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	89.68 146	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 71	97.64 2	97.94 1

DVP-MVS		90.38 3	91.87 1	85.88 82	92.83 74	64.03 185	93.06 99	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 78	91.02 15	97.75 1	96.43 22

CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 22	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 68	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 61	74.11 16	98.91 13	87.26 43	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 10	69.38 42	96.89 4	94.44 40	71.65 183	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	84.72 122	93.00 71	58.16 273	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 37	69.71 38	96.53 11	93.78 61	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

MSP-MVS		89.41 10	89.73 11	88.45 18	96.40 13	69.99 30	96.64 8	94.52 36	71.92 169	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 28	68.23 70	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 8	69.05 49	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 17	67.56 85	94.17 52	94.15 54	68.77 228	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 65	68.72 57	93.85 72	94.03 57	74.18 116	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 92	76.72 1	95.75 19	93.26 86	83.86 10	89.55 16	96.06 28	53.55 189	97.89 44	91.10 13	93.31 50	94.54 88

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 104	68.04 73	90.36 201	93.55 74	82.89 13	91.29 9	92.89 109	72.27 25	96.03 127	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 59	65.32 147	95.15 34	93.84 60	78.17 66	85.93 38	94.80 65	75.80 11	98.21 31	89.38 21	88.78 97	96.59 13

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 23	68.32 65	95.74 20	94.11 56	83.82 11	83.49 62	96.19 26	64.53 75	98.44 26	83.42 70	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	91.41 116	76.43 3	95.74 20	93.12 95	83.53 12	89.55 16	95.95 29	53.45 193	97.68 50	91.07 14	92.62 58	94.54 88

EPNet		87.84 20	88.38 15	86.23 74	93.30 62	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 70	66.79 53	97.34 70	83.89 67	91.68 72	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	89.24 156	71.18 17	96.57 10	92.90 103	82.70 16	87.13 26	95.27 47	64.99 70	95.80 132	89.34 22	91.80 70	95.93 36

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 56	67.21 96	94.31 50	94.14 55	72.92 144	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 20	66.30 124	94.84 44	93.78 61	69.35 219	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 55	68.82 55	93.90 69	92.63 114	76.86 82	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 76	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 32	67.35 92	95.10 36	93.10 96	75.40 99	85.25 47	95.61 37	67.94 40	96.84 100	87.47 38	94.77 23	95.05 70

alignmvs		87.28 26	86.97 34	88.24 20	91.30 117	71.14 19	95.61 24	93.56 73	79.30 47	87.07 29	95.25 49	68.43 34	96.93 98	87.87 36	84.33 133	96.65 11

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 26	65.83 137	93.95 65	95.12 15	82.11 21	84.25 53	95.73 33	67.88 43	98.35 29	85.60 53	88.64 99	94.26 96

train_agg		87.21 28	87.42 28	86.60 57	94.18 39	67.28 94	94.16 53	93.51 75	71.87 174	85.52 43	95.33 42	68.19 36	97.27 76	89.09 25	94.90 19	95.25 63

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 48	67.89 77	95.05 38	84.72 294	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 61	92.94 106	64.34 77	96.94 96	75.19 126	94.09 36	95.66 42

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 94	67.48 90	92.97 104	94.21 52	71.48 194	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 80	67.45 91	93.64 80	93.76 64	70.78 206	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 43	66.97 105	94.08 58	93.31 84	71.85 176	84.49 51	95.39 40	68.91 33	96.75 104	88.84 29	94.32 34	95.13 66

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 26	64.73 164	93.95 65	94.95 20	81.69 26	84.03 59	95.73 33	67.35 47	98.19 33	85.40 55	88.64 99	94.20 98

CSCG		86.87 35	86.26 41	88.72 12	95.05 29	70.79 21	93.83 76	95.33 11	68.48 232	77.63 114	94.35 77	73.04 20	98.45 25	84.92 59	93.71 45	96.92 9

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 103	71.92 13	93.54 85	91.71 148	80.26 38	87.55 24	95.25 49	63.59 89	96.93 98	88.18 34	84.34 132	97.11 6

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 60	65.55 144	95.39 29	95.10 17	71.77 180	85.69 42	96.52 15	62.07 103	98.77 17	86.06 51	95.60 10	96.03 33

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 132	66.38 121	96.09 15	93.87 59	77.73 71	84.01 60	95.66 35	63.39 91	97.94 39	87.40 40	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 50	67.53 87	96.33 14	93.61 71	82.34 18	81.00 80	93.08 101	63.19 94	97.29 73	87.08 44	91.38 77	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	67.74 234	86.19 34	96.39 20	64.38 76	97.91 42	87.33 41	93.14 53	95.90 38

CS-MVS		86.61 41	86.85 37	85.88 82	91.52 112	66.25 126	95.42 27	92.25 124	80.36 37	84.10 58	94.82 64	62.88 98	96.08 123	88.25 33	92.07 68	95.30 55

jason		86.40 42	86.17 43	87.11 41	86.16 213	70.54 24	95.71 23	92.19 131	82.00 23	84.58 50	94.34 78	61.86 105	95.53 150	87.76 37	90.89 83	95.27 60
jason: jason.
WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 76	69.37 44	95.20 32	95.25 12	82.71 15	81.91 69	94.73 66	67.93 42	97.63 56	79.55 98	82.25 144	96.54 16

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 93	68.97 52	95.04 40	92.70 108	79.04 55	81.50 73	96.50 17	58.98 133	96.78 102	83.49 69	93.93 39	96.29 26

VNet		86.20 45	85.65 52	87.84 23	93.92 46	69.99 30	95.73 22	95.94 6	78.43 62	86.00 37	93.07 103	58.22 136	97.00 88	85.22 56	84.33 133	96.52 17

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 67	69.79 36	93.99 63	93.76 64	79.08 54	78.88 103	93.99 87	62.25 102	98.15 34	85.93 52	91.15 81	94.15 103

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 108	67.53 87	91.79 149	93.49 77	74.93 107	84.61 49	95.30 44	59.42 126	97.92 41	86.13 50	94.92 17	94.94 75

ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 137	67.02 104	93.43 90	91.92 139	81.21 29	84.13 57	94.07 86	60.93 112	95.63 142	89.28 23	89.81 91	94.46 94

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 22	65.21 150	93.59 83	92.58 116	66.54 243	86.17 35	95.88 30	63.83 83	97.00 88	86.39 49	92.94 54	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	88.20 180	72.42 12	92.41 125	92.77 106	82.11 21	80.34 86	93.07 103	68.27 35	95.02 158	78.39 108	93.59 47	94.09 106

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 41	65.09 157	92.95 106	95.11 16	81.13 30	81.68 71	95.04 53	65.82 62	98.32 30	83.02 72	84.36 130	92.97 140

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 31	67.19 97	93.89 70	92.83 105	70.90 202	83.09 64	95.28 45	63.62 87	97.36 68	80.63 92	94.18 35	94.84 77

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 165	66.64 114	92.15 130	93.68 68	81.07 31	76.91 124	93.64 92	62.59 100	98.44 26	85.50 54	92.84 56	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	84.73 120	94.17 41	63.23 202	92.95 106	94.83 23	80.66 34	81.29 74	95.04 53	65.12 68	98.08 36	82.74 74	84.36 130	92.88 144

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 178	69.07 48	93.04 101	91.76 145	81.27 28	80.84 82	92.07 126	64.23 78	96.06 125	84.98 58	87.43 108	95.39 48

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 70	65.65 141	93.89 70	93.41 82	73.75 126	79.94 89	94.68 68	60.61 115	98.03 37	82.63 77	93.72 44	94.52 90

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 114	65.59 143	91.54 160	92.51 118	74.56 110	80.62 83	95.64 36	59.15 130	97.00 88	86.94 46	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 21	68.29 66	91.85 147	93.00 100	76.59 86	79.03 101	95.00 55	61.59 106	97.61 58	78.16 109	89.00 96	95.63 43

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 82	64.27 181	93.24 94	92.27 123	73.13 138	79.63 94	94.43 71	61.90 104	97.17 79	85.00 57	92.56 59	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	88.33 175	68.73 56	90.24 205	91.82 144	81.05 32	81.18 76	92.50 116	63.69 86	96.08 123	84.45 62	86.71 114	95.32 53

#test#		84.98 61	84.74 61	85.72 90	93.75 52	65.01 158	94.09 57	93.19 90	73.55 132	79.22 98	94.93 58	59.04 131	97.67 51	82.66 75	92.21 63	94.49 92

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 141	75.14 5	92.07 136	92.32 121	81.87 24	75.68 131	88.27 171	60.18 118	98.60 21	80.46 94	90.27 90	94.96 74

EIA-MVS		84.84 63	84.88 59	84.69 123	91.30 117	62.36 220	93.85 72	92.04 134	79.45 45	79.33 97	94.28 81	62.42 101	96.35 113	80.05 95	91.25 80	95.38 49

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 99	65.16 152	91.55 159	92.23 125	75.32 101	80.53 84	95.21 51	56.06 165	97.16 80	84.86 60	92.55 60	94.18 99

HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 52	65.01 158	93.50 87	93.19 90	72.19 163	79.22 98	94.93 58	59.04 131	97.67 51	81.55 83	92.21 63	94.49 92

MVS		84.66 66	82.86 88	90.06 2	90.93 123	74.56 6	87.91 246	95.54 9	68.55 230	72.35 167	94.71 67	59.78 122	98.90 14	81.29 89	94.69 28	96.74 10

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 76	65.27 148	93.04 101	93.13 94	73.20 136	78.89 102	94.18 83	59.41 127	97.85 46	81.45 85	92.48 62	93.86 117

