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.

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 151	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 72	97.64 2	97.94 1

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 100	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22
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		90.32 4	90.89 4	88.61 16	96.76 7	70.65 23	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 78	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 44	95.94 7	97.03 8
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
SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 43	96.89 4	94.44 40	71.65 185	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 123	93.00 73	58.16 279	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 39	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 31	96.64 8	94.52 36	71.92 171	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26
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
HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 71	95.24 31	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 50	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 86	94.17 52	94.15 53	68.77 232	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25

SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 58	93.85 73	94.03 56	74.18 117	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
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
PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 95	76.72 1	95.75 19	93.26 86	83.86 10	89.55 16	96.06 28	53.55 194	97.89 44	91.10 13	93.31 50	94.54 88

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 109	68.04 74	90.36 205	93.55 73	82.89 13	91.29 9	92.89 113	72.27 25	96.03 128	87.99 35	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 34	93.84 59	78.17 66	85.93 38	94.80 67	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 66	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 71	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 121	76.43 3	95.74 20	93.12 95	83.53 12	89.55 16	95.95 29	53.45 198	97.68 50	91.07 14	92.62 59	94.54 88

EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 68	91.68 73	95.29 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 21	87.77 21	87.63 30	89.24 161	71.18 18	96.57 10	92.90 103	82.70 16	87.13 26	95.27 47	64.99 70	95.80 133	89.34 22	91.80 71	95.93 36

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 97	94.31 50	94.14 54	72.92 146	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 68

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 223	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 56	93.90 70	92.63 114	76.86 82	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
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
test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 93	95.10 36	93.10 96	75.40 99	85.25 48	95.61 37	67.94 40	96.84 101	87.47 39	94.77 23	95.05 70

alignmvs		87.28 26	86.97 34	88.24 20	91.30 122	71.14 20	95.61 24	93.56 72	79.30 47	87.07 29	95.25 49	68.43 34	96.93 99	87.87 36	84.33 137	96.65 11

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 137	93.95 66	95.12 15	82.11 21	84.25 54	95.73 33	67.88 43	98.35 29	85.60 54	88.64 100	94.26 96

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 95	94.16 53	93.51 74	71.87 176	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 63

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 78	95.05 38	84.72 302	78.19 64	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

MG-MVS		87.11 30	86.27 40	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 60	83.87 62	92.94 110	64.34 78	96.94 97	75.19 130	94.09 36	95.66 42

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 99	67.48 91	92.97 105	94.21 51	71.48 196	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 84

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 92	93.64 81	93.76 63	70.78 208	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 106	94.08 59	93.31 84	71.85 178	84.49 52	95.39 40	68.91 33	96.75 105	88.84 29	94.32 34	95.13 66

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 27	64.73 166	93.95 66	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 56	88.64 100	94.20 98

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 22	93.83 77	95.33 11	68.48 236	77.63 118	94.35 81	73.04 20	98.45 25	84.92 60	93.71 45	96.92 9

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 108	71.92 13	93.54 86	91.71 148	80.26 38	87.55 24	95.25 49	63.59 90	96.93 99	88.18 34	84.34 136	97.11 6

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 144	95.39 29	95.10 17	71.77 182	85.69 42	96.52 15	62.07 104	98.77 17	86.06 52	95.60 10	96.03 33

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 137	66.38 121	96.09 15	93.87 58	77.73 71	84.01 61	95.66 35	63.39 92	97.94 39	87.40 41	93.55 48	95.42 47
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PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 88	96.33 14	93.61 70	82.34 18	81.00 81	93.08 105	63.19 95	97.29 73	87.08 45	91.38 78	94.13 104

testtj		86.62 40	86.66 39	86.50 63	96.95 5	65.70 139	94.41 48	93.45 78	67.74 238	86.19 34	96.39 20	64.38 77	97.91 42	87.33 42	93.14 53	95.90 38

CS-MVS		86.61 41	86.85 37	85.88 82	91.52 117	66.25 126	95.42 27	92.25 124	80.36 37	84.10 59	94.82 66	62.88 99	96.08 124	88.25 33	92.07 69	95.30 55

jason		86.40 42	86.17 43	87.11 41	86.16 219	70.54 25	95.71 23	92.19 131	82.00 23	84.58 51	94.34 82	61.86 106	95.53 153	87.76 37	90.89 84	95.27 60
jason: jason.
WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 78	69.37 45	95.20 32	95.25 12	82.71 15	81.91 70	94.73 68	67.93 42	97.63 56	79.55 100	82.25 148	96.54 16

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 98	68.97 53	95.04 40	92.70 108	79.04 55	81.50 74	96.50 17	58.98 135	96.78 103	83.49 70	93.93 39	96.29 26

VNet		86.20 45	85.65 52	87.84 23	93.92 47	69.99 31	95.73 22	95.94 6	78.43 62	86.00 37	93.07 107	58.22 138	97.00 89	85.22 57	84.33 137	96.52 17

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 69	69.79 37	93.99 64	93.76 63	79.08 54	78.88 107	93.99 91	62.25 103	98.15 34	85.93 53	91.15 82	94.15 103

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 113	67.53 88	91.79 150	93.49 77	74.93 107	84.61 50	95.30 44	59.42 128	97.92 41	86.13 51	94.92 17	94.94 75

ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 142	67.02 105	93.43 91	91.92 139	81.21 29	84.13 58	94.07 90	60.93 113	95.63 143	89.28 23	89.81 92	94.46 94

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 23	65.21 151	93.59 84	92.58 116	66.54 249	86.17 35	95.88 30	63.83 84	97.00 89	86.39 50	92.94 55	95.06 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 50	85.46 53	87.18 39	88.20 185	72.42 12	92.41 126	92.77 106	82.11 21	80.34 87	93.07 107	68.27 35	95.02 163	78.39 112	93.59 47	94.09 106

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 42	65.09 159	92.95 107	95.11 16	81.13 30	81.68 72	95.04 54	65.82 62	98.32 30	83.02 73	84.36 134	92.97 141

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 98	93.89 71	92.83 105	70.90 204	83.09 65	95.28 45	63.62 88	97.36 68	80.63 94	94.18 35	94.84 77

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 170	66.64 115	92.15 131	93.68 67	81.07 31	76.91 128	93.64 96	62.59 101	98.44 26	85.50 55	92.84 57	94.03 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-4		85.45 54	85.69 51	84.73 121	94.17 42	63.23 204	92.95 107	94.83 23	80.66 34	81.29 75	95.04 54	65.12 68	98.08 36	82.74 75	84.36 134	92.88 145

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 183	69.07 49	93.04 102	91.76 145	81.27 28	80.84 83	92.07 130	64.23 79	96.06 126	84.98 59	87.43 110	95.39 48

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 141	93.89 71	93.41 82	73.75 128	79.94 91	94.68 70	60.61 116	98.03 37	82.63 78	93.72 44	94.52 90

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 119	65.59 143	91.54 162	92.51 118	74.56 110	80.62 84	95.64 36	59.15 132	97.00 89	86.94 47	93.80 41	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 67	91.85 148	93.00 100	76.59 86	79.03 103	95.00 56	61.59 107	97.61 58	78.16 114	89.00 97	95.63 43

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 84	64.27 183	93.24 95	92.27 123	73.13 140	79.63 96	94.43 75	61.90 105	97.17 80	85.00 58	92.56 60	94.06 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 60	84.44 64	86.71 53	88.33 180	68.73 57	90.24 209	91.82 144	81.05 32	81.18 77	92.50 120	63.69 87	96.08 124	84.45 63	86.71 118	95.32 53

#test#		84.98 61	84.74 61	85.72 90	93.75 53	65.01 160	94.09 58	93.19 90	73.55 134	79.22 100	94.93 60	59.04 133	97.67 51	82.66 76	92.21 64	94.49 92

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 146	75.14 5	92.07 137	92.32 121	81.87 24	75.68 135	88.27 176	60.18 119	98.60 21	80.46 96	90.27 91	94.96 74

EIA-MVS		84.84 63	84.88 59	84.69 124	91.30 122	62.36 222	93.85 73	92.04 134	79.45 45	79.33 99	94.28 85	62.42 102	96.35 114	80.05 97	91.25 81	95.38 49

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 104	65.16 153	91.55 161	92.23 125	75.32 101	80.53 85	95.21 51	56.06 167	97.16 81	84.86 61	92.55 61	94.18 99

HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 53	65.01 160	93.50 88	93.19 90	72.19 165	79.22 100	94.93 60	59.04 133	97.67 51	81.55 85	92.21 64	94.49 92

MVS		84.66 66	82.86 88	90.06 2	90.93 128	74.56 6	87.91 253	95.54 9	68.55 234	72.35 172	94.71 69	59.78 124	98.90 14	81.29 91	94.69 28	96.74 10

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 78	65.27 149	93.04 102	93.13 94	73.20 138	78.89 104	94.18 87	59.41 129	97.85 46	81.45 87	92.48 63	93.86 117

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 58	64.37 178	93.50 88	93.15 93	72.19 165	78.85 109	94.86 64	56.69 159	97.45 62	81.55 85	92.20 66	94.02 111

region2R		84.36 69	84.03 68	85.36 104	93.54 59	64.31 180	93.43 91	92.95 101	72.16 168	78.86 108	94.84 65	56.97 154	97.53 60	81.38 89	92.11 68	94.24 97

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 140	71.90 173	82.16 69	93.49 100	47.98 244	97.05 84	82.55 79	84.82 130	97.25 5

test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 226	81.09 78	92.88 114	57.00 152	97.44 63	81.11 92	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 226	81.09 78	92.88 114	57.00 152	97.44 63	81.11 92	81.76 152	96.23 28

diffmvs		84.28 71	83.83 69	85.61 95	87.40 200	68.02 75	90.88 190	89.24 236	80.54 35	81.64 73	92.52 119	59.83 123	94.52 182	87.32 43	85.11 128	94.29 95

