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	94.37 272	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	85.90 184	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	91.37 109	95.55 7	95.63 137	88.73 6	97.07 19	96.77 86	90.84 24	84.02 308	96.62 89	75.95 194	99.34 38	87.77 160	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	93.01 316	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	87.13 152	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	92.66 288	96.56 106	83.44 256	91.68 130	95.04 163	86.60 43	98.99 76	85.60 193	97.92 80	96.93 162

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 87	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	91.43 113	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	94.60 187	82.33 105	98.62 125	92.40 80	92.86 209	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	86.26 176	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	94.60 187	82.33 105	98.62 125	92.40 80	92.86 209	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 98	98.56 50	98.47 34

X-MVStestdata		88.31 212	86.13 261	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 32	92.85 89	23.41 459	85.02 65	99.49 26	91.99 98	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	94.87 171	80.56 129	98.66 117	92.42 79	93.10 205	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	90.87 122	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 96	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	85.68 191	95.05 49	97.18 60	87.31 35	99.07 59	91.90 104	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 90	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	87.45 145	93.26 78	97.33 50	84.62 74	99.51 24	90.75 124	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	91.44 112	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	85.15 214	93.56 75	96.28 99	85.60 55	99.31 43	92.45 77	98.79 24	98.12 75

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	90.69 146	95.88 122	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	81.86 298	92.77 94	96.20 102	87.63 29	99.12 57	92.14 91	98.69 35	97.94 88

CDPH-MVS		92.83 89	92.30 96	94.44 45	97.79 54	86.11 51	94.06 217	96.66 98	80.09 329	92.77 94	96.63 88	86.62 41	99.04 63	87.40 166	98.66 41	98.17 69

3Dnovator	86.66 5	91.73 110	90.82 123	94.44 45	94.59 199	86.37 41	97.18 13	97.02 57	89.20 82	84.31 303	96.66 84	73.74 232	99.17 51	86.74 176	97.96 78	97.79 102

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	86.90 160	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	93.93 290	89.77 62	94.21 57	95.59 137	87.35 34	98.61 127	92.72 72	96.15 129	97.83 99

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	77.97 164	98.84 99	90.75 124	98.26 59	98.07 77

test12				94.34 53	97.13 75	86.15 50		96.29 125		91.04 142		85.08 63	99.01 69		98.13 70	97.86 96

SymmetryMVS		92.81 91	92.31 95	94.32 54	96.15 103	86.20 48	96.30 42	94.43 268	91.65 16	92.68 99	96.13 108	77.97 164	98.84 99	90.75 124	94.72 160	97.92 91

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	90.15 159	97.03 68	81.44 122	99.51 24	90.85 123	95.74 136	98.04 83
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	89.56 165	97.37 49	82.51 102	99.38 31	92.20 88	98.30 57	97.57 116
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	94.66 184	82.11 112	98.50 133	92.33 85	92.82 212	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	91.02 118	94.10 60	90.10 385	85.25 75	96.03 71	92.05 343	92.83 5	87.39 214	95.78 129	79.39 146	99.01 69	88.13 155	97.48 94	98.05 82
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 87

DELS-MVS		93.43 74	93.25 76	93.97 63	95.42 146	85.04 78	93.06 274	97.13 49	90.74 29	91.84 124	95.09 162	86.32 46	99.21 49	91.22 114	98.45 52	97.65 111
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	91.28 112	93.96 64	98.33 29	85.92 59	94.66 171	96.66 98	82.69 276	90.03 161	95.82 127	82.30 107	99.03 64	84.57 211	96.48 122	96.91 164

HPM-MVS_fast		93.40 75	93.22 77	93.94 65	98.36 27	84.83 82	97.15 14	96.80 83	85.77 188	92.47 107	97.13 63	82.38 103	99.07 59	90.51 129	98.40 54	97.92 91

test_fmvsmconf0.1_n		94.20 42	94.31 38	93.88 66	92.46 301	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 96

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	96.91 289	94.38 47	98.85 20	98.03 84
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	93.24 265	97.24 36	88.76 99	91.60 131	95.85 125	86.07 50	98.66 117	91.91 102	98.16 67	98.03 84

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	87.46 143	93.75 69	97.43 45	84.24 78	99.01 69	92.73 70	97.80 86	97.88 94

test_prior				93.82 69	97.29 72	84.49 93		96.88 73					98.87 93			98.11 76

APD-MVS_3200maxsize		93.78 58	93.77 62	93.80 71	97.92 45	84.19 106	96.30 42	96.87 74	86.96 156	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	86.56 168	96.84 22	97.81 35	87.56 32	98.77 108	97.14 13	96.82 112	97.16 143

CSCG		93.23 80	93.05 81	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 237	90.05 160	95.66 134	87.77 26	99.15 55	89.91 134	98.27 58	98.07 77

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	95.89 121	73.44 236	98.65 119	90.22 132	96.03 131	97.91 93

BP-MVS1		92.48 96	92.07 99	93.72 75	94.50 208	84.39 101	95.90 82	94.30 275	90.39 36	92.67 101	95.94 118	74.46 215	98.65 119	93.14 64	97.35 98	98.13 72

test_fmvsmconf0.01_n		93.19 81	93.02 82	93.71 76	89.25 398	84.42 100	96.06 68	96.29 125	89.06 86	94.68 51	98.13 6	79.22 148	98.98 80	97.22 12	97.24 100	97.74 105

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	84.47 213	95.47 143	97.45 122

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	86.31 174	96.75 24	97.86 33	87.40 33	98.74 112	97.07 15	97.02 105	97.07 148

QAPM		89.51 171	88.15 196	93.59 79	94.92 174	84.58 88	96.82 30	96.70 96	78.43 356	83.41 324	96.19 105	73.18 241	99.30 44	77.11 324	96.54 119	96.89 165

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	97.17 139

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	91.30 110	93.39 82	94.72 190	83.36 133	95.45 114	96.37 120	90.33 38	92.17 112	96.03 113	72.32 253	98.75 109	87.94 158	96.34 124	98.07 77

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 93	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	97.25 133

EI-MVSNet-Vis-set		93.01 87	92.92 84	93.29 85	95.01 165	83.51 128	94.48 180	95.77 178	90.87 23	92.52 105	96.67 83	84.50 75	99.00 74	91.99 98	94.44 172	97.36 124

Vis-MVSNet		91.75 109	91.23 113	93.29 85	95.32 149	83.78 118	96.14 59	95.98 159	89.89 51	90.45 150	96.58 91	75.09 206	98.31 160	84.75 205	96.90 108	97.78 103
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	95.51 139	84.50 75	98.79 106	94.83 42	98.86 19	97.72 107

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	95.77 130	85.02 65	98.33 157	93.03 66	98.62 46	98.13 72

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	95.90 120	77.85 170	98.17 167	88.90 146	93.38 194	98.13 72

VDD-MVS		90.74 129	89.92 143	93.20 90	96.27 100	83.02 150	95.73 96	93.86 294	88.42 112	92.53 104	96.84 75	62.09 359	98.64 122	90.95 120	92.62 219	97.93 90

Elysia		90.12 148	89.10 166	93.18 91	93.16 274	84.05 110	95.22 130	96.27 129	85.16 212	90.59 147	94.68 180	64.64 342	98.37 150	86.38 182	95.77 134	97.12 145

StellarMVS		90.12 148	89.10 166	93.18 91	93.16 274	84.05 110	95.22 130	96.27 129	85.16 212	90.59 147	94.68 180	64.64 342	98.37 150	86.38 182	95.77 134	97.12 145

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		91.08 123	90.39 127	93.17 93	93.07 280	86.91 22	96.41 38	96.26 133	88.30 115	88.37 190	94.85 174	82.19 111	97.64 218	91.09 115	82.95 341	94.96 250

MVSMamba_PlusPlus		93.44 70	93.54 71	93.14 95	96.58 89	83.05 148	96.06 68	96.50 111	84.42 234	94.09 61	95.56 138	85.01 68	98.69 116	94.96 40	98.66 41	97.67 110

EI-MVSNet-UG-set		92.74 92	92.62 91	93.12 96	94.86 179	83.20 138	94.40 188	95.74 181	90.71 31	92.05 115	96.60 90	84.00 80	98.99 76	91.55 110	93.63 185	97.17 139

test_fmvsmvis_n_1920		93.44 70	93.55 70	93.10 97	93.67 260	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	96.32 190

新几何1				93.10 97	97.30 71	84.35 103		95.56 195	71.09 422	91.26 139	96.24 100	82.87 98	98.86 95	79.19 303	98.10 71	96.07 206

OMC-MVS		91.23 118	90.62 126	93.08 99	96.27 100	84.07 108	93.52 247	95.93 164	86.95 157	89.51 166	96.13 108	78.50 158	98.35 154	85.84 191	92.90 208	96.83 172

OpenMVS	83.78 11	88.74 199	87.29 218	93.08 99	92.70 296	85.39 73	96.57 36	96.43 114	78.74 351	80.85 356	96.07 111	69.64 288	99.01 69	78.01 315	96.65 117	94.83 258

MAR-MVS		90.30 144	89.37 159	93.07 101	96.61 86	84.48 94	95.68 99	95.67 187	82.36 281	87.85 201	92.85 254	76.63 183	98.80 104	80.01 291	96.68 116	95.91 212
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		90.92 124	90.21 131	93.03 102	93.86 245	83.88 115	92.81 285	93.86 294	79.84 332	91.76 127	94.29 201	77.92 167	98.04 185	90.48 130	97.11 101	97.17 139

Effi-MVS+		91.59 113	91.11 115	93.01 103	94.35 221	83.39 132	94.60 173	95.10 230	87.10 153	90.57 149	93.10 249	81.43 123	98.07 183	89.29 141	94.48 170	97.59 115

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	87.27 148	95.98 35	98.05 23	83.07 95	98.45 143	96.68 21	95.51 140	96.88 166

MVS_111021_LR		92.47 97	92.29 97	92.98 105	95.99 119	84.43 98	93.08 271	96.09 150	88.20 120	91.12 141	95.72 133	81.33 124	97.76 207	91.74 106	97.37 97	96.75 174

fmvsm_s_conf0.1_n_a		93.19 81	93.26 75	92.97 106	92.49 299	83.62 124	96.02 72	95.72 184	86.78 162	96.04 33	98.19 3	82.30 107	98.43 147	96.38 23	95.42 146	96.86 167

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	91.19 315	84.06 79	98.34 155	91.72 107	96.54 119	96.54 185

LFMVS		90.08 151	89.13 165	92.95 108	96.71 82	82.32 176	96.08 64	89.91 399	86.79 161	92.15 114	96.81 78	62.60 357	98.34 155	87.18 170	93.90 180	98.19 67

UGNet		89.95 158	88.95 172	92.95 108	94.51 207	83.31 134	95.70 98	95.23 223	89.37 74	87.58 208	93.94 217	64.00 347	98.78 107	83.92 220	96.31 125	96.74 175
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		90.80 127	90.10 135	92.90 110	93.04 283	83.53 127	93.08 271	94.15 283	80.22 326	91.41 136	94.91 168	76.87 177	97.93 198	90.28 131	96.90 108	97.24 134
jason: jason.
DP-MVS		87.25 253	85.36 290	92.90 110	97.65 60	83.24 136	94.81 160	92.00 345	74.99 390	81.92 345	95.00 164	72.66 246	99.05 61	66.92 404	92.33 224	96.40 187

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	96.92 163

fmvsm_s_conf0.1_n		93.46 67	93.66 68	92.85 114	93.75 252	83.13 141	96.02 72	95.74 181	87.68 140	95.89 36	98.17 4	82.78 99	98.46 139	96.71 20	96.17 128	96.98 157

