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.

HSP-MVS		90.38 2	91.89 1	85.84 68	92.83 55	64.03 168	93.06 74	94.52 32	82.19 19	93.65 1	96.15 10	85.89 1	97.19 57	91.02 8	97.75 1	96.29 16

TSAR-MVS + MP.		88.11 11	88.64 9	86.54 47	91.73 83	68.04 61	90.36 173	93.55 56	82.89 14	91.29 2	92.89 87	72.27 10	96.03 100	87.99 20	94.77 13	95.54 31

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 103	93.00 52	58.16 255	96.72 3	94.41 37	86.50 5	90.25 3	97.83 1	75.46 5	98.67 12	92.78 2	95.49 7	97.32 1

CANet		89.61 6	89.99 6	88.46 13	94.39 24	69.71 32	96.53 5	93.78 45	86.89 4	89.68 4	95.78 15	65.94 40	99.10 2	92.99 1	93.91 25	96.58 11

xiu_mvs_v2_base		87.92 14	87.38 22	89.55 7	91.41 97	76.43 2	95.74 12	93.12 76	83.53 13	89.55 5	95.95 12	53.45 174	97.68 32	91.07 7	92.62 42	94.54 66

PS-MVSNAJ		88.14 10	87.61 17	89.71 4	92.06 72	76.72 1	95.75 11	93.26 68	83.86 11	89.55 5	96.06 11	53.55 169	97.89 30	91.10 6	93.31 35	94.54 66

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 23	84.83 9	89.07 7	96.80 2	70.86 14	99.06 3	92.64 4	95.71 5	96.12 18

HPM-MVS++		89.37 7	89.95 7	87.64 20	95.10 17	68.23 59	95.24 22	94.49 34	82.43 17	88.90 8	96.35 5	71.89 13	98.63 13	88.76 18	96.40 2	96.06 19

MVS_0304		88.39 9	88.35 12	88.50 12	93.01 51	70.11 23	95.90 10	92.20 109	86.27 6	88.70 9	95.92 13	56.76 121	99.02 4	92.68 3	93.76 28	96.37 15

APDe-MVS		87.54 17	87.84 14	86.65 42	96.07 9	66.30 121	94.84 32	93.78 45	69.35 199	88.39 10	96.34 6	67.74 28	97.66 36	90.62 9	93.44 34	96.01 22

EPNet		87.84 15	88.38 10	86.23 61	93.30 44	66.05 125	95.26 21	94.84 22	87.09 3	88.06 11	94.53 52	66.79 33	97.34 50	83.89 48	91.68 54	95.29 38
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SD-MVS		87.49 18	87.49 19	87.50 24	93.60 39	68.82 46	93.90 53	92.63 94	76.86 71	87.90 12	95.76 16	66.17 36	97.63 38	89.06 15	91.48 58	96.05 20

canonicalmvs		86.85 28	86.25 31	88.66 10	91.80 82	71.92 10	93.54 63	91.71 127	80.26 31	87.55 13	95.25 32	63.59 63	96.93 77	88.18 19	84.34 111	97.11 3

旧先验2							92.00 112		59.37 275	87.54 14			93.47 196	75.39 99

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 130	71.18 14	89.07 200	90.69 159	65.80 232	87.13 15	94.34 60	64.99 49	92.67 216	72.83 113	91.80 52	95.27 40

lupinMVS		87.74 16	87.77 15	87.63 22	89.24 130	71.18 14	96.57 4	92.90 84	82.70 16	87.13 15	95.27 30	64.99 49	95.80 106	89.34 12	91.80 52	95.93 24

alignmvs		87.28 20	86.97 25	88.24 15	91.30 98	71.14 16	95.61 16	93.56 55	79.30 38	87.07 17	95.25 32	68.43 19	96.93 77	87.87 21	84.33 112	96.65 8

NCCC		89.07 8	89.46 8	87.91 16	96.60 5	69.05 40	96.38 7	94.64 31	84.42 10	86.74 18	96.20 8	66.56 35	98.76 11	89.03 16	94.56 18	95.92 25

APD-MVS		85.93 38	85.99 33	85.76 73	95.98 11	65.21 140	93.59 61	92.58 96	66.54 227	86.17 19	95.88 14	63.83 58	97.00 67	86.39 32	92.94 38	95.06 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CANet_DTU		84.09 58	83.52 53	85.81 69	90.30 111	66.82 93	91.87 122	89.01 221	85.27 7	86.09 20	93.74 70	47.71 221	96.98 71	77.90 87	89.78 73	93.65 97

VNet		86.20 35	85.65 40	87.84 18	93.92 34	69.99 26	95.73 14	95.94 11	78.43 53	86.00 21	93.07 81	58.22 105	97.00 67	85.22 39	84.33 112	96.52 13

TSAR-MVS + GP.		87.96 12	88.37 11	86.70 41	93.51 42	65.32 138	95.15 25	93.84 44	78.17 55	85.93 22	94.80 48	75.80 4	98.21 21	89.38 11	88.78 77	96.59 10

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 12	89.33 1	85.77 23	96.26 7	72.84 9	99.38 1	92.64 4	95.93 4	97.08 4

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 49	73.88 6	97.01 2	94.40 38	88.32 2	85.71 24	94.91 45	74.11 7	98.91 6	87.26 26	95.94 3	97.03 5
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
PHI-MVS		86.83 29	86.85 28	86.78 40	93.47 43	65.55 135	95.39 20	95.10 19	71.77 164	85.69 25	96.52 3	62.07 74	98.77 10	86.06 34	95.60 6	96.03 21

TEST9					94.18 27	67.28 77	94.16 36	93.51 57	71.75 165	85.52 26	95.33 26	68.01 23	97.27 54

train_agg		87.21 22	87.42 21	86.60 44	94.18 27	67.28 77	94.16 36	93.51 57	71.87 157	85.52 26	95.33 26	68.19 21	97.27 54	89.09 13	94.90 10	95.25 43

test_8					94.19 26	67.19 79	94.15 38	93.42 62	71.87 157	85.38 28	95.35 25	68.19 21	96.95 74

testdata				81.34 181	89.02 134	57.72 259		89.84 191	58.65 278	85.32 29	94.09 64	57.03 116	93.28 198	69.34 147	90.56 69	93.03 114

test_prior3		87.38 19	87.70 16	86.42 53	94.71 21	67.35 75	95.10 27	93.10 77	75.40 89	85.25 30	95.61 21	67.94 24	96.84 79	87.47 23	94.77 13	95.05 50

test_prior2							95.10 27		75.40 89	85.25 30	95.61 21	67.94 24		87.47 23	94.77 13

agg_prior3		86.93 26	87.08 24	86.48 50	94.21 25	66.95 88	94.14 39	93.40 63	71.80 162	84.86 32	95.13 36	66.16 37	97.25 56	89.09 13	94.90 10	95.25 43

ACMMP_Plus		86.05 37	85.80 37	86.80 39	91.58 86	67.53 72	91.79 126	93.49 59	74.93 96	84.61 33	95.30 28	59.42 96	97.92 28	86.13 33	94.92 9	94.94 56

jason		86.40 32	86.17 32	87.11 33	86.16 180	70.54 20	95.71 15	92.19 111	82.00 24	84.58 34	94.34 60	61.86 76	95.53 122	87.76 22	90.89 64	95.27 40
jason: jason.
agg_prior1		87.02 24	87.26 23	86.28 60	94.16 31	66.97 86	94.08 42	93.31 66	71.85 159	84.49 35	95.39 24	68.91 17	96.75 83	88.84 17	94.32 20	95.13 47

agg_prior					94.16 31	66.97 86		93.31 66		84.49 35			96.75 83

Regformer-1		87.24 21	87.60 18	86.15 62	95.14 15	65.83 131	93.95 49	95.12 17	82.11 21	84.25 37	95.73 17	67.88 27	98.35 19	85.60 36	88.64 78	94.26 73

xiu_mvs_v1_base_debu		82.16 84	81.12 85	85.26 89	86.42 174	68.72 47	92.59 93	90.44 164	73.12 131	84.20 38	94.36 55	38.04 268	95.73 110	84.12 45	86.81 89	91.33 142

xiu_mvs_v1_base		82.16 84	81.12 85	85.26 89	86.42 174	68.72 47	92.59 93	90.44 164	73.12 131	84.20 38	94.36 55	38.04 268	95.73 110	84.12 45	86.81 89	91.33 142

xiu_mvs_v1_base_debi		82.16 84	81.12 85	85.26 89	86.42 174	68.72 47	92.59 93	90.44 164	73.12 131	84.20 38	94.36 55	38.04 268	95.73 110	84.12 45	86.81 89	91.33 142

Regformer-2		87.00 25	87.43 20	85.71 76	95.14 15	64.73 150	93.95 49	94.95 20	81.69 26	84.03 41	95.73 17	67.35 31	98.19 23	85.40 38	88.64 78	94.20 75

SteuartSystems-ACMMP		86.82 30	86.90 26	86.58 46	90.42 108	66.38 118	96.09 9	93.87 43	77.73 60	84.01 42	95.66 19	63.39 64	97.94 27	87.40 25	93.55 33	95.42 32
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		87.11 23	86.27 29	89.62 5	97.79 1	76.27 3	94.96 31	94.49 34	78.74 51	83.87 43	92.94 84	64.34 54	96.94 75	75.19 100	94.09 22	95.66 27

DeepC-MVS_fast	79.48 2	87.95 13	88.00 13	87.79 19	95.86 12	68.32 55	95.74 12	94.11 41	83.82 12	83.49 44	96.19 9	64.53 53	98.44 17	83.42 51	94.88 12	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 123	69.40 36	91.35 145	86.12 268	72.59 138	83.22 45	92.81 89	59.60 95	96.01 102	81.76 60	87.80 84	95.56 30

CDPH-MVS		85.71 41	85.46 41	86.46 51	94.75 20	67.19 79	93.89 54	92.83 86	70.90 178	83.09 46	95.28 29	63.62 61	97.36 48	80.63 68	94.18 21	94.84 58

MVS_Test		84.16 57	83.20 62	87.05 35	91.56 87	69.82 29	89.99 181	92.05 114	77.77 59	82.84 47	86.57 164	63.93 57	96.09 97	74.91 106	89.18 75	95.25 43

test12				87.09 34	94.60 23	68.86 44		92.91 83		82.67 48		65.44 45	97.55 41		93.69 31	94.84 58

