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.

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 102	99.60 94	99.45 155	99.01 13	99.90 1	99.83 40	98.98 19	99.93 56	99.59 2	99.95 6	99.86 6

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 64	99.78 36	99.15 101	99.61 91	99.45 155	99.01 13	99.89 2	99.82 47	99.01 12	99.92 65	99.56 5	99.95 6	99.85 9

ESAPD		99.46 21	99.32 26	99.91 2	99.78 36	99.88 2	99.36 202	99.51 86	98.73 44	99.88 3	99.84 36	98.72 48	99.96 19	98.16 139	99.87 39	99.88 4

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 45	99.41 74	99.58 103	99.49 106	99.02 10	99.88 3	99.80 68	99.00 18	99.94 41	99.45 15	99.92 12	99.84 13

SD-MVS		99.41 34	99.52 6	99.05 153	99.74 68	99.68 33	99.46 161	99.52 77	99.11 7	99.88 3	99.91 5	99.43 1	97.70 338	98.72 87	99.93 11	99.77 51

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 42	99.89 1	99.75 36	99.56 49	99.02 10	99.88 3	99.85 27	99.18 5	99.96 19	99.22 34	99.92 12	99.90 1

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 45	99.29 86	99.58 103	99.44 164	99.01 13	99.87 7	99.80 68	98.97 20	99.91 75	99.44 16	99.92 12	99.83 24

Vis-MVSNet		99.12 70	98.97 75	99.56 77	99.78 36	99.10 105	99.68 57	99.66 25	98.49 57	99.86 8	99.87 20	94.77 192	99.84 122	99.19 36	99.41 109	99.74 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
xiu_mvs_v1_base_debu		99.29 48	99.27 42	99.34 111	99.63 112	98.97 130	99.12 259	99.51 86	98.86 31	99.84 9	99.47 208	98.18 76	99.99 1	99.50 8	99.31 115	99.08 174

xiu_mvs_v1_base		99.29 48	99.27 42	99.34 111	99.63 112	98.97 130	99.12 259	99.51 86	98.86 31	99.84 9	99.47 208	98.18 76	99.99 1	99.50 8	99.31 115	99.08 174

xiu_mvs_v1_base_debi		99.29 48	99.27 42	99.34 111	99.63 112	98.97 130	99.12 259	99.51 86	98.86 31	99.84 9	99.47 208	98.18 76	99.99 1	99.50 8	99.31 115	99.08 174

Regformer-1		99.53 9	99.47 8	99.72 49	99.71 83	99.44 71	99.49 148	99.46 143	98.95 24	99.83 12	99.76 91	99.01 12	99.93 56	99.17 39	99.87 39	99.80 41

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 83	99.52 61	99.49 148	99.49 106	98.94 26	99.83 12	99.76 91	99.01 12	99.94 41	99.15 42	99.87 39	99.80 41

DeepPCF-MVS	98.18 3	98.81 116	99.37 17	97.12 311	99.60 122	91.75 339	98.61 329	99.44 164	99.35 1	99.83 12	99.85 27	98.70 50	99.81 143	99.02 52	99.91 17	99.81 35

TSAR-MVS + GP.		99.36 40	99.36 19	99.36 109	99.67 95	98.61 190	99.07 270	99.33 222	99.00 17	99.82 15	99.81 57	99.06 9	99.84 122	99.09 46	99.42 108	99.65 92

abl_6		99.44 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 68	99.59 38	98.13 84	99.82 15	99.81 57	98.60 56	99.96 19	98.46 120	99.88 35	99.79 45

MVSFormer		99.17 61	99.12 55	99.29 123	99.51 137	98.94 138	99.88 1	99.46 143	97.55 153	99.80 17	99.65 137	97.39 96	99.28 259	99.03 50	99.85 54	99.65 92

lupinMVS		99.13 65	99.01 70	99.46 99	99.51 137	98.94 138	99.05 276	99.16 260	97.86 119	99.80 17	99.56 171	97.39 96	99.86 110	98.94 59	99.85 54	99.58 114

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 18	99.76 45	99.83 8	99.63 82	99.54 63	98.36 66	99.79 19	99.82 47	98.86 30	99.95 34	98.62 98	99.81 68	99.78 49

jason		99.13 65	99.03 66	99.45 100	99.46 151	98.87 146	99.12 259	99.26 249	98.03 103	99.79 19	99.65 137	97.02 108	99.85 116	99.02 52	99.90 24	99.65 92
jason: jason.
SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 7	99.82 29	99.81 14	99.59 96	99.51 86	98.62 50	99.79 19	99.83 40	99.28 3	99.97 11	98.48 117	99.90 24	99.84 13
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 112	99.59 49	99.36 202	99.46 143	99.07 9	99.79 19	99.82 47	98.85 31	99.92 65	98.68 92	99.87 39	99.82 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		99.44 26	99.30 34	99.85 18	99.73 73	99.83 8	99.56 116	99.47 133	97.45 163	99.78 23	99.82 47	99.18 5	99.91 75	98.79 79	99.89 32	99.81 35

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 82	99.39 187	98.91 29	99.78 23	99.85 27	99.36 2	99.94 41	98.84 72	99.88 35	99.82 31

test_part2					99.81 32	99.83 8				99.77 25

v1.0		41.40 338	55.20 339	0.00 355	99.81 32	0.00 370	0.00 361	99.48 117	97.97 111	99.77 25	99.78 82	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

HSP-MVS		99.41 34	99.26 45	99.85 18	99.89 8	99.80 15	99.67 59	99.37 201	98.70 46	99.77 25	99.49 198	98.21 75	99.95 34	98.46 120	99.77 76	99.81 35

UA-Net		99.42 31	99.29 38	99.80 31	99.62 116	99.55 54	99.50 139	99.70 15	98.79 40	99.77 25	99.96 1	97.45 95	99.96 19	98.92 60	99.90 24	99.89 2

APD-MVS		99.27 51	99.08 59	99.84 23	99.75 57	99.79 19	99.50 139	99.50 101	97.16 189	99.77 25	99.82 47	98.78 37	99.94 41	97.56 193	99.86 50	99.80 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		99.47 20	99.34 24	99.88 5	99.87 15	99.86 4	99.47 158	99.48 117	98.05 100	99.76 30	99.86 23	98.82 33	99.93 56	98.82 78	99.91 17	99.84 13

HPM-MVS_fast		99.51 12	99.40 14	99.85 18	99.91 1	99.79 19	99.76 28	99.56 49	97.72 138	99.76 30	99.75 96	99.13 7	99.92 65	99.07 48	99.92 12	99.85 9

VNet		99.11 75	98.90 85	99.73 47	99.52 135	99.56 52	99.41 182	99.39 187	99.01 13	99.74 32	99.78 82	95.56 149	99.92 65	99.52 7	98.18 189	99.72 71

xiu_mvs_v2_base		99.26 53	99.25 46	99.29 123	99.53 133	98.91 143	99.02 285	99.45 155	98.80 39	99.71 33	99.26 264	98.94 25	99.98 5	99.34 23	99.23 119	98.98 187

PS-MVSNAJ		99.32 44	99.32 26	99.30 120	99.57 127	98.94 138	98.97 298	99.46 143	98.92 28	99.71 33	99.24 267	99.01 12	99.98 5	99.35 19	99.66 97	98.97 188

PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 23	99.58 103	99.65 30	97.84 124	99.71 33	99.80 68	99.12 8	99.97 11	98.33 130	99.87 39	99.83 24

114514_t		98.93 100	98.67 113	99.72 49	99.85 23	99.53 58	99.62 85	99.59 38	92.65 330	99.71 33	99.78 82	98.06 80	99.90 88	98.84 72	99.91 17	99.74 60

PVSNet_Blended_VisFu		99.36 40	99.28 40	99.61 68	99.86 20	99.07 110	99.47 158	99.93 2	97.66 146	99.71 33	99.86 23	97.73 88	99.96 19	99.47 13	99.82 67	99.79 45

tfpn1000		98.33 144	98.02 158	99.25 131	99.78 36	98.73 175	99.70 45	97.55 352	97.48 159	99.69 38	99.53 184	92.37 272	99.85 116	97.82 166	98.26 184	99.16 165

zzz-MVS		99.49 13	99.36 19	99.89 3	99.90 3	99.86 4	99.36 202	99.47 133	98.79 40	99.68 39	99.81 57	98.43 63	99.97 11	98.88 62	99.90 24	99.83 24

MTAPA		99.52 11	99.39 15	99.89 3	99.90 3	99.86 4	99.66 68	99.47 133	98.79 40	99.68 39	99.81 57	98.43 63	99.97 11	98.88 62	99.90 24	99.83 24

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 68	99.67 22	98.15 82	99.68 39	99.69 121	99.06 9	99.96 19	98.69 90	99.87 39	99.84 13

#test#		99.43 29	99.29 38	99.86 13	99.87 15	99.80 15	99.55 122	99.67 22	97.83 125	99.68 39	99.69 121	99.06 9	99.96 19	98.39 123	99.87 39	99.84 13

VDDNet		97.55 252	97.02 268	99.16 141	99.49 145	98.12 219	99.38 196	99.30 231	95.35 290	99.68 39	99.90 7	82.62 349	99.93 56	99.31 26	98.13 197	99.42 150

HPM-MVS		99.42 31	99.28 40	99.83 24	99.90 3	99.72 28	99.81 15	99.54 63	97.59 148	99.68 39	99.63 148	98.91 27	99.94 41	98.58 104	99.91 17	99.84 13
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDD-MVS		97.73 235	97.35 249	98.88 192	99.47 149	97.12 252	99.34 210	98.85 296	98.19 78	99.67 45	99.85 27	82.98 347	99.92 65	99.49 12	98.32 178	99.60 107

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 68	99.67 22	98.15 82	99.67 45	99.69 121	98.95 24	99.96 19	98.69 90	99.87 39	99.84 13

PVSNet_BlendedMVS		98.86 106	98.80 100	99.03 154	99.76 45	98.79 169	99.28 223	99.91 3	97.42 168	99.67 45	99.37 235	97.53 92	99.88 103	98.98 55	97.29 242	98.42 304

PVSNet_Blended		99.08 82	98.97 75	99.42 106	99.76 45	98.79 169	98.78 318	99.91 3	96.74 224	99.67 45	99.49 198	97.53 92	99.88 103	98.98 55	99.85 54	99.60 107

sss		99.17 61	99.05 61	99.53 83	99.62 116	98.97 130	99.36 202	99.62 31	97.83 125	99.67 45	99.65 137	97.37 99	99.95 34	99.19 36	99.19 122	99.68 84

region2R		99.48 17	99.35 22	99.87 7	99.88 11	99.80 15	99.65 78	99.66 25	98.13 84	99.66 50	99.68 126	98.96 21	99.96 19	98.62 98	99.87 39	99.84 13

