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

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

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

MVS_0304		88.39 9	88.35 12	88.50 12	93.01 52	70.11 23	95.90 10	92.20 116	86.27 6	88.70 10	95.92 13	56.76 128	99.02 4	92.68 3	93.76 29	96.37 15

QAPM		79.95 116	77.39 139	87.64 20	89.63 128	71.41 12	93.30 68	93.70 51	65.34 238	67.39 205	91.75 105	47.83 226	98.96 5	57.71 231	89.81 73	92.54 127

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
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	59.78 100	98.90 7	81.29 67	94.69 18	96.74 7

API-MVS		82.28 82	80.53 93	87.54 23	96.13 9	70.59 19	93.63 59	91.04 159	65.72 235	75.45 109	92.83 88	56.11 139	98.89 8	64.10 191	89.75 75	93.15 111

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	53.99 171	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
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 81	98.77 10	86.06 34	95.60 6	96.03 22

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

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

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

CHOSEN 1792x2688		84.98 47	83.45 56	89.57 6	89.94 119	75.14 4	92.07 107	92.32 108	81.87 25	75.68 104	88.27 146	60.18 97	98.60 14	80.46 70	90.27 72	94.96 56

3Dnovator	73.91 6	82.69 78	80.82 88	88.31 14	89.57 129	71.26 13	92.60 91	94.39 39	78.84 48	67.89 198	92.48 93	48.42 220	98.52 15	68.80 152	94.40 20	95.15 47

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

DeepC-MVS	77.85 3	85.52 42	85.24 43	86.37 56	88.80 146	66.64 109	92.15 101	93.68 52	81.07 28	76.91 99	93.64 71	62.59 79	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
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 60	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
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

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

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

DP-MVS Recon		82.73 75	81.65 80	85.98 64	97.31 3	67.06 84	95.15 25	91.99 123	69.08 202	76.50 102	93.89 68	54.48 165	98.20 22	70.76 136	85.66 102	92.69 122

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

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 80	98.15 24	85.93 35	91.15 63	94.15 81

OpenMVS	70.45 11	78.54 144	75.92 158	86.41 55	85.93 193	71.68 11	92.74 84	92.51 105	66.49 228	64.56 225	91.96 100	43.88 251	98.10 25	54.61 239	90.65 68	89.44 165

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

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 71	97.94 27	87.40 25	93.55 34	95.42 33
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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	59.42 103	97.92 28	86.13 33	94.92 10	94.94 57

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 72	97.90 29	79.35 74	79.36 139	90.74 151

PS-MVSNAJ		88.14 10	87.61 17	89.71 4	92.06 73	76.72 1	95.75 11	93.26 74	83.86 11	89.55 6	96.06 11	53.55 176	97.89 30	91.10 6	93.31 36	94.54 67

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 78	97.76 31	78.07 85	78.01 150	90.05 158

xiu_mvs_v2_base		87.92 14	87.38 22	89.55 7	91.41 98	76.43 2	95.74 12	93.12 83	83.53 13	89.55 6	95.95 12	53.45 181	97.68 32	91.07 7	92.62 43	94.54 67

HFP-MVS		84.73 49	84.40 49	85.72 74	93.75 38	65.01 148	93.50 64	93.19 79	72.19 149	79.22 73	94.93 42	59.04 107	97.67 33	81.55 62	92.21 47	94.49 71

#test#		84.98 47	84.74 46	85.72 74	93.75 38	65.01 148	94.09 41	93.19 79	73.55 124	79.22 73	94.93 42	59.04 107	97.67 33	82.66 54	92.21 47	94.49 71

IB-MVS	77.80 4	82.18 83	80.46 94	87.35 27	89.14 139	70.28 22	95.59 17	95.17 16	78.85 47	70.19 160	85.82 178	70.66 17	97.67 33	72.19 122	66.52 233	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
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

3Dnovator+	73.60 7	82.10 87	80.60 92	86.60 44	90.89 106	66.80 100	95.20 23	93.44 66	74.05 110	67.42 203	92.49 92	49.46 211	97.65 37	70.80 135	91.68 55	95.33 36

SD-MVS		87.49 18	87.49 19	87.50 24	93.60 40	68.82 46	93.90 53	92.63 101	76.86 71	87.90 13	95.76 16	66.17 39	97.63 38	89.06 15	91.48 59	96.05 21

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

PAPR		85.15 45	84.47 47	87.18 30	96.02 11	68.29 56	91.85 124	93.00 88	76.59 76	79.03 76	95.00 39	61.59 84	97.61 40	78.16 84	89.00 77	95.63 29

test12				87.09 34	94.60 24	68.86 44		92.91 90		82.67 49		65.44 48	97.55 41		93.69 32	94.84 59

region2R		84.36 53	84.03 51	85.36 86	93.54 42	64.31 163	93.43 67	92.95 89	72.16 152	78.86 78	94.84 47	56.97 125	97.53 42	81.38 65	92.11 51	94.24 75

PAPM_NR		82.97 73	81.84 77	86.37 56	94.10 34	66.76 101	87.66 229	92.84 92	69.96 194	74.07 120	93.57 72	63.10 76	97.50 43	70.66 137	90.58 69	94.85 58

ACMMPR		84.37 52	84.06 50	85.28 88	93.56 41	64.37 161	93.50 64	93.15 82	72.19 149	78.85 79	94.86 46	56.69 132	97.45 44	81.55 62	92.20 49	94.02 90

XVS		83.87 62	83.47 55	85.05 94	93.22 46	63.78 172	92.92 80	92.66 99	73.99 111	78.18 83	94.31 62	55.25 145	97.41 45	79.16 76	91.58 57	93.95 92

X-MVStestdata		76.86 173	74.13 187	85.05 94	93.22 46	63.78 172	92.92 80	92.66 99	73.99 111	78.18 83	10.19 348	55.25 145	97.41 45	79.16 76	91.58 57	93.95 92

gm-plane-assit					88.42 155	67.04 86			78.62 52		91.83 102		97.37 47	76.57 93

CDPH-MVS		85.71 41	85.46 41	86.46 51	94.75 21	67.19 80	93.89 54	92.83 93	70.90 178	83.09 47	95.28 29	63.62 68	97.36 48	80.63 68	94.18 22	94.84 59

AdaColmap		78.94 133	77.00 146	84.76 100	96.34 6	65.86 130	92.66 90	87.97 249	62.18 263	70.56 152	92.37 96	43.53 252	97.35 49	64.50 188	82.86 121	91.05 149

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
PGM-MVS		83.25 68	82.70 70	84.92 96	92.81 59	64.07 168	90.44 170	92.20 116	71.28 174	77.23 95	94.43 53	55.17 149	97.31 51	79.33 75	91.38 60	93.37 103

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	85.14 184	63.19 74	97.29 52	87.08 27	73.91 183	84.83 246

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 74	97.29 52	87.08 27	91.38 60	94.13 82

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

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

agg_prior3		86.93 26	87.08 24	86.48 50	94.21 26	66.95 89	94.14 39	93.40 68	71.80 162	84.86 33	95.13 36	66.16 40	97.25 56	89.09 13	94.90 11	95.25 44

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

MP-MVS		85.02 46	84.97 45	85.17 92	92.60 62	64.27 166	93.24 69	92.27 110	73.13 130	79.63 70	94.43 53	61.90 82	97.17 58	85.00 40	92.56 44	94.06 88

MPTG		84.73 49	84.47 47	85.50 78	91.89 79	65.16 142	91.55 137	92.23 111	75.32 91	80.53 61	95.21 34	56.06 140	97.16 59	84.86 42	92.55 45	94.18 77

MTAPA		83.91 61	83.38 60	85.50 78	91.89 79	65.16 142	81.75 275	92.23 111	75.32 91	80.53 61	95.21 34	56.06 140	97.16 59	84.86 42	92.55 45	94.18 77

VDD-MVS		83.06 72	81.81 78	86.81 38	90.86 107	67.70 68	95.40 19	91.50 142	75.46 86	81.78 53	92.34 97	40.09 265	97.13 61	86.85 30	82.04 127	95.60 30

LFMVS		84.34 54	82.73 69	89.18 8	94.76 20	73.25 8	94.99 30	91.89 127	71.90 155	82.16 51	93.49 74	47.98 225	97.05 62	82.55 55	84.82 106	97.25 2

sss		82.71 77	82.38 72	83.73 122	89.25 135	59.58 243	92.24 100	94.89 21	77.96 57	79.86 67	92.38 95	56.70 131	97.05 62	77.26 90	80.86 133	94.55 65

1314		80.70 101	78.95 116	85.94 66	87.77 167	67.56 72	87.91 220	92.55 104	72.17 151	67.44 202	93.09 78	50.27 204	97.04 64	71.68 125	87.64 86	93.23 109

无先验							92.71 85	92.61 102	62.03 264				97.01 65	66.63 166		93.97 91

1121		81.25 95	80.05 95	84.87 98	92.30 67	64.31 163	87.91 220	91.39 146	59.44 280	79.94 66	92.91 85	57.09 121	97.01 65	66.63 166	92.81 42	93.29 107