HY-MVS	76.49 5	84.28 55	83.36 61	87.02 36	92.22 69	67.74 66	84.65 248	94.50 33	79.15 42	82.23 49	87.93 146	66.88 32	96.94 75	80.53 69	82.20 125	96.39 14

LFMVS		84.34 54	82.73 69	89.18 8	94.76 19	73.25 8	94.99 30	91.89 120	71.90 155	82.16 50	93.49 74	47.98 218	97.05 62	82.55 55	84.82 105	97.25 2

WTY-MVS		86.32 33	85.81 36	87.85 17	92.82 57	69.37 37	95.20 23	95.25 15	82.71 15	81.91 51	94.73 49	67.93 26	97.63 38	79.55 73	82.25 124	96.54 12

VDD-MVS		83.06 72	81.81 78	86.81 38	90.86 105	67.70 67	95.40 19	91.50 135	75.46 86	81.78 52	92.34 97	40.09 258	97.13 61	86.85 30	82.04 126	95.60 29

Regformer-3		85.80 40	85.92 34	85.46 80	94.17 29	65.09 146	92.95 78	95.11 18	81.13 27	81.68 53	95.04 37	65.82 42	98.32 20	83.02 52	84.36 109	92.97 116

MSLP-MVS++		86.27 34	85.91 35	87.35 27	92.01 73	68.97 43	95.04 29	92.70 89	79.04 46	81.50 54	96.50 4	58.98 102	96.78 81	83.49 50	93.93 24	96.29 16

Regformer-4		85.45 43	85.69 39	84.73 101	94.17 29	63.23 183	92.95 78	94.83 23	80.66 29	81.29 55	95.04 37	65.12 47	98.08 26	82.74 53	84.36 109	92.88 120

UA-Net		80.02 114	79.65 102	81.11 189	89.33 126	57.72 259	86.33 241	89.00 222	77.44 65	81.01 56	89.15 130	59.33 97	95.90 103	61.01 215	84.28 114	89.73 161

PVSNet_BlendedMVS		83.38 67	83.43 57	83.22 131	93.76 35	67.53 72	94.06 43	93.61 53	79.13 43	81.00 57	85.14 178	63.19 67	97.29 52	87.08 27	73.91 181	84.83 239

PVSNet_Blended		86.73 31	86.86 27	86.31 59	93.76 35	67.53 72	96.33 8	93.61 53	82.34 18	81.00 57	93.08 79	63.19 67	97.29 52	87.08 27	91.38 59	94.13 81

MP-MVS-pluss		85.24 44	85.13 44	85.56 77	91.42 95	65.59 134	91.54 138	92.51 98	74.56 99	80.62 59	95.64 20	59.15 99	97.00 67	86.94 29	93.80 26	94.07 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MPTG		84.73 49	84.47 47	85.50 78	91.89 78	65.16 141	91.55 137	92.23 104	75.32 91	80.53 60	95.21 34	56.06 133	97.16 59	84.86 42	92.55 44	94.18 76

MTAPA		83.91 61	83.38 60	85.50 78	91.89 78	65.16 141	81.75 269	92.23 104	75.32 91	80.53 60	95.21 34	56.06 133	97.16 59	84.86 42	92.55 44	94.18 76

PAPM		85.89 39	85.46 41	87.18 30	88.20 152	72.42 9	92.41 97	92.77 87	82.11 21	80.34 62	93.07 81	68.27 20	95.02 130	78.39 83	93.59 32	94.09 84

CostFormer		82.33 81	81.15 84	85.86 67	89.01 135	68.46 51	82.39 266	93.01 79	75.59 84	80.25 63	81.57 219	72.03 12	94.96 132	79.06 78	77.48 159	94.16 79

PMMVS		81.98 89	82.04 75	81.78 173	89.76 120	56.17 269	91.13 156	90.69 159	77.96 57	80.09 64	93.57 72	46.33 231	94.99 131	81.41 64	87.46 86	94.17 78

1121		81.25 95	80.05 95	84.87 98	92.30 66	64.31 162	87.91 218	91.39 139	59.44 274	79.94 65	92.91 85	57.09 114	97.01 65	66.63 166	92.81 41	93.29 106

sss		82.71 77	82.38 72	83.73 122	89.25 128	59.58 242	92.24 100	94.89 21	77.96 57	79.86 66	92.38 95	56.70 124	97.05 62	77.26 90	80.86 132	94.55 64

新几何1				84.73 101	92.32 65	64.28 164		91.46 137	59.56 273	79.77 67	92.90 86	56.95 119	96.57 88	63.40 195	92.91 39	93.34 103

APD-MVS_3200maxsize		81.64 91	81.32 83	82.59 144	92.36 63	58.74 252	91.39 141	91.01 153	63.35 248	79.72 68	94.62 51	51.82 185	96.14 95	79.71 71	87.93 83	92.89 119

MP-MVS		85.02 46	84.97 45	85.17 92	92.60 61	64.27 165	93.24 69	92.27 103	73.13 130	79.63 69	94.43 53	61.90 75	97.17 58	85.00 40	92.56 43	94.06 87

原ACMM1				84.42 110	93.21 47	64.27 165		93.40 63	65.39 235	79.51 70	92.50 91	58.11 107	96.69 85	65.27 182	93.96 23	92.32 130

VDDNet		80.50 104	78.26 123	87.21 29	86.19 179	69.79 30	94.48 34	91.31 142	60.42 268	79.34 71	90.91 111	38.48 264	96.56 89	82.16 56	81.05 130	95.27 40

HFP-MVS		84.73 49	84.40 49	85.72 74	93.75 37	65.01 147	93.50 64	93.19 72	72.19 149	79.22 72	94.93 42	59.04 100	97.67 33	81.55 62	92.21 46	94.49 70

#test#		84.98 47	84.74 46	85.72 74	93.75 37	65.01 147	94.09 41	93.19 72	73.55 124	79.22 72	94.93 42	59.04 100	97.67 33	82.66 54	92.21 46	94.49 70

MAR-MVS		84.18 56	83.43 57	86.44 52	96.25 7	65.93 128	94.28 35	94.27 40	74.41 100	79.16 74	95.61 21	53.99 164	98.88 9	69.62 144	93.26 36	94.50 69
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
PAPR		85.15 45	84.47 47	87.18 30	96.02 10	68.29 56	91.85 124	93.00 81	76.59 76	79.03 75	95.00 39	61.59 77	97.61 40	78.16 84	89.00 76	95.63 28

MVS_111021_HR		86.19 36	85.80 37	87.37 26	93.17 48	69.79 30	93.99 47	93.76 48	79.08 45	78.88 76	93.99 66	62.25 73	98.15 24	85.93 35	91.15 62	94.15 80

region2R		84.36 53	84.03 51	85.36 86	93.54 41	64.31 162	93.43 67	92.95 82	72.16 152	78.86 77	94.84 47	56.97 118	97.53 42	81.38 65	92.11 50	94.24 74

ACMMPR		84.37 52	84.06 50	85.28 88	93.56 40	64.37 160	93.50 64	93.15 75	72.19 149	78.85 78	94.86 46	56.69 125	97.45 44	81.55 62	92.20 48	94.02 89

UGNet		79.87 117	78.68 117	83.45 130	89.96 115	61.51 212	92.13 102	90.79 156	76.83 72	78.85 78	86.33 167	38.16 266	96.17 94	67.93 156	87.17 87	92.67 122
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
diffmvs		80.18 109	78.55 120	85.07 93	88.56 142	66.93 89	86.70 239	88.62 229	70.42 188	78.69 80	85.26 176	56.93 120	94.77 137	68.68 153	83.09 118	93.51 100

GG-mvs-BLEND				86.53 49	91.91 77	69.67 34	75.02 303	94.75 26		78.67 81	90.85 112	77.91 2	94.56 145	72.25 119	93.74 29	95.36 34

XVS		83.87 62	83.47 55	85.05 94	93.22 45	63.78 171	92.92 80	92.66 92	73.99 111	78.18 82	94.31 62	55.25 138	97.41 45	79.16 76	91.58 56	93.95 91

X-MVStestdata		76.86 173	74.13 186	85.05 94	93.22 45	63.78 171	92.92 80	92.66 92	73.99 111	78.18 82	10.19 342	55.25 138	97.41 45	79.16 76	91.58 56	93.95 91

EI-MVSNet-Vis-set		83.77 64	83.67 52	84.06 115	92.79 59	63.56 180	91.76 129	94.81 25	79.65 36	77.87 84	94.09 64	63.35 65	97.90 29	79.35 74	79.36 138	90.74 150

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 145	61.80 209	93.44 66	88.26 238	73.96 114	77.73 85	91.76 104	49.94 200	94.76 138	65.84 177	90.37 70	94.65 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
abl_6		79.82 118	79.20 113	81.70 177	89.85 117	58.34 254	88.47 208	90.07 184	62.56 255	77.71 86	93.08 79	47.65 222	96.78 81	77.94 86	85.45 103	89.99 158

DWT-MVSNet_test		83.95 60	82.80 67	87.41 25	92.90 54	70.07 25	89.12 199	94.42 36	82.15 20	77.64 87	91.77 103	70.81 15	96.22 92	65.03 183	81.36 128	95.94 23

CSCG		86.87 27	86.26 30	88.72 9	95.05 18	70.79 17	93.83 56	95.33 14	68.48 212	77.63 88	94.35 59	73.04 8	98.45 16	84.92 41	93.71 30	96.92 6

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 153	63.74 173	92.70 86	93.77 47	79.30 38	77.61 89	87.57 152	58.19 106	94.08 174	73.91 109	86.68 92	93.33 105

tpm2		79.80 119	77.95 128	85.34 87	88.28 150	68.26 58	81.56 274	91.42 138	70.11 192	77.59 90	80.50 235	67.40 29	94.26 165	67.34 161	77.35 160	93.51 100

CP-MVS		83.71 66	83.40 59	84.65 104	93.14 49	63.84 169	94.59 33	92.28 102	71.03 176	77.41 91	94.92 44	55.21 141	96.19 93	81.32 66	90.70 66	93.91 93

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 147	65.49 137	83.00 263	92.67 91	71.82 161	77.36 92	85.01 179	54.50 156	96.59 86	76.35 95	75.63 169	95.32 37

test222					89.77 119	61.60 211	89.55 190	89.42 205	56.83 287	77.28 93	92.43 94	52.76 178			91.14 63	93.09 112