RPSCF		98.22 156	98.62 122	96.99 312	99.82 29	91.58 340	99.72 42	99.44 164	96.61 233	99.66 50	99.89 10	95.92 140	99.82 139	97.46 204	99.10 128	99.57 115

OMC-MVS		99.08 82	99.04 64	99.20 138	99.67 95	98.22 213	99.28 223	99.52 77	98.07 95	99.66 50	99.81 57	97.79 86	99.78 158	97.79 169	99.81 68	99.60 107

LFMVS		97.90 206	97.35 249	99.54 78	99.52 135	99.01 123	99.39 191	98.24 333	97.10 197	99.65 53	99.79 76	84.79 343	99.91 75	99.28 28	98.38 174	99.69 80

MVS_111021_LR		99.41 34	99.33 25	99.65 59	99.77 42	99.51 63	98.94 306	99.85 6	98.82 35	99.65 53	99.74 101	98.51 58	99.80 147	98.83 75	99.89 32	99.64 98

CPTT-MVS		99.11 75	98.90 85	99.74 45	99.80 34	99.46 68	99.59 96	99.49 106	97.03 208	99.63 55	99.69 121	97.27 101	99.96 19	97.82 166	99.84 59	99.81 35

ACMMP		99.45 23	99.32 26	99.82 26	99.89 8	99.67 35	99.62 85	99.69 18	98.12 86	99.63 55	99.84 36	98.73 47	99.96 19	98.55 112	99.83 63	99.81 35
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
DeepC-MVS	98.35 2	99.30 46	99.19 49	99.64 64	99.82 29	99.23 93	99.62 85	99.55 56	98.94 26	99.63 55	99.95 2	95.82 144	99.94 41	99.37 18	99.97 3	99.73 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		99.12 70	99.13 54	99.08 149	99.66 105	97.89 227	98.43 336	99.71 13	98.88 30	99.62 58	99.76 91	96.63 121	99.70 190	99.46 14	99.99 1	99.66 88

PHI-MVS		99.30 46	99.17 51	99.70 51	99.56 131	99.52 61	99.58 103	99.80 8	97.12 193	99.62 58	99.73 106	98.58 57	99.90 88	98.61 100	99.91 17	99.68 84

0601test		98.86 106	98.63 118	99.54 78	99.49 145	99.18 97	99.50 139	99.07 271	98.22 77	99.61 60	99.51 192	95.37 154	99.84 122	98.60 102	98.33 175	99.59 111

tfpn_ndepth		98.17 163	97.84 181	99.15 143	99.75 57	98.76 173	99.61 91	97.39 354	96.92 215	99.61 60	99.38 231	92.19 274	99.86 110	97.57 191	98.13 197	98.82 205

MG-MVS		99.13 65	99.02 69	99.45 100	99.57 127	98.63 185	99.07 270	99.34 214	98.99 18	99.61 60	99.82 47	97.98 82	99.87 106	97.00 229	99.80 70	99.85 9

MP-MVS-pluss		99.37 39	99.20 48	99.88 5	99.90 3	99.87 3	99.30 217	99.52 77	97.18 187	99.60 63	99.79 76	98.79 36	99.95 34	98.83 75	99.91 17	99.83 24
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CDPH-MVS		99.13 65	98.91 84	99.80 31	99.75 57	99.71 29	99.15 255	99.41 177	96.60 235	99.60 63	99.55 174	98.83 32	99.90 88	97.48 201	99.83 63	99.78 49

EPP-MVSNet		99.13 65	98.99 72	99.53 83	99.65 108	99.06 111	99.81 15	99.33 222	97.43 165	99.60 63	99.88 15	97.14 104	99.84 122	99.13 43	98.94 142	99.69 80

HyFIR lowres test		99.11 75	98.92 82	99.65 59	99.90 3	99.37 77	99.02 285	99.91 3	97.67 144	99.59 66	99.75 96	95.90 141	99.73 174	99.53 6	99.02 134	99.86 6

MVS_0304		99.06 84	98.86 93	99.66 55	99.51 137	99.36 78	99.22 243	99.51 86	98.95 24	99.58 67	99.65 137	93.74 234	99.98 5	99.66 1	99.95 6	99.64 98

MVS_Test		99.10 78	98.97 75	99.48 92	99.49 145	99.14 102	99.67 59	99.34 214	97.31 176	99.58 67	99.76 91	97.65 91	99.82 139	98.87 66	99.07 131	99.46 143

MDTV_nov1_ep13_2view						95.18 312	99.35 207		96.84 220	99.58 67		95.19 164		97.82 166		99.46 143

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 77	99.40 76	99.05 276	99.66 25	99.14 6	99.57 70	99.80 68	98.46 61	99.94 41	99.57 4	99.84 59	99.60 107
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
CR-MVSNet		98.17 163	97.93 168	98.87 196	99.18 210	98.49 201	99.22 243	99.33 222	96.96 211	99.56 71	99.38 231	94.33 211	99.00 299	94.83 292	98.58 163	99.14 166

RPMNet		96.61 279	95.85 287	98.87 196	99.18 210	98.49 201	99.22 243	99.08 268	88.72 346	99.56 71	97.38 341	94.08 222	99.00 299	86.87 344	98.58 163	99.14 166

IS-MVSNet		99.05 86	98.87 89	99.57 75	99.73 73	99.32 82	99.75 36	99.20 256	98.02 104	99.56 71	99.86 23	96.54 123	99.67 195	98.09 144	99.13 125	99.73 65

conf0.01		98.21 159	97.89 173	99.15 143	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 150	96.90 239	98.12 199	98.61 283

conf0.002		98.21 159	97.89 173	99.15 143	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 150	96.90 239	98.12 199	98.61 283

thresconf0.02		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 150	96.90 239	98.12 199	98.97 188

tfpn_n400		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 150	96.90 239	98.12 199	98.97 188

tfpnconf		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 150	96.90 239	98.12 199	98.97 188

tfpnview11		98.24 152	97.89 173	99.27 126	99.76 45	99.04 113	99.67 59	97.71 344	97.10 197	99.55 74	99.54 177	92.70 254	99.79 150	96.90 239	98.12 199	98.97 188

MVS_111021_HR		99.41 34	99.32 26	99.66 55	99.72 77	99.47 67	98.95 304	99.85 6	98.82 35	99.54 80	99.73 106	98.51 58	99.74 167	98.91 61	99.88 35	99.77 51

CP-MVS		99.45 23	99.32 26	99.85 18	99.83 28	99.75 24	99.69 48	99.52 77	98.07 95	99.53 81	99.63 148	98.93 26	99.97 11	98.74 83	99.91 17	99.83 24

WTY-MVS		99.06 84	98.88 88	99.61 68	99.62 116	99.16 98	99.37 198	99.56 49	98.04 101	99.53 81	99.62 153	96.84 113	99.94 41	98.85 71	98.49 170	99.72 71

MCST-MVS		99.43 29	99.30 34	99.82 26	99.79 35	99.74 27	99.29 221	99.40 184	98.79 40	99.52 83	99.62 153	98.91 27	99.90 88	98.64 95	99.75 79	99.82 31

casdiffmvs1		99.23 56	99.11 57	99.58 73	99.53 133	99.36 78	99.76 28	99.43 172	97.99 109	99.52 83	99.84 36	97.50 94	99.77 160	99.42 17	98.97 139	99.61 106

PatchT		97.03 276	96.44 277	98.79 210	98.99 244	98.34 209	99.16 252	99.07 271	92.13 331	99.52 83	97.31 343	94.54 204	98.98 301	88.54 337	98.73 159	99.03 181

CANet		99.25 54	99.14 53	99.59 70	99.41 161	99.16 98	99.35 207	99.57 44	98.82 35	99.51 86	99.61 157	96.46 125	99.95 34	99.59 2	99.98 2	99.65 92

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 48	99.48 117	98.12 86	99.50 87	99.75 96	98.78 37	99.97 11	98.57 106	99.89 32	99.83 24

PatchMatch-RL		98.84 115	98.62 122	99.52 87	99.71 83	99.28 87	99.06 274	99.77 9	97.74 136	99.50 87	99.53 184	95.41 153	99.84 122	97.17 221	99.64 100	99.44 147

PVSNet	96.02 17	98.85 113	98.84 96	98.89 185	99.73 73	97.28 245	98.32 340	99.60 35	97.86 119	99.50 87	99.57 169	96.75 118	99.86 110	98.56 109	99.70 91	99.54 118

LS3D		99.27 51	99.12 55	99.74 45	99.18 210	99.75 24	99.56 116	99.57 44	98.45 60	99.49 90	99.85 27	97.77 87	99.94 41	98.33 130	99.84 59	99.52 124

MP-MVS		99.33 43	99.15 52	99.87 7	99.88 11	99.82 13	99.66 68	99.46 143	98.09 91	99.48 91	99.74 101	98.29 72	99.96 19	97.93 158	99.87 39	99.82 31
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
旧先验2							98.96 300		96.70 227	99.47 92			99.94 41	98.19 136

MSDG		98.98 96	98.80 100	99.53 83	99.76 45	99.19 95	98.75 321	99.55 56	97.25 181	99.47 92	99.77 88	97.82 85	99.87 106	96.93 236	99.90 24	99.54 118

CDS-MVSNet		99.09 79	99.03 66	99.25 131	99.42 158	98.73 175	99.45 162	99.46 143	98.11 88	99.46 94	99.77 88	98.01 81	99.37 236	98.70 88	98.92 145	99.66 88
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSLP-MVS++		99.46 21	99.47 8	99.44 103	99.60 122	99.16 98	99.41 182	99.71 13	98.98 19	99.45 95	99.78 82	99.19 4	99.54 215	99.28 28	99.84 59	99.63 102

XVG-OURS		98.73 124	98.68 112	98.88 192	99.70 89	97.73 240	98.92 307	99.55 56	98.52 56	99.45 95	99.84 36	95.27 158	99.91 75	98.08 148	98.84 152	99.00 184

tpmrst		98.33 144	98.48 131	97.90 287	99.16 217	94.78 317	99.31 215	99.11 265	97.27 179	99.45 95	99.59 162	95.33 155	99.84 122	98.48 117	98.61 160	99.09 173

TAMVS		99.12 70	99.08 59	99.24 134	99.46 151	98.55 192	99.51 134	99.46 143	98.09 91	99.45 95	99.82 47	98.34 70	99.51 216	98.70 88	98.93 143	99.67 87

CANet_DTU		98.97 98	98.87 89	99.25 131	99.33 178	98.42 208	99.08 269	99.30 231	99.16 5	99.43 99	99.75 96	95.27 158	99.97 11	98.56 109	99.95 6	99.36 154

