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 3	97.66 2	73.37 8	97.13 2	95.58 8	89.33 1	85.77 32	96.26 16	72.84 17	99.38 1	92.64 3	95.93 6	97.08 5

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 135	76.68 2	97.29 1	95.35 10	82.87 14	91.58 4	97.22 3	79.93 2	99.10 3	83.12 63	97.64 2	97.94 1

MSP-MVS		90.38 3	91.87 1	85.88 76	92.83 66	64.03 172	93.06 91	94.33 43	82.19 19	93.65 1	96.15 19	85.89 1	97.19 69	91.02 10	97.75 1	96.43 17

CNVR-MVS		90.32 4	90.89 4	88.61 13	96.76 5	70.65 20	96.47 10	94.83 22	84.83 8	89.07 14	96.80 7	70.86 24	99.06 7	92.64 3	95.71 7	96.12 25

DELS-MVS		90.05 5	90.09 6	89.94 3	93.14 61	73.88 7	97.01 3	94.40 40	88.32 2	85.71 33	94.91 53	74.11 11	98.91 9	87.26 35	95.94 5	97.03 6
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
DeepPCF-MVS	81.17 1	89.72 6	91.38 3	84.72 116	93.00 63	58.16 259	96.72 5	94.41 39	86.50 5	90.25 10	97.83 1	75.46 9	98.67 15	92.78 2	95.49 9	97.32 2

CANet		89.61 7	89.99 7	88.46 14	94.39 32	69.71 36	96.53 9	93.78 54	86.89 4	89.68 11	95.78 23	65.94 51	99.10 3	92.99 1	93.91 32	96.58 12

DVP-MVS		89.41 8	89.73 9	88.45 15	96.40 8	69.99 28	96.64 6	94.52 34	71.92 161	90.55 8	96.93 5	73.77 12	99.08 5	91.91 5	94.90 14	96.29 21

HPM-MVS++		89.37 9	89.95 8	87.64 23	95.10 23	68.23 67	95.24 28	94.49 36	82.43 17	88.90 15	96.35 14	71.89 23	98.63 16	88.76 24	96.40 3	96.06 26

NCCC		89.07 10	89.46 10	87.91 18	96.60 6	69.05 46	96.38 11	94.64 31	84.42 9	86.74 25	96.20 17	66.56 46	98.76 14	89.03 22	94.56 24	95.92 32

DPE-MVS		88.77 11	89.21 11	87.45 30	96.26 12	67.56 81	94.17 47	94.15 48	68.77 214	90.74 7	97.27 2	76.09 7	98.49 19	90.58 12	94.91 13	96.30 20

SMA-MVS		88.14 12	88.29 15	87.67 22	93.21 58	68.72 54	93.85 66	94.03 50	74.18 113	91.74 3	96.67 8	65.61 56	98.42 23	89.24 19	96.08 4	95.88 34

PS-MVSNAJ		88.14 12	87.61 21	89.71 5	92.06 84	76.72 1	95.75 16	93.26 77	83.86 10	89.55 12	96.06 20	53.55 176	97.89 36	91.10 8	93.31 41	94.54 81

TSAR-MVS + MP.		88.11 14	88.64 12	86.54 56	91.73 94	68.04 70	90.36 188	93.55 65	82.89 13	91.29 5	92.89 100	72.27 20	96.03 119	87.99 27	94.77 19	95.54 41
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 15	88.37 14	86.70 49	93.51 52	65.32 138	95.15 31	93.84 53	78.17 65	85.93 31	94.80 57	75.80 8	98.21 26	89.38 16	88.78 88	96.59 11

DeepC-MVS_fast	79.48 2	87.95 16	88.00 16	87.79 21	95.86 18	68.32 62	95.74 17	94.11 49	83.82 11	83.49 54	96.19 18	64.53 65	98.44 21	83.42 62	94.88 17	96.61 10
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 17	87.38 26	89.55 8	91.41 106	76.43 3	95.74 17	93.12 86	83.53 12	89.55 12	95.95 21	53.45 180	97.68 40	91.07 9	92.62 49	94.54 81

EPNet		87.84 18	88.38 13	86.23 69	93.30 55	66.05 121	95.26 27	94.84 21	87.09 3	88.06 17	94.53 61	66.79 43	97.34 60	83.89 59	91.68 63	95.29 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 19	87.77 18	87.63 27	89.24 145	71.18 15	96.57 8	92.90 94	82.70 16	87.13 22	95.27 39	64.99 61	95.80 125	89.34 17	91.80 61	95.93 31

APDe-MVS		87.54 20	87.84 17	86.65 50	96.07 15	66.30 116	94.84 40	93.78 54	69.35 205	88.39 16	96.34 15	67.74 39	97.66 44	90.62 11	93.44 40	96.01 29

SD-MVS		87.49 21	87.49 23	87.50 29	93.60 49	68.82 52	93.90 64	92.63 105	76.86 80	87.90 18	95.76 24	66.17 48	97.63 46	89.06 21	91.48 67	96.05 27
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 22	87.70 19	86.42 61	94.71 27	67.35 86	95.10 33	93.10 87	75.40 97	85.25 39	95.61 29	67.94 35	96.84 91	87.47 30	94.77 19	95.05 64

alignmvs		87.28 23	86.97 28	88.24 17	91.30 107	71.14 17	95.61 21	93.56 64	79.30 47	87.07 24	95.25 41	68.43 30	96.93 89	87.87 28	84.33 125	96.65 9

Regformer-1		87.24 24	87.60 22	86.15 71	95.14 21	65.83 129	93.95 60	95.12 14	82.11 21	84.25 45	95.73 25	67.88 38	98.35 24	85.60 45	88.64 90	94.26 89

train_agg		87.21 25	87.42 25	86.60 52	94.18 34	67.28 88	94.16 48	93.51 66	71.87 166	85.52 35	95.33 34	68.19 32	97.27 67	89.09 20	94.90 14	95.25 57

MG-MVS		87.11 26	86.27 34	89.62 6	97.79 1	76.27 4	94.96 38	94.49 36	78.74 60	83.87 53	92.94 97	64.34 67	96.94 87	75.19 117	94.09 28	95.66 37

agg_prior1		87.02 27	87.26 27	86.28 68	94.16 38	66.97 98	94.08 53	93.31 75	71.85 168	84.49 43	95.39 32	68.91 28	96.75 95	88.84 23	94.32 26	95.13 60

Regformer-2		87.00 28	87.43 24	85.71 86	95.14 21	64.73 153	93.95 60	94.95 19	81.69 27	84.03 51	95.73 25	67.35 41	98.19 28	85.40 47	88.64 90	94.20 91

CSCG		86.87 29	86.26 35	88.72 10	95.05 24	70.79 19	93.83 70	95.33 11	68.48 218	77.63 105	94.35 68	73.04 15	98.45 20	84.92 51	93.71 36	96.92 7

canonicalmvs		86.85 30	86.25 36	88.66 12	91.80 93	71.92 11	93.54 78	91.71 140	80.26 38	87.55 20	95.25 41	63.59 78	96.93 89	88.18 26	84.34 124	97.11 4

PHI-MVS		86.83 31	86.85 31	86.78 47	93.47 53	65.55 135	95.39 26	95.10 16	71.77 172	85.69 34	96.52 9	62.07 91	98.77 13	86.06 43	95.60 8	96.03 28

SteuartSystems-ACMMP		86.82 32	86.90 29	86.58 54	90.42 121	66.38 113	96.09 13	93.87 52	77.73 70	84.01 52	95.66 27	63.39 79	97.94 32	87.40 32	93.55 39	95.42 42
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 33	86.86 30	86.31 67	93.76 44	67.53 83	96.33 12	93.61 62	82.34 18	81.00 72	93.08 92	63.19 82	97.29 64	87.08 36	91.38 68	94.13 97

testtj		86.62 34	86.66 33	86.50 58	96.95 4	65.70 131	94.41 44	93.45 69	67.74 220	86.19 27	96.39 13	64.38 66	97.91 34	87.33 33	93.14 44	95.90 33

CS-MVS		86.61 35	86.85 31	85.88 76	91.52 102	66.25 118	95.42 24	92.25 115	80.36 37	84.10 50	94.82 56	62.88 86	96.08 114	88.25 25	92.07 59	95.30 51

jason		86.40 36	86.17 37	87.11 39	86.16 200	70.54 22	95.71 20	92.19 122	82.00 24	84.58 42	94.34 69	61.86 93	95.53 143	87.76 29	90.89 74	95.27 54
jason: jason.
WTY-MVS		86.32 37	85.81 42	87.85 19	92.82 68	69.37 41	95.20 29	95.25 12	82.71 15	81.91 61	94.73 58	67.93 37	97.63 46	79.55 90	82.25 136	96.54 13

MSLP-MVS++		86.27 38	85.91 41	87.35 33	92.01 85	68.97 49	95.04 36	92.70 99	79.04 55	81.50 65	96.50 11	58.98 120	96.78 93	83.49 61	93.93 31	96.29 21

VNet		86.20 39	85.65 46	87.84 20	93.92 41	69.99 28	95.73 19	95.94 6	78.43 62	86.00 30	93.07 94	58.22 123	97.00 79	85.22 48	84.33 125	96.52 14

MVS_111021_HR		86.19 40	85.80 43	87.37 32	93.17 60	69.79 34	93.99 58	93.76 57	79.08 54	78.88 94	93.99 78	62.25 90	98.15 29	85.93 44	91.15 72	94.15 96

ACMMP_NAP		86.05 41	85.80 43	86.80 46	91.58 98	67.53 83	91.79 140	93.49 68	74.93 104	84.61 41	95.30 36	59.42 113	97.92 33	86.13 42	94.92 12	94.94 69

