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 75					99.61 4	96.03 25	99.06 9	99.07 5

No_MVS				96.52 1	97.78 56	90.86 1		96.85 75					99.61 4	96.03 25	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 44	90.85 3	97.13 15				97.08 64	92.59 2	98.94 86	92.25 86	98.99 14	98.84 15

HPM-MVS++		95.14 10	94.91 22	95.83 4	98.25 31	89.65 4	95.92 81	96.96 63	91.75 12	94.02 65	96.83 76	88.12 24	99.55 16	93.41 60	98.94 16	98.28 57

MM		95.10 11	94.91 22	95.68 5	96.09 111	88.34 9	96.68 34	94.37 276	95.08 1	94.68 51	97.72 37	82.94 96	99.64 1	97.85 4	98.76 29	99.06 7

SMA-MVS		95.20 8	95.07 16	95.59 6	98.14 37	88.48 8	96.26 49	97.28 35	85.90 188	97.67 4	98.10 12	88.41 20	99.56 12	94.66 44	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 98	91.37 111	95.55 7	95.63 137	88.73 6	97.07 19	96.77 86	90.84 24	84.02 312	96.62 89	75.95 196	99.34 38	87.77 164	97.68 91	98.59 25

CNVR-MVS		95.40 7	95.37 9	95.50 8	98.11 38	88.51 7	95.29 123	96.96 63	92.09 9	95.32 43	97.08 64	89.49 15	99.33 41	95.10 39	98.85 20	98.66 22

MVS_0304		94.18 45	93.80 59	95.34 9	94.91 176	87.62 14	95.97 76	93.01 320	92.58 6	94.22 56	97.20 58	80.56 129	99.59 8	97.04 18	98.68 37	98.81 18

ACMMP_NAP		94.74 22	94.56 28	95.28 10	98.02 43	87.70 11	95.68 99	97.34 26	88.28 116	95.30 44	97.67 39	85.90 51	99.54 20	93.91 52	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 85	97.14 14	97.91 30	91.64 7	99.62 2	94.61 45	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 24	95.20 12	97.84 52	87.76 10	96.65 35	97.48 12	87.76 138	95.71 38	97.70 38	88.28 23	99.35 37	93.89 53	98.78 26	98.48 31

MCST-MVS		94.45 30	94.20 46	95.19 13	98.46 19	87.50 16	95.00 146	97.12 50	87.13 154	92.51 106	96.30 98	89.24 17	99.34 38	93.46 57	98.62 46	98.73 19

NCCC		94.81 19	94.69 27	95.17 14	97.83 53	87.46 17	95.66 102	96.93 67	92.34 7	93.94 66	96.58 91	87.74 27	99.44 29	92.83 69	98.40 54	98.62 23

DPM-MVS		92.58 94	91.74 104	95.08 15	96.19 102	89.31 5	92.66 291	96.56 106	83.44 260	91.68 131	95.04 167	86.60 43	98.99 76	85.60 197	97.92 80	96.93 166

ZNCC-MVS		94.47 29	94.28 40	95.03 16	98.52 15	86.96 20	96.85 29	97.32 30	88.24 117	93.15 81	97.04 67	86.17 48	99.62 2	92.40 80	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 32	99.08 7	98.99 9

MTAPA		94.42 34	94.22 43	95.00 18	98.42 21	86.95 21	94.36 196	96.97 60	91.07 20	93.14 82	97.56 41	84.30 77	99.56 12	93.43 58	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 31	96.69 81	89.90 12	99.30 44	94.70 43	98.04 75	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 32	94.27 42	94.92 20	98.65 8	86.67 30	96.92 25	97.23 38	88.60 107	93.58 73	97.27 52	85.22 60	99.54 20	92.21 88	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 90	96.20 30	98.10 12	89.39 16	99.34 38	95.88 27	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 32	94.28 40	94.91 21	98.63 9	86.69 28	96.94 21	97.32 30	88.63 104	93.53 76	97.26 54	85.04 64	99.54 20	92.35 83	98.78 26	98.50 28

GST-MVS		94.21 40	93.97 55	94.90 23	98.41 22	86.82 24	96.54 37	97.19 39	88.24 117	93.26 78	96.83 76	85.48 57	99.59 8	91.43 114	98.40 54	98.30 51

HFP-MVS		94.52 27	94.40 33	94.86 24	98.61 10	86.81 25	96.94 21	97.34 26	88.63 104	93.65 71	97.21 56	86.10 49	99.49 26	92.35 83	98.77 28	98.30 51

sasdasda		93.27 77	92.75 87	94.85 25	95.70 132	87.66 12	96.33 40	96.41 116	90.00 49	94.09 61	94.60 191	82.33 105	98.62 125	92.40 80	92.86 211	98.27 59

MP-MVS-pluss		94.21 40	94.00 54	94.85 25	98.17 35	86.65 31	94.82 159	97.17 44	86.26 180	92.83 91	97.87 32	85.57 56	99.56 12	94.37 48	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 77	92.75 87	94.85 25	95.70 132	87.66 12	96.33 40	96.41 116	90.00 49	94.09 61	94.60 191	82.33 105	98.62 125	92.40 80	92.86 211	98.27 59

XVS		94.45 30	94.32 36	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 32	92.85 89	97.16 62	85.02 65	99.49 26	91.99 99	98.56 50	98.47 34

X-MVStestdata		88.31 216	86.13 265	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 32	92.85 89	23.41 463	85.02 65	99.49 26	91.99 99	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 18	95.39 42	97.46 44	88.98 19	99.40 30	94.12 49	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 27	97.62 7	98.06 20	92.59 2	99.61 4	95.64 30	99.02 12	98.86 12

alignmvs		93.08 85	92.50 93	94.81 32	95.62 138	87.61 15	95.99 74	96.07 152	89.77 62	94.12 60	94.87 175	80.56 129	98.66 117	92.42 79	93.10 207	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 39	97.71 2	98.07 18	92.31 4	99.58 10	95.66 28	99.13 3	98.84 15

DeepC-MVS_fast	89.43 2	94.04 48	93.79 60	94.80 33	97.48 66	86.78 26	95.65 104	96.89 72	89.40 73	92.81 92	96.97 69	85.37 59	99.24 47	90.87 123	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 37	94.07 51	94.77 35	98.47 18	86.31 44	96.71 32	96.98 59	89.04 88	91.98 117	97.19 59	85.43 58	99.56 12	92.06 97	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 38	94.07 51	94.75 36	98.06 41	86.90 23	95.88 83	96.94 66	85.68 195	95.05 49	97.18 60	87.31 35	99.07 59	91.90 105	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 35	94.21 45	94.74 37	98.39 25	86.64 32	97.60 5	97.24 36	88.53 109	92.73 97	97.23 55	85.20 61	99.32 42	92.15 91	98.83 22	98.25 64

PGM-MVS		93.96 53	93.72 65	94.68 38	98.43 20	86.22 47	95.30 121	97.78 1	87.45 146	93.26 78	97.33 50	84.62 74	99.51 24	90.75 125	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 92	90.27 43	97.04 18	98.05 23	91.47 8	99.55 16	95.62 32	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 51	93.78 61	94.63 40	98.50 16	85.90 62	96.87 27	96.91 70	88.70 102	91.83 126	97.17 61	83.96 81	99.55 16	91.44 113	98.64 45	98.43 39

PHI-MVS		93.89 55	93.65 69	94.62 41	96.84 80	86.43 39	96.69 33	97.49 8	85.15 218	93.56 75	96.28 99	85.60 55	99.31 43	92.45 77	98.79 24	98.12 76

TSAR-MVS + MP.		94.85 16	94.94 20	94.58 42	98.25 31	86.33 42	96.11 62	96.62 101	88.14 122	96.10 31	96.96 70	89.09 18	98.94 86	94.48 46	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 64	93.20 78	94.55 43	95.65 135	85.73 67	94.94 149	96.69 97	91.89 11	90.69 148	95.88 123	81.99 117	99.54 20	93.14 64	97.95 79	98.39 41

train_agg		93.44 70	93.08 80	94.52 44	97.53 63	86.49 37	94.07 215	96.78 84	81.86 302	92.77 94	96.20 102	87.63 29	99.12 57	92.14 92	98.69 35	97.94 89

CDPH-MVS		92.83 89	92.30 96	94.44 45	97.79 54	86.11 51	94.06 217	96.66 98	80.09 333	92.77 94	96.63 88	86.62 41	99.04 63	87.40 170	98.66 41	98.17 69

3Dnovator	86.66 5	91.73 110	90.82 125	94.44 45	94.59 199	86.37 41	97.18 13	97.02 57	89.20 82	84.31 307	96.66 84	73.74 236	99.17 51	86.74 180	97.96 78	97.79 103

SR-MVS		94.23 39	94.17 49	94.43 47	98.21 34	85.78 65	96.40 39	96.90 71	88.20 120	94.33 55	97.40 47	84.75 73	99.03 64	93.35 61	97.99 77	98.48 31

HPM-MVS		94.02 49	93.88 56	94.43 47	98.39 25	85.78 65	97.25 11	97.07 55	86.90 164	92.62 103	96.80 80	84.85 71	99.17 51	92.43 78	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 62	93.41 73	94.41 49	96.59 87	86.78 26	94.40 188	93.93 294	89.77 62	94.21 57	95.59 140	87.35 34	98.61 127	92.72 72	96.15 129	97.83 100

reproduce-ours		94.82 17	94.97 18	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 55	95.23 46	98.10 12	87.09 37	99.37 33	95.30 36	98.25 63	98.30 51

our_new_method		94.82 17	94.97 18	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 55	95.23 46	98.10 12	87.09 37	99.37 33	95.30 36	98.25 63	98.30 51

NormalMVS		93.46 67	93.16 79	94.37 52	98.40 23	86.20 48	96.30 42	96.27 129	91.65 16	92.68 99	96.13 108	77.97 166	98.84 99	90.75 125	98.26 59	98.07 78

test12				94.34 53	97.13 75	86.15 50		96.29 125		91.04 144		85.08 63	99.01 69		98.13 70	97.86 97

SymmetryMVS		92.81 91	92.31 95	94.32 54	96.15 103	86.20 48	96.30 42	94.43 272	91.65 16	92.68 99	96.13 108	77.97 166	98.84 99	90.75 125	94.72 161	97.92 92

ACMMP		93.24 79	92.88 85	94.30 55	98.09 40	85.33 74	96.86 28	97.45 16	88.33 113	90.15 161	97.03 68	81.44 122	99.51 24	90.85 124	95.74 136	98.04 84
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 21	94.29 56	97.92 45	85.18 76	95.95 79	97.19 39	89.67 65	95.27 45	98.16 5	86.53 44	99.36 36	95.42 35	98.15 68	98.33 46

DeepC-MVS	88.79 3	93.31 76	92.99 83	94.26 57	96.07 113	85.83 63	94.89 152	96.99 58	89.02 91	89.56 169	97.37 49	82.51 102	99.38 31	92.20 89	98.30 57	97.57 117
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 86	92.63 90	94.23 58	95.62 138	85.92 59	96.08 64	96.33 123	89.86 53	93.89 68	94.66 188	82.11 112	98.50 133	92.33 85	92.82 214	98.27 59

fmvsm_l_conf0.5_n_3		94.80 20	95.01 17	94.15 59	95.64 136	85.08 77	96.09 63	97.36 24	90.98 22	97.09 16	98.12 9	84.98 69	98.94 86	97.07 15	97.80 86	98.43 39

EPNet		91.79 106	91.02 120	94.10 60	90.10 389	85.25 75	96.03 71	92.05 347	92.83 5	87.39 218	95.78 132	79.39 148	99.01 69	88.13 159	97.48 94	98.05 83
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 19	96.22 29	98.08 17	86.64 40	99.37 33	94.91 41	98.26 59	98.29 56

test_fmvsmconf_n		94.60 25	94.81 25	93.98 62	94.62 197	84.96 80	96.15 57	97.35 25	89.37 74	96.03 34	98.11 10	86.36 45	99.01 69	97.45 9	97.83 84	97.96 88

DELS-MVS		93.43 74	93.25 76	93.97 63	95.42 146	85.04 78	93.06 275	97.13 49	90.74 29	91.84 124	95.09 166	86.32 46	99.21 49	91.22 115	98.45 52	97.65 112
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		91.95 103	91.28 114	93.96 64	98.33 29	85.92 59	94.66 171	96.66 98	82.69 280	90.03 163	95.82 128	82.30 107	99.03 64	84.57 215	96.48 122	96.91 168

HPM-MVS_fast		93.40 75	93.22 77	93.94 65	98.36 27	84.83 82	97.15 14	96.80 83	85.77 192	92.47 107	97.13 63	82.38 103	99.07 59	90.51 130	98.40 54	97.92 92

test_fmvsmconf0.1_n		94.20 42	94.31 38	93.88 66	92.46 305	84.80 83	96.18 54	96.82 80	89.29 79	95.68 39	98.11 10	85.10 62	98.99 76	97.38 10	97.75 90	97.86 97

SD-MVS		94.96 15	95.33 10	93.88 66	97.25 74	86.69 28	96.19 52	97.11 53	90.42 35	96.95 20	97.27 52	89.53 14	96.91 293	94.38 47	98.85 20	98.03 85
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 69	93.31 74	93.84 68	96.99 77	84.84 81	93.24 266	97.24 36	88.76 99	91.60 132	95.85 126	86.07 50	98.66 117	91.91 103	98.16 67	98.03 85

