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	98.44 264	99.65 69	95.35 329	99.82 3	99.94 2	99.83 7	99.42 110	99.94 2	98.13 119	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	98.08 189	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	98.80 186	99.75 34	96.59 272	97.97 219	99.86 16	98.22 192	99.88 21	99.71 22	98.59 64	99.84 174	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n_2		99.20 51	99.38 29	98.65 219	99.69 59	96.08 297	97.49 292	99.90 11	99.53 42	99.88 21	99.64 37	98.51 73	99.90 81	99.83 10	99.98 12	99.97 4

mmtdpeth		99.30 34	99.42 25	98.92 167	99.58 90	96.89 259	99.48 13	99.92 7	99.92 2	98.26 307	99.80 11	98.33 91	99.91 74	99.56 41	99.95 38	99.97 4

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 221	99.71 47	96.10 292	97.87 232	99.85 18	98.56 168	99.90 14	99.68 25	98.69 54	99.85 156	99.72 30	99.98 12	99.97 4

test_fmvs3		99.12 70	99.41 26	98.25 286	99.76 30	95.07 341	99.05 67	99.94 2	97.78 238	99.82 34	99.84 3	98.56 70	99.71 298	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	97.77 246	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		98.67 155	98.87 106	98.05 308	99.72 43	95.59 312	98.51 133	99.81 31	96.30 353	99.78 40	99.82 5	96.14 263	98.63 468	99.82 12	99.93 56	99.95 9

test_fmvs2		98.70 142	98.97 94	97.89 316	99.54 118	94.05 372	98.55 124	99.92 7	96.78 329	99.72 48	99.78 13	96.60 244	99.67 325	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	98.07 122	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	97.80 235	99.91 12	99.67 30	97.15 206	98.91 461	99.76 23	99.56 265	99.92 12

fmvsm_s_conf0.5_n_2		99.14 63	99.31 42	98.63 225	99.49 141	96.08 297	97.38 306	99.81 31	99.48 45	99.84 30	99.57 49	98.46 78	99.89 97	99.82 12	99.97 21	99.91 13

MVStest1		95.86 375	95.60 371	96.63 401	95.87 479	91.70 427	97.93 221	98.94 311	98.03 216	99.56 74	99.66 32	71.83 464	98.26 472	99.35 59	99.24 333	99.91 13

fmvsm_s_conf0.5_n_a		99.10 72	99.20 59	98.78 193	99.55 113	96.59 272	97.79 242	99.82 30	98.21 194	99.81 37	99.53 65	98.46 78	99.84 174	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n_9		99.17 53	99.38 29	98.53 252	99.51 127	95.82 307	97.62 271	99.78 36	99.72 15	99.90 14	99.48 76	98.66 56	99.89 97	99.85 6	99.93 56	99.89 16

fmvsm_s_conf0.5_n		99.09 73	99.26 51	98.61 231	99.55 113	96.09 295	97.74 253	99.81 31	98.55 169	99.85 27	99.55 57	98.60 63	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	97.68 260	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	99.56 107	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	98.80 186	99.48 149	96.56 277	97.97 219	99.69 54	99.63 29	99.84 30	99.54 63	98.21 109	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	98.74 206	99.51 127	96.44 284	97.65 266	99.65 69	99.66 24	99.78 40	99.48 76	97.92 136	99.93 54	99.72 30	99.95 38	99.87 22

fmvsm_s_conf0.5_n_7		98.83 117	99.04 83	98.20 293	99.30 199	94.83 347	97.23 323	99.36 195	98.64 152	99.84 30	99.43 89	98.10 121	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 88	98.21 136	97.82 237	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		97.91 258	98.02 243	97.58 350	98.69 349	94.10 371	98.13 179	98.90 320	97.95 222	97.32 380	99.58 47	95.95 279	98.75 466	96.41 307	99.22 337	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		97.66 283	98.50 168	95.13 438	99.63 81	85.84 469	98.35 157	98.21 381	98.23 191	99.54 79	99.46 81	95.02 305	99.68 321	98.24 141	99.87 98	99.87 22

