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.

fmvsm_s_conf0.1_n_a		99.26 91	99.06 110	99.85 43	99.52 235	99.62 84	99.54 175	99.62 52	98.69 108	99.99 2	99.96 1	94.47 305	99.94 91	99.88 26	99.92 38	99.98 2

UA-Net		99.42 55	99.29 65	99.80 64	99.62 183	99.55 98	99.50 207	99.70 18	98.79 96	99.77 90	99.96 1	97.45 125	99.96 41	98.92 162	99.90 56	99.89 30

fmvsm_s_conf0.1_n		99.29 84	99.10 99	99.86 34	99.70 123	99.65 76	99.53 184	99.62 52	98.74 102	99.99 2	99.95 3	94.53 303	99.94 91	99.89 25	99.96 17	99.97 4

test_fmvs1_n		98.41 240	98.14 253	99.21 229	99.82 53	97.71 326	99.74 48	99.49 201	99.32 30	99.99 2	99.95 3	85.32 473	99.97 29	99.82 29	99.84 102	99.96 7

DeepC-MVS	98.35 2	99.30 82	99.19 87	99.64 102	99.82 53	99.23 150	99.62 110	99.55 100	98.94 79	99.63 154	99.95 3	95.82 225	99.94 91	99.37 81	99.97 9	99.73 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n_2		99.37 68	99.22 82	99.81 60	99.77 79	99.75 52	99.46 246	99.60 68	99.47 6	99.98 13	99.94 6	94.98 261	99.95 76	99.97 2	99.79 133	99.73 128

test_cas_vis1_n_1920		99.16 112	99.01 137	99.61 110	99.81 58	98.86 229	99.65 90	99.64 42	99.39 24	99.97 25	99.94 6	93.20 348	99.98 20	99.55 50	99.91 45	99.99 1

test_vis1_n		97.92 303	97.44 344	99.34 200	99.53 229	98.08 301	99.74 48	99.49 201	99.15 38	100.00 1	99.94 6	79.51 498	99.98 20	99.88 26	99.76 142	99.97 4

OurMVSNet-221017-0		97.88 308	97.77 299	98.19 373	98.71 437	96.53 394	99.88 4	99.00 414	97.79 259	98.78 354	99.94 6	91.68 389	99.35 374	97.21 365	96.99 374	98.69 367

fmvsm_s_conf0.5_n_11		99.32 78	99.16 91	99.80 64	99.83 47	99.70 61	99.57 147	99.56 90	99.45 13	99.99 2	99.93 10	94.18 318	99.99 4	99.96 13	99.98 4	99.73 128

fmvsm_s_conf0.5_n_3		99.37 68	99.20 85	99.87 22	99.75 93	99.70 61	99.48 232	99.66 32	99.45 13	99.99 2	99.93 10	94.64 295	99.97 29	99.94 21	99.97 9	99.95 11

fmvsm_s_conf0.5_n_2		99.32 78	99.13 94	99.89 12	99.80 64	99.77 49	99.44 257	99.58 78	99.47 6	99.99 2	99.93 10	94.04 323	99.96 41	99.96 13	99.93 32	99.93 22

test_fmvsmconf0.01_n		99.22 99	99.03 118	99.79 68	98.42 463	99.48 113	99.55 170	99.51 162	99.39 24	99.78 86	99.93 10	94.80 276	99.95 76	99.93 23	99.95 22	99.94 17

test2506		96.81 397	96.65 393	97.29 440	99.74 101	92.21 487	99.60 118	85.06 544	99.13 41	99.77 90	99.93 10	87.82 452	99.85 192	99.38 80	99.38 185	99.80 88

test1111		98.04 283	98.11 257	97.83 411	99.74 101	93.82 470	99.58 139	95.40 521	99.12 46	99.65 146	99.93 10	90.73 411	99.84 202	99.43 71	99.38 185	99.82 72

ECVR-MVS		98.04 283	98.05 266	98.00 389	99.74 101	94.37 465	99.59 129	94.98 522	99.13 41	99.66 136	99.93 10	90.67 412	99.84 202	99.40 74	99.38 185	99.80 88

SixPastTwentyTwo		97.50 366	97.33 362	98.03 384	98.65 445	96.23 406	99.77 35	98.68 466	97.14 333	97.90 432	99.93 10	90.45 413	99.18 411	97.00 380	96.43 384	98.67 380

fmvsm_s_conf0.5_n_10		99.41 59	99.24 77	99.92 1	99.83 47	99.84 20	99.53 184	99.56 90	99.45 13	99.99 2	99.92 18	94.92 267	99.99 4	99.97 2	99.97 9	99.95 11

fmvsm_s_conf0.5_n_9		99.41 59	99.28 68	99.81 60	99.84 38	99.52 107	99.48 232	99.62 52	99.46 9	99.99 2	99.92 18	95.24 254	99.96 41	99.97 2	99.97 9	99.96 7

MVSMamba_PlusPlus		99.46 42	99.41 36	99.64 102	99.68 136	99.50 110	99.75 43	99.50 187	98.27 158	99.87 48	99.92 18	98.09 109	99.94 91	99.65 41	99.95 22	99.47 258

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 82	99.52 186	99.65 39	99.10 48	99.98 13	99.92 18	97.35 130	99.96 41	99.94 21	99.92 38	99.95 11

fmvsm_s_conf0.5_n		99.51 29	99.40 37	99.85 43	99.84 38	99.65 76	99.51 196	99.67 27	99.13 41	99.98 13	99.92 18	96.60 174	99.96 41	99.95 16	99.96 17	99.95 11

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 269	99.65 76	99.50 207	99.61 61	99.45 13	99.87 48	99.92 18	97.31 131	99.97 29	99.95 16	99.99 1	99.97 4

test_fmvsmconf_n		99.70 3	99.64 4	99.87 22	99.80 64	99.66 72	99.48 232	99.64 42	99.45 13	99.92 30	99.92 18	98.62 77	99.99 4	99.96 13	99.99 1	99.96 7

test_fmvsmvis_n_1920		99.65 8	99.61 8	99.77 74	99.38 289	99.37 125	99.58 139	99.62 52	99.41 23	99.87 48	99.92 18	98.81 49	100.00 1	99.97 2	99.93 32	99.94 17

E5new		99.14 125	99.02 129	99.50 153	99.69 129	98.91 210	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.26 198	99.84 202	99.30 98	99.10 234	99.76 107

E6new		99.15 117	99.03 118	99.50 153	99.66 151	98.90 215	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.31 192	99.84 202	99.30 98	99.10 234	99.76 107

E6		99.15 117	99.03 118	99.50 153	99.66 151	98.90 215	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.31 192	99.84 202	99.30 98	99.10 234	99.76 107

E5		99.14 125	99.02 129	99.50 153	99.69 129	98.91 210	99.60 118	99.53 125	98.13 191	99.72 108	99.91 26	96.26 198	99.84 202	99.30 98	99.10 234	99.76 107

test_fmvsm_n_1920		99.69 6	99.66 3	99.78 71	99.84 38	99.44 118	99.58 139	99.69 22	99.43 19	99.98 13	99.91 26	98.62 77	100.00 1	99.97 2	99.95 22	99.90 27

test_vis1_n_1920		98.63 228	98.40 236	99.31 208	99.86 25	97.94 314	99.67 77	99.62 52	99.43 19	99.99 2	99.91 26	87.29 454	100.00 1	99.92 24	99.92 38	99.98 2

mvsany_test1		99.50 31	99.46 28	99.62 109	99.61 194	99.09 169	98.94 430	99.48 213	99.10 48	99.96 27	99.91 26	98.85 43	99.96 41	99.72 32	99.58 171	99.82 72

test_fmvs1		98.88 186	98.79 189	99.16 234	99.69 129	97.61 330	99.55 170	99.49 201	99.32 30	99.98 13	99.91 26	91.41 398	99.96 41	99.82 29	99.92 38	99.90 27

SD-MVS		99.41 59	99.52 14	99.05 246	99.74 101	99.68 65	99.46 246	99.52 134	99.11 47	99.88 42	99.91 26	99.43 1	97.70 495	98.72 197	99.93 32	99.77 100
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMH	97.28 8	98.10 270	97.99 272	98.44 348	99.41 277	96.96 369	99.60 118	99.56 90	98.09 206	98.15 420	99.91 26	90.87 410	99.70 300	98.88 166	97.45 355	98.67 380
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
E4		99.13 129	99.01 137	99.49 160	99.68 136	98.90 215	99.52 186	99.52 134	98.13 191	99.71 118	99.90 36	96.32 190	99.84 202	99.21 116	99.11 225	99.75 113

viewdifsd2359ckpt11		98.78 210	98.74 195	98.89 275	99.67 139	97.04 359	99.50 207	99.58 78	98.26 161	99.56 176	99.90 36	94.36 308	99.87 177	99.49 61	98.32 300	99.77 100

viewmsd2359difaftdt		98.78 210	98.74 195	98.90 271	99.67 139	97.04 359	99.50 207	99.58 78	98.26 161	99.56 176	99.90 36	94.36 308	99.87 177	99.49 61	98.32 300	99.77 100

fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 12	99.83 47	99.74 55	99.51 196	99.62 52	99.46 9	99.99 2	99.90 36	96.60 174	99.98 20	99.95 16	99.95 22	99.96 7

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 71	99.88 10	99.56 155	99.55 100	99.15 38	99.90 34	99.90 36	99.00 23	99.97 29	99.11 132	99.91 45	99.86 43

patch_mono-2		99.26 91	99.62 7	98.16 375	99.81 58	94.59 461	99.52 186	99.64 42	99.33 29	99.73 103	99.90 36	99.00 23	99.99 4	99.69 34	99.98 4	99.89 30

VDDNet		97.55 360	97.02 383	99.16 234	99.49 252	98.12 299	99.38 292	99.30 356	95.35 431	99.68 125	99.90 36	82.62 488	99.93 109	99.31 95	98.13 316	99.42 270

QAPM		98.67 223	98.30 243	99.80 64	99.20 338	99.67 69	99.77 35	99.72 14	94.74 447	98.73 358	99.90 36	95.78 229	99.98 20	96.96 384	99.88 73	99.76 107

3Dnovator	97.25 9	99.24 96	99.05 113	99.81 60	99.12 360	99.66 72	99.84 12	99.74 13	99.09 55	98.92 329	99.90 36	95.94 218	99.98 20	98.95 156	99.92 38	99.79 92

PRO-TEST		98.69 220	98.70 201	98.65 316	99.39 285	96.74 383	99.64 98	99.34 327	98.20 176	99.53 185	99.89 45	93.26 344	99.90 149	99.32 92	99.78 135	99.32 288

MED-MVS		99.70 3	99.63 5	99.90 8	99.88 13	99.81 34	99.69 63	99.87 6	99.48 3	99.90 34	99.89 45	99.30 4	99.95 76	98.83 182	99.88 73	99.93 22

TestfortrainingZip a		99.70 3	99.63 5	99.92 1	99.88 13	99.90 2	99.69 63	99.79 11	99.48 3	99.93 29	99.89 45	98.78 53	99.93 109	99.32 92	99.88 73	99.93 22

viewmacassd2359aftdt		99.08 154	98.94 158	99.50 153	99.66 151	98.96 193	99.51 196	99.54 109	98.27 158	99.42 210	99.89 45	95.88 223	99.80 246	99.20 117	99.11 225	99.76 107

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 29	99.47 242	99.63 46	99.45 13	99.98 13	99.89 45	97.02 149	99.99 4	99.98 1	99.96 17	99.95 11

LuminaMVS		99.23 97	99.10 99	99.61 110	99.35 296	99.31 137	99.46 246	99.13 394	98.61 114	99.86 52	99.89 45	96.41 188	99.91 136	99.67 37	99.51 176	99.63 196

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 29	99.54 175	99.66 32	99.46 9	99.98 13	99.89 45	97.27 134	99.99 4	99.97 2	99.95 22	99.95 11

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 83	99.88 10	99.52 186	99.54 109	99.13 41	99.89 39	99.89 45	98.96 26	99.96 41	99.04 142	99.90 56	99.85 47

our_new_method		99.61 10	99.52 14	99.90 8	99.76 83	99.88 10	99.52 186	99.54 109	99.13 41	99.89 39	99.89 45	98.96 26	99.96 41	99.04 142	99.90 56	99.85 47

Anonymous20240529		98.09 271	97.68 311	99.34 200	99.66 151	98.44 282	99.40 283	99.43 279	93.67 458	99.22 269	99.89 45	90.23 418	99.93 109	99.26 112	98.33 296	99.66 177

CHOSEN 1792x2688		99.19 101	99.10 99	99.45 175	99.89 8	98.52 272	99.39 287	99.94 1	98.73 103	99.11 292	99.89 45	95.50 240	99.94 91	99.50 57	99.97 9	99.89 30

RPSCF		98.22 256	98.62 217	96.99 447	99.82 53	91.58 489	99.72 54	99.44 268	96.61 379	99.66 136	99.89 45	95.92 219	99.82 233	97.46 345	99.10 234	99.57 222

3Dnovator+	97.12 13	99.18 104	98.97 148	99.82 57	99.17 352	99.68 65	99.81 20	99.51 162	99.20 34	98.72 359	99.89 45	95.68 234	99.97 29	98.86 174	99.86 87	99.81 79

COLMAP_ROB	97.56 6	98.86 192	98.75 193	99.17 233	99.88 13	98.53 268	99.34 312	99.59 73	97.55 290	98.70 366	99.89 45	95.83 224	99.90 149	98.10 275	99.90 56	99.08 312
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
dtuplus		99.03 166	98.92 161	99.36 196	99.60 201	98.62 259	99.35 307	99.51 162	97.99 233	99.38 224	99.88 59	96.04 209	99.79 253	99.37 81	99.17 207	99.68 163

hybridcas		99.13 129	99.00 141	99.51 147	99.70 123	99.04 178	99.65 90	99.52 134	98.20 176	99.75 100	99.88 59	95.78 229	99.78 261	99.41 72	99.16 208	99.71 150

casdiffseed414692147		98.97 179	98.78 190	99.53 135	99.66 151	99.16 158	99.61 116	99.52 134	98.01 231	99.21 272	99.88 59	94.82 273	99.70 300	99.29 104	99.04 246	99.74 118

E2		99.15 117	99.03 118	99.49 160	99.65 163	98.93 208	99.49 224	99.52 134	98.14 188	99.72 108	99.88 59	96.57 178	99.84 202	99.17 124	99.13 218	99.72 138

E3		99.15 117	99.03 118	99.49 160	99.62 183	98.91 210	99.49 224	99.52 134	98.13 191	99.72 108	99.88 59	96.61 173	99.84 202	99.17 124	99.13 218	99.72 138

AstraMVS		99.09 152	99.03 118	99.25 223	99.66 151	98.13 297	99.57 147	98.24 485	98.82 90	99.91 31	99.88 59	95.81 226	99.90 149	99.72 32	99.67 160	99.74 118

fmvsm_s_conf0.5_n_7		99.34 75	99.29 65	99.48 165	99.70 123	98.63 257	99.42 270	99.63 46	99.46 9	99.98 13	99.88 59	95.59 237	99.96 41	99.97 2	99.98 4	99.85 47

SDMVSNet		99.11 145	98.90 167	99.75 77	99.81 58	99.59 90	99.81 20	99.65 39	98.78 99	99.64 151	99.88 59	94.56 298	99.93 109	99.67 37	98.26 304	99.72 138

sd_testset		98.75 215	98.57 224	99.29 216	99.81 58	98.26 290	99.56 155	99.62 52	98.78 99	99.64 151	99.88 59	92.02 380	99.88 170	99.54 51	98.26 304	99.72 138

dcpmvs_2		99.23 97	99.58 9	98.16 375	99.83 47	94.68 457	99.76 38	99.52 134	99.07 58	99.98 13	99.88 59	98.56 81	99.93 109	99.67 37	99.98 4	99.87 41

RRT-MVS		98.91 184	98.75 193	99.39 194	99.46 262	98.61 262	99.76 38	99.50 187	98.06 215	99.81 72	99.88 59	93.91 330	99.94 91	99.11 132	99.27 196	99.61 201

test_djsdf		98.67 223	98.57 224	98.98 254	98.70 438	98.91 210	99.88 4	99.46 248	97.55 290	99.22 269	99.88 59	95.73 232	99.28 384	99.03 144	97.62 338	98.75 350

DP-MVS		99.16 112	98.95 156	99.78 71	99.77 79	99.53 103	99.41 275	99.50 187	97.03 348	99.04 309	99.88 59	97.39 126	99.92 124	98.66 206	99.90 56	99.87 41

