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.

UA-Net		99.42 38	99.29 44	99.80 40	99.62 125	99.55 74	99.50 123	99.70 15	98.79 47	99.77 33	99.96 1	97.45 116	99.96 18	98.92 66	99.90 23	99.89 2

DeepC-MVS	98.35 2	99.30 55	99.19 60	99.64 77	99.82 37	99.23 113	99.62 63	99.55 64	98.94 33	99.63 70	99.95 2	95.82 172	99.94 53	99.37 18	99.97 3	99.73 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OurMVSNet-221017-0		97.88 214	97.77 207	98.19 267	98.71 306	96.53 285	99.88 1	99.00 288	97.79 144	98.78 243	99.94 3	91.68 287	99.35 257	97.21 239	96.99 256	98.69 244

SixPastTwentyTwo		97.50 265	97.33 263	98.03 275	98.65 311	96.23 295	99.77 21	98.68 322	97.14 208	97.90 303	99.93 4	90.45 302	99.18 286	97.00 253	96.43 265	98.67 257

SD-MVS		99.41 42	99.52 6	99.05 163	99.74 70	99.68 49	99.46 147	99.52 89	99.11 7	99.88 5	99.91 5	99.43 1	97.70 339	98.72 98	99.93 10	99.77 63
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMH	97.28 8	98.10 184	97.99 183	98.44 245	99.41 176	96.96 270	99.60 70	99.56 56	98.09 110	98.15 294	99.91 5	90.87 301	99.70 199	98.88 70	97.45 240	98.67 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		97.55 259	97.02 275	99.16 154	99.49 158	98.12 218	99.38 185	99.30 254	95.35 303	99.68 53	99.90 7	82.62 343	99.93 68	99.31 26	98.13 212	99.42 169

QAPM		98.67 143	98.30 160	99.80 40	99.20 229	99.67 52	99.77 21	99.72 11	94.74 310	98.73 247	99.90 7	95.78 173	99.98 5	96.96 257	99.88 36	99.76 68

3Dnovator	97.25 9	99.24 65	99.05 74	99.81 38	99.12 247	99.66 54	99.84 6	99.74 10	99.09 10	98.92 222	99.90 7	95.94 166	99.98 5	98.95 61	99.92 11	99.79 53

Anonymous20240529		98.09 185	97.68 217	99.34 127	99.66 109	98.44 203	99.40 176	99.43 194	93.67 320	99.22 166	99.89 10	90.23 307	99.93 68	99.26 32	98.33 197	99.66 108

CHOSEN 1792x2688		99.19 69	99.10 69	99.45 115	99.89 8	98.52 195	99.39 180	99.94 1	98.73 51	99.11 187	99.89 10	95.50 181	99.94 53	99.50 8	99.97 3	99.89 2

RPSCF		98.22 170	98.62 138	96.99 309	99.82 37	91.58 342	99.72 29	99.44 187	96.61 250	99.66 64	99.89 10	95.92 167	99.82 152	97.46 226	99.10 158	99.57 137

3Dnovator+	97.12 13	99.18 71	98.97 91	99.82 35	99.17 240	99.68 49	99.81 12	99.51 102	99.20 4	98.72 248	99.89 10	95.68 177	99.97 10	98.86 77	99.86 51	99.81 41

COLMAP_ROB	97.56 6	98.86 119	98.75 121	99.17 153	99.88 11	98.53 191	99.34 200	99.59 44	97.55 168	98.70 255	99.89 10	95.83 171	99.90 105	98.10 166	99.90 23	99.08 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_djsdf		98.67 143	98.57 144	98.98 172	98.70 307	98.91 158	99.88 1	99.46 167	97.55 168	99.22 166	99.88 15	95.73 175	99.28 267	99.03 52	97.62 224	98.75 229

DP-MVS		99.16 75	98.95 95	99.78 45	99.77 49	99.53 79	99.41 168	99.50 120	97.03 221	99.04 202	99.88 15	97.39 117	99.92 79	98.66 107	99.90 23	99.87 10

TDRefinement		95.42 301	94.57 307	97.97 281	89.83 351	96.11 297	99.48 139	98.75 311	96.74 239	96.68 322	99.88 15	88.65 321	99.71 193	98.37 145	82.74 345	98.09 323

EPP-MVSNet		99.13 79	98.99 87	99.53 98	99.65 114	99.06 134	99.81 12	99.33 239	97.43 183	99.60 80	99.88 15	97.14 126	99.84 136	99.13 44	98.94 169	99.69 98

OpenMVS	96.50 16	98.47 151	98.12 169	99.52 103	99.04 263	99.53 79	99.82 10	99.72 11	94.56 313	98.08 296	99.88 15	94.73 210	99.98 5	97.47 225	99.76 95	99.06 199

lessismore_v0				97.79 293	98.69 308	95.44 311		94.75 351		95.71 330	99.87 20	88.69 320	99.32 262	95.89 287	94.93 302	98.62 279

Vis-MVSNet		99.12 85	98.97 91	99.56 90	99.78 44	99.10 130	99.68 40	99.66 27	98.49 65	99.86 11	99.87 20	94.77 207	99.84 136	99.19 37	99.41 135	99.74 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	97.24 10	97.92 211	97.78 205	98.32 257	99.46 165	96.68 281	99.56 95	99.54 71	98.41 73	97.79 308	99.87 20	90.18 308	99.66 207	98.05 175	97.18 252	98.62 279

ACMMP_NAP		99.47 22	99.34 26	99.88 6	99.87 15	99.86 10	99.47 144	99.48 139	98.05 120	99.76 37	99.86 23	98.82 44	99.93 68	98.82 88	99.91 16	99.84 18

RRT_MVS		98.60 148	98.44 149	99.05 163	98.88 281	99.14 125	99.49 133	99.38 214	97.76 147	99.29 148	99.86 23	95.38 184	99.36 253	98.81 89	97.16 253	98.64 269

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

PVSNet_Blended_VisFu		99.36 49	99.28 48	99.61 82	99.86 21	99.07 133	99.47 144	99.93 2	97.66 159	99.71 46	99.86 23	97.73 111	99.96 18	99.47 13	99.82 78	99.79 53

IS-MVSNet		99.05 100	98.87 104	99.57 88	99.73 75	99.32 101	99.75 25	99.20 269	98.02 124	99.56 88	99.86 23	96.54 147	99.67 204	98.09 167	99.13 154	99.73 80

USDC		97.34 271	97.20 270	97.75 294	99.07 257	95.20 314	98.51 329	99.04 286	97.99 125	98.31 287	99.86 23	89.02 316	99.55 225	95.67 294	97.36 247	98.49 299

TSAR-MVS + MP.		99.58 4	99.50 8	99.81 38	99.91 1	99.66 54	99.63 57	99.39 208	98.91 36	99.78 31	99.85 29	99.36 2	99.94 53	98.84 81	99.88 36	99.82 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tmp_tt		82.80 317	81.52 320	86.66 330	66.61 358	68.44 356	92.79 350	97.92 335	68.96 349	80.04 351	99.85 29	85.77 336	96.15 347	97.86 186	43.89 352	95.39 343

AllTest		98.87 116	98.72 122	99.31 133	99.86 21	98.48 201	99.56 95	99.61 36	97.85 135	99.36 135	99.85 29	95.95 164	99.85 131	96.66 274	99.83 72	99.59 132

TestCases				99.31 133	99.86 21	98.48 201		99.61 36	97.85 135	99.36 135	99.85 29	95.95 164	99.85 131	96.66 274	99.83 72	99.59 132

VDD-MVS		97.73 241	97.35 258	98.88 194	99.47 164	97.12 254	99.34 200	98.85 306	98.19 97	99.67 59	99.85 29	82.98 341	99.92 79	99.49 12	98.32 201	99.60 128

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

DeepPCF-MVS	98.18 3	98.81 130	99.37 19	97.12 308	99.60 132	91.75 341	98.61 322	99.44 187	99.35 1	99.83 17	99.85 29	98.70 62	99.81 156	99.02 54	99.91 16	99.81 41

ACMM	97.58 5	98.37 161	98.34 156	98.48 236	99.41 176	97.10 255	99.56 95	99.45 179	98.53 62	99.04 202	99.85 29	93.00 253	99.71 193	98.74 94	97.45 240	98.64 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 60	99.12 67	99.74 56	99.18 234	99.75 38	99.56 95	99.57 51	98.45 69	99.49 103	99.85 29	97.77 110	99.94 53	98.33 150	99.84 65	99.52 146

DPE-MVS		99.46 24	99.32 30	99.91 2	99.78 44	99.88 7	99.36 192	99.51 102	98.73 51	99.88 5	99.84 38	98.72 60	99.96 18	98.16 163	99.87 40	99.88 5

XVG-OURS		98.73 138	98.68 127	98.88 194	99.70 93	97.73 237	98.92 294	99.55 64	98.52 63	99.45 108	99.84 38	95.27 189	99.91 90	98.08 171	98.84 177	99.00 204

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

ACMMP		99.45 26	99.32 30	99.82 35	99.89 8	99.67 52	99.62 63	99.69 18	98.12 105	99.63 70	99.84 38	98.73 59	99.96 18	98.55 128	99.83 72	99.81 41
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 77	99.78 44	99.14 125	99.60 70	99.45 179	99.01 18	99.90 3	99.83 42	98.98 23	99.93 68	99.59 1	99.95 6	99.86 11

EI-MVSNet		98.67 143	98.67 128	98.68 220	99.35 190	97.97 223	99.50 123	99.38 214	96.93 230	99.20 172	99.83 42	97.87 106	99.36 253	98.38 143	97.56 229	98.71 236

CVMVSNet		98.57 149	98.67 128	98.30 259	99.35 190	95.59 304	99.50 123	99.55 64	98.60 59	99.39 127	99.83 42	94.48 221	99.45 231	98.75 93	98.56 190	99.85 14