SR-MVS-dyc-post		93.82 57	93.82 58	93.82 69	97.92 45	84.57 89	96.28 46	96.76 87	87.46 144	93.75 69	97.43 45	84.24 78	99.01 69	92.73 70	97.80 86	97.88 95

test_prior				93.82 69	97.29 72	84.49 93		96.88 73					98.87 93			98.11 77

APD-MVS_3200maxsize		93.78 58	93.77 62	93.80 71	97.92 45	84.19 106	96.30 42	96.87 74	86.96 160	93.92 67	97.47 43	83.88 82	98.96 83	92.71 73	97.87 82	98.26 63

fmvsm_l_conf0.5_n		94.29 36	94.46 31	93.79 72	95.28 151	85.43 72	95.68 99	96.43 114	86.56 172	96.84 22	97.81 35	87.56 32	98.77 108	97.14 13	96.82 112	97.16 147

CSCG		93.23 80	93.05 81	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 241	90.05 162	95.66 137	87.77 26	99.15 55	89.91 135	98.27 58	98.07 78

GDP-MVS		92.04 101	91.46 108	93.75 74	94.55 205	84.69 86	95.60 110	96.56 106	87.83 135	93.07 85	95.89 122	73.44 240	98.65 119	90.22 133	96.03 131	97.91 94

BP-MVS1		92.48 96	92.07 99	93.72 75	94.50 208	84.39 101	95.90 82	94.30 279	90.39 36	92.67 101	95.94 119	74.46 219	98.65 119	93.14 64	97.35 98	98.13 73

test_fmvsmconf0.01_n		93.19 81	93.02 82	93.71 76	89.25 402	84.42 100	96.06 68	96.29 125	89.06 86	94.68 51	98.13 6	79.22 150	98.98 80	97.22 12	97.24 100	97.74 106

UA-Net		92.83 89	92.54 92	93.68 77	96.10 110	84.71 85	95.66 102	96.39 118	91.92 10	93.22 80	96.49 94	83.16 91	98.87 93	84.47 217	95.47 143	97.45 123

fmvsm_l_conf0.5_n_a		94.20 42	94.40 33	93.60 78	95.29 150	84.98 79	95.61 107	96.28 128	86.31 178	96.75 24	97.86 33	87.40 33	98.74 112	97.07 15	97.02 105	97.07 152

QAPM		89.51 173	88.15 200	93.59 79	94.92 174	84.58 88	96.82 30	96.70 96	78.43 360	83.41 328	96.19 105	73.18 245	99.30 44	77.11 328	96.54 119	96.89 169

test_fmvsm_n_1920		94.71 23	95.11 15	93.50 80	95.79 127	84.62 87	96.15 57	97.64 3	89.85 54	97.19 13	97.89 31	86.28 47	98.71 115	97.11 14	98.08 74	97.17 141

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 68	93.37 3	97.63 6	98.43 1	84.82 72	99.16 54	98.15 1	97.92 80	98.90 11

KinetiMVS		91.82 105	91.30 112	93.39 82	94.72 190	83.36 133	95.45 114	96.37 120	90.33 38	92.17 112	96.03 114	72.32 257	98.75 109	87.94 162	96.34 124	98.07 78

casdiffmvs_mvg		92.96 88	92.83 86	93.35 83	94.59 199	83.40 131	95.00 146	96.34 122	90.30 41	92.05 115	96.05 112	83.43 85	98.15 169	92.07 94	95.67 137	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 53	94.18 48	93.30 84	94.79 183	83.81 117	95.77 92	96.74 91	88.02 125	96.23 28	97.84 34	83.36 89	98.83 102	97.49 7	97.34 99	97.25 135

EI-MVSNet-Vis-set		93.01 87	92.92 84	93.29 85	95.01 165	83.51 128	94.48 180	95.77 179	90.87 23	92.52 105	96.67 83	84.50 75	99.00 74	91.99 99	94.44 174	97.36 126

Vis-MVSNet		91.75 109	91.23 115	93.29 85	95.32 149	83.78 118	96.14 59	95.98 159	89.89 51	90.45 152	96.58 91	75.09 208	98.31 160	84.75 209	96.90 108	97.78 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 52	94.22 43	93.26 87	96.13 105	83.29 135	96.27 48	96.52 109	89.82 55	95.56 41	95.51 143	84.50 75	98.79 106	94.83 42	98.86 19	97.72 108

SPE-MVS-test		94.02 49	94.29 39	93.24 88	96.69 83	83.24 136	97.49 6	96.92 68	92.14 8	92.90 87	95.77 133	85.02 65	98.33 157	93.03 66	98.62 46	98.13 73

VNet		92.24 100	91.91 101	93.24 88	96.59 87	83.43 129	94.84 158	96.44 113	89.19 83	94.08 64	95.90 121	77.85 172	98.17 167	88.90 150	93.38 196	98.13 73

VDD-MVS		90.74 131	89.92 145	93.20 90	96.27 100	83.02 150	95.73 96	93.86 298	88.42 112	92.53 104	96.84 75	62.09 363	98.64 122	90.95 121	92.62 221	97.93 91

Elysia		90.12 150	89.10 168	93.18 91	93.16 275	84.05 110	95.22 130	96.27 129	85.16 216	90.59 149	94.68 184	64.64 346	98.37 150	86.38 186	95.77 134	97.12 149

StellarMVS		90.12 150	89.10 168	93.18 91	93.16 275	84.05 110	95.22 130	96.27 129	85.16 216	90.59 149	94.68 184	64.64 346	98.37 150	86.38 186	95.77 134	97.12 149

CS-MVS		94.12 46	94.44 32	93.17 93	96.55 90	83.08 147	97.63 4	96.95 65	91.71 14	93.50 77	96.21 101	85.61 54	98.24 162	93.64 55	98.17 66	98.19 67

nrg030		91.08 125	90.39 129	93.17 93	93.07 282	86.91 22	96.41 38	96.26 133	88.30 115	88.37 194	94.85 178	82.19 111	97.64 219	91.09 116	82.95 345	94.96 254

MVSMamba_PlusPlus		93.44 70	93.54 71	93.14 95	96.58 89	83.05 148	96.06 68	96.50 111	84.42 238	94.09 61	95.56 142	85.01 68	98.69 116	94.96 40	98.66 41	97.67 111

EI-MVSNet-UG-set		92.74 92	92.62 91	93.12 96	94.86 179	83.20 138	94.40 188	95.74 182	90.71 31	92.05 115	96.60 90	84.00 80	98.99 76	91.55 111	93.63 187	97.17 141

test_fmvsmvis_n_1920		93.44 70	93.55 70	93.10 97	93.67 261	84.26 104	95.83 88	96.14 143	89.00 92	92.43 108	97.50 42	83.37 88	98.72 113	96.61 22	97.44 95	96.32 194

新几何1				93.10 97	97.30 71	84.35 103		95.56 197	71.09 426	91.26 141	96.24 100	82.87 98	98.86 95	79.19 307	98.10 71	96.07 210

OMC-MVS		91.23 120	90.62 128	93.08 99	96.27 100	84.07 108	93.52 248	95.93 165	86.95 161	89.51 170	96.13 108	78.50 160	98.35 154	85.84 195	92.90 210	96.83 176

OpenMVS	83.78 11	88.74 203	87.29 222	93.08 99	92.70 300	85.39 73	96.57 36	96.43 114	78.74 355	80.85 360	96.07 111	69.64 292	99.01 69	78.01 319	96.65 117	94.83 262

MAR-MVS		90.30 146	89.37 161	93.07 101	96.61 86	84.48 94	95.68 99	95.67 188	82.36 285	87.85 205	92.85 258	76.63 185	98.80 104	80.01 295	96.68 116	95.91 216
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		90.92 126	90.21 133	93.03 102	93.86 246	83.88 115	92.81 286	93.86 298	79.84 336	91.76 128	94.29 205	77.92 169	98.04 185	90.48 131	97.11 101	97.17 141

Effi-MVS+		91.59 114	91.11 117	93.01 103	94.35 221	83.39 132	94.60 173	95.10 232	87.10 155	90.57 151	93.10 253	81.43 123	98.07 183	89.29 142	94.48 172	97.59 116

fmvsm_s_conf0.5_n_a		93.57 63	93.76 63	93.00 104	95.02 164	83.67 121	96.19 52	96.10 149	87.27 149	95.98 35	98.05 23	83.07 95	98.45 143	96.68 21	95.51 140	96.88 170

MVS_111021_LR		92.47 97	92.29 97	92.98 105	95.99 119	84.43 98	93.08 272	96.09 150	88.20 120	91.12 143	95.72 136	81.33 124	97.76 208	91.74 107	97.37 97	96.75 178

fmvsm_s_conf0.1_n_a		93.19 81	93.26 75	92.97 106	92.49 303	83.62 124	96.02 72	95.72 185	86.78 166	96.04 33	98.19 3	82.30 107	98.43 147	96.38 23	95.42 146	96.86 171

ETV-MVS		92.74 92	92.66 89	92.97 106	95.20 157	84.04 112	95.07 142	96.51 110	90.73 30	92.96 86	91.19 319	84.06 79	98.34 155	91.72 108	96.54 119	96.54 189

LFMVS		90.08 153	89.13 167	92.95 108	96.71 82	82.32 176	96.08 64	89.91 403	86.79 165	92.15 114	96.81 78	62.60 361	98.34 155	87.18 174	93.90 182	98.19 67

UGNet		89.95 160	88.95 176	92.95 108	94.51 207	83.31 134	95.70 98	95.23 225	89.37 74	87.58 212	93.94 221	64.00 351	98.78 107	83.92 224	96.31 125	96.74 179
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		90.80 129	90.10 137	92.90 110	93.04 285	83.53 127	93.08 272	94.15 287	80.22 330	91.41 138	94.91 172	76.87 179	97.93 199	90.28 132	96.90 108	97.24 136
jason: jason.
DP-MVS		87.25 257	85.36 294	92.90 110	97.65 60	83.24 136	94.81 160	92.00 349	74.99 394	81.92 349	95.00 168	72.66 250	99.05 61	66.92 408	92.33 226	96.40 191

fmvsm_s_conf0.5_n_8		94.56 26	95.12 14	92.87 112	95.96 122	81.32 201	95.76 94	97.57 5	93.48 2	97.53 9	98.32 2	81.78 121	99.13 56	97.91 2	97.81 85	98.16 70

fmvsm_s_conf0.5_n		93.76 59	94.06 53	92.86 113	95.62 138	83.17 139	96.14 59	96.12 147	88.13 123	95.82 37	98.04 26	83.43 85	98.48 135	96.97 19	96.23 126	96.92 167

fmvsm_s_conf0.1_n		93.46 67	93.66 68	92.85 114	93.75 253	83.13 141	96.02 72	95.74 182	87.68 141	95.89 36	98.17 4	82.78 99	98.46 139	96.71 20	96.17 128	96.98 161

CANet_DTU		90.26 148	89.41 160	92.81 115	93.46 268	83.01 151	93.48 249	94.47 271	89.43 72	87.76 210	94.23 210	70.54 280	99.03 64	84.97 204	96.39 123	96.38 192

MVSFormer		91.68 112	91.30 112	92.80 116	93.86 246	83.88 115	95.96 77	95.90 169	84.66 234	91.76 128	94.91 172	77.92 169	97.30 260	89.64 138	97.11 101	97.24 136

PVSNet_Blended_VisFu		91.38 117	90.91 122	92.80 116	96.39 97	83.17 139	94.87 154	96.66 98	83.29 265	89.27 176	94.46 200	80.29 132	99.17 51	87.57 167	95.37 147	96.05 213

fmvsm_l_conf0.5_n_9		94.65 24	95.28 12	92.77 118	95.95 123	81.83 186	95.53 112	97.12 50	91.68 15	97.89 1	98.06 20	85.71 53	98.65 119	97.32 11	98.26 59	97.83 100

LuminaMVS		90.55 142	89.81 147	92.77 118	92.78 298	84.21 105	94.09 213	94.17 286	85.82 189	91.54 133	94.14 212	69.93 286	97.92 200	91.62 110	94.21 177	96.18 202

fmvsm_s_conf0.5_n_6		94.11 47	94.56 28	92.76 120	94.98 169	81.96 184	95.79 90	97.29 34	89.31 77	97.52 10	97.61 40	83.25 90	98.88 92	97.05 17	98.22 65	97.43 125

VDDNet		89.56 172	88.49 191	92.76 120	95.07 163	82.09 179	96.30 42	93.19 315	81.05 324	91.88 122	96.86 74	61.16 379	98.33 157	88.43 156	92.49 225	97.84 99

h-mvs33		90.80 129	90.15 136	92.75 122	96.01 115	82.66 164	95.43 115	95.53 201	89.80 58	93.08 83	95.64 138	75.77 197	99.00 74	92.07 94	78.05 402	96.60 184

casdiffmvs		92.51 95	92.43 94	92.74 123	94.41 216	81.98 182	94.54 177	96.23 137	89.57 68	91.96 119	96.17 106	82.58 101	98.01 187	90.95 121	95.45 145	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		90.69 134	90.02 143	92.71 124	95.72 130	82.41 174	94.11 209	95.12 230	85.63 196	91.49 135	94.70 182	74.75 212	98.42 148	86.13 190	92.53 223	97.31 127

DCV-MVSNet		90.69 134	90.02 143	92.71 124	95.72 130	82.41 174	94.11 209	95.12 230	85.63 196	91.49 135	94.70 182	74.75 212	98.42 148	86.13 190	92.53 223	97.31 127

PCF-MVS	84.11 10	87.74 231	86.08 269	92.70 126	94.02 235	84.43 98	89.27 380	95.87 173	73.62 408	84.43 299	94.33 202	78.48 162	98.86 95	70.27 382	94.45 173	94.81 263
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		90.73 132	90.08 138	92.69 127	95.00 168	83.13 141	94.32 197	95.00 240	85.41 206	89.84 164	95.35 150	76.13 188	97.98 191	85.46 200	94.18 178	96.95 163

baseline		92.39 99	92.29 97	92.69 127	94.46 211	81.77 188	94.14 206	96.27 129	89.22 81	91.88 122	96.00 115	82.35 104	97.99 189	91.05 117	95.27 151	98.30 51

