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.

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 22	94.66 3	96.79 3	98.78 2	86.42 10	99.95 2	97.59 2	99.18 3	99.00 12

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 23	95.17 2	92.11 45	98.46 8	87.33 6	99.97 1	97.21 4	99.31 1	99.63 2

NCCC		95.63 3	95.94 5	94.69 19	99.21 6	85.15 42	99.16 3	96.96 31	94.11 6	95.59 9	98.64 4	85.07 13	99.91 3	95.61 16	99.10 5	99.00 12

HSP-MVS		95.55 4	96.51 2	92.66 83	98.31 37	80.10 139	97.42 65	96.46 75	92.20 13	97.11 2	98.29 11	93.46 1	99.10 77	96.01 11	99.30 2	98.77 20

HPM-MVS++		95.32 5	95.48 6	94.85 16	98.62 22	86.04 27	97.81 37	96.93 34	92.45 11	95.69 8	98.50 6	85.38 12	99.85 10	94.75 21	99.18 3	98.65 26

DELS-MVS		94.98 6	94.49 12	96.44 2	96.42 77	90.59 3	99.21 2	97.02 26	94.40 5	91.46 52	97.08 76	83.32 28	99.69 25	92.83 41	98.70 19	99.04 10
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
CANet		94.89 7	94.64 10	95.63 8	97.55 58	88.12 11	99.06 5	96.39 85	94.07 7	95.34 11	97.80 45	76.83 86	99.87 8	97.08 5	97.64 49	98.89 15

SD-MVS		94.84 8	95.02 8	94.29 24	97.87 51	84.61 47	97.76 43	96.19 99	89.59 32	96.66 4	98.17 19	84.33 18	99.60 34	96.09 10	98.50 24	98.66 25

TSAR-MVS + MP.		94.79 9	95.17 7	93.64 43	97.66 53	84.10 56	95.85 165	96.42 79	91.26 17	97.49 1	96.80 86	86.50 9	98.49 101	95.54 17	99.03 7	98.33 37

DeepPCF-MVS	89.82 1	94.61 10	96.17 3	89.91 159	97.09 72	70.21 276	98.99 8	96.69 51	95.57 1	95.08 15	99.23 1	86.40 11	99.87 8	97.84 1	98.66 20	99.65 1

APDe-MVS		94.56 11	94.75 9	93.96 32	98.84 9	83.40 70	98.04 28	96.41 80	85.79 80	95.00 17	98.28 12	84.32 21	99.18 70	97.35 3	98.77 15	99.28 5

DeepC-MVS_fast	89.06 2	94.48 12	94.30 18	95.02 14	98.86 8	85.68 33	98.06 27	96.64 57	93.64 8	91.74 50	98.54 5	80.17 47	99.90 4	92.28 48	98.75 16	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		94.35 13	94.50 11	93.89 33	97.38 66	83.04 76	98.10 26	95.29 146	91.57 15	93.81 30	97.45 60	86.64 7	99.43 49	96.28 9	94.01 94	99.20 8

train_agg		94.28 14	94.45 13	93.74 38	98.64 19	83.71 63	97.82 35	96.65 54	84.50 111	95.16 12	98.09 26	84.33 18	99.36 53	95.91 13	98.96 10	98.16 47

MSLP-MVS++		94.28 14	94.39 15	93.97 31	98.30 38	84.06 57	98.64 13	96.93 34	90.71 22	93.08 37	98.70 3	79.98 49	99.21 63	94.12 28	99.07 6	98.63 27

MG-MVS		94.25 16	93.72 23	95.85 7	99.38 3	89.35 6	97.98 30	98.09 11	89.99 29	92.34 44	96.97 79	81.30 37	98.99 83	88.54 80	98.88 12	99.20 8

PS-MVSNAJ		94.17 17	93.52 27	96.10 4	95.65 95	92.35 1	98.21 23	95.79 120	92.42 12	96.24 5	98.18 15	71.04 148	99.17 71	96.77 7	97.39 56	96.79 121

SteuartSystems-ACMMP		94.13 18	94.44 14	93.20 62	95.41 100	81.35 111	99.02 7	96.59 63	89.50 33	94.18 28	98.36 10	83.68 26	99.45 48	94.77 20	98.45 26	98.81 18
Skip Steuart: Steuart Systems R&D Blog.
agg_prior3		94.10 19	94.29 19	93.53 51	98.62 22	83.03 77	97.80 39	96.64 57	84.28 120	95.01 16	98.03 30	83.40 27	99.41 50	95.91 13	98.96 10	98.16 47

agg_prior1		94.10 19	94.31 17	93.48 54	98.59 24	83.13 73	97.77 40	96.56 65	84.38 115	94.19 26	98.13 21	84.66 15	99.16 72	95.74 15	98.74 17	98.15 49

EPNet		94.06 21	94.15 20	93.76 37	97.27 69	84.35 52	98.29 20	97.64 14	94.57 4	95.36 10	96.88 82	79.96 50	99.12 76	91.30 54	96.11 74	97.82 75
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_prior3		94.03 22	94.34 16	93.09 67	98.68 13	81.91 96	98.37 18	96.40 82	86.08 75	94.57 23	98.02 31	83.14 29	99.06 79	95.05 18	98.79 13	98.29 41

Regformer-1		94.00 23	94.04 21	93.87 34	98.41 32	84.29 54	97.43 63	97.04 25	89.50 33	92.75 41	98.13 21	82.60 32	99.26 58	93.55 31	96.99 62	98.06 55

xiu_mvs_v2_base		93.92 24	93.26 29	95.91 6	95.07 110	92.02 2	98.19 24	95.68 124	92.06 14	96.01 7	98.14 20	70.83 151	98.96 85	96.74 8	96.57 70	96.76 124

Regformer-2		93.92 24	94.01 22	93.67 42	98.41 32	83.75 62	97.43 63	97.00 27	89.43 35	92.69 42	98.13 21	82.48 33	99.22 61	93.51 32	96.99 62	98.04 56

lupinMVS		93.87 26	93.58 26	94.75 18	93.00 152	88.08 12	99.15 4	95.50 133	91.03 19	94.90 18	97.66 48	78.84 60	97.56 132	94.64 24	97.46 51	98.62 28

MVS_0304		93.82 27	93.11 33	95.95 5	96.79 74	89.15 7	98.56 15	95.30 145	93.61 9	94.82 20	98.02 31	66.60 183	99.88 7	96.94 6	97.39 56	98.81 18

APD-MVS		93.61 28	93.59 25	93.69 41	98.76 10	83.26 71	97.21 72	96.09 104	82.41 152	94.65 22	98.21 14	81.96 35	98.81 93	94.65 23	98.36 33	99.01 11
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PHI-MVS		93.59 29	93.63 24	93.48 54	98.05 45	81.76 102	98.64 13	97.13 21	82.60 150	94.09 29	98.49 7	80.35 42	99.85 10	94.74 22	98.62 21	98.83 17

ACMMP_Plus		93.46 30	93.23 30	94.17 27	97.16 70	84.28 55	96.82 106	96.65 54	86.24 72	94.27 25	97.99 34	77.94 72	99.83 12	93.39 33	98.57 22	98.39 35

MVS_111021_HR		93.41 31	93.39 28	93.47 57	97.34 67	82.83 81	97.56 54	98.27 9	89.16 36	89.71 71	97.14 73	79.77 51	99.56 39	93.65 30	97.94 44	98.02 58

Regformer-3		93.19 32	93.19 31	93.19 63	98.10 43	83.01 78	97.08 90	96.98 29	88.98 37	91.35 57	97.89 41	80.80 39	99.23 59	92.30 47	95.20 84	97.32 101

PVSNet_Blended		93.13 33	92.98 35	93.57 47	97.47 59	83.86 59	99.32 1	96.73 45	91.02 20	89.53 76	96.21 94	76.42 91	99.57 37	94.29 26	95.81 81	97.29 106

CDPH-MVS		93.12 34	92.91 36	93.74 38	98.65 18	83.88 58	97.67 48	96.26 94	83.00 145	93.22 36	98.24 13	81.31 36	99.21 63	89.12 76	98.74 17	98.14 50

Regformer-4		93.06 35	93.12 32	92.89 73	98.10 43	82.20 91	97.08 90	96.92 36	88.87 39	91.23 59	97.89 41	80.57 41	99.19 68	92.21 49	95.20 84	97.29 106

#test#		92.99 36	92.99 34	92.98 70	98.71 11	81.12 114	97.77 40	96.70 49	85.75 81	91.75 48	97.97 38	78.47 65	99.71 21	91.36 53	98.41 28	98.12 52

alignmvs		92.97 37	92.26 47	95.12 13	95.54 96	87.77 15	98.67 11	96.38 86	88.04 50	93.01 38	97.45 60	79.20 57	98.60 95	93.25 38	88.76 135	98.99 14

HFP-MVS		92.89 38	92.86 37	92.98 70	98.71 11	81.12 114	97.58 52	96.70 49	85.20 94	91.75 48	97.97 38	78.47 65	99.71 21	90.95 57	98.41 28	98.12 52

PAPM		92.87 39	92.40 44	94.30 23	92.25 169	87.85 14	96.40 136	96.38 86	91.07 18	88.72 85	96.90 80	82.11 34	97.37 142	90.05 67	97.70 48	97.67 83

MPTG		92.74 40	92.71 38	92.86 74	97.90 47	80.85 120	96.47 125	96.33 90	87.92 52	90.20 68	98.18 15	76.71 89	99.76 13	92.57 45	98.09 38	97.96 66

PAPR		92.74 40	92.17 49	94.45 20	98.89 7	84.87 45	97.20 74	96.20 97	87.73 57	88.40 88	98.12 24	78.71 63	99.76 13	87.99 88	96.28 72	98.74 21

jason		92.73 42	92.23 48	94.21 26	90.50 200	87.30 21	98.65 12	95.09 150	90.61 23	92.76 40	97.13 74	75.28 116	97.30 145	93.32 36	96.75 69	98.02 58
jason: jason.
region2R		92.72 43	92.70 40	92.79 77	98.68 13	80.53 129	97.53 56	96.51 70	85.22 92	91.94 46	97.98 36	77.26 79	99.67 29	90.83 60	98.37 32	98.18 46