MP-MVS-pluss		85.24 44	85.13 44	85.56 77	91.42 96	65.59 135	91.54 138	92.51 105	74.56 99	80.62 60	95.64 20	59.15 106	97.00 67	86.94 29	93.80 27	94.07 87
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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	58.22 112	97.00 67	85.22 39	84.33 113	96.52 13

APD-MVS		85.93 38	85.99 33	85.76 73	95.98 12	65.21 141	93.59 61	92.58 103	66.54 227	86.17 20	95.88 14	63.83 65	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
mPP-MVS		82.96 74	82.44 71	84.52 108	92.83 56	62.92 192	92.76 83	91.85 129	71.52 171	75.61 107	94.24 63	53.48 180	96.99 70	78.97 79	90.73 66	93.64 99

CANet_DTU		84.09 58	83.52 53	85.81 69	90.30 113	66.82 94	91.87 122	89.01 228	85.27 7	86.09 21	93.74 70	47.71 228	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	91.73 132	77.43 66	77.08 98	89.81 132	63.77 67	96.97 72	79.67 72	88.21 82	92.60 125

ACMMP		81.49 92	80.67 90	83.93 117	91.71 85	62.90 193	92.13 102	92.22 115	71.79 163	71.68 148	93.49 74	50.32 202	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
test_8					94.19 27	67.19 80	94.15 38	93.42 67	71.87 157	85.38 29	95.35 25	68.19 22	96.95 74

HY-MVS	76.49 5	84.28 55	83.36 61	87.02 36	92.22 70	67.74 67	84.65 254	94.50 33	79.15 42	82.23 50	87.93 152	66.88 34	96.94 75	80.53 69	82.20 126	96.39 14

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 61	96.94 75	75.19 100	94.09 23	95.66 28

canonicalmvs		86.85 28	86.25 31	88.66 10	91.80 83	71.92 10	93.54 63	91.71 134	80.26 31	87.55 14	95.25 32	63.59 70	96.93 77	88.18 19	84.34 112	97.11 3

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

test_prior3		87.38 19	87.70 16	86.42 53	94.71 22	67.35 76	95.10 27	93.10 84	75.40 89	85.25 31	95.61 21	67.94 25	96.84 79	87.47 23	94.77 14	95.05 51

test_prior				86.42 53	94.71 22	67.35 76		93.10 84					96.84 79			95.05 51

MSLP-MVS++		86.27 34	85.91 35	87.35 27	92.01 74	68.97 43	95.04 29	92.70 96	79.04 46	81.50 55	96.50 4	58.98 109	96.78 81	83.49 50	93.93 25	96.29 16

abl_6		79.82 118	79.20 113	81.70 177	89.85 120	58.34 256	88.47 210	90.07 191	62.56 261	77.71 87	93.08 79	47.65 229	96.78 81	77.94 86	85.45 104	89.99 159

agg_prior1		87.02 24	87.26 23	86.28 60	94.16 32	66.97 87	94.08 42	93.31 72	71.85 159	84.49 36	95.39 24	68.91 18	96.75 83	88.84 17	94.32 21	95.13 48

agg_prior					94.16 32	66.97 87		93.31 72		84.49 36			96.75 83

原ACMM1				84.42 110	93.21 48	64.27 166		93.40 68	65.39 236	79.51 71	92.50 91	58.11 114	96.69 85	65.27 182	93.96 24	92.32 131

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 154	65.49 138	83.00 269	92.67 98	71.82 161	77.36 93	85.01 185	54.50 163	96.59 86	76.35 95	75.63 170	95.32 38

PCF-MVS	73.15 9	79.29 127	77.63 133	84.29 112	86.06 188	65.96 128	87.03 237	91.10 156	69.86 195	69.79 167	90.64 114	57.54 119	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
新几何1				84.73 101	92.32 66	64.28 165		91.46 144	59.56 279	79.77 68	92.90 86	56.95 126	96.57 88	63.40 195	92.91 40	93.34 104

VDDNet		80.50 104	78.26 123	87.21 29	86.19 186	69.79 30	94.48 34	91.31 149	60.42 274	79.34 72	90.91 111	38.48 271	96.56 89	82.16 56	81.05 131	95.27 41

cascas		78.18 149	75.77 160	85.41 84	87.14 174	69.11 39	92.96 77	91.15 154	66.71 226	70.47 153	86.07 175	37.49 281	96.48 90	70.15 140	79.80 136	90.65 152

HPM-MVS		83.25 68	82.95 64	84.17 113	92.25 69	62.88 194	90.91 159	91.86 128	70.30 191	77.12 96	93.96 67	56.75 130	96.28 91	82.04 58	91.34 62	93.34 104

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

CP-MVS		83.71 66	83.40 59	84.65 104	93.14 50	63.84 170	94.59 33	92.28 109	71.03 176	77.41 92	94.92 44	55.21 148	96.19 93	81.32 66	90.70 67	93.91 94

UGNet		79.87 117	78.68 117	83.45 130	89.96 118	61.51 213	92.13 102	90.79 163	76.83 72	78.85 79	86.33 173	38.16 273	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
APD-MVS_3200maxsize		81.64 91	81.32 83	82.59 144	92.36 64	58.74 254	91.39 141	91.01 160	63.35 254	79.72 69	94.62 51	51.82 192	96.14 95	79.71 71	87.93 84	92.89 120

BH-RMVSNet		79.46 126	77.65 132	84.89 97	91.68 86	65.66 134	93.55 62	88.09 246	72.93 134	73.37 124	91.12 110	46.20 240	96.12 96	56.28 235	85.61 103	92.91 119

testdata2													96.09 97	61.26 214

MVS_Test		84.16 57	83.20 62	87.05 35	91.56 88	69.82 29	89.99 182	92.05 121	77.77 59	82.84 48	86.57 170	63.93 64	96.09 97	74.91 106	89.18 76	95.25 44

PatchFormer-LS_test		83.14 70	81.81 78	87.12 32	92.34 65	69.92 28	88.64 207	93.32 71	82.07 23	74.87 113	91.62 107	68.91 18	96.08 99	66.07 174	78.45 149	95.37 34

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

MSDG		69.54 252	65.73 256	80.96 194	85.11 199	63.71 176	84.19 256	83.28 298	56.95 291	54.50 278	84.03 193	31.50 305	96.03 100	42.87 283	69.13 216	83.14 265

Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 130	69.40 36	91.35 145	86.12 275	72.59 138	83.22 46	92.81 89	59.60 102	96.01 102	81.76 60	87.80 85	95.56 31

UA-Net		80.02 114	79.65 102	81.11 189	89.33 133	57.72 261	86.33 247	89.00 229	77.44 65	81.01 57	89.15 136	59.33 104	95.90 103	61.01 215	84.28 115	89.73 162

DI_MVS_plusplus_test		79.78 120	77.50 136	86.62 43	80.90 240	69.46 35	90.69 165	91.97 125	77.00 68	59.07 257	82.34 210	46.82 232	95.88 104	82.14 57	86.59 95	94.53 69

test_normal		79.66 121	77.36 141	86.54 47	80.72 244	69.21 38	90.68 166	92.16 120	76.99 69	58.63 261	82.03 219	46.70 234	95.86 105	81.74 61	86.63 94	94.56 64

lupinMVS		87.74 16	87.77 15	87.63 22	89.24 137	71.18 14	96.57 4	92.90 91	82.70 16	87.13 16	95.27 30	64.99 56	95.80 106	89.34 12	91.80 53	95.93 25

MS-PatchMatch		77.90 154	76.50 151	82.12 166	85.99 189	69.95 27	91.75 131	92.70 96	73.97 113	62.58 243	84.44 192	41.11 262	95.78 107	63.76 192	92.17 50	80.62 295

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 165	67.65 70	95.45 18	92.18 119	85.06 8	72.58 133	92.27 98	52.46 189	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
HPM-MVS_fast		80.25 108	79.55 106	82.33 156	91.55 89	59.95 238	91.32 147	89.16 221	65.23 239	74.71 114	93.07 81	47.81 227	95.74 109	74.87 108	88.23 81	91.31 147

xiu_mvs_v1_base_debu		82.16 84	81.12 85	85.26 89	86.42 181	68.72 47	92.59 93	90.44 171	73.12 131	84.20 39	94.36 55	38.04 275	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	86.42 181	68.72 47	92.59 93	90.44 171	73.12 131	84.20 39	94.36 55	38.04 275	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	86.42 181	68.72 47	92.59 93	90.44 171	73.12 131	84.20 39	94.36 55	38.04 275	95.73 110	84.12 45	86.81 90	91.33 143

DP-MVS		69.90 247	66.48 253	80.14 203	95.36 14	62.93 190	89.56 190	76.11 316	50.27 309	57.69 267	85.23 183	39.68 266	95.73 110	33.35 319	71.05 203	81.78 276

114514_t		79.17 129	77.67 131	83.68 124	95.32 15	65.53 137	92.85 82	91.60 138	63.49 253	67.92 197	90.63 116	46.65 235	95.72 114	67.01 164	83.54 118	89.79 160

TR-MVS		78.77 138	77.37 140	82.95 134	90.49 109	60.88 218	93.67 58	90.07 191	70.08 193	74.51 115	91.37 108	45.69 241	95.70 115	60.12 220	80.32 134	92.29 132

