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.

MVS_0304		99.06 81	98.86 89	99.66 55	99.51 132	99.36 77	99.22 236	99.51 85	98.95 24	99.58 65	99.65 131	93.74 229	99.98 5	99.66 1	99.95 6	99.64 97

CANet		99.25 54	99.14 52	99.59 70	99.41 153	99.16 96	99.35 199	99.57 44	98.82 35	99.51 83	99.61 151	96.46 120	99.95 33	99.59 2	99.98 2	99.65 91

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 64	99.78 36	99.14 100	99.60 90	99.45 151	99.01 13	99.90 1	99.83 37	98.98 19	99.93 57	99.59 2	99.95 6	99.86 5

DELS-MVS		99.48 17	99.42 11	99.65 59	99.72 76	99.40 75	99.05 269	99.66 25	99.14 6	99.57 68	99.80 65	98.46 62	99.94 42	99.57 4	99.84 58	99.60 105
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 3	99.56 3	99.64 64	99.78 36	99.15 99	99.61 88	99.45 151	99.01 13	99.89 2	99.82 44	99.01 12	99.92 65	99.56 5	99.95 6	99.85 8

HyFIR lowres test		99.11 73	98.92 79	99.65 59	99.90 3	99.37 76	99.02 278	99.91 3	97.67 141	99.59 64	99.75 93	95.90 137	99.73 169	99.53 6	99.02 135	99.86 5

VNet		99.11 73	98.90 82	99.73 47	99.52 130	99.56 52	99.41 175	99.39 181	99.01 13	99.74 31	99.78 78	95.56 145	99.92 65	99.52 7	98.18 184	99.72 72

xiu_mvs_v1_base_debu		99.29 48	99.27 41	99.34 107	99.63 109	98.97 126	99.12 252	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

xiu_mvs_v1_base		99.29 48	99.27 41	99.34 107	99.63 109	98.97 126	99.12 252	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

xiu_mvs_v1_base_debi		99.29 48	99.27 41	99.34 107	99.63 109	98.97 126	99.12 252	99.51 85	98.86 31	99.84 8	99.47 201	98.18 77	99.99 1	99.50 8	99.31 116	99.08 169

CHOSEN 1792x2688		99.19 57	99.10 57	99.45 96	99.89 8	98.52 192	99.39 182	99.94 1	98.73 44	99.11 174	99.89 10	95.50 147	99.94 42	99.50 8	99.97 3	99.89 2

VDD-MVS		97.73 227	97.35 240	98.88 184	99.47 143	97.12 244	99.34 202	98.85 288	98.19 76	99.67 44	99.85 26	82.98 339	99.92 65	99.49 12	98.32 174	99.60 105

PVSNet_Blended_VisFu		99.36 39	99.28 39	99.61 68	99.86 20	99.07 106	99.47 151	99.93 2	97.66 142	99.71 32	99.86 22	97.73 89	99.96 19	99.47 13	99.82 68	99.79 46

CHOSEN 280x420		99.12 69	99.13 53	99.08 142	99.66 103	97.89 219	98.43 328	99.71 13	98.88 30	99.62 57	99.76 88	96.63 117	99.70 185	99.46 14	99.99 1	99.66 88

Regformer-4		99.59 2	99.54 4	99.73 47	99.76 44	99.41 73	99.58 99	99.49 105	99.02 10	99.88 3	99.80 65	99.00 18	99.94 42	99.45 15	99.92 12	99.84 12

Regformer-3		99.57 6	99.53 5	99.68 52	99.76 44	99.29 84	99.58 99	99.44 159	99.01 13	99.87 6	99.80 65	98.97 20	99.91 74	99.44 16	99.92 12	99.83 23

DeepC-MVS	98.35 2	99.30 46	99.19 48	99.64 64	99.82 29	99.23 91	99.62 82	99.55 55	98.94 26	99.63 54	99.95 2	95.82 140	99.94 42	99.37 17	99.97 3	99.73 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
alignmvs		98.81 111	98.56 124	99.58 73	99.43 149	99.42 72	99.51 129	98.96 275	98.61 50	99.35 117	98.92 284	94.78 183	99.77 156	99.35 18	98.11 200	99.54 115

PS-MVSNAJ		99.32 43	99.32 27	99.30 115	99.57 124	98.94 134	98.97 291	99.46 139	98.92 28	99.71 32	99.24 258	99.01 12	99.98 5	99.35 18	99.66 98	98.97 183

VPA-MVSNet		98.29 143	97.95 160	99.30 115	99.16 209	99.54 55	99.50 134	99.58 43	98.27 71	99.35 117	99.37 228	92.53 259	99.65 194	99.35 18	94.46 293	98.72 215

mvs_anonymous		99.03 86	98.99 70	99.16 134	99.38 161	98.52 192	99.51 129	99.38 187	97.79 127	99.38 108	99.81 54	97.30 99	99.45 215	99.35 18	98.99 137	99.51 125

xiu_mvs_v2_base		99.26 53	99.25 45	99.29 118	99.53 129	98.91 139	99.02 278	99.45 151	98.80 39	99.71 32	99.26 256	98.94 27	99.98 5	99.34 22	99.23 120	98.98 182

nrg030		98.64 127	98.42 129	99.28 120	99.05 229	99.69 32	99.81 15	99.46 139	98.04 99	99.01 192	99.82 44	96.69 116	99.38 226	99.34 22	94.59 292	98.78 204

UGNet		98.87 99	98.69 107	99.40 104	99.22 194	98.72 172	99.44 159	99.68 19	99.24 3	99.18 166	99.42 212	92.74 244	99.96 19	99.34 22	99.94 10	99.53 119
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		98.40 137	98.23 140	98.91 171	98.67 296	98.51 194	99.66 65	99.53 72	98.19 76	98.65 245	99.81 54	92.75 242	99.44 220	99.31 25	97.48 227	98.77 207

VDDNet		97.55 243	97.02 259	99.16 134	99.49 139	98.12 211	99.38 187	99.30 224	95.35 283	99.68 38	99.90 7	82.62 341	99.93 57	99.31 25	98.13 192	99.42 144

LFMVS		97.90 199	97.35 240	99.54 77	99.52 130	99.01 119	99.39 182	98.24 324	97.10 192	99.65 52	99.79 73	84.79 335	99.91 74	99.28 27	98.38 172	99.69 80

MSLP-MVS++		99.46 22	99.47 8	99.44 99	99.60 119	99.16 96	99.41 175	99.71 13	98.98 19	99.45 92	99.78 78	99.19 5	99.54 210	99.28 27	99.84 58	99.63 101

canonicalmvs		99.02 87	98.86 89	99.51 87	99.42 150	99.32 80	99.80 19	99.48 114	98.63 48	99.31 123	98.81 293	97.09 103	99.75 160	99.27 29	97.90 206	99.47 135

EPNet		98.86 102	98.71 105	99.30 115	97.20 328	98.18 207	99.62 82	98.91 282	99.28 2	98.63 247	99.81 54	95.96 132	99.99 1	99.24 30	99.72 86	99.73 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
jajsoiax		98.43 134	98.28 138	98.88 184	98.60 302	98.43 199	99.82 13	99.53 72	98.19 76	98.63 247	99.80 65	93.22 234	99.44 220	99.22 31	97.50 223	98.77 207

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 41	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 6	99.96 19	99.22 31	99.92 12	99.90 1

VPNet		97.84 205	97.44 228	99.01 149	99.21 195	98.94 134	99.48 147	99.57 44	98.38 64	99.28 132	99.73 101	88.89 309	99.39 225	99.19 33	93.27 310	98.71 217

sss		99.17 60	99.05 60	99.53 81	99.62 113	98.97 126	99.36 195	99.62 31	97.83 122	99.67 44	99.65 131	97.37 98	99.95 33	99.19 33	99.19 123	99.68 84

Vis-MVSNet		99.12 69	98.97 73	99.56 76	99.78 36	99.10 103	99.68 54	99.66 25	98.49 56	99.86 7	99.87 19	94.77 187	99.84 119	99.19 33	99.41 110	99.74 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		99.53 9	99.47 8	99.72 49	99.71 82	99.44 70	99.49 142	99.46 139	98.95 24	99.83 12	99.76 88	99.01 12	99.93 57	99.17 36	99.87 39	99.80 42

ab-mvs		98.86 102	98.63 114	99.54 77	99.64 106	99.19 93	99.44 159	99.54 62	97.77 129	99.30 124	99.81 54	94.20 210	99.93 57	99.17 36	98.82 152	99.49 129

Regformer-2		99.54 7	99.47 8	99.75 40	99.71 82	99.52 61	99.49 142	99.49 105	98.94 26	99.83 12	99.76 88	99.01 12	99.94 42	99.15 38	99.87 39	99.80 42

PS-MVSNAJss		98.92 97	98.92 79	98.90 175	98.78 281	98.53 189	99.78 22	99.54 62	98.07 93	99.00 199	99.76 88	99.01 12	99.37 229	99.13 39	97.23 238	98.81 201

EPP-MVSNet		99.13 64	98.99 70	99.53 81	99.65 105	99.06 107	99.81 15	99.33 215	97.43 161	99.60 61	99.88 14	97.14 102	99.84 119	99.13 39	98.94 142	99.69 80

Effi-MVS+		98.81 111	98.59 122	99.48 90	99.46 144	99.12 102	98.08 338	99.50 99	97.50 155	99.38 108	99.41 215	96.37 124	99.81 139	99.11 41	98.54 165	99.51 125

TSAR-MVS + GP.		99.36 39	99.36 19	99.36 106	99.67 93	98.61 185	99.07 263	99.33 215	99.00 17	99.82 15	99.81 54	99.06 9	99.84 119	99.09 42	99.42 109	99.65 91

FIs		98.78 115	98.63 114	99.23 130	99.18 202	99.54 55	99.83 12	99.59 38	98.28 70	98.79 223	99.81 54	96.75 114	99.37 229	99.08 43	96.38 253	98.78 204

FC-MVSNet-test		98.75 118	98.62 117	99.15 136	99.08 223	99.45 69	99.86 8	99.60 35	98.23 75	98.70 236	99.82 44	96.80 110	99.22 267	99.07 44	96.38 253	98.79 203

HPM-MVS_fast		99.51 12	99.40 14	99.85 19	99.91 1	99.79 19	99.76 27	99.56 48	97.72 135	99.76 29	99.75 93	99.13 7	99.92 65	99.07 44	99.92 12	99.85 8

