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.

test_fmvsmconf0.01_n		98.57 21	98.74 19	98.06 101	99.39 50	94.63 177	96.70 174	99.82 1	95.44 221	99.64 13	99.52 12	98.96 4	99.74 95	99.38 7	99.86 35	99.81 10

mvs5depth		98.06 60	98.58 29	96.51 252	98.97 133	89.65 356	99.43 4	99.81 2	99.30 9	98.36 145	99.86 2	93.15 269	99.88 22	98.50 44	99.84 50	99.99 1

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 3	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 6

mmtdpeth		98.33 36	98.53 31	97.71 128	99.07 111	93.44 230	98.80 15	99.78 4	99.10 15	96.61 325	99.63 10	95.42 187	99.73 101	98.53 43	99.86 35	99.95 2

test_fmvsmconf0.1_n		98.41 34	98.54 30	98.03 106	99.16 93	94.61 178	96.18 217	99.73 5	95.05 240	99.60 17	99.34 29	98.68 8	99.72 112	99.21 12	99.85 47	99.76 21

fmvsm_s_conf0.1_n_2		97.68 115	98.18 57	96.20 286	99.06 113	89.08 375	95.51 282	99.72 6	96.06 175	99.48 21	99.24 36	95.18 199	99.60 200	99.45 4	99.88 28	99.94 3

test_vis1_n_1920		95.77 274	96.41 237	93.85 431	98.55 218	84.86 476	95.91 250	99.71 7	92.72 354	97.67 235	98.90 85	87.44 393	98.73 428	97.96 61	98.85 342	97.96 406

CS-MVS		98.09 56	98.01 76	98.32 72	98.45 239	96.69 59	98.52 29	99.69 8	98.07 59	96.07 365	97.19 326	96.88 99	99.86 27	97.50 84	99.73 85	98.41 350

PDCNetPlus		89.44 477	88.28 482	92.93 471	91.75 538	85.02 472	87.69 529	99.67 9	82.69 511	95.89 380	97.02 343	51.15 549	95.27 515	88.79 439	99.86 35	98.50 342

test_vis3_rt		97.04 178	96.98 186	97.23 185	98.44 240	95.88 104	96.82 158	99.67 9	90.30 423	99.27 39	99.33 31	94.04 241	96.03 512	97.14 101	97.83 430	99.78 14

SPE-MVS-test		97.91 84	97.84 97	98.14 94	98.52 222	96.03 100	98.38 38	99.67 9	98.11 57	95.50 400	96.92 355	96.81 105	99.87 25	96.87 115	99.76 72	98.51 339

EC-MVSNet		97.90 86	97.94 88	97.79 121	98.66 195	95.14 158	98.31 43	99.66 12	97.57 79	95.95 371	97.01 347	96.99 84	99.82 38	97.66 78	99.64 117	98.39 353

test_fmvsmvis_n_1920		98.08 57	98.47 32	96.93 211	99.03 122	93.29 236	96.32 204	99.65 13	95.59 210	99.71 7	99.01 67	97.66 38	99.60 200	99.44 5	99.83 55	97.90 411

dcpmvs_2		97.12 174	97.99 78	94.51 405	99.11 105	84.00 489	97.75 87	99.65 13	97.38 96	99.14 49	98.42 152	95.16 201	99.96 2	95.52 197	99.78 69	99.58 51

LCM-MVSNet-Re		97.33 158	97.33 159	97.32 175	98.13 288	93.79 215	96.99 146	99.65 13	96.74 127	99.47 23	98.93 78	96.91 94	99.84 33	90.11 418	99.06 315	98.32 364

test_fmvsmconf_n		98.30 40	98.41 39	97.99 109	98.94 137	94.60 179	96.00 237	99.64 16	94.99 245	99.43 27	99.18 45	98.51 12	99.71 128	99.13 20	99.84 50	99.67 36

fmvsm_l_conf0.5_n_3		98.29 41	98.46 33	97.79 121	98.90 148	94.05 205	96.06 229	99.63 17	96.07 174	99.37 32	98.93 78	98.29 16	99.68 152	99.11 22	99.79 65	99.65 41

test_fmvs3		97.38 153	97.56 138	96.84 222	98.63 205	92.81 250	97.60 103	99.61 18	90.87 410	98.76 95	99.66 6	94.03 242	97.90 486	99.24 11	99.68 104	99.81 10

fmvsm_l_conf0.5_n_9		97.92 80	98.37 40	96.57 245	98.94 137	90.54 326	95.39 292	99.58 19	96.82 123	99.56 18	98.77 95	97.23 67	99.61 197	99.17 17	99.86 35	99.57 59

fmvsm_s_conf0.5_n_5		97.63 122	97.83 100	97.04 201	98.77 176	92.33 265	95.63 276	99.58 19	93.53 312	99.10 52	98.66 116	96.44 131	99.65 173	99.12 21	99.68 104	99.12 220

test_fmvsm_n_1920		98.08 57	98.29 52	97.43 165	98.88 150	93.95 209	96.17 221	99.57 21	95.66 205	99.52 20	98.71 110	97.04 80	99.64 179	99.21 12	99.87 33	98.69 315

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 40	99.71 10	96.99 48	99.69 2	99.57 21	99.02 21	99.62 15	99.36 26	98.53 11	99.52 227	98.58 42	99.95 5	99.66 38
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
fmvsm_s_conf0.5_n_11		97.90 86	98.34 45	96.60 240	98.75 178	90.50 330	96.28 206	99.56 23	97.05 110	99.15 48	99.11 54	96.31 138	99.69 145	98.97 29	99.84 50	99.62 45

ANet_high		98.31 39	98.94 9	96.41 268	99.33 60	89.64 357	97.92 74	99.56 23	99.27 10	99.66 12	99.50 14	97.67 36	99.83 35	97.55 82	99.98 2	99.77 15

tt0320-xc		99.10 4	99.31 3	98.49 57	99.57 20	96.09 93	98.91 11	99.55 25	99.67 3	99.78 3	99.69 4	98.63 10	99.77 69	98.02 58	99.93 11	99.60 47

fmvsm_s_conf0.5_n_2		97.59 128	98.07 68	96.17 290	98.78 173	89.10 374	95.33 300	99.55 25	95.96 184	99.41 30	99.10 56	95.18 199	99.59 202	99.43 6	99.86 35	99.81 10

fmvsm_s_conf0.5_n_9		97.98 65	98.32 48	96.96 208	98.92 143	91.45 300	95.87 253	99.53 27	97.44 87	99.56 18	99.05 62	95.34 190	99.67 162	99.52 2	99.70 97	99.77 15

tt0320		99.07 6	99.29 4	98.43 62	99.55 24	95.92 103	98.97 10	99.53 27	99.67 3	99.79 2	99.71 3	98.33 14	99.78 58	98.11 52	99.92 15	99.57 59

fmvsm_s_conf0.5_n_10		97.74 106	98.11 62	96.62 236	98.72 183	90.95 316	95.99 240	99.50 29	96.22 158	99.20 44	98.93 78	95.13 203	99.77 69	99.49 3	99.76 72	99.15 206

fmvsm_s_conf0.5_n_3		97.88 89	98.37 40	96.41 268	98.73 180	89.82 350	95.94 247	99.49 30	96.81 124	99.09 53	99.03 65	97.09 73	99.65 173	99.37 8	99.76 72	99.76 21

Vis-MVSNet		98.27 42	98.34 45	98.07 99	99.33 60	95.21 154	98.04 64	99.46 31	97.32 100	97.82 227	99.11 54	96.75 108	99.86 27	97.84 67	99.36 249	99.15 206
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvs2		96.38 238	96.45 234	96.16 292	97.85 313	91.30 303	96.81 159	99.45 32	89.24 442	98.49 126	99.38 23	88.68 370	97.62 491	98.83 31	99.32 267	99.57 59

TDRefinement		98.90 8	98.86 11	99.02 9	99.54 28	98.06 8	99.34 5	99.44 33	98.85 27	99.00 62	99.20 40	97.42 52	99.59 202	97.21 96	99.76 72	99.40 134

test_fmvs1_n		95.21 312	95.28 295	94.99 374	98.15 283	89.13 373	96.81 159	99.43 34	86.97 476	97.21 269	98.92 81	83.00 446	97.13 497	98.09 54	98.94 326	98.72 310

fmvsm_s_conf0.5_n_8		97.66 118	98.12 60	96.27 280	98.79 169	89.43 363	95.76 261	99.42 35	97.49 85	99.16 47	99.04 63	94.56 225	99.69 145	99.18 16	99.73 85	99.70 33

testf1		98.57 21	98.45 36	98.93 21	99.79 3	98.78 2	97.69 96	99.42 35	97.69 75	98.92 72	98.77 95	97.80 30	99.25 349	96.27 149	99.69 99	98.76 305

APD_test2		98.57 21	98.45 36	98.93 21	99.79 3	98.78 2	97.69 96	99.42 35	97.69 75	98.92 72	98.77 95	97.80 30	99.25 349	96.27 149	99.69 99	98.76 305

fmvsm_l_conf0.5_n		97.68 115	97.81 103	97.27 179	98.92 143	92.71 257	95.89 251	99.41 38	93.36 319	99.00 62	98.44 150	96.46 130	99.65 173	99.09 23	99.76 72	99.45 112

fmvsm_s_conf0.5_n_6		97.45 144	97.79 105	96.44 261	98.58 213	90.31 338	95.77 260	99.33 39	94.52 268	98.85 81	98.44 150	95.68 173	99.62 189	99.15 19	99.81 59	99.38 143

fmvsm_l_conf0.5_n_a		97.60 125	97.76 111	97.11 192	98.92 143	92.28 269	95.83 256	99.32 40	93.22 326	98.91 74	98.49 141	96.31 138	99.64 179	99.07 24	99.76 72	99.40 134

UA-Net		98.88 10	98.76 16	99.22 2	99.11 105	97.89 16	99.47 3	99.32 40	99.08 16	97.87 223	99.67 5	96.47 128	99.92 5	97.88 64	99.98 2	99.85 6

aaatest				98.17 88	99.36 54	95.35 137	97.75 87	99.30 42	94.02 295	98.88 77	97.54 290		99.73 101	95.36 216	99.53 176	99.44 122

MED-MVS		98.14 50	98.09 66	98.27 78	99.36 54	95.35 137	97.75 87	99.30 42	97.28 103	98.88 77	98.41 155	96.99 84	99.73 101	95.36 216	99.51 189	99.74 26

patch_mono-2		96.59 219	96.93 191	95.55 340	98.88 150	87.12 435	94.47 356	99.30 42	94.12 289	96.65 323	98.41 155	94.98 209	99.87 25	95.81 180	99.78 69	99.66 38

pmmvs6		99.07 6	99.24 7	98.56 51	99.81 2	96.38 74	98.87 12	99.30 42	99.01 22	99.63 14	99.66 6	99.27 2	99.68 152	97.75 73	99.89 26	99.62 45

GDP-MVS		95.39 302	94.89 318	96.90 215	98.26 265	91.91 287	96.48 190	99.28 46	95.06 239	96.54 333	97.12 336	74.83 494	99.82 38	97.19 99	99.27 278	98.96 260

test_vis1_n		95.67 284	95.89 273	95.03 370	98.18 276	89.89 348	96.94 148	99.28 46	88.25 459	98.20 173	98.92 81	86.69 406	97.19 496	97.70 77	98.82 348	98.00 404

fmvsm_s_conf0.5_n_4		97.43 148	97.77 110	96.39 272	98.48 234	89.89 348	95.65 271	99.26 48	94.73 257	98.72 100	98.58 129	95.58 179	99.57 211	99.28 9	99.67 108	99.73 28

test_cas_vis1_n_1920		95.34 306	95.67 284	94.35 414	98.21 270	86.83 441	95.61 277	99.26 48	90.45 417	98.17 179	98.96 74	84.43 432	98.31 471	96.74 119	99.17 295	97.90 411

FOURS1					99.59 18	98.20 7	99.03 8	99.25 50	98.96 24	98.87 79

Casviewmamba		97.95 72	98.20 56	97.18 186	98.85 157	92.74 255	96.71 172	99.23 51	98.07 59	98.55 118	98.47 146	97.38 54	99.44 265	96.95 112	99.62 123	99.38 143

E5new		97.59 128	97.96 86	96.45 257	99.01 124	90.45 332	96.50 184	99.23 51	96.19 163	98.27 160	98.72 103	97.49 46	99.47 247	96.64 122	99.62 123	99.42 127

E6new		97.59 128	97.97 80	96.45 257	99.01 124	90.45 332	96.50 184	99.23 51	96.20 159	98.27 160	98.72 103	97.49 46	99.47 247	96.64 122	99.62 123	99.42 127

E6		97.59 128	97.97 80	96.45 257	99.01 124	90.45 332	96.50 184	99.23 51	96.20 159	98.27 160	98.72 103	97.49 46	99.47 247	96.64 122	99.62 123	99.42 127

E5		97.59 128	97.96 86	96.45 257	99.01 124	90.45 332	96.50 184	99.23 51	96.19 163	98.27 160	98.72 103	97.49 46	99.47 247	96.64 122	99.62 123	99.42 127

mvs_tets		98.90 8	98.94 9	98.75 34	99.69 11	96.48 69	98.54 26	99.22 56	96.23 157	99.71 7	99.48 15	98.77 7	99.93 3	98.89 30	99.95 5	99.84 8

FC-MVSNet-test		98.16 49	98.37 40	97.56 142	99.49 36	93.10 242	98.35 39	99.21 57	98.43 42	98.89 75	98.83 90	94.30 236	99.81 43	97.87 65	99.91 19	99.77 15

PS-MVSNAJss		98.53 27	98.63 23	98.21 87	99.68 12	94.82 169	98.10 60	99.21 57	96.91 120	99.75 5	99.45 18	95.82 164	99.92 5	98.80 32	99.96 4	99.89 4

hybridcas		97.73 107	98.10 65	96.62 236	98.84 159	91.10 308	96.46 192	99.20 59	97.53 83	98.65 106	98.42 152	97.41 53	99.38 298	96.79 118	99.59 144	99.37 152

UniMVSNet_ETH3D		99.12 3	99.28 5	98.65 45	99.77 5	96.34 78	99.18 6	99.20 59	99.67 3	99.73 6	99.65 8	99.15 3	99.86 27	97.22 95	99.92 15	99.77 15

ACMH+	93.58 10	98.23 45	98.31 49	97.98 110	99.39 50	95.22 152	97.55 108	99.20 59	98.21 54	99.25 41	98.51 140	98.21 18	99.40 285	94.79 268	99.72 90	99.32 160

sc_t1		99.09 5	99.28 5	98.53 54	99.72 8	96.21 86	98.87 12	99.19 62	99.71 2	99.76 4	99.65 8	98.64 9	99.79 53	98.07 56	99.90 25	99.58 51

anonymousdsp		98.72 17	98.63 23	98.99 13	99.62 16	97.29 41	98.65 22	99.19 62	95.62 208	99.35 35	99.37 24	97.38 54	99.90 17	98.59 41	99.91 19	99.77 15

E4		97.28 161	97.55 141	96.46 256	98.86 155	90.53 328	95.28 308	99.18 64	95.82 198	98.01 201	98.59 128	96.78 106	99.46 254	95.86 176	99.56 159	99.38 143

viewmacassd2359aftdt		97.25 164	97.52 144	96.43 263	98.83 160	90.49 331	95.45 285	99.18 64	95.44 221	97.98 208	98.47 146	96.90 96	99.37 305	95.93 169	99.55 166	99.43 125

casdiffmvs_mvg		97.83 95	98.11 62	97.00 206	98.57 215	92.10 280	95.97 243	99.18 64	97.67 78	99.00 62	98.48 145	97.64 39	99.50 232	96.96 111	99.54 172	99.40 134
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffseed414692147		97.67 117	97.88 94	97.03 203	98.82 162	92.32 267	96.55 181	99.17 67	96.99 111	98.01 201	98.67 115	97.64 39	99.38 298	95.45 206	99.66 111	99.40 134

WR-MVS_H		98.65 18	98.62 25	98.75 34	99.51 32	96.61 64	98.55 25	99.17 67	99.05 19	99.17 46	98.79 91	95.47 184	99.89 20	97.95 62	99.91 19	99.75 24

SSM_0407		97.39 152	97.67 120	96.54 250	98.51 224	90.96 313	96.40 194	99.16 69	96.95 116	98.27 160	98.09 215	97.05 78	99.67 162	95.21 227	99.40 236	98.98 255

SSM_0404		97.47 142	97.75 113	96.64 235	98.81 163	91.26 305	96.57 178	99.16 69	96.95 116	98.44 134	98.09 215	97.05 78	99.72 112	95.21 227	99.44 217	98.95 263

