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.

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 180	99.39 169	99.94 1	98.73 44	99.11 160	99.89 10	95.50 143	99.94 40	99.50 8	99.97 3	99.89 2

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 102	99.47 138	99.93 2	97.66 137	99.71 29	99.86 22	97.73 86	99.96 19	99.47 13	99.82 65	99.79 43

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 137	99.76 41	98.79 155	99.28 201	99.91 3	97.42 156	99.67 40	99.37 215	97.53 89	99.88 99	98.98 51	97.29 223	98.42 280

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 41	98.79 155	98.78 294	99.91 3	96.74 203	99.67 40	99.49 179	97.53 89	99.88 99	98.98 51	99.85 52	99.60 102

HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 73	99.02 263	99.91 3	97.67 136	99.59 59	99.75 90	95.90 133	99.73 155	99.53 6	99.02 132	99.86 5

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 38	99.51 60	98.94 284	99.85 6	98.82 35	99.65 48	99.74 95	98.51 56	99.80 135	98.83 68	99.89 32	99.64 94

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 65	99.47 64	98.95 282	99.85 6	98.82 35	99.54 67	99.73 98	98.51 56	99.74 147	98.91 56	99.88 34	99.77 49

PHI-MVS		99.30 44	99.17 49	99.70 49	99.56 113	99.52 58	99.58 87	99.80 8	97.12 181	99.62 53	99.73 98	98.58 55	99.90 84	98.61 92	99.91 17	99.68 81

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 68	99.28 82	99.06 252	99.77 9	97.74 128	99.50 73	99.53 167	95.41 145	99.84 113	97.17 209	99.64 98	99.44 136

3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 200	99.66 34	99.84 9	99.74 10	99.09 8	98.92 193	99.90 7	95.94 131	99.98 5	98.95 53	99.92 12	99.79 43

QAPM		98.67 122	98.30 135	99.80 29	99.20 183	99.67 32	99.77 24	99.72 11	94.74 274	98.73 214	99.90 7	95.78 137	99.98 5	96.96 221	99.88 34	99.76 52

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 215	99.53 55	99.82 13	99.72 11	94.56 280	98.08 259	99.88 14	94.73 186	99.98 5	97.47 191	99.76 76	99.06 168

CHOSEN 280x420		99.12 67	99.13 52	99.08 132	99.66 89	97.89 207	98.43 312	99.71 13	98.88 30	99.62 53	99.76 85	96.63 114	99.70 171	99.46 14	99.99 1	99.66 85

MSLP-MVS++		99.46 21	99.47 8	99.44 97	99.60 105	99.16 92	99.41 162	99.71 13	98.98 19	99.45 81	99.78 77	99.19 4	99.54 196	99.28 27	99.84 57	99.63 98

UA-Net		99.42 29	99.29 36	99.80 29	99.62 99	99.55 51	99.50 121	99.70 15	98.79 40	99.77 23	99.96 1	97.45 91	99.96 19	98.92 55	99.90 24	99.89 2

PVSNet_0	94.43 19	96.09 274	95.47 276	97.94 261	99.31 164	94.34 298	97.81 324	99.70 15	97.12 181	97.46 273	98.75 284	89.71 286	99.79 138	97.69 172	81.69 328	99.68 81

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 113	99.54 52	99.18 228	99.70 15	98.18 79	99.35 106	99.63 138	96.32 121	99.90 84	97.48 189	99.77 74	99.55 110

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 32	99.62 74	99.69 18	98.12 84	99.63 50	99.84 35	98.73 46	99.96 19	98.55 103	99.83 61	99.81 34
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
XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	99.74 95	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 256	95.45 277	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	64.01 341	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

UGNet		98.87 97	98.69 105	99.40 102	99.22 180	98.72 160	99.44 146	99.68 19	99.24 3	99.18 152	99.42 200	92.74 239	99.96 19	99.34 22	99.94 10	99.53 116
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
HFP-MVS		99.49 13	99.37 17	99.86 12	99.87 15	99.80 12	99.66 58	99.67 22	98.15 80	99.68 34	99.69 111	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

#test#		99.43 27	99.29 36	99.86 12	99.87 15	99.80 12	99.55 105	99.67 22	97.83 117	99.68 34	99.69 111	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

ACMMPR		99.49 13	99.36 19	99.86 12	99.87 15	99.79 16	99.66 58	99.67 22	98.15 80	99.67 40	99.69 111	98.95 23	99.96 19	98.69 82	99.87 38	99.84 12

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 12	99.65 68	99.66 25	98.13 82	99.66 45	99.68 116	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

EU-MVSNet		97.98 175	98.03 151	97.81 272	98.72 274	96.65 256	99.66 58	99.66 25	98.09 89	98.35 249	99.82 44	95.25 152	98.01 307	97.41 196	95.30 256	98.78 193

DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 65	99.40 72	99.05 254	99.66 25	99.14 6	99.57 63	99.80 64	98.46 59	99.94 40	99.57 4	99.84 57	99.60 102
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
Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 34	99.10 99	99.68 53	99.66 25	98.49 56	99.86 7	99.87 19	94.77 183	99.84 113	99.19 33	99.41 107	99.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 191	99.71 41	99.66 25	98.11 86	99.41 90	99.80 64	98.37 67	99.96 19	98.99 50	99.96 5	99.72 69

PGM-MVS		99.45 22	99.31 31	99.86 12	99.87 15	99.78 20	99.58 87	99.65 30	97.84 116	99.71 29	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

sss		99.17 58	99.05 58	99.53 79	99.62 99	98.97 116	99.36 180	99.62 31	97.83 117	99.67 40	99.65 127	97.37 95	99.95 33	99.19 33	99.19 120	99.68 81

AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 185	99.56 100	99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

TestCases				99.31 110	99.86 20	98.48 185		99.61 32	97.85 114	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

FC-MVSNet-test		98.75 116	98.62 115	99.15 129	99.08 208	99.45 66	99.86 8	99.60 34	98.23 75	98.70 222	99.82 44	96.80 107	99.22 252	99.07 44	96.38 239	98.79 192

PVSNet	96.02 17	98.85 106	98.84 90	98.89 166	99.73 62	97.28 222	98.32 316	99.60 34	97.86 112	99.50 73	99.57 158	96.75 111	99.86 104	98.56 100	99.70 89	99.54 112

LTVRE_ROB	97.16 12	98.02 170	97.90 160	98.40 225	99.23 178	96.80 251	99.70 42	99.60 34	97.12 181	98.18 255	99.70 106	91.73 264	99.72 159	98.39 114	97.45 214	98.68 220
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
FIs		98.78 113	98.63 112	99.23 123	99.18 187	99.54 52	99.83 12	99.59 37	98.28 70	98.79 209	99.81 53	96.75 111	99.37 214	99.08 43	96.38 239	98.78 193

WR-MVS_H		98.13 151	97.87 165	98.90 164	99.02 218	98.84 136	99.70 42	99.59 37	97.27 167	98.40 245	99.19 249	95.53 142	99.23 249	98.34 120	93.78 290	98.61 264

abl_6		99.44 25	99.31 31	99.83 22	99.85 23	99.75 21	99.66 58	99.59 37	98.13 82	99.82 14	99.81 53	98.60 54	99.96 19	98.46 111	99.88 34	99.79 43

114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 55	99.62 74	99.59 37	92.65 305	99.71 29	99.78 77	98.06 78	99.90 84	98.84 66	99.91 17	99.74 58

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 126	99.88 11	98.53 177	99.34 187	99.59 37	97.55 144	98.70 222	99.89 10	95.83 135	99.90 84	98.10 133	99.90 24	99.08 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VPA-MVSNet		98.29 140	97.95 157	99.30 113	99.16 194	99.54 52	99.50 121	99.58 42	98.27 71	99.35 106	99.37 215	92.53 248	99.65 180	99.35 18	94.46 277	98.72 204

CANet		99.25 52	99.14 51	99.59 68	99.41 139	99.16 92	99.35 184	99.57 43	98.82 35	99.51 72	99.61 146	96.46 117	99.95 33	99.59 2	99.98 2	99.65 88

VPNet		97.84 195	97.44 213	99.01 139	99.21 181	98.94 124	99.48 134	99.57 43	98.38 64	99.28 121	99.73 98	88.89 293	99.39 210	99.19 33	93.27 294	98.71 206

DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 57	99.70 28	99.27 204	99.57 43	96.40 232	99.42 88	99.68 116	98.75 44	99.80 135	97.98 144	99.72 83	99.44 136

LS3D		99.27 49	99.12 53	99.74 43	99.18 187	99.75 21	99.56 100	99.57 43	98.45 59	99.49 76	99.85 26	97.77 85	99.94 40	98.33 121	99.84 57	99.52 117

test_prior3		99.21 54	99.05 58	99.68 50	99.67 79	99.48 62	98.96 278	99.56 47	98.34 66	99.01 178	99.52 171	98.68 49	99.83 120	97.96 145	99.74 79	99.74 58

test_prior				99.68 50	99.67 79	99.48 62		99.56 47					99.83 120			99.74 58

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 38	99.89 1	99.75 34	99.56 47	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

HPM-MVS_fast		99.51 12	99.40 14	99.85 17	99.91 1	99.79 16	99.76 27	99.56 47	97.72 130	99.76 26	99.75 90	99.13 6	99.92 63	99.07 44	99.92 12	99.85 8

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 99	99.16 92	99.37 176	99.56 47	98.04 99	99.53 68	99.62 143	96.84 106	99.94 40	98.85 65	98.49 165	99.72 69

