High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

APDe-MVS		99.02 3	98.84 2	99.55 6	99.57 33	98.96 12	99.39 5	98.93 37	97.38 24	99.41 11	99.54 1	96.66 13	99.84 52	98.86 1	99.85 3	99.87 1

CANet		98.05 56	97.76 61	98.90 71	98.73 129	97.27 91	98.35 175	98.78 95	97.37 26	97.72 113	98.96 93	91.53 138	99.92 21	98.79 2	99.65 58	99.51 89

Regformer-4		98.64 14	98.53 11	98.99 63	99.43 57	97.37 87	98.40 171	98.79 92	97.46 19	99.09 30	99.31 35	95.86 42	99.80 79	98.64 3	99.76 32	99.79 10

VDD-MVS		95.82 156	95.23 166	97.61 156	98.84 123	93.98 230	98.68 129	97.40 291	95.02 133	97.95 98	99.34 31	74.37 342	99.78 95	98.64 3	96.80 182	99.08 147

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 78	99.46 51	96.49 125	98.30 186	98.69 117	97.21 36	98.84 46	99.36 26	95.41 54	99.78 95	98.62 5	99.65 58	99.80 9

Regformer-3		98.59 20	98.50 14	98.86 73	99.43 57	97.05 101	98.40 171	98.68 120	97.43 20	99.06 31	99.31 35	95.80 43	99.77 100	98.62 5	99.76 32	99.78 13

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 83	99.45 55	96.32 133	98.28 189	98.68 120	97.17 39	98.74 53	99.37 22	95.25 66	99.79 91	98.57 7	99.54 84	99.73 36

CHOSEN 280x420		97.18 106	97.18 89	97.20 173	98.81 125	93.27 256	95.78 330	99.15 18	95.25 120	96.79 154	98.11 178	92.29 115	99.07 188	98.56 8	99.85 3	99.25 126

xiu_mvs_v1_base_debu		97.60 78	97.56 69	97.72 145	98.35 157	95.98 143	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base		97.60 78	97.56 69	97.72 145	98.35 157	95.98 143	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base_debi		97.60 78	97.56 69	97.72 145	98.35 157	95.98 143	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

VNet		97.79 69	97.40 81	98.96 67	98.88 118	97.55 81	98.63 137	98.93 37	96.74 55	99.02 34	98.84 106	90.33 162	99.83 55	98.53 9	96.66 186	99.50 91

MSLP-MVS++		98.56 28	98.57 8	98.55 87	99.26 85	96.80 110	98.71 122	99.05 24	97.28 29	98.84 46	99.28 40	96.47 18	99.40 154	98.52 13	99.70 51	99.47 98

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 74	99.22 94	97.25 95	98.11 214	98.29 204	97.19 38	98.99 38	99.02 80	96.22 20	99.67 121	98.52 13	98.56 135	99.51 89

DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 68	98.58 146	97.62 11	99.45 9	99.46 9	97.42 6	99.94 3	98.47 15	99.81 10	99.69 51
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									97.32 27	99.45 9	99.46 9	97.88 1	99.94 3	98.47 15	99.86 1	99.85 2

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 68	98.88 49					99.94 3	98.47 15	99.81 10	99.84 4

SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 58	98.87 55	97.65 9	99.73 1	99.48 6	97.53 4	99.94 3	98.43 18	99.81 10	99.70 48

test_241102_TWO								98.87 55	97.65 9	99.53 8	99.48 6	97.34 8	99.94 3	98.43 18	99.80 17	99.83 5

Regformer-1		98.66 12	98.51 13	99.12 57	99.35 60	97.81 74	98.37 173	98.76 99	97.49 17	99.20 22	99.21 48	96.08 29	99.79 91	98.42 20	99.73 43	99.75 28

DELS-MVS		98.40 42	98.20 44	98.99 63	99.00 109	97.66 76	97.75 247	98.89 46	97.71 8	98.33 78	98.97 87	94.97 74	99.88 43	98.42 20	99.76 32	99.42 107
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
Regformer-2		98.69 11	98.52 12	99.19 43	99.35 60	98.01 62	98.37 173	98.81 76	97.48 18	99.21 21	99.21 48	96.13 27	99.80 79	98.40 22	99.73 43	99.75 28

alignmvs		97.56 84	97.07 94	99.01 62	98.66 138	98.37 41	98.83 94	98.06 247	96.74 55	98.00 96	97.65 218	90.80 153	99.48 149	98.37 23	96.56 190	99.19 132

IU-MVS					99.71 20	99.23 6		98.64 136	95.28 118	99.63 4				98.35 24	99.81 10	99.83 5

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 40	99.69 25	98.28 48	99.14 36	98.66 131	96.84 51	99.56 5	99.31 35	96.34 19	99.70 114	98.32 25	99.73 43	99.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	96.37 2	97.93 63	98.48 17	96.30 242	99.00 109	89.54 312	97.43 264	98.87 55	98.16 2	99.26 18	99.38 21	96.12 28	99.64 125	98.30 26	99.77 26	99.72 40

canonicalmvs		97.67 74	97.23 87	98.98 65	98.70 134	98.38 35	99.34 11	98.39 184	96.76 54	97.67 116	97.40 239	92.26 116	99.49 145	98.28 27	96.28 202	99.08 147

SD-MVS		98.64 14	98.68 5	98.53 91	99.33 65	98.36 42	98.90 78	98.85 64	97.28 29	99.72 3	99.39 14	96.63 15	97.60 318	98.17 28	99.85 3	99.64 70
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
diffmvs		97.58 82	97.40 81	98.13 120	98.32 164	95.81 160	98.06 217	98.37 187	96.20 75	98.74 53	98.89 101	91.31 143	99.25 164	98.16 29	98.52 136	99.34 111

casdiffmvs		97.63 77	97.41 80	98.28 108	98.33 162	96.14 140	98.82 97	98.32 194	96.38 70	97.95 98	99.21 48	91.23 145	99.23 167	98.12 30	98.37 144	99.48 96

baseline		97.64 76	97.44 79	98.25 112	98.35 157	96.20 137	99.00 62	98.32 194	96.33 72	98.03 89	99.17 56	91.35 141	99.16 173	98.10 31	98.29 149	99.39 108

MP-MVS-pluss		98.31 52	97.92 57	99.49 9	99.72 12	98.88 14	98.43 167	98.78 95	94.10 166	97.69 115	99.42 12	95.25 66	99.92 21	98.09 32	99.80 17	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		98.58 23	98.25 38	99.56 5	99.51 39	99.04 11	98.95 72	98.80 87	93.67 196	99.37 13	99.52 3	96.52 17	99.89 35	98.06 33	99.81 10	99.76 26
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
CNVR-MVS		98.78 6	98.56 9	99.45 14	99.32 68	98.87 15	98.47 161	98.81 76	97.72 6	98.76 52	99.16 61	97.05 10	99.78 95	98.06 33	99.66 57	99.69 51

MVS_111021_HR		98.47 38	98.34 28	98.88 72	99.22 94	97.32 88	97.91 231	99.58 3	97.20 37	98.33 78	99.00 85	95.99 35	99.64 125	98.05 35	99.76 32	99.69 51

VDDNet		95.36 179	94.53 196	97.86 134	98.10 181	95.13 185	98.85 90	97.75 264	90.46 295	98.36 76	99.39 14	73.27 344	99.64 125	97.98 36	96.58 189	98.81 166

MCST-MVS		98.65 13	98.37 21	99.48 10	99.60 31	98.87 15	98.41 170	98.68 120	97.04 46	98.52 67	98.80 110	96.78 12	99.83 55	97.93 37	99.61 67	99.74 33

zzz-MVS		98.55 30	98.25 38	99.46 12	99.76 1	98.64 22	98.55 151	98.74 103	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

MTAPA		98.58 23	98.29 35	99.46 12	99.76 1	98.64 22	98.90 78	98.74 103	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

MVS_111021_LR		98.34 48	98.23 42	98.67 80	99.27 83	96.90 107	97.95 227	99.58 3	97.14 41	98.44 72	99.01 84	95.03 73	99.62 130	97.91 38	99.75 38	99.50 91

ACMMP_NAP		98.61 17	98.30 34	99.55 6	99.62 30	98.95 13	98.82 97	98.81 76	95.80 90	99.16 26	99.47 8	95.37 57	99.92 21	97.89 41	99.75 38	99.79 10

PS-MVSNAJ		97.73 71	97.77 60	97.62 155	98.68 137	95.58 165	97.34 273	98.51 161	97.29 28	98.66 60	97.88 197	94.51 85	99.90 33	97.87 42	99.17 109	97.39 217

test1172		98.56 28	98.35 24	99.16 50	99.53 36	97.94 66	99.09 46	98.83 68	96.52 64	99.05 32	99.34 31	95.34 59	99.82 63	97.86 43	99.64 62	99.73 36

XVS		98.70 9	98.49 16	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	99.20 52	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

X-MVStestdata		94.06 262	92.30 282	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	43.50 357	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

xiu_mvs_v2_base		97.66 75	97.70 63	97.56 159	98.61 143	95.46 171	97.44 262	98.46 171	97.15 40	98.65 61	98.15 175	94.33 91	99.80 79	97.84 46	98.66 131	97.41 215

DeepC-MVS	95.98 3	97.88 64	97.58 67	98.77 75	99.25 86	96.93 105	98.83 94	98.75 102	96.96 49	96.89 148	99.50 4	90.46 159	99.87 44	97.84 46	99.76 32	99.52 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSP-MVS		98.74 8	98.55 10	99.29 31	99.75 3	98.23 49	99.26 18	98.88 49	97.52 15	99.41 11	98.78 112	96.00 34	99.79 91	97.79 48	99.59 71	99.85 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CP-MVS		98.57 26	98.36 22	99.19 43	99.66 27	97.86 68	99.34 11	98.87 55	95.96 85	98.60 64	99.13 64	96.05 32	99.94 3	97.77 49	99.86 1	99.77 20

SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 21	99.22 94	98.43 33	99.10 45	98.87 55	97.38 24	99.35 14	99.40 13	97.78 3	99.87 44	97.77 49	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 53	99.50 41	97.92 67	99.15 35	98.81 76	96.24 73	99.20 22	99.37 22	95.30 62	99.80 79	97.73 51	99.67 54	99.72 40

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 54	99.49 45	97.86 68	99.11 42	98.80 87	96.49 65	99.17 24	99.35 28	95.34 59	99.82 63	97.72 52	99.65 58	99.71 44

