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.

MSP-MVS		95.62 6	96.54 1	92.86 92	98.31 49	80.10 166	97.42 88	96.78 45	92.20 13	97.11 8	98.29 28	93.46 1	99.10 95	96.01 20	99.30 3	99.38 10
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
CNVR-MVS		96.30 1	96.54 1	95.55 12	99.31 5	87.69 19	99.06 9	97.12 22	94.66 3	96.79 9	98.78 11	86.42 24	99.95 3	97.59 9	99.18 5	99.00 23

SED-MVS		95.88 4	96.22 3	94.87 19	99.03 13	85.03 58	99.12 6	96.78 45	88.72 51	97.79 3	98.91 3	88.48 14	99.82 16	98.15 2	98.97 15	99.74 1

DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 183	97.09 97	70.21 308	98.99 14	96.69 62	95.57 1	95.08 28	99.23 1	86.40 25	99.87 8	97.84 7	98.66 30	99.65 4

MCST-MVS		96.17 3	96.12 5	96.32 5	99.42 2	89.36 8	98.94 15	97.10 24	95.17 2	92.11 66	98.46 24	87.33 20	99.97 2	97.21 12	99.31 2	99.63 5

NCCC		95.63 5	95.94 6	94.69 24	99.21 7	85.15 56	99.16 3	96.96 33	94.11 6	95.59 21	98.64 19	85.07 28	99.91 4	95.61 27	99.10 7	99.00 23

DVP-MVS		95.58 7	95.91 7	94.57 26	99.05 10	85.18 51	99.06 9	96.46 95	88.75 49	96.69 10	98.76 12	87.69 18	99.76 20	97.90 5	98.85 20	98.77 30
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
DPE-MVS		95.32 9	95.55 8	94.64 25	98.79 21	84.87 63	97.77 56	96.74 55	86.11 93	96.54 13	98.89 7	88.39 16	99.74 28	97.67 8	99.05 10	99.31 14
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		96.21 2	95.53 9	98.26 1	96.26 105	95.09 1	99.15 4	96.98 30	93.39 9	96.45 14	98.79 10	90.17 7	99.99 1	89.33 108	99.25 4	99.70 3

HPM-MVS++		95.32 9	95.48 10	94.85 20	98.62 34	86.04 32	97.81 54	96.93 36	92.45 11	95.69 20	98.50 22	85.38 27	99.85 10	94.75 38	99.18 5	98.65 38

ETH3 D test6400		95.56 8	95.41 11	96.00 7	99.02 16	89.42 7	98.75 18	96.80 44	87.28 79	95.88 19	98.95 2	85.92 26	99.41 62	97.15 13	98.95 18	99.18 20

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 75	84.10 74	95.85 193	96.42 100	91.26 20	97.49 7	96.80 111	86.50 23	98.49 127	95.54 28	99.03 11	98.33 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		94.84 13	95.02 13	94.29 34	97.87 70	84.61 67	97.76 60	96.19 123	89.59 38	96.66 12	98.17 36	84.33 32	99.60 46	96.09 18	98.50 37	98.66 37
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
APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 20	83.40 88	98.04 42	96.41 101	85.79 101	95.00 31	98.28 29	84.32 35	99.18 87	97.35 11	98.77 25	99.28 15

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 64	85.94 35	97.47 80	96.77 51	85.32 112	97.92 2	98.70 16	83.09 47	99.84 12	95.79 24	99.08 8	98.49 46
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
CANet		94.89 12	94.64 16	95.63 10	97.55 81	88.12 13	99.06 9	96.39 107	94.07 7	95.34 24	97.80 65	76.83 114	99.87 8	97.08 14	97.64 68	98.89 26

xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 52	83.34 89	98.61 23	92.69 289	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 91	83.04 97	98.10 37	95.29 175	91.57 16	93.81 46	97.45 82	86.64 21	99.43 61	96.28 17	94.01 119	99.20 18

DELS-MVS		94.98 11	94.49 19	96.44 4	96.42 103	90.59 5	99.21 2	97.02 27	94.40 5	91.46 75	97.08 100	83.32 43	99.69 36	92.83 63	98.70 29	99.04 21
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
train_agg		94.28 22	94.45 20	93.74 51	98.64 31	83.71 81	97.82 52	96.65 68	84.50 136	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 69

SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 126	81.35 134	99.02 13	96.59 78	89.50 39	94.18 43	98.36 27	83.68 40	99.45 60	94.77 37	98.45 40	98.81 29
Skip Steuart: Steuart Systems R&D Blog.
ETH3D-3000-0.1		94.43 19	94.42 22	94.45 28	97.78 71	85.78 38	97.98 44	96.53 87	85.29 115	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 66

MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 50	84.06 75	98.64 21	96.93 36	90.71 25	93.08 55	98.70 16	79.98 71	99.21 80	94.12 45	99.07 9	98.63 39

test_prior3		94.03 31	94.34 24	93.09 81	98.68 25	81.91 117	98.37 27	96.40 104	86.08 95	94.57 38	98.02 49	83.14 44	99.06 97	95.05 35	98.79 23	98.29 60

agg_prior1		94.10 28	94.31 25	93.48 67	98.59 35	83.13 94	97.77 56	96.56 82	84.38 140	94.19 41	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 71

DeepC-MVS_fast	89.06 2	94.48 18	94.30 26	95.02 17	98.86 19	85.68 42	98.06 40	96.64 71	93.64 8	91.74 72	98.54 20	80.17 70	99.90 5	92.28 70	98.75 26	99.49 6
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			94.26 27		98.10 60		98.14 34	96.52 88	84.74 127	94.83 34	98.80 9	82.80 51	99.37 68	95.95 22	98.42 42

EPNet		94.06 30	94.15 28	93.76 50	97.27 94	84.35 68	98.29 29	97.64 13	94.57 4	95.36 23	96.88 106	79.96 72	99.12 94	91.30 78	96.11 98	97.82 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 91	97.59 71	96.61 74	83.60 165	94.77 36	98.46 24	82.72 52	99.64 42	95.29 33	98.42 42	99.32 13

SF-MVS		94.17 25	94.05 30	94.55 27	97.56 80	85.95 33	97.73 62	96.43 99	84.02 150	95.07 29	98.74 14	82.93 48	99.38 64	95.42 30	98.51 34	98.32 55

Regformer-1		94.00 32	94.04 31	93.87 47	98.41 43	84.29 70	97.43 86	97.04 26	89.50 39	92.75 59	98.13 38	82.60 54	99.26 75	93.55 50	96.99 82	98.06 78

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 43	83.75 80	97.43 86	97.00 28	89.43 41	92.69 60	98.13 38	82.48 55	99.22 78	93.51 51	96.99 82	98.04 79

ETH3D cwj APD-0.16		93.91 35	93.76 33	94.36 31	96.70 101	85.74 39	97.22 95	96.41 101	83.94 153	94.13 44	98.69 18	83.13 46	99.37 68	95.25 34	98.39 47	97.97 89

MG-MVS		94.25 24	93.72 34	95.85 9	99.38 3	89.35 9	97.98 44	98.09 8	89.99 34	92.34 63	96.97 103	81.30 59	98.99 101	88.54 113	98.88 19	99.20 18

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 63	81.76 125	98.64 21	97.13 21	82.60 184	94.09 45	98.49 23	80.35 65	99.85 10	94.74 39	98.62 31	98.83 28

APD-MVS		93.61 37	93.59 36	93.69 54	98.76 22	83.26 92	97.21 97	96.09 128	82.41 186	94.65 37	98.21 31	81.96 57	98.81 114	94.65 40	98.36 50	99.01 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
lupinMVS		93.87 36	93.58 37	94.75 23	93.00 191	88.08 14	99.15 4	95.50 160	91.03 22	94.90 32	97.66 69	78.84 83	97.56 159	94.64 41	97.46 70	98.62 40

PS-MVSNAJ		94.17 25	93.52 38	96.10 6	95.65 121	92.35 2	98.21 32	95.79 145	92.42 12	96.24 15	98.18 32	71.04 194	99.17 88	96.77 15	97.39 75	96.79 148

MVS_111021_HR		93.41 40	93.39 39	93.47 70	97.34 92	82.83 100	97.56 74	98.27 6	89.16 44	89.71 101	97.14 97	79.77 73	99.56 51	93.65 48	97.94 62	98.02 81

xiu_mvs_v2_base		93.92 33	93.26 40	95.91 8	95.07 138	92.02 4	98.19 33	95.68 150	92.06 14	96.01 18	98.14 37	70.83 197	98.96 103	96.74 16	96.57 94	96.76 151

ACMMP_NAP		93.46 39	93.23 41	94.17 39	97.16 95	84.28 71	96.82 135	96.65 68	86.24 91	94.27 40	97.99 52	77.94 96	99.83 15	93.39 52	98.57 32	98.39 52

Regformer-3		93.19 41	93.19 42	93.19 77	98.10 60	83.01 98	97.08 116	96.98 30	88.98 45	91.35 80	97.89 59	80.80 61	99.23 76	92.30 69	95.20 109	97.32 130

Regformer-4		93.06 44	93.12 43	92.89 91	98.10 60	82.20 111	97.08 116	96.92 38	88.87 47	91.23 82	97.89 59	80.57 64	99.19 85	92.21 71	95.20 109	97.29 134

CS-MVS		92.88 48	93.09 44	92.26 115	95.21 132	80.70 149	98.84 16	95.26 177	88.83 48	92.50 61	97.48 81	77.49 103	97.63 155	95.34 32	96.88 87	98.46 47

#test#		92.99 45	92.99 45	92.98 86	98.71 23	81.12 137	97.77 56	96.70 60	85.75 102	91.75 70	97.97 56	78.47 88	99.71 32	91.36 77	98.41 44	98.12 74

PVSNet_Blended		93.13 42	92.98 46	93.57 61	97.47 82	83.86 77	99.32 1	96.73 56	91.02 23	89.53 106	96.21 120	76.42 121	99.57 49	94.29 43	95.81 105	97.29 134

CDPH-MVS		93.12 43	92.91 47	93.74 51	98.65 30	83.88 76	97.67 66	96.26 117	83.00 175	93.22 53	98.24 30	81.31 58	99.21 80	89.12 109	98.74 27	98.14 72

ETV-MVS		92.72 54	92.87 48	92.28 114	94.54 153	81.89 119	97.98 44	95.21 179	89.77 37	93.11 54	96.83 108	77.23 110	97.50 166	95.74 25	95.38 107	97.44 124

HFP-MVS		92.89 47	92.86 49	92.98 86	98.71 23	81.12 137	97.58 72	96.70 60	85.20 118	91.75 70	97.97 56	78.47 88	99.71 32	90.95 81	98.41 44	98.12 74

zzz-MVS		92.74 51	92.71 50	92.86 92	97.90 66	80.85 145	96.47 154	96.33 112	87.92 66	90.20 96	98.18 32	76.71 117	99.76 20	92.57 67	98.09 56	97.96 90

