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.

APDe-MVS		97.82 2	97.73 1	98.08 9	99.15 26	94.82 13	98.81 2	98.30 23	94.76 25	98.30 6	98.90 2	93.77 7	99.68 37	97.93 1	99.69 2	99.75 1

ESAPD		97.86 1	97.65 2	98.47 1	99.17 25	95.78 3	97.21 127	98.35 19	95.16 13	98.71 3	98.80 3	95.05 1	99.89 3	96.70 14	99.73 1	99.73 2

ACMMP_Plus		97.20 11	96.86 18	98.23 5	99.09 27	95.16 9	97.60 88	98.19 34	92.82 78	97.93 12	98.74 5	91.60 38	99.86 7	96.26 23	99.52 17	99.67 3

SteuartSystems-ACMMP		97.62 4	97.53 3	97.87 14	98.39 61	94.25 23	98.43 16	98.27 25	95.34 9	98.11 7	98.56 9	94.53 2	99.71 29	96.57 18	99.62 7	99.65 4
Skip Steuart: Steuart Systems R&D Blog.
region2R		97.07 18	96.84 19	97.77 23	99.46 1	93.79 38	98.52 10	98.24 29	93.19 63	97.14 25	98.34 28	91.59 39	99.87 6	95.46 48	99.59 9	99.64 5

SMA-MVS		97.35 8	97.03 10	98.30 4	99.06 30	95.42 5	97.94 45	98.18 36	90.57 150	98.85 2	98.94 1	93.33 10	99.83 14	96.72 13	99.68 3	99.63 6

XVS		97.18 12	96.96 14	97.81 18	99.38 8	94.03 32	98.59 7	98.20 32	94.85 18	96.59 40	98.29 37	91.70 36	99.80 20	95.66 40	99.40 32	99.62 7

X-MVStestdata		91.71 185	89.67 243	97.81 18	99.38 8	94.03 32	98.59 7	98.20 32	94.85 18	96.59 40	32.69 363	91.70 36	99.80 20	95.66 40	99.40 32	99.62 7

ACMMPR		97.07 18	96.84 19	97.79 20	99.44 2	93.88 34	98.52 10	98.31 22	93.21 60	97.15 24	98.33 31	91.35 41	99.86 7	95.63 43	99.59 9	99.62 7

mPP-MVS		96.86 27	96.60 29	97.64 33	99.40 5	93.44 48	98.50 13	98.09 51	93.27 59	95.95 63	98.33 31	91.04 45	99.88 4	95.20 50	99.57 13	99.60 10

zzz-MVS		97.07 18	96.77 25	97.97 12	99.37 10	94.42 19	97.15 134	98.08 52	95.07 15	96.11 54	98.59 7	90.88 49	99.90 1	96.18 30	99.50 21	99.58 11

MTAPA		97.08 17	96.78 24	97.97 12	99.37 10	94.42 19	97.24 121	98.08 52	95.07 15	96.11 54	98.59 7	90.88 49	99.90 1	96.18 30	99.50 21	99.58 11

PGM-MVS		96.81 29	96.53 32	97.65 31	99.35 13	93.53 46	97.65 75	98.98 1	92.22 90	97.14 25	98.44 17	91.17 43	99.85 10	94.35 70	99.46 25	99.57 13

CNVR-MVS		97.68 3	97.44 6	98.37 3	98.90 34	95.86 2	97.27 119	98.08 52	95.81 3	97.87 13	98.31 34	94.26 3	99.68 37	97.02 4	99.49 23	99.57 13

Regformer-2		97.16 14	96.99 12	97.67 30	98.32 67	93.84 36	96.83 160	98.10 49	95.24 10	97.49 15	98.25 40	92.57 19	99.61 47	96.80 9	99.29 43	99.56 15

NCCC		97.30 10	97.03 10	98.11 8	98.77 37	95.06 11	97.34 113	98.04 66	95.96 2	97.09 29	97.88 55	93.18 11	99.71 29	95.84 38	99.17 53	99.56 15

Regformer-1		97.10 16	96.96 14	97.54 38	98.32 67	93.48 47	96.83 160	97.99 78	95.20 12	97.46 16	98.25 40	92.48 22	99.58 55	96.79 11	99.29 43	99.55 17

MCST-MVS		97.18 12	96.84 19	98.20 6	99.30 16	95.35 6	97.12 136	98.07 57	93.54 53	96.08 56	97.69 69	93.86 6	99.71 29	96.50 19	99.39 34	99.55 17

HFP-MVS		97.14 15	96.92 16	97.83 16	99.42 3	94.12 28	98.52 10	98.32 20	93.21 60	97.18 22	98.29 37	92.08 28	99.83 14	95.63 43	99.59 9	99.54 19

#test#		97.02 21	96.75 26	97.83 16	99.42 3	94.12 28	98.15 29	98.32 20	92.57 84	97.18 22	98.29 37	92.08 28	99.83 14	95.12 53	99.59 9	99.54 19

CP-MVS		97.02 21	96.81 22	97.64 33	99.33 14	93.54 45	98.80 3	98.28 24	92.99 69	96.45 47	98.30 36	91.90 33	99.85 10	95.61 45	99.68 3	99.54 19

APD-MVS		96.95 24	96.60 29	98.01 10	99.03 31	94.93 12	97.72 66	98.10 49	91.50 115	98.01 10	98.32 33	92.33 23	99.58 55	94.85 62	99.51 19	99.53 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		96.81 29	96.71 27	97.12 56	99.01 32	92.31 74	97.98 43	98.06 59	93.11 66	97.44 17	98.55 11	90.93 47	99.55 66	96.06 32	99.25 46	99.51 23

agg_prior2														93.94 77	99.38 35	99.50 24

Regformer-4		96.97 23	96.87 17	97.25 49	98.34 64	92.66 67	96.96 146	98.01 71	95.12 14	97.14 25	98.42 19	91.82 34	99.61 47	96.90 6	99.13 56	99.50 24

MP-MVS		96.77 31	96.45 36	97.72 26	99.39 7	93.80 37	98.41 17	98.06 59	93.37 55	95.54 79	98.34 28	90.59 52	99.88 4	94.83 63	99.54 15	99.49 26
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		96.69 34	96.45 36	97.40 41	99.36 12	93.11 56	98.87 1	98.06 59	91.17 128	96.40 48	97.99 51	90.99 46	99.58 55	95.61 45	99.61 8	99.49 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HSP-MVS		97.53 5	97.49 5	97.63 35	99.40 5	93.77 41	98.53 9	97.85 91	95.55 5	98.56 5	97.81 62	93.90 5	99.65 41	96.62 15	99.21 50	99.48 28

Regformer-3		96.85 28	96.80 23	97.01 58	98.34 64	92.02 85	96.96 146	97.76 94	95.01 17	97.08 30	98.42 19	91.71 35	99.54 68	96.80 9	99.13 56	99.48 28

test9_res														94.81 64	99.38 35	99.45 30

DeepPCF-MVS	93.97 1	96.61 37	97.09 8	95.15 143	98.09 85	86.63 265	96.00 237	98.15 40	95.43 7	97.95 11	98.56 9	93.40 9	99.36 93	96.77 12	99.48 24	99.45 30

TSAR-MVS + MP.		97.42 6	97.33 7	97.69 29	99.25 20	94.24 24	98.07 34	97.85 91	93.72 47	98.57 4	98.35 25	93.69 8	99.40 89	97.06 3	99.46 25	99.44 32

3Dnovator+	91.43 4	95.40 62	94.48 81	98.16 7	96.90 143	95.34 7	98.48 14	97.87 88	94.65 29	88.53 244	98.02 48	83.69 131	99.71 29	93.18 94	98.96 66	99.44 32

DeepC-MVS_fast	93.89 2	96.93 26	96.64 28	97.78 21	98.64 48	94.30 21	97.41 104	98.04 66	94.81 23	96.59 40	98.37 24	91.24 42	99.64 46	95.16 51	99.52 17	99.42 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		97.34 9	96.97 13	98.47 1	99.08 28	96.16 1	97.55 95	97.97 80	95.59 4	96.61 38	97.89 53	92.57 19	99.84 13	95.95 35	99.51 19	99.40 35

train_agg		96.30 45	95.83 48	97.72 26	98.70 40	94.19 25	96.41 203	98.02 69	88.58 204	96.03 57	97.56 84	92.73 15	99.59 52	95.04 55	99.37 39	99.39 36

agg_prior3		96.16 49	95.67 50	97.62 36	98.67 42	93.88 34	96.41 203	98.00 73	87.93 229	95.81 67	97.47 88	92.33 23	99.59 52	95.04 55	99.37 39	99.39 36

CDPH-MVS		95.97 54	95.38 57	97.77 23	98.93 33	94.44 18	96.35 211	97.88 86	86.98 254	96.65 36	97.89 53	91.99 32	99.47 80	92.26 102	99.46 25	99.39 36

MP-MVS-pluss		96.70 33	96.27 40	97.98 11	99.23 23	94.71 14	96.96 146	98.06 59	90.67 140	95.55 78	98.78 4	91.07 44	99.86 7	96.58 17	99.55 14	99.38 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS_fast		96.51 39	96.27 40	97.22 52	99.32 15	92.74 64	98.74 4	98.06 59	90.57 150	96.77 32	98.35 25	90.21 56	99.53 71	94.80 65	99.63 6	99.38 39

ACMMP		96.27 46	95.93 46	97.28 47	99.24 21	92.62 68	98.25 25	98.81 3	92.99 69	94.56 92	98.39 23	88.96 66	99.85 10	94.57 69	97.63 96	99.36 41
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PHI-MVS		96.77 31	96.46 35	97.71 28	98.40 59	94.07 30	98.21 28	98.45 15	89.86 160	97.11 28	98.01 49	92.52 21	99.69 35	96.03 34	99.53 16	99.36 41

agg_prior1		96.22 48	95.77 49	97.56 37	98.67 42	93.79 38	96.28 219	98.00 73	88.76 201	95.68 72	97.55 86	92.70 17	99.57 63	95.01 57	99.32 41	99.32 43

SD-MVS		97.41 7	97.53 3	97.06 57	98.57 53	94.46 17	97.92 47	98.14 42	94.82 22	99.01 1	98.55 11	94.18 4	97.41 287	96.94 5	99.64 5	99.32 43

CANet		96.39 43	96.02 45	97.50 39	97.62 114	93.38 50	97.02 141	97.96 81	95.42 8	94.86 86	97.81 62	87.38 92	99.82 18	96.88 7	99.20 51	99.29 45

test_prior3		96.46 41	96.20 43	97.23 50	98.67 42	92.99 58	96.35 211	98.00 73	92.80 79	96.03 57	97.59 80	92.01 30	99.41 87	95.01 57	99.38 35	99.29 45

test_prior				97.23 50	98.67 42	92.99 58		98.00 73					99.41 87			99.29 45

MVS_111021_HR		96.68 36	96.58 31	96.99 59	98.46 55	92.31 74	96.20 227	98.90 2	94.30 36	95.86 65	97.74 67	92.33 23	99.38 92	96.04 33	99.42 30	99.28 48