LPG-MVS_test		98.22 170	98.13 168	98.49 234	99.33 195	97.05 261	99.58 83	99.55 64	97.46 177	99.24 161	99.83 42	92.58 268	99.72 187	98.09 167	97.51 233	98.68 249

LGP-MVS_train				98.49 234	99.33 195	97.05 261		99.55 64	97.46 177	99.24 161	99.83 42	92.58 268	99.72 187	98.09 167	97.51 233	98.68 249

SteuartSystems-ACMMP		99.54 9	99.42 13	99.87 11	99.82 37	99.81 24	99.59 76	99.51 102	98.62 57	99.79 26	99.83 42	99.28 3	99.97 10	98.48 133	99.90 23	99.84 18
Skip Steuart: Steuart Systems R&D Blog.
XXY-MVS		98.38 160	98.09 173	99.24 147	99.26 215	99.32 101	99.56 95	99.55 64	97.45 180	98.71 249	99.83 42	93.23 249	99.63 217	98.88 70	96.32 268	98.76 227

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

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

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

SMA-MVS		99.44 30	99.30 40	99.85 25	99.73 75	99.83 14	99.56 95	99.47 157	97.45 180	99.78 31	99.82 49	99.18 8	99.91 90	98.79 90	99.89 33	99.81 41
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
nrg030		98.64 146	98.42 151	99.28 142	99.05 262	99.69 47	99.81 12	99.46 167	98.04 121	99.01 205	99.82 49	96.69 143	99.38 246	99.34 23	94.59 305	98.78 222

FC-MVSNet-test		98.75 137	98.62 138	99.15 156	99.08 256	99.45 90	99.86 5	99.60 41	98.23 93	98.70 255	99.82 49	96.80 137	99.22 278	99.07 50	96.38 266	98.79 221

EI-MVSNet-Vis-set		99.58 4	99.56 3	99.64 77	99.78 44	99.15 124	99.61 69	99.45 179	99.01 18	99.89 4	99.82 49	99.01 16	99.92 79	99.56 4	99.95 6	99.85 14

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

EU-MVSNet		97.98 203	98.03 179	97.81 292	98.72 304	96.65 282	99.66 46	99.66 27	98.09 110	98.35 285	99.82 49	95.25 192	98.01 332	97.41 231	95.30 293	98.78 222

APD-MVS		99.27 60	99.08 72	99.84 32	99.75 62	99.79 30	99.50 123	99.50 120	97.16 207	99.77 33	99.82 49	98.78 48	99.94 53	97.56 216	99.86 51	99.80 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 85	99.08 72	99.24 147	99.46 165	98.55 189	99.51 117	99.46 167	98.09 110	99.45 108	99.82 49	98.34 89	99.51 227	98.70 100	98.93 170	99.67 105

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 47	99.63 119	99.59 67	99.36 192	99.46 167	99.07 13	99.79 26	99.82 49	98.85 41	99.92 79	98.68 105	99.87 40	99.82 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.13 79	99.02 82	99.45 115	99.57 137	98.63 183	99.07 258	99.34 232	98.99 25	99.61 76	99.82 49	97.98 105	99.87 122	97.00 253	99.80 84	99.85 14

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

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

OPM-MVS		98.19 174	98.10 170	98.45 242	98.88 281	97.07 259	99.28 212	99.38 214	98.57 60	99.22 166	99.81 62	92.12 279	99.66 207	98.08 171	97.54 231	98.61 288

zzz-MVS		99.49 15	99.36 21	99.89 4	99.90 3	99.86 10	99.36 192	99.47 157	98.79 47	99.68 53	99.81 62	98.43 81	99.97 10	98.88 70	99.90 23	99.83 29

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

FIs		98.78 134	98.63 133	99.23 149	99.18 234	99.54 76	99.83 9	99.59 44	98.28 86	98.79 242	99.81 62	96.75 141	99.37 249	99.08 49	96.38 266	98.78 222

mvs_tets		98.40 159	98.23 163	98.91 186	98.67 310	98.51 197	99.66 46	99.53 83	98.19 97	98.65 264	99.81 62	92.75 259	99.44 236	99.31 26	97.48 239	98.77 225

mvs_anonymous		99.03 103	98.99 87	99.16 154	99.38 185	98.52 195	99.51 117	99.38 214	97.79 144	99.38 130	99.81 62	97.30 122	99.45 231	99.35 19	98.99 167	99.51 152

TSAR-MVS + GP.		99.36 49	99.36 21	99.36 126	99.67 100	98.61 186	99.07 258	99.33 239	99.00 22	99.82 20	99.81 62	99.06 13	99.84 136	99.09 48	99.42 134	99.65 112

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

RRT_test8_iter05		97.72 243	97.60 225	98.08 272	99.23 221	96.08 298	99.63 57	99.49 128	97.54 171	98.94 219	99.81 62	87.99 329	99.35 257	99.21 36	96.51 263	98.81 219

EPNet		98.86 119	98.71 124	99.30 137	97.20 340	98.18 213	99.62 63	98.91 300	99.28 2	98.63 266	99.81 62	95.96 163	99.99 1	99.24 33	99.72 103	99.73 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ab-mvs		98.86 119	98.63 133	99.54 92	99.64 116	99.19 115	99.44 152	99.54 71	97.77 146	99.30 145	99.81 62	94.20 229	99.93 68	99.17 40	98.82 178	99.49 156

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

xxxxxxxxxxxxxcwj		99.43 33	99.32 30	99.75 51	99.76 52	99.59 67	99.14 246	99.53 83	99.00 22	99.71 46	99.80 76	98.95 28	99.93 68	98.19 158	99.84 65	99.74 73

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

DVP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 88	99.37 222	99.10 8	99.81 22	99.80 76	98.94 31	99.96 18	98.93 64	99.86 51	99.81 41

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

jajsoiax		98.43 154	98.28 161	98.88 194	98.60 317	98.43 204	99.82 10	99.53 83	98.19 97	98.63 266	99.80 76	93.22 251	99.44 236	99.22 34	97.50 235	98.77 225

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

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

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

TransMVSNet (Re)		97.15 276	96.58 280	98.86 201	99.12 247	98.85 164	99.49 133	98.91 300	95.48 301	97.16 317	99.80 76	93.38 247	99.11 296	94.16 317	91.73 333	98.62 279

K. test v3		97.10 278	96.79 279	98.01 278	98.72 304	96.33 292	99.87 4	97.05 343	97.59 163	96.16 327	99.80 76	88.71 319	99.04 302	96.69 272	96.55 262	98.65 267

DELS-MVS		99.48 19	99.42 13	99.65 72	99.72 80	99.40 96	99.05 263	99.66 27	99.14 6	99.57 87	99.80 76	98.46 79	99.94 53	99.57 3	99.84 65	99.60 128
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CSCG		99.32 53	99.32 30	99.32 132	99.85 25	98.29 209	99.71 31	99.66 27	98.11 107	99.41 120	99.80 76	98.37 88	99.96 18	98.99 56	99.96 5	99.72 86

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

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

MP-MVS-pluss		99.37 48	99.20 59	99.88 6	99.90 3	99.87 9	99.30 206	99.52 89	97.18 205	99.60 80	99.79 88	98.79 47	99.95 42	98.83 84	99.91 16	99.83 29
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pm-mvs1		97.68 250	97.28 267	98.88 194	99.06 259	98.62 184	99.50 123	99.45 179	96.32 272	97.87 304	99.79 88	92.47 272	99.35 257	97.54 218	93.54 320	98.67 257

LFMVS		97.90 213	97.35 258	99.54 92	99.52 147	99.01 139	99.39 180	98.24 330	97.10 215	99.65 67	99.79 88	84.79 339	99.91 90	99.28 29	98.38 196	99.69 98

TinyColmap		97.12 277	96.89 277	97.83 290	99.07 257	95.52 308	98.57 325	98.74 314	97.58 165	97.81 307	99.79 88	88.16 327	99.56 223	95.10 304	97.21 250	98.39 313

ACMP	97.20 11	98.06 188	97.94 190	98.45 242	99.37 187	97.01 264	99.44 152	99.49 128	97.54 171	98.45 278	99.79 88	91.95 281	99.72 187	97.91 182	97.49 238	98.62 279
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
9.14			99.10 69		99.72 80		99.40 176	99.51 102	97.53 173	99.64 69	99.78 95	98.84 42	99.91 90	97.63 207	99.82 78

pmmvs6		96.53 286	96.09 289	97.82 291	98.69 308	95.47 309	99.37 188	99.47 157	93.46 324	97.41 312	99.78 95	87.06 333	99.33 261	96.92 262	92.70 330	98.65 267

MSLP-MVS++		99.46 24	99.47 9	99.44 120	99.60 132	99.16 120	99.41 168	99.71 13	98.98 27	99.45 108	99.78 95	99.19 7	99.54 226	99.28 29	99.84 65	99.63 122

VNet		99.11 90	98.90 100	99.73 58	99.52 147	99.56 72	99.41 168	99.39 208	99.01 18	99.74 41	99.78 95	95.56 179	99.92 79	99.52 6	98.18 207	99.72 86

114514_t		98.93 113	98.67 128	99.72 61	99.85 25	99.53 79	99.62 63	99.59 44	92.65 328	99.71 46	99.78 95	98.06 103	99.90 105	98.84 81	99.91 16	99.74 73

Vis-MVSNet (Re-imp)		98.87 116	98.72 122	99.31 133	99.71 86	98.88 160	99.80 16	99.44 187	97.91 131	99.36 135	99.78 95	95.49 182	99.43 241	97.91 182	99.11 155	99.62 124

UniMVSNet_ETH3D		97.32 272	96.81 278	98.87 198	99.40 181	97.46 244	99.51 117	99.53 83	95.86 298	98.54 274	99.77 101	82.44 344	99.66 207	98.68 105	97.52 232	99.50 155

anonymousdsp		98.44 153	98.28 161	98.94 178	98.50 322	98.96 149	99.77 21	99.50 120	97.07 216	98.87 230	99.77 101	94.76 208	99.28 267	98.66 107	97.60 225	98.57 294

