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.

SMA-MVS		95.20 5	95.10 7	95.51 3	98.14 25	88.26 4	96.26 28	97.31 17	86.04 116	97.82 1	98.10 1	88.43 11	99.56 3	94.66 4	99.13 1	98.71 4

SD-MVS		94.96 8	95.33 5	93.88 50	97.25 53	86.69 22	96.19 30	97.11 29	90.42 24	96.95 2	97.27 14	89.53 5	96.91 218	94.38 6	98.85 9	98.03 50

test_part2					98.55 5	87.22 11				96.40 3

ESAPD		95.32 3	95.38 3	95.17 7	98.55 5	87.22 11	95.99 36	97.45 6	88.25 66	96.40 3	97.60 5	91.93 1	99.62 1	93.18 19	99.02 3	98.67 5

APDe-MVS		95.46 1	95.64 1	94.91 13	98.26 20	86.29 39	97.46 2	97.40 9	89.03 47	96.20 5	98.10 1	89.39 7	99.34 24	95.88 1	99.03 2	99.10 1

HSP-MVS		95.30 4	95.48 2	94.76 25	98.49 10	86.52 29	96.91 15	96.73 55	91.73 9	96.10 6	96.69 39	89.90 3	99.30 30	94.70 3	98.04 50	98.45 19

TSAR-MVS + MP.		94.85 9	94.94 8	94.58 32	98.25 21	86.33 35	96.11 32	96.62 66	88.14 70	96.10 6	96.96 29	89.09 9	98.94 66	94.48 5	98.68 25	98.48 14

DeepPCF-MVS	89.96 1	94.20 26	94.77 10	92.49 89	96.52 67	80.00 173	94.00 169	97.08 30	90.05 26	95.65 8	97.29 13	89.66 4	98.97 62	93.95 9	98.71 20	98.50 12

SteuartSystems-ACMMP		95.20 5	95.32 6	94.85 17	96.99 56	86.33 35	97.33 3	97.30 18	91.38 12	95.39 9	97.46 10	88.98 10	99.40 22	94.12 8	98.89 8	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		95.40 2	95.37 4	95.50 4	98.11 26	88.51 3	95.29 65	96.96 38	92.09 3	95.32 10	97.08 26	89.49 6	99.33 27	95.10 2	98.85 9	98.66 7

ACMMP_Plus		94.74 11	94.56 12	95.28 5	98.02 31	87.70 5	95.68 50	97.34 11	88.28 65	95.30 11	97.67 4	85.90 34	99.54 11	93.91 10	98.95 5	98.60 9

APD-MVS		94.24 23	94.07 25	94.75 26	98.06 29	86.90 16	95.88 42	96.94 40	85.68 122	95.05 12	97.18 22	87.31 20	99.07 45	91.90 46	98.61 34	98.28 29
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-2		94.33 20	94.22 18	94.68 28	95.54 102	86.75 21	94.57 118	96.70 59	91.84 6	94.41 13	96.56 48	87.19 21	99.13 41	93.50 12	97.65 58	98.16 39

旧先验2							93.36 199		71.25 318	94.37 14			97.13 201	86.74 101

Regformer-1		94.22 24	94.13 23	94.51 35	95.54 102	86.36 34	94.57 118	96.44 73	91.69 10	94.32 15	96.56 48	87.05 23	99.03 51	93.35 17	97.65 58	98.15 40

TSAR-MVS + GP.		93.66 36	93.41 37	94.41 39	96.59 64	86.78 19	94.40 129	93.93 219	89.77 32	94.21 16	95.59 82	87.35 19	98.61 86	92.72 24	96.15 81	97.83 62

alignmvs		93.08 51	92.50 54	94.81 22	95.62 101	87.61 7	95.99 36	96.07 97	89.77 32	94.12 17	94.87 97	80.56 81	98.66 81	92.42 28	93.10 130	98.15 40

canonicalmvs		93.27 45	92.75 50	94.85 17	95.70 98	87.66 6	96.33 25	96.41 76	90.00 28	94.09 18	94.60 107	82.33 63	98.62 85	92.40 29	92.86 135	98.27 32

VNet		92.24 58	91.91 57	93.24 62	96.59 64	83.43 92	94.84 98	96.44 73	89.19 43	94.08 19	95.90 72	77.85 113	98.17 106	88.90 73	93.38 124	98.13 42

HPM-MVS++		95.14 7	94.91 9	95.83 1	98.25 21	89.65 1	95.92 41	96.96 38	91.75 8	94.02 20	96.83 33	88.12 12	99.55 8	93.41 16	98.94 6	98.28 29

NCCC		94.81 10	94.69 11	95.17 7	97.83 33	87.46 10	95.66 52	96.93 41	92.34 2	93.94 21	96.58 46	87.74 15	99.44 21	92.83 23	98.40 40	98.62 8

APD-MVS_3200maxsize		93.78 33	93.77 32	93.80 55	97.92 32	84.19 76	96.30 26	96.87 46	86.96 97	93.92 22	97.47 9	83.88 54	98.96 65	92.71 25	97.87 53	98.26 34

Regformer-4		93.91 31	93.81 29	94.19 45	95.36 108	85.47 52	94.68 110	96.41 76	91.60 11	93.75 23	96.71 37	85.95 33	99.10 44	93.21 18	96.65 73	98.01 52

HFP-MVS		94.52 12	94.40 13	94.86 15	98.61 3	86.81 17	96.94 10	97.34 11	88.63 56	93.65 24	97.21 19	86.10 30	99.49 17	92.35 30	98.77 15	98.30 27

#test#		94.32 21	94.14 22	94.86 15	98.61 3	86.81 17	96.43 23	97.34 11	87.51 84	93.65 24	97.21 19	86.10 30	99.49 17	91.68 48	98.77 15	98.30 27

testdata				90.49 162	96.40 68	77.89 244		95.37 155	72.51 310	93.63 26	96.69 39	82.08 69	97.65 145	83.08 138	97.39 61	95.94 119

Regformer-3		93.68 35	93.64 35	93.81 54	95.36 108	84.61 61	94.68 110	95.83 115	91.27 13	93.60 27	96.71 37	85.75 35	98.86 71	92.87 22	96.65 73	97.96 53

region2R		94.43 16	94.27 17	94.92 12	98.65 1	86.67 24	96.92 14	97.23 22	88.60 58	93.58 28	97.27 14	85.22 40	99.54 11	92.21 32	98.74 19	98.56 11

MSLP-MVS++		93.72 34	94.08 24	92.65 83	97.31 47	83.43 92	95.79 45	97.33 14	90.03 27	93.58 28	96.96 29	84.87 46	97.76 140	92.19 34	98.66 29	96.76 95

PHI-MVS		93.89 32	93.65 34	94.62 31	96.84 59	86.43 32	96.69 21	97.49 4	85.15 133	93.56 30	96.28 56	85.60 36	99.31 29	92.45 26	98.79 12	98.12 43

ACMMPR		94.43 16	94.28 16	94.91 13	98.63 2	86.69 22	96.94 10	97.32 16	88.63 56	93.53 31	97.26 16	85.04 43	99.54 11	92.35 30	98.78 14	98.50 12

PGM-MVS		93.96 30	93.72 33	94.68 28	98.43 13	86.22 40	95.30 63	97.78 1	87.45 85	93.26 32	97.33 12	84.62 48	99.51 15	90.75 61	98.57 35	98.32 26

UA-Net		92.83 53	92.54 53	93.68 57	96.10 84	84.71 60	95.66 52	96.39 78	91.92 4	93.22 33	96.49 50	83.16 56	98.87 68	84.47 122	95.47 89	97.45 74

abl_6		93.18 50	93.05 43	93.57 59	97.52 39	84.27 75	95.53 58	96.67 62	87.85 76	93.20 34	97.22 18	80.35 82	99.18 35	91.91 43	97.21 63	97.26 76

zzz-MVS		94.47 13	94.30 15	95.00 10	98.42 14	86.95 13	95.06 84	96.97 35	91.07 14	93.14 35	97.56 7	84.30 50	99.56 3	93.43 14	98.75 17	98.47 15

MTAPA		94.42 18	94.22 18	95.00 10	98.42 14	86.95 13	94.36 139	96.97 35	91.07 14	93.14 35	97.56 7	84.30 50	99.56 3	93.43 14	98.75 17	98.47 15

XVS		94.45 14	94.32 14	94.85 17	98.54 7	86.60 27	96.93 12	97.19 23	90.66 22	92.85 37	97.16 24	85.02 44	99.49 17	91.99 39	98.56 36	98.47 15

X-MVStestdata		88.31 137	86.13 184	94.85 17	98.54 7	86.60 27	96.93 12	97.19 23	90.66 22	92.85 37	23.41 355	85.02 44	99.49 17	91.99 39	98.56 36	98.47 15

MP-MVS-pluss		94.21 25	94.00 27	94.85 17	98.17 24	86.65 25	94.82 99	97.17 25	86.26 111	92.83 39	97.87 3	85.57 37	99.56 3	94.37 7	98.92 7	98.34 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS_fast	89.43 2	94.04 27	93.79 30	94.80 23	97.48 42	86.78 19	95.65 55	96.89 43	89.40 38	92.81 40	96.97 28	85.37 39	99.24 32	90.87 59	98.69 22	98.38 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					97.53 37	86.49 30	94.07 161	96.78 51	81.61 228	92.77 41	96.20 60	87.71 16	99.12 42

train_agg		93.44 40	93.08 42	94.52 34	97.53 37	86.49 30	94.07 161	96.78 51	81.86 223	92.77 41	96.20 60	87.63 17	99.12 42	92.14 36	98.69 22	97.94 54

CDPH-MVS		92.83 53	92.30 55	94.44 36	97.79 34	86.11 43	94.06 164	96.66 63	80.09 242	92.77 41	96.63 43	86.62 26	99.04 50	87.40 91	98.66 29	98.17 38

CP-MVS		94.34 19	94.21 20	94.74 27	98.39 16	86.64 26	97.60 1	97.24 20	88.53 60	92.73 44	97.23 17	85.20 41	99.32 28	92.15 35	98.83 11	98.25 35

test_8					97.49 40	86.30 38	94.02 167	96.76 54	81.86 223	92.70 45	96.20 60	87.63 17	99.02 54

test_prior3		93.60 37	93.53 36	93.82 52	97.29 49	84.49 65	94.12 153	96.88 44	87.67 81	92.63 46	96.39 53	86.62 26	98.87 68	91.50 50	98.67 27	98.11 44

test_prior2							94.12 153		87.67 81	92.63 46	96.39 53	86.62 26		91.50 50	98.67 27

HPM-MVS		94.02 28	93.88 28	94.43 38	98.39 16	85.78 50	97.25 5	97.07 31	86.90 101	92.62 48	96.80 36	84.85 47	99.17 36	92.43 27	98.65 31	98.33 25
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDD-MVS		90.74 78	89.92 86	93.20 63	96.27 72	83.02 103	95.73 47	93.86 220	88.42 62	92.53 49	96.84 32	62.09 289	98.64 83	90.95 58	92.62 137	97.93 57