MVSFormer		99.17 60	99.12 55	99.29 118	99.51 132	98.94 134	99.88 1	99.46 139	97.55 150	99.80 17	99.65 131	97.39 95	99.28 252	99.03 46	99.85 53	99.65 91

test_djsdf		98.67 124	98.57 123	98.98 153	98.70 292	98.91 139	99.88 1	99.46 139	97.55 150	99.22 156	99.88 14	95.73 143	99.28 252	99.03 46	97.62 213	98.75 210

jason		99.13 64	99.03 65	99.45 96	99.46 144	98.87 142	99.12 252	99.26 241	98.03 101	99.79 19	99.65 131	97.02 105	99.85 113	99.02 48	99.90 25	99.65 91
jason: jason.
DeepPCF-MVS	98.18 3	98.81 111	99.37 17	97.12 302	99.60 119	91.75 330	98.61 321	99.44 159	99.35 1	99.83 12	99.85 26	98.70 51	99.81 139	99.02 48	99.91 17	99.81 36

CSCG		99.32 43	99.32 27	99.32 111	99.85 23	98.29 203	99.71 41	99.66 25	98.11 86	99.41 101	99.80 65	98.37 70	99.96 19	98.99 50	99.96 5	99.72 72

PVSNet_BlendedMVS		98.86 102	98.80 96	99.03 147	99.76 44	98.79 165	99.28 216	99.91 3	97.42 163	99.67 44	99.37 228	97.53 92	99.88 102	98.98 51	97.29 237	98.42 296

PVSNet_Blended		99.08 79	98.97 73	99.42 103	99.76 44	98.79 165	98.78 310	99.91 3	96.74 217	99.67 44	99.49 191	97.53 92	99.88 102	98.98 51	99.85 53	99.60 105

3Dnovator	97.25 9	99.24 55	99.05 60	99.81 29	99.12 215	99.66 37	99.84 9	99.74 10	99.09 8	98.92 207	99.90 7	95.94 135	99.98 5	98.95 53	99.92 12	99.79 46

lupinMVS		99.13 64	99.01 69	99.46 95	99.51 132	98.94 134	99.05 269	99.16 252	97.86 117	99.80 17	99.56 165	97.39 95	99.86 107	98.94 54	99.85 53	99.58 111

UA-Net		99.42 30	99.29 37	99.80 31	99.62 113	99.55 54	99.50 134	99.70 15	98.79 40	99.77 24	99.96 1	97.45 94	99.96 19	98.92 55	99.90 25	99.89 2

MVS_111021_HR		99.41 33	99.32 27	99.66 55	99.72 76	99.47 67	98.95 297	99.85 6	98.82 35	99.54 78	99.73 101	98.51 59	99.74 161	98.91 56	99.88 35	99.77 52

zzz-MVS		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 195	99.47 130	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 57	99.90 25	99.83 23

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 65	99.47 130	98.79 40	99.68 38	99.81 54	98.43 64	99.97 11	98.88 57	99.90 25	99.83 23

XXY-MVS		98.38 138	98.09 148	99.24 128	99.26 189	99.32 80	99.56 112	99.55 55	97.45 160	98.71 230	99.83 37	93.23 233	99.63 201	98.88 57	96.32 255	98.76 209

ACMH	97.28 8	98.10 166	97.99 157	98.44 236	99.41 153	96.96 260	99.60 90	99.56 48	98.09 89	98.15 272	99.91 5	90.87 292	99.70 185	98.88 57	97.45 228	98.67 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_Test		99.10 76	98.97 73	99.48 90	99.49 139	99.14 100	99.67 56	99.34 207	97.31 171	99.58 65	99.76 88	97.65 91	99.82 135	98.87 61	99.07 132	99.46 138

MVSTER		98.49 130	98.32 135	99.00 151	99.35 166	99.02 117	99.54 121	99.38 187	97.41 164	99.20 161	99.73 101	93.86 224	99.36 233	98.87 61	97.56 218	98.62 266

1112_ss		98.98 92	98.77 99	99.59 70	99.68 92	99.02 117	99.25 229	99.48 114	97.23 179	99.13 170	99.58 159	96.93 108	99.90 87	98.87 61	98.78 155	99.84 12

3Dnovator+	97.12 13	99.18 59	98.97 73	99.82 26	99.17 207	99.68 33	99.81 15	99.51 85	99.20 4	98.72 229	99.89 10	95.68 144	99.97 11	98.86 64	99.86 49	99.81 36

WTY-MVS		99.06 81	98.88 85	99.61 68	99.62 113	99.16 96	99.37 189	99.56 48	98.04 99	99.53 79	99.62 147	96.84 109	99.94 42	98.85 65	98.49 168	99.72 72

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 29	99.91 1	99.66 37	99.63 79	99.39 181	98.91 29	99.78 23	99.85 26	99.36 2	99.94 42	98.84 66	99.88 35	99.82 32

114514_t		98.93 96	98.67 109	99.72 49	99.85 23	99.53 58	99.62 82	99.59 38	92.65 321	99.71 32	99.78 78	98.06 81	99.90 87	98.84 66	99.91 17	99.74 61

MP-MVS-pluss		99.37 38	99.20 47	99.88 4	99.90 3	99.87 2	99.30 209	99.52 76	97.18 182	99.60 61	99.79 73	98.79 38	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Test_1112_low_res		98.89 98	98.66 112	99.57 74	99.69 89	98.95 131	99.03 275	99.47 130	96.98 205	99.15 169	99.23 259	96.77 113	99.89 95	98.83 68	98.78 155	99.86 5

MVS_111021_LR		99.41 33	99.33 26	99.65 59	99.77 41	99.51 63	98.94 299	99.85 6	98.82 35	99.65 52	99.74 98	98.51 59	99.80 143	98.83 68	99.89 33	99.64 97

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 151	99.48 114	98.05 98	99.76 29	99.86 22	98.82 35	99.93 57	98.82 71	99.91 17	99.84 12

SMA-MVS		99.47 20	99.34 24	99.86 13	99.73 72	99.85 6	99.56 112	99.50 99	97.61 144	99.84 8	99.82 44	99.28 3	99.91 74	98.79 72	99.91 17	99.81 36

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 8	99.69 45	99.68 19	98.98 19	99.37 110	99.74 98	98.81 36	99.94 42	98.79 72	99.86 49	99.84 12

X-MVStestdata		96.55 271	95.45 292	99.87 6	99.85 23	99.83 8	99.69 45	99.68 19	98.98 19	99.37 110	64.01 357	98.81 36	99.94 42	98.79 72	99.86 49	99.84 12

CVMVSNet		98.57 129	98.67 109	98.30 246	99.35 166	95.59 291	99.50 134	99.55 55	98.60 51	99.39 106	99.83 37	94.48 201	99.45 215	98.75 75	98.56 164	99.85 8

CP-MVS		99.45 23	99.32 27	99.85 19	99.83 28	99.75 24	99.69 45	99.52 76	98.07 93	99.53 79	99.63 142	98.93 28	99.97 11	98.74 76	99.91 17	99.83 23

ACMM	97.58 5	98.37 139	98.34 133	98.48 229	99.41 153	97.10 245	99.56 112	99.45 151	98.53 54	99.04 189	99.85 26	93.00 236	99.71 179	98.74 76	97.45 228	98.64 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		98.78 115	98.89 84	98.47 231	99.33 170	96.91 262	99.57 105	99.30 224	98.47 57	99.41 101	98.99 278	96.78 111	99.74 161	98.73 78	99.38 111	98.74 213

mvs-test1		98.86 102	98.84 92	98.89 177	99.33 170	97.77 230	99.44 159	99.30 224	98.47 57	99.10 177	99.43 210	96.78 111	99.95 33	98.73 78	99.02 135	98.96 189

SD-MVS		99.41 33	99.52 6	99.05 146	99.74 67	99.68 33	99.46 154	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 331	98.72 80	99.93 11	99.77 52

CDS-MVSNet		99.09 77	99.03 65	99.25 125	99.42 150	98.73 170	99.45 155	99.46 139	98.11 86	99.46 91	99.77 85	98.01 82	99.37 229	98.70 81	98.92 145	99.66 88
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		99.12 69	99.08 58	99.24 128	99.46 144	98.55 187	99.51 129	99.46 139	98.09 89	99.45 92	99.82 44	98.34 71	99.51 211	98.70 81	98.93 143	99.67 87

HFP-MVS		99.49 13	99.37 17	99.86 13	99.87 15	99.80 15	99.66 65	99.67 22	98.15 80	99.68 38	99.69 115	99.06 9	99.96 19	98.69 83	99.87 39	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 13	99.87 15	99.79 19	99.66 65	99.67 22	98.15 80	99.67 44	99.69 115	98.95 26	99.96 19	98.69 83	99.87 39	99.84 12

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 37	99.63 109	99.59 49	99.36 195	99.46 139	99.07 9	99.79 19	99.82 44	98.85 33	99.92 65	98.68 85	99.87 39	99.82 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
anonymousdsp		98.44 133	98.28 138	98.94 159	98.50 307	98.96 130	99.77 24	99.50 99	97.07 199	98.87 213	99.77 85	94.76 188	99.28 252	98.66 86	97.60 214	98.57 287

DP-MVS		99.16 62	98.95 77	99.78 35	99.77 41	99.53 58	99.41 175	99.50 99	97.03 203	99.04 189	99.88 14	97.39 95	99.92 65	98.66 86	99.90 25	99.87 4

MCST-MVS		99.43 28	99.30 34	99.82 26	99.79 35	99.74 27	99.29 213	99.40 178	98.79 40	99.52 81	99.62 147	98.91 29	99.90 87	98.64 88	99.75 80	99.82 32

CP-MVSNet		98.09 167	97.78 182	99.01 149	98.97 243	99.24 90	99.67 56	99.46 139	97.25 176	98.48 256	99.64 138	93.79 225	99.06 284	98.63 89	94.10 300	98.74 213

DI_MVS_plusplus_test		97.45 254	96.79 263	99.44 99	97.76 319	99.04 109	99.21 239	98.61 316	97.74 133	94.01 321	98.83 291	87.38 326	99.83 126	98.63 89	98.90 147	99.44 141

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 15	99.65 75	99.66 25	98.13 82	99.66 49	99.68 120	98.96 21	99.96 19	98.62 91	99.87 39	99.84 12

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 19	99.76 44	99.83 8	99.63 79	99.54 62	98.36 65	99.79 19	99.82 44	98.86 32	99.95 33	98.62 91	99.81 69	99.78 50

test_normal		97.44 255	96.77 265	99.44 99	97.75 320	99.00 121	99.10 260	98.64 313	97.71 136	93.93 324	98.82 292	87.39 325	99.83 126	98.61 93	98.97 139	99.49 129

