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.

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

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 40	99.71 10	96.99 48	99.69 2	99.57 22	99.02 21	99.62 16	99.36 27	98.53 11	99.52 227	98.58 43	99.95 5	99.66 38
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UA-Net		98.88 10	98.76 16	99.22 2	99.11 105	97.89 16	99.47 3	99.32 41	99.08 16	97.87 224	99.67 5	96.47 128	99.92 5	97.88 65	99.98 2	99.85 6

mvs5depth		98.06 60	98.58 29	96.51 252	98.97 134	89.65 357	99.43 4	99.81 2	99.30 9	98.36 146	99.86 2	93.15 270	99.88 22	98.50 45	99.84 51	99.99 1

TDRefinement		98.90 8	98.86 11	99.02 9	99.54 28	98.06 8	99.34 5	99.44 34	98.85 27	99.00 63	99.20 41	97.42 52	99.59 202	97.21 97	99.76 73	99.40 135

UniMVSNet_ETH3D		99.12 3	99.28 5	98.65 45	99.77 5	96.34 78	99.18 6	99.20 60	99.67 3	99.73 7	99.65 8	99.15 3	99.86 27	97.22 96	99.92 15	99.77 15

OurMVSNet-221017-0		98.61 19	98.61 27	98.63 47	99.77 5	96.35 77	99.17 7	99.05 110	98.05 61	99.61 17	99.52 13	93.72 255	99.88 22	98.72 39	99.88 28	99.65 41

DVP-MVS++		97.96 68	97.90 90	98.12 96	97.75 347	95.40 132	99.03 8	98.89 162	96.62 131	98.62 110	98.30 179	96.97 86	99.75 85	95.70 182	99.25 283	99.21 195

FOURS1					99.59 18	98.20 7	99.03 8	99.25 51	98.96 24	98.87 80

tt0320		99.07 6	99.29 4	98.43 62	99.55 24	95.92 103	98.97 10	99.53 28	99.67 3	99.79 2	99.71 3	98.33 14	99.78 58	98.11 53	99.92 15	99.57 60

tt0320-xc		99.10 4	99.31 3	98.49 57	99.57 20	96.09 93	98.91 11	99.55 26	99.67 3	99.78 3	99.69 4	98.63 10	99.77 69	98.02 59	99.93 11	99.60 47

sc_t1		99.09 5	99.28 5	98.53 54	99.72 8	96.21 86	98.87 12	99.19 63	99.71 2	99.76 4	99.65 8	98.64 9	99.79 53	98.07 57	99.90 25	99.58 52

pmmvs6		99.07 6	99.24 7	98.56 51	99.81 2	96.38 74	98.87 12	99.30 43	99.01 22	99.63 15	99.66 6	99.27 2	99.68 151	97.75 74	99.89 26	99.62 45

Anonymous20231211		98.55 24	98.76 16	97.94 113	98.79 170	94.37 191	98.84 14	99.15 76	99.37 6	99.67 11	99.43 21	95.61 178	99.72 111	98.12 52	99.86 35	99.73 28

mmtdpeth		98.33 36	98.53 31	97.71 128	99.07 111	93.44 230	98.80 15	99.78 4	99.10 15	96.61 326	99.63 10	95.42 188	99.73 101	98.53 44	99.86 35	99.95 2

MIMVSNet1		98.51 28	98.45 36	98.67 43	99.72 8	96.71 57	98.76 16	98.89 162	98.49 40	99.38 32	99.14 53	95.44 187	99.84 33	96.47 134	99.80 64	99.47 107

EPP-MVSNet		96.84 198	96.58 220	97.65 136	99.18 91	93.78 216	98.68 17	96.34 416	97.91 64	97.30 262	98.06 225	88.46 373	99.85 30	93.85 314	99.40 237	99.32 161

v7n		98.73 14	98.99 8	97.95 112	99.64 14	94.20 200	98.67 18	99.14 79	99.08 16	99.42 29	99.23 39	96.53 123	99.91 13	99.27 10	99.93 11	99.73 28

MVSFormer		96.14 253	96.36 242	95.49 345	97.68 357	87.81 422	98.67 18	99.02 123	96.50 142	94.48 432	96.15 407	86.90 403	99.92 5	98.73 37	99.13 301	98.74 308

test_djsdf		98.73 14	98.74 19	98.69 42	99.63 15	96.30 82	98.67 18	99.02 123	96.50 142	99.32 37	99.44 20	97.43 51	99.92 5	98.73 37	99.95 5	99.86 5

tt0805		97.44 147	97.56 139	97.11 192	99.55 24	96.36 76	98.66 21	95.66 430	98.31 47	97.09 286	95.45 445	97.17 69	98.50 460	98.67 40	97.45 458	96.48 482

anonymousdsp		98.72 17	98.63 23	98.99 13	99.62 16	97.29 41	98.65 22	99.19 63	95.62 209	99.35 36	99.37 25	97.38 54	99.90 17	98.59 42	99.91 19	99.77 15

HPM-MVS		98.11 55	97.83 101	98.92 24	99.42 45	97.46 35	98.57 23	99.05 110	95.43 224	97.41 258	97.50 296	97.98 23	99.79 53	95.58 196	99.57 155	99.50 89
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
IS-MVSNet		96.93 189	96.68 211	97.70 130	99.25 71	94.00 207	98.57 23	96.74 408	98.36 45	98.14 185	97.98 237	88.23 380	99.71 127	93.10 345	99.72 91	99.38 144

WR-MVS_H		98.65 18	98.62 25	98.75 34	99.51 32	96.61 64	98.55 25	99.17 68	99.05 19	99.17 47	98.79 92	95.47 185	99.89 20	97.95 63	99.91 19	99.75 24

FE-MVS		92.95 416	92.22 422	95.11 366	97.21 404	88.33 401	98.54 26	93.66 473	89.91 436	96.21 357	98.14 206	70.33 516	99.50 233	87.79 456	98.24 408	97.51 442

test2506		89.86 473	89.16 478	91.97 497	98.95 135	76.83 539	98.54 26	61.07 559	96.20 160	97.07 287	99.16 50	55.19 546	99.69 144	96.43 139	99.83 56	99.38 144

mvs_tets		98.90 8	98.94 9	98.75 34	99.69 11	96.48 69	98.54 26	99.22 57	96.23 158	99.71 8	99.48 16	98.77 7	99.93 3	98.89 31	99.95 5	99.84 8

CS-MVS		98.09 56	98.01 77	98.32 72	98.45 241	96.69 59	98.52 29	99.69 8	98.07 59	96.07 365	97.19 326	96.88 99	99.86 27	97.50 85	99.73 86	98.41 351

Gipuma		98.07 59	98.31 49	97.36 172	99.76 7	96.28 83	98.51 30	99.10 90	98.76 29	96.79 309	99.34 30	96.61 117	98.82 420	96.38 141	99.50 198	96.98 460
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PS-CasMVS		98.73 14	98.85 13	98.39 66	99.55 24	95.47 130	98.49 31	99.13 81	99.22 12	99.22 44	98.96 75	97.35 56	99.92 5	97.79 71	99.93 11	99.79 13

3Dnovator	96.53 2	97.61 125	97.64 127	97.50 154	97.74 350	93.65 223	98.49 31	98.88 169	96.86 122	97.11 280	98.55 134	95.82 165	99.73 101	95.94 169	99.42 231	99.13 215

DTE-MVSNet		98.79 11	98.86 11	98.59 49	99.55 24	96.12 91	98.48 33	99.10 90	99.36 7	99.29 39	99.06 62	97.27 60	99.93 3	97.71 76	99.91 19	99.70 33

jajsoiax		98.77 12	98.79 15	98.74 37	99.66 13	96.48 69	98.45 34	99.12 82	95.83 198	99.67 11	99.37 25	98.25 17	99.92 5	98.77 34	99.94 8	99.82 9

PEN-MVS		98.75 13	98.85 13	98.44 61	99.58 19	95.67 114	98.45 34	99.15 76	99.33 8	99.30 38	99.00 69	97.27 60	99.92 5	97.64 80	99.92 15	99.75 24

LS3D		97.77 104	97.50 149	98.57 50	96.24 441	97.58 27	98.45 34	98.85 181	98.58 36	97.51 247	97.94 241	95.74 172	99.63 184	95.19 230	98.97 320	98.51 340

balanced_ft_v1		96.29 242	96.60 218	95.38 354	96.77 421	88.73 389	98.44 37	98.44 275	94.97 248	95.91 373	98.77 96	91.03 323	99.75 85	96.16 156	98.91 333	97.65 433

SPE-MVS-test		97.91 84	97.84 98	98.14 94	98.52 223	96.03 100	98.38 38	99.67 9	98.11 57	95.50 400	96.92 355	96.81 105	99.87 25	96.87 116	99.76 73	98.51 340

FC-MVSNet-test		98.16 49	98.37 40	97.56 142	99.49 36	93.10 242	98.35 39	99.21 58	98.43 42	98.89 76	98.83 91	94.30 237	99.81 43	97.87 66	99.91 19	99.77 15

HPM-MVS_fast		98.32 38	98.13 60	98.88 26	99.54 28	97.48 34	98.35 39	99.03 119	95.88 193	97.88 221	98.22 197	98.15 20	99.74 95	96.50 133	99.62 124	99.42 128

ab-mvs		96.59 220	96.59 219	96.60 240	98.64 197	92.21 272	98.35 39	97.67 357	94.45 277	96.99 294	98.79 92	94.96 212	99.49 239	90.39 416	99.07 312	98.08 393

EGC-MVSNET		83.08 511	77.93 516	98.53 54	99.57 20	97.55 29	98.33 42	98.57 256	4.71 557	10.38 560	98.90 86	95.60 179	99.50 233	95.69 184	99.61 135	98.55 333

test1111		94.53 354	94.81 328	93.72 441	99.06 113	81.94 509	98.31 43	83.87 548	96.37 149	98.49 127	99.17 49	81.49 455	99.73 101	96.64 123	99.86 35	99.49 97

ECVR-MVS		94.37 361	94.48 347	94.05 430	98.95 135	83.10 499	98.31 43	82.48 550	96.20 160	98.23 172	99.16 50	81.18 459	99.66 169	95.95 168	99.83 56	99.38 144

EC-MVSNet		97.90 86	97.94 89	97.79 121	98.66 196	95.14 158	98.31 43	99.66 12	97.57 79	95.95 371	97.01 347	96.99 84	99.82 38	97.66 79	99.64 118	98.39 354

pm-mvs1		98.47 31	98.67 21	97.86 117	99.52 31	94.58 180	98.28 46	99.00 135	97.57 79	99.27 40	99.22 40	98.32 15	99.50 233	97.09 104	99.75 83	99.50 89

SixPastTwentyTwo		97.49 141	97.57 138	97.26 181	99.56 22	92.33 265	98.28 46	96.97 398	98.30 49	99.45 25	99.35 29	88.43 374	99.89 20	98.01 60	99.76 73	99.54 74

FA-MVS(test-final)		94.91 329	94.89 319	94.99 376	97.51 380	88.11 413	98.27 48	95.20 446	92.40 365	96.68 318	98.60 127	83.44 442	99.28 342	93.34 335	98.53 386	97.59 439

CP-MVSNet		98.42 33	98.46 33	98.30 75	99.46 40	95.22 152	98.27 48	98.84 185	99.05 19	99.01 61	98.65 120	95.37 190	99.90 17	97.57 82	99.91 19	99.77 15

GG-mvs-BLEND				90.60 510	91.00 543	84.21 492	98.23 50	72.63 558		82.76 545	84.11 545	56.14 539	96.79 508	72.20 545	92.09 535	90.78 540

GBi-Net		96.99 182	96.80 204	97.56 142	97.96 304	93.67 219	98.23 50	98.66 240	95.59 211	97.99 204	99.19 42	89.51 355	99.73 101	94.60 280	99.44 218	99.30 167

test1		96.99 182	96.80 204	97.56 142	97.96 304	93.67 219	98.23 50	98.66 240	95.59 211	97.99 204	99.19 42	89.51 355	99.73 101	94.60 280	99.44 218	99.30 167

FMVSNet1		97.95 72	98.08 68	97.56 142	99.14 103	93.67 219	98.23 50	98.66 240	97.41 93	99.00 63	99.19 42	95.47 185	99.73 101	95.83 179	99.76 73	99.30 167

ACMH	93.61 9	98.44 32	98.76 16	97.51 148	99.43 43	93.54 225	98.23 50	99.05 110	97.40 94	99.37 33	99.08 61	98.79 6	99.47 248	97.74 75	99.71 94	99.50 89
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TransMVSNet (Re)		98.38 35	98.67 21	97.51 148	99.51 32	93.39 234	98.20 55	98.87 171	98.23 53	99.48 22	99.27 35	98.47 13	99.55 218	96.52 132	99.53 177	99.60 47

gg-mvs-nofinetune		88.28 495	86.96 500	92.23 494	92.84 537	84.44 487	98.19 56	74.60 555	99.08 16	87.01 540	99.47 17	56.93 536	98.23 479	78.91 531	95.61 514	94.01 518

QAPM		95.88 268	95.57 289	96.80 225	97.90 312	91.84 290	98.18 57	98.73 222	88.41 459	96.42 340	98.13 208	94.73 214	99.75 85	88.72 443	98.94 326	98.81 291

NR-MVSNet		97.96 68	97.86 97	98.26 79	98.73 181	95.54 122	98.14 58	98.73 222	97.79 66	99.42 29	97.83 255	94.40 232	99.78 58	95.91 172	99.76 73	99.46 109

MIMVSNet		93.42 397	92.86 403	95.10 368	98.17 281	88.19 404	98.13 59	93.69 470	92.07 371	95.04 415	98.21 198	80.95 462	99.03 396	81.42 522	98.06 416	98.07 395

PS-MVSNAJss		98.53 27	98.63 23	98.21 87	99.68 12	94.82 169	98.10 60	99.21 58	96.91 120	99.75 5	99.45 19	95.82 165	99.92 5	98.80 33	99.96 4	99.89 4

ACMMP		98.05 61	97.75 114	98.93 21	99.23 75	97.60 25	98.09 61	98.96 147	95.75 203	97.91 218	98.06 225	96.89 97	99.76 77	95.32 222	99.57 155	99.43 126
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
lecture		98.59 20	98.60 28	98.55 52	99.48 37	96.38 74	98.08 62	99.09 95	98.46 41	98.68 106	98.73 102	97.88 27	99.80 50	97.43 88	99.59 145	99.48 103

APDe-MVS		98.14 50	98.03 74	98.47 60	98.72 184	96.04 96	98.07 63	99.10 90	95.96 185	98.59 115	98.69 113	96.94 88	99.81 43	96.64 123	99.58 151	99.57 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
Vis-MVSNet		98.27 42	98.34 45	98.07 99	99.33 60	95.21 154	98.04 64	99.46 32	97.32 100	97.82 228	99.11 55	96.75 108	99.86 27	97.84 68	99.36 250	99.15 207
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	96.13 3	97.73 108	97.59 136	98.15 93	98.11 291	95.60 117	98.04 64	98.70 231	98.13 56	96.93 300	98.45 148	95.30 195	99.62 189	95.64 189	98.96 323	99.24 189

MVSMamba_PlusPlus		97.43 149	97.98 80	95.78 317	98.88 151	89.70 354	98.03 66	98.85 181	99.18 13	96.84 308	99.12 54	93.04 276	99.91 13	98.38 48	99.55 167	97.73 428

FIs		97.93 79	98.07 69	97.48 159	99.38 52	92.95 246	98.03 66	99.11 85	98.04 62	98.62 110	98.66 116	93.75 254	99.78 58	97.23 95	99.84 51	99.73 28

sd_testset		97.97 66	98.12 61	97.51 148	99.41 46	93.44 230	97.96 68	98.25 300	98.58 36	98.78 90	99.39 22	98.21 18	99.56 213	92.65 352	99.86 35	99.52 82

COLMAP_ROB	94.48 6	98.25 44	98.11 63	98.64 46	99.21 85	97.35 39	97.96 68	99.16 70	98.34 46	98.78 90	98.52 137	97.32 57	99.45 263	94.08 300	99.67 109	99.13 215
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
BridgeMVS		96.88 194	97.29 163	95.63 331	97.66 362	89.47 362	97.95 70	98.89 162	95.94 188	97.77 232	98.55 134	92.23 302	99.68 151	97.05 109	99.61 135	97.73 428

usedtu_dtu_shiyan2		97.54 136	97.26 166	98.37 67	99.54 28	96.04 96	97.94 71	98.06 333	97.36 98	98.62 110	98.20 199	95.52 182	99.73 101	90.90 394	99.18 293	99.33 159

VDDNet		96.98 185	96.84 200	97.41 168	99.40 49	93.26 238	97.94 71	95.31 443	99.26 11	98.39 142	99.18 46	87.85 387	99.62 189	95.13 240	99.09 309	99.35 158

