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.

SED-MVS		99.28 5	99.11 6	99.77 6	99.93 26	99.30 8	99.96 23	98.43 113	97.27 20	99.80 16	99.94 4	96.71 20	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 26	99.31 7		98.41 128	97.71 8	99.84 8				100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 45	99.80 2	99.96 23				99.80 58	97.44 11	100.00 1	100.00 1	99.98 33	100.00 1

test_241102_TWO								98.43 113	97.27 20	99.80 16	99.94 4	97.18 17	100.00 1	100.00 1	100.00 1	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 8	99.93 26	99.29 10	99.95 40	98.32 148	97.28 18	99.83 10	99.91 13	97.22 15	100.00 1	99.99 5	100.00 1	99.89 90

test_0728_SECOND				99.82 5	99.94 14	99.47 5	99.95 40	98.43 113					100.00 1	99.99 5	100.00 1	100.00 1

DPE-MVS		99.26 6	99.10 7	99.74 7	99.89 45	99.24 14	99.87 87	98.44 105	97.48 15	99.64 34	99.94 4	96.68 22	99.99 36	99.99 5	100.00 1	99.99 20

ETH3 D test6400		98.81 22	98.54 26	99.59 18	99.93 26	98.93 22	99.93 61	98.46 102	94.56 93	99.84 8	99.92 11	94.32 80	99.86 90	99.96 8	99.98 33	100.00 1

test_0728_THIRD									96.48 40	99.83 10	99.91 13	97.87 4	100.00 1	99.92 9	100.00 1	100.00 1

DeepPCF-MVS	95.94 2	97.71 84	98.98 10	93.92 260	99.63 88	81.76 332	99.96 23	98.56 76	99.47 1	99.19 74	99.99 1	94.16 87	100.00 1	99.92 9	99.93 63	100.00 1

TSAR-MVS + GP.		98.60 33	98.51 28	98.86 87	99.73 81	96.63 122	99.97 16	97.92 195	98.07 5	98.76 91	99.55 106	95.00 57	99.94 68	99.91 11	97.68 148	99.99 20

APDe-MVS		99.06 10	98.91 12	99.51 28	99.94 14	98.76 40	99.91 69	98.39 132	97.20 24	99.46 49	99.85 33	95.53 42	99.79 109	99.86 12	100.00 1	99.99 20

xxxxxxxxxxxxxcwj		98.98 14	98.79 15	99.54 23	99.82 65	98.79 33	99.96 23	97.52 231	97.66 10	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

SF-MVS		98.67 30	98.40 35	99.50 29	99.77 73	98.67 44	99.90 73	98.21 164	93.53 138	99.81 12	99.89 19	94.70 65	99.86 90	99.84 13	99.93 63	99.96 70

9.14			98.38 38		99.87 52		99.91 69	98.33 146	93.22 146	99.78 22	99.89 19	94.57 68	99.85 94	99.84 13	99.97 44

ETH3D-3000-0.1		98.68 29	98.42 31	99.47 34	99.83 63	98.57 51	99.90 73	98.37 139	93.81 129	99.81 12	99.90 17	94.34 76	99.86 90	99.84 13	99.98 33	99.97 63

SD-MVS		98.92 16	98.70 17	99.56 21	99.70 85	98.73 41	99.94 55	98.34 145	96.38 44	99.81 12	99.76 72	94.59 67	99.98 42	99.84 13	99.96 48	99.97 63
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		98.93 15	98.77 16	99.41 39	99.74 77	98.67 44	99.77 126	98.38 136	96.73 35	99.88 3	99.74 81	94.89 62	99.59 139	99.80 18	99.98 33	99.97 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PHI-MVS		98.41 49	98.21 49	99.03 75	99.86 54	97.10 110	99.98 8	98.80 49	90.78 223	99.62 37	99.78 66	95.30 46	100.00 1	99.80 18	99.93 63	99.99 20

test_prior3		98.99 13	98.84 14	99.43 35	99.94 14	98.49 57	99.95 40	98.65 59	95.78 58	99.73 26	99.76 72	96.00 29	99.80 106	99.78 20	100.00 1	99.99 20

test_prior2							99.95 40		95.78 58	99.73 26	99.76 72	96.00 29		99.78 20	100.00 1

CANet		98.27 59	97.82 70	99.63 12	99.72 83	99.10 17	99.98 8	98.51 94	97.00 28	98.52 101	99.71 86	87.80 187	99.95 60	99.75 22	99.38 113	99.83 96

CNVR-MVS		99.40 1	99.26 1	99.84 4	99.98 2	99.51 4	99.98 8	98.69 54	98.20 3	99.93 1	99.98 2	96.82 19	100.00 1	99.75 22	100.00 1	99.99 20

ETH3D cwj APD-0.16		98.40 51	98.07 59	99.40 41	99.59 90	98.41 60	99.86 98	98.24 160	92.18 185	99.73 26	99.87 26	93.47 103	99.85 94	99.74 24	99.95 51	99.93 81

SMA-MVS		98.76 26	98.48 29	99.62 15	99.87 52	98.87 27	99.86 98	98.38 136	93.19 147	99.77 23	99.94 4	95.54 40	100.00 1	99.74 24	99.99 20	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MCST-MVS		99.32 3	99.14 4	99.86 3	99.97 3	99.59 3	99.97 16	98.64 62	98.47 2	99.13 76	99.92 11	96.38 26	100.00 1	99.74 24	100.00 1	100.00 1

CHOSEN 280x420		99.01 12	99.03 8	98.95 83	99.38 104	98.87 27	98.46 273	99.42 20	97.03 27	99.02 80	99.09 138	99.35 1	98.21 213	99.73 27	99.78 88	99.77 104

agg_prior1		98.88 19	98.66 19	99.54 23	99.93 26	98.77 36	99.96 23	98.43 113	94.63 92	99.63 35	99.85 33	95.79 37	99.85 94	99.72 28	99.99 20	99.99 20

test9_res														99.71 29	99.99 20	100.00 1

ZD-MVS					99.92 35	98.57 51		98.52 87	92.34 181	99.31 63	99.83 49	95.06 52	99.80 106	99.70 30	99.97 44

train_agg		98.88 19	98.65 20	99.59 18	99.92 35	98.92 23	99.96 23	98.43 113	94.35 103	99.71 30	99.86 29	95.94 31	99.85 94	99.69 31	99.98 33	99.99 20

testtj		98.89 18	98.69 18	99.52 26	99.94 14	98.56 53	99.90 73	98.55 80	95.14 77	99.72 29	99.84 46	95.46 43	100.00 1	99.65 32	99.99 20	99.99 20

NCCC		99.37 2	99.25 2	99.71 10	99.96 8	99.15 16	99.97 16	98.62 66	98.02 6	99.90 2	99.95 3	97.33 13	100.00 1	99.54 33	100.00 1	100.00 1

MSLP-MVS++		99.13 7	99.01 9	99.49 31	99.94 14	98.46 59	99.98 8	98.86 44	97.10 25	99.80 16	99.94 4	95.92 33	100.00 1	99.51 34	100.00 1	100.00 1

MSP-MVS		99.09 8	99.12 5	98.98 80	99.93 26	97.24 103	99.95 40	98.42 124	97.50 14	99.52 47	99.88 22	97.43 12	99.71 128	99.50 35	99.98 33	100.00 1

agg_prior2														99.48 36	100.00 1	100.00 1

PAPM		98.60 33	98.42 31	99.14 63	96.05 241	98.96 20	99.90 73	99.35 23	96.68 37	98.35 110	99.66 97	96.45 25	98.51 185	99.45 37	99.89 74	99.96 70

SteuartSystems-ACMMP		99.02 11	98.97 11	99.18 54	98.72 136	97.71 83	99.98 8	98.44 105	96.85 29	99.80 16	99.91 13	97.57 6	99.85 94	99.44 38	99.99 20	99.99 20
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		98.62 32	98.35 43	99.41 39	99.90 42	98.51 56	99.87 87	98.36 141	94.08 114	99.74 25	99.73 83	94.08 88	99.74 124	99.42 39	99.99 20	99.99 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-MVSNAJ		98.44 47	98.20 50	99.16 59	98.80 133	98.92 23	99.54 175	98.17 170	97.34 16	99.85 6	99.85 33	91.20 148	99.89 79	99.41 40	99.67 95	98.69 199

xiu_mvs_v2_base		98.23 63	97.97 64	99.02 77	98.69 137	98.66 46	99.52 177	98.08 181	97.05 26	99.86 4	99.86 29	90.65 158	99.71 128	99.39 41	98.63 128	98.69 199

test1172		98.38 53	98.25 47	98.77 90	99.88 49	96.56 126	99.80 119	98.36 141	94.68 89	99.20 71	99.80 58	93.28 110	99.78 111	99.34 42	99.92 67	99.98 51

HPM-MVS++		99.07 9	98.88 13	99.63 12	99.90 42	99.02 19	99.95 40	98.56 76	97.56 13	99.44 51	99.85 33	95.38 45	100.00 1	99.31 43	99.99 20	99.87 93

SR-MVS		98.46 45	98.30 46	98.93 84	99.88 49	97.04 111	99.84 105	98.35 143	94.92 80	99.32 62	99.80 58	93.35 105	99.78 111	99.30 44	99.95 51	99.96 70

MVS_111021_HR		98.72 27	98.62 22	99.01 78	99.36 105	97.18 106	99.93 61	99.90 1	96.81 33	98.67 95	99.77 68	93.92 92	99.89 79	99.27 45	99.94 57	99.96 70

MVS_111021_LR		98.42 48	98.38 38	98.53 111	99.39 103	95.79 155	99.87 87	99.86 2	96.70 36	98.78 90	99.79 62	92.03 138	99.90 75	99.17 46	99.86 79	99.88 92

PVSNet_BlendedMVS		96.05 139	95.82 137	96.72 177	99.59 90	96.99 113	99.95 40	99.10 28	94.06 117	98.27 113	95.80 253	89.00 178	99.95 60	99.12 47	87.53 249	93.24 309

PVSNet_Blended		97.94 71	97.64 75	98.83 88	99.59 90	96.99 113	100.00 1	99.10 28	95.38 70	98.27 113	99.08 139	89.00 178	99.95 60	99.12 47	99.25 116	99.57 137