CDS-MVSNet		99.09 94	99.03 79	99.25 145	99.42 173	98.73 175	99.45 148	99.46 167	98.11 107	99.46 107	99.77 101	98.01 104	99.37 249	98.70 100	98.92 172	99.66 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.98 109	98.80 115	99.53 98	99.76 52	99.19 115	98.75 311	99.55 64	97.25 199	99.47 105	99.77 101	97.82 108	99.87 122	96.93 260	99.90 23	99.54 141

test_part1		96.83 281	96.34 285	98.33 254	99.46 165	96.71 278	99.52 112	99.63 33	91.48 332	97.75 309	99.76 105	87.49 332	99.44 236	98.37 145	93.55 319	98.82 218

CHOSEN 280x420		99.12 85	99.13 65	99.08 159	99.66 109	97.89 229	98.43 332	99.71 13	98.88 37	99.62 74	99.76 105	96.63 144	99.70 199	99.46 14	99.99 1	99.66 108

PS-MVSNAJss		98.92 114	98.92 97	98.90 188	98.78 296	98.53 191	99.78 19	99.54 71	98.07 115	99.00 210	99.76 105	99.01 16	99.37 249	99.13 44	97.23 249	98.81 219

Regformer-1		99.53 11	99.47 9	99.72 61	99.71 86	99.44 91	99.49 133	99.46 167	98.95 32	99.83 17	99.76 105	99.01 16	99.93 68	99.17 40	99.87 40	99.80 49

Regformer-2		99.54 9	99.47 9	99.75 51	99.71 86	99.52 82	99.49 133	99.49 128	98.94 33	99.83 17	99.76 105	99.01 16	99.94 53	99.15 43	99.87 40	99.80 49

MVS_Test		99.10 93	98.97 91	99.48 109	99.49 158	99.14 125	99.67 42	99.34 232	97.31 193	99.58 85	99.76 105	97.65 113	99.82 152	98.87 74	99.07 161	99.46 164

ETH3D-3000-0.1		99.21 66	99.02 82	99.77 47	99.73 75	99.69 47	99.38 185	99.51 102	97.45 180	99.61 76	99.75 111	98.51 75	99.91 90	97.45 228	99.83 72	99.71 93

CANet_DTU		98.97 111	98.87 104	99.25 145	99.33 195	98.42 206	99.08 257	99.30 254	99.16 5	99.43 113	99.75 111	95.27 189	99.97 10	98.56 125	99.95 6	99.36 174

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

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

HyFIR lowres test		99.11 90	98.92 97	99.65 72	99.90 3	99.37 97	99.02 272	99.91 3	97.67 158	99.59 83	99.75 111	95.90 169	99.73 183	99.53 5	99.02 165	99.86 11

ITE_SJBPF				98.08 272	99.29 208	96.37 290		98.92 297	98.34 80	98.83 236	99.75 111	91.09 298	99.62 218	95.82 288	97.40 245	98.25 320

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

testtj		99.12 85	98.87 104	99.86 18	99.72 80	99.79 30	99.44 152	99.51 102	97.29 195	99.59 83	99.74 117	98.15 100	99.96 18	96.74 268	99.69 109	99.81 41

Anonymous202405211		98.30 166	97.98 184	99.26 144	99.57 137	98.16 214	99.41 168	98.55 326	96.03 296	99.19 175	99.74 117	91.87 282	99.92 79	99.16 42	98.29 202	99.70 95

tttt0517		98.42 155	98.14 167	99.28 142	99.66 109	98.38 207	99.74 28	96.85 344	97.68 156	99.79 26	99.74 117	91.39 294	99.89 113	98.83 84	99.56 127	99.57 137

XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 132	99.74 117	98.81 45	99.94 53	98.79 90	99.86 51	99.84 18

MP-MVS		99.33 52	99.15 63	99.87 11	99.88 11	99.82 20	99.66 46	99.46 167	98.09 110	99.48 104	99.74 117	98.29 92	99.96 18	97.93 181	99.87 40	99.82 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_LR		99.41 42	99.33 28	99.65 72	99.77 49	99.51 84	98.94 293	99.85 6	98.82 42	99.65 67	99.74 117	98.51 75	99.80 161	98.83 84	99.89 33	99.64 118

VPNet		97.84 222	97.44 246	99.01 168	99.21 227	98.94 154	99.48 139	99.57 51	98.38 75	99.28 150	99.73 124	88.89 318	99.39 244	99.19 37	93.27 323	98.71 236

MVSTER		98.49 150	98.32 158	99.00 170	99.35 190	99.02 137	99.54 106	99.38 214	97.41 186	99.20 172	99.73 124	93.86 241	99.36 253	98.87 74	97.56 229	98.62 279

MVS_111021_HR		99.41 42	99.32 30	99.66 68	99.72 80	99.47 88	98.95 291	99.85 6	98.82 42	99.54 93	99.73 124	98.51 75	99.74 176	98.91 67	99.88 36	99.77 63

PHI-MVS		99.30 55	99.17 62	99.70 64	99.56 141	99.52 82	99.58 83	99.80 8	97.12 211	99.62 74	99.73 124	98.58 70	99.90 105	98.61 114	99.91 16	99.68 102

IterMVS-SCA-FT		97.82 227	97.75 211	98.06 274	99.57 137	96.36 291	99.02 272	99.49 128	97.18 205	98.71 249	99.72 128	92.72 262	99.14 288	97.44 229	95.86 280	98.67 257

diffmvs		99.14 77	99.02 82	99.51 105	99.61 129	98.96 149	99.28 212	99.49 128	98.46 68	99.72 45	99.71 129	96.50 148	99.88 118	99.31 26	99.11 155	99.67 105

XVG-OURS-SEG-HR		98.69 141	98.62 138	98.89 191	99.71 86	97.74 236	99.12 248	99.54 71	98.44 72	99.42 116	99.71 129	94.20 229	99.92 79	98.54 130	98.90 174	99.00 204

EPNet_dtu		98.03 194	97.96 186	98.23 265	98.27 326	95.54 307	99.23 229	98.75 311	99.02 15	97.82 306	99.71 129	96.11 159	99.48 228	93.04 327	99.65 119	99.69 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		99.42 38	99.30 40	99.78 45	99.62 125	99.71 44	99.26 224	99.52 89	98.82 42	99.39 127	99.71 129	98.96 25	99.85 131	98.59 119	99.80 84	99.77 63

OPU-MVS				99.64 77	99.56 141	99.72 42	99.60 70				99.70 133	99.27 4	99.42 242	98.24 155	99.80 84	99.79 53

tfpnnormal		97.84 222	97.47 238	98.98 172	99.20 229	99.22 114	99.64 55	99.61 36	96.32 272	98.27 290	99.70 133	93.35 248	99.44 236	95.69 292	95.40 291	98.27 318

v7n		97.87 216	97.52 232	98.92 182	98.76 300	98.58 187	99.84 6	99.46 167	96.20 282	98.91 223	99.70 133	94.89 199	99.44 236	96.03 285	93.89 316	98.75 229

testdata				99.54 92	99.75 62	98.95 151		99.51 102	97.07 216	99.43 113	99.70 133	98.87 39	99.94 53	97.76 195	99.64 120	99.72 86

IterMVS		97.83 224	97.77 207	98.02 277	99.58 135	96.27 294	99.02 272	99.48 139	97.22 203	98.71 249	99.70 133	92.75 259	99.13 291	97.46 226	96.00 274	98.67 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	97.08 14	97.66 254	97.06 274	99.47 112	99.61 129	99.09 131	98.04 342	99.25 264	91.24 334	98.51 275	99.70 133	94.55 219	99.91 90	92.76 329	99.85 58	99.42 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LTVRE_ROB	97.16 12	98.02 196	97.90 193	98.40 249	99.23 221	96.80 276	99.70 33	99.60 41	97.12 211	98.18 293	99.70 133	91.73 286	99.72 187	98.39 141	97.45 240	98.68 249
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
HFP-MVS		99.49 15	99.37 19	99.86 18	99.87 15	99.80 26	99.66 46	99.67 22	98.15 101	99.68 53	99.69 140	99.06 13	99.96 18	98.69 103	99.87 40	99.84 18

#test#		99.43 33	99.29 44	99.86 18	99.87 15	99.80 26	99.55 103	99.67 22	97.83 138	99.68 53	99.69 140	99.06 13	99.96 18	98.39 141	99.87 40	99.84 18

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

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

CPTT-MVS		99.11 90	98.90 100	99.74 56	99.80 41	99.46 89	99.59 76	99.49 128	97.03 221	99.63 70	99.69 140	97.27 124	99.96 18	97.82 190	99.84 65	99.81 41

GST-MVS		99.40 45	99.24 55	99.85 25	99.86 21	99.79 30	99.60 70	99.67 22	97.97 126	99.63 70	99.68 145	98.52 74	99.95 42	98.38 143	99.86 51	99.81 41

Anonymous20231211		97.88 214	97.54 231	98.90 188	99.71 86	98.53 191	99.48 139	99.57 51	94.16 316	98.81 238	99.68 145	93.23 249	99.42 242	98.84 81	94.42 308	98.76 227

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

PS-CasMVS		97.93 208	97.59 227	98.95 177	98.99 269	99.06 134	99.68 40	99.52 89	97.13 209	98.31 287	99.68 145	92.44 276	99.05 301	98.51 131	94.08 314	98.75 229

HY-MVS	97.30 7	98.85 127	98.64 132	99.47 112	99.42 173	99.08 132	99.62 63	99.36 223	97.39 188	99.28 150	99.68 145	96.44 151	99.92 79	98.37 145	98.22 203	99.40 172

DP-MVS Recon		99.12 85	98.95 95	99.65 72	99.74 70	99.70 46	99.27 216	99.57 51	96.40 270	99.42 116	99.68 145	98.75 56	99.80 161	97.98 177	99.72 103	99.44 167