PGM-MVS		83.25 68	82.70 70	84.92 96	92.81 58	64.07 167	90.44 170	92.20 109	71.28 174	77.23 94	94.43 53	55.17 142	97.31 51	79.33 75	91.38 59	93.37 102

gg-mvs-nofinetune		77.18 167	74.31 183	85.80 70	91.42 95	68.36 54	71.78 306	94.72 27	49.61 304	77.12 95	45.92 327	77.41 3	93.98 181	67.62 159	93.16 37	95.05 50

HPM-MVS		83.25 68	82.95 64	84.17 113	92.25 68	62.88 193	90.91 159	91.86 121	70.30 191	77.12 95	93.96 67	56.75 123	96.28 91	82.04 58	91.34 61	93.34 103

PVSNet_Blended_VisFu		83.97 59	83.50 54	85.39 85	90.02 114	66.59 111	93.77 57	91.73 125	77.43 66	77.08 97	89.81 127	63.77 60	96.97 72	79.67 72	88.21 81	92.60 124

DeepC-MVS	77.85 3	85.52 42	85.24 43	86.37 56	88.80 139	66.64 108	92.15 101	93.68 51	81.07 28	76.91 98	93.64 71	62.59 72	98.44 17	85.50 37	92.84 40	94.03 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-UG-set		83.14 70	82.96 63	83.67 125	92.28 67	63.19 186	91.38 143	94.68 28	79.22 40	76.60 99	93.75 69	62.64 71	97.76 31	78.07 85	78.01 149	90.05 157

EPNet_dtu		78.80 136	79.26 112	77.43 251	88.06 154	49.71 299	91.96 114	91.95 119	77.67 61	76.56 100	91.28 109	58.51 104	90.20 257	56.37 234	80.95 131	92.39 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DP-MVS Recon		82.73 75	81.65 80	85.98 64	97.31 3	67.06 83	95.15 25	91.99 116	69.08 202	76.50 101	93.89 68	54.48 158	98.20 22	70.76 136	85.66 101	92.69 121

tpmrst		80.57 102	79.14 115	84.84 99	90.10 113	68.28 57	81.70 270	89.72 198	77.63 62	75.96 102	79.54 249	64.94 51	92.71 214	75.43 98	77.28 162	93.55 99

CHOSEN 1792x2688		84.98 47	83.45 56	89.57 6	89.94 116	75.14 4	92.07 107	92.32 101	81.87 25	75.68 103	88.27 140	60.18 90	98.60 14	80.46 70	90.27 71	94.96 55

test-LLR		80.10 112	79.56 104	81.72 175	86.93 171	61.17 214	92.70 86	91.54 132	71.51 172	75.62 104	86.94 161	53.83 165	92.38 224	72.21 120	84.76 107	91.60 139

test-mter		79.96 115	79.38 110	81.72 175	86.93 171	61.17 214	92.70 86	91.54 132	73.85 116	75.62 104	86.94 161	49.84 202	92.38 224	72.21 120	84.76 107	91.60 139

mPP-MVS		82.96 74	82.44 71	84.52 108	92.83 55	62.92 191	92.76 83	91.85 122	71.52 171	75.61 106	94.24 63	53.48 173	96.99 70	78.97 79	90.73 65	93.64 98

MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 148	62.88 193	89.77 188	88.93 223	76.78 73	75.55 107	93.10 77	50.31 196	95.38 124	83.82 49	87.02 88	92.26 133

API-MVS		82.28 82	80.53 93	87.54 23	96.13 8	70.59 19	93.63 59	91.04 152	65.72 234	75.45 108	92.83 88	56.11 132	98.89 8	64.10 191	89.75 74	93.15 110

Fast-Effi-MVS+		81.14 96	80.01 96	84.51 109	90.24 112	65.86 129	94.12 40	89.15 215	73.81 118	75.37 109	88.26 141	57.26 113	94.53 148	66.97 165	84.92 104	93.15 110

tpmp4_e23		78.85 134	76.55 150	85.77 72	89.25 128	68.39 53	81.63 273	91.38 140	70.40 189	75.21 110	79.22 251	67.37 30	94.79 136	58.98 227	75.51 170	94.13 81

nrg030		80.93 99	79.86 99	84.13 114	83.69 210	68.83 45	93.23 70	91.20 145	75.55 85	75.06 111	88.22 144	63.04 70	94.74 140	81.88 59	66.88 223	88.82 168

PatchFormer-LS_test		83.14 70	81.81 78	87.12 32	92.34 64	69.92 28	88.64 206	93.32 65	82.07 23	74.87 112	91.62 107	68.91 17	96.08 99	66.07 174	78.45 148	95.37 33

HPM-MVS_fast		80.25 108	79.55 106	82.33 156	91.55 88	59.95 237	91.32 147	89.16 214	65.23 238	74.71 113	93.07 81	47.81 220	95.74 109	74.87 108	88.23 80	91.31 146

TR-MVS		78.77 138	77.37 140	82.95 134	90.49 107	60.88 217	93.67 58	90.07 184	70.08 193	74.51 114	91.37 108	45.69 234	95.70 115	60.12 220	80.32 133	92.29 131

HQP-NCC					87.54 161		94.06 43		79.80 33	74.18 115

ACMP_Plane					87.54 161		94.06 43		79.80 33	74.18 115

HQP4-MVS										74.18 115			95.61 116			88.63 170

HQP-MVS		81.14 96	80.64 91	82.64 142	87.54 161	63.66 178	94.06 43	91.70 128	79.80 33	74.18 115	90.30 120	51.63 189	95.61 116	77.63 88	78.90 142	88.63 170

PAPM_NR		82.97 73	81.84 77	86.37 56	94.10 33	66.76 100	87.66 223	92.84 85	69.96 194	74.07 119	93.57 72	63.10 69	97.50 43	70.66 137	90.58 68	94.85 57

VPA-MVSNet		79.03 130	78.00 127	82.11 169	85.95 183	64.48 154	93.22 71	94.66 29	75.05 95	74.04 120	84.95 180	52.17 184	93.52 194	74.90 107	67.04 222	88.32 177

CDS-MVSNet		81.43 93	80.74 89	83.52 127	86.26 178	64.45 155	92.09 105	90.65 162	75.83 83	73.95 121	89.81 127	63.97 56	92.91 208	71.27 129	82.82 121	93.20 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		78.58 143	77.03 143	83.22 131	85.94 185	64.56 151	83.21 261	91.14 148	78.31 54	73.67 122	79.68 246	64.01 55	92.09 232	66.07 174	71.26 199	93.03 114

BH-RMVSNet		79.46 126	77.65 132	84.89 97	91.68 85	65.66 133	93.55 62	88.09 239	72.93 134	73.37 123	91.12 110	46.20 233	96.12 96	56.28 235	85.61 102	92.91 118

thres200		79.66 121	78.33 121	83.66 126	92.54 62	65.82 132	93.06 74	96.31 8	74.90 97	73.30 124	88.66 133	59.67 94	95.61 116	47.84 264	78.67 145	89.56 163

CPTT-MVS		79.59 123	79.16 114	80.89 196	91.54 89	59.80 239	92.10 104	88.54 232	60.42 268	72.96 125	93.28 76	48.27 214	92.80 211	78.89 80	86.50 96	90.06 156

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 159	68.11 60	90.18 177	90.01 187	70.65 186	72.95 126	86.06 170	63.61 62	94.50 149	75.01 104	79.75 136	93.67 96

EPP-MVSNet		81.79 90	81.52 81	82.61 143	88.77 140	60.21 232	93.02 76	93.66 52	68.52 211	72.90 127	90.39 119	72.19 11	94.96 132	74.93 105	79.29 140	92.67 122

MDTV_nov1_ep13_2view						59.90 238	80.13 285		67.65 220	72.79 128		54.33 160		59.83 221		92.58 125

mvs-test1		78.74 139	77.95 128	81.14 188	83.22 215	57.13 265	93.96 48	87.78 243	75.42 87	72.68 129	90.80 113	45.08 238	94.54 147	75.08 102	77.49 158	91.74 138

TAMVS		80.37 106	79.45 107	83.13 133	85.14 191	63.37 181	91.23 150	90.76 158	74.81 98	72.65 130	88.49 135	60.63 86	92.95 204	69.41 146	81.95 127	93.08 113

VPNet		78.82 135	77.53 135	82.70 139	84.52 198	66.44 117	93.93 51	92.23 104	80.46 30	72.60 131	88.38 138	49.18 207	93.13 200	72.47 118	63.97 249	88.55 172

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 158	67.65 69	95.45 18	92.18 112	85.06 8	72.58 132	92.27 98	52.46 182	95.78 107	84.18 44	79.06 141	88.16 179
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS		80.34 107	79.75 101	82.12 166	86.94 169	62.42 198	93.13 72	91.31 142	78.81 49	72.53 133	89.14 131	50.66 193	95.55 120	76.74 91	78.53 146	88.39 175

plane_prior3						61.95 208			79.09 44	72.53 133

EPMVS		78.49 145	75.98 157	86.02 63	91.21 99	69.68 33	80.23 283	91.20 145	75.25 93	72.48 135	78.11 256	54.65 155	93.69 191	57.66 232	83.04 119	94.69 61

1112_ss		80.56 103	79.83 100	82.77 137	88.65 141	60.78 219	92.29 98	88.36 234	72.58 139	72.46 136	94.95 40	65.09 48	93.42 197	66.38 170	77.71 151	94.10 83

PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 103	64.97 149	92.47 96	91.05 151	79.18 41	72.43 137	90.51 118	37.05 280	94.06 175	68.06 154	86.00 99	93.90 94

OMC-MVS		78.67 142	77.91 130	80.95 195	85.76 187	57.40 263	88.49 207	88.67 227	73.85 116	72.43 137	92.10 99	49.29 206	94.55 146	72.73 115	77.89 150	90.91 149

MVS		84.66 51	82.86 66	90.06 1	90.93 102	74.56 5	87.91 218	95.54 13	68.55 210	72.35 139	94.71 50	59.78 93	98.90 7	81.29 67	94.69 17	96.74 7