API-MVS		99.04 82	99.03 63	99.06 134	99.40 144	99.31 80	99.55 105	99.56 47	98.54 53	99.33 110	99.39 211	98.76 41	99.78 140	96.98 219	99.78 72	98.07 290

ACMH	97.28 8	98.10 156	97.99 154	98.44 222	99.41 139	96.96 245	99.60 81	99.56 47	98.09 89	98.15 256	99.91 5	90.87 276	99.70 171	98.88 57	97.45 214	98.67 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CVMVSNet		98.57 127	98.67 107	98.30 232	99.35 152	95.59 276	99.50 121	99.55 54	98.60 51	99.39 95	99.83 37	94.48 197	99.45 201	98.75 74	98.56 161	99.85 8

XVG-OURS		98.73 117	98.68 106	98.88 173	99.70 73	97.73 217	98.92 285	99.55 54	98.52 55	99.45 81	99.84 35	95.27 149	99.91 72	98.08 138	98.84 148	99.00 173

LPG-MVS_test		98.22 143	98.13 142	98.49 213	99.33 156	97.05 236	99.58 87	99.55 54	97.46 150	99.24 135	99.83 37	92.58 246	99.72 159	98.09 134	97.51 207	98.68 220

LGP-MVS_train				98.49 213	99.33 156	97.05 236		99.55 54	97.46 150	99.24 135	99.83 37	92.58 246	99.72 159	98.09 134	97.51 207	98.68 220

XXY-MVS		98.38 136	98.09 146	99.24 121	99.26 175	99.32 77	99.56 100	99.55 54	97.45 153	98.71 216	99.83 37	93.23 228	99.63 187	98.88 57	96.32 241	98.76 198

DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 88	99.62 74	99.55 54	98.94 26	99.63 50	99.95 2	95.82 136	99.94 40	99.37 17	99.97 3	99.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		98.98 90	98.80 94	99.53 79	99.76 41	99.19 89	98.75 297	99.55 54	97.25 169	99.47 78	99.77 82	97.82 83	99.87 101	96.93 224	99.90 24	99.54 112

PS-MVSNAJss		98.92 95	98.92 77	98.90 164	98.78 266	98.53 177	99.78 22	99.54 61	98.07 93	99.00 185	99.76 85	99.01 11	99.37 214	99.13 39	97.23 224	98.81 190

新几何1				99.75 38	99.75 47	99.59 46		99.54 61	96.76 202	99.29 117	99.64 134	98.43 61	99.94 40	96.92 225	99.66 95	99.72 69

旧先验1					99.74 57	99.59 46		99.54 61			99.69 111	98.47 58			99.68 93	99.73 63

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 17	99.76 41	99.83 7	99.63 71	99.54 61	98.36 65	99.79 18	99.82 44	98.86 29	99.95 33	98.62 90	99.81 66	99.78 47

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 166	99.71 68	97.74 216	99.12 237	99.54 61	98.44 62	99.42 88	99.71 103	94.20 206	99.92 63	98.54 105	98.90 144	99.00 173

HPM-MVS		99.42 29	99.28 38	99.83 22	99.90 3	99.72 25	99.81 15	99.54 61	97.59 139	99.68 34	99.63 138	98.91 26	99.94 40	98.58 95	99.91 17	99.84 12

ab-mvs		98.86 100	98.63 112	99.54 75	99.64 92	99.19 89	99.44 146	99.54 61	97.77 124	99.30 113	99.81 53	94.20 206	99.93 55	99.17 36	98.82 149	99.49 124

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 34	99.27 84	99.42 158	99.54 61	97.29 166	99.41 90	99.59 151	98.42 64	99.93 55	98.19 127	99.69 90	99.73 63

ACMH+	97.24 10	97.92 187	97.78 172	98.32 230	99.46 130	96.68 255	99.56 100	99.54 61	98.41 63	97.79 271	99.87 19	90.18 283	99.66 178	98.05 142	97.18 227	98.62 255

MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 149	99.66 34	99.45 142	99.54 61	96.61 212	99.01 178	99.40 207	97.09 100	99.86 104	97.68 174	99.53 103	99.10 158
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
pcd1.5k->3k		40.85 315	43.49 317	32.93 329	98.95 233	0.00 346	0.00 337	99.53 71	0.00 341	0.00 342	0.27 343	95.32 147	0.00 344	0.00 341	97.30 222	98.80 191

jajsoiax		98.43 132	98.28 136	98.88 173	98.60 286	98.43 187	99.82 13	99.53 71	98.19 76	98.63 233	99.80 64	93.22 229	99.44 206	99.22 31	97.50 209	98.77 196

mvs_tets		98.40 135	98.23 138	98.91 160	98.67 281	98.51 182	99.66 58	99.53 71	98.19 76	98.65 231	99.81 53	92.75 237	99.44 206	99.31 25	97.48 213	98.77 196

UniMVSNet_NR-MVSNet		98.22 143	97.97 155	98.96 145	98.92 243	98.98 113	99.48 134	99.53 71	97.76 125	98.71 216	99.46 193	96.43 119	99.22 252	98.57 97	92.87 299	98.69 215

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 194	99.52 75	97.18 175	99.60 56	99.79 72	98.79 35	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		99.41 32	99.52 6	99.05 136	99.74 57	99.68 30	99.46 141	99.52 75	99.11 7	99.88 3	99.91 5	99.43 1	97.70 315	98.72 79	99.93 11	99.77 49

PS-CasMVS		97.93 184	97.59 197	98.95 147	98.99 221	99.06 103	99.68 53	99.52 75	97.13 179	98.31 251	99.68 116	92.44 254	99.05 270	98.51 106	94.08 285	98.75 199

XVG-ACMP-BASELINE		97.83 196	97.71 185	98.20 247	99.11 202	96.33 265	99.41 162	99.52 75	98.06 97	99.05 174	99.50 176	89.64 287	99.73 155	97.73 166	97.38 220	98.53 273

CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 99	99.71 26	99.26 212	99.52 75	98.82 35	99.39 95	99.71 103	98.96 20	99.85 108	98.59 94	99.80 68	99.77 49

CP-MVS		99.45 22	99.32 26	99.85 17	99.83 28	99.75 21	99.69 44	99.52 75	98.07 93	99.53 68	99.63 138	98.93 25	99.97 11	98.74 75	99.91 17	99.83 23

FMVSNet5		96.43 259	96.19 256	97.15 285	99.11 202	95.89 273	99.32 189	99.52 75	94.47 284	98.34 250	99.07 258	87.54 308	97.07 318	92.61 303	95.72 250	98.47 277

OMC-MVS		99.08 77	99.04 61	99.20 125	99.67 79	98.22 194	99.28 201	99.52 75	98.07 93	99.66 45	99.81 53	97.79 84	99.78 140	97.79 158	99.81 66	99.60 102

PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 89	99.01 109	99.24 216	99.52 75	96.85 199	99.27 125	99.48 185	98.25 72	99.91 72	97.76 162	99.62 101	99.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_0304		99.06 79	98.86 87	99.66 53	99.51 118	99.36 74	99.22 221	99.51 84	98.95 24	99.58 60	99.65 127	93.74 225	99.98 5	99.66 1	99.95 6	99.64 94

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 95	98.97 116	99.12 237	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 95	98.97 116	99.12 237	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 95	98.97 116	99.12 237	99.51 84	98.86 31	99.84 8	99.47 189	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

cdsmvs_eth3d_5k		24.64 319	32.85 320	0.00 332	0.00 345	0.00 346	0.00 337	99.51 84	0.00 341	0.00 342	99.56 160	96.58 115	0.00 344	0.00 341	0.00 342	0.00 340

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 47	99.84 6	99.43 151	99.51 84	98.68 47	99.27 125	99.53 167	98.64 52	99.96 19	98.44 113	99.80 68	99.79 43

无先验							98.99 269	99.51 84	96.89 197				99.93 55	97.53 184		99.72 69

testdata				99.54 75	99.75 47	98.95 121		99.51 84	97.07 186	99.43 85	99.70 106	98.87 28	99.94 40	97.76 162	99.64 98	99.72 69

PEN-MVS		97.76 207	97.44 213	98.72 196	98.77 269	98.54 176	99.78 22	99.51 84	97.06 188	98.29 253	99.64 134	92.63 245	98.89 287	98.09 134	93.16 295	98.72 204

UniMVSNet (Re)		98.29 140	98.00 153	99.13 130	99.00 220	99.36 74	99.49 129	99.51 84	97.95 108	98.97 188	99.13 253	96.30 122	99.38 211	98.36 119	93.34 293	98.66 242

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 11	99.59 83	99.51 84	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
UnsupCasMVSNet_eth		96.44 258	96.12 257	97.40 284	98.65 282	95.65 274	99.36 180	99.51 84	97.13 179	96.04 293	98.99 265	88.40 302	98.17 296	96.71 233	90.27 307	98.40 282

3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 192	99.68 30	99.81 15	99.51 84	99.20 4	98.72 215	99.89 10	95.68 140	99.97 11	98.86 64	99.86 48	99.81 34

TAPA-MVS	97.07 15	97.74 212	97.34 228	98.94 148	99.70 73	97.53 219	99.25 214	99.51 84	91.90 309	99.30 113	99.63 138	98.78 36	99.64 182	88.09 316	99.87 38	99.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 130	99.12 98	98.08 322	99.50 98	97.50 149	99.38 97	99.41 203	96.37 120	99.81 131	99.11 41	98.54 162	99.51 120

anonymousdsp		98.44 131	98.28 136	98.94 148	98.50 291	98.96 120	99.77 24	99.50 98	97.07 186	98.87 199	99.77 82	94.76 184	99.28 237	98.66 85	97.60 200	98.57 271