MVS_0304		96.05 51	95.45 54	97.85 15	97.75 107	94.50 16	96.87 156	97.95 83	95.46 6	95.60 76	98.01 49	80.96 196	99.83 14	97.23 2	99.25 46	99.23 49

test12				97.65 31	98.46 55	94.26 22		97.66 107		95.52 80		90.89 48	99.46 81		99.25 46	99.22 50

CHOSEN 1792x2688		94.15 95	93.51 101	96.06 99	98.27 70	89.38 177	95.18 272	98.48 14	85.60 275	93.76 106	97.11 102	83.15 138	99.61 47	91.33 129	98.72 72	99.19 51

3Dnovator	91.36 5	95.19 71	94.44 83	97.44 40	96.56 159	93.36 52	98.65 6	98.36 16	94.12 38	89.25 234	98.06 46	82.20 177	99.77 22	93.41 91	99.32 41	99.18 52

旧先验1					98.38 62	93.38 50		97.75 95			98.09 44	92.30 27			99.01 64	99.16 53

VNet		95.89 56	95.45 54	97.21 53	98.07 86	92.94 61	97.50 98	98.15 40	93.87 42	97.52 14	97.61 79	85.29 114	99.53 71	95.81 39	95.27 148	99.16 53

CSCG		96.05 51	95.91 47	96.46 79	99.24 21	90.47 134	98.30 21	98.57 11	89.01 186	93.97 103	97.57 82	92.62 18	99.76 23	94.66 68	99.27 45	99.15 55

IS-MVSNet		94.90 80	94.52 79	96.05 100	97.67 111	90.56 131	98.44 15	96.22 228	93.21 60	93.99 101	97.74 67	85.55 112	98.45 174	89.98 142	97.86 90	99.14 56

EI-MVSNet-Vis-set		96.51 39	96.47 34	96.63 66	98.24 73	91.20 111	96.89 155	97.73 97	94.74 26	96.49 44	98.49 14	90.88 49	99.58 55	96.44 20	98.32 80	99.13 57

MG-MVS		95.61 60	95.38 57	96.31 89	98.42 58	90.53 132	96.04 233	97.48 125	93.47 54	95.67 75	98.10 43	89.17 64	99.25 99	91.27 131	98.77 70	99.13 57

LFMVS		93.60 115	92.63 125	96.52 71	98.13 84	91.27 107	97.94 45	93.39 326	90.57 150	96.29 49	98.31 34	69.00 316	99.16 106	94.18 71	95.87 138	99.12 59

UA-Net		95.95 55	95.53 53	97.20 54	97.67 111	92.98 60	97.65 75	98.13 43	94.81 23	96.61 38	98.35 25	88.87 67	99.51 76	90.36 140	97.35 108	99.11 60

EPNet		95.20 70	94.56 76	97.14 55	92.80 317	92.68 66	97.85 54	94.87 294	96.64 1	92.46 138	97.80 64	86.23 103	99.65 41	93.72 83	98.62 74	99.10 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		96.69 34	96.49 33	97.27 48	98.31 69	93.39 49	96.79 167	96.72 205	94.17 37	97.44 17	97.66 72	92.76 13	99.33 94	96.86 8	97.76 95	99.08 62

HyFIR lowres test		93.66 113	92.92 116	95.87 106	98.24 73	89.88 148	94.58 279	98.49 12	85.06 282	93.78 105	95.78 170	82.86 159	98.67 157	91.77 118	95.71 143	99.07 63

casdiffmvs1		95.77 58	95.55 52	96.44 80	97.30 128	91.43 103	97.57 93	97.58 115	91.21 127	96.65 36	96.60 131	89.18 63	98.83 142	96.27 22	97.60 97	99.05 64

mvs_anonymous		93.82 108	93.74 92	94.06 196	96.44 168	85.41 277	95.81 245	97.05 176	89.85 162	90.09 200	96.36 141	87.44 91	97.75 264	93.97 75	96.69 124	99.02 65

abl_6		96.40 42	96.21 42	96.98 60	98.89 35	92.20 79	97.89 49	98.03 68	93.34 58	97.22 21	98.42 19	87.93 80	99.72 28	95.10 54	99.07 61	99.02 65

CPTT-MVS		95.57 61	95.19 62	96.70 63	99.27 19	91.48 99	98.33 20	98.11 47	87.79 233	95.17 83	98.03 47	87.09 95	99.61 47	93.51 86	99.42 30	99.02 65

Vis-MVSNet (Re-imp)		94.15 95	93.88 89	94.95 156	97.61 115	87.92 236	98.10 31	95.80 250	92.22 90	93.02 129	97.45 89	84.53 124	97.91 251	88.24 177	97.97 88	99.02 65

Anonymous202405211		92.07 173	90.83 194	95.76 110	98.19 80	88.75 199	97.58 91	95.00 284	86.00 271	93.64 107	97.45 89	66.24 329	99.53 71	90.68 138	92.71 193	99.01 69

Vis-MVSNet		95.23 67	94.81 68	96.51 74	97.18 132	91.58 98	98.26 24	98.12 44	94.38 34	94.90 85	98.15 42	82.28 174	98.92 133	91.45 128	98.58 76	99.01 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DELS-MVS		96.61 37	96.38 38	97.30 45	97.79 104	93.19 54	95.96 238	98.18 36	95.23 11	95.87 64	97.65 73	91.45 40	99.70 34	95.87 36	99.44 29	99.00 71
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
PAPM_NR		95.01 73	94.59 75	96.26 94	98.89 35	90.68 129	97.24 121	97.73 97	91.80 109	92.93 135	96.62 129	89.13 65	99.14 109	89.21 160	97.78 93	98.97 72

MSLP-MVS++		96.94 25	97.06 9	96.59 69	98.72 39	91.86 89	97.67 72	98.49 12	94.66 28	97.24 20	98.41 22	92.31 26	98.94 132	96.61 16	99.46 25	98.96 73

DeepC-MVS	93.07 3	96.06 50	95.66 51	97.29 46	97.96 92	93.17 55	97.30 118	98.06 59	93.92 41	93.38 116	98.66 6	86.83 97	99.73 25	95.60 47	99.22 49	98.96 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		95.87 57	95.23 61	97.78 21	97.56 119	95.19 8	97.86 51	97.17 160	94.39 33	96.47 45	96.40 139	85.89 108	99.20 101	96.21 28	95.11 151	98.95 75

114514_t		93.95 104	93.06 113	96.63 66	99.07 29	91.61 95	97.46 103	97.96 81	77.99 336	93.00 130	97.57 82	86.14 107	99.33 94	89.22 159	99.15 54	98.94 76

WTY-MVS		94.71 86	94.02 87	96.79 62	97.71 110	92.05 83	96.59 193	97.35 149	90.61 147	94.64 91	96.93 107	86.41 102	99.39 90	91.20 133	94.71 159	98.94 76

EPP-MVSNet		95.22 69	95.04 65	95.76 110	97.49 126	89.56 163	98.67 5	97.00 184	90.69 139	94.24 98	97.62 78	89.79 61	98.81 144	93.39 92	96.49 130	98.92 78

canonicalmvs		96.02 53	95.45 54	97.75 25	97.59 117	95.15 10	98.28 22	97.60 112	94.52 30	96.27 50	96.12 150	87.65 86	99.18 104	96.20 29	94.82 155	98.91 79

EI-MVSNet-UG-set		96.34 44	96.30 39	96.47 77	98.20 78	90.93 122	96.86 157	97.72 100	94.67 27	96.16 53	98.46 15	90.43 53	99.58 55	96.23 24	97.96 89	98.90 80

PAPR		94.18 94	93.42 107	96.48 76	97.64 113	91.42 104	95.55 255	97.71 104	88.99 187	92.34 143	95.82 165	89.19 62	99.11 111	86.14 218	97.38 106	98.90 80

无先验							95.79 246	97.87 88	83.87 298				99.65 41	87.68 190		98.89 82

DP-MVS		92.76 144	91.51 167	96.52 71	98.77 37	90.99 118	97.38 110	96.08 233	82.38 309	89.29 231	97.87 56	83.77 130	99.69 35	81.37 290	96.69 124	98.89 82

diffmvs1		94.99 77	94.79 69	95.60 120	96.52 162	89.20 189	96.43 200	97.36 147	92.59 83	94.85 87	96.10 153	87.85 82	98.74 152	93.99 74	97.41 105	98.86 84

MVSFormer		95.37 63	95.16 63	95.99 103	96.34 171	91.21 109	98.22 26	97.57 116	91.42 119	96.22 51	97.32 92	86.20 105	97.92 248	94.07 72	99.05 62	98.85 85

jason		94.84 83	94.39 84	96.18 97	95.52 201	90.93 122	96.09 231	96.52 217	89.28 173	96.01 61	97.32 92	84.70 121	98.77 148	95.15 52	98.91 68	98.85 85
jason: jason.
Effi-MVS+		94.93 79	94.45 82	96.36 87	96.61 153	91.47 100	96.41 203	97.41 141	91.02 133	94.50 93	95.92 159	87.53 89	98.78 146	93.89 79	96.81 119	98.84 87

lupinMVS		94.99 77	94.56 76	96.29 92	96.34 171	91.21 109	95.83 244	96.27 224	88.93 192	96.22 51	96.88 110	86.20 105	98.85 140	95.27 49	99.05 62	98.82 88

0601test		94.78 85	94.23 85	96.43 81	97.74 108	91.22 108	96.85 158	97.10 169	91.23 126	95.71 71	96.93 107	84.30 126	99.31 96	93.10 95	95.12 150	98.75 89

CVMVSNet		91.23 217	91.75 150	89.67 319	95.77 195	74.69 339	96.44 198	94.88 291	85.81 272	92.18 146	97.64 76	79.07 229	95.58 330	88.06 180	95.86 139	98.74 90

1121		94.71 86	93.83 90	97.34 43	98.57 53	93.64 43	96.04 233	97.73 97	81.56 318	95.68 72	97.85 59	90.23 55	99.65 41	87.68 190	99.12 59	98.73 91

test222					98.24 73	92.21 77	95.33 264	97.60 112	79.22 331	95.25 81	97.84 61	88.80 69			99.15 54	98.72 92

MVS_Test		94.89 81	94.62 74	95.68 117	96.83 148	89.55 164	96.70 180	97.17 160	91.17 128	95.60 76	96.11 152	87.87 81	98.76 150	93.01 98	97.17 112	98.72 92

VDD-MVS		93.82 108	93.08 112	96.02 101	97.88 101	89.96 146	97.72 66	95.85 247	92.43 87	95.86 65	98.44 17	68.42 320	99.39 90	96.31 21	94.85 153	98.71 94

casdiffmvs		95.23 67	94.84 67	96.40 82	96.90 143	91.71 90	97.36 111	97.30 153	91.02 133	94.81 88	96.18 146	87.74 83	98.77 148	95.65 42	96.55 128	98.71 94

