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		98.64 11	98.68 3	98.53 75	99.33 45	98.36 24	98.90 74	98.85 53	97.28 21	99.72 1	99.39 8	96.63 10	97.60 299	98.17 23	99.85 2	99.64 56

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 27	99.69 17	98.28 30	99.14 44	98.66 108	96.84 43	99.56 2	99.31 22	96.34 13	99.70 94	98.32 20	99.73 37	99.73 30

SMA-MVS		98.64 11	98.33 25	99.59 2	99.51 28	99.11 3	98.95 69	98.83 58	93.77 161	99.52 3	99.52 3	96.94 5	99.89 29	98.06 25	99.84 7	99.76 20

HSP-MVS		98.70 5	98.52 8	99.24 27	99.75 3	98.23 31	99.26 17	98.58 121	97.52 7	99.41 4	98.78 88	96.00 26	99.79 72	97.79 39	99.59 55	99.69 38

APDe-MVS		99.02 1	98.84 1	99.55 3	99.57 25	98.96 5	99.39 5	98.93 36	97.38 17	99.41 4	99.54 1	96.66 8	99.84 45	98.86 2	99.85 2	99.87 1

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 14	99.22 74	98.43 19	99.10 51	98.87 49	97.38 17	99.35 6	99.40 7	97.78 1	99.87 38	97.77 40	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
test_part2					99.63 21	99.18 1				99.27 7

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 153	98.84 54	96.40 57	99.27 7	99.31 22	97.38 2	99.93 9	96.37 96	99.78 15	99.76 20

abl_6		98.30 42	98.03 40	99.13 40	99.56 26	97.76 54	99.13 47	98.82 59	96.14 63	99.26 9	99.37 13	93.33 79	99.93 9	96.96 68	99.67 42	99.69 38

DeepPCF-MVS	96.37 2	97.93 50	98.48 13	96.30 232	99.00 89	89.54 296	97.43 265	98.87 49	98.16 2	99.26 9	99.38 12	96.12 20	99.64 103	98.30 21	99.77 20	99.72 33

APD-MVS		98.35 37	98.00 42	99.42 11	99.51 28	98.72 10	98.80 103	98.82 59	94.52 133	99.23 11	99.25 31	95.54 40	99.80 60	96.52 90	99.77 20	99.74 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-2		98.69 8	98.52 8	99.19 30	99.35 40	98.01 44	98.37 175	98.81 62	97.48 11	99.21 12	99.21 35	96.13 19	99.80 60	98.40 18	99.73 37	99.75 23

Regformer-1		98.66 9	98.51 10	99.12 42	99.35 40	97.81 53	98.37 175	98.76 76	97.49 10	99.20 13	99.21 35	96.08 22	99.79 72	98.42 16	99.73 37	99.75 23

APD-MVS_3200maxsize		98.53 27	98.33 25	99.15 39	99.50 30	97.92 48	99.15 43	98.81 62	96.24 60	99.20 13	99.37 13	95.30 46	99.80 60	97.73 42	99.67 42	99.72 33

ACMMP_Plus		98.61 15	98.30 27	99.55 3	99.62 23	98.95 6	98.82 94	98.81 62	95.80 74	99.16 15	99.47 5	95.37 43	99.92 15	97.89 33	99.75 32	99.79 4

Regformer-4		98.64 11	98.53 7	98.99 49	99.43 38	97.37 66	98.40 173	98.79 70	97.46 12	99.09 16	99.31 22	95.86 34	99.80 60	98.64 4	99.76 26	99.79 4

Regformer-3		98.59 18	98.50 11	98.86 59	99.43 38	97.05 77	98.40 173	98.68 98	97.43 13	99.06 17	99.31 22	95.80 35	99.77 82	98.62 6	99.76 26	99.78 7

PGM-MVS		98.49 29	98.23 34	99.27 25	99.72 11	98.08 41	98.99 63	99.49 5	95.43 88	99.03 18	99.32 21	95.56 38	99.94 3	96.80 80	99.77 20	99.78 7

VNet		97.79 56	97.40 63	98.96 53	98.88 108	97.55 60	98.63 140	98.93 36	96.74 46	99.02 19	98.84 83	90.33 130	99.83 46	98.53 10	96.66 159	99.50 74

xiu_mvs_v1_base_debu		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

xiu_mvs_v1_base		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

xiu_mvs_v1_base_debi		97.60 63	97.56 53	97.72 121	98.35 137	95.98 114	97.86 237	98.51 133	97.13 34	99.01 20	98.40 120	91.56 109	99.80 60	98.53 10	98.68 105	97.37 200

TSAR-MVS + GP.		98.38 34	98.24 33	98.81 60	99.22 74	97.25 72	98.11 209	98.29 168	97.19 30	98.99 23	99.02 61	96.22 14	99.67 99	98.52 14	98.56 113	99.51 72

HFP-MVS		98.63 14	98.40 14	99.32 18	99.72 11	98.29 28	99.23 22	98.96 31	96.10 67	98.94 24	99.17 42	96.06 23	99.92 15	97.62 46	99.78 15	99.75 23

region2R		98.61 15	98.38 17	99.29 20	99.74 7	98.16 37	99.23 22	98.93 36	96.15 62	98.94 24	99.17 42	95.91 31	99.94 3	97.55 51	99.79 11	99.78 7

#test#		98.54 26	98.27 29	99.32 18	99.72 11	98.29 28	98.98 66	98.96 31	95.65 80	98.94 24	99.17 42	96.06 23	99.92 15	97.21 61	99.78 15	99.75 23

HPM-MVS_fast		98.38 34	98.13 37	99.12 42	99.75 3	97.86 49	99.44 4	98.82 59	94.46 137	98.94 24	99.20 38	95.16 51	99.74 88	97.58 48	99.85 2	99.77 14

ACMMPR		98.59 18	98.36 19	99.29 20	99.74 7	98.15 38	99.23 22	98.95 33	96.10 67	98.93 28	99.19 41	95.70 36	99.94 3	97.62 46	99.79 11	99.78 7

DeepC-MVS_fast	96.70 1	98.55 24	98.34 22	99.18 34	99.25 67	98.04 42	98.50 163	98.78 72	97.72 4	98.92 29	99.28 28	95.27 47	99.82 51	97.55 51	99.77 20	99.69 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		98.47 30	98.39 15	98.69 64	99.46 35	96.49 99	98.30 186	98.69 95	97.21 28	98.84 30	99.36 17	95.41 42	99.78 77	98.62 6	99.65 46	99.80 3

MSLP-MVS++		98.56 23	98.57 5	98.55 73	99.26 66	96.80 86	98.71 125	99.05 23	97.28 21	98.84 30	99.28 28	96.47 12	99.40 135	98.52 14	99.70 40	99.47 80

PHI-MVS		98.34 38	98.06 39	99.18 34	99.15 81	98.12 40	99.04 59	99.09 19	93.32 191	98.83 32	99.10 51	96.54 11	99.83 46	97.70 44	99.76 26	99.59 64

MVSFormer		97.57 66	97.49 58	97.84 114	98.07 157	95.76 140	99.47 2	98.40 153	94.98 116	98.79 33	98.83 84	92.34 89	98.41 254	96.91 70	99.59 55	99.34 91

lupinMVS		97.44 73	97.22 70	98.12 100	98.07 157	95.76 140	97.68 251	97.76 229	94.50 134	98.79 33	98.61 103	92.34 89	99.30 141	97.58 48	99.59 55	99.31 94

CDPH-MVS		97.94 49	97.49 58	99.28 22	99.47 34	98.44 17	97.91 229	98.67 105	92.57 215	98.77 35	98.85 82	95.93 30	99.72 89	95.56 121	99.69 41	99.68 44

CNVR-MVS		98.78 3	98.56 6	99.45 10	99.32 48	98.87 8	98.47 166	98.81 62	97.72 4	98.76 36	99.16 45	97.05 4	99.78 77	98.06 25	99.66 45	99.69 38

EI-MVSNet-UG-set		98.41 32	98.34 22	98.61 69	99.45 36	96.32 106	98.28 188	98.68 98	97.17 31	98.74 37	99.37 13	95.25 48	99.79 72	98.57 8	99.54 67	99.73 30

UA-Net		97.96 47	97.62 50	98.98 51	98.86 110	97.47 63	98.89 78	99.08 20	96.67 49	98.72 38	99.54 1	93.15 82	99.81 53	94.87 137	98.83 101	99.65 53

旧先验2							97.57 259		91.30 260	98.67 39			99.80 60	95.70 118

PS-MVSNAJ		97.73 57	97.77 46	97.62 132	98.68 124	95.58 145	97.34 274	98.51 133	97.29 20	98.66 40	97.88 164	94.51 63	99.90 27	97.87 34	99.17 89	97.39 198

xiu_mvs_v2_base		97.66 62	97.70 49	97.56 140	98.61 130	95.46 151	97.44 263	98.46 143	97.15 32	98.65 41	98.15 144	94.33 69	99.80 60	97.84 37	98.66 109	97.41 196

LFMVS		95.86 131	94.98 153	98.47 80	98.87 109	96.32 106	98.84 91	96.02 316	93.40 188	98.62 42	99.20 38	74.99 324	99.63 106	97.72 43	97.20 151	99.46 84

HPM-MVS		98.36 36	98.10 38	99.13 40	99.74 7	97.82 52	99.53 1	98.80 69	94.63 130	98.61 43	98.97 68	95.13 52	99.77 82	97.65 45	99.83 8	99.79 4
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testdata				98.26 91	99.20 77	95.36 154		98.68 98	91.89 240	98.60 44	99.10 51	94.44 68	99.82 51	94.27 154	99.44 77	99.58 66

CP-MVS		98.57 22	98.36 19	99.19 30	99.66 19	97.86 49	99.34 11	98.87 49	95.96 70	98.60 44	99.13 47	96.05 25	99.94 3	97.77 40	99.86 1	99.77 14

jason		97.32 81	97.08 75	98.06 106	97.45 196	95.59 144	97.87 236	97.91 224	94.79 123	98.55 46	98.83 84	91.12 117	99.23 147	97.58 48	99.60 52	99.34 91
jason: jason.
MCST-MVS		98.65 10	98.37 18	99.48 7	99.60 24	98.87 8	98.41 172	98.68 98	97.04 38	98.52 47	98.80 87	96.78 7	99.83 46	97.93 29	99.61 51	99.74 28

XVS		98.70 5	98.49 12	99.34 15	99.70 15	98.35 25	99.29 14	98.88 47	97.40 14	98.46 48	99.20 38	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

X-MVStestdata		94.06 244	92.30 264	99.34 15	99.70 15	98.35 25	99.29 14	98.88 47	97.40 14	98.46 48	43.50 354	95.90 32	99.89 29	97.85 35	99.74 35	99.78 7