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	88.66 139	59.67 101	95.61 116	47.84 264	78.67 146	89.56 164

HQP4-MVS										74.18 116			95.61 116			88.63 172

BH-w/o		80.49 105	79.30 111	84.05 116	90.83 108	64.36 162	93.60 60	89.42 212	74.35 105	69.09 178	90.15 123	55.23 147	95.61 116	64.61 186	86.43 98	92.17 135

HQP-MVS		81.14 96	80.64 91	82.64 142	87.54 168	63.66 179	94.06 43	91.70 135	79.80 33	74.18 116	90.30 121	51.63 196	95.61 116	77.63 88	78.90 143	88.63 172

HQP_MVS		80.34 107	79.75 101	82.12 166	86.94 176	62.42 199	93.13 72	91.31 149	78.81 49	72.53 134	89.14 137	50.66 200	95.55 120	76.74 91	78.53 147	88.39 177

plane_prior5								91.31 149					95.55 120	76.74 91	78.53 147	88.39 177

jason		86.40 32	86.17 32	87.11 33	86.16 187	70.54 20	95.71 15	92.19 118	82.00 24	84.58 35	94.34 60	61.86 83	95.53 122	87.76 22	90.89 65	95.27 41
jason: jason.
BH-untuned		78.68 140	77.08 142	83.48 129	89.84 121	63.74 174	92.70 86	88.59 237	71.57 169	66.83 211	88.65 140	51.75 194	95.39 123	59.03 225	84.77 107	91.32 146

MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 155	62.88 194	89.77 189	88.93 230	76.78 73	75.55 108	93.10 77	50.31 203	95.38 124	83.82 49	87.02 89	92.26 134

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	87.89 153	60.31 94	95.32 125	45.16 273	77.58 155	88.27 180

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	87.89 153	60.31 94	95.32 125	45.16 273	77.58 155	88.83 168

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	88.26 147	60.07 98	95.32 125	45.16 273	77.58 155	88.83 168

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	88.26 147	60.07 98	95.32 125	45.16 273	77.58 155	87.48 192

OPM-MVS		79.00 131	78.09 125	81.73 174	83.52 220	63.83 171	91.64 136	90.30 181	76.36 79	71.97 143	89.93 131	46.30 239	95.17 129	75.10 101	77.70 153	86.19 219

PAPM		85.89 39	85.46 41	87.18 30	88.20 159	72.42 9	92.41 97	92.77 94	82.11 21	80.34 63	93.07 81	68.27 21	95.02 130	78.39 83	93.59 33	94.09 85

PMMVS		81.98 89	82.04 75	81.78 173	89.76 123	56.17 276	91.13 156	90.69 166	77.96 57	80.09 65	93.57 72	46.33 238	94.99 131	81.41 64	87.46 87	94.17 79

CostFormer		82.33 81	81.15 84	85.86 67	89.01 142	68.46 51	82.39 272	93.01 86	75.59 84	80.25 64	81.57 225	72.03 13	94.96 132	79.06 78	77.48 160	94.16 80

EPP-MVSNet		81.79 90	81.52 81	82.61 143	88.77 147	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

ACMH	63.93 17	68.62 256	64.81 262	80.03 207	85.22 197	63.25 183	87.72 224	84.66 285	60.83 272	51.57 296	79.43 256	27.29 317	94.96 132	41.76 286	64.84 248	81.88 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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	87.89 153	60.31 94	94.92 135	42.64 285	76.64 165	87.48 192

tpmp4_e23		78.85 134	76.55 150	85.77 72	89.25 135	68.39 53	81.63 279	91.38 147	70.40 189	75.21 111	79.22 257	67.37 31	94.79 136	58.98 227	75.51 171	94.13 82

diffmvs		80.18 109	78.55 120	85.07 93	88.56 149	66.93 90	86.70 245	88.62 236	70.42 188	78.69 81	85.26 182	56.93 127	94.77 137	68.68 153	83.09 119	93.51 101

XXY-MVS		77.94 153	76.44 152	82.43 149	82.60 228	64.44 157	92.01 110	91.83 130	73.59 123	70.00 163	85.82 178	54.43 166	94.76 138	69.63 143	68.02 224	88.10 182

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 152	61.80 210	93.44 66	88.26 245	73.96 114	77.73 86	91.76 104	49.94 207	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
nrg030		80.93 99	79.86 99	84.13 114	83.69 217	68.83 45	93.23 70	91.20 152	75.55 85	75.06 112	88.22 150	63.04 77	94.74 140	81.88 59	66.88 230	88.82 170

GA-MVS		78.33 147	76.23 154	84.65 104	83.65 218	66.30 122	91.44 139	90.14 189	76.01 81	70.32 158	84.02 194	42.50 255	94.72 141	70.98 133	77.00 164	92.94 118

EI-MVSNet		78.97 132	78.22 124	81.25 182	85.33 195	62.73 197	89.53 193	93.21 76	72.39 143	72.14 141	90.13 124	60.99 86	94.72 141	67.73 158	72.49 193	86.29 217

MVSTER		82.47 79	82.05 74	83.74 120	92.68 61	69.01 41	91.90 121	93.21 76	79.83 32	72.14 141	85.71 180	74.72 7	94.72 141	75.72 96	72.49 193	87.50 191

PLC	68.80 14	75.23 197	73.68 194	79.86 212	92.93 54	58.68 255	90.64 167	88.30 242	60.90 271	64.43 228	90.53 117	42.38 256	94.57 144	56.52 233	76.54 166	86.33 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GG-mvs-BLEND				86.53 49	91.91 78	69.67 34	75.02 309	94.75 26		78.67 82	90.85 112	77.91 2	94.56 145	72.25 119	93.74 30	95.36 35

OMC-MVS		78.67 142	77.91 130	80.95 195	85.76 194	57.40 266	88.49 209	88.67 234	73.85 116	72.43 138	92.10 99	49.29 213	94.55 146	72.73 115	77.89 151	90.91 150

mvs-test1		78.74 139	77.95 128	81.14 188	83.22 222	57.13 268	93.96 48	87.78 250	75.42 87	72.68 130	90.80 113	45.08 245	94.54 147	75.08 102	77.49 159	91.74 139

Fast-Effi-MVS+		81.14 96	80.01 96	84.51 109	90.24 115	65.86 130	94.12 40	89.15 222	73.81 118	75.37 110	88.26 147	57.26 120	94.53 148	66.97 165	84.92 105	93.15 111

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 166	68.11 61	90.18 177	90.01 194	70.65 186	72.95 127	86.06 176	63.61 69	94.50 149	75.01 104	79.75 137	93.67 97

v1neww		77.39 159	75.71 162	82.44 146	80.69 246	66.83 92	91.94 118	90.18 186	74.19 107	69.60 168	82.51 206	54.99 156	94.44 150	71.68 125	65.60 236	86.05 224

v7new		77.39 159	75.71 162	82.44 146	80.69 246	66.83 92	91.94 118	90.18 186	74.19 107	69.60 168	82.51 206	54.99 156	94.44 150	71.68 125	65.60 236	86.05 224

v6		77.39 159	75.71 162	82.44 146	80.67 248	66.82 94	91.94 118	90.18 186	74.19 107	69.60 168	82.50 209	55.00 155	94.44 150	71.68 125	65.60 236	86.05 224

v2v482		77.42 158	75.65 165	82.73 138	80.38 258	67.13 83	91.85 124	90.23 183	75.09 94	69.37 174	83.39 200	53.79 174	94.44 150	71.77 123	65.00 247	86.63 215

v7		76.83 175	75.01 176	82.29 158	80.35 259	66.70 106	91.68 133	89.97 195	73.47 127	69.22 176	82.22 213	52.52 187	94.43 154	69.73 142	65.96 235	85.74 235

v1144		76.73 177	74.88 177	82.27 159	80.23 266	66.60 110	91.68 133	90.21 185	73.69 120	69.06 179	81.89 221	52.73 186	94.40 155	69.21 148	65.23 240	85.80 231

v1141		77.28 164	75.57 166	82.42 152	80.63 252	66.73 102	91.96 114	90.42 174	74.41 100	69.46 171	82.12 216	55.09 152	94.40 155	70.99 132	65.05 243	86.12 221

divwei89l23v2f112		77.28 164	75.57 166	82.42 152	80.62 253	66.72 104	91.96 114	90.42 174	74.41 100	69.46 171	82.12 216	55.11 151	94.40 155	71.00 130	65.04 244	86.12 221

v1		77.29 163	75.57 166	82.42 152	80.61 256	66.73 102	91.96 114	90.42 174	74.41 100	69.46 171	82.12 216	55.14 150	94.40 155	71.00 130	65.04 244	86.13 220

Test4		76.45 180	73.45 201	85.45 82	76.07 302	67.61 71	88.38 212	90.83 162	76.71 74	53.06 290	79.65 254	31.61 304	94.35 159	78.47 81	86.22 99	94.40 73

TAPA-MVS	70.22 12	74.94 202	73.53 199	79.17 231	90.40 111	52.07 294	89.19 198	89.61 207	62.69 260	70.07 161	92.67 90	48.89 219	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
LPG-MVS_test		75.82 189	74.58 180	79.56 219	84.31 210	59.37 246	90.44 170	89.73 203	69.49 197	64.86 222	88.42 142	38.65 269	94.30 161	72.56 116	72.76 190	85.01 244

