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.

mvs5depth		99.30 34	99.59 12	98.44 266	99.65 69	95.35 331	99.82 3	99.94 2	99.83 7	99.42 110	99.94 2	98.13 121	99.96 14	99.63 36	99.96 28	100.00 1

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 142	98.08 192	99.95 1	99.45 51	99.98 2	99.75 16	99.80 1	99.97 7	99.82 12	99.99 5	99.99 2

fmvsm_s_conf0.1_n_a		99.17 53	99.30 45	98.80 188	99.75 34	96.59 274	97.97 222	99.86 16	98.22 195	99.88 21	99.71 22	98.59 66	99.84 174	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n_2		99.20 51	99.38 29	98.65 221	99.69 59	96.08 299	97.49 295	99.90 11	99.53 42	99.88 21	99.64 37	98.51 75	99.90 81	99.83 10	99.98 12	99.97 4

mmtdpeth		99.30 34	99.42 25	98.92 167	99.58 92	96.89 261	99.48 13	99.92 7	99.92 2	98.26 309	99.80 11	98.33 93	99.91 74	99.56 41	99.95 38	99.97 4

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 223	99.71 47	96.10 294	97.87 235	99.85 18	98.56 171	99.90 14	99.68 25	98.69 56	99.85 156	99.72 30	99.98 12	99.97 4

test_fmvs3		99.12 70	99.41 26	98.25 288	99.76 30	95.07 343	99.05 67	99.94 2	97.78 241	99.82 34	99.84 3	98.56 72	99.71 302	99.96 1	99.96 28	99.97 4

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 143	97.77 249	99.90 11	99.33 66	99.97 3	99.66 32	99.71 3	99.96 14	99.79 19	99.99 5	99.96 8

test_f		98.67 157	98.87 108	98.05 310	99.72 43	95.59 314	98.51 134	99.81 31	96.30 356	99.78 40	99.82 5	96.14 265	98.63 474	99.82 12	99.93 56	99.95 9

test_fmvs2		98.70 144	98.97 96	97.89 321	99.54 120	94.05 378	98.55 125	99.92 7	96.78 332	99.72 48	99.78 13	96.60 246	99.67 331	99.91 2	99.90 86	99.94 10

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 139	99.20 48	99.65 69	99.48 45	99.92 8	99.71 22	98.07 124	99.96 14	99.53 48	100.00 1	99.93 11

test_vis3_rt		99.14 63	99.17 61	99.07 135	99.78 24	98.38 119	98.92 82	99.94 2	97.80 238	99.91 12	99.67 30	97.15 208	98.91 467	99.76 23	99.56 267	99.92 12

fmvsm_s_conf0.5_n_2		99.14 63	99.31 42	98.63 227	99.49 143	96.08 299	97.38 309	99.81 31	99.48 45	99.84 30	99.57 49	98.46 80	99.89 97	99.82 12	99.97 21	99.91 13

MVStest1		95.86 380	95.60 374	96.63 407	95.87 485	91.70 433	97.93 224	98.94 314	98.03 219	99.56 74	99.66 32	71.83 470	98.26 478	99.35 59	99.24 335	99.91 13

fmvsm_s_conf0.5_n_a		99.10 72	99.20 59	98.78 195	99.55 115	96.59 274	97.79 245	99.82 30	98.21 197	99.81 37	99.53 65	98.46 80	99.84 174	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n_9		99.17 53	99.38 29	98.53 254	99.51 129	95.82 309	97.62 274	99.78 36	99.72 15	99.90 14	99.48 76	98.66 58	99.89 97	99.85 6	99.93 56	99.89 16

fmvsm_s_conf0.5_n		99.09 73	99.26 51	98.61 233	99.55 115	96.09 297	97.74 256	99.81 31	98.55 172	99.85 27	99.55 57	98.60 65	99.84 174	99.69 35	99.98 12	99.89 16

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 75	98.10 145	97.68 263	99.84 22	99.29 72	99.92 8	99.57 49	99.60 5	99.96 14	99.74 27	99.98 12	99.89 16

test_djsdf		99.52 13	99.51 15	99.53 39	99.86 14	98.74 92	99.39 20	99.56 110	99.11 98	99.70 52	99.73 20	99.00 27	99.97 7	99.26 66	99.98 12	99.89 16

fmvsm_s_conf0.5_n_11		99.21 48	99.34 36	98.80 188	99.48 151	96.56 279	97.97 222	99.69 54	99.63 29	99.84 30	99.54 63	98.21 111	99.94 42	99.76 23	99.95 38	99.88 20

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 102	99.34 23	99.71 47	99.27 74	99.90 14	99.74 18	99.68 4	99.97 7	99.55 43	99.99 5	99.88 20

fmvsm_s_conf0.5_n_8		99.13 67	99.26 51	98.74 208	99.51 129	96.44 286	97.65 269	99.65 69	99.66 24	99.78 40	99.48 76	97.92 138	99.93 54	99.72 30	99.95 38	99.87 22

fmvsm_s_conf0.5_n_7		98.83 119	99.04 85	98.20 295	99.30 201	94.83 352	97.23 326	99.36 198	98.64 155	99.84 30	99.43 89	98.10 123	99.91 74	99.56 41	99.96 28	99.87 22

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 90	98.21 136	97.82 240	99.84 22	99.41 58	99.92 8	99.41 94	99.51 8	99.95 26	99.84 9	99.97 21	99.87 22

ttmdpeth		97.91 260	98.02 245	97.58 356	98.69 351	94.10 377	98.13 182	98.90 323	97.95 225	97.32 382	99.58 47	95.95 281	98.75 472	96.41 313	99.22 339	99.87 22

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 102	99.28 40	99.66 65	99.09 108	99.89 18	99.68 25	99.53 7	99.97 7	99.50 51	99.99 5	99.87 22

EU-MVSNet		97.66 285	98.50 170	95.13 444	99.63 81	85.84 475	98.35 160	98.21 387	98.23 194	99.54 79	99.46 81	95.02 307	99.68 327	98.24 143	99.87 98	99.87 22

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 195	99.46 157	96.58 277	97.65 269	99.72 45	99.47 48	99.86 24	99.50 69	98.94 30	99.89 97	99.75 26	99.97 21	99.86 28

UA-Net		99.47 16	99.40 27	99.70 2	99.49 143	99.29 25	99.80 4	99.72 45	99.82 8	99.04 191	99.81 8	98.05 127	99.96 14	98.85 99	99.99 5	99.86 28

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 155	99.59 90	97.18 239	97.44 304	99.83 25	99.56 40	99.91 12	99.34 113	99.36 13	99.93 54	99.83 10	99.98 12	99.85 30

MM		98.22 230	97.99 248	98.91 168	98.66 361	96.97 253	97.89 231	94.44 462	99.54 41	98.95 211	99.14 171	93.50 343	99.92 65	99.80 17	99.96 28	99.85 30

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 30

fmvsm_l_conf0.5_n_a		99.19 52	99.27 48	98.94 161	99.65 69	97.05 248	97.80 244	99.76 39	98.70 153	99.78 40	99.11 177	98.79 42	99.95 26	99.85 6	99.96 28	99.83 33

fmvsm_l_conf0.5_n		99.21 48	99.28 47	99.02 148	99.64 75	97.28 227	97.82 240	99.76 39	98.73 146	99.82 34	99.09 185	98.81 38	99.95 26	99.86 4	99.96 28	99.83 33

mvsany_test3		98.87 109	98.92 100	98.74 208	99.38 179	96.94 257	98.58 122	99.10 288	96.49 344	99.96 4	99.81 8	98.18 114	99.45 423	98.97 90	99.79 151	99.83 33

fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 195	99.47 154	96.56 279	97.75 255	99.71 47	99.60 36	99.74 47	99.44 86	97.96 135	99.95 26	99.86 4	99.94 50	99.82 36

SSC-MVS		98.71 139	98.74 123	98.62 229	99.72 43	96.08 299	98.74 98	98.64 364	99.74 13	99.67 60	99.24 142	94.57 321	99.95 26	99.11 78	99.24 335	99.82 36

anonymousdsp		99.51 14	99.47 21	99.62 10	99.88 9	99.08 70	99.34 23	99.69 54	98.93 129	99.65 64	99.72 21	98.93 32	99.95 26	99.11 78	100.00 1	99.82 36

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 146	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 42	99.31 62	100.00 1	99.82 36

fmvsm_s_conf0.5_n_4		99.01 87	99.22 55	98.38 273	99.31 197	95.48 323	97.56 285	99.73 44	98.87 137	99.75 45	99.27 129	98.80 40	99.86 143	99.80 17	99.90 86	99.81 40

PS-CasMVS		99.40 26	99.33 38	99.62 10	99.71 47	99.10 66	99.29 36	99.53 123	99.53 42	99.46 101	99.41 94	98.23 106	99.95 26	98.89 97	99.95 38	99.81 40

VortexMVS		97.98 258	98.31 206	97.02 389	98.88 312	91.45 437	98.03 203	99.47 151	98.65 154	99.55 77	99.47 79	91.49 375	99.81 222	99.32 61	99.91 78	99.80 42

FC-MVSNet-test		99.27 38	99.25 53	99.34 83	99.77 27	98.37 121	99.30 35	99.57 101	99.61 35	99.40 115	99.50 69	97.12 209	99.85 156	99.02 87	99.94 50	99.80 42

test_cas_vis1_n_1920		98.33 215	98.68 138	97.27 378	99.69 59	92.29 427	98.03 203	99.85 18	97.62 251	99.96 4	99.62 40	93.98 336	99.74 284	99.52 50	99.86 105	99.79 44

test_vis1_n_1920		98.40 201	98.92 100	96.81 402	99.74 36	90.76 453	98.15 180	99.91 9	98.33 183	99.89 18	99.55 57	95.07 306	99.88 115	99.76 23	99.93 56	99.79 44

CP-MVSNet		99.21 48	99.09 80	99.56 27	99.65 69	98.96 78	99.13 58	99.34 210	99.42 56	99.33 130	99.26 135	97.01 217	99.94 42	98.74 108	99.93 56	99.79 44

fmvsm_s_conf0.5_n_5		99.07 79	99.10 78	98.99 151	99.47 154	97.22 233	97.40 306	99.83 25	97.61 254	99.85 27	99.30 123	98.80 40	99.95 26	99.71 32	99.90 86	99.78 47

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 20	99.69 5	99.58 94	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 88	99.95 38	99.78 47

CVMVSNet		96.25 366	97.21 307	93.38 465	99.10 259	80.56 493	97.20 331	98.19 390	96.94 320	99.00 196	99.02 200	89.50 394	99.80 231	96.36 317	99.59 255	99.78 47

TestfortrainingZip a		98.95 97	98.72 127	99.64 9	99.58 92	99.32 22	98.68 108	99.60 84	96.46 347	99.53 83	98.77 274	97.87 145	99.83 192	98.39 136	99.64 234	99.77 50

reproduce_monomvs		95.00 403	95.25 391	94.22 453	97.51 448	83.34 485	97.86 236	98.44 377	98.51 173	99.29 140	99.30 123	67.68 478	99.56 387	98.89 97	99.81 134	99.77 50

Anonymous20231211		99.27 38	99.27 48	99.26 101	99.29 204	98.18 137	99.49 12	99.51 129	99.70 16	99.80 38	99.68 25	96.84 226	99.83 192	99.21 71	99.91 78	99.77 50

PEN-MVS		99.41 25	99.34 36	99.62 10	99.73 37	99.14 58	99.29 36	99.54 119	99.62 33	99.56 74	99.42 90	98.16 118	99.96 14	98.78 103	99.93 56	99.77 50

WR-MVS_H		99.33 31	99.22 55	99.65 8	99.71 47	99.24 31	99.32 26	99.55 114	99.46 50	99.50 94	99.34 113	97.30 197	99.93 54	98.90 95	99.93 56	99.77 50

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 27	99.85 16	99.11 65	99.90 1	99.78 36	99.63 29	99.78 40	99.67 30	99.48 10	99.81 222	99.30 63	99.97 21	99.77 50
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
WB-MVS		98.52 188	98.55 161	98.43 267	99.65 69	95.59 314	98.52 129	98.77 349	99.65 26	99.52 88	99.00 215	94.34 327	99.93 54	98.65 115	98.83 383	99.76 56

patch_mono-2		98.51 189	98.63 148	98.17 298	99.38 179	94.78 354	97.36 314	99.69 54	98.16 207	98.49 290	99.29 126	97.06 212	99.97 7	98.29 142	99.91 78	99.76 56

nrg030		99.40 26	99.35 34	99.54 32	99.58 92	99.13 61	98.98 75	99.48 142	99.68 20	99.46 101	99.26 135	98.62 63	99.73 291	99.17 75	99.92 69	99.76 56

FIs		99.14 63	99.09 80	99.29 95	99.70 55	98.28 127	99.13 58	99.52 128	99.48 45	99.24 158	99.41 94	96.79 233	99.82 205	98.69 113	99.88 94	99.76 56

v7n		99.53 12	99.57 13	99.41 70	99.88 9	98.54 110	99.45 14	99.61 82	99.66 24	99.68 58	99.66 32	98.44 82	99.95 26	99.73 28	99.96 28	99.75 60

APDe-MVS		98.99 90	98.79 119	99.60 16	99.21 229	99.15 53	98.87 88	99.48 142	97.57 258	99.35 125	99.24 142	97.83 148	99.89 97	97.88 176	99.70 209	99.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 47	99.30 23	99.31 30	99.51 129	99.64 27	99.56 74	99.46 81	98.23 106	99.97 7	98.78 103	99.93 56	99.72 62

MSC_two_6792asdad				99.32 91	98.43 390	98.37 121		98.86 334					99.89 97	97.14 238	99.60 251	99.71 63

No_MVS				99.32 91	98.43 390	98.37 121		98.86 334					99.89 97	97.14 238	99.60 251	99.71 63

PMMVS2		98.07 247	98.08 239	98.04 311	99.41 174	94.59 363	94.59 458	99.40 186	97.50 267	98.82 241	98.83 261	96.83 228	99.84 174	97.50 212	99.81 134	99.71 63

Baseline_NR-MVSNet		98.98 93	98.86 112	99.36 74	99.82 19	98.55 107	97.47 300	99.57 101	99.37 61	99.21 164	99.61 43	96.76 236	99.83 192	98.06 158	99.83 123	99.71 63