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 92	67.74 82	84.65 276	94.50 37	79.15 51	82.23 68	87.93 183	66.88 51	96.94 97	80.53 95	82.20 149	96.39 24

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 134	94.28 51	94.27 50	74.41 111	79.16 102	95.61 37	53.99 189	98.88 16	69.62 175	93.26 51	94.50 91
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
MVS_Test		84.16 76	83.20 81	87.05 43	91.56 114	69.82 36	89.99 218	92.05 133	77.77 70	82.84 66	86.57 198	63.93 83	96.09 122	74.91 136	89.18 96	95.25 63

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 140	66.82 110	91.87 146	89.01 249	85.27 6	86.09 36	93.74 95	47.71 247	96.98 93	77.90 117	89.78 94	93.65 121

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 134	70.89 21	94.74 45	94.62 34	81.44 27	58.19 294	93.64 96	73.64 19	92.35 255	82.66 76	78.66 174	96.50 19

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 144	66.59 118	93.77 78	91.73 146	77.43 77	77.08 127	89.81 162	63.77 86	96.97 94	79.67 99	88.21 103	92.60 149

DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 30	89.12 235	94.42 42	82.15 20	77.64 117	91.77 134	70.81 30	96.22 117	65.03 221	81.36 156	95.94 35

MTAPA		83.91 81	83.38 79	85.50 97	91.89 104	65.16 153	81.75 295	92.23 125	75.32 101	80.53 85	95.21 51	56.06 167	97.16 81	84.86 61	92.55 61	94.18 99

XVS		83.87 82	83.47 73	85.05 111	93.22 65	63.78 190	92.92 109	92.66 111	73.99 120	78.18 112	94.31 84	55.25 173	97.41 65	79.16 103	91.58 75	93.95 113

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 154	69.40 42	91.35 172	86.12 292	72.59 151	83.22 64	92.81 117	59.60 126	96.01 130	81.76 83	87.80 107	95.56 45

EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 139	92.79 81	63.56 200	91.76 153	94.81 25	79.65 44	77.87 114	94.09 88	63.35 93	97.90 43	79.35 101	79.36 166	90.74 182

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 161	71.18 18	89.07 236	90.69 184	65.80 254	87.13 26	94.34 82	64.99 70	92.67 241	72.83 145	91.80 71	95.27 60

CP-MVS		83.71 86	83.40 78	84.65 125	93.14 70	63.84 188	94.59 46	92.28 122	71.03 202	77.41 121	94.92 62	55.21 176	96.19 118	81.32 90	90.70 86	93.91 115

baseline2		83.68 87	83.42 77	84.48 131	87.37 201	66.00 131	90.06 213	95.93 7	79.71 43	69.08 207	90.39 152	77.92 4	96.28 115	78.91 107	81.38 155	91.16 178

thisisatest0515		83.41 88	82.49 94	86.16 75	89.46 157	68.26 69	93.54 86	94.70 30	74.31 114	75.75 134	90.92 142	72.62 23	96.52 112	69.64 173	81.50 154	93.71 119

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 157	93.76 51	67.53 88	94.06 60	93.61 70	79.13 52	81.00 81	85.14 212	63.19 95	97.29 73	87.08 45	73.91 205	84.83 272

PGM-MVS		83.25 90	82.70 92	84.92 115	92.81 80	64.07 186	90.44 201	92.20 130	71.28 197	77.23 124	94.43 75	55.17 177	97.31 72	79.33 102	91.38 78	93.37 126

HPM-MVS		83.25 90	82.95 86	84.17 137	92.25 91	62.88 215	90.91 187	91.86 141	70.30 213	77.12 125	93.96 92	56.75 157	96.28 115	82.04 81	91.34 80	93.34 127
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 149	92.28 90	63.19 206	91.38 170	94.68 31	79.22 49	76.60 129	93.75 94	62.64 100	97.76 49	78.07 115	78.01 177	90.05 190

VDD-MVS		83.06 93	81.81 103	86.81 49	90.86 132	67.70 83	95.40 28	91.50 157	75.46 96	81.78 71	92.34 127	40.09 280	97.13 83	86.85 48	82.04 150	95.60 44

PAPM_NR		82.97 94	81.84 101	86.37 69	94.10 46	66.76 113	87.66 257	92.84 104	69.96 216	74.07 151	93.57 98	63.10 97	97.50 61	70.66 168	90.58 88	94.85 76

mPP-MVS		82.96 95	82.44 95	84.52 129	92.83 76	62.92 213	92.76 112	91.85 142	71.52 193	75.61 138	94.24 86	53.48 197	96.99 92	78.97 106	90.73 85	93.64 122

SR-MVS		82.81 96	82.58 93	83.50 154	93.35 62	61.16 238	92.23 130	91.28 166	64.48 262	81.27 76	95.28 45	53.71 193	95.86 132	82.87 74	88.77 99	93.49 125

DP-MVS Recon		82.73 97	81.65 104	85.98 79	97.31 4	67.06 102	95.15 34	91.99 136	69.08 229	76.50 131	93.89 93	54.48 185	98.20 32	70.76 166	85.66 125	92.69 146

CLD-MVS		82.73 97	82.35 97	83.86 142	87.90 192	67.65 85	95.45 26	92.18 132	85.06 7	72.58 165	92.27 128	52.46 204	95.78 134	84.18 64	79.06 169	88.16 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 99	82.38 96	83.73 146	89.25 160	59.58 264	92.24 129	94.89 21	77.96 68	79.86 93	92.38 125	56.70 158	97.05 84	77.26 120	80.86 160	94.55 86

3Dnovator	73.91 6	82.69 100	80.82 113	88.31 19	89.57 153	71.26 17	92.60 120	94.39 46	78.84 57	67.89 226	92.48 123	48.42 239	98.52 22	68.80 185	94.40 33	95.15 65

MVSTER		82.47 101	82.05 98	83.74 144	92.68 83	69.01 51	91.90 145	93.21 87	79.83 39	72.14 173	85.71 208	74.72 13	94.72 173	75.72 127	72.49 215	87.50 219

TESTMET0.1,1		82.41 102	81.98 100	83.72 147	88.08 186	63.74 192	92.70 115	93.77 62	79.30 47	77.61 119	87.57 188	58.19 139	94.08 196	73.91 140	86.68 119	93.33 129

CostFormer		82.33 103	81.15 108	85.86 85	89.01 166	68.46 63	82.39 293	93.01 98	75.59 94	80.25 88	81.57 253	72.03 27	94.96 165	79.06 105	77.48 186	94.16 102

API-MVS		82.28 104	80.53 119	87.54 31	96.13 20	70.59 24	93.63 82	91.04 177	65.72 256	75.45 140	92.83 116	56.11 166	98.89 15	64.10 226	89.75 95	93.15 135

IB-MVS	77.80 4	82.18 105	80.46 121	87.35 36	89.14 163	70.28 28	95.59 25	95.17 14	78.85 56	70.19 194	85.82 206	70.66 31	97.67 51	72.19 155	66.52 254	94.09 106
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
xiu_mvs_v1_base_debu		82.16 106	81.12 109	85.26 107	86.42 213	68.72 58	92.59 122	90.44 192	73.12 141	84.20 55	94.36 77	38.04 291	95.73 137	84.12 65	86.81 113	91.33 172

xiu_mvs_v1_base		82.16 106	81.12 109	85.26 107	86.42 213	68.72 58	92.59 122	90.44 192	73.12 141	84.20 55	94.36 77	38.04 291	95.73 137	84.12 65	86.81 113	91.33 172

xiu_mvs_v1_base_debi		82.16 106	81.12 109	85.26 107	86.42 213	68.72 58	92.59 122	90.44 192	73.12 141	84.20 55	94.36 77	38.04 291	95.73 137	84.12 65	86.81 113	91.33 172

3Dnovator+	73.60 7	82.10 109	80.60 118	86.60 57	90.89 131	66.80 112	95.20 32	93.44 80	74.05 119	67.42 232	92.49 122	49.46 229	97.65 55	70.80 165	91.68 73	95.33 51

MVS_111021_LR		82.02 110	81.52 105	83.51 153	88.42 178	62.88 215	89.77 221	88.93 251	76.78 84	75.55 139	93.10 103	50.31 221	95.38 156	83.82 69	87.02 112	92.26 161

PMMVS		81.98 111	82.04 99	81.78 192	89.76 150	56.17 296	91.13 183	90.69 184	77.96 68	80.09 90	93.57 98	46.33 255	94.99 164	81.41 88	87.46 109	94.17 101

test1172		81.90 112	81.83 102	82.13 184	93.23 64	57.52 287	91.61 160	90.98 179	64.32 264	80.20 89	95.00 56	51.26 214	95.61 145	81.73 84	88.13 104	93.26 131

baseline1		81.84 113	81.03 112	84.28 136	91.60 112	66.62 116	91.08 184	91.66 151	81.87 24	74.86 143	91.67 138	69.98 32	94.92 168	71.76 159	64.75 267	91.29 177

EPP-MVSNet		81.79 114	81.52 105	82.61 168	88.77 171	60.21 256	93.02 104	93.66 69	68.52 235	72.90 160	90.39 152	72.19 26	94.96 165	74.93 135	79.29 168	92.67 147

APD-MVS_3200maxsize		81.64 115	81.32 107	82.59 169	92.36 87	58.74 273	91.39 168	91.01 178	63.35 270	79.72 95	94.62 71	51.82 207	96.14 120	79.71 98	87.93 106	92.89 144