MG-MVS		97.81 55	97.60 51	98.44 82	99.12 83	95.97 118	97.75 246	98.78 72	96.89 42	98.46 48	99.22 34	93.90 76	99.68 98	94.81 140	99.52 69	99.67 49

NCCC		98.61 15	98.35 21	99.38 12	99.28 63	98.61 13	98.45 167	98.76 76	97.82 3	98.45 51	98.93 76	96.65 9	99.83 46	97.38 58	99.41 79	99.71 35

MVS_Test		97.28 82	97.00 78	98.13 99	98.33 141	95.97 118	98.74 119	98.07 214	94.27 140	98.44 52	98.07 149	92.48 88	99.26 144	96.43 93	98.19 128	99.16 114

MVS_111021_LR		98.34 38	98.23 34	98.67 66	99.27 64	96.90 83	97.95 224	99.58 3	97.14 33	98.44 52	99.01 65	95.03 54	99.62 108	97.91 30	99.75 32	99.50 74

VDDNet		95.36 169	94.53 182	97.86 113	98.10 156	95.13 163	98.85 88	97.75 230	90.46 272	98.36 54	99.39 8	73.27 331	99.64 103	97.98 28	96.58 162	98.81 141

mPP-MVS		98.51 28	98.26 30	99.25 26	99.75 3	98.04 42	99.28 16	98.81 62	96.24 60	98.35 55	99.23 32	95.46 41	99.94 3	97.42 56	99.81 9	99.77 14

DELS-MVS		98.40 33	98.20 36	98.99 49	99.00 89	97.66 55	97.75 246	98.89 44	97.71 6	98.33 56	98.97 68	94.97 55	99.88 37	98.42 16	99.76 26	99.42 88
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
MVS_111021_HR		98.47 30	98.34 22	98.88 58	99.22 74	97.32 67	97.91 229	99.58 3	97.20 29	98.33 56	99.00 66	95.99 27	99.64 103	98.05 27	99.76 26	99.69 38

HPM-MVS++		98.58 20	98.25 31	99.55 3	99.50 30	99.08 4	98.72 124	98.66 108	97.51 8	98.15 58	98.83 84	95.70 36	99.92 15	97.53 53	99.67 42	99.66 51

新几何1				99.16 37	99.34 42	98.01 44		98.69 95	90.06 281	98.13 59	98.95 74	94.60 61	99.89 29	91.97 216	99.47 72	99.59 64

API-MVS		97.41 76	97.25 67	97.91 111	98.70 121	96.80 86	98.82 94	98.69 95	94.53 132	98.11 60	98.28 134	94.50 66	99.57 117	94.12 158	99.49 70	97.37 200

CPTT-MVS		97.72 58	97.32 65	98.92 55	99.64 20	97.10 76	99.12 49	98.81 62	92.34 229	98.09 61	99.08 57	93.01 83	99.92 15	96.06 102	99.77 20	99.75 23

test12				99.18 34	99.16 79	98.19 35		98.53 129		98.07 62		95.13 52	99.72 89		99.56 64	99.63 58

test222					99.23 73	97.17 75	97.40 266	98.66 108	88.68 305	98.05 63	98.96 72	94.14 72			99.53 68	99.61 59

DP-MVS Recon		97.86 53	97.46 60	99.06 47	99.53 27	98.35 25	98.33 179	98.89 44	92.62 212	98.05 63	98.94 75	95.34 45	99.65 101	96.04 103	99.42 78	99.19 109

Vis-MVSNet		97.42 75	97.11 73	98.34 88	98.66 125	96.23 109	99.22 28	99.00 26	96.63 51	98.04 65	99.21 35	88.05 190	99.35 140	96.01 105	99.21 87	99.45 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		97.70 59	97.25 67	99.07 45	98.90 99	97.83 51	98.20 194	98.74 80	97.51 8	98.03 66	99.06 59	86.12 228	99.93 9	99.02 1	99.64 48	99.44 87

zzz-MVS		98.55 24	98.25 31	99.46 8	99.76 1	98.64 11	98.55 153	98.74 80	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

MTAPA		98.58 20	98.29 28	99.46 8	99.76 1	98.64 11	98.90 74	98.74 80	97.27 25	98.02 67	99.39 8	94.81 57	99.96 1	97.91 30	99.79 11	99.77 14

1121		97.37 79	96.77 89	99.16 37	99.34 42	97.99 47	98.19 198	98.68 98	90.14 279	98.01 69	98.97 68	94.80 59	99.87 38	93.36 175	99.46 75	99.61 59

sss		97.39 77	96.98 79	98.61 69	98.60 131	96.61 94	98.22 192	98.93 36	93.97 151	98.01 69	98.48 115	91.98 102	99.85 43	96.45 92	98.15 129	99.39 89

alignmvs		97.56 67	97.07 76	99.01 48	98.66 125	98.37 23	98.83 92	98.06 216	96.74 46	98.00 71	97.65 185	90.80 124	99.48 133	98.37 19	96.56 163	99.19 109

OMC-MVS		97.55 68	97.34 64	98.20 94	99.33 45	95.92 132	98.28 188	98.59 116	95.52 85	97.97 72	99.10 51	93.28 81	99.49 129	95.09 135	98.88 97	99.19 109

VDD-MVS		95.82 133	95.23 143	97.61 137	98.84 113	93.98 229	98.68 133	97.40 264	95.02 115	97.95 73	99.34 20	74.37 329	99.78 77	98.64 4	96.80 157	99.08 123

PVSNet_BlendedMVS		96.73 102	96.60 95	97.12 165	99.25 67	95.35 156	98.26 190	99.26 8	94.28 139	97.94 74	97.46 196	92.74 86	99.81 53	96.88 75	93.32 233	96.20 292

PVSNet_Blended		97.38 78	97.12 72	98.14 97	99.25 67	95.35 156	97.28 278	99.26 8	93.13 197	97.94 74	98.21 141	92.74 86	99.81 53	96.88 75	99.40 81	99.27 101

MP-MVS		98.33 40	98.01 41	99.28 22	99.75 3	98.18 36	99.22 28	98.79 70	96.13 64	97.92 76	99.23 32	94.54 62	99.94 3	96.74 82	99.78 15	99.73 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDTV_nov1_ep13_2view						84.26 327	96.89 297		90.97 268	97.90 77		89.89 135		93.91 162		99.18 113

test_prior3		98.22 44	97.90 45	99.19 30	99.31 50	98.22 33	97.80 242	98.84 54	96.12 65	97.89 78	98.69 95	95.96 28	99.70 94	96.89 72	99.60 52	99.65 53

test_prior2							97.80 242		96.12 65	97.89 78	98.69 95	95.96 28		96.89 72	99.60 52

原ACMM1				98.65 67	99.32 48	96.62 92		98.67 105	93.27 194	97.81 80	98.97 68	95.18 50	99.83 46	93.84 164	99.46 75	99.50 74

114514_t		96.93 95	96.27 106	98.92 55	99.50 30	97.63 57	98.85 88	98.90 42	84.80 326	97.77 81	99.11 49	92.84 84	99.66 100	94.85 138	99.77 20	99.47 80

PMMVS		96.60 105	96.33 104	97.41 151	97.90 168	93.93 230	97.35 273	98.41 151	92.84 208	97.76 82	97.45 198	91.10 119	99.20 155	96.26 98	97.91 135	99.11 119

PVSNet	91.96 18	96.35 115	96.15 110	96.96 174	99.17 78	92.05 262	96.08 315	98.68 98	93.69 170	97.75 83	97.80 174	88.86 157	99.69 97	94.26 155	99.01 92	99.15 115

TEST9					99.31 50	98.50 15	97.92 226	98.73 85	92.63 211	97.74 84	98.68 97	96.20 15	99.80 60

train_agg		97.97 46	97.52 56	99.33 17	99.31 50	98.50 15	97.92 226	98.73 85	92.98 202	97.74 84	98.68 97	96.20 15	99.80 60	96.59 86	99.57 58	99.68 44

CANet		98.05 45	97.76 47	98.90 57	98.73 118	97.27 69	98.35 177	98.78 72	97.37 19	97.72 86	98.96 72	91.53 113	99.92 15	98.79 3	99.65 46	99.51 72

test_8					99.29 58	98.44 17	97.89 234	98.72 87	92.98 202	97.70 87	98.66 100	96.20 15	99.80 60

MP-MVS-pluss		98.31 41	97.92 44	99.49 6	99.72 11	98.88 7	98.43 170	98.78 72	94.10 143	97.69 88	99.42 6	95.25 48	99.92 15	98.09 24	99.80 10	99.67 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		97.67 61	97.23 69	98.98 51	98.70 121	98.38 20	99.34 11	98.39 155	96.76 45	97.67 89	97.40 200	92.26 92	99.49 129	98.28 22	96.28 181	99.08 123

PVSNet_Blended_VisFu		97.70 59	97.46 60	98.44 82	99.27 64	95.91 134	98.63 140	99.16 17	94.48 136	97.67 89	98.88 80	92.80 85	99.91 24	97.11 63	99.12 90	99.50 74

WTY-MVS		97.37 79	96.92 81	98.72 63	98.86 110	96.89 85	98.31 184	98.71 92	95.26 103	97.67 89	98.56 109	92.21 95	99.78 77	95.89 107	96.85 156	99.48 79

Effi-MVS+		97.12 89	96.69 91	98.39 86	98.19 150	96.72 90	97.37 270	98.43 150	93.71 167	97.65 92	98.02 152	92.20 96	99.25 145	96.87 78	97.79 141	99.19 109

HyFIR lowres test		96.90 97	96.49 100	98.14 97	99.33 45	95.56 147	97.38 268	99.65 2	92.34 229	97.61 93	98.20 142	89.29 142	99.10 169	96.97 66	97.60 147	99.77 14

ACMMP		98.23 43	97.95 43	99.09 44	99.74 7	97.62 58	99.03 60	99.41 6	95.98 69	97.60 94	99.36 17	94.45 67	99.93 9	97.14 62	98.85 100	99.70 37
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
agg_prior3		97.87 52	97.42 62	99.23 29	99.29 58	98.23 31	97.92 226	98.72 87	92.38 228	97.59 95	98.64 102	96.09 21	99.79 72	96.59 86	99.57 58	99.68 44

agg_prior1		97.95 48	97.51 57	99.28 22	99.30 55	98.38 20	97.81 241	98.72 87	93.16 196	97.57 96	98.66 100	96.14 18	99.81 53	96.63 85	99.56 64	99.66 51

agg_prior					99.30 55	98.38 20		98.72 87		97.57 96			99.81 53

tpmrst		95.63 142	95.69 127	95.44 263	97.54 188	88.54 312	96.97 288	97.56 238	93.50 179	97.52 98	96.93 252	89.49 137	99.16 157	95.25 132	96.42 168	98.64 152