XXY-MVS		99.14 63	99.15 68	99.10 128	99.76 30	97.74 191	98.85 92	99.62 79	98.48 175	99.37 120	99.49 75	98.75 46	99.86 143	98.20 148	99.80 145	99.71 63

test_0728_THIRD									98.17 204	99.08 179	99.02 200	97.89 143	99.88 115	97.07 244	99.71 202	99.70 68

MSP-MVS		98.40 201	98.00 247	99.61 14	99.57 101	99.25 30	98.57 123	99.35 204	97.55 262	99.31 138	97.71 391	94.61 320	99.88 115	96.14 330	99.19 346	99.70 68
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
SSC-MVS3.2		98.53 184	98.79 119	97.74 336	99.46 157	93.62 404	96.45 374	99.34 210	99.33 66	98.93 219	98.70 293	97.90 139	99.90 81	99.12 77	99.92 69	99.69 70

NormalMVS		98.26 225	97.97 252	99.15 121	99.64 75	97.83 178	98.28 164	99.43 173	99.24 76	98.80 245	98.85 254	89.76 390	99.94 42	98.04 160	99.67 223	99.68 71

KinetiMVS		99.03 85	99.02 88	99.03 145	99.70 55	97.48 208	98.43 147	99.29 239	99.70 16	99.60 71	99.07 187	96.13 266	99.94 42	99.42 56	99.87 98	99.68 71

dcpmvs_2		98.78 130	99.11 72	97.78 329	99.56 109	93.67 401	99.06 65	99.86 16	99.50 44	99.66 61	99.26 135	97.21 205	99.99 2	98.00 165	99.91 78	99.68 71

test_0728_SECOND				99.60 16	99.50 135	99.23 32	98.02 206	99.32 218					99.88 115	96.99 251	99.63 241	99.68 71

OurMVSNet-221017-0		99.37 29	99.31 42	99.53 39	99.91 3	98.98 72	99.63 7	99.58 94	99.44 53	99.78 40	99.76 15	96.39 254	99.92 65	99.44 55	99.92 69	99.68 71

fmvsm_s_conf0.5_n_6		99.08 77	99.21 58	98.69 216	99.36 186	96.51 281	97.62 274	99.68 60	98.43 177	99.85 27	99.10 180	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

CHOSEN 1792x2688		97.49 297	97.14 312	98.54 252	99.68 62	96.09 297	96.50 372	99.62 79	91.58 453	98.84 237	98.97 224	92.36 362	99.88 115	96.76 274	99.95 38	99.67 76

reproduce_model		99.15 58	98.97 96	99.67 4	99.33 195	99.44 10	98.15 180	99.47 151	99.12 97	99.52 88	99.32 121	98.31 94	99.90 81	97.78 184	99.73 185	99.66 78

IU-MVS					99.49 143	99.15 53		98.87 329	92.97 438	99.41 112				96.76 274	99.62 244	99.66 78

test_241102_TWO								99.30 231	98.03 219	99.26 148	99.02 200	97.51 182	99.88 115	96.91 257	99.60 251	99.66 78

DPE-MVS		98.59 171	98.26 214	99.57 22	99.27 210	99.15 53	97.01 341	99.39 188	97.67 247	99.44 105	98.99 217	97.53 179	99.89 97	95.40 360	99.68 217	99.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 105	99.27 42	99.57 101	99.39 59	99.75 45	99.62 40	99.17 20	99.83 192	99.06 83	99.62 244	99.66 78

EI-MVSNet-UG-set		98.69 148	98.71 132	98.62 229	99.10 259	96.37 288	97.23 326	98.87 329	99.20 83	99.19 166	98.99 217	97.30 197	99.85 156	98.77 106	99.79 151	99.65 83

Elysia		99.15 58	99.14 69	99.18 113	99.63 81	97.92 169	98.50 136	99.43 173	99.67 21	99.70 52	99.13 173	96.66 242	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 58	99.14 69	99.18 113	99.63 81	97.92 169	98.50 136	99.43 173	99.67 21	99.70 52	99.13 173	96.66 242	99.98 4	99.54 44	99.96 28	99.64 84

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 28	99.53 9	99.76 39	99.64 27	99.84 30	99.83 4	99.50 9	99.87 134	99.36 58	99.92 69	99.64 84

EI-MVSNet-Vis-set		98.68 154	98.70 135	98.63 227	99.09 262	96.40 287	97.23 326	98.86 334	99.20 83	99.18 170	98.97 224	97.29 199	99.85 156	98.72 110	99.78 156	99.64 84

ACMH	96.65 7	99.25 41	99.24 54	99.26 101	99.72 43	98.38 119	99.07 64	99.55 114	98.30 187	99.65 64	99.45 85	99.22 17	99.76 267	98.44 129	99.77 162	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 100	98.81 118	99.28 96	99.21 229	98.45 116	98.46 144	99.33 216	99.63 29	99.48 96	99.15 168	97.23 203	99.75 278	97.17 234	99.66 231	99.63 89

reproduce-ours		99.09 73	98.90 102	99.67 4	99.27 210	99.49 6	98.00 210	99.42 179	99.05 115	99.48 96	99.27 129	98.29 96	99.89 97	97.61 201	99.71 202	99.62 90

our_new_method		99.09 73	98.90 102	99.67 4	99.27 210	99.49 6	98.00 210	99.42 179	99.05 115	99.48 96	99.27 129	98.29 96	99.89 97	97.61 201	99.71 202	99.62 90

test_fmvs1_n		98.09 245	98.28 210	97.52 364	99.68 62	93.47 406	98.63 115	99.93 5	95.41 393	99.68 58	99.64 37	91.88 371	99.48 415	99.82 12	99.87 98	99.62 90

test1111		96.49 358	96.82 332	95.52 437	99.42 171	87.08 472	99.22 45	87.14 488	99.11 98	99.46 101	99.58 47	88.69 398	99.86 143	98.80 101	99.95 38	99.62 90

VPA-MVSNet		99.30 34	99.30 45	99.28 96	99.49 143	98.36 124	99.00 72	99.45 159	99.63 29	99.52 88	99.44 86	98.25 104	99.88 115	99.09 80	99.84 112	99.62 90

LPG-MVS_test		98.71 139	98.46 180	99.47 61	99.57 101	98.97 74	98.23 170	99.48 142	96.60 339	99.10 177	99.06 188	98.71 50	99.83 192	95.58 356	99.78 156	99.62 90

LGP-MVS_train				99.47 61	99.57 101	98.97 74		99.48 142	96.60 339	99.10 177	99.06 188	98.71 50	99.83 192	95.58 356	99.78 156	99.62 90

Test_1112_low_res		96.99 339	96.55 350	98.31 282	99.35 191	95.47 326	95.84 415	99.53 123	91.51 455	96.80 409	98.48 332	91.36 376	99.83 192	96.58 295	99.53 277	99.62 90

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 43	98.98 72	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 38	99.61 98

v10		98.97 94	99.11 72	98.55 247	99.44 164	96.21 293	98.90 83	99.55 114	98.73 146	99.48 96	99.60 45	96.63 245	99.83 192	99.70 33	99.99 5	99.61 98

sc_t1		99.62 7	99.66 8	99.53 39	99.82 19	99.09 69	99.50 11	99.63 73	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 53	99.93 56	99.60 100

test_vis1_n		98.31 218	98.50 170	97.73 339	99.76 30	94.17 374	98.68 108	99.91 9	96.31 354	99.79 39	99.57 49	92.85 356	99.42 428	99.79 19	99.84 112	99.60 100

v8		99.01 87	99.16 63	98.57 240	99.47 154	96.31 291	98.90 83	99.47 151	99.03 118	99.52 88	99.57 49	96.93 222	99.81 222	99.60 37	99.98 12	99.60 100

EI-MVSNet		98.40 201	98.51 167	98.04 311	99.10 259	94.73 357	97.20 331	98.87 329	98.97 124	99.06 181	99.02 200	96.00 273	99.80 231	98.58 118	99.82 128	99.60 100

SixPastTwentyTwo		98.75 135	98.62 150	99.16 118	99.83 18	97.96 166	99.28 40	98.20 388	99.37 61	99.70 52	99.65 36	92.65 360	99.93 54	99.04 85	99.84 112	99.60 100

IterMVS-LS		98.55 179	98.70 135	98.09 303	99.48 151	94.73 357	97.22 330	99.39 188	98.97 124	99.38 118	99.31 122	96.00 273	99.93 54	98.58 118	99.97 21	99.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		97.19 324	96.60 348	98.96 158	99.62 85	97.28 227	95.17 440	99.50 132	94.21 420	99.01 195	98.32 350	86.61 412	99.99 2	97.10 242	99.84 112	99.60 100

lecture		99.25 41	99.12 71	99.62 10	99.64 75	99.40 12	98.89 87	99.51 129	99.19 88	99.37 120	99.25 140	98.36 87	99.88 115	98.23 145	99.67 223	99.59 107

tt0320		99.61 8	99.65 9	99.48 57	99.71 47	98.94 79	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 81	99.54 44	99.95 38	99.59 107

ACMMP_NAP		98.75 135	98.48 176	99.57 22	99.58 92	99.29 25	97.82 240	99.25 252	96.94 320	98.78 247	99.12 176	98.02 128	99.84 174	97.13 240	99.67 223	99.59 107

VPNet		98.87 109	98.83 115	99.01 149	99.70 55	97.62 200	98.43 147	99.35 204	99.47 48	99.28 142	99.05 195	96.72 239	99.82 205	98.09 155	99.36 314	99.59 107

WR-MVS		98.40 201	98.19 225	99.03 145	99.00 287	97.65 197	96.85 351	98.94 314	98.57 168	98.89 226	98.50 329	95.60 291	99.85 156	97.54 208	99.85 107	99.59 107

HPM-MVS		98.79 128	98.53 165	99.59 20	99.65 69	99.29 25	99.16 54	99.43 173	96.74 334	98.61 271	98.38 342	98.62 63	99.87 134	96.47 309	99.67 223	99.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EG-PatchMatch MVS		98.99 90	99.01 90	98.94 161	99.50 135	97.47 209	98.04 201	99.59 91	98.15 212	99.40 115	99.36 108	98.58 71	99.76 267	98.78 103	99.68 217	99.59 107

Vis-MVSNet		99.34 30	99.36 33	99.27 99	99.73 37	98.26 128	99.17 53	99.78 36	99.11 98	99.27 144	99.48 76	98.82 37	99.95 26	98.94 92	99.93 56	99.59 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MED-MVS test				99.45 64	99.58 92	98.93 80	98.68 108	99.60 84	96.46 347	99.53 83	98.77 274		99.83 192	96.67 286	99.64 234	99.58 115

MED-MVS		98.90 104	98.72 127	99.45 64	99.58 92	98.93 80	98.68 108	99.60 84	98.14 213	99.53 83	98.77 274	97.87 145	99.83 192	96.67 286	99.64 234	99.58 115

ME-MVS		98.61 167	98.33 204	99.44 66	99.24 221	98.93 80	97.45 302	99.06 293	98.14 213	99.06 181	98.77 274	96.97 220	99.82 205	96.67 286	99.64 234	99.58 115

MP-MVS-pluss		98.57 174	98.23 219	99.60 16	99.69 59	99.35 17	97.16 336	99.38 190	94.87 405	98.97 205	98.99 217	98.01 129	99.88 115	97.29 227	99.70 209	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 148	98.40 188	99.54 32	99.53 123	99.17 45	98.52 129	99.31 223	97.46 275	98.44 294	98.51 325	97.83 148	99.88 115	96.46 310	99.58 260	99.58 115

ACMMPR		98.70 144	98.42 186	99.54 32	99.52 126	99.14 58	98.52 129	99.31 223	97.47 270	98.56 281	98.54 320	97.75 157	99.88 115	96.57 297	99.59 255	99.58 115

PGM-MVS		98.66 158	98.37 195	99.55 29	99.53 123	99.18 44	98.23 170	99.49 140	97.01 317	98.69 258	98.88 248	98.00 130	99.89 97	95.87 342	99.59 255	99.58 115

SteuartSystems-ACMMP		98.79 128	98.54 163	99.54 32	99.73 37	99.16 49	98.23 170	99.31 223	97.92 229	98.90 223	98.90 241	98.00 130	99.88 115	96.15 329	99.72 193	99.58 115
Skip Steuart: Steuart Systems R&D Blog.
SDMVSNet		99.23 46	99.32 40	98.96 158	99.68 62	97.35 216	98.84 94	99.48 142	99.69 18	99.63 67	99.68 25	99.03 24	99.96 14	97.97 169	99.92 69	99.57 123

sd_testset		99.28 37	99.31 42	99.19 112	99.68 62	98.06 155	99.41 17	99.30 231	99.69 18	99.63 67	99.68 25	99.25 16	99.96 14	97.25 230	99.92 69	99.57 123

TranMVSNet+NR-MVSNet		99.17 53	99.07 83	99.46 63	99.37 185	98.87 85	98.39 156	99.42 179	99.42 56	99.36 123	99.06 188	98.38 86	99.95 26	98.34 139	99.90 86	99.57 123

mPP-MVS		98.64 161	98.34 199	99.54 32	99.54 120	99.17 45	98.63 115	99.24 257	97.47 270	98.09 323	98.68 297	97.62 168	99.89 97	96.22 324	99.62 244	99.57 123

PVSNet_Blended_VisFu		98.17 239	98.15 231	98.22 294	99.73 37	95.15 339	97.36 314	99.68 60	94.45 415	98.99 200	99.27 129	96.87 225	99.94 42	97.13 240	99.91 78	99.57 123

1112_ss		97.29 316	96.86 328	98.58 237	99.34 194	96.32 290	96.75 357	99.58 94	93.14 436	96.89 404	97.48 405	92.11 368	99.86 143	96.91 257	99.54 273	99.57 123

MTAPA		98.88 108	98.64 146	99.61 14	99.67 66	99.36 16	98.43 147	99.20 263	98.83 144	98.89 226	98.90 241	96.98 219	99.92 65	97.16 235	99.70 209	99.56 129

XVS		98.72 138	98.45 181	99.53 39	99.46 157	99.21 34	98.65 113	99.34 210	98.62 160	97.54 365	98.63 309	97.50 183	99.83 192	96.79 270	99.53 277	99.56 129

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 99	99.29 36	99.63 73	99.30 71	99.65 64	99.60 45	99.16 22	99.82 205	99.07 81	99.83 123	99.56 129

X-MVStestdata		94.32 410	92.59 429	99.53 39	99.46 157	99.21 34	98.65 113	99.34 210	98.62 160	97.54 365	45.85 490	97.50 183	99.83 192	96.79 270	99.53 277	99.56 129