Regformer-1		98.79 24	98.60 23	99.36 45	99.85 55	98.34 62	99.87 87	98.52 87	96.05 53	99.41 54	99.79 62	94.93 60	99.76 117	99.07 49	99.90 72	99.99 20

xiu_mvs_v1_base_debu		97.43 89	97.06 93	98.55 106	97.74 185	98.14 67	99.31 205	97.86 201	96.43 41	99.62 37	99.69 91	85.56 208	99.68 132	99.05 50	98.31 135	97.83 208

xiu_mvs_v1_base		97.43 89	97.06 93	98.55 106	97.74 185	98.14 67	99.31 205	97.86 201	96.43 41	99.62 37	99.69 91	85.56 208	99.68 132	99.05 50	98.31 135	97.83 208

xiu_mvs_v1_base_debi		97.43 89	97.06 93	98.55 106	97.74 185	98.14 67	99.31 205	97.86 201	96.43 41	99.62 37	99.69 91	85.56 208	99.68 132	99.05 50	98.31 135	97.83 208

Regformer-2		98.78 25	98.59 24	99.36 45	99.85 55	98.32 63	99.87 87	98.52 87	96.04 54	99.41 54	99.79 62	94.92 61	99.76 117	99.05 50	99.90 72	99.98 51

CP-MVS		98.45 46	98.32 44	98.87 86	99.96 8	96.62 123	99.97 16	98.39 132	94.43 98	98.90 86	99.87 26	94.30 81	100.00 1	99.04 54	99.99 20	99.99 20

DeepC-MVS_fast	96.59 1	98.81 22	98.54 26	99.62 15	99.90 42	98.85 29	99.24 212	98.47 100	98.14 4	99.08 77	99.91 13	93.09 116	100.00 1	99.04 54	99.99 20	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETV-MVS		97.92 73	97.80 71	98.25 127	98.14 162	96.48 127	99.98 8	97.63 214	95.61 66	99.29 67	99.46 114	92.55 129	98.82 165	99.02 56	98.54 129	99.46 152

CS-MVS		97.84 76	97.69 73	98.31 124	98.28 151	96.27 135	100.00 1	97.52 231	95.29 73	99.25 70	99.65 99	91.18 151	98.94 162	98.96 57	99.04 121	99.73 108

VDD-MVS		93.77 190	92.94 196	96.27 190	98.55 140	90.22 275	98.77 256	97.79 206	90.85 221	96.82 145	99.42 116	61.18 336	99.77 115	98.95 58	94.13 204	98.82 195

APD-MVS_3200maxsize		98.25 62	98.08 58	98.78 89	99.81 68	96.60 124	99.82 112	98.30 153	93.95 123	99.37 60	99.77 68	92.84 121	99.76 117	98.95 58	99.92 67	99.97 63

VNet		97.21 100	96.57 110	99.13 68	98.97 119	97.82 81	99.03 234	99.21 27	94.31 106	99.18 75	98.88 160	86.26 203	99.89 79	98.93 60	94.32 202	99.69 114

XVS		98.70 28	98.55 25	99.15 61	99.94 14	97.50 94	99.94 55	98.42 124	96.22 49	99.41 54	99.78 66	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

X-MVStestdata		93.83 186	92.06 213	99.15 61	99.94 14	97.50 94	99.94 55	98.42 124	96.22 49	99.41 54	41.37 356	94.34 76	99.96 53	98.92 61	99.95 51	99.99 20

MP-MVS-pluss		98.07 68	97.64 75	99.38 44	99.74 77	98.41 60	99.74 137	98.18 169	93.35 142	96.45 154	99.85 33	92.64 127	99.97 51	98.91 63	99.89 74	99.77 104
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SR-MVS-dyc-post		98.31 56	98.17 52	98.71 93	99.79 70	96.37 133	99.76 131	98.31 150	94.43 98	99.40 58	99.75 77	93.28 110	99.78 111	98.90 64	99.92 67	99.97 63

RE-MVS-def			98.13 55		99.79 70	96.37 133	99.76 131	98.31 150	94.43 98	99.40 58	99.75 77	92.95 119		98.90 64	99.92 67	99.97 63

HPM-MVS		97.96 70	97.72 72	98.68 95	99.84 60	96.39 132	99.90 73	98.17 170	92.61 169	98.62 98	99.57 105	91.87 141	99.67 135	98.87 66	99.99 20	99.99 20
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS		98.23 63	97.97 64	99.03 75	99.94 14	97.17 109	99.95 40	98.39 132	94.70 88	98.26 115	99.81 57	91.84 142	100.00 1	98.85 67	99.97 44	99.93 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_yl		97.83 77	97.37 84	99.21 51	99.18 108	97.98 75	99.64 160	99.27 25	91.43 208	97.88 124	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

DCV-MVSNet		97.83 77	97.37 84	99.21 51	99.18 108	97.98 75	99.64 160	99.27 25	91.43 208	97.88 124	98.99 147	95.84 35	99.84 103	98.82 68	95.32 194	99.79 100

Regformer-3		98.58 36	98.41 33	99.10 69	99.84 60	97.57 88	99.66 153	98.52 87	95.79 57	99.01 81	99.77 68	94.40 71	99.75 120	98.82 68	99.83 81	99.98 51

Regformer-4		98.56 37	98.39 37	99.08 71	99.84 60	97.52 91	99.66 153	98.52 87	95.76 60	99.01 81	99.77 68	94.33 79	99.75 120	98.80 71	99.83 81	99.98 51

#test#		98.59 35	98.41 33	99.14 63	99.96 8	97.43 98	99.95 40	98.61 68	95.00 79	99.31 63	99.85 33	94.22 83	100.00 1	98.78 72	99.98 33	99.98 51

PVSNet_0	88.03 19	91.80 231	90.27 243	96.38 188	98.27 154	90.46 271	99.94 55	99.61 11	93.99 120	86.26 295	97.39 209	71.13 306	99.89 79	98.77 73	67.05 341	98.79 197

MG-MVS		98.91 17	98.65 20	99.68 11	99.94 14	99.07 18	99.64 160	99.44 18	97.33 17	99.00 83	99.72 84	94.03 90	99.98 42	98.73 74	100.00 1	100.00 1

HFP-MVS		98.56 37	98.37 40	99.14 63	99.96 8	97.43 98	99.95 40	98.61 68	94.77 85	99.31 63	99.85 33	94.22 83	100.00 1	98.70 75	99.98 33	99.98 51

ACMMPR		98.50 42	98.32 44	99.05 73	99.96 8	97.18 106	99.95 40	98.60 70	94.77 85	99.31 63	99.84 46	93.73 98	100.00 1	98.70 75	99.98 33	99.98 51

zzz-MVS		98.33 55	98.00 62	99.30 47	99.85 55	97.93 78	99.80 119	98.28 155	95.76 60	97.18 137	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

MTAPA		98.29 58	97.96 67	99.30 47	99.85 55	97.93 78	99.39 195	98.28 155	95.76 60	97.18 137	99.88 22	92.74 124	100.00 1	98.67 77	99.88 76	99.99 20

region2R		98.54 39	98.37 40	99.05 73	99.96 8	97.18 106	99.96 23	98.55 80	94.87 83	99.45 50	99.85 33	94.07 89	100.00 1	98.67 77	100.00 1	99.98 51

ACMMP_NAP		98.49 43	98.14 54	99.54 23	99.66 87	98.62 50	99.85 101	98.37 139	94.68 89	99.53 44	99.83 49	92.87 120	100.00 1	98.66 80	99.84 80	99.99 20

mPP-MVS		98.39 52	98.20 50	98.97 81	99.97 3	96.92 116	99.95 40	98.38 136	95.04 78	98.61 99	99.80 58	93.39 104	100.00 1	98.64 81	100.00 1	99.98 51

DELS-MVS		98.54 39	98.22 48	99.50 29	99.15 111	98.65 48	100.00 1	98.58 72	97.70 9	98.21 117	99.24 132	92.58 128	99.94 68	98.63 82	99.94 57	99.92 87
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
alignmvs		97.81 79	97.33 87	99.25 49	98.77 135	98.66 46	99.99 4	98.44 105	94.40 102	98.41 106	99.47 112	93.65 100	99.42 150	98.57 83	94.26 203	99.67 117

CDPH-MVS		98.65 31	98.36 42	99.49 31	99.94 14	98.73 41	99.87 87	98.33 146	93.97 121	99.76 24	99.87 26	94.99 58	99.75 120	98.55 84	100.00 1	99.98 51

EI-MVSNet-Vis-set		98.27 59	98.11 57	98.75 92	99.83 63	96.59 125	99.40 191	98.51 94	95.29 73	98.51 102	99.76 72	93.60 102	99.71 128	98.53 85	99.52 106	99.95 78

canonicalmvs		97.09 104	96.32 116	99.39 43	98.93 123	98.95 21	99.72 145	97.35 250	94.45 96	97.88 124	99.42 116	86.71 198	99.52 141	98.48 86	93.97 207	99.72 111

API-MVS		97.86 74	97.66 74	98.47 114	99.52 96	95.41 167	99.47 185	98.87 43	91.68 199	98.84 87	99.85 33	92.34 132	99.99 36	98.44 87	99.96 48	100.00 1

lupinMVS		97.85 75	97.60 77	98.62 100	97.28 209	97.70 85	99.99 4	97.55 225	95.50 69	99.43 52	99.67 95	90.92 156	98.71 175	98.40 88	99.62 98	99.45 154

EI-MVSNet-UG-set		98.14 65	97.99 63	98.60 102	99.80 69	96.27 135	99.36 200	98.50 98	95.21 76	98.30 112	99.75 77	93.29 109	99.73 127	98.37 89	99.30 115	99.81 98

diffmvs		97.00 105	96.64 107	98.09 133	97.64 192	96.17 144	99.81 114	97.19 260	94.67 91	98.95 84	99.28 124	86.43 201	98.76 171	98.37 89	97.42 154	99.33 167

CPTT-MVS		97.64 86	97.32 88	98.58 105	99.97 3	95.77 156	99.96 23	98.35 143	89.90 236	98.36 109	99.79 62	91.18 151	99.99 36	98.37 89	99.99 20	99.99 20

ZNCC-MVS		98.31 56	98.03 60	99.17 57	99.88 49	97.59 87	99.94 55	98.44 105	94.31 106	98.50 103	99.82 53	93.06 117	99.99 36	98.30 92	99.99 20	99.93 81

DP-MVS Recon		98.41 49	98.02 61	99.56 21	99.97 3	98.70 43	99.92 65	98.44 105	92.06 190	98.40 108	99.84 46	95.68 38	100.00 1	98.19 93	99.71 93	99.97 63

GG-mvs-BLEND				98.54 109	98.21 157	98.01 73	93.87 331	98.52 87		97.92 122	97.92 199	99.02 2	97.94 227	98.17 94	99.58 103	99.67 117

GST-MVS		98.27 59	97.97 64	99.17 57	99.92 35	97.57 88	99.93 61	98.39 132	94.04 119	98.80 89	99.74 81	92.98 118	100.00 1	98.16 95	99.76 89	99.93 81

