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 56	90.86 1		96.85 74					99.61 4	96.03 24	99.06 9	99.07 5

No_MVS				96.52 1	97.78 56	90.86 1		96.85 74					99.61 4	96.03 24	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 44	90.85 3	97.13 15				97.08 63	92.59 2	98.94 86	92.25 85	98.99 14	98.84 15

HPM-MVS++		95.14 10	94.91 21	95.83 4	98.25 31	89.65 4	95.92 81	96.96 62	91.75 12	94.02 64	96.83 75	88.12 24	99.55 16	93.41 59	98.94 16	98.28 57

MM		95.10 11	94.91 21	95.68 5	96.09 111	88.34 9	96.68 34	94.37 264	95.08 1	94.68 50	97.72 36	82.94 95	99.64 1	97.85 4	98.76 29	99.06 7

SMA-MVS		95.20 8	95.07 15	95.59 6	98.14 37	88.48 8	96.26 49	97.28 35	85.90 182	97.67 3	98.10 12	88.41 20	99.56 12	94.66 43	99.19 1	98.71 21
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 97	91.37 107	95.55 7	95.63 136	88.73 6	97.07 19	96.77 85	90.84 23	84.02 300	96.62 88	75.95 186	99.34 38	87.77 158	97.68 90	98.59 25

CNVR-MVS		95.40 7	95.37 9	95.50 8	98.11 38	88.51 7	95.29 122	96.96 62	92.09 9	95.32 42	97.08 63	89.49 15	99.33 41	95.10 38	98.85 20	98.66 22

MVS_0304		94.18 44	93.80 58	95.34 9	94.91 175	87.62 14	95.97 76	93.01 308	92.58 6	94.22 55	97.20 57	80.56 127	99.59 8	97.04 17	98.68 37	98.81 18

ACMMP_NAP		94.74 22	94.56 27	95.28 10	98.02 43	87.70 11	95.68 99	97.34 26	88.28 115	95.30 43	97.67 38	85.90 51	99.54 20	93.91 51	98.95 15	98.60 24

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 27	87.28 18	95.56 111	97.51 7	89.13 84	97.14 13	97.91 29	91.64 7	99.62 2	94.61 44	99.17 2	98.86 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 14	94.90 23	95.20 12	97.84 52	87.76 10	96.65 35	97.48 12	87.76 137	95.71 37	97.70 37	88.28 23	99.35 37	93.89 52	98.78 26	98.48 31

MCST-MVS		94.45 29	94.20 45	95.19 13	98.46 19	87.50 16	95.00 145	97.12 50	87.13 150	92.51 105	96.30 97	89.24 17	99.34 38	93.46 56	98.62 46	98.73 19

NCCC		94.81 19	94.69 26	95.17 14	97.83 53	87.46 17	95.66 102	96.93 66	92.34 7	93.94 65	96.58 90	87.74 27	99.44 29	92.83 68	98.40 54	98.62 23

DPM-MVS		92.58 93	91.74 103	95.08 15	96.19 102	89.31 5	92.66 284	96.56 105	83.44 249	91.68 129	95.04 159	86.60 43	98.99 76	85.60 190	97.92 79	96.93 157

ZNCC-MVS		94.47 28	94.28 39	95.03 16	98.52 15	86.96 20	96.85 29	97.32 30	88.24 116	93.15 80	97.04 66	86.17 48	99.62 2	92.40 79	98.81 23	98.52 27

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 17	97.49 8					99.61 4	95.62 31	99.08 7	98.99 9

MTAPA		94.42 33	94.22 42	95.00 18	98.42 21	86.95 21	94.36 195	96.97 59	91.07 19	93.14 81	97.56 40	84.30 76	99.56 12	93.43 57	98.75 30	98.47 34

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 26	97.47 13	91.73 13	96.10 30	96.69 80	89.90 12	99.30 44	94.70 42	98.04 74	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 31	94.27 41	94.92 20	98.65 8	86.67 30	96.92 25	97.23 38	88.60 106	93.58 72	97.27 51	85.22 59	99.54 20	92.21 86	98.74 31	98.56 26

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 30	86.29 46	97.46 7	97.40 22	89.03 89	96.20 29	98.10 12	89.39 16	99.34 38	95.88 26	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 31	94.28 39	94.91 21	98.63 9	86.69 28	96.94 21	97.32 30	88.63 103	93.53 75	97.26 53	85.04 63	99.54 20	92.35 82	98.78 26	98.50 28

GST-MVS		94.21 39	93.97 54	94.90 23	98.41 22	86.82 24	96.54 37	97.19 39	88.24 116	93.26 77	96.83 75	85.48 56	99.59 8	91.43 112	98.40 54	98.30 51

HFP-MVS		94.52 26	94.40 32	94.86 24	98.61 10	86.81 25	96.94 21	97.34 26	88.63 103	93.65 70	97.21 55	86.10 49	99.49 26	92.35 82	98.77 28	98.30 51

sasdasda		93.27 76	92.75 86	94.85 25	95.70 131	87.66 12	96.33 40	96.41 115	90.00 48	94.09 60	94.60 181	82.33 104	98.62 124	92.40 79	92.86 203	98.27 59

MP-MVS-pluss		94.21 39	94.00 53	94.85 25	98.17 35	86.65 31	94.82 158	97.17 44	86.26 174	92.83 90	97.87 31	85.57 55	99.56 12	94.37 47	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 76	92.75 86	94.85 25	95.70 131	87.66 12	96.33 40	96.41 115	90.00 48	94.09 60	94.60 181	82.33 104	98.62 124	92.40 79	92.86 203	98.27 59

XVS		94.45 29	94.32 35	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	97.16 61	85.02 64	99.49 26	91.99 97	98.56 50	98.47 34

X-MVStestdata		88.31 204	86.13 253	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	23.41 451	85.02 64	99.49 26	91.99 97	98.56 50	98.47 34

SteuartSystems-ACMMP		95.20 8	95.32 11	94.85 25	96.99 77	86.33 42	97.33 8	97.30 32	91.38 17	95.39 41	97.46 43	88.98 19	99.40 30	94.12 48	98.89 18	98.82 17
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 56	86.00 52	98.29 1	97.49 8	90.75 26	97.62 6	98.06 20	92.59 2	99.61 4	95.64 29	99.02 12	98.86 12

alignmvs		93.08 84	92.50 92	94.81 32	95.62 137	87.61 15	95.99 74	96.07 151	89.77 61	94.12 59	94.87 165	80.56 127	98.66 117	92.42 78	93.10 199	98.15 71

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 54	97.13 15	97.44 17	90.31 38	97.71 1	98.07 18	92.31 4	99.58 10	95.66 27	99.13 3	98.84 15

DeepC-MVS_fast	89.43 2	94.04 47	93.79 59	94.80 33	97.48 66	86.78 26	95.65 104	96.89 71	89.40 72	92.81 91	96.97 68	85.37 58	99.24 47	90.87 121	98.69 35	98.38 43
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 36	94.07 50	94.77 35	98.47 18	86.31 44	96.71 32	96.98 58	89.04 87	91.98 116	97.19 58	85.43 57	99.56 12	92.06 95	98.79 24	98.44 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 37	94.07 50	94.75 36	98.06 41	86.90 23	95.88 83	96.94 65	85.68 189	95.05 48	97.18 59	87.31 35	99.07 59	91.90 103	98.61 48	98.28 57
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 34	94.21 44	94.74 37	98.39 25	86.64 32	97.60 5	97.24 36	88.53 108	92.73 96	97.23 54	85.20 60	99.32 42	92.15 89	98.83 22	98.25 64

PGM-MVS		93.96 52	93.72 64	94.68 38	98.43 20	86.22 47	95.30 120	97.78 1	87.45 144	93.26 77	97.33 49	84.62 73	99.51 24	90.75 123	98.57 49	98.32 50

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 57	97.09 17	96.73 91	90.27 42	97.04 17	98.05 22	91.47 8	99.55 16	95.62 31	99.08 7	98.45 37
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 50	93.78 60	94.63 40	98.50 16	85.90 62	96.87 27	96.91 69	88.70 101	91.83 125	97.17 60	83.96 80	99.55 16	91.44 111	98.64 45	98.43 39

PHI-MVS		93.89 54	93.65 68	94.62 41	96.84 80	86.43 39	96.69 33	97.49 8	85.15 207	93.56 74	96.28 98	85.60 54	99.31 43	92.45 76	98.79 24	98.12 75

TSAR-MVS + MP.		94.85 16	94.94 19	94.58 42	98.25 31	86.33 42	96.11 62	96.62 100	88.14 121	96.10 30	96.96 69	89.09 18	98.94 86	94.48 45	98.68 37	98.48 31
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 63	93.20 77	94.55 43	95.65 134	85.73 67	94.94 148	96.69 96	91.89 11	90.69 144	95.88 121	81.99 116	99.54 20	93.14 63	97.95 78	98.39 41

train_agg		93.44 69	93.08 79	94.52 44	97.53 63	86.49 37	94.07 214	96.78 83	81.86 290	92.77 93	96.20 101	87.63 29	99.12 57	92.14 90	98.69 35	97.94 87

CDPH-MVS		92.83 88	92.30 95	94.44 45	97.79 54	86.11 51	94.06 216	96.66 97	80.09 321	92.77 93	96.63 87	86.62 41	99.04 63	87.40 163	98.66 41	98.17 69

3Dnovator	86.66 5	91.73 108	90.82 121	94.44 45	94.59 195	86.37 41	97.18 13	97.02 56	89.20 81	84.31 295	96.66 83	73.74 224	99.17 51	86.74 173	97.96 77	97.79 100

SR-MVS		94.23 38	94.17 48	94.43 47	98.21 34	85.78 65	96.40 39	96.90 70	88.20 119	94.33 54	97.40 46	84.75 72	99.03 64	93.35 60	97.99 76	98.48 31

HPM-MVS		94.02 48	93.88 55	94.43 47	98.39 25	85.78 65	97.25 11	97.07 54	86.90 158	92.62 102	96.80 79	84.85 70	99.17 51	92.43 77	98.65 44	98.33 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 61	93.41 72	94.41 49	96.59 87	86.78 26	94.40 187	93.93 282	89.77 61	94.21 56	95.59 135	87.35 34	98.61 126	92.72 71	96.15 128	97.83 98

reproduce-ours		94.82 17	94.97 17	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 54	95.23 45	98.10 12	87.09 37	99.37 33	95.30 35	98.25 62	98.30 51

our_new_method		94.82 17	94.97 17	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 54	95.23 45	98.10 12	87.09 37	99.37 33	95.30 35	98.25 62	98.30 51

NormalMVS		93.46 66	93.16 78	94.37 52	98.40 23	86.20 48	96.30 42	96.27 128	91.65 15	92.68 98	96.13 107	77.97 161	98.84 99	90.75 123	98.26 59	98.07 77

test12				94.34 53	97.13 75	86.15 50		96.29 124		91.04 140		85.08 62	99.01 69		98.13 69	97.86 95

SymmetryMVS		92.81 90	92.31 94	94.32 54	96.15 103	86.20 48	96.30 42	94.43 260	91.65 15	92.68 98	96.13 107	77.97 161	98.84 99	90.75 123	94.72 158	97.92 90

ACMMP		93.24 78	92.88 84	94.30 55	98.09 40	85.33 74	96.86 28	97.45 16	88.33 112	90.15 156	97.03 67	81.44 121	99.51 24	90.85 122	95.74 135	98.04 82
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 21	94.92 20	94.29 56	97.92 45	85.18 76	95.95 79	97.19 39	89.67 64	95.27 44	98.16 5	86.53 44	99.36 36	95.42 34	98.15 67	98.33 46

DeepC-MVS	88.79 3	93.31 75	92.99 82	94.26 57	96.07 113	85.83 63	94.89 151	96.99 57	89.02 90	89.56 162	97.37 48	82.51 101	99.38 31	92.20 87	98.30 57	97.57 114
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 85	92.63 89	94.23 58	95.62 137	85.92 59	96.08 64	96.33 122	89.86 52	93.89 67	94.66 178	82.11 111	98.50 132	92.33 84	92.82 206	98.27 59

fmvsm_l_conf0.5_n_3		94.80 20	95.01 16	94.15 59	95.64 135	85.08 77	96.09 63	97.36 24	90.98 21	97.09 15	98.12 9	84.98 68	98.94 86	97.07 14	97.80 85	98.43 39

EPNet		91.79 105	91.02 116	94.10 60	90.10 377	85.25 75	96.03 71	92.05 335	92.83 5	87.39 206	95.78 127	79.39 143	99.01 69	88.13 153	97.48 93	98.05 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 11	95.46 8	94.01 61	98.40 23	84.36 102	97.70 3	97.78 1	91.19 18	96.22 28	98.08 17	86.64 40	99.37 33	94.91 40	98.26 59	98.29 56

test_fmvsmconf_n		94.60 24	94.81 24	93.98 62	94.62 193	84.96 80	96.15 57	97.35 25	89.37 73	96.03 33	98.11 10	86.36 45	99.01 69	97.45 9	97.83 83	97.96 86

DELS-MVS		93.43 73	93.25 75	93.97 63	95.42 145	85.04 78	93.06 270	97.13 49	90.74 28	91.84 123	95.09 158	86.32 46	99.21 49	91.22 113	98.45 52	97.65 109
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		91.95 102	91.28 110	93.96 64	98.33 29	85.92 59	94.66 170	96.66 97	82.69 269	90.03 158	95.82 125	82.30 106	99.03 64	84.57 205	96.48 121	96.91 159