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 57	64.37 176	93.50 87	93.15 93	72.19 163	78.85 105	94.86 62	56.69 157	97.45 62	81.55 83	92.20 65	94.02 111

region2R		84.36 69	84.03 68	85.36 104	93.54 58	64.31 178	93.43 90	92.95 101	72.16 166	78.86 104	94.84 63	56.97 152	97.53 60	81.38 87	92.11 67	94.24 97

LFMVS		84.34 70	82.73 91	89.18 11	94.76 30	73.25 10	94.99 41	91.89 140	71.90 171	82.16 68	93.49 96	47.98 235	97.05 83	82.55 78	84.82 126	97.25 5

test_yl		84.28 71	83.16 82	87.64 26	94.52 35	69.24 45	95.78 17	95.09 18	69.19 222	81.09 77	92.88 110	57.00 150	97.44 63	81.11 90	81.76 148	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 35	69.24 45	95.78 17	95.09 18	69.19 222	81.09 77	92.88 110	57.00 150	97.44 63	81.11 90	81.76 148	96.23 28

diffmvs		84.28 71	83.83 69	85.61 95	87.40 195	68.02 74	90.88 186	89.24 228	80.54 35	81.64 72	92.52 115	59.83 121	94.52 176	87.32 42	85.11 124	94.29 95

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 89	67.74 81	84.65 269	94.50 37	79.15 51	82.23 67	87.93 178	66.88 51	96.94 96	80.53 93	82.20 145	96.39 24

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 18	65.93 134	94.28 51	94.27 50	74.41 111	79.16 100	95.61 37	53.99 184	98.88 16	69.62 169	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	91.56 109	69.82 35	89.99 213	92.05 133	77.77 70	82.84 65	86.57 193	63.93 82	96.09 121	74.91 132	89.18 95	95.25 63

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 135	66.82 109	91.87 145	89.01 241	85.27 6	86.09 36	93.74 91	47.71 238	96.98 92	77.90 112	89.78 93	93.65 121

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 129	70.89 20	94.74 45	94.62 34	81.44 27	58.19 286	93.64 92	73.64 19	92.35 249	82.66 75	78.66 170	96.50 19

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 139	66.59 117	93.77 77	91.73 146	77.43 77	77.08 123	89.81 157	63.77 85	96.97 93	79.67 97	88.21 102	92.60 148

DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 73	70.07 29	89.12 229	94.42 42	82.15 20	77.64 113	91.77 130	70.81 30	96.22 116	65.03 215	81.36 152	95.94 35

MTAPA		83.91 81	83.38 79	85.50 97	91.89 99	65.16 152	81.75 289	92.23 125	75.32 101	80.53 84	95.21 51	56.06 165	97.16 80	84.86 60	92.55 60	94.18 99

XVS		83.87 82	83.47 73	85.05 111	93.22 63	63.78 188	92.92 108	92.66 111	73.99 118	78.18 108	94.31 80	55.25 170	97.41 65	79.16 101	91.58 74	93.95 113

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 149	69.40 41	91.35 170	86.12 284	72.59 149	83.22 63	92.81 113	59.60 124	96.01 129	81.76 82	87.80 105	95.56 45

EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 137	92.79 79	63.56 198	91.76 152	94.81 25	79.65 44	77.87 110	94.09 84	63.35 92	97.90 43	79.35 99	79.36 162	90.74 178

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 156	71.18 17	89.07 230	90.69 182	65.80 248	87.13 26	94.34 78	64.99 70	92.67 236	72.83 140	91.80 70	95.27 60

CP-MVS		83.71 86	83.40 78	84.65 124	93.14 68	63.84 186	94.59 46	92.28 122	71.03 200	77.41 117	94.92 60	55.21 173	96.19 117	81.32 88	90.70 85	93.91 115

baseline2		83.68 87	83.42 77	84.48 129	87.37 196	66.00 131	90.06 209	95.93 7	79.71 43	69.08 200	90.39 147	77.92 4	96.28 114	78.91 105	81.38 151	91.16 174

thisisatest0515		83.41 88	82.49 94	86.16 75	89.46 152	68.26 68	93.54 85	94.70 30	74.31 114	75.75 130	90.92 138	72.62 23	96.52 111	69.64 167	81.50 150	93.71 119

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 155	93.76 50	67.53 87	94.06 59	93.61 71	79.13 52	81.00 80	85.14 207	63.19 94	97.29 73	87.08 44	73.91 201	84.83 267

PGM-MVS		83.25 90	82.70 92	84.92 114	92.81 78	64.07 184	90.44 197	92.20 130	71.28 195	77.23 120	94.43 71	55.17 174	97.31 72	79.33 100	91.38 77	93.37 126

HPM-MVS		83.25 90	82.95 86	84.17 135	92.25 88	62.88 213	90.91 185	91.86 141	70.30 210	77.12 121	93.96 88	56.75 155	96.28 114	82.04 80	91.34 79	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	83.67 147	92.28 87	63.19 204	91.38 168	94.68 31	79.22 49	76.60 125	93.75 90	62.64 99	97.76 49	78.07 110	78.01 173	90.05 186

VDD-MVS		83.06 93	81.81 102	86.81 49	90.86 127	67.70 82	95.40 28	91.50 157	75.46 96	81.78 70	92.34 123	40.09 270	97.13 82	86.85 47	82.04 146	95.60 44

PAPM_NR		82.97 94	81.84 101	86.37 69	94.10 45	66.76 112	87.66 250	92.84 104	69.96 213	74.07 146	93.57 94	63.10 96	97.50 61	70.66 162	90.58 87	94.85 76

mPP-MVS		82.96 95	82.44 95	84.52 127	92.83 74	62.92 211	92.76 111	91.85 142	71.52 191	75.61 134	94.24 82	53.48 192	96.99 91	78.97 104	90.73 84	93.64 122

SR-MVS		82.81 96	82.58 93	83.50 152	93.35 61	61.16 236	92.23 129	91.28 166	64.48 256	81.27 75	95.28 45	53.71 188	95.86 131	82.87 73	88.77 98	93.49 125

DP-MVS Recon		82.73 97	81.65 103	85.98 79	97.31 4	67.06 101	95.15 34	91.99 136	69.08 225	76.50 127	93.89 89	54.48 180	98.20 32	70.76 160	85.66 121	92.69 145

CLD-MVS		82.73 97	82.35 97	83.86 140	87.90 187	67.65 84	95.45 26	92.18 132	85.06 7	72.58 160	92.27 124	52.46 199	95.78 133	84.18 63	79.06 165	88.16 209
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	83.73 144	89.25 155	59.58 260	92.24 128	94.89 21	77.96 68	79.86 91	92.38 121	56.70 156	97.05 83	77.26 115	80.86 156	94.55 86

3Dnovator	73.91 6	82.69 100	80.82 112	88.31 19	89.57 148	71.26 16	92.60 119	94.39 46	78.84 57	67.89 219	92.48 119	48.42 230	98.52 22	68.80 179	94.40 33	95.15 65

MVSTER		82.47 101	82.05 98	83.74 142	92.68 81	69.01 50	91.90 144	93.21 87	79.83 39	72.14 168	85.71 203	74.72 13	94.72 168	75.72 122	72.49 211	87.50 214

TESTMET0.1,1		82.41 102	81.98 100	83.72 145	88.08 181	63.74 190	92.70 114	93.77 63	79.30 47	77.61 115	87.57 183	58.19 137	94.08 190	73.91 135	86.68 115	93.33 129

CostFormer		82.33 103	81.15 107	85.86 85	89.01 161	68.46 62	82.39 287	93.01 98	75.59 94	80.25 87	81.57 246	72.03 27	94.96 160	79.06 103	77.48 182	94.16 102

API-MVS		82.28 104	80.53 117	87.54 31	96.13 19	70.59 23	93.63 81	91.04 175	65.72 250	75.45 136	92.83 112	56.11 164	98.89 15	64.10 220	89.75 94	93.15 134

IB-MVS	77.80 4	82.18 105	80.46 118	87.35 36	89.14 158	70.28 27	95.59 25	95.17 14	78.85 56	70.19 188	85.82 201	70.66 31	97.67 51	72.19 150	66.52 251	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	81.12 108	85.26 107	86.42 207	68.72 57	92.59 121	90.44 187	73.12 139	84.20 54	94.36 73	38.04 281	95.73 136	84.12 64	86.81 111	91.33 168

xiu_mvs_v1_base		82.16 106	81.12 108	85.26 107	86.42 207	68.72 57	92.59 121	90.44 187	73.12 139	84.20 54	94.36 73	38.04 281	95.73 136	84.12 64	86.81 111	91.33 168

xiu_mvs_v1_base_debi		82.16 106	81.12 108	85.26 107	86.42 207	68.72 57	92.59 121	90.44 187	73.12 139	84.20 54	94.36 73	38.04 281	95.73 136	84.12 64	86.81 111	91.33 168

3Dnovator+	73.60 7	82.10 109	80.60 116	86.60 57	90.89 126	66.80 111	95.20 32	93.44 80	74.05 117	67.42 225	92.49 118	49.46 221	97.65 55	70.80 159	91.68 72	95.33 51

MVS_111021_LR		82.02 110	81.52 104	83.51 151	88.42 173	62.88 213	89.77 216	88.93 243	76.78 84	75.55 135	93.10 99	50.31 213	95.38 153	83.82 68	87.02 110	92.26 158