CP-MVS		97.92 80	97.56 139	98.99 13	98.99 130	97.82 18	97.93 73	98.96 147	96.11 170	96.89 304	97.45 300	96.85 102	99.78 58	95.19 230	99.63 121	99.38 144

mvsmamba		94.91 329	94.41 352	96.40 272	97.65 364	91.30 303	97.92 74	95.32 442	91.50 394	95.54 397	98.38 161	83.06 446	99.68 151	92.46 358	97.84 431	98.23 380

ANet_high		98.31 39	98.94 9	96.41 269	99.33 60	89.64 358	97.92 74	99.56 24	99.27 10	99.66 13	99.50 15	97.67 36	99.83 35	97.55 83	99.98 2	99.77 15

nrg030		98.54 25	98.62 25	98.32 72	99.22 78	95.66 115	97.90 76	99.08 99	98.31 47	99.02 60	98.74 101	97.68 35	99.61 197	97.77 73	99.85 48	99.70 33

ambc				96.56 247	98.23 271	91.68 294	97.88 77	98.13 323		98.42 137	98.56 133	94.22 239	99.04 393	94.05 303	99.35 256	98.95 264

Anonymous20240529		97.96 68	98.04 73	97.71 128	98.69 193	94.28 198	97.86 78	98.31 297	98.79 28	99.23 43	98.86 90	95.76 171	99.61 197	95.49 199	99.36 250	99.23 191

sasdasda		97.23 166	97.21 171	97.30 176	97.65 364	94.39 188	97.84 79	99.05 110	97.42 89	96.68 318	93.85 478	97.63 41	99.33 319	96.29 148	98.47 394	98.18 387

canonicalmvs		97.23 166	97.21 171	97.30 176	97.65 364	94.39 188	97.84 79	99.05 110	97.42 89	96.68 318	93.85 478	97.63 41	99.33 319	96.29 148	98.47 394	98.18 387

tfpnnormal		97.72 111	97.97 81	96.94 210	99.26 68	92.23 271	97.83 81	98.45 271	98.25 52	99.13 51	98.66 116	96.65 114	99.69 144	93.92 311	99.62 124	98.91 275

MGCFI-Net		97.20 168	97.23 169	97.08 197	97.68 357	93.71 218	97.79 82	99.09 95	97.40 94	96.59 327	93.96 475	97.67 36	99.35 314	96.43 139	98.50 391	98.17 389

Anonymous20240521		97.07 178	97.51 147	95.76 318	99.35 58	88.18 407	97.78 83	98.40 283	97.11 108	98.34 150	99.04 64	89.58 350	99.79 53	98.09 55	99.93 11	99.30 167

XVS		97.96 68	97.63 129	98.94 18	99.15 96	97.66 22	97.77 84	98.83 192	97.42 89	96.32 345	97.64 282	96.49 126	99.72 111	95.66 187	99.37 245	99.45 113

X-MVStestdata		92.86 417	90.83 456	98.94 18	99.15 96	97.66 22	97.77 84	98.83 192	97.42 89	96.32 345	36.50 555	96.49 126	99.72 111	95.66 187	99.37 245	99.45 113

VPA-MVSNet		98.27 42	98.46 33	97.70 130	99.06 113	93.80 214	97.76 86	99.00 135	98.40 44	99.07 57	98.98 72	96.89 97	99.75 85	97.19 100	99.79 66	99.55 72

aaatest				98.17 88	99.36 54	95.35 137	97.75 87	99.30 43	94.02 296	98.88 78	97.54 290		99.73 101	95.36 217	99.53 177	99.44 123

MED-MVS		98.14 50	98.09 67	98.27 78	99.36 54	95.35 137	97.75 87	99.30 43	97.28 103	98.88 78	98.41 155	96.99 84	99.73 101	95.36 217	99.51 190	99.74 26

TestfortrainingZip a		98.22 46	98.18 57	98.33 71	99.36 54	95.49 128	97.75 87	98.86 175	97.28 103	98.87 80	98.41 155	96.31 138	99.77 69	97.40 89	99.38 243	99.74 26

TestfortrainingZip				97.39 170	97.24 403	94.58 180	97.75 87	97.64 365	96.08 174	96.48 336	96.31 396	92.56 291	99.27 345		96.62 485	98.31 367

dcpmvs_2		97.12 175	97.99 79	94.51 408	99.11 105	84.00 493	97.75 87	99.65 13	97.38 96	99.14 50	98.42 152	95.16 202	99.96 2	95.52 198	99.78 70	99.58 52

UGNet		96.81 203	96.56 223	97.58 141	96.64 424	93.84 213	97.75 87	97.12 386	96.47 146	93.62 461	98.88 88	93.22 268	99.53 224	95.61 193	99.69 100	99.36 154
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
mPP-MVS		97.91 84	97.53 144	99.04 7	99.22 78	97.87 17	97.74 93	98.78 210	96.04 179	97.10 281	97.73 273	96.53 123	99.78 58	95.16 235	99.50 198	99.46 109

OpenMVS	94.22 8	95.48 296	95.20 298	96.32 278	97.16 406	91.96 286	97.74 93	98.84 185	87.26 473	94.36 434	98.01 233	93.95 247	99.67 161	90.70 407	98.75 363	97.35 449

RRT-MVS		95.78 273	96.25 248	94.35 418	96.68 423	84.47 486	97.72 95	99.11 85	97.23 105	97.27 264	98.72 103	86.39 412	99.79 53	95.49 199	97.67 445	98.80 292

testf1		98.57 21	98.45 36	98.93 21	99.79 3	98.78 2	97.69 96	99.42 36	97.69 75	98.92 73	98.77 96	97.80 30	99.25 350	96.27 150	99.69 100	98.76 306

APD_test2		98.57 21	98.45 36	98.93 21	99.79 3	98.78 2	97.69 96	99.42 36	97.69 75	98.92 73	98.77 96	97.80 30	99.25 350	96.27 150	99.69 100	98.76 306

MonoMVSNet		93.30 405	93.96 371	91.33 505	94.14 523	81.33 515	97.68 98	96.69 410	95.38 226	96.32 345	98.42 152	84.12 436	96.76 510	90.78 400	92.12 534	95.89 494

MSP-MVS		97.45 145	96.92 194	99.03 8	99.26 68	97.70 21	97.66 99	98.89 162	95.65 207	98.51 124	96.46 384	92.15 304	99.81 43	95.14 238	98.58 384	99.58 52
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
LFMVS		95.32 309	94.88 321	96.62 236	98.03 294	91.47 298	97.65 100	90.72 520	99.11 14	97.89 220	98.31 173	79.20 471	99.48 242	93.91 312	99.12 304	98.93 271

K. test v3		96.44 233	96.28 247	96.95 209	99.41 46	91.53 295	97.65 100	90.31 526	98.89 26	98.93 72	99.36 27	84.57 432	99.92 5	97.81 69	99.56 160	99.39 142

TSAR-MVS + MP.		97.42 151	97.23 169	98.00 108	99.38 52	95.00 162	97.63 102	98.20 307	93.00 343	98.16 181	98.06 225	95.89 160	99.72 111	95.67 186	99.10 308	99.28 175
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_fmvs3		97.38 154	97.56 139	96.84 222	98.63 206	92.81 250	97.60 103	99.61 19	90.87 414	98.76 96	99.66 6	94.03 243	97.90 490	99.24 11	99.68 105	99.81 10

region2R		97.92 80	97.59 136	98.92 24	99.22 78	97.55 29	97.60 103	98.84 185	96.00 182	97.22 268	97.62 284	96.87 101	99.76 77	95.48 203	99.43 228	99.46 109

HFP-MVS		97.94 76	97.64 127	98.83 28	99.15 96	97.50 33	97.59 105	98.84 185	96.05 177	97.49 249	97.54 290	97.07 75	99.70 136	95.61 193	99.46 212	99.30 167

ACMMPR		97.95 72	97.62 131	98.94 18	99.20 87	97.56 28	97.59 105	98.83 192	96.05 177	97.46 255	97.63 283	96.77 107	99.76 77	95.61 193	99.46 212	99.49 97

RPSCF		97.87 91	97.51 147	98.95 17	99.15 96	98.43 6	97.56 107	99.06 104	96.19 164	98.48 129	98.70 112	94.72 215	99.24 354	94.37 289	99.33 266	99.17 203

KD-MVS_self_test		97.86 93	98.07 69	97.25 182	99.22 78	92.81 250	97.55 108	98.94 152	97.10 109	98.85 82	98.88 88	95.03 207	99.67 161	97.39 91	99.65 114	99.26 181

SR-MVS-dyc-post		98.14 50	97.84 98	99.02 9	98.81 164	98.05 9	97.55 108	98.86 175	97.77 67	98.20 174	98.07 219	96.60 119	99.76 77	95.49 199	99.20 288	99.26 181

RE-MVS-def			97.88 95		98.81 164	98.05 9	97.55 108	98.86 175	97.77 67	98.20 174	98.07 219	96.94 88		95.49 199	99.20 288	99.26 181

APD-MVS_3200maxsize		98.13 54	97.90 90	98.79 32	98.79 170	97.31 40	97.55 108	98.92 156	97.72 72	98.25 169	98.13 208	97.10 71	99.75 85	95.44 208	99.24 286	99.32 161

ACMH+	93.58 10	98.23 45	98.31 49	97.98 110	99.39 50	95.22 152	97.55 108	99.20 60	98.21 54	99.25 42	98.51 140	98.21 18	99.40 286	94.79 269	99.72 91	99.32 161

Vis-MVSNet (Re-imp)		95.11 320	94.85 324	95.87 314	99.12 104	89.17 368	97.54 113	94.92 451	96.50 142	96.58 328	97.27 320	83.64 441	99.48 242	88.42 449	99.67 109	98.97 260

MP-MVS		97.64 121	97.18 175	99.00 12	99.32 62	97.77 20	97.49 114	98.73 222	96.27 153	95.59 394	97.75 268	96.30 141	99.78 58	93.70 325	99.48 206	99.45 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SD_0403		93.73 385	93.43 386	94.64 397	97.85 315	86.35 452	97.47 115	97.94 337	93.50 315	93.71 457	96.73 368	93.77 253	98.84 418	73.48 543	96.39 491	98.72 311

ZNCC-MVS		97.92 80	97.62 131	98.83 28	99.32 62	97.24 43	97.45 116	98.84 185	95.76 201	96.93 300	97.43 302	97.26 64	99.79 53	96.06 158	99.53 177	99.45 113

tttt0517		93.31 403	92.56 415	95.57 335	98.71 188	87.86 418	97.44 117	87.17 542	95.79 200	97.47 254	96.84 359	64.12 524	99.81 43	96.20 153	99.32 268	99.02 248

v10		97.55 135	97.97 81	96.31 279	98.60 210	89.64 358	97.44 117	99.02 123	96.60 133	98.72 101	99.16 50	93.48 261	99.72 111	98.76 35	99.92 15	99.58 52

v8		97.60 126	98.06 72	96.23 284	98.71 188	89.44 363	97.43 119	98.82 200	97.29 102	98.74 98	99.10 57	93.86 249	99.68 151	98.61 41	99.94 8	99.56 68

PMVS	89.60 17	96.71 214	96.97 188	95.95 307	99.51 32	97.81 19	97.42 120	97.49 371	97.93 63	95.95 371	98.58 129	96.88 99	96.91 506	89.59 430	99.36 250	93.12 524
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
usedtu_blend_shiyan5		93.74 383	93.08 395	95.71 326	94.99 502	89.17 368	97.38 121	98.93 154	96.40 147	94.75 422	87.24 538	80.36 465	99.40 286	91.84 370	95.85 503	98.55 333

SR-MVS		98.00 64	97.66 123	99.01 11	98.77 177	97.93 14	97.38 121	98.83 192	97.32 100	98.06 195	97.85 252	96.65 114	99.77 69	95.00 250	99.11 305	99.32 161

Elysia		98.19 47	98.37 40	97.66 134	99.28 64	93.52 226	97.35 123	98.90 158	98.63 32	99.45 25	98.32 171	94.31 235	99.91 13	99.19 14	99.88 28	99.54 74

StellarMVS		98.19 47	98.37 40	97.66 134	99.28 64	93.52 226	97.35 123	98.90 158	98.63 32	99.45 25	98.32 171	94.31 235	99.91 13	99.19 14	99.88 28	99.54 74

FMVSNet5		93.39 398	92.35 419	96.50 253	95.83 469	90.81 320	97.31 125	98.27 298	92.74 354	96.27 352	98.28 185	62.23 526	99.67 161	90.86 396	99.36 250	99.03 245

HY-MVS	91.43 15	92.58 425	91.81 432	94.90 383	96.49 430	88.87 382	97.31 125	94.62 456	85.92 489	90.50 512	96.84 359	85.05 426	99.40 286	83.77 512	95.78 511	96.43 485

CSCG		97.40 152	97.30 162	97.69 132	98.95 135	94.83 168	97.28 127	98.99 140	96.35 152	98.13 186	95.95 423	95.99 155	99.66 169	94.36 291	99.73 86	98.59 328

MTAPA		98.14 50	97.84 98	99.06 6	99.44 42	97.90 15	97.25 128	98.73 222	97.69 75	97.90 219	97.96 238	95.81 169	99.82 38	96.13 157	99.61 135	99.45 113

CPTT-MVS		96.69 215	96.08 257	98.49 57	98.89 150	96.64 62	97.25 128	98.77 212	92.89 350	96.01 369	97.13 334	92.23 302	99.67 161	92.24 361	99.34 261	99.17 203

EU-MVSNet		94.25 363	94.47 348	93.60 445	98.14 287	82.60 504	97.24 130	92.72 490	85.08 499	98.48 129	98.94 78	82.59 450	98.76 428	97.47 87	99.53 177	99.44 123

XXY-MVS		97.54 136	97.70 116	97.07 198	99.46 40	92.21 272	97.22 131	99.00 135	94.93 251	98.58 116	98.92 82	97.31 58	99.41 284	94.44 284	99.43 228	99.59 51

APD_test1		97.95 72	97.68 120	98.75 34	99.60 17	98.60 5	97.21 132	99.08 99	96.57 140	98.07 194	98.38 161	96.22 146	99.14 373	94.71 277	99.31 271	98.52 339

GST-MVS		97.82 98	97.49 151	98.81 30	99.23 75	97.25 42	97.16 133	98.79 206	95.96 185	97.53 245	97.40 304	96.93 90	99.77 69	95.04 244	99.35 256	99.42 128

SteuartSystems-ACMMP		98.02 63	97.76 112	98.79 32	99.43 43	97.21 45	97.15 134	98.90 158	96.58 137	98.08 192	97.87 251	97.02 82	99.76 77	95.25 225	99.59 145	99.40 135
Skip Steuart: Steuart Systems R&D Blog.
FMVSNet2		96.72 212	96.67 212	96.87 218	97.96 304	91.88 288	97.15 134	98.06 333	95.59 211	98.50 126	98.62 122	89.51 355	99.65 173	94.99 256	99.60 142	99.07 236

AllTest		97.20 168	96.92 194	98.06 101	99.08 109	96.16 88	97.14 136	99.16 70	94.35 281	97.78 230	98.07 219	95.84 162	99.12 378	91.41 380	99.42 231	98.91 275

DP-MVS		97.87 91	97.89 93	97.81 120	98.62 208	94.82 169	97.13 137	98.79 206	98.98 23	98.74 98	98.49 141	95.80 170	99.49 239	95.04 244	99.44 218	99.11 226

GeoE		97.75 106	97.70 116	97.89 115	98.88 151	94.53 183	97.10 138	98.98 143	95.75 203	97.62 239	97.59 286	97.61 43	99.77 69	96.34 144	99.44 218	99.36 154

PGM-MVS		97.88 89	97.52 145	98.96 16	99.20 87	97.62 24	97.09 139	99.06 104	95.45 219	97.55 244	97.94 241	97.11 70	99.78 58	94.77 272	99.46 212	99.48 103

LPG-MVS_test		97.94 76	97.67 121	98.74 37	99.15 96	97.02 46	97.09 139	99.02 123	95.15 235	98.34 150	98.23 194	97.91 25	99.70 136	94.41 286	99.73 86	99.50 89

SF-MVS		97.60 126	97.39 154	98.22 84	98.93 142	95.69 112	97.05 141	99.10 90	95.32 228	97.83 227	97.88 248	96.44 131	99.72 111	94.59 283	99.39 241	99.25 188

FE-MVSNET2		97.69 113	97.97 81	96.85 219	99.19 89	91.46 299	97.04 142	99.11 85	95.85 196	98.73 100	99.02 67	96.66 111	99.68 151	96.31 146	99.86 35	99.40 135

reproduce_model		98.54 25	98.33 47	99.15 3	99.06 113	98.04 11	97.04 142	99.09 95	98.42 43	99.03 58	98.71 110	96.93 90	99.83 35	97.09 104	99.63 121	99.56 68

