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.

SMA-MVS		98.82 18	98.55 22	99.65 8	99.87 39	98.95 14	99.86 86	98.35 133	93.19 122	99.83 7	99.94 4	96.17 23	100.00 1	99.74 13	99.99 13	100.00 1

test_part3							99.88 66		96.14 43		99.91 7		100.00 1	99.99 1

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 122	96.14 43	99.49 33	99.91 7	97.20 11	100.00 1	99.99 1	99.99 13	99.99 12

ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 67	98.62 41	99.85 88	98.37 131	94.68 77	99.53 29	99.83 37	92.87 99	100.00 1	98.66 60	99.84 61	99.99 12

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 41	97.93 67	99.80 101	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

MTAPA		98.29 46	97.96 52	99.30 38	99.85 41	97.93 67	99.39 180	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

HFP-MVS		98.56 30	98.37 32	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	94.77 73	99.31 46	99.85 21	94.22 69	100.00 1	98.70 55	99.98 26	99.98 44

region2R		98.54 32	98.37 32	99.05 66	99.96 8	97.18 98	99.96 19	98.55 88	94.87 71	99.45 36	99.85 21	94.07 75	100.00 1	98.67 57	100.00 1	99.98 44

#test#		98.59 28	98.41 27	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	95.00 68	99.31 46	99.85 21	94.22 69	100.00 1	98.78 52	99.98 26	99.98 44

HPM-MVS++		99.07 6	98.88 11	99.63 9	99.90 33	99.02 12	99.95 31	98.56 84	97.56 9	99.44 37	99.85 21	95.38 39	100.00 1	99.31 30	99.99 13	99.87 75

新几何1				99.42 30	99.75 56	98.27 57		98.63 73	92.69 140	99.55 28	99.82 40	94.40 60	100.00 1	91.21 190	99.94 44	99.99 12

无先验							99.49 168	98.71 61	93.46 117				100.00 1	94.36 143		99.99 12

1121		98.03 56	97.57 62	99.40 33	99.74 57	98.21 58	98.31 274	98.62 74	92.78 135	99.53 29	99.83 37	95.08 44	100.00 1	94.36 143	99.92 51	99.99 12

MSLP-MVS++		99.13 5	99.01 6	99.49 23	99.94 14	98.46 52	99.98 6	98.86 53	97.10 15	99.80 9	99.94 4	95.92 30	100.00 1	99.51 22	100.00 1	100.00 1

ACMMPR		98.50 35	98.32 36	99.05 66	99.96 8	97.18 98	99.95 31	98.60 78	94.77 73	99.31 46	99.84 35	93.73 85	100.00 1	98.70 55	99.98 26	99.98 44

MP-MVS		98.23 50	97.97 50	99.03 68	99.94 14	97.17 101	99.95 31	98.39 126	94.70 76	98.26 92	99.81 43	91.84 119	100.00 1	98.85 49	99.97 35	99.93 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		98.34 44	98.13 44	98.99 72	99.92 27	97.00 103	99.75 117	99.50 21	93.90 105	99.37 44	99.76 56	93.24 95	100.00 1	97.75 91	99.96 37	99.98 44

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 70	98.47 2	99.13 55	99.92 6	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

mPP-MVS		98.39 43	98.20 40	98.97 73	99.97 3	96.92 107	99.95 31	98.38 129	95.04 67	98.61 77	99.80 44	93.39 90	100.00 1	98.64 61	100.00 1	99.98 44

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 63	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 12

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 74	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 21	100.00 1	100.00 1

CP-MVS		98.45 38	98.32 36	98.87 79	99.96 8	96.62 113	99.97 12	98.39 126	94.43 83	98.90 64	99.87 15	94.30 67	100.00 1	99.04 40	99.99 13	99.99 12

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 52	98.44 107	92.06 170	98.40 85	99.84 35	95.68 33	100.00 1	98.19 71	99.71 73	99.97 54

PHI-MVS		98.41 41	98.21 39	99.03 68	99.86 40	97.10 102	99.98 6	98.80 58	90.78 204	99.62 23	99.78 50	95.30 40	100.00 1	99.80 7	99.93 49	99.99 12

DeepPCF-MVS	95.94 2	97.71 67	98.98 8	93.92 248	99.63 68	81.76 323	99.96 19	98.56 84	99.47 1	99.19 53	99.99 1	94.16 73	100.00 1	99.92 3	99.93 49	100.00 1

DeepC-MVS_fast	96.59 1	98.81 19	98.54 24	99.62 12	99.90 33	98.85 21	99.24 197	98.47 103	98.14 4	99.08 56	99.91 7	93.09 98	100.00 1	99.04 40	99.99 13	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		97.23 81	96.80 82	98.51 102	99.99 1	95.60 148	99.09 207	98.84 55	93.32 120	96.74 121	99.72 65	86.04 179	100.00 1	98.01 79	99.43 92	99.94 65

testdata2													99.99 28	90.54 203

CPTT-MVS		97.64 69	97.32 68	98.58 96	99.97 3	95.77 140	99.96 19	98.35 133	89.90 215	98.36 86	99.79 45	91.18 128	99.99 28	98.37 69	99.99 13	99.99 12

API-MVS		97.86 60	97.66 57	98.47 108	99.52 76	95.41 152	99.47 171	98.87 52	91.68 178	98.84 65	99.85 21	92.34 109	99.99 28	98.44 67	99.96 37	100.00 1

ACMMP		97.74 66	97.44 64	98.66 89	99.92 27	96.13 131	99.18 201	99.45 22	94.84 72	96.41 130	99.71 67	91.40 122	99.99 28	97.99 81	98.03 121	99.87 75
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
CANet_DTU		96.76 97	96.15 98	98.60 94	98.78 115	97.53 77	99.84 91	97.63 198	97.25 13	99.20 51	99.64 80	81.36 228	99.98 32	92.77 175	98.89 101	98.28 190

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 65	98.73 33	99.94 45	98.34 135	96.38 34	99.81 8	99.76 56	94.59 57	99.98 32	99.84 6	99.96 37	99.97 54

abl_6		97.67 68	97.34 66	98.66 89	99.68 66	96.11 135	99.68 139	98.14 159	93.80 108	99.27 49	99.70 69	88.65 158	99.98 32	97.46 94	99.72 72	99.89 72

PAPM_NR		98.12 53	97.93 53	98.70 86	99.94 14	96.13 131	99.82 96	98.43 112	94.56 79	97.52 106	99.70 69	94.40 60	99.98 32	97.00 105	99.98 26	99.99 12

PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 31	98.43 112	95.35 62	98.03 97	99.75 61	94.03 76	99.98 32	98.11 75	99.83 62	99.99 12

CSCG		97.10 85	97.04 76	97.27 154	99.89 36	91.92 236	99.90 59	99.07 33	88.67 234	95.26 152	99.82 40	93.17 97	99.98 32	98.15 73	99.47 89	99.90 71

CNLPA		97.76 65	97.38 65	98.92 77	99.53 75	96.84 108	99.87 71	98.14 159	93.78 109	96.55 124	99.69 72	92.28 110	99.98 32	97.13 101	99.44 91	99.93 66

MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 151	99.44 23	97.33 12	99.00 62	99.72 65	94.03 76	99.98 32	98.73 54	100.00 1	100.00 1

MAR-MVS		97.43 72	97.19 70	98.15 126	99.47 79	94.79 167	99.05 219	98.76 59	92.65 144	98.66 74	99.82 40	88.52 159	99.98 32	98.12 74	99.63 77	99.67 97
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
MP-MVS-pluss		98.07 55	97.64 58	99.38 35	99.74 57	98.41 53	99.74 120	98.18 152	93.35 119	96.45 127	99.85 21	92.64 106	99.97 41	98.91 47	99.89 55	99.77 85
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PLC	95.54 3	97.93 59	97.89 54	98.05 130	99.82 50	94.77 168	99.92 52	98.46 105	93.93 104	97.20 111	99.27 102	95.44 38	99.97 41	97.41 95	99.51 88	99.41 134
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_0304		97.52 71	96.79 83	99.69 6	99.59 70	99.30 4	99.97 12	98.01 168	96.99 19	98.84 65	99.79 45	78.90 259	99.96 43	99.74 13	99.32 95	99.81 80

XVS		98.70 23	98.55 22	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	99.78 50	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

X-MVStestdata		93.83 174	92.06 196	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	41.37 359	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

原ACMM1				98.96 74	99.73 61	96.99 104		98.51 97	94.06 98	99.62 23	99.85 21	94.97 51	99.96 43	95.11 128	99.95 40	99.92 69

1314		96.84 93	95.96 107	99.48 25	96.74 201	98.52 48	98.31 274	98.86 53	95.82 48	89.91 211	98.98 120	87.49 165	99.96 43	97.80 87	99.73 71	99.96 58

MVS		96.60 106	95.56 131	99.72 4	96.85 194	99.22 8	98.31 274	98.94 38	91.57 180	90.90 197	99.61 82	86.66 174	99.96 43	97.36 96	99.88 57	99.99 12

UGNet		95.33 145	94.57 151	97.62 142	98.55 126	94.85 163	98.67 250	99.32 28	95.75 55	96.80 120	96.27 222	72.18 299	99.96 43	94.58 140	99.05 100	98.04 194
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
QAPM		95.40 144	94.17 158	99.10 58	96.92 190	97.71 72	99.40 177	98.68 64	89.31 220	88.94 239	98.89 126	82.48 205	99.96 43	93.12 173	99.83 62	99.62 105

CANet		98.27 47	97.82 55	99.63 9	99.72 63	99.10 10	99.98 6	98.51 97	97.00 18	98.52 79	99.71 67	87.80 162	99.95 51	99.75 11	99.38 93	99.83 78

旧先验2							99.46 173		94.21 90	99.85 5			99.95 51	96.96 107

PVSNet_BlendedMVS		96.05 130	95.82 122	96.72 167	99.59 70	96.99 104	99.95 31	99.10 30	94.06 98	98.27 90	95.80 230	89.00 153	99.95 51	99.12 33	87.53 238	93.24 292