PHI-MVS		99.30 46	99.17 50	99.70 51	99.56 127	99.52 61	99.58 99	99.80 8	97.12 188	99.62 57	99.73 101	98.58 58	99.90 87	98.61 93	99.91 17	99.68 84

CNVR-MVS		99.42 30	99.30 34	99.78 35	99.62 113	99.71 29	99.26 227	99.52 76	98.82 35	99.39 106	99.71 106	98.96 21	99.85 113	98.59 95	99.80 71	99.77 52

WR-MVS		98.06 169	97.73 193	99.06 144	98.86 271	99.25 89	99.19 242	99.35 199	97.30 172	98.66 239	99.43 210	93.94 220	99.21 271	98.58 96	94.28 296	98.71 217

HPM-MVS		99.42 30	99.28 39	99.83 24	99.90 3	99.72 28	99.81 15	99.54 62	97.59 145	99.68 38	99.63 142	98.91 29	99.94 42	98.58 96	99.91 17	99.84 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
UniMVSNet_NR-MVSNet		98.22 150	97.97 158	98.96 156	98.92 258	98.98 123	99.48 147	99.53 72	97.76 130	98.71 230	99.46 205	96.43 123	99.22 267	98.57 98	92.87 315	98.69 226

DU-MVS		98.08 168	97.79 180	98.96 156	98.87 268	98.98 123	99.41 175	99.45 151	97.87 116	98.71 230	99.50 188	94.82 180	99.22 267	98.57 98	92.87 315	98.68 231

mPP-MVS		99.44 26	99.30 34	99.86 13	99.88 11	99.79 19	99.69 45	99.48 114	98.12 84	99.50 84	99.75 93	98.78 39	99.97 11	98.57 98	99.89 33	99.83 23

CANet_DTU		98.97 94	98.87 86	99.25 125	99.33 170	98.42 201	99.08 262	99.30 224	99.16 5	99.43 96	99.75 93	95.27 153	99.97 11	98.56 101	99.95 6	99.36 149

PMMVS		98.80 114	98.62 117	99.34 107	99.27 187	98.70 173	98.76 312	99.31 222	97.34 168	99.21 158	99.07 271	97.20 101	99.82 135	98.56 101	98.87 149	99.52 120

PVSNet	96.02 17	98.85 108	98.84 92	98.89 177	99.73 72	97.28 237	98.32 332	99.60 35	97.86 117	99.50 84	99.57 163	96.75 114	99.86 107	98.56 101	99.70 92	99.54 115

PatchFormer-LS_test		98.01 183	98.05 152	97.87 280	99.15 212	94.76 309	99.42 171	98.93 277	97.12 188	98.84 219	98.59 305	93.74 229	99.80 143	98.55 104	98.17 190	99.06 174

ACMMP		99.45 23	99.32 27	99.82 26	99.89 8	99.67 35	99.62 82	99.69 18	98.12 84	99.63 54	99.84 35	98.73 49	99.96 19	98.55 104	99.83 64	99.81 36
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		98.69 122	98.62 117	98.89 177	99.71 82	97.74 231	99.12 252	99.54 62	98.44 62	99.42 99	99.71 106	94.20 210	99.92 65	98.54 106	98.90 147	99.00 179

PS-CasMVS		97.93 194	97.59 207	98.95 158	98.99 236	99.06 107	99.68 54	99.52 76	97.13 186	98.31 265	99.68 120	92.44 265	99.05 285	98.51 107	94.08 301	98.75 210

CostFormer		97.72 229	97.73 193	97.71 291	99.15 212	94.02 316	99.54 121	99.02 269	94.67 291	99.04 189	99.35 239	92.35 267	99.77 156	98.50 108	97.94 205	99.34 151

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 14	99.59 92	99.51 85	98.62 49	99.79 19	99.83 37	99.28 3	99.97 11	98.48 109	99.90 25	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
tpmrst		98.33 140	98.48 127	97.90 279	99.16 209	94.78 308	99.31 207	99.11 257	97.27 174	99.45 92	99.59 156	95.33 150	99.84 119	98.48 109	98.61 158	99.09 168

IB-MVS	95.67 18	96.22 285	95.44 293	98.57 220	99.21 195	96.70 268	98.65 320	97.74 334	96.71 219	97.27 292	98.54 307	86.03 329	99.92 65	98.47 111	86.30 340	99.10 164
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
HSP-MVS		99.41 33	99.26 44	99.85 19	99.89 8	99.80 15	99.67 56	99.37 194	98.70 45	99.77 24	99.49 191	98.21 76	99.95 33	98.46 112	99.77 77	99.81 36

abl_6		99.44 26	99.31 32	99.83 24	99.85 23	99.75 24	99.66 65	99.59 38	98.13 82	99.82 15	99.81 54	98.60 57	99.96 19	98.46 112	99.88 35	99.79 46

HPM-MVS++		99.39 37	99.23 46	99.87 6	99.75 56	99.84 7	99.43 164	99.51 85	98.68 47	99.27 136	99.53 178	98.64 55	99.96 19	98.44 114	99.80 71	99.79 46

#test#		99.43 28	99.29 37	99.86 13	99.87 15	99.80 15	99.55 118	99.67 22	97.83 122	99.68 38	99.69 115	99.06 9	99.96 19	98.39 115	99.87 39	99.84 12

LTVRE_ROB	97.16 12	98.02 180	97.90 163	98.40 239	99.23 192	96.80 266	99.70 42	99.60 35	97.12 188	98.18 271	99.70 109	91.73 280	99.72 173	98.39 115	97.45 228	98.68 231
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
EI-MVSNet		98.67 124	98.67 109	98.68 212	99.35 166	97.97 215	99.50 134	99.38 187	96.93 209	99.20 161	99.83 37	97.87 84	99.36 233	98.38 117	97.56 218	98.71 217

HY-MVS	97.30 7	98.85 108	98.64 113	99.47 93	99.42 150	99.08 105	99.62 82	99.36 195	97.39 166	99.28 132	99.68 120	96.44 122	99.92 65	98.37 118	98.22 180	99.40 146

TDRefinement		95.42 296	94.57 301	97.97 274	89.83 347	96.11 285	99.48 147	98.75 297	96.74 217	96.68 301	99.88 14	88.65 314	99.71 179	98.37 118	82.74 343	98.09 307

UniMVSNet (Re)		98.29 143	98.00 156	99.13 140	99.00 235	99.36 77	99.49 142	99.51 85	97.95 110	98.97 202	99.13 266	96.30 126	99.38 226	98.36 120	93.34 309	98.66 253

WR-MVS_H		98.13 161	97.87 174	98.90 175	99.02 233	98.84 146	99.70 42	99.59 38	97.27 174	98.40 259	99.19 262	95.53 146	99.23 264	98.34 121	93.78 306	98.61 275

PGM-MVS		99.45 23	99.31 32	99.86 13	99.87 15	99.78 23	99.58 99	99.65 30	97.84 121	99.71 32	99.80 65	99.12 8	99.97 11	98.33 122	99.87 39	99.83 23

LS3D		99.27 51	99.12 55	99.74 45	99.18 202	99.75 24	99.56 112	99.57 44	98.45 59	99.49 87	99.85 26	97.77 88	99.94 42	98.33 122	99.84 58	99.52 120

IterMVS-LS		98.46 132	98.42 129	98.58 219	99.59 121	98.00 213	99.37 189	99.43 167	96.94 208	99.07 183	99.59 156	97.87 84	99.03 288	98.32 124	95.62 267	98.71 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		98.16 159	98.10 146	98.33 243	99.29 182	96.82 265	98.75 313	99.44 159	97.83 122	99.13 170	99.55 168	92.92 238	99.67 190	98.32 124	97.69 210	98.48 292
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 41	99.19 48	99.79 34	99.61 117	99.65 40	99.30 209	99.48 114	98.86 31	99.21 158	99.63 142	98.72 50	99.90 87	98.25 126	99.63 103	99.80 42

testing_2		94.44 304	92.93 310	98.98 153	94.16 338	99.00 121	99.42 171	99.28 235	96.60 228	84.86 341	96.84 336	70.91 344	99.27 255	98.23 127	96.08 259	98.68 231

旧先验2							98.96 293		96.70 220	99.47 89			99.94 42	98.19 128

F-COLMAP		99.19 57	99.04 63	99.64 64	99.78 36	99.27 87	99.42 171	99.54 62	97.29 173	99.41 101	99.59 156	98.42 67	99.93 57	98.19 128	99.69 93	99.73 66

LCM-MVSNet-Re		97.83 207	98.15 142	96.87 307	99.30 179	92.25 329	99.59 92	98.26 323	97.43 161	96.20 305	99.13 266	96.27 127	98.73 307	98.17 130	98.99 137	99.64 97

cascas		97.69 233	97.43 231	98.48 229	98.60 302	97.30 236	98.18 337	99.39 181	92.96 318	98.41 258	98.78 296	93.77 226	99.27 255	98.16 131	98.61 158	98.86 198

diffmvs		98.72 120	98.49 126	99.43 102	99.48 142	99.19 93	99.62 82	99.42 168	95.58 281	99.37 110	99.67 124	96.14 130	99.74 161	98.14 132	98.96 140	99.37 148

DWT-MVSNet_test		97.53 245	97.40 234	97.93 276	99.03 232	94.86 307	99.57 105	98.63 314	96.59 230	98.36 262	98.79 294	89.32 305	99.74 161	98.14 132	98.16 191	99.20 159

COLMAP_ROB	97.56 6	98.86 102	98.75 102	99.17 133	99.88 11	98.53 189	99.34 202	99.59 38	97.55 150	98.70 236	99.89 10	95.83 139	99.90 87	98.10 134	99.90 25	99.08 169
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		97.76 220	97.44 228	98.72 209	98.77 284	98.54 188	99.78 22	99.51 85	97.06 201	98.29 267	99.64 138	92.63 256	98.89 303	98.09 135	93.16 311	98.72 215

LPG-MVS_test		98.22 150	98.13 144	98.49 227	99.33 170	97.05 251	99.58 99	99.55 55	97.46 157	99.24 149	99.83 37	92.58 257	99.72 173	98.09 135	97.51 221	98.68 231

LGP-MVS_train				98.49 227	99.33 170	97.05 251		99.55 55	97.46 157	99.24 149	99.83 37	92.58 257	99.72 173	98.09 135	97.51 221	98.68 231

IS-MVSNet		99.05 83	98.87 86	99.57 74	99.73 72	99.32 80	99.75 34	99.20 248	98.02 102	99.56 69	99.86 22	96.54 119	99.67 190	98.09 135	99.13 126	99.73 66