TDRefinement		95.42 429	94.57 439	97.97 392	89.83 546	96.11 410	99.48 232	98.75 453	96.74 367	96.68 463	99.88 59	88.65 439	99.71 292	98.37 249	82.74 514	98.09 462

EPP-MVSNet		99.13 129	98.99 143	99.53 135	99.65 163	99.06 175	99.81 20	99.33 336	97.43 307	99.60 166	99.88 59	97.14 139	99.84 202	99.13 129	98.94 253	99.69 157

OpenMVS	96.50 16	98.47 234	98.12 256	99.52 142	99.04 383	99.53 103	99.82 16	99.72 14	94.56 450	98.08 422	99.88 59	94.73 286	99.98 20	97.47 344	99.76 142	99.06 318

onestephybrid01		99.17 109	99.06 110	99.49 160	99.60 201	98.98 185	99.38 292	99.50 187	98.52 123	99.81 72	99.87 75	96.27 195	99.81 238	99.47 66	99.10 234	99.67 170

Casviewmamba		99.16 112	99.02 129	99.59 114	99.66 151	99.21 152	99.68 73	99.52 134	98.31 153	99.60 166	99.87 75	95.96 214	99.85 192	99.40 74	99.16 208	99.72 138

viewcassd2359sk11		99.18 104	99.08 105	99.49 160	99.65 163	98.95 199	99.48 232	99.51 162	98.10 205	99.72 108	99.87 75	97.13 140	99.84 202	99.13 129	99.14 215	99.69 157

Elysia		98.88 186	98.65 209	99.58 118	99.58 207	99.34 129	99.65 90	99.52 134	98.26 161	99.83 66	99.87 75	93.37 341	99.90 149	97.81 304	99.91 45	99.49 249

StellarMVS		98.88 186	98.65 209	99.58 118	99.58 207	99.34 129	99.65 90	99.52 134	98.26 161	99.83 66	99.87 75	93.37 341	99.90 149	97.81 304	99.91 45	99.49 249

guyue		99.16 112	99.04 115	99.52 142	99.69 129	98.92 209	99.59 129	98.81 446	98.73 103	99.90 34	99.87 75	95.34 247	99.88 170	99.66 40	99.81 121	99.74 118

balanced_ft_v1		99.02 168	98.98 146	99.15 238	99.39 285	98.12 299	99.79 31	99.51 162	98.20 176	99.66 136	99.87 75	94.84 272	99.93 109	99.69 34	99.84 102	99.41 273

lessismore_v0				97.79 415	98.69 441	95.44 437		94.75 525		95.71 473	99.87 75	88.69 437	99.32 379	95.89 418	94.93 425	98.62 402

casdiffmvs_mvg		99.15 117	99.02 129	99.55 126	99.66 151	99.09 169	99.64 98	99.56 90	98.26 161	99.45 199	99.87 75	96.03 211	99.81 238	99.54 51	99.15 214	99.73 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		99.12 139	98.97 148	99.56 124	99.78 71	99.10 168	99.68 73	99.66 32	98.49 127	99.86 52	99.87 75	94.77 281	99.84 202	99.19 118	99.41 184	99.74 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH+	97.24 10	97.92 303	97.78 297	98.32 360	99.46 262	96.68 389	99.56 155	99.54 109	98.41 138	97.79 438	99.87 75	90.18 421	99.66 312	98.05 284	97.18 370	98.62 402

fmvsm_s_conf0.5_n_5		99.37 68	99.21 83	99.86 34	99.80 64	99.68 65	99.42 270	99.61 61	99.37 26	99.97 25	99.86 86	94.96 262	99.99 4	99.97 2	99.93 32	99.92 25

fmvsm_s_conf0.5_n_4		99.36 72	99.24 77	99.73 83	99.78 71	99.53 103	99.49 224	99.60 68	99.42 22	99.99 2	99.86 86	95.15 257	99.95 76	99.95 16	99.89 67	99.73 128

ACMMP_NAP		99.47 40	99.34 49	99.88 16	99.87 20	99.86 18	99.47 242	99.48 213	98.05 218	99.76 96	99.86 86	98.82 48	99.93 109	98.82 189	99.91 45	99.84 54

casdiffmvs		99.13 129	98.98 146	99.56 124	99.65 163	99.16 158	99.56 155	99.50 187	98.33 149	99.41 215	99.86 86	95.92 219	99.83 224	99.45 70	99.16 208	99.70 154
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		99.36 72	99.28 68	99.61 110	99.86 25	99.07 174	99.47 242	99.93 2	97.66 278	99.71 118	99.86 86	97.73 120	99.96 41	99.47 66	99.82 118	99.79 92

IS-MVSNet		99.05 163	98.87 175	99.57 122	99.73 108	99.32 133	99.75 43	99.20 385	98.02 230	99.56 176	99.86 86	96.54 179	99.67 309	98.09 276	99.13 218	99.73 128

USDC		97.34 378	97.20 374	97.75 418	99.07 374	95.20 442	98.51 483	99.04 407	97.99 233	98.31 408	99.86 86	89.02 431	99.55 337	95.67 427	97.36 363	98.49 432

SSM_0407		99.13 129	99.03 118	99.43 184	99.62 183	98.88 221	99.51 196	99.50 187	98.14 188	99.37 227	99.85 93	96.85 156	99.83 224	99.19 118	99.25 199	99.60 204

viewmambaseed2359dif		99.01 173	98.90 167	99.32 206	99.58 207	98.51 274	99.33 314	99.54 109	97.85 249	99.44 204	99.85 93	96.01 212	99.79 253	99.41 72	99.13 218	99.67 170

SSM_0404		99.16 112	99.06 110	99.44 180	99.65 163	98.96 193	99.49 224	99.50 187	98.14 188	99.62 158	99.85 93	96.85 156	99.85 192	99.19 118	99.26 198	99.52 235

lecture		99.60 14	99.50 19	99.89 12	99.89 8	99.90 2	99.75 43	99.59 73	99.06 61	99.88 42	99.85 93	98.41 94	99.96 41	99.28 106	99.84 102	99.83 64

sc_t1		95.75 420	95.05 428	97.87 401	98.83 417	94.61 460	99.21 364	99.45 259	87.45 503	97.97 429	99.85 93	81.19 494	99.43 356	98.27 259	93.20 458	99.57 222

APD_test1		95.87 417	96.49 397	94.00 476	99.53 229	84.01 510	99.54 175	99.32 347	95.91 425	97.99 427	99.85 93	85.49 471	99.88 170	91.96 479	98.84 266	98.12 460

TSAR-MVS + MP.		99.58 16	99.50 19	99.81 60	99.91 1	99.66 72	99.63 105	99.39 294	98.91 83	99.78 86	99.85 93	99.36 2	99.94 91	98.84 179	99.88 73	99.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tmp_tt		82.80 491	81.52 495	86.66 509	66.61 556	68.44 540	92.79 538	97.92 491	68.96 526	80.04 532	99.85 93	85.77 467	96.15 512	97.86 297	43.89 544	95.39 517

AllTest		98.87 189	98.72 197	99.31 208	99.86 25	98.48 279	99.56 155	99.61 61	97.85 249	99.36 233	99.85 93	95.95 216	99.85 192	96.66 400	99.83 114	99.59 215

TestCases				99.31 208	99.86 25	98.48 279		99.61 61	97.85 249	99.36 233	99.85 93	95.95 216	99.85 192	96.66 400	99.83 114	99.59 215

VDD-MVS		97.73 339	97.35 356	98.88 280	99.47 260	97.12 349	99.34 312	98.85 441	98.19 179	99.67 131	99.85 93	82.98 486	99.92 124	99.49 61	98.32 300	99.60 204

APDe-MVS		99.66 7	99.57 10	99.92 1	99.77 79	99.89 6	99.75 43	99.56 90	99.02 62	99.88 42	99.85 93	99.18 11	99.96 41	99.22 114	99.92 38	99.90 27
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepPCF-MVS	98.18 3	98.81 205	99.37 43	97.12 444	99.60 201	91.75 488	98.61 471	99.44 268	99.35 27	99.83 66	99.85 93	98.70 70	99.81 238	99.02 146	99.91 45	99.81 79

ACMM	97.58 5	98.37 246	98.34 239	98.48 337	99.41 277	97.10 350	99.56 155	99.45 259	98.53 122	99.04 309	99.85 93	93.00 350	99.71 292	98.74 194	97.45 355	98.64 393
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 88	99.12 96	99.74 80	99.18 344	99.75 52	99.56 155	99.57 85	98.45 132	99.49 194	99.85 93	97.77 119	99.94 91	98.33 254	99.84 102	99.52 235

viewmamba		99.20 100	99.12 96	99.44 180	99.61 194	98.87 225	99.42 270	99.52 134	98.42 136	99.84 56	99.84 108	96.85 156	99.78 261	99.46 68	99.11 225	99.67 170

hybridnocas07		99.13 129	99.03 118	99.46 173	99.63 173	98.90 215	99.38 292	99.52 134	98.41 138	99.82 70	99.84 108	96.09 206	99.80 246	99.40 74	99.16 208	99.68 163

hybrid		99.11 145	99.01 137	99.41 187	99.64 168	98.76 245	99.35 307	99.52 134	98.31 153	99.80 78	99.84 108	96.16 202	99.79 253	99.40 74	99.06 243	99.68 163

E3new		99.18 104	99.08 105	99.48 165	99.63 173	98.94 203	99.46 246	99.50 187	98.06 215	99.72 108	99.84 108	97.27 134	99.84 202	99.10 135	99.13 218	99.67 170

BridgeMVS		99.46 42	99.39 39	99.67 91	99.55 221	99.58 95	99.74 48	99.51 162	98.42 136	99.87 48	99.84 108	98.05 112	99.91 136	99.58 47	99.94 30	99.52 235

DPE-MVS		99.46 42	99.32 53	99.91 6	99.78 71	99.88 10	99.36 302	99.51 162	98.73 103	99.88 42	99.84 108	98.72 68	99.96 41	98.16 269	99.87 79	99.88 36
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVG-OURS		98.73 218	98.68 203	98.88 280	99.70 123	97.73 322	98.92 432	99.55 100	98.52 123	99.45 199	99.84 108	95.27 250	99.91 136	98.08 280	98.84 266	99.00 324

baseline		99.15 117	99.02 129	99.53 135	99.66 151	99.14 164	99.72 54	99.48 213	98.35 146	99.42 210	99.84 108	96.07 207	99.79 253	99.51 56	99.14 215	99.67 170

ACMMP		99.45 46	99.32 53	99.82 57	99.89 8	99.67 69	99.62 110	99.69 22	98.12 199	99.63 154	99.84 108	98.73 67	99.96 41	98.55 229	99.83 114	99.81 79
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
MED-MVS test				99.87 22	99.88 13	99.81 34	99.69 63	99.87 6	99.34 28	99.90 34	99.83 117		99.95 76	98.83 182	99.89 67	99.83 64

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 64	99.81 34	99.37 296	99.70 18	99.18 35	99.83 66	99.83 117	98.74 66	99.93 109	98.83 182	99.89 67	99.83 64

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 43	99.86 25	99.61 87	99.56 155	99.63 46	99.48 3	99.98 13	99.83 117	98.75 61	99.99 4	99.97 2	99.96 17	99.94 17

EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 102	99.78 71	99.14 164	99.60 118	99.45 259	99.01 64	99.90 34	99.83 117	98.98 25	99.93 109	99.59 45	99.95 22	99.86 43

EI-MVSNet		98.67 223	98.67 204	98.68 313	99.35 296	97.97 307	99.50 207	99.38 303	96.93 357	99.20 276	99.83 117	97.87 115	99.36 371	98.38 247	97.56 343	98.71 358

CVMVSNet		98.57 230	98.67 204	98.30 362	99.35 296	95.59 428	99.50 207	99.55 100	98.60 116	99.39 222	99.83 117	94.48 304	99.45 347	98.75 193	98.56 284	99.85 47

mvsmamba		99.06 159	98.96 152	99.36 196	99.47 260	98.64 256	99.70 59	99.05 406	97.61 283	99.65 146	99.83 117	96.54 179	99.92 124	99.19 118	99.62 167	99.51 244

LPG-MVS_test		98.22 256	98.13 255	98.49 335	99.33 302	97.05 356	99.58 139	99.55 100	97.46 300	99.24 264	99.83 117	92.58 366	99.72 286	98.09 276	97.51 348	98.68 372

LGP-MVS_train				98.49 335	99.33 302	97.05 356		99.55 100	97.46 300	99.24 264	99.83 117	92.58 366	99.72 286	98.09 276	97.51 348	98.68 372

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 22	99.82 53	99.81 34	99.59 129	99.51 162	98.62 113	99.79 81	99.83 117	99.28 5	99.97 29	98.48 233	99.90 56	99.84 54
Skip Steuart: Steuart Systems R&D Blog.
XXY-MVS		98.38 244	98.09 261	99.24 226	99.26 323	99.32 133	99.56 155	99.55 100	97.45 303	98.71 360	99.83 117	93.23 345	99.63 327	98.88 166	96.32 387	98.76 348

viewmanbaseed2359cas		99.18 104	99.07 109	99.50 153	99.62 183	99.01 182	99.50 207	99.52 134	98.25 166	99.68 125	99.82 128	96.93 154	99.80 246	99.15 128	99.11 225	99.70 154

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 43	99.84 38	99.63 83	99.56 155	99.63 46	99.47 6	99.98 13	99.82 128	98.75 61	99.99 4	99.97 2	99.97 9	99.94 17

SR-MVS-dyc-post		99.45 46	99.31 59	99.85 43	99.76 83	99.82 29	99.63 105	99.52 134	98.38 141	99.76 96	99.82 128	98.53 84	99.95 76	98.61 214	99.81 121	99.77 100

RE-MVS-def			99.34 49		99.76 83	99.82 29	99.63 105	99.52 134	98.38 141	99.76 96	99.82 128	98.75 61		98.61 214	99.81 121	99.77 100

test0726					99.85 31	99.89 6	99.62 110	99.50 187	99.10 48	99.86 52	99.82 128	98.94 33

SMA-MVS		99.44 50	99.30 61	99.85 43	99.73 108	99.83 23	99.56 155	99.47 235	97.45 303	99.78 86	99.82 128	99.18 11	99.91 136	98.79 190	99.89 67	99.81 79
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
nrg030		98.64 227	98.42 234	99.28 220	99.05 381	99.69 64	99.81 20	99.46 248	98.04 225	99.01 312	99.82 128	96.69 169	99.38 364	99.34 88	94.59 432	98.78 342

FC-MVSNet-test		98.75 215	98.62 217	99.15 238	99.08 371	99.45 117	99.86 11	99.60 68	98.23 171	98.70 366	99.82 128	96.80 164	99.22 402	99.07 139	96.38 385	98.79 340

EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 102	99.78 71	99.15 163	99.61 116	99.45 259	99.01 64	99.89 39	99.82 128	99.01 19	99.92 124	99.56 49	99.95 22	99.85 47

APD-MVS_3200maxsize		99.48 37	99.35 47	99.85 43	99.76 83	99.83 23	99.63 105	99.54 109	98.36 145	99.79 81	99.82 128	98.86 42	99.95 76	98.62 211	99.81 121	99.78 98

EU-MVSNet		97.98 294	98.03 268	97.81 414	98.72 434	96.65 390	99.66 84	99.66 32	98.09 206	98.35 405	99.82 128	95.25 253	98.01 487	97.41 351	95.30 416	98.78 342

APD-MVS		99.27 88	99.08 105	99.84 55	99.75 93	99.79 42	99.50 207	99.50 187	97.16 332	99.77 90	99.82 128	98.78 53	99.94 91	97.56 333	99.86 87	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.12 139	99.08 105	99.24 226	99.46 262	98.55 266	99.51 196	99.46 248	98.09 206	99.45 199	99.82 128	98.34 98	99.51 340	98.70 199	98.93 254	99.67 170

DeepC-MVS_fast	98.69 1	99.49 33	99.39 39	99.77 74	99.63 173	99.59 90	99.36 302	99.46 248	99.07 58	99.79 81	99.82 128	98.85 43	99.92 124	98.68 204	99.87 79	99.82 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.13 129	99.02 129	99.45 175	99.57 213	98.63 257	99.07 395	99.34 327	98.99 69	99.61 163	99.82 128	97.98 114	99.87 177	97.00 380	99.80 126	99.85 47

viewdifsd2359ckpt07		99.11 145	99.00 141	99.43 184	99.63 173	98.73 247	99.45 250	99.54 109	98.33 149	99.62 158	99.81 143	96.17 201	99.87 177	99.27 109	99.14 215	99.69 157