ACMMP		81.49 116	80.67 116	83.93 141	91.71 110	62.90 214	92.13 132	92.22 129	71.79 181	71.68 181	93.49 100	50.32 220	96.96 95	78.47 111	84.22 141	91.93 164
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
CDS-MVSNet		81.43 117	80.74 114	83.52 152	86.26 217	64.45 172	92.09 135	90.65 187	75.83 93	73.95 153	89.81 162	63.97 82	92.91 231	71.27 162	82.82 145	93.20 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 118	79.99 125	85.46 99	90.39 139	68.40 64	86.88 267	90.61 188	74.41 111	70.31 193	84.67 218	63.79 85	92.32 256	73.13 142	85.70 124	95.67 41

1121		81.25 119	80.05 123	84.87 118	92.30 89	64.31 180	87.91 253	91.39 161	59.44 301	79.94 91	92.91 111	57.09 148	97.01 87	66.63 200	92.81 58	93.29 130

thisisatest0530		81.15 120	80.07 122	84.39 133	88.26 182	65.63 142	91.40 166	94.62 34	71.27 198	70.93 185	89.18 165	72.47 24	96.04 127	65.62 215	76.89 192	91.49 169

Fast-Effi-MVS+		81.14 121	80.01 124	84.51 130	90.24 141	65.86 135	94.12 57	89.15 242	73.81 127	75.37 141	88.26 177	57.26 146	94.53 181	66.97 199	84.92 129	93.15 135

HQP-MVS		81.14 121	80.64 117	82.64 167	87.54 196	63.66 197	94.06 60	91.70 149	79.80 40	74.18 147	90.30 154	51.63 211	95.61 145	77.63 118	78.90 170	88.63 204

SR-MVS-dyc-post		81.06 123	80.70 115	82.15 182	92.02 96	58.56 275	90.90 188	90.45 189	62.76 276	78.89 104	94.46 73	51.26 214	95.61 145	78.77 109	86.77 116	92.28 158

HyFIR lowres test		81.03 124	79.56 133	85.43 101	87.81 193	68.11 73	90.18 210	90.01 213	70.65 210	72.95 159	86.06 204	63.61 89	94.50 183	75.01 134	79.75 164	93.67 120

nrg030		80.93 125	79.86 127	84.13 138	83.69 257	68.83 55	93.23 96	91.20 167	75.55 95	75.06 142	88.22 180	63.04 98	94.74 172	81.88 82	66.88 251	88.82 202

Vis-MVSNet		80.92 126	79.98 126	83.74 144	88.48 175	61.80 228	93.44 90	88.26 271	73.96 123	77.73 115	91.76 135	49.94 225	94.76 170	65.84 212	90.37 90	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
1314		80.70 127	78.95 145	85.94 81	87.77 194	67.56 86	87.91 253	92.55 117	72.17 167	67.44 231	93.09 104	50.27 222	97.04 86	71.68 161	87.64 108	93.23 133

RRT_test8_iter05		80.61 128	79.62 131	83.60 151	91.87 107	66.90 108	93.42 93	93.68 67	77.09 80	68.83 213	85.63 209	66.82 52	95.42 154	76.46 125	62.74 280	88.48 207

tpmrst		80.57 129	79.14 144	84.84 119	90.10 143	68.28 68	81.70 296	89.72 224	77.63 73	75.96 133	79.54 284	64.94 72	92.71 238	75.43 128	77.28 189	93.55 123

1112_ss		80.56 130	79.83 128	82.77 163	88.65 172	60.78 244	92.29 127	88.36 266	72.58 152	72.46 169	94.95 58	65.09 69	93.42 219	66.38 206	77.71 179	94.10 105

VDDNet		80.50 131	78.26 152	87.21 38	86.19 218	69.79 37	94.48 47	91.31 163	60.42 293	79.34 98	90.91 143	38.48 287	96.56 111	82.16 80	81.05 158	95.27 60

BH-w/o		80.49 132	79.30 140	84.05 140	90.83 133	64.36 179	93.60 83	89.42 231	74.35 113	69.09 206	90.15 157	55.23 175	95.61 145	64.61 223	86.43 122	92.17 162

TAMVS		80.37 133	79.45 136	83.13 159	85.14 234	63.37 201	91.23 177	90.76 183	74.81 109	72.65 163	88.49 171	60.63 115	92.95 226	69.41 177	81.95 151	93.08 138

HQP_MVS		80.34 134	79.75 129	82.12 185	86.94 207	62.42 220	93.13 98	91.31 163	78.81 58	72.53 166	89.14 167	50.66 218	95.55 151	76.74 121	78.53 175	88.39 210

HPM-MVS_fast		80.25 135	79.55 135	82.33 175	91.55 115	59.95 259	91.32 174	89.16 241	65.23 260	74.71 144	93.07 107	47.81 246	95.74 136	74.87 138	88.23 102	91.31 176

ab-mvs		80.18 136	78.31 151	85.80 87	88.44 177	65.49 147	83.00 290	92.67 110	71.82 180	77.36 122	85.01 213	54.50 182	96.59 108	76.35 126	75.63 198	95.32 53

IS-MVSNet		80.14 137	79.41 137	82.33 175	87.91 191	60.08 258	91.97 143	88.27 269	72.90 147	71.44 183	91.73 137	61.44 108	93.66 214	62.47 238	86.53 120	93.24 132

test-LLR		80.10 138	79.56 133	81.72 194	86.93 209	61.17 236	92.70 115	91.54 154	71.51 194	75.62 136	86.94 195	53.83 190	92.38 252	72.21 153	84.76 132	91.60 167

PVSNet	73.49 8	80.05 139	78.63 147	84.31 134	90.92 129	64.97 162	92.47 125	91.05 176	79.18 50	72.43 170	90.51 150	37.05 303	94.06 198	68.06 188	86.00 123	93.90 116

UA-Net		80.02 140	79.65 130	81.11 209	89.33 158	57.72 283	86.33 270	89.00 250	77.44 76	81.01 80	89.15 166	59.33 130	95.90 131	61.01 245	84.28 139	89.73 194

test-mter		79.96 141	79.38 139	81.72 194	86.93 209	61.17 236	92.70 115	91.54 154	73.85 125	75.62 136	86.94 195	49.84 227	92.38 252	72.21 153	84.76 132	91.60 167

QAPM		79.95 142	77.39 170	87.64 26	89.63 152	71.41 16	93.30 94	93.70 66	65.34 259	67.39 234	91.75 136	47.83 245	98.96 12	57.71 260	89.81 92	92.54 151

UGNet		79.87 143	78.68 146	83.45 156	89.96 145	61.51 233	92.13 132	90.79 182	76.83 83	78.85 109	86.33 201	38.16 289	96.17 119	67.93 190	87.17 111	92.67 147
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
abl_6		79.82 144	79.20 142	81.70 196	89.85 147	58.34 277	88.47 246	90.07 208	62.56 279	77.71 116	93.08 105	47.65 248	96.78 103	77.94 116	85.45 127	89.99 191

tpm2		79.80 145	77.95 158	85.34 105	88.28 181	68.26 69	81.56 298	91.42 160	70.11 214	77.59 120	80.50 271	67.40 46	94.26 191	67.34 195	77.35 187	93.51 124

thres200		79.66 146	78.33 150	83.66 150	92.54 85	65.82 138	93.06 100	96.31 3	74.90 108	73.30 156	88.66 169	59.67 125	95.61 145	47.84 296	78.67 173	89.56 197

test_part1		79.63 147	77.86 161	84.93 114	92.50 86	71.43 15	94.15 55	91.08 174	72.51 154	70.66 186	84.98 214	59.84 122	95.07 162	72.07 156	62.94 278	88.30 213

CPTT-MVS		79.59 148	79.16 143	80.89 218	91.54 116	59.80 261	92.10 134	88.54 264	60.42 293	72.96 158	93.28 102	48.27 240	92.80 235	78.89 108	86.50 121	90.06 189

Test_1112_low_res		79.56 149	78.60 148	82.43 171	88.24 184	60.39 253	92.09 135	87.99 275	72.10 169	71.84 177	87.42 190	64.62 75	93.04 223	65.80 213	77.30 188	93.85 118

tttt0517		79.50 150	78.53 149	82.41 174	87.22 203	61.43 235	89.75 222	94.76 27	69.29 224	67.91 225	88.06 182	72.92 21	95.63 143	62.91 234	73.90 206	90.16 188

FIs		79.47 151	79.41 137	79.67 241	85.95 222	59.40 266	91.68 157	93.94 57	78.06 67	68.96 210	88.28 175	66.61 55	91.77 267	66.20 209	74.99 199	87.82 216

BH-RMVSNet		79.46 152	77.65 163	84.89 116	91.68 111	65.66 140	93.55 85	88.09 273	72.93 145	73.37 155	91.12 141	46.20 257	96.12 121	56.28 265	85.61 126	92.91 143

PCF-MVS	73.15 9	79.29 153	77.63 164	84.29 135	86.06 220	65.96 133	87.03 263	91.10 172	69.86 218	69.79 202	90.64 146	57.54 145	96.59 108	64.37 225	82.29 147	90.32 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 154	79.57 132	78.24 261	88.46 176	52.29 315	90.41 203	89.12 244	74.24 116	69.13 205	91.91 132	65.77 63	90.09 291	59.00 257	88.09 105	92.33 155

114514_t		79.17 155	77.67 162	83.68 148	95.32 26	65.53 145	92.85 111	91.60 153	63.49 269	67.92 224	90.63 148	46.65 252	95.72 141	67.01 198	83.54 142	89.79 192

VPA-MVSNet		79.03 156	78.00 156	82.11 188	85.95 222	64.48 171	93.22 97	94.66 32	75.05 106	74.04 152	84.95 215	52.17 206	93.52 216	74.90 137	67.04 250	88.32 212

OPM-MVS		79.00 157	78.09 154	81.73 193	83.52 260	63.83 189	91.64 159	90.30 199	76.36 89	71.97 176	89.93 161	46.30 256	95.17 161	75.10 131	77.70 180	86.19 246