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 47	99.79 16	99.50 121	99.50 98	97.16 177	99.77 23	99.82 44	98.78 36	99.94 40	97.56 181	99.86 48	99.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MIMVSNet1		95.51 279	95.04 282	96.92 291	97.38 307	95.60 275	99.52 112	99.50 98	93.65 295	96.97 284	99.17 250	85.28 317	96.56 322	88.36 315	95.55 254	98.60 266

DP-MVS		99.16 60	98.95 75	99.78 33	99.77 38	99.53 55	99.41 162	99.50 98	97.03 190	99.04 175	99.88 14	97.39 92	99.92 63	98.66 85	99.90 24	99.87 4

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 203	99.41 139	96.99 241	99.52 112	99.49 103	98.11 86	99.24 135	99.34 229	96.96 104	99.79 138	97.95 147	99.45 104	99.02 172

semantic-postprocess				98.06 253	99.57 110	96.36 264		99.49 103	97.18 175	98.71 216	99.72 102	92.70 243	99.14 259	97.44 194	95.86 248	98.67 231

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 41	99.41 70	99.58 87	99.49 103	99.02 10	99.88 3	99.80 64	99.00 17	99.94 40	99.45 15	99.92 12	99.84 12

Regformer-2		99.54 7	99.47 8	99.75 38	99.71 68	99.52 58	99.49 129	99.49 103	98.94 26	99.83 11	99.76 85	99.01 11	99.94 40	99.15 38	99.87 38	99.80 39

test222					99.75 47	99.49 61	98.91 287	99.49 103	96.42 229	99.34 109	99.65 127	98.28 71			99.69 90	99.72 69

1314		98.68 121	98.54 123	99.11 131	98.89 249	98.65 166	99.27 204	99.49 103	96.89 197	97.99 264	99.56 160	97.72 87	99.83 120	97.74 165	99.27 116	98.84 189

TranMVSNet+NR-MVSNet		97.93 184	97.66 187	98.76 194	98.78 266	98.62 170	99.65 68	99.49 103	97.76 125	98.49 241	99.60 149	94.23 205	98.97 285	98.00 143	92.90 297	98.70 210

CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 32	99.46 65	99.59 83	99.49 103	97.03 190	99.63 50	99.69 111	97.27 97	99.96 19	97.82 156	99.84 57	99.81 34

ACMP	97.20 11	98.06 159	97.94 158	98.45 219	99.37 149	97.01 239	99.44 146	99.49 103	97.54 147	98.45 243	99.79 72	91.95 257	99.72 159	97.91 149	97.49 212	98.62 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 138	99.48 112	98.05 98	99.76 26	99.86 22	98.82 32	99.93 55	98.82 71	99.91 17	99.84 12

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 136	99.32 77	99.80 19	99.48 112	98.63 48	99.31 112	98.81 280	97.09 100	99.75 146	99.27 29	97.90 192	99.47 130

1121		99.09 75	98.87 84	99.75 38	99.74 57	99.60 44	99.27 204	99.48 112	96.82 201	99.25 130	99.65 127	98.38 65	99.93 55	97.53 184	99.67 94	99.73 63

testgi		97.65 226	97.50 204	98.13 251	99.36 151	96.45 261	99.42 158	99.48 112	97.76 125	97.87 267	99.45 196	91.09 273	98.81 289	94.53 276	98.52 163	99.13 157

DTE-MVSNet		97.51 235	97.19 240	98.46 218	98.63 284	98.13 198	99.84 9	99.48 112	96.68 207	97.97 265	99.67 120	92.92 233	98.56 293	96.88 227	92.60 302	98.70 210

mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 16	99.69 44	99.48 112	98.12 84	99.50 73	99.75 90	98.78 36	99.97 11	98.57 97	99.89 32	99.83 23

NCCC		99.34 40	99.19 47	99.79 32	99.61 103	99.65 37	99.30 194	99.48 112	98.86 31	99.21 144	99.63 138	98.72 47	99.90 84	98.25 125	99.63 100	99.80 39

GBi-Net		97.68 221	97.48 206	98.29 233	99.51 118	97.26 224	99.43 151	99.48 112	96.49 219	99.07 169	99.32 234	90.26 280	98.98 278	97.10 211	96.65 232	98.62 255

UnsupCasMVSNet_bld		93.53 294	92.51 296	96.58 297	97.38 307	93.82 301	98.24 318	99.48 112	91.10 313	93.10 313	96.66 321	74.89 327	98.37 294	94.03 292	87.71 317	97.56 315

test1		97.68 221	97.48 206	98.29 233	99.51 118	97.26 224	99.43 151	99.48 112	96.49 219	99.07 169	99.32 234	90.26 280	98.98 278	97.10 211	96.65 232	98.62 255

FMVSNet1		96.84 253	96.36 254	98.29 233	99.32 163	97.26 224	99.43 151	99.48 112	95.11 269	98.55 238	99.32 234	83.95 322	98.98 278	95.81 254	96.26 242	98.62 255

1112_ss		98.98 90	98.77 97	99.59 68	99.68 78	99.02 107	99.25 214	99.48 112	97.23 172	99.13 156	99.58 154	96.93 105	99.90 84	98.87 61	98.78 152	99.84 12

IterMVS		97.83 196	97.77 176	98.02 256	99.58 108	96.27 267	99.02 263	99.48 112	97.22 173	98.71 216	99.70 106	92.75 237	99.13 262	97.46 192	96.00 246	98.67 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 291	94.90 283	91.84 311	97.24 311	80.01 331	98.52 309	99.48 112	89.01 319	91.99 317	99.67 120	85.67 315	99.13 262	95.44 261	97.03 229	96.39 319
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ESAPD								99.47 126

MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 180	99.47 126	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

MTGPA								99.47 126

pmmvs6		96.53 257	96.09 258	97.82 271	98.69 278	95.47 281	99.37 176	99.47 126	93.46 299	97.41 274	99.78 77	87.06 311	99.33 225	96.92 225	92.70 301	98.65 245

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 118	99.28 82	99.52 112	99.47 126	96.11 254	99.01 178	99.34 229	96.20 125	99.84 113	97.88 151	98.82 149	99.39 142

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 58	99.47 126	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

原ACMM1				99.65 57	99.73 62	99.33 76		99.47 126	97.46 150	99.12 158	99.66 126	98.67 51	99.91 72	97.70 171	99.69 90	99.71 76

HQP_MVS		98.27 142	98.22 139	98.44 222	99.29 168	96.97 243	99.39 169	99.47 126	98.97 22	99.11 160	99.61 146	92.71 241	99.69 174	97.78 159	97.63 197	98.67 231

plane_prior5								99.47 126					99.69 174	97.78 159	97.63 197	98.67 231

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 75	98.95 121	99.03 260	99.47 126	96.98 192	99.15 155	99.23 246	96.77 110	99.89 92	98.83 68	98.78 152	99.86 5

nrg030		98.64 125	98.42 127	99.28 118	99.05 214	99.69 29	99.81 15	99.46 136	98.04 99	99.01 178	99.82 44	96.69 113	99.38 211	99.34 22	94.59 276	98.78 193

v7n		97.87 191	97.52 200	98.92 156	98.76 270	98.58 174	99.84 9	99.46 136	96.20 245	98.91 194	99.70 106	94.89 172	99.44 206	96.03 250	93.89 289	98.75 199

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 110	98.94 124	98.97 276	99.46 136	98.92 28	99.71 29	99.24 245	99.01 11	99.98 5	99.35 18	99.66 95	98.97 177

Regformer-1		99.53 9	99.47 8	99.72 47	99.71 68	99.44 67	99.49 129	99.46 136	98.95 24	99.83 11	99.76 85	99.01 11	99.93 55	99.17 36	99.87 38	99.80 39

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 10	99.66 58	99.46 136	98.09 89	99.48 77	99.74 95	98.29 70	99.96 19	97.93 148	99.87 38	99.82 30

CP-MVSNet		98.09 157	97.78 172	99.01 139	98.97 228	99.24 87	99.67 55	99.46 136	97.25 169	98.48 242	99.64 134	93.79 221	99.06 269	98.63 88	94.10 284	98.74 202

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 118	98.94 124	99.88 1	99.46 136	97.55 144	99.80 16	99.65 127	97.39 92	99.28 237	99.03 46	99.85 52	99.65 88

test_djsdf		98.67 122	98.57 121	98.98 143	98.70 277	98.91 129	99.88 1	99.46 136	97.55 144	99.22 142	99.88 14	95.73 139	99.28 237	99.03 46	97.62 199	98.75 199

CDS-MVSNet		99.09 75	99.03 63	99.25 119	99.42 136	98.73 159	99.45 142	99.46 136	98.11 86	99.46 80	99.77 82	98.01 79	99.37 214	98.70 80	98.92 142	99.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		99.12 67	99.08 56	99.24 121	99.46 130	98.55 175	99.51 116	99.46 136	98.09 89	99.45 81	99.82 44	98.34 68	99.51 197	98.70 80	98.93 140	99.67 84

