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	94.37 270	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	84.02 306	96.62 88	75.95 192	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	93.01 314	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	92.66 286	96.56 105	83.44 254	91.68 129	95.04 161	86.60 43	98.99 76	85.60 191	97.92 79	96.93 160

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	94.60 185	82.33 104	98.62 124	92.40 79	92.86 207	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	94.60 185	82.33 104	98.62 124	92.40 79	92.86 207	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		88.31 210	86.13 259	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	23.41 457	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	94.87 169	80.56 127	98.66 117	92.42 78	93.10 203	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	85.15 212	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	81.86 296	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	80.09 327	92.77 93	96.63 87	86.62 41	99.04 63	87.40 164	98.66 41	98.17 69

3Dnovator	86.66 5	91.73 108	90.82 121	94.44 45	94.59 198	86.37 41	97.18 13	97.02 56	89.20 81	84.31 301	96.66 83	73.74 230	99.17 51	86.74 174	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	93.93 288	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	77.97 162	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	94.43 266	91.65 15	92.68 98	96.13 107	77.97 162	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	90.15 157	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	89.56 163	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	94.66 182	82.11 111	98.50 132	92.33 84	92.82 210	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	90.10 383	85.25 75	96.03 71	92.05 341	92.83 5	87.39 212	95.78 127	79.39 144	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	94.62 196	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	93.06 272	97.13 49	90.74 28	91.84 123	95.09 160	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	82.69 274	90.03 159	95.82 125	82.30 106	99.03 64	84.57 209	96.48 121	96.91 162

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	92.46 299	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	96.91 287	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	93.24 263	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	97.16 141

CSCG		93.23 79	93.05 80	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 235	90.05 158	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	94.55 204	84.69 86	95.60 110	96.56 105	87.83 134	93.07 84	95.89 120	73.44 234	98.65 119	90.22 131	96.03 130	97.91 92

BP-MVS1		92.48 95	92.07 98	93.72 75	94.50 207	84.39 101	95.90 82	94.30 273	90.39 35	92.67 100	95.94 117	74.46 213	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	89.25 396	84.42 100	96.06 68	96.29 124	89.06 85	94.68 50	98.13 6	79.22 146	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	84.47 211	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	97.07 146

QAPM		89.51 169	88.15 194	93.59 79	94.92 173	84.58 88	96.82 30	96.70 95	78.43 354	83.41 322	96.19 104	73.18 239	99.30 44	77.11 322	96.54 118	96.89 163

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	97.17 137

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 177	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	94.72 189	83.36 133	95.45 113	96.37 119	90.33 37	92.17 111	96.03 112	72.32 251	98.75 109	87.94 156	96.34 123	98.07 77

casdiffmvs_mvg		92.96 87	92.83 85	93.35 83	94.59 198	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	97.25 131

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	75.09 204	98.31 159	84.75 203	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	77.85 168	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	93.86 292	88.42 111	92.53 103	96.84 74	62.09 357	98.64 121	90.95 119	92.62 217	97.93 89

Elysia		90.12 146	89.10 164	93.18 91	93.16 272	84.05 110	95.22 129	96.27 128	85.16 210	90.59 145	94.68 178	64.64 340	98.37 149	86.38 180	95.77 133	97.12 143

StellarMVS		90.12 146	89.10 164	93.18 91	93.16 272	84.05 110	95.22 129	96.27 128	85.16 210	90.59 145	94.68 178	64.64 340	98.37 149	86.38 180	95.77 133	97.12 143

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	93.07 278	86.91 22	96.41 38	96.26 132	88.30 114	88.37 188	94.85 172	82.19 110	97.64 216	91.09 114	82.95 339	94.96 248

MVSMamba_PlusPlus		93.44 69	93.54 70	93.14 95	96.58 89	83.05 148	96.06 68	96.50 110	84.42 232	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	97.17 137

test_fmvsmvis_n_1920		93.44 69	93.55 69	93.10 97	93.67 258	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	96.32 188

新几何1				93.10 97	97.30 71	84.35 103		95.56 193	71.09 420	91.26 137	96.24 99	82.87 97	98.86 95	79.19 301	98.10 70	96.07 204

OMC-MVS		91.23 116	90.62 124	93.08 99	96.27 100	84.07 108	93.52 245	95.93 162	86.95 155	89.51 164	96.13 107	78.50 156	98.35 153	85.84 189	92.90 206	96.83 170

OpenMVS	83.78 11	88.74 197	87.29 216	93.08 99	92.70 294	85.39 73	96.57 36	96.43 113	78.74 349	80.85 354	96.07 110	69.64 286	99.01 69	78.01 313	96.65 116	94.83 256

MAR-MVS		90.30 142	89.37 157	93.07 101	96.61 86	84.48 94	95.68 99	95.67 185	82.36 279	87.85 199	92.85 252	76.63 181	98.80 104	80.01 289	96.68 115	95.91 210
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	93.86 243	83.88 115	92.81 283	93.86 292	79.84 330	91.76 126	94.29 199	77.92 165	98.04 184	90.48 129	97.11 100	97.17 137

Effi-MVS+		91.59 111	91.11 113	93.01 103	94.35 220	83.39 132	94.60 172	95.10 228	87.10 151	90.57 147	93.10 247	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	96.88 164

MVS_111021_LR		92.47 96	92.29 96	92.98 105	95.99 119	84.43 98	93.08 269	96.09 149	88.20 119	91.12 139	95.72 131	81.33 123	97.76 205	91.74 105	97.37 96	96.75 172

fmvsm_s_conf0.1_n_a		93.19 80	93.26 74	92.97 106	92.49 297	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	96.86 165

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	91.19 313	84.06 78	98.34 154	91.72 106	96.54 118	96.54 183

LFMVS		90.08 149	89.13 163	92.95 108	96.71 82	82.32 176	96.08 64	89.91 397	86.79 159	92.15 113	96.81 77	62.60 355	98.34 154	87.18 168	93.90 178	98.19 67

UGNet		89.95 156	88.95 170	92.95 108	94.51 206	83.31 134	95.70 98	95.23 221	89.37 73	87.58 206	93.94 215	64.00 345	98.78 107	83.92 218	96.31 124	96.74 173
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	93.04 281	83.53 127	93.08 269	94.15 281	80.22 324	91.41 134	94.91 166	76.87 175	97.93 196	90.28 130	96.90 107	97.24 132
jason: jason.
DP-MVS		87.25 251	85.36 288	92.90 110	97.65 60	83.24 136	94.81 159	92.00 343	74.99 388	81.92 343	95.00 162	72.66 244	99.05 61	66.92 402	92.33 222	96.40 185

fmvsm_s_conf0.5_n_8		94.56 25	95.12 13	92.87 112	95.96 122	81.32 200	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	96.92 161

fmvsm_s_conf0.1_n		93.46 66	93.66 67	92.85 114	93.75 250	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	96.98 155

CANet_DTU		90.26 144	89.41 156	92.81 115	93.46 265	83.01 151	93.48 246	94.47 265	89.43 71	87.76 204	94.23 204	70.54 274	99.03 64	84.97 198	96.39 122	96.38 186

MVSFormer		91.68 110	91.30 108	92.80 116	93.86 243	83.88 115	95.96 77	95.90 166	84.66 228	91.76 126	94.91 166	77.92 165	97.30 254	89.64 135	97.11 100	97.24 132

PVSNet_Blended_VisFu		91.38 113	90.91 118	92.80 116	96.39 97	83.17 139	94.87 153	96.66 97	83.29 259	89.27 170	94.46 194	80.29 130	99.17 51	87.57 161	95.37 146	96.05 207

LuminaMVS		90.55 138	89.81 143	92.77 118	92.78 292	84.21 105	94.09 212	94.17 280	85.82 183	91.54 131	94.14 206	69.93 280	97.92 197	91.62 108	94.21 173	96.18 196

fmvsm_s_conf0.5_n_6		94.11 46	94.56 27	92.76 119	94.98 168	81.96 184	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		89.56 168	88.49 185	92.76 119	95.07 162	82.09 179	96.30 42	93.19 309	81.05 318	91.88 121	96.86 73	61.16 373	98.33 156	88.43 150	92.49 221	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	75.77 193	99.00 74	92.07 92	78.05 396	96.60 178

casdiffmvs		92.51 94	92.43 93	92.74 122	94.41 215	81.98 182	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	82.41 174	94.11 208	95.12 226	85.63 190	91.49 132	94.70 176	74.75 208	98.42 147	86.13 184	92.53 219	97.31 123

DCV-MVSNet		90.69 130	90.02 139	92.71 123	95.72 129	82.41 174	94.11 208	95.12 226	85.63 190	91.49 132	94.70 176	74.75 208	98.42 147	86.13 184	92.53 219	97.31 123

PCF-MVS	84.11 10	87.74 225	86.08 263	92.70 125	94.02 233	84.43 98	89.27 374	95.87 170	73.62 402	84.43 293	94.33 196	78.48 158	98.86 95	70.27 376	94.45 169	94.81 257
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	95.00 236	85.41 200	89.84 160	95.35 144	76.13 184	97.98 190	85.46 194	94.18 174	96.95 157

baseline		92.39 98	92.29 96	92.69 126	94.46 210	81.77 187	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	97.76 205	92.19 88	98.66 41	96.76 171

EC-MVSNet		93.44 69	93.71 65	92.63 129	95.21 155	82.43 171	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		89.41 174	88.35 187	92.60 130	95.15 160	82.65 168	92.20 304	95.60 192	83.97 239	88.55 184	93.70 229	74.16 221	98.21 165	82.46 242	89.37 266	96.94 159

LS3D		87.89 220	86.32 252	92.59 131	96.07 113	82.92 154	95.23 127	94.92 243	75.66 380	82.89 329	95.98 115	72.48 248	99.21 49	68.43 390	95.23 151	95.64 224

Anonymous20240529		88.09 216	86.59 241	92.58 132	96.53 92	81.92 185	95.99 74	95.84 172	74.11 397	89.06 174	95.21 154	61.44 365	98.81 103	83.67 225	87.47 297	97.01 153

fmvsm_s_conf0.5_n_3		94.49 27	95.13 12	92.56 133	95.49 143	81.10 210	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	81.98 182	96.76 31	96.49 111	81.89 295	90.24 151	96.44 95	78.59 154	98.61 126	89.68 134	97.85 82	97.06 147

114514_t		89.51 169	88.50 183	92.54 135	98.11 38	81.99 181	95.16 137	96.36 120	70.19 424	85.81 246	95.25 150	76.70 179	98.63 123	82.07 252	96.86 110	97.00 154

PAPM_NR		91.22 117	90.78 122	92.52 136	97.60 61	81.46 196	94.37 193	96.24 135	86.39 171	87.41 209	94.80 174	82.06 114	98.48 134	82.80 237	95.37 146	97.61 111

mamba_0408		89.06 187	87.92 201	92.50 137	94.76 183	82.66 164	79.84 443	94.64 261	85.18 205	88.96 176	95.00 162	76.00 189	97.98 190	83.74 222	93.15 200	96.85 166