新几何1				97.32 44	98.60 49	93.59 44		97.75 95	81.58 316	95.75 70	97.85 59	90.04 58	99.67 39	86.50 213	99.13 56	98.69 96

sss		94.51 88	93.80 91	96.64 64	97.07 137	91.97 87	96.32 215	98.06 59	88.94 191	94.50 93	96.78 112	84.60 122	99.27 98	91.90 114	96.02 134	98.68 97

testdata				95.46 130	98.18 82	88.90 197		97.66 107	82.73 307	97.03 31	98.07 45	90.06 57	98.85 140	89.67 149	98.98 65	98.64 98

diffmvs		94.47 89	94.23 85	95.18 137	96.32 173	88.22 214	96.27 220	97.04 179	92.55 85	93.60 108	95.94 158	86.79 98	98.70 156	92.98 99	96.61 126	98.63 99

MVS_111021_LR		96.24 47	96.19 44	96.39 84	98.23 77	91.35 105	96.24 225	98.79 4	93.99 40	95.80 68	97.65 73	89.92 60	99.24 100	95.87 36	99.20 51	98.58 100

test_normal		92.01 174	90.75 197	95.80 109	93.24 306	89.97 144	95.93 240	96.24 227	90.62 145	81.63 313	93.45 283	74.98 284	98.89 137	93.61 84	97.04 115	98.55 101

PVSNet_Blended_VisFu		95.27 66	94.91 66	96.38 85	98.20 78	90.86 124	97.27 119	98.25 28	90.21 154	94.18 99	97.27 94	87.48 90	99.73 25	93.53 85	97.77 94	98.55 101

TAMVS		94.01 103	93.46 103	95.64 118	96.16 181	90.45 135	96.71 177	96.89 198	89.27 174	93.46 114	96.92 109	87.29 93	97.94 244	88.70 174	95.74 141	98.53 103

Test4		89.48 261	87.50 272	95.44 131	90.76 333	89.72 152	95.78 248	97.09 170	90.28 153	77.67 338	91.74 311	55.42 349	98.08 209	91.92 113	96.83 118	98.52 104

PatchmatchNet		91.91 179	91.35 169	93.59 229	95.38 207	84.11 291	93.15 312	95.39 263	89.54 166	92.10 148	93.68 273	82.82 161	98.13 201	84.81 239	95.32 147	98.52 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DI_MVS_plusplus_test		92.01 174	90.77 195	95.73 115	93.34 302	89.78 151	96.14 229	96.18 230	90.58 149	81.80 312	93.50 280	74.95 285	98.90 135	93.51 86	96.94 116	98.51 106

QAPM		93.45 120	92.27 138	96.98 60	96.77 150	92.62 68	98.39 18	98.12 44	84.50 290	88.27 250	97.77 65	82.39 173	99.81 19	85.40 232	98.81 69	98.51 106

1112_ss		93.37 121	92.42 135	96.21 96	97.05 140	90.99 118	96.31 216	96.72 205	86.87 260	89.83 209	96.69 119	86.51 101	99.14 109	88.12 179	93.67 177	98.50 108

ab-mvs		93.57 117	92.55 129	96.64 64	97.28 129	91.96 88	95.40 262	97.45 134	89.81 164	93.22 124	96.28 143	79.62 223	99.46 81	90.74 136	93.11 189	98.50 108

原ACMM1				96.38 85	98.59 50	91.09 117		97.89 84	87.41 242	95.22 82	97.68 70	90.25 54	99.54 68	87.95 183	99.12 59	98.49 110

Test_1112_low_res		92.84 142	91.84 148	95.85 107	97.04 141	89.97 144	95.53 257	96.64 213	85.38 276	89.65 219	95.18 198	85.86 109	99.10 117	87.70 188	93.58 182	98.49 110

Patchmatch-test		89.42 263	87.99 267	93.70 223	95.27 214	85.11 279	88.98 344	94.37 309	81.11 319	87.10 273	93.69 272	82.28 174	97.50 280	74.37 326	94.76 156	98.48 112

VDDNet		93.05 131	92.07 140	96.02 101	96.84 146	90.39 136	98.08 33	95.85 247	86.22 268	95.79 69	98.46 15	67.59 323	99.19 102	94.92 61	94.85 153	98.47 113

PVSNet	86.66 18	92.24 167	91.74 152	93.73 220	97.77 106	83.69 296	92.88 316	96.72 205	87.91 231	93.00 130	94.86 211	78.51 248	99.05 127	86.53 211	97.45 104	98.47 113

GSMVS																98.45 115

sam_mvs1												82.76 162				98.45 115

CDS-MVSNet		94.14 97	93.54 99	95.93 104	96.18 179	91.46 101	96.33 214	97.04 179	88.97 190	93.56 109	96.51 134	87.55 88	97.89 252	89.80 145	95.95 136	98.44 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DP-MVS Recon		95.68 59	95.12 64	97.37 42	99.19 24	94.19 25	97.03 139	98.08 52	88.35 218	95.09 84	97.65 73	89.97 59	99.48 79	92.08 111	98.59 75	98.44 117

Patchmatch-RL test		87.38 292	86.24 291	90.81 306	88.74 341	78.40 334	88.12 347	93.17 327	87.11 249	82.17 308	89.29 329	81.95 182	95.60 329	88.64 175	77.02 328	98.41 119

Patchmatch-test1		91.54 203	90.85 191	93.59 229	95.59 199	84.95 283	94.72 277	95.58 258	90.82 135	92.25 145	93.58 277	75.80 277	97.41 287	83.35 261	95.98 135	98.40 120

LCM-MVSNet-Re		92.50 151	92.52 132	92.44 266	96.82 149	81.89 309	96.92 153	93.71 321	92.41 88	84.30 297	94.60 225	85.08 117	97.03 300	91.51 125	97.36 107	98.40 120

PVSNet_Blended		94.87 82	94.56 76	95.81 108	98.27 70	89.46 170	95.47 260	98.36 16	88.84 195	94.36 95	96.09 154	88.02 77	99.58 55	93.44 89	98.18 83	98.40 120

tttt0517		92.96 134	92.33 137	94.87 161	97.11 135	87.16 253	97.97 44	92.09 343	90.63 144	93.88 104	97.01 106	76.50 272	99.06 126	90.29 141	95.45 145	98.38 123

MDTV_nov1_ep13_2view						70.35 346	93.10 314		83.88 297	93.55 110		82.47 171		86.25 216		98.38 123

BH-RMVSNet		92.72 145	91.97 145	94.97 154	97.16 133	87.99 231	96.15 228	95.60 256	90.62 145	91.87 152	97.15 101	78.41 250	98.57 165	83.16 264	97.60 97	98.36 125

OMC-MVS		95.09 72	94.70 73	96.25 95	98.46 55	91.28 106	96.43 200	97.57 116	92.04 104	94.77 90	97.96 52	87.01 96	99.09 120	91.31 130	96.77 120	98.36 125

Anonymous20240529		91.98 178	90.73 198	95.73 115	98.14 83	89.40 176	97.99 42	97.72 100	79.63 328	93.54 111	97.41 91	69.94 314	99.56 65	91.04 134	91.11 220	98.22 127

GA-MVS		91.38 210	90.31 216	94.59 175	94.65 247	87.62 243	94.34 285	96.19 229	90.73 138	90.35 188	93.83 267	71.84 301	97.96 242	87.22 203	93.61 180	98.21 128

TAPA-MVS	90.10 7	92.30 163	91.22 178	95.56 122	98.33 66	89.60 161	96.79 167	97.65 109	81.83 313	91.52 158	97.23 97	87.94 79	98.91 134	71.31 335	98.37 79	98.17 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UGNet		94.04 102	93.28 110	96.31 89	96.85 145	91.19 112	97.88 50	97.68 106	94.40 32	93.00 130	96.18 146	73.39 297	99.61 47	91.72 119	98.46 77	98.13 130
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
Fast-Effi-MVS+		93.46 119	92.75 121	95.59 121	96.77 150	90.03 138	96.81 164	97.13 165	88.19 223	91.30 166	94.27 254	86.21 104	98.63 159	87.66 192	96.46 132	98.12 131

tpm		90.25 247	89.74 242	91.76 291	93.92 284	79.73 327	93.98 294	93.54 325	88.28 219	91.99 150	93.25 287	77.51 268	97.44 284	87.30 202	87.94 253	98.12 131

PMMVS		92.86 140	92.34 136	94.42 184	94.92 236	86.73 261	94.53 281	96.38 220	84.78 287	94.27 97	95.12 202	83.13 140	98.40 182	91.47 127	96.49 130	98.12 131

EPMVS		90.70 237	89.81 238	93.37 241	94.73 245	84.21 289	93.67 301	88.02 355	89.50 168	92.38 141	93.49 281	77.82 266	97.78 261	86.03 222	92.68 194	98.11 134

LS3D		93.57 117	92.61 127	96.47 77	97.59 117	91.61 95	97.67 72	97.72 100	85.17 280	90.29 189	98.34 28	84.60 122	99.73 25	83.85 259	98.27 81	98.06 135

HY-MVS	89.66 9	93.87 106	92.95 115	96.63 66	97.10 136	92.49 72	95.64 253	96.64 213	89.05 185	93.00 130	95.79 169	85.77 111	99.45 83	89.16 162	94.35 160	97.96 136

DWT-MVSNet_test		90.76 231	89.89 234	93.38 240	95.04 229	83.70 295	95.85 243	94.30 312	88.19 223	90.46 185	92.80 291	73.61 295	98.50 170	88.16 178	90.58 228	97.95 137

CNLPA		94.28 92	93.53 100	96.52 71	98.38 62	92.55 70	96.59 193	96.88 199	90.13 156	91.91 151	97.24 96	85.21 115	99.09 120	87.64 193	97.83 91	97.92 138

CostFormer		91.18 221	90.70 200	92.62 264	94.84 240	81.76 310	94.09 293	94.43 306	84.15 293	92.72 137	93.77 270	79.43 225	98.20 195	90.70 137	92.18 202	97.90 139

tpmrst		91.44 207	91.32 171	91.79 288	95.15 223	79.20 331	93.42 306	95.37 265	88.55 206	93.49 113	93.67 274	82.49 169	98.27 192	90.41 139	89.34 241	97.90 139

EPNet_dtu		91.71 185	91.28 174	92.99 253	93.76 290	83.71 294	96.69 182	95.28 270	93.15 64	87.02 275	95.95 157	83.37 135	97.38 290	79.46 308	96.84 117	97.88 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thisisatest0515		92.29 164	91.30 173	95.25 135	96.60 154	88.90 197	94.36 284	92.32 341	87.92 230	93.43 115	94.57 226	77.28 269	99.00 129	89.42 154	95.86 139	97.86 142

ADS-MVSNet2		89.45 262	88.59 260	92.03 281	95.86 190	82.26 307	90.93 333	94.32 311	83.23 304	91.28 169	91.81 309	79.01 234	95.99 322	79.52 305	91.39 216	97.84 143

ADS-MVSNet		89.89 255	88.68 259	93.53 233	95.86 190	84.89 284	90.93 333	95.07 282	83.23 304	91.28 169	91.81 309	79.01 234	97.85 254	79.52 305	91.39 216	97.84 143