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 95	99.59 46	99.36 180	99.46 136	99.07 9	99.79 18	99.82 44	98.85 30	99.92 63	98.68 84	99.87 38	99.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 115	98.91 129	99.02 263	99.45 147	98.80 39	99.71 29	99.26 243	98.94 24	99.98 5	99.34 22	99.23 117	98.98 176

v748		97.52 232	97.23 238	98.41 224	98.69 278	97.23 227	99.87 4	99.45 147	95.72 262	98.51 239	99.53 167	94.13 210	99.30 234	96.78 230	92.39 303	98.70 210

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 62	99.78 34	99.14 96	99.60 81	99.45 147	99.01 13	99.90 1	99.83 37	98.98 18	99.93 55	99.59 2	99.95 6	99.86 5

EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 62	99.78 34	99.15 95	99.61 80	99.45 147	99.01 13	99.89 2	99.82 44	99.01 11	99.92 63	99.56 5	99.95 6	99.85 8

pm-mvs1		97.68 221	97.28 235	98.88 173	99.06 211	98.62 170	99.50 121	99.45 147	96.32 235	97.87 267	99.79 72	92.47 250	99.35 221	97.54 183	93.54 292	98.67 231

DU-MVS		98.08 158	97.79 170	98.96 145	98.87 253	98.98 113	99.41 162	99.45 147	97.87 111	98.71 216	99.50 176	94.82 176	99.22 252	98.57 97	92.87 299	98.68 220

ACMM	97.58 5	98.37 137	98.34 131	98.48 215	99.41 139	97.10 230	99.56 100	99.45 147	98.53 54	99.04 175	99.85 26	93.00 231	99.71 165	98.74 75	97.45 214	98.64 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Gipuma		90.99 299	90.15 300	93.51 304	98.73 272	90.12 317	93.98 333	99.45 147	79.32 328	92.28 316	94.91 325	69.61 329	97.98 308	87.42 317	95.67 251	92.45 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-3		99.57 6	99.53 5	99.68 50	99.76 41	99.29 81	99.58 87	99.44 155	99.01 13	99.87 6	99.80 64	98.97 19	99.91 72	99.44 16	99.92 12	99.83 23

v52		97.79 204	97.50 204	98.66 201	98.80 260	98.62 170	99.87 4	99.44 155	95.87 260	99.01 178	99.46 193	94.44 200	99.33 225	96.65 239	93.96 288	98.05 291

V4		97.80 202	97.51 202	98.67 200	98.79 262	98.63 168	99.87 4	99.44 155	95.87 260	99.01 178	99.46 193	94.52 196	99.33 225	96.64 240	93.97 287	98.05 291

RPSCF		98.22 143	98.62 115	96.99 288	99.82 29	91.58 315	99.72 39	99.44 155	96.61 212	99.66 45	99.89 10	95.92 132	99.82 127	97.46 192	99.10 126	99.57 109

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 68	98.88 131	99.80 19	99.44 155	97.91 110	99.36 103	99.78 77	95.49 144	99.43 209	97.91 149	99.11 124	99.62 100

CNLPA		99.14 61	98.99 68	99.59 68	99.58 108	99.41 70	99.16 230	99.44 155	98.45 59	99.19 150	99.49 179	98.08 77	99.89 92	97.73 166	99.75 77	99.48 126

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 287	99.60 105	91.75 314	98.61 305	99.44 155	99.35 1	99.83 11	99.85 26	98.70 48	99.81 131	99.02 48	99.91 17	99.81 34

CLD-MVS		98.16 149	98.10 144	98.33 229	99.29 168	96.82 250	98.75 297	99.44 155	97.83 117	99.13 156	99.55 163	92.92 233	99.67 176	98.32 123	97.69 196	98.48 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IterMVS-LS		98.46 130	98.42 127	98.58 205	99.59 107	98.00 201	99.37 176	99.43 163	96.94 195	99.07 169	99.59 151	97.87 81	99.03 273	98.32 123	95.62 252	98.71 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs		98.72 118	98.49 124	99.43 100	99.48 128	99.19 89	99.62 74	99.42 164	95.58 265	99.37 99	99.67 120	96.14 126	99.74 147	98.14 131	98.96 137	99.37 143

NR-MVSNet		97.97 178	97.61 195	99.02 138	98.87 253	99.26 85	99.47 138	99.42 164	97.63 138	97.08 280	99.50 176	95.07 159	99.13 262	97.86 153	93.59 291	98.68 220

FMVSNet2		97.72 215	97.36 223	98.80 190	99.51 118	98.84 136	99.45 142	99.42 164	96.49 219	98.86 204	99.29 239	90.26 280	98.98 278	96.44 243	96.56 235	98.58 270

TEST9					99.67 79	99.65 37	99.05 254	99.41 167	96.22 244	98.95 189	99.49 179	98.77 39	99.91 72

train_agg		99.02 85	98.77 97	99.77 35	99.67 79	99.65 37	99.05 254	99.41 167	96.28 237	98.95 189	99.49 179	98.76 41	99.91 72	97.63 175	99.72 83	99.75 53

test_8					99.67 79	99.61 42	99.03 260	99.41 167	96.28 237	98.93 192	99.48 185	98.76 41	99.91 72

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 79	99.60 44	99.04 259	99.41 167	95.93 259	98.87 199	99.48 185	98.61 53	99.91 72	97.63 175	99.72 83	99.75 53

v8		97.95 183	97.63 194	98.93 151	98.95 233	98.81 148	99.80 19	99.41 167	96.03 258	99.10 163	99.42 200	94.92 169	99.30 234	96.94 223	94.08 285	98.66 242

v10		97.85 193	97.52 200	98.86 181	98.99 221	98.67 163	99.75 34	99.41 167	95.70 263	98.98 187	99.41 203	94.75 185	99.23 249	96.01 251	94.63 275	98.67 231

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 47	99.71 26	99.15 233	99.41 167	96.60 214	99.60 56	99.55 163	98.83 31	99.90 84	97.48 189	99.83 61	99.78 47

agg_prior1		99.01 88	98.76 99	99.76 37	99.67 79	99.62 40	98.99 269	99.40 174	96.26 240	98.87 199	99.49 179	98.77 39	99.91 72	97.69 172	99.72 83	99.75 53

agg_prior					99.67 79	99.62 40		99.40 174		98.87 199			99.91 72

MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 33	99.74 24	99.29 198	99.40 174	98.79 40	99.52 70	99.62 143	98.91 26	99.90 84	98.64 87	99.75 77	99.82 30

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 27	99.91 1	99.66 34	99.63 71	99.39 177	98.91 29	99.78 22	99.85 26	99.36 2	99.94 40	98.84 66	99.88 34	99.82 30

MVS		97.28 245	96.55 252	99.48 88	98.78 266	98.95 121	99.27 204	99.39 177	83.53 325	98.08 259	99.54 166	96.97 103	99.87 101	94.23 289	99.16 121	99.63 98

VNet		99.11 71	98.90 80	99.73 45	99.52 116	99.56 49	99.41 162	99.39 177	99.01 13	99.74 28	99.78 77	95.56 141	99.92 63	99.52 7	98.18 180	99.72 69

HQP3-MVS								99.39 177							97.58 202

cascas		97.69 219	97.43 216	98.48 215	98.60 286	97.30 221	98.18 321	99.39 177	92.96 302	98.41 244	98.78 283	93.77 222	99.27 240	98.16 130	98.61 155	98.86 188

HQP-MVS		98.02 170	97.90 160	98.37 227	99.19 184	96.83 248	98.98 273	99.39 177	98.24 72	98.66 225	99.40 207	92.47 250	99.64 182	97.19 206	97.58 202	98.64 247

OPM-MVS		98.19 147	98.10 144	98.45 219	98.88 250	97.07 234	99.28 201	99.38 183	98.57 52	99.22 142	99.81 53	92.12 256	99.66 178	98.08 138	97.54 206	98.61 264

EI-MVSNet		98.67 122	98.67 107	98.68 198	99.35 152	97.97 203	99.50 121	99.38 183	96.93 196	99.20 147	99.83 37	97.87 81	99.36 218	98.38 116	97.56 204	98.71 206

test20.03		96.12 273	95.96 262	96.63 295	97.44 306	95.45 282	99.51 116	99.38 183	96.55 217	96.16 290	99.25 244	93.76 223	96.17 323	87.35 319	94.22 282	98.27 286

mvs_anonymous		99.03 84	98.99 68	99.16 127	99.38 147	98.52 180	99.51 116	99.38 183	97.79 122	99.38 97	99.81 53	97.30 96	99.45 201	99.35 18	98.99 134	99.51 120

MVSTER		98.49 128	98.32 133	99.00 141	99.35 152	99.02 107	99.54 108	99.38 183	97.41 157	99.20 147	99.73 98	93.86 220	99.36 218	98.87 61	97.56 204	98.62 255

FMVSNet3		98.03 168	97.76 179	98.84 185	99.39 146	98.98 113	99.40 168	99.38 183	96.67 208	99.07 169	99.28 240	92.93 232	98.98 278	97.10 211	96.65 232	98.56 272

PAPM_NR		99.04 82	98.84 90	99.66 53	99.74 57	99.44 67	99.39 169	99.38 183	97.70 133	99.28 121	99.28 240	98.34 68	99.85 108	96.96 221	99.45 104	99.69 77

HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 12	99.67 55	99.37 190	98.70 45	99.77 23	99.49 179	98.21 73	99.95 33	98.46 111	99.77 74	99.81 34

v1240		97.69 219	97.32 231	98.79 191	98.85 257	98.43 187	99.48 134	99.36 191	96.11 254	99.27 125	99.36 222	93.76 223	99.24 248	94.46 278	95.23 257	98.70 210

v2v482		98.06 159	97.77 176	98.92 156	98.90 246	98.82 146	99.57 93	99.36 191	96.65 209	99.19 150	99.35 226	94.20 206	99.25 246	97.72 170	94.97 264	98.69 215

HY-MVS	97.30 7	98.85 106	98.64 111	99.47 91	99.42 136	99.08 101	99.62 74	99.36 191	97.39 159	99.28 121	99.68 116	96.44 118	99.92 63	98.37 117	98.22 176	99.40 141

PAPR		98.63 126	98.34 131	99.51 85	99.40 144	99.03 106	98.80 293	99.36 191	96.33 234	99.00 185	99.12 256	98.46 59	99.84 113	95.23 266	99.37 112	99.66 85

v1144		97.98 175	97.69 186	98.85 184	98.87 253	98.66 165	99.54 108	99.35 195	96.27 239	99.23 140	99.35 226	94.67 189	99.23 249	96.73 232	95.16 259	98.68 220

v1141		98.05 165	97.76 179	98.91 160	98.91 245	98.78 157	99.57 93	99.35 195	96.41 231	99.23 140	99.36 222	94.93 168	99.27 240	97.38 197	94.72 270	98.68 220

v1neww		98.12 153	97.84 166	98.93 151	98.97 228	98.81 148	99.66 58	99.35 195	96.49 219	99.29 117	99.37 215	95.02 161	99.32 228	97.73 166	94.73 268	98.67 231

v7new		98.12 153	97.84 166	98.93 151	98.97 228	98.81 148	99.66 58	99.35 195	96.49 219	99.29 117	99.37 215	95.02 161	99.32 228	97.73 166	94.73 268	98.67 231

divwei89l23v2f112		98.06 159	97.78 172	98.91 160	98.90 246	98.77 158	99.57 93	99.35 195	96.45 226	99.24 135	99.37 215	94.92 169	99.27 240	97.50 187	94.71 272	98.68 220

v1		98.05 165	97.76 179	98.93 151	98.92 243	98.80 153	99.57 93	99.35 195	96.39 233	99.28 121	99.36 222	94.86 174	99.32 228	97.38 197	94.72 270	98.68 220

WR-MVS		98.06 159	97.73 183	99.06 134	98.86 256	99.25 86	99.19 227	99.35 195	97.30 165	98.66 225	99.43 198	93.94 216	99.21 256	98.58 95	94.28 280	98.71 206

test11								99.35 195

v144192		97.92 187	97.60 196	98.87 177	98.83 259	98.65 166	99.55 105	99.34 203	96.20 245	99.32 111	99.40 207	94.36 201	99.26 245	96.37 246	95.03 263	98.70 210

v1921920		97.80 202	97.45 210	98.84 185	98.80 260	98.53 177	99.52 112	99.34 203	96.15 251	99.24 135	99.47 189	93.98 215	99.29 236	95.40 263	95.13 261	98.69 215

v1192		97.81 200	97.44 213	98.91 160	98.88 250	98.68 162	99.51 116	99.34 203	96.18 247	99.20 147	99.34 229	94.03 214	99.36 218	95.32 265	95.18 258	98.69 215

v7		98.05 165	97.78 172	98.87 177	98.99 221	98.67 163	99.64 70	99.34 203	96.31 236	99.29 117	99.51 174	94.78 179	99.27 240	97.03 215	95.15 260	98.66 242

v6		98.12 153	97.84 166	98.94 148	98.94 236	98.83 139	99.66 58	99.34 203	96.49 219	99.30 113	99.37 215	94.95 165	99.34 224	97.77 161	94.74 267	98.67 231

V42		98.06 159	97.79 170	98.86 181	98.98 225	98.84 136	99.69 44	99.34 203	96.53 218	99.30 113	99.37 215	94.67 189	99.32 228	97.57 180	94.66 273	98.42 280

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 125	99.14 96	99.67 55	99.34 203	97.31 164	99.58 60	99.76 85	97.65 88	99.82 127	98.87 61	99.07 129	99.46 133

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 110	98.63 168	99.07 248	99.34 203	98.99 18	99.61 55	99.82 44	97.98 80	99.87 101	97.00 217	99.80 68	99.85 8

v148		97.79 204	97.55 198	98.50 212	98.74 271	97.72 218	99.54 108	99.33 211	96.26 240	98.90 196	99.51 174	94.68 188	99.14 259	97.83 155	93.15 296	98.63 253

MDA-MVSNet-bldmvs		94.96 285	93.98 290	97.92 263	98.24 297	97.27 223	99.15 233	99.33 211	93.80 294	80.09 331	99.03 263	88.31 303	97.86 311	93.49 295	94.36 279	98.62 255

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 79	98.61 173	99.07 248	99.33 211	99.00 17	99.82 14	99.81 53	99.06 8	99.84 113	99.09 42	99.42 106	99.65 88

CR-MVSNet		98.17 148	97.93 159	98.87 177	99.18 187	98.49 183	99.22 221	99.33 211	96.96 193	99.56 64	99.38 212	94.33 202	99.00 276	94.83 272	98.58 158	99.14 155

Patchmtry		97.75 210	97.40 219	98.81 188	99.10 205	98.87 132	99.11 243	99.33 211	94.83 272	98.81 207	99.38 212	94.33 202	99.02 274	96.10 248	95.57 253	98.53 273

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 91	99.06 103	99.81 15	99.33 211	97.43 154	99.60 56	99.88 14	97.14 99	99.84 113	99.13 39	98.94 139	99.69 77

MS-PatchMatch		97.24 247	97.32 231	96.99 288	98.45 293	93.51 307	98.82 292	99.32 217	97.41 157	98.13 257	99.30 237	88.99 292	99.56 193	95.68 257	99.80 68	97.90 301

tpm cat1		97.39 242	97.36 223	97.50 282	99.17 192	93.73 302	99.43 151	99.31 218	91.27 311	98.71 216	99.08 257	94.31 204	99.77 142	96.41 245	98.50 164	99.00 173

PMMVS		98.80 112	98.62 115	99.34 105	99.27 173	98.70 161	98.76 296	99.31 218	97.34 161	99.21 144	99.07 258	97.20 98	99.82 127	98.56 100	98.87 146	99.52 117

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 217	99.33 156	96.91 247	99.57 93	99.30 220	98.47 57	99.41 90	98.99 265	96.78 108	99.74 147	98.73 77	99.38 108	98.74 202

CANet_DTU		98.97 92	98.87 84	99.25 119	99.33 156	98.42 189	99.08 247	99.30 220	99.16 5	99.43 85	99.75 90	95.27 149	99.97 11	98.56 100	99.95 6	99.36 144

mvs-test1		98.86 100	98.84 90	98.89 166	99.33 156	97.77 215	99.44 146	99.30 220	98.47 57	99.10 163	99.43 198	96.78 108	99.95 33	98.73 77	99.02 132	98.96 179

VDDNet		97.55 229	97.02 244	99.16 127	99.49 125	98.12 199	99.38 174	99.30 220	95.35 267	99.68 34	99.90 7	82.62 325	99.93 55	99.31 25	98.13 185	99.42 139

v15		96.28 264	95.62 270	98.25 240	98.94 236	98.83 139	99.76 27	99.29 224	94.52 282	94.02 304	97.61 311	95.02 161	98.13 301	94.53 276	86.92 319	97.80 304

v13		96.24 267	95.58 272	98.25 240	98.98 225	98.83 139	99.75 34	99.29 224	94.35 287	93.89 309	97.60 312	95.17 156	98.11 303	94.27 288	86.86 322	97.81 302

v12		96.24 267	95.58 272	98.23 243	98.96 231	98.81 148	99.76 27	99.29 224	94.42 286	93.85 310	97.60 312	95.12 157	98.09 304	94.32 285	86.85 323	97.80 304

v11		96.23 269	95.57 275	98.21 246	98.93 241	98.83 139	99.72 39	99.29 224	94.29 288	94.05 303	97.64 309	94.88 173	98.04 305	92.89 300	88.43 312	97.77 310

V14		96.26 265	95.60 271	98.26 236	98.94 236	98.83 139	99.76 27	99.29 224	94.49 283	93.96 306	97.66 307	94.99 164	98.13 301	94.41 279	86.90 320	97.80 304

V9		96.25 266	95.58 272	98.26 236	98.94 236	98.83 139	99.75 34	99.29 224	94.45 285	93.96 306	97.62 310	94.94 166	98.14 300	94.40 280	86.87 321	97.81 302

test12				99.75 38	99.64 92	99.61 42		99.29 224		99.21 144		98.38 65	99.89 92		99.74 79	99.74 58

new-patchmatchnet		94.48 288	94.08 289	95.67 300	95.08 319	92.41 311	99.18 228	99.28 231	94.55 281	93.49 312	97.37 318	87.86 307	97.01 319	91.57 305	88.36 313	97.61 313

testing_2		94.44 289	92.93 295	98.98 143	94.16 322	99.00 111	99.42 158	99.28 231	96.60 214	84.86 325	96.84 320	70.91 328	99.27 240	98.23 126	96.08 245	98.68 220

v18		96.42 260	95.80 267	98.26 236	98.95 233	98.82 146	99.76 27	99.28 231	94.58 277	94.12 300	97.70 304	95.22 154	98.16 297	94.83 272	87.80 314	97.79 309

v17		96.42 260	95.81 265	98.25 240	98.94 236	98.80 153	99.76 27	99.28 231	94.57 278	94.18 299	97.71 303	95.23 153	98.16 297	94.86 270	87.73 316	97.80 304

v16		96.39 262	95.76 268	98.26 236	98.96 231	98.81 148	99.76 27	99.28 231	94.57 278	94.10 301	97.70 304	95.04 160	98.16 297	94.70 274	87.77 315	97.80 304