RE-MVS-def			98.34 28		99.49 45	97.86 68	99.11 42	98.80 87	96.49 65	99.17 24	99.35 28	95.29 63		97.72 52	99.65 58	99.71 44

xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 30	99.46 51	98.38 35	98.21 195	98.52 158	97.95 3	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

SF-MVS		98.59 20	98.32 33	99.41 16	99.54 35	98.71 18	99.04 53	98.81 76	95.12 127	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

LFMVS		95.86 153	94.98 178	98.47 96	98.87 119	96.32 133	98.84 93	96.02 327	93.40 206	98.62 62	99.20 52	74.99 338	99.63 128	97.72 52	97.20 176	99.46 102

SR-MVS		98.57 26	98.35 24	99.24 40	99.53 36	98.18 53	99.09 46	98.82 70	96.58 61	99.10 29	99.32 33	95.39 55	99.82 63	97.70 57	99.63 64	99.72 40

PHI-MVS		98.34 48	98.06 49	99.18 47	99.15 101	98.12 58	99.04 53	99.09 20	93.32 209	98.83 48	99.10 69	96.54 16	99.83 55	97.70 57	99.76 32	99.59 80

HPM-MVS		98.36 45	98.10 48	99.13 54	99.74 7	97.82 72	99.53 1	98.80 87	94.63 151	98.61 63	98.97 87	95.13 70	99.77 100	97.65 59	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 169	98.91 43	97.58 14	99.54 7	99.46 9	97.10 9	99.94 3	97.64 60	99.84 8	99.83 5

ETV-MVS		97.96 58	97.81 59	98.40 103	98.42 153	97.27 91	98.73 117	98.55 151	96.84 51	98.38 75	97.44 236	95.39 55	99.35 158	97.62 61	98.89 118	98.58 184

CS-MVS		97.81 67	97.61 65	98.41 102	98.52 149	97.15 99	99.09 46	98.55 151	96.18 76	97.61 122	97.20 251	94.59 83	99.39 155	97.62 61	99.10 111	98.70 172

HFP-MVS		98.63 16	98.40 18	99.32 28	99.72 12	98.29 46	99.23 21	98.96 32	96.10 82	98.94 39	99.17 56	96.06 30	99.92 21	97.62 61	99.78 23	99.75 28

ACMMPR		98.59 20	98.36 22	99.29 31	99.74 7	98.15 56	99.23 21	98.95 34	96.10 82	98.93 43	99.19 55	95.70 44	99.94 3	97.62 61	99.79 19	99.78 13

jason		97.32 99	97.08 93	98.06 125	97.45 228	95.59 164	97.87 237	97.91 258	94.79 142	98.55 66	98.83 107	91.12 146	99.23 167	97.58 65	99.60 68	99.34 111
jason: jason.
lupinMVS		97.44 91	97.22 88	98.12 122	98.07 182	95.76 161	97.68 251	97.76 263	94.50 156	98.79 49	98.61 127	92.34 113	99.30 161	97.58 65	99.59 71	99.31 117

HPM-MVS_fast		98.38 43	98.13 46	99.12 57	99.75 3	97.86 68	99.44 4	98.82 70	94.46 158	98.94 39	99.20 52	95.16 69	99.74 106	97.58 65	99.85 3	99.77 20

ZNCC-MVS		98.49 36	98.20 44	99.35 22	99.73 11	98.39 34	99.19 31	98.86 61	95.77 91	98.31 80	99.10 69	95.46 51	99.93 15	97.57 68	99.81 10	99.74 33

region2R		98.61 17	98.38 20	99.29 31	99.74 7	98.16 55	99.23 21	98.93 37	96.15 77	98.94 39	99.17 56	95.91 39	99.94 3	97.55 69	99.79 19	99.78 13

DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 47	99.25 86	98.04 60	98.50 158	98.78 95	97.72 6	98.92 44	99.28 40	95.27 64	99.82 63	97.55 69	99.77 26	99.69 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		98.58 23	98.25 38	99.55 6	99.50 41	99.08 9	98.72 121	98.66 131	97.51 16	98.15 81	98.83 107	95.70 44	99.92 21	97.53 71	99.67 54	99.66 65

nrg030		96.28 139	95.72 143	97.96 131	96.90 264	98.15 56	99.39 5	98.31 196	95.47 106	94.42 214	98.35 155	92.09 123	98.69 229	97.50 72	89.05 298	97.04 227

CSCG		97.85 66	97.74 62	98.20 115	99.67 26	95.16 181	99.22 25	99.32 7	93.04 219	97.02 141	98.92 99	95.36 58	99.91 30	97.43 73	99.64 62	99.52 85

mPP-MVS		98.51 35	98.26 37	99.25 39	99.75 3	98.04 60	99.28 16	98.81 76	96.24 73	98.35 77	99.23 45	95.46 51	99.94 3	97.42 74	99.81 10	99.77 20

mvs_anonymous		96.70 123	96.53 121	97.18 175	98.19 173	93.78 235	98.31 184	98.19 215	94.01 171	94.47 208	98.27 167	92.08 124	98.46 252	97.39 75	97.91 157	99.31 117

EIA-MVS		97.75 70	97.58 67	98.27 109	98.38 155	96.44 127	99.01 60	98.60 139	95.88 87	97.26 130	97.53 229	94.97 74	99.33 160	97.38 76	99.20 107	99.05 149

NCCC		98.61 17	98.35 24	99.38 17	99.28 82	98.61 24	98.45 162	98.76 99	97.82 5	98.45 71	98.93 97	96.65 14	99.83 55	97.38 76	99.41 97	99.71 44

VPA-MVSNet		95.75 158	95.11 172	97.69 149	97.24 239	97.27 91	98.94 74	99.23 12	95.13 126	95.51 185	97.32 242	85.73 253	98.91 208	97.33 78	89.55 291	96.89 243

OPU-MVS				99.37 20	99.24 92	99.05 10	99.02 58				99.16 61	97.81 2	99.37 157	97.24 79	99.73 43	99.70 48

3Dnovator	94.51 5	97.46 87	96.93 100	99.07 60	97.78 199	97.64 77	99.35 10	99.06 22	97.02 47	93.75 246	99.16 61	89.25 177	99.92 21	97.22 80	99.75 38	99.64 70

#test#		98.54 32	98.27 36	99.32 28	99.72 12	98.29 46	98.98 67	98.96 32	95.65 98	98.94 39	99.17 56	96.06 30	99.92 21	97.21 81	99.78 23	99.75 28

ACMMP		98.23 54	97.95 55	99.09 59	99.74 7	97.62 79	99.03 55	99.41 6	95.98 84	97.60 124	99.36 26	94.45 89	99.93 15	97.14 82	98.85 122	99.70 48
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
PVSNet_Blended_VisFu		97.70 73	97.46 77	98.44 98	99.27 83	95.91 156	98.63 137	99.16 17	94.48 157	97.67 116	98.88 102	92.80 108	99.91 30	97.11 83	99.12 110	99.50 91

mvs_tets		95.41 175	95.00 176	96.65 208	95.58 316	94.42 217	99.00 62	98.55 151	95.73 93	93.21 264	98.38 152	83.45 292	98.63 236	97.09 84	94.00 230	96.91 240

GST-MVS		98.43 40	98.12 47	99.34 23	99.72 12	98.38 35	99.09 46	98.82 70	95.71 94	98.73 55	99.06 78	95.27 64	99.93 15	97.07 85	99.63 64	99.72 40

9.14			98.06 49		99.47 48		98.71 122	98.82 70	94.36 160	99.16 26	99.29 39	96.05 32	99.81 70	97.00 86	99.71 50

EPNet		97.28 100	96.87 103	98.51 92	94.98 326	96.14 140	98.90 78	97.02 308	98.28 1	95.99 181	99.11 67	91.36 140	99.89 35	96.98 87	99.19 108	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		96.90 117	96.49 122	98.14 118	99.33 65	95.56 166	97.38 267	99.65 2	92.34 244	97.61 122	98.20 172	89.29 176	99.10 185	96.97 88	97.60 170	99.77 20

3Dnovator+	94.38 6	97.43 92	96.78 107	99.38 17	97.83 197	98.52 27	99.37 7	98.71 113	97.09 45	92.99 272	99.13 64	89.36 174	99.89 35	96.97 88	99.57 75	99.71 44

abl_6		98.30 53	98.03 51	99.13 54	99.56 34	97.76 75	99.13 39	98.82 70	96.14 78	99.26 18	99.37 22	93.33 102	99.93 15	96.96 90	99.67 54	99.69 51

jajsoiax		95.45 171	95.03 175	96.73 202	95.42 323	94.63 207	99.14 36	98.52 158	95.74 92	93.22 263	98.36 154	83.87 288	98.65 235	96.95 91	94.04 228	96.91 240

ET-MVSNet_ETH3D		94.13 255	92.98 270	97.58 157	98.22 169	96.20 137	97.31 276	95.37 335	94.53 153	79.56 346	97.63 222	86.51 239	97.53 321	96.91 92	90.74 276	99.02 151

MVSFormer		97.57 83	97.49 75	97.84 135	98.07 182	95.76 161	99.47 2	98.40 182	94.98 134	98.79 49	98.83 107	92.34 113	98.41 264	96.91 92	99.59 71	99.34 111

test_djsdf		96.00 147	95.69 148	96.93 191	95.72 312	95.49 170	99.47 2	98.40 182	94.98 134	94.58 204	97.86 199	89.16 180	98.41 264	96.91 92	94.12 227	96.88 244

test_prior3		98.22 55	97.90 58	99.19 43	99.31 70	98.22 50	97.80 243	98.84 65	96.12 80	97.89 105	98.69 119	95.96 36	99.70 114	96.89 95	99.60 68	99.65 67

test_prior2							97.80 243		96.12 80	97.89 105	98.69 119	95.96 36		96.89 95	99.60 68

EPP-MVSNet		97.46 87	97.28 85	97.99 128	98.64 140	95.38 173	99.33 13	98.31 196	93.61 199	97.19 132	99.07 77	94.05 95	99.23 167	96.89 95	98.43 143	99.37 110

PS-MVSNAJss		96.43 132	96.26 129	96.92 193	95.84 310	95.08 187	99.16 34	98.50 166	95.87 88	93.84 242	98.34 159	94.51 85	98.61 237	96.88 98	93.45 243	97.06 226

PVSNet_BlendedMVS		96.73 122	96.60 117	97.12 179	99.25 86	95.35 176	98.26 192	99.26 8	94.28 161	97.94 100	97.46 233	92.74 109	99.81 70	96.88 98	93.32 246	96.20 310

PVSNet_Blended		97.38 96	97.12 90	98.14 118	99.25 86	95.35 176	97.28 278	99.26 8	93.13 217	97.94 100	98.21 171	92.74 109	99.81 70	96.88 98	99.40 99	99.27 124