XVS		92.69 44	92.71 38	92.63 86	98.52 27	80.29 132	97.37 67	96.44 77	87.04 67	91.38 53	97.83 44	77.24 81	99.59 35	90.46 63	98.07 40	98.02 58

ACMMPR		92.69 44	92.67 41	92.75 79	98.66 16	80.57 126	97.58 52	96.69 51	85.20 94	91.57 51	97.92 40	77.01 83	99.67 29	90.95 57	98.41 28	98.00 63

WTY-MVS		92.65 46	91.68 54	95.56 9	96.00 84	88.90 8	98.23 22	97.65 13	88.57 40	89.82 70	97.22 71	79.29 53	99.06 79	89.57 72	88.73 136	98.73 23

MP-MVS		92.61 47	92.67 41	92.42 91	98.13 42	79.73 147	97.33 69	96.20 97	85.63 83	90.53 64	97.66 48	78.14 70	99.70 24	92.12 50	98.30 35	97.85 73

MP-MVS-pluss		92.58 48	92.35 45	93.29 59	97.30 68	82.53 85	96.44 130	96.04 108	84.68 106	89.12 81	98.37 9	77.48 77	99.74 18	93.31 37	98.38 31	97.59 90
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 49	92.60 43	92.34 94	98.50 29	79.90 142	98.40 17	96.40 82	84.75 104	90.48 66	98.09 26	77.40 78	99.21 63	91.15 56	98.23 37	97.92 69

MTAPA		92.45 50	92.31 46	92.86 74	97.90 47	80.85 120	92.88 233	96.33 90	87.92 52	90.20 68	98.18 15	76.71 89	99.76 13	92.57 45	98.09 38	97.96 66

canonicalmvs		92.27 51	91.22 59	95.41 11	95.80 92	88.31 9	97.09 88	94.64 176	88.49 43	92.99 39	97.31 66	72.68 133	98.57 97	93.38 35	88.58 138	99.36 4

VNet		92.11 52	91.22 59	94.79 17	96.91 73	86.98 22	97.91 31	97.96 12	86.38 71	93.65 32	95.74 99	70.16 156	98.95 87	93.39 33	88.87 134	98.43 33

CSCG		92.02 53	91.65 55	93.12 65	98.53 26	80.59 125	97.47 59	97.18 20	77.06 236	84.64 120	97.98 36	83.98 23	99.52 41	90.72 61	97.33 58	99.23 7

PGM-MVS		91.93 54	91.80 52	92.32 95	98.27 39	79.74 146	95.28 178	97.27 17	83.83 130	90.89 63	97.78 46	76.12 97	99.56 39	88.82 78	97.93 46	97.66 84

mPP-MVS		91.88 55	91.82 51	92.07 101	98.38 34	78.63 181	97.29 70	96.09 104	85.12 96	88.45 87	97.66 48	75.53 103	99.68 27	89.83 69	98.02 43	97.88 70

EI-MVSNet-Vis-set		91.84 56	91.77 53	92.04 103	97.60 55	81.17 113	96.61 120	96.87 38	88.20 48	89.19 80	97.55 58	78.69 64	99.14 74	90.29 65	90.94 124	95.80 145

DP-MVS Recon		91.72 57	90.85 63	94.34 22	99.50 1	85.00 43	98.51 16	95.96 111	80.57 182	88.08 93	97.63 53	76.84 85	99.89 6	85.67 102	94.88 88	98.13 51

CHOSEN 280x420		91.71 58	91.85 50	91.29 123	94.94 112	82.69 83	87.89 283	96.17 100	85.94 77	87.27 99	94.31 128	90.27 4	95.65 218	94.04 29	95.86 79	95.53 151

HY-MVS	84.06 6	91.63 59	90.37 67	95.39 12	96.12 82	88.25 10	90.22 265	97.58 15	88.33 46	90.50 65	91.96 157	79.26 55	99.06 79	90.29 65	89.07 132	98.88 16

HPM-MVS		91.62 60	91.53 57	91.89 107	97.88 50	79.22 158	96.99 94	95.73 122	82.07 156	89.50 78	97.19 72	75.59 102	98.93 90	90.91 59	97.94 44	97.54 91

MVS_111021_LR		91.60 61	91.64 56	91.47 120	95.74 93	78.79 178	96.15 149	96.77 43	88.49 43	88.64 86	97.07 77	72.33 136	99.19 68	93.13 39	96.48 71	96.43 132

DeepC-MVS	86.58 3	91.53 62	91.06 62	92.94 72	94.52 121	81.89 98	95.95 157	95.98 110	90.76 21	83.76 130	96.76 87	73.24 130	99.71 21	91.67 52	96.96 64	97.22 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPM_NR		91.46 63	90.82 64	93.37 58	98.50 29	81.81 101	95.03 186	96.13 101	84.65 107	86.10 108	97.65 52	79.24 56	99.75 16	83.20 128	96.88 67	98.56 30

MVSFormer		91.36 64	90.57 66	93.73 40	93.00 152	88.08 12	94.80 191	94.48 181	80.74 178	94.90 18	97.13 74	78.84 60	95.10 242	83.77 117	97.46 51	98.02 58

EI-MVSNet-UG-set		91.35 65	91.22 59	91.73 115	97.39 63	80.68 123	96.47 125	96.83 40	87.92 52	88.30 91	97.36 65	77.84 74	99.13 75	89.43 75	89.45 130	95.37 154

PVSNet_Blended_VisFu		91.24 66	90.77 65	92.66 83	95.09 108	82.40 87	97.77 40	95.87 117	88.26 47	86.39 104	93.94 135	76.77 87	99.27 56	88.80 79	94.00 95	96.31 138

APD-MVS_3200maxsize		91.23 67	91.35 58	90.89 135	97.89 49	76.35 227	96.30 144	95.52 132	79.82 201	91.03 62	97.88 43	74.70 122	98.54 98	92.11 51	96.89 66	97.77 78

CHOSEN 1792x2688		91.07 68	90.21 69	93.64 43	95.18 106	83.53 67	96.26 146	96.13 101	88.92 38	84.90 114	93.10 149	72.86 132	99.62 33	88.86 77	95.67 82	97.79 77

CANet_DTU		90.98 69	90.04 71	93.83 35	94.76 116	86.23 26	96.32 140	93.12 246	93.11 10	93.71 31	96.82 85	63.08 211	99.48 46	84.29 112	95.12 87	95.77 146

sss		90.87 70	89.96 73	93.60 46	94.15 129	83.84 61	97.14 82	98.13 10	85.93 78	89.68 72	96.09 95	71.67 140	99.30 55	87.69 90	89.16 131	97.66 84

Effi-MVS+		90.70 71	89.90 76	93.09 67	93.61 141	83.48 68	95.20 181	92.79 250	83.22 140	91.82 47	95.70 101	71.82 139	97.48 139	91.25 55	93.67 99	98.32 38

1121		90.66 72	89.82 78	93.16 64	97.39 63	81.71 106	93.33 220	96.66 53	74.45 262	91.38 53	97.55 58	79.27 54	99.52 41	79.95 147	98.43 27	98.26 44

MAR-MVS		90.63 73	90.22 68	91.86 112	98.47 31	78.20 197	97.18 76	96.61 61	83.87 129	88.18 92	98.18 15	68.71 161	99.75 16	83.66 122	97.15 60	97.63 87
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS		90.60 74	88.64 92	96.50 1	94.25 127	90.53 4	93.33 220	97.21 19	77.59 228	78.88 177	97.31 66	71.52 143	99.69 25	89.60 71	98.03 42	99.27 6

xiu_mvs_v1_base_debu		90.54 75	89.54 82	93.55 48	92.31 162	87.58 18	96.99 94	94.87 159	87.23 62	93.27 33	97.56 55	57.43 251	98.32 105	92.72 42	93.46 102	94.74 162

xiu_mvs_v1_base		90.54 75	89.54 82	93.55 48	92.31 162	87.58 18	96.99 94	94.87 159	87.23 62	93.27 33	97.56 55	57.43 251	98.32 105	92.72 42	93.46 102	94.74 162

xiu_mvs_v1_base_debi		90.54 75	89.54 82	93.55 48	92.31 162	87.58 18	96.99 94	94.87 159	87.23 62	93.27 33	97.56 55	57.43 251	98.32 105	92.72 42	93.46 102	94.74 162

DWT-MVSNet_test		90.52 78	89.80 79	92.70 82	95.73 94	82.20 91	93.69 211	96.55 67	88.34 45	87.04 102	95.34 108	86.53 8	97.55 134	76.32 183	88.66 137	98.34 36

ACMMP		90.39 79	89.97 72	91.64 117	97.58 57	78.21 196	96.78 108	96.72 47	84.73 105	84.72 118	97.23 70	71.22 145	99.63 32	88.37 85	92.41 110	97.08 112
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
HPM-MVS_fast		90.38 80	90.17 70	91.03 131	97.61 54	77.35 216	97.15 81	95.48 134	79.51 206	88.79 84	96.90 80	71.64 142	98.81 93	87.01 97	97.44 53	96.94 115

MVS_Test		90.29 81	89.18 87	93.62 45	95.23 104	84.93 44	94.41 197	94.66 173	84.31 117	90.37 67	91.02 170	75.13 117	97.82 122	83.11 130	94.42 90	98.12 52

API-MVS		90.18 82	88.97 88	93.80 36	98.66 16	82.95 80	97.50 58	95.63 127	75.16 249	86.31 105	97.69 47	72.49 134	99.90 4	81.26 139	96.07 75	98.56 30

PatchFormer-LS_test		90.14 83	89.30 86	92.65 85	95.43 98	82.46 86	93.46 216	96.35 88	88.56 41	84.82 115	95.22 109	84.63 16	97.55 134	78.40 159	86.81 149	97.94 68

PVSNet_BlendedMVS		90.05 84	89.96 73	90.33 144	97.47 59	83.86 59	98.02 29	96.73 45	87.98 51	89.53 76	89.61 190	76.42 91	99.57 37	94.29 26	79.59 199	87.57 258

TESTMET0.1,1		89.83 85	89.34 85	91.31 121	92.54 160	80.19 137	97.11 84	96.57 64	86.15 73	86.85 103	91.83 161	79.32 52	96.95 162	81.30 138	92.35 111	96.77 123

abl_6		89.80 86	89.71 81	90.07 150	96.53 76	75.52 233	94.48 194	95.04 153	81.12 169	89.22 79	97.00 78	68.83 160	98.96 85	89.86 68	95.27 83	95.73 147