EIA-MVS		96.04 257	95.77 281	96.85 219	97.80 333	92.98 244	96.12 224	99.16 69	94.65 261	93.77 452	91.69 508	95.68 173	99.67 162	94.18 296	98.85 342	97.91 409

AllTest		97.20 167	96.92 193	98.06 101	99.08 109	96.16 88	97.14 136	99.16 69	94.35 280	97.78 229	98.07 219	95.84 161	99.12 377	91.41 379	99.42 230	98.91 274

TestCases				98.06 101	99.08 109	96.16 88		99.16 69	94.35 280	97.78 229	98.07 219	95.84 161	99.12 377	91.41 379	99.42 230	98.91 274

COLMAP_ROB	94.48 6	98.25 44	98.11 62	98.64 46	99.21 85	97.35 39	97.96 68	99.16 69	98.34 46	98.78 89	98.52 137	97.32 57	99.45 262	94.08 300	99.67 108	99.13 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.1_n_a		97.80 101	98.01 76	97.18 186	99.17 92	92.51 260	96.57 178	99.15 75	93.68 308	98.89 75	99.30 32	96.42 133	99.37 305	99.03 25	99.83 55	99.66 38

Anonymous20231211		98.55 24	98.76 16	97.94 113	98.79 169	94.37 191	98.84 14	99.15 75	99.37 6	99.67 10	99.43 20	95.61 177	99.72 112	98.12 51	99.86 35	99.73 28

PEN-MVS		98.75 13	98.85 13	98.44 61	99.58 19	95.67 114	98.45 34	99.15 75	99.33 8	99.30 37	99.00 68	97.27 60	99.92 5	97.64 79	99.92 15	99.75 24

diffmvs_AUTHOR		96.50 225	96.81 201	95.57 334	98.03 292	88.26 400	93.73 402	99.14 78	94.92 250	97.24 266	97.84 254	94.62 221	99.33 318	96.44 137	99.37 244	99.13 214

v7n		98.73 14	98.99 8	97.95 112	99.64 14	94.20 200	98.67 18	99.14 78	99.08 16	99.42 28	99.23 38	96.53 123	99.91 13	99.27 10	99.93 11	99.73 28

PS-CasMVS		98.73 14	98.85 13	98.39 66	99.55 24	95.47 130	98.49 31	99.13 80	99.22 12	99.22 43	98.96 74	97.35 56	99.92 5	97.79 70	99.93 11	99.79 13

E2		96.97 185	97.19 172	96.33 274	98.64 196	90.34 336	95.07 323	99.12 81	95.00 243	97.66 236	98.31 173	96.19 148	99.43 269	95.35 219	99.35 255	99.23 190

E3		96.97 185	97.19 172	96.33 274	98.64 196	90.34 336	95.07 323	99.12 81	95.00 243	97.66 236	98.31 173	96.19 148	99.43 269	95.35 219	99.35 255	99.23 190

jajsoiax		98.77 12	98.79 15	98.74 37	99.66 13	96.48 69	98.45 34	99.12 81	95.83 197	99.67 10	99.37 24	98.25 17	99.92 5	98.77 33	99.94 8	99.82 9

FE-MVSNET2		97.69 112	97.97 80	96.85 219	99.19 89	91.46 299	97.04 142	99.11 84	95.85 195	98.73 99	99.02 66	96.66 111	99.68 152	96.31 145	99.86 35	99.40 134

viewdifsd2359ckpt07		97.10 176	97.55 141	95.76 317	98.64 196	88.58 389	94.54 354	99.11 84	96.96 115	98.54 119	98.18 203	96.91 94	99.44 265	95.58 195	99.49 200	99.26 180

fmvsm_s_conf0.1_n		97.73 107	98.02 74	96.85 219	99.09 108	91.43 302	96.37 200	99.11 84	94.19 286	99.01 60	99.25 35	96.30 141	99.38 298	99.00 26	99.88 28	99.73 28

FIs		97.93 79	98.07 68	97.48 159	99.38 52	92.95 246	98.03 66	99.11 84	98.04 62	98.62 109	98.66 116	93.75 253	99.78 58	97.23 94	99.84 50	99.73 28

RRT-MVS		95.78 273	96.25 247	94.35 414	96.68 422	84.47 482	97.72 95	99.11 84	97.23 105	97.27 263	98.72 103	86.39 411	99.79 53	95.49 198	97.67 443	98.80 291

SF-MVS		97.60 125	97.39 153	98.22 84	98.93 141	95.69 112	97.05 141	99.10 89	95.32 227	97.83 226	97.88 248	96.44 131	99.72 112	94.59 283	99.39 240	99.25 187

Effi-MVS+		96.19 250	96.01 261	96.71 231	97.43 387	92.19 276	96.12 224	99.10 89	95.45 218	93.33 471	94.71 461	97.23 67	99.56 213	93.21 342	97.54 450	98.37 356

APDe-MVS		98.14 50	98.03 73	98.47 60	98.72 183	96.04 96	98.07 63	99.10 89	95.96 184	98.59 114	98.69 113	96.94 88	99.81 43	96.64 122	99.58 150	99.57 59
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DTE-MVSNet		98.79 11	98.86 11	98.59 49	99.55 24	96.12 91	98.48 33	99.10 89	99.36 7	99.29 38	99.06 61	97.27 60	99.93 3	97.71 75	99.91 19	99.70 33

Gipuma		98.07 59	98.31 49	97.36 172	99.76 7	96.28 83	98.51 30	99.10 89	98.76 29	96.79 308	99.34 29	96.61 117	98.82 418	96.38 140	99.50 197	96.98 456
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
lecture		98.59 20	98.60 28	98.55 52	99.48 37	96.38 74	98.08 62	99.09 94	98.46 41	98.68 105	98.73 102	97.88 27	99.80 50	97.43 87	99.59 144	99.48 102

reproduce_model		98.54 25	98.33 47	99.15 3	99.06 113	98.04 11	97.04 142	99.09 94	98.42 43	99.03 57	98.71 110	96.93 90	99.83 35	97.09 103	99.63 120	99.56 67

MGCFI-Net		97.20 167	97.23 168	97.08 197	97.68 355	93.71 218	97.79 82	99.09 94	97.40 94	96.59 326	93.96 473	97.67 36	99.35 313	96.43 138	98.50 390	98.17 386

casdiffmvs		97.50 139	97.81 103	96.56 247	98.51 224	91.04 310	95.83 256	99.09 94	97.23 105	98.33 152	98.30 179	97.03 81	99.37 305	96.58 130	99.38 242	99.28 174
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD_test1		97.95 72	97.68 119	98.75 34	99.60 17	98.60 5	97.21 132	99.08 98	96.57 139	98.07 193	98.38 161	96.22 146	99.14 372	94.71 277	99.31 270	98.52 338

nrg030		98.54 25	98.62 25	98.32 72	99.22 78	95.66 115	97.90 76	99.08 98	98.31 47	99.02 59	98.74 101	97.68 35	99.61 197	97.77 72	99.85 47	99.70 33

diffmvs		96.04 257	96.23 248	95.46 346	97.35 392	88.03 411	93.42 417	99.08 98	94.09 292	96.66 321	96.93 352	93.85 249	99.29 336	96.01 164	98.67 373	99.06 238
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		95.95 263	95.80 279	96.42 265	99.28 64	90.62 322	95.31 303	99.08 98	88.40 456	96.97 297	98.17 205	92.11 304	99.78 58	93.64 326	99.21 286	98.86 285

viewcassd2359sk11		96.73 209	96.89 197	96.24 282	98.46 238	90.20 340	94.94 332	99.07 102	94.43 277	97.33 260	98.05 228	95.69 172	99.40 285	94.98 257	99.11 304	99.12 220

mamba_0408		97.17 169	97.38 155	96.55 249	98.51 224	90.96 313	95.19 313	99.06 103	96.60 132	98.27 160	97.78 263	96.58 120	99.72 112	95.04 243	99.40 236	98.98 255

SSM_04072		97.14 170	97.38 155	96.42 265	98.51 224	90.96 313	95.19 313	99.06 103	96.60 132	98.27 160	97.78 263	96.58 120	99.31 328	95.04 243	99.40 236	98.98 255

fmvsm_s_conf0.5_n_a		97.65 119	97.83 100	97.13 191	98.80 166	92.51 260	96.25 212	99.06 103	93.67 309	98.64 107	99.00 68	96.23 145	99.36 309	98.99 27	99.80 63	99.53 78

fmvsm_s_conf0.5_n		97.62 123	97.89 92	96.80 225	98.79 169	91.44 301	96.14 223	99.06 103	94.19 286	98.82 86	98.98 71	96.22 146	99.38 298	98.98 28	99.86 35	99.58 51

PGM-MVS		97.88 89	97.52 144	98.96 16	99.20 87	97.62 24	97.09 139	99.06 103	95.45 218	97.55 243	97.94 241	97.11 70	99.78 58	94.77 271	99.46 211	99.48 102

RPSCF		97.87 91	97.51 146	98.95 17	99.15 96	98.43 6	97.56 107	99.06 103	96.19 163	98.48 128	98.70 112	94.72 214	99.24 353	94.37 289	99.33 265	99.17 202

FE-MVSNET		96.59 219	96.65 212	96.41 268	98.94 137	90.51 329	96.07 227	99.05 109	92.94 347	98.03 198	98.00 235	93.08 272	99.42 273	94.04 304	99.74 84	99.30 166

sasdasda		97.23 165	97.21 170	97.30 176	97.65 362	94.39 188	97.84 79	99.05 109	97.42 89	96.68 317	93.85 476	97.63 41	99.33 318	96.29 147	98.47 393	98.18 384

canonicalmvs		97.23 165	97.21 170	97.30 176	97.65 362	94.39 188	97.84 79	99.05 109	97.42 89	96.68 317	93.85 476	97.63 41	99.33 318	96.29 147	98.47 393	98.18 384

TranMVSNet+NR-MVSNet		98.33 36	98.30 51	98.43 62	99.07 111	95.87 105	96.73 171	99.05 109	98.67 30	98.84 83	98.45 148	97.58 44	99.88 22	96.45 136	99.86 35	99.54 73

OurMVSNet-221017-0		98.61 19	98.61 27	98.63 47	99.77 5	96.35 77	99.17 7	99.05 109	98.05 61	99.61 16	99.52 12	93.72 254	99.88 22	98.72 38	99.88 28	99.65 41

HPM-MVS		98.11 55	97.83 100	98.92 24	99.42 45	97.46 35	98.57 23	99.05 109	95.43 223	97.41 257	97.50 296	97.98 23	99.79 53	95.58 195	99.57 154	99.50 88
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		97.12 174	96.74 207	98.26 79	98.99 129	97.45 36	93.82 396	99.05 109	95.19 232	98.32 153	97.70 276	95.22 197	98.41 463	94.27 293	98.13 410	98.93 270

ACMH	93.61 9	98.44 32	98.76 16	97.51 148	99.43 43	93.54 225	98.23 50	99.05 109	97.40 94	99.37 32	99.08 60	98.79 6	99.47 247	97.74 74	99.71 93	99.50 88
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		97.83 95	97.65 123	98.35 70	98.80 166	95.86 106	95.92 249	99.04 117	97.51 84	98.22 172	97.81 260	94.68 218	99.78 58	97.14 101	99.75 82	99.41 133

viewdifsd2359ckpt09		96.23 247	96.04 259	96.82 223	98.29 257	92.06 283	95.25 309	99.03 118	91.51 391	96.19 359	97.01 347	94.41 229	99.40 285	93.76 319	98.90 334	99.00 248

viewmanbaseed2359cas		96.77 205	96.94 190	96.27 280	98.41 247	90.24 339	95.11 318	99.03 118	94.28 283	97.45 255	97.85 252	95.92 158	99.32 326	95.18 231	99.19 291	99.24 188

HPM-MVS_fast		98.32 38	98.13 59	98.88 26	99.54 28	97.48 34	98.35 39	99.03 118	95.88 192	97.88 220	98.22 197	98.15 20	99.74 95	96.50 132	99.62 123	99.42 127

baseline		97.44 146	97.78 109	96.43 263	98.52 222	90.75 321	96.84 156	99.03 118	96.51 140	97.86 224	98.02 231	96.67 110	99.36 309	97.09 103	99.47 208	99.19 198

E3new		96.50 225	96.61 215	96.17 290	98.28 260	90.09 341	94.85 338	99.02 122	93.95 299	97.01 291	97.74 271	95.19 198	99.39 294	94.70 278	98.77 361	99.04 242

reproduce-ours		98.48 29	98.27 53	99.12 4	98.99 129	98.02 12	96.81 159	99.02 122	98.29 50	98.97 66	98.61 123	97.27 60	99.82 38	96.86 116	99.61 134	99.51 85

our_new_method		98.48 29	98.27 53	99.12 4	98.99 129	98.02 12	96.81 159	99.02 122	98.29 50	98.97 66	98.61 123	97.27 60	99.82 38	96.86 116	99.61 134	99.51 85

test_fmvs1		94.51 354	94.60 338	94.26 419	95.91 459	87.92 412	95.35 298	99.02 122	86.56 480	96.79 308	98.52 137	82.64 448	97.00 501	97.87 65	98.71 368	97.88 413

v10		97.55 134	97.97 80	96.31 278	98.60 209	89.64 357	97.44 117	99.02 122	96.60 132	98.72 100	99.16 49	93.48 260	99.72 112	98.76 34	99.92 15	99.58 51

UniMVSNet_NR-MVSNet		97.83 95	97.65 123	98.37 67	98.72 183	95.78 108	95.66 269	99.02 122	98.11 57	98.31 155	97.69 277	94.65 220	99.85 30	97.02 109	99.71 93	99.48 102

XVG-OURS-SEG-HR		97.38 153	97.07 180	98.30 75	99.01 124	97.41 38	94.66 349	99.02 122	95.20 231	98.15 182	97.52 294	98.83 5	98.43 462	94.87 261	96.41 487	99.07 235

MVSFormer		96.14 252	96.36 241	95.49 344	97.68 355	87.81 420	98.67 18	99.02 122	96.50 141	94.48 430	96.15 406	86.90 402	99.92 5	98.73 36	99.13 300	98.74 307

test_djsdf		98.73 14	98.74 19	98.69 42	99.63 15	96.30 82	98.67 18	99.02 122	96.50 141	99.32 36	99.44 19	97.43 51	99.92 5	98.73 36	99.95 5	99.86 5

LPG-MVS_test		97.94 76	97.67 120	98.74 37	99.15 96	97.02 46	97.09 139	99.02 122	95.15 234	98.34 149	98.23 194	97.91 25	99.70 137	94.41 286	99.73 85	99.50 88

LGP-MVS_train				98.74 37	99.15 96	97.02 46		99.02 122	95.15 234	98.34 149	98.23 194	97.91 25	99.70 137	94.41 286	99.73 85	99.50 88

DeepC-MVS	95.41 4	97.82 98	97.70 115	98.16 90	98.78 173	95.72 110	96.23 215	99.02 122	93.92 300	98.62 109	98.99 70	97.69 34	99.62 189	96.18 154	99.87 33	99.15 206
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
viewmamba		96.62 218	96.92 193	95.74 319	97.85 313	88.83 383	94.25 366	99.00 134	95.69 204	97.18 273	97.90 247	95.34 190	99.29 336	96.20 152	98.85 342	99.11 225

fmvsm_s_conf0.5_n_7		97.13 171	97.50 148	96.04 298	98.43 243	89.03 378	94.92 333	99.00 134	94.51 269	98.42 136	98.96 74	94.97 210	99.54 221	98.42 46	99.85 47	99.56 67

pm-mvs1		98.47 31	98.67 21	97.86 117	99.52 31	94.58 180	98.28 46	99.00 134	97.57 79	99.27 39	99.22 39	98.32 15	99.50 232	97.09 103	99.75 82	99.50 88

VPA-MVSNet		98.27 42	98.46 33	97.70 130	99.06 113	93.80 214	97.76 86	99.00 134	98.40 44	99.07 56	98.98 71	96.89 97	99.75 85	97.19 99	99.79 65	99.55 71

XXY-MVS		97.54 135	97.70 115	97.07 198	99.46 40	92.21 272	97.22 131	99.00 134	94.93 249	98.58 115	98.92 81	97.31 58	99.41 283	94.44 284	99.43 227	99.59 50

DPE-MVS		97.64 120	97.35 158	98.50 56	98.85 157	96.18 87	95.21 312	98.99 139	95.84 196	98.78 89	98.08 217	96.84 103	99.81 43	93.98 308	99.57 154	99.52 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		97.69 112	97.36 157	98.70 41	99.50 35	96.84 52	95.38 294	98.99 139	92.45 359	98.11 186	98.31 173	97.25 65	99.77 69	96.60 128	99.62 123	99.48 102
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CSCG		97.40 151	97.30 161	97.69 132	98.95 134	94.83 168	97.28 127	98.99 139	96.35 151	98.13 185	95.95 422	95.99 155	99.66 170	94.36 291	99.73 85	98.59 327