Effi-MVS+		97.12 109	96.69 113	98.39 104	98.19 173	96.72 114	97.37 269	98.43 178	93.71 189	97.65 119	98.02 183	92.20 120	99.25 164	96.87 101	97.79 162	99.19 132

CHOSEN 1792x2688		97.12 109	96.80 104	98.08 123	99.30 75	94.56 214	98.05 218	99.71 1	93.57 200	97.09 135	98.91 100	88.17 206	99.89 35	96.87 101	99.56 80	99.81 8

test_yl		97.22 102	96.78 107	98.54 89	98.73 129	96.60 119	98.45 162	98.31 196	94.70 144	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

DCV-MVSNet		97.22 102	96.78 107	98.54 89	98.73 129	96.60 119	98.45 162	98.31 196	94.70 144	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

ETH3D-3000-0.1		98.35 46	98.00 53	99.38 17	99.47 48	98.68 21	98.67 132	98.84 65	94.66 150	99.11 28	99.25 43	95.46 51	99.81 70	96.80 105	99.73 43	99.63 73

PGM-MVS		98.49 36	98.23 42	99.27 38	99.72 12	98.08 59	98.99 64	99.49 5	95.43 108	99.03 33	99.32 33	95.56 47	99.94 3	96.80 105	99.77 26	99.78 13

RRT_test8_iter05		94.56 228	94.19 215	95.67 267	97.60 211	91.34 286	98.93 75	98.42 179	94.75 143	93.39 258	97.87 198	79.00 317	98.61 237	96.78 107	90.99 274	97.07 225

XVG-OURS-SEG-HR		96.51 130	96.34 125	97.02 184	98.77 127	93.76 236	97.79 245	98.50 166	95.45 107	96.94 143	99.09 74	87.87 216	99.55 141	96.76 108	95.83 212	97.74 208

MP-MVS		98.33 50	98.01 52	99.28 35	99.75 3	98.18 53	99.22 25	98.79 92	96.13 79	97.92 103	99.23 45	94.54 84	99.94 3	96.74 109	99.78 23	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
agg_prior1		97.95 61	97.51 74	99.28 35	99.30 75	98.38 35	97.81 242	98.72 109	93.16 216	97.57 125	98.66 124	96.14 26	99.81 70	96.63 110	99.56 80	99.66 65

train_agg		97.97 57	97.52 72	99.33 27	99.31 70	98.50 29	97.92 229	98.73 107	92.98 221	97.74 111	98.68 121	96.20 23	99.80 79	96.59 111	99.57 75	99.68 57

MVSTER		96.06 144	95.72 143	97.08 182	98.23 168	95.93 154	98.73 117	98.27 205	94.86 140	95.07 190	98.09 179	88.21 204	98.54 245	96.59 111	93.46 241	96.79 253

bset_n11_16_dypcd		94.89 208	94.27 211	96.76 200	94.41 333	95.15 183	95.67 331	95.64 334	95.53 102	94.65 202	97.52 230	87.10 229	98.29 280	96.58 113	91.35 266	96.83 251

UGNet		96.78 121	96.30 127	98.19 117	98.24 167	95.89 158	98.88 85	98.93 37	97.39 23	96.81 152	97.84 202	82.60 294	99.90 33	96.53 114	99.49 88	98.79 167
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
APD-MVS		98.35 46	98.00 53	99.42 15	99.51 39	98.72 17	98.80 104	98.82 70	94.52 155	99.23 20	99.25 43	95.54 49	99.80 79	96.52 115	99.77 26	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPNet		94.99 200	94.19 215	97.40 167	97.16 248	96.57 121	98.71 122	98.97 30	95.67 96	94.84 196	98.24 170	80.36 309	98.67 233	96.46 116	87.32 318	96.96 232

ETH3D cwj APD-0.16		97.96 58	97.52 72	99.29 31	99.05 105	98.52 27	98.33 178	98.68 120	93.18 214	98.68 57	99.13 64	94.62 81	99.83 55	96.45 117	99.55 83	99.52 85

sss		97.39 95	96.98 99	98.61 83	98.60 144	96.61 118	98.22 194	98.93 37	93.97 174	98.01 94	98.48 141	91.98 126	99.85 49	96.45 117	98.15 151	99.39 108

MVS_Test		97.28 100	97.00 97	98.13 120	98.33 162	95.97 148	98.74 113	98.07 242	94.27 162	98.44 72	98.07 180	92.48 111	99.26 163	96.43 119	98.19 150	99.16 137

FIs		96.51 130	96.12 133	97.67 151	97.13 250	97.54 82	99.36 8	99.22 14	95.89 86	94.03 234	98.35 155	91.98 126	98.44 255	96.40 120	92.76 253	97.01 228

test9_res														96.39 121	99.57 75	99.69 51

Anonymous20240529		95.10 194	94.22 213	97.75 143	99.01 108	94.26 224	98.87 87	98.83 68	85.79 335	96.64 157	98.97 87	78.73 318	99.85 49	96.27 122	94.89 216	99.12 142

PMMVS		96.60 125	96.33 126	97.41 165	97.90 193	93.93 231	97.35 272	98.41 180	92.84 228	97.76 109	97.45 235	91.10 148	99.20 170	96.26 123	97.91 157	99.11 143

CLD-MVS		95.62 165	95.34 160	96.46 232	97.52 221	93.75 238	97.27 279	98.46 171	95.53 102	94.42 214	98.00 186	86.21 246	98.97 198	96.25 124	94.37 217	96.66 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous202405211		95.28 184	94.49 198	97.67 151	99.00 109	93.75 238	98.70 126	97.04 305	90.66 291	96.49 168	98.80 110	78.13 323	99.83 55	96.21 125	95.36 215	99.44 105

RRT_MVS		96.04 145	95.53 151	97.56 159	97.07 254	97.32 88	98.57 148	98.09 238	95.15 125	95.02 192	98.44 144	88.20 205	98.58 243	96.17 126	93.09 250	96.79 253

ZD-MVS					99.46 51	98.70 19		98.79 92	93.21 213	98.67 58	98.97 87	95.70 44	99.83 55	96.07 127	99.58 74

HQP_MVS		96.14 142	95.90 139	96.85 196	97.42 229	94.60 212	98.80 104	98.56 149	97.28 29	95.34 186	98.28 164	87.09 230	99.03 193	96.07 127	94.27 219	96.92 235

plane_prior5								98.56 149					99.03 193	96.07 127	94.27 219	96.92 235

CPTT-MVS		97.72 72	97.32 84	98.92 69	99.64 28	97.10 100	99.12 41	98.81 76	92.34 244	98.09 84	99.08 76	93.01 106	99.92 21	96.06 130	99.77 26	99.75 28

DP-MVS Recon		97.86 65	97.46 77	99.06 61	99.53 36	98.35 43	98.33 178	98.89 46	92.62 233	98.05 86	98.94 96	95.34 59	99.65 123	96.04 131	99.42 96	99.19 132

FC-MVSNet-test		96.42 133	96.05 134	97.53 161	96.95 259	97.27 91	99.36 8	99.23 12	95.83 89	93.93 236	98.37 153	92.00 125	98.32 273	96.02 132	92.72 254	97.00 229

Vis-MVSNet		97.42 93	97.11 91	98.34 106	98.66 138	96.23 136	99.22 25	99.00 27	96.63 60	98.04 88	99.21 48	88.05 211	99.35 158	96.01 133	99.21 106	99.45 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ab-mvs		96.42 133	95.71 146	98.55 87	98.63 141	96.75 113	97.88 236	98.74 103	93.84 180	96.54 165	98.18 174	85.34 261	99.75 104	95.93 134	96.35 196	99.15 138

WTY-MVS		97.37 97	96.92 101	98.72 77	98.86 120	96.89 109	98.31 184	98.71 113	95.26 119	97.67 116	98.56 135	92.21 119	99.78 95	95.89 135	96.85 181	99.48 96

XVG-OURS		96.55 129	96.41 123	96.99 185	98.75 128	93.76 236	97.50 261	98.52 158	95.67 96	96.83 149	99.30 38	88.95 190	99.53 142	95.88 136	96.26 203	97.69 211

agg_prior2														95.87 137	99.57 75	99.68 57

UniMVSNet_NR-MVSNet		95.71 160	95.15 169	97.40 167	96.84 267	96.97 103	98.74 113	99.24 10	95.16 124	93.88 239	97.72 213	91.68 131	98.31 275	95.81 138	87.25 319	96.92 235

DU-MVS		95.42 173	94.76 186	97.40 167	96.53 282	96.97 103	98.66 135	98.99 29	95.43 108	93.88 239	97.69 214	88.57 196	98.31 275	95.81 138	87.25 319	96.92 235

testtj		98.33 50	97.95 55	99.47 11	99.49 45	98.70 19	98.83 94	98.86 61	95.48 105	98.91 45	99.17 56	95.48 50	99.93 15	95.80 140	99.53 85	99.76 26

UniMVSNet (Re)		95.78 157	95.19 168	97.58 157	96.99 258	97.47 84	98.79 108	99.18 16	95.60 99	93.92 237	97.04 268	91.68 131	98.48 249	95.80 140	87.66 314	96.79 253

cascas		94.63 223	93.86 238	96.93 191	96.91 263	94.27 223	96.00 327	98.51 161	85.55 336	94.54 205	96.23 309	84.20 281	98.87 215	95.80 140	96.98 180	97.66 212

Effi-MVS+-dtu		96.29 137	96.56 118	95.51 270	97.89 194	90.22 305	98.80 104	98.10 234	96.57 62	96.45 171	96.66 293	90.81 151	98.91 208	95.72 143	97.99 155	97.40 216

mvs-test1		96.60 125	96.68 115	96.37 237	97.89 194	91.81 276	98.56 149	98.10 234	96.57 62	96.52 167	97.94 191	90.81 151	99.45 152	95.72 143	98.01 154	97.86 205

LPG-MVS_test		95.62 165	95.34 160	96.47 229	97.46 224	93.54 245	98.99 64	98.54 154	94.67 148	94.36 216	98.77 114	85.39 258	99.11 182	95.71 145	94.15 225	96.76 257

LGP-MVS_train				96.47 229	97.46 224	93.54 245		98.54 154	94.67 148	94.36 216	98.77 114	85.39 258	99.11 182	95.71 145	94.15 225	96.76 257