EPP-MVSNet		89.76 87	89.72 80	89.87 160	93.78 136	76.02 230	97.22 71	96.51 70	79.35 208	85.11 112	95.01 121	84.82 14	97.10 157	87.46 93	88.21 141	96.50 130

CPTT-MVS		89.72 88	89.87 77	89.29 167	98.33 36	73.30 247	97.70 46	95.35 143	75.68 241	87.40 96	97.44 63	70.43 153	98.25 108	89.56 73	96.90 65	96.33 137

3Dnovator+	82.88 8	89.63 89	87.85 101	94.99 15	94.49 124	86.76 23	97.84 34	95.74 121	86.10 74	75.47 219	96.02 96	65.00 200	99.51 44	82.91 132	97.07 61	98.72 24

CDS-MVSNet		89.50 90	88.96 89	91.14 129	91.94 182	80.93 118	97.09 88	95.81 119	84.26 121	84.72 118	94.20 130	80.31 43	95.64 219	83.37 127	88.96 133	96.85 120
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 91	89.92 75	88.06 190	94.64 117	69.57 282	96.22 147	94.95 156	87.27 61	91.37 56	96.54 91	65.88 188	97.39 141	88.54 80	93.89 96	97.23 109

HyFIR lowres test		89.36 92	88.60 93	91.63 118	94.91 114	80.76 122	95.60 172	95.53 130	82.56 151	84.03 124	91.24 167	78.03 71	96.81 170	87.07 96	88.41 139	97.32 101

3Dnovator	82.32 10	89.33 93	87.64 106	94.42 21	93.73 140	85.70 32	97.73 45	96.75 44	86.73 70	76.21 210	95.93 97	62.17 215	99.68 27	81.67 137	97.81 47	97.88 70

LFMVS		89.27 94	87.64 106	94.16 29	97.16 70	85.52 36	97.18 76	94.66 173	79.17 213	89.63 74	96.57 90	55.35 267	98.22 109	89.52 74	89.54 129	98.74 21

MVSTER		89.25 95	88.92 90	90.24 146	95.98 85	84.66 46	96.79 107	95.36 141	87.19 65	80.33 167	90.61 177	90.02 5	95.97 200	85.38 105	78.64 208	90.09 205

CostFormer		89.08 96	88.39 96	91.15 128	93.13 150	79.15 161	88.61 278	96.11 103	83.14 141	89.58 75	86.93 222	83.83 25	96.87 167	88.22 86	85.92 157	97.42 97

PVSNet	82.34 9	89.02 97	87.79 103	92.71 81	95.49 97	81.50 109	97.70 46	97.29 16	87.76 56	85.47 110	95.12 118	56.90 256	98.90 91	80.33 142	94.02 93	97.71 81

test-mter		88.95 98	88.60 93	89.98 155	92.26 167	77.23 218	97.11 84	95.96 111	85.32 90	86.30 106	91.38 164	76.37 93	96.78 172	80.82 140	91.92 117	95.94 142

1314		88.94 99	87.20 117	94.17 27	93.21 147	85.73 31	93.33 220	96.64 57	82.89 146	75.98 212	96.36 92	66.83 179	99.39 51	83.52 126	96.02 77	97.39 99

UA-Net		88.92 100	88.48 95	90.24 146	94.06 132	77.18 220	93.04 230	94.66 173	87.39 60	91.09 61	93.89 136	74.92 120	98.18 112	75.83 187	91.43 121	95.35 155

thres200		88.92 100	87.65 105	92.73 80	96.30 78	85.62 34	97.85 33	98.86 1	84.38 115	84.82 115	93.99 134	75.12 118	98.01 113	70.86 224	86.67 150	94.56 165

Vis-MVSNet (Re-imp)		88.88 102	88.87 91	88.91 173	93.89 135	74.43 241	96.93 102	94.19 193	84.39 114	83.22 134	95.67 103	78.24 68	94.70 251	78.88 156	94.40 91	97.61 89

AdaColmap		88.81 103	87.61 109	92.39 93	99.33 4	79.95 140	96.70 115	95.58 128	77.51 229	83.05 136	96.69 89	61.90 222	99.72 20	84.29 112	93.47 101	97.50 93

OMC-MVS		88.80 104	88.16 97	90.72 137	95.30 103	77.92 205	94.81 190	94.51 180	86.80 69	84.97 113	96.85 83	67.53 165	98.60 95	85.08 106	87.62 144	95.63 149

114514_t		88.79 105	87.57 110	92.45 90	98.21 40	81.74 103	96.99 94	95.45 138	75.16 249	82.48 139	95.69 102	68.59 162	98.50 100	80.33 142	95.18 86	97.10 111

mvs_anonymous		88.68 106	87.62 108	91.86 112	94.80 115	81.69 107	93.53 215	94.92 157	82.03 157	78.87 178	90.43 180	75.77 101	95.34 233	85.04 107	93.16 105	98.55 32

Vis-MVSNet		88.67 107	87.82 102	91.24 126	92.68 155	78.82 175	96.95 100	93.85 214	87.55 58	87.07 101	95.13 117	63.43 209	97.21 150	77.58 169	96.15 73	97.70 82
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 107	88.16 97	90.20 148	93.61 141	76.86 222	96.77 110	93.07 247	84.02 125	83.62 131	95.60 105	74.69 123	96.24 188	78.43 158	93.66 100	97.49 94

IB-MVS	85.34 4	88.67 107	87.14 121	93.26 60	93.12 151	84.32 53	98.76 10	97.27 17	87.19 65	79.36 174	90.45 179	83.92 24	98.53 99	84.41 111	69.79 255	96.93 116
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
1112_ss		88.60 110	87.47 113	92.00 104	93.21 147	80.97 117	96.47 125	92.46 253	83.64 135	80.86 160	97.30 68	80.24 45	97.62 130	77.60 168	85.49 162	97.40 98

tfpn200view9		88.48 111	87.15 119	92.47 89	96.21 79	85.30 39	97.44 60	98.85 2	83.37 138	83.99 125	93.82 137	75.36 113	97.93 115	69.04 234	86.24 155	94.17 166

test-LLR		88.48 111	87.98 99	89.98 155	92.26 167	77.23 218	97.11 84	95.96 111	83.76 132	86.30 106	91.38 164	72.30 137	96.78 172	80.82 140	91.92 117	95.94 142

TAMVS		88.48 111	87.79 103	90.56 140	91.09 192	79.18 159	96.45 128	95.88 116	83.64 135	83.12 135	93.33 145	75.94 99	95.74 217	82.40 133	88.27 140	96.75 125

thres400		88.42 114	87.15 119	92.23 96	96.21 79	85.30 39	97.44 60	98.85 2	83.37 138	83.99 125	93.82 137	75.36 113	97.93 115	69.04 234	86.24 155	93.45 177

tpmrst		88.36 115	87.38 115	91.31 121	94.36 125	79.92 141	87.32 287	95.26 148	85.32 90	88.34 89	86.13 242	80.60 40	96.70 174	83.78 116	85.34 169	97.30 104

VDD-MVS		88.28 116	87.02 123	92.06 102	95.09 108	80.18 138	97.55 55	94.45 184	83.09 142	89.10 82	95.92 98	47.97 290	98.49 101	93.08 40	86.91 148	97.52 92

BH-w/o		88.24 117	87.47 113	90.54 141	95.03 111	78.54 184	97.41 66	93.82 215	84.08 123	78.23 182	94.51 127	69.34 159	97.21 150	80.21 145	94.58 89	95.87 144

thres600view7		88.06 118	86.70 126	92.15 99	96.10 83	85.17 41	97.14 82	98.85 2	82.70 149	83.41 132	93.66 140	75.43 111	97.82 122	67.13 247	85.88 158	93.45 177

Test_1112_low_res		88.03 119	86.73 125	91.94 106	93.15 149	80.88 119	96.44 130	92.41 254	83.59 137	80.74 162	91.16 168	80.18 46	97.59 131	77.48 170	85.40 163	97.36 100

PLC	83.97 7	88.00 120	87.38 115	89.83 162	98.02 46	76.46 225	97.16 80	94.43 185	79.26 212	81.98 153	96.28 93	69.36 158	99.27 56	77.71 167	92.25 114	93.77 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 121	87.48 112	89.44 165	92.16 172	80.54 128	98.14 25	94.92 157	91.41 16	79.43 173	95.40 107	62.34 214	97.27 148	90.60 62	82.90 186	90.50 195
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
diffmvs		87.96 122	86.47 128	92.42 91	94.26 126	82.70 82	92.79 237	94.03 206	77.94 223	88.99 83	89.98 187	70.72 152	97.56 132	77.75 161	91.80 119	96.98 113

Fast-Effi-MVS+		87.93 123	86.94 124	90.92 134	94.04 133	79.16 160	98.26 21	93.72 223	81.29 167	83.94 128	92.90 150	69.83 157	96.68 175	76.70 179	91.74 120	96.93 116

HQP-MVS		87.91 124	87.55 111	88.98 172	92.08 173	78.48 186	97.63 49	94.80 164	90.52 24	82.30 142	94.56 125	65.40 196	97.32 143	87.67 91	83.01 183	91.13 188

UGNet		87.73 125	86.55 127	91.27 124	95.16 107	79.11 162	96.35 138	96.23 96	88.14 49	87.83 95	90.48 178	50.65 279	99.09 78	80.13 146	94.03 92	95.60 150
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
EPNet_dtu		87.65 126	87.89 100	86.93 217	94.57 119	71.37 267	96.72 111	96.50 72	88.56 41	87.12 100	95.02 120	75.91 100	94.01 264	66.62 250	90.00 128	95.42 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		87.50 127	87.09 122	88.74 177	91.86 183	77.96 202	97.18 76	94.69 169	89.89 30	81.33 156	94.15 131	64.77 201	97.30 145	87.08 94	82.82 187	90.96 190