GeoE		97.75 105	97.70 115	97.89 115	98.88 150	94.53 183	97.10 138	98.98 142	95.75 202	97.62 238	97.59 286	97.61 43	99.77 69	96.34 143	99.44 217	99.36 153

9.14			96.69 209		98.53 221		96.02 235	98.98 142	93.23 325	97.18 273	97.46 299	96.47 128	99.62 189	92.99 346	99.32 267

XVG-ACMP-BASELINE		97.58 133	97.28 164	98.49 57	99.16 93	96.90 51	96.39 196	98.98 142	95.05 240	98.06 194	98.02 231	95.86 160	99.56 213	94.37 289	99.64 117	99.00 248

EG-PatchMatch MVS		97.69 112	97.79 105	97.40 169	99.06 113	93.52 226	95.96 245	98.97 145	94.55 267	98.82 86	98.76 100	97.31 58	99.29 336	97.20 98	99.44 217	99.38 143

CP-MVS		97.92 80	97.56 138	98.99 13	98.99 129	97.82 18	97.93 73	98.96 146	96.11 169	96.89 303	97.45 300	96.85 102	99.78 58	95.19 229	99.63 120	99.38 143

ACMMP		98.05 61	97.75 113	98.93 21	99.23 75	97.60 25	98.09 61	98.96 146	95.75 202	97.91 217	98.06 225	96.89 97	99.76 77	95.32 221	99.57 154	99.43 125
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
viewdifsd2359ckpt11		97.13 171	97.62 130	95.67 327	98.64 196	88.36 396	94.84 339	98.95 148	96.24 155	98.70 102	98.61 123	96.66 111	99.29 336	96.46 134	99.45 214	99.36 153

viewmsd2359difaftdt		97.13 171	97.62 130	95.67 327	98.64 196	88.36 396	94.84 339	98.95 148	96.24 155	98.70 102	98.61 123	96.66 111	99.29 336	96.46 134	99.45 214	99.36 153

ETV-MVS		96.13 253	95.90 272	96.82 223	97.76 343	93.89 210	95.40 291	98.95 148	95.87 193	95.58 395	91.00 514	96.36 137	99.72 112	93.36 334	98.83 346	96.85 463

KD-MVS_self_test		97.86 93	98.07 68	97.25 182	99.22 78	92.81 250	97.55 108	98.94 151	97.10 109	98.85 81	98.88 87	95.03 206	99.67 162	97.39 90	99.65 113	99.26 180

114514_t		93.96 375	93.22 389	96.19 288	99.06 113	90.97 312	95.99 240	98.94 151	73.88 543	93.43 468	96.93 352	92.38 299	99.37 305	89.09 435	99.28 276	98.25 376

hybridnocas07		96.00 261	96.21 250	95.39 352	97.56 372	87.89 414	93.70 404	98.93 153	93.96 298	96.48 335	97.65 280	93.38 263	99.19 361	95.39 215	98.81 350	99.08 232

usedtu_blend_shiyan5		93.74 381	93.08 393	95.71 325	94.99 498	89.17 367	97.38 121	98.93 153	96.40 146	94.75 420	87.24 534	80.36 464	99.40 285	91.84 369	95.85 499	98.55 332

SD-MVS		97.37 155	97.70 115	96.35 273	98.14 285	95.13 159	96.54 183	98.92 155	95.94 187	99.19 45	98.08 217	97.74 33	95.06 520	95.24 225	99.54 172	98.87 284
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
APD-MVS_3200maxsize		98.13 54	97.90 89	98.79 32	98.79 169	97.31 40	97.55 108	98.92 155	97.72 72	98.25 168	98.13 208	97.10 71	99.75 85	95.44 207	99.24 285	99.32 160

NormalMVS		96.87 194	96.39 238	98.30 75	99.48 37	95.57 119	96.87 154	98.90 157	96.94 118	96.85 305	97.88 248	85.36 422	99.76 77	95.63 189	99.59 144	99.57 59

Elysia		98.19 47	98.37 40	97.66 134	99.28 64	93.52 226	97.35 123	98.90 157	98.63 32	99.45 24	98.32 171	94.31 234	99.91 13	99.19 14	99.88 28	99.54 73

StellarMVS		98.19 47	98.37 40	97.66 134	99.28 64	93.52 226	97.35 123	98.90 157	98.63 32	99.45 24	98.32 171	94.31 234	99.91 13	99.19 14	99.88 28	99.54 73

SteuartSystems-ACMMP		98.02 63	97.76 111	98.79 32	99.43 43	97.21 45	97.15 134	98.90 157	96.58 136	98.08 191	97.87 251	97.02 82	99.76 77	95.25 224	99.59 144	99.40 134
Skip Steuart: Steuart Systems R&D Blog.
BridgeMVS		96.88 193	97.29 162	95.63 330	97.66 360	89.47 361	97.95 70	98.89 161	95.94 187	97.77 231	98.55 134	92.23 300	99.68 152	97.05 108	99.61 134	97.73 425

DVP-MVS++		97.96 68	97.90 89	98.12 96	97.75 345	95.40 132	99.03 8	98.89 161	96.62 130	98.62 109	98.30 179	96.97 86	99.75 85	95.70 181	99.25 282	99.21 194

test_0728_SECOND				98.25 82	99.23 75	95.49 128	96.74 167	98.89 161					99.75 85	95.48 202	99.52 183	99.53 78

test0726					99.24 72	95.51 124	96.89 152	98.89 161	95.92 189	98.64 107	98.31 173	97.06 76

MSP-MVS		97.45 144	96.92 193	99.03 8	99.26 68	97.70 21	97.66 99	98.89 161	95.65 206	98.51 123	96.46 384	92.15 302	99.81 43	95.14 237	98.58 383	99.58 51
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MIMVSNet1		98.51 28	98.45 36	98.67 43	99.72 8	96.71 57	98.76 16	98.89 161	98.49 40	99.38 31	99.14 52	95.44 186	99.84 33	96.47 133	99.80 63	99.47 106

ACMP	92.54 13	97.47 142	97.10 177	98.55 52	99.04 121	96.70 58	96.24 214	98.89 161	93.71 304	97.97 210	97.75 268	97.44 50	99.63 184	93.22 341	99.70 97	99.32 160
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1240		96.74 207	97.02 185	95.91 309	98.18 276	88.52 390	95.39 292	98.88 168	93.15 336	98.46 131	98.40 160	92.80 281	99.71 128	98.45 45	99.49 200	99.49 96

3Dnovator	96.53 2	97.61 124	97.64 126	97.50 154	97.74 348	93.65 223	98.49 31	98.88 168	96.86 122	97.11 279	98.55 134	95.82 164	99.73 101	95.94 168	99.42 230	99.13 214

ALIKED-LG		94.42 356	93.57 379	96.97 207	96.80 419	97.51 32	96.56 180	98.87 170	90.23 427	96.16 361	96.93 352	83.76 439	97.07 498	84.00 504	98.80 351	96.33 482

test_one_0601					99.05 119	95.50 127		98.87 170	97.21 107	98.03 198	98.30 179	96.93 90

TransMVSNet (Re)		98.38 35	98.67 21	97.51 148	99.51 32	93.39 234	98.20 55	98.87 170	98.23 53	99.48 21	99.27 34	98.47 13	99.55 218	96.52 131	99.53 176	99.60 47

DU-MVS		97.79 102	97.60 134	98.36 69	98.73 180	95.78 108	95.65 271	98.87 170	97.57 79	98.31 155	97.83 255	94.69 216	99.85 30	97.02 109	99.71 93	99.46 108

hybrid		95.77 274	95.95 269	95.23 358	97.54 375	87.44 427	93.65 406	98.86 174	93.17 334	96.06 367	97.65 280	93.14 270	99.20 359	94.94 259	98.57 384	99.04 242

TestfortrainingZip a		98.22 46	98.18 57	98.33 71	99.36 54	95.49 128	97.75 87	98.86 174	97.28 103	98.87 79	98.41 155	96.31 138	99.77 69	97.40 88	99.38 242	99.74 26

SR-MVS-dyc-post		98.14 50	97.84 97	99.02 9	98.81 163	98.05 9	97.55 108	98.86 174	97.77 67	98.20 173	98.07 219	96.60 119	99.76 77	95.49 198	99.20 287	99.26 180

RE-MVS-def			97.88 94		98.81 163	98.05 9	97.55 108	98.86 174	97.77 67	98.20 173	98.07 219	96.94 88		95.49 198	99.20 287	99.26 180

Baseline_NR-MVSNet		97.72 110	97.79 105	97.50 154	99.56 22	93.29 236	95.44 286	98.86 174	98.20 55	98.37 142	99.24 36	94.69 216	99.55 218	95.98 166	99.79 65	99.65 41

RPMNet		94.68 342	94.60 338	94.90 380	95.44 483	88.15 406	96.18 217	98.86 174	97.43 88	94.10 441	98.49 141	79.40 469	99.76 77	95.69 183	95.81 503	96.81 467

MVSMamba_PlusPlus		97.43 148	97.98 79	95.78 316	98.88 150	89.70 353	98.03 66	98.85 180	99.18 13	96.84 307	99.12 53	93.04 274	99.91 13	98.38 47	99.55 166	97.73 425

1112_ss		94.12 368	93.42 385	96.23 283	98.59 211	90.85 317	94.24 368	98.85 180	85.49 490	92.97 477	94.94 455	86.01 414	99.64 179	91.78 373	97.92 422	98.20 381

PHI-MVS		96.96 187	96.53 229	98.25 82	97.48 381	96.50 67	96.76 165	98.85 180	93.52 313	96.19 359	96.85 358	95.94 156	99.42 273	93.79 318	99.43 227	98.83 288

LS3D		97.77 104	97.50 148	98.57 50	96.24 438	97.58 27	98.45 34	98.85 180	98.58 36	97.51 246	97.94 241	95.74 171	99.63 184	95.19 229	98.97 320	98.51 339

viewdifsd2359ckpt13		96.47 229	96.42 236	96.61 239	98.35 252	91.50 297	95.31 303	98.84 184	93.21 328	96.73 314	97.58 288	95.28 195	99.26 346	94.02 306	98.45 395	99.07 235

ZNCC-MVS		97.92 80	97.62 130	98.83 28	99.32 62	97.24 43	97.45 116	98.84 184	95.76 200	96.93 299	97.43 302	97.26 64	99.79 53	96.06 157	99.53 176	99.45 112

HFP-MVS		97.94 76	97.64 126	98.83 28	99.15 96	97.50 33	97.59 105	98.84 184	96.05 176	97.49 248	97.54 290	97.07 75	99.70 137	95.61 192	99.46 211	99.30 166

region2R		97.92 80	97.59 135	98.92 24	99.22 78	97.55 29	97.60 103	98.84 184	96.00 181	97.22 267	97.62 284	96.87 101	99.76 77	95.48 202	99.43 227	99.46 108

MSLP-MVS++		96.42 235	96.71 208	95.57 334	97.82 327	90.56 325	95.71 263	98.84 184	94.72 258	96.71 316	97.39 309	94.91 212	98.10 480	95.28 222	99.02 317	98.05 399

CP-MVSNet		98.42 33	98.46 33	98.30 75	99.46 40	95.22 152	98.27 48	98.84 184	99.05 19	99.01 60	98.65 120	95.37 189	99.90 17	97.57 81	99.91 19	99.77 15

OpenMVS	94.22 8	95.48 296	95.20 297	96.32 277	97.16 404	91.96 286	97.74 93	98.84 184	87.26 469	94.36 432	98.01 233	93.95 246	99.67 162	90.70 405	98.75 363	97.35 446

SED-MVS		97.94 76	97.90 89	98.07 99	99.22 78	95.35 137	96.79 163	98.83 191	96.11 169	99.08 54	98.24 192	97.87 28	99.72 112	95.44 207	99.51 189	99.14 212

test_241102_TWO								98.83 191	96.11 169	98.62 109	98.24 192	96.92 93	99.72 112	95.44 207	99.49 200	99.49 96

test_241102_ONE					99.22 78	95.35 137		98.83 191	96.04 178	99.08 54	98.13 208	97.87 28	99.33 318

SR-MVS		98.00 64	97.66 122	99.01 11	98.77 176	97.93 14	97.38 121	98.83 191	97.32 100	98.06 194	97.85 252	96.65 114	99.77 69	95.00 249	99.11 304	99.32 160

XVS		97.96 68	97.63 128	98.94 18	99.15 96	97.66 22	97.77 84	98.83 191	97.42 89	96.32 344	97.64 282	96.49 126	99.72 112	95.66 186	99.37 244	99.45 112

X-MVStestdata		92.86 415	90.83 454	98.94 18	99.15 96	97.66 22	97.77 84	98.83 191	97.42 89	96.32 344	36.50 548	96.49 126	99.72 112	95.66 186	99.37 244	99.45 112

ACMMPR		97.95 72	97.62 130	98.94 18	99.20 87	97.56 28	97.59 105	98.83 191	96.05 176	97.46 254	97.63 283	96.77 107	99.76 77	95.61 192	99.46 211	99.49 96

ACMM	93.33 11	98.05 61	97.79 105	98.85 27	99.15 96	97.55 29	96.68 175	98.83 191	95.21 230	98.36 145	98.13 208	98.13 22	99.62 189	96.04 160	99.54 172	99.39 141
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
icg_test_0407_2		95.88 268	96.39 238	94.36 412	97.83 323	86.11 452	91.82 467	98.82 199	94.48 270	97.57 241	97.14 330	96.08 152	98.20 478	95.00 249	98.78 354	98.78 294

IMVS_0407		96.35 239	96.88 198	94.74 391	97.83 323	86.11 452	96.25 212	98.82 199	94.48 270	97.57 241	97.14 330	96.08 152	99.33 318	95.00 249	98.78 354	98.78 294

IMVS_0404		95.66 286	96.03 260	94.55 402	97.83 323	86.11 452	93.24 424	98.82 199	94.48 270	95.51 399	97.14 330	93.49 259	98.78 422	95.00 249	98.78 354	98.78 294

IMVS_0403		96.27 243	96.77 206	94.76 389	97.83 323	86.11 452	96.00 237	98.82 199	94.48 270	97.49 248	97.14 330	95.38 188	99.40 285	95.00 249	98.78 354	98.78 294

v8		97.60 125	98.06 71	96.23 283	98.71 187	89.44 362	97.43 119	98.82 199	97.29 102	98.74 97	99.10 56	93.86 248	99.68 152	98.61 40	99.94 8	99.56 67

LF4IMVS		96.07 254	95.63 287	97.36 172	98.19 273	95.55 121	95.44 286	98.82 199	92.29 364	95.70 390	96.55 378	92.63 287	98.69 435	91.75 375	99.33 265	97.85 415

GST-MVS		97.82 98	97.49 150	98.81 30	99.23 75	97.25 42	97.16 133	98.79 205	95.96 184	97.53 244	97.40 304	96.93 90	99.77 69	95.04 243	99.35 255	99.42 127

ACMMP_NAP		97.89 88	97.63 128	98.67 43	99.35 58	96.84 52	96.36 201	98.79 205	95.07 238	97.88 220	98.35 165	97.24 66	99.72 112	96.05 159	99.58 150	99.45 112

v1921920		96.72 211	96.96 189	95.99 301	98.21 270	88.79 385	95.42 288	98.79 205	93.22 326	98.19 177	98.26 190	92.68 284	99.70 137	98.34 49	99.55 166	99.49 96

DP-MVS		97.87 91	97.89 92	97.81 120	98.62 207	94.82 169	97.13 137	98.79 205	98.98 23	98.74 97	98.49 141	95.80 169	99.49 238	95.04 243	99.44 217	99.11 225

PMatch-Up-SfM		95.95 263	95.43 292	97.51 148	97.90 310	95.17 156	93.40 419	98.78 209	92.45 359	98.24 169	98.07 219	87.10 400	99.18 364	94.87 261	98.10 411	98.19 382

mPP-MVS		97.91 84	97.53 143	99.04 7	99.22 78	97.87 17	97.74 93	98.78 209	96.04 178	97.10 280	97.73 273	96.53 123	99.78 58	95.16 234	99.50 197	99.46 108

onestephybrid01		96.25 245	96.31 244	96.07 297	97.54 375	90.01 346	94.06 383	98.77 211	94.74 254	96.32 344	97.74 271	94.03 242	99.20 359	94.81 266	98.79 352	98.98 255

v144192		96.69 214	96.90 196	96.03 299	98.25 266	88.92 379	95.49 283	98.77 211	93.05 339	98.09 189	98.29 183	92.51 296	99.70 137	98.11 52	99.56 159	99.47 106

v1192		96.83 200	97.06 181	96.15 293	98.28 260	89.29 365	95.36 295	98.77 211	93.73 303	98.11 186	98.34 167	93.02 278	99.67 162	98.35 48	99.58 150	99.50 88