PVSNet_Blended		97.94 58	97.64 58	98.83 81	99.59 70	96.99 104	100.00 1	99.10 30	95.38 61	98.27 90	99.08 113	89.00 153	99.95 51	99.12 33	99.25 97	99.57 115

DP-MVS		94.54 163	93.42 174	97.91 134	99.46 81	94.04 179	98.93 230	97.48 217	81.15 315	90.04 208	99.55 85	87.02 171	99.95 51	88.97 225	98.11 117	99.73 90

PVSNet	91.05 13	97.13 84	96.69 86	98.45 110	99.52 76	95.81 138	99.95 31	99.65 16	94.73 75	99.04 58	99.21 108	84.48 192	99.95 51	94.92 130	98.74 105	99.58 114

3Dnovator	91.47 12	96.28 127	95.34 136	99.08 60	96.82 196	97.47 83	99.45 174	98.81 56	95.52 59	89.39 229	99.00 119	81.97 214	99.95 51	97.27 98	99.83 62	99.84 77

LS3D		95.84 135	95.11 143	98.02 131	99.85 41	95.10 160	98.74 243	98.50 101	87.22 259	93.66 176	99.86 17	87.45 166	99.95 51	90.94 197	99.81 68	99.02 179

testdata				98.42 113	99.47 79	95.33 154		98.56 84	93.78 109	99.79 11	99.85 21	93.64 88	99.94 59	94.97 129	99.94 44	100.00 1

TSAR-MVS + GP.		98.60 26	98.51 25	98.86 80	99.73 61	96.63 112	99.97 12	97.92 177	98.07 5	98.76 69	99.55 85	95.00 49	99.94 59	99.91 4	97.68 125	99.99 12

DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 87	98.65 39	100.00 1	98.58 80	97.70 7	98.21 94	99.24 106	92.58 107	99.94 59	98.63 62	99.94 44	99.92 69
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
F-COLMAP		96.93 90	96.95 78	96.87 162	99.71 64	91.74 242	99.85 88	97.95 174	93.11 125	95.72 145	99.16 110	92.35 108	99.94 59	95.32 126	99.35 94	98.92 181

3Dnovator+	91.53 11	96.31 124	95.24 138	99.52 20	96.88 193	98.64 40	99.72 131	98.24 145	95.27 65	88.42 247	98.98 120	82.76 204	99.94 59	97.10 103	99.83 62	99.96 58

OpenMVS	90.15 15	94.77 157	93.59 168	98.33 119	96.07 210	97.48 82	99.56 159	98.57 82	90.46 206	86.51 267	98.95 124	78.57 262	99.94 59	93.86 153	99.74 70	97.57 200

EPNet		98.49 36	98.40 29	98.77 83	99.62 69	96.80 110	99.90 59	99.51 20	97.60 8	99.20 51	99.36 100	93.71 86	99.91 65	97.99 81	98.71 106	99.61 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		96.81 94	96.53 91	97.64 141	98.91 103	93.07 208	99.65 147	99.80 3	95.64 57	95.39 149	98.86 132	84.35 194	99.90 66	96.98 106	99.16 99	99.95 63

MVS_111021_LR		98.42 40	98.38 31	98.53 101	99.39 82	95.79 139	99.87 71	99.86 2	96.70 27	98.78 68	99.79 45	92.03 115	99.90 66	99.17 32	99.86 60	99.88 74

DeepC-MVS	94.51 4	96.92 91	96.40 93	98.45 110	99.16 86	95.90 137	99.66 144	98.06 165	96.37 37	94.37 171	99.49 90	83.29 201	99.90 66	97.63 92	99.61 81	99.55 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PS-MVSNAJ		98.44 39	98.20 40	99.16 46	98.80 114	98.92 16	99.54 163	98.17 153	97.34 11	99.85 5	99.85 21	91.20 125	99.89 69	99.41 28	99.67 75	98.69 187

VNet		97.21 82	96.57 90	99.13 57	98.97 96	97.82 70	99.03 221	99.21 29	94.31 87	99.18 54	98.88 128	86.26 178	99.89 69	98.93 44	94.32 184	99.69 95

sss		97.57 70	97.03 77	99.18 43	98.37 132	98.04 64	99.73 126	99.38 26	93.46 117	98.76 69	99.06 114	91.21 124	99.89 69	96.33 113	97.01 143	99.62 105

MVS_111021_HR		98.72 22	98.62 18	99.01 71	99.36 84	97.18 98	99.93 50	99.90 1	96.81 24	98.67 73	99.77 52	93.92 78	99.89 69	99.27 31	99.94 44	99.96 58

PVSNet_0	88.03 19	91.80 211	90.27 223	96.38 176	98.27 136	90.46 263	99.94 45	99.61 17	93.99 100	86.26 273	97.39 187	71.13 305	99.89 69	98.77 53	67.05 330	98.79 185

PCF-MVS	94.20 5	95.18 147	94.10 159	98.43 112	98.55 126	95.99 136	97.91 291	97.31 234	90.35 208	89.48 228	99.22 107	85.19 189	99.89 69	90.40 206	98.47 109	99.41 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
AllTest		92.48 199	91.64 199	95.00 204	99.01 91	88.43 287	98.94 229	96.82 283	86.50 268	88.71 240	98.47 167	74.73 289	99.88 75	85.39 266	96.18 152	96.71 203

TestCases				95.00 204	99.01 91	88.43 287		96.82 283	86.50 268	88.71 240	98.47 167	74.73 289	99.88 75	85.39 266	96.18 152	96.71 203

PVSNet_Blended_VisFu		97.27 79	96.81 81	98.66 89	98.81 113	96.67 111	99.92 52	98.64 70	94.51 80	96.38 131	98.49 163	89.05 152	99.88 75	97.10 103	98.34 111	99.43 132

MSDG		94.37 169	93.36 178	97.40 150	98.88 108	93.95 181	99.37 182	97.38 228	85.75 281	90.80 198	99.17 109	84.11 196	99.88 75	86.35 257	98.43 110	98.36 189

TEST9					99.92 27	98.92 16	99.96 19	98.43 112	93.90 105	99.71 16	99.86 17	95.88 31	99.85 79

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 16	99.96 19	98.43 112	94.35 85	99.71 16	99.86 17	95.94 28	99.85 79	99.69 19	99.98 26	99.99 12

test_8					99.92 27	98.88 19	99.96 19	98.43 112	94.35 85	99.69 18	99.85 21	95.94 28	99.85 79

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 19	98.43 112	94.07 95	99.67 19	99.85 21	96.05 24	99.85 79	99.69 19	99.98 26	99.99 12

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 19	98.43 112	94.63 78	99.63 21	99.85 21	95.79 32	99.85 79	99.72 17	99.99 13	99.99 12

agg_prior					99.93 24	98.77 26		98.43 112		99.63 21			99.85 79

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 43	98.72 117	97.71 72	99.98 6	98.44 107	96.85 20	99.80 9	99.91 7	97.57 4	99.85 79	99.44 26	99.99 13	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
COLMAP_ROB	90.47 14	92.18 205	91.49 203	94.25 236	99.00 93	88.04 292	98.42 269	96.70 286	82.30 305	88.43 245	99.01 117	76.97 270	99.85 79	86.11 260	96.50 150	94.86 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchMatch-RL		96.04 131	95.40 133	97.95 132	99.59 70	95.22 159	99.52 165	99.07 33	93.96 102	96.49 125	98.35 169	82.28 206	99.82 87	90.15 210	99.22 98	98.81 184

test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 31	98.65 67	95.78 50	99.73 14	99.76 56	96.00 26	99.80 88	99.78 9	100.00 1	99.99 12

test_prior				99.43 27	99.94 14	98.49 50		98.65 67					99.80 88			99.99 12

APDe-MVS		99.06 8	98.91 10	99.51 21	99.94 14	98.76 32	99.91 56	98.39 126	97.20 14	99.46 35	99.85 21	95.53 37	99.79 90	99.86 5	100.00 1	99.99 12

XVG-OURS-SEG-HR		94.79 155	94.70 149	95.08 200	98.05 148	89.19 278	99.08 209	97.54 208	93.66 113	94.87 165	99.58 83	78.78 260	99.79 90	97.31 97	93.40 201	96.25 206

VDD-MVS		93.77 178	92.94 181	96.27 178	98.55 126	90.22 267	98.77 242	97.79 189	90.85 202	96.82 119	99.42 93	61.18 333	99.77 92	98.95 42	94.13 188	98.82 183

HY-MVS	92.50 7	97.79 64	97.17 72	99.63 9	98.98 95	99.32 3	97.49 295	99.52 18	95.69 56	98.32 88	97.41 186	93.32 92	99.77 92	98.08 78	95.75 163	99.81 80

Regformer-1		98.79 20	98.60 20	99.36 36	99.85 41	98.34 54	99.87 71	98.52 91	96.05 45	99.41 40	99.79 45	94.93 52	99.76 94	99.07 35	99.90 53	99.99 12

Regformer-2		98.78 21	98.59 21	99.36 36	99.85 41	98.32 55	99.87 71	98.52 91	96.04 46	99.41 40	99.79 45	94.92 53	99.76 94	99.05 36	99.90 53	99.98 44

APD-MVS_3200maxsize		98.25 49	98.08 46	98.78 82	99.81 51	96.60 114	99.82 96	98.30 139	93.95 103	99.37 44	99.77 52	92.84 100	99.76 94	98.95 42	99.92 51	99.97 54

Regformer-3		98.58 29	98.41 27	99.10 58	99.84 46	97.57 76	99.66 144	98.52 91	95.79 49	99.01 60	99.77 52	94.40 60	99.75 97	98.82 50	99.83 62	99.98 44

Regformer-4		98.56 30	98.39 30	99.08 60	99.84 46	97.52 78	99.66 144	98.52 91	95.76 52	99.01 60	99.77 52	94.33 66	99.75 97	98.80 51	99.83 62	99.98 44

CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 71	98.33 136	93.97 101	99.76 12	99.87 15	94.99 50	99.75 97	98.55 64	100.00 1	99.98 44

test12				99.43 27	99.74 57	98.56 46		98.40 124		99.65 20		94.76 55	99.75 97		99.98 26	99.99 12

XVG-OURS		94.82 154	94.74 148	95.06 201	98.00 149	89.19 278	99.08 209	97.55 206	94.10 93	94.71 166	99.62 81	80.51 242	99.74 101	96.04 117	93.06 206	96.25 206

APD-MVS		98.62 25	98.35 35	99.41 31	99.90 33	98.51 49	99.87 71	98.36 132	94.08 94	99.74 13	99.73 64	94.08 74	99.74 101	99.42 27	99.99 13	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WTY-MVS		98.10 54	97.60 60	99.60 13	98.92 101	99.28 5	99.89 64	99.52 18	95.58 58	98.24 93	99.39 97	93.33 91	99.74 101	97.98 83	95.58 166	99.78 84

EI-MVSNet-UG-set		98.14 52	97.99 49	98.60 94	99.80 52	96.27 122	99.36 184	98.50 101	95.21 66	98.30 89	99.75 61	93.29 94	99.73 104	98.37 69	99.30 96	99.81 80

HSP-MVS		99.07 6	99.11 4	98.95 75	99.93 24	97.24 95	99.95 31	98.32 137	97.50 10	99.52 32	99.88 12	97.43 6	99.71 105	99.50 23	99.98 26	99.89 72

xiu_mvs_v2_base		98.23 50	97.97 50	99.02 70	98.69 118	98.66 37	99.52 165	98.08 164	97.05 16	99.86 4	99.86 17	90.65 133	99.71 105	99.39 29	98.63 107	98.69 187

EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 84	99.83 49	96.59 115	99.40 177	98.51 97	95.29 64	98.51 80	99.76 56	93.60 89	99.71 105	98.53 65	99.52 86	99.95 63

ab-mvs		94.69 159	93.42 174	98.51 102	98.07 147	96.26 123	96.49 310	98.68 64	90.31 209	94.54 167	97.00 200	76.30 277	99.71 105	95.98 118	93.38 202	99.56 116

xiu_mvs_v1_base_debu		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 189	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 189	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base_debi		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 189	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

HPM-MVS		97.96 57	97.72 56	98.68 87	99.84 46	96.39 121	99.90 59	98.17 153	92.61 146	98.62 76	99.57 84	91.87 118	99.67 112	98.87 48	99.99 13	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
UA-Net		96.54 107	95.96 107	98.27 121	98.23 139	95.71 145	98.00 289	98.45 106	93.72 111	98.41 83	99.27 102	88.71 157	99.66 113	91.19 191	97.69 124	99.44 131

HPM-MVS_fast		97.80 63	97.50 63	98.68 87	99.79 53	96.42 118	99.88 66	98.16 156	91.75 177	98.94 63	99.54 87	91.82 120	99.65 114	97.62 93	99.99 13	99.99 12

114514_t		97.41 76	96.83 80	99.14 52	99.51 78	97.83 69	99.89 64	98.27 144	88.48 237	99.06 57	99.66 78	90.30 136	99.64 115	96.32 114	99.97 35	99.96 58

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 57	98.67 36	99.77 110	98.38 129	96.73 26	99.88 3	99.74 63	94.89 54	99.59 116	99.80 7	99.98 26	99.97 54

LFMVS		94.75 158	93.56 170	98.30 120	99.03 90	95.70 146	98.74 243	97.98 171	87.81 246	98.47 81	99.39 97	67.43 317	99.53 117	98.01 79	95.20 170	99.67 97

canonicalmvs		97.09 86	96.32 94	99.39 34	98.93 100	98.95 14	99.72 131	97.35 230	94.45 81	97.88 100	99.42 93	86.71 173	99.52 118	98.48 66	93.97 197	99.72 92

thres200		96.96 88	96.21 97	99.22 41	98.97 96	98.84 22	99.85 88	99.71 5	93.17 123	96.26 132	98.88 128	89.87 139	99.51 119	94.26 147	94.91 172	99.31 149

OMC-MVS		97.28 78	97.23 69	97.41 149	99.76 54	93.36 202	99.65 147	97.95 174	96.03 47	97.41 108	99.70 69	89.61 141	99.51 119	96.73 111	98.25 116	99.38 141

tfpn111		96.69 102	95.87 120	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.20 160

conf200view11		96.73 101	95.92 110	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.20 160

thres100view900		96.74 99	95.92 110	99.18 43	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.27 154

tfpn200view9		96.79 95	95.99 102	99.19 42	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.27 154

thres600view7		96.69 102	95.87 120	99.14 52	98.90 104	98.78 25	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.16 166

thres400		96.78 96	95.99 102	99.16 46	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.16 166

VDDNet		93.12 188	91.91 197	96.76 165	96.67 204	92.65 221	98.69 247	98.21 148	82.81 300	97.75 102	99.28 101	61.57 331	99.48 127	98.09 77	94.09 189	98.15 192

RPSCF		91.80 211	92.79 184	88.83 312	98.15 144	69.87 335	98.11 285	96.60 290	83.93 296	94.33 172	99.27 102	79.60 250	99.46 128	91.99 183	93.16 205	97.18 201

view600		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

view800		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

conf0.05thres1000		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

tfpn		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

alignmvs		97.81 62	97.33 67	99.25 40	98.77 116	98.66 37	99.99 3	98.44 107	94.40 84	98.41 83	99.47 91	93.65 87	99.42 133	98.57 63	94.26 186	99.67 97

Test_1112_low_res		95.72 136	94.83 146	98.42 113	97.79 163	96.41 119	99.65 147	96.65 288	92.70 139	92.86 186	96.13 226	92.15 113	99.30 134	91.88 185	93.64 199	99.55 117

1112_ss		96.01 132	95.20 140	98.42 113	97.80 162	96.41 119	99.65 147	96.66 287	92.71 138	92.88 185	99.40 95	92.16 112	99.30 134	91.92 184	93.66 198	99.55 117

cascas		94.64 161	93.61 165	97.74 138	97.82 161	96.26 123	99.96 19	97.78 190	85.76 278	94.00 175	97.54 183	76.95 271	99.21 136	97.23 99	95.43 168	97.76 199

TAPA-MVS	92.12 8	94.42 167	93.60 167	96.90 161	99.33 85	91.78 240	99.78 105	98.00 169	89.89 216	94.52 168	99.47 91	91.97 116	99.18 137	69.90 326	99.52 86	99.73 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IB-MVS	92.85 6	94.99 153	93.94 161	98.16 123	97.72 171	95.69 147	99.99 3	98.81 56	94.28 88	92.70 187	96.90 202	95.08 44	99.17 138	96.07 116	73.88 320	99.60 109
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
tfpn_ndepth		97.21 82	96.63 87	98.92 77	99.06 88	98.28 56	99.95 31	98.91 42	92.96 127	96.49 125	98.67 152	97.40 7	99.07 139	91.87 186	94.38 179	99.41 134

DI_MVS_plusplus_test		92.48 199	90.60 213	98.11 128	91.88 306	96.13 131	99.64 151	97.73 191	92.69 140	76.02 314	93.79 296	70.49 306	99.07 139	95.88 120	97.26 136	99.14 172

test_normal		92.44 202	90.54 214	98.12 127	91.85 307	96.18 130	99.68 139	97.73 191	92.66 142	75.76 318	93.74 298	70.49 306	99.04 141	95.71 124	97.27 135	99.13 174

mvs-test1		95.53 141	95.97 106	94.20 237	97.77 164	85.44 306	99.95 31	97.06 248	94.92 69	96.58 123	98.72 150	85.81 181	98.98 142	94.80 134	98.11 117	98.18 191

MVS_Test		96.46 116	95.74 123	98.61 93	98.18 142	97.23 96	99.31 189	97.15 244	91.07 197	98.84 65	97.05 198	88.17 161	98.97 143	94.39 142	97.50 128	99.61 107

tpmvs		94.28 170	93.57 169	96.40 175	98.55 126	91.50 251	95.70 323	98.55 88	87.47 254	92.15 189	94.26 289	91.42 121	98.95 144	88.15 231	95.85 160	98.76 186

diffmvs		95.25 146	94.26 156	98.23 122	98.13 145	96.59 115	99.12 204	97.18 240	85.78 277	97.64 103	96.70 210	85.92 180	98.87 145	90.40 206	97.45 129	99.24 159

tpm cat1		93.51 183	92.52 189	96.47 172	97.77 164	91.47 252	96.13 316	98.06 165	80.98 316	92.91 184	93.78 297	89.66 140	98.87 145	87.03 249	96.39 151	99.09 177

tfpn1000		96.90 92	96.29 95	98.74 85	99.00 93	98.09 62	99.92 52	98.91 42	92.08 167	95.85 138	98.65 154	97.39 8	98.83 147	90.56 201	94.23 187	99.31 149

BH-RMVSNet		95.18 147	94.31 155	97.80 135	98.17 143	95.23 158	99.76 116	97.53 210	92.52 154	94.27 173	99.25 105	76.84 272	98.80 148	90.89 199	99.54 85	99.35 146

gm-plane-assit					96.97 189	93.76 188			91.47 184		98.96 122		98.79 149	94.92 130

DWT-MVSNet_test		97.31 77	97.19 70	97.66 140	98.24 138	94.67 169	98.86 238	98.20 151	93.60 115	98.09 95	98.89 126	97.51 5	98.78 150	94.04 151	97.28 134	99.55 117

TR-MVS		94.54 163	93.56 170	97.49 145	97.96 151	94.34 173	98.71 245	97.51 214	90.30 210	94.51 169	98.69 151	75.56 282	98.77 151	92.82 174	95.99 156	99.35 146

Vis-MVSNet		95.72 136	95.15 142	97.45 147	97.62 173	94.28 174	99.28 194	98.24 145	94.27 89	96.84 118	98.94 125	79.39 251	98.76 152	93.25 167	98.49 108	99.30 151
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tpmrst		96.27 128	95.98 104	97.13 156	97.96 151	93.15 207	96.34 313	98.17 153	92.07 168	98.71 72	95.12 256	93.91 80	98.73 153	94.91 132	96.62 147	99.50 126