CANet_DTU		90.26 146	89.41 158	92.81 115	93.46 267	83.01 151	93.48 248	94.47 267	89.43 72	87.76 206	94.23 206	70.54 276	99.03 64	84.97 200	96.39 123	96.38 188

MVSFormer		91.68 112	91.30 110	92.80 116	93.86 245	83.88 115	95.96 77	95.90 168	84.66 230	91.76 127	94.91 168	77.92 167	97.30 256	89.64 137	97.11 101	97.24 134

PVSNet_Blended_VisFu		91.38 115	90.91 120	92.80 116	96.39 97	83.17 139	94.87 154	96.66 98	83.29 261	89.27 172	94.46 196	80.29 132	99.17 51	87.57 163	95.37 147	96.05 209

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 99

LuminaMVS		90.55 140	89.81 145	92.77 118	92.78 294	84.21 105	94.09 213	94.17 282	85.82 185	91.54 132	94.14 208	69.93 282	97.92 199	91.62 109	94.21 175	96.18 198

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	97.43 123

VDDNet		89.56 170	88.49 187	92.76 120	95.07 163	82.09 179	96.30 42	93.19 311	81.05 320	91.88 122	96.86 74	61.16 375	98.33 157	88.43 152	92.49 223	97.84 98

h-mvs33		90.80 127	90.15 134	92.75 122	96.01 115	82.66 164	95.43 115	95.53 199	89.80 58	93.08 83	95.64 135	75.77 195	99.00 74	92.07 93	78.05 398	96.60 180

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	90.95 120	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		90.69 132	90.02 141	92.71 124	95.72 130	82.41 174	94.11 209	95.12 228	85.63 192	91.49 134	94.70 178	74.75 210	98.42 148	86.13 186	92.53 221	97.31 125

DCV-MVSNet		90.69 132	90.02 141	92.71 124	95.72 130	82.41 174	94.11 209	95.12 228	85.63 192	91.49 134	94.70 178	74.75 210	98.42 148	86.13 186	92.53 221	97.31 125

PCF-MVS	84.11 10	87.74 227	86.08 265	92.70 126	94.02 235	84.43 98	89.27 376	95.87 172	73.62 404	84.43 295	94.33 198	78.48 160	98.86 95	70.27 378	94.45 171	94.81 259
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mamba_0404		90.73 130	90.08 136	92.69 127	95.00 168	83.13 141	94.32 197	95.00 238	85.41 202	89.84 162	95.35 146	76.13 186	97.98 191	85.46 196	94.18 176	96.95 159

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	96.00 114	82.35 104	97.99 189	91.05 116	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	97.76 207	92.19 89	98.66 41	96.76 173

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	95.80 128	83.16 91	98.16 168	93.68 54	98.14 69	97.31 125

ab-mvs		89.41 176	88.35 189	92.60 131	95.15 161	82.65 168	92.20 306	95.60 194	83.97 241	88.55 186	93.70 231	74.16 223	98.21 166	82.46 244	89.37 268	96.94 161

LS3D		87.89 222	86.32 254	92.59 132	96.07 113	82.92 154	95.23 128	94.92 245	75.66 382	82.89 331	95.98 116	72.48 250	99.21 49	68.43 392	95.23 152	95.64 226

Anonymous20240529		88.09 218	86.59 243	92.58 133	96.53 92	81.92 185	95.99 74	95.84 174	74.11 399	89.06 176	95.21 156	61.44 367	98.81 103	83.67 227	87.47 299	97.01 155

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	97.78 103

CPTT-MVS		91.99 102	91.80 102	92.55 135	98.24 33	81.98 182	96.76 31	96.49 112	81.89 297	90.24 153	96.44 96	78.59 156	98.61 127	89.68 136	97.85 83	97.06 149

114514_t		89.51 171	88.50 185	92.54 136	98.11 38	81.99 181	95.16 138	96.36 121	70.19 426	85.81 248	95.25 152	76.70 181	98.63 124	82.07 254	96.86 111	97.00 156

PAPM_NR		91.22 119	90.78 124	92.52 137	97.60 61	81.46 197	94.37 194	96.24 136	86.39 173	87.41 211	94.80 176	82.06 115	98.48 135	82.80 239	95.37 147	97.61 113

mamba_0408		89.06 189	87.92 203	92.50 138	94.76 184	82.66 164	79.84 445	94.64 263	85.18 207	88.96 178	95.00 164	76.00 191	97.98 191	83.74 224	93.15 202	96.85 168

DeepPCF-MVS	89.96 1	94.20 42	94.77 26	92.49 139	96.52 93	80.00 248	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

mamba_test_0407		90.47 142	89.80 146	92.46 140	94.76 184	82.66 164	93.98 225	95.00 238	85.41 202	88.96 178	95.35 146	76.13 186	97.88 202	85.46 196	93.15 202	96.85 168

IS-MVSNet		91.43 114	91.09 117	92.46 140	95.87 126	81.38 200	96.95 20	93.69 302	89.72 64	89.50 168	95.98 116	78.57 157	97.77 206	83.02 233	96.50 121	98.22 66

API-MVS		90.66 135	90.07 137	92.45 142	96.36 98	84.57 89	96.06 68	95.22 225	82.39 279	89.13 173	94.27 204	80.32 131	98.46 139	80.16 290	96.71 115	94.33 282

xiu_mvs_v1_base_debu		90.64 136	90.05 138	92.40 143	93.97 241	84.46 95	93.32 256	95.46 203	85.17 209	92.25 109	94.03 209	70.59 272	98.57 130	90.97 117	94.67 162	94.18 285

xiu_mvs_v1_base		90.64 136	90.05 138	92.40 143	93.97 241	84.46 95	93.32 256	95.46 203	85.17 209	92.25 109	94.03 209	70.59 272	98.57 130	90.97 117	94.67 162	94.18 285

xiu_mvs_v1_base_debi		90.64 136	90.05 138	92.40 143	93.97 241	84.46 95	93.32 256	95.46 203	85.17 209	92.25 109	94.03 209	70.59 272	98.57 130	90.97 117	94.67 162	94.18 285

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	77.47 173	98.96 83	97.79 5	96.58 118	97.03 152

viewmanbaseed2359cas		91.78 107	91.58 106	92.37 147	94.32 222	81.07 212	93.76 237	95.96 162	87.26 149	91.50 133	95.88 122	80.92 128	97.97 193	89.70 135	94.92 156	98.07 77

fmvsm_s_conf0.1_n_2		93.16 83	93.42 72	92.37 147	94.62 197	81.13 209	95.23 128	95.89 170	90.30 41	96.74 25	98.02 28	76.14 185	98.95 85	97.64 6	96.21 127	97.03 152

AdaColmap		89.89 161	89.07 168	92.37 147	97.41 67	83.03 149	94.42 187	95.92 165	82.81 273	86.34 237	94.65 185	73.89 228	99.02 67	80.69 281	95.51 140	95.05 245

CNLPA		89.07 188	87.98 200	92.34 150	96.87 79	84.78 84	94.08 214	93.24 308	81.41 311	84.46 293	95.13 161	75.57 202	96.62 302	77.21 322	93.84 182	95.61 229

fmvsm_s_conf0.5_n_4		93.86 56	94.37 35	92.33 151	95.13 162	80.95 217	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	97.13 144

ET-MVSNet_ETH3D		87.51 241	85.91 273	92.32 152	93.70 259	83.93 113	92.33 300	90.94 377	84.16 236	72.09 425	92.52 267	69.90 283	95.85 349	89.20 142	88.36 286	97.17 139

Anonymous202405211		87.68 228	86.13 261	92.31 153	96.66 84	80.74 224	94.87 154	91.49 362	80.47 325	89.46 169	95.44 142	54.72 411	98.23 163	82.19 250	89.89 258	97.97 86

CHOSEN 1792x2688		88.84 195	87.69 208	92.30 154	96.14 104	81.42 199	90.01 363	95.86 173	74.52 395	87.41 211	93.94 217	75.46 203	98.36 152	80.36 286	95.53 139	97.12 145

HY-MVS	83.01 12	89.03 191	87.94 202	92.29 155	94.86 179	82.77 156	92.08 311	94.49 266	81.52 310	86.93 218	92.79 260	78.32 162	98.23 163	79.93 292	90.55 245	95.88 215

CDS-MVSNet		89.45 174	88.51 184	92.29 155	93.62 262	83.61 126	93.01 275	94.68 261	81.95 291	87.82 204	93.24 243	78.69 154	96.99 283	80.34 287	93.23 199	96.28 193
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 154	89.27 164	92.29 155	95.78 128	80.95 217	92.68 287	96.22 138	81.91 293	86.66 228	93.75 229	82.23 109	98.44 145	79.40 302	94.79 159	97.48 120

mvsmamba		90.33 143	89.69 149	92.25 158	95.17 158	81.64 190	95.27 126	93.36 307	84.88 221	89.51 166	94.27 204	69.29 297	97.42 241	89.34 140	96.12 130	97.68 109

PLC	84.53 7	89.06 189	88.03 198	92.15 159	97.27 73	82.69 163	94.29 198	95.44 208	79.71 334	84.01 309	94.18 207	76.68 182	98.75 109	77.28 321	93.41 193	95.02 246
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	92.13 160	95.88 124	80.50 231	97.33 8	95.25 222	86.15 179	89.76 164	95.60 136	83.42 87	98.32 159	87.37 168	93.25 198	97.56 117

patch_mono-2		93.74 60	94.32 36	92.01 161	97.54 62	78.37 290	93.40 252	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				92.01 161	97.34 69	81.05 213		96.81 82	78.89 345	90.45 150	95.92 119	82.65 100	98.84 99	80.68 282	98.26 59	96.14 200

UniMVSNet (Re)		89.80 164	89.07 168	92.01 161	93.60 263	84.52 92	94.78 162	97.47 13	89.26 80	86.44 234	92.32 273	82.10 113	97.39 252	84.81 204	80.84 375	94.12 289

MG-MVS		91.77 108	91.70 105	92.00 164	97.08 76	80.03 246	93.60 245	95.18 226	87.85 134	90.89 144	96.47 95	82.06 115	98.36 152	85.07 199	97.04 104	97.62 112

EIA-MVS		91.95 103	91.94 100	91.98 165	95.16 159	80.01 247	95.36 116	96.73 92	88.44 110	89.34 170	92.16 278	83.82 83	98.45 143	89.35 139	97.06 103	97.48 120

PVSNet_Blended		90.73 130	90.32 129	91.98 165	96.12 106	81.25 203	92.55 292	96.83 78	82.04 289	89.10 174	92.56 266	81.04 126	98.85 97	86.72 178	95.91 132	95.84 217

guyue		91.12 122	90.84 122	91.96 167	94.59 199	80.57 229	94.87 154	93.71 301	88.96 93	91.14 140	95.22 153	73.22 240	97.76 207	92.01 97	93.81 183	97.54 119

PS-MVSNAJ		91.18 120	90.92 119	91.96 167	95.26 154	82.60 170	92.09 310	95.70 185	86.27 175	91.84 124	92.46 268	79.70 140	98.99 76	89.08 143	95.86 133	94.29 283

TAMVS		89.21 182	88.29 193	91.96 167	93.71 257	82.62 169	93.30 260	94.19 280	82.22 284	87.78 205	93.94 217	78.83 151	96.95 286	77.70 317	92.98 207	96.32 190

SDMVSNet		90.19 147	89.61 152	91.93 170	96.00 116	83.09 146	92.89 282	95.98 159	88.73 100	86.85 224	95.20 157	72.09 255	97.08 275	88.90 146	89.85 260	95.63 227