MSLP-MVS++		93.72 61	94.08 50	92.65 129	97.31 70	83.43 129	95.79 90	97.33 28	90.03 48	93.58 73	96.96 70	84.87 70	97.76 208	92.19 90	98.66 41	96.76 177

EC-MVSNet		93.44 70	93.71 66	92.63 130	95.21 156	82.43 171	97.27 10	96.71 95	90.57 34	92.88 88	95.80 129	83.16 91	98.16 168	93.68 54	98.14 69	97.31 127

ab-mvs		89.41 180	88.35 193	92.60 131	95.15 161	82.65 168	92.20 310	95.60 195	83.97 245	88.55 190	93.70 235	74.16 227	98.21 166	82.46 248	89.37 270	96.94 165

LS3D		87.89 226	86.32 258	92.59 132	96.07 113	82.92 154	95.23 128	94.92 247	75.66 386	82.89 335	95.98 117	72.48 254	99.21 49	68.43 396	95.23 152	95.64 230

Anonymous20240529		88.09 222	86.59 247	92.58 133	96.53 92	81.92 185	95.99 74	95.84 175	74.11 403	89.06 180	95.21 160	61.44 371	98.81 103	83.67 231	87.47 301	97.01 159

fmvsm_s_conf0.5_n_3		94.49 28	95.13 13	92.56 134	95.49 144	81.10 211	95.93 80	97.16 45	92.96 4	97.39 11	98.13 6	83.63 84	98.80 104	97.89 3	97.61 93	97.78 104

CPTT-MVS		91.99 102	91.80 102	92.55 135	98.24 33	81.98 182	96.76 31	96.49 112	81.89 301	90.24 155	96.44 96	78.59 158	98.61 127	89.68 137	97.85 83	97.06 153

114514_t		89.51 173	88.50 189	92.54 136	98.11 38	81.99 181	95.16 138	96.36 121	70.19 430	85.81 252	95.25 156	76.70 183	98.63 124	82.07 258	96.86 111	97.00 160

PAPM_NR		91.22 121	90.78 126	92.52 137	97.60 61	81.46 197	94.37 194	96.24 136	86.39 177	87.41 215	94.80 180	82.06 115	98.48 135	82.80 243	95.37 147	97.61 114

mamba_0408		89.06 193	87.92 207	92.50 138	94.76 184	82.66 164	79.84 449	94.64 265	85.18 211	88.96 182	95.00 168	76.00 193	97.98 191	83.74 228	93.15 204	96.85 172

DeepPCF-MVS	89.96 1	94.20 42	94.77 26	92.49 139	96.52 93	80.00 250	94.00 223	97.08 54	90.05 47	95.65 40	97.29 51	89.66 13	98.97 81	93.95 51	98.71 32	98.50 28

SSM_0407		90.47 144	89.80 148	92.46 140	94.76 184	82.66 164	93.98 225	95.00 240	85.41 206	88.96 182	95.35 150	76.13 188	97.88 203	85.46 200	93.15 204	96.85 172

IS-MVSNet		91.43 116	91.09 119	92.46 140	95.87 126	81.38 200	96.95 20	93.69 306	89.72 64	89.50 172	95.98 117	78.57 159	97.77 207	83.02 237	96.50 121	98.22 66

API-MVS		90.66 137	90.07 139	92.45 142	96.36 98	84.57 89	96.06 68	95.22 227	82.39 283	89.13 177	94.27 208	80.32 131	98.46 139	80.16 294	96.71 115	94.33 286

xiu_mvs_v1_base_debu		90.64 138	90.05 140	92.40 143	93.97 241	84.46 95	93.32 257	95.46 205	85.17 213	92.25 109	94.03 213	70.59 276	98.57 130	90.97 118	94.67 163	94.18 289

xiu_mvs_v1_base		90.64 138	90.05 140	92.40 143	93.97 241	84.46 95	93.32 257	95.46 205	85.17 213	92.25 109	94.03 213	70.59 276	98.57 130	90.97 118	94.67 163	94.18 289

xiu_mvs_v1_base_debi		90.64 138	90.05 140	92.40 143	93.97 241	84.46 95	93.32 257	95.46 205	85.17 213	92.25 109	94.03 213	70.59 276	98.57 130	90.97 118	94.67 163	94.18 289

fmvsm_s_conf0.5_n_2		93.47 66	93.83 57	92.39 146	95.36 147	81.19 207	95.20 135	96.56 106	90.37 37	97.13 15	98.03 27	77.47 175	98.96 83	97.79 5	96.58 118	97.03 156

viewmacassd2359aftdt		91.67 113	91.43 110	92.37 147	93.95 244	81.00 215	93.90 233	95.97 162	87.75 139	91.45 137	96.04 113	79.92 137	97.97 193	89.26 143	94.67 163	98.14 72

viewmanbaseed2359cas		91.78 107	91.58 106	92.37 147	94.32 222	81.07 212	93.76 238	95.96 163	87.26 150	91.50 134	95.88 123	80.92 128	97.97 193	89.70 136	94.92 157	98.07 78

fmvsm_s_conf0.1_n_2		93.16 83	93.42 72	92.37 147	94.62 197	81.13 209	95.23 128	95.89 171	90.30 41	96.74 25	98.02 28	76.14 187	98.95 85	97.64 6	96.21 127	97.03 156

AdaColmap		89.89 163	89.07 170	92.37 147	97.41 67	83.03 149	94.42 187	95.92 166	82.81 277	86.34 241	94.65 189	73.89 232	99.02 67	80.69 285	95.51 140	95.05 249

CNLPA		89.07 192	87.98 204	92.34 151	96.87 79	84.78 84	94.08 214	93.24 312	81.41 315	84.46 297	95.13 165	75.57 204	96.62 306	77.21 326	93.84 184	95.61 233

fmvsm_s_conf0.5_n_4		93.86 56	94.37 35	92.33 152	95.13 162	80.95 218	95.64 105	96.97 60	89.60 67	96.85 21	97.77 36	83.08 94	98.92 89	97.49 7	96.78 113	97.13 148

ET-MVSNet_ETH3D		87.51 245	85.91 277	92.32 153	93.70 260	83.93 113	92.33 304	90.94 381	84.16 240	72.09 429	92.52 271	69.90 287	95.85 353	89.20 144	88.36 288	97.17 141

Anonymous202405211		87.68 232	86.13 265	92.31 154	96.66 84	80.74 225	94.87 154	91.49 366	80.47 329	89.46 173	95.44 146	54.72 415	98.23 163	82.19 254	89.89 260	97.97 87

CHOSEN 1792x2688		88.84 199	87.69 212	92.30 155	96.14 104	81.42 199	90.01 367	95.86 174	74.52 399	87.41 215	93.94 221	75.46 205	98.36 152	80.36 290	95.53 139	97.12 149

HY-MVS	83.01 12	89.03 195	87.94 206	92.29 156	94.86 179	82.77 156	92.08 315	94.49 270	81.52 314	86.93 222	92.79 264	78.32 164	98.23 163	79.93 296	90.55 247	95.88 219

CDS-MVSNet		89.45 176	88.51 188	92.29 156	93.62 263	83.61 126	93.01 276	94.68 263	81.95 295	87.82 208	93.24 247	78.69 156	96.99 287	80.34 291	93.23 201	96.28 197
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 156	89.27 166	92.29 156	95.78 128	80.95 218	92.68 290	96.22 138	81.91 297	86.66 232	93.75 233	82.23 109	98.44 145	79.40 306	94.79 160	97.48 121

mvsmamba		90.33 145	89.69 151	92.25 159	95.17 158	81.64 190	95.27 126	93.36 311	84.88 225	89.51 170	94.27 208	69.29 301	97.42 245	89.34 141	96.12 130	97.68 110

PLC	84.53 7	89.06 193	88.03 202	92.15 160	97.27 73	82.69 163	94.29 198	95.44 210	79.71 338	84.01 313	94.18 211	76.68 184	98.75 109	77.28 325	93.41 195	95.02 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 111	91.56 107	92.13 161	95.88 124	80.50 232	97.33 8	95.25 224	86.15 183	89.76 168	95.60 139	83.42 87	98.32 159	87.37 172	93.25 200	97.56 118

patch_mono-2		93.74 60	94.32 36	92.01 162	97.54 62	78.37 292	93.40 253	97.19 39	88.02 125	94.99 50	97.21 56	88.35 21	98.44 145	94.07 50	98.09 72	99.23 1

原ACMM1				92.01 162	97.34 69	81.05 213		96.81 82	78.89 349	90.45 152	95.92 120	82.65 100	98.84 99	80.68 286	98.26 59	96.14 204

UniMVSNet (Re)		89.80 166	89.07 170	92.01 162	93.60 264	84.52 92	94.78 162	97.47 13	89.26 80	86.44 238	92.32 277	82.10 113	97.39 256	84.81 208	80.84 379	94.12 293

MG-MVS		91.77 108	91.70 105	92.00 165	97.08 76	80.03 248	93.60 246	95.18 228	87.85 134	90.89 146	96.47 95	82.06 115	98.36 152	85.07 203	97.04 104	97.62 113

EIA-MVS		91.95 103	91.94 100	91.98 166	95.16 159	80.01 249	95.36 116	96.73 92	88.44 110	89.34 174	92.16 282	83.82 83	98.45 143	89.35 140	97.06 103	97.48 121

PVSNet_Blended		90.73 132	90.32 131	91.98 166	96.12 106	81.25 203	92.55 295	96.83 78	82.04 293	89.10 178	92.56 270	81.04 126	98.85 97	86.72 182	95.91 132	95.84 221

guyue		91.12 124	90.84 124	91.96 168	94.59 199	80.57 230	94.87 154	93.71 305	88.96 93	91.14 142	95.22 157	73.22 244	97.76 208	92.01 98	93.81 185	97.54 120

PS-MVSNAJ		91.18 122	90.92 121	91.96 168	95.26 154	82.60 170	92.09 314	95.70 186	86.27 179	91.84 124	92.46 272	79.70 142	98.99 76	89.08 145	95.86 133	94.29 287

TAMVS		89.21 186	88.29 197	91.96 168	93.71 258	82.62 169	93.30 261	94.19 284	82.22 288	87.78 209	93.94 221	78.83 153	96.95 290	77.70 321	92.98 209	96.32 194

SDMVSNet		90.19 149	89.61 154	91.93 171	96.00 116	83.09 146	92.89 283	95.98 159	88.73 100	86.85 228	95.20 161	72.09 259	97.08 279	88.90 150	89.85 262	95.63 231

FA-MVS(test-final)		89.66 168	88.91 178	91.93 171	94.57 203	80.27 236	91.36 331	94.74 260	84.87 226	89.82 165	92.61 269	74.72 215	98.47 138	83.97 223	93.53 190	97.04 155

MVS_Test		91.31 119	91.11 117	91.93 171	94.37 217	80.14 241	93.46 251	95.80 177	86.46 175	91.35 140	93.77 231	82.21 110	98.09 180	87.57 167	94.95 156	97.55 119

NR-MVSNet		88.58 209	87.47 218	91.93 171	93.04 285	84.16 107	94.77 163	96.25 135	89.05 87	80.04 374	93.29 245	79.02 152	97.05 284	81.71 269	80.05 389	94.59 270

HyFIR lowres test		88.09 222	86.81 235	91.93 171	96.00 116	80.63 227	90.01 367	95.79 178	73.42 410	87.68 211	92.10 288	73.86 233	97.96 195	80.75 284	91.70 230	97.19 140

GeoE		90.05 154	89.43 159	91.90 176	95.16 159	80.37 235	95.80 89	94.65 264	83.90 246	87.55 214	94.75 181	78.18 165	97.62 221	81.28 274	93.63 187	97.71 109

thisisatest0530		88.67 204	87.61 214	91.86 177	94.87 178	80.07 244	94.63 172	89.90 404	84.00 244	88.46 192	93.78 230	66.88 325	98.46 139	83.30 233	92.65 216	97.06 153

xiu_mvs_v2_base		91.13 123	90.89 123	91.86 177	94.97 170	82.42 172	92.24 307	95.64 193	86.11 187	91.74 130	93.14 251	79.67 145	98.89 91	89.06 146	95.46 144	94.28 288

DU-MVS		89.34 185	88.50 189	91.85 179	93.04 285	83.72 119	94.47 183	96.59 103	89.50 69	86.46 235	93.29 245	77.25 177	97.23 269	84.92 205	81.02 375	94.59 270

AstraMVS		90.69 134	90.30 132	91.84 180	93.81 249	79.85 255	94.76 164	92.39 335	88.96 93	91.01 145	95.87 125	70.69 274	97.94 198	92.49 76	92.70 215	97.73 107

OPM-MVS		90.12 150	89.56 155	91.82 181	93.14 277	83.90 114	94.16 205	95.74 182	88.96 93	87.86 204	95.43 148	72.48 254	97.91 201	88.10 161	90.18 254	93.65 324
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 141	90.19 134	91.82 181	94.70 193	82.73 160	95.85 86	96.22 138	90.81 25	86.91 224	94.86 176	74.23 223	98.12 170	88.15 157	89.99 256	94.63 267

UniMVSNet_NR-MVSNet		89.92 162	89.29 164	91.81 183	93.39 270	83.72 119	94.43 186	97.12 50	89.80 58	86.46 235	93.32 242	83.16 91	97.23 269	84.92 205	81.02 375	94.49 280

diffmvs		91.37 118	91.23 115	91.77 184	93.09 280	80.27 236	92.36 301	95.52 202	87.03 157	91.40 139	94.93 171	80.08 134	97.44 243	92.13 93	94.56 169	97.61 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.51 115	91.44 109	91.73 185	93.09 280	80.27 236	92.51 296	95.58 196	87.22 151	91.80 127	95.57 141	79.96 136	97.48 235	92.23 87	94.97 155	97.45 123