EIA-MVS		86.01 42	86.11 38	85.70 87	90.21 126	67.02 97	93.43 83	91.92 131	81.21 30	84.13 49	94.07 77	60.93 100	95.63 135	89.28 18	89.81 82	94.46 87

APD-MVS		85.93 43	85.99 39	85.76 83	95.98 17	65.21 141	93.59 76	92.58 107	66.54 229	86.17 28	95.88 22	63.83 72	97.00 79	86.39 41	92.94 45	95.06 63
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 44	85.46 47	87.18 36	88.20 169	72.42 10	92.41 116	92.77 97	82.11 21	80.34 78	93.07 94	68.27 31	95.02 150	78.39 100	93.59 38	94.09 99

Regformer-3		85.80 45	85.92 40	85.46 93	94.17 36	65.09 147	92.95 97	95.11 15	81.13 31	81.68 63	95.04 45	65.82 53	98.32 25	83.02 64	84.36 122	92.97 133

CDPH-MVS		85.71 46	85.46 47	86.46 59	94.75 26	67.19 90	93.89 65	92.83 96	70.90 189	83.09 56	95.28 37	63.62 76	97.36 58	80.63 84	94.18 27	94.84 71

DeepC-MVS	77.85 3	85.52 47	85.24 49	86.37 64	88.80 154	66.64 106	92.15 121	93.68 60	81.07 32	76.91 115	93.64 83	62.59 88	98.44 21	85.50 46	92.84 47	94.03 103
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 48	85.69 45	84.73 114	94.17 36	63.23 188	92.95 97	94.83 22	80.66 34	81.29 66	95.04 45	65.12 59	98.08 31	82.74 66	84.36 122	92.88 137

casdiffmvs		85.37 49	84.87 53	86.84 44	88.25 167	69.07 45	93.04 93	91.76 137	81.27 29	80.84 74	92.07 117	64.23 68	96.06 117	84.98 50	87.43 99	95.39 43

MP-MVS-pluss		85.24 50	85.13 50	85.56 90	91.42 104	65.59 134	91.54 151	92.51 109	74.56 107	80.62 75	95.64 28	59.15 117	97.00 79	86.94 38	93.80 33	94.07 101
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		85.15 51	84.47 55	87.18 36	96.02 16	68.29 63	91.85 138	93.00 91	76.59 84	79.03 92	95.00 47	61.59 94	97.61 48	78.16 101	89.00 87	95.63 38

MP-MVS		85.02 52	84.97 51	85.17 104	92.60 73	64.27 169	93.24 86	92.27 114	73.13 134	79.63 85	94.43 62	61.90 92	97.17 70	85.00 49	92.56 50	94.06 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 53	84.44 57	86.71 48	88.33 164	68.73 53	90.24 192	91.82 136	81.05 33	81.18 68	92.50 107	63.69 75	96.08 114	84.45 54	86.71 105	95.32 49

#test#		84.98 54	84.74 54	85.72 84	93.75 46	65.01 148	94.09 52	93.19 81	73.55 128	79.22 89	94.93 50	59.04 118	97.67 41	82.66 67	92.21 54	94.49 85

CHOSEN 1792x2688		84.98 54	83.45 67	89.57 7	89.94 130	75.14 5	92.07 127	92.32 112	81.87 25	75.68 122	88.27 163	60.18 105	98.60 17	80.46 86	90.27 81	94.96 68

ETV-MVS		84.84 56	84.88 52	84.69 117	91.30 107	62.36 206	93.85 66	92.04 125	79.45 45	79.33 88	94.28 72	62.42 89	96.35 104	80.05 87	91.25 71	95.38 44

zzz-MVS		84.73 57	84.47 55	85.50 91	91.89 90	65.16 142	91.55 150	92.23 116	75.32 99	80.53 76	95.21 43	56.06 152	97.16 71	84.86 52	92.55 51	94.18 92

HFP-MVS		84.73 57	84.40 58	85.72 84	93.75 46	65.01 148	93.50 80	93.19 81	72.19 155	79.22 89	94.93 50	59.04 118	97.67 41	81.55 75	92.21 54	94.49 85

MVS		84.66 59	82.86 81	90.06 2	90.93 113	74.56 6	87.91 234	95.54 9	68.55 216	72.35 159	94.71 59	59.78 109	98.90 10	81.29 81	94.69 23	96.74 8

GST-MVS		84.63 60	84.29 59	85.66 88	92.82 68	65.27 139	93.04 93	93.13 85	73.20 132	78.89 93	94.18 74	59.41 114	97.85 38	81.45 77	92.48 53	93.86 110

ACMMPR		84.37 61	84.06 60	85.28 100	93.56 50	64.37 164	93.50 80	93.15 84	72.19 155	78.85 96	94.86 54	56.69 144	97.45 52	81.55 75	92.20 56	94.02 104

region2R		84.36 62	84.03 61	85.36 98	93.54 51	64.31 166	93.43 83	92.95 92	72.16 158	78.86 95	94.84 55	56.97 139	97.53 50	81.38 79	92.11 58	94.24 90

LFMVS		84.34 63	82.73 84	89.18 9	94.76 25	73.25 9	94.99 37	91.89 132	71.90 163	82.16 60	93.49 87	47.98 222	97.05 74	82.55 69	84.82 118	97.25 3

test_yl		84.28 64	83.16 75	87.64 23	94.52 30	69.24 42	95.78 14	95.09 17	69.19 208	81.09 69	92.88 101	57.00 137	97.44 53	81.11 82	81.76 140	96.23 23

DCV-MVSNet		84.28 64	83.16 75	87.64 23	94.52 30	69.24 42	95.78 14	95.09 17	69.19 208	81.09 69	92.88 101	57.00 137	97.44 53	81.11 82	81.76 140	96.23 23

diffmvs		84.28 64	83.83 62	85.61 89	87.40 182	68.02 71	90.88 175	89.24 219	80.54 35	81.64 64	92.52 106	59.83 108	94.52 168	87.32 34	85.11 116	94.29 88

HY-MVS	76.49 5	84.28 64	83.36 73	87.02 42	92.22 81	67.74 77	84.65 257	94.50 35	79.15 51	82.23 59	87.93 170	66.88 42	96.94 87	80.53 85	82.20 137	96.39 19

MAR-MVS		84.18 68	83.43 68	86.44 60	96.25 13	65.93 126	94.28 46	94.27 45	74.41 108	79.16 91	95.61 29	53.99 171	98.88 12	69.62 156	93.26 42	94.50 84
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 69	83.20 74	87.05 41	91.56 99	69.82 33	89.99 200	92.05 124	77.77 69	82.84 57	86.57 184	63.93 71	96.09 112	74.91 123	89.18 86	95.25 57

CANet_DTU		84.09 70	83.52 64	85.81 80	90.30 124	66.82 101	91.87 136	89.01 231	85.27 6	86.09 29	93.74 82	47.71 225	96.98 83	77.90 104	89.78 84	93.65 114

ET-MVSNet_ETH3D		84.01 71	83.15 77	86.58 54	90.78 118	70.89 18	94.74 41	94.62 32	81.44 28	58.19 273	93.64 83	73.64 14	92.35 237	82.66 67	78.66 162	96.50 16

PVSNet_Blended_VisFu		83.97 72	83.50 65	85.39 97	90.02 128	66.59 109	93.77 71	91.73 138	77.43 76	77.08 114	89.81 149	63.77 74	96.97 84	79.67 89	88.21 93	92.60 141

DWT-MVSNet_test		83.95 73	82.80 82	87.41 31	92.90 65	70.07 27	89.12 216	94.42 38	82.15 20	77.64 104	91.77 121	70.81 25	96.22 107	65.03 202	81.36 144	95.94 30

MTAPA		83.91 74	83.38 72	85.50 91	91.89 90	65.16 142	81.75 277	92.23 116	75.32 99	80.53 76	95.21 43	56.06 152	97.16 71	84.86 52	92.55 51	94.18 92

XVS		83.87 75	83.47 66	85.05 105	93.22 56	63.78 175	92.92 99	92.66 102	73.99 115	78.18 99	94.31 71	55.25 157	97.41 55	79.16 93	91.58 65	93.95 106

Effi-MVS+		83.82 76	82.76 83	86.99 43	89.56 138	69.40 40	91.35 160	86.12 272	72.59 144	83.22 55	92.81 104	59.60 111	96.01 121	81.76 74	87.80 96	95.56 40

EI-MVSNet-Vis-set		83.77 77	83.67 63	84.06 131	92.79 71	63.56 184	91.76 143	94.81 24	79.65 44	77.87 101	94.09 75	63.35 80	97.90 35	79.35 91	79.36 154	90.74 171

MVSFormer		83.75 78	82.88 80	86.37 64	89.24 145	71.18 15	89.07 217	90.69 173	65.80 235	87.13 22	94.34 69	64.99 61	92.67 226	72.83 130	91.80 61	95.27 54

CP-MVS		83.71 79	83.40 71	84.65 118	93.14 61	63.84 173	94.59 42	92.28 113	71.03 187	77.41 108	94.92 52	55.21 160	96.19 108	81.32 80	90.70 76	93.91 108

baseline2		83.68 80	83.42 70	84.48 123	87.37 183	66.00 123	90.06 196	95.93 7	79.71 43	69.08 191	90.39 139	77.92 3	96.28 105	78.91 97	81.38 143	91.16 167

thisisatest0515		83.41 81	82.49 87	86.16 70	89.46 141	68.26 65	93.54 78	94.70 28	74.31 111	75.75 121	90.92 130	72.62 18	96.52 102	69.64 154	81.50 142	93.71 112

