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		94.96 6	95.33 4	93.88 48	97.25 51	86.69 20	96.19 29	97.11 28	90.42 24	96.95 1	97.27 11	89.53 4	96.91 213	94.38 5	98.85 7	98.03 48

test_part2					98.55 5	87.22 10				96.40 2

APDe-MVS		95.46 1	95.64 1	94.91 11	98.26 19	86.29 37	97.46 2	97.40 9	89.03 47	96.20 3	98.10 1	89.39 6	99.34 21	95.88 1	99.03 1	99.10 1

HSP-MVS		95.30 3	95.48 2	94.76 23	98.49 9	86.52 27	96.91 15	96.73 54	91.73 9	96.10 4	96.69 36	89.90 2	99.30 27	94.70 3	98.04 48	98.45 17

TSAR-MVS + MP.		94.85 7	94.94 6	94.58 30	98.25 20	86.33 33	96.11 31	96.62 65	88.14 68	96.10 4	96.96 26	89.09 8	98.94 63	94.48 4	98.68 23	98.48 12

DeepPCF-MVS	89.96 1	94.20 24	94.77 8	92.49 87	96.52 65	80.00 171	94.00 162	97.08 29	90.05 26	95.65 6	97.29 10	89.66 3	98.97 59	93.95 8	98.71 18	98.50 10

SteuartSystems-ACMMP		95.20 4	95.32 5	94.85 15	96.99 54	86.33 33	97.33 3	97.30 17	91.38 12	95.39 7	97.46 7	88.98 9	99.40 19	94.12 7	98.89 6	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		95.40 2	95.37 3	95.50 3	98.11 24	88.51 3	95.29 61	96.96 37	92.09 3	95.32 8	97.08 23	89.49 5	99.33 24	95.10 2	98.85 7	98.66 5

ACMMP_Plus		94.74 9	94.56 10	95.28 4	98.02 29	87.70 4	95.68 47	97.34 11	88.28 65	95.30 9	97.67 3	85.90 32	99.54 8	93.91 9	98.95 3	98.60 7

APD-MVS		94.24 21	94.07 23	94.75 24	98.06 27	86.90 14	95.88 39	96.94 39	85.68 118	95.05 10	97.18 19	87.31 18	99.07 42	91.90 43	98.61 32	98.28 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-2		94.33 18	94.22 16	94.68 26	95.54 99	86.75 19	94.57 113	96.70 58	91.84 6	94.41 11	96.56 45	87.19 19	99.13 38	93.50 11	97.65 56	98.16 37

旧先验2							93.36 192		71.25 309	94.37 12			97.13 197	86.74 98

Regformer-1		94.22 22	94.13 21	94.51 33	95.54 99	86.36 32	94.57 113	96.44 72	91.69 10	94.32 13	96.56 45	87.05 21	99.03 48	93.35 16	97.65 56	98.15 38

TSAR-MVS + GP.		93.66 34	93.41 35	94.41 37	96.59 62	86.78 17	94.40 124	93.93 217	89.77 32	94.21 14	95.59 79	87.35 17	98.61 83	92.72 21	96.15 79	97.83 60

alignmvs		93.08 49	92.50 52	94.81 20	95.62 98	87.61 6	95.99 35	96.07 96	89.77 32	94.12 15	94.87 94	80.56 79	98.66 78	92.42 25	93.10 128	98.15 38

canonicalmvs		93.27 43	92.75 48	94.85 15	95.70 95	87.66 5	96.33 25	96.41 75	90.00 28	94.09 16	94.60 104	82.33 61	98.62 82	92.40 26	92.86 133	98.27 30

VNet		92.24 56	91.91 55	93.24 60	96.59 62	83.43 90	94.84 93	96.44 72	89.19 43	94.08 17	95.90 69	77.85 111	98.17 103	88.90 70	93.38 122	98.13 40

HPM-MVS++		95.14 5	94.91 7	95.83 1	98.25 20	89.65 1	95.92 38	96.96 37	91.75 8	94.02 18	96.83 30	88.12 10	99.55 5	93.41 15	98.94 4	98.28 27

NCCC		94.81 8	94.69 9	95.17 6	97.83 31	87.46 9	95.66 49	96.93 40	92.34 2	93.94 19	96.58 43	87.74 13	99.44 18	92.83 20	98.40 38	98.62 6

APD-MVS_3200maxsize		93.78 31	93.77 30	93.80 53	97.92 30	84.19 74	96.30 26	96.87 45	86.96 94	93.92 20	97.47 6	83.88 52	98.96 62	92.71 22	97.87 51	98.26 32

Regformer-4		93.91 29	93.81 27	94.19 43	95.36 105	85.47 50	94.68 105	96.41 75	91.60 11	93.75 21	96.71 34	85.95 31	99.10 41	93.21 17	96.65 71	98.01 50

HFP-MVS		94.52 10	94.40 11	94.86 13	98.61 3	86.81 15	96.94 10	97.34 11	88.63 56	93.65 22	97.21 16	86.10 28	99.49 14	92.35 27	98.77 13	98.30 25

#test#		94.32 19	94.14 20	94.86 13	98.61 3	86.81 15	96.43 23	97.34 11	87.51 82	93.65 22	97.21 16	86.10 28	99.49 14	91.68 45	98.77 13	98.30 25

testdata				90.49 160	96.40 66	77.89 240		95.37 153	72.51 301	93.63 24	96.69 36	82.08 67	97.65 141	83.08 135	97.39 59	95.94 115

Regformer-3		93.68 33	93.64 33	93.81 52	95.36 105	84.61 59	94.68 105	95.83 113	91.27 13	93.60 25	96.71 34	85.75 33	98.86 68	92.87 19	96.65 71	97.96 51

region2R		94.43 14	94.27 15	94.92 10	98.65 1	86.67 22	96.92 14	97.23 21	88.60 58	93.58 26	97.27 11	85.22 38	99.54 8	92.21 29	98.74 17	98.56 9

MSLP-MVS++		93.72 32	94.08 22	92.65 81	97.31 45	83.43 90	95.79 42	97.33 14	90.03 27	93.58 26	96.96 26	84.87 44	97.76 136	92.19 31	98.66 27	96.76 93

PHI-MVS		93.89 30	93.65 32	94.62 29	96.84 57	86.43 30	96.69 21	97.49 4	85.15 129	93.56 28	96.28 53	85.60 34	99.31 26	92.45 23	98.79 10	98.12 41

ACMMPR		94.43 14	94.28 14	94.91 11	98.63 2	86.69 20	96.94 10	97.32 16	88.63 56	93.53 29	97.26 13	85.04 41	99.54 8	92.35 27	98.78 12	98.50 10

PGM-MVS		93.96 28	93.72 31	94.68 26	98.43 12	86.22 38	95.30 59	97.78 1	87.45 83	93.26 30	97.33 9	84.62 46	99.51 12	90.75 58	98.57 33	98.32 24

UA-Net		92.83 51	92.54 51	93.68 55	96.10 81	84.71 58	95.66 49	96.39 77	91.92 4	93.22 31	96.49 47	83.16 54	98.87 65	84.47 119	95.47 87	97.45 72

abl_6		93.18 48	93.05 41	93.57 57	97.52 37	84.27 73	95.53 54	96.67 61	87.85 74	93.20 32	97.22 15	80.35 80	99.18 32	91.91 40	97.21 61	97.26 74

MPTG		94.47 11	94.30 13	95.00 8	98.42 13	86.95 11	95.06 80	96.97 34	91.07 14	93.14 33	97.56 4	84.30 48	99.56 1	93.43 13	98.75 15	98.47 13

MTAPA		94.42 16	94.22 16	95.00 8	98.42 13	86.95 11	94.36 134	96.97 34	91.07 14	93.14 33	97.56 4	84.30 48	99.56 1	93.43 13	98.75 15	98.47 13

XVS		94.45 12	94.32 12	94.85 15	98.54 6	86.60 25	96.93 12	97.19 22	90.66 22	92.85 35	97.16 21	85.02 42	99.49 14	91.99 36	98.56 34	98.47 13

X-MVStestdata		88.31 135	86.13 181	94.85 15	98.54 6	86.60 25	96.93 12	97.19 22	90.66 22	92.85 35	23.41 346	85.02 42	99.49 14	91.99 36	98.56 34	98.47 13

MP-MVS-pluss		94.21 23	94.00 25	94.85 15	98.17 23	86.65 23	94.82 94	97.17 24	86.26 108	92.83 37	97.87 2	85.57 35	99.56 1	94.37 6	98.92 5	98.34 22
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS_fast	89.43 2	94.04 25	93.79 28	94.80 21	97.48 40	86.78 17	95.65 51	96.89 42	89.40 38	92.81 38	96.97 25	85.37 37	99.24 29	90.87 56	98.69 20	98.38 21
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 35	86.49 28	94.07 154	96.78 50	81.61 221	92.77 39	96.20 57	87.71 14	99.12 39

train_agg		93.44 38	93.08 40	94.52 32	97.53 35	86.49 28	94.07 154	96.78 50	81.86 216	92.77 39	96.20 57	87.63 15	99.12 39	92.14 33	98.69 20	97.94 52

CDPH-MVS		92.83 51	92.30 53	94.44 34	97.79 32	86.11 41	94.06 157	96.66 62	80.09 235	92.77 39	96.63 40	86.62 24	99.04 47	87.40 88	98.66 27	98.17 36

CP-MVS		94.34 17	94.21 18	94.74 25	98.39 15	86.64 24	97.60 1	97.24 19	88.53 60	92.73 42	97.23 14	85.20 39	99.32 25	92.15 32	98.83 9	98.25 33

test_8					97.49 38	86.30 36	94.02 160	96.76 53	81.86 216	92.70 43	96.20 57	87.63 15	99.02 51

test_prior3		93.60 35	93.53 34	93.82 50	97.29 47	84.49 63	94.12 146	96.88 43	87.67 79	92.63 44	96.39 50	86.62 24	98.87 65	91.50 47	98.67 25	98.11 42

test_prior2							94.12 146		87.67 79	92.63 44	96.39 50	86.62 24		91.50 47	98.67 25

HPM-MVS		94.02 26	93.88 26	94.43 36	98.39 15	85.78 48	97.25 5	97.07 30	86.90 98	92.62 46	96.80 33	84.85 45	99.17 33	92.43 24	98.65 29	98.33 23