PMMVS		81.98 111	82.04 99	81.78 190	89.76 145	56.17 288	91.13 181	90.69 182	77.96 68	80.09 88	93.57 94	46.33 246	94.99 159	81.41 86	87.46 107	94.17 101

baseline1		81.84 112	81.03 111	84.28 134	91.60 107	66.62 115	91.08 182	91.66 151	81.87 24	74.86 139	91.67 134	69.98 32	94.92 163	71.76 153	64.75 263	91.29 173

EPP-MVSNet		81.79 113	81.52 104	82.61 167	88.77 166	60.21 252	93.02 103	93.66 70	68.52 231	72.90 155	90.39 147	72.19 26	94.96 160	74.93 131	79.29 164	92.67 146

APD-MVS_3200maxsize		81.64 114	81.32 106	82.59 168	92.36 84	58.74 269	91.39 166	91.01 176	63.35 263	79.72 93	94.62 69	51.82 202	96.14 119	79.71 96	87.93 104	92.89 143

ACMMP		81.49 115	80.67 114	83.93 139	91.71 105	62.90 212	92.13 131	92.22 129	71.79 179	71.68 176	93.49 96	50.32 212	96.96 94	78.47 107	84.22 137	91.93 161
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		81.43 116	80.74 113	83.52 150	86.26 211	64.45 170	92.09 134	90.65 185	75.83 93	73.95 148	89.81 157	63.97 81	92.91 226	71.27 156	82.82 141	93.20 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 117	79.99 122	85.46 99	90.39 134	68.40 63	86.88 260	90.61 186	74.41 111	70.31 187	84.67 212	63.79 84	92.32 250	73.13 137	85.70 120	95.67 41

1121		81.25 118	80.05 120	84.87 117	92.30 86	64.31 178	87.91 246	91.39 161	59.44 292	79.94 89	92.91 107	57.09 146	97.01 86	66.63 194	92.81 57	93.29 130

thisisatest0530		81.15 119	80.07 119	84.39 131	88.26 177	65.63 142	91.40 164	94.62 34	71.27 196	70.93 180	89.18 160	72.47 24	96.04 126	65.62 209	76.89 188	91.49 166

Fast-Effi-MVS+		81.14 120	80.01 121	84.51 128	90.24 136	65.86 135	94.12 56	89.15 234	73.81 125	75.37 137	88.26 172	57.26 144	94.53 175	66.97 193	84.92 125	93.15 134

HQP-MVS		81.14 120	80.64 115	82.64 166	87.54 191	63.66 195	94.06 59	91.70 149	79.80 40	74.18 142	90.30 149	51.63 206	95.61 144	77.63 113	78.90 166	88.63 200

HyFIR lowres test		81.03 122	79.56 130	85.43 101	87.81 188	68.11 72	90.18 206	90.01 206	70.65 207	72.95 154	86.06 199	63.61 88	94.50 177	75.01 130	79.75 160	93.67 120

nrg030		80.93 123	79.86 124	84.13 136	83.69 249	68.83 54	93.23 95	91.20 167	75.55 95	75.06 138	88.22 175	63.04 97	94.74 167	81.88 81	66.88 248	88.82 198

Vis-MVSNet		80.92 124	79.98 123	83.74 142	88.48 170	61.80 226	93.44 89	88.26 263	73.96 121	77.73 111	91.76 131	49.94 217	94.76 165	65.84 206	90.37 89	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
1314		80.70 125	78.95 142	85.94 81	87.77 189	67.56 85	87.91 246	92.55 117	72.17 165	67.44 224	93.09 100	50.27 214	97.04 85	71.68 155	87.64 106	93.23 132

RRT_test8_iter05		80.61 126	79.62 128	83.60 149	91.87 102	66.90 107	93.42 92	93.68 68	77.09 80	68.83 206	85.63 204	66.82 52	95.42 151	76.46 120	62.74 275	88.48 203

tpmrst		80.57 127	79.14 141	84.84 118	90.10 138	68.28 67	81.70 290	89.72 216	77.63 73	75.96 129	79.54 277	64.94 72	92.71 233	75.43 124	77.28 185	93.55 123

1112_ss		80.56 128	79.83 125	82.77 161	88.65 167	60.78 240	92.29 126	88.36 258	72.58 150	72.46 164	94.95 56	65.09 69	93.42 214	66.38 200	77.71 175	94.10 105

VDDNet		80.50 129	78.26 149	87.21 38	86.19 212	69.79 36	94.48 47	91.31 163	60.42 284	79.34 96	90.91 139	38.48 277	96.56 110	82.16 79	81.05 154	95.27 60

BH-w/o		80.49 130	79.30 137	84.05 138	90.83 128	64.36 177	93.60 82	89.42 223	74.35 113	69.09 199	90.15 152	55.23 172	95.61 144	64.61 217	86.43 118	92.17 159

TAMVS		80.37 131	79.45 133	83.13 157	85.14 226	63.37 199	91.23 175	90.76 181	74.81 109	72.65 158	88.49 166	60.63 114	92.95 221	69.41 171	81.95 147	93.08 137

HQP_MVS		80.34 132	79.75 126	82.12 183	86.94 202	62.42 218	93.13 97	91.31 163	78.81 58	72.53 161	89.14 162	50.66 210	95.55 148	76.74 116	78.53 171	88.39 206

HPM-MVS_fast		80.25 133	79.55 132	82.33 175	91.55 110	59.95 255	91.32 172	89.16 233	65.23 254	74.71 140	93.07 103	47.81 237	95.74 135	74.87 134	88.23 101	91.31 172

ab-mvs		80.18 134	78.31 148	85.80 87	88.44 172	65.49 146	83.00 284	92.67 110	71.82 178	77.36 118	85.01 208	54.50 179	96.59 107	76.35 121	75.63 194	95.32 53

IS-MVSNet		80.14 135	79.41 134	82.33 175	87.91 186	60.08 254	91.97 142	88.27 261	72.90 145	71.44 178	91.73 133	61.44 107	93.66 209	62.47 232	86.53 116	93.24 131

test-LLR		80.10 136	79.56 130	81.72 192	86.93 204	61.17 234	92.70 114	91.54 154	71.51 192	75.62 132	86.94 190	53.83 185	92.38 246	72.21 148	84.76 128	91.60 164

PVSNet	73.49 8	80.05 137	78.63 144	84.31 132	90.92 124	64.97 160	92.47 124	91.05 174	79.18 50	72.43 165	90.51 146	37.05 293	94.06 192	68.06 182	86.00 119	93.90 116

UA-Net		80.02 138	79.65 127	81.11 205	89.33 153	57.72 277	86.33 263	89.00 242	77.44 76	81.01 79	89.15 161	59.33 128	95.90 130	61.01 239	84.28 135	89.73 190

test-mter		79.96 139	79.38 136	81.72 192	86.93 204	61.17 234	92.70 114	91.54 154	73.85 123	75.62 132	86.94 190	49.84 219	92.38 246	72.21 148	84.76 128	91.60 164

QAPM		79.95 140	77.39 165	87.64 26	89.63 147	71.41 15	93.30 93	93.70 67	65.34 253	67.39 227	91.75 132	47.83 236	98.96 12	57.71 254	89.81 91	92.54 150

UGNet		79.87 141	78.68 143	83.45 154	89.96 140	61.51 231	92.13 131	90.79 179	76.83 83	78.85 105	86.33 196	38.16 279	96.17 118	67.93 184	87.17 109	92.67 146
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		79.82 142	79.20 139	81.70 194	89.85 142	58.34 271	88.47 239	90.07 201	62.56 270	77.71 112	93.08 101	47.65 239	96.78 102	77.94 111	85.45 123	89.99 187

tpm2		79.80 143	77.95 155	85.34 105	88.28 176	68.26 68	81.56 292	91.42 160	70.11 211	77.59 116	80.50 264	67.40 46	94.26 185	67.34 189	77.35 183	93.51 124

thres200		79.66 144	78.33 147	83.66 148	92.54 83	65.82 138	93.06 99	96.31 3	74.90 108	73.30 151	88.66 164	59.67 123	95.61 144	47.84 286	78.67 169	89.56 193

CPTT-MVS		79.59 145	79.16 140	80.89 214	91.54 111	59.80 257	92.10 133	88.54 256	60.42 284	72.96 153	93.28 98	48.27 231	92.80 230	78.89 106	86.50 117	90.06 185

Test_1112_low_res		79.56 146	78.60 145	82.43 170	88.24 179	60.39 249	92.09 134	87.99 267	72.10 167	71.84 172	87.42 185	64.62 74	93.04 218	65.80 207	77.30 184	93.85 118

tttt0517		79.50 147	78.53 146	82.41 173	87.22 198	61.43 233	89.75 217	94.76 27	69.29 220	67.91 218	88.06 177	72.92 21	95.63 142	62.91 228	73.90 202	90.16 184

FIs		79.47 148	79.41 134	79.67 237	85.95 216	59.40 262	91.68 156	93.94 58	78.06 67	68.96 203	88.28 170	66.61 55	91.77 261	66.20 203	74.99 195	87.82 211

BH-RMVSNet		79.46 149	77.65 159	84.89 115	91.68 106	65.66 140	93.55 84	88.09 265	72.93 143	73.37 150	91.12 137	46.20 248	96.12 120	56.28 259	85.61 122	92.91 142

PCF-MVS	73.15 9	79.29 150	77.63 160	84.29 133	86.06 214	65.96 133	87.03 256	91.10 171	69.86 214	69.79 195	90.64 142	57.54 143	96.59 107	64.37 219	82.29 143	90.32 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 151	79.57 129	78.24 257	88.46 171	52.29 306	90.41 199	89.12 236	74.24 115	69.13 198	91.91 128	65.77 63	90.09 285	59.00 251	88.09 103	92.33 154