EI-MVSNet-Vis-set		93.01 52	92.92 47	93.29 60	95.01 124	83.51 91	94.48 121	95.77 119	90.87 16	92.52 50	96.67 41	84.50 49	99.00 59	91.99 39	94.44 108	97.36 75

MCST-MVS		94.45 14	94.20 21	95.19 6	98.46 12	87.50 9	95.00 88	97.12 27	87.13 90	92.51 51	96.30 55	89.24 8	99.34 24	93.46 13	98.62 33	98.73 3

HPM-MVS_fast		93.40 42	93.22 40	93.94 49	98.36 18	84.83 58	97.15 7	96.80 50	85.77 119	92.47 52	97.13 25	82.38 62	99.07 45	90.51 63	98.40 40	97.92 58

agg_prior3		93.27 45	92.89 48	94.40 40	97.49 40	86.12 42	94.07 161	96.73 55	81.46 231	92.46 53	96.05 68	86.90 24	99.15 39	92.14 36	98.69 22	97.94 54

xiu_mvs_v1_base_debu		90.64 81	90.05 83	92.40 92	93.97 166	84.46 68	93.32 200	95.46 143	85.17 130	92.25 54	94.03 120	70.59 209	98.57 88	90.97 55	94.67 98	94.18 192

xiu_mvs_v1_base		90.64 81	90.05 83	92.40 92	93.97 166	84.46 68	93.32 200	95.46 143	85.17 130	92.25 54	94.03 120	70.59 209	98.57 88	90.97 55	94.67 98	94.18 192

xiu_mvs_v1_base_debi		90.64 81	90.05 83	92.40 92	93.97 166	84.46 68	93.32 200	95.46 143	85.17 130	92.25 54	94.03 120	70.59 209	98.57 88	90.97 55	94.67 98	94.18 192

agg_prior1		93.29 44	92.97 46	94.26 43	97.38 44	85.92 45	93.92 172	96.72 57	81.96 210	92.16 57	96.23 58	87.85 13	98.97 62	91.95 42	98.55 38	97.90 59

agg_prior					97.38 44	85.92 45		96.72 57		92.16 57			98.97 62

LFMVS		90.08 91	89.13 100	92.95 74	96.71 61	82.32 122	96.08 33	89.91 308	86.79 102	92.15 59	96.81 34	62.60 286	98.34 99	87.18 95	93.90 113	98.19 37

EI-MVSNet-UG-set		92.74 55	92.62 51	93.12 66	94.86 132	83.20 97	94.40 129	95.74 122	90.71 21	92.05 60	96.60 45	84.00 52	98.99 60	91.55 49	93.63 117	97.17 82

MP-MVS		94.25 22	94.07 25	94.77 24	98.47 11	86.31 37	96.71 20	96.98 34	89.04 46	91.98 61	97.19 21	85.43 38	99.56 3	92.06 38	98.79 12	98.44 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
VDDNet		89.56 104	88.49 115	92.76 81	95.07 123	82.09 124	96.30 26	93.19 230	81.05 236	91.88 62	96.86 31	61.16 298	98.33 100	88.43 78	92.49 138	97.84 61

PS-MVSNAJ		91.18 73	90.92 69	91.96 109	95.26 114	82.60 119	92.09 247	95.70 124	86.27 110	91.84 63	92.46 174	79.70 91	98.99 60	89.08 71	95.86 83	94.29 190

DELS-MVS		93.43 41	93.25 39	93.97 47	95.42 107	85.04 56	93.06 216	97.13 26	90.74 20	91.84 63	95.09 93	86.32 29	99.21 33	91.22 53	98.45 39	97.65 66
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
mPP-MVS		93.99 29	93.78 31	94.63 30	98.50 9	85.90 48	96.87 16	96.91 42	88.70 54	91.83 65	97.17 23	83.96 53	99.55 8	91.44 52	98.64 32	98.43 21

MVSFormer		91.68 66	91.30 62	92.80 79	93.86 169	83.88 81	95.96 39	95.90 110	84.66 143	91.76 66	94.91 95	77.92 110	97.30 184	89.64 68	97.11 64	97.24 77

lupinMVS		90.92 76	90.21 78	93.03 71	93.86 169	83.88 81	92.81 223	93.86 220	79.84 244	91.76 66	94.29 114	77.92 110	98.04 127	90.48 64	97.11 64	97.17 82

xiu_mvs_v2_base		91.13 74	90.89 71	91.86 114	94.97 127	82.42 120	92.24 241	95.64 130	86.11 115	91.74 68	93.14 152	79.67 94	98.89 67	89.06 72	95.46 90	94.28 191

MVS_111021_HR		93.45 39	93.31 38	93.84 51	96.99 56	84.84 57	93.24 209	97.24 20	88.76 53	91.60 69	95.85 74	86.07 32	98.66 81	91.91 43	98.16 46	98.03 50

MVS_0304		93.25 47	92.62 51	95.14 9	95.72 97	87.58 8	94.71 109	96.59 68	91.78 7	91.46 70	96.18 64	75.45 147	99.55 8	93.53 11	98.19 45	98.28 29

jason		90.80 77	90.10 81	92.90 76	93.04 193	83.53 90	93.08 214	94.15 206	80.22 240	91.41 71	94.91 95	76.87 116	97.93 134	90.28 65	96.90 67	97.24 77
jason: jason.
MVS_Test		91.31 70	91.11 65	91.93 111	94.37 150	80.14 167	93.46 198	95.80 117	86.46 107	91.35 72	93.77 136	82.21 66	98.09 123	87.57 89	94.95 96	97.55 72

新几何1				93.10 67	97.30 48	84.35 74		95.56 133	71.09 320	91.26 73	96.24 57	82.87 59	98.86 71	79.19 206	98.10 48	96.07 115

1121		90.42 86	89.49 90	93.20 63	97.27 51	84.46 68	92.63 228	95.51 140	71.01 321	91.20 74	96.21 59	82.92 58	99.05 47	80.56 178	98.07 49	96.10 113

MVS_111021_LR		92.47 56	92.29 56	92.98 73	95.99 89	84.43 72	93.08 214	96.09 95	88.20 69	91.12 75	95.72 79	81.33 77	97.76 140	91.74 47	97.37 62	96.75 96

test12				94.34 41	97.13 54	86.15 41		96.29 82		91.04 76		85.08 42	99.01 56		98.13 47	97.86 60

MG-MVS		91.77 62	91.70 59	92.00 107	97.08 55	80.03 172	93.60 193	95.18 169	87.85 76	90.89 77	96.47 51	82.06 70	98.36 96	85.07 113	97.04 66	97.62 67

CANet		93.54 38	93.20 41	94.55 33	95.65 99	85.73 51	94.94 91	96.69 61	91.89 5	90.69 78	95.88 73	81.99 72	99.54 11	93.14 21	97.95 52	98.39 22

Effi-MVS+		91.59 67	91.11 65	93.01 72	94.35 153	83.39 94	94.60 115	95.10 171	87.10 91	90.57 79	93.10 154	81.43 76	98.07 125	89.29 70	94.48 105	97.59 69

原ACMM1				92.01 105	97.34 46	81.05 147		96.81 49	78.89 252	90.45 80	95.92 71	82.65 60	98.84 76	80.68 176	98.26 44	96.14 109

Vis-MVSNet		91.75 63	91.23 64	93.29 60	95.32 111	83.78 83	96.14 31	95.98 103	89.89 29	90.45 80	96.58 46	75.09 151	98.31 102	84.75 119	96.90 67	97.78 65
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		91.99 59	91.80 58	92.55 86	98.24 23	81.98 127	96.76 19	96.49 72	81.89 215	90.24 82	96.44 52	78.59 102	98.61 86	89.68 67	97.85 54	97.06 87

test222					96.55 66	81.70 129	92.22 242	95.01 174	68.36 327	90.20 83	96.14 65	80.26 85			97.80 55	96.05 117

ACMMP		93.24 48	92.88 49	94.30 42	98.09 28	85.33 54	96.86 17	97.45 6	88.33 63	90.15 84	97.03 27	81.44 75	99.51 15	90.85 60	95.74 84	98.04 49
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
CSCG		93.23 49	93.05 43	93.76 56	98.04 30	84.07 78	96.22 29	97.37 10	84.15 154	90.05 85	95.66 80	87.77 14	99.15 39	89.91 66	98.27 43	98.07 46

DP-MVS Recon		91.95 60	91.28 63	93.96 48	98.33 19	85.92 45	94.66 113	96.66 63	82.69 199	90.03 86	95.82 75	82.30 64	99.03 51	84.57 121	96.48 78	96.91 91

EPP-MVSNet		91.70 65	91.56 60	92.13 104	95.88 92	80.50 163	97.33 3	95.25 162	86.15 113	89.76 87	95.60 81	83.42 55	98.32 101	87.37 93	93.25 127	97.56 71

DeepC-MVS	88.79 3	93.31 43	92.99 45	94.26 43	96.07 86	85.83 49	94.89 94	96.99 33	89.02 48	89.56 88	97.37 11	82.51 61	99.38 23	92.20 33	98.30 42	97.57 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OMC-MVS		91.23 71	90.62 74	93.08 68	96.27 72	84.07 78	93.52 195	95.93 106	86.95 98	89.51 89	96.13 66	78.50 104	98.35 98	85.84 108	92.90 134	96.83 94

IS-MVSNet		91.43 68	91.09 67	92.46 90	95.87 94	81.38 138	96.95 9	93.69 224	89.72 34	89.50 90	95.98 69	78.57 103	97.77 139	83.02 140	96.50 77	98.22 36

PVSNet_Blended_VisFu		91.38 69	90.91 70	92.80 79	96.39 69	83.17 98	94.87 97	96.66 63	83.29 176	89.27 91	94.46 109	80.29 84	99.17 36	87.57 89	95.37 91	96.05 117

API-MVS		90.66 80	90.07 82	92.45 91	96.36 70	84.57 63	96.06 34	95.22 168	82.39 201	89.13 92	94.27 117	80.32 83	98.46 93	80.16 187	96.71 71	94.33 189

PVSNet_BlendedMVS		89.98 93	89.70 87	90.82 148	96.12 79	81.25 140	93.92 172	96.83 47	83.49 170	89.10 93	92.26 186	81.04 79	98.85 74	86.72 104	87.86 206	92.35 276

PVSNet_Blended		90.73 79	90.32 77	91.98 108	96.12 79	81.25 140	92.55 232	96.83 47	82.04 209	89.10 93	92.56 173	81.04 79	98.85 74	86.72 104	95.91 82	95.84 124

