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.

SD-MVS		97.41 7	97.53 2	97.06 56	98.57 51	94.46 16	97.92 42	98.14 40	94.82 21	99.01 1	98.55 9	94.18 5	97.41 276	96.94 5	99.64 3	99.32 43

TSAR-MVS + MP.		97.42 6	97.33 6	97.69 28	99.25 20	94.24 23	98.07 34	97.85 88	93.72 47	98.57 2	98.35 24	93.69 9	99.40 87	97.06 3	99.46 25	99.44 32

HSP-MVS		97.53 5	97.49 4	97.63 34	99.40 5	93.77 40	98.53 9	97.85 88	95.55 5	98.56 3	97.81 61	93.90 6	99.65 41	96.62 13	99.21 50	99.48 28

APDe-MVS		97.82 1	97.73 1	98.08 8	99.15 25	94.82 12	98.81 2	98.30 22	94.76 24	98.30 4	98.90 1	93.77 8	99.68 37	97.93 1	99.69 1	99.75 1

SteuartSystems-ACMMP		97.62 3	97.53 2	97.87 13	98.39 59	94.25 22	98.43 16	98.27 24	95.34 9	98.11 5	98.56 7	94.53 3	99.71 29	96.57 16	99.62 7	99.65 3
Skip Steuart: Steuart Systems R&D Blog.
test_part2					99.28 17	95.74 3				98.10 6

ESAPD		97.57 4	97.29 7	98.41 2	99.28 17	95.74 3	97.50 89	98.26 25	93.81 45	98.10 6	98.53 11	95.31 1	99.87 5	95.19 47	99.63 4	99.63 5

APD-MVS		96.95 23	96.60 28	98.01 9	99.03 29	94.93 11	97.72 60	98.10 47	91.50 112	98.01 8	98.32 32	92.33 23	99.58 55	94.85 60	99.51 19	99.53 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepPCF-MVS	93.97 1	96.61 36	97.09 8	95.15 135	98.09 80	86.63 253	96.00 226	98.15 38	95.43 7	97.95 9	98.56 7	93.40 10	99.36 91	96.77 12	99.48 24	99.45 30

ACMMP_Plus		97.20 10	96.86 17	98.23 4	99.09 26	95.16 8	97.60 81	98.19 33	92.82 78	97.93 10	98.74 3	91.60 38	99.86 8	96.26 20	99.52 17	99.67 2

CNVR-MVS		97.68 2	97.44 5	98.37 3	98.90 32	95.86 2	97.27 111	98.08 50	95.81 3	97.87 11	98.31 33	94.26 4	99.68 37	97.02 4	99.49 23	99.57 13

VNet		95.89 55	95.45 52	97.21 52	98.07 81	92.94 60	97.50 89	98.15 38	93.87 41	97.52 12	97.61 78	85.29 110	99.53 70	95.81 36	95.27 143	99.16 53

Regformer-2		97.16 13	96.99 11	97.67 29	98.32 65	93.84 35	96.83 150	98.10 47	95.24 10	97.49 13	98.25 39	92.57 19	99.61 47	96.80 9	99.29 43	99.56 15

Regformer-1		97.10 15	96.96 13	97.54 37	98.32 65	93.48 46	96.83 150	97.99 76	95.20 12	97.46 14	98.25 39	92.48 22	99.58 55	96.79 11	99.29 43	99.55 17

APD-MVS_3200maxsize		96.81 28	96.71 26	97.12 55	99.01 30	92.31 73	97.98 40	98.06 57	93.11 66	97.44 15	98.55 9	90.93 47	99.55 65	96.06 29	99.25 46	99.51 23

TSAR-MVS + GP.		96.69 33	96.49 32	97.27 47	98.31 67	93.39 48	96.79 157	96.72 196	94.17 36	97.44 15	97.66 71	92.76 13	99.33 92	96.86 8	97.76 95	99.08 62

旧先验2							95.94 228		81.66 302	97.34 17			98.82 136	92.26 97

MSLP-MVS++		96.94 24	97.06 9	96.59 68	98.72 37	91.86 88	97.67 65	98.49 12	94.66 27	97.24 18	98.41 21	92.31 26	98.94 126	96.61 14	99.46 25	98.96 71

abl_6		96.40 41	96.21 41	96.98 59	98.89 33	92.20 78	97.89 44	98.03 66	93.34 58	97.22 19	98.42 18	87.93 79	99.72 28	95.10 52	99.07 61	99.02 64

HFP-MVS		97.14 14	96.92 15	97.83 15	99.42 3	94.12 27	98.52 10	98.32 19	93.21 60	97.18 20	98.29 36	92.08 28	99.83 15	95.63 39	99.59 9	99.54 19

#test#		97.02 20	96.75 25	97.83 15	99.42 3	94.12 27	98.15 29	98.32 19	92.57 83	97.18 20	98.29 36	92.08 28	99.83 15	95.12 51	99.59 9	99.54 19

ACMMPR		97.07 17	96.84 18	97.79 19	99.44 2	93.88 33	98.52 10	98.31 21	93.21 60	97.15 22	98.33 30	91.35 41	99.86 8	95.63 39	99.59 9	99.62 7

region2R		97.07 17	96.84 18	97.77 22	99.46 1	93.79 37	98.52 10	98.24 28	93.19 63	97.14 23	98.34 27	91.59 39	99.87 5	95.46 44	99.59 9	99.64 4

Regformer-4		96.97 22	96.87 16	97.25 48	98.34 62	92.66 66	96.96 137	98.01 69	95.12 13	97.14 23	98.42 18	91.82 34	99.61 47	96.90 6	99.13 56	99.50 24

PGM-MVS		96.81 28	96.53 31	97.65 30	99.35 13	93.53 45	97.65 68	98.98 1	92.22 88	97.14 23	98.44 16	91.17 43	99.85 11	94.35 68	99.46 25	99.57 13

PHI-MVS		96.77 30	96.46 34	97.71 27	98.40 57	94.07 29	98.21 28	98.45 15	89.86 152	97.11 26	98.01 48	92.52 21	99.69 35	96.03 31	99.53 16	99.36 41

NCCC		97.30 9	97.03 10	98.11 7	98.77 35	95.06 10	97.34 105	98.04 64	95.96 2	97.09 27	97.88 54	93.18 11	99.71 29	95.84 35	99.17 53	99.56 15

Regformer-3		96.85 27	96.80 22	97.01 57	98.34 62	92.02 84	96.96 137	97.76 91	95.01 16	97.08 28	98.42 18	91.71 35	99.54 67	96.80 9	99.13 56	99.48 28

testdata				95.46 124	98.18 78	88.90 189		97.66 103	82.73 295	97.03 29	98.07 44	90.06 57	98.85 134	89.67 140	98.98 65	98.64 93

HPM-MVS_fast		96.51 38	96.27 39	97.22 51	99.32 15	92.74 63	98.74 4	98.06 57	90.57 143	96.77 30	98.35 24	90.21 56	99.53 70	94.80 63	99.63 4	99.38 39

xiu_mvs_v1_base_debu		95.01 70	94.76 66	95.75 107	96.58 145	91.71 89	96.25 210	97.35 142	92.99 69	96.70 31	96.63 121	82.67 159	99.44 82	96.22 22	97.46 99	96.11 193

xiu_mvs_v1_base		95.01 70	94.76 66	95.75 107	96.58 145	91.71 89	96.25 210	97.35 142	92.99 69	96.70 31	96.63 121	82.67 159	99.44 82	96.22 22	97.46 99	96.11 193

xiu_mvs_v1_base_debi		95.01 70	94.76 66	95.75 107	96.58 145	91.71 89	96.25 210	97.35 142	92.99 69	96.70 31	96.63 121	82.67 159	99.44 82	96.22 22	97.46 99	96.11 193

CDPH-MVS		95.97 53	95.38 55	97.77 22	98.93 31	94.44 17	96.35 200	97.88 83	86.98 244	96.65 34	97.89 52	91.99 32	99.47 78	92.26 97	99.46 25	99.39 36

UA-Net		95.95 54	95.53 51	97.20 53	97.67 104	92.98 59	97.65 68	98.13 41	94.81 22	96.61 35	98.35 24	88.87 66	99.51 74	90.36 133	97.35 106	99.11 60

HPM-MVS++		97.34 8	96.97 12	98.47 1	99.08 27	96.16 1	97.55 86	97.97 78	95.59 4	96.61 35	97.89 52	92.57 19	99.84 14	95.95 32	99.51 19	99.40 35

XVS		97.18 11	96.96 13	97.81 17	99.38 8	94.03 31	98.59 7	98.20 31	94.85 17	96.59 37	98.29 36	91.70 36	99.80 20	95.66 37	99.40 32	99.62 7

X-MVStestdata		91.71 175	89.67 232	97.81 17	99.38 8	94.03 31	98.59 7	98.20 31	94.85 17	96.59 37	32.69 350	91.70 36	99.80 20	95.66 37	99.40 32	99.62 7

DeepC-MVS_fast	93.89 2	96.93 25	96.64 27	97.78 20	98.64 46	94.30 20	97.41 97	98.04 64	94.81 22	96.59 37	98.37 23	91.24 42	99.64 46	95.16 49	99.52 17	99.42 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PS-MVSNAJ		95.37 61	95.33 57	95.49 120	97.35 120	90.66 126	95.31 256	97.48 119	93.85 42	96.51 40	95.70 167	88.65 70	99.65 41	94.80 63	98.27 81	96.17 188

EI-MVSNet-Vis-set		96.51 38	96.47 33	96.63 65	98.24 71	91.20 107	96.89 146	97.73 94	94.74 25	96.49 41	98.49 13	90.88 49	99.58 55	96.44 18	98.32 80	99.13 57

alignmvs		95.87 56	95.23 59	97.78 20	97.56 112	95.19 7	97.86 46	97.17 152	94.39 32	96.47 42	96.40 133	85.89 104	99.20 98	96.21 25	95.11 145	98.95 73

xiu_mvs_v2_base		95.32 63	95.29 58	95.40 126	97.22 122	90.50 129	95.44 251	97.44 131	93.70 49	96.46 43	96.18 140	88.59 73	99.53 70	94.79 65	97.81 92	96.17 188

CP-MVS		97.02 20	96.81 21	97.64 32	99.33 14	93.54 44	98.80 3	98.28 23	92.99 69	96.45 44	98.30 35	91.90 33	99.85 11	95.61 41	99.68 2	99.54 19

HPM-MVS		96.69 33	96.45 35	97.40 40	99.36 12	93.11 55	98.87 1	98.06 57	91.17 123	96.40 45	97.99 50	90.99 46	99.58 55	95.61 41	99.61 8	99.49 26

