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 2	97.66 2	73.37 7	97.13 1	95.58 12	89.33 1	85.77 24	96.26 7	72.84 10	99.38 1	92.64 4	95.93 4	97.08 4

HSP-MVS		90.38 2	91.89 1	85.84 68	92.83 56	64.03 169	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

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 8	96.80 2	70.86 15	99.06 3	92.64 4	95.71 5	96.12 18

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 50	73.88 6	97.01 2	94.40 38	88.32 2	85.71 25	94.91 45	74.11 8	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
DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 103	93.00 53	58.16 257	96.72 3	94.41 37	86.50 5	90.25 4	97.83 1	75.46 6	98.67 12	92.78 2	95.49 7	97.32 1

CANet		89.61 6	89.99 6	88.46 13	94.39 25	69.71 32	96.53 5	93.78 46	86.89 4	89.68 5	95.78 15	65.94 43	99.10 2	92.99 1	93.91 26	96.58 11

HPM-MVS++		89.37 7	89.95 7	87.64 20	95.10 18	68.23 59	95.24 22	94.49 34	82.43 17	88.90 9	96.35 5	71.89 14	98.63 13	88.76 18	96.40 2	96.06 20

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 19	96.20 8	66.56 38	98.76 11	89.03 16	94.56 19	95.92 26

MVS_0304		88.39 9	88.35 12	88.50 12	93.01 52	70.11 23	95.90 10	92.20 114	86.27 6	88.70 10	95.92 13	56.76 126	99.02 4	92.68 3	93.76 29	96.37 15

PS-MVSNAJ		88.14 10	87.61 17	89.71 4	92.06 73	76.72 1	95.75 11	93.26 72	83.86 11	89.55 6	96.06 11	53.55 174	97.89 30	91.10 6	93.31 36	94.54 67

TSAR-MVS + MP.		88.11 11	88.64 9	86.54 47	91.73 84	68.04 62	90.36 173	93.55 57	82.89 14	91.29 2	92.89 87	72.27 11	96.03 100	87.99 20	94.77 14	95.54 32

TSAR-MVS + GP.		87.96 12	88.37 11	86.70 41	93.51 43	65.32 139	95.15 25	93.84 45	78.17 55	85.93 23	94.80 48	75.80 5	98.21 21	89.38 11	88.78 78	96.59 10

DeepC-MVS_fast	79.48 2	87.95 13	88.00 13	87.79 19	95.86 13	68.32 55	95.74 12	94.11 42	83.82 12	83.49 45	96.19 9	64.53 58	98.44 17	83.42 51	94.88 13	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
xiu_mvs_v2_base		87.92 14	87.38 22	89.55 7	91.41 98	76.43 2	95.74 12	93.12 81	83.53 13	89.55 6	95.95 12	53.45 179	97.68 32	91.07 7	92.62 43	94.54 67

EPNet		87.84 15	88.38 10	86.23 61	93.30 45	66.05 126	95.26 21	94.84 22	87.09 3	88.06 12	94.53 52	66.79 35	97.34 50	83.89 48	91.68 55	95.29 39
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 16	87.77 15	87.63 22	89.24 135	71.18 14	96.57 4	92.90 89	82.70 16	87.13 16	95.27 30	64.99 54	95.80 106	89.34 12	91.80 53	95.93 25

APDe-MVS		87.54 17	87.84 14	86.65 42	96.07 10	66.30 122	94.84 32	93.78 46	69.35 199	88.39 11	96.34 6	67.74 29	97.66 36	90.62 9	93.44 35	96.01 23

SD-MVS		87.49 18	87.49 19	87.50 24	93.60 40	68.82 46	93.90 53	92.63 99	76.86 71	87.90 13	95.76 16	66.17 39	97.63 38	89.06 15	91.48 59	96.05 21

test_prior3		87.38 19	87.70 16	86.42 53	94.71 22	67.35 76	95.10 27	93.10 82	75.40 89	85.25 31	95.61 21	67.94 25	96.84 79	87.47 23	94.77 14	95.05 51

alignmvs		87.28 20	86.97 25	88.24 15	91.30 99	71.14 16	95.61 16	93.56 56	79.30 38	87.07 18	95.25 32	68.43 20	96.93 77	87.87 21	84.33 113	96.65 8

Regformer-1		87.24 21	87.60 18	86.15 62	95.14 16	65.83 132	93.95 49	95.12 17	82.11 21	84.25 38	95.73 17	67.88 28	98.35 19	85.60 36	88.64 79	94.26 74

train_agg		87.21 22	87.42 21	86.60 44	94.18 28	67.28 78	94.16 36	93.51 58	71.87 157	85.52 27	95.33 26	68.19 22	97.27 54	89.09 13	94.90 11	95.25 44

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 44	92.94 84	64.34 59	96.94 75	75.19 100	94.09 23	95.66 28

agg_prior1		87.02 24	87.26 23	86.28 60	94.16 32	66.97 87	94.08 42	93.31 70	71.85 159	84.49 36	95.39 24	68.91 18	96.75 83	88.84 17	94.32 21	95.13 48

Regformer-2		87.00 25	87.43 20	85.71 76	95.14 16	64.73 151	93.95 49	94.95 20	81.69 26	84.03 42	95.73 17	67.35 32	98.19 23	85.40 38	88.64 79	94.20 76

agg_prior3		86.93 26	87.08 24	86.48 50	94.21 26	66.95 89	94.14 39	93.40 66	71.80 162	84.86 33	95.13 36	66.16 40	97.25 56	89.09 13	94.90 11	95.25 44

CSCG		86.87 27	86.26 30	88.72 9	95.05 19	70.79 17	93.83 56	95.33 14	68.48 212	77.63 89	94.35 59	73.04 9	98.45 16	84.92 41	93.71 31	96.92 6

canonicalmvs		86.85 28	86.25 31	88.66 10	91.80 83	71.92 10	93.54 63	91.71 132	80.26 31	87.55 14	95.25 32	63.59 68	96.93 77	88.18 19	84.34 112	97.11 3

PHI-MVS		86.83 29	86.85 28	86.78 40	93.47 44	65.55 136	95.39 20	95.10 19	71.77 164	85.69 26	96.52 3	62.07 79	98.77 10	86.06 34	95.60 6	96.03 22

SteuartSystems-ACMMP		86.82 30	86.90 26	86.58 46	90.42 110	66.38 119	96.09 9	93.87 44	77.73 60	84.01 43	95.66 19	63.39 69	97.94 27	87.40 25	93.55 34	95.42 33
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 31	86.86 27	86.31 59	93.76 36	67.53 73	96.33 8	93.61 54	82.34 18	81.00 58	93.08 79	63.19 72	97.29 52	87.08 27	91.38 60	94.13 82

jason		86.40 32	86.17 32	87.11 33	86.16 185	70.54 20	95.71 15	92.19 116	82.00 24	84.58 35	94.34 60	61.86 81	95.53 122	87.76 22	90.89 65	95.27 41
jason: jason.
WTY-MVS		86.32 33	85.81 36	87.85 17	92.82 58	69.37 37	95.20 23	95.25 15	82.71 15	81.91 52	94.73 49	67.93 27	97.63 38	79.55 73	82.25 125	96.54 12

MSLP-MVS++		86.27 34	85.91 35	87.35 27	92.01 74	68.97 43	95.04 29	92.70 94	79.04 46	81.50 55	96.50 4	58.98 107	96.78 81	83.49 50	93.93 25	96.29 16

VNet		86.20 35	85.65 40	87.84 18	93.92 35	69.99 26	95.73 14	95.94 11	78.43 53	86.00 22	93.07 81	58.22 110	97.00 67	85.22 39	84.33 113	96.52 13

MVS_111021_HR		86.19 36	85.80 37	87.37 26	93.17 49	69.79 30	93.99 47	93.76 49	79.08 45	78.88 77	93.99 66	62.25 78	98.15 24	85.93 35	91.15 63	94.15 81

ACMMP_Plus		86.05 37	85.80 37	86.80 39	91.58 87	67.53 73	91.79 126	93.49 60	74.93 96	84.61 34	95.30 28	59.42 101	97.92 28	86.13 33	94.92 10	94.94 57

APD-MVS		85.93 38	85.99 33	85.76 73	95.98 12	65.21 141	93.59 61	92.58 101	66.54 227	86.17 20	95.88 14	63.83 63	97.00 67	86.39 32	92.94 39	95.06 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 39	85.46 41	87.18 30	88.20 157	72.42 9	92.41 97	92.77 92	82.11 21	80.34 63	93.07 81	68.27 21	95.02 130	78.39 83	93.59 33	94.09 85

Regformer-3		85.80 40	85.92 34	85.46 80	94.17 30	65.09 147	92.95 78	95.11 18	81.13 27	81.68 54	95.04 37	65.82 45	98.32 20	83.02 52	84.36 110	92.97 117

CDPH-MVS		85.71 41	85.46 41	86.46 51	94.75 21	67.19 80	93.89 54	92.83 91	70.90 178	83.09 47	95.28 29	63.62 66	97.36 48	80.63 68	94.18 22	94.84 59

DeepC-MVS	77.85 3	85.52 42	85.24 43	86.37 56	88.80 144	66.64 109	92.15 101	93.68 52	81.07 28	76.91 99	93.64 71	62.59 77	98.44 17	85.50 37	92.84 41	94.03 89
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 43	85.69 39	84.73 101	94.17 30	63.23 184	92.95 78	94.83 23	80.66 29	81.29 56	95.04 37	65.12 50	98.08 26	82.74 53	84.36 110	92.88 121

MP-MVS-pluss		85.24 44	85.13 44	85.56 77	91.42 96	65.59 135	91.54 138	92.51 103	74.56 99	80.62 60	95.64 20	59.15 104	97.00 67	86.94 29	93.80 27	94.07 87
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		85.15 45	84.47 47	87.18 30	96.02 11	68.29 56	91.85 124	93.00 86	76.59 76	79.03 76	95.00 39	61.59 82	97.61 40	78.16 84	89.00 77	95.63 29

MP-MVS		85.02 46	84.97 45	85.17 92	92.60 62	64.27 166	93.24 69	92.27 108	73.13 130	79.63 70	94.43 53	61.90 80	97.17 58	85.00 40	92.56 44	94.06 88