Patchmatch-test1		98.16 165	98.14 147	98.22 267	99.30 187	95.55 300	99.07 270	98.97 281	97.57 151	99.43 99	99.60 160	92.72 251	99.60 209	97.38 209	99.20 121	99.50 132

testdata				99.54 78	99.75 57	98.95 135		99.51 86	97.07 204	99.43 99	99.70 115	98.87 29	99.94 41	97.76 173	99.64 100	99.72 71

XVG-OURS-SEG-HR		98.69 126	98.62 122	98.89 185	99.71 83	97.74 239	99.12 259	99.54 63	98.44 63	99.42 102	99.71 112	94.20 215	99.92 65	98.54 114	98.90 147	99.00 184

DP-MVS Recon		99.12 70	98.95 80	99.65 59	99.74 68	99.70 31	99.27 226	99.57 44	96.40 253	99.42 102	99.68 126	98.75 45	99.80 147	97.98 154	99.72 85	99.44 147

Effi-MVS+-dtu		98.78 120	98.89 87	98.47 239	99.33 178	96.91 270	99.57 109	99.30 231	98.47 58	99.41 104	98.99 287	96.78 115	99.74 167	98.73 85	99.38 110	98.74 219

MIMVSNet		97.73 235	97.45 231	98.57 228	99.45 155	97.50 243	99.02 285	98.98 280	96.11 276	99.41 104	99.14 274	90.28 302	98.74 314	95.74 273	98.93 143	99.47 139

CSCG		99.32 44	99.32 26	99.32 116	99.85 23	98.29 210	99.71 44	99.66 25	98.11 88	99.41 104	99.80 68	98.37 69	99.96 19	98.99 54	99.96 5	99.72 71

F-COLMAP		99.19 58	99.04 64	99.64 64	99.78 36	99.27 89	99.42 178	99.54 63	97.29 178	99.41 104	99.59 162	98.42 66	99.93 56	98.19 136	99.69 92	99.73 65

MDTV_nov1_ep13			98.32 139		99.11 225	94.44 321	99.27 226	98.74 308	97.51 157	99.40 108	99.62 153	94.78 188	99.76 165	97.59 188	98.81 156

CVMVSNet		98.57 133	98.67 113	98.30 254	99.35 174	95.59 299	99.50 139	99.55 56	98.60 52	99.39 109	99.83 40	94.48 206	99.45 220	98.75 82	98.56 166	99.85 9

CNVR-MVS		99.42 31	99.30 34	99.78 35	99.62 116	99.71 29	99.26 234	99.52 77	98.82 35	99.39 109	99.71 112	98.96 21	99.85 116	98.59 103	99.80 70	99.77 51

Effi-MVS+		98.81 116	98.59 127	99.48 92	99.46 151	99.12 104	98.08 346	99.50 101	97.50 158	99.38 111	99.41 222	96.37 129	99.81 143	99.11 45	98.54 167	99.51 129

mvs_anonymous		99.03 89	98.99 72	99.16 141	99.38 169	98.52 198	99.51 134	99.38 193	97.79 130	99.38 111	99.81 57	97.30 100	99.45 220	99.35 19	98.99 136	99.51 129

XVS		99.53 9	99.42 11	99.87 7	99.85 23	99.83 8	99.69 48	99.68 19	98.98 19	99.37 113	99.74 101	98.81 34	99.94 41	98.79 79	99.86 50	99.84 13

X-MVStestdata		96.55 280	95.45 301	99.87 7	99.85 23	99.83 8	99.69 48	99.68 19	98.98 19	99.37 113	64.01 365	98.81 34	99.94 41	98.79 79	99.86 50	99.84 13

PatchmatchNet		98.31 146	98.36 135	98.19 270	99.16 217	95.32 307	99.27 226	98.92 287	97.37 172	99.37 113	99.58 165	94.90 180	99.70 190	97.43 207	99.21 120	99.54 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AllTest		98.87 103	98.72 107	99.31 117	99.86 20	98.48 203	99.56 116	99.61 32	97.85 122	99.36 116	99.85 27	95.95 137	99.85 116	96.66 255	99.83 63	99.59 111

TestCases				99.31 117	99.86 20	98.48 203		99.61 32	97.85 122	99.36 116	99.85 27	95.95 137	99.85 116	96.66 255	99.83 63	99.59 111

diffmvs1		99.12 70	99.00 71	99.48 92	99.51 137	99.10 105	99.61 91	99.49 106	97.67 144	99.36 116	99.74 101	97.67 90	99.88 103	98.95 57	98.99 136	99.47 139

Vis-MVSNet (Re-imp)		98.87 103	98.72 107	99.31 117	99.71 83	98.88 145	99.80 19	99.44 164	97.91 117	99.36 116	99.78 82	95.49 152	99.43 229	97.91 159	99.11 126	99.62 104

alignmvs		98.81 116	98.56 129	99.58 73	99.43 157	99.42 73	99.51 134	98.96 283	98.61 51	99.35 120	98.92 293	94.78 188	99.77 160	99.35 19	98.11 205	99.54 118

VPA-MVSNet		98.29 149	97.95 166	99.30 120	99.16 217	99.54 55	99.50 139	99.58 43	98.27 72	99.35 120	99.37 235	92.53 265	99.65 199	99.35 19	94.46 300	98.72 221

AdaColmap		99.01 93	98.80 100	99.66 55	99.56 131	99.54 55	99.18 250	99.70 15	98.18 81	99.35 120	99.63 148	96.32 130	99.90 88	97.48 201	99.77 76	99.55 116

test222					99.75 57	99.49 64	98.91 309	99.49 106	96.42 250	99.34 123	99.65 137	98.28 73			99.69 92	99.72 71

API-MVS		99.04 87	99.03 66	99.06 151	99.40 166	99.31 85	99.55 122	99.56 49	98.54 54	99.33 124	99.39 230	98.76 42	99.78 158	96.98 231	99.78 74	98.07 316

v144192		97.92 204	97.60 214	98.87 196	98.83 283	98.65 183	99.55 122	99.34 214	96.20 267	99.32 125	99.40 226	94.36 210	99.26 267	96.37 264	95.03 285	98.70 228

casdiffmvs		99.09 79	98.97 75	99.47 96	99.47 149	99.10 105	99.74 41	99.38 193	97.86 119	99.32 125	99.79 76	97.08 107	99.77 160	99.24 32	98.82 153	99.54 118

canonicalmvs		99.02 90	98.86 93	99.51 89	99.42 158	99.32 82	99.80 19	99.48 117	98.63 49	99.31 127	98.81 302	97.09 105	99.75 166	99.27 30	97.90 211	99.47 139

v6		98.12 169	97.84 181	98.94 166	98.94 260	98.83 153	99.66 68	99.34 214	96.49 240	99.30 128	99.37 235	94.95 174	99.34 246	97.77 172	94.74 290	98.67 249

V42		98.06 176	97.79 186	98.86 200	98.98 248	98.84 150	99.69 48	99.34 214	96.53 239	99.30 128	99.37 235	94.67 198	99.32 250	97.57 191	94.66 296	98.42 304

ab-mvs		98.86 106	98.63 118	99.54 78	99.64 109	99.19 95	99.44 166	99.54 63	97.77 132	99.30 128	99.81 57	94.20 215	99.93 56	99.17 39	98.82 153	99.49 133

TAPA-MVS	97.07 15	97.74 234	97.34 252	98.94 166	99.70 89	97.53 242	99.25 236	99.51 86	91.90 334	99.30 128	99.63 148	98.78 37	99.64 201	88.09 339	99.87 39	99.65 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1neww		98.12 169	97.84 181	98.93 169	98.97 252	98.81 162	99.66 68	99.35 206	96.49 240	99.29 132	99.37 235	95.02 170	99.32 250	97.73 177	94.73 291	98.67 249

v7new		98.12 169	97.84 181	98.93 169	98.97 252	98.81 162	99.66 68	99.35 206	96.49 240	99.29 132	99.37 235	95.02 170	99.32 250	97.73 177	94.73 291	98.67 249

新几何1				99.75 40	99.75 57	99.59 49		99.54 63	96.76 223	99.29 132	99.64 144	98.43 63	99.94 41	96.92 237	99.66 97	99.72 71

v7		98.05 182	97.78 188	98.87 196	98.99 244	98.67 180	99.64 80	99.34 214	96.31 258	99.29 132	99.51 192	94.78 188	99.27 262	97.03 227	95.15 282	98.66 260

VPNet		97.84 213	97.44 237	99.01 156	99.21 203	98.94 138	99.48 153	99.57 44	98.38 65	99.28 136	99.73 106	88.89 317	99.39 231	99.19 36	93.27 318	98.71 223

v1		98.05 182	97.76 195	98.93 169	98.92 267	98.80 167	99.57 109	99.35 206	96.39 254	99.28 136	99.36 242	94.86 183	99.32 250	97.38 209	94.72 293	98.68 238

HY-MVS	97.30 7	98.85 113	98.64 117	99.47 96	99.42 158	99.08 109	99.62 85	99.36 202	97.39 171	99.28 136	99.68 126	96.44 127	99.92 65	98.37 126	98.22 185	99.40 152

PAPM_NR		99.04 87	98.84 96	99.66 55	99.74 68	99.44 71	99.39 191	99.38 193	97.70 141	99.28 136	99.28 261	98.34 70	99.85 116	96.96 233	99.45 106	99.69 80

HPM-MVS++		99.39 38	99.23 47	99.87 7	99.75 57	99.84 7	99.43 171	99.51 86	98.68 48	99.27 140	99.53 184	98.64 54	99.96 19	98.44 122	99.80 70	99.79 45

v1240		97.69 241	97.32 255	98.79 210	98.85 281	98.43 206	99.48 153	99.36 202	96.11 276	99.27 140	99.36 242	93.76 232	99.24 270	94.46 298	95.23 279	98.70 228

thres600view7		97.86 210	97.51 221	98.92 174	99.72 77	97.95 226	99.59 96	98.74 308	97.94 113	99.27 140	98.62 309	91.75 283	99.86 110	93.73 314	98.19 188	98.96 194

PLC	97.94 4	99.02 90	98.85 95	99.53 83	99.66 105	99.01 123	99.24 238	99.52 77	96.85 219	99.27 140	99.48 204	98.25 74	99.91 75	97.76 173	99.62 103	99.65 92
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpn111		97.81 219	97.49 225	98.78 212	99.72 77	97.86 229	99.59 96	98.74 308	97.93 114	99.26 144	98.62 309	91.75 283	99.86 110	93.57 315	98.18 189	98.61 283

conf200view11		97.78 226	97.45 231	98.77 213	99.72 77	97.86 229	99.59 96	98.74 308	97.93 114	99.26 144	98.62 309	91.75 283	99.83 130	93.22 319	98.18 189	98.61 283

thres100view900		97.76 228	97.45 231	98.69 219	99.72 77	97.86 229	99.59 96	98.74 308	97.93 114	99.26 144	98.62 309	91.75 283	99.83 130	93.22 319	98.18 189	98.37 308