HPM-MVS_fast		93.40 74	93.22 76	93.94 65	98.36 27	84.83 82	97.15 14	96.80 82	85.77 186	92.47 106	97.13 62	82.38 102	99.07 59	90.51 128	98.40 54	97.92 90

test_fmvsmconf0.1_n		94.20 41	94.31 37	93.88 66	92.46 293	84.80 83	96.18 54	96.82 79	89.29 78	95.68 38	98.11 10	85.10 61	98.99 76	97.38 10	97.75 89	97.86 95

SD-MVS		94.96 15	95.33 10	93.88 66	97.25 74	86.69 28	96.19 52	97.11 52	90.42 34	96.95 19	97.27 51	89.53 14	96.91 283	94.38 46	98.85 20	98.03 83
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 68	93.31 73	93.84 68	96.99 77	84.84 81	93.24 262	97.24 36	88.76 98	91.60 130	95.85 123	86.07 50	98.66 117	91.91 101	98.16 66	98.03 83

SR-MVS-dyc-post		93.82 56	93.82 57	93.82 69	97.92 45	84.57 89	96.28 46	96.76 86	87.46 142	93.75 68	97.43 44	84.24 77	99.01 69	92.73 69	97.80 85	97.88 93

test_prior				93.82 69	97.29 72	84.49 93		96.88 72					98.87 93			98.11 76

APD-MVS_3200maxsize		93.78 57	93.77 61	93.80 71	97.92 45	84.19 106	96.30 42	96.87 73	86.96 154	93.92 66	97.47 42	83.88 81	98.96 83	92.71 72	97.87 81	98.26 63

fmvsm_l_conf0.5_n		94.29 35	94.46 30	93.79 72	95.28 150	85.43 72	95.68 99	96.43 113	86.56 166	96.84 21	97.81 34	87.56 32	98.77 108	97.14 12	96.82 111	97.16 138

CSCG		93.23 79	93.05 80	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 230	90.05 157	95.66 132	87.77 26	99.15 55	89.91 133	98.27 58	98.07 77

GDP-MVS		92.04 100	91.46 106	93.75 74	94.55 201	84.69 86	95.60 110	96.56 105	87.83 134	93.07 84	95.89 120	73.44 228	98.65 119	90.22 131	96.03 130	97.91 92

BP-MVS1		92.48 95	92.07 98	93.72 75	94.50 204	84.39 101	95.90 82	94.30 267	90.39 35	92.67 100	95.94 117	74.46 207	98.65 119	93.14 63	97.35 97	98.13 72

test_fmvsmconf0.01_n		93.19 80	93.02 81	93.71 76	89.25 390	84.42 100	96.06 68	96.29 124	89.06 85	94.68 50	98.13 6	79.22 145	98.98 80	97.22 11	97.24 99	97.74 103

UA-Net		92.83 88	92.54 91	93.68 77	96.10 110	84.71 85	95.66 102	96.39 117	91.92 10	93.22 79	96.49 93	83.16 90	98.87 93	84.47 207	95.47 142	97.45 120

fmvsm_l_conf0.5_n_a		94.20 41	94.40 32	93.60 78	95.29 149	84.98 79	95.61 107	96.28 127	86.31 172	96.75 23	97.86 32	87.40 33	98.74 112	97.07 14	97.02 104	97.07 143

QAPM		89.51 166	88.15 190	93.59 79	94.92 173	84.58 88	96.82 30	96.70 95	78.43 348	83.41 316	96.19 104	73.18 233	99.30 44	77.11 316	96.54 118	96.89 160

test_fmvsm_n_1920		94.71 23	95.11 14	93.50 80	95.79 126	84.62 87	96.15 57	97.64 3	89.85 53	97.19 12	97.89 30	86.28 47	98.71 115	97.11 13	98.08 73	97.17 134

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 174	95.76 94	96.92 67	93.37 3	97.63 5	98.43 1	84.82 71	99.16 54	98.15 1	97.92 79	98.90 11

KinetiMVS		91.82 104	91.30 108	93.39 82	94.72 186	83.36 133	95.45 113	96.37 119	90.33 37	92.17 111	96.03 112	72.32 245	98.75 109	87.94 156	96.34 123	98.07 77

casdiffmvs_mvg		92.96 87	92.83 85	93.35 83	94.59 195	83.40 131	95.00 145	96.34 121	90.30 40	92.05 114	96.05 111	83.43 84	98.15 168	92.07 92	95.67 136	98.49 30
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 52	94.18 47	93.30 84	94.79 182	83.81 117	95.77 92	96.74 90	88.02 124	96.23 27	97.84 33	83.36 88	98.83 102	97.49 7	97.34 98	97.25 129

EI-MVSNet-Vis-set		93.01 86	92.92 83	93.29 85	95.01 164	83.51 128	94.48 179	95.77 176	90.87 22	92.52 104	96.67 82	84.50 74	99.00 74	91.99 97	94.44 170	97.36 122

Vis-MVSNet		91.75 107	91.23 111	93.29 85	95.32 148	83.78 118	96.14 59	95.98 158	89.89 50	90.45 148	96.58 90	75.09 198	98.31 159	84.75 201	96.90 107	97.78 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 51	94.22 42	93.26 87	96.13 105	83.29 135	96.27 48	96.52 108	89.82 54	95.56 40	95.51 137	84.50 74	98.79 106	94.83 41	98.86 19	97.72 105

SPE-MVS-test		94.02 48	94.29 38	93.24 88	96.69 83	83.24 136	97.49 6	96.92 67	92.14 8	92.90 86	95.77 128	85.02 64	98.33 156	93.03 65	98.62 46	98.13 72

VNet		92.24 99	91.91 100	93.24 88	96.59 87	83.43 129	94.84 157	96.44 112	89.19 82	94.08 63	95.90 119	77.85 167	98.17 166	88.90 144	93.38 192	98.13 72

VDD-MVS		90.74 127	89.92 141	93.20 90	96.27 100	83.02 150	95.73 96	93.86 286	88.42 111	92.53 103	96.84 74	62.09 351	98.64 121	90.95 119	92.62 211	97.93 89

Elysia		90.12 145	89.10 161	93.18 91	93.16 267	84.05 110	95.22 129	96.27 128	85.16 205	90.59 145	94.68 174	64.64 334	98.37 149	86.38 179	95.77 133	97.12 140

StellarMVS		90.12 145	89.10 161	93.18 91	93.16 267	84.05 110	95.22 129	96.27 128	85.16 205	90.59 145	94.68 174	64.64 334	98.37 149	86.38 179	95.77 133	97.12 140

CS-MVS		94.12 45	94.44 31	93.17 93	96.55 90	83.08 147	97.63 4	96.95 64	91.71 14	93.50 76	96.21 100	85.61 53	98.24 161	93.64 54	98.17 65	98.19 67

nrg030		91.08 121	90.39 125	93.17 93	93.07 273	86.91 22	96.41 38	96.26 132	88.30 114	88.37 184	94.85 168	82.19 110	97.64 214	91.09 114	82.95 333	94.96 242

MVSMamba_PlusPlus		93.44 69	93.54 70	93.14 95	96.58 89	83.05 148	96.06 68	96.50 110	84.42 227	94.09 60	95.56 136	85.01 67	98.69 116	94.96 39	98.66 41	97.67 108

EI-MVSNet-UG-set		92.74 91	92.62 90	93.12 96	94.86 178	83.20 138	94.40 187	95.74 179	90.71 30	92.05 114	96.60 89	84.00 79	98.99 76	91.55 109	93.63 183	97.17 134

test_fmvsmvis_n_1920		93.44 69	93.55 69	93.10 97	93.67 253	84.26 104	95.83 88	96.14 142	89.00 91	92.43 107	97.50 41	83.37 87	98.72 113	96.61 21	97.44 94	96.32 182

新几何1				93.10 97	97.30 71	84.35 103		95.56 193	71.09 414	91.26 137	96.24 99	82.87 97	98.86 95	79.19 295	98.10 70	96.07 198

OMC-MVS		91.23 116	90.62 124	93.08 99	96.27 100	84.07 108	93.52 244	95.93 162	86.95 155	89.51 163	96.13 107	78.50 155	98.35 153	85.84 188	92.90 202	96.83 164

OpenMVS	83.78 11	88.74 192	87.29 210	93.08 99	92.70 288	85.39 73	96.57 36	96.43 113	78.74 343	80.85 348	96.07 110	69.64 280	99.01 69	78.01 307	96.65 116	94.83 250

MAR-MVS		90.30 141	89.37 154	93.07 101	96.61 86	84.48 94	95.68 99	95.67 185	82.36 274	87.85 193	92.85 246	76.63 178	98.80 104	80.01 283	96.68 115	95.91 204
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		90.92 122	90.21 129	93.03 102	93.86 240	83.88 115	92.81 281	93.86 286	79.84 324	91.76 126	94.29 193	77.92 164	98.04 184	90.48 129	97.11 100	97.17 134

Effi-MVS+		91.59 111	91.11 113	93.01 103	94.35 217	83.39 132	94.60 172	95.10 226	87.10 151	90.57 147	93.10 241	81.43 122	98.07 182	89.29 139	94.48 168	97.59 113

fmvsm_s_conf0.5_n_a		93.57 62	93.76 62	93.00 104	95.02 163	83.67 121	96.19 52	96.10 148	87.27 147	95.98 34	98.05 22	83.07 94	98.45 142	96.68 20	95.51 139	96.88 161

MVS_111021_LR		92.47 96	92.29 96	92.98 105	95.99 119	84.43 98	93.08 268	96.09 149	88.20 119	91.12 139	95.72 131	81.33 123	97.76 203	91.74 105	97.37 96	96.75 166

fmvsm_s_conf0.1_n_a		93.19 80	93.26 74	92.97 106	92.49 291	83.62 124	96.02 72	95.72 182	86.78 160	96.04 32	98.19 3	82.30 106	98.43 146	96.38 22	95.42 145	96.86 162

ETV-MVS		92.74 91	92.66 88	92.97 106	95.20 156	84.04 112	95.07 141	96.51 109	90.73 29	92.96 85	91.19 307	84.06 78	98.34 154	91.72 106	96.54 118	96.54 177

LFMVS		90.08 148	89.13 160	92.95 108	96.71 82	82.32 173	96.08 64	89.91 391	86.79 159	92.15 113	96.81 77	62.60 349	98.34 154	87.18 167	93.90 178	98.19 67

UGNet		89.95 154	88.95 166	92.95 108	94.51 203	83.31 134	95.70 98	95.23 219	89.37 73	87.58 200	93.94 209	64.00 339	98.78 107	83.92 214	96.31 124	96.74 167
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		90.80 125	90.10 133	92.90 110	93.04 276	83.53 127	93.08 268	94.15 275	80.22 318	91.41 134	94.91 162	76.87 172	97.93 195	90.28 130	96.90 107	97.24 130
jason: jason.
DP-MVS		87.25 245	85.36 282	92.90 110	97.65 60	83.24 136	94.81 159	92.00 337	74.99 382	81.92 337	95.00 160	72.66 238	99.05 61	66.92 396	92.33 216	96.40 179

fmvsm_s_conf0.5_n_8		94.56 25	95.12 13	92.87 112	95.96 122	81.32 197	95.76 94	97.57 5	93.48 2	97.53 8	98.32 2	81.78 120	99.13 56	97.91 2	97.81 84	98.16 70

fmvsm_s_conf0.5_n		93.76 58	94.06 52	92.86 113	95.62 137	83.17 139	96.14 59	96.12 146	88.13 122	95.82 36	98.04 25	83.43 84	98.48 134	96.97 18	96.23 125	96.92 158

fmvsm_s_conf0.1_n		93.46 66	93.66 67	92.85 114	93.75 247	83.13 141	96.02 72	95.74 179	87.68 139	95.89 35	98.17 4	82.78 98	98.46 138	96.71 19	96.17 127	96.98 152

CANet_DTU		90.26 143	89.41 153	92.81 115	93.46 260	83.01 151	93.48 245	94.47 259	89.43 71	87.76 198	94.23 198	70.54 268	99.03 64	84.97 196	96.39 122	96.38 180

MVSFormer		91.68 110	91.30 108	92.80 116	93.86 240	83.88 115	95.96 77	95.90 166	84.66 223	91.76 126	94.91 162	77.92 164	97.30 250	89.64 135	97.11 100	97.24 130

PVSNet_Blended_VisFu		91.38 113	90.91 118	92.80 116	96.39 97	83.17 139	94.87 153	96.66 97	83.29 254	89.27 169	94.46 188	80.29 130	99.17 51	87.57 161	95.37 146	96.05 201

LuminaMVS		90.55 138	89.81 143	92.77 118	92.78 286	84.21 105	94.09 212	94.17 274	85.82 183	91.54 131	94.14 200	69.93 274	97.92 196	91.62 108	94.21 173	96.18 190

fmvsm_s_conf0.5_n_6		94.11 46	94.56 27	92.76 119	94.98 168	81.96 181	95.79 90	97.29 34	89.31 76	97.52 9	97.61 39	83.25 89	98.88 92	97.05 16	98.22 64	97.43 121

VDDNet		89.56 165	88.49 181	92.76 119	95.07 162	82.09 176	96.30 42	93.19 303	81.05 312	91.88 121	96.86 73	61.16 367	98.33 156	88.43 150	92.49 215	97.84 97

h-mvs33		90.80 125	90.15 132	92.75 121	96.01 115	82.66 164	95.43 114	95.53 197	89.80 57	93.08 82	95.64 133	75.77 187	99.00 74	92.07 92	78.05 390	96.60 172

casdiffmvs		92.51 94	92.43 93	92.74 122	94.41 212	81.98 179	94.54 176	96.23 136	89.57 67	91.96 118	96.17 105	82.58 100	98.01 186	90.95 119	95.45 144	98.23 65
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		90.69 130	90.02 139	92.71 123	95.72 129	82.41 171	94.11 208	95.12 224	85.63 190	91.49 132	94.70 172	74.75 202	98.42 147	86.13 183	92.53 213	97.31 123