PVSNet_BlendedMVS		83.38 82	83.43 68	83.22 148	93.76 44	67.53 83	94.06 54	93.61 62	79.13 52	81.00 72	85.14 197	63.19 82	97.29 64	87.08 36	73.91 194	84.83 254

PGM-MVS		83.25 83	82.70 85	84.92 108	92.81 70	64.07 171	90.44 185	92.20 121	71.28 182	77.23 111	94.43 62	55.17 161	97.31 63	79.33 92	91.38 68	93.37 119

HPM-MVS		83.25 83	82.95 79	84.17 129	92.25 80	62.88 199	90.91 174	91.86 133	70.30 196	77.12 112	93.96 79	56.75 142	96.28 105	82.04 72	91.34 70	93.34 120
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 85	82.96 78	83.67 141	92.28 79	63.19 190	91.38 158	94.68 29	79.22 49	76.60 116	93.75 81	62.64 87	97.76 39	78.07 102	78.01 166	90.05 179

PatchFormer-LS_test		83.14 85	81.81 95	87.12 38	92.34 76	69.92 32	88.64 224	93.32 74	82.07 23	74.87 130	91.62 126	68.91 28	96.08 114	66.07 190	78.45 165	95.37 45

VDD-MVS		83.06 87	81.81 95	86.81 45	90.86 116	67.70 78	95.40 25	91.50 149	75.46 94	81.78 62	92.34 114	40.09 257	97.13 73	86.85 39	82.04 138	95.60 39

PAPM_NR		82.97 88	81.84 94	86.37 64	94.10 40	66.76 104	87.66 238	92.84 95	69.96 199	74.07 138	93.57 85	63.10 84	97.50 51	70.66 149	90.58 78	94.85 70

mPP-MVS		82.96 89	82.44 88	84.52 121	92.83 66	62.92 197	92.76 102	91.85 134	71.52 179	75.61 125	94.24 73	53.48 179	96.99 82	78.97 96	90.73 75	93.64 115

SR-MVS		82.81 90	82.58 86	83.50 145	93.35 54	61.16 222	92.23 120	91.28 158	64.48 243	81.27 67	95.28 37	53.71 175	95.86 124	82.87 65	88.77 89	93.49 118

DP-MVS Recon		82.73 91	81.65 97	85.98 73	97.31 3	67.06 94	95.15 31	91.99 127	69.08 211	76.50 118	93.89 80	54.48 167	98.20 27	70.76 148	85.66 113	92.69 138

CLD-MVS		82.73 91	82.35 90	83.86 134	87.90 175	67.65 80	95.45 23	92.18 123	85.06 7	72.58 152	92.27 115	52.46 186	95.78 126	84.18 55	79.06 157	88.16 201
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 93	82.38 89	83.73 138	89.25 144	59.58 246	92.24 119	94.89 20	77.96 67	79.86 82	92.38 112	56.70 143	97.05 74	77.26 107	80.86 148	94.55 79

3Dnovator	73.91 6	82.69 94	80.82 106	88.31 16	89.57 137	71.26 14	92.60 110	94.39 41	78.84 57	67.89 208	92.48 110	48.42 217	98.52 18	68.80 165	94.40 25	95.15 59

MVSTER		82.47 95	82.05 91	83.74 136	92.68 72	69.01 47	91.90 135	93.21 78	79.83 39	72.14 160	85.71 194	74.72 10	94.72 160	75.72 113	72.49 204	87.50 206

TESTMET0.1,1		82.41 96	81.98 93	83.72 139	88.08 170	63.74 177	92.70 105	93.77 56	79.30 47	77.61 106	87.57 175	58.19 124	94.08 182	73.91 126	86.68 106	93.33 122

CostFormer		82.33 97	81.15 101	85.86 79	89.01 150	68.46 59	82.39 275	93.01 89	75.59 92	80.25 79	81.57 233	72.03 22	94.96 152	79.06 95	77.48 175	94.16 95

API-MVS		82.28 98	80.53 111	87.54 28	96.13 14	70.59 21	93.63 74	91.04 167	65.72 237	75.45 127	92.83 103	56.11 151	98.89 11	64.10 207	89.75 85	93.15 127

IB-MVS	77.80 4	82.18 99	80.46 112	87.35 33	89.14 147	70.28 25	95.59 22	95.17 13	78.85 56	70.19 179	85.82 192	70.66 26	97.67 41	72.19 139	66.52 243	94.09 99
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		82.16 100	81.12 102	85.26 101	86.42 194	68.72 54	92.59 112	90.44 178	73.12 135	84.20 46	94.36 64	38.04 268	95.73 129	84.12 56	86.81 102	91.33 161

xiu_mvs_v1_base		82.16 100	81.12 102	85.26 101	86.42 194	68.72 54	92.59 112	90.44 178	73.12 135	84.20 46	94.36 64	38.04 268	95.73 129	84.12 56	86.81 102	91.33 161

xiu_mvs_v1_base_debi		82.16 100	81.12 102	85.26 101	86.42 194	68.72 54	92.59 112	90.44 178	73.12 135	84.20 46	94.36 64	38.04 268	95.73 129	84.12 56	86.81 102	91.33 161

3Dnovator+	73.60 7	82.10 103	80.60 110	86.60 52	90.89 115	66.80 103	95.20 29	93.44 71	74.05 114	67.42 213	92.49 109	49.46 208	97.65 45	70.80 147	91.68 63	95.33 47

MVS_111021_LR		82.02 104	81.52 98	83.51 144	88.42 162	62.88 199	89.77 203	88.93 233	76.78 82	75.55 126	93.10 90	50.31 200	95.38 145	83.82 60	87.02 101	92.26 151

PMMVS		81.98 105	82.04 92	81.78 183	89.76 134	56.17 274	91.13 170	90.69 173	77.96 67	80.09 80	93.57 85	46.33 234	94.99 151	81.41 78	87.46 98	94.17 94

baseline1		81.84 106	81.03 105	84.28 128	91.60 97	66.62 107	91.08 171	91.66 143	81.87 25	74.86 131	91.67 125	69.98 27	94.92 155	71.76 141	64.75 252	91.29 166

EPP-MVSNet		81.79 107	81.52 98	82.61 160	88.77 155	60.21 238	93.02 95	93.66 61	68.52 217	72.90 147	90.39 139	72.19 21	94.96 152	74.93 122	79.29 156	92.67 139

APD-MVS_3200maxsize		81.64 108	81.32 100	82.59 161	92.36 75	58.74 255	91.39 156	91.01 168	63.35 250	79.72 84	94.62 60	51.82 189	96.14 110	79.71 88	87.93 95	92.89 136

ACMMP		81.49 109	80.67 108	83.93 133	91.71 95	62.90 198	92.13 122	92.22 120	71.79 171	71.68 167	93.49 87	50.32 199	96.96 85	78.47 99	84.22 129	91.93 154
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		81.43 110	80.74 107	83.52 143	86.26 198	64.45 159	92.09 125	90.65 176	75.83 91	73.95 140	89.81 149	63.97 70	92.91 218	71.27 144	82.82 133	93.20 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 111	79.99 116	85.46 93	90.39 123	68.40 60	86.88 248	90.61 177	74.41 108	70.31 178	84.67 202	63.79 73	92.32 238	73.13 127	85.70 112	95.67 36

1121		81.25 112	80.05 114	84.87 111	92.30 78	64.31 166	87.91 234	91.39 153	59.44 279	79.94 81	92.91 98	57.09 133	97.01 77	66.63 180	92.81 48	93.29 123

thisisatest0530		81.15 113	80.07 113	84.39 125	88.26 166	65.63 133	91.40 154	94.62 32	71.27 183	70.93 171	89.18 152	72.47 19	96.04 118	65.62 196	76.89 181	91.49 159

Fast-Effi-MVS+		81.14 114	80.01 115	84.51 122	90.24 125	65.86 127	94.12 51	89.15 225	73.81 122	75.37 128	88.26 164	57.26 131	94.53 167	66.97 179	84.92 117	93.15 127

HQP-MVS		81.14 114	80.64 109	82.64 159	87.54 178	63.66 182	94.06 54	91.70 141	79.80 40	74.18 134	90.30 141	51.63 193	95.61 137	77.63 105	78.90 158	88.63 193

HyFIR lowres test		81.03 116	79.56 123	85.43 95	87.81 176	68.11 69	90.18 193	90.01 197	70.65 193	72.95 146	86.06 190	63.61 77	94.50 169	75.01 121	79.75 152	93.67 113

nrg030		80.93 117	79.86 118	84.13 130	83.69 234	68.83 51	93.23 87	91.20 159	75.55 93	75.06 129	88.22 167	63.04 85	94.74 159	81.88 73	66.88 240	88.82 191

Vis-MVSNet		80.92 118	79.98 117	83.74 136	88.48 159	61.80 212	93.44 82	88.26 251	73.96 118	77.73 102	91.76 122	49.94 204	94.76 157	65.84 193	90.37 80	94.65 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
1314		80.70 119	78.95 135	85.94 75	87.77 177	67.56 81	87.91 234	92.55 108	72.17 157	67.44 212	93.09 91	50.27 201	97.04 76	71.68 143	87.64 97	93.23 125

tpmrst		80.57 120	79.14 134	84.84 112	90.10 127	68.28 64	81.70 278	89.72 207	77.63 72	75.96 120	79.54 264	64.94 63	92.71 223	75.43 115	77.28 178	93.55 116