ETH3D cwj APD-0.16		99.06 98	98.84 110	99.72 61	99.51 149	99.60 64	99.23 229	99.44 187	97.04 219	99.39 127	99.67 151	98.30 91	99.92 79	97.27 235	99.69 109	99.64 118

ADS-MVSNet2		98.02 196	98.07 177	97.87 287	99.33 195	95.19 315	99.23 229	99.08 281	96.24 279	99.10 190	99.67 151	94.11 233	98.93 320	96.81 265	99.05 162	99.48 157

ADS-MVSNet		98.20 173	98.08 174	98.56 228	99.33 195	96.48 287	99.23 229	99.15 273	96.24 279	99.10 190	99.67 151	94.11 233	99.71 193	96.81 265	99.05 162	99.48 157

DTE-MVSNet		97.51 264	97.19 271	98.46 241	98.63 313	98.13 217	99.84 6	99.48 139	96.68 243	97.97 302	99.67 151	92.92 255	98.56 327	96.88 264	92.60 331	98.70 240

Baseline_NR-MVSNet		97.76 234	97.45 241	98.68 220	99.09 254	98.29 209	99.41 168	98.85 306	95.65 300	98.63 266	99.67 151	94.82 201	99.10 298	98.07 174	92.89 327	98.64 269

CMPMVS	69.68 23	94.13 309	94.90 304	91.84 327	97.24 339	80.01 350	98.52 328	99.48 139	89.01 338	91.99 340	99.67 151	85.67 337	99.13 291	95.44 297	97.03 255	96.39 341
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
原ACMM1				99.65 72	99.73 75	99.33 100		99.47 157	97.46 177	99.12 185	99.66 157	98.67 66	99.91 90	97.70 203	99.69 109	99.71 93

thisisatest0530		98.35 162	98.03 179	99.31 133	99.63 119	98.56 188	99.54 106	96.75 346	97.53 173	99.73 43	99.65 158	91.25 297	99.89 113	98.62 111	99.56 127	99.48 157

test222					99.75 62	99.49 85	98.91 296	99.49 128	96.42 268	99.34 141	99.65 158	98.28 93			99.69 109	99.72 86

1121		99.09 94	98.87 104	99.75 51	99.74 70	99.60 64	99.27 216	99.48 139	96.82 237	99.25 160	99.65 158	98.38 86	99.93 68	97.53 219	99.67 116	99.73 80

MVSFormer		99.17 73	99.12 67	99.29 140	99.51 149	98.94 154	99.88 1	99.46 167	97.55 168	99.80 24	99.65 158	97.39 117	99.28 267	99.03 52	99.85 58	99.65 112

jason		99.13 79	99.03 79	99.45 115	99.46 165	98.87 161	99.12 248	99.26 262	98.03 123	99.79 26	99.65 158	97.02 131	99.85 131	99.02 54	99.90 23	99.65 112
jason: jason.
BH-RMVSNet		98.41 157	98.08 174	99.40 122	99.41 176	98.83 168	99.30 206	98.77 310	97.70 154	98.94 219	99.65 158	92.91 257	99.74 176	96.52 276	99.55 129	99.64 118

sss		99.17 73	99.05 74	99.53 98	99.62 125	98.97 145	99.36 192	99.62 34	97.83 138	99.67 59	99.65 158	97.37 121	99.95 42	99.19 37	99.19 149	99.68 102

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

新几何1				99.75 51	99.75 62	99.59 67		99.54 71	96.76 238	99.29 148	99.64 165	98.43 81	99.94 53	96.92 262	99.66 117	99.72 86

PEN-MVS		97.76 234	97.44 246	98.72 217	98.77 299	98.54 190	99.78 19	99.51 102	97.06 218	98.29 289	99.64 165	92.63 267	98.89 323	98.09 167	93.16 324	98.72 234

CP-MVSNet		98.09 185	97.78 205	99.01 168	98.97 274	99.24 112	99.67 42	99.46 167	97.25 199	98.48 277	99.64 165	93.79 242	99.06 300	98.63 110	94.10 313	98.74 232

LF4IMVS		97.52 262	97.46 240	97.70 297	98.98 272	95.55 305	99.29 210	98.82 309	98.07 115	98.66 258	99.64 165	89.97 309	99.61 219	97.01 252	96.68 257	97.94 330

HPM-MVS		99.42 38	99.28 48	99.83 33	99.90 3	99.72 42	99.81 12	99.54 71	97.59 163	99.68 53	99.63 170	98.91 36	99.94 53	98.58 120	99.91 16	99.84 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NCCC		99.34 51	99.19 60	99.79 43	99.61 129	99.65 57	99.30 206	99.48 139	98.86 38	99.21 169	99.63 170	98.72 60	99.90 105	98.25 154	99.63 122	99.80 49

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

AdaColmap		99.01 107	98.80 115	99.66 68	99.56 141	99.54 76	99.18 238	99.70 15	98.18 100	99.35 138	99.63 170	96.32 154	99.90 105	97.48 223	99.77 92	99.55 139

TAPA-MVS	97.07 15	97.74 240	97.34 261	98.94 178	99.70 93	97.53 242	99.25 226	99.51 102	91.90 330	99.30 145	99.63 170	98.78 48	99.64 212	88.09 341	99.87 40	99.65 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ppachtmachnet_test		97.49 267	97.45 241	97.61 298	98.62 314	95.24 313	98.80 306	99.46 167	96.11 291	98.22 291	99.62 175	96.45 150	98.97 318	93.77 319	95.97 278	98.61 288

MCST-MVS		99.43 33	99.30 40	99.82 35	99.79 42	99.74 41	99.29 210	99.40 204	98.79 47	99.52 97	99.62 175	98.91 36	99.90 105	98.64 109	99.75 96	99.82 36

WTY-MVS		99.06 98	98.88 103	99.61 82	99.62 125	99.16 120	99.37 188	99.56 56	98.04 121	99.53 95	99.62 175	96.84 136	99.94 53	98.85 79	98.49 194	99.72 86

MDTV_nov1_ep13			98.32 158		99.11 249	94.44 325	99.27 216	98.74 314	97.51 175	99.40 125	99.62 175	94.78 204	99.76 173	97.59 210	98.81 180

CANet		99.25 64	99.14 64	99.59 84	99.41 176	99.16 120	99.35 197	99.57 51	98.82 42	99.51 99	99.61 179	96.46 149	99.95 42	99.59 1	99.98 2	99.65 112

HQP_MVS		98.27 169	98.22 164	98.44 245	99.29 208	96.97 268	99.39 180	99.47 157	98.97 30	99.11 187	99.61 179	92.71 264	99.69 202	97.78 193	97.63 222	98.67 257

plane_prior4											99.61 179

ETH3 D test6400		98.70 139	98.35 155	99.73 58	99.69 95	99.60 64	99.16 240	99.45 179	95.42 302	99.27 153	99.60 182	97.39 117	99.91 90	95.36 301	99.83 72	99.70 95

baseline1		98.31 164	97.95 188	99.38 125	99.50 156	98.74 174	99.59 76	98.93 295	98.41 73	99.14 182	99.60 182	94.59 216	99.79 164	98.48 133	93.29 322	99.61 126

TranMVSNet+NR-MVSNet		97.93 208	97.66 219	98.76 215	98.78 296	98.62 184	99.65 53	99.49 128	97.76 147	98.49 276	99.60 182	94.23 228	98.97 318	98.00 176	92.90 326	98.70 240

tpmrst		98.33 163	98.48 148	97.90 286	99.16 242	94.78 322	99.31 204	99.11 277	97.27 197	99.45 108	99.59 185	95.33 187	99.84 136	98.48 133	98.61 184	99.09 194

IterMVS-LS		98.46 152	98.42 151	98.58 225	99.59 134	98.00 221	99.37 188	99.43 194	96.94 229	99.07 196	99.59 185	97.87 106	99.03 304	98.32 152	95.62 286	98.71 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		99.19 69	99.04 77	99.64 77	99.78 44	99.27 109	99.42 165	99.54 71	97.29 195	99.41 120	99.59 185	98.42 84	99.93 68	98.19 158	99.69 109	99.73 80

pmmvs4		98.13 181	97.90 193	98.81 209	98.61 316	98.87 161	98.99 279	99.21 268	96.44 266	99.06 200	99.58 188	95.90 169	99.11 296	97.18 245	96.11 271	98.46 306

1112_ss		98.98 109	98.77 118	99.59 84	99.68 99	99.02 137	99.25 226	99.48 139	97.23 202	99.13 183	99.58 188	96.93 135	99.90 105	98.87 74	98.78 181	99.84 18

ab-mvs-re		8.30 327	11.06 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	99.58 188	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

PatchmatchNet		98.31 164	98.36 153	98.19 267	99.16 242	95.32 312	99.27 216	98.92 297	97.37 189	99.37 132	99.58 188	94.90 198	99.70 199	97.43 230	99.21 147	99.54 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		98.19 174	98.16 165	98.27 264	99.30 204	95.55 305	99.07 258	98.97 291	97.57 166	99.43 113	99.57 192	92.72 262	99.74 176	97.58 211	99.20 148	99.52 146

Patchmatch-test		97.93 208	97.65 220	98.77 214	99.18 234	97.07 259	99.03 269	99.14 275	96.16 286	98.74 246	99.57 192	94.56 218	99.72 187	93.36 323	99.11 155	99.52 146

PVSNet	96.02 17	98.85 127	98.84 110	98.89 191	99.73 75	97.28 248	98.32 335	99.60 41	97.86 133	99.50 100	99.57 192	96.75 141	99.86 125	98.56 125	99.70 108	99.54 141

cdsmvs_eth3d_5k		24.64 326	32.85 329	0.00 340	0.00 361	0.00 362	0.00 352	99.51 102	0.00 357	0.00 358	99.56 195	96.58 145	0.00 358	0.00 356	0.00 356	0.00 354