EI-MVSNet		78.97 158	78.22 153	81.25 204	85.33 230	62.73 218	89.53 226	93.21 87	72.39 159	72.14 173	90.13 158	60.99 111	94.72 173	67.73 192	72.49 215	86.29 243

AdaColmap		78.94 159	77.00 175	84.76 120	96.34 16	65.86 135	92.66 119	87.97 276	62.18 282	70.56 187	92.37 126	43.53 269	97.35 69	64.50 224	82.86 144	91.05 180

miper_enhance_ethall		78.86 160	77.97 157	81.54 198	88.00 190	65.17 152	91.41 164	89.15 242	75.19 104	68.79 214	83.98 225	67.17 48	92.82 233	72.73 147	65.30 258	86.62 239

VPNet		78.82 161	77.53 166	82.70 165	84.52 244	66.44 120	93.93 68	92.23 125	80.46 36	72.60 164	88.38 174	49.18 233	93.13 222	72.47 151	63.97 275	88.55 206

EPNet_dtu		78.80 162	79.26 141	77.43 269	88.06 187	49.71 328	91.96 144	91.95 138	77.67 72	76.56 130	91.28 140	58.51 137	90.20 289	56.37 264	80.95 159	92.39 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 163	77.43 167	82.88 161	92.21 93	64.49 169	92.05 138	96.28 4	73.48 135	71.75 179	88.26 177	60.07 120	95.32 157	45.16 305	77.58 182	88.83 200

TR-MVS		78.77 164	77.37 171	82.95 160	90.49 136	60.88 242	93.67 80	90.07 208	70.08 215	74.51 145	91.37 139	45.69 258	95.70 142	60.12 251	80.32 161	92.29 157

mvs-test1		78.74 165	77.95 158	81.14 207	83.22 262	57.13 291	93.96 65	87.78 277	75.42 97	72.68 162	90.80 145	45.08 262	94.54 180	75.08 132	77.49 185	91.74 166

thres400		78.68 166	77.43 167	82.43 171	92.21 93	64.49 169	92.05 138	96.28 4	73.48 135	71.75 179	88.26 177	60.07 120	95.32 157	45.16 305	77.58 182	87.48 220

BH-untuned		78.68 166	77.08 172	83.48 155	89.84 148	63.74 192	92.70 115	88.59 262	71.57 191	66.83 240	88.65 170	51.75 209	95.39 155	59.03 256	84.77 131	91.32 175

OMC-MVS		78.67 168	77.91 160	80.95 216	85.76 226	57.40 289	88.49 245	88.67 259	73.85 125	72.43 170	92.10 129	49.29 232	94.55 179	72.73 147	77.89 178	90.91 181

tpm		78.58 169	77.03 173	83.22 157	85.94 224	64.56 167	83.21 288	91.14 171	78.31 63	73.67 154	79.68 282	64.01 81	92.09 261	66.07 210	71.26 225	93.03 139

OpenMVS	70.45 11	78.54 170	75.92 189	86.41 68	85.93 225	71.68 14	92.74 113	92.51 118	66.49 250	64.56 255	91.96 131	43.88 268	98.10 35	54.61 269	90.65 87	89.44 198

EPMVS		78.49 171	75.98 188	86.02 78	91.21 124	69.68 40	80.23 308	91.20 167	75.25 103	72.48 168	78.11 292	54.65 181	93.69 213	57.66 261	83.04 143	94.69 80

AUN-MVS		78.37 172	77.43 167	81.17 206	86.60 212	57.45 288	89.46 228	91.16 169	74.11 118	74.40 146	90.49 151	55.52 172	94.57 176	74.73 139	60.43 303	91.48 170

thres100view900		78.37 172	77.01 174	82.46 170	91.89 104	63.21 205	91.19 181	96.33 1	72.28 162	70.45 190	87.89 184	60.31 117	95.32 157	45.16 305	77.58 182	88.83 200

GA-MVS		78.33 174	76.23 185	84.65 125	83.65 258	66.30 124	91.44 163	90.14 206	76.01 91	70.32 192	84.02 224	42.50 272	94.72 173	70.98 163	77.00 191	92.94 142

cascas		78.18 175	75.77 191	85.41 102	87.14 205	69.11 48	92.96 106	91.15 170	66.71 248	70.47 188	86.07 203	37.49 297	96.48 113	70.15 171	79.80 163	90.65 183

UniMVSNet_NR-MVSNet		78.15 176	77.55 165	79.98 232	84.46 246	60.26 254	92.25 128	93.20 89	77.50 75	68.88 211	86.61 197	66.10 58	92.13 259	66.38 206	62.55 281	87.54 218

thres600view7		78.00 177	76.66 179	82.03 190	91.93 101	63.69 195	91.30 175	96.33 1	72.43 157	70.46 189	87.89 184	60.31 117	94.92 168	42.64 317	76.64 193	87.48 220

FC-MVSNet-test		77.99 178	78.08 155	77.70 264	84.89 239	55.51 301	90.27 207	93.75 65	76.87 81	66.80 241	87.59 187	65.71 64	90.23 288	62.89 235	73.94 204	87.37 223

Anonymous202405211		77.96 179	75.33 195	85.87 84	93.73 55	64.52 168	94.85 43	85.36 298	62.52 280	76.11 132	90.18 156	29.43 328	97.29 73	68.51 186	77.24 190	95.81 40

cl-mvsnet2		77.94 180	76.78 177	81.42 200	87.57 195	64.93 164	90.67 196	88.86 254	72.45 156	67.63 230	82.68 238	64.07 80	92.91 231	71.79 157	65.30 258	86.44 240

XXY-MVS		77.94 180	76.44 182	82.43 171	82.60 268	64.44 173	92.01 140	91.83 143	73.59 133	70.00 197	85.82 206	54.43 186	94.76 170	69.63 174	68.02 244	88.10 215

MS-PatchMatch		77.90 182	76.50 181	82.12 185	85.99 221	69.95 34	91.75 155	92.70 108	73.97 122	62.58 274	84.44 221	41.11 277	95.78 134	63.76 229	92.17 67	80.62 316

FMVSNet3		77.73 183	76.04 187	82.80 162	91.20 125	68.99 52	91.87 146	91.99 136	73.35 137	67.04 237	83.19 234	56.62 160	92.14 258	59.80 253	69.34 233	87.28 226

miper_ehance_all_eth		77.60 184	76.44 182	81.09 213	85.70 227	64.41 176	90.65 197	88.64 261	72.31 160	67.37 235	82.52 239	64.77 74	92.64 244	70.67 167	65.30 258	86.24 245

UniMVSNet (Re)		77.58 185	76.78 177	79.98 232	84.11 252	60.80 243	91.76 153	93.17 92	76.56 87	69.93 200	84.78 217	63.32 94	92.36 254	64.89 222	62.51 283	86.78 233

PatchmatchNet		77.46 186	74.63 201	85.96 80	89.55 155	70.35 27	79.97 312	89.55 227	72.23 164	70.94 184	76.91 303	57.03 150	92.79 236	54.27 271	81.17 157	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 187	75.65 193	82.73 164	80.38 285	67.13 101	91.85 148	90.23 203	75.09 105	69.37 203	83.39 232	53.79 192	94.44 184	71.77 158	65.00 264	86.63 238

RRT_MVS		77.38 188	76.59 180	79.77 239	90.91 130	63.61 199	91.15 182	90.91 180	72.28 162	72.06 175	87.28 193	43.92 267	89.04 298	73.32 141	67.47 248	86.67 234

CHOSEN 280x420		77.35 189	76.95 176	78.55 256	87.07 206	62.68 219	69.71 334	82.95 318	68.80 231	71.48 182	87.27 194	66.03 59	84.00 327	76.47 124	82.81 146	88.95 199

PS-MVSNAJss		77.26 190	76.31 184	80.13 229	80.64 283	59.16 270	90.63 200	91.06 175	72.80 148	68.58 218	84.57 220	53.55 194	93.96 205	72.97 143	71.96 219	87.27 227

gg-mvs-nofinetune		77.18 191	74.31 208	85.80 87	91.42 119	68.36 65	71.78 329	94.72 29	49.61 331	77.12 125	45.92 347	77.41 6	93.98 204	67.62 193	93.16 52	95.05 70

MVP-Stereo		77.12 192	76.23 185	79.79 238	81.72 274	66.34 123	89.29 229	90.88 181	70.56 211	62.01 277	82.88 235	49.34 230	94.13 193	65.55 217	93.80 41	78.88 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
X-MVStestdata		76.86 193	74.13 213	85.05 111	93.22 65	63.78 190	92.92 109	92.66 111	73.99 120	78.18 112	10.19 359	55.25 173	97.41 65	79.16 103	91.58 75	93.95 113

DU-MVS		76.86 193	75.84 190	79.91 234	82.96 266	60.26 254	91.26 176	91.54 154	76.46 88	68.88 211	86.35 199	56.16 164	92.13 259	66.38 206	62.55 281	87.35 224

Anonymous20240529		76.84 195	74.15 212	84.88 117	91.02 126	64.95 163	93.84 76	91.09 173	53.57 322	73.00 157	87.42 190	35.91 307	97.32 71	69.14 181	72.41 217	92.36 154

cl_fuxian		76.83 196	75.47 194	80.93 217	85.02 237	64.18 185	90.39 204	88.11 272	71.66 184	66.65 242	81.64 251	63.58 91	92.56 245	69.31 179	62.86 279	86.04 251

WR-MVS		76.76 197	75.74 192	79.82 237	84.60 242	62.27 225	92.60 120	92.51 118	76.06 90	67.87 227	85.34 210	56.76 156	90.24 287	62.20 239	63.69 277	86.94 231

v1144		76.73 198	74.88 198	82.27 177	80.23 290	66.60 117	91.68 157	90.21 205	73.69 130	69.06 208	81.89 246	52.73 202	94.40 185	69.21 180	65.23 261	85.80 257

