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.

CANet		99.25 64	99.14 64	99.59 84	99.41 177	99.16 120	99.35 198	99.57 51	98.82 42	99.51 99	99.61 179	96.46 149	99.95 42	99.59 1	99.98 2	99.65 112

EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 77	99.78 44	99.14 125	99.60 70	99.45 179	99.01 18	99.90 3	99.83 42	98.98 23	99.93 68	99.59 1	99.95 6	99.86 11

DELS-MVS		99.48 19	99.42 13	99.65 72	99.72 80	99.40 96	99.05 265	99.66 27	99.14 6	99.57 87	99.80 76	98.46 79	99.94 53	99.57 3	99.84 65	99.60 128
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
EI-MVSNet-Vis-set		99.58 4	99.56 3	99.64 77	99.78 44	99.15 124	99.61 69	99.45 179	99.01 18	99.89 4	99.82 49	99.01 16	99.92 79	99.56 4	99.95 6	99.85 14

HyFIR lowres test		99.11 90	98.92 97	99.65 72	99.90 3	99.37 97	99.02 274	99.91 3	97.67 158	99.59 83	99.75 111	95.90 169	99.73 183	99.53 5	99.02 165	99.86 11

VNet		99.11 90	98.90 100	99.73 58	99.52 147	99.56 72	99.41 169	99.39 209	99.01 18	99.74 41	99.78 95	95.56 179	99.92 79	99.52 6	98.18 207	99.72 86

baseline		99.15 76	99.02 82	99.53 98	99.66 109	99.14 125	99.72 29	99.48 139	98.35 79	99.42 116	99.84 38	96.07 160	99.79 164	99.51 7	99.14 153	99.67 105

xiu_mvs_v1_base_debu		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 250	99.51 102	98.86 38	99.84 13	99.47 229	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

xiu_mvs_v1_base		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 250	99.51 102	98.86 38	99.84 13	99.47 229	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

xiu_mvs_v1_base_debi		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 250	99.51 102	98.86 38	99.84 13	99.47 229	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

CHOSEN 1792x2688		99.19 69	99.10 69	99.45 115	99.89 8	98.52 195	99.39 181	99.94 1	98.73 51	99.11 187	99.89 10	95.50 181	99.94 53	99.50 8	99.97 3	99.89 2

VDD-MVS		97.73 241	97.35 258	98.88 194	99.47 165	97.12 254	99.34 201	98.85 308	98.19 97	99.67 59	99.85 29	82.98 343	99.92 79	99.49 12	98.32 201	99.60 128

PVSNet_Blended_VisFu		99.36 49	99.28 48	99.61 82	99.86 21	99.07 133	99.47 145	99.93 2	97.66 159	99.71 46	99.86 23	97.73 111	99.96 18	99.47 13	99.82 78	99.79 53

CHOSEN 280x420		99.12 85	99.13 65	99.08 159	99.66 109	97.89 229	98.43 334	99.71 13	98.88 37	99.62 74	99.76 105	96.63 144	99.70 199	99.46 14	99.99 1	99.66 108

Regformer-4		99.59 3	99.54 4	99.73 58	99.76 52	99.41 94	99.58 83	99.49 128	99.02 15	99.88 5	99.80 76	99.00 22	99.94 53	99.45 15	99.92 11	99.84 18

casdiffmvs		99.13 79	98.98 90	99.56 90	99.65 114	99.16 120	99.56 95	99.50 120	98.33 83	99.41 120	99.86 23	95.92 167	99.83 145	99.45 15	99.16 150	99.70 95

Regformer-3		99.57 7	99.53 5	99.68 65	99.76 52	99.29 106	99.58 83	99.44 187	99.01 18	99.87 10	99.80 76	98.97 24	99.91 90	99.44 17	99.92 11	99.83 29

DeepC-MVS	98.35 2	99.30 55	99.19 60	99.64 77	99.82 37	99.23 113	99.62 63	99.55 64	98.94 33	99.63 70	99.95 2	95.82 172	99.94 53	99.37 18	99.97 3	99.73 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		98.81 130	98.56 145	99.58 87	99.43 173	99.42 93	99.51 118	98.96 295	98.61 58	99.35 138	98.92 314	94.78 204	99.77 170	99.35 19	98.11 213	99.54 141

PS-MVSNAJ		99.32 53	99.32 30	99.30 137	99.57 137	98.94 154	98.97 288	99.46 167	98.92 35	99.71 46	99.24 283	99.01 16	99.98 5	99.35 19	99.66 117	98.97 209

VPA-MVSNet		98.29 167	97.95 188	99.30 137	99.16 243	99.54 76	99.50 124	99.58 50	98.27 88	99.35 138	99.37 254	92.53 270	99.65 211	99.35 19	94.46 306	98.72 235

mvs_anonymous		99.03 103	98.99 87	99.16 154	99.38 186	98.52 195	99.51 118	99.38 215	97.79 144	99.38 130	99.81 62	97.30 122	99.45 232	99.35 19	98.99 167	99.51 152

xiu_mvs_v2_base		99.26 62	99.25 54	99.29 140	99.53 145	98.91 158	99.02 274	99.45 179	98.80 46	99.71 46	99.26 281	98.94 31	99.98 5	99.34 23	99.23 146	98.98 208

nrg030		98.64 146	98.42 151	99.28 142	99.05 263	99.69 47	99.81 12	99.46 167	98.04 121	99.01 205	99.82 49	96.69 143	99.38 247	99.34 23	94.59 305	98.78 223

UGNet		98.87 116	98.69 126	99.40 122	99.22 226	98.72 176	99.44 153	99.68 19	99.24 3	99.18 178	99.42 240	92.74 261	99.96 18	99.34 23	99.94 9	99.53 145
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
mvs_tets		98.40 159	98.23 163	98.91 186	98.67 311	98.51 197	99.66 46	99.53 83	98.19 97	98.65 264	99.81 62	92.75 259	99.44 237	99.31 26	97.48 239	98.77 226

VDDNet		97.55 259	97.02 275	99.16 154	99.49 158	98.12 218	99.38 186	99.30 255	95.35 304	99.68 53	99.90 7	82.62 345	99.93 68	99.31 26	98.13 212	99.42 169

diffmvs		99.14 77	99.02 82	99.51 105	99.61 129	98.96 149	99.28 213	99.49 128	98.46 68	99.72 45	99.71 129	96.50 148	99.88 118	99.31 26	99.11 155	99.67 105

LFMVS		97.90 213	97.35 258	99.54 92	99.52 147	99.01 139	99.39 181	98.24 332	97.10 216	99.65 67	99.79 88	84.79 341	99.91 90	99.28 29	98.38 196	99.69 98

MSLP-MVS++		99.46 24	99.47 9	99.44 120	99.60 132	99.16 120	99.41 169	99.71 13	98.98 27	99.45 108	99.78 95	99.19 7	99.54 227	99.28 29	99.84 65	99.63 122

canonicalmvs		99.02 104	98.86 108	99.51 105	99.42 174	99.32 101	99.80 16	99.48 139	98.63 56	99.31 144	98.81 317	97.09 128	99.75 175	99.27 31	97.90 217	99.47 162

Anonymous20240529		98.09 185	97.68 217	99.34 127	99.66 109	98.44 203	99.40 177	99.43 195	93.67 322	99.22 166	99.89 10	90.23 309	99.93 68	99.26 32	98.33 197	99.66 108

EPNet		98.86 119	98.71 124	99.30 137	97.20 341	98.18 213	99.62 63	98.91 302	99.28 2	98.63 266	99.81 62	95.96 163	99.99 1	99.24 33	99.72 103	99.73 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
jajsoiax		98.43 154	98.28 161	98.88 194	98.60 318	98.43 204	99.82 10	99.53 83	98.19 97	98.63 266	99.80 76	93.22 251	99.44 237	99.22 34	97.50 235	98.77 226

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 49	99.89 3	99.75 25	99.56 56	99.02 15	99.88 5	99.85 29	99.18 8	99.96 18	99.22 34	99.92 11	99.90 1

RRT_test8_iter05		97.72 243	97.60 225	98.08 273	99.23 222	96.08 299	99.63 57	99.49 128	97.54 171	98.94 219	99.81 62	87.99 331	99.35 258	99.21 36	96.51 263	98.81 220

VPNet		97.84 222	97.44 246	99.01 168	99.21 228	98.94 154	99.48 140	99.57 51	98.38 75	99.28 150	99.73 124	88.89 320	99.39 245	99.19 37	93.27 323	98.71 237

sss		99.17 73	99.05 74	99.53 98	99.62 125	98.97 145	99.36 193	99.62 34	97.83 138	99.67 59	99.65 158	97.37 121	99.95 42	99.19 37	99.19 149	99.68 102

Vis-MVSNet		99.12 85	98.97 91	99.56 90	99.78 44	99.10 130	99.68 40	99.66 27	98.49 65	99.86 11	99.87 20	94.77 207	99.84 136	99.19 37	99.41 135	99.74 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		99.53 11	99.47 9	99.72 61	99.71 86	99.44 91	99.49 134	99.46 167	98.95 32	99.83 17	99.76 105	99.01 16	99.93 68	99.17 40	99.87 40	99.80 49

ab-mvs		98.86 119	98.63 133	99.54 92	99.64 116	99.19 115	99.44 153	99.54 71	97.77 146	99.30 145	99.81 62	94.20 229	99.93 68	99.17 40	98.82 178	99.49 156

Anonymous202405211		98.30 166	97.98 184	99.26 144	99.57 137	98.16 214	99.41 169	98.55 328	96.03 297	99.19 175	99.74 117	91.87 283	99.92 79	99.16 42	98.29 202	99.70 95

Regformer-2		99.54 9	99.47 9	99.75 51	99.71 86	99.52 82	99.49 134	99.49 128	98.94 33	99.83 17	99.76 105	99.01 16	99.94 53	99.15 43	99.87 40	99.80 49

PS-MVSNAJss		98.92 114	98.92 97	98.90 188	98.78 297	98.53 191	99.78 19	99.54 71	98.07 115	99.00 210	99.76 105	99.01 16	99.37 250	99.13 44	97.23 249	98.81 220

EPP-MVSNet		99.13 79	98.99 87	99.53 98	99.65 114	99.06 134	99.81 12	99.33 240	97.43 184	99.60 80	99.88 15	97.14 126	99.84 136	99.13 44	98.94 169	99.69 98

Effi-MVS+		98.81 130	98.59 143	99.48 109	99.46 166	99.12 129	98.08 343	99.50 120	97.50 176	99.38 130	99.41 243	96.37 153	99.81 156	99.11 46	98.54 191	99.51 152

ETV-MVS		99.26 62	99.21 58	99.40 122	99.46 166	99.30 105	99.56 95	99.52 89	98.52 63	99.44 112	99.27 280	98.41 85	99.86 125	99.10 47	99.59 126	99.04 201