旧先验2							97.57 259		91.30 279	98.67 58			99.80 79	95.70 147

LCM-MVSNet-Re		95.22 187	95.32 163	94.91 288	98.18 175	87.85 335	98.75 110	95.66 333	95.11 128	88.96 323	96.85 286	90.26 164	97.65 316	95.65 148	98.44 141	99.22 128

anonymousdsp		95.42 173	94.91 181	96.94 190	95.10 325	95.90 157	99.14 36	98.41 180	93.75 184	93.16 265	97.46 233	87.50 224	98.41 264	95.63 149	94.03 229	96.50 296

CDPH-MVS		97.94 62	97.49 75	99.28 35	99.47 48	98.44 31	97.91 231	98.67 128	92.57 236	98.77 51	98.85 104	95.93 38	99.72 108	95.56 150	99.69 52	99.68 57

CostFormer		94.95 204	94.73 188	95.60 269	97.28 237	89.06 319	97.53 260	96.89 316	89.66 311	96.82 151	96.72 291	86.05 249	98.95 205	95.53 151	96.13 208	98.79 167

ACMM	93.85 9	95.69 162	95.38 158	96.61 213	97.61 210	93.84 234	98.91 77	98.44 175	95.25 120	94.28 220	98.47 142	86.04 251	99.12 179	95.50 152	93.95 232	96.87 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	93.49 10	95.34 181	94.98 178	96.43 234	97.67 206	93.48 248	98.73 117	98.44 175	94.94 139	92.53 285	98.53 136	84.50 275	99.14 177	95.48 153	94.00 230	96.66 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tttt0517		96.07 143	95.51 153	97.78 140	98.41 154	94.84 198	99.28 16	94.33 346	94.26 163	97.64 120	98.64 126	84.05 283	99.47 150	95.34 154	97.60 170	99.03 150

TAMVS		97.02 112	96.79 106	97.70 148	98.06 184	95.31 178	98.52 153	98.31 196	93.95 175	97.05 140	98.61 127	93.49 101	98.52 247	95.33 155	97.81 161	99.29 122

BP-MVS														95.30 156

HQP-MVS		95.72 159	95.40 154	96.69 206	97.20 243	94.25 225	98.05 218	98.46 171	96.43 67	94.45 209	97.73 211	86.75 236	98.96 201	95.30 156	94.18 223	96.86 248

thisisatest0530		96.01 146	95.36 159	97.97 129	98.38 155	95.52 169	98.88 85	94.19 348	94.04 168	97.64 120	98.31 162	83.82 290	99.46 151	95.29 158	97.70 167	98.93 160

WR-MVS		95.15 191	94.46 201	97.22 172	96.67 277	96.45 126	98.21 195	98.81 76	94.15 164	93.16 265	97.69 214	87.51 222	98.30 277	95.29 158	88.62 304	96.90 242

tpmrst		95.63 164	95.69 148	95.44 274	97.54 218	88.54 327	96.97 295	97.56 274	93.50 202	97.52 127	96.93 281	89.49 170	99.16 173	95.25 160	96.42 195	98.64 180

CDS-MVSNet		96.99 113	96.69 113	97.90 133	98.05 185	95.98 143	98.20 198	98.33 193	93.67 196	96.95 142	98.49 140	93.54 100	98.42 257	95.24 161	97.74 165	99.31 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OPM-MVS		95.69 162	95.33 162	96.76 200	96.16 299	94.63 207	98.43 167	98.39 184	96.64 59	95.02 192	98.78 112	85.15 263	99.05 189	95.21 162	94.20 222	96.60 277

OMC-MVS		97.55 85	97.34 83	98.20 115	99.33 65	95.92 155	98.28 189	98.59 141	95.52 104	97.97 97	99.10 69	93.28 104	99.49 145	95.09 163	98.88 119	99.19 132

UniMVSNet_ETH3D		94.24 248	93.33 264	96.97 188	97.19 246	93.38 253	98.74 113	98.57 147	91.21 285	93.81 243	98.58 132	72.85 345	98.77 226	95.05 164	93.93 233	98.77 169

CANet_DTU		96.96 114	96.55 119	98.21 114	98.17 177	96.07 142	97.98 225	98.21 212	97.24 35	97.13 134	98.93 97	86.88 235	99.91 30	95.00 165	99.37 101	98.66 178

UA-Net		97.96 58	97.62 64	98.98 65	98.86 120	97.47 84	98.89 82	99.08 21	96.67 58	98.72 56	99.54 1	93.15 105	99.81 70	94.87 166	98.83 123	99.65 67

114514_t		96.93 115	96.27 128	98.92 69	99.50 41	97.63 78	98.85 90	98.90 44	84.80 338	97.77 108	99.11 67	92.84 107	99.66 122	94.85 167	99.77 26	99.47 98

Anonymous20231211		94.10 258	93.26 267	96.61 213	99.11 104	94.28 222	99.01 60	98.88 49	86.43 329	92.81 275	97.57 226	81.66 300	98.68 232	94.83 168	89.02 300	96.88 244

XXY-MVS		95.20 189	94.45 203	97.46 162	96.75 272	96.56 122	98.86 89	98.65 135	93.30 211	93.27 262	98.27 167	84.85 268	98.87 215	94.82 169	91.26 270	96.96 232

MG-MVS		97.81 67	97.60 66	98.44 98	99.12 103	95.97 148	97.75 247	98.78 95	96.89 50	98.46 68	99.22 47	93.90 99	99.68 120	94.81 170	99.52 87	99.67 61

test_part1		94.82 210	93.82 240	97.82 138	98.84 123	97.82 72	99.03 55	98.81 76	92.31 248	92.51 287	97.89 196	81.96 297	98.67 233	94.80 171	88.24 307	96.98 230

EI-MVSNet		95.96 148	95.83 141	96.36 238	97.93 191	93.70 242	98.12 212	98.27 205	93.70 191	95.07 190	99.02 80	92.23 118	98.54 245	94.68 172	93.46 241	96.84 249

thisisatest0515		95.61 167	94.89 182	97.76 142	98.15 178	95.15 183	96.77 311	94.41 344	92.95 223	97.18 133	97.43 237	84.78 269	99.45 152	94.63 173	97.73 166	98.68 175

IterMVS-LS		95.46 169	95.21 167	96.22 245	98.12 179	93.72 241	98.32 183	98.13 229	93.71 189	94.26 221	97.31 243	92.24 117	98.10 292	94.63 173	90.12 282	96.84 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.25 141	95.73 142	97.79 139	97.13 250	95.55 168	98.19 202	98.59 141	93.47 203	92.03 298	97.82 206	91.33 142	99.49 145	94.62 175	98.44 141	98.32 194

baseline1		95.84 154	95.12 171	98.01 127	98.49 151	95.98 143	98.73 117	97.03 306	95.37 113	96.22 175	98.19 173	89.96 167	99.16 173	94.60 176	87.48 315	98.90 162

IS-MVSNet		97.22 102	96.88 102	98.25 112	98.85 122	96.36 131	99.19 31	97.97 252	95.39 110	97.23 131	98.99 86	91.11 147	98.93 206	94.60 176	98.59 133	99.47 98

NR-MVSNet		94.98 202	94.16 218	97.44 163	96.53 282	97.22 96	98.74 113	98.95 34	94.96 136	89.25 322	97.69 214	89.32 175	98.18 286	94.59 178	87.40 317	96.92 235

IB-MVS	91.98 17	93.27 275	91.97 286	97.19 174	97.47 223	93.41 251	97.09 290	95.99 328	93.32 209	92.47 289	95.73 319	78.06 324	99.53 142	94.59 178	82.98 333	98.62 181
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
DWT-MVSNet_test		94.82 210	94.36 208	96.20 246	97.35 234	90.79 297	98.34 176	96.57 326	92.91 225	95.33 188	96.44 303	82.00 296	99.12 179	94.52 180	95.78 213	98.70 172

HY-MVS	93.96 8	96.82 120	96.23 131	98.57 85	98.46 152	97.00 102	98.14 209	98.21 212	93.95 175	96.72 155	97.99 187	91.58 133	99.76 102	94.51 181	96.54 191	98.95 159

D2MVS		95.18 190	95.08 173	95.48 271	97.10 252	92.07 272	98.30 186	99.13 19	94.02 170	92.90 273	96.73 290	89.48 171	98.73 228	94.48 182	93.60 240	95.65 322

Baseline_NR-MVSNet		94.35 241	93.81 241	95.96 255	96.20 295	94.05 229	98.61 140	96.67 324	91.44 272	93.85 241	97.60 223	88.57 196	98.14 289	94.39 183	86.93 322	95.68 321

AdaColmap		97.15 108	96.70 112	98.48 95	99.16 99	96.69 115	98.01 222	98.89 46	94.44 159	96.83 149	98.68 121	90.69 156	99.76 102	94.36 184	99.29 105	98.98 155

AUN-MVS		94.53 231	93.73 249	96.92 193	98.50 150	93.52 247	98.34 176	98.10 234	93.83 182	95.94 183	97.98 188	85.59 256	99.03 193	94.35 185	80.94 340	98.22 196

1112_ss		96.63 124	96.00 137	98.50 93	98.56 145	96.37 130	98.18 206	98.10 234	92.92 224	94.84 196	98.43 145	92.14 121	99.58 133	94.35 185	96.51 192	99.56 84

CP-MVSNet		94.94 206	94.30 210	96.83 197	96.72 274	95.56 166	99.11 42	98.95 34	93.89 177	92.42 291	97.90 194	87.19 228	98.12 291	94.32 187	88.21 308	96.82 252

CNLPA		97.45 90	97.03 95	98.73 76	99.05 105	97.44 86	98.07 216	98.53 156	95.32 116	96.80 153	98.53 136	93.32 103	99.72 108	94.31 188	99.31 104	99.02 151

testdata				98.26 111	99.20 97	95.36 174		98.68 120	91.89 259	98.60 64	99.10 69	94.44 90	99.82 63	94.27 189	99.44 95	99.58 82

PVSNet	91.96 18	96.35 135	96.15 132	96.96 189	99.17 98	92.05 273	96.08 323	98.68 120	93.69 192	97.75 110	97.80 208	88.86 191	99.69 119	94.26 190	99.01 113	99.15 138

miper_enhance_ethall		95.10 194	94.75 187	96.12 250	97.53 220	93.73 240	96.61 317	98.08 240	92.20 253	93.89 238	96.65 295	92.44 112	98.30 277	94.21 191	91.16 271	96.34 304

Test_1112_low_res		96.34 136	95.66 150	98.36 105	98.56 145	95.94 151	97.71 249	98.07 242	92.10 254	94.79 200	97.29 244	91.75 130	99.56 136	94.17 192	96.50 193	99.58 82