CSCG		97.10 102	97.04 96	97.27 164	99.89 45	91.92 242	99.90 73	99.07 31	88.67 256	95.26 176	99.82 53	93.17 115	99.98 42	98.15 96	99.47 109	99.90 89

MAR-MVS		97.43 89	97.19 90	98.15 132	99.47 100	94.79 185	99.05 232	98.76 50	92.65 167	98.66 96	99.82 53	88.52 184	99.98 42	98.12 97	99.63 97	99.67 117
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
PAPR		98.52 41	98.16 53	99.58 20	99.97 3	98.77 36	99.95 40	98.43 113	95.35 71	98.03 120	99.75 77	94.03 90	99.98 42	98.11 98	99.83 81	99.99 20

CLD-MVS		94.06 184	93.90 175	94.55 234	96.02 242	90.69 264	99.98 8	97.72 208	96.62 39	91.05 216	98.85 167	77.21 269	98.47 186	98.11 98	89.51 224	94.48 229
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VDDNet		93.12 202	91.91 216	96.76 175	96.67 234	92.65 227	98.69 262	98.21 164	82.81 315	97.75 127	99.28 124	61.57 334	99.48 148	98.09 100	94.09 205	98.15 204

HY-MVS	92.50 7	97.79 81	97.17 92	99.63 12	98.98 118	99.32 6	97.49 303	99.52 13	95.69 64	98.32 111	97.41 207	93.32 107	99.77 115	98.08 101	95.75 187	99.81 98

EIA-MVS		97.53 88	97.46 81	97.76 145	98.04 166	94.84 182	99.98 8	97.61 219	94.41 101	97.90 123	99.59 103	92.40 130	98.87 163	98.04 102	99.13 119	99.59 130

LFMVS		94.75 167	93.56 184	98.30 125	99.03 114	95.70 161	98.74 257	97.98 188	87.81 269	98.47 104	99.39 120	67.43 318	99.53 140	98.01 103	95.20 196	99.67 117

AdaColmap		97.23 99	96.80 103	98.51 112	99.99 1	95.60 163	99.09 221	98.84 46	93.32 143	96.74 147	99.72 84	86.04 204	100.00 1	98.01 103	99.43 112	99.94 80

EPNet		98.49 43	98.40 35	98.77 90	99.62 89	96.80 119	99.90 73	99.51 15	97.60 12	99.20 71	99.36 123	93.71 99	99.91 74	97.99 105	98.71 127	99.61 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		97.74 83	97.44 82	98.66 97	99.92 35	96.13 145	99.18 216	99.45 17	94.84 84	96.41 157	99.71 86	91.40 145	99.99 36	97.99 105	98.03 144	99.87 93
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
WTY-MVS		98.10 67	97.60 77	99.60 17	98.92 125	99.28 12	99.89 81	99.52 13	95.58 67	98.24 116	99.39 120	93.33 106	99.74 124	97.98 107	95.58 190	99.78 103

jason		97.24 98	96.86 100	98.38 122	95.73 253	97.32 102	99.97 16	97.40 247	95.34 72	98.60 100	99.54 108	87.70 188	98.56 182	97.94 108	99.47 109	99.25 174
jason: jason.
BP-MVS														97.92 109

HQP-MVS		94.61 172	94.50 164	94.92 220	95.78 247	91.85 243	99.87 87	97.89 197	96.82 30	93.37 195	98.65 173	80.65 247	98.39 197	97.92 109	89.60 219	94.53 225

1314		96.84 111	95.96 130	99.48 33	96.74 231	98.52 55	98.31 280	98.86 44	95.82 56	89.91 228	98.98 149	87.49 190	99.96 53	97.80 111	99.73 91	99.96 70

HQP_MVS		94.49 177	94.36 166	94.87 221	95.71 256	91.74 247	99.84 105	97.87 199	96.38 44	93.01 199	98.59 177	80.47 251	98.37 202	97.79 112	89.55 222	94.52 227

plane_prior5								97.87 199					98.37 202	97.79 112	89.55 222	94.52 227

gg-mvs-nofinetune		93.51 196	91.86 218	98.47 114	97.72 189	97.96 77	92.62 335	98.51 94	74.70 337	97.33 134	69.59 348	98.91 3	97.79 230	97.77 114	99.56 104	99.67 117

casdiffmvs		96.42 129	95.97 128	97.77 144	97.30 208	94.98 178	99.84 105	97.09 269	93.75 133	96.58 150	99.26 130	85.07 213	98.78 168	97.77 114	97.04 163	99.54 143

PGM-MVS		98.34 54	98.13 55	98.99 79	99.92 35	97.00 112	99.75 134	99.50 16	93.90 126	99.37 60	99.76 72	93.24 113	100.00 1	97.75 116	99.96 48	99.98 51

DeepC-MVS	94.51 4	96.92 109	96.40 115	98.45 116	99.16 110	95.90 152	99.66 153	98.06 182	96.37 47	94.37 185	99.49 111	83.29 226	99.90 75	97.63 117	99.61 101	99.55 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS_fast		97.80 80	97.50 80	98.68 95	99.79 70	96.42 129	99.88 84	98.16 173	91.75 198	98.94 85	99.54 108	91.82 143	99.65 137	97.62 118	99.99 20	99.99 20

baseline		96.43 128	95.98 125	97.76 145	97.34 204	95.17 176	99.51 179	97.17 263	93.92 125	96.90 143	99.28 124	85.37 211	98.64 179	97.50 119	96.86 168	99.46 152

abl_6		97.67 85	97.34 86	98.66 97	99.68 86	96.11 148	99.68 150	98.14 176	93.80 130	99.27 68	99.70 88	88.65 183	99.98 42	97.46 120	99.72 92	99.89 90

PLC	95.54 3	97.93 72	97.89 69	98.05 135	99.82 65	94.77 186	99.92 65	98.46 102	93.93 124	97.20 136	99.27 127	95.44 44	99.97 51	97.41 121	99.51 108	99.41 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		96.60 123	95.56 142	99.72 9	96.85 224	99.22 15	98.31 280	98.94 36	91.57 202	90.90 217	99.61 102	86.66 199	99.96 53	97.36 122	99.88 76	99.99 20

XVG-OURS-SEG-HR		94.79 164	94.70 162	95.08 214	98.05 165	89.19 286	99.08 223	97.54 227	93.66 135	94.87 179	99.58 104	78.78 262	99.79 109	97.31 123	93.40 211	96.25 219

3Dnovator	91.47 12	96.28 136	95.34 147	99.08 71	96.82 226	97.47 97	99.45 188	98.81 47	95.52 68	89.39 242	99.00 146	81.97 231	99.95 60	97.27 124	99.83 81	99.84 95

cascas		94.64 171	93.61 179	97.74 147	97.82 179	96.26 137	99.96 23	97.78 207	85.76 294	94.00 190	97.54 204	76.95 272	99.21 153	97.23 125	95.43 192	97.76 212

LCM-MVSNet-Re		92.31 220	92.60 202	91.43 297	97.53 196	79.27 340	99.02 235	91.83 346	92.07 188	80.31 319	94.38 310	83.50 224	95.48 317	97.22 126	97.58 150	99.54 143

CNLPA		97.76 82	97.38 83	98.92 85	99.53 95	96.84 117	99.87 87	98.14 176	93.78 131	96.55 152	99.69 91	92.28 133	99.98 42	97.13 127	99.44 111	99.93 81

Effi-MVS+		96.30 134	95.69 139	98.16 129	97.85 177	96.26 137	97.41 304	97.21 259	90.37 228	98.65 97	98.58 179	86.61 200	98.70 176	97.11 128	97.37 156	99.52 146

PVSNet_Blended_VisFu		97.27 97	96.81 102	98.66 97	98.81 132	96.67 121	99.92 65	98.64 62	94.51 95	96.38 158	98.49 183	89.05 177	99.88 85	97.10 129	98.34 133	99.43 157

3Dnovator+	91.53 11	96.31 133	95.24 149	99.52 26	96.88 223	98.64 49	99.72 145	98.24 160	95.27 75	88.42 265	98.98 149	82.76 228	99.94 68	97.10 129	99.83 81	99.96 70

PAPM_NR		98.12 66	97.93 68	98.70 94	99.94 14	96.13 145	99.82 112	98.43 113	94.56 93	97.52 130	99.70 88	94.40 71	99.98 42	97.00 131	99.98 33	99.99 20

CHOSEN 1792x2688		96.81 112	96.53 111	97.64 149	98.91 127	93.07 214	99.65 156	99.80 3	95.64 65	95.39 173	98.86 164	84.35 219	99.90 75	96.98 132	99.16 118	99.95 78

旧先验2							99.46 187		94.21 110	99.85 6			99.95 60	96.96 133

PMMVS		96.76 115	96.76 104	96.76 175	98.28 151	92.10 237	99.91 69	97.98 188	94.12 112	99.53 44	99.39 120	86.93 197	98.73 173	96.95 134	97.73 146	99.45 154

EPP-MVSNet		96.69 120	96.60 108	96.96 169	97.74 185	93.05 216	99.37 198	98.56 76	88.75 254	95.83 167	99.01 144	96.01 28	98.56 182	96.92 135	97.20 160	99.25 174

ET-MVSNet_ETH3D		94.37 179	93.28 193	97.64 149	98.30 149	97.99 74	99.99 4	97.61 219	94.35 103	71.57 338	99.45 115	96.23 27	95.34 320	96.91 136	85.14 266	99.59 130

HyFIR lowres test		96.66 122	96.43 114	97.36 161	99.05 113	93.91 200	99.70 147	99.80 3	90.54 225	96.26 159	98.08 194	92.15 136	98.23 212	96.84 137	95.46 191	99.93 81

OMC-MVS		97.28 96	97.23 89	97.41 157	99.76 74	93.36 212	99.65 156	97.95 191	96.03 55	97.41 133	99.70 88	89.61 169	99.51 142	96.73 138	98.25 138	99.38 161

CostFormer		96.10 138	95.88 135	96.78 174	97.03 216	92.55 229	97.08 308	97.83 204	90.04 235	98.72 93	94.89 296	95.01 56	98.29 207	96.54 139	95.77 185	99.50 149

sss		97.57 87	97.03 97	99.18 54	98.37 147	98.04 72	99.73 142	99.38 21	93.46 140	98.76 91	99.06 140	91.21 147	99.89 79	96.33 140	97.01 164	99.62 125

114514_t		97.41 93	96.83 101	99.14 63	99.51 98	97.83 80	99.89 81	98.27 158	88.48 260	99.06 78	99.66 97	90.30 162	99.64 138	96.32 141	99.97 44	99.96 70