VDD-MVS		90.74 76	89.92 84	93.20 61	96.27 70	83.02 101	95.73 44	93.86 218	88.42 62	92.53 47	96.84 29	62.09 281	98.64 80	90.95 55	92.62 135	97.93 55

EI-MVSNet-Vis-set		93.01 50	92.92 45	93.29 58	95.01 119	83.51 89	94.48 116	95.77 117	90.87 16	92.52 48	96.67 38	84.50 47	99.00 56	91.99 36	94.44 106	97.36 73

MCST-MVS		94.45 12	94.20 19	95.19 5	98.46 11	87.50 8	95.00 83	97.12 26	87.13 87	92.51 49	96.30 52	89.24 7	99.34 21	93.46 12	98.62 31	98.73 3

HPM-MVS_fast		93.40 40	93.22 38	93.94 47	98.36 17	84.83 56	97.15 7	96.80 49	85.77 115	92.47 50	97.13 22	82.38 60	99.07 42	90.51 60	98.40 38	97.92 56

agg_prior3		93.27 43	92.89 46	94.40 38	97.49 38	86.12 40	94.07 154	96.73 54	81.46 224	92.46 51	96.05 65	86.90 22	99.15 36	92.14 33	98.69 20	97.94 52

xiu_mvs_v1_base_debu		90.64 79	90.05 81	92.40 90	93.97 161	84.46 66	93.32 193	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

xiu_mvs_v1_base		90.64 79	90.05 81	92.40 90	93.97 161	84.46 66	93.32 193	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

xiu_mvs_v1_base_debi		90.64 79	90.05 81	92.40 90	93.97 161	84.46 66	93.32 193	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

agg_prior1		93.29 42	92.97 44	94.26 41	97.38 42	85.92 43	93.92 165	96.72 56	81.96 205	92.16 55	96.23 55	87.85 11	98.97 59	91.95 39	98.55 36	97.90 57

agg_prior					97.38 42	85.92 43		96.72 56		92.16 55			98.97 59

LFMVS		90.08 89	89.13 98	92.95 72	96.71 59	82.32 120	96.08 32	89.91 303	86.79 99	92.15 57	96.81 31	62.60 278	98.34 96	87.18 92	93.90 111	98.19 35

EI-MVSNet-UG-set		92.74 53	92.62 49	93.12 64	94.86 127	83.20 95	94.40 124	95.74 120	90.71 21	92.05 58	96.60 42	84.00 50	98.99 57	91.55 46	93.63 115	97.17 80

MP-MVS		94.25 20	94.07 23	94.77 22	98.47 10	86.31 35	96.71 20	96.98 33	89.04 46	91.98 59	97.19 18	85.43 36	99.56 1	92.06 35	98.79 10	98.44 18

VDDNet		89.56 102	88.49 113	92.76 79	95.07 118	82.09 122	96.30 26	93.19 228	81.05 229	91.88 60	96.86 28	61.16 290	98.33 97	88.43 75	92.49 136	97.84 59

PS-MVSNAJ		91.18 71	90.92 67	91.96 107	95.26 111	82.60 117	92.09 240	95.70 122	86.27 107	91.84 61	92.46 171	79.70 89	98.99 57	89.08 68	95.86 81	94.29 183

DELS-MVS		93.43 39	93.25 37	93.97 45	95.42 104	85.04 54	93.06 209	97.13 25	90.74 20	91.84 61	95.09 90	86.32 27	99.21 30	91.22 50	98.45 37	97.65 64
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 27	93.78 29	94.63 28	98.50 8	85.90 46	96.87 16	96.91 41	88.70 54	91.83 63	97.17 20	83.96 51	99.55 5	91.44 49	98.64 30	98.43 19

MVSFormer		91.68 64	91.30 60	92.80 77	93.86 164	83.88 79	95.96 36	95.90 108	84.66 138	91.76 64	94.91 92	77.92 108	97.30 180	89.64 65	97.11 62	97.24 75

lupinMVS		90.92 74	90.21 76	93.03 69	93.86 164	83.88 79	92.81 216	93.86 218	79.84 237	91.76 64	94.29 111	77.92 108	98.04 123	90.48 61	97.11 62	97.17 80

xiu_mvs_v2_base		91.13 72	90.89 69	91.86 112	94.97 122	82.42 118	92.24 234	95.64 128	86.11 112	91.74 66	93.14 149	79.67 92	98.89 64	89.06 69	95.46 88	94.28 184

MVS_111021_HR		93.45 37	93.31 36	93.84 49	96.99 54	84.84 55	93.24 202	97.24 19	88.76 53	91.60 67	95.85 71	86.07 30	98.66 78	91.91 40	98.16 44	98.03 48

MVS_0304		93.25 45	92.62 49	95.14 7	95.72 94	87.58 7	94.71 104	96.59 67	91.78 7	91.46 68	96.18 61	75.45 145	99.55 5	93.53 10	98.19 43	98.28 27

jason		90.80 75	90.10 79	92.90 74	93.04 188	83.53 88	93.08 207	94.15 204	80.22 233	91.41 69	94.91 92	76.87 114	97.93 130	90.28 62	96.90 65	97.24 75
jason: jason.
MVS_Test		91.31 68	91.11 63	91.93 109	94.37 145	80.14 165	93.46 191	95.80 115	86.46 104	91.35 70	93.77 133	82.21 64	98.09 119	87.57 86	94.95 94	97.55 70

新几何1				93.10 65	97.30 46	84.35 72		95.56 131	71.09 311	91.26 71	96.24 54	82.87 57	98.86 68	79.19 203	98.10 46	96.07 111

1121		90.42 84	89.49 88	93.20 61	97.27 49	84.46 66	92.63 221	95.51 138	71.01 312	91.20 72	96.21 56	82.92 56	99.05 44	80.56 175	98.07 47	96.10 109

MVS_111021_LR		92.47 54	92.29 54	92.98 71	95.99 86	84.43 70	93.08 207	96.09 94	88.20 67	91.12 73	95.72 76	81.33 75	97.76 136	91.74 44	97.37 60	96.75 94

test12				94.34 39	97.13 52	86.15 39		96.29 81		91.04 74		85.08 40	99.01 53		98.13 45	97.86 58

MG-MVS		91.77 60	91.70 57	92.00 105	97.08 53	80.03 170	93.60 186	95.18 167	87.85 74	90.89 75	96.47 48	82.06 68	98.36 93	85.07 110	97.04 64	97.62 65

CANet		93.54 36	93.20 39	94.55 31	95.65 96	85.73 49	94.94 86	96.69 60	91.89 5	90.69 76	95.88 70	81.99 70	99.54 8	93.14 18	97.95 50	98.39 20

Effi-MVS+		91.59 65	91.11 63	93.01 70	94.35 148	83.39 92	94.60 110	95.10 169	87.10 88	90.57 77	93.10 151	81.43 74	98.07 121	89.29 67	94.48 103	97.59 67

原ACMM1				92.01 103	97.34 44	81.05 145		96.81 48	78.89 245	90.45 78	95.92 68	82.65 58	98.84 73	80.68 173	98.26 42	96.14 107

Vis-MVSNet		91.75 61	91.23 62	93.29 58	95.32 108	83.78 81	96.14 30	95.98 101	89.89 29	90.45 78	96.58 43	75.09 149	98.31 99	84.75 116	96.90 65	97.78 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		91.99 57	91.80 56	92.55 84	98.24 22	81.98 125	96.76 19	96.49 71	81.89 210	90.24 80	96.44 49	78.59 100	98.61 83	89.68 64	97.85 52	97.06 85

test222					96.55 64	81.70 127	92.22 235	95.01 172	68.36 318	90.20 81	96.14 62	80.26 83			97.80 53	96.05 113

ACMMP		93.24 46	92.88 47	94.30 40	98.09 26	85.33 52	96.86 17	97.45 7	88.33 63	90.15 82	97.03 24	81.44 73	99.51 12	90.85 57	95.74 82	98.04 47
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 47	93.05 41	93.76 54	98.04 28	84.07 76	96.22 28	97.37 10	84.15 149	90.05 83	95.66 77	87.77 12	99.15 36	89.91 63	98.27 41	98.07 44

DP-MVS Recon		91.95 58	91.28 61	93.96 46	98.33 18	85.92 43	94.66 108	96.66 62	82.69 194	90.03 84	95.82 72	82.30 62	99.03 48	84.57 118	96.48 76	96.91 89

EPP-MVSNet		91.70 63	91.56 58	92.13 102	95.88 89	80.50 161	97.33 3	95.25 160	86.15 110	89.76 85	95.60 78	83.42 53	98.32 98	87.37 90	93.25 125	97.56 69

DeepC-MVS	88.79 3	93.31 41	92.99 43	94.26 41	96.07 83	85.83 47	94.89 89	96.99 32	89.02 48	89.56 86	97.37 8	82.51 59	99.38 20	92.20 30	98.30 40	97.57 68
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 69	90.62 72	93.08 66	96.27 70	84.07 76	93.52 188	95.93 104	86.95 95	89.51 87	96.13 63	78.50 102	98.35 95	85.84 105	92.90 132	96.83 92

IS-MVSNet		91.43 66	91.09 65	92.46 88	95.87 91	81.38 136	96.95 9	93.69 222	89.72 34	89.50 88	95.98 66	78.57 101	97.77 135	83.02 137	96.50 75	98.22 34

PVSNet_Blended_VisFu		91.38 67	90.91 68	92.80 77	96.39 67	83.17 96	94.87 92	96.66 62	83.29 171	89.27 89	94.46 106	80.29 82	99.17 33	87.57 86	95.37 89	96.05 113

API-MVS		90.66 78	90.07 80	92.45 89	96.36 68	84.57 61	96.06 33	95.22 166	82.39 196	89.13 90	94.27 114	80.32 81	98.46 90	80.16 184	96.71 69	94.33 182

PVSNet_BlendedMVS		89.98 91	89.70 85	90.82 146	96.12 77	81.25 138	93.92 165	96.83 46	83.49 165	89.10 91	92.26 181	81.04 77	98.85 71	86.72 101	87.86 201	92.35 268

PVSNet_Blended		90.73 77	90.32 75	91.98 106	96.12 77	81.25 138	92.55 225	96.83 46	82.04 204	89.10 91	92.56 170	81.04 77	98.85 71	86.72 101	95.91 80	95.84 120