PMMVS		96.76 97	96.76 85	96.76 165	98.28 135	92.10 231	99.91 56	97.98 171	94.12 92	99.53 29	99.39 97	86.93 172	98.73 153	96.95 108	97.73 123	99.45 129

lupinMVS		97.85 61	97.60 60	98.62 92	97.28 181	97.70 74	99.99 3	97.55 206	95.50 60	99.43 38	99.67 76	90.92 131	98.71 155	98.40 68	99.62 78	99.45 129

Effi-MVS+		96.30 125	95.69 124	98.16 123	97.85 158	96.26 123	97.41 296	97.21 238	90.37 207	98.65 75	98.58 160	86.61 175	98.70 156	97.11 102	97.37 133	99.52 123

PatchFormer-LS_test		97.01 87	96.79 83	97.69 139	98.26 137	94.80 165	98.66 253	98.13 161	93.70 112	97.86 101	98.80 144	95.54 35	98.67 157	94.12 150	96.00 155	99.60 109

BH-w/o		95.71 138	95.38 135	96.68 168	98.49 130	92.28 227	99.84 91	97.50 215	92.12 166	92.06 190	98.79 149	84.69 190	98.67 157	95.29 127	99.66 76	99.09 177

conf0.01		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

conf0.002		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

thresconf0.02		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpn_n400		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnconf		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnview11		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

MDTV_nov1_ep13			95.69 124		97.90 154	94.15 176	95.98 319	98.44 107	93.12 124	97.98 98	95.74 232	95.10 43	98.58 165	90.02 211	96.92 145

jason		97.24 80	96.86 79	98.38 118	95.73 223	97.32 94	99.97 12	97.40 226	95.34 63	98.60 78	99.54 87	87.70 163	98.56 166	97.94 84	99.47 89	99.25 156
jason: jason.
EPP-MVSNet		96.69 102	96.60 88	96.96 159	97.74 167	93.05 210	99.37 182	98.56 84	88.75 233	95.83 143	99.01 117	96.01 25	98.56 166	96.92 109	97.20 139	99.25 156

BH-untuned		95.18 147	94.83 146	96.22 179	98.36 133	91.22 253	99.80 101	97.32 233	90.91 200	91.08 195	98.67 152	83.51 198	98.54 168	94.23 148	99.61 81	98.92 181

tpmp4_e23		95.15 150	94.69 150	96.55 171	97.84 159	91.77 241	97.10 302	97.91 178	88.33 240	97.19 112	95.06 260	93.92 78	98.51 169	89.64 214	95.19 171	99.37 143

PAPM		98.60 26	98.42 26	99.14 52	96.05 211	98.96 13	99.90 59	99.35 27	96.68 28	98.35 87	99.66 78	96.45 21	98.51 169	99.45 25	99.89 55	99.96 58

OPM-MVS		93.21 187	92.80 183	94.44 230	93.12 284	90.85 258	99.77 110	97.61 203	96.19 41	91.56 192	98.65 154	75.16 287	98.47 171	93.78 159	89.39 214	93.99 256

ACMP	92.05 9	92.74 194	92.42 191	93.73 251	95.91 216	88.72 282	99.81 98	97.53 210	94.13 91	87.00 260	98.23 171	74.07 293	98.47 171	96.22 115	88.86 220	93.99 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		94.06 172	93.90 162	94.55 227	96.02 212	90.69 259	99.98 6	97.72 193	96.62 30	91.05 196	98.85 137	77.21 268	98.47 171	98.11 75	89.51 213	94.48 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMM	91.95 10	92.88 192	92.52 189	93.98 247	95.75 222	89.08 280	99.77 110	97.52 212	93.00 126	89.95 210	97.99 177	76.17 279	98.46 174	93.63 162	88.87 219	94.39 224
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
dp		95.05 151	94.43 153	96.91 160	97.99 150	92.73 217	96.29 314	97.98 171	89.70 218	95.93 137	94.67 280	93.83 84	98.45 175	86.91 253	96.53 149	99.54 121

ACMH+	89.98 16	90.35 246	89.54 240	92.78 271	95.99 213	86.12 300	98.81 240	97.18 240	89.38 219	83.14 292	97.76 181	68.42 314	98.43 176	89.11 224	86.05 245	93.78 275

ITE_SJBPF				92.38 282	95.69 228	85.14 307		95.71 305	92.81 132	89.33 232	98.11 173	70.23 308	98.42 177	85.91 261	88.16 231	93.59 283

Fast-Effi-MVS+		95.02 152	94.19 157	97.52 144	97.88 155	94.55 170	99.97 12	97.08 247	88.85 232	94.47 170	97.96 178	84.59 191	98.41 178	89.84 212	97.10 141	99.59 111

ACMH	89.72 17	90.64 239	89.63 237	93.66 255	95.64 229	88.64 285	98.55 257	97.45 218	89.03 224	81.62 297	97.61 182	69.75 309	98.41 178	89.37 221	87.62 237	93.92 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		92.96 190	92.71 185	93.71 253	95.43 231	88.67 283	99.75 117	97.62 200	92.81 132	90.05 205	98.49 163	75.24 285	98.40 180	95.84 122	89.12 215	94.07 245

LGP-MVS_train				93.71 253	95.43 231	88.67 283		97.62 200	92.81 132	90.05 205	98.49 163	75.24 285	98.40 180	95.84 122	89.12 215	94.07 245

XVG-ACMP-BASELINE		91.22 227	90.75 210	92.63 273	93.73 256	85.61 303	98.52 261	97.44 219	92.77 136	89.90 212	96.85 206	66.64 319	98.39 182	92.29 177	88.61 224	93.89 268

HQP4-MVS										93.37 177			98.39 182			94.53 212

HQP-MVS		94.61 162	94.50 152	94.92 210	95.78 217	91.85 237	99.87 71	97.89 180	96.82 21	93.37 177	98.65 154	80.65 240	98.39 182	97.92 85	89.60 208	94.53 212

TDRefinement		84.76 299	82.56 303	91.38 294	74.58 345	84.80 310	97.36 297	94.56 331	84.73 290	80.21 302	96.12 227	63.56 327	98.39 182	87.92 234	63.97 340	90.95 319

EPMVS		96.53 108	96.01 101	98.09 129	98.43 131	96.12 134	96.36 312	99.43 24	93.53 116	97.64 103	95.04 262	94.41 59	98.38 186	91.13 192	98.11 117	99.75 87

HQP_MVS		94.49 166	94.36 154	94.87 213	95.71 226	91.74 242	99.84 91	97.87 182	96.38 34	93.01 181	98.59 158	80.47 244	98.37 187	97.79 88	89.55 211	94.52 214

plane_prior5								97.87 182					98.37 187	97.79 88	89.55 211	94.52 214

TinyColmap		87.87 276	86.51 277	91.94 289	95.05 237	85.57 304	97.65 293	94.08 334	84.40 294	81.82 296	96.85 206	62.14 330	98.33 189	80.25 297	86.37 244	91.91 310

CMPMVS	61.59 21	84.75 300	85.14 282	83.57 320	90.32 321	62.54 344	96.98 305	97.59 205	74.33 333	69.95 333	96.66 211	64.17 325	98.32 190	87.88 235	88.41 228	89.84 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		90.00 255	88.96 253	93.10 265	94.81 240	88.16 291	98.71 245	95.54 311	93.66 113	83.75 290	97.20 191	65.58 321	98.31 191	83.96 277	87.49 239	92.85 301

TESTMET0.1,1		96.74 99	96.26 96	98.16 123	97.36 180	96.48 117	99.96 19	98.29 140	91.93 172	95.77 144	98.07 175	95.54 35	98.29 192	90.55 202	98.89 101	99.70 93

CostFormer		96.10 129	95.88 113	96.78 164	97.03 186	92.55 223	97.08 303	97.83 187	90.04 214	98.72 71	94.89 272	95.01 48	98.29 192	96.54 112	95.77 162	99.50 126

LTVRE_ROB	88.28 18	90.29 249	89.05 252	94.02 243	95.08 235	90.15 269	97.19 301	97.43 220	84.91 288	83.99 288	97.06 197	74.00 294	98.28 194	84.08 274	87.71 235	93.62 282
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
test-LLR		96.47 115	96.04 100	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	94.07 95	95.55 146	96.38 218	93.90 81	98.27 195	90.42 204	98.83 103	99.64 103

test-mter		96.39 121	95.93 109	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	91.81 176	95.55 146	96.38 218	95.17 41	98.27 195	90.42 204	98.83 103	99.64 103

HyFIR lowres test		96.66 105	96.43 92	97.36 152	99.05 89	93.91 182	99.70 133	99.80 3	90.54 205	96.26 132	98.08 174	92.15 113	98.23 197	96.84 110	95.46 167	99.93 66

CHOSEN 280x420		99.01 10	99.03 5	98.95 75	99.38 83	98.87 20	98.46 264	99.42 25	97.03 17	99.02 59	99.09 112	99.35 1	98.21 198	99.73 16	99.78 69	99.77 85

ADS-MVSNet		94.79 155	94.02 160	97.11 158	97.87 156	93.79 184	94.24 325	98.16 156	90.07 212	96.43 128	94.48 285	90.29 137	98.19 199	87.44 239	97.23 137	99.36 144

test_post											63.35 354	94.43 58	98.13 200

LF4IMVS		89.25 266	88.85 254	90.45 302	92.81 292	81.19 325	98.12 284	94.79 328	91.44 185	86.29 272	97.11 193	65.30 323	98.11 201	88.53 228	85.25 250	92.07 306

IS-MVSNet		96.29 126	95.90 112	97.45 147	98.13 145	94.80 165	99.08 209	97.61 203	92.02 171	95.54 148	98.96 122	90.64 134	98.08 202	93.73 161	97.41 132	99.47 128