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 165	99.64 168	98.85 230	99.32 317	99.48 213	98.50 126	99.81 72	99.81 143	96.82 162	99.88 170	99.40 74	99.12 223	99.71 150

mamba_0408		99.08 154	98.96 152	99.44 180	99.62 183	98.88 221	99.25 350	99.47 235	98.05 218	99.37 227	99.81 143	96.85 156	99.85 192	98.98 149	99.25 199	99.60 204

SSM_04072		99.06 159	98.96 152	99.35 199	99.62 183	98.88 221	99.25 350	99.47 235	98.05 218	99.37 227	99.81 143	96.85 156	99.58 332	98.98 149	99.25 199	99.60 204

KinetiMVS		99.12 139	98.92 161	99.70 87	99.67 139	99.40 123	99.67 77	99.63 46	98.73 103	99.94 28	99.81 143	94.54 301	99.96 41	98.40 245	99.93 32	99.74 118

DVP-MVS++		99.59 15	99.50 19	99.88 16	99.51 238	99.88 10	99.87 8	99.51 162	98.99 69	99.88 42	99.81 143	99.27 6	99.96 41	98.85 176	99.80 126	99.81 79

test_one_0601					99.81 58	99.88 10		99.49 201	98.97 76	99.65 146	99.81 143	99.09 15

SED-MVS		99.61 10	99.52 14	99.88 16	99.84 38	99.90 2	99.60 118	99.48 213	99.08 56	99.91 31	99.81 143	99.20 8	99.96 41	98.91 163	99.85 94	99.79 92

test_241102_TWO								99.48 213	99.08 56	99.88 42	99.81 143	98.94 33	99.96 41	98.91 163	99.84 102	99.88 36

OPM-MVS		98.19 260	98.10 258	98.45 345	98.88 407	97.07 354	99.28 334	99.38 303	98.57 118	99.22 269	99.81 143	92.12 378	99.66 312	98.08 280	97.54 345	98.61 411
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MTAPA		99.52 28	99.39 39	99.89 12	99.90 4	99.86 18	99.66 84	99.47 235	98.79 96	99.68 125	99.81 143	98.43 91	99.97 29	98.88 166	99.90 56	99.83 64

FIs		98.78 210	98.63 212	99.23 228	99.18 344	99.54 100	99.83 15	99.59 73	98.28 156	98.79 353	99.81 143	96.75 167	99.37 367	99.08 138	96.38 385	98.78 342

mvs_tets		98.40 243	98.23 247	98.91 269	98.67 443	98.51 274	99.66 84	99.53 125	98.19 179	98.65 375	99.81 143	92.75 356	99.44 352	99.31 95	97.48 354	98.77 346

mvs_anonymous		99.03 166	98.99 143	99.16 234	99.38 289	98.52 272	99.51 196	99.38 303	97.79 259	99.38 224	99.81 143	97.30 132	99.45 347	99.35 83	98.99 251	99.51 244

TSAR-MVS + GP.		99.36 72	99.36 45	99.36 196	99.67 139	98.61 262	99.07 395	99.33 336	99.00 67	99.82 70	99.81 143	99.06 17	99.84 202	99.09 137	99.42 183	99.65 184

EPNet		98.86 192	98.71 199	99.30 213	97.20 493	98.18 293	99.62 110	98.91 430	99.28 32	98.63 378	99.81 143	95.96 214	99.99 4	99.24 113	99.72 150	99.73 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ab-mvs		98.86 192	98.63 212	99.54 127	99.64 168	99.19 153	99.44 257	99.54 109	97.77 262	99.30 247	99.81 143	94.20 315	99.93 109	99.17 124	98.82 268	99.49 249

OMC-MVS		99.08 154	99.04 115	99.20 230	99.67 139	98.22 292	99.28 334	99.52 134	98.07 211	99.66 136	99.81 143	97.79 118	99.78 261	97.79 306	99.81 121	99.60 204

viewdifsd2359ckpt13		99.06 159	98.93 160	99.45 175	99.63 173	98.96 193	99.50 207	99.51 162	97.83 253	99.28 251	99.80 161	96.68 171	99.71 292	99.05 141	99.12 223	99.68 163

icg_test_0407_2		98.79 209	98.86 178	98.57 324	99.55 221	96.93 370	99.07 395	99.44 268	98.05 218	99.66 136	99.80 161	97.13 140	99.18 411	98.15 271	98.92 256	99.60 204

IMVS_0407		98.86 192	98.91 165	98.72 306	99.55 221	96.93 370	99.50 207	99.44 268	98.05 218	99.66 136	99.80 161	97.13 140	99.65 317	98.15 271	98.92 256	99.60 204

IMVS_0404		98.53 231	98.52 229	98.55 330	99.55 221	96.93 370	99.20 367	99.44 268	98.05 218	98.96 323	99.80 161	94.66 293	99.13 419	98.15 271	98.92 256	99.60 204

IMVS_0403		98.86 192	98.89 171	98.78 301	99.55 221	96.93 370	99.58 139	99.44 268	98.05 218	99.68 125	99.80 161	96.81 163	99.80 246	98.15 271	98.92 256	99.60 204

MM		99.40 64	99.28 68	99.74 80	99.67 139	99.31 137	99.52 186	98.87 438	99.55 1	99.74 101	99.80 161	96.47 182	99.98 20	99.97 2	99.97 9	99.94 17

test_fmvs2		97.25 383	97.30 366	97.09 445	99.43 270	93.31 479	99.73 52	98.87 438	98.83 89	99.28 251	99.80 161	84.45 478	99.66 312	97.88 294	97.45 355	98.30 449

tt0805		97.97 297	97.77 299	98.57 324	99.59 205	96.61 392	99.45 250	99.08 400	98.21 174	98.88 335	99.80 161	88.66 438	99.70 300	98.58 220	97.72 333	99.39 277

SF-MVS		99.38 67	99.24 77	99.79 68	99.79 69	99.68 65	99.57 147	99.54 109	97.82 258	99.71 118	99.80 161	98.95 31	99.93 109	98.19 265	99.84 102	99.74 118

DVP-MVS		99.57 20	99.47 24	99.88 16	99.85 31	99.89 6	99.57 147	99.37 313	99.10 48	99.81 72	99.80 161	98.94 33	99.96 41	98.93 160	99.86 87	99.81 79
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
test_0728_THIRD									98.99 69	99.81 72	99.80 161	99.09 15	99.96 41	98.85 176	99.90 56	99.88 36

jajsoiax		98.43 237	98.28 244	98.88 280	98.60 452	98.43 283	99.82 16	99.53 125	98.19 179	98.63 378	99.80 161	93.22 347	99.44 352	99.22 114	97.50 350	98.77 346

PGM-MVS		99.45 46	99.31 59	99.86 34	99.87 20	99.78 48	99.58 139	99.65 39	97.84 252	99.71 118	99.80 161	99.12 14	99.97 29	98.33 254	99.87 79	99.83 64

TransMVSNet (Re)		97.15 387	96.58 394	98.86 287	99.12 360	98.85 230	99.49 224	98.91 430	95.48 430	97.16 454	99.80 161	93.38 340	99.11 425	94.16 453	91.73 474	98.62 402

K. test v3		97.10 389	96.79 390	98.01 387	98.72 434	96.33 401	99.87 8	97.05 506	97.59 284	96.16 469	99.80 161	88.71 436	99.04 436	96.69 398	96.55 382	98.65 391

DELS-MVS		99.48 37	99.42 32	99.65 96	99.72 112	99.40 123	99.05 402	99.66 32	99.14 40	99.57 174	99.80 161	98.46 89	99.94 91	99.57 48	99.84 102	99.60 204
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CSCG		99.32 78	99.32 53	99.32 206	99.85 31	98.29 288	99.71 58	99.66 32	98.11 201	99.41 215	99.80 161	98.37 97	99.96 41	98.99 148	99.96 17	99.72 138

mvs5depth		96.66 399	96.22 404	97.97 392	97.00 498	96.28 403	98.66 467	99.03 410	96.61 379	96.93 460	99.79 178	87.20 455	99.47 343	96.65 402	94.13 442	98.16 458

SR-MVS		99.43 53	99.29 65	99.86 34	99.75 93	99.83 23	99.59 129	99.62 52	98.21 174	99.73 103	99.79 178	98.68 71	99.96 41	98.44 240	99.77 139	99.79 92

MP-MVS-pluss		99.37 68	99.20 85	99.88 16	99.90 4	99.87 17	99.30 323	99.52 134	97.18 330	99.60 166	99.79 178	98.79 52	99.95 76	98.83 182	99.91 45	99.83 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pm-mvs1		97.68 349	97.28 369	98.88 280	99.06 377	98.62 259	99.50 207	99.45 259	96.32 400	97.87 434	99.79 178	92.47 370	99.35 374	97.54 335	93.54 452	98.67 380

LFMVS		97.90 306	97.35 356	99.54 127	99.52 235	99.01 182	99.39 287	98.24 485	97.10 340	99.65 146	99.79 178	84.79 476	99.91 136	99.28 106	98.38 293	99.69 157

TinyColmap		97.12 388	96.89 388	97.83 411	99.07 374	95.52 432	98.57 475	98.74 457	97.58 286	97.81 437	99.79 178	88.16 446	99.56 335	95.10 438	97.21 368	98.39 445

ACMP	97.20 11	98.06 277	97.94 279	98.45 345	99.37 292	97.01 363	99.44 257	99.49 201	97.54 293	98.45 396	99.79 178	91.95 382	99.72 286	97.91 292	97.49 353	98.62 402
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 238	99.67 69	99.50 207	99.64 42	99.43 19	99.98 13	99.78 185	97.26 137	99.95 76	99.95 16	99.93 32	99.92 25

GeoE		98.85 201	98.62 217	99.53 135	99.61 194	99.08 172	99.80 25	99.51 162	97.10 340	99.31 243	99.78 185	95.23 255	99.77 266	98.21 263	99.03 247	99.75 113

9.14			99.10 99		99.72 112		99.40 283	99.51 162	97.53 294	99.64 151	99.78 185	98.84 45	99.91 136	97.63 324	99.82 118

MGCNet		99.15 117	98.96 152	99.73 83	98.92 401	99.37 125	99.37 296	96.92 508	99.51 2	99.66 136	99.78 185	96.69 169	99.97 29	99.84 28	99.97 9	99.84 54

pmmvs6		96.53 403	96.09 408	97.82 413	98.69 441	95.47 433	99.37 296	99.47 235	93.46 463	97.41 444	99.78 185	87.06 459	99.33 377	96.92 389	92.70 468	98.65 391

MSLP-MVS++		99.46 42	99.47 24	99.44 180	99.60 201	99.16 158	99.41 275	99.71 16	98.98 72	99.45 199	99.78 185	99.19 10	99.54 338	99.28 106	99.84 102	99.63 196

VNet		99.11 145	98.90 167	99.73 83	99.52 235	99.56 96	99.41 275	99.39 294	99.01 64	99.74 101	99.78 185	95.56 238	99.92 124	99.52 55	98.18 312	99.72 138

114514_t		98.93 182	98.67 204	99.72 86	99.85 31	99.53 103	99.62 110	99.59 73	92.65 475	99.71 118	99.78 185	98.06 111	99.90 149	98.84 179	99.91 45	99.74 118

Vis-MVSNet (Re-imp)		98.87 189	98.72 197	99.31 208	99.71 118	98.88 221	99.80 25	99.44 268	97.91 241	99.36 233	99.78 185	95.49 241	99.43 356	97.91 292	99.11 225	99.62 199

UniMVSNet_ETH3D		97.32 380	96.81 389	98.87 284	99.40 282	97.46 334	99.51 196	99.53 125	95.86 426	98.54 388	99.77 194	82.44 489	99.66 312	98.68 204	97.52 347	99.50 248

anonymousdsp		98.44 236	98.28 244	98.94 261	98.50 459	98.96 193	99.77 35	99.50 187	97.07 342	98.87 338	99.77 194	94.76 282	99.28 384	98.66 206	97.60 339	98.57 423

CDS-MVSNet		99.09 152	99.03 118	99.25 223	99.42 272	98.73 247	99.45 250	99.46 248	98.11 201	99.46 198	99.77 194	98.01 113	99.37 367	98.70 199	98.92 256	99.66 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSDG		98.98 177	98.80 186	99.53 135	99.76 83	99.19 153	98.75 457	99.55 100	97.25 323	99.47 196	99.77 194	97.82 117	99.87 177	96.93 387	99.90 56	99.54 229

NormalMVS		99.27 88	99.19 87	99.52 142	99.89 8	98.83 235	99.65 90	99.52 134	99.10 48	99.84 56	99.76 198	95.80 227	99.99 4	99.30 98	99.84 102	99.74 118

SymmetryMVS		99.15 117	99.02 129	99.52 142	99.72 112	98.83 235	99.65 90	99.34 327	99.10 48	99.84 56	99.76 198	95.80 227	99.99 4	99.30 98	98.72 274	99.73 128

CHOSEN 280x420		99.12 139	99.13 94	99.08 242	99.66 151	97.89 315	98.43 489	99.71 16	98.88 84	99.62 158	99.76 198	96.63 172	99.70 300	99.46 68	99.99 1	99.66 177

PS-MVSNAJss		98.92 183	98.92 161	98.90 271	98.78 423	98.53 268	99.78 33	99.54 109	98.07 211	99.00 316	99.76 198	99.01 19	99.37 367	99.13 129	97.23 367	98.81 339

MVS_Test		99.10 151	98.97 148	99.48 165	99.49 252	99.14 164	99.67 77	99.34 327	97.31 318	99.58 171	99.76 198	97.65 122	99.82 233	98.87 169	99.07 242	99.46 263

TestfortrainingZip				99.69 89	99.58 207	99.62 84	99.69 63	99.38 303	98.98 72	99.84 56	99.75 203	98.84 45	99.78 261		99.21 203	99.66 177

CANet_DTU		98.97 179	98.87 175	99.25 223	99.33 302	98.42 285	99.08 394	99.30 356	99.16 37	99.43 207	99.75 203	95.27 250	99.97 29	98.56 226	99.95 22	99.36 282

mPP-MVS		99.44 50	99.30 61	99.86 34	99.88 13	99.79 42	99.69 63	99.48 213	98.12 199	99.50 191	99.75 203	98.78 53	99.97 29	98.57 223	99.89 67	99.83 64

HPM-MVS_fast		99.51 29	99.40 37	99.85 43	99.91 1	99.79 42	99.76 38	99.56 90	97.72 269	99.76 96	99.75 203	99.13 13	99.92 124	99.07 139	99.92 38	99.85 47

HyFIR lowres test		99.11 145	98.92 161	99.65 96	99.90 4	99.37 125	99.02 410	99.91 3	97.67 277	99.59 170	99.75 203	95.90 221	99.73 282	99.53 53	99.02 249	99.86 43

ITE_SJBPF				98.08 382	99.29 315	96.37 399		98.92 425	98.34 147	98.83 346	99.75 203	91.09 407	99.62 328	95.82 419	97.40 361	98.25 453

test_241102_ONE					99.84 38	99.90 2		99.48 213	99.07 58	99.91 31	99.74 209	99.20 8	99.76 270

Anonymous202405211		98.30 252	97.98 273	99.26 222	99.57 213	98.16 294	99.41 275	98.55 475	96.03 423	99.19 279	99.74 209	91.87 383	99.92 124	99.16 127	98.29 303	99.70 154

tttt0517		98.42 238	98.14 253	99.28 220	99.66 151	98.38 286	99.74 48	96.85 509	97.68 275	99.79 81	99.74 209	91.39 399	99.89 165	98.83 182	99.56 172	99.57 222

XVS		99.53 27	99.42 32	99.87 22	99.85 31	99.83 23	99.69 63	99.68 24	98.98 72	99.37 227	99.74 209	98.81 49	99.94 91	98.79 190	99.86 87	99.84 54

MP-MVS		99.33 77	99.15 92	99.87 22	99.88 13	99.82 29	99.66 84	99.46 248	98.09 206	99.48 195	99.74 209	98.29 100	99.96 41	97.93 291	99.87 79	99.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_LR		99.41 59	99.33 51	99.65 96	99.77 79	99.51 109	98.94 430	99.85 8	98.82 90	99.65 146	99.74 209	98.51 86	99.80 246	98.83 182	99.89 67	99.64 191