114514_t		79.17 152	77.67 158	83.68 146	95.32 25	65.53 145	92.85 110	91.60 153	63.49 262	67.92 217	90.63 144	46.65 243	95.72 140	67.01 192	83.54 138	89.79 188

VPA-MVSNet		79.03 153	78.00 153	82.11 186	85.95 216	64.48 169	93.22 96	94.66 32	75.05 106	74.04 147	84.95 209	52.17 201	93.52 211	74.90 133	67.04 247	88.32 208

OPM-MVS		79.00 154	78.09 151	81.73 191	83.52 252	63.83 187	91.64 158	90.30 194	76.36 89	71.97 171	89.93 156	46.30 247	95.17 157	75.10 127	77.70 176	86.19 241

EI-MVSNet		78.97 155	78.22 150	81.25 201	85.33 222	62.73 216	89.53 221	93.21 87	72.39 156	72.14 168	90.13 153	60.99 110	94.72 168	67.73 186	72.49 211	86.29 238

AdaColmap		78.94 156	77.00 170	84.76 119	96.34 15	65.86 135	92.66 118	87.97 268	62.18 273	70.56 181	92.37 122	43.53 260	97.35 69	64.50 218	82.86 140	91.05 176

miper_enhance_ethall		78.86 157	77.97 154	81.54 196	88.00 185	65.17 151	91.41 162	89.15 234	75.19 104	68.79 207	83.98 219	67.17 48	92.82 228	72.73 142	65.30 254	86.62 235

VPNet		78.82 158	77.53 162	82.70 163	84.52 236	66.44 120	93.93 67	92.23 125	80.46 36	72.60 159	88.38 169	49.18 224	93.13 217	72.47 146	63.97 271	88.55 202

EPNet_dtu		78.80 159	79.26 138	77.43 265	88.06 182	49.71 319	91.96 143	91.95 138	77.67 72	76.56 126	91.28 136	58.51 135	90.20 283	56.37 258	80.95 155	92.39 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 160	77.43 163	82.88 159	92.21 90	64.49 167	92.05 137	96.28 4	73.48 133	71.75 174	88.26 172	60.07 119	95.32 154	45.16 295	77.58 178	88.83 196

TR-MVS		78.77 161	77.37 166	82.95 158	90.49 131	60.88 238	93.67 79	90.07 201	70.08 212	74.51 141	91.37 135	45.69 249	95.70 141	60.12 245	80.32 157	92.29 156

mvs-test1		78.74 162	77.95 155	81.14 203	83.22 254	57.13 283	93.96 64	87.78 269	75.42 97	72.68 157	90.80 141	45.08 253	94.54 174	75.08 128	77.49 181	91.74 163

thres400		78.68 163	77.43 163	82.43 170	92.21 90	64.49 167	92.05 137	96.28 4	73.48 133	71.75 174	88.26 172	60.07 119	95.32 154	45.16 295	77.58 178	87.48 215

BH-untuned		78.68 163	77.08 167	83.48 153	89.84 143	63.74 190	92.70 114	88.59 254	71.57 189	66.83 233	88.65 165	51.75 204	95.39 152	59.03 250	84.77 127	91.32 171

OMC-MVS		78.67 165	77.91 157	80.95 212	85.76 220	57.40 281	88.49 238	88.67 251	73.85 123	72.43 165	92.10 125	49.29 223	94.55 173	72.73 142	77.89 174	90.91 177

tpm		78.58 166	77.03 168	83.22 155	85.94 218	64.56 165	83.21 282	91.14 170	78.31 63	73.67 149	79.68 275	64.01 80	92.09 255	66.07 204	71.26 221	93.03 138

OpenMVS	70.45 11	78.54 167	75.92 184	86.41 68	85.93 219	71.68 14	92.74 112	92.51 118	66.49 244	64.56 248	91.96 127	43.88 259	98.10 35	54.61 263	90.65 86	89.44 194

EPMVS		78.49 168	75.98 183	86.02 78	91.21 119	69.68 39	80.23 300	91.20 167	75.25 103	72.48 163	78.11 285	54.65 178	93.69 208	57.66 255	83.04 139	94.69 80

thres100view900		78.37 169	77.01 169	82.46 169	91.89 99	63.21 203	91.19 179	96.33 1	72.28 159	70.45 184	87.89 179	60.31 116	95.32 154	45.16 295	77.58 178	88.83 196

GA-MVS		78.33 170	76.23 180	84.65 124	83.65 250	66.30 124	91.44 161	90.14 199	76.01 91	70.32 186	84.02 218	42.50 263	94.72 168	70.98 157	77.00 187	92.94 141

cascas		78.18 171	75.77 186	85.41 102	87.14 200	69.11 47	92.96 105	91.15 169	66.71 242	70.47 182	86.07 198	37.49 287	96.48 112	70.15 165	79.80 159	90.65 179

UniMVSNet_NR-MVSNet		78.15 172	77.55 161	79.98 228	84.46 238	60.26 250	92.25 127	93.20 89	77.50 75	68.88 204	86.61 192	66.10 58	92.13 253	66.38 200	62.55 276	87.54 213

thres600view7		78.00 173	76.66 174	82.03 188	91.93 96	63.69 193	91.30 173	96.33 1	72.43 154	70.46 183	87.89 179	60.31 116	94.92 163	42.64 307	76.64 189	87.48 215

FC-MVSNet-test		77.99 174	78.08 152	77.70 260	84.89 231	55.51 292	90.27 203	93.75 66	76.87 81	66.80 234	87.59 182	65.71 64	90.23 282	62.89 229	73.94 200	87.37 218

Anonymous202405211		77.96 175	75.33 190	85.87 84	93.73 54	64.52 166	94.85 43	85.36 290	62.52 271	76.11 128	90.18 151	29.43 317	97.29 73	68.51 180	77.24 186	95.81 40

cl-mvsnet2		77.94 176	76.78 172	81.42 198	87.57 190	64.93 162	90.67 192	88.86 246	72.45 153	67.63 223	82.68 231	64.07 79	92.91 226	71.79 151	65.30 254	86.44 236

XXY-MVS		77.94 176	76.44 177	82.43 170	82.60 260	64.44 171	92.01 139	91.83 143	73.59 131	70.00 191	85.82 201	54.43 181	94.76 165	69.63 168	68.02 241	88.10 210

MS-PatchMatch		77.90 178	76.50 176	82.12 183	85.99 215	69.95 33	91.75 154	92.70 108	73.97 120	62.58 267	84.44 215	41.11 267	95.78 133	63.76 223	92.17 66	80.62 308

FMVSNet3		77.73 179	76.04 182	82.80 160	91.20 120	68.99 51	91.87 145	91.99 136	73.35 135	67.04 230	83.19 227	56.62 158	92.14 252	59.80 247	69.34 230	87.28 221

miper_ehance_all_eth		77.60 180	76.44 177	81.09 209	85.70 221	64.41 174	90.65 193	88.64 253	72.31 157	67.37 228	82.52 232	64.77 73	92.64 239	70.67 161	65.30 254	86.24 240

UniMVSNet (Re)		77.58 181	76.78 172	79.98 228	84.11 244	60.80 239	91.76 152	93.17 92	76.56 87	69.93 194	84.78 211	63.32 93	92.36 248	64.89 216	62.51 278	86.78 229

PatchmatchNet		77.46 182	74.63 196	85.96 80	89.55 150	70.35 26	79.97 304	89.55 219	72.23 161	70.94 179	76.91 296	57.03 148	92.79 231	54.27 265	81.17 153	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 183	75.65 188	82.73 162	80.38 277	67.13 100	91.85 147	90.23 196	75.09 105	69.37 196	83.39 225	53.79 187	94.44 178	71.77 152	65.00 260	86.63 234

RRT_MVS		77.38 184	76.59 175	79.77 235	90.91 125	63.61 197	91.15 180	90.91 177	72.28 159	72.06 170	87.28 188	43.92 258	89.04 293	73.32 136	67.47 245	86.67 230

CHOSEN 280x420		77.35 185	76.95 171	78.55 252	87.07 201	62.68 217	69.71 325	82.95 309	68.80 227	71.48 177	87.27 189	66.03 59	84.00 319	76.47 119	82.81 142	88.95 195

PS-MVSNAJss		77.26 186	76.31 179	80.13 225	80.64 275	59.16 266	90.63 196	91.06 173	72.80 146	68.58 211	84.57 214	53.55 189	93.96 199	72.97 138	71.96 215	87.27 222

gg-mvs-nofinetune		77.18 187	74.31 203	85.80 87	91.42 114	68.36 64	71.78 320	94.72 29	49.61 321	77.12 121	45.92 337	77.41 6	93.98 198	67.62 187	93.16 52	95.05 70

MVP-Stereo		77.12 188	76.23 180	79.79 234	81.72 266	66.34 123	89.29 223	90.88 178	70.56 208	62.01 270	82.88 228	49.34 222	94.13 187	65.55 211	93.80 41	78.88 319
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
X-MVStestdata		76.86 189	74.13 207	85.05 111	93.22 63	63.78 188	92.92 108	92.66 111	73.99 118	78.18 108	10.19 349	55.25 170	97.41 65	79.16 101	91.58 74	93.95 113

DU-MVS		76.86 189	75.84 185	79.91 230	82.96 258	60.26 250	91.26 174	91.54 154	76.46 88	68.88 204	86.35 194	56.16 162	92.13 253	66.38 200	62.55 276	87.35 219