EPMVS		97.82 218	97.65 210	98.35 250	98.88 274	95.98 294	99.49 148	94.71 360	97.57 151	99.26 144	99.48 204	92.46 270	99.71 184	97.87 162	99.08 130	99.35 155

view600		97.97 195	97.66 205	98.89 185	99.75 57	97.81 233	99.69 48	98.80 300	98.02 104	99.25 148	98.88 294	91.95 276	99.89 96	94.36 301	98.29 179	98.96 194

view800		97.97 195	97.66 205	98.89 185	99.75 57	97.81 233	99.69 48	98.80 300	98.02 104	99.25 148	98.88 294	91.95 276	99.89 96	94.36 301	98.29 179	98.96 194

conf0.05thres1000		97.97 195	97.66 205	98.89 185	99.75 57	97.81 233	99.69 48	98.80 300	98.02 104	99.25 148	98.88 294	91.95 276	99.89 96	94.36 301	98.29 179	98.96 194

tfpn		97.97 195	97.66 205	98.89 185	99.75 57	97.81 233	99.69 48	98.80 300	98.02 104	99.25 148	98.88 294	91.95 276	99.89 96	94.36 301	98.29 179	98.96 194

1121		99.09 79	98.87 89	99.75 40	99.74 68	99.60 47	99.27 226	99.48 117	96.82 222	99.25 148	99.65 137	98.38 67	99.93 56	97.53 196	99.67 96	99.73 65

Fast-Effi-MVS+-dtu		98.77 122	98.83 99	98.60 225	99.41 161	96.99 264	99.52 130	99.49 106	98.11 88	99.24 153	99.34 249	96.96 111	99.79 150	97.95 157	99.45 106	99.02 183

v1921920		97.80 222	97.45 231	98.84 204	98.80 284	98.53 194	99.52 130	99.34 214	96.15 273	99.24 153	99.47 208	93.98 224	99.29 258	95.40 282	95.13 283	98.69 233

divwei89l23v2f112		98.06 176	97.78 188	98.91 178	98.90 270	98.77 172	99.57 109	99.35 206	96.45 247	99.24 153	99.37 235	94.92 178	99.27 262	97.50 199	94.71 295	98.68 238

LPG-MVS_test		98.22 156	98.13 148	98.49 235	99.33 178	97.05 259	99.58 103	99.55 56	97.46 160	99.24 153	99.83 40	92.58 263	99.72 178	98.09 144	97.51 226	98.68 238

LGP-MVS_train				98.49 235	99.33 178	97.05 259		99.55 56	97.46 160	99.24 153	99.83 40	92.58 263	99.72 178	98.09 144	97.51 226	98.68 238

v1144		97.98 192	97.69 202	98.85 203	98.87 277	98.66 182	99.54 125	99.35 206	96.27 261	99.23 158	99.35 246	94.67 198	99.23 271	96.73 250	95.16 281	98.68 238

v1141		98.05 182	97.76 195	98.91 178	98.91 269	98.78 171	99.57 109	99.35 206	96.41 252	99.23 158	99.36 242	94.93 177	99.27 262	97.38 209	94.72 293	98.68 238

Anonymous20240529		98.09 173	97.68 203	99.34 111	99.66 105	98.44 205	99.40 189	99.43 172	93.67 319	99.22 160	99.89 10	90.23 306	99.93 56	99.26 31	98.33 175	99.66 88

OPM-MVS		98.19 162	98.10 150	98.45 241	98.88 274	97.07 257	99.28 223	99.38 193	98.57 53	99.22 160	99.81 57	92.12 275	99.66 197	98.08 148	97.54 225	98.61 283

test_djsdf		98.67 128	98.57 128	98.98 160	98.70 301	98.91 143	99.88 1	99.46 143	97.55 153	99.22 160	99.88 15	95.73 147	99.28 259	99.03 50	97.62 218	98.75 216

test12				99.75 40	99.64 109	99.61 45		99.29 236		99.21 163		98.38 67	99.89 96		99.74 81	99.74 60

NCCC		99.34 42	99.19 49	99.79 34	99.61 120	99.65 40	99.30 217	99.48 117	98.86 31	99.21 163	99.63 148	98.72 48	99.90 88	98.25 134	99.63 102	99.80 41

PMMVS		98.80 119	98.62 122	99.34 111	99.27 195	98.70 178	98.76 320	99.31 229	97.34 173	99.21 163	99.07 280	97.20 103	99.82 139	98.56 109	98.87 150	99.52 124

v1192		97.81 219	97.44 237	98.91 178	98.88 274	98.68 179	99.51 134	99.34 214	96.18 269	99.20 166	99.34 249	94.03 223	99.36 240	95.32 284	95.18 280	98.69 233

EI-MVSNet		98.67 128	98.67 113	98.68 220	99.35 174	97.97 223	99.50 139	99.38 193	96.93 214	99.20 166	99.83 40	97.87 83	99.36 240	98.38 125	97.56 223	98.71 223

MVSTER		98.49 134	98.32 139	99.00 158	99.35 174	99.02 121	99.54 125	99.38 193	97.41 169	99.20 166	99.73 106	93.86 229	99.36 240	98.87 66	97.56 223	98.62 274

Anonymous202405211		98.30 147	97.98 163	99.26 130	99.57 127	98.16 215	99.41 182	98.55 327	96.03 281	99.19 169	99.74 101	91.87 281	99.92 65	99.16 41	98.29 179	99.70 79

v2v482		98.06 176	97.77 192	98.92 174	98.90 270	98.82 160	99.57 109	99.36 202	96.65 230	99.19 169	99.35 246	94.20 215	99.25 268	97.72 181	94.97 286	98.69 233

CNLPA		99.14 64	98.99 72	99.59 70	99.58 125	99.41 74	99.16 252	99.44 164	98.45 60	99.19 169	99.49 198	98.08 79	99.89 96	97.73 177	99.75 79	99.48 135

UGNet		98.87 103	98.69 111	99.40 107	99.22 202	98.72 177	99.44 166	99.68 19	99.24 3	99.18 172	99.42 219	92.74 250	99.96 19	99.34 23	99.94 10	99.53 123
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
tfpn200view9		97.72 237	97.38 245	98.72 217	99.69 91	97.96 224	99.50 139	98.73 317	97.83 125	99.17 173	98.45 318	91.67 289	99.83 130	93.22 319	98.18 189	98.37 308

thres400		97.77 227	97.38 245	98.92 174	99.69 91	97.96 224	99.50 139	98.73 317	97.83 125	99.17 173	98.45 318	91.67 289	99.83 130	93.22 319	98.18 189	98.96 194

Test_1112_low_res		98.89 102	98.66 116	99.57 75	99.69 91	98.95 135	99.03 282	99.47 133	96.98 210	99.15 175	99.23 268	96.77 117	99.89 96	98.83 75	98.78 157	99.86 6

diffmvs		98.99 95	98.87 89	99.35 110	99.45 155	98.74 174	99.62 85	99.45 155	97.43 165	99.13 176	99.72 110	97.23 102	99.87 106	98.86 69	98.90 147	99.45 146

1112_ss		98.98 96	98.77 103	99.59 70	99.68 94	99.02 121	99.25 236	99.48 117	97.23 184	99.13 176	99.58 165	96.93 112	99.90 88	98.87 66	98.78 157	99.84 13

CLD-MVS		98.16 165	98.10 150	98.33 251	99.29 190	96.82 273	98.75 321	99.44 164	97.83 125	99.13 176	99.55 174	92.92 244	99.67 195	98.32 132	97.69 215	98.48 300
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
原ACMM1				99.65 59	99.73 73	99.33 81		99.47 133	97.46 160	99.12 179	99.66 136	98.67 53	99.91 75	97.70 182	99.69 92	99.71 78

tpm		97.67 246	97.55 216	98.03 276	99.02 241	95.01 314	99.43 171	98.54 328	96.44 248	99.12 179	99.34 249	91.83 282	99.60 209	97.75 175	96.46 256	99.48 135

HQP_MVS		98.27 151	98.22 145	98.44 244	99.29 190	96.97 266	99.39 191	99.47 133	98.97 22	99.11 181	99.61 157	92.71 252	99.69 193	97.78 170	97.63 216	98.67 249

plane_prior3						97.00 263			98.69 47	99.11 181

CHOSEN 1792x2688		99.19 58	99.10 58	99.45 100	99.89 8	98.52 198	99.39 191	99.94 1	98.73 44	99.11 181	99.89 10	95.50 151	99.94 41	99.50 8	99.97 3	99.89 2

mvs-test1		98.86 106	98.84 96	98.89 185	99.33 178	97.77 238	99.44 166	99.30 231	98.47 58	99.10 184	99.43 217	96.78 115	99.95 34	98.73 85	99.02 134	98.96 194

v8		97.95 200	97.63 212	98.93 169	98.95 257	98.81 162	99.80 19	99.41 177	96.03 281	99.10 184	99.42 219	94.92 178	99.30 256	96.94 235	94.08 309	98.66 260

ADS-MVSNet2		98.02 187	98.07 155	97.87 288	99.33 178	95.19 311	99.23 239	99.08 268	96.24 264	99.10 184	99.67 131	94.11 220	98.93 310	96.81 246	99.05 132	99.48 135

ADS-MVSNet		98.20 161	98.08 153	98.56 230	99.33 178	96.48 283	99.23 239	99.15 261	96.24 264	99.10 184	99.67 131	94.11 220	99.71 184	96.81 246	99.05 132	99.48 135

thres200		97.61 250	97.28 259	98.62 224	99.64 109	98.03 220	99.26 234	98.74 308	97.68 143	99.09 188	98.32 320	91.66 291	99.81 143	92.88 325	98.22 185	98.03 320

dp		97.75 232	97.80 185	97.59 302	99.10 228	93.71 329	99.32 212	98.88 294	96.48 246	99.08 189	99.55 174	92.67 261	99.82 139	96.52 259	98.58 163	99.24 162

GBi-Net		97.68 243	97.48 226	98.29 255	99.51 137	97.26 247	99.43 171	99.48 117	96.49 240	99.07 190	99.32 255	90.26 303	98.98 301	97.10 223	96.65 251	98.62 274

test1		97.68 243	97.48 226	98.29 255	99.51 137	97.26 247	99.43 171	99.48 117	96.49 240	99.07 190	99.32 255	90.26 303	98.98 301	97.10 223	96.65 251	98.62 274

FMVSNet3		98.03 185	97.76 195	98.84 204	99.39 168	98.98 127	99.40 189	99.38 193	96.67 229	99.07 190	99.28 261	92.93 243	98.98 301	97.10 223	96.65 251	98.56 296