FA-MVS(test-final)		89.66 166	88.91 174	91.93 170	94.57 203	80.27 235	91.36 327	94.74 258	84.87 222	89.82 163	92.61 265	74.72 213	98.47 138	83.97 219	93.53 188	97.04 151

MVS_Test		91.31 117	91.11 115	91.93 170	94.37 217	80.14 239	93.46 250	95.80 176	86.46 171	91.35 138	93.77 227	82.21 110	98.09 180	87.57 163	94.95 155	97.55 118

NR-MVSNet		88.58 205	87.47 214	91.93 170	93.04 283	84.16 107	94.77 163	96.25 135	89.05 87	80.04 370	93.29 241	79.02 150	97.05 280	81.71 265	80.05 385	94.59 266

HyFIR lowres test		88.09 218	86.81 231	91.93 170	96.00 116	80.63 226	90.01 363	95.79 177	73.42 406	87.68 207	92.10 284	73.86 229	97.96 194	80.75 280	91.70 228	97.19 138

GeoE		90.05 152	89.43 157	91.90 175	95.16 159	80.37 234	95.80 89	94.65 262	83.90 242	87.55 210	94.75 177	78.18 163	97.62 220	81.28 270	93.63 185	97.71 108

thisisatest0530		88.67 200	87.61 210	91.86 176	94.87 178	80.07 242	94.63 172	89.90 400	84.00 240	88.46 188	93.78 226	66.88 321	98.46 139	83.30 229	92.65 214	97.06 149

xiu_mvs_v2_base		91.13 121	90.89 121	91.86 176	94.97 170	82.42 172	92.24 303	95.64 192	86.11 183	91.74 129	93.14 247	79.67 143	98.89 91	89.06 144	95.46 144	94.28 284

DU-MVS		89.34 181	88.50 185	91.85 178	93.04 283	83.72 119	94.47 183	96.59 103	89.50 69	86.46 231	93.29 241	77.25 175	97.23 265	84.92 201	81.02 371	94.59 266

AstraMVS		90.69 132	90.30 130	91.84 179	93.81 248	79.85 253	94.76 164	92.39 331	88.96 93	91.01 143	95.87 124	70.69 270	97.94 197	92.49 76	92.70 213	97.73 106

OPM-MVS		90.12 148	89.56 153	91.82 180	93.14 276	83.90 114	94.16 205	95.74 181	88.96 93	87.86 200	95.43 144	72.48 250	97.91 200	88.10 157	90.18 252	93.65 320
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 139	90.19 132	91.82 180	94.70 193	82.73 160	95.85 86	96.22 138	90.81 25	86.91 220	94.86 172	74.23 219	98.12 170	88.15 153	89.99 254	94.63 263

UniMVSNet_NR-MVSNet		89.92 160	89.29 162	91.81 182	93.39 269	83.72 119	94.43 186	97.12 50	89.80 58	86.46 231	93.32 238	83.16 91	97.23 265	84.92 201	81.02 371	94.49 276

diffmvs		91.37 116	91.23 113	91.77 183	93.09 279	80.27 235	92.36 297	95.52 200	87.03 155	91.40 137	94.93 167	80.08 134	97.44 239	92.13 92	94.56 167	97.61 113
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		88.42 207	87.33 217	91.72 184	94.92 174	80.98 215	92.97 279	94.54 265	78.16 362	83.82 312	93.88 222	78.78 153	97.91 200	79.45 298	89.41 267	96.26 194

Fast-Effi-MVS+		89.41 176	88.64 180	91.71 185	94.74 187	80.81 222	93.54 246	95.10 230	83.11 265	86.82 226	90.67 338	79.74 139	97.75 211	80.51 285	93.55 187	96.57 183

WTY-MVS		89.60 168	88.92 173	91.67 186	95.47 145	81.15 208	92.38 296	94.78 256	83.11 265	89.06 176	94.32 199	78.67 155	96.61 305	81.57 266	90.89 241	97.24 134

TAPA-MVS	84.62 6	88.16 216	87.01 226	91.62 187	96.64 85	80.65 225	94.39 190	96.21 141	76.38 375	86.19 241	95.44 142	79.75 138	98.08 182	62.75 422	95.29 149	96.13 201
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 167	88.96 171	91.60 188	93.86 245	82.89 155	95.46 113	97.33 28	87.91 129	88.43 189	93.31 239	74.17 222	97.40 249	87.32 169	82.86 346	94.52 271

FE-MVS		87.40 246	86.02 267	91.57 189	94.56 204	79.69 257	90.27 350	93.72 300	80.57 323	88.80 182	91.62 304	65.32 337	98.59 129	74.97 347	94.33 174	96.44 186

XVG-OURS		89.40 178	88.70 179	91.52 190	94.06 233	81.46 197	91.27 331	96.07 152	86.14 180	88.89 181	95.77 130	68.73 306	97.26 262	87.39 167	89.96 256	95.83 218

hse-mvs2		89.88 162	89.34 160	91.51 191	94.83 181	81.12 210	93.94 227	93.91 293	89.80 58	93.08 83	93.60 232	75.77 195	97.66 215	92.07 93	77.07 405	95.74 222

TranMVSNet+NR-MVSNet		88.84 195	87.95 201	91.49 192	92.68 297	83.01 151	94.92 151	96.31 124	89.88 52	85.53 257	93.85 224	76.63 183	96.96 285	81.91 258	79.87 388	94.50 274

AUN-MVS		87.78 226	86.54 246	91.48 193	94.82 182	81.05 213	93.91 231	93.93 290	83.00 268	86.93 218	93.53 233	69.50 291	97.67 213	86.14 184	77.12 404	95.73 224

XVG-OURS-SEG-HR		89.95 158	89.45 155	91.47 194	94.00 239	81.21 206	91.87 315	96.06 154	85.78 187	88.55 186	95.73 132	74.67 214	97.27 260	88.71 149	89.64 265	95.91 212

MVS		87.44 244	86.10 264	91.44 195	92.61 298	83.62 124	92.63 289	95.66 189	67.26 432	81.47 348	92.15 279	77.95 166	98.22 165	79.71 294	95.48 142	92.47 363

F-COLMAP		87.95 221	86.80 232	91.40 196	96.35 99	80.88 220	94.73 166	95.45 206	79.65 335	82.04 343	94.61 186	71.13 262	98.50 133	76.24 334	91.05 239	94.80 260

dcpmvs_2		93.49 65	94.19 47	91.38 197	97.69 59	76.78 329	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		87.33 249	85.99 268	91.37 198	93.49 265	79.55 258	90.63 345	89.56 408	80.17 327	87.56 209	90.86 328	67.07 318	98.28 161	81.50 267	93.02 206	96.29 192

HQP-MVS		89.80 164	89.28 163	91.34 199	94.17 228	81.56 191	94.39 190	96.04 155	88.81 96	85.43 266	93.97 216	73.83 230	97.96 194	87.11 173	89.77 263	94.50 274

fmvsm_s_conf0.5_n_7		93.15 84	93.76 63	91.31 200	94.42 215	79.48 260	94.52 178	97.14 48	89.33 76	94.17 59	98.09 16	81.83 119	97.49 231	96.33 24	98.02 76	96.95 159

RRT-MVS		90.85 126	90.70 125	91.30 201	94.25 224	76.83 328	94.85 157	96.13 146	89.04 88	90.23 154	94.88 170	70.15 281	98.72 113	91.86 105	94.88 157	98.34 44

FMVSNet3		87.40 246	86.11 263	91.30 201	93.79 251	83.64 123	94.20 204	94.81 254	83.89 243	84.37 296	91.87 295	68.45 309	96.56 310	78.23 312	85.36 316	93.70 319

FMVSNet2		87.19 259	85.82 276	91.30 201	94.01 236	83.67 121	94.79 161	94.94 240	83.57 251	83.88 311	92.05 288	66.59 326	96.51 314	77.56 319	85.01 319	93.73 317

RPMNet		83.95 336	81.53 347	91.21 204	90.58 375	79.34 266	85.24 423	96.76 87	71.44 420	85.55 255	82.97 432	70.87 267	98.91 90	61.01 426	89.36 269	95.40 233

IB-MVS	80.51 15	85.24 313	83.26 331	91.19 205	92.13 310	79.86 252	91.75 318	91.29 367	83.28 262	80.66 360	88.49 385	61.28 369	98.46 139	80.99 276	79.46 392	95.25 239
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		89.47 173	88.90 175	91.18 206	94.22 226	82.07 180	92.13 308	96.09 150	87.90 130	85.37 272	92.45 269	74.38 217	97.56 225	87.15 171	90.43 247	93.93 298
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		89.45 174	88.90 175	91.12 207	94.47 209	81.49 195	95.30 121	96.14 143	86.73 164	85.45 263	95.16 159	69.89 284	98.10 172	87.70 161	89.23 272	93.77 313

LGP-MVS_train				91.12 207	94.47 209	81.49 195		96.14 143	86.73 164	85.45 263	95.16 159	69.89 284	98.10 172	87.70 161	89.23 272	93.77 313

ACMM	84.12 9	89.14 184	88.48 188	91.12 207	94.65 196	81.22 205	95.31 119	96.12 147	85.31 206	85.92 246	94.34 197	70.19 280	98.06 184	85.65 192	88.86 277	94.08 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 202	87.78 207	91.11 210	94.96 171	77.81 307	95.35 117	89.69 403	85.09 216	88.05 198	94.59 189	66.93 319	98.48 135	83.27 230	92.13 226	97.03 152

GBi-Net		87.26 251	85.98 269	91.08 211	94.01 236	83.10 143	95.14 139	94.94 240	83.57 251	84.37 296	91.64 300	66.59 326	96.34 327	78.23 312	85.36 316	93.79 308

test1		87.26 251	85.98 269	91.08 211	94.01 236	83.10 143	95.14 139	94.94 240	83.57 251	84.37 296	91.64 300	66.59 326	96.34 327	78.23 312	85.36 316	93.79 308

FMVSNet1		85.85 298	84.11 318	91.08 211	92.81 292	83.10 143	95.14 139	94.94 240	81.64 305	82.68 333	91.64 300	59.01 391	96.34 327	75.37 341	83.78 330	93.79 308

Test_1112_low_res		87.65 230	86.51 247	91.08 211	94.94 173	79.28 270	91.77 317	94.30 275	76.04 380	83.51 322	92.37 271	77.86 169	97.73 212	78.69 307	89.13 274	96.22 195

PS-MVSNAJss		89.97 156	89.62 151	91.02 215	91.90 319	80.85 221	95.26 127	95.98 159	86.26 176	86.21 240	94.29 201	79.70 140	97.65 216	88.87 148	88.10 288	94.57 268

BH-RMVSNet		88.37 210	87.48 213	91.02 215	95.28 151	79.45 262	92.89 282	93.07 314	85.45 201	86.91 220	94.84 175	70.35 277	97.76 207	73.97 355	94.59 166	95.85 216

UniMVSNet_ETH3D		87.53 240	86.37 251	91.00 217	92.44 302	78.96 275	94.74 165	95.61 193	84.07 239	85.36 273	94.52 191	59.78 383	97.34 254	82.93 234	87.88 293	96.71 176

FIs		90.51 141	90.35 128	90.99 218	93.99 240	80.98 215	95.73 96	97.54 6	89.15 84	86.72 227	94.68 180	81.83 119	97.24 264	85.18 198	88.31 287	94.76 261