#test#		84.98 47	84.74 46	85.72 74	93.75 38	65.01 148	94.09 41	93.19 77	73.55 124	79.22 73	94.93 42	59.04 105	97.67 33	82.66 54	92.21 47	94.49 71

CHOSEN 1792x2688		84.98 47	83.45 56	89.57 6	89.94 119	75.14 4	92.07 107	92.32 106	81.87 25	75.68 104	88.27 144	60.18 95	98.60 14	80.46 70	90.27 72	94.96 56

MPTG		84.73 49	84.47 47	85.50 78	91.89 79	65.16 142	91.55 137	92.23 109	75.32 91	80.53 61	95.21 34	56.06 138	97.16 59	84.86 42	92.55 45	94.18 77

HFP-MVS		84.73 49	84.40 49	85.72 74	93.75 38	65.01 148	93.50 64	93.19 77	72.19 149	79.22 73	94.93 42	59.04 105	97.67 33	81.55 62	92.21 47	94.49 71

MVS		84.66 51	82.86 66	90.06 1	90.93 104	74.56 5	87.91 220	95.54 13	68.55 210	72.35 140	94.71 50	59.78 98	98.90 7	81.29 67	94.69 18	96.74 7

ACMMPR		84.37 52	84.06 50	85.28 88	93.56 41	64.37 161	93.50 64	93.15 80	72.19 149	78.85 79	94.86 46	56.69 130	97.45 44	81.55 62	92.20 49	94.02 90

region2R		84.36 53	84.03 51	85.36 86	93.54 42	64.31 163	93.43 67	92.95 87	72.16 152	78.86 78	94.84 47	56.97 123	97.53 42	81.38 65	92.11 51	94.24 75

LFMVS		84.34 54	82.73 69	89.18 8	94.76 20	73.25 8	94.99 30	91.89 125	71.90 155	82.16 51	93.49 74	47.98 223	97.05 62	82.55 55	84.82 106	97.25 2

HY-MVS	76.49 5	84.28 55	83.36 61	87.02 36	92.22 70	67.74 67	84.65 252	94.50 33	79.15 42	82.23 50	87.93 150	66.88 34	96.94 75	80.53 69	82.20 126	96.39 14

MAR-MVS		84.18 56	83.43 57	86.44 52	96.25 8	65.93 129	94.28 35	94.27 40	74.41 100	79.16 75	95.61 21	53.99 169	98.88 9	69.62 144	93.26 37	94.50 70
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 57	83.20 62	87.05 35	91.56 88	69.82 29	89.99 182	92.05 119	77.77 59	82.84 48	86.57 168	63.93 62	96.09 97	74.91 106	89.18 76	95.25 44

CANet_DTU		84.09 58	83.52 53	85.81 69	90.30 113	66.82 94	91.87 122	89.01 226	85.27 7	86.09 21	93.74 70	47.71 226	96.98 71	77.90 87	89.78 74	93.65 98

PVSNet_Blended_VisFu		83.97 59	83.50 54	85.39 85	90.02 117	66.59 112	93.77 57	91.73 130	77.43 66	77.08 98	89.81 130	63.77 65	96.97 72	79.67 72	88.21 82	92.60 125

DWT-MVSNet_test		83.95 60	82.80 67	87.41 25	92.90 55	70.07 25	89.12 200	94.42 36	82.15 20	77.64 88	91.77 103	70.81 16	96.22 92	65.03 183	81.36 129	95.94 24

MTAPA		83.91 61	83.38 60	85.50 78	91.89 79	65.16 142	81.75 273	92.23 109	75.32 91	80.53 61	95.21 34	56.06 138	97.16 59	84.86 42	92.55 45	94.18 77

XVS		83.87 62	83.47 55	85.05 94	93.22 46	63.78 172	92.92 80	92.66 97	73.99 111	78.18 83	94.31 62	55.25 143	97.41 45	79.16 76	91.58 57	93.95 92

Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 128	69.40 36	91.35 145	86.12 273	72.59 138	83.22 46	92.81 89	59.60 100	96.01 102	81.76 60	87.80 85	95.56 31

EI-MVSNet-Vis-set		83.77 64	83.67 52	84.06 115	92.79 60	63.56 181	91.76 129	94.81 25	79.65 36	77.87 85	94.09 64	63.35 70	97.90 29	79.35 74	79.36 139	90.74 151

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 135	71.18 14	89.07 201	90.69 164	65.80 233	87.13 16	94.34 60	64.99 54	92.67 216	72.83 113	91.80 53	95.27 41

CP-MVS		83.71 66	83.40 59	84.65 104	93.14 50	63.84 170	94.59 33	92.28 107	71.03 176	77.41 92	94.92 44	55.21 146	96.19 93	81.32 66	90.70 67	93.91 94

PVSNet_BlendedMVS		83.38 67	83.43 57	83.22 131	93.76 36	67.53 73	94.06 43	93.61 54	79.13 43	81.00 58	85.14 182	63.19 72	97.29 52	87.08 27	73.91 183	84.83 244

PGM-MVS		83.25 68	82.70 70	84.92 96	92.81 59	64.07 168	90.44 170	92.20 114	71.28 174	77.23 95	94.43 53	55.17 147	97.31 51	79.33 75	91.38 60	93.37 103

HPM-MVS		83.25 68	82.95 64	84.17 113	92.25 69	62.88 194	90.91 159	91.86 126	70.30 191	77.12 96	93.96 67	56.75 128	96.28 91	82.04 58	91.34 62	93.34 104

EI-MVSNet-UG-set		83.14 70	82.96 63	83.67 125	92.28 68	63.19 187	91.38 143	94.68 28	79.22 40	76.60 100	93.75 69	62.64 76	97.76 31	78.07 85	78.01 150	90.05 158

PatchFormer-LS_test		83.14 70	81.81 78	87.12 32	92.34 65	69.92 28	88.64 207	93.32 69	82.07 23	74.87 113	91.62 107	68.91 18	96.08 99	66.07 174	78.45 149	95.37 34

VDD-MVS		83.06 72	81.81 78	86.81 38	90.86 107	67.70 68	95.40 19	91.50 140	75.46 86	81.78 53	92.34 97	40.09 263	97.13 61	86.85 30	82.04 127	95.60 30

PAPM_NR		82.97 73	81.84 77	86.37 56	94.10 34	66.76 101	87.66 227	92.84 90	69.96 194	74.07 120	93.57 72	63.10 74	97.50 43	70.66 137	90.58 69	94.85 58

mPP-MVS		82.96 74	82.44 71	84.52 108	92.83 56	62.92 192	92.76 83	91.85 127	71.52 171	75.61 107	94.24 63	53.48 178	96.99 70	78.97 79	90.73 66	93.64 99

DP-MVS Recon		82.73 75	81.65 80	85.98 64	97.31 3	67.06 84	95.15 25	91.99 121	69.08 202	76.50 102	93.89 68	54.48 163	98.20 22	70.76 136	85.66 102	92.69 122

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 163	67.65 70	95.45 18	92.18 117	85.06 8	72.58 133	92.27 98	52.46 187	95.78 107	84.18 44	79.06 142	88.16 181
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 77	82.38 72	83.73 122	89.25 133	59.58 243	92.24 100	94.89 21	77.96 57	79.86 67	92.38 95	56.70 129	97.05 62	77.26 90	80.86 133	94.55 65

3Dnovator	73.91 6	82.69 78	80.82 88	88.31 14	89.57 127	71.26 13	92.60 91	94.39 39	78.84 48	67.89 198	92.48 93	48.42 218	98.52 15	68.80 152	94.40 20	95.15 47

MVSTER		82.47 79	82.05 74	83.74 120	92.68 61	69.01 41	91.90 121	93.21 74	79.83 32	72.14 141	85.71 178	74.72 7	94.72 141	75.72 96	72.49 193	87.50 189

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 158	63.74 174	92.70 86	93.77 48	79.30 38	77.61 90	87.57 156	58.19 111	94.08 174	73.91 109	86.68 93	93.33 106

CostFormer		82.33 81	81.15 84	85.86 67	89.01 140	68.46 51	82.39 270	93.01 84	75.59 84	80.25 64	81.57 223	72.03 13	94.96 132	79.06 78	77.48 160	94.16 80

API-MVS		82.28 82	80.53 93	87.54 23	96.13 9	70.59 19	93.63 59	91.04 157	65.72 235	75.45 109	92.83 88	56.11 137	98.89 8	64.10 191	89.75 75	93.15 111

IB-MVS	77.80 4	82.18 83	80.46 94	87.35 27	89.14 137	70.28 22	95.59 17	95.17 16	78.85 47	70.19 160	85.82 176	70.66 17	97.67 33	72.19 122	66.52 231	94.09 85
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 84	81.12 85	85.26 89	86.42 179	68.72 47	92.59 93	90.44 169	73.12 131	84.20 39	94.36 55	38.04 273	95.73 110	84.12 45	86.81 90	91.33 143

xiu_mvs_v1_base		82.16 84	81.12 85	85.26 89	86.42 179	68.72 47	92.59 93	90.44 169	73.12 131	84.20 39	94.36 55	38.04 273	95.73 110	84.12 45	86.81 90	91.33 143

xiu_mvs_v1_base_debi		82.16 84	81.12 85	85.26 89	86.42 179	68.72 47	92.59 93	90.44 169	73.12 131	84.20 39	94.36 55	38.04 273	95.73 110	84.12 45	86.81 90	91.33 143

3Dnovator+	73.60 7	82.10 87	80.60 92	86.60 44	90.89 106	66.80 100	95.20 23	93.44 64	74.05 110	67.42 203	92.49 92	49.46 209	97.65 37	70.80 135	91.68 55	95.33 36

MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 153	62.88 194	89.77 189	88.93 228	76.78 73	75.55 108	93.10 77	50.31 201	95.38 124	83.82 49	87.02 89	92.26 134

PMMVS		81.98 89	82.04 75	81.78 173	89.76 123	56.17 274	91.13 156	90.69 164	77.96 57	80.09 65	93.57 72	46.33 236	94.99 131	81.41 64	87.46 87	94.17 79

EPP-MVSNet		81.79 90	81.52 81	82.61 143	88.77 145	60.21 233	93.02 76	93.66 53	68.52 211	72.90 128	90.39 120	72.19 12	94.96 132	74.93 105	79.29 141	92.67 123