DCV-MVSNet		90.69 130	90.02 139	92.71 123	95.72 129	82.41 171	94.11 208	95.12 224	85.63 190	91.49 132	94.70 172	74.75 202	98.42 147	86.13 183	92.53 213	97.31 123

PCF-MVS	84.11 10	87.74 219	86.08 257	92.70 125	94.02 230	84.43 98	89.27 371	95.87 170	73.62 396	84.43 287	94.33 190	78.48 157	98.86 95	70.27 370	94.45 169	94.81 251
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mamba_0404		90.73 128	90.08 134	92.69 126	95.00 167	83.13 141	94.32 196	95.00 233	85.41 198	89.84 159	95.35 143	76.13 181	97.98 190	85.46 193	94.18 174	96.95 154

baseline		92.39 98	92.29 96	92.69 126	94.46 207	81.77 184	94.14 205	96.27 128	89.22 80	91.88 121	96.00 113	82.35 103	97.99 188	91.05 115	95.27 150	98.30 51

MSLP-MVS++		93.72 60	94.08 49	92.65 128	97.31 70	83.43 129	95.79 90	97.33 28	90.03 47	93.58 72	96.96 69	84.87 69	97.76 203	92.19 88	98.66 41	96.76 165

EC-MVSNet		93.44 69	93.71 65	92.63 129	95.21 155	82.43 168	97.27 10	96.71 94	90.57 33	92.88 87	95.80 126	83.16 90	98.16 167	93.68 53	98.14 68	97.31 123

ab-mvs		89.41 171	88.35 183	92.60 130	95.15 160	82.65 165	92.20 301	95.60 192	83.97 234	88.55 180	93.70 223	74.16 215	98.21 165	82.46 236	89.37 260	96.94 156

LS3D		87.89 214	86.32 246	92.59 131	96.07 113	82.92 154	95.23 127	94.92 239	75.66 374	82.89 323	95.98 115	72.48 242	99.21 49	68.43 384	95.23 151	95.64 218

Anonymous20240529		88.09 210	86.59 235	92.58 132	96.53 92	81.92 182	95.99 74	95.84 172	74.11 391	89.06 173	95.21 152	61.44 359	98.81 103	83.67 219	87.47 291	97.01 150

fmvsm_s_conf0.5_n_3		94.49 27	95.13 12	92.56 133	95.49 143	81.10 207	95.93 80	97.16 45	92.96 4	97.39 10	98.13 6	83.63 83	98.80 104	97.89 3	97.61 92	97.78 101

CPTT-MVS		91.99 101	91.80 101	92.55 134	98.24 33	81.98 179	96.76 31	96.49 111	81.89 289	90.24 151	96.44 95	78.59 153	98.61 126	89.68 134	97.85 82	97.06 144

114514_t		89.51 166	88.50 179	92.54 135	98.11 38	81.99 178	95.16 137	96.36 120	70.19 418	85.81 240	95.25 148	76.70 176	98.63 123	82.07 246	96.86 110	97.00 151

PAPM_NR		91.22 117	90.78 122	92.52 136	97.60 61	81.46 193	94.37 193	96.24 135	86.39 171	87.41 203	94.80 170	82.06 114	98.48 134	82.80 231	95.37 146	97.61 111

DeepPCF-MVS	89.96 1	94.20 41	94.77 25	92.49 137	96.52 93	80.00 243	94.00 222	97.08 53	90.05 46	95.65 39	97.29 50	89.66 13	98.97 81	93.95 50	98.71 32	98.50 28

IS-MVSNet		91.43 112	91.09 115	92.46 138	95.87 125	81.38 196	96.95 20	93.69 294	89.72 63	89.50 165	95.98 115	78.57 154	97.77 202	83.02 225	96.50 120	98.22 66

API-MVS		90.66 133	90.07 135	92.45 139	96.36 98	84.57 89	96.06 68	95.22 221	82.39 272	89.13 170	94.27 196	80.32 129	98.46 138	80.16 282	96.71 114	94.33 274

xiu_mvs_v1_base_debu		90.64 134	90.05 136	92.40 140	93.97 236	84.46 95	93.32 253	95.46 201	85.17 202	92.25 108	94.03 201	70.59 264	98.57 129	90.97 116	94.67 160	94.18 277

xiu_mvs_v1_base		90.64 134	90.05 136	92.40 140	93.97 236	84.46 95	93.32 253	95.46 201	85.17 202	92.25 108	94.03 201	70.59 264	98.57 129	90.97 116	94.67 160	94.18 277

xiu_mvs_v1_base_debi		90.64 134	90.05 136	92.40 140	93.97 236	84.46 95	93.32 253	95.46 201	85.17 202	92.25 108	94.03 201	70.59 264	98.57 129	90.97 116	94.67 160	94.18 277

fmvsm_s_conf0.5_n_2		93.47 65	93.83 56	92.39 143	95.36 146	81.19 203	95.20 134	96.56 105	90.37 36	97.13 14	98.03 26	77.47 168	98.96 83	97.79 5	96.58 117	97.03 147

fmvsm_s_conf0.1_n_2		93.16 82	93.42 71	92.37 144	94.62 193	81.13 205	95.23 127	95.89 168	90.30 40	96.74 24	98.02 27	76.14 180	98.95 85	97.64 6	96.21 126	97.03 147

AdaColmap		89.89 157	89.07 163	92.37 144	97.41 67	83.03 149	94.42 186	95.92 163	82.81 266	86.34 229	94.65 179	73.89 220	99.02 67	80.69 273	95.51 139	95.05 237

CNLPA		89.07 182	87.98 194	92.34 146	96.87 79	84.78 84	94.08 213	93.24 300	81.41 303	84.46 285	95.13 157	75.57 194	96.62 296	77.21 314	93.84 180	95.61 221

fmvsm_s_conf0.5_n_4		93.86 55	94.37 34	92.33 147	95.13 161	80.95 212	95.64 105	96.97 59	89.60 66	96.85 20	97.77 35	83.08 93	98.92 89	97.49 7	96.78 112	97.13 139

ET-MVSNet_ETH3D		87.51 233	85.91 265	92.32 148	93.70 252	83.93 113	92.33 296	90.94 369	84.16 229	72.09 417	92.52 259	69.90 275	95.85 343	89.20 140	88.36 278	97.17 134

Anonymous202405211		87.68 220	86.13 253	92.31 149	96.66 84	80.74 219	94.87 153	91.49 354	80.47 317	89.46 166	95.44 139	54.72 403	98.23 162	82.19 242	89.89 250	97.97 85

CHOSEN 1792x2688		88.84 188	87.69 200	92.30 150	96.14 104	81.42 195	90.01 358	95.86 171	74.52 387	87.41 203	93.94 209	75.46 195	98.36 151	80.36 278	95.53 138	97.12 140

HY-MVS	83.01 12	89.03 184	87.94 196	92.29 151	94.86 178	82.77 156	92.08 306	94.49 258	81.52 302	86.93 210	92.79 252	78.32 159	98.23 162	79.93 284	90.55 237	95.88 207

CDS-MVSNet		89.45 169	88.51 178	92.29 151	93.62 255	83.61 126	93.01 271	94.68 255	81.95 284	87.82 196	93.24 235	78.69 151	96.99 277	80.34 279	93.23 197	96.28 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 150	89.27 159	92.29 151	95.78 127	80.95 212	92.68 283	96.22 137	81.91 286	86.66 220	93.75 221	82.23 108	98.44 144	79.40 294	94.79 157	97.48 118

mvsmamba		90.33 140	89.69 145	92.25 154	95.17 157	81.64 186	95.27 125	93.36 299	84.88 214	89.51 163	94.27 196	69.29 289	97.42 236	89.34 138	96.12 129	97.68 107

PLC	84.53 7	89.06 183	88.03 192	92.15 155	97.27 73	82.69 163	94.29 197	95.44 206	79.71 326	84.01 301	94.18 199	76.68 177	98.75 109	77.28 313	93.41 191	95.02 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 109	91.56 105	92.13 156	95.88 123	80.50 226	97.33 8	95.25 218	86.15 177	89.76 161	95.60 134	83.42 86	98.32 158	87.37 165	93.25 196	97.56 115

patch_mono-2		93.74 59	94.32 35	92.01 157	97.54 62	78.37 285	93.40 249	97.19 39	88.02 124	94.99 49	97.21 55	88.35 21	98.44 144	94.07 49	98.09 71	99.23 1

原ACMM1				92.01 157	97.34 69	81.05 208		96.81 81	78.89 337	90.45 148	95.92 118	82.65 99	98.84 99	80.68 274	98.26 59	96.14 192

UniMVSNet (Re)		89.80 159	89.07 163	92.01 157	93.60 256	84.52 92	94.78 161	97.47 13	89.26 79	86.44 226	92.32 265	82.10 112	97.39 247	84.81 200	80.84 367	94.12 281

MG-MVS		91.77 106	91.70 104	92.00 160	97.08 76	80.03 241	93.60 242	95.18 222	87.85 133	90.89 142	96.47 94	82.06 114	98.36 151	85.07 195	97.04 103	97.62 110

EIA-MVS		91.95 102	91.94 99	91.98 161	95.16 158	80.01 242	95.36 115	96.73 91	88.44 109	89.34 167	92.16 270	83.82 82	98.45 142	89.35 137	97.06 102	97.48 118

PVSNet_Blended		90.73 128	90.32 127	91.98 161	96.12 106	81.25 199	92.55 288	96.83 77	82.04 282	89.10 171	92.56 258	81.04 125	98.85 97	86.72 175	95.91 131	95.84 209

guyue		91.12 120	90.84 120	91.96 163	94.59 195	80.57 224	94.87 153	93.71 293	88.96 92	91.14 138	95.22 149	73.22 232	97.76 203	92.01 96	93.81 181	97.54 117

PS-MVSNAJ		91.18 118	90.92 117	91.96 163	95.26 153	82.60 167	92.09 305	95.70 183	86.27 173	91.84 123	92.46 260	79.70 138	98.99 76	89.08 141	95.86 132	94.29 275

TAMVS		89.21 177	88.29 187	91.96 163	93.71 250	82.62 166	93.30 257	94.19 272	82.22 277	87.78 197	93.94 209	78.83 148	96.95 280	77.70 309	92.98 201	96.32 182

SDMVSNet		90.19 144	89.61 148	91.93 166	96.00 116	83.09 146	92.89 278	95.98 158	88.73 99	86.85 216	95.20 153	72.09 247	97.08 269	88.90 144	89.85 252	95.63 219

FA-MVS(test-final)		89.66 161	88.91 168	91.93 166	94.57 199	80.27 230	91.36 322	94.74 252	84.87 215	89.82 160	92.61 257	74.72 205	98.47 137	83.97 213	93.53 186	97.04 146

MVS_Test		91.31 115	91.11 113	91.93 166	94.37 213	80.14 234	93.46 247	95.80 174	86.46 169	91.35 136	93.77 219	82.21 109	98.09 179	87.57 161	94.95 154	97.55 116

NR-MVSNet		88.58 198	87.47 206	91.93 166	93.04 276	84.16 107	94.77 162	96.25 134	89.05 86	80.04 362	93.29 233	79.02 147	97.05 274	81.71 257	80.05 377	94.59 258

HyFIR lowres test		88.09 210	86.81 223	91.93 166	96.00 116	80.63 221	90.01 358	95.79 175	73.42 398	87.68 199	92.10 276	73.86 221	97.96 191	80.75 272	91.70 220	97.19 133

GeoE		90.05 149	89.43 152	91.90 171	95.16 158	80.37 229	95.80 89	94.65 256	83.90 235	87.55 202	94.75 171	78.18 160	97.62 216	81.28 262	93.63 183	97.71 106

thisisatest0530		88.67 193	87.61 202	91.86 172	94.87 177	80.07 237	94.63 171	89.90 392	84.00 233	88.46 182	93.78 218	66.88 313	98.46 138	83.30 221	92.65 208	97.06 144

xiu_mvs_v2_base		91.13 119	90.89 119	91.86 172	94.97 169	82.42 169	92.24 299	95.64 190	86.11 181	91.74 128	93.14 239	79.67 141	98.89 91	89.06 142	95.46 143	94.28 276

DU-MVS		89.34 176	88.50 179	91.85 174	93.04 276	83.72 119	94.47 182	96.59 102	89.50 68	86.46 223	93.29 233	77.25 170	97.23 259	84.92 197	81.02 363	94.59 258

AstraMVS		90.69 130	90.30 128	91.84 175	93.81 243	79.85 248	94.76 163	92.39 323	88.96 92	91.01 141	95.87 122	70.69 262	97.94 194	92.49 75	92.70 207	97.73 104

OPM-MVS		90.12 145	89.56 149	91.82 176	93.14 269	83.90 114	94.16 204	95.74 179	88.96 92	87.86 192	95.43 141	72.48 242	97.91 197	88.10 155	90.18 244	93.65 312
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 137	90.19 130	91.82 176	94.70 189	82.73 160	95.85 86	96.22 137	90.81 24	86.91 212	94.86 166	74.23 211	98.12 169	88.15 151	89.99 246	94.63 255