WTY-MVS		89.60 102	88.92 105	91.67 122	95.47 106	81.15 145	92.38 237	94.78 189	83.11 179	89.06 95	94.32 112	78.67 101	96.61 236	81.57 164	90.89 157	97.24 77

XVG-OURS		89.40 113	88.70 109	91.52 125	94.06 158	81.46 135	91.27 262	96.07 97	86.14 114	88.89 96	95.77 77	68.73 242	97.26 190	87.39 92	89.96 168	95.83 125

sss		88.93 125	88.26 124	90.94 147	94.05 159	80.78 156	91.71 253	95.38 153	81.55 229	88.63 97	93.91 131	75.04 152	95.47 283	82.47 149	91.61 142	96.57 100

XVG-OURS-SEG-HR		89.95 95	89.45 91	91.47 127	94.00 164	81.21 143	91.87 249	96.06 99	85.78 118	88.55 98	95.73 78	74.67 155	97.27 188	88.71 75	89.64 173	95.91 120

ab-mvs		89.41 111	88.35 117	92.60 84	95.15 122	82.65 117	92.20 243	95.60 131	83.97 156	88.55 98	93.70 139	74.16 163	98.21 105	82.46 150	89.37 176	96.94 90

VPA-MVSNet		89.62 101	88.96 103	91.60 124	93.86 169	82.89 108	95.46 59	97.33 14	87.91 73	88.43 100	93.31 144	74.17 162	97.40 177	87.32 94	82.86 249	94.52 180

nrg030		91.08 75	90.39 75	93.17 65	93.07 191	86.91 15	96.41 24	96.26 83	88.30 64	88.37 101	94.85 100	82.19 67	97.64 147	91.09 54	82.95 247	94.96 151

tfpn200view9		87.58 173	86.64 172	90.41 167	95.99 89	78.64 217	94.58 116	91.98 257	86.94 99	88.09 102	91.77 203	69.18 230	98.10 116	70.13 273	91.10 145	94.48 185

thres400		87.62 167	86.64 172	90.57 152	95.99 89	78.64 217	94.58 116	91.98 257	86.94 99	88.09 102	91.77 203	69.18 230	98.10 116	70.13 273	91.10 145	94.96 151

thres600view7		87.65 160	86.67 167	90.59 151	96.08 85	78.72 215	94.88 96	91.58 266	87.06 96	88.08 104	92.30 182	68.91 233	98.10 116	70.05 277	91.10 145	94.96 151

tfpn111		87.63 164	86.68 166	90.47 164	96.12 79	78.55 219	95.03 85	91.58 266	87.15 87	88.06 105	92.29 183	68.91 233	98.15 109	69.88 278	91.10 145	94.71 166

conf200view11		87.65 160	86.71 163	90.46 166	96.12 79	78.55 219	95.03 85	91.58 266	87.15 87	88.06 105	92.29 183	68.91 233	98.10 116	70.13 273	91.10 145	94.71 166

thres100view900		87.63 164	86.71 163	90.38 170	96.12 79	78.55 219	95.03 85	91.58 266	87.15 87	88.06 105	92.29 183	68.91 233	98.10 116	70.13 273	91.10 145	94.48 185

thres200		87.21 186	86.24 183	90.12 183	95.36 108	78.53 222	93.26 207	92.10 250	86.42 108	88.00 108	91.11 238	69.24 229	98.00 129	69.58 279	91.04 152	93.83 214

OPM-MVS		90.12 90	89.56 89	91.82 117	93.14 189	83.90 80	94.16 152	95.74 122	88.96 49	87.86 109	95.43 84	72.48 187	97.91 135	88.10 83	90.18 165	93.65 229

MAR-MVS		90.30 87	89.37 94	93.07 70	96.61 63	84.48 67	95.68 50	95.67 125	82.36 203	87.85 110	92.85 163	76.63 121	98.80 77	80.01 188	96.68 72	95.91 120
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
view600		87.62 167	86.65 168	90.53 154	96.19 74	78.52 223	95.29 65	91.09 279	87.08 92	87.84 111	93.03 157	68.86 237	98.11 112	69.44 280	91.02 153	94.96 151

view800		87.62 167	86.65 168	90.53 154	96.19 74	78.52 223	95.29 65	91.09 279	87.08 92	87.84 111	93.03 157	68.86 237	98.11 112	69.44 280	91.02 153	94.96 151

conf0.05thres1000		87.62 167	86.65 168	90.53 154	96.19 74	78.52 223	95.29 65	91.09 279	87.08 92	87.84 111	93.03 157	68.86 237	98.11 112	69.44 280	91.02 153	94.96 151

tfpn		87.62 167	86.65 168	90.53 154	96.19 74	78.52 223	95.29 65	91.09 279	87.08 92	87.84 111	93.03 157	68.86 237	98.11 112	69.44 280	91.02 153	94.96 151

Vis-MVSNet (Re-imp)		89.59 103	89.44 92	90.03 192	95.74 96	75.85 273	95.61 56	90.80 292	87.66 83	87.83 115	95.40 85	76.79 118	96.46 244	78.37 211	96.73 70	97.80 63

CDS-MVSNet		89.45 108	88.51 112	92.29 98	93.62 177	83.61 89	93.01 217	94.68 191	81.95 211	87.82 116	93.24 148	78.69 100	96.99 210	80.34 183	93.23 128	96.28 105
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		89.21 116	88.29 122	91.96 109	93.71 175	82.62 118	93.30 204	94.19 204	82.22 204	87.78 117	93.94 127	78.83 98	96.95 215	77.70 219	92.98 132	96.32 104

CANet_DTU		90.26 89	89.41 93	92.81 78	93.46 181	83.01 104	93.48 196	94.47 196	89.43 37	87.76 118	94.23 118	70.54 213	99.03 51	84.97 114	96.39 79	96.38 103

HyFIR lowres test		88.09 144	86.81 158	91.93 111	96.00 88	80.63 158	90.01 274	95.79 118	73.42 301	87.68 119	92.10 192	73.86 167	97.96 131	80.75 174	91.70 141	97.19 81

UGNet		89.95 95	88.95 104	92.95 74	94.51 145	83.31 95	95.70 49	95.23 166	89.37 39	87.58 120	93.94 127	64.00 282	98.78 78	83.92 132	96.31 80	96.74 97
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
CHOSEN 1792x2688		88.84 126	87.69 132	92.30 97	96.14 78	81.42 137	90.01 274	95.86 114	74.52 295	87.41 121	93.94 127	75.46 146	98.36 96	80.36 182	95.53 86	97.12 85

PAPM_NR		91.22 72	90.78 73	92.52 88	97.60 36	81.46 135	94.37 135	96.24 86	86.39 109	87.41 121	94.80 102	82.06 70	98.48 92	82.80 144	95.37 91	97.61 68

EPNet		91.79 61	91.02 68	94.10 46	90.10 291	85.25 55	96.03 35	92.05 253	92.83 1	87.39 123	95.78 76	79.39 96	99.01 56	88.13 82	97.48 60	98.05 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		89.07 119	88.32 120	91.34 130	93.24 186	79.79 178	92.29 240	94.98 177	80.24 239	87.38 124	92.45 175	78.02 108	97.33 182	83.29 137	92.93 133	96.91 91

EI-MVSNet		89.10 118	88.86 108	89.80 202	91.84 216	78.30 233	93.70 188	95.01 174	85.73 120	87.15 125	95.28 86	79.87 88	97.21 196	83.81 134	87.36 210	93.88 209

MVSTER		88.84 126	88.29 122	90.51 161	92.95 197	80.44 164	93.73 184	95.01 174	84.66 143	87.15 125	93.12 153	72.79 181	97.21 196	87.86 85	87.36 210	93.87 210

mvs-test1		89.45 108	89.14 99	90.38 170	93.33 183	77.63 253	94.95 90	94.36 199	87.70 79	87.10 127	92.81 167	73.45 173	98.03 128	85.57 110	93.04 131	95.48 135

VPNet		88.20 140	87.47 136	90.39 168	93.56 179	79.46 187	94.04 165	95.54 136	88.67 55	86.96 128	94.58 108	69.33 225	97.15 198	84.05 131	80.53 285	94.56 178

HY-MVS	83.01 12	89.03 122	87.94 130	92.29 98	94.86 132	82.77 109	92.08 248	94.49 195	81.52 230	86.93 129	92.79 169	78.32 107	98.23 103	79.93 191	90.55 158	95.88 122

HQP_MVS		90.60 84	90.19 79	91.82 117	94.70 138	82.73 113	95.85 43	96.22 87	90.81 18	86.91 130	94.86 98	74.23 159	98.12 110	88.15 80	89.99 166	94.63 171

plane_prior3						82.75 110			90.26 25	86.91 130

BH-RMVSNet		88.37 135	87.48 135	91.02 143	95.28 112	79.45 189	92.89 222	93.07 232	85.45 126	86.91 130	94.84 101	70.35 214	97.76 140	73.97 252	94.59 102	95.85 123

Fast-Effi-MVS+		89.41 111	88.64 110	91.71 121	94.74 134	80.81 155	93.54 194	95.10 171	83.11 179	86.82 133	90.67 245	79.74 90	97.75 143	80.51 180	93.55 118	96.57 100

FIs		90.51 85	90.35 76	90.99 145	93.99 165	80.98 149	95.73 47	97.54 3	89.15 44	86.72 134	94.68 103	81.83 74	97.24 192	85.18 112	88.31 201	94.76 165

PAPR		90.02 92	89.27 98	92.29 98	95.78 95	80.95 151	92.68 227	96.22 87	81.91 213	86.66 135	93.75 138	82.23 65	98.44 95	79.40 205	94.79 97	97.48 73

PMMVS		85.71 225	84.96 211	87.95 264	88.90 306	77.09 262	88.68 294	90.06 304	72.32 311	86.47 136	90.76 244	72.15 190	94.40 304	81.78 162	93.49 120	92.36 275

UniMVSNet_NR-MVSNet		89.92 97	89.29 96	91.81 119	93.39 182	83.72 84	94.43 127	97.12 27	89.80 31	86.46 137	93.32 143	83.16 56	97.23 194	84.92 115	81.02 276	94.49 184

DU-MVS		89.34 115	88.50 113	91.85 115	93.04 193	83.72 84	94.47 124	96.59 68	89.50 36	86.46 137	93.29 146	77.25 114	97.23 194	84.92 115	81.02 276	94.59 175

CostFormer		85.77 219	84.94 212	88.26 257	91.16 258	72.58 300	89.47 283	91.04 286	76.26 279	86.45 139	89.97 260	70.74 207	96.86 221	82.35 151	87.07 215	95.34 141

UniMVSNet (Re)		89.80 99	89.07 101	92.01 105	93.60 178	84.52 64	94.78 102	97.47 5	89.26 41	86.44 140	92.32 181	82.10 68	97.39 180	84.81 118	80.84 280	94.12 196