XVS		92.69 56	92.71 50	92.63 102	98.52 38	80.29 159	97.37 91	96.44 97	87.04 86	91.38 76	97.83 64	77.24 108	99.59 47	90.46 91	98.07 58	98.02 81

region2R		92.72 54	92.70 52	92.79 95	98.68 25	80.53 156	97.53 76	96.51 89	85.22 116	91.94 68	97.98 54	77.26 106	99.67 40	90.83 85	98.37 49	98.18 67

ACMMPR		92.69 56	92.67 53	92.75 96	98.66 28	80.57 153	97.58 72	96.69 62	85.20 118	91.57 74	97.92 58	77.01 111	99.67 40	90.95 81	98.41 44	98.00 86

MP-MVS		92.61 59	92.67 53	92.42 109	98.13 59	79.73 175	97.33 93	96.20 121	85.63 104	90.53 91	97.66 69	78.14 94	99.70 35	92.12 72	98.30 53	97.85 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		92.75 50	92.60 55	93.23 75	98.24 52	81.82 123	97.63 67	96.50 91	85.00 123	91.05 85	97.74 67	78.38 90	99.80 19	90.48 90	98.34 51	98.07 77

CP-MVS		92.54 61	92.60 55	92.34 111	98.50 40	79.90 169	98.40 26	96.40 104	84.75 126	90.48 93	98.09 43	77.40 105	99.21 80	91.15 80	98.23 55	97.92 92

PAPM		92.87 49	92.40 57	94.30 33	92.25 212	87.85 16	96.40 163	96.38 108	91.07 21	88.72 116	96.90 104	82.11 56	97.37 172	90.05 98	97.70 67	97.67 109

MP-MVS-pluss		92.58 60	92.35 58	93.29 72	97.30 93	82.53 104	96.44 159	96.04 132	84.68 130	89.12 111	98.37 26	77.48 104	99.74 28	93.31 56	98.38 48	97.59 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MTAPA		92.45 62	92.31 59	92.86 92	97.90 66	80.85 145	92.88 274	96.33 112	87.92 66	90.20 96	98.18 32	76.71 117	99.76 20	92.57 67	98.09 56	97.96 90

SR-MVS		92.16 65	92.27 60	91.83 131	98.37 46	78.41 206	96.67 147	95.76 146	82.19 190	91.97 67	98.07 47	76.44 120	98.64 118	93.71 47	97.27 77	98.45 49

alignmvs		92.97 46	92.26 61	95.12 16	95.54 123	87.77 17	98.67 19	96.38 108	88.04 64	93.01 56	97.45 82	79.20 80	98.60 121	93.25 57	88.76 161	98.99 25

jason		92.73 53	92.23 62	94.21 38	90.50 251	87.30 23	98.65 20	95.09 182	90.61 26	92.76 58	97.13 98	75.28 148	97.30 175	93.32 55	96.75 93	98.02 81
jason: jason.
GST-MVS		92.43 63	92.22 63	93.04 84	98.17 57	81.64 130	97.40 90	96.38 108	84.71 129	90.90 87	97.40 87	77.55 102	99.76 20	89.75 102	97.74 66	97.72 105

PAPR		92.74 51	92.17 64	94.45 28	98.89 18	84.87 63	97.20 99	96.20 121	87.73 72	88.40 120	98.12 41	78.71 86	99.76 20	87.99 120	96.28 96	98.74 31

EIA-MVS		91.73 71	92.05 65	90.78 158	94.52 154	76.40 250	98.06 40	95.34 172	89.19 43	88.90 114	97.28 93	77.56 101	97.73 152	90.77 86	96.86 90	98.20 65

test1172		91.64 74	92.00 66	90.54 163	98.20 56	74.48 272	96.45 157	95.65 151	81.97 194	91.63 73	98.02 49	75.76 133	98.61 119	93.16 58	97.17 79	98.52 45

CHOSEN 280x420		91.71 73	91.85 67	91.29 143	94.94 143	82.69 101	87.89 315	96.17 124	85.94 98	87.27 131	94.31 164	90.27 6	95.65 248	94.04 46	95.86 103	95.53 181

mPP-MVS		91.88 69	91.82 68	92.07 121	98.38 45	78.63 200	97.29 94	96.09 128	85.12 120	88.45 119	97.66 69	75.53 138	99.68 38	89.83 100	98.02 61	97.88 93

PGM-MVS		91.93 68	91.80 69	92.32 113	98.27 51	79.74 174	95.28 210	97.27 17	83.83 158	90.89 88	97.78 66	76.12 127	99.56 51	88.82 111	97.93 64	97.66 110

EI-MVSNet-Vis-set		91.84 70	91.77 70	92.04 123	97.60 77	81.17 136	96.61 148	96.87 40	88.20 62	89.19 110	97.55 79	78.69 87	99.14 91	90.29 96	90.94 148	95.80 174

WTY-MVS		92.65 58	91.68 71	95.56 11	96.00 112	88.90 10	98.23 31	97.65 12	88.57 54	89.82 100	97.22 95	79.29 76	99.06 97	89.57 104	88.73 162	98.73 35

CSCG		92.02 67	91.65 72	93.12 79	98.53 37	80.59 152	97.47 80	97.18 20	77.06 273	84.64 152	97.98 54	83.98 37	99.52 53	90.72 87	97.33 76	99.23 17

MVS_111021_LR		91.60 77	91.64 73	91.47 140	95.74 117	78.79 198	96.15 177	96.77 51	88.49 56	88.64 117	97.07 101	72.33 180	99.19 85	93.13 61	96.48 95	96.43 159

HPM-MVS		91.62 76	91.53 74	91.89 127	97.88 69	79.22 185	96.99 121	95.73 148	82.07 191	89.50 108	97.19 96	75.59 137	98.93 109	90.91 83	97.94 62	97.54 117
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SR-MVS-dyc-post		91.29 84	91.45 75	90.80 156	97.76 73	76.03 255	96.20 175	95.44 164	80.56 213	90.72 89	97.84 62	75.76 133	98.61 119	91.99 74	96.79 91	97.75 103

APD-MVS_3200maxsize		91.23 86	91.35 76	90.89 154	97.89 68	76.35 251	96.30 169	95.52 159	79.82 231	91.03 86	97.88 61	74.70 155	98.54 124	92.11 73	96.89 86	97.77 102

canonicalmvs		92.27 64	91.22 77	95.41 13	95.80 116	88.31 11	97.09 114	94.64 209	88.49 56	92.99 57	97.31 89	72.68 177	98.57 123	93.38 54	88.58 164	99.36 12

EI-MVSNet-UG-set		91.35 83	91.22 77	91.73 132	97.39 88	80.68 150	96.47 154	96.83 43	87.92 66	88.30 123	97.36 88	77.84 98	99.13 93	89.43 107	89.45 155	95.37 184

VNet		92.11 66	91.22 77	94.79 21	96.91 98	86.98 24	97.91 47	97.96 9	86.38 90	93.65 48	95.74 128	70.16 202	98.95 106	93.39 52	88.87 160	98.43 50

RE-MVS-def			91.18 80		97.76 73	76.03 255	96.20 175	95.44 164	80.56 213	90.72 89	97.84 62	73.36 172		91.99 74	96.79 91	97.75 103

DeepC-MVS	86.58 3	91.53 78	91.06 81	92.94 89	94.52 154	81.89 119	95.95 185	95.98 134	90.76 24	83.76 164	96.76 112	73.24 173	99.71 32	91.67 76	96.96 84	97.22 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS Recon		91.72 72	90.85 82	94.34 32	99.50 1	85.00 60	98.51 25	95.96 135	80.57 212	88.08 125	97.63 74	76.84 113	99.89 7	85.67 135	94.88 113	98.13 73

PAPM_NR		91.46 79	90.82 83	93.37 71	98.50 40	81.81 124	95.03 224	96.13 125	84.65 132	86.10 141	97.65 73	79.24 79	99.75 26	83.20 163	96.88 87	98.56 42

PVSNet_Blended_VisFu		91.24 85	90.77 84	92.66 101	95.09 136	82.40 107	97.77 56	95.87 142	88.26 61	86.39 137	93.94 174	76.77 115	99.27 73	88.80 112	94.00 120	96.31 165

diffmvs		91.17 87	90.74 85	92.44 108	93.11 190	82.50 106	96.25 172	93.62 259	87.79 70	90.40 94	95.93 125	73.44 171	97.42 169	93.62 49	92.55 134	97.41 126

MVSFormer		91.36 82	90.57 86	93.73 53	93.00 191	88.08 14	94.80 229	94.48 216	80.74 208	94.90 32	97.13 98	78.84 83	95.10 277	83.77 150	97.46 70	98.02 81

test_yl		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 204	89.85 98	96.14 121	75.61 135	98.81 114	90.42 94	88.56 165	98.74 31

DCV-MVSNet		91.46 79	90.53 87	94.24 36	97.41 86	85.18 51	98.08 38	97.72 10	80.94 204	89.85 98	96.14 121	75.61 135	98.81 114	90.42 94	88.56 165	98.74 31

casdiffmvs		90.95 90	90.39 89	92.63 102	92.82 196	82.53 104	96.83 134	94.47 218	87.69 73	88.47 118	95.56 136	74.04 163	97.54 163	90.90 84	92.74 132	97.83 98

HY-MVS	84.06 6	91.63 75	90.37 90	95.39 14	96.12 109	88.25 12	90.22 298	97.58 14	88.33 60	90.50 92	91.96 195	79.26 78	99.06 97	90.29 96	89.07 157	98.88 27

thisisatest0515		90.95 90	90.26 91	93.01 85	94.03 170	84.27 72	97.91 47	96.67 64	83.18 170	86.87 135	95.51 137	88.66 13	97.85 148	80.46 178	89.01 158	96.92 144

MAR-MVS		90.63 96	90.22 92	91.86 128	98.47 42	78.20 216	97.18 101	96.61 74	83.87 157	88.18 124	98.18 32	68.71 207	99.75 26	83.66 155	97.15 80	97.63 113
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
baseline2		90.39 102	90.21 93	90.93 152	90.86 245	80.99 141	95.20 215	97.41 15	86.03 97	80.07 206	94.61 159	90.58 4	97.47 168	87.29 125	89.86 153	94.35 200

CHOSEN 1792x2688		91.07 88	90.21 93	93.64 57	95.18 134	83.53 85	96.26 171	96.13 125	88.92 46	84.90 147	93.10 185	72.86 175	99.62 45	88.86 110	95.67 106	97.79 101

HPM-MVS_fast		90.38 104	90.17 95	91.03 150	97.61 76	77.35 236	97.15 106	95.48 161	79.51 236	88.79 115	96.90 104	71.64 188	98.81 114	87.01 129	97.44 72	96.94 141

baseline		90.76 93	90.10 96	92.74 97	92.90 195	82.56 103	94.60 231	94.56 214	87.69 73	89.06 113	95.67 132	73.76 166	97.51 165	90.43 93	92.23 140	98.16 69

