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 72					99.61 4	96.03 23	99.06 9	99.07 5

No_MVS				96.52 1	97.78 54	90.86 1		96.85 72					99.61 4	96.03 23	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 61	92.59 2	98.94 84	92.25 83	98.99 14	98.84 14

HPM-MVS++		95.14 10	94.91 19	95.83 4	98.25 29	89.65 4	95.92 78	96.96 61	91.75 11	94.02 62	96.83 73	88.12 24	99.55 16	93.41 57	98.94 16	98.28 55

MM		95.10 11	94.91 19	95.68 5	96.09 107	88.34 9	96.68 33	94.37 253	95.08 1	94.68 48	97.72 34	82.94 93	99.64 1	97.85 3	98.76 29	99.06 7

SMA-MVS		95.20 8	95.07 13	95.59 6	98.14 35	88.48 8	96.26 46	97.28 34	85.90 176	97.67 3	98.10 11	88.41 20	99.56 12	94.66 41	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 93	91.37 103	95.55 7	95.63 132	88.73 6	97.07 18	96.77 83	90.84 19	84.02 289	96.62 86	75.95 180	99.34 37	87.77 151	97.68 87	98.59 24

CNVR-MVS		95.40 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 117	96.96 61	92.09 8	95.32 40	97.08 61	89.49 15	99.33 40	95.10 37	98.85 20	98.66 21

MVS_0304		94.18 42	93.80 56	95.34 9	94.91 170	87.62 14	95.97 73	93.01 295	92.58 5	94.22 53	97.20 55	80.56 125	99.59 8	97.04 16	98.68 37	98.81 17

ACMMP_NAP		94.74 20	94.56 25	95.28 10	98.02 41	87.70 11	95.68 95	97.34 25	88.28 110	95.30 41	97.67 36	85.90 50	99.54 20	93.91 49	98.95 15	98.60 23

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 25	87.28 18	95.56 107	97.51 6	89.13 79	97.14 12	97.91 27	91.64 7	99.62 2	94.61 42	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 21	95.20 12	97.84 50	87.76 10	96.65 34	97.48 11	87.76 132	95.71 35	97.70 35	88.28 23	99.35 36	93.89 50	98.78 26	98.48 30

MCST-MVS		94.45 27	94.20 43	95.19 13	98.46 19	87.50 16	95.00 138	97.12 49	87.13 145	92.51 101	96.30 95	89.24 17	99.34 37	93.46 54	98.62 46	98.73 18

NCCC		94.81 17	94.69 24	95.17 14	97.83 51	87.46 17	95.66 98	96.93 65	92.34 6	93.94 63	96.58 88	87.74 27	99.44 29	92.83 66	98.40 54	98.62 22

DPM-MVS		92.58 89	91.74 99	95.08 15	96.19 99	89.31 5	92.66 273	96.56 103	83.44 236	91.68 124	95.04 153	86.60 42	98.99 74	85.60 181	97.92 77	96.93 147

ZNCC-MVS		94.47 26	94.28 37	95.03 16	98.52 15	86.96 20	96.85 28	97.32 29	88.24 111	93.15 78	97.04 64	86.17 47	99.62 2	92.40 77	98.81 23	98.52 26

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 16	97.49 7					99.61 4	95.62 30	99.08 7	98.99 9

MTAPA		94.42 31	94.22 40	95.00 18	98.42 21	86.95 21	94.36 188	96.97 58	91.07 15	93.14 79	97.56 38	84.30 74	99.56 12	93.43 55	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 12	91.73 12	96.10 28	96.69 78	89.90 12	99.30 43	94.70 40	98.04 72	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 29	94.27 39	94.92 20	98.65 8	86.67 30	96.92 24	97.23 37	88.60 101	93.58 70	97.27 49	85.22 58	99.54 20	92.21 84	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 21	89.03 84	96.20 27	98.10 11	89.39 16	99.34 37	95.88 25	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 29	94.28 37	94.91 21	98.63 9	86.69 28	96.94 20	97.32 29	88.63 98	93.53 73	97.26 51	85.04 62	99.54 20	92.35 80	98.78 26	98.50 27

GST-MVS		94.21 37	93.97 52	94.90 23	98.41 22	86.82 24	96.54 36	97.19 38	88.24 111	93.26 75	96.83 73	85.48 55	99.59 8	91.43 109	98.40 54	98.30 50

HFP-MVS		94.52 24	94.40 30	94.86 24	98.61 10	86.81 25	96.94 20	97.34 25	88.63 98	93.65 68	97.21 53	86.10 48	99.49 26	92.35 80	98.77 28	98.30 50

sasdasda		93.27 73	92.75 83	94.85 25	95.70 127	87.66 12	96.33 39	96.41 113	90.00 43	94.09 58	94.60 173	82.33 102	98.62 119	92.40 77	92.86 194	98.27 57

MP-MVS-pluss		94.21 37	94.00 51	94.85 25	98.17 33	86.65 31	94.82 151	97.17 43	86.26 168	92.83 88	97.87 29	85.57 54	99.56 12	94.37 45	98.92 17	98.34 43
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 73	92.75 83	94.85 25	95.70 127	87.66 12	96.33 39	96.41 113	90.00 43	94.09 58	94.60 173	82.33 102	98.62 119	92.40 77	92.86 194	98.27 57

XVS		94.45 27	94.32 33	94.85 25	98.54 13	86.60 34	96.93 22	97.19 38	90.66 27	92.85 86	97.16 59	85.02 63	99.49 26	91.99 95	98.56 50	98.47 33

X-MVStestdata		88.31 193	86.13 241	94.85 25	98.54 13	86.60 34	96.93 22	97.19 38	90.66 27	92.85 86	23.41 438	85.02 63	99.49 26	91.99 95	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 31	91.38 14	95.39 39	97.46 41	88.98 19	99.40 30	94.12 46	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 7	90.75 22	97.62 5	98.06 18	92.59 2	99.61 4	95.64 28	99.02 12	98.86 11

alignmvs		93.08 81	92.50 89	94.81 32	95.62 133	87.61 15	95.99 71	96.07 145	89.77 56	94.12 57	94.87 159	80.56 125	98.66 112	92.42 76	93.10 190	98.15 69

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 52	97.13 14	97.44 16	90.31 33	97.71 1	98.07 16	92.31 4	99.58 10	95.66 26	99.13 3	98.84 14

DeepC-MVS_fast	89.43 2	94.04 45	93.79 57	94.80 33	97.48 64	86.78 26	95.65 100	96.89 69	89.40 67	92.81 89	96.97 66	85.37 57	99.24 46	90.87 118	98.69 35	98.38 42
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 34	94.07 48	94.77 35	98.47 18	86.31 44	96.71 31	96.98 57	89.04 82	91.98 111	97.19 56	85.43 56	99.56 12	92.06 93	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 35	94.07 48	94.75 36	98.06 39	86.90 23	95.88 80	96.94 64	85.68 182	95.05 46	97.18 57	87.31 35	99.07 57	91.90 101	98.61 48	98.28 55
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 32	94.21 42	94.74 37	98.39 23	86.64 32	97.60 4	97.24 35	88.53 103	92.73 94	97.23 52	85.20 59	99.32 41	92.15 87	98.83 22	98.25 62

PGM-MVS		93.96 50	93.72 62	94.68 38	98.43 20	86.22 47	95.30 115	97.78 1	87.45 139	93.26 75	97.33 47	84.62 71	99.51 24	90.75 120	98.57 49	98.32 49

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 55	97.09 16	96.73 89	90.27 37	97.04 16	98.05 20	91.47 8	99.55 16	95.62 30	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 48	93.78 58	94.63 40	98.50 16	85.90 60	96.87 26	96.91 67	88.70 96	91.83 120	97.17 58	83.96 78	99.55 16	91.44 108	98.64 45	98.43 38

PHI-MVS		93.89 52	93.65 66	94.62 41	96.84 78	86.43 39	96.69 32	97.49 7	85.15 195	93.56 72	96.28 96	85.60 53	99.31 42	92.45 74	98.79 24	98.12 73

TSAR-MVS + MP.		94.85 14	94.94 17	94.58 42	98.25 29	86.33 42	96.11 59	96.62 98	88.14 116	96.10 28	96.96 67	89.09 18	98.94 84	94.48 43	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 61	93.20 75	94.55 43	95.65 130	85.73 65	94.94 141	96.69 94	91.89 10	90.69 138	95.88 116	81.99 114	99.54 20	93.14 61	97.95 76	98.39 40

train_agg		93.44 66	93.08 76	94.52 44	97.53 61	86.49 37	94.07 205	96.78 81	81.86 277	92.77 91	96.20 99	87.63 29	99.12 55	92.14 88	98.69 35	97.94 83

CDPH-MVS		92.83 85	92.30 91	94.44 45	97.79 52	86.11 49	94.06 207	96.66 95	80.09 308	92.77 91	96.63 85	86.62 40	99.04 61	87.40 156	98.66 41	98.17 67

3Dnovator	86.66 5	91.73 103	90.82 116	94.44 45	94.59 189	86.37 41	97.18 12	97.02 55	89.20 76	84.31 284	96.66 81	73.74 217	99.17 50	86.74 166	97.96 75	97.79 95

SR-MVS		94.23 36	94.17 46	94.43 47	98.21 32	85.78 63	96.40 38	96.90 68	88.20 114	94.33 52	97.40 44	84.75 70	99.03 62	93.35 58	97.99 74	98.48 30

HPM-MVS		94.02 46	93.88 53	94.43 47	98.39 23	85.78 63	97.25 10	97.07 53	86.90 153	92.62 98	96.80 77	84.85 69	99.17 50	92.43 75	98.65 44	98.33 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 59	93.41 70	94.41 49	96.59 85	86.78 26	94.40 180	93.93 270	89.77 56	94.21 54	95.59 130	87.35 34	98.61 121	92.72 69	96.15 124	97.83 93

reproduce-ours		94.82 15	94.97 15	94.38 50	97.91 47	85.46 68	95.86 81	97.15 45	89.82 49	95.23 43	98.10 11	87.09 37	99.37 33	95.30 34	98.25 60	98.30 50

our_new_method		94.82 15	94.97 15	94.38 50	97.91 47	85.46 68	95.86 81	97.15 45	89.82 49	95.23 43	98.10 11	87.09 37	99.37 33	95.30 34	98.25 60	98.30 50

test12				94.34 52	97.13 73	86.15 48		96.29 121		91.04 134		85.08 61	99.01 67		98.13 67	97.86 90

ACMMP		93.24 75	92.88 81	94.30 53	98.09 38	85.33 72	96.86 27	97.45 15	88.33 107	90.15 148	97.03 65	81.44 119	99.51 24	90.85 119	95.74 129	98.04 78
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 19	94.92 18	94.29 54	97.92 43	85.18 74	95.95 76	97.19 38	89.67 59	95.27 42	98.16 4	86.53 43	99.36 35	95.42 33	98.15 65	98.33 45

DeepC-MVS	88.79 3	93.31 72	92.99 79	94.26 55	96.07 109	85.83 61	94.89 144	96.99 56	89.02 85	89.56 153	97.37 46	82.51 99	99.38 31	92.20 85	98.30 57	97.57 109
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 82	92.63 86	94.23 56	95.62 133	85.92 57	96.08 61	96.33 119	89.86 47	93.89 65	94.66 170	82.11 109	98.50 127	92.33 82	92.82 197	98.27 57

fmvsm_l_conf0.5_n_3		94.80 18	95.01 14	94.15 57	95.64 131	85.08 75	96.09 60	97.36 23	90.98 17	97.09 14	98.12 8	84.98 67	98.94 84	97.07 13	97.80 82	98.43 38

EPNet		91.79 100	91.02 111	94.10 58	90.10 364	85.25 73	96.03 68	92.05 322	92.83 4	87.39 196	95.78 122	79.39 141	99.01 67	88.13 147	97.48 90	98.05 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		94.60 22	94.81 22	93.98 59	94.62 187	84.96 78	96.15 54	97.35 24	89.37 68	96.03 31	98.11 9	86.36 44	99.01 67	97.45 8	97.83 80	97.96 82

DELS-MVS		93.43 70	93.25 73	93.97 60	95.42 141	85.04 76	93.06 260	97.13 48	90.74 24	91.84 118	95.09 152	86.32 45	99.21 48	91.22 110	98.45 52	97.65 104
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 98	91.28 105	93.96 61	98.33 27	85.92 57	94.66 163	96.66 95	82.69 256	90.03 150	95.82 120	82.30 104	99.03 62	84.57 193	96.48 118	96.91 149

HPM-MVS_fast		93.40 71	93.22 74	93.94 62	98.36 25	84.83 80	97.15 13	96.80 80	85.77 179	92.47 102	97.13 60	82.38 100	99.07 57	90.51 123	98.40 54	97.92 86

test_fmvsmconf0.1_n		94.20 39	94.31 35	93.88 63	92.46 280	84.80 81	96.18 51	96.82 77	89.29 73	95.68 36	98.11 9	85.10 60	98.99 74	97.38 9	97.75 86	97.86 90