APD-MVS		97.00 180	96.53 229	98.41 64	98.55 218	96.31 80	96.32 204	98.77 211	92.96 346	97.44 256	97.58 288	95.84 161	99.74 95	91.96 364	99.35 255	99.19 198
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CPTT-MVS		96.69 214	96.08 257	98.49 57	98.89 149	96.64 62	97.25 128	98.77 211	92.89 348	96.01 369	97.13 334	92.23 300	99.67 162	92.24 360	99.34 260	99.17 202

test-260524					98.88 150	95.35 137		98.76 216		98.18 178		95.58 179	99.73 101	96.66 121	99.51 189

HQP_MVS		96.66 216	96.33 243	97.68 133	98.70 189	94.29 195	96.50 184	98.75 217	96.36 149	96.16 361	96.77 365	91.91 312	99.46 254	92.59 353	99.20 287	99.28 174

plane_prior5								98.75 217					99.46 254	92.59 353	99.20 287	99.28 174

Patchmatch-RL test		94.66 343	94.49 345	95.19 360	98.54 220	88.91 380	92.57 441	98.74 219	91.46 397	98.32 153	97.75 268	77.31 482	98.81 420	96.06 157	99.61 134	97.85 415

SMA-MVS		97.48 141	97.11 176	98.60 48	98.83 160	96.67 60	96.74 167	98.73 220	91.61 383	98.48 128	98.36 163	96.53 123	99.68 152	95.17 232	99.54 172	99.45 112
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
Fast-Effi-MVS+-dtu		96.44 232	96.12 254	97.39 170	97.18 403	94.39 188	95.46 284	98.73 220	96.03 180	94.72 423	94.92 457	96.28 144	99.69 145	93.81 317	97.98 418	98.09 389

MTGPA								98.73 220

MTAPA		98.14 50	97.84 97	99.06 6	99.44 42	97.90 15	97.25 128	98.73 220	97.69 75	97.90 218	97.96 238	95.81 168	99.82 38	96.13 156	99.61 134	99.45 112

MP-MVS		97.64 120	97.18 174	99.00 12	99.32 62	97.77 20	97.49 114	98.73 220	96.27 152	95.59 394	97.75 268	96.30 141	99.78 58	93.70 325	99.48 205	99.45 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NR-MVSNet		97.96 68	97.86 96	98.26 79	98.73 180	95.54 122	98.14 58	98.73 220	97.79 66	99.42 28	97.83 255	94.40 231	99.78 58	95.91 171	99.76 72	99.46 108

QAPM		95.88 268	95.57 289	96.80 225	97.90 310	91.84 290	98.18 57	98.73 220	88.41 455	96.42 339	98.13 208	94.73 213	99.75 85	88.72 441	98.94 326	98.81 290

test_0402		97.84 94	97.97 80	97.47 161	99.19 89	94.07 203	96.71 172	98.73 220	98.66 31	98.56 117	98.41 155	96.84 103	99.69 145	94.82 265	99.81 59	98.64 319

TAPA-MVS	93.32 12	94.93 327	94.23 357	97.04 201	98.18 276	94.51 184	95.22 311	98.73 220	81.22 522	96.25 353	95.95 422	93.80 251	98.98 400	89.89 423	98.87 339	97.62 433
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator+	96.13 3	97.73 107	97.59 135	98.15 93	98.11 289	95.60 117	98.04 64	98.70 229	98.13 56	96.93 299	98.45 148	95.30 194	99.62 189	95.64 188	98.96 323	99.24 188

Test_1112_low_res		93.53 393	92.86 401	95.54 341	98.60 209	88.86 382	92.75 435	98.69 230	82.66 513	92.65 488	96.92 355	84.75 428	99.56 213	90.94 391	97.76 435	98.19 382

DP-MVS Recon		95.55 292	95.13 302	96.80 225	98.51 224	93.99 208	94.60 351	98.69 230	90.20 428	95.78 386	96.21 402	92.73 283	98.98 400	90.58 409	98.86 341	97.42 443

CHOSEN 1792x2688		94.10 369	93.41 386	96.18 289	99.16 93	90.04 344	92.15 456	98.68 232	79.90 527	96.22 355	97.83 255	87.92 385	99.42 273	89.18 434	99.65 113	99.08 232

PVSNet_BlendedMVS		95.02 326	94.93 315	95.27 356	97.79 338	87.40 430	94.14 378	98.68 232	88.94 447	94.51 428	98.01 233	93.04 274	99.30 332	89.77 425	99.49 200	99.11 225

PVSNet_Blended		93.96 375	93.65 377	94.91 378	97.79 338	87.40 430	91.43 474	98.68 232	84.50 504	94.51 428	94.48 467	93.04 274	99.30 332	89.77 425	98.61 380	98.02 402

VortexMVS		96.04 257	96.56 222	94.49 407	97.60 369	84.36 484	96.05 230	98.67 235	94.74 254	98.95 70	98.78 94	87.13 399	99.50 232	97.37 92	99.76 72	99.60 47

v1144		96.84 197	97.08 179	96.13 294	98.42 245	89.28 366	95.41 290	98.67 235	94.21 284	97.97 210	98.31 173	93.06 273	99.65 173	98.06 57	99.62 123	99.45 112

CLD-MVS		95.47 297	95.07 305	96.69 233	98.27 263	92.53 259	91.36 475	98.67 235	91.22 403	95.78 386	94.12 471	95.65 176	98.98 400	90.81 396	99.72 90	98.57 328
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GBi-Net		96.99 181	96.80 203	97.56 142	97.96 302	93.67 219	98.23 50	98.66 238	95.59 210	97.99 203	99.19 41	89.51 354	99.73 101	94.60 280	99.44 217	99.30 166

test1		96.99 181	96.80 203	97.56 142	97.96 302	93.67 219	98.23 50	98.66 238	95.59 210	97.99 203	99.19 41	89.51 354	99.73 101	94.60 280	99.44 217	99.30 166

FMVSNet1		97.95 72	98.08 67	97.56 142	99.14 103	93.67 219	98.23 50	98.66 238	97.41 93	99.00 62	99.19 41	95.47 184	99.73 101	95.83 178	99.76 72	99.30 166

IterMVS-LS		96.92 189	97.29 162	95.79 315	98.51 224	88.13 408	95.10 319	98.66 238	96.99 111	98.46 131	98.68 114	92.55 291	99.74 95	96.91 113	99.79 65	99.50 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		95.30 309	94.38 352	98.05 105	98.64 196	96.04 96	95.61 277	98.66 238	89.00 446	93.22 472	96.40 389	92.90 279	99.35 313	87.45 464	97.53 451	98.77 303

USDC		94.56 351	94.57 343	94.55 402	97.78 341	86.43 446	92.75 435	98.65 243	85.96 484	96.91 302	97.93 243	90.82 326	98.74 427	90.71 404	99.59 144	98.47 345

PM-MVS		97.36 157	97.10 177	98.14 94	98.91 146	96.77 54	96.20 216	98.63 244	93.82 301	98.54 119	98.33 168	93.98 244	99.05 389	95.99 165	99.45 214	98.61 326

cascas		91.89 443	91.35 441	93.51 443	94.27 515	85.60 459	88.86 526	98.61 245	79.32 530	92.16 495	91.44 510	89.22 362	98.12 479	90.80 397	97.47 455	96.82 466

dtuplus		95.73 279	95.86 275	95.33 354	97.72 350	87.82 419	93.74 400	98.60 246	92.12 367	97.27 263	97.92 244	94.35 232	99.13 376	92.24 360	98.83 346	99.05 240

aaEdge-Enhanced		97.53 138	97.32 160	98.16 90	98.70 189	95.35 137	96.04 232	98.60 246	96.16 168	97.99 203	97.54 290	95.94 156	99.70 137	95.36 216	99.53 176	99.44 122

SDMVSNet		97.97 66	98.26 55	97.11 192	99.41 46	92.21 272	96.92 149	98.60 246	98.58 36	98.78 89	99.39 21	97.80 30	99.62 189	94.98 257	99.86 35	99.52 81

Fast-Effi-MVS+		95.49 294	95.07 305	96.75 229	97.67 359	92.82 248	94.22 371	98.60 246	91.61 383	93.42 469	92.90 489	96.73 109	99.70 137	92.60 352	97.89 427	97.74 424

DeepPCF-MVS	94.58 5	96.90 191	96.43 235	98.31 74	97.48 381	97.23 44	92.56 442	98.60 246	92.84 349	98.54 119	97.40 304	96.64 116	98.78 422	94.40 288	99.41 235	98.93 270

OMC-MVS		96.48 228	96.00 262	97.91 114	98.30 256	96.01 101	94.86 337	98.60 246	91.88 375	97.18 273	97.21 325	96.11 151	99.04 392	90.49 413	99.34 260	98.69 315

viewmambaseed2359dif		95.68 283	95.85 276	95.17 362	97.51 378	87.41 429	93.61 410	98.58 252	91.06 406	96.68 317	97.66 279	94.71 215	99.11 380	93.93 310	98.94 326	98.99 252

testgi		96.07 254	96.50 232	94.80 386	99.26 68	87.69 423	95.96 245	98.58 252	95.08 237	98.02 200	96.25 400	97.92 24	97.60 492	88.68 443	98.74 364	99.11 225

EGC-MVSNET		83.08 507	77.93 512	98.53 54	99.57 20	97.55 29	98.33 42	98.57 254	4.71 551	10.38 553	98.90 85	95.60 178	99.50 232	95.69 183	99.61 134	98.55 332

DKM-HiRes		96.47 229	95.93 270	98.09 98	98.86 155	96.41 73	94.38 359	98.56 255	94.05 293	96.93 299	97.48 297	87.73 388	98.55 451	95.86 176	99.48 205	99.31 165

ZD-MVS					98.43 243	95.94 102		98.56 255	90.72 412	96.66 321	97.07 339	95.02 207	99.74 95	91.08 386	98.93 331

VPNet		97.26 163	97.49 150	96.59 242	99.47 39	90.58 323	96.27 208	98.53 257	97.77 67	98.46 131	98.41 155	94.59 222	99.68 152	94.61 279	99.29 275	99.52 81

DELS-MVS		96.17 251	96.23 248	95.99 301	97.55 374	90.04 344	92.38 451	98.52 258	94.13 288	96.55 332	97.06 340	94.99 208	99.58 205	95.62 191	99.28 276	98.37 356
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
HyFIR lowres test		93.72 384	92.65 410	96.91 214	98.93 141	91.81 291	91.23 483	98.52 258	82.69 511	96.46 338	96.52 382	80.38 463	99.90 17	90.36 415	98.79 352	99.03 244

PMatch-SfM		95.65 287	95.03 308	97.51 148	97.96 302	95.00 162	93.49 415	98.51 260	92.24 365	97.80 228	98.03 229	83.97 438	99.19 361	94.77 271	98.50 390	98.35 362

DKM		96.39 237	95.99 263	97.59 140	98.44 240	96.42 72	94.42 358	98.51 260	92.81 350	98.15 182	97.47 298	89.37 360	97.26 495	95.02 248	99.68 104	99.09 231

ITE_SJBPF				97.85 118	98.64 196	96.66 61		98.51 260	95.63 207	97.22 267	97.30 319	95.52 181	98.55 451	90.97 390	98.90 334	98.34 363

eth_miper_zixun_eth		94.89 330	94.93 315	94.75 390	95.99 456	86.12 451	91.35 476	98.49 263	93.40 317	97.12 278	97.25 323	86.87 404	99.35 313	95.08 242	98.82 348	98.78 294

TinyColmap		96.00 261	96.34 242	94.96 377	97.90 310	87.91 413	94.13 379	98.49 263	94.41 278	98.16 180	97.76 265	96.29 143	98.68 438	90.52 410	99.42 230	98.30 369

PRO-TEST		95.94 265	96.20 251	95.16 364	97.04 410	87.84 418	96.89 152	98.48 265	94.45 275	96.21 356	98.77 95	90.09 342	99.73 101	94.76 274	99.07 311	97.91 409

dtuonlycased		95.11 319	95.70 283	93.35 446	99.05 119	81.45 509	91.13 489	98.48 265	93.11 338	97.98 208	97.27 320	96.15 150	99.32 326	89.61 427	98.50 390	99.27 178

RoMa-HiRes		97.28 161	97.05 183	97.98 110	98.78 173	96.22 85	96.48 190	98.47 267	93.69 306	98.97 66	97.73 273	93.48 260	98.47 459	96.31 145	99.51 189	99.26 180

SP-LightGlue		95.19 314	94.96 312	95.89 311	95.10 495	94.93 166	94.29 362	98.47 267	94.91 251	94.92 418	95.51 441	86.69 406	95.61 514	97.08 106	97.67 443	97.12 451

OPM-MVS		97.54 135	97.25 166	98.41 64	99.11 105	96.61 64	95.24 310	98.46 269	94.58 266	98.10 188	98.07 219	97.09 73	99.39 294	95.16 234	99.44 217	99.21 194
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
RoMa-SfM		96.87 194	96.56 222	97.79 121	98.50 230	96.46 71	95.89 251	98.45 270	91.48 394	98.84 83	97.40 304	93.93 247	97.96 483	94.99 255	99.58 150	98.96 260

tfpnnormal		97.72 110	97.97 80	96.94 210	99.26 68	92.23 271	97.83 81	98.45 270	98.25 52	99.13 50	98.66 116	96.65 114	99.69 145	93.92 311	99.62 123	98.91 274

UnsupCasMVSNet_eth		95.91 267	95.73 282	96.44 261	98.48 234	91.52 296	95.31 303	98.45 270	95.76 200	97.48 251	97.54 290	89.53 353	98.69 435	94.43 285	94.61 519	99.13 214

PCF-MVS	89.43 18	92.12 437	90.64 458	96.57 245	97.80 333	93.48 229	89.88 512	98.45 270	74.46 542	96.04 368	95.68 433	90.71 329	99.31 328	73.73 538	99.01 319	96.91 460
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
balanced_ft_v1		96.29 241	96.60 217	95.38 353	96.77 420	88.73 388	98.44 37	98.44 274	94.97 246	95.91 373	98.77 95	91.03 321	99.75 85	96.16 155	98.91 333	97.65 430

HQP3-MVS								98.43 275							98.74 364

HQP-MVS		95.17 317	94.58 341	96.92 212	97.85 313	92.47 262	94.26 363	98.43 275	93.18 331	92.86 482	95.08 451	90.33 335	99.23 355	90.51 411	98.74 364	99.05 240

DeepC-MVS_fast	94.34 7	96.74 207	96.51 231	97.44 164	97.69 354	94.15 201	96.02 235	98.43 275	93.17 334	97.30 261	97.38 311	95.48 183	99.28 341	93.74 320	99.34 260	98.88 282
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior				97.46 162	97.79 338	94.26 199		98.42 278					99.34 316			98.79 293

ArgMatch-SfM		95.74 278	95.15 301	97.49 157	97.82 327	95.16 157	94.03 384	98.41 279	89.33 438	97.58 240	96.65 373	90.07 343	98.89 409	93.17 343	99.30 274	98.44 349

save fliter					98.48 234	94.71 171	94.53 355	98.41 279	95.02 242

CANet		95.86 270	95.65 286	96.49 254	96.41 433	90.82 318	94.36 360	98.41 279	94.94 247	92.62 491	96.73 368	92.68 284	99.71 128	95.12 240	99.60 141	98.94 266

Anonymous20240521		97.07 177	97.51 146	95.76 317	99.35 58	88.18 405	97.78 83	98.40 282	97.11 108	98.34 149	99.04 63	89.58 349	99.79 53	98.09 54	99.93 11	99.30 166

TEST9					97.84 320	95.23 149	93.62 408	98.39 283	86.81 477	93.78 450	95.99 418	94.68 218	99.52 227

train_agg		95.46 298	94.66 332	97.88 116	97.84 320	95.23 149	93.62 408	98.39 283	87.04 473	93.78 450	95.99 418	94.58 223	99.52 227	91.76 374	98.90 334	98.89 278

KinetiMVS		97.82 98	98.02 74	97.24 184	99.24 72	92.32 267	96.92 149	98.38 285	98.56 39	99.03 57	98.33 168	93.22 267	99.83 35	98.74 35	99.71 93	99.57 59

test_8					97.81 329	95.07 161	93.54 413	98.38 285	87.04 473	93.71 455	95.96 421	94.58 223	99.52 227