UniMVSNet_NR-MVSNet		89.92 156	89.29 157	91.81 178	93.39 262	83.72 119	94.43 185	97.12 50	89.80 57	86.46 223	93.32 230	83.16 90	97.23 259	84.92 197	81.02 363	94.49 268

diffmvs		91.37 114	91.23 111	91.77 179	93.09 272	80.27 230	92.36 293	95.52 198	87.03 153	91.40 135	94.93 161	80.08 132	97.44 234	92.13 91	94.56 165	97.61 111
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		88.42 199	87.33 209	91.72 180	94.92 173	80.98 210	92.97 275	94.54 257	78.16 354	83.82 304	93.88 214	78.78 150	97.91 197	79.45 290	89.41 259	96.26 186

Fast-Effi-MVS+		89.41 171	88.64 174	91.71 181	94.74 183	80.81 217	93.54 243	95.10 226	83.11 258	86.82 218	90.67 330	79.74 137	97.75 207	80.51 277	93.55 185	96.57 175

WTY-MVS		89.60 163	88.92 167	91.67 182	95.47 144	81.15 204	92.38 292	94.78 250	83.11 258	89.06 173	94.32 191	78.67 152	96.61 299	81.57 258	90.89 233	97.24 130

TAPA-MVS	84.62 6	88.16 208	87.01 218	91.62 183	96.64 85	80.65 220	94.39 189	96.21 140	76.38 367	86.19 233	95.44 139	79.75 136	98.08 181	62.75 414	95.29 148	96.13 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 162	88.96 165	91.60 184	93.86 240	82.89 155	95.46 112	97.33 28	87.91 128	88.43 183	93.31 231	74.17 214	97.40 244	87.32 166	82.86 338	94.52 263

FE-MVS		87.40 238	86.02 259	91.57 185	94.56 200	79.69 252	90.27 345	93.72 292	80.57 315	88.80 176	91.62 296	65.32 329	98.59 128	74.97 339	94.33 172	96.44 178

XVG-OURS		89.40 173	88.70 173	91.52 186	94.06 228	81.46 193	91.27 326	96.07 151	86.14 178	88.89 175	95.77 128	68.73 298	97.26 256	87.39 164	89.96 248	95.83 210

hse-mvs2		89.88 158	89.34 155	91.51 187	94.83 180	81.12 206	93.94 225	93.91 285	89.80 57	93.08 82	93.60 224	75.77 187	97.66 211	92.07 92	77.07 397	95.74 214

TranMVSNet+NR-MVSNet		88.84 188	87.95 195	91.49 188	92.68 289	83.01 151	94.92 150	96.31 123	89.88 51	85.53 249	93.85 216	76.63 178	96.96 279	81.91 250	79.87 380	94.50 266

AUN-MVS		87.78 218	86.54 238	91.48 189	94.82 181	81.05 208	93.91 229	93.93 282	83.00 261	86.93 210	93.53 225	69.50 283	97.67 209	86.14 181	77.12 396	95.73 216

XVG-OURS-SEG-HR		89.95 154	89.45 150	91.47 190	94.00 234	81.21 202	91.87 310	96.06 153	85.78 185	88.55 180	95.73 130	74.67 206	97.27 254	88.71 147	89.64 257	95.91 204

MVS		87.44 236	86.10 256	91.44 191	92.61 290	83.62 124	92.63 285	95.66 187	67.26 424	81.47 340	92.15 271	77.95 163	98.22 164	79.71 286	95.48 141	92.47 355

F-COLMAP		87.95 213	86.80 224	91.40 192	96.35 99	80.88 215	94.73 165	95.45 204	79.65 327	82.04 335	94.61 180	71.13 254	98.50 132	76.24 326	91.05 231	94.80 252

dcpmvs_2		93.49 64	94.19 46	91.38 193	97.69 59	76.78 321	94.25 199	96.29 124	88.33 112	94.46 52	96.88 72	88.07 25	98.64 121	93.62 55	98.09 71	98.73 19

thisisatest0515		87.33 241	85.99 260	91.37 194	93.49 258	79.55 253	90.63 340	89.56 400	80.17 319	87.56 201	90.86 320	67.07 310	98.28 160	81.50 259	93.02 200	96.29 184

HQP-MVS		89.80 159	89.28 158	91.34 195	94.17 223	81.56 187	94.39 189	96.04 154	88.81 95	85.43 258	93.97 208	73.83 222	97.96 191	87.11 170	89.77 255	94.50 266

fmvsm_s_conf0.5_n_7		93.15 83	93.76 62	91.31 196	94.42 211	79.48 255	94.52 177	97.14 48	89.33 75	94.17 58	98.09 16	81.83 118	97.49 226	96.33 23	98.02 75	96.95 154

RRT-MVS		90.85 124	90.70 123	91.30 197	94.25 219	76.83 320	94.85 156	96.13 145	89.04 87	90.23 152	94.88 164	70.15 273	98.72 113	91.86 104	94.88 155	98.34 44

FMVSNet3		87.40 238	86.11 255	91.30 197	93.79 246	83.64 123	94.20 203	94.81 248	83.89 236	84.37 288	91.87 287	68.45 301	96.56 304	78.23 304	85.36 308	93.70 311

FMVSNet2		87.19 251	85.82 268	91.30 197	94.01 231	83.67 121	94.79 160	94.94 234	83.57 244	83.88 303	92.05 280	66.59 318	96.51 308	77.56 311	85.01 311	93.73 309

RPMNet		83.95 328	81.53 339	91.21 200	90.58 367	79.34 261	85.24 417	96.76 86	71.44 412	85.55 247	82.97 424	70.87 259	98.91 90	61.01 418	89.36 261	95.40 225

IB-MVS	80.51 15	85.24 305	83.26 323	91.19 201	92.13 302	79.86 247	91.75 313	91.29 359	83.28 255	80.66 352	88.49 377	61.28 361	98.46 138	80.99 268	79.46 384	95.25 231
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		89.47 168	88.90 169	91.18 202	94.22 221	82.07 177	92.13 303	96.09 149	87.90 129	85.37 264	92.45 261	74.38 209	97.56 220	87.15 168	90.43 239	93.93 290
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		89.45 169	88.90 169	91.12 203	94.47 205	81.49 191	95.30 120	96.14 142	86.73 162	85.45 255	95.16 155	69.89 276	98.10 171	87.70 159	89.23 264	93.77 305

LGP-MVS_train				91.12 203	94.47 205	81.49 191		96.14 142	86.73 162	85.45 255	95.16 155	69.89 276	98.10 171	87.70 159	89.23 264	93.77 305

ACMM	84.12 9	89.14 178	88.48 182	91.12 203	94.65 192	81.22 201	95.31 118	96.12 146	85.31 201	85.92 238	94.34 189	70.19 272	98.06 183	85.65 189	88.86 269	94.08 285
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 195	87.78 199	91.11 206	94.96 170	77.81 301	95.35 116	89.69 395	85.09 209	88.05 190	94.59 183	66.93 311	98.48 134	83.27 222	92.13 218	97.03 147

GBi-Net		87.26 243	85.98 261	91.08 207	94.01 231	83.10 143	95.14 138	94.94 234	83.57 244	84.37 288	91.64 292	66.59 318	96.34 321	78.23 304	85.36 308	93.79 300

test1		87.26 243	85.98 261	91.08 207	94.01 231	83.10 143	95.14 138	94.94 234	83.57 244	84.37 288	91.64 292	66.59 318	96.34 321	78.23 304	85.36 308	93.79 300

FMVSNet1		85.85 290	84.11 310	91.08 207	92.81 284	83.10 143	95.14 138	94.94 234	81.64 297	82.68 325	91.64 292	59.01 383	96.34 321	75.37 333	83.78 322	93.79 300

Test_1112_low_res		87.65 222	86.51 239	91.08 207	94.94 172	79.28 265	91.77 312	94.30 267	76.04 372	83.51 314	92.37 263	77.86 166	97.73 208	78.69 299	89.13 266	96.22 187

PS-MVSNAJss		89.97 152	89.62 147	91.02 211	91.90 311	80.85 216	95.26 126	95.98 158	86.26 174	86.21 232	94.29 193	79.70 138	97.65 212	88.87 146	88.10 280	94.57 260

BH-RMVSNet		88.37 202	87.48 205	91.02 211	95.28 150	79.45 257	92.89 278	93.07 306	85.45 197	86.91 212	94.84 169	70.35 269	97.76 203	73.97 347	94.59 164	95.85 208

UniMVSNet_ETH3D		87.53 232	86.37 243	91.00 213	92.44 294	78.96 270	94.74 164	95.61 191	84.07 232	85.36 265	94.52 185	59.78 375	97.34 249	82.93 226	87.88 285	96.71 168

FIs		90.51 139	90.35 126	90.99 214	93.99 235	80.98 210	95.73 96	97.54 6	89.15 83	86.72 219	94.68 174	81.83 118	97.24 258	85.18 194	88.31 279	94.76 253

ACMP	84.23 8	89.01 186	88.35 183	90.99 214	94.73 184	81.27 198	95.07 141	95.89 168	86.48 167	83.67 309	94.30 192	69.33 285	97.99 188	87.10 172	88.55 271	93.72 310
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 273	85.13 288	90.98 216	96.52 93	81.50 189	96.14 59	96.16 141	73.78 394	83.65 310	92.15 271	63.26 345	97.37 248	82.82 230	81.74 352	94.06 286

icg_test_0403		89.97 152	89.64 146	90.96 217	93.72 248	77.75 306	93.00 272	95.34 215	85.53 195	88.77 177	94.49 186	78.49 156	97.84 199	84.75 201	92.65 208	97.28 126

sss		88.93 187	88.26 189	90.94 218	94.05 229	80.78 218	91.71 314	95.38 210	81.55 301	88.63 179	93.91 213	75.04 199	95.47 362	82.47 235	91.61 221	96.57 175

sd_testset		88.59 197	87.85 198	90.83 219	96.00 116	80.42 228	92.35 294	94.71 253	88.73 99	86.85 216	95.20 153	67.31 305	96.43 315	79.64 288	89.85 252	95.63 219

PVSNet_BlendedMVS		89.98 151	89.70 144	90.82 220	96.12 106	81.25 199	93.92 227	96.83 77	83.49 248	89.10 171	92.26 268	81.04 125	98.85 97	86.72 175	87.86 286	92.35 361

cascas		86.43 281	84.98 291	90.80 221	92.10 304	80.92 214	90.24 349	95.91 165	73.10 401	83.57 313	88.39 378	65.15 331	97.46 230	84.90 199	91.43 223	94.03 288

ECVR-MVS		89.09 181	88.53 177	90.77 222	95.62 137	75.89 334	96.16 55	84.22 426	87.89 131	90.20 153	96.65 84	63.19 346	98.10 171	85.90 186	96.94 105	98.33 46

GA-MVS		86.61 271	85.27 285	90.66 223	91.33 334	78.71 274	90.40 344	93.81 289	85.34 200	85.12 268	89.57 359	61.25 362	97.11 268	80.99 268	89.59 258	96.15 191

thres600view7		87.65 222	86.67 230	90.59 224	96.08 112	78.72 272	94.88 152	91.58 350	87.06 152	88.08 188	92.30 266	68.91 295	98.10 171	70.05 377	91.10 226	94.96 242

thres400		87.62 227	86.64 231	90.57 225	95.99 119	78.64 275	94.58 173	91.98 339	86.94 156	88.09 186	91.77 288	69.18 291	98.10 171	70.13 374	91.10 226	94.96 242

baseline1		88.10 209	87.28 211	90.57 225	94.96 170	80.07 237	94.27 198	91.29 359	86.74 161	87.41 203	94.00 206	76.77 175	96.20 326	80.77 271	79.31 386	95.44 223

FC-MVSNet-test		90.27 142	90.18 131	90.53 227	93.71 250	79.85 248	95.77 92	97.59 4	89.31 76	86.27 230	94.67 177	81.93 117	97.01 276	84.26 209	88.09 282	94.71 254

PAPM		86.68 270	85.39 280	90.53 227	93.05 275	79.33 264	89.79 361	94.77 251	78.82 340	81.95 336	93.24 235	76.81 173	97.30 250	66.94 394	93.16 198	94.95 246

WR-MVS		88.38 201	87.67 201	90.52 229	93.30 264	80.18 232	93.26 260	95.96 161	88.57 107	85.47 254	92.81 250	76.12 182	96.91 283	81.24 263	82.29 343	94.47 271

MVSTER		88.84 188	88.29 187	90.51 230	92.95 281	80.44 227	93.73 236	95.01 230	84.66 223	87.15 207	93.12 240	72.79 237	97.21 261	87.86 157	87.36 294	93.87 295

testdata				90.49 231	96.40 96	77.89 298		95.37 212	72.51 406	93.63 71	96.69 80	82.08 113	97.65 212	83.08 223	97.39 95	95.94 203

test1111		89.10 179	88.64 174	90.48 232	95.53 142	74.97 344	96.08 64	84.89 424	88.13 122	90.16 155	96.65 84	63.29 344	98.10 171	86.14 181	96.90 107	98.39 41

tt0805		86.92 259	85.74 274	90.48 232	92.22 298	79.98 244	95.63 106	94.88 242	83.83 238	84.74 277	92.80 251	57.61 389	97.67 209	85.48 192	84.42 315	93.79 300

jajsoiax		88.24 206	87.50 204	90.48 232	90.89 355	80.14 234	95.31 118	95.65 189	84.97 212	84.24 296	94.02 204	65.31 330	97.42 236	88.56 148	88.52 273	93.89 291