SD-MVS		94.96 13	95.33 8	93.88 63	97.25 72	86.69 28	96.19 49	97.11 51	90.42 30	96.95 18	97.27 49	89.53 14	96.91 273	94.38 44	98.85 20	98.03 79
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 65	93.31 71	93.84 65	96.99 75	84.84 79	93.24 252	97.24 35	88.76 93	91.60 125	95.85 118	86.07 49	98.66 112	91.91 99	98.16 64	98.03 79

SR-MVS-dyc-post		93.82 54	93.82 55	93.82 66	97.92 43	84.57 87	96.28 43	96.76 84	87.46 137	93.75 66	97.43 42	84.24 75	99.01 67	92.73 67	97.80 82	97.88 88

test_prior				93.82 66	97.29 70	84.49 91		96.88 70					98.87 91			98.11 74

APD-MVS_3200maxsize		93.78 55	93.77 59	93.80 68	97.92 43	84.19 102	96.30 41	96.87 71	86.96 149	93.92 64	97.47 40	83.88 79	98.96 81	92.71 70	97.87 78	98.26 61

fmvsm_l_conf0.5_n		94.29 33	94.46 28	93.79 69	95.28 146	85.43 70	95.68 95	96.43 111	86.56 160	96.84 20	97.81 32	87.56 32	98.77 104	97.14 11	96.82 108	97.16 131

CSCG		93.23 76	93.05 77	93.76 70	98.04 40	84.07 104	96.22 48	97.37 22	84.15 218	90.05 149	95.66 127	87.77 26	99.15 53	89.91 128	98.27 58	98.07 75

GDP-MVS		92.04 96	91.46 102	93.75 71	94.55 195	84.69 84	95.60 106	96.56 103	87.83 129	93.07 82	95.89 115	73.44 221	98.65 114	90.22 126	96.03 126	97.91 87

BP-MVS1		92.48 91	92.07 94	93.72 72	94.50 198	84.39 99	95.90 79	94.30 256	90.39 31	92.67 96	95.94 112	74.46 201	98.65 114	93.14 61	97.35 94	98.13 70

test_fmvsmconf0.01_n		93.19 77	93.02 78	93.71 73	89.25 377	84.42 98	96.06 65	96.29 121	89.06 80	94.68 48	98.13 5	79.22 143	98.98 78	97.22 10	97.24 96	97.74 98

UA-Net		92.83 85	92.54 88	93.68 74	96.10 106	84.71 83	95.66 98	96.39 115	91.92 9	93.22 77	96.49 91	83.16 88	98.87 91	84.47 195	95.47 136	97.45 115

fmvsm_l_conf0.5_n_a		94.20 39	94.40 30	93.60 75	95.29 145	84.98 77	95.61 103	96.28 124	86.31 166	96.75 22	97.86 30	87.40 33	98.74 107	97.07 13	97.02 101	97.07 134

QAPM		89.51 156	88.15 180	93.59 76	94.92 168	84.58 86	96.82 29	96.70 93	78.43 335	83.41 305	96.19 102	73.18 226	99.30 43	77.11 304	96.54 115	96.89 150

test_fmvsm_n_1920		94.71 21	95.11 12	93.50 77	95.79 122	84.62 85	96.15 54	97.64 2	89.85 48	97.19 11	97.89 28	86.28 46	98.71 110	97.11 12	98.08 71	97.17 127

casdiffmvs_mvg		92.96 84	92.83 82	93.35 78	94.59 189	83.40 125	95.00 138	96.34 118	90.30 35	92.05 109	96.05 107	83.43 82	98.15 161	92.07 90	95.67 130	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
fmvsm_s_conf0.5_n_5		93.96 50	94.18 45	93.30 79	94.79 177	83.81 111	95.77 89	96.74 88	88.02 119	96.23 26	97.84 31	83.36 86	98.83 98	97.49 6	97.34 95	97.25 122

EI-MVSNet-Vis-set		93.01 83	92.92 80	93.29 80	95.01 160	83.51 122	94.48 172	95.77 170	90.87 18	92.52 100	96.67 80	84.50 72	99.00 72	91.99 95	94.44 163	97.36 117

Vis-MVSNet		91.75 102	91.23 106	93.29 80	95.32 144	83.78 112	96.14 56	95.98 152	89.89 45	90.45 140	96.58 88	75.09 192	98.31 152	84.75 191	96.90 104	97.78 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 49	94.22 40	93.26 82	96.13 101	83.29 128	96.27 45	96.52 106	89.82 49	95.56 38	95.51 132	84.50 72	98.79 102	94.83 39	98.86 19	97.72 100

SPE-MVS-test		94.02 46	94.29 36	93.24 83	96.69 81	83.24 129	97.49 5	96.92 66	92.14 7	92.90 84	95.77 123	85.02 63	98.33 149	93.03 63	98.62 46	98.13 70

VNet		92.24 95	91.91 96	93.24 83	96.59 85	83.43 123	94.84 150	96.44 110	89.19 77	94.08 61	95.90 114	77.85 162	98.17 159	88.90 138	93.38 183	98.13 70

VDD-MVS		90.74 122	89.92 135	93.20 85	96.27 97	83.02 142	95.73 92	93.86 274	88.42 106	92.53 99	96.84 72	62.09 338	98.64 116	90.95 116	92.62 200	97.93 85

CS-MVS		94.12 43	94.44 29	93.17 86	96.55 88	83.08 139	97.63 3	96.95 63	91.71 13	93.50 74	96.21 98	85.61 52	98.24 154	93.64 52	98.17 63	98.19 65

nrg030		91.08 116	90.39 120	93.17 86	93.07 262	86.91 22	96.41 37	96.26 126	88.30 109	88.37 174	94.85 162	82.19 108	97.64 204	91.09 111	82.95 320	94.96 230

MVSMamba_PlusPlus		93.44 66	93.54 68	93.14 88	96.58 87	83.05 140	96.06 65	96.50 108	84.42 215	94.09 58	95.56 131	85.01 66	98.69 111	94.96 38	98.66 41	97.67 103

EI-MVSNet-UG-set		92.74 87	92.62 87	93.12 89	94.86 173	83.20 131	94.40 180	95.74 173	90.71 26	92.05 109	96.60 87	84.00 77	98.99 74	91.55 106	93.63 174	97.17 127

test_fmvsmvis_n_1920		93.44 66	93.55 67	93.10 90	93.67 244	84.26 101	95.83 85	96.14 136	89.00 86	92.43 103	97.50 39	83.37 85	98.72 108	96.61 20	97.44 91	96.32 172

新几何1				93.10 90	97.30 69	84.35 100		95.56 187	71.09 401	91.26 131	96.24 97	82.87 95	98.86 93	79.19 283	98.10 68	96.07 187

OMC-MVS		91.23 111	90.62 119	93.08 92	96.27 97	84.07 104	93.52 234	95.93 156	86.95 150	89.51 154	96.13 105	78.50 153	98.35 146	85.84 179	92.90 193	96.83 154

OpenMVS	83.78 11	88.74 182	87.29 199	93.08 92	92.70 275	85.39 71	96.57 35	96.43 111	78.74 330	80.85 337	96.07 106	69.64 270	99.01 67	78.01 295	96.65 113	94.83 238

MAR-MVS		90.30 134	89.37 146	93.07 94	96.61 84	84.48 92	95.68 95	95.67 179	82.36 261	87.85 183	92.85 234	76.63 173	98.80 100	80.01 271	96.68 112	95.91 193
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		90.92 117	90.21 124	93.03 95	93.86 233	83.88 109	92.81 270	93.86 274	79.84 311	91.76 121	94.29 183	77.92 159	98.04 177	90.48 124	97.11 97	97.17 127

Effi-MVS+		91.59 106	91.11 108	93.01 96	94.35 211	83.39 126	94.60 165	95.10 217	87.10 146	90.57 139	93.10 229	81.43 120	98.07 175	89.29 134	94.48 161	97.59 108

fmvsm_s_conf0.5_n_a		93.57 60	93.76 60	93.00 97	95.02 159	83.67 115	96.19 49	96.10 142	87.27 142	95.98 32	98.05 20	83.07 92	98.45 137	96.68 19	95.51 133	96.88 151

MVS_111021_LR		92.47 92	92.29 92	92.98 98	95.99 115	84.43 96	93.08 258	96.09 143	88.20 114	91.12 133	95.72 126	81.33 121	97.76 193	91.74 103	97.37 93	96.75 156

fmvsm_s_conf0.1_n_a		93.19 77	93.26 72	92.97 99	92.49 278	83.62 118	96.02 69	95.72 176	86.78 155	96.04 30	98.19 2	82.30 104	98.43 141	96.38 21	95.42 139	96.86 152

ETV-MVS		92.74 87	92.66 85	92.97 99	95.20 152	84.04 106	95.07 134	96.51 107	90.73 25	92.96 83	91.19 294	84.06 76	98.34 147	91.72 104	96.54 115	96.54 167

LFMVS		90.08 139	89.13 152	92.95 101	96.71 80	82.32 165	96.08 61	89.91 378	86.79 154	92.15 108	96.81 75	62.60 336	98.34 147	87.18 160	93.90 169	98.19 65

UGNet		89.95 144	88.95 156	92.95 101	94.51 197	83.31 127	95.70 94	95.23 210	89.37 68	87.58 190	93.94 198	64.00 326	98.78 103	83.92 202	96.31 120	96.74 157
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		90.80 120	90.10 128	92.90 103	93.04 265	83.53 121	93.08 258	94.15 263	80.22 305	91.41 128	94.91 156	76.87 167	97.93 187	90.28 125	96.90 104	97.24 123
jason: jason.
DP-MVS		87.25 233	85.36 270	92.90 103	97.65 58	83.24 129	94.81 152	92.00 324	74.99 369	81.92 326	95.00 154	72.66 231	99.05 59	66.92 384	92.33 205	96.40 169

fmvsm_s_conf0.5_n_8		94.56 23	95.12 11	92.87 105	95.96 118	81.32 188	95.76 91	97.57 4	93.48 2	97.53 7	98.32 1	81.78 118	99.13 54	97.91 1	97.81 81	98.16 68

fmvsm_s_conf0.5_n		93.76 56	94.06 50	92.86 106	95.62 133	83.17 132	96.14 56	96.12 140	88.13 117	95.82 34	98.04 23	83.43 82	98.48 129	96.97 17	96.23 121	96.92 148

fmvsm_s_conf0.1_n		93.46 64	93.66 65	92.85 107	93.75 240	83.13 134	96.02 69	95.74 173	87.68 134	95.89 33	98.17 3	82.78 96	98.46 133	96.71 18	96.17 123	96.98 143

CANet_DTU		90.26 136	89.41 145	92.81 108	93.46 251	83.01 143	93.48 235	94.47 249	89.43 66	87.76 188	94.23 188	70.54 259	99.03 62	84.97 186	96.39 119	96.38 170

MVSFormer		91.68 105	91.30 104	92.80 109	93.86 233	83.88 109	95.96 74	95.90 160	84.66 211	91.76 121	94.91 156	77.92 159	97.30 240	89.64 130	97.11 97	97.24 123

PVSNet_Blended_VisFu		91.38 108	90.91 113	92.80 109	96.39 94	83.17 132	94.87 146	96.66 95	83.29 241	89.27 160	94.46 178	80.29 128	99.17 50	87.57 154	95.37 140	96.05 190

fmvsm_s_conf0.5_n_6		94.11 44	94.56 25	92.76 111	94.98 163	81.96 172	95.79 87	97.29 33	89.31 71	97.52 8	97.61 37	83.25 87	98.88 90	97.05 15	98.22 62	97.43 116

VDDNet		89.56 155	88.49 171	92.76 111	95.07 158	82.09 167	96.30 41	93.19 290	81.05 299	91.88 116	96.86 71	61.16 354	98.33 149	88.43 144	92.49 204	97.84 92

h-mvs33		90.80 120	90.15 127	92.75 113	96.01 111	82.66 156	95.43 109	95.53 191	89.80 52	93.08 80	95.64 128	75.77 181	99.00 72	92.07 90	78.05 377	96.60 162

casdiffmvs		92.51 90	92.43 90	92.74 114	94.41 206	81.98 170	94.54 169	96.23 130	89.57 62	91.96 113	96.17 103	82.58 98	98.01 179	90.95 116	95.45 138	98.23 63
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		90.69 124	90.02 133	92.71 115	95.72 125	82.41 163	94.11 200	95.12 215	85.63 183	91.49 126	94.70 166	74.75 196	98.42 142	86.13 174	92.53 202	97.31 118

DCV-MVSNet		90.69 124	90.02 133	92.71 115	95.72 125	82.41 163	94.11 200	95.12 215	85.63 183	91.49 126	94.70 166	74.75 196	98.42 142	86.13 174	92.53 202	97.31 118

PCF-MVS	84.11 10	87.74 208	86.08 245	92.70 117	94.02 224	84.43 96	89.27 359	95.87 164	73.62 383	84.43 276	94.33 180	78.48 154	98.86 93	70.27 358	94.45 162	94.81 239
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline		92.39 94	92.29 92	92.69 118	94.46 201	81.77 175	94.14 197	96.27 125	89.22 75	91.88 116	96.00 108	82.35 101	97.99 181	91.05 112	95.27 144	98.30 50