VDD-MVS		97.37 156	97.25 167	97.74 126	98.69 193	94.50 186	97.04 142	95.61 434	98.59 35	98.51 124	98.72 103	92.54 295	99.58 205	96.02 163	99.49 201	99.12 221

wuyk23d		93.25 407	95.20 298	87.40 528	96.07 457	95.38 134	97.04 142	94.97 449	95.33 227	99.70 10	98.11 213	98.14 21	91.94 545	77.76 536	99.68 105	74.89 549

LCM-MVSNet-Re		97.33 159	97.33 160	97.32 175	98.13 290	93.79 215	96.99 146	99.65 13	96.74 128	99.47 24	98.93 79	96.91 94	99.84 33	90.11 420	99.06 315	98.32 365

MAR-MVS		94.21 366	93.03 397	97.76 125	96.94 416	97.44 37	96.97 147	97.15 384	87.89 469	92.00 498	92.73 497	92.14 305	99.12 378	83.92 508	97.51 454	96.73 474
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
test_vis1_n		95.67 284	95.89 273	95.03 372	98.18 278	89.89 349	96.94 148	99.28 47	88.25 463	98.20 174	98.92 82	86.69 407	97.19 500	97.70 78	98.82 348	98.00 407

KinetiMVS		97.82 98	98.02 75	97.24 184	99.24 72	92.32 267	96.92 149	98.38 286	98.56 39	99.03 58	98.33 168	93.22 268	99.83 35	98.74 36	99.71 94	99.57 60

SDMVSNet		97.97 66	98.26 55	97.11 192	99.41 46	92.21 272	96.92 149	98.60 248	98.58 36	98.78 90	99.39 22	97.80 30	99.62 189	94.98 258	99.86 35	99.52 82

h-mvs33		96.29 242	95.63 287	98.26 79	98.50 231	96.11 92	96.90 151	97.09 390	96.58 137	97.21 270	98.19 200	84.14 434	99.78 58	95.89 173	96.17 498	98.89 279

test0726					99.24 72	95.51 124	96.89 152	98.89 162	95.92 190	98.64 108	98.31 173	97.06 76

NormalMVS		96.87 195	96.39 239	98.30 75	99.48 37	95.57 119	96.87 153	98.90 158	96.94 118	96.85 306	97.88 248	85.36 423	99.76 77	95.63 190	99.59 145	99.57 60

SymmetryMVS		96.43 235	95.85 276	98.17 88	98.58 214	95.57 119	96.87 153	95.29 444	96.94 118	96.85 306	97.88 248	85.36 423	99.76 77	95.63 190	99.27 279	99.19 199

baseline		97.44 147	97.78 110	96.43 264	98.52 223	90.75 321	96.84 155	99.03 119	96.51 141	97.86 225	98.02 231	96.67 110	99.36 310	97.09 104	99.47 209	99.19 199

API-MVS		95.09 323	95.01 310	95.31 356	96.61 425	94.02 206	96.83 156	97.18 383	95.60 210	95.79 384	94.33 471	94.54 227	98.37 472	85.70 486	98.52 387	93.52 520

test_vis3_rt		97.04 179	96.98 187	97.23 185	98.44 242	95.88 104	96.82 157	99.67 9	90.30 427	99.27 40	99.33 32	94.04 242	96.03 516	97.14 102	97.83 432	99.78 14

reproduce-ours		98.48 29	98.27 53	99.12 4	98.99 130	98.02 12	96.81 158	99.02 123	98.29 50	98.97 67	98.61 123	97.27 60	99.82 38	96.86 117	99.61 135	99.51 86

our_new_method		98.48 29	98.27 53	99.12 4	98.99 130	98.02 12	96.81 158	99.02 123	98.29 50	98.97 67	98.61 123	97.27 60	99.82 38	96.86 117	99.61 135	99.51 86

test_fmvs1_n		95.21 313	95.28 296	94.99 376	98.15 285	89.13 374	96.81 158	99.43 35	86.97 480	97.21 270	98.92 82	83.00 447	97.13 501	98.09 55	98.94 326	98.72 311

test_fmvs2		96.38 239	96.45 235	96.16 293	97.85 315	91.30 303	96.81 158	99.45 33	89.24 446	98.49 127	99.38 24	88.68 371	97.62 495	98.83 32	99.32 268	99.57 60

SED-MVS		97.94 76	97.90 90	98.07 99	99.22 78	95.35 137	96.79 162	98.83 192	96.11 170	99.08 55	98.24 192	97.87 28	99.72 111	95.44 208	99.51 190	99.14 213

OPU-MVS				97.64 137	98.01 298	95.27 147	96.79 162				97.35 314	96.97 86	98.51 459	91.21 386	99.25 283	99.14 213

BP-MVS1		95.36 304	94.86 322	96.89 216	98.35 254	91.72 292	96.76 164	95.21 445	96.48 145	96.23 355	97.19 326	75.97 492	99.80 50	97.91 64	99.60 142	99.15 207

PHI-MVS		96.96 188	96.53 230	98.25 82	97.48 383	96.50 67	96.76 164	98.85 181	93.52 314	96.19 359	96.85 358	95.94 157	99.42 274	93.79 318	99.43 228	98.83 289

DVP-MVS		97.78 103	97.65 124	98.16 90	99.24 72	95.51 124	96.74 166	98.23 303	95.92 190	98.40 140	98.28 185	97.06 76	99.71 127	95.48 203	99.52 184	99.26 181
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_SECOND				98.25 82	99.23 75	95.49 128	96.74 166	98.89 162					99.75 85	95.48 203	99.52 184	99.53 79

Anonymous202405211		96.34 241	95.98 265	97.43 165	98.25 268	93.85 212	96.74 166	94.41 460	97.72 72	98.37 143	98.03 229	87.15 399	99.53 224	94.06 301	99.07 312	98.92 274

SMA-MVS		97.48 142	97.11 177	98.60 48	98.83 161	96.67 60	96.74 166	98.73 222	91.61 385	98.48 129	98.36 163	96.53 123	99.68 151	95.17 233	99.54 173	99.45 113
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
TranMVSNet+NR-MVSNet		98.33 36	98.30 51	98.43 62	99.07 111	95.87 105	96.73 170	99.05 110	98.67 30	98.84 84	98.45 148	97.58 44	99.88 22	96.45 137	99.86 35	99.54 74

Casviewmamba		97.95 72	98.20 56	97.18 186	98.85 158	92.74 255	96.71 171	99.23 52	98.07 59	98.55 119	98.47 146	97.38 54	99.44 266	96.95 113	99.62 124	99.38 144

test_0402		97.84 94	97.97 81	97.47 161	99.19 89	94.07 203	96.71 171	98.73 222	98.66 31	98.56 118	98.41 155	96.84 103	99.69 144	94.82 266	99.81 60	98.64 320

testing915		94.01 376	93.64 380	95.13 365	98.48 235	88.13 410	96.70 173	93.57 476	95.09 238	95.00 416	96.39 391	77.97 476	99.01 397	90.87 395	98.69 371	98.26 377

test_fmvsmconf0.01_n		98.57 21	98.74 19	98.06 101	99.39 50	94.63 177	96.70 173	99.82 1	95.44 222	99.64 14	99.52 13	98.96 4	99.74 95	99.38 7	99.86 35	99.81 10

SSC-MVS		95.92 266	97.03 185	92.58 485	99.28 64	78.39 528	96.68 175	95.12 447	98.90 25	99.11 52	98.66 116	91.36 319	99.68 151	95.00 250	99.16 297	99.67 36

ACMM	93.33 11	98.05 61	97.79 106	98.85 27	99.15 96	97.55 29	96.68 175	98.83 192	95.21 231	98.36 146	98.13 208	98.13 22	99.62 189	96.04 161	99.54 173	99.39 142
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		93.14 410	92.64 413	94.62 400	97.34 396	87.20 436	96.67 177	93.02 484	94.71 261	96.51 335	95.83 429	81.64 454	98.60 450	90.00 423	88.06 542	98.07 395

SSM_0404		97.47 143	97.75 114	96.64 235	98.81 164	91.26 305	96.57 178	99.16 70	96.95 116	98.44 135	98.09 215	97.05 78	99.72 111	95.21 228	99.44 218	98.95 264

fmvsm_s_conf0.1_n_a		97.80 101	98.01 77	97.18 186	99.17 92	92.51 260	96.57 178	99.15 76	93.68 309	98.89 76	99.30 33	96.42 133	99.37 306	99.03 25	99.83 56	99.66 38

ALIKED-LG		94.42 357	93.57 381	96.97 207	96.80 420	97.51 32	96.56 180	98.87 171	90.23 431	96.16 361	96.93 352	83.76 440	97.07 502	84.00 507	98.80 351	96.33 486

casdiffseed414692147		97.67 118	97.88 95	97.03 203	98.82 163	92.32 267	96.55 181	99.17 68	96.99 111	98.01 202	98.67 115	97.64 39	99.38 299	95.45 207	99.66 112	99.40 135

MTMP							96.55 181	74.60 555

SD-MVS		97.37 156	97.70 116	96.35 274	98.14 287	95.13 159	96.54 183	98.92 156	95.94 188	99.19 46	98.08 217	97.74 33	95.06 524	95.24 226	99.54 173	98.87 285
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
E5new		97.59 129	97.96 87	96.45 258	99.01 125	90.45 332	96.50 184	99.23 52	96.19 164	98.27 161	98.72 103	97.49 46	99.47 248	96.64 123	99.62 124	99.42 128

E6new		97.59 129	97.97 81	96.45 258	99.01 125	90.45 332	96.50 184	99.23 52	96.20 160	98.27 161	98.72 103	97.49 46	99.47 248	96.64 123	99.62 124	99.42 128

E6		97.59 129	97.97 81	96.45 258	99.01 125	90.45 332	96.50 184	99.23 52	96.20 160	98.27 161	98.72 103	97.49 46	99.47 248	96.64 123	99.62 124	99.42 128

E5		97.59 129	97.96 87	96.45 258	99.01 125	90.45 332	96.50 184	99.23 52	96.19 164	98.27 161	98.72 103	97.49 46	99.47 248	96.64 123	99.62 124	99.42 128

HQP_MVS		96.66 217	96.33 244	97.68 133	98.70 190	94.29 195	96.50 184	98.75 218	96.36 150	96.16 361	96.77 365	91.91 314	99.46 255	92.59 354	99.20 288	99.28 175

plane_prior2							96.50 184		96.36 150

RoMa-HiRes		97.28 162	97.05 184	97.98 110	98.78 174	96.22 85	96.48 190	98.47 268	93.69 307	98.97 67	97.73 273	93.48 261	98.47 463	96.31 146	99.51 190	99.26 181

GDP-MVS		95.39 302	94.89 319	96.90 215	98.26 267	91.91 287	96.48 190	99.28 47	95.06 241	96.54 334	97.12 336	74.83 496	99.82 38	97.19 100	99.27 279	98.96 261

hybridcas		97.73 108	98.10 66	96.62 236	98.84 160	91.10 308	96.46 192	99.20 60	97.53 83	98.65 107	98.42 152	97.41 53	99.38 299	96.79 119	99.59 145	99.37 153

Effi-MVS+-dtu		96.81 203	96.09 256	98.99 13	96.90 418	98.69 4	96.42 193	98.09 326	95.86 195	95.15 409	95.54 440	94.26 238	99.81 43	94.06 301	98.51 390	98.47 346

SSM_0407		97.39 153	97.67 121	96.54 250	98.51 225	90.96 313	96.40 194	99.16 70	96.95 116	98.27 161	98.09 215	97.05 78	99.67 161	95.21 228	99.40 237	98.98 256

guyue		96.21 249	96.29 246	95.98 304	98.80 167	89.14 373	96.40 194	94.34 462	95.99 184	98.58 116	98.13 208	87.42 395	99.64 179	97.39 91	99.55 167	99.16 206

MM		96.87 195	96.62 214	97.62 138	97.72 352	93.30 235	96.39 196	92.61 493	97.90 65	96.76 314	98.64 121	90.46 334	99.81 43	99.16 18	99.94 8	99.76 21

thres100view900		91.76 448	91.26 448	93.26 457	98.21 272	84.50 485	96.39 196	90.39 523	96.87 121	96.33 344	93.08 486	73.44 507	99.42 274	78.85 532	97.74 438	95.85 496

XVG-ACMP-BASELINE		97.58 134	97.28 165	98.49 57	99.16 93	96.90 51	96.39 196	98.98 143	95.05 242	98.06 195	98.02 231	95.86 161	99.56 213	94.37 289	99.64 118	99.00 249

Patchmtry		95.03 326	94.59 341	96.33 275	94.83 510	90.82 318	96.38 199	97.20 381	96.59 136	97.49 249	98.57 131	77.67 479	99.38 299	92.95 348	99.62 124	98.80 292

fmvsm_s_conf0.1_n		97.73 108	98.02 75	96.85 219	99.09 108	91.43 302	96.37 200	99.11 85	94.19 287	99.01 61	99.25 36	96.30 141	99.38 299	99.00 26	99.88 28	99.73 28

ACMMP_NAP		97.89 88	97.63 129	98.67 43	99.35 58	96.84 52	96.36 201	98.79 206	95.07 240	97.88 221	98.35 165	97.24 66	99.72 111	96.05 160	99.58 151	99.45 113

VNet		96.84 198	96.83 201	96.88 217	98.06 293	92.02 284	96.35 202	97.57 370	97.70 74	97.88 221	97.80 261	92.40 300	99.54 221	94.73 275	98.96 323	99.08 233

V42		97.04 179	97.16 176	96.68 234	98.59 212	91.05 309	96.33 203	98.36 289	94.60 265	97.99 204	98.30 179	93.32 265	99.62 189	97.40 89	99.53 177	99.38 144

test_fmvsmvis_n_1920		98.08 57	98.47 32	96.93 211	99.03 122	93.29 236	96.32 204	99.65 13	95.59 211	99.71 8	99.01 68	97.66 38	99.60 200	99.44 5	99.83 56	97.90 413

APD-MVS		97.00 181	96.53 230	98.41 64	98.55 219	96.31 80	96.32 204	98.77 212	92.96 348	97.44 257	97.58 288	95.84 162	99.74 95	91.96 365	99.35 256	99.19 199
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_s_conf0.5_n_11		97.90 86	98.34 45	96.60 240	98.75 179	90.50 330	96.28 206	99.56 24	97.05 110	99.15 49	99.11 55	96.31 138	99.69 144	98.97 29	99.84 51	99.62 45

AstraMVS		96.41 237	96.48 234	96.20 287	98.91 147	89.69 355	96.28 206	93.29 480	96.11 170	98.70 103	98.36 163	89.41 359	99.66 169	97.60 81	99.63 121	99.26 181

VPNet		97.26 164	97.49 151	96.59 242	99.47 39	90.58 323	96.27 208	98.53 259	97.77 67	98.46 132	98.41 155	94.59 223	99.68 151	94.61 279	99.29 276	99.52 82

thres600view7		92.03 443	91.43 441	93.82 436	98.19 275	84.61 484	96.27 208	90.39 523	96.81 124	96.37 343	93.11 482	73.44 507	99.49 239	80.32 526	97.95 423	97.36 447

EPNet		93.72 386	92.62 414	97.03 203	87.61 554	92.25 270	96.27 208	91.28 511	96.74 128	87.65 537	97.39 309	85.00 427	99.64 179	92.14 363	99.48 206	99.20 198
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DSMNet-mixed		92.19 437	91.83 431	93.25 458	96.18 448	83.68 497	96.27 208	93.68 472	76.97 544	92.54 494	99.18 46	89.20 364	98.55 454	83.88 509	98.60 383	97.51 442

IMVS_0407		96.35 240	96.88 199	94.74 394	97.83 325	86.11 456	96.25 212	98.82 200	94.48 272	97.57 242	97.14 330	96.08 152	99.33 319	95.00 250	98.78 354	98.78 295

fmvsm_s_conf0.5_n_a		97.65 120	97.83 101	97.13 191	98.80 167	92.51 260	96.25 212	99.06 104	93.67 310	98.64 108	99.00 69	96.23 145	99.36 310	98.99 27	99.80 64	99.53 79

ACMP	92.54 13	97.47 143	97.10 178	98.55 52	99.04 121	96.70 58	96.24 214	98.89 162	93.71 305	97.97 211	97.75 268	97.44 50	99.63 184	93.22 341	99.70 98	99.32 161
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS	95.41 4	97.82 98	97.70 116	98.16 90	98.78 174	95.72 110	96.23 215	99.02 123	93.92 301	98.62 110	98.99 71	97.69 34	99.62 189	96.18 155	99.87 33	99.15 207
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PM-MVS		97.36 158	97.10 178	98.14 94	98.91 147	96.77 54	96.20 216	98.63 246	93.82 302	98.54 120	98.33 168	93.98 245	99.05 390	95.99 166	99.45 215	98.61 327

test_fmvsmconf0.1_n		98.41 34	98.54 30	98.03 106	99.16 93	94.61 178	96.18 217	99.73 5	95.05 242	99.60 18	99.34 30	98.68 8	99.72 111	99.21 12	99.85 48	99.76 21