DeepMVS_CX				82.92 324	95.98 215	58.66 348		96.01 301	92.72 137	78.34 308	95.51 238	58.29 337	98.08 202	82.57 285	85.29 249	92.03 308

PatchmatchNet		95.94 133	95.45 132	97.39 151	97.83 160	94.41 172	96.05 318	98.40 124	92.86 128	97.09 115	95.28 253	94.21 72	98.07 204	89.26 223	98.11 117	99.70 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		90.65 238	90.30 220	91.71 292	94.22 248	85.50 305	98.24 279	97.70 194	88.67 234	86.42 270	96.37 220	67.82 316	98.03 205	83.62 279	99.62 78	91.60 313

Patchmatch-test		92.65 198	91.50 202	96.10 182	96.85 194	90.49 262	91.50 339	97.19 239	82.76 301	90.23 202	95.59 237	95.02 47	98.00 206	77.41 314	96.98 144	99.82 79

tpm2		95.47 143	95.18 141	96.35 177	96.91 191	91.70 246	96.96 306	97.93 176	88.04 244	98.44 82	95.40 241	93.32 92	97.97 207	94.00 152	95.61 165	99.38 141

JIA-IIPM		91.76 214	90.70 211	94.94 208	96.11 209	87.51 294	93.16 332	98.13 161	75.79 329	97.58 105	77.68 344	92.84 100	97.97 207	88.47 229	96.54 148	99.33 148

VPA-MVSNet		92.70 195	91.55 201	96.16 180	95.09 234	96.20 128	98.88 233	99.00 35	91.02 199	91.82 191	95.29 252	76.05 281	97.96 209	95.62 125	81.19 270	94.30 232

patchmatchnet-post											91.70 311	95.12 42	97.95 210

GG-mvs-BLEND				98.54 100	98.21 140	98.01 65	93.87 329	98.52 91		97.92 99	97.92 179	99.02 2	97.94 211	98.17 72	99.58 83	99.67 97

Effi-MVS+-dtu		94.53 165	95.30 137	92.22 286	97.77 164	82.54 317	99.59 155	97.06 248	94.92 69	95.29 151	95.37 246	85.81 181	97.89 212	94.80 134	97.07 142	96.23 208

XXY-MVS		91.82 208	90.46 215	95.88 186	93.91 253	95.40 153	98.87 236	97.69 195	88.63 236	87.87 252	97.08 195	74.38 292	97.89 212	91.66 188	84.07 257	94.35 229

gg-mvs-nofinetune		93.51 183	91.86 198	98.47 108	97.72 171	97.96 66	92.62 334	98.51 97	74.70 332	97.33 109	69.59 348	98.91 3	97.79 214	97.77 90	99.56 84	99.67 97

test_post1							95.78 322				59.23 357	93.20 96	97.74 215	91.06 194

nrg030		93.51 183	92.53 188	96.45 173	94.36 245	97.20 97	99.81 98	97.16 243	91.60 179	89.86 214	97.46 184	86.37 177	97.68 216	95.88 120	80.31 281	94.46 217

Fast-Effi-MVS+-dtu		93.72 180	93.86 164	93.29 260	97.06 185	86.16 299	99.80 101	96.83 281	92.66 142	92.58 188	97.83 180	81.39 227	97.67 217	89.75 213	96.87 146	96.05 210

GA-MVS		93.83 174	92.84 182	96.80 163	95.73 223	93.57 190	99.88 66	97.24 237	92.57 152	92.92 183	96.66 211	78.73 261	97.67 217	87.75 236	94.06 196	99.17 165

VPNet		91.81 209	90.46 215	95.85 188	94.74 241	95.54 149	98.98 224	98.59 79	92.14 165	90.77 199	97.44 185	68.73 312	97.54 219	94.89 133	77.89 301	94.46 217

MVS-HIRNet		86.22 286	83.19 301	95.31 195	96.71 203	90.29 266	92.12 336	97.33 232	62.85 343	86.82 263	70.37 347	69.37 310	97.49 220	75.12 321	97.99 122	98.15 192

Vis-MVSNet (Re-imp)		96.32 123	95.98 104	97.35 153	97.93 153	94.82 164	99.47 171	98.15 158	91.83 175	95.09 163	99.11 111	91.37 123	97.47 221	93.47 163	97.43 130	99.74 88

Test4		88.80 270	85.91 279	97.48 146	87.33 329	95.72 144	99.29 193	97.04 257	92.82 131	70.35 332	91.46 312	44.37 347	97.43 222	93.37 166	97.17 140	99.29 153

tfpnnormal		89.29 265	87.61 271	94.34 234	94.35 246	94.13 177	98.95 228	98.94 38	83.94 295	84.47 285	95.51 238	74.84 288	97.39 223	77.05 317	80.41 279	91.48 315

v6		91.44 217	90.27 223	94.93 209	93.44 266	93.44 193	99.73 126	97.05 252	87.57 247	90.05 205	95.10 259	81.87 217	97.39 223	87.45 238	80.17 282	93.98 260

jajsoiax		91.92 207	91.18 206	94.15 238	91.35 313	90.95 256	99.00 223	97.42 223	92.61 146	87.38 256	97.08 195	72.46 298	97.36 225	94.53 141	88.77 221	94.13 242

EPNet_dtu		95.71 138	95.39 134	96.66 169	98.92 101	93.41 198	99.57 157	98.90 50	96.19 41	97.52 106	98.56 161	92.65 105	97.36 225	77.89 311	98.33 112	99.20 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test1		94.39 168	93.46 172	97.17 155	97.10 183	94.44 171	98.86 238	98.32 137	93.30 121	96.17 134	95.38 244	76.48 276	97.34 227	88.12 233	97.43 130	99.74 88

V42		91.28 225	90.12 231	94.74 218	93.42 268	93.46 192	99.68 139	97.02 258	87.36 256	89.85 215	95.05 261	81.31 229	97.34 227	87.34 244	80.07 285	93.40 286

v1		91.36 221	90.14 229	95.04 202	93.35 272	93.80 183	99.77 110	97.05 252	87.53 251	89.77 217	94.91 270	81.99 211	97.33 229	86.90 255	79.98 288	94.00 253

Anonymous20240521		90.82 235	89.48 244	94.82 216	93.53 260	94.07 178	99.07 213	97.43 220	87.10 260	84.14 286	94.60 282	83.11 203	97.32 230	85.73 263	80.61 278	93.08 295

v1neww		91.44 217	90.28 221	94.91 211	93.50 262	93.43 194	99.73 126	97.06 248	87.55 248	90.08 203	95.11 257	81.98 212	97.32 230	87.41 241	80.15 283	93.99 256

mvs_tets		91.81 209	91.08 207	94.00 245	91.63 311	90.58 260	98.67 250	97.43 220	92.43 157	87.37 257	97.05 198	71.76 300	97.32 230	94.75 137	88.68 223	94.11 243

v7new		91.44 217	90.28 221	94.91 211	93.50 262	93.43 194	99.73 126	97.06 248	87.55 248	90.08 203	95.11 257	81.98 212	97.32 230	87.41 241	80.15 283	93.99 256

EI-MVSNet		93.73 179	93.40 177	94.74 218	96.80 197	92.69 218	99.06 216	97.67 196	88.96 228	91.39 193	99.02 115	88.75 156	97.30 234	91.07 193	87.85 233	94.22 237

MVSTER		95.53 141	95.22 139	96.45 173	98.56 125	97.72 71	99.91 56	97.67 196	92.38 158	91.39 193	97.14 192	97.24 10	97.30 234	94.80 134	87.85 233	94.34 230

TAMVS		95.85 134	95.58 130	96.65 170	97.07 184	93.50 191	99.17 202	97.82 188	91.39 187	95.02 164	98.01 176	92.20 111	97.30 234	93.75 160	95.83 161	99.14 172

PS-MVSNAJss		93.64 182	93.31 179	94.61 223	92.11 301	92.19 229	99.12 204	97.38 228	92.51 155	88.45 243	96.99 201	91.20 125	97.29 237	94.36 143	87.71 235	94.36 226

OurMVSNet-221017-0		89.81 256	89.48 244	90.83 298	91.64 310	81.21 324	98.17 283	95.38 320	91.48 183	85.65 279	97.31 188	72.66 297	97.29 237	88.15 231	84.83 253	93.97 261

MVP-Stereo		90.93 231	90.45 217	92.37 283	91.25 315	88.76 281	98.05 288	96.17 298	87.27 258	84.04 287	95.30 249	78.46 264	97.27 239	83.78 278	99.70 74	91.09 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1141		91.36 221	90.14 229	95.00 204	93.33 274	93.79 184	99.78 105	97.05 252	87.52 252	89.75 218	94.89 272	82.13 208	97.21 240	86.84 256	80.00 287	94.00 253

divwei89l23v2f112		91.37 220	90.15 228	95.00 204	93.35 272	93.78 187	99.78 105	97.05 252	87.54 250	89.73 219	94.89 272	82.24 207	97.21 240	86.91 253	79.90 289	94.00 253

v8		90.54 242	89.17 248	94.66 221	93.43 267	93.40 201	99.20 199	96.94 272	85.76 278	87.56 254	94.51 283	81.96 215	97.19 242	84.94 270	78.25 298	93.38 288

testing_2		85.10 298	81.72 305	95.22 197	82.25 338	94.16 175	97.54 294	97.01 261	88.15 241	62.23 340	86.43 337	44.43 346	97.18 243	92.28 182	85.20 252	94.31 231

v52		89.55 260	88.41 261	92.98 266	92.32 298	90.01 271	98.88 233	96.89 276	84.51 292	86.89 261	94.22 290	79.23 253	97.16 244	84.46 272	78.27 297	91.76 311

mvs_anonymous		95.65 140	95.03 144	97.53 143	98.19 141	95.74 142	99.33 186	97.49 216	90.87 201	90.47 201	97.10 194	88.23 160	97.16 244	95.92 119	97.66 126	99.68 96

