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	92.20 115	86.27 6	88.70 10	95.92 13	56.76 127	99.02 4	92.68 3	93.76 29	96.37 15

QAPM		79.95 116	77.39 139	87.64 20	89.63 127	71.41 12	93.30 68	93.70 51	65.34 238	67.39 205	91.75 105	47.83 225	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 99	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	91.04 158	65.72 235	75.45 109	92.83 88	56.11 138	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	53.99 170	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 80	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	92.32 107	81.87 25	75.68 104	88.27 145	60.18 96	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	89.57 128	71.26 13	92.60 91	94.39 39	78.84 48	67.89 198	92.48 93	48.42 219	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	88.80 145	66.64 109	92.15 101	93.68 52	81.07 28	76.91 99	93.64 71	62.59 78	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 59	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	91.99 122	69.08 202	76.50 102	93.89 68	54.48 164	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 79	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	85.93 192	71.68 11	92.74 84	92.51 104	66.49 228	64.56 224	91.96 100	43.88 250	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 70	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	59.42 102	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 71	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 73	83.86 11	89.55 6	96.06 11	53.55 175	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 77	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 82	83.53 13	89.55 6	95.95 12	53.45 180	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 78	72.19 149	79.22 73	94.93 42	59.04 106	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 78	73.55 124	79.22 73	94.93 42	59.04 106	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	89.14 138	70.28 22	95.59 17	95.17 16	78.85 47	70.19 160	85.82 177	70.66 17	97.67 33	72.19 122	66.52 232	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 65	74.05 110	67.42 203	92.49 92	49.46 210	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	92.63 100	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 87	76.59 76	79.03 76	95.00 39	61.59 83	97.61 40	78.16 84	89.00 77	95.63 29

test12				87.09 34	94.60 24	68.86 44		92.91 89		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 88	72.16 152	78.86 78	94.84 47	56.97 124	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	87.66 228	92.84 91	69.96 194	74.07 120	93.57 72	63.10 75	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 81	72.19 149	78.85 79	94.86 46	56.69 131	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 98	73.99 111	78.18 83	94.31 62	55.25 144	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 98	73.99 111	78.18 83	10.19 347	55.25 144	97.41 45	79.16 76	91.58 57	93.95 92

gm-plane-assit					88.42 154	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 92	70.90 178	83.09 47	95.28 29	63.62 67	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	87.97 248	62.18 262	70.56 152	92.37 96	43.53 251	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	92.20 115	71.28 174	77.23 95	94.43 53	55.17 148	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	85.14 183	63.19 73	97.29 52	87.08 27	73.91 183	84.83 245

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 73	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 67	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	92.27 109	73.13 130	79.63 70	94.43 53	61.90 81	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	92.23 110	75.32 91	80.53 61	95.21 34	56.06 139	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	81.75 274	92.23 110	75.32 91	80.53 61	95.21 34	56.06 139	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	91.50 141	75.46 86	81.78 53	92.34 97	40.09 264	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	91.89 126	71.90 155	82.16 51	93.49 74	47.98 224	97.05 62	82.55 55	84.82 106	97.25 2

sss		82.71 77	82.38 72	83.73 122	89.25 134	59.58 243	92.24 100	94.89 21	77.96 57	79.86 67	92.38 95	56.70 130	97.05 62	77.26 90	80.86 133	94.55 65

1314		80.70 101	78.95 116	85.94 66	87.77 166	67.56 72	87.91 220	92.55 103	72.17 151	67.44 202	93.09 78	50.27 203	97.04 64	71.68 125	87.64 86	93.23 109

无先验							92.71 85	92.61 101	62.03 263				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	91.39 145	59.44 279	79.94 66	92.91 85	57.09 120	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	92.51 104	74.56 99	80.62 60	95.64 20	59.15 105	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	58.22 111	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	92.58 102	66.54 227	86.17 20	95.88 14	63.83 64	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	91.85 128	71.52 171	75.61 107	94.24 63	53.48 179	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	89.01 227	85.27 7	86.09 21	93.74 70	47.71 227	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	91.73 131	77.43 66	77.08 98	89.81 131	63.77 66	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	92.22 114	71.79 163	71.68 148	93.49 74	50.32 201	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 66	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	84.65 253	94.50 33	79.15 42	82.23 50	87.93 151	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 60	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	91.71 133	80.26 31	87.55 14	95.25 32	63.59 69	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 83	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 83					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 95	79.04 46	81.50 55	96.50 4	58.98 108	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	90.07 190	62.56 260	77.71 87	93.08 79	47.65 228	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 71	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 71		84.49 36			96.75 83

原ACMM1				84.42 110	93.21 48	64.27 166		93.40 67	65.39 236	79.51 71	92.50 91	58.11 113	96.69 85	65.27 182	93.96 24	92.32 131

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 153	65.49 138	83.00 268	92.67 97	71.82 161	77.36 93	85.01 184	54.50 162	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	86.06 187	65.96 128	87.03 236	91.10 155	69.86 195	69.79 167	90.64 114	57.54 118	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		91.46 143	59.56 278	79.77 68	92.90 86	56.95 125	96.57 88	63.40 195	92.91 40	93.34 104

VDDNet		80.50 104	78.26 123	87.21 29	86.19 185	69.79 30	94.48 34	91.31 148	60.42 273	79.34 72	90.91 111	38.48 270	96.56 89	82.16 56	81.05 131	95.27 41

cascas		78.18 149	75.77 160	85.41 84	87.14 173	69.11 39	92.96 77	91.15 153	66.71 226	70.47 153	86.07 174	37.49 280	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	91.86 127	70.30 191	77.12 96	93.96 67	56.75 129	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	92.28 108	71.03 176	77.41 92	94.92 44	55.21 147	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	90.79 162	76.83 72	78.85 79	86.33 172	38.16 272	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	91.01 159	63.35 253	79.72 69	94.62 51	51.82 191	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	88.09 245	72.93 134	73.37 124	91.12 110	46.20 239	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	92.05 120	77.77 59	82.84 48	86.57 169	63.93 63	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 70	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		69.54 251	65.73 255	80.96 194	85.11 198	63.71 176	84.19 255	83.28 297	56.95 290	54.50 277	84.03 192	31.50 304	96.03 100	42.87 283	69.13 215	83.14 264

Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 129	69.40 36	91.35 145	86.12 274	72.59 138	83.22 46	92.81 89	59.60 101	96.01 102	81.76 60	87.80 85	95.56 31

UA-Net		80.02 114	79.65 102	81.11 189	89.33 132	57.72 261	86.33 246	89.00 228	77.44 65	81.01 57	89.15 135	59.33 103	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	80.90 239	69.46 35	90.69 165	91.97 124	77.00 68	59.07 256	82.34 209	46.82 231	95.88 104	82.14 57	86.59 95	94.53 69

test_normal		79.66 121	77.36 141	86.54 47	80.72 243	69.21 38	90.68 166	92.16 119	76.99 69	58.63 260	82.03 218	46.70 233	95.86 105	81.74 61	86.63 94	94.56 64

lupinMVS		87.74 16	87.77 15	87.63 22	89.24 136	71.18 14	96.57 4	92.90 90	82.70 16	87.13 16	95.27 30	64.99 55	95.80 106	89.34 12	91.80 53	95.93 25

MS-PatchMatch		77.90 154	76.50 151	82.12 166	85.99 188	69.95 27	91.75 131	92.70 95	73.97 113	62.58 242	84.44 191	41.11 261	95.78 107	63.76 192	92.17 50	80.62 294