TR-MVS		86.78 195	85.76 195	89.82 199	94.37 150	78.41 230	92.47 234	92.83 235	81.11 235	86.36 141	92.40 177	68.73 242	97.48 155	73.75 255	89.85 170	93.57 237

AdaColmap		89.89 98	89.07 101	92.37 95	97.41 43	83.03 102	94.42 128	95.92 107	82.81 195	86.34 142	94.65 105	73.89 166	99.02 54	80.69 175	95.51 87	95.05 145

FC-MVSNet-test		90.27 88	90.18 80	90.53 154	93.71 175	79.85 177	95.77 46	97.59 2	89.31 40	86.27 143	94.67 104	81.93 73	97.01 209	84.26 127	88.09 204	94.71 166

PS-MVSNAJss		89.97 94	89.62 88	91.02 143	91.90 214	80.85 154	95.26 72	95.98 103	86.26 111	86.21 144	94.29 114	79.70 91	97.65 145	88.87 74	88.10 202	94.57 177

TAPA-MVS	84.62 6	88.16 141	87.01 152	91.62 123	96.64 62	80.65 157	94.39 131	96.21 90	76.38 276	86.19 145	95.44 83	79.75 89	98.08 124	62.75 320	95.29 93	96.13 110
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CVMVSNet		84.69 250	84.79 217	84.37 308	91.84 216	64.92 332	93.70 188	91.47 273	66.19 333	86.16 146	95.28 86	67.18 261	93.33 316	80.89 173	90.42 160	94.88 160

tpmrst		85.35 229	84.99 208	86.43 292	90.88 271	67.88 324	88.71 293	91.43 274	80.13 241	86.08 147	88.80 275	73.05 178	96.02 260	82.48 148	83.40 246	95.40 138

ACMM	84.12 9	89.14 117	88.48 116	91.12 136	94.65 141	81.22 142	95.31 61	96.12 94	85.31 129	85.92 148	94.34 110	70.19 217	98.06 126	85.65 109	88.86 191	94.08 200
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		89.51 105	88.50 113	92.54 87	98.11 26	81.99 126	95.16 78	96.36 80	70.19 323	85.81 149	95.25 88	76.70 119	98.63 84	82.07 155	96.86 69	97.00 88

v6		87.98 146	87.25 143	90.16 177	91.36 238	79.39 196	94.37 135	95.27 161	84.48 146	85.78 150	91.51 217	76.15 124	97.46 157	84.46 123	81.88 262	93.62 233

v1neww		87.98 146	87.25 143	90.16 177	91.38 235	79.41 191	94.37 135	95.28 158	84.48 146	85.77 151	91.53 215	76.12 125	97.45 159	84.45 124	81.89 260	93.61 234

v7new		87.98 146	87.25 143	90.16 177	91.38 235	79.41 191	94.37 135	95.28 158	84.48 146	85.77 151	91.53 215	76.12 125	97.45 159	84.45 124	81.89 260	93.61 234

v7		87.75 157	86.96 153	90.12 183	91.20 253	79.50 182	94.28 141	95.46 143	83.45 171	85.75 153	91.56 214	75.13 149	97.43 168	83.60 135	82.18 255	93.42 243

tpm		84.73 247	84.02 231	86.87 289	90.33 286	68.90 321	89.06 289	89.94 307	80.85 237	85.75 153	89.86 262	68.54 244	95.97 262	77.76 218	84.05 236	95.75 129

Baseline_NR-MVSNet		87.07 189	86.63 174	88.40 253	91.44 228	77.87 245	94.23 144	92.57 242	84.12 155	85.74 155	92.08 193	77.25 114	96.04 258	82.29 153	79.94 293	91.30 295

divwei89l23v2f112		87.84 152	87.09 147	90.10 190	91.23 250	79.24 207	94.09 157	95.24 163	84.44 150	85.70 156	91.31 228	75.91 137	97.44 166	84.17 129	81.73 268	93.64 230

v1		87.85 151	87.10 146	90.11 188	91.21 252	79.24 207	94.09 157	95.24 163	84.44 150	85.70 156	91.31 228	75.96 135	97.45 159	84.18 128	81.73 268	93.64 230

v1141		87.84 152	87.09 147	90.11 188	91.23 250	79.25 205	94.08 159	95.24 163	84.44 150	85.69 158	91.31 228	75.91 137	97.44 166	84.17 129	81.74 267	93.63 232

V42		87.68 159	86.86 155	90.15 181	90.58 280	80.14 167	94.24 143	95.28 158	83.66 163	85.67 159	91.33 225	74.73 154	97.41 175	84.43 126	81.83 264	92.89 259

v1144		87.61 172	86.79 160	90.06 191	91.01 261	79.34 199	93.95 171	95.42 152	83.36 175	85.66 160	91.31 228	74.98 153	97.42 170	83.37 136	82.06 256	93.42 243

PatchT		82.68 268	81.27 266	86.89 288	90.09 292	70.94 312	84.06 327	90.15 301	74.91 291	85.63 161	83.57 321	69.37 224	94.87 302	65.19 310	88.50 196	94.84 161

CR-MVSNet		85.35 229	83.76 235	90.12 183	90.58 280	79.34 199	85.24 320	91.96 259	78.27 262	85.55 162	87.87 292	71.03 202	95.61 274	73.96 253	89.36 177	95.40 138

RPMNet		83.18 265	80.87 271	90.12 183	90.58 280	79.34 199	85.24 320	90.78 293	71.44 316	85.55 162	82.97 325	70.87 204	95.61 274	61.01 324	89.36 177	95.40 138

v2v482		87.84 152	87.06 150	90.17 176	90.99 262	79.23 209	94.00 169	95.13 170	84.87 137	85.53 164	92.07 195	74.45 156	97.45 159	84.71 120	81.75 266	93.85 213

TranMVSNet+NR-MVSNet		88.84 126	87.95 129	91.49 126	92.68 203	83.01 104	94.92 93	96.31 81	89.88 30	85.53 164	93.85 134	76.63 121	96.96 213	81.91 159	79.87 295	94.50 182

v144192		87.19 187	86.35 178	89.74 203	90.64 279	78.24 236	93.92 172	95.43 150	81.93 212	85.51 166	91.05 240	74.21 161	97.45 159	82.86 142	81.56 270	93.53 238

Patchmatch-test1		85.81 218	84.71 218	89.12 230	92.15 209	76.60 266	91.12 265	91.69 264	83.53 169	85.50 167	88.56 280	66.79 263	95.00 300	72.69 259	90.35 161	95.76 128

v1192		87.25 183	86.33 179	90.00 195	90.76 274	79.04 211	93.80 178	95.48 142	82.57 200	85.48 168	91.18 234	73.38 176	97.42 170	82.30 152	82.06 256	93.53 238

WR-MVS		88.38 134	87.67 133	90.52 160	93.30 185	80.18 165	93.26 207	95.96 105	88.57 59	85.47 169	92.81 167	76.12 125	96.91 218	81.24 166	82.29 253	94.47 187

mvs_anonymous		89.37 114	89.32 95	89.51 215	93.47 180	74.22 280	91.65 256	94.83 187	82.91 193	85.45 170	93.79 135	81.23 78	96.36 249	86.47 107	94.09 111	97.94 54

LPG-MVS_test		89.45 108	88.90 106	91.12 136	94.47 146	81.49 133	95.30 63	96.14 91	86.73 103	85.45 170	95.16 90	69.89 218	98.10 116	87.70 87	89.23 180	93.77 219

LGP-MVS_train				91.12 136	94.47 146	81.49 133		96.14 91	86.73 103	85.45 170	95.16 90	69.89 218	98.10 116	87.70 87	89.23 180	93.77 219

tfpn_ndepth		86.10 208	84.98 209	89.43 218	95.52 105	78.29 234	94.62 114	89.60 314	81.88 222	85.43 173	90.54 249	68.47 247	96.85 222	68.46 289	90.34 162	93.15 252

Effi-MVS+-dtu		88.65 130	88.35 117	89.54 212	93.33 183	76.39 268	94.47 124	94.36 199	87.70 79	85.43 173	89.56 267	73.45 173	97.26 190	85.57 110	91.28 144	94.97 148

v1240		86.78 195	85.85 193	89.56 211	90.45 285	77.79 247	93.61 192	95.37 155	81.65 225	85.43 173	91.15 236	71.50 197	97.43 168	81.47 165	82.05 258	93.47 242

HQP-NCC					94.17 155		94.39 131		88.81 50	85.43 173

ACMP_Plane					94.17 155		94.39 131		88.81 50	85.43 173

HQP4-MVS										85.43 173			97.96 131			94.51 181

HQP-MVS		89.80 99	89.28 97	91.34 130	94.17 155	81.56 130	94.39 131	96.04 101	88.81 50	85.43 173	93.97 126	73.83 168	97.96 131	87.11 98	89.77 171	94.50 182

CLD-MVS		89.47 107	88.90 106	91.18 135	94.22 154	82.07 125	92.13 245	96.09 95	87.90 74	85.37 180	92.45 175	74.38 157	97.56 150	87.15 96	90.43 159	93.93 205
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn1000		86.06 209	84.92 213	89.49 216	95.54 102	77.79 247	94.72 107	89.07 323	82.05 207	85.36 181	91.94 199	68.32 256	96.65 232	67.04 296	90.24 163	94.02 203

v1921920		86.97 191	86.06 188	89.69 208	90.53 284	78.11 239	93.80 178	95.43 150	81.90 214	85.33 182	91.05 240	72.66 183	97.41 175	82.05 156	81.80 265	93.53 238

test_djsdf		89.03 122	88.64 110	90.21 175	90.74 275	79.28 203	95.96 39	95.90 110	84.66 143	85.33 182	92.94 162	74.02 165	97.30 184	89.64 68	88.53 194	94.05 201

GA-MVS		86.61 199	85.27 206	90.66 150	91.33 243	78.71 216	90.40 268	93.81 223	85.34 128	85.12 184	89.57 266	61.25 295	97.11 202	80.99 171	89.59 174	96.15 108

PatchmatchNet		85.85 214	84.70 219	89.29 226	91.76 219	75.54 275	88.49 296	91.30 276	81.63 227	85.05 185	88.70 277	71.71 191	96.24 253	74.61 248	89.05 189	96.08 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		83.90 258	82.70 259	87.51 271	90.23 290	72.67 296	88.62 295	81.96 346	81.37 233	85.01 186	88.34 283	66.31 268	94.45 303	75.30 241	87.12 213	95.43 137