TSAR-MVS + GP.		99.36 49	99.36 21	99.36 126	99.67 100	98.61 186	99.07 260	99.33 240	99.00 22	99.82 20	99.81 62	99.06 13	99.84 136	99.09 48	99.42 134	99.65 112

FIs		98.78 134	98.63 133	99.23 149	99.18 235	99.54 76	99.83 9	99.59 44	98.28 86	98.79 242	99.81 62	96.75 141	99.37 250	99.08 49	96.38 266	98.78 223

FC-MVSNet-test		98.75 137	98.62 138	99.15 156	99.08 257	99.45 90	99.86 5	99.60 41	98.23 93	98.70 255	99.82 49	96.80 137	99.22 279	99.07 50	96.38 266	98.79 222

HPM-MVS_fast		99.51 14	99.40 16	99.85 25	99.91 1	99.79 30	99.76 24	99.56 56	97.72 152	99.76 37	99.75 111	99.13 10	99.92 79	99.07 50	99.92 11	99.85 14

MVSFormer		99.17 73	99.12 67	99.29 140	99.51 149	98.94 154	99.88 1	99.46 167	97.55 168	99.80 24	99.65 158	97.39 117	99.28 268	99.03 52	99.85 58	99.65 112

test_djsdf		98.67 143	98.57 144	98.98 172	98.70 308	98.91 158	99.88 1	99.46 167	97.55 168	99.22 166	99.88 15	95.73 175	99.28 268	99.03 52	97.62 224	98.75 230

jason		99.13 79	99.03 79	99.45 115	99.46 166	98.87 161	99.12 250	99.26 263	98.03 123	99.79 26	99.65 158	97.02 131	99.85 131	99.02 54	99.90 23	99.65 112
jason: jason.
DeepPCF-MVS	98.18 3	98.81 130	99.37 19	97.12 309	99.60 132	91.75 343	98.61 324	99.44 187	99.35 1	99.83 17	99.85 29	98.70 62	99.81 156	99.02 54	99.91 16	99.81 41

CSCG		99.32 53	99.32 30	99.32 132	99.85 25	98.29 209	99.71 31	99.66 27	98.11 107	99.41 120	99.80 76	98.37 88	99.96 18	98.99 56	99.96 5	99.72 86

ET-MVSNet_ETH3D		96.49 288	95.64 298	99.05 163	99.53 145	98.82 169	98.84 304	97.51 343	97.63 161	84.77 346	99.21 289	92.09 280	98.91 322	98.98 57	92.21 332	99.41 171

CS-MVS		99.21 66	99.13 65	99.45 115	99.54 144	99.34 99	99.71 31	99.54 71	98.26 89	98.99 212	99.24 283	98.25 94	99.88 118	98.98 57	99.63 122	99.12 189

PVSNet_BlendedMVS		98.86 119	98.80 115	99.03 166	99.76 52	98.79 172	99.28 213	99.91 3	97.42 186	99.67 59	99.37 254	97.53 114	99.88 118	98.98 57	97.29 248	98.42 310

PVSNet_Blended		99.08 96	98.97 91	99.42 121	99.76 52	98.79 172	98.78 310	99.91 3	96.74 240	99.67 59	99.49 220	97.53 114	99.88 118	98.98 57	99.85 58	99.60 128

3Dnovator	97.25 9	99.24 65	99.05 74	99.81 38	99.12 248	99.66 54	99.84 6	99.74 10	99.09 10	98.92 222	99.90 7	95.94 166	99.98 5	98.95 61	99.92 11	99.79 53

EIA-MVS		99.18 71	99.09 71	99.45 115	99.49 158	99.18 117	99.67 42	99.53 83	97.66 159	99.40 125	99.44 235	98.10 101	99.81 156	98.94 62	99.62 124	99.35 175

lupinMVS		99.13 79	99.01 86	99.46 114	99.51 149	98.94 154	99.05 265	99.16 274	97.86 133	99.80 24	99.56 195	97.39 117	99.86 125	98.94 62	99.85 58	99.58 136

DVP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 88	99.37 223	99.10 8	99.81 22	99.80 76	98.94 31	99.96 18	98.93 64	99.86 51	99.81 41

test_0728_SECOND				99.91 2	99.84 32	99.89 3	99.57 88	99.51 102					99.96 18	98.93 64	99.86 51	99.88 5

UA-Net		99.42 38	99.29 44	99.80 40	99.62 125	99.55 74	99.50 124	99.70 15	98.79 47	99.77 33	99.96 1	97.45 116	99.96 18	98.92 66	99.90 23	99.89 2

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 32	99.90 1	99.60 70	99.48 139	99.08 11	99.91 1	99.81 62	99.20 5	99.96 18	98.91 67	99.85 58	99.79 53

test_241102_TWO								99.48 139	99.08 11	99.88 5	99.81 62	98.94 31	99.96 18	98.91 67	99.84 65	99.88 5

MVS_111021_HR		99.41 42	99.32 30	99.66 68	99.72 80	99.47 88	98.95 293	99.85 6	98.82 42	99.54 93	99.73 124	98.51 75	99.74 176	98.91 67	99.88 36	99.77 63

zzz-MVS		99.49 15	99.36 21	99.89 4	99.90 3	99.86 10	99.36 193	99.47 157	98.79 47	99.68 53	99.81 62	98.43 81	99.97 10	98.88 70	99.90 23	99.83 29

MTAPA		99.52 13	99.39 17	99.89 4	99.90 3	99.86 10	99.66 46	99.47 157	98.79 47	99.68 53	99.81 62	98.43 81	99.97 10	98.88 70	99.90 23	99.83 29

XXY-MVS		98.38 160	98.09 173	99.24 147	99.26 216	99.32 101	99.56 95	99.55 64	97.45 180	98.71 249	99.83 42	93.23 249	99.63 218	98.88 70	96.32 268	98.76 228

ACMH	97.28 8	98.10 184	97.99 183	98.44 246	99.41 177	96.96 271	99.60 70	99.56 56	98.09 110	98.15 295	99.91 5	90.87 303	99.70 199	98.88 70	97.45 240	98.67 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_Test		99.10 93	98.97 91	99.48 109	99.49 158	99.14 125	99.67 42	99.34 233	97.31 194	99.58 85	99.76 105	97.65 113	99.82 152	98.87 74	99.07 161	99.46 164

MVSTER		98.49 150	98.32 158	99.00 170	99.35 191	99.02 137	99.54 107	99.38 215	97.41 187	99.20 172	99.73 124	93.86 241	99.36 254	98.87 74	97.56 229	98.62 280

1112_ss		98.98 109	98.77 118	99.59 84	99.68 99	99.02 137	99.25 228	99.48 139	97.23 203	99.13 183	99.58 188	96.93 135	99.90 105	98.87 74	98.78 181	99.84 18

IU-MVS					99.84 32	99.88 7		99.32 248	98.30 85	99.84 13				98.86 77	99.85 58	99.89 2

3Dnovator+	97.12 13	99.18 71	98.97 91	99.82 35	99.17 241	99.68 49	99.81 12	99.51 102	99.20 4	98.72 248	99.89 10	95.68 177	99.97 10	98.86 77	99.86 51	99.81 41

test_0728_THIRD									98.99 25	99.81 22	99.80 76	99.09 12	99.96 18	98.85 79	99.90 23	99.88 5

WTY-MVS		99.06 98	98.88 103	99.61 82	99.62 125	99.16 120	99.37 189	99.56 56	98.04 121	99.53 95	99.62 175	96.84 136	99.94 53	98.85 79	98.49 194	99.72 86

TSAR-MVS + MP.		99.58 4	99.50 8	99.81 38	99.91 1	99.66 54	99.63 57	99.39 209	98.91 36	99.78 31	99.85 29	99.36 2	99.94 53	98.84 81	99.88 36	99.82 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous20231211		97.88 214	97.54 231	98.90 188	99.71 86	98.53 191	99.48 140	99.57 51	94.16 318	98.81 238	99.68 145	93.23 249	99.42 243	98.84 81	94.42 308	98.76 228

114514_t		98.93 113	98.67 128	99.72 61	99.85 25	99.53 79	99.62 63	99.59 44	92.65 330	99.71 46	99.78 95	98.06 103	99.90 105	98.84 81	99.91 16	99.74 73

tttt0517		98.42 155	98.14 167	99.28 142	99.66 109	98.38 207	99.74 28	96.85 346	97.68 156	99.79 26	99.74 117	91.39 296	99.89 113	98.83 84	99.56 127	99.57 137

MP-MVS-pluss		99.37 48	99.20 59	99.88 6	99.90 3	99.87 9	99.30 207	99.52 89	97.18 206	99.60 80	99.79 88	98.79 47	99.95 42	98.83 84	99.91 16	99.83 29
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Test_1112_low_res		98.89 115	98.66 131	99.57 88	99.69 95	98.95 151	99.03 271	99.47 157	96.98 224	99.15 181	99.23 285	96.77 140	99.89 113	98.83 84	98.78 181	99.86 11

MVS_111021_LR		99.41 42	99.33 28	99.65 72	99.77 49	99.51 84	98.94 295	99.85 6	98.82 42	99.65 67	99.74 117	98.51 75	99.80 161	98.83 84	99.89 33	99.64 118

ACMMP_NAP		99.47 22	99.34 26	99.88 6	99.87 15	99.86 10	99.47 145	99.48 139	98.05 120	99.76 37	99.86 23	98.82 44	99.93 68	98.82 88	99.91 16	99.84 18

RRT_MVS		98.60 148	98.44 149	99.05 163	98.88 282	99.14 125	99.49 134	99.38 215	97.76 147	99.29 148	99.86 23	95.38 184	99.36 254	98.81 89	97.16 253	98.64 270

SMA-MVS		99.44 30	99.30 40	99.85 25	99.73 75	99.83 14	99.56 95	99.47 157	97.45 180	99.78 31	99.82 49	99.18 8	99.91 90	98.79 90	99.89 33	99.81 41
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
XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 132	99.74 117	98.81 45	99.94 53	98.79 90	99.86 51	99.84 18

X-MVStestdata		96.55 286	95.45 300	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 132	64.01 358	98.81 45	99.94 53	98.79 90	99.86 51	99.84 18

CVMVSNet		98.57 149	98.67 128	98.30 260	99.35 191	95.59 305	99.50 124	99.55 64	98.60 59	99.39 127	99.83 42	94.48 221	99.45 232	98.75 93	98.56 190	99.85 14

CP-MVS		99.45 26	99.32 30	99.85 25	99.83 36	99.75 38	99.69 35	99.52 89	98.07 115	99.53 95	99.63 170	98.93 35	99.97 10	98.74 94	99.91 16	99.83 29