MSLP-MVS++		93.72 58	94.08 47	92.65 119	97.31 68	83.43 123	95.79 87	97.33 27	90.03 42	93.58 70	96.96 67	84.87 68	97.76 193	92.19 86	98.66 41	96.76 155

EC-MVSNet		93.44 66	93.71 63	92.63 120	95.21 151	82.43 160	97.27 9	96.71 92	90.57 29	92.88 85	95.80 121	83.16 88	98.16 160	93.68 51	98.14 66	97.31 118

ab-mvs		89.41 161	88.35 173	92.60 121	95.15 156	82.65 157	92.20 290	95.60 186	83.97 222	88.55 170	93.70 211	74.16 209	98.21 158	82.46 224	89.37 248	96.94 146

LS3D		87.89 203	86.32 234	92.59 122	96.07 109	82.92 146	95.23 122	94.92 229	75.66 361	82.89 312	95.98 110	72.48 234	99.21 48	68.43 372	95.23 145	95.64 206

Anonymous20240529		88.09 199	86.59 223	92.58 123	96.53 90	81.92 173	95.99 71	95.84 166	74.11 378	89.06 164	95.21 146	61.44 346	98.81 99	83.67 207	87.47 279	97.01 141

fmvsm_s_conf0.5_n_3		94.49 25	95.13 10	92.56 124	95.49 139	81.10 198	95.93 77	97.16 44	92.96 3	97.39 9	98.13 5	83.63 81	98.80 100	97.89 2	97.61 89	97.78 96

CPTT-MVS		91.99 97	91.80 97	92.55 125	98.24 31	81.98 170	96.76 30	96.49 109	81.89 276	90.24 143	96.44 93	78.59 151	98.61 121	89.68 129	97.85 79	97.06 135

114514_t		89.51 156	88.50 169	92.54 126	98.11 36	81.99 169	95.16 130	96.36 117	70.19 405	85.81 230	95.25 142	76.70 171	98.63 118	82.07 234	96.86 107	97.00 142

PAPM_NR		91.22 112	90.78 117	92.52 127	97.60 59	81.46 184	94.37 186	96.24 129	86.39 165	87.41 193	94.80 164	82.06 112	98.48 129	82.80 219	95.37 140	97.61 106

DeepPCF-MVS	89.96 1	94.20 39	94.77 23	92.49 128	96.52 91	80.00 234	94.00 213	97.08 52	90.05 41	95.65 37	97.29 48	89.66 13	98.97 79	93.95 48	98.71 32	98.50 27

IS-MVSNet		91.43 107	91.09 110	92.46 129	95.87 121	81.38 187	96.95 19	93.69 282	89.72 58	89.50 156	95.98 110	78.57 152	97.77 192	83.02 213	96.50 117	98.22 64

API-MVS		90.66 127	90.07 129	92.45 130	96.36 95	84.57 87	96.06 65	95.22 212	82.39 259	89.13 161	94.27 186	80.32 127	98.46 133	80.16 270	96.71 111	94.33 262

xiu_mvs_v1_base_debu		90.64 128	90.05 130	92.40 131	93.97 230	84.46 93	93.32 243	95.46 194	85.17 192	92.25 104	94.03 190	70.59 255	98.57 124	90.97 113	94.67 153	94.18 265

xiu_mvs_v1_base		90.64 128	90.05 130	92.40 131	93.97 230	84.46 93	93.32 243	95.46 194	85.17 192	92.25 104	94.03 190	70.59 255	98.57 124	90.97 113	94.67 153	94.18 265

xiu_mvs_v1_base_debi		90.64 128	90.05 130	92.40 131	93.97 230	84.46 93	93.32 243	95.46 194	85.17 192	92.25 104	94.03 190	70.59 255	98.57 124	90.97 113	94.67 153	94.18 265

fmvsm_s_conf0.5_n_2		93.47 63	93.83 54	92.39 134	95.36 142	81.19 194	95.20 127	96.56 103	90.37 32	97.13 13	98.03 24	77.47 163	98.96 81	97.79 4	96.58 114	97.03 138

fmvsm_s_conf0.1_n_2		93.16 79	93.42 69	92.37 135	94.62 187	81.13 196	95.23 122	95.89 162	90.30 35	96.74 23	98.02 25	76.14 175	98.95 83	97.64 5	96.21 122	97.03 138

AdaColmap		89.89 147	89.07 153	92.37 135	97.41 65	83.03 141	94.42 179	95.92 157	82.81 253	86.34 219	94.65 171	73.89 213	99.02 65	80.69 261	95.51 133	95.05 225

CNLPA		89.07 172	87.98 183	92.34 137	96.87 77	84.78 82	94.08 204	93.24 288	81.41 290	84.46 274	95.13 151	75.57 188	96.62 285	77.21 302	93.84 171	95.61 209

fmvsm_s_conf0.5_n_4		93.86 53	94.37 32	92.33 138	95.13 157	80.95 203	95.64 101	96.97 58	89.60 61	96.85 19	97.77 33	83.08 91	98.92 87	97.49 6	96.78 109	97.13 132

ET-MVSNet_ETH3D		87.51 221	85.91 253	92.32 139	93.70 243	83.93 107	92.33 285	90.94 356	84.16 217	72.09 404	92.52 247	69.90 265	95.85 332	89.20 135	88.36 266	97.17 127

Anonymous202405211		87.68 209	86.13 241	92.31 140	96.66 82	80.74 210	94.87 146	91.49 341	80.47 304	89.46 157	95.44 134	54.72 390	98.23 155	82.19 230	89.89 238	97.97 81

CHOSEN 1792x2688		88.84 178	87.69 189	92.30 141	96.14 100	81.42 186	90.01 346	95.86 165	74.52 374	87.41 193	93.94 198	75.46 189	98.36 144	80.36 266	95.53 132	97.12 133

HY-MVS	83.01 12	89.03 174	87.94 185	92.29 142	94.86 173	82.77 148	92.08 295	94.49 248	81.52 289	86.93 200	92.79 240	78.32 156	98.23 155	79.93 272	90.55 226	95.88 195

CDS-MVSNet		89.45 159	88.51 168	92.29 142	93.62 246	83.61 120	93.01 261	94.68 245	81.95 271	87.82 186	93.24 223	78.69 149	96.99 267	80.34 267	93.23 188	96.28 175
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 141	89.27 151	92.29 142	95.78 123	80.95 203	92.68 272	96.22 131	81.91 273	86.66 210	93.75 210	82.23 106	98.44 139	79.40 282	94.79 151	97.48 113

mvsmamba		90.33 133	89.69 138	92.25 145	95.17 153	81.64 177	95.27 120	93.36 287	84.88 202	89.51 154	94.27 186	69.29 279	97.42 226	89.34 133	96.12 125	97.68 102

PLC	84.53 7	89.06 173	88.03 182	92.15 146	97.27 71	82.69 155	94.29 189	95.44 199	79.71 313	84.01 290	94.18 189	76.68 172	98.75 105	77.28 301	93.41 182	95.02 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 104	91.56 101	92.13 147	95.88 119	80.50 217	97.33 7	95.25 209	86.15 171	89.76 152	95.60 129	83.42 84	98.32 151	87.37 158	93.25 187	97.56 110

patch_mono-2		93.74 57	94.32 33	92.01 148	97.54 60	78.37 275	93.40 239	97.19 38	88.02 119	94.99 47	97.21 53	88.35 21	98.44 139	94.07 47	98.09 69	99.23 1

原ACMM1				92.01 148	97.34 67	81.05 199		96.81 79	78.89 324	90.45 140	95.92 113	82.65 97	98.84 97	80.68 262	98.26 59	96.14 181

UniMVSNet (Re)		89.80 149	89.07 153	92.01 148	93.60 247	84.52 90	94.78 154	97.47 12	89.26 74	86.44 216	92.32 253	82.10 110	97.39 237	84.81 190	80.84 354	94.12 269

MG-MVS		91.77 101	91.70 100	92.00 151	97.08 74	80.03 232	93.60 232	95.18 213	87.85 128	90.89 136	96.47 92	82.06 112	98.36 144	85.07 185	97.04 100	97.62 105

EIA-MVS		91.95 98	91.94 95	91.98 152	95.16 154	80.01 233	95.36 110	96.73 89	88.44 104	89.34 158	92.16 258	83.82 80	98.45 137	89.35 132	97.06 99	97.48 113

PVSNet_Blended		90.73 123	90.32 122	91.98 152	96.12 102	81.25 190	92.55 277	96.83 75	82.04 269	89.10 162	92.56 246	81.04 123	98.85 95	86.72 168	95.91 127	95.84 197

guyue		91.12 115	90.84 115	91.96 154	94.59 189	80.57 215	94.87 146	93.71 281	88.96 87	91.14 132	95.22 143	73.22 225	97.76 193	92.01 94	93.81 172	97.54 112

PS-MVSNAJ		91.18 113	90.92 112	91.96 154	95.26 149	82.60 159	92.09 294	95.70 177	86.27 167	91.84 118	92.46 248	79.70 136	98.99 74	89.08 136	95.86 128	94.29 263

TAMVS		89.21 167	88.29 177	91.96 154	93.71 241	82.62 158	93.30 247	94.19 261	82.22 264	87.78 187	93.94 198	78.83 146	96.95 270	77.70 297	92.98 192	96.32 172

SDMVSNet		90.19 137	89.61 140	91.93 157	96.00 112	83.09 138	92.89 267	95.98 152	88.73 94	86.85 206	95.20 147	72.09 238	97.08 259	88.90 138	89.85 240	95.63 207

FA-MVS(test-final)		89.66 151	88.91 158	91.93 157	94.57 193	80.27 221	91.36 310	94.74 242	84.87 203	89.82 151	92.61 245	74.72 199	98.47 132	83.97 201	93.53 177	97.04 137

MVS_Test		91.31 110	91.11 108	91.93 157	94.37 207	80.14 225	93.46 237	95.80 168	86.46 163	91.35 130	93.77 208	82.21 107	98.09 172	87.57 154	94.95 148	97.55 111

NR-MVSNet		88.58 188	87.47 195	91.93 157	93.04 265	84.16 103	94.77 155	96.25 128	89.05 81	80.04 351	93.29 221	79.02 145	97.05 264	81.71 245	80.05 364	94.59 246

HyFIR lowres test		88.09 199	86.81 211	91.93 157	96.00 112	80.63 212	90.01 346	95.79 169	73.42 385	87.68 189	92.10 264	73.86 214	97.96 183	80.75 260	91.70 209	97.19 126

GeoE		90.05 140	89.43 144	91.90 162	95.16 154	80.37 220	95.80 86	94.65 246	83.90 223	87.55 192	94.75 165	78.18 157	97.62 206	81.28 250	93.63 174	97.71 101

thisisatest0530		88.67 183	87.61 191	91.86 163	94.87 172	80.07 228	94.63 164	89.90 379	84.00 221	88.46 172	93.78 207	66.88 303	98.46 133	83.30 209	92.65 199	97.06 135

xiu_mvs_v2_base		91.13 114	90.89 114	91.86 163	94.97 164	82.42 161	92.24 288	95.64 184	86.11 175	91.74 123	93.14 227	79.67 139	98.89 89	89.06 137	95.46 137	94.28 264

DU-MVS		89.34 166	88.50 169	91.85 165	93.04 265	83.72 113	94.47 175	96.59 100	89.50 63	86.46 213	93.29 221	77.25 165	97.23 249	84.92 187	81.02 350	94.59 246

AstraMVS		90.69 124	90.30 123	91.84 166	93.81 236	79.85 239	94.76 156	92.39 310	88.96 87	91.01 135	95.87 117	70.69 253	97.94 186	92.49 73	92.70 198	97.73 99

OPM-MVS		90.12 138	89.56 141	91.82 167	93.14 258	83.90 108	94.16 196	95.74 173	88.96 87	87.86 182	95.43 136	72.48 234	97.91 188	88.10 149	90.18 233	93.65 300
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 131	90.19 125	91.82 167	94.70 183	82.73 152	95.85 83	96.22 131	90.81 20	86.91 202	94.86 160	74.23 205	98.12 162	88.15 145	89.99 234	94.63 243

UniMVSNet_NR-MVSNet		89.92 146	89.29 149	91.81 169	93.39 253	83.72 113	94.43 178	97.12 49	89.80 52	86.46 213	93.32 218	83.16 88	97.23 249	84.92 187	81.02 350	94.49 256

diffmvs		91.37 109	91.23 106	91.77 170	93.09 261	80.27 221	92.36 282	95.52 192	87.03 148	91.40 129	94.93 155	80.08 130	97.44 224	92.13 89	94.56 158	97.61 106
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		88.42 189	87.33 198	91.72 171	94.92 168	80.98 201	92.97 264	94.54 247	78.16 341	83.82 293	93.88 203	78.78 148	97.91 188	79.45 278	89.41 247	96.26 176

Fast-Effi-MVS+		89.41 161	88.64 164	91.71 172	94.74 178	80.81 208	93.54 233	95.10 217	83.11 245	86.82 208	90.67 317	79.74 135	97.75 197	80.51 265	93.55 176	96.57 165

WTY-MVS		89.60 153	88.92 157	91.67 173	95.47 140	81.15 195	92.38 281	94.78 240	83.11 245	89.06 164	94.32 181	78.67 150	96.61 288	81.57 246	90.89 222	97.24 123