CANet_DTU		90.98 89	90.04 97	93.83 48	94.76 148	86.23 30	96.32 168	93.12 282	93.11 10	93.71 47	96.82 110	63.08 239	99.48 58	84.29 145	95.12 112	95.77 175

ACMMP		90.39 102	89.97 98	91.64 134	97.58 79	78.21 215	96.78 138	96.72 58	84.73 128	84.72 150	97.23 94	71.22 191	99.63 44	88.37 118	92.41 137	97.08 139
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
PVSNet_BlendedMVS		90.05 107	89.96 99	90.33 169	97.47 82	83.86 77	98.02 43	96.73 56	87.98 65	89.53 106	89.61 229	76.42 121	99.57 49	94.29 43	79.59 227	87.57 296

sss		90.87 92	89.96 99	93.60 60	94.15 164	83.84 79	97.14 107	98.13 7	85.93 99	89.68 102	96.09 123	71.67 186	99.30 72	87.69 121	89.16 156	97.66 110

PMMVS		89.46 116	89.92 101	88.06 220	94.64 149	69.57 315	96.22 173	94.95 189	87.27 80	91.37 79	96.54 117	65.88 222	97.39 171	88.54 113	93.89 121	97.23 136

Effi-MVS+		90.70 94	89.90 102	93.09 81	93.61 176	83.48 86	95.20 215	92.79 287	83.22 169	91.82 69	95.70 130	71.82 185	97.48 167	91.25 79	93.67 124	98.32 55

CPTT-MVS		89.72 112	89.87 103	89.29 194	98.33 48	73.30 281	97.70 64	95.35 171	75.68 279	87.40 128	97.44 85	70.43 199	98.25 135	89.56 105	96.90 85	96.33 164

1121		90.66 95	89.82 104	93.16 78	97.39 88	81.71 128	93.33 261	96.66 67	74.45 289	91.38 76	97.55 79	79.27 77	99.52 53	79.95 184	98.43 41	98.26 63

DWT-MVSNet_test		90.52 101	89.80 105	92.70 100	95.73 119	82.20 111	93.69 252	96.55 84	88.34 59	87.04 134	95.34 140	86.53 22	97.55 160	76.32 222	88.66 163	98.34 53

EPP-MVSNet		89.76 111	89.72 106	89.87 184	93.78 172	76.02 257	97.22 95	96.51 89	79.35 238	85.11 145	95.01 153	84.82 29	97.10 187	87.46 124	88.21 169	96.50 157

abl_6		89.80 110	89.71 107	90.07 175	96.53 102	75.52 263	94.48 232	95.04 185	81.12 202	89.22 109	97.00 102	68.83 206	98.96 103	89.86 99	95.27 108	95.73 176

xiu_mvs_v1_base_debu		90.54 98	89.54 108	93.55 62	92.31 205	87.58 20	96.99 121	94.87 193	87.23 81	93.27 50	97.56 76	57.43 278	98.32 132	92.72 64	93.46 127	94.74 194

xiu_mvs_v1_base		90.54 98	89.54 108	93.55 62	92.31 205	87.58 20	96.99 121	94.87 193	87.23 81	93.27 50	97.56 76	57.43 278	98.32 132	92.72 64	93.46 127	94.74 194

xiu_mvs_v1_base_debi		90.54 98	89.54 108	93.55 62	92.31 205	87.58 20	96.99 121	94.87 193	87.23 81	93.27 50	97.56 76	57.43 278	98.32 132	92.72 64	93.46 127	94.74 194

TESTMET0.1,1		89.83 109	89.34 111	91.31 141	92.54 203	80.19 164	97.11 110	96.57 80	86.15 92	86.85 136	91.83 199	79.32 75	96.95 191	81.30 173	92.35 138	96.77 150

MVS_Test		90.29 105	89.18 112	93.62 59	95.23 130	84.93 61	94.41 235	94.66 206	84.31 142	90.37 95	91.02 208	75.13 150	97.82 149	83.11 165	94.42 115	98.12 74

ET-MVSNet_ETH3D		90.01 108	89.03 113	92.95 88	94.38 160	86.77 26	98.14 34	96.31 115	89.30 42	63.33 324	96.72 114	90.09 8	93.63 306	90.70 88	82.29 217	98.46 47

thisisatest0530		89.65 113	89.02 114	91.53 138	93.46 182	80.78 147	96.52 151	96.67 64	81.69 197	83.79 163	94.90 155	88.85 12	97.68 153	77.80 201	87.49 175	96.14 168

API-MVS		90.18 106	88.97 115	93.80 49	98.66 28	82.95 99	97.50 79	95.63 154	75.16 283	86.31 138	97.69 68	72.49 178	99.90 5	81.26 174	96.07 99	98.56 42

CDS-MVSNet		89.50 115	88.96 116	91.14 148	91.94 228	80.93 143	97.09 114	95.81 144	84.26 145	84.72 150	94.20 169	80.31 66	95.64 249	83.37 161	88.96 159	96.85 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
hse-mvs3		89.30 119	88.95 117	90.36 167	95.07 138	76.04 254	96.96 127	97.11 23	90.39 30	92.22 64	95.10 150	74.70 155	98.86 111	93.14 59	65.89 317	96.16 167

MVSTER		89.25 121	88.92 118	90.24 171	95.98 113	84.66 66	96.79 137	95.36 169	87.19 84	80.33 201	90.61 215	90.02 9	95.97 226	85.38 138	78.64 236	90.09 239

Vis-MVSNet (Re-imp)		88.88 128	88.87 119	88.91 200	93.89 171	74.43 273	96.93 130	94.19 228	84.39 139	83.22 169	95.67 132	78.24 92	94.70 287	78.88 197	94.40 116	97.61 115

MVS		90.60 97	88.64 120	96.50 3	94.25 162	90.53 6	93.33 261	97.21 19	77.59 264	78.88 213	97.31 89	71.52 189	99.69 36	89.60 103	98.03 60	99.27 16

test-mter		88.95 124	88.60 121	89.98 179	92.26 210	77.23 238	97.11 110	95.96 135	85.32 112	86.30 139	91.38 202	76.37 123	96.78 202	80.82 175	91.92 142	95.94 171

HyFIR lowres test		89.36 117	88.60 121	91.63 136	94.91 145	80.76 148	95.60 201	95.53 157	82.56 185	84.03 157	91.24 205	78.03 95	96.81 200	87.07 128	88.41 167	97.32 130

UA-Net		88.92 126	88.48 123	90.24 171	94.06 167	77.18 240	93.04 270	94.66 206	87.39 77	91.09 84	93.89 175	74.92 153	98.18 139	75.83 227	91.43 145	95.35 185

CostFormer		89.08 122	88.39 124	91.15 147	93.13 188	79.15 188	88.61 309	96.11 127	83.14 171	89.58 105	86.93 266	83.83 39	96.87 197	88.22 119	85.92 187	97.42 125

hse-mvs2		88.22 147	88.21 125	88.25 216	93.54 179	73.41 278	95.41 208	95.89 139	90.39 30	92.22 64	94.22 167	74.70 155	96.66 207	93.14 59	64.37 322	94.69 198

tttt0517		88.57 138	88.19 126	89.71 189	93.00 191	75.99 258	95.67 198	96.67 64	80.78 207	81.82 188	94.40 163	88.97 11	97.58 158	76.05 225	86.31 181	95.57 180

IS-MVSNet		88.67 134	88.16 127	90.20 173	93.61 176	76.86 243	96.77 140	93.07 283	84.02 150	83.62 165	95.60 135	74.69 158	96.24 220	78.43 200	93.66 125	97.49 123

OMC-MVS		88.80 131	88.16 127	90.72 159	95.30 129	77.92 224	94.81 228	94.51 215	86.80 88	84.97 146	96.85 107	67.53 212	98.60 121	85.08 140	87.62 172	95.63 178

test-LLR		88.48 139	87.98 129	89.98 179	92.26 210	77.23 238	97.11 110	95.96 135	83.76 160	86.30 139	91.38 202	72.30 181	96.78 202	80.82 175	91.92 142	95.94 171

EPNet_dtu		87.65 155	87.89 130	86.93 245	94.57 151	71.37 302	96.72 142	96.50 91	88.56 55	87.12 132	95.02 152	75.91 131	94.01 299	66.62 282	90.00 152	95.42 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	82.88 8	89.63 114	87.85 131	94.99 18	94.49 158	86.76 27	97.84 51	95.74 147	86.10 94	75.47 257	96.02 124	65.00 230	99.51 56	82.91 167	97.07 81	98.72 36

Vis-MVSNet		88.67 134	87.82 132	91.24 145	92.68 197	78.82 195	96.95 128	93.85 245	87.55 75	87.07 133	95.13 148	63.43 237	97.21 180	77.58 207	96.15 97	97.70 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS		88.48 139	87.79 133	90.56 162	91.09 240	79.18 186	96.45 157	95.88 140	83.64 163	83.12 170	93.33 181	75.94 130	95.74 244	82.40 168	88.27 168	96.75 152

PVSNet	82.34 9	89.02 123	87.79 133	92.71 99	95.49 124	81.50 132	97.70 64	97.29 16	87.76 71	85.47 143	95.12 149	56.90 282	98.90 110	80.33 179	94.02 118	97.71 107

thres200		88.92 126	87.65 135	92.73 98	96.30 104	85.62 43	97.85 50	98.86 1	84.38 140	84.82 148	93.99 173	75.12 151	98.01 140	70.86 265	86.67 178	94.56 199

LFMVS		89.27 120	87.64 136	94.16 41	97.16 95	85.52 45	97.18 101	94.66 206	79.17 244	89.63 104	96.57 116	55.35 293	98.22 136	89.52 106	89.54 154	98.74 31

3Dnovator	82.32 10	89.33 118	87.64 136	94.42 30	93.73 175	85.70 41	97.73 62	96.75 54	86.73 89	76.21 245	95.93 125	62.17 244	99.68 38	81.67 172	97.81 65	97.88 93

mvs_anonymous		88.68 133	87.62 138	91.86 128	94.80 147	81.69 129	93.53 257	94.92 190	82.03 192	78.87 214	90.43 219	75.77 132	95.34 262	85.04 141	93.16 130	98.55 44

AdaColmap		88.81 130	87.61 139	92.39 110	99.33 4	79.95 167	96.70 146	95.58 155	77.51 265	83.05 172	96.69 115	61.90 251	99.72 31	84.29 145	93.47 126	97.50 122

114514_t		88.79 132	87.57 140	92.45 107	98.21 55	81.74 126	96.99 121	95.45 163	75.16 283	82.48 175	95.69 131	68.59 208	98.50 126	80.33 179	95.18 111	97.10 138