VPNet		97.84 317	97.44 344	99.01 250	99.21 336	98.94 203	99.48 232	99.57 85	98.38 141	99.28 251	99.73 215	88.89 433	99.39 362	99.19 118	93.27 456	98.71 358

MVSTER		98.49 232	98.32 241	99.00 252	99.35 296	99.02 180	99.54 175	99.38 303	97.41 310	99.20 276	99.73 215	93.86 332	99.36 371	98.87 169	97.56 343	98.62 402

MVS_111021_HR		99.41 59	99.32 53	99.66 92	99.72 112	99.47 115	98.95 428	99.85 8	98.82 90	99.54 183	99.73 215	98.51 86	99.74 276	98.91 163	99.88 73	99.77 100

PHI-MVS		99.30 82	99.17 90	99.70 87	99.56 217	99.52 107	99.58 139	99.80 10	97.12 336	99.62 158	99.73 215	98.58 79	99.90 149	98.61 214	99.91 45	99.68 163

tt0320-xc		95.31 433	94.59 437	97.45 433	98.92 401	94.73 454	99.20 367	99.31 351	86.74 505	97.23 450	99.72 219	81.14 495	98.95 460	97.08 376	91.98 473	98.67 380

tt0320		95.71 422	95.07 427	97.62 425	99.05 381	95.02 447	99.25 350	99.52 134	86.81 504	97.97 429	99.72 219	83.58 483	99.15 414	96.38 410	93.35 453	98.68 372

IterMVS-SCA-FT		97.82 323	97.75 304	98.06 383	99.57 213	96.36 400	99.02 410	99.49 201	97.18 330	98.71 360	99.72 219	92.72 359	99.14 416	97.44 349	95.86 401	98.67 380

diffmvs		99.14 125	99.02 129	99.51 147	99.61 194	98.96 193	99.28 334	99.49 201	98.46 130	99.72 108	99.71 222	96.50 181	99.88 170	99.31 95	99.11 225	99.67 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XVG-OURS-SEG-HR		98.69 220	98.62 217	98.89 275	99.71 118	97.74 321	99.12 385	99.54 109	98.44 135	99.42 210	99.71 222	94.20 315	99.92 124	98.54 230	98.90 262	99.00 324

EPNet_dtu		98.03 285	97.96 275	98.23 371	98.27 466	95.54 431	99.23 358	98.75 453	99.02 62	97.82 436	99.71 222	96.11 205	99.48 341	93.04 470	99.65 163	99.69 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		99.42 55	99.30 61	99.78 71	99.62 183	99.71 59	99.26 348	99.52 134	98.82 90	99.39 222	99.71 222	98.96 26	99.85 192	98.59 219	99.80 126	99.77 100

VortexMVS		98.67 223	98.66 207	98.68 313	99.62 183	97.96 309	99.59 129	99.41 284	98.13 191	99.31 243	99.70 226	95.48 242	99.27 387	99.40 74	97.32 364	98.79 340

FE-MVS		98.48 233	98.17 249	99.40 189	99.54 228	98.96 193	99.68 73	98.81 446	95.54 429	99.62 158	99.70 226	93.82 333	99.93 109	97.35 355	99.46 180	99.32 288

PC_three_1452									98.18 182	99.84 56	99.70 226	99.31 3	98.52 477	98.30 258	99.80 126	99.81 79

OPU-MVS				99.64 102	99.56 217	99.72 57	99.60 118				99.70 226	99.27 6	99.42 359	98.24 262	99.80 126	99.79 92

CS-MVS		99.50 31	99.48 22	99.54 127	99.76 83	99.42 120	99.90 1	99.55 100	98.56 119	99.78 86	99.70 226	98.65 75	99.79 253	99.65 41	99.78 135	99.41 273

tfpnnormal		97.84 317	97.47 336	98.98 254	99.20 338	99.22 151	99.64 98	99.61 61	96.32 400	98.27 412	99.70 226	93.35 343	99.44 352	95.69 425	95.40 414	98.27 451

v7n		97.87 310	97.52 328	98.92 265	98.76 430	98.58 264	99.84 12	99.46 248	96.20 409	98.91 330	99.70 226	94.89 270	99.44 352	96.03 415	93.89 448	98.75 350

testdata				99.54 127	99.75 93	98.95 199		99.51 162	97.07 342	99.43 207	99.70 226	98.87 41	99.94 91	97.76 311	99.64 164	99.72 138

IterMVS		97.83 320	97.77 299	98.02 386	99.58 207	96.27 404	99.02 410	99.48 213	97.22 327	98.71 360	99.70 226	92.75 356	99.13 419	97.46 345	96.00 395	98.67 380
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS	97.08 14	97.66 353	97.06 382	99.47 171	99.61 194	99.09 169	98.04 507	99.25 374	91.24 489	98.51 390	99.70 226	94.55 300	99.91 136	92.76 475	99.85 94	99.42 270
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LTVRE_ROB	97.16 12	98.02 287	97.90 282	98.40 353	99.23 331	96.80 382	99.70 59	99.60 68	97.12 336	98.18 418	99.70 226	91.73 388	99.72 286	98.39 246	97.45 355	98.68 372
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
viewdifsd2359ckpt09		99.01 173	98.87 175	99.40 189	99.62 183	98.79 241	99.44 257	99.51 162	97.76 264	99.35 236	99.69 237	96.42 187	99.75 273	98.97 154	99.11 225	99.66 177

BP-MVS1		99.12 139	98.94 158	99.65 96	99.51 238	99.30 140	99.67 77	98.92 425	98.48 128	99.84 56	99.69 237	94.96 262	99.92 124	99.62 44	99.79 133	99.71 150

SPE-MVS-test		99.49 33	99.48 22	99.54 127	99.78 71	99.30 140	99.89 2	99.58 78	98.56 119	99.73 103	99.69 237	98.55 82	99.82 233	99.69 34	99.85 94	99.48 252

HFP-MVS		99.49 33	99.37 43	99.86 34	99.87 20	99.80 39	99.66 84	99.67 27	98.15 184	99.68 125	99.69 237	99.06 17	99.96 41	98.69 202	99.87 79	99.84 54

旧先验1					99.74 101	99.59 90		99.54 109			99.69 237	98.47 88			99.68 158	99.73 128

ACMMPR		99.49 33	99.36 45	99.86 34	99.87 20	99.79 42	99.66 84	99.67 27	98.15 184	99.67 131	99.69 237	98.95 31	99.96 41	98.69 202	99.87 79	99.84 54

CPTT-MVS		99.11 145	98.90 167	99.74 80	99.80 64	99.46 116	99.59 129	99.49 201	97.03 348	99.63 154	99.69 237	97.27 134	99.96 41	97.82 302	99.84 102	99.81 79

EC-MVSNet		99.44 50	99.39 39	99.58 118	99.56 217	99.49 111	99.88 4	99.58 78	98.38 141	99.73 103	99.69 237	98.20 104	99.70 300	99.64 43	99.82 118	99.54 229

dtuonly		98.37 246	98.26 246	98.69 311	99.07 374	96.81 381	98.51 483	98.75 453	97.77 262	99.57 174	99.68 245	96.12 204	99.71 292	95.76 422	99.11 225	99.57 222

GST-MVS		99.40 64	99.24 77	99.85 43	99.86 25	99.79 42	99.60 118	99.67 27	97.97 236	99.63 154	99.68 245	98.52 85	99.95 76	98.38 247	99.86 87	99.81 79

Anonymous20231211		97.88 308	97.54 326	98.90 271	99.71 118	98.53 268	99.48 232	99.57 85	94.16 453	98.81 349	99.68 245	93.23 345	99.42 359	98.84 179	94.42 436	98.76 348

region2R		99.48 37	99.35 47	99.87 22	99.88 13	99.80 39	99.65 90	99.66 32	98.13 191	99.66 136	99.68 245	98.96 26	99.96 41	98.62 211	99.87 79	99.84 54

PS-CasMVS		97.93 300	97.59 322	98.95 259	98.99 391	99.06 175	99.68 73	99.52 134	97.13 334	98.31 408	99.68 245	92.44 374	99.05 435	98.51 231	94.08 445	98.75 350

HY-MVS	97.30 7	98.85 201	98.64 211	99.47 171	99.42 272	99.08 172	99.62 110	99.36 315	97.39 312	99.28 251	99.68 245	96.44 185	99.92 124	98.37 249	98.22 307	99.40 276

DP-MVS Recon		99.12 139	98.95 156	99.65 96	99.74 101	99.70 61	99.27 339	99.57 85	96.40 398	99.42 210	99.68 245	98.75 61	99.80 246	97.98 288	99.72 150	99.44 268

ADS-MVSNet2		98.02 287	98.07 265	97.87 401	99.33 302	95.19 443	99.23 358	99.08 400	96.24 406	99.10 295	99.67 252	94.11 320	98.93 462	96.81 392	99.05 244	99.48 252

ADS-MVSNet		98.20 259	98.08 262	98.56 328	99.33 302	96.48 396	99.23 358	99.15 391	96.24 406	99.10 295	99.67 252	94.11 320	99.71 292	96.81 392	99.05 244	99.48 252

DTE-MVSNet		97.51 365	97.19 375	98.46 343	98.63 447	98.13 297	99.84 12	99.48 213	96.68 371	97.97 429	99.67 252	92.92 352	98.56 476	96.88 391	92.60 470	98.70 363

Baseline_NR-MVSNet		97.76 331	97.45 339	98.68 313	99.09 368	98.29 288	99.41 275	98.85 441	95.65 428	98.63 378	99.67 252	94.82 273	99.10 428	98.07 283	92.89 465	98.64 393

CMPMVS	69.68 23	94.13 452	94.90 430	91.84 488	97.24 492	80.01 525	98.52 481	99.48 213	89.01 498	91.99 502	99.67 252	85.67 468	99.13 419	95.44 431	97.03 373	96.39 512
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GDP-MVS		99.08 154	98.89 171	99.64 102	99.53 229	99.34 129	99.64 98	99.48 213	98.32 151	99.77 90	99.66 257	95.14 258	99.93 109	98.97 154	99.50 178	99.64 191

原ACMM1				99.65 96	99.73 108	99.33 132		99.47 235	97.46 300	99.12 290	99.66 257	98.67 73	99.91 136	97.70 321	99.69 155	99.71 150

thisisatest0530		98.35 248	98.03 268	99.31 208	99.63 173	98.56 265	99.54 175	96.75 511	97.53 294	99.73 103	99.65 259	91.25 403	99.89 165	98.62 211	99.56 172	99.48 252

test222					99.75 93	99.49 111	98.91 435	99.49 201	96.42 396	99.34 240	99.65 259	98.28 101			99.69 155	99.72 138

MVSFormer		99.17 109	99.12 96	99.29 216	99.51 238	98.94 203	99.88 4	99.46 248	97.55 290	99.80 78	99.65 259	97.39 126	99.28 384	99.03 144	99.85 94	99.65 184

jason		99.13 129	99.03 118	99.45 175	99.46 262	98.87 225	99.12 385	99.26 371	98.03 227	99.79 81	99.65 259	97.02 149	99.85 192	99.02 146	99.90 56	99.65 184
jason: jason.
BH-RMVSNet		98.41 240	98.08 262	99.40 189	99.41 277	98.83 235	99.30 323	98.77 452	97.70 273	98.94 327	99.65 259	92.91 354	99.74 276	96.52 404	99.55 174	99.64 191

sss		99.17 109	99.05 113	99.53 135	99.62 183	98.97 189	99.36 302	99.62 52	97.83 253	99.67 131	99.65 259	97.37 129	99.95 76	99.19 118	99.19 206	99.68 163

h-mvs33		97.70 345	97.28 369	98.97 256	99.70 123	97.27 341	99.36 302	99.45 259	98.94 79	99.66 136	99.64 265	94.93 265	99.99 4	99.48 64	84.36 504	99.65 184

ZNCC-MVS		99.47 40	99.33 51	99.87 22	99.87 20	99.81 34	99.64 98	99.67 27	98.08 210	99.55 182	99.64 265	98.91 38	99.96 41	98.72 197	99.90 56	99.82 72

新几何1				99.75 77	99.75 93	99.59 90		99.54 109	96.76 366	99.29 250	99.64 265	98.43 91	99.94 91	96.92 389	99.66 161	99.72 138

PEN-MVS		97.76 331	97.44 344	98.72 306	98.77 428	98.54 267	99.78 33	99.51 162	97.06 344	98.29 411	99.64 265	92.63 365	98.89 466	98.09 276	93.16 459	98.72 356

CP-MVSNet		98.09 271	97.78 297	99.01 250	98.97 396	99.24 149	99.67 77	99.46 248	97.25 323	98.48 393	99.64 265	93.79 334	99.06 434	98.63 210	94.10 444	98.74 354

LF4IMVS		97.52 363	97.46 338	97.70 422	98.98 394	95.55 429	99.29 328	98.82 444	98.07 211	98.66 369	99.64 265	89.97 422	99.61 329	97.01 379	96.68 377	97.94 477

HPM-MVS		99.42 55	99.28 68	99.83 56	99.90 4	99.72 57	99.81 20	99.54 109	97.59 284	99.68 125	99.63 271	98.91 38	99.94 91	98.58 220	99.91 45	99.84 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NCCC		99.34 75	99.19 87	99.79 68	99.61 194	99.65 76	99.30 323	99.48 213	98.86 85	99.21 272	99.63 271	98.72 68	99.90 149	98.25 261	99.63 166	99.80 88

CP-MVS		99.45 46	99.32 53	99.85 43	99.83 47	99.75 52	99.69 63	99.52 134	98.07 211	99.53 185	99.63 271	98.93 37	99.97 29	98.74 194	99.91 45	99.83 64

AdaColmap		99.01 173	98.80 186	99.66 92	99.56 217	99.54 100	99.18 372	99.70 18	98.18 182	99.35 236	99.63 271	96.32 190	99.90 149	97.48 342	99.77 139	99.55 227

TAPA-MVS	97.07 15	97.74 337	97.34 359	98.94 261	99.70 123	97.53 331	99.25 350	99.51 162	91.90 484	99.30 247	99.63 271	98.78 53	99.64 321	88.09 500	99.87 79	99.65 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ppachtmachnet_test		97.49 371	97.45 339	97.61 428	98.62 448	95.24 441	98.80 450	99.46 248	96.11 418	98.22 415	99.62 276	96.45 184	98.97 457	93.77 457	95.97 399	98.61 411

MCST-MVS		99.43 53	99.30 61	99.82 57	99.79 69	99.74 55	99.29 328	99.40 291	98.79 96	99.52 188	99.62 276	98.91 38	99.90 149	98.64 208	99.75 144	99.82 72

WTY-MVS		99.06 159	98.88 174	99.61 110	99.62 183	99.16 158	99.37 296	99.56 90	98.04 225	99.53 185	99.62 276	96.84 161	99.94 91	98.85 176	98.49 289	99.72 138

MDTV_nov1_ep13			98.32 241		99.11 362	94.44 463	99.27 339	98.74 457	97.51 297	99.40 220	99.62 276	94.78 278	99.76 270	97.59 327	98.81 270

CANet		99.25 95	99.14 93	99.59 114	99.41 277	99.16 158	99.35 307	99.57 85	98.82 90	99.51 190	99.61 280	96.46 183	99.95 76	99.59 45	99.98 4	99.65 184

HQP_MVS		98.27 255	98.22 248	98.44 348	99.29 315	96.97 367	99.39 287	99.47 235	98.97 76	99.11 292	99.61 280	92.71 361	99.69 306	97.78 307	97.63 336	98.67 380

plane_prior4											99.61 280

baseline1		98.31 250	97.95 277	99.38 195	99.50 250	98.74 246	99.59 129	98.93 422	98.41 138	99.14 287	99.60 283	94.59 296	99.79 253	98.48 233	93.29 455	99.61 201

TranMVSNet+NR-MVSNet		97.93 300	97.66 313	98.76 303	98.78 423	98.62 259	99.65 90	99.49 201	97.76 264	98.49 392	99.60 283	94.23 314	98.97 457	98.00 287	92.90 464	98.70 363

FA-MVS(test-final)		98.75 215	98.53 228	99.41 187	99.55 221	99.05 177	99.80 25	99.01 413	96.59 384	99.58 171	99.59 285	95.39 244	99.90 149	97.78 307	99.49 179	99.28 292

tpmrst		98.33 249	98.48 231	97.90 399	99.16 354	94.78 453	99.31 321	99.11 396	97.27 321	99.45 199	99.59 285	95.33 248	99.84 202	98.48 233	98.61 278	99.09 311