Test4		95.05 284	93.67 292	99.22 124	96.07 314	98.94 124	99.20 226	99.27 236	97.71 131	89.96 323	97.59 314	66.18 331	99.25 246	98.06 141	98.96 137	99.47 130

jason		99.13 62	99.03 63	99.45 94	99.46 130	98.87 132	99.12 237	99.26 237	98.03 101	99.79 18	99.65 127	97.02 102	99.85 108	99.02 48	99.90 24	99.65 88
jason: jason.
test_0402		96.64 254	96.24 255	97.85 268	98.85 257	96.43 262	99.44 146	99.26 237	93.52 297	96.98 283	99.52 171	88.52 300	99.20 257	92.58 304	97.50 209	97.93 299

PCF-MVS	97.08 14	97.66 225	97.06 243	99.47 91	99.61 103	99.09 100	98.04 323	99.25 239	91.24 312	98.51 239	99.70 106	94.55 194	99.91 72	92.76 302	99.85 52	99.42 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MDA-MVSNet_test_wron		95.45 280	94.60 285	98.01 257	98.16 298	97.21 228	99.11 243	99.24 240	93.49 298	80.73 330	98.98 268	93.02 230	98.18 295	94.22 290	94.45 278	98.64 247

YYNet1		95.36 282	94.51 287	97.92 263	97.89 300	97.10 230	99.10 245	99.23 241	93.26 301	80.77 329	99.04 262	92.81 236	98.02 306	94.30 286	94.18 283	98.64 247

DeepMVS_CX				93.34 305	99.29 168	82.27 329		99.22 242	85.15 323	96.33 288	99.05 261	90.97 275	99.73 155	93.57 294	97.77 195	98.01 295

pmmvs4		98.13 151	97.90 160	98.81 188	98.61 285	98.87 132	98.99 269	99.21 243	96.44 227	99.06 173	99.58 154	95.90 133	99.11 265	97.18 208	96.11 244	98.46 279

tpmvs		97.98 175	98.02 152	97.84 269	99.04 215	94.73 294	99.31 192	99.20 244	96.10 257	98.76 212	99.42 200	94.94 166	99.81 131	96.97 220	98.45 166	98.97 177

new_pmnet		96.38 263	96.03 259	97.41 283	98.13 299	95.16 289	99.05 254	99.20 244	93.94 292	97.39 275	98.79 281	91.61 269	99.04 271	90.43 309	95.77 249	98.05 291

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 62	99.32 77	99.75 34	99.20 244	98.02 102	99.56 64	99.86 22	96.54 116	99.67 176	98.09 134	99.13 123	99.73 63

tpmp4_e23		97.34 243	97.29 234	97.52 280	99.25 177	93.73 302	99.58 87	99.19 247	94.00 291	98.20 254	99.41 203	90.74 277	99.74 147	97.13 210	98.07 187	99.07 167

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 118	98.94 124	99.05 254	99.16 248	97.86 112	99.80 16	99.56 160	97.39 92	99.86 104	98.94 54	99.85 52	99.58 108

GA-MVS		97.85 193	97.47 208	99.00 141	99.38 147	97.99 202	98.57 307	99.15 249	97.04 189	98.90 196	99.30 237	89.83 285	99.38 211	96.70 234	98.33 170	99.62 100

ADS-MVSNet		98.20 146	98.08 147	98.56 208	99.33 156	96.48 260	99.23 217	99.15 249	96.24 242	99.10 163	99.67 120	94.11 211	99.71 165	96.81 228	99.05 130	99.48 126

Patchmatch-test		97.93 184	97.65 192	98.77 193	99.18 187	97.07 234	99.03 260	99.14 251	96.16 249	98.74 213	99.57 158	94.56 193	99.72 159	93.36 296	99.11 124	99.52 117

BH-untuned		98.42 133	98.36 129	98.59 204	99.49 125	96.70 253	99.27 204	99.13 252	97.24 171	98.80 208	99.38 212	95.75 138	99.74 147	97.07 214	99.16 121	99.33 147

tpmrst		98.33 138	98.48 125	97.90 265	99.16 194	94.78 292	99.31 192	99.11 253	97.27 167	99.45 81	99.59 151	95.33 146	99.84 113	98.48 108	98.61 155	99.09 162

pmmvs-eth3d		95.34 283	94.73 284	97.15 285	95.53 317	95.94 272	99.35 184	99.10 254	95.13 268	93.55 311	97.54 315	88.15 306	97.91 309	94.58 275	89.69 310	97.61 313

PAPM		97.59 228	97.09 242	99.07 133	99.06 211	98.26 193	98.30 317	99.10 254	94.88 271	98.08 259	99.34 229	96.27 123	99.64 182	89.87 310	98.92 142	99.31 148

Anonymous20231206		96.22 270	96.03 259	96.79 294	97.31 310	94.14 299	99.63 71	99.08 256	96.17 248	97.04 281	99.06 260	93.94 216	97.76 314	86.96 320	95.06 262	98.47 277

ADS-MVSNet2		98.02 170	98.07 149	97.87 266	99.33 156	95.19 287	99.23 217	99.08 256	96.24 242	99.10 163	99.67 120	94.11 211	98.93 286	96.81 228	99.05 130	99.48 126

RPMNet		96.61 255	95.85 263	98.87 177	99.18 187	98.49 183	99.22 221	99.08 256	88.72 321	99.56 64	97.38 317	94.08 213	99.00 276	86.87 321	98.58 158	99.14 155

Anonymous20231211		90.69 300	89.39 301	94.58 302	94.25 321	88.18 319	99.29 198	99.07 259	82.45 327	92.95 314	97.65 308	63.96 334	97.79 312	89.27 312	85.63 325	97.77 310

PatchT		97.03 252	96.44 253	98.79 191	98.99 221	98.34 190	99.16 230	99.07 259	92.13 306	99.52 70	97.31 319	94.54 195	98.98 278	88.54 314	98.73 154	99.03 170

test2356		94.07 293	94.46 288	92.89 307	95.18 318	86.13 322	97.60 327	99.06 261	93.61 296	96.15 292	98.28 296	85.60 316	93.95 329	86.68 322	98.00 189	98.59 267

LP		97.04 251	96.80 247	97.77 274	98.90 246	95.23 285	98.97 276	99.06 261	94.02 290	98.09 258	99.41 203	93.88 218	98.82 288	90.46 308	98.42 168	99.26 151

USDC		97.34 243	97.20 239	97.75 275	99.07 209	95.20 286	98.51 310	99.04 263	97.99 107	98.31 251	99.86 22	89.02 291	99.55 195	95.67 258	97.36 221	98.49 275

testus		94.61 287	95.30 280	92.54 309	96.44 313	84.18 324	98.36 313	99.03 264	94.18 289	96.49 286	98.57 290	88.74 294	95.09 327	87.41 318	98.45 166	98.36 285

CostFormer		97.72 215	97.73 183	97.71 277	99.15 197	94.02 300	99.54 108	99.02 265	94.67 275	99.04 175	99.35 226	92.35 255	99.77 142	98.50 107	97.94 191	99.34 146

OurMVSNet-221017-0		97.88 190	97.77 176	98.19 248	98.71 276	96.53 258	99.88 1	99.00 266	97.79 122	98.78 210	99.94 3	91.68 265	99.35 221	97.21 204	96.99 230	98.69 215

LCM-MVSNet		86.80 303	85.22 306	91.53 314	87.81 333	80.96 330	98.23 320	98.99 267	71.05 331	90.13 322	96.51 322	48.45 339	96.88 320	90.51 307	85.30 326	96.76 317

MIMVSNet		97.73 213	97.45 210	98.57 206	99.45 134	97.50 220	99.02 263	98.98 268	96.11 254	99.41 90	99.14 252	90.28 279	98.74 290	95.74 255	98.93 140	99.47 130

Patchmatch-test1		98.16 149	98.14 141	98.22 245	99.30 165	95.55 277	99.07 248	98.97 269	97.57 142	99.43 85	99.60 149	92.72 240	99.60 190	97.38 197	99.20 119	99.50 123

JIA-IIPM		97.50 236	97.02 244	98.93 151	98.73 272	97.80 214	99.30 194	98.97 269	91.73 310	98.91 194	94.86 326	95.10 158	99.71 165	97.58 178	97.98 190	99.28 150

alignmvs		98.81 109	98.56 122	99.58 71	99.43 135	99.42 69	99.51 116	98.96 271	98.61 50	99.35 106	98.92 271	94.78 179	99.77 142	99.35 18	98.11 186	99.54 112

tpm2		97.44 240	97.34 228	97.74 276	99.15 197	94.36 297	99.45 142	98.94 272	93.45 300	98.90 196	99.44 197	91.35 271	99.59 192	97.31 200	98.07 187	99.29 149

PatchFormer-LS_test		98.01 173	98.05 150	97.87 266	99.15 197	94.76 293	99.42 158	98.93 273	97.12 181	98.84 205	98.59 289	93.74 225	99.80 135	98.55 103	98.17 183	99.06 168