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 193	99.46 155	96.58 275	97.65 266	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	99.49 141	99.29 25	99.80 4	99.72 45	99.82 8	99.04 189	99.81 8	98.05 125	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 88	97.18 237	97.44 301	99.83 25	99.56 40	99.91 12	99.34 111	99.36 13	99.93 54	99.83 10	99.98 12	99.85 30

MM		98.22 228	97.99 246	98.91 168	98.66 359	96.97 251	97.89 228	94.44 456	99.54 41	98.95 209	99.14 169	93.50 341	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	97.05 246	97.80 241	99.76 39	98.70 150	99.78 40	99.11 175	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	97.28 225	97.82 237	99.76 39	98.73 145	99.82 34	99.09 183	98.81 38	99.95 26	99.86 4	99.96 28	99.83 33

mvsany_test3		98.87 107	98.92 98	98.74 206	99.38 177	96.94 255	98.58 121	99.10 285	96.49 341	99.96 4	99.81 8	98.18 112	99.45 417	98.97 90	99.79 149	99.83 33

fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 193	99.47 152	96.56 277	97.75 252	99.71 47	99.60 36	99.74 47	99.44 86	97.96 133	99.95 26	99.86 4	99.94 50	99.82 36

SSC-MVS		98.71 137	98.74 121	98.62 227	99.72 43	96.08 297	98.74 97	98.64 361	99.74 13	99.67 60	99.24 140	94.57 319	99.95 26	99.11 78	99.24 333	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 85	99.22 55	98.38 271	99.31 195	95.48 321	97.56 282	99.73 44	98.87 136	99.75 45	99.27 127	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	99.53 120	99.53 42	99.46 101	99.41 94	98.23 104	99.95 26	98.89 97	99.95 38	99.81 40

VortexMVS		97.98 256	98.31 204	97.02 383	98.88 310	91.45 431	98.03 200	99.47 148	98.65 151	99.55 77	99.47 79	91.49 373	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 98	99.61 35	99.40 115	99.50 69	97.12 207	99.85 156	99.02 87	99.94 50	99.80 42

test_cas_vis1_n_1920		98.33 213	98.68 136	97.27 372	99.69 59	92.29 421	98.03 200	99.85 18	97.62 248	99.96 4	99.62 40	93.98 334	99.74 281	99.52 50	99.86 105	99.79 44

test_vis1_n_1920		98.40 199	98.92 98	96.81 396	99.74 36	90.76 447	98.15 177	99.91 9	98.33 180	99.89 18	99.55 57	95.07 304	99.88 115	99.76 23	99.93 56	99.79 44

CP-MVSNet		99.21 48	99.09 78	99.56 27	99.65 69	98.96 78	99.13 58	99.34 207	99.42 56	99.33 130	99.26 133	97.01 215	99.94 42	98.74 108	99.93 56	99.79 44

fmvsm_s_conf0.5_n_5		99.07 79	99.10 76	98.99 151	99.47 152	97.22 231	97.40 303	99.83 25	97.61 251	99.85 27	99.30 121	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 91	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		96.25 364	97.21 305	93.38 459	99.10 257	80.56 487	97.20 328	98.19 384	96.94 317	99.00 194	99.02 198	89.50 392	99.80 231	96.36 311	99.59 253	99.78 47

TestfortrainingZip a		98.95 95	98.72 125	99.64 9	99.58 90	99.32 22	98.68 107	99.60 82	96.46 344	99.53 83	98.77 272	97.87 143	99.83 192	98.39 134	99.64 232	99.77 50

reproduce_monomvs		95.00 397	95.25 386	94.22 447	97.51 446	83.34 479	97.86 233	98.44 371	98.51 170	99.29 140	99.30 121	67.68 472	99.56 381	98.89 97	99.81 132	99.77 50