ACMP	84.23 8	89.01 193	88.35 189	90.99 218	94.73 188	81.27 202	95.07 142	95.89 170	86.48 169	83.67 317	94.30 200	69.33 293	97.99 189	87.10 175	88.55 279	93.72 318
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 281	85.13 296	90.98 220	96.52 93	81.50 193	96.14 59	96.16 142	73.78 402	83.65 318	92.15 279	63.26 353	97.37 253	82.82 238	81.74 360	94.06 294

icg_test_0403		89.97 156	89.64 150	90.96 221	93.72 253	77.75 312	93.00 276	95.34 217	85.53 197	88.77 183	94.49 192	78.49 159	97.84 203	84.75 205	92.65 214	97.28 128

sss		88.93 194	88.26 195	90.94 222	94.05 234	80.78 223	91.71 319	95.38 212	81.55 309	88.63 185	93.91 221	75.04 207	95.47 368	82.47 243	91.61 229	96.57 183

icg_test_0407		89.85 163	89.51 154	90.88 223	93.72 253	77.75 312	93.07 273	95.34 217	85.53 197	88.34 191	94.49 192	77.69 171	97.60 221	84.75 205	92.65 214	97.28 128

viewmambaseed2359dif		90.04 153	89.78 147	90.83 224	92.85 291	77.92 301	92.23 304	95.01 234	81.90 294	90.20 155	95.45 141	79.64 145	97.34 254	87.52 165	93.17 200	97.23 137

sd_testset		88.59 204	87.85 206	90.83 224	96.00 116	80.42 233	92.35 298	94.71 259	88.73 100	86.85 224	95.20 157	67.31 313	96.43 321	79.64 296	89.85 260	95.63 227

PVSNet_BlendedMVS		89.98 155	89.70 148	90.82 226	96.12 106	81.25 203	93.92 229	96.83 78	83.49 255	89.10 174	92.26 276	81.04 126	98.85 97	86.72 178	87.86 294	92.35 369

cascas		86.43 289	84.98 299	90.80 227	92.10 312	80.92 219	90.24 354	95.91 167	73.10 409	83.57 321	88.39 386	65.15 339	97.46 235	84.90 203	91.43 231	94.03 296

ECVR-MVS		89.09 187	88.53 183	90.77 228	95.62 138	75.89 342	96.16 55	84.22 434	87.89 132	90.20 155	96.65 85	63.19 354	98.10 172	85.90 189	96.94 106	98.33 46

GA-MVS		86.61 279	85.27 293	90.66 229	91.33 342	78.71 279	90.40 349	93.81 297	85.34 205	85.12 276	89.57 367	61.25 370	97.11 274	80.99 276	89.59 266	96.15 199

thres600view7		87.65 230	86.67 238	90.59 230	96.08 112	78.72 277	94.88 153	91.58 358	87.06 154	88.08 196	92.30 274	68.91 303	98.10 172	70.05 385	91.10 234	94.96 250

thres400		87.62 235	86.64 239	90.57 231	95.99 119	78.64 280	94.58 174	91.98 347	86.94 158	88.09 194	91.77 296	69.18 299	98.10 172	70.13 382	91.10 234	94.96 250

baseline1		88.10 217	87.28 219	90.57 231	94.96 171	80.07 242	94.27 199	91.29 367	86.74 163	87.41 211	94.00 214	76.77 180	96.20 332	80.77 279	79.31 394	95.44 231

FC-MVSNet-test		90.27 145	90.18 133	90.53 233	93.71 257	79.85 253	95.77 92	97.59 4	89.31 77	86.27 238	94.67 183	81.93 118	97.01 282	84.26 215	88.09 290	94.71 262

PAPM		86.68 278	85.39 288	90.53 233	93.05 282	79.33 269	89.79 366	94.77 257	78.82 348	81.95 344	93.24 243	76.81 178	97.30 256	66.94 402	93.16 201	94.95 254

WR-MVS		88.38 209	87.67 209	90.52 235	93.30 271	80.18 237	93.26 263	95.96 162	88.57 108	85.47 262	92.81 258	76.12 188	96.91 289	81.24 271	82.29 351	94.47 279

mamba_test_0407_2		88.57 206	87.92 203	90.51 236	94.76 184	82.66 164	79.84 445	94.64 263	85.18 207	88.96 178	95.00 164	76.00 191	92.03 416	83.74 224	93.15 202	96.85 168

MVSTER		88.84 195	88.29 193	90.51 236	92.95 288	80.44 232	93.73 239	95.01 234	84.66 230	87.15 215	93.12 248	72.79 245	97.21 267	87.86 159	87.36 302	93.87 303

testdata				90.49 238	96.40 96	77.89 304		95.37 214	72.51 414	93.63 72	96.69 81	82.08 114	97.65 216	83.08 231	97.39 96	95.94 211

test1111		89.10 185	88.64 180	90.48 239	95.53 143	74.97 352	96.08 64	84.89 432	88.13 123	90.16 158	96.65 85	63.29 352	98.10 172	86.14 184	96.90 108	98.39 41

tt0805		86.92 267	85.74 282	90.48 239	92.22 306	79.98 249	95.63 106	94.88 248	83.83 245	84.74 285	92.80 259	57.61 397	97.67 213	85.48 195	84.42 323	93.79 308

jajsoiax		88.24 214	87.50 212	90.48 239	90.89 363	80.14 239	95.31 119	95.65 191	84.97 219	84.24 304	94.02 212	65.31 338	97.42 241	88.56 150	88.52 281	93.89 299

PatchMatch-RL		86.77 275	85.54 284	90.47 242	95.88 124	82.71 162	90.54 347	92.31 335	79.82 333	84.32 301	91.57 308	68.77 305	96.39 323	73.16 361	93.48 192	92.32 370

tfpn200view9		87.58 238	86.64 239	90.41 243	95.99 119	78.64 280	94.58 174	91.98 347	86.94 158	88.09 194	91.77 296	69.18 299	98.10 172	70.13 382	91.10 234	94.48 277

VPNet		88.20 215	87.47 214	90.39 244	93.56 264	79.46 261	94.04 218	95.54 198	88.67 103	86.96 217	94.58 190	69.33 293	97.15 269	84.05 218	80.53 380	94.56 269

ACMH	80.38 17	85.36 308	83.68 325	90.39 244	94.45 212	80.63 226	94.73 166	94.85 250	82.09 286	77.24 395	92.65 263	60.01 381	97.58 223	72.25 366	84.87 320	92.96 348
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 233	86.71 235	90.38 246	96.12 106	78.55 283	95.03 145	91.58 358	87.15 151	88.06 197	92.29 275	68.91 303	98.10 172	70.13 382	91.10 234	94.48 277

mvs_tets		88.06 220	87.28 219	90.38 246	90.94 359	79.88 251	95.22 130	95.66 189	85.10 215	84.21 305	93.94 217	63.53 350	97.40 249	88.50 151	88.40 285	93.87 303

1314		87.51 241	86.57 244	90.34 248	92.42 303	79.74 256	92.63 289	95.35 216	78.35 357	80.14 367	91.62 304	74.05 224	97.15 269	81.05 272	93.53 188	94.12 289

LTVRE_ROB	82.13 13	86.26 292	84.90 302	90.34 248	94.44 213	81.50 193	92.31 302	94.89 246	83.03 267	79.63 377	92.67 262	69.69 287	97.79 205	71.20 371	86.26 311	91.72 380
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		89.03 191	88.64 180	90.21 250	90.74 370	79.28 270	95.96 77	95.90 168	84.66 230	85.33 274	92.94 253	74.02 225	97.30 256	89.64 137	88.53 280	94.05 295

v2v482		87.84 223	87.06 223	90.17 251	90.99 355	79.23 273	94.00 223	95.13 227	84.87 222	85.53 257	92.07 287	74.45 216	97.45 236	84.71 210	81.75 359	93.85 306

pmmvs4		85.43 306	83.86 323	90.16 252	90.02 388	82.97 153	90.27 350	92.67 326	75.93 381	80.73 358	91.74 298	71.05 263	95.73 357	78.85 306	83.46 337	91.78 379

V42		87.68 228	86.86 228	90.15 253	90.58 375	80.14 239	94.24 202	95.28 221	83.66 249	85.67 252	91.33 310	74.73 212	97.41 247	84.43 214	81.83 357	92.89 351

MSDG		84.86 321	83.09 334	90.14 254	93.80 249	80.05 244	89.18 379	93.09 313	78.89 345	78.19 387	91.91 293	65.86 336	97.27 260	68.47 391	88.45 283	93.11 343

sc_t1		81.53 360	78.67 381	90.12 255	90.78 367	78.64 280	93.91 231	90.20 390	68.42 429	80.82 357	89.88 360	46.48 434	96.76 294	76.03 337	71.47 419	94.96 250

anonymousdsp		87.84 223	87.09 222	90.12 255	89.13 399	80.54 230	94.67 170	95.55 196	82.05 287	83.82 312	92.12 281	71.47 260	97.15 269	87.15 171	87.80 297	92.67 357

thres200		87.21 257	86.24 258	90.12 255	95.36 147	78.53 284	93.26 263	92.10 341	86.42 172	88.00 199	91.11 321	69.24 298	98.00 188	69.58 386	91.04 240	93.83 307

CR-MVSNet		85.35 309	83.76 324	90.12 255	90.58 375	79.34 266	85.24 423	91.96 349	78.27 359	85.55 255	87.87 396	71.03 264	95.61 360	73.96 356	89.36 269	95.40 233

v1144		87.61 236	86.79 233	90.06 259	91.01 354	79.34 266	93.95 226	95.42 211	83.36 260	85.66 253	91.31 313	74.98 208	97.42 241	83.37 228	82.06 353	93.42 329

XXY-MVS		87.65 230	86.85 229	90.03 260	92.14 309	80.60 228	93.76 237	95.23 223	82.94 270	84.60 287	94.02 212	74.27 218	95.49 367	81.04 273	83.68 333	94.01 297

Vis-MVSNet (Re-imp)		89.59 169	89.44 156	90.03 260	95.74 129	75.85 343	95.61 107	90.80 381	87.66 142	87.83 203	95.40 145	76.79 179	96.46 319	78.37 308	96.73 114	97.80 101

test2506		87.21 257	86.28 256	90.02 262	95.62 138	73.64 368	96.25 50	71.38 457	87.89 132	90.45 150	96.65 85	55.29 408	98.09 180	86.03 188	96.94 106	98.33 46

BH-untuned		88.60 203	88.13 197	90.01 263	95.24 155	78.50 286	93.29 261	94.15 283	84.75 227	84.46 293	93.40 235	75.76 197	97.40 249	77.59 318	94.52 169	94.12 289

v1192		87.25 253	86.33 253	90.00 264	90.76 369	79.04 274	93.80 235	95.48 201	82.57 277	85.48 261	91.18 317	73.38 239	97.42 241	82.30 247	82.06 353	93.53 323

v7n		86.81 270	85.76 280	89.95 265	90.72 371	79.25 272	95.07 142	95.92 165	84.45 233	82.29 337	90.86 328	72.60 249	97.53 227	79.42 301	80.52 381	93.08 345

testing91		87.11 262	86.18 259	89.92 266	94.43 214	75.38 351	91.53 324	92.27 337	86.48 169	86.50 229	90.24 346	61.19 373	97.53 227	82.10 252	90.88 242	96.84 171

ICG_test_0404		87.60 237	86.84 230	89.89 267	93.72 253	77.75 312	88.56 388	95.34 217	85.53 197	79.98 371	94.49 192	66.54 329	94.64 381	84.75 205	92.65 214	97.28 128

v8		87.50 243	86.71 235	89.89 267	91.37 339	79.40 263	94.50 179	95.38 212	84.81 225	83.60 320	91.33 310	76.05 189	97.42 241	82.84 237	80.51 382	92.84 353

v10		87.25 253	86.38 250	89.85 269	91.19 345	79.50 259	94.48 180	95.45 206	83.79 247	83.62 319	91.19 315	75.13 205	97.42 241	81.94 257	80.60 377	92.63 359

baseline2		86.50 285	85.39 288	89.84 270	91.12 350	76.70 331	91.88 314	88.58 412	82.35 282	79.95 372	90.95 326	73.42 237	97.63 219	80.27 289	89.95 257	95.19 240

pm-mvs1		86.61 279	85.54 284	89.82 271	91.44 334	80.18 237	95.28 125	94.85 250	83.84 244	81.66 346	92.62 264	72.45 252	96.48 316	79.67 295	78.06 397	92.82 354