PatchMatch-RL		86.77 267	85.54 276	90.47 235	95.88 123	82.71 162	90.54 342	92.31 327	79.82 325	84.32 293	91.57 300	68.77 297	96.39 317	73.16 353	93.48 190	92.32 362

tfpn200view9		87.58 230	86.64 231	90.41 236	95.99 119	78.64 275	94.58 173	91.98 339	86.94 156	88.09 186	91.77 288	69.18 291	98.10 171	70.13 374	91.10 226	94.48 269

VPNet		88.20 207	87.47 206	90.39 237	93.56 257	79.46 256	94.04 217	95.54 196	88.67 102	86.96 209	94.58 184	69.33 285	97.15 263	84.05 212	80.53 372	94.56 261

ACMH	80.38 17	85.36 300	83.68 317	90.39 237	94.45 208	80.63 221	94.73 165	94.85 244	82.09 279	77.24 387	92.65 255	60.01 373	97.58 218	72.25 358	84.87 312	92.96 340
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 225	86.71 227	90.38 239	96.12 106	78.55 278	95.03 144	91.58 350	87.15 149	88.06 189	92.29 267	68.91 295	98.10 171	70.13 374	91.10 226	94.48 269

mvs_tets		88.06 212	87.28 211	90.38 239	90.94 351	79.88 246	95.22 129	95.66 187	85.10 208	84.21 297	93.94 209	63.53 342	97.40 244	88.50 149	88.40 277	93.87 295

1314		87.51 233	86.57 236	90.34 241	92.42 295	79.74 251	92.63 285	95.35 214	78.35 349	80.14 359	91.62 296	74.05 216	97.15 263	81.05 264	93.53 186	94.12 281

LTVRE_ROB	82.13 13	86.26 284	84.90 294	90.34 241	94.44 209	81.50 189	92.31 298	94.89 240	83.03 260	79.63 369	92.67 254	69.69 279	97.79 201	71.20 363	86.26 303	91.72 372
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		89.03 184	88.64 174	90.21 243	90.74 362	79.28 265	95.96 77	95.90 166	84.66 223	85.33 266	92.94 245	74.02 217	97.30 250	89.64 135	88.53 272	94.05 287

v2v482		87.84 215	87.06 215	90.17 244	90.99 347	79.23 268	94.00 222	95.13 223	84.87 215	85.53 249	92.07 279	74.45 208	97.45 231	84.71 204	81.75 351	93.85 298

pmmvs4		85.43 298	83.86 315	90.16 245	90.02 380	82.97 153	90.27 345	92.67 318	75.93 373	80.73 350	91.74 290	71.05 255	95.73 351	78.85 298	83.46 329	91.78 371

V42		87.68 220	86.86 220	90.15 246	90.58 367	80.14 234	94.24 201	95.28 217	83.66 242	85.67 244	91.33 302	74.73 204	97.41 242	84.43 208	81.83 349	92.89 343

MSDG		84.86 313	83.09 326	90.14 247	93.80 244	80.05 239	89.18 374	93.09 305	78.89 337	78.19 379	91.91 285	65.86 328	97.27 254	68.47 383	88.45 275	93.11 335

sc_t1		81.53 352	78.67 373	90.12 248	90.78 359	78.64 275	93.91 229	90.20 382	68.42 421	80.82 349	89.88 352	46.48 426	96.76 288	76.03 329	71.47 411	94.96 242

anonymousdsp		87.84 215	87.09 214	90.12 248	89.13 391	80.54 225	94.67 169	95.55 194	82.05 280	83.82 304	92.12 273	71.47 252	97.15 263	87.15 168	87.80 289	92.67 349

thres200		87.21 249	86.24 250	90.12 248	95.36 146	78.53 279	93.26 260	92.10 333	86.42 170	88.00 191	91.11 313	69.24 290	98.00 187	69.58 378	91.04 232	93.83 299

CR-MVSNet		85.35 301	83.76 316	90.12 248	90.58 367	79.34 261	85.24 417	91.96 341	78.27 351	85.55 247	87.87 388	71.03 256	95.61 354	73.96 348	89.36 261	95.40 225

v1144		87.61 228	86.79 225	90.06 252	91.01 346	79.34 261	93.95 224	95.42 209	83.36 253	85.66 245	91.31 305	74.98 200	97.42 236	83.37 220	82.06 345	93.42 321

XXY-MVS		87.65 222	86.85 221	90.03 253	92.14 301	80.60 223	93.76 235	95.23 219	82.94 263	84.60 279	94.02 204	74.27 210	95.49 361	81.04 265	83.68 325	94.01 289

Vis-MVSNet (Re-imp)		89.59 164	89.44 151	90.03 253	95.74 128	75.85 335	95.61 107	90.80 373	87.66 141	87.83 195	95.40 142	76.79 174	96.46 313	78.37 300	96.73 113	97.80 99

test2506		87.21 249	86.28 248	90.02 255	95.62 137	73.64 360	96.25 50	71.38 449	87.89 131	90.45 148	96.65 84	55.29 400	98.09 179	86.03 185	96.94 105	98.33 46

BH-untuned		88.60 196	88.13 191	90.01 256	95.24 154	78.50 281	93.29 258	94.15 275	84.75 220	84.46 285	93.40 227	75.76 189	97.40 244	77.59 310	94.52 167	94.12 281

v1192		87.25 245	86.33 245	90.00 257	90.76 361	79.04 269	93.80 233	95.48 199	82.57 270	85.48 253	91.18 309	73.38 231	97.42 236	82.30 239	82.06 345	93.53 315

v7n		86.81 262	85.76 272	89.95 258	90.72 363	79.25 267	95.07 141	95.92 163	84.45 226	82.29 329	90.86 320	72.60 241	97.53 222	79.42 293	80.52 373	93.08 337

testing91		87.11 254	86.18 251	89.92 259	94.43 210	75.38 343	91.53 319	92.27 329	86.48 167	86.50 221	90.24 338	61.19 365	97.53 222	82.10 244	90.88 234	96.84 163

ICG_test_0404		87.60 229	86.84 222	89.89 260	93.72 248	77.75 306	88.56 383	95.34 215	85.53 195	79.98 363	94.49 186	66.54 321	94.64 375	84.75 201	92.65 208	97.28 126

v8		87.50 235	86.71 227	89.89 260	91.37 331	79.40 258	94.50 178	95.38 210	84.81 218	83.60 312	91.33 302	76.05 183	97.42 236	82.84 229	80.51 374	92.84 345

v10		87.25 245	86.38 242	89.85 262	91.19 337	79.50 254	94.48 179	95.45 204	83.79 240	83.62 311	91.19 307	75.13 197	97.42 236	81.94 249	80.60 369	92.63 351

baseline2		86.50 277	85.39 280	89.84 263	91.12 342	76.70 323	91.88 309	88.58 404	82.35 275	79.95 364	90.95 318	73.42 229	97.63 215	80.27 281	89.95 249	95.19 232

pm-mvs1		86.61 271	85.54 276	89.82 264	91.44 326	80.18 232	95.28 124	94.85 244	83.84 237	81.66 338	92.62 256	72.45 244	96.48 310	79.67 287	78.06 389	92.82 346

TR-MVS		86.78 264	85.76 272	89.82 264	94.37 213	78.41 283	92.47 289	92.83 312	81.11 311	86.36 227	92.40 262	68.73 298	97.48 227	73.75 351	89.85 252	93.57 314

ACMH+	81.04 14	85.05 308	83.46 320	89.82 264	94.66 191	79.37 259	94.44 184	94.12 278	82.19 278	78.04 381	92.82 249	58.23 386	97.54 221	73.77 350	82.90 337	92.54 352

EI-MVSNet		89.10 179	88.86 171	89.80 267	91.84 313	78.30 287	93.70 239	95.01 230	85.73 187	87.15 207	95.28 146	79.87 135	97.21 261	83.81 216	87.36 294	93.88 294

v144192		87.19 251	86.35 244	89.74 268	90.64 365	78.24 289	93.92 227	95.43 207	81.93 285	85.51 251	91.05 316	74.21 213	97.45 231	82.86 228	81.56 353	93.53 315

COLMAP_ROB	80.39 16	83.96 327	82.04 336	89.74 268	95.28 150	79.75 250	94.25 199	92.28 328	75.17 380	78.02 382	93.77 219	58.60 385	97.84 199	65.06 405	85.92 304	91.63 374
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 283	85.18 287	89.73 270	92.15 300	76.60 324	91.12 330	91.69 346	83.53 247	85.50 252	88.81 371	66.79 314	96.48 310	76.65 319	90.35 241	96.12 194

IterMVS-LS		88.36 203	87.91 197	89.70 271	93.80 244	78.29 288	93.73 236	95.08 228	85.73 187	84.75 276	91.90 286	79.88 134	96.92 282	83.83 215	82.51 339	93.89 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 280	85.35 283	89.69 272	94.29 218	75.40 342	91.30 324	90.53 377	84.76 219	85.06 270	90.13 344	58.95 384	97.45 231	82.08 245	91.09 230	96.21 189

testing99		86.72 268	85.73 275	89.69 272	94.23 220	74.91 346	91.35 323	90.97 367	86.14 178	86.36 227	90.22 339	59.41 378	97.48 227	82.24 241	90.66 236	96.69 170

v1921920		86.97 258	86.06 258	89.69 272	90.53 370	78.11 292	93.80 233	95.43 207	81.90 287	85.33 266	91.05 316	72.66 238	97.41 242	82.05 247	81.80 350	93.53 315

VortexMVS		88.42 199	88.01 193	89.63 275	93.89 239	78.82 271	93.82 232	95.47 200	86.67 164	84.53 283	91.99 282	72.62 240	96.65 294	89.02 143	84.09 319	93.41 322

Fast-Effi-MVS+-dtu		87.44 236	86.72 226	89.63 275	92.04 305	77.68 309	94.03 218	93.94 281	85.81 184	82.42 328	91.32 304	70.33 270	97.06 272	80.33 280	90.23 243	94.14 280

v1240		86.78 264	85.85 267	89.56 277	90.45 372	77.79 303	93.61 241	95.37 212	81.65 296	85.43 258	91.15 311	71.50 251	97.43 235	81.47 260	82.05 347	93.47 319

Effi-MVS+-dtu		88.65 194	88.35 183	89.54 278	93.33 263	76.39 328	94.47 182	94.36 265	87.70 138	85.43 258	89.56 360	73.45 227	97.26 256	85.57 191	91.28 225	94.97 239

AllTest		83.42 334	81.39 340	89.52 279	95.01 164	77.79 303	93.12 264	90.89 371	77.41 358	76.12 396	93.34 228	54.08 406	97.51 224	68.31 385	84.27 317	93.26 325

TestCases				89.52 279	95.01 164	77.79 303		90.89 371	77.41 358	76.12 396	93.34 228	54.08 406	97.51 224	68.31 385	84.27 317	93.26 325

mvs_anonymous		89.37 175	89.32 156	89.51 281	93.47 259	74.22 353	91.65 317	94.83 246	82.91 264	85.45 255	93.79 217	81.23 124	96.36 320	86.47 177	94.09 175	97.94 87

XVG-ACMP-BASELINE		86.00 286	84.84 296	89.45 282	91.20 336	78.00 294	91.70 315	95.55 194	85.05 210	82.97 322	92.25 269	54.49 404	97.48 227	82.93 226	87.45 293	92.89 343

testing222		84.84 314	83.32 321	89.43 283	94.15 226	75.94 333	91.09 331	89.41 402	84.90 213	85.78 241	89.44 361	52.70 411	96.28 324	70.80 369	91.57 222	96.07 198

MVP-Stereo		85.97 287	84.86 295	89.32 284	90.92 353	82.19 175	92.11 304	94.19 272	78.76 342	78.77 378	91.63 295	68.38 302	96.56 304	75.01 338	93.95 177	89.20 412
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 290	84.70 298	89.29 285	91.76 317	75.54 339	88.49 384	91.30 358	81.63 298	85.05 271	88.70 375	71.71 248	96.24 325	74.61 343	89.05 267	96.08 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 256	86.32 246	89.21 286	90.94 351	77.26 314	93.71 238	94.43 260	84.84 217	84.36 291	90.80 324	76.04 184	97.05 274	82.12 243	79.60 383	93.31 324

tfpnnormal		84.72 316	83.23 324	89.20 287	92.79 285	80.05 239	94.48 179	95.81 173	82.38 273	81.08 346	91.21 306	69.01 294	96.95 280	61.69 416	80.59 370	90.58 399

cl22		86.78 264	85.98 261	89.18 288	92.34 296	77.62 310	90.84 336	94.13 277	81.33 305	83.97 302	90.15 343	73.96 218	96.60 301	84.19 210	82.94 334	93.33 323

BH-w/o		87.57 231	87.05 216	89.12 289	94.90 176	77.90 297	92.41 290	93.51 296	82.89 265	83.70 308	91.34 301	75.75 190	97.07 271	75.49 331	93.49 188	92.39 359

WR-MVS_H		87.80 217	87.37 208	89.10 290	93.23 265	78.12 291	95.61 107	97.30 32	87.90 129	83.72 307	92.01 281	79.65 142	96.01 335	76.36 323	80.54 371	93.16 333

miper_enhance_ethall		86.90 260	86.18 251	89.06 291	91.66 322	77.58 311	90.22 351	94.82 247	79.16 333	84.48 284	89.10 365	79.19 146	96.66 293	84.06 211	82.94 334	92.94 341

c3_l		87.14 253	86.50 240	89.04 292	92.20 299	77.26 314	91.22 329	94.70 254	82.01 283	84.34 292	90.43 335	78.81 149	96.61 299	83.70 218	81.09 360	93.25 327

miper_ehance_all_eth		87.22 248	86.62 234	89.02 293	92.13 302	77.40 313	90.91 335	94.81 248	81.28 306	84.32 293	90.08 346	79.26 144	96.62 296	83.81 216	82.94 334	93.04 338

gg-mvs-nofinetune		81.77 346	79.37 361	88.99 294	90.85 357	77.73 308	86.29 409	79.63 437	74.88 385	83.19 321	69.05 440	60.34 370	96.11 330	75.46 332	94.64 163	93.11 335