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 164	67.65 70	95.45 18	92.18 118	85.06 8	72.58 133	92.27 98	52.46 188	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	89.16 220	65.23 239	74.71 114	93.07 81	47.81 226	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	86.42 180	68.72 47	92.59 93	90.44 170	73.12 131	84.20 39	94.36 55	38.04 274	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	86.42 180	68.72 47	92.59 93	90.44 170	73.12 131	84.20 39	94.36 55	38.04 274	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	86.42 180	68.72 47	92.59 93	90.44 170	73.12 131	84.20 39	94.36 55	38.04 274	95.73 110	84.12 45	86.81 90	91.33 143

DP-MVS		69.90 246	66.48 252	80.14 203	95.36 14	62.93 190	89.56 190	76.11 315	50.27 308	57.69 266	85.23 182	39.68 265	95.73 110	33.35 318	71.05 203	81.78 275

114514_t		79.17 129	77.67 131	83.68 124	95.32 15	65.53 137	92.85 82	91.60 137	63.49 252	67.92 197	90.63 116	46.65 234	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	90.07 190	70.08 193	74.51 115	91.37 108	45.69 240	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	88.66 138	59.67 100	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	89.42 211	74.35 105	69.09 178	90.15 123	55.23 146	95.61 116	64.61 186	86.43 98	92.17 135

HQP-MVS		81.14 96	80.64 91	82.64 142	87.54 167	63.66 179	94.06 43	91.70 134	79.80 33	74.18 116	90.30 121	51.63 195	95.61 116	77.63 88	78.90 143	88.63 172

HQP_MVS		80.34 107	79.75 101	82.12 166	86.94 175	62.42 199	93.13 72	91.31 148	78.81 49	72.53 134	89.14 136	50.66 199	95.55 120	76.74 91	78.53 147	88.39 177

plane_prior5								91.31 148					95.55 120	76.74 91	78.53 147	88.39 177

jason		86.40 32	86.17 32	87.11 33	86.16 186	70.54 20	95.71 15	92.19 117	82.00 24	84.58 35	94.34 60	61.86 82	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	88.59 236	71.57 169	66.83 211	88.65 139	51.75 193	95.39 123	59.03 225	84.77 107	91.32 146

MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 154	62.88 194	89.77 189	88.93 229	76.78 73	75.55 108	93.10 77	50.31 202	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	87.89 152	60.31 93	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	87.89 152	60.31 93	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	88.26 146	60.07 97	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	88.26 146	60.07 97	95.32 125	45.16 273	77.58 155	87.48 191

OPM-MVS		79.00 131	78.09 125	81.73 174	83.52 219	63.83 171	91.64 136	90.30 180	76.36 79	71.97 143	89.93 130	46.30 238	95.17 129	75.10 101	77.70 153	86.19 218

PAPM		85.89 39	85.46 41	87.18 30	88.20 158	72.42 9	92.41 97	92.77 93	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	56.17 275	91.13 156	90.69 165	77.96 57	80.09 65	93.57 72	46.33 237	94.99 131	81.41 64	87.46 87	94.17 79

CostFormer		82.33 81	81.15 84	85.86 67	89.01 141	68.46 51	82.39 271	93.01 85	75.59 84	80.25 64	81.57 224	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	88.77 146	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	68.62 255	64.81 261	80.03 207	85.22 196	63.25 183	87.72 224	84.66 284	60.83 271	51.57 295	79.43 255	27.29 316	94.96 132	41.76 286	64.84 247	81.88 274
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	87.89 152	60.31 93	94.92 135	42.64 285	76.64 165	87.48 191

tpmp4_e23		78.85 134	76.55 150	85.77 72	89.25 134	68.39 53	81.63 278	91.38 146	70.40 189	75.21 111	79.22 256	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	88.56 148	66.93 90	86.70 244	88.62 235	70.42 188	78.69 81	85.26 181	56.93 126	94.77 137	68.68 153	83.09 119	93.51 101

XXY-MVS		77.94 153	76.44 152	82.43 149	82.60 227	64.44 157	92.01 110	91.83 129	73.59 123	70.00 163	85.82 177	54.43 165	94.76 138	69.63 143	68.02 223	88.10 182

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 151	61.80 210	93.44 66	88.26 244	73.96 114	77.73 86	91.76 104	49.94 206	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	83.69 216	68.83 45	93.23 70	91.20 151	75.55 85	75.06 112	88.22 149	63.04 76	94.74 140	81.88 59	66.88 229	88.82 170

GA-MVS		78.33 147	76.23 154	84.65 104	83.65 217	66.30 122	91.44 139	90.14 188	76.01 81	70.32 158	84.02 193	42.50 254	94.72 141	70.98 133	77.00 164	92.94 118

EI-MVSNet		78.97 132	78.22 124	81.25 182	85.33 194	62.73 197	89.53 193	93.21 75	72.39 143	72.14 141	90.13 124	60.99 85	94.72 141	67.73 158	72.49 193	86.29 216

MVSTER		82.47 79	82.05 74	83.74 120	92.68 61	69.01 41	91.90 121	93.21 75	79.83 32	72.14 141	85.71 179	74.72 7	94.72 141	75.72 96	72.49 193	87.50 190

PLC	68.80 14	75.23 197	73.68 194	79.86 212	92.93 54	58.68 255	90.64 167	88.30 241	60.90 270	64.43 227	90.53 117	42.38 255	94.57 144	56.52 233	76.54 166	86.33 215
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	75.02 308	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	85.76 193	57.40 266	88.49 209	88.67 233	73.85 116	72.43 138	92.10 99	49.29 212	94.55 146	72.73 115	77.89 151	90.91 150

mvs-test1		78.74 139	77.95 128	81.14 188	83.22 221	57.13 268	93.96 48	87.78 249	75.42 87	72.68 130	90.80 113	45.08 244	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	89.15 221	73.81 118	75.37 110	88.26 146	57.26 119	94.53 148	66.97 165	84.92 105	93.15 111

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 165	68.11 61	90.18 177	90.01 193	70.65 186	72.95 127	86.06 175	63.61 68	94.50 149	75.01 104	79.75 137	93.67 97

v1neww		77.39 159	75.71 162	82.44 146	80.69 245	66.83 92	91.94 118	90.18 185	74.19 107	69.60 168	82.51 205	54.99 155	94.44 150	71.68 125	65.60 235	86.05 223

v7new		77.39 159	75.71 162	82.44 146	80.69 245	66.83 92	91.94 118	90.18 185	74.19 107	69.60 168	82.51 205	54.99 155	94.44 150	71.68 125	65.60 235	86.05 223

v6		77.39 159	75.71 162	82.44 146	80.67 247	66.82 94	91.94 118	90.18 185	74.19 107	69.60 168	82.50 208	55.00 154	94.44 150	71.68 125	65.60 235	86.05 223

v2v482		77.42 158	75.65 165	82.73 138	80.38 257	67.13 83	91.85 124	90.23 182	75.09 94	69.37 174	83.39 199	53.79 173	94.44 150	71.77 123	65.00 246	86.63 214

v7		76.83 175	75.01 176	82.29 158	80.35 258	66.70 106	91.68 133	89.97 194	73.47 127	69.22 176	82.22 212	52.52 186	94.43 154	69.73 142	65.96 234	85.74 234

v1144		76.73 177	74.88 177	82.27 159	80.23 265	66.60 110	91.68 133	90.21 184	73.69 120	69.06 179	81.89 220	52.73 185	94.40 155	69.21 148	65.23 239	85.80 230

v1141		77.28 164	75.57 166	82.42 152	80.63 251	66.73 102	91.96 114	90.42 173	74.41 100	69.46 171	82.12 215	55.09 151	94.40 155	70.99 132	65.05 242	86.12 220

divwei89l23v2f112		77.28 164	75.57 166	82.42 152	80.62 252	66.72 104	91.96 114	90.42 173	74.41 100	69.46 171	82.12 215	55.11 150	94.40 155	71.00 130	65.04 243	86.12 220

v1		77.29 163	75.57 166	82.42 152	80.61 255	66.73 102	91.96 114	90.42 173	74.41 100	69.46 171	82.12 215	55.14 149	94.40 155	71.00 130	65.04 243	86.13 219