IterMVS-LS		98.46 136	98.42 133	98.58 227	99.59 124	98.00 221	99.37 198	99.43 172	96.94 213	99.07 190	99.59 162	97.87 83	99.03 295	98.32 132	95.62 273	98.71 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		98.13 167	97.90 169	98.81 207	98.61 311	98.87 146	98.99 291	99.21 255	96.44 248	99.06 194	99.58 165	95.90 141	99.11 287	97.18 220	96.11 263	98.46 303

XVG-ACMP-BASELINE		97.83 215	97.71 201	98.20 269	99.11 225	96.33 288	99.41 182	99.52 77	98.06 99	99.05 195	99.50 195	89.64 311	99.73 174	97.73 177	97.38 239	98.53 297

CostFormer		97.72 237	97.73 199	97.71 299	99.15 220	94.02 325	99.54 125	99.02 277	94.67 298	99.04 196	99.35 246	92.35 273	99.77 160	98.50 116	97.94 210	99.34 156

DP-MVS		99.16 63	98.95 80	99.78 35	99.77 42	99.53 58	99.41 182	99.50 101	97.03 208	99.04 196	99.88 15	97.39 96	99.92 65	98.66 93	99.90 24	99.87 5

ACMM	97.58 5	98.37 143	98.34 137	98.48 237	99.41 161	97.10 253	99.56 116	99.45 155	98.53 55	99.04 196	99.85 27	93.00 242	99.71 184	98.74 83	97.45 233	98.64 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		98.70 125	98.43 132	99.51 89	99.51 137	99.28 87	99.52 130	99.47 133	96.11 276	99.01 199	99.34 249	96.20 134	99.84 122	97.88 161	98.82 153	99.39 153

nrg030		98.64 131	98.42 133	99.28 125	99.05 237	99.69 32	99.81 15	99.46 143	98.04 101	99.01 199	99.82 47	96.69 120	99.38 232	99.34 23	94.59 299	98.78 209

test_prior3		99.21 57	99.05 61	99.68 52	99.67 95	99.48 65	98.96 300	99.56 49	98.34 67	99.01 199	99.52 189	98.68 51	99.83 130	97.96 155	99.74 81	99.74 60

test_prior2							98.96 300		98.34 67	99.01 199	99.52 189	98.68 51		97.96 155	99.74 81

v52		97.79 224	97.50 223	98.66 223	98.80 284	98.62 187	99.87 4	99.44 164	95.87 284	99.01 199	99.46 212	94.44 209	99.33 247	96.65 257	93.96 312	98.05 317

V4		97.80 222	97.51 221	98.67 222	98.79 286	98.63 185	99.87 4	99.44 164	95.87 284	99.01 199	99.46 212	94.52 205	99.33 247	96.64 258	93.97 311	98.05 317

MAR-MVS		98.86 106	98.63 118	99.54 78	99.37 171	99.66 37	99.45 162	99.54 63	96.61 233	99.01 199	99.40 226	97.09 105	99.86 110	97.68 185	99.53 105	99.10 169
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
PS-MVSNAJss		98.92 101	98.92 82	98.90 182	98.78 290	98.53 194	99.78 22	99.54 63	98.07 95	99.00 206	99.76 91	99.01 12	99.37 236	99.13 43	97.23 243	98.81 206

PAPR		98.63 132	98.34 137	99.51 89	99.40 166	99.03 120	98.80 316	99.36 202	96.33 255	99.00 206	99.12 278	98.46 61	99.84 122	95.23 285	99.37 114	99.66 88

v10		97.85 211	97.52 219	98.86 200	98.99 244	98.67 180	99.75 36	99.41 177	95.70 287	98.98 208	99.41 222	94.75 194	99.23 271	96.01 269	94.63 298	98.67 249

UniMVSNet (Re)		98.29 149	98.00 160	99.13 147	99.00 243	99.36 78	99.49 148	99.51 86	97.95 112	98.97 209	99.13 275	96.30 131	99.38 232	98.36 128	93.34 317	98.66 260

TEST9					99.67 95	99.65 40	99.05 276	99.41 177	96.22 266	98.95 210	99.49 198	98.77 40	99.91 75

train_agg		99.02 90	98.77 103	99.77 37	99.67 95	99.65 40	99.05 276	99.41 177	96.28 259	98.95 210	99.49 198	98.76 42	99.91 75	97.63 186	99.72 85	99.75 55

BH-RMVSNet		98.41 140	98.08 153	99.40 107	99.41 161	98.83 153	99.30 217	98.77 304	97.70 141	98.94 212	99.65 137	92.91 246	99.74 167	96.52 259	99.55 104	99.64 98

test_8					99.67 95	99.61 45	99.03 282	99.41 177	96.28 259	98.93 213	99.48 204	98.76 42	99.91 75

3Dnovator	97.25 9	99.24 55	99.05 61	99.81 29	99.12 223	99.66 37	99.84 9	99.74 10	99.09 8	98.92 214	99.90 7	95.94 139	99.98 5	98.95 57	99.92 12	99.79 45

v7n		97.87 209	97.52 219	98.92 174	98.76 294	98.58 191	99.84 9	99.46 143	96.20 267	98.91 215	99.70 115	94.89 181	99.44 225	96.03 268	93.89 313	98.75 216

JIA-IIPM		97.50 259	97.02 268	98.93 169	98.73 296	97.80 237	99.30 217	98.97 281	91.73 335	98.91 215	94.86 350	95.10 167	99.71 184	97.58 189	97.98 209	99.28 160

v148		97.79 224	97.55 216	98.50 234	98.74 295	97.72 241	99.54 125	99.33 222	96.26 262	98.90 217	99.51 192	94.68 197	99.14 281	97.83 165	93.15 320	98.63 272

GA-MVS		97.85 211	97.47 228	99.00 158	99.38 169	97.99 222	98.57 331	99.15 261	97.04 207	98.90 217	99.30 258	89.83 309	99.38 232	96.70 252	98.33 175	99.62 104

tpm2		97.44 264	97.34 252	97.74 298	99.15 220	94.36 322	99.45 162	98.94 284	93.45 325	98.90 217	99.44 216	91.35 294	99.59 211	97.31 212	98.07 206	99.29 159

agg_prior3		98.97 98	98.71 109	99.75 40	99.67 95	99.60 47	99.04 281	99.41 177	95.93 283	98.87 220	99.48 204	98.61 55	99.91 75	97.63 186	99.72 85	99.75 55

agg_prior1		99.01 93	98.76 105	99.76 39	99.67 95	99.62 43	98.99 291	99.40 184	96.26 262	98.87 220	99.49 198	98.77 40	99.91 75	97.69 183	99.72 85	99.75 55

agg_prior					99.67 95	99.62 43		99.40 184		98.87 220			99.91 75

anonymousdsp		98.44 137	98.28 142	98.94 166	98.50 317	98.96 134	99.77 25	99.50 101	97.07 204	98.87 220	99.77 88	94.76 193	99.28 259	98.66 93	97.60 219	98.57 295

DSMNet-mixed		97.25 270	97.35 249	96.95 314	97.84 327	93.61 331	99.57 109	96.63 356	96.13 275	98.87 220	98.61 313	94.59 201	97.70 338	95.08 287	98.86 151	99.55 116

FMVSNet2		97.72 237	97.36 247	98.80 209	99.51 137	98.84 150	99.45 162	99.42 175	96.49 240	98.86 225	99.29 260	90.26 303	98.98 301	96.44 261	96.56 254	98.58 294

PatchFormer-LS_test		98.01 190	98.05 156	97.87 288	99.15 220	94.76 318	99.42 178	98.93 285	97.12 193	98.84 226	98.59 314	93.74 234	99.80 147	98.55 112	98.17 195	99.06 179

ITE_SJBPF				98.08 274	99.29 190	96.37 286		98.92 287	98.34 67	98.83 227	99.75 96	91.09 296	99.62 207	95.82 271	97.40 237	98.25 313

Anonymous20231211		97.88 207	97.54 218	98.90 182	99.71 83	98.53 194	99.48 153	99.57 44	94.16 313	98.81 228	99.68 126	93.23 238	99.42 230	98.84 72	94.42 302	98.76 214

Patchmtry		97.75 232	97.40 243	98.81 207	99.10 228	98.87 146	99.11 265	99.33 222	94.83 295	98.81 228	99.38 231	94.33 211	99.02 296	96.10 266	95.57 274	98.53 297

BH-untuned		98.42 139	98.36 135	98.59 226	99.49 145	96.70 276	99.27 226	99.13 264	97.24 183	98.80 230	99.38 231	95.75 146	99.74 167	97.07 226	99.16 123	99.33 157

FIs		98.78 120	98.63 118	99.23 136	99.18 210	99.54 55	99.83 12	99.59 38	98.28 71	98.79 231	99.81 57	96.75 118	99.37 236	99.08 47	96.38 258	98.78 209

OurMVSNet-221017-0		97.88 207	97.77 192	98.19 270	98.71 300	96.53 281	99.88 1	99.00 278	97.79 130	98.78 232	99.94 3	91.68 288	99.35 243	97.21 216	96.99 249	98.69 233

MVS-HIRNet		95.75 301	95.16 305	97.51 305	99.30 187	93.69 330	98.88 311	95.78 357	85.09 349	98.78 232	92.65 352	91.29 295	99.37 236	94.85 291	99.85 54	99.46 143

tpmvs		97.98 192	98.02 158	97.84 291	99.04 238	94.73 319	99.31 215	99.20 256	96.10 280	98.76 234	99.42 219	94.94 175	99.81 143	96.97 232	98.45 171	98.97 188

Patchmatch-test		97.93 201	97.65 210	98.77 213	99.18 210	97.07 257	99.03 282	99.14 263	96.16 271	98.74 235	99.57 169	94.56 202	99.72 178	93.36 318	99.11 126	99.52 124

QAPM		98.67 128	98.30 141	99.80 31	99.20 205	99.67 35	99.77 25	99.72 11	94.74 297	98.73 236	99.90 7	95.78 145	99.98 5	96.96 233	99.88 35	99.76 54

3Dnovator+	97.12 13	99.18 60	98.97 75	99.82 26	99.17 215	99.68 33	99.81 15	99.51 86	99.20 4	98.72 237	99.89 10	95.68 148	99.97 11	98.86 69	99.86 50	99.81 35

semantic-postprocess				98.06 275	99.57 127	96.36 287		99.49 106	97.18 187	98.71 238	99.72 110	92.70 254	99.14 281	97.44 206	95.86 269	98.67 249

UniMVSNet_NR-MVSNet		98.22 156	97.97 164	98.96 163	98.92 267	98.98 127	99.48 153	99.53 73	97.76 133	98.71 238	99.46 212	96.43 128	99.22 274	98.57 106	92.87 323	98.69 233