Anonymous20240529		76.84 191	74.15 206	84.88 116	91.02 121	64.95 161	93.84 75	91.09 172	53.57 312	73.00 152	87.42 185	35.91 297	97.32 71	69.14 175	72.41 213	92.36 153

cl_fuxian		76.83 192	75.47 189	80.93 213	85.02 229	64.18 183	90.39 200	88.11 264	71.66 182	66.65 235	81.64 244	63.58 90	92.56 240	69.31 173	62.86 274	86.04 246

WR-MVS		76.76 193	75.74 187	79.82 233	84.60 234	62.27 223	92.60 119	92.51 118	76.06 90	67.87 220	85.34 205	56.76 154	90.24 281	62.20 233	63.69 273	86.94 227

v1144		76.73 194	74.88 193	82.27 177	80.23 282	66.60 116	91.68 156	90.21 198	73.69 128	69.06 201	81.89 239	52.73 197	94.40 179	69.21 174	65.23 257	85.80 252

IterMVS-LS		76.49 195	75.18 192	80.43 218	84.49 237	62.74 215	90.64 194	88.80 247	72.40 155	65.16 243	81.72 242	60.98 111	92.27 251	67.74 185	64.65 265	86.29 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 196	74.55 199	82.19 181	79.14 294	67.82 79	90.26 204	89.42 223	73.75 126	68.63 210	81.89 239	51.31 208	94.09 189	71.69 154	64.84 261	84.66 268

v148		76.19 197	74.47 201	81.36 199	80.05 283	64.44 171	91.75 154	90.23 196	73.68 129	67.13 229	80.84 259	55.92 168	93.86 205	68.95 177	61.73 287	85.76 255

Effi-MVS+-dtu		76.14 198	75.28 191	78.72 251	83.22 254	55.17 294	89.87 214	87.78 269	75.42 97	67.98 216	81.43 248	45.08 253	92.52 242	75.08 128	71.63 216	88.48 203

cl-mvsnet_		76.07 199	74.67 194	80.28 221	85.15 225	61.76 227	90.12 207	88.73 249	71.16 197	65.43 240	81.57 246	61.15 108	92.95 221	66.54 197	62.17 280	86.13 244

cl-mvsnet1		76.07 199	74.67 194	80.28 221	85.14 226	61.75 228	90.12 207	88.73 249	71.16 197	65.42 241	81.60 245	61.15 108	92.94 225	66.54 197	62.16 282	86.14 242

FMVSNet2		76.07 199	74.01 209	82.26 179	88.85 162	67.66 83	91.33 171	91.61 152	70.84 203	65.98 237	82.25 235	48.03 232	92.00 257	58.46 252	68.73 236	87.10 223

v144192		76.05 202	74.03 208	82.12 183	79.50 288	66.55 118	91.39 166	89.71 217	72.30 158	68.17 214	81.33 251	51.75 204	94.03 196	67.94 183	64.19 267	85.77 253

NR-MVSNet		76.05 202	74.59 197	80.44 217	82.96 258	62.18 224	90.83 188	91.73 146	77.12 79	60.96 272	86.35 194	59.28 129	91.80 260	60.74 240	61.34 291	87.35 219

v1192		75.98 204	73.92 210	82.15 182	79.73 284	66.24 127	91.22 176	89.75 211	72.67 148	68.49 212	81.42 249	49.86 218	94.27 183	67.08 191	65.02 259	85.95 249

eth_miper_zixun_eth		75.96 205	74.40 202	80.66 215	84.66 233	63.02 206	89.28 224	88.27 261	71.88 173	65.73 238	81.65 243	59.45 125	92.81 229	68.13 181	60.53 296	86.14 242

TranMVSNet+NR-MVSNet		75.86 206	74.52 200	79.89 231	82.44 261	60.64 246	91.37 169	91.37 162	76.63 85	67.65 222	86.21 197	52.37 200	91.55 265	61.84 235	60.81 294	87.48 215

SCA		75.82 207	72.76 221	85.01 113	86.63 206	70.08 28	81.06 294	89.19 231	71.60 188	70.01 190	77.09 294	45.53 250	90.25 278	60.43 242	73.27 204	94.68 81

LPG-MVS_test		75.82 207	74.58 198	79.56 241	84.31 241	59.37 263	90.44 197	89.73 214	69.49 217	64.86 244	88.42 167	38.65 275	94.30 181	72.56 144	72.76 208	85.01 265

GBi-Net		75.65 209	73.83 211	81.10 206	88.85 162	65.11 154	90.01 210	90.32 190	70.84 203	67.04 230	80.25 269	48.03 232	91.54 266	59.80 247	69.34 230	86.64 231

test1		75.65 209	73.83 211	81.10 206	88.85 162	65.11 154	90.01 210	90.32 190	70.84 203	67.04 230	80.25 269	48.03 232	91.54 266	59.80 247	69.34 230	86.64 231

v1921920		75.63 211	73.49 215	82.06 187	79.38 289	66.35 122	91.07 184	89.48 220	71.98 168	67.99 215	81.22 254	49.16 226	93.90 202	66.56 196	64.56 266	85.92 251

ACMP	71.68 10	75.58 212	74.23 205	79.62 239	84.97 230	59.64 258	90.80 189	89.07 239	70.39 209	62.95 263	87.30 187	38.28 278	93.87 203	72.89 139	71.45 219	85.36 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 213	73.26 217	81.61 195	80.67 274	66.82 109	89.54 220	89.27 227	71.65 183	63.30 261	80.30 268	54.99 176	94.06 192	67.33 190	62.33 279	83.94 273

tpm cat1		75.30 214	72.21 229	84.58 126	88.52 168	67.77 80	78.16 313	88.02 266	61.88 277	68.45 213	76.37 297	60.65 113	94.03 196	53.77 268	74.11 198	91.93 161

PLC	68.80 14	75.23 215	73.68 213	79.86 232	92.93 72	58.68 270	90.64 194	88.30 259	60.90 281	64.43 252	90.53 145	42.38 264	94.57 171	56.52 257	76.54 190	86.33 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 216	72.98 219	81.88 189	79.20 291	66.00 131	90.75 191	89.11 237	71.63 187	67.41 226	81.22 254	47.36 240	93.87 203	65.46 212	64.72 264	85.77 253

Fast-Effi-MVS+-dtu		75.04 217	73.37 216	80.07 226	80.86 271	59.52 261	91.20 178	85.38 289	71.90 171	65.20 242	84.84 210	41.46 266	92.97 220	66.50 199	72.96 207	87.73 212

dp		75.01 218	72.09 230	83.76 141	89.28 154	66.22 128	79.96 305	89.75 211	71.16 197	67.80 221	77.19 293	51.81 203	92.54 241	50.39 276	71.44 220	92.51 151

TAPA-MVS	70.22 12	74.94 219	73.53 214	79.17 246	90.40 133	52.07 307	89.19 227	89.61 218	62.69 269	70.07 189	92.67 114	48.89 229	94.32 180	38.26 321	79.97 158	91.12 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 220	72.54 226	81.46 197	80.33 280	66.71 113	89.15 228	89.08 238	70.94 201	63.08 262	79.86 273	52.52 198	94.04 195	65.70 208	62.17 280	83.64 275

XVG-OURS-SEG-HR		74.70 221	73.08 218	79.57 240	78.25 304	57.33 282	80.49 296	87.32 273	63.22 265	68.76 208	90.12 155	44.89 255	91.59 264	70.55 163	74.09 199	89.79 188

ACMM	69.62 13	74.34 222	72.73 222	79.17 246	84.25 243	57.87 275	90.36 201	89.93 207	63.17 266	65.64 239	86.04 200	37.79 285	94.10 188	65.89 205	71.52 218	85.55 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 223	72.30 228	80.32 219	91.49 113	61.66 229	90.85 187	80.72 315	56.67 305	63.85 256	90.64 142	46.75 242	90.84 273	53.79 267	75.99 193	88.47 205

XVG-OURS		74.25 224	72.46 227	79.63 238	78.45 303	57.59 280	80.33 298	87.39 272	63.86 260	68.76 208	89.62 159	40.50 269	91.72 262	69.00 176	74.25 197	89.58 191

CVMVSNet		74.04 225	74.27 204	73.33 293	85.33 222	43.94 333	89.53 221	88.39 257	54.33 311	70.37 185	90.13 153	49.17 225	84.05 317	61.83 236	79.36 162	91.99 160

Baseline_NR-MVSNet		73.99 226	72.83 220	77.48 264	80.78 272	59.29 265	91.79 149	84.55 296	68.85 226	68.99 202	80.70 260	56.16 162	92.04 256	62.67 230	60.98 293	81.11 302

pmmvs4		73.92 227	71.81 233	80.25 223	79.17 292	65.24 149	87.43 253	87.26 275	67.64 238	63.46 259	83.91 220	48.96 228	91.53 269	62.94 227	65.49 253	83.96 272

D2MVS		73.80 228	72.02 231	79.15 248	79.15 293	62.97 207	88.58 237	90.07 201	72.94 142	59.22 280	78.30 282	42.31 265	92.70 235	65.59 210	72.00 214	81.79 299

CR-MVSNet		73.79 229	70.82 240	82.70 163	83.15 256	67.96 75	70.25 322	84.00 301	73.67 130	69.97 192	72.41 312	57.82 140	89.48 289	52.99 271	73.13 205	90.64 180

test_djsdf		73.76 230	72.56 225	77.39 266	77.00 312	53.93 299	89.07 230	90.69 182	65.80 248	63.92 254	82.03 238	43.14 262	92.67 236	72.83 140	68.53 237	85.57 257

pmmvs5		73.35 231	71.52 235	78.86 250	78.64 301	60.61 247	91.08 182	86.90 276	67.69 235	63.32 260	83.64 221	44.33 257	90.53 275	62.04 234	66.02 252	85.46 259