EI-MVSNet		78.97 132	78.22 124	81.25 182	85.33 188	62.73 196	89.53 192	93.21 69	72.39 143	72.14 140	90.13 123	60.99 79	94.72 141	67.73 158	72.49 190	86.29 210

MVSTER		82.47 79	82.05 74	83.74 120	92.68 60	69.01 41	91.90 121	93.21 69	79.83 32	72.14 140	85.71 174	74.72 6	94.72 141	75.72 96	72.49 190	87.50 184

OPM-MVS		79.00 131	78.09 125	81.73 174	83.52 213	63.83 170	91.64 136	90.30 174	76.36 79	71.97 142	89.93 126	46.30 232	95.17 129	75.10 101	77.70 152	86.19 212

Test_1112_low_res		79.56 124	78.60 119	82.43 149	88.24 151	60.39 228	92.09 105	87.99 241	72.10 153	71.84 143	87.42 154	64.62 52	93.04 201	65.80 178	77.30 161	93.85 95

MDTV_nov1_ep13			72.61 203		89.06 133	68.48 50	80.33 281	90.11 183	71.84 160	71.81 144	75.92 273	53.01 176	93.92 184	48.04 263	73.38 182

tfpn200view9		78.79 137	77.43 137	82.88 135	92.21 70	64.49 152	92.05 108	96.28 9	73.48 125	71.75 145	88.26 141	60.07 91	95.32 125	45.16 273	77.58 154	88.83 166

thres400		78.68 140	77.43 137	82.43 149	92.21 70	64.49 152	92.05 108	96.28 9	73.48 125	71.75 145	88.26 141	60.07 91	95.32 125	45.16 273	77.58 154	87.48 185

ACMMP		81.49 92	80.67 90	83.93 117	91.71 84	62.90 192	92.13 102	92.22 108	71.79 163	71.68 147	93.49 74	50.32 195	96.96 73	78.47 81	84.22 116	91.93 136
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
CHOSEN 280x420		77.35 162	76.95 147	78.55 235	87.07 168	62.68 197	69.71 312	82.95 294	68.80 205	71.48 148	87.27 160	66.03 39	84.00 301	76.47 94	82.81 122	88.95 165

IS-MVSNet		80.14 111	79.41 108	82.33 156	87.91 157	60.08 236	91.97 113	88.27 237	72.90 135	71.44 149	91.73 106	61.44 78	93.66 192	62.47 208	86.53 95	93.24 107

PatchmatchNet		77.46 157	74.63 177	85.96 65	89.55 124	70.35 21	79.97 287	89.55 201	72.23 147	70.94 150	76.91 268	57.03 116	92.79 212	54.27 241	81.17 129	94.74 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AdaColmap		78.94 133	77.00 146	84.76 100	96.34 6	65.86 129	92.66 90	87.97 242	62.18 257	70.56 151	92.37 96	43.53 245	97.35 49	64.50 188	82.86 120	91.05 148

cascas		78.18 149	75.77 160	85.41 84	87.14 167	69.11 39	92.96 77	91.15 147	66.71 226	70.47 152	86.07 169	37.49 274	96.48 90	70.15 140	79.80 135	90.65 151

thres600view7		78.00 151	76.66 149	82.03 171	91.93 75	63.69 176	91.30 148	96.33 5	72.43 141	70.46 153	87.89 147	60.31 87	94.92 135	42.64 285	76.64 164	87.48 185

conf200view11		78.32 148	77.01 144	82.27 159	91.89 78	63.21 184	91.19 154	96.33 5	72.28 145	70.45 154	87.89 147	60.31 87	95.32 125	45.16 273	77.58 154	88.27 178

thres100view900		78.37 146	77.01 144	82.46 145	91.89 78	63.21 184	91.19 154	96.33 5	72.28 145	70.45 154	87.89 147	60.31 87	95.32 125	45.16 273	77.58 154	88.83 166

CVMVSNet		74.04 206	74.27 184	73.33 277	85.33 188	43.94 313	89.53 192	88.39 233	54.33 294	70.37 156	90.13 123	49.17 208	84.05 298	61.83 212	79.36 138	91.99 135

GA-MVS		78.33 147	76.23 154	84.65 104	83.65 211	66.30 121	91.44 139	90.14 182	76.01 81	70.32 157	84.02 188	42.50 248	94.72 141	70.98 133	77.00 163	92.94 117

mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 110	68.40 52	86.88 236	90.61 163	74.41 100	70.31 158	84.67 183	63.79 59	92.32 227	73.13 110	85.70 100	95.67 26

IB-MVS	77.80 4	82.18 83	80.46 94	87.35 27	89.14 132	70.28 22	95.59 17	95.17 16	78.85 47	70.19 159	85.82 172	70.66 16	97.67 33	72.19 122	66.52 226	94.09 84
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
TAPA-MVS	70.22 12	74.94 200	73.53 193	79.17 230	90.40 109	52.07 287	89.19 197	89.61 200	62.69 254	70.07 160	92.67 90	48.89 212	94.32 160	38.26 303	79.97 134	91.12 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Patchmatch-test1		75.00 199	71.80 212	84.58 106	86.63 173	70.08 24	81.06 276	89.19 212	71.60 168	70.01 161	77.16 266	45.53 235	88.63 275	51.79 250	73.27 183	95.02 54

XXY-MVS		77.94 153	76.44 152	82.43 149	82.60 221	64.44 156	92.01 110	91.83 123	73.59 123	70.00 162	85.82 172	54.43 159	94.76 138	69.63 143	68.02 217	88.10 180

CR-MVSNet		73.79 209	70.82 218	82.70 139	83.15 217	67.96 62	70.25 309	84.00 284	73.67 122	69.97 163	72.41 288	57.82 109	89.48 271	52.99 248	73.13 184	90.64 152

RPMNet		69.58 245	65.21 255	82.70 139	83.15 217	67.96 62	70.25 309	86.15 267	46.83 312	69.97 163	65.10 313	56.48 129	89.48 271	35.79 309	73.13 184	90.64 152

UniMVSNet (Re)		77.58 156	76.78 148	79.98 207	84.11 206	60.80 218	91.76 129	93.17 74	76.56 77	69.93 165	84.78 182	63.32 66	92.36 226	64.89 184	62.51 254	86.78 204

PCF-MVS	73.15 9	79.29 127	77.63 133	84.29 112	86.06 181	65.96 127	87.03 231	91.10 149	69.86 195	69.79 166	90.64 114	57.54 112	96.59 86	64.37 190	82.29 123	90.32 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1neww		77.39 159	75.71 162	82.44 146	80.69 239	66.83 91	91.94 118	90.18 179	74.19 107	69.60 167	82.51 200	54.99 149	94.44 150	71.68 125	65.60 229	86.05 217

v7new		77.39 159	75.71 162	82.44 146	80.69 239	66.83 91	91.94 118	90.18 179	74.19 107	69.60 167	82.51 200	54.99 149	94.44 150	71.68 125	65.60 229	86.05 217

v6		77.39 159	75.71 162	82.44 146	80.67 241	66.82 93	91.94 118	90.18 179	74.19 107	69.60 167	82.50 203	55.00 148	94.44 150	71.68 125	65.60 229	86.05 217

v1141		77.28 164	75.57 166	82.42 152	80.63 245	66.73 101	91.96 114	90.42 167	74.41 100	69.46 170	82.12 210	55.09 145	94.40 155	70.99 132	65.05 236	86.12 214

divwei89l23v2f112		77.28 164	75.57 166	82.42 152	80.62 246	66.72 103	91.96 114	90.42 167	74.41 100	69.46 170	82.12 210	55.11 144	94.40 155	71.00 130	65.04 237	86.12 214

v1		77.29 163	75.57 166	82.42 152	80.61 249	66.73 101	91.96 114	90.42 167	74.41 100	69.46 170	82.12 210	55.14 143	94.40 155	71.00 130	65.04 237	86.13 213

v2v482		77.42 158	75.65 165	82.73 138	80.38 251	67.13 82	91.85 124	90.23 176	75.09 94	69.37 173	83.39 194	53.79 167	94.44 150	71.77 123	65.00 240	86.63 208

PatchT		69.11 249	65.37 254	80.32 200	82.07 225	63.68 177	67.96 318	87.62 245	50.86 302	69.37 173	65.18 312	57.09 114	88.53 279	41.59 288	66.60 225	88.74 169

v7		76.83 175	75.01 175	82.29 158	80.35 252	66.70 105	91.68 133	89.97 188	73.47 127	69.22 175	82.22 207	52.52 180	94.43 154	69.73 142	65.96 228	85.74 228

Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 241	88.46 146	52.29 286	90.41 172	89.12 216	74.24 106	69.13 176	91.91 101	65.77 43	90.09 262	59.00 226	88.09 82	92.33 129

BH-w/o		80.49 105	79.30 111	84.05 116	90.83 106	64.36 161	93.60 60	89.42 205	74.35 105	69.09 177	90.15 122	55.23 140	95.61 116	64.61 186	86.43 97	92.17 134

v1144		76.73 177	74.88 176	82.27 159	80.23 259	66.60 109	91.68 133	90.21 178	73.69 120	69.06 178	81.89 215	52.73 179	94.40 155	69.21 148	65.23 233	85.80 224

Baseline_NR-MVSNet		73.99 207	72.83 200	77.48 250	80.78 235	59.29 247	91.79 126	84.55 279	68.85 204	68.99 179	80.70 232	56.16 130	92.04 233	62.67 206	60.98 267	81.11 282

FIs		79.47 125	79.41 108	79.67 214	85.95 183	59.40 244	91.68 133	93.94 42	78.06 56	68.96 180	88.28 139	66.61 34	91.77 237	66.20 173	74.99 175	87.82 181

UniMVSNet_NR-MVSNet		78.15 150	77.55 134	79.98 207	84.46 200	60.26 230	92.25 99	93.20 71	77.50 64	68.88 181	86.61 163	66.10 38	92.13 230	66.38 170	62.55 252	87.54 183

DU-MVS		76.86 173	75.84 159	79.91 209	82.96 219	60.26 230	91.26 149	91.54 132	76.46 78	68.88 181	86.35 165	56.16 130	92.13 230	66.38 170	62.55 252	87.35 194

XVG-OURS-SEG-HR		74.70 202	73.08 198	79.57 217	78.25 281	57.33 264	80.49 279	87.32 249	63.22 250	68.76 183	90.12 125	44.89 241	91.59 240	70.55 138	74.09 179	89.79 159