DeepPCF-MVS	89.96 1	94.20 41	94.77 25	92.49 138	96.52 93	80.00 246	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

mamba_test_0407		90.47 140	89.80 144	92.46 139	94.76 183	82.66 164	93.98 224	95.00 236	85.41 200	88.96 176	95.35 144	76.13 184	97.88 200	85.46 194	93.15 200	96.85 166

IS-MVSNet		91.43 112	91.09 115	92.46 139	95.87 125	81.38 199	96.95 20	93.69 300	89.72 63	89.50 166	95.98 115	78.57 155	97.77 204	83.02 231	96.50 120	98.22 66

API-MVS		90.66 133	90.07 135	92.45 141	96.36 98	84.57 89	96.06 68	95.22 223	82.39 277	89.13 171	94.27 202	80.32 129	98.46 138	80.16 288	96.71 114	94.33 280

xiu_mvs_v1_base_debu		90.64 134	90.05 136	92.40 142	93.97 239	84.46 95	93.32 254	95.46 201	85.17 207	92.25 108	94.03 207	70.59 270	98.57 129	90.97 116	94.67 160	94.18 283

xiu_mvs_v1_base		90.64 134	90.05 136	92.40 142	93.97 239	84.46 95	93.32 254	95.46 201	85.17 207	92.25 108	94.03 207	70.59 270	98.57 129	90.97 116	94.67 160	94.18 283

xiu_mvs_v1_base_debi		90.64 134	90.05 136	92.40 142	93.97 239	84.46 95	93.32 254	95.46 201	85.17 207	92.25 108	94.03 207	70.59 270	98.57 129	90.97 116	94.67 160	94.18 283

fmvsm_s_conf0.5_n_2		93.47 65	93.83 56	92.39 145	95.36 146	81.19 206	95.20 134	96.56 105	90.37 36	97.13 14	98.03 26	77.47 171	98.96 83	97.79 5	96.58 117	97.03 150

fmvsm_s_conf0.1_n_2		93.16 82	93.42 71	92.37 146	94.62 196	81.13 208	95.23 127	95.89 168	90.30 40	96.74 24	98.02 27	76.14 183	98.95 85	97.64 6	96.21 126	97.03 150

AdaColmap		89.89 159	89.07 166	92.37 146	97.41 67	83.03 149	94.42 186	95.92 163	82.81 271	86.34 235	94.65 183	73.89 226	99.02 67	80.69 279	95.51 139	95.05 243

CNLPA		89.07 186	87.98 198	92.34 148	96.87 79	84.78 84	94.08 213	93.24 306	81.41 309	84.46 291	95.13 159	75.57 200	96.62 300	77.21 320	93.84 180	95.61 227

fmvsm_s_conf0.5_n_4		93.86 55	94.37 34	92.33 149	95.13 161	80.95 215	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	97.13 142

ET-MVSNet_ETH3D		87.51 239	85.91 271	92.32 150	93.70 257	83.93 113	92.33 298	90.94 375	84.16 234	72.09 423	92.52 265	69.90 281	95.85 347	89.20 140	88.36 284	97.17 137

Anonymous202405211		87.68 226	86.13 259	92.31 151	96.66 84	80.74 222	94.87 153	91.49 360	80.47 323	89.46 167	95.44 140	54.72 409	98.23 162	82.19 248	89.89 256	97.97 85

CHOSEN 1792x2688		88.84 193	87.69 206	92.30 152	96.14 104	81.42 198	90.01 361	95.86 171	74.52 393	87.41 209	93.94 215	75.46 201	98.36 151	80.36 284	95.53 138	97.12 143

HY-MVS	83.01 12	89.03 189	87.94 200	92.29 153	94.86 178	82.77 156	92.08 309	94.49 264	81.52 308	86.93 216	92.79 258	78.32 160	98.23 162	79.93 290	90.55 243	95.88 213

CDS-MVSNet		89.45 172	88.51 182	92.29 153	93.62 260	83.61 126	93.01 273	94.68 259	81.95 289	87.82 202	93.24 241	78.69 152	96.99 281	80.34 285	93.23 197	96.28 191
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 152	89.27 162	92.29 153	95.78 127	80.95 215	92.68 285	96.22 137	81.91 291	86.66 226	93.75 227	82.23 108	98.44 144	79.40 300	94.79 157	97.48 118

mvsmamba		90.33 141	89.69 147	92.25 156	95.17 157	81.64 189	95.27 125	93.36 305	84.88 219	89.51 164	94.27 202	69.29 295	97.42 239	89.34 138	96.12 129	97.68 107

PLC	84.53 7	89.06 187	88.03 196	92.15 157	97.27 73	82.69 163	94.29 197	95.44 206	79.71 332	84.01 307	94.18 205	76.68 180	98.75 109	77.28 319	93.41 191	95.02 244
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	92.13 158	95.88 123	80.50 229	97.33 8	95.25 220	86.15 177	89.76 162	95.60 134	83.42 86	98.32 158	87.37 166	93.25 196	97.56 115

patch_mono-2		93.74 59	94.32 35	92.01 159	97.54 62	78.37 288	93.40 250	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				92.01 159	97.34 69	81.05 211		96.81 81	78.89 343	90.45 148	95.92 118	82.65 99	98.84 99	80.68 280	98.26 59	96.14 198

UniMVSNet (Re)		89.80 162	89.07 166	92.01 159	93.60 261	84.52 92	94.78 161	97.47 13	89.26 79	86.44 232	92.32 271	82.10 112	97.39 250	84.81 202	80.84 373	94.12 287

MG-MVS		91.77 106	91.70 104	92.00 162	97.08 76	80.03 244	93.60 243	95.18 224	87.85 133	90.89 142	96.47 94	82.06 114	98.36 151	85.07 197	97.04 103	97.62 110

EIA-MVS		91.95 102	91.94 99	91.98 163	95.16 158	80.01 245	95.36 115	96.73 91	88.44 109	89.34 168	92.16 276	83.82 82	98.45 142	89.35 137	97.06 102	97.48 118

PVSNet_Blended		90.73 128	90.32 127	91.98 163	96.12 106	81.25 202	92.55 290	96.83 77	82.04 287	89.10 172	92.56 264	81.04 125	98.85 97	86.72 176	95.91 131	95.84 215

guyue		91.12 120	90.84 120	91.96 165	94.59 198	80.57 227	94.87 153	93.71 299	88.96 92	91.14 138	95.22 151	73.22 238	97.76 205	92.01 96	93.81 181	97.54 117

PS-MVSNAJ		91.18 118	90.92 117	91.96 165	95.26 153	82.60 170	92.09 308	95.70 183	86.27 173	91.84 123	92.46 266	79.70 138	98.99 76	89.08 141	95.86 132	94.29 281

TAMVS		89.21 180	88.29 191	91.96 165	93.71 255	82.62 169	93.30 258	94.19 278	82.22 282	87.78 203	93.94 215	78.83 149	96.95 284	77.70 315	92.98 205	96.32 188

SDMVSNet		90.19 145	89.61 150	91.93 168	96.00 116	83.09 146	92.89 280	95.98 158	88.73 99	86.85 222	95.20 155	72.09 253	97.08 273	88.90 144	89.85 258	95.63 225

FA-MVS(test-final)		89.66 164	88.91 172	91.93 168	94.57 202	80.27 233	91.36 325	94.74 256	84.87 220	89.82 161	92.61 263	74.72 211	98.47 137	83.97 217	93.53 186	97.04 149

MVS_Test		91.31 115	91.11 113	91.93 168	94.37 216	80.14 237	93.46 248	95.80 174	86.46 169	91.35 136	93.77 225	82.21 109	98.09 179	87.57 161	94.95 154	97.55 116

NR-MVSNet		88.58 203	87.47 212	91.93 168	93.04 281	84.16 107	94.77 162	96.25 134	89.05 86	80.04 368	93.29 239	79.02 148	97.05 278	81.71 263	80.05 383	94.59 264

HyFIR lowres test		88.09 216	86.81 229	91.93 168	96.00 116	80.63 224	90.01 361	95.79 175	73.42 404	87.68 205	92.10 282	73.86 227	97.96 192	80.75 278	91.70 226	97.19 136

GeoE		90.05 150	89.43 155	91.90 173	95.16 158	80.37 232	95.80 89	94.65 260	83.90 240	87.55 208	94.75 175	78.18 161	97.62 218	81.28 268	93.63 183	97.71 106

thisisatest0530		88.67 198	87.61 208	91.86 174	94.87 177	80.07 240	94.63 171	89.90 398	84.00 238	88.46 186	93.78 224	66.88 319	98.46 138	83.30 227	92.65 212	97.06 147

xiu_mvs_v2_base		91.13 119	90.89 119	91.86 174	94.97 169	82.42 172	92.24 301	95.64 190	86.11 181	91.74 128	93.14 245	79.67 141	98.89 91	89.06 142	95.46 143	94.28 282

DU-MVS		89.34 179	88.50 183	91.85 176	93.04 281	83.72 119	94.47 182	96.59 102	89.50 68	86.46 229	93.29 239	77.25 173	97.23 263	84.92 199	81.02 369	94.59 264

AstraMVS		90.69 130	90.30 128	91.84 177	93.81 246	79.85 251	94.76 163	92.39 329	88.96 92	91.01 141	95.87 122	70.69 268	97.94 195	92.49 75	92.70 211	97.73 104

OPM-MVS		90.12 146	89.56 151	91.82 178	93.14 274	83.90 114	94.16 204	95.74 179	88.96 92	87.86 198	95.43 142	72.48 248	97.91 198	88.10 155	90.18 250	93.65 318
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 137	90.19 130	91.82 178	94.70 192	82.73 160	95.85 86	96.22 137	90.81 24	86.91 218	94.86 170	74.23 217	98.12 169	88.15 151	89.99 252	94.63 261

UniMVSNet_NR-MVSNet		89.92 158	89.29 160	91.81 180	93.39 267	83.72 119	94.43 185	97.12 50	89.80 57	86.46 229	93.32 236	83.16 90	97.23 263	84.92 199	81.02 369	94.49 274

diffmvs		91.37 114	91.23 111	91.77 181	93.09 277	80.27 233	92.36 295	95.52 198	87.03 153	91.40 135	94.93 165	80.08 132	97.44 237	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		88.42 205	87.33 215	91.72 182	94.92 173	80.98 213	92.97 277	94.54 263	78.16 360	83.82 310	93.88 220	78.78 151	97.91 198	79.45 296	89.41 265	96.26 192

Fast-Effi-MVS+		89.41 174	88.64 178	91.71 183	94.74 186	80.81 220	93.54 244	95.10 228	83.11 263	86.82 224	90.67 336	79.74 137	97.75 209	80.51 283	93.55 185	96.57 181

WTY-MVS		89.60 166	88.92 171	91.67 184	95.47 144	81.15 207	92.38 294	94.78 254	83.11 263	89.06 174	94.32 197	78.67 153	96.61 303	81.57 264	90.89 239	97.24 132