TAPA-MVS	84.62 6	88.16 197	87.01 207	91.62 174	96.64 83	80.65 211	94.39 182	96.21 134	76.38 354	86.19 223	95.44 134	79.75 134	98.08 174	62.75 401	95.29 142	96.13 182
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 152	88.96 155	91.60 175	93.86 233	82.89 147	95.46 108	97.33 27	87.91 123	88.43 173	93.31 219	74.17 208	97.40 234	87.32 159	82.86 325	94.52 251

FE-MVS		87.40 226	86.02 247	91.57 176	94.56 194	79.69 243	90.27 333	93.72 280	80.57 302	88.80 167	91.62 283	65.32 318	98.59 123	74.97 327	94.33 165	96.44 168

XVG-OURS		89.40 163	88.70 163	91.52 177	94.06 222	81.46 184	91.27 314	96.07 145	86.14 172	88.89 166	95.77 123	68.73 288	97.26 246	87.39 157	89.96 236	95.83 198

hse-mvs2		89.88 148	89.34 147	91.51 178	94.83 175	81.12 197	93.94 216	93.91 273	89.80 52	93.08 80	93.60 212	75.77 181	97.66 201	92.07 90	77.07 384	95.74 202

TranMVSNet+NR-MVSNet		88.84 178	87.95 184	91.49 179	92.68 276	83.01 143	94.92 143	96.31 120	89.88 46	85.53 239	93.85 205	76.63 173	96.96 269	81.91 238	79.87 367	94.50 254

AUN-MVS		87.78 207	86.54 226	91.48 180	94.82 176	81.05 199	93.91 220	93.93 270	83.00 248	86.93 200	93.53 213	69.50 273	97.67 199	86.14 172	77.12 383	95.73 204

XVG-OURS-SEG-HR		89.95 144	89.45 142	91.47 181	94.00 228	81.21 193	91.87 298	96.06 147	85.78 178	88.55 170	95.73 125	74.67 200	97.27 244	88.71 141	89.64 245	95.91 193

MVS		87.44 224	86.10 244	91.44 182	92.61 277	83.62 118	92.63 274	95.66 181	67.26 411	81.47 329	92.15 259	77.95 158	98.22 157	79.71 274	95.48 135	92.47 342

F-COLMAP		87.95 202	86.80 212	91.40 183	96.35 96	80.88 206	94.73 158	95.45 197	79.65 314	82.04 324	94.61 172	71.13 245	98.50 127	76.24 314	91.05 220	94.80 240

dcpmvs_2		93.49 62	94.19 44	91.38 184	97.69 57	76.78 309	94.25 191	96.29 121	88.33 107	94.46 50	96.88 70	88.07 25	98.64 116	93.62 53	98.09 69	98.73 18

thisisatest0515		87.33 229	85.99 248	91.37 185	93.49 249	79.55 244	90.63 328	89.56 387	80.17 306	87.56 191	90.86 307	67.07 300	98.28 153	81.50 247	93.02 191	96.29 174

HQP-MVS		89.80 149	89.28 150	91.34 186	94.17 217	81.56 178	94.39 182	96.04 148	88.81 90	85.43 248	93.97 197	73.83 215	97.96 183	87.11 163	89.77 243	94.50 254

fmvsm_s_conf0.5_n_7		93.15 80	93.76 60	91.31 187	94.42 205	79.48 246	94.52 170	97.14 47	89.33 70	94.17 56	98.09 15	81.83 116	97.49 216	96.33 22	98.02 73	96.95 145

RRT-MVS		90.85 119	90.70 118	91.30 188	94.25 213	76.83 308	94.85 149	96.13 139	89.04 82	90.23 144	94.88 158	70.15 264	98.72 108	91.86 102	94.88 149	98.34 43

FMVSNet3		87.40 226	86.11 243	91.30 188	93.79 239	83.64 117	94.20 195	94.81 238	83.89 224	84.37 277	91.87 274	68.45 291	96.56 293	78.23 292	85.36 296	93.70 299

FMVSNet2		87.19 239	85.82 256	91.30 188	94.01 225	83.67 115	94.79 153	94.94 224	83.57 231	83.88 292	92.05 268	66.59 308	96.51 297	77.56 299	85.01 299	93.73 297

RPMNet		83.95 315	81.53 326	91.21 191	90.58 354	79.34 252	85.24 404	96.76 84	71.44 399	85.55 237	82.97 411	70.87 250	98.91 88	61.01 405	89.36 249	95.40 213

IB-MVS	80.51 15	85.24 293	83.26 310	91.19 192	92.13 289	79.86 238	91.75 301	91.29 346	83.28 242	80.66 341	88.49 364	61.28 348	98.46 133	80.99 256	79.46 371	95.25 219
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		89.47 158	88.90 159	91.18 193	94.22 215	82.07 168	92.13 292	96.09 143	87.90 124	85.37 254	92.45 249	74.38 203	97.56 210	87.15 161	90.43 228	93.93 278
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		89.45 159	88.90 159	91.12 194	94.47 199	81.49 182	95.30 115	96.14 136	86.73 157	85.45 245	95.16 149	69.89 266	98.10 164	87.70 152	89.23 252	93.77 293

LGP-MVS_train				91.12 194	94.47 199	81.49 182		96.14 136	86.73 157	85.45 245	95.16 149	69.89 266	98.10 164	87.70 152	89.23 252	93.77 293

ACMM	84.12 9	89.14 168	88.48 172	91.12 194	94.65 186	81.22 192	95.31 113	96.12 140	85.31 191	85.92 228	94.34 179	70.19 263	98.06 176	85.65 180	88.86 257	94.08 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 185	87.78 188	91.11 197	94.96 165	77.81 291	95.35 111	89.69 382	85.09 197	88.05 180	94.59 175	66.93 301	98.48 129	83.27 210	92.13 207	97.03 138

GBi-Net		87.26 231	85.98 249	91.08 198	94.01 225	83.10 135	95.14 131	94.94 224	83.57 231	84.37 277	91.64 279	66.59 308	96.34 310	78.23 292	85.36 296	93.79 288

test1		87.26 231	85.98 249	91.08 198	94.01 225	83.10 135	95.14 131	94.94 224	83.57 231	84.37 277	91.64 279	66.59 308	96.34 310	78.23 292	85.36 296	93.79 288

FMVSNet1		85.85 278	84.11 297	91.08 198	92.81 272	83.10 135	95.14 131	94.94 224	81.64 284	82.68 314	91.64 279	59.01 370	96.34 310	75.37 321	83.78 309	93.79 288

Test_1112_low_res		87.65 211	86.51 227	91.08 198	94.94 167	79.28 256	91.77 300	94.30 256	76.04 359	83.51 303	92.37 251	77.86 161	97.73 198	78.69 287	89.13 254	96.22 177

PS-MVSNAJss		89.97 143	89.62 139	91.02 202	91.90 298	80.85 207	95.26 121	95.98 152	86.26 168	86.21 222	94.29 183	79.70 136	97.65 202	88.87 140	88.10 268	94.57 248

BH-RMVSNet		88.37 191	87.48 194	91.02 202	95.28 146	79.45 248	92.89 267	93.07 293	85.45 188	86.91 202	94.84 163	70.35 260	97.76 193	73.97 335	94.59 157	95.85 196

UniMVSNet_ETH3D		87.53 220	86.37 231	91.00 204	92.44 281	78.96 261	94.74 157	95.61 185	84.07 220	85.36 255	94.52 177	59.78 362	97.34 239	82.93 214	87.88 273	96.71 158

FIs		90.51 132	90.35 121	90.99 205	93.99 229	80.98 201	95.73 92	97.54 5	89.15 78	86.72 209	94.68 168	81.83 116	97.24 248	85.18 184	88.31 267	94.76 241

ACMP	84.23 8	89.01 176	88.35 173	90.99 205	94.73 179	81.27 189	95.07 134	95.89 162	86.48 161	83.67 298	94.30 182	69.33 275	97.99 181	87.10 165	88.55 259	93.72 298
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 261	85.13 276	90.98 207	96.52 91	81.50 180	96.14 56	96.16 135	73.78 381	83.65 299	92.15 259	63.26 332	97.37 238	82.82 218	81.74 339	94.06 274

sss		88.93 177	88.26 179	90.94 208	94.05 223	80.78 209	91.71 302	95.38 203	81.55 288	88.63 169	93.91 202	75.04 193	95.47 351	82.47 223	91.61 210	96.57 165

sd_testset		88.59 187	87.85 187	90.83 209	96.00 112	80.42 219	92.35 283	94.71 243	88.73 94	86.85 206	95.20 147	67.31 295	96.43 304	79.64 276	89.85 240	95.63 207

PVSNet_BlendedMVS		89.98 142	89.70 137	90.82 210	96.12 102	81.25 190	93.92 218	96.83 75	83.49 235	89.10 162	92.26 256	81.04 123	98.85 95	86.72 168	87.86 274	92.35 348

cascas		86.43 269	84.98 279	90.80 211	92.10 291	80.92 205	90.24 337	95.91 159	73.10 388	83.57 302	88.39 365	65.15 320	97.46 220	84.90 189	91.43 212	94.03 276

ECVR-MVS		89.09 171	88.53 167	90.77 212	95.62 133	75.89 322	96.16 52	84.22 413	87.89 126	90.20 145	96.65 82	63.19 333	98.10 164	85.90 177	96.94 102	98.33 45

GA-MVS		86.61 259	85.27 273	90.66 213	91.33 321	78.71 264	90.40 332	93.81 277	85.34 190	85.12 258	89.57 346	61.25 349	97.11 258	80.99 256	89.59 246	96.15 180

thres600view7		87.65 211	86.67 218	90.59 214	96.08 108	78.72 262	94.88 145	91.58 337	87.06 147	88.08 178	92.30 254	68.91 285	98.10 164	70.05 365	91.10 215	94.96 230

thres400		87.62 216	86.64 219	90.57 215	95.99 115	78.64 265	94.58 166	91.98 326	86.94 151	88.09 176	91.77 275	69.18 281	98.10 164	70.13 362	91.10 215	94.96 230

baseline1		88.10 198	87.28 200	90.57 215	94.96 165	80.07 228	94.27 190	91.29 346	86.74 156	87.41 193	94.00 195	76.77 170	96.20 315	80.77 259	79.31 373	95.44 211

FC-MVSNet-test		90.27 135	90.18 126	90.53 217	93.71 241	79.85 239	95.77 89	97.59 3	89.31 71	86.27 220	94.67 169	81.93 115	97.01 266	84.26 197	88.09 270	94.71 242

PAPM		86.68 258	85.39 268	90.53 217	93.05 264	79.33 255	89.79 349	94.77 241	78.82 327	81.95 325	93.24 223	76.81 168	97.30 240	66.94 382	93.16 189	94.95 234

WR-MVS		88.38 190	87.67 190	90.52 219	93.30 255	80.18 223	93.26 250	95.96 155	88.57 102	85.47 244	92.81 238	76.12 176	96.91 273	81.24 251	82.29 330	94.47 259

MVSTER		88.84 178	88.29 177	90.51 220	92.95 270	80.44 218	93.73 226	95.01 221	84.66 211	87.15 197	93.12 228	72.79 230	97.21 251	87.86 150	87.36 282	93.87 283

testdata				90.49 221	96.40 93	77.89 288		95.37 205	72.51 393	93.63 69	96.69 78	82.08 111	97.65 202	83.08 211	97.39 92	95.94 192

test1111		89.10 169	88.64 164	90.48 222	95.53 138	74.97 332	96.08 61	84.89 411	88.13 117	90.16 147	96.65 82	63.29 331	98.10 164	86.14 172	96.90 104	98.39 40

tt0805		86.92 247	85.74 262	90.48 222	92.22 285	79.98 235	95.63 102	94.88 232	83.83 226	84.74 267	92.80 239	57.61 376	97.67 199	85.48 183	84.42 303	93.79 288

jajsoiax		88.24 195	87.50 193	90.48 222	90.89 342	80.14 225	95.31 113	95.65 183	84.97 200	84.24 285	94.02 193	65.31 319	97.42 226	88.56 142	88.52 261	93.89 279

PatchMatch-RL		86.77 255	85.54 264	90.47 225	95.88 119	82.71 154	90.54 330	92.31 314	79.82 312	84.32 282	91.57 287	68.77 287	96.39 306	73.16 341	93.48 181	92.32 349

tfpn200view9		87.58 218	86.64 219	90.41 226	95.99 115	78.64 265	94.58 166	91.98 326	86.94 151	88.09 176	91.77 275	69.18 281	98.10 164	70.13 362	91.10 215	94.48 257

VPNet		88.20 196	87.47 195	90.39 227	93.56 248	79.46 247	94.04 208	95.54 190	88.67 97	86.96 199	94.58 176	69.33 275	97.15 253	84.05 200	80.53 359	94.56 249