LGP-MVS_train				79.56 219	84.31 210	59.37 246		89.73 203	69.49 197	64.86 222	88.42 142	38.65 269	94.30 161	72.56 116	72.76 190	85.01 244

v1192		75.98 187	73.92 191	82.15 165	79.73 271	66.24 124	91.22 151	89.75 200	72.67 137	68.49 192	81.42 227	49.86 208	94.27 163	67.08 163	65.02 246	85.95 228

tpmvs		72.88 221	69.76 229	82.22 163	90.98 102	67.05 85	78.22 303	88.30 242	63.10 258	64.35 229	74.98 282	55.09 152	94.27 163	43.25 279	69.57 212	85.34 242

tpm2		79.80 119	77.95 128	85.34 87	88.28 157	68.26 58	81.56 280	91.42 145	70.11 192	77.59 91	80.50 241	67.40 30	94.26 165	67.34 161	77.35 161	93.51 101

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	87.31 161	60.79 88	94.19 166	38.90 298	75.31 172	87.48 192

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	87.31 161	60.79 88	94.19 166	38.90 298	75.31 172	87.48 192

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	87.31 161	60.79 88	94.19 166	38.90 298	75.31 172	87.48 192

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	87.31 161	60.79 88	94.19 166	38.90 298	75.31 172	87.48 192

PVSNet_0	68.08 15	71.81 228	68.32 242	82.27 159	84.68 202	62.31 202	88.68 206	90.31 180	75.84 82	57.93 264	80.65 240	37.85 278	94.19 166	69.94 141	29.05 337	90.31 156

MVP-Stereo		77.12 168	76.23 154	79.79 214	81.72 234	66.34 121	89.29 195	90.88 161	70.56 187	62.01 246	82.88 203	49.34 212	94.13 171	65.55 180	93.80 27	78.88 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMM	69.62 13	74.34 209	72.73 207	79.17 231	84.25 212	57.87 259	90.36 173	89.93 196	63.17 257	65.64 218	86.04 177	37.79 279	94.10 172	65.89 176	71.52 199	85.55 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V42		76.46 179	74.55 181	82.19 164	79.14 280	67.82 65	90.26 176	89.42 212	73.75 119	68.63 186	81.89 221	51.31 198	94.09 173	71.69 124	64.84 248	84.66 247

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 160	63.74 174	92.70 86	93.77 48	79.30 38	77.61 90	87.57 158	58.19 113	94.08 174	73.91 109	86.68 93	93.33 106

v8		75.35 194	73.26 203	81.61 178	80.67 248	66.82 94	89.54 192	89.27 216	71.65 166	63.30 237	80.30 245	54.99 156	94.06 175	67.33 162	62.33 262	83.94 252

EG-PatchMatch MVS		68.55 257	65.41 259	77.96 251	78.69 285	62.93 190	89.86 188	89.17 220	60.55 273	50.27 301	77.73 265	22.60 325	94.06 175	47.18 267	72.65 192	76.88 316

PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 105	64.97 150	92.47 96	91.05 158	79.18 41	72.43 138	90.51 119	37.05 287	94.06 175	68.06 154	86.00 100	93.90 95

v10		74.77 207	72.54 211	81.46 179	80.33 263	66.71 105	89.15 199	89.08 225	70.94 177	63.08 238	79.86 250	52.52 187	94.04 178	65.70 179	62.17 263	83.64 254

v144192		76.05 185	74.03 188	82.12 166	79.50 275	66.55 114	91.39 141	89.71 206	72.30 144	68.17 194	81.33 229	51.75 194	94.03 179	67.94 155	64.19 253	85.77 232

tpm cat1		75.30 195	72.21 214	84.58 106	88.52 150	67.77 66	78.16 304	88.02 247	61.88 267	68.45 193	76.37 275	60.65 92	94.03 179	53.77 244	74.11 180	91.93 137

gg-mvs-nofinetune		77.18 167	74.31 184	85.80 70	91.42 96	68.36 54	71.78 312	94.72 27	49.61 310	77.12 96	45.92 333	77.41 3	93.98 181	67.62 159	93.16 38	95.05 51

PS-MVSNAJss		77.26 166	76.31 153	80.13 205	80.64 251	59.16 249	90.63 169	91.06 157	72.80 136	68.58 187	84.57 191	53.55 176	93.96 182	72.97 111	71.96 196	87.27 204

OpenMVS_ROB	61.12 18	66.39 270	62.92 275	76.80 264	76.51 297	57.77 260	89.22 196	83.41 296	55.48 298	53.86 286	77.84 264	26.28 320	93.95 183	34.90 316	68.76 218	78.68 310

MDTV_nov1_ep13			72.61 209		89.06 140	68.48 50	80.33 287	90.11 190	71.84 160	71.81 145	75.92 279	53.01 183	93.92 184	48.04 263	73.38 184

v1921920		75.63 192	73.49 200	82.06 170	79.38 276	66.35 120	91.07 158	89.48 209	71.98 154	67.99 195	81.22 232	49.16 216	93.90 185	66.56 168	64.56 252	85.92 230

v1240		75.21 198	72.98 205	81.88 172	79.20 278	66.00 127	90.75 164	89.11 224	71.63 167	67.41 204	81.22 232	47.36 230	93.87 186	65.46 181	64.72 250	85.77 232

ACMP	71.68 10	75.58 193	74.23 186	79.62 217	84.97 200	59.64 241	90.80 162	89.07 226	70.39 190	62.95 239	87.30 165	38.28 272	93.87 186	72.89 112	71.45 200	85.36 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v148		76.19 182	74.47 183	81.36 180	80.05 270	64.44 157	91.75 131	90.23 183	73.68 121	67.13 207	80.84 237	55.92 143	93.86 188	68.95 150	61.73 268	85.76 234

testing_2		71.09 237	67.32 250	82.40 155	69.82 319	66.52 116	83.64 259	90.77 164	72.21 148	45.12 316	71.07 308	27.60 316	93.74 189	75.71 97	69.96 208	86.95 208

LS3D		69.17 254	66.40 254	77.50 255	91.92 77	56.12 277	85.12 251	80.37 309	46.96 316	56.50 272	87.51 159	37.25 282	93.71 190	32.52 324	79.40 138	82.68 271

EPMVS		78.49 145	75.98 157	86.02 63	91.21 100	69.68 33	80.23 289	91.20 152	75.25 93	72.48 136	78.11 262	54.65 162	93.69 191	57.66 232	83.04 120	94.69 62

IS-MVSNet		80.14 111	79.41 108	82.33 156	87.91 164	60.08 237	91.97 113	88.27 244	72.90 135	71.44 150	91.73 106	61.44 85	93.66 192	62.47 208	86.53 96	93.24 108

v7n		71.31 233	68.65 236	79.28 226	76.40 298	60.77 221	86.71 244	89.45 210	64.17 249	58.77 260	78.24 261	44.59 249	93.54 193	57.76 230	61.75 267	83.52 257

VPA-MVSNet		79.03 130	78.00 127	82.11 169	85.95 190	64.48 155	93.22 71	94.66 29	75.05 95	74.04 121	84.95 186	52.17 191	93.52 194	74.90 107	67.04 229	88.32 179

tfpnnormal		70.10 245	67.36 248	78.32 243	83.45 221	60.97 217	88.85 203	92.77 94	64.85 245	60.83 248	78.53 260	43.52 253	93.48 195	31.73 325	61.70 269	80.52 296

旧先验2							92.00 112		59.37 281	87.54 15			93.47 196	75.39 99

1112_ss		80.56 103	79.83 100	82.77 137	88.65 148	60.78 220	92.29 98	88.36 241	72.58 139	72.46 137	94.95 40	65.09 51	93.42 197	66.38 170	77.71 152	94.10 84

testdata				81.34 181	89.02 141	57.72 261		89.84 198	58.65 284	85.32 30	94.09 64	57.03 123	93.28 198	69.34 147	90.56 70	93.03 115

LTVRE_ROB	59.60 19	66.27 271	63.54 270	74.45 276	84.00 215	51.55 296	67.08 325	83.53 294	58.78 283	54.94 276	80.31 244	34.54 295	93.23 199	40.64 292	68.03 223	78.58 311
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
VPNet		78.82 135	77.53 135	82.70 139	84.52 205	66.44 118	93.93 51	92.23 111	80.46 30	72.60 132	88.38 144	49.18 214	93.13 200	72.47 118	63.97 256	88.55 174

Test_1112_low_res		79.56 124	78.60 119	82.43 149	88.24 158	60.39 229	92.09 105	87.99 248	72.10 153	71.84 144	87.42 160	64.62 59	93.04 201	65.80 178	77.30 162	93.85 96

PatchMatch-RL		72.06 226	69.98 225	78.28 244	89.51 132	55.70 279	83.49 261	83.39 297	61.24 270	63.72 233	82.76 204	34.77 294	93.03 202	53.37 247	77.59 154	86.12 221