ACMP	92.05 9	92.74 210	92.42 208	93.73 264	95.91 246	88.72 291	99.81 114	97.53 229	94.13 111	87.00 283	98.23 191	74.07 294	98.47 186	96.22 142	88.86 231	93.99 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	92.85 6	94.99 162	93.94 174	98.16 129	97.72 189	95.69 162	99.99 4	98.81 47	94.28 108	92.70 205	96.90 224	95.08 50	99.17 155	96.07 143	73.88 332	99.60 129
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
XVG-OURS		94.82 163	94.74 161	95.06 215	98.00 167	89.19 286	99.08 223	97.55 225	94.10 113	94.71 180	99.62 101	80.51 249	99.74 124	96.04 144	93.06 215	96.25 219

ab-mvs		94.69 168	93.42 187	98.51 112	98.07 164	96.26 137	96.49 315	98.68 55	90.31 230	94.54 181	97.00 222	76.30 278	99.71 128	95.98 145	93.38 212	99.56 138

mvs_anonymous		95.65 150	95.03 155	97.53 152	98.19 158	95.74 158	99.33 202	97.49 236	90.87 220	90.47 221	97.10 216	88.23 185	97.16 258	95.92 146	97.66 149	99.68 115

nrg030		93.51 196	92.53 205	96.45 184	94.36 278	97.20 105	99.81 114	97.16 265	91.60 201	89.86 230	97.46 205	86.37 202	97.68 233	95.88 147	80.31 302	94.46 230

LPG-MVS_test		92.96 205	92.71 200	93.71 266	95.43 262	88.67 292	99.75 134	97.62 216	92.81 156	90.05 223	98.49 183	75.24 286	98.40 195	95.84 148	89.12 226	94.07 268

LGP-MVS_train				93.71 266	95.43 262	88.67 292		97.62 216	92.81 156	90.05 223	98.49 183	75.24 286	98.40 195	95.84 148	89.12 226	94.07 268

VPA-MVSNet		92.70 211	91.55 223	96.16 192	95.09 267	96.20 142	98.88 247	99.00 33	91.02 218	91.82 209	95.29 283	76.05 282	97.96 224	95.62 150	81.19 291	94.30 245

F-COLMAP		96.93 108	96.95 99	96.87 172	99.71 84	91.74 247	99.85 101	97.95 191	93.11 150	95.72 169	99.16 136	92.35 131	99.94 68	95.32 151	99.35 114	98.92 189

BH-w/o		95.71 148	95.38 146	96.68 178	98.49 144	92.28 233	99.84 105	97.50 235	92.12 187	92.06 208	98.79 168	84.69 215	98.67 178	95.29 152	99.66 96	99.09 185

原ACMM1				98.96 82	99.73 81	96.99 113		98.51 94	94.06 117	99.62 37	99.85 33	94.97 59	99.96 53	95.11 153	99.95 51	99.92 87

Anonymous202405211		93.10 203	91.99 214	96.40 186	99.10 112	89.65 283	98.88 247	97.93 193	83.71 311	94.00 190	98.75 169	68.79 311	99.88 85	95.08 154	91.71 217	99.68 115

testdata				98.42 119	99.47 100	95.33 169		98.56 76	93.78 131	99.79 21	99.85 33	93.64 101	99.94 68	94.97 155	99.94 57	100.00 1

gm-plane-assit					96.97 219	93.76 203			91.47 206		98.96 153		98.79 167	94.92 156

PVSNet	91.05 13	97.13 101	96.69 106	98.45 116	99.52 96	95.81 154	99.95 40	99.65 10	94.73 87	99.04 79	99.21 134	84.48 217	99.95 60	94.92 156	98.74 126	99.58 136

tpmrst		96.27 137	95.98 125	97.13 166	97.96 169	93.15 213	96.34 317	98.17 170	92.07 188	98.71 94	95.12 287	93.91 93	98.73 173	94.91 158	96.62 169	99.50 149

VPNet		91.81 228	90.46 237	95.85 200	94.74 273	95.54 164	98.98 237	98.59 71	92.14 186	90.77 219	97.44 206	68.73 313	97.54 238	94.89 159	77.89 315	94.46 230

baseline2		96.71 119	96.49 112	97.37 160	95.63 260	95.96 151	99.74 137	98.88 42	92.94 152	91.61 210	98.97 151	97.72 5	98.62 180	94.83 160	98.08 143	97.53 214

Effi-MVS+-dtu		94.53 176	95.30 148	92.22 289	97.77 182	82.54 326	99.59 166	97.06 272	94.92 80	95.29 175	95.37 277	85.81 205	97.89 228	94.80 161	97.07 162	96.23 221

mvs-test1		95.53 151	95.97 128	94.20 248	97.77 182	85.44 316	99.95 40	97.06 272	94.92 80	96.58 150	98.72 170	85.81 205	98.98 159	94.80 161	98.11 139	98.18 203

MVSTER		95.53 151	95.22 150	96.45 184	98.56 139	97.72 82	99.91 69	97.67 212	92.38 180	91.39 212	97.14 214	97.24 14	97.30 248	94.80 161	87.85 244	94.34 243

thisisatest0515		97.41 93	97.02 98	98.59 104	97.71 191	97.52 91	99.97 16	98.54 84	91.83 195	97.45 132	99.04 141	97.50 8	99.10 156	94.75 164	96.37 174	99.16 179

mvs_tets		91.81 228	91.08 230	94.00 257	91.63 321	90.58 268	98.67 264	97.43 241	92.43 179	87.37 280	97.05 220	71.76 301	97.32 247	94.75 164	88.68 234	94.11 266

RRT_test8_iter05		94.58 173	94.11 170	95.98 196	97.88 173	96.11 148	99.89 81	97.45 238	91.66 200	88.28 266	96.71 232	96.53 24	97.40 241	94.73 166	83.85 277	94.45 235

Anonymous20240529		92.10 224	90.65 235	96.47 182	98.82 131	90.61 267	98.72 259	98.67 58	75.54 335	93.90 192	98.58 179	66.23 321	99.90 75	94.70 167	90.67 218	98.90 192

MVSFormer		96.94 107	96.60 108	97.95 137	97.28 209	97.70 85	99.55 173	97.27 256	91.17 212	99.43 52	99.54 108	90.92 156	96.89 277	94.67 168	99.62 98	99.25 174

test_djsdf		92.83 208	92.29 209	94.47 239	91.90 317	92.46 230	99.55 173	97.27 256	91.17 212	89.96 226	96.07 250	81.10 240	96.89 277	94.67 168	88.91 228	94.05 270

UGNet		95.33 155	94.57 163	97.62 151	98.55 140	94.85 181	98.67 264	99.32 24	95.75 63	96.80 146	96.27 244	72.18 300	99.96 53	94.58 170	99.05 120	98.04 206
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
jajsoiax		91.92 226	91.18 229	94.15 249	91.35 323	90.95 261	99.00 236	97.42 243	92.61 169	87.38 279	97.08 217	72.46 299	97.36 243	94.53 171	88.77 232	94.13 265

MVS_Test		96.46 127	95.74 138	98.61 101	98.18 159	97.23 104	99.31 205	97.15 266	91.07 216	98.84 87	97.05 220	88.17 186	98.97 160	94.39 172	97.50 151	99.61 127

PS-MVSNAJss		93.64 195	93.31 192	94.61 230	92.11 314	92.19 235	99.12 219	97.38 248	92.51 177	88.45 260	96.99 223	91.20 148	97.29 251	94.36 173	87.71 246	94.36 239

无先验							99.49 182	98.71 52	93.46 140				100.00 1	94.36 173		99.99 20

1121		98.03 69	97.57 79	99.40 41	99.74 77	98.21 66	98.31 280	98.62 66	92.78 159	99.53 44	99.83 49	95.08 50	100.00 1	94.36 173	99.92 67	99.99 20

MDTV_nov1_ep13_2view						96.26 137	96.11 320		91.89 193	98.06 119		94.40 71		94.30 176		99.67 117

thres200		96.96 106	96.21 118	99.22 50	98.97 119	98.84 30	99.85 101	99.71 5	93.17 148	96.26 159	98.88 160	89.87 167	99.51 142	94.26 177	94.91 197	99.31 169

BH-untuned		95.18 157	94.83 158	96.22 191	98.36 148	91.22 258	99.80 119	97.32 253	90.91 219	91.08 215	98.67 172	83.51 223	98.54 184	94.23 178	99.61 101	98.92 189

FIs		94.10 183	93.43 186	96.11 193	94.70 274	96.82 118	99.58 167	98.93 39	92.54 175	89.34 244	97.31 210	87.62 189	97.10 264	94.22 179	86.58 254	94.40 237

DWT-MVSNet_test		97.31 95	97.19 90	97.66 148	98.24 155	94.67 187	98.86 251	98.20 168	93.60 137	98.09 118	98.89 158	97.51 7	98.78 168	94.04 180	97.28 157	99.55 139

tpm2		95.47 153	95.18 152	96.35 189	96.91 221	91.70 251	96.96 311	97.93 193	88.04 266	98.44 105	95.40 273	93.32 107	97.97 222	94.00 181	95.61 189	99.38 161

OpenMVS	90.15 15	94.77 166	93.59 182	98.33 123	96.07 240	97.48 96	99.56 171	98.57 74	90.46 226	86.51 289	98.95 155	78.57 264	99.94 68	93.86 182	99.74 90	97.57 213

thres100view900		96.74 117	95.92 133	99.18 54	98.90 128	98.77 36	99.74 137	99.71 5	92.59 171	95.84 165	98.86 164	89.25 174	99.50 144	93.84 183	94.57 198	99.27 172

tfpn200view9		96.79 113	95.99 123	99.19 53	98.94 121	98.82 31	99.78 123	99.71 5	92.86 153	96.02 162	98.87 162	89.33 172	99.50 144	93.84 183	94.57 198	99.27 172

thres400		96.78 114	95.99 123	99.16 59	98.94 121	98.82 31	99.78 123	99.71 5	92.86 153	96.02 162	98.87 162	89.33 172	99.50 144	93.84 183	94.57 198	99.16 179

DPM-MVS		98.83 21	98.46 30	99.97 1	99.33 106	99.92 1	99.96 23	98.44 105	97.96 7	99.55 42	99.94 4	97.18 17	100.00 1	93.81 186	99.94 57	99.98 51

CDS-MVSNet		96.34 131	96.07 120	97.13 166	97.37 202	94.96 179	99.53 176	97.91 196	91.55 203	95.37 174	98.32 190	95.05 53	97.13 261	93.80 187	95.75 187	99.30 170
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		95.78 145	94.86 157	98.54 109	98.47 145	98.07 70	99.06 228	97.99 186	92.68 165	94.13 189	98.62 176	93.28 110	98.69 177	93.79 188	85.76 258	98.84 194