MDTV_nov1_ep13			95.40 131		97.48 191	88.34 314	96.85 300	97.29 273	93.74 164	97.48 99	97.26 210	89.18 145	99.05 173	91.92 218	97.43 149

EPMVS		94.99 185	94.48 183	96.52 215	97.22 209	91.75 268	97.23 280	91.66 349	94.11 142	97.28 100	96.81 263	85.70 242	98.84 201	93.04 185	97.28 150	98.97 131

IS-MVSNet		97.22 84	96.88 82	98.25 92	98.85 112	96.36 104	99.19 34	97.97 221	95.39 90	97.23 101	98.99 67	91.11 118	98.93 190	94.60 144	98.59 111	99.47 80

EPP-MVSNet		97.46 69	97.28 66	97.99 108	98.64 127	95.38 153	99.33 13	98.31 163	93.61 176	97.19 102	99.07 58	94.05 73	99.23 147	96.89 72	98.43 120	99.37 90

CANet_DTU		96.96 94	96.55 97	98.21 93	98.17 154	96.07 113	97.98 221	98.21 179	97.24 27	97.13 103	98.93 76	86.88 217	99.91 24	95.00 136	99.37 83	98.66 150

CHOSEN 1792x2688		97.12 89	96.80 84	98.08 103	99.30 55	94.56 213	98.05 214	99.71 1	93.57 177	97.09 104	98.91 79	88.17 184	99.89 29	96.87 78	99.56 64	99.81 2

PatchT		93.06 265	91.97 267	96.35 228	96.69 240	92.67 255	94.48 336	97.08 281	86.62 314	97.08 105	92.23 336	87.94 192	97.90 289	78.89 330	96.69 158	98.49 158

PatchmatchNet		95.71 138	95.52 130	96.29 233	97.58 185	90.72 282	96.84 301	97.52 244	94.06 145	97.08 105	96.96 245	89.24 144	98.90 195	92.03 214	98.37 121	99.26 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MAR-MVS		96.91 96	96.40 102	98.45 81	98.69 123	96.90 83	98.66 138	98.68 98	92.40 227	97.07 107	97.96 157	91.54 112	99.75 86	93.68 168	98.92 95	98.69 147
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PAPM_NR		97.46 69	97.11 73	98.50 77	99.50 30	96.41 102	98.63 140	98.60 115	95.18 107	97.06 108	98.06 150	94.26 71	99.57 117	93.80 166	98.87 99	99.52 69

TAMVS		97.02 92	96.79 86	97.70 126	98.06 159	95.31 158	98.52 158	98.31 163	93.95 152	97.05 109	98.61 103	93.49 78	98.52 230	95.33 127	97.81 140	99.29 99

CSCG		97.85 54	97.74 48	98.20 94	99.67 18	95.16 161	99.22 28	99.32 7	93.04 199	97.02 110	98.92 78	95.36 44	99.91 24	97.43 55	99.64 48	99.52 69

tfpn_ndepth		95.53 151	94.90 162	97.39 156	98.96 96	95.88 137	99.05 57	95.27 331	93.80 160	96.95 111	96.93 252	85.53 244	99.40 135	91.54 227	96.10 189	96.89 227

CDS-MVSNet		96.99 93	96.69 91	97.90 112	98.05 160	95.98 114	98.20 194	98.33 162	93.67 174	96.95 111	98.49 114	93.54 77	98.42 247	95.24 133	97.74 144	99.31 94
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XVG-OURS-SEG-HR		96.51 110	96.34 103	97.02 170	98.77 116	93.76 235	97.79 244	98.50 138	95.45 87	96.94 113	99.09 55	87.87 196	99.55 125	96.76 81	95.83 199	97.74 187

CR-MVSNet		94.76 200	94.15 200	96.59 205	97.00 221	93.43 243	94.96 329	97.56 238	92.46 216	96.93 114	96.24 282	88.15 185	97.88 293	87.38 300	96.65 160	98.46 159

RPMNet		92.52 269	91.17 272	96.59 205	97.00 221	93.43 243	94.96 329	97.26 276	82.27 333	96.93 114	92.12 337	86.98 215	97.88 293	76.32 335	96.65 160	98.46 159

Patchmatch-test1		95.32 173	94.97 155	96.35 228	97.67 178	91.29 274	97.33 275	97.60 236	94.68 125	96.92 116	96.95 246	83.97 277	98.50 233	91.33 232	98.32 124	99.25 103

PatchMatch-RL		96.59 107	96.03 114	98.27 90	99.31 50	96.51 98	97.91 229	99.06 21	93.72 166	96.92 116	98.06 150	88.50 179	99.65 101	91.77 222	99.00 93	98.66 150

DeepC-MVS	95.98 3	97.88 51	97.58 52	98.77 61	99.25 67	96.93 81	98.83 92	98.75 79	96.96 41	96.89 118	99.50 4	90.46 127	99.87 38	97.84 37	99.76 26	99.52 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
diffmvs		96.32 117	95.74 120	98.07 105	98.26 144	96.14 111	98.53 157	98.23 177	90.10 280	96.88 119	97.73 177	90.16 133	99.15 158	93.90 163	97.85 139	98.91 137

tfpn1000		95.72 136	95.11 147	97.58 138	99.00 89	95.73 142	99.24 20	95.49 330	94.08 144	96.87 120	97.45 198	85.81 240	99.30 141	91.78 221	96.22 186	97.71 190

XVG-OURS		96.55 109	96.41 101	96.99 171	98.75 117	93.76 235	97.50 262	98.52 131	95.67 78	96.83 121	99.30 27	88.95 154	99.53 126	95.88 108	96.26 182	97.69 191

AdaColmap		97.15 88	96.70 90	98.48 79	99.16 79	96.69 91	98.01 218	98.89 44	94.44 138	96.83 121	98.68 97	90.69 125	99.76 84	94.36 150	99.29 86	98.98 130

CostFormer		94.95 189	94.73 174	95.60 256	97.28 205	89.06 303	97.53 260	96.89 300	89.66 294	96.82 123	96.72 266	86.05 236	98.95 189	95.53 122	96.13 188	98.79 142

UGNet		96.78 101	96.30 105	98.19 96	98.24 145	95.89 136	98.88 80	98.93 36	97.39 16	96.81 124	97.84 168	82.60 286	99.90 27	96.53 89	99.49 70	98.79 142
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
CNLPA		97.45 72	97.03 77	98.73 62	99.05 84	97.44 65	98.07 213	98.53 129	95.32 101	96.80 125	98.53 110	93.32 80	99.72 89	94.31 153	99.31 85	99.02 126

CHOSEN 280x420		97.18 86	97.18 71	97.20 159	98.81 114	93.27 246	95.78 323	99.15 18	95.25 104	96.79 126	98.11 147	92.29 91	99.07 172	98.56 9	99.85 2	99.25 103

HY-MVS	93.96 8	96.82 100	96.23 109	98.57 71	98.46 136	97.00 78	98.14 204	98.21 179	93.95 152	96.72 127	97.99 156	91.58 108	99.76 84	94.51 148	96.54 164	98.95 135

PAPR		96.84 99	96.24 108	98.65 67	98.72 120	96.92 82	97.36 272	98.57 122	93.33 190	96.67 128	97.57 192	94.30 70	99.56 119	91.05 237	98.59 111	99.47 80

thres600view7		95.49 156	94.77 171	97.67 129	98.98 92	95.02 166	98.85 88	96.90 296	95.38 91	96.63 129	96.90 254	84.29 267	99.59 110	88.65 286	96.33 174	98.40 162

tfpn111		95.43 160	94.74 173	97.51 142	98.98 92	94.92 174	98.87 81	96.90 296	95.38 91	96.61 130	96.88 257	84.29 267	99.59 110	88.43 287	96.32 175	98.02 177

conf200view11		95.40 165	94.70 175	97.50 147	98.98 92	94.92 174	98.87 81	96.90 296	95.38 91	96.61 130	96.88 257	84.29 267	99.56 119	88.11 293	96.29 177	98.02 177

thres100view900		95.38 166	94.70 175	97.41 151	98.98 92	94.92 174	98.87 81	96.90 296	95.38 91	96.61 130	96.88 257	84.29 267	99.56 119	88.11 293	96.29 177	97.76 185

Vis-MVSNet (Re-imp)		96.87 98	96.55 97	97.83 115	98.73 118	95.46 151	99.20 32	98.30 166	94.96 118	96.60 133	98.87 81	90.05 134	98.59 219	93.67 169	98.60 110	99.46 84

CVMVSNet		95.43 160	96.04 113	93.57 305	97.93 166	83.62 328	98.12 207	98.59 116	95.68 77	96.56 134	99.02 61	87.51 206	97.51 302	93.56 172	97.44 148	99.60 62

RPSCF		94.87 193	95.40 131	93.26 309	98.89 107	82.06 334	98.33 179	98.06 216	90.30 276	96.56 134	99.26 30	87.09 212	99.49 129	93.82 165	96.32 175	98.24 172

tfpn200view9		95.32 173	94.62 178	97.43 150	98.94 97	94.98 170	98.68 133	96.93 294	95.33 99	96.55 136	96.53 273	84.23 272	99.56 119	88.11 293	96.29 177	97.76 185

thres400		95.38 166	94.62 178	97.65 131	98.94 97	94.98 170	98.68 133	96.93 294	95.33 99	96.55 136	96.53 273	84.23 272	99.56 119	88.11 293	96.29 177	98.40 162

thres200		95.25 175	94.57 180	97.28 157	98.81 114	94.92 174	98.20 194	97.11 280	95.24 106	96.54 138	96.22 286	84.58 259	99.53 126	87.93 298	96.50 166	97.39 198

ab-mvs		96.42 113	95.71 125	98.55 73	98.63 128	96.75 89	97.88 235	98.74 80	93.84 157	96.54 138	98.18 143	85.34 249	99.75 86	95.93 106	96.35 173	99.15 115

view600		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 288	95.36 95	96.52 140	97.37 201	84.55 260	99.59 110	89.07 277	96.39 169	98.40 162

view800		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 288	95.36 95	96.52 140	97.37 201	84.55 260	99.59 110	89.07 277	96.39 169	98.40 162

conf0.05thres1000		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 288	95.36 95	96.52 140	97.37 201	84.55 260	99.59 110	89.07 277	96.39 169	98.40 162

tfpn		95.60 145	94.93 157	97.62 132	99.05 84	94.85 180	99.09 52	97.01 288	95.36 95	96.52 140	97.37 201	84.55 260	99.59 110	89.07 277	96.39 169	98.40 162

mvs-test1		96.60 105	96.68 93	96.37 226	97.89 169	91.81 265	98.56 151	98.10 209	96.57 52	96.52 140	97.94 159	90.81 122	99.45 134	95.72 114	98.01 132	97.86 184

conf0.01		95.56 149	94.84 165	97.72 121	98.90 99	95.93 125	99.17 35	95.70 322	93.42 182	96.50 145	97.16 216	86.12 228	99.22 149	90.51 245	96.06 190	98.02 177

conf0.002		95.56 149	94.84 165	97.72 121	98.90 99	95.93 125	99.17 35	95.70 322	93.42 182	96.50 145	97.16 216	86.12 228	99.22 149	90.51 245	96.06 190	98.02 177

thresconf0.02		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 322	93.42 182	96.50 145	97.16 216	86.12 228	99.22 149	90.51 245	96.06 190	97.37 200

tfpn_n400		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 322	93.42 182	96.50 145	97.16 216	86.12 228	99.22 149	90.51 245	96.06 190	97.37 200

tfpnconf		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 322	93.42 182	96.50 145	97.16 216	86.12 228	99.22 149	90.51 245	96.06 190	97.37 200

tfpnview11		95.50 152	94.84 165	97.51 142	98.90 99	95.93 125	99.17 35	95.70 322	93.42 182	96.50 145	97.16 216	86.12 228	99.22 149	90.51 245	96.06 190	97.37 200