IterMVS-LS		76.49 199	75.18 197	80.43 222	84.49 245	62.74 217	90.64 198	88.80 255	72.40 158	65.16 250	81.72 249	60.98 112	92.27 257	67.74 191	64.65 269	86.29 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 200	74.55 204	82.19 181	79.14 302	67.82 80	90.26 208	89.42 231	73.75 128	68.63 217	81.89 246	51.31 213	94.09 195	71.69 160	64.84 265	84.66 273

v148		76.19 201	74.47 206	81.36 201	80.05 291	64.44 173	91.75 155	90.23 203	73.68 131	67.13 236	80.84 266	55.92 170	93.86 211	68.95 183	61.73 292	85.76 260

Effi-MVS+-dtu		76.14 202	75.28 196	78.72 255	83.22 262	55.17 303	89.87 219	87.78 277	75.42 97	67.98 223	81.43 255	45.08 262	92.52 247	75.08 132	71.63 220	88.48 207

cl-mvsnet_		76.07 203	74.67 199	80.28 225	85.15 233	61.76 229	90.12 211	88.73 257	71.16 199	65.43 247	81.57 253	61.15 109	92.95 226	66.54 203	62.17 285	86.13 249

cl-mvsnet1		76.07 203	74.67 199	80.28 225	85.14 234	61.75 230	90.12 211	88.73 257	71.16 199	65.42 248	81.60 252	61.15 109	92.94 230	66.54 203	62.16 287	86.14 247

FMVSNet2		76.07 203	74.01 215	82.26 179	88.85 167	67.66 84	91.33 173	91.61 152	70.84 205	65.98 244	82.25 242	48.03 241	92.00 263	58.46 258	68.73 239	87.10 228

v144192		76.05 206	74.03 214	82.12 185	79.50 296	66.55 119	91.39 168	89.71 225	72.30 161	68.17 221	81.33 258	51.75 209	94.03 202	67.94 189	64.19 271	85.77 258

NR-MVSNet		76.05 206	74.59 202	80.44 221	82.96 266	62.18 226	90.83 192	91.73 146	77.12 79	60.96 280	86.35 199	59.28 131	91.80 266	60.74 246	61.34 296	87.35 224

v1192		75.98 208	73.92 216	82.15 182	79.73 292	66.24 127	91.22 178	89.75 219	72.67 150	68.49 219	81.42 256	49.86 226	94.27 189	67.08 197	65.02 263	85.95 254

eth_miper_zixun_eth		75.96 209	74.40 207	80.66 219	84.66 241	63.02 208	89.28 230	88.27 269	71.88 175	65.73 245	81.65 250	59.45 127	92.81 234	68.13 187	60.53 301	86.14 247

bset_n11_16_dypcd		75.95 210	74.16 211	81.30 203	76.91 321	65.14 155	88.89 238	87.48 280	74.30 115	69.90 201	83.40 231	42.16 275	92.42 250	78.39 112	66.03 255	86.32 242

TranMVSNet+NR-MVSNet		75.86 211	74.52 205	79.89 235	82.44 269	60.64 250	91.37 171	91.37 162	76.63 85	67.65 229	86.21 202	52.37 205	91.55 271	61.84 241	60.81 299	87.48 220

SCA		75.82 212	72.76 227	85.01 113	86.63 211	70.08 29	81.06 302	89.19 239	71.60 190	70.01 196	77.09 301	45.53 259	90.25 284	60.43 248	73.27 208	94.68 81

LPG-MVS_test		75.82 212	74.58 203	79.56 245	84.31 249	59.37 267	90.44 201	89.73 222	69.49 221	64.86 251	88.42 172	38.65 285	94.30 187	72.56 149	72.76 212	85.01 270

GBi-Net		75.65 214	73.83 217	81.10 210	88.85 167	65.11 156	90.01 215	90.32 195	70.84 205	67.04 237	80.25 276	48.03 241	91.54 272	59.80 253	69.34 233	86.64 235

test1		75.65 214	73.83 217	81.10 210	88.85 167	65.11 156	90.01 215	90.32 195	70.84 205	67.04 237	80.25 276	48.03 241	91.54 272	59.80 253	69.34 233	86.64 235

v1921920		75.63 216	73.49 221	82.06 189	79.38 297	66.35 122	91.07 186	89.48 228	71.98 170	67.99 222	81.22 261	49.16 235	93.90 208	66.56 202	64.56 270	85.92 256

ACMP	71.68 10	75.58 217	74.23 210	79.62 243	84.97 238	59.64 262	90.80 193	89.07 247	70.39 212	62.95 270	87.30 192	38.28 288	93.87 209	72.89 144	71.45 223	85.36 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 218	73.26 223	81.61 197	80.67 282	66.82 110	89.54 225	89.27 235	71.65 185	63.30 268	80.30 275	54.99 179	94.06 198	67.33 196	62.33 284	83.94 278

tpm cat1		75.30 219	72.21 235	84.58 128	88.52 173	67.77 81	78.16 321	88.02 274	61.88 286	68.45 220	76.37 307	60.65 114	94.03 202	53.77 274	74.11 202	91.93 164

PLC	68.80 14	75.23 220	73.68 219	79.86 236	92.93 74	58.68 274	90.64 198	88.30 267	60.90 290	64.43 259	90.53 149	42.38 273	94.57 176	56.52 263	76.54 194	86.33 241
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 221	72.98 225	81.88 191	79.20 299	66.00 131	90.75 195	89.11 245	71.63 189	67.41 233	81.22 261	47.36 249	93.87 209	65.46 218	64.72 268	85.77 258

Fast-Effi-MVS+-dtu		75.04 222	73.37 222	80.07 230	80.86 279	59.52 265	91.20 180	85.38 297	71.90 173	65.20 249	84.84 216	41.46 276	92.97 225	66.50 205	72.96 211	87.73 217

dp		75.01 223	72.09 236	83.76 143	89.28 159	66.22 128	79.96 313	89.75 219	71.16 199	67.80 228	77.19 300	51.81 208	92.54 246	50.39 282	71.44 224	92.51 152

TAPA-MVS	70.22 12	74.94 224	73.53 220	79.17 250	90.40 138	52.07 316	89.19 233	89.61 226	62.69 278	70.07 195	92.67 118	48.89 238	94.32 186	38.26 331	79.97 162	91.12 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 225	72.54 232	81.46 199	80.33 288	66.71 114	89.15 234	89.08 246	70.94 203	63.08 269	79.86 280	52.52 203	94.04 201	65.70 214	62.17 285	83.64 280

XVG-OURS-SEG-HR		74.70 226	73.08 224	79.57 244	78.25 312	57.33 290	80.49 304	87.32 282	63.22 272	68.76 215	90.12 160	44.89 264	91.59 270	70.55 169	74.09 203	89.79 192

ACMM	69.62 13	74.34 227	72.73 228	79.17 250	84.25 251	57.87 281	90.36 205	89.93 214	63.17 273	65.64 246	86.04 205	37.79 295	94.10 194	65.89 211	71.52 222	85.55 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 228	72.30 234	80.32 223	91.49 118	61.66 231	90.85 191	80.72 324	56.67 315	63.85 263	90.64 146	46.75 251	90.84 279	53.79 273	75.99 197	88.47 209

XVG-OURS		74.25 229	72.46 233	79.63 242	78.45 311	57.59 286	80.33 306	87.39 281	63.86 267	68.76 215	89.62 164	40.50 279	91.72 268	69.00 182	74.25 201	89.58 195

CVMVSNet		74.04 230	74.27 209	73.33 300	85.33 230	43.94 342	89.53 226	88.39 265	54.33 321	70.37 191	90.13 158	49.17 234	84.05 325	61.83 242	79.36 166	91.99 163

Baseline_NR-MVSNet		73.99 231	72.83 226	77.48 268	80.78 280	59.29 269	91.79 150	84.55 305	68.85 230	68.99 209	80.70 267	56.16 164	92.04 262	62.67 236	60.98 298	81.11 310

pmmvs4		73.92 232	71.81 239	80.25 227	79.17 300	65.24 150	87.43 260	87.26 284	67.64 242	63.46 266	83.91 226	48.96 237	91.53 275	62.94 233	65.49 257	83.96 277

D2MVS		73.80 233	72.02 237	79.15 252	79.15 301	62.97 209	88.58 244	90.07 208	72.94 144	59.22 288	78.30 289	42.31 274	92.70 240	65.59 216	72.00 218	81.79 307

CR-MVSNet		73.79 234	70.82 246	82.70 165	83.15 264	67.96 76	70.25 331	84.00 310	73.67 132	69.97 198	72.41 322	57.82 142	89.48 295	52.99 277	73.13 209	90.64 184

test_djsdf		73.76 235	72.56 231	77.39 270	77.00 320	53.93 308	89.07 236	90.69 184	65.80 254	63.92 261	82.03 245	43.14 271	92.67 241	72.83 145	68.53 240	85.57 262

pmmvs5		73.35 236	71.52 241	78.86 254	78.64 309	60.61 251	91.08 184	86.90 285	67.69 239	63.32 267	83.64 227	44.33 266	90.53 281	62.04 240	66.02 256	85.46 264

Anonymous20231211		73.08 237	70.39 248	81.13 208	90.62 135	63.33 202	91.40 166	90.06 211	51.84 326	64.46 258	80.67 269	36.49 305	94.07 197	63.83 228	64.17 272	85.98 253

miper_lstm_enhance		73.05 238	71.73 240	77.03 274	83.80 255	58.32 278	81.76 294	88.88 252	69.80 219	61.01 279	78.23 291	57.19 147	87.51 313	65.34 219	59.53 305	85.27 269

jajsoiax		73.05 238	71.51 242	77.67 265	77.46 317	54.83 304	88.81 240	90.04 212	69.13 228	62.85 272	83.51 229	31.16 323	92.75 237	70.83 164	69.80 229	85.43 265