TranMVSNet+NR-MVSNet		95.14 192	94.48 199	97.11 180	96.45 287	96.36 131	99.03 55	99.03 25	95.04 132	93.58 249	97.93 192	88.27 203	98.03 299	94.13 193	86.90 324	96.95 234

API-MVS		97.41 94	97.25 86	97.91 132	98.70 134	96.80 110	98.82 97	98.69 117	94.53 153	98.11 83	98.28 164	94.50 88	99.57 134	94.12 194	99.49 88	97.37 219

ETH3 D test6400		97.59 81	97.01 96	99.34 23	99.40 59	98.56 25	98.20 198	98.81 76	91.63 267	98.44 72	98.85 104	93.98 98	99.82 63	94.11 195	99.69 52	99.64 70

cl-mvsnet2		94.68 218	94.19 215	96.13 249	98.11 180	93.60 243	96.94 297	98.31 196	92.43 241	93.32 261	96.87 285	86.51 239	98.28 282	94.10 196	91.16 271	96.51 294

PLC	95.07 4	97.20 105	96.78 107	98.44 98	99.29 78	96.31 135	98.14 209	98.76 99	92.41 242	96.39 172	98.31 162	94.92 76	99.78 95	94.06 197	98.77 126	99.23 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
XVG-ACMP-BASELINE		94.54 230	94.14 220	95.75 265	96.55 281	91.65 282	98.11 214	98.44 175	94.96 136	94.22 224	97.90 194	79.18 316	99.11 182	94.05 198	93.85 234	96.48 298

F-COLMAP		97.09 111	96.80 104	97.97 129	99.45 55	94.95 195	98.55 151	98.62 138	93.02 220	96.17 177	98.58 132	94.01 96	99.81 70	93.95 199	98.90 117	99.14 140

MDTV_nov1_ep13_2view						84.26 343	96.89 305		90.97 289	97.90 104		89.89 168		93.91 200		99.18 136

baseline2		95.11 193	94.52 197	96.87 195	96.65 278	93.56 244	98.27 191	94.10 350	93.45 204	92.02 299	97.43 237	87.45 226	99.19 171	93.88 201	97.41 174	97.87 204

原ACMM1				98.65 81	99.32 68	96.62 116		98.67 128	93.27 212	97.81 107	98.97 87	95.18 68	99.83 55	93.84 202	99.46 93	99.50 91

RPSCF		94.87 209	95.40 154	93.26 318	98.89 117	82.06 349	98.33 178	98.06 247	90.30 300	96.56 161	99.26 42	87.09 230	99.49 145	93.82 203	96.32 198	98.24 195

PAPM_NR		97.46 87	97.11 91	98.50 93	99.50 41	96.41 129	98.63 137	98.60 139	95.18 123	97.06 139	98.06 181	94.26 93	99.57 134	93.80 204	98.87 121	99.52 85

ACMH	92.88 16	94.55 229	93.95 232	96.34 240	97.63 209	93.26 257	98.81 103	98.49 170	93.43 205	89.74 318	98.53 136	81.91 298	99.08 187	93.69 205	93.30 247	96.70 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_ehance_all_eth		95.01 198	94.69 190	95.97 254	97.70 205	93.31 255	97.02 293	98.07 242	92.23 250	93.51 254	96.96 277	91.85 128	98.15 288	93.68 206	91.16 271	96.44 301

MAR-MVS		96.91 116	96.40 124	98.45 97	98.69 136	96.90 107	98.66 135	98.68 120	92.40 243	97.07 138	97.96 189	91.54 137	99.75 104	93.68 206	98.92 116	98.69 174
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
Vis-MVSNet (Re-imp)		96.87 118	96.55 119	97.83 136	98.73 129	95.46 171	99.20 29	98.30 202	94.96 136	96.60 160	98.87 103	90.05 165	98.59 241	93.67 208	98.60 132	99.46 102

LS3D		97.16 107	96.66 116	98.68 79	98.53 148	97.19 97	98.93 75	98.90 44	92.83 229	95.99 181	99.37 22	92.12 122	99.87 44	93.67 208	99.57 75	98.97 156

PS-CasMVS		94.67 221	93.99 230	96.71 203	96.68 276	95.26 179	99.13 39	99.03 25	93.68 194	92.33 292	97.95 190	85.35 260	98.10 292	93.59 210	88.16 310	96.79 253

cl_fuxian		94.79 213	94.43 205	95.89 259	97.75 200	93.12 262	97.16 287	98.03 249	92.23 250	93.46 257	97.05 267	91.39 139	98.01 300	93.58 211	89.21 296	96.53 288

CVMVSNet		95.43 172	96.04 135	93.57 313	97.93 191	83.62 344	98.12 212	98.59 141	95.68 95	96.56 161	99.02 80	87.51 222	97.51 322	93.56 212	97.44 172	99.60 78

OurMVSNet-221017-0		94.21 249	94.00 228	94.85 291	95.60 315	89.22 317	98.89 82	97.43 289	95.29 117	92.18 295	98.52 139	82.86 293	98.59 241	93.46 213	91.76 262	96.74 259

eth_miper_zixun_eth		94.68 218	94.41 206	95.47 272	97.64 208	91.71 281	96.73 314	98.07 242	92.71 231	93.64 247	97.21 250	90.54 158	98.17 287	93.38 214	89.76 286	96.54 286

OpenMVS	93.04 13	95.83 155	95.00 176	98.32 107	97.18 247	97.32 88	99.21 28	98.97 30	89.96 305	91.14 306	99.05 79	86.64 238	99.92 21	93.38 214	99.47 90	97.73 209

无先验							97.58 258	98.72 109	91.38 273				99.87 44	93.36 216		99.60 78

1121		97.37 97	96.77 111	99.16 50	99.34 62	97.99 65	98.19 202	98.68 120	90.14 303	98.01 94	98.97 87	94.80 79	99.87 44	93.36 216	99.46 93	99.61 75

gm-plane-assit					95.88 308	87.47 336			89.74 310		96.94 280		99.19 171	93.32 218

WR-MVS_H		95.05 197	94.46 201	96.81 198	96.86 266	95.82 159	99.24 20	99.24 10	93.87 179	92.53 285	96.84 287	90.37 160	98.24 284	93.24 219	87.93 311	96.38 303

tpm		94.13 255	93.80 242	95.12 282	96.50 284	87.91 334	97.44 262	95.89 332	92.62 233	96.37 173	96.30 306	84.13 282	98.30 277	93.24 219	91.66 264	99.14 140

Fast-Effi-MVS+-dtu		95.87 152	95.85 140	95.91 257	97.74 203	91.74 280	98.69 128	98.15 226	95.56 101	94.92 194	97.68 217	88.98 188	98.79 224	93.19 221	97.78 163	97.20 223

pmmvs5		93.65 269	92.97 271	95.68 266	95.49 319	92.37 268	98.20 198	97.28 296	89.66 311	92.58 283	97.26 245	82.14 295	98.09 294	93.18 222	90.95 275	96.58 279

TESTMET0.1,1		94.18 253	93.69 252	95.63 268	96.92 261	89.12 318	96.91 300	94.78 341	93.17 215	94.88 195	96.45 302	78.52 319	98.92 207	93.09 223	98.50 138	98.85 163

test-LLR		95.10 194	94.87 183	95.80 262	96.77 269	89.70 309	96.91 300	95.21 336	95.11 128	94.83 198	95.72 321	87.71 218	98.97 198	93.06 224	98.50 138	98.72 170

test-mter		94.08 260	93.51 259	95.80 262	96.77 269	89.70 309	96.91 300	95.21 336	92.89 226	94.83 198	95.72 321	77.69 326	98.97 198	93.06 224	98.50 138	98.72 170

BH-untuned		95.95 149	95.72 143	96.65 208	98.55 147	92.26 269	98.23 193	97.79 262	93.73 187	94.62 203	98.01 185	88.97 189	99.00 197	93.04 226	98.51 137	98.68 175

EPMVS		94.99 200	94.48 199	96.52 225	97.22 241	91.75 279	97.23 280	91.66 354	94.11 165	97.28 129	96.81 288	85.70 254	98.84 218	93.04 226	97.28 175	98.97 156

pmmvs4		94.69 216	93.99 230	96.81 198	95.74 311	95.94 151	97.40 265	97.67 267	90.42 297	93.37 259	97.59 224	89.08 183	98.20 285	92.97 228	91.67 263	96.30 308

v2v482		94.69 216	94.03 224	96.65 208	96.17 297	94.79 203	98.67 132	98.08 240	92.72 230	94.00 235	97.16 253	87.69 221	98.45 253	92.91 229	88.87 302	96.72 262

Fast-Effi-MVS+		96.28 139	95.70 147	98.03 126	98.29 166	95.97 148	98.58 143	98.25 210	91.74 262	95.29 189	97.23 248	91.03 150	99.15 176	92.90 230	97.96 156	98.97 156

V42		94.78 214	94.14 220	96.70 205	96.33 292	95.22 180	98.97 68	98.09 238	92.32 246	94.31 219	97.06 265	88.39 201	98.55 244	92.90 230	88.87 302	96.34 304

DP-MVS		96.59 127	95.93 138	98.57 85	99.34 62	96.19 139	98.70 126	98.39 184	89.45 314	94.52 206	99.35 28	91.85 128	99.85 49	92.89 232	98.88 119	99.68 57

TDRefinement		91.06 297	89.68 302	95.21 279	85.35 353	91.49 285	98.51 157	97.07 303	91.47 270	88.83 326	97.84 202	77.31 330	99.09 186	92.79 233	77.98 342	95.04 332

ACMH+	92.99 14	94.30 244	93.77 245	95.88 260	97.81 198	92.04 274	98.71 122	98.37 187	93.99 173	90.60 312	98.47 142	80.86 306	99.05 189	92.75 234	92.40 256	96.55 285

cl-mvsnet_		94.51 233	94.01 227	96.02 251	97.58 213	93.40 252	97.05 291	97.96 254	91.73 264	92.76 277	97.08 261	89.06 184	98.13 290	92.61 235	90.29 281	96.52 291

cl-mvsnet1		94.52 232	94.03 224	95.99 252	97.57 217	93.38 253	97.05 291	97.94 255	91.74 262	92.81 275	97.10 255	89.12 181	98.07 296	92.60 236	90.30 280	96.53 288