ETVMVS		84.43 321	82.92 330	88.97 295	94.37 213	74.67 347	91.23 328	88.35 406	83.37 252	86.06 236	89.04 366	55.38 398	95.67 353	67.12 392	91.34 224	96.58 174

pmmvs6		83.42 334	81.60 338	88.87 296	88.01 406	77.87 299	94.96 147	94.24 271	74.67 386	78.80 377	91.09 314	60.17 372	96.49 309	77.06 318	75.40 403	92.23 364

test_cas_vis1_n_1920		88.83 191	88.85 172	88.78 297	91.15 341	76.72 322	93.85 231	94.93 238	83.23 257	92.81 91	96.00 113	61.17 366	94.45 376	91.67 107	94.84 156	95.17 233

MIMVSNet		82.59 340	80.53 345	88.76 298	91.51 324	78.32 286	86.57 408	90.13 385	79.32 329	80.70 351	88.69 376	52.98 410	93.07 401	66.03 400	88.86 269	94.90 247

cl____		86.52 276	85.78 269	88.75 299	92.03 306	76.46 326	90.74 337	94.30 267	81.83 292	83.34 318	90.78 325	75.74 192	96.57 302	81.74 255	81.54 354	93.22 329

DIV-MVS_self_test		86.53 275	85.78 269	88.75 299	92.02 307	76.45 327	90.74 337	94.30 267	81.83 292	83.34 318	90.82 323	75.75 190	96.57 302	81.73 256	81.52 355	93.24 328

CP-MVSNet		87.63 225	87.26 213	88.74 301	93.12 270	76.59 325	95.29 122	96.58 103	88.43 110	83.49 315	92.98 244	75.28 196	95.83 344	78.97 296	81.15 359	93.79 300

eth_miper_zixun_eth		86.50 277	85.77 271	88.68 302	91.94 308	75.81 336	90.47 343	94.89 240	82.05 280	84.05 299	90.46 334	75.96 185	96.77 287	82.76 232	79.36 385	93.46 320

CHOSEN 280x420		85.15 306	83.99 313	88.65 303	92.47 292	78.40 284	79.68 439	92.76 315	74.90 384	81.41 342	89.59 358	69.85 278	95.51 358	79.92 285	95.29 148	92.03 367

PS-CasMVS		87.32 242	86.88 219	88.63 304	92.99 279	76.33 330	95.33 117	96.61 101	88.22 118	83.30 320	93.07 242	73.03 235	95.79 348	78.36 301	81.00 365	93.75 307

TransMVSNet (Re)		84.43 321	83.06 328	88.54 305	91.72 318	78.44 282	95.18 135	92.82 314	82.73 268	79.67 368	92.12 273	73.49 226	95.96 337	71.10 367	68.73 421	91.21 386

tt0320-xc		79.63 375	76.66 384	88.52 306	91.03 345	78.72 272	93.00 272	89.53 401	66.37 425	76.11 398	87.11 399	46.36 428	95.32 366	72.78 355	67.67 422	91.51 378

EG-PatchMatch MVS		82.37 342	80.34 348	88.46 307	90.27 374	79.35 260	92.80 282	94.33 266	77.14 362	73.26 414	90.18 342	47.47 423	96.72 289	70.25 371	87.32 296	89.30 409

PEN-MVS		86.80 263	86.27 249	88.40 308	92.32 297	75.71 338	95.18 135	96.38 118	87.97 126	82.82 324	93.15 238	73.39 230	95.92 339	76.15 327	79.03 388	93.59 313

Baseline_NR-MVSNet		87.07 255	86.63 233	88.40 308	91.44 326	77.87 299	94.23 202	92.57 320	84.12 231	85.74 243	92.08 277	77.25 170	96.04 331	82.29 240	79.94 378	91.30 384

UBG		85.51 296	84.57 303	88.35 310	94.21 222	71.78 384	90.07 356	89.66 397	82.28 276	85.91 239	89.01 367	61.30 360	97.06 272	76.58 322	92.06 219	96.22 187

D2MVS		85.90 288	85.09 289	88.35 310	90.79 358	77.42 312	91.83 311	95.70 183	80.77 314	80.08 361	90.02 348	66.74 316	96.37 318	81.88 251	87.97 284	91.26 385

pmmvs5		84.21 323	82.84 333	88.34 312	88.95 393	76.94 318	92.41 290	91.91 343	75.63 375	80.28 356	91.18 309	64.59 336	95.57 355	77.09 317	83.47 328	92.53 353

mamv4		90.92 122	91.78 102	88.33 313	95.67 133	70.75 397	92.92 277	96.02 157	81.90 287	88.11 185	95.34 144	85.88 52	96.97 278	95.22 37	95.01 153	97.26 128

tt0320		80.13 368	77.41 377	88.29 314	90.50 371	78.02 293	93.10 267	90.71 375	66.06 428	76.75 391	86.97 400	49.56 418	95.40 363	71.65 359	71.41 412	91.46 381

LCM-MVSNet-Re		88.30 205	88.32 186	88.27 315	94.71 188	72.41 379	93.15 263	90.98 366	87.77 136	79.25 372	91.96 283	78.35 158	95.75 349	83.04 224	95.62 137	96.65 171

CostFormer		85.77 293	84.94 293	88.26 316	91.16 340	72.58 377	89.47 369	91.04 365	76.26 370	86.45 225	89.97 350	70.74 261	96.86 286	82.35 238	87.07 299	95.34 229

ITE_SJBPF				88.24 317	91.88 312	77.05 317		92.92 309	85.54 193	80.13 360	93.30 232	57.29 390	96.20 326	72.46 357	84.71 313	91.49 379

PVSNet	78.82 18	85.55 295	84.65 299	88.23 318	94.72 186	71.93 380	87.12 404	92.75 316	78.80 341	84.95 273	90.53 332	64.43 337	96.71 291	74.74 341	93.86 179	96.06 200

IterMVS-SCA-FT		85.45 297	84.53 304	88.18 319	91.71 319	76.87 319	90.19 353	92.65 319	85.40 199	81.44 341	90.54 331	66.79 314	95.00 372	81.04 265	81.05 361	92.66 350

EPNet_dtu		86.49 279	85.94 264	88.14 320	90.24 375	72.82 369	94.11 208	92.20 331	86.66 165	79.42 371	92.36 264	73.52 225	95.81 346	71.26 362	93.66 182	95.80 212
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 338	80.93 344	88.06 321	90.05 379	76.37 329	84.74 422	91.96 341	72.28 409	81.32 344	87.87 388	71.03 256	95.50 360	68.97 380	80.15 376	92.32 362

test_vis1_n_1920		89.39 174	89.84 142	88.04 322	92.97 280	72.64 374	94.71 167	96.03 156	86.18 176	91.94 120	96.56 92	61.63 355	95.74 350	93.42 58	95.11 152	95.74 214

DTE-MVSNet		86.11 285	85.48 278	87.98 323	91.65 323	74.92 345	94.93 149	95.75 178	87.36 146	82.26 330	93.04 243	72.85 236	95.82 345	74.04 346	77.46 394	93.20 331

PMMVS		85.71 294	84.96 292	87.95 324	88.90 394	77.09 316	88.68 381	90.06 387	72.32 408	86.47 222	90.76 326	72.15 246	94.40 378	81.78 254	93.49 188	92.36 360

GG-mvs-BLEND				87.94 325	89.73 386	77.91 296	87.80 393	78.23 442		80.58 353	83.86 417	59.88 374	95.33 365	71.20 363	92.22 217	90.60 398

MonoMVSNet		86.89 261	86.55 237	87.92 326	89.46 389	73.75 357	94.12 206	93.10 304	87.82 135	85.10 269	90.76 326	69.59 281	94.94 373	86.47 177	82.50 340	95.07 236

reproduce_monomvs		86.37 282	85.87 266	87.87 327	93.66 254	73.71 358	93.44 248	95.02 229	88.61 105	82.64 327	91.94 284	57.88 388	96.68 292	89.96 132	79.71 382	93.22 329

pmmvs-eth3d		80.97 361	78.72 372	87.74 328	84.99 424	79.97 245	90.11 355	91.65 348	75.36 377	73.51 412	86.03 407	59.45 377	93.96 388	75.17 335	72.21 408	89.29 411

MS-PatchMatch		85.05 308	84.16 308	87.73 329	91.42 329	78.51 280	91.25 327	93.53 295	77.50 357	80.15 358	91.58 298	61.99 352	95.51 358	75.69 330	94.35 171	89.16 413

mmtdpeth		85.04 310	84.15 309	87.72 330	93.11 271	75.74 337	94.37 193	92.83 312	84.98 211	89.31 168	86.41 404	61.61 357	97.14 266	92.63 74	62.11 432	90.29 400

test_0402		81.30 357	79.17 366	87.67 331	93.19 266	78.17 290	92.98 274	91.71 344	75.25 379	76.02 399	90.31 337	59.23 379	96.37 318	50.22 435	83.63 326	88.47 420

IterMVS		84.88 312	83.98 314	87.60 332	91.44 326	76.03 332	90.18 354	92.41 322	83.24 256	81.06 347	90.42 336	66.60 317	94.28 382	79.46 289	80.98 366	92.48 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 355	79.30 362	87.58 333	90.92 353	74.16 355	80.99 434	87.68 411	70.52 416	76.63 393	88.81 371	71.21 253	92.76 404	60.01 422	86.93 300	95.83 210

EPMVS		83.90 330	82.70 334	87.51 334	90.23 376	72.67 372	88.62 382	81.96 432	81.37 304	85.01 272	88.34 379	66.31 322	94.45 376	75.30 334	87.12 297	95.43 224

ADS-MVSNet2		81.66 349	79.71 358	87.50 335	91.35 332	74.19 354	83.33 427	88.48 405	72.90 403	82.24 331	85.77 410	64.98 332	93.20 399	64.57 407	83.74 323	95.12 234

OurMVSNet-221017-0		85.35 301	84.64 301	87.49 336	90.77 360	72.59 376	94.01 220	94.40 263	84.72 221	79.62 370	93.17 237	61.91 353	96.72 289	81.99 248	81.16 357	93.16 333

tpm2		84.08 325	82.94 329	87.48 337	91.39 330	71.27 389	89.23 373	90.37 379	71.95 410	84.64 278	89.33 362	67.30 306	96.55 306	75.17 335	87.09 298	94.63 255

RPSCF		85.07 307	84.27 305	87.48 337	92.91 283	70.62 399	91.69 316	92.46 321	76.20 371	82.67 326	95.22 149	63.94 340	97.29 253	77.51 312	85.80 305	94.53 262

myMVS_eth3d28		85.80 292	85.26 286	87.42 339	94.73 184	69.92 404	90.60 341	90.95 368	87.21 148	86.06 236	90.04 347	59.47 376	96.02 333	74.89 340	93.35 195	96.33 181

WBMVS		84.97 311	84.18 307	87.34 340	94.14 227	71.62 388	90.20 352	92.35 324	81.61 299	84.06 298	90.76 326	61.82 354	96.52 307	78.93 297	83.81 321	93.89 291

miper_lstm_enhance		85.27 304	84.59 302	87.31 341	91.28 335	74.63 348	87.69 398	94.09 279	81.20 310	81.36 343	89.85 354	74.97 201	94.30 381	81.03 267	79.84 381	93.01 339

FMVSNet5		81.52 353	79.60 359	87.27 342	91.17 338	77.95 295	91.49 320	92.26 330	76.87 363	76.16 395	87.91 387	51.67 412	92.34 407	67.74 389	81.16 357	91.52 377

USDC		82.76 337	81.26 342	87.26 343	91.17 338	74.55 349	89.27 371	93.39 298	78.26 352	75.30 403	92.08 277	54.43 405	96.63 295	71.64 360	85.79 306	90.61 396

test-LLR		85.87 289	85.41 279	87.25 344	90.95 349	71.67 386	89.55 365	89.88 393	83.41 250	84.54 281	87.95 385	67.25 307	95.11 369	81.82 252	93.37 193	94.97 239

test-mter		84.54 320	83.64 318	87.25 344	90.95 349	71.67 386	89.55 365	89.88 393	79.17 332	84.54 281	87.95 385	55.56 396	95.11 369	81.82 252	93.37 193	94.97 239

JIA-IIPM		81.04 358	78.98 370	87.25 344	88.64 395	73.48 362	81.75 433	89.61 399	73.19 400	82.05 334	73.71 436	66.07 327	95.87 342	71.18 365	84.60 314	92.41 358