ACMM	97.58 5	98.37 161	98.34 156	98.48 237	99.41 177	97.10 255	99.56 95	99.45 179	98.53 62	99.04 202	99.85 29	93.00 253	99.71 193	98.74 94	97.45 240	98.64 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		98.78 134	98.89 102	98.47 241	99.33 196	96.91 273	99.57 88	99.30 255	98.47 66	99.41 120	98.99 308	96.78 138	99.74 176	98.73 96	99.38 136	98.74 233

mvs-test1		98.86 119	98.84 110	98.89 191	99.33 196	97.77 235	99.44 153	99.30 255	98.47 66	99.10 190	99.43 237	96.78 138	99.95 42	98.73 96	99.02 165	98.96 211

ZNCC-MVS		99.47 22	99.33 28	99.87 11	99.87 15	99.81 24	99.64 55	99.67 22	98.08 114	99.55 92	99.64 165	98.91 36	99.96 18	98.72 98	99.90 23	99.82 36

SD-MVS		99.41 42	99.52 6	99.05 163	99.74 70	99.68 49	99.46 148	99.52 89	99.11 7	99.88 5	99.91 5	99.43 1	97.70 340	98.72 98	99.93 10	99.77 63
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
D2MVS		98.41 157	98.50 147	98.15 271	99.26 216	96.62 284	99.40 177	99.61 36	97.71 153	98.98 213	99.36 257	96.04 161	99.67 204	98.70 100	97.41 244	98.15 324

CDS-MVSNet		99.09 94	99.03 79	99.25 145	99.42 174	98.73 175	99.45 149	99.46 167	98.11 107	99.46 107	99.77 101	98.01 104	99.37 250	98.70 100	98.92 172	99.66 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		99.12 85	99.08 72	99.24 147	99.46 166	98.55 189	99.51 118	99.46 167	98.09 110	99.45 108	99.82 49	98.34 89	99.51 228	98.70 100	98.93 170	99.67 105

HFP-MVS		99.49 15	99.37 19	99.86 18	99.87 15	99.80 26	99.66 46	99.67 22	98.15 101	99.68 53	99.69 140	99.06 13	99.96 18	98.69 103	99.87 40	99.84 18

ACMMPR		99.49 15	99.36 21	99.86 18	99.87 15	99.79 30	99.66 46	99.67 22	98.15 101	99.67 59	99.69 140	98.95 28	99.96 18	98.69 103	99.87 40	99.84 18

UniMVSNet_ETH3D		97.32 272	96.81 278	98.87 198	99.40 182	97.46 244	99.51 118	99.53 83	95.86 299	98.54 274	99.77 101	82.44 346	99.66 207	98.68 105	97.52 232	99.50 155

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 47	99.63 119	99.59 67	99.36 193	99.46 167	99.07 13	99.79 26	99.82 49	98.85 41	99.92 79	98.68 105	99.87 40	99.82 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
anonymousdsp		98.44 153	98.28 161	98.94 178	98.50 323	98.96 149	99.77 21	99.50 120	97.07 217	98.87 230	99.77 101	94.76 208	99.28 268	98.66 107	97.60 225	98.57 295

DP-MVS		99.16 75	98.95 95	99.78 45	99.77 49	99.53 79	99.41 169	99.50 120	97.03 222	99.04 202	99.88 15	97.39 117	99.92 79	98.66 107	99.90 23	99.87 10

MCST-MVS		99.43 33	99.30 40	99.82 35	99.79 42	99.74 41	99.29 211	99.40 205	98.79 47	99.52 97	99.62 175	98.91 36	99.90 105	98.64 109	99.75 96	99.82 36

CP-MVSNet		98.09 185	97.78 205	99.01 168	98.97 275	99.24 112	99.67 42	99.46 167	97.25 200	98.48 278	99.64 165	93.79 242	99.06 301	98.63 110	94.10 313	98.74 233

thisisatest0530		98.35 162	98.03 179	99.31 133	99.63 119	98.56 188	99.54 107	96.75 348	97.53 173	99.73 43	99.65 158	91.25 299	99.89 113	98.62 111	99.56 127	99.48 157

region2R		99.48 19	99.35 24	99.87 11	99.88 11	99.80 26	99.65 53	99.66 27	98.13 103	99.66 64	99.68 145	98.96 25	99.96 18	98.62 111	99.87 40	99.84 18

APD-MVS_3200maxsize		99.48 19	99.35 24	99.85 25	99.76 52	99.83 14	99.63 57	99.54 71	98.36 78	99.79 26	99.82 49	98.86 40	99.95 42	98.62 111	99.81 80	99.78 61

SR-MVS-dyc-post		99.45 26	99.31 37	99.85 25	99.76 52	99.82 20	99.63 57	99.52 89	98.38 75	99.76 37	99.82 49	98.53 72	99.95 42	98.61 114	99.81 80	99.77 63

RE-MVS-def			99.34 26		99.76 52	99.82 20	99.63 57	99.52 89	98.38 75	99.76 37	99.82 49	98.75 56		98.61 114	99.81 80	99.77 63

PHI-MVS		99.30 55	99.17 62	99.70 64	99.56 141	99.52 82	99.58 83	99.80 8	97.12 212	99.62 74	99.73 124	98.58 70	99.90 105	98.61 114	99.91 16	99.68 102

test_yl		98.86 119	98.63 133	99.54 92	99.49 158	99.18 117	99.50 124	99.07 285	98.22 94	99.61 76	99.51 214	95.37 185	99.84 136	98.60 117	98.33 197	99.59 132

DCV-MVSNet		98.86 119	98.63 133	99.54 92	99.49 158	99.18 117	99.50 124	99.07 285	98.22 94	99.61 76	99.51 214	95.37 185	99.84 136	98.60 117	98.33 197	99.59 132

CNVR-MVS		99.42 38	99.30 40	99.78 45	99.62 125	99.71 44	99.26 226	99.52 89	98.82 42	99.39 127	99.71 129	98.96 25	99.85 131	98.59 119	99.80 84	99.77 63

WR-MVS		98.06 188	97.73 213	99.06 161	98.86 289	99.25 111	99.19 239	99.35 229	97.30 195	98.66 258	99.43 237	93.94 238	99.21 284	98.58 120	94.28 310	98.71 237

HPM-MVS		99.42 38	99.28 48	99.83 33	99.90 3	99.72 42	99.81 12	99.54 71	97.59 163	99.68 53	99.63 170	98.91 36	99.94 53	98.58 120	99.91 16	99.84 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
UniMVSNet_NR-MVSNet		98.22 170	97.97 185	98.96 175	98.92 279	98.98 142	99.48 140	99.53 83	97.76 147	98.71 249	99.46 233	96.43 152	99.22 279	98.57 122	92.87 328	98.69 245

DU-MVS		98.08 187	97.79 202	98.96 175	98.87 286	98.98 142	99.41 169	99.45 179	97.87 132	98.71 249	99.50 217	94.82 201	99.22 279	98.57 122	92.87 328	98.68 250

mPP-MVS		99.44 30	99.30 40	99.86 18	99.88 11	99.79 30	99.69 35	99.48 139	98.12 105	99.50 100	99.75 111	98.78 48	99.97 10	98.57 122	99.89 33	99.83 29

CANet_DTU		98.97 111	98.87 104	99.25 145	99.33 196	98.42 206	99.08 259	99.30 255	99.16 5	99.43 113	99.75 111	95.27 189	99.97 10	98.56 125	99.95 6	99.36 174

PMMVS		98.80 133	98.62 138	99.34 127	99.27 214	98.70 177	98.76 312	99.31 251	97.34 191	99.21 169	99.07 301	97.20 125	99.82 152	98.56 125	98.87 175	99.52 146

PVSNet	96.02 17	98.85 127	98.84 110	98.89 191	99.73 75	97.28 248	98.32 337	99.60 41	97.86 133	99.50 100	99.57 192	96.75 141	99.86 125	98.56 125	99.70 108	99.54 141

test1172		99.43 33	99.29 44	99.85 25	99.75 62	99.82 20	99.60 70	99.56 56	98.28 86	99.74 41	99.79 88	98.53 72	99.95 42	98.55 128	99.78 89	99.79 53

ACMMP		99.45 26	99.32 30	99.82 35	99.89 8	99.67 52	99.62 63	99.69 18	98.12 105	99.63 70	99.84 38	98.73 59	99.96 18	98.55 128	99.83 72	99.81 41
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
XVG-OURS-SEG-HR		98.69 141	98.62 138	98.89 191	99.71 86	97.74 236	99.12 250	99.54 71	98.44 72	99.42 116	99.71 129	94.20 229	99.92 79	98.54 130	98.90 174	99.00 205

PS-CasMVS		97.93 208	97.59 227	98.95 177	98.99 270	99.06 134	99.68 40	99.52 89	97.13 210	98.31 288	99.68 145	92.44 276	99.05 302	98.51 131	94.08 314	98.75 230

CostFormer		97.72 243	97.73 213	97.71 297	99.15 246	94.02 331	99.54 107	99.02 289	94.67 313	99.04 202	99.35 260	92.35 278	99.77 170	98.50 132	97.94 216	99.34 177

baseline1		98.31 164	97.95 188	99.38 125	99.50 156	98.74 174	99.59 76	98.93 297	98.41 73	99.14 182	99.60 182	94.59 216	99.79 164	98.48 133	93.29 322	99.61 126

SteuartSystems-ACMMP		99.54 9	99.42 13	99.87 11	99.82 37	99.81 24	99.59 76	99.51 102	98.62 57	99.79 26	99.83 42	99.28 3	99.97 10	98.48 133	99.90 23	99.84 18
Skip Steuart: Steuart Systems R&D Blog.
tpmrst		98.33 163	98.48 148	97.90 287	99.16 243	94.78 324	99.31 205	99.11 279	97.27 198	99.45 108	99.59 185	95.33 187	99.84 136	98.48 133	98.61 184	99.09 194

IB-MVS	95.67 18	96.22 292	95.44 301	98.57 227	99.21 228	96.70 280	98.65 322	97.74 341	96.71 242	97.27 315	98.54 327	86.03 337	99.92 79	98.47 136	86.30 343	99.10 190
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
MSP-MVS		99.42 38	99.27 50	99.88 6	99.89 8	99.80 26	99.67 42	99.50 120	98.70 53	99.77 33	99.49 220	98.21 96	99.95 42	98.46 137	99.77 92	99.88 5

abl_6		99.44 30	99.31 37	99.83 33	99.85 25	99.75 38	99.66 46	99.59 44	98.13 103	99.82 20	99.81 62	98.60 69	99.96 18	98.46 137	99.88 36	99.79 53