OPM-MVS		93.21 200	92.80 198	94.44 241	93.12 300	90.85 263	99.77 126	97.61 219	96.19 51	91.56 211	98.65 173	75.16 288	98.47 186	93.78 189	89.39 225	93.99 276

TAMVS		95.85 143	95.58 141	96.65 180	97.07 213	93.50 207	99.17 217	97.82 205	91.39 211	95.02 178	98.01 196	92.20 134	97.30 248	93.75 190	95.83 184	99.14 182

thisisatest0530		97.10 102	96.72 105	98.22 128	97.60 194	96.70 120	99.92 65	98.54 84	91.11 215	97.07 140	98.97 151	97.47 9	99.03 157	93.73 191	96.09 177	98.92 189

IS-MVSNet		96.29 135	95.90 134	97.45 155	98.13 163	94.80 184	99.08 223	97.61 219	92.02 191	95.54 172	98.96 153	90.64 159	98.08 217	93.73 191	97.41 155	99.47 151

RRT_MVS		95.23 156	94.77 160	96.61 181	98.28 151	98.32 63	99.81 114	97.41 245	92.59 171	91.28 214	97.76 201	95.02 54	97.23 255	93.65 193	87.14 251	94.28 247

ACMM	91.95 10	92.88 207	92.52 206	93.98 259	95.75 252	89.08 289	99.77 126	97.52 231	93.00 151	89.95 227	97.99 197	76.17 280	98.46 189	93.63 194	88.87 230	94.39 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		96.32 132	95.98 125	97.35 162	97.93 171	94.82 183	99.47 185	98.15 175	91.83 195	95.09 177	99.11 137	91.37 146	97.47 240	93.47 195	97.43 152	99.74 107

thres600view7		96.69 120	95.87 136	99.14 63	98.90 128	98.78 35	99.74 137	99.71 5	92.59 171	95.84 165	98.86 164	89.25 174	99.50 144	93.44 196	94.50 201	99.16 179

Vis-MVSNet		95.72 146	95.15 153	97.45 155	97.62 193	94.28 193	99.28 209	98.24 160	94.27 109	96.84 144	98.94 156	79.39 257	98.76 171	93.25 197	98.49 130	99.30 170
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FC-MVSNet-test		93.81 188	93.15 195	95.80 201	94.30 280	96.20 142	99.42 190	98.89 41	92.33 182	89.03 253	97.27 212	87.39 192	96.83 281	93.20 198	86.48 255	94.36 239

UniMVSNet_NR-MVSNet		92.95 206	92.11 211	95.49 203	94.61 276	95.28 171	99.83 111	99.08 30	91.49 204	89.21 248	96.86 227	87.14 194	96.73 285	93.20 198	77.52 318	94.46 230

DU-MVS		92.46 217	91.45 226	95.49 203	94.05 283	95.28 171	99.81 114	98.74 51	92.25 184	89.21 248	96.64 235	81.66 234	96.73 285	93.20 198	77.52 318	94.46 230

WR-MVS		92.31 220	91.25 228	95.48 206	94.45 277	95.29 170	99.60 165	98.68 55	90.10 232	88.07 269	96.89 225	80.68 246	96.80 283	93.14 201	79.67 306	94.36 239

UniMVSNet (Re)		93.07 204	92.13 210	95.88 198	94.84 271	96.24 141	99.88 84	98.98 34	92.49 178	89.25 246	95.40 273	87.09 195	97.14 260	93.13 202	78.16 313	94.26 248

QAPM		95.40 154	94.17 169	99.10 69	96.92 220	97.71 83	99.40 191	98.68 55	89.31 241	88.94 254	98.89 158	82.48 229	99.96 53	93.12 203	99.83 81	99.62 125

tttt0517		96.85 110	96.49 112	97.92 139	97.48 200	95.89 153	99.85 101	98.54 84	90.72 224	96.63 149	98.93 157	97.47 9	99.02 158	93.03 204	95.76 186	98.85 193

TR-MVS		94.54 174	93.56 184	97.49 154	97.96 169	94.34 192	98.71 260	97.51 234	90.30 231	94.51 183	98.69 171	75.56 283	98.77 170	92.82 205	95.99 179	99.35 165

CANet_DTU		96.76 115	96.15 119	98.60 102	98.78 134	97.53 90	99.84 105	97.63 214	97.25 23	99.20 71	99.64 100	81.36 238	99.98 42	92.77 206	98.89 122	98.28 202

anonymousdsp		91.79 233	90.92 232	94.41 244	90.76 328	92.93 218	98.93 243	97.17 263	89.08 243	87.46 278	95.30 280	78.43 267	96.92 276	92.38 207	88.73 233	93.39 305

XVG-ACMP-BASELINE		91.22 241	90.75 233	92.63 286	93.73 289	85.61 313	98.52 272	97.44 240	92.77 160	89.90 229	96.85 228	66.64 320	98.39 197	92.29 208	88.61 235	93.89 285

testing_2		85.10 299	81.72 307	95.22 211	82.25 345	94.16 194	97.54 302	97.01 278	88.15 263	62.23 342	86.43 339	44.43 348	97.18 257	92.28 209	85.20 265	94.31 244

miper_enhance_ethall		94.36 181	93.98 173	95.49 203	98.68 138	95.24 173	99.73 142	97.29 255	93.28 145	89.86 230	95.97 251	94.37 75	97.05 267	92.20 210	84.45 270	94.19 254

RPSCF		91.80 231	92.79 199	88.83 315	98.15 161	69.87 344	98.11 291	96.60 306	83.93 309	94.33 186	99.27 127	79.60 256	99.46 149	91.99 211	93.16 214	97.18 215

cl-mvsnet2		93.77 190	93.25 194	95.33 208	99.49 99	94.43 190	99.61 164	98.09 179	90.38 227	89.16 251	95.61 261	90.56 160	97.34 245	91.93 212	84.45 270	94.21 253

1112_ss		96.01 141	95.20 151	98.42 119	97.80 180	96.41 130	99.65 156	96.66 304	92.71 162	92.88 203	99.40 118	92.16 135	99.30 151	91.92 213	93.66 208	99.55 139

Test_1112_low_res		95.72 146	94.83 158	98.42 119	97.79 181	96.41 130	99.65 156	96.65 305	92.70 163	92.86 204	96.13 248	92.15 136	99.30 151	91.88 214	93.64 209	99.55 139

tmp_tt		65.23 317	62.94 320	72.13 330	44.90 357	50.03 353	81.05 347	89.42 352	38.45 349	48.51 351	99.90 17	54.09 343	78.70 350	91.84 215	18.26 352	87.64 342

XXY-MVS		91.82 227	90.46 237	95.88 198	93.91 286	95.40 168	98.87 250	97.69 211	88.63 258	87.87 271	97.08 217	74.38 293	97.89 228	91.66 216	84.07 274	94.35 242

D2MVS		92.76 209	92.59 204	93.27 274	95.13 266	89.54 285	99.69 148	99.38 21	92.26 183	87.59 274	94.61 304	85.05 214	97.79 230	91.59 217	88.01 243	92.47 318

UniMVSNet_ETH3D		90.06 268	88.58 274	94.49 238	94.67 275	88.09 302	97.81 299	97.57 224	83.91 310	88.44 261	97.41 207	57.44 340	97.62 236	91.41 218	88.59 237	97.77 211

NR-MVSNet		91.56 236	90.22 244	95.60 202	94.05 283	95.76 157	98.25 284	98.70 53	91.16 214	80.78 318	96.64 235	83.23 227	96.57 291	91.41 218	77.73 317	94.46 230

新几何1				99.42 38	99.75 76	98.27 65		98.63 65	92.69 164	99.55 42	99.82 53	94.40 71	100.00 1	91.21 220	99.94 57	99.99 20

UA-Net		96.54 124	95.96 130	98.27 126	98.23 156	95.71 160	98.00 295	98.45 104	93.72 134	98.41 106	99.27 127	88.71 182	99.66 136	91.19 221	97.69 147	99.44 156

EPMVS		96.53 125	96.01 122	98.09 133	98.43 146	96.12 147	96.36 316	99.43 19	93.53 138	97.64 128	95.04 289	94.41 70	98.38 201	91.13 222	98.11 139	99.75 106

EI-MVSNet		93.73 192	93.40 190	94.74 225	96.80 227	92.69 224	99.06 228	97.67 212	88.96 249	91.39 212	99.02 142	88.75 181	97.30 248	91.07 223	87.85 244	94.22 251

test_post1							95.78 325				59.23 355	93.20 114	97.74 232	91.06 224

SCA		94.69 168	93.81 178	97.33 163	97.10 212	94.44 189	98.86 251	98.32 148	93.30 144	96.17 161	95.59 263	76.48 276	97.95 225	91.06 224	97.43 152	99.59 130

Baseline_NR-MVSNet		90.33 260	89.51 258	92.81 284	92.84 306	89.95 279	99.77 126	93.94 342	84.69 306	89.04 252	95.66 259	81.66 234	96.52 292	90.99 226	76.98 324	91.97 323

IterMVS-LS		92.69 212	92.11 211	94.43 243	96.80 227	92.74 221	99.45 188	96.89 291	88.98 247	89.65 237	95.38 276	88.77 180	96.34 299	90.98 227	82.04 285	94.22 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LS3D		95.84 144	95.11 154	98.02 136	99.85 55	95.10 177	98.74 257	98.50 98	87.22 276	93.66 194	99.86 29	87.45 191	99.95 60	90.94 228	99.81 87	99.02 187

CVMVSNet		94.68 170	94.94 156	93.89 262	96.80 227	86.92 308	99.06 228	98.98 34	94.45 96	94.23 188	99.02 142	85.60 207	95.31 321	90.91 229	95.39 193	99.43 157

BH-RMVSNet		95.18 157	94.31 167	97.80 141	98.17 160	95.23 174	99.76 131	97.53 229	92.52 176	94.27 187	99.25 131	76.84 273	98.80 166	90.89 230	99.54 105	99.35 165

Anonymous20231211		89.86 270	88.44 276	94.13 251	98.93 123	90.68 265	98.54 270	98.26 159	76.28 331	86.73 285	95.54 265	70.60 307	97.56 237	90.82 231	80.27 303	94.15 261

miper_ehance_all_eth		93.16 201	92.60 202	94.82 224	97.57 195	93.56 206	99.50 180	97.07 271	88.75 254	88.85 255	95.52 267	90.97 155	96.74 284	90.77 232	84.45 270	94.17 255

tpm		93.70 194	93.41 189	94.58 232	95.36 264	87.41 306	97.01 309	96.90 290	90.85 221	96.72 148	94.14 312	90.40 161	96.84 280	90.75 233	88.54 238	99.51 147