V4		89.55 260	88.41 261	92.98 266	92.21 300	90.03 270	98.87 236	96.91 274	84.51 292	86.84 262	94.21 291	79.37 252	97.15 246	84.45 273	78.28 296	91.76 311

v2v482		91.30 223	90.07 232	95.01 203	93.13 282	93.79 184	99.77 110	97.02 258	88.05 243	89.25 233	95.37 246	80.73 238	97.15 246	87.28 245	80.04 286	94.09 244

v7		91.20 228	89.99 233	94.82 216	93.57 258	93.41 198	99.57 157	96.98 264	86.83 265	89.88 213	95.22 254	81.01 233	97.14 248	85.53 264	81.31 269	93.90 266

UniMVSNet (Re)		93.07 189	92.13 193	95.88 186	94.84 239	96.24 127	99.88 66	98.98 36	92.49 156	89.25 233	95.40 241	87.09 170	97.14 248	93.13 172	78.16 299	94.26 234

v7n		89.65 258	88.29 264	93.72 252	92.22 299	90.56 261	99.07 213	97.10 246	85.42 286	86.73 264	94.72 276	80.06 247	97.13 250	81.14 293	78.12 300	93.49 284

CDS-MVSNet		96.34 122	96.07 99	97.13 156	97.37 179	94.96 161	99.53 164	97.91 178	91.55 181	95.37 150	98.32 170	95.05 46	97.13 250	93.80 158	95.75 163	99.30 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EG-PatchMatch MVS		85.35 297	83.81 298	89.99 307	90.39 320	81.89 322	98.21 282	96.09 300	81.78 312	74.73 319	93.72 299	51.56 344	97.12 252	79.16 305	88.61 224	90.96 318

v144192		90.79 236	89.52 241	94.59 224	93.11 285	92.77 215	99.56 159	96.99 262	86.38 270	89.82 216	94.95 269	80.50 243	97.10 253	83.98 276	80.41 279	93.90 266

FIs		94.10 171	93.43 173	96.11 181	94.70 242	96.82 109	99.58 156	98.93 41	92.54 153	89.34 231	97.31 188	87.62 164	97.10 253	94.22 149	86.58 242	94.40 223

v1192		90.62 241	89.25 247	94.72 220	93.13 282	93.07 208	99.50 167	97.02 258	86.33 271	89.56 227	95.01 263	79.22 254	97.09 255	82.34 287	81.16 271	94.01 250

v1144		91.09 229	89.83 234	94.87 213	93.25 279	93.69 189	99.62 153	96.98 264	86.83 265	89.64 224	94.99 266	80.94 234	97.05 256	85.08 269	81.16 271	93.87 270

v148		90.70 237	89.63 237	93.92 248	92.97 288	90.97 255	99.75 117	96.89 276	87.51 253	88.27 248	95.01 263	81.67 220	97.04 257	87.40 243	77.17 309	93.75 276

v748		88.94 269	87.72 270	92.61 274	91.91 304	87.50 295	99.07 213	96.97 267	84.76 289	85.79 278	93.63 300	79.19 255	97.04 257	83.16 282	75.03 319	93.28 290

pm-mvs1		89.36 264	87.81 269	94.01 244	93.40 270	91.93 235	98.62 254	96.48 294	86.25 272	83.86 289	96.14 225	73.68 295	97.04 257	86.16 259	75.73 316	93.04 297

v1921920		90.46 243	89.12 249	94.50 228	92.96 289	92.46 224	99.49 168	96.98 264	86.10 273	89.61 226	95.30 249	78.55 263	97.03 260	82.17 288	80.89 277	94.01 250

v1240		90.20 251	88.79 256	94.44 230	93.05 287	92.27 228	99.38 181	96.92 273	85.89 275	89.36 230	94.87 275	77.89 267	97.03 260	80.66 295	81.08 273	94.01 250

v10		90.25 250	88.82 255	94.57 226	93.53 260	93.43 194	99.08 209	96.87 279	85.00 287	87.34 258	94.51 283	80.93 235	97.02 262	82.85 284	79.23 291	93.26 291

lessismore_v0				90.53 299	90.58 319	80.90 327		95.80 304		77.01 310	95.84 229	66.15 320	96.95 263	83.03 283	75.05 318	93.74 279

OpenMVS_ROB	79.82 20	83.77 306	81.68 306	90.03 306	88.30 327	82.82 315	98.46 264	95.22 324	73.92 335	76.00 315	91.29 313	55.00 340	96.94 264	68.40 329	88.51 227	90.34 321

anonymousdsp		91.79 213	90.92 209	94.41 233	90.76 318	92.93 213	98.93 230	97.17 242	89.08 222	87.46 255	95.30 249	78.43 265	96.92 265	92.38 176	88.73 222	93.39 287

MVSFormer		96.94 89	96.60 88	97.95 132	97.28 181	97.70 74	99.55 161	97.27 235	91.17 194	99.43 38	99.54 87	90.92 131	96.89 266	94.67 138	99.62 78	99.25 156

test_djsdf		92.83 193	92.29 192	94.47 229	91.90 305	92.46 224	99.55 161	97.27 235	91.17 194	89.96 209	96.07 228	81.10 231	96.89 266	94.67 138	88.91 217	94.05 247

pmmvs6		85.69 292	83.84 297	91.26 295	90.00 323	84.41 311	97.82 292	96.15 299	75.86 328	81.29 298	95.39 243	61.21 332	96.87 268	83.52 281	73.29 322	92.50 303

tpm		93.70 181	93.41 176	94.58 225	95.36 233	87.41 296	97.01 304	96.90 275	90.85 202	96.72 122	94.14 292	90.40 135	96.84 269	90.75 200	88.54 226	99.51 124

FC-MVSNet-test		93.81 176	93.15 180	95.80 189	94.30 247	96.20 128	99.42 176	98.89 51	92.33 159	89.03 238	97.27 190	87.39 167	96.83 270	93.20 168	86.48 243	94.36 226

pmmvs4		92.10 206	91.07 208	95.18 198	92.82 291	94.96 161	99.48 170	96.83 281	87.45 255	88.66 242	96.56 216	83.78 197	96.83 270	89.29 222	84.77 254	93.75 276

WR-MVS		92.31 203	91.25 205	95.48 193	94.45 244	95.29 155	99.60 154	98.68 64	90.10 211	88.07 250	96.89 203	80.68 239	96.80 272	93.14 171	79.67 290	94.36 226

UniMVSNet_NR-MVSNet		92.95 191	92.11 194	95.49 191	94.61 243	95.28 156	99.83 95	99.08 32	91.49 182	89.21 235	96.86 205	87.14 169	96.73 273	93.20 168	77.52 305	94.46 217

DU-MVS		92.46 201	91.45 204	95.49 191	94.05 250	95.28 156	99.81 98	98.74 60	92.25 160	89.21 235	96.64 213	81.66 221	96.73 273	93.20 168	77.52 305	94.46 217

SixPastTwentyTwo		88.73 271	88.01 268	90.88 296	91.85 307	82.24 319	98.22 281	95.18 326	88.97 227	82.26 295	96.89 203	71.75 301	96.67 275	84.00 275	82.98 261	93.72 280

WR-MVS_H		91.30 223	90.35 218	94.15 238	94.17 249	92.62 222	99.17 202	98.94 38	88.87 231	86.48 269	94.46 287	84.36 193	96.61 276	88.19 230	78.51 295	93.21 293

NR-MVSNet		91.56 216	90.22 225	95.60 190	94.05 250	95.76 141	98.25 278	98.70 62	91.16 196	80.78 300	96.64 213	83.23 202	96.57 277	91.41 189	77.73 303	94.46 217

Baseline_NR-MVSNet		90.33 247	89.51 242	92.81 270	92.84 290	89.95 273	99.77 110	93.94 336	84.69 291	89.04 237	95.66 235	81.66 221	96.52 278	90.99 195	76.98 310	91.97 309

pmmvs5		90.17 253	89.09 250	93.40 258	92.10 302	89.77 276	99.74 120	95.58 309	85.88 276	87.24 259	95.74 232	73.41 296	96.48 279	88.54 227	83.56 260	93.95 262

TransMVSNet (Re)		87.25 277	85.28 281	93.16 262	93.56 259	91.03 254	98.54 259	94.05 335	83.69 297	81.09 299	96.16 224	75.32 284	96.40 280	76.69 318	68.41 327	92.06 307

CP-MVSNet		91.23 226	90.22 225	94.26 235	93.96 252	92.39 226	99.09 207	98.57 82	88.95 229	86.42 270	96.57 215	79.19 255	96.37 281	90.29 208	78.95 292	94.02 248

ambc				83.23 321	77.17 344	62.61 343	87.38 346	94.55 332		76.72 312	86.65 336	30.16 351	96.36 282	84.85 271	69.86 323	90.73 320

IterMVS-LS		92.69 196	92.11 194	94.43 232	96.80 197	92.74 216	99.45 174	96.89 276	88.98 226	89.65 223	95.38 244	88.77 155	96.34 283	90.98 196	82.04 265	94.22 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-CasMVS		90.63 240	89.51 242	93.99 246	93.83 254	91.70 246	98.98 224	98.52 91	88.48 237	86.15 274	96.53 217	75.46 283	96.31 284	88.83 226	78.86 294	93.95 262

FMVSNet3		92.69 196	91.58 200	95.99 183	98.29 134	97.42 86	99.26 196	97.62 200	89.80 217	89.68 220	95.32 248	81.62 223	96.27 285	87.01 250	85.65 246	94.29 233

test_0402		85.58 293	83.94 296	90.50 300	93.81 255	85.04 308	98.55 257	95.20 325	76.01 327	79.72 304	95.13 255	64.15 326	96.26 286	66.04 335	86.88 241	90.21 326

FMVSNet2		91.02 230	89.56 239	95.41 194	97.53 175	95.74 142	98.98 224	97.41 225	87.05 261	88.43 245	95.00 265	71.34 302	96.24 287	85.12 268	85.21 251	94.25 236

TranMVSNet+NR-MVSNet		91.68 215	90.61 212	94.87 213	93.69 257	93.98 180	99.69 134	98.65 67	91.03 198	88.44 244	96.83 209	80.05 248	96.18 288	90.26 209	76.89 312	94.45 222