SR-MVS		99.43 33	99.29 44	99.86 18	99.75 62	99.83 14	99.59 76	99.62 34	98.21 96	99.73 43	99.79 88	98.68 63	99.96 18	98.44 139	99.77 92	99.79 53

HPM-MVS++		99.39 46	99.23 57	99.87 11	99.75 62	99.84 13	99.43 159	99.51 102	98.68 55	99.27 153	99.53 207	98.64 68	99.96 18	98.44 139	99.80 84	99.79 53

#test#		99.43 33	99.29 44	99.86 18	99.87 15	99.80 26	99.55 104	99.67 22	97.83 138	99.68 53	99.69 140	99.06 13	99.96 18	98.39 141	99.87 40	99.84 18

LTVRE_ROB	97.16 12	98.02 196	97.90 193	98.40 250	99.23 222	96.80 277	99.70 33	99.60 41	97.12 212	98.18 294	99.70 133	91.73 288	99.72 187	98.39 141	97.45 240	98.68 250
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
GST-MVS		99.40 45	99.24 55	99.85 25	99.86 21	99.79 30	99.60 70	99.67 22	97.97 126	99.63 70	99.68 145	98.52 74	99.95 42	98.38 143	99.86 51	99.81 41

EI-MVSNet		98.67 143	98.67 128	98.68 220	99.35 191	97.97 223	99.50 124	99.38 215	96.93 231	99.20 172	99.83 42	97.87 106	99.36 254	98.38 143	97.56 229	98.71 237

test_part1		96.83 282	96.34 285	98.33 255	99.46 166	96.71 279	99.52 113	99.63 33	91.48 334	97.75 310	99.76 105	87.49 334	99.44 237	98.37 145	93.55 319	98.82 219

HY-MVS	97.30 7	98.85 127	98.64 132	99.47 112	99.42 174	99.08 132	99.62 63	99.36 224	97.39 189	99.28 150	99.68 145	96.44 151	99.92 79	98.37 145	98.22 203	99.40 172

TDRefinement		95.42 302	94.57 308	97.97 282	89.83 353	96.11 298	99.48 140	98.75 313	96.74 240	96.68 324	99.88 15	88.65 323	99.71 193	98.37 145	82.74 346	98.09 325

UniMVSNet (Re)		98.29 167	98.00 182	99.13 157	99.00 269	99.36 98	99.49 134	99.51 102	97.95 127	98.97 215	99.13 296	96.30 155	99.38 247	98.36 148	93.34 321	98.66 266

WR-MVS_H		98.13 181	97.87 198	98.90 188	99.02 267	98.84 165	99.70 33	99.59 44	97.27 198	98.40 282	99.19 290	95.53 180	99.23 276	98.34 149	93.78 317	98.61 289

PGM-MVS		99.45 26	99.31 37	99.86 18	99.87 15	99.78 37	99.58 83	99.65 32	97.84 137	99.71 46	99.80 76	99.12 11	99.97 10	98.33 150	99.87 40	99.83 29

LS3D		99.27 60	99.12 67	99.74 56	99.18 235	99.75 38	99.56 95	99.57 51	98.45 69	99.49 103	99.85 29	97.77 110	99.94 53	98.33 150	99.84 65	99.52 146

IterMVS-LS		98.46 152	98.42 151	98.58 226	99.59 134	98.00 221	99.37 189	99.43 195	96.94 230	99.07 196	99.59 185	97.87 106	99.03 305	98.32 152	95.62 286	98.71 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		98.16 178	98.10 170	98.33 255	99.29 209	96.82 276	98.75 313	99.44 187	97.83 138	99.13 183	99.55 198	92.92 255	99.67 204	98.32 152	97.69 221	98.48 301
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
NCCC		99.34 51	99.19 60	99.79 43	99.61 129	99.65 57	99.30 207	99.48 139	98.86 38	99.21 169	99.63 170	98.72 60	99.90 105	98.25 154	99.63 122	99.80 49

OPU-MVS				99.64 77	99.56 141	99.72 42	99.60 70				99.70 133	99.27 4	99.42 243	98.24 155	99.80 84	99.79 53

testing_2		94.44 309	92.93 315	98.98 172	94.16 348	99.00 141	99.42 166	99.28 261	96.60 253	84.86 345	96.84 341	70.91 350	99.27 271	98.23 156	96.08 272	98.68 250

cl-mvsnet2		97.85 219	97.64 222	98.48 237	99.09 255	97.87 230	98.60 326	99.33 240	97.11 215	98.87 230	99.22 286	92.38 277	99.17 288	98.21 157	95.99 275	98.42 310

xxxxxxxxxxxxxcwj		99.43 33	99.32 30	99.75 51	99.76 52	99.59 67	99.14 248	99.53 83	99.00 22	99.71 46	99.80 76	98.95 28	99.93 68	98.19 158	99.84 65	99.74 73

SF-MVS		99.38 47	99.24 55	99.79 43	99.79 42	99.68 49	99.57 88	99.54 71	97.82 143	99.71 46	99.80 76	98.95 28	99.93 68	98.19 158	99.84 65	99.74 73

旧先验2							98.96 289		96.70 243	99.47 105			99.94 53	98.19 158

F-COLMAP		99.19 69	99.04 77	99.64 77	99.78 44	99.27 109	99.42 166	99.54 71	97.29 196	99.41 120	99.59 185	98.42 84	99.93 68	98.19 158	99.69 109	99.73 80

LCM-MVSNet-Re		97.83 224	98.15 166	96.87 315	99.30 205	92.25 342	99.59 76	98.26 331	97.43 184	96.20 328	99.13 296	96.27 156	98.73 327	98.17 162	98.99 167	99.64 118

DPE-MVS		99.46 24	99.32 30	99.91 2	99.78 44	99.88 7	99.36 193	99.51 102	98.73 51	99.88 5	99.84 38	98.72 60	99.96 18	98.16 163	99.87 40	99.88 5

cascas		97.69 248	97.43 249	98.48 237	98.60 318	97.30 247	98.18 342	99.39 209	92.96 329	98.41 281	98.78 320	93.77 243	99.27 271	98.16 163	98.61 184	98.86 216

DWT-MVSNet_test		97.53 261	97.40 252	97.93 284	99.03 266	94.86 323	99.57 88	98.63 325	96.59 256	98.36 285	98.79 318	89.32 316	99.74 176	98.14 165	98.16 211	99.20 185

COLMAP_ROB	97.56 6	98.86 119	98.75 121	99.17 153	99.88 11	98.53 191	99.34 201	99.59 44	97.55 168	98.70 255	99.89 10	95.83 171	99.90 105	98.10 166	99.90 23	99.08 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		97.76 234	97.44 246	98.72 217	98.77 300	98.54 190	99.78 19	99.51 102	97.06 219	98.29 290	99.64 165	92.63 267	98.89 324	98.09 167	93.16 324	98.72 235

LPG-MVS_test		98.22 170	98.13 168	98.49 235	99.33 196	97.05 261	99.58 83	99.55 64	97.46 177	99.24 161	99.83 42	92.58 268	99.72 187	98.09 167	97.51 233	98.68 250

LGP-MVS_train				98.49 235	99.33 196	97.05 261		99.55 64	97.46 177	99.24 161	99.83 42	92.58 268	99.72 187	98.09 167	97.51 233	98.68 250

IS-MVSNet		99.05 100	98.87 104	99.57 88	99.73 75	99.32 101	99.75 25	99.20 271	98.02 124	99.56 88	99.86 23	96.54 147	99.67 204	98.09 167	99.13 154	99.73 80

thisisatest0515		98.14 180	97.79 202	99.19 151	99.50 156	98.50 198	98.61 324	96.82 347	96.95 228	99.54 93	99.43 237	91.66 292	99.86 125	98.08 171	99.51 131	99.22 183

OPM-MVS		98.19 174	98.10 170	98.45 243	98.88 282	97.07 259	99.28 213	99.38 215	98.57 60	99.22 166	99.81 62	92.12 279	99.66 207	98.08 171	97.54 231	98.61 289

XVG-OURS		98.73 138	98.68 127	98.88 194	99.70 93	97.73 237	98.92 296	99.55 64	98.52 63	99.45 108	99.84 38	95.27 189	99.91 90	98.08 171	98.84 177	99.00 205

Baseline_NR-MVSNet		97.76 234	97.45 241	98.68 220	99.09 255	98.29 209	99.41 169	98.85 308	95.65 301	98.63 266	99.67 151	94.82 201	99.10 299	98.07 174	92.89 327	98.64 270

ACMH+	97.24 10	97.92 211	97.78 205	98.32 258	99.46 166	96.68 282	99.56 95	99.54 71	98.41 73	97.79 309	99.87 20	90.18 310	99.66 207	98.05 175	97.18 252	98.62 280

TranMVSNet+NR-MVSNet		97.93 208	97.66 219	98.76 215	98.78 297	98.62 184	99.65 53	99.49 128	97.76 147	98.49 277	99.60 182	94.23 228	98.97 319	98.00 176	92.90 326	98.70 241

DP-MVS Recon		99.12 85	98.95 95	99.65 72	99.74 70	99.70 46	99.27 217	99.57 51	96.40 271	99.42 116	99.68 145	98.75 56	99.80 161	97.98 177	99.72 103	99.44 167

test_prior3		99.21 66	99.05 74	99.68 65	99.67 100	99.48 86	98.96 289	99.56 56	98.34 80	99.01 205	99.52 210	98.68 63	99.83 145	97.96 178	99.74 99	99.74 73

test_prior2							98.96 289		98.34 80	99.01 205	99.52 210	98.68 63		97.96 178	99.74 99

Fast-Effi-MVS+-dtu		98.77 136	98.83 114	98.60 223	99.41 177	96.99 267	99.52 113	99.49 128	98.11 107	99.24 161	99.34 263	96.96 134	99.79 164	97.95 180	99.45 132	99.02 204

MP-MVS		99.33 52	99.15 63	99.87 11	99.88 11	99.82 20	99.66 46	99.46 167	98.09 110	99.48 104	99.74 117	98.29 92	99.96 18	97.93 181	99.87 40	99.82 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Vis-MVSNet (Re-imp)		98.87 116	98.72 122	99.31 133	99.71 86	98.88 160	99.80 16	99.44 187	97.91 131	99.36 135	99.78 95	95.49 182	99.43 242	97.91 182	99.11 155	99.62 124

ACMP	97.20 11	98.06 188	97.94 190	98.45 243	99.37 188	97.01 265	99.44 153	99.49 128	97.54 171	98.45 279	99.79 88	91.95 282	99.72 187	97.91 182	97.49 238	98.62 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Fast-Effi-MVS+		98.70 139	98.43 150	99.51 105	99.51 149	99.28 107	99.52 113	99.47 157	96.11 292	99.01 205	99.34 263	96.20 158	99.84 136	97.88 184	98.82 178	99.39 173