XVG-OURS		74.25 205	72.46 206	79.63 215	78.45 280	57.59 262	80.33 281	87.39 246	63.86 246	68.76 183	89.62 129	40.50 257	91.72 238	69.00 149	74.25 177	89.58 162

V42		76.46 179	74.55 180	82.19 164	79.14 273	67.82 64	90.26 176	89.42 205	73.75 119	68.63 185	81.89 215	51.31 191	94.09 173	71.69 124	64.84 241	84.66 240

PS-MVSNAJss		77.26 166	76.31 153	80.13 204	80.64 244	59.16 248	90.63 169	91.06 150	72.80 136	68.58 186	84.57 185	53.55 169	93.96 182	72.97 111	71.96 193	87.27 197

view600		76.93 169	75.50 169	81.23 183	91.44 91	62.00 204	89.94 182	96.56 1	70.68 182	68.54 187	87.31 155	60.79 81	94.19 166	38.90 298	75.31 171	87.48 185

view800		76.93 169	75.50 169	81.23 183	91.44 91	62.00 204	89.94 182	96.56 1	70.68 182	68.54 187	87.31 155	60.79 81	94.19 166	38.90 298	75.31 171	87.48 185

conf0.05thres1000		76.93 169	75.50 169	81.23 183	91.44 91	62.00 204	89.94 182	96.56 1	70.68 182	68.54 187	87.31 155	60.79 81	94.19 166	38.90 298	75.31 171	87.48 185

tfpn		76.93 169	75.50 169	81.23 183	91.44 91	62.00 204	89.94 182	96.56 1	70.68 182	68.54 187	87.31 155	60.79 81	94.19 166	38.90 298	75.31 171	87.48 185

v1192		75.98 186	73.92 189	82.15 165	79.73 264	66.24 123	91.22 151	89.75 193	72.67 137	68.49 191	81.42 221	49.86 201	94.27 163	67.08 163	65.02 239	85.95 221

tpm cat1		75.30 194	72.21 208	84.58 106	88.52 143	67.77 65	78.16 298	88.02 240	61.88 261	68.45 192	76.37 269	60.65 85	94.03 179	53.77 244	74.11 178	91.93 136

v144192		76.05 184	74.03 187	82.12 166	79.50 268	66.55 113	91.39 141	89.71 199	72.30 144	68.17 193	81.33 223	51.75 187	94.03 179	67.94 155	64.19 246	85.77 225

v1921920		75.63 191	73.49 194	82.06 170	79.38 269	66.35 119	91.07 158	89.48 202	71.98 154	67.99 194	81.22 226	49.16 209	93.90 185	66.56 168	64.56 245	85.92 223

Effi-MVS+-dtu		76.14 182	75.28 173	78.72 234	83.22 215	55.17 275	89.87 186	87.78 243	75.42 87	67.98 195	81.43 220	45.08 238	92.52 220	75.08 102	71.63 194	88.48 173

114514_t		79.17 129	77.67 131	83.68 124	95.32 14	65.53 136	92.85 82	91.60 131	63.49 247	67.92 196	90.63 116	46.65 228	95.72 114	67.01 164	83.54 117	89.79 159

3Dnovator	73.91 6	82.69 78	80.82 88	88.31 14	89.57 122	71.26 13	92.60 91	94.39 39	78.84 48	67.89 197	92.48 93	48.42 213	98.52 15	68.80 152	94.40 19	95.15 46

WR-MVS		76.76 176	75.74 161	79.82 212	84.60 196	62.27 202	92.60 91	92.51 98	76.06 80	67.87 198	85.34 175	56.76 121	90.24 255	62.20 209	63.69 251	86.94 202

dp		75.01 198	72.09 209	83.76 119	89.28 127	66.22 124	79.96 288	89.75 193	71.16 175	67.80 199	77.19 264	51.81 186	92.54 219	50.39 254	71.44 198	92.51 127

TranMVSNet+NR-MVSNet		75.86 187	74.52 181	79.89 210	82.44 222	60.64 225	91.37 144	91.37 141	76.63 75	67.65 200	86.21 168	52.37 183	91.55 241	61.84 211	60.81 268	87.48 185

1314		80.70 101	78.95 116	85.94 66	87.77 160	67.56 71	87.91 218	92.55 97	72.17 151	67.44 201	93.09 78	50.27 197	97.04 64	71.68 125	87.64 85	93.23 108

3Dnovator+	73.60 7	82.10 87	80.60 92	86.60 44	90.89 104	66.80 99	95.20 23	93.44 61	74.05 110	67.42 202	92.49 92	49.46 204	97.65 37	70.80 135	91.68 54	95.33 35

v1240		75.21 196	72.98 199	81.88 172	79.20 271	66.00 126	90.75 164	89.11 217	71.63 167	67.41 203	81.22 226	47.36 223	93.87 186	65.46 181	64.72 243	85.77 225

QAPM		79.95 116	77.39 139	87.64 20	89.63 121	71.41 12	93.30 68	93.70 50	65.34 237	67.39 204	91.75 105	47.83 219	98.96 5	57.71 231	89.81 72	92.54 126

v148		76.19 181	74.47 182	81.36 180	80.05 263	64.44 156	91.75 131	90.23 176	73.68 121	67.13 205	80.84 231	55.92 136	93.86 188	68.95 150	61.73 261	85.76 227

GBi-Net		75.65 189	73.83 190	81.10 190	88.85 136	65.11 143	90.01 178	90.32 170	70.84 179	67.04 206	80.25 240	48.03 215	91.54 242	59.80 222	69.34 206	86.64 205

test1		75.65 189	73.83 190	81.10 190	88.85 136	65.11 143	90.01 178	90.32 170	70.84 179	67.04 206	80.25 240	48.03 215	91.54 242	59.80 222	69.34 206	86.64 205

FMVSNet3		77.73 155	76.04 156	82.80 136	91.20 100	68.99 42	91.87 122	91.99 116	73.35 128	67.04 206	83.19 196	56.62 126	92.14 229	59.80 222	69.34 206	87.28 196

BH-untuned		78.68 140	77.08 142	83.48 129	89.84 118	63.74 173	92.70 86	88.59 230	71.57 169	66.83 209	88.65 134	51.75 187	95.39 123	59.03 225	84.77 106	91.32 145

FC-MVSNet-test		77.99 152	78.08 126	77.70 246	84.89 194	55.51 273	90.27 175	93.75 49	76.87 70	66.80 210	87.59 151	65.71 44	90.23 256	62.89 203	73.94 180	87.37 192

FMVSNet2		76.07 183	74.01 188	82.26 162	88.85 136	67.66 68	91.33 146	91.61 130	70.84 179	65.98 211	82.25 206	48.03 215	92.00 234	58.46 228	68.73 212	87.10 198

ACMM	69.62 13	74.34 203	72.73 201	79.17 230	84.25 205	57.87 257	90.36 173	89.93 189	63.17 251	65.64 212	86.04 171	37.79 272	94.10 172	65.89 176	71.52 196	85.55 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+-dtu		75.04 197	73.37 196	80.07 205	80.86 234	59.52 243	91.20 153	85.38 275	71.90 155	65.20 213	84.84 181	41.46 254	92.97 203	66.50 169	72.96 186	87.73 182

IterMVS-LS		76.49 178	75.18 174	80.43 199	84.49 199	62.74 195	90.64 167	88.80 225	72.40 142	65.16 214	81.72 218	60.98 80	92.27 228	67.74 157	64.65 244	86.29 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LPG-MVS_test		75.82 188	74.58 179	79.56 218	84.31 203	59.37 245	90.44 170	89.73 196	69.49 197	64.86 215	88.42 136	38.65 262	94.30 161	72.56 116	72.76 187	85.01 237

LGP-MVS_train				79.56 218	84.31 203	59.37 245		89.73 196	69.49 197	64.86 215	88.42 136	38.65 262	94.30 161	72.56 116	72.76 187	85.01 237

MIMVSNet		71.64 225	68.44 234	81.23 183	81.97 226	64.44 156	73.05 305	88.80 225	69.67 196	64.59 217	74.79 277	32.79 291	87.82 284	53.99 242	76.35 166	91.42 141

OpenMVS	70.45 11	78.54 144	75.92 158	86.41 55	85.93 186	71.68 11	92.74 84	92.51 98	66.49 228	64.56 218	91.96 100	43.88 244	98.10 25	54.61 239	90.65 67	89.44 164

ADS-MVSNet2		66.90 262	63.44 265	77.26 255	88.06 154	60.70 223	68.01 316	75.56 314	57.57 280	64.48 219	69.87 303	38.68 260	84.10 297	40.87 290	67.89 218	86.97 199

ADS-MVSNet		68.54 252	64.38 262	81.03 193	88.06 154	66.90 90	68.01 316	84.02 283	57.57 280	64.48 219	69.87 303	38.68 260	89.21 274	40.87 290	67.89 218	86.97 199

PLC	68.80 14	75.23 195	73.68 192	79.86 211	92.93 53	58.68 253	90.64 167	88.30 235	60.90 265	64.43 221	90.53 117	42.38 249	94.57 144	56.52 233	76.54 165	86.33 209
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpmvs		72.88 215	69.76 223	82.22 163	90.98 101	67.05 84	78.22 297	88.30 235	63.10 252	64.35 222	74.98 276	55.09 145	94.27 163	43.25 279	69.57 205	85.34 235

test_djsdf		73.76 210	72.56 204	77.39 252	77.00 289	53.93 280	89.07 200	90.69 159	65.80 232	63.92 223	82.03 213	43.14 247	92.67 216	72.83 113	68.53 213	85.57 230

JIA-IIPM		66.06 266	62.45 272	76.88 257	81.42 231	54.45 279	57.49 330	88.67 227	49.36 305	63.86 224	46.86 326	56.06 133	90.25 253	49.53 258	68.83 210	85.95 221

CNLPA		74.31 204	72.30 207	80.32 200	91.49 90	61.66 210	90.85 160	80.72 301	56.67 288	63.85 225	90.64 114	46.75 226	90.84 249	53.79 243	75.99 168	88.47 174

PatchMatch-RL		72.06 220	69.98 219	78.28 238	89.51 125	55.70 272	83.49 255	83.39 290	61.24 264	63.72 226	82.76 198	34.77 287	93.03 202	53.37 247	77.59 153	86.12 214