1112_ss		80.56 121	79.83 119	82.77 154	88.65 156	60.78 226	92.29 117	88.36 246	72.58 145	72.46 156	94.95 48	65.09 60	93.42 206	66.38 186	77.71 168	94.10 98

VDDNet		80.50 122	78.26 142	87.21 35	86.19 199	69.79 34	94.48 43	91.31 155	60.42 271	79.34 87	90.91 131	38.48 264	96.56 101	82.16 70	81.05 146	95.27 54

BH-w/o		80.49 123	79.30 130	84.05 132	90.83 117	64.36 165	93.60 75	89.42 214	74.35 110	69.09 190	90.15 144	55.23 159	95.61 137	64.61 204	86.43 110	92.17 152

TAMVS		80.37 124	79.45 126	83.13 150	85.14 212	63.37 185	91.23 165	90.76 172	74.81 106	72.65 150	88.49 158	60.63 102	92.95 213	69.41 158	81.95 139	93.08 130

HQP_MVS		80.34 125	79.75 120	82.12 176	86.94 189	62.42 204	93.13 89	91.31 155	78.81 58	72.53 153	89.14 154	50.66 197	95.55 141	76.74 108	78.53 163	88.39 198

HPM-MVS_fast		80.25 126	79.55 125	82.33 168	91.55 100	59.95 241	91.32 162	89.16 224	65.23 241	74.71 132	93.07 94	47.81 224	95.74 128	74.87 125	88.23 92	91.31 165

ab-mvs		80.18 127	78.31 141	85.80 81	88.44 161	65.49 137	83.00 272	92.67 101	71.82 170	77.36 109	85.01 198	54.50 166	96.59 98	76.35 112	75.63 187	95.32 49

IS-MVSNet		80.14 128	79.41 127	82.33 168	87.91 174	60.08 240	91.97 133	88.27 249	72.90 140	71.44 169	91.73 124	61.44 95	93.66 201	62.47 220	86.53 108	93.24 124

test-LLR		80.10 129	79.56 123	81.72 185	86.93 191	61.17 220	92.70 105	91.54 146	71.51 180	75.62 123	86.94 181	53.83 172	92.38 234	72.21 137	84.76 120	91.60 157

PVSNet	73.49 8	80.05 130	78.63 137	84.31 126	90.92 114	64.97 150	92.47 115	91.05 166	79.18 50	72.43 157	90.51 138	37.05 280	94.06 184	68.06 168	86.00 111	93.90 109

UA-Net		80.02 131	79.65 121	81.11 196	89.33 142	57.72 263	86.33 251	89.00 232	77.44 75	81.01 71	89.15 153	59.33 115	95.90 122	61.01 227	84.28 127	89.73 183

test-mter		79.96 132	79.38 129	81.72 185	86.93 191	61.17 220	92.70 105	91.54 146	73.85 120	75.62 123	86.94 181	49.84 206	92.38 234	72.21 137	84.76 120	91.60 157

QAPM		79.95 133	77.39 158	87.64 23	89.63 136	71.41 13	93.30 85	93.70 59	65.34 240	67.39 215	91.75 123	47.83 223	98.96 8	57.71 242	89.81 82	92.54 143

UGNet		79.87 134	78.68 136	83.45 147	89.96 129	61.51 217	92.13 122	90.79 170	76.83 81	78.85 96	86.33 187	38.16 266	96.17 109	67.93 170	87.17 100	92.67 139
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		79.82 135	79.20 132	81.70 187	89.85 131	58.34 257	88.47 227	90.07 192	62.56 257	77.71 103	93.08 92	47.65 226	96.78 93	77.94 103	85.45 115	89.99 180

tpm2		79.80 136	77.95 147	85.34 99	88.28 165	68.26 65	81.56 280	91.42 152	70.11 197	77.59 107	80.50 251	67.40 40	94.26 177	67.34 175	77.35 176	93.51 117

DI_MVS_plusplus_test		79.78 137	77.50 155	86.62 51	80.90 256	69.46 39	90.69 181	91.97 129	77.00 78	59.07 269	82.34 221	46.82 229	95.88 123	82.14 71	86.59 107	94.53 83

thres200		79.66 138	78.33 140	83.66 142	92.54 74	65.82 130	93.06 91	96.31 3	74.90 105	73.30 143	88.66 156	59.67 110	95.61 137	47.84 274	78.67 161	89.56 186

CPTT-MVS		79.59 139	79.16 133	80.89 203	91.54 101	59.80 243	92.10 124	88.54 244	60.42 271	72.96 145	93.28 89	48.27 218	92.80 220	78.89 98	86.50 109	90.06 178

Test_1112_low_res		79.56 140	78.60 138	82.43 163	88.24 168	60.39 235	92.09 125	87.99 255	72.10 159	71.84 163	87.42 177	64.62 64	93.04 210	65.80 194	77.30 177	93.85 111

tttt0517		79.50 141	78.53 139	82.41 166	87.22 185	61.43 219	89.75 204	94.76 25	69.29 206	67.91 207	88.06 169	72.92 16	95.63 135	62.91 215	73.90 195	90.16 177

FIs		79.47 142	79.41 127	79.67 225	85.95 203	59.40 248	91.68 147	93.94 51	78.06 66	68.96 194	88.28 162	66.61 45	91.77 249	66.20 189	74.99 188	87.82 203

BH-RMVSNet		79.46 143	77.65 151	84.89 109	91.68 96	65.66 132	93.55 77	88.09 252	72.93 139	73.37 142	91.12 129	46.20 236	96.12 111	56.28 247	85.61 114	92.91 135

PCF-MVS	73.15 9	79.29 144	77.63 152	84.29 127	86.06 201	65.96 125	87.03 244	91.10 163	69.86 200	69.79 186	90.64 134	57.54 130	96.59 98	64.37 206	82.29 135	90.32 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 145	79.57 122	78.24 245	88.46 160	52.29 292	90.41 187	89.12 226	74.24 112	69.13 189	91.91 119	65.77 54	90.09 274	59.00 239	88.09 94	92.33 147

114514_t		79.17 146	77.67 150	83.68 140	95.32 20	65.53 136	92.85 101	91.60 145	63.49 249	67.92 206	90.63 136	46.65 231	95.72 133	67.01 178	83.54 130	89.79 181

VPA-MVSNet		79.03 147	78.00 146	82.11 179	85.95 203	64.48 158	93.22 88	94.66 30	75.05 103	74.04 139	84.95 199	52.17 188	93.52 203	74.90 124	67.04 239	88.32 200

OPM-MVS		79.00 148	78.09 144	81.73 184	83.52 237	63.83 174	91.64 149	90.30 185	76.36 87	71.97 162	89.93 148	46.30 235	95.17 149	75.10 118	77.70 169	86.19 229

EI-MVSNet		78.97 149	78.22 143	81.25 192	85.33 208	62.73 202	89.53 208	93.21 78	72.39 150	72.14 160	90.13 145	60.99 98	94.72 160	67.73 172	72.49 204	86.29 227

AdaColmap		78.94 150	77.00 163	84.76 113	96.34 10	65.86 127	92.66 109	87.97 256	62.18 260	70.56 172	92.37 113	43.53 247	97.35 59	64.50 205	82.86 132	91.05 169

VPNet		78.82 151	77.53 154	82.70 156	84.52 221	66.44 112	93.93 62	92.23 116	80.46 36	72.60 151	88.38 161	49.18 211	93.13 209	72.47 135	63.97 260	88.55 195

EPNet_dtu		78.80 152	79.26 131	77.43 253	88.06 171	49.71 305	91.96 134	91.95 130	77.67 71	76.56 117	91.28 128	58.51 122	90.20 272	56.37 246	80.95 147	92.39 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 153	77.43 156	82.88 152	92.21 82	64.49 156	92.05 128	96.28 4	73.48 129	71.75 165	88.26 164	60.07 106	95.32 146	45.16 283	77.58 171	88.83 189

TR-MVS		78.77 154	77.37 159	82.95 151	90.49 120	60.88 224	93.67 73	90.07 192	70.08 198	74.51 133	91.37 127	45.69 237	95.70 134	60.12 233	80.32 149	92.29 149

mvs-test1		78.74 155	77.95 147	81.14 194	83.22 239	57.13 269	93.96 59	87.78 257	75.42 95	72.68 149	90.80 133	45.08 241	94.54 166	75.08 119	77.49 174	91.74 156

thres400		78.68 156	77.43 156	82.43 163	92.21 82	64.49 156	92.05 128	96.28 4	73.48 129	71.75 165	88.26 164	60.07 106	95.32 146	45.16 283	77.58 171	87.48 207

BH-untuned		78.68 156	77.08 160	83.48 146	89.84 132	63.74 177	92.70 105	88.59 242	71.57 177	66.83 220	88.65 157	51.75 191	95.39 144	59.03 238	84.77 119	91.32 164

OMC-MVS		78.67 158	77.91 149	80.95 202	85.76 207	57.40 267	88.49 226	88.67 240	73.85 120	72.43 157	92.10 116	49.29 210	94.55 165	72.73 132	77.89 167	90.91 170

tpm		78.58 159	77.03 161	83.22 148	85.94 205	64.56 154	83.21 270	91.14 162	78.31 63	73.67 141	79.68 262	64.01 69	92.09 243	66.07 190	71.26 214	93.03 131

OpenMVS	70.45 11	78.54 160	75.92 174	86.41 63	85.93 206	71.68 12	92.74 103	92.51 109	66.49 231	64.56 234	91.96 118	43.88 246	98.10 30	54.61 251	90.65 77	89.44 187