GBi-Net		90.88 233	89.82 235	94.08 240	97.53 175	91.97 232	98.43 266	96.95 269	87.05 261	89.68 220	94.72 276	71.34 302	96.11 289	87.01 250	85.65 246	94.17 239

test1		90.88 233	89.82 235	94.08 240	97.53 175	91.97 232	98.43 266	96.95 269	87.05 261	89.68 220	94.72 276	71.34 302	96.11 289	87.01 250	85.65 246	94.17 239

FMVSNet1		88.50 272	86.64 276	94.08 240	95.62 230	91.97 232	98.43 266	96.95 269	83.00 299	86.08 275	94.72 276	59.09 336	96.11 289	81.82 291	84.07 257	94.17 239

our_test_3		90.39 244	89.48 244	93.12 263	92.40 296	89.57 277	99.33 186	96.35 295	87.84 245	85.30 280	94.99 266	84.14 195	96.09 292	80.38 296	84.56 255	93.71 281

PatchT		90.38 245	88.75 257	95.25 196	95.99 213	90.16 268	91.22 341	97.54 208	76.80 326	97.26 110	86.01 339	91.88 117	96.07 293	66.16 334	95.91 159	99.51 124

CR-MVSNet		93.45 186	92.62 186	95.94 184	96.29 206	92.66 219	92.01 337	96.23 296	92.62 145	96.94 116	93.31 303	91.04 129	96.03 294	79.23 304	95.96 157	99.13 174

Patchmtry		89.70 257	88.49 260	93.33 259	96.24 208	89.94 275	91.37 340	96.23 296	78.22 323	87.69 253	93.31 303	91.04 129	96.03 294	80.18 298	82.10 264	94.02 248

RPMNet		89.39 263	87.20 275	95.94 184	96.29 206	92.66 219	92.01 337	97.63 198	70.19 340	96.94 116	85.87 340	87.25 168	96.03 294	62.69 337	95.96 157	99.13 174

ppachtmachnet_test		89.58 259	88.35 263	93.25 261	92.40 296	90.44 264	99.33 186	96.73 285	85.49 284	85.90 277	95.77 231	81.09 232	96.00 297	76.00 320	82.49 263	93.30 289

PEN-MVS		90.19 252	89.06 251	93.57 256	93.06 286	90.90 257	99.06 216	98.47 103	88.11 242	85.91 276	96.30 221	76.67 273	95.94 298	87.07 247	76.91 311	93.89 268

testpf		89.10 267	88.73 258	90.24 303	97.59 174	83.48 314	74.22 352	97.39 227	79.66 320	89.64 224	93.92 293	86.38 176	95.76 299	85.42 265	94.31 185	91.49 314

N_pmnet		80.06 311	80.78 308	77.89 328	91.94 303	45.28 357	98.80 241	56.82 361	78.10 324	80.08 303	93.33 301	77.03 269	95.76 299	68.14 330	82.81 262	92.64 302

LCM-MVSNet-Re		92.31 203	92.60 187	91.43 293	97.53 175	79.27 331	99.02 222	91.83 345	92.07 168	80.31 301	94.38 288	83.50 199	95.48 301	97.22 100	97.58 127	99.54 121

K. test v3		88.05 275	87.24 274	90.47 301	91.82 309	82.23 320	98.96 227	97.42 223	89.05 223	76.93 311	95.60 236	68.49 313	95.42 302	85.87 262	81.01 275	93.75 276

ADS-MVSNet2		93.80 177	93.88 163	93.55 257	97.87 156	85.94 301	94.24 325	96.84 280	90.07 212	96.43 128	94.48 285	90.29 137	95.37 303	87.44 239	97.23 137	99.36 144

CVMVSNet		94.68 160	94.94 145	93.89 250	96.80 197	86.92 298	99.06 216	98.98 36	94.45 81	94.23 174	99.02 115	85.60 183	95.31 304	90.91 198	95.39 169	99.43 132

DTE-MVSNet		89.40 262	88.24 265	92.88 269	92.66 294	89.95 273	99.10 206	98.22 147	87.29 257	85.12 282	96.22 223	76.27 278	95.30 305	83.56 280	75.74 315	93.41 285

LP		86.76 279	84.85 283	92.50 277	95.08 235	85.89 302	89.97 342	96.97 267	75.28 331	84.97 283	90.68 315	80.78 237	95.13 306	61.64 339	88.31 229	96.46 205

IterMVS		90.91 232	90.17 227	93.12 263	96.78 200	90.42 265	98.89 232	97.05 252	89.03 224	86.49 268	95.42 240	76.59 274	95.02 307	87.22 246	84.09 256	93.93 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				92.93 268	96.72 202	89.96 272		96.99 262	88.95 229	86.63 265	95.67 234	76.50 275	95.00 308	87.04 248	84.04 259	93.84 272

test0.0.03 1		93.86 173	93.61 165	94.64 222	95.02 238	92.18 230	99.93 50	98.58 80	94.07 95	87.96 251	98.50 162	93.90 81	94.96 309	81.33 292	93.17 204	96.78 202

UnsupCasMVSNet_bld		79.97 312	77.03 315	88.78 313	85.62 333	81.98 321	93.66 330	97.35 230	75.51 330	70.79 331	83.05 341	48.70 345	94.91 310	78.31 310	60.29 345	89.46 338

MIMVSNet		90.30 248	88.67 259	95.17 199	96.45 205	91.64 248	92.39 335	97.15 244	85.99 274	90.50 200	93.19 305	66.95 318	94.86 311	82.01 289	93.43 200	99.01 180

new_pmnet		84.49 302	82.92 302	89.21 310	90.03 322	82.60 316	96.89 307	95.62 308	80.59 317	75.77 317	89.17 317	65.04 324	94.79 312	72.12 323	81.02 274	90.23 325

testgi		89.01 268	88.04 267	91.90 290	93.49 264	84.89 309	99.73 126	95.66 307	93.89 107	85.14 281	98.17 172	59.68 335	94.66 313	77.73 312	88.88 218	96.16 209

v18		86.59 280	84.57 284	92.65 272	93.41 269	93.43 194	98.69 247	95.55 310	82.44 303	74.71 320	87.68 326	82.11 209	94.21 314	80.14 299	66.37 333	90.32 322

v16		86.52 281	84.49 285	92.60 275	93.45 265	93.31 203	98.60 256	95.52 313	82.30 305	74.59 322	87.70 325	81.95 216	94.18 315	79.93 301	66.38 332	90.30 323

v17		86.51 282	84.49 285	92.57 276	93.38 271	93.29 204	98.61 255	95.54 311	82.32 304	74.69 321	87.63 327	82.03 210	94.17 316	80.02 300	66.17 334	90.26 324

v15		86.26 285	84.19 288	92.47 278	93.29 276	93.28 205	98.53 260	95.47 314	82.24 307	74.34 323	87.34 329	81.71 219	94.07 317	79.39 302	65.42 335	90.06 330

V9		86.16 288	84.07 290	92.43 279	93.27 278	93.04 211	98.40 270	95.45 316	81.98 310	74.18 326	87.31 330	81.58 225	94.06 318	79.12 306	65.33 338	90.20 327

V14		86.22 286	84.15 289	92.41 281	93.30 275	93.16 206	98.47 263	95.47 314	82.10 308	74.27 324	87.41 328	81.73 218	94.02 319	79.26 303	65.37 337	90.04 331

pmmvs-eth3d		84.03 304	81.97 304	90.20 304	84.15 335	87.09 297	98.10 286	94.73 330	83.05 298	74.10 327	87.77 324	65.56 322	94.01 320	81.08 294	69.24 326	89.49 337

v13		86.06 291	83.97 295	92.34 285	93.25 279	92.85 214	98.26 277	95.44 318	81.70 314	74.02 329	87.11 334	81.58 225	94.00 321	78.94 308	65.41 336	90.18 328

v12		86.10 289	84.01 291	92.37 283	93.23 281	92.96 212	98.33 273	95.45 316	81.87 311	74.05 328	87.15 332	81.60 224	93.98 322	79.09 307	65.28 339	90.18 328

UnsupCasMVSNet_eth		85.52 294	83.99 292	90.10 305	89.36 325	83.51 313	96.65 308	97.99 170	89.14 221	75.89 316	93.83 295	63.25 328	93.92 323	81.92 290	67.90 329	92.88 300

PM-MVS		80.47 309	78.88 311	85.26 319	83.79 336	72.22 334	95.89 321	91.08 346	85.71 282	76.56 313	88.30 318	36.64 348	93.90 324	82.39 286	69.57 325	89.66 335

MDA-MVSNet_test_wron		85.51 295	83.32 300	92.10 287	90.96 316	88.58 286	99.20 199	96.52 292	79.70 319	57.12 345	92.69 307	79.11 257	93.86 325	77.10 316	77.46 307	93.86 271

v11		86.09 290	83.98 294	92.42 280	93.29 276	93.41 198	98.52 261	95.30 321	81.73 313	74.27 324	87.20 331	81.24 230	93.85 326	77.68 313	66.61 331	90.00 332

YYNet1		85.50 296	83.33 299	92.00 288	90.89 317	88.38 290	99.22 198	96.55 291	79.60 321	57.26 344	92.72 306	79.09 258	93.78 327	77.25 315	77.37 308	93.84 272

Patchmatch-RL test		86.90 278	85.98 278	89.67 308	84.45 334	75.59 332	89.71 343	92.43 342	86.89 264	77.83 309	90.94 314	94.22 69	93.63 328	87.75 236	69.61 324	99.79 83

MDA-MVSNet-bldmvs		84.09 303	81.52 307	91.81 291	91.32 314	88.00 293	98.67 250	95.92 303	80.22 318	55.60 346	93.32 302	68.29 315	93.60 329	73.76 322	76.61 313	93.82 274

Anonymous20231206		86.32 284	85.42 280	89.02 311	89.11 326	80.53 329	99.05 219	95.28 322	85.43 285	82.82 293	93.92 293	74.40 291	93.44 330	66.99 332	81.83 267	93.08 295