FMVSNet1		72.71 217	69.91 221	81.10 190	83.60 212	65.11 143	90.01 178	90.32 170	63.92 245	63.56 227	80.25 240	36.35 282	91.54 242	54.46 240	66.75 224	86.64 205

pmmvs4		73.92 208	71.81 211	80.25 202	79.17 272	65.24 139	87.43 226	87.26 251	67.64 221	63.46 228	83.91 189	48.96 211	91.53 245	62.94 202	65.49 232	83.96 244

pmmvs5		73.35 211	71.52 213	78.86 233	78.64 279	60.61 226	91.08 157	86.90 252	67.69 218	63.32 229	83.64 190	44.33 243	90.53 250	62.04 210	66.02 227	85.46 232

v8		75.35 193	73.26 197	81.61 178	80.67 241	66.82 93	89.54 191	89.27 209	71.65 166	63.30 230	80.30 239	54.99 149	94.06 175	67.33 162	62.33 255	83.94 245

v10		74.77 201	72.54 205	81.46 179	80.33 256	66.71 104	89.15 198	89.08 218	70.94 177	63.08 231	79.86 244	52.52 180	94.04 178	65.70 179	62.17 256	83.64 247

ACMP	71.68 10	75.58 192	74.23 185	79.62 216	84.97 193	59.64 240	90.80 162	89.07 219	70.39 190	62.95 232	87.30 159	38.28 265	93.87 186	72.89 112	71.45 197	85.36 234
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pm-mvs1		72.89 214	71.09 216	78.26 240	79.10 275	57.62 261	90.80 162	89.30 208	67.66 219	62.91 233	81.78 217	49.11 210	92.95 204	60.29 219	58.89 277	84.22 243

jajsoiax		73.05 212	71.51 214	77.67 247	77.46 286	54.83 276	88.81 203	90.04 186	69.13 201	62.85 234	83.51 192	31.16 300	92.75 213	70.83 134	69.80 202	85.43 233

mvs_tets		72.71 217	71.11 215	77.52 248	77.41 287	54.52 278	88.45 209	89.76 192	68.76 206	62.70 235	83.26 195	29.49 304	92.71 214	70.51 139	69.62 204	85.34 235

MS-PatchMatch		77.90 154	76.50 151	82.12 166	85.99 182	69.95 27	91.75 131	92.70 89	73.97 113	62.58 236	84.44 186	41.11 255	95.78 107	63.76 192	92.17 49	80.62 288

test0.0.03 1		72.76 216	72.71 202	72.88 281	80.25 258	47.99 304	91.22 151	89.45 203	71.51 172	62.51 237	87.66 150	53.83 165	85.06 294	50.16 255	67.84 220	85.58 229

anonymousdsp		71.14 230	69.37 225	76.45 259	72.95 301	54.71 277	84.19 250	88.88 224	61.92 260	62.15 238	79.77 245	38.14 267	91.44 247	68.90 151	67.45 221	83.21 256

MVP-Stereo		77.12 168	76.23 154	79.79 213	81.72 227	66.34 120	89.29 194	90.88 154	70.56 187	62.01 239	82.88 197	49.34 205	94.13 171	65.55 180	93.80 26	78.88 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
NR-MVSNet		76.05 184	74.59 178	80.44 198	82.96 219	62.18 203	90.83 161	91.73 125	77.12 67	60.96 240	86.35 165	59.28 98	91.80 236	60.74 216	61.34 265	87.35 194

tfpnnormal		70.10 239	67.36 242	78.32 237	83.45 214	60.97 216	88.85 202	92.77 87	64.85 240	60.83 241	78.53 254	43.52 246	93.48 195	31.73 319	61.70 262	80.52 289

IterMVS		72.65 219	70.83 217	78.09 244	82.17 223	62.96 188	87.64 224	86.28 264	71.56 170	60.44 242	78.85 253	45.42 237	86.66 289	63.30 196	61.83 258	84.65 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS_H		70.59 236	69.94 220	72.53 283	81.03 232	51.43 290	87.35 228	92.03 115	67.38 223	60.23 243	80.70 232	55.84 137	83.45 305	46.33 269	58.58 278	82.72 263

TransMVSNet (Re)		70.07 240	67.66 241	77.31 254	80.62 246	59.13 249	91.78 128	84.94 277	65.97 231	60.08 244	80.44 236	50.78 192	91.87 235	48.84 260	45.46 314	80.94 284

CP-MVSNet		70.50 238	69.91 221	72.26 286	80.71 238	51.00 293	87.23 229	90.30 174	67.84 217	59.64 245	82.69 199	50.23 198	82.30 312	51.28 251	59.28 272	83.46 252

semantic-postprocess				76.32 260	81.48 228	60.67 224		85.99 270	66.17 230	59.50 246	78.88 252	45.51 236	83.65 303	62.58 207	61.93 257	84.63 242

Patchmtry		67.53 259	63.93 263	78.34 236	82.12 224	64.38 159	68.72 313	84.00 284	48.23 309	59.24 247	72.41 288	57.82 109	89.27 273	46.10 270	56.68 284	81.36 276

PS-CasMVS		69.86 242	69.13 228	72.07 289	80.35 252	50.57 295	87.02 232	89.75 193	67.27 224	59.19 248	82.28 205	46.58 229	82.24 313	50.69 253	59.02 275	83.39 254

PEN-MVS		69.46 247	68.56 232	72.17 288	79.27 270	49.71 299	86.90 235	89.24 211	67.24 225	59.08 249	82.51 200	47.23 224	83.54 304	48.42 262	57.12 280	83.25 255

DI_MVS_plusplus_test		79.78 120	77.50 136	86.62 43	80.90 233	69.46 35	90.69 165	91.97 118	77.00 68	59.07 250	82.34 204	46.82 225	95.88 104	82.14 57	86.59 94	94.53 68

RPSCF		64.24 274	61.98 275	71.01 293	76.10 293	45.00 310	75.83 302	75.94 311	46.94 311	58.96 251	84.59 184	31.40 299	82.00 314	47.76 265	60.33 271	86.04 220

XVG-ACMP-BASELINE		68.04 255	65.53 252	75.56 264	74.06 300	52.37 285	78.43 294	85.88 272	62.03 258	58.91 252	81.21 228	20.38 321	91.15 248	60.69 217	68.18 215	83.16 257

v7n		71.31 227	68.65 230	79.28 225	76.40 291	60.77 220	86.71 238	89.45 203	64.17 244	58.77 253	78.24 255	44.59 242	93.54 193	57.76 230	61.75 260	83.52 250

test_normal		79.66 121	77.36 141	86.54 47	80.72 237	69.21 38	90.68 166	92.16 113	76.99 69	58.63 254	82.03 213	46.70 227	95.86 105	81.74 61	86.63 93	94.56 63

DTE-MVSNet		68.46 253	67.33 243	71.87 292	77.94 284	49.00 302	86.16 243	88.58 231	66.36 229	58.19 255	82.21 208	46.36 230	83.87 302	44.97 277	55.17 287	82.73 262

Anonymous20231206		67.53 259	65.78 249	72.79 282	74.95 297	47.59 306	88.23 211	87.32 249	61.75 263	58.07 256	77.29 262	37.79 272	87.29 287	42.91 281	63.71 250	83.48 251

PVSNet_0	68.08 15	71.81 222	68.32 236	82.27 159	84.68 195	62.31 201	88.68 205	90.31 173	75.84 82	57.93 257	80.65 234	37.85 271	94.19 166	69.94 141	29.05 330	90.31 155

v52		69.80 243	67.01 246	78.15 242	71.84 305	60.10 234	82.02 267	87.39 246	64.48 241	57.80 258	75.97 272	41.47 253	92.90 209	63.00 200	59.13 274	81.45 273

V4		69.80 243	67.02 245	78.15 242	71.86 304	60.10 234	82.02 267	87.39 246	64.48 241	57.78 259	75.98 271	41.49 252	92.90 209	63.00 200	59.16 273	81.44 274

DP-MVS		69.90 241	66.48 247	80.14 203	95.36 13	62.93 189	89.56 189	76.11 309	50.27 303	57.69 260	85.23 177	39.68 259	95.73 110	33.35 313	71.05 200	81.78 269

pmmvs6		67.57 258	64.76 257	76.00 263	72.82 303	53.37 282	88.71 204	86.78 253	53.19 295	57.58 261	78.03 257	35.33 285	92.41 223	55.56 237	54.88 289	82.21 266

F-COLMAP		70.66 235	68.44 234	77.32 253	86.37 177	55.91 271	88.00 214	86.32 263	56.94 286	57.28 262	88.07 145	33.58 289	92.49 221	51.02 252	68.37 214	83.55 248

Patchmatch-RL test		68.17 254	64.49 260	79.19 229	71.22 307	53.93 280	70.07 311	71.54 325	69.22 200	56.79 263	62.89 316	56.58 127	88.61 276	69.53 145	52.61 293	95.03 53

v748		70.55 237	67.97 240	78.27 239	75.75 296	58.78 251	86.29 242	89.25 210	65.12 239	56.66 264	77.17 265	45.05 240	92.95 204	58.13 229	58.33 279	83.10 259

LS3D		69.17 248	66.40 248	77.50 249	91.92 76	56.12 270	85.12 245	80.37 302	46.96 310	56.50 265	87.51 153	37.25 275	93.71 190	32.52 318	79.40 137	82.68 264

v18		71.94 221	69.43 224	79.50 220	80.74 236	66.82 93	88.16 212	86.66 254	68.95 203	55.55 266	72.66 283	55.03 147	90.15 258	64.78 185	52.30 294	81.54 270

v16		71.81 222	69.26 226	79.47 221	80.66 243	66.81 97	87.93 216	86.63 256	68.70 208	55.35 267	72.51 284	54.75 153	90.12 260	64.51 187	52.28 295	81.47 271

v17		71.77 224	69.20 227	79.46 222	80.62 246	66.81 97	87.93 216	86.63 256	68.71 207	55.25 268	72.49 285	54.72 154	90.11 261	64.50 188	51.97 296	81.47 271

LTVRE_ROB	59.60 19	66.27 265	63.54 264	74.45 270	84.00 208	51.55 289	67.08 319	83.53 287	58.78 277	54.94 269	80.31 238	34.54 288	93.23 199	40.64 292	68.03 216	78.58 304
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
v15		71.40 226	68.75 229	79.35 223	80.39 250	66.70 105	87.57 225	86.64 255	68.66 209	54.68 270	72.00 292	54.50 156	89.98 263	63.69 193	50.66 301	81.38 275