TAPA-MVS	84.62 6	88.16 214	87.01 224	91.62 185	96.64 85	80.65 223	94.39 189	96.21 140	76.38 373	86.19 239	95.44 140	79.75 136	98.08 181	62.75 420	95.29 148	96.13 199
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 165	88.96 169	91.60 186	93.86 243	82.89 155	95.46 112	97.33 28	87.91 128	88.43 187	93.31 237	74.17 220	97.40 247	87.32 167	82.86 344	94.52 269

FE-MVS		87.40 244	86.02 265	91.57 187	94.56 203	79.69 255	90.27 348	93.72 298	80.57 321	88.80 180	91.62 302	65.32 335	98.59 128	74.97 345	94.33 172	96.44 184

XVG-OURS		89.40 176	88.70 177	91.52 188	94.06 231	81.46 196	91.27 329	96.07 151	86.14 178	88.89 179	95.77 128	68.73 304	97.26 260	87.39 165	89.96 254	95.83 216

hse-mvs2		89.88 160	89.34 158	91.51 189	94.83 180	81.12 209	93.94 226	93.91 291	89.80 57	93.08 82	93.60 230	75.77 193	97.66 213	92.07 92	77.07 403	95.74 220

TranMVSNet+NR-MVSNet		88.84 193	87.95 199	91.49 190	92.68 295	83.01 151	94.92 150	96.31 123	89.88 51	85.53 255	93.85 222	76.63 181	96.96 283	81.91 256	79.87 386	94.50 272

AUN-MVS		87.78 224	86.54 244	91.48 191	94.82 181	81.05 211	93.91 230	93.93 288	83.00 266	86.93 216	93.53 231	69.50 289	97.67 211	86.14 182	77.12 402	95.73 222

XVG-OURS-SEG-HR		89.95 156	89.45 153	91.47 192	94.00 237	81.21 205	91.87 313	96.06 153	85.78 185	88.55 184	95.73 130	74.67 212	97.27 258	88.71 147	89.64 263	95.91 210

MVS		87.44 242	86.10 262	91.44 193	92.61 296	83.62 124	92.63 287	95.66 187	67.26 430	81.47 346	92.15 277	77.95 164	98.22 164	79.71 292	95.48 141	92.47 361

F-COLMAP		87.95 219	86.80 230	91.40 194	96.35 99	80.88 218	94.73 165	95.45 204	79.65 333	82.04 341	94.61 184	71.13 260	98.50 132	76.24 332	91.05 237	94.80 258

dcpmvs_2		93.49 64	94.19 46	91.38 195	97.69 59	76.78 327	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		87.33 247	85.99 266	91.37 196	93.49 263	79.55 256	90.63 343	89.56 406	80.17 325	87.56 207	90.86 326	67.07 316	98.28 160	81.50 265	93.02 204	96.29 190

HQP-MVS		89.80 162	89.28 161	91.34 197	94.17 226	81.56 190	94.39 189	96.04 154	88.81 95	85.43 264	93.97 214	73.83 228	97.96 192	87.11 171	89.77 261	94.50 272

fmvsm_s_conf0.5_n_7		93.15 83	93.76 62	91.31 198	94.42 214	79.48 258	94.52 177	97.14 48	89.33 75	94.17 58	98.09 16	81.83 118	97.49 229	96.33 23	98.02 75	96.95 157

RRT-MVS		90.85 124	90.70 123	91.30 199	94.25 222	76.83 326	94.85 156	96.13 145	89.04 87	90.23 152	94.88 168	70.15 279	98.72 113	91.86 104	94.88 155	98.34 44

FMVSNet3		87.40 244	86.11 261	91.30 199	93.79 249	83.64 123	94.20 203	94.81 252	83.89 241	84.37 294	91.87 293	68.45 307	96.56 308	78.23 310	85.36 314	93.70 317

FMVSNet2		87.19 257	85.82 274	91.30 199	94.01 234	83.67 121	94.79 160	94.94 238	83.57 249	83.88 309	92.05 286	66.59 324	96.51 312	77.56 317	85.01 317	93.73 315

RPMNet		83.95 334	81.53 345	91.21 202	90.58 373	79.34 264	85.24 421	96.76 86	71.44 418	85.55 253	82.97 430	70.87 265	98.91 90	61.01 424	89.36 267	95.40 231

IB-MVS	80.51 15	85.24 311	83.26 329	91.19 203	92.13 308	79.86 250	91.75 316	91.29 365	83.28 260	80.66 358	88.49 383	61.28 367	98.46 138	80.99 274	79.46 390	95.25 237
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		89.47 171	88.90 173	91.18 204	94.22 224	82.07 180	92.13 306	96.09 149	87.90 129	85.37 270	92.45 267	74.38 215	97.56 223	87.15 169	90.43 245	93.93 296
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		89.45 172	88.90 173	91.12 205	94.47 208	81.49 194	95.30 120	96.14 142	86.73 162	85.45 261	95.16 157	69.89 282	98.10 171	87.70 159	89.23 270	93.77 311

LGP-MVS_train				91.12 205	94.47 208	81.49 194		96.14 142	86.73 162	85.45 261	95.16 157	69.89 282	98.10 171	87.70 159	89.23 270	93.77 311

ACMM	84.12 9	89.14 182	88.48 186	91.12 205	94.65 195	81.22 204	95.31 118	96.12 146	85.31 204	85.92 244	94.34 195	70.19 278	98.06 183	85.65 190	88.86 275	94.08 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 200	87.78 205	91.11 208	94.96 170	77.81 305	95.35 116	89.69 401	85.09 214	88.05 196	94.59 187	66.93 317	98.48 134	83.27 228	92.13 224	97.03 150

GBi-Net		87.26 249	85.98 267	91.08 209	94.01 234	83.10 143	95.14 138	94.94 238	83.57 249	84.37 294	91.64 298	66.59 324	96.34 325	78.23 310	85.36 314	93.79 306

test1		87.26 249	85.98 267	91.08 209	94.01 234	83.10 143	95.14 138	94.94 238	83.57 249	84.37 294	91.64 298	66.59 324	96.34 325	78.23 310	85.36 314	93.79 306

FMVSNet1		85.85 296	84.11 316	91.08 209	92.81 290	83.10 143	95.14 138	94.94 238	81.64 303	82.68 331	91.64 298	59.01 389	96.34 325	75.37 339	83.78 328	93.79 306

Test_1112_low_res		87.65 228	86.51 245	91.08 209	94.94 172	79.28 268	91.77 315	94.30 273	76.04 378	83.51 320	92.37 269	77.86 167	97.73 210	78.69 305	89.13 272	96.22 193

PS-MVSNAJss		89.97 154	89.62 149	91.02 213	91.90 317	80.85 219	95.26 126	95.98 158	86.26 174	86.21 238	94.29 199	79.70 138	97.65 214	88.87 146	88.10 286	94.57 266

BH-RMVSNet		88.37 208	87.48 211	91.02 213	95.28 150	79.45 260	92.89 280	93.07 312	85.45 199	86.91 218	94.84 173	70.35 275	97.76 205	73.97 353	94.59 164	95.85 214

UniMVSNet_ETH3D		87.53 238	86.37 249	91.00 215	92.44 300	78.96 273	94.74 164	95.61 191	84.07 237	85.36 271	94.52 189	59.78 381	97.34 252	82.93 232	87.88 291	96.71 174

FIs		90.51 139	90.35 126	90.99 216	93.99 238	80.98 213	95.73 96	97.54 6	89.15 83	86.72 225	94.68 178	81.83 118	97.24 262	85.18 196	88.31 285	94.76 259

ACMP	84.23 8	89.01 191	88.35 187	90.99 216	94.73 187	81.27 201	95.07 141	95.89 168	86.48 167	83.67 315	94.30 198	69.33 291	97.99 188	87.10 173	88.55 277	93.72 316
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 279	85.13 294	90.98 218	96.52 93	81.50 192	96.14 59	96.16 141	73.78 400	83.65 316	92.15 277	63.26 351	97.37 251	82.82 236	81.74 358	94.06 292

icg_test_0403		89.97 154	89.64 148	90.96 219	93.72 251	77.75 310	93.00 274	95.34 215	85.53 195	88.77 181	94.49 190	78.49 157	97.84 201	84.75 203	92.65 212	97.28 126

sss		88.93 192	88.26 193	90.94 220	94.05 232	80.78 221	91.71 317	95.38 210	81.55 307	88.63 183	93.91 219	75.04 205	95.47 366	82.47 241	91.61 227	96.57 181

icg_test_0407		89.85 161	89.51 152	90.88 221	93.72 251	77.75 310	93.07 271	95.34 215	85.53 195	88.34 189	94.49 190	77.69 169	97.60 219	84.75 203	92.65 212	97.28 126

viewmambaseed2359dif		90.04 151	89.78 145	90.83 222	92.85 289	77.92 299	92.23 302	95.01 232	81.90 292	90.20 153	95.45 139	79.64 143	97.34 252	87.52 163	93.17 198	97.23 135

sd_testset		88.59 202	87.85 204	90.83 222	96.00 116	80.42 231	92.35 296	94.71 257	88.73 99	86.85 222	95.20 155	67.31 311	96.43 319	79.64 294	89.85 258	95.63 225

PVSNet_BlendedMVS		89.98 153	89.70 146	90.82 224	96.12 106	81.25 202	93.92 228	96.83 77	83.49 253	89.10 172	92.26 274	81.04 125	98.85 97	86.72 176	87.86 292	92.35 367

cascas		86.43 287	84.98 297	90.80 225	92.10 310	80.92 217	90.24 352	95.91 165	73.10 407	83.57 319	88.39 384	65.15 337	97.46 233	84.90 201	91.43 229	94.03 294

ECVR-MVS		89.09 185	88.53 181	90.77 226	95.62 137	75.89 340	96.16 55	84.22 432	87.89 131	90.20 153	96.65 84	63.19 352	98.10 171	85.90 187	96.94 105	98.33 46

GA-MVS		86.61 277	85.27 291	90.66 227	91.33 340	78.71 277	90.40 347	93.81 295	85.34 203	85.12 274	89.57 365	61.25 368	97.11 272	80.99 274	89.59 264	96.15 197

thres600view7		87.65 228	86.67 236	90.59 228	96.08 112	78.72 275	94.88 152	91.58 356	87.06 152	88.08 194	92.30 272	68.91 301	98.10 171	70.05 383	91.10 232	94.96 248

thres400		87.62 233	86.64 237	90.57 229	95.99 119	78.64 278	94.58 173	91.98 345	86.94 156	88.09 192	91.77 294	69.18 297	98.10 171	70.13 380	91.10 232	94.96 248

baseline1		88.10 215	87.28 217	90.57 229	94.96 170	80.07 240	94.27 198	91.29 365	86.74 161	87.41 209	94.00 212	76.77 178	96.20 330	80.77 277	79.31 392	95.44 229