HPM-MVS_fast		99.01 87	98.82 116	99.57 22	99.71 47	99.35 17	99.00 72	99.50 132	97.33 287	98.94 218	98.86 251	98.75 46	99.82 205	97.53 209	99.71 202	99.56 129

K. test v3		98.00 254	97.66 279	99.03 145	99.79 23	97.56 202	99.19 52	92.47 474	99.62 33	99.52 88	99.66 32	89.61 392	99.96 14	99.25 68	99.81 134	99.56 129

CP-MVS		98.70 144	98.42 186	99.52 45	99.36 186	99.12 63	98.72 103	99.36 198	97.54 264	98.30 303	98.40 339	97.86 147	99.89 97	96.53 306	99.72 193	99.56 129

viewmacassd2359aftdt		98.86 113	98.87 108	98.83 181	99.53 123	97.32 220	97.70 261	99.64 71	98.22 195	99.25 156	99.27 129	98.40 84	99.61 368	97.98 168	99.87 98	99.55 136

FE-MVSNET		98.59 171	98.50 170	98.87 172	99.58 92	97.30 221	98.08 192	99.74 43	96.94 320	98.97 205	99.10 180	96.94 221	99.74 284	97.33 225	99.86 105	99.55 136

ZNCC-MVS		98.68 154	98.40 188	99.54 32	99.57 101	99.21 34	98.46 144	99.29 239	97.28 293	98.11 321	98.39 340	98.00 130	99.87 134	96.86 267	99.64 234	99.55 136

v1192		98.60 169	98.66 143	98.41 269	99.27 210	95.88 305	97.52 290	99.36 198	97.41 279	99.33 130	99.20 151	96.37 257	99.82 205	99.57 39	99.92 69	99.55 136

v1240		98.55 179	98.62 150	98.32 280	99.22 227	95.58 316	97.51 292	99.45 159	97.16 308	99.45 104	99.24 142	96.12 268	99.85 156	99.60 37	99.88 94	99.55 136

UGNet		98.53 184	98.45 181	98.79 192	97.94 419	96.96 255	99.08 61	98.54 372	99.10 105	96.82 408	99.47 79	96.55 248	99.84 174	98.56 123	99.94 50	99.55 136
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
E5new		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 309	98.43 131	99.84 112	99.54 142

E6new		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 309	98.43 131	99.84 112	99.54 142

E6		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 309	98.43 131	99.84 112	99.54 142

E5		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 309	98.43 131	99.84 112	99.54 142

AstraMVS		98.16 241	98.07 241	98.41 269	99.51 129	95.86 306	98.00 210	95.14 457	98.97 124	99.43 106	99.24 142	93.25 344	99.84 174	99.21 71	99.87 98	99.54 142

WBMVS		95.18 398	94.78 403	96.37 413	97.68 436	89.74 460	95.80 416	98.73 357	97.54 264	98.30 303	98.44 336	70.06 472	99.82 205	96.62 292	99.87 98	99.54 142

test2506		92.39 441	91.89 443	93.89 458	99.38 179	82.28 489	99.32 26	66.03 496	99.08 112	98.77 250	99.57 49	66.26 482	99.84 174	98.71 111	99.95 38	99.54 142

ECVR-MVS		96.42 360	96.61 346	95.85 429	99.38 179	88.18 467	99.22 45	86.00 490	99.08 112	99.36 123	99.57 49	88.47 403	99.82 205	98.52 126	99.95 38	99.54 142

v144192		98.54 182	98.57 159	98.45 264	99.21 229	95.98 302	97.63 273	99.36 198	97.15 310	99.32 136	99.18 158	95.84 285	99.84 174	99.50 51	99.91 78	99.54 142

v1921920		98.54 182	98.60 155	98.38 273	99.20 233	95.76 312	97.56 285	99.36 198	97.23 302	99.38 118	99.17 162	96.02 271	99.84 174	99.57 39	99.90 86	99.54 142

MP-MVS		98.46 194	98.09 236	99.54 32	99.57 101	99.22 33	98.50 136	99.19 267	97.61 254	97.58 361	98.66 302	97.40 191	99.88 115	94.72 375	99.60 251	99.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MIMVSNet1		99.38 28	99.32 40	99.55 29	99.86 14	99.19 43	99.41 17	99.59 91	99.59 37	99.71 50	99.57 49	97.12 209	99.90 81	99.21 71	99.87 98	99.54 142

ACMMP		98.75 135	98.50 170	99.52 45	99.56 109	99.16 49	98.87 88	99.37 194	97.16 308	98.82 241	99.01 211	97.71 159	99.87 134	96.29 321	99.69 212	99.54 142
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
SMA-MVS		98.40 201	98.03 244	99.51 49	99.16 248	99.21 34	98.05 199	99.22 260	94.16 421	98.98 201	99.10 180	97.52 181	99.79 244	96.45 311	99.64 234	99.53 155
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
HFP-MVS		98.71 139	98.44 183	99.51 49	99.49 143	99.16 49	98.52 129	99.31 223	97.47 270	98.58 277	98.50 329	97.97 134	99.85 156	96.57 297	99.59 255	99.53 155

UniMVSNet_NR-MVSNet		98.86 113	98.68 138	99.40 72	99.17 246	98.74 92	97.68 263	99.40 186	99.14 96	99.06 181	98.59 316	96.71 240	99.93 54	98.57 120	99.77 162	99.53 155

E4		98.87 109	98.88 105	98.81 185	99.52 126	97.23 230	97.62 274	99.61 82	98.58 166	99.18 170	99.33 116	98.29 96	99.69 317	97.99 167	99.83 123	99.52 158

GST-MVS		98.61 167	98.30 207	99.52 45	99.51 129	99.20 40	98.26 168	99.25 252	97.44 278	98.67 261	98.39 340	97.68 160	99.85 156	96.00 334	99.51 283	99.52 158

MGCNet		97.44 302	97.01 319	98.72 212	96.42 478	96.74 269	97.20 331	91.97 478	98.46 176	98.30 303	98.79 270	92.74 358	99.91 74	99.30 63	99.94 50	99.52 158

TDRefinement		99.42 24	99.38 29	99.55 29	99.76 30	99.33 21	99.68 6	99.71 47	99.38 60	99.53 83	99.61 43	98.64 60	99.80 231	98.24 143	99.84 112	99.52 158

FE-MVSNET2		99.15 58	99.22 55	98.94 161	99.70 55	97.49 205	98.62 117	99.67 64	98.85 142	99.34 127	99.54 63	98.47 76	99.81 222	98.93 93	99.91 78	99.51 162

v1144		98.60 169	98.66 143	98.41 269	99.36 186	95.90 304	97.58 283	99.34 210	97.51 266	99.27 144	99.15 168	96.34 259	99.80 231	99.47 54	99.93 56	99.51 162

v2v482		98.56 175	98.62 150	98.37 276	99.42 171	95.81 310	97.58 283	99.16 278	97.90 231	99.28 142	99.01 211	95.98 278	99.79 244	99.33 60	99.90 86	99.51 162

CPTT-MVS		97.84 274	97.36 298	99.27 99	99.31 197	98.46 115	98.29 163	99.27 246	94.90 404	97.83 345	98.37 343	94.90 309	99.84 174	93.85 403	99.54 273	99.51 162

viewdifsd2359ckpt11		98.84 116	99.04 85	98.24 290	99.56 109	95.51 319	97.38 309	99.70 52	99.16 93	99.57 72	99.40 97	98.26 102	99.71 302	98.55 124	99.82 128	99.50 166

viewmsd2359difaftdt		98.84 116	99.04 85	98.24 290	99.56 109	95.51 319	97.38 309	99.70 52	99.16 93	99.57 72	99.40 97	98.26 102	99.71 302	98.55 124	99.82 128	99.50 166

LuminaMVS		98.39 207	98.20 221	98.98 155	99.50 135	97.49 205	97.78 246	97.69 403	98.75 145	99.49 95	99.25 140	92.30 364	99.94 42	99.14 76	99.88 94	99.50 166

DU-MVS		98.82 122	98.63 148	99.39 73	99.16 248	98.74 92	97.54 288	99.25 252	98.84 143	99.06 181	98.76 280	96.76 236	99.93 54	98.57 120	99.77 162	99.50 166

NR-MVSNet		98.95 97	98.82 116	99.36 74	99.16 248	98.72 97	99.22 45	99.20 263	99.10 105	99.72 48	98.76 280	96.38 256	99.86 143	98.00 165	99.82 128	99.50 166

casdiffmvs_mvg		99.12 70	99.16 63	98.99 151	99.43 169	97.73 193	98.00 210	99.62 79	99.22 79	99.55 77	99.22 148	98.93 32	99.75 278	98.66 114	99.81 134	99.50 166
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMH+	96.62 9	99.08 77	99.00 92	99.33 89	99.71 47	98.83 87	98.60 120	99.58 94	99.11 98	99.53 83	99.18 158	98.81 38	99.67 331	96.71 281	99.77 162	99.50 166

SymmetryMVS		98.05 249	97.71 274	99.09 132	99.29 204	97.83 178	98.28 164	97.64 408	99.24 76	98.80 245	98.85 254	89.76 390	99.94 42	98.04 160	99.50 291	99.49 173

DVP-MVS++		98.90 104	98.70 135	99.51 49	98.43 390	99.15 53	99.43 15	99.32 218	98.17 204	99.26 148	99.02 200	98.18 114	99.88 115	97.07 244	99.45 298	99.49 173

PC_three_1452									93.27 434	99.40 115	98.54 320	98.22 109	97.00 485	95.17 363	99.45 298	99.49 173

GeoE		99.05 80	98.99 94	99.25 104	99.44 164	98.35 125	98.73 102	99.56 110	98.42 178	98.91 222	98.81 267	98.94 30	99.91 74	98.35 138	99.73 185	99.49 173

h-mvs33		97.77 277	97.33 301	99.10 128	99.21 229	97.84 177	98.35 160	98.57 370	99.11 98	98.58 277	99.02 200	88.65 401	99.96 14	98.11 153	96.34 461	99.49 173

IterMVS-SCA-FT		97.85 273	98.18 226	96.87 398	99.27 210	91.16 447	95.53 425	99.25 252	99.10 105	99.41 112	99.35 109	93.10 349	99.96 14	98.65 115	99.94 50	99.49 173

new-patchmatchnet		98.35 210	98.74 123	97.18 381	99.24 221	92.23 429	96.42 378	99.48 142	98.30 187	99.69 56	99.53 65	97.44 189	99.82 205	98.84 100	99.77 162	99.49 173

APD-MVS		98.10 243	97.67 276	99.42 68	99.11 257	98.93 80	97.76 252	99.28 243	94.97 402	98.72 256	98.77 274	97.04 213	99.85 156	93.79 404	99.54 273	99.49 173
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 219	98.04 243	99.07 135	99.56 109	97.83 178	99.29 36	98.07 394	99.03 118	98.59 275	99.13 173	92.16 366	99.90 81	96.87 265	99.68 217	99.49 173

DeepC-MVS	97.60 4	98.97 94	98.93 99	99.10 128	99.35 191	97.98 162	98.01 209	99.46 155	97.56 260	99.54 79	99.50 69	98.97 28	99.84 174	98.06 158	99.92 69	99.49 173
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	96.08 12	98.91 102	98.73 125	99.48 57	99.55 115	99.14 58	98.07 196	99.37 194	97.62 251	99.04 191	98.96 227	98.84 36	99.79 244	97.43 219	99.65 232	99.49 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
guyue		98.01 253	97.93 257	98.26 286	99.45 162	95.48 323	98.08 192	96.24 440	98.89 135	99.34 127	99.14 171	91.32 377	99.82 205	99.07 81	99.83 123	99.48 184

DVP-MVS		98.77 133	98.52 166	99.52 45	99.50 135	99.21 34	98.02 206	98.84 338	97.97 223	99.08 179	99.02 200	97.61 170	99.88 115	96.99 251	99.63 241	99.48 184
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
SR-MVS		98.71 139	98.43 184	99.57 22	99.18 244	99.35 17	98.36 159	99.29 239	98.29 190	98.88 230	98.85 254	97.53 179	99.87 134	96.14 330	99.31 323	99.48 184

TSAR-MVS + MP.		98.63 163	98.49 175	99.06 141	99.64 75	97.90 172	98.51 134	98.94 314	96.96 318	99.24 158	98.89 247	97.83 148	99.81 222	96.88 264	99.49 293	99.48 184
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 232	97.95 253	99.01 149	99.58 92	97.74 191	99.01 70	97.29 416	99.67 21	98.97 205	99.50 69	90.45 385	99.80 231	97.88 176	99.20 343	99.48 184

IterMVS		97.73 279	98.11 235	96.57 408	99.24 221	90.28 456	95.52 427	99.21 261	98.86 139	99.33 130	99.33 116	93.11 348	99.94 42	98.49 127	99.94 50	99.48 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 235	97.90 261	99.08 133	99.57 101	97.97 163	99.31 30	98.32 383	99.01 120	98.98 201	99.03 199	91.59 373	99.79 244	95.49 358	99.80 145	99.48 184

ACMP	95.32 15	98.41 198	98.09 236	99.36 74	99.51 129	98.79 90	97.68 263	99.38 190	95.76 380	98.81 243	98.82 264	98.36 87	99.82 205	94.75 372	99.77 162	99.48 184
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 254	97.63 282	99.10 128	99.24 221	98.17 138	96.89 350	98.73 357	95.66 381	97.92 336	97.70 393	97.17 207	99.66 344	96.18 328	99.23 338	99.47 192

3Dnovator+	97.89 3	98.69 148	98.51 167	99.24 106	98.81 327	98.40 117	99.02 69	99.19 267	98.99 121	98.07 325	99.28 127	97.11 211	99.84 174	96.84 268	99.32 321	99.47 192

diffmvs_AUTHOR		98.50 190	98.59 157	98.23 293	99.35 191	95.48 323	96.61 365	99.60 84	98.37 179	98.90 223	99.00 215	97.37 193	99.76 267	98.22 146	99.85 107	99.46 194

HPM-MVS++		98.10 243	97.64 281	99.48 57	99.09 262	99.13 61	97.52 290	98.75 354	97.46 275	96.90 403	97.83 385	96.01 272	99.84 174	95.82 346	99.35 316	99.46 194

V42		98.78 130	98.78 121	98.76 202	99.44 164	97.04 249	98.27 167	99.19 267	97.87 233	99.25 156	99.16 164	96.84 226	99.78 255	99.21 71	99.84 112	99.46 194