DU-MVS		98.08 175	97.79 186	98.96 163	98.87 277	98.98 127	99.41 182	99.45 155	97.87 118	98.71 238	99.50 195	94.82 185	99.22 274	98.57 106	92.87 323	98.68 238

tpm cat1		97.39 266	97.36 247	97.50 306	99.17 215	93.73 327	99.43 171	99.31 229	91.27 336	98.71 238	99.08 279	94.31 213	99.77 160	96.41 263	98.50 169	99.00 184

XXY-MVS		98.38 142	98.09 152	99.24 134	99.26 197	99.32 82	99.56 116	99.55 56	97.45 163	98.71 238	99.83 40	93.23 238	99.63 206	98.88 62	96.32 260	98.76 214

IterMVS		97.83 215	97.77 192	98.02 278	99.58 125	96.27 290	99.02 285	99.48 117	97.22 185	98.71 238	99.70 115	92.75 248	99.13 284	97.46 204	96.00 266	98.67 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test		98.75 123	98.62 122	99.15 143	99.08 231	99.45 70	99.86 8	99.60 35	98.23 76	98.70 244	99.82 47	96.80 114	99.22 274	99.07 48	96.38 258	98.79 208

COLMAP_ROB	97.56 6	98.86 106	98.75 106	99.17 140	99.88 11	98.53 194	99.34 210	99.59 38	97.55 153	98.70 244	99.89 10	95.83 143	99.90 88	98.10 143	99.90 24	99.08 174
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		97.76 228	97.41 242	98.82 206	99.06 234	97.87 228	98.87 312	98.56 326	96.63 232	98.68 246	99.22 269	92.49 266	99.65 199	95.40 282	97.79 213	98.95 201

WR-MVS		98.06 176	97.73 199	99.06 151	98.86 280	99.25 91	99.19 249	99.35 206	97.30 177	98.66 247	99.43 217	93.94 225	99.21 278	98.58 104	94.28 304	98.71 223

HQP-NCC					99.19 207		98.98 295		98.24 73	98.66 247

ACMP_Plane					99.19 207		98.98 295		98.24 73	98.66 247

HQP4-MVS										98.66 247			99.64 201			98.64 265

HQP-MVS		98.02 187	97.90 169	98.37 249	99.19 207	96.83 271	98.98 295	99.39 187	98.24 73	98.66 247	99.40 226	92.47 267	99.64 201	97.19 218	97.58 221	98.64 265

LF4IMVS		97.52 255	97.46 230	97.70 300	98.98 248	95.55 300	99.29 221	98.82 299	98.07 95	98.66 247	99.64 144	89.97 308	99.61 208	97.01 228	96.68 250	97.94 324

mvs_tets		98.40 141	98.23 144	98.91 178	98.67 305	98.51 200	99.66 68	99.53 73	98.19 78	98.65 253	99.81 57	92.75 248	99.44 225	99.31 26	97.48 232	98.77 212

TESTMET0.1,1		97.55 252	97.27 261	98.40 247	98.93 265	96.53 281	98.67 325	97.61 351	96.96 211	98.64 254	99.28 261	88.63 323	99.45 220	97.30 213	99.38 110	99.21 163

jajsoiax		98.43 138	98.28 142	98.88 192	98.60 312	98.43 206	99.82 13	99.53 73	98.19 78	98.63 255	99.80 68	93.22 240	99.44 225	99.22 34	97.50 228	98.77 212

Baseline_NR-MVSNet		97.76 228	97.45 231	98.68 220	99.09 230	98.29 210	99.41 182	98.85 296	95.65 288	98.63 255	99.67 131	94.82 185	99.10 289	98.07 150	92.89 322	98.64 265

EPNet		98.86 106	98.71 109	99.30 120	97.20 338	98.18 214	99.62 85	98.91 290	99.28 2	98.63 255	99.81 57	95.96 136	99.99 1	99.24 32	99.72 85	99.73 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-LLR		98.06 176	97.90 169	98.55 232	98.79 286	97.10 253	98.67 325	97.75 341	97.34 173	98.61 258	98.85 298	94.45 207	99.45 220	97.25 214	99.38 110	99.10 169

test-mter		97.49 261	97.13 265	98.55 232	98.79 286	97.10 253	98.67 325	97.75 341	96.65 230	98.61 258	98.85 298	88.23 328	99.45 220	97.25 214	99.38 110	99.10 169

FMVSNet1		96.84 277	96.36 278	98.29 255	99.32 185	97.26 247	99.43 171	99.48 117	95.11 292	98.55 260	99.32 255	83.95 346	98.98 301	95.81 272	96.26 261	98.62 274

v748		97.52 255	97.23 262	98.41 246	98.69 302	97.23 250	99.87 4	99.45 155	95.72 286	98.51 261	99.53 184	94.13 219	99.30 256	96.78 248	92.39 327	98.70 228

PCF-MVS	97.08 14	97.66 247	97.06 267	99.47 96	99.61 120	99.09 108	98.04 347	99.25 251	91.24 337	98.51 261	99.70 115	94.55 203	99.91 75	92.76 326	99.85 54	99.42 150
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		97.93 201	97.66 205	98.76 215	98.78 290	98.62 187	99.65 78	99.49 106	97.76 133	98.49 263	99.60 160	94.23 214	98.97 308	98.00 153	92.90 321	98.70 228

CP-MVSNet		98.09 173	97.78 188	99.01 156	98.97 252	99.24 92	99.67 59	99.46 143	97.25 181	98.48 264	99.64 144	93.79 230	99.06 291	98.63 96	94.10 308	98.74 219

ACMP	97.20 11	98.06 176	97.94 167	98.45 241	99.37 171	97.01 262	99.44 166	99.49 106	97.54 156	98.45 265	99.79 76	91.95 276	99.72 178	97.91 159	97.49 231	98.62 274
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		97.69 241	97.43 240	98.48 237	98.60 312	97.30 244	98.18 345	99.39 187	92.96 327	98.41 266	98.78 305	93.77 231	99.27 262	98.16 139	98.61 160	98.86 203

WR-MVS_H		98.13 167	97.87 180	98.90 182	99.02 241	98.84 150	99.70 45	99.59 38	97.27 179	98.40 267	99.19 271	95.53 150	99.23 271	98.34 129	93.78 314	98.61 283

BH-w/o		98.00 191	97.89 173	98.32 252	99.35 174	96.20 292	99.01 289	98.90 292	96.42 250	98.38 268	99.00 286	95.26 160	99.72 178	96.06 267	98.61 160	99.03 181

pmmvs5		97.52 255	97.30 257	98.16 272	98.57 314	96.73 275	99.27 226	98.90 292	96.14 274	98.37 269	99.53 184	91.54 293	99.14 281	97.51 198	95.87 268	98.63 272

DWT-MVSNet_test		97.53 254	97.40 243	97.93 284	99.03 240	94.86 316	99.57 109	98.63 322	96.59 237	98.36 270	98.79 303	89.32 313	99.74 167	98.14 142	98.16 196	99.20 164

EU-MVSNet		97.98 192	98.03 157	97.81 294	98.72 298	96.65 279	99.66 68	99.66 25	98.09 91	98.35 271	99.82 47	95.25 161	98.01 331	97.41 208	95.30 278	98.78 209

FMVSNet5		96.43 283	96.19 280	97.15 309	99.11 225	95.89 296	99.32 212	99.52 77	94.47 307	98.34 272	99.07 280	87.54 332	97.07 341	92.61 327	95.72 271	98.47 301

PS-CasMVS		97.93 201	97.59 215	98.95 165	98.99 244	99.06 111	99.68 57	99.52 77	97.13 191	98.31 273	99.68 126	92.44 271	99.05 292	98.51 115	94.08 309	98.75 216

USDC		97.34 267	97.20 263	97.75 297	99.07 232	95.20 310	98.51 334	99.04 275	97.99 109	98.31 273	99.86 23	89.02 315	99.55 214	95.67 277	97.36 240	98.49 299

PEN-MVS		97.76 228	97.44 237	98.72 217	98.77 293	98.54 193	99.78 22	99.51 86	97.06 206	98.29 275	99.64 144	92.63 262	98.89 311	98.09 144	93.16 319	98.72 221

tfpnnormal		97.84 213	97.47 228	98.98 160	99.20 205	99.22 94	99.64 80	99.61 32	96.32 256	98.27 276	99.70 115	93.35 237	99.44 225	95.69 275	95.40 276	98.27 311

ppachtmachnet_test		97.49 261	97.45 231	97.61 301	98.62 309	95.24 308	98.80 316	99.46 143	96.11 276	98.22 277	99.62 153	96.45 126	98.97 308	93.77 313	95.97 267	98.61 283

tpmp4_e23		97.34 267	97.29 258	97.52 304	99.25 199	93.73 327	99.58 103	99.19 259	94.00 315	98.20 278	99.41 222	90.74 300	99.74 167	97.13 222	98.07 206	99.07 178

our_test_3		97.65 248	97.68 203	97.55 303	98.62 309	94.97 315	98.84 314	99.30 231	96.83 221	98.19 279	99.34 249	97.01 109	99.02 296	95.00 289	96.01 265	98.64 265

LTVRE_ROB	97.16 12	98.02 187	97.90 169	98.40 247	99.23 200	96.80 274	99.70 45	99.60 35	97.12 193	98.18 280	99.70 115	91.73 287	99.72 178	98.39 123	97.45 233	98.68 238
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
ACMH	97.28 8	98.10 172	97.99 162	98.44 244	99.41 161	96.96 268	99.60 94	99.56 49	98.09 91	98.15 281	99.91 5	90.87 299	99.70 190	98.88 62	97.45 233	98.67 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		97.24 271	97.32 255	96.99 312	98.45 319	93.51 332	98.82 315	99.32 228	97.41 169	98.13 282	99.30 258	88.99 316	99.56 212	95.68 276	99.80 70	97.90 327

LP		97.04 275	96.80 271	97.77 296	98.90 270	95.23 309	98.97 298	99.06 273	94.02 314	98.09 283	99.41 222	93.88 227	98.82 312	90.46 332	98.42 173	99.26 161

MVS		97.28 269	96.55 276	99.48 92	98.78 290	98.95 135	99.27 226	99.39 187	83.53 350	98.08 284	99.54 177	96.97 110	99.87 106	94.23 309	99.16 123	99.63 102

PAPM		97.59 251	97.09 266	99.07 150	99.06 234	98.26 212	98.30 341	99.10 266	94.88 294	98.08 284	99.34 249	96.27 132	99.64 201	89.87 334	98.92 145	99.31 158

OpenMVS	96.50 16	98.47 135	98.12 149	99.52 87	99.04 238	99.53 58	99.82 13	99.72 11	94.56 303	98.08 284	99.88 15	94.73 195	99.98 5	97.47 203	99.76 78	99.06 179

gg-mvs-nofinetune		96.17 296	95.32 303	98.73 216	98.79 286	98.14 217	99.38 196	94.09 361	91.07 339	98.07 287	91.04 356	89.62 312	99.35 243	96.75 249	99.09 129	98.68 238

test0.0.03 1		97.71 240	97.42 241	98.56 230	98.41 320	97.82 232	98.78 318	98.63 322	97.34 173	98.05 288	98.98 290	94.45 207	98.98 301	95.04 288	97.15 247	98.89 202