WTY-MVS		89.60 100	88.92 103	91.67 120	95.47 103	81.15 143	92.38 230	94.78 187	83.11 174	89.06 93	94.32 109	78.67 99	96.61 229	81.57 161	90.89 154	97.24 75

XVG-OURS		89.40 111	88.70 107	91.52 123	94.06 153	81.46 133	91.27 255	96.07 96	86.14 111	88.89 94	95.77 74	68.73 238	97.26 186	87.39 89	89.96 165	95.83 121

sss		88.93 123	88.26 122	90.94 145	94.05 154	80.78 154	91.71 246	95.38 151	81.55 222	88.63 95	93.91 128	75.04 150	95.47 276	82.47 146	91.61 140	96.57 98

XVG-OURS-SEG-HR		89.95 93	89.45 89	91.47 125	94.00 159	81.21 141	91.87 242	96.06 98	85.78 114	88.55 96	95.73 75	74.67 153	97.27 184	88.71 72	89.64 170	95.91 116

ab-mvs		89.41 109	88.35 115	92.60 82	95.15 117	82.65 115	92.20 236	95.60 129	83.97 151	88.55 96	93.70 136	74.16 161	98.21 102	82.46 147	89.37 173	96.94 88

VPA-MVSNet		89.62 99	88.96 101	91.60 122	93.86 164	82.89 106	95.46 55	97.33 14	87.91 71	88.43 98	93.31 141	74.17 160	97.40 173	87.32 91	82.86 244	94.52 173

nrg030		91.08 73	90.39 73	93.17 63	93.07 186	86.91 13	96.41 24	96.26 82	88.30 64	88.37 99	94.85 97	82.19 65	97.64 143	91.09 51	82.95 242	94.96 147

tfpn200view9		87.58 170	86.64 169	90.41 164	95.99 86	78.64 214	94.58 111	91.98 254	86.94 96	88.09 100	91.77 198	69.18 227	98.10 112	70.13 269	91.10 143	94.48 178

thres400		87.62 164	86.64 169	90.57 150	95.99 86	78.64 214	94.58 111	91.98 254	86.94 96	88.09 100	91.77 198	69.18 227	98.10 112	70.13 269	91.10 143	94.96 147

thres600view7		87.65 158	86.67 164	90.59 149	96.08 82	78.72 212	94.88 91	91.58 263	87.06 93	88.08 102	92.30 178	68.91 230	98.10 112	70.05 273	91.10 143	94.96 147

conf200view11		87.65 158	86.71 161	90.46 163	96.12 77	78.55 216	95.03 81	91.58 263	87.15 85	88.06 103	92.29 179	68.91 230	98.10 112	70.13 269	91.10 143	94.71 162

thres100view900		87.63 162	86.71 161	90.38 167	96.12 77	78.55 216	95.03 81	91.58 263	87.15 85	88.06 103	92.29 179	68.91 230	98.10 112	70.13 269	91.10 143	94.48 178

thres200		87.21 183	86.24 180	90.12 180	95.36 105	78.53 218	93.26 200	92.10 247	86.42 105	88.00 105	91.11 231	69.24 226	98.00 125	69.58 274	91.04 149	93.83 207

OPM-MVS		90.12 88	89.56 87	91.82 115	93.14 184	83.90 78	94.16 145	95.74 120	88.96 49	87.86 106	95.43 81	72.48 185	97.91 131	88.10 80	90.18 162	93.65 222

MAR-MVS		90.30 85	89.37 92	93.07 68	96.61 61	84.48 65	95.68 47	95.67 123	82.36 198	87.85 107	92.85 160	76.63 119	98.80 74	80.01 185	96.68 70	95.91 116
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		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

view800		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

conf0.05thres1000		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

tfpn		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

Vis-MVSNet (Re-imp)		89.59 101	89.44 90	90.03 189	95.74 93	75.85 267	95.61 52	90.80 287	87.66 81	87.83 112	95.40 82	76.79 116	96.46 237	78.37 208	96.73 68	97.80 61

CDS-MVSNet		89.45 106	88.51 110	92.29 96	93.62 172	83.61 87	93.01 210	94.68 189	81.95 206	87.82 113	93.24 145	78.69 98	96.99 206	80.34 180	93.23 126	96.28 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		89.21 114	88.29 120	91.96 107	93.71 170	82.62 116	93.30 197	94.19 202	82.22 199	87.78 114	93.94 124	78.83 96	96.95 210	77.70 216	92.98 130	96.32 102

CANet_DTU		90.26 87	89.41 91	92.81 76	93.46 176	83.01 102	93.48 189	94.47 194	89.43 37	87.76 115	94.23 115	70.54 210	99.03 48	84.97 111	96.39 77	96.38 101

HyFIR lowres test		88.09 142	86.81 156	91.93 109	96.00 85	80.63 156	90.01 267	95.79 116	73.42 292	87.68 116	92.10 187	73.86 165	97.96 127	80.75 171	91.70 139	97.19 79

UGNet		89.95 93	88.95 102	92.95 72	94.51 140	83.31 93	95.70 46	95.23 164	89.37 39	87.58 117	93.94 124	64.00 274	98.78 75	83.92 129	96.31 78	96.74 95
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		88.84 124	87.69 130	92.30 95	96.14 76	81.42 135	90.01 267	95.86 112	74.52 286	87.41 118	93.94 124	75.46 144	98.36 93	80.36 179	95.53 84	97.12 83

PAPM_NR		91.22 70	90.78 71	92.52 86	97.60 34	81.46 133	94.37 130	96.24 85	86.39 106	87.41 118	94.80 99	82.06 68	98.48 89	82.80 141	95.37 89	97.61 66

EPNet		91.79 59	91.02 66	94.10 44	90.10 285	85.25 53	96.03 34	92.05 250	92.83 1	87.39 120	95.78 73	79.39 94	99.01 53	88.13 79	97.48 58	98.05 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		89.07 117	88.32 118	91.34 128	93.24 181	79.79 176	92.29 233	94.98 175	80.24 232	87.38 121	92.45 172	78.02 106	97.33 178	83.29 134	92.93 131	96.91 89

EI-MVSNet		89.10 116	88.86 106	89.80 199	91.84 210	78.30 229	93.70 181	95.01 172	85.73 116	87.15 122	95.28 83	79.87 86	97.21 192	83.81 131	87.36 205	93.88 202

MVSTER		88.84 124	88.29 120	90.51 159	92.95 192	80.44 162	93.73 177	95.01 172	84.66 138	87.15 122	93.12 150	72.79 179	97.21 192	87.86 82	87.36 205	93.87 203

mvs-test1		89.45 106	89.14 97	90.38 167	93.33 178	77.63 249	94.95 85	94.36 197	87.70 77	87.10 124	92.81 164	73.45 171	98.03 124	85.57 107	93.04 129	95.48 131

VPNet		88.20 138	87.47 134	90.39 165	93.56 174	79.46 185	94.04 158	95.54 134	88.67 55	86.96 125	94.58 105	69.33 222	97.15 194	84.05 128	80.53 279	94.56 171

HY-MVS	83.01 12	89.03 120	87.94 128	92.29 96	94.86 127	82.77 107	92.08 241	94.49 193	81.52 223	86.93 126	92.79 166	78.32 105	98.23 100	79.93 188	90.55 155	95.88 118

HQP_MVS		90.60 82	90.19 77	91.82 115	94.70 133	82.73 111	95.85 40	96.22 86	90.81 18	86.91 127	94.86 95	74.23 157	98.12 106	88.15 77	89.99 163	94.63 164

plane_prior3						82.75 108			90.26 25	86.91 127

BH-RMVSNet		88.37 133	87.48 133	91.02 141	95.28 109	79.45 187	92.89 215	93.07 230	85.45 122	86.91 127	94.84 98	70.35 211	97.76 136	73.97 248	94.59 100	95.85 119

Fast-Effi-MVS+		89.41 109	88.64 108	91.71 119	94.74 129	80.81 153	93.54 187	95.10 169	83.11 174	86.82 130	90.67 238	79.74 88	97.75 139	80.51 177	93.55 116	96.57 98

FIs		90.51 83	90.35 74	90.99 143	93.99 160	80.98 147	95.73 44	97.54 3	89.15 44	86.72 131	94.68 100	81.83 72	97.24 188	85.18 109	88.31 196	94.76 161

PAPR		90.02 90	89.27 96	92.29 96	95.78 92	80.95 149	92.68 220	96.22 86	81.91 208	86.66 132	93.75 135	82.23 63	98.44 92	79.40 202	94.79 95	97.48 71

PMMVS		85.71 219	84.96 207	87.95 258	88.90 300	77.09 256	88.68 286	90.06 299	72.32 302	86.47 133	90.76 237	72.15 188	94.40 296	81.78 159	93.49 118	92.36 267

UniMVSNet_NR-MVSNet		89.92 95	89.29 94	91.81 117	93.39 177	83.72 82	94.43 122	97.12 26	89.80 31	86.46 134	93.32 140	83.16 54	97.23 190	84.92 112	81.02 270	94.49 177

DU-MVS		89.34 113	88.50 111	91.85 113	93.04 188	83.72 82	94.47 119	96.59 67	89.50 36	86.46 134	93.29 143	77.25 112	97.23 190	84.92 112	81.02 270	94.59 168

CostFormer		85.77 213	84.94 208	88.26 251	91.16 252	72.58 292	89.47 276	91.04 281	76.26 271	86.45 136	89.97 253	70.74 204	96.86 216	82.35 148	87.07 210	95.34 137

UniMVSNet (Re)		89.80 97	89.07 99	92.01 103	93.60 173	84.52 62	94.78 97	97.47 5	89.26 41	86.44 137	92.32 177	82.10 66	97.39 176	84.81 115	80.84 274	94.12 189

TR-MVS		86.78 191	85.76 191	89.82 196	94.37 145	78.41 226	92.47 227	92.83 233	81.11 228	86.36 138	92.40 174	68.73 238	97.48 151	73.75 251	89.85 167	93.57 230

AdaColmap		89.89 96	89.07 99	92.37 93	97.41 41	83.03 100	94.42 123	95.92 105	82.81 190	86.34 139	94.65 102	73.89 164	99.02 51	80.69 172	95.51 85	95.05 141