PVSNet	78.82 18	85.55 226	84.65 220	88.23 259	94.72 136	71.93 302	87.12 308	92.75 238	78.80 255	84.95 187	90.53 251	64.43 281	96.71 231	74.74 246	93.86 114	96.06 116

conf0.01		85.83 216	84.54 222	89.71 205	95.26 114	77.63 253	94.21 145	89.33 316	81.89 215	84.94 188	91.51 217	68.43 249	96.80 223	66.05 302	89.23 180	94.71 166

conf0.002		85.83 216	84.54 222	89.71 205	95.26 114	77.63 253	94.21 145	89.33 316	81.89 215	84.94 188	91.51 217	68.43 249	96.80 223	66.05 302	89.23 180	94.71 166

thresconf0.02		85.75 220	84.54 222	89.38 221	95.26 114	77.63 253	94.21 145	89.33 316	81.89 215	84.94 188	91.51 217	68.43 249	96.80 223	66.05 302	89.23 180	93.70 224

tfpn_n400		85.75 220	84.54 222	89.38 221	95.26 114	77.63 253	94.21 145	89.33 316	81.89 215	84.94 188	91.51 217	68.43 249	96.80 223	66.05 302	89.23 180	93.70 224

tfpnconf		85.75 220	84.54 222	89.38 221	95.26 114	77.63 253	94.21 145	89.33 316	81.89 215	84.94 188	91.51 217	68.43 249	96.80 223	66.05 302	89.23 180	93.70 224

tfpnview11		85.75 220	84.54 222	89.38 221	95.26 114	77.63 253	94.21 145	89.33 316	81.89 215	84.94 188	91.51 217	68.43 249	96.80 223	66.05 302	89.23 180	93.70 224

MDTV_nov1_ep13			83.56 243		91.69 223	69.93 318	87.75 304	91.54 271	78.60 258	84.86 194	88.90 273	69.54 223	96.03 259	70.25 270	88.93 190

IterMVS-LS		88.36 136	87.91 131	89.70 207	93.80 172	78.29 234	93.73 184	95.08 173	85.73 120	84.75 195	91.90 201	79.88 87	96.92 217	83.83 133	82.51 251	93.89 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm2		84.08 255	82.94 255	87.48 274	91.39 234	71.27 306	89.23 287	90.37 297	71.95 314	84.64 196	89.33 268	67.30 258	96.55 239	75.17 242	87.09 214	94.63 171

XXY-MVS		87.65 160	86.85 156	90.03 192	92.14 210	80.60 160	93.76 181	95.23 166	82.94 191	84.60 197	94.02 123	74.27 158	95.49 282	81.04 168	83.68 240	94.01 204

MDTV_nov1_ep13_2view						55.91 346	87.62 306		73.32 302	84.59 198		70.33 215		74.65 247		95.50 134

test-LLR		85.87 213	85.41 202	87.25 278	90.95 264	71.67 304	89.55 279	89.88 309	83.41 172	84.54 199	87.95 289	67.25 259	95.11 297	81.82 160	93.37 125	94.97 148

test-mter		84.54 252	83.64 242	87.25 278	90.95 264	71.67 304	89.55 279	89.88 309	79.17 249	84.54 199	87.95 289	55.56 317	95.11 297	81.82 160	93.37 125	94.97 148

BH-untuned		88.60 131	88.13 126	90.01 194	95.24 121	78.50 228	93.29 205	94.15 206	84.75 141	84.46 201	93.40 140	75.76 141	97.40 177	77.59 220	94.52 104	94.12 196

CNLPA		89.07 119	87.98 128	92.34 96	96.87 58	84.78 59	94.08 159	93.24 229	81.41 232	84.46 201	95.13 92	75.57 144	96.62 234	77.21 224	93.84 115	95.61 133

PCF-MVS	84.11 10	87.74 158	86.08 187	92.70 82	94.02 160	84.43 72	89.27 285	95.87 113	73.62 300	84.43 203	94.33 111	78.48 105	98.86 71	70.27 269	94.45 107	94.81 163
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 181	85.98 189	91.08 139	94.01 161	83.10 99	95.14 79	94.94 178	83.57 166	84.37 204	91.64 206	66.59 265	96.34 250	78.23 214	85.36 224	93.79 215

test1		87.26 181	85.98 189	91.08 139	94.01 161	83.10 99	95.14 79	94.94 178	83.57 166	84.37 204	91.64 206	66.59 265	96.34 250	78.23 214	85.36 224	93.79 215

FMVSNet3		87.40 179	86.11 185	91.30 132	93.79 174	83.64 87	94.20 151	94.81 188	83.89 157	84.37 204	91.87 202	68.45 248	96.56 237	78.23 214	85.36 224	93.70 224

v148		87.04 190	86.32 180	89.21 228	90.94 266	77.26 261	93.71 187	94.43 197	84.84 138	84.36 207	90.80 243	76.04 131	97.05 207	82.12 154	79.60 296	93.31 245

PatchMatch-RL		86.77 197	85.54 197	90.47 164	95.88 92	82.71 115	90.54 267	92.31 245	79.82 245	84.32 208	91.57 213	68.77 241	96.39 247	73.16 257	93.48 122	92.32 277

3Dnovator	86.66 5	91.73 64	90.82 72	94.44 36	94.59 142	86.37 33	97.18 6	97.02 32	89.20 42	84.31 209	96.66 42	73.74 170	99.17 36	86.74 101	97.96 51	97.79 64

PatchFormer-LS_test		86.02 210	85.13 207	88.70 238	91.52 225	74.12 283	91.19 264	92.09 251	82.71 198	84.30 210	87.24 298	70.87 204	96.98 211	81.04 168	85.17 227	95.00 147

jajsoiax		88.24 139	87.50 134	90.48 163	90.89 270	80.14 167	95.31 61	95.65 129	84.97 136	84.24 211	94.02 123	65.31 276	97.42 170	88.56 76	88.52 195	93.89 207

mvs_tets		88.06 145	87.28 141	90.38 170	90.94 266	79.88 175	95.22 74	95.66 127	85.10 134	84.21 212	93.94 127	63.53 284	97.40 177	88.50 77	88.40 200	93.87 210

3Dnovator+	87.14 4	92.42 57	91.37 61	95.55 2	95.63 100	88.73 2	97.07 8	96.77 53	90.84 17	84.02 213	96.62 44	75.95 136	99.34 24	87.77 86	97.68 56	98.59 10

PLC	84.53 7	89.06 121	88.03 127	92.15 102	97.27 51	82.69 116	94.29 140	95.44 149	79.71 246	84.01 214	94.18 119	76.68 120	98.75 79	77.28 223	93.41 123	95.02 146
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FMVSNet2		87.19 187	85.82 194	91.30 132	94.01 161	83.67 86	94.79 101	94.94 178	83.57 166	83.88 215	92.05 196	66.59 265	96.51 240	77.56 221	85.01 228	93.73 222

DWT-MVSNet_test		84.95 239	83.68 240	88.77 235	91.43 231	73.75 286	91.74 252	90.98 287	80.66 238	83.84 216	87.36 296	62.44 287	97.11 202	78.84 209	85.81 220	95.46 136

anonymousdsp		87.84 152	87.09 147	90.12 183	89.13 302	80.54 161	94.67 112	95.55 134	82.05 207	83.82 217	92.12 189	71.47 198	97.15 198	87.15 96	87.80 207	92.67 265

1112_ss		88.42 133	87.33 139	91.72 120	94.92 129	80.98 149	92.97 220	94.54 194	78.16 265	83.82 217	93.88 132	78.78 99	97.91 135	79.45 201	89.41 175	96.26 106

WR-MVS_H		87.80 156	87.37 138	89.10 232	93.23 187	78.12 238	95.61 56	97.30 18	87.90 74	83.72 219	92.01 197	79.65 95	96.01 261	76.36 231	80.54 284	93.16 250

BH-w/o		87.57 174	87.05 151	89.12 230	94.90 131	77.90 243	92.41 235	93.51 226	82.89 194	83.70 220	91.34 224	75.75 142	97.07 205	75.49 238	93.49 120	92.39 274

ACMP	84.23 8	89.01 124	88.35 117	90.99 145	94.73 135	81.27 139	95.07 82	95.89 112	86.48 106	83.67 221	94.30 113	69.33 225	97.99 130	87.10 100	88.55 193	93.72 223
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		87.25 183	86.38 177	89.85 198	91.19 255	79.50 182	94.48 121	95.45 147	83.79 161	83.62 222	91.19 233	75.13 149	97.42 170	81.94 158	80.60 282	92.63 267

v8		87.50 176	86.71 163	89.89 197	91.37 237	79.40 195	94.50 120	95.38 153	84.81 139	83.60 223	91.33 225	76.05 129	97.42 170	82.84 143	80.51 287	92.84 261

cascas		86.43 204	84.98 209	90.80 149	92.10 212	80.92 152	90.24 270	95.91 109	73.10 304	83.57 224	88.39 282	65.15 277	97.46 157	84.90 117	91.43 143	94.03 202

Test_1112_low_res		87.65 160	86.51 176	91.08 139	94.94 128	79.28 203	91.77 250	94.30 202	76.04 281	83.51 225	92.37 179	77.86 112	97.73 144	78.69 210	89.13 188	96.22 107

CP-MVSNet		87.63 164	87.26 142	88.74 237	93.12 190	76.59 267	95.29 65	96.58 70	88.43 61	83.49 226	92.98 161	75.28 148	95.83 268	78.97 207	81.15 273	93.79 215

QAPM		89.51 105	88.15 125	93.59 58	94.92 129	84.58 62	96.82 18	96.70 59	78.43 260	83.41 227	96.19 63	73.18 177	99.30 30	77.11 226	96.54 76	96.89 93

TESTMET0.1,1		83.74 260	82.85 257	86.42 293	89.96 295	71.21 308	89.55 279	87.88 330	77.41 268	83.37 228	87.31 297	56.71 314	93.65 312	80.62 177	92.85 136	94.40 188

PS-CasMVS		87.32 180	86.88 154	88.63 240	92.99 196	76.33 270	95.33 60	96.61 67	88.22 68	83.30 229	93.07 155	73.03 179	95.79 271	78.36 212	81.00 278	93.75 221

gg-mvs-nofinetune		81.77 275	79.37 285	88.99 233	90.85 272	77.73 251	86.29 312	79.63 350	74.88 293	83.19 230	69.05 343	60.34 301	96.11 257	75.46 239	94.64 101	93.11 253

XVG-ACMP-BASELINE		86.00 211	84.84 216	89.45 217	91.20 253	78.00 240	91.70 254	95.55 134	85.05 135	82.97 231	92.25 187	54.49 321	97.48 155	82.93 141	87.45 209	92.89 259

LS3D		87.89 150	86.32 180	92.59 85	96.07 86	82.92 107	95.23 73	94.92 182	75.66 283	82.89 232	95.98 69	72.48 187	99.21 33	68.43 290	95.23 95	95.64 132

PEN-MVS		86.80 194	86.27 182	88.40 253	92.32 208	75.71 274	95.18 76	96.38 79	87.97 71	82.82 233	93.15 151	73.39 175	95.92 264	76.15 235	79.03 299	93.59 236