APD-MVS_3200maxsize		98.84 116	98.61 154	99.53 39	99.19 236	99.27 28	98.49 139	99.33 216	98.64 155	99.03 194	98.98 222	97.89 143	99.85 156	96.54 305	99.42 307	99.46 194

UniMVSNet (Re)		98.87 109	98.71 132	99.35 80	99.24 221	98.73 95	97.73 258	99.38 190	98.93 129	99.12 173	98.73 283	96.77 234	99.86 143	98.63 117	99.80 145	99.46 194

SR-MVS-dyc-post		98.81 124	98.55 161	99.57 22	99.20 233	99.38 13	98.48 142	99.30 231	98.64 155	98.95 211	98.96 227	97.49 186	99.86 143	96.56 301	99.39 310	99.45 199

RE-MVS-def			98.58 158		99.20 233	99.38 13	98.48 142	99.30 231	98.64 155	98.95 211	98.96 227	97.75 157		96.56 301	99.39 310	99.45 199

HQP_MVS		97.99 257	97.67 276	98.93 164	99.19 236	97.65 197	97.77 249	99.27 246	98.20 201	97.79 348	97.98 375	94.90 309	99.70 309	94.42 384	99.51 283	99.45 199

plane_prior5								99.27 246					99.70 309	94.42 384	99.51 283	99.45 199

lessismore_v0				98.97 157	99.73 37	97.53 204		86.71 489		99.37 120	99.52 68	89.93 388	99.92 65	98.99 89	99.72 193	99.44 203

TAMVS		98.24 229	98.05 242	98.80 188	99.07 266	97.18 239	97.88 232	98.81 343	96.66 338	99.17 172	99.21 149	94.81 315	99.77 261	96.96 255	99.88 94	99.44 203

DeepPCF-MVS	96.93 5	98.32 216	98.01 246	99.23 108	98.39 395	98.97 74	95.03 444	99.18 271	96.88 325	99.33 130	98.78 272	98.16 118	99.28 449	96.74 276	99.62 244	99.44 203

3Dnovator	98.27 2	98.81 124	98.73 125	99.05 142	98.76 332	97.81 186	99.25 43	99.30 231	98.57 168	98.55 283	99.33 116	97.95 136	99.90 81	97.16 235	99.67 223	99.44 203

E2		98.70 144	98.68 138	98.73 210	99.40 176	97.10 246	97.48 296	99.57 101	98.09 216	99.00 196	99.20 151	97.90 139	99.67 331	97.73 192	99.77 162	99.43 207

E3		98.69 148	98.68 138	98.73 210	99.40 176	97.10 246	97.48 296	99.57 101	98.09 216	99.00 196	99.20 151	97.90 139	99.67 331	97.73 192	99.77 162	99.43 207

MVSFormer		98.26 225	98.43 184	97.77 330	98.88 312	93.89 394	99.39 20	99.56 110	99.11 98	98.16 315	98.13 361	93.81 339	99.97 7	99.26 66	99.57 264	99.43 207

jason		97.45 301	97.35 299	97.76 333	99.24 221	93.93 390	95.86 412	98.42 379	94.24 419	98.50 289	98.13 361	94.82 313	99.91 74	97.22 231	99.73 185	99.43 207
jason: jason.
NCCC		97.86 268	97.47 293	99.05 142	98.61 366	98.07 152	96.98 343	98.90 323	97.63 250	97.04 393	97.93 380	95.99 277	99.66 344	95.31 361	98.82 385	99.43 207

Anonymous20240521		98.69 148	98.87 108	98.16 300	99.77 27	95.11 342	99.08 61	99.44 167	99.34 65	99.33 130	99.55 57	94.10 335	99.94 42	99.25 68	99.96 28	99.42 212

MVS_111021_HR		98.25 228	98.08 239	98.75 204	99.09 262	97.46 210	95.97 403	99.27 246	97.60 256	97.99 333	98.25 353	98.15 120	99.38 434	96.87 265	99.57 264	99.42 212

COLMAP_ROB	96.50 10	98.99 90	98.85 114	99.41 70	99.58 92	99.10 66	98.74 98	99.56 110	99.09 108	99.33 130	99.19 154	98.40 84	99.72 301	95.98 336	99.76 177	99.42 212
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SED-MVS		98.91 102	98.72 127	99.49 55	99.49 143	99.17 45	98.10 189	99.31 223	98.03 219	99.66 61	99.02 200	98.36 87	99.88 115	96.91 257	99.62 244	99.41 215

OPU-MVS				98.82 183	98.59 371	98.30 126	98.10 189				98.52 324	98.18 114	98.75 472	94.62 376	99.48 294	99.41 215

our_test_3		97.39 307	97.73 272	96.34 414	98.70 346	89.78 459	94.61 457	98.97 313	96.50 343	99.04 191	98.85 254	95.98 278	99.84 174	97.26 229	99.67 223	99.41 215

casdiffmvs		98.95 97	99.00 92	98.81 185	99.38 179	97.33 218	97.82 240	99.57 101	99.17 92	99.35 125	99.17 162	98.35 91	99.69 317	98.46 128	99.73 185	99.41 215
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
YYNet1		97.60 288	97.67 276	97.39 374	99.04 275	93.04 413	95.27 436	98.38 382	97.25 296	98.92 221	98.95 231	95.48 297	99.73 291	96.99 251	98.74 387	99.41 215

MDA-MVSNet_test_wron		97.60 288	97.66 279	97.41 373	99.04 275	93.09 409	95.27 436	98.42 379	97.26 295	98.88 230	98.95 231	95.43 298	99.73 291	97.02 247	98.72 389	99.41 215

GBi-Net		98.65 159	98.47 178	99.17 115	98.90 306	98.24 130	99.20 48	99.44 167	98.59 163	98.95 211	99.55 57	94.14 331	99.86 143	97.77 185	99.69 212	99.41 215

test1		98.65 159	98.47 178	99.17 115	98.90 306	98.24 130	99.20 48	99.44 167	98.59 163	98.95 211	99.55 57	94.14 331	99.86 143	97.77 185	99.69 212	99.41 215

FMVSNet1		99.17 53	99.17 61	99.17 115	99.55 115	98.24 130	99.20 48	99.44 167	99.21 81	99.43 106	99.55 57	97.82 151	99.86 143	98.42 135	99.89 92	99.41 215

test_fmvs1		97.72 280	97.94 255	97.07 388	98.66 361	92.39 424	97.68 263	99.81 31	95.20 398	99.54 79	99.44 86	91.56 374	99.41 429	99.78 21	99.77 162	99.40 224

viewdifsd2359ckpt07		98.71 139	98.86 112	98.26 286	99.43 169	95.65 313	97.20 331	99.66 65	99.20 83	99.29 140	99.01 211	98.29 96	99.73 291	97.92 172	99.75 181	99.39 225

viewmanbaseed2359cas		98.58 173	98.54 163	98.70 214	99.28 207	97.13 245	97.47 300	99.55 114	97.55 262	98.96 210	98.92 235	97.77 155	99.59 375	97.59 204	99.77 162	99.39 225

KD-MVS_self_test		99.25 41	99.18 60	99.44 66	99.63 81	99.06 71	98.69 107	99.54 119	99.31 69	99.62 70	99.53 65	97.36 194	99.86 143	99.24 70	99.71 202	99.39 225

v148		98.45 195	98.60 155	98.00 313	99.44 164	94.98 345	97.44 304	99.06 293	98.30 187	99.32 136	98.97 224	96.65 244	99.62 361	98.37 137	99.85 107	99.39 225

test20.03		98.78 130	98.77 122	98.78 195	99.46 157	97.20 236	97.78 246	99.24 257	99.04 117	99.41 112	98.90 241	97.65 163	99.76 267	97.70 194	99.79 151	99.39 225

CDPH-MVS		97.26 317	96.66 344	99.07 135	99.00 287	98.15 139	96.03 401	99.01 308	91.21 459	97.79 348	97.85 384	96.89 224	99.69 317	92.75 427	99.38 313	99.39 225

EPNet		96.14 370	95.44 382	98.25 288	90.76 494	95.50 322	97.92 227	94.65 460	98.97 124	92.98 476	98.85 254	89.12 396	99.87 134	95.99 335	99.68 217	99.39 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 239	97.87 263	99.07 135	98.67 356	98.24 130	97.01 341	98.93 317	97.25 296	97.62 357	98.34 347	97.27 200	99.57 384	96.42 312	99.33 319	99.39 225

DeepC-MVS_fast	96.85 6	98.30 219	98.15 231	98.75 204	98.61 366	97.23 230	97.76 252	99.09 290	97.31 290	98.75 253	98.66 302	97.56 174	99.64 355	96.10 333	99.55 271	99.39 225
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		98.53 184	98.27 213	99.32 91	99.31 197	98.75 91	98.19 174	99.41 183	96.77 333	98.83 238	98.90 241	97.80 153	99.82 205	95.68 352	99.52 280	99.38 234

test9_res														93.28 416	99.15 351	99.38 234

BP-MVS1		97.40 306	96.97 320	98.71 213	99.07 266	96.81 264	98.34 162	97.18 418	98.58 166	98.17 312	98.61 313	84.01 437	99.94 42	98.97 90	99.78 156	99.37 236

OPM-MVS		98.56 175	98.32 205	99.25 104	99.41 174	98.73 95	97.13 338	99.18 271	97.10 311	98.75 253	98.92 235	98.18 114	99.65 351	96.68 285	99.56 267	99.37 236
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 432	99.16 349	99.37 236

AllTest		98.44 196	98.20 221	99.16 118	99.50 135	98.55 107	98.25 169	99.58 94	96.80 330	98.88 230	99.06 188	97.65 163	99.57 384	94.45 382	99.61 249	99.37 236

TestCases				99.16 118	99.50 135	98.55 107		99.58 94	96.80 330	98.88 230	99.06 188	97.65 163	99.57 384	94.45 382	99.61 249	99.37 236

MDA-MVSNet-bldmvs		97.94 259	97.91 260	98.06 308	99.44 164	94.96 346	96.63 364	99.15 283	98.35 181	98.83 238	99.11 177	94.31 328	99.85 156	96.60 294	98.72 389	99.37 236

MVSTER		96.86 343	96.55 350	97.79 328	97.91 421	94.21 372	97.56 285	98.87 329	97.49 269	99.06 181	99.05 195	80.72 450	99.80 231	98.44 129	99.82 128	99.37 236

viewcassd2359sk11		98.55 179	98.51 167	98.67 219	99.29 204	96.99 252	97.39 307	99.54 119	97.73 243	98.81 243	99.08 186	97.55 175	99.66 344	97.52 211	99.67 223	99.36 243

pmmvs5		97.64 286	97.49 290	98.08 306	99.14 253	95.12 341	96.70 360	99.05 297	93.77 428	98.62 269	98.83 261	93.23 345	99.75 278	98.33 141	99.76 177	99.36 243

Anonymous20231206		98.21 232	98.21 220	98.20 295	99.51 129	95.43 328	98.13 182	99.32 218	96.16 363	98.93 219	98.82 264	96.00 273	99.83 192	97.32 226	99.73 185	99.36 243

train_agg		97.10 329	96.45 354	99.07 135	98.71 342	98.08 150	95.96 405	99.03 302	91.64 451	95.85 439	97.53 401	96.47 251	99.76 267	93.67 406	99.16 349	99.36 243

PVSNet_BlendedMVS		97.55 293	97.53 287	97.60 354	98.92 302	93.77 398	96.64 363	99.43 173	94.49 411	97.62 357	99.18 158	96.82 229	99.67 331	94.73 373	99.93 56	99.36 243

Anonymous20240529		98.93 100	98.87 108	99.12 124	99.19 236	98.22 135	99.01 70	98.99 311	99.25 75	99.54 79	99.37 104	97.04 213	99.80 231	97.89 173	99.52 280	99.35 248

F-COLMAP		97.30 314	96.68 341	99.14 122	99.19 236	98.39 118	97.27 325	99.30 231	92.93 439	96.62 417	98.00 373	95.73 288	99.68 327	92.62 430	98.46 406	99.35 248

viewdifsd2359ckpt13		98.39 207	98.29 209	98.70 214	99.26 219	97.19 237	97.51 292	99.48 142	96.94 320	98.58 277	98.82 264	97.47 188	99.55 391	97.21 232	99.33 319	99.34 250

ppachtmachnet_test		97.50 294	97.74 270	96.78 404	98.70 346	91.23 446	94.55 459	99.05 297	96.36 351	99.21 164	98.79 270	96.39 254	99.78 255	96.74 276	99.82 128	99.34 250

VDD-MVS		98.56 175	98.39 191	99.07 135	99.13 255	98.07 152	98.59 121	97.01 423	99.59 37	99.11 174	99.27 129	94.82 313	99.79 244	98.34 139	99.63 241	99.34 250

testgi		98.32 216	98.39 191	98.13 301	99.57 101	95.54 317	97.78 246	99.49 140	97.37 284	99.19 166	97.65 395	98.96 29	99.49 412	96.50 308	98.99 371	99.34 250

diffmvs		98.22 230	98.24 218	98.17 298	99.00 287	95.44 327	96.38 380	99.58 94	97.79 240	98.53 286	98.50 329	96.76 236	99.74 284	97.95 171	99.64 234	99.34 250
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UnsupCasMVSNet_eth		97.89 263	97.60 284	98.75 204	99.31 197	97.17 241	97.62 274	99.35 204	98.72 152	98.76 252	98.68 297	92.57 361	99.74 284	97.76 189	95.60 473	99.34 250

viewmambaseed2359dif		98.19 235	98.26 214	97.99 314	99.02 284	95.03 344	96.59 367	99.53 123	96.21 358	99.00 196	98.99 217	97.62 168	99.61 368	97.62 200	99.72 193	99.33 256

baseline		98.96 96	99.02 88	98.76 202	99.38 179	97.26 229	98.49 139	99.50 132	98.86 139	99.19 166	99.06 188	98.23 106	99.69 317	98.71 111	99.76 177	99.33 256

MG-MVS		96.77 347	96.61 346	97.26 379	98.31 399	93.06 410	95.93 408	98.12 393	96.45 349	97.92 336	98.73 283	93.77 341	99.39 432	91.19 451	99.04 363	99.33 256