Anonymous20231211		73.08 232	70.39 242	81.13 204	90.62 130	63.33 200	91.40 164	90.06 204	51.84 316	64.46 251	80.67 262	36.49 295	94.07 191	63.83 222	64.17 268	85.98 248

miper_lstm_enhance		73.05 233	71.73 234	77.03 270	83.80 247	58.32 272	81.76 288	88.88 244	69.80 215	61.01 271	78.23 284	57.19 145	87.51 305	65.34 213	59.53 299	85.27 264

jajsoiax		73.05 233	71.51 236	77.67 261	77.46 309	54.83 295	88.81 233	90.04 205	69.13 224	62.85 265	83.51 223	31.16 313	92.75 232	70.83 158	69.80 226	85.43 260

LCM-MVSNet-Re		72.93 235	71.84 232	76.18 277	88.49 169	48.02 323	80.07 303	70.17 335	73.96 121	52.25 306	80.09 272	49.98 216	88.24 299	67.35 188	84.23 136	92.28 157

pm-mvs1		72.89 236	71.09 238	78.26 256	79.10 295	57.62 279	90.80 189	89.30 226	67.66 236	62.91 264	81.78 241	49.11 227	92.95 221	60.29 244	58.89 302	84.22 271

tpmvs		72.88 237	69.76 248	82.22 180	90.98 122	67.05 102	78.22 312	88.30 259	63.10 267	64.35 253	74.98 304	55.09 175	94.27 183	43.25 301	69.57 229	85.34 262

test0.0.03 1		72.76 238	72.71 223	72.88 297	80.25 281	47.99 324	91.22 176	89.45 221	71.51 192	62.51 268	87.66 181	53.83 185	85.06 314	50.16 277	67.84 244	85.58 256

UniMVSNet_ETH3D		72.74 239	70.53 241	79.36 243	78.62 302	56.64 286	85.01 267	89.20 230	63.77 261	64.84 246	84.44 215	34.05 302	91.86 259	63.94 221	70.89 223	89.57 192

mvs_tets		72.71 240	71.11 237	77.52 262	77.41 310	54.52 297	88.45 240	89.76 210	68.76 229	62.70 266	83.26 226	29.49 316	92.71 233	70.51 164	69.62 228	85.34 262

FMVSNet1		72.71 240	69.91 246	81.10 206	83.60 251	65.11 154	90.01 210	90.32 190	63.92 259	63.56 258	80.25 269	36.35 296	91.54 266	54.46 264	66.75 249	86.64 231

IterMVS		72.65 242	70.83 239	78.09 258	82.17 262	62.96 208	87.64 251	86.28 280	71.56 190	60.44 274	78.85 280	45.42 252	86.66 309	63.30 225	61.83 284	84.65 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		72.06 243	69.98 243	78.28 255	89.51 151	55.70 291	83.49 276	83.39 307	61.24 280	63.72 257	82.76 229	34.77 300	93.03 219	53.37 270	77.59 177	86.12 245

PVSNet_0	68.08 15	71.81 244	68.32 255	82.27 177	84.68 232	62.31 222	88.68 235	90.31 193	75.84 92	57.93 289	80.65 263	37.85 284	94.19 186	69.94 166	29.05 340	90.31 183

MIMVSNet		71.64 245	68.44 253	81.23 202	81.97 265	64.44 171	73.05 319	88.80 247	69.67 216	64.59 247	74.79 305	32.79 305	87.82 303	53.99 266	76.35 191	91.42 167

IterMVS-SCA-FT		71.55 246	69.97 244	76.32 275	81.48 267	60.67 245	87.64 251	85.99 285	66.17 246	59.50 278	78.88 279	45.53 250	83.65 321	62.58 231	61.93 283	84.63 270

v7n		71.31 247	68.65 251	79.28 244	76.40 314	60.77 241	86.71 261	89.45 221	64.17 258	58.77 285	78.24 283	44.59 256	93.54 210	57.76 253	61.75 286	83.52 278

anonymousdsp		71.14 248	69.37 249	76.45 274	72.95 322	54.71 296	84.19 271	88.88 244	61.92 276	62.15 269	79.77 274	38.14 280	91.44 271	68.90 178	67.45 246	83.21 284

testing_2		71.09 249	67.32 259	82.40 174	69.82 331	66.52 119	83.64 274	90.77 180	72.21 162	45.12 327	71.07 320	27.60 322	93.74 206	75.71 123	69.96 225	86.95 226

F-COLMAP		70.66 250	68.44 253	77.32 267	86.37 210	55.91 290	88.00 244	86.32 279	56.94 303	57.28 292	88.07 176	33.58 303	92.49 243	51.02 274	68.37 238	83.55 276

WR-MVS_H		70.59 251	69.94 245	72.53 299	81.03 270	51.43 310	87.35 254	92.03 135	67.38 239	60.23 275	80.70 260	55.84 169	83.45 323	46.33 291	58.58 303	82.72 290

CP-MVSNet		70.50 252	69.91 246	72.26 302	80.71 273	51.00 313	87.23 255	90.30 194	67.84 233	59.64 277	82.69 230	50.23 215	82.30 330	51.28 273	59.28 300	83.46 280

tfpnnormal		70.10 253	67.36 257	78.32 254	83.45 253	60.97 237	88.85 232	92.77 106	64.85 255	60.83 273	78.53 281	43.52 261	93.48 212	31.73 336	61.70 288	80.52 309

TransMVSNet (Re)		70.07 254	67.66 256	77.31 268	80.62 276	59.13 267	91.78 151	84.94 293	65.97 247	60.08 276	80.44 265	50.78 209	91.87 258	48.84 282	45.46 327	80.94 304

DP-MVS		69.90 255	66.48 261	80.14 224	95.36 24	62.93 209	89.56 218	76.11 322	50.27 320	57.69 290	85.23 206	39.68 271	95.73 136	33.35 330	71.05 222	81.78 300

PS-CasMVS		69.86 256	69.13 250	72.07 305	80.35 279	50.57 315	87.02 257	89.75 211	67.27 240	59.19 281	82.28 234	46.58 244	82.24 331	50.69 275	59.02 301	83.39 282

RPMNet		69.58 257	65.21 269	82.70 163	83.15 256	67.96 75	70.25 322	86.15 283	46.83 329	69.97 192	65.10 328	56.48 161	89.48 289	35.79 326	73.13 205	90.64 180

MSDG		69.54 258	65.73 264	80.96 211	85.11 228	63.71 192	84.19 271	83.28 308	56.95 302	54.50 297	84.03 217	31.50 311	96.03 127	42.87 305	69.13 233	83.14 286

PEN-MVS		69.46 259	68.56 252	72.17 304	79.27 290	49.71 319	86.90 259	89.24 228	67.24 241	59.08 282	82.51 233	47.23 241	83.54 322	48.42 284	57.12 304	83.25 283

LS3D		69.17 260	66.40 262	77.50 263	91.92 97	56.12 289	85.12 266	80.37 316	46.96 327	56.50 294	87.51 184	37.25 288	93.71 207	32.52 335	79.40 161	82.68 292

PatchT		69.11 261	65.37 268	80.32 219	82.07 264	63.68 194	67.96 331	87.62 271	50.86 319	69.37 196	65.18 327	57.09 146	88.53 297	41.59 310	66.60 250	88.74 199

MVS_0304		68.99 262	67.23 260	74.28 288	80.36 278	52.54 304	87.01 258	86.36 278	59.89 290	66.22 236	73.56 308	24.25 327	88.03 301	57.34 256	70.11 224	82.27 295

ACMH	63.93 17	68.62 263	64.81 270	80.03 227	85.22 224	63.25 201	87.72 249	84.66 295	60.83 282	51.57 309	79.43 278	27.29 323	94.96 160	41.76 308	64.84 261	81.88 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 264	65.41 267	77.96 259	78.69 300	62.93 209	89.86 215	89.17 232	60.55 283	50.27 314	77.73 288	22.60 332	94.06 192	47.18 289	72.65 210	76.88 326

ADS-MVSNet		68.54 265	64.38 277	81.03 210	88.06 182	66.90 107	68.01 329	84.02 300	57.57 298	64.48 249	69.87 321	38.68 273	89.21 292	40.87 312	67.89 242	86.97 224

DTE-MVSNet		68.46 266	67.33 258	71.87 307	77.94 307	49.00 322	86.16 264	88.58 255	66.36 245	58.19 286	82.21 236	46.36 245	83.87 320	44.97 298	55.17 311	82.73 289

our_test_3		68.29 267	64.69 272	79.11 249	78.92 296	64.85 163	88.40 241	85.06 291	60.32 286	52.68 304	76.12 299	40.81 268	89.80 288	44.25 300	55.65 309	82.67 293

Patchmatch-RL test		68.17 268	64.49 275	79.19 245	71.22 326	53.93 299	70.07 324	71.54 334	69.22 221	56.79 293	62.89 330	56.58 159	88.61 294	69.53 170	52.61 317	95.03 73

XVG-ACMP-BASELINE		68.04 269	65.53 266	75.56 279	74.06 321	52.37 305	78.43 309	85.88 286	62.03 274	58.91 284	81.21 256	20.38 335	91.15 272	60.69 241	68.18 239	83.16 285

FMVSNet5		68.04 269	65.66 265	75.18 281	84.43 239	57.89 274	83.54 275	86.26 281	61.83 278	53.64 302	73.30 309	37.15 291	85.08 313	48.99 281	61.77 285	82.56 294

ppachtmachnet_test		67.72 271	63.70 279	79.77 235	78.92 296	66.04 130	88.68 235	82.90 310	60.11 288	55.45 295	75.96 300	39.19 272	90.55 274	39.53 316	52.55 318	82.71 291