FMVSNet1		85.85 214	84.11 230	91.08 139	92.81 200	83.10 99	95.14 79	94.94 178	81.64 226	82.68 234	91.64 206	59.01 308	96.34 250	75.37 240	83.78 237	93.79 215

RPSCF		85.07 234	84.27 228	87.48 274	92.91 198	70.62 314	91.69 255	92.46 243	76.20 280	82.67 235	95.22 89	63.94 283	97.29 187	77.51 222	85.80 221	94.53 179

Fast-Effi-MVS+-dtu		87.44 177	86.72 162	89.63 210	92.04 213	77.68 252	94.03 166	93.94 218	85.81 117	82.42 236	91.32 227	70.33 215	97.06 206	80.33 184	90.23 164	94.14 195

v7n		86.81 193	85.76 195	89.95 196	90.72 276	79.25 205	95.07 82	95.92 107	84.45 149	82.29 237	90.86 242	72.60 185	97.53 152	79.42 204	80.52 286	93.08 255

DTE-MVSNet		86.11 207	85.48 201	87.98 263	91.65 224	74.92 277	94.93 92	95.75 121	87.36 86	82.26 238	93.04 156	72.85 180	95.82 269	74.04 251	77.46 305	93.20 248

ADS-MVSNet2		81.66 277	79.71 283	87.50 272	91.35 241	74.19 281	83.33 331	88.48 327	72.90 307	82.24 239	85.77 313	64.98 278	93.20 318	64.57 314	83.74 238	95.12 143

ADS-MVSNet		81.56 279	79.78 281	86.90 287	91.35 241	71.82 303	83.33 331	89.16 322	72.90 307	82.24 239	85.77 313	64.98 278	93.76 310	64.57 314	83.74 238	95.12 143

v52		86.50 201	85.53 200	89.39 219	89.17 301	78.99 212	94.72 107	95.54 136	83.59 164	82.10 241	90.60 248	71.59 194	97.45 159	82.52 146	79.99 292	91.73 286

V4		86.50 201	85.54 197	89.39 219	89.13 302	78.99 212	94.73 104	95.54 136	83.59 164	82.10 241	90.61 247	71.60 193	97.45 159	82.52 146	80.01 291	91.74 285

JIA-IIPM		81.04 285	78.98 291	87.25 278	88.64 307	73.48 288	81.75 336	89.61 313	73.19 303	82.05 243	73.71 340	66.07 274	95.87 267	71.18 267	84.60 231	92.41 273

F-COLMAP		87.95 149	86.80 159	91.40 129	96.35 71	80.88 153	94.73 104	95.45 147	79.65 247	82.04 244	94.61 106	71.13 200	98.50 91	76.24 234	91.05 151	94.80 164

PAPM		86.68 198	85.39 203	90.53 154	93.05 192	79.33 202	89.79 278	94.77 190	78.82 254	81.95 245	93.24 148	76.81 117	97.30 184	66.94 297	93.16 129	94.95 158

Anonymous20240521		86.87 192	85.95 191	89.64 209	92.89 199	78.88 214	95.66 52	96.05 100	84.77 140	81.92 246	92.39 178	71.54 195	96.96 213	76.46 230	81.87 263	93.08 255

DP-MVS		87.25 183	85.36 204	92.90 76	97.65 35	83.24 96	94.81 100	92.00 255	74.99 290	81.92 246	95.00 94	72.66 183	99.05 47	66.92 299	92.33 139	96.40 102

tpmp4_e23		83.87 259	82.33 260	88.48 250	91.46 227	72.82 293	89.82 277	91.57 270	73.02 306	81.86 248	89.05 270	66.20 270	96.97 212	71.57 263	86.39 217	95.66 131

pm-mvs1		86.61 199	85.54 197	89.82 199	91.44 228	80.18 165	95.28 71	94.85 185	83.84 158	81.66 249	92.62 172	72.45 189	96.48 242	79.67 198	78.06 301	92.82 263

MVS		87.44 177	86.10 186	91.44 128	92.61 204	83.62 88	92.63 228	95.66 127	67.26 331	81.47 250	92.15 188	77.95 109	98.22 104	79.71 197	95.48 88	92.47 271

semantic-postprocess				88.18 260	91.71 221	76.87 265		92.65 241	85.40 127	81.44 251	90.54 249	66.21 269	95.00 300	81.04 168	81.05 274	92.66 266

CHOSEN 280x420		85.15 233	83.99 232	88.65 239	92.47 205	78.40 231	79.68 340	92.76 237	74.90 292	81.41 252	89.59 265	69.85 220	95.51 279	79.92 192	95.29 93	92.03 281

v748		86.27 205	85.28 205	89.25 227	90.26 288	77.58 260	94.89 94	95.50 141	84.28 153	81.41 252	90.46 253	72.57 186	97.32 183	79.81 196	78.36 300	92.84 261

Patchmtry		82.71 267	80.93 270	88.06 262	90.05 293	76.37 269	84.74 322	91.96 259	72.28 312	81.32 254	87.87 292	71.03 202	95.50 281	68.97 285	80.15 289	92.32 277

dp		81.47 281	80.23 276	85.17 303	89.92 296	65.49 331	86.74 309	90.10 303	76.30 278	81.10 255	87.12 300	62.81 285	95.92 264	68.13 293	79.88 294	94.09 199

tfpnnormal		84.72 248	83.23 252	89.20 229	92.79 201	80.05 170	94.48 121	95.81 116	82.38 202	81.08 256	91.21 232	69.01 232	96.95 215	61.69 322	80.59 283	90.58 314

IterMVS		84.88 241	83.98 233	87.60 269	91.44 228	76.03 272	90.18 272	92.41 244	83.24 178	81.06 257	90.42 254	66.60 264	94.28 306	79.46 200	80.98 279	92.48 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS	83.78 11	88.74 129	87.29 140	93.08 68	92.70 202	85.39 53	96.57 22	96.43 75	78.74 257	80.85 258	96.07 67	69.64 222	99.01 56	78.01 217	96.65 73	94.83 162

pmmvs4		85.43 227	83.86 234	90.16 177	90.02 294	82.97 106	90.27 269	92.67 240	75.93 282	80.73 259	91.74 205	71.05 201	95.73 273	78.85 208	83.46 244	91.78 284

MIMVSNet		82.59 269	80.53 272	88.76 236	91.51 226	78.32 232	86.57 311	90.13 302	79.32 248	80.70 260	88.69 278	52.98 325	93.07 321	66.03 308	88.86 191	94.90 159

IB-MVS	80.51 15	85.24 232	83.26 251	91.19 134	92.13 211	79.86 176	91.75 251	91.29 277	83.28 177	80.66 261	88.49 281	61.28 294	98.46 93	80.99 171	79.46 297	95.25 142
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
GG-mvs-BLEND				87.94 265	89.73 299	77.91 242	87.80 302	78.23 352		80.58 262	83.86 319	59.88 305	95.33 294	71.20 265	92.22 140	90.60 313

EU-MVSNet		81.32 283	80.95 269	82.42 316	88.50 309	63.67 333	93.32 200	91.33 275	64.02 337	80.57 263	92.83 165	61.21 297	92.27 324	76.34 232	80.38 288	91.32 294

tpmvs		83.35 264	82.07 261	87.20 282	91.07 260	71.00 311	88.31 299	91.70 263	78.91 251	80.49 264	87.18 299	69.30 228	97.08 204	68.12 294	83.56 242	93.51 241

pmmvs5		84.21 254	82.84 258	88.34 255	88.95 305	76.94 264	92.41 235	91.91 261	75.63 284	80.28 265	91.18 234	64.59 280	95.57 276	77.09 227	83.47 243	92.53 269

tpm cat1		81.96 272	80.27 275	87.01 284	91.09 259	71.02 310	87.38 307	91.53 272	66.25 332	80.17 266	86.35 310	68.22 257	96.15 256	69.16 284	82.29 253	93.86 212

MS-PatchMatch		85.05 235	84.16 229	87.73 267	91.42 232	78.51 227	91.25 263	93.53 225	77.50 267	80.15 267	91.58 211	61.99 290	95.51 279	75.69 237	94.35 110	89.16 322

1314		87.51 175	86.57 175	90.34 173	92.42 206	79.74 180	92.63 228	95.35 157	78.35 261	80.14 268	91.62 210	74.05 164	97.15 198	81.05 167	93.53 119	94.12 196

ITE_SJBPF				88.24 258	91.88 215	77.05 263		92.92 233	85.54 124	80.13 269	93.30 145	57.29 313	96.20 254	72.46 260	84.71 230	91.49 291

NR-MVSNet		88.58 132	87.47 136	91.93 111	93.04 193	84.16 77	94.77 103	96.25 85	89.05 45	80.04 270	93.29 146	79.02 97	97.05 207	81.71 163	80.05 290	94.59 175

test0.0.03 1		82.41 270	81.69 263	84.59 306	88.23 313	72.89 292	90.24 270	87.83 331	83.41 172	79.86 271	89.78 263	67.25 259	88.99 334	65.18 311	83.42 245	91.90 283

TransMVSNet (Re)		84.43 253	83.06 254	88.54 249	91.72 220	78.44 229	95.18 76	92.82 236	82.73 197	79.67 272	92.12 189	73.49 172	95.96 263	71.10 268	68.73 334	91.21 296

LTVRE_ROB	82.13 13	86.26 206	84.90 214	90.34 173	94.44 149	81.50 132	92.31 239	94.89 183	83.03 186	79.63 273	92.67 170	69.69 221	97.79 138	71.20 265	86.26 218	91.72 287
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
OurMVSNet-221017-0		85.35 229	84.64 221	87.49 273	90.77 273	72.59 299	94.01 168	94.40 198	84.72 142	79.62 274	93.17 150	61.91 291	96.72 229	81.99 157	81.16 271	93.16 250

EPNet_dtu		86.49 203	85.94 192	88.14 261	90.24 289	72.82 293	94.11 155	92.20 248	86.66 105	79.42 275	92.36 180	73.52 171	95.81 270	71.26 264	93.66 116	95.80 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		88.30 138	88.32 120	88.27 256	94.71 137	72.41 301	93.15 210	90.98 287	87.77 78	79.25 276	91.96 198	78.35 106	95.75 272	83.04 139	95.62 85	96.65 98

pmmvs6		83.42 261	81.60 264	88.87 234	88.01 317	77.87 245	94.96 89	94.24 203	74.67 294	78.80 277	91.09 239	60.17 303	96.49 241	77.06 228	75.40 310	92.23 279

MVP-Stereo		85.97 212	84.86 215	89.32 225	90.92 268	82.19 123	92.11 246	94.19 204	78.76 256	78.77 278	91.63 209	68.38 255	96.56 237	75.01 245	93.95 112	89.20 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		84.86 242	83.09 253	90.14 182	93.80 172	80.05 170	89.18 288	93.09 231	78.89 252	78.19 279	91.91 200	65.86 275	97.27 188	68.47 288	88.45 197	93.11 253

testgi		80.94 288	80.20 277	83.18 312	87.96 318	66.29 328	91.28 261	90.70 295	83.70 162	78.12 280	92.84 164	51.37 327	90.82 331	63.34 317	82.46 252	92.43 272