MVS_Test		96.27 244	96.79 206	94.73 395	96.94 416	86.63 447	96.18 217	98.33 293	94.94 249	96.07 365	98.28 185	95.25 197	99.26 347	97.21 97	97.90 428	98.30 370

CR-MVSNet		93.29 406	92.79 406	94.78 391	95.44 487	88.15 408	96.18 217	97.20 381	84.94 504	94.10 443	98.57 131	77.67 479	99.39 295	95.17 233	95.81 507	96.81 471

RPMNet		94.68 343	94.60 339	94.90 383	95.44 487	88.15 408	96.18 217	98.86 175	97.43 88	94.10 443	98.49 141	79.40 470	99.76 77	95.69 184	95.81 507	96.81 471

LuminaMVS		96.76 207	96.58 220	97.30 176	98.94 138	92.96 245	96.17 221	96.15 418	95.54 215	98.96 70	98.18 203	87.73 389	99.80 50	97.98 61	99.61 135	99.15 207

test_fmvsm_n_1920		98.08 57	98.29 52	97.43 165	98.88 151	93.95 209	96.17 221	99.57 22	95.66 206	99.52 21	98.71 110	97.04 80	99.64 179	99.21 12	99.87 33	98.69 316

fmvsm_s_conf0.5_n		97.62 124	97.89 93	96.80 225	98.79 170	91.44 301	96.14 223	99.06 104	94.19 287	98.82 87	98.98 72	96.22 146	99.38 299	98.98 28	99.86 35	99.58 52

WB-MVS		95.50 293	96.62 214	92.11 496	99.21 85	77.26 538	96.12 224	95.40 441	98.62 34	98.84 84	98.26 190	91.08 322	99.50 233	93.37 333	98.70 370	99.58 52

EIA-MVS		96.04 258	95.77 281	96.85 219	97.80 335	92.98 244	96.12 224	99.16 70	94.65 263	93.77 454	91.69 510	95.68 174	99.67 161	94.18 296	98.85 342	97.91 412

Effi-MVS+		96.19 251	96.01 261	96.71 231	97.43 389	92.19 276	96.12 224	99.10 90	95.45 219	93.33 473	94.71 463	97.23 67	99.56 213	93.21 342	97.54 452	98.37 357

FE-MVSNET		96.59 220	96.65 213	96.41 269	98.94 138	90.51 329	96.07 227	99.05 110	92.94 349	98.03 199	98.00 235	93.08 274	99.42 274	94.04 304	99.74 85	99.30 167

alignmvs		96.01 261	95.52 291	97.50 154	97.77 344	94.71 171	96.07 227	96.84 402	97.48 86	96.78 313	94.28 472	85.50 422	99.40 286	96.22 152	98.73 367	98.40 352

fmvsm_l_conf0.5_n_3		98.29 41	98.46 33	97.79 121	98.90 149	94.05 205	96.06 229	99.63 17	96.07 175	99.37 33	98.93 79	98.29 16	99.68 151	99.11 22	99.79 66	99.65 41

VortexMVS		96.04 258	96.56 223	94.49 410	97.60 371	84.36 488	96.05 230	98.67 237	94.74 256	98.95 71	98.78 95	87.13 400	99.50 233	97.37 93	99.76 73	99.60 47

PatchT		93.75 382	93.57 381	94.29 422	95.05 500	87.32 434	96.05 230	92.98 485	97.54 82	94.25 435	98.72 103	75.79 493	99.24 354	95.92 171	95.81 507	96.32 487

aaEdge-Enhanced		97.53 139	97.32 161	98.16 90	98.70 190	95.35 137	96.04 232	98.60 248	96.16 169	97.99 204	97.54 290	95.94 157	99.70 136	95.36 217	99.53 177	99.44 123

Patchmatch-test		93.60 393	93.25 390	94.63 399	96.14 453	87.47 428	96.04 232	94.50 458	93.57 311	96.47 338	96.97 349	76.50 487	98.61 448	90.67 409	98.41 400	97.81 421

thisisatest0530		92.71 421	91.76 436	95.56 340	98.42 247	88.23 403	96.03 234	87.35 541	94.04 295	96.56 331	95.47 444	64.03 525	99.77 69	94.78 271	99.11 305	98.68 319

9.14			96.69 210		98.53 222		96.02 235	98.98 143	93.23 326	97.18 274	97.46 299	96.47 128	99.62 189	92.99 346	99.32 268

DeepC-MVS_fast	94.34 7	96.74 208	96.51 232	97.44 164	97.69 356	94.15 201	96.02 235	98.43 276	93.17 336	97.30 262	97.38 311	95.48 184	99.28 342	93.74 320	99.34 261	98.88 283
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IMVS_0403		96.27 244	96.77 207	94.76 392	97.83 325	86.11 456	96.00 237	98.82 200	94.48 272	97.49 249	97.14 330	95.38 189	99.40 286	95.00 250	98.78 354	98.78 295

ttmdpeth		94.05 373	94.15 364	93.75 440	95.81 471	85.32 468	96.00 237	94.93 450	92.07 371	94.19 438	99.09 59	85.73 418	96.41 513	90.98 390	98.52 387	99.53 79

test_fmvsmconf_n		98.30 40	98.41 39	97.99 109	98.94 138	94.60 179	96.00 237	99.64 16	94.99 247	99.43 28	99.18 46	98.51 12	99.71 127	99.13 20	99.84 51	99.67 36

fmvsm_s_conf0.5_n_10		97.74 107	98.11 63	96.62 236	98.72 184	90.95 316	95.99 240	99.50 30	96.22 159	99.20 45	98.93 79	95.13 204	99.77 69	99.49 3	99.76 73	99.15 207

114514_t		93.96 377	93.22 391	96.19 289	99.06 113	90.97 312	95.99 240	98.94 152	73.88 547	93.43 470	96.93 352	92.38 301	99.37 306	89.09 437	99.28 277	98.25 378

FMVSNet3		95.26 312	94.94 314	96.22 286	96.53 428	90.06 343	95.99 240	97.66 359	94.11 291	97.99 204	97.91 246	80.22 469	99.63 184	94.60 280	99.44 218	98.96 261

HPM-MVS++		96.99 182	96.38 241	98.81 30	98.64 197	97.59 26	95.97 243	98.20 307	95.51 216	95.06 412	96.53 380	94.10 241	99.70 136	94.29 292	99.15 298	99.13 215

casdiffmvs_mvg		97.83 95	98.11 63	97.00 206	98.57 216	92.10 280	95.97 243	99.18 65	97.67 78	99.00 63	98.48 145	97.64 39	99.50 233	96.96 112	99.54 173	99.40 135
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testgi		96.07 255	96.50 233	94.80 389	99.26 68	87.69 425	95.96 245	98.58 254	95.08 239	98.02 201	96.25 401	97.92 24	97.60 496	88.68 445	98.74 364	99.11 226

EG-PatchMatch MVS		97.69 113	97.79 106	97.40 169	99.06 113	93.52 226	95.96 245	98.97 146	94.55 269	98.82 87	98.76 100	97.31 58	99.29 337	97.20 99	99.44 218	99.38 144

fmvsm_s_conf0.5_n_3		97.88 89	98.37 40	96.41 269	98.73 181	89.82 351	95.94 247	99.49 31	96.81 124	99.09 54	99.03 66	97.09 73	99.65 173	99.37 8	99.76 73	99.76 21

PAPM_NR		94.61 348	94.17 363	95.96 305	98.36 253	91.23 306	95.93 248	97.95 336	92.98 344	93.42 471	94.43 470	90.53 332	98.38 470	87.60 461	96.29 495	98.27 374

UniMVSNet (Re)		97.83 95	97.65 124	98.35 70	98.80 167	95.86 106	95.92 249	99.04 118	97.51 84	98.22 173	97.81 260	94.68 219	99.78 58	97.14 102	99.75 83	99.41 134

test_vis1_n_1920		95.77 274	96.41 238	93.85 435	98.55 219	84.86 480	95.91 250	99.71 7	92.72 356	97.67 236	98.90 86	87.44 394	98.73 430	97.96 62	98.85 342	97.96 409

RoMa-SfM		96.87 195	96.56 223	97.79 121	98.50 231	96.46 71	95.89 251	98.45 271	91.48 396	98.84 84	97.40 304	93.93 248	97.96 487	94.99 256	99.58 151	98.96 261

fmvsm_l_conf0.5_n		97.68 116	97.81 104	97.27 179	98.92 144	92.71 257	95.89 251	99.41 39	93.36 320	99.00 63	98.44 150	96.46 130	99.65 173	99.09 23	99.76 73	99.45 113

fmvsm_s_conf0.5_n_9		97.98 65	98.32 48	96.96 208	98.92 144	91.45 300	95.87 253	99.53 28	97.44 87	99.56 19	99.05 63	95.34 191	99.67 161	99.52 2	99.70 98	99.77 15

PRO-TEST		95.35 306	95.48 292	94.95 380	96.49 430	87.11 439	95.86 254	98.74 220	93.21 329	95.07 410	95.57 439	93.10 273	99.51 231	92.89 351	98.37 401	98.24 379

1314		92.38 430	92.30 420	92.64 484	95.42 489	85.15 473	95.86 254	96.97 398	85.40 497	90.62 509	93.06 487	91.12 321	97.80 493	86.74 473	95.49 516	94.97 509

MVS		90.02 468	89.20 475	92.47 488	94.71 511	86.90 443	95.86 254	96.74 408	64.72 549	90.62 509	92.77 495	92.54 295	98.39 469	79.30 529	95.56 515	92.12 525

fmvsm_l_conf0.5_n_a		97.60 126	97.76 112	97.11 192	98.92 144	92.28 269	95.83 257	99.32 41	93.22 327	98.91 75	98.49 141	96.31 138	99.64 179	99.07 24	99.76 73	99.40 135

casdiffmvs		97.50 140	97.81 104	96.56 247	98.51 225	91.04 310	95.83 257	99.09 95	97.23 105	98.33 153	98.30 179	97.03 81	99.37 306	96.58 131	99.38 243	99.28 175
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVStest1		91.89 445	91.45 440	93.21 462	89.01 548	84.87 479	95.82 259	95.05 448	91.50 394	98.75 97	99.19 42	57.56 532	95.11 522	97.78 72	98.37 401	99.64 44

tpmvs		90.79 462	90.87 454	90.57 511	92.75 538	76.30 540	95.79 260	93.64 474	91.04 410	91.91 499	96.26 399	77.19 485	98.86 417	89.38 434	89.85 539	96.56 479

fmvsm_s_conf0.5_n_6		97.45 145	97.79 106	96.44 262	98.58 214	90.31 338	95.77 261	99.33 40	94.52 270	98.85 82	98.44 150	95.68 174	99.62 189	99.15 19	99.81 60	99.38 144

fmvsm_s_conf0.5_n_8		97.66 119	98.12 61	96.27 281	98.79 170	89.43 364	95.76 262	99.42 36	97.49 85	99.16 48	99.04 64	94.56 226	99.69 144	99.18 16	99.73 86	99.70 33

mvsany_test3		96.21 249	95.93 270	97.05 199	97.40 391	94.33 193	95.76 262	94.20 464	89.10 447	99.36 35	99.60 11	93.97 246	97.85 491	95.40 215	98.63 379	98.99 253

MSLP-MVS++		96.42 236	96.71 209	95.57 335	97.82 329	90.56 325	95.71 264	98.84 185	94.72 260	96.71 317	97.39 309	94.91 213	98.10 484	95.28 223	99.02 317	98.05 402

tfpn200view9		91.55 450	91.00 450	93.21 462	98.02 296	84.35 489	95.70 265	90.79 517	96.26 154	95.90 377	92.13 505	73.62 504	99.42 274	78.85 532	97.74 438	95.85 496

Anonymous20231206		95.27 311	95.06 308	95.88 313	98.72 184	89.37 365	95.70 265	97.85 345	88.00 467	96.98 297	97.62 284	91.95 311	99.34 317	89.21 435	99.53 177	98.94 267

thres400		91.68 449	91.00 450	93.71 442	98.02 296	84.35 489	95.70 265	90.79 517	96.26 154	95.90 377	92.13 505	73.62 504	99.42 274	78.85 532	97.74 438	97.36 447

reproduce_monomvs		92.05 442	92.26 421	91.43 502	95.42 489	75.72 543	95.68 268	97.05 393	94.47 276	97.95 214	98.35 165	55.58 543	99.05 390	96.36 142	99.44 218	99.51 86

test20.03		96.58 223	96.61 216	96.48 256	98.49 233	91.72 292	95.68 268	97.69 356	96.81 124	98.27 161	97.92 244	94.18 240	98.71 435	90.78 400	99.66 112	99.00 249

hse-mvs2		95.77 274	95.09 305	97.79 121	97.84 322	95.51 124	95.66 270	95.43 440	96.58 137	97.21 270	96.16 406	84.14 434	99.54 221	95.89 173	96.92 469	98.32 365

UniMVSNet_NR-MVSNet		97.83 95	97.65 124	98.37 67	98.72 184	95.78 108	95.66 270	99.02 123	98.11 57	98.31 156	97.69 277	94.65 221	99.85 30	97.02 110	99.71 94	99.48 103

fmvsm_s_conf0.5_n_4		97.43 149	97.77 111	96.39 273	98.48 235	89.89 349	95.65 272	99.26 49	94.73 259	98.72 101	98.58 129	95.58 180	99.57 211	99.28 9	99.67 109	99.73 28

dmvs_re		92.08 441	91.27 446	94.51 408	97.16 406	92.79 253	95.65 272	92.64 492	94.11 291	92.74 487	90.98 518	83.41 444	94.44 533	80.72 525	94.07 527	96.29 488

DU-MVS		97.79 102	97.60 135	98.36 69	98.73 181	95.78 108	95.65 272	98.87 171	97.57 79	98.31 156	97.83 255	94.69 217	99.85 30	97.02 110	99.71 94	99.46 109

EPMVS		89.26 482	88.55 482	91.39 504	92.36 540	79.11 526	95.65 272	79.86 551	88.60 457	93.12 476	96.53 380	70.73 515	98.10 484	90.75 402	89.32 540	96.98 460

MVP-Stereo		95.69 281	95.28 296	96.92 212	98.15 285	93.03 243	95.64 276	98.20 307	90.39 424	96.63 325	97.73 273	91.63 316	99.10 385	91.84 370	97.31 463	98.63 322
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_5		97.63 123	97.83 101	97.04 201	98.77 177	92.33 265	95.63 277	99.58 20	93.53 313	99.10 53	98.66 116	96.44 131	99.65 173	99.12 21	99.68 105	99.12 221

test_cas_vis1_n_1920		95.34 307	95.67 284	94.35 418	98.21 272	86.83 445	95.61 278	99.26 49	90.45 421	98.17 180	98.96 75	84.43 433	98.31 475	96.74 120	99.17 296	97.90 413

test_f		95.82 272	95.88 274	95.66 330	97.61 369	93.21 241	95.61 278	98.17 314	86.98 479	98.42 137	99.47 17	90.46 334	94.74 528	97.71 76	98.45 396	99.03 245

F-COLMAP		95.30 310	94.38 353	98.05 105	98.64 197	96.04 96	95.61 278	98.66 240	89.00 450	93.22 474	96.40 389	92.90 281	99.35 314	87.45 467	97.53 453	98.77 304

AUN-MVS		93.95 379	92.69 411	97.74 126	97.80 335	95.38 134	95.57 281	95.46 439	91.26 404	92.64 491	96.10 413	74.67 497	99.55 218	93.72 324	96.97 468	98.30 370

ALIKED-MNN		93.09 413	92.12 426	96.00 301	96.50 429	96.72 56	95.52 282	98.20 307	82.37 519	90.90 507	96.15 407	87.02 402	96.30 514	83.03 516	99.42 231	94.99 508

fmvsm_s_conf0.1_n_2		97.68 116	98.18 57	96.20 287	99.06 113	89.08 376	95.51 283	99.72 6	96.06 176	99.48 22	99.24 37	95.18 200	99.60 200	99.45 4	99.88 28	99.94 3

v144192		96.69 215	96.90 197	96.03 300	98.25 268	88.92 380	95.49 284	98.77 212	93.05 341	98.09 190	98.29 183	92.51 298	99.70 136	98.11 53	99.56 160	99.47 107

Fast-Effi-MVS+-dtu		96.44 233	96.12 254	97.39 170	97.18 405	94.39 188	95.46 285	98.73 222	96.03 181	94.72 425	94.92 459	96.28 144	99.69 144	93.81 317	97.98 420	98.09 392

viewmacassd2359aftdt		97.25 165	97.52 145	96.43 264	98.83 161	90.49 331	95.45 286	99.18 65	95.44 222	97.98 209	98.47 146	96.90 96	99.37 306	95.93 170	99.55 167	99.43 126

Baseline_NR-MVSNet		97.72 111	97.79 106	97.50 154	99.56 22	93.29 236	95.44 287	98.86 175	98.20 55	98.37 143	99.24 37	94.69 217	99.55 218	95.98 167	99.79 66	99.65 41