IterMVS-LS		98.46 235	98.42 234	98.58 323	99.59 205	98.00 305	99.37 296	99.43 279	96.94 356	99.07 301	99.59 285	97.87 115	99.03 438	98.32 256	95.62 408	98.71 358
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
F-COLMAP		99.19 101	99.04 115	99.64 102	99.78 71	99.27 145	99.42 270	99.54 109	97.29 320	99.41 215	99.59 285	98.42 93	99.93 109	98.19 265	99.69 155	99.73 128

ttmdpeth		97.80 327	97.63 318	98.29 363	98.77 428	97.38 337	99.64 98	99.36 315	98.78 99	96.30 467	99.58 289	92.34 377	99.39 362	98.36 251	95.58 409	98.10 461

pmmvs4		98.13 267	97.90 282	98.81 296	98.61 450	98.87 225	98.99 418	99.21 384	96.44 394	99.06 306	99.58 289	95.90 221	99.11 425	97.18 371	96.11 392	98.46 438

1112_ss		98.98 177	98.77 191	99.59 114	99.68 136	99.02 180	99.25 350	99.48 213	97.23 326	99.13 288	99.58 289	96.93 154	99.90 149	98.87 169	98.78 271	99.84 54

ab-mvs-re		8.30 518	11.06 521	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	99.58 289	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

PatchmatchNet		98.31 250	98.36 237	98.19 373	99.16 354	95.32 440	99.27 339	98.92 425	97.37 313	99.37 227	99.58 289	94.90 269	99.70 300	97.43 350	99.21 203	99.54 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		98.19 260	98.16 250	98.27 368	99.30 311	95.55 429	99.07 395	98.97 418	97.57 287	99.43 207	99.57 294	92.72 359	99.74 276	97.58 328	99.20 205	99.52 235

Patchmatch-test		97.93 300	97.65 314	98.77 302	99.18 344	97.07 354	99.03 407	99.14 393	96.16 413	98.74 357	99.57 294	94.56 298	99.72 286	93.36 465	99.11 225	99.52 235

PVSNet	96.02 17	98.85 201	98.84 183	98.89 275	99.73 108	97.28 340	98.32 495	99.60 68	97.86 246	99.50 191	99.57 294	96.75 167	99.86 184	98.56 226	99.70 154	99.54 229

cdsmvs_eth3d_5k		24.64 517	32.85 520	0.00 535	0.00 559	0.00 561	0.00 546	99.51 162	0.00 553	0.00 555	99.56 297	96.58 176	0.00 555	0.00 553	0.00 553	0.00 550

1314		98.68 222	98.54 227	99.11 241	98.89 405	98.65 254	99.27 339	99.49 201	96.89 358	97.99 427	99.56 297	97.72 121	99.83 224	97.74 314	99.27 196	98.84 338

lupinMVS		99.13 129	99.01 137	99.46 173	99.51 238	98.94 203	99.05 402	99.16 390	97.86 246	99.80 78	99.56 297	97.39 126	99.86 184	98.94 157	99.85 94	99.58 219

miper_lstm_enhance		98.00 292	97.91 281	98.28 367	99.34 301	97.43 335	98.88 437	99.36 315	96.48 391	98.80 351	99.55 300	95.98 213	98.91 463	97.27 361	95.50 413	98.51 431

DPM-MVS		98.95 181	98.71 199	99.66 92	99.63 173	99.55 98	98.64 469	99.10 397	97.93 239	99.42 210	99.55 300	98.67 73	99.80 246	95.80 421	99.68 158	99.61 201

CDPH-MVS		99.13 129	98.91 165	99.80 64	99.75 93	99.71 59	99.15 378	99.41 284	96.60 382	99.60 166	99.55 300	98.83 47	99.90 149	97.48 342	99.83 114	99.78 98

dp		97.75 335	97.80 293	97.59 429	99.10 365	93.71 473	99.32 317	98.88 436	96.48 391	99.08 300	99.55 300	92.67 364	99.82 233	96.52 404	98.58 281	99.24 298

CLD-MVS		98.16 264	98.10 258	98.33 358	99.29 315	96.82 380	98.75 457	99.44 268	97.83 253	99.13 288	99.55 300	92.92 352	99.67 309	98.32 256	97.69 334	98.48 433
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ArgMatch-SfM		96.18 411	95.78 416	97.38 437	99.08 371	94.64 459	99.20 367	99.33 336	98.01 231	98.54 388	99.54 305	83.13 485	99.43 356	93.86 456	91.29 476	98.08 463

ZD-MVS					99.71 118	99.79 42		99.61 61	96.84 361	99.56 176	99.54 305	98.58 79	99.96 41	96.93 387	99.75 144

cl____		98.01 290	97.84 290	98.55 330	99.25 327	97.97 307	98.71 462	99.34 327	96.47 393	98.59 385	99.54 305	95.65 235	99.21 407	97.21 365	95.77 402	98.46 438

DIV-MVS_self_test		98.01 290	97.85 289	98.48 337	99.24 329	97.95 312	98.71 462	99.35 322	96.50 387	98.60 384	99.54 305	95.72 233	99.03 438	97.21 365	95.77 402	98.46 438

MVS		97.28 381	96.55 395	99.48 165	98.78 423	98.95 199	99.27 339	99.39 294	83.53 512	98.08 422	99.54 305	96.97 152	99.87 177	94.23 451	99.16 208	99.63 196

ArgMatch-Sym		96.59 401	96.31 401	97.42 434	98.89 405	94.84 452	99.16 374	99.39 294	98.11 201	98.35 405	99.53 310	84.38 479	99.40 361	94.16 453	94.85 429	98.03 468

SSC-MVS3.2		97.34 378	97.15 376	97.93 396	99.02 385	95.76 423	99.48 232	99.58 78	97.62 282	99.09 298	99.53 310	87.95 448	99.27 387	96.42 407	95.66 407	98.75 350

pmmvs5		97.52 363	97.30 366	98.16 375	98.57 455	96.73 384	99.27 339	98.90 432	96.14 416	98.37 401	99.53 310	91.54 395	99.14 416	97.51 339	95.87 400	98.63 400

HPM-MVS++		99.39 66	99.23 81	99.87 22	99.75 93	99.84 20	99.43 263	99.51 162	98.68 110	99.27 257	99.53 310	98.64 76	99.96 41	98.44 240	99.80 126	99.79 92

PatchMatch-RL		98.84 204	98.62 217	99.52 142	99.71 118	99.28 143	99.06 399	99.77 12	97.74 268	99.50 191	99.53 310	95.41 243	99.84 202	97.17 372	99.64 164	99.44 268

MonoMVSNet		98.38 244	98.47 232	98.12 380	98.59 454	96.19 408	99.72 54	98.79 450	97.89 243	99.44 204	99.52 315	96.13 203	98.90 465	98.64 208	97.54 345	99.28 292

eth_miper_zixun_eth		98.05 282	97.96 275	98.33 358	99.26 323	97.38 337	98.56 479	99.31 351	96.65 374	98.88 335	99.52 315	96.58 176	99.12 424	97.39 352	95.53 412	98.47 435

test_prior2							98.96 425		98.34 147	99.01 312	99.52 315	98.68 71		97.96 289	99.74 147

test_0402		96.64 400	96.24 403	97.85 405	98.85 414	96.43 398	99.44 257	99.26 371	93.52 461	96.98 458	99.52 315	88.52 442	99.20 409	92.58 478	97.50 350	97.93 478

test_yl		98.86 192	98.63 212	99.54 127	99.49 252	99.18 155	99.50 207	99.07 403	98.22 172	99.61 163	99.51 319	95.37 245	99.84 202	98.60 217	98.33 296	99.59 215

DCV-MVSNet		98.86 192	98.63 212	99.54 127	99.49 252	99.18 155	99.50 207	99.07 403	98.22 172	99.61 163	99.51 319	95.37 245	99.84 202	98.60 217	98.33 296	99.59 215

v148		97.79 329	97.55 323	98.50 334	98.74 431	97.72 323	99.54 175	99.33 336	96.26 405	98.90 332	99.51 319	94.68 290	99.14 416	97.83 301	93.15 460	98.63 400

DU-MVS		98.08 275	97.79 294	98.96 257	98.87 410	98.98 185	99.41 275	99.45 259	97.87 245	98.71 360	99.50 322	94.82 273	99.22 402	98.57 223	92.87 466	98.68 372

NR-MVSNet		97.97 297	97.61 320	99.02 249	98.87 410	99.26 146	99.47 242	99.42 281	97.63 280	97.08 456	99.50 322	95.07 260	99.13 419	97.86 297	93.59 451	98.68 372

XVG-ACMP-BASELINE		97.83 320	97.71 308	98.20 372	99.11 362	96.33 401	99.41 275	99.52 134	98.06 215	99.05 308	99.50 322	89.64 427	99.73 282	97.73 315	97.38 362	98.53 427

dtuonlycased		97.04 391	97.33 362	96.16 463	99.08 371	90.59 494	98.79 452	99.38 303	97.19 329	96.91 461	99.49 325	90.22 420	98.75 471	97.04 378	97.89 325	99.14 303

RoMa-SfM		94.36 450	93.86 451	95.88 467	98.61 450	90.62 493	98.85 441	99.04 407	91.63 486	94.14 486	99.49 325	77.16 499	99.09 430	92.66 476	93.13 461	97.91 480

reproduce_monomvs		97.89 307	97.87 287	97.96 394	99.51 238	95.45 435	99.60 118	99.25 374	99.17 36	98.85 345	99.49 325	89.29 430	99.64 321	99.35 83	96.31 388	98.78 342

MSP-MVS		99.42 55	99.27 72	99.88 16	99.89 8	99.80 39	99.67 77	99.50 187	98.70 107	99.77 90	99.49 325	98.21 103	99.95 76	98.46 238	99.77 139	99.88 36
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
TEST9					99.67 139	99.65 76	99.05 402	99.41 284	96.22 408	98.95 325	99.49 325	98.77 57	99.91 136

train_agg		99.02 168	98.77 191	99.77 74	99.67 139	99.65 76	99.05 402	99.41 284	96.28 402	98.95 325	99.49 325	98.76 58	99.91 136	97.63 324	99.72 150	99.75 113

PVSNet_Blended		99.08 154	98.97 148	99.42 186	99.76 83	98.79 241	98.78 453	99.91 3	96.74 367	99.67 131	99.49 325	97.53 123	99.88 170	98.98 149	99.85 94	99.60 204

CNLPA		99.14 125	98.99 143	99.59 114	99.58 207	99.41 122	99.16 374	99.44 268	98.45 132	99.19 279	99.49 325	98.08 110	99.89 165	97.73 315	99.75 144	99.48 252

test_8					99.67 139	99.61 87	99.03 407	99.41 284	96.28 402	98.93 328	99.48 333	98.76 58	99.91 136

EPMVS		97.82 323	97.65 314	98.35 357	98.88 407	95.98 411	99.49 224	94.71 527	97.57 287	99.26 262	99.48 333	92.46 373	99.71 292	97.87 296	99.08 241	99.35 283

PLC	97.94 4	99.02 168	98.85 181	99.53 135	99.66 151	99.01 182	99.24 355	99.52 134	96.85 360	99.27 257	99.48 333	98.25 102	99.91 136	97.76 311	99.62 167	99.65 184
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu		99.29 84	99.27 72	99.34 200	99.63 173	98.97 189	99.12 385	99.51 162	98.86 85	99.84 56	99.47 336	98.18 105	99.99 4	99.50 57	99.31 193	99.08 312

xiu_mvs_v1_base		99.29 84	99.27 72	99.34 200	99.63 173	98.97 189	99.12 385	99.51 162	98.86 85	99.84 56	99.47 336	98.18 105	99.99 4	99.50 57	99.31 193	99.08 312

xiu_mvs_v1_base_debi		99.29 84	99.27 72	99.34 200	99.63 173	98.97 189	99.12 385	99.51 162	98.86 85	99.84 56	99.47 336	98.18 105	99.99 4	99.50 57	99.31 193	99.08 312

v1921920		97.80 327	97.45 339	98.84 291	98.80 419	98.53 268	99.52 186	99.34 327	96.15 415	99.24 264	99.47 336	93.98 326	99.29 383	95.40 433	95.13 420	98.69 367

MVStest1		96.08 415	95.48 420	97.89 400	98.93 399	96.70 385	99.56 155	99.35 322	92.69 474	91.81 503	99.46 340	89.90 423	98.96 459	95.00 441	92.61 469	98.00 473

UniMVSNet_NR-MVSNet		98.22 256	97.97 274	98.96 257	98.92 401	98.98 185	99.48 232	99.53 125	97.76 264	98.71 360	99.46 340	96.43 186	99.22 402	98.57 223	92.87 466	98.69 367

testgi		97.65 354	97.50 331	98.13 379	99.36 295	96.45 397	99.42 270	99.48 213	97.76 264	97.87 434	99.45 342	91.09 407	98.81 468	94.53 446	98.52 287	99.13 306

EIA-MVS		99.18 104	99.09 104	99.45 175	99.49 252	99.18 155	99.67 77	99.53 125	97.66 278	99.40 220	99.44 343	98.10 108	99.81 238	98.94 157	99.62 167	99.35 283

tpm2		97.44 373	97.34 359	97.74 420	99.15 358	94.36 466	99.45 250	98.94 421	93.45 464	98.90 332	99.44 343	91.35 400	99.59 331	97.31 356	98.07 318	99.29 291

thisisatest0515		98.14 266	97.79 294	99.19 231	99.50 250	98.50 276	98.61 471	96.82 510	96.95 354	99.54 183	99.43 345	91.66 392	99.86 184	98.08 280	99.51 176	99.22 300

WR-MVS		98.06 277	97.73 306	99.06 244	98.86 413	99.25 148	99.19 370	99.35 322	97.30 319	98.66 369	99.43 345	93.94 327	99.21 407	98.58 220	94.28 439	98.71 358

hse-mvs2		97.50 366	97.14 377	98.59 320	99.49 252	97.05 356	99.28 334	99.22 380	98.94 79	99.66 136	99.42 347	94.93 265	99.65 317	99.48 64	83.80 508	99.08 312

v8		97.95 299	97.63 318	98.93 263	98.95 398	98.81 240	99.80 25	99.41 284	96.03 423	99.10 295	99.42 347	94.92 267	99.30 382	96.94 386	94.08 445	98.66 389

tpmvs		97.98 294	98.02 270	97.84 408	99.04 383	94.73 454	99.31 321	99.20 385	96.10 422	98.76 356	99.42 347	94.94 264	99.81 238	96.97 383	98.45 290	98.97 330

UGNet		98.87 189	98.69 202	99.40 189	99.22 335	98.72 249	99.44 257	99.68 24	99.24 33	99.18 283	99.42 347	92.74 358	99.96 41	99.34 88	99.94 30	99.53 234
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
RoMa-HiRes		92.56 463	92.07 466	94.02 475	97.77 483	87.59 502	98.87 439	98.46 478	89.82 493	92.47 498	99.41 351	71.58 510	97.29 500	90.47 488	89.79 491	97.17 499

WBMVS		97.74 337	97.50 331	98.46 343	99.24 329	97.43 335	99.21 364	99.42 281	97.45 303	98.96 323	99.41 351	88.83 434	99.23 395	98.94 157	96.02 393	98.71 358

AUN-MVS		96.88 395	96.31 401	98.59 320	99.48 259	97.04 359	99.27 339	99.22 380	97.44 306	98.51 390	99.41 351	91.97 381	99.66 312	97.71 318	83.83 507	99.07 317

Effi-MVS+		98.81 205	98.59 223	99.48 165	99.46 262	99.12 167	98.08 506	99.50 187	97.50 298	99.38 224	99.41 351	96.37 189	99.81 238	99.11 132	98.54 286	99.51 244

v10		97.85 313	97.52 328	98.86 287	98.99 391	98.67 252	99.75 43	99.41 284	95.70 427	98.98 319	99.41 351	94.75 283	99.23 395	96.01 417	94.63 431	98.67 380

v144192		97.92 303	97.60 321	98.87 284	98.83 417	98.65 254	99.55 170	99.34 327	96.20 409	99.32 242	99.40 356	94.36 308	99.26 390	96.37 411	95.03 422	98.70 363

NP-MVS					99.23 331	96.92 374					99.40 356

HQP-MVS		98.02 287	97.90 282	98.37 356	99.19 341	96.83 378	98.98 421	99.39 294	98.24 168	98.66 369	99.40 356	92.47 370	99.64 321	97.19 369	97.58 341	98.64 393