ACMH	80.38 17	85.36 288	83.68 304	90.39 227	94.45 202	80.63 212	94.73 158	94.85 234	82.09 266	77.24 374	92.65 243	60.01 360	97.58 208	72.25 346	84.87 300	92.96 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 214	86.71 215	90.38 229	96.12 102	78.55 268	95.03 137	91.58 337	87.15 144	88.06 179	92.29 255	68.91 285	98.10 164	70.13 362	91.10 215	94.48 257

mvs_tets		88.06 201	87.28 200	90.38 229	90.94 338	79.88 237	95.22 124	95.66 181	85.10 196	84.21 286	93.94 198	63.53 329	97.40 234	88.50 143	88.40 265	93.87 283

1314		87.51 221	86.57 224	90.34 231	92.42 282	79.74 242	92.63 274	95.35 207	78.35 336	80.14 348	91.62 283	74.05 210	97.15 253	81.05 252	93.53 177	94.12 269

LTVRE_ROB	82.13 13	86.26 272	84.90 282	90.34 231	94.44 203	81.50 180	92.31 287	94.89 230	83.03 247	79.63 357	92.67 242	69.69 269	97.79 191	71.20 351	86.26 291	91.72 359
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		89.03 174	88.64 164	90.21 233	90.74 349	79.28 256	95.96 74	95.90 160	84.66 211	85.33 256	92.94 233	74.02 211	97.30 240	89.64 130	88.53 260	94.05 275

v2v482		87.84 204	87.06 204	90.17 234	90.99 334	79.23 259	94.00 213	95.13 214	84.87 203	85.53 239	92.07 267	74.45 202	97.45 221	84.71 192	81.75 338	93.85 286

pmmvs4		85.43 286	83.86 302	90.16 235	90.02 367	82.97 145	90.27 333	92.67 305	75.93 360	80.73 339	91.74 277	71.05 246	95.73 340	78.85 286	83.46 316	91.78 358

V42		87.68 209	86.86 209	90.15 236	90.58 354	80.14 225	94.24 193	95.28 208	83.66 229	85.67 234	91.33 289	74.73 198	97.41 232	84.43 196	81.83 336	92.89 330

MSDG		84.86 301	83.09 313	90.14 237	93.80 237	80.05 230	89.18 362	93.09 292	78.89 324	78.19 366	91.91 272	65.86 317	97.27 244	68.47 371	88.45 263	93.11 322

sc_t1		81.53 339	78.67 360	90.12 238	90.78 346	78.64 265	93.91 220	90.20 369	68.42 408	80.82 338	89.88 339	46.48 413	96.76 278	76.03 317	71.47 398	94.96 230

anonymousdsp		87.84 204	87.09 203	90.12 238	89.13 378	80.54 216	94.67 162	95.55 188	82.05 267	83.82 293	92.12 261	71.47 243	97.15 253	87.15 161	87.80 277	92.67 336

thres200		87.21 237	86.24 238	90.12 238	95.36 142	78.53 269	93.26 250	92.10 320	86.42 164	88.00 181	91.11 300	69.24 280	98.00 180	69.58 366	91.04 221	93.83 287

CR-MVSNet		85.35 289	83.76 303	90.12 238	90.58 354	79.34 252	85.24 404	91.96 328	78.27 338	85.55 237	87.87 375	71.03 247	95.61 343	73.96 336	89.36 249	95.40 213

v1144		87.61 217	86.79 213	90.06 242	91.01 333	79.34 252	93.95 215	95.42 202	83.36 240	85.66 235	91.31 292	74.98 194	97.42 226	83.37 208	82.06 332	93.42 309

XXY-MVS		87.65 211	86.85 210	90.03 243	92.14 288	80.60 214	93.76 225	95.23 210	82.94 250	84.60 269	94.02 193	74.27 204	95.49 350	81.04 253	83.68 312	94.01 277

Vis-MVSNet (Re-imp)		89.59 154	89.44 143	90.03 243	95.74 124	75.85 323	95.61 103	90.80 360	87.66 136	87.83 185	95.40 137	76.79 169	96.46 302	78.37 288	96.73 110	97.80 94

test2506		87.21 237	86.28 236	90.02 245	95.62 133	73.64 348	96.25 47	71.38 436	87.89 126	90.45 140	96.65 82	55.29 387	98.09 172	86.03 176	96.94 102	98.33 45

BH-untuned		88.60 186	88.13 181	90.01 246	95.24 150	78.50 271	93.29 248	94.15 263	84.75 208	84.46 274	93.40 215	75.76 183	97.40 234	77.59 298	94.52 160	94.12 269

v1192		87.25 233	86.33 233	90.00 247	90.76 348	79.04 260	93.80 223	95.48 193	82.57 257	85.48 243	91.18 296	73.38 224	97.42 226	82.30 227	82.06 332	93.53 303

v7n		86.81 250	85.76 260	89.95 248	90.72 350	79.25 258	95.07 134	95.92 157	84.45 214	82.29 318	90.86 307	72.60 233	97.53 212	79.42 281	80.52 360	93.08 324

testing91		87.11 242	86.18 239	89.92 249	94.43 204	75.38 331	91.53 307	92.27 316	86.48 161	86.50 211	90.24 325	61.19 352	97.53 212	82.10 232	90.88 223	96.84 153

v8		87.50 223	86.71 215	89.89 250	91.37 318	79.40 249	94.50 171	95.38 203	84.81 206	83.60 301	91.33 289	76.05 177	97.42 226	82.84 217	80.51 361	92.84 332

v10		87.25 233	86.38 230	89.85 251	91.19 324	79.50 245	94.48 172	95.45 197	83.79 227	83.62 300	91.19 294	75.13 191	97.42 226	81.94 237	80.60 356	92.63 338

baseline2		86.50 265	85.39 268	89.84 252	91.12 329	76.70 311	91.88 297	88.58 391	82.35 262	79.95 352	90.95 305	73.42 222	97.63 205	80.27 269	89.95 237	95.19 220

pm-mvs1		86.61 259	85.54 264	89.82 253	91.44 313	80.18 223	95.28 119	94.85 234	83.84 225	81.66 327	92.62 244	72.45 236	96.48 299	79.67 275	78.06 376	92.82 333

TR-MVS		86.78 252	85.76 260	89.82 253	94.37 207	78.41 273	92.47 278	92.83 299	81.11 298	86.36 217	92.40 250	68.73 288	97.48 217	73.75 339	89.85 240	93.57 302

ACMH+	81.04 14	85.05 296	83.46 307	89.82 253	94.66 185	79.37 250	94.44 177	94.12 266	82.19 265	78.04 368	92.82 237	58.23 373	97.54 211	73.77 338	82.90 324	92.54 339

EI-MVSNet		89.10 169	88.86 161	89.80 256	91.84 300	78.30 277	93.70 229	95.01 221	85.73 180	87.15 197	95.28 140	79.87 133	97.21 251	83.81 204	87.36 282	93.88 282

v144192		87.19 239	86.35 232	89.74 257	90.64 352	78.24 279	93.92 218	95.43 200	81.93 272	85.51 241	91.05 303	74.21 207	97.45 221	82.86 216	81.56 340	93.53 303

COLMAP_ROB	80.39 16	83.96 314	82.04 323	89.74 257	95.28 146	79.75 241	94.25 191	92.28 315	75.17 367	78.02 369	93.77 208	58.60 372	97.84 190	65.06 393	85.92 292	91.63 361
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 271	85.18 275	89.73 259	92.15 287	76.60 312	91.12 318	91.69 333	83.53 234	85.50 242	88.81 358	66.79 304	96.48 299	76.65 307	90.35 230	96.12 183

IterMVS-LS		88.36 192	87.91 186	89.70 260	93.80 237	78.29 278	93.73 226	95.08 219	85.73 180	84.75 266	91.90 273	79.88 132	96.92 272	83.83 203	82.51 326	93.89 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 268	85.35 271	89.69 261	94.29 212	75.40 330	91.30 312	90.53 364	84.76 207	85.06 260	90.13 331	58.95 371	97.45 221	82.08 233	91.09 219	96.21 179

testing99		86.72 256	85.73 263	89.69 261	94.23 214	74.91 334	91.35 311	90.97 354	86.14 172	86.36 217	90.22 326	59.41 365	97.48 217	82.24 229	90.66 225	96.69 160

v1921920		86.97 246	86.06 246	89.69 261	90.53 357	78.11 282	93.80 223	95.43 200	81.90 274	85.33 256	91.05 303	72.66 231	97.41 232	82.05 235	81.80 337	93.53 303

Fast-Effi-MVS+-dtu		87.44 224	86.72 214	89.63 264	92.04 292	77.68 297	94.03 209	93.94 269	85.81 177	82.42 317	91.32 291	70.33 261	97.06 262	80.33 268	90.23 232	94.14 268

v1240		86.78 252	85.85 255	89.56 265	90.45 359	77.79 293	93.61 231	95.37 205	81.65 283	85.43 248	91.15 298	71.50 242	97.43 225	81.47 248	82.05 334	93.47 307

Effi-MVS+-dtu		88.65 184	88.35 173	89.54 266	93.33 254	76.39 316	94.47 175	94.36 254	87.70 133	85.43 248	89.56 347	73.45 220	97.26 246	85.57 182	91.28 214	94.97 227

AllTest		83.42 321	81.39 327	89.52 267	95.01 160	77.79 293	93.12 254	90.89 358	77.41 345	76.12 383	93.34 216	54.08 393	97.51 214	68.31 373	84.27 305	93.26 312

TestCases				89.52 267	95.01 160	77.79 293		90.89 358	77.41 345	76.12 383	93.34 216	54.08 393	97.51 214	68.31 373	84.27 305	93.26 312

mvs_anonymous		89.37 165	89.32 148	89.51 269	93.47 250	74.22 341	91.65 305	94.83 236	82.91 251	85.45 245	93.79 206	81.23 122	96.36 309	86.47 170	94.09 166	97.94 83

XVG-ACMP-BASELINE		86.00 274	84.84 284	89.45 270	91.20 323	78.00 284	91.70 303	95.55 188	85.05 198	82.97 311	92.25 257	54.49 391	97.48 217	82.93 214	87.45 281	92.89 330

testing222		84.84 302	83.32 308	89.43 271	94.15 220	75.94 321	91.09 319	89.41 389	84.90 201	85.78 231	89.44 348	52.70 398	96.28 313	70.80 357	91.57 211	96.07 187

MVP-Stereo		85.97 275	84.86 283	89.32 272	90.92 340	82.19 166	92.11 293	94.19 261	78.76 329	78.77 365	91.63 282	68.38 292	96.56 293	75.01 326	93.95 168	89.20 399
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 278	84.70 286	89.29 273	91.76 304	75.54 327	88.49 371	91.30 345	81.63 285	85.05 261	88.70 362	71.71 239	96.24 314	74.61 331	89.05 255	96.08 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 244	86.32 234	89.21 274	90.94 338	77.26 302	93.71 228	94.43 250	84.84 205	84.36 280	90.80 311	76.04 178	97.05 264	82.12 231	79.60 370	93.31 311

tfpnnormal		84.72 304	83.23 311	89.20 275	92.79 273	80.05 230	94.48 172	95.81 167	82.38 260	81.08 335	91.21 293	69.01 284	96.95 270	61.69 403	80.59 357	90.58 386

cl22		86.78 252	85.98 249	89.18 276	92.34 283	77.62 298	90.84 324	94.13 265	81.33 292	83.97 291	90.15 330	73.96 212	96.60 290	84.19 198	82.94 321	93.33 310

BH-w/o		87.57 219	87.05 205	89.12 277	94.90 171	77.90 287	92.41 279	93.51 284	82.89 252	83.70 297	91.34 288	75.75 184	97.07 261	75.49 319	93.49 179	92.39 346

WR-MVS_H		87.80 206	87.37 197	89.10 278	93.23 256	78.12 281	95.61 103	97.30 31	87.90 124	83.72 296	92.01 269	79.65 140	96.01 324	76.36 311	80.54 358	93.16 320

miper_enhance_ethall		86.90 248	86.18 239	89.06 279	91.66 309	77.58 299	90.22 339	94.82 237	79.16 320	84.48 273	89.10 352	79.19 144	96.66 283	84.06 199	82.94 321	92.94 328

c3_l		87.14 241	86.50 228	89.04 280	92.20 286	77.26 302	91.22 317	94.70 244	82.01 270	84.34 281	90.43 322	78.81 147	96.61 288	83.70 206	81.09 347	93.25 314

miper_ehance_all_eth		87.22 236	86.62 222	89.02 281	92.13 289	77.40 301	90.91 323	94.81 238	81.28 293	84.32 282	90.08 333	79.26 142	96.62 285	83.81 204	82.94 321	93.04 325

gg-mvs-nofinetune		81.77 333	79.37 348	88.99 282	90.85 344	77.73 296	86.29 396	79.63 424	74.88 372	83.19 310	69.05 427	60.34 357	96.11 319	75.46 320	94.64 156	93.11 322

ETVMVS		84.43 308	82.92 317	88.97 283	94.37 207	74.67 335	91.23 316	88.35 393	83.37 239	86.06 226	89.04 353	55.38 385	95.67 342	67.12 380	91.34 213	96.58 164

pmmvs6		83.42 321	81.60 325	88.87 284	88.01 393	77.87 289	94.96 140	94.24 260	74.67 373	78.80 364	91.09 301	60.17 359	96.49 298	77.06 306	75.40 390	92.23 351

test_cas_vis1_n_1920		88.83 181	88.85 162	88.78 285	91.15 328	76.72 310	93.85 222	94.93 228	83.23 244	92.81 89	96.00 108	61.17 353	94.45 364	91.67 105	94.84 150	95.17 221