EPMVS		78.49 161	75.98 173	86.02 72	91.21 109	69.68 37	80.23 288	91.20 159	75.25 101	72.48 155	78.11 272	54.65 165	93.69 200	57.66 243	83.04 131	94.69 74

thres100view900		78.37 162	77.01 162	82.46 162	91.89 90	63.21 189	91.19 169	96.33 1	72.28 152	70.45 175	87.89 171	60.31 103	95.32 146	45.16 283	77.58 171	88.83 189

GA-MVS		78.33 163	76.23 170	84.65 118	83.65 235	66.30 116	91.44 152	90.14 190	76.01 89	70.32 177	84.02 208	42.50 250	94.72 160	70.98 145	77.00 180	92.94 134

cascas		78.18 164	75.77 176	85.41 96	87.14 187	69.11 44	92.96 96	91.15 161	66.71 228	70.47 173	86.07 189	37.49 274	96.48 103	70.15 152	79.80 151	90.65 172

UniMVSNet_NR-MVSNet		78.15 165	77.55 153	79.98 217	84.46 223	60.26 236	92.25 118	93.20 80	77.50 74	68.88 195	86.61 183	66.10 49	92.13 241	66.38 186	62.55 264	87.54 205

thres600view7		78.00 166	76.66 166	82.03 181	91.93 87	63.69 180	91.30 163	96.33 1	72.43 148	70.46 174	87.89 171	60.31 103	94.92 155	42.64 295	76.64 182	87.48 207

FC-MVSNet-test		77.99 167	78.08 145	77.70 248	84.89 216	55.51 278	90.27 190	93.75 58	76.87 79	66.80 221	87.59 174	65.71 55	90.23 271	62.89 216	73.94 193	87.37 210

Anonymous202405211		77.96 168	75.33 179	85.87 78	93.73 48	64.52 155	94.85 39	85.36 278	62.52 258	76.11 119	90.18 143	29.43 304	97.29 64	68.51 166	77.24 179	95.81 35

XXY-MVS		77.94 169	76.44 168	82.43 163	82.60 245	64.44 160	92.01 130	91.83 135	73.59 127	70.00 182	85.82 192	54.43 168	94.76 157	69.63 155	68.02 234	88.10 202

MS-PatchMatch		77.90 170	76.50 167	82.12 176	85.99 202	69.95 31	91.75 145	92.70 99	73.97 117	62.58 253	84.44 205	41.11 254	95.78 126	63.76 210	92.17 57	80.62 296

FMVSNet3		77.73 171	76.04 172	82.80 153	91.20 110	68.99 48	91.87 136	91.99 127	73.35 131	67.04 217	83.19 216	56.62 145	92.14 240	59.80 235	69.34 223	87.28 213

UniMVSNet (Re)		77.58 172	76.78 165	79.98 217	84.11 229	60.80 225	91.76 143	93.17 83	76.56 85	69.93 185	84.78 201	63.32 81	92.36 236	64.89 203	62.51 266	86.78 221

PatchmatchNet		77.46 173	74.63 185	85.96 74	89.55 139	70.35 24	79.97 292	89.55 210	72.23 153	70.94 170	76.91 283	57.03 135	92.79 221	54.27 253	81.17 145	94.74 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 174	75.65 178	82.73 155	80.38 263	67.13 93	91.85 138	90.23 187	75.09 102	69.37 187	83.39 214	53.79 174	94.44 170	71.77 140	65.00 249	86.63 225

CHOSEN 280x420		77.35 175	76.95 164	78.55 240	87.07 188	62.68 203	69.71 313	82.95 296	68.80 213	71.48 168	87.27 180	66.03 50	84.00 307	76.47 111	82.81 134	88.95 188

PS-MVSNAJss		77.26 176	76.31 169	80.13 214	80.64 261	59.16 252	90.63 184	91.06 165	72.80 141	68.58 200	84.57 204	53.55 176	93.96 191	72.97 128	71.96 208	87.27 214

gg-mvs-nofinetune		77.18 177	74.31 192	85.80 81	91.42 104	68.36 61	71.78 308	94.72 27	49.61 308	77.12 112	45.92 325	77.41 5	93.98 190	67.62 173	93.16 43	95.05 64

MVP-Stereo		77.12 178	76.23 170	79.79 223	81.72 251	66.34 115	89.29 210	90.88 169	70.56 194	62.01 256	82.88 217	49.34 209	94.13 179	65.55 198	93.80 33	78.88 307
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
X-MVStestdata		76.86 179	74.13 196	85.05 105	93.22 56	63.78 175	92.92 99	92.66 102	73.99 115	78.18 99	10.19 337	55.25 157	97.41 55	79.16 93	91.58 65	93.95 106

DU-MVS		76.86 179	75.84 175	79.91 219	82.96 243	60.26 236	91.26 164	91.54 146	76.46 86	68.88 195	86.35 185	56.16 149	92.13 241	66.38 186	62.55 264	87.35 211

Anonymous20240529		76.84 181	74.15 195	84.88 110	91.02 111	64.95 151	93.84 69	91.09 164	53.57 299	73.00 144	87.42 177	35.91 284	97.32 62	69.14 161	72.41 206	92.36 146

WR-MVS		76.76 182	75.74 177	79.82 222	84.60 219	62.27 209	92.60 110	92.51 109	76.06 88	67.87 209	85.34 195	56.76 141	90.24 270	62.20 221	63.69 262	86.94 219

v1144		76.73 183	74.88 182	82.27 170	80.23 268	66.60 108	91.68 147	90.21 189	73.69 124	69.06 192	81.89 227	52.73 184	94.40 171	69.21 160	65.23 246	85.80 239

IterMVS-LS		76.49 184	75.18 181	80.43 207	84.49 222	62.74 201	90.64 182	88.80 236	72.40 149	65.16 229	81.72 230	60.98 99	92.27 239	67.74 171	64.65 254	86.29 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 185	74.55 188	82.19 174	79.14 280	67.82 75	90.26 191	89.42 214	73.75 123	68.63 199	81.89 227	51.31 195	94.09 181	71.69 142	64.84 250	84.66 255

v148		76.19 186	74.47 190	81.36 190	80.05 269	64.44 160	91.75 145	90.23 187	73.68 125	67.13 216	80.84 246	55.92 155	93.86 197	68.95 163	61.73 275	85.76 242

Effi-MVS+-dtu		76.14 187	75.28 180	78.72 239	83.22 239	55.17 280	89.87 201	87.78 257	75.42 95	67.98 205	81.43 235	45.08 241	92.52 230	75.08 119	71.63 209	88.48 196

cl-mvsnet_		76.07 188	74.67 183	80.28 210	85.15 211	61.76 213	90.12 194	88.73 238	71.16 184	65.43 226	81.57 233	61.15 96	92.95 213	66.54 183	62.17 268	86.13 232

cl-mvsnet1		76.07 188	74.67 183	80.28 210	85.14 212	61.75 214	90.12 194	88.73 238	71.16 184	65.42 227	81.60 232	61.15 96	92.94 217	66.54 183	62.16 270	86.14 230

FMVSNet2		76.07 188	74.01 198	82.26 172	88.85 151	67.66 79	91.33 161	91.61 144	70.84 190	65.98 223	82.25 223	48.03 219	92.00 245	58.46 240	68.73 229	87.10 215

v144192		76.05 191	74.03 197	82.12 176	79.50 274	66.55 110	91.39 156	89.71 208	72.30 151	68.17 203	81.33 238	51.75 191	94.03 188	67.94 169	64.19 256	85.77 240

NR-MVSNet		76.05 191	74.59 186	80.44 206	82.96 243	62.18 210	90.83 177	91.73 138	77.12 77	60.96 258	86.35 185	59.28 116	91.80 248	60.74 228	61.34 279	87.35 211

v1192		75.98 193	73.92 199	82.15 175	79.73 270	66.24 119	91.22 166	89.75 202	72.67 143	68.49 201	81.42 236	49.86 205	94.27 175	67.08 177	65.02 248	85.95 236

eth_miper_zixun_eth		75.96 194	74.40 191	80.66 204	84.66 218	63.02 192	89.28 211	88.27 249	71.88 165	65.73 224	81.65 231	59.45 112	92.81 219	68.13 167	60.53 284	86.14 230

TranMVSNet+NR-MVSNet		75.86 195	74.52 189	79.89 220	82.44 246	60.64 232	91.37 159	91.37 154	76.63 83	67.65 211	86.21 188	52.37 187	91.55 253	61.84 223	60.81 282	87.48 207

SCA		75.82 196	72.76 210	85.01 107	86.63 193	70.08 26	81.06 282	89.19 222	71.60 176	70.01 181	77.09 281	45.53 238	90.25 267	60.43 230	73.27 197	94.68 75

LPG-MVS_test		75.82 196	74.58 187	79.56 229	84.31 226	59.37 249	90.44 185	89.73 205	69.49 203	64.86 230	88.42 159	38.65 262	94.30 173	72.56 133	72.76 201	85.01 252

GBi-Net		75.65 198	73.83 200	81.10 197	88.85 151	65.11 144	90.01 197	90.32 181	70.84 190	67.04 217	80.25 256	48.03 219	91.54 254	59.80 235	69.34 223	86.64 222

test1		75.65 198	73.83 200	81.10 197	88.85 151	65.11 144	90.01 197	90.32 181	70.84 190	67.04 217	80.25 256	48.03 219	91.54 254	59.80 235	69.34 223	86.64 222

v1921920		75.63 200	73.49 204	82.06 180	79.38 275	66.35 114	91.07 173	89.48 211	71.98 160	67.99 204	81.22 241	49.16 213	93.90 194	66.56 182	64.56 255	85.92 238