HQP-MVS		87.91 153	87.55 141	88.98 199	92.08 219	78.48 202	97.63 67	94.80 198	90.52 27	82.30 178	94.56 160	65.40 226	97.32 173	87.67 122	83.01 208	91.13 221

baseline1		88.85 129	87.49 142	92.93 90	95.21 132	86.85 25	95.47 205	94.61 211	87.29 78	83.11 171	94.99 154	80.70 62	96.89 195	82.28 169	73.72 257	95.05 188

CLD-MVS		87.97 151	87.48 143	89.44 191	92.16 217	80.54 155	98.14 34	94.92 190	91.41 17	79.43 209	95.40 139	62.34 242	97.27 178	90.60 89	82.90 211	90.50 229
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-w/o		88.24 146	87.47 144	90.54 163	95.03 141	78.54 201	97.41 89	93.82 246	84.08 148	78.23 220	94.51 162	69.34 205	97.21 180	80.21 182	94.58 114	95.87 173

1112_ss		88.60 137	87.47 144	92.00 124	93.21 185	80.97 142	96.47 154	92.46 291	83.64 163	80.86 194	97.30 91	80.24 68	97.62 156	77.60 206	85.49 192	97.40 127

tpmrst		88.36 143	87.38 146	91.31 141	94.36 161	79.92 168	87.32 319	95.26 177	85.32 112	88.34 121	86.13 282	80.60 63	96.70 204	83.78 149	85.34 195	97.30 133

PLC	83.97 7	88.00 150	87.38 146	89.83 186	98.02 64	76.46 248	97.16 105	94.43 221	79.26 243	81.98 185	96.28 119	69.36 204	99.27 73	77.71 205	92.25 139	93.77 209
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1314		88.94 125	87.20 148	94.17 39	93.21 185	85.73 40	93.33 261	96.64 71	82.89 177	75.98 248	96.36 118	66.83 219	99.39 63	83.52 160	96.02 101	97.39 128

mvs-test1		86.83 166	87.17 149	85.81 261	91.96 225	65.24 328	97.90 49	93.34 273	85.57 105	84.51 154	95.14 147	61.99 248	97.19 182	83.55 156	90.55 150	95.00 189

tfpn200view9		88.48 139	87.15 150	92.47 106	96.21 106	85.30 49	97.44 82	98.85 2	83.37 167	83.99 158	93.82 176	75.36 145	97.93 142	69.04 271	86.24 184	94.17 201

thres400		88.42 142	87.15 150	92.23 116	96.21 106	85.30 49	97.44 82	98.85 2	83.37 167	83.99 158	93.82 176	75.36 145	97.93 142	69.04 271	86.24 184	93.45 214

IB-MVS	85.34 4	88.67 134	87.14 152	93.26 73	93.12 189	84.32 69	98.76 17	97.27 17	87.19 84	79.36 210	90.45 218	83.92 38	98.53 125	84.41 144	69.79 284	96.93 142
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
HQP_MVS		87.50 156	87.09 153	88.74 205	91.86 229	77.96 221	97.18 101	94.69 202	89.89 35	81.33 190	94.15 170	64.77 231	97.30 175	87.08 126	82.82 212	90.96 223

VDD-MVS		88.28 145	87.02 154	92.06 122	95.09 136	80.18 165	97.55 75	94.45 220	83.09 172	89.10 112	95.92 127	47.97 315	98.49 127	93.08 62	86.91 177	97.52 121

thres100view900		88.30 144	86.95 155	92.33 112	96.10 110	84.90 62	97.14 107	98.85 2	82.69 182	83.41 166	93.66 179	75.43 142	97.93 142	69.04 271	86.24 184	94.17 201

Fast-Effi-MVS+		87.93 152	86.94 156	90.92 153	94.04 168	79.16 187	98.26 30	93.72 255	81.29 200	83.94 161	92.90 186	69.83 203	96.68 205	76.70 216	91.74 144	96.93 142

Test_1112_low_res		88.03 149	86.73 157	91.94 126	93.15 187	80.88 144	96.44 159	92.41 292	83.59 166	80.74 196	91.16 206	80.18 69	97.59 157	77.48 209	85.40 193	97.36 129

thres600view7		88.06 148	86.70 158	92.15 119	96.10 110	85.17 55	97.14 107	98.85 2	82.70 181	83.41 166	93.66 179	75.43 142	97.82 149	67.13 280	85.88 188	93.45 214

UGNet		87.73 154	86.55 159	91.27 144	95.16 135	79.11 189	96.35 165	96.23 119	88.14 63	87.83 127	90.48 216	50.65 305	99.09 96	80.13 183	94.03 117	95.60 179
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
RRT_test8_iter05		87.14 159	86.41 160	89.32 193	94.41 159	81.10 139	97.06 118	95.33 173	84.67 131	76.27 243	90.48 216	83.60 41	96.33 215	85.10 139	70.78 273	90.53 228

tpm2		87.35 158	86.26 161	90.62 161	92.93 194	78.67 199	88.06 314	95.99 133	79.33 239	87.40 128	86.43 277	80.28 67	96.40 212	80.23 181	85.73 191	96.79 148

FIs		86.73 170	86.10 162	88.61 207	90.05 259	80.21 163	96.14 178	96.95 34	85.56 108	78.37 219	92.30 190	76.73 116	95.28 266	79.51 188	79.27 230	90.35 231

RRT_MVS		86.89 163	85.96 163	89.68 190	95.01 142	84.13 73	96.33 167	94.98 188	84.20 147	80.10 205	92.07 193	70.52 198	95.01 281	83.30 162	77.14 245	89.91 243

BH-untuned		86.95 162	85.94 164	89.99 178	94.52 154	77.46 233	96.78 138	93.37 272	81.80 195	76.62 236	93.81 178	66.64 220	97.02 189	76.06 224	93.88 122	95.48 182

EPMVS		87.47 157	85.90 165	92.18 118	95.41 126	82.26 110	87.00 321	96.28 116	85.88 100	84.23 155	85.57 288	75.07 152	96.26 218	71.14 263	92.50 135	98.03 80

AUN-MVS		86.25 175	85.57 166	88.26 215	93.57 178	73.38 279	95.45 206	95.88 140	83.94 153	85.47 143	94.21 168	73.70 169	96.67 206	83.54 158	64.41 321	94.73 197

CVMVSNet		84.83 194	85.57 166	82.63 303	91.55 233	60.38 342	95.13 218	95.03 186	80.60 211	82.10 184	94.71 157	66.40 221	90.19 338	74.30 241	90.32 151	97.31 132

nrg030		86.79 168	85.43 168	90.87 155	88.76 274	85.34 47	97.06 118	94.33 223	84.31 142	80.45 199	91.98 194	72.36 179	96.36 214	88.48 116	71.13 270	90.93 225

FC-MVSNet-test		85.96 177	85.39 169	87.66 227	89.38 271	78.02 219	95.65 200	96.87 40	85.12 120	77.34 225	91.94 197	76.28 125	94.74 286	77.09 211	78.82 234	90.21 235

CNLPA		86.96 161	85.37 170	91.72 133	97.59 78	79.34 183	97.21 97	91.05 310	74.22 290	78.90 212	96.75 113	67.21 216	98.95 106	74.68 236	90.77 149	96.88 146

BH-RMVSNet		86.84 165	85.28 171	91.49 139	95.35 128	80.26 162	96.95 128	92.21 293	82.86 179	81.77 189	95.46 138	59.34 264	97.64 154	69.79 269	93.81 123	96.57 156

miper_enhance_ethall		85.95 178	85.20 172	88.19 219	94.85 146	79.76 171	96.00 182	94.06 237	82.98 176	77.74 223	88.76 239	79.42 74	95.46 258	80.58 177	72.42 265	89.36 254

EI-MVSNet		85.80 180	85.20 172	87.59 229	91.55 233	77.41 234	95.13 218	95.36 169	80.43 218	80.33 201	94.71 157	73.72 167	95.97 226	76.96 214	78.64 236	89.39 249

XVG-OURS-SEG-HR		85.74 182	85.16 174	87.49 234	90.22 255	71.45 301	91.29 292	94.09 235	81.37 199	83.90 162	95.22 141	60.30 257	97.53 164	85.58 136	84.42 199	93.50 212

PatchmatchNet		86.83 166	85.12 175	91.95 125	94.12 165	82.27 109	86.55 325	95.64 153	84.59 134	82.98 173	84.99 300	77.26 106	95.96 229	68.61 275	91.34 146	97.64 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS		85.84 179	85.10 176	88.06 220	88.34 280	77.83 227	95.72 196	94.20 227	87.89 69	80.45 199	94.05 172	58.57 269	97.26 179	83.88 148	82.76 214	89.09 260

PCF-MVS	84.09 5	86.77 169	85.00 177	92.08 120	92.06 222	83.07 96	92.14 282	94.47 218	79.63 235	76.90 232	94.78 156	71.15 192	99.20 84	72.87 249	91.05 147	93.98 206
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ab-mvs		87.08 160	84.94 178	93.48 67	93.34 184	83.67 83	88.82 306	95.70 149	81.18 201	84.55 153	90.14 225	62.72 240	98.94 108	85.49 137	82.54 216	97.85 96

TR-MVS		86.30 173	84.93 179	90.42 165	94.63 150	77.58 231	96.57 150	93.82 246	80.30 221	82.42 177	95.16 145	58.74 268	97.55 160	74.88 234	87.82 171	96.13 169

Effi-MVS+-dtu		84.61 198	84.90 180	83.72 292	91.96 225	63.14 335	94.95 225	93.34 273	85.57 105	79.79 207	87.12 263	61.99 248	95.61 252	83.55 156	85.83 189	92.41 217

UniMVSNet_NR-MVSNet		85.49 185	84.59 181	88.21 218	89.44 270	79.36 181	96.71 144	96.41 101	85.22 116	78.11 221	90.98 210	76.97 112	95.14 273	79.14 194	68.30 298	90.12 237

VDDNet		86.44 172	84.51 182	92.22 117	91.56 232	81.83 122	97.10 113	94.64 209	69.50 319	87.84 126	95.19 143	48.01 314	97.92 147	89.82 101	86.92 176	96.89 145

QAPM		86.88 164	84.51 182	93.98 43	94.04 168	85.89 36	97.19 100	96.05 131	73.62 294	75.12 260	95.62 134	62.02 247	99.74 28	70.88 264	96.06 100	96.30 166

cascas		86.50 171	84.48 184	92.55 105	92.64 201	85.95 33	97.04 120	95.07 184	75.32 281	80.50 197	91.02 208	54.33 300	97.98 141	86.79 130	87.62 172	93.71 210

tpm		85.55 184	84.47 185	88.80 204	90.19 256	75.39 265	88.79 307	94.69 202	84.83 125	83.96 160	85.21 294	78.22 93	94.68 288	76.32 222	78.02 243	96.34 162