OPM-MVS		98.19 156	98.10 146	98.45 233	98.88 265	97.07 249	99.28 216	99.38 187	98.57 52	99.22 156	99.81 54	92.12 269	99.66 192	98.08 139	97.54 220	98.61 275

XVG-OURS		98.73 119	98.68 108	98.88 184	99.70 87	97.73 232	98.92 300	99.55 55	98.52 55	99.45 92	99.84 35	95.27 153	99.91 74	98.08 139	98.84 151	99.00 179

Baseline_NR-MVSNet		97.76 220	97.45 222	98.68 212	99.09 222	98.29 203	99.41 175	98.85 288	95.65 280	98.63 247	99.67 124	94.82 180	99.10 282	98.07 141	92.89 314	98.64 258

Test4		95.05 299	93.67 307	99.22 131	96.07 330	98.94 134	99.20 241	99.27 240	97.71 136	89.96 339	97.59 330	66.18 347	99.25 261	98.06 142	98.96 140	99.47 135

ACMH+	97.24 10	97.92 197	97.78 182	98.32 244	99.46 144	96.68 270	99.56 112	99.54 62	98.41 63	97.79 287	99.87 19	90.18 299	99.66 192	98.05 143	97.18 241	98.62 266

TranMVSNet+NR-MVSNet		97.93 194	97.66 197	98.76 207	98.78 281	98.62 182	99.65 75	99.49 105	97.76 130	98.49 255	99.60 154	94.23 209	98.97 300	98.00 144	92.90 313	98.70 221

DP-MVS Recon		99.12 69	98.95 77	99.65 59	99.74 67	99.70 31	99.27 219	99.57 44	96.40 246	99.42 99	99.68 120	98.75 47	99.80 143	97.98 145	99.72 86	99.44 141

test_prior3		99.21 56	99.05 60	99.68 52	99.67 93	99.48 65	98.96 293	99.56 48	98.34 66	99.01 192	99.52 183	98.68 52	99.83 126	97.96 146	99.74 82	99.74 61

test_prior2							98.96 293		98.34 66	99.01 192	99.52 183	98.68 52		97.96 146	99.74 82

Fast-Effi-MVS+-dtu		98.77 117	98.83 95	98.60 217	99.41 153	96.99 256	99.52 125	99.49 105	98.11 86	99.24 149	99.34 242	96.96 107	99.79 146	97.95 148	99.45 107	99.02 178

MP-MVS		99.33 42	99.15 51	99.87 6	99.88 11	99.82 13	99.66 65	99.46 139	98.09 89	99.48 88	99.74 98	98.29 73	99.96 19	97.93 149	99.87 39	99.82 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Vis-MVSNet (Re-imp)		98.87 99	98.72 103	99.31 112	99.71 82	98.88 141	99.80 19	99.44 159	97.91 115	99.36 114	99.78 78	95.49 148	99.43 224	97.91 150	99.11 127	99.62 103

ACMP	97.20 11	98.06 169	97.94 161	98.45 233	99.37 163	97.01 254	99.44 159	99.49 105	97.54 153	98.45 257	99.79 73	91.95 270	99.72 173	97.91 150	97.49 226	98.62 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Fast-Effi-MVS+		98.70 121	98.43 128	99.51 87	99.51 132	99.28 85	99.52 125	99.47 130	96.11 269	99.01 192	99.34 242	96.20 129	99.84 119	97.88 152	98.82 152	99.39 147

EPMVS		97.82 210	97.65 202	98.35 242	98.88 265	95.98 286	99.49 142	94.71 351	97.57 148	99.26 140	99.48 197	92.46 264	99.71 179	97.87 153	99.08 131	99.35 150

tmp_tt		82.80 322	81.52 322	86.66 333	66.61 358	68.44 356	92.79 351	97.92 329	68.96 349	80.04 348	99.85 26	85.77 330	96.15 340	97.86 154	43.89 353	95.39 341

NR-MVSNet		97.97 188	97.61 205	99.02 148	98.87 268	99.26 88	99.47 151	99.42 168	97.63 143	97.08 296	99.50 188	95.07 163	99.13 277	97.86 154	93.59 307	98.68 231

v148		97.79 216	97.55 208	98.50 226	98.74 286	97.72 233	99.54 121	99.33 215	96.26 255	98.90 210	99.51 186	94.68 192	99.14 274	97.83 156	93.15 312	98.63 264

tfpn1000		98.33 140	98.02 154	99.25 125	99.78 36	98.73 170	99.70 42	97.55 343	97.48 156	99.69 37	99.53 178	92.37 266	99.85 113	97.82 157	98.26 179	99.16 160

CPTT-MVS		99.11 73	98.90 82	99.74 45	99.80 34	99.46 68	99.59 92	99.49 105	97.03 203	99.63 54	99.69 115	97.27 100	99.96 19	97.82 157	99.84 58	99.81 36

MDTV_nov1_ep13_2view						95.18 304	99.35 199		96.84 214	99.58 65		95.19 159		97.82 157		99.46 138

OMC-MVS		99.08 79	99.04 63	99.20 132	99.67 93	98.22 206	99.28 216	99.52 76	98.07 93	99.66 49	99.81 54	97.79 87	99.78 154	97.79 160	99.81 69	99.60 105

HQP_MVS		98.27 145	98.22 141	98.44 236	99.29 182	96.97 258	99.39 182	99.47 130	98.97 22	99.11 174	99.61 151	92.71 246	99.69 188	97.78 161	97.63 211	98.67 242

plane_prior5								99.47 130					99.69 188	97.78 161	97.63 211	98.67 242

v6		98.12 163	97.84 175	98.94 159	98.94 251	98.83 149	99.66 65	99.34 207	96.49 233	99.30 124	99.37 228	94.95 169	99.34 239	97.77 163	94.74 283	98.67 242

testdata				99.54 77	99.75 56	98.95 131		99.51 85	97.07 199	99.43 96	99.70 109	98.87 31	99.94 42	97.76 164	99.64 101	99.72 72

PLC	97.94 4	99.02 87	98.85 91	99.53 81	99.66 103	99.01 119	99.24 231	99.52 76	96.85 213	99.27 136	99.48 197	98.25 75	99.91 74	97.76 164	99.62 104	99.65 91
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		97.67 238	97.55 208	98.03 268	99.02 233	95.01 306	99.43 164	98.54 319	96.44 241	99.12 172	99.34 242	91.83 275	99.60 204	97.75 166	96.46 251	99.48 131

1314		98.68 123	98.54 125	99.11 141	98.89 264	98.65 178	99.27 219	99.49 105	96.89 211	97.99 280	99.56 165	97.72 90	99.83 126	97.74 167	99.27 119	98.84 199

v1neww		98.12 163	97.84 175	98.93 162	98.97 243	98.81 158	99.66 65	99.35 199	96.49 233	99.29 128	99.37 228	95.02 165	99.32 243	97.73 168	94.73 284	98.67 242

v7new		98.12 163	97.84 175	98.93 162	98.97 243	98.81 158	99.66 65	99.35 199	96.49 233	99.29 128	99.37 228	95.02 165	99.32 243	97.73 168	94.73 284	98.67 242

XVG-ACMP-BASELINE		97.83 207	97.71 195	98.20 261	99.11 217	96.33 280	99.41 175	99.52 76	98.06 97	99.05 188	99.50 188	89.64 303	99.73 169	97.73 168	97.38 234	98.53 289

CNLPA		99.14 63	98.99 70	99.59 70	99.58 122	99.41 73	99.16 245	99.44 159	98.45 59	99.19 164	99.49 191	98.08 80	99.89 95	97.73 168	99.75 80	99.48 131

v2v482		98.06 169	97.77 186	98.92 167	98.90 261	98.82 156	99.57 105	99.36 195	96.65 223	99.19 164	99.35 239	94.20 210	99.25 261	97.72 172	94.97 280	98.69 226

原ACMM1				99.65 59	99.73 72	99.33 79		99.47 130	97.46 157	99.12 172	99.66 130	98.67 54	99.91 74	97.70 173	99.69 93	99.71 79

agg_prior1		99.01 90	98.76 101	99.76 39	99.67 93	99.62 43	98.99 284	99.40 178	96.26 255	98.87 213	99.49 191	98.77 42	99.91 74	97.69 174	99.72 86	99.75 56

PVSNet_0	94.43 19	96.09 289	95.47 291	97.94 275	99.31 178	94.34 314	97.81 340	99.70 15	97.12 188	97.46 289	98.75 297	89.71 302	99.79 146	97.69 174	81.69 344	99.68 84

MAR-MVS		98.86 102	98.63 114	99.54 77	99.37 163	99.66 37	99.45 155	99.54 62	96.61 226	99.01 192	99.40 219	97.09 103	99.86 107	97.68 176	99.53 106	99.10 164
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
train_agg		99.02 87	98.77 99	99.77 37	99.67 93	99.65 40	99.05 269	99.41 171	96.28 252	98.95 203	99.49 191	98.76 44	99.91 74	97.63 177	99.72 86	99.75 56

agg_prior3		98.97 94	98.71 105	99.75 40	99.67 93	99.60 47	99.04 274	99.41 171	95.93 275	98.87 213	99.48 197	98.61 56	99.91 74	97.63 177	99.72 86	99.75 56

MDTV_nov1_ep13			98.32 135		99.11 217	94.44 312	99.27 219	98.74 300	97.51 154	99.40 105	99.62 147	94.78 183	99.76 159	97.59 179	98.81 154

test_post1							99.23 232				65.14 356	94.18 213	99.71 179	97.58 180

JIA-IIPM		97.50 250	97.02 259	98.93 162	98.73 287	97.80 229	99.30 209	98.97 273	91.73 326	98.91 208	94.86 342	95.10 162	99.71 179	97.58 180	97.98 204	99.28 155

tfpn_ndepth		98.17 157	97.84 175	99.15 136	99.75 56	98.76 169	99.61 88	97.39 345	96.92 210	99.61 59	99.38 224	92.19 268	99.86 107	97.57 182	98.13 192	98.82 200

V42		98.06 169	97.79 180	98.86 192	98.98 240	98.84 146	99.69 45	99.34 207	96.53 232	99.30 124	99.37 228	94.67 193	99.32 243	97.57 182	94.66 289	98.42 296

gm-plane-assit					98.54 306	92.96 325			94.65 292		99.15 264		99.64 196	97.56 184

APD-MVS		99.27 51	99.08 58	99.84 23	99.75 56	99.79 19	99.50 134	99.50 99	97.16 184	99.77 24	99.82 44	98.78 39	99.94 42	97.56 184	99.86 49	99.80 42
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
pm-mvs1		97.68 235	97.28 250	98.88 184	99.06 226	98.62 182	99.50 134	99.45 151	96.32 249	97.87 283	99.79 73	92.47 261	99.35 236	97.54 186	93.54 308	98.67 242