MAR-MVS		98.86 192	98.63 212	99.54 127	99.37 292	99.66 72	99.45 250	99.54 109	96.61 379	99.01 312	99.40 356	97.09 144	99.86 184	97.68 323	99.53 175	99.10 307
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
SD_0403		97.55 360	97.53 327	97.62 425	99.61 194	93.64 476	99.72 54	99.44 268	98.03 227	98.62 381	99.39 360	96.06 208	99.57 333	87.88 502	99.01 250	99.66 177

dongtai		93.26 457	92.93 461	94.25 474	99.39 285	85.68 506	97.68 514	93.27 531	92.87 472	96.85 462	99.39 360	82.33 490	97.48 498	76.78 523	97.80 330	99.58 219

API-MVS		99.04 164	99.03 118	99.06 244	99.40 282	99.31 137	99.55 170	99.56 90	98.54 121	99.33 241	99.39 360	98.76 58	99.78 261	96.98 382	99.78 135	98.07 464

CR-MVSNet		98.17 263	97.93 280	98.87 284	99.18 344	98.49 277	99.22 362	99.33 336	96.96 352	99.56 176	99.38 363	94.33 311	99.00 447	94.83 444	98.58 281	99.14 303

Patchmtry		97.75 335	97.40 351	98.81 296	99.10 365	98.87 225	99.11 391	99.33 336	94.83 445	98.81 349	99.38 363	94.33 311	99.02 442	96.10 413	95.57 410	98.53 427

BH-untuned		98.42 238	98.36 237	98.59 320	99.49 252	96.70 385	99.27 339	99.13 394	97.24 325	98.80 351	99.38 363	95.75 231	99.74 276	97.07 377	99.16 208	99.33 287

V42		98.06 277	97.79 294	98.86 287	98.98 394	98.84 232	99.69 63	99.34 327	96.53 386	99.30 247	99.37 366	94.67 291	99.32 379	97.57 332	94.66 430	98.42 441

VPA-MVSNet		98.29 253	97.95 277	99.30 213	99.16 354	99.54 100	99.50 207	99.58 78	98.27 158	99.35 236	99.37 366	92.53 368	99.65 317	99.35 83	94.46 433	98.72 356

PVSNet_BlendedMVS		98.86 192	98.80 186	99.03 248	99.76 83	98.79 241	99.28 334	99.91 3	97.42 309	99.67 131	99.37 366	97.53 123	99.88 170	98.98 149	97.29 365	98.42 441

D2MVS		98.41 240	98.50 230	98.15 378	99.26 323	96.62 391	99.40 283	99.61 61	97.71 270	98.98 319	99.36 369	96.04 209	99.67 309	98.70 199	97.41 360	98.15 459

MVP-Stereo		97.81 325	97.75 304	97.99 390	97.53 485	96.60 393	98.96 425	98.85 441	97.22 327	97.23 450	99.36 369	95.28 249	99.46 345	95.51 429	99.78 135	97.92 479
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1240		97.69 346	97.32 364	98.79 299	98.85 414	98.43 283	99.48 232	99.36 315	96.11 418	99.27 257	99.36 369	93.76 336	99.24 394	94.46 447	95.23 417	98.70 363

dmvs_re		98.08 275	98.16 250	97.85 405	99.55 221	94.67 458	99.70 59	98.92 425	98.15 184	99.06 306	99.35 372	93.67 338	99.25 392	97.77 310	97.25 366	99.64 191

v1144		97.98 294	97.69 310	98.85 290	98.87 410	98.66 253	99.54 175	99.35 322	96.27 404	99.23 268	99.35 372	94.67 291	99.23 395	96.73 395	95.16 419	98.68 372

v2v482		98.06 277	97.77 299	98.92 265	98.90 404	98.82 238	99.57 147	99.36 315	96.65 374	99.19 279	99.35 372	94.20 315	99.25 392	97.72 317	94.97 423	98.69 367

CostFormer		97.72 341	97.73 306	97.71 421	99.15 358	94.02 469	99.54 175	99.02 411	94.67 448	99.04 309	99.35 372	92.35 376	99.77 266	98.50 232	97.94 322	99.34 286

testing3-2		97.84 317	97.70 309	98.24 370	99.53 229	95.37 439	99.55 170	98.67 469	98.46 130	99.27 257	99.34 376	86.58 461	99.83 224	99.32 92	98.63 277	99.52 235

our_test_3		97.65 354	97.68 311	97.55 430	98.62 448	94.97 449	98.84 445	99.30 356	96.83 363	98.19 417	99.34 376	97.01 151	99.02 442	95.00 441	96.01 394	98.64 393

c3_l		98.12 269	98.04 267	98.38 355	99.30 311	97.69 327	98.81 449	99.33 336	96.67 372	98.83 346	99.34 376	97.11 143	98.99 449	97.58 328	95.34 415	98.48 433

Fast-Effi-MVS+-dtu		98.77 214	98.83 185	98.60 319	99.41 277	96.99 365	99.52 186	99.49 201	98.11 201	99.24 264	99.34 376	96.96 153	99.79 253	97.95 290	99.45 181	99.02 323

Fast-Effi-MVS+		98.70 219	98.43 233	99.51 147	99.51 238	99.28 143	99.52 186	99.47 235	96.11 418	99.01 312	99.34 376	96.20 200	99.84 202	97.88 294	98.82 268	99.39 277

v1192		97.81 325	97.44 344	98.91 269	98.88 407	98.68 251	99.51 196	99.34 327	96.18 411	99.20 276	99.34 376	94.03 324	99.36 371	95.32 435	95.18 418	98.69 367

tpm		97.67 352	97.55 323	98.03 384	99.02 385	95.01 448	99.43 263	98.54 476	96.44 394	99.12 290	99.34 376	91.83 385	99.60 330	97.75 313	96.46 383	99.48 252

PAPM		97.59 358	97.09 381	99.07 243	99.06 377	98.26 290	98.30 496	99.10 397	94.88 443	98.08 422	99.34 376	96.27 195	99.64 321	89.87 491	98.92 256	99.31 290

GBi-Net		97.68 349	97.48 333	98.29 363	99.51 238	97.26 343	99.43 263	99.48 213	96.49 388	99.07 301	99.32 384	90.26 415	98.98 450	97.10 373	96.65 378	98.62 402

test1		97.68 349	97.48 333	98.29 363	99.51 238	97.26 343	99.43 263	99.48 213	96.49 388	99.07 301	99.32 384	90.26 415	98.98 450	97.10 373	96.65 378	98.62 402

FMVSNet1		96.84 396	96.36 400	98.29 363	99.32 309	97.26 343	99.43 263	99.48 213	95.11 436	98.55 387	99.32 384	83.95 481	98.98 450	95.81 420	96.26 389	98.62 402

MS-PatchMatch		97.24 385	97.32 364	96.99 447	98.45 462	93.51 478	98.82 448	99.32 347	97.41 310	98.13 421	99.30 387	88.99 432	99.56 335	95.68 426	99.80 126	97.90 481

GA-MVS		97.85 313	97.47 336	99.00 252	99.38 289	97.99 306	98.57 475	99.15 391	97.04 347	98.90 332	99.30 387	89.83 424	99.38 364	96.70 397	98.33 296	99.62 199

miper_ehance_all_eth		98.18 262	98.10 258	98.41 351	99.23 331	97.72 323	98.72 461	99.31 351	96.60 382	98.88 335	99.29 389	97.29 133	99.13 419	97.60 326	95.99 396	98.38 446

FMVSNet2		97.72 341	97.36 354	98.80 298	99.51 238	98.84 232	99.45 250	99.42 281	96.49 388	98.86 344	99.29 389	90.26 415	98.98 450	96.44 406	96.56 381	98.58 421

TESTMET0.1,1		97.55 360	97.27 372	98.40 353	98.93 399	96.53 394	98.67 464	97.61 499	96.96 352	98.64 376	99.28 391	88.63 441	99.45 347	97.30 359	99.38 185	99.21 301

FMVSNet3		98.03 285	97.76 303	98.84 291	99.39 285	98.98 185	99.40 283	99.38 303	96.67 372	99.07 301	99.28 391	92.93 351	98.98 450	97.10 373	96.65 378	98.56 424

PAPM_NR		99.04 164	98.84 183	99.66 92	99.74 101	99.44 118	99.39 287	99.38 303	97.70 273	99.28 251	99.28 391	98.34 98	99.85 192	96.96 384	99.45 181	99.69 157

EGC-MVSNET		82.80 491	77.86 498	97.62 425	97.91 474	96.12 409	99.33 314	99.28 362	8.40 552	25.05 554	99.27 394	84.11 480	99.33 377	89.20 494	98.22 307	97.42 495

ETV-MVS		99.26 91	99.21 83	99.40 189	99.46 262	99.30 140	99.56 155	99.52 134	98.52 123	99.44 204	99.27 394	98.41 94	99.86 184	99.10 135	99.59 170	99.04 320

xiu_mvs_v2_base		99.26 91	99.25 76	99.29 216	99.53 229	98.91 210	99.02 410	99.45 259	98.80 95	99.71 118	99.26 396	98.94 33	99.98 20	99.34 88	99.23 202	98.98 328

test20.03		96.12 413	95.96 411	96.63 456	97.44 486	95.45 435	99.51 196	99.38 303	96.55 385	96.16 469	99.25 397	93.76 336	96.17 511	87.35 506	94.22 440	98.27 451

PS-MVSNAJ		99.32 78	99.32 53	99.30 213	99.57 213	98.94 203	98.97 424	99.46 248	98.92 82	99.71 118	99.24 398	99.01 19	99.98 20	99.35 83	99.66 161	98.97 330

Test_1112_low_res		98.89 185	98.66 207	99.57 122	99.69 129	98.95 199	99.03 407	99.47 235	96.98 350	99.15 286	99.23 399	96.77 166	99.89 165	98.83 182	98.78 271	99.86 43

cl22		97.85 313	97.64 317	98.48 337	99.09 368	97.87 316	98.60 474	99.33 336	97.11 339	98.87 338	99.22 400	92.38 375	99.17 413	98.21 263	95.99 396	98.42 441

EG-PatchMatch MVS		95.97 416	95.69 417	96.81 454	97.78 480	92.79 482	99.16 374	98.93 422	96.16 413	94.08 488	99.22 400	82.72 487	99.47 343	95.67 427	97.50 350	98.17 457

TR-MVS		97.76 331	97.41 350	98.82 293	99.06 377	97.87 316	98.87 439	98.56 473	96.63 378	98.68 368	99.22 400	92.49 369	99.65 317	95.40 433	97.79 331	98.95 334

ET-MVSNet_ETH3D		96.49 404	95.64 419	99.05 246	99.53 229	98.82 238	98.84 445	97.51 502	97.63 280	84.77 519	99.21 403	92.09 379	98.91 463	98.98 149	92.21 472	99.41 273

WR-MVS_H		98.13 267	97.87 287	98.90 271	99.02 385	98.84 232	99.70 59	99.59 73	97.27 321	98.40 399	99.19 404	95.53 239	99.23 395	98.34 253	93.78 450	98.61 411

miper_enhance_ethall		98.16 264	98.08 262	98.41 351	98.96 397	97.72 323	98.45 488	99.32 347	96.95 354	98.97 321	99.17 405	97.06 147	99.22 402	97.86 297	95.99 396	98.29 450

baseline2		97.87 310	97.55 323	98.82 293	99.18 344	98.02 304	99.41 275	96.58 515	96.97 351	96.51 464	99.17 405	93.43 339	99.57 333	97.71 318	99.03 247	98.86 336

MIMVSNet1		95.51 425	95.04 429	96.92 452	97.38 488	95.60 427	99.52 186	99.50 187	93.65 459	96.97 459	99.17 405	85.28 474	96.56 508	88.36 499	95.55 411	98.60 414

gm-plane-assit					98.54 457	92.96 481			94.65 449		99.15 408		99.64 321	97.56 333

MIMVSNet		97.73 339	97.45 339	98.57 324	99.45 268	97.50 333	99.02 410	98.98 417	96.11 418	99.41 215	99.14 409	90.28 414	98.74 472	95.74 423	98.93 254	99.47 258

MASt3R-SfM		94.79 443	95.11 426	93.81 479	97.96 473	85.14 508	98.52 481	98.99 415	95.33 432	97.53 442	99.13 410	79.99 497	99.48 341	93.66 460	94.90 427	96.80 505

LCM-MVSNet-Re		97.83 320	98.15 252	96.87 453	99.30 311	92.25 486	99.59 129	98.26 483	97.43 307	96.20 468	99.13 410	96.27 195	98.73 473	98.17 268	98.99 251	99.64 191

UniMVSNet (Re)		98.29 253	98.00 271	99.13 240	99.00 388	99.36 128	99.49 224	99.51 162	97.95 237	98.97 321	99.13 410	96.30 194	99.38 364	98.36 251	93.34 454	98.66 389

N_pmnet		94.95 441	95.83 414	92.31 487	98.47 460	79.33 528	99.12 385	92.81 535	93.87 455	97.68 439	99.13 410	93.87 331	99.01 445	91.38 484	96.19 390	98.59 420

PAPR		98.63 228	98.34 239	99.51 147	99.40 282	99.03 179	98.80 450	99.36 315	96.33 399	99.00 316	99.12 414	98.46 89	99.84 202	95.23 437	99.37 192	99.66 177

tpm cat1		97.39 375	97.36 354	97.50 432	99.17 352	93.73 472	99.43 263	99.31 351	91.27 488	98.71 360	99.08 415	94.31 313	99.77 266	96.41 409	98.50 288	99.00 324

FMVSNet5		96.43 406	96.19 405	97.15 441	99.11 362	95.89 418	99.32 317	99.52 134	94.47 452	98.34 407	99.07 416	87.54 453	97.07 502	92.61 477	95.72 405	98.47 435

PMMVS		98.80 208	98.62 217	99.34 200	99.27 320	98.70 250	98.76 456	99.31 351	97.34 315	99.21 272	99.07 416	97.20 138	99.82 233	98.56 226	98.87 263	99.52 235

DKM		93.17 459	92.50 463	95.21 471	98.53 458	90.26 496	98.74 460	98.90 432	93.00 470	92.61 497	99.06 418	70.06 514	97.74 494	91.92 480	89.65 493	97.62 488

Anonymous20231206		96.22 408	96.03 409	96.79 455	97.31 491	94.14 468	99.63 105	99.08 400	96.17 412	97.04 457	99.06 418	93.94 327	97.76 493	86.96 509	95.06 421	98.47 435

usedtu_dtu_shiyan1		98.09 271	97.82 291	98.89 275	98.70 438	98.90 215	98.57 475	99.47 235	96.78 364	98.87 338	99.05 420	94.75 283	99.23 395	97.45 347	96.74 375	98.53 427

FE-MVSNET3		98.09 271	97.82 291	98.89 275	98.70 438	98.90 215	98.57 475	99.47 235	96.78 364	98.87 338	99.05 420	94.75 283	99.23 395	97.45 347	96.74 375	98.53 427

DeepMVS_CX				93.34 482	99.29 315	82.27 514		99.22 380	85.15 510	96.33 466	99.05 420	90.97 409	99.73 282	93.57 462	97.77 332	98.01 470

YYNet1		95.36 431	94.51 440	97.92 397	97.89 476	97.10 350	99.10 393	99.23 378	93.26 466	80.77 529	99.04 423	92.81 355	98.02 486	94.30 448	94.18 441	98.64 393

Anonymous20240521		96.20 410	95.89 413	97.13 443	97.72 484	94.96 450	99.79 31	99.29 360	93.01 469	97.20 453	99.03 424	89.69 426	98.36 480	91.16 485	96.13 391	98.07 464

MDA-MVSNet-bldmvs		94.96 440	93.98 448	97.92 397	98.24 467	97.27 341	99.15 378	99.33 336	93.80 457	80.09 531	99.03 424	88.31 444	97.86 491	93.49 463	94.36 437	98.62 402

test_method		91.10 469	91.36 469	90.31 498	95.85 512	73.72 537	94.89 525	99.25 374	68.39 527	95.82 472	99.02 426	80.50 496	98.95 460	93.64 461	94.89 428	98.25 453

DenseAffine		94.28 451	93.53 457	96.52 459	98.72 434	92.31 485	98.78 453	99.02 411	93.14 468	94.45 484	99.01 427	74.73 503	99.20 409	90.98 486	92.94 463	98.04 467

UWE-MVS		97.58 359	97.29 368	98.48 337	99.09 368	96.25 405	99.01 415	96.61 514	97.86 246	99.19 279	99.01 427	88.72 435	99.90 149	97.38 353	98.69 275	99.28 292