LF4IMVS		96.07 255	95.63 287	97.36 172	98.19 275	95.55 121	95.44 287	98.82 200	92.29 366	95.70 390	96.55 378	92.63 289	98.69 438	91.75 376	99.33 266	97.85 417

v1921920		96.72 212	96.96 190	95.99 302	98.21 272	88.79 386	95.42 289	98.79 206	93.22 327	98.19 178	98.26 190	92.68 286	99.70 136	98.34 50	99.55 167	99.49 97

plane_prior						94.29 195	95.42 289		94.31 283						98.93 331

v1144		96.84 198	97.08 180	96.13 295	98.42 247	89.28 367	95.41 291	98.67 237	94.21 285	97.97 211	98.31 173	93.06 275	99.65 173	98.06 58	99.62 124	99.45 113

ETV-MVS		96.13 254	95.90 272	96.82 223	97.76 345	93.89 210	95.40 292	98.95 149	95.87 194	95.58 395	91.00 517	96.36 137	99.72 111	93.36 334	98.83 346	96.85 467

fmvsm_l_conf0.5_n_9		97.92 80	98.37 40	96.57 245	98.94 138	90.54 326	95.39 293	99.58 20	96.82 123	99.56 19	98.77 96	97.23 67	99.61 197	99.17 17	99.86 35	99.57 60

v1240		96.74 208	97.02 186	95.91 310	98.18 278	88.52 392	95.39 293	98.88 169	93.15 338	98.46 132	98.40 160	92.80 283	99.71 127	98.45 46	99.49 201	99.49 97

MP-MVS-pluss		97.69 113	97.36 158	98.70 41	99.50 35	96.84 52	95.38 295	98.99 140	92.45 361	98.11 187	98.31 173	97.25 65	99.77 69	96.60 129	99.62 124	99.48 103
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MGCNet		95.71 280	95.18 300	97.33 174	94.85 508	92.82 248	95.36 296	90.89 516	95.51 216	95.61 393	97.82 258	88.39 375	99.78 58	98.23 51	99.91 19	99.40 135

v1192		96.83 201	97.06 182	96.15 294	98.28 262	89.29 366	95.36 296	98.77 212	93.73 304	98.11 187	98.34 167	93.02 280	99.67 161	98.35 49	99.58 151	99.50 89

v2v482		96.78 205	97.06 182	95.95 307	98.57 216	88.77 387	95.36 296	98.26 299	95.18 234	97.85 226	98.23 194	92.58 290	99.63 184	97.80 70	99.69 100	99.45 113

test_fmvs1		94.51 355	94.60 339	94.26 423	95.91 463	87.92 415	95.35 299	99.02 123	86.56 484	96.79 309	98.52 137	82.64 449	97.00 505	97.87 66	98.71 368	97.88 415

EI-MVSNet-Vis-set		97.32 160	97.39 154	97.11 192	97.36 393	92.08 281	95.34 300	97.65 361	97.74 70	98.29 159	98.11 213	95.05 205	99.68 151	97.50 85	99.50 198	99.56 68

fmvsm_s_conf0.5_n_2		97.59 129	98.07 69	96.17 291	98.78 174	89.10 375	95.33 301	99.55 26	95.96 185	99.41 31	99.10 57	95.18 200	99.59 202	99.43 6	99.86 35	99.81 10

EI-MVSNet-UG-set		97.32 160	97.40 153	97.09 196	97.34 396	92.01 285	95.33 301	97.65 361	97.74 70	98.30 158	98.14 206	95.04 206	99.69 144	97.55 83	99.52 184	99.58 52

CostFormer		89.75 475	89.25 471	91.26 506	94.69 512	78.00 532	95.32 303	91.98 502	81.50 524	90.55 511	96.96 351	71.06 513	98.89 411	88.59 446	92.63 532	96.87 465

viewdifsd2359ckpt13		96.47 230	96.42 237	96.61 239	98.35 254	91.50 297	95.31 304	98.84 185	93.21 329	96.73 315	97.58 288	95.28 196	99.26 347	94.02 306	98.45 396	99.07 236

PVSNet_Blended_VisFu		95.95 264	95.80 279	96.42 266	99.28 64	90.62 322	95.31 304	99.08 99	88.40 460	96.97 298	98.17 205	92.11 306	99.78 58	93.64 326	99.21 287	98.86 286

UnsupCasMVSNet_eth		95.91 267	95.73 282	96.44 262	98.48 235	91.52 296	95.31 304	98.45 271	95.76 201	97.48 252	97.54 290	89.53 354	98.69 438	94.43 285	94.61 523	99.13 215

EI-MVSNet		96.63 218	96.93 192	95.74 320	97.26 401	88.13 410	95.29 307	97.65 361	96.99 111	97.94 216	98.19 200	92.55 293	99.58 205	96.91 114	99.56 160	99.50 89

CVMVSNet		92.33 432	92.79 406	90.95 507	97.26 401	75.84 542	95.29 307	92.33 497	81.86 521	96.27 352	98.19 200	81.44 457	98.46 465	94.23 295	98.29 406	98.55 333

E4		97.28 162	97.55 142	96.46 257	98.86 156	90.53 328	95.28 309	99.18 65	95.82 199	98.01 202	98.59 128	96.78 106	99.46 255	95.86 177	99.56 160	99.38 144

viewdifsd2359ckpt09		96.23 248	96.04 259	96.82 223	98.29 259	92.06 283	95.25 310	99.03 119	91.51 393	96.19 359	97.01 347	94.41 230	99.40 286	93.76 319	98.90 334	99.00 249

OPM-MVS		97.54 136	97.25 167	98.41 64	99.11 105	96.61 64	95.24 311	98.46 270	94.58 268	98.10 189	98.07 219	97.09 73	99.39 295	95.16 235	99.44 218	99.21 195
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TAPA-MVS	93.32 12	94.93 328	94.23 358	97.04 201	98.18 278	94.51 184	95.22 312	98.73 222	81.22 526	96.25 354	95.95 423	93.80 252	98.98 402	89.89 425	98.87 339	97.62 436
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DPE-MVS		97.64 121	97.35 159	98.50 56	98.85 158	96.18 87	95.21 313	98.99 140	95.84 197	98.78 90	98.08 217	96.84 103	99.81 43	93.98 308	99.57 155	99.52 82
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mamba_0408		97.17 170	97.38 156	96.55 249	98.51 225	90.96 313	95.19 314	99.06 104	96.60 133	98.27 161	97.78 263	96.58 120	99.72 111	95.04 244	99.40 237	98.98 256

SSM_04072		97.14 171	97.38 156	96.42 266	98.51 225	90.96 313	95.19 314	99.06 104	96.60 133	98.27 161	97.78 263	96.58 120	99.31 329	95.04 244	99.40 237	98.98 256

MVSTER		94.21 366	93.93 372	95.05 371	95.83 469	86.46 448	95.18 316	97.65 361	92.41 364	97.94 216	98.00 235	72.39 509	99.58 205	96.36 142	99.56 160	99.12 221

testing3-2		90.09 467	90.38 464	89.24 518	98.07 292	69.88 555	95.12 317	90.71 521	96.65 130	93.60 464	94.03 474	55.81 542	99.33 319	90.69 408	98.71 368	98.51 340

PatchmatchNet		91.98 444	91.87 430	92.30 492	94.60 514	79.71 523	95.12 317	93.59 475	89.52 440	93.61 462	97.02 343	77.94 477	99.18 365	90.84 397	94.57 525	98.01 406
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
viewmanbaseed2359cas		96.77 206	96.94 191	96.27 281	98.41 249	90.24 339	95.11 319	99.03 119	94.28 284	97.45 256	97.85 252	95.92 159	99.32 327	95.18 232	99.19 292	99.24 189

IterMVS-LS		96.92 190	97.29 163	95.79 316	98.51 225	88.13 410	95.10 320	98.66 240	96.99 111	98.46 132	98.68 114	92.55 293	99.74 95	96.91 114	99.79 66	99.50 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		96.58 223	96.97 188	95.42 348	98.63 206	87.57 426	95.09 321	97.90 341	95.91 192	98.24 170	97.96 238	93.42 263	99.39 295	96.04 161	99.52 184	99.29 174

tpm2		88.47 491	87.69 494	90.79 509	94.98 505	77.34 536	95.09 321	91.83 503	77.51 543	89.40 526	96.41 387	67.83 521	98.73 430	83.58 514	92.60 533	96.29 488

OpenMVS_ROB	91.80 14	93.64 392	93.05 396	95.42 348	97.31 400	91.21 307	95.08 323	96.68 411	81.56 523	96.88 305	96.41 387	90.44 336	99.25 350	85.39 492	97.67 445	95.80 498

E2		96.97 186	97.19 173	96.33 275	98.64 197	90.34 336	95.07 324	99.12 82	95.00 245	97.66 237	98.31 173	96.19 148	99.43 270	95.35 220	99.35 256	99.23 191

E3		96.97 186	97.19 173	96.33 275	98.64 197	90.34 336	95.07 324	99.12 82	95.00 245	97.66 237	98.31 173	96.19 148	99.43 270	95.35 220	99.35 256	99.23 191

TAMVS		95.49 294	94.94 314	97.16 188	98.31 257	93.41 233	95.07 324	96.82 404	91.09 408	97.51 247	97.82 258	89.96 345	99.42 274	88.42 449	99.44 218	98.64 320

tpmrst		90.31 465	90.61 461	89.41 517	94.06 524	72.37 552	95.06 327	93.69 470	88.01 466	92.32 496	96.86 357	77.45 481	98.82 420	91.04 388	87.01 543	97.04 459

ADS-MVSNet2		91.47 452	90.51 462	94.36 415	95.51 485	85.63 462	95.05 328	95.70 429	83.46 513	92.69 488	96.84 359	79.15 472	99.41 284	85.66 488	90.52 536	98.04 403

ADS-MVSNet		90.95 460	90.26 465	93.04 468	95.51 485	82.37 505	95.05 328	93.41 477	83.46 513	92.69 488	96.84 359	79.15 472	98.70 436	85.66 488	90.52 536	98.04 403

tpm		91.08 458	90.85 455	91.75 499	95.33 492	78.09 530	95.03 330	91.27 512	88.75 453	93.53 466	97.40 304	71.24 511	99.30 333	91.25 385	93.87 528	97.87 416

NCCC		96.52 225	95.99 263	98.10 97	97.81 331	95.68 113	95.00 331	98.20 307	95.39 225	95.40 404	96.36 392	93.81 251	99.45 263	93.55 330	98.42 399	99.17 203

test_post1							94.98 332				10.37 560	76.21 490	99.04 393	89.47 432

FBQ-MVS		89.51 480	87.89 490	94.36 415	96.47 433	87.19 437	94.96 333	92.96 486	91.01 413	90.38 514	88.46 532	57.42 534	98.55 454	83.35 515	96.03 501	97.35 449

viewcassd2359sk11		96.73 210	96.89 198	96.24 283	98.46 240	90.20 341	94.94 334	99.07 103	94.43 278	97.33 261	98.05 228	95.69 173	99.40 286	94.98 258	99.11 305	99.12 221

fmvsm_s_conf0.5_n_7		97.13 172	97.50 149	96.04 299	98.43 245	89.03 379	94.92 335	99.00 135	94.51 271	98.42 137	98.96 75	94.97 211	99.54 221	98.42 47	99.85 48	99.56 68

AdaColmap		95.11 320	94.62 338	96.58 243	97.33 398	94.45 187	94.92 335	98.08 328	93.15 338	93.98 450	95.53 442	94.34 234	99.10 385	85.69 487	98.61 381	96.20 490

MDTV_nov1_ep13_2view						57.28 559	94.89 337		80.59 528	94.02 448		78.66 474		85.50 490		97.82 419

CNVR-MVS		96.92 190	96.55 227	98.03 106	98.00 302	95.54 122	94.87 338	98.17 314	94.60 265	96.38 342	97.05 341	95.67 176	99.36 310	95.12 241	99.08 310	99.19 199

OMC-MVS		96.48 229	96.00 262	97.91 114	98.30 258	96.01 101	94.86 339	98.60 248	91.88 377	97.18 274	97.21 325	96.11 151	99.04 393	90.49 415	99.34 261	98.69 316

E3new		96.50 226	96.61 216	96.17 291	98.28 262	90.09 342	94.85 340	99.02 123	93.95 300	97.01 292	97.74 271	95.19 199	99.39 295	94.70 278	98.77 361	99.04 243

viewdifsd2359ckpt11		97.13 172	97.62 131	95.67 328	98.64 197	88.36 398	94.84 341	98.95 149	96.24 156	98.70 103	98.61 123	96.66 111	99.29 337	96.46 135	99.45 215	99.36 154

viewmsd2359difaftdt		97.13 172	97.62 131	95.67 328	98.64 197	88.36 398	94.84 341	98.95 149	96.24 156	98.70 103	98.61 123	96.66 111	99.29 337	96.46 135	99.45 215	99.36 154

testing3		89.72 476	88.26 486	94.10 427	97.66 362	84.30 491	94.80 343	88.25 536	94.66 262	95.07 410	92.51 500	41.15 557	99.43 270	91.81 373	98.44 398	98.55 333

EPNet_dtu		91.39 454	90.75 457	93.31 456	90.48 546	82.61 503	94.80 343	92.88 487	93.39 319	81.74 547	94.90 460	81.36 458	99.11 381	88.28 451	98.87 339	98.21 383
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			91.28 445		94.31 517	73.51 550	94.80 343	93.16 481	86.75 483	93.45 469	97.40 304	76.37 488	98.55 454	88.85 440	96.43 489

LoFTR		95.39 302	95.01 310	96.52 251	97.16 406	95.19 155	94.77 346	96.95 400	90.31 426	98.78 90	98.29 183	86.71 406	97.91 489	92.56 356	99.57 155	96.46 484

pmmvs-eth3d		96.49 228	96.18 253	97.42 167	98.25 268	94.29 195	94.77 346	98.07 332	89.81 437	97.97 211	98.33 168	93.11 272	99.08 387	95.46 206	99.84 51	98.89 279

test_yl		94.40 358	94.00 368	95.59 333	96.95 414	89.52 360	94.75 348	95.55 437	96.18 167	96.79 309	96.14 410	81.09 460	99.18 365	90.75 402	97.77 434	98.07 395

DCV-MVSNet		94.40 358	94.00 368	95.59 333	96.95 414	89.52 360	94.75 348	95.55 437	96.18 167	96.79 309	96.14 410	81.09 460	99.18 365	90.75 402	97.77 434	98.07 395

dmvs_testset		87.30 503	86.99 499	88.24 524	96.71 422	77.48 535	94.68 350	86.81 544	92.64 357	89.61 525	87.01 541	85.91 416	93.12 542	61.04 550	88.49 541	94.13 517

MCST-MVS		96.24 247	95.80 279	97.56 142	98.75 179	94.13 202	94.66 351	98.17 314	90.17 433	96.21 357	96.10 413	95.14 203	99.43 270	94.13 299	98.85 342	99.13 215

XVG-OURS-SEG-HR		97.38 154	97.07 181	98.30 75	99.01 125	97.41 38	94.66 351	99.02 123	95.20 232	98.15 183	97.52 294	98.83 5	98.43 466	94.87 262	96.41 490	99.07 236

mvs_anonymous		95.36 304	96.07 258	93.21 462	96.29 440	81.56 511	94.60 353	97.66 359	93.30 324	96.95 299	98.91 85	93.03 279	99.38 299	96.60 129	97.30 464	98.69 316

DP-MVS Recon		95.55 292	95.13 303	96.80 225	98.51 225	93.99 208	94.60 353	98.69 232	90.20 432	95.78 386	96.21 403	92.73 285	98.98 402	90.58 411	98.86 341	97.42 446

ELoFTR		95.12 319	94.86 322	95.91 310	98.39 250	93.23 240	94.57 355	97.21 380	87.26 473	98.53 123	98.52 137	86.67 409	97.37 497	93.24 340	99.36 250	97.12 455

viewdifsd2359ckpt07		97.10 177	97.55 142	95.76 318	98.64 197	88.58 391	94.54 356	99.11 85	96.96 115	98.54 120	98.18 203	96.91 94	99.44 266	95.58 196	99.49 201	99.26 181

save fliter					98.48 235	94.71 171	94.53 357	98.41 280	95.02 244

patch_mono-2		96.59 220	96.93 192	95.55 341	98.88 151	87.12 438	94.47 358	99.30 43	94.12 290	96.65 324	98.41 155	94.98 210	99.87 25	95.81 181	99.78 70	99.66 38

tpm cat1		88.01 497	87.33 496	90.05 516	94.48 515	76.28 541	94.47 358	94.35 461	73.84 548	89.26 527	95.61 438	73.64 503	98.30 476	84.13 505	86.20 544	95.57 503

DKM		96.39 238	95.99 263	97.59 140	98.44 242	96.42 72	94.42 360	98.51 262	92.81 352	98.15 183	97.47 298	89.37 361	97.26 499	95.02 249	99.68 105	99.09 232