APD-MVS_3200maxsize		81.64 91	81.32 83	82.59 144	92.36 64	58.74 254	91.39 141	91.01 158	63.35 252	79.72 69	94.62 51	51.82 190	96.14 95	79.71 71	87.93 84	92.89 120

ACMMP		81.49 92	80.67 90	83.93 117	91.71 85	62.90 193	92.13 102	92.22 113	71.79 163	71.68 148	93.49 74	50.32 200	96.96 73	78.47 81	84.22 117	91.93 137
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 93	80.74 89	83.52 127	86.26 183	64.45 156	92.09 105	90.65 167	75.83 83	73.95 122	89.81 130	63.97 61	92.91 208	71.27 129	82.82 122	93.20 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 112	68.40 52	86.88 240	90.61 168	74.41 100	70.31 159	84.67 187	63.79 64	92.32 227	73.13 110	85.70 101	95.67 27

1121		81.25 95	80.05 95	84.87 98	92.30 67	64.31 163	87.91 220	91.39 144	59.44 278	79.94 66	92.91 85	57.09 119	97.01 65	66.63 166	92.81 42	93.29 107

Fast-Effi-MVS+		81.14 96	80.01 96	84.51 109	90.24 115	65.86 130	94.12 40	89.15 220	73.81 118	75.37 110	88.26 145	57.26 118	94.53 148	66.97 165	84.92 105	93.15 111

HQP-MVS		81.14 96	80.64 91	82.64 142	87.54 166	63.66 179	94.06 43	91.70 133	79.80 33	74.18 116	90.30 121	51.63 194	95.61 116	77.63 88	78.90 143	88.63 172

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 164	68.11 61	90.18 177	90.01 192	70.65 186	72.95 127	86.06 174	63.61 67	94.50 149	75.01 104	79.75 137	93.67 97

nrg030		80.93 99	79.86 99	84.13 114	83.69 215	68.83 45	93.23 70	91.20 150	75.55 85	75.06 112	88.22 148	63.04 75	94.74 140	81.88 59	66.88 228	88.82 170

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 150	61.80 210	93.44 66	88.26 243	73.96 114	77.73 86	91.76 104	49.94 205	94.76 138	65.84 177	90.37 71	94.65 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
1314		80.70 101	78.95 116	85.94 66	87.77 165	67.56 72	87.91 220	92.55 102	72.17 151	67.44 202	93.09 78	50.27 202	97.04 64	71.68 125	87.64 86	93.23 109

tpmrst		80.57 102	79.14 115	84.84 99	90.10 116	68.28 57	81.70 274	89.72 203	77.63 62	75.96 103	79.54 253	64.94 56	92.71 214	75.43 98	77.28 163	93.55 100

1112_ss		80.56 103	79.83 100	82.77 137	88.65 146	60.78 220	92.29 98	88.36 239	72.58 139	72.46 137	94.95 40	65.09 51	93.42 197	66.38 170	77.71 152	94.10 84

VDDNet		80.50 104	78.26 123	87.21 29	86.19 184	69.79 30	94.48 34	91.31 147	60.42 272	79.34 72	90.91 111	38.48 269	96.56 89	82.16 56	81.05 131	95.27 41

BH-w/o		80.49 105	79.30 111	84.05 116	90.83 108	64.36 162	93.60 60	89.42 210	74.35 105	69.09 178	90.15 123	55.23 145	95.61 116	64.61 186	86.43 98	92.17 135

TAMVS		80.37 106	79.45 107	83.13 133	85.14 196	63.37 182	91.23 150	90.76 163	74.81 98	72.65 131	88.49 139	60.63 91	92.95 204	69.41 146	81.95 128	93.08 114

HQP_MVS		80.34 107	79.75 101	82.12 166	86.94 174	62.42 199	93.13 72	91.31 147	78.81 49	72.53 134	89.14 135	50.66 198	95.55 120	76.74 91	78.53 147	88.39 177

HPM-MVS_fast		80.25 108	79.55 106	82.33 156	91.55 89	59.95 238	91.32 147	89.16 219	65.23 239	74.71 114	93.07 81	47.81 225	95.74 109	74.87 108	88.23 81	91.31 147

diffmvs		80.18 109	78.55 120	85.07 93	88.56 147	66.93 90	86.70 243	88.62 234	70.42 188	78.69 81	85.26 180	56.93 125	94.77 137	68.68 153	83.09 119	93.51 101

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 152	65.49 138	83.00 267	92.67 96	71.82 161	77.36 93	85.01 183	54.50 161	96.59 86	76.35 95	75.63 170	95.32 38

IS-MVSNet		80.14 111	79.41 108	82.33 156	87.91 162	60.08 237	91.97 113	88.27 242	72.90 135	71.44 150	91.73 106	61.44 83	93.66 192	62.47 208	86.53 96	93.24 108

test-LLR		80.10 112	79.56 104	81.72 175	86.93 176	61.17 215	92.70 86	91.54 137	71.51 172	75.62 105	86.94 165	53.83 170	92.38 224	72.21 120	84.76 108	91.60 140

PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 105	64.97 150	92.47 96	91.05 156	79.18 41	72.43 138	90.51 119	37.05 285	94.06 175	68.06 154	86.00 100	93.90 95

UA-Net		80.02 114	79.65 102	81.11 189	89.33 131	57.72 261	86.33 245	89.00 227	77.44 65	81.01 57	89.15 134	59.33 102	95.90 103	61.01 215	84.28 115	89.73 162

test-mter		79.96 115	79.38 110	81.72 175	86.93 176	61.17 215	92.70 86	91.54 137	73.85 116	75.62 105	86.94 165	49.84 207	92.38 224	72.21 120	84.76 108	91.60 140

QAPM		79.95 116	77.39 139	87.64 20	89.63 126	71.41 12	93.30 68	93.70 51	65.34 238	67.39 205	91.75 105	47.83 224	98.96 5	57.71 231	89.81 73	92.54 127

UGNet		79.87 117	78.68 117	83.45 130	89.96 118	61.51 213	92.13 102	90.79 161	76.83 72	78.85 79	86.33 171	38.16 271	96.17 94	67.93 156	87.17 88	92.67 123
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		79.82 118	79.20 113	81.70 177	89.85 120	58.34 256	88.47 210	90.07 189	62.56 259	77.71 87	93.08 79	47.65 227	96.78 81	77.94 86	85.45 104	89.99 159

tpm2		79.80 119	77.95 128	85.34 87	88.28 155	68.26 58	81.56 278	91.42 143	70.11 192	77.59 91	80.50 239	67.40 30	94.26 165	67.34 161	77.35 161	93.51 101

DI_MVS_plusplus_test		79.78 120	77.50 136	86.62 43	80.90 238	69.46 35	90.69 165	91.97 123	77.00 68	59.07 255	82.34 208	46.82 230	95.88 104	82.14 57	86.59 95	94.53 69

test_normal		79.66 121	77.36 141	86.54 47	80.72 242	69.21 38	90.68 166	92.16 118	76.99 69	58.63 259	82.03 217	46.70 232	95.86 105	81.74 61	86.63 94	94.56 64

thres200		79.66 121	78.33 121	83.66 126	92.54 63	65.82 133	93.06 74	96.31 8	74.90 97	73.30 125	88.66 137	59.67 99	95.61 116	47.84 264	78.67 146	89.56 164

CPTT-MVS		79.59 123	79.16 114	80.89 196	91.54 90	59.80 240	92.10 104	88.54 237	60.42 272	72.96 126	93.28 76	48.27 219	92.80 211	78.89 80	86.50 97	90.06 157

Test_1112_low_res		79.56 124	78.60 119	82.43 149	88.24 156	60.39 229	92.09 105	87.99 246	72.10 153	71.84 144	87.42 158	64.62 57	93.04 201	65.80 178	77.30 162	93.85 96

FIs		79.47 125	79.41 108	79.67 215	85.95 188	59.40 245	91.68 133	93.94 43	78.06 56	68.96 181	88.28 143	66.61 37	91.77 238	66.20 173	74.99 176	87.82 186

BH-RMVSNet		79.46 126	77.65 132	84.89 97	91.68 86	65.66 134	93.55 62	88.09 244	72.93 134	73.37 124	91.12 110	46.20 238	96.12 96	56.28 235	85.61 103	92.91 119

PCF-MVS	73.15 9	79.29 127	77.63 133	84.29 112	86.06 186	65.96 128	87.03 235	91.10 154	69.86 195	69.79 167	90.64 114	57.54 117	96.59 86	64.37 190	82.29 124	90.32 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 245	88.46 151	52.29 291	90.41 172	89.12 221	74.24 106	69.13 177	91.91 101	65.77 46	90.09 266	59.00 226	88.09 83	92.33 130

114514_t		79.17 129	77.67 131	83.68 124	95.32 15	65.53 137	92.85 82	91.60 136	63.49 251	67.92 197	90.63 116	46.65 233	95.72 114	67.01 164	83.54 118	89.79 160

VPA-MVSNet		79.03 130	78.00 127	82.11 169	85.95 188	64.48 155	93.22 71	94.66 29	75.05 95	74.04 121	84.95 184	52.17 189	93.52 194	74.90 107	67.04 227	88.32 179

OPM-MVS		79.00 131	78.09 125	81.73 174	83.52 218	63.83 171	91.64 136	90.30 179	76.36 79	71.97 143	89.93 129	46.30 237	95.17 129	75.10 101	77.70 153	86.19 217

EI-MVSNet		78.97 132	78.22 124	81.25 182	85.33 193	62.73 197	89.53 193	93.21 74	72.39 143	72.14 141	90.13 124	60.99 84	94.72 141	67.73 158	72.49 193	86.29 215

AdaColmap		78.94 133	77.00 146	84.76 100	96.34 6	65.86 130	92.66 90	87.97 247	62.18 261	70.56 152	92.37 96	43.53 250	97.35 49	64.50 188	82.86 121	91.05 149

tpmp4_e23		78.85 134	76.55 150	85.77 72	89.25 133	68.39 53	81.63 277	91.38 145	70.40 189	75.21 111	79.22 255	67.37 31	94.79 136	58.98 227	75.51 171	94.13 82

VPNet		78.82 135	77.53 135	82.70 139	84.52 203	66.44 118	93.93 51	92.23 109	80.46 30	72.60 132	88.38 142	49.18 212	93.13 200	72.47 118	63.97 254	88.55 174