FC-MVSNet-test		90.27 143	90.18 131	90.53 231	93.71 255	79.85 251	95.77 92	97.59 4	89.31 76	86.27 236	94.67 181	81.93 117	97.01 280	84.26 213	88.09 288	94.71 260

PAPM		86.68 276	85.39 286	90.53 231	93.05 280	79.33 267	89.79 364	94.77 255	78.82 346	81.95 342	93.24 241	76.81 176	97.30 254	66.94 400	93.16 199	94.95 252

WR-MVS		88.38 207	87.67 207	90.52 233	93.30 269	80.18 235	93.26 261	95.96 161	88.57 107	85.47 260	92.81 256	76.12 186	96.91 287	81.24 269	82.29 349	94.47 277

mamba_test_0407_2		88.57 204	87.92 201	90.51 234	94.76 183	82.66 164	79.84 443	94.64 261	85.18 205	88.96 176	95.00 162	76.00 189	92.03 414	83.74 222	93.15 200	96.85 166

MVSTER		88.84 193	88.29 191	90.51 234	92.95 286	80.44 230	93.73 237	95.01 232	84.66 228	87.15 213	93.12 246	72.79 243	97.21 265	87.86 157	87.36 300	93.87 301

testdata				90.49 236	96.40 96	77.89 302		95.37 212	72.51 412	93.63 71	96.69 80	82.08 113	97.65 214	83.08 229	97.39 95	95.94 209

test1111		89.10 183	88.64 178	90.48 237	95.53 142	74.97 350	96.08 64	84.89 430	88.13 122	90.16 156	96.65 84	63.29 350	98.10 171	86.14 182	96.90 107	98.39 41

tt0805		86.92 265	85.74 280	90.48 237	92.22 304	79.98 247	95.63 106	94.88 246	83.83 243	84.74 283	92.80 257	57.61 395	97.67 211	85.48 193	84.42 321	93.79 306

jajsoiax		88.24 212	87.50 210	90.48 237	90.89 361	80.14 237	95.31 118	95.65 189	84.97 217	84.24 302	94.02 210	65.31 336	97.42 239	88.56 148	88.52 279	93.89 297

PatchMatch-RL		86.77 273	85.54 282	90.47 240	95.88 123	82.71 162	90.54 345	92.31 333	79.82 331	84.32 299	91.57 306	68.77 303	96.39 321	73.16 359	93.48 190	92.32 368

tfpn200view9		87.58 236	86.64 237	90.41 241	95.99 119	78.64 278	94.58 173	91.98 345	86.94 156	88.09 192	91.77 294	69.18 297	98.10 171	70.13 380	91.10 232	94.48 275

VPNet		88.20 213	87.47 212	90.39 242	93.56 262	79.46 259	94.04 217	95.54 196	88.67 102	86.96 215	94.58 188	69.33 291	97.15 267	84.05 216	80.53 378	94.56 267

ACMH	80.38 17	85.36 306	83.68 323	90.39 242	94.45 211	80.63 224	94.73 165	94.85 248	82.09 284	77.24 393	92.65 261	60.01 379	97.58 221	72.25 364	84.87 318	92.96 346
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 231	86.71 233	90.38 244	96.12 106	78.55 281	95.03 144	91.58 356	87.15 149	88.06 195	92.29 273	68.91 301	98.10 171	70.13 380	91.10 232	94.48 275

mvs_tets		88.06 218	87.28 217	90.38 244	90.94 357	79.88 249	95.22 129	95.66 187	85.10 213	84.21 303	93.94 215	63.53 348	97.40 247	88.50 149	88.40 283	93.87 301

1314		87.51 239	86.57 242	90.34 246	92.42 301	79.74 254	92.63 287	95.35 214	78.35 355	80.14 365	91.62 302	74.05 222	97.15 267	81.05 270	93.53 186	94.12 287

LTVRE_ROB	82.13 13	86.26 290	84.90 300	90.34 246	94.44 212	81.50 192	92.31 300	94.89 244	83.03 265	79.63 375	92.67 260	69.69 285	97.79 203	71.20 369	86.26 309	91.72 378
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		89.03 189	88.64 178	90.21 248	90.74 368	79.28 268	95.96 77	95.90 166	84.66 228	85.33 272	92.94 251	74.02 223	97.30 254	89.64 135	88.53 278	94.05 293

v2v482		87.84 221	87.06 221	90.17 249	90.99 353	79.23 271	94.00 222	95.13 225	84.87 220	85.53 255	92.07 285	74.45 214	97.45 234	84.71 208	81.75 357	93.85 304

pmmvs4		85.43 304	83.86 321	90.16 250	90.02 386	82.97 153	90.27 348	92.67 324	75.93 379	80.73 356	91.74 296	71.05 261	95.73 355	78.85 304	83.46 335	91.78 377

V42		87.68 226	86.86 226	90.15 251	90.58 373	80.14 237	94.24 201	95.28 219	83.66 247	85.67 250	91.33 308	74.73 210	97.41 245	84.43 212	81.83 355	92.89 349

MSDG		84.86 319	83.09 332	90.14 252	93.80 247	80.05 242	89.18 377	93.09 311	78.89 343	78.19 385	91.91 291	65.86 334	97.27 258	68.47 389	88.45 281	93.11 341

sc_t1		81.53 358	78.67 379	90.12 253	90.78 365	78.64 278	93.91 230	90.20 388	68.42 427	80.82 355	89.88 358	46.48 432	96.76 292	76.03 335	71.47 417	94.96 248

anonymousdsp		87.84 221	87.09 220	90.12 253	89.13 397	80.54 228	94.67 169	95.55 194	82.05 285	83.82 310	92.12 279	71.47 258	97.15 267	87.15 169	87.80 295	92.67 355

thres200		87.21 255	86.24 256	90.12 253	95.36 146	78.53 282	93.26 261	92.10 339	86.42 170	88.00 197	91.11 319	69.24 296	98.00 187	69.58 384	91.04 238	93.83 305

CR-MVSNet		85.35 307	83.76 322	90.12 253	90.58 373	79.34 264	85.24 421	91.96 347	78.27 357	85.55 253	87.87 394	71.03 262	95.61 358	73.96 354	89.36 267	95.40 231

v1144		87.61 234	86.79 231	90.06 257	91.01 352	79.34 264	93.95 225	95.42 209	83.36 258	85.66 251	91.31 311	74.98 206	97.42 239	83.37 226	82.06 351	93.42 327

XXY-MVS		87.65 228	86.85 227	90.03 258	92.14 307	80.60 226	93.76 236	95.23 221	82.94 268	84.60 285	94.02 210	74.27 216	95.49 365	81.04 271	83.68 331	94.01 295

Vis-MVSNet (Re-imp)		89.59 167	89.44 154	90.03 258	95.74 128	75.85 341	95.61 107	90.80 379	87.66 141	87.83 201	95.40 143	76.79 177	96.46 317	78.37 306	96.73 113	97.80 99

test2506		87.21 255	86.28 254	90.02 260	95.62 137	73.64 366	96.25 50	71.38 455	87.89 131	90.45 148	96.65 84	55.29 406	98.09 179	86.03 186	96.94 105	98.33 46

BH-untuned		88.60 201	88.13 195	90.01 261	95.24 154	78.50 284	93.29 259	94.15 281	84.75 225	84.46 291	93.40 233	75.76 195	97.40 247	77.59 316	94.52 167	94.12 287

v1192		87.25 251	86.33 251	90.00 262	90.76 367	79.04 272	93.80 234	95.48 199	82.57 275	85.48 259	91.18 315	73.38 237	97.42 239	82.30 245	82.06 351	93.53 321

v7n		86.81 268	85.76 278	89.95 263	90.72 369	79.25 270	95.07 141	95.92 163	84.45 231	82.29 335	90.86 326	72.60 247	97.53 225	79.42 299	80.52 379	93.08 343

testing91		87.11 260	86.18 257	89.92 264	94.43 213	75.38 349	91.53 322	92.27 335	86.48 167	86.50 227	90.24 344	61.19 371	97.53 225	82.10 250	90.88 240	96.84 169

ICG_test_0404		87.60 235	86.84 228	89.89 265	93.72 251	77.75 310	88.56 386	95.34 215	85.53 195	79.98 369	94.49 190	66.54 327	94.64 379	84.75 203	92.65 212	97.28 126

v8		87.50 241	86.71 233	89.89 265	91.37 337	79.40 261	94.50 178	95.38 210	84.81 223	83.60 318	91.33 308	76.05 187	97.42 239	82.84 235	80.51 380	92.84 351

v10		87.25 251	86.38 248	89.85 267	91.19 343	79.50 257	94.48 179	95.45 204	83.79 245	83.62 317	91.19 313	75.13 203	97.42 239	81.94 255	80.60 375	92.63 357

baseline2		86.50 283	85.39 286	89.84 268	91.12 348	76.70 329	91.88 312	88.58 410	82.35 280	79.95 370	90.95 324	73.42 235	97.63 217	80.27 287	89.95 255	95.19 238

pm-mvs1		86.61 277	85.54 282	89.82 269	91.44 332	80.18 235	95.28 124	94.85 248	83.84 242	81.66 344	92.62 262	72.45 250	96.48 314	79.67 293	78.06 395	92.82 352

TR-MVS		86.78 270	85.76 278	89.82 269	94.37 216	78.41 286	92.47 291	92.83 318	81.11 317	86.36 233	92.40 268	68.73 304	97.48 230	73.75 357	89.85 258	93.57 320

ACMH+	81.04 14	85.05 314	83.46 326	89.82 269	94.66 194	79.37 262	94.44 184	94.12 284	82.19 283	78.04 387	92.82 255	58.23 392	97.54 224	73.77 356	82.90 343	92.54 358

EI-MVSNet		89.10 183	88.86 175	89.80 272	91.84 319	78.30 290	93.70 240	95.01 232	85.73 187	87.15 213	95.28 148	79.87 135	97.21 265	83.81 220	87.36 300	93.88 300

v144192		87.19 257	86.35 250	89.74 273	90.64 371	78.24 292	93.92 228	95.43 207	81.93 290	85.51 257	91.05 322	74.21 219	97.45 234	82.86 234	81.56 359	93.53 321

COLMAP_ROB	80.39 16	83.96 333	82.04 342	89.74 273	95.28 150	79.75 253	94.25 199	92.28 334	75.17 386	78.02 388	93.77 225	58.60 391	97.84 201	65.06 411	85.92 310	91.63 380
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 289	85.18 293	89.73 275	92.15 306	76.60 330	91.12 333	91.69 352	83.53 252	85.50 258	88.81 377	66.79 320	96.48 314	76.65 325	90.35 247	96.12 200