FC-MVSNet-test		90.27 86	90.18 78	90.53 152	93.71 170	79.85 175	95.77 43	97.59 2	89.31 40	86.27 140	94.67 101	81.93 71	97.01 205	84.26 124	88.09 199	94.71 162

PS-MVSNAJss		89.97 92	89.62 86	91.02 141	91.90 208	80.85 152	95.26 68	95.98 101	86.26 108	86.21 141	94.29 111	79.70 89	97.65 141	88.87 71	88.10 197	94.57 170

TAPA-MVS	84.62 6	88.16 139	87.01 150	91.62 121	96.64 60	80.65 155	94.39 126	96.21 89	76.38 268	86.19 142	95.44 80	79.75 87	98.08 120	62.75 311	95.29 91	96.13 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CVMVSNet		84.69 244	84.79 213	84.37 300	91.84 210	64.92 324	93.70 181	91.47 269	66.19 324	86.16 143	95.28 83	67.18 255	93.33 307	80.89 170	90.42 157	94.88 156

tpmrst		85.35 223	84.99 204	86.43 285	90.88 265	67.88 316	88.71 285	91.43 270	80.13 234	86.08 144	88.80 267	73.05 176	96.02 253	82.48 145	83.40 241	95.40 134

ACMM	84.12 9	89.14 115	88.48 114	91.12 134	94.65 136	81.22 140	95.31 57	96.12 93	85.31 125	85.92 145	94.34 107	70.19 214	98.06 122	85.65 106	88.86 186	94.08 193
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		89.51 103	88.50 111	92.54 85	98.11 24	81.99 124	95.16 74	96.36 79	70.19 314	85.81 146	95.25 85	76.70 117	98.63 81	82.07 152	96.86 67	97.00 86

v6		87.98 144	87.25 141	90.16 174	91.36 232	79.39 194	94.37 130	95.27 159	84.48 141	85.78 147	91.51 212	76.15 122	97.46 153	84.46 120	81.88 257	93.62 226

v1neww		87.98 144	87.25 141	90.16 174	91.38 229	79.41 189	94.37 130	95.28 156	84.48 141	85.77 148	91.53 210	76.12 123	97.45 155	84.45 121	81.89 255	93.61 227

v7new		87.98 144	87.25 141	90.16 174	91.38 229	79.41 189	94.37 130	95.28 156	84.48 141	85.77 148	91.53 210	76.12 123	97.45 155	84.45 121	81.89 255	93.61 227

v7		87.75 155	86.96 151	90.12 180	91.20 247	79.50 180	94.28 136	95.46 141	83.45 166	85.75 150	91.56 209	75.13 147	97.43 164	83.60 132	82.18 250	93.42 236

tpm		84.73 241	84.02 225	86.87 282	90.33 280	68.90 313	89.06 282	89.94 302	80.85 230	85.75 150	89.86 255	68.54 240	95.97 255	77.76 215	84.05 231	95.75 125

Baseline_NR-MVSNet		87.07 186	86.63 171	88.40 247	91.44 222	77.87 241	94.23 139	92.57 240	84.12 150	85.74 152	92.08 188	77.25 112	96.04 251	82.29 150	79.94 287	91.30 286

divwei89l23v2f112		87.84 150	87.09 145	90.10 187	91.23 244	79.24 205	94.09 150	95.24 161	84.44 145	85.70 153	91.31 221	75.91 135	97.44 162	84.17 126	81.73 262	93.64 223

v1		87.85 149	87.10 144	90.11 185	91.21 246	79.24 205	94.09 150	95.24 161	84.44 145	85.70 153	91.31 221	75.96 133	97.45 155	84.18 125	81.73 262	93.64 223

v1141		87.84 150	87.09 145	90.11 185	91.23 244	79.25 203	94.08 152	95.24 161	84.44 145	85.69 155	91.31 221	75.91 135	97.44 162	84.17 126	81.74 261	93.63 225

V42		87.68 157	86.86 153	90.15 178	90.58 274	80.14 165	94.24 138	95.28 156	83.66 158	85.67 156	91.33 218	74.73 152	97.41 171	84.43 123	81.83 258	92.89 251

v1144		87.61 169	86.79 158	90.06 188	91.01 255	79.34 197	93.95 164	95.42 150	83.36 170	85.66 157	91.31 221	74.98 151	97.42 166	83.37 133	82.06 251	93.42 236

PatchT		82.68 262	81.27 260	86.89 281	90.09 286	70.94 304	84.06 318	90.15 296	74.91 282	85.63 158	83.57 312	69.37 221	94.87 294	65.19 302	88.50 191	94.84 157

CR-MVSNet		85.35 223	83.76 229	90.12 180	90.58 274	79.34 197	85.24 311	91.96 256	78.27 255	85.55 159	87.87 283	71.03 199	95.61 267	73.96 249	89.36 174	95.40 134

RPMNet		83.18 259	80.87 265	90.12 180	90.58 274	79.34 197	85.24 311	90.78 288	71.44 307	85.55 159	82.97 316	70.87 201	95.61 267	61.01 315	89.36 174	95.40 134

v2v482		87.84 150	87.06 148	90.17 173	90.99 256	79.23 207	94.00 162	95.13 168	84.87 133	85.53 161	92.07 190	74.45 154	97.45 155	84.71 117	81.75 260	93.85 206

TranMVSNet+NR-MVSNet		88.84 124	87.95 127	91.49 124	92.68 197	83.01 102	94.92 88	96.31 80	89.88 30	85.53 161	93.85 131	76.63 119	96.96 209	81.91 156	79.87 289	94.50 175

v144192		87.19 184	86.35 175	89.74 200	90.64 273	78.24 232	93.92 165	95.43 148	81.93 207	85.51 163	91.05 233	74.21 159	97.45 155	82.86 139	81.56 264	93.53 231

Patchmatch-test1		85.81 212	84.71 214	89.12 224	92.15 203	76.60 260	91.12 258	91.69 261	83.53 164	85.50 164	88.56 272	66.79 256	95.00 292	72.69 255	90.35 158	95.76 124

v1192		87.25 180	86.33 176	90.00 192	90.76 268	79.04 209	93.80 171	95.48 140	82.57 195	85.48 165	91.18 227	73.38 174	97.42 166	82.30 149	82.06 251	93.53 231

WR-MVS		88.38 132	87.67 131	90.52 158	93.30 180	80.18 163	93.26 200	95.96 103	88.57 59	85.47 166	92.81 164	76.12 123	96.91 213	81.24 163	82.29 248	94.47 180

mvs_anonymous		89.37 112	89.32 93	89.51 209	93.47 175	74.22 273	91.65 249	94.83 185	82.91 188	85.45 167	93.79 132	81.23 76	96.36 242	86.47 104	94.09 109	97.94 52

LPG-MVS_test		89.45 106	88.90 104	91.12 134	94.47 141	81.49 131	95.30 59	96.14 90	86.73 100	85.45 167	95.16 87	69.89 215	98.10 112	87.70 84	89.23 177	93.77 212

LGP-MVS_train				91.12 134	94.47 141	81.49 131		96.14 90	86.73 100	85.45 167	95.16 87	69.89 215	98.10 112	87.70 84	89.23 177	93.77 212

tfpn_ndepth		86.10 204	84.98 205	89.43 212	95.52 102	78.29 230	94.62 109	89.60 309	81.88 215	85.43 170	90.54 242	68.47 243	96.85 217	68.46 283	90.34 159	93.15 245

Effi-MVS+-dtu		88.65 128	88.35 115	89.54 206	93.33 178	76.39 262	94.47 119	94.36 197	87.70 77	85.43 170	89.56 260	73.45 171	97.26 186	85.57 107	91.28 142	94.97 144

v1240		86.78 191	85.85 189	89.56 205	90.45 279	77.79 243	93.61 185	95.37 153	81.65 218	85.43 170	91.15 229	71.50 194	97.43 164	81.47 162	82.05 253	93.47 235

HQP-NCC					94.17 150		94.39 126		88.81 50	85.43 170

ACMP_Plane					94.17 150		94.39 126		88.81 50	85.43 170

HQP4-MVS										85.43 170			97.96 127			94.51 174

HQP-MVS		89.80 97	89.28 95	91.34 128	94.17 150	81.56 128	94.39 126	96.04 99	88.81 50	85.43 170	93.97 123	73.83 166	97.96 127	87.11 95	89.77 168	94.50 175

CLD-MVS		89.47 105	88.90 104	91.18 133	94.22 149	82.07 123	92.13 238	96.09 94	87.90 72	85.37 177	92.45 172	74.38 155	97.56 146	87.15 93	90.43 156	93.93 198
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn1000		86.06 205	84.92 209	89.49 210	95.54 99	77.79 243	94.72 102	89.07 316	82.05 202	85.36 178	91.94 194	68.32 250	96.65 225	67.04 290	90.24 160	94.02 196

v1921920		86.97 188	86.06 185	89.69 203	90.53 278	78.11 235	93.80 171	95.43 148	81.90 209	85.33 179	91.05 233	72.66 181	97.41 171	82.05 153	81.80 259	93.53 231

test_djsdf		89.03 120	88.64 108	90.21 172	90.74 269	79.28 201	95.96 36	95.90 108	84.66 138	85.33 179	92.94 159	74.02 163	97.30 180	89.64 65	88.53 189	94.05 194

GA-MVS		86.61 195	85.27 202	90.66 148	91.33 237	78.71 213	90.40 261	93.81 221	85.34 124	85.12 181	89.57 259	61.25 287	97.11 198	80.99 168	89.59 171	96.15 106

PatchmatchNet		85.85 210	84.70 215	89.29 220	91.76 213	75.54 269	88.49 288	91.30 272	81.63 220	85.05 182	88.70 269	71.71 189	96.24 246	74.61 244	89.05 184	96.08 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		83.90 252	82.70 253	87.51 265	90.23 284	72.67 288	88.62 287	81.96 339	81.37 226	85.01 183	88.34 275	66.31 261	94.45 295	75.30 237	87.12 208	95.43 133

PVSNet	78.82 18	85.55 220	84.65 216	88.23 253	94.72 131	71.93 294	87.12 299	92.75 236	78.80 248	84.95 184	90.53 244	64.43 273	96.71 224	74.74 242	93.86 112	96.06 112

thresconf0.02		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

tfpn_n400		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

tfpnconf		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

tfpnview11		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

MDTV_nov1_ep13			83.56 237		91.69 217	69.93 310	87.75 295	91.54 267	78.60 251	84.86 189	88.90 265	69.54 220	96.03 252	70.25 266	88.93 185