MAR-MVS		94.22 93	93.46 103	96.51 74	98.00 87	92.19 80	97.67 72	97.47 128	88.13 227	93.00 130	95.84 163	84.86 120	99.51 76	87.99 182	98.17 84	97.83 145
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
CANet_DTU		94.37 90	93.65 96	96.55 70	96.46 167	92.13 81	96.21 226	96.67 212	94.38 34	93.53 112	97.03 105	79.34 226	99.71 29	90.76 135	98.45 78	97.82 146

tpmp4_e23		89.58 260	88.59 260	92.54 265	95.16 222	81.53 311	94.11 292	95.09 280	81.66 314	88.60 242	93.44 284	75.11 282	98.33 189	82.45 274	91.72 209	97.75 147

PLC	91.00 6	94.11 98	93.43 105	96.13 98	98.58 52	91.15 116	96.69 182	97.39 142	87.29 245	91.37 161	96.71 115	88.39 75	99.52 75	87.33 201	97.13 113	97.73 148
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dp		88.90 268	88.26 266	90.81 306	94.58 251	76.62 336	92.85 317	94.93 289	85.12 281	90.07 202	93.07 288	75.81 276	98.12 204	80.53 301	87.42 259	97.71 149

AdaColmap		94.34 91	93.68 95	96.31 89	98.59 50	91.68 94	96.59 193	97.81 93	89.87 159	92.15 147	97.06 104	83.62 132	99.54 68	89.34 155	98.07 86	97.70 150

test-LLR		91.42 208	91.19 179	92.12 278	94.59 249	80.66 316	94.29 287	92.98 334	91.11 130	90.76 181	92.37 299	79.02 232	98.07 213	88.81 172	96.74 121	97.63 151

test-mter		90.19 250	89.54 246	92.12 278	94.59 249	80.66 316	94.29 287	92.98 334	87.68 237	90.76 181	92.37 299	67.67 322	98.07 213	88.81 172	96.74 121	97.63 151

PAPM		91.52 204	90.30 217	95.20 136	95.30 213	89.83 149	93.38 307	96.85 201	86.26 267	88.59 243	95.80 166	84.88 119	98.15 200	75.67 323	95.93 137	97.63 151

F-COLMAP		93.58 116	92.98 114	95.37 133	98.40 59	88.98 195	97.18 131	97.29 154	87.75 235	90.49 184	97.10 103	85.21 115	99.50 78	86.70 210	96.72 123	97.63 151

TESTMET0.1,1		90.06 252	89.42 248	91.97 282	94.41 256	80.62 318	94.29 287	91.97 345	87.28 246	90.44 186	92.47 298	68.79 317	97.67 269	88.50 176	96.60 127	97.61 155

CR-MVSNet		90.82 230	89.77 239	93.95 205	94.45 254	87.19 251	90.23 338	95.68 254	86.89 259	92.40 139	92.36 302	80.91 200	97.05 298	81.09 299	93.95 173	97.60 156

RPMNet		88.52 277	86.72 290	93.95 205	94.45 254	87.19 251	90.23 338	94.99 286	77.87 338	92.40 139	87.55 342	80.17 215	97.05 298	68.84 339	93.95 173	97.60 156

MIMVSNet		88.50 279	86.76 288	93.72 222	94.84 240	87.77 240	91.39 328	94.05 317	86.41 265	87.99 255	92.59 295	63.27 335	95.82 326	77.44 315	92.84 192	97.57 158

PatchT		88.87 269	87.42 275	93.22 247	94.08 276	85.10 280	89.51 342	94.64 300	81.92 312	92.36 142	88.15 338	80.05 216	97.01 302	72.43 331	93.65 178	97.54 159

tpm2		89.96 253	89.21 252	92.23 272	94.91 238	81.25 313	93.78 297	94.42 307	80.62 324	91.56 157	93.44 284	76.44 274	97.94 244	85.60 229	92.08 206	97.49 160

IB-MVS	87.33 17	89.91 254	88.28 265	94.79 167	95.26 217	87.70 242	95.12 273	93.95 320	89.35 172	87.03 274	92.49 297	70.74 309	99.19 102	89.18 161	81.37 318	97.49 160
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
PatchFormer-LS_test		91.68 195	91.18 180	93.19 249	95.24 218	83.63 297	95.53 257	95.44 262	89.82 163	91.37 161	92.58 296	80.85 204	98.52 168	89.65 151	90.16 234	97.42 162

CHOSEN 280x420		93.12 128	92.72 123	94.34 187	96.71 152	87.27 247	90.29 337	97.72 100	86.61 264	91.34 163	95.29 194	84.29 127	98.41 181	93.25 93	98.94 67	97.35 163

BH-untuned		92.94 136	92.62 126	93.92 209	97.22 130	86.16 269	96.40 207	96.25 226	90.06 157	89.79 211	96.17 149	83.19 136	98.35 186	87.19 204	97.27 110	97.24 164

1314		92.81 143	92.03 142	95.14 144	95.33 212	89.52 167	96.04 233	97.44 137	87.72 236	86.25 282	95.33 193	83.84 129	98.79 145	89.26 157	97.05 114	97.11 165

PCF-MVS	89.48 11	91.56 201	89.95 232	96.36 87	96.60 154	92.52 71	92.51 321	97.26 155	79.41 329	88.90 236	96.56 132	84.04 128	99.55 66	77.01 319	97.30 109	97.01 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
view600		92.55 147	91.68 153	95.18 137	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 125	95.52 185	78.14 256	99.11 111	81.61 279	94.04 169	96.98 167

view800		92.55 147	91.68 153	95.18 137	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 125	95.52 185	78.14 256	99.11 111	81.61 279	94.04 169	96.98 167

conf0.05thres1000		92.55 147	91.68 153	95.18 137	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 125	95.52 185	78.14 256	99.11 111	81.61 279	94.04 169	96.98 167

tfpn		92.55 147	91.68 153	95.18 137	97.98 88	89.44 172	98.00 38	94.57 301	92.09 98	93.17 125	95.52 185	78.14 256	99.11 111	81.61 279	94.04 169	96.98 167

thres600view7		92.49 153	91.60 159	95.18 137	97.91 99	89.47 168	97.65 75	94.66 296	92.18 97	93.33 117	94.91 206	78.06 260	99.10 117	81.61 279	94.06 167	96.98 167

thres400		92.42 157	91.52 165	95.12 146	97.85 102	89.29 184	97.41 104	94.88 291	92.19 95	93.27 122	94.46 232	78.17 253	99.08 122	81.40 286	94.08 163	96.98 167

XVG-OURS-SEG-HR		93.86 107	93.55 98	94.81 164	97.06 139	88.53 204	95.28 267	97.45 134	91.68 112	94.08 100	97.68 70	82.41 172	98.90 135	93.84 81	92.47 196	96.98 167

MSDG		91.42 208	90.24 221	94.96 155	97.15 134	88.91 196	93.69 300	96.32 222	85.72 274	86.93 276	96.47 136	80.24 213	98.98 131	80.57 300	95.05 152	96.98 167

XVG-OURS		93.72 112	93.35 108	94.80 165	97.07 137	88.61 202	94.79 276	97.46 130	91.97 107	93.99 101	97.86 58	81.74 186	98.88 139	92.64 101	92.67 195	96.92 175

PatchMatch-RL		92.90 138	92.02 143	95.56 122	98.19 80	90.80 126	95.27 269	97.18 158	87.96 228	91.86 153	95.68 177	80.44 209	98.99 130	84.01 255	97.54 99	96.89 176

mvs-test1		93.63 114	93.69 94	93.46 237	96.02 187	84.61 287	97.24 121	96.72 205	93.85 43	92.30 144	95.76 171	83.08 144	98.89 137	91.69 122	96.54 129	96.87 177

tpmvs		89.83 258	89.15 254	91.89 284	94.92 236	80.30 322	93.11 313	95.46 261	86.28 266	88.08 253	92.65 293	80.44 209	98.52 168	81.47 285	89.92 237	96.84 178

TR-MVS		91.48 205	90.59 210	94.16 193	96.40 169	87.33 245	95.67 250	95.34 269	87.68 237	91.46 159	95.52 185	76.77 271	98.35 186	82.85 269	93.61 180	96.79 179

OpenMVS	89.19 12	92.86 140	91.68 153	96.40 82	95.34 209	92.73 65	98.27 23	98.12 44	84.86 285	85.78 285	97.75 66	78.89 245	99.74 24	87.50 197	98.65 73	96.73 180

tpm cat1		88.36 284	87.21 284	91.81 287	95.13 225	80.55 319	92.58 320	95.70 251	74.97 344	87.45 263	91.96 307	78.01 264	98.17 199	80.39 302	88.74 247	96.72 181

tfpn111		92.45 154	91.58 160	95.06 147	97.92 96	89.37 178	97.71 68	94.66 296	92.20 92	93.31 118	94.90 207	78.06 260	99.11 111	81.37 290	94.06 167	96.70 182

conf0.01		91.74 183	90.67 202	94.94 159	97.55 120	89.68 153	97.64 79	93.14 328	88.43 209	91.24 171	94.30 244	78.91 238	98.45 174	81.28 292	93.57 183	96.70 182

conf0.002		91.74 183	90.67 202	94.94 159	97.55 120	89.68 153	97.64 79	93.14 328	88.43 209	91.24 171	94.30 244	78.91 238	98.45 174	81.28 292	93.57 183	96.70 182

conf200view11		92.45 154	91.58 160	95.05 148	97.92 96	89.37 178	97.71 68	94.66 296	92.20 92	93.31 118	94.90 207	78.06 260	99.08 122	81.40 286	94.08 163	96.70 182

DSMNet-mixed		86.34 300	86.12 294	87.00 327	89.88 337	70.43 344	94.93 275	90.08 352	77.97 337	85.42 290	92.78 292	74.44 288	93.96 338	74.43 325	95.14 149	96.62 186

API-MVS		94.84 83	94.49 80	95.90 105	97.90 100	92.00 86	97.80 57	97.48 125	89.19 176	94.81 88	96.71 115	88.84 68	99.17 105	88.91 168	98.76 71	96.53 187

gg-mvs-nofinetune		87.82 289	85.61 296	94.44 182	94.46 253	89.27 188	91.21 332	84.61 361	80.88 321	89.89 206	74.98 352	71.50 303	97.53 278	85.75 227	97.21 111	96.51 188

Effi-MVS+-dtu		93.08 129	93.21 111	92.68 263	96.02 187	83.25 300	97.14 135	96.72 205	93.85 43	91.20 178	93.44 284	83.08 144	98.30 191	91.69 122	95.73 142	96.50 189

thres100view900		92.43 156	91.58 160	94.98 153	97.92 96	89.37 178	97.71 68	94.66 296	92.20 92	93.31 118	94.90 207	78.06 260	99.08 122	81.40 286	94.08 163	96.48 190

tfpn200view9		92.38 159	91.52 165	94.95 156	97.85 102	89.29 184	97.41 104	94.88 291	92.19 95	93.27 122	94.46 232	78.17 253	99.08 122	81.40 286	94.08 163	96.48 190

tfpn1000		91.99 177	91.05 181	94.80 165	97.78 105	89.66 159	97.91 48	92.90 337	88.99 187	91.73 154	94.84 212	78.99 237	98.33 189	82.41 275	93.91 175	96.40 192