HQP4-MVS										95.56 444			99.54 397			99.32 259

CDS-MVSNet		97.69 282	97.35 299	98.69 216	98.73 336	97.02 251	96.92 349	98.75 354	95.89 375	98.59 275	98.67 299	92.08 369	99.74 284	96.72 279	99.81 134	99.32 259
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 338	96.49 353	98.55 247	98.67 356	96.79 265	96.29 386	99.04 300	96.05 366	95.55 445	96.84 422	93.84 337	99.54 397	92.82 424	99.26 333	99.32 259

RPSCF		98.62 166	98.36 196	99.42 68	99.65 69	99.42 11	98.55 125	99.57 101	97.72 245	98.90 223	99.26 135	96.12 268	99.52 403	95.72 349	99.71 202	99.32 259

E3new		98.41 198	98.34 199	98.62 229	99.19 236	96.90 260	97.32 317	99.50 132	97.40 281	98.63 266	98.92 235	97.21 205	99.65 351	97.34 223	99.52 280	99.31 263

MVP-Stereo		98.08 246	97.92 258	98.57 240	98.96 294	96.79 265	97.90 230	99.18 271	96.41 350	98.46 292	98.95 231	95.93 282	99.60 371	96.51 307	98.98 374	99.31 263
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 201	98.68 138	97.54 362	98.96 294	97.99 159	97.88 232	99.36 198	98.20 201	99.63 67	99.04 197	98.76 45	95.33 489	96.56 301	99.74 182	99.31 263
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
VNet		98.42 197	98.30 207	98.79 192	98.79 331	97.29 226	98.23 170	98.66 361	99.31 69	98.85 235	98.80 268	94.80 316	99.78 255	98.13 152	99.13 354	99.31 263

test_prior				98.95 160	98.69 351	97.95 167		99.03 302					99.59 375			99.30 267

USDC		97.41 305	97.40 294	97.44 371	98.94 296	93.67 401	95.17 440	99.53 123	94.03 425	98.97 205	99.10 180	95.29 300	99.34 439	95.84 345	99.73 185	99.30 267

viewdifsd2359ckpt09		98.13 242	97.92 258	98.77 200	99.18 244	97.35 216	97.29 321	99.53 123	95.81 378	98.09 323	98.47 333	96.34 259	99.66 344	97.02 247	99.51 283	99.29 269

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 151	99.57 101	97.73 193	97.93 224	99.83 25	99.22 79	99.93 6	99.30 123	99.42 11	99.96 14	99.85 6	99.99 5	99.29 269

FMVSNet2		98.49 191	98.40 188	98.75 204	98.90 306	97.14 244	98.61 119	99.13 284	98.59 163	99.19 166	99.28 127	94.14 331	99.82 205	97.97 169	99.80 145	99.29 269

XVG-OURS-SEG-HR		98.49 191	98.28 210	99.14 122	99.49 143	98.83 87	96.54 368	99.48 142	97.32 289	99.11 174	98.61 313	99.33 15	99.30 445	96.23 323	98.38 407	99.28 272

mamba_0408		98.80 126	98.88 105	98.55 247	99.27 210	96.50 282	98.00 210	99.60 84	98.93 129	99.22 161	98.84 259	98.59 66	99.89 97	97.74 190	99.72 193	99.27 273

SSM_04072		98.80 126	98.88 105	98.56 245	99.27 210	96.50 282	98.00 210	99.60 84	98.93 129	99.22 161	98.84 259	98.59 66	99.90 81	97.74 190	99.72 193	99.27 273

SSM_0407		98.86 113	98.96 98	98.55 247	99.27 210	96.50 282	98.04 201	99.66 65	99.09 108	99.22 161	99.02 200	98.79 42	99.87 134	97.87 178	99.72 193	99.27 273

test12				98.93 164	98.58 373	97.83 178		98.66 361		96.53 421		95.51 295	99.69 317		99.13 354	99.27 273

DSMNet-mixed		97.42 304	97.60 284	96.87 398	99.15 252	91.46 436	98.54 127	99.12 285	92.87 441	97.58 361	99.63 39	96.21 263	99.90 81	95.74 348	99.54 273	99.27 273

N_pmnet		97.63 287	97.17 308	98.99 151	99.27 210	97.86 175	95.98 402	93.41 471	95.25 395	99.47 100	98.90 241	95.63 290	99.85 156	96.91 257	99.73 185	99.27 273

ambc				98.24 290	98.82 324	95.97 303	98.62 117	99.00 310		99.27 144	99.21 149	96.99 218	99.50 409	96.55 304	99.50 291	99.26 279

LFMVS		97.20 323	96.72 338	98.64 223	98.72 338	96.95 256	98.93 81	94.14 468	99.74 13	98.78 247	99.01 211	84.45 432	99.73 291	97.44 218	99.27 330	99.25 280

FMVSNet5		96.01 373	95.20 394	98.41 269	97.53 443	96.10 294	98.74 98	99.50 132	97.22 305	98.03 330	99.04 197	69.80 473	99.88 115	97.27 228	99.71 202	99.25 280

BH-RMVSNet		96.83 344	96.58 349	97.58 356	98.47 384	94.05 378	96.67 361	97.36 412	96.70 337	97.87 341	97.98 375	95.14 304	99.44 425	90.47 459	98.58 403	99.25 280

testf1		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 105	99.69 54	98.90 133	99.43 106	99.35 109	98.86 34	99.67 331	97.81 181	99.81 134	99.24 283

APD_test2		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 105	99.69 54	98.90 133	99.43 106	99.35 109	98.86 34	99.67 331	97.81 181	99.81 134	99.24 283

SSM_0404		98.90 104	99.01 90	98.57 240	99.42 171	96.59 274	98.13 182	99.66 65	99.09 108	99.30 139	99.02 200	98.79 42	99.89 97	97.87 178	99.80 145	99.23 285

旧先验1					98.82 324	97.45 211		98.76 351			98.34 347	95.50 296			99.01 368	99.23 285

test222					98.92 302	96.93 258	95.54 424	98.78 348	85.72 479	96.86 406	98.11 364	94.43 323			99.10 359	99.23 285

XVG-ACMP-BASELINE		98.56 175	98.34 199	99.22 109	99.54 120	98.59 104	97.71 259	99.46 155	97.25 296	98.98 201	98.99 217	97.54 177	99.84 174	95.88 339	99.74 182	99.23 285

FMVSNet3		97.50 294	97.24 305	98.29 284	98.08 414	95.83 308	97.86 236	98.91 322	97.89 232	98.95 211	98.95 231	87.06 409	99.81 222	97.77 185	99.69 212	99.23 285

icg_test_0407_2		98.20 234	98.38 193	97.65 347	99.03 278	94.03 381	95.78 417	99.45 159	98.16 207	99.06 181	98.71 286	98.27 100	99.68 327	97.50 212	99.45 298	99.22 290

IMVS_0407		98.39 207	98.64 146	97.66 345	99.03 278	94.03 381	98.10 189	99.45 159	98.16 207	99.06 181	98.71 286	98.27 100	99.71 302	97.50 212	99.45 298	99.22 290

IMVS_0404		98.07 247	98.20 221	97.69 341	99.03 278	94.03 381	96.67 361	99.45 159	98.16 207	98.03 330	98.71 286	96.80 232	99.82 205	97.50 212	99.45 298	99.22 290

IMVS_0403		98.34 211	98.56 160	97.66 345	99.03 278	94.03 381	97.98 218	99.45 159	98.16 207	98.89 226	98.71 286	97.90 139	99.74 284	97.50 212	99.45 298	99.22 290

无先验							95.74 419	98.74 356	89.38 470				99.73 291	92.38 434		99.22 290

blended_shiyan8		95.98 376	95.33 388	97.94 317	97.05 463	94.87 351	95.34 434	98.59 367	96.17 359	97.09 389	92.39 481	87.62 408	99.76 267	97.65 197	96.05 471	99.20 295

tttt0517		95.64 388	94.98 398	97.64 350	99.36 186	93.81 396	98.72 103	90.47 482	98.08 218	98.67 261	98.34 347	73.88 468	99.92 65	97.77 185	99.51 283	99.20 295

pmmvs-eth3d		98.47 193	98.34 199	98.86 174	99.30 201	97.76 189	97.16 336	99.28 243	95.54 386	99.42 110	99.19 154	97.27 200	99.63 358	97.89 173	99.97 21	99.20 295

MS-PatchMatch		97.68 283	97.75 269	97.45 370	98.23 405	93.78 397	97.29 321	98.84 338	96.10 365	98.64 265	98.65 304	96.04 270	99.36 435	96.84 268	99.14 352	99.20 295

新几何1				98.91 168	98.94 296	97.76 189		98.76 351	87.58 476	96.75 411	98.10 365	94.80 316	99.78 255	92.73 428	99.00 369	99.20 295

PHI-MVS		98.29 222	97.95 253	99.34 83	98.44 389	99.16 49	98.12 186	99.38 190	96.01 370	98.06 326	98.43 337	97.80 153	99.67 331	95.69 351	99.58 260	99.20 295

blended_shiyan6		95.99 375	95.33 388	97.95 316	97.06 461	94.89 349	95.34 434	98.58 368	96.17 359	97.06 391	92.41 480	87.64 407	99.76 267	97.64 198	96.09 466	99.19 301

GDP-MVS		97.50 294	97.11 314	98.67 219	99.02 284	96.85 262	98.16 179	99.71 47	98.32 185	98.52 288	98.54 320	83.39 441	99.95 26	98.79 102	99.56 267	99.19 301

Anonymous202405211		97.90 261	97.50 289	99.08 133	98.90 306	98.25 129	98.53 128	96.16 441	98.87 137	99.11 174	98.86 251	90.40 386	99.78 255	97.36 222	99.31 323	99.19 301

CANet		97.87 267	97.76 268	98.19 297	97.75 427	95.51 319	96.76 356	99.05 297	97.74 242	96.93 397	98.21 357	95.59 292	99.89 97	97.86 180	99.93 56	99.19 301

XVG-OURS		98.53 184	98.34 199	99.11 126	99.50 135	98.82 89	95.97 403	99.50 132	97.30 291	99.05 189	98.98 222	99.35 14	99.32 442	95.72 349	99.68 217	99.18 305

WTY-MVS		96.67 350	96.27 360	97.87 323	98.81 327	94.61 362	96.77 355	97.92 398	94.94 403	97.12 386	97.74 390	91.11 379	99.82 205	93.89 400	98.15 419	99.18 305

Vis-MVSNet (Re-imp)		97.46 299	97.16 309	98.34 279	99.55 115	96.10 294	98.94 80	98.44 377	98.32 185	98.16 315	98.62 311	88.76 397	99.73 291	93.88 401	99.79 151	99.18 305

TinyColmap		97.89 263	97.98 249	97.60 354	98.86 315	94.35 368	96.21 390	99.44 167	97.45 277	99.06 181	98.88 248	97.99 133	99.28 449	94.38 388	99.58 260	99.18 305

FE-blended-shiyan7		95.48 393	94.74 405	97.68 342	96.53 473	94.12 376	94.17 467	98.57 370	95.84 376	96.71 412	91.16 486	86.05 419	99.76 267	97.57 205	96.09 466	99.17 309

usedtu_blend_shiyan5		96.20 369	95.62 372	97.94 317	96.53 473	94.93 347	98.83 95	99.59 91	98.89 135	96.71 412	91.16 486	86.05 419	99.73 291	96.70 282	96.09 466	99.17 309

testdata				98.09 303	98.93 298	95.40 329		98.80 345	90.08 467	97.45 374	98.37 343	95.26 301	99.70 309	93.58 409	98.95 377	99.17 309

lupinMVS		97.06 332	96.86 328	97.65 347	98.88 312	93.89 394	95.48 428	97.97 396	93.53 431	98.16 315	97.58 399	93.81 339	99.91 74	96.77 273	99.57 264	99.17 309

Patchmtry		97.35 310	96.97 320	98.50 260	97.31 454	96.47 285	98.18 175	98.92 320	98.95 128	98.78 247	99.37 104	85.44 426	99.85 156	95.96 337	99.83 123	99.17 309

FE-MVSNET3		97.37 308	97.13 313	98.11 302	99.03 278	95.40 329	94.47 461	98.99 311	96.87 326	97.97 334	97.81 386	92.12 367	99.75 278	97.49 217	99.43 306	99.16 314

SD_0403		96.28 364	95.83 365	97.64 350	98.72 338	94.30 369	98.87 88	98.77 349	97.80 238	96.53 421	98.02 372	97.34 195	99.47 418	76.93 487	99.48 294	99.16 314

RRT-MVS		97.88 265	97.98 249	97.61 353	98.15 409	93.77 398	98.97 76	99.64 71	99.16 93	98.69 258	99.42 90	91.60 372	99.89 97	97.63 199	98.52 405	99.16 314

sss		97.21 322	96.93 322	98.06 308	98.83 321	95.22 337	96.75 357	98.48 376	94.49 411	97.27 383	97.90 381	92.77 357	99.80 231	96.57 297	99.32 321	99.16 314

CSCG		98.68 154	98.50 170	99.20 110	99.45 162	98.63 99	98.56 124	99.57 101	97.87 233	98.85 235	98.04 371	97.66 162	99.84 174	96.72 279	99.81 134	99.13 318

MVS_111021_LR		98.30 219	98.12 234	98.83 181	99.16 248	98.03 157	96.09 399	99.30 231	97.58 257	98.10 322	98.24 354	98.25 104	99.34 439	96.69 284	99.65 232	99.12 319

miper_lstm_enhance		97.18 325	97.16 309	97.25 380	98.16 408	92.85 415	95.15 442	99.31 223	97.25 296	98.74 255	98.78 272	90.07 387	99.78 255	97.19 233	99.80 145	99.11 320

testing3		93.51 425	92.09 436	97.75 334	98.60 368	94.40 366	97.32 317	95.26 456	97.56 260	96.79 410	95.50 450	53.57 494	99.77 261	95.26 362	98.97 375	99.08 321

原ACMM1				98.35 278	98.90 306	96.25 292		98.83 342	92.48 445	96.07 435	98.10 365	95.39 299	99.71 302	92.61 431	98.99 371	99.08 321

QAPM		97.31 313	96.81 334	98.82 183	98.80 330	97.49 205	99.06 65	99.19 267	90.22 465	97.69 354	99.16 164	96.91 223	99.90 81	90.89 456	99.41 308	99.07 323

PAPM_NR		96.82 346	96.32 357	98.30 283	99.07 266	96.69 272	97.48 296	98.76 351	95.81 378	96.61 418	96.47 431	94.12 334	99.17 456	90.82 457	97.78 432	99.06 324

eth_miper_zixun_eth		97.23 321	97.25 304	97.17 383	98.00 417	92.77 417	94.71 451	99.18 271	97.27 294	98.56 281	98.74 282	91.89 370	99.69 317	97.06 246	99.81 134	99.05 325