IterMVS-LS		88.36 134	87.91 129	89.70 202	93.80 167	78.29 230	93.73 177	95.08 171	85.73 116	84.75 190	91.90 196	79.88 85	96.92 212	83.83 130	82.51 246	93.89 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm2		84.08 249	82.94 249	87.48 268	91.39 228	71.27 298	89.23 280	90.37 292	71.95 305	84.64 191	89.33 261	67.30 252	96.55 232	75.17 238	87.09 209	94.63 164

XXY-MVS		87.65 158	86.85 154	90.03 189	92.14 204	80.60 158	93.76 174	95.23 164	82.94 186	84.60 192	94.02 120	74.27 156	95.49 275	81.04 165	83.68 235	94.01 197

MDTV_nov1_ep13_2view						55.91 338	87.62 297		73.32 293	84.59 193		70.33 212		74.65 243		95.50 130

test-LLR		85.87 209	85.41 198	87.25 272	90.95 258	71.67 296	89.55 272	89.88 304	83.41 167	84.54 194	87.95 280	67.25 253	95.11 289	81.82 157	93.37 123	94.97 144

test-mter		84.54 246	83.64 236	87.25 272	90.95 258	71.67 296	89.55 272	89.88 304	79.17 242	84.54 194	87.95 280	55.56 309	95.11 289	81.82 157	93.37 123	94.97 144

BH-untuned		88.60 129	88.13 124	90.01 191	95.24 116	78.50 224	93.29 198	94.15 204	84.75 136	84.46 196	93.40 137	75.76 139	97.40 173	77.59 217	94.52 102	94.12 189

CNLPA		89.07 117	87.98 126	92.34 94	96.87 56	84.78 57	94.08 152	93.24 227	81.41 225	84.46 196	95.13 89	75.57 142	96.62 227	77.21 221	93.84 113	95.61 129

PCF-MVS	84.11 10	87.74 156	86.08 184	92.70 80	94.02 155	84.43 70	89.27 278	95.87 111	73.62 291	84.43 198	94.33 108	78.48 103	98.86 68	70.27 265	94.45 105	94.81 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 178	85.98 186	91.08 137	94.01 156	83.10 97	95.14 75	94.94 176	83.57 161	84.37 199	91.64 201	66.59 258	96.34 243	78.23 211	85.36 219	93.79 208

test1		87.26 178	85.98 186	91.08 137	94.01 156	83.10 97	95.14 75	94.94 176	83.57 161	84.37 199	91.64 201	66.59 258	96.34 243	78.23 211	85.36 219	93.79 208

FMVSNet3		87.40 176	86.11 182	91.30 130	93.79 169	83.64 85	94.20 144	94.81 186	83.89 152	84.37 199	91.87 197	68.45 244	96.56 230	78.23 211	85.36 219	93.70 217

v148		87.04 187	86.32 177	89.21 222	90.94 260	77.26 255	93.71 180	94.43 195	84.84 134	84.36 202	90.80 236	76.04 129	97.05 203	82.12 151	79.60 290	93.31 238

PatchMatch-RL		86.77 193	85.54 193	90.47 162	95.88 89	82.71 113	90.54 260	92.31 243	79.82 238	84.32 203	91.57 208	68.77 237	96.39 240	73.16 253	93.48 120	92.32 269

3Dnovator	86.66 5	91.73 62	90.82 70	94.44 34	94.59 137	86.37 31	97.18 6	97.02 31	89.20 42	84.31 204	96.66 39	73.74 168	99.17 33	86.74 98	97.96 49	97.79 62

PatchFormer-LS_test		86.02 206	85.13 203	88.70 232	91.52 219	74.12 276	91.19 257	92.09 248	82.71 193	84.30 205	87.24 289	70.87 201	96.98 207	81.04 165	85.17 222	95.00 143

jajsoiax		88.24 137	87.50 132	90.48 161	90.89 264	80.14 165	95.31 57	95.65 127	84.97 132	84.24 206	94.02 120	65.31 268	97.42 166	88.56 73	88.52 190	93.89 200

mvs_tets		88.06 143	87.28 139	90.38 167	90.94 260	79.88 173	95.22 70	95.66 125	85.10 130	84.21 207	93.94 124	63.53 276	97.40 173	88.50 74	88.40 195	93.87 203

3Dnovator+	87.14 4	92.42 55	91.37 59	95.55 2	95.63 97	88.73 2	97.07 8	96.77 52	90.84 17	84.02 208	96.62 41	75.95 134	99.34 21	87.77 83	97.68 54	98.59 8

PLC	84.53 7	89.06 119	88.03 125	92.15 100	97.27 49	82.69 114	94.29 135	95.44 147	79.71 239	84.01 209	94.18 116	76.68 118	98.75 76	77.28 220	93.41 121	95.02 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FMVSNet2		87.19 184	85.82 190	91.30 130	94.01 156	83.67 84	94.79 96	94.94 176	83.57 161	83.88 210	92.05 191	66.59 258	96.51 233	77.56 218	85.01 223	93.73 215

DWT-MVSNet_test		84.95 233	83.68 234	88.77 229	91.43 225	73.75 279	91.74 245	90.98 282	80.66 231	83.84 211	87.36 287	62.44 279	97.11 198	78.84 206	85.81 215	95.46 132

anonymousdsp		87.84 150	87.09 145	90.12 180	89.13 296	80.54 159	94.67 107	95.55 132	82.05 202	83.82 212	92.12 184	71.47 195	97.15 194	87.15 93	87.80 202	92.67 257

1112_ss		88.42 131	87.33 137	91.72 118	94.92 124	80.98 147	92.97 213	94.54 192	78.16 258	83.82 212	93.88 129	78.78 97	97.91 131	79.45 198	89.41 172	96.26 104

WR-MVS_H		87.80 154	87.37 136	89.10 226	93.23 182	78.12 234	95.61 52	97.30 17	87.90 72	83.72 214	92.01 192	79.65 93	96.01 254	76.36 227	80.54 278	93.16 243

BH-w/o		87.57 171	87.05 149	89.12 224	94.90 126	77.90 239	92.41 228	93.51 224	82.89 189	83.70 215	91.34 217	75.75 140	97.07 201	75.49 234	93.49 118	92.39 266

ACMP	84.23 8	89.01 122	88.35 115	90.99 143	94.73 130	81.27 137	95.07 78	95.89 110	86.48 103	83.67 216	94.30 110	69.33 222	97.99 126	87.10 97	88.55 188	93.72 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		87.25 180	86.38 174	89.85 195	91.19 249	79.50 180	94.48 116	95.45 145	83.79 156	83.62 217	91.19 226	75.13 147	97.42 166	81.94 155	80.60 276	92.63 259

v8		87.50 173	86.71 161	89.89 194	91.37 231	79.40 193	94.50 115	95.38 151	84.81 135	83.60 218	91.33 218	76.05 127	97.42 166	82.84 140	80.51 281	92.84 253

cascas		86.43 200	84.98 205	90.80 147	92.10 206	80.92 150	90.24 263	95.91 107	73.10 295	83.57 219	88.39 274	65.15 269	97.46 153	84.90 114	91.43 141	94.03 195

Test_1112_low_res		87.65 158	86.51 173	91.08 137	94.94 123	79.28 201	91.77 243	94.30 200	76.04 273	83.51 220	92.37 175	77.86 110	97.73 140	78.69 207	89.13 183	96.22 105

CP-MVSNet		87.63 162	87.26 140	88.74 231	93.12 185	76.59 261	95.29 61	96.58 69	88.43 61	83.49 221	92.98 158	75.28 146	95.83 261	78.97 204	81.15 267	93.79 208

QAPM		89.51 103	88.15 123	93.59 56	94.92 124	84.58 60	96.82 18	96.70 58	78.43 253	83.41 222	96.19 60	73.18 175	99.30 27	77.11 223	96.54 74	96.89 91

TESTMET0.1,1		83.74 254	82.85 251	86.42 286	89.96 289	71.21 300	89.55 272	87.88 323	77.41 261	83.37 223	87.31 288	56.71 306	93.65 303	80.62 174	92.85 134	94.40 181

PS-CasMVS		87.32 177	86.88 152	88.63 234	92.99 191	76.33 264	95.33 56	96.61 66	88.22 66	83.30 224	93.07 152	73.03 177	95.79 264	78.36 209	81.00 272	93.75 214

gg-mvs-nofinetune		81.77 267	79.37 277	88.99 227	90.85 266	77.73 247	86.29 303	79.63 343	74.88 284	83.19 225	69.05 334	60.34 293	96.11 250	75.46 235	94.64 99	93.11 246

XVG-ACMP-BASELINE		86.00 207	84.84 212	89.45 211	91.20 247	78.00 236	91.70 247	95.55 132	85.05 131	82.97 226	92.25 182	54.49 313	97.48 151	82.93 138	87.45 204	92.89 251

LS3D		87.89 148	86.32 177	92.59 83	96.07 83	82.92 105	95.23 69	94.92 180	75.66 275	82.89 227	95.98 66	72.48 185	99.21 30	68.43 284	95.23 93	95.64 128

PEN-MVS		86.80 190	86.27 179	88.40 247	92.32 202	75.71 268	95.18 72	96.38 78	87.97 69	82.82 228	93.15 148	73.39 173	95.92 257	76.15 231	79.03 292	93.59 229

FMVSNet1		85.85 210	84.11 224	91.08 137	92.81 194	83.10 97	95.14 75	94.94 176	81.64 219	82.68 229	91.64 201	59.01 300	96.34 243	75.37 236	83.78 232	93.79 208

RPSCF		85.07 228	84.27 222	87.48 268	92.91 193	70.62 306	91.69 248	92.46 241	76.20 272	82.67 230	95.22 86	63.94 275	97.29 183	77.51 219	85.80 216	94.53 172

Fast-Effi-MVS+-dtu		87.44 174	86.72 160	89.63 204	92.04 207	77.68 248	94.03 159	93.94 216	85.81 113	82.42 231	91.32 220	70.33 212	97.06 202	80.33 181	90.23 161	94.14 188

v7n		86.81 189	85.76 191	89.95 193	90.72 270	79.25 203	95.07 78	95.92 105	84.45 144	82.29 232	90.86 235	72.60 183	97.53 148	79.42 201	80.52 280	93.08 248

