91 395	87.82 396

test_fmvs3		77.67 358	77.16 355	79.22 380	79.52 410	61.14 406	92.34 272	91.64 327	73.98 367	78.86 349	86.59 373	27.38 416	87.03 404	88.12 140	75.97 376	89.50 378

DSMNet-mixed		76.94 360	76.29 359	78.89 381	83.10 402	56.11 417	87.78 367	79.77 409	60.65 407	75.64 373	88.71 348	61.56 336	88.34 403	60.07 394	89.29 240	92.21 339

EGC-MVSNET		61.97 378	56.37 383	78.77 382	89.63 360	73.50 338	89.12 349	82.79 402	0.21 428	1.24 429	84.80 386	39.48 407	90.04 396	44.13 411	75.94 377	72.79 410

new_pmnet		72.15 368	70.13 371	78.20 383	82.95 403	65.68 393	83.91 397	82.40 404	62.94 405	64.47 402	79.82 402	42.85 405	86.26 408	57.41 401	74.44 379	82.65 403

MVS-HIRNet		73.70 366	72.20 369	78.18 384	91.81 292	56.42 416	82.94 402	82.58 403	55.24 410	68.88 397	66.48 413	55.32 374	95.13 343	58.12 399	88.42 253	83.01 401

LCM-MVSNet		66.00 375	62.16 380	77.51 385	64.51 425	58.29 411	83.87 398	90.90 347	48.17 414	54.69 411	73.31 409	16.83 425	86.75 405	65.47 375	61.67 405	87.48 397

APD_test1		69.04 371	66.26 377	77.36 386	80.51 408	62.79 405	85.46 388	83.51 401	54.11 412	59.14 409	84.79 387	23.40 419	89.61 398	55.22 403	70.24 387	79.68 407

test_f		71.95 369	70.87 370	75.21 387	74.21 417	59.37 410	85.07 391	85.82 391	65.25 401	70.42 395	83.13 393	23.62 417	82.93 415	78.32 281	71.94 385	83.33 400

ANet_high		58.88 382	54.22 387	72.86 388	56.50 428	56.67 413	80.75 407	86.00 390	73.09 377	37.39 420	64.63 416	22.17 420	79.49 418	43.51 412	23.96 422	82.43 404

test_vis3_rt		65.12 376	62.60 378	72.69 389	71.44 418	60.71 407	87.17 375	65.55 422	63.80 404	53.22 412	65.65 415	14.54 426	89.44 400	76.65 298	65.38 398	67.91 413

FPMVS		64.63 377	62.55 379	70.88 390	70.80 419	56.71 412	84.42 395	84.42 398	51.78 413	49.57 413	81.61 399	23.49 418	81.48 416	40.61 416	76.25 375	74.46 409

dmvs_testset		74.57 365	75.81 363	70.86 391	87.72 382	40.47 426	87.05 377	77.90 416	82.75 243	71.15 394	85.47 384	67.98 286	84.12 413	45.26 410	76.98 373	88.00 394

N_pmnet		68.89 372	68.44 374	70.23 392	89.07 365	28.79 431	88.06 362	19.50 431	69.47 394	71.86 391	84.93 385	61.24 341	91.75 386	54.70 404	77.15 370	90.15 373

testf1		59.54 380	56.11 384	69.85 393	69.28 420	56.61 414	80.37 408	76.55 419	42.58 417	45.68 416	75.61 404	11.26 427	84.18 411	43.20 413	60.44 407	68.75 411

APD_test2		59.54 380	56.11 384	69.85 393	69.28 420	56.61 414	80.37 408	76.55 419	42.58 417	45.68 416	75.61 404	11.26 427	84.18 411	43.20 413	60.44 407	68.75 411

WB-MVS		67.92 373	67.49 375	69.21 395	81.09 406	41.17 425	88.03 363	78.00 415	73.50 372	62.63 404	83.11 395	63.94 319	86.52 406	25.66 421	51.45 413	79.94 406

PMMVS2		59.60 379	56.40 382	69.21 395	68.83 422	46.58 421	73.02 416	77.48 417	55.07 411	49.21 414	72.95 410	17.43 424	80.04 417	49.32 408	44.33 417	80.99 405

SSC-MVS		67.06 374	66.56 376	68.56 397	80.54 407	40.06 427	87.77 368	77.37 418	72.38 382	61.75 406	82.66 397	63.37 322	86.45 407	24.48 422	48.69 416	79.16 408