JIA-IIPM		88.26 286	87.04 287	91.91 283	93.52 296	81.42 312	89.38 343	94.38 308	80.84 322	90.93 180	80.74 349	79.22 228	97.92 248	82.76 270	91.62 211	96.38 193

cascas		91.20 218	90.08 226	94.58 179	94.97 231	89.16 193	93.65 302	97.59 114	79.90 327	89.40 226	92.92 290	75.36 281	98.36 185	92.14 107	94.75 157	96.23 194

RPSCF		90.75 233	90.86 190	90.42 313	96.84 146	76.29 337	95.61 254	96.34 221	83.89 296	91.38 160	97.87 56	76.45 273	98.78 146	87.16 206	92.23 199	96.20 195

thres200		92.23 168	91.39 168	94.75 169	97.61 115	89.03 194	96.60 192	95.09 280	92.08 103	93.28 121	94.00 262	78.39 251	99.04 128	81.26 298	94.18 162	96.19 196

xiu_mvs_v2_base		95.32 65	95.29 60	95.40 132	97.22 130	90.50 133	95.44 261	97.44 137	93.70 49	96.46 46	96.18 146	88.59 74	99.53 71	94.79 67	97.81 92	96.17 197

PS-MVSNAJ		95.37 63	95.33 59	95.49 127	97.35 127	90.66 130	95.31 266	97.48 125	93.85 43	96.51 43	95.70 176	88.65 71	99.65 41	94.80 65	98.27 81	96.17 197

AllTest		90.23 248	88.98 255	93.98 200	97.94 94	86.64 262	96.51 197	95.54 259	85.38 276	85.49 288	96.77 113	70.28 311	99.15 107	80.02 303	92.87 190	96.15 199

TestCases				93.98 200	97.94 94	86.64 262		95.54 259	85.38 276	85.49 288	96.77 113	70.28 311	99.15 107	80.02 303	92.87 190	96.15 199

BH-w/o		92.14 172	91.75 150	93.31 243	96.99 142	85.73 272	95.67 250	95.69 252	88.73 202	89.26 233	94.82 215	82.97 154	98.07 213	85.26 235	96.32 133	96.13 201

thresconf0.02		91.69 190	90.67 202	94.75 169	97.55 120	89.68 153	97.64 79	93.14 328	88.43 209	91.24 171	94.30 244	78.91 238	98.45 174	81.28 292	93.57 183	96.11 202

tfpn_n400		91.69 190	90.67 202	94.75 169	97.55 120	89.68 153	97.64 79	93.14 328	88.43 209	91.24 171	94.30 244	78.91 238	98.45 174	81.28 292	93.57 183	96.11 202

tfpnconf		91.69 190	90.67 202	94.75 169	97.55 120	89.68 153	97.64 79	93.14 328	88.43 209	91.24 171	94.30 244	78.91 238	98.45 174	81.28 292	93.57 183	96.11 202

tfpnview11		91.69 190	90.67 202	94.75 169	97.55 120	89.68 153	97.64 79	93.14 328	88.43 209	91.24 171	94.30 244	78.91 238	98.45 174	81.28 292	93.57 183	96.11 202

xiu_mvs_v1_base_debu		95.01 73	94.76 70	95.75 112	96.58 156	91.71 90	96.25 222	97.35 149	92.99 69	96.70 33	96.63 126	82.67 163	99.44 84	96.22 25	97.46 100	96.11 202

xiu_mvs_v1_base		95.01 73	94.76 70	95.75 112	96.58 156	91.71 90	96.25 222	97.35 149	92.99 69	96.70 33	96.63 126	82.67 163	99.44 84	96.22 25	97.46 100	96.11 202

xiu_mvs_v1_base_debi		95.01 73	94.76 70	95.75 112	96.58 156	91.71 90	96.25 222	97.35 149	92.99 69	96.70 33	96.63 126	82.67 163	99.44 84	96.22 25	97.46 100	96.11 202

Fast-Effi-MVS+-dtu		92.29 164	91.99 144	93.21 248	95.27 214	85.52 276	97.03 139	96.63 215	92.09 98	89.11 235	95.14 200	80.33 212	98.08 209	87.54 196	94.74 158	96.03 209

nrg030		94.05 101	93.31 109	96.27 93	95.22 219	94.59 15	98.34 19	97.46 130	92.93 76	91.21 177	96.64 122	87.23 94	98.22 194	94.99 60	85.80 269	95.98 210

pcd1.5k->3k		38.37 341	40.51 342	31.96 353	94.29 260	0.00 372	0.00 363	97.69 105	0.00 367	0.00 369	0.00 369	81.45 190	0.00 369	0.00 366	91.11 220	95.89 211

PS-MVSNAJss		93.74 111	93.51 101	94.44 182	93.91 285	89.28 186	97.75 60	97.56 119	92.50 86	89.94 203	96.54 133	88.65 71	98.18 198	93.83 82	90.90 224	95.86 212

HQP_MVS		93.78 110	93.43 105	94.82 162	96.21 176	89.99 141	97.74 62	97.51 123	94.85 18	91.34 163	96.64 122	81.32 192	98.60 162	93.02 96	92.23 199	95.86 212

plane_prior5								97.51 123					98.60 162	93.02 96	92.23 199	95.86 212

FIs		94.09 99	93.70 93	95.27 134	95.70 197	92.03 84	98.10 31	98.68 7	93.36 57	90.39 187	96.70 117	87.63 87	97.94 244	92.25 104	90.50 231	95.84 215

FC-MVSNet-test		93.94 105	93.57 97	95.04 149	95.48 203	91.45 102	98.12 30	98.71 5	93.37 55	90.23 190	96.70 117	87.66 85	97.85 254	91.49 126	90.39 232	95.83 216

MVS		91.71 185	90.44 212	95.51 125	95.20 221	91.59 97	96.04 233	97.45 134	73.44 348	87.36 267	95.60 180	85.42 113	99.10 117	85.97 223	97.46 100	95.83 216

VPNet		92.23 168	91.31 172	94.99 151	95.56 200	90.96 120	97.22 126	97.86 90	92.96 75	90.96 179	96.62 129	75.06 283	98.20 195	91.90 114	83.65 305	95.80 218

DU-MVS		92.90 138	92.04 141	95.49 127	94.95 233	92.83 62	97.16 133	98.24 29	93.02 68	90.13 195	95.71 174	83.47 133	97.85 254	91.71 120	83.93 299	95.78 219

NR-MVSNet		92.34 160	91.27 175	95.53 124	94.95 233	93.05 57	97.39 108	98.07 57	92.65 82	84.46 295	95.71 174	85.00 118	97.77 263	89.71 147	83.52 306	95.78 219

HQP4-MVS										90.14 191			98.50 170			95.78 219

HQP-MVS		93.19 127	92.74 122	94.54 180	95.86 190	89.33 181	96.65 185	97.39 142	93.55 50	90.14 191	95.87 161	80.95 197	98.50 170	92.13 108	92.10 204	95.78 219

VPA-MVSNet		93.24 125	92.48 134	95.51 125	95.70 197	92.39 73	97.86 51	98.66 9	92.30 89	92.09 149	95.37 192	80.49 208	98.40 182	93.95 76	85.86 268	95.75 223

TranMVSNet+NR-MVSNet		92.50 151	91.63 158	95.14 144	94.76 243	92.07 82	97.53 96	98.11 47	92.90 77	89.56 222	96.12 150	83.16 137	97.60 275	89.30 156	83.20 309	95.75 223

UniMVSNet_NR-MVSNet		93.37 121	92.67 124	95.47 129	95.34 209	92.83 62	97.17 132	98.58 10	92.98 74	90.13 195	95.80 166	88.37 76	97.85 254	91.71 120	83.93 299	95.73 225

WR-MVS		92.34 160	91.53 164	94.77 168	95.13 225	90.83 125	96.40 207	97.98 79	91.88 108	89.29 231	95.54 184	82.50 168	97.80 259	89.79 146	85.27 276	95.69 226

XXY-MVS		92.16 170	91.23 177	94.95 156	94.75 244	90.94 121	97.47 102	97.43 139	89.14 183	88.90 236	96.43 138	79.71 221	98.24 193	89.56 152	87.68 255	95.67 227

ACMM	89.79 8	92.96 134	92.50 133	94.35 186	96.30 174	88.71 200	97.58 91	97.36 147	91.40 121	90.53 183	96.65 121	79.77 220	98.75 151	91.24 132	91.64 210	95.59 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		90.63 240	89.42 248	94.27 189	98.24 73	89.19 192	98.05 35	97.89 84	79.95 326	88.25 251	94.96 203	72.56 299	98.13 201	89.70 148	85.14 278	95.49 229

jajsoiax		92.42 157	91.89 147	94.03 198	93.33 304	88.50 205	97.73 64	97.53 120	92.00 106	88.85 238	96.50 135	75.62 280	98.11 205	93.88 80	91.56 213	95.48 230

testgi		87.97 287	87.21 284	90.24 315	92.86 315	80.76 315	96.67 184	94.97 287	91.74 110	85.52 287	95.83 164	62.66 337	94.47 336	76.25 320	88.36 251	95.48 230

MVSTER		93.20 126	92.81 118	94.37 185	96.56 159	89.59 162	97.06 138	97.12 166	91.24 125	91.30 166	95.96 156	82.02 180	98.05 221	93.48 88	90.55 229	95.47 232

UniMVSNet (Re)		93.31 123	92.55 129	95.61 119	95.39 206	93.34 53	97.39 108	98.71 5	93.14 65	90.10 199	94.83 214	87.71 84	98.03 227	91.67 124	83.99 298	95.46 233

tfpn_ndepth		91.88 181	90.96 185	94.62 174	97.73 109	89.93 147	97.75 60	92.92 336	88.93 192	91.73 154	93.80 269	78.91 238	98.49 173	83.02 267	93.86 176	95.45 234

mvs_tets		92.31 162	91.76 149	93.94 208	93.41 300	88.29 208	97.63 86	97.53 120	92.04 104	88.76 239	96.45 137	74.62 287	98.09 208	93.91 78	91.48 214	95.45 234

testing_2		87.33 293	85.03 300	94.22 190	87.77 345	89.32 183	94.97 274	97.11 168	89.22 175	71.64 346	88.73 332	55.16 350	97.94 244	91.95 112	88.73 248	95.41 236

EI-MVSNet		93.03 132	92.88 117	93.48 235	95.77 195	86.98 257	96.44 198	97.12 166	90.66 142	91.30 166	97.64 76	86.56 100	98.05 221	89.91 143	90.55 229	95.41 236

EU-MVSNet		88.72 271	88.90 256	88.20 322	93.15 312	74.21 340	96.63 189	94.22 315	85.18 279	87.32 268	95.97 155	76.16 275	94.98 334	85.27 234	86.17 265	95.41 236

test0.0.03 1		89.37 264	88.70 258	91.41 298	92.47 322	85.63 274	95.22 271	92.70 339	91.11 130	86.91 277	93.65 275	79.02 232	93.19 342	78.00 314	89.18 242	95.41 236

test_djsdf		93.07 130	92.76 119	94.00 199	93.49 298	88.70 201	98.22 26	97.57 116	91.42 119	90.08 201	95.55 183	82.85 160	97.92 248	94.07 72	91.58 212	95.40 240