MSDG		95.33 307	95.13 302	95.94 308	97.40 389	91.85 289	91.02 491	98.37 287	95.30 228	96.31 349	95.99 418	94.51 227	98.38 466	89.59 428	97.65 447	97.60 435

agg_prior					97.80 333	94.96 164		98.36 288		93.49 465			99.53 224

V42		97.04 178	97.16 175	96.68 234	98.59 211	91.05 309	96.33 203	98.36 288	94.60 263	97.99 203	98.30 179	93.32 264	99.62 189	97.40 88	99.53 176	99.38 143

MVS_111021_HR		96.73 209	96.54 228	97.27 179	98.35 252	93.66 222	93.42 417	98.36 288	94.74 254	96.58 327	96.76 367	96.54 122	98.99 398	94.87 261	99.27 278	99.15 206

c3_l		95.20 313	95.32 294	94.83 385	96.19 443	86.43 446	91.83 466	98.35 291	93.47 316	97.36 259	97.26 322	88.69 369	99.28 341	95.41 213	99.36 249	98.78 294

test_vis1_rt		94.03 374	93.65 377	95.17 362	95.76 472	93.42 232	93.97 390	98.33 292	84.68 501	93.17 473	95.89 425	92.53 295	94.79 522	93.50 331	94.97 515	97.31 448

MVS_Test		96.27 243	96.79 205	94.73 392	96.94 415	86.63 443	96.18 217	98.33 292	94.94 247	96.07 365	98.28 185	95.25 196	99.26 346	97.21 96	97.90 426	98.30 369

CDPH-MVS		95.45 299	94.65 333	97.84 119	98.28 260	94.96 164	93.73 402	98.33 292	85.03 497	95.44 401	96.60 376	95.31 193	99.44 265	90.01 420	99.13 300	99.11 225

MVS_111021_LR		96.82 201	96.55 226	97.62 138	98.27 263	95.34 143	93.81 398	98.33 292	94.59 265	96.56 330	96.63 375	96.61 117	98.73 428	94.80 267	99.34 260	98.78 294

Anonymous20240529		97.96 68	98.04 72	97.71 128	98.69 192	94.28 198	97.86 78	98.31 296	98.79 28	99.23 42	98.86 89	95.76 170	99.61 197	95.49 198	99.36 249	99.23 190

FMVSNet5		93.39 396	92.35 417	96.50 253	95.83 465	90.81 320	97.31 125	98.27 297	92.74 352	96.27 351	98.28 185	62.23 524	99.67 162	90.86 394	99.36 249	99.03 244

v2v482		96.78 204	97.06 181	95.95 306	98.57 215	88.77 386	95.36 295	98.26 298	95.18 233	97.85 225	98.23 194	92.58 288	99.63 184	97.80 69	99.69 99	99.45 112

sd_testset		97.97 66	98.12 60	97.51 148	99.41 46	93.44 230	97.96 68	98.25 299	98.58 36	98.78 89	99.39 21	98.21 18	99.56 213	92.65 351	99.86 35	99.52 81

PLC	91.02 16	94.05 372	92.90 400	97.51 148	98.00 300	95.12 160	94.25 366	98.25 299	86.17 482	91.48 502	95.25 449	91.01 322	99.19 361	85.02 496	96.69 480	98.22 379
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
miper_ehance_all_eth		94.69 340	94.70 331	94.64 394	95.77 471	86.22 449	91.32 479	98.24 301	91.67 380	97.05 287	96.65 373	88.39 374	99.22 357	94.88 260	98.34 401	98.49 344

DVP-MVS		97.78 103	97.65 123	98.16 90	99.24 72	95.51 124	96.74 167	98.23 302	95.92 189	98.40 139	98.28 185	97.06 76	99.71 128	95.48 202	99.52 183	99.26 180
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
xiu_mvs_v1_base_debu		95.62 288	95.96 266	94.60 398	98.01 296	88.42 393	93.99 387	98.21 303	92.98 342	95.91 373	94.53 464	96.39 134	99.72 112	95.43 210	98.19 407	95.64 496

xiu_mvs_v1_base		95.62 288	95.96 266	94.60 398	98.01 296	88.42 393	93.99 387	98.21 303	92.98 342	95.91 373	94.53 464	96.39 134	99.72 112	95.43 210	98.19 407	95.64 496

xiu_mvs_v1_base_debi		95.62 288	95.96 266	94.60 398	98.01 296	88.42 393	93.99 387	98.21 303	92.98 342	95.91 373	94.53 464	96.39 134	99.72 112	95.43 210	98.19 407	95.64 496

ALIKED-MNN		93.09 411	92.12 424	96.00 300	96.50 428	96.72 56	95.52 281	98.20 306	82.37 515	90.90 505	96.15 406	87.02 401	96.30 510	83.03 512	99.42 230	94.99 504

miper_lstm_enhance		94.81 334	94.80 328	94.85 383	96.16 446	86.45 445	91.14 487	98.20 306	93.49 315	97.03 288	97.37 313	84.97 427	99.26 346	95.28 222	99.56 159	98.83 288

TSAR-MVS + MP.		97.42 150	97.23 168	98.00 108	99.38 52	95.00 162	97.63 102	98.20 306	93.00 341	98.16 180	98.06 225	95.89 159	99.72 112	95.67 185	99.10 307	99.28 174
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MVP-Stereo		95.69 281	95.28 295	96.92 212	98.15 283	93.03 243	95.64 275	98.20 306	90.39 420	96.63 324	97.73 273	91.63 314	99.10 384	91.84 369	97.31 461	98.63 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS++		96.99 181	96.38 240	98.81 30	98.64 196	97.59 26	95.97 243	98.20 306	95.51 215	95.06 411	96.53 380	94.10 240	99.70 137	94.29 292	99.15 297	99.13 214

NCCC		96.52 224	95.99 263	98.10 97	97.81 329	95.68 113	95.00 330	98.20 306	95.39 224	95.40 404	96.36 391	93.81 250	99.45 262	93.55 330	98.42 398	99.17 202

new-patchmatchnet		95.67 284	96.58 219	92.94 470	97.48 381	80.21 518	92.96 430	98.19 312	94.83 252	98.82 86	98.79 91	93.31 265	99.51 231	95.83 178	99.04 316	99.12 220

test_f		95.82 272	95.88 274	95.66 329	97.61 367	93.21 241	95.61 277	98.17 313	86.98 475	98.42 136	99.47 16	90.46 332	94.74 524	97.71 75	98.45 395	99.03 244

MCST-MVS		96.24 246	95.80 279	97.56 142	98.75 178	94.13 202	94.66 349	98.17 313	90.17 429	96.21 356	96.10 412	95.14 202	99.43 269	94.13 299	98.85 342	99.13 214

door-mid								98.17 313

CNVR-MVS		96.92 189	96.55 226	98.03 106	98.00 300	95.54 122	94.87 336	98.17 313	94.60 263	96.38 341	97.05 341	95.67 175	99.36 309	95.12 240	99.08 309	99.19 198

MSC_two_6792asdad				98.22 84	97.75 345	95.34 143		98.16 317					99.75 85	95.87 174	99.51 189	99.57 59

No_MVS				98.22 84	97.75 345	95.34 143		98.16 317					99.75 85	95.87 174	99.51 189	99.57 59

原ACMM1				96.58 243	98.16 281	92.12 277		98.15 319	85.90 486	93.49 465	96.43 386	92.47 297	99.38 298	87.66 457	98.62 379	98.23 377

DenseAffine		96.06 256	95.57 289	97.53 147	98.44 240	95.79 107	94.20 373	98.14 320	92.44 361	97.95 213	97.18 328	88.87 367	97.96 483	93.41 332	99.52 183	98.85 287

IU-MVS					99.22 78	95.40 132		98.14 320	85.77 488	98.36 145				95.23 226	99.51 189	99.49 96

ambc				96.56 247	98.23 269	91.68 294	97.88 77	98.13 322		98.42 136	98.56 133	94.22 238	99.04 392	94.05 303	99.35 255	98.95 263

WR-MVS		96.90 191	96.81 201	97.16 188	98.56 217	92.20 275	94.33 361	98.12 323	97.34 99	98.20 173	97.33 316	92.81 280	99.75 85	94.79 268	99.81 59	99.54 73

cdsmvs_eth3d_5k		24.22 515	32.30 518	0.00 536	0.00 560	0.00 563	0.00 548	98.10 324	0.00 555	0.00 556	95.06 453	97.54 45	0.00 557	0.00 555	0.00 555	0.00 552

dtuonly		92.30 432	93.44 383	88.89 516	95.60 479	69.49 552	89.18 523	98.09 325	88.17 460	94.19 436	96.35 392	88.98 365	98.72 431	91.74 376	98.69 371	98.45 348

Effi-MVS+-dtu		96.81 202	96.09 256	98.99 13	96.90 417	98.69 4	96.42 193	98.09 325	95.86 194	95.15 409	95.54 438	94.26 237	99.81 43	94.06 301	98.51 389	98.47 345

cl____		94.73 335	94.64 334	95.01 372	95.85 464	87.00 437	91.33 477	98.08 327	93.34 321	97.10 280	97.33 316	84.01 437	99.30 332	95.14 237	99.56 159	98.71 314

DIV-MVS_self_test		94.73 335	94.64 334	95.01 372	95.86 463	87.00 437	91.33 477	98.08 327	93.34 321	97.10 280	97.34 315	84.02 436	99.31 328	95.15 236	99.55 166	98.72 310

test11								98.08 327

AdaColmap		95.11 319	94.62 337	96.58 243	97.33 396	94.45 187	94.92 333	98.08 327	93.15 336	93.98 448	95.53 440	94.34 233	99.10 384	85.69 484	98.61 380	96.20 486

pmmvs-eth3d		96.49 227	96.18 253	97.42 167	98.25 266	94.29 195	94.77 344	98.07 331	89.81 433	97.97 210	98.33 168	93.11 271	99.08 386	95.46 205	99.84 50	98.89 278

usedtu_dtu_shiyan2		97.54 135	97.26 165	98.37 67	99.54 28	96.04 96	97.94 71	98.06 332	97.36 98	98.62 109	98.20 199	95.52 181	99.73 101	90.90 393	99.18 292	99.33 158

FMVSNet2		96.72 211	96.67 211	96.87 218	97.96 302	91.88 288	97.15 134	98.06 332	95.59 210	98.50 125	98.62 122	89.51 354	99.65 173	94.99 255	99.60 141	99.07 235

UnsupCasMVSNet_bld		94.72 339	94.26 356	96.08 296	98.62 207	90.54 326	93.38 420	98.05 334	90.30 423	97.02 289	96.80 364	89.54 350	99.16 370	88.44 446	96.18 494	98.56 329

PAPM_NR		94.61 347	94.17 362	95.96 304	98.36 251	91.23 306	95.93 248	97.95 335	92.98 342	93.42 469	94.43 468	90.53 330	98.38 466	87.60 458	96.29 492	98.27 373

SD_0403		93.73 383	93.43 384	94.64 394	97.85 313	86.35 448	97.47 115	97.94 336	93.50 314	93.71 455	96.73 368	93.77 252	98.84 416	73.48 539	96.39 488	98.72 310

D2MVS		95.18 315	95.17 300	95.21 359	97.76 343	87.76 422	94.15 376	97.94 336	89.77 434	96.99 293	97.68 278	87.45 391	99.14 372	95.03 247	99.81 59	98.74 307

SP-SuperGlue		95.41 301	95.38 293	95.51 342	94.92 503	94.67 174	94.09 381	97.93 338	95.45 218	95.62 391	96.26 398	89.54 350	95.26 516	96.70 120	97.92 422	96.61 474

无先验							93.20 426	97.91 339	80.78 523				99.40 285	87.71 455		97.94 408

v148		96.58 222	96.97 187	95.42 347	98.63 205	87.57 424	95.09 320	97.90 340	95.91 191	98.24 169	97.96 238	93.42 262	99.39 294	96.04 160	99.52 183	99.29 173

ArgMatch-Sym		95.60 291	94.97 311	97.48 159	97.70 353	95.41 131	93.60 412	97.89 341	89.33 438	97.70 233	96.03 417	91.00 324	98.66 440	92.25 359	99.18 292	98.39 353

CNLPA		95.04 323	94.47 347	96.75 229	97.81 329	95.25 148	94.12 380	97.89 341	94.41 278	94.57 426	95.69 432	90.30 338	98.35 469	86.72 471	98.76 362	96.64 471

PAPR		92.22 434	91.27 444	95.07 368	95.73 474	88.81 384	91.97 462	97.87 343	85.80 487	90.91 504	92.73 495	91.16 318	98.33 470	79.48 524	95.76 508	98.08 390

miper_enhance_ethall		93.14 408	92.78 406	94.20 420	93.65 524	85.29 466	89.97 508	97.85 344	85.05 496	96.15 364	94.56 463	85.74 416	99.14 372	93.74 320	98.34 401	98.17 386

Anonymous20231206		95.27 310	95.06 307	95.88 312	98.72 183	89.37 364	95.70 264	97.85 344	88.00 463	96.98 296	97.62 284	91.95 309	99.34 316	89.21 433	99.53 176	98.94 266

SP-DiffGlue		94.64 345	94.54 344	94.97 376	93.53 527	94.33 193	93.94 392	97.84 346	93.35 320	96.58 327	95.54 438	88.87 367	94.71 525	93.73 322	97.44 457	95.87 491

xiu_mvs_v2_base		94.22 363	94.63 336	92.99 468	97.32 397	84.84 477	92.12 458	97.84 346	91.96 373	94.17 438	93.43 478	96.07 154	99.71 128	91.27 382	97.48 453	94.42 510

PS-MVSNAJ		94.10 369	94.47 347	93.00 467	97.35 392	84.88 474	91.86 465	97.84 346	91.96 373	94.17 438	92.50 499	95.82 164	99.71 128	91.27 382	97.48 453	94.40 511

CANet_DTU		94.65 344	94.21 360	95.96 304	95.90 460	89.68 355	93.92 393	97.83 349	93.19 330	90.12 516	95.64 435	88.52 371	99.57 211	93.27 339	99.47 208	98.62 322

door								97.81 350

test12				97.46 162	97.61 367	94.07 203		97.78 351		93.57 463		93.31 265	99.42 273		98.78 354	98.89 278

旧先验1					97.80 333	93.87 211		97.75 352			97.04 342	93.57 257			98.68 372	98.72 310

新几何1				97.25 182	98.29 257	94.70 173		97.73 353	77.98 536	94.83 419	96.67 372	92.08 306	99.45 262	88.17 452	98.65 377	97.61 434

testdata				95.70 326	98.16 281	90.58 323		97.72 354	80.38 525	95.62 391	97.02 343	92.06 307	98.98 400	89.06 437	98.52 386	97.54 438

test20.03		96.58 222	96.61 215	96.48 255	98.49 232	91.72 292	95.68 267	97.69 355	96.81 124	98.27 160	97.92 244	94.18 239	98.71 432	90.78 398	99.66 111	99.00 248

ab-mvs		96.59 219	96.59 218	96.60 240	98.64 196	92.21 272	98.35 39	97.67 356	94.45 275	96.99 293	98.79 91	94.96 211	99.49 238	90.39 414	99.07 311	98.08 390

CMPMVS	73.10 23	92.74 418	91.39 440	96.77 228	93.57 526	94.67 174	94.21 372	97.67 356	80.36 526	93.61 460	96.60 376	82.85 447	97.35 494	84.86 498	98.78 354	98.29 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvs_anonymous		95.36 304	96.07 258	93.21 458	96.29 437	81.56 507	94.60 351	97.66 358	93.30 323	96.95 298	98.91 84	93.03 277	99.38 298	96.60 128	97.30 462	98.69 315

FMVSNet3		95.26 311	94.94 313	96.22 285	96.53 427	90.06 342	95.99 240	97.66 358	94.11 290	97.99 203	97.91 246	80.22 468	99.63 184	94.60 280	99.44 217	98.96 260

EI-MVSNet-UG-set		97.32 159	97.40 152	97.09 196	97.34 394	92.01 285	95.33 300	97.65 360	97.74 70	98.30 157	98.14 206	95.04 205	99.69 145	97.55 82	99.52 183	99.58 51

EI-MVSNet-Vis-set		97.32 159	97.39 153	97.11 192	97.36 391	92.08 281	95.34 299	97.65 360	97.74 70	98.29 158	98.11 213	95.05 204	99.68 152	97.50 84	99.50 197	99.56 67

EI-MVSNet		96.63 217	96.93 191	95.74 319	97.26 399	88.13 408	95.29 306	97.65 360	96.99 111	97.94 215	98.19 200	92.55 291	99.58 205	96.91 113	99.56 159	99.50 88