Anonymous20231211		99.27 38	99.27 48	99.26 101	99.29 202	98.18 137	99.49 12	99.51 126	99.70 16	99.80 38	99.68 25	96.84 224	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	99.54 116	99.62 33	99.56 74	99.42 90	98.16 116	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	99.55 111	99.46 50	99.50 94	99.34 111	97.30 195	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		98.52 186	98.55 159	98.43 265	99.65 69	95.59 312	98.52 128	98.77 346	99.65 26	99.52 88	99.00 213	94.34 325	99.93 54	98.65 115	98.83 381	99.76 56

patch_mono-2		98.51 187	98.63 146	98.17 296	99.38 177	94.78 349	97.36 311	99.69 54	98.16 204	98.49 288	99.29 124	97.06 210	99.97 7	98.29 140	99.91 78	99.76 56

nrg030		99.40 26	99.35 34	99.54 32	99.58 90	99.13 61	98.98 75	99.48 139	99.68 20	99.46 101	99.26 133	98.62 61	99.73 288	99.17 75	99.92 69	99.76 56

FIs		99.14 63	99.09 78	99.29 95	99.70 55	98.28 127	99.13 58	99.52 125	99.48 45	99.24 156	99.41 94	96.79 231	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 80	99.66 24	99.68 58	99.66 32	98.44 80	99.95 26	99.73 28	99.96 28	99.75 60

APDe-MVS		98.99 88	98.79 117	99.60 16	99.21 227	99.15 53	98.87 88	99.48 139	97.57 255	99.35 125	99.24 140	97.83 146	99.89 97	97.88 174	99.70 207	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	99.51 126	99.64 27	99.56 74	99.46 81	98.23 104	99.97 7	98.78 103	99.93 56	99.72 62

MSC_two_6792asdad				99.32 91	98.43 388	98.37 121		98.86 331					99.89 97	97.14 233	99.60 249	99.71 63

No_MVS				99.32 91	98.43 388	98.37 121		98.86 331					99.89 97	97.14 233	99.60 249	99.71 63

PMMVS2		98.07 245	98.08 237	98.04 309	99.41 172	94.59 358	94.59 453	99.40 183	97.50 264	98.82 239	98.83 259	96.83 226	99.84 174	97.50 207	99.81 132	99.71 63

Baseline_NR-MVSNet		98.98 91	98.86 110	99.36 74	99.82 19	98.55 107	97.47 297	99.57 98	99.37 61	99.21 162	99.61 43	96.76 234	99.83 192	98.06 156	99.83 121	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 77	98.48 172	99.37 120	99.49 75	98.75 46	99.86 143	98.20 146	99.80 143	99.71 63

test_0728_THIRD									98.17 201	99.08 177	99.02 198	97.89 141	99.88 115	97.07 239	99.71 200	99.70 68

MSP-MVS		98.40 199	98.00 245	99.61 14	99.57 99	99.25 30	98.57 122	99.35 201	97.55 259	99.31 138	97.71 389	94.61 318	99.88 115	96.14 324	99.19 344	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		98.53 182	98.79 117	97.74 331	99.46 155	93.62 398	96.45 371	99.34 207	99.33 66	98.93 217	98.70 291	97.90 137	99.90 81	99.12 77	99.92 69	99.69 70

NormalMVS		98.26 223	97.97 250	99.15 121	99.64 75	97.83 178	98.28 161	99.43 170	99.24 76	98.80 243	98.85 252	89.76 388	99.94 42	98.04 158	99.67 221	99.68 71

KinetiMVS		99.03 83	99.02 86	99.03 145	99.70 55	97.48 208	98.43 146	99.29 236	99.70 16	99.60 71	99.07 185	96.13 264	99.94 42	99.42 56	99.87 98	99.68 71

dcpmvs_2		98.78 128	99.11 72	97.78 324	99.56 107	93.67 395	99.06 65	99.86 16	99.50 44	99.66 61	99.26 133	97.21 203	99.99 2	98.00 163	99.91 78	99.68 71

test_0728_SECOND				99.60 16	99.50 133	99.23 32	98.02 203	99.32 215					99.88 115	96.99 246	99.63 239	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 91	99.44 53	99.78 40	99.76 15	96.39 252	99.92 65	99.44 55	99.92 69	99.68 71

fmvsm_s_conf0.5_n_6		99.08 77	99.21 58	98.69 214	99.36 184	96.51 279	97.62 271	99.68 60	98.43 174	99.85 27	99.10 178	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