1314		98.68 142	98.54 146	99.11 158	98.89 280	98.65 181	99.27 216	99.49 128	96.89 231	97.99 301	99.56 195	97.72 112	99.83 145	97.74 198	99.27 144	98.84 217

lupinMVS		99.13 79	99.01 86	99.46 114	99.51 149	98.94 154	99.05 263	99.16 272	97.86 133	99.80 24	99.56 195	97.39 117	99.86 125	98.94 62	99.85 58	99.58 136

miper_lstm_enhance		98.00 201	97.91 192	98.28 263	99.34 194	97.43 245	98.88 298	99.36 223	96.48 263	98.80 240	99.55 198	95.98 162	98.91 321	97.27 235	95.50 290	98.51 298

DPM-MVS		98.95 112	98.71 124	99.66 68	99.63 119	99.55 74	98.64 321	99.10 278	97.93 129	99.42 116	99.55 198	98.67 66	99.80 161	95.80 290	99.68 114	99.61 126

CDPH-MVS		99.13 79	98.91 99	99.80 40	99.75 62	99.71 44	99.15 244	99.41 198	96.60 252	99.60 80	99.55 198	98.83 43	99.90 105	97.48 223	99.83 72	99.78 61

dp		97.75 238	97.80 201	97.59 299	99.10 252	93.71 332	99.32 202	98.88 304	96.48 263	99.08 195	99.55 198	92.67 266	99.82 152	96.52 276	98.58 187	99.24 182

CLD-MVS		98.16 178	98.10 170	98.33 254	99.29 208	96.82 275	98.75 311	99.44 187	97.83 138	99.13 183	99.55 198	92.92 255	99.67 204	98.32 152	97.69 221	98.48 300
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ZD-MVS					99.71 86	99.79 30		99.61 36	96.84 234	99.56 88	99.54 203	98.58 70	99.96 18	96.93 260	99.75 96

cl-mvsnet_		98.01 199	97.84 200	98.55 230	99.25 219	97.97 223	98.71 315	99.34 232	96.47 265	98.59 272	99.54 203	95.65 178	99.21 283	97.21 239	95.77 281	98.46 306

cl-mvsnet1		98.01 199	97.85 199	98.48 236	99.24 220	97.95 227	98.71 315	99.35 228	96.50 258	98.60 271	99.54 203	95.72 176	99.03 304	97.21 239	95.77 281	98.46 306

MVS		97.28 273	96.55 281	99.48 109	98.78 296	98.95 151	99.27 216	99.39 208	83.53 344	98.08 296	99.54 203	96.97 133	99.87 122	94.23 315	99.16 150	99.63 122

pmmvs5		97.52 262	97.30 266	98.16 269	98.57 319	96.73 277	99.27 216	98.90 302	96.14 289	98.37 283	99.53 207	91.54 293	99.14 288	97.51 221	95.87 279	98.63 277

HPM-MVS++		99.39 46	99.23 57	99.87 11	99.75 62	99.84 13	99.43 158	99.51 102	98.68 55	99.27 153	99.53 207	98.64 68	99.96 18	98.44 139	99.80 84	99.79 53

PatchMatch-RL		98.84 129	98.62 138	99.52 103	99.71 86	99.28 107	99.06 261	99.77 9	97.74 151	99.50 100	99.53 207	95.41 183	99.84 136	97.17 246	99.64 120	99.44 167

eth_miper_zixun_eth		98.05 193	97.96 186	98.33 254	99.26 215	97.38 246	98.56 327	99.31 250	96.65 246	98.88 228	99.52 210	96.58 145	99.12 295	97.39 232	95.53 289	98.47 302

test_prior3		99.21 66	99.05 74	99.68 65	99.67 100	99.48 86	98.96 287	99.56 56	98.34 80	99.01 205	99.52 210	98.68 63	99.83 145	97.96 178	99.74 99	99.74 73

test_prior2							98.96 287		98.34 80	99.01 205	99.52 210	98.68 63		97.96 178	99.74 99

test_0402		96.64 284	96.24 286	97.85 288	98.85 289	96.43 289	99.44 152	99.26 262	93.52 322	96.98 320	99.52 210	88.52 323	99.20 285	92.58 331	97.50 235	97.93 331

test_yl		98.86 119	98.63 133	99.54 92	99.49 158	99.18 117	99.50 123	99.07 283	98.22 94	99.61 76	99.51 214	95.37 185	99.84 136	98.60 117	98.33 197	99.59 132

DCV-MVSNet		98.86 119	98.63 133	99.54 92	99.49 158	99.18 117	99.50 123	99.07 283	98.22 94	99.61 76	99.51 214	95.37 185	99.84 136	98.60 117	98.33 197	99.59 132

v148		97.79 232	97.55 228	98.50 233	98.74 301	97.72 238	99.54 106	99.33 239	96.26 277	98.90 225	99.51 214	94.68 212	99.14 288	97.83 189	93.15 325	98.63 277

DU-MVS		98.08 187	97.79 202	98.96 175	98.87 285	98.98 142	99.41 168	99.45 179	97.87 132	98.71 249	99.50 217	94.82 201	99.22 278	98.57 122	92.87 328	98.68 249

NR-MVSNet		97.97 206	97.61 224	99.02 167	98.87 285	99.26 110	99.47 144	99.42 196	97.63 161	97.08 318	99.50 217	95.07 195	99.13 291	97.86 186	93.59 318	98.68 249

XVG-ACMP-BASELINE		97.83 224	97.71 215	98.20 266	99.11 249	96.33 292	99.41 168	99.52 89	98.06 119	99.05 201	99.50 217	89.64 312	99.73 183	97.73 199	97.38 246	98.53 296

MSP-MVS		99.42 38	99.27 50	99.88 6	99.89 8	99.80 26	99.67 42	99.50 120	98.70 53	99.77 33	99.49 220	98.21 96	99.95 42	98.46 137	99.77 92	99.88 5

TEST9					99.67 100	99.65 57	99.05 263	99.41 198	96.22 281	98.95 217	99.49 220	98.77 51	99.91 90

train_agg		99.02 104	98.77 118	99.77 47	99.67 100	99.65 57	99.05 263	99.41 198	96.28 274	98.95 217	99.49 220	98.76 53	99.91 90	97.63 207	99.72 103	99.75 69

agg_prior1		99.01 107	98.76 120	99.76 50	99.67 100	99.62 60	98.99 279	99.40 204	96.26 277	98.87 230	99.49 220	98.77 51	99.91 90	97.69 204	99.72 103	99.75 69

PVSNet_Blended		99.08 96	98.97 91	99.42 121	99.76 52	98.79 172	98.78 308	99.91 3	96.74 239	99.67 59	99.49 220	97.53 114	99.88 118	98.98 57	99.85 58	99.60 128

CNLPA		99.14 77	98.99 87	99.59 84	99.58 135	99.41 94	99.16 240	99.44 187	98.45 69	99.19 175	99.49 220	98.08 102	99.89 113	97.73 199	99.75 96	99.48 157

test_8					99.67 100	99.61 62	99.03 269	99.41 198	96.28 274	98.93 221	99.48 226	98.76 53	99.91 90

EPMVS		97.82 227	97.65 220	98.35 253	98.88 281	95.98 299	99.49 133	94.71 352	97.57 166	99.26 158	99.48 226	92.46 275	99.71 193	97.87 185	99.08 160	99.35 175

PLC	97.94 4	99.02 104	98.85 109	99.53 98	99.66 109	99.01 139	99.24 228	99.52 89	96.85 233	99.27 153	99.48 226	98.25 94	99.91 90	97.76 195	99.62 124	99.65 112
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 248	99.51 102	98.86 38	99.84 13	99.47 229	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

xiu_mvs_v1_base		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 248	99.51 102	98.86 38	99.84 13	99.47 229	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

xiu_mvs_v1_base_debi		99.29 57	99.27 50	99.34 127	99.63 119	98.97 145	99.12 248	99.51 102	98.86 38	99.84 13	99.47 229	98.18 97	99.99 1	99.50 8	99.31 141	99.08 195

v1921920		97.80 231	97.45 241	98.84 205	98.80 292	98.53 191	99.52 112	99.34 232	96.15 288	99.24 161	99.47 229	93.98 237	99.29 266	95.40 299	95.13 297	98.69 244

UniMVSNet_NR-MVSNet		98.22 170	97.97 185	98.96 175	98.92 278	98.98 142	99.48 139	99.53 83	97.76 147	98.71 249	99.46 233	96.43 152	99.22 278	98.57 122	92.87 328	98.69 244

testgi		97.65 255	97.50 235	98.13 271	99.36 189	96.45 288	99.42 165	99.48 139	97.76 147	97.87 304	99.45 234	91.09 298	98.81 324	94.53 311	98.52 192	99.13 188

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

tpm2		97.44 269	97.34 261	97.74 295	99.15 245	94.36 326	99.45 148	98.94 294	93.45 325	98.90 225	99.44 235	91.35 295	99.59 221	97.31 233	98.07 214	99.29 180

thisisatest0515		98.14 180	97.79 202	99.19 151	99.50 156	98.50 198	98.61 322	96.82 345	96.95 227	99.54 93	99.43 237	91.66 290	99.86 125	98.08 171	99.51 131	99.22 183

mvs-test1		98.86 119	98.84 110	98.89 191	99.33 195	97.77 235	99.44 152	99.30 254	98.47 66	99.10 190	99.43 237	96.78 138	99.95 42	98.73 96	99.02 165	98.96 210

WR-MVS		98.06 188	97.73 213	99.06 161	98.86 288	99.25 111	99.19 237	99.35 228	97.30 194	98.66 258	99.43 237	93.94 238	99.21 283	98.58 120	94.28 310	98.71 236

v8		97.95 207	97.63 223	98.93 180	98.95 276	98.81 171	99.80 16	99.41 198	96.03 296	99.10 190	99.42 240	94.92 197	99.30 265	96.94 259	94.08 314	98.66 265

tpmvs		97.98 203	98.02 181	97.84 289	99.04 263	94.73 323	99.31 204	99.20 269	96.10 295	98.76 245	99.42 240	94.94 196	99.81 156	96.97 256	98.45 195	98.97 208