Test4		76.45 180	73.45 200	85.45 82	76.07 301	67.61 71	88.38 212	90.83 161	76.71 74	53.06 289	79.65 253	31.61 303	94.35 159	78.47 81	86.22 99	94.40 73

TAPA-MVS	70.22 12	74.94 202	73.53 198	79.17 231	90.40 111	52.07 293	89.19 198	89.61 206	62.69 259	70.07 161	92.67 90	48.89 218	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	84.31 209	59.37 246	90.44 170	89.73 202	69.49 197	64.86 221	88.42 141	38.65 268	94.30 161	72.56 116	72.76 190	85.01 243

LGP-MVS_train				79.56 219	84.31 209	59.37 246		89.73 202	69.49 197	64.86 221	88.42 141	38.65 268	94.30 161	72.56 116	72.76 190	85.01 243

v1192		75.98 187	73.92 191	82.15 165	79.73 270	66.24 124	91.22 151	89.75 199	72.67 137	68.49 192	81.42 226	49.86 207	94.27 163	67.08 163	65.02 245	85.95 227

tpmvs		72.88 220	69.76 228	82.22 163	90.98 102	67.05 85	78.22 302	88.30 241	63.10 257	64.35 228	74.98 281	55.09 151	94.27 163	43.25 279	69.57 211	85.34 241

tpm2		79.80 119	77.95 128	85.34 87	88.28 156	68.26 58	81.56 279	91.42 144	70.11 192	77.59 91	80.50 240	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	87.31 160	60.79 87	94.19 166	38.90 298	75.31 172	87.48 191

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	87.31 160	60.79 87	94.19 166	38.90 298	75.31 172	87.48 191

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	87.31 160	60.79 87	94.19 166	38.90 298	75.31 172	87.48 191

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	87.31 160	60.79 87	94.19 166	38.90 298	75.31 172	87.48 191

PVSNet_0	68.08 15	71.81 227	68.32 241	82.27 159	84.68 201	62.31 202	88.68 206	90.31 179	75.84 82	57.93 263	80.65 239	37.85 277	94.19 166	69.94 141	29.05 336	90.31 156

MVP-Stereo		77.12 168	76.23 154	79.79 214	81.72 233	66.34 121	89.29 195	90.88 160	70.56 187	62.01 245	82.88 202	49.34 211	94.13 171	65.55 180	93.80 27	78.88 307
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMM	69.62 13	74.34 208	72.73 206	79.17 231	84.25 211	57.87 259	90.36 173	89.93 195	63.17 256	65.64 217	86.04 176	37.79 278	94.10 172	65.89 176	71.52 199	85.55 237
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	79.14 279	67.82 65	90.26 176	89.42 211	73.75 119	68.63 186	81.89 220	51.31 197	94.09 173	71.69 124	64.84 247	84.66 246

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 159	63.74 174	92.70 86	93.77 48	79.30 38	77.61 90	87.57 157	58.19 112	94.08 174	73.91 109	86.68 93	93.33 106

v8		75.35 194	73.26 202	81.61 178	80.67 247	66.82 94	89.54 192	89.27 215	71.65 166	63.30 236	80.30 244	54.99 155	94.06 175	67.33 162	62.33 261	83.94 251

EG-PatchMatch MVS		68.55 256	65.41 258	77.96 250	78.69 284	62.93 190	89.86 188	89.17 219	60.55 272	50.27 300	77.73 264	22.60 324	94.06 175	47.18 267	72.65 192	76.88 315

PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 105	64.97 150	92.47 96	91.05 157	79.18 41	72.43 138	90.51 119	37.05 286	94.06 175	68.06 154	86.00 100	93.90 95

v10		74.77 206	72.54 210	81.46 179	80.33 262	66.71 105	89.15 199	89.08 224	70.94 177	63.08 237	79.86 249	52.52 186	94.04 178	65.70 179	62.17 262	83.64 253

v144192		76.05 185	74.03 188	82.12 166	79.50 274	66.55 114	91.39 141	89.71 205	72.30 144	68.17 194	81.33 228	51.75 193	94.03 179	67.94 155	64.19 252	85.77 231

tpm cat1		75.30 195	72.21 213	84.58 106	88.52 149	67.77 66	78.16 303	88.02 246	61.88 266	68.45 193	76.37 274	60.65 91	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	71.78 311	94.72 27	49.61 309	77.12 96	45.92 332	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	80.64 250	59.16 249	90.63 169	91.06 156	72.80 136	68.58 187	84.57 190	53.55 175	93.96 182	72.97 111	71.96 196	87.27 203

OpenMVS_ROB	61.12 18	66.39 269	62.92 274	76.80 263	76.51 296	57.77 260	89.22 196	83.41 295	55.48 297	53.86 285	77.84 263	26.28 319	93.95 183	34.90 315	68.76 217	78.68 309

MDTV_nov1_ep13			72.61 208		89.06 139	68.48 50	80.33 286	90.11 189	71.84 160	71.81 145	75.92 278	53.01 182	93.92 184	48.04 263	73.38 184

v1921920		75.63 192	73.49 199	82.06 170	79.38 275	66.35 120	91.07 158	89.48 208	71.98 154	67.99 195	81.22 231	49.16 215	93.90 185	66.56 168	64.56 251	85.92 229

v1240		75.21 198	72.98 204	81.88 172	79.20 277	66.00 127	90.75 164	89.11 223	71.63 167	67.41 204	81.22 231	47.36 229	93.87 186	65.46 181	64.72 249	85.77 231

ACMP	71.68 10	75.58 193	74.23 186	79.62 217	84.97 199	59.64 241	90.80 162	89.07 225	70.39 190	62.95 238	87.30 164	38.28 271	93.87 186	72.89 112	71.45 200	85.36 240
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v148		76.19 182	74.47 183	81.36 180	80.05 269	64.44 157	91.75 131	90.23 182	73.68 121	67.13 207	80.84 236	55.92 142	93.86 188	68.95 150	61.73 267	85.76 233

testing_2		71.09 236	67.32 249	82.40 155	69.82 318	66.52 116	83.64 258	90.77 163	72.21 148	45.12 315	71.07 307	27.60 315	93.74 189	75.71 97	69.96 207	86.95 207

LS3D		69.17 253	66.40 253	77.50 254	91.92 77	56.12 276	85.12 250	80.37 308	46.96 315	56.50 271	87.51 158	37.25 281	93.71 190	32.52 323	79.40 138	82.68 270

EPMVS		78.49 145	75.98 157	86.02 63	91.21 100	69.68 33	80.23 288	91.20 151	75.25 93	72.48 136	78.11 261	54.65 161	93.69 191	57.66 232	83.04 120	94.69 62

IS-MVSNet		80.14 111	79.41 108	82.33 156	87.91 163	60.08 237	91.97 113	88.27 243	72.90 135	71.44 150	91.73 106	61.44 84	93.66 192	62.47 208	86.53 96	93.24 108

v7n		71.31 232	68.65 235	79.28 226	76.40 297	60.77 221	86.71 243	89.45 209	64.17 248	58.77 259	78.24 260	44.59 248	93.54 193	57.76 230	61.75 266	83.52 256