LFMVS		93.60 109	92.63 120	96.52 70	98.13 79	91.27 104	97.94 41	93.39 314	90.57 143	96.29 46	98.31 33	69.00 304	99.16 103	94.18 69	95.87 135	99.12 59

canonicalmvs		96.02 52	95.45 52	97.75 24	97.59 110	95.15 9	98.28 22	97.60 108	94.52 29	96.27 47	96.12 143	87.65 83	99.18 101	96.20 26	94.82 149	98.91 77

MVSFormer		95.37 61	95.16 61	95.99 99	96.34 160	91.21 105	98.22 26	97.57 111	91.42 116	96.22 48	97.32 89	86.20 101	97.92 237	94.07 70	99.05 62	98.85 82

lupinMVS		94.99 74	94.56 72	96.29 88	96.34 160	91.21 105	95.83 233	96.27 215	88.93 183	96.22 48	96.88 105	86.20 101	98.85 134	95.27 45	99.05 62	98.82 85

EI-MVSNet-UG-set		96.34 43	96.30 38	96.47 76	98.20 75	90.93 118	96.86 148	97.72 97	94.67 26	96.16 50	98.46 14	90.43 53	99.58 55	96.23 21	97.96 89	98.90 78

MPTG		97.07 17	96.77 24	97.97 11	99.37 10	94.42 18	97.15 125	98.08 50	95.07 14	96.11 51	98.59 5	90.88 49	99.90 1	96.18 27	99.50 21	99.58 11

MTAPA		97.08 16	96.78 23	97.97 11	99.37 10	94.42 18	97.24 113	98.08 50	95.07 14	96.11 51	98.59 5	90.88 49	99.90 1	96.18 27	99.50 21	99.58 11

MCST-MVS		97.18 11	96.84 18	98.20 5	99.30 16	95.35 5	97.12 127	98.07 55	93.54 53	96.08 53	97.69 68	93.86 7	99.71 29	96.50 17	99.39 34	99.55 17

TEST9					98.70 38	94.19 24	96.41 192	98.02 67	88.17 216	96.03 54	97.56 83	92.74 14	99.59 52

train_agg		96.30 44	95.83 47	97.72 25	98.70 38	94.19 24	96.41 192	98.02 67	88.58 195	96.03 54	97.56 83	92.73 15	99.59 52	95.04 53	99.37 39	99.39 36

test_prior3		96.46 40	96.20 42	97.23 49	98.67 40	92.99 57	96.35 200	98.00 71	92.80 79	96.03 54	97.59 79	92.01 30	99.41 85	95.01 55	99.38 35	99.29 45

test_prior2							96.35 200		92.80 79	96.03 54	97.59 79	92.01 30		95.01 55	99.38 35

jason		94.84 79	94.39 80	96.18 93	95.52 189	90.93 118	96.09 219	96.52 208	89.28 164	96.01 58	97.32 89	84.70 118	98.77 141	95.15 50	98.91 68	98.85 82
jason: jason.
test_8					98.67 40	94.06 30	96.37 199	98.01 69	88.58 195	95.98 59	97.55 85	92.73 15	99.58 55

mPP-MVS		96.86 26	96.60 28	97.64 32	99.40 5	93.44 47	98.50 13	98.09 49	93.27 59	95.95 60	98.33 30	91.04 45	99.88 3	95.20 46	99.57 13	99.60 10

DELS-MVS		96.61 36	96.38 37	97.30 44	97.79 98	93.19 53	95.96 227	98.18 35	95.23 11	95.87 61	97.65 72	91.45 40	99.70 34	95.87 33	99.44 29	99.00 69
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
VDD-MVS		93.82 102	93.08 106	96.02 97	97.88 95	89.96 142	97.72 60	95.85 237	92.43 85	95.86 62	98.44 16	68.42 308	99.39 88	96.31 19	94.85 147	98.71 90

MVS_111021_HR		96.68 35	96.58 30	96.99 58	98.46 53	92.31 73	96.20 215	98.90 2	94.30 35	95.86 62	97.74 66	92.33 23	99.38 90	96.04 30	99.42 30	99.28 48

agg_prior3		96.16 48	95.67 49	97.62 35	98.67 40	93.88 33	96.41 192	98.00 71	87.93 220	95.81 64	97.47 87	92.33 23	99.59 52	95.04 53	99.37 39	99.39 36

MVS_111021_LR		96.24 46	96.19 43	96.39 80	98.23 74	91.35 102	96.24 213	98.79 4	93.99 39	95.80 65	97.65 72	89.92 60	99.24 97	95.87 33	99.20 51	98.58 94

VDDNet		93.05 126	92.07 134	96.02 97	96.84 136	90.39 132	98.08 33	95.85 237	86.22 258	95.79 66	98.46 14	67.59 311	99.19 99	94.92 59	94.85 147	98.47 107

新几何1				97.32 43	98.60 47	93.59 43		97.75 92	81.58 304	95.75 67	97.85 58	90.04 58	99.67 39	86.50 204	99.13 56	98.69 91

agg_prior1		96.22 47	95.77 48	97.56 36	98.67 40	93.79 37	96.28 208	98.00 71	88.76 192	95.68 68	97.55 85	92.70 17	99.57 63	95.01 55	99.32 41	99.32 43

agg_prior					98.67 40	93.79 37		98.00 71		95.68 68			99.57 63

1121		94.71 81	93.83 84	97.34 42	98.57 51	93.64 42	96.04 222	97.73 94	81.56 306	95.68 68	97.85 58	90.23 55	99.65 41	87.68 181	99.12 59	98.73 87

MG-MVS		95.61 58	95.38 55	96.31 85	98.42 56	90.53 128	96.04 222	97.48 119	93.47 54	95.67 71	98.10 42	89.17 63	99.25 96	91.27 126	98.77 70	99.13 57

MVS_0304		96.05 50	95.45 52	97.85 14	97.75 101	94.50 15	96.87 147	97.95 81	95.46 6	95.60 72	98.01 48	80.96 191	99.83 15	97.23 2	99.25 46	99.23 49

MVS_Test		94.89 77	94.62 70	95.68 111	96.83 138	89.55 161	96.70 170	97.17 152	91.17 123	95.60 72	96.11 145	87.87 80	98.76 142	93.01 94	97.17 110	98.72 88

MP-MVS-pluss		96.70 32	96.27 39	97.98 10	99.23 23	94.71 13	96.96 137	98.06 57	90.67 134	95.55 74	98.78 2	91.07 44	99.86 8	96.58 15	99.55 14	99.38 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		96.77 30	96.45 35	97.72 25	99.39 7	93.80 36	98.41 17	98.06 57	93.37 55	95.54 75	98.34 27	90.59 52	99.88 3	94.83 61	99.54 15	99.49 26

test12				97.65 30	98.46 53	94.26 21		97.66 103		95.52 76		90.89 48	99.46 79		99.25 46	99.22 50

test222					98.24 71	92.21 76	95.33 254	97.60 108	79.22 317	95.25 77	97.84 60	88.80 68			99.15 54	98.72 88

原ACMM1				96.38 81	98.59 48	91.09 113		97.89 82	87.41 232	95.22 78	97.68 69	90.25 54	99.54 67	87.95 174	99.12 59	98.49 104

CPTT-MVS		95.57 59	95.19 60	96.70 62	99.27 19	91.48 97	98.33 20	98.11 45	87.79 223	95.17 79	98.03 46	87.09 92	99.61 47	93.51 83	99.42 30	99.02 64

DP-MVS Recon		95.68 57	95.12 62	97.37 41	99.19 24	94.19 24	97.03 130	98.08 50	88.35 209	95.09 80	97.65 72	89.97 59	99.48 77	92.08 106	98.59 75	98.44 111

Vis-MVSNet		95.23 65	94.81 65	96.51 73	97.18 124	91.58 96	98.26 24	98.12 42	94.38 33	94.90 81	98.15 41	82.28 170	98.92 127	91.45 123	98.58 76	99.01 68
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CANet		96.39 42	96.02 44	97.50 38	97.62 107	93.38 49	97.02 132	97.96 79	95.42 8	94.86 82	97.81 61	87.38 89	99.82 18	96.88 7	99.20 51	99.29 45

API-MVS		94.84 79	94.49 76	95.90 101	97.90 94	92.00 85	97.80 51	97.48 119	89.19 167	94.81 83	96.71 110	88.84 67	99.17 102	88.91 159	98.76 71	96.53 178

OMC-MVS		95.09 69	94.70 69	96.25 91	98.46 53	91.28 103	96.43 190	97.57 111	92.04 101	94.77 84	97.96 51	87.01 93	99.09 116	91.31 125	96.77 118	98.36 118

WTY-MVS		94.71 81	94.02 81	96.79 61	97.71 103	92.05 82	96.59 183	97.35 142	90.61 140	94.64 85	96.93 103	86.41 98	99.39 88	91.20 128	94.71 153	98.94 74

ACMMP		96.27 45	95.93 45	97.28 46	99.24 21	92.62 67	98.25 25	98.81 3	92.99 69	94.56 86	98.39 22	88.96 65	99.85 11	94.57 67	97.63 96	99.36 41
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
Effi-MVS+		94.93 75	94.45 78	96.36 83	96.61 143	91.47 98	96.41 192	97.41 135	91.02 128	94.50 87	95.92 150	87.53 86	98.78 139	93.89 76	96.81 117	98.84 84

sss		94.51 83	93.80 85	96.64 63	97.07 128	91.97 86	96.32 204	98.06 57	88.94 182	94.50 87	96.78 107	84.60 119	99.27 95	91.90 109	96.02 131	98.68 92

PVSNet_BlendedMVS		94.06 94	93.92 82	94.47 171	98.27 68	89.46 167	96.73 162	98.36 16	90.17 147	94.36 89	95.24 189	88.02 76	99.58 55	93.44 86	90.72 218	94.36 284

PVSNet_Blended		94.87 78	94.56 72	95.81 104	98.27 68	89.46 167	95.47 250	98.36 16	88.84 186	94.36 89	96.09 146	88.02 76	99.58 55	93.44 86	98.18 83	98.40 114

PMMVS		92.86 134	92.34 131	94.42 174	94.92 223	86.73 249	94.53 271	96.38 211	84.78 276	94.27 91	95.12 194	83.13 136	98.40 173	91.47 122	96.49 126	98.12 124

EPP-MVSNet		95.22 66	95.04 63	95.76 106	97.49 119	89.56 160	98.67 5	97.00 175	90.69 133	94.24 92	97.62 77	89.79 61	98.81 137	93.39 89	96.49 126	98.92 76