EPNet_dtu		78.80 136	79.26 112	77.43 255	88.06 159	49.71 304	91.96 114	91.95 124	77.67 61	76.56 101	91.28 109	58.51 109	90.20 261	56.37 234	80.95 132	92.39 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 137	77.43 137	82.88 135	92.21 71	64.49 153	92.05 108	96.28 9	73.48 125	71.75 146	88.26 145	60.07 96	95.32 125	45.16 273	77.58 155	88.83 168

TR-MVS		78.77 138	77.37 140	82.95 134	90.49 109	60.88 218	93.67 58	90.07 189	70.08 193	74.51 115	91.37 108	45.69 239	95.70 115	60.12 220	80.32 134	92.29 132

mvs-test1		78.74 139	77.95 128	81.14 188	83.22 220	57.13 268	93.96 48	87.78 248	75.42 87	72.68 130	90.80 113	45.08 243	94.54 147	75.08 102	77.49 159	91.74 139

thres400		78.68 140	77.43 137	82.43 149	92.21 71	64.49 153	92.05 108	96.28 9	73.48 125	71.75 146	88.26 145	60.07 96	95.32 125	45.16 273	77.58 155	87.48 190

BH-untuned		78.68 140	77.08 142	83.48 129	89.84 121	63.74 174	92.70 86	88.59 235	71.57 169	66.83 211	88.65 138	51.75 192	95.39 123	59.03 225	84.77 107	91.32 146

OMC-MVS		78.67 142	77.91 130	80.95 195	85.76 192	57.40 266	88.49 209	88.67 232	73.85 116	72.43 138	92.10 99	49.29 211	94.55 146	72.73 115	77.89 151	90.91 150

tpm		78.58 143	77.03 143	83.22 131	85.94 190	64.56 152	83.21 265	91.14 153	78.31 54	73.67 123	79.68 250	64.01 60	92.09 233	66.07 174	71.26 202	93.03 115

OpenMVS	70.45 11	78.54 144	75.92 158	86.41 55	85.93 191	71.68 11	92.74 84	92.51 103	66.49 228	64.56 223	91.96 100	43.88 249	98.10 25	54.61 239	90.65 68	89.44 165

EPMVS		78.49 145	75.98 157	86.02 63	91.21 100	69.68 33	80.23 287	91.20 150	75.25 93	72.48 136	78.11 260	54.65 160	93.69 191	57.66 232	83.04 120	94.69 62

thres100view900		78.37 146	77.01 144	82.46 145	91.89 79	63.21 185	91.19 154	96.33 5	72.28 145	70.45 155	87.89 151	60.31 92	95.32 125	45.16 273	77.58 155	88.83 168

GA-MVS		78.33 147	76.23 154	84.65 104	83.65 216	66.30 122	91.44 139	90.14 187	76.01 81	70.32 158	84.02 192	42.50 253	94.72 141	70.98 133	77.00 164	92.94 118

conf200view11		78.32 148	77.01 144	82.27 159	91.89 79	63.21 185	91.19 154	96.33 5	72.28 145	70.45 155	87.89 151	60.31 92	95.32 125	45.16 273	77.58 155	88.27 180

cascas		78.18 149	75.77 160	85.41 84	87.14 172	69.11 39	92.96 77	91.15 152	66.71 226	70.47 153	86.07 173	37.49 279	96.48 90	70.15 140	79.80 136	90.65 152

UniMVSNet_NR-MVSNet		78.15 150	77.55 134	79.98 208	84.46 205	60.26 231	92.25 99	93.20 76	77.50 64	68.88 182	86.61 167	66.10 41	92.13 231	66.38 170	62.55 257	87.54 188

thres600view7		78.00 151	76.66 149	82.03 171	91.93 76	63.69 177	91.30 148	96.33 5	72.43 141	70.46 154	87.89 151	60.31 92	94.92 135	42.64 285	76.64 165	87.48 190

FC-MVSNet-test		77.99 152	78.08 126	77.70 250	84.89 199	55.51 278	90.27 175	93.75 50	76.87 70	66.80 212	87.59 155	65.71 47	90.23 260	62.89 203	73.94 182	87.37 197

XXY-MVS		77.94 153	76.44 152	82.43 149	82.60 226	64.44 157	92.01 110	91.83 128	73.59 123	70.00 163	85.82 176	54.43 164	94.76 138	69.63 143	68.02 222	88.10 182

MS-PatchMatch		77.90 154	76.50 151	82.12 166	85.99 187	69.95 27	91.75 131	92.70 94	73.97 113	62.58 241	84.44 190	41.11 260	95.78 107	63.76 192	92.17 50	80.62 293

FMVSNet3		77.73 155	76.04 156	82.80 136	91.20 101	68.99 42	91.87 122	91.99 121	73.35 128	67.04 208	83.19 200	56.62 131	92.14 230	59.80 222	69.34 211	87.28 201

UniMVSNet (Re)		77.58 156	76.78 148	79.98 208	84.11 211	60.80 219	91.76 129	93.17 79	76.56 77	69.93 166	84.78 186	63.32 71	92.36 226	64.89 184	62.51 259	86.78 209

PatchmatchNet		77.46 157	74.63 178	85.96 65	89.55 129	70.35 21	79.97 291	89.55 206	72.23 147	70.94 151	76.91 272	57.03 121	92.79 212	54.27 241	81.17 130	94.74 61
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 158	75.65 165	82.73 138	80.38 256	67.13 83	91.85 124	90.23 181	75.09 94	69.37 174	83.39 198	53.79 172	94.44 150	71.77 123	65.00 245	86.63 213

v1neww		77.39 159	75.71 162	82.44 146	80.69 244	66.83 92	91.94 118	90.18 184	74.19 107	69.60 168	82.51 204	54.99 154	94.44 150	71.68 125	65.60 234	86.05 222

v7new		77.39 159	75.71 162	82.44 146	80.69 244	66.83 92	91.94 118	90.18 184	74.19 107	69.60 168	82.51 204	54.99 154	94.44 150	71.68 125	65.60 234	86.05 222

v6		77.39 159	75.71 162	82.44 146	80.67 246	66.82 94	91.94 118	90.18 184	74.19 107	69.60 168	82.50 207	55.00 153	94.44 150	71.68 125	65.60 234	86.05 222

CHOSEN 280x420		77.35 162	76.95 147	78.55 239	87.07 173	62.68 198	69.71 316	82.95 299	68.80 205	71.48 149	87.27 164	66.03 42	84.00 305	76.47 94	82.81 123	88.95 167

v1		77.29 163	75.57 166	82.42 152	80.61 254	66.73 102	91.96 114	90.42 172	74.41 100	69.46 171	82.12 214	55.14 148	94.40 155	71.00 130	65.04 242	86.13 218

v1141		77.28 164	75.57 166	82.42 152	80.63 250	66.73 102	91.96 114	90.42 172	74.41 100	69.46 171	82.12 214	55.09 150	94.40 155	70.99 132	65.05 241	86.12 219

divwei89l23v2f112		77.28 164	75.57 166	82.42 152	80.62 251	66.72 104	91.96 114	90.42 172	74.41 100	69.46 171	82.12 214	55.11 149	94.40 155	71.00 130	65.04 242	86.12 219

PS-MVSNAJss		77.26 166	76.31 153	80.13 205	80.64 249	59.16 249	90.63 169	91.06 155	72.80 136	68.58 187	84.57 189	53.55 174	93.96 182	72.97 111	71.96 196	87.27 202

gg-mvs-nofinetune		77.18 167	74.31 184	85.80 70	91.42 96	68.36 54	71.78 310	94.72 27	49.61 308	77.12 96	45.92 331	77.41 3	93.98 181	67.62 159	93.16 38	95.05 51

MVP-Stereo		77.12 168	76.23 154	79.79 214	81.72 232	66.34 121	89.29 195	90.88 159	70.56 187	62.01 244	82.88 201	49.34 210	94.13 171	65.55 180	93.80 27	78.88 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
view600		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 159	60.79 86	94.19 166	38.90 298	75.31 172	87.48 190

view800		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 159	60.79 86	94.19 166	38.90 298	75.31 172	87.48 190

conf0.05thres1000		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 159	60.79 86	94.19 166	38.90 298	75.31 172	87.48 190

tfpn		76.93 169	75.50 169	81.23 183	91.44 92	62.00 205	89.94 183	96.56 1	70.68 182	68.54 188	87.31 159	60.79 86	94.19 166	38.90 298	75.31 172	87.48 190

X-MVStestdata		76.86 173	74.13 187	85.05 94	93.22 46	63.78 172	92.92 80	92.66 97	73.99 111	78.18 83	10.19 346	55.25 143	97.41 45	79.16 76	91.58 57	93.95 92

DU-MVS		76.86 173	75.84 159	79.91 210	82.96 224	60.26 231	91.26 149	91.54 137	76.46 78	68.88 182	86.35 169	56.16 135	92.13 231	66.38 170	62.55 257	87.35 199

v7		76.83 175	75.01 176	82.29 158	80.35 257	66.70 106	91.68 133	89.97 193	73.47 127	69.22 176	82.22 211	52.52 185	94.43 154	69.73 142	65.96 233	85.74 233

WR-MVS		76.76 176	75.74 161	79.82 213	84.60 201	62.27 203	92.60 91	92.51 103	76.06 80	67.87 199	85.34 179	56.76 126	90.24 259	62.20 209	63.69 256	86.94 207

v1144		76.73 177	74.88 177	82.27 159	80.23 264	66.60 110	91.68 133	90.21 183	73.69 120	69.06 179	81.89 219	52.73 184	94.40 155	69.21 148	65.23 238	85.80 229

IterMVS-LS		76.49 178	75.18 175	80.43 199	84.49 204	62.74 196	90.64 167	88.80 230	72.40 142	65.16 219	81.72 222	60.98 85	92.27 229	67.74 157	64.65 249	86.29 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 179	74.55 181	82.19 164	79.14 278	67.82 65	90.26 176	89.42 210	73.75 119	68.63 186	81.89 219	51.31 196	94.09 173	71.69 124	64.84 246	84.66 245

tfpn_ndepth		76.45 180	75.22 174	80.14 203	90.97 103	58.92 251	90.11 178	93.24 73	65.96 232	67.37 206	90.52 118	66.67 36	92.29 228	37.71 304	74.44 178	89.21 166