ADS-MVSNet2		94.58 216	94.40 189	95.11 277	98.00 161	88.74 307	96.04 316	97.30 272	90.15 277	96.47 151	96.64 270	87.89 194	97.56 301	90.08 256	97.06 152	99.02 126

ADS-MVSNet		95.00 184	94.45 187	96.63 200	98.00 161	91.91 264	96.04 316	97.74 231	90.15 277	96.47 151	96.64 270	87.89 194	98.96 185	90.08 256	97.06 152	99.02 126

Effi-MVS+-dtu		96.29 118	96.56 96	95.51 257	97.89 169	90.22 289	98.80 103	98.10 209	96.57 52	96.45 153	96.66 268	90.81 122	98.91 192	95.72 114	97.99 133	97.40 197

PLC	95.07 4	97.20 85	96.78 87	98.44 82	99.29 58	96.31 108	98.14 204	98.76 76	92.41 226	96.39 154	98.31 133	94.92 56	99.78 77	94.06 159	98.77 104	99.23 105
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		94.13 240	93.80 222	95.12 276	96.50 248	87.91 318	97.44 263	95.89 321	92.62 212	96.37 155	96.30 281	84.13 275	98.30 267	93.24 178	91.66 253	99.14 117

TAPA-MVS	93.98 7	95.35 170	94.56 181	97.74 120	99.13 82	94.83 191	98.33 179	98.64 113	86.62 314	96.29 156	98.61 103	94.00 75	99.29 143	80.00 326	99.41 79	99.09 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpm2		94.19 234	93.76 227	95.46 261	97.23 208	89.04 304	97.31 277	96.85 303	87.08 313	96.21 157	96.79 264	83.75 282	98.74 209	92.43 206	96.23 184	98.59 154

F-COLMAP		97.09 91	96.80 84	97.97 109	99.45 36	94.95 173	98.55 153	98.62 114	93.02 200	96.17 158	98.58 108	94.01 74	99.81 53	93.95 161	98.90 96	99.14 117

PatchFormer-LS_test		95.47 157	95.27 142	96.08 241	97.59 184	90.66 283	98.10 211	97.34 268	93.98 150	96.08 159	96.15 288	87.65 204	99.12 162	95.27 131	95.24 203	98.44 161

JIA-IIPM		93.35 256	92.49 261	95.92 244	96.48 250	90.65 284	95.01 328	96.96 292	85.93 320	96.08 159	87.33 341	87.70 202	98.78 208	91.35 231	95.58 201	98.34 169

BH-RMVSNet		95.92 129	95.32 139	97.69 127	98.32 142	94.64 205	98.19 198	97.45 259	94.56 131	96.03 161	98.61 103	85.02 252	99.12 162	90.68 241	99.06 91	99.30 97

dp		94.15 239	93.90 217	94.90 282	97.31 204	86.82 324	96.97 288	97.19 279	91.22 265	96.02 162	96.61 272	85.51 245	99.02 180	90.00 260	94.30 206	98.85 138

EPNet		97.28 82	96.87 83	98.51 76	94.98 314	96.14 111	98.90 74	97.02 286	98.28 1	95.99 163	99.11 49	91.36 114	99.89 29	96.98 65	99.19 88	99.50 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		97.16 87	96.66 94	98.68 65	98.53 135	97.19 74	98.93 72	98.90 42	92.83 209	95.99 163	99.37 13	92.12 98	99.87 38	93.67 169	99.57 58	98.97 131

TR-MVS		94.94 191	94.20 197	97.17 162	97.75 175	94.14 226	97.59 257	97.02 286	92.28 233	95.75 165	97.64 187	83.88 279	98.96 185	89.77 262	96.15 187	98.40 162

VPA-MVSNet		95.75 135	95.11 147	97.69 127	97.24 207	97.27 69	98.94 71	99.23 12	95.13 109	95.51 166	97.32 207	85.73 241	98.91 192	97.33 59	89.55 271	96.89 227

HQP_MVS		96.14 123	95.90 117	96.85 180	97.42 197	94.60 211	98.80 103	98.56 123	97.28 21	95.34 167	98.28 134	87.09 212	99.03 178	96.07 100	94.27 207	96.92 219

plane_prior3						94.61 209			97.02 39	95.34 167

DWT-MVSNet_test		94.82 197	94.36 190	96.20 236	97.35 202	90.79 280	98.34 178	96.57 311	92.91 205	95.33 169	96.44 278	82.00 288	99.12 162	94.52 147	95.78 200	98.70 146

Fast-Effi-MVS+		96.28 120	95.70 126	98.03 107	98.29 143	95.97 118	98.58 146	98.25 174	91.74 244	95.29 170	97.23 213	91.03 121	99.15 158	92.90 192	97.96 134	98.97 131

EI-MVSNet		95.96 126	95.83 119	96.36 227	97.93 166	93.70 239	98.12 207	98.27 169	93.70 169	95.07 171	99.02 61	92.23 94	98.54 223	94.68 141	93.46 228	96.84 233

MVSTER		96.06 124	95.72 122	97.08 168	98.23 146	95.93 125	98.73 122	98.27 169	94.86 122	95.07 171	98.09 148	88.21 183	98.54 223	96.59 86	93.46 228	96.79 237

OPM-MVS		95.69 140	95.33 138	96.76 184	96.16 281	94.63 206	98.43 170	98.39 155	96.64 50	95.02 173	98.78 88	85.15 251	99.05 173	95.21 134	94.20 210	96.60 267

Fast-Effi-MVS+-dtu		95.87 130	95.85 118	95.91 245	97.74 176	91.74 269	98.69 129	98.15 194	95.56 83	94.92 174	97.68 184	88.98 152	98.79 207	93.19 180	97.78 142	97.20 208

TESTMET0.1,1		94.18 236	93.69 231	95.63 255	96.92 226	89.12 302	96.91 292	94.78 337	93.17 195	94.88 175	96.45 277	78.52 308	98.92 191	93.09 182	98.50 115	98.85 138

VPNet		94.99 185	94.19 198	97.40 153	97.16 215	96.57 95	98.71 125	98.97 29	95.67 78	94.84 176	98.24 140	80.36 301	98.67 213	96.46 91	87.32 302	96.96 216

1112_ss		96.63 104	96.00 115	98.50 77	98.56 132	96.37 103	98.18 202	98.10 209	92.92 204	94.84 176	98.43 118	92.14 97	99.58 116	94.35 151	96.51 165	99.56 68

test-LLR		95.10 182	94.87 163	95.80 250	96.77 234	89.70 293	96.91 292	95.21 332	95.11 110	94.83 178	95.72 299	87.71 200	98.97 182	93.06 183	98.50 115	98.72 144

test-mter		94.08 242	93.51 242	95.80 250	96.77 234	89.70 293	96.91 292	95.21 332	92.89 206	94.83 178	95.72 299	77.69 312	98.97 182	93.06 183	98.50 115	98.72 144

Test_1112_low_res		96.34 116	95.66 129	98.36 87	98.56 132	95.94 122	97.71 248	98.07 214	92.10 235	94.79 180	97.29 209	91.75 105	99.56 119	94.17 156	96.50 166	99.58 66

GA-MVS		94.81 198	94.03 208	97.14 163	97.15 216	93.86 232	96.76 303	97.58 237	94.00 148	94.76 181	97.04 237	80.91 294	98.48 234	91.79 220	96.25 183	99.09 120

BH-untuned		95.95 127	95.72 122	96.65 197	98.55 134	92.26 259	98.23 191	97.79 228	93.73 165	94.62 182	98.01 154	88.97 153	99.00 181	93.04 185	98.51 114	98.68 148

test_djsdf		96.00 125	95.69 127	96.93 177	95.72 298	95.49 150	99.47 2	98.40 153	94.98 116	94.58 183	97.86 165	89.16 146	98.41 254	96.91 70	94.12 215	96.88 229

cascas		94.63 212	93.86 219	96.93 177	96.91 228	94.27 223	96.00 319	98.51 133	85.55 322	94.54 184	96.23 284	84.20 274	98.87 198	95.80 112	96.98 155	97.66 192

DP-MVS		96.59 107	95.93 116	98.57 71	99.34 42	96.19 110	98.70 128	98.39 155	89.45 298	94.52 185	99.35 19	91.85 104	99.85 43	92.89 194	98.88 97	99.68 44

gg-mvs-nofinetune		92.21 272	90.58 286	97.13 164	96.75 237	95.09 164	95.85 321	89.40 352	85.43 323	94.50 186	81.98 345	80.80 297	98.40 260	92.16 208	98.33 123	97.88 183

mvs_anonymous		96.70 103	96.53 99	97.18 161	98.19 150	93.78 234	98.31 184	98.19 183	94.01 147	94.47 187	98.27 137	92.08 100	98.46 239	97.39 57	97.91 135	99.31 94

v1neww		94.83 194	94.22 194	96.68 192	96.39 254	94.85 180	98.87 81	98.11 204	92.45 221	94.45 188	97.06 232	88.82 162	98.54 223	92.93 189	88.91 281	96.65 260

v7new		94.83 194	94.22 194	96.68 192	96.39 254	94.85 180	98.87 81	98.11 204	92.45 221	94.45 188	97.06 232	88.82 162	98.54 223	92.93 189	88.91 281	96.65 260