XVG-OURS		85.18 189	84.38 186	87.59 229	90.42 253	71.73 298	91.06 295	94.07 236	82.00 193	83.29 168	95.08 151	56.42 287	97.55 160	83.70 154	83.42 204	93.49 213

PS-MVSNAJss		84.91 193	84.30 187	86.74 246	85.89 306	74.40 274	94.95 225	94.16 230	83.93 155	76.45 238	90.11 226	71.04 194	95.77 239	83.16 164	79.02 233	90.06 241

UniMVSNet (Re)		85.31 188	84.23 188	88.55 208	89.75 262	80.55 154	96.72 142	96.89 39	85.42 109	78.40 218	88.93 237	75.38 144	95.52 256	78.58 198	68.02 301	89.57 247

cl-mvsnet2		85.11 190	84.17 189	87.92 222	95.06 140	78.82 195	95.51 203	94.22 226	79.74 233	76.77 233	87.92 252	75.96 129	95.68 245	79.93 186	72.42 265	89.27 255

X-MVStestdata		86.26 174	84.14 190	92.63 102	98.52 38	80.29 159	97.37 91	96.44 97	87.04 86	91.38 76	20.73 362	77.24 108	99.59 47	90.46 91	98.07 58	98.02 81

GA-MVS		85.79 181	84.04 191	91.02 151	89.47 269	80.27 161	96.90 131	94.84 196	85.57 105	80.88 193	89.08 233	56.56 286	96.47 211	77.72 204	85.35 194	96.34 162

VPA-MVSNet		85.32 187	83.83 192	89.77 188	90.25 254	82.63 102	96.36 164	97.07 25	83.03 174	81.21 192	89.02 235	61.58 252	96.31 217	85.02 142	70.95 272	90.36 230

MDTV_nov1_ep13			83.69 193		94.09 166	81.01 140	86.78 323	96.09 128	83.81 159	84.75 149	84.32 305	74.44 159	96.54 208	63.88 296	85.07 196

TAPA-MVS	81.61 12	85.02 191	83.67 194	89.06 196	96.79 99	73.27 283	95.92 187	94.79 200	74.81 286	80.47 198	96.83 108	71.07 193	98.19 138	49.82 342	92.57 133	95.71 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 192	83.66 195	89.02 198	95.86 115	74.55 271	92.49 278	93.60 260	79.30 241	79.29 211	91.47 200	58.53 270	98.45 129	70.22 268	92.17 141	94.07 205

SCA		85.63 183	83.64 196	91.60 137	92.30 208	81.86 121	92.88 274	95.56 156	84.85 124	82.52 174	85.12 298	58.04 273	95.39 259	73.89 244	87.58 174	97.54 117

OpenMVS	79.58 14	86.09 176	83.62 197	93.50 65	90.95 242	86.71 28	97.44 82	95.83 143	75.35 280	72.64 279	95.72 129	57.42 281	99.64 42	71.41 258	95.85 104	94.13 204

miper_ehance_all_eth		84.57 199	83.60 198	87.50 233	92.64 201	78.25 211	95.40 209	93.47 264	79.28 242	76.41 239	87.64 255	76.53 119	95.24 268	78.58 198	72.42 265	89.01 265

LCM-MVSNet-Re		83.75 211	83.54 199	84.39 285	93.54 179	64.14 331	92.51 277	84.03 347	83.90 156	66.14 313	86.59 271	67.36 214	92.68 313	84.89 143	92.87 131	96.35 161

LPG-MVS_test		84.20 206	83.49 200	86.33 252	90.88 243	73.06 284	95.28 210	94.13 231	82.20 188	76.31 240	93.20 182	54.83 298	96.95 191	83.72 152	80.83 220	88.98 266

F-COLMAP		84.50 201	83.44 201	87.67 226	95.22 131	72.22 288	95.95 185	93.78 251	75.74 278	76.30 242	95.18 144	59.50 262	98.45 129	72.67 251	86.59 180	92.35 218

DU-MVS		84.57 199	83.33 202	88.28 214	88.76 274	79.36 181	96.43 161	95.41 168	85.42 109	78.11 221	90.82 211	67.61 210	95.14 273	79.14 194	68.30 298	90.33 232

ACMP	81.66 11	84.00 207	83.22 203	86.33 252	91.53 235	72.95 286	95.91 189	93.79 250	83.70 162	73.79 267	92.22 191	54.31 301	96.89 195	83.98 147	79.74 226	89.16 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
WR-MVS		84.32 204	82.96 204	88.41 210	89.38 271	80.32 158	96.59 149	96.25 118	83.97 152	76.63 235	90.36 220	67.53 212	94.86 284	75.82 228	70.09 282	90.06 241

VPNet		84.69 197	82.92 205	90.01 177	89.01 273	83.45 87	96.71 144	95.46 162	85.71 103	79.65 208	92.18 192	56.66 285	96.01 225	83.05 166	67.84 304	90.56 227

gg-mvs-nofinetune		85.48 186	82.90 206	93.24 74	94.51 157	85.82 37	79.22 340	96.97 32	61.19 340	87.33 130	53.01 352	90.58 4	96.07 222	86.07 133	97.23 78	97.81 100

test_part1		84.72 195	82.85 207	90.34 168	95.73 119	84.79 65	96.75 141	94.10 234	79.05 250	75.97 249	89.51 230	67.69 209	95.94 230	79.34 190	67.50 307	90.30 234

ACMM	80.70 13	83.72 212	82.85 207	86.31 255	91.19 238	72.12 291	95.88 190	94.29 224	80.44 216	77.02 230	91.96 195	55.24 294	97.14 186	79.30 192	80.38 222	89.67 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
bset_n11_16_dypcd		84.35 203	82.83 209	88.91 200	82.54 330	82.07 113	94.12 246	93.47 264	85.39 111	78.55 216	88.98 236	62.23 243	95.11 275	86.75 131	73.42 259	89.55 248

IterMVS-LS		83.93 208	82.80 210	87.31 238	91.46 236	77.39 235	95.66 199	93.43 267	80.44 216	75.51 256	87.26 260	73.72 167	95.16 272	76.99 212	70.72 275	89.39 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		84.71 196	82.71 211	90.70 160	94.55 152	87.71 18	95.92 187	94.67 205	81.73 196	75.82 252	88.08 250	66.99 217	94.47 291	71.23 260	75.38 251	89.91 243

cl_fuxian		83.80 210	82.65 212	87.25 240	92.10 218	77.74 229	95.25 213	93.04 284	78.58 254	76.01 247	87.21 262	75.25 149	95.11 275	77.54 208	68.89 292	88.91 271

Fast-Effi-MVS+-dtu		83.33 217	82.60 213	85.50 267	89.55 267	69.38 316	96.09 181	91.38 302	82.30 187	75.96 250	91.41 201	56.71 283	95.58 254	75.13 233	84.90 197	91.54 219

test0.0.03 1		82.79 228	82.48 214	83.74 291	86.81 293	72.22 288	96.52 151	95.03 186	83.76 160	73.00 275	93.20 182	72.30 181	88.88 341	64.15 295	77.52 244	90.12 237

test_djsdf		83.00 226	82.45 215	84.64 278	84.07 325	69.78 312	94.80 229	94.48 216	80.74 208	75.41 258	87.70 254	61.32 254	95.10 277	83.77 150	79.76 224	89.04 263

dp		84.30 205	82.31 216	90.28 170	94.24 163	77.97 220	86.57 324	95.53 157	79.94 230	80.75 195	85.16 296	71.49 190	96.39 213	63.73 297	83.36 205	96.48 158

cl-mvsnet_		83.27 218	82.12 217	86.74 246	92.20 213	75.95 259	95.11 220	93.27 276	78.44 257	74.82 262	87.02 265	74.19 161	95.19 270	74.67 237	69.32 288	89.09 260

cl-mvsnet1		83.27 218	82.12 217	86.74 246	92.19 214	75.92 260	95.11 220	93.26 277	78.44 257	74.81 263	87.08 264	74.19 161	95.19 270	74.66 238	69.30 289	89.11 259

eth_miper_zixun_eth		83.12 222	82.01 219	86.47 251	91.85 231	74.80 268	94.33 238	93.18 279	79.11 245	75.74 255	87.25 261	72.71 176	95.32 264	76.78 215	67.13 311	89.27 255

XXY-MVS		83.84 209	82.00 220	89.35 192	87.13 291	81.38 133	95.72 196	94.26 225	80.15 225	75.92 251	90.63 214	61.96 250	96.52 209	78.98 196	73.28 263	90.14 236

Anonymous202405211		84.41 202	81.93 221	91.85 130	96.78 100	78.41 206	97.44 82	91.34 305	70.29 315	84.06 156	94.26 166	41.09 337	98.96 103	79.46 189	82.65 215	98.17 68

v2v482		83.46 215	81.86 222	88.25 216	86.19 300	79.65 176	96.34 166	94.02 238	81.56 198	77.32 226	88.23 247	65.62 223	96.03 223	77.77 202	69.72 286	89.09 260

MS-PatchMatch		83.05 223	81.82 223	86.72 250	89.64 265	79.10 190	94.88 227	94.59 213	79.70 234	70.67 291	89.65 228	50.43 307	96.82 199	70.82 267	95.99 102	84.25 330

TranMVSNet+NR-MVSNet		83.24 220	81.71 224	87.83 223	87.71 287	78.81 197	96.13 180	94.82 197	84.52 135	76.18 246	90.78 213	64.07 234	94.60 289	74.60 239	66.59 316	90.09 239

MVP-Stereo		82.65 231	81.67 225	85.59 266	86.10 303	78.29 209	93.33 261	92.82 286	77.75 262	69.17 301	87.98 251	59.28 265	95.76 240	71.77 255	96.88 87	82.73 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		82.67 230	81.55 226	86.04 259	87.77 286	76.47 247	95.21 214	96.58 79	82.66 183	70.26 294	85.46 291	60.39 256	95.80 238	76.40 220	79.18 231	85.83 320

V42		83.04 224	81.53 227	87.57 231	86.27 299	79.09 191	95.87 191	94.11 233	80.35 220	77.22 228	86.79 269	65.32 228	96.02 224	77.74 203	70.14 278	87.61 295

NR-MVSNet		83.35 216	81.52 228	88.84 202	88.76 274	81.31 135	94.45 234	95.16 180	84.65 132	67.81 303	90.82 211	70.36 200	94.87 283	74.75 235	66.89 314	90.33 232

tpm cat1		83.63 213	81.38 229	90.39 166	93.53 181	78.19 217	85.56 331	95.09 182	70.78 313	78.51 217	83.28 313	74.80 154	97.03 188	66.77 281	84.05 200	95.95 170