MIMVSNet		82.59 327	80.53 332	88.76 286	91.51 311	78.32 276	86.57 395	90.13 372	79.32 316	80.70 340	88.69 363	52.98 397	93.07 389	66.03 388	88.86 257	94.90 235

cl____		86.52 264	85.78 257	88.75 287	92.03 293	76.46 314	90.74 325	94.30 256	81.83 279	83.34 307	90.78 312	75.74 186	96.57 291	81.74 243	81.54 341	93.22 316

DIV-MVS_self_test		86.53 263	85.78 257	88.75 287	92.02 294	76.45 315	90.74 325	94.30 256	81.83 279	83.34 307	90.82 310	75.75 184	96.57 291	81.73 244	81.52 342	93.24 315

CP-MVSNet		87.63 214	87.26 202	88.74 289	93.12 259	76.59 313	95.29 117	96.58 101	88.43 105	83.49 304	92.98 232	75.28 190	95.83 333	78.97 284	81.15 346	93.79 288

eth_miper_zixun_eth		86.50 265	85.77 259	88.68 290	91.94 295	75.81 324	90.47 331	94.89 230	82.05 267	84.05 288	90.46 321	75.96 179	96.77 277	82.76 220	79.36 372	93.46 308

CHOSEN 280x420		85.15 294	83.99 300	88.65 291	92.47 279	78.40 274	79.68 426	92.76 302	74.90 371	81.41 331	89.59 345	69.85 268	95.51 347	79.92 273	95.29 142	92.03 354

PS-CasMVS		87.32 230	86.88 208	88.63 292	92.99 268	76.33 318	95.33 112	96.61 99	88.22 113	83.30 309	93.07 230	73.03 228	95.79 337	78.36 289	81.00 352	93.75 295

TransMVSNet (Re)		84.43 308	83.06 315	88.54 293	91.72 305	78.44 272	95.18 128	92.82 301	82.73 255	79.67 356	92.12 261	73.49 219	95.96 326	71.10 355	68.73 408	91.21 373

tt0320-xc		79.63 362	76.66 371	88.52 294	91.03 332	78.72 262	93.00 262	89.53 388	66.37 412	76.11 385	87.11 386	46.36 415	95.32 355	72.78 343	67.67 409	91.51 365

EG-PatchMatch MVS		82.37 329	80.34 335	88.46 295	90.27 361	79.35 251	92.80 271	94.33 255	77.14 349	73.26 401	90.18 329	47.47 410	96.72 279	70.25 359	87.32 284	89.30 396

PEN-MVS		86.80 251	86.27 237	88.40 296	92.32 284	75.71 326	95.18 128	96.38 116	87.97 121	82.82 313	93.15 226	73.39 223	95.92 328	76.15 315	79.03 375	93.59 301

Baseline_NR-MVSNet		87.07 243	86.63 221	88.40 296	91.44 313	77.87 289	94.23 194	92.57 307	84.12 219	85.74 233	92.08 265	77.25 165	96.04 320	82.29 228	79.94 365	91.30 371

UBG		85.51 284	84.57 290	88.35 298	94.21 216	71.78 372	90.07 344	89.66 384	82.28 263	85.91 229	89.01 354	61.30 347	97.06 262	76.58 310	92.06 208	96.22 177

D2MVS		85.90 276	85.09 277	88.35 298	90.79 345	77.42 300	91.83 299	95.70 177	80.77 301	80.08 350	90.02 335	66.74 306	96.37 307	81.88 239	87.97 272	91.26 372

pmmvs5		84.21 310	82.84 320	88.34 300	88.95 380	76.94 306	92.41 279	91.91 330	75.63 362	80.28 345	91.18 296	64.59 323	95.57 344	77.09 305	83.47 315	92.53 340

mamv4		90.92 117	91.78 98	88.33 301	95.67 129	70.75 385	92.92 266	96.02 151	81.90 274	88.11 175	95.34 138	85.88 51	96.97 268	95.22 36	95.01 147	97.26 121

tt0320		80.13 355	77.41 364	88.29 302	90.50 358	78.02 283	93.10 257	90.71 362	66.06 415	76.75 378	86.97 387	49.56 405	95.40 352	71.65 347	71.41 399	91.46 368

LCM-MVSNet-Re		88.30 194	88.32 176	88.27 303	94.71 182	72.41 367	93.15 253	90.98 353	87.77 131	79.25 360	91.96 270	78.35 155	95.75 338	83.04 212	95.62 131	96.65 161

CostFormer		85.77 281	84.94 281	88.26 304	91.16 327	72.58 365	89.47 357	91.04 352	76.26 357	86.45 215	89.97 337	70.74 252	96.86 276	82.35 226	87.07 287	95.34 217

ITE_SJBPF				88.24 305	91.88 299	77.05 305		92.92 296	85.54 186	80.13 349	93.30 220	57.29 377	96.20 315	72.46 345	84.71 301	91.49 366

PVSNet	78.82 18	85.55 283	84.65 287	88.23 306	94.72 181	71.93 368	87.12 391	92.75 303	78.80 328	84.95 263	90.53 319	64.43 324	96.71 281	74.74 329	93.86 170	96.06 189

IterMVS-SCA-FT		85.45 285	84.53 291	88.18 307	91.71 306	76.87 307	90.19 341	92.65 306	85.40 189	81.44 330	90.54 318	66.79 304	95.00 361	81.04 253	81.05 348	92.66 337

EPNet_dtu		86.49 267	85.94 252	88.14 308	90.24 362	72.82 357	94.11 200	92.20 318	86.66 159	79.42 359	92.36 252	73.52 218	95.81 335	71.26 350	93.66 173	95.80 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 325	80.93 331	88.06 309	90.05 366	76.37 317	84.74 409	91.96 328	72.28 396	81.32 333	87.87 375	71.03 247	95.50 349	68.97 368	80.15 363	92.32 349

test_vis1_n_1920		89.39 164	89.84 136	88.04 310	92.97 269	72.64 362	94.71 160	96.03 150	86.18 170	91.94 115	96.56 90	61.63 342	95.74 339	93.42 56	95.11 146	95.74 202

DTE-MVSNet		86.11 273	85.48 266	87.98 311	91.65 310	74.92 333	94.93 142	95.75 172	87.36 141	82.26 319	93.04 231	72.85 229	95.82 334	74.04 334	77.46 381	93.20 318

PMMVS		85.71 282	84.96 280	87.95 312	88.90 381	77.09 304	88.68 369	90.06 374	72.32 395	86.47 212	90.76 313	72.15 237	94.40 366	81.78 242	93.49 179	92.36 347

GG-mvs-BLEND				87.94 313	89.73 373	77.91 286	87.80 380	78.23 429		80.58 342	83.86 404	59.88 361	95.33 354	71.20 351	92.22 206	90.60 385

MonoMVSNet		86.89 249	86.55 225	87.92 314	89.46 376	73.75 345	94.12 198	93.10 291	87.82 130	85.10 259	90.76 313	69.59 271	94.94 362	86.47 170	82.50 327	95.07 224

reproduce_monomvs		86.37 270	85.87 254	87.87 315	93.66 245	73.71 346	93.44 238	95.02 220	88.61 100	82.64 316	91.94 271	57.88 375	96.68 282	89.96 127	79.71 369	93.22 316

pmmvs-eth3d		80.97 348	78.72 359	87.74 316	84.99 411	79.97 236	90.11 343	91.65 335	75.36 364	73.51 399	86.03 394	59.45 364	93.96 376	75.17 323	72.21 395	89.29 398

MS-PatchMatch		85.05 296	84.16 295	87.73 317	91.42 316	78.51 270	91.25 315	93.53 283	77.50 344	80.15 347	91.58 285	61.99 339	95.51 347	75.69 318	94.35 164	89.16 400

mmtdpeth		85.04 298	84.15 296	87.72 318	93.11 260	75.74 325	94.37 186	92.83 299	84.98 199	89.31 159	86.41 391	61.61 344	97.14 256	92.63 72	62.11 419	90.29 387

test_0402		81.30 344	79.17 353	87.67 319	93.19 257	78.17 280	92.98 263	91.71 331	75.25 366	76.02 386	90.31 324	59.23 366	96.37 307	50.22 422	83.63 313	88.47 407

IterMVS		84.88 300	83.98 301	87.60 320	91.44 313	76.03 320	90.18 342	92.41 309	83.24 243	81.06 336	90.42 323	66.60 307	94.28 370	79.46 277	80.98 353	92.48 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 342	79.30 349	87.58 321	90.92 340	74.16 343	80.99 421	87.68 398	70.52 403	76.63 380	88.81 358	71.21 244	92.76 391	60.01 409	86.93 288	95.83 198

EPMVS		83.90 317	82.70 321	87.51 322	90.23 363	72.67 360	88.62 370	81.96 419	81.37 291	85.01 262	88.34 366	66.31 311	94.45 364	75.30 322	87.12 285	95.43 212

ADS-MVSNet2		81.66 336	79.71 345	87.50 323	91.35 319	74.19 342	83.33 414	88.48 392	72.90 390	82.24 320	85.77 397	64.98 321	93.20 387	64.57 395	83.74 310	95.12 222

OurMVSNet-221017-0		85.35 289	84.64 288	87.49 324	90.77 347	72.59 364	94.01 211	94.40 252	84.72 209	79.62 358	93.17 225	61.91 340	96.72 279	81.99 236	81.16 344	93.16 320

tpm2		84.08 312	82.94 316	87.48 325	91.39 317	71.27 377	89.23 361	90.37 366	71.95 397	84.64 268	89.33 349	67.30 296	96.55 295	75.17 323	87.09 286	94.63 243

RPSCF		85.07 295	84.27 292	87.48 325	92.91 271	70.62 387	91.69 304	92.46 308	76.20 358	82.67 315	95.22 143	63.94 327	97.29 243	77.51 300	85.80 293	94.53 250

myMVS_eth3d28		85.80 280	85.26 274	87.42 327	94.73 179	69.92 392	90.60 329	90.95 355	87.21 143	86.06 226	90.04 334	59.47 363	96.02 322	74.89 328	93.35 186	96.33 171

WBMVS		84.97 299	84.18 294	87.34 328	94.14 221	71.62 376	90.20 340	92.35 311	81.61 286	84.06 287	90.76 313	61.82 341	96.52 296	78.93 285	83.81 308	93.89 279

miper_lstm_enhance		85.27 292	84.59 289	87.31 329	91.28 322	74.63 336	87.69 385	94.09 267	81.20 297	81.36 332	89.85 341	74.97 195	94.30 369	81.03 255	79.84 368	93.01 326

FMVSNet5		81.52 340	79.60 346	87.27 330	91.17 325	77.95 285	91.49 308	92.26 317	76.87 350	76.16 382	87.91 374	51.67 399	92.34 394	67.74 377	81.16 344	91.52 364

USDC		82.76 324	81.26 329	87.26 331	91.17 325	74.55 337	89.27 359	93.39 286	78.26 339	75.30 390	92.08 265	54.43 392	96.63 284	71.64 348	85.79 294	90.61 383

test-LLR		85.87 277	85.41 267	87.25 332	90.95 336	71.67 374	89.55 353	89.88 380	83.41 237	84.54 271	87.95 372	67.25 297	95.11 358	81.82 240	93.37 184	94.97 227

test-mter		84.54 307	83.64 305	87.25 332	90.95 336	71.67 374	89.55 353	89.88 380	79.17 319	84.54 271	87.95 372	55.56 383	95.11 358	81.82 240	93.37 184	94.97 227

JIA-IIPM		81.04 345	78.98 357	87.25 332	88.64 382	73.48 350	81.75 420	89.61 386	73.19 387	82.05 323	73.71 423	66.07 316	95.87 331	71.18 353	84.60 302	92.41 345

TDRefinement		79.81 359	77.34 365	87.22 335	79.24 426	75.48 328	93.12 254	92.03 323	76.45 353	75.01 391	91.58 285	49.19 406	96.44 303	70.22 361	69.18 405	89.75 392

tpmvs		83.35 323	82.07 322	87.20 336	91.07 331	71.00 383	88.31 374	91.70 332	78.91 322	80.49 344	87.18 384	69.30 278	97.08 259	68.12 376	83.56 314	93.51 306

ppachtmachnet_test		81.84 332	80.07 340	87.15 337	88.46 386	74.43 340	89.04 365	92.16 319	75.33 365	77.75 371	88.99 355	66.20 313	95.37 353	65.12 392	77.60 379	91.65 360

dmvs_re		84.20 311	83.22 312	87.14 338	91.83 302	77.81 291	90.04 345	90.19 370	84.70 210	81.49 328	89.17 351	64.37 325	91.13 405	71.58 349	85.65 295	92.46 343

tpm cat1		81.96 330	80.27 336	87.01 339	91.09 330	71.02 382	87.38 389	91.53 340	66.25 413	80.17 346	86.35 393	68.22 293	96.15 318	69.16 367	82.29 330	93.86 285

test_fmvs1_n		87.03 245	87.04 206	86.97 340	89.74 372	71.86 369	94.55 168	94.43 250	78.47 333	91.95 114	95.50 133	51.16 401	93.81 377	93.02 64	94.56 158	95.26 218