TESTMET0.1,1		96.74 117	96.26 117	98.16 129	97.36 203	96.48 127	99.96 23	98.29 154	91.93 192	95.77 168	98.07 195	95.54 40	98.29 207	90.55 234	98.89 122	99.70 112

testdata2													99.99 36	90.54 235

cl_fuxian		92.53 215	91.87 217	94.52 235	97.40 201	92.99 217	99.40 191	96.93 288	87.86 267	88.69 258	95.44 271	89.95 166	96.44 295	90.45 236	80.69 299	94.14 264

test-LLR		96.47 126	96.04 121	97.78 142	97.02 217	95.44 165	99.96 23	98.21 164	94.07 115	95.55 170	96.38 240	93.90 94	98.27 210	90.42 237	98.83 124	99.64 123

test-mter		96.39 130	95.93 132	97.78 142	97.02 217	95.44 165	99.96 23	98.21 164	91.81 197	95.55 170	96.38 240	95.17 47	98.27 210	90.42 237	98.83 124	99.64 123

PCF-MVS	94.20 5	95.18 157	94.10 171	98.43 118	98.55 140	95.99 150	97.91 297	97.31 254	90.35 229	89.48 241	99.22 133	85.19 212	99.89 79	90.40 239	98.47 131	99.41 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CP-MVSNet		91.23 240	90.22 244	94.26 246	93.96 285	92.39 232	99.09 221	98.57 74	88.95 250	86.42 292	96.57 237	79.19 259	96.37 297	90.29 240	78.95 308	94.02 271

TranMVSNet+NR-MVSNet		91.68 235	90.61 236	94.87 221	93.69 290	93.98 198	99.69 148	98.65 59	91.03 217	88.44 261	96.83 231	80.05 254	96.18 305	90.26 241	76.89 326	94.45 235

PatchMatch-RL		96.04 140	95.40 144	97.95 137	99.59 90	95.22 175	99.52 177	99.07 31	93.96 122	96.49 153	98.35 189	82.28 230	99.82 105	90.15 242	99.22 117	98.81 196

MDTV_nov1_ep13			95.69 139		97.90 172	94.15 195	95.98 322	98.44 105	93.12 149	97.98 121	95.74 255	95.10 49	98.58 181	90.02 243	96.92 166

eth_miper_zixun_eth		92.41 218	91.93 215	93.84 263	97.28 209	90.68 265	98.83 253	96.97 283	88.57 259	89.19 250	95.73 257	89.24 176	96.69 287	89.97 244	81.55 288	94.15 261

Fast-Effi-MVS+		95.02 161	94.19 168	97.52 153	97.88 173	94.55 188	99.97 16	97.08 270	88.85 253	94.47 184	97.96 198	84.59 216	98.41 193	89.84 245	97.10 161	99.59 130

test_part1		87.55 288	85.69 292	93.15 277	95.35 265	88.21 300	98.30 283	97.70 209	71.40 341	83.19 310	95.62 260	62.37 332	97.28 253	89.77 246	77.47 320	93.96 279

Fast-Effi-MVS+-dtu		93.72 193	93.86 177	93.29 273	97.06 214	86.16 310	99.80 119	96.83 295	92.66 166	92.58 206	97.83 200	81.39 237	97.67 234	89.75 247	96.87 167	96.05 223

ACMH	89.72 17	90.64 252	89.63 253	93.66 268	95.64 259	88.64 294	98.55 268	97.45 238	89.03 245	81.62 315	97.61 203	69.75 309	98.41 193	89.37 248	87.62 248	93.92 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		92.10 224	91.07 231	95.18 212	92.82 307	94.96 179	99.48 184	96.83 295	87.45 272	88.66 259	96.56 238	83.78 222	96.83 281	89.29 249	84.77 268	93.75 294

PatchmatchNet		95.94 142	95.45 143	97.39 159	97.83 178	94.41 191	96.05 321	98.40 129	92.86 153	97.09 139	95.28 284	94.21 86	98.07 219	89.26 250	98.11 139	99.70 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH+	89.98 16	90.35 259	89.54 256	92.78 285	95.99 243	86.12 311	98.81 254	97.18 262	89.38 240	83.14 311	97.76 201	68.42 315	98.43 191	89.11 251	86.05 257	93.78 293

DP-MVS		94.54 174	93.42 187	97.91 140	99.46 102	94.04 197	98.93 243	97.48 237	81.15 321	90.04 225	99.55 106	87.02 196	99.95 60	88.97 252	98.11 139	99.73 108

PS-CasMVS		90.63 253	89.51 258	93.99 258	93.83 287	91.70 251	98.98 237	98.52 87	88.48 260	86.15 296	96.53 239	75.46 284	96.31 300	88.83 253	78.86 310	93.95 280

cl-mvsnet_		92.31 220	91.58 221	94.52 235	97.33 206	92.77 219	99.57 169	96.78 300	86.97 281	87.56 275	95.51 268	89.43 171	96.62 289	88.60 254	82.44 282	94.16 260

cl-mvsnet1		92.32 219	91.60 220	94.47 239	97.31 207	92.74 221	99.58 167	96.75 301	86.99 280	87.64 273	95.54 265	89.55 170	96.50 293	88.58 255	82.44 282	94.17 255

pmmvs5		90.17 266	89.09 265	93.40 271	92.10 315	89.77 282	99.74 137	95.58 325	85.88 293	87.24 282	95.74 255	73.41 297	96.48 294	88.54 256	83.56 278	93.95 280

LF4IMVS		89.25 280	88.85 269	90.45 306	92.81 308	81.19 334	98.12 290	94.79 335	91.44 207	86.29 294	97.11 215	65.30 325	98.11 216	88.53 257	85.25 263	92.07 320

JIA-IIPM		91.76 234	90.70 234	94.94 219	96.11 239	87.51 305	93.16 334	98.13 178	75.79 334	97.58 129	77.68 345	92.84 121	97.97 222	88.47 258	96.54 170	99.33 167

miper_lstm_enhance		91.81 228	91.39 227	93.06 281	97.34 204	89.18 288	99.38 196	96.79 299	86.70 284	87.47 277	95.22 285	90.00 165	95.86 315	88.26 259	81.37 290	94.15 261

WR-MVS_H		91.30 237	90.35 240	94.15 249	94.17 282	92.62 228	99.17 217	98.94 36	88.87 252	86.48 291	94.46 309	84.36 218	96.61 290	88.19 260	78.51 311	93.21 310

tpmvs		94.28 182	93.57 183	96.40 186	98.55 140	91.50 256	95.70 326	98.55 80	87.47 271	92.15 207	94.26 311	91.42 144	98.95 161	88.15 261	95.85 183	98.76 198

OurMVSNet-221017-0		89.81 271	89.48 260	90.83 302	91.64 320	81.21 333	98.17 289	95.38 328	91.48 205	85.65 300	97.31 210	72.66 298	97.29 251	88.15 261	84.83 267	93.97 278

TDRefinement		84.76 300	82.56 305	91.38 298	74.58 348	84.80 320	97.36 305	94.56 338	84.73 305	80.21 320	96.12 249	63.56 329	98.39 197	87.92 263	63.97 342	90.95 330

CMPMVS	61.59 21	84.75 301	85.14 295	83.57 323	90.32 331	62.54 347	96.98 310	97.59 223	74.33 338	69.95 340	96.66 233	64.17 327	98.32 205	87.88 264	88.41 240	89.84 336
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		86.90 290	85.98 291	89.67 311	84.45 341	75.59 341	89.71 343	92.43 344	86.89 282	77.83 327	90.94 330	94.22 83	93.63 335	87.75 265	69.61 335	99.79 100

GA-MVS		93.83 186	92.84 197	96.80 173	95.73 253	93.57 205	99.88 84	97.24 258	92.57 174	92.92 201	96.66 233	78.73 263	97.67 234	87.75 265	94.06 206	99.17 178

ADS-MVSNet2		93.80 189	93.88 176	93.55 270	97.87 175	85.94 312	94.24 327	96.84 294	90.07 233	96.43 155	94.48 307	90.29 163	95.37 319	87.44 267	97.23 158	99.36 163

ADS-MVSNet		94.79 164	94.02 172	97.11 168	97.87 175	93.79 201	94.24 327	98.16 173	90.07 233	96.43 155	94.48 307	90.29 163	98.19 214	87.44 267	97.23 158	99.36 163

v148		90.70 250	89.63 253	93.92 260	92.97 304	90.97 260	99.75 134	96.89 291	87.51 270	88.27 267	95.01 290	81.67 233	97.04 269	87.40 269	77.17 323	93.75 294

V42		91.28 239	90.12 248	94.74 225	93.42 295	93.46 208	99.68 150	97.02 275	87.36 273	89.85 232	95.05 288	81.31 239	97.34 245	87.34 270	80.07 304	93.40 304

v2v482		91.30 237	90.07 249	95.01 216	93.13 298	93.79 201	99.77 126	97.02 275	88.05 265	89.25 246	95.37 277	80.73 245	97.15 259	87.28 271	80.04 305	94.09 267

IterMVS		90.91 245	90.17 246	93.12 278	96.78 230	90.42 273	98.89 245	97.05 274	89.03 245	86.49 290	95.42 272	76.59 275	95.02 323	87.22 272	84.09 273	93.93 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		90.19 265	89.06 266	93.57 269	93.06 302	90.90 262	99.06 228	98.47 100	88.11 264	85.91 298	96.30 243	76.67 274	95.94 314	87.07 273	76.91 325	93.89 285

IterMVS-SCA-FT		90.85 248	90.16 247	92.93 282	96.72 232	89.96 278	98.89 245	96.99 279	88.95 250	86.63 287	95.67 258	76.48 276	95.00 324	87.04 274	84.04 276	93.84 289

tpm cat1		93.51 196	92.52 206	96.47 182	97.77 182	91.47 257	96.13 319	98.06 182	80.98 322	92.91 202	93.78 315	89.66 168	98.87 163	87.03 275	96.39 173	99.09 185

GBi-Net		90.88 246	89.82 251	94.08 252	97.53 196	91.97 238	98.43 275	96.95 284	87.05 277	89.68 234	94.72 298	71.34 303	96.11 306	87.01 276	85.65 259	94.17 255

test1		90.88 246	89.82 251	94.08 252	97.53 196	91.97 238	98.43 275	96.95 284	87.05 277	89.68 234	94.72 298	71.34 303	96.11 306	87.01 276	85.65 259	94.17 255

FMVSNet3		92.69 212	91.58 221	95.99 195	98.29 150	97.42 100	99.26 211	97.62 216	89.80 238	89.68 234	95.32 279	81.62 236	96.27 302	87.01 276	85.65 259	94.29 246

dp		95.05 160	94.43 165	96.91 170	97.99 168	92.73 223	96.29 318	97.98 188	89.70 239	95.93 164	94.67 302	93.83 97	98.45 190	86.91 279	96.53 171	99.54 143