DKM-HiRes		96.47 230	95.93 270	98.09 98	98.86 156	96.41 73	94.38 361	98.56 257	94.05 294	96.93 300	97.48 297	87.73 389	98.55 454	95.86 177	99.48 206	99.31 166

CANet		95.86 270	95.65 286	96.49 254	96.41 436	90.82 318	94.36 362	98.41 280	94.94 249	92.62 493	96.73 368	92.68 286	99.71 127	95.12 241	99.60 142	98.94 267

WR-MVS		96.90 192	96.81 202	97.16 188	98.56 218	92.20 275	94.33 363	98.12 324	97.34 99	98.20 174	97.33 316	92.81 282	99.75 85	94.79 269	99.81 60	99.54 74

SP-LightGlue		95.19 315	94.96 313	95.89 312	95.10 499	94.93 166	94.29 364	98.47 268	94.91 253	94.92 420	95.51 443	86.69 407	95.61 518	97.08 107	97.67 445	97.12 455

HQP-NCC					97.85 315		94.26 365		93.18 333	92.86 484

ACMP_Plane					97.85 315		94.26 365		93.18 333	92.86 484

HQP-MVS		95.17 318	94.58 342	96.92 212	97.85 315	92.47 262	94.26 365	98.43 276	93.18 333	92.86 484	95.08 453	90.33 337	99.23 356	90.51 413	98.74 364	99.05 241

viewmamba		96.62 219	96.92 194	95.74 320	97.85 315	88.83 384	94.25 368	99.00 135	95.69 205	97.18 274	97.90 247	95.34 191	99.29 337	96.20 153	98.85 342	99.11 226

PLC	91.02 16	94.05 373	92.90 402	97.51 148	98.00 302	95.12 160	94.25 368	98.25 300	86.17 486	91.48 504	95.25 451	91.01 324	99.19 362	85.02 499	96.69 483	98.22 382
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MatchFormer		93.37 400	93.14 393	94.07 428	96.06 458	92.91 247	94.24 370	94.92 451	85.51 493	98.29 159	97.79 262	85.70 419	96.13 515	86.23 479	99.51 190	93.18 523

1112_ss		94.12 369	93.42 387	96.23 284	98.59 212	90.85 317	94.24 370	98.85 181	85.49 494	92.97 479	94.94 457	86.01 415	99.64 179	91.78 374	97.92 424	98.20 384

MS-PatchMatch		94.83 333	94.91 318	94.57 404	96.81 419	87.10 440	94.23 372	97.34 376	88.74 454	97.14 277	97.11 337	91.94 312	98.23 479	92.99 346	97.92 424	98.37 357

Fast-Effi-MVS+		95.49 294	95.07 306	96.75 229	97.67 361	92.82 248	94.22 373	98.60 248	91.61 385	93.42 471	92.90 491	96.73 109	99.70 136	92.60 353	97.89 429	97.74 427

CMPMVS	73.10 23	92.74 420	91.39 442	96.77 228	93.57 530	94.67 174	94.21 374	97.67 357	80.36 530	93.61 462	96.60 376	82.85 448	97.35 498	84.86 501	98.78 354	98.29 373
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DenseAffine		96.06 257	95.57 289	97.53 147	98.44 242	95.79 107	94.20 375	98.14 321	92.44 363	97.95 214	97.18 328	88.87 368	97.96 487	93.41 332	99.52 184	98.85 288

dp		88.08 496	88.05 488	88.16 526	92.85 536	68.81 557	94.17 376	92.88 487	85.47 495	91.38 505	96.14 410	68.87 520	98.81 422	86.88 472	83.80 546	96.87 465

JIA-IIPM		91.79 447	90.69 459	95.11 366	93.80 527	90.98 311	94.16 377	91.78 505	96.38 148	90.30 517	99.30 33	72.02 510	98.90 410	88.28 451	90.17 538	95.45 504

D2MVS		95.18 316	95.17 301	95.21 360	97.76 345	87.76 424	94.15 378	97.94 337	89.77 438	96.99 294	97.68 278	87.45 392	99.14 373	95.03 248	99.81 60	98.74 308

TSAR-MVS + GP.		96.47 230	96.12 254	97.49 157	97.74 350	95.23 149	94.15 378	96.90 401	93.26 325	98.04 198	96.70 370	94.41 230	98.89 411	94.77 272	99.14 299	98.37 357

PVSNet_BlendedMVS		95.02 327	94.93 316	95.27 357	97.79 340	87.40 432	94.14 380	98.68 234	88.94 451	94.51 430	98.01 233	93.04 276	99.30 333	89.77 427	99.49 201	99.11 226

TinyColmap		96.00 262	96.34 243	94.96 379	97.90 312	87.91 416	94.13 381	98.49 265	94.41 279	98.16 181	97.76 265	96.29 143	98.68 441	90.52 412	99.42 231	98.30 370

CNLPA		95.04 324	94.47 348	96.75 229	97.81 331	95.25 148	94.12 382	97.89 342	94.41 279	94.57 428	95.69 433	90.30 340	98.35 473	86.72 474	98.76 362	96.64 475

SP-SuperGlue		95.41 301	95.38 294	95.51 343	94.92 507	94.67 174	94.09 383	97.93 339	95.45 219	95.62 391	96.26 399	89.54 351	95.26 520	96.70 121	97.92 424	96.61 478

BH-untuned		94.69 341	94.75 331	94.52 407	97.95 308	87.53 427	94.07 384	97.01 396	93.99 297	97.10 281	95.65 435	92.65 288	98.95 407	87.60 461	96.74 479	97.09 457

fmvsm_l_mol_unc0.5_1		97.76 105	98.18 57	96.49 254	99.02 124	90.21 340	94.06 385	99.63 17	96.81 124	99.74 6	99.60 11	95.96 156	99.66 169	98.92 30	99.86 35	99.60 47

onestephybrid01		96.25 246	96.31 245	96.07 298	97.54 377	90.01 347	94.06 385	98.77 212	94.74 256	96.32 345	97.74 271	94.03 243	99.20 360	94.81 267	98.79 352	98.98 256

ArgMatch-SfM		95.74 278	95.15 302	97.49 157	97.82 329	95.16 157	94.03 387	98.41 280	89.33 442	97.58 241	96.65 373	90.07 344	98.89 411	93.17 343	99.30 275	98.44 350

pmmvs5		94.63 347	94.34 354	95.50 344	97.63 368	88.34 400	94.02 388	97.13 385	87.15 476	95.22 408	97.15 329	87.50 391	99.27 345	93.99 307	99.26 282	98.88 283

thres200		91.00 459	90.42 463	92.77 480	97.47 387	83.98 494	94.01 389	91.18 513	95.12 237	95.44 401	91.21 515	73.93 500	99.31 329	77.76 536	97.63 450	95.01 507

xiu_mvs_v1_base_debu		95.62 288	95.96 266	94.60 401	98.01 298	88.42 395	93.99 390	98.21 304	92.98 344	95.91 373	94.53 466	96.39 134	99.72 111	95.43 211	98.19 409	95.64 500

xiu_mvs_v1_base		95.62 288	95.96 266	94.60 401	98.01 298	88.42 395	93.99 390	98.21 304	92.98 344	95.91 373	94.53 466	96.39 134	99.72 111	95.43 211	98.19 409	95.64 500

xiu_mvs_v1_base_debi		95.62 288	95.96 266	94.60 401	98.01 298	88.42 395	93.99 390	98.21 304	92.98 344	95.91 373	94.53 466	96.39 134	99.72 111	95.43 211	98.19 409	95.64 500

test_vis1_rt		94.03 375	93.65 378	95.17 363	95.76 476	93.42 232	93.97 393	98.33 293	84.68 505	93.17 475	95.89 426	92.53 297	94.79 526	93.50 331	94.97 519	97.31 452

CDS-MVSNet		94.88 332	94.12 365	97.14 190	97.64 367	93.57 224	93.96 394	97.06 392	90.05 434	96.30 351	96.55 378	86.10 414	99.47 248	90.10 421	99.31 271	98.40 352
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SP-DiffGlue		94.64 346	94.54 345	94.97 378	93.53 531	94.33 193	93.94 395	97.84 347	93.35 321	96.58 328	95.54 440	88.87 368	94.71 529	93.73 322	97.44 459	95.87 495

CANet_DTU		94.65 345	94.21 361	95.96 305	95.90 464	89.68 356	93.92 396	97.83 350	93.19 332	90.12 520	95.64 436	88.52 372	99.57 211	93.27 339	99.47 209	98.62 323

WTY-MVS		93.55 394	93.00 399	95.19 361	97.81 331	87.86 418	93.89 397	96.00 422	89.02 449	94.07 445	95.44 446	86.27 413	99.33 319	87.69 459	96.82 475	98.39 354

sss		94.22 364	93.72 376	95.74 320	97.71 354	89.95 348	93.84 398	96.98 397	88.38 461	93.75 455	95.74 432	87.94 382	98.89 411	91.02 389	98.10 413	98.37 357

baseline2		89.65 478	88.44 484	93.25 458	95.62 482	82.71 501	93.82 399	85.94 545	88.89 452	87.35 539	92.54 499	71.23 512	99.33 319	86.01 482	94.60 524	97.72 430

XVG-OURS		97.12 175	96.74 208	98.26 79	98.99 130	97.45 36	93.82 399	99.05 110	95.19 233	98.32 154	97.70 276	95.22 198	98.41 467	94.27 293	98.13 412	98.93 271

MVS_111021_LR		96.82 202	96.55 227	97.62 138	98.27 265	95.34 143	93.81 401	98.33 293	94.59 267	96.56 331	96.63 375	96.61 117	98.73 430	94.80 268	99.34 261	98.78 295

BH-RMVSNet		94.56 352	94.44 351	94.91 381	97.57 372	87.44 429	93.78 402	96.26 417	93.69 307	96.41 341	96.50 383	92.10 307	99.00 398	85.96 484	97.71 441	98.31 367

dtuplus		95.73 279	95.86 275	95.33 355	97.72 352	87.82 421	93.74 403	98.60 248	92.12 369	97.27 264	97.92 244	94.35 233	99.13 377	92.24 361	98.83 346	99.05 241

SP-MNN		94.33 362	94.22 360	94.67 396	94.94 506	92.73 256	93.74 403	96.59 415	92.73 355	93.75 455	95.38 448	88.24 378	95.08 523	94.86 265	97.78 433	96.20 490

diffmvs_AUTHOR		96.50 226	96.81 202	95.57 335	98.03 294	88.26 402	93.73 405	99.14 79	94.92 252	97.24 267	97.84 254	94.62 222	99.33 319	96.44 138	99.37 245	99.13 215

CDPH-MVS		95.45 299	94.65 334	97.84 119	98.28 262	94.96 164	93.73 405	98.33 293	85.03 501	95.44 401	96.60 376	95.31 194	99.44 266	90.01 422	99.13 301	99.11 226

hybridnocas07		96.00 262	96.21 251	95.39 353	97.56 374	87.89 417	93.70 407	98.93 154	93.96 299	96.48 336	97.65 280	93.38 264	99.19 362	95.39 216	98.81 350	99.08 233

PatchMatch-RL		94.61 348	93.81 373	97.02 205	98.19 275	95.72 110	93.66 408	97.23 379	88.17 464	94.94 418	95.62 437	91.43 317	98.57 451	87.36 468	97.68 444	96.76 473

hybrid		95.77 274	95.95 269	95.23 359	97.54 377	87.44 429	93.65 409	98.86 175	93.17 336	96.06 367	97.65 280	93.14 271	99.20 360	94.94 260	98.57 385	99.04 243

UWE-MVS-28		83.78 509	82.36 512	88.03 527	90.72 545	71.58 553	93.64 410	77.87 552	87.62 471	85.91 543	92.89 492	59.94 528	95.99 517	56.06 552	96.56 488	96.52 480

TEST9					97.84 322	95.23 149	93.62 411	98.39 284	86.81 481	93.78 452	95.99 419	94.68 219	99.52 227

train_agg		95.46 298	94.66 333	97.88 116	97.84 322	95.23 149	93.62 411	98.39 284	87.04 477	93.78 452	95.99 419	94.58 224	99.52 227	91.76 375	98.90 334	98.89 279

viewmambaseed2359dif		95.68 283	95.85 276	95.17 363	97.51 380	87.41 431	93.61 413	98.58 254	91.06 409	96.68 318	97.66 279	94.71 216	99.11 381	93.93 310	98.94 326	98.99 253

test_prior4						95.38 134	93.61 413

ArgMatch-Sym		95.60 291	94.97 312	97.48 159	97.70 355	95.41 131	93.60 415	97.89 342	89.33 442	97.70 234	96.03 418	91.00 326	98.66 443	92.25 360	99.18 293	98.39 354

test_8					97.81 331	95.07 161	93.54 416	98.38 286	87.04 477	93.71 457	95.96 422	94.58 224	99.52 227

TR-MVS		92.54 426	92.20 423	93.57 446	96.49 430	86.66 446	93.51 417	94.73 454	89.96 435	94.95 417	93.87 477	90.24 342	98.61 448	81.18 524	94.88 520	95.45 504

PMatch-SfM		95.65 287	95.03 309	97.51 148	97.96 304	95.00 162	93.49 418	98.51 262	92.24 367	97.80 229	98.03 229	83.97 439	99.19 362	94.77 272	98.50 391	98.35 363

新几何2							93.43 419

diffmvs		96.04 258	96.23 249	95.46 347	97.35 394	88.03 414	93.42 420	99.08 99	94.09 293	96.66 322	96.93 352	93.85 250	99.29 337	96.01 165	98.67 374	99.06 239
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_111021_HR		96.73 210	96.54 229	97.27 179	98.35 254	93.66 222	93.42 420	98.36 289	94.74 256	96.58 328	96.76 367	96.54 122	98.99 400	94.87 262	99.27 279	99.15 207

PMatch-Up-SfM		95.95 264	95.43 293	97.51 148	97.90 312	95.17 156	93.40 422	98.78 210	92.45 361	98.24 170	98.07 219	87.10 401	99.18 365	94.87 262	98.10 413	98.19 385

UnsupCasMVSNet_bld		94.72 340	94.26 357	96.08 297	98.62 208	90.54 326	93.38 423	98.05 335	90.30 427	97.02 290	96.80 364	89.54 351	99.16 371	88.44 448	96.18 497	98.56 330

旧先验2							93.35 424		77.95 541	95.77 388			98.67 442	90.74 405

test_prior2							93.33 425		94.21 285	94.02 448	96.25 401	93.64 257		91.90 367	98.96 323

WB-MVSnew		91.50 451	91.29 444	92.14 495	94.85 508	80.32 521	93.29 426	88.77 533	88.57 458	94.03 447	92.21 503	92.56 291	98.28 477	80.21 527	97.08 466	97.81 421

IMVS_0404		95.66 286	96.03 260	94.55 405	97.83 325	86.11 456	93.24 427	98.82 200	94.48 272	95.51 399	97.14 330	93.49 260	98.78 424	95.00 250	98.78 354	98.78 295

SCA		93.38 399	93.52 383	92.96 473	96.24 441	81.40 514	93.24 427	94.00 465	91.58 392	94.57 428	96.97 349	87.94 382	99.42 274	89.47 432	97.66 448	98.06 399

无先验							93.20 429	97.91 340	80.78 527				99.40 286	87.71 457		97.94 411

MG-MVS		94.08 372	94.00 368	94.32 420	97.09 410	85.89 461	93.19 430	95.96 424	92.52 358	94.93 419	97.51 295	89.54 351	98.77 426	87.52 465	97.71 441	98.31 367

ALIKED-NN		90.94 461	89.58 470	95.02 373	94.61 513	96.31 80	93.16 431	97.27 377	79.38 533	86.25 542	95.27 450	83.42 443	94.29 534	79.08 530	97.77 434	94.46 512

nomal-1		90.42 464	88.88 480	95.06 370	96.01 459	88.66 390	93.13 432	92.16 498	91.23 405	90.46 513	91.32 514	61.17 527	98.72 433	87.70 458	96.70 482	97.79 424

MVS-HIRNet		88.40 492	90.20 466	82.99 530	97.01 412	60.04 558	93.11 433	85.61 546	84.45 509	88.72 532	99.09 59	84.72 430	98.23 479	82.52 518	96.59 487	90.69 541

new-patchmatchnet		95.67 284	96.58 220	92.94 474	97.48 383	80.21 522	92.96 434	98.19 313	94.83 254	98.82 87	98.79 92	93.31 266	99.51 231	95.83 179	99.04 316	99.12 221

ETVMVS		87.62 500	85.75 507	93.22 461	96.15 452	83.26 498	92.94 435	90.37 525	91.39 401	90.37 515	88.45 533	51.93 552	98.64 445	73.76 541	96.38 492	97.75 426