CR-MVSNet		83.53 214	81.36 230	90.06 176	90.16 257	79.75 172	79.02 342	91.12 307	84.24 146	82.27 182	80.35 327	75.45 140	93.67 305	63.37 300	86.25 182	96.75 152

v1144		82.90 227	81.27 231	87.78 225	86.29 298	79.07 192	96.14 178	93.93 240	80.05 227	77.38 224	86.80 268	65.50 224	95.93 232	75.21 232	70.13 279	88.33 281

jajsoiax		82.12 239	81.15 232	85.03 272	84.19 323	70.70 304	94.22 244	93.95 239	83.07 173	73.48 269	89.75 227	49.66 310	95.37 261	82.24 170	79.76 224	89.02 264

v148		82.41 236	80.89 233	86.99 244	86.18 301	76.81 244	96.27 170	93.82 246	80.49 215	75.28 259	86.11 283	67.32 215	95.75 241	75.48 230	67.03 313	88.42 279

pmmvs4		82.54 232	80.79 234	87.79 224	86.11 302	80.49 157	93.55 256	93.18 279	77.29 268	73.35 271	89.40 232	65.26 229	95.05 280	75.32 231	73.61 258	87.83 289

tpmvs		83.04 224	80.77 235	89.84 185	95.43 125	77.96 221	85.59 330	95.32 174	75.31 282	76.27 243	83.70 310	73.89 164	97.41 170	59.53 311	81.93 218	94.14 203

v144192		82.43 233	80.73 236	87.54 232	85.81 307	78.22 212	95.98 183	93.78 251	79.09 246	77.11 229	86.49 273	64.66 233	95.91 233	74.20 242	69.42 287	88.49 275

mvs_tets		81.74 242	80.71 237	84.84 273	84.22 322	70.29 307	93.91 249	93.78 251	82.77 180	73.37 270	89.46 231	47.36 319	95.31 265	81.99 171	79.55 229	88.92 270

miper_lstm_enhance		81.66 245	80.66 238	84.67 277	91.19 238	71.97 294	91.94 284	93.19 278	77.86 261	72.27 282	85.26 292	73.46 170	93.42 308	73.71 247	67.05 312	88.61 273

Anonymous20240529		83.15 221	80.60 239	90.80 156	95.74 117	78.27 210	96.81 136	94.92 190	60.10 345	81.89 187	92.54 189	45.82 322	98.82 113	79.25 193	78.32 241	95.31 186

v1192		82.31 237	80.55 240	87.60 228	85.94 304	78.47 205	95.85 193	93.80 249	79.33 239	76.97 231	86.51 272	63.33 238	95.87 234	73.11 248	70.13 279	88.46 277

FMVSNet2		82.79 228	80.44 241	89.83 186	92.66 198	85.43 46	95.42 207	94.35 222	79.06 247	74.46 264	87.28 258	56.38 288	94.31 294	69.72 270	74.68 254	89.76 245

GBi-Net		82.42 234	80.43 242	88.39 211	92.66 198	81.95 114	94.30 240	93.38 269	79.06 247	75.82 252	85.66 284	56.38 288	93.84 301	71.23 260	75.38 251	89.38 251

test1		82.42 234	80.43 242	88.39 211	92.66 198	81.95 114	94.30 240	93.38 269	79.06 247	75.82 252	85.66 284	56.38 288	93.84 301	71.23 260	75.38 251	89.38 251

v1921920		82.02 240	80.23 244	87.41 235	85.62 308	77.92 224	95.79 195	93.69 256	78.86 251	76.67 234	86.44 275	62.50 241	95.83 236	72.69 250	69.77 285	88.47 276

WR-MVS_H		81.02 251	80.09 245	83.79 289	88.08 284	71.26 303	94.46 233	96.54 85	80.08 226	72.81 278	86.82 267	70.36 200	92.65 314	64.18 294	67.50 307	87.46 300

CP-MVSNet		81.01 252	80.08 246	83.79 289	87.91 285	70.51 305	94.29 243	95.65 151	80.83 206	72.54 281	88.84 238	63.71 235	92.32 317	68.58 276	68.36 297	88.55 274

Baseline_NR-MVSNet		81.22 250	80.07 247	84.68 276	85.32 314	75.12 267	96.48 153	88.80 328	76.24 277	77.28 227	86.40 278	67.61 210	94.39 293	75.73 229	66.73 315	84.54 327

v8		81.88 241	80.06 248	87.32 237	86.63 294	79.04 193	94.41 235	93.65 258	78.77 252	73.19 274	85.57 288	66.87 218	95.81 237	73.84 246	67.61 306	87.11 303

anonymousdsp		80.98 253	79.97 249	84.01 286	81.73 331	70.44 306	92.49 278	93.58 262	77.10 272	72.98 276	86.31 279	57.58 277	94.90 282	79.32 191	78.63 238	86.69 308

LS3D		82.22 238	79.94 250	89.06 196	97.43 85	74.06 277	93.20 268	92.05 294	61.90 336	73.33 272	95.21 142	59.35 263	99.21 80	54.54 330	92.48 136	93.90 208

v1240		81.70 243	79.83 251	87.30 239	85.50 309	77.70 230	95.48 204	93.44 266	78.46 256	76.53 237	86.44 275	60.85 255	95.84 235	71.59 257	70.17 277	88.35 280

pmmvs5		81.34 248	79.54 252	86.73 249	85.02 316	76.91 242	96.22 173	91.65 300	77.65 263	73.55 268	88.61 241	55.70 291	94.43 292	74.12 243	73.35 262	88.86 272

v10		81.43 247	79.53 253	87.11 242	86.38 295	78.87 194	94.31 239	93.43 267	77.88 260	73.24 273	85.26 292	65.44 225	95.75 241	72.14 254	67.71 305	86.72 307

PS-CasMVS		80.27 258	79.18 254	83.52 296	87.56 289	69.88 310	94.08 247	95.29 175	80.27 223	72.08 283	88.51 245	59.22 266	92.23 319	67.49 278	68.15 300	88.45 278

IterMVS		80.67 255	79.16 255	85.20 270	89.79 261	76.08 253	92.97 272	91.86 296	80.28 222	71.20 287	85.14 297	57.93 276	91.34 328	72.52 252	70.74 274	88.18 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		80.51 257	79.10 256	84.73 275	89.63 266	74.66 269	92.98 271	91.81 298	80.05 227	71.06 289	85.18 295	58.04 273	91.40 327	72.48 253	70.70 276	88.12 285

PVSNet_0	77.72 15	81.70 243	78.95 257	89.94 182	90.77 248	76.72 246	95.96 184	96.95 34	85.01 122	70.24 295	88.53 244	52.32 302	98.20 137	86.68 132	44.08 351	94.89 190

UniMVSNet_ETH3D		80.86 254	78.75 258	87.22 241	86.31 297	72.02 292	91.95 283	93.76 254	73.51 295	75.06 261	90.16 224	43.04 331	95.66 246	76.37 221	78.55 239	93.98 206

ADS-MVSNet		81.26 249	78.36 259	89.96 181	93.78 172	79.78 170	79.48 338	93.60 260	73.09 300	80.14 203	79.99 330	62.15 245	95.24 268	59.49 312	83.52 202	94.85 191

DP-MVS		81.47 246	78.28 260	91.04 149	98.14 58	78.48 202	95.09 223	86.97 335	61.14 341	71.12 288	92.78 188	59.59 260	99.38 64	53.11 334	86.61 179	95.27 187

PEN-MVS		79.47 265	78.26 261	83.08 299	86.36 296	68.58 318	93.85 250	94.77 201	79.76 232	71.37 285	88.55 242	59.79 258	92.46 315	64.50 293	65.40 318	88.19 283

pm-mvs1		80.05 259	78.02 262	86.15 257	85.42 310	75.81 261	95.11 220	92.69 289	77.13 270	70.36 293	87.43 257	58.44 271	95.27 267	71.36 259	64.25 323	87.36 301

XVG-ACMP-BASELINE		79.38 266	77.90 263	83.81 288	84.98 317	67.14 325	89.03 305	93.18 279	80.26 224	72.87 277	88.15 249	38.55 340	96.26 218	76.05 225	78.05 242	88.02 286

MSDG		80.62 256	77.77 264	89.14 195	93.43 183	77.24 237	91.89 285	90.18 317	69.86 318	68.02 302	91.94 197	52.21 303	98.84 112	59.32 314	83.12 206	91.35 220

ADS-MVSNet2		79.57 263	77.53 265	85.71 264	93.78 172	72.13 290	79.48 338	86.11 340	73.09 300	80.14 203	79.99 330	62.15 245	90.14 339	59.49 312	83.52 202	94.85 191

v7n		79.32 267	77.34 266	85.28 269	84.05 326	72.89 287	93.38 259	93.87 244	75.02 285	70.68 290	84.37 304	59.58 261	95.62 251	67.60 277	67.50 307	87.32 302

JIA-IIPM		79.00 269	77.20 267	84.40 284	89.74 264	64.06 332	75.30 348	95.44 164	62.15 335	81.90 186	59.08 350	78.92 82	95.59 253	66.51 285	85.78 190	93.54 211

Anonymous20231211		79.72 262	77.19 268	87.33 236	95.59 122	77.16 241	95.18 217	94.18 229	59.31 347	72.57 280	86.20 281	47.89 316	95.66 246	74.53 240	69.24 290	89.18 257

DTE-MVSNet		78.37 272	77.06 269	82.32 306	85.22 315	67.17 324	93.40 258	93.66 257	78.71 253	70.53 292	88.29 246	59.06 267	92.23 319	61.38 307	63.28 327	87.56 297

EU-MVSNet		76.92 286	76.95 270	76.83 325	84.10 324	54.73 352	91.77 287	92.71 288	72.74 303	69.57 298	88.69 240	58.03 275	87.43 346	64.91 292	70.00 283	88.33 281

PatchT		79.75 261	76.85 271	88.42 209	89.55 267	75.49 264	77.37 346	94.61 211	63.07 332	82.46 176	73.32 344	75.52 139	93.41 309	51.36 337	84.43 198	96.36 160

MVS_0304		78.43 271	76.70 272	83.60 294	88.22 282	69.81 311	92.91 273	95.10 181	72.32 307	78.71 215	80.29 329	33.78 348	93.37 310	68.77 274	80.23 223	87.63 293

RPSCF		77.73 278	76.63 273	81.06 311	88.66 278	55.76 350	87.77 316	87.88 333	64.82 331	74.14 266	92.79 187	49.22 311	96.81 200	67.47 279	76.88 246	90.62 226

FMVSNet1		79.50 264	76.54 274	88.39 211	88.47 279	81.95 114	94.30 240	93.38 269	73.14 299	72.04 284	85.66 284	43.86 325	93.84 301	65.48 289	72.53 264	89.38 251

USDC		78.65 270	76.25 275	85.85 260	87.58 288	74.60 270	89.58 301	90.58 316	84.05 149	63.13 325	88.23 247	40.69 339	96.86 198	66.57 284	75.81 249	86.09 316

OurMVSNet-221017-0		77.18 284	76.06 276	80.55 314	83.78 327	60.00 343	90.35 297	91.05 310	77.01 274	66.62 311	87.92 252	47.73 317	94.03 298	71.63 256	68.44 296	87.62 294