EG-PatchMatch MVS		95.97 275	95.69 269	96.81 293	97.78 302	92.79 310	99.16 230	98.93 273	96.16 249	94.08 302	99.22 247	82.72 324	99.47 199	95.67 258	97.50 209	98.17 288

PatchmatchNet		98.31 139	98.36 129	98.19 248	99.16 194	95.32 284	99.27 204	98.92 275	97.37 160	99.37 99	99.58 154	94.90 171	99.70 171	97.43 195	99.21 118	99.54 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ITE_SJBPF				98.08 252	99.29 168	96.37 263		98.92 275	98.34 66	98.83 206	99.75 90	91.09 273	99.62 188	95.82 253	97.40 218	98.25 287

FPMVS		84.93 304	85.65 304	82.75 324	86.77 335	63.39 341	98.35 315	98.92 275	74.11 330	83.39 327	98.98 268	50.85 337	92.40 334	84.54 324	94.97 264	92.46 327

TransMVSNet (Re)		97.15 248	96.58 251	98.86 181	99.12 200	98.85 135	99.49 129	98.91 278	95.48 266	97.16 279	99.80 64	93.38 227	99.11 265	94.16 291	91.73 304	98.62 255

EPNet		98.86 100	98.71 103	99.30 113	97.20 312	98.18 195	99.62 74	98.91 278	99.28 2	98.63 233	99.81 53	95.96 128	99.99 1	99.24 30	99.72 83	99.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		97.52 232	97.30 233	98.16 250	98.57 288	96.73 252	99.27 204	98.90 280	96.14 252	98.37 247	99.53 167	91.54 270	99.14 259	97.51 186	95.87 247	98.63 253

BH-w/o		98.00 174	97.89 164	98.32 230	99.35 152	96.20 269	99.01 267	98.90 280	96.42 229	98.38 246	99.00 264	95.26 151	99.72 159	96.06 249	98.61 155	99.03 170

MTMP								98.88 282

dp		97.75 210	97.80 169	97.59 279	99.10 205	93.71 304	99.32 189	98.88 282	96.48 225	99.08 168	99.55 163	92.67 244	99.82 127	96.52 241	98.58 158	99.24 152

MVP-Stereo		97.81 200	97.75 182	97.99 259	97.53 305	96.60 257	98.96 278	98.85 284	97.22 173	97.23 277	99.36 222	95.28 148	99.46 200	95.51 260	99.78 72	97.92 300
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
VDD-MVS		97.73 213	97.35 225	98.88 173	99.47 129	97.12 229	99.34 187	98.85 284	98.19 76	99.67 40	99.85 26	82.98 323	99.92 63	99.49 12	98.32 171	99.60 102

Baseline_NR-MVSNet		97.76 207	97.45 210	98.68 198	99.09 207	98.29 191	99.41 162	98.85 284	95.65 264	98.63 233	99.67 120	94.82 176	99.10 267	98.07 140	92.89 298	98.64 247

LF4IMVS		97.52 232	97.46 209	97.70 278	98.98 225	95.55 277	99.29 198	98.82 287	98.07 93	98.66 225	99.64 134	89.97 284	99.61 189	97.01 216	96.68 231	97.94 298

view600		97.97 178	97.66 187	98.89 166	99.75 47	97.81 210	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

view800		97.97 178	97.66 187	98.89 166	99.75 47	97.81 210	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

conf0.05thres1000		97.97 178	97.66 187	98.89 166	99.75 47	97.81 210	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

tfpn		97.97 178	97.66 187	98.89 166	99.75 47	97.81 210	99.69 44	98.80 288	98.02 102	99.25 130	98.88 272	91.95 257	99.89 92	94.36 281	98.29 172	98.96 179

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 139	98.83 139	99.30 194	98.77 292	97.70 133	98.94 191	99.65 127	92.91 235	99.74 147	96.52 241	99.55 102	99.64 94

EPNet_dtu		98.03 168	97.96 156	98.23 243	98.27 296	95.54 279	99.23 217	98.75 293	99.02 10	97.82 269	99.71 103	96.11 127	99.48 198	93.04 299	99.65 97	99.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TDRefinement		95.42 281	94.57 286	97.97 260	89.83 331	96.11 270	99.48 134	98.75 293	96.74 203	96.68 285	99.88 14	88.65 298	99.71 165	98.37 117	82.74 327	98.09 289

OpenMVS_ROB	92.34 20	94.38 290	93.70 291	96.41 298	97.38 307	93.17 308	99.06 252	98.75 293	86.58 322	94.84 298	98.26 297	81.53 326	99.32 228	89.01 313	97.87 193	96.76 317

thres600view7		97.86 192	97.51 202	98.92 156	99.72 65	97.95 206	99.59 83	98.74 296	97.94 109	99.27 125	98.62 287	91.75 263	99.86 104	93.73 293	98.19 179	98.96 179

1111		92.30 297	92.21 298	92.55 308	93.30 323	86.27 320	99.15 233	98.74 296	91.94 307	90.85 320	97.82 301	84.18 320	95.21 325	79.65 328	94.27 281	96.19 320

.test1245		83.42 305	86.17 303	75.15 327	93.30 323	86.27 320	99.15 233	98.74 296	91.94 307	90.85 320	97.82 301	84.18 320	95.21 325	79.65 328	39.90 338	43.98 337

thres200		97.61 227	97.28 235	98.62 202	99.64 92	98.03 200	99.26 212	98.74 296	97.68 135	99.09 167	98.32 295	91.66 268	99.81 131	92.88 301	98.22 176	98.03 294

MDTV_nov1_ep13			98.32 133		99.11 202	94.44 296	99.27 204	98.74 296	97.51 148	99.40 94	99.62 143	94.78 179	99.76 145	97.59 177	98.81 151

TinyColmap		97.12 249	96.89 246	97.83 270	99.07 209	95.52 280	98.57 307	98.74 296	97.58 141	97.81 270	99.79 72	88.16 305	99.56 193	95.10 267	97.21 225	98.39 283

tfpn200view9		97.72 215	97.38 221	98.72 196	99.69 75	97.96 204	99.50 121	98.73 302	97.83 117	99.17 153	98.45 293	91.67 266	99.83 120	93.22 297	98.18 180	98.37 284

ambc				93.06 306	92.68 326	82.36 328	98.47 311	98.73 302		95.09 296	97.41 316	55.55 336	99.10 267	96.42 244	91.32 305	97.71 312

thres400		97.77 206	97.38 221	98.92 156	99.69 75	97.96 204	99.50 121	98.73 302	97.83 117	99.17 153	98.45 293	91.67 266	99.83 120	93.22 297	98.18 180	98.96 179

SixPastTwentyTwo		97.50 236	97.33 230	98.03 254	98.65 282	96.23 268	99.77 24	98.68 305	97.14 178	97.90 266	99.93 4	90.45 278	99.18 258	97.00 217	96.43 238	98.67 231

test_normal		97.44 240	96.77 250	99.44 97	97.75 304	99.00 111	99.10 245	98.64 306	97.71 131	93.93 308	98.82 279	87.39 309	99.83 120	98.61 92	98.97 136	99.49 124

test0.0.03 1		97.71 218	97.42 217	98.56 208	98.41 294	97.82 209	98.78 294	98.63 307	97.34 161	98.05 263	98.98 268	94.45 198	98.98 278	95.04 269	97.15 228	98.89 187

DWT-MVSNet_test		97.53 231	97.40 219	97.93 262	99.03 217	94.86 291	99.57 93	98.63 307	96.59 216	98.36 248	98.79 281	89.32 289	99.74 147	98.14 131	98.16 184	99.20 154

DI_MVS_plusplus_test		97.45 239	96.79 248	99.44 97	97.76 303	99.04 105	99.21 224	98.61 309	97.74 128	94.01 305	98.83 278	87.38 310	99.83 120	98.63 88	98.90 144	99.44 136

test1235678		92.91 296	93.30 293	91.71 313	93.14 325	83.01 326	98.75 297	98.58 310	92.80 304	92.45 315	97.91 300	88.51 301	93.54 330	82.26 326	95.35 255	98.59 267

TR-MVS		97.76 207	97.41 218	98.82 187	99.06 211	97.87 208	98.87 290	98.56 311	96.63 211	98.68 224	99.22 247	92.49 249	99.65 180	95.40 263	97.79 194	98.95 186

tpm		97.67 224	97.55 198	98.03 254	99.02 218	95.01 290	99.43 151	98.54 312	96.44 227	99.12 158	99.34 229	91.83 262	99.60 190	97.75 164	96.46 237	99.48 126

Patchmatch-RL test		95.84 276	95.81 265	95.95 299	95.61 315	90.57 316	98.24 318	98.39 313	95.10 270	95.20 295	98.67 286	94.78 179	97.77 313	96.28 247	90.02 308	99.51 120

no-one		83.04 306	80.12 308	91.79 312	89.44 332	85.65 323	99.32 189	98.32 314	89.06 318	79.79 333	89.16 334	44.86 340	96.67 321	84.33 325	46.78 336	93.05 325

test12356		91.74 298	92.19 299	90.37 316	91.22 327	82.41 327	98.61 305	98.28 315	90.66 315	91.82 318	97.92 299	84.90 318	92.61 331	81.64 327	94.66 273	96.09 321

LCM-MVSNet-Re		97.83 196	98.15 140	96.87 292	99.30 165	92.25 313	99.59 83	98.26 316	97.43 154	96.20 289	99.13 253	96.27 123	98.73 291	98.17 129	98.99 134	99.64 94