MSDG		94.37 179	93.36 191	97.40 158	98.88 130	93.95 199	99.37 198	97.38 248	85.75 296	90.80 218	99.17 135	84.11 221	99.88 85	86.35 280	98.43 132	98.36 201

EU-MVSNet		90.14 267	90.34 241	89.54 312	92.55 310	81.06 335	98.69 262	98.04 184	91.41 210	86.59 288	96.84 230	80.83 244	93.31 338	86.20 281	81.91 286	94.26 248

pm-mvs1		89.36 277	87.81 284	94.01 256	93.40 296	91.93 241	98.62 267	96.48 310	86.25 289	83.86 308	96.14 247	73.68 296	97.04 269	86.16 282	75.73 330	93.04 313

COLMAP_ROB	90.47 14	92.18 223	91.49 225	94.25 247	99.00 117	88.04 303	98.42 278	96.70 303	82.30 318	88.43 263	99.01 144	76.97 271	99.85 94	86.11 283	96.50 172	94.86 224
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ITE_SJBPF				92.38 287	95.69 258	85.14 317		95.71 321	92.81 156	89.33 245	98.11 193	70.23 308	98.42 192	85.91 284	88.16 242	93.59 301

K. test v3		88.05 286	87.24 288	90.47 305	91.82 319	82.23 329	98.96 240	97.42 243	89.05 244	76.93 329	95.60 262	68.49 314	95.42 318	85.87 285	81.01 296	93.75 294

AllTest		92.48 216	91.64 219	95.00 217	99.01 115	88.43 296	98.94 242	96.82 297	86.50 285	88.71 256	98.47 187	74.73 290	99.88 85	85.39 286	96.18 175	96.71 217

TestCases				95.00 217	99.01 115	88.43 296		96.82 297	86.50 285	88.71 256	98.47 187	74.73 290	99.88 85	85.39 286	96.18 175	96.71 217

FMVSNet2		91.02 243	89.56 255	95.41 207	97.53 196	95.74 158	98.98 237	97.41 245	87.05 277	88.43 263	95.00 292	71.34 303	96.24 304	85.12 288	85.21 264	94.25 250

v1144		91.09 242	89.83 250	94.87 221	93.25 297	93.69 204	99.62 163	96.98 281	86.83 283	89.64 238	94.99 293	80.94 242	97.05 267	85.08 289	81.16 292	93.87 287

v8		90.54 255	89.17 263	94.66 228	93.43 294	93.40 211	99.20 214	96.94 287	85.76 294	87.56 275	94.51 305	81.96 232	97.19 256	84.94 290	78.25 312	93.38 306

ambc				83.23 324	77.17 347	62.61 346	87.38 345	94.55 339		76.72 330	86.65 338	30.16 350	96.36 298	84.85 291	69.86 334	90.73 331

MVS_0304		89.28 279	88.31 278	92.21 290	97.05 215	86.53 309	97.76 300	99.57 12	85.58 299	93.86 193	92.71 323	51.04 346	96.30 301	84.49 292	92.72 216	93.79 292

LTVRE_ROB	88.28 18	90.29 262	89.05 267	94.02 255	95.08 268	90.15 277	97.19 307	97.43 241	84.91 304	83.99 307	97.06 219	74.00 295	98.28 209	84.08 293	87.71 246	93.62 300
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
SixPastTwentyTwo		88.73 282	88.01 283	90.88 300	91.85 318	82.24 328	98.22 287	95.18 333	88.97 248	82.26 313	96.89 225	71.75 302	96.67 288	84.00 294	82.98 279	93.72 298

v144192		90.79 249	89.52 257	94.59 231	93.11 301	92.77 219	99.56 171	96.99 279	86.38 287	89.82 233	94.95 295	80.50 250	97.10 264	83.98 295	80.41 300	93.90 284

USDC		90.00 269	88.96 268	93.10 280	94.81 272	88.16 301	98.71 260	95.54 326	93.66 135	83.75 309	97.20 213	65.58 323	98.31 206	83.96 296	87.49 250	92.85 315

MVP-Stereo		90.93 244	90.45 239	92.37 288	91.25 325	88.76 290	98.05 294	96.17 314	87.27 275	84.04 306	95.30 280	78.46 266	97.27 254	83.78 297	99.70 94	91.09 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		90.65 251	90.30 242	91.71 296	94.22 281	85.50 315	98.24 285	97.70 209	88.67 256	86.42 292	96.37 242	67.82 317	98.03 220	83.62 298	99.62 98	91.60 325

DTE-MVSNet		89.40 276	88.24 280	92.88 283	92.66 309	89.95 279	99.10 220	98.22 163	87.29 274	85.12 303	96.22 245	76.27 279	95.30 322	83.56 299	75.74 329	93.41 303

pmmvs6		85.69 293	83.84 299	91.26 299	90.00 333	84.41 321	97.82 298	96.15 315	75.86 333	81.29 316	95.39 275	61.21 335	96.87 279	83.52 300	73.29 333	92.50 317

lessismore_v0				90.53 303	90.58 329	80.90 336		95.80 320		77.01 328	95.84 252	66.15 322	96.95 274	83.03 301	75.05 331	93.74 297

v10		90.25 263	88.82 270	94.57 233	93.53 292	93.43 209	99.08 223	96.87 293	85.00 303	87.34 281	94.51 305	80.93 243	97.02 273	82.85 302	79.23 307	93.26 308

DeepMVS_CX				82.92 325	95.98 245	58.66 349		96.01 317	92.72 161	78.34 326	95.51 268	58.29 339	98.08 217	82.57 303	85.29 262	92.03 322

PM-MVS		80.47 309	78.88 313	85.26 322	83.79 343	72.22 343	95.89 324	91.08 347	85.71 297	76.56 331	88.30 333	36.64 349	93.90 332	82.39 304	69.57 336	89.66 337

v1192		90.62 254	89.25 262	94.72 227	93.13 298	93.07 214	99.50 180	97.02 275	86.33 288	89.56 240	95.01 290	79.22 258	97.09 266	82.34 305	81.16 292	94.01 273

v1921920		90.46 256	89.12 264	94.50 237	92.96 305	92.46 230	99.49 182	96.98 281	86.10 290	89.61 239	95.30 280	78.55 265	97.03 271	82.17 306	80.89 298	94.01 273

MIMVSNet		90.30 261	88.67 273	95.17 213	96.45 235	91.64 253	92.39 336	97.15 266	85.99 291	90.50 220	93.19 321	66.95 319	94.86 327	82.01 307	93.43 210	99.01 188

UnsupCasMVSNet_eth		85.52 295	83.99 296	90.10 308	89.36 335	83.51 323	96.65 313	97.99 186	89.14 242	75.89 333	93.83 314	63.25 330	93.92 331	81.92 308	67.90 340	92.88 314

FMVSNet1		88.50 283	86.64 289	94.08 252	95.62 261	91.97 238	98.43 275	96.95 284	83.00 314	86.08 297	94.72 298	59.09 338	96.11 306	81.82 309	84.07 274	94.17 255

test0.0.03 1		93.86 185	93.61 179	94.64 229	95.02 270	92.18 236	99.93 61	98.58 72	94.07 115	87.96 270	98.50 182	93.90 94	94.96 325	81.33 310	93.17 213	96.78 216

v7n		89.65 274	88.29 279	93.72 265	92.22 313	90.56 269	99.07 227	97.10 268	85.42 302	86.73 285	94.72 298	80.06 253	97.13 261	81.14 311	78.12 314	93.49 302

pmmvs-eth3d		84.03 305	81.97 306	90.20 307	84.15 342	87.09 307	98.10 292	94.73 337	83.05 313	74.10 336	87.77 335	65.56 324	94.01 330	81.08 312	69.24 337	89.49 338

v1240		90.20 264	88.79 271	94.44 241	93.05 303	92.27 234	99.38 196	96.92 289	85.89 292	89.36 243	94.87 297	77.89 268	97.03 271	80.66 313	81.08 294	94.01 273

our_test_3		90.39 257	89.48 260	93.12 278	92.40 311	89.57 284	99.33 202	96.35 311	87.84 268	85.30 301	94.99 293	84.14 220	96.09 309	80.38 314	84.56 269	93.71 299

TinyColmap		87.87 287	86.51 290	91.94 293	95.05 269	85.57 314	97.65 301	94.08 340	84.40 307	81.82 314	96.85 228	62.14 333	98.33 204	80.25 315	86.37 256	91.91 324

Patchmtry		89.70 273	88.49 275	93.33 272	96.24 238	89.94 281	91.37 341	96.23 312	78.22 328	87.69 272	93.31 319	91.04 153	96.03 311	80.18 316	82.10 284	94.02 271

CR-MVSNet		93.45 199	92.62 201	95.94 197	96.29 236	92.66 225	92.01 338	96.23 312	92.62 168	96.94 141	93.31 319	91.04 153	96.03 311	79.23 317	95.96 180	99.13 183

EG-PatchMatch MVS		85.35 298	83.81 300	89.99 310	90.39 330	81.89 331	98.21 288	96.09 316	81.78 320	74.73 335	93.72 316	51.56 345	97.12 263	79.16 318	88.61 235	90.96 329

DSMNet-mixed		88.28 285	88.24 280	88.42 318	89.64 334	75.38 342	98.06 293	89.86 349	85.59 298	88.20 268	92.14 327	76.15 281	91.95 339	78.46 319	96.05 178	97.92 207

UnsupCasMVSNet_bld		79.97 312	77.03 315	88.78 316	85.62 340	81.98 330	93.66 332	97.35 250	75.51 336	70.79 339	83.05 342	48.70 347	94.91 326	78.31 320	60.29 345	89.46 339

EPNet_dtu		95.71 148	95.39 145	96.66 179	98.92 125	93.41 210	99.57 169	98.90 40	96.19 51	97.52 130	98.56 181	92.65 126	97.36 243	77.89 321	98.33 134	99.20 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testgi		89.01 281	88.04 282	91.90 294	93.49 293	84.89 319	99.73 142	95.66 323	93.89 128	85.14 302	98.17 192	59.68 337	94.66 329	77.73 322	88.88 229	96.16 222

Patchmatch-test		92.65 214	91.50 224	96.10 194	96.85 224	90.49 270	91.50 340	97.19 260	82.76 316	90.23 222	95.59 263	95.02 54	98.00 221	77.41 323	96.98 165	99.82 97