CHOSEN 1792x2688		97.49 295	97.14 310	98.54 250	99.68 62	96.09 295	96.50 369	99.62 77	91.58 447	98.84 235	98.97 222	92.36 360	99.88 115	96.76 269	99.95 38	99.67 76

reproduce_model		99.15 58	98.97 94	99.67 4	99.33 193	99.44 10	98.15 177	99.47 148	99.12 97	99.52 88	99.32 119	98.31 92	99.90 81	97.78 182	99.73 183	99.66 78

IU-MVS					99.49 141	99.15 53		98.87 326	92.97 432	99.41 112				96.76 269	99.62 242	99.66 78

test_241102_TWO								99.30 228	98.03 216	99.26 148	99.02 198	97.51 180	99.88 115	96.91 252	99.60 249	99.66 78

DPE-MVS		98.59 169	98.26 212	99.57 22	99.27 208	99.15 53	97.01 338	99.39 185	97.67 244	99.44 105	98.99 215	97.53 177	99.89 97	95.40 354	99.68 215	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 98	99.39 59	99.75 45	99.62 40	99.17 20	99.83 192	99.06 83	99.62 242	99.66 78

EI-MVSNet-UG-set		98.69 146	98.71 130	98.62 227	99.10 257	96.37 286	97.23 323	98.87 326	99.20 83	99.19 164	98.99 215	97.30 195	99.85 156	98.77 106	99.79 149	99.65 83

Elysia		99.15 58	99.14 69	99.18 113	99.63 81	97.92 169	98.50 135	99.43 170	99.67 21	99.70 52	99.13 171	96.66 240	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	98.50 135	99.43 170	99.67 21	99.70 52	99.13 171	96.66 240	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		98.68 152	98.70 133	98.63 225	99.09 260	96.40 285	97.23 323	98.86 331	99.20 83	99.18 168	98.97 222	97.29 197	99.85 156	98.72 110	99.78 154	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	99.55 111	98.30 184	99.65 64	99.45 85	99.22 17	99.76 267	98.44 129	99.77 160	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 98	98.81 116	99.28 96	99.21 227	98.45 116	98.46 143	99.33 213	99.63 29	99.48 96	99.15 166	97.23 201	99.75 275	97.17 229	99.66 229	99.63 89

reproduce-ours		99.09 73	98.90 100	99.67 4	99.27 208	99.49 6	98.00 207	99.42 176	99.05 115	99.48 96	99.27 127	98.29 94	99.89 97	97.61 197	99.71 200	99.62 90

our_new_method		99.09 73	98.90 100	99.67 4	99.27 208	99.49 6	98.00 207	99.42 176	99.05 115	99.48 96	99.27 127	98.29 94	99.89 97	97.61 197	99.71 200	99.62 90

test_fmvs1_n		98.09 243	98.28 208	97.52 358	99.68 62	93.47 400	98.63 114	99.93 5	95.41 387	99.68 58	99.64 37	91.88 369	99.48 409	99.82 12	99.87 98	99.62 90

test1111		96.49 356	96.82 330	95.52 431	99.42 169	87.08 466	99.22 45	87.14 482	99.11 98	99.46 101	99.58 47	88.69 396	99.86 143	98.80 101	99.95 38	99.62 90

VPA-MVSNet		99.30 34	99.30 45	99.28 96	99.49 141	98.36 124	99.00 72	99.45 156	99.63 29	99.52 88	99.44 86	98.25 102	99.88 115	99.09 80	99.84 112	99.62 90

LPG-MVS_test		98.71 137	98.46 178	99.47 61	99.57 99	98.97 74	98.23 167	99.48 139	96.60 336	99.10 175	99.06 186	98.71 50	99.83 192	95.58 350	99.78 154	99.62 90

LGP-MVS_train				99.47 61	99.57 99	98.97 74		99.48 139	96.60 336	99.10 175	99.06 186	98.71 50	99.83 192	95.58 350	99.78 154	99.62 90