TR-MVS		86.78 272	85.76 280	89.82 271	94.37 217	78.41 288	92.47 293	92.83 320	81.11 319	86.36 235	92.40 270	68.73 306	97.48 232	73.75 359	89.85 260	93.57 322

ACMH+	81.04 14	85.05 316	83.46 328	89.82 271	94.66 195	79.37 264	94.44 185	94.12 286	82.19 285	78.04 389	92.82 257	58.23 394	97.54 226	73.77 358	82.90 345	92.54 360

EI-MVSNet		89.10 185	88.86 177	89.80 274	91.84 321	78.30 292	93.70 242	95.01 234	85.73 189	87.15 215	95.28 150	79.87 137	97.21 267	83.81 222	87.36 302	93.88 302

v144192		87.19 259	86.35 252	89.74 275	90.64 373	78.24 294	93.92 229	95.43 209	81.93 292	85.51 259	91.05 324	74.21 221	97.45 236	82.86 236	81.56 361	93.53 323

COLMAP_ROB	80.39 16	83.96 335	82.04 344	89.74 275	95.28 151	79.75 255	94.25 200	92.28 336	75.17 388	78.02 390	93.77 227	58.60 393	97.84 203	65.06 413	85.92 312	91.63 382
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 291	85.18 295	89.73 277	92.15 308	76.60 332	91.12 335	91.69 354	83.53 254	85.50 260	88.81 379	66.79 322	96.48 316	76.65 327	90.35 249	96.12 202

IterMVS-LS		88.36 211	87.91 205	89.70 278	93.80 249	78.29 293	93.73 239	95.08 232	85.73 189	84.75 284	91.90 294	79.88 136	96.92 288	83.83 221	82.51 347	93.89 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 288	85.35 291	89.69 279	94.29 223	75.40 350	91.30 329	90.53 385	84.76 226	85.06 278	90.13 352	58.95 392	97.45 236	82.08 253	91.09 238	96.21 197

testing99		86.72 276	85.73 283	89.69 279	94.23 225	74.91 354	91.35 328	90.97 375	86.14 180	86.36 235	90.22 347	59.41 386	97.48 232	82.24 249	90.66 244	96.69 178

v1921920		86.97 266	86.06 266	89.69 279	90.53 378	78.11 297	93.80 235	95.43 209	81.90 294	85.33 274	91.05 324	72.66 246	97.41 247	82.05 255	81.80 358	93.53 323

icg_test_0407_2		89.15 183	88.97 170	89.68 282	93.72 253	77.75 312	88.26 393	95.34 217	85.53 197	88.34 191	94.49 192	77.69 171	93.99 392	84.75 205	92.65 214	97.28 128

VortexMVS		88.42 207	88.01 199	89.63 283	93.89 244	78.82 276	93.82 234	95.47 202	86.67 166	84.53 291	91.99 290	72.62 248	96.65 300	89.02 145	84.09 327	93.41 330

Fast-Effi-MVS+-dtu		87.44 244	86.72 234	89.63 283	92.04 313	77.68 317	94.03 219	93.94 289	85.81 186	82.42 336	91.32 312	70.33 278	97.06 278	80.33 288	90.23 251	94.14 288

v1240		86.78 272	85.85 275	89.56 285	90.45 380	77.79 309	93.61 244	95.37 214	81.65 304	85.43 266	91.15 319	71.50 259	97.43 240	81.47 268	82.05 355	93.47 327

Effi-MVS+-dtu		88.65 201	88.35 189	89.54 286	93.33 270	76.39 336	94.47 183	94.36 273	87.70 139	85.43 266	89.56 368	73.45 235	97.26 262	85.57 194	91.28 233	94.97 247

AllTest		83.42 342	81.39 348	89.52 287	95.01 165	77.79 309	93.12 267	90.89 379	77.41 366	76.12 404	93.34 236	54.08 414	97.51 229	68.31 393	84.27 325	93.26 333

TestCases				89.52 287	95.01 165	77.79 309		90.89 379	77.41 366	76.12 404	93.34 236	54.08 414	97.51 229	68.31 393	84.27 325	93.26 333

mvs_anonymous		89.37 180	89.32 161	89.51 289	93.47 266	74.22 361	91.65 322	94.83 252	82.91 271	85.45 263	93.79 225	81.23 125	96.36 326	86.47 180	94.09 177	97.94 88

XVG-ACMP-BASELINE		86.00 294	84.84 304	89.45 290	91.20 344	78.00 299	91.70 320	95.55 196	85.05 217	82.97 330	92.25 277	54.49 412	97.48 232	82.93 234	87.45 301	92.89 351

testing222		84.84 322	83.32 329	89.43 291	94.15 231	75.94 341	91.09 336	89.41 410	84.90 220	85.78 249	89.44 369	52.70 419	96.28 330	70.80 377	91.57 230	96.07 206

MVP-Stereo		85.97 295	84.86 303	89.32 292	90.92 361	82.19 178	92.11 309	94.19 280	78.76 350	78.77 386	91.63 303	68.38 310	96.56 310	75.01 346	93.95 179	89.20 420
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 298	84.70 306	89.29 293	91.76 325	75.54 347	88.49 389	91.30 366	81.63 306	85.05 279	88.70 383	71.71 256	96.24 331	74.61 351	89.05 275	96.08 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 264	86.32 254	89.21 294	90.94 359	77.26 322	93.71 241	94.43 268	84.84 224	84.36 299	90.80 332	76.04 190	97.05 280	82.12 251	79.60 391	93.31 332

tfpnnormal		84.72 324	83.23 332	89.20 295	92.79 293	80.05 244	94.48 180	95.81 175	82.38 280	81.08 354	91.21 314	69.01 302	96.95 286	61.69 424	80.59 378	90.58 407

cl22		86.78 272	85.98 269	89.18 296	92.34 304	77.62 318	90.84 341	94.13 285	81.33 313	83.97 310	90.15 351	73.96 226	96.60 307	84.19 216	82.94 342	93.33 331

BH-w/o		87.57 239	87.05 224	89.12 297	94.90 177	77.90 303	92.41 294	93.51 304	82.89 272	83.70 316	91.34 309	75.75 198	97.07 277	75.49 339	93.49 190	92.39 367

WR-MVS_H		87.80 225	87.37 216	89.10 298	93.23 272	78.12 296	95.61 107	97.30 32	87.90 130	83.72 315	92.01 289	79.65 144	96.01 341	76.36 331	80.54 379	93.16 341

miper_enhance_ethall		86.90 268	86.18 259	89.06 299	91.66 330	77.58 319	90.22 356	94.82 253	79.16 341	84.48 292	89.10 373	79.19 149	96.66 299	84.06 217	82.94 342	92.94 349

c3_l		87.14 261	86.50 248	89.04 300	92.20 307	77.26 322	91.22 334	94.70 260	82.01 290	84.34 300	90.43 343	78.81 152	96.61 305	83.70 226	81.09 368	93.25 335

miper_ehance_all_eth		87.22 256	86.62 242	89.02 301	92.13 310	77.40 321	90.91 340	94.81 254	81.28 314	84.32 301	90.08 354	79.26 147	96.62 302	83.81 222	82.94 342	93.04 346

gg-mvs-nofinetune		81.77 354	79.37 369	88.99 302	90.85 365	77.73 316	86.29 415	79.63 445	74.88 393	83.19 329	69.05 448	60.34 378	96.11 336	75.46 340	94.64 165	93.11 343

ETVMVS		84.43 329	82.92 338	88.97 303	94.37 217	74.67 355	91.23 333	88.35 414	83.37 259	86.06 244	89.04 374	55.38 406	95.67 359	67.12 400	91.34 232	96.58 182

pmmvs6		83.42 342	81.60 346	88.87 304	88.01 414	77.87 305	94.96 148	94.24 279	74.67 394	78.80 385	91.09 322	60.17 380	96.49 315	77.06 326	75.40 411	92.23 372

test_cas_vis1_n_1920		88.83 198	88.85 178	88.78 305	91.15 349	76.72 330	93.85 233	94.93 244	83.23 264	92.81 92	96.00 114	61.17 374	94.45 382	91.67 108	94.84 158	95.17 241

MIMVSNet		82.59 348	80.53 353	88.76 306	91.51 332	78.32 291	86.57 414	90.13 393	79.32 337	80.70 359	88.69 384	52.98 418	93.07 408	66.03 408	88.86 277	94.90 255

cl____		86.52 284	85.78 277	88.75 307	92.03 314	76.46 334	90.74 342	94.30 275	81.83 300	83.34 326	90.78 333	75.74 200	96.57 308	81.74 263	81.54 362	93.22 337

DIV-MVS_self_test		86.53 283	85.78 277	88.75 307	92.02 315	76.45 335	90.74 342	94.30 275	81.83 300	83.34 326	90.82 331	75.75 198	96.57 308	81.73 264	81.52 363	93.24 336

CP-MVSNet		87.63 233	87.26 221	88.74 309	93.12 277	76.59 333	95.29 123	96.58 104	88.43 111	83.49 323	92.98 252	75.28 204	95.83 350	78.97 304	81.15 367	93.79 308

eth_miper_zixun_eth		86.50 285	85.77 279	88.68 310	91.94 316	75.81 344	90.47 348	94.89 246	82.05 287	84.05 307	90.46 342	75.96 193	96.77 293	82.76 240	79.36 393	93.46 328

CHOSEN 280x420		85.15 314	83.99 321	88.65 311	92.47 300	78.40 289	79.68 447	92.76 323	74.90 392	81.41 350	89.59 366	69.85 286	95.51 364	79.92 293	95.29 149	92.03 375

PS-CasMVS		87.32 250	86.88 227	88.63 312	92.99 286	76.33 338	95.33 118	96.61 102	88.22 119	83.30 328	93.07 250	73.03 243	95.79 354	78.36 309	81.00 373	93.75 315

TransMVSNet (Re)		84.43 329	83.06 336	88.54 313	91.72 326	78.44 287	95.18 136	92.82 322	82.73 275	79.67 376	92.12 281	73.49 234	95.96 343	71.10 375	68.73 429	91.21 394

tt0320-xc		79.63 383	76.66 392	88.52 314	91.03 353	78.72 277	93.00 276	89.53 409	66.37 433	76.11 406	87.11 407	46.36 436	95.32 372	72.78 363	67.67 430	91.51 386

EG-PatchMatch MVS		82.37 350	80.34 356	88.46 315	90.27 382	79.35 265	92.80 286	94.33 274	77.14 370	73.26 422	90.18 350	47.47 431	96.72 295	70.25 379	87.32 304	89.30 417

PEN-MVS		86.80 271	86.27 257	88.40 316	92.32 305	75.71 346	95.18 136	96.38 119	87.97 127	82.82 332	93.15 246	73.39 238	95.92 345	76.15 335	79.03 396	93.59 321

Baseline_NR-MVSNet		87.07 263	86.63 241	88.40 316	91.44 334	77.87 305	94.23 203	92.57 328	84.12 238	85.74 251	92.08 285	77.25 175	96.04 337	82.29 248	79.94 386	91.30 392