Fast-Effi-MVS+-dtu		75.04 199	73.37 202	80.07 206	80.86 241	59.52 244	91.20 153	85.38 282	71.90 155	65.20 219	84.84 187	41.46 261	92.97 203	66.50 169	72.96 189	87.73 189

v748		70.55 243	67.97 246	78.27 245	75.75 303	58.78 253	86.29 248	89.25 217	65.12 244	56.66 271	77.17 271	45.05 247	92.95 204	58.13 229	58.33 286	83.10 266

pm-mvs1		72.89 220	71.09 222	78.26 246	79.10 282	57.62 263	90.80 162	89.30 215	67.66 219	62.91 240	81.78 223	49.11 217	92.95 204	60.29 219	58.89 284	84.22 250

TAMVS		80.37 106	79.45 107	83.13 133	85.14 198	63.37 182	91.23 150	90.76 165	74.81 98	72.65 131	88.49 141	60.63 93	92.95 204	69.41 146	81.95 128	93.08 114

ACMH+	65.35 16	67.65 263	64.55 264	76.96 262	84.59 204	57.10 269	88.08 215	80.79 307	58.59 285	53.00 291	81.09 236	26.63 319	92.95 204	46.51 268	61.69 270	80.82 292

CDS-MVSNet		81.43 93	80.74 89	83.52 127	86.26 185	64.45 156	92.09 105	90.65 169	75.83 83	73.95 122	89.81 132	63.97 63	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
v52		69.80 249	67.01 252	78.15 248	71.84 312	60.10 235	82.02 273	87.39 253	64.48 246	57.80 265	75.97 278	41.47 260	92.90 209	63.00 200	59.13 281	81.45 280

V4		69.80 249	67.02 251	78.15 248	71.86 311	60.10 235	82.02 273	87.39 253	64.48 246	57.78 266	75.98 277	41.49 259	92.90 209	63.00 200	59.16 280	81.44 281

CPTT-MVS		79.59 123	79.16 114	80.89 196	91.54 90	59.80 240	92.10 104	88.54 239	60.42 274	72.96 126	93.28 76	48.27 221	92.80 211	78.89 80	86.50 97	90.06 157

PatchmatchNet		77.46 157	74.63 178	85.96 65	89.55 131	70.35 21	79.97 293	89.55 208	72.23 147	70.94 151	76.91 274	57.03 123	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.
jajsoiax		73.05 218	71.51 220	77.67 253	77.46 293	54.83 283	88.81 204	90.04 193	69.13 201	62.85 241	83.51 198	31.16 307	92.75 213	70.83 134	69.80 209	85.43 240

mvs_tets		72.71 223	71.11 221	77.52 254	77.41 294	54.52 285	88.45 211	89.76 199	68.76 206	62.70 242	83.26 201	29.49 311	92.71 214	70.51 139	69.62 211	85.34 242

tpmrst		80.57 102	79.14 115	84.84 99	90.10 116	68.28 57	81.70 276	89.72 205	77.63 62	75.96 103	79.54 255	64.94 58	92.71 214	75.43 98	77.28 163	93.55 100

test_post											23.01 342	56.49 135	92.67 216

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 137	71.18 14	89.07 201	90.69 166	65.80 233	87.13 16	94.34 60	64.99 56	92.67 216	72.83 113	91.80 53	95.27 41

test_djsdf		73.76 216	72.56 210	77.39 258	77.00 296	53.93 287	89.07 201	90.69 166	65.80 233	63.92 230	82.03 219	43.14 254	92.67 216	72.83 113	68.53 220	85.57 237

dp		75.01 200	72.09 215	83.76 119	89.28 134	66.22 125	79.96 294	89.75 200	71.16 175	67.80 200	77.19 270	51.81 193	92.54 219	50.39 254	71.44 201	92.51 128

Effi-MVS+-dtu		76.14 183	75.28 173	78.72 240	83.22 222	55.17 282	89.87 187	87.78 250	75.42 87	67.98 196	81.43 226	45.08 245	92.52 220	75.08 102	71.63 197	88.48 175

F-COLMAP		70.66 241	68.44 240	77.32 259	86.37 184	55.91 278	88.00 216	86.32 270	56.94 292	57.28 269	88.07 151	33.58 296	92.49 221	51.02 252	68.37 221	83.55 255

USDC		67.43 267	64.51 265	76.19 267	77.94 291	55.29 281	78.38 301	85.00 283	73.17 129	48.36 306	80.37 243	21.23 327	92.48 222	52.15 249	64.02 255	80.81 293

pmmvs6		67.57 264	64.76 263	76.00 269	72.82 310	53.37 289	88.71 205	86.78 260	53.19 301	57.58 268	78.03 263	35.33 292	92.41 223	55.56 237	54.88 296	82.21 273

test-LLR		80.10 112	79.56 104	81.72 175	86.93 178	61.17 215	92.70 86	91.54 139	71.51 172	75.62 105	86.94 167	53.83 172	92.38 224	72.21 120	84.76 108	91.60 140

test-mter		79.96 115	79.38 110	81.72 175	86.93 178	61.17 215	92.70 86	91.54 139	73.85 116	75.62 105	86.94 167	49.84 209	92.38 224	72.21 120	84.76 108	91.60 140

UniMVSNet (Re)		77.58 156	76.78 148	79.98 208	84.11 213	60.80 219	91.76 129	93.17 81	76.56 77	69.93 166	84.78 188	63.32 73	92.36 226	64.89 184	62.51 261	86.78 211

mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 112	68.40 52	86.88 242	90.61 170	74.41 100	70.31 159	84.67 189	63.79 66	92.32 227	73.13 110	85.70 101	95.67 27

tfpn_ndepth		76.45 180	75.22 174	80.14 203	90.97 103	58.92 251	90.11 178	93.24 75	65.96 232	67.37 206	90.52 118	66.67 36	92.29 228	37.71 304	74.44 178	89.21 166

IterMVS-LS		76.49 178	75.18 175	80.43 199	84.49 206	62.74 196	90.64 167	88.80 232	72.40 142	65.16 221	81.72 224	60.98 87	92.27 229	67.74 157	64.65 251	86.29 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		77.73 155	76.04 156	82.80 136	91.20 101	68.99 42	91.87 122	91.99 123	73.35 128	67.04 208	83.19 202	56.62 133	92.14 230	59.80 222	69.34 213	87.28 203

UniMVSNet_NR-MVSNet		78.15 150	77.55 134	79.98 208	84.46 207	60.26 231	92.25 99	93.20 78	77.50 64	68.88 182	86.61 169	66.10 41	92.13 231	66.38 170	62.55 259	87.54 190

DU-MVS		76.86 173	75.84 159	79.91 210	82.96 226	60.26 231	91.26 149	91.54 139	76.46 78	68.88 182	86.35 171	56.16 137	92.13 231	66.38 170	62.55 259	87.35 201

tpm		78.58 143	77.03 143	83.22 131	85.94 192	64.56 152	83.21 267	91.14 155	78.31 54	73.67 123	79.68 252	64.01 62	92.09 233	66.07 174	71.26 202	93.03 115

Baseline_NR-MVSNet		73.99 213	72.83 206	77.48 256	80.78 242	59.29 248	91.79 126	84.55 286	68.85 204	68.99 180	80.70 238	56.16 137	92.04 234	62.67 206	60.98 274	81.11 289

FMVSNet2		76.07 184	74.01 189	82.26 162	88.85 143	67.66 69	91.33 146	91.61 137	70.84 179	65.98 213	82.25 212	48.03 222	92.00 235	58.46 228	68.73 219	87.10 205

TransMVSNet (Re)		70.07 246	67.66 247	77.31 260	80.62 253	59.13 250	91.78 128	84.94 284	65.97 231	60.08 251	80.44 242	50.78 199	91.87 236	48.84 260	45.46 321	80.94 291

NR-MVSNet		76.05 185	74.59 179	80.44 198	82.96 226	62.18 204	90.83 161	91.73 132	77.12 67	60.96 247	86.35 171	59.28 105	91.80 237	60.74 216	61.34 272	87.35 201

FIs		79.47 125	79.41 108	79.67 215	85.95 190	59.40 245	91.68 133	93.94 43	78.06 56	68.96 181	88.28 145	66.61 37	91.77 238	66.20 173	74.99 176	87.82 188

XVG-OURS		74.25 211	72.46 212	79.63 216	78.45 287	57.59 264	80.33 287	87.39 253	63.86 252	68.76 184	89.62 135	40.50 264	91.72 239	69.00 149	74.25 179	89.58 163

thresconf0.02		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

tfpn_n400		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

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

test_0402		64.54 278	61.09 283	74.92 273	84.10 214	60.75 222	87.95 217	79.71 311	52.03 304	52.41 292	77.20 269	32.21 302	91.64 244	23.14 335	61.03 273	72.36 323

XVG-OURS-SEG-HR		74.70 208	73.08 204	79.57 218	78.25 288	57.33 267	80.49 285	87.32 256	63.22 256	68.76 184	90.12 126	44.89 248	91.59 245	70.55 138	74.09 181	89.79 160

TranMVSNet+NR-MVSNet		75.86 188	74.52 182	79.89 211	82.44 229	60.64 226	91.37 144	91.37 148	76.63 75	67.65 201	86.21 174	52.37 190	91.55 246	61.84 211	60.81 275	87.48 192