无先验							98.99 284	99.51 85	96.89 211				99.93 57	97.53 187		99.72 72

1121		99.09 77	98.87 86	99.75 40	99.74 67	99.60 47	99.27 219	99.48 114	96.82 215	99.25 144	99.65 131	98.38 68	99.93 57	97.53 187	99.67 97	99.73 66

pmmvs5		97.52 246	97.30 248	98.16 264	98.57 304	96.73 267	99.27 219	98.90 284	96.14 267	98.37 261	99.53 178	91.54 286	99.14 274	97.51 189	95.87 262	98.63 264

divwei89l23v2f112		98.06 169	97.78 182	98.91 171	98.90 261	98.77 168	99.57 105	99.35 199	96.45 240	99.24 149	99.37 228	94.92 173	99.27 255	97.50 190	94.71 288	98.68 231

test9_res														97.49 191	99.72 86	99.75 56

CDPH-MVS		99.13 64	98.91 81	99.80 31	99.75 56	99.71 29	99.15 248	99.41 171	96.60 228	99.60 61	99.55 168	98.83 34	99.90 87	97.48 192	99.83 64	99.78 50

AdaColmap		99.01 90	98.80 96	99.66 55	99.56 127	99.54 55	99.18 243	99.70 15	98.18 79	99.35 117	99.63 142	96.32 125	99.90 87	97.48 192	99.77 77	99.55 113

OpenMVS	96.50 16	98.47 131	98.12 145	99.52 85	99.04 230	99.53 58	99.82 13	99.72 11	94.56 296	98.08 275	99.88 14	94.73 190	99.98 5	97.47 194	99.76 79	99.06 174

IterMVS		97.83 207	97.77 186	98.02 270	99.58 122	96.27 282	99.02 278	99.48 114	97.22 180	98.71 230	99.70 109	92.75 242	99.13 277	97.46 195	96.00 260	98.67 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		98.22 150	98.62 117	96.99 303	99.82 29	91.58 331	99.72 39	99.44 159	96.61 226	99.66 49	99.89 10	95.92 136	99.82 135	97.46 195	99.10 129	99.57 112

semantic-postprocess				98.06 267	99.57 124	96.36 279		99.49 105	97.18 182	98.71 230	99.72 105	92.70 248	99.14 274	97.44 197	95.86 263	98.67 242

PatchmatchNet		98.31 142	98.36 131	98.19 262	99.16 209	95.32 299	99.27 219	98.92 279	97.37 167	99.37 110	99.58 159	94.90 175	99.70 185	97.43 198	99.21 121	99.54 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EU-MVSNet		97.98 185	98.03 153	97.81 286	98.72 289	96.65 271	99.66 65	99.66 25	98.09 89	98.35 263	99.82 44	95.25 156	98.01 323	97.41 199	95.30 272	98.78 204

Patchmatch-test1		98.16 159	98.14 143	98.22 259	99.30 179	95.55 292	99.07 263	98.97 273	97.57 148	99.43 96	99.60 154	92.72 245	99.60 204	97.38 200	99.20 122	99.50 128

v1141		98.05 175	97.76 189	98.91 171	98.91 260	98.78 167	99.57 105	99.35 199	96.41 245	99.23 154	99.36 235	94.93 172	99.27 255	97.38 200	94.72 286	98.68 231

v1		98.05 175	97.76 189	98.93 162	98.92 258	98.80 163	99.57 105	99.35 199	96.39 247	99.28 132	99.36 235	94.86 178	99.32 243	97.38 200	94.72 286	98.68 231

tpm2		97.44 255	97.34 243	97.74 290	99.15 212	94.36 313	99.45 155	98.94 276	93.45 316	98.90 210	99.44 209	91.35 287	99.59 206	97.31 203	98.07 201	99.29 154

TESTMET0.1,1		97.55 243	97.27 252	98.40 239	98.93 256	96.53 273	98.67 317	97.61 342	96.96 206	98.64 246	99.28 253	88.63 315	99.45 215	97.30 204	99.38 111	99.21 158

test-LLR		98.06 169	97.90 163	98.55 224	98.79 277	97.10 245	98.67 317	97.75 332	97.34 168	98.61 250	98.85 289	94.45 202	99.45 215	97.25 205	99.38 111	99.10 164

test-mter		97.49 252	97.13 256	98.55 224	98.79 277	97.10 245	98.67 317	97.75 332	96.65 223	98.61 250	98.85 289	88.23 320	99.45 215	97.25 205	99.38 111	99.10 164

agg_prior2														97.21 207	99.73 85	99.75 56

OurMVSNet-221017-0		97.88 200	97.77 186	98.19 262	98.71 291	96.53 273	99.88 1	99.00 270	97.79 127	98.78 224	99.94 3	91.68 281	99.35 236	97.21 207	96.99 244	98.69 226

BP-MVS														97.19 209

HQP-MVS		98.02 180	97.90 163	98.37 241	99.19 199	96.83 263	98.98 288	99.39 181	98.24 72	98.66 239	99.40 219	92.47 261	99.64 196	97.19 209	97.58 216	98.64 258

pmmvs4		98.13 161	97.90 163	98.81 199	98.61 301	98.87 142	98.99 284	99.21 247	96.44 241	99.06 187	99.58 159	95.90 137	99.11 280	97.18 211	96.11 258	98.46 295

PatchMatch-RL		98.84 110	98.62 117	99.52 85	99.71 82	99.28 85	99.06 267	99.77 9	97.74 133	99.50 84	99.53 178	95.41 149	99.84 119	97.17 212	99.64 101	99.44 141

test_part3							99.37 189		97.97 108		99.78 78		99.95 33	97.15 213

ESAPD		99.31 45	99.13 53	99.87 6	99.81 32	99.83 8	99.37 189	99.48 114	97.97 108	99.77 24	99.78 78	98.96 21	99.95 33	97.15 213	99.84 58	99.83 23

tpmp4_e23		97.34 258	97.29 249	97.52 295	99.25 191	93.73 318	99.58 99	99.19 251	94.00 307	98.20 270	99.41 215	90.74 293	99.74 161	97.13 215	98.07 201	99.07 173

GBi-Net		97.68 235	97.48 217	98.29 247	99.51 132	97.26 239	99.43 164	99.48 114	96.49 233	99.07 183	99.32 247	90.26 296	98.98 293	97.10 216	96.65 246	98.62 266

test1		97.68 235	97.48 217	98.29 247	99.51 132	97.26 239	99.43 164	99.48 114	96.49 233	99.07 183	99.32 247	90.26 296	98.98 293	97.10 216	96.65 246	98.62 266

FMVSNet3		98.03 178	97.76 189	98.84 196	99.39 160	98.98 123	99.40 181	99.38 187	96.67 222	99.07 183	99.28 253	92.93 237	98.98 293	97.10 216	96.65 246	98.56 288

BH-untuned		98.42 135	98.36 131	98.59 218	99.49 139	96.70 268	99.27 219	99.13 256	97.24 178	98.80 222	99.38 224	95.75 142	99.74 161	97.07 219	99.16 124	99.33 152

v7		98.05 175	97.78 182	98.87 188	98.99 236	98.67 175	99.64 77	99.34 207	96.31 251	99.29 128	99.51 186	94.78 183	99.27 255	97.03 220	95.15 276	98.66 253

LF4IMVS		97.52 246	97.46 221	97.70 292	98.98 240	95.55 292	99.29 213	98.82 291	98.07 93	98.66 239	99.64 138	89.97 300	99.61 203	97.01 221	96.68 245	97.94 316

SixPastTwentyTwo		97.50 250	97.33 245	98.03 268	98.65 297	96.23 283	99.77 24	98.68 312	97.14 185	97.90 282	99.93 4	90.45 294	99.18 273	97.00 222	96.43 252	98.67 242

MG-MVS		99.13 64	99.02 68	99.45 96	99.57 124	98.63 180	99.07 263	99.34 207	98.99 18	99.61 59	99.82 44	97.98 83	99.87 104	97.00 222	99.80 71	99.85 8

API-MVS		99.04 84	99.03 65	99.06 144	99.40 158	99.31 83	99.55 118	99.56 48	98.54 53	99.33 121	99.39 223	98.76 44	99.78 154	96.98 224	99.78 75	98.07 308

tpmvs		97.98 185	98.02 154	97.84 283	99.04 230	94.73 310	99.31 207	99.20 248	96.10 273	98.76 226	99.42 212	94.94 170	99.81 139	96.97 225	98.45 169	98.97 183

QAPM		98.67 124	98.30 137	99.80 31	99.20 197	99.67 35	99.77 24	99.72 11	94.74 290	98.73 228	99.90 7	95.78 141	99.98 5	96.96 226	99.88 35	99.76 55

PAPM_NR		99.04 84	98.84 92	99.66 55	99.74 67	99.44 70	99.39 182	99.38 187	97.70 138	99.28 132	99.28 253	98.34 71	99.85 113	96.96 226	99.45 107	99.69 80

v8		97.95 193	97.63 204	98.93 162	98.95 248	98.81 158	99.80 19	99.41 171	96.03 274	99.10 177	99.42 212	94.92 173	99.30 249	96.94 228	94.08 301	98.66 253

MSDG		98.98 92	98.80 96	99.53 81	99.76 44	99.19 93	98.75 313	99.55 55	97.25 176	99.47 89	99.77 85	97.82 86	99.87 104	96.93 229	99.90 25	99.54 115

pmmvs6		96.53 272	96.09 273	97.82 285	98.69 293	95.47 296	99.37 189	99.47 130	93.46 315	97.41 290	99.78 78	87.06 327	99.33 240	96.92 230	92.70 317	98.65 256

新几何1				99.75 40	99.75 56	99.59 49		99.54 62	96.76 216	99.29 128	99.64 138	98.43 64	99.94 42	96.92 230	99.66 98	99.72 72

conf0.01		98.21 153	97.89 167	99.15 136	99.76 44	99.04 109	99.67 56	97.71 335	97.10 192	99.55 72	99.54 171	92.70 248	99.79 146	96.90 232	98.12 194	98.61 275

conf0.002		98.21 153	97.89 167	99.15 136	99.76 44	99.04 109	99.67 56	97.71 335	97.10 192	99.55 72	99.54 171	92.70 248	99.79 146	96.90 232	98.12 194	98.61 275

thresconf0.02		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 335	97.10 192	99.55 72	99.54 171	92.70 248	99.79 146	96.90 232	98.12 194	98.97 183

tfpn_n400		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 335	97.10 192	99.55 72	99.54 171	92.70 248	99.79 146	96.90 232	98.12 194	98.97 183

tfpnconf		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 335	97.10 192	99.55 72	99.54 171	92.70 248	99.79 146	96.90 232	98.12 194	98.97 183

tfpnview11		98.24 146	97.89 167	99.27 121	99.76 44	99.04 109	99.67 56	97.71 335	97.10 192	99.55 72	99.54 171	92.70 248	99.79 146	96.90 232	98.12 194	98.97 183