UWE-MVS-28		97.36 376	97.24 373	97.75 418	98.84 416	94.44 463	99.24 355	97.58 501	97.98 235	99.00 316	99.00 429	91.35 400	99.53 339	93.75 458	98.39 292	99.27 296

BH-w/o		98.00 292	97.89 286	98.32 360	99.35 296	96.20 407	99.01 415	98.90 432	96.42 396	98.38 400	99.00 429	95.26 252	99.72 286	96.06 414	98.61 278	99.03 321

Effi-MVS+-dtu		98.78 210	98.89 171	98.47 342	99.33 302	96.91 375	99.57 147	99.30 356	98.47 129	99.41 215	98.99 431	96.78 165	99.74 276	98.73 196	99.38 185	98.74 354

UnsupCasMVSNet_eth		96.44 405	96.12 406	97.40 436	98.65 445	95.65 426	99.36 302	99.51 162	97.13 334	96.04 471	98.99 431	88.40 443	98.17 483	96.71 396	90.27 486	98.40 444

test0.0.03 1		97.71 344	97.42 349	98.56 328	98.41 464	97.82 319	98.78 453	98.63 471	97.34 315	98.05 426	98.98 433	94.45 306	98.98 450	95.04 440	97.15 371	98.89 335

MDA-MVSNet_test_wron		95.45 426	94.60 436	98.01 387	98.16 471	97.21 346	99.11 391	99.24 377	93.49 462	80.73 530	98.98 433	93.02 349	98.18 482	94.22 452	94.45 435	98.64 393

FPMVS		84.93 488	85.65 488	82.75 514	86.77 550	63.39 542	98.35 491	98.92 425	74.11 519	83.39 524	98.98 433	50.85 533	92.40 529	84.54 516	94.97 423	92.46 522

testing3		97.28 381	96.76 391	98.82 293	99.37 292	98.07 302	99.45 250	99.36 315	97.56 289	97.89 433	98.95 436	83.70 482	98.82 467	96.03 415	98.56 284	99.58 219

WB-MVSnew		97.65 354	97.65 314	97.63 424	98.78 423	97.62 329	99.13 382	98.33 481	97.36 314	99.07 301	98.94 437	95.64 236	99.15 414	92.95 471	98.68 276	96.12 515

SSC-MVS		92.73 462	93.73 452	89.72 504	95.02 523	81.38 519	99.76 38	99.23 378	94.87 444	92.80 496	98.93 438	94.71 288	91.37 532	74.49 529	93.80 449	96.42 511

testf1		90.42 473	90.68 473	89.65 505	97.78 480	73.97 535	99.13 382	98.81 446	89.62 495	91.80 504	98.93 438	62.23 525	98.80 469	86.61 511	91.17 477	96.19 513

APD_test2		90.42 473	90.68 473	89.65 505	97.78 480	73.97 535	99.13 382	98.81 446	89.62 495	91.80 504	98.93 438	62.23 525	98.80 469	86.61 511	91.17 477	96.19 513

alignmvs		98.81 205	98.56 226	99.58 118	99.43 270	99.42 120	99.51 196	98.96 420	98.61 114	99.35 236	98.92 441	94.78 278	99.77 266	99.35 83	98.11 317	99.54 229

WB-MVS		93.10 460	94.10 445	90.12 501	95.51 519	81.88 516	99.73 52	99.27 369	95.05 439	93.09 495	98.91 442	94.70 289	91.89 530	76.62 524	94.02 447	96.58 510

DKM-HiRes		92.13 464	91.58 468	93.78 480	98.24 467	88.09 500	98.61 471	98.68 466	91.39 487	90.36 507	98.90 443	67.97 519	96.01 513	91.39 483	88.65 495	97.24 497

test-LLR		98.06 277	97.90 282	98.55 330	98.79 420	97.10 350	98.67 464	97.75 494	97.34 315	98.61 382	98.85 444	94.45 306	99.45 347	97.25 363	99.38 185	99.10 307

test-mter		97.49 371	97.13 379	98.55 330	98.79 420	97.10 350	98.67 464	97.75 494	96.65 374	98.61 382	98.85 444	88.23 445	99.45 347	97.25 363	99.38 185	99.10 307

dmvs_testset		95.02 438	96.12 406	91.72 489	99.10 365	80.43 524	99.58 139	97.87 493	97.47 299	95.22 475	98.82 446	93.99 325	95.18 518	88.09 500	94.91 426	99.56 226

MGCFI-Net		99.01 173	98.85 181	99.50 153	99.42 272	99.26 146	99.82 16	99.48 213	98.60 116	99.28 251	98.81 447	97.04 148	99.76 270	99.29 104	97.87 327	99.47 258

sasdasda		99.02 168	98.86 178	99.51 147	99.42 272	99.32 133	99.80 25	99.48 213	98.63 111	99.31 243	98.81 447	97.09 144	99.75 273	99.27 109	97.90 323	99.47 258

canonicalmvs		99.02 168	98.86 178	99.51 147	99.42 272	99.32 133	99.80 25	99.48 213	98.63 111	99.31 243	98.81 447	97.09 144	99.75 273	99.27 109	97.90 323	99.47 258

new_pmnet		96.38 407	96.03 409	97.41 435	98.13 472	95.16 445	99.05 402	99.20 385	93.94 454	97.39 447	98.79 450	91.61 394	99.04 436	90.43 489	95.77 402	98.05 466

cascas		97.69 346	97.43 348	98.48 337	98.60 452	97.30 339	98.18 501	99.39 294	92.96 471	98.41 398	98.78 451	93.77 335	99.27 387	98.16 269	98.61 278	98.86 336

PVSNet_0	94.43 19	96.09 414	95.47 421	97.94 395	99.31 310	94.34 467	97.81 512	99.70 18	97.12 336	97.46 443	98.75 452	89.71 425	99.79 253	97.69 322	81.69 517	99.68 163

patchmatchnet-post											98.70 453	94.79 277	99.74 276

Patchmatch-RL test		95.84 418	95.81 415	95.95 466	95.61 515	90.57 495	98.24 497	98.39 479	95.10 438	95.20 476	98.67 454	94.78 278	97.77 492	96.28 412	90.02 487	99.51 244

thres100view900		97.76 331	97.45 339	98.69 311	99.72 112	97.86 318	99.59 129	98.74 457	97.93 239	99.26 262	98.62 455	91.75 386	99.83 224	93.22 467	98.18 312	98.37 447

thres600view7		97.86 312	97.51 330	98.92 265	99.72 112	97.95 312	99.59 129	98.74 457	97.94 238	99.27 257	98.62 455	91.75 386	99.86 184	93.73 459	98.19 311	98.96 332

DSMNet-mixed		97.25 383	97.35 356	96.95 450	97.84 478	93.61 477	99.57 147	96.63 513	96.13 417	98.87 338	98.61 457	94.59 296	97.70 495	95.08 439	98.86 264	99.55 227

mmtdpeth		96.95 393	96.71 392	97.67 423	99.33 302	94.90 451	99.89 2	99.28 362	98.15 184	99.72 108	98.57 458	86.56 462	99.90 149	99.82 29	89.02 494	98.20 456

IB-MVS	95.67 18	96.22 408	95.44 423	98.57 324	99.21 336	96.70 385	98.65 468	97.74 496	96.71 369	97.27 449	98.54 459	86.03 466	99.92 124	98.47 236	86.30 501	99.10 307
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
myMVS_eth3d28		97.69 346	97.34 359	98.73 304	99.27 320	97.52 332	99.33 314	98.78 451	98.03 227	98.82 348	98.49 460	86.64 460	99.46 345	98.44 240	98.24 306	99.23 299

GG-mvs-BLEND				98.45 345	98.55 456	98.16 294	99.43 263	93.68 530		97.23 450	98.46 461	89.30 429	99.22 402	95.43 432	98.22 307	97.98 475

tfpn200view9		97.72 341	97.38 352	98.72 306	99.69 129	97.96 309	99.50 207	98.73 463	97.83 253	99.17 284	98.45 462	91.67 390	99.83 224	93.22 467	98.18 312	98.37 447

thres400		97.77 330	97.38 352	98.92 265	99.69 129	97.96 309	99.50 207	98.73 463	97.83 253	99.17 284	98.45 462	91.67 390	99.83 224	93.22 467	98.18 312	98.96 332

testing11		97.50 366	97.10 380	98.71 309	99.20 338	96.91 375	99.29 328	98.82 444	97.89 243	98.21 416	98.40 464	85.63 469	99.83 224	98.45 239	98.04 319	99.37 281

kuosan		90.92 471	90.11 476	93.34 482	98.78 423	85.59 507	98.15 504	93.16 533	89.37 497	92.07 501	98.38 465	81.48 493	95.19 517	62.54 535	97.04 372	99.25 297

KD-MVS_2432*1600		94.62 445	93.72 453	97.31 438	97.19 494	95.82 421	98.34 492	99.20 385	95.00 441	97.57 440	98.35 466	87.95 448	98.10 484	92.87 473	77.00 531	98.01 470

miper_refine_blended		94.62 445	93.72 453	97.31 438	97.19 494	95.82 421	98.34 492	99.20 385	95.00 441	97.57 440	98.35 466	87.95 448	98.10 484	92.87 473	77.00 531	98.01 470

thres200		97.61 357	97.28 369	98.62 318	99.64 168	98.03 303	99.26 348	98.74 457	97.68 275	99.09 298	98.32 468	91.66 392	99.81 238	92.88 472	98.22 307	98.03 468

testing91		97.44 373	97.02 383	98.71 309	99.18 344	96.89 377	99.19 370	99.04 407	97.78 261	98.31 408	98.29 469	85.41 472	99.85 192	98.01 286	97.95 321	99.39 277

usedtu_dtu_shiyan2		91.34 468	89.96 477	95.47 470	93.61 533	90.81 492	99.15 378	98.68 466	86.37 507	95.19 477	98.27 470	72.64 506	97.05 503	85.40 514	80.32 527	98.54 425

testing99		97.36 376	96.94 386	98.63 317	99.18 344	96.70 385	99.30 323	98.93 422	97.71 270	98.23 413	98.26 471	84.92 475	99.84 202	98.04 285	97.85 329	99.35 283

OpenMVS_ROB	92.34 20	94.38 449	93.70 455	96.41 460	97.38 488	93.17 480	99.06 399	98.75 453	86.58 506	94.84 483	98.26 471	81.53 492	99.32 379	89.01 496	97.87 327	96.76 506

UBG		97.85 313	97.48 333	98.95 259	99.25 327	97.64 328	99.24 355	98.74 457	97.90 242	98.64 376	98.20 473	88.65 439	99.81 238	98.27 259	98.40 291	99.42 270

LoFTR		93.25 458	92.33 464	95.99 465	97.91 474	90.83 491	99.06 399	98.56 473	92.19 477	90.24 508	98.18 474	72.97 504	99.26 390	89.37 493	92.52 471	97.89 482

testing222		97.16 386	96.50 396	99.16 234	99.16 354	98.47 281	99.27 339	98.66 470	97.71 270	98.23 413	98.15 475	82.28 491	99.84 202	97.36 354	97.66 335	99.18 302

Syy-MVS		97.09 390	97.14 377	96.95 450	99.00 388	92.73 483	99.29 328	99.39 294	97.06 344	97.41 444	98.15 475	93.92 329	98.68 474	91.71 481	98.34 294	99.45 266

myMVS_eth3d		96.89 394	96.37 399	98.43 350	99.00 388	97.16 347	99.29 328	99.39 294	97.06 344	97.41 444	98.15 475	83.46 484	98.68 474	95.27 436	98.34 294	99.45 266

CL-MVSNet_self_test		94.49 447	93.97 449	96.08 464	96.16 508	93.67 475	98.33 494	99.38 303	95.13 434	97.33 448	98.15 475	92.69 363	96.57 507	88.67 497	79.87 529	97.99 474

test_vis1_rt		95.81 419	95.65 418	96.32 461	99.67 139	91.35 490	99.49 224	96.74 512	98.25 166	95.24 474	98.10 479	74.96 500	99.90 149	99.53 53	98.85 265	97.70 487

ETVMVS		97.50 366	96.90 387	99.29 216	99.23 331	98.78 244	99.32 317	98.90 432	97.52 296	98.56 386	98.09 480	84.72 477	99.69 306	97.86 297	97.88 326	99.39 277

pmmvs3		94.09 453	93.25 460	96.60 457	94.76 525	94.49 462	98.92 432	98.18 489	89.66 494	96.48 465	98.06 481	86.28 464	97.33 499	89.68 492	87.20 500	97.97 476

mvsany_test3		93.77 455	93.45 458	94.74 473	95.78 513	88.01 501	99.64 98	98.25 484	98.28 156	94.31 485	97.97 482	68.89 517	98.51 478	97.50 340	90.37 484	97.71 484

PMatch-Up-SfM		86.75 487	85.43 489	90.73 496	94.97 524	81.39 518	97.55 517	94.92 523	86.33 508	83.10 525	97.95 483	46.03 542	93.97 525	87.59 503	80.39 526	96.83 504

PMatch-SfM		88.28 480	86.92 485	92.38 486	95.93 510	84.56 509	97.84 511	96.01 517	88.80 500	84.11 521	97.95 483	49.73 536	95.66 516	89.15 495	82.72 515	96.91 503

blended_shiyan6		95.54 424	94.78 432	97.84 408	96.60 502	95.89 418	98.85 441	99.28 362	92.17 481	98.43 397	97.95 483	91.44 396	99.02 442	97.30 359	80.97 521	98.60 414

blended_shiyan8		95.56 423	94.79 431	97.87 401	96.60 502	95.90 417	98.85 441	99.27 369	92.19 477	98.47 394	97.94 486	91.43 397	99.11 425	97.26 362	81.09 520	98.60 414

MatchFormer		91.94 466	90.72 471	95.58 469	97.82 479	89.79 499	98.92 432	98.87 438	88.24 502	88.03 513	97.92 487	70.39 512	99.23 395	85.21 515	91.12 479	97.72 483

blend_shiyan4		95.25 434	94.39 442	97.84 408	96.70 501	95.92 415	98.84 445	99.28 362	92.21 476	98.16 419	97.84 488	87.10 458	99.07 431	97.53 336	81.87 516	98.54 425

ELoFTR		89.95 475	88.65 480	93.85 477	95.93 510	85.85 505	98.64 469	98.31 482	90.34 492	85.03 518	97.76 489	60.28 527	99.01 445	87.27 507	84.26 505	96.71 509

PM-MVS		92.96 461	92.23 465	95.14 472	95.61 515	89.98 498	99.37 296	98.21 487	94.80 446	95.04 480	97.69 490	65.06 521	97.90 490	94.30 448	89.98 488	97.54 493

wanda-best-256-512		95.43 427	94.66 434	97.77 416	96.45 504	95.68 424	98.48 485	99.28 362	92.18 479	98.36 402	97.68 491	91.20 404	99.03 438	97.31 356	80.97 521	98.60 414

FE-blended-shiyan7		95.43 427	94.66 434	97.77 416	96.45 504	95.68 424	98.48 485	99.28 362	92.18 479	98.36 402	97.68 491	91.20 404	99.03 438	97.31 356	80.97 521	98.60 414

usedtu_blend_shiyan5		95.04 437	94.10 445	97.86 404	96.45 504	95.92 415	99.29 328	99.22 380	86.17 509	98.36 402	97.68 491	91.20 404	99.07 431	97.53 336	80.97 521	98.60 414

FE-MVSNET2		95.10 436	94.44 441	97.08 446	95.08 521	95.97 412	99.51 196	99.37 313	95.02 440	94.10 487	97.57 494	86.18 465	97.66 497	93.28 466	89.86 489	97.61 489

FE-MVSNET		94.07 454	93.36 459	96.22 462	94.05 529	94.71 456	99.56 155	98.36 480	93.15 467	93.76 491	97.55 495	86.47 463	96.49 509	87.48 504	89.83 490	97.48 494

pmmvs-eth3d		95.34 432	94.73 433	97.15 441	95.53 517	95.94 414	99.35 307	99.10 397	95.13 434	93.55 492	97.54 496	88.15 447	97.91 489	94.58 445	89.69 492	97.61 489

gbinet_0.2-2-1-0.02		95.40 430	94.58 438	97.85 405	96.11 509	95.97 412	98.56 479	99.26 371	92.12 483	98.47 394	97.49 497	90.23 418	99.00 447	97.71 318	81.25 518	98.58 421

ambc				93.06 485	92.68 537	82.36 513	98.47 487	98.73 463		95.09 479	97.41 498	55.55 528	99.10 428	96.42 407	91.32 475	97.71 484