GBi-Net		75.65 190	73.83 192	81.10 190	88.85 143	65.11 144	90.01 179	90.32 177	70.84 179	67.04 208	80.25 246	48.03 222	91.54 247	59.80 222	69.34 213	86.64 212

test1		75.65 190	73.83 192	81.10 190	88.85 143	65.11 144	90.01 179	90.32 177	70.84 179	67.04 208	80.25 246	48.03 222	91.54 247	59.80 222	69.34 213	86.64 212

FMVSNet1		72.71 223	69.91 227	81.10 190	83.60 219	65.11 144	90.01 179	90.32 177	63.92 251	63.56 234	80.25 246	36.35 289	91.54 247	54.46 240	66.75 231	86.64 212

pmmvs4		73.92 214	71.81 217	80.25 202	79.17 279	65.24 140	87.43 232	87.26 258	67.64 221	63.46 235	83.91 195	48.96 218	91.53 250	62.94 202	65.49 239	83.96 251

test_post1							78.95 297				20.70 346	53.05 182	91.50 251	60.43 218

tfpn1000		75.25 196	74.00 190	79.03 234	90.30 113	57.56 265	88.55 208	93.36 70	64.14 250	65.17 220	89.76 134	67.06 33	91.46 252	34.54 317	73.09 188	88.06 183

anonymousdsp		71.14 236	69.37 231	76.45 265	72.95 308	54.71 284	84.19 256	88.88 231	61.92 266	62.15 245	79.77 251	38.14 274	91.44 253	68.90 151	67.45 228	83.21 263

XVG-ACMP-BASELINE		68.04 261	65.53 258	75.56 270	74.06 307	52.37 292	78.43 300	85.88 279	62.03 264	58.91 259	81.21 234	20.38 328	91.15 254	60.69 217	68.18 222	83.16 264

CNLPA		74.31 210	72.30 213	80.32 200	91.49 91	61.66 211	90.85 160	80.72 308	56.67 294	63.85 232	90.64 114	46.75 233	90.84 255	53.79 243	75.99 169	88.47 176

pmmvs5		73.35 217	71.52 219	78.86 235	78.64 286	60.61 227	91.08 157	86.90 259	67.69 218	63.32 236	83.64 196	44.33 250	90.53 256	62.04 210	66.02 234	85.46 239

SixPastTwentyTwo		64.92 276	61.78 282	74.34 278	78.74 284	49.76 305	83.42 264	79.51 312	62.86 259	50.27 301	77.35 266	30.92 309	90.49 257	45.89 271	47.06 318	82.78 267

COLMAP_ROB	57.96 20	62.98 287	59.65 287	72.98 286	81.44 237	53.00 291	83.75 258	75.53 322	48.34 314	48.81 305	81.40 228	24.14 321	90.30 258	32.95 321	60.52 277	75.65 319
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
patchmatchnet-post											67.62 312	57.62 118	90.25 259

JIA-IIPM		66.06 272	62.45 278	76.88 263	81.42 238	54.45 286	57.49 336	88.67 234	49.36 311	63.86 231	46.86 332	56.06 140	90.25 259	49.53 258	68.83 217	85.95 228

WR-MVS		76.76 176	75.74 161	79.82 213	84.60 203	62.27 203	92.60 91	92.51 105	76.06 80	67.87 199	85.34 181	56.76 128	90.24 261	62.20 209	63.69 258	86.94 209

FC-MVSNet-test		77.99 152	78.08 126	77.70 252	84.89 201	55.51 280	90.27 175	93.75 50	76.87 70	66.80 212	87.59 157	65.71 47	90.23 262	62.89 203	73.94 182	87.37 199

EPNet_dtu		78.80 136	79.26 112	77.43 257	88.06 161	49.71 306	91.96 114	91.95 126	77.67 61	76.56 101	91.28 109	58.51 111	90.20 263	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
v18		71.94 227	69.43 230	79.50 221	80.74 243	66.82 94	88.16 214	86.66 261	68.95 203	55.55 273	72.66 289	55.03 154	90.15 264	64.78 185	52.30 301	81.54 277

CMPMVS	48.56 21	66.77 269	64.41 267	73.84 280	70.65 317	50.31 303	77.79 305	85.73 281	45.54 321	44.76 317	82.14 215	35.40 291	90.14 265	63.18 198	74.54 177	81.07 290
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v16		71.81 228	69.26 232	79.47 222	80.66 250	66.81 98	87.93 218	86.63 263	68.70 208	55.35 274	72.51 290	54.75 160	90.12 266	64.51 187	52.28 302	81.47 278

v17		71.77 230	69.20 233	79.46 223	80.62 253	66.81 98	87.93 218	86.63 263	68.71 207	55.25 275	72.49 291	54.72 161	90.11 267	64.50 188	51.97 303	81.47 278

Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 247	88.46 153	52.29 293	90.41 172	89.12 223	74.24 106	69.13 177	91.91 101	65.77 46	90.09 268	59.00 226	88.09 83	92.33 130

v15		71.40 232	68.75 235	79.35 224	80.39 257	66.70 106	87.57 231	86.64 262	68.66 209	54.68 277	72.00 298	54.50 163	89.98 269	63.69 193	50.66 308	81.38 282

V14		71.29 234	68.61 237	79.31 225	80.34 261	66.65 108	87.39 233	86.61 265	68.41 213	54.49 279	71.91 299	54.25 168	89.96 270	63.50 194	50.62 309	81.33 284

V9		71.16 235	68.46 239	79.27 227	80.26 264	66.60 110	87.21 236	86.56 266	68.17 214	54.26 282	71.81 301	54.00 170	89.93 271	63.28 197	50.57 310	81.27 285

v12		71.02 239	68.29 244	79.22 229	80.18 267	66.53 115	87.01 239	86.54 268	67.90 216	54.00 285	71.70 302	53.66 175	89.91 272	63.09 199	50.51 311	81.21 286

v11		71.05 238	68.32 242	79.23 228	80.34 261	66.57 113	87.01 239	86.55 267	68.11 215	54.40 280	71.66 303	52.94 184	89.91 272	62.71 205	51.12 306	81.21 286

v13		70.90 240	68.15 245	79.15 233	80.08 268	66.45 117	86.83 243	86.50 269	67.62 222	53.78 287	71.61 304	53.51 179	89.87 274	62.89 203	50.50 312	81.14 288

lessismore_v0				73.72 281	72.93 309	47.83 312		61.72 343		45.86 313	73.76 285	28.63 314	89.81 275	47.75 266	31.37 336	83.53 256

MVS-HIRNet		60.25 292	55.55 298	74.35 277	84.37 209	56.57 275	71.64 313	74.11 325	34.44 334	45.54 315	42.24 336	31.11 308	89.81 275	40.36 293	76.10 168	76.67 317

CR-MVSNet		73.79 215	70.82 224	82.70 139	83.15 224	67.96 63	70.25 315	84.00 291	73.67 122	69.97 164	72.41 294	57.82 116	89.48 277	52.99 248	73.13 186	90.64 153

RPMNet		69.58 251	65.21 261	82.70 139	83.15 224	67.96 63	70.25 315	86.15 274	46.83 318	69.97 164	65.10 319	56.48 136	89.48 277	35.79 310	73.13 186	90.64 153

Patchmtry		67.53 265	63.93 269	78.34 242	82.12 231	64.38 160	68.72 319	84.00 291	48.23 315	59.24 254	72.41 294	57.82 116	89.27 279	46.10 270	56.68 291	81.36 283

ADS-MVSNet		68.54 258	64.38 268	81.03 193	88.06 161	66.90 91	68.01 322	84.02 290	57.57 286	64.48 226	69.87 309	38.68 267	89.21 280	40.87 290	67.89 225	86.97 206

Patchmatch-test1		75.00 201	71.80 218	84.58 106	86.63 180	70.08 24	81.06 282	89.19 219	71.60 168	70.01 162	77.16 272	45.53 242	88.63 281	51.79 250	73.27 185	95.02 55

Patchmatch-RL test		68.17 260	64.49 266	79.19 230	71.22 314	53.93 287	70.07 317	71.54 332	69.22 200	56.79 270	62.89 322	56.58 134	88.61 282	69.53 145	52.61 300	95.03 54

UnsupCasMVSNet_bld		61.60 289	57.71 290	73.29 284	68.73 323	51.64 295	78.61 299	89.05 227	57.20 290	46.11 310	61.96 324	28.70 313	88.60 283	50.08 256	38.90 330	79.63 304

OurMVSNet-221017-0		64.68 277	62.17 280	72.21 293	76.08 301	47.35 314	80.67 284	81.02 306	56.19 295	51.60 295	79.66 253	27.05 318	88.56 284	53.60 245	53.63 299	80.71 294

PatchT		69.11 255	65.37 260	80.32 200	82.07 232	63.68 178	67.96 324	87.62 252	50.86 308	69.37 174	65.18 318	57.09 121	88.53 285	41.59 288	66.60 232	88.74 171

TinyColmap		60.32 291	56.42 297	72.00 296	78.78 283	53.18 290	78.36 302	75.64 319	52.30 303	41.59 327	75.82 280	14.76 336	88.35 286	35.84 308	54.71 297	74.46 320