EPMVS		97.82 227	97.65 220	98.35 254	98.88 282	95.98 300	99.49 134	94.71 354	97.57 166	99.26 158	99.48 226	92.46 275	99.71 193	97.87 185	99.08 160	99.35 175

miper_enhance_ethall		98.16 178	98.08 174	98.41 248	98.96 276	97.72 238	98.45 333	99.32 248	96.95 228	98.97 215	99.17 291	97.06 130	99.22 279	97.86 186	95.99 275	98.29 318

tmp_tt		82.80 319	81.52 322	86.66 332	66.61 360	68.44 358	92.79 352	97.92 337	68.96 351	80.04 353	99.85 29	85.77 338	96.15 349	97.86 186	43.89 354	95.39 345

NR-MVSNet		97.97 206	97.61 224	99.02 167	98.87 286	99.26 110	99.47 145	99.42 197	97.63 161	97.08 320	99.50 217	95.07 195	99.13 292	97.86 186	93.59 318	98.68 250

v148		97.79 232	97.55 228	98.50 234	98.74 302	97.72 238	99.54 107	99.33 240	96.26 278	98.90 225	99.51 214	94.68 212	99.14 289	97.83 189	93.15 325	98.63 278

CPTT-MVS		99.11 90	98.90 100	99.74 56	99.80 41	99.46 89	99.59 76	99.49 128	97.03 222	99.63 70	99.69 140	97.27 124	99.96 18	97.82 190	99.84 65	99.81 41

MDTV_nov1_ep13_2view						95.18 318	99.35 198		96.84 235	99.58 85		95.19 193		97.82 190		99.46 164

OMC-MVS		99.08 96	99.04 77	99.20 150	99.67 100	98.22 212	99.28 213	99.52 89	98.07 115	99.66 64	99.81 62	97.79 109	99.78 168	97.79 192	99.81 80	99.60 128

HQP_MVS		98.27 169	98.22 164	98.44 246	99.29 209	96.97 269	99.39 181	99.47 157	98.97 30	99.11 187	99.61 179	92.71 264	99.69 202	97.78 193	97.63 222	98.67 258

plane_prior5								99.47 157					99.69 202	97.78 193	97.63 222	98.67 258

testdata				99.54 92	99.75 62	98.95 151		99.51 102	97.07 217	99.43 113	99.70 133	98.87 39	99.94 53	97.76 195	99.64 120	99.72 86

PLC	97.94 4	99.02 104	98.85 109	99.53 98	99.66 109	99.01 139	99.24 230	99.52 89	96.85 234	99.27 153	99.48 226	98.25 94	99.91 90	97.76 195	99.62 124	99.65 112
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		97.67 253	97.55 228	98.03 276	99.02 267	95.01 320	99.43 159	98.54 329	96.44 267	99.12 185	99.34 263	91.83 285	99.60 221	97.75 197	96.46 264	99.48 157

1314		98.68 142	98.54 146	99.11 158	98.89 281	98.65 181	99.27 217	99.49 128	96.89 232	97.99 302	99.56 195	97.72 112	99.83 145	97.74 198	99.27 144	98.84 218

XVG-ACMP-BASELINE		97.83 224	97.71 215	98.20 267	99.11 250	96.33 293	99.41 169	99.52 89	98.06 119	99.05 201	99.50 217	89.64 314	99.73 183	97.73 199	97.38 246	98.53 297

CNLPA		99.14 77	98.99 87	99.59 84	99.58 135	99.41 94	99.16 242	99.44 187	98.45 69	99.19 175	99.49 220	98.08 102	99.89 113	97.73 199	99.75 96	99.48 157

v2v482		98.06 188	97.77 207	98.92 182	98.90 280	98.82 169	99.57 88	99.36 224	96.65 247	99.19 175	99.35 260	94.20 229	99.25 274	97.72 201	94.97 300	98.69 245

AUN-MVS		96.88 280	96.31 286	98.59 224	99.48 164	97.04 263	99.27 217	99.22 268	97.44 183	98.51 275	99.41 243	91.97 281	99.66 207	97.71 202	83.83 345	99.07 199

baseline2		97.87 216	97.55 228	98.82 207	99.18 235	98.02 220	99.41 169	96.58 350	96.97 225	96.51 325	99.17 291	93.43 246	99.57 223	97.71 202	99.03 164	98.86 216

原ACMM1				99.65 72	99.73 75	99.33 100		99.47 157	97.46 177	99.12 185	99.66 157	98.67 66	99.91 90	97.70 204	99.69 109	99.71 93

agg_prior1		99.01 107	98.76 120	99.76 50	99.67 100	99.62 60	98.99 281	99.40 205	96.26 278	98.87 230	99.49 220	98.77 51	99.91 90	97.69 205	99.72 103	99.75 69

PVSNet_0	94.43 19	96.09 296	95.47 299	97.94 283	99.31 204	94.34 329	97.81 345	99.70 15	97.12 212	97.46 312	98.75 321	89.71 313	99.79 164	97.69 205	81.69 347	99.68 102

MAR-MVS		98.86 119	98.63 133	99.54 92	99.37 188	99.66 54	99.45 149	99.54 71	96.61 251	99.01 205	99.40 246	97.09 128	99.86 125	97.68 207	99.53 130	99.10 190
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
9.14			99.10 69		99.72 80		99.40 177	99.51 102	97.53 173	99.64 69	99.78 95	98.84 42	99.91 90	97.63 208	99.82 78

train_agg		99.02 104	98.77 118	99.77 47	99.67 100	99.65 57	99.05 265	99.41 199	96.28 275	98.95 217	99.49 220	98.76 53	99.91 90	97.63 208	99.72 103	99.75 69

miper_ehance_all_eth		98.18 176	98.10 170	98.41 248	99.23 222	97.72 238	98.72 316	99.31 251	96.60 253	98.88 228	99.29 275	97.29 123	99.13 292	97.60 210	95.99 275	98.38 315

MDTV_nov1_ep13			98.32 158		99.11 250	94.44 327	99.27 217	98.74 316	97.51 175	99.40 125	99.62 175	94.78 204	99.76 173	97.59 211	98.81 180

cl_fuxian		98.12 183	98.04 178	98.38 252	99.30 205	97.69 241	98.81 307	99.33 240	96.67 245	98.83 236	99.34 263	97.11 127	98.99 311	97.58 212	95.34 292	98.48 301

test_post1							99.23 231				65.14 357	94.18 232	99.71 193	97.58 212

SCA		98.19 174	98.16 165	98.27 265	99.30 205	95.55 306	99.07 260	98.97 293	97.57 166	99.43 113	99.57 192	92.72 262	99.74 176	97.58 212	99.20 148	99.52 146

JIA-IIPM		97.50 265	97.02 275	98.93 180	98.73 303	97.80 234	99.30 207	98.97 293	91.73 333	98.91 223	94.86 346	95.10 194	99.71 193	97.58 212	97.98 215	99.28 181

V42		98.06 188	97.79 202	98.86 201	98.98 273	98.84 165	99.69 35	99.34 233	96.53 258	99.30 145	99.37 254	94.67 213	99.32 263	97.57 216	94.66 303	98.42 310

gm-plane-assit					98.54 322	92.96 339			94.65 314		99.15 294		99.64 213	97.56 217

APD-MVS		99.27 60	99.08 72	99.84 32	99.75 62	99.79 30	99.50 124	99.50 120	97.16 208	99.77 33	99.82 49	98.78 48	99.94 53	97.56 217	99.86 51	99.80 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
pm-mvs1		97.68 250	97.28 267	98.88 194	99.06 260	98.62 184	99.50 124	99.45 179	96.32 273	97.87 305	99.79 88	92.47 272	99.35 258	97.54 219	93.54 320	98.67 258

无先验							98.99 281	99.51 102	96.89 232				99.93 68	97.53 220		99.72 86

1121		99.09 94	98.87 104	99.75 51	99.74 70	99.60 64	99.27 217	99.48 139	96.82 238	99.25 160	99.65 158	98.38 86	99.93 68	97.53 220	99.67 116	99.73 80

pmmvs5		97.52 262	97.30 266	98.16 270	98.57 320	96.73 278	99.27 217	98.90 304	96.14 290	98.37 284	99.53 207	91.54 295	99.14 289	97.51 222	95.87 279	98.63 278

test9_res														97.49 223	99.72 103	99.75 69

CDPH-MVS		99.13 79	98.91 99	99.80 40	99.75 62	99.71 44	99.15 246	99.41 199	96.60 253	99.60 80	99.55 198	98.83 43	99.90 105	97.48 224	99.83 72	99.78 61

AdaColmap		99.01 107	98.80 115	99.66 68	99.56 141	99.54 76	99.18 240	99.70 15	98.18 100	99.35 138	99.63 170	96.32 154	99.90 105	97.48 224	99.77 92	99.55 139

OpenMVS	96.50 16	98.47 151	98.12 169	99.52 103	99.04 264	99.53 79	99.82 10	99.72 11	94.56 315	98.08 297	99.88 15	94.73 210	99.98 5	97.47 226	99.76 95	99.06 200

IterMVS		97.83 224	97.77 207	98.02 278	99.58 135	96.27 295	99.02 274	99.48 139	97.22 204	98.71 249	99.70 133	92.75 259	99.13 292	97.46 227	96.00 274	98.67 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		98.22 170	98.62 138	96.99 310	99.82 37	91.58 344	99.72 29	99.44 187	96.61 251	99.66 64	99.89 10	95.92 167	99.82 152	97.46 227	99.10 158	99.57 137

ETH3D-3000-0.1		99.21 66	99.02 82	99.77 47	99.73 75	99.69 47	99.38 186	99.51 102	97.45 180	99.61 76	99.75 111	98.51 75	99.91 90	97.45 229	99.83 72	99.71 93

IterMVS-SCA-FT		97.82 227	97.75 211	98.06 275	99.57 137	96.36 292	99.02 274	99.49 128	97.18 206	98.71 249	99.72 128	92.72 262	99.14 289	97.44 230	95.86 280	98.67 258

PatchmatchNet		98.31 164	98.36 153	98.19 268	99.16 243	95.32 314	99.27 217	98.92 299	97.37 190	99.37 132	99.58 188	94.90 198	99.70 199	97.43 231	99.21 147	99.54 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EU-MVSNet		97.98 203	98.03 179	97.81 293	98.72 305	96.65 283	99.66 46	99.66 27	98.09 110	98.35 286	99.82 49	95.25 192	98.01 333	97.41 232	95.30 293	98.78 223

eth_miper_zixun_eth		98.05 193	97.96 186	98.33 255	99.26 216	97.38 246	98.56 329	99.31 251	96.65 247	98.88 228	99.52 210	96.58 145	99.12 296	97.39 233	95.53 289	98.47 303

tpm2		97.44 269	97.34 261	97.74 296	99.15 246	94.36 328	99.45 149	98.94 296	93.45 327	98.90 225	99.44 235	91.35 297	99.59 222	97.31 234	98.07 214	99.29 180