Test4		76.45 180	73.45 199	85.45 82	76.07 300	67.61 71	88.38 212	90.83 160	76.71 74	53.06 288	79.65 252	31.61 302	94.35 159	78.47 81	86.22 99	94.40 73

v148		76.19 182	74.47 183	81.36 180	80.05 268	64.44 157	91.75 131	90.23 181	73.68 121	67.13 207	80.84 235	55.92 141	93.86 188	68.95 150	61.73 266	85.76 232

Effi-MVS+-dtu		76.14 183	75.28 173	78.72 238	83.22 220	55.17 280	89.87 187	87.78 248	75.42 87	67.98 196	81.43 224	45.08 243	92.52 220	75.08 102	71.63 197	88.48 175

FMVSNet2		76.07 184	74.01 189	82.26 162	88.85 141	67.66 69	91.33 146	91.61 135	70.84 179	65.98 213	82.25 210	48.03 220	92.00 235	58.46 228	68.73 217	87.10 203

v144192		76.05 185	74.03 188	82.12 166	79.50 273	66.55 114	91.39 141	89.71 204	72.30 144	68.17 194	81.33 227	51.75 192	94.03 179	67.94 155	64.19 251	85.77 230

NR-MVSNet		76.05 185	74.59 179	80.44 198	82.96 224	62.18 204	90.83 161	91.73 130	77.12 67	60.96 245	86.35 169	59.28 103	91.80 237	60.74 216	61.34 270	87.35 199

v1192		75.98 187	73.92 191	82.15 165	79.73 269	66.24 124	91.22 151	89.75 198	72.67 137	68.49 192	81.42 225	49.86 206	94.27 163	67.08 163	65.02 244	85.95 226

TranMVSNet+NR-MVSNet		75.86 188	74.52 182	79.89 211	82.44 227	60.64 226	91.37 144	91.37 146	76.63 75	67.65 201	86.21 172	52.37 188	91.55 244	61.84 211	60.81 273	87.48 190

LPG-MVS_test		75.82 189	74.58 180	79.56 219	84.31 208	59.37 246	90.44 170	89.73 201	69.49 197	64.86 220	88.42 140	38.65 267	94.30 161	72.56 116	72.76 190	85.01 242

GBi-Net		75.65 190	73.83 192	81.10 190	88.85 141	65.11 144	90.01 179	90.32 175	70.84 179	67.04 208	80.25 244	48.03 220	91.54 245	59.80 222	69.34 211	86.64 210

test1		75.65 190	73.83 192	81.10 190	88.85 141	65.11 144	90.01 179	90.32 175	70.84 179	67.04 208	80.25 244	48.03 220	91.54 245	59.80 222	69.34 211	86.64 210

v1921920		75.63 192	73.49 198	82.06 170	79.38 274	66.35 120	91.07 158	89.48 207	71.98 154	67.99 195	81.22 230	49.16 214	93.90 185	66.56 168	64.56 250	85.92 228

ACMP	71.68 10	75.58 193	74.23 186	79.62 217	84.97 198	59.64 241	90.80 162	89.07 224	70.39 190	62.95 237	87.30 163	38.28 270	93.87 186	72.89 112	71.45 200	85.36 239
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 194	73.26 201	81.61 178	80.67 246	66.82 94	89.54 192	89.27 214	71.65 166	63.30 235	80.30 243	54.99 154	94.06 175	67.33 162	62.33 260	83.94 250

tpm cat1		75.30 195	72.21 212	84.58 106	88.52 148	67.77 66	78.16 302	88.02 245	61.88 265	68.45 193	76.37 273	60.65 90	94.03 179	53.77 244	74.11 180	91.93 137

tfpn1000		75.25 196	74.00 190	79.03 234	90.30 113	57.56 265	88.55 208	93.36 68	64.14 248	65.17 218	89.76 132	67.06 33	91.46 250	34.54 315	73.09 188	88.06 183

PLC	68.80 14	75.23 197	73.68 194	79.86 212	92.93 54	58.68 255	90.64 167	88.30 240	60.90 269	64.43 226	90.53 117	42.38 254	94.57 144	56.52 233	76.54 166	86.33 214
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 198	72.98 203	81.88 172	79.20 276	66.00 127	90.75 164	89.11 222	71.63 167	67.41 204	81.22 230	47.36 228	93.87 186	65.46 181	64.72 248	85.77 230

Fast-Effi-MVS+-dtu		75.04 199	73.37 200	80.07 206	80.86 239	59.52 244	91.20 153	85.38 280	71.90 155	65.20 217	84.84 185	41.46 259	92.97 203	66.50 169	72.96 189	87.73 187

dp		75.01 200	72.09 213	83.76 119	89.28 132	66.22 125	79.96 292	89.75 198	71.16 175	67.80 200	77.19 268	51.81 191	92.54 219	50.39 254	71.44 201	92.51 128

Patchmatch-test1		75.00 201	71.80 216	84.58 106	86.63 178	70.08 24	81.06 280	89.19 217	71.60 168	70.01 162	77.16 270	45.53 240	88.63 279	51.79 250	73.27 185	95.02 55

TAPA-MVS	70.22 12	74.94 202	73.53 197	79.17 231	90.40 111	52.07 292	89.19 198	89.61 205	62.69 258	70.07 161	92.67 90	48.89 217	94.32 160	38.26 303	79.97 135	91.12 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpnconf		74.92 203	73.61 195	78.85 236	89.65 124	56.94 271	87.72 224	93.45 61	65.14 240	65.68 214	89.99 127	65.09 51	91.67 240	35.16 311	70.61 204	87.94 184

tfpnview11		74.92 203	73.61 195	78.85 236	89.65 124	56.94 271	87.72 224	93.45 61	65.14 240	65.68 214	89.99 127	65.09 51	91.67 240	35.16 311	70.61 204	87.94 184

v10		74.77 205	72.54 209	81.46 179	80.33 261	66.71 105	89.15 199	89.08 223	70.94 177	63.08 236	79.86 248	52.52 185	94.04 178	65.70 179	62.17 261	83.64 252

XVG-OURS-SEG-HR		74.70 206	73.08 202	79.57 218	78.25 286	57.33 267	80.49 283	87.32 254	63.22 254	68.76 184	90.12 126	44.89 246	91.59 243	70.55 138	74.09 181	89.79 160

ACMM	69.62 13	74.34 207	72.73 205	79.17 231	84.25 210	57.87 259	90.36 173	89.93 194	63.17 255	65.64 216	86.04 175	37.79 277	94.10 172	65.89 176	71.52 199	85.55 236
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 208	72.30 211	80.32 200	91.49 91	61.66 211	90.85 160	80.72 306	56.67 292	63.85 230	90.64 114	46.75 231	90.84 253	53.79 243	75.99 169	88.47 176

XVG-OURS		74.25 209	72.46 210	79.63 216	78.45 285	57.59 264	80.33 285	87.39 251	63.86 250	68.76 184	89.62 133	40.50 262	91.72 239	69.00 149	74.25 179	89.58 163

CVMVSNet		74.04 210	74.27 185	73.33 281	85.33 193	43.94 318	89.53 193	88.39 238	54.33 298	70.37 157	90.13 124	49.17 213	84.05 302	61.83 212	79.36 139	91.99 136

Baseline_NR-MVSNet		73.99 211	72.83 204	77.48 254	80.78 240	59.29 248	91.79 126	84.55 284	68.85 204	68.99 180	80.70 236	56.16 135	92.04 234	62.67 206	60.98 272	81.11 287

pmmvs4		73.92 212	71.81 215	80.25 202	79.17 277	65.24 140	87.43 230	87.26 256	67.64 221	63.46 233	83.91 193	48.96 216	91.53 248	62.94 202	65.49 237	83.96 249

CR-MVSNet		73.79 213	70.82 222	82.70 139	83.15 222	67.96 63	70.25 313	84.00 289	73.67 122	69.97 164	72.41 292	57.82 114	89.48 275	52.99 248	73.13 186	90.64 153

test_djsdf		73.76 214	72.56 208	77.39 256	77.00 294	53.93 285	89.07 201	90.69 164	65.80 233	63.92 228	82.03 217	43.14 252	92.67 216	72.83 113	68.53 218	85.57 235

pmmvs5		73.35 215	71.52 217	78.86 235	78.64 284	60.61 227	91.08 157	86.90 257	67.69 218	63.32 234	83.64 194	44.33 248	90.53 254	62.04 210	66.02 232	85.46 237

jajsoiax		73.05 216	71.51 218	77.67 251	77.46 291	54.83 281	88.81 204	90.04 191	69.13 201	62.85 239	83.51 196	31.16 305	92.75 213	70.83 134	69.80 207	85.43 238

LCM-MVSNet-Re		72.93 217	71.84 214	76.18 266	88.49 149	48.02 308	80.07 290	70.17 331	73.96 114	52.25 291	80.09 247	49.98 204	88.24 285	67.35 160	84.23 116	92.28 133

pm-mvs1		72.89 218	71.09 220	78.26 244	79.10 280	57.62 263	90.80 162	89.30 213	67.66 219	62.91 238	81.78 221	49.11 215	92.95 204	60.29 219	58.89 282	84.22 248

tpmvs		72.88 219	69.76 227	82.22 163	90.98 102	67.05 85	78.22 301	88.30 240	63.10 256	64.35 227	74.98 280	55.09 150	94.27 163	43.25 279	69.57 210	85.34 240

test0.0.03 1		72.76 220	72.71 206	72.88 285	80.25 263	47.99 309	91.22 151	89.45 208	71.51 172	62.51 242	87.66 154	53.83 170	85.06 298	50.16 255	67.84 225	85.58 234

mvs_tets		72.71 221	71.11 219	77.52 252	77.41 292	54.52 283	88.45 211	89.76 197	68.76 206	62.70 240	83.26 199	29.49 309	92.71 214	70.51 139	69.62 209	85.34 240

FMVSNet1		72.71 221	69.91 225	81.10 190	83.60 217	65.11 144	90.01 179	90.32 175	63.92 249	63.56 232	80.25 244	36.35 287	91.54 245	54.46 240	66.75 229	86.64 210