HQP-NCC					97.20 211		98.05 214		96.43 54	94.45 188

ACMP_Plane					97.20 211		98.05 214		96.43 54	94.45 188

HQP4-MVS										94.45 188			98.96 185			96.87 230

HQP-MVS		95.72 136	95.40 131	96.69 189	97.20 211	94.25 224	98.05 214	98.46 143	96.43 54	94.45 188	97.73 177	86.75 218	98.96 185	95.30 128	94.18 211	96.86 232

v6		94.83 194	94.21 196	96.69 189	96.36 258	94.85 180	98.87 81	98.11 204	92.46 216	94.44 194	97.05 236	88.76 168	98.57 221	92.95 188	88.92 280	96.65 260

MSDG		95.93 128	95.30 141	97.83 115	98.90 99	95.36 154	96.83 302	98.37 158	91.32 259	94.43 195	98.73 94	90.27 131	99.60 109	90.05 258	98.82 102	98.52 156

nrg030		96.28 120	95.72 122	97.96 110	96.90 229	98.15 38	99.39 5	98.31 163	95.47 86	94.42 196	98.35 126	92.09 99	98.69 210	97.50 54	89.05 277	97.04 212

CLD-MVS		95.62 143	95.34 136	96.46 222	97.52 190	93.75 237	97.27 279	98.46 143	95.53 84	94.42 196	98.00 155	86.21 226	98.97 182	96.25 99	94.37 205	96.66 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		95.62 143	95.34 136	96.47 219	97.46 193	93.54 240	98.99 63	98.54 126	94.67 126	94.36 198	98.77 90	85.39 246	99.11 166	95.71 116	94.15 213	96.76 240

LGP-MVS_train				96.47 219	97.46 193	93.54 240		98.54 126	94.67 126	94.36 198	98.77 90	85.39 246	99.11 166	95.71 116	94.15 213	96.76 240

v144192		94.39 225	93.70 230	96.48 218	96.06 284	94.35 220	98.58 146	98.16 193	91.45 250	94.33 200	97.02 239	87.50 208	98.45 241	91.08 234	89.11 276	96.63 263

v1		94.75 202	94.11 205	96.69 189	96.27 273	94.87 178	98.69 129	98.12 199	92.43 224	94.32 201	96.94 248	88.71 171	98.54 223	92.66 198	88.84 286	96.67 255

v1141		94.75 202	94.11 205	96.67 195	96.27 273	94.86 179	98.69 129	98.12 199	92.43 224	94.31 202	96.94 248	88.78 167	98.48 234	92.63 199	88.85 285	96.67 255

V42		94.78 199	94.14 201	96.70 188	96.33 265	95.22 160	98.97 67	98.09 212	92.32 231	94.31 202	97.06 232	88.39 180	98.55 222	92.90 192	88.87 283	96.34 288

v7		94.69 206	94.04 207	96.62 202	96.41 253	94.79 199	98.78 110	98.13 197	91.89 240	94.30 204	97.16 216	88.13 187	98.45 241	91.96 217	89.65 268	96.61 265

divwei89l23v2f112		94.76 200	94.12 204	96.67 195	96.28 271	94.85 180	98.69 129	98.12 199	92.44 223	94.29 205	96.94 248	88.85 159	98.48 234	92.67 197	88.79 287	96.67 255

ACMM	93.85 9	95.69 140	95.38 135	96.61 203	97.61 182	93.84 233	98.91 73	98.44 147	95.25 104	94.28 206	98.47 116	86.04 238	99.12 162	95.50 123	93.95 220	96.87 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		95.46 158	95.21 144	96.22 235	98.12 155	93.72 238	98.32 183	98.13 197	93.71 167	94.26 207	97.31 208	92.24 93	98.10 276	94.63 142	90.12 263	96.84 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		94.20 233	93.47 244	96.40 225	95.98 287	94.08 227	98.52 158	98.15 194	91.33 258	94.25 208	97.20 215	86.41 223	98.42 247	90.04 259	89.39 274	96.69 254

BH-w/o		95.38 166	95.08 149	96.26 234	98.34 140	91.79 266	97.70 249	97.43 261	92.87 207	94.24 209	97.22 214	88.66 172	98.84 201	91.55 226	97.70 145	98.16 174

XVG-ACMP-BASELINE		94.54 218	94.14 201	95.75 253	96.55 245	91.65 270	98.11 209	98.44 147	94.96 118	94.22 210	97.90 162	79.18 307	99.11 166	94.05 160	93.85 221	96.48 282

v1144		94.59 215	93.92 215	96.60 204	96.21 275	94.78 201	98.59 144	98.14 196	91.86 243	94.21 211	97.02 239	87.97 191	98.41 254	91.72 223	89.57 269	96.61 265

v1192		94.32 227	93.58 237	96.53 214	96.10 282	94.45 215	98.50 163	98.17 191	91.54 248	94.19 212	97.06 232	86.95 216	98.43 246	90.14 254	89.57 269	96.70 249

PAPM		94.95 189	94.00 210	97.78 119	97.04 220	95.65 143	96.03 318	98.25 174	91.23 264	94.19 212	97.80 174	91.27 116	98.86 200	82.61 321	97.61 146	98.84 140

tpmp4_e23		93.91 248	93.42 247	95.38 269	97.62 181	88.59 311	97.52 261	97.34 268	87.94 309	94.17 214	96.79 264	82.91 284	99.05 173	90.62 243	95.91 197	98.50 157

Patchmatch-test		94.42 223	93.68 232	96.63 200	97.60 183	91.76 267	94.83 333	97.49 256	89.45 298	94.14 215	97.10 225	88.99 149	98.83 203	85.37 315	98.13 130	99.29 99

v1240		94.06 244	93.29 249	96.34 230	96.03 286	93.90 231	98.44 168	98.17 191	91.18 266	94.13 216	97.01 241	86.05 236	98.42 247	89.13 276	89.50 272	96.70 249

GBi-Net		94.49 219	93.80 222	96.56 210	98.21 147	95.00 167	98.82 94	98.18 186	92.46 216	94.09 217	97.07 229	81.16 291	97.95 285	92.08 210	92.14 244	96.72 245

test1		94.49 219	93.80 222	96.56 210	98.21 147	95.00 167	98.82 94	98.18 186	92.46 216	94.09 217	97.07 229	81.16 291	97.95 285	92.08 210	92.14 244	96.72 245

FMVSNet3		94.97 188	94.26 193	97.11 166	98.18 152	96.62 92	98.56 151	98.26 173	93.67 174	94.09 217	97.10 225	84.25 271	98.01 282	92.08 210	92.14 244	96.70 249

MIMVSNet		93.26 260	92.21 265	96.41 224	97.73 177	93.13 251	95.65 324	97.03 285	91.27 263	94.04 220	96.06 290	75.33 322	97.19 307	86.56 305	96.23 184	98.92 136

FIs		96.51 110	96.12 111	97.67 129	97.13 217	97.54 61	99.36 8	99.22 14	95.89 71	94.03 221	98.35 126	91.98 102	98.44 244	96.40 94	92.76 240	97.01 213

v2v482		94.69 206	94.03 208	96.65 197	96.17 278	94.79 199	98.67 136	98.08 213	92.72 210	94.00 222	97.16 216	87.69 203	98.45 241	92.91 191	88.87 283	96.72 245

FC-MVSNet-test		96.42 113	96.05 112	97.53 141	96.95 224	97.27 69	99.36 8	99.23 12	95.83 73	93.93 223	98.37 124	92.00 101	98.32 263	96.02 104	92.72 241	97.00 214

UniMVSNet (Re)		95.78 134	95.19 145	97.58 138	96.99 223	97.47 63	98.79 108	99.18 16	95.60 81	93.92 224	97.04 237	91.68 106	98.48 234	95.80 112	87.66 299	96.79 237

UniMVSNet_NR-MVSNet		95.71 138	95.15 146	97.40 153	96.84 232	96.97 79	98.74 119	99.24 10	95.16 108	93.88 225	97.72 180	91.68 106	98.31 265	95.81 110	87.25 304	96.92 219

DU-MVS		95.42 162	94.76 172	97.40 153	96.53 246	96.97 79	98.66 138	98.99 28	95.43 88	93.88 225	97.69 181	88.57 174	98.31 265	95.81 110	87.25 304	96.92 219

Baseline_NR-MVSNet		94.35 226	93.81 221	95.96 243	96.20 276	94.05 228	98.61 143	96.67 308	91.44 251	93.85 227	97.60 189	88.57 174	98.14 274	94.39 149	86.93 307	95.68 305

PS-MVSNAJss		96.43 112	96.26 107	96.92 179	95.84 294	95.08 165	99.16 42	98.50 138	95.87 72	93.84 228	98.34 130	94.51 63	98.61 216	96.88 75	93.45 230	97.06 210

tpmvs		94.60 213	94.36 190	95.33 272	97.46 193	88.60 310	96.88 298	97.68 232	91.29 261	93.80 229	96.42 279	88.58 173	99.24 146	91.06 235	96.04 196	98.17 173

3Dnovator	94.51 5	97.46 69	96.93 80	99.07 45	97.78 174	97.64 56	99.35 10	99.06 21	97.02 39	93.75 230	99.16 45	89.25 143	99.92 15	97.22 60	99.75 32	99.64 56

ITE_SJBPF				95.44 263	97.42 197	91.32 273		97.50 250	95.09 113	93.59 231	98.35 126	81.70 290	98.88 197	89.71 265	93.39 232	96.12 294

TranMVSNet+NR-MVSNet		95.14 181	94.48 183	97.11 166	96.45 251	96.36 104	99.03 60	99.03 24	95.04 114	93.58 232	97.93 160	88.27 182	98.03 281	94.13 157	86.90 309	96.95 218

COLMAP_ROB	93.27 12	95.33 172	94.87 163	96.71 186	99.29 58	93.24 248	98.58 146	98.11 204	89.92 286	93.57 233	99.10 51	86.37 224	99.79 72	90.78 239	98.10 131	97.09 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.36 255	92.80 255	95.07 278	97.58 185	87.97 317	96.76 303	97.86 226	82.17 334	93.53 234	96.04 291	86.13 227	99.13 161	89.24 274	95.87 198	98.10 175

AllTest		95.24 176	94.65 177	96.99 171	99.25 67	93.21 249	98.59 144	98.18 186	91.36 255	93.52 235	98.77 90	84.67 257	99.72 89	89.70 266	97.87 137	98.02 177