MDA-MVSNet-bldmvs		95.69 281	95.67 284	95.74 320	98.48 235	88.76 388	92.84 436	97.25 378	96.00 182	97.59 240	97.95 240	91.38 318	99.46 255	93.16 344	96.35 493	98.99 253

原ACMM2							92.82 437

testdata1							92.77 438		93.78 303

Test_1112_low_res		93.53 395	92.86 403	95.54 342	98.60 210	88.86 383	92.75 439	98.69 232	82.66 517	92.65 490	96.92 355	84.75 429	99.56 213	90.94 392	97.76 437	98.19 385

USDC		94.56 352	94.57 344	94.55 405	97.78 343	86.43 450	92.75 439	98.65 245	85.96 488	96.91 303	97.93 243	90.82 328	98.74 429	90.71 406	99.59 145	98.47 346

test222					98.17 281	93.24 239	92.74 441	97.61 369	75.17 545	94.65 427	96.69 371	90.96 327			98.66 376	97.66 432

jason		94.39 360	94.04 367	95.41 350	98.29 259	87.85 420	92.74 441	96.75 407	85.38 498	95.29 406	96.15 407	88.21 381	99.65 173	94.24 294	99.34 261	98.74 308
jason: jason.
testing91		89.67 477	88.55 482	93.04 468	95.90 464	81.80 510	92.71 443	93.71 469	93.71 305	90.18 518	90.15 523	57.11 535	99.22 358	87.17 471	96.32 494	98.12 391

testing99		89.21 483	88.04 489	92.70 482	95.78 474	81.00 518	92.65 444	92.03 500	93.20 331	89.90 523	90.08 525	55.25 544	99.14 373	87.54 463	95.95 502	97.97 408

Patchmatch-RL test		94.66 344	94.49 346	95.19 361	98.54 221	88.91 381	92.57 445	98.74 220	91.46 399	98.32 154	97.75 268	77.31 484	98.81 422	96.06 158	99.61 135	97.85 417

DeepPCF-MVS	94.58 5	96.90 192	96.43 236	98.31 74	97.48 383	97.23 44	92.56 446	98.60 248	92.84 351	98.54 120	97.40 304	96.64 116	98.78 424	94.40 288	99.41 236	98.93 271

N_pmnet		95.18 316	94.23 358	98.06 101	97.85 315	96.55 66	92.49 447	91.63 506	89.34 441	98.09 190	97.41 303	90.33 337	99.06 389	91.58 378	99.31 271	98.56 330

testing11		88.93 485	87.63 495	92.80 479	95.87 466	81.49 512	92.48 448	91.54 507	91.62 384	88.27 535	90.24 521	55.12 547	99.11 381	87.30 469	96.28 496	97.81 421

Syy-MVS		92.09 440	91.80 433	92.93 475	95.19 496	82.65 502	92.46 449	91.35 509	90.67 418	91.76 501	87.61 535	85.64 421	98.50 460	94.73 275	96.84 473	97.65 433

myMVS_eth3d		87.16 505	85.61 508	91.82 498	95.19 496	79.32 524	92.46 449	91.35 509	90.67 418	91.76 501	87.61 535	41.96 556	98.50 460	82.66 517	96.84 473	97.65 433

BH-w/o		92.14 438	91.94 428	92.73 481	97.13 409	85.30 469	92.46 449	95.64 431	89.33 442	94.21 437	92.74 496	89.60 349	98.24 478	81.68 521	94.66 522	94.66 511

IterMVS-SCA-FT		95.86 270	96.19 252	94.85 386	97.68 357	85.53 464	92.42 452	97.63 368	96.99 111	98.36 146	98.54 136	87.94 382	99.75 85	97.07 108	99.08 310	99.27 179

IterMVS		95.42 300	95.83 278	94.20 424	97.52 379	83.78 496	92.41 453	97.47 373	95.49 218	98.06 195	98.49 141	87.94 382	99.58 205	96.02 163	99.02 317	99.23 191
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing222		87.35 502	85.50 509	92.93 475	95.79 473	82.83 500	92.40 454	90.10 529	92.80 353	88.87 531	89.02 528	48.34 555	98.70 436	75.40 540	96.74 479	97.27 453

DELS-MVS		96.17 252	96.23 249	95.99 302	97.55 376	90.04 345	92.38 455	98.52 260	94.13 289	96.55 333	97.06 340	94.99 209	99.58 205	95.62 192	99.28 277	98.37 357
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
blended_shiyan8		93.34 401	92.55 416	95.73 324	95.69 479	89.08 376	92.36 456	97.11 387	91.47 397	95.42 403	88.94 531	82.26 452	99.48 242	93.84 315	95.81 507	98.62 323

blended_shiyan6		93.34 401	92.54 417	95.73 324	95.68 480	89.08 376	92.35 457	97.10 388	91.47 397	95.37 405	88.96 530	82.26 452	99.48 242	93.83 316	95.85 503	98.62 323

new_pmnet		92.34 431	91.69 439	94.32 420	96.23 443	89.16 371	92.27 458	92.88 487	84.39 510	95.29 406	96.35 393	85.66 420	96.74 511	84.53 503	97.56 451	97.05 458

myMVS_eth3d28		88.32 493	87.73 493	90.11 515	96.42 434	74.96 547	92.21 459	92.37 496	93.56 312	90.14 519	89.61 526	56.13 540	98.05 486	81.84 519	97.26 465	97.33 451

CHOSEN 1792x2688		94.10 370	93.41 388	96.18 290	99.16 93	90.04 345	92.15 460	98.68 234	79.90 531	96.22 356	97.83 255	87.92 386	99.42 274	89.18 436	99.65 114	99.08 233

blend_shiyan4		88.73 489	86.43 504	95.61 332	95.31 493	89.17 368	92.13 461	97.10 388	91.59 391	94.15 442	87.38 537	52.97 551	99.40 286	91.84 370	75.42 551	98.27 374

xiu_mvs_v2_base		94.22 364	94.63 337	92.99 472	97.32 399	84.84 481	92.12 462	97.84 347	91.96 375	94.17 440	93.43 480	96.07 154	99.71 127	91.27 383	97.48 455	94.42 514

lupinMVS		93.77 381	93.28 389	95.24 358	97.68 357	87.81 422	92.12 462	96.05 420	84.52 507	94.48 432	95.06 455	86.90 403	99.63 184	93.62 329	99.13 301	98.27 374

pmmvs4		94.82 334	94.19 362	96.70 232	97.42 390	92.75 254	92.09 464	96.76 406	86.80 482	95.73 389	97.22 324	89.28 362	98.89 411	93.28 338	99.14 299	98.46 348

SP-NN		92.63 424	92.38 418	93.37 449	93.30 532	92.36 264	92.04 465	94.24 463	91.60 389	89.19 528	93.92 476	87.21 398	91.28 546	93.73 322	96.17 498	96.48 482

PAPR		92.22 436	91.27 446	95.07 369	95.73 478	88.81 385	91.97 466	97.87 344	85.80 491	90.91 506	92.73 497	91.16 320	98.33 474	79.48 528	95.76 512	98.08 393

UWE-MVS		87.57 501	86.72 502	90.13 514	95.21 495	73.56 549	91.94 467	83.78 549	88.73 455	93.00 478	92.87 493	55.22 545	99.25 350	81.74 520	97.96 422	97.59 439

gbinet_0.2-2-1-0.02		92.86 417	91.78 435	96.13 295	94.34 516	90.06 343	91.90 468	96.63 414	91.73 379	94.24 436	86.22 544	80.26 468	99.56 213	93.87 313	96.80 477	98.77 304

PS-MVSNAJ		94.10 370	94.47 348	93.00 471	97.35 394	84.88 478	91.86 469	97.84 347	91.96 375	94.17 440	92.50 501	95.82 165	99.71 127	91.27 383	97.48 455	94.40 515

c3_l		95.20 314	95.32 295	94.83 388	96.19 446	86.43 450	91.83 470	98.35 292	93.47 317	97.36 260	97.26 322	88.69 370	99.28 342	95.41 214	99.36 250	98.78 295

icg_test_0407_2		95.88 268	96.39 239	94.36 415	97.83 325	86.11 456	91.82 471	98.82 200	94.48 272	97.57 242	97.14 330	96.08 152	98.20 482	95.00 250	98.78 354	98.78 295

test0.0.03 1		90.11 466	89.21 474	92.83 478	93.89 526	86.87 444	91.74 472	88.74 534	92.02 373	94.71 426	91.14 516	73.92 501	94.48 532	83.75 513	92.94 530	97.16 454

UBG		88.29 494	87.17 497	91.63 500	96.08 455	78.21 529	91.61 473	91.50 508	89.67 439	89.71 524	88.97 529	59.01 530	98.91 408	81.28 523	96.72 481	97.77 425

SSC-MVS3.2		95.75 277	96.56 223	93.34 451	98.69 193	80.75 519	91.60 474	97.43 375	97.37 97	96.99 294	97.02 343	93.69 256	99.71 127	96.32 145	99.89 26	99.55 72

FPMVS		89.92 472	88.63 481	93.82 436	98.37 252	96.94 49	91.58 475	93.34 479	88.00 467	90.32 516	97.10 338	70.87 514	91.13 548	71.91 546	96.16 500	93.39 522

ET-MVSNet_ETH3D		91.12 455	89.67 469	95.47 346	96.41 436	89.15 372	91.54 476	90.23 527	89.07 448	86.78 541	92.84 494	69.39 518	99.44 266	94.16 297	96.61 486	97.82 419

WBMVS		91.11 456	90.72 458	92.26 493	95.99 460	77.98 533	91.47 477	95.90 426	91.63 383	95.90 377	96.45 385	59.60 529	99.46 255	89.97 424	99.59 145	99.33 159

PVSNet_Blended		93.96 377	93.65 378	94.91 381	97.79 340	87.40 432	91.43 478	98.68 234	84.50 508	94.51 430	94.48 469	93.04 276	99.30 333	89.77 427	98.61 381	98.02 405

CLD-MVS		95.47 297	95.07 306	96.69 233	98.27 265	92.53 259	91.36 479	98.67 237	91.22 406	95.78 386	94.12 473	95.65 177	98.98 402	90.81 398	99.72 91	98.57 329
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
eth_miper_zixun_eth		94.89 331	94.93 316	94.75 393	95.99 460	86.12 455	91.35 480	98.49 265	93.40 318	97.12 279	97.25 323	86.87 405	99.35 314	95.08 243	98.82 348	98.78 295

cl____		94.73 336	94.64 335	95.01 374	95.85 468	87.00 441	91.33 481	98.08 328	93.34 322	97.10 281	97.33 316	84.01 438	99.30 333	95.14 238	99.56 160	98.71 315

DIV-MVS_self_test		94.73 336	94.64 335	95.01 374	95.86 467	87.00 441	91.33 481	98.08 328	93.34 322	97.10 281	97.34 315	84.02 437	99.31 329	95.15 237	99.55 167	98.72 311

miper_ehance_all_eth		94.69 341	94.70 332	94.64 397	95.77 475	86.22 453	91.32 483	98.24 302	91.67 382	97.05 288	96.65 373	88.39 375	99.22 358	94.88 261	98.34 403	98.49 345

usedtu_dtu_shiyan1		94.61 348	94.29 355	95.57 335	97.93 309	88.45 393	91.30 484	97.64 365	91.61 385	95.85 382	95.79 430	86.65 410	99.48 242	92.92 349	98.97 320	98.78 295

FE-MVSNET3		94.61 348	94.29 355	95.57 335	97.93 309	88.45 393	91.30 484	97.64 365	91.61 385	95.85 382	95.79 430	86.65 410	99.48 242	92.92 349	98.97 320	98.78 295

pmmvs3		90.00 469	88.90 479	93.32 455	94.20 522	85.34 467	91.25 486	92.56 495	78.59 538	93.82 451	95.17 452	67.36 522	98.69 438	89.08 438	98.03 418	95.92 492

SIFT-NCM-Cal		93.81 380	93.73 374	94.05 430	96.55 426	96.75 55	91.23 487	93.80 467	91.44 400	95.86 381	96.27 398	90.82 328	93.76 536	88.26 453	99.37 245	91.63 531

SIFT-MNN		93.13 412	92.91 401	93.79 438	96.42 434	96.49 68	91.23 487	93.73 468	92.18 368	95.52 398	96.08 416	84.66 431	93.04 543	87.49 466	98.94 326	91.84 527

HyFIR lowres test		93.72 386	92.65 412	96.91 214	98.93 142	91.81 291	91.23 487	98.52 260	82.69 515	96.46 339	96.52 382	80.38 464	99.90 17	90.36 417	98.79 352	99.03 245

DPM-MVS		93.68 389	92.77 409	96.42 266	97.91 311	92.54 258	91.17 490	97.47 373	84.99 503	93.08 477	94.74 462	89.90 346	99.00 398	87.54 463	98.09 415	97.72 430

CL-MVSNet_self_test		95.04 324	94.79 330	95.82 315	97.51 380	89.79 352	91.14 491	96.82 404	93.05 341	96.72 316	96.40 389	90.82 328	99.16 371	91.95 366	98.66 376	98.50 343

miper_lstm_enhance		94.81 335	94.80 329	94.85 386	96.16 449	86.45 449	91.14 491	98.20 307	93.49 316	97.03 289	97.37 313	84.97 428	99.26 347	95.28 223	99.56 160	98.83 289

dtuonlycased		95.11 320	95.70 283	93.35 450	99.05 119	81.45 513	91.13 493	98.48 267	93.11 340	97.98 209	97.27 320	96.15 150	99.32 327	89.61 429	98.50 391	99.27 179

cl22		93.25 407	92.84 405	94.46 412	94.30 518	86.00 460	91.09 494	96.64 413	90.74 415	95.79 384	96.31 396	78.24 475	98.77 426	94.15 298	98.34 403	98.62 323

MSDG		95.33 308	95.13 303	95.94 309	97.40 391	91.85 289	91.02 495	98.37 288	95.30 229	96.31 350	95.99 419	94.51 228	98.38 470	89.59 430	97.65 449	97.60 438

wanda-best-256-512		92.66 422	91.75 437	95.40 351	94.99 502	88.19 404	90.89 496	97.05 393	91.02 411	94.75 422	87.24 538	80.36 465	99.46 255	93.63 327	95.85 503	98.55 333

FE-blended-shiyan7		92.66 422	91.75 437	95.40 351	94.99 502	88.19 404	90.89 496	97.05 393	91.02 411	94.75 422	87.24 538	80.36 465	99.46 255	93.63 327	95.85 503	98.55 333

IB-MVS	85.98 20	88.63 490	86.95 501	93.68 443	95.12 498	84.82 482	90.85 498	90.17 528	87.55 472	88.48 534	91.34 513	58.01 531	99.59 202	87.24 470	93.80 529	96.63 477
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
mvsany_test1		93.47 396	93.03 397	94.79 390	94.05 525	92.12 277	90.82 499	90.01 530	85.02 502	97.26 266	98.28 185	93.57 258	97.03 503	92.51 357	95.75 513	95.23 506

test123		12.59 524	15.49 527	3.87 541	6.07 565	2.55 567	90.75 500	2.59 568	2.52 558	5.20 562	13.02 558	4.96 564	1.85 562	5.20 560	9.09 560	7.23 557

SIFT-UMatch		93.66 390	93.67 377	93.63 444	96.30 439	96.15 90	90.62 501	94.47 459	92.12 369	97.39 259	96.18 404	87.74 388	93.63 538	88.59 446	99.64 118	91.12 535

ppachtmachnet_test		94.49 356	94.84 325	93.46 448	96.16 449	82.10 506	90.59 502	97.48 372	90.53 420	97.01 292	97.59 286	91.01 324	99.36 310	93.97 309	99.18 293	98.94 267

SIFT-ConvMatch		93.72 386	93.47 384	94.48 411	96.22 445	96.63 63	90.58 503	93.91 466	91.70 380	97.70 234	96.17 405	89.03 365	95.12 521	86.29 478	99.65 114	91.69 530

PMMVS		92.39 429	91.08 449	96.30 280	93.12 534	92.81 250	90.58 503	95.96 424	79.17 535	91.85 500	92.27 502	90.29 341	98.66 443	89.85 426	96.68 484	97.43 445

our_test_3		94.20 368	94.58 342	93.07 466	96.16 449	81.20 516	90.42 505	96.84 402	90.72 416	97.14 277	97.13 334	90.47 333	99.11 381	94.04 304	98.25 407	98.91 275

SIFT-NN-NCMNet		92.32 433	91.79 434	93.89 434	96.32 438	96.91 50	90.32 506	90.69 522	90.36 425	91.72 503	95.43 447	88.98 366	94.27 535	84.23 504	98.06 416	90.49 543

YYNet1		94.73 336	94.84 325	94.41 414	97.47 387	85.09 475	90.29 507	95.85 428	92.52 358	97.53 245	97.76 265	91.97 310	99.18 365	93.31 337	96.86 472	98.95 264

MDA-MVSNet_test_wron		94.73 336	94.83 327	94.42 413	97.48 383	85.15 473	90.28 508	95.87 427	92.52 358	97.48 252	97.76 265	91.92 313	99.17 370	93.32 336	96.80 477	98.94 267