LCM-MVSNet-Re		72.93 219	71.84 216	76.18 268	88.49 151	48.02 310	80.07 292	70.17 333	73.96 114	52.25 293	80.09 249	49.98 206	88.24 287	67.35 160	84.23 116	92.28 133

ambc				69.61 301	61.38 333	41.35 325	49.07 339	85.86 280		50.18 303	66.40 314	10.16 341	88.14 288	45.73 272	44.20 322	79.32 307

Patchmatch-test		65.86 273	60.94 284	80.62 197	83.75 216	58.83 252	58.91 335	75.26 323	44.50 325	50.95 300	77.09 273	58.81 110	87.90 289	35.13 315	64.03 254	95.12 49

MIMVSNet		71.64 231	68.44 240	81.23 183	81.97 233	64.44 157	73.05 311	88.80 232	69.67 196	64.59 224	74.79 283	32.79 298	87.82 290	53.99 242	76.35 167	91.42 142

K. test v3		63.09 286	59.61 288	73.53 282	76.26 299	49.38 308	83.27 265	77.15 315	64.35 248	47.77 307	72.32 296	28.73 312	87.79 291	49.93 257	36.69 332	83.41 260

UnsupCasMVSNet_eth		65.79 274	63.10 273	73.88 279	70.71 316	50.29 304	81.09 281	89.88 197	72.58 139	49.25 304	74.77 284	32.57 300	87.43 292	55.96 236	41.04 327	83.90 253

Anonymous20231206		67.53 265	65.78 255	72.79 288	74.95 304	47.59 313	88.23 213	87.32 256	61.75 269	58.07 263	77.29 268	37.79 279	87.29 293	42.91 281	63.71 257	83.48 258

pmmvs-eth3d		65.53 275	62.32 279	75.19 271	69.39 321	59.59 242	82.80 270	83.43 295	62.52 262	51.30 298	72.49 291	32.86 297	87.16 294	55.32 238	50.73 307	78.83 309

IterMVS		72.65 225	70.83 223	78.09 250	82.17 230	62.96 189	87.64 230	86.28 271	71.56 170	60.44 249	78.85 259	45.42 244	86.66 295	63.30 196	61.83 265	84.65 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		61.66 288	58.06 289	72.46 290	79.57 272	51.42 298	80.17 290	68.61 335	51.25 306	45.88 311	81.23 230	19.86 329	86.58 296	38.98 296	57.01 289	79.39 305

TestCases				72.46 290	79.57 272	51.42 298		68.61 335	51.25 306	45.88 311	81.23 230	19.86 329	86.58 296	38.98 296	57.01 289	79.39 305

MDA-MVSNet-bldmvs		61.54 290	57.70 291	73.05 285	79.53 274	57.00 270	83.08 268	81.23 304	57.57 286	34.91 332	72.45 293	32.79 298	86.26 298	35.81 309	41.95 325	75.89 318

FMVSNet5		68.04 261	65.66 257	75.18 272	84.43 208	57.89 258	83.54 260	86.26 272	61.83 268	53.64 288	73.30 286	37.15 285	85.08 299	48.99 259	61.77 266	82.56 272

test0.0.03 1		72.76 222	72.71 208	72.88 287	80.25 265	47.99 311	91.22 151	89.45 210	71.51 172	62.51 244	87.66 156	53.83 172	85.06 300	50.16 255	67.84 227	85.58 236

testgi		64.48 279	62.87 276	69.31 302	71.24 313	40.62 327	85.49 250	79.92 310	65.36 237	54.18 283	83.49 199	23.74 323	84.55 301	41.60 287	60.79 276	82.77 268

Anonymous20231211		53.57 305	49.43 307	66.00 311	65.01 326	42.08 322	80.95 283	72.60 328	38.46 332	41.65 326	64.48 321	15.72 333	84.23 302	25.78 332	40.24 329	71.68 324

ADS-MVSNet2		66.90 268	63.44 271	77.26 261	88.06 161	60.70 224	68.01 322	75.56 321	57.57 286	64.48 226	69.87 309	38.68 267	84.10 303	40.87 290	67.89 225	86.97 206

CVMVSNet		74.04 212	74.27 185	73.33 283	85.33 195	43.94 320	89.53 193	88.39 240	54.33 300	70.37 157	90.13 124	49.17 215	84.05 304	61.83 212	79.36 139	91.99 136

LP		56.71 299	51.64 303	71.91 297	80.08 268	60.33 230	61.72 330	75.61 320	43.87 327	43.76 321	60.30 326	30.46 310	84.05 304	22.94 336	46.06 320	71.34 325

ITE_SJBPF				70.43 300	74.44 305	47.06 315		77.32 314	60.16 276	54.04 284	83.53 197	23.30 324	84.01 306	43.07 280	61.58 271	80.21 300

CHOSEN 280x420		77.35 162	76.95 147	78.55 241	87.07 175	62.68 198	69.71 318	82.95 301	68.80 205	71.48 149	87.27 166	66.03 42	84.00 307	76.47 94	82.81 123	88.95 167

DTE-MVSNet		68.46 259	67.33 249	71.87 298	77.94 291	49.00 309	86.16 249	88.58 238	66.36 229	58.19 262	82.21 214	46.36 237	83.87 308	44.97 277	55.17 294	82.73 269

semantic-postprocess				76.32 266	81.48 235	60.67 225		85.99 277	66.17 230	59.50 253	78.88 258	45.51 243	83.65 309	62.58 207	61.93 264	84.63 249

PEN-MVS		69.46 253	68.56 238	72.17 294	79.27 277	49.71 306	86.90 241	89.24 218	67.24 225	59.08 256	82.51 206	47.23 231	83.54 310	48.42 262	57.12 287	83.25 262

WR-MVS_H		70.59 242	69.94 226	72.53 289	81.03 239	51.43 297	87.35 234	92.03 122	67.38 223	60.23 250	80.70 238	55.84 144	83.45 311	46.33 269	58.58 285	82.72 270

YYNet1		63.76 285	60.14 286	74.62 275	78.06 290	60.19 234	83.46 263	83.99 293	56.18 296	39.25 328	71.56 307	37.18 284	83.34 312	42.90 282	48.70 316	80.32 298

PM-MVS		59.40 294	56.59 295	67.84 304	63.63 327	41.86 324	76.76 306	63.22 341	59.01 282	51.07 299	72.27 297	11.72 338	83.25 313	61.34 213	50.28 313	78.39 312

MDA-MVSNet_test_wron		63.78 284	60.16 285	74.64 274	78.15 289	60.41 228	83.49 261	84.03 289	56.17 297	39.17 329	71.59 306	37.22 283	83.24 314	42.87 283	48.73 315	80.26 299

N_pmnet		50.55 306	49.11 308	54.88 323	77.17 295	4.02 353	84.36 255	2.00 354	48.59 312	45.86 313	68.82 311	32.22 301	82.80 315	31.58 326	51.38 305	77.81 314

test20.03		63.83 283	62.65 277	67.38 307	70.58 318	39.94 328	86.57 246	84.17 288	63.29 255	51.86 294	77.30 267	37.09 286	82.47 316	38.87 302	54.13 298	79.73 303

TDRefinement		55.28 303	51.58 304	66.39 310	59.53 335	46.15 316	76.23 307	72.80 327	44.60 324	42.49 323	76.28 276	15.29 334	82.39 317	33.20 320	43.75 323	70.62 327

CP-MVSNet		70.50 244	69.91 227	72.26 292	80.71 245	51.00 300	87.23 235	90.30 181	67.84 217	59.64 252	82.69 205	50.23 205	82.30 318	51.28 251	59.28 279	83.46 259

PS-CasMVS		69.86 248	69.13 234	72.07 295	80.35 259	50.57 302	87.02 238	89.75 200	67.27 224	59.19 255	82.28 211	46.58 236	82.24 319	50.69 253	59.02 282	83.39 261

RPSCF		64.24 280	61.98 281	71.01 299	76.10 300	45.00 317	75.83 308	75.94 318	46.94 317	58.96 258	84.59 190	31.40 306	82.00 320	47.76 265	60.33 278	86.04 227

new-patchmatchnet		59.30 296	56.48 296	67.79 305	65.86 325	44.19 318	82.47 271	81.77 302	59.94 277	43.65 322	66.20 315	27.67 315	81.68 321	39.34 295	41.40 326	77.50 315

MIMVSNet1		60.16 293	57.33 293	68.67 303	69.71 320	44.13 319	78.92 298	84.21 287	55.05 299	44.63 318	71.85 300	23.91 322	81.54 322	32.63 323	55.03 295	80.35 297

test2356		64.16 281	63.28 272	66.81 309	69.37 322	39.86 330	87.76 223	86.02 276	59.83 278	53.54 289	73.23 287	34.94 293	80.67 323	39.66 294	65.20 241	79.89 301

EU-MVSNet		64.01 282	63.01 274	67.02 308	74.40 306	38.86 332	83.27 265	86.19 273	45.11 322	54.27 281	81.15 235	36.91 288	80.01 324	48.79 261	57.02 288	82.19 274

no-one		44.13 312	38.39 313	61.34 316	45.91 344	41.94 323	61.67 331	75.07 324	45.05 323	20.07 338	40.68 339	11.58 339	79.82 325	30.18 328	15.30 340	62.26 333

testpf		57.17 297	56.93 294	57.88 319	79.13 281	42.40 321	34.23 342	85.97 278	52.64 302	47.66 309	66.50 313	36.33 290	79.65 326	53.60 245	56.31 292	51.60 336