TestCases				96.99 171	99.25 67	93.21 249		98.18 186	91.36 255	93.52 235	98.77 90	84.67 257	99.72 89	89.70 266	97.87 137	98.02 177

FMVSNet2		94.47 221	93.61 235	97.04 169	98.21 147	96.43 101	98.79 108	98.27 169	92.46 216	93.50 237	97.09 227	81.16 291	98.00 283	91.09 233	91.93 248	96.70 249

v148		94.29 229	93.76 227	95.91 245	96.10 282	92.93 253	98.58 146	97.97 221	92.59 214	93.47 238	96.95 246	88.53 177	98.32 263	92.56 201	87.06 306	96.49 281

pmmvs4		94.69 206	93.99 212	96.81 182	95.74 296	95.94 122	97.40 266	97.67 233	90.42 274	93.37 239	97.59 190	89.08 148	98.20 272	92.97 187	91.67 252	96.30 290

PCF-MVS	93.45 11	94.68 209	93.43 245	98.42 85	98.62 129	96.77 88	95.48 325	98.20 182	84.63 327	93.34 240	98.32 132	88.55 176	99.81 53	84.80 317	98.96 94	98.68 148
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XXY-MVS		95.20 179	94.45 187	97.46 148	96.75 237	96.56 96	98.86 87	98.65 112	93.30 193	93.27 241	98.27 137	84.85 256	98.87 198	94.82 139	91.26 258	96.96 216

jajsoiax		95.45 159	95.03 150	96.73 185	95.42 309	94.63 206	99.14 44	98.52 131	95.74 75	93.22 242	98.36 125	83.87 280	98.65 214	96.95 69	94.04 216	96.91 224

mvs_tets		95.41 164	95.00 151	96.65 197	95.58 302	94.42 216	99.00 62	98.55 125	95.73 76	93.21 243	98.38 123	83.45 283	98.63 215	97.09 64	94.00 218	96.91 224

anonymousdsp		95.42 162	94.91 161	96.94 176	95.10 313	95.90 135	99.14 44	98.41 151	93.75 162	93.16 244	97.46 196	87.50 208	98.41 254	95.63 120	94.03 217	96.50 280

v8		94.47 221	93.77 225	96.57 209	96.36 258	94.83 191	99.05 57	98.19 183	91.92 239	93.16 244	96.97 244	88.82 162	98.48 234	91.69 224	87.79 297	96.39 285

WR-MVS		95.15 180	94.46 185	97.22 158	96.67 242	96.45 100	98.21 193	98.81 62	94.15 141	93.16 244	97.69 181	87.51 206	98.30 267	95.29 130	88.62 288	96.90 226

EPNet_dtu		95.21 178	94.95 156	95.99 242	96.17 278	90.45 287	98.16 203	97.27 275	96.77 44	93.14 247	98.33 131	90.34 129	98.42 247	85.57 312	98.81 103	99.09 120
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		96.29 118	95.40 131	98.96 53	97.85 171	97.60 59	99.23 22	98.93 36	89.76 290	93.11 248	99.02 61	89.11 147	99.93 9	91.99 215	99.62 50	99.34 91

GG-mvs-BLEND				96.59 205	96.34 261	94.98 170	96.51 313	88.58 353		93.10 249	94.34 313	80.34 302	98.05 280	89.53 269	96.99 154	96.74 242

v10		94.29 229	93.55 238	96.51 216	96.39 254	94.80 196	98.99 63	98.19 183	91.35 257	93.02 250	96.99 242	88.09 188	98.41 254	90.50 251	88.41 290	96.33 289

3Dnovator+	94.38 6	97.43 74	96.78 87	99.38 12	97.83 172	98.52 14	99.37 7	98.71 92	97.09 37	92.99 251	99.13 47	89.36 140	99.89 29	96.97 66	99.57 58	99.71 35

Patchmtry		93.22 261	92.35 263	95.84 248	96.77 234	93.09 252	94.66 335	97.56 238	87.37 312	92.90 252	96.24 282	88.15 185	97.90 289	87.37 301	90.10 264	96.53 276

v52		94.18 236	93.52 240	96.13 239	95.95 289	94.29 222	99.23 22	98.21 179	91.42 252	92.84 253	96.89 255	87.85 197	98.53 229	91.51 228	87.81 295	95.57 308

V4		94.18 236	93.52 240	96.13 239	95.89 291	94.31 221	99.23 22	98.22 178	91.42 252	92.82 254	96.89 255	87.93 193	98.52 230	91.51 228	87.81 295	95.58 307

v7n		94.19 234	93.43 245	96.47 219	95.90 290	94.38 219	99.26 17	98.34 161	91.99 237	92.76 255	97.13 224	88.31 181	98.52 230	89.48 271	87.70 298	96.52 277

MVS		94.67 210	93.54 239	98.08 103	96.88 230	96.56 96	98.19 198	98.50 138	78.05 340	92.69 256	98.02 152	91.07 120	99.63 106	90.09 255	98.36 122	98.04 176

DSMNet-mixed		92.52 269	92.58 260	92.33 313	94.15 322	82.65 332	98.30 186	94.26 342	89.08 303	92.65 257	95.73 297	85.01 253	95.76 331	86.24 307	97.76 143	98.59 154

EU-MVSNet		93.66 251	94.14 201	92.25 314	95.96 288	83.38 329	98.52 158	98.12 199	94.69 124	92.61 258	98.13 146	87.36 210	96.39 328	91.82 219	90.00 265	96.98 215

semantic-postprocess				94.85 284	97.98 165	90.56 286		98.11 204	93.75 162	92.58 259	97.48 195	83.91 278	97.41 304	92.48 205	91.30 256	96.58 269

pmmvs5		93.65 253	92.97 253	95.68 254	95.49 305	92.37 258	98.20 194	97.28 274	89.66 294	92.58 259	97.26 210	82.14 287	98.09 278	93.18 181	90.95 259	96.58 269

WR-MVS_H		95.05 183	94.46 185	96.81 182	96.86 231	95.82 139	99.24 20	99.24 10	93.87 156	92.53 261	96.84 262	90.37 128	98.24 271	93.24 178	87.93 294	96.38 286

ACMP	93.49 10	95.34 171	94.98 153	96.43 223	97.67 178	93.48 242	98.73 122	98.44 147	94.94 121	92.53 261	98.53 110	84.50 265	99.14 160	95.48 124	94.00 218	96.66 258
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0.0.03 1		94.08 242	93.51 242	95.80 250	95.53 304	92.89 254	97.38 268	95.97 318	95.11 110	92.51 263	96.66 268	87.71 200	96.94 310	87.03 303	93.67 223	97.57 193

IB-MVS	91.98 17	93.27 259	91.97 267	97.19 160	97.47 192	93.41 245	97.09 286	95.99 317	93.32 191	92.47 264	95.73 297	78.06 310	99.53 126	94.59 145	82.98 321	98.62 153
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
IterMVS		94.09 241	93.85 220	94.80 287	97.99 163	90.35 288	97.18 283	98.12 199	93.68 172	92.46 265	97.34 205	84.05 276	97.41 304	92.51 204	91.33 255	96.62 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 191	94.30 192	96.83 181	96.72 239	95.56 147	99.11 50	98.95 33	93.89 154	92.42 266	97.90 162	87.19 211	98.12 275	94.32 152	88.21 291	96.82 236

PS-CasMVS		94.67 210	93.99 212	96.71 186	96.68 241	95.26 159	99.13 47	99.03 24	93.68 172	92.33 267	97.95 158	85.35 248	98.10 276	93.59 171	88.16 293	96.79 237

FMVSNet1		93.19 263	92.07 266	96.56 210	97.54 188	95.00 167	98.82 94	98.18 186	90.38 275	92.27 268	97.07 229	73.68 330	97.95 285	89.36 273	91.30 256	96.72 245

PEN-MVS		94.42 223	93.73 229	96.49 217	96.28 271	94.84 189	99.17 35	99.00 26	93.51 178	92.23 269	97.83 171	86.10 235	97.90 289	92.55 202	86.92 308	96.74 242

v748		93.75 250	93.06 251	95.82 249	95.73 297	92.64 256	99.25 19	98.24 176	91.60 247	92.22 270	96.52 275	87.60 205	98.46 239	90.64 242	85.72 316	96.36 287

OurMVSNet-221017-0		94.21 232	94.00 210	94.85 284	95.60 301	89.22 301	98.89 78	97.43 261	95.29 102	92.18 271	98.52 113	82.86 285	98.59 219	93.46 173	91.76 251	96.74 242

MS-PatchMatch		93.84 249	93.63 233	94.46 297	96.18 277	89.45 297	97.76 245	98.27 169	92.23 234	92.13 272	97.49 194	79.50 304	98.69 210	89.75 264	99.38 82	95.25 310

ppachtmachnet_test		93.22 261	92.63 259	94.97 280	95.45 307	90.84 278	96.88 298	97.88 225	90.60 270	92.08 273	97.26 210	88.08 189	97.86 295	85.12 316	90.33 262	96.22 291

1314		96.25 122	95.73 121	97.79 118	97.13 217	95.55 149	98.19 198	98.59 116	93.47 180	92.03 274	97.82 172	91.33 115	99.49 129	94.62 143	98.44 118	98.32 171

DTE-MVSNet		93.98 246	93.26 250	96.14 238	96.06 284	94.39 218	99.20 32	98.86 52	93.06 198	91.78 275	97.81 173	85.87 239	97.58 300	90.53 244	86.17 313	96.46 284

LF4IMVS		93.14 264	92.79 256	94.20 300	95.88 292	88.67 309	97.66 253	97.07 282	93.81 159	91.71 276	97.65 185	77.96 311	98.81 205	91.47 230	91.92 249	95.12 311

our_test_3		93.65 253	93.30 248	94.69 289	95.45 307	89.68 295	96.91 292	97.65 234	91.97 238	91.66 277	96.88 257	89.67 136	97.93 288	88.02 297	91.49 254	96.48 282

testgi		93.06 265	92.45 262	94.88 283	96.43 252	89.90 290	98.75 115	97.54 243	95.60 81	91.63 278	97.91 161	74.46 328	97.02 309	86.10 308	93.67 223	97.72 189

tfpnnormal		93.66 251	92.70 258	96.55 213	96.94 225	95.94 122	98.97 67	99.19 15	91.04 267	91.38 279	97.34 205	84.94 254	98.61 216	85.45 314	89.02 279	95.11 312