IterMVS		72.65 223	70.83 221	78.09 248	82.17 228	62.96 189	87.64 228	86.28 269	71.56 170	60.44 247	78.85 257	45.42 242	86.66 293	63.30 196	61.83 263	84.65 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		72.06 224	69.98 223	78.28 242	89.51 130	55.70 277	83.49 259	83.39 295	61.24 268	63.72 231	82.76 202	34.77 292	93.03 202	53.37 247	77.59 154	86.12 219

v18		71.94 225	69.43 228	79.50 221	80.74 241	66.82 94	88.16 214	86.66 259	68.95 203	55.55 271	72.66 287	55.03 152	90.15 262	64.78 185	52.30 299	81.54 275

v16		71.81 226	69.26 230	79.47 222	80.66 248	66.81 98	87.93 218	86.63 261	68.70 208	55.35 272	72.51 288	54.75 158	90.12 264	64.51 187	52.28 300	81.47 276

PVSNet_0	68.08 15	71.81 226	68.32 240	82.27 159	84.68 200	62.31 202	88.68 206	90.31 178	75.84 82	57.93 262	80.65 238	37.85 276	94.19 166	69.94 141	29.05 335	90.31 156

v17		71.77 228	69.20 231	79.46 223	80.62 251	66.81 98	87.93 218	86.63 261	68.71 207	55.25 273	72.49 289	54.72 159	90.11 265	64.50 188	51.97 301	81.47 276

MIMVSNet		71.64 229	68.44 238	81.23 183	81.97 231	64.44 157	73.05 309	88.80 230	69.67 196	64.59 222	74.79 281	32.79 296	87.82 288	53.99 242	76.35 167	91.42 142

v15		71.40 230	68.75 233	79.35 224	80.39 255	66.70 106	87.57 229	86.64 260	68.66 209	54.68 275	72.00 296	54.50 161	89.98 267	63.69 193	50.66 306	81.38 280

v7n		71.31 231	68.65 234	79.28 226	76.40 296	60.77 221	86.71 242	89.45 208	64.17 247	58.77 258	78.24 259	44.59 247	93.54 193	57.76 230	61.75 265	83.52 255

V14		71.29 232	68.61 235	79.31 225	80.34 259	66.65 108	87.39 231	86.61 263	68.41 213	54.49 277	71.91 297	54.25 166	89.96 268	63.50 194	50.62 307	81.33 282

V9		71.16 233	68.46 237	79.27 227	80.26 262	66.60 110	87.21 234	86.56 264	68.17 214	54.26 280	71.81 299	54.00 168	89.93 269	63.28 197	50.57 308	81.27 283

anonymousdsp		71.14 234	69.37 229	76.45 263	72.95 306	54.71 282	84.19 254	88.88 229	61.92 264	62.15 243	79.77 249	38.14 272	91.44 251	68.90 151	67.45 226	83.21 261

testing_2		71.09 235	67.32 248	82.40 155	69.82 317	66.52 116	83.64 257	90.77 162	72.21 148	45.12 314	71.07 306	27.60 314	93.74 189	75.71 97	69.96 206	86.95 206

v11		71.05 236	68.32 240	79.23 228	80.34 259	66.57 113	87.01 237	86.55 265	68.11 215	54.40 278	71.66 301	52.94 182	89.91 270	62.71 205	51.12 304	81.21 284

v12		71.02 237	68.29 242	79.22 229	80.18 265	66.53 115	87.01 237	86.54 266	67.90 216	54.00 283	71.70 300	53.66 173	89.91 270	63.09 199	50.51 309	81.21 284

v13		70.90 238	68.15 243	79.15 233	80.08 266	66.45 117	86.83 241	86.50 267	67.62 222	53.78 285	71.61 302	53.51 177	89.87 272	62.89 203	50.50 310	81.14 286

F-COLMAP		70.66 239	68.44 238	77.32 257	86.37 182	55.91 276	88.00 216	86.32 268	56.94 290	57.28 267	88.07 149	33.58 294	92.49 221	51.02 252	68.37 219	83.55 253

WR-MVS_H		70.59 240	69.94 224	72.53 287	81.03 237	51.43 295	87.35 232	92.03 120	67.38 223	60.23 248	80.70 236	55.84 142	83.45 309	46.33 269	58.58 283	82.72 268

v748		70.55 241	67.97 244	78.27 243	75.75 301	58.78 253	86.29 246	89.25 215	65.12 242	56.66 269	77.17 269	45.05 245	92.95 204	58.13 229	58.33 284	83.10 264

CP-MVSNet		70.50 242	69.91 225	72.26 290	80.71 243	51.00 298	87.23 233	90.30 179	67.84 217	59.64 250	82.69 203	50.23 203	82.30 316	51.28 251	59.28 277	83.46 257

tfpnnormal		70.10 243	67.36 246	78.32 241	83.45 219	60.97 217	88.85 203	92.77 92	64.85 243	60.83 246	78.53 258	43.52 251	93.48 195	31.73 323	61.70 267	80.52 294

TransMVSNet (Re)		70.07 244	67.66 245	77.31 258	80.62 251	59.13 250	91.78 128	84.94 282	65.97 231	60.08 249	80.44 240	50.78 197	91.87 236	48.84 260	45.46 319	80.94 289

DP-MVS		69.90 245	66.48 251	80.14 203	95.36 14	62.93 190	89.56 190	76.11 314	50.27 307	57.69 265	85.23 181	39.68 264	95.73 110	33.35 317	71.05 203	81.78 274

PS-CasMVS		69.86 246	69.13 232	72.07 293	80.35 257	50.57 300	87.02 236	89.75 198	67.27 224	59.19 253	82.28 209	46.58 234	82.24 317	50.69 253	59.02 280	83.39 259

v52		69.80 247	67.01 250	78.15 246	71.84 310	60.10 235	82.02 271	87.39 251	64.48 244	57.80 263	75.97 276	41.47 258	92.90 209	63.00 200	59.13 279	81.45 278

V4		69.80 247	67.02 249	78.15 246	71.86 309	60.10 235	82.02 271	87.39 251	64.48 244	57.78 264	75.98 275	41.49 257	92.90 209	63.00 200	59.16 278	81.44 279

RPMNet		69.58 249	65.21 259	82.70 139	83.15 222	67.96 63	70.25 313	86.15 272	46.83 316	69.97 164	65.10 317	56.48 134	89.48 275	35.79 310	73.13 186	90.64 153

MSDG		69.54 250	65.73 254	80.96 194	85.11 197	63.71 176	84.19 254	83.28 296	56.95 289	54.50 276	84.03 191	31.50 303	96.03 100	42.87 283	69.13 214	83.14 263

PEN-MVS		69.46 251	68.56 236	72.17 292	79.27 275	49.71 304	86.90 239	89.24 216	67.24 225	59.08 254	82.51 204	47.23 229	83.54 308	48.42 262	57.12 285	83.25 260

LS3D		69.17 252	66.40 252	77.50 253	91.92 77	56.12 275	85.12 249	80.37 307	46.96 314	56.50 270	87.51 157	37.25 280	93.71 190	32.52 322	79.40 138	82.68 269

PatchT		69.11 253	65.37 258	80.32 200	82.07 230	63.68 178	67.96 322	87.62 250	50.86 306	69.37 174	65.18 316	57.09 119	88.53 283	41.59 288	66.60 230	88.74 171

ACMH	63.93 17	68.62 254	64.81 260	80.03 207	85.22 195	63.25 183	87.72 224	84.66 283	60.83 270	51.57 294	79.43 254	27.29 315	94.96 132	41.76 286	64.84 246	81.88 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 255	65.41 257	77.96 249	78.69 283	62.93 190	89.86 188	89.17 218	60.55 271	50.27 299	77.73 263	22.60 323	94.06 175	47.18 267	72.65 192	76.88 314

ADS-MVSNet		68.54 256	64.38 266	81.03 193	88.06 159	66.90 91	68.01 320	84.02 288	57.57 284	64.48 224	69.87 307	38.68 265	89.21 278	40.87 290	67.89 223	86.97 204

DTE-MVSNet		68.46 257	67.33 247	71.87 296	77.94 289	49.00 307	86.16 247	88.58 236	66.36 229	58.19 260	82.21 212	46.36 235	83.87 306	44.97 277	55.17 292	82.73 267

Patchmatch-RL test		68.17 258	64.49 264	79.19 230	71.22 312	53.93 285	70.07 315	71.54 330	69.22 200	56.79 268	62.89 320	56.58 132	88.61 280	69.53 145	52.61 298	95.03 54

XVG-ACMP-BASELINE		68.04 259	65.53 256	75.56 268	74.06 305	52.37 290	78.43 298	85.88 277	62.03 262	58.91 257	81.21 232	20.38 326	91.15 252	60.69 217	68.18 220	83.16 262

FMVSNet5		68.04 259	65.66 255	75.18 270	84.43 206	57.89 258	83.54 258	86.26 270	61.83 266	53.64 286	73.30 284	37.15 283	85.08 297	48.99 259	61.77 264	82.56 270

ACMH+	65.35 16	67.65 261	64.55 262	76.96 260	84.59 202	57.10 269	88.08 215	80.79 305	58.59 283	53.00 289	81.09 234	26.63 317	92.95 204	46.51 268	61.69 268	80.82 290

pmmvs6		67.57 262	64.76 261	76.00 267	72.82 308	53.37 287	88.71 205	86.78 258	53.19 299	57.58 266	78.03 261	35.33 290	92.41 223	55.56 237	54.88 294	82.21 271

Anonymous20231206		67.53 263	65.78 253	72.79 286	74.95 302	47.59 311	88.23 213	87.32 254	61.75 267	58.07 261	77.29 266	37.79 277	87.29 291	42.91 281	63.71 255	83.48 256

Patchmtry		67.53 263	63.93 267	78.34 240	82.12 229	64.38 160	68.72 317	84.00 289	48.23 313	59.24 252	72.41 292	57.82 114	89.27 277	46.10 270	56.68 289	81.36 281

USDC		67.43 265	64.51 263	76.19 265	77.94 289	55.29 279	78.38 299	85.00 281	73.17 129	48.36 304	80.37 241	21.23 325	92.48 222	52.15 249	64.02 253	80.81 291

ADS-MVSNet2		66.90 266	63.44 269	77.26 259	88.06 159	60.70 224	68.01 320	75.56 319	57.57 284	64.48 224	69.87 307	38.68 265	84.10 301	40.87 290	67.89 223	86.97 204