PVSNet_Blended_VisFu		95.27 64	94.91 64	96.38 81	98.20 75	90.86 120	97.27 111	98.25 27	90.21 146	94.18 93	97.27 91	87.48 87	99.73 25	93.53 82	97.77 94	98.55 95

XVG-OURS-SEG-HR		93.86 101	93.55 92	94.81 154	97.06 130	88.53 194	95.28 257	97.45 128	91.68 109	94.08 94	97.68 69	82.41 168	98.90 129	93.84 78	92.47 188	96.98 159

XVG-OURS		93.72 106	93.35 102	94.80 155	97.07 128	88.61 192	94.79 266	97.46 124	91.97 104	93.99 95	97.86 57	81.74 182	98.88 133	92.64 96	92.67 187	96.92 167

IS-MVSNet		94.90 76	94.52 75	96.05 96	97.67 104	90.56 127	98.44 15	96.22 219	93.21 60	93.99 95	97.74 66	85.55 108	98.45 165	89.98 134	97.86 90	99.14 56

CSCG		96.05 50	95.91 46	96.46 78	99.24 21	90.47 130	98.30 21	98.57 11	89.01 177	93.97 97	97.57 81	92.62 18	99.76 23	94.66 66	99.27 45	99.15 55

HyFIR lowres test		93.66 107	92.92 110	95.87 102	98.24 71	89.88 144	94.58 269	98.49 12	85.06 271	93.78 98	95.78 161	82.86 155	98.67 147	91.77 113	95.71 139	99.07 63

CHOSEN 1792x2688		94.15 89	93.51 95	96.06 95	98.27 68	89.38 173	95.18 262	98.48 14	85.60 264	93.76 99	97.11 99	83.15 134	99.61 47	91.33 124	98.72 72	99.19 51

CDS-MVSNet		94.14 91	93.54 93	95.93 100	96.18 167	91.46 99	96.33 203	97.04 171	88.97 181	93.56 100	96.51 128	87.55 85	97.89 241	89.80 137	95.95 133	98.44 111
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view						70.35 333	93.10 301		83.88 285	93.55 101		82.47 167		86.25 207		98.38 117

CANet_DTU		94.37 84	93.65 90	96.55 69	96.46 156	92.13 80	96.21 214	96.67 203	94.38 33	93.53 102	97.03 102	79.34 220	99.71 29	90.76 129	98.45 78	97.82 138

tpmrst		91.44 197	91.32 164	91.79 276	95.15 211	79.20 317	93.42 293	95.37 254	88.55 197	93.49 103	93.67 260	82.49 165	98.27 183	90.41 132	89.34 232	97.90 132

TAMVS		94.01 97	93.46 97	95.64 112	96.16 169	90.45 131	96.71 167	96.89 189	89.27 165	93.46 104	96.92 104	87.29 90	97.94 233	88.70 165	95.74 137	98.53 97

DeepC-MVS	93.07 3	96.06 49	95.66 50	97.29 45	97.96 87	93.17 54	97.30 110	98.06 57	93.92 40	93.38 105	98.66 4	86.83 94	99.73 25	95.60 43	99.22 49	98.96 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thres600view7		92.49 147	91.60 153	95.18 130	97.91 93	89.47 165	97.65 68	94.66 284	92.18 94	93.33 106	94.91 197	78.06 253	99.10 113	81.61 269	94.06 161	96.98 159

conf200view11		92.45 148	91.58 154	95.05 139	97.92 91	89.37 174	97.71 62	94.66 284	92.20 90	93.31 107	94.90 198	78.06 253	99.08 118	81.40 276	94.08 157	96.70 174

thres100view900		92.43 149	91.58 154	94.98 144	97.92 91	89.37 174	97.71 62	94.66 284	92.20 90	93.31 107	94.90 198	78.06 253	99.08 118	81.40 276	94.08 157	96.48 181

thres200		92.23 160	91.39 161	94.75 159	97.61 108	89.03 186	96.60 182	95.09 269	92.08 100	93.28 109	94.00 249	78.39 244	99.04 123	81.26 287	94.18 156	96.19 187

tfpn200view9		92.38 152	91.52 158	94.95 147	97.85 96	89.29 179	97.41 97	94.88 279	92.19 92	93.27 110	94.46 220	78.17 246	99.08 118	81.40 276	94.08 157	96.48 181

thres400		92.42 150	91.52 158	95.12 138	97.85 96	89.29 179	97.41 97	94.88 279	92.19 92	93.27 110	94.46 220	78.17 246	99.08 118	81.40 276	94.08 157	96.98 159

ab-mvs		93.57 111	92.55 124	96.64 63	97.28 121	91.96 87	95.40 252	97.45 128	89.81 156	93.22 112	96.28 137	79.62 217	99.46 79	90.74 130	93.11 182	98.50 102

view600		92.55 141	91.68 147	95.18 130	97.98 83	89.44 169	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 249	99.11 108	81.61 269	94.04 162	96.98 159

view800		92.55 141	91.68 147	95.18 130	97.98 83	89.44 169	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 249	99.11 108	81.61 269	94.04 162	96.98 159

conf0.05thres1000		92.55 141	91.68 147	95.18 130	97.98 83	89.44 169	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 249	99.11 108	81.61 269	94.04 162	96.98 159

tfpn		92.55 141	91.68 147	95.18 130	97.98 83	89.44 169	98.00 36	94.57 288	92.09 95	93.17 113	95.52 176	78.14 249	99.11 108	81.61 269	94.04 162	96.98 159

diffmvs		93.43 115	92.75 115	95.48 122	96.47 155	89.61 157	96.09 219	97.14 156	85.97 261	93.09 117	95.35 184	84.87 116	98.55 157	89.51 144	96.26 130	98.28 120

Vis-MVSNet (Re-imp)		94.15 89	93.88 83	94.95 147	97.61 108	87.92 225	98.10 31	95.80 240	92.22 88	93.02 118	97.45 88	84.53 121	97.91 240	88.24 168	97.97 88	99.02 64

114514_t		93.95 98	93.06 107	96.63 65	99.07 28	91.61 93	97.46 96	97.96 79	77.99 322	93.00 119	97.57 81	86.14 103	99.33 92	89.22 150	99.15 54	98.94 74

UGNet		94.04 96	93.28 104	96.31 85	96.85 135	91.19 108	97.88 45	97.68 102	94.40 31	93.00 119	96.18 140	73.39 287	99.61 47	91.72 114	98.46 77	98.13 123
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
HY-MVS	89.66 9	93.87 100	92.95 109	96.63 65	97.10 127	92.49 71	95.64 242	96.64 204	89.05 176	93.00 119	95.79 160	85.77 107	99.45 81	89.16 153	94.35 154	97.96 129

PVSNet	86.66 18	92.24 159	91.74 146	93.73 208	97.77 100	83.69 284	92.88 303	96.72 196	87.91 221	93.00 119	94.86 201	78.51 241	99.05 122	86.53 202	97.45 103	98.47 107

MAR-MVS		94.22 87	93.46 97	96.51 73	98.00 82	92.19 79	97.67 65	97.47 122	88.13 218	93.00 119	95.84 154	84.86 117	99.51 74	87.99 173	98.17 84	97.83 137
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
PAPM_NR		95.01 70	94.59 71	96.26 90	98.89 33	90.68 125	97.24 113	97.73 94	91.80 106	92.93 124	96.62 124	89.13 64	99.14 106	89.21 151	97.78 93	98.97 70

MDTV_nov1_ep13			90.76 187		95.22 207	80.33 307	93.03 302	95.28 259	88.14 217	92.84 125	93.83 254	81.34 186	98.08 199	82.86 258	94.34 155

CostFormer		91.18 210	90.70 190	92.62 252	94.84 227	81.76 296	94.09 282	94.43 293	84.15 281	92.72 126	93.77 257	79.43 219	98.20 186	90.70 131	92.18 194	97.90 132

EPNet		95.20 67	94.56 72	97.14 54	92.80 304	92.68 65	97.85 48	94.87 282	96.64 1	92.46 127	97.80 63	86.23 99	99.65 41	93.72 80	98.62 74	99.10 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		90.82 219	89.77 228	93.95 193	94.45 241	87.19 240	90.23 325	95.68 243	86.89 249	92.40 128	92.36 288	80.91 195	97.05 287	81.09 288	93.95 166	97.60 148

RPMNet		88.52 264	86.72 276	93.95 193	94.45 241	87.19 240	90.23 325	94.99 274	77.87 324	92.40 128	87.55 329	80.17 210	97.05 287	68.84 326	93.95 166	97.60 148

EPMVS		90.70 226	89.81 227	93.37 229	94.73 232	84.21 277	93.67 289	88.02 341	89.50 160	92.38 130	93.49 267	77.82 258	97.78 250	86.03 213	92.68 186	98.11 127

PatchT		88.87 257	87.42 262	93.22 235	94.08 263	85.10 268	89.51 329	94.64 287	81.92 300	92.36 131	88.15 324	80.05 211	97.01 291	72.43 318	93.65 171	97.54 151

PAPR		94.18 88	93.42 101	96.48 75	97.64 106	91.42 101	95.55 245	97.71 100	88.99 178	92.34 132	95.82 156	89.19 62	99.11 108	86.14 209	97.38 104	98.90 78

mvs-test1		93.63 108	93.69 88	93.46 225	96.02 175	84.61 275	97.24 113	96.72 196	93.85 42	92.30 133	95.76 162	83.08 140	98.89 131	91.69 117	96.54 125	96.87 169

Patchmatch-test1		91.54 193	90.85 183	93.59 217	95.59 187	84.95 271	94.72 267	95.58 247	90.82 129	92.25 134	93.58 263	75.80 267	97.41 276	83.35 251	95.98 132	98.40 114

CVMVSNet		91.23 206	91.75 144	89.67 305	95.77 183	74.69 325	96.44 188	94.88 279	85.81 262	92.18 135	97.64 75	79.07 223	95.58 317	88.06 171	95.86 136	98.74 86

AdaColmap		94.34 85	93.68 89	96.31 85	98.59 48	91.68 92	96.59 183	97.81 90	89.87 151	92.15 136	97.06 101	83.62 128	99.54 67	89.34 146	98.07 86	97.70 142

PatchmatchNet		91.91 169	91.35 162	93.59 217	95.38 195	84.11 279	93.15 299	95.39 252	89.54 158	92.10 137	93.68 259	82.82 157	98.13 192	84.81 229	95.32 142	98.52 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		93.24 120	92.48 129	95.51 118	95.70 185	92.39 72	97.86 46	98.66 9	92.30 87	92.09 138	95.37 183	80.49 203	98.40 173	93.95 73	85.86 258	95.75 214

tpm		90.25 235	89.74 231	91.76 279	93.92 271	79.73 313	93.98 283	93.54 313	88.28 210	91.99 139	93.25 273	77.51 260	97.44 273	87.30 193	87.94 244	98.12 124