UBG		85.51 304	84.57 311	88.35 318	94.21 227	71.78 392	90.07 361	89.66 405	82.28 283	85.91 247	89.01 375	61.30 368	97.06 278	76.58 330	92.06 227	96.22 195

D2MVS		85.90 296	85.09 297	88.35 318	90.79 366	77.42 320	91.83 316	95.70 185	80.77 322	80.08 369	90.02 356	66.74 324	96.37 324	81.88 259	87.97 292	91.26 393

pmmvs5		84.21 331	82.84 341	88.34 320	88.95 401	76.94 326	92.41 294	91.91 351	75.63 383	80.28 364	91.18 317	64.59 344	95.57 361	77.09 325	83.47 336	92.53 361

mamv4		90.92 124	91.78 103	88.33 321	95.67 134	70.75 405	92.92 281	96.02 158	81.90 294	88.11 193	95.34 148	85.88 52	96.97 284	95.22 38	95.01 154	97.26 132

tt0320		80.13 376	77.41 385	88.29 322	90.50 379	78.02 298	93.10 270	90.71 383	66.06 436	76.75 399	86.97 408	49.56 426	95.40 369	71.65 367	71.41 420	91.46 389

LCM-MVSNet-Re		88.30 213	88.32 192	88.27 323	94.71 192	72.41 387	93.15 266	90.98 374	87.77 137	79.25 380	91.96 291	78.35 161	95.75 355	83.04 232	95.62 138	96.65 179

CostFormer		85.77 301	84.94 301	88.26 324	91.16 348	72.58 385	89.47 374	91.04 373	76.26 378	86.45 233	89.97 358	70.74 269	96.86 292	82.35 246	87.07 307	95.34 237

ITE_SJBPF				88.24 325	91.88 320	77.05 325		92.92 317	85.54 195	80.13 368	93.30 240	57.29 398	96.20 332	72.46 365	84.71 321	91.49 387

PVSNet	78.82 18	85.55 303	84.65 307	88.23 326	94.72 190	71.93 388	87.12 410	92.75 324	78.80 349	84.95 281	90.53 340	64.43 345	96.71 297	74.74 349	93.86 181	96.06 208

IterMVS-SCA-FT		85.45 305	84.53 312	88.18 327	91.71 327	76.87 327	90.19 358	92.65 327	85.40 204	81.44 349	90.54 339	66.79 322	95.00 378	81.04 273	81.05 369	92.66 358

EPNet_dtu		86.49 287	85.94 272	88.14 328	90.24 383	72.82 377	94.11 209	92.20 339	86.66 167	79.42 379	92.36 272	73.52 233	95.81 352	71.26 370	93.66 184	95.80 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 346	80.93 352	88.06 329	90.05 387	76.37 337	84.74 428	91.96 349	72.28 417	81.32 352	87.87 396	71.03 264	95.50 366	68.97 388	80.15 384	92.32 370

test_vis1_n_1920		89.39 179	89.84 144	88.04 330	92.97 287	72.64 382	94.71 168	96.03 157	86.18 178	91.94 121	96.56 93	61.63 363	95.74 356	93.42 59	95.11 153	95.74 222

DTE-MVSNet		86.11 293	85.48 286	87.98 331	91.65 331	74.92 353	94.93 150	95.75 180	87.36 147	82.26 338	93.04 251	72.85 244	95.82 351	74.04 354	77.46 402	93.20 339

PMMVS		85.71 302	84.96 300	87.95 332	88.90 402	77.09 324	88.68 386	90.06 395	72.32 416	86.47 230	90.76 334	72.15 254	94.40 384	81.78 262	93.49 190	92.36 368

GG-mvs-BLEND				87.94 333	89.73 394	77.91 302	87.80 399	78.23 450		80.58 361	83.86 425	59.88 382	95.33 371	71.20 371	92.22 225	90.60 406

MonoMVSNet		86.89 269	86.55 245	87.92 334	89.46 397	73.75 365	94.12 207	93.10 312	87.82 136	85.10 277	90.76 334	69.59 289	94.94 379	86.47 180	82.50 348	95.07 244

reproduce_monomvs		86.37 290	85.87 274	87.87 335	93.66 261	73.71 366	93.44 251	95.02 233	88.61 106	82.64 335	91.94 292	57.88 396	96.68 298	89.96 133	79.71 390	93.22 337

pmmvs-eth3d		80.97 369	78.72 380	87.74 336	84.99 432	79.97 250	90.11 360	91.65 356	75.36 385	73.51 420	86.03 415	59.45 385	93.96 395	75.17 343	72.21 416	89.29 419

MS-PatchMatch		85.05 316	84.16 316	87.73 337	91.42 337	78.51 285	91.25 332	93.53 303	77.50 365	80.15 366	91.58 306	61.99 360	95.51 364	75.69 338	94.35 173	89.16 421

mmtdpeth		85.04 318	84.15 317	87.72 338	93.11 278	75.74 345	94.37 194	92.83 320	84.98 218	89.31 171	86.41 412	61.61 365	97.14 272	92.63 75	62.11 440	90.29 408

test_0402		81.30 365	79.17 374	87.67 339	93.19 273	78.17 295	92.98 278	91.71 352	75.25 387	76.02 407	90.31 345	59.23 387	96.37 324	50.22 443	83.63 334	88.47 428

IterMVS		84.88 320	83.98 322	87.60 340	91.44 334	76.03 340	90.18 359	92.41 330	83.24 263	81.06 355	90.42 344	66.60 325	94.28 388	79.46 297	80.98 374	92.48 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 363	79.30 370	87.58 341	90.92 361	74.16 363	80.99 440	87.68 419	70.52 424	76.63 401	88.81 379	71.21 261	92.76 411	60.01 430	86.93 308	95.83 218

EPMVS		83.90 338	82.70 342	87.51 342	90.23 384	72.67 380	88.62 387	81.96 440	81.37 312	85.01 280	88.34 387	66.31 330	94.45 382	75.30 342	87.12 305	95.43 232

ADS-MVSNet2		81.66 357	79.71 366	87.50 343	91.35 340	74.19 362	83.33 433	88.48 413	72.90 411	82.24 339	85.77 418	64.98 340	93.20 406	64.57 415	83.74 331	95.12 242

OurMVSNet-221017-0		85.35 309	84.64 309	87.49 344	90.77 368	72.59 384	94.01 221	94.40 271	84.72 228	79.62 378	93.17 245	61.91 361	96.72 295	81.99 256	81.16 365	93.16 341

tpm2		84.08 333	82.94 337	87.48 345	91.39 338	71.27 397	89.23 378	90.37 387	71.95 418	84.64 286	89.33 370	67.30 314	96.55 312	75.17 343	87.09 306	94.63 263

RPSCF		85.07 315	84.27 313	87.48 345	92.91 290	70.62 407	91.69 321	92.46 329	76.20 379	82.67 334	95.22 153	63.94 348	97.29 259	77.51 320	85.80 313	94.53 270

myMVS_eth3d28		85.80 300	85.26 294	87.42 347	94.73 188	69.92 412	90.60 346	90.95 376	87.21 150	86.06 244	90.04 355	59.47 384	96.02 339	74.89 348	93.35 197	96.33 189

WBMVS		84.97 319	84.18 315	87.34 348	94.14 232	71.62 396	90.20 357	92.35 332	81.61 307	84.06 306	90.76 334	61.82 362	96.52 313	78.93 305	83.81 329	93.89 299

miper_lstm_enhance		85.27 312	84.59 310	87.31 349	91.28 343	74.63 356	87.69 404	94.09 287	81.20 318	81.36 351	89.85 362	74.97 209	94.30 387	81.03 275	79.84 389	93.01 347

FMVSNet5		81.52 361	79.60 367	87.27 350	91.17 346	77.95 300	91.49 325	92.26 338	76.87 371	76.16 403	87.91 395	51.67 420	92.34 414	67.74 397	81.16 365	91.52 385

USDC		82.76 345	81.26 350	87.26 351	91.17 346	74.55 357	89.27 376	93.39 306	78.26 360	75.30 411	92.08 285	54.43 413	96.63 301	71.64 368	85.79 314	90.61 404

test-LLR		85.87 297	85.41 287	87.25 352	90.95 357	71.67 394	89.55 370	89.88 401	83.41 257	84.54 289	87.95 393	67.25 315	95.11 375	81.82 260	93.37 195	94.97 247

test-mter		84.54 328	83.64 326	87.25 352	90.95 357	71.67 394	89.55 370	89.88 401	79.17 340	84.54 289	87.95 393	55.56 404	95.11 375	81.82 260	93.37 195	94.97 247

JIA-IIPM		81.04 366	78.98 378	87.25 352	88.64 403	73.48 370	81.75 439	89.61 407	73.19 408	82.05 342	73.71 444	66.07 335	95.87 348	71.18 373	84.60 322	92.41 366

TDRefinement		79.81 380	77.34 386	87.22 355	79.24 447	75.48 348	93.12 267	92.03 344	76.45 374	75.01 412	91.58 306	49.19 427	96.44 320	70.22 381	69.18 426	89.75 413

tpmvs		83.35 344	82.07 343	87.20 356	91.07 352	71.00 403	88.31 392	91.70 353	78.91 343	80.49 363	87.18 405	69.30 296	97.08 275	68.12 396	83.56 335	93.51 326

ppachtmachnet_test		81.84 353	80.07 361	87.15 357	88.46 407	74.43 360	89.04 382	92.16 340	75.33 386	77.75 392	88.99 376	66.20 332	95.37 370	65.12 412	77.60 400	91.65 381

dmvs_re		84.20 332	83.22 333	87.14 358	91.83 323	77.81 307	90.04 362	90.19 391	84.70 229	81.49 347	89.17 372	64.37 346	91.13 426	71.58 369	85.65 315	92.46 364

tpm cat1		81.96 351	80.27 357	87.01 359	91.09 351	71.02 402	87.38 408	91.53 361	66.25 434	80.17 365	86.35 414	68.22 311	96.15 335	69.16 387	82.29 351	93.86 305

test_fmvs1_n		87.03 265	87.04 225	86.97 360	89.74 393	71.86 389	94.55 176	94.43 268	78.47 354	91.95 120	95.50 140	51.16 422	93.81 396	93.02 67	94.56 167	95.26 238

OpenMVS_ROB	74.94 19	79.51 384	77.03 391	86.93 361	87.00 420	76.23 339	92.33 300	90.74 382	68.93 428	74.52 416	88.23 390	49.58 425	96.62 302	57.64 435	84.29 324	87.94 431

SixPastTwentyTwo		83.91 337	82.90 339	86.92 362	90.99 355	70.67 406	93.48 248	91.99 346	85.54 195	77.62 394	92.11 283	60.59 377	96.87 291	76.05 336	77.75 399	93.20 339

ADS-MVSNet		81.56 359	79.78 363	86.90 363	91.35 340	71.82 390	83.33 433	89.16 411	72.90 411	82.24 339	85.77 418	64.98 340	93.76 397	64.57 415	83.74 331	95.12 242

PatchT		82.68 347	81.27 349	86.89 364	90.09 386	70.94 404	84.06 430	90.15 392	74.91 391	85.63 254	83.57 427	69.37 292	94.87 380	65.19 410	88.50 282	94.84 257

tpm		84.73 323	84.02 320	86.87 365	90.33 381	68.90 415	89.06 381	89.94 398	80.85 321	85.75 250	89.86 361	68.54 308	95.97 342	77.76 316	84.05 328	95.75 221