DTE-MVSNet		97.51 249	97.19 255	98.46 232	98.63 299	98.13 210	99.84 9	99.48 114	96.68 221	97.97 281	99.67 124	92.92 238	98.56 309	96.88 238	92.60 318	98.70 221

ADS-MVSNet2		98.02 180	98.07 151	97.87 280	99.33 170	95.19 303	99.23 232	99.08 260	96.24 257	99.10 177	99.67 124	94.11 215	98.93 302	96.81 239	99.05 133	99.48 131

ADS-MVSNet		98.20 155	98.08 149	98.56 222	99.33 170	96.48 275	99.23 232	99.15 253	96.24 257	99.10 177	99.67 124	94.11 215	99.71 179	96.81 239	99.05 133	99.48 131

v748		97.52 246	97.23 253	98.41 238	98.69 293	97.23 242	99.87 4	99.45 151	95.72 278	98.51 253	99.53 178	94.13 214	99.30 249	96.78 241	92.39 319	98.70 221

gg-mvs-nofinetune		96.17 287	95.32 294	98.73 208	98.79 277	98.14 209	99.38 187	94.09 352	91.07 330	98.07 278	91.04 348	89.62 304	99.35 236	96.75 242	99.09 130	98.68 231

v1144		97.98 185	97.69 196	98.85 195	98.87 268	98.66 177	99.54 121	99.35 199	96.27 254	99.23 154	99.35 239	94.67 193	99.23 264	96.73 243	95.16 275	98.68 231

UnsupCasMVSNet_eth		96.44 273	96.12 272	97.40 299	98.65 297	95.65 289	99.36 195	99.51 85	97.13 186	96.04 309	98.99 278	88.40 318	98.17 312	96.71 244	90.27 323	98.40 298

GA-MVS		97.85 203	97.47 219	99.00 151	99.38 161	97.99 214	98.57 323	99.15 253	97.04 202	98.90 210	99.30 250	89.83 301	99.38 226	96.70 245	98.33 173	99.62 103

K. test v3		97.10 265	96.79 263	98.01 271	98.72 289	96.33 280	99.87 4	97.05 346	97.59 145	96.16 306	99.80 65	88.71 311	99.04 286	96.69 246	96.55 250	98.65 256

testdata2													99.95 33	96.67 247

AllTest		98.87 99	98.72 103	99.31 112	99.86 20	98.48 197	99.56 112	99.61 32	97.85 119	99.36 114	99.85 26	95.95 133	99.85 113	96.66 248	99.83 64	99.59 109

TestCases				99.31 112	99.86 20	98.48 197		99.61 32	97.85 119	99.36 114	99.85 26	95.95 133	99.85 113	96.66 248	99.83 64	99.59 109

v52		97.79 216	97.50 214	98.66 215	98.80 275	98.62 182	99.87 4	99.44 159	95.87 276	99.01 192	99.46 205	94.44 204	99.33 240	96.65 250	93.96 304	98.05 309

V4		97.80 214	97.51 212	98.67 214	98.79 277	98.63 180	99.87 4	99.44 159	95.87 276	99.01 192	99.46 205	94.52 200	99.33 240	96.64 251	93.97 303	98.05 309

dp		97.75 224	97.80 179	97.59 294	99.10 220	93.71 320	99.32 204	98.88 286	96.48 239	99.08 182	99.55 168	92.67 255	99.82 135	96.52 252	98.58 161	99.24 157

BH-RMVSNet		98.41 136	98.08 149	99.40 104	99.41 153	98.83 149	99.30 209	98.77 296	97.70 138	98.94 205	99.65 131	92.91 240	99.74 161	96.52 252	99.55 105	99.64 97

FMVSNet2		97.72 229	97.36 238	98.80 201	99.51 132	98.84 146	99.45 155	99.42 168	96.49 233	98.86 218	99.29 252	90.26 296	98.98 293	96.44 254	96.56 249	98.58 286

ambc				93.06 321	92.68 342	82.36 344	98.47 327	98.73 309		95.09 312	97.41 332	55.55 352	99.10 282	96.42 255	91.32 321	97.71 330

tpm cat1		97.39 257	97.36 238	97.50 297	99.17 207	93.73 318	99.43 164	99.31 222	91.27 327	98.71 230	99.08 270	94.31 208	99.77 156	96.41 256	98.50 167	99.00 179

v144192		97.92 197	97.60 206	98.87 188	98.83 274	98.65 178	99.55 118	99.34 207	96.20 260	99.32 122	99.40 219	94.36 205	99.26 260	96.37 257	95.03 279	98.70 221

Patchmatch-RL test		95.84 291	95.81 280	95.95 314	95.61 331	90.57 332	98.24 334	98.39 320	95.10 286	95.20 311	98.67 299	94.78 183	97.77 329	96.28 258	90.02 324	99.51 125

Patchmtry		97.75 224	97.40 234	98.81 199	99.10 220	98.87 142	99.11 258	99.33 215	94.83 288	98.81 221	99.38 224	94.33 206	99.02 289	96.10 259	95.57 268	98.53 289

BH-w/o		98.00 184	97.89 167	98.32 244	99.35 166	96.20 284	99.01 282	98.90 284	96.42 243	98.38 260	99.00 277	95.26 155	99.72 173	96.06 260	98.61 158	99.03 176

v7n		97.87 201	97.52 210	98.92 167	98.76 285	98.58 186	99.84 9	99.46 139	96.20 260	98.91 208	99.70 109	94.89 176	99.44 220	96.03 261	93.89 305	98.75 210

v10		97.85 203	97.52 210	98.86 192	98.99 236	98.67 175	99.75 34	99.41 171	95.70 279	98.98 201	99.41 215	94.75 189	99.23 264	96.01 262	94.63 291	98.67 242

lessismore_v0				97.79 287	98.69 293	95.44 298		94.75 350		95.71 310	99.87 19	88.69 312	99.32 243	95.89 263	94.93 282	98.62 266

ITE_SJBPF				98.08 266	99.29 182	96.37 278		98.92 279	98.34 66	98.83 220	99.75 93	91.09 289	99.62 202	95.82 264	97.40 232	98.25 305

FMVSNet1		96.84 268	96.36 269	98.29 247	99.32 177	97.26 239	99.43 164	99.48 114	95.11 285	98.55 252	99.32 247	83.95 338	98.98 293	95.81 265	96.26 256	98.62 266

MIMVSNet		97.73 227	97.45 222	98.57 220	99.45 148	97.50 235	99.02 278	98.98 272	96.11 269	99.41 101	99.14 265	90.28 295	98.74 306	95.74 266	98.93 143	99.47 135

testpf		95.66 293	96.02 276	94.58 317	98.35 311	92.32 328	97.25 345	97.91 331	92.83 319	97.03 298	98.99 278	88.69 312	98.61 308	95.72 267	97.40 232	92.80 344

tfpnnormal		97.84 205	97.47 219	98.98 153	99.20 197	99.22 92	99.64 77	99.61 32	96.32 249	98.27 268	99.70 109	93.35 232	99.44 220	95.69 268	95.40 270	98.27 303

MS-PatchMatch		97.24 262	97.32 246	96.99 303	98.45 309	93.51 323	98.82 307	99.32 221	97.41 164	98.13 273	99.30 250	88.99 308	99.56 207	95.68 269	99.80 71	97.90 319

EG-PatchMatch MVS		95.97 290	95.69 284	96.81 308	97.78 318	92.79 326	99.16 245	98.93 277	96.16 264	94.08 318	99.22 260	82.72 340	99.47 213	95.67 270	97.50 223	98.17 306

USDC		97.34 258	97.20 254	97.75 289	99.07 224	95.20 302	98.51 326	99.04 267	97.99 107	98.31 265	99.86 22	89.02 307	99.55 209	95.67 270	97.36 235	98.49 291

MVP-Stereo		97.81 211	97.75 192	97.99 273	97.53 321	96.60 272	98.96 293	98.85 288	97.22 180	97.23 293	99.36 235	95.28 152	99.46 214	95.51 272	99.78 75	97.92 318
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	69.68 23	94.13 306	94.90 298	91.84 326	97.24 327	80.01 347	98.52 325	99.48 114	89.01 335	91.99 333	99.67 124	85.67 331	99.13 277	95.44 273	97.03 243	96.39 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GG-mvs-BLEND				98.45 233	98.55 305	98.16 208	99.43 164	93.68 353		97.23 293	98.46 308	89.30 306	99.22 267	95.43 274	98.22 180	97.98 314

v1921920		97.80 214	97.45 222	98.84 196	98.80 275	98.53 189	99.52 125	99.34 207	96.15 266	99.24 149	99.47 201	93.98 219	99.29 251	95.40 275	95.13 277	98.69 226

TR-MVS		97.76 220	97.41 233	98.82 198	99.06 226	97.87 220	98.87 305	98.56 318	96.63 225	98.68 238	99.22 260	92.49 260	99.65 194	95.40 275	97.79 208	98.95 196

v1192		97.81 211	97.44 228	98.91 171	98.88 265	98.68 174	99.51 129	99.34 207	96.18 262	99.20 161	99.34 242	94.03 218	99.36 233	95.32 277	95.18 274	98.69 226

PAPR		98.63 128	98.34 133	99.51 87	99.40 158	99.03 116	98.80 308	99.36 195	96.33 248	99.00 199	99.12 269	98.46 62	99.84 119	95.23 278	99.37 115	99.66 88

TinyColmap		97.12 264	96.89 261	97.83 284	99.07 224	95.52 295	98.57 323	98.74 300	97.58 147	97.81 286	99.79 73	88.16 321	99.56 207	95.10 279	97.21 239	98.39 299

DSMNet-mixed		97.25 261	97.35 240	96.95 305	97.84 317	93.61 322	99.57 105	96.63 347	96.13 268	98.87 213	98.61 304	94.59 196	97.70 331	95.08 280	98.86 150	99.55 113

test0.0.03 1		97.71 232	97.42 232	98.56 222	98.41 310	97.82 224	98.78 310	98.63 314	97.34 168	98.05 279	98.98 281	94.45 202	98.98 293	95.04 281	97.15 242	98.89 197

v17		96.42 275	95.81 280	98.25 254	98.94 251	98.80 163	99.76 27	99.28 235	94.57 294	94.18 315	97.71 319	95.23 157	98.16 313	94.86 282	87.73 332	97.80 322