EU-MVSNet		90.14 254	90.34 219	89.54 309	92.55 295	81.06 326	98.69 247	98.04 167	91.41 186	86.59 266	96.84 208	80.83 236	93.31 331	86.20 258	81.91 266	94.26 234

Anonymous20231211		74.17 316	71.17 318	83.17 322	80.58 339	67.02 340	96.27 315	94.45 333	57.31 345	69.60 334	86.25 338	33.67 349	92.96 332	61.86 338	60.50 344	89.54 336

DSMNet-mixed		88.28 274	88.24 265	88.42 315	89.64 324	75.38 333	98.06 287	89.86 350	85.59 283	88.20 249	92.14 310	76.15 280	91.95 333	78.46 309	96.05 154	97.92 195

FMVSNet5		88.32 273	87.47 273	90.88 296	96.90 192	88.39 289	97.28 300	95.68 306	82.60 302	84.67 284	92.40 309	79.83 249	91.16 334	76.39 319	81.51 268	93.09 294

pmmvs3		80.27 310	77.77 314	87.76 316	80.32 340	82.43 318	98.23 280	91.97 344	72.74 336	78.75 306	87.97 321	57.30 338	90.99 335	70.31 325	62.37 342	89.87 333

new-patchmatchnet		81.19 308	79.34 310	86.76 318	82.86 337	80.36 330	97.92 290	95.27 323	82.09 309	72.02 330	86.87 335	62.81 329	90.74 336	71.10 324	63.08 341	89.19 339

MIMVSNet1		82.58 307	80.51 309	88.78 313	86.68 330	84.20 312	96.65 308	95.41 319	78.75 322	78.59 307	92.44 308	51.88 343	89.76 337	65.26 336	78.95 292	92.38 304

test20.03		84.72 301	83.99 292	86.91 317	88.19 328	80.62 328	98.88 233	95.94 302	88.36 239	78.87 305	94.62 281	68.75 311	89.11 338	66.52 333	75.82 314	91.00 317

1111		79.11 313	78.74 312	80.23 326	78.33 341	67.13 338	97.31 298	93.65 338	71.34 337	68.35 336	87.87 322	85.42 187	88.46 339	52.93 346	73.46 321	85.11 342

.test1245		71.48 317	71.80 317	70.51 336	78.33 341	67.13 338	97.31 298	93.65 338	71.34 337	68.35 336	87.87 322	85.42 187	88.46 339	52.93 346	11.01 356	55.94 355

testus		83.91 305	84.49 285	82.17 325	85.68 332	66.11 341	99.68 139	93.53 340	86.55 267	82.60 294	94.91 270	56.70 339	88.19 341	68.46 328	92.31 207	92.21 305

no-one		63.48 324	59.26 325	76.14 330	66.71 350	65.06 342	92.75 333	89.92 349	68.96 341	46.96 351	66.55 351	21.74 357	87.68 342	57.07 344	22.69 354	75.68 348

Gipuma		66.95 322	65.00 321	72.79 333	91.52 312	67.96 337	66.16 353	95.15 327	47.89 347	58.54 343	67.99 350	29.74 352	87.54 343	50.20 348	77.83 302	62.87 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		67.77 319	64.73 322	76.87 329	62.95 355	56.25 350	89.37 344	93.74 337	44.53 349	61.99 341	80.74 342	20.42 358	86.53 344	69.37 327	59.50 346	87.84 340

test2356		86.43 283	87.59 272	82.95 323	85.90 331	69.43 336	99.79 104	96.63 289	85.76 278	83.44 291	94.99 266	80.45 246	86.52 345	68.12 331	93.21 203	92.90 298

test1235678		78.45 314	77.88 313	80.16 327	77.83 343	62.18 345	98.36 271	93.45 341	77.46 325	69.08 335	88.23 319	60.33 334	85.41 346	58.46 342	77.68 304	92.90 298

PMMVS2		67.15 321	64.15 323	76.14 330	70.56 349	62.07 346	93.89 328	87.52 354	58.09 344	60.02 342	78.32 343	22.38 356	84.54 347	59.56 341	47.03 347	81.80 344

test12356		75.26 315	75.12 316	75.67 332	74.02 346	60.60 347	96.43 311	92.15 343	74.17 334	66.35 338	88.11 320	52.29 342	84.36 348	57.41 343	75.12 317	82.05 343

FPMVS		68.72 318	68.72 319	68.71 337	65.95 351	44.27 359	95.97 320	94.74 329	51.13 346	53.26 348	90.50 316	25.11 355	83.00 349	60.80 340	80.97 276	78.87 346

testmv		67.54 320	65.93 320	72.37 334	64.46 354	54.05 351	95.09 324	90.07 348	68.90 342	55.16 347	77.63 345	30.39 350	82.61 350	49.42 349	62.26 343	80.45 345

PMVS	49.05 23	53.75 327	51.34 329	60.97 342	40.80 360	34.68 360	74.82 351	89.62 352	37.55 352	28.67 357	72.12 346	7.09 363	81.63 351	43.17 354	68.21 328	66.59 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		65.23 323	62.94 324	72.13 335	44.90 359	50.03 355	81.05 348	89.42 353	38.45 351	48.51 350	99.90 11	54.09 341	78.70 352	91.84 187	18.26 355	87.64 341

PNet_i23d		56.44 325	53.54 326	65.14 340	65.34 352	50.33 354	89.06 345	79.57 356	45.77 348	35.75 355	68.95 349	10.75 362	74.40 353	48.48 350	38.20 348	70.70 349

wuykxyi23d		50.36 331	45.43 332	65.16 339	51.13 357	51.75 352	77.46 350	78.42 357	41.45 350	26.98 358	54.30 358	6.13 364	74.03 354	46.82 352	26.19 350	69.71 350

MVE	53.74 22	51.54 329	47.86 331	62.60 341	59.56 356	50.93 353	79.41 349	77.69 358	35.69 354	36.27 354	61.76 355	5.79 366	69.63 355	37.97 355	36.61 349	67.24 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 326	52.24 327	67.66 338	49.27 358	56.82 349	83.94 347	82.02 355	70.47 339	33.28 356	64.54 352	17.23 360	69.16 356	45.59 353	23.85 353	77.02 347

E-PMN		52.30 328	52.18 328	52.67 343	71.51 347	45.40 356	93.62 331	76.60 359	36.01 353	43.50 352	64.13 353	27.11 354	67.31 357	31.06 356	26.06 351	45.30 358

EMVS		51.44 330	51.22 330	52.11 344	70.71 348	44.97 358	94.04 327	75.66 360	35.34 355	42.40 353	61.56 356	28.93 353	65.87 358	27.64 357	24.73 352	45.49 357

wuyk23d		20.37 336	20.84 337	18.99 348	65.34 352	27.73 361	50.43 354	7.67 364	9.50 358	8.01 359	6.34 360	6.13 364	26.24 359	23.40 358	10.69 358	2.99 359

test123		37.68 333	39.14 335	33.31 345	19.94 361	24.83 362	98.36 271	9.75 363	15.53 357	51.31 349	87.14 333	19.62 359	17.74 360	47.10 351	3.47 359	57.36 354

testmvs		40.60 332	44.45 333	29.05 347	19.49 362	14.11 363	99.68 139	18.47 362	20.74 356	64.59 339	98.48 166	10.95 361	17.09 361	56.66 345	11.01 356	55.94 355

cdsmvs_eth3d_5k		23.43 335	31.24 336	0.00 349	0.00 363	0.00 364	0.00 355	98.09 163	0.00 359	0.00 360	99.67 76	83.37 200	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		7.60 338	10.13 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	91.20 125	0.00 362	0.00 359	0.00 360	0.00 360

pcd1.5k->3k		37.58 334	39.62 334	31.46 346	92.73 293	0.00 364	0.00 355	97.52 212	0.00 359	0.00 360	0.00 361	78.40 266	0.00 362	0.00 359	87.90 232	94.37 225

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.28 337	11.04 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	99.40 95	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																99.59 111

test_part2					99.89 36	99.25 6				99.49 33

test_part1								98.41 122				97.20 11			99.99 13	99.99 12

sam_mvs1												94.72 56				99.59 111

sam_mvs												94.25 68

MTGPA								98.28 141

MTMP								96.49 293

test9_res														99.71 18	99.99 13	100.00 1

agg_prior2														99.48 24	100.00 1	100.00 1

test_prior4						98.05 63	99.94 45

test_prior2							99.95 31		95.78 50	99.73 14	99.76 56	96.00 26		99.78 9	100.00 1

新几何2							99.40 177

旧先验1					99.76 54	97.52 78		98.64 70			99.85 21	95.63 34			99.94 44	99.99 12

原ACMM2							99.90 59

test222					99.55 74	97.41 87	99.34 185	98.55 88	91.86 174	99.27 49	99.83 37	93.84 83			99.95 40	99.99 12

segment_acmp												96.68 14

testdata1							99.28 194		96.35 38

plane_prior7					95.71 226	91.59 250

plane_prior6					95.76 221	91.72 245						80.47 244

plane_prior4											98.59 158

plane_prior3						91.64 248			96.63 29	93.01 181

plane_prior2							99.84 91		96.38 34

plane_prior1					95.73 223

plane_prior						91.74 242	99.86 86		96.76 25						89.59 210

n2								0.00 365

nn								0.00 365

door-mid								89.69 351

test11								98.44 107

door								90.31 347

HQP5-MVS						91.85 237

HQP-NCC					95.78 217		99.87 71		96.82 21	93.37 177

ACMP_Plane					95.78 217		99.87 71		96.82 21	93.37 177

BP-MVS														97.92 85

HQP3-MVS								97.89 180							89.60 208

HQP2-MVS												80.65 240

NP-MVS					95.77 220	91.79 239					98.65 154

MDTV_nov1_ep13_2view						96.26 123	96.11 317		91.89 173	98.06 96		94.40 60		94.30 146		99.67 97

ACMMP++_ref															87.04 240

ACMMP++															88.23 230

Test By Simon												92.82 102

High-resolution multi-view benchmark