D2MVS		97.84 274	97.84 265	97.83 325	99.14 253	94.74 356	96.94 345	98.88 327	95.84 376	98.89 226	98.96 227	94.40 325	99.69 317	97.55 206	99.95 38	99.05 325

c3_l		97.36 309	97.37 297	97.31 375	98.09 413	93.25 408	95.01 445	99.16 278	97.05 313	98.77 250	98.72 285	92.88 354	99.64 355	96.93 256	99.76 177	99.05 325

PLC	94.65 16	96.51 355	95.73 368	98.85 175	98.75 334	97.91 171	96.42 378	99.06 293	90.94 462	95.59 442	97.38 411	94.41 324	99.59 375	90.93 454	98.04 428	99.05 325
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 104	98.90 102	98.91 168	99.67 66	97.82 183	99.00 72	99.44 167	99.45 51	99.51 93	99.24 142	98.20 113	99.86 143	95.92 338	99.69 212	99.04 329

CANet_DTU		97.26 317	97.06 316	97.84 324	97.57 438	94.65 361	96.19 392	98.79 346	97.23 302	95.14 454	98.24 354	93.22 346	99.84 174	97.34 223	99.84 112	99.04 329

PM-MVS		98.82 122	98.72 127	99.12 124	99.64 75	98.54 110	97.98 218	99.68 60	97.62 251	99.34 127	99.18 158	97.54 177	99.77 261	97.79 183	99.74 182	99.04 329

TSAR-MVS + GP.		98.18 237	97.98 249	98.77 200	98.71 342	97.88 173	96.32 384	98.66 361	96.33 352	99.23 160	98.51 325	97.48 187	99.40 430	97.16 235	99.46 296	99.02 332

DIV-MVS_self_test		97.02 335	96.84 330	97.58 356	97.82 425	94.03 381	94.66 454	99.16 278	97.04 314	98.63 266	98.71 286	88.69 398	99.69 317	97.00 249	99.81 134	99.01 333

mamv4		99.44 19	99.39 28	99.58 21	99.30 201	99.74 2	99.04 68	99.81 31	99.77 10	99.82 34	99.57 49	97.82 151	99.98 4	99.53 48	99.89 92	99.01 333

GA-MVS		95.86 380	95.32 390	97.49 367	98.60 368	94.15 375	93.83 473	97.93 397	95.49 388	96.68 414	97.42 409	83.21 442	99.30 445	96.22 324	98.55 404	99.01 333

OMC-MVS		97.88 265	97.49 290	99.04 144	98.89 311	98.63 99	96.94 345	99.25 252	95.02 400	98.53 286	98.51 325	97.27 200	99.47 418	93.50 412	99.51 283	99.01 333

cl____		97.02 335	96.83 331	97.58 356	97.82 425	94.04 380	94.66 454	99.16 278	97.04 314	98.63 266	98.71 286	88.68 400	99.69 317	97.00 249	99.81 134	99.00 337

pmmvs4		97.58 291	97.28 302	98.51 256	98.84 319	96.93 258	95.40 432	98.52 374	93.60 430	98.61 271	98.65 304	95.10 305	99.60 371	96.97 254	99.79 151	98.99 338

blend_shiyan4		92.09 447	90.16 454	97.88 322	96.78 468	94.93 347	95.24 438	98.58 368	96.22 357	96.07 435	91.42 485	63.46 490	99.73 291	96.70 282	76.98 489	98.98 339

EPNet_dtu		94.93 404	94.78 403	95.38 442	93.58 490	87.68 469	96.78 354	95.69 453	97.35 286	89.14 487	98.09 367	88.15 405	99.49 412	94.95 369	99.30 326	98.98 339
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 357	95.77 366	98.69 216	99.48 151	97.43 213	97.84 239	99.55 114	81.42 485	96.51 424	98.58 317	95.53 293	99.67 331	93.41 414	99.58 260	98.98 339

PVSNet_Blended		96.88 342	96.68 341	97.47 369	98.92 302	93.77 398	94.71 451	99.43 173	90.98 461	97.62 357	97.36 413	96.82 229	99.67 331	94.73 373	99.56 267	98.98 339

APD_test1		98.83 119	98.66 143	99.34 83	99.78 24	99.47 9	98.42 150	99.45 159	98.28 192	98.98 201	99.19 154	97.76 156	99.58 382	96.57 297	99.55 271	98.97 343

PAPR		95.29 395	94.47 406	97.75 334	97.50 449	95.14 340	94.89 448	98.71 359	91.39 457	95.35 452	95.48 452	94.57 321	99.14 459	84.95 475	97.37 445	98.97 343

EGC-MVSNET		85.24 453	80.54 456	99.34 83	99.77 27	99.20 40	99.08 61	99.29 239	12.08 492	20.84 493	99.42 90	97.55 175	99.85 156	97.08 243	99.72 193	98.96 345

thisisatest0530		95.27 396	94.45 407	97.74 336	99.19 236	94.37 367	97.86 236	90.20 483	97.17 307	98.22 310	97.65 395	73.53 469	99.90 81	96.90 262	99.35 316	98.95 346

mvs_anonymous		97.83 276	98.16 230	96.87 398	98.18 407	91.89 431	97.31 319	98.90 323	97.37 284	98.83 238	99.46 81	96.28 261	99.79 244	98.90 95	98.16 418	98.95 346

baseline1		95.96 378	95.44 382	97.52 364	98.51 382	93.99 388	98.39 156	96.09 444	98.21 197	98.40 301	97.76 389	86.88 410	99.63 358	95.42 359	89.27 486	98.95 346

CLD-MVS		97.49 297	97.16 309	98.48 261	99.07 266	97.03 250	94.71 451	99.21 261	94.46 413	98.06 326	97.16 417	97.57 173	99.48 415	94.46 381	99.78 156	98.95 346
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 251	98.14 233	97.64 350	98.58 373	95.19 338	97.48 296	99.23 259	97.47 270	97.90 338	98.62 311	97.04 213	98.81 470	97.55 206	99.41 308	98.94 350

DELS-MVS		98.27 223	98.20 221	98.48 261	98.86 315	96.70 271	95.60 423	99.20 263	97.73 243	98.45 293	98.71 286	97.50 183	99.82 205	98.21 147	99.59 255	98.93 351
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
cl22		95.79 383	95.39 385	96.98 392	96.77 469	92.79 416	94.40 463	98.53 373	94.59 410	97.89 339	98.17 360	82.82 446	99.24 451	96.37 315	99.03 364	98.92 352

LS3D		98.63 163	98.38 193	99.36 74	97.25 455	99.38 13	99.12 60	99.32 218	99.21 81	98.44 294	98.88 248	97.31 196	99.80 231	96.58 295	99.34 318	98.92 352

CMPMVS	75.91 23	96.29 363	95.44 382	98.84 180	96.25 481	98.69 98	97.02 340	99.12 285	88.90 472	97.83 345	98.86 251	89.51 393	98.90 468	91.92 435	99.51 283	98.92 352
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 161	98.48 176	99.11 126	98.85 318	98.51 112	98.49 139	99.83 25	98.37 179	99.69 56	99.46 81	98.21 111	99.92 65	94.13 394	99.30 326	98.91 355

mvsmamba		97.57 292	97.26 303	98.51 256	98.69 351	96.73 270	98.74 98	97.25 417	97.03 316	97.88 340	99.23 147	90.95 380	99.87 134	96.61 293	99.00 369	98.91 355

DPM-MVS		96.32 362	95.59 376	98.51 256	98.76 332	97.21 235	94.54 460	98.26 385	91.94 450	96.37 428	97.25 415	93.06 351	99.43 426	91.42 446	98.74 387	98.89 357

test_yl		96.69 348	96.29 358	97.90 319	98.28 400	95.24 335	97.29 321	97.36 412	98.21 197	98.17 312	97.86 382	86.27 414	99.55 391	94.87 370	98.32 408	98.89 357

DCV-MVSNet		96.69 348	96.29 358	97.90 319	98.28 400	95.24 335	97.29 321	97.36 412	98.21 197	98.17 312	97.86 382	86.27 414	99.55 391	94.87 370	98.32 408	98.89 357

SPE-MVS-test		99.13 67	99.09 80	99.26 101	99.13 255	98.97 74	99.31 30	99.88 14	99.44 53	98.16 315	98.51 325	98.64 60	99.93 54	98.91 94	99.85 107	98.88 360

UnsupCasMVSNet_bld		97.30 314	96.92 324	98.45 264	99.28 207	96.78 268	96.20 391	99.27 246	95.42 390	98.28 307	98.30 351	93.16 347	99.71 302	94.99 366	97.37 445	98.87 361

Effi-MVS+		98.02 251	97.82 266	98.62 229	98.53 380	97.19 237	97.33 316	99.68 60	97.30 291	96.68 414	97.46 407	98.56 72	99.80 231	96.63 291	98.20 414	98.86 362

test_0402		98.76 134	98.71 132	98.93 164	99.56 109	98.14 141	98.45 146	99.34 210	99.28 73	98.95 211	98.91 238	98.34 92	99.79 244	95.63 353	99.91 78	98.86 362

PatchmatchNet		95.58 389	95.67 371	95.30 443	97.34 453	87.32 471	97.65 269	96.65 433	95.30 394	97.07 390	98.69 295	84.77 429	99.75 278	94.97 368	98.64 398	98.83 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		93.78 421	93.91 413	93.39 464	98.82 324	81.72 491	97.76 252	95.28 455	98.60 162	96.54 420	96.66 426	65.85 485	99.62 361	96.65 290	98.99 371	98.82 365

test_vis1_rt		97.75 278	97.72 273	97.83 325	98.81 327	96.35 289	97.30 320	99.69 54	94.61 409	97.87 341	98.05 370	96.26 262	98.32 477	98.74 108	98.18 415	98.82 365

CL-MVSNet_self_test		97.44 302	97.22 306	98.08 306	98.57 375	95.78 311	94.30 465	98.79 346	96.58 341	98.60 273	98.19 359	94.74 319	99.64 355	96.41 313	98.84 382	98.82 365

miper_ehance_all_eth		97.06 332	97.03 317	97.16 385	97.83 424	93.06 410	94.66 454	99.09 290	95.99 371	98.69 258	98.45 335	92.73 359	99.61 368	96.79 270	99.03 364	98.82 365

MIMVSNet		96.62 353	96.25 361	97.71 340	99.04 275	94.66 360	99.16 54	96.92 429	97.23 302	97.87 341	99.10 180	86.11 418	99.65 351	91.65 441	99.21 342	98.82 365

hse-mvs2		97.46 299	97.07 315	98.64 223	98.73 336	97.33 218	97.45 302	97.64 408	99.11 98	98.58 277	97.98 375	88.65 401	99.79 244	98.11 153	97.39 444	98.81 370

GSMVS																98.81 370

sam_mvs1												84.74 430				98.81 370

SCA		96.41 361	96.66 344	95.67 433	98.24 403	88.35 465	95.85 414	96.88 430	96.11 364	97.67 355	98.67 299	93.10 349	99.85 156	94.16 390	99.22 339	98.81 370

Patchmatch-RL test		97.26 317	97.02 318	97.99 314	99.52 126	95.53 318	96.13 397	99.71 47	97.47 270	99.27 144	99.16 164	84.30 435	99.62 361	97.89 173	99.77 162	98.81 370

AUN-MVS		96.24 368	95.45 381	98.60 235	98.70 346	97.22 233	97.38 309	97.65 406	95.95 373	95.53 449	97.96 379	82.11 449	99.79 244	96.31 319	97.44 441	98.80 375

ITE_SJBPF				98.87 172	99.22 227	98.48 114		99.35 204	97.50 267	98.28 307	98.60 315	97.64 166	99.35 438	93.86 402	99.27 330	98.79 376

tpm		94.67 406	94.34 410	95.66 434	97.68 436	88.42 464	97.88 232	94.90 458	94.46 413	96.03 438	98.56 319	78.66 460	99.79 244	95.88 339	95.01 476	98.78 377

Patchmatch-test		96.55 354	96.34 356	97.17 383	98.35 396	93.06 410	98.40 155	97.79 399	97.33 287	98.41 297	98.67 299	83.68 440	99.69 317	95.16 364	99.31 323	98.77 378

EC-MVSNet		99.09 73	99.05 84	99.20 110	99.28 207	98.93 80	99.24 44	99.84 22	99.08 112	98.12 320	98.37 343	98.72 49	99.90 81	99.05 84	99.77 162	98.77 378

PMMVS		96.51 355	95.98 362	98.09 303	97.53 443	95.84 307	94.92 447	98.84 338	91.58 453	96.05 437	95.58 447	95.68 289	99.66 344	95.59 355	98.09 422	98.76 380

test_method		79.78 454	79.50 457	80.62 471	80.21 496	45.76 499	70.82 488	98.41 381	31.08 491	80.89 491	97.71 391	84.85 428	97.37 484	91.51 445	80.03 487	98.75 381

ab-mvs		98.41 198	98.36 196	98.59 236	99.19 236	97.23 230	99.32 26	98.81 343	97.66 248	98.62 269	99.40 97	96.82 229	99.80 231	95.88 339	99.51 283	98.75 381

CHOSEN 280x420		95.51 392	95.47 379	95.65 435	98.25 402	88.27 466	93.25 477	98.88 327	93.53 431	94.65 460	97.15 418	86.17 416	99.93 54	97.41 220	99.93 56	98.73 383

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 252	99.66 68	96.97 253	98.00 210	99.85 18	99.24 76	99.92 8	99.50 69	99.39 12	99.95 26	99.89 3	99.98 12	98.71 384

MVS_Test		98.18 237	98.36 196	97.67 343	98.48 383	94.73 357	98.18 175	99.02 305	97.69 246	98.04 329	99.11 177	97.22 204	99.56 387	98.57 120	98.90 381	98.71 384

PVSNet	93.40 17	95.67 386	95.70 369	95.57 436	98.83 321	88.57 463	92.50 480	97.72 401	92.69 443	96.49 427	96.44 432	93.72 342	99.43 426	93.61 407	99.28 329	98.71 384

alignmvs		97.35 310	96.88 327	98.78 195	98.54 378	98.09 146	97.71 259	97.69 403	99.20 83	97.59 360	95.90 442	88.12 406	99.55 391	98.18 149	98.96 376	98.70 387