SIFT-NN-PointCN		92.48 428	92.19 424	93.33 454	95.40 491	95.65 116	90.19 509	93.07 483	88.67 456	92.90 480	95.95 423	89.38 360	93.20 541	85.21 495	98.94 326	91.15 534

SIFT-NN-UMatch		92.28 435	91.93 429	93.34 451	96.13 454	96.04 96	90.05 510	92.08 499	90.41 422	92.88 482	95.29 449	87.36 397	93.63 538	85.33 493	97.87 430	90.34 544

GA-MVS		92.83 419	92.15 425	94.87 385	96.97 413	87.27 435	90.03 511	96.12 419	91.83 378	94.05 446	94.57 464	76.01 491	98.97 406	92.46 358	97.34 462	98.36 362

miper_enhance_ethall		93.14 410	92.78 408	94.20 424	93.65 528	85.29 470	89.97 512	97.85 345	85.05 500	96.15 364	94.56 465	85.74 417	99.14 373	93.74 320	98.34 403	98.17 389

test-LLR		89.97 471	89.90 467	90.16 512	94.24 520	74.98 544	89.89 513	89.06 531	92.02 373	89.97 521	90.77 519	73.92 501	98.57 451	91.88 368	97.36 460	96.92 462

TESTMET0.1,1		87.20 504	86.57 503	89.07 519	93.62 529	72.84 551	89.89 513	87.01 543	85.46 496	89.12 529	90.20 522	56.00 541	97.72 494	90.91 393	96.92 469	96.64 475

test-mter		87.92 498	87.17 497	90.16 512	94.24 520	74.98 544	89.89 513	89.06 531	86.44 485	89.97 521	90.77 519	54.96 548	98.57 451	91.88 368	97.36 460	96.92 462

PCF-MVS	89.43 18	92.12 439	90.64 460	96.57 245	97.80 335	93.48 229	89.88 516	98.45 271	74.46 546	96.04 368	95.68 434	90.71 331	99.31 329	73.73 542	99.01 319	96.91 464
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SIFT-CM-Cal		93.31 403	93.10 394	93.95 433	96.19 446	96.32 79	89.81 517	93.40 478	91.16 407	97.19 273	96.07 417	88.24 378	94.58 531	86.11 480	99.69 100	90.94 538

SIFT-UM-Cal		93.74 383	93.73 374	93.78 439	95.97 462	96.07 94	89.78 518	96.67 412	91.69 381	97.77 232	96.09 415	89.51 355	94.75 527	86.68 475	99.39 241	90.52 542

MASt3R-SfM		91.42 453	90.88 453	93.06 467	92.40 539	92.08 281	89.76 519	93.15 482	78.62 537	95.98 370	97.33 316	82.42 451	91.17 547	90.23 419	97.98 420	95.92 492

SIFT-PointCN		93.04 414	92.72 410	94.01 432	95.80 472	95.33 146	89.76 519	92.60 494	90.24 430	96.32 345	95.87 427	87.45 392	94.70 530	86.65 476	99.77 72	92.01 526

thisisatest0515		90.43 463	89.18 477	94.17 426	97.07 411	85.44 465	89.75 521	87.58 540	88.28 462	93.69 460	91.72 509	65.27 523	99.58 205	90.59 410	98.67 374	97.50 444

SIFT-PCN-Cal		93.02 415	92.95 400	93.23 460	95.63 481	94.57 182	89.68 522	94.71 455	90.40 423	97.02 290	95.84 428	88.33 377	93.66 537	85.26 494	99.65 114	91.45 533

PatchmatchNet2					0.00 567	78.83 527	89.63 523	94.76 453	87.65 470
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
KD-MVS_2432*1600		88.93 485	87.74 491	92.49 486	88.04 552	81.99 507	89.63 523	95.62 432	91.35 402	95.06 412	93.11 482	56.58 537	98.63 446	85.19 496	95.07 517	96.85 467

miper_refine_blended		88.93 485	87.74 491	92.49 486	88.04 552	81.99 507	89.63 523	95.62 432	91.35 402	95.06 412	93.11 482	56.58 537	98.63 446	85.19 496	95.07 517	96.85 467

SIFT-NN-CMatch		92.54 426	92.03 427	94.07 428	96.08 455	96.27 84	89.47 526	90.90 515	90.26 429	92.89 481	94.83 461	90.17 343	94.95 525	84.92 500	98.78 354	90.99 537

dtuonly		92.30 434	93.44 385	88.89 520	95.60 483	69.49 556	89.18 527	98.09 326	88.17 464	94.19 438	96.35 393	88.98 366	98.72 433	91.74 377	98.69 371	98.45 349

testmvs		12.33 525	15.23 528	3.64 542	5.77 566	2.23 568	88.99 528	3.62 566	2.30 559	5.29 561	13.09 557	4.52 565	1.95 561	5.16 561	8.32 561	6.75 558

SIFT-NN		89.78 474	89.23 472	91.41 503	95.04 501	94.89 167	88.98 529	90.76 519	89.26 445	89.11 530	92.97 489	81.45 456	88.25 549	78.47 535	97.06 467	91.08 536

cascas		91.89 445	91.35 443	93.51 447	94.27 519	85.60 463	88.86 530	98.61 247	79.32 534	92.16 497	91.44 512	89.22 363	98.12 483	90.80 399	97.47 457	96.82 470

PAPM		87.64 499	85.84 506	93.04 468	96.54 427	84.99 477	88.42 531	95.57 436	79.52 532	83.82 544	93.05 488	80.57 463	98.41 467	62.29 549	92.79 531	95.71 499

SIFT-NCMNet		93.23 409	93.19 392	93.34 451	95.31 493	95.59 118	88.29 532	95.60 435	91.60 389	98.43 136	96.34 395	89.80 348	93.57 540	83.82 511	99.57 155	90.85 539

PDCNetPlus		89.44 481	88.28 485	92.93 475	91.75 542	85.02 476	87.69 533	99.67 9	82.69 515	95.89 380	97.02 343	51.15 553	95.27 519	88.79 441	99.86 35	98.50 343

PVSNet	86.72 19	91.10 457	90.97 452	91.49 501	97.56 374	78.04 531	87.17 534	94.60 457	84.65 506	92.34 495	92.20 504	87.37 396	98.47 463	85.17 498	97.69 443	97.96 409

0.4-1-1-0.1		83.64 510	80.50 513	93.08 465	90.32 547	85.42 466	86.48 535	87.71 539	83.60 512	80.38 550	75.45 549	53.19 550	98.91 408	86.46 477	80.88 548	94.93 510

PMMVS2		93.66 390	94.07 366	92.45 489	97.57 372	80.67 520	86.46 536	96.00 422	93.99 297	97.10 281	97.38 311	89.90 346	97.82 492	88.76 442	99.47 209	98.86 286

CHOSEN 280x420		89.98 470	89.19 476	92.37 490	95.60 483	81.13 517	86.22 537	97.09 390	81.44 525	87.44 538	93.15 481	73.99 499	99.47 248	88.69 444	99.07 312	96.52 480

dongtai		63.43 516	63.37 519	63.60 534	83.91 556	53.17 560	85.14 538	43.40 563	77.91 542	80.96 548	79.17 548	36.36 558	77.10 554	37.88 555	45.63 556	60.54 550

kuosan		54.81 518	54.94 521	54.42 535	74.43 557	50.03 561	84.98 539	44.27 562	61.80 550	62.49 556	70.43 552	35.16 559	58.04 556	19.30 557	41.61 557	55.19 551

0.3-1-1-0.015		82.33 513	78.89 515	92.66 483	88.57 549	84.69 483	84.76 540	88.02 538	82.48 518	77.55 552	72.96 550	49.60 554	98.87 416	86.05 481	80.02 550	94.43 513

XFeat-MNN		88.85 488	88.16 487	90.91 508	88.38 550	89.73 353	84.46 541	91.81 504	83.72 511	95.56 396	92.95 490	74.60 498	92.68 544	84.01 506	97.99 419	90.32 545

tmp_tt		57.23 517	62.50 520	41.44 536	34.77 562	49.21 562	83.93 542	60.22 560	15.31 555	71.11 554	79.37 547	70.09 517	44.86 558	64.76 548	82.93 547	30.25 552

0.4-1-1-0.2		82.53 512	79.25 514	92.37 490	88.10 551	83.96 495	83.72 543	88.15 537	82.14 520	78.97 551	72.49 551	53.22 549	98.84 418	85.99 483	80.50 549	94.30 516

PVSNet_0	81.89 21	84.49 507	83.21 511	88.34 523	95.76 476	74.97 546	83.49 544	92.70 491	78.47 539	87.94 536	86.90 543	83.38 445	96.63 512	73.44 544	66.86 553	93.40 521

E-PMN		89.52 479	89.78 468	88.73 521	93.14 533	77.61 534	83.26 545	92.02 501	94.82 255	93.71 457	93.11 482	75.31 494	96.81 507	85.81 485	96.81 476	91.77 529

EMVS		89.06 484	89.22 473	88.61 522	93.00 535	77.34 536	82.91 546	90.92 514	94.64 264	92.63 492	91.81 508	76.30 489	97.02 504	83.83 510	96.90 471	91.48 532

MVE	73.61 22	86.48 506	85.92 505	88.18 525	96.23 443	85.28 471	81.78 547	75.79 554	86.01 487	82.53 546	91.88 507	92.74 284	87.47 551	71.42 547	94.86 521	91.78 528
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
XFeat-NN		84.28 508	83.52 510	86.54 529	85.42 555	86.22 453	78.86 548	88.43 535	79.17 535	90.71 508	89.11 527	69.18 519	85.27 553	76.68 538	94.13 526	88.13 546

GLUNet-SfM		74.13 514	71.69 517	81.46 531	63.16 558	74.17 548	66.80 549	76.03 553	58.10 551	88.60 533	86.99 542	57.56 532	86.25 552	50.03 553	97.91 427	83.95 547

test_method		66.88 515	66.13 518	69.11 533	62.68 559	25.73 565	49.76 550	96.04 421	14.32 556	64.27 555	91.69 510	73.45 506	88.05 550	76.06 539	66.94 552	93.54 519

MVS_clip		42.92 519	47.56 522	28.98 538	56.50 560	40.01 563	44.33 551	12.68 564	16.97 554	74.98 553	81.47 546	34.48 560	17.21 559	43.66 554	63.00 554	29.72 553

VLMVS_CLIP		41.19 520	42.85 523	36.20 537	35.69 561	29.96 564	41.27 552	59.71 561	20.51 553	51.77 557	61.89 553	24.86 561	51.47 557	37.87 556	52.12 555	27.15 554

VLMVS		16.27 523	17.60 526	12.26 539	17.44 564	14.02 566	13.33 553	7.39 565	0.97 560	23.14 559	32.55 556	21.01 562	8.58 560	7.93 559	34.66 559	14.18 555

MVS_baseline		16.43 522	20.39 525	4.55 540	19.03 563	1.35 569	10.44 554	3.04 567	0.59 561	41.63 558	49.56 554	10.52 563	0.00 563	9.18 558	39.56 558	12.29 556

mmdepth		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

monomultidepth		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

test_blank		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uanet_test		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

DCPMVS		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

cdsmvs_eth3d_5k		24.22 521	32.30 524	0.00 543	0.00 567	0.00 570	0.00 555	98.10 325	0.00 562	0.00 563	95.06 455	97.54 45	0.00 563	0.00 562	0.00 562	0.00 559

pcd_1.5k_mvsjas		7.98 526	10.65 529	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	95.82 165	0.00 563	0.00 562	0.00 562	0.00 559

sosnet-low-res		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

sosnet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uncertanet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

Regformer		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

ab-mvs-re		7.91 527	10.55 530	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	94.94 457	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uanet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

Meshroom													0.00 563
: In preparation.
AliceVision / Meshro													0.00 563
: In preparation.
AliceVision_Meshroom													0.00 563
: In preparation.
PatchmatchNet1														91.55 379	99.31 271	98.56 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.05 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					98.88 151	95.35 137		98.76 217		98.18 179		95.58 180	99.73 101	96.66 122	99.51 190

WAC-MVS						79.32 524								85.41 491

MSC_two_6792asdad				98.22 84	97.75 347	95.34 143		98.16 318					99.75 85	95.87 175	99.51 190	99.57 60

PC_three_1452									87.24 475	98.37 143	97.44 301	97.00 83	96.78 509	92.01 364	99.25 283	99.21 195

No_MVS				98.22 84	97.75 347	95.34 143		98.16 318					99.75 85	95.87 175	99.51 190	99.57 60

test_one_0601					99.05 119	95.50 127		98.87 171	97.21 107	98.03 199	98.30 179	96.93 90

eth-test2					0.00 567

eth-test					0.00 567

ZD-MVS					98.43 245	95.94 102		98.56 257	90.72 416	96.66 322	97.07 339	95.02 208	99.74 95	91.08 387	98.93 331

IU-MVS					99.22 78	95.40 132		98.14 321	85.77 492	98.36 146				95.23 227	99.51 190	99.49 97

test_241102_TWO								98.83 192	96.11 170	98.62 110	98.24 192	96.92 93	99.72 111	95.44 208	99.49 201	99.49 97

test_241102_ONE					99.22 78	95.35 137		98.83 192	96.04 179	99.08 55	98.13 208	97.87 28	99.33 319

test_0728_THIRD									96.62 131	98.40 140	98.28 185	97.10 71	99.71 127	95.70 182	99.62 124	99.58 52

GSMVS																98.06 399

test_part2					99.03 122	96.07 94				98.08 192

sam_mvs1												77.80 478				98.06 399

sam_mvs												77.38 482

MTGPA								98.73 222

test_post											10.87 559	76.83 486	99.07 388

patchmatchnet-post											96.84 359	77.36 483	99.42 274

gm-plane-assit					91.79 541	71.40 554			81.67 522		90.11 524		98.99 400	84.86 501

test9_res														91.29 382	98.89 338	99.00 249

agg_prior2														90.34 418	98.90 334	99.10 231

agg_prior					97.80 335	94.96 164		98.36 289		93.49 467			99.53 224

TestCases				98.06 101	99.08 109	96.16 88		99.16 70	94.35 281	97.78 230	98.07 219	95.84 162	99.12 378	91.41 380	99.42 231	98.91 275

test_prior				97.46 162	97.79 340	94.26 199		98.42 279					99.34 317			98.79 294

新几何1				97.25 182	98.29 259	94.70 173		97.73 354	77.98 540	94.83 421	96.67 372	92.08 308	99.45 263	88.17 454	98.65 378	97.61 437

旧先验1					97.80 335	93.87 211		97.75 353			97.04 342	93.57 258			98.68 373	98.72 311

原ACMM1				96.58 243	98.16 283	92.12 277		98.15 320	85.90 490	93.49 467	96.43 386	92.47 299	99.38 299	87.66 460	98.62 380	98.23 380

testdata2													99.46 255	87.84 455

segment_acmp												95.34 191

testdata				95.70 327	98.16 283	90.58 323		97.72 355	80.38 529	95.62 391	97.02 343	92.06 309	98.98 402	89.06 439	98.52 387	97.54 441

test12				97.46 162	97.61 369	94.07 203		97.78 352		93.57 465		93.31 266	99.42 274		98.78 354	98.89 279

plane_prior7					98.70 190	94.67 174

plane_prior6					98.38 251	94.37 191						91.91 314

plane_prior5								98.75 218					99.46 255	92.59 354	99.20 288	99.28 175

plane_prior4											96.77 365

plane_prior3						94.51 184			95.29 230	96.16 361

plane_prior1					98.49 233

n2								0.00 569

nn								0.00 569

door-mid								98.17 314

lessismore_v0				97.05 199	99.36 54	92.12 277		84.07 547		98.77 95	98.98 72	85.36 423	99.74 95	97.34 94	99.37 245	99.30 167

LGP-MVS_train				98.74 37	99.15 96	97.02 46		99.02 123	95.15 235	98.34 150	98.23 194	97.91 25	99.70 136	94.41 286	99.73 86	99.50 89

test11								98.08 328

door								97.81 351

HQP5-MVS						92.47 262

BP-MVS														90.51 413

HQP4-MVS										92.87 483			99.23 356			99.06 239

HQP3-MVS								98.43 276							98.74 364

HQP2-MVS												90.33 337

NP-MVS					98.14 287	93.72 217					95.08 453

ACMMP++_ref															99.52 184

ACMMP++															99.55 167

Test By Simon												94.51 228

ITE_SJBPF				97.85 118	98.64 197	96.66 61		98.51 262	95.63 208	97.22 268	97.30 319	95.52 182	98.55 454	90.97 391	98.90 334	98.34 364

DeepMVS_CX				77.17 532	90.94 544	85.28 471		74.08 557	52.51 552	80.87 549	88.03 534	75.25 495	70.63 555	59.23 551	84.94 545	75.62 548

High-resolution multi-view benchmark