MSDG		69.54 246	65.73 250	80.96 194	85.11 192	63.71 175	84.19 250	83.28 291	56.95 285	54.50 271	84.03 187	31.50 298	96.03 100	42.87 283	69.13 209	83.14 258

V14		71.29 228	68.61 231	79.31 224	80.34 254	66.65 107	87.39 227	86.61 258	68.41 213	54.49 272	71.91 293	54.25 161	89.96 264	63.50 194	50.62 302	81.33 277

v11		71.05 232	68.32 236	79.23 227	80.34 254	66.57 112	87.01 233	86.55 260	68.11 215	54.40 273	71.66 297	52.94 177	89.91 266	62.71 205	51.12 299	81.21 279

EU-MVSNet		64.01 276	63.01 268	67.02 302	74.40 299	38.86 325	83.27 259	86.19 266	45.11 316	54.27 274	81.15 229	36.91 281	80.01 318	48.79 261	57.02 281	82.19 267

V9		71.16 229	68.46 233	79.27 226	80.26 257	66.60 109	87.21 230	86.56 259	68.17 214	54.26 275	71.81 295	54.00 163	89.93 265	63.28 197	50.57 303	81.27 278

testgi		64.48 273	62.87 270	69.31 296	71.24 306	40.62 320	85.49 244	79.92 303	65.36 236	54.18 276	83.49 193	23.74 316	84.55 295	41.60 287	60.79 269	82.77 261

ITE_SJBPF				70.43 294	74.44 298	47.06 308		77.32 307	60.16 270	54.04 277	83.53 191	23.30 317	84.01 300	43.07 280	61.58 264	80.21 293

v12		71.02 233	68.29 238	79.22 228	80.18 260	66.53 114	87.01 233	86.54 261	67.90 216	54.00 278	71.70 296	53.66 168	89.91 266	63.09 199	50.51 304	81.21 279

OpenMVS_ROB	61.12 18	66.39 264	62.92 269	76.80 258	76.51 290	57.77 258	89.22 195	83.41 289	55.48 292	53.86 279	77.84 258	26.28 313	93.95 183	34.90 311	68.76 211	78.68 303

v13		70.90 234	68.15 239	79.15 232	80.08 261	66.45 116	86.83 237	86.50 262	67.62 222	53.78 280	71.61 298	53.51 172	89.87 268	62.89 203	50.50 305	81.14 281

FMVSNet5		68.04 255	65.66 251	75.18 266	84.43 201	57.89 256	83.54 254	86.26 265	61.83 262	53.64 281	73.30 280	37.15 278	85.08 293	48.99 259	61.77 259	82.56 265

test2356		64.16 275	63.28 266	66.81 303	69.37 315	39.86 323	87.76 221	86.02 269	59.83 272	53.54 282	73.23 281	34.94 286	80.67 317	39.66 294	65.20 234	79.89 294

Test4		76.45 180	73.45 195	85.45 82	76.07 295	67.61 70	88.38 210	90.83 155	76.71 74	53.06 283	79.65 248	31.61 297	94.35 159	78.47 81	86.22 98	94.40 72

ACMH+	65.35 16	67.65 257	64.55 258	76.96 256	84.59 197	57.10 266	88.08 213	80.79 300	58.59 279	53.00 284	81.09 230	26.63 312	92.95 204	46.51 268	61.69 263	80.82 285

test_0402		64.54 272	61.09 277	74.92 267	84.10 207	60.75 221	87.95 215	79.71 304	52.03 298	52.41 285	77.20 263	32.21 295	91.64 239	23.14 329	61.03 266	72.36 316

LCM-MVSNet-Re		72.93 213	71.84 210	76.18 262	88.49 144	48.02 303	80.07 286	70.17 326	73.96 114	52.25 286	80.09 243	49.98 199	88.24 281	67.35 160	84.23 115	92.28 132

test20.03		63.83 277	62.65 271	67.38 301	70.58 311	39.94 321	86.57 240	84.17 281	63.29 249	51.86 287	77.30 261	37.09 279	82.47 310	38.87 302	54.13 291	79.73 296

OurMVSNet-221017-0		64.68 271	62.17 274	72.21 287	76.08 294	47.35 307	80.67 278	81.02 299	56.19 289	51.60 288	79.66 247	27.05 311	88.56 278	53.60 245	53.63 292	80.71 287

ACMH	63.93 17	68.62 250	64.81 256	80.03 206	85.22 190	63.25 182	87.72 222	84.66 278	60.83 266	51.57 289	79.43 250	27.29 310	94.96 132	41.76 286	64.84 241	81.88 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DSMNet-mixed		56.78 292	54.44 294	63.79 308	63.21 321	29.44 334	64.43 322	64.10 333	42.12 324	51.32 290	71.60 299	31.76 296	75.04 327	36.23 306	65.20 234	86.87 203

pmmvs-eth3d		65.53 269	62.32 273	75.19 265	69.39 314	59.59 241	82.80 264	83.43 288	62.52 256	51.30 291	72.49 285	32.86 290	87.16 288	55.32 238	50.73 300	78.83 302

PM-MVS		59.40 288	56.59 289	67.84 298	63.63 320	41.86 317	76.76 300	63.22 334	59.01 276	51.07 292	72.27 291	11.72 331	83.25 307	61.34 213	50.28 306	78.39 305

Patchmatch-test		65.86 267	60.94 278	80.62 197	83.75 209	58.83 250	58.91 329	75.26 316	44.50 319	50.95 293	77.09 267	58.81 103	87.90 283	35.13 310	64.03 247	95.12 48

SixPastTwentyTwo		64.92 270	61.78 276	74.34 272	78.74 277	49.76 298	83.42 258	79.51 305	62.86 253	50.27 294	77.35 260	30.92 302	90.49 251	45.89 271	47.06 311	82.78 260

EG-PatchMatch MVS		68.55 251	65.41 253	77.96 245	78.69 278	62.93 189	89.86 187	89.17 213	60.55 267	50.27 294	77.73 259	22.60 318	94.06 175	47.18 267	72.65 189	76.88 309

ambc				69.61 295	61.38 326	41.35 318	49.07 333	85.86 273		50.18 296	66.40 308	10.16 334	88.14 282	45.73 272	44.20 315	79.32 300

UnsupCasMVSNet_eth		65.79 268	63.10 267	73.88 273	70.71 309	50.29 297	81.09 275	89.88 190	72.58 139	49.25 297	74.77 278	32.57 293	87.43 286	55.96 236	41.04 320	83.90 246

COLMAP_ROB	57.96 20	62.98 281	59.65 281	72.98 280	81.44 230	53.00 284	83.75 252	75.53 315	48.34 308	48.81 298	81.40 222	24.14 314	90.30 252	32.95 315	60.52 270	75.65 312
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
USDC		67.43 261	64.51 259	76.19 261	77.94 284	55.29 274	78.38 295	85.00 276	73.17 129	48.36 299	80.37 237	21.23 320	92.48 222	52.15 249	64.02 248	80.81 286

K. test v3		63.09 280	59.61 282	73.53 276	76.26 292	49.38 301	83.27 259	77.15 308	64.35 243	47.77 300	72.32 290	28.73 305	87.79 285	49.93 257	36.69 325	83.41 253

testus		59.36 289	57.51 286	64.90 306	66.72 317	37.56 326	84.98 246	81.09 298	57.46 283	47.72 301	72.76 282	11.43 333	78.78 324	36.56 304	58.91 276	78.36 306

testpf		57.17 291	56.93 288	57.88 313	79.13 274	42.40 314	34.23 336	85.97 271	52.64 296	47.66 302	66.50 307	36.33 283	79.65 320	53.60 245	56.31 285	51.60 329

UnsupCasMVSNet_bld		61.60 283	57.71 284	73.29 278	68.73 316	51.64 288	78.61 293	89.05 220	57.20 284	46.11 303	61.96 318	28.70 306	88.60 277	50.08 256	38.90 323	79.63 297

AllTest		61.66 282	58.06 283	72.46 284	79.57 265	51.42 291	80.17 284	68.61 328	51.25 300	45.88 304	81.23 224	19.86 322	86.58 290	38.98 296	57.01 282	79.39 298

TestCases				72.46 284	79.57 265	51.42 291		68.61 328	51.25 300	45.88 304	81.23 224	19.86 322	86.58 290	38.98 296	57.01 282	79.39 298

lessismore_v0				73.72 275	72.93 302	47.83 305		61.72 336		45.86 306	73.76 279	28.63 307	89.81 269	47.75 266	31.37 329	83.53 249

N_pmnet		50.55 300	49.11 302	54.88 317	77.17 288	4.02 346	84.36 249	2.00 347	48.59 306	45.86 306	68.82 305	32.22 294	82.80 309	31.58 320	51.38 298	77.81 307

MVS-HIRNet		60.25 286	55.55 292	74.35 271	84.37 202	56.57 268	71.64 307	74.11 318	34.44 328	45.54 308	42.24 330	31.11 301	89.81 269	40.36 293	76.10 167	76.67 310

testing_2		71.09 231	67.32 244	82.40 155	69.82 312	66.52 115	83.64 253	90.77 157	72.21 148	45.12 309	71.07 302	27.60 309	93.74 189	75.71 97	69.96 201	86.95 201

CMPMVS	48.56 21	66.77 263	64.41 261	73.84 274	70.65 310	50.31 296	77.79 299	85.73 274	45.54 315	44.76 310	82.14 209	35.40 284	90.14 259	63.18 198	74.54 176	81.07 283
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		60.16 287	57.33 287	68.67 297	69.71 313	44.13 312	78.92 292	84.21 280	55.05 293	44.63 311	71.85 294	23.91 315	81.54 316	32.63 317	55.03 288	80.35 290

LF4IMVS		54.01 298	52.12 296	59.69 311	62.41 323	39.91 322	68.59 314	68.28 330	42.96 322	44.55 312	75.18 275	14.09 330	68.39 331	41.36 289	51.68 297	70.78 319

pmmvs3		55.51 296	51.50 299	67.53 300	57.90 329	50.93 294	80.37 280	73.66 319	40.63 325	44.15 313	64.75 314	16.30 325	78.97 323	44.77 278	40.98 321	72.69 315