ACMH+	65.35 16	67.65 272	64.55 273	76.96 271	84.59 235	57.10 284	88.08 243	80.79 314	58.59 297	53.00 303	81.09 258	26.63 325	92.95 221	46.51 290	61.69 289	80.82 305

pmmvs6		67.57 273	64.76 271	76.00 278	72.82 324	53.37 301	88.71 234	86.78 277	53.19 313	57.58 291	78.03 286	35.33 299	92.41 245	55.56 261	54.88 313	82.21 296

Anonymous20231206		67.53 274	65.78 263	72.79 298	74.95 318	47.59 326	88.23 242	87.32 273	61.75 279	58.07 288	77.29 291	37.79 285	87.29 307	42.91 303	63.71 272	83.48 279

Patchmtry		67.53 274	63.93 278	78.34 253	82.12 263	64.38 175	68.72 326	84.00 301	48.23 326	59.24 279	72.41 312	57.82 140	89.27 291	46.10 292	56.68 308	81.36 301

USDC		67.43 276	64.51 274	76.19 276	77.94 307	55.29 293	78.38 310	85.00 292	73.17 137	48.36 319	80.37 266	21.23 334	92.48 244	52.15 272	64.02 270	80.81 306

ADS-MVSNet2		66.90 277	63.44 281	77.26 269	88.06 182	60.70 244	68.01 329	75.56 325	57.57 298	64.48 249	69.87 321	38.68 273	84.10 316	40.87 312	67.89 242	86.97 224

CMPMVS	48.56 21	66.77 278	64.41 276	73.84 290	70.65 329	50.31 316	77.79 314	85.73 288	45.54 330	44.76 328	82.14 237	35.40 298	90.14 284	63.18 226	74.54 196	81.07 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 279	62.92 284	76.80 273	76.51 313	57.77 276	89.22 225	83.41 306	55.48 309	53.86 301	77.84 287	26.28 326	93.95 200	34.90 328	68.76 235	78.68 321

LTVRE_ROB	59.60 19	66.27 280	63.54 280	74.45 285	84.00 246	51.55 309	67.08 332	83.53 304	58.78 295	54.94 296	80.31 267	34.54 301	93.23 216	40.64 314	68.03 240	78.58 322
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		66.06 281	62.45 287	76.88 272	81.42 269	54.45 298	57.49 340	88.67 251	49.36 322	63.86 255	46.86 336	56.06 165	90.25 278	49.53 280	68.83 234	85.95 249

Patchmatch-test		65.86 282	60.94 293	80.62 216	83.75 248	58.83 268	58.91 339	75.26 327	44.50 333	50.95 313	77.09 294	58.81 134	87.90 302	35.13 327	64.03 269	95.12 67

UnsupCasMVSNet_eth		65.79 283	63.10 282	73.88 289	70.71 328	50.29 317	81.09 293	89.88 208	72.58 150	49.25 317	74.77 306	32.57 307	87.43 306	55.96 260	41.04 333	83.90 274

pmmvs-eth3d		65.53 284	62.32 288	75.19 280	69.39 333	59.59 259	82.80 285	83.43 305	62.52 271	51.30 311	72.49 310	32.86 304	87.16 308	55.32 262	50.73 321	78.83 320

SixPastTwentyTwo		64.92 285	61.78 291	74.34 287	78.74 299	49.76 318	83.42 279	79.51 319	62.86 268	50.27 314	77.35 289	30.92 315	90.49 276	45.89 293	47.06 325	82.78 287

OurMVSNet-221017-0		64.68 286	62.17 289	72.21 303	76.08 317	47.35 327	80.67 295	81.02 313	56.19 306	51.60 308	79.66 276	27.05 324	88.56 296	53.60 269	53.63 316	80.71 307

test_0402		64.54 287	61.09 292	74.92 282	84.10 245	60.75 242	87.95 245	79.71 318	52.03 315	52.41 305	77.20 292	32.21 309	91.64 263	23.14 339	61.03 292	72.36 332

testgi		64.48 288	62.87 285	69.31 311	71.24 325	40.62 337	85.49 265	79.92 317	65.36 252	54.18 299	83.49 224	23.74 330	84.55 315	41.60 309	60.79 295	82.77 288

RPSCF		64.24 289	61.98 290	71.01 308	76.10 316	45.00 330	75.83 317	75.94 323	46.94 328	58.96 283	84.59 213	31.40 312	82.00 332	47.76 287	60.33 298	86.04 246

EU-MVSNet		64.01 290	63.01 283	67.02 317	74.40 320	38.86 341	83.27 280	86.19 282	45.11 331	54.27 298	81.15 257	36.91 294	80.01 335	48.79 283	57.02 305	82.19 297

test20.03		63.83 291	62.65 286	67.38 316	70.58 330	39.94 338	86.57 262	84.17 298	63.29 264	51.86 307	77.30 290	37.09 292	82.47 328	38.87 320	54.13 315	79.73 314

MDA-MVSNet_test_wron		63.78 292	60.16 294	74.64 283	78.15 305	60.41 248	83.49 276	84.03 299	56.17 308	39.17 336	71.59 318	37.22 289	83.24 326	42.87 305	48.73 323	80.26 312

YYNet1		63.76 293	60.14 295	74.62 284	78.06 306	60.19 253	83.46 278	83.99 303	56.18 307	39.25 335	71.56 319	37.18 290	83.34 324	42.90 304	48.70 324	80.32 311

K. test v3		63.09 294	59.61 297	73.53 292	76.26 315	49.38 321	83.27 280	77.15 321	64.35 257	47.77 320	72.32 314	28.73 318	87.79 304	49.93 279	36.69 337	83.41 281

COLMAP_ROB	57.96 20	62.98 295	59.65 296	72.98 296	81.44 268	53.00 303	83.75 273	75.53 326	48.34 325	48.81 318	81.40 250	24.14 328	90.30 277	32.95 332	60.52 297	75.65 329
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		61.66 296	58.06 298	72.46 300	79.57 285	51.42 311	80.17 301	68.61 337	51.25 317	45.88 322	81.23 252	19.86 336	86.58 310	38.98 318	57.01 306	79.39 316

UnsupCasMVSNet_bld		61.60 297	57.71 299	73.29 294	68.73 334	51.64 308	78.61 308	89.05 240	57.20 301	46.11 321	61.96 331	28.70 319	88.60 295	50.08 278	38.90 335	79.63 315

MDA-MVSNet-bldmvs		61.54 298	57.70 300	73.05 295	79.53 287	57.00 285	83.08 283	81.23 312	57.57 298	34.91 338	72.45 311	32.79 305	86.26 312	35.81 325	41.95 331	75.89 328

TinyColmap		60.32 299	56.42 304	72.00 306	78.78 298	53.18 302	78.36 311	75.64 324	52.30 314	41.59 334	75.82 302	14.76 341	88.35 298	35.84 324	54.71 314	74.46 330

MVS-HIRNet		60.25 300	55.55 305	74.35 286	84.37 240	56.57 287	71.64 321	74.11 328	34.44 338	45.54 326	42.24 340	31.11 314	89.81 286	40.36 315	76.10 192	76.67 327

MIMVSNet1		60.16 301	57.33 301	68.67 312	69.71 332	44.13 332	78.92 307	84.21 297	55.05 310	44.63 329	71.85 316	23.91 329	81.54 334	32.63 334	55.03 312	80.35 310

PM-MVS		59.40 302	56.59 302	67.84 313	63.63 336	41.86 334	76.76 315	63.22 343	59.01 294	51.07 312	72.27 315	11.72 343	83.25 325	61.34 237	50.28 322	78.39 323

new-patchmatchnet		59.30 303	56.48 303	67.79 314	65.86 335	44.19 331	82.47 286	81.77 311	59.94 289	43.65 332	66.20 326	27.67 321	81.68 333	39.34 317	41.40 332	77.50 325

DSMNet-mixed		56.78 304	54.44 306	63.79 319	63.21 337	29.44 345	64.43 334	64.10 342	42.12 335	51.32 310	71.60 317	31.76 310	75.04 337	36.23 323	65.20 258	86.87 228

pmmvs3		55.51 305	51.50 309	67.53 315	57.90 342	50.93 314	80.37 297	73.66 329	40.63 336	44.15 331	64.75 329	16.30 338	78.97 336	44.77 299	40.98 334	72.69 331

TDRefinement		55.28 306	51.58 308	66.39 318	59.53 341	46.15 329	76.23 316	72.80 330	44.60 332	42.49 333	76.28 298	15.29 339	82.39 329	33.20 331	43.75 329	70.62 334

LF4IMVS		54.01 307	52.12 307	59.69 320	62.41 339	39.91 339	68.59 327	68.28 339	42.96 334	44.55 330	75.18 303	14.09 342	68.39 340	41.36 311	51.68 319	70.78 333

N_pmnet		50.55 308	49.11 310	54.88 323	77.17 311	4.02 354	84.36 270	2.00 354	48.59 323	45.86 324	68.82 323	32.22 308	82.80 327	31.58 337	51.38 320	77.81 324

new_pmnet		49.31 309	46.44 311	57.93 321	62.84 338	40.74 336	68.47 328	62.96 344	36.48 337	35.09 337	57.81 333	14.97 340	72.18 338	32.86 333	46.44 326	60.88 338

FPMVS		45.64 310	43.10 312	53.23 325	51.42 344	36.46 342	64.97 333	71.91 332	29.13 340	27.53 339	61.55 332	9.83 345	65.01 344	16.00 342	55.58 310	58.22 339