DTE-MVSNet		86.11 203	85.48 197	87.98 257	91.65 218	74.92 271	94.93 87	95.75 119	87.36 84	82.26 233	93.04 153	72.85 178	95.82 262	74.04 247	77.46 297	93.20 241

ADS-MVSNet2		81.66 269	79.71 275	87.50 266	91.35 235	74.19 274	83.33 322	88.48 320	72.90 298	82.24 234	85.77 304	64.98 270	93.20 309	64.57 305	83.74 233	95.12 139

ADS-MVSNet		81.56 271	79.78 273	86.90 280	91.35 235	71.82 295	83.33 322	89.16 315	72.90 298	82.24 234	85.77 304	64.98 270	93.76 301	64.57 305	83.74 233	95.12 139

v52		86.50 197	85.53 196	89.39 213	89.17 295	78.99 210	94.72 102	95.54 134	83.59 159	82.10 236	90.60 241	71.59 192	97.45 155	82.52 143	79.99 286	91.73 278

V4		86.50 197	85.54 193	89.39 213	89.13 296	78.99 210	94.73 99	95.54 134	83.59 159	82.10 236	90.61 240	71.60 191	97.45 155	82.52 143	80.01 285	91.74 277

JIA-IIPM		81.04 277	78.98 283	87.25 272	88.64 301	73.48 281	81.75 327	89.61 308	73.19 294	82.05 238	73.71 331	66.07 266	95.87 260	71.18 263	84.60 226	92.41 265

F-COLMAP		87.95 147	86.80 157	91.40 127	96.35 69	80.88 151	94.73 99	95.45 145	79.65 240	82.04 239	94.61 103	71.13 197	98.50 88	76.24 230	91.05 148	94.80 160

PAPM		86.68 194	85.39 199	90.53 152	93.05 187	79.33 200	89.79 271	94.77 188	78.82 247	81.95 240	93.24 145	76.81 115	97.30 180	66.94 291	93.16 127	94.95 154

DP-MVS		87.25 180	85.36 200	92.90 74	97.65 33	83.24 94	94.81 95	92.00 252	74.99 281	81.92 241	95.00 91	72.66 181	99.05 44	66.92 293	92.33 137	96.40 100

tpmp4_e23		83.87 253	82.33 254	88.48 244	91.46 221	72.82 285	89.82 270	91.57 266	73.02 297	81.86 242	89.05 263	66.20 263	96.97 208	71.57 259	86.39 212	95.66 127

pm-mvs1		86.61 195	85.54 193	89.82 196	91.44 222	80.18 163	95.28 67	94.85 183	83.84 153	81.66 243	92.62 169	72.45 187	96.48 235	79.67 195	78.06 294	92.82 255

MVS		87.44 174	86.10 183	91.44 126	92.61 198	83.62 86	92.63 221	95.66 125	67.26 322	81.47 244	92.15 183	77.95 107	98.22 101	79.71 194	95.48 86	92.47 263

semantic-postprocess				88.18 254	91.71 215	76.87 259		92.65 239	85.40 123	81.44 245	90.54 242	66.21 262	95.00 292	81.04 165	81.05 268	92.66 258

CHOSEN 280x420		85.15 227	83.99 226	88.65 233	92.47 199	78.40 227	79.68 331	92.76 235	74.90 283	81.41 246	89.59 258	69.85 217	95.51 272	79.92 189	95.29 91	92.03 273

v748		86.27 201	85.28 201	89.25 221	90.26 282	77.58 254	94.89 89	95.50 139	84.28 148	81.41 246	90.46 246	72.57 184	97.32 179	79.81 193	78.36 293	92.84 253

Patchmtry		82.71 261	80.93 264	88.06 256	90.05 287	76.37 263	84.74 313	91.96 256	72.28 303	81.32 248	87.87 283	71.03 199	95.50 274	68.97 280	80.15 283	92.32 269

dp		81.47 273	80.23 269	85.17 295	89.92 290	65.49 323	86.74 300	90.10 298	76.30 270	81.10 249	87.12 291	62.81 277	95.92 257	68.13 287	79.88 288	94.09 192

tfpnnormal		84.72 242	83.23 246	89.20 223	92.79 195	80.05 168	94.48 116	95.81 114	82.38 197	81.08 250	91.21 225	69.01 229	96.95 210	61.69 313	80.59 277	90.58 304

IterMVS		84.88 235	83.98 227	87.60 263	91.44 222	76.03 266	90.18 265	92.41 242	83.24 173	81.06 251	90.42 247	66.60 257	94.28 297	79.46 197	80.98 273	92.48 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS	83.78 11	88.74 127	87.29 138	93.08 66	92.70 196	85.39 51	96.57 22	96.43 74	78.74 250	80.85 252	96.07 64	69.64 219	99.01 53	78.01 214	96.65 71	94.83 158

pmmvs4		85.43 221	83.86 228	90.16 174	90.02 288	82.97 104	90.27 262	92.67 238	75.93 274	80.73 253	91.74 200	71.05 198	95.73 266	78.85 205	83.46 239	91.78 276

MIMVSNet		82.59 263	80.53 266	88.76 230	91.51 220	78.32 228	86.57 302	90.13 297	79.32 241	80.70 254	88.69 270	52.98 317	93.07 312	66.03 300	88.86 186	94.90 155

IB-MVS	80.51 15	85.24 226	83.26 245	91.19 132	92.13 205	79.86 174	91.75 244	91.29 273	83.28 172	80.66 255	88.49 273	61.28 286	98.46 90	80.99 168	79.46 291	95.25 138
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				87.94 259	89.73 293	77.91 238	87.80 293	78.23 345		80.58 256	83.86 310	59.88 297	95.33 286	71.20 261	92.22 138	90.60 303

EU-MVSNet		81.32 275	80.95 263	82.42 308	88.50 303	63.67 325	93.32 193	91.33 271	64.02 328	80.57 257	92.83 162	61.21 289	92.27 315	76.34 228	80.38 282	91.32 285

tpmvs		83.35 258	82.07 255	87.20 276	91.07 254	71.00 303	88.31 290	91.70 260	78.91 244	80.49 258	87.18 290	69.30 225	97.08 200	68.12 288	83.56 237	93.51 234

pmmvs5		84.21 248	82.84 252	88.34 249	88.95 299	76.94 258	92.41 228	91.91 258	75.63 276	80.28 259	91.18 227	64.59 272	95.57 269	77.09 224	83.47 238	92.53 261

tpm cat1		81.96 266	80.27 268	87.01 277	91.09 253	71.02 302	87.38 298	91.53 268	66.25 323	80.17 260	86.35 301	68.22 251	96.15 249	69.16 279	82.29 248	93.86 205

MS-PatchMatch		85.05 229	84.16 223	87.73 261	91.42 226	78.51 223	91.25 256	93.53 223	77.50 260	80.15 261	91.58 206	61.99 282	95.51 272	75.69 233	94.35 108	89.16 312

1314		87.51 172	86.57 172	90.34 170	92.42 200	79.74 178	92.63 221	95.35 155	78.35 254	80.14 262	91.62 205	74.05 162	97.15 194	81.05 164	93.53 117	94.12 189

ITE_SJBPF				88.24 252	91.88 209	77.05 257		92.92 231	85.54 120	80.13 263	93.30 142	57.29 305	96.20 247	72.46 256	84.71 225	91.49 282

NR-MVSNet		88.58 130	87.47 134	91.93 109	93.04 188	84.16 75	94.77 98	96.25 84	89.05 45	80.04 264	93.29 143	79.02 95	97.05 203	81.71 160	80.05 284	94.59 168

test0.0.03 1		82.41 264	81.69 257	84.59 298	88.23 305	72.89 284	90.24 263	87.83 324	83.41 167	79.86 265	89.78 256	67.25 253	88.99 325	65.18 303	83.42 240	91.90 275

TransMVSNet (Re)		84.43 247	83.06 248	88.54 243	91.72 214	78.44 225	95.18 72	92.82 234	82.73 192	79.67 266	92.12 184	73.49 170	95.96 256	71.10 264	68.73 326	91.21 287

LTVRE_ROB	82.13 13	86.26 202	84.90 210	90.34 170	94.44 144	81.50 130	92.31 232	94.89 181	83.03 181	79.63 267	92.67 167	69.69 218	97.79 134	71.20 261	86.26 213	91.72 279
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		85.35 223	84.64 217	87.49 267	90.77 267	72.59 291	94.01 161	94.40 196	84.72 137	79.62 268	93.17 147	61.91 283	96.72 222	81.99 154	81.16 265	93.16 243

EPNet_dtu		86.49 199	85.94 188	88.14 255	90.24 283	72.82 285	94.11 148	92.20 246	86.66 102	79.42 269	92.36 176	73.52 169	95.81 263	71.26 260	93.66 114	95.80 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		88.30 136	88.32 118	88.27 250	94.71 132	72.41 293	93.15 203	90.98 282	87.77 76	79.25 270	91.96 193	78.35 104	95.75 265	83.04 136	95.62 83	96.65 96

pmmvs6		83.42 255	81.60 258	88.87 228	88.01 309	77.87 241	94.96 84	94.24 201	74.67 285	78.80 271	91.09 232	60.17 295	96.49 234	77.06 225	75.40 302	92.23 271

MVP-Stereo		85.97 208	84.86 211	89.32 219	90.92 262	82.19 121	92.11 239	94.19 202	78.76 249	78.77 272	91.63 204	68.38 249	96.56 230	75.01 241	93.95 110	89.20 311
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		84.86 236	83.09 247	90.14 179	93.80 167	80.05 168	89.18 281	93.09 229	78.89 245	78.19 273	91.91 195	65.86 267	97.27 184	68.47 282	88.45 192	93.11 246

testgi		80.94 280	80.20 270	83.18 304	87.96 310	66.29 320	91.28 254	90.70 290	83.70 157	78.12 274	92.84 161	51.37 319	90.82 322	63.34 308	82.46 247	92.43 264

ACMH+	81.04 14	85.05 229	83.46 241	89.82 196	94.66 135	79.37 195	94.44 121	94.12 206	82.19 200	78.04 275	92.82 163	58.23 302	97.54 147	73.77 250	82.90 243	92.54 260