MVSTER		94.21 365	93.93 371	95.05 369	95.83 465	86.46 444	95.18 315	97.65 360	92.41 362	97.94 215	98.00 235	72.39 507	99.58 205	96.36 141	99.56 159	99.12 220

usedtu_dtu_shiyan1		94.61 347	94.29 354	95.57 334	97.93 307	88.45 391	91.30 480	97.64 364	91.61 383	95.85 382	95.79 429	86.65 409	99.48 241	92.92 349	98.97 320	98.78 294

FE-MVSNET3		94.61 347	94.29 354	95.57 334	97.93 307	88.45 391	91.30 480	97.64 364	91.61 383	95.85 382	95.79 429	86.65 409	99.48 241	92.92 349	98.97 320	98.78 294

TestfortrainingZip				97.39 170	97.24 401	94.58 180	97.75 87	97.64 364	96.08 173	96.48 335	96.31 395	92.56 289	99.27 344		96.62 482	98.31 366

IterMVS-SCA-FT		95.86 270	96.19 252	94.85 383	97.68 355	85.53 460	92.42 448	97.63 367	96.99 111	98.36 145	98.54 136	87.94 381	99.75 85	97.07 107	99.08 309	99.27 178

test222					98.17 279	93.24 239	92.74 437	97.61 368	75.17 541	94.65 425	96.69 371	90.96 325			98.66 375	97.66 429

VNet		96.84 197	96.83 200	96.88 217	98.06 291	92.02 284	96.35 202	97.57 369	97.70 74	97.88 220	97.80 261	92.40 298	99.54 221	94.73 275	98.96 323	99.08 232

PMVS	89.60 17	96.71 213	96.97 187	95.95 306	99.51 32	97.81 19	97.42 120	97.49 370	97.93 63	95.95 371	98.58 129	96.88 99	96.91 502	89.59 428	99.36 249	93.12 520
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ppachtmachnet_test		94.49 355	94.84 324	93.46 444	96.16 446	82.10 502	90.59 498	97.48 371	90.53 416	97.01 291	97.59 286	91.01 322	99.36 309	93.97 309	99.18 292	98.94 266

DPM-MVS		93.68 387	92.77 407	96.42 265	97.91 309	92.54 258	91.17 486	97.47 372	84.99 499	93.08 475	94.74 460	89.90 345	99.00 396	87.54 460	98.09 413	97.72 427

IterMVS		95.42 300	95.83 278	94.20 420	97.52 377	83.78 492	92.41 449	97.47 372	95.49 217	98.06 194	98.49 141	87.94 381	99.58 205	96.02 162	99.02 317	99.23 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SSC-MVS3.2		95.75 277	96.56 222	93.34 447	98.69 192	80.75 515	91.60 470	97.43 374	97.37 97	96.99 293	97.02 343	93.69 255	99.71 128	96.32 144	99.89 26	99.55 71

MS-PatchMatch		94.83 332	94.91 317	94.57 401	96.81 418	87.10 436	94.23 370	97.34 375	88.74 450	97.14 276	97.11 337	91.94 310	98.23 475	92.99 346	97.92 422	98.37 356

ALIKED-NN		90.94 459	89.58 468	95.02 371	94.61 509	96.31 80	93.16 428	97.27 376	79.38 529	86.25 538	95.27 448	83.42 442	94.29 530	79.08 526	97.77 432	94.46 508

MDA-MVSNet-bldmvs		95.69 281	95.67 284	95.74 319	98.48 234	88.76 387	92.84 432	97.25 377	96.00 181	97.59 239	97.95 240	91.38 316	99.46 254	93.16 344	96.35 490	98.99 252

PatchMatch-RL		94.61 347	93.81 372	97.02 205	98.19 273	95.72 110	93.66 405	97.23 378	88.17 460	94.94 416	95.62 436	91.43 315	98.57 448	87.36 465	97.68 442	96.76 469

ELoFTR		95.12 318	94.86 321	95.91 309	98.39 248	93.23 240	94.57 353	97.21 379	87.26 469	98.53 122	98.52 137	86.67 408	97.37 493	93.24 340	99.36 249	97.12 451

CR-MVSNet		93.29 404	92.79 404	94.78 388	95.44 483	88.15 406	96.18 217	97.20 380	84.94 500	94.10 441	98.57 131	77.67 477	99.39 294	95.17 232	95.81 503	96.81 467

Patchmtry		95.03 325	94.59 340	96.33 274	94.83 506	90.82 318	96.38 199	97.20 380	96.59 135	97.49 248	98.57 131	77.67 477	99.38 298	92.95 348	99.62 123	98.80 291

API-MVS		95.09 322	95.01 309	95.31 355	96.61 424	94.02 206	96.83 157	97.18 382	95.60 209	95.79 384	94.33 469	94.54 226	98.37 468	85.70 483	98.52 386	93.52 516

MAR-MVS		94.21 365	93.03 395	97.76 125	96.94 415	97.44 37	96.97 147	97.15 383	87.89 465	92.00 496	92.73 495	92.14 303	99.12 377	83.92 505	97.51 452	96.73 470
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
pmmvs5		94.63 346	94.34 353	95.50 343	97.63 366	88.34 398	94.02 385	97.13 384	87.15 472	95.22 408	97.15 329	87.50 390	99.27 344	93.99 307	99.26 281	98.88 282

UGNet		96.81 202	96.56 222	97.58 141	96.64 423	93.84 213	97.75 87	97.12 385	96.47 145	93.62 459	98.88 87	93.22 267	99.53 224	95.61 192	99.69 99	99.36 153
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
blended_shiyan8		93.34 399	92.55 414	95.73 323	95.69 475	89.08 375	92.36 452	97.11 386	91.47 395	95.42 403	88.94 528	82.26 451	99.48 241	93.84 315	95.81 503	98.62 322

blended_shiyan6		93.34 399	92.54 415	95.73 323	95.68 476	89.08 375	92.35 453	97.10 387	91.47 395	95.37 405	88.96 527	82.26 451	99.48 241	93.83 316	95.85 499	98.62 322

blend_shiyan4		88.73 485	86.43 500	95.61 331	95.31 489	89.17 367	92.13 457	97.10 387	91.59 389	94.15 440	87.38 533	52.97 547	99.40 285	91.84 369	75.42 547	98.27 373

h-mvs33		96.29 241	95.63 287	98.26 79	98.50 230	96.11 92	96.90 151	97.09 389	96.58 136	97.21 269	98.19 200	84.14 433	99.78 58	95.89 172	96.17 495	98.89 278

CHOSEN 280x420		89.98 467	89.19 474	92.37 486	95.60 479	81.13 513	86.22 533	97.09 389	81.44 521	87.44 534	93.15 479	73.99 497	99.47 247	88.69 442	99.07 311	96.52 476

CDS-MVSNet		94.88 331	94.12 364	97.14 190	97.64 365	93.57 224	93.96 391	97.06 391	90.05 430	96.30 350	96.55 378	86.10 413	99.47 247	90.10 419	99.31 270	98.40 351
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
wanda-best-256-512		92.66 420	91.75 435	95.40 350	94.99 498	88.19 402	90.89 492	97.05 392	91.02 408	94.75 420	87.24 534	80.36 464	99.46 254	93.63 327	95.85 499	98.55 332

FE-blended-shiyan7		92.66 420	91.75 435	95.40 350	94.99 498	88.19 402	90.89 492	97.05 392	91.02 408	94.75 420	87.24 534	80.36 464	99.46 254	93.63 327	95.85 499	98.55 332

reproduce_monomvs		92.05 440	92.26 419	91.43 498	95.42 485	75.72 539	95.68 267	97.05 392	94.47 274	97.95 213	98.35 165	55.58 539	99.05 389	96.36 141	99.44 217	99.51 85

BH-untuned		94.69 340	94.75 330	94.52 404	97.95 306	87.53 425	94.07 382	97.01 395	93.99 296	97.10 280	95.65 434	92.65 286	98.95 405	87.60 458	96.74 477	97.09 453

sss		94.22 363	93.72 375	95.74 319	97.71 352	89.95 347	93.84 395	96.98 396	88.38 457	93.75 453	95.74 431	87.94 381	98.89 409	91.02 388	98.10 411	98.37 356

1314		92.38 428	92.30 418	92.64 480	95.42 485	85.15 469	95.86 254	96.97 397	85.40 493	90.62 507	93.06 485	91.12 319	97.80 489	86.74 470	95.49 512	94.97 505

SixPastTwentyTwo		97.49 140	97.57 137	97.26 181	99.56 22	92.33 265	98.28 46	96.97 397	98.30 49	99.45 24	99.35 28	88.43 373	99.89 20	98.01 59	99.76 72	99.54 73

LoFTR		95.39 302	95.01 309	96.52 251	97.16 404	95.19 155	94.77 344	96.95 399	90.31 422	98.78 89	98.29 183	86.71 405	97.91 485	92.56 355	99.57 154	96.46 480

TSAR-MVS + GP.		96.47 229	96.12 254	97.49 157	97.74 348	95.23 149	94.15 376	96.90 400	93.26 324	98.04 197	96.70 370	94.41 229	98.89 409	94.77 271	99.14 298	98.37 356

our_test_3		94.20 367	94.58 341	93.07 462	96.16 446	81.20 512	90.42 501	96.84 401	90.72 412	97.14 276	97.13 334	90.47 331	99.11 380	94.04 304	98.25 405	98.91 274

alignmvs		96.01 260	95.52 291	97.50 154	97.77 342	94.71 171	96.07 227	96.84 401	97.48 86	96.78 312	94.28 470	85.50 421	99.40 285	96.22 151	98.73 367	98.40 351

CL-MVSNet_self_test		95.04 323	94.79 329	95.82 314	97.51 378	89.79 351	91.14 487	96.82 403	93.05 339	96.72 315	96.40 389	90.82 326	99.16 370	91.95 365	98.66 375	98.50 342

TAMVS		95.49 294	94.94 313	97.16 188	98.31 255	93.41 233	95.07 323	96.82 403	91.09 405	97.51 246	97.82 258	89.96 344	99.42 273	88.42 447	99.44 217	98.64 319

pmmvs4		94.82 333	94.19 361	96.70 232	97.42 388	92.75 254	92.09 460	96.76 405	86.80 478	95.73 389	97.22 324	89.28 361	98.89 409	93.28 338	99.14 298	98.46 347

jason		94.39 359	94.04 366	95.41 349	98.29 257	87.85 417	92.74 437	96.75 406	85.38 494	95.29 406	96.15 406	88.21 380	99.65 173	94.24 294	99.34 260	98.74 307
jason: jason.
MVS		90.02 465	89.20 473	92.47 484	94.71 507	86.90 439	95.86 254	96.74 407	64.72 545	90.62 507	92.77 493	92.54 293	98.39 465	79.30 525	95.56 511	92.12 521

IS-MVSNet		96.93 188	96.68 210	97.70 130	99.25 71	94.00 207	98.57 23	96.74 407	98.36 45	98.14 184	97.98 237	88.23 379	99.71 128	93.10 345	99.72 90	99.38 143

MonoMVSNet		93.30 403	93.96 370	91.33 501	94.14 519	81.33 511	97.68 98	96.69 409	95.38 225	96.32 344	98.42 152	84.12 435	96.76 506	90.78 398	92.12 530	95.89 490

OpenMVS_ROB	91.80 14	93.64 390	93.05 394	95.42 347	97.31 398	91.21 307	95.08 322	96.68 410	81.56 519	96.88 304	96.41 387	90.44 334	99.25 349	85.39 489	97.67 443	95.80 494

SIFT-UM-Cal		93.74 381	93.73 373	93.78 435	95.97 458	96.07 94	89.78 514	96.67 411	91.69 379	97.77 231	96.09 414	89.51 354	94.75 523	86.68 472	99.39 240	90.52 538

cl22		93.25 405	92.84 403	94.46 409	94.30 514	86.00 456	91.09 490	96.64 412	90.74 411	95.79 384	96.31 395	78.24 474	98.77 424	94.15 298	98.34 401	98.62 322

gbinet_0.2-2-1-0.02		92.86 415	91.78 433	96.13 294	94.34 512	90.06 342	91.90 464	96.63 413	91.73 377	94.24 434	86.22 540	80.26 467	99.56 213	93.87 313	96.80 475	98.77 303

SP-MNN		94.33 361	94.22 359	94.67 393	94.94 502	92.73 256	93.74 400	96.59 414	92.73 353	93.75 453	95.38 446	88.24 377	95.08 519	94.86 264	97.78 431	96.20 486

EPP-MVSNet		96.84 197	96.58 219	97.65 136	99.18 91	93.78 216	98.68 17	96.34 415	97.91 64	97.30 261	98.06 225	88.46 372	99.85 30	93.85 314	99.40 236	99.32 160

BH-RMVSNet		94.56 351	94.44 350	94.91 378	97.57 370	87.44 427	93.78 399	96.26 416	93.69 306	96.41 340	96.50 383	92.10 305	99.00 396	85.96 481	97.71 439	98.31 366

LuminaMVS		96.76 206	96.58 219	97.30 176	98.94 137	92.96 245	96.17 221	96.15 417	95.54 214	98.96 69	98.18 203	87.73 388	99.80 50	97.98 60	99.61 134	99.15 206

GA-MVS		92.83 417	92.15 423	94.87 382	96.97 412	87.27 433	90.03 507	96.12 418	91.83 376	94.05 444	94.57 462	76.01 489	98.97 404	92.46 357	97.34 460	98.36 361

lupinMVS		93.77 379	93.28 387	95.24 357	97.68 355	87.81 420	92.12 458	96.05 419	84.52 503	94.48 430	95.06 453	86.90 402	99.63 184	93.62 329	99.13 300	98.27 373

test_method		66.88 511	66.13 514	69.11 529	62.68 555	25.73 559	49.76 546	96.04 420	14.32 550	64.27 550	91.69 508	73.45 504	88.05 546	76.06 535	66.94 548	93.54 515

PMMVS2		93.66 388	94.07 365	92.45 485	97.57 370	80.67 516	86.46 532	96.00 421	93.99 296	97.10 280	97.38 311	89.90 345	97.82 488	88.76 440	99.47 208	98.86 285

WTY-MVS		93.55 392	93.00 397	95.19 360	97.81 329	87.86 415	93.89 394	96.00 421	89.02 445	94.07 443	95.44 444	86.27 412	99.33 318	87.69 456	96.82 473	98.39 353

PMMVS		92.39 427	91.08 447	96.30 279	93.12 530	92.81 250	90.58 499	95.96 423	79.17 531	91.85 498	92.27 500	90.29 339	98.66 440	89.85 424	96.68 481	97.43 442

MG-MVS		94.08 371	94.00 367	94.32 416	97.09 408	85.89 457	93.19 427	95.96 423	92.52 356	94.93 417	97.51 295	89.54 350	98.77 424	87.52 462	97.71 439	98.31 366

WBMVS		91.11 454	90.72 456	92.26 489	95.99 456	77.98 529	91.47 473	95.90 425	91.63 381	95.90 377	96.45 385	59.60 526	99.46 254	89.97 422	99.59 144	99.33 158

MDA-MVSNet_test_wron		94.73 335	94.83 326	94.42 410	97.48 381	85.15 469	90.28 504	95.87 426	92.52 356	97.48 251	97.76 265	91.92 311	99.17 369	93.32 336	96.80 475	98.94 266

YYNet1		94.73 335	94.84 324	94.41 411	97.47 385	85.09 471	90.29 503	95.85 427	92.52 356	97.53 244	97.76 265	91.97 308	99.18 364	93.31 337	96.86 470	98.95 263

ADS-MVSNet2		91.47 450	90.51 460	94.36 412	95.51 481	85.63 458	95.05 327	95.70 428	83.46 509	92.69 486	96.84 359	79.15 471	99.41 283	85.66 485	90.52 532	98.04 400

tt0805		97.44 146	97.56 138	97.11 192	99.55 24	96.36 76	98.66 21	95.66 429	98.31 47	97.09 285	95.45 443	97.17 69	98.50 456	98.67 39	97.45 456	96.48 478

BH-w/o		92.14 436	91.94 426	92.73 477	97.13 407	85.30 465	92.46 445	95.64 430	89.33 438	94.21 435	92.74 494	89.60 348	98.24 474	81.68 517	94.66 518	94.66 507

KD-MVS_2432*1600		88.93 481	87.74 487	92.49 482	88.04 548	81.99 503	89.63 519	95.62 431	91.35 400	95.06 411	93.11 480	56.58 533	98.63 443	85.19 493	95.07 513	96.85 463

miper_refine_blended		88.93 481	87.74 487	92.49 482	88.04 548	81.99 503	89.63 519	95.62 431	91.35 400	95.06 411	93.11 480	56.58 533	98.63 443	85.19 493	95.07 513	96.85 463

VDD-MVS		97.37 155	97.25 166	97.74 126	98.69 192	94.50 186	97.04 142	95.61 433	98.59 35	98.51 123	98.72 103	92.54 293	99.58 205	96.02 162	99.49 200	99.12 220