MVS-HIRNet		95.75 292	95.16 296	97.51 296	99.30 179	93.69 321	98.88 304	95.78 348	85.09 340	98.78 224	92.65 344	91.29 288	99.37 229	94.85 283	99.85 53	99.46 138

v18		96.42 275	95.80 282	98.26 250	98.95 248	98.82 156	99.76 27	99.28 235	94.58 293	94.12 316	97.70 320	95.22 158	98.16 313	94.83 284	87.80 330	97.79 327

CR-MVSNet		98.17 157	97.93 162	98.87 188	99.18 202	98.49 195	99.22 236	99.33 215	96.96 206	99.56 69	99.38 224	94.33 206	99.00 291	94.83 284	98.58 161	99.14 161

v16		96.39 277	95.76 283	98.26 250	98.96 246	98.81 158	99.76 27	99.28 235	94.57 294	94.10 317	97.70 320	95.04 164	98.16 313	94.70 286	87.77 331	97.80 322

pmmvs-eth3d		95.34 298	94.73 299	97.15 300	95.53 333	95.94 287	99.35 199	99.10 258	95.13 284	93.55 327	97.54 331	88.15 322	97.91 325	94.58 287	89.69 326	97.61 331

v15		96.28 279	95.62 285	98.25 254	98.94 251	98.83 149	99.76 27	99.29 228	94.52 298	94.02 320	97.61 327	95.02 165	98.13 317	94.53 288	86.92 335	97.80 322

testgi		97.65 240	97.50 214	98.13 265	99.36 165	96.45 276	99.42 171	99.48 114	97.76 130	97.87 283	99.45 208	91.09 289	98.81 305	94.53 288	98.52 166	99.13 163

v1240		97.69 233	97.32 246	98.79 202	98.85 272	98.43 199	99.48 147	99.36 195	96.11 269	99.27 136	99.36 235	93.76 227	99.24 263	94.46 290	95.23 273	98.70 221

V14		96.26 280	95.60 286	98.26 250	98.94 251	98.83 149	99.76 27	99.29 228	94.49 299	93.96 322	97.66 323	94.99 168	98.13 317	94.41 291	86.90 336	97.80 322

V9		96.25 281	95.58 287	98.26 250	98.94 251	98.83 149	99.75 34	99.29 228	94.45 301	93.96 322	97.62 326	94.94 170	98.14 316	94.40 292	86.87 337	97.81 320

view600		97.97 188	97.66 197	98.89 177	99.75 56	97.81 225	99.69 45	98.80 292	98.02 102	99.25 144	98.88 285	91.95 270	99.89 95	94.36 293	98.29 175	98.96 189

view800		97.97 188	97.66 197	98.89 177	99.75 56	97.81 225	99.69 45	98.80 292	98.02 102	99.25 144	98.88 285	91.95 270	99.89 95	94.36 293	98.29 175	98.96 189

conf0.05thres1000		97.97 188	97.66 197	98.89 177	99.75 56	97.81 225	99.69 45	98.80 292	98.02 102	99.25 144	98.88 285	91.95 270	99.89 95	94.36 293	98.29 175	98.96 189

tfpn		97.97 188	97.66 197	98.89 177	99.75 56	97.81 225	99.69 45	98.80 292	98.02 102	99.25 144	98.88 285	91.95 270	99.89 95	94.36 293	98.29 175	98.96 189

v12		96.24 282	95.58 287	98.23 257	98.96 246	98.81 158	99.76 27	99.29 228	94.42 302	93.85 326	97.60 328	95.12 161	98.09 320	94.32 297	86.85 339	97.80 322

YYNet1		95.36 297	94.51 302	97.92 277	97.89 316	97.10 245	99.10 260	99.23 245	93.26 317	80.77 345	99.04 275	92.81 241	98.02 322	94.30 298	94.18 299	98.64 258

PM-MVS		92.96 310	92.23 312	95.14 316	95.61 331	89.98 334	99.37 189	98.21 325	94.80 289	95.04 313	97.69 322	65.06 348	97.90 326	94.30 298	89.98 325	97.54 334

v13		96.24 282	95.58 287	98.25 254	98.98 240	98.83 149	99.75 34	99.29 228	94.35 303	93.89 325	97.60 328	95.17 160	98.11 319	94.27 300	86.86 338	97.81 320

MVS		97.28 260	96.55 267	99.48 90	98.78 281	98.95 131	99.27 219	99.39 181	83.53 341	98.08 275	99.54 171	96.97 106	99.87 104	94.23 301	99.16 124	99.63 101

MDA-MVSNet_test_wron		95.45 295	94.60 300	98.01 271	98.16 314	97.21 243	99.11 258	99.24 244	93.49 314	80.73 346	98.98 281	93.02 235	98.18 311	94.22 302	94.45 294	98.64 258

TransMVSNet (Re)		97.15 263	96.58 266	98.86 192	99.12 215	98.85 145	99.49 142	98.91 282	95.48 282	97.16 295	99.80 65	93.38 231	99.11 280	94.16 303	91.73 320	98.62 266

UnsupCasMVSNet_bld		93.53 309	92.51 311	96.58 312	97.38 323	93.82 317	98.24 334	99.48 114	91.10 329	93.10 329	96.66 337	74.89 343	98.37 310	94.03 304	87.71 333	97.56 333

ppachtmachnet_test		97.49 252	97.45 222	97.61 293	98.62 300	95.24 300	98.80 308	99.46 139	96.11 269	98.22 269	99.62 147	96.45 121	98.97 300	93.77 305	95.97 261	98.61 275

thres600view7		97.86 202	97.51 212	98.92 167	99.72 76	97.95 218	99.59 92	98.74 300	97.94 111	99.27 136	98.62 300	91.75 276	99.86 107	93.73 306	98.19 183	98.96 189

tfpn111		97.81 211	97.49 216	98.78 204	99.72 76	97.86 221	99.59 92	98.74 300	97.93 112	99.26 140	98.62 300	91.75 276	99.86 107	93.57 307	98.18 184	98.61 275

DeepMVS_CX				93.34 320	99.29 182	82.27 345		99.22 246	85.15 339	96.33 304	99.05 274	90.97 291	99.73 169	93.57 307	97.77 209	98.01 313

MDA-MVSNet-bldmvs		94.96 300	93.98 305	97.92 277	98.24 313	97.27 238	99.15 248	99.33 215	93.80 310	80.09 347	99.03 276	88.31 319	97.86 327	93.49 309	94.36 295	98.62 266

Patchmatch-test		97.93 194	97.65 202	98.77 205	99.18 202	97.07 249	99.03 275	99.14 255	96.16 264	98.74 227	99.57 163	94.56 197	99.72 173	93.36 310	99.11 127	99.52 120

conf200view11		97.78 218	97.45 222	98.77 205	99.72 76	97.86 221	99.59 92	98.74 300	97.93 112	99.26 140	98.62 300	91.75 276	99.83 126	93.22 311	98.18 184	98.61 275

thres100view900		97.76 220	97.45 222	98.69 211	99.72 76	97.86 221	99.59 92	98.74 300	97.93 112	99.26 140	98.62 300	91.75 276	99.83 126	93.22 311	98.18 184	98.37 300

tfpn200view9		97.72 229	97.38 236	98.72 209	99.69 89	97.96 216	99.50 134	98.73 309	97.83 122	99.17 167	98.45 309	91.67 282	99.83 126	93.22 311	98.18 184	98.37 300

thres400		97.77 219	97.38 236	98.92 167	99.69 89	97.96 216	99.50 134	98.73 309	97.83 122	99.17 167	98.45 309	91.67 282	99.83 126	93.22 311	98.18 184	98.96 189

EPNet_dtu		98.03 178	97.96 159	98.23 257	98.27 312	95.54 294	99.23 232	98.75 297	99.02 10	97.82 285	99.71 106	96.11 131	99.48 212	93.04 315	99.65 100	99.69 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v11		96.23 284	95.57 290	98.21 260	98.93 256	98.83 149	99.72 39	99.29 228	94.29 304	94.05 319	97.64 325	94.88 177	98.04 321	92.89 316	88.43 328	97.77 328

thres200		97.61 241	97.28 250	98.62 216	99.64 106	98.03 212	99.26 227	98.74 300	97.68 140	99.09 181	98.32 311	91.66 284	99.81 139	92.88 317	98.22 180	98.03 312

PCF-MVS	97.08 14	97.66 239	97.06 258	99.47 93	99.61 117	99.09 104	98.04 339	99.25 243	91.24 328	98.51 253	99.70 109	94.55 198	99.91 74	92.76 318	99.85 53	99.42 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet5		96.43 274	96.19 271	97.15 300	99.11 217	95.89 288	99.32 204	99.52 76	94.47 300	98.34 264	99.07 271	87.54 324	97.07 334	92.61 319	95.72 265	98.47 293

test_0402		96.64 269	96.24 270	97.85 282	98.85 272	96.43 277	99.44 159	99.26 241	93.52 313	96.98 299	99.52 183	88.52 316	99.20 272	92.58 320	97.50 223	97.93 317

new-patchmatchnet		94.48 303	94.08 304	95.67 315	95.08 335	92.41 327	99.18 243	99.28 235	94.55 297	93.49 328	97.37 334	87.86 323	97.01 335	91.57 321	88.36 329	97.61 331

N_pmnet		94.95 301	95.83 279	92.31 325	98.47 308	79.33 348	99.12 252	92.81 357	93.87 309	97.68 288	99.13 266	93.87 223	99.01 290	91.38 322	96.19 257	98.59 283

LCM-MVSNet		86.80 318	85.22 321	91.53 329	87.81 349	80.96 346	98.23 336	98.99 271	71.05 347	90.13 338	96.51 338	48.45 355	96.88 336	90.51 323	85.30 342	96.76 335

LP		97.04 266	96.80 262	97.77 288	98.90 261	95.23 301	98.97 291	99.06 265	94.02 306	98.09 274	99.41 215	93.88 222	98.82 304	90.46 324	98.42 171	99.26 156

new_pmnet		96.38 278	96.03 274	97.41 298	98.13 315	95.16 305	99.05 269	99.20 248	93.94 308	97.39 291	98.79 294	91.61 285	99.04 286	90.43 325	95.77 264	98.05 309