ACMP	71.68 10	75.58 201	74.23 194	79.62 227	84.97 215	59.64 244	90.80 178	89.07 229	70.39 195	62.95 249	87.30 179	38.28 265	93.87 195	72.89 129	71.45 212	85.36 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 202	73.26 206	81.61 188	80.67 260	66.82 101	89.54 207	89.27 218	71.65 174	63.30 247	80.30 255	54.99 163	94.06 184	67.33 176	62.33 267	83.94 260

tpm cat1		75.30 203	72.21 218	84.58 120	88.52 157	67.77 76	78.16 301	88.02 254	61.88 264	68.45 202	76.37 284	60.65 101	94.03 188	53.77 256	74.11 191	91.93 154

PLC	68.80 14	75.23 204	73.68 202	79.86 221	92.93 64	58.68 256	90.64 182	88.30 247	60.90 268	64.43 238	90.53 137	42.38 251	94.57 163	56.52 245	76.54 183	86.33 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 205	72.98 208	81.88 182	79.20 277	66.00 123	90.75 180	89.11 227	71.63 175	67.41 214	81.22 241	47.36 227	93.87 195	65.46 199	64.72 253	85.77 240

Fast-Effi-MVS+-dtu		75.04 206	73.37 205	80.07 215	80.86 257	59.52 247	91.20 168	85.38 277	71.90 163	65.20 228	84.84 200	41.46 253	92.97 212	66.50 185	72.96 200	87.73 204

dp		75.01 207	72.09 219	83.76 135	89.28 143	66.22 120	79.96 293	89.75 202	71.16 184	67.80 210	77.19 280	51.81 190	92.54 229	50.39 264	71.44 213	92.51 144

TAPA-MVS	70.22 12	74.94 208	73.53 203	79.17 234	90.40 122	52.07 293	89.19 214	89.61 209	62.69 256	70.07 180	92.67 105	48.89 216	94.32 172	38.26 309	79.97 150	91.12 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 209	72.54 215	81.46 189	80.33 266	66.71 105	89.15 215	89.08 228	70.94 188	63.08 248	79.86 260	52.52 185	94.04 187	65.70 195	62.17 268	83.64 262

XVG-OURS-SEG-HR		74.70 210	73.08 207	79.57 228	78.25 290	57.33 268	80.49 284	87.32 261	63.22 252	68.76 197	90.12 147	44.89 243	91.59 252	70.55 150	74.09 192	89.79 181

ACMM	69.62 13	74.34 211	72.73 211	79.17 234	84.25 228	57.87 261	90.36 188	89.93 198	63.17 253	65.64 225	86.04 191	37.79 272	94.10 180	65.89 192	71.52 211	85.55 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 212	72.30 217	80.32 208	91.49 103	61.66 215	90.85 176	80.72 302	56.67 292	63.85 242	90.64 134	46.75 230	90.84 261	53.79 255	75.99 186	88.47 197

XVG-OURS		74.25 213	72.46 216	79.63 226	78.45 289	57.59 266	80.33 286	87.39 260	63.86 247	68.76 197	89.62 151	40.50 256	91.72 250	69.00 162	74.25 190	89.58 184

CVMVSNet		74.04 214	74.27 193	73.33 281	85.33 208	43.94 320	89.53 208	88.39 245	54.33 298	70.37 176	90.13 145	49.17 212	84.05 305	61.83 224	79.36 154	91.99 153

Baseline_NR-MVSNet		73.99 215	72.83 209	77.48 252	80.78 258	59.29 251	91.79 140	84.55 283	68.85 212	68.99 193	80.70 247	56.16 149	92.04 244	62.67 217	60.98 281	81.11 290

pmmvs4		73.92 216	71.81 222	80.25 212	79.17 278	65.24 140	87.43 241	87.26 263	67.64 224	63.46 245	83.91 209	48.96 215	91.53 257	62.94 214	65.49 245	83.96 259

D2MVS		73.80 217	72.02 220	79.15 236	79.15 279	62.97 193	88.58 225	90.07 192	72.94 138	59.22 266	78.30 269	42.31 252	92.70 225	65.59 197	72.00 207	81.79 287

CR-MVSNet		73.79 218	70.82 229	82.70 156	83.15 241	67.96 72	70.25 310	84.00 288	73.67 126	69.97 183	72.41 299	57.82 127	89.48 278	52.99 259	73.13 198	90.64 173

test_djsdf		73.76 219	72.56 214	77.39 254	77.00 298	53.93 285	89.07 217	90.69 173	65.80 235	63.92 240	82.03 226	43.14 249	92.67 226	72.83 130	68.53 230	85.57 244

pmmvs5		73.35 220	71.52 224	78.86 238	78.64 287	60.61 233	91.08 171	86.90 264	67.69 221	63.32 246	83.64 210	44.33 245	90.53 264	62.04 222	66.02 244	85.46 246

Anonymous20231211		73.08 221	70.39 231	81.13 195	90.62 119	63.33 186	91.40 154	90.06 195	51.84 303	64.46 237	80.67 249	36.49 282	94.07 183	63.83 209	64.17 257	85.98 235

miper_lstm_enhance		73.05 222	71.73 223	77.03 258	83.80 232	58.32 258	81.76 276	88.88 234	69.80 201	61.01 257	78.23 271	57.19 132	87.51 293	65.34 200	59.53 287	85.27 251

jajsoiax		73.05 222	71.51 225	77.67 249	77.46 295	54.83 281	88.81 220	90.04 196	69.13 210	62.85 251	83.51 212	31.16 300	92.75 222	70.83 146	69.80 219	85.43 247

LCM-MVSNet-Re		72.93 224	71.84 221	76.18 265	88.49 158	48.02 309	80.07 291	70.17 322	73.96 118	52.25 293	80.09 259	49.98 203	88.24 287	67.35 174	84.23 128	92.28 150

pm-mvs1		72.89 225	71.09 227	78.26 244	79.10 281	57.62 265	90.80 178	89.30 217	67.66 222	62.91 250	81.78 229	49.11 214	92.95 213	60.29 232	58.89 290	84.22 258

tpmvs		72.88 226	69.76 237	82.22 173	90.98 112	67.05 95	78.22 300	88.30 247	63.10 254	64.35 239	74.98 291	55.09 162	94.27 175	43.25 289	69.57 222	85.34 249

test0.0.03 1		72.76 227	72.71 212	72.88 286	80.25 267	47.99 310	91.22 166	89.45 212	71.51 180	62.51 254	87.66 173	53.83 172	85.06 302	50.16 265	67.84 237	85.58 243

UniMVSNet_ETH3D		72.74 228	70.53 230	79.36 231	78.62 288	56.64 272	85.01 255	89.20 221	63.77 248	64.84 232	84.44 205	34.05 289	91.86 247	63.94 208	70.89 216	89.57 185

mvs_tets		72.71 229	71.11 226	77.52 250	77.41 296	54.52 283	88.45 228	89.76 201	68.76 215	62.70 252	83.26 215	29.49 303	92.71 223	70.51 151	69.62 221	85.34 249

FMVSNet1		72.71 229	69.91 235	81.10 197	83.60 236	65.11 144	90.01 197	90.32 181	63.92 246	63.56 244	80.25 256	36.35 283	91.54 254	54.46 252	66.75 241	86.64 222

IterMVS		72.65 231	70.83 228	78.09 246	82.17 247	62.96 194	87.64 239	86.28 268	71.56 178	60.44 260	78.85 267	45.42 240	86.66 297	63.30 212	61.83 272	84.65 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		72.06 232	69.98 232	78.28 243	89.51 140	55.70 277	83.49 264	83.39 294	61.24 267	63.72 243	82.76 218	34.77 287	93.03 211	53.37 258	77.59 170	86.12 233

PVSNet_0	68.08 15	71.81 233	68.32 244	82.27 170	84.68 217	62.31 208	88.68 222	90.31 184	75.84 90	57.93 276	80.65 250	37.85 271	94.19 178	69.94 153	29.05 328	90.31 176

MIMVSNet		71.64 234	68.44 242	81.23 193	81.97 250	64.44 160	73.05 307	88.80 236	69.67 202	64.59 233	74.79 292	32.79 292	87.82 291	53.99 254	76.35 184	91.42 160

IterMVS-SCA-FT		71.55 235	69.97 233	76.32 263	81.48 252	60.67 231	87.64 239	85.99 273	66.17 233	59.50 264	78.88 266	45.53 238	83.65 309	62.58 218	61.93 271	84.63 257

v7n		71.31 236	68.65 240	79.28 232	76.40 300	60.77 227	86.71 249	89.45 212	64.17 245	58.77 272	78.24 270	44.59 244	93.54 202	57.76 241	61.75 274	83.52 265

anonymousdsp		71.14 237	69.37 238	76.45 262	72.95 308	54.71 282	84.19 259	88.88 234	61.92 263	62.15 255	79.77 261	38.14 267	91.44 259	68.90 164	67.45 238	83.21 271

testing_2		71.09 238	67.32 248	82.40 167	69.82 317	66.52 111	83.64 262	90.77 171	72.21 154	45.12 314	71.07 307	27.60 309	93.74 198	75.71 114	69.96 218	86.95 218

F-COLMAP		70.66 239	68.44 242	77.32 255	86.37 197	55.91 276	88.00 232	86.32 267	56.94 290	57.28 279	88.07 168	33.58 290	92.49 231	51.02 262	68.37 231	83.55 263

WR-MVS_H		70.59 240	69.94 234	72.53 288	81.03 255	51.43 296	87.35 242	92.03 126	67.38 225	60.23 261	80.70 247	55.84 156	83.45 311	46.33 279	58.58 291	82.72 277