SIFT-NCMNet		93.23 407	93.19 390	93.34 447	95.31 489	95.59 118	88.29 528	95.60 434	91.60 387	98.43 135	96.34 394	89.80 347	93.57 536	83.82 508	99.57 154	90.85 535

PAPM		87.64 495	85.84 502	93.04 464	96.54 426	84.99 473	88.42 527	95.57 435	79.52 528	83.82 540	93.05 486	80.57 462	98.41 463	62.29 545	92.79 527	95.71 495

test_yl		94.40 357	94.00 367	95.59 332	96.95 413	89.52 359	94.75 346	95.55 436	96.18 166	96.79 308	96.14 409	81.09 459	99.18 364	90.75 400	97.77 432	98.07 392

DCV-MVSNet		94.40 357	94.00 367	95.59 332	96.95 413	89.52 359	94.75 346	95.55 436	96.18 166	96.79 308	96.14 409	81.09 459	99.18 364	90.75 400	97.77 432	98.07 392

AUN-MVS		93.95 377	92.69 409	97.74 126	97.80 333	95.38 134	95.57 280	95.46 438	91.26 402	92.64 489	96.10 412	74.67 495	99.55 218	93.72 324	96.97 466	98.30 369

hse-mvs2		95.77 274	95.09 304	97.79 121	97.84 320	95.51 124	95.66 269	95.43 439	96.58 136	97.21 269	96.16 405	84.14 433	99.54 221	95.89 172	96.92 467	98.32 364

WB-MVS		95.50 293	96.62 213	92.11 492	99.21 85	77.26 534	96.12 224	95.40 440	98.62 34	98.84 83	98.26 190	91.08 320	99.50 232	93.37 333	98.70 370	99.58 51

mvsmamba		94.91 328	94.41 351	96.40 271	97.65 362	91.30 303	97.92 74	95.32 441	91.50 392	95.54 397	98.38 161	83.06 445	99.68 152	92.46 357	97.84 429	98.23 377

VDDNet		96.98 184	96.84 199	97.41 168	99.40 49	93.26 238	97.94 71	95.31 442	99.26 11	98.39 141	99.18 45	87.85 386	99.62 189	95.13 239	99.09 308	99.35 157

SymmetryMVS		96.43 234	95.85 276	98.17 88	98.58 213	95.57 119	96.87 154	95.29 443	96.94 118	96.85 305	97.88 248	85.36 422	99.76 77	95.63 189	99.27 278	99.19 198

BP-MVS1		95.36 304	94.86 321	96.89 216	98.35 252	91.72 292	96.76 165	95.21 444	96.48 144	96.23 354	97.19 326	75.97 490	99.80 50	97.91 63	99.60 141	99.15 206

FA-MVS(test-final)		94.91 328	94.89 318	94.99 374	97.51 378	88.11 410	98.27 48	95.20 445	92.40 363	96.68 317	98.60 127	83.44 441	99.28 341	93.34 335	98.53 385	97.59 436

SSC-MVS		95.92 266	97.03 184	92.58 481	99.28 64	78.39 524	96.68 175	95.12 446	98.90 25	99.11 51	98.66 116	91.36 317	99.68 152	95.00 249	99.16 296	99.67 36

MVStest1		91.89 443	91.45 438	93.21 458	89.01 544	84.87 475	95.82 258	95.05 447	91.50 392	98.75 96	99.19 41	57.56 529	95.11 518	97.78 71	98.37 400	99.64 44

wuyk23d		93.25 405	95.20 297	87.40 524	96.07 454	95.38 134	97.04 142	94.97 448	95.33 226	99.70 9	98.11 213	98.14 21	91.94 541	77.76 532	99.68 104	74.89 545

ttmdpeth		94.05 372	94.15 363	93.75 436	95.81 467	85.32 464	96.00 237	94.93 449	92.07 369	94.19 436	99.09 58	85.73 417	96.41 509	90.98 389	98.52 386	99.53 78

MatchFormer		93.37 398	93.14 391	94.07 424	96.06 455	92.91 247	94.24 368	94.92 450	85.51 489	98.29 158	97.79 262	85.70 418	96.13 511	86.23 476	99.51 189	93.18 519

Vis-MVSNet (Re-imp)		95.11 319	94.85 323	95.87 313	99.12 104	89.17 367	97.54 113	94.92 450	96.50 141	96.58 327	97.27 320	83.64 440	99.48 241	88.42 447	99.67 108	98.97 259

PatchmatchNet2					0.00 560	78.83 523	89.63 519	94.76 452	87.65 466
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TR-MVS		92.54 424	92.20 421	93.57 442	96.49 429	86.66 442	93.51 414	94.73 453	89.96 431	94.95 415	93.87 475	90.24 340	98.61 445	81.18 520	94.88 516	95.45 500

SIFT-PCN-Cal		93.02 413	92.95 398	93.23 456	95.63 477	94.57 182	89.68 518	94.71 454	90.40 419	97.02 289	95.84 427	88.33 376	93.66 533	85.26 491	99.65 113	91.45 529

HY-MVS	91.43 15	92.58 423	91.81 430	94.90 380	96.49 429	88.87 381	97.31 125	94.62 455	85.92 485	90.50 510	96.84 359	85.05 425	99.40 285	83.77 509	95.78 507	96.43 481

PVSNet	86.72 19	91.10 455	90.97 450	91.49 497	97.56 372	78.04 527	87.17 530	94.60 456	84.65 502	92.34 493	92.20 502	87.37 395	98.47 459	85.17 495	97.69 441	97.96 406

Patchmatch-test		93.60 391	93.25 388	94.63 396	96.14 450	87.47 426	96.04 232	94.50 457	93.57 310	96.47 337	96.97 349	76.50 485	98.61 445	90.67 407	98.41 399	97.81 419

SIFT-UMatch		93.66 388	93.67 376	93.63 440	96.30 436	96.15 90	90.62 497	94.47 458	92.12 367	97.39 258	96.18 403	87.74 387	93.63 534	88.59 444	99.64 117	91.12 531

Anonymous202405211		96.34 240	95.98 265	97.43 165	98.25 266	93.85 212	96.74 167	94.41 459	97.72 72	98.37 142	98.03 229	87.15 398	99.53 224	94.06 301	99.07 311	98.92 273

tpm cat1		88.01 493	87.33 492	90.05 512	94.48 511	76.28 537	94.47 356	94.35 460	73.84 544	89.26 523	95.61 437	73.64 501	98.30 472	84.13 502	86.20 540	95.57 499

guyue		96.21 248	96.29 245	95.98 303	98.80 166	89.14 372	96.40 194	94.34 461	95.99 183	98.58 115	98.13 208	87.42 394	99.64 179	97.39 90	99.55 166	99.16 205

SP-NN		92.63 422	92.38 416	93.37 445	93.30 528	92.36 264	92.04 461	94.24 462	91.60 387	89.19 524	93.92 474	87.21 397	91.28 542	93.73 322	96.17 495	96.48 478

mvsany_test3		96.21 248	95.93 270	97.05 199	97.40 389	94.33 193	95.76 261	94.20 463	89.10 443	99.36 34	99.60 11	93.97 245	97.85 487	95.40 214	98.63 378	98.99 252

SCA		93.38 397	93.52 381	92.96 469	96.24 438	81.40 510	93.24 424	94.00 464	91.58 390	94.57 426	96.97 349	87.94 381	99.42 273	89.47 430	97.66 446	98.06 396

SIFT-ConvMatch		93.72 384	93.47 382	94.48 408	96.22 442	96.63 63	90.58 499	93.91 465	91.70 378	97.70 233	96.17 404	89.03 364	95.12 517	86.29 475	99.65 113	91.69 526

SIFT-NCM-Cal		93.81 378	93.73 373	94.05 426	96.55 425	96.75 55	91.23 483	93.80 466	91.44 398	95.86 381	96.27 397	90.82 326	93.76 532	88.26 451	99.37 244	91.63 527

SIFT-MNN		93.13 410	92.91 399	93.79 434	96.42 431	96.49 68	91.23 483	93.73 467	92.18 366	95.52 398	96.08 415	84.66 430	93.04 539	87.49 463	98.94 326	91.84 523

testing91		89.67 474	88.55 479	93.04 464	95.90 460	81.80 506	92.71 439	93.71 468	93.71 304	90.18 514	90.15 520	57.11 531	99.22 357	87.17 468	96.32 491	98.12 388

tpmrst		90.31 462	90.61 459	89.41 513	94.06 520	72.37 548	95.06 326	93.69 469	88.01 462	92.32 494	96.86 357	77.45 479	98.82 418	91.04 387	87.01 539	97.04 455

MIMVSNet		93.42 395	92.86 401	95.10 367	98.17 279	88.19 402	98.13 59	93.69 469	92.07 369	95.04 414	98.21 198	80.95 461	99.03 395	81.42 518	98.06 414	98.07 392

DSMNet-mixed		92.19 435	91.83 429	93.25 454	96.18 445	83.68 493	96.27 208	93.68 471	76.97 540	92.54 492	99.18 45	89.20 363	98.55 451	83.88 506	98.60 382	97.51 439

FE-MVS		92.95 414	92.22 420	95.11 365	97.21 402	88.33 399	98.54 26	93.66 472	89.91 432	96.21 356	98.14 206	70.33 514	99.50 232	87.79 454	98.24 406	97.51 439

tpmvs		90.79 460	90.87 452	90.57 507	92.75 534	76.30 536	95.79 259	93.64 473	91.04 407	91.91 497	96.26 398	77.19 483	98.86 415	89.38 432	89.85 535	96.56 475

PatchmatchNet		91.98 442	91.87 428	92.30 488	94.60 510	79.71 519	95.12 316	93.59 474	89.52 436	93.61 460	97.02 343	77.94 475	99.18 364	90.84 395	94.57 521	98.01 403
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		90.95 458	90.26 463	93.04 464	95.51 481	82.37 501	95.05 327	93.41 475	83.46 509	92.69 486	96.84 359	79.15 471	98.70 433	85.66 485	90.52 532	98.04 400

SIFT-CM-Cal		93.31 401	93.10 392	93.95 429	96.19 443	96.32 79	89.81 513	93.40 476	91.16 404	97.19 272	96.07 416	88.24 377	94.58 527	86.11 477	99.69 99	90.94 534

FPMVS		89.92 469	88.63 478	93.82 432	98.37 250	96.94 49	91.58 471	93.34 477	88.00 463	90.32 512	97.10 338	70.87 512	91.13 544	71.91 542	96.16 497	93.39 518

AstraMVS		96.41 236	96.48 233	96.20 286	98.91 146	89.69 354	96.28 206	93.29 478	96.11 169	98.70 102	98.36 163	89.41 358	99.66 170	97.60 80	99.63 120	99.26 180

MDTV_nov1_ep13			91.28 443		94.31 513	73.51 546	94.80 341	93.16 479	86.75 479	93.45 467	97.40 304	76.37 486	98.55 451	88.85 438	96.43 486

MASt3R-SfM		91.42 451	90.88 451	93.06 463	92.40 535	92.08 281	89.76 515	93.15 480	78.62 533	95.98 370	97.33 316	82.42 450	91.17 543	90.23 417	97.98 418	95.92 488

SIFT-NN-PointCN		92.48 426	92.19 422	93.33 450	95.40 487	95.65 116	90.19 505	93.07 481	88.67 452	92.90 478	95.95 422	89.38 359	93.20 537	85.21 492	98.94 326	91.15 530

baseline1		93.14 408	92.64 411	94.62 397	97.34 394	87.20 434	96.67 177	93.02 482	94.71 259	96.51 334	95.83 428	81.64 453	98.60 447	90.00 421	88.06 538	98.07 392

PatchT		93.75 380	93.57 379	94.29 418	95.05 496	87.32 432	96.05 230	92.98 483	97.54 82	94.25 433	98.72 103	75.79 491	99.24 353	95.92 170	95.81 503	96.32 483

EPNet_dtu		91.39 452	90.75 455	93.31 452	90.48 542	82.61 499	94.80 341	92.88 484	93.39 318	81.74 543	94.90 458	81.36 457	99.11 380	88.28 449	98.87 339	98.21 380
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
new_pmnet		92.34 429	91.69 437	94.32 416	96.23 440	89.16 370	92.27 454	92.88 484	84.39 506	95.29 406	96.35 392	85.66 419	96.74 507	84.53 500	97.56 449	97.05 454

dp		88.08 492	88.05 485	88.16 522	92.85 532	68.81 553	94.17 374	92.88 484	85.47 491	91.38 503	96.14 409	68.87 518	98.81 420	86.88 469	83.80 542	96.87 461

EU-MVSNet		94.25 362	94.47 347	93.60 441	98.14 285	82.60 500	97.24 130	92.72 487	85.08 495	98.48 128	98.94 77	82.59 449	98.76 426	97.47 86	99.53 176	99.44 122

PVSNet_0	81.89 21	84.49 503	83.21 507	88.34 519	95.76 472	74.97 542	83.49 540	92.70 488	78.47 535	87.94 532	86.90 539	83.38 444	96.63 508	73.44 540	66.86 549	93.40 517

dmvs_re		92.08 439	91.27 444	94.51 405	97.16 404	92.79 253	95.65 271	92.64 489	94.11 290	92.74 485	90.98 515	83.41 443	94.44 529	80.72 521	94.07 523	96.29 484

MM		96.87 194	96.62 213	97.62 138	97.72 350	93.30 235	96.39 196	92.61 490	97.90 65	96.76 313	98.64 121	90.46 332	99.81 43	99.16 18	99.94 8	99.76 21

SIFT-PointCN		93.04 412	92.72 408	94.01 428	95.80 468	95.33 146	89.76 515	92.60 491	90.24 426	96.32 344	95.87 426	87.45 391	94.70 526	86.65 473	99.77 71	92.01 522

pmmvs3		90.00 466	88.90 477	93.32 451	94.20 518	85.34 463	91.25 482	92.56 492	78.59 534	93.82 449	95.17 450	67.36 520	98.69 435	89.08 436	98.03 416	95.92 488

myMVS_eth3d28		88.32 489	87.73 489	90.11 511	96.42 431	74.96 543	92.21 455	92.37 493	93.56 311	90.14 515	89.61 523	56.13 536	98.05 482	81.84 515	97.26 463	97.33 447

CVMVSNet		92.33 430	92.79 404	90.95 503	97.26 399	75.84 538	95.29 306	92.33 494	81.86 517	96.27 351	98.19 200	81.44 456	98.46 461	94.23 295	98.29 404	98.55 332

SIFT-NN-UMatch		92.28 433	91.93 427	93.34 447	96.13 451	96.04 96	90.05 506	92.08 495	90.41 418	92.88 480	95.29 447	87.36 396	93.63 534	85.33 490	97.87 428	90.34 540

testing99		89.21 479	88.04 486	92.70 478	95.78 470	81.00 514	92.65 440	92.03 496	93.20 329	89.90 519	90.08 522	55.25 540	99.14 372	87.54 460	95.95 498	97.97 405

E-PMN		89.52 476	89.78 466	88.73 517	93.14 529	77.61 530	83.26 541	92.02 497	94.82 253	93.71 455	93.11 480	75.31 492	96.81 503	85.81 482	96.81 474	91.77 525

CostFormer		89.75 472	89.25 469	91.26 502	94.69 508	78.00 528	95.32 302	91.98 498	81.50 520	90.55 509	96.96 351	71.06 511	98.89 409	88.59 444	92.63 528	96.87 461

tpm2		88.47 487	87.69 490	90.79 505	94.98 501	77.34 532	95.09 320	91.83 499	77.51 539	89.40 522	96.41 387	67.83 519	98.73 428	83.58 511	92.60 529	96.29 484

XFeat-MNN		88.85 484	88.16 484	90.91 504	88.38 546	89.73 352	84.46 537	91.81 500	83.72 507	95.56 396	92.95 488	74.60 496	92.68 540	84.01 503	97.99 417	90.32 541

JIA-IIPM		91.79 445	90.69 457	95.11 365	93.80 523	90.98 311	94.16 375	91.78 501	96.38 147	90.30 513	99.30 32	72.02 508	98.90 408	88.28 449	90.17 534	95.45 500

N_pmnet		95.18 315	94.23 357	98.06 101	97.85 313	96.55 66	92.49 443	91.63 502	89.34 437	98.09 189	97.41 303	90.33 335	99.06 388	91.58 377	99.31 270	98.56 329

testing11		88.93 481	87.63 491	92.80 475	95.87 462	81.49 508	92.48 444	91.54 503	91.62 382	88.27 531	90.24 518	55.12 543	99.11 380	87.30 466	96.28 493	97.81 419