UGNet		98.87 116	98.69 126	99.40 122	99.22 225	98.72 176	99.44 152	99.68 19	99.24 3	99.18 178	99.42 240	92.74 261	99.96 18	99.34 23	99.94 9	99.53 145
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Effi-MVS+		98.81 130	98.59 143	99.48 109	99.46 165	99.12 129	98.08 341	99.50 120	97.50 176	99.38 130	99.41 243	96.37 153	99.81 156	99.11 46	98.54 191	99.51 152

v10		97.85 219	97.52 232	98.86 201	98.99 269	98.67 179	99.75 25	99.41 198	95.70 299	98.98 213	99.41 243	94.75 209	99.23 275	96.01 286	94.63 304	98.67 257

v144192		97.92 211	97.60 225	98.87 198	98.83 291	98.65 181	99.55 103	99.34 232	96.20 282	99.32 143	99.40 245	94.36 224	99.26 272	96.37 281	95.03 299	98.70 240

NP-MVS					99.23 221	96.92 271					99.40 245

HQP-MVS		98.02 196	97.90 193	98.37 252	99.19 231	96.83 273	98.98 283	99.39 208	98.24 90	98.66 258	99.40 245	92.47 272	99.64 212	97.19 243	97.58 227	98.64 269

MAR-MVS		98.86 119	98.63 133	99.54 92	99.37 187	99.66 54	99.45 148	99.54 71	96.61 250	99.01 205	99.40 245	97.09 128	99.86 125	97.68 206	99.53 130	99.10 190
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
API-MVS		99.04 101	99.03 79	99.06 161	99.40 181	99.31 104	99.55 103	99.56 56	98.54 61	99.33 142	99.39 249	98.76 53	99.78 168	96.98 255	99.78 89	98.07 324

CR-MVSNet		98.17 177	97.93 191	98.87 198	99.18 234	98.49 199	99.22 234	99.33 239	96.96 225	99.56 88	99.38 250	94.33 225	99.00 309	94.83 309	98.58 187	99.14 186

Patchmtry		97.75 238	97.40 252	98.81 209	99.10 252	98.87 161	99.11 254	99.33 239	94.83 308	98.81 238	99.38 250	94.33 225	99.02 306	96.10 283	95.57 287	98.53 296

BH-untuned		98.42 155	98.36 153	98.59 224	99.49 158	96.70 279	99.27 216	99.13 276	97.24 201	98.80 240	99.38 250	95.75 174	99.74 176	97.07 251	99.16 150	99.33 178

V42		98.06 188	97.79 202	98.86 201	98.98 272	98.84 165	99.69 35	99.34 232	96.53 257	99.30 145	99.37 253	94.67 213	99.32 262	97.57 215	94.66 303	98.42 309

VPA-MVSNet		98.29 167	97.95 188	99.30 137	99.16 242	99.54 76	99.50 123	99.58 50	98.27 88	99.35 138	99.37 253	92.53 270	99.65 210	99.35 19	94.46 306	98.72 234

PVSNet_BlendedMVS		98.86 119	98.80 115	99.03 166	99.76 52	98.79 172	99.28 212	99.91 3	97.42 185	99.67 59	99.37 253	97.53 114	99.88 118	98.98 57	97.29 248	98.42 309

D2MVS		98.41 157	98.50 147	98.15 270	99.26 215	96.62 283	99.40 176	99.61 36	97.71 153	98.98 213	99.36 256	96.04 161	99.67 204	98.70 100	97.41 244	98.15 322

MVP-Stereo		97.81 229	97.75 211	97.99 280	97.53 333	96.60 284	98.96 287	98.85 306	97.22 203	97.23 315	99.36 256	95.28 188	99.46 230	95.51 296	99.78 89	97.92 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1240		97.69 248	97.32 264	98.79 212	98.85 289	98.43 204	99.48 139	99.36 223	96.11 291	99.27 153	99.36 256	93.76 244	99.24 274	94.46 312	95.23 294	98.70 240

v1144		97.98 203	97.69 216	98.85 204	98.87 285	98.66 180	99.54 106	99.35 228	96.27 276	99.23 165	99.35 259	94.67 213	99.23 275	96.73 269	95.16 296	98.68 249

v2v482		98.06 188	97.77 207	98.92 182	98.90 279	98.82 169	99.57 88	99.36 223	96.65 246	99.19 175	99.35 259	94.20 229	99.25 273	97.72 201	94.97 300	98.69 244

CostFormer		97.72 243	97.73 213	97.71 296	99.15 245	94.02 329	99.54 106	99.02 287	94.67 311	99.04 202	99.35 259	92.35 278	99.77 170	98.50 132	97.94 216	99.34 177

our_test_3		97.65 255	97.68 217	97.55 301	98.62 314	94.97 319	98.84 302	99.30 254	96.83 236	98.19 292	99.34 262	97.01 132	99.02 306	95.00 307	96.01 273	98.64 269

cl_fuxian		98.12 183	98.04 178	98.38 251	99.30 204	97.69 241	98.81 305	99.33 239	96.67 244	98.83 236	99.34 262	97.11 127	98.99 310	97.58 211	95.34 292	98.48 300

Fast-Effi-MVS+-dtu		98.77 136	98.83 114	98.60 223	99.41 176	96.99 266	99.52 112	99.49 128	98.11 107	99.24 161	99.34 262	96.96 134	99.79 164	97.95 180	99.45 132	99.02 203

Fast-Effi-MVS+		98.70 139	98.43 150	99.51 105	99.51 149	99.28 107	99.52 112	99.47 157	96.11 291	99.01 205	99.34 262	96.20 158	99.84 136	97.88 184	98.82 178	99.39 173

v1192		97.81 229	97.44 246	98.91 186	98.88 281	98.68 178	99.51 117	99.34 232	96.18 284	99.20 172	99.34 262	94.03 236	99.36 253	95.32 302	95.18 295	98.69 244

tpm		97.67 253	97.55 228	98.03 275	99.02 266	95.01 318	99.43 158	98.54 327	96.44 266	99.12 185	99.34 262	91.83 283	99.60 220	97.75 197	96.46 264	99.48 157

PAPM		97.59 258	97.09 273	99.07 160	99.06 259	98.26 211	98.30 336	99.10 278	94.88 307	98.08 296	99.34 262	96.27 156	99.64 212	89.87 336	98.92 172	99.31 179

GBi-Net		97.68 250	97.48 236	98.29 260	99.51 149	97.26 250	99.43 158	99.48 139	96.49 259	99.07 196	99.32 269	90.26 304	98.98 311	97.10 248	96.65 258	98.62 279

test1		97.68 250	97.48 236	98.29 260	99.51 149	97.26 250	99.43 158	99.48 139	96.49 259	99.07 196	99.32 269	90.26 304	98.98 311	97.10 248	96.65 258	98.62 279

FMVSNet1		96.84 280	96.36 284	98.29 260	99.32 202	97.26 250	99.43 158	99.48 139	95.11 305	98.55 273	99.32 269	83.95 340	98.98 311	95.81 289	96.26 269	98.62 279

MS-PatchMatch		97.24 275	97.32 264	96.99 309	98.45 324	93.51 335	98.82 304	99.32 247	97.41 186	98.13 295	99.30 272	88.99 317	99.56 223	95.68 293	99.80 84	97.90 333

GA-MVS		97.85 219	97.47 238	99.00 170	99.38 185	97.99 222	98.57 325	99.15 273	97.04 219	98.90 225	99.30 272	89.83 310	99.38 246	96.70 271	98.33 197	99.62 124

miper_ehance_all_eth		98.18 176	98.10 170	98.41 247	99.23 221	97.72 238	98.72 314	99.31 250	96.60 252	98.88 228	99.29 274	97.29 123	99.13 291	97.60 209	95.99 275	98.38 314

FMVSNet2		97.72 243	97.36 256	98.80 211	99.51 149	98.84 165	99.45 148	99.42 196	96.49 259	98.86 235	99.29 274	90.26 304	98.98 311	96.44 278	96.56 261	98.58 293

TESTMET0.1,1		97.55 259	97.27 269	98.40 249	98.93 277	96.53 285	98.67 317	97.61 340	96.96 225	98.64 265	99.28 276	88.63 322	99.45 231	97.30 234	99.38 136	99.21 184

FMVSNet3		98.03 194	97.76 210	98.84 205	99.39 184	98.98 142	99.40 176	99.38 214	96.67 244	99.07 196	99.28 276	92.93 254	98.98 311	97.10 248	96.65 258	98.56 295

PAPM_NR		99.04 101	98.84 110	99.66 68	99.74 70	99.44 91	99.39 180	99.38 214	97.70 154	99.28 150	99.28 276	98.34 89	99.85 131	96.96 257	99.45 132	99.69 98

ETV-MVS		99.26 62	99.21 58	99.40 122	99.46 165	99.30 105	99.56 95	99.52 89	98.52 63	99.44 112	99.27 279	98.41 85	99.86 125	99.10 47	99.59 126	99.04 200

xiu_mvs_v2_base		99.26 62	99.25 54	99.29 140	99.53 145	98.91 158	99.02 272	99.45 179	98.80 46	99.71 46	99.26 280	98.94 31	99.98 5	99.34 23	99.23 146	98.98 207

test20.03		96.12 294	95.96 292	96.63 316	97.44 334	95.45 310	99.51 117	99.38 214	96.55 256	96.16 327	99.25 281	93.76 244	96.17 346	87.35 343	94.22 311	98.27 318

CS-MVS		99.21 66	99.13 65	99.45 115	99.54 144	99.34 99	99.71 31	99.54 71	98.26 89	98.99 212	99.24 282	98.25 94	99.88 118	98.98 57	99.63 122	99.12 189

PS-MVSNAJ		99.32 53	99.32 30	99.30 137	99.57 137	98.94 154	98.97 286	99.46 167	98.92 35	99.71 46	99.24 282	99.01 16	99.98 5	99.35 19	99.66 117	98.97 208