1112_ss		88.42 211	87.33 221	91.72 186	94.92 174	80.98 216	92.97 280	94.54 267	78.16 366	83.82 316	93.88 226	78.78 155	97.91 201	79.45 302	89.41 269	96.26 198

Fast-Effi-MVS+		89.41 180	88.64 184	91.71 187	94.74 187	80.81 223	93.54 247	95.10 232	83.11 269	86.82 230	90.67 342	79.74 141	97.75 212	80.51 289	93.55 189	96.57 187

WTY-MVS		89.60 170	88.92 177	91.67 188	95.47 145	81.15 208	92.38 300	94.78 258	83.11 269	89.06 180	94.32 203	78.67 157	96.61 309	81.57 270	90.89 243	97.24 136

TAPA-MVS	84.62 6	88.16 220	87.01 230	91.62 189	96.64 85	80.65 226	94.39 190	96.21 141	76.38 379	86.19 245	95.44 146	79.75 140	98.08 182	62.75 426	95.29 149	96.13 205
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 169	88.96 175	91.60 190	93.86 246	82.89 155	95.46 113	97.33 28	87.91 129	88.43 193	93.31 243	74.17 226	97.40 253	87.32 173	82.86 350	94.52 275

FE-MVS		87.40 250	86.02 271	91.57 191	94.56 204	79.69 259	90.27 354	93.72 304	80.57 327	88.80 186	91.62 308	65.32 341	98.59 129	74.97 351	94.33 176	96.44 190

XVG-OURS		89.40 182	88.70 183	91.52 192	94.06 233	81.46 197	91.27 335	96.07 152	86.14 184	88.89 185	95.77 133	68.73 310	97.26 266	87.39 171	89.96 258	95.83 222

hse-mvs2		89.88 164	89.34 162	91.51 193	94.83 181	81.12 210	93.94 227	93.91 297	89.80 58	93.08 83	93.60 236	75.77 197	97.66 216	92.07 94	77.07 409	95.74 226

TranMVSNet+NR-MVSNet		88.84 199	87.95 205	91.49 194	92.68 301	83.01 151	94.92 151	96.31 124	89.88 52	85.53 261	93.85 228	76.63 185	96.96 289	81.91 262	79.87 392	94.50 278

AUN-MVS		87.78 230	86.54 250	91.48 195	94.82 182	81.05 213	93.91 231	93.93 294	83.00 272	86.93 222	93.53 237	69.50 295	97.67 214	86.14 188	77.12 408	95.73 228

XVG-OURS-SEG-HR		89.95 160	89.45 157	91.47 196	94.00 239	81.21 206	91.87 319	96.06 154	85.78 191	88.55 190	95.73 135	74.67 216	97.27 264	88.71 153	89.64 267	95.91 216

MVS		87.44 248	86.10 268	91.44 197	92.61 302	83.62 124	92.63 292	95.66 190	67.26 436	81.47 352	92.15 283	77.95 168	98.22 165	79.71 298	95.48 142	92.47 367

F-COLMAP		87.95 225	86.80 236	91.40 198	96.35 99	80.88 221	94.73 166	95.45 208	79.65 339	82.04 347	94.61 190	71.13 266	98.50 133	76.24 338	91.05 241	94.80 264

dcpmvs_2		93.49 65	94.19 47	91.38 199	97.69 59	76.78 333	94.25 200	96.29 125	88.33 113	94.46 53	96.88 73	88.07 25	98.64 122	93.62 56	98.09 72	98.73 19

thisisatest0515		87.33 253	85.99 272	91.37 200	93.49 266	79.55 260	90.63 349	89.56 412	80.17 331	87.56 213	90.86 332	67.07 322	98.28 161	81.50 271	93.02 208	96.29 196

HQP-MVS		89.80 166	89.28 165	91.34 201	94.17 228	81.56 191	94.39 190	96.04 155	88.81 96	85.43 270	93.97 220	73.83 234	97.96 195	87.11 177	89.77 265	94.50 278

fmvsm_s_conf0.5_n_7		93.15 84	93.76 63	91.31 202	94.42 215	79.48 262	94.52 178	97.14 48	89.33 76	94.17 59	98.09 16	81.83 119	97.49 234	96.33 24	98.02 76	96.95 163

RRT-MVS		90.85 128	90.70 127	91.30 203	94.25 224	76.83 332	94.85 157	96.13 146	89.04 88	90.23 156	94.88 174	70.15 285	98.72 113	91.86 106	94.88 158	98.34 44

FMVSNet3		87.40 250	86.11 267	91.30 203	93.79 252	83.64 123	94.20 204	94.81 256	83.89 247	84.37 300	91.87 299	68.45 313	96.56 314	78.23 316	85.36 318	93.70 323

FMVSNet2		87.19 263	85.82 280	91.30 203	94.01 236	83.67 121	94.79 161	94.94 242	83.57 255	83.88 315	92.05 292	66.59 330	96.51 318	77.56 323	85.01 321	93.73 321

RPMNet		83.95 340	81.53 351	91.21 206	90.58 379	79.34 268	85.24 427	96.76 87	71.44 424	85.55 259	82.97 436	70.87 271	98.91 90	61.01 430	89.36 271	95.40 237

IB-MVS	80.51 15	85.24 317	83.26 335	91.19 207	92.13 314	79.86 254	91.75 322	91.29 371	83.28 266	80.66 364	88.49 389	61.28 373	98.46 139	80.99 280	79.46 396	95.25 243
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		89.47 175	88.90 179	91.18 208	94.22 226	82.07 180	92.13 312	96.09 150	87.90 130	85.37 276	92.45 273	74.38 221	97.56 226	87.15 175	90.43 249	93.93 302
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		89.45 176	88.90 179	91.12 209	94.47 209	81.49 195	95.30 121	96.14 143	86.73 168	85.45 267	95.16 163	69.89 288	98.10 172	87.70 165	89.23 274	93.77 317

LGP-MVS_train				91.12 209	94.47 209	81.49 195		96.14 143	86.73 168	85.45 267	95.16 163	69.89 288	98.10 172	87.70 165	89.23 274	93.77 317

ACMM	84.12 9	89.14 188	88.48 192	91.12 209	94.65 196	81.22 205	95.31 119	96.12 147	85.31 210	85.92 250	94.34 201	70.19 284	98.06 184	85.65 196	88.86 279	94.08 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 206	87.78 211	91.11 212	94.96 171	77.81 309	95.35 117	89.69 407	85.09 220	88.05 202	94.59 193	66.93 323	98.48 135	83.27 234	92.13 228	97.03 156

GBi-Net		87.26 255	85.98 273	91.08 213	94.01 236	83.10 143	95.14 139	94.94 242	83.57 255	84.37 300	91.64 304	66.59 330	96.34 331	78.23 316	85.36 318	93.79 312

test1		87.26 255	85.98 273	91.08 213	94.01 236	83.10 143	95.14 139	94.94 242	83.57 255	84.37 300	91.64 304	66.59 330	96.34 331	78.23 316	85.36 318	93.79 312

FMVSNet1		85.85 302	84.11 322	91.08 213	92.81 296	83.10 143	95.14 139	94.94 242	81.64 309	82.68 337	91.64 304	59.01 395	96.34 331	75.37 345	83.78 334	93.79 312

Test_1112_low_res		87.65 234	86.51 251	91.08 213	94.94 173	79.28 272	91.77 321	94.30 279	76.04 384	83.51 326	92.37 275	77.86 171	97.73 213	78.69 311	89.13 276	96.22 199

PS-MVSNAJss		89.97 158	89.62 153	91.02 217	91.90 323	80.85 222	95.26 127	95.98 159	86.26 180	86.21 244	94.29 205	79.70 142	97.65 217	88.87 152	88.10 290	94.57 272

BH-RMVSNet		88.37 214	87.48 217	91.02 217	95.28 151	79.45 264	92.89 283	93.07 318	85.45 205	86.91 224	94.84 179	70.35 281	97.76 208	73.97 359	94.59 168	95.85 220

UniMVSNet_ETH3D		87.53 244	86.37 255	91.00 219	92.44 306	78.96 277	94.74 165	95.61 194	84.07 243	85.36 277	94.52 195	59.78 387	97.34 258	82.93 238	87.88 295	96.71 180

FIs		90.51 143	90.35 130	90.99 220	93.99 240	80.98 216	95.73 96	97.54 6	89.15 84	86.72 231	94.68 184	81.83 119	97.24 268	85.18 202	88.31 289	94.76 265

ACMP	84.23 8	89.01 197	88.35 193	90.99 220	94.73 188	81.27 202	95.07 142	95.89 171	86.48 173	83.67 321	94.30 204	69.33 297	97.99 189	87.10 179	88.55 281	93.72 322
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 285	85.13 300	90.98 222	96.52 93	81.50 193	96.14 59	96.16 142	73.78 406	83.65 322	92.15 283	63.26 357	97.37 257	82.82 242	81.74 364	94.06 298

IMVS_0403		89.97 158	89.64 152	90.96 223	93.72 254	77.75 314	93.00 277	95.34 219	85.53 201	88.77 187	94.49 196	78.49 161	97.84 204	84.75 209	92.65 216	97.28 130

sss		88.93 198	88.26 199	90.94 224	94.05 234	80.78 224	91.71 323	95.38 214	81.55 313	88.63 189	93.91 225	75.04 209	95.47 372	82.47 247	91.61 231	96.57 187

IMVS_0407		89.85 165	89.51 156	90.88 225	93.72 254	77.75 314	93.07 274	95.34 219	85.53 201	88.34 195	94.49 196	77.69 173	97.60 222	84.75 209	92.65 216	97.28 130

viewmambaseed2359dif		90.04 155	89.78 149	90.83 226	92.85 295	77.92 303	92.23 308	95.01 236	81.90 298	90.20 157	95.45 145	79.64 147	97.34 258	87.52 169	93.17 202	97.23 139

sd_testset		88.59 208	87.85 210	90.83 226	96.00 116	80.42 234	92.35 302	94.71 261	88.73 100	86.85 228	95.20 161	67.31 317	96.43 325	79.64 300	89.85 262	95.63 231

PVSNet_BlendedMVS		89.98 157	89.70 150	90.82 228	96.12 106	81.25 203	93.92 229	96.83 78	83.49 259	89.10 178	92.26 280	81.04 126	98.85 97	86.72 182	87.86 296	92.35 373

cascas		86.43 293	84.98 303	90.80 229	92.10 316	80.92 220	90.24 358	95.91 168	73.10 413	83.57 325	88.39 390	65.15 343	97.46 239	84.90 207	91.43 233	94.03 300

ECVR-MVS		89.09 191	88.53 187	90.77 230	95.62 138	75.89 346	96.16 55	84.22 438	87.89 132	90.20 157	96.65 85	63.19 358	98.10 172	85.90 193	96.94 106	98.33 46

GA-MVS		86.61 283	85.27 297	90.66 231	91.33 346	78.71 281	90.40 353	93.81 301	85.34 209	85.12 280	89.57 371	61.25 374	97.11 278	80.99 280	89.59 268	96.15 203

thres600view7		87.65 234	86.67 242	90.59 232	96.08 112	78.72 279	94.88 153	91.58 362	87.06 156	88.08 200	92.30 278	68.91 307	98.10 172	70.05 389	91.10 236	94.96 254

thres400		87.62 239	86.64 243	90.57 233	95.99 119	78.64 282	94.58 174	91.98 351	86.94 162	88.09 198	91.77 300	69.18 303	98.10 172	70.13 386	91.10 236	94.96 254

baseline1		88.10 221	87.28 223	90.57 233	94.96 171	80.07 244	94.27 199	91.29 371	86.74 167	87.41 215	94.00 218	76.77 182	96.20 336	80.77 283	79.31 398	95.44 235

viewdifsd2359ckpt11		89.43 178	89.05 172	90.56 235	92.89 293	77.00 328	92.81 286	94.52 268	87.03 157	89.77 166	95.79 130	74.67 216	97.51 230	88.97 148	84.98 322	97.17 141

viewmsd2359difaftdt		89.43 178	89.05 172	90.56 235	92.89 293	77.00 328	92.81 286	94.52 268	87.03 157	89.77 166	95.79 130	74.67 216	97.51 230	88.97 148	84.98 322	97.17 141

FC-MVSNet-test		90.27 147	90.18 135	90.53 237	93.71 258	79.85 255	95.77 92	97.59 4	89.31 77	86.27 242	94.67 187	81.93 118	97.01 286	84.26 219	88.09 292	94.71 266

PAPM		86.68 282	85.39 292	90.53 237	93.05 284	79.33 271	89.79 370	94.77 259	78.82 352	81.95 348	93.24 247	76.81 180	97.30 260	66.94 406	93.16 203	94.95 258

WR-MVS		88.38 213	87.67 213	90.52 239	93.30 272	80.18 239	93.26 264	95.96 163	88.57 108	85.47 266	92.81 262	76.12 190	96.91 293	81.24 275	82.29 355	94.47 283

SSM_04072		88.57 210	87.92 207	90.51 240	94.76 184	82.66 164	79.84 449	94.64 265	85.18 211	88.96 182	95.00 168	76.00 193	92.03 420	83.74 228	93.15 204	96.85 172