ACMH+	81.04 14	85.05 235	83.46 247	89.82 199	94.66 140	79.37 197	94.44 126	94.12 208	82.19 205	78.04 281	92.82 166	58.23 310	97.54 151	73.77 254	82.90 248	92.54 268

COLMAP_ROB	80.39 16	83.96 256	82.04 262	89.74 203	95.28 112	79.75 179	94.25 142	92.28 246	75.17 288	78.02 282	93.77 136	58.60 309	97.84 137	65.06 313	85.92 219	91.63 289
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DI_MVS_plusplus_test		88.15 142	86.82 157	92.14 103	90.67 278	81.07 146	93.01 217	94.59 193	83.83 160	77.78 283	90.63 246	68.51 245	98.16 107	88.02 84	94.37 109	97.17 82

ppachtmachnet_test		81.84 274	80.07 279	87.15 283	88.46 310	74.43 279	89.04 290	92.16 249	75.33 286	77.75 284	88.99 271	66.20 270	95.37 288	65.12 312	77.60 303	91.65 288

Anonymous20231206		81.03 286	79.77 282	84.82 305	87.85 320	70.26 316	91.42 259	92.08 252	73.67 299	77.75 284	89.25 269	62.43 288	93.08 320	61.50 323	82.00 259	91.12 299

SixPastTwentyTwo		83.91 257	82.90 256	86.92 286	90.99 262	70.67 313	93.48 196	91.99 256	85.54 124	77.62 286	92.11 191	60.59 300	96.87 220	76.05 236	77.75 302	93.20 248

test_normal		88.13 143	86.78 161	92.18 101	90.55 283	81.19 144	92.74 225	94.64 192	83.84 158	77.49 287	90.51 252	68.49 246	98.16 107	88.22 79	94.55 103	97.21 80

ACMH	80.38 17	85.36 228	83.68 240	90.39 168	94.45 148	80.63 158	94.73 104	94.85 185	82.09 206	77.24 288	92.65 171	60.01 304	97.58 148	72.25 261	84.87 229	92.96 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmatch-RL test		81.67 276	79.96 280	86.81 290	85.42 325	71.23 307	82.17 335	87.50 335	78.47 259	77.19 289	82.50 326	70.81 206	93.48 314	82.66 145	72.89 315	95.71 130

our_test_3		81.93 273	80.46 273	86.33 294	88.46 310	73.48 288	88.46 297	91.11 278	76.46 274	76.69 290	88.25 285	66.89 262	94.36 305	68.75 286	79.08 298	91.14 298

Patchmatch-test		81.37 282	79.30 286	87.58 270	90.92 268	74.16 282	80.99 337	87.68 333	70.52 322	76.63 291	88.81 274	71.21 199	92.76 322	60.01 328	86.93 216	95.83 125

v18		84.97 237	83.76 235	88.60 243	91.36 238	79.41 191	93.82 177	94.04 209	83.00 189	76.61 292	86.60 301	76.19 123	95.43 284	80.39 181	71.79 319	90.96 301

v16		84.96 238	83.74 237	88.62 241	91.40 233	79.48 185	93.83 175	94.04 209	83.03 186	76.54 293	86.59 302	76.11 128	95.42 285	80.33 184	71.80 318	90.95 303

v17		84.93 240	83.70 239	88.62 241	91.36 238	79.48 185	93.83 175	94.03 211	83.04 185	76.51 294	86.57 303	76.05 129	95.42 285	80.31 186	71.65 320	90.96 301

v15		84.79 243	83.53 244	88.57 247	91.30 249	79.41 191	93.70 188	94.01 212	83.06 182	76.27 295	86.42 307	76.03 132	95.38 287	80.01 188	71.00 323	90.92 304

V14		84.79 243	83.52 245	88.57 247	91.32 245	79.43 190	93.72 186	94.01 212	83.06 182	76.22 296	86.43 304	76.01 133	95.37 288	79.96 190	70.99 324	90.91 305

v11		84.67 251	83.41 250	88.44 252	91.32 245	79.13 210	93.69 191	93.99 217	82.81 195	76.20 297	86.24 311	75.48 145	95.35 292	79.53 199	71.48 322	90.85 309

FMVSNet5		81.52 280	79.60 284	87.27 276	91.17 256	77.95 241	91.49 258	92.26 247	76.87 273	76.16 298	87.91 291	51.67 326	92.34 323	67.74 295	81.16 271	91.52 290

AllTest		83.42 261	81.39 265	89.52 213	95.01 124	77.79 247	93.12 211	90.89 290	77.41 268	76.12 299	93.34 141	54.08 323	97.51 153	68.31 291	84.27 234	93.26 246

TestCases				89.52 213	95.01 124	77.79 247		90.89 290	77.41 268	76.12 299	93.34 141	54.08 323	97.51 153	68.31 291	84.27 234	93.26 246

V9		84.77 245	83.50 246	88.58 244	91.33 243	79.46 187	93.75 182	94.00 215	83.07 181	76.07 301	86.43 304	75.97 134	95.37 288	79.91 193	70.93 326	90.91 305

test_0402		81.30 284	79.17 288	87.67 268	93.19 188	78.17 237	92.98 219	91.71 262	75.25 287	76.02 302	90.31 255	59.23 307	96.37 248	50.22 338	83.63 241	88.47 332

v12		84.74 246	83.46 247	88.58 244	91.32 245	79.50 182	93.75 182	94.01 212	83.06 182	75.98 303	86.41 308	75.82 140	95.36 291	79.87 194	70.89 327	90.89 307

v13		84.72 248	83.44 249	88.58 244	91.31 248	79.52 181	93.77 180	94.00 215	83.03 186	75.85 304	86.38 309	75.84 139	95.35 292	79.83 195	70.95 325	90.87 308

DSMNet-mixed		76.94 302	76.29 301	78.89 319	83.10 333	56.11 345	87.78 303	79.77 349	60.65 341	75.64 305	88.71 276	61.56 293	88.34 336	60.07 327	89.29 179	92.21 280

USDC		82.76 266	81.26 267	87.26 277	91.17 256	74.55 278	89.27 285	93.39 228	78.26 263	75.30 306	92.08 193	54.43 322	96.63 233	71.64 262	85.79 222	90.61 311

TDRefinement		79.81 293	77.34 295	87.22 281	79.24 342	75.48 276	93.12 211	92.03 254	76.45 275	75.01 307	91.58 211	49.19 332	96.44 245	70.22 272	69.18 331	89.75 317

LF4IMVS		80.37 290	79.07 290	84.27 310	86.64 322	69.87 319	89.39 284	91.05 285	76.38 276	74.97 308	90.00 259	47.85 334	94.25 307	74.55 249	80.82 281	88.69 327

PM-MVS		78.11 300	76.12 302	84.09 311	83.54 332	70.08 317	88.97 291	85.27 340	79.93 243	74.73 309	86.43 304	34.70 346	93.48 314	79.43 203	72.06 317	88.72 326

OpenMVS_ROB	74.94 19	79.51 295	77.03 299	86.93 285	87.00 321	76.23 271	92.33 238	90.74 294	68.93 326	74.52 310	88.23 286	49.58 330	96.62 234	57.64 330	84.29 233	87.94 334

test20.03		79.95 292	79.08 289	82.55 315	85.79 324	67.74 325	91.09 266	91.08 283	81.23 234	74.48 311	89.96 261	61.63 292	90.15 332	60.08 326	76.38 307	89.76 316

ambc				83.06 313	79.99 339	63.51 334	77.47 343	92.86 234		74.34 312	84.45 317	28.74 348	95.06 299	73.06 258	68.89 333	90.61 311

PVSNet_0	73.20 20	77.22 301	74.83 304	84.37 308	90.70 277	71.10 309	83.09 333	89.67 312	72.81 309	73.93 313	83.13 324	60.79 299	93.70 311	68.54 287	50.84 346	88.30 333

pmmvs-eth3d		80.97 287	78.72 292	87.74 266	84.99 328	79.97 174	90.11 273	91.65 265	75.36 285	73.51 314	86.03 312	59.45 306	93.96 309	75.17 242	72.21 316	89.29 320

K. test v3		81.59 278	80.15 278	85.91 297	89.89 297	69.42 320	92.57 231	87.71 332	85.56 123	73.44 315	89.71 264	55.58 316	95.52 278	77.17 225	69.76 330	92.78 264

Test4		85.75 220	83.72 238	91.83 116	88.08 316	81.03 148	92.48 233	95.54 136	83.38 174	73.40 316	88.57 279	50.99 328	97.37 181	86.61 106	94.47 106	97.09 86

EG-PatchMatch MVS		82.37 271	80.34 274	88.46 251	90.27 287	79.35 198	92.80 224	94.33 201	77.14 272	73.26 317	90.18 257	47.47 335	96.72 229	70.25 270	87.32 212	89.30 319

lessismore_v0				86.04 295	88.46 310	68.78 322		80.59 348		73.01 318	90.11 258	55.39 318	96.43 246	75.06 244	65.06 336	92.90 258

testus		74.41 307	73.35 305	77.59 324	82.49 337	57.08 341	86.02 313	90.21 300	72.28 312	72.89 319	84.32 318	37.08 344	86.96 340	52.24 334	82.65 250	88.73 325

MIMVSNet1		79.38 296	77.28 296	85.69 298	86.35 323	73.67 287	91.61 257	92.75 238	78.11 266	72.64 320	88.12 287	48.16 333	91.97 327	60.32 325	77.49 304	91.43 293

test2356		74.50 306	73.27 306	78.20 320	80.81 338	59.84 336	83.76 330	88.33 329	71.43 317	72.37 321	81.84 329	45.60 338	86.26 342	50.97 336	84.32 232	88.50 329

TinyColmap		79.76 294	77.69 294	85.97 296	91.71 221	73.12 290	89.55 279	90.36 298	75.03 289	72.03 322	90.19 256	46.22 337	96.19 255	63.11 318	81.03 275	88.59 328

N_pmnet		68.89 315	68.44 316	70.23 331	89.07 304	28.79 360	88.06 300	19.50 361	69.47 325	71.86 323	84.93 316	61.24 296	91.75 328	54.70 332	77.15 306	90.15 315

UnsupCasMVSNet_eth		80.07 291	78.27 293	85.46 300	85.24 326	72.63 298	88.45 298	94.87 184	82.99 190	71.64 324	88.07 288	56.34 315	91.75 328	73.48 256	63.36 341	92.01 282