1111		56.66 300	54.98 299	61.69 315	61.99 331	31.38 337	79.81 295	83.17 299	45.66 319	41.94 324	65.44 316	41.50 257	79.56 327	27.64 329	47.68 317	74.14 321

.test1245		46.52 310	49.68 306	37.02 332	61.99 331	31.38 337	79.81 295	83.17 299	45.66 319	41.94 324	65.44 316	41.50 257	79.56 327	27.64 329	0.01 349	0.13 348

pmmvs3		55.51 302	51.50 305	67.53 306	57.90 336	50.93 301	80.37 286	73.66 326	40.63 331	44.15 320	64.75 320	16.30 332	78.97 329	44.77 278	40.98 328	72.69 322

testus		59.36 295	57.51 292	64.90 312	66.72 324	37.56 333	84.98 252	81.09 305	57.46 289	47.72 308	72.76 288	11.43 340	78.78 330	36.56 305	58.91 283	78.36 313

test1235678		55.73 301	52.74 301	64.68 313	60.16 334	35.56 335	81.65 277	81.46 303	51.27 305	38.93 330	62.82 323	17.44 331	78.58 331	30.87 327	50.09 314	79.89 301

test12356		47.51 308	44.82 310	55.56 321	52.53 337	21.09 348	71.45 314	76.03 317	44.14 326	30.69 333	58.18 327	9.01 344	76.14 332	26.95 331	34.43 335	69.46 329

DSMNet-mixed		56.78 298	54.44 300	63.79 314	63.21 328	29.44 341	64.43 328	64.10 340	42.12 330	51.32 297	71.60 305	31.76 303	75.04 333	36.23 307	65.20 241	86.87 210

new_pmnet		49.31 307	46.44 309	57.93 318	62.84 329	40.74 326	68.47 321	62.96 342	36.48 333	35.09 331	57.81 328	14.97 335	72.18 334	32.86 322	46.44 319	60.88 334

testmv		46.98 309	43.53 311	57.35 320	47.75 342	30.41 340	74.99 310	77.69 313	42.84 329	28.03 334	53.36 329	8.18 345	71.18 335	24.36 334	34.55 333	70.46 328

Gipuma		34.91 316	31.44 318	45.30 328	70.99 315	39.64 331	19.85 345	72.56 329	20.10 341	16.16 342	21.47 344	5.08 349	71.16 336	13.07 342	43.70 324	25.08 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		54.01 304	52.12 302	59.69 317	62.41 330	39.91 329	68.59 320	68.28 337	42.96 328	44.55 319	75.18 281	14.09 337	68.39 337	41.36 289	51.68 304	70.78 326

PMVS	26.43 22	31.84 318	28.16 320	42.89 329	25.87 351	27.58 344	50.92 338	49.78 347	21.37 340	14.17 344	40.81 338	2.01 352	66.62 338	9.61 344	38.88 331	34.49 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LCM-MVSNet		40.54 313	35.79 314	54.76 324	36.92 347	30.81 339	51.41 337	69.02 334	22.07 338	24.63 336	45.37 334	4.56 350	65.81 339	33.67 318	34.50 334	67.67 330

PMMVS2		37.93 315	33.61 316	50.92 326	46.31 343	24.76 346	60.55 334	50.05 345	28.94 337	20.93 337	47.59 331	4.41 351	65.13 340	25.14 333	18.55 339	62.87 332

FPMVS		45.64 311	43.10 312	53.23 325	51.42 339	36.46 334	64.97 327	71.91 330	29.13 336	27.53 335	61.55 325	9.83 342	65.01 341	16.00 341	55.58 293	58.22 335

ANet_high		40.27 314	35.20 315	55.47 322	34.74 348	34.47 336	63.84 329	71.56 331	48.42 313	18.80 340	41.08 337	9.52 343	64.45 342	20.18 338	8.66 347	67.49 331

wuykxyi23d		29.03 320	23.09 325	46.84 327	31.67 350	28.82 343	43.46 340	57.72 344	14.39 344	7.52 348	20.84 345	0.64 355	60.29 343	21.57 337	10.04 344	51.40 337

PNet_i23d		32.77 317	29.98 319	41.11 330	48.05 340	29.17 342	65.82 326	50.02 346	21.42 339	14.74 343	37.19 340	1.11 354	55.11 344	19.75 339	11.77 342	39.06 338

MVE	24.84 23	24.35 322	19.77 326	38.09 331	34.56 349	26.92 345	26.57 343	38.87 350	11.73 345	11.37 345	27.44 341	1.37 353	50.42 345	11.41 343	14.60 341	36.93 339
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 321	24.00 322	26.45 335	43.74 345	18.44 350	60.86 332	39.66 348	15.11 342	9.53 346	22.10 343	6.52 347	46.94 346	8.31 345	10.14 343	13.98 344

EMVS		23.76 323	23.20 324	25.46 336	41.52 346	16.90 351	60.56 333	38.79 351	14.62 343	8.99 347	20.24 347	7.35 346	45.82 347	7.25 346	9.46 345	13.64 345

DeepMVS_CX				34.71 333	51.45 338	24.73 347		28.48 353	31.46 335	17.49 341	52.75 330	5.80 348	42.60 348	18.18 340	19.42 338	36.81 340

tmp_tt		22.26 324	23.75 323	17.80 337	5.23 352	12.06 352	35.26 341	39.48 349	2.82 347	18.94 339	44.20 335	22.23 326	24.64 349	36.30 306	9.31 346	16.69 343

wuyk23d		11.30 326	10.95 327	12.33 338	48.05 340	19.89 349	25.89 344	1.92 355	3.58 346	3.12 349	1.37 349	0.64 355	15.77 350	6.23 347	7.77 348	1.35 346

testmvs		7.23 328	9.62 329	0.06 340	0.04 353	0.02 355	84.98 252	0.02 356	0.03 348	0.18 350	1.21 350	0.01 358	0.02 351	0.14 348	0.01 349	0.13 348

test123		6.92 329	9.21 330	0.08 339	0.03 354	0.05 354	81.65 277	0.01 357	0.02 349	0.14 351	0.85 351	0.03 357	0.02 351	0.12 349	0.00 351	0.16 347

cdsmvs_eth3d_5k		19.86 325	26.47 321	0.00 341	0.00 355	0.00 356	0.00 346	93.45 61	0.00 350	0.00 352	95.27 30	49.56 210	0.00 353	0.00 350	0.00 351	0.00 350

pcd_1.5k_mvsjas		4.46 330	5.95 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	53.55 176	0.00 353	0.00 350	0.00 351	0.00 350

pcd1.5k->3k		31.17 319	31.85 317	29.12 334	81.48 235	0.00 356	0.00 346	91.79 131	0.00 350	0.00 352	0.00 352	41.05 263	0.00 353	0.00 350	72.34 195	87.36 200

sosnet-low-res		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 351	0.00 350

sosnet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 351	0.00 350

uncertanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 351	0.00 350

Regformer		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 351	0.00 350

ab-mvs-re		7.91 327	10.55 328	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	94.95 40	0.00 359	0.00 353	0.00 350	0.00 351	0.00 350

uanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 351	0.00 350

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_all								94.66 29

sam_mvs1												57.85 115

sam_mvs												54.91 159

MTGPA								92.23 111

MTMP								32.52 352

test9_res														89.41 10	94.96 9	95.29 39

agg_prior2														86.41 31	94.75 17	95.33 36

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

新几何2							91.41 140

旧先验1					91.94 75	60.74 223		91.50 142			94.36 55	65.23 49			91.84 52	94.55 65

原ACMM2							92.01 110

test222					89.77 122	61.60 212	89.55 191	89.42 212	56.83 293	77.28 94	92.43 94	52.76 185			91.14 64	93.09 113

segment_acmp												65.94 43

testdata1							89.21 197		77.55 63

plane_prior7					86.94 176	61.51 213

plane_prior6					87.23 172	62.32 201						50.66 200

plane_prior4											89.14 137

plane_prior3						61.95 209			79.09 44	72.53 134

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 173

plane_prior						62.42 199	93.85 55		79.38 37						78.80 145

n2								0.00 358

nn								0.00 358

door-mid								66.01 339

test11								93.01 86

door								66.57 338

HQP5-MVS						63.66 179

HQP-NCC					87.54 168		94.06 43		79.80 33	74.18 116

ACMP_Plane					87.54 168		94.06 43		79.80 33	74.18 116

BP-MVS														77.63 88

HQP3-MVS								91.70 135							78.90 143

HQP2-MVS												51.63 196

NP-MVS					87.41 171	63.04 188					90.30 121

MDTV_nov1_ep13_2view						59.90 239	80.13 291		67.65 220	72.79 129		54.33 167		59.83 221		92.58 126

ACMMP++_ref															71.63 197

ACMMP++															69.72 210

Test By Simon												54.21 169

High-resolution multi-view benchmark