CMPMVS	48.56 21	66.77 267	64.41 265	73.84 278	70.65 315	50.31 301	77.79 303	85.73 279	45.54 319	44.76 315	82.14 213	35.40 289	90.14 263	63.18 198	74.54 177	81.07 288
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 268	62.92 273	76.80 262	76.51 295	57.77 260	89.22 196	83.41 294	55.48 296	53.86 284	77.84 262	26.28 318	93.95 183	34.90 314	68.76 216	78.68 308

LTVRE_ROB	59.60 19	66.27 269	63.54 268	74.45 274	84.00 213	51.55 294	67.08 323	83.53 292	58.78 281	54.94 274	80.31 242	34.54 293	93.23 199	40.64 292	68.03 221	78.58 309
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	76.88 261	81.42 236	54.45 284	57.49 334	88.67 232	49.36 309	63.86 229	46.86 330	56.06 138	90.25 257	49.53 258	68.83 215	85.95 226

Patchmatch-test		65.86 271	60.94 282	80.62 197	83.75 214	58.83 252	58.91 333	75.26 321	44.50 323	50.95 298	77.09 271	58.81 108	87.90 287	35.13 313	64.03 252	95.12 49

UnsupCasMVSNet_eth		65.79 272	63.10 271	73.88 277	70.71 314	50.29 302	81.09 279	89.88 195	72.58 139	49.25 302	74.77 282	32.57 298	87.43 290	55.96 236	41.04 325	83.90 251

pmmvs-eth3d		65.53 273	62.32 277	75.19 269	69.39 319	59.59 242	82.80 268	83.43 293	62.52 260	51.30 296	72.49 289	32.86 295	87.16 292	55.32 238	50.73 305	78.83 307

SixPastTwentyTwo		64.92 274	61.78 280	74.34 276	78.74 282	49.76 303	83.42 262	79.51 310	62.86 257	50.27 299	77.35 264	30.92 307	90.49 255	45.89 271	47.06 316	82.78 265

OurMVSNet-221017-0		64.68 275	62.17 278	72.21 291	76.08 299	47.35 312	80.67 282	81.02 304	56.19 293	51.60 293	79.66 251	27.05 316	88.56 282	53.60 245	53.63 297	80.71 292

test_0402		64.54 276	61.09 281	74.92 271	84.10 212	60.75 222	87.95 217	79.71 309	52.03 302	52.41 290	77.20 267	32.21 300	91.64 242	23.14 333	61.03 271	72.36 321

testgi		64.48 277	62.87 274	69.31 300	71.24 311	40.62 325	85.49 248	79.92 308	65.36 237	54.18 281	83.49 197	23.74 321	84.55 299	41.60 287	60.79 274	82.77 266

RPSCF		64.24 278	61.98 279	71.01 297	76.10 298	45.00 315	75.83 306	75.94 316	46.94 315	58.96 256	84.59 188	31.40 304	82.00 318	47.76 265	60.33 276	86.04 225

test2356		64.16 279	63.28 270	66.81 307	69.37 320	39.86 328	87.76 223	86.02 274	59.83 276	53.54 287	73.23 285	34.94 291	80.67 321	39.66 294	65.20 239	79.89 299

EU-MVSNet		64.01 280	63.01 272	67.02 306	74.40 304	38.86 330	83.27 263	86.19 271	45.11 320	54.27 279	81.15 233	36.91 286	80.01 322	48.79 261	57.02 286	82.19 272

test20.03		63.83 281	62.65 275	67.38 305	70.58 316	39.94 326	86.57 244	84.17 286	63.29 253	51.86 292	77.30 265	37.09 284	82.47 314	38.87 302	54.13 296	79.73 301

MDA-MVSNet_test_wron		63.78 282	60.16 283	74.64 272	78.15 287	60.41 228	83.49 259	84.03 287	56.17 295	39.17 327	71.59 304	37.22 281	83.24 312	42.87 283	48.73 313	80.26 297

YYNet1		63.76 283	60.14 284	74.62 273	78.06 288	60.19 234	83.46 261	83.99 291	56.18 294	39.25 326	71.56 305	37.18 282	83.34 310	42.90 282	48.70 314	80.32 296

K. test v3		63.09 284	59.61 286	73.53 280	76.26 297	49.38 306	83.27 263	77.15 313	64.35 246	47.77 305	72.32 294	28.73 310	87.79 289	49.93 257	36.69 330	83.41 258

COLMAP_ROB	57.96 20	62.98 285	59.65 285	72.98 284	81.44 235	53.00 289	83.75 256	75.53 320	48.34 312	48.81 303	81.40 226	24.14 319	90.30 256	32.95 319	60.52 275	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		61.66 286	58.06 287	72.46 288	79.57 270	51.42 296	80.17 288	68.61 333	51.25 304	45.88 309	81.23 228	19.86 327	86.58 294	38.98 296	57.01 287	79.39 303

UnsupCasMVSNet_bld		61.60 287	57.71 288	73.29 282	68.73 321	51.64 293	78.61 297	89.05 225	57.20 288	46.11 308	61.96 322	28.70 311	88.60 281	50.08 256	38.90 328	79.63 302

MDA-MVSNet-bldmvs		61.54 288	57.70 289	73.05 283	79.53 272	57.00 270	83.08 266	81.23 302	57.57 284	34.91 330	72.45 291	32.79 296	86.26 296	35.81 309	41.95 323	75.89 316

TinyColmap		60.32 289	56.42 295	72.00 294	78.78 281	53.18 288	78.36 300	75.64 317	52.30 301	41.59 325	75.82 278	14.76 334	88.35 284	35.84 308	54.71 295	74.46 318

MVS-HIRNet		60.25 290	55.55 296	74.35 275	84.37 207	56.57 273	71.64 311	74.11 323	34.44 332	45.54 313	42.24 334	31.11 306	89.81 273	40.36 293	76.10 168	76.67 315

MIMVSNet1		60.16 291	57.33 291	68.67 301	69.71 318	44.13 317	78.92 296	84.21 285	55.05 297	44.63 316	71.85 298	23.91 320	81.54 320	32.63 321	55.03 293	80.35 295

PM-MVS		59.40 292	56.59 293	67.84 302	63.63 325	41.86 322	76.76 304	63.22 339	59.01 280	51.07 297	72.27 295	11.72 336	83.25 311	61.34 213	50.28 311	78.39 310

testus		59.36 293	57.51 290	64.90 310	66.72 322	37.56 331	84.98 250	81.09 303	57.46 287	47.72 306	72.76 286	11.43 338	78.78 328	36.56 305	58.91 281	78.36 311

new-patchmatchnet		59.30 294	56.48 294	67.79 303	65.86 323	44.19 316	82.47 269	81.77 300	59.94 275	43.65 320	66.20 313	27.67 313	81.68 319	39.34 295	41.40 324	77.50 313

testpf		57.17 295	56.93 292	57.88 317	79.13 279	42.40 319	34.23 340	85.97 276	52.64 300	47.66 307	66.50 311	36.33 288	79.65 324	53.60 245	56.31 290	51.60 334

DSMNet-mixed		56.78 296	54.44 298	63.79 312	63.21 326	29.44 339	64.43 326	64.10 338	42.12 328	51.32 295	71.60 303	31.76 301	75.04 331	36.23 307	65.20 239	86.87 208

LP		56.71 297	51.64 301	71.91 295	80.08 266	60.33 230	61.72 328	75.61 318	43.87 325	43.76 319	60.30 324	30.46 308	84.05 302	22.94 334	46.06 318	71.34 323

1111		56.66 298	54.98 297	61.69 313	61.99 329	31.38 335	79.81 293	83.17 297	45.66 317	41.94 322	65.44 314	41.50 255	79.56 325	27.64 327	47.68 315	74.14 319

test1235678		55.73 299	52.74 299	64.68 311	60.16 332	35.56 333	81.65 275	81.46 301	51.27 303	38.93 328	62.82 321	17.44 329	78.58 329	30.87 325	50.09 312	79.89 299

pmmvs3		55.51 300	51.50 303	67.53 304	57.90 334	50.93 299	80.37 284	73.66 324	40.63 329	44.15 318	64.75 318	16.30 330	78.97 327	44.77 278	40.98 326	72.69 320

TDRefinement		55.28 301	51.58 302	66.39 308	59.53 333	46.15 314	76.23 305	72.80 325	44.60 322	42.49 321	76.28 274	15.29 332	82.39 315	33.20 318	43.75 321	70.62 325

LF4IMVS		54.01 302	52.12 300	59.69 315	62.41 328	39.91 327	68.59 318	68.28 335	42.96 326	44.55 317	75.18 279	14.09 335	68.39 335	41.36 289	51.68 302	70.78 324

Anonymous20231211		53.57 303	49.43 305	66.00 309	65.01 324	42.08 320	80.95 281	72.60 326	38.46 330	41.65 324	64.48 319	15.72 331	84.23 300	25.78 330	40.24 327	71.68 322

N_pmnet		50.55 304	49.11 306	54.88 321	77.17 293	4.02 351	84.36 253	2.00 352	48.59 310	45.86 311	68.82 309	32.22 299	82.80 313	31.58 324	51.38 303	77.81 312

new_pmnet		49.31 305	46.44 307	57.93 316	62.84 327	40.74 324	68.47 319	62.96 340	36.48 331	35.09 329	57.81 326	14.97 333	72.18 332	32.86 320	46.44 317	60.88 332

test12356		47.51 306	44.82 308	55.56 319	52.53 335	21.09 346	71.45 312	76.03 315	44.14 324	30.69 331	58.18 325	9.01 342	76.14 330	26.95 329	34.43 333	69.46 327

testmv		46.98 307	43.53 309	57.35 318	47.75 340	30.41 338	74.99 308	77.69 311	42.84 327	28.03 332	53.36 327	8.18 343	71.18 333	24.36 332	34.55 331	70.46 326

.test1245		46.52 308	49.68 304	37.02 330	61.99 329	31.38 335	79.81 293	83.17 297	45.66 317	41.94 322	65.44 314	41.50 255	79.56 325	27.64 327	0.01 347	0.13 346