IterMVS-LS		92.29 164	91.94 146	93.34 242	96.25 175	86.97 258	96.57 196	97.05 176	90.67 140	89.50 225	94.80 217	86.59 99	97.64 272	89.91 143	86.11 267	95.40 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		92.98 133	92.53 131	94.32 188	96.12 185	89.20 189	95.28 267	97.47 128	92.66 81	89.90 204	95.62 179	80.58 206	98.40 182	92.73 100	92.40 197	95.38 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CP-MVSNet		91.89 180	91.24 176	93.82 211	95.05 228	88.57 203	97.82 56	98.19 34	91.70 111	88.21 252	95.76 171	81.96 181	97.52 279	87.86 184	84.65 292	95.37 243

FMVSNet3		91.78 182	90.69 201	95.03 150	96.53 161	92.27 76	97.02 141	96.93 194	89.79 165	89.35 228	94.65 223	77.01 270	97.47 282	86.12 219	88.82 244	95.35 244

FMVSNet2		91.31 215	90.08 226	94.99 151	96.51 163	92.21 77	97.41 104	96.95 192	88.82 197	88.62 241	94.75 219	73.87 291	97.42 286	85.20 236	88.55 250	95.35 244

PS-CasMVS		91.55 202	90.84 193	93.69 224	94.96 232	88.28 209	97.84 55	98.24 29	91.46 117	88.04 254	95.80 166	79.67 222	97.48 281	87.02 207	84.54 294	95.31 246

LPG-MVS_test		92.94 136	92.56 128	94.10 194	96.16 181	88.26 210	97.65 75	97.46 130	91.29 122	90.12 197	97.16 99	79.05 230	98.73 153	92.25 104	91.89 207	95.31 246

LGP-MVS_train				94.10 194	96.16 181	88.26 210		97.46 130	91.29 122	90.12 197	97.16 99	79.05 230	98.73 153	92.25 104	91.89 207	95.31 246

GBi-Net		91.35 212	90.27 219	94.59 175	96.51 163	91.18 113	97.50 98	96.93 194	88.82 197	89.35 228	94.51 228	73.87 291	97.29 294	86.12 219	88.82 244	95.31 246

test1		91.35 212	90.27 219	94.59 175	96.51 163	91.18 113	97.50 98	96.93 194	88.82 197	89.35 228	94.51 228	73.87 291	97.29 294	86.12 219	88.82 244	95.31 246

FMVSNet1		89.88 256	88.31 264	94.59 175	95.41 205	91.18 113	97.50 98	96.93 194	86.62 263	87.41 265	94.51 228	65.94 331	97.29 294	83.04 266	87.43 258	95.31 246

PVSNet_0	82.17 19	85.46 307	83.64 308	90.92 304	95.27 214	79.49 328	90.55 336	95.60 256	83.76 299	83.00 306	89.95 316	71.09 306	97.97 238	82.75 271	60.79 353	95.31 246

ACMP	89.59 10	92.62 146	92.14 139	94.05 197	96.40 169	88.20 217	97.36 111	97.25 157	91.52 114	88.30 248	96.64 122	78.46 249	98.72 155	91.86 117	91.48 214	95.23 253
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v2v482		91.59 199	90.85 191	93.80 212	93.87 287	88.17 219	96.94 152	96.88 199	89.54 166	89.53 223	94.90 207	81.70 188	98.02 230	89.25 158	85.04 285	95.20 254

PEN-MVS		91.20 218	90.44 212	93.48 235	94.49 252	87.91 238	97.76 59	98.18 36	91.29 122	87.78 257	95.74 173	80.35 211	97.33 292	85.46 231	82.96 310	95.19 255

OurMVSNet-221017-0		90.51 243	90.19 225	91.44 297	93.41 300	81.25 313	96.98 145	96.28 223	91.68 112	86.55 280	96.30 142	74.20 290	97.98 235	88.96 167	87.40 260	95.09 256

divwei89l23v2f112		91.61 196	90.89 186	93.78 214	94.01 280	88.22 214	96.96 146	96.96 189	89.17 180	89.75 213	94.28 252	83.02 150	98.03 227	88.86 169	84.98 289	95.08 257

v1		91.61 196	90.89 186	93.78 214	94.01 280	88.21 216	96.96 146	96.96 189	89.17 180	89.78 212	94.29 250	82.97 154	98.05 221	88.85 170	84.99 287	95.08 257

OPM-MVS		93.28 124	92.76 119	94.82 162	94.63 248	90.77 128	96.65 185	97.18 158	93.72 47	91.68 156	97.26 95	79.33 227	98.63 159	92.13 108	92.28 198	95.07 259

v1141		91.61 196	90.89 186	93.78 214	94.01 280	88.24 212	96.96 146	96.96 189	89.17 180	89.75 213	94.29 250	82.99 152	98.03 227	88.85 170	85.00 286	95.07 259

v6		91.69 190	91.00 184	93.75 217	94.14 267	88.12 224	97.20 128	96.98 185	89.19 176	89.90 204	94.42 236	83.04 148	98.07 213	89.07 163	85.10 280	95.07 259

v1neww		91.70 188	91.01 182	93.75 217	94.19 262	88.14 222	97.20 128	96.98 185	89.18 178	89.87 207	94.44 234	83.10 142	98.06 218	89.06 164	85.09 281	95.06 262

v7new		91.70 188	91.01 182	93.75 217	94.19 262	88.14 222	97.20 128	96.98 185	89.18 178	89.87 207	94.44 234	83.10 142	98.06 218	89.06 164	85.09 281	95.06 262

ACMH	87.59 16	90.53 242	89.42 248	93.87 210	96.21 176	87.92 236	97.24 121	96.94 193	88.45 208	83.91 303	96.27 144	71.92 300	98.62 161	84.43 247	89.43 240	95.05 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		91.07 222	90.23 222	93.58 231	93.70 291	87.82 239	96.73 172	97.07 173	87.77 234	89.58 220	94.32 242	80.90 203	97.97 238	86.52 212	85.48 270	94.95 265

COLMAP_ROB	87.81 15	90.40 244	89.28 251	93.79 213	97.95 93	87.13 254	96.92 153	95.89 246	82.83 306	86.88 278	97.18 98	73.77 294	99.29 97	78.44 313	93.62 179	94.95 265
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		90.85 229	90.03 229	93.29 244	93.55 294	86.96 259	96.74 171	97.04 179	87.36 243	89.52 224	94.34 240	80.23 214	97.97 238	86.27 215	85.21 277	94.94 267

SixPastTwentyTwo		89.15 265	88.54 262	90.98 302	93.49 298	80.28 323	96.70 180	94.70 295	90.78 136	84.15 300	95.57 181	71.78 302	97.71 267	84.63 243	85.07 283	94.94 267

v144192		91.06 223	90.28 218	93.39 239	93.66 293	87.23 250	96.83 160	97.07 173	87.43 241	89.69 217	94.28 252	81.48 189	98.00 234	87.18 205	84.92 290	94.93 269

v1240		90.70 237	89.85 236	93.23 246	93.51 297	86.80 260	96.61 190	97.02 183	87.16 248	89.58 220	94.31 243	79.55 224	97.98 235	85.52 230	85.44 271	94.90 270

v7		91.47 206	90.73 198	93.68 225	94.13 268	88.16 220	97.09 137	97.05 176	88.38 216	89.80 210	94.52 227	82.21 176	98.01 231	88.00 181	85.42 272	94.87 271

pmmvs5		89.86 257	88.87 257	92.82 255	92.86 315	86.23 268	96.26 221	95.39 263	84.24 292	87.12 271	94.51 228	74.27 289	97.36 291	87.61 195	87.57 256	94.86 272

v1144		91.37 211	90.60 209	93.68 225	93.89 286	88.23 213	96.84 159	97.03 182	88.37 217	89.69 217	94.39 237	82.04 179	97.98 235	87.80 186	85.37 274	94.84 273

LP		84.13 311	81.85 316	90.97 303	93.20 310	82.12 308	87.68 348	94.27 314	76.80 339	81.93 310	88.52 333	72.97 298	95.95 323	59.53 349	81.73 315	94.84 273

K. test v3		87.64 291	86.75 289	90.32 314	93.02 314	79.48 329	96.61 190	92.08 344	90.66 142	80.25 332	94.09 260	67.21 326	96.65 308	85.96 224	80.83 321	94.83 275

IterMVS		90.15 251	89.67 243	91.61 293	95.48 203	83.72 293	94.33 286	96.12 232	89.99 158	87.31 269	94.15 259	75.78 278	96.27 312	86.97 208	86.89 262	94.83 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				91.82 286	95.52 201	84.20 290		96.15 231	90.61 147	87.39 266	94.27 254	75.63 279	96.44 309	87.34 200	86.88 263	94.82 277

WR-MVS_H		92.00 176	91.35 169	93.95 205	95.09 227	89.47 168	98.04 36	98.68 7	91.46 117	88.34 246	94.68 221	85.86 109	97.56 276	85.77 226	84.24 296	94.82 277

GG-mvs-BLEND				93.62 227	93.69 292	89.20 189	92.39 324	83.33 362		87.98 256	89.84 318	71.00 307	96.87 305	82.08 278	95.40 146	94.80 279

v148		90.99 225	90.38 215	92.81 258	93.83 288	85.80 271	96.78 169	96.68 210	89.45 170	88.75 240	93.93 265	82.96 156	97.82 258	87.83 185	83.25 307	94.80 279

XVG-ACMP-BASELINE		90.93 227	90.21 224	93.09 250	94.31 259	85.89 270	95.33 264	97.26 155	91.06 132	89.38 227	95.44 191	68.61 318	98.60 162	89.46 153	91.05 222	94.79 281

DTE-MVSNet		90.56 241	89.75 241	93.01 252	93.95 283	87.25 248	97.64 79	97.65 109	90.74 137	87.12 271	95.68 177	79.97 218	97.00 303	83.33 263	81.66 317	94.78 282

ACMH+	87.92 14	90.20 249	89.18 253	93.25 245	96.48 166	86.45 266	96.99 144	96.68 210	88.83 196	84.79 294	96.22 145	70.16 313	98.53 167	84.42 248	88.04 252	94.77 283

lessismore_v0				90.45 312	91.96 328	79.09 332		87.19 358		80.32 330	94.39 237	66.31 328	97.55 277	84.00 256	76.84 329	94.70 284

Patchmtry		88.64 275	87.25 280	92.78 259	94.09 274	86.64 262	89.82 341	95.68 254	80.81 323	87.63 262	92.36 302	80.91 200	97.03 300	78.86 311	85.12 279	94.67 285

v7n		90.76 231	89.86 235	93.45 238	93.54 295	87.60 244	97.70 71	97.37 145	88.85 194	87.65 261	94.08 261	81.08 194	98.10 206	84.68 242	83.79 304	94.66 286