CP-MVSNet		70.50 241	69.91 235	72.26 291	80.71 259	51.00 299	87.23 243	90.30 185	67.84 219	59.64 263	82.69 219	50.23 202	82.30 318	51.28 261	59.28 288	83.46 267

tfpnnormal		70.10 242	67.36 246	78.32 242	83.45 238	60.97 223	88.85 219	92.77 97	64.85 242	60.83 259	78.53 268	43.52 248	93.48 204	31.73 324	61.70 276	80.52 297

TransMVSNet (Re)		70.07 243	67.66 245	77.31 256	80.62 262	59.13 253	91.78 142	84.94 281	65.97 234	60.08 262	80.44 252	50.78 196	91.87 246	48.84 270	45.46 315	80.94 292

DP-MVS		69.90 244	66.48 250	80.14 213	95.36 19	62.93 195	89.56 205	76.11 309	50.27 307	57.69 277	85.23 196	39.68 258	95.73 129	33.35 318	71.05 215	81.78 288

PS-CasMVS		69.86 245	69.13 239	72.07 294	80.35 265	50.57 301	87.02 245	89.75 202	67.27 226	59.19 267	82.28 222	46.58 232	82.24 319	50.69 263	59.02 289	83.39 269

RPMNet		69.58 246	65.21 258	82.70 156	83.15 241	67.96 72	70.25 310	86.15 271	46.83 316	69.97 183	65.10 315	56.48 148	89.48 278	35.79 314	73.13 198	90.64 173

MSDG		69.54 247	65.73 253	80.96 201	85.11 214	63.71 179	84.19 259	83.28 295	56.95 289	54.50 284	84.03 207	31.50 298	96.03 119	42.87 293	69.13 226	83.14 273

PEN-MVS		69.46 248	68.56 241	72.17 293	79.27 276	49.71 305	86.90 247	89.24 219	67.24 227	59.08 268	82.51 220	47.23 228	83.54 310	48.42 272	57.12 292	83.25 270

LS3D		69.17 249	66.40 251	77.50 251	91.92 88	56.12 275	85.12 254	80.37 303	46.96 314	56.50 281	87.51 176	37.25 275	93.71 199	32.52 323	79.40 153	82.68 280

PatchT		69.11 250	65.37 257	80.32 208	82.07 249	63.68 181	67.96 319	87.62 259	50.86 306	69.37 187	65.18 314	57.09 133	88.53 285	41.59 298	66.60 242	88.74 192

MVS_0304		68.99 251	67.23 249	74.28 276	80.36 264	52.54 290	87.01 246	86.36 266	59.89 277	66.22 222	73.56 295	24.25 314	88.03 289	57.34 244	70.11 217	82.27 283

ACMH	63.93 17	68.62 252	64.81 259	80.03 216	85.22 210	63.25 187	87.72 237	84.66 282	60.83 269	51.57 296	79.43 265	27.29 310	94.96 152	41.76 296	64.84 250	81.88 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 253	65.41 256	77.96 247	78.69 286	62.93 195	89.86 202	89.17 223	60.55 270	50.27 301	77.73 275	22.60 319	94.06 184	47.18 277	72.65 203	76.88 314

ADS-MVSNet		68.54 254	64.38 266	81.03 200	88.06 171	66.90 100	68.01 317	84.02 287	57.57 285	64.48 235	69.87 308	38.68 260	89.21 281	40.87 300	67.89 235	86.97 216

DTE-MVSNet		68.46 255	67.33 247	71.87 296	77.94 293	49.00 308	86.16 252	88.58 243	66.36 232	58.19 273	82.21 224	46.36 233	83.87 308	44.97 286	55.17 299	82.73 276

our_test_3		68.29 256	64.69 261	79.11 237	78.92 282	64.85 152	88.40 229	85.06 279	60.32 273	52.68 291	76.12 286	40.81 255	89.80 277	44.25 288	55.65 297	82.67 281

Patchmatch-RL test		68.17 257	64.49 264	79.19 233	71.22 312	53.93 285	70.07 312	71.54 321	69.22 207	56.79 280	62.89 317	56.58 146	88.61 282	69.53 157	52.61 305	95.03 67

XVG-ACMP-BASELINE		68.04 258	65.53 255	75.56 267	74.06 307	52.37 291	78.43 297	85.88 274	62.03 261	58.91 271	81.21 243	20.38 322	91.15 260	60.69 229	68.18 232	83.16 272

FMVSNet5		68.04 258	65.66 254	75.18 269	84.43 224	57.89 260	83.54 263	86.26 269	61.83 265	53.64 289	73.30 296	37.15 278	85.08 301	48.99 269	61.77 273	82.56 282

ppachtmachnet_test		67.72 260	63.70 268	79.77 224	78.92 282	66.04 122	88.68 222	82.90 297	60.11 275	55.45 282	75.96 287	39.19 259	90.55 263	39.53 304	52.55 306	82.71 278

ACMH+	65.35 16	67.65 261	64.55 262	76.96 259	84.59 220	57.10 270	88.08 231	80.79 301	58.59 284	53.00 290	81.09 245	26.63 312	92.95 213	46.51 278	61.69 277	80.82 293

pmmvs6		67.57 262	64.76 260	76.00 266	72.82 310	53.37 287	88.71 221	86.78 265	53.19 300	57.58 278	78.03 273	35.33 286	92.41 233	55.56 249	54.88 301	82.21 284

Anonymous20231206		67.53 263	65.78 252	72.79 287	74.95 304	47.59 312	88.23 230	87.32 261	61.75 266	58.07 275	77.29 278	37.79 272	87.29 295	42.91 291	63.71 261	83.48 266

Patchmtry		67.53 263	63.93 267	78.34 241	82.12 248	64.38 163	68.72 314	84.00 288	48.23 313	59.24 265	72.41 299	57.82 127	89.27 280	46.10 280	56.68 296	81.36 289

USDC		67.43 265	64.51 263	76.19 264	77.94 293	55.29 279	78.38 298	85.00 280	73.17 133	48.36 306	80.37 253	21.23 321	92.48 232	52.15 260	64.02 259	80.81 294

ADS-MVSNet2		66.90 266	63.44 270	77.26 257	88.06 171	60.70 230	68.01 317	75.56 312	57.57 285	64.48 235	69.87 308	38.68 260	84.10 304	40.87 300	67.89 235	86.97 216

CMPMVS	48.56 21	66.77 267	64.41 265	73.84 278	70.65 315	50.31 302	77.79 302	85.73 276	45.54 317	44.76 315	82.14 225	35.40 285	90.14 273	63.18 213	74.54 189	81.07 291
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 268	62.92 273	76.80 261	76.51 299	57.77 262	89.22 212	83.41 293	55.48 296	53.86 288	77.84 274	26.28 313	93.95 192	34.90 316	68.76 228	78.68 309

LTVRE_ROB	59.60 19	66.27 269	63.54 269	74.45 273	84.00 231	51.55 295	67.08 320	83.53 291	58.78 282	54.94 283	80.31 254	34.54 288	93.23 208	40.64 302	68.03 233	78.58 310
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		66.06 270	62.45 276	76.88 260	81.42 254	54.45 284	57.49 328	88.67 240	49.36 309	63.86 241	46.86 324	56.06 152	90.25 267	49.53 268	68.83 227	85.95 236

Patchmatch-test		65.86 271	60.94 282	80.62 205	83.75 233	58.83 254	58.91 327	75.26 314	44.50 320	50.95 300	77.09 281	58.81 121	87.90 290	35.13 315	64.03 258	95.12 61

UnsupCasMVSNet_eth		65.79 272	63.10 271	73.88 277	70.71 314	50.29 303	81.09 281	89.88 199	72.58 145	49.25 304	74.77 293	32.57 294	87.43 294	55.96 248	41.04 321	83.90 261

pmmvs-eth3d		65.53 273	62.32 277	75.19 268	69.39 319	59.59 245	82.80 273	83.43 292	62.52 258	51.30 298	72.49 297	32.86 291	87.16 296	55.32 250	50.73 309	78.83 308

SixPastTwentyTwo		64.92 274	61.78 280	74.34 275	78.74 285	49.76 304	83.42 267	79.51 306	62.86 255	50.27 301	77.35 276	30.92 302	90.49 265	45.89 281	47.06 313	82.78 274

OurMVSNet-221017-0		64.68 275	62.17 278	72.21 292	76.08 303	47.35 313	80.67 283	81.02 300	56.19 293	51.60 295	79.66 263	27.05 311	88.56 284	53.60 257	53.63 304	80.71 295

test_0402		64.54 276	61.09 281	74.92 270	84.10 230	60.75 228	87.95 233	79.71 305	52.03 302	52.41 292	77.20 279	32.21 296	91.64 251	23.14 327	61.03 280	72.36 320

testgi		64.48 277	62.87 274	69.31 300	71.24 311	40.62 324	85.49 253	79.92 304	65.36 239	54.18 286	83.49 213	23.74 317	84.55 303	41.60 297	60.79 283	82.77 275

RPSCF		64.24 278	61.98 279	71.01 297	76.10 302	45.00 317	75.83 305	75.94 310	46.94 315	58.96 270	84.59 203	31.40 299	82.00 320	47.76 275	60.33 286	86.04 234

EU-MVSNet		64.01 279	63.01 272	67.02 306	74.40 306	38.86 328	83.27 268	86.19 270	45.11 318	54.27 285	81.15 244	36.91 281	80.01 323	48.79 271	57.02 293	82.19 285

test20.03		63.83 280	62.65 275	67.38 305	70.58 316	39.94 325	86.57 250	84.17 285	63.29 251	51.86 294	77.30 277	37.09 279	82.47 316	38.87 308	54.13 303	79.73 302