FPMVS		45.64 309	43.10 310	53.23 323	51.42 337	36.46 332	64.97 325	71.91 328	29.13 334	27.53 333	61.55 323	9.83 340	65.01 339	16.00 339	55.58 291	58.22 333

no-one		44.13 310	38.39 311	61.34 314	45.91 342	41.94 321	61.67 329	75.07 322	45.05 321	20.07 336	40.68 337	11.58 337	79.82 323	30.18 326	15.30 338	62.26 331

LCM-MVSNet		40.54 311	35.79 312	54.76 322	36.92 345	30.81 337	51.41 335	69.02 332	22.07 336	24.63 334	45.37 332	4.56 348	65.81 337	33.67 316	34.50 332	67.67 328

ANet_high		40.27 312	35.20 313	55.47 320	34.74 346	34.47 334	63.84 327	71.56 329	48.42 311	18.80 338	41.08 335	9.52 341	64.45 340	20.18 336	8.66 345	67.49 329

PMMVS2		37.93 313	33.61 314	50.92 324	46.31 341	24.76 344	60.55 332	50.05 343	28.94 335	20.93 335	47.59 329	4.41 349	65.13 338	25.14 331	18.55 337	62.87 330

Gipuma		34.91 314	31.44 316	45.30 326	70.99 313	39.64 329	19.85 343	72.56 327	20.10 339	16.16 340	21.47 342	5.08 347	71.16 334	13.07 340	43.70 322	25.08 340
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PNet_i23d		32.77 315	29.98 317	41.11 328	48.05 338	29.17 340	65.82 324	50.02 344	21.42 337	14.74 341	37.19 338	1.11 352	55.11 342	19.75 337	11.77 340	39.06 336

PMVS	26.43 22	31.84 316	28.16 318	42.89 327	25.87 349	27.58 342	50.92 336	49.78 345	21.37 338	14.17 342	40.81 336	2.01 350	66.62 336	9.61 342	38.88 329	34.49 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pcd1.5k->3k		31.17 317	31.85 315	29.12 332	81.48 233	0.00 354	0.00 344	91.79 129	0.00 348	0.00 350	0.00 350	41.05 261	0.00 351	0.00 348	72.34 195	87.36 198

wuykxyi23d		29.03 318	23.09 323	46.84 325	31.67 348	28.82 341	43.46 338	57.72 342	14.39 342	7.52 346	20.84 343	0.64 353	60.29 341	21.57 335	10.04 342	51.40 335

E-PMN		24.61 319	24.00 320	26.45 333	43.74 343	18.44 348	60.86 330	39.66 346	15.11 340	9.53 344	22.10 341	6.52 345	46.94 344	8.31 343	10.14 341	13.98 342

MVE	24.84 23	24.35 320	19.77 324	38.09 329	34.56 347	26.92 343	26.57 341	38.87 348	11.73 343	11.37 343	27.44 339	1.37 351	50.42 343	11.41 341	14.60 339	36.93 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 321	23.20 322	25.46 334	41.52 344	16.90 349	60.56 331	38.79 349	14.62 341	8.99 345	20.24 345	7.35 344	45.82 345	7.25 344	9.46 343	13.64 343

tmp_tt		22.26 322	23.75 321	17.80 335	5.23 350	12.06 350	35.26 339	39.48 347	2.82 345	18.94 337	44.20 333	22.23 324	24.64 347	36.30 306	9.31 344	16.69 341

cdsmvs_eth3d_5k		19.86 323	26.47 319	0.00 339	0.00 353	0.00 354	0.00 344	93.45 61	0.00 348	0.00 350	95.27 30	49.56 208	0.00 351	0.00 348	0.00 349	0.00 348

wuyk23d		11.30 324	10.95 325	12.33 336	48.05 338	19.89 347	25.89 342	1.92 353	3.58 344	3.12 347	1.37 347	0.64 353	15.77 348	6.23 345	7.77 346	1.35 344

ab-mvs-re		7.91 325	10.55 326	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	94.95 40	0.00 357	0.00 351	0.00 348	0.00 349	0.00 348

testmvs		7.23 326	9.62 327	0.06 338	0.04 351	0.02 353	84.98 250	0.02 354	0.03 346	0.18 348	1.21 348	0.01 356	0.02 349	0.14 346	0.01 347	0.13 346

test123		6.92 327	9.21 328	0.08 337	0.03 352	0.05 352	81.65 275	0.01 355	0.02 347	0.14 349	0.85 349	0.03 355	0.02 349	0.12 347	0.00 349	0.16 345

pcd_1.5k_mvsjas		4.46 328	5.95 329	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	53.55 174	0.00 351	0.00 348	0.00 349	0.00 348

sosnet-low-res		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 349	0.00 348

sosnet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 349	0.00 348

uncertanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 349	0.00 348

Regformer		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 349	0.00 348

uanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 349	0.00 348

test_part2					96.29 7	68.16 60				90.78 3

test_part1								94.26 41				77.03 4			95.18 8	96.11 19

test_full								94.66 29

sam_mvs1												57.85 113

sam_mvs												54.91 157

semantic-postprocess				76.32 264	81.48 233	60.67 225		85.99 275	66.17 230	59.50 251	78.88 256	45.51 241	83.65 307	62.58 207	61.93 262	84.63 247

ambc				69.61 299	61.38 331	41.35 323	49.07 337	85.86 278		50.18 301	66.40 312	10.16 339	88.14 286	45.73 272	44.20 320	79.32 305

MTGPA								92.23 109

test_post1							78.95 295				20.70 344	53.05 180	91.50 249	60.43 218

test_post											23.01 340	56.49 133	92.67 216

patchmatchnet-post											67.62 310	57.62 116	90.25 257

GG-mvs-BLEND				86.53 49	91.91 78	69.67 34	75.02 307	94.75 26		78.67 82	90.85 112	77.91 2	94.56 145	72.25 119	93.74 30	95.36 35

MTMP								32.52 350

gm-plane-assit					88.42 153	67.04 86			78.62 52		91.83 102		97.37 47	76.57 93

test9_res														89.41 10	94.96 9	95.29 39

TEST9					94.18 28	67.28 78	94.16 36	93.51 58	71.75 165	85.52 27	95.33 26	68.01 24	97.27 54

test_8					94.19 27	67.19 80	94.15 38	93.42 65	71.87 157	85.38 29	95.35 25	68.19 22	96.95 74

agg_prior2														86.41 31	94.75 17	95.33 36

agg_prior					94.16 32	66.97 87		93.31 70		84.49 36			96.75 83

TestCases				72.46 288	79.57 270	51.42 296		68.61 333	51.25 304	45.88 309	81.23 228	19.86 327	86.58 294	38.98 296	57.01 287	79.39 303

test_prior4						67.18 82	93.92 52

test_prior2							95.10 27		75.40 89	85.25 31	95.61 21	67.94 25		87.47 23	94.77 14

test_prior				86.42 53	94.71 22	67.35 76		93.10 82					96.84 79			95.05 51

旧先验2							92.00 112		59.37 279	87.54 15			93.47 196	75.39 99

新几何2							91.41 140

新几何1				84.73 101	92.32 66	64.28 165		91.46 142	59.56 277	79.77 68	92.90 86	56.95 124	96.57 88	63.40 195	92.91 40	93.34 104

旧先验1					91.94 75	60.74 223		91.50 140			94.36 55	65.23 49			91.84 52	94.55 65

无先验							92.71 85	92.61 100	62.03 262				97.01 65	66.63 166		93.97 91

原ACMM2							92.01 110

原ACMM1				84.42 110	93.21 48	64.27 166		93.40 66	65.39 236	79.51 71	92.50 91	58.11 112	96.69 85	65.27 182	93.96 24	92.32 131

test222					89.77 122	61.60 212	89.55 191	89.42 210	56.83 291	77.28 94	92.43 94	52.76 183			91.14 64	93.09 113

testdata2													96.09 97	61.26 214

segment_acmp												65.94 43

testdata				81.34 181	89.02 139	57.72 261		89.84 196	58.65 282	85.32 30	94.09 64	57.03 121	93.28 198	69.34 147	90.56 70	93.03 115

testdata1							89.21 197		77.55 63

test12				87.09 34	94.60 24	68.86 44		92.91 88		82.67 49		65.44 48	97.55 41		93.69 32	94.84 59

plane_prior7					86.94 174	61.51 213

plane_prior6					87.23 170	62.32 201						50.66 198

plane_prior5								91.31 147					95.55 120	76.74 91	78.53 147	88.39 177

plane_prior4											89.14 135

plane_prior3						61.95 209			79.09 44	72.53 134

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 171

plane_prior						62.42 199	93.85 55		79.38 37						78.80 145

n2								0.00 356

nn								0.00 356

door-mid								66.01 337

lessismore_v0				73.72 279	72.93 307	47.83 310		61.72 341		45.86 311	73.76 283	28.63 312	89.81 273	47.75 266	31.37 334	83.53 254

LGP-MVS_train				79.56 219	84.31 208	59.37 246		89.73 201	69.49 197	64.86 220	88.42 140	38.65 267	94.30 161	72.56 116	72.76 190	85.01 242

test11								93.01 84

door								66.57 336

HQP5-MVS						63.66 179

HQP-NCC					87.54 166		94.06 43		79.80 33	74.18 116

ACMP_Plane					87.54 166		94.06 43		79.80 33	74.18 116

BP-MVS														77.63 88

HQP4-MVS										74.18 116			95.61 116			88.63 172

HQP3-MVS								91.70 133							78.90 143

HQP2-MVS												51.63 194

NP-MVS					87.41 169	63.04 188					90.30 121

MDTV_nov1_ep13_2view						59.90 239	80.13 289		67.65 220	72.79 129		54.33 165		59.83 221		92.58 126

MDTV_nov1_ep13			72.61 207		89.06 138	68.48 50	80.33 285	90.11 188	71.84 160	71.81 145	75.92 277	53.01 181	93.92 184	48.04 263	73.38 184

ACMMP++_ref															71.63 197

ACMMP++															69.72 208

Test By Simon												54.21 167

ITE_SJBPF				70.43 298	74.44 303	47.06 313		77.32 312	60.16 274	54.04 282	83.53 195	23.30 322	84.01 304	43.07 280	61.58 269	80.21 298

DeepMVS_CX				34.71 331	51.45 336	24.73 345		28.48 351	31.46 333	17.49 339	52.75 328	5.80 346	42.60 346	18.18 338	19.42 336	36.81 338

High-resolution multi-view benchmark