CNLPA		94.28 86	93.53 94	96.52 70	98.38 60	92.55 69	96.59 183	96.88 190	90.13 148	91.91 140	97.24 93	85.21 111	99.09 116	87.64 184	97.83 91	97.92 131

BH-RMVSNet		92.72 139	91.97 139	94.97 145	97.16 125	87.99 220	96.15 216	95.60 245	90.62 138	91.87 141	97.15 98	78.41 243	98.57 155	83.16 254	97.60 97	98.36 118

PatchMatch-RL		92.90 132	92.02 137	95.56 115	98.19 77	90.80 122	95.27 259	97.18 150	87.96 219	91.86 142	95.68 168	80.44 204	98.99 124	84.01 245	97.54 98	96.89 168

tfpn1000		91.99 168	91.05 173	94.80 155	97.78 99	89.66 155	97.91 43	92.90 325	88.99 178	91.73 143	94.84 202	78.99 230	98.33 180	82.41 265	93.91 168	96.40 183

tfpn_ndepth		91.88 171	90.96 177	94.62 164	97.73 102	89.93 143	97.75 54	92.92 324	88.93 183	91.73 143	93.80 256	78.91 231	98.49 164	83.02 257	93.86 169	95.45 224

OPM-MVS		93.28 119	92.76 113	94.82 152	94.63 235	90.77 124	96.65 175	97.18 150	93.72 47	91.68 145	97.26 92	79.33 221	98.63 149	92.13 103	92.28 190	95.07 249

tpm2		89.96 241	89.21 240	92.23 260	94.91 225	81.25 299	93.78 286	94.42 294	80.62 312	91.56 146	93.44 270	76.44 264	97.94 233	85.60 220	92.08 198	97.49 152

TAPA-MVS	90.10 7	92.30 156	91.22 170	95.56 115	98.33 64	89.60 158	96.79 157	97.65 105	81.83 301	91.52 147	97.23 94	87.94 78	98.91 128	71.31 322	98.37 79	98.17 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		91.48 195	90.59 200	94.16 182	96.40 158	87.33 234	95.67 239	95.34 258	87.68 227	91.46 148	95.52 176	76.77 262	98.35 177	82.85 259	93.61 173	96.79 171

RPSCF		90.75 222	90.86 182	90.42 299	96.84 136	76.29 323	95.61 244	96.34 212	83.89 284	91.38 149	97.87 55	76.45 263	98.78 139	87.16 197	92.23 191	96.20 186

PatchFormer-LS_test		91.68 185	91.18 172	93.19 237	95.24 206	83.63 285	95.53 247	95.44 251	89.82 155	91.37 150	92.58 282	80.85 199	98.52 159	89.65 142	90.16 225	97.42 154

PLC	91.00 6	94.11 92	93.43 99	96.13 94	98.58 50	91.15 112	96.69 172	97.39 136	87.29 235	91.37 150	96.71 110	88.39 74	99.52 73	87.33 192	97.13 111	97.73 140
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		93.12 123	92.72 118	94.34 177	96.71 142	87.27 236	90.29 324	97.72 97	86.61 254	91.34 152	95.29 186	84.29 123	98.41 172	93.25 90	98.94 67	97.35 155

HQP_MVS		93.78 104	93.43 99	94.82 152	96.21 164	89.99 137	97.74 56	97.51 117	94.85 17	91.34 152	96.64 117	81.32 187	98.60 152	93.02 92	92.23 191	95.86 203

plane_prior3						90.00 135			94.46 30	91.34 152

Fast-Effi-MVS+		93.46 113	92.75 115	95.59 114	96.77 140	90.03 134	96.81 154	97.13 158	88.19 214	91.30 155	94.27 242	86.21 100	98.63 149	87.66 183	96.46 128	98.12 124

EI-MVSNet		93.03 127	92.88 111	93.48 223	95.77 183	86.98 245	96.44 188	97.12 159	90.66 136	91.30 155	97.64 75	86.56 96	98.05 211	89.91 135	90.55 220	95.41 226

MVSTER		93.20 121	92.81 112	94.37 175	96.56 148	89.59 159	97.06 129	97.12 159	91.24 122	91.30 155	95.96 148	82.02 176	98.05 211	93.48 85	90.55 220	95.47 222

ADS-MVSNet2		89.45 250	88.59 248	92.03 269	95.86 178	82.26 293	90.93 320	94.32 298	83.23 292	91.28 158	91.81 295	79.01 228	95.99 309	79.52 294	91.39 208	97.84 135

ADS-MVSNet		89.89 243	88.68 247	93.53 221	95.86 178	84.89 272	90.93 320	95.07 271	83.23 292	91.28 158	91.81 295	79.01 228	97.85 243	79.52 294	91.39 208	97.84 135

conf0.01		91.74 173	90.67 192	94.94 150	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.70 174

conf0.002		91.74 173	90.67 192	94.94 150	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.70 174

thresconf0.02		91.69 180	90.67 192	94.75 159	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 193

tfpn_n400		91.69 180	90.67 192	94.75 159	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 193

tfpnconf		91.69 180	90.67 192	94.75 159	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 193

tfpnview11		91.69 180	90.67 192	94.75 159	97.55 113	89.68 149	97.64 72	93.14 316	88.43 200	91.24 160	94.30 232	78.91 231	98.45 165	81.28 281	93.57 176	96.11 193

nrg030		94.05 95	93.31 103	96.27 89	95.22 207	94.59 14	98.34 19	97.46 124	92.93 76	91.21 166	96.64 117	87.23 91	98.22 185	94.99 58	85.80 259	95.98 201

Effi-MVS+-dtu		93.08 124	93.21 105	92.68 251	96.02 175	83.25 287	97.14 126	96.72 196	93.85 42	91.20 167	93.44 270	83.08 140	98.30 182	91.69 117	95.73 138	96.50 180

VPNet		92.23 160	91.31 165	94.99 142	95.56 188	90.96 116	97.22 118	97.86 87	92.96 75	90.96 168	96.62 124	75.06 273	98.20 186	91.90 109	83.65 292	95.80 209

JIA-IIPM		88.26 272	87.04 273	91.91 271	93.52 283	81.42 298	89.38 330	94.38 295	80.84 310	90.93 169	80.74 336	79.22 222	97.92 237	82.76 260	91.62 203	96.38 184

test-LLR		91.42 198	91.19 171	92.12 266	94.59 236	80.66 302	94.29 276	92.98 322	91.11 125	90.76 170	92.37 285	79.02 226	98.07 203	88.81 163	96.74 119	97.63 143

test-mter		90.19 238	89.54 235	92.12 266	94.59 236	80.66 302	94.29 276	92.98 322	87.68 227	90.76 170	92.37 285	67.67 310	98.07 203	88.81 163	96.74 119	97.63 143

ACMM	89.79 8	92.96 129	92.50 128	94.35 176	96.30 162	88.71 190	97.58 84	97.36 141	91.40 118	90.53 172	96.65 116	79.77 214	98.75 143	91.24 127	91.64 202	95.59 219
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
F-COLMAP		93.58 110	92.98 108	95.37 127	98.40 57	88.98 187	97.18 122	97.29 146	87.75 225	90.49 173	97.10 100	85.21 111	99.50 76	86.70 201	96.72 121	97.63 143

DWT-MVSNet_test		90.76 220	89.89 223	93.38 228	95.04 217	83.70 283	95.85 232	94.30 299	88.19 214	90.46 174	92.80 277	73.61 285	98.50 161	88.16 169	90.58 219	97.95 130

TESTMET0.1,1		90.06 240	89.42 237	91.97 270	94.41 243	80.62 304	94.29 276	91.97 331	87.28 236	90.44 175	92.47 284	68.79 305	97.67 258	88.50 167	96.60 124	97.61 147

FIs		94.09 93	93.70 87	95.27 128	95.70 185	92.03 83	98.10 31	98.68 7	93.36 57	90.39 176	96.70 112	87.63 84	97.94 233	92.25 99	90.50 222	95.84 206

GA-MVS		91.38 200	90.31 205	94.59 165	94.65 234	87.62 232	94.34 274	96.19 220	90.73 132	90.35 177	93.83 254	71.84 290	97.96 231	87.22 194	93.61 173	98.21 121

LS3D		93.57 111	92.61 122	96.47 76	97.59 110	91.61 93	97.67 65	97.72 97	85.17 269	90.29 178	98.34 27	84.60 119	99.73 25	83.85 249	98.27 81	98.06 128

FC-MVSNet-test		93.94 99	93.57 91	95.04 140	95.48 191	91.45 100	98.12 30	98.71 5	93.37 55	90.23 179	96.70 112	87.66 82	97.85 243	91.49 121	90.39 223	95.83 207

HQP-NCC					95.86 178		96.65 175		93.55 50	90.14 180

ACMP_Plane					95.86 178		96.65 175		93.55 50	90.14 180

HQP4-MVS										90.14 180			98.50 161			95.78 210

HQP-MVS		93.19 122	92.74 117	94.54 170	95.86 178	89.33 176	96.65 175	97.39 136	93.55 50	90.14 180	95.87 152	80.95 192	98.50 161	92.13 103	92.10 196	95.78 210

UniMVSNet_NR-MVSNet		93.37 116	92.67 119	95.47 123	95.34 197	92.83 61	97.17 123	98.58 10	92.98 74	90.13 184	95.80 157	88.37 75	97.85 243	91.71 115	83.93 286	95.73 216

DU-MVS		92.90 132	92.04 135	95.49 120	94.95 221	92.83 61	97.16 124	98.24 28	93.02 68	90.13 184	95.71 165	83.47 129	97.85 243	91.71 115	83.93 286	95.78 210

LPG-MVS_test		92.94 130	92.56 123	94.10 183	96.16 169	88.26 200	97.65 68	97.46 124	91.29 119	90.12 186	97.16 96	79.05 224	98.73 144	92.25 99	91.89 199	95.31 236

LGP-MVS_train				94.10 183	96.16 169	88.26 200		97.46 124	91.29 119	90.12 186	97.16 96	79.05 224	98.73 144	92.25 99	91.89 199	95.31 236