V42		91.58 200	90.87 189	93.73 220	94.05 279	88.50 205	97.32 116	96.97 188	88.80 200	89.71 215	94.33 241	82.54 167	98.05 221	89.01 166	85.07 283	94.64 287

Anonymous20240521		91.32 214	90.43 214	93.98 200	94.93 235	89.28 186	98.04 36	97.53 120	89.49 169	86.68 279	94.82 215	81.72 187	98.05 221	85.31 233	85.39 273	94.61 288

v8		91.29 216	90.53 211	93.57 232	94.15 266	88.12 224	97.34 113	97.06 175	88.99 187	88.32 247	94.26 256	83.08 144	98.01 231	87.62 194	83.92 301	94.57 289

v748		90.34 245	89.54 246	92.75 260	93.25 305	85.71 273	97.61 87	97.17 160	88.54 207	87.20 270	93.54 278	81.02 195	98.01 231	85.73 228	81.80 314	94.52 290

anonymousdsp		92.16 170	91.55 163	93.97 203	92.58 321	89.55 164	97.51 97	97.42 140	89.42 171	88.40 245	94.84 212	80.66 205	97.88 253	91.87 116	91.28 218	94.48 291

pm-mvs1		90.72 235	89.65 245	93.96 204	94.29 260	89.63 160	97.79 58	96.82 202	89.07 184	86.12 284	95.48 190	78.61 247	97.78 261	86.97 208	81.67 316	94.46 292

LTVRE_ROB	88.41 13	90.99 225	89.92 233	94.19 191	96.18 179	89.55 164	96.31 216	97.09 170	87.88 232	85.67 286	95.91 160	78.79 246	98.57 165	81.50 284	89.98 235	94.44 293
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
YYNet1		85.87 304	84.23 306	90.78 309	92.38 325	82.46 305	93.17 310	95.14 278	82.12 311	67.69 347	92.36 302	78.16 255	95.50 332	77.31 317	79.73 323	94.39 294

PVSNet_BlendedMVS		94.06 100	93.92 88	94.47 181	98.27 70	89.46 170	96.73 172	98.36 16	90.17 155	94.36 95	95.24 197	88.02 77	99.58 55	93.44 89	90.72 227	94.36 295

v10		91.04 224	90.23 222	93.49 234	94.12 270	88.16 220	97.32 116	97.08 172	88.26 220	88.29 249	94.22 257	82.17 178	97.97 238	86.45 214	84.12 297	94.33 296

MDA-MVSNet-bldmvs		85.00 308	82.95 310	91.17 301	93.13 313	83.33 299	94.56 280	95.00 284	84.57 289	65.13 352	92.65 293	70.45 310	95.85 324	73.57 329	77.49 327	94.33 296

MDA-MVSNet_test_wron		85.87 304	84.23 306	90.80 308	92.38 325	82.57 302	93.17 310	95.15 277	82.15 310	67.65 348	92.33 305	78.20 252	95.51 331	77.33 316	79.74 322	94.31 298

our_test_3		88.78 270	87.98 268	91.20 300	92.45 323	82.53 303	93.61 304	95.69 252	85.77 273	84.88 292	93.71 271	79.99 217	96.78 307	79.47 307	86.24 264	94.28 299

pmmvs4		90.93 227	89.85 236	94.17 192	93.34 302	90.79 127	94.60 278	96.02 234	84.62 288	87.45 263	95.15 199	81.88 184	97.45 283	87.70 188	87.87 254	94.27 300

ppachtmachnet_test		88.35 285	87.29 278	91.53 294	92.45 323	83.57 298	93.75 298	95.97 235	84.28 291	85.32 291	94.18 258	79.00 236	96.93 304	75.71 322	84.99 287	94.10 301

UnsupCasMVSNet_eth		85.99 303	84.45 304	90.62 310	89.97 336	82.40 306	93.62 303	97.37 145	89.86 160	78.59 337	92.37 299	65.25 333	95.35 333	82.27 277	70.75 347	94.10 301

pmmvs6		87.81 290	86.19 292	92.69 262	91.32 330	86.30 267	97.34 113	96.41 219	80.59 325	84.05 302	94.37 239	67.37 325	97.67 269	84.75 240	79.51 324	94.09 303

ITE_SJBPF				92.43 267	95.34 209	85.37 278		95.92 239	91.47 116	87.75 258	96.39 140	71.00 307	97.96 242	82.36 276	89.86 238	93.97 304

FMVSNet5		87.29 294	85.79 295	91.78 289	94.80 242	87.28 246	95.49 259	95.28 270	84.09 294	83.85 304	91.82 308	62.95 336	94.17 337	78.48 312	85.34 275	93.91 305

Anonymous20231206		87.09 295	86.14 293	89.93 318	91.22 331	80.35 320	96.11 230	95.35 266	83.57 301	84.16 299	93.02 289	73.54 296	95.61 328	72.16 332	86.14 266	93.84 306

USDC		88.94 266	87.83 269	92.27 268	94.66 246	84.96 282	93.86 296	95.90 241	87.34 244	83.40 305	95.56 182	67.43 324	98.19 197	82.64 273	89.67 239	93.66 307

N_pmnet		78.73 322	78.71 321	78.79 339	92.80 317	46.50 366	94.14 291	43.71 370	78.61 334	80.83 316	91.66 312	74.94 286	96.36 310	67.24 340	84.45 295	93.50 308

MIMVSNet1		84.93 309	83.05 309	90.56 311	89.56 339	84.84 285	95.40 262	95.35 266	83.91 295	80.38 328	92.21 306	57.23 344	93.34 341	70.69 338	82.75 313	93.50 308

TransMVSNet (Re)		88.94 266	87.56 270	93.08 251	94.35 257	88.45 207	97.73 64	95.23 274	87.47 240	84.26 298	95.29 194	79.86 219	97.33 292	79.44 309	74.44 344	93.45 310

V4		90.71 236	90.00 230	92.82 255	93.21 309	87.03 255	97.59 90	97.16 163	88.21 221	87.69 259	93.92 266	80.93 199	98.06 218	87.39 198	83.90 302	93.39 311

Baseline_NR-MVSNet		91.20 218	90.62 208	92.95 254	93.83 288	88.03 230	97.01 143	95.12 279	88.42 215	89.70 216	95.13 201	83.47 133	97.44 284	89.66 150	83.24 308	93.37 312

v52		90.70 237	90.00 230	92.82 255	93.24 306	87.03 255	97.60 88	97.14 164	88.21 221	87.69 259	93.94 264	80.91 200	98.07 213	87.39 198	83.87 303	93.36 313

TDRefinement		86.53 298	84.76 303	91.85 285	82.23 354	84.25 288	96.38 209	95.35 266	84.97 284	84.09 301	94.94 204	65.76 332	98.34 188	84.60 246	74.52 342	92.97 314

ambc				86.56 328	83.60 351	70.00 348	85.69 351	94.97 287		80.60 323	88.45 334	37.42 357	96.84 306	82.69 272	75.44 333	92.86 315

test2356		82.77 315	82.14 313	84.65 330	85.77 348	70.36 345	91.22 331	93.69 324	81.58 316	81.82 311	89.00 331	60.63 341	90.77 349	64.74 343	90.80 226	92.82 316

test1235678		79.82 321	78.53 322	83.69 332	82.55 353	67.55 351	92.50 322	94.13 316	79.28 330	72.10 345	86.45 345	57.27 343	90.68 350	61.60 347	80.90 320	92.82 316

MS-PatchMatch		90.27 246	89.77 239	91.78 289	94.33 258	84.72 286	95.55 255	96.73 204	86.17 269	86.36 281	95.28 196	71.28 305	97.80 259	84.09 252	98.14 85	92.81 318

tfpnnormal		89.70 259	88.40 263	93.60 228	95.15 223	90.10 137	97.56 94	98.16 39	87.28 246	86.16 283	94.63 224	77.57 267	98.05 221	74.48 324	84.59 293	92.65 319

testus		82.63 316	82.15 312	84.07 331	87.31 346	67.67 350	93.18 308	94.29 313	82.47 308	82.14 309	90.69 314	53.01 351	91.94 346	66.30 342	89.96 236	92.62 320

EG-PatchMatch MVS		87.02 296	85.44 297	91.76 291	92.67 319	85.00 281	96.08 232	96.45 218	83.41 303	79.52 334	93.49 281	57.10 345	97.72 266	79.34 310	90.87 225	92.56 321

TinyColmap		86.82 297	85.35 299	91.21 299	94.91 238	82.99 301	93.94 295	94.02 319	83.58 300	81.56 314	94.68 221	62.34 338	98.13 201	75.78 321	87.35 261	92.52 322

v18		88.71 272	87.52 271	92.27 268	94.16 265	88.11 226	96.82 163	95.96 236	87.03 250	80.76 319	89.81 319	83.15 138	96.22 313	84.69 241	75.31 335	92.49 323

v17		88.67 274	87.47 274	92.26 270	94.13 268	88.09 228	96.81 164	95.95 237	87.02 251	80.72 320	89.75 321	83.11 141	96.20 314	84.61 244	75.15 337	92.49 323

v16		88.69 273	87.50 272	92.26 270	94.19 262	88.11 226	96.81 164	95.95 237	87.01 252	80.71 321	89.80 320	83.08 144	96.20 314	84.61 244	75.34 334	92.48 325

CMPMVS	62.92 21	85.62 306	84.92 301	87.74 324	89.14 340	73.12 342	94.17 290	96.80 203	73.98 346	73.65 342	94.93 205	66.36 327	97.61 274	83.95 257	91.28 218	92.48 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
V9		88.49 280	87.26 279	92.18 274	94.12 270	87.97 234	96.73 172	95.90 241	86.95 256	80.40 327	89.61 323	82.98 153	96.13 316	84.14 251	74.55 341	92.44 327

v12		88.46 281	87.23 282	92.17 275	94.10 273	87.99 231	96.71 177	95.90 241	86.91 257	80.34 329	89.58 326	82.92 157	96.11 320	84.09 252	74.50 343	92.42 328

v15		88.53 276	87.31 276	92.20 273	94.09 274	88.05 229	96.72 175	95.90 241	87.01 252	80.53 324	89.60 325	83.02 150	96.13 316	84.29 249	74.64 338	92.41 329

v13		88.45 282	87.22 283	92.16 277	94.08 276	87.95 235	96.71 177	95.90 241	86.86 261	80.27 331	89.55 327	82.92 157	96.12 318	84.02 254	74.63 339	92.40 330

V14		88.52 277	87.30 277	92.17 275	94.12 270	87.99 231	96.72 175	95.91 240	86.98 254	80.50 325	89.63 322	83.03 149	96.12 318	84.23 250	74.60 340	92.40 330

v11		88.41 283	87.19 286	92.08 280	94.08 276	87.77 240	96.75 170	95.85 247	86.74 262	80.50 325	89.50 328	82.49 169	96.08 321	83.55 260	75.20 336	92.38 332

test20.03		86.14 302	85.40 298	88.35 320	90.12 334	80.06 325	95.90 241	95.20 275	88.59 203	81.29 315	93.62 276	71.43 304	92.65 343	71.26 336	81.17 319	92.34 333