MVSTER		88.84 199	88.29 197	90.51 240	92.95 290	80.44 233	93.73 240	95.01 236	84.66 234	87.15 219	93.12 252	72.79 249	97.21 271	87.86 163	87.36 304	93.87 307

testdata				90.49 242	96.40 96	77.89 306		95.37 216	72.51 418	93.63 72	96.69 81	82.08 114	97.65 217	83.08 235	97.39 96	95.94 215

test1111		89.10 189	88.64 184	90.48 243	95.53 143	74.97 356	96.08 64	84.89 436	88.13 123	90.16 160	96.65 85	63.29 356	98.10 172	86.14 188	96.90 108	98.39 41

tt0805		86.92 271	85.74 286	90.48 243	92.22 310	79.98 251	95.63 106	94.88 250	83.83 249	84.74 289	92.80 263	57.61 401	97.67 214	85.48 199	84.42 327	93.79 312

jajsoiax		88.24 218	87.50 216	90.48 243	90.89 367	80.14 241	95.31 119	95.65 192	84.97 223	84.24 308	94.02 216	65.31 342	97.42 245	88.56 154	88.52 283	93.89 303

PatchMatch-RL		86.77 279	85.54 288	90.47 246	95.88 124	82.71 162	90.54 351	92.31 339	79.82 337	84.32 305	91.57 312	68.77 309	96.39 327	73.16 365	93.48 194	92.32 374

tfpn200view9		87.58 242	86.64 243	90.41 247	95.99 119	78.64 282	94.58 174	91.98 351	86.94 162	88.09 198	91.77 300	69.18 303	98.10 172	70.13 386	91.10 236	94.48 281

VPNet		88.20 219	87.47 218	90.39 248	93.56 265	79.46 263	94.04 218	95.54 200	88.67 103	86.96 221	94.58 194	69.33 297	97.15 273	84.05 222	80.53 384	94.56 273

ACMH	80.38 17	85.36 312	83.68 329	90.39 248	94.45 212	80.63 227	94.73 166	94.85 252	82.09 290	77.24 399	92.65 267	60.01 385	97.58 224	72.25 370	84.87 324	92.96 352
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 237	86.71 239	90.38 250	96.12 106	78.55 285	95.03 145	91.58 362	87.15 153	88.06 201	92.29 279	68.91 307	98.10 172	70.13 386	91.10 236	94.48 281

mvs_tets		88.06 224	87.28 223	90.38 250	90.94 363	79.88 253	95.22 130	95.66 190	85.10 219	84.21 309	93.94 221	63.53 354	97.40 253	88.50 155	88.40 287	93.87 307

1314		87.51 245	86.57 248	90.34 252	92.42 307	79.74 258	92.63 292	95.35 218	78.35 361	80.14 371	91.62 308	74.05 228	97.15 273	81.05 276	93.53 190	94.12 293

LTVRE_ROB	82.13 13	86.26 296	84.90 306	90.34 252	94.44 213	81.50 193	92.31 306	94.89 248	83.03 271	79.63 381	92.67 266	69.69 291	97.79 206	71.20 375	86.26 313	91.72 384
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		89.03 195	88.64 184	90.21 254	90.74 374	79.28 272	95.96 77	95.90 169	84.66 234	85.33 278	92.94 257	74.02 229	97.30 260	89.64 138	88.53 282	94.05 299

v2v482		87.84 227	87.06 227	90.17 255	90.99 359	79.23 275	94.00 223	95.13 229	84.87 226	85.53 261	92.07 291	74.45 220	97.45 240	84.71 214	81.75 363	93.85 310

pmmvs4		85.43 310	83.86 327	90.16 256	90.02 392	82.97 153	90.27 354	92.67 330	75.93 385	80.73 362	91.74 302	71.05 267	95.73 361	78.85 310	83.46 341	91.78 383

V42		87.68 232	86.86 232	90.15 257	90.58 379	80.14 241	94.24 202	95.28 223	83.66 253	85.67 256	91.33 314	74.73 214	97.41 251	84.43 218	81.83 361	92.89 355

MSDG		84.86 325	83.09 338	90.14 258	93.80 250	80.05 246	89.18 383	93.09 317	78.89 349	78.19 391	91.91 297	65.86 340	97.27 264	68.47 395	88.45 285	93.11 347

sc_t1		81.53 364	78.67 385	90.12 259	90.78 371	78.64 282	93.91 231	90.20 394	68.42 433	80.82 361	89.88 364	46.48 438	96.76 298	76.03 341	71.47 423	94.96 254

anonymousdsp		87.84 227	87.09 226	90.12 259	89.13 403	80.54 231	94.67 170	95.55 198	82.05 291	83.82 316	92.12 285	71.47 264	97.15 273	87.15 175	87.80 299	92.67 361

thres200		87.21 261	86.24 262	90.12 259	95.36 147	78.53 286	93.26 264	92.10 345	86.42 176	88.00 203	91.11 325	69.24 302	98.00 188	69.58 390	91.04 242	93.83 311

CR-MVSNet		85.35 313	83.76 328	90.12 259	90.58 379	79.34 268	85.24 427	91.96 353	78.27 363	85.55 259	87.87 400	71.03 268	95.61 364	73.96 360	89.36 271	95.40 237

v1144		87.61 240	86.79 237	90.06 263	91.01 358	79.34 268	93.95 226	95.42 213	83.36 264	85.66 257	91.31 317	74.98 210	97.42 245	83.37 232	82.06 357	93.42 333

XXY-MVS		87.65 234	86.85 233	90.03 264	92.14 313	80.60 229	93.76 238	95.23 225	82.94 274	84.60 291	94.02 216	74.27 222	95.49 371	81.04 277	83.68 337	94.01 301

Vis-MVSNet (Re-imp)		89.59 171	89.44 158	90.03 264	95.74 129	75.85 347	95.61 107	90.80 385	87.66 143	87.83 207	95.40 149	76.79 181	96.46 323	78.37 312	96.73 114	97.80 102

test2506		87.21 261	86.28 260	90.02 266	95.62 138	73.64 372	96.25 50	71.38 461	87.89 132	90.45 152	96.65 85	55.29 412	98.09 180	86.03 192	96.94 106	98.33 46

BH-untuned		88.60 207	88.13 201	90.01 267	95.24 155	78.50 288	93.29 262	94.15 287	84.75 231	84.46 297	93.40 239	75.76 199	97.40 253	77.59 322	94.52 171	94.12 293

v1192		87.25 257	86.33 257	90.00 268	90.76 373	79.04 276	93.80 236	95.48 203	82.57 281	85.48 265	91.18 321	73.38 243	97.42 245	82.30 251	82.06 357	93.53 327

v7n		86.81 274	85.76 284	89.95 269	90.72 375	79.25 274	95.07 142	95.92 166	84.45 237	82.29 341	90.86 332	72.60 253	97.53 228	79.42 305	80.52 385	93.08 349

testing91		87.11 266	86.18 263	89.92 270	94.43 214	75.38 355	91.53 328	92.27 341	86.48 173	86.50 233	90.24 350	61.19 377	97.53 228	82.10 256	90.88 244	96.84 175

IMVS_0404		87.60 241	86.84 234	89.89 271	93.72 254	77.75 314	88.56 392	95.34 219	85.53 201	79.98 375	94.49 196	66.54 333	94.64 385	84.75 209	92.65 216	97.28 130

v8		87.50 247	86.71 239	89.89 271	91.37 343	79.40 265	94.50 179	95.38 214	84.81 229	83.60 324	91.33 314	76.05 191	97.42 245	82.84 241	80.51 386	92.84 357

v10		87.25 257	86.38 254	89.85 273	91.19 349	79.50 261	94.48 180	95.45 208	83.79 251	83.62 323	91.19 319	75.13 207	97.42 245	81.94 261	80.60 381	92.63 363

baseline2		86.50 289	85.39 292	89.84 274	91.12 354	76.70 335	91.88 318	88.58 416	82.35 286	79.95 376	90.95 330	73.42 241	97.63 220	80.27 293	89.95 259	95.19 244

pm-mvs1		86.61 283	85.54 288	89.82 275	91.44 338	80.18 239	95.28 125	94.85 252	83.84 248	81.66 350	92.62 268	72.45 256	96.48 320	79.67 299	78.06 401	92.82 358

TR-MVS		86.78 276	85.76 284	89.82 275	94.37 217	78.41 290	92.47 297	92.83 324	81.11 323	86.36 239	92.40 274	68.73 310	97.48 235	73.75 363	89.85 262	93.57 326

ACMH+	81.04 14	85.05 320	83.46 332	89.82 275	94.66 195	79.37 266	94.44 185	94.12 290	82.19 289	78.04 393	92.82 261	58.23 398	97.54 227	73.77 362	82.90 349	92.54 364

EI-MVSNet		89.10 189	88.86 181	89.80 278	91.84 325	78.30 294	93.70 243	95.01 236	85.73 193	87.15 219	95.28 154	79.87 139	97.21 271	83.81 226	87.36 304	93.88 306

v144192		87.19 263	86.35 256	89.74 279	90.64 377	78.24 296	93.92 229	95.43 211	81.93 296	85.51 263	91.05 328	74.21 225	97.45 240	82.86 240	81.56 365	93.53 327

COLMAP_ROB	80.39 16	83.96 339	82.04 348	89.74 279	95.28 151	79.75 257	94.25 200	92.28 340	75.17 392	78.02 394	93.77 231	58.60 397	97.84 204	65.06 417	85.92 314	91.63 386
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 295	85.18 299	89.73 281	92.15 312	76.60 336	91.12 339	91.69 358	83.53 258	85.50 264	88.81 383	66.79 326	96.48 320	76.65 331	90.35 251	96.12 206

IterMVS-LS		88.36 215	87.91 209	89.70 282	93.80 250	78.29 295	93.73 240	95.08 234	85.73 193	84.75 288	91.90 298	79.88 138	96.92 292	83.83 225	82.51 351	93.89 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 292	85.35 295	89.69 283	94.29 223	75.40 354	91.30 333	90.53 389	84.76 230	85.06 282	90.13 356	58.95 396	97.45 240	82.08 257	91.09 240	96.21 201

testing99		86.72 280	85.73 287	89.69 283	94.23 225	74.91 358	91.35 332	90.97 379	86.14 184	86.36 239	90.22 351	59.41 390	97.48 235	82.24 253	90.66 246	96.69 182

v1921920		86.97 270	86.06 270	89.69 283	90.53 382	78.11 299	93.80 236	95.43 211	81.90 298	85.33 278	91.05 328	72.66 250	97.41 251	82.05 259	81.80 362	93.53 327

icg_test_0407_2		89.15 187	88.97 174	89.68 286	93.72 254	77.75 314	88.26 397	95.34 219	85.53 201	88.34 195	94.49 196	77.69 173	93.99 396	84.75 209	92.65 216	97.28 130

VortexMVS		88.42 211	88.01 203	89.63 287	93.89 245	78.82 278	93.82 235	95.47 204	86.67 170	84.53 295	91.99 294	72.62 252	96.65 304	89.02 147	84.09 331	93.41 334

Fast-Effi-MVS+-dtu		87.44 248	86.72 238	89.63 287	92.04 317	77.68 319	94.03 219	93.94 293	85.81 190	82.42 340	91.32 316	70.33 282	97.06 282	80.33 292	90.23 253	94.14 292

v1240		86.78 276	85.85 279	89.56 289	90.45 384	77.79 311	93.61 245	95.37 216	81.65 308	85.43 270	91.15 323	71.50 263	97.43 244	81.47 272	82.05 359	93.47 331

Effi-MVS+-dtu		88.65 205	88.35 193	89.54 290	93.33 271	76.39 340	94.47 183	94.36 277	87.70 140	85.43 270	89.56 372	73.45 239	97.26 266	85.57 198	91.28 235	94.97 251

AllTest		83.42 346	81.39 352	89.52 291	95.01 165	77.79 311	93.12 268	90.89 383	77.41 370	76.12 408	93.34 240	54.08 418	97.51 230	68.31 397	84.27 329	93.26 337

TestCases				89.52 291	95.01 165	77.79 311		90.89 383	77.41 370	76.12 408	93.34 240	54.08 418	97.51 230	68.31 397	84.27 329	93.26 337

mvs_anonymous		89.37 184	89.32 163	89.51 293	93.47 267	74.22 365	91.65 326	94.83 254	82.91 275	85.45 267	93.79 229	81.23 125	96.36 330	86.47 184	94.09 179	97.94 89

XVG-ACMP-BASELINE		86.00 298	84.84 308	89.45 294	91.20 348	78.00 301	91.70 324	95.55 198	85.05 221	82.97 334	92.25 281	54.49 416	97.48 235	82.93 238	87.45 303	92.89 355

testing222		84.84 326	83.32 333	89.43 295	94.15 231	75.94 345	91.09 340	89.41 414	84.90 224	85.78 253	89.44 373	52.70 423	96.28 334	70.80 381	91.57 232	96.07 210

MVP-Stereo		85.97 299	84.86 307	89.32 296	90.92 365	82.19 178	92.11 313	94.19 284	78.76 354	78.77 390	91.63 307	68.38 314	96.56 314	75.01 350	93.95 181	89.20 424
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 302	84.70 310	89.29 297	91.76 329	75.54 351	88.49 393	91.30 370	81.63 310	85.05 283	88.70 387	71.71 260	96.24 335	74.61 355	89.05 277	96.08 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 268	86.32 258	89.21 298	90.94 363	77.26 324	93.71 242	94.43 272	84.84 228	84.36 303	90.80 336	76.04 192	97.05 284	82.12 255	79.60 395	93.31 336

tfpnnormal		84.72 328	83.23 336	89.20 299	92.79 297	80.05 246	94.48 180	95.81 176	82.38 284	81.08 358	91.21 318	69.01 306	96.95 290	61.69 428	80.59 382	90.58 411

cl22		86.78 276	85.98 273	89.18 300	92.34 308	77.62 320	90.84 345	94.13 289	81.33 317	83.97 314	90.15 355	73.96 230	96.60 311	84.19 220	82.94 346	93.33 335