UniMVSNet (Re)		93.31 118	92.55 124	95.61 113	95.39 194	93.34 52	97.39 101	98.71 5	93.14 65	90.10 188	94.83 204	87.71 81	98.03 216	91.67 119	83.99 285	95.46 223

mvs_anonymous		93.82 102	93.74 86	94.06 185	96.44 157	85.41 265	95.81 234	97.05 168	89.85 154	90.09 189	96.36 135	87.44 88	97.75 253	93.97 72	96.69 122	99.02 64

test_djsdf		93.07 125	92.76 113	94.00 188	93.49 285	88.70 191	98.22 26	97.57 111	91.42 116	90.08 190	95.55 174	82.85 156	97.92 237	94.07 70	91.58 204	95.40 230

dp		88.90 256	88.26 254	90.81 292	94.58 238	76.62 322	92.85 304	94.93 277	85.12 270	90.07 191	93.07 274	75.81 266	98.12 194	80.53 290	87.42 250	97.71 141

PS-MVSNAJss		93.74 105	93.51 95	94.44 172	93.91 272	89.28 181	97.75 54	97.56 114	92.50 84	89.94 192	96.54 127	88.65 70	98.18 189	93.83 79	90.90 215	95.86 203

v6		91.69 180	91.00 176	93.75 205	94.14 254	88.12 213	97.20 119	96.98 176	89.19 167	89.90 193	94.42 224	83.04 144	98.07 203	89.07 154	85.10 268	95.07 249

CLD-MVS		92.98 128	92.53 126	94.32 178	96.12 173	89.20 183	95.28 257	97.47 122	92.66 81	89.90 193	95.62 170	80.58 201	98.40 173	92.73 95	92.40 189	95.38 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
gg-mvs-nofinetune		87.82 275	85.61 282	94.44 172	94.46 240	89.27 182	91.21 319	84.61 347	80.88 309	89.89 195	74.98 339	71.50 292	97.53 267	85.75 218	97.21 109	96.51 179

v1neww		91.70 178	91.01 174	93.75 205	94.19 249	88.14 211	97.20 119	96.98 176	89.18 169	89.87 196	94.44 222	83.10 138	98.06 208	89.06 155	85.09 269	95.06 252

v7new		91.70 178	91.01 174	93.75 205	94.19 249	88.14 211	97.20 119	96.98 176	89.18 169	89.87 196	94.44 222	83.10 138	98.06 208	89.06 155	85.09 269	95.06 252

1112_ss		93.37 116	92.42 130	96.21 92	97.05 131	90.99 114	96.31 205	96.72 196	86.87 250	89.83 198	96.69 114	86.51 97	99.14 106	88.12 170	93.67 170	98.50 102

v7		91.47 196	90.73 189	93.68 213	94.13 255	88.16 209	97.09 128	97.05 168	88.38 207	89.80 199	94.52 215	82.21 172	98.01 220	88.00 172	85.42 262	94.87 261

BH-untuned		92.94 130	92.62 121	93.92 197	97.22 122	86.16 257	96.40 196	96.25 217	90.06 149	89.79 200	96.17 142	83.19 132	98.35 177	87.19 195	97.27 108	97.24 156

v1		91.61 186	90.89 178	93.78 202	94.01 267	88.21 205	96.96 137	96.96 180	89.17 171	89.78 201	94.29 238	82.97 150	98.05 211	88.85 161	84.99 275	95.08 247

v1141		91.61 186	90.89 178	93.78 202	94.01 267	88.24 202	96.96 137	96.96 180	89.17 171	89.75 202	94.29 238	82.99 148	98.03 216	88.85 161	85.00 274	95.07 249

divwei89l23v2f112		91.61 186	90.89 178	93.78 202	94.01 267	88.22 204	96.96 137	96.96 180	89.17 171	89.75 202	94.28 240	83.02 146	98.03 216	88.86 160	84.98 276	95.08 247

V42		91.58 190	90.87 181	93.73 208	94.05 266	88.50 195	97.32 108	96.97 179	88.80 191	89.71 204	94.33 229	82.54 163	98.05 211	89.01 157	85.07 271	94.64 277

Baseline_NR-MVSNet		91.20 207	90.62 198	92.95 242	93.83 275	88.03 219	97.01 134	95.12 268	88.42 206	89.70 205	95.13 193	83.47 129	97.44 273	89.66 141	83.24 295	93.37 299

v144192		91.06 212	90.28 207	93.39 227	93.66 280	87.23 239	96.83 150	97.07 165	87.43 231	89.69 206	94.28 240	81.48 184	98.00 223	87.18 196	84.92 277	94.93 259

v1144		91.37 201	90.60 199	93.68 213	93.89 273	88.23 203	96.84 149	97.03 173	88.37 208	89.69 206	94.39 225	82.04 175	97.98 224	87.80 177	85.37 263	94.84 263

Test_1112_low_res		92.84 136	91.84 142	95.85 103	97.04 132	89.97 140	95.53 247	96.64 204	85.38 265	89.65 208	95.18 190	85.86 105	99.10 113	87.70 179	93.58 175	98.49 104

v1192		91.07 211	90.23 211	93.58 219	93.70 278	87.82 228	96.73 162	97.07 165	87.77 224	89.58 209	94.32 230	80.90 198	97.97 227	86.52 203	85.48 260	94.95 255

v1240		90.70 226	89.85 225	93.23 234	93.51 284	86.80 248	96.61 180	97.02 174	87.16 238	89.58 209	94.31 231	79.55 218	97.98 224	85.52 221	85.44 261	94.90 260

TranMVSNet+NR-MVSNet		92.50 145	91.63 152	95.14 136	94.76 230	92.07 81	97.53 87	98.11 45	92.90 77	89.56 211	96.12 143	83.16 133	97.60 264	89.30 147	83.20 296	95.75 214

v2v482		91.59 189	90.85 183	93.80 200	93.87 274	88.17 208	96.94 143	96.88 190	89.54 158	89.53 212	94.90 198	81.70 183	98.02 219	89.25 149	85.04 273	95.20 244

v1921920		90.85 218	90.03 218	93.29 232	93.55 281	86.96 247	96.74 161	97.04 171	87.36 233	89.52 213	94.34 228	80.23 209	97.97 227	86.27 206	85.21 266	94.94 257

IterMVS-LS		92.29 157	91.94 140	93.34 230	96.25 163	86.97 246	96.57 186	97.05 168	90.67 134	89.50 214	94.80 206	86.59 95	97.64 261	89.91 135	86.11 257	95.40 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cascas		91.20 207	90.08 215	94.58 169	94.97 219	89.16 185	93.65 290	97.59 110	79.90 314	89.40 215	92.92 276	75.36 271	98.36 176	92.14 102	94.75 151	96.23 185

XVG-ACMP-BASELINE		90.93 216	90.21 213	93.09 238	94.31 246	85.89 258	95.33 254	97.26 147	91.06 127	89.38 216	95.44 182	68.61 306	98.60 152	89.46 145	91.05 213	94.79 271

GBi-Net		91.35 202	90.27 208	94.59 165	96.51 151	91.18 109	97.50 89	96.93 185	88.82 188	89.35 217	94.51 216	73.87 281	97.29 283	86.12 210	88.82 235	95.31 236

test1		91.35 202	90.27 208	94.59 165	96.51 151	91.18 109	97.50 89	96.93 185	88.82 188	89.35 217	94.51 216	73.87 281	97.29 283	86.12 210	88.82 235	95.31 236

FMVSNet3		91.78 172	90.69 191	95.03 141	96.53 150	92.27 75	97.02 132	96.93 185	89.79 157	89.35 217	94.65 212	77.01 261	97.47 271	86.12 210	88.82 235	95.35 234

WR-MVS		92.34 153	91.53 157	94.77 158	95.13 213	90.83 121	96.40 196	97.98 77	91.88 105	89.29 220	95.54 175	82.50 164	97.80 248	89.79 138	85.27 265	95.69 217

DP-MVS		92.76 138	91.51 160	96.52 70	98.77 35	90.99 114	97.38 103	96.08 224	82.38 297	89.29 220	97.87 55	83.77 126	99.69 35	81.37 280	96.69 122	98.89 80

BH-w/o		92.14 164	91.75 144	93.31 231	96.99 133	85.73 260	95.67 239	95.69 242	88.73 193	89.26 222	94.82 205	82.97 150	98.07 203	85.26 225	96.32 129	96.13 192

3Dnovator	91.36 5	95.19 68	94.44 79	97.44 39	96.56 148	93.36 51	98.65 6	98.36 16	94.12 37	89.25 223	98.06 45	82.20 173	99.77 22	93.41 88	99.32 41	99.18 52

Fast-Effi-MVS+-dtu		92.29 157	91.99 138	93.21 236	95.27 202	85.52 264	97.03 130	96.63 206	92.09 95	89.11 224	95.14 192	80.33 207	98.08 199	87.54 187	94.74 152	96.03 200

XXY-MVS		92.16 162	91.23 169	94.95 147	94.75 231	90.94 117	97.47 95	97.43 133	89.14 174	88.90 225	96.43 132	79.71 215	98.24 184	89.56 143	87.68 246	95.67 218

PCF-MVS	89.48 11	91.56 191	89.95 221	96.36 83	96.60 144	92.52 70	92.51 308	97.26 147	79.41 315	88.90 225	96.56 126	84.04 124	99.55 65	77.01 307	97.30 107	97.01 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
jajsoiax		92.42 150	91.89 141	94.03 187	93.33 291	88.50 195	97.73 58	97.53 115	92.00 103	88.85 227	96.50 129	75.62 270	98.11 195	93.88 77	91.56 205	95.48 220

mvs_tets		92.31 155	91.76 143	93.94 196	93.41 287	88.29 198	97.63 79	97.53 115	92.04 101	88.76 228	96.45 131	74.62 277	98.09 198	93.91 75	91.48 206	95.45 224

v148		90.99 214	90.38 204	92.81 246	93.83 275	85.80 259	96.78 159	96.68 201	89.45 161	88.75 229	93.93 252	82.96 152	97.82 247	87.83 176	83.25 294	94.80 269

FMVSNet2		91.31 204	90.08 215	94.99 142	96.51 151	92.21 76	97.41 97	96.95 183	88.82 188	88.62 230	94.75 208	73.87 281	97.42 275	85.20 226	88.55 241	95.35 234

tpmp4_e23		89.58 248	88.59 248	92.54 253	95.16 210	81.53 297	94.11 281	95.09 269	81.66 302	88.60 231	93.44 270	75.11 272	98.33 180	82.45 264	91.72 201	97.75 139

PAPM		91.52 194	90.30 206	95.20 129	95.30 201	89.83 145	93.38 294	96.85 192	86.26 257	88.59 232	95.80 157	84.88 115	98.15 191	75.67 310	95.93 134	97.63 143