Anonymous20240521		98.30 147	98.00 160	99.18 139	98.98 248	99.46 68	99.78 22	99.49 106	96.91 216	98.00 289	99.25 265	96.51 124	99.38 232	98.15 141	94.95 288	98.71 223

1314		98.68 127	98.54 130	99.11 148	98.89 273	98.65 183	99.27 226	99.49 106	96.89 217	97.99 290	99.56 171	97.72 89	99.83 130	97.74 176	99.27 118	98.84 204

DTE-MVSNet		97.51 258	97.19 264	98.46 240	98.63 308	98.13 218	99.84 9	99.48 117	96.68 228	97.97 291	99.67 131	92.92 244	98.56 317	96.88 245	92.60 326	98.70 228

SixPastTwentyTwo		97.50 259	97.33 254	98.03 276	98.65 306	96.23 291	99.77 25	98.68 320	97.14 190	97.90 292	99.93 4	90.45 301	99.18 280	97.00 229	96.43 257	98.67 249

pm-mvs1		97.68 243	97.28 259	98.88 192	99.06 234	98.62 187	99.50 139	99.45 155	96.32 256	97.87 293	99.79 76	92.47 267	99.35 243	97.54 195	93.54 316	98.67 249

testgi		97.65 248	97.50 223	98.13 273	99.36 173	96.45 284	99.42 178	99.48 117	97.76 133	97.87 293	99.45 215	91.09 296	98.81 313	94.53 296	98.52 168	99.13 168

EPNet_dtu		98.03 185	97.96 165	98.23 265	98.27 322	95.54 302	99.23 239	98.75 305	99.02 10	97.82 295	99.71 112	96.11 135	99.48 217	93.04 323	99.65 99	99.69 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 273	96.89 270	97.83 292	99.07 232	95.52 303	98.57 331	98.74 308	97.58 150	97.81 296	99.79 76	88.16 329	99.56 212	95.10 286	97.21 244	98.39 307

ACMH+	97.24 10	97.92 204	97.78 188	98.32 252	99.46 151	96.68 278	99.56 116	99.54 63	98.41 64	97.79 297	99.87 20	90.18 307	99.66 197	98.05 152	97.18 246	98.62 274

N_pmnet		94.95 310	95.83 288	92.31 333	98.47 318	79.33 356	99.12 259	92.81 366	93.87 317	97.68 298	99.13 275	93.87 228	99.01 298	91.38 330	96.19 262	98.59 291

PVSNet_0	94.43 19	96.09 298	95.47 300	97.94 283	99.31 186	94.34 323	97.81 348	99.70 15	97.12 193	97.46 299	98.75 306	89.71 310	99.79 150	97.69 183	81.69 351	99.68 84

pmmvs6		96.53 281	96.09 282	97.82 293	98.69 302	95.47 304	99.37 198	99.47 133	93.46 324	97.41 300	99.78 82	87.06 335	99.33 247	96.92 237	92.70 325	98.65 263

new_pmnet		96.38 287	96.03 283	97.41 307	98.13 325	95.16 313	99.05 276	99.20 256	93.94 316	97.39 301	98.79 303	91.61 292	99.04 293	90.43 333	95.77 270	98.05 317

IB-MVS	95.67 18	96.22 294	95.44 302	98.57 228	99.21 203	96.70 276	98.65 328	97.74 343	96.71 226	97.27 302	98.54 316	86.03 337	99.92 65	98.47 119	86.30 348	99.10 169
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
GG-mvs-BLEND				98.45 241	98.55 315	98.16 215	99.43 171	93.68 362		97.23 303	98.46 317	89.30 314	99.22 274	95.43 281	98.22 185	97.98 322

MVP-Stereo		97.81 219	97.75 198	97.99 281	97.53 331	96.60 280	98.96 300	98.85 296	97.22 185	97.23 303	99.36 242	95.28 157	99.46 219	95.51 279	99.78 74	97.92 326
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TransMVSNet (Re)		97.15 272	96.58 275	98.86 200	99.12 223	98.85 149	99.49 148	98.91 290	95.48 289	97.16 305	99.80 68	93.38 236	99.11 287	94.16 311	91.73 328	98.62 274

NR-MVSNet		97.97 195	97.61 213	99.02 155	98.87 277	99.26 90	99.47 158	99.42 175	97.63 147	97.08 306	99.50 195	95.07 168	99.13 284	97.86 163	93.59 315	98.68 238

Anonymous20231206		96.22 294	96.03 283	96.79 318	97.31 336	94.14 324	99.63 82	99.08 268	96.17 270	97.04 307	99.06 282	93.94 225	97.76 337	86.96 343	95.06 284	98.47 301

testpf		95.66 302	96.02 285	94.58 326	98.35 321	92.32 337	97.25 353	97.91 340	92.83 328	97.03 308	98.99 287	88.69 320	98.61 316	95.72 274	97.40 237	92.80 351

test_0402		96.64 278	96.24 279	97.85 290	98.85 281	96.43 285	99.44 166	99.26 249	93.52 322	96.98 309	99.52 189	88.52 324	99.20 279	92.58 328	97.50 228	97.93 325

MIMVSNet1		95.51 303	95.04 306	96.92 315	97.38 333	95.60 298	99.52 130	99.50 101	93.65 320	96.97 310	99.17 272	85.28 341	96.56 345	88.36 338	95.55 275	98.60 290

TDRefinement		95.42 305	94.57 310	97.97 282	89.83 356	96.11 293	99.48 153	98.75 305	96.74 224	96.68 311	99.88 15	88.65 322	99.71 184	98.37 126	82.74 350	98.09 315

testus		94.61 311	95.30 304	92.54 332	96.44 339	84.18 348	98.36 337	99.03 276	94.18 312	96.49 312	98.57 315	88.74 318	95.09 350	87.41 341	98.45 171	98.36 310

pmmvs3		94.09 316	93.25 318	96.60 320	94.76 346	94.49 320	98.92 307	98.18 336	89.66 341	96.48 313	98.06 323	86.28 336	97.33 340	89.68 335	87.20 342	97.97 323

DeepMVS_CX				93.34 328	99.29 190	82.27 353		99.22 254	85.15 348	96.33 314	99.05 283	90.97 298	99.73 174	93.57 315	97.77 214	98.01 321

LCM-MVSNet-Re		97.83 215	98.15 146	96.87 316	99.30 187	92.25 338	99.59 96	98.26 332	97.43 165	96.20 315	99.13 275	96.27 132	98.73 315	98.17 138	98.99 136	99.64 98

test20.03		96.12 297	95.96 286	96.63 319	97.44 332	95.45 305	99.51 134	99.38 193	96.55 238	96.16 316	99.25 265	93.76 232	96.17 346	87.35 342	94.22 306	98.27 311

K. test v3		97.10 274	96.79 272	98.01 279	98.72 298	96.33 288	99.87 4	97.05 355	97.59 148	96.16 316	99.80 68	88.71 319	99.04 293	96.69 253	96.55 255	98.65 263

test2356		94.07 317	94.46 312	92.89 330	95.18 344	86.13 346	97.60 351	99.06 273	93.61 321	96.15 318	98.28 321	85.60 340	93.95 352	86.68 345	98.00 208	98.59 291

UnsupCasMVSNet_eth		96.44 282	96.12 281	97.40 308	98.65 306	95.65 297	99.36 202	99.51 86	97.13 191	96.04 319	98.99 287	88.40 326	98.17 320	96.71 251	90.27 331	98.40 306

lessismore_v0				97.79 295	98.69 302	95.44 306		94.75 359		95.71 320	99.87 20	88.69 320	99.32 250	95.89 270	94.93 289	98.62 274

Patchmatch-RL test		95.84 300	95.81 289	95.95 323	95.61 341	90.57 341	98.24 342	98.39 329	95.10 293	95.20 321	98.67 308	94.78 188	97.77 336	96.28 265	90.02 332	99.51 129

ambc				93.06 329	92.68 351	82.36 352	98.47 335	98.73 317		95.09 322	97.41 340	55.55 359	99.10 289	96.42 262	91.32 329	97.71 337

PM-MVS		92.96 319	92.23 321	95.14 325	95.61 341	89.98 343	99.37 198	98.21 334	94.80 296	95.04 323	97.69 331	65.06 356	97.90 334	94.30 306	89.98 333	97.54 341

OpenMVS_ROB	92.34 20	94.38 314	93.70 315	96.41 322	97.38 333	93.17 333	99.06 274	98.75 305	86.58 347	94.84 324	98.26 322	81.53 350	99.32 250	89.01 336	97.87 212	96.76 342

v17		96.42 284	95.81 289	98.25 262	98.94 260	98.80 167	99.76 28	99.28 243	94.57 301	94.18 325	97.71 328	95.23 162	98.16 321	94.86 290	87.73 340	97.80 330

v18		96.42 284	95.80 291	98.26 258	98.95 257	98.82 160	99.76 28	99.28 243	94.58 300	94.12 326	97.70 329	95.22 163	98.16 321	94.83 292	87.80 338	97.79 335

v16		96.39 286	95.76 292	98.26 258	98.96 255	98.81 162	99.76 28	99.28 243	94.57 301	94.10 327	97.70 329	95.04 169	98.16 321	94.70 294	87.77 339	97.80 330

EG-PatchMatch MVS		95.97 299	95.69 293	96.81 317	97.78 328	92.79 335	99.16 252	98.93 285	96.16 271	94.08 328	99.22 269	82.72 348	99.47 218	95.67 277	97.50 228	98.17 314

v11		96.23 293	95.57 299	98.21 268	98.93 265	98.83 153	99.72 42	99.29 236	94.29 311	94.05 329	97.64 333	94.88 182	98.04 329	92.89 324	88.43 336	97.77 336

v15		96.28 288	95.62 294	98.25 262	98.94 260	98.83 153	99.76 28	99.29 236	94.52 305	94.02 330	97.61 335	95.02 170	98.13 325	94.53 296	86.92 343	97.80 330

DI_MVS_plusplus_test		97.45 263	96.79 272	99.44 103	97.76 329	99.04 113	99.21 246	98.61 324	97.74 136	94.01 331	98.83 300	87.38 334	99.83 130	98.63 96	98.90 147	99.44 147

V14		96.26 289	95.60 295	98.26 258	98.94 260	98.83 153	99.76 28	99.29 236	94.49 306	93.96 332	97.66 332	94.99 173	98.13 325	94.41 299	86.90 344	97.80 330