VPA-MVSNet		79.03 130	78.00 127	82.11 169	85.95 189	64.48 155	93.22 71	94.66 29	75.05 95	74.04 121	84.95 185	52.17 190	93.52 194	74.90 107	67.04 228	88.32 179

tfpnnormal		70.10 244	67.36 247	78.32 242	83.45 220	60.97 217	88.85 203	92.77 93	64.85 244	60.83 247	78.53 259	43.52 252	93.48 195	31.73 324	61.70 268	80.52 295

旧先验2							92.00 112		59.37 280	87.54 15			93.47 196	75.39 99

1112_ss		80.56 103	79.83 100	82.77 137	88.65 147	60.78 220	92.29 98	88.36 240	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	89.02 140	57.72 261		89.84 197	58.65 283	85.32 30	94.09 64	57.03 122	93.28 198	69.34 147	90.56 70	93.03 115

LTVRE_ROB	59.60 19	66.27 270	63.54 269	74.45 275	84.00 214	51.55 295	67.08 324	83.53 293	58.78 282	54.94 275	80.31 243	34.54 294	93.23 199	40.64 292	68.03 222	78.58 310
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
VPNet		78.82 135	77.53 135	82.70 139	84.52 204	66.44 118	93.93 51	92.23 110	80.46 30	72.60 132	88.38 143	49.18 213	93.13 200	72.47 118	63.97 255	88.55 174

Test_1112_low_res		79.56 124	78.60 119	82.43 149	88.24 157	60.39 229	92.09 105	87.99 247	72.10 153	71.84 144	87.42 159	64.62 58	93.04 201	65.80 178	77.30 162	93.85 96

PatchMatch-RL		72.06 225	69.98 224	78.28 243	89.51 131	55.70 278	83.49 260	83.39 296	61.24 269	63.72 232	82.76 203	34.77 293	93.03 202	53.37 247	77.59 154	86.12 220

Fast-Effi-MVS+-dtu		75.04 199	73.37 201	80.07 206	80.86 240	59.52 244	91.20 153	85.38 281	71.90 155	65.20 218	84.84 186	41.46 260	92.97 203	66.50 169	72.96 189	87.73 188

v748		70.55 242	67.97 245	78.27 244	75.75 302	58.78 253	86.29 247	89.25 216	65.12 243	56.66 270	77.17 270	45.05 246	92.95 204	58.13 229	58.33 285	83.10 265

pm-mvs1		72.89 219	71.09 221	78.26 245	79.10 281	57.62 263	90.80 162	89.30 214	67.66 219	62.91 239	81.78 222	49.11 216	92.95 204	60.29 219	58.89 283	84.22 249

TAMVS		80.37 106	79.45 107	83.13 133	85.14 197	63.37 182	91.23 150	90.76 164	74.81 98	72.65 131	88.49 140	60.63 92	92.95 204	69.41 146	81.95 128	93.08 114

ACMH+	65.35 16	67.65 262	64.55 263	76.96 261	84.59 203	57.10 269	88.08 215	80.79 306	58.59 284	53.00 290	81.09 235	26.63 318	92.95 204	46.51 268	61.69 269	80.82 291

CDS-MVSNet		81.43 93	80.74 89	83.52 127	86.26 184	64.45 156	92.09 105	90.65 168	75.83 83	73.95 122	89.81 131	63.97 62	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		69.80 248	67.01 251	78.15 247	71.84 311	60.10 235	82.02 272	87.39 252	64.48 245	57.80 264	75.97 277	41.47 259	92.90 209	63.00 200	59.13 280	81.45 279

V4		69.80 248	67.02 250	78.15 247	71.86 310	60.10 235	82.02 272	87.39 252	64.48 245	57.78 265	75.98 276	41.49 258	92.90 209	63.00 200	59.16 279	81.44 280

CPTT-MVS		79.59 123	79.16 114	80.89 196	91.54 90	59.80 240	92.10 104	88.54 238	60.42 273	72.96 126	93.28 76	48.27 220	92.80 211	78.89 80	86.50 97	90.06 157

PatchmatchNet		77.46 157	74.63 178	85.96 65	89.55 130	70.35 21	79.97 292	89.55 207	72.23 147	70.94 151	76.91 273	57.03 122	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		73.05 217	71.51 219	77.67 252	77.46 292	54.83 282	88.81 204	90.04 192	69.13 201	62.85 240	83.51 197	31.16 306	92.75 213	70.83 134	69.80 208	85.43 239

mvs_tets		72.71 222	71.11 220	77.52 253	77.41 293	54.52 284	88.45 211	89.76 198	68.76 206	62.70 241	83.26 200	29.49 310	92.71 214	70.51 139	69.62 210	85.34 241

tpmrst		80.57 102	79.14 115	84.84 99	90.10 116	68.28 57	81.70 275	89.72 204	77.63 62	75.96 103	79.54 254	64.94 57	92.71 214	75.43 98	77.28 163	93.55 100

test_post											23.01 341	56.49 134	92.67 216

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 136	71.18 14	89.07 201	90.69 165	65.80 233	87.13 16	94.34 60	64.99 55	92.67 216	72.83 113	91.80 53	95.27 41

test_djsdf		73.76 215	72.56 209	77.39 257	77.00 295	53.93 286	89.07 201	90.69 165	65.80 233	63.92 229	82.03 218	43.14 253	92.67 216	72.83 113	68.53 219	85.57 236

dp		75.01 200	72.09 214	83.76 119	89.28 133	66.22 125	79.96 293	89.75 199	71.16 175	67.80 200	77.19 269	51.81 192	92.54 219	50.39 254	71.44 201	92.51 128

Effi-MVS+-dtu		76.14 183	75.28 173	78.72 239	83.22 221	55.17 281	89.87 187	87.78 249	75.42 87	67.98 196	81.43 225	45.08 244	92.52 220	75.08 102	71.63 197	88.48 175

F-COLMAP		70.66 240	68.44 239	77.32 258	86.37 183	55.91 277	88.00 216	86.32 269	56.94 291	57.28 268	88.07 150	33.58 295	92.49 221	51.02 252	68.37 220	83.55 254

USDC		67.43 266	64.51 264	76.19 266	77.94 290	55.29 280	78.38 300	85.00 282	73.17 129	48.36 305	80.37 242	21.23 326	92.48 222	52.15 249	64.02 254	80.81 292

pmmvs6		67.57 263	64.76 262	76.00 268	72.82 309	53.37 288	88.71 205	86.78 259	53.19 300	57.58 267	78.03 262	35.33 291	92.41 223	55.56 237	54.88 295	82.21 272

test-LLR		80.10 112	79.56 104	81.72 175	86.93 177	61.17 215	92.70 86	91.54 138	71.51 172	75.62 105	86.94 166	53.83 171	92.38 224	72.21 120	84.76 108	91.60 140

test-mter		79.96 115	79.38 110	81.72 175	86.93 177	61.17 215	92.70 86	91.54 138	73.85 116	75.62 105	86.94 166	49.84 208	92.38 224	72.21 120	84.76 108	91.60 140

UniMVSNet (Re)		77.58 156	76.78 148	79.98 208	84.11 212	60.80 219	91.76 129	93.17 80	76.56 77	69.93 166	84.78 187	63.32 72	92.36 226	64.89 184	62.51 260	86.78 210

mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 112	68.40 52	86.88 241	90.61 169	74.41 100	70.31 159	84.67 188	63.79 65	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 74	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	84.49 205	62.74 196	90.64 167	88.80 231	72.40 142	65.16 220	81.72 223	60.98 86	92.27 229	67.74 157	64.65 250	86.29 216
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	91.99 122	73.35 128	67.04 208	83.19 201	56.62 132	92.14 230	59.80 222	69.34 212	87.28 202