OpenMVS_ROB	74.94 19	79.51 363	77.03 370	86.93 341	87.00 399	76.23 319	92.33 285	90.74 361	68.93 407	74.52 395	88.23 369	49.58 404	96.62 285	57.64 414	84.29 304	87.94 410

SixPastTwentyTwo		83.91 316	82.90 318	86.92 342	90.99 334	70.67 386	93.48 235	91.99 325	85.54 186	77.62 373	92.11 263	60.59 356	96.87 275	76.05 316	77.75 378	93.20 318

ADS-MVSNet		81.56 338	79.78 342	86.90 343	91.35 319	71.82 370	83.33 414	89.16 390	72.90 390	82.24 320	85.77 397	64.98 321	93.76 378	64.57 395	83.74 310	95.12 222

PatchT		82.68 326	81.27 328	86.89 344	90.09 365	70.94 384	84.06 411	90.15 371	74.91 370	85.63 236	83.57 406	69.37 274	94.87 363	65.19 390	88.50 262	94.84 237

tpm		84.73 303	84.02 299	86.87 345	90.33 360	68.90 395	89.06 364	89.94 377	80.85 300	85.75 232	89.86 340	68.54 290	95.97 325	77.76 296	84.05 307	95.75 201

Patchmatch-RL test		81.67 335	79.96 341	86.81 346	85.42 409	71.23 378	82.17 419	87.50 399	78.47 333	77.19 375	82.50 413	70.81 251	93.48 382	82.66 221	72.89 394	95.71 205

test_vis1_n		86.56 262	86.49 229	86.78 347	88.51 383	72.69 359	94.68 161	93.78 279	79.55 315	90.70 137	95.31 139	48.75 407	93.28 385	93.15 60	93.99 167	94.38 261

testing3-2		86.72 256	86.71 215	86.74 348	96.11 105	65.92 406	93.39 240	89.65 385	89.46 64	87.84 184	92.79 240	59.17 368	97.60 207	81.31 249	90.72 224	96.70 159

test_fmvs1		87.34 228	87.56 192	86.68 349	90.59 353	71.80 371	94.01 211	94.04 268	78.30 337	91.97 112	95.22 143	56.28 381	93.71 379	92.89 65	94.71 152	94.52 251

MDA-MVSNet-bldmvs		78.85 368	76.31 373	86.46 350	89.76 371	73.88 344	88.79 367	90.42 365	79.16 320	59.18 423	88.33 367	60.20 358	94.04 372	62.00 402	68.96 406	91.48 367

mvs5depth		80.98 347	79.15 354	86.45 351	84.57 412	73.29 352	87.79 381	91.67 334	80.52 303	82.20 322	89.72 343	55.14 388	95.93 327	73.93 337	66.83 411	90.12 389

tpmrst		85.35 289	84.99 278	86.43 352	90.88 343	67.88 400	88.71 368	91.43 343	80.13 307	86.08 225	88.80 360	73.05 227	96.02 322	82.48 222	83.40 318	95.40 213

TESTMET0.1,1		83.74 319	82.85 319	86.42 353	89.96 368	71.21 379	89.55 353	87.88 395	77.41 345	83.37 306	87.31 380	56.71 379	93.65 381	80.62 263	92.85 196	94.40 260

our_test_3		81.93 331	80.46 334	86.33 354	88.46 386	73.48 350	88.46 372	91.11 348	76.46 352	76.69 379	88.25 368	66.89 302	94.36 367	68.75 369	79.08 374	91.14 375

lessismore_v0				86.04 355	88.46 386	68.78 396		80.59 422		73.01 402	90.11 332	55.39 384	96.43 304	75.06 325	65.06 414	92.90 329

TinyColmap		79.76 360	77.69 363	85.97 356	91.71 306	73.12 353	89.55 353	90.36 367	75.03 368	72.03 405	90.19 328	46.22 416	96.19 317	63.11 399	81.03 349	88.59 406

KD-MVS_2432*1600		78.50 369	76.02 376	85.93 357	86.22 402	74.47 338	84.80 407	92.33 312	79.29 317	76.98 376	85.92 395	53.81 395	93.97 374	67.39 378	57.42 424	89.36 394

miper_refine_blended		78.50 369	76.02 376	85.93 357	86.22 402	74.47 338	84.80 407	92.33 312	79.29 317	76.98 376	85.92 395	53.81 395	93.97 374	67.39 378	57.42 424	89.36 394

K. test v3		81.59 337	80.15 339	85.91 359	89.89 370	69.42 394	92.57 276	87.71 397	85.56 185	73.44 400	89.71 344	55.58 382	95.52 346	77.17 303	69.76 402	92.78 334

SSC-MVS3.2		84.60 306	84.19 293	85.85 360	92.74 274	68.07 397	88.15 376	93.81 277	87.42 140	83.76 295	91.07 302	62.91 334	95.73 340	74.56 332	83.24 319	93.75 295

mvsany_test1		85.42 287	85.30 272	85.77 361	87.95 395	75.41 329	87.61 388	80.97 421	76.82 351	88.68 168	95.83 119	77.44 164	90.82 407	85.90 177	86.51 289	91.08 379

MIMVSNet1		79.38 364	77.28 366	85.69 362	86.35 401	73.67 347	91.61 306	92.75 303	78.11 342	72.64 403	88.12 370	48.16 408	91.97 399	60.32 406	77.49 380	91.43 369

UWE-MVS		83.69 320	83.09 313	85.48 363	93.06 263	65.27 411	90.92 322	86.14 403	79.90 310	86.26 221	90.72 316	57.17 378	95.81 335	71.03 356	92.62 200	95.35 216

UnsupCasMVSNet_eth		80.07 356	78.27 362	85.46 364	85.24 410	72.63 363	88.45 373	94.87 233	82.99 249	71.64 407	88.07 371	56.34 380	91.75 400	73.48 340	63.36 417	92.01 355

CL-MVSNet_self_test		81.74 334	80.53 332	85.36 365	85.96 404	72.45 366	90.25 335	93.07 293	81.24 295	79.85 355	87.29 381	70.93 249	92.52 392	66.95 381	69.23 404	91.11 377

MDA-MVSNet_test_wron		79.21 366	77.19 368	85.29 366	88.22 390	72.77 358	85.87 398	90.06 374	74.34 375	62.62 420	87.56 378	66.14 314	91.99 398	66.90 385	73.01 392	91.10 378

YYNet1		79.22 365	77.20 367	85.28 367	88.20 391	72.66 361	85.87 398	90.05 376	74.33 376	62.70 418	87.61 377	66.09 315	92.03 396	66.94 382	72.97 393	91.15 374

WB-MVSnew		83.77 318	83.28 309	85.26 368	91.48 312	71.03 381	91.89 296	87.98 394	78.91 322	84.78 265	90.22 326	69.11 283	94.02 373	64.70 394	90.44 227	90.71 381

dp		81.47 341	80.23 337	85.17 369	89.92 369	65.49 409	86.74 393	90.10 373	76.30 356	81.10 334	87.12 385	62.81 335	95.92 328	68.13 375	79.88 366	94.09 272

UnsupCasMVSNet_bld		76.23 378	73.27 382	85.09 370	83.79 414	72.92 355	85.65 401	93.47 285	71.52 398	68.84 413	79.08 418	49.77 403	93.21 386	66.81 386	60.52 421	89.13 402

Anonymous20231206		81.03 346	79.77 344	84.82 371	87.85 396	70.26 389	91.42 309	92.08 321	73.67 382	77.75 371	89.25 350	62.43 337	93.08 388	61.50 404	82.00 335	91.12 376

test0.0.03 1		82.41 328	81.69 324	84.59 372	88.23 389	72.89 356	90.24 337	87.83 396	83.41 237	79.86 354	89.78 342	67.25 297	88.99 417	65.18 391	83.42 317	91.90 357

CMPMVS	59.16 21	80.52 350	79.20 352	84.48 373	83.98 413	67.63 403	89.95 348	93.84 276	64.79 417	66.81 415	91.14 299	57.93 374	95.17 356	76.25 313	88.10 268	90.65 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 305	84.79 285	84.37 374	91.84 300	64.92 412	93.70 229	91.47 342	66.19 414	86.16 224	95.28 140	67.18 299	93.33 384	80.89 258	90.42 229	94.88 236

PVSNet_0	73.20 20	77.22 374	74.83 380	84.37 374	90.70 351	71.10 380	83.09 416	89.67 383	72.81 392	73.93 398	83.13 408	60.79 355	93.70 380	68.54 370	50.84 429	88.30 408

LF4IMVS		80.37 353	79.07 356	84.27 376	86.64 400	69.87 393	89.39 358	91.05 351	76.38 354	74.97 392	90.00 336	47.85 409	94.25 371	74.55 333	80.82 355	88.69 405

Anonymous20240521		80.44 352	79.21 351	84.11 377	85.75 407	67.89 399	92.86 269	93.23 289	75.61 363	75.59 389	87.47 379	50.03 402	94.33 368	71.14 354	81.21 343	90.12 389

PM-MVS		78.11 371	76.12 375	84.09 378	83.54 415	70.08 390	88.97 366	85.27 410	79.93 309	74.73 394	86.43 390	34.70 427	93.48 382	79.43 280	72.06 396	88.72 404

test_fmvs2		83.98 313	84.03 298	83.83 379	87.16 398	67.53 404	93.93 217	92.89 297	77.62 343	86.89 205	93.53 213	47.18 411	92.02 397	90.54 121	86.51 289	91.93 356

testgi		80.94 349	80.20 338	83.18 380	87.96 394	66.29 405	91.28 313	90.70 363	83.70 228	78.12 367	92.84 235	51.37 400	90.82 407	63.34 398	82.46 328	92.43 344

KD-MVS_self_test		80.20 354	79.24 350	83.07 381	85.64 408	65.29 410	91.01 321	93.93 270	78.71 331	76.32 381	86.40 392	59.20 367	92.93 390	72.59 344	69.35 403	91.00 380

testing3		80.46 351	79.59 347	83.06 382	93.44 252	64.64 413	93.33 242	85.47 408	84.34 216	79.93 353	90.84 309	44.35 419	92.39 393	57.06 416	87.56 278	92.16 353

ambc				83.06 382	79.99 424	63.51 417	77.47 427	92.86 298		74.34 397	84.45 403	28.74 428	95.06 360	73.06 342	68.89 407	90.61 383

test20.03		79.95 358	79.08 355	82.55 384	85.79 406	67.74 402	91.09 319	91.08 349	81.23 296	74.48 396	89.96 338	61.63 342	90.15 409	60.08 407	76.38 386	89.76 391

MVStest1		72.91 382	69.70 387	82.54 385	78.14 427	73.05 354	88.21 375	86.21 402	60.69 421	64.70 416	90.53 319	46.44 414	85.70 424	58.78 412	53.62 426	88.87 403

test_vis1_rt		77.96 372	76.46 372	82.48 386	85.89 405	71.74 373	90.25 335	78.89 425	71.03 402	71.30 408	81.35 415	42.49 421	91.05 406	84.55 194	82.37 329	84.65 413

EU-MVSNet		81.32 343	80.95 330	82.42 387	88.50 385	63.67 416	93.32 243	91.33 344	64.02 418	80.57 343	92.83 236	61.21 351	92.27 395	76.34 312	80.38 362	91.32 370

myMVS_eth3d		79.67 361	78.79 358	82.32 388	91.92 296	64.08 414	89.75 351	87.40 400	81.72 281	78.82 362	87.20 382	45.33 417	91.29 403	59.09 411	87.84 275	91.60 362

ttmdpeth		76.55 376	74.64 381	82.29 389	82.25 420	67.81 401	89.76 350	85.69 406	70.35 404	75.76 387	91.69 278	46.88 412	89.77 411	66.16 387	63.23 418	89.30 396

pmmvs3		71.81 385	68.71 388	81.11 390	75.86 429	70.42 388	86.74 393	83.66 414	58.95 424	68.64 414	80.89 416	36.93 425	89.52 413	63.10 400	63.59 416	83.39 414

Syy-MVS		80.07 356	79.78 342	80.94 391	91.92 296	59.93 423	89.75 351	87.40 400	81.72 281	78.82 362	87.20 382	66.29 312	91.29 403	47.06 424	87.84 275	91.60 362

UWE-MVS-28		78.98 367	78.38 361	80.80 392	88.18 392	60.66 422	90.65 327	78.51 426	78.84 326	77.93 370	90.93 306	59.08 369	89.02 416	50.96 421	90.33 231	92.72 335

new-patchmatchnet		76.41 377	75.17 379	80.13 393	82.65 419	59.61 424	87.66 386	91.08 349	78.23 340	69.85 411	83.22 407	54.76 389	91.63 402	64.14 397	64.89 415	89.16 400

mvsany_test3		74.95 379	73.26 383	80.02 394	74.61 430	63.16 418	85.53 402	78.42 427	74.16 377	74.89 393	86.46 389	36.02 426	89.09 415	82.39 225	66.91 410	87.82 411

test_fmvs3		77.67 373	77.16 369	79.22 395	79.52 425	61.14 420	92.34 284	91.64 336	73.98 379	78.86 361	86.59 388	27.38 431	87.03 419	88.12 148	75.97 388	89.50 393