LP		56.71 293	51.64 297	71.91 291	80.08 261	60.33 229	61.72 324	75.61 313	43.87 321	43.76 314	60.30 320	30.46 303	84.05 298	22.94 330	46.06 313	71.34 318

new-patchmatchnet		59.30 290	56.48 290	67.79 299	65.86 318	44.19 311	82.47 265	81.77 295	59.94 271	43.65 315	66.20 309	27.67 308	81.68 315	39.34 295	41.40 319	77.50 308

TDRefinement		55.28 297	51.58 298	66.39 304	59.53 328	46.15 309	76.23 301	72.80 320	44.60 318	42.49 316	76.28 270	15.29 327	82.39 311	33.20 314	43.75 316	70.62 320

1111		56.66 294	54.98 293	61.69 309	61.99 324	31.38 330	79.81 289	83.17 292	45.66 313	41.94 317	65.44 310	41.50 250	79.56 321	27.64 323	47.68 310	74.14 314

.test1245		46.52 304	49.68 300	37.02 326	61.99 324	31.38 330	79.81 289	83.17 292	45.66 313	41.94 317	65.44 310	41.50 250	79.56 321	27.64 323	0.01 342	0.13 341

Anonymous20231211		53.57 299	49.43 301	66.00 305	65.01 319	42.08 315	80.95 277	72.60 321	38.46 326	41.65 319	64.48 315	15.72 326	84.23 296	25.78 326	40.24 322	71.68 317

TinyColmap		60.32 285	56.42 291	72.00 290	78.78 276	53.18 283	78.36 296	75.64 312	52.30 297	41.59 320	75.82 274	14.76 329	88.35 280	35.84 307	54.71 290	74.46 313

YYNet1		63.76 279	60.14 280	74.62 269	78.06 283	60.19 233	83.46 257	83.99 286	56.18 290	39.25 321	71.56 301	37.18 277	83.34 306	42.90 282	48.70 309	80.32 291

MDA-MVSNet_test_wron		63.78 278	60.16 279	74.64 268	78.15 282	60.41 227	83.49 255	84.03 282	56.17 291	39.17 322	71.59 300	37.22 276	83.24 308	42.87 283	48.73 308	80.26 292

test1235678		55.73 295	52.74 295	64.68 307	60.16 327	35.56 328	81.65 271	81.46 296	51.27 299	38.93 323	62.82 317	17.44 324	78.58 325	30.87 321	50.09 307	79.89 294

new_pmnet		49.31 301	46.44 303	57.93 312	62.84 322	40.74 319	68.47 315	62.96 335	36.48 327	35.09 324	57.81 322	14.97 328	72.18 328	32.86 316	46.44 312	60.88 327

MDA-MVSNet-bldmvs		61.54 284	57.70 285	73.05 279	79.53 267	57.00 267	83.08 262	81.23 297	57.57 280	34.91 325	72.45 287	32.79 291	86.26 292	35.81 308	41.95 318	75.89 311

test12356		47.51 302	44.82 304	55.56 315	52.53 330	21.09 341	71.45 308	76.03 310	44.14 320	30.69 326	58.18 321	9.01 337	76.14 326	26.95 325	34.43 328	69.46 322

testmv		46.98 303	43.53 305	57.35 314	47.75 335	30.41 333	74.99 304	77.69 306	42.84 323	28.03 327	53.36 323	8.18 338	71.18 329	24.36 328	34.55 326	70.46 321

FPMVS		45.64 305	43.10 306	53.23 319	51.42 332	36.46 327	64.97 321	71.91 323	29.13 330	27.53 328	61.55 319	9.83 335	65.01 335	16.00 335	55.58 286	58.22 328

LCM-MVSNet		40.54 307	35.79 308	54.76 318	36.92 340	30.81 332	51.41 331	69.02 327	22.07 332	24.63 329	45.37 328	4.56 343	65.81 333	33.67 312	34.50 327	67.67 323

PMMVS2		37.93 309	33.61 310	50.92 320	46.31 336	24.76 339	60.55 328	50.05 338	28.94 331	20.93 330	47.59 325	4.41 344	65.13 334	25.14 327	18.55 332	62.87 325

no-one		44.13 306	38.39 307	61.34 310	45.91 337	41.94 316	61.67 325	75.07 317	45.05 317	20.07 331	40.68 333	11.58 332	79.82 319	30.18 322	15.30 333	62.26 326

tmp_tt		22.26 318	23.75 317	17.80 331	5.23 345	12.06 345	35.26 335	39.48 342	2.82 341	18.94 332	44.20 329	22.23 319	24.64 343	36.30 305	9.31 339	16.69 336

ANet_high		40.27 308	35.20 309	55.47 316	34.74 341	34.47 329	63.84 323	71.56 324	48.42 307	18.80 333	41.08 331	9.52 336	64.45 336	20.18 332	8.66 340	67.49 324

DeepMVS_CX				34.71 327	51.45 331	24.73 340		28.48 346	31.46 329	17.49 334	52.75 324	5.80 341	42.60 342	18.18 334	19.42 331	36.81 333

Gipuma		34.91 310	31.44 312	45.30 322	70.99 308	39.64 324	19.85 339	72.56 322	20.10 335	16.16 335	21.47 338	5.08 342	71.16 330	13.07 336	43.70 317	25.08 335
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		32.77 311	29.98 313	41.11 324	48.05 333	29.17 335	65.82 320	50.02 339	21.42 333	14.74 336	37.19 334	1.11 347	55.11 338	19.75 333	11.77 335	39.06 331

PMVS	26.43 22	31.84 312	28.16 314	42.89 323	25.87 344	27.58 337	50.92 332	49.78 340	21.37 334	14.17 337	40.81 332	2.01 345	66.62 332	9.61 338	38.88 324	34.49 334
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 316	19.77 320	38.09 325	34.56 342	26.92 338	26.57 337	38.87 343	11.73 339	11.37 338	27.44 335	1.37 346	50.42 339	11.41 337	14.60 334	36.93 332
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 315	24.00 316	26.45 329	43.74 338	18.44 343	60.86 326	39.66 341	15.11 336	9.53 339	22.10 337	6.52 340	46.94 340	8.31 339	10.14 336	13.98 337

EMVS		23.76 317	23.20 318	25.46 330	41.52 339	16.90 344	60.56 327	38.79 344	14.62 337	8.99 340	20.24 341	7.35 339	45.82 341	7.25 340	9.46 338	13.64 338

wuykxyi23d		29.03 314	23.09 319	46.84 321	31.67 343	28.82 336	43.46 334	57.72 337	14.39 338	7.52 341	20.84 339	0.64 348	60.29 337	21.57 331	10.04 337	51.40 330

wuyk23d		11.30 320	10.95 321	12.33 332	48.05 333	19.89 342	25.89 338	1.92 348	3.58 340	3.12 342	1.37 343	0.64 348	15.77 344	6.23 341	7.77 341	1.35 339

testmvs		7.23 322	9.62 323	0.06 334	0.04 346	0.02 348	84.98 246	0.02 349	0.03 342	0.18 343	1.21 344	0.01 351	0.02 345	0.14 342	0.01 342	0.13 341

test123		6.92 323	9.21 324	0.08 333	0.03 347	0.05 347	81.65 271	0.01 350	0.02 343	0.14 344	0.85 345	0.03 350	0.02 345	0.12 343	0.00 344	0.16 340

cdsmvs_eth3d_5k		19.86 319	26.47 315	0.00 335	0.00 348	0.00 349	0.00 340	93.45 60	0.00 344	0.00 345	95.27 30	49.56 203	0.00 347	0.00 344	0.00 344	0.00 343

pcd_1.5k_mvsjas		4.46 324	5.95 325	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	53.55 169	0.00 347	0.00 344	0.00 344	0.00 343

pcd1.5k->3k		31.17 313	31.85 311	29.12 328	81.48 228	0.00 349	0.00 340	91.79 124	0.00 344	0.00 345	0.00 346	41.05 256	0.00 347	0.00 344	72.34 192	87.36 193

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 344	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 344	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 344	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 344	0.00 343

ab-mvs-re		7.91 321	10.55 322	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	94.95 40	0.00 352	0.00 347	0.00 344	0.00 344	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 349	0.00 340	0.00 351	0.00 344	0.00 345	0.00 346	0.00 352	0.00 347	0.00 344	0.00 344	0.00 343

ESAPD								94.66 29

sam_mvs1												57.85 108

sam_mvs												54.91 152

MTGPA								92.23 104

test_post1							78.95 291				20.70 340	53.05 175	91.50 246	60.43 218

test_post											23.01 336	56.49 128	92.67 216

patchmatchnet-post											67.62 306	57.62 111	90.25 253

MTMP								32.52 345

gm-plane-assit					88.42 148	67.04 85			78.62 52		91.83 102		97.37 47	76.57 93

test9_res														89.41 10	94.96 8	95.29 38

agg_prior2														86.41 31	94.75 16	95.33 35

test_prior4						67.18 81	93.92 52

test_prior				86.42 53	94.71 21	67.35 75		93.10 77					96.84 79			95.05 50

新几何2							91.41 140

旧先验1					91.94 74	60.74 222		91.50 135			94.36 55	65.23 46			91.84 51	94.55 64

无先验							92.71 85	92.61 95	62.03 258				97.01 65	66.63 166		93.97 90

原ACMM2							92.01 110

testdata2													96.09 97	61.26 214

segment_acmp												65.94 40

testdata1							89.21 196		77.55 63

plane_prior7					86.94 169	61.51 212

plane_prior6					87.23 165	62.32 200						50.66 193

plane_prior5								91.31 142					95.55 120	76.74 91	78.53 146	88.39 175

plane_prior4											89.14 131

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 166

plane_prior						62.42 198	93.85 55		79.38 37						78.80 144

n2								0.00 351

nn								0.00 351

door-mid								66.01 332

test11								93.01 79

door								66.57 331

HQP5-MVS						63.66 178

BP-MVS														77.63 88

HQP3-MVS								91.70 128							78.90 142

HQP2-MVS												51.63 189

NP-MVS					87.41 164	63.04 187					90.30 120

ACMMP++_ref															71.63 194

ACMMP++															69.72 203

Test By Simon												54.21 162

High-resolution multi-view benchmark