UniMVSNet_NR-MVSNet		78.15 150	77.55 134	79.98 208	84.46 206	60.26 231	92.25 99	93.20 77	77.50 64	68.88 182	86.61 168	66.10 41	92.13 231	66.38 170	62.55 258	87.54 189

DU-MVS		76.86 173	75.84 159	79.91 210	82.96 225	60.26 231	91.26 149	91.54 138	76.46 78	68.88 182	86.35 170	56.16 136	92.13 231	66.38 170	62.55 258	87.35 200

tpm		78.58 143	77.03 143	83.22 131	85.94 191	64.56 152	83.21 266	91.14 154	78.31 54	73.67 123	79.68 251	64.01 61	92.09 233	66.07 174	71.26 202	93.03 115

Baseline_NR-MVSNet		73.99 212	72.83 205	77.48 255	80.78 241	59.29 248	91.79 126	84.55 285	68.85 204	68.99 180	80.70 237	56.16 136	92.04 234	62.67 206	60.98 273	81.11 288

FMVSNet2		76.07 184	74.01 189	82.26 162	88.85 142	67.66 69	91.33 146	91.61 136	70.84 179	65.98 213	82.25 211	48.03 221	92.00 235	58.46 228	68.73 218	87.10 204

TransMVSNet (Re)		70.07 245	67.66 246	77.31 259	80.62 252	59.13 250	91.78 128	84.94 283	65.97 231	60.08 250	80.44 241	50.78 198	91.87 236	48.84 260	45.46 320	80.94 290

NR-MVSNet		76.05 185	74.59 179	80.44 198	82.96 225	62.18 204	90.83 161	91.73 131	77.12 67	60.96 246	86.35 170	59.28 104	91.80 237	60.74 216	61.34 271	87.35 200

FIs		79.47 125	79.41 108	79.67 215	85.95 189	59.40 245	91.68 133	93.94 43	78.06 56	68.96 181	88.28 144	66.61 37	91.77 238	66.20 173	74.99 176	87.82 187

XVG-OURS		74.25 210	72.46 211	79.63 216	78.45 286	57.59 264	80.33 286	87.39 252	63.86 251	68.76 184	89.62 134	40.50 263	91.72 239	69.00 149	74.25 179	89.58 163

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		64.54 277	61.09 282	74.92 272	84.10 213	60.75 222	87.95 217	79.71 310	52.03 303	52.41 291	77.20 268	32.21 301	91.64 243	23.14 334	61.03 272	72.36 322

XVG-OURS-SEG-HR		74.70 207	73.08 203	79.57 218	78.25 287	57.33 267	80.49 284	87.32 255	63.22 255	68.76 184	90.12 126	44.89 247	91.59 244	70.55 138	74.09 181	89.79 160

TranMVSNet+NR-MVSNet		75.86 188	74.52 182	79.89 211	82.44 228	60.64 226	91.37 144	91.37 147	76.63 75	67.65 201	86.21 173	52.37 189	91.55 245	61.84 211	60.81 274	87.48 191

GBi-Net		75.65 190	73.83 192	81.10 190	88.85 142	65.11 144	90.01 179	90.32 176	70.84 179	67.04 208	80.25 245	48.03 221	91.54 246	59.80 222	69.34 212	86.64 211

test1		75.65 190	73.83 192	81.10 190	88.85 142	65.11 144	90.01 179	90.32 176	70.84 179	67.04 208	80.25 245	48.03 221	91.54 246	59.80 222	69.34 212	86.64 211

FMVSNet1		72.71 222	69.91 226	81.10 190	83.60 218	65.11 144	90.01 179	90.32 176	63.92 250	63.56 233	80.25 245	36.35 288	91.54 246	54.46 240	66.75 230	86.64 211

pmmvs4		73.92 213	71.81 216	80.25 202	79.17 278	65.24 140	87.43 231	87.26 257	67.64 221	63.46 234	83.91 194	48.96 217	91.53 249	62.94 202	65.49 238	83.96 250

test_post1							78.95 296				20.70 345	53.05 181	91.50 250	60.43 218

tfpn1000		75.25 196	74.00 190	79.03 234	90.30 113	57.56 265	88.55 208	93.36 69	64.14 249	65.17 219	89.76 133	67.06 33	91.46 251	34.54 316	73.09 188	88.06 183

anonymousdsp		71.14 235	69.37 230	76.45 264	72.95 307	54.71 283	84.19 255	88.88 230	61.92 265	62.15 244	79.77 250	38.14 273	91.44 252	68.90 151	67.45 227	83.21 262

XVG-ACMP-BASELINE		68.04 260	65.53 257	75.56 269	74.06 306	52.37 291	78.43 299	85.88 278	62.03 263	58.91 258	81.21 233	20.38 327	91.15 253	60.69 217	68.18 221	83.16 263

CNLPA		74.31 209	72.30 212	80.32 200	91.49 91	61.66 211	90.85 160	80.72 307	56.67 293	63.85 231	90.64 114	46.75 232	90.84 254	53.79 243	75.99 169	88.47 176

pmmvs5		73.35 216	71.52 218	78.86 235	78.64 285	60.61 227	91.08 157	86.90 258	67.69 218	63.32 235	83.64 195	44.33 249	90.53 255	62.04 210	66.02 233	85.46 238

SixPastTwentyTwo		64.92 275	61.78 281	74.34 277	78.74 283	49.76 304	83.42 263	79.51 311	62.86 258	50.27 300	77.35 265	30.92 308	90.49 256	45.89 271	47.06 317	82.78 266

COLMAP_ROB	57.96 20	62.98 286	59.65 286	72.98 285	81.44 236	53.00 290	83.75 257	75.53 321	48.34 313	48.81 304	81.40 227	24.14 320	90.30 257	32.95 320	60.52 276	75.65 318
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
patchmatchnet-post											67.62 311	57.62 117	90.25 258

JIA-IIPM		66.06 271	62.45 277	76.88 262	81.42 237	54.45 285	57.49 335	88.67 233	49.36 310	63.86 230	46.86 331	56.06 139	90.25 258	49.53 258	68.83 216	85.95 227

WR-MVS		76.76 176	75.74 161	79.82 213	84.60 202	62.27 203	92.60 91	92.51 104	76.06 80	67.87 199	85.34 180	56.76 127	90.24 260	62.20 209	63.69 257	86.94 208

FC-MVSNet-test		77.99 152	78.08 126	77.70 251	84.89 200	55.51 279	90.27 175	93.75 50	76.87 70	66.80 212	87.59 156	65.71 47	90.23 261	62.89 203	73.94 182	87.37 198

EPNet_dtu		78.80 136	79.26 112	77.43 256	88.06 160	49.71 305	91.96 114	91.95 125	77.67 61	76.56 101	91.28 109	58.51 110	90.20 262	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		71.94 226	69.43 229	79.50 221	80.74 242	66.82 94	88.16 214	86.66 260	68.95 203	55.55 272	72.66 288	55.03 153	90.15 263	64.78 185	52.30 300	81.54 276

CMPMVS	48.56 21	66.77 268	64.41 266	73.84 279	70.65 316	50.31 302	77.79 304	85.73 280	45.54 320	44.76 316	82.14 214	35.40 290	90.14 264	63.18 198	74.54 177	81.07 289
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v16		71.81 227	69.26 231	79.47 222	80.66 249	66.81 98	87.93 218	86.63 262	68.70 208	55.35 273	72.51 289	54.75 159	90.12 265	64.51 187	52.28 301	81.47 277

v17		71.77 229	69.20 232	79.46 223	80.62 252	66.81 98	87.93 218	86.63 262	68.71 207	55.25 274	72.49 290	54.72 160	90.11 266	64.50 188	51.97 302	81.47 277

Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 246	88.46 152	52.29 292	90.41 172	89.12 222	74.24 106	69.13 177	91.91 101	65.77 46	90.09 267	59.00 226	88.09 83	92.33 130

v15		71.40 231	68.75 234	79.35 224	80.39 256	66.70 106	87.57 230	86.64 261	68.66 209	54.68 276	72.00 297	54.50 162	89.98 268	63.69 193	50.66 307	81.38 281

V14		71.29 233	68.61 236	79.31 225	80.34 260	66.65 108	87.39 232	86.61 264	68.41 213	54.49 278	71.91 298	54.25 167	89.96 269	63.50 194	50.62 308	81.33 283

V9		71.16 234	68.46 238	79.27 227	80.26 263	66.60 110	87.21 235	86.56 265	68.17 214	54.26 281	71.81 300	54.00 169	89.93 270	63.28 197	50.57 309	81.27 284

v12		71.02 238	68.29 243	79.22 229	80.18 266	66.53 115	87.01 238	86.54 267	67.90 216	54.00 284	71.70 301	53.66 174	89.91 271	63.09 199	50.51 310	81.21 285

v11		71.05 237	68.32 241	79.23 228	80.34 260	66.57 113	87.01 238	86.55 266	68.11 215	54.40 279	71.66 302	52.94 183	89.91 271	62.71 205	51.12 305	81.21 285

v13		70.90 239	68.15 244	79.15 233	80.08 267	66.45 117	86.83 242	86.50 268	67.62 222	53.78 286	71.61 303	53.51 178	89.87 273	62.89 203	50.50 311	81.14 287

lessismore_v0				73.72 280	72.93 308	47.83 311		61.72 342		45.86 312	73.76 284	28.63 313	89.81 274	47.75 266	31.37 335	83.53 255

MVS-HIRNet		60.25 291	55.55 297	74.35 276	84.37 208	56.57 274	71.64 312	74.11 324	34.44 333	45.54 314	42.24 335	31.11 307	89.81 274	40.36 293	76.10 168	76.67 316

CR-MVSNet		73.79 214	70.82 223	82.70 139	83.15 223	67.96 63	70.25 314	84.00 290	73.67 122	69.97 164	72.41 293	57.82 115	89.48 276	52.99 248	73.13 186	90.64 153

RPMNet		69.58 250	65.21 260	82.70 139	83.15 223	67.96 63	70.25 314	86.15 273	46.83 317	69.97 164	65.10 318	56.48 135	89.48 276	35.79 310	73.13 186	90.64 153

Patchmtry		67.53 264	63.93 268	78.34 241	82.12 230	64.38 160	68.72 318	84.00 290	48.23 314	59.24 253	72.41 293	57.82 115	89.27 278	46.10 270	56.68 290	81.36 282

ADS-MVSNet		68.54 257	64.38 267	81.03 193	88.06 160	66.90 91	68.01 321	84.02 289	57.57 285	64.48 225	69.87 308	38.68 266	89.21 279	40.87 290	67.89 224	86.97 205

Patchmatch-test1		75.00 201	71.80 217	84.58 106	86.63 179	70.08 24	81.06 281	89.19 218	71.60 168	70.01 162	77.16 271	45.53 241	88.63 280	51.79 250	73.27 185	95.02 55

Patchmatch-RL test		68.17 259	64.49 265	79.19 230	71.22 313	53.93 286	70.07 316	71.54 331	69.22 200	56.79 269	62.89 321	56.58 133	88.61 281	69.53 145	52.61 299	95.03 54

UnsupCasMVSNet_bld		61.60 288	57.71 289	73.29 283	68.73 322	51.64 294	78.61 298	89.05 226	57.20 289	46.11 309	61.96 323	28.70 312	88.60 282	50.08 256	38.90 329	79.63 303

OurMVSNet-221017-0		64.68 276	62.17 279	72.21 292	76.08 300	47.35 313	80.67 283	81.02 305	56.19 294	51.60 294	79.66 252	27.05 317	88.56 283	53.60 245	53.63 298	80.71 293

PatchT		69.11 254	65.37 259	80.32 200	82.07 231	63.68 178	67.96 323	87.62 251	50.86 307	69.37 174	65.18 317	57.09 120	88.53 284	41.59 288	66.60 231	88.74 171

TinyColmap		60.32 290	56.42 296	72.00 295	78.78 282	53.18 289	78.36 301	75.64 318	52.30 302	41.59 326	75.82 279	14.76 335	88.35 285	35.84 308	54.71 296	74.46 319

LCM-MVSNet-Re		72.93 218	71.84 215	76.18 267	88.49 150	48.02 309	80.07 291	70.17 332	73.96 114	52.25 292	80.09 248	49.98 205	88.24 286	67.35 160	84.23 116	92.28 133

ambc				69.61 300	61.38 332	41.35 324	49.07 338	85.86 279		50.18 302	66.40 313	10.16 340	88.14 287	45.73 272	44.20 321	79.32 306

Patchmatch-test		65.86 272	60.94 283	80.62 197	83.75 215	58.83 252	58.91 334	75.26 322	44.50 324	50.95 299	77.09 272	58.81 109	87.90 288	35.13 314	64.03 253	95.12 49

MIMVSNet		71.64 230	68.44 239	81.23 183	81.97 232	64.44 157	73.05 310	88.80 231	69.67 196	64.59 223	74.79 282	32.79 297	87.82 289	53.99 242	76.35 167	91.42 142

K. test v3		63.09 285	59.61 287	73.53 281	76.26 298	49.38 307	83.27 264	77.15 314	64.35 247	47.77 306	72.32 295	28.73 311	87.79 290	49.93 257	36.69 331	83.41 259

UnsupCasMVSNet_eth		65.79 273	63.10 272	73.88 278	70.71 315	50.29 303	81.09 280	89.88 196	72.58 139	49.25 303	74.77 283	32.57 299	87.43 291	55.96 236	41.04 326	83.90 252

Anonymous20231206		67.53 264	65.78 254	72.79 287	74.95 303	47.59 312	88.23 213	87.32 255	61.75 268	58.07 262	77.29 267	37.79 278	87.29 292	42.91 281	63.71 256	83.48 257

pmmvs-eth3d		65.53 274	62.32 278	75.19 270	69.39 320	59.59 242	82.80 269	83.43 294	62.52 261	51.30 297	72.49 290	32.86 296	87.16 293	55.32 238	50.73 306	78.83 308

IterMVS		72.65 224	70.83 222	78.09 249	82.17 229	62.96 189	87.64 229	86.28 270	71.56 170	60.44 248	78.85 258	45.42 243	86.66 294	63.30 196	61.83 264	84.65 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		61.66 287	58.06 288	72.46 289	79.57 271	51.42 297	80.17 289	68.61 334	51.25 305	45.88 310	81.23 229	19.86 328	86.58 295	38.98 296	57.01 288	79.39 304

TestCases				72.46 289	79.57 271	51.42 297		68.61 334	51.25 305	45.88 310	81.23 229	19.86 328	86.58 295	38.98 296	57.01 288	79.39 304

MDA-MVSNet-bldmvs		61.54 289	57.70 290	73.05 284	79.53 273	57.00 270	83.08 267	81.23 303	57.57 285	34.91 331	72.45 292	32.79 297	86.26 297	35.81 309	41.95 324	75.89 317