BH-w/o		87.57 243	87.05 228	89.12 301	94.90 177	77.90 305	92.41 298	93.51 308	82.89 276	83.70 320	91.34 313	75.75 200	97.07 281	75.49 343	93.49 192	92.39 371

WR-MVS_H		87.80 229	87.37 220	89.10 302	93.23 273	78.12 298	95.61 107	97.30 32	87.90 130	83.72 319	92.01 293	79.65 146	96.01 345	76.36 335	80.54 383	93.16 345

miper_enhance_ethall		86.90 272	86.18 263	89.06 303	91.66 334	77.58 321	90.22 360	94.82 255	79.16 345	84.48 296	89.10 377	79.19 151	96.66 303	84.06 221	82.94 346	92.94 353

c3_l		87.14 265	86.50 252	89.04 304	92.20 311	77.26 324	91.22 338	94.70 262	82.01 294	84.34 304	90.43 347	78.81 154	96.61 309	83.70 230	81.09 372	93.25 339

miper_ehance_all_eth		87.22 260	86.62 246	89.02 305	92.13 314	77.40 323	90.91 344	94.81 256	81.28 318	84.32 305	90.08 358	79.26 149	96.62 306	83.81 226	82.94 346	93.04 350

gg-mvs-nofinetune		81.77 358	79.37 373	88.99 306	90.85 369	77.73 318	86.29 419	79.63 449	74.88 397	83.19 333	69.05 452	60.34 382	96.11 340	75.46 344	94.64 167	93.11 347

ETVMVS		84.43 333	82.92 342	88.97 307	94.37 217	74.67 359	91.23 337	88.35 418	83.37 263	86.06 248	89.04 378	55.38 410	95.67 363	67.12 404	91.34 234	96.58 186

pmmvs6		83.42 346	81.60 350	88.87 308	88.01 418	77.87 307	94.96 148	94.24 283	74.67 398	78.80 389	91.09 326	60.17 384	96.49 319	77.06 330	75.40 415	92.23 376

test_cas_vis1_n_1920		88.83 202	88.85 182	88.78 309	91.15 353	76.72 334	93.85 234	94.93 246	83.23 268	92.81 92	96.00 115	61.17 378	94.45 386	91.67 109	94.84 159	95.17 245

MIMVSNet		82.59 352	80.53 357	88.76 310	91.51 336	78.32 293	86.57 418	90.13 397	79.32 341	80.70 363	88.69 388	52.98 422	93.07 412	66.03 412	88.86 279	94.90 259

cl____		86.52 288	85.78 281	88.75 311	92.03 318	76.46 338	90.74 346	94.30 279	81.83 304	83.34 330	90.78 337	75.74 202	96.57 312	81.74 267	81.54 366	93.22 341

DIV-MVS_self_test		86.53 287	85.78 281	88.75 311	92.02 319	76.45 339	90.74 346	94.30 279	81.83 304	83.34 330	90.82 335	75.75 200	96.57 312	81.73 268	81.52 367	93.24 340

CP-MVSNet		87.63 237	87.26 225	88.74 313	93.12 278	76.59 337	95.29 123	96.58 104	88.43 111	83.49 327	92.98 256	75.28 206	95.83 354	78.97 308	81.15 371	93.79 312

eth_miper_zixun_eth		86.50 289	85.77 283	88.68 314	91.94 320	75.81 348	90.47 352	94.89 248	82.05 291	84.05 311	90.46 346	75.96 195	96.77 297	82.76 244	79.36 397	93.46 332

CHOSEN 280x420		85.15 318	83.99 325	88.65 315	92.47 304	78.40 291	79.68 451	92.76 327	74.90 396	81.41 354	89.59 370	69.85 290	95.51 368	79.92 297	95.29 149	92.03 379

PS-CasMVS		87.32 254	86.88 231	88.63 316	92.99 288	76.33 342	95.33 118	96.61 102	88.22 119	83.30 332	93.07 254	73.03 247	95.79 358	78.36 313	81.00 377	93.75 319

TransMVSNet (Re)		84.43 333	83.06 340	88.54 317	91.72 330	78.44 289	95.18 136	92.82 326	82.73 279	79.67 380	92.12 285	73.49 238	95.96 347	71.10 379	68.73 433	91.21 398

tt0320-xc		79.63 387	76.66 396	88.52 318	91.03 357	78.72 279	93.00 277	89.53 413	66.37 437	76.11 410	87.11 411	46.36 440	95.32 376	72.78 367	67.67 434	91.51 390

EG-PatchMatch MVS		82.37 354	80.34 360	88.46 319	90.27 386	79.35 267	92.80 289	94.33 278	77.14 374	73.26 426	90.18 354	47.47 435	96.72 299	70.25 383	87.32 306	89.30 421

PEN-MVS		86.80 275	86.27 261	88.40 320	92.32 309	75.71 350	95.18 136	96.38 119	87.97 127	82.82 336	93.15 250	73.39 242	95.92 349	76.15 339	79.03 400	93.59 325

Baseline_NR-MVSNet		87.07 267	86.63 245	88.40 320	91.44 338	77.87 307	94.23 203	92.57 332	84.12 242	85.74 255	92.08 289	77.25 177	96.04 341	82.29 252	79.94 390	91.30 396

UBG		85.51 308	84.57 315	88.35 322	94.21 227	71.78 396	90.07 365	89.66 409	82.28 287	85.91 251	89.01 379	61.30 372	97.06 282	76.58 334	92.06 229	96.22 199

D2MVS		85.90 300	85.09 301	88.35 322	90.79 370	77.42 322	91.83 320	95.70 186	80.77 326	80.08 373	90.02 360	66.74 328	96.37 328	81.88 263	87.97 294	91.26 397

pmmvs5		84.21 335	82.84 345	88.34 324	88.95 405	76.94 330	92.41 298	91.91 355	75.63 387	80.28 368	91.18 321	64.59 348	95.57 365	77.09 329	83.47 340	92.53 365

mamv4		90.92 126	91.78 103	88.33 325	95.67 134	70.75 409	92.92 282	96.02 158	81.90 298	88.11 197	95.34 152	85.88 52	96.97 288	95.22 38	95.01 154	97.26 134

tt0320		80.13 380	77.41 389	88.29 326	90.50 383	78.02 300	93.10 271	90.71 387	66.06 440	76.75 403	86.97 412	49.56 430	95.40 373	71.65 371	71.41 424	91.46 393

LCM-MVSNet-Re		88.30 217	88.32 196	88.27 327	94.71 192	72.41 391	93.15 267	90.98 378	87.77 137	79.25 384	91.96 295	78.35 163	95.75 359	83.04 236	95.62 138	96.65 183

CostFormer		85.77 305	84.94 305	88.26 328	91.16 352	72.58 389	89.47 378	91.04 377	76.26 382	86.45 237	89.97 362	70.74 273	96.86 296	82.35 250	87.07 309	95.34 241

ITE_SJBPF				88.24 329	91.88 324	77.05 327		92.92 321	85.54 199	80.13 372	93.30 244	57.29 402	96.20 336	72.46 369	84.71 325	91.49 391

PVSNet	78.82 18	85.55 307	84.65 311	88.23 330	94.72 190	71.93 392	87.12 414	92.75 328	78.80 353	84.95 285	90.53 344	64.43 349	96.71 301	74.74 353	93.86 183	96.06 212

IterMVS-SCA-FT		85.45 309	84.53 316	88.18 331	91.71 331	76.87 331	90.19 362	92.65 331	85.40 208	81.44 353	90.54 343	66.79 326	95.00 382	81.04 277	81.05 373	92.66 362

EPNet_dtu		86.49 291	85.94 276	88.14 332	90.24 387	72.82 381	94.11 209	92.20 343	86.66 171	79.42 383	92.36 276	73.52 237	95.81 356	71.26 374	93.66 186	95.80 224
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 350	80.93 356	88.06 333	90.05 391	76.37 341	84.74 432	91.96 353	72.28 421	81.32 356	87.87 400	71.03 268	95.50 370	68.97 392	80.15 388	92.32 374

test_vis1_n_1920		89.39 183	89.84 146	88.04 334	92.97 289	72.64 386	94.71 168	96.03 157	86.18 182	91.94 121	96.56 93	61.63 367	95.74 360	93.42 59	95.11 153	95.74 226

DTE-MVSNet		86.11 297	85.48 290	87.98 335	91.65 335	74.92 357	94.93 150	95.75 181	87.36 148	82.26 342	93.04 255	72.85 248	95.82 355	74.04 358	77.46 406	93.20 343

PMMVS		85.71 306	84.96 304	87.95 336	88.90 406	77.09 326	88.68 390	90.06 399	72.32 420	86.47 234	90.76 338	72.15 258	94.40 388	81.78 266	93.49 192	92.36 372

GG-mvs-BLEND				87.94 337	89.73 398	77.91 304	87.80 403	78.23 454		80.58 365	83.86 429	59.88 386	95.33 375	71.20 375	92.22 227	90.60 410

MonoMVSNet		86.89 273	86.55 249	87.92 338	89.46 401	73.75 369	94.12 207	93.10 316	87.82 136	85.10 281	90.76 338	69.59 293	94.94 383	86.47 184	82.50 352	95.07 248

reproduce_monomvs		86.37 294	85.87 278	87.87 339	93.66 262	73.71 370	93.44 252	95.02 235	88.61 106	82.64 339	91.94 296	57.88 400	96.68 302	89.96 134	79.71 394	93.22 341

pmmvs-eth3d		80.97 373	78.72 384	87.74 340	84.99 436	79.97 252	90.11 364	91.65 360	75.36 389	73.51 424	86.03 419	59.45 389	93.96 399	75.17 347	72.21 420	89.29 423

MS-PatchMatch		85.05 320	84.16 320	87.73 341	91.42 341	78.51 287	91.25 336	93.53 307	77.50 369	80.15 370	91.58 310	61.99 364	95.51 368	75.69 342	94.35 175	89.16 425

mmtdpeth		85.04 322	84.15 321	87.72 342	93.11 279	75.74 349	94.37 194	92.83 324	84.98 222	89.31 175	86.41 416	61.61 369	97.14 276	92.63 75	62.11 444	90.29 412

test_0402		81.30 369	79.17 378	87.67 343	93.19 274	78.17 297	92.98 279	91.71 356	75.25 391	76.02 411	90.31 349	59.23 391	96.37 328	50.22 447	83.63 338	88.47 432

IterMVS		84.88 324	83.98 326	87.60 344	91.44 338	76.03 344	90.18 363	92.41 334	83.24 267	81.06 359	90.42 348	66.60 329	94.28 392	79.46 301	80.98 378	92.48 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 367	79.30 374	87.58 345	90.92 365	74.16 367	80.99 444	87.68 423	70.52 428	76.63 405	88.81 383	71.21 265	92.76 415	60.01 434	86.93 310	95.83 222

EPMVS		83.90 342	82.70 346	87.51 346	90.23 388	72.67 384	88.62 391	81.96 444	81.37 316	85.01 284	88.34 391	66.31 334	94.45 386	75.30 346	87.12 307	95.43 236

ADS-MVSNet2		81.66 361	79.71 370	87.50 347	91.35 344	74.19 366	83.33 437	88.48 417	72.90 415	82.24 343	85.77 422	64.98 344	93.20 410	64.57 419	83.74 335	95.12 246

OurMVSNet-221017-0		85.35 313	84.64 313	87.49 348	90.77 372	72.59 388	94.01 221	94.40 275	84.72 232	79.62 382	93.17 249	61.91 365	96.72 299	81.99 260	81.16 369	93.16 345

tpm2		84.08 337	82.94 341	87.48 349	91.39 342	71.27 401	89.23 382	90.37 391	71.95 422	84.64 290	89.33 374	67.30 318	96.55 316	75.17 347	87.09 308	94.63 267

RPSCF		85.07 319	84.27 317	87.48 349	92.91 292	70.62 411	91.69 325	92.46 333	76.20 383	82.67 338	95.22 157	63.94 352	97.29 263	77.51 324	85.80 315	94.53 274

myMVS_eth3d28		85.80 304	85.26 298	87.42 351	94.73 188	69.92 416	90.60 350	90.95 380	87.21 152	86.06 248	90.04 359	59.47 388	96.02 343	74.89 352	93.35 199	96.33 193

WBMVS		84.97 323	84.18 319	87.34 352	94.14 232	71.62 400	90.20 361	92.35 336	81.61 311	84.06 310	90.76 338	61.82 366	96.52 317	78.93 309	83.81 333	93.89 303

miper_lstm_enhance		85.27 316	84.59 314	87.31 353	91.28 347	74.63 360	87.69 408	94.09 291	81.20 322	81.36 355	89.85 366	74.97 211	94.30 391	81.03 279	79.84 393	93.01 351

FMVSNet5		81.52 365	79.60 371	87.27 354	91.17 350	77.95 302	91.49 329	92.26 342	76.87 375	76.16 407	87.91 399	51.67 424	92.34 418	67.74 401	81.16 369	91.52 389

USDC		82.76 349	81.26 354	87.26 355	91.17 350	74.55 361	89.27 380	93.39 310	78.26 364	75.30 415	92.08 289	54.43 417	96.63 305	71.64 372	85.79 316	90.61 408

test-LLR		85.87 301	85.41 291	87.25 356	90.95 361	71.67 398	89.55 374	89.88 405	83.41 261	84.54 293	87.95 397	67.25 319	95.11 379	81.82 264	93.37 197	94.97 251

test-mter		84.54 332	83.64 330	87.25 356	90.95 361	71.67 398	89.55 374	89.88 405	79.17 344	84.54 293	87.95 397	55.56 408	95.11 379	81.82 264	93.37 197	94.97 251

JIA-IIPM		81.04 370	78.98 382	87.25 356	88.64 407	73.48 374	81.75 443	89.61 411	73.19 412	82.05 346	73.71 448	66.07 339	95.87 352	71.18 377	84.60 326	92.41 370