TESTMET0.1,1		97.55 259	97.27 269	98.40 250	98.93 278	96.53 286	98.67 319	97.61 342	96.96 226	98.64 265	99.28 277	88.63 324	99.45 232	97.30 235	99.38 136	99.21 184

ETH3D cwj APD-0.16		99.06 98	98.84 110	99.72 61	99.51 149	99.60 64	99.23 231	99.44 187	97.04 220	99.39 127	99.67 151	98.30 91	99.92 79	97.27 236	99.69 109	99.64 118

miper_lstm_enhance		98.00 201	97.91 192	98.28 264	99.34 195	97.43 245	98.88 300	99.36 224	96.48 264	98.80 240	99.55 198	95.98 162	98.91 322	97.27 236	95.50 290	98.51 299

test-LLR		98.06 188	97.90 193	98.55 231	98.79 294	97.10 255	98.67 319	97.75 339	97.34 191	98.61 269	98.85 315	94.45 222	99.45 232	97.25 238	99.38 136	99.10 190

test-mter		97.49 267	97.13 272	98.55 231	98.79 294	97.10 255	98.67 319	97.75 339	96.65 247	98.61 269	98.85 315	88.23 328	99.45 232	97.25 238	99.38 136	99.10 190

cl-mvsnet_		98.01 199	97.84 200	98.55 231	99.25 220	97.97 223	98.71 317	99.34 233	96.47 266	98.59 272	99.54 203	95.65 178	99.21 284	97.21 240	95.77 281	98.46 307

cl-mvsnet1		98.01 199	97.85 199	98.48 237	99.24 221	97.95 227	98.71 317	99.35 229	96.50 259	98.60 271	99.54 203	95.72 176	99.03 305	97.21 240	95.77 281	98.46 307

agg_prior2														97.21 240	99.73 102	99.75 69

OurMVSNet-221017-0		97.88 214	97.77 207	98.19 268	98.71 307	96.53 286	99.88 1	99.00 290	97.79 144	98.78 243	99.94 3	91.68 289	99.35 258	97.21 240	96.99 256	98.69 245

BP-MVS														97.19 244

HQP-MVS		98.02 196	97.90 193	98.37 253	99.19 232	96.83 274	98.98 285	99.39 209	98.24 90	98.66 258	99.40 246	92.47 272	99.64 213	97.19 244	97.58 227	98.64 270

pmmvs4		98.13 181	97.90 193	98.81 209	98.61 317	98.87 161	98.99 281	99.21 270	96.44 267	99.06 200	99.58 188	95.90 169	99.11 297	97.18 246	96.11 271	98.46 307

PatchMatch-RL		98.84 129	98.62 138	99.52 103	99.71 86	99.28 107	99.06 263	99.77 9	97.74 151	99.50 100	99.53 207	95.41 183	99.84 136	97.17 247	99.64 120	99.44 167

MVS_0304		96.79 283	96.52 282	97.59 300	99.22 226	94.92 322	99.04 270	99.59 44	96.49 260	98.43 280	98.99 308	80.48 348	99.39 245	97.15 248	99.27 144	98.47 303

GBi-Net		97.68 250	97.48 236	98.29 261	99.51 149	97.26 250	99.43 159	99.48 139	96.49 260	99.07 196	99.32 270	90.26 306	98.98 312	97.10 249	96.65 258	98.62 280

test1		97.68 250	97.48 236	98.29 261	99.51 149	97.26 250	99.43 159	99.48 139	96.49 260	99.07 196	99.32 270	90.26 306	98.98 312	97.10 249	96.65 258	98.62 280

FMVSNet3		98.03 194	97.76 210	98.84 205	99.39 185	98.98 142	99.40 177	99.38 215	96.67 245	99.07 196	99.28 277	92.93 254	98.98 312	97.10 249	96.65 258	98.56 296

BH-untuned		98.42 155	98.36 153	98.59 224	99.49 158	96.70 280	99.27 217	99.13 278	97.24 202	98.80 240	99.38 251	95.75 174	99.74 176	97.07 252	99.16 150	99.33 178

LF4IMVS		97.52 262	97.46 240	97.70 298	98.98 273	95.55 306	99.29 211	98.82 311	98.07 115	98.66 258	99.64 165	89.97 311	99.61 220	97.01 253	96.68 257	97.94 332

SixPastTwentyTwo		97.50 265	97.33 263	98.03 276	98.65 312	96.23 296	99.77 21	98.68 324	97.14 209	97.90 304	99.93 4	90.45 304	99.18 287	97.00 254	96.43 265	98.67 258

MG-MVS		99.13 79	99.02 82	99.45 115	99.57 137	98.63 183	99.07 260	99.34 233	98.99 25	99.61 76	99.82 49	97.98 105	99.87 122	97.00 254	99.80 84	99.85 14

API-MVS		99.04 101	99.03 79	99.06 161	99.40 182	99.31 104	99.55 104	99.56 56	98.54 61	99.33 142	99.39 250	98.76 53	99.78 168	96.98 256	99.78 89	98.07 326

tpmvs		97.98 203	98.02 181	97.84 290	99.04 264	94.73 325	99.31 205	99.20 271	96.10 296	98.76 245	99.42 240	94.94 196	99.81 156	96.97 257	98.45 195	98.97 209

QAPM		98.67 143	98.30 160	99.80 40	99.20 230	99.67 52	99.77 21	99.72 11	94.74 312	98.73 247	99.90 7	95.78 173	99.98 5	96.96 258	99.88 36	99.76 68

PAPM_NR		99.04 101	98.84 110	99.66 68	99.74 70	99.44 91	99.39 181	99.38 215	97.70 154	99.28 150	99.28 277	98.34 89	99.85 131	96.96 258	99.45 132	99.69 98

v8		97.95 207	97.63 223	98.93 180	98.95 277	98.81 171	99.80 16	99.41 199	96.03 297	99.10 190	99.42 240	94.92 197	99.30 266	96.94 260	94.08 314	98.66 266

ZD-MVS					99.71 86	99.79 30		99.61 36	96.84 235	99.56 88	99.54 203	98.58 70	99.96 18	96.93 261	99.75 96

MSDG		98.98 109	98.80 115	99.53 98	99.76 52	99.19 115	98.75 313	99.55 64	97.25 200	99.47 105	99.77 101	97.82 108	99.87 122	96.93 261	99.90 23	99.54 141

pmmvs6		96.53 287	96.09 290	97.82 292	98.69 309	95.47 310	99.37 189	99.47 157	93.46 326	97.41 313	99.78 95	87.06 335	99.33 262	96.92 263	92.70 330	98.65 268

新几何1				99.75 51	99.75 62	99.59 67		99.54 71	96.76 239	99.29 148	99.64 165	98.43 81	99.94 53	96.92 263	99.66 117	99.72 86

DTE-MVSNet		97.51 264	97.19 271	98.46 242	98.63 314	98.13 217	99.84 6	99.48 139	96.68 244	97.97 303	99.67 151	92.92 255	98.56 328	96.88 265	92.60 331	98.70 241

ADS-MVSNet2		98.02 196	98.07 177	97.87 288	99.33 196	95.19 317	99.23 231	99.08 283	96.24 280	99.10 190	99.67 151	94.11 233	98.93 321	96.81 266	99.05 162	99.48 157

ADS-MVSNet		98.20 173	98.08 174	98.56 229	99.33 196	96.48 288	99.23 231	99.15 275	96.24 280	99.10 190	99.67 151	94.11 233	99.71 193	96.81 266	99.05 162	99.48 157

gg-mvs-nofinetune		96.17 294	95.32 302	98.73 216	98.79 294	98.14 216	99.38 186	94.09 355	91.07 338	98.07 300	91.04 351	89.62 315	99.35 258	96.75 268	99.09 159	98.68 250

testtj		99.12 85	98.87 104	99.86 18	99.72 80	99.79 30	99.44 153	99.51 102	97.29 196	99.59 83	99.74 117	98.15 100	99.96 18	96.74 269	99.69 109	99.81 41

v1144		97.98 203	97.69 216	98.85 204	98.87 286	98.66 180	99.54 107	99.35 229	96.27 277	99.23 165	99.35 260	94.67 213	99.23 276	96.73 270	95.16 296	98.68 250

UnsupCasMVSNet_eth		96.44 289	96.12 289	97.40 306	98.65 312	95.65 303	99.36 193	99.51 102	97.13 210	96.04 331	98.99 308	88.40 326	98.17 331	96.71 271	90.27 336	98.40 313

GA-MVS		97.85 219	97.47 238	99.00 170	99.38 186	97.99 222	98.57 327	99.15 275	97.04 220	98.90 225	99.30 273	89.83 312	99.38 247	96.70 272	98.33 197	99.62 124

K. test v3		97.10 278	96.79 279	98.01 279	98.72 305	96.33 293	99.87 4	97.05 345	97.59 163	96.16 329	99.80 76	88.71 321	99.04 303	96.69 273	96.55 262	98.65 268

testdata2													99.95 42	96.67 274

AllTest		98.87 116	98.72 122	99.31 133	99.86 21	98.48 201	99.56 95	99.61 36	97.85 135	99.36 135	99.85 29	95.95 164	99.85 131	96.66 275	99.83 72	99.59 132

TestCases				99.31 133	99.86 21	98.48 201		99.61 36	97.85 135	99.36 135	99.85 29	95.95 164	99.85 131	96.66 275	99.83 72	99.59 132

dp		97.75 238	97.80 201	97.59 300	99.10 253	93.71 334	99.32 203	98.88 306	96.48 264	99.08 195	99.55 198	92.67 266	99.82 152	96.52 277	98.58 187	99.24 182

BH-RMVSNet		98.41 157	98.08 174	99.40 122	99.41 177	98.83 168	99.30 207	98.77 312	97.70 154	98.94 219	99.65 158	92.91 257	99.74 176	96.52 277	99.55 129	99.64 118

FMVSNet2		97.72 243	97.36 256	98.80 211	99.51 149	98.84 165	99.45 149	99.42 197	96.49 260	98.86 235	99.29 275	90.26 306	98.98 312	96.44 279	96.56 261	98.58 294

ambc				93.06 327	92.68 349	82.36 349	98.47 332	98.73 321		95.09 334	97.41 336	55.55 355	99.10 299	96.42 280	91.32 334	97.71 336

tpm cat1		97.39 270	97.36 256	97.50 304	99.17 241	93.73 333	99.43 159	99.31 251	91.27 335	98.71 249	99.08 300	94.31 227	99.77 170	96.41 281	98.50 193	99.00 205

v144192		97.92 211	97.60 225	98.87 198	98.83 292	98.65 181	99.55 104	99.34 233	96.20 283	99.32 143	99.40 246	94.36 224	99.26 273	96.37 282	95.03 299	98.70 241