EPMVS		87.47 128	85.90 136	92.18 98	95.41 100	82.26 90	87.00 291	96.28 93	85.88 79	84.23 123	85.57 249	75.07 119	96.26 186	71.14 222	92.50 108	98.03 57

view600		87.45 129	85.98 131	91.88 108	95.90 87	84.52 48	96.68 116	98.85 2	81.85 159	82.30 142	93.39 141	75.44 107	97.66 125	64.02 265	85.36 164	93.45 177

view800		87.45 129	85.98 131	91.88 108	95.90 87	84.52 48	96.68 116	98.85 2	81.85 159	82.30 142	93.39 141	75.44 107	97.66 125	64.02 265	85.36 164	93.45 177

conf0.05thres1000		87.45 129	85.98 131	91.88 108	95.90 87	84.52 48	96.68 116	98.85 2	81.85 159	82.30 142	93.39 141	75.44 107	97.66 125	64.02 265	85.36 164	93.45 177

tfpn		87.45 129	85.98 131	91.88 108	95.90 87	84.52 48	96.68 116	98.85 2	81.85 159	82.30 142	93.39 141	75.44 107	97.66 125	64.02 265	85.36 164	93.45 177

tpm2		87.35 133	86.26 129	90.62 139	92.93 154	78.67 179	88.06 282	95.99 109	79.33 209	87.40 96	86.43 238	80.28 44	96.40 180	80.23 144	85.73 161	96.79 121

ab-mvs		87.08 134	84.94 148	93.48 54	93.34 146	83.67 65	88.82 275	95.70 123	81.18 168	84.55 121	90.14 185	62.72 212	98.94 89	85.49 104	82.54 190	97.85 73

CNLPA		86.96 135	85.37 140	91.72 116	97.59 56	79.34 154	97.21 72	91.05 270	74.22 263	78.90 176	96.75 88	67.21 169	98.95 87	74.68 197	90.77 125	96.88 119

BH-untuned		86.95 136	85.94 135	89.99 154	94.52 121	77.46 213	96.78 108	93.37 239	81.80 163	76.62 203	93.81 139	66.64 182	97.02 160	76.06 185	93.88 97	95.48 152

QAPM		86.88 137	84.51 152	93.98 30	94.04 133	85.89 29	97.19 75	96.05 107	73.62 267	75.12 222	95.62 104	62.02 218	99.74 18	70.88 223	96.06 76	96.30 139

BH-RMVSNet		86.84 138	85.28 141	91.49 119	95.35 102	80.26 135	96.95 100	92.21 255	82.86 147	81.77 155	95.46 106	59.34 234	97.64 129	69.79 231	93.81 98	96.57 129

mvs-test1		86.83 139	87.17 118	85.81 227	91.96 179	65.24 294	97.90 32	93.34 240	85.57 84	84.51 122	95.14 116	61.99 219	97.19 152	83.55 123	90.55 126	95.00 157

PatchmatchNet		86.83 139	85.12 144	91.95 105	94.12 130	82.27 89	86.55 295	95.64 126	84.59 109	82.98 137	84.99 258	77.26 79	95.96 204	68.61 240	91.34 122	97.64 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 141	85.43 138	90.87 136	88.76 224	85.34 38	97.06 92	94.33 187	84.31 117	80.45 165	91.98 156	72.36 135	96.36 182	88.48 83	71.13 238	90.93 192

PCF-MVS	84.09 5	86.77 142	85.00 146	92.08 100	92.06 176	83.07 75	92.14 249	94.47 183	79.63 205	76.90 200	94.78 122	71.15 146	99.20 67	72.87 205	91.05 123	93.98 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 143	86.10 130	88.61 179	90.05 208	80.21 136	96.14 150	96.95 32	85.56 87	78.37 181	92.30 153	76.73 88	95.28 236	79.51 150	79.27 203	90.35 197

cascas		86.50 144	84.48 154	92.55 88	92.64 159	85.95 28	97.04 93	95.07 152	75.32 246	80.50 163	91.02 170	54.33 274	97.98 114	86.79 98	87.62 144	93.71 173

tpmp4_e23		86.46 145	84.95 147	90.98 133	93.74 139	78.60 183	88.13 281	95.90 115	79.65 204	85.42 111	85.67 244	80.08 48	97.06 158	71.71 214	84.26 174	97.28 108

VDDNet		86.44 146	84.51 152	92.22 97	91.56 185	81.83 100	97.10 87	94.64 176	69.50 287	87.84 94	95.19 112	48.01 289	97.92 119	89.82 70	86.92 147	96.89 118

TR-MVS		86.30 147	84.93 149	90.42 142	94.63 118	77.58 211	96.57 122	93.82 215	80.30 189	82.42 141	95.16 114	58.74 238	97.55 134	74.88 195	87.82 143	96.13 140

X-MVStestdata		86.26 148	84.14 159	92.63 86	98.52 27	80.29 132	97.37 67	96.44 77	87.04 67	91.38 53	20.73 339	77.24 81	99.59 35	90.46 63	98.07 40	98.02 58

OpenMVS	79.58 14	86.09 149	83.62 165	93.50 52	90.95 194	86.71 24	97.44 60	95.83 118	75.35 245	72.64 238	95.72 100	57.42 254	99.64 31	71.41 217	95.85 80	94.13 168

FC-MVSNet-test		85.96 150	85.39 139	87.66 203	89.38 220	78.02 200	95.65 171	96.87 38	85.12 96	77.34 193	91.94 159	76.28 95	94.74 250	77.09 175	78.82 206	90.21 200

DI_MVS_plusplus_test		85.92 151	83.61 166	92.86 74	86.43 253	83.20 72	95.57 173	95.46 135	85.10 99	65.99 268	86.84 226	56.70 258	97.89 120	88.10 87	92.33 112	97.48 95

OPM-MVS		85.84 152	85.10 145	88.06 190	88.34 230	77.83 208	95.72 168	94.20 191	87.89 55	80.45 165	94.05 133	58.57 239	97.26 149	83.88 115	82.76 189	89.09 220

test_normal		85.83 153	83.51 168	92.78 78	86.33 258	83.01 78	95.56 175	95.46 135	85.11 98	65.73 270	86.63 231	56.62 260	97.86 121	87.87 89	92.29 113	97.47 96

EI-MVSNet		85.80 154	85.20 142	87.59 205	91.55 186	77.41 214	95.13 182	95.36 141	80.43 186	80.33 167	94.71 123	73.72 128	95.97 200	76.96 178	78.64 208	89.39 214

GA-MVS		85.79 155	84.04 160	91.02 132	89.47 218	80.27 134	96.90 103	94.84 162	85.57 84	80.88 159	89.08 193	56.56 261	96.47 179	77.72 166	85.35 168	96.34 135

XVG-OURS-SEG-HR		85.74 156	85.16 143	87.49 209	90.22 204	71.45 266	91.29 259	94.09 204	81.37 166	83.90 129	95.22 109	60.30 226	97.53 138	85.58 103	84.42 173	93.50 175

tpm		85.55 157	84.47 155	88.80 176	90.19 205	75.39 235	88.79 276	94.69 169	84.83 103	83.96 127	85.21 252	78.22 69	94.68 252	76.32 183	78.02 213	96.34 135

UniMVSNet_NR-MVSNet		85.49 158	84.59 151	88.21 189	89.44 219	79.36 152	96.71 113	96.41 80	85.22 92	78.11 183	90.98 172	76.97 84	95.14 240	79.14 153	68.30 267	90.12 203

gg-mvs-nofinetune		85.48 159	82.90 175	93.24 61	94.51 123	85.82 30	79.22 312	96.97 30	61.19 309	87.33 98	53.01 326	90.58 3	96.07 193	86.07 100	97.23 59	97.81 76

VPA-MVSNet		85.32 160	83.83 161	89.77 164	90.25 203	82.63 84	96.36 137	97.07 24	83.03 144	81.21 158	89.02 195	61.58 223	96.31 184	85.02 108	70.95 240	90.36 196

UniMVSNet (Re)		85.31 161	84.23 158	88.55 180	89.75 211	80.55 127	96.72 111	96.89 37	85.42 88	78.40 180	88.93 196	75.38 112	95.52 226	78.58 157	68.02 270	89.57 212

XVG-OURS		85.18 162	84.38 156	87.59 205	90.42 202	71.73 263	91.06 262	94.07 205	82.00 158	83.29 133	95.08 119	56.42 262	97.55 134	83.70 121	83.42 179	93.49 176

TAPA-MVS	81.61 12	85.02 163	83.67 163	89.06 169	96.79 74	73.27 249	95.92 159	94.79 166	74.81 256	80.47 164	96.83 84	71.07 147	98.19 111	49.82 310	92.57 107	95.71 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 164	83.66 164	89.02 171	95.86 91	74.55 240	92.49 241	93.60 228	79.30 211	79.29 175	91.47 162	58.53 240	98.45 103	70.22 227	92.17 115	94.07 169

PS-MVSNAJss		84.91 165	84.30 157	86.74 218	85.89 272	74.40 242	94.95 187	94.16 197	83.93 127	76.45 205	90.11 186	71.04 148	95.77 213	83.16 129	79.02 205	90.06 207

Patchmatch-test1		84.89 166	82.76 178	91.27 124	92.30 165	81.86 99	92.88 233	95.56 129	84.85 102	82.52 138	85.19 253	58.04 245	94.21 260	65.93 256	87.58 146	97.74 79

CVMVSNet		84.83 167	85.57 137	82.63 276	91.55 186	60.38 307	95.13 182	95.03 154	80.60 181	82.10 152	94.71 123	66.40 185	90.19 305	74.30 199	90.32 127	97.31 103

FMVSNet3		84.71 168	82.71 179	90.70 138	94.55 120	87.71 16	95.92 159	94.67 172	81.73 164	75.82 215	88.08 208	66.99 177	94.47 255	71.23 219	75.38 221	89.91 209

VPNet		84.69 169	82.92 174	90.01 153	89.01 222	83.45 69	96.71 113	95.46 135	85.71 82	79.65 172	92.18 155	56.66 259	96.01 199	83.05 131	67.84 271	90.56 194

Effi-MVS+-dtu		84.61 170	84.90 150	83.72 266	91.96 179	63.14 301	94.95 187	93.34 240	85.57 84	79.79 171	87.12 220	61.99 219	95.61 222	83.55 123	85.83 159	92.41 184

DU-MVS		84.57 171	83.33 171	88.28 187	88.76 224	79.36 152	96.43 134	95.41 140	85.42 88	78.11 183	90.82 173	67.61 163	95.14 240	79.14 153	68.30 267	90.33 198