Patchmatch-RL test		81.67 356	79.96 362	86.81 366	85.42 430	71.23 398	82.17 438	87.50 420	78.47 354	77.19 396	82.50 434	70.81 268	93.48 401	82.66 241	72.89 415	95.71 225

test_vis1_n		86.56 282	86.49 249	86.78 367	88.51 404	72.69 379	94.68 169	93.78 299	79.55 336	90.70 145	95.31 149	48.75 428	93.28 404	93.15 63	93.99 178	94.38 281

testing3-2		86.72 276	86.71 235	86.74 368	96.11 109	65.92 427	93.39 253	89.65 406	89.46 70	87.84 202	92.79 260	59.17 389	97.60 221	81.31 269	90.72 243	96.70 177

test_fmvs1		87.34 248	87.56 211	86.68 369	90.59 374	71.80 391	94.01 221	94.04 288	78.30 358	91.97 118	95.22 153	56.28 402	93.71 398	92.89 68	94.71 161	94.52 271

MDA-MVSNet-bldmvs		78.85 389	76.31 394	86.46 370	89.76 392	73.88 364	88.79 384	90.42 386	79.16 341	59.18 444	88.33 388	60.20 379	94.04 390	62.00 423	68.96 427	91.48 388

mvs5depth		80.98 368	79.15 375	86.45 371	84.57 433	73.29 372	87.79 400	91.67 355	80.52 324	82.20 341	89.72 364	55.14 409	95.93 344	73.93 357	66.83 432	90.12 410

tpmrst		85.35 309	84.99 298	86.43 372	90.88 364	67.88 420	88.71 385	91.43 364	80.13 328	86.08 243	88.80 381	73.05 242	96.02 339	82.48 242	83.40 339	95.40 233

TESTMET0.1,1		83.74 340	82.85 340	86.42 373	89.96 389	71.21 399	89.55 370	87.88 416	77.41 366	83.37 325	87.31 401	56.71 400	93.65 400	80.62 283	92.85 211	94.40 280

our_test_3		81.93 352	80.46 355	86.33 374	88.46 407	73.48 370	88.46 390	91.11 369	76.46 373	76.69 400	88.25 389	66.89 320	94.36 385	68.75 389	79.08 395	91.14 396

lessismore_v0				86.04 375	88.46 407	68.78 416		80.59 443		73.01 423	90.11 353	55.39 405	96.43 321	75.06 345	65.06 435	92.90 350

TinyColmap		79.76 381	77.69 384	85.97 376	91.71 327	73.12 373	89.55 370	90.36 388	75.03 389	72.03 426	90.19 349	46.22 437	96.19 334	63.11 419	81.03 370	88.59 427

KD-MVS_2432*1600		78.50 390	76.02 397	85.93 377	86.22 423	74.47 358	84.80 426	92.33 333	79.29 338	76.98 397	85.92 416	53.81 416	93.97 393	67.39 398	57.42 445	89.36 415

miper_refine_blended		78.50 390	76.02 397	85.93 377	86.22 423	74.47 358	84.80 426	92.33 333	79.29 338	76.98 397	85.92 416	53.81 416	93.97 393	67.39 398	57.42 445	89.36 415

K. test v3		81.59 358	80.15 360	85.91 379	89.89 391	69.42 414	92.57 291	87.71 418	85.56 194	73.44 421	89.71 365	55.58 403	95.52 363	77.17 323	69.76 423	92.78 355

SSC-MVS3.2		84.60 327	84.19 314	85.85 380	92.74 295	68.07 417	88.15 395	93.81 297	87.42 146	83.76 314	91.07 323	62.91 355	95.73 357	74.56 352	83.24 340	93.75 315

mvsany_test1		85.42 307	85.30 292	85.77 381	87.95 416	75.41 349	87.61 407	80.97 442	76.82 372	88.68 184	95.83 126	77.44 174	90.82 428	85.90 189	86.51 309	91.08 400

MIMVSNet1		79.38 385	77.28 387	85.69 382	86.35 422	73.67 367	91.61 323	92.75 324	78.11 363	72.64 424	88.12 391	48.16 429	91.97 420	60.32 427	77.49 401	91.43 390

UWE-MVS		83.69 341	83.09 334	85.48 383	93.06 281	65.27 432	90.92 339	86.14 424	79.90 331	86.26 239	90.72 337	57.17 399	95.81 352	71.03 376	92.62 219	95.35 236

UnsupCasMVSNet_eth		80.07 377	78.27 383	85.46 384	85.24 431	72.63 383	88.45 391	94.87 249	82.99 269	71.64 428	88.07 392	56.34 401	91.75 421	73.48 360	63.36 438	92.01 376

CL-MVSNet_self_test		81.74 355	80.53 353	85.36 385	85.96 425	72.45 386	90.25 352	93.07 314	81.24 316	79.85 375	87.29 402	70.93 266	92.52 412	66.95 401	69.23 425	91.11 398

MDA-MVSNet_test_wron		79.21 387	77.19 389	85.29 386	88.22 411	72.77 378	85.87 417	90.06 395	74.34 396	62.62 441	87.56 399	66.14 333	91.99 419	66.90 405	73.01 413	91.10 399

YYNet1		79.22 386	77.20 388	85.28 387	88.20 412	72.66 381	85.87 417	90.05 397	74.33 397	62.70 439	87.61 398	66.09 334	92.03 416	66.94 402	72.97 414	91.15 395

WB-MVSnew		83.77 339	83.28 330	85.26 388	91.48 333	71.03 401	91.89 313	87.98 415	78.91 343	84.78 283	90.22 347	69.11 301	94.02 391	64.70 414	90.44 246	90.71 402

dp		81.47 362	80.23 358	85.17 389	89.92 390	65.49 430	86.74 412	90.10 394	76.30 377	81.10 353	87.12 406	62.81 356	95.92 345	68.13 395	79.88 387	94.09 292

UnsupCasMVSNet_bld		76.23 399	73.27 403	85.09 390	83.79 435	72.92 375	85.65 420	93.47 305	71.52 419	68.84 434	79.08 439	49.77 424	93.21 405	66.81 406	60.52 442	89.13 423

SD_0403		84.71 325	84.65 307	84.92 391	92.95 288	65.95 426	92.07 312	93.23 309	83.82 246	79.03 381	93.73 230	73.90 227	92.91 410	63.02 421	90.05 253	95.89 214

Anonymous20231206		81.03 367	79.77 365	84.82 392	87.85 417	70.26 409	91.42 326	92.08 342	73.67 403	77.75 392	89.25 371	62.43 358	93.08 407	61.50 425	82.00 356	91.12 397

test0.0.03 1		82.41 349	81.69 345	84.59 393	88.23 410	72.89 376	90.24 354	87.83 417	83.41 257	79.86 374	89.78 363	67.25 315	88.99 438	65.18 411	83.42 338	91.90 378

CMPMVS	59.16 21	80.52 371	79.20 373	84.48 394	83.98 434	67.63 423	89.95 365	93.84 296	64.79 438	66.81 436	91.14 320	57.93 395	95.17 373	76.25 333	88.10 288	90.65 403
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 326	84.79 305	84.37 395	91.84 321	64.92 433	93.70 242	91.47 363	66.19 435	86.16 242	95.28 150	67.18 317	93.33 403	80.89 278	90.42 248	94.88 256

PVSNet_0	73.20 20	77.22 395	74.83 401	84.37 395	90.70 372	71.10 400	83.09 435	89.67 404	72.81 413	73.93 419	83.13 429	60.79 376	93.70 399	68.54 390	50.84 450	88.30 429

LF4IMVS		80.37 374	79.07 377	84.27 397	86.64 421	69.87 413	89.39 375	91.05 372	76.38 375	74.97 413	90.00 357	47.85 430	94.25 389	74.55 353	80.82 376	88.69 426

Anonymous20240521		80.44 373	79.21 372	84.11 398	85.75 428	67.89 419	92.86 284	93.23 309	75.61 384	75.59 410	87.47 400	50.03 423	94.33 386	71.14 374	81.21 364	90.12 410

PM-MVS		78.11 392	76.12 396	84.09 399	83.54 436	70.08 410	88.97 383	85.27 431	79.93 330	74.73 415	86.43 411	34.70 448	93.48 401	79.43 300	72.06 417	88.72 425

test_fmvs2		83.98 334	84.03 319	83.83 400	87.16 419	67.53 424	93.93 228	92.89 318	77.62 364	86.89 223	93.53 233	47.18 432	92.02 418	90.54 127	86.51 309	91.93 377

testgi		80.94 370	80.20 359	83.18 401	87.96 415	66.29 425	91.28 330	90.70 384	83.70 248	78.12 388	92.84 255	51.37 421	90.82 428	63.34 418	82.46 349	92.43 365

KD-MVS_self_test		80.20 375	79.24 371	83.07 402	85.64 429	65.29 431	91.01 338	93.93 290	78.71 352	76.32 402	86.40 413	59.20 388	92.93 409	72.59 364	69.35 424	91.00 401

testing3		80.46 372	79.59 368	83.06 403	93.44 268	64.64 434	93.33 255	85.47 429	84.34 235	79.93 373	90.84 330	44.35 440	92.39 413	57.06 437	87.56 298	92.16 374

ambc				83.06 403	79.99 445	63.51 438	77.47 448	92.86 319		74.34 418	84.45 424	28.74 449	95.06 377	73.06 362	68.89 428	90.61 404

test20.03		79.95 379	79.08 376	82.55 405	85.79 427	67.74 422	91.09 336	91.08 370	81.23 317	74.48 417	89.96 359	61.63 363	90.15 430	60.08 428	76.38 407	89.76 412

MVStest1		72.91 403	69.70 408	82.54 406	78.14 448	73.05 374	88.21 394	86.21 423	60.69 442	64.70 437	90.53 340	46.44 435	85.70 445	58.78 433	53.62 447	88.87 424

test_vis1_rt		77.96 393	76.46 393	82.48 407	85.89 426	71.74 393	90.25 352	78.89 446	71.03 423	71.30 429	81.35 436	42.49 442	91.05 427	84.55 212	82.37 350	84.65 434

EU-MVSNet		81.32 364	80.95 351	82.42 408	88.50 406	63.67 437	93.32 256	91.33 365	64.02 439	80.57 362	92.83 256	61.21 372	92.27 415	76.34 332	80.38 383	91.32 391

myMVS_eth3d		79.67 382	78.79 379	82.32 409	91.92 317	64.08 435	89.75 368	87.40 421	81.72 302	78.82 383	87.20 403	45.33 438	91.29 424	59.09 432	87.84 295	91.60 383

ttmdpeth		76.55 397	74.64 402	82.29 410	82.25 441	67.81 421	89.76 367	85.69 427	70.35 425	75.76 408	91.69 299	46.88 433	89.77 432	66.16 407	63.23 439	89.30 417

pmmvs3		71.81 406	68.71 409	81.11 411	75.86 450	70.42 408	86.74 412	83.66 435	58.95 445	68.64 435	80.89 437	36.93 446	89.52 434	63.10 420	63.59 437	83.39 435

Syy-MVS		80.07 377	79.78 363	80.94 412	91.92 317	59.93 444	89.75 368	87.40 421	81.72 302	78.82 383	87.20 403	66.29 331	91.29 424	47.06 445	87.84 295	91.60 383

UWE-MVS-28		78.98 388	78.38 382	80.80 413	88.18 413	60.66 443	90.65 344	78.51 447	78.84 347	77.93 391	90.93 327	59.08 390	89.02 437	50.96 442	90.33 250	92.72 356

new-patchmatchnet		76.41 398	75.17 400	80.13 414	82.65 440	59.61 445	87.66 405	91.08 370	78.23 361	69.85 432	83.22 428	54.76 410	91.63 423	64.14 417	64.89 436	89.16 421

mvsany_test3		74.95 400	73.26 404	80.02 415	74.61 451	63.16 439	85.53 421	78.42 448	74.16 398	74.89 414	86.46 410	36.02 447	89.09 436	82.39 245	66.91 431	87.82 432

test_fmvs3		77.67 394	77.16 390	79.22 416	79.52 446	61.14 441	92.34 299	91.64 357	73.98 400	78.86 382	86.59 409	27.38 452	87.03 440	88.12 156	75.97 409	89.50 414