LCM-MVSNet-Re		72.93 240	71.84 238	76.18 283	88.49 174	48.02 332	80.07 311	70.17 344	73.96 123	52.25 316	80.09 279	49.98 224	88.24 304	67.35 194	84.23 140	92.28 158

pm-mvs1		72.89 241	71.09 244	78.26 260	79.10 303	57.62 285	90.80 193	89.30 234	67.66 240	62.91 271	81.78 248	49.11 236	92.95 226	60.29 250	58.89 308	84.22 276

tpmvs		72.88 242	69.76 254	82.22 180	90.98 127	67.05 103	78.22 320	88.30 267	63.10 274	64.35 260	74.98 314	55.09 178	94.27 189	43.25 311	69.57 232	85.34 267

test0.0.03 1		72.76 243	72.71 229	72.88 304	80.25 289	47.99 333	91.22 178	89.45 229	71.51 194	62.51 275	87.66 186	53.83 190	85.06 322	50.16 283	67.84 247	85.58 261

UniMVSNet_ETH3D		72.74 244	70.53 247	79.36 247	78.62 310	56.64 294	85.01 274	89.20 238	63.77 268	64.84 253	84.44 221	34.05 312	91.86 265	63.94 227	70.89 227	89.57 196

mvs_tets		72.71 245	71.11 243	77.52 266	77.41 318	54.52 306	88.45 247	89.76 218	68.76 233	62.70 273	83.26 233	29.49 327	92.71 238	70.51 170	69.62 231	85.34 267

FMVSNet1		72.71 245	69.91 252	81.10 210	83.60 259	65.11 156	90.01 215	90.32 195	63.92 266	63.56 265	80.25 276	36.35 306	91.54 272	54.46 270	66.75 252	86.64 235

IterMVS		72.65 247	70.83 245	78.09 262	82.17 270	62.96 210	87.64 258	86.28 289	71.56 192	60.44 282	78.85 287	45.42 261	86.66 317	63.30 231	61.83 289	84.65 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		72.06 248	69.98 249	78.28 259	89.51 156	55.70 300	83.49 282	83.39 316	61.24 289	63.72 264	82.76 236	34.77 310	93.03 224	53.37 276	77.59 181	86.12 250

PVSNet_0	68.08 15	71.81 249	68.32 261	82.27 177	84.68 240	62.31 224	88.68 242	90.31 198	75.84 92	57.93 299	80.65 270	37.85 294	94.19 192	69.94 172	29.05 350	90.31 187

MIMVSNet		71.64 250	68.44 259	81.23 205	81.97 273	64.44 173	73.05 328	88.80 255	69.67 220	64.59 254	74.79 315	32.79 315	87.82 308	53.99 272	76.35 195	91.42 171

IterMVS-SCA-FT		71.55 251	69.97 250	76.32 281	81.48 275	60.67 249	87.64 258	85.99 293	66.17 252	59.50 286	78.88 286	45.53 259	83.65 329	62.58 237	61.93 288	84.63 275

v7n		71.31 252	68.65 257	79.28 248	76.40 323	60.77 245	86.71 268	89.45 229	64.17 265	58.77 293	78.24 290	44.59 265	93.54 215	57.76 259	61.75 291	83.52 283

anonymousdsp		71.14 253	69.37 255	76.45 280	72.95 332	54.71 305	84.19 278	88.88 252	61.92 285	62.15 276	79.77 281	38.14 290	91.44 277	68.90 184	67.45 249	83.21 289

F-COLMAP		70.66 254	68.44 259	77.32 271	86.37 216	55.91 298	88.00 251	86.32 288	56.94 313	57.28 302	88.07 181	33.58 313	92.49 248	51.02 280	68.37 241	83.55 281

WR-MVS_H		70.59 255	69.94 251	72.53 306	81.03 278	51.43 319	87.35 261	92.03 135	67.38 243	60.23 283	80.70 267	55.84 171	83.45 331	46.33 301	58.58 309	82.72 295

CP-MVSNet		70.50 256	69.91 252	72.26 309	80.71 281	51.00 322	87.23 262	90.30 199	67.84 237	59.64 285	82.69 237	50.23 223	82.30 339	51.28 279	59.28 306	83.46 285

RPMNet		70.42 257	65.68 273	84.63 127	83.15 264	67.96 76	70.25 331	90.45 189	46.83 339	69.97 198	65.10 338	56.48 163	95.30 160	35.79 336	73.13 209	90.64 184

tfpnnormal		70.10 258	67.36 264	78.32 258	83.45 261	60.97 241	88.85 239	92.77 106	64.85 261	60.83 281	78.53 288	43.52 270	93.48 217	31.73 346	61.70 293	80.52 317

TransMVSNet (Re)		70.07 259	67.66 263	77.31 272	80.62 284	59.13 271	91.78 152	84.94 301	65.97 253	60.08 284	80.44 272	50.78 217	91.87 264	48.84 289	45.46 335	80.94 312

CL-MVSNet_2432*1600		69.92 260	68.09 262	75.41 286	73.25 331	55.90 299	90.05 214	89.90 215	69.96 216	61.96 278	76.54 304	51.05 216	87.64 310	49.51 287	50.59 328	82.70 297

DP-MVS		69.90 261	66.48 267	80.14 228	95.36 25	62.93 211	89.56 223	76.11 331	50.27 330	57.69 300	85.23 211	39.68 281	95.73 137	33.35 340	71.05 226	81.78 308

PS-CasMVS		69.86 262	69.13 256	72.07 312	80.35 287	50.57 324	87.02 264	89.75 219	67.27 244	59.19 289	82.28 241	46.58 253	82.24 340	50.69 281	59.02 307	83.39 287

MSDG		69.54 263	65.73 272	80.96 215	85.11 236	63.71 194	84.19 278	83.28 317	56.95 312	54.50 307	84.03 223	31.50 321	96.03 128	42.87 315	69.13 236	83.14 291

PEN-MVS		69.46 264	68.56 258	72.17 311	79.27 298	49.71 328	86.90 266	89.24 236	67.24 247	59.08 290	82.51 240	47.23 250	83.54 330	48.42 291	57.12 310	83.25 288

LS3D		69.17 265	66.40 268	77.50 267	91.92 102	56.12 297	85.12 273	80.37 325	46.96 337	56.50 304	87.51 189	37.25 298	93.71 212	32.52 345	79.40 165	82.68 298

PatchT		69.11 266	65.37 277	80.32 223	82.07 272	63.68 196	67.96 340	87.62 279	50.86 329	69.37 203	65.18 337	57.09 148	88.53 302	41.59 320	66.60 253	88.74 203

KD-MVS_2432*1600		69.03 267	66.37 269	77.01 275	85.56 228	61.06 239	81.44 299	90.25 201	67.27 244	58.00 297	76.53 305	54.49 183	87.63 311	48.04 293	35.77 346	82.34 301

miper_refine_blended		69.03 267	66.37 269	77.01 275	85.56 228	61.06 239	81.44 299	90.25 201	67.27 244	58.00 297	76.53 305	54.49 183	87.63 311	48.04 293	35.77 346	82.34 301

MVS_0304		68.99 269	67.23 266	74.28 295	80.36 286	52.54 313	87.01 265	86.36 287	59.89 299	66.22 243	73.56 318	24.25 337	88.03 306	57.34 262	70.11 228	82.27 303

ACMH	63.93 17	68.62 270	64.81 278	80.03 231	85.22 232	63.25 203	87.72 256	84.66 304	60.83 291	51.57 319	79.43 285	27.29 333	94.96 165	41.76 318	64.84 265	81.88 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 271	65.41 276	77.96 263	78.69 308	62.93 211	89.86 220	89.17 240	60.55 292	50.27 324	77.73 295	22.60 342	94.06 198	47.18 299	72.65 214	76.88 335

ADS-MVSNet		68.54 272	64.38 285	81.03 214	88.06 187	66.90 108	68.01 338	84.02 309	57.57 307	64.48 256	69.87 330	38.68 283	89.21 297	40.87 322	67.89 245	86.97 229

DTE-MVSNet		68.46 273	67.33 265	71.87 314	77.94 315	49.00 331	86.16 271	88.58 263	66.36 251	58.19 294	82.21 243	46.36 254	83.87 328	44.97 308	55.17 317	82.73 294

our_test_3		68.29 274	64.69 280	79.11 253	78.92 304	64.85 165	88.40 248	85.06 299	60.32 295	52.68 314	76.12 309	40.81 278	89.80 294	44.25 310	55.65 315	82.67 299

Patchmatch-RL test		68.17 275	64.49 283	79.19 249	71.22 336	53.93 308	70.07 333	71.54 343	69.22 225	56.79 303	62.89 340	56.58 161	88.61 299	69.53 176	52.61 323	95.03 73

XVG-ACMP-BASELINE		68.04 276	65.53 275	75.56 285	74.06 330	52.37 314	78.43 317	85.88 294	62.03 283	58.91 292	81.21 263	20.38 345	91.15 278	60.69 247	68.18 242	83.16 290

FMVSNet5		68.04 276	65.66 274	75.18 288	84.43 247	57.89 280	83.54 281	86.26 290	61.83 287	53.64 312	73.30 319	37.15 301	85.08 321	48.99 288	61.77 290	82.56 300

ppachtmachnet_test		67.72 278	63.70 287	79.77 239	78.92 304	66.04 130	88.68 242	82.90 319	60.11 297	55.45 305	75.96 310	39.19 282	90.55 280	39.53 326	52.55 324	82.71 296