3Dnovator+	91.43 4	95.40 60	94.48 77	98.16 6	96.90 134	95.34 6	98.48 14	97.87 85	94.65 28	88.53 233	98.02 47	83.69 127	99.71 29	93.18 91	98.96 66	99.44 32

anonymousdsp		92.16 162	91.55 156	93.97 191	92.58 308	89.55 161	97.51 88	97.42 134	89.42 162	88.40 234	94.84 202	80.66 200	97.88 242	91.87 111	91.28 210	94.48 280

WR-MVS_H		92.00 167	91.35 162	93.95 193	95.09 215	89.47 165	98.04 35	98.68 7	91.46 114	88.34 235	94.68 210	85.86 105	97.56 265	85.77 217	84.24 283	94.82 267

v8		91.29 205	90.53 201	93.57 220	94.15 253	88.12 213	97.34 105	97.06 167	88.99 178	88.32 236	94.26 244	83.08 140	98.01 220	87.62 185	83.92 288	94.57 278

ACMP	89.59 10	92.62 140	92.14 133	94.05 186	96.40 158	88.20 206	97.36 104	97.25 149	91.52 111	88.30 237	96.64 117	78.46 242	98.72 146	91.86 112	91.48 206	95.23 243
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		91.04 213	90.23 211	93.49 222	94.12 257	88.16 209	97.32 108	97.08 164	88.26 211	88.29 238	94.22 245	82.17 174	97.97 227	86.45 205	84.12 284	94.33 285

QAPM		93.45 114	92.27 132	96.98 59	96.77 140	92.62 67	98.39 18	98.12 42	84.50 279	88.27 239	97.77 64	82.39 169	99.81 19	85.40 223	98.81 69	98.51 100

CP-MVSNet		91.89 170	91.24 168	93.82 199	95.05 216	88.57 193	97.82 50	98.19 33	91.70 108	88.21 240	95.76 162	81.96 177	97.52 268	87.86 175	84.65 279	95.37 233

tpmvs		89.83 246	89.15 242	91.89 272	94.92 223	80.30 308	93.11 300	95.46 250	86.28 256	88.08 241	92.65 279	80.44 204	98.52 159	81.47 275	89.92 228	96.84 170

PS-CasMVS		91.55 192	90.84 185	93.69 212	94.96 220	88.28 199	97.84 49	98.24 28	91.46 114	88.04 242	95.80 157	79.67 216	97.48 270	87.02 198	84.54 281	95.31 236

MIMVSNet		88.50 266	86.76 274	93.72 210	94.84 227	87.77 229	91.39 315	94.05 304	86.41 255	87.99 243	92.59 281	63.27 322	95.82 313	77.44 303	92.84 185	97.57 150

GG-mvs-BLEND				93.62 215	93.69 279	89.20 183	92.39 311	83.33 348		87.98 244	89.84 304	71.00 296	96.87 293	82.08 268	95.40 141	94.80 269

PEN-MVS		91.20 207	90.44 202	93.48 223	94.49 239	87.91 227	97.76 53	98.18 35	91.29 119	87.78 245	95.74 164	80.35 206	97.33 281	85.46 222	82.96 297	95.19 245

ITE_SJBPF				92.43 255	95.34 197	85.37 266		95.92 229	91.47 113	87.75 246	96.39 134	71.00 296	97.96 231	82.36 266	89.86 229	93.97 291

v52		90.70 226	90.00 219	92.82 243	93.24 293	87.03 243	97.60 81	97.14 156	88.21 212	87.69 247	93.94 251	80.91 195	98.07 203	87.39 189	83.87 290	93.36 300

V4		90.71 225	90.00 219	92.82 243	93.21 296	87.03 243	97.59 83	97.16 155	88.21 212	87.69 247	93.92 253	80.93 194	98.06 208	87.39 189	83.90 289	93.39 298

v7n		90.76 220	89.86 224	93.45 226	93.54 282	87.60 233	97.70 64	97.37 139	88.85 185	87.65 249	94.08 248	81.08 189	98.10 196	84.68 232	83.79 291	94.66 276

Patchmtry		88.64 262	87.25 266	92.78 247	94.09 261	86.64 250	89.82 328	95.68 243	80.81 311	87.63 250	92.36 288	80.91 195	97.03 289	78.86 299	85.12 267	94.67 275

pmmvs4		90.93 216	89.85 225	94.17 181	93.34 289	90.79 123	94.60 268	96.02 225	84.62 277	87.45 251	95.15 191	81.88 180	97.45 272	87.70 179	87.87 245	94.27 288

tpm cat1		88.36 271	87.21 270	91.81 275	95.13 213	80.55 305	92.58 307	95.70 241	74.97 330	87.45 251	91.96 293	78.01 256	98.17 190	80.39 291	88.74 238	96.72 173

FMVSNet1		89.88 244	88.31 252	94.59 165	95.41 193	91.18 109	97.50 89	96.93 185	86.62 253	87.41 253	94.51 216	65.94 318	97.29 283	83.04 256	87.43 249	95.31 236

semantic-postprocess				91.82 274	95.52 189	84.20 278		96.15 222	90.61 140	87.39 254	94.27 242	75.63 269	96.44 296	87.34 191	86.88 254	94.82 267

MVS		91.71 175	90.44 202	95.51 118	95.20 209	91.59 95	96.04 222	97.45 128	73.44 334	87.36 255	95.60 171	85.42 109	99.10 113	85.97 214	97.46 99	95.83 207

EU-MVSNet		88.72 258	88.90 244	88.20 308	93.15 299	74.21 326	96.63 179	94.22 302	85.18 268	87.32 256	95.97 147	76.16 265	94.98 322	85.27 224	86.17 255	95.41 226

IterMVS		90.15 239	89.67 232	91.61 281	95.48 191	83.72 281	94.33 275	96.12 223	89.99 150	87.31 257	94.15 246	75.78 268	96.27 299	86.97 199	86.89 253	94.83 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v748		90.34 233	89.54 235	92.75 248	93.25 292	85.71 261	97.61 80	97.17 152	88.54 198	87.20 258	93.54 264	81.02 190	98.01 220	85.73 219	81.80 301	94.52 279

pmmvs5		89.86 245	88.87 245	92.82 243	92.86 302	86.23 256	96.26 209	95.39 252	84.24 280	87.12 259	94.51 216	74.27 279	97.36 280	87.61 186	87.57 247	94.86 262

DTE-MVSNet		90.56 229	89.75 230	93.01 240	93.95 270	87.25 237	97.64 72	97.65 105	90.74 131	87.12 259	95.68 168	79.97 212	97.00 292	83.33 253	81.66 304	94.78 272

Patchmatch-test		89.42 251	87.99 255	93.70 211	95.27 202	85.11 267	88.98 331	94.37 296	81.11 307	87.10 261	93.69 258	82.28 170	97.50 269	74.37 313	94.76 150	98.48 106

IB-MVS	87.33 17	89.91 242	88.28 253	94.79 157	95.26 205	87.70 231	95.12 263	93.95 307	89.35 163	87.03 262	92.49 283	70.74 298	99.19 99	89.18 152	81.37 305	97.49 152
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
EPNet_dtu		91.71 175	91.28 166	92.99 241	93.76 277	83.71 282	96.69 172	95.28 259	93.15 64	87.02 263	95.95 149	83.37 131	97.38 279	79.46 296	96.84 115	97.88 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		91.42 198	90.24 210	94.96 146	97.15 126	88.91 188	93.69 288	96.32 213	85.72 263	86.93 264	96.47 130	80.24 208	98.98 125	80.57 289	95.05 146	96.98 159

test0.0.03 1		89.37 252	88.70 246	91.41 285	92.47 309	85.63 262	95.22 261	92.70 327	91.11 125	86.91 265	93.65 261	79.02 226	93.19 330	78.00 302	89.18 233	95.41 226

COLMAP_ROB	87.81 15	90.40 232	89.28 239	93.79 201	97.95 88	87.13 242	96.92 144	95.89 236	82.83 294	86.88 266	97.18 95	73.77 284	99.29 94	78.44 301	93.62 172	94.95 255
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		90.51 231	90.19 214	91.44 284	93.41 287	81.25 299	96.98 136	96.28 214	91.68 109	86.55 267	96.30 136	74.20 280	97.98 224	88.96 158	87.40 251	95.09 246

MS-PatchMatch		90.27 234	89.77 228	91.78 277	94.33 245	84.72 274	95.55 245	96.73 195	86.17 259	86.36 268	95.28 188	71.28 294	97.80 248	84.09 242	98.14 85	92.81 305

1314		92.81 137	92.03 136	95.14 136	95.33 200	89.52 164	96.04 222	97.44 131	87.72 226	86.25 269	95.33 185	83.84 125	98.79 138	89.26 148	97.05 112	97.11 157

tfpnnormal		89.70 247	88.40 251	93.60 216	95.15 211	90.10 133	97.56 85	98.16 37	87.28 236	86.16 270	94.63 213	77.57 259	98.05 211	74.48 311	84.59 280	92.65 306

pm-mvs1		90.72 224	89.65 234	93.96 192	94.29 247	89.63 156	97.79 52	96.82 193	89.07 175	86.12 271	95.48 181	78.61 240	97.78 250	86.97 199	81.67 303	94.46 281

OpenMVS	89.19 12	92.86 134	91.68 147	96.40 79	95.34 197	92.73 64	98.27 23	98.12 42	84.86 274	85.78 272	97.75 65	78.89 238	99.74 24	87.50 188	98.65 73	96.73 172

LTVRE_ROB	88.41 13	90.99 214	89.92 222	94.19 180	96.18 167	89.55 161	96.31 205	97.09 162	87.88 222	85.67 273	95.91 151	78.79 239	98.57 155	81.50 274	89.98 226	94.44 282
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
testgi		87.97 273	87.21 270	90.24 301	92.86 302	80.76 301	96.67 174	94.97 275	91.74 107	85.52 274	95.83 155	62.66 324	94.47 324	76.25 308	88.36 242	95.48 220

AllTest		90.23 236	88.98 243	93.98 189	97.94 89	86.64 250	96.51 187	95.54 248	85.38 265	85.49 275	96.77 108	70.28 300	99.15 104	80.02 292	92.87 183	96.15 190

TestCases				93.98 189	97.94 89	86.64 250		95.54 248	85.38 265	85.49 275	96.77 108	70.28 300	99.15 104	80.02 292	92.87 183	96.15 190

DSMNet-mixed		86.34 286	86.12 280	87.00 313	89.88 322	70.43 331	94.93 265	90.08 338	77.97 323	85.42 277	92.78 278	74.44 278	93.96 326	74.43 312	95.14 144	96.62 177