MDA-MVSNet_test_wron		63.78 281	60.16 283	74.64 271	78.15 291	60.41 234	83.49 264	84.03 286	56.17 295	39.17 323	71.59 305	37.22 276	83.24 314	42.87 293	48.73 311	80.26 300

YYNet1		63.76 282	60.14 284	74.62 272	78.06 292	60.19 239	83.46 266	83.99 290	56.18 294	39.25 322	71.56 306	37.18 277	83.34 312	42.90 292	48.70 312	80.32 299

K. test v3		63.09 283	59.61 286	73.53 280	76.26 301	49.38 307	83.27 268	77.15 308	64.35 244	47.77 307	72.32 301	28.73 305	87.79 292	49.93 267	36.69 325	83.41 268

COLMAP_ROB	57.96 20	62.98 284	59.65 285	72.98 285	81.44 253	53.00 289	83.75 261	75.53 313	48.34 312	48.81 305	81.40 237	24.14 315	90.30 266	32.95 320	60.52 285	75.65 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		61.66 285	58.06 287	72.46 289	79.57 271	51.42 297	80.17 289	68.61 324	51.25 304	45.88 309	81.23 239	19.86 324	86.58 298	38.98 306	57.01 294	79.39 304

UnsupCasMVSNet_bld		61.60 286	57.71 288	73.29 282	68.73 320	51.64 294	78.61 296	89.05 230	57.20 288	46.11 308	61.96 318	28.70 306	88.60 283	50.08 266	38.90 323	79.63 303

MDA-MVSNet-bldmvs		61.54 287	57.70 289	73.05 284	79.53 273	57.00 271	83.08 271	81.23 299	57.57 285	34.91 325	72.45 298	32.79 292	86.26 300	35.81 313	41.95 319	75.89 316

TinyColmap		60.32 288	56.42 293	72.00 295	78.78 284	53.18 288	78.36 299	75.64 311	52.30 301	41.59 321	75.82 289	14.76 329	88.35 286	35.84 312	54.71 302	74.46 318

MVS-HIRNet		60.25 289	55.55 294	74.35 274	84.37 225	56.57 273	71.64 309	74.11 315	34.44 325	45.54 313	42.24 328	31.11 301	89.81 275	40.36 303	76.10 185	76.67 315

MIMVSNet1		60.16 290	57.33 290	68.67 301	69.71 318	44.13 319	78.92 295	84.21 284	55.05 297	44.63 316	71.85 303	23.91 316	81.54 322	32.63 322	55.03 300	80.35 298

PM-MVS		59.40 291	56.59 291	67.84 302	63.63 322	41.86 321	76.76 303	63.22 330	59.01 281	51.07 299	72.27 302	11.72 331	83.25 313	61.34 225	50.28 310	78.39 311

new-patchmatchnet		59.30 292	56.48 292	67.79 303	65.86 321	44.19 318	82.47 274	81.77 298	59.94 276	43.65 319	66.20 313	27.67 308	81.68 321	39.34 305	41.40 320	77.50 313

test_normal		57.36 293	52.06 297	73.28 283	60.52 327	46.40 315	26.00 334	88.06 253	66.54 229	32.27 326	49.48 322	19.92 323	90.75 262	62.55 219	63.18 263	82.70 279

DSMNet-mixed		56.78 294	54.44 295	63.79 308	63.21 323	29.44 332	64.43 322	64.10 329	42.12 322	51.32 297	71.60 304	31.76 297	75.04 325	36.23 311	65.20 247	86.87 220

pmmvs3		55.51 295	51.50 299	67.53 304	57.90 329	50.93 300	80.37 285	73.66 316	40.63 323	44.15 318	64.75 316	16.30 326	78.97 324	44.77 287	40.98 322	72.69 319

TDRefinement		55.28 296	51.58 298	66.39 307	59.53 328	46.15 316	76.23 304	72.80 317	44.60 319	42.49 320	76.28 285	15.29 327	82.39 317	33.20 319	43.75 317	70.62 322

LF4IMVS		54.01 297	52.12 296	59.69 309	62.41 325	39.91 326	68.59 315	68.28 326	42.96 321	44.55 317	75.18 290	14.09 330	68.39 328	41.36 299	51.68 307	70.78 321

N_pmnet		50.55 298	49.11 300	54.88 312	77.17 297	4.02 341	84.36 258	2.00 341	48.59 310	45.86 311	68.82 310	32.22 295	82.80 315	31.58 325	51.38 308	77.81 312

new_pmnet		49.31 299	46.44 301	57.93 310	62.84 324	40.74 323	68.47 316	62.96 331	36.48 324	35.09 324	57.81 320	14.97 328	72.18 326	32.86 321	46.44 314	60.88 326

FPMVS		45.64 300	43.10 302	53.23 314	51.42 331	36.46 329	64.97 321	71.91 319	29.13 327	27.53 327	61.55 319	9.83 333	65.01 332	16.00 330	55.58 298	58.22 327

LCM-MVSNet		40.54 301	35.79 303	54.76 313	36.92 336	30.81 331	51.41 329	69.02 323	22.07 329	24.63 328	45.37 326	4.56 339	65.81 330	33.67 317	34.50 326	67.67 323

ANet_high		40.27 302	35.20 304	55.47 311	34.74 337	34.47 330	63.84 323	71.56 320	48.42 311	18.80 331	41.08 329	9.52 334	64.45 333	20.18 328	8.66 335	67.49 324

PMMVS2		37.93 303	33.61 305	50.92 315	46.31 333	24.76 335	60.55 326	50.05 333	28.94 328	20.93 329	47.59 323	4.41 340	65.13 331	25.14 326	18.55 330	62.87 325

Gipuma		34.91 304	31.44 306	45.30 316	70.99 313	39.64 327	19.85 336	72.56 318	20.10 331	16.16 333	21.47 334	5.08 338	71.16 327	13.07 331	43.70 318	25.08 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	26.43 22	31.84 305	28.16 307	42.89 317	25.87 339	27.58 333	50.92 330	49.78 334	21.37 330	14.17 334	40.81 330	2.01 341	66.62 329	9.61 333	38.88 324	34.49 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 306	24.00 309	26.45 320	43.74 334	18.44 338	60.86 324	39.66 335	15.11 332	9.53 336	22.10 333	6.52 336	46.94 335	8.31 334	10.14 332	13.98 333

MVE	24.84 23	24.35 307	19.77 312	38.09 318	34.56 338	26.92 334	26.57 333	38.87 337	11.73 334	11.37 335	27.44 331	1.37 342	50.42 334	11.41 332	14.60 331	36.93 328
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 308	23.20 311	25.46 321	41.52 335	16.90 339	60.56 325	38.79 338	14.62 333	8.99 337	20.24 336	7.35 335	45.82 336	7.25 335	9.46 333	13.64 334

tmp_tt		22.26 309	23.75 310	17.80 322	5.23 340	12.06 340	35.26 332	39.48 336	2.82 336	18.94 330	44.20 327	22.23 320	24.64 338	36.30 310	9.31 334	16.69 332

cdsmvs_eth3d_5k		19.86 310	26.47 308	0.00 326	0.00 343	0.00 344	0.00 337	93.45 69	0.00 339	0.00 341	95.27 39	49.56 207	0.00 342	0.00 339	0.00 338	0.00 338

wuyk23d		11.30 311	10.95 313	12.33 323	48.05 332	19.89 337	25.89 335	1.92 342	3.58 335	3.12 338	1.37 338	0.64 343	15.77 339	6.23 336	7.77 336	1.35 335

ab-mvs-re		7.91 312	10.55 314	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	94.95 48	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

testmvs		7.23 313	9.62 315	0.06 325	0.04 341	0.02 343	84.98 256	0.02 343	0.03 337	0.18 339	1.21 339	0.01 345	0.02 340	0.14 337	0.01 337	0.13 337

test123		6.92 314	9.21 316	0.08 324	0.03 342	0.05 342	81.65 279	0.01 344	0.02 338	0.14 340	0.85 340	0.03 344	0.02 340	0.12 338	0.00 338	0.16 336

pcd_1.5k_mvsjas		4.46 315	5.95 317	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	53.55 176	0.00 342	0.00 339	0.00 338	0.00 338

uanet_test		0.00 316	0.00 318	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

sosnet-low-res		0.00 316	0.00 318	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

sosnet		0.00 316	0.00 318	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

uncertanet		0.00 316	0.00 318	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

Regformer		0.00 316	0.00 318	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

uanet		0.00 316	0.00 318	0.00 326	0.00 343	0.00 344	0.00 337	0.00 345	0.00 339	0.00 341	0.00 341	0.00 346	0.00 342	0.00 339	0.00 338	0.00 338

9.14			87.63 20		93.86 42		94.41 44	94.18 47	72.76 142	86.21 26	96.51 10	66.64 44	97.88 37	90.08 14	94.04 29

save filter2										87.82 19	96.42 12	66.33 47	97.33 61	90.39 13	94.81 18	95.11 62

save fliter					93.84 43	67.89 74	95.05 35	92.66 102	78.19 64

test_0728_THIRD									72.48 147	90.55 8	96.93 5	76.24 6	99.08 5	91.53 7	94.99 10	96.43 17

test_0728_SECOND				88.70 11	96.45 7	70.43 23	96.64 6	94.37 42					99.15 2	91.91 5	94.90 14	96.51 15

test0726

High-resolution multi-view benchmark