LF4IMVS		87.94 288	87.25 280	89.98 317	92.38 325	80.05 326	94.38 283	95.25 273	87.59 239	84.34 296	94.74 220	64.31 334	97.66 271	84.83 238	87.45 257	92.23 334

MVS-HIRNet		82.47 317	81.21 318	86.26 329	95.38 207	69.21 349	88.96 345	89.49 354	66.28 351	80.79 318	74.08 354	68.48 319	97.39 289	71.93 333	95.47 144	92.18 335

MVP-Stereo		90.74 234	90.08 226	92.71 261	93.19 311	88.20 217	95.86 242	96.27 224	86.07 270	84.86 293	94.76 218	77.84 265	97.75 264	83.88 258	98.01 87	92.17 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs-eth3d		86.22 301	84.45 304	91.53 294	88.34 342	87.25 248	94.47 282	95.01 283	83.47 302	79.51 335	89.61 323	69.75 315	95.71 327	83.13 265	76.73 330	91.64 337

UnsupCasMVSNet_bld		82.13 318	79.46 320	90.14 316	88.00 343	82.47 304	90.89 335	96.62 216	78.94 332	75.61 340	84.40 347	56.63 346	96.31 311	77.30 318	66.77 352	91.63 338

test_0402		86.46 299	84.79 302	91.45 296	95.02 230	85.55 275	96.29 218	94.89 290	80.90 320	82.21 307	93.97 263	68.21 321	97.29 294	62.98 345	88.68 249	91.51 339

PM-MVS		83.48 312	81.86 315	88.31 321	87.83 344	77.59 335	93.43 305	91.75 346	86.91 257	80.63 322	89.91 317	44.42 356	95.84 325	85.17 237	76.73 330	91.50 340

new-patchmatchnet		83.18 313	81.87 314	87.11 326	86.88 347	75.99 338	93.70 299	95.18 276	85.02 283	77.30 339	88.40 335	65.99 330	93.88 339	74.19 328	70.18 348	91.47 341

OpenMVS_ROB	81.14 20	84.42 310	82.28 311	90.83 305	90.06 335	84.05 292	95.73 249	94.04 318	73.89 347	80.17 333	91.53 313	59.15 342	97.64 272	66.92 341	89.05 243	90.80 342

LCM-MVSNet		72.55 325	69.39 328	82.03 333	70.81 364	65.42 354	90.12 340	94.36 310	55.02 355	65.88 351	81.72 348	24.16 366	89.96 351	74.32 327	68.10 350	90.71 343

new_pmnet		82.89 314	81.12 319	88.18 323	89.63 338	80.18 324	91.77 327	92.57 340	76.79 340	75.56 341	88.23 337	61.22 340	94.48 335	71.43 334	82.92 311	89.87 344

pmmvs3		79.97 320	77.50 324	87.39 325	82.80 352	79.38 330	92.70 319	90.75 350	70.69 350	78.66 336	87.47 343	51.34 353	93.40 340	73.39 330	69.65 349	89.38 345

1111		78.29 323	77.55 323	80.50 335	83.89 349	59.98 358	91.89 325	93.71 321	75.06 342	73.60 343	87.67 340	55.66 347	92.60 344	58.54 351	77.92 326	88.93 346

test12356		74.97 324	74.13 325	77.49 340	78.81 355	56.23 362	88.53 346	92.75 338	75.14 341	67.50 349	85.07 346	44.88 355	89.96 351	58.71 350	75.75 332	86.26 347

testmv		72.22 326	70.02 326	78.82 338	73.06 362	61.75 356	91.24 330	92.31 342	74.45 345	61.06 354	80.51 350	34.21 358	88.63 354	55.31 354	68.07 351	86.06 348

PMMVS2		70.19 328	66.92 330	80.01 336	76.35 356	65.67 353	86.22 350	87.58 357	64.83 353	62.38 353	80.29 351	26.78 364	88.49 355	63.79 344	54.07 354	85.88 349

ANet_high		63.94 332	59.58 333	77.02 341	61.24 367	66.06 352	85.66 352	87.93 356	78.53 335	42.94 359	71.04 356	25.42 365	80.71 359	52.60 356	30.83 360	84.28 350

FPMVS		71.27 327	69.85 327	75.50 342	74.64 357	59.03 360	91.30 329	91.50 347	58.80 354	57.92 355	88.28 336	29.98 362	85.53 357	53.43 355	82.84 312	81.95 351

no-one		68.12 329	63.78 332	81.13 334	74.01 359	70.22 347	87.61 349	90.71 351	72.63 349	53.13 357	71.89 355	30.29 360	91.45 347	61.53 348	32.21 358	81.72 352

DeepMVS_CX				74.68 344	90.84 332	64.34 355		81.61 365	65.34 352	67.47 350	88.01 339	48.60 354	80.13 360	62.33 346	73.68 346	79.58 353

wuykxyi23d		56.92 335	51.11 340	74.38 345	62.30 366	61.47 357	80.09 356	84.87 360	49.62 358	30.80 365	57.20 362	7.03 369	82.94 358	55.69 353	32.36 357	78.72 354

testpf		80.97 319	81.40 317	79.65 337	91.53 329	72.43 343	73.47 359	89.55 353	78.63 333	80.81 317	89.06 330	61.36 339	91.36 348	83.34 262	84.89 291	75.15 355

PNet_i23d		59.01 333	55.87 334	68.44 346	73.98 360	51.37 363	81.36 355	82.41 363	52.37 357	42.49 361	70.39 357	11.39 367	79.99 361	49.77 357	38.71 356	73.97 356

PMVS	53.92 22	58.58 334	55.40 335	68.12 347	51.00 368	48.64 364	78.86 357	87.10 359	46.77 359	35.84 364	74.28 353	8.76 368	86.34 356	42.07 359	73.91 345	69.38 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.73 23	53.25 337	48.81 341	66.58 348	65.34 365	57.50 361	72.49 360	70.94 368	40.15 362	39.28 363	63.51 359	6.89 371	73.48 364	38.29 360	42.38 355	68.76 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		67.86 330	65.41 331	75.18 343	92.66 320	73.45 341	66.50 361	94.52 305	53.33 356	57.80 356	66.07 358	30.81 359	89.20 353	48.15 358	78.88 325	62.90 359
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		53.28 336	52.56 338	55.43 349	74.43 358	47.13 365	83.63 354	76.30 366	42.23 360	42.59 360	62.22 360	28.57 363	74.40 362	31.53 361	31.51 359	44.78 360

EMVS		52.08 338	51.31 339	54.39 350	72.62 363	45.39 367	83.84 353	75.51 367	41.13 361	40.77 362	59.65 361	30.08 361	73.60 363	28.31 362	29.90 361	44.18 361

tmp_tt		51.94 339	53.82 337	46.29 352	33.73 369	45.30 368	78.32 358	67.24 369	18.02 363	50.93 358	87.05 344	52.99 352	53.11 365	70.76 337	25.29 362	40.46 362

test123		13.04 345	15.66 346	5.18 355	4.51 371	3.45 370	92.50 322	1.81 373	2.50 366	7.58 368	20.15 365	3.67 372	2.18 368	7.13 365	1.07 366	9.90 363

.test1245		65.38 331	69.22 329	53.86 351	83.89 349	59.98 358	91.89 325	93.71 321	75.06 342	73.60 343	87.67 340	55.66 347	92.60 344	58.54 351	2.96 364	9.00 364

testmvs		13.36 344	16.33 345	4.48 356	5.04 370	2.26 371	93.18 308	3.28 372	2.70 365	8.24 367	21.66 364	2.29 373	2.19 367	7.58 364	2.96 364	9.00 364

wuyk23d		25.11 342	24.57 344	26.74 354	73.98 360	39.89 369	57.88 362	9.80 371	12.27 364	10.39 366	6.97 368	7.03 369	36.44 366	25.43 363	17.39 363	3.89 366

test_part1				0.00 357		0.00 372	0.00 363	98.26 26				0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

v1.0		40.67 340	54.22 336	0.00 357	99.28 17	0.00 372	0.00 363	98.26 26	93.81 46	98.10 8	98.53 13	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

cdsmvs_eth3d_5k		23.24 343	30.99 343	0.00 357	0.00 372	0.00 372	0.00 363	97.63 111	0.00 367	0.00 369	96.88 110	84.38 125	0.00 369	0.00 366	0.00 367	0.00 367

pcd_1.5k_mvsjas		7.39 347	9.85 348	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	88.65 71	0.00 369	0.00 366	0.00 367	0.00 367

sosnet-low-res		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sosnet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uncertanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

Regformer		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

ab-mvs-re		8.06 346	10.74 347	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	96.69 119	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

test_part2					99.28 17	95.74 4				98.10 8

sam_mvs												81.94 183

MTGPA								98.08 52

test_post1							92.81 318				16.58 367	80.53 207	97.68 268	86.20 217

test_post											17.58 366	81.76 185	98.08 209

patchmatchnet-post											90.45 315	82.65 166	98.10 206

MTMP							97.86 51	82.03 364

gm-plane-assit					93.22 308	78.89 333			84.82 286		93.52 279		98.64 158	87.72 187

TEST9					98.70 40	94.19 25	96.41 203	98.02 69	88.17 225	96.03 57	97.56 84	92.74 14	99.59 52

test_8					98.67 42	94.06 31	96.37 210	98.01 71	88.58 204	95.98 62	97.55 86	92.73 15	99.58 55

agg_prior					98.67 42	93.79 38		98.00 73		95.68 72			99.57 63

test_prior4						93.66 42	96.42 202

test_prior2							96.35 211		92.80 79	96.03 57	97.59 80	92.01 30		95.01 57	99.38 35

旧先验2							95.94 239		81.66 314	97.34 19			98.82 143	92.26 102

新几何2							95.79 246

原ACMM2							95.67 250

testdata2													99.67 39	85.96 224

segment_acmp												92.89 12

testdata1							95.26 270		93.10 67

plane_prior7					96.21 176	89.98 143

plane_prior6					96.10 186	90.00 139						81.32 192

plane_prior4											96.64 122

plane_prior3						90.00 139			94.46 31	91.34 163

plane_prior2							97.74 62		94.85 18

plane_prior1					96.14 184

plane_prior						89.99 141	97.24 121		94.06 39						92.16 203

n2								0.00 374

nn								0.00 374

door-mid								91.06 349

test11								97.88 86

door								91.13 348

HQP5-MVS						89.33 181

HQP-NCC					95.86 190		96.65 185		93.55 50	90.14 191

ACMP_Plane					95.86 190		96.65 185		93.55 50	90.14 191

BP-MVS														92.13 108

HQP3-MVS								97.39 142							92.10 204

HQP2-MVS												80.95 197

NP-MVS					95.99 189	89.81 150					95.87 161

MDTV_nov1_ep13			90.76 196		95.22 219	80.33 321	93.03 315	95.28 270	88.14 226	92.84 136	93.83 267	81.34 191	98.08 209	82.86 268	94.34 161

ACMMP++_ref															90.30 233

ACMMP++															91.02 223

Test By Simon												88.73 70

High-resolution multi-view benchmark