DPM-MVS		97.55 85	96.99 98	99.23 42	99.04 107	98.55 26	97.17 286	98.35 190	94.85 141	97.93 102	98.58 132	95.07 72	99.71 113	92.60 236	99.34 102	99.43 106

test_post1							96.68 315				30.43 361	87.85 217	98.69 229	92.59 238

SCA		95.46 169	95.13 170	96.46 232	97.67 206	91.29 290	97.33 274	97.60 272	94.68 147	96.92 146	97.10 255	83.97 285	98.89 212	92.59 238	98.32 148	99.20 129

v148		94.29 245	93.76 247	95.91 257	96.10 300	92.93 264	98.58 143	97.97 252	92.59 235	93.47 256	96.95 279	88.53 199	98.32 273	92.56 240	87.06 321	96.49 297

PEN-MVS		94.42 238	93.73 249	96.49 227	96.28 293	94.84 198	99.17 33	99.00 27	93.51 201	92.23 294	97.83 205	86.10 248	97.90 308	92.55 241	86.92 323	96.74 259

Patchmatch-RL test		91.49 293	90.85 294	93.41 314	91.37 347	84.40 342	92.81 347	95.93 331	91.87 260	87.25 331	94.87 329	88.99 185	96.53 338	92.54 242	82.00 335	99.30 120

miper_lstm_enhance		94.33 242	94.07 223	95.11 283	97.75 200	90.97 294	97.22 281	98.03 249	91.67 266	92.76 277	96.97 275	90.03 166	97.78 314	92.51 243	89.64 288	96.56 283

IterMVS		94.09 259	93.85 239	94.80 294	97.99 188	90.35 304	97.18 284	98.12 230	93.68 194	92.46 290	97.34 240	84.05 283	97.41 323	92.51 243	91.33 267	96.62 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		94.11 257	93.87 237	94.85 291	97.98 190	90.56 302	97.18 284	98.11 232	93.75 184	92.58 283	97.48 232	83.97 285	97.41 323	92.48 245	91.30 268	96.58 279

tpm2		94.19 251	93.76 247	95.46 273	97.23 240	89.04 320	97.31 276	96.85 319	87.08 326	96.21 176	96.79 289	83.75 291	98.74 227	92.43 246	96.23 205	98.59 182

PVSNet_0	88.72 19	91.28 295	90.03 300	95.00 286	97.99 188	87.29 338	94.84 340	98.50 166	92.06 255	89.86 317	95.19 326	79.81 312	99.39 155	92.27 247	69.79 349	98.33 193

gg-mvs-nofinetune		92.21 289	90.58 296	97.13 178	96.75 272	95.09 186	95.85 328	89.40 357	85.43 337	94.50 207	81.98 351	80.80 307	98.40 270	92.16 248	98.33 147	97.88 203

pm-mvs1		93.94 265	93.06 269	96.59 216	96.49 285	95.16 181	98.95 72	98.03 249	92.32 246	91.08 307	97.84 202	84.54 274	98.41 264	92.16 248	86.13 330	96.19 311

K. test v3		92.55 286	91.91 288	94.48 303	95.64 314	89.24 316	99.07 50	94.88 340	94.04 168	86.78 333	97.59 224	77.64 329	97.64 317	92.08 250	89.43 293	96.57 281

GBi-Net		94.49 234	93.80 242	96.56 220	98.21 170	95.00 189	98.82 97	98.18 218	92.46 237	94.09 230	97.07 262	81.16 301	97.95 304	92.08 250	92.14 257	96.72 262

test1		94.49 234	93.80 242	96.56 220	98.21 170	95.00 189	98.82 97	98.18 218	92.46 237	94.09 230	97.07 262	81.16 301	97.95 304	92.08 250	92.14 257	96.72 262

FMVSNet3		94.97 203	94.26 212	97.11 180	98.18 175	96.62 116	98.56 149	98.26 209	93.67 196	94.09 230	97.10 255	84.25 278	98.01 300	92.08 250	92.14 257	96.70 266

PatchmatchNet		95.71 160	95.52 152	96.29 243	97.58 213	90.72 299	96.84 309	97.52 280	94.06 167	97.08 136	96.96 277	89.24 178	98.90 211	92.03 254	98.37 144	99.26 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
QAPM		96.29 137	95.40 154	98.96 67	97.85 196	97.60 80	99.23 21	98.93 37	89.76 309	93.11 269	99.02 80	89.11 182	99.93 15	91.99 255	99.62 66	99.34 111

新几何1				99.16 50	99.34 62	98.01 62		98.69 117	90.06 304	98.13 82	98.95 95	94.60 82	99.89 35	91.97 256	99.47 90	99.59 80

MDTV_nov1_ep13			95.40 154		97.48 222	88.34 329	96.85 308	97.29 295	93.74 186	97.48 128	97.26 245	89.18 179	99.05 189	91.92 257	97.43 173

EU-MVSNet		93.66 267	94.14 220	92.25 324	95.96 306	83.38 345	98.52 153	98.12 230	94.69 146	92.61 282	98.13 177	87.36 227	96.39 340	91.82 258	90.00 284	96.98 230

GA-MVS		94.81 212	94.03 224	97.14 177	97.15 249	93.86 233	96.76 312	97.58 273	94.00 172	94.76 201	97.04 268	80.91 304	98.48 249	91.79 259	96.25 204	99.09 144

PatchMatch-RL		96.59 127	96.03 136	98.27 109	99.31 70	96.51 124	97.91 231	99.06 22	93.72 188	96.92 146	98.06 181	88.50 200	99.65 123	91.77 260	99.00 114	98.66 178

v1144		94.59 226	93.92 233	96.60 215	96.21 294	94.78 204	98.59 141	98.14 228	91.86 261	94.21 225	97.02 270	87.97 212	98.41 264	91.72 261	89.57 289	96.61 276

v8		94.47 236	93.77 245	96.57 219	96.36 290	94.83 200	99.05 52	98.19 215	91.92 258	93.16 265	96.97 275	88.82 193	98.48 249	91.69 262	87.79 312	96.39 302

testdata2													99.89 35	91.65 263

BH-w/o		95.38 176	95.08 173	96.26 244	98.34 161	91.79 277	97.70 250	97.43 289	92.87 227	94.24 223	97.22 249	88.66 194	98.84 218	91.55 264	97.70 167	98.16 198

LF4IMVS		93.14 280	92.79 274	94.20 308	95.88 308	88.67 325	97.66 253	97.07 303	93.81 183	91.71 301	97.65 218	77.96 325	98.81 222	91.47 265	91.92 261	95.12 329

JIA-IIPM		93.35 272	92.49 279	95.92 256	96.48 286	90.65 300	95.01 336	96.96 310	85.93 333	96.08 178	87.33 348	87.70 220	98.78 225	91.35 266	95.58 214	98.34 192

FMVSNet2		94.47 236	93.61 255	97.04 183	98.21 170	96.43 128	98.79 108	98.27 205	92.46 237	93.50 255	97.09 259	81.16 301	98.00 302	91.09 267	91.93 260	96.70 266

v144192		94.39 240	93.70 251	96.48 228	96.06 302	94.35 221	98.58 143	98.16 225	91.45 271	94.33 218	97.02 270	87.50 224	98.45 253	91.08 268	89.11 297	96.63 274

tpmvs		94.60 224	94.36 208	95.33 277	97.46 224	88.60 326	96.88 306	97.68 266	91.29 280	93.80 244	96.42 304	88.58 195	99.24 166	91.06 269	96.04 210	98.17 197

LTVRE_ROB	92.95 15	94.60 224	93.90 235	96.68 207	97.41 232	94.42 217	98.52 153	98.59 141	91.69 265	91.21 305	98.35 155	84.87 267	99.04 192	91.06 269	93.44 244	96.60 277
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
PAPR		96.84 119	96.24 130	98.65 81	98.72 133	96.92 106	97.36 271	98.57 147	93.33 208	96.67 156	97.57 226	94.30 92	99.56 136	91.05 271	98.59 133	99.47 98

SixPastTwentyTwo		93.34 273	92.86 272	94.75 295	95.67 313	89.41 315	98.75 110	96.67 324	93.89 177	90.15 316	98.25 169	80.87 305	98.27 283	90.90 272	90.64 277	96.57 281

MVS_0304		92.81 283	92.01 285	95.23 278	97.46 224	91.33 288	98.17 207	98.81 76	91.13 287	93.80 244	95.68 324	66.08 351	98.06 297	90.79 273	96.13 208	96.32 307

COLMAP_ROB	93.27 12	95.33 182	94.87 183	96.71 203	99.29 78	93.24 258	98.58 143	98.11 232	89.92 306	93.57 250	99.10 69	86.37 244	99.79 91	90.78 274	98.10 153	97.09 224
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs6		91.77 291	90.63 295	95.17 281	94.69 332	91.24 291	98.67 132	97.92 257	86.14 331	89.62 319	97.56 228	75.79 335	98.34 271	90.75 275	84.56 332	95.94 317

BH-RMVSNet		95.92 151	95.32 163	97.69 149	98.32 164	94.64 206	98.19 202	97.45 287	94.56 152	96.03 179	98.61 127	85.02 264	99.12 179	90.68 276	99.06 112	99.30 120

DTE-MVSNet		93.98 264	93.26 267	96.14 248	96.06 302	94.39 219	99.20 29	98.86 61	93.06 218	91.78 300	97.81 207	85.87 252	97.58 319	90.53 277	86.17 328	96.46 300

v10		94.29 245	93.55 257	96.51 226	96.39 289	94.80 202	98.99 64	98.19 215	91.35 276	93.02 271	96.99 273	88.09 209	98.41 264	90.50 278	88.41 306	96.33 306

ambc				89.49 329	86.66 352	75.78 352	92.66 348	96.72 321		86.55 335	92.50 342	46.01 355	97.90 308	90.32 279	82.09 334	94.80 335

lessismore_v0				94.45 306	94.93 328	88.44 328		91.03 355		86.77 334	97.64 220	76.23 333	98.42 257	90.31 280	85.64 331	96.51 294

v1192		94.32 243	93.58 256	96.53 224	96.10 300	94.45 216	98.50 158	98.17 223	91.54 269	94.19 226	97.06 265	86.95 234	98.43 256	90.14 281	89.57 289	96.70 266

MVS		94.67 221	93.54 258	98.08 123	96.88 265	96.56 122	98.19 202	98.50 166	78.05 347	92.69 280	98.02 183	91.07 149	99.63 128	90.09 282	98.36 146	98.04 200

ADS-MVSNet2		94.58 227	94.40 207	95.11 283	98.00 186	88.74 324	96.04 324	97.30 294	90.15 301	96.47 169	96.64 296	87.89 214	97.56 320	90.08 283	97.06 177	99.02 151

ADS-MVSNet		95.00 199	94.45 203	96.63 211	98.00 186	91.91 275	96.04 324	97.74 265	90.15 301	96.47 169	96.64 296	87.89 214	98.96 201	90.08 283	97.06 177	99.02 151