COLMAP_ROB	80.39 16	83.96 250	82.04 256	89.74 200	95.28 109	79.75 177	94.25 137	92.28 244	75.17 279	78.02 276	93.77 133	58.60 301	97.84 133	65.06 304	85.92 214	91.63 280
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		88.15 140	86.82 155	92.14 101	90.67 272	81.07 144	93.01 210	94.59 191	83.83 155	77.78 277	90.63 239	68.51 241	98.16 104	88.02 81	94.37 107	97.17 80

Anonymous20231206		81.03 278	79.77 274	84.82 297	87.85 312	70.26 308	91.42 252	92.08 249	73.67 290	77.75 278	89.25 262	62.43 280	93.08 311	61.50 314	82.00 254	91.12 289

SixPastTwentyTwo		83.91 251	82.90 250	86.92 279	90.99 256	70.67 305	93.48 189	91.99 253	85.54 120	77.62 279	92.11 186	60.59 292	96.87 215	76.05 232	77.75 295	93.20 241

test_normal		88.13 141	86.78 159	92.18 99	90.55 277	81.19 142	92.74 218	94.64 190	83.84 153	77.49 280	90.51 245	68.49 242	98.16 104	88.22 76	94.55 101	97.21 78

ACMH	80.38 17	85.36 222	83.68 234	90.39 165	94.45 143	80.63 156	94.73 99	94.85 183	82.09 201	77.24 281	92.65 168	60.01 296	97.58 144	72.25 257	84.87 224	92.96 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmatch-RL test		81.67 268	79.96 272	86.81 283	85.42 317	71.23 299	82.17 326	87.50 328	78.47 252	77.19 282	82.50 317	70.81 203	93.48 305	82.66 142	72.89 307	95.71 126

Patchmatch-test		81.37 274	79.30 278	87.58 264	90.92 262	74.16 275	80.99 328	87.68 326	70.52 313	76.63 283	88.81 266	71.21 196	92.76 313	60.01 319	86.93 211	95.83 121

v18		84.97 231	83.76 229	88.60 237	91.36 232	79.41 189	93.82 170	94.04 207	83.00 184	76.61 284	86.60 292	76.19 121	95.43 277	80.39 178	71.79 311	90.96 291

v16		84.96 232	83.74 231	88.62 235	91.40 227	79.48 183	93.83 168	94.04 207	83.03 181	76.54 285	86.59 293	76.11 126	95.42 278	80.33 181	71.80 310	90.95 293

v17		84.93 234	83.70 233	88.62 235	91.36 232	79.48 183	93.83 168	94.03 209	83.04 180	76.51 286	86.57 294	76.05 127	95.42 278	80.31 183	71.65 312	90.96 291

v15		84.79 237	83.53 238	88.57 241	91.30 243	79.41 189	93.70 181	94.01 210	83.06 177	76.27 287	86.42 298	76.03 130	95.38 280	80.01 185	71.00 315	90.92 294

V14		84.79 237	83.52 239	88.57 241	91.32 239	79.43 188	93.72 179	94.01 210	83.06 177	76.22 288	86.43 295	76.01 131	95.37 281	79.96 187	70.99 316	90.91 295

v11		84.67 245	83.41 244	88.44 246	91.32 239	79.13 208	93.69 184	93.99 215	82.81 190	76.20 289	86.24 302	75.48 143	95.35 284	79.53 196	71.48 314	90.85 299

FMVSNet5		81.52 272	79.60 276	87.27 270	91.17 250	77.95 237	91.49 251	92.26 245	76.87 266	76.16 290	87.91 282	51.67 318	92.34 314	67.74 289	81.16 265	91.52 281

AllTest		83.42 255	81.39 259	89.52 207	95.01 119	77.79 243	93.12 204	90.89 285	77.41 261	76.12 291	93.34 138	54.08 315	97.51 149	68.31 285	84.27 229	93.26 239

TestCases				89.52 207	95.01 119	77.79 243		90.89 285	77.41 261	76.12 291	93.34 138	54.08 315	97.51 149	68.31 285	84.27 229	93.26 239

V9		84.77 239	83.50 240	88.58 238	91.33 237	79.46 185	93.75 175	94.00 213	83.07 176	76.07 293	86.43 295	75.97 132	95.37 281	79.91 190	70.93 318	90.91 295

test_0402		81.30 276	79.17 280	87.67 262	93.19 183	78.17 233	92.98 212	91.71 259	75.25 278	76.02 294	90.31 248	59.23 299	96.37 241	50.22 329	83.63 236	88.47 322

v12		84.74 240	83.46 241	88.58 238	91.32 239	79.50 180	93.75 175	94.01 210	83.06 177	75.98 295	86.41 299	75.82 138	95.36 283	79.87 191	70.89 319	90.89 297

v13		84.72 242	83.44 243	88.58 238	91.31 242	79.52 179	93.77 173	94.00 213	83.03 181	75.85 296	86.38 300	75.84 137	95.35 284	79.83 192	70.95 317	90.87 298

DSMNet-mixed		76.94 294	76.29 293	78.89 311	83.10 325	56.11 337	87.78 294	79.77 342	60.65 332	75.64 297	88.71 268	61.56 285	88.34 327	60.07 318	89.29 176	92.21 272

USDC		82.76 260	81.26 261	87.26 271	91.17 250	74.55 272	89.27 278	93.39 226	78.26 256	75.30 298	92.08 188	54.43 314	96.63 226	71.64 258	85.79 217	90.61 301

TDRefinement		79.81 285	77.34 287	87.22 275	79.24 334	75.48 270	93.12 204	92.03 251	76.45 267	75.01 299	91.58 206	49.19 324	96.44 238	70.22 268	69.18 323	89.75 307

LF4IMVS		80.37 282	79.07 282	84.27 302	86.64 314	69.87 311	89.39 277	91.05 280	76.38 268	74.97 300	90.00 252	47.85 326	94.25 298	74.55 245	80.82 275	88.69 317

PM-MVS		78.11 292	76.12 294	84.09 303	83.54 324	70.08 309	88.97 283	85.27 333	79.93 236	74.73 301	86.43 295	34.70 338	93.48 305	79.43 200	72.06 309	88.72 316

OpenMVS_ROB	74.94 19	79.51 287	77.03 291	86.93 278	87.00 313	76.23 265	92.33 231	90.74 289	68.93 317	74.52 302	88.23 277	49.58 322	96.62 227	57.64 321	84.29 228	87.94 324

test20.03		79.95 284	79.08 281	82.55 307	85.79 316	67.74 317	91.09 259	91.08 278	81.23 227	74.48 303	89.96 254	61.63 284	90.15 323	60.08 317	76.38 299	89.76 306

ambc				83.06 305	79.99 331	63.51 326	77.47 334	92.86 232		74.34 304	84.45 308	28.74 340	95.06 291	73.06 254	68.89 325	90.61 301

PVSNet_0	73.20 20	77.22 293	74.83 296	84.37 300	90.70 271	71.10 301	83.09 324	89.67 307	72.81 300	73.93 305	83.13 315	60.79 291	93.70 302	68.54 281	50.84 338	88.30 323

pmmvs-eth3d		80.97 279	78.72 284	87.74 260	84.99 320	79.97 172	90.11 266	91.65 262	75.36 277	73.51 306	86.03 303	59.45 298	93.96 300	75.17 238	72.21 308	89.29 310

K. test v3		81.59 270	80.15 271	85.91 289	89.89 291	69.42 312	92.57 224	87.71 325	85.56 119	73.44 307	89.71 257	55.58 308	95.52 271	77.17 222	69.76 322	92.78 256

Test4		85.75 214	83.72 232	91.83 114	88.08 308	81.03 146	92.48 226	95.54 134	83.38 169	73.40 308	88.57 271	50.99 320	97.37 177	86.61 103	94.47 104	97.09 84

EG-PatchMatch MVS		82.37 265	80.34 267	88.46 245	90.27 281	79.35 196	92.80 217	94.33 199	77.14 265	73.26 309	90.18 250	47.47 327	96.72 222	70.25 266	87.32 207	89.30 309

lessismore_v0				86.04 287	88.46 304	68.78 314		80.59 341		73.01 310	90.11 251	55.39 310	96.43 239	75.06 240	65.06 328	92.90 250

testus		74.41 299	73.35 297	77.59 316	82.49 329	57.08 333	86.02 304	90.21 295	72.28 303	72.89 311	84.32 309	37.08 336	86.96 331	52.24 325	82.65 245	88.73 315

MIMVSNet1		79.38 288	77.28 288	85.69 290	86.35 315	73.67 280	91.61 250	92.75 236	78.11 259	72.64 312	88.12 278	48.16 325	91.97 318	60.32 316	77.49 296	91.43 284

test2356		74.50 298	73.27 298	78.20 312	80.81 330	59.84 328	83.76 321	88.33 322	71.43 308	72.37 313	81.84 320	45.60 330	86.26 333	50.97 327	84.32 227	88.50 319

TinyColmap		79.76 286	77.69 286	85.97 288	91.71 215	73.12 282	89.55 272	90.36 293	75.03 280	72.03 314	90.19 249	46.22 329	96.19 248	63.11 309	81.03 269	88.59 318

N_pmnet		68.89 307	68.44 308	70.23 323	89.07 298	28.79 352	88.06 291	19.50 354	69.47 316	71.86 315	84.93 307	61.24 288	91.75 319	54.70 323	77.15 298	90.15 305

UnsupCasMVSNet_eth		80.07 283	78.27 285	85.46 292	85.24 318	72.63 290	88.45 289	94.87 182	82.99 185	71.64 316	88.07 279	56.34 307	91.75 319	73.48 252	63.36 333	92.01 274

LP		75.51 297	72.15 301	85.61 291	87.86 311	73.93 277	80.20 330	88.43 321	67.39 319	70.05 317	80.56 324	58.18 303	93.18 310	46.28 335	70.36 321	89.71 308

new-patchmatchnet		76.41 295	75.17 295	80.13 310	82.65 328	59.61 330	87.66 296	91.08 278	78.23 257	69.85 318	83.22 314	54.76 312	91.63 321	64.14 307	64.89 329	89.16 312

MVS-HIRNet		73.70 300	72.20 300	78.18 314	91.81 212	56.42 336	82.94 325	82.58 337	55.24 334	68.88 319	66.48 335	55.32 311	95.13 288	58.12 320	88.42 194	83.01 329