F-COLMAP		84.50 172	83.44 170	87.67 202	95.22 105	72.22 254	95.95 157	93.78 220	75.74 239	76.30 208	95.18 113	59.50 231	98.45 103	72.67 207	86.59 152	92.35 185

WR-MVS		84.32 173	82.96 173	88.41 182	89.38 220	80.32 131	96.59 121	96.25 95	83.97 126	76.63 202	90.36 181	67.53 165	94.86 248	75.82 188	70.09 250	90.06 207

dp		84.30 174	82.31 183	90.28 145	94.24 128	77.97 201	86.57 294	95.53 130	79.94 199	80.75 161	85.16 255	71.49 144	96.39 181	63.73 270	83.36 180	96.48 131

LPG-MVS_test		84.20 175	83.49 169	86.33 221	90.88 195	73.06 250	95.28 178	94.13 198	82.20 154	76.31 206	93.20 146	54.83 272	96.95 162	83.72 119	80.83 193	88.98 224

ACMP	81.66 11	84.00 176	83.22 172	86.33 221	91.53 188	72.95 252	95.91 161	93.79 219	83.70 134	73.79 226	92.22 154	54.31 275	96.89 166	83.98 114	79.74 198	89.16 219
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 177	82.80 177	87.31 212	91.46 189	77.39 215	95.66 170	93.43 233	80.44 184	75.51 218	87.26 216	73.72 128	95.16 239	76.99 176	70.72 242	89.39 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 178	82.00 184	89.35 166	87.13 239	81.38 110	95.72 168	94.26 189	80.15 194	75.92 214	90.63 176	61.96 221	96.52 177	78.98 155	73.28 232	90.14 201

LCM-MVSNet-Re		83.75 179	83.54 167	84.39 256	93.54 143	64.14 297	92.51 240	84.03 320	83.90 128	66.14 267	86.59 232	67.36 167	92.68 274	84.89 109	92.87 106	96.35 134

ACMM	80.70 13	83.72 180	82.85 176	86.31 224	91.19 191	72.12 257	95.88 162	94.29 188	80.44 184	77.02 198	91.96 157	55.24 268	97.14 156	79.30 152	80.38 195	89.67 211
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 181	81.38 198	90.39 143	93.53 144	78.19 198	85.56 301	95.09 150	70.78 283	78.51 179	83.28 274	74.80 121	97.03 159	66.77 249	84.05 175	95.95 141

CR-MVSNet		83.53 182	81.36 199	90.06 151	90.16 206	79.75 144	79.02 314	91.12 267	84.24 122	82.27 150	80.35 286	75.45 105	93.67 270	63.37 273	86.25 153	96.75 125

v2v482		83.46 183	81.86 188	88.25 188	86.19 264	79.65 148	96.34 139	94.02 207	81.56 165	77.32 194	88.23 205	65.62 191	96.03 195	77.77 160	69.72 257	89.09 220

v1neww		83.45 184	81.95 185	87.95 195	86.66 243	79.04 166	96.32 140	94.17 194	80.76 175	77.56 186	87.25 217	67.02 175	96.08 191	77.73 163	70.07 251	88.74 234

v7new		83.45 184	81.95 185	87.95 195	86.66 243	79.04 166	96.32 140	94.17 194	80.76 175	77.56 186	87.25 217	67.02 175	96.08 191	77.73 163	70.07 251	88.74 234

v6		83.45 184	81.94 187	87.95 195	86.62 247	79.03 169	96.32 140	94.17 194	80.76 175	77.57 185	87.23 219	67.03 174	96.09 190	77.73 163	70.06 253	88.75 232

v1		83.37 187	81.82 189	88.01 192	86.58 251	79.24 156	96.45 128	94.13 198	80.88 171	77.48 190	86.88 223	67.15 170	96.04 194	77.15 172	69.67 259	88.76 230

v1141		83.36 188	81.81 191	88.01 192	86.61 249	79.26 155	96.44 130	94.12 201	80.88 171	77.48 190	86.87 224	67.08 172	96.03 195	77.14 173	69.69 258	88.75 232

divwei89l23v2f112		83.36 188	81.81 191	88.01 192	86.60 250	79.23 157	96.44 130	94.12 201	80.88 171	77.49 188	86.87 224	67.08 172	96.03 195	77.14 173	69.67 259	88.76 230

NR-MVSNet		83.35 190	81.52 196	88.84 174	88.76 224	81.31 112	94.45 196	95.16 149	84.65 107	67.81 260	90.82 173	70.36 154	94.87 247	74.75 196	66.89 278	90.33 198

Fast-Effi-MVS+-dtu		83.33 191	82.60 180	85.50 231	89.55 216	69.38 283	96.09 154	91.38 264	82.30 153	75.96 213	91.41 163	56.71 257	95.58 224	75.13 194	84.90 171	91.54 186

TranMVSNet+NR-MVSNet		83.24 192	81.71 193	87.83 198	87.71 235	78.81 177	96.13 152	94.82 163	84.52 110	76.18 211	90.78 175	64.07 204	94.60 253	74.60 198	66.59 282	90.09 205

MS-PatchMatch		83.05 193	81.82 189	86.72 220	89.64 214	79.10 163	94.88 189	94.59 179	79.70 203	70.67 248	89.65 189	50.43 281	96.82 169	70.82 226	95.99 78	84.25 289

V42		83.04 194	81.53 195	87.57 207	86.27 262	79.09 164	95.87 163	94.11 203	80.35 188	77.22 196	86.79 229	65.32 198	96.02 198	77.74 162	70.14 246	87.61 257

tpmvs		83.04 194	80.77 204	89.84 161	95.43 98	77.96 202	85.59 300	95.32 144	75.31 247	76.27 209	83.70 270	73.89 126	97.41 140	59.53 281	81.93 191	94.14 167

test_djsdf		83.00 196	82.45 182	84.64 247	84.07 289	69.78 279	94.80 191	94.48 181	80.74 178	75.41 220	87.70 211	61.32 224	95.10 242	83.77 117	79.76 196	89.04 222

v7		82.99 197	81.41 197	87.73 201	86.41 254	78.86 174	96.10 153	93.98 208	79.88 200	77.49 188	87.11 221	65.44 194	95.97 200	75.69 190	70.59 244	88.36 242

v1144		82.90 198	81.27 200	87.78 200	86.29 260	79.07 165	96.14 150	93.93 210	80.05 196	77.38 192	86.80 228	65.50 192	95.93 206	75.21 193	70.13 247	88.33 244

test0.0.03 1		82.79 199	82.48 181	83.74 265	86.81 241	72.22 254	96.52 123	95.03 154	83.76 132	73.00 234	93.20 146	72.30 137	88.88 308	64.15 264	77.52 215	90.12 203

FMVSNet2		82.79 199	80.44 208	89.83 162	92.66 156	85.43 37	95.42 177	94.35 186	79.06 215	74.46 223	87.28 214	56.38 263	94.31 258	69.72 232	74.68 225	89.76 210

MVP-Stereo		82.65 201	81.67 194	85.59 230	86.10 268	78.29 192	93.33 220	92.82 249	77.75 226	69.17 258	87.98 209	59.28 235	95.76 214	71.77 213	96.88 67	82.73 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 202	80.79 203	87.79 199	86.11 267	80.49 130	93.55 214	93.18 243	77.29 232	73.35 230	89.40 192	65.26 199	95.05 245	75.32 192	73.61 228	87.83 252

v144192		82.43 203	80.73 205	87.54 208	85.81 273	78.22 194	95.98 155	93.78 220	79.09 214	77.11 197	86.49 234	64.66 203	95.91 207	74.20 200	69.42 261	88.49 237

GBi-Net		82.42 204	80.43 209	88.39 183	92.66 156	81.95 93	94.30 200	93.38 236	79.06 215	75.82 215	85.66 245	56.38 263	93.84 266	71.23 219	75.38 221	89.38 216

test1		82.42 204	80.43 209	88.39 183	92.66 156	81.95 93	94.30 200	93.38 236	79.06 215	75.82 215	85.66 245	56.38 263	93.84 266	71.23 219	75.38 221	89.38 216

v148		82.41 206	80.89 202	86.99 216	86.18 265	76.81 223	96.27 145	93.82 215	80.49 183	75.28 221	86.11 243	67.32 168	95.75 215	75.48 191	67.03 277	88.42 241

v1192		82.31 207	80.55 207	87.60 204	85.94 270	78.47 189	95.85 165	93.80 218	79.33 209	76.97 199	86.51 233	63.33 210	95.87 208	73.11 204	70.13 247	88.46 239

Test4		82.30 208	79.15 223	91.78 114	81.84 295	81.74 103	94.04 206	94.20 191	84.86 101	59.75 301	83.88 265	37.14 313	96.28 185	84.60 110	92.00 116	97.30 104

LS3D		82.22 209	79.94 217	89.06 169	97.43 62	74.06 245	93.20 228	92.05 257	61.90 305	73.33 231	95.21 111	59.35 233	99.21 63	54.54 297	92.48 109	93.90 171

jajsoiax		82.12 210	81.15 201	85.03 235	84.19 287	70.70 272	94.22 204	93.95 209	83.07 143	73.48 228	89.75 188	49.66 284	95.37 232	82.24 135	79.76 196	89.02 223

v1921920		82.02 211	80.23 211	87.41 210	85.62 274	77.92 205	95.79 167	93.69 224	78.86 218	76.67 201	86.44 236	62.50 213	95.83 210	72.69 206	69.77 256	88.47 238

v8		81.88 212	80.06 215	87.32 211	86.63 246	79.04 166	94.41 197	93.65 226	78.77 219	73.19 233	85.57 249	66.87 178	95.81 211	73.84 203	67.61 273	87.11 265

mvs_tets		81.74 213	80.71 206	84.84 239	84.22 286	70.29 275	93.91 207	93.78 220	82.77 148	73.37 229	89.46 191	47.36 293	95.31 235	81.99 136	79.55 202	88.92 228

v1240		81.70 214	79.83 218	87.30 213	85.50 275	77.70 210	95.48 176	93.44 231	78.46 222	76.53 204	86.44 236	60.85 225	95.84 209	71.59 216	70.17 245	88.35 243

PVSNet_0	77.72 15	81.70 214	78.95 224	89.94 158	90.77 197	76.72 224	95.96 156	96.95 32	85.01 100	70.24 253	88.53 202	52.32 276	98.20 110	86.68 99	44.08 326	94.89 158