Patchmatch-RL test		95.84 298	95.81 296	95.95 322	95.61 343	90.57 345	98.24 339	98.39 330	95.10 308	95.20 333	98.67 323	94.78 204	97.77 338	96.28 283	90.02 337	99.51 152

Patchmtry		97.75 238	97.40 252	98.81 209	99.10 253	98.87 161	99.11 256	99.33 240	94.83 310	98.81 238	99.38 251	94.33 225	99.02 307	96.10 284	95.57 287	98.53 297

BH-w/o		98.00 201	97.89 197	98.32 258	99.35 191	96.20 297	99.01 279	98.90 304	96.42 269	98.38 283	99.00 307	95.26 191	99.72 187	96.06 285	98.61 184	99.03 202

v7n		97.87 216	97.52 232	98.92 182	98.76 301	98.58 187	99.84 6	99.46 167	96.20 283	98.91 223	99.70 133	94.89 199	99.44 237	96.03 286	93.89 316	98.75 230

v10		97.85 219	97.52 232	98.86 201	98.99 270	98.67 179	99.75 25	99.41 199	95.70 300	98.98 213	99.41 243	94.75 209	99.23 276	96.01 287	94.63 304	98.67 258

lessismore_v0				97.79 294	98.69 309	95.44 312		94.75 353		95.71 332	99.87 20	88.69 322	99.32 263	95.89 288	94.93 302	98.62 280

ITE_SJBPF				98.08 273	99.29 209	96.37 291		98.92 299	98.34 80	98.83 236	99.75 111	91.09 300	99.62 219	95.82 289	97.40 245	98.25 321

FMVSNet1		96.84 281	96.36 284	98.29 261	99.32 203	97.26 250	99.43 159	99.48 139	95.11 306	98.55 273	99.32 270	83.95 342	98.98 312	95.81 290	96.26 269	98.62 280

DPM-MVS		98.95 112	98.71 124	99.66 68	99.63 119	99.55 74	98.64 323	99.10 280	97.93 129	99.42 116	99.55 198	98.67 66	99.80 161	95.80 291	99.68 114	99.61 126

MIMVSNet		97.73 241	97.45 241	98.57 227	99.45 172	97.50 243	99.02 274	98.98 292	96.11 292	99.41 120	99.14 295	90.28 305	98.74 326	95.74 292	98.93 170	99.47 162

tfpnnormal		97.84 222	97.47 238	98.98 172	99.20 230	99.22 114	99.64 55	99.61 36	96.32 273	98.27 291	99.70 133	93.35 248	99.44 237	95.69 293	95.40 291	98.27 319

MS-PatchMatch		97.24 275	97.32 264	96.99 310	98.45 325	93.51 337	98.82 306	99.32 248	97.41 187	98.13 296	99.30 273	88.99 319	99.56 224	95.68 294	99.80 84	97.90 335

EG-PatchMatch MVS		95.97 297	95.69 297	96.81 316	97.78 333	92.79 340	99.16 242	98.93 297	96.16 287	94.08 337	99.22 286	82.72 344	99.47 230	95.67 295	97.50 235	98.17 323

USDC		97.34 271	97.20 270	97.75 295	99.07 258	95.20 316	98.51 331	99.04 288	97.99 125	98.31 288	99.86 23	89.02 318	99.55 226	95.67 295	97.36 247	98.49 300

MVP-Stereo		97.81 229	97.75 211	97.99 281	97.53 334	96.60 285	98.96 289	98.85 308	97.22 204	97.23 316	99.36 257	95.28 188	99.46 231	95.51 297	99.78 89	97.92 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	69.68 23	94.13 311	94.90 305	91.84 329	97.24 340	80.01 352	98.52 330	99.48 139	89.01 340	91.99 342	99.67 151	85.67 339	99.13 292	95.44 298	97.03 255	96.39 343
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GG-mvs-BLEND				98.45 243	98.55 321	98.16 214	99.43 159	93.68 356		97.23 316	98.46 328	89.30 317	99.22 279	95.43 299	98.22 203	97.98 330

v1921920		97.80 231	97.45 241	98.84 205	98.80 293	98.53 191	99.52 113	99.34 233	96.15 289	99.24 161	99.47 229	93.98 237	99.29 267	95.40 300	95.13 297	98.69 245

TR-MVS		97.76 234	97.41 251	98.82 207	99.06 260	97.87 230	98.87 302	98.56 327	96.63 250	98.68 257	99.22 286	92.49 271	99.65 211	95.40 300	97.79 219	98.95 214

ETH3 D test6400		98.70 139	98.35 155	99.73 58	99.69 95	99.60 64	99.16 242	99.45 179	95.42 303	99.27 153	99.60 182	97.39 117	99.91 90	95.36 302	99.83 72	99.70 95

v1192		97.81 229	97.44 246	98.91 186	98.88 282	98.68 178	99.51 118	99.34 233	96.18 285	99.20 172	99.34 263	94.03 236	99.36 254	95.32 303	95.18 295	98.69 245

PAPR		98.63 147	98.34 156	99.51 105	99.40 182	99.03 136	98.80 308	99.36 224	96.33 272	99.00 210	99.12 299	98.46 79	99.84 136	95.23 304	99.37 140	99.66 108

TinyColmap		97.12 277	96.89 277	97.83 291	99.07 258	95.52 309	98.57 327	98.74 316	97.58 165	97.81 308	99.79 88	88.16 329	99.56 224	95.10 305	97.21 250	98.39 314

DSMNet-mixed		97.25 274	97.35 258	96.95 313	97.84 332	93.61 336	99.57 88	96.63 349	96.13 291	98.87 230	98.61 326	94.59 216	97.70 340	95.08 306	98.86 176	99.55 139

test0.0.03 1		97.71 247	97.42 250	98.56 229	98.41 326	97.82 233	98.78 310	98.63 325	97.34 191	98.05 301	98.98 311	94.45 222	98.98 312	95.04 307	97.15 254	98.89 215

our_test_3		97.65 255	97.68 217	97.55 302	98.62 315	94.97 321	98.84 304	99.30 255	96.83 237	98.19 293	99.34 263	97.01 132	99.02 307	95.00 308	96.01 273	98.64 270

MVS-HIRNet		95.75 299	95.16 303	97.51 303	99.30 205	93.69 335	98.88 300	95.78 351	85.09 345	98.78 243	92.65 348	91.29 298	99.37 250	94.85 309	99.85 58	99.46 164

CR-MVSNet		98.17 177	97.93 191	98.87 198	99.18 235	98.49 199	99.22 236	99.33 240	96.96 226	99.56 88	99.38 251	94.33 225	99.00 310	94.83 310	98.58 187	99.14 186

pmmvs-eth3d		95.34 304	94.73 306	97.15 307	95.53 345	95.94 301	99.35 198	99.10 280	95.13 305	93.55 338	97.54 335	88.15 330	97.91 335	94.58 311	89.69 339	97.61 337

testgi		97.65 255	97.50 235	98.13 272	99.36 190	96.45 289	99.42 166	99.48 139	97.76 147	97.87 305	99.45 234	91.09 300	98.81 325	94.53 312	98.52 192	99.13 188

v1240		97.69 248	97.32 264	98.79 212	98.85 290	98.43 204	99.48 140	99.36 224	96.11 292	99.27 153	99.36 257	93.76 244	99.24 275	94.46 313	95.23 294	98.70 241

YYNet1		95.36 303	94.51 309	97.92 285	97.89 331	97.10 255	99.10 258	99.23 267	93.26 328	80.77 350	99.04 305	92.81 258	98.02 332	94.30 314	94.18 312	98.64 270

PM-MVS		92.96 314	92.23 317	95.14 324	95.61 343	89.98 347	99.37 189	98.21 333	94.80 311	95.04 335	97.69 334	65.06 352	97.90 336	94.30 314	89.98 338	97.54 340

MVS		97.28 273	96.55 281	99.48 109	98.78 297	98.95 151	99.27 217	99.39 209	83.53 346	98.08 297	99.54 203	96.97 133	99.87 122	94.23 316	99.16 150	99.63 122

MDA-MVSNet_test_wron		95.45 301	94.60 307	98.01 279	98.16 329	97.21 253	99.11 256	99.24 266	93.49 325	80.73 351	98.98 311	93.02 252	98.18 330	94.22 317	94.45 307	98.64 270

TransMVSNet (Re)		97.15 276	96.58 280	98.86 201	99.12 248	98.85 164	99.49 134	98.91 302	95.48 302	97.16 318	99.80 76	93.38 247	99.11 297	94.16 318	91.73 333	98.62 280

UnsupCasMVSNet_bld		93.53 313	92.51 316	96.58 320	97.38 336	93.82 332	98.24 339	99.48 139	91.10 337	93.10 340	96.66 342	74.89 349	98.37 329	94.03 319	87.71 341	97.56 339

ppachtmachnet_test		97.49 267	97.45 241	97.61 299	98.62 315	95.24 315	98.80 308	99.46 167	96.11 292	98.22 292	99.62 175	96.45 150	98.97 319	93.77 320	95.97 278	98.61 289

thres600view7		97.86 218	97.51 234	98.92 182	99.72 80	97.95 227	99.59 76	98.74 316	97.94 128	99.27 153	98.62 324	91.75 286	99.86 125	93.73 321	98.19 206	98.96 211

DeepMVS_CX				93.34 326	99.29 209	82.27 350		99.22 268	85.15 344	96.33 327	99.05 304	90.97 302	99.73 183	93.57 322	97.77 220	98.01 329

MDA-MVSNet-bldmvs		94.96 306	93.98 312	97.92 285	98.24 328	97.27 249	99.15 246	99.33 240	93.80 321	80.09 352	99.03 306	88.31 327	97.86 337	93.49 323	94.36 309	98.62 280

Patchmatch-test		97.93 208	97.65 220	98.77 214	99.18 235	97.07 259	99.03 271	99.14 277	96.16 287	98.74 246	99.57 192	94.56 218	99.72 187	93.36 324	99.11 155	99.52 146

thres100view900		97.76 234	97.45 241	98.69 219	99.72 80	97.86 232	99.59 76	98.74 316	97.93 129	99.26 158	98.62 324	91.75 286	99.83 145	93.22 325	98.18 207	98.37 316

tfpn200view9		97.72 243	97.38 254	98.72 217	99.69 95	97.96 225	99.50 124	98.73 321	97.83 138	99.17 179	98.45 329	91.67 290	99.83 145	93.22 325	98.18 207	98.37 316

thres400		97.77 233	97.38 254	98.92 182	99.69 95	97.96 225	99.50 124	98.73 321	97.83 138	99.17 179	98.45 329	91.67 290	99.83 145	93.22 325	98.18 207	98.96 211