TDRefinement		79.81 372	77.34 378	87.22 347	79.24 439	75.48 340	93.12 264	92.03 336	76.45 366	75.01 404	91.58 298	49.19 419	96.44 314	70.22 373	69.18 418	89.75 405

tpmvs		83.35 336	82.07 335	87.20 348	91.07 344	71.00 395	88.31 387	91.70 345	78.91 335	80.49 355	87.18 397	69.30 288	97.08 269	68.12 388	83.56 327	93.51 318

ppachtmachnet_test		81.84 345	80.07 353	87.15 349	88.46 399	74.43 352	89.04 377	92.16 332	75.33 378	77.75 384	88.99 368	66.20 324	95.37 364	65.12 404	77.60 392	91.65 373

dmvs_re		84.20 324	83.22 325	87.14 350	91.83 315	77.81 301	90.04 357	90.19 383	84.70 222	81.49 339	89.17 364	64.37 338	91.13 418	71.58 361	85.65 307	92.46 356

tpm cat1		81.96 343	80.27 349	87.01 351	91.09 343	71.02 394	87.38 402	91.53 353	66.25 426	80.17 357	86.35 406	68.22 303	96.15 329	69.16 379	82.29 343	93.86 297

test_fmvs1_n		87.03 257	87.04 217	86.97 352	89.74 385	71.86 381	94.55 175	94.43 260	78.47 346	91.95 119	95.50 138	51.16 414	93.81 389	93.02 66	94.56 165	95.26 230

OpenMVS_ROB	74.94 19	79.51 376	77.03 383	86.93 353	87.00 412	76.23 331	92.33 296	90.74 374	68.93 420	74.52 408	88.23 382	49.58 417	96.62 296	57.64 427	84.29 316	87.94 423

SixPastTwentyTwo		83.91 329	82.90 331	86.92 354	90.99 347	70.67 398	93.48 245	91.99 338	85.54 193	77.62 386	92.11 275	60.59 369	96.87 285	76.05 328	77.75 391	93.20 331

ADS-MVSNet		81.56 351	79.78 355	86.90 355	91.35 332	71.82 382	83.33 427	89.16 403	72.90 403	82.24 331	85.77 410	64.98 332	93.76 390	64.57 407	83.74 323	95.12 234

PatchT		82.68 339	81.27 341	86.89 356	90.09 378	70.94 396	84.06 424	90.15 384	74.91 383	85.63 246	83.57 419	69.37 284	94.87 374	65.19 402	88.50 274	94.84 249

tpm		84.73 315	84.02 312	86.87 357	90.33 373	68.90 407	89.06 376	89.94 390	80.85 313	85.75 242	89.86 353	68.54 300	95.97 336	77.76 308	84.05 320	95.75 213

Patchmatch-RL test		81.67 348	79.96 354	86.81 358	85.42 422	71.23 390	82.17 432	87.50 412	78.47 346	77.19 388	82.50 426	70.81 260	93.48 394	82.66 233	72.89 407	95.71 217

test_vis1_n		86.56 274	86.49 241	86.78 359	88.51 396	72.69 371	94.68 168	93.78 291	79.55 328	90.70 143	95.31 145	48.75 420	93.28 397	93.15 62	93.99 176	94.38 273

testing3-2		86.72 268	86.71 227	86.74 360	96.11 109	65.92 419	93.39 250	89.65 398	89.46 69	87.84 194	92.79 252	59.17 381	97.60 217	81.31 261	90.72 235	96.70 169

test_fmvs1		87.34 240	87.56 203	86.68 361	90.59 366	71.80 383	94.01 220	94.04 280	78.30 350	91.97 117	95.22 149	56.28 394	93.71 391	92.89 67	94.71 159	94.52 263

MDA-MVSNet-bldmvs		78.85 381	76.31 386	86.46 362	89.76 384	73.88 356	88.79 379	90.42 378	79.16 333	59.18 436	88.33 380	60.20 371	94.04 384	62.00 415	68.96 419	91.48 380

mvs5depth		80.98 360	79.15 367	86.45 363	84.57 425	73.29 364	87.79 394	91.67 347	80.52 316	82.20 333	89.72 356	55.14 401	95.93 338	73.93 349	66.83 424	90.12 402

tpmrst		85.35 301	84.99 290	86.43 364	90.88 356	67.88 412	88.71 380	91.43 356	80.13 320	86.08 235	88.80 373	73.05 234	96.02 333	82.48 234	83.40 331	95.40 225

TESTMET0.1,1		83.74 332	82.85 332	86.42 365	89.96 381	71.21 391	89.55 365	87.88 408	77.41 358	83.37 317	87.31 393	56.71 392	93.65 393	80.62 275	92.85 205	94.40 272

our_test_3		81.93 344	80.46 347	86.33 366	88.46 399	73.48 362	88.46 385	91.11 361	76.46 365	76.69 392	88.25 381	66.89 312	94.36 379	68.75 381	79.08 387	91.14 388

lessismore_v0				86.04 367	88.46 399	68.78 408		80.59 435		73.01 415	90.11 345	55.39 397	96.43 315	75.06 337	65.06 427	92.90 342

TinyColmap		79.76 373	77.69 376	85.97 368	91.71 319	73.12 365	89.55 365	90.36 380	75.03 381	72.03 418	90.19 341	46.22 429	96.19 328	63.11 411	81.03 362	88.59 419

KD-MVS_2432*1600		78.50 382	76.02 389	85.93 369	86.22 415	74.47 350	84.80 420	92.33 325	79.29 330	76.98 389	85.92 408	53.81 408	93.97 386	67.39 390	57.42 437	89.36 407

miper_refine_blended		78.50 382	76.02 389	85.93 369	86.22 415	74.47 350	84.80 420	92.33 325	79.29 330	76.98 389	85.92 408	53.81 408	93.97 386	67.39 390	57.42 437	89.36 407

K. test v3		81.59 350	80.15 352	85.91 371	89.89 383	69.42 406	92.57 287	87.71 410	85.56 192	73.44 413	89.71 357	55.58 395	95.52 357	77.17 315	69.76 415	92.78 347

SSC-MVS3.2		84.60 319	84.19 306	85.85 372	92.74 287	68.07 409	88.15 389	93.81 289	87.42 145	83.76 306	91.07 315	62.91 347	95.73 351	74.56 344	83.24 332	93.75 307

mvsany_test1		85.42 299	85.30 284	85.77 373	87.95 408	75.41 341	87.61 401	80.97 434	76.82 364	88.68 178	95.83 124	77.44 169	90.82 420	85.90 186	86.51 301	91.08 392

MIMVSNet1		79.38 377	77.28 379	85.69 374	86.35 414	73.67 359	91.61 318	92.75 316	78.11 355	72.64 416	88.12 383	48.16 421	91.97 412	60.32 419	77.49 393	91.43 382

UWE-MVS		83.69 333	83.09 326	85.48 375	93.06 274	65.27 424	90.92 334	86.14 416	79.90 323	86.26 231	90.72 329	57.17 391	95.81 346	71.03 368	92.62 211	95.35 228

UnsupCasMVSNet_eth		80.07 369	78.27 375	85.46 376	85.24 423	72.63 375	88.45 386	94.87 243	82.99 262	71.64 420	88.07 384	56.34 393	91.75 413	73.48 352	63.36 430	92.01 368

CL-MVSNet_self_test		81.74 347	80.53 345	85.36 377	85.96 417	72.45 378	90.25 347	93.07 306	81.24 308	79.85 367	87.29 394	70.93 258	92.52 405	66.95 393	69.23 417	91.11 390

MDA-MVSNet_test_wron		79.21 379	77.19 381	85.29 378	88.22 403	72.77 370	85.87 411	90.06 387	74.34 388	62.62 433	87.56 391	66.14 325	91.99 411	66.90 397	73.01 405	91.10 391

YYNet1		79.22 378	77.20 380	85.28 379	88.20 404	72.66 373	85.87 411	90.05 389	74.33 389	62.70 431	87.61 390	66.09 326	92.03 409	66.94 394	72.97 406	91.15 387

WB-MVSnew		83.77 331	83.28 322	85.26 380	91.48 325	71.03 393	91.89 308	87.98 407	78.91 335	84.78 275	90.22 339	69.11 293	94.02 385	64.70 406	90.44 238	90.71 394

dp		81.47 354	80.23 350	85.17 381	89.92 382	65.49 422	86.74 406	90.10 386	76.30 369	81.10 345	87.12 398	62.81 348	95.92 339	68.13 387	79.88 379	94.09 284

UnsupCasMVSNet_bld		76.23 391	73.27 395	85.09 382	83.79 427	72.92 367	85.65 414	93.47 297	71.52 411	68.84 426	79.08 431	49.77 416	93.21 398	66.81 398	60.52 434	89.13 415

SD_0403		84.71 317	84.65 299	84.92 383	92.95 281	65.95 418	92.07 307	93.23 301	83.82 239	79.03 373	93.73 222	73.90 219	92.91 403	63.02 413	90.05 245	95.89 206

Anonymous20231206		81.03 359	79.77 357	84.82 384	87.85 409	70.26 401	91.42 321	92.08 334	73.67 395	77.75 384	89.25 363	62.43 350	93.08 400	61.50 417	82.00 348	91.12 389

test0.0.03 1		82.41 341	81.69 337	84.59 385	88.23 402	72.89 368	90.24 349	87.83 409	83.41 250	79.86 366	89.78 355	67.25 307	88.99 430	65.18 403	83.42 330	91.90 370

CMPMVS	59.16 21	80.52 363	79.20 365	84.48 386	83.98 426	67.63 415	89.95 360	93.84 288	64.79 430	66.81 428	91.14 312	57.93 387	95.17 367	76.25 325	88.10 280	90.65 395
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 318	84.79 297	84.37 387	91.84 313	64.92 425	93.70 239	91.47 355	66.19 427	86.16 234	95.28 146	67.18 309	93.33 396	80.89 270	90.42 240	94.88 248

PVSNet_0	73.20 20	77.22 387	74.83 393	84.37 387	90.70 364	71.10 392	83.09 429	89.67 396	72.81 405	73.93 411	83.13 421	60.79 368	93.70 392	68.54 382	50.84 442	88.30 421

LF4IMVS		80.37 366	79.07 369	84.27 389	86.64 413	69.87 405	89.39 370	91.05 364	76.38 367	74.97 405	90.00 349	47.85 422	94.25 383	74.55 345	80.82 368	88.69 418

Anonymous20240521		80.44 365	79.21 364	84.11 390	85.75 420	67.89 411	92.86 280	93.23 301	75.61 376	75.59 402	87.47 392	50.03 415	94.33 380	71.14 366	81.21 356	90.12 402

PM-MVS		78.11 384	76.12 388	84.09 391	83.54 428	70.08 402	88.97 378	85.27 423	79.93 322	74.73 407	86.43 403	34.70 440	93.48 394	79.43 292	72.06 409	88.72 417

test_fmvs2		83.98 326	84.03 311	83.83 392	87.16 411	67.53 416	93.93 226	92.89 310	77.62 356	86.89 215	93.53 225	47.18 424	92.02 410	90.54 126	86.51 301	91.93 369

testgi		80.94 362	80.20 351	83.18 393	87.96 407	66.29 417	91.28 325	90.70 376	83.70 241	78.12 380	92.84 247	51.37 413	90.82 420	63.34 410	82.46 341	92.43 357

KD-MVS_self_test		80.20 367	79.24 363	83.07 394	85.64 421	65.29 423	91.01 333	93.93 282	78.71 344	76.32 394	86.40 405	59.20 380	92.93 402	72.59 356	69.35 416	91.00 393

testing3		80.46 364	79.59 360	83.06 395	93.44 261	64.64 426	93.33 252	85.47 421	84.34 228	79.93 365	90.84 322	44.35 432	92.39 406	57.06 429	87.56 290	92.16 366

ambc				83.06 395	79.99 437	63.51 430	77.47 440	92.86 311		74.34 410	84.45 416	28.74 441	95.06 371	73.06 354	68.89 420	90.61 396

test20.03		79.95 371	79.08 368	82.55 397	85.79 419	67.74 414	91.09 331	91.08 362	81.23 309	74.48 409	89.96 351	61.63 355	90.15 422	60.08 420	76.38 399	89.76 404

MVStest1		72.91 395	69.70 400	82.54 398	78.14 440	73.05 366	88.21 388	86.21 415	60.69 434	64.70 429	90.53 332	46.44 427	85.70 437	58.78 425	53.62 439	88.87 416

test_vis1_rt		77.96 385	76.46 385	82.48 399	85.89 418	71.74 385	90.25 347	78.89 438	71.03 415	71.30 421	81.35 428	42.49 434	91.05 419	84.55 206	82.37 342	84.65 426

EU-MVSNet		81.32 356	80.95 343	82.42 400	88.50 398	63.67 429	93.32 253	91.33 357	64.02 431	80.57 354	92.83 248	61.21 364	92.27 408	76.34 324	80.38 375	91.32 383

myMVS_eth3d		79.67 374	78.79 371	82.32 401	91.92 309	64.08 427	89.75 363	87.40 413	81.72 294	78.82 375	87.20 395	45.33 430	91.29 416	59.09 424	87.84 287	91.60 375

ttmdpeth		76.55 389	74.64 394	82.29 402	82.25 433	67.81 413	89.76 362	85.69 419	70.35 417	75.76 400	91.69 291	46.88 425	89.77 424	66.16 399	63.23 431	89.30 409

pmmvs3		71.81 398	68.71 401	81.11 403	75.86 442	70.42 400	86.74 406	83.66 427	58.95 437	68.64 427	80.89 429	36.93 438	89.52 426	63.10 412	63.59 429	83.39 427

Syy-MVS		80.07 369	79.78 355	80.94 404	91.92 309	59.93 436	89.75 363	87.40 413	81.72 294	78.82 375	87.20 395	66.29 323	91.29 416	47.06 437	87.84 287	91.60 375