Gipuma		57.99 384	54.91 386	67.24 398	88.51 369	65.59 394	52.21 419	90.33 357	43.58 416	42.84 419	51.18 420	20.29 422	85.07 410	34.77 417	70.45 386	51.05 419
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 386	48.46 390	63.48 399	45.72 430	46.20 422	73.41 415	78.31 413	41.03 419	30.06 422	65.68 414	6.05 429	83.43 414	30.04 419	65.86 397	60.80 414
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 383	58.24 381	60.56 400	83.13 401	45.09 424	82.32 403	48.22 430	67.61 397	61.70 407	69.15 411	38.75 408	76.05 419	32.01 418	41.31 418	60.55 415

MVE	39.65 23	43.39 388	38.59 394	57.77 401	56.52 427	48.77 420	55.38 418	58.64 426	29.33 422	28.96 423	52.65 419	4.68 430	64.62 423	28.11 420	33.07 420	59.93 416
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 387	48.47 389	56.66 402	52.26 429	18.98 433	41.51 421	81.40 406	10.10 423	44.59 418	75.01 407	28.51 414	68.16 420	53.54 405	49.31 415	82.83 402

DeepMVS_CX				56.31 403	74.23 416	51.81 419		56.67 427	44.85 415	48.54 415	75.16 406	27.87 415	58.74 425	40.92 415	52.22 412	58.39 417

kuosan		53.51 385	53.30 388	54.13 404	76.06 413	45.36 423	80.11 410	48.36 429	59.63 408	54.84 410	63.43 417	37.41 409	62.07 424	20.73 424	39.10 419	54.96 418

E-PMN		43.23 389	42.29 391	46.03 405	65.58 424	37.41 428	73.51 414	64.62 423	33.99 420	28.47 424	47.87 421	19.90 423	67.91 421	22.23 423	24.45 421	32.77 420

EMVS		42.07 390	41.12 392	44.92 406	63.45 426	35.56 430	73.65 413	63.48 424	33.05 421	26.88 425	45.45 422	21.27 421	67.14 422	19.80 425	23.02 423	32.06 421

tmp_tt		35.64 391	39.24 393	24.84 407	14.87 431	23.90 432	62.71 417	51.51 428	6.58 425	36.66 421	62.08 418	44.37 403	30.34 427	52.40 406	22.00 424	20.27 422

wuyk23d		21.27 393	20.48 396	23.63 408	68.59 423	36.41 429	49.57 420	6.85 432	9.37 424	7.89 426	4.46 428	4.03 431	31.37 426	17.47 426	16.07 425	3.12 423

test123		8.76 395	11.22 398	1.39 409	0.85 433	0.97 434	85.76 385	0.35 434	0.54 427	2.45 428	8.14 427	0.60 432	0.48 428	2.16 428	0.17 427	2.71 424

testmvs		8.92 394	11.52 397	1.12 410	1.06 432	0.46 435	86.02 382	0.65 433	0.62 426	2.74 427	9.52 426	0.31 433	0.45 429	2.38 427	0.39 426	2.46 425

mmdepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

monomultidepth		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

test_blank		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uanet_test		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

DCPMVS		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

cdsmvs_eth3d_5k		22.14 392	29.52 395	0.00 411	0.00 434	0.00 436	0.00 422	95.76 165	0.00 429	0.00 430	94.29 175	75.66 181	0.00 430	0.00 429	0.00 428	0.00 426

pcd_1.5k_mvsjas		6.64 397	8.86 400	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	79.70 130	0.00 430	0.00 429	0.00 428	0.00 426

sosnet-low-res		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

sosnet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uncertanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

Regformer		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

ab-mvs-re		7.82 396	10.43 399	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	93.88 195	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

uanet		0.00 398	0.00 401	0.00 411	0.00 434	0.00 436	0.00 422	0.00 435	0.00 429	0.00 430	0.00 429	0.00 434	0.00 430	0.00 429	0.00 428	0.00 426

WAC-MVS						64.08 400								59.14 396

FOURS1					98.86 1	85.54 67	98.29 1	97.49 6	89.79 53	96.29 21

PC_three_1452									82.47 247	97.09 12	97.07 57	92.72 1	98.04 172	92.70 65	99.02 12	98.86 11

test_one_0601					98.58 11	85.83 61		97.44 15	91.05 15	96.78 17	98.06 15	91.45 11

eth-test2					0.00 434

eth-test					0.00 434

ZD-MVS					98.15 34	86.62 33		97.07 49	83.63 219	94.19 50	96.91 63	87.57 31	99.26 45	91.99 87	98.44 53

RE-MVS-def			93.68 58		97.92 43	84.57 86	96.28 43	96.76 79	87.46 128	93.75 60	97.43 36	82.94 89		92.73 61	97.80 79	97.88 86