YYNet1		85.50 297	83.33 301	92.00 292	90.89 327	88.38 299	99.22 213	96.55 307	79.60 326	57.26 346	92.72 322	79.09 261	93.78 334	77.25 324	77.37 322	93.84 289

MDA-MVSNet_test_wron		85.51 296	83.32 302	92.10 291	90.96 326	88.58 295	99.20 214	96.52 308	79.70 325	57.12 347	92.69 324	79.11 260	93.86 333	77.10 325	77.46 321	93.86 288

tfpnnormal		89.29 278	87.61 285	94.34 245	94.35 279	94.13 196	98.95 241	98.94 36	83.94 308	84.47 305	95.51 268	74.84 289	97.39 242	77.05 326	80.41 300	91.48 326

TransMVSNet (Re)		87.25 289	85.28 294	93.16 276	93.56 291	91.03 259	98.54 270	94.05 341	83.69 312	81.09 317	96.16 246	75.32 285	96.40 296	76.69 327	68.41 338	92.06 321

FMVSNet5		88.32 284	87.47 286	90.88 300	96.90 222	88.39 298	97.28 306	95.68 322	82.60 317	84.67 304	92.40 326	79.83 255	91.16 340	76.39 328	81.51 289	93.09 311

ppachtmachnet_test		89.58 275	88.35 277	93.25 275	92.40 311	90.44 272	99.33 202	96.73 302	85.49 300	85.90 299	95.77 254	81.09 241	96.00 313	76.00 329	82.49 281	93.30 307

MVS-HIRNet		86.22 292	83.19 303	95.31 209	96.71 233	90.29 274	92.12 337	97.33 252	62.85 344	86.82 284	70.37 347	69.37 310	97.49 239	75.12 330	97.99 145	98.15 204

MDA-MVSNet-bldmvs		84.09 304	81.52 309	91.81 295	91.32 324	88.00 304	98.67 264	95.92 319	80.22 324	55.60 348	93.32 318	68.29 316	93.60 336	73.76 331	76.61 327	93.82 291

new_pmnet		84.49 303	82.92 304	89.21 313	90.03 332	82.60 325	96.89 312	95.62 324	80.59 323	75.77 334	89.17 332	65.04 326	94.79 328	72.12 332	81.02 295	90.23 333

new-patchmatchnet		81.19 308	79.34 312	86.76 321	82.86 344	80.36 339	97.92 296	95.27 330	82.09 319	72.02 337	86.87 337	62.81 331	90.74 342	71.10 333	63.08 343	89.19 340

pmmvs3		80.27 310	77.77 314	87.76 319	80.32 346	82.43 327	98.23 286	91.97 345	72.74 340	78.75 324	87.97 334	57.30 341	90.99 341	70.31 334	62.37 344	89.87 335

TAPA-MVS	92.12 8	94.42 178	93.60 181	96.90 171	99.33 106	91.78 246	99.78 123	98.00 185	89.89 237	94.52 182	99.47 112	91.97 139	99.18 154	69.90 335	99.52 106	99.73 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet		67.77 314	64.73 318	76.87 327	62.95 354	56.25 351	89.37 344	93.74 343	44.53 348	61.99 343	80.74 343	20.42 356	86.53 346	69.37 336	59.50 346	87.84 341

OpenMVS_ROB	79.82 20	83.77 306	81.68 308	90.03 309	88.30 337	82.82 324	98.46 273	95.22 331	73.92 339	76.00 332	91.29 329	55.00 342	96.94 275	68.40 337	88.51 239	90.34 332

N_pmnet		80.06 311	80.78 310	77.89 326	91.94 316	45.28 355	98.80 255	56.82 358	78.10 329	80.08 321	93.33 317	77.03 270	95.76 316	68.14 338	82.81 280	92.64 316

Anonymous20231206		86.32 291	85.42 293	89.02 314	89.11 336	80.53 338	99.05 232	95.28 329	85.43 301	82.82 312	93.92 313	74.40 292	93.44 337	66.99 339	81.83 287	93.08 312

test20.03		84.72 302	83.99 296	86.91 320	88.19 338	80.62 337	98.88 247	95.94 318	88.36 262	78.87 323	94.62 303	68.75 312	89.11 344	66.52 340	75.82 328	91.00 328

PatchT		90.38 258	88.75 272	95.25 210	95.99 243	90.16 276	91.22 342	97.54 227	76.80 330	97.26 135	86.01 340	91.88 140	96.07 310	66.16 341	95.91 182	99.51 147

test_0402		85.58 294	83.94 298	90.50 304	93.81 288	85.04 318	98.55 268	95.20 332	76.01 332	79.72 322	95.13 286	64.15 328	96.26 303	66.04 342	86.88 253	90.21 334

MIMVSNet1		82.58 307	80.51 311	88.78 316	86.68 339	84.20 322	96.65 313	95.41 327	78.75 327	78.59 325	92.44 325	51.88 344	89.76 343	65.26 343	78.95 308	92.38 319

RPMNet		89.76 272	87.28 287	97.19 165	96.29 236	92.66 225	92.01 338	98.31 150	70.19 343	96.94 141	85.87 341	87.25 193	99.78 111	62.69 344	95.96 180	99.13 183

FPMVS		68.72 313	68.72 316	68.71 331	65.95 352	44.27 357	95.97 323	94.74 336	51.13 346	53.26 349	90.50 331	25.11 354	83.00 348	60.80 345	80.97 297	78.87 344

PMMVS2		67.15 315	64.15 319	76.14 328	70.56 351	62.07 348	93.89 330	87.52 353	58.09 345	60.02 344	78.32 344	22.38 355	84.54 347	59.56 346	47.03 347	81.80 343

testmvs		40.60 323	44.45 326	29.05 338	19.49 360	14.11 361	99.68 150	18.47 359	20.74 354	64.59 341	98.48 186	10.95 359	17.09 357	56.66 347	11.01 353	55.94 350

Gipuma		66.95 316	65.00 317	72.79 329	91.52 322	67.96 345	66.16 350	95.15 334	47.89 347	58.54 345	67.99 349	29.74 351	87.54 345	50.20 348	77.83 316	62.87 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test123		37.68 324	39.14 327	33.31 337	19.94 359	24.83 360	98.36 279	9.75 360	15.53 355	51.31 350	87.14 336	19.62 357	17.74 356	47.10 349	3.47 355	57.36 349

ANet_high		56.10 318	52.24 321	67.66 332	49.27 356	56.82 350	83.94 346	82.02 354	70.47 342	33.28 355	64.54 350	17.23 358	69.16 352	45.59 350	23.85 351	77.02 345

PMVS	49.05 23	53.75 319	51.34 323	60.97 334	40.80 358	34.68 358	74.82 349	89.62 351	37.55 350	28.67 356	72.12 346	7.09 360	81.63 349	43.17 351	68.21 339	66.59 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	53.74 22	51.54 321	47.86 325	62.60 333	59.56 355	50.93 352	79.41 348	77.69 355	35.69 352	36.27 354	61.76 353	5.79 362	69.63 351	37.97 352	36.61 348	67.24 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		52.30 320	52.18 322	52.67 335	71.51 349	45.40 354	93.62 333	76.60 356	36.01 351	43.50 352	64.13 351	27.11 353	67.31 353	31.06 353	26.06 349	45.30 352

EMVS		51.44 322	51.22 324	52.11 336	70.71 350	44.97 356	94.04 329	75.66 357	35.34 353	42.40 353	61.56 354	28.93 352	65.87 354	27.64 354	24.73 350	45.49 351

wuyk23d		20.37 326	20.84 329	18.99 339	65.34 353	27.73 359	50.43 351	7.67 361	9.50 356	8.01 357	6.34 357	6.13 361	26.24 355	23.40 355	10.69 354	2.99 353

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		23.43 325	31.24 328	0.00 340	0.00 361	0.00 362	0.00 352	98.09 179	0.00 357	0.00 358	99.67 95	83.37 225	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		7.60 328	10.13 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	91.20 148	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		8.28 327	11.04 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	99.40 118	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

test_241102_ONE					99.93 26	99.30 8		98.43 113	97.26 22	99.80 16	99.88 22	96.71 20	100.00 1

save fliter					99.82 65	98.79 33	99.96 23	98.40 129	97.66 10

test0726					99.93 26	99.29 10	99.96 23	98.42 124	97.28 18	99.86 4	99.94 4	97.22 15

GSMVS																99.59 130

test_part2					99.89 45	99.25 13				99.49 48

sam_mvs1												94.72 64				99.59 130

sam_mvs												94.25 82

MTGPA								98.28 155

test_post											63.35 352	94.43 69	98.13 215

patchmatchnet-post											91.70 328	95.12 48	97.95 225

MTMP							99.87 87	96.49 309

TEST9					99.92 35	98.92 23	99.96 23	98.43 113	93.90 126	99.71 30	99.86 29	95.88 34	99.85 94

test_8					99.92 35	98.88 26	99.96 23	98.43 113	94.35 103	99.69 32	99.85 33	95.94 31	99.85 94

agg_prior					99.93 26	98.77 36		98.43 113		99.63 35			99.85 94

test_prior4						98.05 71	99.94 55

test_prior				99.43 35	99.94 14	98.49 57		98.65 59					99.80 106			99.99 20

新几何2							99.40 191

旧先验1					99.76 74	97.52 91		98.64 62			99.85 33	95.63 39			99.94 57	99.99 20

原ACMM2							99.90 73

test222					99.55 94	97.41 101	99.34 201	98.55 80	91.86 194	99.27 68	99.83 49	93.84 96			99.95 51	99.99 20

segment_acmp												96.68 22

testdata1							99.28 209		96.35 48

test12				99.43 35	99.74 77	98.56 53		98.40 129		99.65 33		94.76 63	99.75 120		99.98 33	99.99 20

plane_prior7					95.71 256	91.59 255

plane_prior6					95.76 251	91.72 250						80.47 251

plane_prior4											98.59 177

plane_prior3						91.64 253			96.63 38	93.01 199

plane_prior2							99.84 105		96.38 44

plane_prior1					95.73 253

plane_prior						91.74 247	99.86 98		96.76 34						89.59 221

n2								0.00 362

nn								0.00 362

door-mid								89.69 350

test11								98.44 105

door								90.31 348

HQP5-MVS						91.85 243

HQP-NCC					95.78 247		99.87 87		96.82 30	93.37 195

ACMP_Plane					95.78 247		99.87 87		96.82 30	93.37 195

HQP4-MVS										93.37 195			98.39 197			94.53 225

HQP3-MVS								97.89 197							89.60 219

HQP2-MVS												80.65 247

NP-MVS					95.77 250	91.79 245					98.65 173

ACMMP++_ref															87.04 252

ACMMP++															88.23 241

Test By Simon												92.82 123

High-resolution multi-view benchmark