LTVRE_ROB	92.95 15	94.60 213	93.90 217	96.68 192	97.41 200	94.42 216	98.52 158	98.59 116	91.69 245	91.21 280	98.35 126	84.87 255	99.04 177	91.06 235	93.44 231	96.60 267
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
OpenMVS	93.04 13	95.83 132	95.00 151	98.32 89	97.18 214	97.32 67	99.21 31	98.97 29	89.96 283	91.14 281	99.05 60	86.64 220	99.92 15	93.38 174	99.47 72	97.73 188

pm-mvs1		93.94 247	93.06 251	96.59 205	96.49 249	95.16 161	98.95 69	98.03 220	92.32 231	91.08 282	97.84 168	84.54 264	98.41 254	92.16 208	86.13 315	96.19 293

LP		91.12 290	89.99 292	94.53 293	96.35 260	88.70 308	93.86 340	97.35 267	84.88 325	90.98 283	94.77 308	84.40 266	97.43 303	75.41 338	91.89 250	97.47 194

MVS-HIRNet		89.46 302	88.40 304	92.64 311	97.58 185	82.15 333	94.16 339	93.05 348	75.73 342	90.90 284	82.52 344	79.42 305	98.33 262	83.53 319	98.68 105	97.43 195

FMVSNet5		91.81 283	90.92 277	94.49 294	97.21 210	92.09 261	98.00 220	97.55 242	89.31 301	90.86 285	95.61 302	74.48 327	95.32 333	85.57 312	89.70 267	96.07 296

USDC		93.33 258	92.71 257	95.21 273	96.83 233	90.83 279	96.91 292	97.50 250	93.84 157	90.72 286	98.14 145	77.69 312	98.82 204	89.51 270	93.21 237	95.97 298

MVP-Stereo		94.28 231	93.92 215	95.35 271	94.95 315	92.60 257	97.97 222	97.65 234	91.61 246	90.68 287	97.09 227	86.32 225	98.42 247	89.70 266	99.34 84	95.02 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	92.99 14	94.30 228	93.77 225	95.88 247	97.81 173	92.04 263	98.71 125	98.37 158	93.99 149	90.60 288	98.47 116	80.86 296	99.05 173	92.75 196	92.40 243	96.55 274

testpf		88.74 305	89.09 298	87.69 323	95.78 295	83.16 331	84.05 351	94.13 345	85.22 324	90.30 289	94.39 312	74.92 325	95.80 330	89.77 262	93.28 236	84.10 347

Anonymous20231206		91.66 285	91.10 273	93.33 307	94.02 324	87.35 321	98.58 146	97.26 276	90.48 271	90.16 290	96.31 280	83.83 281	96.53 326	79.36 328	89.90 266	96.12 294

SixPastTwentyTwo		93.34 257	92.86 254	94.75 288	95.67 299	89.41 299	98.75 115	96.67 308	93.89 154	90.15 291	98.25 139	80.87 295	98.27 270	90.90 238	90.64 260	96.57 271

PVSNet_0	88.72 19	91.28 288	90.03 291	95.00 279	97.99 163	87.29 322	94.84 332	98.50 138	92.06 236	89.86 292	95.19 303	79.81 303	99.39 137	92.27 207	69.79 345	98.33 170

ACMH	92.88 16	94.55 217	93.95 214	96.34 230	97.63 180	93.26 247	98.81 100	98.49 142	93.43 181	89.74 293	98.53 110	81.91 289	99.08 171	93.69 167	93.30 234	96.70 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		91.77 284	90.63 285	95.17 275	94.69 320	91.24 275	98.67 136	97.92 223	86.14 317	89.62 294	97.56 193	75.79 321	98.34 261	90.75 240	84.56 320	95.94 299

TinyColmap		92.31 271	91.53 270	94.65 291	96.92 226	89.75 292	96.92 290	96.68 307	90.45 273	89.62 294	97.85 167	76.06 320	98.81 205	86.74 304	92.51 242	95.41 309

TransMVSNet (Re)		92.67 267	91.51 271	96.15 237	96.58 244	94.65 204	98.90 74	96.73 304	90.86 269	89.46 296	97.86 165	85.62 243	98.09 278	86.45 306	81.12 326	95.71 304

testus		88.91 304	89.08 299	88.40 322	91.39 331	76.05 340	96.56 309	96.48 312	89.38 300	89.39 297	95.17 305	70.94 334	93.56 340	77.04 334	95.41 202	95.61 306

NR-MVSNet		94.98 187	94.16 199	97.44 149	96.53 246	97.22 73	98.74 119	98.95 33	94.96 118	89.25 298	97.69 181	89.32 141	98.18 273	94.59 145	87.40 301	96.92 219

test2356		88.68 306	88.61 302	88.87 321	89.90 337	78.23 337	95.11 327	96.66 310	88.66 306	89.06 299	94.33 314	73.14 332	92.56 344	75.56 337	95.11 204	95.81 302

LCM-MVSNet-Re		95.22 177	95.32 139	94.91 281	98.18 152	87.85 319	98.75 115	95.66 328	95.11 110	88.96 300	96.85 261	90.26 132	97.65 297	95.65 119	98.44 118	99.22 106

TDRefinement		91.06 291	89.68 294	95.21 273	85.35 344	91.49 271	98.51 162	97.07 282	91.47 249	88.83 301	97.84 168	77.31 316	99.09 170	92.79 195	77.98 338	95.04 314

N_pmnet		87.12 310	87.77 307	85.17 330	95.46 306	61.92 353	97.37 270	70.66 360	85.83 321	88.73 302	96.04 291	85.33 250	97.76 296	80.02 325	90.48 261	95.84 300

test_0402		91.32 287	90.27 289	94.48 295	96.60 243	91.12 276	98.50 163	97.22 278	86.10 318	88.30 303	96.98 243	77.65 314	97.99 284	78.13 332	92.94 239	94.34 328

test20.03		90.89 293	90.38 287	92.43 312	93.48 325	88.14 316	98.33 179	97.56 238	93.40 188	87.96 304	96.71 267	80.69 298	94.13 337	79.15 329	86.17 313	95.01 316

MIMVSNet1		89.67 301	88.28 306	93.82 303	92.81 329	91.08 277	98.01 218	97.45 259	87.95 308	87.90 305	95.87 295	67.63 340	94.56 336	78.73 331	88.18 292	95.83 301

Patchmatch-RL test		91.49 286	90.85 278	93.41 306	91.37 332	84.40 326	92.81 341	95.93 320	91.87 242	87.25 306	94.87 307	88.99 149	96.53 326	92.54 203	82.00 323	99.30 97

pmmvs3		86.67 311	84.86 313	92.11 315	88.16 339	87.19 323	96.63 306	94.75 338	79.88 338	87.22 307	92.75 331	66.56 341	95.20 334	81.24 324	76.56 341	93.96 334

K. test v3		92.55 268	91.91 269	94.48 295	95.64 300	89.24 300	99.07 56	94.88 336	94.04 146	86.78 308	97.59 190	77.64 315	97.64 298	92.08 210	89.43 273	96.57 271

lessismore_v0				94.45 298	94.93 316	88.44 313		91.03 350		86.77 309	97.64 187	76.23 319	98.42 247	90.31 253	85.64 317	96.51 279

ambc				89.49 320	86.66 343	75.78 341	92.66 342	96.72 305		86.55 310	92.50 333	46.01 350	97.90 289	90.32 252	82.09 322	94.80 317

PM-MVS		87.77 308	86.55 310	91.40 317	91.03 334	83.36 330	96.92 290	95.18 334	91.28 262	86.48 311	93.42 316	53.27 347	96.74 320	89.43 272	81.97 324	94.11 331

DI_MVS_plusplus_test		94.74 204	93.62 234	98.09 102	95.34 310	95.92 132	98.09 212	97.34 268	94.66 128	85.89 312	95.91 293	80.49 300	99.38 138	96.66 84	98.22 126	98.97 131

OpenMVS_ROB	86.42 20	89.00 303	87.43 309	93.69 304	93.08 327	89.42 298	97.91 229	96.89 300	78.58 339	85.86 313	94.69 309	69.48 336	98.29 269	77.13 333	93.29 235	93.36 337

UnsupCasMVSNet_eth		90.99 292	89.92 293	94.19 301	94.08 323	89.83 291	97.13 285	98.67 105	93.69 170	85.83 314	96.19 287	75.15 323	96.74 320	89.14 275	79.41 332	96.00 297

test_normal		94.72 205	93.59 236	98.11 101	95.30 311	95.95 121	97.91 229	97.39 266	94.64 129	85.70 315	95.88 294	80.52 299	99.36 139	96.69 83	98.30 125	99.01 129

new_pmnet		90.06 298	89.00 301	93.22 310	94.18 321	88.32 315	96.42 314	96.89 300	86.19 316	85.67 316	93.62 315	77.18 317	97.10 308	81.61 323	89.29 275	94.23 329

v17		92.08 275	90.94 275	95.48 260	96.34 261	94.83 191	98.81 100	97.52 244	89.95 284	85.32 317	93.24 319	88.91 155	96.91 312	88.76 283	79.63 331	94.71 320

EG-PatchMatch MVS		91.13 289	90.12 290	94.17 302	94.73 319	89.00 305	98.13 206	97.81 227	89.22 302	85.32 317	96.46 276	67.71 339	98.42 247	87.89 299	93.82 222	95.08 313

v18		92.10 274	90.97 274	95.50 258	96.34 261	94.85 180	98.82 94	97.52 244	89.99 282	85.31 319	93.26 318	88.90 156	96.92 311	88.82 282	79.77 330	94.73 318

v16		92.08 275	90.94 275	95.49 259	96.38 257	94.84 189	98.81 100	97.51 247	89.94 285	85.25 320	93.28 317	88.86 157	96.91 312	88.70 284	79.78 329	94.72 319

v15		91.94 277	90.77 279	95.43 265	96.31 269	94.83 191	98.77 111	97.50 250	89.92 286	85.13 321	93.08 322	88.76 168	96.86 314	88.40 288	79.10 333	94.61 324

v11		91.85 282	90.68 284	95.36 270	96.34 261	94.74 203	98.80 103	97.43 261	89.60 296	85.09 322	93.03 324	88.53 177	96.75 319	87.37 301	79.96 328	94.58 326

V14		91.93 278	90.76 280	95.42 268	96.33 265	94.81 195	98.77 111	97.51 247	89.86 288	85.09 322	93.13 320	88.80 166	96.83 316	88.32 289	79.06 335	94.60 325

V9		91.91 279	90.73 281	95.45 262	96.32 268	94.80 196	98.77 111	97.50 250	89.81 289	85.03 324	93.08 322	88.76 168	96.86 314	88.24 290	79.03 336	94.69 321

v12		91.89 280	90.70 282	95.43 265	96.31 269	94.80 196	98.76 114	97.50 250	89.76 290	84.95 325	93.00 325	88.82 162	96.82 318	88.23 291	79.00 337	94.68 323

v13		91.88 281	90.69 283	95.43 265	96.33 265	94.78 201	98.75 115	97.50 250	89.68 293	84.93 326	92.98 326	88.84 160	96.83 316	88.14 292	79.09 334	94.69 321

pmmvs-eth3d		90.36 297	89.05 300	94.32 299	91.10 333	92.12 260	97.63 256	96.95 293	88.86 304	84.91 327	93.13 320	78.32 309	96.74 320	88.70 284	81.81 325	94.09 332