ADS-MVSNet2		95.43 394	94.98 398	96.76 405	98.14 410	91.74 432	97.92 227	97.76 400	90.23 463	96.51 424	98.91 238	85.61 423	99.85 156	92.88 422	96.90 454	98.69 388

ADS-MVSNet		95.24 397	94.93 401	96.18 422	98.14 410	90.10 458	97.92 227	97.32 415	90.23 463	96.51 424	98.91 238	85.61 423	99.74 284	92.88 422	96.90 454	98.69 388

MDTV_nov1_ep13_2view						74.92 495	97.69 262		90.06 468	97.75 351		85.78 422		93.52 410		98.69 388

MSDG		97.71 281	97.52 288	98.28 285	98.91 305	96.82 263	94.42 462	99.37 194	97.65 249	98.37 302	98.29 352	97.40 191	99.33 441	94.09 395	99.22 339	98.68 391

mvsany_test1		97.60 288	97.54 286	97.77 330	97.72 428	95.35 331	95.36 433	97.13 421	94.13 422	99.71 50	99.33 116	97.93 137	99.30 445	97.60 203	98.94 378	98.67 392

CS-MVS		99.13 67	99.10 78	99.24 106	99.06 271	99.15 53	99.36 22	99.88 14	99.36 64	98.21 311	98.46 334	98.68 57	99.93 54	99.03 86	99.85 107	98.64 393

Syy-MVS		96.04 372	95.56 378	97.49 367	97.10 459	94.48 364	96.18 394	96.58 435	95.65 382	94.77 457	92.29 483	91.27 378	99.36 435	98.17 151	98.05 426	98.63 394

myMVS_eth3d		91.92 449	90.45 450	96.30 415	97.10 459	90.90 450	96.18 394	96.58 435	95.65 382	94.77 457	92.29 483	53.88 493	99.36 435	89.59 463	98.05 426	98.63 394

balanced_conf03		98.63 163	98.72 127	98.38 273	98.66 361	96.68 273	98.90 83	99.42 179	98.99 121	98.97 205	99.19 154	95.81 286	99.85 156	98.77 106	99.77 162	98.60 396

miper_enhance_ethall		96.01 373	95.74 367	96.81 402	96.41 479	92.27 428	93.69 475	98.89 326	91.14 460	98.30 303	97.35 414	90.58 384	99.58 382	96.31 319	99.03 364	98.60 396

Effi-MVS+-dtu		98.26 225	97.90 261	99.35 80	98.02 416	99.49 6	98.02 206	99.16 278	98.29 190	97.64 356	97.99 374	96.44 253	99.95 26	96.66 289	98.93 379	98.60 396

new_pmnet		96.99 339	96.76 336	97.67 343	98.72 338	94.89 349	95.95 407	98.20 388	92.62 444	98.55 283	98.54 320	94.88 312	99.52 403	93.96 398	99.44 305	98.59 399

MVSMamba_PlusPlus		98.83 119	98.98 95	98.36 277	99.32 196	96.58 277	98.90 83	99.41 183	99.75 11	98.72 256	99.50 69	96.17 264	99.94 42	99.27 65	99.78 156	98.57 400

testing91		93.32 428	92.27 433	96.47 411	97.54 441	91.25 444	96.17 396	96.76 432	97.18 306	93.65 474	93.50 473	65.11 487	99.63 358	93.04 419	97.45 440	98.53 401

EIA-MVS		98.00 254	97.74 270	98.80 188	98.72 338	98.09 146	98.05 199	99.60 84	97.39 282	96.63 416	95.55 448	97.68 160	99.80 231	96.73 278	99.27 330	98.52 402

PatchMatch-RL		97.24 320	96.78 335	98.61 233	99.03 278	97.83 178	96.36 381	99.06 293	93.49 433	97.36 381	97.78 387	95.75 287	99.49 412	93.44 413	98.77 386	98.52 402

sasdasda		98.34 211	98.26 214	98.58 237	98.46 386	97.82 183	98.96 77	99.46 155	99.19 88	97.46 372	95.46 453	98.59 66	99.46 421	98.08 156	98.71 391	98.46 404

ET-MVSNet_ETH3D		94.30 412	93.21 423	97.58 356	98.14 410	94.47 365	94.78 450	93.24 473	94.72 407	89.56 485	95.87 443	78.57 462	99.81 222	96.91 257	97.11 453	98.46 404

canonicalmvs		98.34 211	98.26 214	98.58 237	98.46 386	97.82 183	98.96 77	99.46 155	99.19 88	97.46 372	95.46 453	98.59 66	99.46 421	98.08 156	98.71 391	98.46 404

UBG		93.25 430	92.32 431	96.04 427	97.72 428	90.16 457	95.92 410	95.91 448	96.03 369	93.95 471	93.04 477	69.60 474	99.52 403	90.72 458	97.98 429	98.45 407

tt0805		98.69 148	98.62 150	98.90 171	99.75 34	99.30 23	99.15 56	96.97 425	98.86 139	98.87 234	97.62 398	98.63 62	98.96 464	99.41 57	98.29 411	98.45 407

TAPA-MVS	96.21 11	96.63 352	95.95 363	98.65 221	98.93 298	98.09 146	96.93 347	99.28 243	83.58 482	98.13 319	97.78 387	96.13 266	99.40 430	93.52 410	99.29 328	98.45 407
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net		98.34 211	98.28 210	98.51 256	98.47 384	97.59 201	98.96 77	99.48 142	99.18 91	97.40 377	95.50 450	98.66 58	99.50 409	98.18 149	98.71 391	98.44 410

BH-untuned		96.83 344	96.75 337	97.08 386	98.74 335	93.33 407	96.71 359	98.26 385	96.72 335	98.44 294	97.37 412	95.20 302	99.47 418	91.89 436	97.43 442	98.44 410

WB-MVSnew		95.73 385	95.57 377	96.23 420	96.70 470	90.70 454	96.07 400	93.86 469	95.60 384	97.04 393	95.45 456	96.00 273	99.55 391	91.04 452	98.31 410	98.43 412

pmmvs3		95.03 401	94.40 408	96.93 394	97.70 433	92.53 421	95.08 443	97.71 402	88.57 473	97.71 352	98.08 368	79.39 457	99.82 205	96.19 326	99.11 358	98.43 412

DP-MVS Recon		97.33 312	96.92 324	98.57 240	99.09 262	97.99 159	96.79 353	99.35 204	93.18 435	97.71 352	98.07 369	95.00 308	99.31 443	93.97 397	99.13 354	98.42 414

testing99		93.04 434	91.98 441	96.23 420	97.53 443	90.70 454	96.35 382	95.94 447	96.87 326	93.41 475	93.43 475	63.84 489	99.59 375	93.24 417	97.19 450	98.40 415

ETVMVS		92.60 439	91.08 448	97.18 381	97.70 433	93.65 403	96.54 368	95.70 451	96.51 342	94.68 459	92.39 481	61.80 491	99.50 409	86.97 470	97.41 443	98.40 415

Fast-Effi-MVS+-dtu		98.27 223	98.09 236	98.81 185	98.43 390	98.11 143	97.61 279	99.50 132	98.64 155	97.39 379	97.52 403	98.12 122	99.95 26	96.90 262	98.71 391	98.38 417

LF4IMVS		97.90 261	97.69 275	98.52 255	99.17 246	97.66 196	97.19 335	99.47 151	96.31 354	97.85 344	98.20 358	96.71 240	99.52 403	94.62 376	99.72 193	98.38 417

testing11		93.08 433	92.02 438	96.26 418	97.56 439	90.83 452	96.32 384	95.70 451	96.47 346	92.66 478	93.73 470	64.36 488	99.59 375	93.77 405	97.57 436	98.37 419

Fast-Effi-MVS+		97.67 284	97.38 296	98.57 240	98.71 342	97.43 213	97.23 326	99.45 159	94.82 406	96.13 432	96.51 428	98.52 74	99.91 74	96.19 326	98.83 383	98.37 419

test0.0.03 1		94.51 407	93.69 417	96.99 391	96.05 482	93.61 405	94.97 446	93.49 470	96.17 359	97.57 363	94.88 463	82.30 447	99.01 463	93.60 408	94.17 480	98.37 419

UWE-MVS		92.38 442	91.76 445	94.21 454	97.16 457	84.65 480	95.42 431	88.45 486	95.96 372	96.17 431	95.84 445	66.36 481	99.71 302	91.87 437	98.64 398	98.28 422

FE-MVS		95.66 387	94.95 400	97.77 330	98.53 380	95.28 334	99.40 19	96.09 444	93.11 437	97.96 335	99.26 135	79.10 459	99.77 261	92.40 433	98.71 391	98.27 423

baseline2		93.73 422	92.83 428	96.42 412	97.70 433	91.28 443	96.84 352	89.77 484	93.96 427	92.44 479	95.93 441	79.14 458	99.77 261	92.94 420	96.76 458	98.21 424

thisisatest0515		94.12 416	93.16 424	96.97 393	98.60 368	92.90 414	93.77 474	90.61 481	94.10 423	96.91 400	95.87 443	74.99 467	99.80 231	94.52 379	99.12 357	98.20 425

EPMVS		93.72 423	93.27 422	95.09 446	96.04 483	87.76 468	98.13 182	85.01 491	94.69 408	96.92 398	98.64 307	78.47 464	99.31 443	95.04 365	96.46 460	98.20 425

dp		93.47 426	93.59 419	93.13 467	96.64 471	81.62 492	97.66 267	96.42 438	92.80 442	96.11 433	98.64 307	78.55 463	99.59 375	93.31 415	92.18 485	98.16 427

CNLPA		97.17 326	96.71 339	98.55 247	98.56 376	98.05 156	96.33 383	98.93 317	96.91 324	97.06 391	97.39 410	94.38 326	99.45 423	91.66 440	99.18 348	98.14 428

dmvs_re		95.98 376	95.39 385	97.74 336	98.86 315	97.45 211	98.37 158	95.69 453	97.95 225	96.56 419	95.95 440	90.70 383	97.68 483	88.32 466	96.13 465	98.11 429

HY-MVS	95.94 13	95.90 379	95.35 387	97.55 361	97.95 418	94.79 353	98.81 97	96.94 428	92.28 448	95.17 453	98.57 318	89.90 389	99.75 278	91.20 450	97.33 449	98.10 430

CostFormer		93.97 418	93.78 416	94.51 450	97.53 443	85.83 476	97.98 218	95.96 446	89.29 471	94.99 456	98.63 309	78.63 461	99.62 361	94.54 378	96.50 459	98.09 431

FA-MVS(test-final)		96.99 339	96.82 332	97.50 366	98.70 346	94.78 354	99.34 23	96.99 424	95.07 399	98.48 291	99.33 116	88.41 404	99.65 351	96.13 332	98.92 380	98.07 432

AdaColmap		97.14 328	96.71 339	98.46 263	98.34 397	97.80 187	96.95 344	98.93 317	95.58 385	96.92 398	97.66 394	95.87 284	99.53 399	90.97 453	99.14 352	98.04 433

KD-MVS_2432*1600		92.87 437	91.99 439	95.51 438	91.37 492	89.27 461	94.07 468	98.14 391	95.42 390	97.25 384	96.44 432	67.86 476	99.24 451	91.28 448	96.08 469	98.02 434

miper_refine_blended		92.87 437	91.99 439	95.51 438	91.37 492	89.27 461	94.07 468	98.14 391	95.42 390	97.25 384	96.44 432	67.86 476	99.24 451	91.28 448	96.08 469	98.02 434

TESTMET0.1,1		92.19 446	91.77 444	93.46 462	96.48 477	82.80 488	94.05 470	91.52 480	94.45 415	94.00 469	94.88 463	66.65 480	99.56 387	95.78 347	98.11 421	98.02 434

testing222		91.96 448	90.37 451	96.72 406	97.47 450	92.59 419	96.11 398	94.76 459	96.83 329	92.90 477	92.87 478	57.92 492	99.55 391	86.93 471	97.52 437	98.00 437

PCF-MVS	92.86 18	94.36 409	93.00 427	98.42 268	98.70 346	97.56 202	93.16 478	99.11 287	79.59 486	97.55 364	97.43 408	92.19 365	99.73 291	79.85 484	99.45 298	97.97 438
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		90.22 452	89.28 455	93.02 468	94.50 489	82.87 487	96.52 371	87.51 487	95.21 397	92.36 480	96.04 437	71.57 471	98.25 479	72.04 489	97.77 433	97.94 439

myMVS_eth3d28		92.92 436	92.31 432	94.77 447	97.84 423	87.59 470	96.19 392	96.11 443	97.08 312	94.27 463	93.49 474	66.07 484	98.78 471	91.78 438	97.93 431	97.92 440

OpenMVS	96.65 7	97.09 330	96.68 341	98.32 280	98.32 398	97.16 242	98.86 91	99.37 194	89.48 469	96.29 430	99.15 168	96.56 247	99.90 81	92.90 421	99.20 343	97.89 441

Gipuma		99.03 85	99.16 63	98.64 223	99.94 2	98.51 112	99.32 26	99.75 42	99.58 39	98.60 273	99.62 40	98.22 109	99.51 408	97.70 194	99.73 185	97.89 441
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 451	90.30 453	93.70 460	97.72 428	84.34 484	90.24 484	97.42 410	90.20 466	93.79 472	93.09 476	90.90 382	98.89 469	86.57 473	72.76 490	97.87 443

test-LLR		93.90 419	93.85 414	94.04 455	96.53 473	84.62 481	94.05 470	92.39 475	96.17 359	94.12 466	95.07 457	82.30 447	99.67 331	95.87 342	98.18 415	97.82 444

test-mter		92.33 444	91.76 445	94.04 455	96.53 473	84.62 481	94.05 470	92.39 475	94.00 426	94.12 466	95.07 457	65.63 486	99.67 331	95.87 342	98.18 415	97.82 444

tpm2		93.09 432	92.58 430	94.62 449	97.56 439	86.53 473	97.66 267	95.79 450	86.15 478	94.07 468	98.23 356	75.95 465	99.53 399	90.91 455	96.86 457	97.81 446

CR-MVSNet		96.28 364	95.95 363	97.28 377	97.71 431	94.22 370	98.11 187	98.92 320	92.31 447	96.91 400	99.37 104	85.44 426	99.81 222	97.39 221	97.36 447	97.81 446