MVP-Stereo		90.74 223	90.08 215	92.71 249	93.19 298	88.20 206	95.86 231	96.27 215	86.07 260	84.86 278	94.76 207	77.84 257	97.75 253	83.88 248	98.01 87	92.17 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	87.92 14	90.20 237	89.18 241	93.25 233	96.48 154	86.45 254	96.99 135	96.68 201	88.83 187	84.79 279	96.22 139	70.16 302	98.53 158	84.42 238	88.04 243	94.77 273

NR-MVSNet		92.34 153	91.27 167	95.53 117	94.95 221	93.05 56	97.39 101	98.07 55	92.65 82	84.46 280	95.71 165	85.00 114	97.77 252	89.71 139	83.52 293	95.78 210

LF4IMVS		87.94 274	87.25 266	89.98 303	92.38 310	80.05 312	94.38 273	95.25 262	87.59 229	84.34 281	94.74 209	64.31 321	97.66 260	84.83 228	87.45 248	92.23 321

LCM-MVSNet-Re		92.50 145	92.52 127	92.44 254	96.82 139	81.89 295	96.92 144	93.71 309	92.41 86	84.30 282	94.60 214	85.08 113	97.03 289	91.51 120	97.36 105	98.40 114

TransMVSNet (Re)		88.94 254	87.56 257	93.08 239	94.35 244	88.45 197	97.73 58	95.23 263	87.47 230	84.26 283	95.29 186	79.86 213	97.33 281	79.44 297	74.44 331	93.45 297

Anonymous20231206		87.09 281	86.14 279	89.93 304	91.22 316	80.35 306	96.11 218	95.35 255	83.57 289	84.16 284	93.02 275	73.54 286	95.61 315	72.16 319	86.14 256	93.84 293

SixPastTwentyTwo		89.15 253	88.54 250	90.98 288	93.49 285	80.28 309	96.70 170	94.70 283	90.78 130	84.15 285	95.57 172	71.78 291	97.71 256	84.63 233	85.07 271	94.94 257

TDRefinement		86.53 284	84.76 289	91.85 273	82.23 340	84.25 276	96.38 198	95.35 255	84.97 273	84.09 286	94.94 195	65.76 319	98.34 179	84.60 236	74.52 329	92.97 301

pmmvs6		87.81 276	86.19 278	92.69 250	91.32 315	86.30 255	97.34 105	96.41 210	80.59 313	84.05 287	94.37 227	67.37 313	97.67 258	84.75 230	79.51 311	94.09 290

ACMH	87.59 16	90.53 230	89.42 237	93.87 198	96.21 164	87.92 225	97.24 113	96.94 184	88.45 199	83.91 288	96.27 138	71.92 289	98.62 151	84.43 237	89.43 231	95.05 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet5		87.29 280	85.79 281	91.78 277	94.80 229	87.28 235	95.49 249	95.28 259	84.09 282	83.85 289	91.82 294	62.95 323	94.17 325	78.48 300	85.34 264	93.91 292

USDC		88.94 254	87.83 256	92.27 256	94.66 233	84.96 270	93.86 285	95.90 231	87.34 234	83.40 290	95.56 173	67.43 312	98.19 188	82.64 263	89.67 230	93.66 294

PVSNet_0	82.17 19	85.46 293	83.64 294	90.92 290	95.27 202	79.49 314	90.55 323	95.60 245	83.76 287	83.00 291	89.95 302	71.09 295	97.97 227	82.75 261	60.79 341	95.31 236

test_0402		86.46 285	84.79 288	91.45 283	95.02 218	85.55 263	96.29 207	94.89 278	80.90 308	82.21 292	93.97 250	68.21 309	97.29 283	62.98 332	88.68 240	91.51 327

Patchmatch-RL test		87.38 278	86.24 277	90.81 292	88.74 326	78.40 320	88.12 334	93.17 315	87.11 239	82.17 293	89.29 315	81.95 178	95.60 316	88.64 166	77.02 315	98.41 113

testus		82.63 302	82.15 298	84.07 318	87.31 331	67.67 337	93.18 295	94.29 300	82.47 296	82.14 294	90.69 300	53.01 338	91.94 334	66.30 329	89.96 227	92.62 307

LP		84.13 297	81.85 302	90.97 289	93.20 297	82.12 294	87.68 335	94.27 301	76.80 325	81.93 295	88.52 319	72.97 288	95.95 310	59.53 337	81.73 302	94.84 263

test2356		82.77 301	82.14 299	84.65 317	85.77 334	70.36 332	91.22 318	93.69 312	81.58 304	81.82 296	89.00 317	60.63 328	90.77 337	64.74 330	90.80 217	92.82 303

DI_MVS_plusplus_test		92.01 165	90.77 186	95.73 110	93.34 289	89.78 147	96.14 217	96.18 221	90.58 142	81.80 297	93.50 266	74.95 275	98.90 129	93.51 83	96.94 114	98.51 100

test_normal		92.01 165	90.75 188	95.80 105	93.24 293	89.97 140	95.93 229	96.24 218	90.62 138	81.63 298	93.45 269	74.98 274	98.89 131	93.61 81	97.04 113	98.55 95

TinyColmap		86.82 283	85.35 285	91.21 286	94.91 225	82.99 288	93.94 284	94.02 306	83.58 288	81.56 299	94.68 210	62.34 325	98.13 192	75.78 309	87.35 252	92.52 309

test20.03		86.14 288	85.40 284	88.35 306	90.12 319	80.06 311	95.90 230	95.20 264	88.59 194	81.29 300	93.62 262	71.43 293	92.65 331	71.26 323	81.17 306	92.34 320

N_pmnet		78.73 308	78.71 307	78.79 326	92.80 304	46.50 353	94.14 280	43.71 356	78.61 320	80.83 301	91.66 298	74.94 276	96.36 297	67.24 327	84.45 282	93.50 295

testpf		80.97 305	81.40 303	79.65 324	91.53 314	72.43 330	73.47 346	89.55 339	78.63 319	80.81 302	89.06 316	61.36 326	91.36 336	83.34 252	84.89 278	75.15 343

MVS-HIRNet		82.47 303	81.21 304	86.26 316	95.38 195	69.21 336	88.96 332	89.49 340	66.28 338	80.79 303	74.08 341	68.48 307	97.39 278	71.93 320	95.47 140	92.18 322

v18		88.71 259	87.52 258	92.27 256	94.16 252	88.11 215	96.82 153	95.96 226	87.03 240	80.76 304	89.81 305	83.15 134	96.22 300	84.69 231	75.31 322	92.49 310

v17		88.67 261	87.47 261	92.26 258	94.13 255	88.09 217	96.81 154	95.95 227	87.02 241	80.72 305	89.75 307	83.11 137	96.20 301	84.61 234	75.15 324	92.49 310

v16		88.69 260	87.50 259	92.26 258	94.19 249	88.11 215	96.81 154	95.95 227	87.01 242	80.71 306	89.80 306	83.08 140	96.20 301	84.61 234	75.34 321	92.48 312

PM-MVS		83.48 298	81.86 301	88.31 307	87.83 329	77.59 321	93.43 292	91.75 332	86.91 247	80.63 307	89.91 303	44.42 343	95.84 312	85.17 227	76.73 317	91.50 328

ambc				86.56 315	83.60 337	70.00 335	85.69 338	94.97 275		80.60 308	88.45 320	37.42 345	96.84 294	82.69 262	75.44 320	92.86 302

v15		88.53 263	87.31 263	92.20 261	94.09 261	88.05 218	96.72 165	95.90 231	87.01 242	80.53 309	89.60 311	83.02 146	96.13 303	84.29 239	74.64 325	92.41 316

v11		88.41 270	87.19 272	92.08 268	94.08 263	87.77 229	96.75 160	95.85 237	86.74 252	80.50 310	89.50 314	82.49 165	96.08 308	83.55 250	75.20 323	92.38 319

V14		88.52 264	87.30 264	92.17 263	94.12 257	87.99 220	96.72 165	95.91 230	86.98 244	80.50 310	89.63 308	83.03 145	96.12 305	84.23 240	74.60 327	92.40 317

V9		88.49 267	87.26 265	92.18 262	94.12 257	87.97 223	96.73 162	95.90 231	86.95 246	80.40 312	89.61 309	82.98 149	96.13 303	84.14 241	74.55 328	92.44 314

MIMVSNet1		84.93 295	83.05 295	90.56 297	89.56 324	84.84 273	95.40 252	95.35 255	83.91 283	80.38 313	92.21 292	57.23 331	93.34 329	70.69 325	82.75 300	93.50 295

v12		88.46 268	87.23 268	92.17 263	94.10 260	87.99 220	96.71 167	95.90 231	86.91 247	80.34 314	89.58 312	82.92 153	96.11 307	84.09 242	74.50 330	92.42 315

lessismore_v0				90.45 298	91.96 313	79.09 318		87.19 344		80.32 315	94.39 225	66.31 316	97.55 266	84.00 246	76.84 316	94.70 274

v13		88.45 269	87.22 269	92.16 265	94.08 263	87.95 224	96.71 167	95.90 231	86.86 251	80.27 316	89.55 313	82.92 153	96.12 305	84.02 244	74.63 326	92.40 317

K. test v3		87.64 277	86.75 275	90.32 300	93.02 301	79.48 315	96.61 180	92.08 330	90.66 136	80.25 317	94.09 247	67.21 314	96.65 295	85.96 215	80.83 308	94.83 265

OpenMVS_ROB	81.14 20	84.42 296	82.28 297	90.83 291	90.06 320	84.05 280	95.73 238	94.04 305	73.89 333	80.17 318	91.53 299	59.15 329	97.64 261	66.92 328	89.05 234	90.80 330

EG-PatchMatch MVS		87.02 282	85.44 283	91.76 279	92.67 306	85.00 269	96.08 221	96.45 209	83.41 291	79.52 319	93.49 267	57.10 332	97.72 255	79.34 298	90.87 216	92.56 308

pmmvs-eth3d		86.22 287	84.45 290	91.53 282	88.34 327	87.25 237	94.47 272	95.01 272	83.47 290	79.51 320	89.61 309	69.75 303	95.71 314	83.13 255	76.73 317	91.64 325

pmmvs3		79.97 306	77.50 310	87.39 311	82.80 338	79.38 316	92.70 306	90.75 336	70.69 336	78.66 321	87.47 330	51.34 340	93.40 328	73.39 317	69.65 336	89.38 333