Test_1112_low_res		98.89 115	98.66 131	99.57 88	99.69 95	98.95 151	99.03 269	99.47 157	96.98 223	99.15 181	99.23 284	96.77 140	99.89 113	98.83 84	98.78 181	99.86 11

cl-mvsnet2		97.85 219	97.64 222	98.48 236	99.09 254	97.87 230	98.60 324	99.33 239	97.11 214	98.87 230	99.22 285	92.38 277	99.17 287	98.21 157	95.99 275	98.42 309

EG-PatchMatch MVS		95.97 296	95.69 296	96.81 314	97.78 332	92.79 338	99.16 240	98.93 295	96.16 286	94.08 335	99.22 285	82.72 342	99.47 229	95.67 294	97.50 235	98.17 321

TR-MVS		97.76 234	97.41 251	98.82 207	99.06 259	97.87 230	98.87 300	98.56 325	96.63 249	98.68 257	99.22 285	92.49 271	99.65 210	95.40 299	97.79 219	98.95 213

ET-MVSNet_ETH3D		96.49 287	95.64 297	99.05 163	99.53 145	98.82 169	98.84 302	97.51 341	97.63 161	84.77 344	99.21 288	92.09 280	98.91 321	98.98 57	92.21 332	99.41 171

WR-MVS_H		98.13 181	97.87 198	98.90 188	99.02 266	98.84 165	99.70 33	99.59 44	97.27 197	98.40 281	99.19 289	95.53 180	99.23 275	98.34 149	93.78 317	98.61 288

miper_enhance_ethall		98.16 178	98.08 174	98.41 247	98.96 275	97.72 238	98.45 331	99.32 247	96.95 227	98.97 215	99.17 290	97.06 130	99.22 278	97.86 186	95.99 275	98.29 317

baseline2		97.87 216	97.55 228	98.82 207	99.18 234	98.02 220	99.41 168	96.58 348	96.97 224	96.51 323	99.17 290	93.43 246	99.57 222	97.71 202	99.03 164	98.86 215

MIMVSNet1		95.51 299	95.04 303	96.92 312	97.38 335	95.60 303	99.52 112	99.50 120	93.65 321	96.97 321	99.17 290	85.28 338	96.56 345	88.36 340	95.55 288	98.60 291

gm-plane-assit					98.54 321	92.96 337			94.65 312		99.15 293		99.64 212	97.56 216

MIMVSNet		97.73 241	97.45 241	98.57 226	99.45 171	97.50 243	99.02 272	98.98 290	96.11 291	99.41 120	99.14 294	90.28 303	98.74 325	95.74 291	98.93 170	99.47 162

LCM-MVSNet-Re		97.83 224	98.15 166	96.87 313	99.30 204	92.25 340	99.59 76	98.26 329	97.43 183	96.20 326	99.13 295	96.27 156	98.73 326	98.17 162	98.99 167	99.64 118

UniMVSNet (Re)		98.29 167	98.00 182	99.13 157	99.00 268	99.36 98	99.49 133	99.51 102	97.95 127	98.97 215	99.13 295	96.30 155	99.38 246	98.36 148	93.34 321	98.66 265

N_pmnet		94.95 305	95.83 294	92.31 326	98.47 323	79.33 351	99.12 248	92.81 357	93.87 318	97.68 310	99.13 295	93.87 240	99.01 308	91.38 333	96.19 270	98.59 292

PAPR		98.63 147	98.34 156	99.51 105	99.40 181	99.03 136	98.80 306	99.36 223	96.33 271	99.00 210	99.12 298	98.46 79	99.84 136	95.23 303	99.37 140	99.66 108

tpm cat1		97.39 270	97.36 256	97.50 303	99.17 240	93.73 331	99.43 158	99.31 250	91.27 333	98.71 249	99.08 299	94.31 227	99.77 170	96.41 280	98.50 193	99.00 204

FMVSNet5		96.43 289	96.19 287	97.15 306	99.11 249	95.89 301	99.32 202	99.52 89	94.47 315	98.34 286	99.07 300	87.54 331	97.07 342	92.61 330	95.72 284	98.47 302

PMMVS		98.80 133	98.62 138	99.34 127	99.27 213	98.70 177	98.76 310	99.31 250	97.34 190	99.21 169	99.07 300	97.20 125	99.82 152	98.56 125	98.87 175	99.52 146

Anonymous20231206		96.22 291	96.03 290	96.79 315	97.31 338	94.14 328	99.63 57	99.08 281	96.17 285	97.04 319	99.06 302	93.94 238	97.76 338	86.96 344	95.06 298	98.47 302

DeepMVS_CX				93.34 324	99.29 208	82.27 348		99.22 267	85.15 342	96.33 325	99.05 303	90.97 300	99.73 183	93.57 321	97.77 220	98.01 327

YYNet1		95.36 302	94.51 308	97.92 284	97.89 330	97.10 255	99.10 256	99.23 266	93.26 326	80.77 348	99.04 304	92.81 258	98.02 331	94.30 313	94.18 312	98.64 269

MDA-MVSNet-bldmvs		94.96 304	93.98 310	97.92 284	98.24 327	97.27 249	99.15 244	99.33 239	93.80 319	80.09 350	99.03 305	88.31 325	97.86 336	93.49 322	94.36 309	98.62 279

BH-w/o		98.00 201	97.89 197	98.32 257	99.35 190	96.20 296	99.01 277	98.90 302	96.42 268	98.38 282	99.00 306	95.26 191	99.72 187	96.06 284	98.61 184	99.03 201

Effi-MVS+-dtu		98.78 134	98.89 102	98.47 240	99.33 195	96.91 272	99.57 88	99.30 254	98.47 66	99.41 120	98.99 307	96.78 138	99.74 176	98.73 96	99.38 136	98.74 232

MVS_0304		96.79 282	96.52 282	97.59 299	99.22 225	94.92 320	99.04 268	99.59 44	96.49 259	98.43 279	98.99 307	80.48 346	99.39 244	97.15 247	99.27 144	98.47 302

UnsupCasMVSNet_eth		96.44 288	96.12 288	97.40 305	98.65 311	95.65 302	99.36 192	99.51 102	97.13 209	96.04 329	98.99 307	88.40 324	98.17 330	96.71 270	90.27 336	98.40 312

test0.0.03 1		97.71 247	97.42 250	98.56 228	98.41 325	97.82 233	98.78 308	98.63 323	97.34 190	98.05 300	98.98 310	94.45 222	98.98 311	95.04 306	97.15 254	98.89 214

MDA-MVSNet_test_wron		95.45 300	94.60 306	98.01 278	98.16 328	97.21 253	99.11 254	99.24 265	93.49 323	80.73 349	98.98 310	93.02 252	98.18 329	94.22 316	94.45 307	98.64 269

FPMVS		84.93 316	85.65 317	82.75 334	86.77 353	63.39 357	98.35 334	98.92 297	74.11 347	83.39 346	98.98 310	50.85 354	92.40 350	84.54 347	94.97 300	92.46 344

alignmvs		98.81 130	98.56 145	99.58 87	99.43 172	99.42 93	99.51 117	98.96 293	98.61 58	99.35 138	98.92 313	94.78 204	99.77 170	99.35 19	98.11 213	99.54 141

test-LLR		98.06 188	97.90 193	98.55 230	98.79 293	97.10 255	98.67 317	97.75 337	97.34 190	98.61 269	98.85 314	94.45 222	99.45 231	97.25 237	99.38 136	99.10 190

test-mter		97.49 267	97.13 272	98.55 230	98.79 293	97.10 255	98.67 317	97.75 337	96.65 246	98.61 269	98.85 314	88.23 326	99.45 231	97.25 237	99.38 136	99.10 190

canonicalmvs		99.02 104	98.86 108	99.51 105	99.42 173	99.32 101	99.80 16	99.48 139	98.63 56	99.31 144	98.81 316	97.09 128	99.75 175	99.27 31	97.90 217	99.47 162

DWT-MVSNet_test		97.53 261	97.40 252	97.93 283	99.03 265	94.86 321	99.57 88	98.63 323	96.59 255	98.36 284	98.79 317	89.32 314	99.74 176	98.14 165	98.16 211	99.20 185

new_pmnet		96.38 290	96.03 290	97.41 304	98.13 329	95.16 317	99.05 263	99.20 269	93.94 317	97.39 313	98.79 317	91.61 292	99.04 302	90.43 335	95.77 281	98.05 325

cascas		97.69 248	97.43 249	98.48 236	98.60 317	97.30 247	98.18 340	99.39 208	92.96 327	98.41 280	98.78 319	93.77 243	99.27 270	98.16 163	98.61 184	98.86 215

PVSNet_0	94.43 19	96.09 295	95.47 298	97.94 282	99.31 203	94.34 327	97.81 343	99.70 15	97.12 211	97.46 311	98.75 320	89.71 311	99.79 164	97.69 204	81.69 346	99.68 102

patchmatchnet-post											98.70 321	94.79 203	99.74 176

Patchmatch-RL test		95.84 297	95.81 295	95.95 320	95.61 341	90.57 343	98.24 337	98.39 328	95.10 306	95.20 331	98.67 322	94.78 204	97.77 337	96.28 282	90.02 337	99.51 152

thres100view900		97.76 234	97.45 241	98.69 219	99.72 80	97.86 232	99.59 76	98.74 314	97.93 129	99.26 158	98.62 323	91.75 284	99.83 145	93.22 324	98.18 207	98.37 315

thres600view7		97.86 218	97.51 234	98.92 182	99.72 80	97.95 227	99.59 76	98.74 314	97.94 128	99.27 153	98.62 323	91.75 284	99.86 125	93.73 320	98.19 206	98.96 210

DSMNet-mixed		97.25 274	97.35 258	96.95 311	97.84 331	93.61 334	99.57 88	96.63 347	96.13 290	98.87 230	98.61 325	94.59 216	97.70 339	95.08 305	98.86 176	99.55 139