DSMNet-mixed		76.94 375	76.29 374	78.89 396	83.10 417	56.11 432	87.78 382	79.77 423	60.65 422	75.64 388	88.71 361	61.56 345	88.34 418	60.07 408	89.29 251	92.21 352

EGC-MVSNET		61.97 393	56.37 398	78.77 397	89.63 374	73.50 349	89.12 363	82.79 416	0.21 443	1.24 444	84.80 401	39.48 422	90.04 410	44.13 426	75.94 389	72.79 425

new_pmnet		72.15 383	70.13 386	78.20 398	82.95 418	65.68 407	83.91 412	82.40 418	62.94 420	64.47 417	79.82 417	42.85 420	86.26 423	57.41 415	74.44 391	82.65 418

MVS-HIRNet		73.70 381	72.20 384	78.18 399	91.81 303	56.42 431	82.94 417	82.58 417	55.24 425	68.88 412	66.48 428	55.32 386	95.13 357	58.12 413	88.42 264	83.01 416

LCM-MVSNet		66.00 390	62.16 395	77.51 400	64.51 440	58.29 426	83.87 413	90.90 357	48.17 429	54.69 426	73.31 424	16.83 440	86.75 420	65.47 389	61.67 420	87.48 412

APD_test1		69.04 386	66.26 392	77.36 401	80.51 423	62.79 419	85.46 403	83.51 415	54.11 427	59.14 424	84.79 402	23.40 434	89.61 412	55.22 417	70.24 401	79.68 422

test_f		71.95 384	70.87 385	75.21 402	74.21 432	59.37 425	85.07 406	85.82 405	65.25 416	70.42 410	83.13 408	23.62 432	82.93 430	78.32 290	71.94 397	83.33 415

ANet_high		58.88 397	54.22 402	72.86 403	56.50 443	56.67 428	80.75 422	86.00 404	73.09 389	37.39 435	64.63 431	22.17 435	79.49 433	43.51 427	23.96 437	82.43 419

test_vis3_rt		65.12 391	62.60 393	72.69 404	71.44 433	60.71 421	87.17 390	65.55 437	63.80 419	53.22 427	65.65 430	14.54 441	89.44 414	76.65 307	65.38 413	67.91 428

FPMVS		64.63 392	62.55 394	70.88 405	70.80 434	56.71 427	84.42 410	84.42 412	51.78 428	49.57 428	81.61 414	23.49 433	81.48 431	40.61 431	76.25 387	74.46 424

dmvs_testset		74.57 380	75.81 378	70.86 406	87.72 397	40.47 441	87.05 392	77.90 431	82.75 254	71.15 409	85.47 399	67.98 294	84.12 428	45.26 425	76.98 385	88.00 409

N_pmnet		68.89 387	68.44 389	70.23 407	89.07 379	28.79 446	88.06 377	19.50 446	69.47 406	71.86 406	84.93 400	61.24 350	91.75 400	54.70 418	77.15 382	90.15 388

testf1		59.54 395	56.11 399	69.85 408	69.28 435	56.61 429	80.37 423	76.55 434	42.58 432	45.68 431	75.61 419	11.26 442	84.18 426	43.20 428	60.44 422	68.75 426

APD_test2		59.54 395	56.11 399	69.85 408	69.28 435	56.61 429	80.37 423	76.55 434	42.58 432	45.68 431	75.61 419	11.26 442	84.18 426	43.20 428	60.44 422	68.75 426

WB-MVS		67.92 388	67.49 390	69.21 410	81.09 421	41.17 440	88.03 378	78.00 430	73.50 384	62.63 419	83.11 410	63.94 327	86.52 421	25.66 436	51.45 428	79.94 421

PMMVS2		59.60 394	56.40 397	69.21 410	68.83 437	46.58 436	73.02 431	77.48 432	55.07 426	49.21 429	72.95 425	17.43 439	80.04 432	49.32 423	44.33 432	80.99 420

SSC-MVS		67.06 389	66.56 391	68.56 412	80.54 422	40.06 442	87.77 383	77.37 433	72.38 394	61.75 421	82.66 412	63.37 330	86.45 422	24.48 437	48.69 431	79.16 423

Gipuma		57.99 399	54.91 401	67.24 413	88.51 383	65.59 408	52.21 434	90.33 368	43.58 431	42.84 434	51.18 435	20.29 437	85.07 425	34.77 432	70.45 400	51.05 434
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 401	48.46 405	63.48 414	45.72 445	46.20 437	73.41 430	78.31 428	41.03 434	30.06 437	65.68 429	6.05 444	83.43 429	30.04 434	65.86 412	60.80 429
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 398	58.24 396	60.56 415	83.13 416	45.09 439	82.32 418	48.22 445	67.61 410	61.70 422	69.15 426	38.75 423	76.05 434	32.01 433	41.31 433	60.55 430

MVE	39.65 23	43.39 403	38.59 409	57.77 416	56.52 442	48.77 435	55.38 433	58.64 441	29.33 437	28.96 438	52.65 434	4.68 445	64.62 438	28.11 435	33.07 435	59.93 431
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 402	48.47 404	56.66 417	52.26 444	18.98 448	41.51 436	81.40 420	10.10 438	44.59 433	75.01 422	28.51 429	68.16 435	53.54 419	49.31 430	82.83 417

DeepMVS_CX				56.31 418	74.23 431	51.81 434		56.67 442	44.85 430	48.54 430	75.16 421	27.87 430	58.74 440	40.92 430	52.22 427	58.39 432

kuosan		53.51 400	53.30 403	54.13 419	76.06 428	45.36 438	80.11 425	48.36 444	59.63 423	54.84 425	63.43 432	37.41 424	62.07 439	20.73 439	39.10 434	54.96 433

E-PMN		43.23 404	42.29 406	46.03 420	65.58 439	37.41 443	73.51 429	64.62 438	33.99 435	28.47 439	47.87 436	19.90 438	67.91 436	22.23 438	24.45 436	32.77 435

EMVS		42.07 405	41.12 407	44.92 421	63.45 441	35.56 445	73.65 428	63.48 439	33.05 436	26.88 440	45.45 437	21.27 436	67.14 437	19.80 440	23.02 438	32.06 436

tmp_tt		35.64 406	39.24 408	24.84 422	14.87 446	23.90 447	62.71 432	51.51 443	6.58 440	36.66 436	62.08 433	44.37 418	30.34 442	52.40 420	22.00 439	20.27 437

wuyk23d		21.27 408	20.48 411	23.63 423	68.59 438	36.41 444	49.57 435	6.85 447	9.37 439	7.89 441	4.46 443	4.03 446	31.37 441	17.47 441	16.07 440	3.12 438

test123		8.76 410	11.22 413	1.39 424	0.85 448	0.97 449	85.76 400	0.35 449	0.54 442	2.45 443	8.14 442	0.60 447	0.48 443	2.16 443	0.17 442	2.71 439

testmvs		8.92 409	11.52 412	1.12 425	1.06 447	0.46 450	86.02 397	0.65 448	0.62 441	2.74 442	9.52 441	0.31 448	0.45 444	2.38 442	0.39 441	2.46 440

mmdepth		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

monomultidepth		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

test_blank		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uanet_test		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

DCPMVS		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

cdsmvs_eth3d_5k		22.14 407	29.52 410	0.00 426	0.00 449	0.00 451	0.00 437	95.76 171	0.00 444	0.00 445	94.29 183	75.66 187	0.00 445	0.00 444	0.00 443	0.00 441

pcd_1.5k_mvsjas		6.64 412	8.86 415	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	79.70 136	0.00 445	0.00 444	0.00 443	0.00 441

sosnet-low-res		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

sosnet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uncertanet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

Regformer		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

ab-mvs-re		7.82 411	10.43 414	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	93.88 203	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

uanet		0.00 413	0.00 416	0.00 426	0.00 449	0.00 451	0.00 437	0.00 450	0.00 444	0.00 445	0.00 444	0.00 449	0.00 445	0.00 444	0.00 443	0.00 441

WAC-MVS						64.08 414								59.14 410

FOURS1					98.86 1	85.54 67	98.29 1	97.49 7	89.79 55	96.29 25

PC_three_1452									82.47 258	97.09 14	97.07 63	92.72 1	98.04 177	92.70 71	99.02 12	98.86 11

test_one_0601					98.58 11	85.83 61		97.44 16	91.05 16	96.78 21	98.06 18	91.45 11

eth-test2					0.00 449

eth-test					0.00 449

ZD-MVS					98.15 34	86.62 33		97.07 53	83.63 230	94.19 55	96.91 69	87.57 31	99.26 45	91.99 95	98.44 53

RE-MVS-def			93.68 64		97.92 43	84.57 87	96.28 43	96.76 84	87.46 137	93.75 66	97.43 42	82.94 93		92.73 67	97.80 82	97.88 88

IU-MVS					98.77 5	86.00 50		96.84 74	81.26 294	97.26 10				95.50 32	99.13 3	99.03 8

test_241102_TWO								97.44 16	90.31 33	97.62 5	98.07 16	91.46 10	99.58 10	95.66 26	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 52		97.44 16	90.26 39	97.71 1	97.96 26	92.31 4	99.38 31

9.14			94.47 27		97.79 52		96.08 61	97.44 16	86.13 174	95.10 45	97.40 44	88.34 22	99.22 47	93.25 59	98.70 34

save fliter					97.85 49	85.63 66	95.21 125	96.82 77	89.44 65

test_0728_THIRD									90.75 22	97.04 16	98.05 20	92.09 6	99.55 16	95.64 28	99.13 3	99.13 2

test0726					98.78 3	85.93 55	97.19 11	97.47 12	90.27 37	97.64 4	98.13 5	91.47 8

GSMVS																96.12 183

test_part2					98.55 12	87.22 19				96.40 24

sam_mvs1												71.70 240				96.12 183

sam_mvs												70.60 254

MTGPA								96.97 58

test_post1							88.00 379				9.81 440	69.31 277	95.53 345	76.65 307

test_post											10.29 439	70.57 258	95.91 330

patchmatchnet-post											83.76 405	71.53 241	96.48 299

MTMP							96.16 52	60.64 440

gm-plane-assit					89.60 375	68.00 398			77.28 348		88.99 355		97.57 209	79.44 279

test9_res														91.91 99	98.71 32	98.07 75

TEST9					97.53 61	86.49 37	94.07 205	96.78 81	81.61 286	92.77 91	96.20 99	87.71 28	99.12 55

test_8					97.49 63	86.30 45	94.02 210	96.76 84	81.86 277	92.70 95	96.20 99	87.63 29	99.02 65

agg_prior2														90.54 121	98.68 37	98.27 57

agg_prior					97.38 66	85.92 57		96.72 91		92.16 107			98.97 79

test_prior4						85.96 54	94.11 200

test_prior2							94.12 198		87.67 135	92.63 97	96.39 94	86.62 40		91.50 107	98.67 40

旧先验2							93.36 241		71.25 400	94.37 51			97.13 257	86.74 166

新几何2							93.11 256

旧先验1					96.79 79	81.81 174		95.67 179			96.81 75	86.69 39			97.66 88	96.97 144

无先验							93.28 249	96.26 126	73.95 380				99.05 59	80.56 264		96.59 163

原ACMM2							92.94 265

test222					96.55 88	81.70 176	92.22 289	95.01 221	68.36 409	90.20 145	96.14 104	80.26 129			97.80 82	96.05 190

testdata2													98.75 105	78.30 291

segment_acmp												87.16 36

testdata1							92.15 291		87.94 122

plane_prior7					94.70 183	82.74 151

plane_prior6					94.52 196	82.75 149						74.23 205

plane_prior5								96.22 131					98.12 162	88.15 145	89.99 234	94.63 243

plane_prior4											94.86 160

plane_prior3						82.75 149			90.26 39	86.91 202

plane_prior2							95.85 83		90.81 20

plane_prior1					94.59 189

plane_prior						82.73 152	95.21 125		89.66 60						89.88 239

n2								0.00 450

nn								0.00 450

door-mid								85.49 407

test11								96.57 102

door								85.33 409

HQP5-MVS						81.56 178

HQP-NCC					94.17 217		94.39 182		88.81 90	85.43 248

ACMP_Plane					94.17 217		94.39 182		88.81 90	85.43 248

BP-MVS														87.11 163

HQP4-MVS										85.43 248			97.96 183			94.51 253

HQP3-MVS								96.04 148							89.77 243

HQP2-MVS												73.83 215

NP-MVS					94.37 207	82.42 161					93.98 196

MDTV_nov1_ep13_2view						55.91 433	87.62 387		73.32 386	84.59 270		70.33 261		74.65 330		95.50 210

MDTV_nov1_ep13			83.56 306		91.69 308	69.93 391	87.75 384	91.54 339	78.60 332	84.86 264	88.90 357	69.54 272	96.03 321	70.25 359	88.93 256

ACMMP++_ref															87.47 279

ACMMP++															88.01 271

Test By Simon												80.02 131

High-resolution multi-view benchmark