DeepMVS_CX				86.78 326	97.09 219	72.30 346		95.17 335	75.92 341	84.34 328	95.19 303	70.58 335	95.35 332	79.98 327	89.04 278	92.68 338

new-patchmatchnet		88.50 307	87.45 308	91.67 316	90.31 335	85.89 325	97.16 284	97.33 271	89.47 297	83.63 329	92.77 330	76.38 318	95.06 335	82.70 320	77.29 339	94.06 333

UnsupCasMVSNet_bld		87.17 309	85.12 312	93.31 308	91.94 330	88.77 306	94.92 331	98.30 166	84.30 328	82.30 330	90.04 338	63.96 344	97.25 306	85.85 311	74.47 344	93.93 335

Test4		92.21 272	90.34 288	97.82 117	92.83 328	95.87 138	97.94 225	98.05 219	94.50 134	82.12 331	94.48 310	59.54 346	98.54 223	95.39 126	98.22 126	99.06 125

Anonymous20231211		83.69 314	81.50 316	90.26 318	89.23 338	80.10 336	97.97 222	97.06 284	72.79 344	82.05 332	92.57 332	50.28 348	96.32 329	76.15 336	75.38 342	94.37 327

CMPMVS	66.06 21	89.70 300	89.67 295	89.78 319	93.19 326	76.56 339	97.00 287	98.35 160	80.97 336	81.57 333	97.75 176	74.75 326	98.61 216	89.85 261	93.63 225	94.17 330
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test1235678		86.26 312	85.81 311	87.62 324	86.97 342	75.00 344	96.55 311	96.32 315	86.08 319	81.32 334	92.98 326	73.10 333	92.05 345	71.64 341	87.32 302	95.81 302

1111		84.94 313	84.30 314	86.86 325	87.59 340	75.10 342	96.63 306	96.43 313	82.53 331	80.75 335	92.91 328	68.94 337	93.79 338	68.24 344	84.66 319	91.70 339

.test1245		73.05 322	76.31 320	63.27 343	87.59 340	75.10 342	96.63 306	96.43 313	82.53 331	80.75 335	92.91 328	68.94 337	93.79 338	68.24 344	12.72 356	20.91 356

test12356		83.47 315	83.37 315	83.78 331	84.43 345	70.09 349	95.12 326	95.60 329	82.98 329	78.89 337	92.43 335	64.99 342	91.41 347	70.36 342	85.55 318	89.82 341

testing_2		90.61 296	88.50 303	96.95 175	90.08 336	95.57 146	97.69 250	98.06 216	93.02 200	76.55 338	92.48 334	61.18 345	98.44 244	95.45 125	91.98 247	96.84 233

LCM-MVSNet		78.70 317	76.24 321	86.08 327	77.26 354	71.99 347	94.34 337	96.72 305	61.62 348	76.53 339	89.33 339	33.91 357	92.78 343	81.85 322	74.60 343	93.46 336

PMMVS2		77.95 319	75.44 322	85.46 328	82.54 346	74.95 345	94.23 338	93.08 347	72.80 343	74.68 340	87.38 340	36.36 355	91.56 346	73.95 339	63.94 346	89.87 340

Gipuma		78.40 318	76.75 319	83.38 332	95.54 303	80.43 335	79.42 352	97.40 264	64.67 346	73.46 341	80.82 347	45.65 351	93.14 342	66.32 346	87.43 300	76.56 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		90.70 295	89.39 296	94.62 292	94.79 318	90.65 284	97.20 281	97.46 257	87.54 311	72.54 342	95.74 296	86.51 221	96.66 324	86.00 309	86.76 311	96.54 275

MDA-MVSNet_test_wron		90.71 294	89.38 297	94.68 290	94.83 317	90.78 281	97.19 282	97.46 257	87.60 310	72.41 343	95.72 299	86.51 221	96.71 323	85.92 310	86.80 310	96.56 273

MDA-MVSNet-bldmvs		89.97 299	88.35 305	94.83 286	95.21 312	91.34 272	97.64 254	97.51 247	88.36 307	71.17 344	96.13 289	79.22 306	96.63 325	83.65 318	86.27 312	96.52 277

testmv		78.74 316	77.35 317	82.89 333	78.16 353	69.30 350	95.87 320	94.65 339	81.11 335	70.98 345	87.11 342	46.31 349	90.42 348	65.28 347	76.72 340	88.95 342

FPMVS		77.62 320	77.14 318	79.05 335	79.25 350	60.97 354	95.79 322	95.94 319	65.96 345	67.93 346	94.40 311	37.73 354	88.88 350	68.83 343	88.46 289	87.29 343

no-one		74.41 321	70.76 323	85.35 329	79.88 349	76.83 338	94.68 334	94.22 343	80.33 337	63.81 347	79.73 348	35.45 356	93.36 341	71.78 340	36.99 353	85.86 346

tmp_tt		68.90 324	66.97 324	74.68 339	50.78 359	59.95 355	87.13 347	83.47 358	38.80 354	62.21 348	96.23 284	64.70 343	76.91 357	88.91 281	30.49 354	87.19 344

E-PMN		64.94 327	64.25 327	67.02 341	82.28 347	59.36 357	91.83 344	85.63 356	52.69 351	60.22 349	77.28 350	41.06 353	80.12 355	46.15 353	41.14 350	61.57 354

EMVS		64.07 328	63.26 329	66.53 342	81.73 348	58.81 358	91.85 343	84.75 357	51.93 353	59.09 350	75.13 351	43.32 352	79.09 356	42.03 354	39.47 351	61.69 353

PNet_i23d		67.70 325	65.07 326	75.60 337	78.61 351	59.61 356	89.14 346	88.24 354	61.83 347	52.37 351	80.89 346	18.91 359	84.91 352	62.70 349	52.93 348	82.28 348

MVE	62.14 22	63.28 330	59.38 330	74.99 338	74.33 355	65.47 352	85.55 349	80.50 359	52.02 352	51.10 352	75.00 352	10.91 364	80.50 354	51.60 352	53.40 347	78.99 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 323	65.37 325	80.22 334	65.99 357	71.96 348	90.91 345	90.09 351	82.62 330	49.93 353	78.39 349	29.36 358	81.75 353	62.49 350	38.52 352	86.95 345

PMVS	61.03 23	65.95 326	63.57 328	73.09 340	57.90 358	51.22 359	85.05 350	93.93 346	54.45 350	44.32 354	83.57 343	13.22 360	89.15 349	58.68 351	81.00 327	78.91 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		63.73 329	58.86 331	78.35 336	67.62 356	67.90 351	86.56 348	87.81 355	58.26 349	42.49 355	70.28 353	11.55 362	85.05 351	63.66 348	41.50 349	82.11 349

testmvs		21.48 334	24.95 335	11.09 347	14.89 360	6.47 362	96.56 309	9.87 362	7.55 356	17.93 356	39.02 355	9.43 365	5.90 360	16.56 357	12.72 356	20.91 356

test123		20.95 335	23.72 336	12.64 346	13.54 361	8.19 361	96.55 311	6.13 363	7.48 357	16.74 357	37.98 356	12.97 361	6.05 359	16.69 356	5.43 358	23.68 355

wuyk23d		30.17 332	30.18 334	30.16 345	78.61 351	43.29 360	66.79 353	14.21 361	17.31 355	14.82 358	11.93 359	11.55 362	41.43 358	37.08 355	19.30 355	5.76 358

cdsmvs_eth3d_5k		23.98 333	31.98 333	0.00 348	0.00 362	0.00 363	0.00 354	98.59 116	0.00 358	0.00 359	98.61 103	90.60 126	0.00 361	0.00 358	0.00 359	0.00 359

pcd_1.5k_mvsjas		7.88 337	10.50 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	94.51 63	0.00 361	0.00 358	0.00 359	0.00 359

pcd1.5k->3k		39.42 331	41.78 332	32.35 344	96.17 278	0.00 363	0.00 354	98.54 126	0.00 358	0.00 359	0.00 360	87.78 199	0.00 361	0.00 358	93.56 227	97.06 210

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		8.20 336	10.94 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	98.43 118	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																99.20 107

test_part3							98.55 153		96.40 57		99.31 22		99.93 9	96.37 96

test_part1								98.84 54				97.38 2			99.78 15	99.76 20

sam_mvs1												89.45 138				99.20 107

sam_mvs												88.99 149

MTGPA								98.74 80

test_post1							96.68 305				30.43 358	87.85 197	98.69 210	92.59 200

test_post											31.83 357	88.83 161	98.91 192

patchmatchnet-post											95.10 306	89.42 139	98.89 196

MTMP								94.14 344

gm-plane-assit					95.88 292	87.47 320			89.74 292		96.94 248		99.19 156	93.32 177

test9_res														96.39 95	99.57 58	99.69 38

agg_prior2														95.87 109	99.57 58	99.68 44

test_prior4						98.01 44	97.86 237

test_prior				99.19 30	99.31 50	98.22 33		98.84 54					99.70 94			99.65 53

新几何2							97.64 254

旧先验1					99.29 58	97.48 62		98.70 94			99.09 55	95.56 38			99.47 72	99.61 59

无先验							97.58 258	98.72 87	91.38 254				99.87 38	93.36 175		99.60 62

原ACMM2							97.67 252

testdata2													99.89 29	91.65 225

segment_acmp												96.85 6

testdata1							97.32 276		96.34 59

plane_prior7					97.42 197	94.63 206

plane_prior6					97.35 202	94.61 209						87.09 212

plane_prior5								98.56 123					99.03 178	96.07 100	94.27 207	96.92 219

plane_prior4											98.28 134

plane_prior2							98.80 103		97.28 21

plane_prior1					97.37 201

plane_prior						94.60 211	98.44 168		96.74 46						94.22 209

n2								0.00 364

nn								0.00 364

door-mid								94.37 341

test11								98.66 108

door								94.64 340

HQP5-MVS						94.25 224

BP-MVS														95.30 128

HQP3-MVS								98.46 143							94.18 211

HQP2-MVS												86.75 218

NP-MVS					97.28 205	94.51 214					97.73 177

ACMMP++_ref															92.97 238

ACMMP++															93.61 226

Test By Simon												94.64 60

High-resolution multi-view benchmark