LP		75.51 305	72.15 309	85.61 299	87.86 319	73.93 284	80.20 339	88.43 328	67.39 328	70.05 325	80.56 333	58.18 311	93.18 319	46.28 344	70.36 329	89.71 318

new-patchmatchnet		76.41 303	75.17 303	80.13 318	82.65 336	59.61 338	87.66 305	91.08 283	78.23 264	69.85 326	83.22 323	54.76 320	91.63 330	64.14 316	64.89 337	89.16 322

MVS-HIRNet		73.70 308	72.20 308	78.18 322	91.81 218	56.42 344	82.94 334	82.58 344	55.24 343	68.88 327	66.48 344	55.32 319	95.13 296	58.12 329	88.42 199	83.01 339

UnsupCasMVSNet_bld		76.23 304	73.27 306	85.09 304	83.79 331	72.92 291	85.65 319	93.47 227	71.52 315	68.84 328	79.08 336	49.77 329	93.21 317	66.81 301	60.52 343	89.13 324

testpf		71.41 313	72.11 310	69.30 333	84.53 329	59.79 337	62.74 350	83.14 343	71.11 319	68.83 329	81.57 331	46.70 336	84.83 347	74.51 250	75.86 309	63.30 347

pmmvs3		71.81 312	68.71 315	81.11 317	75.86 344	70.42 315	86.74 309	83.66 342	58.95 342	68.64 330	80.89 332	36.93 345	89.52 333	63.10 319	63.59 340	83.39 338

Anonymous20231211		72.97 309	69.63 314	83.00 314	83.05 334	66.91 327	92.69 226	89.45 315	61.06 340	67.50 331	83.46 322	34.34 347	93.61 313	51.11 335	63.97 339	88.48 331

testing_2		83.40 263	81.02 268	90.56 153	85.06 327	80.51 162	91.37 260	95.57 132	82.92 192	67.06 332	85.54 315	49.47 331	97.24 192	86.74 101	85.44 223	93.93 205

CMPMVS	59.16 21	80.52 289	79.20 287	84.48 307	83.98 330	67.63 326	89.95 276	93.84 222	64.79 336	66.81 333	91.14 237	57.93 312	95.17 295	76.25 233	88.10 202	90.65 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		70.54 314	69.71 313	73.04 328	79.30 340	44.83 353	84.23 325	88.96 324	67.33 329	65.42 334	82.28 327	41.11 342	88.11 337	47.12 342	71.60 321	86.19 336

.test1245		57.63 324	61.79 321	45.14 342	79.30 340	44.83 353	84.23 325	88.96 324	67.33 329	65.42 334	82.28 327	41.11 342	88.11 337	47.12 342	0.39 357	2.46 358

new_pmnet		72.15 311	70.13 312	78.20 320	82.95 335	65.68 329	83.91 328	82.40 345	62.94 339	64.47 336	79.82 335	42.85 340	86.26 342	57.41 331	74.44 312	82.65 340

test1235678		72.22 310	70.31 311	77.93 323	78.04 343	58.04 340	85.76 317	89.80 311	70.15 324	63.43 337	80.20 334	42.24 341	87.24 339	48.68 340	74.50 311	88.50 329

YYNet1		79.22 297	77.20 297	85.28 302	88.20 315	72.66 297	85.87 315	90.05 306	74.33 297	62.70 338	87.61 294	66.09 273	92.03 325	66.94 297	72.97 314	91.15 297

MDA-MVSNet_test_wron		79.21 298	77.19 298	85.29 301	88.22 314	72.77 295	85.87 315	90.06 304	74.34 296	62.62 339	87.56 295	66.14 272	91.99 326	66.90 300	73.01 313	91.10 300

MDA-MVSNet-bldmvs		78.85 299	76.31 300	86.46 291	89.76 298	73.88 285	88.79 292	90.42 296	79.16 250	59.18 340	88.33 284	60.20 302	94.04 308	62.00 321	68.96 332	91.48 292

test12356		64.99 318	63.78 317	68.61 335	72.69 346	39.14 356	78.46 341	87.61 334	64.91 335	55.77 341	77.48 337	28.10 349	85.59 344	44.69 345	64.35 338	81.12 342

LCM-MVSNet		66.00 316	62.16 320	77.51 325	64.51 354	58.29 339	83.87 329	90.90 289	48.17 346	54.69 342	73.31 341	16.83 359	86.75 341	65.47 309	61.67 342	87.48 335

testmv		65.49 317	62.66 318	73.96 327	68.78 349	53.14 348	84.70 323	88.56 326	65.94 334	52.35 343	74.65 339	25.02 352	85.14 345	43.54 346	60.40 344	83.60 337

FPMVS		64.63 319	62.55 319	70.88 330	70.80 347	56.71 342	84.42 324	84.42 341	51.78 345	49.57 344	81.61 330	23.49 353	81.48 349	40.61 349	76.25 308	74.46 346

PMMVS2		59.60 321	56.40 323	69.21 334	68.83 348	46.58 351	73.02 348	77.48 353	55.07 344	49.21 345	72.95 342	17.43 358	80.04 350	49.32 339	44.33 347	80.99 343

DeepMVS_CX				56.31 340	74.23 345	51.81 349		56.67 359	44.85 347	48.54 346	75.16 338	27.87 350	58.74 357	40.92 348	52.22 345	58.39 351

no-one		61.56 320	56.58 322	76.49 326	67.80 352	62.76 335	78.13 342	86.11 336	63.16 338	43.24 347	64.70 346	26.12 351	88.95 335	50.84 337	29.15 349	77.77 344

Gipuma		57.99 323	54.91 324	67.24 336	88.51 308	65.59 330	52.21 353	90.33 299	43.58 349	42.84 348	51.18 351	20.29 356	85.07 346	34.77 351	70.45 328	51.05 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		58.88 322	54.22 325	72.86 329	56.50 358	56.67 343	80.75 338	86.00 337	73.09 305	37.39 349	64.63 347	22.17 354	79.49 352	43.51 347	23.96 353	82.43 341

tmp_tt		35.64 332	39.24 331	24.84 345	14.87 360	23.90 361	62.71 351	51.51 360	6.58 356	36.66 350	62.08 348	44.37 339	30.34 359	52.40 333	22.00 355	20.27 355

PNet_i23d		50.48 327	47.18 327	60.36 338	68.59 350	44.56 355	72.75 349	72.61 354	43.92 348	33.91 351	60.19 349	6.16 360	73.52 353	38.50 350	28.04 350	63.01 348

PMVS	47.18 22	52.22 325	48.46 326	63.48 337	45.72 359	46.20 352	73.41 346	78.31 351	41.03 350	30.06 352	65.68 345	6.05 361	83.43 348	30.04 352	65.86 335	60.80 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 328	38.59 333	57.77 339	56.52 357	48.77 350	55.38 352	58.64 358	29.33 354	28.96 353	52.65 350	4.68 362	64.62 356	28.11 353	33.07 348	59.93 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 329	42.29 329	46.03 341	65.58 353	37.41 357	73.51 345	64.62 355	33.99 352	28.47 354	47.87 352	19.90 357	67.91 354	22.23 354	24.45 352	32.77 353

EMVS		42.07 330	41.12 330	44.92 343	63.45 355	35.56 359	73.65 344	63.48 356	33.05 353	26.88 355	45.45 354	21.27 355	67.14 355	19.80 355	23.02 354	32.06 354

wuykxyi23d		50.55 326	44.13 328	69.81 332	56.77 356	54.58 347	73.22 347	80.78 347	39.79 351	22.08 356	46.69 353	4.03 363	79.71 351	47.65 341	26.13 351	75.14 345

wuyk23d		21.27 334	20.48 335	23.63 346	68.59 350	36.41 358	49.57 354	6.85 362	9.37 355	7.89 357	4.46 360	4.03 363	31.37 358	17.47 356	16.07 356	3.12 356

testmvs		8.92 335	11.52 336	1.12 348	1.06 361	0.46 363	86.02 313	0.65 363	0.62 357	2.74 358	9.52 358	0.31 366	0.45 361	2.38 357	0.39 357	2.46 358

test123		8.76 336	11.22 337	1.39 347	0.85 362	0.97 362	85.76 317	0.35 364	0.54 358	2.45 359	8.14 359	0.60 365	0.48 360	2.16 358	0.17 359	2.71 357

cdsmvs_eth3d_5k		22.14 333	29.52 334	0.00 349	0.00 363	0.00 364	0.00 355	95.76 120	0.00 359	0.00 360	94.29 114	75.66 143	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		6.64 338	8.86 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	79.70 91	0.00 362	0.00 359	0.00 360	0.00 360

pcd1.5k->3k		37.02 331	38.84 332	31.53 344	92.33 207	0.00 364	0.00 355	96.13 93	0.00 359	0.00 360	0.00 361	72.70 182	0.00 362	0.00 359	88.43 198	94.60 174

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		7.82 337	10.43 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	93.88 132	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																96.12 111

test_part3							95.99 36		88.25 66		97.60 5		99.62 1	93.18 19

test_part1								97.45 6				91.93 1			99.02 3	98.67 5

sam_mvs1												71.70 192				96.12 111

sam_mvs												70.60 208

MTGPA								96.97 35

test_post1							88.00 301				9.81 357	69.31 227	95.53 277	76.65 229

test_post											10.29 356	70.57 212	95.91 266

patchmatchnet-post											83.76 320	71.53 196	96.48 242

MTMP								60.64 357

gm-plane-assit					89.60 300	68.00 323			77.28 271		88.99 271		97.57 149	79.44 202

test9_res														91.91 43	98.71 20	98.07 46

agg_prior2														90.54 62	98.68 25	98.27 32

test_prior4						85.96 44	94.11 155

test_prior				93.82 52	97.29 49	84.49 65		96.88 44					98.87 68			98.11 44

新几何2							93.11 213

旧先验1					96.79 60	81.81 128		95.67 125			96.81 34	86.69 25			97.66 57	96.97 89

无先验							93.28 206	96.26 83	73.95 298				99.05 47	80.56 178		96.59 99

原ACMM2							92.94 221

testdata2													98.75 79	78.30 213

segment_acmp												87.16 22

testdata1							92.15 244		87.94 72

plane_prior7					94.70 138	82.74 112

plane_prior6					94.52 144	82.75 110						74.23 159

plane_prior5								96.22 87					98.12 110	88.15 80	89.99 166	94.63 171

plane_prior4											94.86 98

plane_prior2							95.85 43		90.81 18

plane_prior1					94.59 142

plane_prior						82.73 113	95.21 75		89.66 35						89.88 169

n2								0.00 365

nn								0.00 365

door-mid								85.49 338

test11								96.57 71

door								85.33 339

HQP5-MVS						81.56 130

BP-MVS														87.11 98

HQP3-MVS								96.04 101							89.77 171

HQP2-MVS												73.83 168

NP-MVS					94.37 150	82.42 120					93.98 125

ACMMP++_ref															87.47 208

ACMMP++															88.01 205

Test By Simon												80.02 86

High-resolution multi-view benchmark