DP-MVS		81.47 216	78.28 226	91.04 130	98.14 41	78.48 186	95.09 185	86.97 306	61.14 310	71.12 245	92.78 152	59.59 229	99.38 52	53.11 301	86.61 151	95.27 156

v10		81.43 217	79.53 220	87.11 214	86.38 255	78.87 173	94.31 199	93.43 233	77.88 225	73.24 232	85.26 251	65.44 194	95.75 215	72.14 210	67.71 272	86.72 269

pmmvs5		81.34 218	79.54 219	86.73 219	85.02 280	76.91 221	96.22 147	91.65 262	77.65 227	73.55 227	88.61 199	55.70 266	94.43 256	74.12 201	73.35 231	88.86 229

ADS-MVSNet		81.26 219	78.36 225	89.96 157	93.78 136	79.78 143	79.48 310	93.60 228	73.09 270	80.14 169	79.99 288	62.15 216	95.24 238	59.49 282	83.52 177	94.85 159

Baseline_NR-MVSNet		81.22 220	80.07 214	84.68 245	85.32 278	75.12 237	96.48 124	88.80 295	76.24 238	77.28 195	86.40 239	67.61 163	94.39 257	75.73 189	66.73 281	84.54 287

WR-MVS_H		81.02 221	80.09 212	83.79 263	88.08 232	71.26 270	94.46 195	96.54 68	80.08 195	72.81 237	86.82 227	70.36 154	92.65 275	64.18 263	67.50 274	87.46 262

CP-MVSNet		81.01 222	80.08 213	83.79 263	87.91 234	70.51 273	94.29 203	95.65 125	80.83 174	72.54 239	88.84 197	63.71 205	92.32 278	68.58 241	68.36 266	88.55 236

anonymousdsp		80.98 223	79.97 216	84.01 258	81.73 296	70.44 274	92.49 241	93.58 230	77.10 235	72.98 235	86.31 240	57.58 250	94.90 246	79.32 151	78.63 210	86.69 270

IterMVS		80.67 224	79.16 222	85.20 233	89.79 210	76.08 229	92.97 232	91.86 259	80.28 190	71.20 244	85.14 256	57.93 249	91.34 296	72.52 208	70.74 241	88.18 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 225	77.77 230	89.14 168	93.43 145	77.24 217	91.89 253	90.18 284	69.86 286	68.02 259	91.94 159	52.21 277	98.84 92	59.32 284	83.12 181	91.35 187

PS-CasMVS		80.27 226	79.18 221	83.52 269	87.56 237	69.88 278	94.08 205	95.29 146	80.27 191	72.08 240	88.51 203	59.22 236	92.23 280	67.49 245	68.15 269	88.45 240

pm-mvs1		80.05 227	78.02 228	86.15 225	85.42 276	75.81 231	95.11 184	92.69 252	77.13 233	70.36 250	87.43 213	58.44 241	95.27 237	71.36 218	64.25 286	87.36 263

PatchT		79.75 228	76.85 238	88.42 181	89.55 216	75.49 234	77.37 318	94.61 178	63.07 299	82.46 140	73.32 313	75.52 104	93.41 273	51.36 304	84.43 172	96.36 133

ADS-MVSNet2		79.57 229	77.53 231	85.71 228	93.78 136	72.13 256	79.48 310	86.11 311	73.09 270	80.14 169	79.99 288	62.15 216	90.14 306	59.49 282	83.52 177	94.85 159

FMVSNet1		79.50 230	76.54 241	88.39 183	88.47 229	81.95 93	94.30 200	93.38 236	73.14 269	72.04 241	85.66 245	43.86 297	93.84 266	65.48 258	72.53 235	89.38 216

PEN-MVS		79.47 231	78.26 227	83.08 272	86.36 257	68.58 285	93.85 208	94.77 167	79.76 202	71.37 242	88.55 200	59.79 227	92.46 276	64.50 262	65.40 283	88.19 246

XVG-ACMP-BASELINE		79.38 232	77.90 229	83.81 262	84.98 281	67.14 291	89.03 274	93.18 243	80.26 192	72.87 236	88.15 207	38.55 310	96.26 186	76.05 186	78.05 212	88.02 249

v7n		79.32 233	77.34 232	85.28 232	84.05 290	72.89 253	93.38 218	93.87 213	75.02 252	70.68 247	84.37 260	59.58 230	95.62 221	67.60 244	67.50 274	87.32 264

RPMNet		79.32 233	75.75 245	90.06 151	90.16 206	79.75 144	79.02 314	93.92 211	58.43 316	82.27 150	72.55 314	73.03 131	93.67 270	46.10 315	86.25 153	96.75 125

MIMVSNet		79.18 235	75.99 244	88.72 178	87.37 238	80.66 124	79.96 309	91.82 260	77.38 231	74.33 224	81.87 279	41.78 305	90.74 301	66.36 255	83.10 182	94.76 161

JIA-IIPM		79.00 236	77.20 233	84.40 255	89.74 213	64.06 298	75.30 320	95.44 139	62.15 304	81.90 154	59.08 324	78.92 59	95.59 223	66.51 253	85.78 160	93.54 174

v52		78.70 237	76.95 235	83.95 259	81.71 297	71.34 268	91.93 252	93.43 233	74.69 259	70.36 250	83.71 269	58.04 245	95.50 227	71.84 211	66.82 280	85.00 284

V4		78.70 237	76.95 235	83.95 259	81.66 298	71.34 268	91.94 251	93.44 231	74.69 259	70.35 252	83.73 268	58.07 244	95.50 227	71.84 211	66.86 279	85.02 283

v748		78.69 239	76.79 239	84.39 256	83.40 293	70.78 271	93.25 226	93.62 227	74.96 253	69.40 255	83.74 267	59.40 232	95.39 230	68.74 238	64.59 285	86.99 268

USDC		78.65 240	76.25 242	85.85 226	87.58 236	74.60 239	89.58 269	90.58 283	84.05 124	63.13 281	88.23 205	40.69 309	96.86 168	66.57 252	75.81 219	86.09 277

DTE-MVSNet		78.37 241	77.06 234	82.32 280	85.22 279	67.17 290	93.40 217	93.66 225	78.71 220	70.53 249	88.29 204	59.06 237	92.23 280	61.38 278	63.28 290	87.56 259

Patchmatch-test		78.25 242	74.72 256	88.83 175	91.20 190	74.10 244	73.91 324	88.70 298	59.89 314	66.82 263	85.12 257	78.38 67	94.54 254	48.84 312	79.58 200	97.86 72

ACMH	75.40 17	77.99 243	74.96 252	87.10 215	90.67 198	76.41 226	93.19 229	91.64 263	72.47 276	63.44 279	87.61 212	43.34 300	97.16 153	58.34 286	73.94 227	87.72 253
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 243	75.74 246	84.74 242	90.45 201	72.02 258	86.41 296	91.12 267	72.57 275	66.63 264	87.27 215	54.95 271	96.98 161	56.29 292	75.98 217	85.21 282
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
v18		77.96 245	75.61 247	84.98 236	86.66 243	79.01 170	93.02 231	90.94 272	75.69 240	63.19 280	77.62 295	67.11 171	92.07 283	70.05 228	56.24 302	83.87 293

v16		77.84 246	75.47 248	84.93 238	86.62 247	78.93 172	92.84 235	90.89 273	75.50 243	63.03 284	77.54 296	66.82 180	92.04 284	69.82 229	56.22 303	83.82 295

v17		77.79 247	75.41 249	84.94 237	86.53 252	78.94 171	92.83 236	90.88 274	75.51 242	62.97 285	77.50 297	66.69 181	92.03 285	69.80 230	56.01 304	83.83 294

RPSCF		77.73 248	76.63 240	81.06 285	88.66 228	55.76 316	87.77 284	87.88 301	64.82 298	74.14 225	92.79 151	49.22 286	96.81 170	67.47 246	76.88 216	90.62 193

v15		77.52 249	75.09 250	84.82 240	86.37 256	78.82 175	92.58 239	90.78 276	75.47 244	62.53 287	77.17 298	66.58 184	91.92 286	69.18 233	55.16 306	83.73 296

ACMH+	76.62 16	77.47 250	74.94 253	85.05 234	91.07 193	71.58 265	93.26 225	90.01 285	71.80 279	64.76 274	88.55 200	41.62 306	96.48 178	62.35 276	71.00 239	87.09 266

V14		77.43 251	74.99 251	84.75 241	86.32 259	78.67 179	92.44 243	90.77 277	75.28 248	62.42 288	77.13 299	66.40 185	91.88 287	69.01 236	55.14 307	83.70 297

Patchmtry		77.36 252	74.59 259	85.67 229	89.75 211	75.75 232	77.85 317	91.12 267	60.28 312	71.23 243	80.35 286	75.45 105	93.56 272	57.94 287	67.34 276	87.68 255

V9		77.32 253	74.87 254	84.69 244	86.26 263	78.52 185	92.33 246	90.72 278	75.11 251	62.21 290	77.08 301	66.19 187	91.87 288	68.84 237	55.06 309	83.69 298

v11		77.21 254	74.72 256	84.68 245	86.29 260	78.62 182	92.30 247	90.63 282	74.86 255	62.32 289	76.73 304	65.49 193	91.83 289	68.17 243	55.72 305	83.59 300

v12		77.20 255	74.73 255	84.63 248	86.15 266	78.41 190	92.17 248	90.71 279	74.92 254	62.05 292	77.00 302	65.83 189	91.83 289	68.69 239	55.01 310	83.64 299

OurMVSNet-221017-0		77.18 256	76.06 243	80.55 287	83.78 291	60.00 308	90.35 264	91.05 270	77.01 237	66.62 265	87.92 210	47.73 291	94.03 263	71.63 215	68.44 265	87.62 256

v13		77.11 257	74.63 258	84.55 250	86.08 269	78.27 193	92.06 250	90.68 281	74.73 257	61.86 295	76.93 303	65.73 190	91.81 292	68.55 242	55.07 308	83.59 300

testing_2		76.96 258	73.18 268	88.30 186	75.87 316	79.64 149	89.92 267	94.21 190	80.16 193	51.23 315	75.94 306	33.94 318	95.81 211	82.28 134	75.12 224	89.46 213