IB-MVS	95.67 18	96.22 291	95.44 300	98.57 226	99.21 227	96.70 279	98.65 320	97.74 339	96.71 241	97.27 314	98.54 326	86.03 335	99.92 79	98.47 136	86.30 343	99.10 190
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
GG-mvs-BLEND				98.45 242	98.55 320	98.16 214	99.43 158	93.68 354		97.23 315	98.46 327	89.30 315	99.22 278	95.43 298	98.22 203	97.98 328

tfpn200view9		97.72 243	97.38 254	98.72 217	99.69 95	97.96 225	99.50 123	98.73 319	97.83 138	99.17 179	98.45 328	91.67 288	99.83 145	93.22 324	98.18 207	98.37 315

thres400		97.77 233	97.38 254	98.92 182	99.69 95	97.96 225	99.50 123	98.73 319	97.83 138	99.17 179	98.45 328	91.67 288	99.83 145	93.22 324	98.18 207	98.96 210

thres200		97.61 257	97.28 267	98.62 222	99.64 116	98.03 219	99.26 224	98.74 314	97.68 156	99.09 194	98.32 330	91.66 290	99.81 156	92.88 328	98.22 203	98.03 326

OpenMVS_ROB	92.34 20	94.38 308	93.70 311	96.41 319	97.38 335	93.17 336	99.06 261	98.75 311	86.58 341	94.84 334	98.26 331	81.53 345	99.32 262	89.01 338	97.87 218	96.76 339

pmmvs3		94.09 310	93.25 312	96.60 317	94.76 345	94.49 324	98.92 294	98.18 333	89.66 337	96.48 324	98.06 332	86.28 334	97.33 341	89.68 337	87.20 342	97.97 329

PM-MVS		92.96 312	92.23 315	95.14 322	95.61 341	89.98 345	99.37 188	98.21 331	94.80 309	95.04 333	97.69 333	65.06 350	97.90 335	94.30 313	89.98 338	97.54 338

pmmvs-eth3d		95.34 303	94.73 305	97.15 306	95.53 343	95.94 300	99.35 197	99.10 278	95.13 304	93.55 336	97.54 334	88.15 328	97.91 334	94.58 310	89.69 339	97.61 335

ambc				93.06 325	92.68 347	82.36 347	98.47 330	98.73 319		95.09 332	97.41 335	55.55 353	99.10 298	96.42 279	91.32 334	97.71 334

RPMNet		96.72 283	95.90 293	99.19 151	99.18 234	98.49 199	99.22 234	99.52 89	88.72 340	99.56 88	97.38 336	94.08 235	99.95 42	86.87 345	98.58 187	99.14 186

new-patchmatchnet		94.48 306	94.08 309	95.67 321	95.08 344	92.41 339	99.18 238	99.28 260	94.55 314	93.49 337	97.37 337	87.86 330	97.01 343	91.57 332	88.36 340	97.61 335

PatchT		97.03 279	96.44 283	98.79 212	98.99 269	98.34 208	99.16 240	99.07 283	92.13 329	99.52 97	97.31 338	94.54 220	98.98 311	88.54 339	98.73 183	99.03 201

testing_2		94.44 307	92.93 313	98.98 172	94.16 346	99.00 141	99.42 165	99.28 260	96.60 252	84.86 343	96.84 339	70.91 348	99.27 270	98.23 156	96.08 272	98.68 249

UnsupCasMVSNet_bld		93.53 311	92.51 314	96.58 318	97.38 335	93.82 330	98.24 337	99.48 139	91.10 335	93.10 338	96.66 340	74.89 347	98.37 328	94.03 318	87.71 341	97.56 337

LCM-MVSNet		86.80 315	85.22 319	91.53 328	87.81 352	80.96 349	98.23 339	98.99 289	71.05 348	90.13 342	96.51 341	48.45 356	96.88 344	90.51 334	85.30 344	96.76 339

PMMVS2		86.87 314	85.37 318	91.35 329	90.21 350	83.80 346	98.89 297	97.45 342	83.13 345	91.67 341	95.03 342	48.49 355	94.70 348	85.86 346	77.62 347	95.54 342

Gipuma		90.99 313	90.15 316	93.51 323	98.73 302	90.12 344	93.98 348	99.45 179	79.32 346	92.28 339	94.91 343	69.61 349	97.98 333	87.42 342	95.67 285	92.45 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
JIA-IIPM		97.50 265	97.02 275	98.93 180	98.73 302	97.80 234	99.30 206	98.97 291	91.73 331	98.91 223	94.86 344	95.10 194	99.71 193	97.58 211	97.98 215	99.28 181

PMVS	70.75 22	75.98 322	74.97 323	79.01 336	70.98 357	55.18 358	93.37 349	98.21 331	65.08 353	61.78 354	93.83 345	21.74 361	92.53 349	78.59 348	91.12 335	89.34 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet		95.75 298	95.16 302	97.51 302	99.30 204	93.69 333	98.88 298	95.78 349	85.09 343	98.78 243	92.65 346	91.29 296	99.37 249	94.85 308	99.85 58	99.46 164

E-PMN		80.61 318	79.88 321	82.81 333	90.75 349	76.38 354	97.69 344	95.76 350	66.44 351	83.52 345	92.25 347	62.54 352	87.16 352	68.53 351	61.40 349	84.89 350

EMVS		80.02 319	79.22 322	82.43 335	91.19 348	76.40 353	97.55 346	92.49 358	66.36 352	83.01 347	91.27 348	64.63 351	85.79 353	65.82 352	60.65 350	85.08 349

gg-mvs-nofinetune		96.17 293	95.32 301	98.73 216	98.79 293	98.14 216	99.38 185	94.09 353	91.07 336	98.07 299	91.04 349	89.62 313	99.35 257	96.75 267	99.09 159	98.68 249

ANet_high		77.30 320	74.86 324	84.62 332	75.88 356	77.61 352	97.63 345	93.15 356	88.81 339	64.27 353	89.29 350	36.51 357	83.93 354	75.89 349	52.31 351	92.33 346

MVE	76.82 21	76.91 321	74.31 325	84.70 331	85.38 355	76.05 355	96.88 347	93.17 355	67.39 350	71.28 352	89.01 351	21.66 362	87.69 351	71.74 350	72.29 348	90.35 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		39.17 324	43.78 326	25.37 339	36.04 360	16.84 361	98.36 333	26.56 359	20.06 355	38.51 356	67.32 352	29.64 359	15.30 357	37.59 354	39.90 353	43.98 352

test123		39.01 325	42.50 327	28.53 338	39.17 359	20.91 360	98.75 311	19.17 361	19.83 356	38.57 355	66.67 353	33.16 358	15.42 356	37.50 355	29.66 354	49.26 351

test_post											65.99 354	94.65 215	99.73 183

test_post1							99.23 229				65.14 355	94.18 232	99.71 193	97.58 211

X-MVStestdata		96.55 285	95.45 299	99.87 11	99.85 25	99.83 14	99.69 35	99.68 19	98.98 27	99.37 132	64.01 356	98.81 45	99.94 53	98.79 90	99.86 51	99.84 18

wuyk23d		40.18 323	41.29 328	36.84 337	86.18 354	49.12 359	79.73 351	22.81 360	27.64 354	25.46 357	28.45 357	21.98 360	48.89 355	55.80 353	23.56 355	12.51 353

uanet_test		0.02 329	0.03 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		8.27 328	11.03 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	99.01 16	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.02 329	0.03 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.02 329	0.03 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.02 329	0.03 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.02 329	0.03 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.02 329	0.03 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.27 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

IU-MVS					99.84 32	99.88 7		99.32 247	98.30 85	99.84 13				98.86 77	99.85 58	99.89 2

save fliter					99.76 52	99.59 67	99.14 246	99.40 204	99.00 22

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

GSMVS																99.52 146

test_part2					99.81 40	99.83 14				99.77 33

sam_mvs1												94.86 200				99.52 146

sam_mvs												94.72 211

MTGPA								99.47 157

MTMP							99.54 106	98.88 304

test9_res														97.49 222	99.72 103	99.75 69

agg_prior2														97.21 239	99.73 102	99.75 69

agg_prior					99.67 100	99.62 60		99.40 204		98.87 230			99.91 90

test_prior4						99.56 72	98.99 279

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

旧先验2							98.96 287		96.70 242	99.47 105			99.94 53	98.19 158

新几何2							99.01 277

无先验							98.99 279	99.51 102	96.89 231				99.93 68	97.53 219		99.72 86

原ACMM2							98.95 291

testdata2													99.95 42	96.67 273

segment_acmp												98.96 25

testdata1							98.85 301		98.32 84

test12				99.75 51	99.64 116	99.61 62		99.29 259		99.21 169		98.38 86	99.89 113		99.74 99	99.74 73

plane_prior7					99.29 208	97.03 263

plane_prior6					99.27 213	96.98 267						92.71 264

plane_prior5								99.47 157					99.69 202	97.78 193	97.63 222	98.67 257

plane_prior3						97.00 265			98.69 54	99.11 187

plane_prior2							99.39 180		98.97 30

plane_prior1					99.26 215

plane_prior						96.97 268	99.21 236		98.45 69						97.60 225

n2								0.00 362

nn								0.00 362

door-mid								98.05 334

test11								99.35 228

door								97.92 335

HQP5-MVS						96.83 273

HQP-NCC					99.19 231		98.98 283		98.24 90	98.66 258

ACMP_Plane					99.19 231		98.98 283		98.24 90	98.66 258

BP-MVS														97.19 243

HQP4-MVS										98.66 258			99.64 212			98.64 269

HQP3-MVS								99.39 208							97.58 227

HQP2-MVS												92.47 272

MDTV_nov1_ep13_2view						95.18 316	99.35 197		96.84 234	99.58 85		95.19 193		97.82 190		99.46 164

ACMMP++_ref															97.19 251

ACMMP++															97.43 243

Test By Simon												98.75 56

High-resolution multi-view benchmark