UWE-MVS-28		78.98 380	78.38 374	80.80 405	88.18 405	60.66 435	90.65 339	78.51 439	78.84 339	77.93 383	90.93 319	59.08 382	89.02 429	50.96 434	90.33 242	92.72 348

new-patchmatchnet		76.41 390	75.17 392	80.13 406	82.65 432	59.61 437	87.66 399	91.08 362	78.23 353	69.85 424	83.22 420	54.76 402	91.63 415	64.14 409	64.89 428	89.16 413

mvsany_test3		74.95 392	73.26 396	80.02 407	74.61 443	63.16 431	85.53 415	78.42 440	74.16 390	74.89 406	86.46 402	36.02 439	89.09 428	82.39 237	66.91 423	87.82 424

test_fmvs3		77.67 386	77.16 382	79.22 408	79.52 438	61.14 433	92.34 295	91.64 349	73.98 392	78.86 374	86.59 401	27.38 444	87.03 432	88.12 154	75.97 401	89.50 406

DSMNet-mixed		76.94 388	76.29 387	78.89 409	83.10 430	56.11 445	87.78 395	79.77 436	60.65 435	75.64 401	88.71 374	61.56 358	88.34 431	60.07 421	89.29 263	92.21 365

EGC-MVSNET		61.97 406	56.37 411	78.77 410	89.63 387	73.50 361	89.12 375	82.79 429	0.21 456	1.24 457	84.80 414	39.48 435	90.04 423	44.13 439	75.94 402	72.79 438

new_pmnet		72.15 396	70.13 399	78.20 411	82.95 431	65.68 420	83.91 425	82.40 431	62.94 433	64.47 430	79.82 430	42.85 433	86.26 436	57.41 428	74.44 404	82.65 431

MVS-HIRNet		73.70 394	72.20 397	78.18 412	91.81 316	56.42 444	82.94 430	82.58 430	55.24 438	68.88 425	66.48 441	55.32 399	95.13 368	58.12 426	88.42 276	83.01 429

LCM-MVSNet		66.00 403	62.16 408	77.51 413	64.51 453	58.29 439	83.87 426	90.90 370	48.17 442	54.69 439	73.31 437	16.83 453	86.75 433	65.47 401	61.67 433	87.48 425

APD_test1		69.04 399	66.26 405	77.36 414	80.51 436	62.79 432	85.46 416	83.51 428	54.11 440	59.14 437	84.79 415	23.40 447	89.61 425	55.22 430	70.24 414	79.68 435

test_f		71.95 397	70.87 398	75.21 415	74.21 445	59.37 438	85.07 419	85.82 418	65.25 429	70.42 423	83.13 421	23.62 445	82.93 443	78.32 302	71.94 410	83.33 428

ANet_high		58.88 410	54.22 415	72.86 416	56.50 456	56.67 441	80.75 435	86.00 417	73.09 402	37.39 448	64.63 444	22.17 448	79.49 446	43.51 440	23.96 450	82.43 432

test_vis3_rt		65.12 404	62.60 406	72.69 417	71.44 446	60.71 434	87.17 403	65.55 450	63.80 432	53.22 440	65.65 443	14.54 454	89.44 427	76.65 319	65.38 426	67.91 441

FPMVS		64.63 405	62.55 407	70.88 418	70.80 447	56.71 440	84.42 423	84.42 425	51.78 441	49.57 441	81.61 427	23.49 446	81.48 444	40.61 444	76.25 400	74.46 437

dmvs_testset		74.57 393	75.81 391	70.86 419	87.72 410	40.47 454	87.05 405	77.90 444	82.75 267	71.15 422	85.47 412	67.98 304	84.12 441	45.26 438	76.98 398	88.00 422

N_pmnet		68.89 400	68.44 402	70.23 420	89.07 392	28.79 459	88.06 390	19.50 459	69.47 419	71.86 419	84.93 413	61.24 363	91.75 413	54.70 431	77.15 395	90.15 401

testf1		59.54 408	56.11 412	69.85 421	69.28 448	56.61 442	80.37 436	76.55 447	42.58 445	45.68 444	75.61 432	11.26 455	84.18 439	43.20 441	60.44 435	68.75 439

APD_test2		59.54 408	56.11 412	69.85 421	69.28 448	56.61 442	80.37 436	76.55 447	42.58 445	45.68 444	75.61 432	11.26 455	84.18 439	43.20 441	60.44 435	68.75 439

WB-MVS		67.92 401	67.49 403	69.21 423	81.09 434	41.17 453	88.03 391	78.00 443	73.50 397	62.63 432	83.11 423	63.94 340	86.52 434	25.66 449	51.45 441	79.94 434

PMMVS2		59.60 407	56.40 410	69.21 423	68.83 450	46.58 449	73.02 444	77.48 445	55.07 439	49.21 442	72.95 438	17.43 452	80.04 445	49.32 436	44.33 445	80.99 433

SSC-MVS		67.06 402	66.56 404	68.56 425	80.54 435	40.06 455	87.77 396	77.37 446	72.38 407	61.75 434	82.66 425	63.37 343	86.45 435	24.48 450	48.69 444	79.16 436

Gipuma		57.99 412	54.91 414	67.24 426	88.51 396	65.59 421	52.21 447	90.33 381	43.58 444	42.84 447	51.18 448	20.29 450	85.07 438	34.77 445	70.45 413	51.05 447
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 414	48.46 418	63.48 427	45.72 458	46.20 450	73.41 443	78.31 441	41.03 447	30.06 450	65.68 442	6.05 457	83.43 442	30.04 447	65.86 425	60.80 442
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 411	58.24 409	60.56 428	83.13 429	45.09 452	82.32 431	48.22 458	67.61 423	61.70 435	69.15 439	38.75 436	76.05 447	32.01 446	41.31 446	60.55 443

MVE	39.65 23	43.39 416	38.59 422	57.77 429	56.52 455	48.77 448	55.38 446	58.64 454	29.33 450	28.96 451	52.65 447	4.68 458	64.62 451	28.11 448	33.07 448	59.93 444
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 415	48.47 417	56.66 430	52.26 457	18.98 461	41.51 449	81.40 433	10.10 451	44.59 446	75.01 435	28.51 442	68.16 448	53.54 432	49.31 443	82.83 430

DeepMVS_CX				56.31 431	74.23 444	51.81 447		56.67 455	44.85 443	48.54 443	75.16 434	27.87 443	58.74 453	40.92 443	52.22 440	58.39 445

kuosan		53.51 413	53.30 416	54.13 432	76.06 441	45.36 451	80.11 438	48.36 457	59.63 436	54.84 438	63.43 445	37.41 437	62.07 452	20.73 452	39.10 447	54.96 446

E-PMN		43.23 417	42.29 419	46.03 433	65.58 452	37.41 456	73.51 442	64.62 451	33.99 448	28.47 452	47.87 449	19.90 451	67.91 449	22.23 451	24.45 449	32.77 448

EMVS		42.07 418	41.12 420	44.92 434	63.45 454	35.56 458	73.65 441	63.48 452	33.05 449	26.88 453	45.45 450	21.27 449	67.14 450	19.80 453	23.02 451	32.06 449

tmp_tt		35.64 419	39.24 421	24.84 435	14.87 459	23.90 460	62.71 445	51.51 456	6.58 453	36.66 449	62.08 446	44.37 431	30.34 455	52.40 433	22.00 452	20.27 450

wuyk23d		21.27 421	20.48 424	23.63 436	68.59 451	36.41 457	49.57 448	6.85 460	9.37 452	7.89 454	4.46 456	4.03 459	31.37 454	17.47 454	16.07 453	3.12 451

test123		8.76 423	11.22 426	1.39 437	0.85 461	0.97 462	85.76 413	0.35 462	0.54 455	2.45 456	8.14 455	0.60 460	0.48 456	2.16 456	0.17 455	2.71 452

testmvs		8.92 422	11.52 425	1.12 438	1.06 460	0.46 463	86.02 410	0.65 461	0.62 454	2.74 455	9.52 454	0.31 461	0.45 457	2.38 455	0.39 454	2.46 453

mmdepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

monomultidepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

test_blank		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet_test		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

DCPMVS		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

cdsmvs_eth3d_5k		22.14 420	29.52 423	0.00 439	0.00 462	0.00 464	0.00 450	95.76 177	0.00 457	0.00 458	94.29 193	75.66 193	0.00 458	0.00 457	0.00 456	0.00 454

pcd_1.5k_mvsjas		6.64 425	8.86 428	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	79.70 138	0.00 458	0.00 457	0.00 456	0.00 454

sosnet-low-res		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

sosnet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uncertanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

Regformer		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

ab-mvs-re		7.82 424	10.43 427	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	93.88 214	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

WAC-MVS						64.08 427								59.14 423

FOURS1					98.86 1	85.54 69	98.29 1	97.49 8	89.79 60	96.29 26

PC_three_1452									82.47 271	97.09 15	97.07 65	92.72 1	98.04 184	92.70 73	99.02 12	98.86 12

test_one_0601					98.58 11	85.83 63		97.44 17	91.05 20	96.78 22	98.06 20	91.45 11

eth-test2					0.00 462

eth-test					0.00 462

ZD-MVS					98.15 36	86.62 33		97.07 54	83.63 243	94.19 57	96.91 71	87.57 31	99.26 46	91.99 97	98.44 53

RE-MVS-def			93.68 66		97.92 45	84.57 89	96.28 46	96.76 86	87.46 142	93.75 68	97.43 44	82.94 95		92.73 69	97.80 85	97.88 93

IU-MVS					98.77 5	86.00 52		96.84 76	81.26 307	97.26 11				95.50 33	99.13 3	99.03 8

test_241102_TWO								97.44 17	90.31 38	97.62 6	98.07 18	91.46 10	99.58 10	95.66 27	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 54		97.44 17	90.26 44	97.71 1	97.96 28	92.31 4	99.38 31

9.14			94.47 29		97.79 54		96.08 64	97.44 17	86.13 180	95.10 47	97.40 46	88.34 22	99.22 48	93.25 61	98.70 34

save fliter					97.85 51	85.63 68	95.21 132	96.82 79	89.44 70

test_0728_THIRD									90.75 26	97.04 17	98.05 22	92.09 6	99.55 16	95.64 29	99.13 3	99.13 2

test0726					98.78 3	85.93 57	97.19 12	97.47 13	90.27 42	97.64 4	98.13 6	91.47 8

GSMVS																96.12 194

test_part2					98.55 12	87.22 19				96.40 25

sam_mvs1												71.70 249				96.12 194

sam_mvs												70.60 263

MTGPA								96.97 59

test_post1							88.00 392				9.81 453	69.31 287	95.53 356	76.65 319

test_post											10.29 452	70.57 267	95.91 341

patchmatchnet-post											83.76 418	71.53 250	96.48 310

MTMP							96.16 55	60.64 453

gm-plane-assit					89.60 388	68.00 410			77.28 361		88.99 368		97.57 219	79.44 291

test9_res														91.91 101	98.71 32	98.07 77

TEST9					97.53 63	86.49 37	94.07 214	96.78 83	81.61 299	92.77 93	96.20 101	87.71 28	99.12 57

test_8					97.49 65	86.30 45	94.02 219	96.76 86	81.86 290	92.70 97	96.20 101	87.63 29	99.02 67

agg_prior2														90.54 126	98.68 37	98.27 59

agg_prior					97.38 68	85.92 59		96.72 93		92.16 112			98.97 81

test_prior4						85.96 56	94.11 208

test_prior2							94.12 206		87.67 140	92.63 101	96.39 96	86.62 41		91.50 110	98.67 40

旧先验2							93.36 251		71.25 413	94.37 53			97.13 267	86.74 173

新几何2							93.11 266

旧先验1					96.79 81	81.81 183		95.67 185			96.81 77	86.69 39			97.66 91	96.97 153

无先验							93.28 259	96.26 132	73.95 393				99.05 61	80.56 276		96.59 173

原ACMM2							92.94 276

test222					96.55 90	81.70 185	92.22 300	95.01 230	68.36 422	90.20 153	96.14 106	80.26 131			97.80 85	96.05 201

testdata2													98.75 109	78.30 303

segment_acmp												87.16 36

testdata1							92.15 302		87.94 127

plane_prior7					94.70 189	82.74 159

plane_prior6					94.52 202	82.75 157						74.23 211

plane_prior5								96.22 137					98.12 169	88.15 151	89.99 246	94.63 255

plane_prior4											94.86 166

plane_prior3						82.75 157			90.26 44	86.91 212

plane_prior2							95.85 86		90.81 24

plane_prior1					94.59 195

plane_prior						82.73 160	95.21 132		89.66 65						89.88 251

n2								0.00 463

nn								0.00 463

door-mid								85.49 420

test11								96.57 104

door								85.33 422

HQP5-MVS						81.56 187

HQP-NCC					94.17 223		94.39 189		88.81 95	85.43 258

ACMP_Plane					94.17 223		94.39 189		88.81 95	85.43 258

BP-MVS														87.11 170

HQP4-MVS										85.43 258			97.96 191			94.51 265

HQP3-MVS								96.04 154							89.77 255

HQP2-MVS												73.83 222

NP-MVS					94.37 213	82.42 169					93.98 207

MDTV_nov1_ep13_2view						55.91 446	87.62 400		73.32 399	84.59 280		70.33 270		74.65 342		95.50 222

MDTV_nov1_ep13			83.56 319		91.69 321	69.93 403	87.75 397	91.54 352	78.60 345	84.86 274	88.90 370	69.54 282	96.03 332	70.25 371	88.93 268

ACMMP++_ref															87.47 291

ACMMP++															88.01 283

Test By Simon												80.02 133

High-resolution multi-view benchmark