UnsupCasMVSNet_eth		85.99 289	84.45 290	90.62 296	89.97 321	82.40 292	93.62 291	97.37 139	89.86 152	78.59 322	92.37 285	65.25 320	95.35 320	82.27 267	70.75 334	94.10 289

Test4		89.48 249	87.50 259	95.44 125	90.76 318	89.72 148	95.78 237	97.09 162	90.28 145	77.67 323	91.74 297	55.42 336	98.08 199	91.92 108	96.83 116	98.52 98

new-patchmatchnet		83.18 299	81.87 300	87.11 312	86.88 332	75.99 324	93.70 287	95.18 265	85.02 272	77.30 324	88.40 321	65.99 317	93.88 327	74.19 315	70.18 335	91.47 329

UnsupCasMVSNet_bld		82.13 304	79.46 306	90.14 302	88.00 328	82.47 290	90.89 322	96.62 207	78.94 318	75.61 325	84.40 334	56.63 333	96.31 298	77.30 306	66.77 340	91.63 326

new_pmnet		82.89 300	81.12 305	88.18 309	89.63 323	80.18 310	91.77 314	92.57 328	76.79 326	75.56 326	88.23 323	61.22 327	94.48 323	71.43 321	82.92 298	89.87 332

Anonymous20231211		78.22 310	75.30 311	86.99 314	86.14 333	74.16 327	95.62 243	93.88 308	66.43 337	74.44 327	87.86 326	41.39 344	95.11 321	62.49 333	69.46 337	91.71 324

CMPMVS	62.92 21	85.62 292	84.92 287	87.74 310	89.14 325	73.12 329	94.17 279	96.80 194	73.98 332	73.65 328	94.93 196	66.36 315	97.61 263	83.95 247	91.28 210	92.48 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		78.29 309	77.55 309	80.50 322	83.89 335	59.98 345	91.89 312	93.71 309	75.06 328	73.60 329	87.67 327	55.66 334	92.60 332	58.54 339	77.92 313	88.93 334

.test1245		65.38 318	69.22 316	53.86 338	83.89 335	59.98 345	91.89 312	93.71 309	75.06 328	73.60 329	87.67 327	55.66 334	92.60 332	58.54 339	2.96 352	9.00 352

test1235678		79.82 307	78.53 308	83.69 319	82.55 339	67.55 338	92.50 309	94.13 303	79.28 316	72.10 331	86.45 332	57.27 330	90.68 338	61.60 335	80.90 307	92.82 303

testing_2		87.33 279	85.03 286	94.22 179	87.77 330	89.32 178	94.97 264	97.11 161	89.22 166	71.64 332	88.73 318	55.16 337	97.94 233	91.95 107	88.73 239	95.41 226

YYNet1		85.87 290	84.23 292	90.78 295	92.38 310	82.46 291	93.17 297	95.14 267	82.12 299	67.69 333	92.36 288	78.16 248	95.50 319	77.31 305	79.73 310	94.39 283

MDA-MVSNet_test_wron		85.87 290	84.23 292	90.80 294	92.38 310	82.57 289	93.17 297	95.15 266	82.15 298	67.65 334	92.33 291	78.20 245	95.51 318	77.33 304	79.74 309	94.31 287

test12356		74.97 311	74.13 312	77.49 327	78.81 341	56.23 349	88.53 333	92.75 326	75.14 327	67.50 335	85.07 333	44.88 342	89.96 339	58.71 338	75.75 319	86.26 335

DeepMVS_CX				74.68 331	90.84 317	64.34 342		81.61 351	65.34 339	67.47 336	88.01 325	48.60 341	80.13 348	62.33 334	73.68 333	79.58 341

LCM-MVSNet		72.55 312	69.39 315	82.03 320	70.81 350	65.42 341	90.12 327	94.36 297	55.02 342	65.88 337	81.72 335	24.16 354	89.96 339	74.32 314	68.10 338	90.71 331

MDA-MVSNet-bldmvs		85.00 294	82.95 296	91.17 287	93.13 300	83.33 286	94.56 270	95.00 273	84.57 278	65.13 338	92.65 279	70.45 299	95.85 311	73.57 316	77.49 314	94.33 285

PMMVS2		70.19 315	66.92 317	80.01 323	76.35 342	65.67 340	86.22 337	87.58 343	64.83 340	62.38 339	80.29 338	26.78 352	88.49 343	63.79 331	54.07 342	85.88 337

testmv		72.22 313	70.02 313	78.82 325	73.06 348	61.75 343	91.24 317	92.31 329	74.45 331	61.06 340	80.51 337	34.21 346	88.63 342	55.31 342	68.07 339	86.06 336

FPMVS		71.27 314	69.85 314	75.50 329	74.64 343	59.03 347	91.30 316	91.50 333	58.80 341	57.92 341	88.28 322	29.98 350	85.53 345	53.43 343	82.84 299	81.95 339

Gipuma		67.86 317	65.41 318	75.18 330	92.66 307	73.45 328	66.50 348	94.52 292	53.33 343	57.80 342	66.07 345	30.81 347	89.20 341	48.15 346	78.88 312	62.90 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		68.12 316	63.78 319	81.13 321	74.01 345	70.22 334	87.61 336	90.71 337	72.63 335	53.13 343	71.89 342	30.29 348	91.45 335	61.53 336	32.21 346	81.72 340

tmp_tt		51.94 326	53.82 323	46.29 339	33.73 355	45.30 355	78.32 345	67.24 355	18.02 350	50.93 344	87.05 331	52.99 339	53.11 353	70.76 324	25.29 350	40.46 350

ANet_high		63.94 319	59.58 320	77.02 328	61.24 353	66.06 339	85.66 339	87.93 342	78.53 321	42.94 345	71.04 343	25.42 353	80.71 347	52.60 344	30.83 348	84.28 338

E-PMN		53.28 323	52.56 324	55.43 336	74.43 344	47.13 352	83.63 341	76.30 352	42.23 347	42.59 346	62.22 347	28.57 351	74.40 350	31.53 349	31.51 347	44.78 348

PNet_i23d		59.01 320	55.87 321	68.44 333	73.98 346	51.37 350	81.36 342	82.41 349	52.37 344	42.49 347	70.39 344	11.39 355	79.99 349	49.77 345	38.71 344	73.97 344

EMVS		52.08 325	51.31 325	54.39 337	72.62 349	45.39 354	83.84 340	75.51 353	41.13 348	40.77 348	59.65 348	30.08 349	73.60 351	28.31 350	29.90 349	44.18 349

MVE	50.73 23	53.25 324	48.81 327	66.58 335	65.34 351	57.50 348	72.49 347	70.94 354	40.15 349	39.28 349	63.51 346	6.89 359	73.48 352	38.29 348	42.38 343	68.76 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	53.92 22	58.58 321	55.40 322	68.12 334	51.00 354	48.64 351	78.86 344	87.10 345	46.77 346	35.84 350	74.28 340	8.76 356	86.34 344	42.07 347	73.91 332	69.38 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		56.92 322	51.11 326	74.38 332	62.30 352	61.47 344	80.09 343	84.87 346	49.62 345	30.80 351	57.20 349	7.03 357	82.94 346	55.69 341	32.36 345	78.72 342

wuyk23d		25.11 328	24.57 330	26.74 341	73.98 346	39.89 356	57.88 349	9.80 357	12.27 351	10.39 352	6.97 355	7.03 357	36.44 354	25.43 351	17.39 351	3.89 354

testmvs		13.36 330	16.33 331	4.48 343	5.04 356	2.26 358	93.18 295	3.28 358	2.70 352	8.24 353	21.66 351	2.29 361	2.19 355	7.58 352	2.96 352	9.00 352

test123		13.04 331	15.66 332	5.18 342	4.51 357	3.45 357	92.50 309	1.81 359	2.50 353	7.58 354	20.15 352	3.67 360	2.18 356	7.13 353	1.07 354	9.90 351

cdsmvs_eth3d_5k		23.24 329	30.99 329	0.00 344	0.00 358	0.00 359	0.00 350	97.63 107	0.00 354	0.00 355	96.88 105	84.38 122	0.00 357	0.00 354	0.00 355	0.00 355

pcd_1.5k_mvsjas		7.39 333	9.85 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	88.65 70	0.00 357	0.00 354	0.00 355	0.00 355

pcd1.5k->3k		38.37 327	40.51 328	31.96 340	94.29 247	0.00 359	0.00 350	97.69 101	0.00 354	0.00 355	0.00 356	81.45 185	0.00 357	0.00 354	91.11 212	95.89 202

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		8.06 332	10.74 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	96.69 114	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																98.45 109

test_part3							97.50 89		93.81 45		98.53 11		99.87 5	95.19 47

test_part1								98.26 25				95.31 1			99.63 4	99.63 5

sam_mvs1												82.76 158				98.45 109

sam_mvs												81.94 179

MTGPA								98.08 50

test_post1							92.81 305				16.58 354	80.53 202	97.68 257	86.20 208

test_post											17.58 353	81.76 181	98.08 199

patchmatchnet-post											90.45 301	82.65 162	98.10 196

MTMP								82.03 350

gm-plane-assit					93.22 295	78.89 319			84.82 275		93.52 265		98.64 148	87.72 178

test9_res														94.81 62	99.38 35	99.45 30

agg_prior2														93.94 74	99.38 35	99.50 24

test_prior4						93.66 41	96.42 191

test_prior				97.23 49	98.67 40	92.99 57		98.00 71					99.41 85			99.29 45

新几何2							95.79 235

旧先验1					98.38 60	93.38 49		97.75 92			98.09 43	92.30 27			99.01 64	99.16 53

无先验							95.79 235	97.87 85	83.87 286				99.65 41	87.68 181		98.89 80

原ACMM2							95.67 239

testdata2													99.67 39	85.96 215

segment_acmp												92.89 12

testdata1							95.26 260		93.10 67

plane_prior7					96.21 164	89.98 139

plane_prior6					96.10 174	90.00 135						81.32 187

plane_prior5								97.51 117					98.60 152	93.02 92	92.23 191	95.86 203

plane_prior4											96.64 117

plane_prior2							97.74 56		94.85 17

plane_prior1					96.14 172

plane_prior						89.99 137	97.24 113		94.06 38						92.16 195

n2								0.00 360

nn								0.00 360

door-mid								91.06 335

test11								97.88 83

door								91.13 334

HQP5-MVS						89.33 176

BP-MVS														92.13 103

HQP3-MVS								97.39 136							92.10 196

HQP2-MVS												80.95 192

NP-MVS					95.99 177	89.81 146					95.87 152

ACMMP++_ref															90.30 224

ACMMP++															91.02 214

Test By Simon												88.73 69

High-resolution multi-view benchmark