LFMVS		97.90 189	97.35 225	99.54 75	99.52 116	99.01 109	99.39 169	98.24 317	97.10 185	99.65 48	99.79 72	84.79 319	99.91 72	99.28 27	98.38 169	99.69 77

PM-MVS		92.96 295	92.23 297	95.14 301	95.61 315	89.98 318	99.37 176	98.21 318	94.80 273	95.04 297	97.69 306	65.06 332	97.90 310	94.30 286	89.98 309	97.54 316

PMVS	70.75 22	75.98 313	74.97 312	79.01 326	70.98 341	55.18 342	93.37 334	98.21 318	65.08 337	61.78 338	93.83 327	21.74 347	92.53 332	78.59 330	91.12 306	89.34 332
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		94.09 292	93.25 294	96.60 296	94.76 320	94.49 295	98.92 285	98.18 320	89.66 316	96.48 287	98.06 298	86.28 312	97.33 317	89.68 311	87.20 318	97.97 297

door-mid								98.05 321

tmp_tt		82.80 307	81.52 307	86.66 318	66.61 342	68.44 340	92.79 335	97.92 322	68.96 333	80.04 332	99.85 26	85.77 314	96.15 324	97.86 153	43.89 337	95.39 323

door								97.92 322

testpf		95.66 278	96.02 261	94.58 302	98.35 295	92.32 312	97.25 329	97.91 324	92.83 303	97.03 282	98.99 265	88.69 296	98.61 292	95.72 256	97.40 218	92.80 326

test-LLR		98.06 159	97.90 160	98.55 210	98.79 262	97.10 230	98.67 301	97.75 325	97.34 161	98.61 236	98.85 276	94.45 198	99.45 201	97.25 202	99.38 108	99.10 158

test-mter		97.49 238	97.13 241	98.55 210	98.79 262	97.10 230	98.67 301	97.75 325	96.65 209	98.61 236	98.85 276	88.23 304	99.45 201	97.25 202	99.38 108	99.10 158

IB-MVS	95.67 18	96.22 270	95.44 278	98.57 206	99.21 181	96.70 253	98.65 304	97.74 327	96.71 205	97.27 276	98.54 291	86.03 313	99.92 63	98.47 110	86.30 324	99.10 158
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
testmv		87.91 301	87.80 302	88.24 317	87.68 334	77.50 334	99.07 248	97.66 328	89.27 317	86.47 324	96.22 323	68.35 330	92.49 333	76.63 332	88.82 311	94.72 324

TESTMET0.1,1		97.55 229	97.27 237	98.40 225	98.93 241	96.53 258	98.67 301	97.61 329	96.96 193	98.64 232	99.28 240	88.63 299	99.45 201	97.30 201	99.38 108	99.21 153

PMMVS2		86.87 302	85.37 305	91.35 315	90.21 330	83.80 325	98.89 288	97.45 330	83.13 326	91.67 319	95.03 324	48.49 338	94.70 328	85.86 323	77.62 329	95.54 322

K. test v3		97.10 250	96.79 248	98.01 257	98.72 274	96.33 265	99.87 4	97.05 331	97.59 139	96.16 290	99.80 64	88.71 295	99.04 271	96.69 235	96.55 236	98.65 245

DSMNet-mixed		97.25 246	97.35 225	96.95 290	97.84 301	93.61 306	99.57 93	96.63 332	96.13 253	98.87 199	98.61 288	94.59 192	97.70 315	95.08 268	98.86 147	99.55 110

MVS-HIRNet		95.75 277	95.16 281	97.51 281	99.30 165	93.69 305	98.88 289	95.78 333	85.09 324	98.78 210	92.65 328	91.29 272	99.37 214	94.85 271	99.85 52	99.46 133

E-PMN		80.61 308	79.88 309	82.81 323	90.75 329	76.38 336	97.69 325	95.76 334	66.44 335	83.52 326	92.25 329	62.54 335	87.16 338	68.53 336	61.40 332	84.89 335

lessismore_v0				97.79 273	98.69 278	95.44 283		94.75 335		95.71 294	99.87 19	88.69 296	99.32 228	95.89 252	94.93 266	98.62 255

EPMVS		97.82 199	97.65 192	98.35 228	98.88 250	95.98 271	99.49 129	94.71 336	97.57 142	99.26 129	99.48 185	92.46 253	99.71 165	97.87 152	99.08 128	99.35 145

gg-mvs-nofinetune		96.17 272	95.32 279	98.73 195	98.79 262	98.14 197	99.38 174	94.09 337	91.07 314	98.07 262	91.04 332	89.62 288	99.35 221	96.75 231	99.09 127	98.68 220

GG-mvs-BLEND				98.45 219	98.55 289	98.16 196	99.43 151	93.68 338		97.23 277	98.46 292	89.30 290	99.22 252	95.43 262	98.22 176	97.98 296

PNet_i23d		79.43 310	77.68 311	84.67 320	86.18 336	71.69 339	96.50 331	93.68 338	75.17 329	71.33 334	91.18 331	32.18 343	90.62 335	78.57 331	74.34 330	91.71 330

MVE	76.82 21	76.91 312	74.31 314	84.70 319	85.38 338	76.05 337	96.88 330	93.17 340	67.39 334	71.28 335	89.01 335	21.66 348	87.69 337	71.74 335	72.29 331	90.35 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 311	74.86 313	84.62 321	75.88 340	77.61 333	97.63 326	93.15 341	88.81 320	64.27 336	89.29 333	36.51 341	83.93 340	75.89 333	52.31 335	92.33 329

N_pmnet		94.95 286	95.83 264	92.31 310	98.47 292	79.33 332	99.12 237	92.81 342	93.87 293	97.68 272	99.13 253	93.87 219	99.01 275	91.38 306	96.19 243	98.59 267

wuykxyi23d		74.42 314	71.19 315	84.14 322	76.16 339	74.29 338	96.00 332	92.57 343	69.57 332	63.84 337	87.49 336	21.98 345	88.86 336	75.56 334	57.50 334	89.26 333

EMVS		80.02 309	79.22 310	82.43 325	91.19 328	76.40 335	97.55 328	92.49 344	66.36 336	83.01 328	91.27 330	64.63 333	85.79 339	65.82 337	60.65 333	85.08 334

testmvs		39.17 317	43.78 316	25.37 331	36.04 344	16.84 345	98.36 313	26.56 345	20.06 339	38.51 340	67.32 337	29.64 344	15.30 343	37.59 339	39.90 338	43.98 337

wuyk23d		40.18 316	41.29 319	36.84 328	86.18 336	49.12 343	79.73 336	22.81 346	27.64 338	25.46 341	28.45 342	21.98 345	48.89 341	55.80 338	23.56 341	12.51 339

test123		39.01 318	42.50 318	28.53 330	39.17 343	20.91 344	98.75 297	19.17 347	19.83 340	38.57 339	66.67 338	33.16 342	15.42 342	37.50 340	29.66 340	49.26 336

pcd_1.5k_mvsjas		8.27 321	11.03 322	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.27 343	99.01 11	0.00 344	0.00 341	0.00 342	0.00 340

sosnet-low-res		0.02 322	0.03 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.27 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sosnet		0.02 322	0.03 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.27 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uncertanet		0.02 322	0.03 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.27 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

Regformer		0.02 322	0.03 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.27 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

n2								0.00 348

nn								0.00 348

ab-mvs-re		8.30 320	11.06 321	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	99.58 154	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uanet		0.02 322	0.03 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.27 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sam_mvs1												94.86 174

sam_mvs												94.72 187

test_post1							99.23 217				65.14 340	94.18 209	99.71 165	97.58 178

test_post											65.99 339	94.65 191	99.73 155

patchmatchnet-post											98.70 285	94.79 178	99.74 147

gm-plane-assit					98.54 290	92.96 309			94.65 276		99.15 251		99.64 182	97.56 181

test9_res														97.49 188	99.72 83	99.75 53

agg_prior2														97.21 204	99.73 82	99.75 53

test_prior4						99.56 49	98.99 269

test_prior2							98.96 278		98.34 66	99.01 178	99.52 171	98.68 49		97.96 145	99.74 79

旧先验2							98.96 278		96.70 206	99.47 78			99.94 40	98.19 127

新几何2							99.01 267

原ACMM2							98.95 282

testdata2													99.95 33	96.67 236

segment_acmp												98.96 20

testdata1							98.85 291		98.32 69

plane_prior7					99.29 168	97.03 238

plane_prior6					99.27 173	96.98 242						92.71 241

plane_prior4											99.61 146

plane_prior3						97.00 240			98.69 46	99.11 160

plane_prior2							99.39 169		98.97 22

plane_prior1					99.26 175

plane_prior						96.97 243	99.21 224		98.45 59						97.60 200

HQP5-MVS						96.83 248

HQP-NCC					99.19 184		98.98 273		98.24 72	98.66 225

ACMP_Plane					99.19 184		98.98 273		98.24 72	98.66 225

BP-MVS														97.19 206

HQP4-MVS										98.66 225			99.64 182			98.64 247

HQP2-MVS												92.47 250

NP-MVS					99.23 178	96.92 246					99.40 207

MDTV_nov1_ep13_2view						95.18 288	99.35 184		96.84 200	99.58 60		95.19 155		97.82 156		99.46 133

ACMMP++_ref															97.19 226

ACMMP++															97.43 217

Test By Simon												98.75 44

High-resolution multi-view benchmark