IterMVS-LS		88.36 209	87.91 203	89.70 276	93.80 247	78.29 291	93.73 237	95.08 230	85.73 187	84.75 282	91.90 292	79.88 134	96.92 286	83.83 219	82.51 345	93.89 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 286	85.35 289	89.69 277	94.29 221	75.40 348	91.30 327	90.53 383	84.76 224	85.06 276	90.13 350	58.95 390	97.45 234	82.08 251	91.09 236	96.21 195

testing99		86.72 274	85.73 281	89.69 277	94.23 223	74.91 352	91.35 326	90.97 373	86.14 178	86.36 233	90.22 345	59.41 384	97.48 230	82.24 247	90.66 242	96.69 176

v1921920		86.97 264	86.06 264	89.69 277	90.53 376	78.11 295	93.80 234	95.43 207	81.90 292	85.33 272	91.05 322	72.66 244	97.41 245	82.05 253	81.80 356	93.53 321

icg_test_0407_2		89.15 181	88.97 168	89.68 280	93.72 251	77.75 310	88.26 391	95.34 215	85.53 195	88.34 189	94.49 190	77.69 169	93.99 390	84.75 203	92.65 212	97.28 126

VortexMVS		88.42 205	88.01 197	89.63 281	93.89 242	78.82 274	93.82 233	95.47 200	86.67 164	84.53 289	91.99 288	72.62 246	96.65 298	89.02 143	84.09 325	93.41 328

Fast-Effi-MVS+-dtu		87.44 242	86.72 232	89.63 281	92.04 311	77.68 315	94.03 218	93.94 287	85.81 184	82.42 334	91.32 310	70.33 276	97.06 276	80.33 286	90.23 249	94.14 286

v1240		86.78 270	85.85 273	89.56 283	90.45 378	77.79 307	93.61 242	95.37 212	81.65 302	85.43 264	91.15 317	71.50 257	97.43 238	81.47 266	82.05 353	93.47 325

Effi-MVS+-dtu		88.65 199	88.35 187	89.54 284	93.33 268	76.39 334	94.47 182	94.36 271	87.70 138	85.43 264	89.56 366	73.45 233	97.26 260	85.57 192	91.28 231	94.97 245

AllTest		83.42 340	81.39 346	89.52 285	95.01 164	77.79 307	93.12 265	90.89 377	77.41 364	76.12 402	93.34 234	54.08 412	97.51 227	68.31 391	84.27 323	93.26 331

TestCases				89.52 285	95.01 164	77.79 307		90.89 377	77.41 364	76.12 402	93.34 234	54.08 412	97.51 227	68.31 391	84.27 323	93.26 331

mvs_anonymous		89.37 178	89.32 159	89.51 287	93.47 264	74.22 359	91.65 320	94.83 250	82.91 269	85.45 261	93.79 223	81.23 124	96.36 324	86.47 178	94.09 175	97.94 87

XVG-ACMP-BASELINE		86.00 292	84.84 302	89.45 288	91.20 342	78.00 297	91.70 318	95.55 194	85.05 215	82.97 328	92.25 275	54.49 410	97.48 230	82.93 232	87.45 299	92.89 349

testing222		84.84 320	83.32 327	89.43 289	94.15 229	75.94 339	91.09 334	89.41 408	84.90 218	85.78 247	89.44 367	52.70 417	96.28 328	70.80 375	91.57 228	96.07 204

MVP-Stereo		85.97 293	84.86 301	89.32 290	90.92 359	82.19 178	92.11 307	94.19 278	78.76 348	78.77 384	91.63 301	68.38 308	96.56 308	75.01 344	93.95 177	89.20 418
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 296	84.70 304	89.29 291	91.76 323	75.54 345	88.49 387	91.30 364	81.63 304	85.05 277	88.70 381	71.71 254	96.24 329	74.61 349	89.05 273	96.08 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 262	86.32 252	89.21 292	90.94 357	77.26 320	93.71 239	94.43 266	84.84 222	84.36 297	90.80 330	76.04 188	97.05 278	82.12 249	79.60 389	93.31 330

tfpnnormal		84.72 322	83.23 330	89.20 293	92.79 291	80.05 242	94.48 179	95.81 173	82.38 278	81.08 352	91.21 312	69.01 300	96.95 284	61.69 422	80.59 376	90.58 405

cl22		86.78 270	85.98 267	89.18 294	92.34 302	77.62 316	90.84 339	94.13 283	81.33 311	83.97 308	90.15 349	73.96 224	96.60 305	84.19 214	82.94 340	93.33 329

BH-w/o		87.57 237	87.05 222	89.12 295	94.90 176	77.90 301	92.41 292	93.51 302	82.89 270	83.70 314	91.34 307	75.75 196	97.07 275	75.49 337	93.49 188	92.39 365

WR-MVS_H		87.80 223	87.37 214	89.10 296	93.23 270	78.12 294	95.61 107	97.30 32	87.90 129	83.72 313	92.01 287	79.65 142	96.01 339	76.36 329	80.54 377	93.16 339

miper_enhance_ethall		86.90 266	86.18 257	89.06 297	91.66 328	77.58 317	90.22 354	94.82 251	79.16 339	84.48 290	89.10 371	79.19 147	96.66 297	84.06 215	82.94 340	92.94 347

c3_l		87.14 259	86.50 246	89.04 298	92.20 305	77.26 320	91.22 332	94.70 258	82.01 288	84.34 298	90.43 341	78.81 150	96.61 303	83.70 224	81.09 366	93.25 333

miper_ehance_all_eth		87.22 254	86.62 240	89.02 299	92.13 308	77.40 319	90.91 338	94.81 252	81.28 312	84.32 299	90.08 352	79.26 145	96.62 300	83.81 220	82.94 340	93.04 344

gg-mvs-nofinetune		81.77 352	79.37 367	88.99 300	90.85 363	77.73 314	86.29 413	79.63 443	74.88 391	83.19 327	69.05 446	60.34 376	96.11 334	75.46 338	94.64 163	93.11 341

ETVMVS		84.43 327	82.92 336	88.97 301	94.37 216	74.67 353	91.23 331	88.35 412	83.37 257	86.06 242	89.04 372	55.38 404	95.67 357	67.12 398	91.34 230	96.58 180

pmmvs6		83.42 340	81.60 344	88.87 302	88.01 412	77.87 303	94.96 147	94.24 277	74.67 392	78.80 383	91.09 320	60.17 378	96.49 313	77.06 324	75.40 409	92.23 370

test_cas_vis1_n_1920		88.83 196	88.85 176	88.78 303	91.15 347	76.72 328	93.85 232	94.93 242	83.23 262	92.81 91	96.00 113	61.17 372	94.45 380	91.67 107	94.84 156	95.17 239

MIMVSNet		82.59 346	80.53 351	88.76 304	91.51 330	78.32 289	86.57 412	90.13 391	79.32 335	80.70 357	88.69 382	52.98 416	93.07 406	66.03 406	88.86 275	94.90 253

cl____		86.52 282	85.78 275	88.75 305	92.03 312	76.46 332	90.74 340	94.30 273	81.83 298	83.34 324	90.78 331	75.74 198	96.57 306	81.74 261	81.54 360	93.22 335

DIV-MVS_self_test		86.53 281	85.78 275	88.75 305	92.02 313	76.45 333	90.74 340	94.30 273	81.83 298	83.34 324	90.82 329	75.75 196	96.57 306	81.73 262	81.52 361	93.24 334

CP-MVSNet		87.63 231	87.26 219	88.74 307	93.12 275	76.59 331	95.29 122	96.58 103	88.43 110	83.49 321	92.98 250	75.28 202	95.83 348	78.97 302	81.15 365	93.79 306

eth_miper_zixun_eth		86.50 283	85.77 277	88.68 308	91.94 314	75.81 342	90.47 346	94.89 244	82.05 285	84.05 305	90.46 340	75.96 191	96.77 291	82.76 238	79.36 391	93.46 326

CHOSEN 280x420		85.15 312	83.99 319	88.65 309	92.47 298	78.40 287	79.68 445	92.76 321	74.90 390	81.41 348	89.59 364	69.85 284	95.51 362	79.92 291	95.29 148	92.03 373

PS-CasMVS		87.32 248	86.88 225	88.63 310	92.99 284	76.33 336	95.33 117	96.61 101	88.22 118	83.30 326	93.07 248	73.03 241	95.79 352	78.36 307	81.00 371	93.75 313

TransMVSNet (Re)		84.43 327	83.06 334	88.54 311	91.72 324	78.44 285	95.18 135	92.82 320	82.73 273	79.67 374	92.12 279	73.49 232	95.96 341	71.10 373	68.73 427	91.21 392

tt0320-xc		79.63 381	76.66 390	88.52 312	91.03 351	78.72 275	93.00 274	89.53 407	66.37 431	76.11 404	87.11 405	46.36 434	95.32 370	72.78 361	67.67 428	91.51 384

EG-PatchMatch MVS		82.37 348	80.34 354	88.46 313	90.27 380	79.35 263	92.80 284	94.33 272	77.14 368	73.26 420	90.18 348	47.47 429	96.72 293	70.25 377	87.32 302	89.30 415

PEN-MVS		86.80 269	86.27 255	88.40 314	92.32 303	75.71 344	95.18 135	96.38 118	87.97 126	82.82 330	93.15 244	73.39 236	95.92 343	76.15 333	79.03 394	93.59 319

Baseline_NR-MVSNet		87.07 261	86.63 239	88.40 314	91.44 332	77.87 303	94.23 202	92.57 326	84.12 236	85.74 249	92.08 283	77.25 173	96.04 335	82.29 246	79.94 384	91.30 390

UBG		85.51 302	84.57 309	88.35 316	94.21 225	71.78 390	90.07 359	89.66 403	82.28 281	85.91 245	89.01 373	61.30 366	97.06 276	76.58 328	92.06 225	96.22 193

D2MVS		85.90 294	85.09 295	88.35 316	90.79 364	77.42 318	91.83 314	95.70 183	80.77 320	80.08 367	90.02 354	66.74 322	96.37 322	81.88 257	87.97 290	91.26 391

pmmvs5		84.21 329	82.84 339	88.34 318	88.95 399	76.94 324	92.41 292	91.91 349	75.63 381	80.28 362	91.18 315	64.59 342	95.57 359	77.09 323	83.47 334	92.53 359

mamv4		90.92 122	91.78 102	88.33 319	95.67 133	70.75 403	92.92 279	96.02 157	81.90 292	88.11 191	95.34 146	85.88 52	96.97 282	95.22 37	95.01 153	97.26 130

tt0320		80.13 374	77.41 383	88.29 320	90.50 377	78.02 296	93.10 268	90.71 381	66.06 434	76.75 397	86.97 406	49.56 424	95.40 367	71.65 365	71.41 418	91.46 387

LCM-MVSNet-Re		88.30 211	88.32 190	88.27 321	94.71 191	72.41 385	93.15 264	90.98 372	87.77 136	79.25 378	91.96 289	78.35 159	95.75 353	83.04 230	95.62 137	96.65 177

CostFormer		85.77 299	84.94 299	88.26 322	91.16 346	72.58 383	89.47 372	91.04 371	76.26 376	86.45 231	89.97 356	70.74 267	96.86 290	82.35 244	87.07 305	95.34 235

ITE_SJBPF				88.24 323	91.88 318	77.05 323		92.92 315	85.54 193	80.13 366	93.30 238	57.29 396	96.20 330	72.46 363	84.71 319	91.49 385