MSDG		95.93 150	95.30 165	97.83 136	98.90 116	95.36 174	96.83 310	98.37 187	91.32 278	94.43 213	98.73 118	90.27 163	99.60 131	90.05 285	98.82 124	98.52 185

v1921920		94.20 250	93.47 261	96.40 236	95.98 305	94.08 228	98.52 153	98.15 226	91.33 277	94.25 222	97.20 251	86.41 243	98.42 257	90.04 286	89.39 294	96.69 271

dp		94.15 254	93.90 235	94.90 289	97.31 236	86.82 340	96.97 295	97.19 300	91.22 284	96.02 180	96.61 298	85.51 257	99.02 196	90.00 287	94.30 218	98.85 163

CMPMVS	66.06 21	89.70 307	89.67 303	89.78 328	93.19 341	76.56 351	97.00 294	98.35 190	80.97 344	81.57 345	97.75 210	74.75 339	98.61 237	89.85 288	93.63 238	94.17 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TR-MVS		94.94 206	94.20 214	97.17 176	97.75 200	94.14 227	97.59 257	97.02 308	92.28 249	95.75 184	97.64 220	83.88 287	98.96 201	89.77 289	96.15 207	98.40 189

MS-PatchMatch		93.84 266	93.63 254	94.46 305	96.18 296	89.45 313	97.76 246	98.27 205	92.23 250	92.13 296	97.49 231	79.50 313	98.69 229	89.75 290	99.38 100	95.25 326

ITE_SJBPF				95.44 274	97.42 229	91.32 289		97.50 282	95.09 131	93.59 248	98.35 155	81.70 299	98.88 214	89.71 291	93.39 245	96.12 312

MVP-Stereo		94.28 247	93.92 233	95.35 276	94.95 327	92.60 267	97.97 226	97.65 268	91.61 268	90.68 311	97.09 259	86.32 245	98.42 257	89.70 292	99.34 102	95.02 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AllTest		95.24 186	94.65 191	96.99 185	99.25 86	93.21 259	98.59 141	98.18 218	91.36 274	93.52 252	98.77 114	84.67 271	99.72 108	89.70 292	97.87 159	98.02 201

TestCases				96.99 185	99.25 86	93.21 259		98.18 218	91.36 274	93.52 252	98.77 114	84.67 271	99.72 108	89.70 292	97.87 159	98.02 201

GG-mvs-BLEND				96.59 216	96.34 291	94.98 192	96.51 320	88.58 358		93.10 270	94.34 335	80.34 310	98.05 298	89.53 295	96.99 179	96.74 259

USDC		93.33 274	92.71 275	95.21 279	96.83 268	90.83 296	96.91 300	97.50 282	93.84 180	90.72 310	98.14 176	77.69 326	98.82 221	89.51 296	93.21 249	95.97 316

v7n		94.19 251	93.43 262	96.47 229	95.90 307	94.38 220	99.26 18	98.34 192	91.99 256	92.76 277	97.13 254	88.31 202	98.52 247	89.48 297	87.70 313	96.52 291

PM-MVS		87.77 314	86.55 318	91.40 327	91.03 349	83.36 346	96.92 298	95.18 338	91.28 281	86.48 336	93.42 338	53.27 354	96.74 332	89.43 298	81.97 336	94.11 339

FMVSNet1		93.19 279	92.07 284	96.56 220	97.54 218	95.00 189	98.82 97	98.18 218	90.38 298	92.27 293	97.07 262	73.68 343	97.95 304	89.36 299	91.30 268	96.72 262

tpm cat1		93.36 271	92.80 273	95.07 285	97.58 213	87.97 333	96.76 312	97.86 260	82.17 343	93.53 251	96.04 315	86.13 247	99.13 178	89.24 300	95.87 211	98.10 199

UnsupCasMVSNet_eth		90.99 298	89.92 301	94.19 309	94.08 336	89.83 307	97.13 289	98.67 128	93.69 192	85.83 338	96.19 312	75.15 337	96.74 332	89.14 301	79.41 341	96.00 315

v1240		94.06 262	93.29 266	96.34 240	96.03 304	93.90 232	98.44 165	98.17 223	91.18 286	94.13 229	97.01 272	86.05 249	98.42 257	89.13 302	89.50 292	96.70 266

tmp_tt		68.90 323	66.97 325	74.68 338	50.78 363	59.95 360	87.13 352	83.47 361	38.80 358	62.21 354	96.23 309	64.70 352	76.91 359	88.91 303	30.49 357	87.19 349

pmmvs-eth3d		90.36 303	89.05 308	94.32 307	91.10 348	92.12 270	97.63 256	96.95 311	88.86 319	84.91 341	93.13 339	78.32 320	96.74 332	88.70 304	81.81 337	94.09 340

thres600view7		95.49 168	94.77 185	97.67 151	98.98 112	95.02 188	98.85 90	96.90 314	95.38 111	96.63 158	96.90 282	84.29 276	99.59 132	88.65 305	96.33 197	98.40 189

thres100view900		95.38 176	94.70 189	97.41 165	98.98 112	94.92 196	98.87 87	96.90 314	95.38 111	96.61 159	96.88 283	84.29 276	99.56 136	88.11 306	96.29 199	97.76 206

tfpn200view9		95.32 183	94.62 192	97.43 164	98.94 114	94.98 192	98.68 129	96.93 312	95.33 114	96.55 163	96.53 299	84.23 279	99.56 136	88.11 306	96.29 199	97.76 206

thres400		95.38 176	94.62 192	97.65 154	98.94 114	94.98 192	98.68 129	96.93 312	95.33 114	96.55 163	96.53 299	84.23 279	99.56 136	88.11 306	96.29 199	98.40 189

our_test_3		93.65 269	93.30 265	94.69 296	95.45 321	89.68 311	96.91 300	97.65 268	91.97 257	91.66 302	96.88 283	89.67 169	97.93 307	88.02 309	91.49 265	96.48 298

thres200		95.25 185	94.57 194	97.28 170	98.81 125	94.92 196	98.20 198	97.11 301	95.24 122	96.54 165	96.22 311	84.58 273	99.53 142	87.93 310	96.50 193	97.39 217

EG-PatchMatch MVS		91.13 296	90.12 299	94.17 310	94.73 331	89.00 321	98.13 211	97.81 261	89.22 317	85.32 340	96.46 301	67.71 348	98.42 257	87.89 311	93.82 235	95.08 331

CR-MVSNet		94.76 215	94.15 219	96.59 216	97.00 256	93.43 249	94.96 337	97.56 274	92.46 237	96.93 144	96.24 307	88.15 207	97.88 312	87.38 312	96.65 187	98.46 187

Patchmtry		93.22 277	92.35 281	95.84 261	96.77 269	93.09 263	94.66 342	97.56 274	87.37 325	92.90 273	96.24 307	88.15 207	97.90 308	87.37 313	90.10 283	96.53 288

test0.0.03 1		94.08 260	93.51 259	95.80 262	95.53 318	92.89 265	97.38 267	95.97 329	95.11 128	92.51 287	96.66 293	87.71 218	96.94 329	87.03 314	93.67 236	97.57 213

TinyColmap		92.31 288	91.53 289	94.65 298	96.92 261	89.75 308	96.92 298	96.68 323	90.45 296	89.62 319	97.85 201	76.06 334	98.81 222	86.74 315	92.51 255	95.41 324

MIMVSNet		93.26 276	92.21 283	96.41 235	97.73 204	93.13 261	95.65 332	97.03 306	91.27 282	94.04 233	96.06 314	75.33 336	97.19 326	86.56 316	96.23 205	98.92 161

TransMVSNet (Re)		92.67 285	91.51 290	96.15 247	96.58 280	94.65 205	98.90 78	96.73 320	90.86 290	89.46 321	97.86 199	85.62 255	98.09 294	86.45 317	81.12 338	95.71 320

DSMNet-mixed		92.52 287	92.58 278	92.33 323	94.15 335	82.65 347	98.30 186	94.26 347	89.08 318	92.65 281	95.73 319	85.01 265	95.76 342	86.24 318	97.76 164	98.59 182

testgi		93.06 281	92.45 280	94.88 290	96.43 288	89.90 306	98.75 110	97.54 279	95.60 99	91.63 303	97.91 193	74.46 341	97.02 328	86.10 319	93.67 236	97.72 210

YYNet1		90.70 301	89.39 304	94.62 299	94.79 330	90.65 300	97.20 282	97.46 285	87.54 324	72.54 350	95.74 318	86.51 239	96.66 336	86.00 320	86.76 326	96.54 286

MDA-MVSNet_test_wron		90.71 300	89.38 305	94.68 297	94.83 329	90.78 298	97.19 283	97.46 285	87.60 323	72.41 351	95.72 321	86.51 239	96.71 335	85.92 321	86.80 325	96.56 283

UnsupCasMVSNet_bld		87.17 315	85.12 319	93.31 317	91.94 345	88.77 323	94.92 339	98.30 202	84.30 340	82.30 344	90.04 345	63.96 353	97.25 325	85.85 322	74.47 348	93.93 343

EPNet_dtu		95.21 188	94.95 180	95.99 252	96.17 297	90.45 303	98.16 208	97.27 297	96.77 53	93.14 268	98.33 160	90.34 161	98.42 257	85.57 323	98.81 125	99.09 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet5		91.81 290	90.92 293	94.49 302	97.21 242	92.09 271	98.00 224	97.55 278	89.31 316	90.86 309	95.61 325	74.48 340	95.32 345	85.57 323	89.70 287	96.07 314

tfpnnormal		93.66 267	92.70 276	96.55 223	96.94 260	95.94 151	98.97 68	99.19 15	91.04 288	91.38 304	97.34 240	84.94 266	98.61 237	85.45 325	89.02 300	95.11 330

Patchmatch-test		94.42 238	93.68 253	96.63 211	97.60 211	91.76 278	94.83 341	97.49 284	89.45 314	94.14 228	97.10 255	88.99 185	98.83 220	85.37 326	98.13 152	99.29 122

ppachtmachnet_test		93.22 277	92.63 277	94.97 287	95.45 321	90.84 295	96.88 306	97.88 259	90.60 292	92.08 297	97.26 245	88.08 210	97.86 313	85.12 327	90.33 279	96.22 309

KD-MVS_2432*1600		89.61 309	87.96 313	94.54 300	94.06 337	91.59 283	95.59 333	97.63 270	89.87 307	88.95 324	94.38 333	78.28 321	96.82 330	84.83 328	68.05 350	95.21 327

miper_refine_blended		89.61 309	87.96 313	94.54 300	94.06 337	91.59 283	95.59 333	97.63 270	89.87 307	88.95 324	94.38 333	78.28 321	96.82 330	84.83 328	68.05 350	95.21 327