EPNet_dtu		98.03 194	97.96 186	98.23 266	98.27 327	95.54 308	99.23 231	98.75 313	99.02 15	97.82 307	99.71 129	96.11 159	99.48 229	93.04 328	99.65 119	99.69 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		97.61 257	97.28 267	98.62 222	99.64 116	98.03 219	99.26 226	98.74 316	97.68 156	99.09 194	98.32 331	91.66 292	99.81 156	92.88 329	98.22 203	98.03 328

PCF-MVS	97.08 14	97.66 254	97.06 274	99.47 112	99.61 129	99.09 131	98.04 344	99.25 265	91.24 336	98.51 275	99.70 133	94.55 219	99.91 90	92.76 330	99.85 58	99.42 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet5		96.43 290	96.19 288	97.15 307	99.11 250	95.89 302	99.32 203	99.52 89	94.47 317	98.34 287	99.07 301	87.54 333	97.07 344	92.61 331	95.72 284	98.47 303

test_0402		96.64 285	96.24 287	97.85 289	98.85 290	96.43 290	99.44 153	99.26 263	93.52 324	96.98 322	99.52 210	88.52 325	99.20 286	92.58 332	97.50 235	97.93 333

new-patchmatchnet		94.48 308	94.08 311	95.67 323	95.08 346	92.41 341	99.18 240	99.28 261	94.55 316	93.49 339	97.37 338	87.86 332	97.01 345	91.57 333	88.36 340	97.61 337

N_pmnet		94.95 307	95.83 295	92.31 328	98.47 324	79.33 353	99.12 250	92.81 359	93.87 320	97.68 311	99.13 296	93.87 240	99.01 309	91.38 334	96.19 270	98.59 293

LCM-MVSNet		86.80 317	85.22 321	91.53 330	87.81 354	80.96 351	98.23 341	98.99 291	71.05 350	90.13 344	96.51 343	48.45 358	96.88 346	90.51 335	85.30 344	96.76 341

new_pmnet		96.38 291	96.03 291	97.41 305	98.13 330	95.16 319	99.05 265	99.20 271	93.94 319	97.39 314	98.79 318	91.61 294	99.04 303	90.43 336	95.77 281	98.05 327

CL-MVSNet_2432*1600		95.00 305	94.34 310	96.96 312	97.07 342	95.39 313	99.56 95	99.44 187	95.11 306	97.13 319	97.32 339	91.86 284	97.27 343	90.35 337	81.23 348	98.23 322

PAPM		97.59 258	97.09 273	99.07 160	99.06 260	98.26 211	98.30 338	99.10 280	94.88 309	98.08 297	99.34 263	96.27 156	99.64 213	89.87 338	98.92 172	99.31 179

pmmvs3		94.09 312	93.25 314	96.60 319	94.76 347	94.49 326	98.92 296	98.18 335	89.66 339	96.48 326	98.06 333	86.28 336	97.33 342	89.68 339	87.20 342	97.97 331

OpenMVS_ROB	92.34 20	94.38 310	93.70 313	96.41 321	97.38 336	93.17 338	99.06 263	98.75 313	86.58 343	94.84 336	98.26 332	81.53 347	99.32 263	89.01 340	97.87 218	96.76 341

PatchT		97.03 279	96.44 283	98.79 212	98.99 270	98.34 208	99.16 242	99.07 285	92.13 331	99.52 97	97.31 340	94.54 220	98.98 312	88.54 341	98.73 183	99.03 202

MIMVSNet1		95.51 300	95.04 304	96.92 314	97.38 336	95.60 304	99.52 113	99.50 120	93.65 323	96.97 323	99.17 291	85.28 340	96.56 347	88.36 342	95.55 288	98.60 292

TAPA-MVS	97.07 15	97.74 240	97.34 261	98.94 178	99.70 93	97.53 242	99.25 228	99.51 102	91.90 332	99.30 145	99.63 170	98.78 48	99.64 213	88.09 343	99.87 40	99.65 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Gipuma		90.99 315	90.15 318	93.51 325	98.73 303	90.12 346	93.98 350	99.45 179	79.32 348	92.28 341	94.91 345	69.61 351	97.98 334	87.42 344	95.67 285	92.45 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		96.12 295	95.96 293	96.63 318	97.44 335	95.45 311	99.51 118	99.38 215	96.55 257	96.16 329	99.25 282	93.76 244	96.17 348	87.35 345	94.22 311	98.27 319

Anonymous20231206		96.22 292	96.03 291	96.79 317	97.31 339	94.14 330	99.63 57	99.08 283	96.17 286	97.04 321	99.06 303	93.94 238	97.76 339	86.96 346	95.06 298	98.47 303

RPMNet		96.72 284	95.90 294	99.19 151	99.18 235	98.49 199	99.22 236	99.52 89	88.72 342	99.56 88	97.38 337	94.08 235	99.95 42	86.87 347	98.58 187	99.14 186

PMMVS2		86.87 316	85.37 320	91.35 331	90.21 352	83.80 348	98.89 299	97.45 344	83.13 347	91.67 343	95.03 344	48.49 357	94.70 350	85.86 348	77.62 349	95.54 344

FPMVS		84.93 318	85.65 319	82.75 336	86.77 355	63.39 359	98.35 336	98.92 299	74.11 349	83.39 348	98.98 311	50.85 356	92.40 352	84.54 349	94.97 300	92.46 346

PMVS	70.75 22	75.98 324	74.97 325	79.01 338	70.98 359	55.18 360	93.37 351	98.21 333	65.08 355	61.78 356	93.83 347	21.74 363	92.53 351	78.59 350	91.12 335	89.34 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ANet_high		77.30 322	74.86 326	84.62 334	75.88 358	77.61 354	97.63 347	93.15 358	88.81 341	64.27 355	89.29 352	36.51 359	83.93 356	75.89 351	52.31 353	92.33 348

MVE	76.82 21	76.91 323	74.31 327	84.70 333	85.38 357	76.05 357	96.88 349	93.17 357	67.39 352	71.28 354	89.01 353	21.66 364	87.69 353	71.74 352	72.29 350	90.35 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 320	79.88 323	82.81 335	90.75 351	76.38 356	97.69 346	95.76 352	66.44 353	83.52 347	92.25 349	62.54 354	87.16 354	68.53 353	61.40 351	84.89 352

EMVS		80.02 321	79.22 324	82.43 337	91.19 350	76.40 355	97.55 348	92.49 360	66.36 354	83.01 349	91.27 350	64.63 353	85.79 355	65.82 354	60.65 352	85.08 351

wuyk23d		40.18 325	41.29 330	36.84 339	86.18 356	49.12 361	79.73 353	22.81 362	27.64 356	25.46 359	28.45 359	21.98 362	48.89 357	55.80 355	23.56 357	12.51 355

testmvs		39.17 326	43.78 328	25.37 341	36.04 362	16.84 363	98.36 335	26.56 361	20.06 357	38.51 358	67.32 354	29.64 361	15.30 359	37.59 356	39.90 355	43.98 354

test123		39.01 327	42.50 329	28.53 340	39.17 361	20.91 362	98.75 313	19.17 363	19.83 358	38.57 357	66.67 355	33.16 360	15.42 358	37.50 357	29.66 356	49.26 353

uanet_test		0.02 331	0.03 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

cdsmvs_eth3d_5k		24.64 328	32.85 331	0.00 342	0.00 363	0.00 364	0.00 354	99.51 102	0.00 359	0.00 360	99.56 195	96.58 145	0.00 360	0.00 358	0.00 358	0.00 356

pcd_1.5k_mvsjas		8.27 330	11.03 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	99.01 16	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		0.02 331	0.03 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		0.02 331	0.03 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		0.02 331	0.03 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		0.02 331	0.03 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		8.30 329	11.06 332	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	99.58 188	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		0.02 331	0.03 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.27 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

test_241102_ONE					99.84 32	99.90 1		99.48 139	99.07 13	99.91 1	99.74 117	99.20 5	99.76 173

save fliter					99.76 52	99.59 67	99.14 248	99.40 205	99.00 22

test0726					99.85 25	99.89 3	99.62 63	99.50 120	99.10 8	99.86 11	99.82 49	98.94 31

GSMVS																99.52 146

test_part2					99.81 40	99.83 14				99.77 33

sam_mvs1												94.86 200				99.52 146

sam_mvs												94.72 211

MTGPA								99.47 157

test_post											65.99 356	94.65 215	99.73 183

patchmatchnet-post											98.70 322	94.79 203	99.74 176

MTMP							99.54 107	98.88 306

TEST9					99.67 100	99.65 57	99.05 265	99.41 199	96.22 282	98.95 217	99.49 220	98.77 51	99.91 90

test_8					99.67 100	99.61 62	99.03 271	99.41 199	96.28 275	98.93 221	99.48 226	98.76 53	99.91 90

agg_prior					99.67 100	99.62 60		99.40 205		98.87 230			99.91 90

test_prior4						99.56 72	98.99 281

test_prior				99.68 65	99.67 100	99.48 86		99.56 56					99.83 145			99.74 73

新几何2							99.01 279

旧先验1					99.74 70	99.59 67		99.54 71			99.69 140	98.47 78			99.68 114	99.73 80

原ACMM2							98.95 293

test222					99.75 62	99.49 85	98.91 298	99.49 128	96.42 269	99.34 141	99.65 158	98.28 93			99.69 109	99.72 86

segment_acmp												98.96 25

testdata1							98.85 303		98.32 84

test12				99.75 51	99.64 116	99.61 62		99.29 260		99.21 169		98.38 86	99.89 113		99.74 99	99.74 73

plane_prior7					99.29 209	97.03 264

plane_prior6					99.27 214	96.98 268						92.71 264

plane_prior4											99.61 179

plane_prior3						97.00 266			98.69 54	99.11 187

plane_prior2							99.39 181		98.97 30

plane_prior1					99.26 216

plane_prior						96.97 269	99.21 238		98.45 69						97.60 225

n2								0.00 364

nn								0.00 364

door-mid								98.05 336

test11								99.35 229

door								97.92 337

HQP5-MVS						96.83 274

HQP-NCC					99.19 232		98.98 285		98.24 90	98.66 258

ACMP_Plane					99.19 232		98.98 285		98.24 90	98.66 258

HQP4-MVS										98.66 258			99.64 213			98.64 270

HQP3-MVS								99.39 209							97.58 227

HQP2-MVS												92.47 272

NP-MVS					99.23 222	96.92 272					99.40 246

ACMMP++_ref															97.19 251

ACMMP++															97.43 243

Test By Simon												98.75 56

High-resolution multi-view benchmark