MIMVSNet		79.18 268	75.99 277	88.72 206	87.37 290	80.66 151	79.96 337	91.82 297	77.38 267	74.33 265	81.87 319	41.78 334	90.74 334	66.36 287	83.10 207	94.76 193

RPMNet		79.85 260	75.92 278	91.64 134	90.16 257	79.75 172	79.02 342	95.44 164	58.43 349	82.27 182	72.55 345	73.03 174	98.41 131	46.10 348	86.25 182	96.75 152

LTVRE_ROB	73.68 18	77.99 275	75.74 279	84.74 274	90.45 252	72.02 292	86.41 326	91.12 307	72.57 305	66.63 310	87.27 259	54.95 297	96.98 190	56.29 325	75.98 247	85.21 324
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
tfpnnormal		78.14 274	75.42 280	86.31 255	88.33 281	79.24 184	94.41 235	96.22 120	73.51 295	69.81 297	85.52 290	55.43 292	95.75 241	47.65 346	67.86 303	83.95 332

our_test_3		77.90 277	75.37 281	85.48 268	85.39 311	76.74 245	93.63 253	91.67 299	73.39 298	65.72 315	84.65 303	58.20 272	93.13 312	57.82 318	67.87 302	86.57 309

ACMH	75.40 17	77.99 275	74.96 282	87.10 243	90.67 249	76.41 249	93.19 269	91.64 301	72.47 306	63.44 323	87.61 256	43.34 328	97.16 183	58.34 316	73.94 256	87.72 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	76.62 16	77.47 281	74.94 283	85.05 271	91.07 241	71.58 300	93.26 266	90.01 318	71.80 309	64.76 318	88.55 242	41.62 335	96.48 210	62.35 303	71.00 271	87.09 304

KD-MVS_2432*1600		77.63 279	74.92 284	85.77 262	90.86 245	79.44 179	88.08 312	93.92 241	76.26 275	67.05 307	82.78 315	72.15 183	91.92 322	61.53 304	41.62 352	85.94 318

miper_refine_blended		77.63 279	74.92 284	85.77 262	90.86 245	79.44 179	88.08 312	93.92 241	76.26 275	67.05 307	82.78 315	72.15 183	91.92 322	61.53 304	41.62 352	85.94 318

Patchmatch-test		78.25 273	74.72 286	88.83 203	91.20 237	74.10 276	73.91 351	88.70 331	59.89 346	66.82 309	85.12 298	78.38 90	94.54 290	48.84 344	79.58 228	97.86 95

Patchmtry		77.36 282	74.59 287	85.67 265	89.75 262	75.75 262	77.85 345	91.12 307	60.28 343	71.23 286	80.35 327	75.45 140	93.56 307	57.94 317	67.34 310	87.68 292

CMPMVS	54.94 21	75.71 293	74.56 288	79.17 320	79.69 337	55.98 348	89.59 300	93.30 275	60.28 343	53.85 347	89.07 234	47.68 318	96.33 215	76.55 217	81.02 219	85.22 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TransMVSNet (Re)		76.94 285	74.38 289	84.62 279	85.92 305	75.25 266	95.28 210	89.18 325	73.88 293	67.22 304	86.46 274	59.64 259	94.10 297	59.24 315	52.57 343	84.50 328

SixPastTwentyTwo		76.04 289	74.32 290	81.22 310	84.54 319	61.43 341	91.16 293	89.30 324	77.89 259	64.04 320	86.31 279	48.23 312	94.29 295	63.54 299	63.84 325	87.93 288

ppachtmachnet_test		77.19 283	74.22 291	86.13 258	85.39 311	78.22 212	93.98 248	91.36 304	71.74 310	67.11 306	84.87 301	56.67 284	93.37 310	52.21 335	64.59 320	86.80 306

FMVSNet5		76.46 288	74.16 292	83.35 298	90.05 259	76.17 252	89.58 301	89.85 319	71.39 312	65.29 317	80.42 326	50.61 306	87.70 345	61.05 309	69.24 290	86.18 314

CL-MVSNet_2432*1600		75.81 291	74.14 293	80.83 313	78.33 341	67.79 321	94.22 244	93.52 263	77.28 269	69.82 296	81.54 321	61.47 253	89.22 340	57.59 320	53.51 339	85.48 322

Patchmatch-RL test		76.65 287	74.01 294	84.55 280	77.37 345	64.23 330	78.49 344	82.84 351	78.48 255	64.63 319	73.40 343	76.05 128	91.70 326	76.99 212	57.84 333	97.72 105

Anonymous20231206		75.29 294	73.64 295	80.22 315	80.75 332	63.38 334	93.36 260	90.71 315	73.09 300	67.12 305	83.70 310	50.33 308	90.85 333	53.63 333	70.10 281	86.44 310

testgi		74.88 296	73.40 296	79.32 319	80.13 336	61.75 338	93.21 267	86.64 338	79.49 237	66.56 312	91.06 207	35.51 346	88.67 342	56.79 324	71.25 269	87.56 297

AllTest		75.92 290	73.06 297	84.47 281	92.18 215	67.29 322	91.07 294	84.43 345	67.63 322	63.48 321	90.18 222	38.20 341	97.16 183	57.04 321	73.37 260	88.97 268

COLMAP_ROB	73.24 19	75.74 292	73.00 298	83.94 287	92.38 204	69.08 317	91.85 286	86.93 336	61.48 339	65.32 316	90.27 221	42.27 333	96.93 194	50.91 339	75.63 250	85.80 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DSMNet-mixed		73.13 303	72.45 299	75.19 331	77.51 344	46.82 355	85.09 332	82.01 352	67.61 326	69.27 300	81.33 322	50.89 304	86.28 348	54.54 330	83.80 201	92.46 216

EG-PatchMatch MVS		74.92 295	72.02 300	83.62 293	83.76 328	73.28 282	93.62 254	92.04 295	68.57 321	58.88 338	83.80 309	31.87 352	95.57 255	56.97 323	78.67 235	82.00 343

pmmvs6		74.65 297	71.67 301	83.60 294	79.13 339	69.94 309	93.31 265	90.88 314	61.05 342	65.83 314	84.15 307	43.43 327	94.83 285	66.62 282	60.63 330	86.02 317

K. test v3		73.62 298	71.59 302	79.69 317	82.98 329	59.85 344	90.85 296	88.83 327	77.13 270	58.90 337	82.11 317	43.62 326	91.72 325	65.83 288	54.10 338	87.50 299

test20.03		72.36 307	71.15 303	75.98 329	77.79 342	59.16 345	92.40 280	89.35 323	74.09 291	61.50 332	84.32 305	48.09 313	85.54 351	50.63 340	62.15 329	83.24 333

LF4IMVS		72.36 307	70.82 304	76.95 324	79.18 338	56.33 347	86.12 327	86.11 340	69.30 320	63.06 326	86.66 270	33.03 350	92.25 318	65.33 290	68.64 294	82.28 341

pmmvs-eth3d		73.59 299	70.66 305	82.38 304	76.40 348	73.38 279	89.39 304	89.43 322	72.69 304	60.34 336	77.79 336	46.43 321	91.26 330	66.42 286	57.06 334	82.51 338

UnsupCasMVSNet_eth		73.25 302	70.57 306	81.30 309	77.53 343	66.33 326	87.24 320	93.89 243	80.38 219	57.90 342	81.59 320	42.91 332	90.56 335	65.18 291	48.51 346	87.01 305

YYNet1		73.53 301	70.43 307	82.85 301	84.52 320	71.73 298	91.69 289	91.37 303	67.63 322	46.79 349	81.21 323	55.04 296	90.43 336	55.93 326	59.70 332	86.38 311

MDA-MVSNet_test_wron		73.54 300	70.43 307	82.86 300	84.55 318	71.85 295	91.74 288	91.32 306	67.63 322	46.73 350	81.09 324	55.11 295	90.42 337	55.91 327	59.76 331	86.31 312

Anonymous20240521		72.06 309	69.91 309	78.50 321	77.11 346	61.67 340	91.62 291	90.97 312	65.52 329	62.37 328	79.05 333	36.32 343	90.96 332	57.75 319	68.52 295	82.87 334

OpenMVS_ROB	68.52 20	73.02 304	69.57 310	83.37 297	80.54 335	71.82 296	93.60 255	88.22 332	62.37 334	61.98 330	83.15 314	35.31 347	95.47 257	45.08 349	75.88 248	82.82 335

test_0402		72.68 305	69.54 311	82.09 307	88.67 277	71.81 297	92.72 276	86.77 337	61.52 338	62.21 329	83.91 308	43.22 329	93.76 304	34.60 353	72.23 268	80.72 345

DIV-MVS_2432*1600		70.97 312	69.31 312	75.95 330	76.24 350	55.39 351	87.45 317	90.94 313	70.20 316	62.96 327	77.48 337	44.01 324	88.09 343	61.25 308	53.26 340	84.37 329

TinyColmap		72.41 306	68.99 313	82.68 302	88.11 283	69.59 314	88.41 310	85.20 342	65.55 328	57.91 341	84.82 302	30.80 354	95.94 230	51.38 336	68.70 293	82.49 340

MDA-MVSNet-bldmvs		71.45 310	67.94 314	81.98 308	85.33 313	68.50 319	92.35 281	88.76 329	70.40 314	42.99 351	81.96 318	46.57 320	91.31 329	48.75 345	54.39 337	86.11 315

MVS-HIRNet		71.36 311	67.00 315	84.46 283	90.58 250	69.74 313	79.15 341	87.74 334	46.09 352	61.96 331	50.50 353	45.14 323	95.64 249	53.74 332	88.11 170	88.00 287

PM-MVS		69.32 314	66.93 316	76.49 326	73.60 352	55.84 349	85.91 328	79.32 356	74.72 287	61.09 333	78.18 335	21.76 356	91.10 331	70.86 265	56.90 335	82.51 338

MIMVSNet1		69.44 313	66.65 317	77.84 322	76.48 347	62.84 336	87.42 318	88.97 326	66.96 327	57.75 343	79.72 332	32.77 351	85.83 350	46.32 347	63.42 326	84.85 326

new-patchmatchnet		68.85 316	65.93 318	77.61 323	73.57 353	63.94 333	90.11 299	88.73 330	71.62 311	55.08 345	73.60 342	40.84 338	87.22 347	51.35 338	48.49 347	81.67 344

TDRefinement		69.20 315	65.78 319	79.48 318	66.04 356	62.21 337	88.21 311	86.12 339	62.92 333	61.03 334	85.61 287	33.23 349	94.16 296	55.82 328	53.02 341	82.08 342