PVSNet	78.82 18	85.55 301	84.65 305	88.23 324	94.72 189	71.93 386	87.12 408	92.75 322	78.80 347	84.95 279	90.53 338	64.43 343	96.71 295	74.74 347	93.86 179	96.06 206

IterMVS-SCA-FT		85.45 303	84.53 310	88.18 325	91.71 325	76.87 325	90.19 356	92.65 325	85.40 202	81.44 347	90.54 337	66.79 320	95.00 376	81.04 271	81.05 367	92.66 356

EPNet_dtu		86.49 285	85.94 270	88.14 326	90.24 381	72.82 375	94.11 208	92.20 337	86.66 165	79.42 377	92.36 270	73.52 231	95.81 350	71.26 368	93.66 182	95.80 218
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 344	80.93 350	88.06 327	90.05 385	76.37 335	84.74 426	91.96 347	72.28 415	81.32 350	87.87 394	71.03 262	95.50 364	68.97 386	80.15 382	92.32 368

test_vis1_n_1920		89.39 177	89.84 142	88.04 328	92.97 285	72.64 380	94.71 167	96.03 156	86.18 176	91.94 120	96.56 92	61.63 361	95.74 354	93.42 58	95.11 152	95.74 220

DTE-MVSNet		86.11 291	85.48 284	87.98 329	91.65 329	74.92 351	94.93 149	95.75 178	87.36 146	82.26 336	93.04 249	72.85 242	95.82 349	74.04 352	77.46 400	93.20 337

PMMVS		85.71 300	84.96 298	87.95 330	88.90 400	77.09 322	88.68 384	90.06 393	72.32 414	86.47 228	90.76 332	72.15 252	94.40 382	81.78 260	93.49 188	92.36 366

GG-mvs-BLEND				87.94 331	89.73 392	77.91 300	87.80 397	78.23 448		80.58 359	83.86 423	59.88 380	95.33 369	71.20 369	92.22 223	90.60 404

MonoMVSNet		86.89 267	86.55 243	87.92 332	89.46 395	73.75 363	94.12 206	93.10 310	87.82 135	85.10 275	90.76 332	69.59 287	94.94 377	86.47 178	82.50 346	95.07 242

reproduce_monomvs		86.37 288	85.87 272	87.87 333	93.66 259	73.71 364	93.44 249	95.02 231	88.61 105	82.64 333	91.94 290	57.88 394	96.68 296	89.96 132	79.71 388	93.22 335

pmmvs-eth3d		80.97 367	78.72 378	87.74 334	84.99 430	79.97 248	90.11 358	91.65 354	75.36 383	73.51 418	86.03 413	59.45 383	93.96 393	75.17 341	72.21 414	89.29 417

MS-PatchMatch		85.05 314	84.16 314	87.73 335	91.42 335	78.51 283	91.25 330	93.53 301	77.50 363	80.15 364	91.58 304	61.99 358	95.51 362	75.69 336	94.35 171	89.16 419

mmtdpeth		85.04 316	84.15 315	87.72 336	93.11 276	75.74 343	94.37 193	92.83 318	84.98 216	89.31 169	86.41 410	61.61 363	97.14 270	92.63 74	62.11 438	90.29 406

test_0402		81.30 363	79.17 372	87.67 337	93.19 271	78.17 293	92.98 276	91.71 350	75.25 385	76.02 405	90.31 343	59.23 385	96.37 322	50.22 441	83.63 332	88.47 426

IterMVS		84.88 318	83.98 320	87.60 338	91.44 332	76.03 338	90.18 357	92.41 328	83.24 261	81.06 353	90.42 342	66.60 323	94.28 386	79.46 295	80.98 372	92.48 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 361	79.30 368	87.58 339	90.92 359	74.16 361	80.99 438	87.68 417	70.52 422	76.63 399	88.81 377	71.21 259	92.76 409	60.01 428	86.93 306	95.83 216

EPMVS		83.90 336	82.70 340	87.51 340	90.23 382	72.67 378	88.62 385	81.96 438	81.37 310	85.01 278	88.34 385	66.31 328	94.45 380	75.30 340	87.12 303	95.43 230

ADS-MVSNet2		81.66 355	79.71 364	87.50 341	91.35 338	74.19 360	83.33 431	88.48 411	72.90 409	82.24 337	85.77 416	64.98 338	93.20 404	64.57 413	83.74 329	95.12 240

OurMVSNet-221017-0		85.35 307	84.64 307	87.49 342	90.77 366	72.59 382	94.01 220	94.40 269	84.72 226	79.62 376	93.17 243	61.91 359	96.72 293	81.99 254	81.16 363	93.16 339

tpm2		84.08 331	82.94 335	87.48 343	91.39 336	71.27 395	89.23 376	90.37 385	71.95 416	84.64 284	89.33 368	67.30 312	96.55 310	75.17 341	87.09 304	94.63 261

RPSCF		85.07 313	84.27 311	87.48 343	92.91 288	70.62 405	91.69 319	92.46 327	76.20 377	82.67 332	95.22 151	63.94 346	97.29 257	77.51 318	85.80 311	94.53 268

myMVS_eth3d28		85.80 298	85.26 292	87.42 345	94.73 187	69.92 410	90.60 344	90.95 374	87.21 148	86.06 242	90.04 353	59.47 382	96.02 337	74.89 346	93.35 195	96.33 187

WBMVS		84.97 317	84.18 313	87.34 346	94.14 230	71.62 394	90.20 355	92.35 330	81.61 305	84.06 304	90.76 332	61.82 360	96.52 311	78.93 303	83.81 327	93.89 297

miper_lstm_enhance		85.27 310	84.59 308	87.31 347	91.28 341	74.63 354	87.69 402	94.09 285	81.20 316	81.36 349	89.85 360	74.97 207	94.30 385	81.03 273	79.84 387	93.01 345

FMVSNet5		81.52 359	79.60 365	87.27 348	91.17 344	77.95 298	91.49 323	92.26 336	76.87 369	76.16 401	87.91 393	51.67 418	92.34 412	67.74 395	81.16 363	91.52 383

USDC		82.76 343	81.26 348	87.26 349	91.17 344	74.55 355	89.27 374	93.39 304	78.26 358	75.30 409	92.08 283	54.43 411	96.63 299	71.64 366	85.79 312	90.61 402

test-LLR		85.87 295	85.41 285	87.25 350	90.95 355	71.67 392	89.55 368	89.88 399	83.41 255	84.54 287	87.95 391	67.25 313	95.11 373	81.82 258	93.37 193	94.97 245

test-mter		84.54 326	83.64 324	87.25 350	90.95 355	71.67 392	89.55 368	89.88 399	79.17 338	84.54 287	87.95 391	55.56 402	95.11 373	81.82 258	93.37 193	94.97 245

JIA-IIPM		81.04 364	78.98 376	87.25 350	88.64 401	73.48 368	81.75 437	89.61 405	73.19 406	82.05 340	73.71 442	66.07 333	95.87 346	71.18 371	84.60 320	92.41 364

TDRefinement		79.81 378	77.34 384	87.22 353	79.24 445	75.48 346	93.12 265	92.03 342	76.45 372	75.01 410	91.58 304	49.19 425	96.44 318	70.22 379	69.18 424	89.75 411

tpmvs		83.35 342	82.07 341	87.20 354	91.07 350	71.00 401	88.31 390	91.70 351	78.91 341	80.49 361	87.18 403	69.30 294	97.08 273	68.12 394	83.56 333	93.51 324

ppachtmachnet_test		81.84 351	80.07 359	87.15 355	88.46 405	74.43 358	89.04 380	92.16 338	75.33 384	77.75 390	88.99 374	66.20 330	95.37 368	65.12 410	77.60 398	91.65 379

dmvs_re		84.20 330	83.22 331	87.14 356	91.83 321	77.81 305	90.04 360	90.19 389	84.70 227	81.49 345	89.17 370	64.37 344	91.13 424	71.58 367	85.65 313	92.46 362

tpm cat1		81.96 349	80.27 355	87.01 357	91.09 349	71.02 400	87.38 406	91.53 359	66.25 432	80.17 363	86.35 412	68.22 309	96.15 333	69.16 385	82.29 349	93.86 303

test_fmvs1_n		87.03 263	87.04 223	86.97 358	89.74 391	71.86 387	94.55 175	94.43 266	78.47 352	91.95 119	95.50 138	51.16 420	93.81 394	93.02 66	94.56 165	95.26 236

OpenMVS_ROB	74.94 19	79.51 382	77.03 389	86.93 359	87.00 418	76.23 337	92.33 298	90.74 380	68.93 426	74.52 414	88.23 388	49.58 423	96.62 300	57.64 433	84.29 322	87.94 429

SixPastTwentyTwo		83.91 335	82.90 337	86.92 360	90.99 353	70.67 404	93.48 246	91.99 344	85.54 193	77.62 392	92.11 281	60.59 375	96.87 289	76.05 334	77.75 397	93.20 337

ADS-MVSNet		81.56 357	79.78 361	86.90 361	91.35 338	71.82 388	83.33 431	89.16 409	72.90 409	82.24 337	85.77 416	64.98 338	93.76 395	64.57 413	83.74 329	95.12 240

PatchT		82.68 345	81.27 347	86.89 362	90.09 384	70.94 402	84.06 428	90.15 390	74.91 389	85.63 252	83.57 425	69.37 290	94.87 378	65.19 408	88.50 280	94.84 255

tpm		84.73 321	84.02 318	86.87 363	90.33 379	68.90 413	89.06 379	89.94 396	80.85 319	85.75 248	89.86 359	68.54 306	95.97 340	77.76 314	84.05 326	95.75 219

Patchmatch-RL test		81.67 354	79.96 360	86.81 364	85.42 428	71.23 396	82.17 436	87.50 418	78.47 352	77.19 394	82.50 432	70.81 266	93.48 399	82.66 239	72.89 413	95.71 223

test_vis1_n		86.56 280	86.49 247	86.78 365	88.51 402	72.69 377	94.68 168	93.78 297	79.55 334	90.70 143	95.31 147	48.75 426	93.28 402	93.15 62	93.99 176	94.38 279

testing3-2		86.72 274	86.71 233	86.74 366	96.11 109	65.92 425	93.39 251	89.65 404	89.46 69	87.84 200	92.79 258	59.17 387	97.60 219	81.31 267	90.72 241	96.70 175

test_fmvs1		87.34 246	87.56 209	86.68 367	90.59 372	71.80 389	94.01 220	94.04 286	78.30 356	91.97 117	95.22 151	56.28 400	93.71 396	92.89 67	94.71 159	94.52 269