PCF-MVS	93.45 11	94.68 218	93.43 262	98.42 101	98.62 142	96.77 112	95.48 335	98.20 214	84.63 339	93.34 260	98.32 161	88.55 198	99.81 70	84.80 330	98.96 115	98.68 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DIV-MVS_2432*1600		90.38 302	89.38 305	93.40 315	92.85 343	88.94 322	97.95 227	97.94 255	90.35 299	90.25 314	93.96 336	79.82 311	95.94 341	84.62 331	76.69 344	95.33 325

MDA-MVSNet-bldmvs		89.97 306	88.35 311	94.83 293	95.21 324	91.34 286	97.64 254	97.51 281	88.36 321	71.17 352	96.13 313	79.22 315	96.63 337	83.65 332	86.27 327	96.52 291

MVS-HIRNet		89.46 311	88.40 310	92.64 321	97.58 213	82.15 348	94.16 346	93.05 353	75.73 349	90.90 308	82.52 350	79.42 314	98.33 272	83.53 333	98.68 127	97.43 214

new-patchmatchnet		88.50 313	87.45 316	91.67 326	90.31 350	85.89 341	97.16 287	97.33 293	89.47 313	83.63 343	92.77 340	76.38 332	95.06 347	82.70 334	77.29 343	94.06 341

PAPM		94.95 204	94.00 228	97.78 140	97.04 255	95.65 163	96.03 326	98.25 210	91.23 283	94.19 226	97.80 208	91.27 144	98.86 217	82.61 335	97.61 169	98.84 165

LCM-MVSNet		78.70 318	76.24 323	86.08 331	77.26 359	71.99 355	94.34 344	96.72 321	61.62 353	76.53 347	89.33 346	33.91 361	92.78 351	81.85 336	74.60 347	93.46 344

new_pmnet		90.06 305	89.00 309	93.22 319	94.18 334	88.32 330	96.42 322	96.89 316	86.19 330	85.67 339	93.62 337	77.18 331	97.10 327	81.61 337	89.29 295	94.23 337

pmmvs3		86.67 317	84.86 320	92.11 325	88.16 351	87.19 339	96.63 316	94.75 342	79.88 345	87.22 332	92.75 341	66.56 350	95.20 346	81.24 338	76.56 345	93.96 342

CL-MVSNet_2432*1600		90.11 304	89.14 307	93.02 320	91.86 346	88.23 331	96.51 320	98.07 242	90.49 293	90.49 313	94.41 331	84.75 270	95.34 344	80.79 339	74.95 346	95.50 323

N_pmnet		87.12 316	87.77 315	85.17 333	95.46 320	61.92 358	97.37 269	70.66 363	85.83 334	88.73 327	96.04 315	85.33 262	97.76 315	80.02 340	90.48 278	95.84 318

TAPA-MVS	93.98 7	95.35 180	94.56 195	97.74 144	99.13 102	94.83 200	98.33 178	98.64 136	86.62 327	96.29 174	98.61 127	94.00 97	99.29 162	80.00 341	99.41 97	99.09 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepMVS_CX				86.78 330	97.09 253	72.30 354		95.17 339	75.92 348	84.34 342	95.19 326	70.58 346	95.35 343	79.98 342	89.04 299	92.68 346

Anonymous20231206		91.66 292	91.10 292	93.33 316	94.02 339	87.35 337	98.58 143	97.26 298	90.48 294	90.16 315	96.31 305	83.83 289	96.53 338	79.36 343	89.90 285	96.12 312

test20.03		90.89 299	90.38 297	92.43 322	93.48 340	88.14 332	98.33 178	97.56 274	93.40 206	87.96 329	96.71 292	80.69 308	94.13 349	79.15 344	86.17 328	95.01 334

PatchT		93.06 281	91.97 286	96.35 239	96.69 275	92.67 266	94.48 343	97.08 302	86.62 327	97.08 136	92.23 343	87.94 213	97.90 308	78.89 345	96.69 185	98.49 186

MIMVSNet1		89.67 308	88.28 312	93.82 311	92.81 344	91.08 293	98.01 222	97.45 287	87.95 322	87.90 330	95.87 317	67.63 349	94.56 348	78.73 346	88.18 309	95.83 319

test_0402		91.32 294	90.27 298	94.48 303	96.60 279	91.12 292	98.50 158	97.22 299	86.10 332	88.30 328	96.98 274	77.65 328	97.99 303	78.13 347	92.94 252	94.34 336

OpenMVS_ROB	86.42 20	89.00 312	87.43 317	93.69 312	93.08 342	89.42 314	97.91 231	96.89 316	78.58 346	85.86 337	94.69 330	69.48 347	98.29 280	77.13 348	93.29 248	93.36 345

RPMNet		92.81 283	91.34 291	97.24 171	97.00 256	93.43 249	94.96 337	98.80 87	82.27 342	96.93 144	92.12 344	86.98 233	99.82 63	76.32 349	96.65 187	98.46 187

PMMVS2		77.95 320	75.44 324	85.46 332	82.54 354	74.95 353	94.23 345	93.08 352	72.80 350	74.68 348	87.38 347	36.36 360	91.56 352	73.95 350	63.94 352	89.87 347

FPMVS		77.62 321	77.14 321	79.05 336	79.25 357	60.97 359	95.79 329	95.94 330	65.96 351	67.93 353	94.40 332	37.73 359	88.88 354	68.83 351	88.46 305	87.29 348

Gipuma		78.40 319	76.75 322	83.38 334	95.54 317	80.43 350	79.42 355	97.40 291	64.67 352	73.46 349	80.82 352	45.65 356	93.14 350	66.32 352	87.43 316	76.56 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high		69.08 322	65.37 326	80.22 335	65.99 361	71.96 356	90.91 351	90.09 356	82.62 341	49.93 358	78.39 353	29.36 362	81.75 355	62.49 353	38.52 356	86.95 350

PMVS	61.03 23	65.95 324	63.57 328	73.09 339	57.90 362	51.22 363	85.05 354	93.93 351	54.45 354	44.32 359	83.57 349	13.22 363	89.15 353	58.68 354	81.00 339	78.91 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	62.14 22	63.28 327	59.38 330	74.99 337	74.33 360	65.47 357	85.55 353	80.50 362	52.02 356	51.10 357	75.00 356	10.91 366	80.50 356	51.60 355	53.40 353	78.99 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		64.94 325	64.25 327	67.02 340	82.28 355	59.36 361	91.83 350	85.63 359	52.69 355	60.22 355	77.28 354	41.06 358	80.12 357	46.15 356	41.14 354	61.57 355

EMVS		64.07 326	63.26 329	66.53 341	81.73 356	58.81 362	91.85 349	84.75 360	51.93 357	59.09 356	75.13 355	43.32 357	79.09 358	42.03 357	39.47 355	61.69 354

wuyk23d		30.17 328	30.18 332	30.16 342	78.61 358	43.29 364	66.79 356	14.21 364	17.31 359	14.82 362	11.93 362	11.55 365	41.43 360	37.08 358	19.30 358	5.76 358

test123		20.95 331	23.72 334	12.64 343	13.54 365	8.19 365	96.55 319	6.13 366	7.48 361	16.74 361	37.98 359	12.97 364	6.05 361	16.69 359	5.43 360	23.68 356

testmvs		21.48 330	24.95 333	11.09 344	14.89 364	6.47 366	96.56 318	9.87 365	7.55 360	17.93 360	39.02 358	9.43 367	5.90 362	16.56 360	12.72 359	20.91 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		23.98 329	31.98 331	0.00 345	0.00 366	0.00 367	0.00 357	98.59 141	0.00 362	0.00 363	98.61 127	90.60 157	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		7.88 333	10.50 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	94.51 85	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.20 332	10.94 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	98.43 145	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 4	99.74 106

save fliter					99.46 51	98.38 35	98.21 195	98.71 113	97.95 3

test0726					99.72 12	99.25 2	99.06 51	98.88 49	97.62 11	99.56 5	99.50 4	97.42 6

GSMVS																99.20 129

test_part2					99.63 29	99.18 8				99.27 17

sam_mvs1												89.45 172				99.20 129

sam_mvs												88.99 185

MTGPA								98.74 103

test_post											31.83 360	88.83 192	98.91 208

patchmatchnet-post											95.10 328	89.42 173	98.89 212

MTMP							98.89 82	94.14 349

TEST9					99.31 70	98.50 29	97.92 229	98.73 107	92.63 232	97.74 111	98.68 121	96.20 23	99.80 79

test_8					99.29 78	98.44 31	97.89 235	98.72 109	92.98 221	97.70 114	98.66 124	96.20 23	99.80 79

agg_prior					99.30 75	98.38 35		98.72 109		97.57 125			99.81 70

test_prior4						98.01 62	97.86 238

test_prior				99.19 43	99.31 70	98.22 50		98.84 65					99.70 114			99.65 67

新几何2							97.64 254

旧先验1					99.29 78	97.48 83		98.70 116			99.09 74	95.56 47			99.47 90	99.61 75

原ACMM2							97.67 252

test222					99.23 93	97.17 98	97.40 265	98.66 131	88.68 320	98.05 86	98.96 93	94.14 94			99.53 85	99.61 75

segment_acmp												96.85 11

testdata1							97.32 275		96.34 71

test12				99.18 47	99.16 99	98.19 52		98.53 156		98.07 85		95.13 70	99.72 108		99.56 80	99.63 73

plane_prior7					97.42 229	94.63 207

plane_prior6					97.35 234	94.61 210						87.09 230

plane_prior4											98.28 164

plane_prior3						94.61 210			97.02 47	95.34 186

plane_prior2							98.80 104		97.28 29

plane_prior1					97.37 233

plane_prior						94.60 212	98.44 165		96.74 55						94.22 221

n2								0.00 367

nn								0.00 367

door-mid								94.37 345

test11								98.66 131

door								94.64 343

HQP5-MVS						94.25 225

HQP-NCC					97.20 243		98.05 218		96.43 67	94.45 209

ACMP_Plane					97.20 243		98.05 218		96.43 67	94.45 209

HQP4-MVS										94.45 209			98.96 201			96.87 246

HQP3-MVS								98.46 171							94.18 223

HQP2-MVS												86.75 236

NP-MVS					97.28 237	94.51 215					97.73 211

ACMMP++_ref															92.97 251

ACMMP++															93.61 239

Test By Simon												94.64 80

High-resolution multi-view benchmark