V9		96.25 290	95.58 296	98.26 258	98.94 260	98.83 153	99.75 36	99.29 236	94.45 308	93.96 332	97.62 334	94.94 175	98.14 324	94.40 300	86.87 345	97.81 328

test_normal		97.44 264	96.77 274	99.44 103	97.75 330	99.00 125	99.10 267	98.64 321	97.71 139	93.93 334	98.82 301	87.39 333	99.83 130	98.61 100	98.97 139	99.49 133

v13		96.24 291	95.58 296	98.25 262	98.98 248	98.83 153	99.75 36	99.29 236	94.35 310	93.89 335	97.60 336	95.17 165	98.11 327	94.27 308	86.86 346	97.81 328

v12		96.24 291	95.58 296	98.23 265	98.96 255	98.81 162	99.76 28	99.29 236	94.42 309	93.85 336	97.60 336	95.12 166	98.09 328	94.32 305	86.85 347	97.80 330

pmmvs-eth3d		95.34 307	94.73 308	97.15 309	95.53 343	95.94 295	99.35 207	99.10 266	95.13 291	93.55 337	97.54 339	88.15 330	97.91 333	94.58 295	89.69 334	97.61 338

new-patchmatchnet		94.48 312	94.08 313	95.67 324	95.08 345	92.41 336	99.18 250	99.28 243	94.55 304	93.49 338	97.37 342	87.86 331	97.01 342	91.57 329	88.36 337	97.61 338

UnsupCasMVSNet_bld		93.53 318	92.51 320	96.58 321	97.38 333	93.82 326	98.24 342	99.48 117	91.10 338	93.10 339	96.66 345	74.89 351	98.37 318	94.03 312	87.71 341	97.56 340

test1235678		92.91 320	93.30 317	91.71 336	93.14 350	83.01 350	98.75 321	98.58 325	92.80 329	92.45 340	97.91 325	88.51 325	93.54 353	82.26 349	95.35 277	98.59 291

Gipuma		90.99 323	90.15 324	93.51 327	98.73 296	90.12 342	93.98 357	99.45 155	79.32 352	92.28 341	94.91 349	69.61 353	97.98 332	87.42 340	95.67 272	92.45 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVS	69.68 23	94.13 315	94.90 307	91.84 334	97.24 337	80.01 355	98.52 333	99.48 117	89.01 344	91.99 342	99.67 131	85.67 339	99.13 284	95.44 280	97.03 248	96.39 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		91.74 322	92.19 323	90.37 339	91.22 352	82.41 351	98.61 329	98.28 331	90.66 340	91.82 343	97.92 324	84.90 342	92.61 354	81.64 350	94.66 296	96.09 346

PMMVS2		86.87 325	85.37 328	91.35 338	90.21 355	83.80 349	98.89 310	97.45 353	83.13 351	91.67 344	95.03 348	48.49 361	94.70 351	85.86 346	77.62 352	95.54 347

1111		92.30 321	92.21 322	92.55 331	93.30 348	86.27 344	99.15 255	98.74 308	91.94 332	90.85 345	97.82 326	84.18 344	95.21 348	79.65 351	94.27 305	96.19 345

.test1245		83.42 328	86.17 326	75.15 350	93.30 348	86.27 344	99.15 255	98.74 308	91.94 332	90.85 345	97.82 326	84.18 344	95.21 348	79.65 351	39.90 361	43.98 362

LCM-MVSNet		86.80 326	85.22 329	91.53 337	87.81 358	80.96 354	98.23 344	98.99 279	71.05 355	90.13 347	96.51 346	48.45 362	96.88 343	90.51 331	85.30 349	96.76 342

Test4		95.05 308	93.67 316	99.22 137	96.07 340	98.94 138	99.20 248	99.27 248	97.71 139	89.96 348	97.59 338	66.18 355	99.25 268	98.06 151	98.96 141	99.47 139

testmv		87.91 324	87.80 325	88.24 340	87.68 359	77.50 358	99.07 270	97.66 350	89.27 342	86.47 349	96.22 347	68.35 354	92.49 356	76.63 355	88.82 335	94.72 349

testing_2		94.44 313	92.93 319	98.98 160	94.16 347	99.00 125	99.42 178	99.28 243	96.60 235	84.86 350	96.84 344	70.91 352	99.27 262	98.23 135	96.08 264	98.68 238

E-PMN		80.61 331	79.88 332	82.81 346	90.75 354	76.38 360	97.69 349	95.76 358	66.44 359	83.52 351	92.25 353	62.54 358	87.16 361	68.53 359	61.40 355	84.89 360

FPMVS		84.93 327	85.65 327	82.75 347	86.77 360	63.39 365	98.35 339	98.92 287	74.11 354	83.39 352	98.98 290	50.85 360	92.40 357	84.54 347	94.97 286	92.46 352

EMVS		80.02 332	79.22 333	82.43 348	91.19 353	76.40 359	97.55 352	92.49 368	66.36 360	83.01 353	91.27 354	64.63 357	85.79 362	65.82 360	60.65 356	85.08 359

YYNet1		95.36 306	94.51 311	97.92 285	97.89 326	97.10 253	99.10 267	99.23 253	93.26 326	80.77 354	99.04 284	92.81 247	98.02 330	94.30 306	94.18 307	98.64 265

MDA-MVSNet_test_wron		95.45 304	94.60 309	98.01 279	98.16 324	97.21 251	99.11 265	99.24 252	93.49 323	80.73 355	98.98 290	93.02 241	98.18 319	94.22 310	94.45 301	98.64 265

MDA-MVSNet-bldmvs		94.96 309	93.98 314	97.92 285	98.24 323	97.27 246	99.15 255	99.33 222	93.80 318	80.09 356	99.03 285	88.31 327	97.86 335	93.49 317	94.36 303	98.62 274

tmp_tt		82.80 330	81.52 330	86.66 341	66.61 367	68.44 364	92.79 359	97.92 338	68.96 357	80.04 357	99.85 27	85.77 338	96.15 347	97.86 163	43.89 360	95.39 348

no-one		83.04 329	80.12 331	91.79 335	89.44 357	85.65 347	99.32 212	98.32 330	89.06 343	79.79 358	89.16 358	44.86 363	96.67 344	84.33 348	46.78 359	93.05 350

PNet_i23d		79.43 333	77.68 334	84.67 343	86.18 361	71.69 363	96.50 355	93.68 362	75.17 353	71.33 359	91.18 355	32.18 366	90.62 358	78.57 354	74.34 353	91.71 355

MVE	76.82 21	76.91 335	74.31 337	84.70 342	85.38 363	76.05 361	96.88 354	93.17 364	67.39 358	71.28 360	89.01 359	21.66 371	87.69 360	71.74 358	72.29 354	90.35 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 334	74.86 336	84.62 344	75.88 365	77.61 357	97.63 350	93.15 365	88.81 345	64.27 361	89.29 357	36.51 364	83.93 363	75.89 356	52.31 358	92.33 354

wuykxyi23d		74.42 337	71.19 338	84.14 345	76.16 364	74.29 362	96.00 356	92.57 367	69.57 356	63.84 362	87.49 360	21.98 368	88.86 359	75.56 357	57.50 357	89.26 358

PMVS	70.75 22	75.98 336	74.97 335	79.01 349	70.98 366	55.18 366	93.37 358	98.21 334	65.08 361	61.78 363	93.83 351	21.74 370	92.53 355	78.59 353	91.12 330	89.34 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		39.01 342	42.50 342	28.53 353	39.17 368	20.91 368	98.75 321	19.17 371	19.83 364	38.57 364	66.67 362	33.16 365	15.42 365	37.50 363	29.66 363	49.26 361

testmvs		39.17 341	43.78 340	25.37 354	36.04 369	16.84 369	98.36 337	26.56 369	20.06 363	38.51 365	67.32 361	29.64 367	15.30 366	37.59 362	39.90 361	43.98 362

wuyk23d		40.18 340	41.29 343	36.84 351	86.18 361	49.12 367	79.73 360	22.81 370	27.64 362	25.46 366	28.45 366	21.98 368	48.89 364	55.80 361	23.56 364	12.51 364

cdsmvs_eth3d_5k		24.64 343	32.85 344	0.00 355	0.00 370	0.00 370	0.00 361	99.51 86	0.00 365	0.00 367	99.56 171	96.58 122	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		8.27 345	11.03 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.27 367	99.01 12	0.00 367	0.00 364	0.00 365	0.00 365

pcd1.5k->3k		40.85 339	43.49 341	32.93 352	98.95 257	0.00 370	0.00 361	99.53 73	0.00 365	0.00 367	0.27 367	95.32 156	0.00 367	0.00 364	97.30 241	98.80 207

sosnet-low-res		0.02 346	0.03 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.27 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.02 346	0.03 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.27 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.02 346	0.03 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.27 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.02 346	0.03 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.27 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		8.30 344	11.06 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	99.58 165	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.02 346	0.03 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.27 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																99.52 124

test_part1				0.00 355		0.00 370	0.00 361	99.48 117				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sam_mvs1												94.86 183				99.52 124

sam_mvs												94.72 196

MTGPA								99.47 133

test_post1							99.23 239				65.14 364	94.18 218	99.71 184	97.58 189

test_post											65.99 363	94.65 200	99.73 174

patchmatchnet-post											98.70 307	94.79 187	99.74 167

MTMP							99.54 125	98.88 294

gm-plane-assit					98.54 316	92.96 334			94.65 299		99.15 273		99.64 201	97.56 193

test9_res														97.49 200	99.72 85	99.75 55

agg_prior2														97.21 216	99.73 84	99.75 55

test_prior4						99.56 52	98.99 291

test_prior				99.68 52	99.67 95	99.48 65		99.56 49					99.83 130			99.74 60

新几何2							99.01 289

旧先验1					99.74 68	99.59 49		99.54 63			99.69 121	98.47 60			99.68 95	99.73 65

无先验							98.99 291	99.51 86	96.89 217				99.93 56	97.53 196		99.72 71

原ACMM2							98.95 304

testdata2													99.95 34	96.67 254

segment_acmp												98.96 21

testdata1							98.85 313		98.32 70

plane_prior7					99.29 190	97.03 261

plane_prior6					99.27 195	96.98 265						92.71 252

plane_prior5								99.47 133					99.69 193	97.78 170	97.63 216	98.67 249

plane_prior4											99.61 157

plane_prior2							99.39 191		98.97 22

plane_prior1					99.26 197

plane_prior						96.97 266	99.21 246		98.45 60						97.60 219

n2								0.00 372

nn								0.00 372

door-mid								98.05 337

test11								99.35 206

door								97.92 338

HQP5-MVS						96.83 271

BP-MVS														97.19 218

HQP3-MVS								99.39 187							97.58 221

HQP2-MVS												92.47 267

NP-MVS					99.23 200	96.92 269					99.40 226

ACMMP++_ref															97.19 245

ACMMP++															97.43 236

Test By Simon												98.75 45

High-resolution multi-view benchmark