DSMNet-mixed		76.94 396	76.29 395	78.89 417	83.10 438	56.11 453	87.78 401	79.77 444	60.65 443	75.64 409	88.71 382	61.56 366	88.34 439	60.07 429	89.29 271	92.21 373

EGC-MVSNET		61.97 414	56.37 419	78.77 418	89.63 395	73.50 369	89.12 380	82.79 437	0.21 464	1.24 465	84.80 422	39.48 443	90.04 431	44.13 447	75.94 410	72.79 446

new_pmnet		72.15 404	70.13 407	78.20 419	82.95 439	65.68 428	83.91 431	82.40 439	62.94 441	64.47 438	79.82 438	42.85 441	86.26 444	57.41 436	74.44 412	82.65 439

MVS-HIRNet		73.70 402	72.20 405	78.18 420	91.81 324	56.42 452	82.94 436	82.58 438	55.24 446	68.88 433	66.48 449	55.32 407	95.13 374	58.12 434	88.42 284	83.01 437

LCM-MVSNet		66.00 411	62.16 416	77.51 421	64.51 461	58.29 447	83.87 432	90.90 378	48.17 450	54.69 447	73.31 445	16.83 461	86.75 441	65.47 409	61.67 441	87.48 433

APD_test1		69.04 407	66.26 413	77.36 422	80.51 444	62.79 440	85.46 422	83.51 436	54.11 448	59.14 445	84.79 423	23.40 455	89.61 433	55.22 438	70.24 422	79.68 443

test_f		71.95 405	70.87 406	75.21 423	74.21 453	59.37 446	85.07 425	85.82 426	65.25 437	70.42 431	83.13 429	23.62 453	82.93 451	78.32 310	71.94 418	83.33 436

ANet_high		58.88 418	54.22 423	72.86 424	56.50 464	56.67 449	80.75 441	86.00 425	73.09 410	37.39 456	64.63 452	22.17 456	79.49 454	43.51 448	23.96 458	82.43 440

test_vis3_rt		65.12 412	62.60 414	72.69 425	71.44 454	60.71 442	87.17 409	65.55 458	63.80 440	53.22 448	65.65 451	14.54 462	89.44 435	76.65 327	65.38 434	67.91 449

FPMVS		64.63 413	62.55 415	70.88 426	70.80 455	56.71 448	84.42 429	84.42 433	51.78 449	49.57 449	81.61 435	23.49 454	81.48 452	40.61 452	76.25 408	74.46 445

dmvs_testset		74.57 401	75.81 399	70.86 427	87.72 418	40.47 462	87.05 411	77.90 452	82.75 274	71.15 430	85.47 420	67.98 312	84.12 449	45.26 446	76.98 406	88.00 430

N_pmnet		68.89 408	68.44 410	70.23 428	89.07 400	28.79 467	88.06 396	19.50 467	69.47 427	71.86 427	84.93 421	61.24 371	91.75 421	54.70 439	77.15 403	90.15 409

testf1		59.54 416	56.11 420	69.85 429	69.28 456	56.61 450	80.37 442	76.55 455	42.58 453	45.68 452	75.61 440	11.26 463	84.18 447	43.20 449	60.44 443	68.75 447

APD_test2		59.54 416	56.11 420	69.85 429	69.28 456	56.61 450	80.37 442	76.55 455	42.58 453	45.68 452	75.61 440	11.26 463	84.18 447	43.20 449	60.44 443	68.75 447

WB-MVS		67.92 409	67.49 411	69.21 431	81.09 442	41.17 461	88.03 397	78.00 451	73.50 405	62.63 440	83.11 431	63.94 348	86.52 442	25.66 457	51.45 449	79.94 442

PMMVS2		59.60 415	56.40 418	69.21 431	68.83 458	46.58 457	73.02 452	77.48 453	55.07 447	49.21 450	72.95 446	17.43 460	80.04 453	49.32 444	44.33 453	80.99 441

SSC-MVS		67.06 410	66.56 412	68.56 433	80.54 443	40.06 463	87.77 402	77.37 454	72.38 415	61.75 442	82.66 433	63.37 351	86.45 443	24.48 458	48.69 452	79.16 444

Gipuma		57.99 420	54.91 422	67.24 434	88.51 404	65.59 429	52.21 455	90.33 389	43.58 452	42.84 455	51.18 456	20.29 458	85.07 446	34.77 453	70.45 421	51.05 455
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 422	48.46 426	63.48 435	45.72 466	46.20 458	73.41 451	78.31 449	41.03 455	30.06 458	65.68 450	6.05 465	83.43 450	30.04 455	65.86 433	60.80 450
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 419	58.24 417	60.56 436	83.13 437	45.09 460	82.32 437	48.22 466	67.61 431	61.70 443	69.15 447	38.75 444	76.05 455	32.01 454	41.31 454	60.55 451

MVE	39.65 23	43.39 424	38.59 430	57.77 437	56.52 463	48.77 456	55.38 454	58.64 462	29.33 458	28.96 459	52.65 455	4.68 466	64.62 459	28.11 456	33.07 456	59.93 452
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 423	48.47 425	56.66 438	52.26 465	18.98 469	41.51 457	81.40 441	10.10 459	44.59 454	75.01 443	28.51 450	68.16 456	53.54 440	49.31 451	82.83 438

DeepMVS_CX				56.31 439	74.23 452	51.81 455		56.67 463	44.85 451	48.54 451	75.16 442	27.87 451	58.74 461	40.92 451	52.22 448	58.39 453

kuosan		53.51 421	53.30 424	54.13 440	76.06 449	45.36 459	80.11 444	48.36 465	59.63 444	54.84 446	63.43 453	37.41 445	62.07 460	20.73 460	39.10 455	54.96 454

E-PMN		43.23 425	42.29 427	46.03 441	65.58 460	37.41 464	73.51 450	64.62 459	33.99 456	28.47 460	47.87 457	19.90 459	67.91 457	22.23 459	24.45 457	32.77 456

EMVS		42.07 426	41.12 428	44.92 442	63.45 462	35.56 466	73.65 449	63.48 460	33.05 457	26.88 461	45.45 458	21.27 457	67.14 458	19.80 461	23.02 459	32.06 457

tmp_tt		35.64 427	39.24 429	24.84 443	14.87 467	23.90 468	62.71 453	51.51 464	6.58 461	36.66 457	62.08 454	44.37 439	30.34 463	52.40 441	22.00 460	20.27 458

wuyk23d		21.27 429	20.48 432	23.63 444	68.59 459	36.41 465	49.57 456	6.85 468	9.37 460	7.89 462	4.46 464	4.03 467	31.37 462	17.47 462	16.07 461	3.12 459

test123		8.76 431	11.22 434	1.39 445	0.85 469	0.97 470	85.76 419	0.35 470	0.54 463	2.45 464	8.14 463	0.60 468	0.48 464	2.16 464	0.17 463	2.71 460

testmvs		8.92 430	11.52 433	1.12 446	1.06 468	0.46 471	86.02 416	0.65 469	0.62 462	2.74 463	9.52 462	0.31 469	0.45 465	2.38 463	0.39 462	2.46 461

mmdepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

monomultidepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

test_blank		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet_test		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

DCPMVS		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

cdsmvs_eth3d_5k		22.14 428	29.52 431	0.00 447	0.00 470	0.00 472	0.00 458	95.76 179	0.00 465	0.00 466	94.29 201	75.66 201	0.00 466	0.00 465	0.00 464	0.00 462

pcd_1.5k_mvsjas		6.64 433	8.86 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	79.70 140	0.00 466	0.00 465	0.00 464	0.00 462

sosnet-low-res		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uncertanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

Regformer		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

ab-mvs-re		7.82 432	10.43 435	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	93.88 222	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

WAC-MVS						64.08 435								59.14 431

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

PC_three_1452									82.47 278	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					0.00 470

eth-test					0.00 470

ZD-MVS					98.15 36	86.62 33		97.07 55	83.63 250	94.19 58	96.91 72	87.57 31	99.26 46	91.99 98	98.44 53

RE-MVS-def			93.68 67		97.92 45	84.57 89	96.28 46	96.76 87	87.46 143	93.75 69	97.43 45	82.94 96		92.73 70	97.80 86	97.88 94

IU-MVS					98.77 5	86.00 52		96.84 77	81.26 315	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	86.13 182	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																96.12 202

test_part2					98.55 12	87.22 19				96.40 26

sam_mvs1												71.70 257				96.12 202

sam_mvs												70.60 271

MTGPA								96.97 60

test_post1							88.00 398				9.81 461	69.31 295	95.53 362	76.65 327

test_post											10.29 460	70.57 275	95.91 347

patchmatchnet-post											83.76 426	71.53 258	96.48 316

MTMP							96.16 55	60.64 461

gm-plane-assit					89.60 396	68.00 418			77.28 369		88.99 376		97.57 224	79.44 299

test9_res														91.91 102	98.71 32	98.07 77

TEST9					97.53 63	86.49 37	94.07 215	96.78 84	81.61 307	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	81.86 298	92.70 98	96.20 102	87.63 29	99.02 67

agg_prior2														90.54 127	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		87.67 141	92.63 102	96.39 97	86.62 41		91.50 111	98.67 40

旧先验2							93.36 254		71.25 421	94.37 54			97.13 273	86.74 176

新几何2							93.11 269

旧先验1					96.79 81	81.81 187		95.67 187			96.81 78	86.69 39			97.66 92	96.97 158

无先验							93.28 262	96.26 133	73.95 401				99.05 61	80.56 284		96.59 181

原ACMM2							92.94 280

test222					96.55 90	81.70 189	92.22 305	95.01 234	68.36 430	90.20 155	96.14 107	80.26 133			97.80 86	96.05 209

testdata2													98.75 109	78.30 311

segment_acmp												87.16 36

testdata1							92.15 307		87.94 128

plane_prior7					94.70 193	82.74 159

plane_prior6					94.52 206	82.75 157						74.23 219

plane_prior5								96.22 138					98.12 170	88.15 153	89.99 254	94.63 263

plane_prior4											94.86 172

plane_prior3						82.75 157			90.26 45	86.91 220

plane_prior2							95.85 86		90.81 25

plane_prior1					94.59 199

plane_prior						82.73 160	95.21 133		89.66 66						89.88 259

n2								0.00 471

nn								0.00 471

door-mid								85.49 428

test11								96.57 105

door								85.33 430

HQP5-MVS						81.56 191

HQP-NCC					94.17 228		94.39 190		88.81 96	85.43 266

ACMP_Plane					94.17 228		94.39 190		88.81 96	85.43 266

BP-MVS														87.11 173

HQP4-MVS										85.43 266			97.96 194			94.51 273

HQP3-MVS								96.04 155							89.77 263

HQP2-MVS												73.83 230

NP-MVS					94.37 217	82.42 172					93.98 215

MDTV_nov1_ep13_2view						55.91 454	87.62 406		73.32 407	84.59 288		70.33 278		74.65 350		95.50 230

MDTV_nov1_ep13			83.56 327		91.69 329	69.93 411	87.75 403	91.54 360	78.60 353	84.86 282	88.90 378	69.54 290	96.03 338	70.25 379	88.93 276

ACMMP++_ref															87.47 299

ACMMP++															88.01 291

Test By Simon												80.02 135

High-resolution multi-view benchmark