MDA-MVSNet-bldmvs		78.85 387	76.31 392	86.46 368	89.76 390	73.88 362	88.79 382	90.42 384	79.16 339	59.18 442	88.33 386	60.20 377	94.04 388	62.00 421	68.96 425	91.48 386

mvs5depth		80.98 366	79.15 373	86.45 369	84.57 431	73.29 370	87.79 398	91.67 353	80.52 322	82.20 339	89.72 362	55.14 407	95.93 342	73.93 355	66.83 430	90.12 408

tpmrst		85.35 307	84.99 296	86.43 370	90.88 362	67.88 418	88.71 383	91.43 362	80.13 326	86.08 241	88.80 379	73.05 240	96.02 337	82.48 240	83.40 337	95.40 231

TESTMET0.1,1		83.74 338	82.85 338	86.42 371	89.96 387	71.21 397	89.55 368	87.88 414	77.41 364	83.37 323	87.31 399	56.71 398	93.65 398	80.62 281	92.85 209	94.40 278

our_test_3		81.93 350	80.46 353	86.33 372	88.46 405	73.48 368	88.46 388	91.11 367	76.46 371	76.69 398	88.25 387	66.89 318	94.36 383	68.75 387	79.08 393	91.14 394

lessismore_v0				86.04 373	88.46 405	68.78 414		80.59 441		73.01 421	90.11 351	55.39 403	96.43 319	75.06 343	65.06 433	92.90 348

TinyColmap		79.76 379	77.69 382	85.97 374	91.71 325	73.12 371	89.55 368	90.36 386	75.03 387	72.03 424	90.19 347	46.22 435	96.19 332	63.11 417	81.03 368	88.59 425

KD-MVS_2432*1600		78.50 388	76.02 395	85.93 375	86.22 421	74.47 356	84.80 424	92.33 331	79.29 336	76.98 395	85.92 414	53.81 414	93.97 391	67.39 396	57.42 443	89.36 413

miper_refine_blended		78.50 388	76.02 395	85.93 375	86.22 421	74.47 356	84.80 424	92.33 331	79.29 336	76.98 395	85.92 414	53.81 414	93.97 391	67.39 396	57.42 443	89.36 413

K. test v3		81.59 356	80.15 358	85.91 377	89.89 389	69.42 412	92.57 289	87.71 416	85.56 192	73.44 419	89.71 363	55.58 401	95.52 361	77.17 321	69.76 421	92.78 353

SSC-MVS3.2		84.60 325	84.19 312	85.85 378	92.74 293	68.07 415	88.15 393	93.81 295	87.42 145	83.76 312	91.07 321	62.91 353	95.73 355	74.56 350	83.24 338	93.75 313

mvsany_test1		85.42 305	85.30 290	85.77 379	87.95 414	75.41 347	87.61 405	80.97 440	76.82 370	88.68 182	95.83 124	77.44 172	90.82 426	85.90 187	86.51 307	91.08 398

MIMVSNet1		79.38 383	77.28 385	85.69 380	86.35 420	73.67 365	91.61 321	92.75 322	78.11 361	72.64 422	88.12 389	48.16 427	91.97 418	60.32 425	77.49 399	91.43 388

UWE-MVS		83.69 339	83.09 332	85.48 381	93.06 279	65.27 430	90.92 337	86.14 422	79.90 329	86.26 237	90.72 335	57.17 397	95.81 350	71.03 374	92.62 217	95.35 234

UnsupCasMVSNet_eth		80.07 375	78.27 381	85.46 382	85.24 429	72.63 381	88.45 389	94.87 247	82.99 267	71.64 426	88.07 390	56.34 399	91.75 419	73.48 358	63.36 436	92.01 374

CL-MVSNet_self_test		81.74 353	80.53 351	85.36 383	85.96 423	72.45 384	90.25 350	93.07 312	81.24 314	79.85 373	87.29 400	70.93 264	92.52 410	66.95 399	69.23 423	91.11 396

MDA-MVSNet_test_wron		79.21 385	77.19 387	85.29 384	88.22 409	72.77 376	85.87 415	90.06 393	74.34 394	62.62 439	87.56 397	66.14 331	91.99 417	66.90 403	73.01 411	91.10 397

YYNet1		79.22 384	77.20 386	85.28 385	88.20 410	72.66 379	85.87 415	90.05 395	74.33 395	62.70 437	87.61 396	66.09 332	92.03 414	66.94 400	72.97 412	91.15 393

WB-MVSnew		83.77 337	83.28 328	85.26 386	91.48 331	71.03 399	91.89 311	87.98 413	78.91 341	84.78 281	90.22 345	69.11 299	94.02 389	64.70 412	90.44 244	90.71 400

dp		81.47 360	80.23 356	85.17 387	89.92 388	65.49 428	86.74 410	90.10 392	76.30 375	81.10 351	87.12 404	62.81 354	95.92 343	68.13 393	79.88 385	94.09 290

UnsupCasMVSNet_bld		76.23 397	73.27 401	85.09 388	83.79 433	72.92 373	85.65 418	93.47 303	71.52 417	68.84 432	79.08 437	49.77 422	93.21 403	66.81 404	60.52 440	89.13 421

SD_0403		84.71 323	84.65 305	84.92 389	92.95 286	65.95 424	92.07 310	93.23 307	83.82 244	79.03 379	93.73 228	73.90 225	92.91 408	63.02 419	90.05 251	95.89 212

Anonymous20231206		81.03 365	79.77 363	84.82 390	87.85 415	70.26 407	91.42 324	92.08 340	73.67 401	77.75 390	89.25 369	62.43 356	93.08 405	61.50 423	82.00 354	91.12 395

test0.0.03 1		82.41 347	81.69 343	84.59 391	88.23 408	72.89 374	90.24 352	87.83 415	83.41 255	79.86 372	89.78 361	67.25 313	88.99 436	65.18 409	83.42 336	91.90 376

CMPMVS	59.16 21	80.52 369	79.20 371	84.48 392	83.98 432	67.63 421	89.95 363	93.84 294	64.79 436	66.81 434	91.14 318	57.93 393	95.17 371	76.25 331	88.10 286	90.65 401
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 324	84.79 303	84.37 393	91.84 319	64.92 431	93.70 240	91.47 361	66.19 433	86.16 240	95.28 148	67.18 315	93.33 401	80.89 276	90.42 246	94.88 254

PVSNet_0	73.20 20	77.22 393	74.83 399	84.37 393	90.70 370	71.10 398	83.09 433	89.67 402	72.81 411	73.93 417	83.13 427	60.79 374	93.70 397	68.54 388	50.84 448	88.30 427

LF4IMVS		80.37 372	79.07 375	84.27 395	86.64 419	69.87 411	89.39 373	91.05 370	76.38 373	74.97 411	90.00 355	47.85 428	94.25 387	74.55 351	80.82 374	88.69 424

Anonymous20240521		80.44 371	79.21 370	84.11 396	85.75 426	67.89 417	92.86 282	93.23 307	75.61 382	75.59 408	87.47 398	50.03 421	94.33 384	71.14 372	81.21 362	90.12 408

PM-MVS		78.11 390	76.12 394	84.09 397	83.54 434	70.08 408	88.97 381	85.27 429	79.93 328	74.73 413	86.43 409	34.70 446	93.48 399	79.43 298	72.06 415	88.72 423

test_fmvs2		83.98 332	84.03 317	83.83 398	87.16 417	67.53 422	93.93 227	92.89 316	77.62 362	86.89 221	93.53 231	47.18 430	92.02 416	90.54 126	86.51 307	91.93 375

testgi		80.94 368	80.20 357	83.18 399	87.96 413	66.29 423	91.28 328	90.70 382	83.70 246	78.12 386	92.84 253	51.37 419	90.82 426	63.34 416	82.46 347	92.43 363

KD-MVS_self_test		80.20 373	79.24 369	83.07 400	85.64 427	65.29 429	91.01 336	93.93 288	78.71 350	76.32 400	86.40 411	59.20 386	92.93 407	72.59 362	69.35 422	91.00 399

testing3		80.46 370	79.59 366	83.06 401	93.44 266	64.64 432	93.33 253	85.47 427	84.34 233	79.93 371	90.84 328	44.35 438	92.39 411	57.06 435	87.56 296	92.16 372

ambc				83.06 401	79.99 443	63.51 436	77.47 446	92.86 317		74.34 416	84.45 422	28.74 447	95.06 375	73.06 360	68.89 426	90.61 402

test20.03		79.95 377	79.08 374	82.55 403	85.79 425	67.74 420	91.09 334	91.08 368	81.23 315	74.48 415	89.96 357	61.63 361	90.15 428	60.08 426	76.38 405	89.76 410

MVStest1		72.91 401	69.70 406	82.54 404	78.14 446	73.05 372	88.21 392	86.21 421	60.69 440	64.70 435	90.53 338	46.44 433	85.70 443	58.78 431	53.62 445	88.87 422

test_vis1_rt		77.96 391	76.46 391	82.48 405	85.89 424	71.74 391	90.25 350	78.89 444	71.03 421	71.30 427	81.35 434	42.49 440	91.05 425	84.55 210	82.37 348	84.65 432

EU-MVSNet		81.32 362	80.95 349	82.42 406	88.50 404	63.67 435	93.32 254	91.33 363	64.02 437	80.57 360	92.83 254	61.21 370	92.27 413	76.34 330	80.38 381	91.32 389

myMVS_eth3d		79.67 380	78.79 377	82.32 407	91.92 315	64.08 433	89.75 366	87.40 419	81.72 300	78.82 381	87.20 401	45.33 436	91.29 422	59.09 430	87.84 293	91.60 381

ttmdpeth		76.55 395	74.64 400	82.29 408	82.25 439	67.81 419	89.76 365	85.69 425	70.35 423	75.76 406	91.69 297	46.88 431	89.77 430	66.16 405	63.23 437	89.30 415

pmmvs3		71.81 404	68.71 407	81.11 409	75.86 448	70.42 406	86.74 410	83.66 433	58.95 443	68.64 433	80.89 435	36.93 444	89.52 432	63.10 418	63.59 435	83.39 433

Syy-MVS		80.07 375	79.78 361	80.94 410	91.92 315	59.93 442	89.75 366	87.40 419	81.72 300	78.82 381	87.20 401	66.29 329	91.29 422	47.06 443	87.84 293	91.60 381

UWE-MVS-28		78.98 386	78.38 380	80.80 411	88.18 411	60.66 441	90.65 342	78.51 445	78.84 345	77.93 389	90.93 325	59.08 388	89.02 435	50.96 440	90.33 248	92.72 354

new-patchmatchnet		76.41 396	75.17 398	80.13 412	82.65 438	59.61 443	87.66 403	91.08 368	78.23 359	69.85 430	83.22 426	54.76 408	91.63 421	64.14 415	64.89 434	89.16 419

mvsany_test3		74.95 398	73.26 402	80.02 413	74.61 449	63.16 437	85.53 419	78.42 446	74.16 396	74.89 412	86.46 408	36.02 445	89.09 434	82.39 243	66.91 429	87.82 430

test_fmvs3		77.67 392	77.16 388	79.22 414	79.52 444	61.14 439	92.34 297	91.64 355	73.98 398	78.86 380	86.59 407	27.38 450	87.03 438	88.12 154	75.97 407	89.50 412