ACMH+	65.35 16	67.65 279	64.55 281	76.96 277	84.59 243	57.10 292	88.08 250	80.79 323	58.59 306	53.00 313	81.09 265	26.63 335	92.95 226	46.51 300	61.69 294	80.82 313

pmmvs6		67.57 280	64.76 279	76.00 284	72.82 334	53.37 310	88.71 241	86.78 286	53.19 323	57.58 301	78.03 293	35.33 309	92.41 251	55.56 267	54.88 319	82.21 304

Anonymous20231206		67.53 281	65.78 271	72.79 305	74.95 327	47.59 335	88.23 249	87.32 282	61.75 288	58.07 296	77.29 298	37.79 295	87.29 315	42.91 313	63.71 276	83.48 284

Patchmtry		67.53 281	63.93 286	78.34 257	82.12 271	64.38 177	68.72 335	84.00 310	48.23 336	59.24 287	72.41 322	57.82 142	89.27 296	46.10 302	56.68 314	81.36 309

USDC		67.43 283	64.51 282	76.19 282	77.94 315	55.29 302	78.38 318	85.00 300	73.17 139	48.36 330	80.37 273	21.23 344	92.48 249	52.15 278	64.02 274	80.81 314

ADS-MVSNet2		66.90 284	63.44 289	77.26 273	88.06 187	60.70 248	68.01 338	75.56 334	57.57 307	64.48 256	69.87 330	38.68 283	84.10 324	40.87 322	67.89 245	86.97 229

CMPMVS	48.56 21	66.77 285	64.41 284	73.84 297	70.65 339	50.31 325	77.79 322	85.73 296	45.54 340	44.76 338	82.14 244	35.40 308	90.14 290	63.18 232	74.54 200	81.07 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 286	62.92 292	76.80 279	76.51 322	57.77 282	89.22 231	83.41 315	55.48 319	53.86 311	77.84 294	26.28 336	93.95 206	34.90 338	68.76 238	78.68 330

LTVRE_ROB	59.60 19	66.27 287	63.54 288	74.45 292	84.00 254	51.55 318	67.08 341	83.53 313	58.78 304	54.94 306	80.31 274	34.54 311	93.23 221	40.64 324	68.03 243	78.58 331
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
JIA-IIPM		66.06 288	62.45 295	76.88 278	81.42 277	54.45 307	57.49 349	88.67 259	49.36 332	63.86 262	46.86 346	56.06 167	90.25 284	49.53 286	68.83 237	85.95 254

Patchmatch-test		65.86 289	60.94 301	80.62 220	83.75 256	58.83 272	58.91 348	75.26 336	44.50 343	50.95 323	77.09 301	58.81 136	87.90 307	35.13 337	64.03 273	95.12 67

UnsupCasMVSNet_eth		65.79 290	63.10 290	73.88 296	70.71 338	50.29 326	81.09 301	89.88 216	72.58 152	49.25 328	74.77 316	32.57 317	87.43 314	55.96 266	41.04 341	83.90 279

pmmvs-eth3d		65.53 291	62.32 296	75.19 287	69.39 342	59.59 263	82.80 291	83.43 314	62.52 280	51.30 321	72.49 320	32.86 314	87.16 316	55.32 268	50.73 327	78.83 329

SixPastTwentyTwo		64.92 292	61.78 299	74.34 294	78.74 307	49.76 327	83.42 285	79.51 328	62.86 275	50.27 324	77.35 296	30.92 325	90.49 282	45.89 303	47.06 333	82.78 292

OurMVSNet-221017-0		64.68 293	62.17 297	72.21 310	76.08 326	47.35 336	80.67 303	81.02 322	56.19 316	51.60 318	79.66 283	27.05 334	88.56 301	53.60 275	53.63 322	80.71 315

test_0402		64.54 294	61.09 300	74.92 289	84.10 253	60.75 246	87.95 252	79.71 327	52.03 325	52.41 315	77.20 299	32.21 319	91.64 269	23.14 349	61.03 297	72.36 341

testgi		64.48 295	62.87 293	69.31 318	71.24 335	40.62 346	85.49 272	79.92 326	65.36 258	54.18 309	83.49 230	23.74 340	84.55 323	41.60 319	60.79 300	82.77 293

RPSCF		64.24 296	61.98 298	71.01 315	76.10 325	45.00 339	75.83 325	75.94 332	46.94 338	58.96 291	84.59 219	31.40 322	82.00 341	47.76 297	60.33 304	86.04 251

EU-MVSNet		64.01 297	63.01 291	67.02 325	74.40 329	38.86 351	83.27 286	86.19 291	45.11 341	54.27 308	81.15 264	36.91 304	80.01 344	48.79 290	57.02 311	82.19 305

test20.03		63.83 298	62.65 294	67.38 324	70.58 340	39.94 347	86.57 269	84.17 307	63.29 271	51.86 317	77.30 297	37.09 302	82.47 337	38.87 330	54.13 321	79.73 323

MDA-MVSNet_test_wron		63.78 299	60.16 302	74.64 290	78.15 313	60.41 252	83.49 282	84.03 308	56.17 318	39.17 346	71.59 328	37.22 299	83.24 334	42.87 315	48.73 330	80.26 320

YYNet1		63.76 300	60.14 303	74.62 291	78.06 314	60.19 257	83.46 284	83.99 312	56.18 317	39.25 345	71.56 329	37.18 300	83.34 332	42.90 314	48.70 331	80.32 319

K. test v3		63.09 301	59.61 305	73.53 299	76.26 324	49.38 330	83.27 286	77.15 330	64.35 263	47.77 331	72.32 324	28.73 329	87.79 309	49.93 285	36.69 345	83.41 286

COLMAP_ROB	57.96 20	62.98 302	59.65 304	72.98 303	81.44 276	53.00 312	83.75 280	75.53 335	48.34 335	48.81 329	81.40 257	24.14 338	90.30 283	32.95 342	60.52 302	75.65 338
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		61.66 303	58.06 307	72.46 307	79.57 293	51.42 320	80.17 309	68.61 346	51.25 327	45.88 333	81.23 259	19.86 346	86.58 318	38.98 328	57.01 312	79.39 325

UnsupCasMVSNet_bld		61.60 304	57.71 308	73.29 301	68.73 343	51.64 317	78.61 316	89.05 248	57.20 311	46.11 332	61.96 341	28.70 330	88.60 300	50.08 284	38.90 343	79.63 324

MDA-MVSNet-bldmvs		61.54 305	57.70 309	73.05 302	79.53 295	57.00 293	83.08 289	81.23 321	57.57 307	34.91 348	72.45 321	32.79 315	86.26 320	35.81 335	41.95 339	75.89 337

DIV-MVS_2432*1600		60.87 306	58.60 306	67.68 322	66.13 344	39.93 348	75.63 326	84.70 303	57.32 310	49.57 327	68.45 333	29.55 326	82.87 335	48.09 292	47.94 332	80.25 321

TinyColmap		60.32 307	56.42 313	72.00 313	78.78 306	53.18 311	78.36 319	75.64 333	52.30 324	41.59 344	75.82 312	14.76 351	88.35 303	35.84 334	54.71 320	74.46 339

MVS-HIRNet		60.25 308	55.55 314	74.35 293	84.37 248	56.57 295	71.64 330	74.11 337	34.44 348	45.54 337	42.24 350	31.11 324	89.81 292	40.36 325	76.10 196	76.67 336

MIMVSNet1		60.16 309	57.33 310	68.67 319	69.71 341	44.13 341	78.92 315	84.21 306	55.05 320	44.63 339	71.85 326	23.91 339	81.54 343	32.63 344	55.03 318	80.35 318

PM-MVS		59.40 310	56.59 311	67.84 320	63.63 346	41.86 343	76.76 323	63.22 352	59.01 303	51.07 322	72.27 325	11.72 353	83.25 333	61.34 243	50.28 329	78.39 332

new-patchmatchnet		59.30 311	56.48 312	67.79 321	65.86 345	44.19 340	82.47 292	81.77 320	59.94 298	43.65 342	66.20 336	27.67 332	81.68 342	39.34 327	41.40 340	77.50 334

DSMNet-mixed		56.78 312	54.44 315	63.79 327	63.21 347	29.44 355	64.43 343	64.10 351	42.12 345	51.32 320	71.60 327	31.76 320	75.04 346	36.23 333	65.20 262	86.87 232

pmmvs3		55.51 313	51.50 318	67.53 323	57.90 352	50.93 323	80.37 305	73.66 338	40.63 346	44.15 341	64.75 339	16.30 348	78.97 345	44.77 309	40.98 342	72.69 340

TDRefinement		55.28 314	51.58 317	66.39 326	59.53 351	46.15 338	76.23 324	72.80 339	44.60 342	42.49 343	76.28 308	15.29 349	82.39 338	33.20 341	43.75 337	70.62 343

LF4IMVS		54.01 315	52.12 316	59.69 328	62.41 349	39.91 349	68.59 336	68.28 348	42.96 344	44.55 340	75.18 313	14.09 352	68.39 349	41.36 321	51.68 325	70.78 342

N_pmnet		50.55 316	49.11 319	54.88 331	77.17 319	4.02 364	84.36 277	2.00 363	48.59 333	45.86 335	68.82 332	32.22 318	82.80 336	31.58 347	51.38 326	77.81 333

new_pmnet		49.31 317	46.44 320	57.93 329	62.84 348	40.74 345	68.47 337	62.96 353	36.48 347	35.09 347	57.81 343	14.97 350	72.18 347	32.86 343	46.44 334	60.88 347

FPMVS		45.64 318	43.10 321	53.23 333	51.42 354	36.46 352	64.97 342	71.91 341	29.13 350	27.53 349	61.55 342	9.83 355	65.01 353	16.00 352	55.58 316	58.22 348