IU-MVS					98.77 5	86.00 50		96.84 69	81.26 283	97.26 8				95.50 26	99.13 3	99.03 8

test_241102_TWO								97.44 15	90.31 31	97.62 5	98.07 13	91.46 10	99.58 10	95.66 20	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 52		97.44 15	90.26 37	97.71 1	97.96 23	92.31 4	99.38 31

9.14			94.47 24		97.79 52		96.08 60	97.44 15	86.13 163	95.10 40	97.40 38	88.34 22	99.22 47	93.25 53	98.70 34

save fliter					97.85 49	85.63 66	95.21 120	96.82 72	89.44 61

test_0728_THIRD									90.75 20	97.04 13	98.05 17	92.09 6	99.55 16	95.64 22	99.13 3	99.13 2

test0726					98.78 3	85.93 55	97.19 11	97.47 11	90.27 35	97.64 4	98.13 4	91.47 8

GSMVS																96.12 173

test_part2					98.55 12	87.22 19				96.40 20

sam_mvs1												71.70 233				96.12 173

sam_mvs												70.60 246

MTGPA								96.97 54

test_post1							88.00 364				9.81 425	69.31 269	95.53 333	76.65 298

test_post											10.29 424	70.57 250	95.91 319

patchmatchnet-post											83.76 390	71.53 234	96.48 289

MTMP							96.16 52	60.64 425

gm-plane-assit					89.60 361	68.00 385			77.28 336		88.99 342		97.57 201	79.44 270

test9_res														91.91 91	98.71 32	98.07 73

TEST9					97.53 61	86.49 37	94.07 197	96.78 76	81.61 275	92.77 85	96.20 93	87.71 28	99.12 54

test_8					97.49 63	86.30 45	94.02 202	96.76 79	81.86 266	92.70 89	96.20 93	87.63 29	99.02 64

agg_prior2														90.54 113	98.68 37	98.27 56

agg_prior					97.38 66	85.92 57		96.72 85		92.16 101			98.97 78

test_prior4						85.96 54	94.11 192

test_prior2							94.12 190		87.67 126	92.63 91	96.39 88	86.62 40		91.50 99	98.67 40

旧先验2							93.36 231		71.25 388	94.37 46			97.13 248	86.74 158

新几何2							93.11 246

旧先验1					96.79 79	81.81 171		95.67 173			96.81 69	86.69 39			97.66 84	96.97 137

无先验							93.28 239	96.26 120	73.95 368				99.05 58	80.56 255		96.59 154

原ACMM2							92.94 253

test222					96.55 88	81.70 173	92.22 277	95.01 215	68.36 396	90.20 137	96.14 98	80.26 123			97.80 79	96.05 180

testdata2													98.75 100	78.30 282

segment_acmp												87.16 36

testdata1							92.15 279		87.94 113

plane_prior7					94.70 176	82.74 149

plane_prior6					94.52 188	82.75 147						74.23 199

plane_prior5								96.22 125					98.12 157	88.15 137	89.99 223	94.63 232

plane_prior4											94.86 152

plane_prior3						82.75 147			90.26 37	86.91 193

plane_prior2							95.85 82		90.81 18

plane_prior1					94.59 182

plane_prior						82.73 150	95.21 120		89.66 58						89.88 228

n2								0.00 435

nn								0.00 435

door-mid								85.49 393

test11								96.57 96

door								85.33 395

HQP5-MVS						81.56 175

HQP-NCC					94.17 208		94.39 174		88.81 82	85.43 238

ACMP_Plane					94.17 208		94.39 174		88.81 82	85.43 238

BP-MVS														87.11 155

HQP4-MVS										85.43 238			97.96 178			94.51 242

HQP3-MVS								96.04 142							89.77 232

HQP2-MVS												73.83 209

NP-MVS					94.37 198	82.42 159					93.98 188

MDTV_nov1_ep13_2view						55.91 418	87.62 372		73.32 374	84.59 260		70.33 253		74.65 319		95.50 200

MDTV_nov1_ep13			83.56 295		91.69 297	69.93 380	87.75 369	91.54 330	78.60 320	84.86 254	88.90 344	69.54 264	96.03 311	70.25 345	88.93 245

ACMMP++_ref															87.47 268

ACMMP++															88.01 260

Test By Simon												80.02 125

High-resolution multi-view benchmark