DSMNet-mixed		76.94 394	76.29 393	78.89 415	83.10 436	56.11 451	87.78 399	79.77 442	60.65 441	75.64 407	88.71 380	61.56 364	88.34 437	60.07 427	89.29 269	92.21 371

EGC-MVSNET		61.97 412	56.37 417	78.77 416	89.63 393	73.50 367	89.12 378	82.79 435	0.21 462	1.24 463	84.80 420	39.48 441	90.04 429	44.13 445	75.94 408	72.79 444

new_pmnet		72.15 402	70.13 405	78.20 417	82.95 437	65.68 426	83.91 429	82.40 437	62.94 439	64.47 436	79.82 436	42.85 439	86.26 442	57.41 434	74.44 410	82.65 437

MVS-HIRNet		73.70 400	72.20 403	78.18 418	91.81 322	56.42 450	82.94 434	82.58 436	55.24 444	68.88 431	66.48 447	55.32 405	95.13 372	58.12 432	88.42 282	83.01 435

LCM-MVSNet		66.00 409	62.16 414	77.51 419	64.51 459	58.29 445	83.87 430	90.90 376	48.17 448	54.69 445	73.31 443	16.83 459	86.75 439	65.47 407	61.67 439	87.48 431

APD_test1		69.04 405	66.26 411	77.36 420	80.51 442	62.79 438	85.46 420	83.51 434	54.11 446	59.14 443	84.79 421	23.40 453	89.61 431	55.22 436	70.24 420	79.68 441

test_f		71.95 403	70.87 404	75.21 421	74.21 451	59.37 444	85.07 423	85.82 424	65.25 435	70.42 429	83.13 427	23.62 451	82.93 449	78.32 308	71.94 416	83.33 434

ANet_high		58.88 416	54.22 421	72.86 422	56.50 462	56.67 447	80.75 439	86.00 423	73.09 408	37.39 454	64.63 450	22.17 454	79.49 452	43.51 446	23.96 456	82.43 438

test_vis3_rt		65.12 410	62.60 412	72.69 423	71.44 452	60.71 440	87.17 407	65.55 456	63.80 438	53.22 446	65.65 449	14.54 460	89.44 433	76.65 325	65.38 432	67.91 447

FPMVS		64.63 411	62.55 413	70.88 424	70.80 453	56.71 446	84.42 427	84.42 431	51.78 447	49.57 447	81.61 433	23.49 452	81.48 450	40.61 450	76.25 406	74.46 443

dmvs_testset		74.57 399	75.81 397	70.86 425	87.72 416	40.47 460	87.05 409	77.90 450	82.75 272	71.15 428	85.47 418	67.98 310	84.12 447	45.26 444	76.98 404	88.00 428

N_pmnet		68.89 406	68.44 408	70.23 426	89.07 398	28.79 465	88.06 394	19.50 465	69.47 425	71.86 425	84.93 419	61.24 369	91.75 419	54.70 437	77.15 401	90.15 407

testf1		59.54 414	56.11 418	69.85 427	69.28 454	56.61 448	80.37 440	76.55 453	42.58 451	45.68 450	75.61 438	11.26 461	84.18 445	43.20 447	60.44 441	68.75 445

APD_test2		59.54 414	56.11 418	69.85 427	69.28 454	56.61 448	80.37 440	76.55 453	42.58 451	45.68 450	75.61 438	11.26 461	84.18 445	43.20 447	60.44 441	68.75 445

WB-MVS		67.92 407	67.49 409	69.21 429	81.09 440	41.17 459	88.03 395	78.00 449	73.50 403	62.63 438	83.11 429	63.94 346	86.52 440	25.66 455	51.45 447	79.94 440

PMMVS2		59.60 413	56.40 416	69.21 429	68.83 456	46.58 455	73.02 450	77.48 451	55.07 445	49.21 448	72.95 444	17.43 458	80.04 451	49.32 442	44.33 451	80.99 439

SSC-MVS		67.06 408	66.56 410	68.56 431	80.54 441	40.06 461	87.77 400	77.37 452	72.38 413	61.75 440	82.66 431	63.37 349	86.45 441	24.48 456	48.69 450	79.16 442

Gipuma		57.99 418	54.91 420	67.24 432	88.51 402	65.59 427	52.21 453	90.33 387	43.58 450	42.84 453	51.18 454	20.29 456	85.07 444	34.77 451	70.45 419	51.05 453
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 420	48.46 424	63.48 433	45.72 464	46.20 456	73.41 449	78.31 447	41.03 453	30.06 456	65.68 448	6.05 463	83.43 448	30.04 453	65.86 431	60.80 448
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 417	58.24 415	60.56 434	83.13 435	45.09 458	82.32 435	48.22 464	67.61 429	61.70 441	69.15 445	38.75 442	76.05 453	32.01 452	41.31 452	60.55 449

MVE	39.65 23	43.39 422	38.59 428	57.77 435	56.52 461	48.77 454	55.38 452	58.64 460	29.33 456	28.96 457	52.65 453	4.68 464	64.62 457	28.11 454	33.07 454	59.93 450
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 421	48.47 423	56.66 436	52.26 463	18.98 467	41.51 455	81.40 439	10.10 457	44.59 452	75.01 441	28.51 448	68.16 454	53.54 438	49.31 449	82.83 436

DeepMVS_CX				56.31 437	74.23 450	51.81 453		56.67 461	44.85 449	48.54 449	75.16 440	27.87 449	58.74 459	40.92 449	52.22 446	58.39 451

kuosan		53.51 419	53.30 422	54.13 438	76.06 447	45.36 457	80.11 442	48.36 463	59.63 442	54.84 444	63.43 451	37.41 443	62.07 458	20.73 458	39.10 453	54.96 452

E-PMN		43.23 423	42.29 425	46.03 439	65.58 458	37.41 462	73.51 448	64.62 457	33.99 454	28.47 458	47.87 455	19.90 457	67.91 455	22.23 457	24.45 455	32.77 454

EMVS		42.07 424	41.12 426	44.92 440	63.45 460	35.56 464	73.65 447	63.48 458	33.05 455	26.88 459	45.45 456	21.27 455	67.14 456	19.80 459	23.02 457	32.06 455

tmp_tt		35.64 425	39.24 427	24.84 441	14.87 465	23.90 466	62.71 451	51.51 462	6.58 459	36.66 455	62.08 452	44.37 437	30.34 461	52.40 439	22.00 458	20.27 456

wuyk23d		21.27 427	20.48 430	23.63 442	68.59 457	36.41 463	49.57 454	6.85 466	9.37 458	7.89 460	4.46 462	4.03 465	31.37 460	17.47 460	16.07 459	3.12 457

test123		8.76 429	11.22 432	1.39 443	0.85 467	0.97 468	85.76 417	0.35 468	0.54 461	2.45 462	8.14 461	0.60 466	0.48 462	2.16 462	0.17 461	2.71 458

testmvs		8.92 428	11.52 431	1.12 444	1.06 466	0.46 469	86.02 414	0.65 467	0.62 460	2.74 461	9.52 460	0.31 467	0.45 463	2.38 461	0.39 460	2.46 459

mmdepth		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

monomultidepth		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

test_blank		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

uanet_test		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

DCPMVS		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

cdsmvs_eth3d_5k		22.14 426	29.52 429	0.00 445	0.00 468	0.00 470	0.00 456	95.76 177	0.00 463	0.00 464	94.29 199	75.66 199	0.00 464	0.00 463	0.00 462	0.00 460

pcd_1.5k_mvsjas		6.64 431	8.86 434	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	79.70 138	0.00 464	0.00 463	0.00 462	0.00 460

sosnet-low-res		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

sosnet		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

uncertanet		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

Regformer		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

ab-mvs-re		7.82 430	10.43 433	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	93.88 220	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

uanet		0.00 432	0.00 435	0.00 445	0.00 468	0.00 470	0.00 456	0.00 469	0.00 463	0.00 464	0.00 463	0.00 468	0.00 464	0.00 463	0.00 462	0.00 460

WAC-MVS						64.08 433								59.14 429

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

PC_three_1452									82.47 276	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					0.00 468

eth-test					0.00 468

ZD-MVS					98.15 36	86.62 33		97.07 54	83.63 248	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	81.26 313	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																96.12 200

test_part2					98.55 12	87.22 19				96.40 25

sam_mvs1												71.70 255				96.12 200

sam_mvs												70.60 269

MTGPA								96.97 59

test_post1							88.00 396				9.81 459	69.31 293	95.53 360	76.65 325

test_post											10.29 458	70.57 273	95.91 345

patchmatchnet-post											83.76 424	71.53 256	96.48 314

MTMP							96.16 55	60.64 459

gm-plane-assit					89.60 394	68.00 416			77.28 367		88.99 374		97.57 222	79.44 297

test9_res														91.91 101	98.71 32	98.07 77

TEST9					97.53 63	86.49 37	94.07 214	96.78 83	81.61 305	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	81.86 296	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							93.36 252		71.25 419	94.37 53			97.13 271	86.74 174

新几何2							93.11 267

旧先验1					96.79 81	81.81 186		95.67 185			96.81 77	86.69 39			97.66 91	96.97 156

无先验							93.28 260	96.26 132	73.95 399				99.05 61	80.56 282		96.59 179

原ACMM2							92.94 278

test222					96.55 90	81.70 188	92.22 303	95.01 232	68.36 428	90.20 153	96.14 106	80.26 131			97.80 85	96.05 207

testdata2													98.75 109	78.30 309

segment_acmp												87.16 36

testdata1							92.15 305		87.94 127

plane_prior7					94.70 192	82.74 159

plane_prior6					94.52 205	82.75 157						74.23 217

plane_prior5								96.22 137					98.12 169	88.15 151	89.99 252	94.63 261

plane_prior4											94.86 170

plane_prior3						82.75 157			90.26 44	86.91 218

plane_prior2							95.85 86		90.81 24

plane_prior1					94.59 198

plane_prior						82.73 160	95.21 132		89.66 65						89.88 257

n2								0.00 469

nn								0.00 469

door-mid								85.49 426

test11								96.57 104

door								85.33 428

HQP5-MVS						81.56 190

HQP-NCC					94.17 226		94.39 189		88.81 95	85.43 264

ACMP_Plane					94.17 226		94.39 189		88.81 95	85.43 264

BP-MVS														87.11 171

HQP4-MVS										85.43 264			97.96 192			94.51 271

HQP3-MVS								96.04 154							89.77 261

HQP2-MVS												73.83 228

NP-MVS					94.37 216	82.42 172					93.98 213

MDTV_nov1_ep13_2view						55.91 452	87.62 404		73.32 405	84.59 286		70.33 276		74.65 348		95.50 228

MDTV_nov1_ep13			83.56 325		91.69 327	69.93 409	87.75 401	91.54 358	78.60 351	84.86 280	88.90 376	69.54 288	96.03 336	70.25 377	88.93 274

ACMMP++_ref															87.47 297

ACMMP++															88.01 289

Test By Simon												80.02 133

High-resolution multi-view benchmark