UnsupCasMVSNet_bld		76.23 296	73.27 298	85.09 296	83.79 323	72.92 283	85.65 310	93.47 225	71.52 306	68.84 320	79.08 327	49.77 321	93.21 308	66.81 295	60.52 335	89.13 314

testpf		71.41 305	72.11 302	69.30 325	84.53 321	59.79 329	62.74 341	83.14 336	71.11 310	68.83 321	81.57 322	46.70 328	84.83 338	74.51 246	75.86 301	63.30 337

pmmvs3		71.81 304	68.71 307	81.11 309	75.86 336	70.42 307	86.74 300	83.66 335	58.95 333	68.64 322	80.89 323	36.93 337	89.52 324	63.10 310	63.59 332	83.39 328

Anonymous20231211		72.97 301	69.63 306	83.00 306	83.05 326	66.91 319	92.69 219	89.45 310	61.06 331	67.50 323	83.46 313	34.34 339	93.61 304	51.11 326	63.97 331	88.48 321

testing_2		83.40 257	81.02 262	90.56 151	85.06 319	80.51 160	91.37 253	95.57 130	82.92 187	67.06 324	85.54 306	49.47 323	97.24 188	86.74 98	85.44 218	93.93 198

CMPMVS	59.16 21	80.52 281	79.20 279	84.48 299	83.98 322	67.63 318	89.95 269	93.84 220	64.79 327	66.81 325	91.14 230	57.93 304	95.17 287	76.25 229	88.10 197	90.65 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1111		70.54 306	69.71 305	73.04 320	79.30 332	44.83 345	84.23 316	88.96 317	67.33 320	65.42 326	82.28 318	41.11 334	88.11 328	47.12 333	71.60 313	86.19 326

.test1245		57.63 316	61.79 313	45.14 334	79.30 332	44.83 345	84.23 316	88.96 317	67.33 320	65.42 326	82.28 318	41.11 334	88.11 328	47.12 333	0.39 349	2.46 348

new_pmnet		72.15 303	70.13 304	78.20 312	82.95 327	65.68 321	83.91 319	82.40 338	62.94 330	64.47 328	79.82 326	42.85 332	86.26 333	57.41 322	74.44 304	82.65 330

test1235678		72.22 302	70.31 303	77.93 315	78.04 335	58.04 332	85.76 308	89.80 306	70.15 315	63.43 329	80.20 325	42.24 333	87.24 330	48.68 331	74.50 303	88.50 319

YYNet1		79.22 289	77.20 289	85.28 294	88.20 307	72.66 289	85.87 306	90.05 301	74.33 288	62.70 330	87.61 285	66.09 265	92.03 316	66.94 291	72.97 306	91.15 288

MDA-MVSNet_test_wron		79.21 290	77.19 290	85.29 293	88.22 306	72.77 287	85.87 306	90.06 299	74.34 287	62.62 331	87.56 286	66.14 264	91.99 317	66.90 294	73.01 305	91.10 290

MDA-MVSNet-bldmvs		78.85 291	76.31 292	86.46 284	89.76 292	73.88 278	88.79 284	90.42 291	79.16 243	59.18 332	88.33 276	60.20 294	94.04 299	62.00 312	68.96 324	91.48 283

test12356		64.99 310	63.78 309	68.61 327	72.69 338	39.14 348	78.46 332	87.61 327	64.91 326	55.77 333	77.48 328	28.10 341	85.59 335	44.69 336	64.35 330	81.12 332

LCM-MVSNet		66.00 308	62.16 312	77.51 317	64.51 346	58.29 331	83.87 320	90.90 284	48.17 337	54.69 334	73.31 332	16.83 351	86.75 332	65.47 301	61.67 334	87.48 325

testmv		65.49 309	62.66 310	73.96 319	68.78 341	53.14 340	84.70 314	88.56 319	65.94 325	52.35 335	74.65 330	25.02 344	85.14 336	43.54 337	60.40 336	83.60 327

FPMVS		64.63 311	62.55 311	70.88 322	70.80 339	56.71 334	84.42 315	84.42 334	51.78 336	49.57 336	81.61 321	23.49 345	81.48 340	40.61 340	76.25 300	74.46 336

PMMVS2		59.60 313	56.40 315	69.21 326	68.83 340	46.58 343	73.02 339	77.48 346	55.07 335	49.21 337	72.95 333	17.43 350	80.04 341	49.32 330	44.33 339	80.99 333

DeepMVS_CX				56.31 332	74.23 337	51.81 341		56.67 352	44.85 338	48.54 338	75.16 329	27.87 342	58.74 348	40.92 339	52.22 337	58.39 341

no-one		61.56 312	56.58 314	76.49 318	67.80 344	62.76 327	78.13 333	86.11 329	63.16 329	43.24 339	64.70 337	26.12 343	88.95 326	50.84 328	29.15 341	77.77 334

Gipuma		57.99 315	54.91 316	67.24 328	88.51 302	65.59 322	52.21 344	90.33 294	43.58 340	42.84 340	51.18 342	20.29 348	85.07 337	34.77 342	70.45 320	51.05 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		58.88 314	54.22 317	72.86 321	56.50 350	56.67 335	80.75 329	86.00 330	73.09 296	37.39 341	64.63 338	22.17 346	79.49 343	43.51 338	23.96 345	82.43 331

tmp_tt		35.64 324	39.24 323	24.84 337	14.87 352	23.90 353	62.71 342	51.51 353	6.58 347	36.66 342	62.08 339	44.37 331	30.34 350	52.40 324	22.00 347	20.27 345

PNet_i23d		50.48 319	47.18 319	60.36 330	68.59 342	44.56 347	72.75 340	72.61 347	43.92 339	33.91 343	60.19 340	6.16 352	73.52 344	38.50 341	28.04 342	63.01 338

PMVS	47.18 22	52.22 317	48.46 318	63.48 329	45.72 351	46.20 344	73.41 337	78.31 344	41.03 341	30.06 344	65.68 336	6.05 353	83.43 339	30.04 343	65.86 327	60.80 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 320	38.59 325	57.77 331	56.52 349	48.77 342	55.38 343	58.64 351	29.33 345	28.96 345	52.65 341	4.68 354	64.62 347	28.11 344	33.07 340	59.93 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 321	42.29 321	46.03 333	65.58 345	37.41 349	73.51 336	64.62 348	33.99 343	28.47 346	47.87 343	19.90 349	67.91 345	22.23 345	24.45 344	32.77 343

EMVS		42.07 322	41.12 322	44.92 335	63.45 347	35.56 351	73.65 335	63.48 349	33.05 344	26.88 347	45.45 345	21.27 347	67.14 346	19.80 346	23.02 346	32.06 344

wuykxyi23d		50.55 318	44.13 320	69.81 324	56.77 348	54.58 339	73.22 338	80.78 340	39.79 342	22.08 348	46.69 344	4.03 355	79.71 342	47.65 332	26.13 343	75.14 335

wuyk23d		21.27 326	20.48 327	23.63 338	68.59 342	36.41 350	49.57 345	6.85 355	9.37 346	7.89 349	4.46 351	4.03 355	31.37 349	17.47 347	16.07 348	3.12 346

testmvs		8.92 327	11.52 328	1.12 340	1.06 353	0.46 355	86.02 304	0.65 356	0.62 348	2.74 350	9.52 349	0.31 358	0.45 352	2.38 348	0.39 349	2.46 348

test123		8.76 328	11.22 329	1.39 339	0.85 354	0.97 354	85.76 308	0.35 357	0.54 349	2.45 351	8.14 350	0.60 357	0.48 351	2.16 349	0.17 351	2.71 347

cdsmvs_eth3d_5k		22.14 325	29.52 326	0.00 341	0.00 355	0.00 356	0.00 346	95.76 118	0.00 350	0.00 352	94.29 111	75.66 141	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		6.64 330	8.86 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	79.70 89	0.00 353	0.00 350	0.00 352	0.00 350

pcd1.5k->3k		37.02 323	38.84 324	31.53 336	92.33 201	0.00 356	0.00 346	96.13 92	0.00 350	0.00 352	0.00 352	72.70 180	0.00 353	0.00 350	88.43 193	94.60 167

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

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

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

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

ab-mvs-re		7.82 329	10.43 330	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	93.88 129	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

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

test_part1								97.45 7				91.93 1			99.02 2	98.67 4

test_all								97.46 6

sam_mvs1												71.70 190

sam_mvs												70.60 205

MTGPA								96.97 34

test_post1							88.00 292				9.81 348	69.31 224	95.53 270	76.65 226

test_post											10.29 347	70.57 209	95.91 259

patchmatchnet-post											83.76 311	71.53 193	96.48 235

MTMP								60.64 350

gm-plane-assit					89.60 294	68.00 315			77.28 264		88.99 264		97.57 145	79.44 199

test9_res														91.91 40	98.71 18	98.07 44

agg_prior2														90.54 59	98.68 23	98.27 30

test_prior4						85.96 42	94.11 148

test_prior				93.82 50	97.29 47	84.49 63		96.88 43					98.87 65			98.11 42

新几何2							93.11 206

旧先验1					96.79 58	81.81 126		95.67 123			96.81 31	86.69 23			97.66 55	96.97 87

无先验							93.28 199	96.26 82	73.95 289				99.05 44	80.56 175		96.59 97

原ACMM2							92.94 214

testdata2													98.75 76	78.30 210

segment_acmp												87.16 20

testdata1							92.15 237		87.94 70

plane_prior7					94.70 133	82.74 110

plane_prior6					94.52 139	82.75 108						74.23 157

plane_prior5								96.22 86					98.12 106	88.15 77	89.99 163	94.63 164

plane_prior4											94.86 95

plane_prior2							95.85 40		90.81 18

plane_prior1					94.59 137

plane_prior						82.73 111	95.21 71		89.66 35						89.88 166

n2								0.00 358

nn								0.00 358

door-mid								85.49 331

test11								96.57 70

door								85.33 332

HQP5-MVS						81.56 128

BP-MVS														87.11 95

HQP3-MVS								96.04 99							89.77 168

HQP2-MVS												73.83 166

NP-MVS					94.37 145	82.42 118					93.98 122

ACMMP++_ref															87.47 203

ACMMP++															88.01 200

Test By Simon												80.02 84

High-resolution multi-view benchmark