TransMVSNet (Re)		76.94 259	74.38 262	84.62 249	85.92 271	75.25 236	95.28 178	89.18 292	73.88 266	67.22 261	86.46 235	59.64 228	94.10 262	59.24 285	52.57 316	84.50 288

EU-MVSNet		76.92 260	76.95 235	76.83 297	84.10 288	54.73 317	91.77 255	92.71 251	72.74 273	69.57 254	88.69 198	58.03 247	87.43 313	64.91 261	70.00 254	88.33 244

Patchmatch-RL test		76.65 261	74.01 265	84.55 250	77.37 311	64.23 296	78.49 316	82.84 325	78.48 221	64.63 275	73.40 312	76.05 98	91.70 294	76.99 176	57.84 298	97.72 80

FMVSNet5		76.46 262	74.16 264	83.35 271	90.05 208	76.17 228	89.58 269	89.85 286	71.39 282	65.29 273	80.42 285	50.61 280	87.70 312	61.05 279	69.24 262	86.18 275

SixPastTwentyTwo		76.04 263	74.32 263	81.22 284	84.54 283	61.43 306	91.16 260	89.30 291	77.89 224	64.04 276	86.31 240	48.23 287	94.29 259	63.54 272	63.84 288	87.93 251

AllTest		75.92 264	73.06 269	84.47 252	92.18 170	67.29 288	91.07 261	84.43 317	67.63 290	63.48 277	90.18 183	38.20 311	97.16 153	57.04 288	73.37 229	88.97 226

COLMAP_ROB	73.24 19	75.74 265	73.00 270	83.94 261	92.38 161	69.08 284	91.85 254	86.93 307	61.48 308	65.32 272	90.27 182	42.27 304	96.93 165	50.91 307	75.63 220	85.80 279
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CMPMVS	54.94 21	75.71 266	74.56 260	79.17 293	79.69 304	55.98 314	89.59 268	93.30 242	60.28 312	53.85 313	89.07 194	47.68 292	96.33 183	76.55 180	81.02 192	85.22 281
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 267	73.64 266	80.22 288	80.75 299	63.38 300	93.36 219	90.71 279	73.09 270	67.12 262	83.70 270	50.33 282	90.85 300	53.63 300	70.10 249	86.44 271

EG-PatchMatch MVS		74.92 268	72.02 272	83.62 267	83.76 292	73.28 248	93.62 212	92.04 258	68.57 289	58.88 303	83.80 266	31.87 322	95.57 225	56.97 290	78.67 207	82.00 312

testgi		74.88 269	73.40 267	79.32 292	80.13 303	61.75 304	93.21 227	86.64 309	79.49 207	66.56 266	91.06 169	35.51 316	88.67 309	56.79 291	71.25 237	87.56 259

pmmvs6		74.65 270	71.67 273	83.60 268	79.13 306	69.94 277	93.31 224	90.88 274	61.05 311	65.83 269	84.15 263	43.43 299	94.83 249	66.62 250	60.63 294	86.02 278

test2356		74.41 271	74.53 261	74.07 304	76.13 315	54.45 318	94.74 193	92.08 256	71.96 278	65.51 271	83.05 276	56.96 255	83.71 323	52.74 302	77.58 214	84.06 291

K. test v3		73.62 272	71.59 274	79.69 290	82.98 294	59.85 309	90.85 263	88.83 294	77.13 233	58.90 302	82.11 277	43.62 298	91.72 293	65.83 257	54.10 313	87.50 261

pmmvs-eth3d		73.59 273	70.66 277	82.38 278	76.40 313	73.38 246	89.39 273	89.43 289	72.69 274	60.34 300	77.79 294	46.43 295	91.26 298	66.42 254	57.06 299	82.51 307

MDA-MVSNet_test_wron		73.54 274	70.43 280	82.86 273	84.55 282	71.85 259	91.74 256	91.32 266	67.63 290	46.73 321	81.09 283	55.11 269	90.42 304	55.91 294	59.76 296	86.31 273

YYNet1		73.53 275	70.43 280	82.85 274	84.52 284	71.73 263	91.69 257	91.37 265	67.63 290	46.79 320	81.21 282	55.04 270	90.43 303	55.93 293	59.70 297	86.38 272

UnsupCasMVSNet_eth		73.25 276	70.57 278	81.30 283	77.53 309	66.33 292	87.24 288	93.89 212	80.38 187	57.90 308	81.59 280	42.91 303	90.56 302	65.18 260	48.51 320	87.01 267

DSMNet-mixed		73.13 277	72.45 271	75.19 302	77.51 310	46.82 325	85.09 302	82.01 326	67.61 294	69.27 257	81.33 281	50.89 278	86.28 316	54.54 297	83.80 176	92.46 183

OpenMVS_ROB	68.52 20	73.02 278	69.57 282	83.37 270	80.54 302	71.82 260	93.60 213	88.22 299	62.37 303	61.98 293	83.15 275	35.31 317	95.47 229	45.08 316	75.88 218	82.82 304

test_0402		72.68 279	69.54 283	82.09 281	88.67 227	71.81 261	92.72 238	86.77 308	61.52 307	62.21 290	83.91 264	43.22 301	93.76 269	34.60 326	72.23 236	80.72 314

TinyColmap		72.41 280	68.99 285	82.68 275	88.11 231	69.59 281	88.41 279	85.20 314	65.55 296	57.91 307	84.82 259	30.80 324	95.94 205	51.38 303	68.70 263	82.49 309

test20.03		72.36 281	71.15 275	75.98 301	77.79 308	59.16 311	92.40 244	89.35 290	74.09 264	61.50 296	84.32 261	48.09 288	85.54 321	50.63 308	62.15 292	83.24 302

LF4IMVS		72.36 281	70.82 276	76.95 296	79.18 305	56.33 313	86.12 297	86.11 311	69.30 288	63.06 283	86.66 230	33.03 320	92.25 279	65.33 259	68.64 264	82.28 310

MDA-MVSNet-bldmvs		71.45 283	67.94 286	81.98 282	85.33 277	68.50 286	92.35 245	88.76 296	70.40 284	42.99 322	81.96 278	46.57 294	91.31 297	48.75 313	54.39 312	86.11 276

MVS-HIRNet		71.36 284	67.00 287	84.46 254	90.58 199	69.74 280	79.15 313	87.74 302	46.09 325	61.96 294	50.50 327	45.14 296	95.64 219	53.74 299	88.11 142	88.00 250

testpf		70.88 285	70.47 279	72.08 307	88.92 223	59.57 310	48.62 334	93.15 245	63.05 300	63.07 282	79.51 291	58.33 242	86.63 315	66.93 248	72.69 234	70.05 324

testus		70.06 286	69.09 284	72.98 306	74.54 318	51.28 323	93.78 209	87.34 303	71.49 281	62.69 286	83.46 272	24.44 327	84.77 322	51.22 306	72.85 233	82.90 303

MIMVSNet1		69.44 287	66.65 289	77.84 294	76.48 312	62.84 302	87.42 286	88.97 293	66.96 295	57.75 309	79.72 290	32.77 321	85.83 318	46.32 314	63.42 289	84.85 286

PM-MVS		69.32 288	66.93 288	76.49 298	73.60 319	55.84 315	85.91 298	79.32 332	74.72 258	61.09 297	78.18 293	21.76 328	91.10 299	70.86 224	56.90 300	82.51 307

TDRefinement		69.20 289	65.78 291	79.48 291	66.04 327	62.21 303	88.21 280	86.12 310	62.92 301	61.03 298	85.61 248	33.23 319	94.16 261	55.82 295	53.02 314	82.08 311

new-patchmatchnet		68.85 290	65.93 290	77.61 295	73.57 320	63.94 299	90.11 266	88.73 297	71.62 280	55.08 311	73.60 309	40.84 308	87.22 314	51.35 305	48.49 321	81.67 313

UnsupCasMVSNet_bld		68.60 291	64.50 292	80.92 286	74.63 317	67.80 287	83.97 303	92.94 248	65.12 297	54.63 312	68.23 321	35.97 314	92.17 282	60.13 280	44.83 324	82.78 305

LP		68.54 292	63.92 294	82.39 277	87.93 233	71.79 262	72.37 327	86.01 313	55.89 319	58.33 306	71.46 318	49.58 285	90.10 307	32.25 328	61.48 293	85.27 280

new_pmnet		66.18 293	63.18 295	75.18 303	76.27 314	61.74 305	83.79 304	84.66 316	56.64 318	51.57 314	71.85 316	31.29 323	87.93 311	49.98 309	62.55 291	75.86 319

pmmvs3		65.75 294	62.18 297	76.45 299	67.12 325	64.54 295	88.68 277	85.05 315	54.77 323	57.54 310	73.79 308	29.40 326	86.21 317	55.49 296	47.77 322	78.62 316

1111		65.60 295	64.33 293	69.41 309	68.26 322	45.11 328	87.06 289	87.32 304	54.99 320	51.20 316	73.45 310	63.57 206	85.70 319	36.53 323	56.59 301	77.42 318

test1235678		64.50 296	62.19 296	71.42 308	66.82 326	48.00 324	89.44 271	87.90 300	62.82 302	49.12 319	71.31 319	30.14 325	82.19 325	41.88 319	60.32 295	84.06 291

N_pmnet		61.30 297	60.20 298	64.60 314	84.32 285	17.00 343	91.67 258	10.98 344	61.77 306	58.45 305	78.55 292	49.89 283	91.83 289	42.27 318	63.94 287	84.97 285

Anonymous20231211		61.03 298	56.76 300	73.82 305	71.24 321	53.47 319	87.60 285	81.65 327	44.19 326	51.08 318	71.82 317	20.79 329	88.46 310	35.45 325	47.07 323	79.52 315

test12356		58.24 299	56.06 301	64.77 312	60.65 328	39.42 334	82.78 307	84.34 319	57.47 317	42.65 323	69.10 320	19.21 330	81.18 326	38.97 322	49.40 317	73.69 320

FPMVS		55.09 300	52.93 302	61.57 317	55.98 329	40.51 333	83.11 306	83.41 324	37.61 328	34.95 327	71.95 315	14.40 335	76.95 329	29.81 330	65.16 284	67.25 326