FMVSNet5		68.04 260	65.66 256	75.18 271	84.43 207	57.89 258	83.54 259	86.26 271	61.83 267	53.64 287	73.30 285	37.15 284	85.08 298	48.99 259	61.77 265	82.56 271

test0.0.03 1		72.76 221	72.71 207	72.88 286	80.25 264	47.99 310	91.22 151	89.45 209	71.51 172	62.51 243	87.66 155	53.83 171	85.06 299	50.16 255	67.84 226	85.58 235

testgi		64.48 278	62.87 275	69.31 301	71.24 312	40.62 326	85.49 249	79.92 309	65.36 237	54.18 282	83.49 198	23.74 322	84.55 300	41.60 287	60.79 275	82.77 267

Anonymous20231211		53.57 304	49.43 306	66.00 310	65.01 325	42.08 321	80.95 282	72.60 327	38.46 331	41.65 325	64.48 320	15.72 332	84.23 301	25.78 331	40.24 328	71.68 323

ADS-MVSNet2		66.90 267	63.44 270	77.26 260	88.06 160	60.70 224	68.01 321	75.56 320	57.57 285	64.48 225	69.87 308	38.68 266	84.10 302	40.87 290	67.89 224	86.97 205

CVMVSNet		74.04 211	74.27 185	73.33 282	85.33 194	43.94 319	89.53 193	88.39 239	54.33 299	70.37 157	90.13 124	49.17 214	84.05 303	61.83 212	79.36 139	91.99 136

LP		56.71 298	51.64 302	71.91 296	80.08 267	60.33 230	61.72 329	75.61 319	43.87 326	43.76 320	60.30 325	30.46 309	84.05 303	22.94 335	46.06 319	71.34 324

ITE_SJBPF				70.43 299	74.44 304	47.06 314		77.32 313	60.16 275	54.04 283	83.53 196	23.30 323	84.01 305	43.07 280	61.58 270	80.21 299

CHOSEN 280x420		77.35 162	76.95 147	78.55 240	87.07 174	62.68 198	69.71 317	82.95 300	68.80 205	71.48 149	87.27 165	66.03 42	84.00 306	76.47 94	82.81 123	88.95 167

DTE-MVSNet		68.46 258	67.33 248	71.87 297	77.94 290	49.00 308	86.16 248	88.58 237	66.36 229	58.19 261	82.21 213	46.36 236	83.87 307	44.97 277	55.17 293	82.73 268

semantic-postprocess				76.32 265	81.48 234	60.67 225		85.99 276	66.17 230	59.50 252	78.88 257	45.51 242	83.65 308	62.58 207	61.93 263	84.63 248

PEN-MVS		69.46 252	68.56 237	72.17 293	79.27 276	49.71 305	86.90 240	89.24 217	67.24 225	59.08 255	82.51 205	47.23 230	83.54 309	48.42 262	57.12 286	83.25 261

WR-MVS_H		70.59 241	69.94 225	72.53 288	81.03 238	51.43 296	87.35 233	92.03 121	67.38 223	60.23 249	80.70 237	55.84 143	83.45 310	46.33 269	58.58 284	82.72 269

YYNet1		63.76 284	60.14 285	74.62 274	78.06 289	60.19 234	83.46 262	83.99 292	56.18 295	39.25 327	71.56 306	37.18 283	83.34 311	42.90 282	48.70 315	80.32 297

PM-MVS		59.40 293	56.59 294	67.84 303	63.63 326	41.86 323	76.76 305	63.22 340	59.01 281	51.07 298	72.27 296	11.72 337	83.25 312	61.34 213	50.28 312	78.39 311

MDA-MVSNet_test_wron		63.78 283	60.16 284	74.64 273	78.15 288	60.41 228	83.49 260	84.03 288	56.17 296	39.17 328	71.59 305	37.22 282	83.24 313	42.87 283	48.73 314	80.26 298

N_pmnet		50.55 305	49.11 307	54.88 322	77.17 294	4.02 352	84.36 254	2.00 353	48.59 311	45.86 312	68.82 310	32.22 300	82.80 314	31.58 325	51.38 304	77.81 313

test20.03		63.83 282	62.65 276	67.38 306	70.58 317	39.94 327	86.57 245	84.17 287	63.29 254	51.86 293	77.30 266	37.09 285	82.47 315	38.87 302	54.13 297	79.73 302

TDRefinement		55.28 302	51.58 303	66.39 309	59.53 334	46.15 315	76.23 306	72.80 326	44.60 323	42.49 322	76.28 275	15.29 333	82.39 316	33.20 319	43.75 322	70.62 326

CP-MVSNet		70.50 243	69.91 226	72.26 291	80.71 244	51.00 299	87.23 234	90.30 180	67.84 217	59.64 251	82.69 204	50.23 204	82.30 317	51.28 251	59.28 278	83.46 258

PS-CasMVS		69.86 247	69.13 233	72.07 294	80.35 258	50.57 301	87.02 237	89.75 199	67.27 224	59.19 254	82.28 210	46.58 235	82.24 318	50.69 253	59.02 281	83.39 260

RPSCF		64.24 279	61.98 280	71.01 298	76.10 299	45.00 316	75.83 307	75.94 317	46.94 316	58.96 257	84.59 189	31.40 305	82.00 319	47.76 265	60.33 277	86.04 226

new-patchmatchnet		59.30 295	56.48 295	67.79 304	65.86 324	44.19 317	82.47 270	81.77 301	59.94 276	43.65 321	66.20 314	27.67 314	81.68 320	39.34 295	41.40 325	77.50 314

MIMVSNet1		60.16 292	57.33 292	68.67 302	69.71 319	44.13 318	78.92 297	84.21 286	55.05 298	44.63 317	71.85 299	23.91 321	81.54 321	32.63 322	55.03 294	80.35 296

test2356		64.16 280	63.28 271	66.81 308	69.37 321	39.86 329	87.76 223	86.02 275	59.83 277	53.54 288	73.23 286	34.94 292	80.67 322	39.66 294	65.20 240	79.89 300

EU-MVSNet		64.01 281	63.01 273	67.02 307	74.40 305	38.86 331	83.27 264	86.19 272	45.11 321	54.27 280	81.15 234	36.91 287	80.01 323	48.79 261	57.02 287	82.19 273

no-one		44.13 311	38.39 312	61.34 315	45.91 343	41.94 322	61.67 330	75.07 323	45.05 322	20.07 337	40.68 338	11.58 338	79.82 324	30.18 327	15.30 339	62.26 332

testpf		57.17 296	56.93 293	57.88 318	79.13 280	42.40 320	34.23 341	85.97 277	52.64 301	47.66 308	66.50 312	36.33 289	79.65 325	53.60 245	56.31 291	51.60 335

1111		56.66 299	54.98 298	61.69 314	61.99 330	31.38 336	79.81 294	83.17 298	45.66 318	41.94 323	65.44 315	41.50 256	79.56 326	27.64 328	47.68 316	74.14 320

.test1245		46.52 309	49.68 305	37.02 331	61.99 330	31.38 336	79.81 294	83.17 298	45.66 318	41.94 323	65.44 315	41.50 256	79.56 326	27.64 328	0.01 348	0.13 347

pmmvs3		55.51 301	51.50 304	67.53 305	57.90 335	50.93 300	80.37 285	73.66 325	40.63 330	44.15 319	64.75 319	16.30 331	78.97 328	44.77 278	40.98 327	72.69 321

testus		59.36 294	57.51 291	64.90 311	66.72 323	37.56 332	84.98 251	81.09 304	57.46 288	47.72 307	72.76 287	11.43 339	78.78 329	36.56 305	58.91 282	78.36 312

test1235678		55.73 300	52.74 300	64.68 312	60.16 333	35.56 334	81.65 276	81.46 302	51.27 304	38.93 329	62.82 322	17.44 330	78.58 330	30.87 326	50.09 313	79.89 300

test12356		47.51 307	44.82 309	55.56 320	52.53 336	21.09 347	71.45 313	76.03 316	44.14 325	30.69 332	58.18 326	9.01 343	76.14 331	26.95 330	34.43 334	69.46 328