LCM-MVSNet		40.54 319	35.79 322	54.76 332	36.92 359	30.81 354	51.41 350	69.02 345	22.07 352	24.63 350	45.37 348	4.56 361	65.81 351	33.67 339	34.50 348	67.67 344

ANet_high		40.27 320	35.20 323	55.47 330	34.74 360	34.47 353	63.84 344	71.56 342	48.42 334	18.80 353	41.08 351	9.52 356	64.45 354	20.18 350	8.66 357	67.49 345

PMMVS2		37.93 321	33.61 324	50.92 334	46.31 356	24.76 358	60.55 347	50.05 355	28.94 351	20.93 351	47.59 345	4.41 362	65.13 352	25.14 348	18.55 352	62.87 346

Gipuma		34.91 322	31.44 325	45.30 335	70.99 337	39.64 350	19.85 356	72.56 340	20.10 354	16.16 355	21.47 356	5.08 360	71.16 348	13.07 353	43.70 338	25.08 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	26.43 22	31.84 323	28.16 326	42.89 336	25.87 362	27.58 356	50.92 351	49.78 356	21.37 353	14.17 356	40.81 352	2.01 363	66.62 350	9.61 355	38.88 344	34.49 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 324	24.00 328	26.45 339	43.74 357	18.44 361	60.86 345	39.66 357	15.11 355	9.53 358	22.10 355	6.52 358	46.94 356	8.31 356	10.14 354	13.98 354

MVE	24.84 23	24.35 325	19.77 331	38.09 337	34.56 361	26.92 357	26.57 354	38.87 359	11.73 357	11.37 357	27.44 353	1.37 364	50.42 355	11.41 354	14.60 353	36.93 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 326	23.20 330	25.46 340	41.52 358	16.90 362	60.56 346	38.79 360	14.62 356	8.99 359	20.24 358	7.35 357	45.82 357	7.25 357	9.46 355	13.64 355

tmp_tt		22.26 327	23.75 329	17.80 341	5.23 363	12.06 363	35.26 353	39.48 358	2.82 359	18.94 352	44.20 349	22.23 343	24.64 359	36.30 332	9.31 356	16.69 353

cdsmvs_eth3d_5k		19.86 328	26.47 327	0.00 345	0.00 366	0.00 367	0.00 357	93.45 78	0.00 362	0.00 363	95.27 47	49.56 228	0.00 363	0.00 361	0.00 360	0.00 359

wuyk23d		11.30 329	10.95 332	12.33 342	48.05 355	19.89 360	25.89 355	1.92 364	3.58 358	3.12 360	1.37 360	0.64 365	15.77 360	6.23 358	7.77 358	1.35 356

ab-mvs-re		7.91 330	10.55 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	94.95 58	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

testmvs		7.23 331	9.62 334	0.06 344	0.04 364	0.02 366	84.98 275	0.02 365	0.03 360	0.18 361	1.21 361	0.01 367	0.02 361	0.14 359	0.01 359	0.13 358

test123		6.92 332	9.21 335	0.08 343	0.03 365	0.05 365	81.65 297	0.01 366	0.02 361	0.14 362	0.85 362	0.03 366	0.02 361	0.12 360	0.00 360	0.16 357

pcd_1.5k_mvsjas		4.46 333	5.95 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	53.55 194	0.00 363	0.00 361	0.00 360	0.00 359

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

ZD-MVS					96.63 8	65.50 146		93.50 76	70.74 209	85.26 47	95.19 53	64.92 73	97.29 73	87.51 38	93.01 54

RE-MVS-def			80.48 120		92.02 96	58.56 275	90.90 188	90.45 189	62.76 276	78.89 104	94.46 73	49.30 231		78.77 109	86.77 116	92.28 158

IU-MVS					96.46 10	69.91 35		95.18 13	80.75 33	95.28 1				92.34 6	95.36 12	96.47 20

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_241102_TWO								94.41 43	71.65 185	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

test_241102_ONE					96.45 11	69.38 43		94.44 40	71.65 185	92.11 3	97.05 8	76.79 7	99.11 4

9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 149	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

save fliter					93.84 49	67.89 78	95.05 38	92.66 111	78.19 64

test_0728_THIRD									72.48 155	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

test_0728_SECOND				88.70 13	96.45 11	70.43 26	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

test0726					96.40 14	69.99 31	96.76 6	94.33 48	71.92 171	91.89 6	97.11 7	73.77 17

GSMVS																94.68 81

test_part2					96.29 17	68.16 72				90.78 10

sam_mvs1												57.85 141				94.68 81

sam_mvs												54.91 180

ambc				69.61 317	61.38 350	41.35 344	49.07 352	85.86 295		50.18 326	66.40 335	10.16 354	88.14 305	45.73 304	44.20 336	79.32 327

MTGPA								92.23 125

test_post1							78.95 314				20.70 357	53.05 199	91.50 276	60.43 248

test_post											23.01 354	56.49 162	92.67 241

patchmatchnet-post											67.62 334	57.62 144	90.25 284

GG-mvs-BLEND				86.53 62	91.91 103	69.67 41	75.02 327	94.75 28		78.67 111	90.85 144	77.91 5	94.56 178	72.25 152	93.74 43	95.36 50

MTMP							93.77 78	32.52 361

gm-plane-assit					88.42 178	67.04 104			78.62 61		91.83 133		97.37 67	76.57 123

test9_res														89.41 20	94.96 16	95.29 56

TEST9					94.18 40	67.28 95	94.16 53	93.51 74	71.75 183	85.52 43	95.33 42	68.01 39	97.27 77

test_8					94.19 39	67.19 98	94.15 55	93.42 81	71.87 176	85.38 45	95.35 41	68.19 36	96.95 96

agg_prior2														86.41 49	94.75 26	95.33 51

agg_prior					94.16 44	66.97 106		93.31 84		84.49 52			96.75 105

TestCases				72.46 307	79.57 293	51.42 320		68.61 346	51.25 327	45.88 333	81.23 259	19.86 346	86.58 318	38.98 328	57.01 312	79.39 325

test_prior4						67.18 100	93.92 69

test_prior2							95.10 36		75.40 99	85.25 48	95.61 37	67.94 40		87.47 39	94.77 23

test_prior				86.42 66	94.71 33	67.35 93		93.10 96					96.84 101			95.05 70

旧先验2							92.00 142		59.37 302	87.54 25			93.47 218	75.39 129

新几何2							91.41 164

新几何1				84.73 121	92.32 88	64.28 182		91.46 159	59.56 300	79.77 94	92.90 112	56.95 155	96.57 110	63.40 230	92.91 56	93.34 127

旧先验1					91.94 100	60.74 247		91.50 157			94.36 77	65.23 67			91.84 70	94.55 86

无先验							92.71 114	92.61 115	62.03 283				97.01 87	66.63 200		93.97 112

原ACMM2							92.01 140

原ACMM1				84.42 132	93.21 67	64.27 183		93.40 83	65.39 257	79.51 97	92.50 120	58.11 140	96.69 107	65.27 220	93.96 38	92.32 156

test222					89.77 149	61.60 232	89.55 224	89.42 231	56.83 314	77.28 123	92.43 124	52.76 201			91.14 83	93.09 137

testdata2													96.09 122	61.26 244

segment_acmp												65.94 60

testdata				81.34 202	89.02 165	57.72 283		89.84 217	58.65 305	85.32 46	94.09 88	57.03 150	93.28 220	69.34 178	90.56 89	93.03 139

testdata1							89.21 232		77.55 74

test12				87.09 42	94.60 35	68.86 54		92.91 102		82.67 67		65.44 66	97.55 59		93.69 46	94.84 77

plane_prior7					86.94 207	61.51 233

plane_prior6					87.23 202	62.32 223						50.66 218

plane_prior5								91.31 163					95.55 151	76.74 121	78.53 175	88.39 210

plane_prior4											89.14 167

plane_prior3						61.95 227			79.09 53	72.53 166

plane_prior2							93.13 98		78.81 58

plane_prior1					87.15 204

plane_prior						62.42 220	93.85 73		79.38 46						78.80 172

n2								0.00 367

nn								0.00 367

door-mid								66.01 350

lessismore_v0				73.72 298	72.93 333	47.83 334		61.72 354		45.86 335	73.76 317	28.63 331	89.81 292	47.75 298	31.37 349	83.53 282

LGP-MVS_train				79.56 245	84.31 249	59.37 267		89.73 222	69.49 221	64.86 251	88.42 172	38.65 285	94.30 187	72.56 149	72.76 212	85.01 270

test11								93.01 98

door								66.57 349

HQP5-MVS						63.66 197

HQP-NCC					87.54 196		94.06 60		79.80 40	74.18 147

ACMP_Plane					87.54 196		94.06 60		79.80 40	74.18 147

BP-MVS														77.63 118

HQP4-MVS										74.18 147			95.61 145			88.63 204

HQP3-MVS								91.70 149							78.90 170

HQP2-MVS												51.63 211

NP-MVS					87.41 199	63.04 207					90.30 154

MDTV_nov1_ep13_2view						59.90 260	80.13 310		67.65 241	72.79 161		54.33 187		59.83 252		92.58 150

MDTV_nov1_ep13			72.61 230		89.06 164	68.48 62	80.33 306	90.11 207	71.84 179	71.81 178	75.92 311	53.01 200	93.92 207	48.04 293	73.38 207

ACMMP++_ref															71.63 220

ACMMP++															69.72 230

Test By Simon												54.21 188

ITE_SJBPF				70.43 316	74.44 328	47.06 337		77.32 329	60.16 296	54.04 310	83.53 228	23.30 341	84.01 326	43.07 312	61.58 295	80.21 322

DeepMVS_CX				34.71 338	51.45 353	24.73 359		28.48 362	31.46 349	17.49 354	52.75 344	5.80 359	42.60 358	18.18 351	19.42 351	36.81 350

High-resolution multi-view benchmark