UnsupCasMVSNet_bld		68.60 317	64.50 320	80.92 312	74.63 351	67.80 320	83.97 333	92.94 285	65.12 330	54.63 346	68.23 348	35.97 344	92.17 321	60.13 310	44.83 349	82.78 336

new_pmnet		66.18 318	63.18 321	75.18 332	76.27 349	61.74 339	83.79 334	84.66 344	56.64 350	51.57 348	71.85 347	31.29 353	87.93 344	49.98 341	62.55 328	75.86 348

pmmvs3		65.75 319	62.18 322	76.45 327	67.12 355	64.54 329	88.68 308	85.05 343	54.77 351	57.54 344	73.79 341	29.40 355	86.21 349	55.49 329	47.77 348	78.62 346

N_pmnet		61.30 320	60.20 323	64.60 335	84.32 321	17.00 367	91.67 290	10.98 366	61.77 337	58.45 340	78.55 334	49.89 309	91.83 324	42.27 351	63.94 324	84.97 325

FPMVS		55.09 321	52.93 324	61.57 337	55.98 357	40.51 360	83.11 335	83.41 350	37.61 354	34.95 354	71.95 346	14.40 359	76.95 353	29.81 354	65.16 319	67.25 352

LCM-MVSNet		52.52 322	48.24 325	65.35 333	47.63 362	41.45 359	72.55 352	83.62 349	31.75 355	37.66 353	57.92 351	9.19 365	76.76 354	49.26 343	44.60 350	77.84 347

PMMVS2		50.90 323	46.31 326	64.67 334	55.53 358	46.67 356	77.30 347	71.02 358	40.89 353	34.16 355	59.32 349	9.83 364	76.14 356	40.09 352	28.63 355	71.21 349

Gipuma		45.11 325	42.05 327	54.30 339	80.69 333	51.30 354	35.80 358	83.81 348	28.13 356	27.94 357	34.53 357	11.41 363	76.70 355	21.45 356	54.65 336	34.90 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 326	41.93 328	40.38 342	20.10 366	26.84 363	61.93 355	59.09 362	14.81 361	28.51 356	80.58 325	35.53 345	48.33 362	63.70 298	13.11 359	45.96 355

ANet_high		46.22 324	41.28 329	61.04 338	39.91 364	46.25 357	70.59 353	76.18 357	58.87 348	23.09 358	48.00 355	12.58 361	66.54 358	28.65 355	13.62 358	70.35 350

PMVS	34.80 23	39.19 327	35.53 330	50.18 340	29.72 365	30.30 362	59.60 356	66.20 361	26.06 357	17.91 360	49.53 354	3.12 366	74.09 357	18.19 358	49.40 344	46.14 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		32.70 329	32.39 331	33.65 343	53.35 360	25.70 364	74.07 350	53.33 364	21.08 358	17.17 361	33.63 359	11.85 362	54.84 360	12.98 359	14.04 357	20.42 357

EMVS		31.70 330	31.45 332	32.48 344	50.72 361	23.95 365	74.78 349	52.30 365	20.36 359	16.08 362	31.48 360	12.80 360	53.60 361	11.39 360	13.10 360	19.88 358

MVE	35.65 22	33.85 328	29.49 333	46.92 341	41.86 363	36.28 361	50.45 357	56.52 363	18.75 360	18.28 359	37.84 356	2.41 367	58.41 359	18.71 357	20.62 356	46.06 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		21.43 331	28.57 334	0.00 348	0.00 369	0.00 370	0.00 360	95.93 138	0.00 365	0.00 366	97.66 69	63.57 236	0.00 366	0.00 364	0.00 364	0.00 362

wuyk23d		14.10 332	13.89 335	14.72 345	55.23 359	22.91 366	33.83 359	3.56 367	4.94 362	4.11 363	2.28 365	2.06 368	19.66 363	10.23 361	8.74 361	1.59 361

testmvs		9.92 333	12.94 336	0.84 347	0.65 367	0.29 369	93.78 251	0.39 368	0.42 363	2.85 364	15.84 363	0.17 370	0.30 365	2.18 362	0.21 362	1.91 360

test123		9.07 334	11.73 337	1.11 346	0.50 368	0.77 368	89.44 303	0.20 369	0.34 364	2.15 365	10.72 364	0.34 369	0.32 364	1.79 363	0.08 363	2.23 359

ab-mvs-re		8.11 335	10.81 338	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	97.30 91	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

pcd_1.5k_mvsjas		5.92 336	7.89 339	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	71.04 194	0.00 366	0.00 364	0.00 364	0.00 362

uanet_test		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

sosnet-low-res		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

sosnet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

uncertanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

Regformer		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

uanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 364	0.00 362

ZD-MVS					99.09 9	83.22 93		96.60 77	82.88 178	93.61 49	98.06 48	82.93 48	99.14 91	95.51 29	98.49 38

IU-MVS					99.03 13	85.34 47		96.86 42	92.05 15	98.74 1				98.15 2	98.97 15	99.42 9

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 20	92.06 2	99.84 12	99.11 1	99.37 1	99.74 1

test_241102_TWO								96.78 45	88.72 51	97.70 5	98.91 3	87.86 17	99.82 16	98.15 2	99.00 13	99.47 7

test_241102_ONE					99.03 13	85.03 58		96.78 45	88.72 51	97.79 3	98.90 6	88.48 14	99.82 16

save fliter					98.24 52	83.34 89	98.61 23	96.57 80	91.32 18

test_0728_THIRD									88.38 58	96.69 10	98.76 12	89.64 10	99.76 20	97.47 10	98.84 22	99.38 10

test_0728_SECOND				95.14 15	99.04 12	86.14 31	99.06 9	96.77 51					99.84 12	97.90 5	98.85 20	99.45 8

test0726					99.05 10	85.18 51	99.11 8	96.78 45	88.75 49	97.65 6	98.91 3	87.69 18

GSMVS																97.54 117

test_part2					98.90 17	85.14 57				96.07 17

sam_mvs1												77.59 100				97.54 117

sam_mvs												75.35 147

ambc				76.02 328	68.11 354	51.43 353	64.97 354	89.59 320		60.49 335	74.49 340	17.17 358	92.46 315	61.50 306	52.85 342	84.17 331

MTGPA								96.33 112

test_post1							85.88 329				30.24 361	73.77 165	95.07 279	73.89 244

test_post											33.80 358	76.17 126	95.97 226

patchmatchnet-post											77.09 338	77.78 99	95.39 259

GG-mvs-BLEND				93.49 66	94.94 143	86.26 29	81.62 336	97.00 28		88.32 122	94.30 165	91.23 3	96.21 221	88.49 115	97.43 73	98.00 86

MTMP							97.53 76	68.16 359

gm-plane-assit					92.27 209	79.64 177			84.47 138		95.15 146		97.93 142	85.81 134

test9_res														96.00 21	99.03 11	98.31 58

TEST9					98.64 31	83.71 81	97.82 52	96.65 68	84.29 144	95.16 25	98.09 43	84.39 31	99.36 70

test_8					98.63 33	83.64 84	97.81 54	96.63 73	84.50 136	95.10 27	98.11 42	84.33 32	99.23 76

agg_prior2														94.30 42	99.00 13	98.57 41

agg_prior					98.59 35	83.13 94		96.56 82		94.19 41			99.16 89

TestCases				84.47 281	92.18 215	67.29 322		84.43 345	67.63 322	63.48 321	90.18 222	38.20 341	97.16 183	57.04 321	73.37 260	88.97 268

test_prior4						82.34 108	97.75 61

test_prior2							98.37 27		86.08 95	94.57 38	98.02 49	83.14 44		95.05 35	98.79 23

test_prior				93.09 81	98.68 25	81.91 117		96.40 104					99.06 97			98.29 60

旧先验2							96.97 126		74.06 292	96.10 16			97.76 151	88.38 117

新几何2							96.42 162

新几何1				93.12 79	97.44 84	81.60 131		96.71 59	74.54 288	91.22 83	97.57 75	79.13 81	99.51 56	77.40 210	98.46 39	98.26 63

旧先验1					97.39 88	79.58 178		96.54 85			98.08 46	84.00 36			97.42 74	97.62 114

无先验							96.87 132	96.78 45	77.39 266				99.52 53	79.95 184		98.43 50

原ACMM2							96.84 133

原ACMM1				91.22 146	97.77 72	78.10 218		96.61 74	81.05 203	91.28 81	97.42 86	77.92 97	98.98 102	79.85 187	98.51 34	96.59 155

test222					96.15 108	78.41 206	95.87 191	96.46 95	71.97 308	89.66 103	97.45 82	76.33 124			98.24 54	98.30 59

testdata2													99.48 58	76.45 219

segment_acmp												82.69 53

testdata				90.13 174	95.92 114	74.17 275		96.49 94	73.49 297	94.82 35	97.99 52	78.80 85	97.93 142	83.53 159	97.52 69	98.29 60

testdata1							95.57 202		87.44 76

test12				94.25 35	98.34 47	85.55 44		96.35 111		92.36 62		80.84 60	99.22 78		98.31 52	97.98 88

plane_prior7					91.86 229	77.55 232

plane_prior6					91.98 224	77.92 224						64.77 231

plane_prior5								94.69 202					97.30 175	87.08 126	82.82 212	90.96 223

plane_prior4											94.15 170

plane_prior3						77.75 228			90.17 33	81.33 190

plane_prior2							97.18 101		89.89 35

plane_prior1					91.95 227

plane_prior						77.96 221	97.52 78		90.36 32						82.96 210

n2								0.00 370

nn								0.00 370

door-mid								79.75 355

lessismore_v0				79.98 316	80.59 334	58.34 346		80.87 353		58.49 339	83.46 312	43.10 330	93.89 300	63.11 301	48.68 345	87.72 290

LGP-MVS_train				86.33 252	90.88 243	73.06 284		94.13 231	82.20 188	76.31 240	93.20 182	54.83 298	96.95 191	83.72 152	80.83 220	88.98 266

test11								96.50 91

door								80.13 354

HQP5-MVS						78.48 202

HQP-NCC					92.08 219		97.63 67		90.52 27	82.30 178

ACMP_Plane					92.08 219		97.63 67		90.52 27	82.30 178

BP-MVS														87.67 122

HQP4-MVS										82.30 178			97.32 173			91.13 221

HQP3-MVS								94.80 198							83.01 208

HQP2-MVS												65.40 226

NP-MVS					92.04 223	78.22 212					94.56 160

MDTV_nov1_ep13_2view						81.74 126	86.80 322		80.65 210	85.65 142		74.26 160		76.52 218		96.98 140

ACMMP++_ref															78.45 240

ACMMP++															79.05 232

Test By Simon												71.65 187

ITE_SJBPF				82.38 304	87.00 292	65.59 327		89.55 321	79.99 229	69.37 299	91.30 204	41.60 336	95.33 263	62.86 302	74.63 255	86.24 313

DeepMVS_CX				64.06 336	78.53 340	43.26 358		68.11 360	69.94 317	38.55 352	76.14 339	18.53 357	79.34 352	43.72 350	41.62 352	69.57 351

High-resolution multi-view benchmark