.test1245		54.61 301	58.07 299	44.24 324	68.26 322	45.11 328	87.06 289	87.32 304	54.99 320	51.20 316	73.45 310	63.57 206	85.70 319	36.53 323	0.21 339	1.91 337

testmv		54.58 302	51.53 303	63.74 316	53.58 333	40.82 332	83.26 305	83.92 321	54.07 324	36.35 326	61.26 322	14.80 334	77.07 328	33.00 327	43.53 327	73.33 321

LCM-MVSNet		52.52 303	48.24 304	65.35 311	47.63 337	41.45 331	72.55 326	83.62 323	31.75 329	37.66 325	57.92 325	9.19 341	76.76 330	49.26 311	44.60 325	77.84 317

no-one		51.12 304	45.81 306	67.03 310	53.16 335	52.22 320	75.21 321	80.40 329	54.89 322	28.26 330	48.50 329	15.54 333	82.81 324	39.29 321	17.06 332	66.07 327

PMMVS2		50.90 305	46.31 305	64.67 313	55.53 330	46.67 326	77.30 319	71.02 334	40.89 327	34.16 328	59.32 323	9.83 340	76.14 332	40.09 320	28.63 329	71.21 322

ANet_high		46.22 306	41.28 309	61.04 318	39.91 340	46.25 327	70.59 328	76.18 333	58.87 315	23.09 332	48.00 330	12.58 337	66.54 335	28.65 331	13.62 335	70.35 323

Gipuma		45.11 307	42.05 307	54.30 320	80.69 300	51.30 322	35.80 335	83.81 322	28.13 331	27.94 331	34.53 333	11.41 339	76.70 331	21.45 333	54.65 311	34.90 333
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 308	41.93 308	40.38 325	20.10 342	26.84 339	61.93 330	59.09 340	14.81 337	28.51 329	80.58 284	35.53 315	48.33 340	63.70 271	13.11 336	45.96 332

PNet_i23d		41.20 309	38.13 310	50.41 321	55.23 331	36.24 337	73.80 325	65.45 339	29.75 330	21.36 333	47.05 331	3.43 342	63.23 336	28.17 332	18.92 331	51.76 329

PMVS	34.80 23	39.19 310	35.53 311	50.18 322	29.72 341	30.30 338	59.60 332	66.20 338	26.06 332	17.91 335	49.53 328	3.12 343	74.09 333	18.19 335	49.40 317	46.14 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		37.75 311	31.85 314	55.46 319	40.00 339	38.01 335	59.81 331	69.47 335	25.46 333	12.42 338	30.55 337	2.06 345	67.08 334	31.81 329	15.03 333	61.29 328

pcd1.5k->3k		34.11 312	35.46 312	30.05 328	86.70 242	0.00 346	0.00 337	94.74 168	0.00 341	0.00 342	0.00 343	58.13 243	0.00 344	0.00 341	79.56 201	90.14 201

MVE	35.65 22	33.85 313	29.49 316	46.92 323	41.86 338	36.28 336	50.45 333	56.52 341	18.75 336	18.28 334	37.84 332	2.41 344	58.41 337	18.71 334	20.62 330	46.06 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 314	32.39 313	33.65 326	53.35 334	25.70 340	74.07 323	53.33 342	21.08 334	17.17 336	33.63 335	11.85 338	54.84 338	12.98 336	14.04 334	20.42 334

EMVS		31.70 315	31.45 315	32.48 327	50.72 336	23.95 341	74.78 322	52.30 343	20.36 335	16.08 337	31.48 336	12.80 336	53.60 339	11.39 337	13.10 337	19.88 335

cdsmvs_eth3d_5k		21.43 316	28.57 317	0.00 332	0.00 345	0.00 346	0.00 337	95.93 114	0.00 341	0.00 342	97.66 48	63.57 206	0.00 344	0.00 341	0.00 342	0.00 340

wuyk23d		14.10 317	13.89 318	14.72 329	55.23 331	22.91 342	33.83 336	3.56 345	4.94 338	4.11 339	2.28 342	2.06 345	19.66 341	10.23 338	8.74 338	1.59 339

testmvs		9.92 318	12.94 319	0.84 331	0.65 343	0.29 345	93.78 209	0.39 346	0.42 339	2.85 340	15.84 340	0.17 348	0.30 343	2.18 339	0.21 339	1.91 337

test123		9.07 319	11.73 320	1.11 330	0.50 344	0.77 344	89.44 271	0.20 347	0.34 340	2.15 341	10.72 341	0.34 347	0.32 342	1.79 340	0.08 341	2.23 336

ab-mvs-re		8.11 320	10.81 321	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	97.30 68	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

pcd_1.5k_mvsjas		5.92 321	7.89 322	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	71.04 148	0.00 344	0.00 341	0.00 342	0.00 340

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ESAPD								96.83 40

sam_mvs1												77.59 76

sam_mvs												75.35 115

semantic-postprocess				84.73 243	89.63 215	74.66 238		91.81 261	80.05 196	71.06 246	85.18 254	57.98 248	91.40 295	72.48 209	70.70 243	88.12 248

ambc				76.02 300	68.11 324	51.43 321	64.97 329	89.59 287		60.49 299	74.49 307	17.17 332	92.46 276	61.50 277	52.85 315	84.17 290

MTGPA								96.33 90

test_post1							85.88 299				30.24 338	73.77 127	95.07 244	73.89 202

test_post											33.80 334	76.17 96	95.97 200

patchmatchnet-post											77.09 300	77.78 75	95.39 230

GG-mvs-BLEND				93.49 53	94.94 112	86.26 25	81.62 308	97.00 27		88.32 90	94.30 129	91.23 2	96.21 189	88.49 82	97.43 54	98.00 63

MTMP								68.16 336

gm-plane-assit					92.27 166	79.64 149			84.47 113		95.15 115		97.93 115	85.81 101

test9_res														96.00 12	99.03 7	98.31 39

TEST9					98.64 19	83.71 63	97.82 35	96.65 54	84.29 119	95.16 12	98.09 26	84.39 17	99.36 53

test_8					98.63 21	83.64 66	97.81 37	96.63 60	84.50 111	95.10 14	98.11 25	84.33 18	99.23 59

agg_prior2														94.30 25	99.00 9	98.57 29

agg_prior					98.59 24	83.13 73		96.56 65		94.19 26			99.16 72

TestCases				84.47 252	92.18 170	67.29 288		84.43 317	67.63 290	63.48 277	90.18 183	38.20 311	97.16 153	57.04 288	73.37 229	88.97 226

test_prior4						82.34 88	97.75 44

test_prior2							98.37 18		86.08 75	94.57 23	98.02 31	83.14 29		95.05 18	98.79 13

test_prior				93.09 67	98.68 13	81.91 96		96.40 82					99.06 79			98.29 41

旧先验2							96.97 99		74.06 265	96.10 6			97.76 124	88.38 84

新几何2							96.42 135

新几何1				93.12 65	97.44 61	81.60 108		96.71 48	74.54 261	91.22 60	97.57 54	79.13 58	99.51 44	77.40 171	98.46 25	98.26 44

旧先验1					97.39 63	79.58 151		96.54 68			98.08 29	84.00 22			97.42 55	97.62 88

无先验							96.87 104	96.78 42	77.39 230				99.52 41	79.95 147		98.43 33

原ACMM2							96.84 105

原ACMM1				91.22 127	97.77 52	78.10 199		96.61 61	81.05 170	91.28 58	97.42 64	77.92 73	98.98 84	79.85 149	98.51 23	96.59 128

test222					96.15 81	78.41 190	95.87 163	96.46 75	71.97 277	89.66 73	97.45 60	76.33 94			98.24 36	98.30 40

testdata2													99.48 46	76.45 182

segment_acmp												82.69 31

testdata				90.13 149	95.92 86	74.17 243		96.49 74	73.49 268	94.82 20	97.99 34	78.80 62	97.93 115	83.53 125	97.52 50	98.29 41

testdata1							95.57 173		87.44 59

test12				94.25 25	98.34 35	85.55 35		96.35 88		92.36 43		80.84 38	99.22 61		98.31 34	97.98 65

plane_prior7					91.86 183	77.55 212

plane_prior6					91.98 178	77.92 205						64.77 201

plane_prior5								94.69 169					97.30 145	87.08 94	82.82 187	90.96 190

plane_prior4											94.15 131

plane_prior3						77.75 209			90.17 28	81.33 156

plane_prior2							97.18 76		89.89 30

plane_prior1					91.95 181

plane_prior						77.96 202	97.52 57		90.36 27						82.96 185

n2								0.00 348

nn								0.00 348

door-mid								79.75 331

lessismore_v0				79.98 289	80.59 301	58.34 312		80.87 328		58.49 304	83.46 272	43.10 302	93.89 265	63.11 274	48.68 319	87.72 253

LGP-MVS_train				86.33 221	90.88 195	73.06 250		94.13 198	82.20 154	76.31 206	93.20 146	54.83 272	96.95 162	83.72 119	80.83 193	88.98 224

test11								96.50 72

door								80.13 330

HQP5-MVS						78.48 186

HQP-NCC					92.08 173		97.63 49		90.52 24	82.30 142

ACMP_Plane					92.08 173		97.63 49		90.52 24	82.30 142

BP-MVS														87.67 91

HQP4-MVS										82.30 142			97.32 143			91.13 188

HQP3-MVS								94.80 164							83.01 183

HQP2-MVS												65.40 196

NP-MVS					92.04 177	78.22 194					94.56 125

MDTV_nov1_ep13_2view						81.74 103	86.80 292		80.65 180	85.65 109		74.26 125		76.52 181		96.98 113

MDTV_nov1_ep13			83.69 162		94.09 131	81.01 116	86.78 293	96.09 104	83.81 131	84.75 117	84.32 261	74.44 124	96.54 176	63.88 269	85.07 170

ACMMP++_ref															78.45 211

ACMMP++															79.05 204

Test By Simon												71.65 141

ITE_SJBPF				82.38 278	87.00 240	65.59 293		89.55 288	79.99 198	69.37 256	91.30 166	41.60 307	95.33 234	62.86 275	74.63 226	86.24 274

DeepMVS_CX				64.06 315	78.53 307	43.26 330		68.11 337	69.94 285	38.55 324	76.14 305	18.53 331	79.34 327	43.72 317	41.62 328	69.57 325

High-resolution multi-view benchmark