DSMNet-mixed		56.78 297	54.44 299	63.79 313	63.21 327	29.44 340	64.43 327	64.10 339	42.12 329	51.32 296	71.60 304	31.76 302	75.04 332	36.23 307	65.20 240	86.87 209

new_pmnet		49.31 306	46.44 308	57.93 317	62.84 328	40.74 325	68.47 320	62.96 341	36.48 332	35.09 330	57.81 327	14.97 334	72.18 333	32.86 321	46.44 318	60.88 333

testmv		46.98 308	43.53 310	57.35 319	47.75 341	30.41 339	74.99 309	77.69 312	42.84 328	28.03 333	53.36 328	8.18 344	71.18 334	24.36 333	34.55 332	70.46 327

Gipuma		34.91 315	31.44 317	45.30 327	70.99 314	39.64 330	19.85 344	72.56 328	20.10 340	16.16 341	21.47 343	5.08 348	71.16 335	13.07 341	43.70 323	25.08 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		54.01 303	52.12 301	59.69 316	62.41 329	39.91 328	68.59 319	68.28 336	42.96 327	44.55 318	75.18 280	14.09 336	68.39 336	41.36 289	51.68 303	70.78 325

PMVS	26.43 22	31.84 317	28.16 319	42.89 328	25.87 350	27.58 343	50.92 337	49.78 346	21.37 339	14.17 343	40.81 337	2.01 351	66.62 337	9.61 343	38.88 330	34.49 340
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LCM-MVSNet		40.54 312	35.79 313	54.76 323	36.92 346	30.81 338	51.41 336	69.02 333	22.07 337	24.63 335	45.37 333	4.56 349	65.81 338	33.67 317	34.50 333	67.67 329

PMMVS2		37.93 314	33.61 315	50.92 325	46.31 342	24.76 345	60.55 333	50.05 344	28.94 336	20.93 336	47.59 330	4.41 350	65.13 339	25.14 332	18.55 338	62.87 331

FPMVS		45.64 310	43.10 311	53.23 324	51.42 338	36.46 333	64.97 326	71.91 329	29.13 335	27.53 334	61.55 324	9.83 341	65.01 340	16.00 340	55.58 292	58.22 334

ANet_high		40.27 313	35.20 314	55.47 321	34.74 347	34.47 335	63.84 328	71.56 330	48.42 312	18.80 339	41.08 336	9.52 342	64.45 341	20.18 337	8.66 346	67.49 330

wuykxyi23d		29.03 319	23.09 324	46.84 326	31.67 349	28.82 342	43.46 339	57.72 343	14.39 343	7.52 347	20.84 344	0.64 354	60.29 342	21.57 336	10.04 343	51.40 336

PNet_i23d		32.77 316	29.98 318	41.11 329	48.05 339	29.17 341	65.82 325	50.02 345	21.42 338	14.74 342	37.19 339	1.11 353	55.11 343	19.75 338	11.77 341	39.06 337

MVE	24.84 23	24.35 321	19.77 325	38.09 330	34.56 348	26.92 344	26.57 342	38.87 349	11.73 344	11.37 344	27.44 340	1.37 352	50.42 344	11.41 342	14.60 340	36.93 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 320	24.00 321	26.45 334	43.74 344	18.44 349	60.86 331	39.66 347	15.11 341	9.53 345	22.10 342	6.52 346	46.94 345	8.31 344	10.14 342	13.98 343

EMVS		23.76 322	23.20 323	25.46 335	41.52 345	16.90 350	60.56 332	38.79 350	14.62 342	8.99 346	20.24 346	7.35 345	45.82 346	7.25 345	9.46 344	13.64 344

DeepMVS_CX				34.71 332	51.45 337	24.73 346		28.48 352	31.46 334	17.49 340	52.75 329	5.80 347	42.60 347	18.18 339	19.42 337	36.81 339

tmp_tt		22.26 323	23.75 322	17.80 336	5.23 351	12.06 351	35.26 340	39.48 348	2.82 346	18.94 338	44.20 334	22.23 325	24.64 348	36.30 306	9.31 345	16.69 342

wuyk23d		11.30 325	10.95 326	12.33 337	48.05 339	19.89 348	25.89 343	1.92 354	3.58 345	3.12 348	1.37 348	0.64 354	15.77 349	6.23 346	7.77 347	1.35 345

testmvs		7.23 327	9.62 328	0.06 339	0.04 352	0.02 354	84.98 251	0.02 355	0.03 347	0.18 349	1.21 349	0.01 357	0.02 350	0.14 347	0.01 348	0.13 347

test123		6.92 328	9.21 329	0.08 338	0.03 353	0.05 353	81.65 276	0.01 356	0.02 348	0.14 350	0.85 350	0.03 356	0.02 350	0.12 348	0.00 350	0.16 346

cdsmvs_eth3d_5k		19.86 324	26.47 320	0.00 340	0.00 354	0.00 355	0.00 345	93.45 61	0.00 349	0.00 351	95.27 30	49.56 209	0.00 352	0.00 349	0.00 350	0.00 349

pcd_1.5k_mvsjas		4.46 329	5.95 330	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	53.55 175	0.00 352	0.00 349	0.00 350	0.00 349

pcd1.5k->3k		31.17 318	31.85 316	29.12 333	81.48 234	0.00 355	0.00 345	91.79 130	0.00 349	0.00 351	0.00 351	41.05 262	0.00 352	0.00 349	72.34 195	87.36 199

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 350	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 350	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 350	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 350	0.00 349

ab-mvs-re		7.91 326	10.55 327	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	94.95 40	0.00 358	0.00 352	0.00 349	0.00 350	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 355	0.00 345	0.00 357	0.00 349	0.00 351	0.00 351	0.00 358	0.00 352	0.00 349	0.00 350	0.00 349

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												57.85 114

sam_mvs												54.91 158

MTGPA								92.23 110

MTMP								32.52 351

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		91.50 141			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	89.42 211	56.83 292	77.28 94	92.43 94	52.76 184			91.14 64	93.09 113

segment_acmp												65.94 43

testdata1							89.21 197		77.55 63

plane_prior7					86.94 175	61.51 213

plane_prior6					87.23 171	62.32 201						50.66 199

plane_prior4											89.14 136

plane_prior3						61.95 209			79.09 44	72.53 134

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 172

plane_prior						62.42 199	93.85 55		79.38 37						78.80 145

n2								0.00 357

nn								0.00 357

door-mid								66.01 338

test11								93.01 85

door								66.57 337

HQP5-MVS						63.66 179

HQP-NCC					87.54 167		94.06 43		79.80 33	74.18 116

ACMP_Plane					87.54 167		94.06 43		79.80 33	74.18 116

BP-MVS														77.63 88

HQP3-MVS								91.70 134							78.90 143

HQP2-MVS												51.63 195

NP-MVS					87.41 170	63.04 188					90.30 121

MDTV_nov1_ep13_2view						59.90 239	80.13 290		67.65 220	72.79 129		54.33 166		59.83 221		92.58 126

ACMMP++_ref															71.63 197

ACMMP++															69.72 209

Test By Simon												54.21 168

High-resolution multi-view benchmark