PAPM		97.59 242	97.09 257	99.07 143	99.06 226	98.26 205	98.30 333	99.10 258	94.88 287	98.08 275	99.34 242	96.27 127	99.64 196	89.87 326	98.92 145	99.31 153

pmmvs3		94.09 307	93.25 309	96.60 311	94.76 336	94.49 311	98.92 300	98.18 327	89.66 332	96.48 303	98.06 314	86.28 328	97.33 333	89.68 327	87.20 334	97.97 315

Anonymous20231211		90.69 315	89.39 316	94.58 317	94.25 337	88.18 335	99.29 213	99.07 263	82.45 343	92.95 330	97.65 324	63.96 350	97.79 328	89.27 328	85.63 341	97.77 328

OpenMVS_ROB	92.34 20	94.38 305	93.70 306	96.41 313	97.38 323	93.17 324	99.06 267	98.75 297	86.58 338	94.84 314	98.26 313	81.53 342	99.32 243	89.01 329	97.87 207	96.76 335

PatchT		97.03 267	96.44 268	98.79 202	98.99 236	98.34 202	99.16 245	99.07 263	92.13 322	99.52 81	97.31 335	94.54 199	98.98 293	88.54 330	98.73 157	99.03 176

MIMVSNet1		95.51 294	95.04 297	96.92 306	97.38 323	95.60 290	99.52 125	99.50 99	93.65 311	96.97 300	99.17 263	85.28 333	96.56 338	88.36 331	95.55 269	98.60 282

TAPA-MVS	97.07 15	97.74 226	97.34 243	98.94 159	99.70 87	97.53 234	99.25 229	99.51 85	91.90 325	99.30 124	99.63 142	98.78 39	99.64 196	88.09 332	99.87 39	99.65 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Gipuma		90.99 314	90.15 315	93.51 319	98.73 287	90.12 333	93.98 349	99.45 151	79.32 344	92.28 332	94.91 341	69.61 345	97.98 324	87.42 333	95.67 266	92.45 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testus		94.61 302	95.30 295	92.54 324	96.44 329	84.18 340	98.36 329	99.03 268	94.18 305	96.49 302	98.57 306	88.74 310	95.09 343	87.41 334	98.45 169	98.36 302

test20.03		96.12 288	95.96 277	96.63 310	97.44 322	95.45 297	99.51 129	99.38 187	96.55 231	96.16 306	99.25 257	93.76 227	96.17 339	87.35 335	94.22 298	98.27 303

Anonymous20231206		96.22 285	96.03 274	96.79 309	97.31 326	94.14 315	99.63 79	99.08 260	96.17 263	97.04 297	99.06 273	93.94 220	97.76 330	86.96 336	95.06 278	98.47 293

RPMNet		96.61 270	95.85 278	98.87 188	99.18 202	98.49 195	99.22 236	99.08 260	88.72 337	99.56 69	97.38 333	94.08 217	99.00 291	86.87 337	98.58 161	99.14 161

test2356		94.07 308	94.46 303	92.89 322	95.18 334	86.13 338	97.60 343	99.06 265	93.61 312	96.15 308	98.28 312	85.60 332	93.95 345	86.68 338	98.00 203	98.59 283

PMMVS2		86.87 317	85.37 320	91.35 330	90.21 346	83.80 341	98.89 303	97.45 344	83.13 342	91.67 335	95.03 340	48.49 354	94.70 344	85.86 339	77.62 345	95.54 340

FPMVS		84.93 319	85.65 319	82.75 339	86.77 351	63.39 357	98.35 331	98.92 279	74.11 346	83.39 343	98.98 281	50.85 353	92.40 350	84.54 340	94.97 280	92.46 345

no-one		83.04 321	80.12 323	91.79 327	89.44 348	85.65 339	99.32 204	98.32 321	89.06 334	79.79 349	89.16 350	44.86 356	96.67 337	84.33 341	46.78 352	93.05 343

test1235678		92.91 311	93.30 308	91.71 328	93.14 341	83.01 342	98.75 313	98.58 317	92.80 320	92.45 331	97.91 316	88.51 317	93.54 346	82.26 342	95.35 271	98.59 283

test12356		91.74 313	92.19 314	90.37 331	91.22 343	82.41 343	98.61 321	98.28 322	90.66 331	91.82 334	97.92 315	84.90 334	92.61 347	81.64 343	94.66 289	96.09 339

1111		92.30 312	92.21 313	92.55 323	93.30 339	86.27 336	99.15 248	98.74 300	91.94 323	90.85 336	97.82 317	84.18 336	95.21 341	79.65 344	94.27 297	96.19 338

.test1245		83.42 320	86.17 318	75.15 342	93.30 339	86.27 336	99.15 248	98.74 300	91.94 323	90.85 336	97.82 317	84.18 336	95.21 341	79.65 344	39.90 354	43.98 355

PMVS	70.75 22	75.98 328	74.97 327	79.01 341	70.98 357	55.18 358	93.37 350	98.21 325	65.08 353	61.78 354	93.83 343	21.74 363	92.53 348	78.59 346	91.12 322	89.34 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PNet_i23d		79.43 325	77.68 326	84.67 335	86.18 352	71.69 355	96.50 347	93.68 353	75.17 345	71.33 350	91.18 347	32.18 359	90.62 351	78.57 347	74.34 346	91.71 348

testmv		87.91 316	87.80 317	88.24 332	87.68 350	77.50 350	99.07 263	97.66 341	89.27 333	86.47 340	96.22 339	68.35 346	92.49 349	76.63 348	88.82 327	94.72 342

ANet_high		77.30 326	74.86 328	84.62 336	75.88 356	77.61 349	97.63 342	93.15 356	88.81 336	64.27 352	89.29 349	36.51 357	83.93 356	75.89 349	52.31 351	92.33 347

wuykxyi23d		74.42 329	71.19 330	84.14 337	76.16 355	74.29 354	96.00 348	92.57 358	69.57 348	63.84 353	87.49 352	21.98 361	88.86 352	75.56 350	57.50 350	89.26 351

MVE	76.82 21	76.91 327	74.31 329	84.70 334	85.38 354	76.05 353	96.88 346	93.17 355	67.39 350	71.28 351	89.01 351	21.66 364	87.69 353	71.74 351	72.29 347	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		80.61 323	79.88 324	82.81 338	90.75 345	76.38 352	97.69 341	95.76 349	66.44 351	83.52 342	92.25 345	62.54 351	87.16 354	68.53 352	61.40 348	84.89 353

EMVS		80.02 324	79.22 325	82.43 340	91.19 344	76.40 351	97.55 344	92.49 359	66.36 352	83.01 344	91.27 346	64.63 349	85.79 355	65.82 353	60.65 349	85.08 352

wuyk23d		40.18 331	41.29 334	36.84 343	86.18 352	49.12 359	79.73 352	22.81 361	27.64 354	25.46 357	28.45 358	21.98 361	48.89 357	55.80 354	23.56 357	12.51 357

testmvs		39.17 332	43.78 331	25.37 346	36.04 360	16.84 361	98.36 329	26.56 360	20.06 355	38.51 356	67.32 353	29.64 360	15.30 359	37.59 355	39.90 354	43.98 355

test123		39.01 333	42.50 333	28.53 345	39.17 359	20.91 360	98.75 313	19.17 362	19.83 356	38.57 355	66.67 354	33.16 358	15.42 358	37.50 356	29.66 356	49.26 354

cdsmvs_eth3d_5k		24.64 334	32.85 335	0.00 347	0.00 361	0.00 362	0.00 353	99.51 85	0.00 357	0.00 358	99.56 165	96.58 118	0.00 360	0.00 357	0.00 358	0.00 358

pcd_1.5k_mvsjas		8.27 336	11.03 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.27 359	99.01 12	0.00 360	0.00 357	0.00 358	0.00 358

pcd1.5k->3k		40.85 330	43.49 332	32.93 344	98.95 248	0.00 362	0.00 353	99.53 72	0.00 357	0.00 358	0.27 359	95.32 151	0.00 360	0.00 357	97.30 236	98.80 202

sosnet-low-res		0.02 337	0.03 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.27 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.02 337	0.03 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.27 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.02 337	0.03 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.27 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.02 337	0.03 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.27 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		8.30 335	11.06 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	99.58 159	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.02 337	0.03 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.27 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																99.52 120

test_part2					99.81 32	99.83 8				99.77 24

test_part1								99.48 114				98.96 21			99.84 58	99.83 23

sam_mvs1												94.86 178				99.52 120

sam_mvs												94.72 191

MTGPA								99.47 130

test_post											65.99 355	94.65 195	99.73 169

patchmatchnet-post											98.70 298	94.79 182	99.74 161

MTMP								98.88 286

TEST9					99.67 93	99.65 40	99.05 269	99.41 171	96.22 259	98.95 203	99.49 191	98.77 42	99.91 74

test_8					99.67 93	99.61 45	99.03 275	99.41 171	96.28 252	98.93 206	99.48 197	98.76 44	99.91 74

agg_prior					99.67 93	99.62 43		99.40 178		98.87 213			99.91 74

test_prior4						99.56 52	98.99 284

test_prior				99.68 52	99.67 93	99.48 65		99.56 48					99.83 126			99.74 61

新几何2							99.01 282

旧先验1					99.74 67	99.59 49		99.54 62			99.69 115	98.47 61			99.68 96	99.73 66

原ACMM2							98.95 297

test222					99.75 56	99.49 64	98.91 302	99.49 105	96.42 243	99.34 120	99.65 131	98.28 74			99.69 93	99.72 72

segment_acmp												98.96 21

testdata1							98.85 306		98.32 69

test12				99.75 40	99.64 106	99.61 45		99.29 228		99.21 158		98.38 68	99.89 95		99.74 82	99.74 61

plane_prior7					99.29 182	97.03 253

plane_prior6					99.27 187	96.98 257						92.71 246

plane_prior4											99.61 151

plane_prior3						97.00 255			98.69 46	99.11 174

plane_prior2							99.39 182		98.97 22

plane_prior1					99.26 189

plane_prior						96.97 258	99.21 239		98.45 59						97.60 214

n2								0.00 363

nn								0.00 363

door-mid								98.05 328

test11								99.35 199

door								97.92 329

HQP5-MVS						96.83 263

HQP-NCC					99.19 199		98.98 288		98.24 72	98.66 239

ACMP_Plane					99.19 199		98.98 288		98.24 72	98.66 239

HQP4-MVS										98.66 239			99.64 196			98.64 258

HQP3-MVS								99.39 181							97.58 216

HQP2-MVS												92.47 261

NP-MVS					99.23 192	96.92 261					99.40 219

ACMMP++_ref															97.19 240

ACMMP++															97.43 231

Test By Simon												98.75 47

High-resolution multi-view benchmark