RPMNet		96.72 398	95.90 412	99.19 231	99.18 344	98.49 277	99.22 362	99.52 134	88.72 501	99.56 176	97.38 499	94.08 322	99.95 76	86.87 510	98.58 281	99.14 303

new-patchmatchnet		94.48 448	94.08 447	95.67 468	95.08 521	92.41 484	99.18 372	99.28 362	94.55 451	93.49 493	97.37 500	87.86 451	97.01 504	91.57 482	88.36 496	97.61 489

PDCNetPlus		84.77 489	83.24 492	89.36 507	94.33 528	83.93 511	98.13 505	76.80 549	83.26 513	86.31 515	97.33 501	62.90 523	92.65 527	87.20 508	62.90 535	91.50 525

KD-MVS_self_test		95.00 439	94.34 443	96.96 449	97.07 497	95.39 438	99.56 155	99.44 268	95.11 436	97.13 455	97.32 502	91.86 384	97.27 501	90.35 490	81.23 519	98.23 455

PatchT		97.03 392	96.44 398	98.79 299	98.99 391	98.34 287	99.16 374	99.07 403	92.13 482	99.52 188	97.31 503	94.54 301	98.98 450	88.54 498	98.73 273	99.03 321

ALIKED-LG		88.17 482	87.32 484	90.75 495	98.67 443	81.68 517	98.16 502	94.72 526	78.63 516	86.08 517	97.07 504	70.16 513	96.62 506	71.97 531	90.37 484	93.95 520

0.4-1-1-0.1		95.23 435	94.22 444	98.26 369	97.39 487	95.86 420	97.59 516	97.62 497	93.85 456	94.97 481	97.03 505	87.20 455	99.87 177	98.47 236	83.84 506	99.05 319

ALIKED-NN		88.27 481	87.61 483	90.24 499	98.46 461	79.97 526	97.04 520	94.61 528	75.25 517	86.99 514	96.90 506	72.78 505	95.78 515	75.45 527	91.01 481	94.97 518

test_fmvs3		92.10 465	91.77 467	93.08 484	96.19 507	86.25 503	99.82 16	98.62 472	96.65 374	95.19 477	96.90 506	55.05 530	95.93 514	96.63 403	90.92 483	97.06 502

UnsupCasMVSNet_bld		93.53 456	92.51 462	96.58 458	97.38 488	93.82 470	98.24 497	99.48 213	91.10 490	93.10 494	96.66 508	74.89 502	98.37 479	94.03 455	87.71 499	97.56 492

0.4-1-1-0.2		94.94 442	93.92 450	97.99 390	96.84 500	95.13 446	96.64 523	97.62 497	93.45 464	94.92 482	96.56 509	87.14 457	99.86 184	98.43 243	83.69 510	98.98 328

0.3-1-1-0.015		94.79 443	93.69 456	98.10 381	96.99 499	95.46 434	97.02 521	97.61 499	93.53 460	94.03 489	96.54 510	85.60 470	99.86 184	98.43 243	83.45 511	98.99 327

LCM-MVSNet		86.80 486	85.22 491	91.53 490	87.81 549	80.96 521	98.23 499	98.99 415	71.05 524	90.13 509	96.51 511	48.45 541	96.88 505	90.51 487	85.30 503	96.76 506

test_f		91.90 467	91.26 470	93.84 478	95.52 518	85.92 504	99.69 63	98.53 477	95.31 433	93.87 490	96.37 512	55.33 529	98.27 481	95.70 424	90.98 482	97.32 496

SP-DiffGlue		90.78 472	90.71 472	90.98 493	95.45 520	81.30 520	97.92 510	97.30 504	75.18 518	92.09 500	95.93 513	74.93 501	94.89 521	93.46 464	94.12 443	96.74 508

ALIKED-MNN		86.97 484	85.90 486	90.16 500	99.06 377	79.59 527	97.93 509	94.82 524	72.37 522	84.41 520	95.46 514	68.55 518	96.43 510	72.40 530	88.11 498	94.47 519

PMMVS2		86.87 485	85.37 490	91.35 491	90.21 543	83.80 512	98.89 436	97.45 503	83.13 514	91.67 506	95.03 515	48.49 540	94.70 523	85.86 513	77.62 530	95.54 516

Gipuma		90.99 470	90.15 475	93.51 481	98.73 432	90.12 497	93.98 530	99.45 259	79.32 515	92.28 499	94.91 516	69.61 515	97.98 488	87.42 505	95.67 406	92.45 523
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
JIA-IIPM		97.50 366	97.02 383	98.93 263	98.73 432	97.80 320	99.30 323	98.97 418	91.73 485	98.91 330	94.86 517	95.10 259	99.71 292	97.58 328	97.98 320	99.28 292

SP-SuperGlue		89.23 477	88.68 478	90.88 494	98.23 469	80.60 523	98.16 502	97.30 504	73.08 520	89.64 510	94.62 518	71.80 509	94.91 520	82.11 519	93.22 457	97.14 501

SP-LightGlue		89.28 476	88.68 478	91.06 492	98.21 470	80.90 522	98.19 500	96.96 507	72.38 521	89.60 511	94.43 519	72.44 507	95.06 519	82.91 517	93.03 462	97.22 498

SP-MNN		88.33 479	87.78 482	89.95 503	98.28 465	77.92 530	98.01 508	95.69 520	70.61 525	86.18 516	94.36 520	71.09 511	94.76 522	81.51 520	94.32 438	97.17 499

XFeat-MNN		82.40 493	82.10 494	83.31 512	93.04 535	68.49 539	95.39 524	90.86 537	60.29 534	81.56 527	94.09 521	66.79 520	91.70 531	76.62 524	80.26 528	89.74 528

SP-NN		88.62 478	88.17 481	89.96 502	97.89 476	78.51 529	97.19 519	96.09 516	71.28 523	88.29 512	94.00 522	71.98 508	93.65 526	82.37 518	94.46 433	97.71 484

PMVS	70.75 22	75.98 500	74.97 503	79.01 517	70.98 555	55.18 554	93.37 533	98.21 487	65.08 532	61.78 543	93.83 523	21.74 556	92.53 528	78.59 522	91.12 479	89.34 530
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
XFeat-NN		82.84 490	83.12 493	82.00 516	94.35 527	67.14 541	93.32 535	89.27 540	62.21 533	84.06 522	93.50 524	69.15 516	89.40 533	78.92 521	83.33 512	89.46 529

GLUNet-SfM		78.99 496	76.32 500	86.99 508	89.16 548	73.30 538	93.36 534	90.45 538	66.38 530	74.95 537	93.30 525	52.29 532	94.61 524	75.35 528	51.65 542	93.07 521

MVS-HIRNet		95.75 420	95.16 425	97.51 431	99.30 311	93.69 474	98.88 437	95.78 518	85.09 511	98.78 354	92.65 526	91.29 402	99.37 367	94.85 443	99.85 94	99.46 263

E-PMN		80.61 494	79.88 496	82.81 513	90.75 541	76.38 533	97.69 513	95.76 519	66.44 529	83.52 523	92.25 527	62.54 524	87.16 541	68.53 533	61.40 536	84.89 532

test_vis3_rt		87.04 483	85.81 487	90.73 496	93.99 530	81.96 515	99.76 38	90.23 539	92.81 473	81.35 528	91.56 528	40.06 547	99.07 431	94.27 450	88.23 497	91.15 526

EMVS		80.02 495	79.22 497	82.43 515	91.19 540	76.40 532	97.55 517	92.49 536	66.36 531	83.01 526	91.27 529	64.63 522	85.79 544	65.82 534	60.65 537	85.08 531

gg-mvs-nofinetune		96.17 412	95.32 424	98.73 304	98.79 420	98.14 296	99.38 292	94.09 529	91.07 491	98.07 425	91.04 530	89.62 428	99.35 374	96.75 394	99.09 240	98.68 372

SIFT-MNN		75.73 501	75.71 501	75.77 519	95.65 514	60.92 545	94.36 527	87.62 541	58.67 536	75.90 535	90.94 531	49.64 538	89.04 535	44.85 542	83.80 508	77.35 533

SIFT-NN		76.99 498	77.37 499	75.84 518	97.10 496	62.39 543	94.15 529	87.21 542	59.41 535	79.90 533	90.73 532	54.60 531	88.56 536	47.22 537	86.03 502	76.57 534

SIFT-ConvMatch		69.43 507	68.09 510	73.45 524	93.86 532	60.02 549	92.57 539	77.69 548	57.58 540	62.69 541	90.53 533	42.14 544	86.65 543	43.98 543	51.72 541	73.67 539

SIFT-NN-NCMNet		75.53 502	75.57 502	75.42 520	93.93 531	61.35 544	94.41 526	86.44 543	58.51 537	76.23 534	90.44 534	50.56 534	89.34 534	46.60 538	83.04 513	75.58 536

SIFT-UMatch		68.14 508	66.40 511	73.38 525	92.20 539	59.42 550	92.84 537	76.01 551	56.87 542	58.37 545	90.35 535	41.97 545	87.16 541	42.64 544	46.35 543	73.55 541

SIFT-NN-CMatch		72.61 503	71.92 506	74.68 521	92.79 536	60.24 547	93.28 536	81.57 547	58.24 539	75.18 536	90.26 536	49.66 537	87.35 540	46.02 539	60.26 538	76.45 535

SIFT-CM-Cal		66.94 509	65.48 512	71.33 527	93.05 534	58.77 551	91.46 542	70.45 553	56.64 545	61.97 542	89.98 537	40.72 546	83.32 547	42.57 545	42.47 545	71.90 542

SIFT-UM-Cal		64.60 510	62.65 513	70.42 528	92.22 538	58.07 553	92.29 540	66.92 554	56.70 543	50.16 548	89.97 538	37.90 548	82.95 548	42.33 546	35.40 548	70.24 544

SIFT-NN-UMatch		71.65 504	70.86 507	74.00 523	90.69 542	60.53 546	93.59 531	81.89 545	58.42 538	60.99 544	89.71 539	50.18 535	87.89 538	45.77 540	66.55 534	73.57 540

SIFT-NCM-Cal		71.65 504	70.76 508	74.34 522	94.61 526	60.18 548	94.16 528	81.72 546	57.21 541	55.36 546	89.56 540	42.48 543	88.45 537	41.31 547	80.41 525	74.39 538

ANet_high		77.30 497	74.86 504	84.62 511	75.88 554	77.61 531	97.63 515	93.15 534	88.81 499	64.27 540	89.29 541	36.51 550	83.93 545	75.89 526	52.31 540	92.33 524

SIFT-NN-PointCN		70.32 506	69.71 509	72.13 526	90.01 544	58.29 552	93.45 532	76.20 550	56.66 544	70.25 539	89.20 542	48.94 539	83.41 546	45.45 541	57.26 539	74.70 537

MVE	76.82 21	76.91 499	74.31 505	84.70 510	85.38 553	76.05 534	96.88 522	93.17 532	67.39 528	71.28 538	89.01 543	21.66 557	87.69 539	71.74 532	72.29 533	90.35 527
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-PCN-Cal		61.29 512	60.21 515	64.54 530	89.88 545	50.56 556	91.21 543	65.73 555	53.15 547	48.59 549	87.20 544	36.60 549	76.52 549	37.37 550	32.17 549	66.54 545

SIFT-PointCN		62.71 511	61.56 514	66.18 529	89.53 547	50.88 555	91.81 541	72.35 552	53.65 546	50.49 547	86.32 545	33.30 551	76.23 550	35.91 551	40.66 546	71.43 543

SIFT-NCMNet		55.02 513	53.54 516	59.46 531	86.55 551	47.35 558	87.85 544	46.22 556	51.77 548	44.11 550	83.50 546	27.88 554	68.75 551	32.81 552	21.14 552	62.27 546

testmvs		39.17 515	43.78 517	25.37 534	36.04 558	16.84 560	98.36 490	26.56 557	20.06 550	38.51 552	67.32 547	29.64 553	15.30 554	37.59 548	39.90 547	43.98 548

test123		39.01 516	42.50 518	28.53 533	39.17 557	20.91 559	98.75 457	19.17 559	19.83 551	38.57 551	66.67 548	33.16 552	15.42 553	37.50 549	29.66 550	49.26 547

test_post											65.99 549	94.65 294	99.73 282

test_post1							99.23 358				65.14 550	94.18 318	99.71 292	97.58 328

X-MVStestdata		96.55 402	95.45 422	99.87 22	99.85 31	99.83 23	99.69 63	99.68 24	98.98 72	99.37 227	64.01 551	98.81 49	99.94 91	98.79 190	99.86 87	99.84 54

wuyk23d		40.18 514	41.29 519	36.84 532	86.18 552	49.12 557	79.73 545	22.81 558	27.64 549	25.46 553	28.45 552	21.98 555	48.89 552	55.80 536	23.56 551	12.51 549

test_blank		0.13 520	0.17 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	1.57 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

mmdepth		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

monomultidepth		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet_test		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

DCPMVS		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

pcd_1.5k_mvsjas		8.27 519	11.03 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	99.01 19	0.00 555	0.00 553	0.00 553	0.00 550

sosnet-low-res		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

sosnet		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uncertanet		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

Regformer		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet		0.02 521	0.03 524	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.27 554	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

test-260524					99.82 53	99.84 20		99.63 46		99.85 55		98.54 83	99.94 91	99.34 88	99.88 73

WAC-MVS						97.16 347								95.47 430

FOURS1					99.91 1	99.93 1	99.87 8	99.56 90	99.10 48	99.81 72

MSC_two_6792asdad				99.87 22	99.51 238	99.76 50		99.33 336					99.96 41	98.87 169	99.84 102	99.89 30

No_MVS				99.87 22	99.51 238	99.76 50		99.33 336					99.96 41	98.87 169	99.84 102	99.89 30

eth-test2					0.00 559

eth-test					0.00 559

IU-MVS					99.84 38	99.88 10		99.32 347	98.30 155	99.84 56				98.86 174	99.85 94	99.89 30

save fliter					99.76 83	99.59 90	99.14 381	99.40 291	99.00 67

test_0728_SECOND				99.91 6	99.84 38	99.89 6	99.57 147	99.51 162					99.96 41	98.93 160	99.86 87	99.88 36

GSMVS																99.52 235

test_part2					99.81 58	99.83 23				99.77 90

sam_mvs1												94.86 271				99.52 235

sam_mvs												94.72 287

MTGPA								99.47 235

MTMP							99.54 175	98.88 436

test9_res														97.49 341	99.72 150	99.75 113

agg_prior2														97.21 365	99.73 149	99.75 113

agg_prior					99.67 139	99.62 84		99.40 291		98.87 338			99.91 136

test_prior4						99.56 96	98.99 418

test_prior				99.68 90	99.67 139	99.48 113		99.56 90					99.83 224			99.74 118

旧先验2							98.96 425		96.70 370	99.47 196			99.94 91	98.19 265

新几何2							99.01 415

无先验							98.99 418	99.51 162	96.89 358				99.93 109	97.53 336		99.72 138

原ACMM2							98.95 428

testdata2													99.95 76	96.67 399

segment_acmp												98.96 26

testdata1							98.85 441		98.32 151

test12				99.75 77	99.64 168	99.61 87		99.29 360		99.21 272		98.38 96	99.89 165		99.74 147	99.74 118

plane_prior7					99.29 315	97.03 362

plane_prior6					99.27 320	96.98 366						92.71 361

plane_prior5								99.47 235					99.69 306	97.78 307	97.63 336	98.67 380

plane_prior3						97.00 364			98.69 108	99.11 292

plane_prior2							99.39 287		98.97 76

plane_prior1					99.26 323

plane_prior						96.97 367	99.21 364		98.45 132						97.60 339

n2								0.00 560

nn								0.00 560

door-mid								98.05 490

test11								99.35 322

door								97.92 491

HQP5-MVS						96.83 378

HQP-NCC					99.19 341		98.98 421		98.24 168	98.66 369

ACMP_Plane					99.19 341		98.98 421		98.24 168	98.66 369

BP-MVS														97.19 369

HQP4-MVS										98.66 369			99.64 321			98.64 393

HQP3-MVS								99.39 294							97.58 341

HQP2-MVS												92.47 370

MDTV_nov1_ep13_2view						95.18 444	99.35 307		96.84 361	99.58 171		95.19 256		97.82 302		99.46 263

ACMMP++_ref															97.19 369

ACMMP++															97.43 359

Test By Simon												98.75 61

High-resolution multi-view benchmark