RPMNet		97.02 335	96.93 322	97.30 376	97.71 431	94.22 370	98.11 187	99.30 231	99.37 61	96.91 400	99.34 113	86.72 411	99.87 134	97.53 209	97.36 447	97.81 446

tpmrst		95.07 400	95.46 380	93.91 457	97.11 458	84.36 483	97.62 274	96.96 426	94.98 401	96.35 429	98.80 268	85.46 425	99.59 375	95.60 354	96.23 463	97.79 449

PAPM		91.88 450	90.34 452	96.51 409	98.06 415	92.56 420	92.44 481	97.17 419	86.35 477	90.38 484	96.01 438	86.61 412	99.21 454	70.65 490	95.43 474	97.75 450

FPMVS		93.44 427	92.23 434	97.08 386	99.25 220	97.86 175	95.61 422	97.16 420	92.90 440	93.76 473	98.65 304	75.94 466	95.66 487	79.30 485	97.49 438	97.73 451

MAR-MVS		96.47 359	95.70 369	98.79 192	97.92 420	99.12 63	98.28 164	98.60 366	92.16 449	95.54 448	96.17 436	94.77 318	99.52 403	89.62 462	98.23 412	97.72 452
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
ETV-MVS		98.03 250	97.86 264	98.56 245	98.69 351	98.07 152	97.51 292	99.50 132	98.10 215	97.50 369	95.51 449	98.41 83	99.88 115	96.27 322	99.24 335	97.71 453

thres600view7		94.45 408	93.83 415	96.29 416	99.06 271	91.53 435	97.99 217	94.24 466	98.34 182	97.44 375	95.01 459	79.84 453	99.67 331	84.33 476	98.23 412	97.66 454

thres400		94.14 415	93.44 420	96.24 419	98.93 298	91.44 438	97.60 280	94.29 464	97.94 227	97.10 387	94.31 468	79.67 455	99.62 361	83.05 478	98.08 423	97.66 454

IB-MVS	91.63 19	92.24 445	90.90 449	96.27 417	97.22 456	91.24 445	94.36 464	93.33 472	92.37 446	92.24 481	94.58 467	66.20 483	99.89 97	93.16 418	94.63 478	97.66 454
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
tpmvs		95.02 402	95.25 391	94.33 451	96.39 480	85.87 474	98.08 192	96.83 431	95.46 389	95.51 450	98.69 295	85.91 421	99.53 399	94.16 390	96.23 463	97.58 457

cascas		94.79 405	94.33 411	96.15 426	96.02 484	92.36 426	92.34 482	99.26 251	85.34 480	95.08 455	94.96 462	92.96 353	98.53 475	94.41 387	98.59 402	97.56 458

PatchT		96.65 351	96.35 355	97.54 362	97.40 451	95.32 333	97.98 218	96.64 434	99.33 66	96.89 404	99.42 90	84.32 434	99.81 222	97.69 196	97.49 438	97.48 459

TR-MVS		95.55 390	95.12 396	96.86 401	97.54 441	93.94 389	96.49 373	96.53 437	94.36 418	97.03 395	96.61 427	94.26 330	99.16 457	86.91 472	96.31 462	97.47 460

dmvs_testset		92.94 435	92.21 435	95.13 444	98.59 371	90.99 449	97.65 269	92.09 477	96.95 319	94.00 469	93.55 472	92.34 363	96.97 486	72.20 488	92.52 483	97.43 461

MonoMVSNet		96.25 366	96.53 352	95.39 441	96.57 472	91.01 448	98.82 96	97.68 405	98.57 168	98.03 330	99.37 104	90.92 381	97.78 482	94.99 366	93.88 481	97.38 462

JIA-IIPM		95.52 391	95.03 397	97.00 390	96.85 466	94.03 381	96.93 347	95.82 449	99.20 83	94.63 461	99.71 22	83.09 443	99.60 371	94.42 384	94.64 477	97.36 463

BH-w/o		95.13 399	94.89 402	95.86 428	98.20 406	91.31 441	95.65 421	97.37 411	93.64 429	96.52 423	95.70 446	93.04 352	99.02 461	88.10 467	95.82 472	97.24 464

tpm cat1		93.29 429	93.13 426	93.75 459	97.39 452	84.74 479	97.39 307	97.65 406	83.39 483	94.16 465	98.41 338	82.86 445	99.39 432	91.56 444	95.35 475	97.14 465

xiu_mvs_v1_base_debu		97.86 268	98.17 227	96.92 395	98.98 291	93.91 391	96.45 374	99.17 275	97.85 235	98.41 297	97.14 419	98.47 76	99.92 65	98.02 162	99.05 360	96.92 466

xiu_mvs_v1_base		97.86 268	98.17 227	96.92 395	98.98 291	93.91 391	96.45 374	99.17 275	97.85 235	98.41 297	97.14 419	98.47 76	99.92 65	98.02 162	99.05 360	96.92 466

xiu_mvs_v1_base_debi		97.86 268	98.17 227	96.92 395	98.98 291	93.91 391	96.45 374	99.17 275	97.85 235	98.41 297	97.14 419	98.47 76	99.92 65	98.02 162	99.05 360	96.92 466

PMVS	91.26 20	97.86 268	97.94 255	97.65 347	99.71 47	97.94 168	98.52 129	98.68 360	98.99 121	97.52 367	99.35 109	97.41 190	98.18 480	91.59 443	99.67 223	96.82 469
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 384	95.60 374	96.17 423	97.53 443	92.75 418	98.07 196	98.31 384	91.22 458	94.25 464	96.68 425	95.53 293	99.03 460	91.64 442	97.18 451	96.74 470

MVS-HIRNet		94.32 410	95.62 372	90.42 470	98.46 386	75.36 494	96.29 386	89.13 485	95.25 395	95.38 451	99.75 16	92.88 354	99.19 455	94.07 396	99.39 310	96.72 471

OpenMVS_ROB	95.38 14	95.84 382	95.18 395	97.81 327	98.41 394	97.15 243	97.37 313	98.62 365	83.86 481	98.65 264	98.37 343	94.29 329	99.68 327	88.41 465	98.62 401	96.60 472

thres100view900		94.19 413	93.67 418	95.75 432	99.06 271	91.35 440	98.03 203	94.24 466	98.33 183	97.40 377	94.98 461	79.84 453	99.62 361	83.05 478	98.08 423	96.29 473

tfpn200view9		94.03 417	93.44 420	95.78 431	98.93 298	91.44 438	97.60 280	94.29 464	97.94 227	97.10 387	94.31 468	79.67 455	99.62 361	83.05 478	98.08 423	96.29 473

MVS		93.19 431	92.09 436	96.50 410	96.91 464	94.03 381	98.07 196	98.06 395	68.01 488	94.56 462	96.48 430	95.96 280	99.30 445	83.84 477	96.89 456	96.17 475

gg-mvs-nofinetune		92.37 443	91.20 447	95.85 429	95.80 486	92.38 425	99.31 30	81.84 493	99.75 11	91.83 482	99.74 18	68.29 475	99.02 461	87.15 469	97.12 452	96.16 476

xiu_mvs_v2_base		97.16 327	97.49 290	96.17 423	98.54 378	92.46 422	95.45 429	98.84 338	97.25 296	97.48 371	96.49 429	98.31 94	99.90 81	96.34 318	98.68 396	96.15 477

PS-MVSNAJ		97.08 331	97.39 295	96.16 425	98.56 376	92.46 422	95.24 438	98.85 337	97.25 296	97.49 370	95.99 439	98.07 124	99.90 81	96.37 315	98.67 397	96.12 478

E-PMN		94.17 414	94.37 409	93.58 461	96.86 465	85.71 477	90.11 486	97.07 422	98.17 204	97.82 347	97.19 416	84.62 431	98.94 465	89.77 461	97.68 435	96.09 479

EMVS		93.83 420	94.02 412	93.23 466	96.83 467	84.96 478	89.77 487	96.32 439	97.92 229	97.43 376	96.36 435	86.17 416	98.93 466	87.68 468	97.73 434	95.81 480

MVE	83.40 22	92.50 440	91.92 442	94.25 452	98.83 321	91.64 434	92.71 479	83.52 492	95.92 374	86.46 490	95.46 453	95.20 302	95.40 488	80.51 483	98.64 398	95.73 481
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 423	93.14 425	95.46 440	98.66 361	91.29 442	96.61 365	94.63 461	97.39 282	96.83 407	93.71 471	79.88 452	99.56 387	82.40 481	98.13 420	95.54 482

API-MVS		97.04 334	96.91 326	97.42 372	97.88 422	98.23 134	98.18 175	98.50 375	97.57 258	97.39 379	96.75 424	96.77 234	99.15 458	90.16 460	99.02 367	94.88 483

GG-mvs-BLEND				94.76 448	94.54 488	92.13 430	99.31 30	80.47 494		88.73 488	91.01 488	67.59 479	98.16 481	82.30 482	94.53 479	93.98 484

DeepMVS_CX				93.44 463	98.24 403	94.21 372		94.34 463	64.28 489	91.34 483	94.87 465	89.45 395	92.77 490	77.54 486	93.14 482	93.35 485

tmp_tt		78.77 455	78.73 458	78.90 472	58.45 497	74.76 496	94.20 466	78.26 495	39.16 490	86.71 489	92.82 479	80.50 451	75.19 492	86.16 474	92.29 484	86.74 486

dongtai		76.24 456	75.95 459	77.12 473	92.39 491	67.91 497	90.16 485	59.44 498	82.04 484	89.42 486	94.67 466	49.68 495	81.74 491	48.06 491	77.66 488	81.72 487

kuosan		69.30 457	68.95 460	70.34 474	87.68 495	65.00 498	91.11 483	59.90 497	69.02 487	74.46 492	88.89 489	48.58 496	68.03 493	28.61 492	72.33 491	77.99 488

wuyk23d		96.06 371	97.62 283	91.38 469	98.65 365	98.57 106	98.85 92	96.95 427	96.86 328	99.90 14	99.16 164	99.18 19	98.40 476	89.23 464	99.77 162	77.18 489

test123		17.04 460	20.11 463	7.82 475	10.25 499	4.91 500	94.80 449	4.47 500	4.93 493	10.00 495	24.28 492	9.69 497	3.64 494	10.14 493	12.43 493	14.92 490

testmvs		17.12 459	20.53 462	6.87 476	12.05 498	4.20 501	93.62 476	6.73 499	4.62 494	10.41 494	24.33 491	8.28 498	3.56 495	9.69 494	15.07 492	12.86 491

mmdepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

monomultidepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

test_blank		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet_test		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

DCPMVS		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

cdsmvs_eth3d_5k		24.66 458	32.88 461	0.00 477	0.00 500	0.00 502	0.00 489	99.10 288	0.00 495	0.00 496	97.58 399	99.21 18	0.00 496	0.00 495	0.00 494	0.00 492

pcd_1.5k_mvsjas		8.17 461	10.90 464	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	98.07 124	0.00 496	0.00 495	0.00 494	0.00 492

sosnet-low-res		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uncertanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

Regformer		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

ab-mvs-re		8.12 462	10.83 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	97.48 405	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

TestfortrainingZip							98.68 108

WAC-MVS						90.90 450								91.37 447

FOURS1					99.73 37	99.67 3	99.43 15	99.54 119	99.43 55	99.26 148

test_one_0601					99.39 178	99.20 40		99.31 223	98.49 174	98.66 263	99.02 200	97.64 166

eth-test2					0.00 500

eth-test					0.00 500

ZD-MVS					99.01 286	98.84 86		99.07 292	94.10 423	98.05 328	98.12 363	96.36 258	99.86 143	92.70 429	99.19 346

test_241102_ONE					99.49 143	99.17 45		99.31 223	97.98 222	99.66 61	98.90 241	98.36 87	99.48 415

9.14			97.78 267		99.07 266		97.53 289	99.32 218	95.53 387	98.54 285	98.70 293	97.58 172	99.76 267	94.32 389	99.46 296

save fliter					99.11 257	97.97 163	96.53 370	99.02 305	98.24 193

test0726					99.50 135	99.21 34	98.17 178	99.35 204	97.97 223	99.26 148	99.06 188	97.61 170

test_part2					99.36 186	99.10 66				99.05 189

sam_mvs												84.29 436

MTGPA								99.20 263

test_post1							97.59 282				20.48 494	83.07 444	99.66 344	94.16 390

test_post											21.25 493	83.86 439	99.70 309

patchmatchnet-post											98.77 274	84.37 433	99.85 156

MTMP							97.93 224	91.91 479

gm-plane-assit					94.83 487	81.97 490			88.07 475		94.99 460		99.60 371	91.76 439

TEST9					98.71 342	98.08 150	95.96 405	99.03 302	91.40 456	95.85 439	97.53 401	96.52 249	99.76 267

test_8					98.67 356	98.01 158	95.91 411	99.02 305	91.64 451	95.79 441	97.50 404	96.47 251	99.76 267

agg_prior					98.68 355	97.99 159		99.01 308		95.59 442			99.77 261

test_prior4						97.97 163	95.86 412

test_prior2							95.74 419		96.48 345	96.11 433	97.63 397	95.92 283		94.16 390	99.20 343

旧先验2							95.76 418		88.56 474	97.52 367			99.66 344	94.48 380

新几何2							95.93 408

原ACMM2							95.53 425

testdata2													99.79 244	92.80 426

segment_acmp												97.02 216

testdata1							95.44 430		96.32 353

plane_prior7					99.19 236	97.87 174

plane_prior6					98.99 290	97.70 195						94.90 309

plane_prior4											97.98 375

plane_prior3						97.78 188			97.41 279	97.79 348

plane_prior2							97.77 249		98.20 201

plane_prior1					99.05 274

plane_prior						97.65 197	97.07 339		96.72 335						99.36 314

n2								0.00 501

nn								0.00 501

door-mid								99.57 101

test11								98.87 329

door								99.41 183

HQP5-MVS						96.79 265

HQP-NCC					98.67 356		96.29 386		96.05 366	95.55 445

ACMP_Plane					98.67 356		96.29 386		96.05 366	95.55 445

BP-MVS														92.82 424

HQP3-MVS								99.04 300							99.26 333

HQP2-MVS												93.84 337

NP-MVS					98.84 319	97.39 215					96.84 422

MDTV_nov1_ep13			95.22 393		97.06 461	83.20 486	97.74 256	96.16 441	94.37 417	96.99 396	98.83 261	83.95 438	99.53 399	93.90 399	97.95 430

ACMMP++_ref															99.77 162

ACMMP++															99.68 217

Test By Simon												96.52 249

High-resolution multi-view benchmark