TDRefinement		79.81 384	77.34 390	87.22 359	79.24 451	75.48 352	93.12 268	92.03 348	76.45 378	75.01 416	91.58 310	49.19 431	96.44 324	70.22 385	69.18 430	89.75 417

tpmvs		83.35 348	82.07 347	87.20 360	91.07 356	71.00 407	88.31 396	91.70 357	78.91 347	80.49 367	87.18 409	69.30 300	97.08 279	68.12 400	83.56 339	93.51 330

ppachtmachnet_test		81.84 357	80.07 365	87.15 361	88.46 411	74.43 364	89.04 386	92.16 344	75.33 390	77.75 396	88.99 380	66.20 336	95.37 374	65.12 416	77.60 404	91.65 385

dmvs_re		84.20 336	83.22 337	87.14 362	91.83 327	77.81 309	90.04 366	90.19 395	84.70 233	81.49 351	89.17 376	64.37 350	91.13 430	71.58 373	85.65 317	92.46 368

tpm cat1		81.96 355	80.27 361	87.01 363	91.09 355	71.02 406	87.38 412	91.53 365	66.25 438	80.17 369	86.35 418	68.22 315	96.15 339	69.16 391	82.29 355	93.86 309

test_fmvs1_n		87.03 269	87.04 229	86.97 364	89.74 397	71.86 393	94.55 176	94.43 272	78.47 358	91.95 120	95.50 144	51.16 426	93.81 400	93.02 67	94.56 169	95.26 242

OpenMVS_ROB	74.94 19	79.51 388	77.03 395	86.93 365	87.00 424	76.23 343	92.33 304	90.74 386	68.93 432	74.52 420	88.23 394	49.58 429	96.62 306	57.64 439	84.29 328	87.94 435

SixPastTwentyTwo		83.91 341	82.90 343	86.92 366	90.99 359	70.67 410	93.48 249	91.99 350	85.54 199	77.62 398	92.11 287	60.59 381	96.87 295	76.05 340	77.75 403	93.20 343

ADS-MVSNet		81.56 363	79.78 367	86.90 367	91.35 344	71.82 394	83.33 437	89.16 415	72.90 415	82.24 343	85.77 422	64.98 344	93.76 401	64.57 419	83.74 335	95.12 246

PatchT		82.68 351	81.27 353	86.89 368	90.09 390	70.94 408	84.06 434	90.15 396	74.91 395	85.63 258	83.57 431	69.37 296	94.87 384	65.19 414	88.50 284	94.84 261

tpm		84.73 327	84.02 324	86.87 369	90.33 385	68.90 419	89.06 385	89.94 402	80.85 325	85.75 254	89.86 365	68.54 312	95.97 346	77.76 320	84.05 332	95.75 225

Patchmatch-RL test		81.67 360	79.96 366	86.81 370	85.42 434	71.23 402	82.17 442	87.50 424	78.47 358	77.19 400	82.50 438	70.81 272	93.48 405	82.66 245	72.89 419	95.71 229

test_vis1_n		86.56 286	86.49 253	86.78 371	88.51 408	72.69 383	94.68 169	93.78 303	79.55 340	90.70 147	95.31 153	48.75 432	93.28 408	93.15 63	93.99 180	94.38 285

testing3-2		86.72 280	86.71 239	86.74 372	96.11 109	65.92 431	93.39 254	89.65 410	89.46 70	87.84 206	92.79 264	59.17 393	97.60 222	81.31 273	90.72 245	96.70 181

test_fmvs1		87.34 252	87.56 215	86.68 373	90.59 378	71.80 395	94.01 221	94.04 292	78.30 362	91.97 118	95.22 157	56.28 406	93.71 402	92.89 68	94.71 162	94.52 275

MDA-MVSNet-bldmvs		78.85 393	76.31 398	86.46 374	89.76 396	73.88 368	88.79 388	90.42 390	79.16 345	59.18 448	88.33 392	60.20 383	94.04 394	62.00 427	68.96 431	91.48 392

mvs5depth		80.98 372	79.15 379	86.45 375	84.57 437	73.29 376	87.79 404	91.67 359	80.52 328	82.20 345	89.72 368	55.14 413	95.93 348	73.93 361	66.83 436	90.12 414

tpmrst		85.35 313	84.99 302	86.43 376	90.88 368	67.88 424	88.71 389	91.43 368	80.13 332	86.08 247	88.80 385	73.05 246	96.02 343	82.48 246	83.40 343	95.40 237

TESTMET0.1,1		83.74 344	82.85 344	86.42 377	89.96 393	71.21 403	89.55 374	87.88 420	77.41 370	83.37 329	87.31 405	56.71 404	93.65 404	80.62 287	92.85 213	94.40 284

our_test_3		81.93 356	80.46 359	86.33 378	88.46 411	73.48 374	88.46 394	91.11 373	76.46 377	76.69 404	88.25 393	66.89 324	94.36 389	68.75 393	79.08 399	91.14 400

lessismore_v0				86.04 379	88.46 411	68.78 420		80.59 447		73.01 427	90.11 357	55.39 409	96.43 325	75.06 349	65.06 439	92.90 354

TinyColmap		79.76 385	77.69 388	85.97 380	91.71 331	73.12 377	89.55 374	90.36 392	75.03 393	72.03 430	90.19 353	46.22 441	96.19 338	63.11 423	81.03 374	88.59 431

KD-MVS_2432*1600		78.50 394	76.02 401	85.93 381	86.22 427	74.47 362	84.80 430	92.33 337	79.29 342	76.98 401	85.92 420	53.81 420	93.97 397	67.39 402	57.42 449	89.36 419

miper_refine_blended		78.50 394	76.02 401	85.93 381	86.22 427	74.47 362	84.80 430	92.33 337	79.29 342	76.98 401	85.92 420	53.81 420	93.97 397	67.39 402	57.42 449	89.36 419

K. test v3		81.59 362	80.15 364	85.91 383	89.89 395	69.42 418	92.57 294	87.71 422	85.56 198	73.44 425	89.71 369	55.58 407	95.52 367	77.17 327	69.76 427	92.78 359

SSC-MVS3.2		84.60 331	84.19 318	85.85 384	92.74 299	68.07 421	88.15 399	93.81 301	87.42 147	83.76 318	91.07 327	62.91 359	95.73 361	74.56 356	83.24 344	93.75 319

mvsany_test1		85.42 311	85.30 296	85.77 385	87.95 420	75.41 353	87.61 411	80.97 446	76.82 376	88.68 188	95.83 127	77.44 176	90.82 432	85.90 193	86.51 311	91.08 404

MIMVSNet1		79.38 389	77.28 391	85.69 386	86.35 426	73.67 371	91.61 327	92.75 328	78.11 367	72.64 428	88.12 395	48.16 433	91.97 424	60.32 431	77.49 405	91.43 394

UWE-MVS		83.69 345	83.09 338	85.48 387	93.06 283	65.27 436	90.92 343	86.14 428	79.90 335	86.26 243	90.72 341	57.17 403	95.81 356	71.03 380	92.62 221	95.35 240

UnsupCasMVSNet_eth		80.07 381	78.27 387	85.46 388	85.24 435	72.63 387	88.45 395	94.87 251	82.99 273	71.64 432	88.07 396	56.34 405	91.75 425	73.48 364	63.36 442	92.01 380

CL-MVSNet_self_test		81.74 359	80.53 357	85.36 389	85.96 429	72.45 390	90.25 356	93.07 318	81.24 320	79.85 379	87.29 406	70.93 270	92.52 416	66.95 405	69.23 429	91.11 402

MDA-MVSNet_test_wron		79.21 391	77.19 393	85.29 390	88.22 415	72.77 382	85.87 421	90.06 399	74.34 400	62.62 445	87.56 403	66.14 337	91.99 423	66.90 409	73.01 417	91.10 403

YYNet1		79.22 390	77.20 392	85.28 391	88.20 416	72.66 385	85.87 421	90.05 401	74.33 401	62.70 443	87.61 402	66.09 338	92.03 420	66.94 406	72.97 418	91.15 399

WB-MVSnew		83.77 343	83.28 334	85.26 392	91.48 337	71.03 405	91.89 317	87.98 419	78.91 347	84.78 287	90.22 351	69.11 305	94.02 395	64.70 418	90.44 248	90.71 406

dp		81.47 366	80.23 362	85.17 393	89.92 394	65.49 434	86.74 416	90.10 398	76.30 381	81.10 357	87.12 410	62.81 360	95.92 349	68.13 399	79.88 391	94.09 296

UnsupCasMVSNet_bld		76.23 403	73.27 407	85.09 394	83.79 439	72.92 379	85.65 424	93.47 309	71.52 423	68.84 438	79.08 443	49.77 428	93.21 409	66.81 410	60.52 446	89.13 427

SD_0403		84.71 329	84.65 311	84.92 395	92.95 290	65.95 430	92.07 316	93.23 313	83.82 250	79.03 385	93.73 234	73.90 231	92.91 414	63.02 425	90.05 255	95.89 218

Anonymous20231206		81.03 371	79.77 369	84.82 396	87.85 421	70.26 413	91.42 330	92.08 346	73.67 407	77.75 396	89.25 375	62.43 362	93.08 411	61.50 429	82.00 360	91.12 401

test0.0.03 1		82.41 353	81.69 349	84.59 397	88.23 414	72.89 380	90.24 358	87.83 421	83.41 261	79.86 378	89.78 367	67.25 319	88.99 442	65.18 415	83.42 342	91.90 382

CMPMVS	59.16 21	80.52 375	79.20 377	84.48 398	83.98 438	67.63 427	89.95 369	93.84 300	64.79 442	66.81 440	91.14 324	57.93 399	95.17 377	76.25 337	88.10 290	90.65 407
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 330	84.79 309	84.37 399	91.84 325	64.92 437	93.70 243	91.47 367	66.19 439	86.16 246	95.28 154	67.18 321	93.33 407	80.89 282	90.42 250	94.88 260

PVSNet_0	73.20 20	77.22 399	74.83 405	84.37 399	90.70 376	71.10 404	83.09 439	89.67 408	72.81 417	73.93 423	83.13 433	60.79 380	93.70 403	68.54 394	50.84 454	88.30 433

LF4IMVS		80.37 378	79.07 381	84.27 401	86.64 425	69.87 417	89.39 379	91.05 376	76.38 379	74.97 417	90.00 361	47.85 434	94.25 393	74.55 357	80.82 380	88.69 430

Anonymous20240521		80.44 377	79.21 376	84.11 402	85.75 432	67.89 423	92.86 285	93.23 313	75.61 388	75.59 414	87.47 404	50.03 427	94.33 390	71.14 378	81.21 368	90.12 414

PM-MVS		78.11 396	76.12 400	84.09 403	83.54 440	70.08 414	88.97 387	85.27 435	79.93 334	74.73 419	86.43 415	34.70 452	93.48 405	79.43 304	72.06 421	88.72 429

test_fmvs2		83.98 338	84.03 323	83.83 404	87.16 423	67.53 428	93.93 228	92.89 322	77.62 368	86.89 227	93.53 237	47.18 436	92.02 422	90.54 128	86.51 311	91.93 381

testgi		80.94 374	80.20 363	83.18 405	87.96 419	66.29 429	91.28 334	90.70 388	83.70 252	78.12 392	92.84 259	51.37 425	90.82 432	63.34 422	82.46 353	92.43 369

KD-MVS_self_test		80.20 379	79.24 375	83.07 406	85.64 433	65.29 435	91.01 342	93.93 294	78.71 356	76.32 406	86.40 417	59.20 392	92.93 413	72.59 368	69.35 428	91.00 405

testing3		80.46 376	79.59 372	83.06 407	93.44 269	64.64 438	93.33 256	85.47 433	84.34 239	79.93 377	90.84 334	44.35 444	92.39 417	57.06 441	87.56 300	92.16 378

ambc				83.06 407	79.99 449	63.51 442	77.47 452	92.86 323		74.34 422	84.45 428	28.74 453	95.06 381	73.06 366	68.89 432	90.61 408

test20.03		79.95 383	79.08 380	82.55 409	85.79 431	67.74 426	91.09 340	91.08 374	81.23 321	74.48 421	89.96 363	61.63 367	90.15 434	60.08 432	76.38 411	89.76 416

MVStest1		72.91 407	69.70 412	82.54 410	78.14 452	73.05 378	88.21 398	86.21 427	60.69 446	64.70 441	90.53 344	46.44 439	85.70 449	58.78 437	53.62 451	88.87 428

test_vis1_rt		77.96 397	76.46 397	82.48 411	85.89 430	71.74 397	90.25 356	78.89 450	71.03 427	71.30 433	81.35 440	42.49 446	91.05 431	84.55 216	82.37 354	84.65 438

EU-MVSNet		81.32 368	80.95 355	82.42 412	88.50 410	63.67 441	93.32 257	91.33 369	64.02 443	80.57 366	92.83 260	61.21 376	92.27 419	76.34 336	80.38 387	91.32 395

myMVS_eth3d		79.67 386	78.79 383	82.32 413	91.92 321	64.08 439	89.75 372	87.40 425	81.72 306	78.82 387	87.20 407	45.33 442	91.29 428	59.09 436	87.84 297	91.60 387

ttmdpeth		76.55 401	74.64 406	82.29 414	82.25 445	67.81 425	89.76 371	85.69 431	70.35 429	75.76 412	91.69 303	46.88 437	89.77 436	66.16 411	63.23 443	89.30 421

pmmvs3		71.81 410	68.71 413	81.11 415	75.86 454	70.42 412	86.74 416	83.66 439	58.95 449	68.64 439	80.89 441	36.93 450	89.52 438	63.10 424	63.59 441	83.39 439