LCM-MVSNet		40.54 311	35.79 313	54.76 324	36.92 349	30.81 344	51.41 341	69.02 336	22.07 342	24.63 340	45.37 338	4.56 351	65.81 342	33.67 329	34.50 338	67.67 335

ANet_high		40.27 312	35.20 314	55.47 322	34.74 350	34.47 343	63.84 335	71.56 333	48.42 324	18.80 343	41.08 341	9.52 346	64.45 345	20.18 340	8.66 347	67.49 336

PMMVS2		37.93 313	33.61 315	50.92 326	46.31 346	24.76 348	60.55 338	50.05 346	28.94 341	20.93 341	47.59 335	4.41 352	65.13 343	25.14 338	18.55 342	62.87 337

Gipuma		34.91 314	31.44 316	45.30 327	70.99 327	39.64 340	19.85 347	72.56 331	20.10 344	16.16 345	21.47 346	5.08 350	71.16 339	13.07 343	43.70 330	25.08 343
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	26.43 22	31.84 315	28.16 317	42.89 328	25.87 352	27.58 346	50.92 342	49.78 347	21.37 343	14.17 346	40.81 342	2.01 353	66.62 341	9.61 345	38.88 336	34.49 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 316	24.00 319	26.45 331	43.74 347	18.44 351	60.86 336	39.66 348	15.11 345	9.53 348	22.10 345	6.52 348	46.94 347	8.31 346	10.14 344	13.98 345

MVE	24.84 23	24.35 317	19.77 322	38.09 329	34.56 351	26.92 347	26.57 345	38.87 350	11.73 347	11.37 347	27.44 343	1.37 354	50.42 346	11.41 344	14.60 343	36.93 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 318	23.20 321	25.46 332	41.52 348	16.90 352	60.56 337	38.79 351	14.62 346	8.99 349	20.24 348	7.35 347	45.82 348	7.25 347	9.46 345	13.64 346

tmp_tt		22.26 319	23.75 320	17.80 333	5.23 353	12.06 353	35.26 344	39.48 349	2.82 349	18.94 342	44.20 339	22.23 333	24.64 350	36.30 322	9.31 346	16.69 344

cdsmvs_eth3d_5k		19.86 320	26.47 318	0.00 337	0.00 356	0.00 357	0.00 348	93.45 78	0.00 352	0.00 353	95.27 47	49.56 220	0.00 354	0.00 351	0.00 350	0.00 350

wuyk23d		11.30 321	10.95 323	12.33 334	48.05 345	19.89 350	25.89 346	1.92 355	3.58 348	3.12 350	1.37 350	0.64 355	15.77 351	6.23 348	7.77 348	1.35 347

ab-mvs-re		7.91 322	10.55 324	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	94.95 56	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

testmvs		7.23 323	9.62 325	0.06 336	0.04 354	0.02 356	84.98 268	0.02 356	0.03 350	0.18 351	1.21 351	0.01 357	0.02 352	0.14 349	0.01 349	0.13 349

test123		6.92 324	9.21 326	0.08 335	0.03 355	0.05 355	81.65 291	0.01 357	0.02 351	0.14 352	0.85 352	0.03 356	0.02 352	0.12 350	0.00 350	0.16 348

pcd_1.5k_mvsjas		4.46 325	5.95 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	53.55 189	0.00 354	0.00 351	0.00 350	0.00 350

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

IU-MVS					96.46 9	69.91 34		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	71.65 183	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 10	69.38 42		94.44 40	71.65 183	92.11 3	97.05 8	76.79 7	99.11 4

9.14			87.63 23		93.86 47		94.41 48	94.18 53	72.76 147	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

save fliter					93.84 48	67.89 77	95.05 38	92.66 111	78.19 64

test_0728_THIRD									72.48 152	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 10	70.43 25	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

test0726					96.40 13	69.99 30	96.76 6	94.33 48	71.92 169	91.89 6	97.11 7	73.77 17

GSMVS																94.68 81

test_part2					96.29 16	68.16 71				90.78 10

test_part1				0.00 337		0.00 357	0.00 348	94.26 51				0.00 358	0.00 354	0.00 351	0.00 350	0.00 350

sam_mvs1												57.85 139				94.68 81

sam_mvs												54.91 177

ambc				69.61 310	61.38 340	41.35 335	49.07 343	85.86 287		50.18 316	66.40 325	10.16 344	88.14 300	45.73 294	44.20 328	79.32 318

MTGPA								92.23 125

test_post1							78.95 306				20.70 347	53.05 194	91.50 270	60.43 242

test_post											23.01 344	56.49 160	92.67 236

patchmatchnet-post											67.62 324	57.62 142	90.25 278

GG-mvs-BLEND				86.53 62	91.91 98	69.67 40	75.02 318	94.75 28		78.67 107	90.85 140	77.91 5	94.56 172	72.25 147	93.74 43	95.36 50

MTMP							93.77 77	32.52 352

gm-plane-assit					88.42 173	67.04 103			78.62 61		91.83 129		97.37 67	76.57 118

test9_res														89.41 20	94.96 16	95.29 56

TEST9					94.18 39	67.28 94	94.16 53	93.51 75	71.75 181	85.52 43	95.33 42	68.01 39	97.27 76

test_8					94.19 38	67.19 97	94.15 55	93.42 81	71.87 174	85.38 45	95.35 41	68.19 36	96.95 95

agg_prior2														86.41 48	94.75 26	95.33 51

agg_prior					94.16 43	66.97 105		93.31 84		84.49 51			96.75 104

TestCases				72.46 300	79.57 285	51.42 311		68.61 337	51.25 317	45.88 322	81.23 252	19.86 336	86.58 310	38.98 318	57.01 306	79.39 316

test_prior4						67.18 99	93.92 68

test_prior2							95.10 36		75.40 99	85.25 47	95.61 37	67.94 40		87.47 38	94.77 23

test_prior				86.42 66	94.71 32	67.35 92		93.10 96					96.84 100			95.05 70

旧先验2							92.00 141		59.37 293	87.54 25			93.47 213	75.39 125

新几何2							91.41 162

新几何1				84.73 120	92.32 85	64.28 180		91.46 159	59.56 291	79.77 92	92.90 108	56.95 153	96.57 109	63.40 224	92.91 55	93.34 127

旧先验1					91.94 95	60.74 243		91.50 157			94.36 73	65.23 67			91.84 69	94.55 86

无先验							92.71 113	92.61 115	62.03 274				97.01 86	66.63 194		93.97 112

原ACMM2							92.01 139

原ACMM1				84.42 130	93.21 65	64.27 181		93.40 83	65.39 251	79.51 95	92.50 116	58.11 138	96.69 106	65.27 214	93.96 38	92.32 155

test222					89.77 144	61.60 230	89.55 219	89.42 223	56.83 304	77.28 119	92.43 120	52.76 196			91.14 82	93.09 136

testdata2													96.09 121	61.26 238

segment_acmp												65.94 60

testdata				81.34 200	89.02 160	57.72 277		89.84 209	58.65 296	85.32 46	94.09 84	57.03 148	93.28 215	69.34 172	90.56 88	93.03 138

testdata1							89.21 226		77.55 74

test12				87.09 42	94.60 34	68.86 53		92.91 102		82.67 66		65.44 66	97.55 59		93.69 46	94.84 77

plane_prior7					86.94 202	61.51 231

plane_prior6					87.23 197	62.32 221						50.66 210

plane_prior5								91.31 163					95.55 148	76.74 116	78.53 171	88.39 206

plane_prior4											89.14 162

plane_prior3						61.95 225			79.09 53	72.53 161

plane_prior2							93.13 97		78.81 58

plane_prior1					87.15 199

plane_prior						62.42 218	93.85 72		79.38 46						78.80 168

n2								0.00 358

nn								0.00 358

door-mid								66.01 341

lessismore_v0				73.72 291	72.93 323	47.83 325		61.72 345		45.86 324	73.76 307	28.63 320	89.81 286	47.75 288	31.37 339	83.53 277

LGP-MVS_train				79.56 241	84.31 241	59.37 263		89.73 214	69.49 217	64.86 244	88.42 167	38.65 275	94.30 181	72.56 144	72.76 208	85.01 265

test11								93.01 98

door								66.57 340

HQP5-MVS						63.66 195

HQP-NCC					87.54 191		94.06 59		79.80 40	74.18 142

ACMP_Plane					87.54 191		94.06 59		79.80 40	74.18 142

BP-MVS														77.63 113

HQP4-MVS										74.18 142			95.61 144			88.63 200

HQP3-MVS								91.70 149							78.90 166

HQP2-MVS												51.63 206

NP-MVS					87.41 194	63.04 205					90.30 149

MDTV_nov1_ep13_2view						59.90 256	80.13 302		67.65 237	72.79 156		54.33 182		59.83 246		92.58 149

MDTV_nov1_ep13			72.61 224		89.06 159	68.48 61	80.33 298	90.11 200	71.84 177	71.81 173	75.92 301	53.01 195	93.92 201	48.04 285	73.38 203

ACMMP++_ref															71.63 216

ACMMP++															69.72 227

Test By Simon												54.21 183

ITE_SJBPF				70.43 309	74.44 319	47.06 328		77.32 320	60.16 287	54.04 300	83.53 222	23.30 331	84.01 318	43.07 302	61.58 290	80.21 313

DeepMVS_CX				34.71 330	51.45 343	24.73 349		28.48 353	31.46 339	17.49 344	52.75 334	5.80 349	42.60 349	18.18 341	19.42 341	36.81 341

High-resolution multi-view benchmark