UBG		88.29 490	87.17 493	91.63 496	96.08 452	78.21 525	91.61 469	91.50 504	89.67 435	89.71 520	88.97 526	59.01 527	98.91 406	81.28 519	96.72 479	97.77 422

Syy-MVS		92.09 438	91.80 431	92.93 471	95.19 492	82.65 498	92.46 445	91.35 505	90.67 414	91.76 499	87.61 531	85.64 420	98.50 456	94.73 275	96.84 471	97.65 430

myMVS_eth3d		87.16 501	85.61 504	91.82 494	95.19 492	79.32 520	92.46 445	91.35 505	90.67 414	91.76 499	87.61 531	41.96 552	98.50 456	82.66 513	96.84 471	97.65 430

EPNet		93.72 384	92.62 412	97.03 203	87.61 550	92.25 270	96.27 208	91.28 507	96.74 127	87.65 533	97.39 309	85.00 426	99.64 179	92.14 362	99.48 205	99.20 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		91.08 456	90.85 453	91.75 495	95.33 488	78.09 526	95.03 329	91.27 508	88.75 449	93.53 464	97.40 304	71.24 509	99.30 332	91.25 384	93.87 524	97.87 414

thres200		91.00 457	90.42 461	92.77 476	97.47 385	83.98 490	94.01 386	91.18 509	95.12 236	95.44 401	91.21 512	73.93 498	99.31 328	77.76 532	97.63 448	95.01 503

EMVS		89.06 480	89.22 471	88.61 518	93.00 531	77.34 532	82.91 542	90.92 510	94.64 262	92.63 490	91.81 506	76.30 487	97.02 500	83.83 507	96.90 469	91.48 528

SIFT-NN-CMatch		92.54 424	92.03 425	94.07 424	96.08 452	96.27 84	89.47 522	90.90 511	90.26 425	92.89 479	94.83 459	90.17 341	94.95 521	84.92 497	98.78 354	90.99 533

MGCNet		95.71 280	95.18 299	97.33 174	94.85 504	92.82 248	95.36 295	90.89 512	95.51 215	95.61 393	97.82 258	88.39 374	99.78 58	98.23 50	99.91 19	99.40 134

tfpn200view9		91.55 448	91.00 448	93.21 458	98.02 294	84.35 485	95.70 264	90.79 513	96.26 153	95.90 377	92.13 503	73.62 502	99.42 273	78.85 528	97.74 436	95.85 492

thres400		91.68 447	91.00 448	93.71 438	98.02 294	84.35 485	95.70 264	90.79 513	96.26 153	95.90 377	92.13 503	73.62 502	99.42 273	78.85 528	97.74 436	97.36 444

SIFT-NN		89.78 471	89.23 470	91.41 499	95.04 497	94.89 167	88.98 525	90.76 515	89.26 441	89.11 526	92.97 487	81.45 455	88.25 545	78.47 531	97.06 465	91.08 532

LFMVS		95.32 308	94.88 320	96.62 236	98.03 292	91.47 298	97.65 100	90.72 516	99.11 14	97.89 219	98.31 173	79.20 470	99.48 241	93.91 312	99.12 303	98.93 270

testing3-2		90.09 464	90.38 462	89.24 514	98.07 290	69.88 551	95.12 316	90.71 517	96.65 129	93.60 462	94.03 472	55.81 538	99.33 318	90.69 406	98.71 368	98.51 339

SIFT-NN-NCMNet		92.32 431	91.79 432	93.89 430	96.32 435	96.91 50	90.32 502	90.69 518	90.36 421	91.72 501	95.43 445	88.98 365	94.27 531	84.23 501	98.06 414	90.49 539

thres100view900		91.76 446	91.26 446	93.26 453	98.21 270	84.50 481	96.39 196	90.39 519	96.87 121	96.33 343	93.08 484	73.44 505	99.42 273	78.85 528	97.74 436	95.85 492

thres600view7		92.03 441	91.43 439	93.82 432	98.19 273	84.61 480	96.27 208	90.39 519	96.81 124	96.37 342	93.11 480	73.44 505	99.49 238	80.32 522	97.95 421	97.36 444

ETVMVS		87.62 496	85.75 503	93.22 457	96.15 449	83.26 494	92.94 431	90.37 521	91.39 399	90.37 511	88.45 529	51.93 548	98.64 442	73.76 537	96.38 489	97.75 423

K. test v3		96.44 232	96.28 246	96.95 209	99.41 46	91.53 295	97.65 100	90.31 522	98.89 26	98.93 71	99.36 26	84.57 431	99.92 5	97.81 68	99.56 159	99.39 141

ET-MVSNet_ETH3D		91.12 453	89.67 467	95.47 345	96.41 433	89.15 371	91.54 472	90.23 523	89.07 444	86.78 537	92.84 492	69.39 516	99.44 265	94.16 297	96.61 483	97.82 417

IB-MVS	85.98 20	88.63 486	86.95 497	93.68 439	95.12 494	84.82 478	90.85 494	90.17 524	87.55 468	88.48 530	91.34 511	58.01 528	99.59 202	87.24 467	93.80 525	96.63 473
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
testing222		87.35 498	85.50 505	92.93 471	95.79 469	82.83 496	92.40 450	90.10 525	92.80 351	88.87 527	89.02 525	48.34 551	98.70 433	75.40 536	96.74 477	97.27 449

mvsany_test1		93.47 394	93.03 395	94.79 387	94.05 521	92.12 277	90.82 495	90.01 526	85.02 498	97.26 265	98.28 185	93.57 257	97.03 499	92.51 356	95.75 509	95.23 502

test-LLR		89.97 468	89.90 465	90.16 508	94.24 516	74.98 540	89.89 509	89.06 527	92.02 371	89.97 517	90.77 516	73.92 499	98.57 448	91.88 367	97.36 458	96.92 458

test-mter		87.92 494	87.17 493	90.16 508	94.24 516	74.98 540	89.89 509	89.06 527	86.44 481	89.97 517	90.77 516	54.96 544	98.57 448	91.88 367	97.36 458	96.92 458

WB-MVSnew		91.50 449	91.29 442	92.14 491	94.85 504	80.32 517	93.29 423	88.77 529	88.57 454	94.03 445	92.21 501	92.56 289	98.28 473	80.21 523	97.08 464	97.81 419

test0.0.03 1		90.11 463	89.21 472	92.83 474	93.89 522	86.87 440	91.74 468	88.74 530	92.02 371	94.71 424	91.14 513	73.92 499	94.48 528	83.75 510	92.94 526	97.16 450

XFeat-NN		84.28 504	83.52 506	86.54 525	85.42 551	86.22 449	78.86 544	88.43 531	79.17 531	90.71 506	89.11 524	69.18 517	85.27 549	76.68 534	94.13 522	88.13 542

testing3		89.72 473	88.26 483	94.10 423	97.66 360	84.30 487	94.80 341	88.25 532	94.66 260	95.07 410	92.51 498	41.15 553	99.43 269	91.81 372	98.44 397	98.55 332

0.4-1-1-0.2		82.53 508	79.25 510	92.37 486	88.10 547	83.96 491	83.72 539	88.15 533	82.14 516	78.97 547	72.49 546	53.22 545	98.84 416	85.99 480	80.50 545	94.30 512

0.3-1-1-0.015		82.33 509	78.89 511	92.66 479	88.57 545	84.69 479	84.76 536	88.02 534	82.48 514	77.55 548	72.96 545	49.60 550	98.87 414	86.05 478	80.02 546	94.43 509

0.4-1-1-0.1		83.64 506	80.50 509	93.08 461	90.32 543	85.42 462	86.48 531	87.71 535	83.60 508	80.38 546	75.45 544	53.19 546	98.91 406	86.46 474	80.88 544	94.93 506

thisisatest0515		90.43 461	89.18 475	94.17 422	97.07 409	85.44 461	89.75 517	87.58 536	88.28 458	93.69 458	91.72 507	65.27 521	99.58 205	90.59 408	98.67 373	97.50 441

thisisatest0530		92.71 419	91.76 434	95.56 339	98.42 245	88.23 401	96.03 234	87.35 537	94.04 294	96.56 330	95.47 442	64.03 523	99.77 69	94.78 270	99.11 304	98.68 318

tttt0517		93.31 401	92.56 413	95.57 334	98.71 187	87.86 415	97.44 117	87.17 538	95.79 199	97.47 253	96.84 359	64.12 522	99.81 43	96.20 152	99.32 267	99.02 247

TESTMET0.1,1		87.20 500	86.57 499	89.07 515	93.62 525	72.84 547	89.89 509	87.01 539	85.46 492	89.12 525	90.20 519	56.00 537	97.72 490	90.91 392	96.92 467	96.64 471

dmvs_testset		87.30 499	86.99 495	88.24 520	96.71 421	77.48 531	94.68 348	86.81 540	92.64 355	89.61 521	87.01 537	85.91 415	93.12 538	61.04 546	88.49 537	94.13 513

baseline2		89.65 475	88.44 481	93.25 454	95.62 478	82.71 497	93.82 396	85.94 541	88.89 448	87.35 535	92.54 497	71.23 510	99.33 318	86.01 479	94.60 520	97.72 427

MVS-HIRNet		88.40 488	90.20 464	82.99 526	97.01 411	60.04 554	93.11 429	85.61 542	84.45 505	88.72 528	99.09 58	84.72 429	98.23 475	82.52 514	96.59 484	90.69 537

lessismore_v0				97.05 199	99.36 54	92.12 277		84.07 543		98.77 94	98.98 71	85.36 422	99.74 95	97.34 93	99.37 244	99.30 166

test1111		94.53 353	94.81 327	93.72 437	99.06 113	81.94 505	98.31 43	83.87 544	96.37 148	98.49 126	99.17 48	81.49 454	99.73 101	96.64 122	99.86 35	99.49 96

UWE-MVS		87.57 497	86.72 498	90.13 510	95.21 491	73.56 545	91.94 463	83.78 545	88.73 451	93.00 476	92.87 491	55.22 541	99.25 349	81.74 516	97.96 420	97.59 436

ECVR-MVS		94.37 360	94.48 346	94.05 426	98.95 134	83.10 495	98.31 43	82.48 546	96.20 159	98.23 171	99.16 49	81.18 458	99.66 170	95.95 167	99.83 55	99.38 143

EPMVS		89.26 478	88.55 479	91.39 500	92.36 536	79.11 522	95.65 271	79.86 547	88.60 453	93.12 474	96.53 380	70.73 513	98.10 480	90.75 400	89.32 536	96.98 456

UWE-MVS-28		83.78 505	82.36 508	88.03 523	90.72 541	71.58 549	93.64 407	77.87 548	87.62 467	85.91 539	92.89 490	59.94 525	95.99 513	56.06 548	96.56 485	96.52 476

GLUNet-SfM		74.13 510	71.69 513	81.46 527	63.16 554	74.17 544	66.80 545	76.03 549	58.10 547	88.60 529	86.99 538	57.56 529	86.25 548	50.03 549	97.91 425	83.95 543

MVE	73.61 22	86.48 502	85.92 501	88.18 521	96.23 440	85.28 467	81.78 543	75.79 550	86.01 483	82.53 542	91.88 505	92.74 282	87.47 547	71.42 543	94.86 517	91.78 524
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							96.55 181	74.60 551

gg-mvs-nofinetune		88.28 491	86.96 496	92.23 490	92.84 533	84.44 483	98.19 56	74.60 551	99.08 16	87.01 536	99.47 16	56.93 532	98.23 475	78.91 527	95.61 510	94.01 514

DeepMVS_CX				77.17 528	90.94 540	85.28 467		74.08 553	52.51 548	80.87 545	88.03 530	75.25 493	70.63 551	59.23 547	84.94 541	75.62 544

GG-mvs-BLEND				90.60 506	91.00 539	84.21 488	98.23 50	72.63 554		82.76 541	84.11 541	56.14 535	96.79 504	72.20 541	92.09 531	90.78 536

test2506		89.86 470	89.16 476	91.97 493	98.95 134	76.83 535	98.54 26	61.07 555	96.20 159	97.07 286	99.16 49	55.19 542	99.69 145	96.43 138	99.83 55	99.38 143

tmp_tt		57.23 513	62.50 516	41.44 532	34.77 556	49.21 558	83.93 538	60.22 556	15.31 549	71.11 549	79.37 542	70.09 515	44.86 553	64.76 544	82.93 543	30.25 548

kuosan		54.81 514	54.94 517	54.42 531	74.43 553	50.03 557	84.98 535	44.27 557	61.80 546	62.49 551	70.43 547	35.16 555	58.04 552	19.30 551	41.61 551	55.19 547

dongtai		63.43 512	63.37 515	63.60 530	83.91 552	53.17 556	85.14 534	43.40 558	77.91 538	80.96 544	79.17 543	36.36 554	77.10 550	37.88 550	45.63 550	60.54 546

VLMVS		16.27 516	17.60 519	12.26 533	17.44 557	14.02 560	13.33 547	7.39 559	0.97 554	23.14 552	32.55 549	21.01 556	8.58 554	7.93 552	34.66 552	14.18 549

testmvs		12.33 518	15.23 521	3.64 535	5.77 559	2.23 562	88.99 524	3.62 560	2.30 553	5.29 554	13.09 550	4.52 558	1.95 555	5.16 554	8.32 554	6.75 551

test123		12.59 517	15.49 520	3.87 534	6.07 558	2.55 561	90.75 496	2.59 561	2.52 552	5.20 555	13.02 551	4.96 557	1.85 556	5.20 553	9.09 553	7.23 550

mmdepth		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

monomultidepth		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

test_blank		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

uanet_test		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

DCPMVS		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

pcd_1.5k_mvsjas		7.98 519	10.65 522	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	95.82 164	0.00 557	0.00 555	0.00 555	0.00 552

sosnet-low-res		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

sosnet		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

uncertanet		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

Regformer		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

n2								0.00 562

nn								0.00 562

ab-mvs-re		7.91 520	10.55 523	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	94.94 455	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

uanet		0.00 521	0.00 524	0.00 536	0.00 560	0.00 563	0.00 548	0.00 562	0.00 555	0.00 556	0.00 554	0.00 559	0.00 557	0.00 555	0.00 555	0.00 552

PatchmatchNet1														91.55 378	99.31 270	98.56 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.05 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						79.32 520								85.41 488

PC_three_1452									87.24 471	98.37 142	97.44 301	97.00 83	96.78 505	92.01 363	99.25 282	99.21 194

eth-test2					0.00 560

eth-test					0.00 560

OPU-MVS				97.64 137	98.01 296	95.27 147	96.79 163				97.35 314	96.97 86	98.51 455	91.21 385	99.25 282	99.14 212

test_0728_THIRD									96.62 130	98.40 139	98.28 185	97.10 71	99.71 128	95.70 181	99.62 123	99.58 51

GSMVS																98.06 396

test_part2					99.03 122	96.07 94				98.08 191

sam_mvs1												77.80 476				98.06 396

sam_mvs												77.38 480

test_post1							94.98 331				10.37 553	76.21 488	99.04 392	89.47 430

test_post											10.87 552	76.83 484	99.07 387

patchmatchnet-post											96.84 359	77.36 481	99.42 273

gm-plane-assit					91.79 537	71.40 550			81.67 518		90.11 521		98.99 398	84.86 498

test9_res														91.29 381	98.89 338	99.00 248

agg_prior2														90.34 416	98.90 334	99.10 230

test_prior4						95.38 134	93.61 410

test_prior2							93.33 422		94.21 284	94.02 446	96.25 400	93.64 256		91.90 366	98.96 323

旧先验2							93.35 421		77.95 537	95.77 388			98.67 439	90.74 403

新几何2							93.43 416

原ACMM2							92.82 433

testdata2													99.46 254	87.84 453

segment_acmp												95.34 190

testdata1							92.77 434		93.78 302

plane_prior7					98.70 189	94.67 174

plane_prior6					98.38 249	94.37 191						91.91 312

plane_prior4											96.77 365

plane_prior3						94.51 184			95.29 229	96.16 361

plane_prior2							96.50 184		96.36 149

plane_prior1					98.49 232

plane_prior						94.29 195	95.42 288		94.31 282						98.93 331

HQP5-MVS						92.47 262

HQP-NCC					97.85 313		94.26 363		93.18 331	92.86 482

ACMP_Plane					97.85 313		94.26 363		93.18 331	92.86 482

BP-MVS														90.51 411

HQP4-MVS										92.87 481			99.23 355			99.06 238

HQP2-MVS												90.33 335

NP-MVS					98.14 285	93.72 217					95.08 451

MDTV_nov1_ep13_2view						57.28 555	94.89 335		80.59 524	94.02 446		78.66 473		85.50 487		97.82 417

ACMMP++_ref															99.52 183

ACMMP++															99.55 166

Test By Simon												94.51 227

High-resolution multi-view benchmark