Syy-MVS		80.07 381	79.78 367	80.94 416	91.92 321	59.93 448	89.75 372	87.40 425	81.72 306	78.82 387	87.20 407	66.29 335	91.29 428	47.06 449	87.84 297	91.60 387

UWE-MVS-28		78.98 392	78.38 386	80.80 417	88.18 417	60.66 447	90.65 348	78.51 451	78.84 351	77.93 395	90.93 331	59.08 394	89.02 441	50.96 446	90.33 252	92.72 360

new-patchmatchnet		76.41 402	75.17 404	80.13 418	82.65 444	59.61 449	87.66 409	91.08 374	78.23 365	69.85 436	83.22 432	54.76 414	91.63 427	64.14 421	64.89 440	89.16 425

mvsany_test3		74.95 404	73.26 408	80.02 419	74.61 455	63.16 443	85.53 425	78.42 452	74.16 402	74.89 418	86.46 414	36.02 451	89.09 440	82.39 249	66.91 435	87.82 436

test_fmvs3		77.67 398	77.16 394	79.22 420	79.52 450	61.14 445	92.34 303	91.64 361	73.98 404	78.86 386	86.59 413	27.38 456	87.03 444	88.12 160	75.97 413	89.50 418

DSMNet-mixed		76.94 400	76.29 399	78.89 421	83.10 442	56.11 457	87.78 405	79.77 448	60.65 447	75.64 413	88.71 386	61.56 370	88.34 443	60.07 433	89.29 273	92.21 377

EGC-MVSNET		61.97 418	56.37 423	78.77 422	89.63 399	73.50 373	89.12 384	82.79 441	0.21 468	1.24 469	84.80 426	39.48 447	90.04 435	44.13 451	75.94 414	72.79 450

new_pmnet		72.15 408	70.13 411	78.20 423	82.95 443	65.68 432	83.91 435	82.40 443	62.94 445	64.47 442	79.82 442	42.85 445	86.26 448	57.41 440	74.44 416	82.65 443

MVS-HIRNet		73.70 406	72.20 409	78.18 424	91.81 328	56.42 456	82.94 440	82.58 442	55.24 450	68.88 437	66.48 453	55.32 411	95.13 378	58.12 438	88.42 286	83.01 441

LCM-MVSNet		66.00 415	62.16 420	77.51 425	64.51 465	58.29 451	83.87 436	90.90 382	48.17 454	54.69 451	73.31 449	16.83 465	86.75 445	65.47 413	61.67 445	87.48 437

APD_test1		69.04 411	66.26 417	77.36 426	80.51 448	62.79 444	85.46 426	83.51 440	54.11 452	59.14 449	84.79 427	23.40 459	89.61 437	55.22 442	70.24 426	79.68 447

test_f		71.95 409	70.87 410	75.21 427	74.21 457	59.37 450	85.07 429	85.82 430	65.25 441	70.42 435	83.13 433	23.62 457	82.93 455	78.32 314	71.94 422	83.33 440

ANet_high		58.88 422	54.22 427	72.86 428	56.50 468	56.67 453	80.75 445	86.00 429	73.09 414	37.39 460	64.63 456	22.17 460	79.49 458	43.51 452	23.96 462	82.43 444

test_vis3_rt		65.12 416	62.60 418	72.69 429	71.44 458	60.71 446	87.17 413	65.55 462	63.80 444	53.22 452	65.65 455	14.54 466	89.44 439	76.65 331	65.38 438	67.91 453

FPMVS		64.63 417	62.55 419	70.88 430	70.80 459	56.71 452	84.42 433	84.42 437	51.78 453	49.57 453	81.61 439	23.49 458	81.48 456	40.61 456	76.25 412	74.46 449

dmvs_testset		74.57 405	75.81 403	70.86 431	87.72 422	40.47 466	87.05 415	77.90 456	82.75 278	71.15 434	85.47 424	67.98 316	84.12 453	45.26 450	76.98 410	88.00 434

N_pmnet		68.89 412	68.44 414	70.23 432	89.07 404	28.79 471	88.06 400	19.50 471	69.47 431	71.86 431	84.93 425	61.24 375	91.75 425	54.70 443	77.15 407	90.15 413

testf1		59.54 420	56.11 424	69.85 433	69.28 460	56.61 454	80.37 446	76.55 459	42.58 457	45.68 456	75.61 444	11.26 467	84.18 451	43.20 453	60.44 447	68.75 451

APD_test2		59.54 420	56.11 424	69.85 433	69.28 460	56.61 454	80.37 446	76.55 459	42.58 457	45.68 456	75.61 444	11.26 467	84.18 451	43.20 453	60.44 447	68.75 451

WB-MVS		67.92 413	67.49 415	69.21 435	81.09 446	41.17 465	88.03 401	78.00 455	73.50 409	62.63 444	83.11 435	63.94 352	86.52 446	25.66 461	51.45 453	79.94 446

PMMVS2		59.60 419	56.40 422	69.21 435	68.83 462	46.58 461	73.02 456	77.48 457	55.07 451	49.21 454	72.95 450	17.43 464	80.04 457	49.32 448	44.33 457	80.99 445

SSC-MVS		67.06 414	66.56 416	68.56 437	80.54 447	40.06 467	87.77 406	77.37 458	72.38 419	61.75 446	82.66 437	63.37 355	86.45 447	24.48 462	48.69 456	79.16 448

Gipuma		57.99 424	54.91 426	67.24 438	88.51 408	65.59 433	52.21 459	90.33 393	43.58 456	42.84 459	51.18 460	20.29 462	85.07 450	34.77 457	70.45 425	51.05 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 426	48.46 430	63.48 439	45.72 470	46.20 462	73.41 455	78.31 453	41.03 459	30.06 462	65.68 454	6.05 469	83.43 454	30.04 459	65.86 437	60.80 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 423	58.24 421	60.56 440	83.13 441	45.09 464	82.32 441	48.22 470	67.61 435	61.70 447	69.15 451	38.75 448	76.05 459	32.01 458	41.31 458	60.55 455

MVE	39.65 23	43.39 428	38.59 434	57.77 441	56.52 467	48.77 460	55.38 458	58.64 466	29.33 462	28.96 463	52.65 459	4.68 470	64.62 463	28.11 460	33.07 460	59.93 456
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 427	48.47 429	56.66 442	52.26 469	18.98 473	41.51 461	81.40 445	10.10 463	44.59 458	75.01 447	28.51 454	68.16 460	53.54 444	49.31 455	82.83 442

DeepMVS_CX				56.31 443	74.23 456	51.81 459		56.67 467	44.85 455	48.54 455	75.16 446	27.87 455	58.74 465	40.92 455	52.22 452	58.39 457

kuosan		53.51 425	53.30 428	54.13 444	76.06 453	45.36 463	80.11 448	48.36 469	59.63 448	54.84 450	63.43 457	37.41 449	62.07 464	20.73 464	39.10 459	54.96 458

E-PMN		43.23 429	42.29 431	46.03 445	65.58 464	37.41 468	73.51 454	64.62 463	33.99 460	28.47 464	47.87 461	19.90 463	67.91 461	22.23 463	24.45 461	32.77 460

EMVS		42.07 430	41.12 432	44.92 446	63.45 466	35.56 470	73.65 453	63.48 464	33.05 461	26.88 465	45.45 462	21.27 461	67.14 462	19.80 465	23.02 463	32.06 461

tmp_tt		35.64 431	39.24 433	24.84 447	14.87 471	23.90 472	62.71 457	51.51 468	6.58 465	36.66 461	62.08 458	44.37 443	30.34 467	52.40 445	22.00 464	20.27 462

wuyk23d		21.27 433	20.48 436	23.63 448	68.59 463	36.41 469	49.57 460	6.85 472	9.37 464	7.89 466	4.46 468	4.03 471	31.37 466	17.47 466	16.07 465	3.12 463

test123		8.76 435	11.22 438	1.39 449	0.85 473	0.97 474	85.76 423	0.35 474	0.54 467	2.45 468	8.14 467	0.60 472	0.48 468	2.16 468	0.17 467	2.71 464

testmvs		8.92 434	11.52 437	1.12 450	1.06 472	0.46 475	86.02 420	0.65 473	0.62 466	2.74 467	9.52 466	0.31 473	0.45 469	2.38 467	0.39 466	2.46 465

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

cdsmvs_eth3d_5k		22.14 432	29.52 435	0.00 451	0.00 474	0.00 476	0.00 462	95.76 180	0.00 469	0.00 470	94.29 205	75.66 203	0.00 470	0.00 469	0.00 468	0.00 466

pcd_1.5k_mvsjas		6.64 437	8.86 440	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	79.70 142	0.00 470	0.00 469	0.00 468	0.00 466

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

ab-mvs-re		7.82 436	10.43 439	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	93.88 226	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

WAC-MVS						64.08 439								59.14 435

FOURS1					98.86 1	85.54 69	98.29 1	97.49 8	89.79 61	96.29 27

PC_three_1452									82.47 282	97.09 16	97.07 66	92.72 1	98.04 185	92.70 74	99.02 12	98.86 12

test_one_0601					98.58 11	85.83 63		97.44 17	91.05 21	96.78 23	98.06 20	91.45 11

eth-test2					0.00 474

eth-test					0.00 474

ZD-MVS					98.15 36	86.62 33		97.07 55	83.63 254	94.19 58	96.91 72	87.57 31	99.26 46	91.99 99	98.44 53

RE-MVS-def			93.68 67		97.92 45	84.57 89	96.28 46	96.76 87	87.46 144	93.75 69	97.43 45	82.94 96		92.73 70	97.80 86	97.88 95

IU-MVS					98.77 5	86.00 52		96.84 77	81.26 319	97.26 12				95.50 34	99.13 3	99.03 8

test_241102_TWO								97.44 17	90.31 39	97.62 7	98.07 18	91.46 10	99.58 10	95.66 28	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 54		97.44 17	90.26 45	97.71 2	97.96 29	92.31 4	99.38 31

9.14			94.47 30		97.79 54		96.08 64	97.44 17	86.13 186	95.10 48	97.40 47	88.34 22	99.22 48	93.25 62	98.70 34

save fliter					97.85 51	85.63 68	95.21 133	96.82 80	89.44 71

test_0728_THIRD									90.75 27	97.04 18	98.05 23	92.09 6	99.55 16	95.64 30	99.13 3	99.13 2

test0726					98.78 3	85.93 57	97.19 12	97.47 13	90.27 43	97.64 5	98.13 6	91.47 8

GSMVS																96.12 206

test_part2					98.55 12	87.22 19				96.40 26

sam_mvs1												71.70 261				96.12 206

sam_mvs												70.60 275

MTGPA								96.97 60

test_post1							88.00 402				9.81 465	69.31 299	95.53 366	76.65 331

test_post											10.29 464	70.57 279	95.91 351

patchmatchnet-post											83.76 430	71.53 262	96.48 320

MTMP							96.16 55	60.64 465

gm-plane-assit					89.60 400	68.00 422			77.28 373		88.99 380		97.57 225	79.44 303

test9_res														91.91 103	98.71 32	98.07 78

TEST9					97.53 63	86.49 37	94.07 215	96.78 84	81.61 311	92.77 94	96.20 102	87.71 28	99.12 57

test_8					97.49 65	86.30 45	94.02 220	96.76 87	81.86 302	92.70 98	96.20 102	87.63 29	99.02 67

agg_prior2														90.54 128	98.68 37	98.27 59

agg_prior					97.38 68	85.92 59		96.72 94		92.16 113			98.97 81

test_prior4						85.96 56	94.11 209

test_prior2							94.12 207		87.67 142	92.63 102	96.39 97	86.62 41		91.50 112	98.67 40

旧先验2							93.36 255		71.25 425	94.37 54			97.13 277	86.74 180

新几何2							93.11 270

旧先验1					96.79 81	81.81 187		95.67 188			96.81 78	86.69 39			97.66 92	96.97 162

无先验							93.28 263	96.26 133	73.95 405				99.05 61	80.56 288		96.59 185

原ACMM2							92.94 281

test222					96.55 90	81.70 189	92.22 309	95.01 236	68.36 434	90.20 157	96.14 107	80.26 133			97.80 86	96.05 213

testdata2													98.75 109	78.30 315

segment_acmp												87.16 36

testdata1							92.15 311		87.94 128

plane_prior7					94.70 193	82.74 159

plane_prior6					94.52 206	82.75 157						74.23 223

plane_prior5								96.22 138					98.12 170	88.15 157	89.99 256	94.63 267

plane_prior4											94.86 176

plane_prior3						82.75 157			90.26 45	86.91 224

plane_prior2							95.85 86		90.81 25

plane_prior1					94.59 199

plane_prior						82.73 160	95.21 133		89.66 66						89.88 261

n2								0.00 475

nn								0.00 475

door-mid								85.49 432

test11								96.57 105

door								85.33 434

HQP5-MVS						81.56 191

HQP-NCC					94.17 228		94.39 190		88.81 96	85.43 270

ACMP_Plane					94.17 228		94.39 190		88.81 96	85.43 270

BP-MVS														87.11 177

HQP4-MVS										85.43 270			97.96 195			94.51 277

HQP3-MVS								96.04 155							89.77 265

HQP2-MVS												73.83 234

NP-MVS					94.37 217	82.42 172					93.98 219

MDTV_nov1_ep13_2view						55.91 458	87.62 410		73.32 411	84.59 292		70.33 282		74.65 354		95.50 234

MDTV_nov1_ep13			83.56 331		91.69 333	69.93 415	87.75 407	91.54 364	78.60 357	84.86 286	88.90 382	69.54 294	96.03 342	70.25 383	88.93 278

ACMMP++_ref															87.47 301

ACMMP++															88.01 293

Test By Simon												80.02 135

High-resolution multi-view benchmark