Test_1112_low_res		96.99 337	96.55 348	98.31 280	99.35 189	95.47 324	95.84 412	99.53 120	91.51 449	96.80 405	98.48 330	91.36 374	99.83 192	96.58 289	99.53 275	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 92	99.11 72	98.55 245	99.44 162	96.21 291	98.90 83	99.55 111	98.73 145	99.48 96	99.60 45	96.63 243	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		98.31 216	98.50 168	97.73 334	99.76 30	94.17 369	98.68 107	99.91 9	96.31 351	99.79 39	99.57 49	92.85 354	99.42 422	99.79 19	99.84 112	99.60 100

v8		99.01 85	99.16 63	98.57 238	99.47 152	96.31 289	98.90 83	99.47 148	99.03 118	99.52 88	99.57 49	96.93 220	99.81 222	99.60 37	99.98 12	99.60 100

EI-MVSNet		98.40 199	98.51 165	98.04 309	99.10 257	94.73 352	97.20 328	98.87 326	98.97 124	99.06 179	99.02 198	96.00 271	99.80 231	98.58 118	99.82 126	99.60 100

SixPastTwentyTwo		98.75 133	98.62 148	99.16 118	99.83 18	97.96 166	99.28 40	98.20 382	99.37 61	99.70 52	99.65 36	92.65 358	99.93 54	99.04 85	99.84 112	99.60 100

IterMVS-LS		98.55 177	98.70 133	98.09 301	99.48 149	94.73 352	97.22 327	99.39 185	98.97 124	99.38 118	99.31 120	96.00 271	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		97.19 322	96.60 346	98.96 158	99.62 85	97.28 225	95.17 435	99.50 129	94.21 414	99.01 193	98.32 348	86.61 408	99.99 2	97.10 237	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	99.51 126	99.19 88	99.37 120	99.25 138	98.36 85	99.88 115	98.23 143	99.67 221	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		98.75 133	98.48 174	99.57 22	99.58 90	99.29 25	97.82 237	99.25 249	96.94 317	98.78 245	99.12 174	98.02 126	99.84 174	97.13 235	99.67 221	99.59 107

VPNet		98.87 107	98.83 113	99.01 149	99.70 55	97.62 200	98.43 146	99.35 201	99.47 48	99.28 142	99.05 193	96.72 237	99.82 205	98.09 153	99.36 312	99.59 107

WR-MVS		98.40 199	98.19 223	99.03 145	99.00 285	97.65 197	96.85 348	98.94 311	98.57 165	98.89 224	98.50 327	95.60 289	99.85 156	97.54 203	99.85 107	99.59 107

HPM-MVS		98.79 126	98.53 163	99.59 20	99.65 69	99.29 25	99.16 54	99.43 170	96.74 331	98.61 269	98.38 340	98.62 61	99.87 134	96.47 303	99.67 221	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 88	99.01 88	98.94 161	99.50 133	97.47 209	98.04 198	99.59 89	98.15 209	99.40 115	99.36 106	98.58 69	99.76 267	98.78 103	99.68 215	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 90	98.93 80	98.68 107	99.60 82	96.46 344	99.53 83	98.77 272		99.83 192	96.67 280	99.64 232	99.58 115

MED-MVS		98.90 102	98.72 125	99.45 64	99.58 90	98.93 80	98.68 107	99.60 82	98.14 210	99.53 83	98.77 272	97.87 143	99.83 192	96.67 280	99.64 232	99.58 115

ME-MVS		98.61 165	98.33 202	99.44 66	99.24 219	98.93 80	97.45 299	99.06 290	98.14 210	99.06 179	98.77 272	96.97 218	99.82 205	96.67 280	99.64 232	99.58 115

MP-MVS-pluss		98.57 172	98.23 217	99.60 16	99.69 59	99.35 17	97.16 333	99.38 187	94.87 399	98.97 203	98.99 215	98.01 127	99.88 115	97.29 222	99.70 207	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 146	98.40 186	99.54 32	99.53 121	99.17 45	98.52 128	99.31 220	97.46 272	98.44 292	98.51 323	97.83 146	99.88 115	96.46 304	99.58 258	99.58 115

ACMMPR		98.70 142	98.42 184	99.54 32	99.52 124	99.14 58	98.52 128	99.31 220	97.47 267	98.56 279	98.54 318	97.75 155	99.88 115	96.57 291	99.59 253	99.58 115

PGM-MVS		98.66 156	98.37 193	99.55 29	99.53 121	99.18 44	98.23 167	99.49 137	97.01 314	98.69 256	98.88 246	98.00 128	99.89 97	95.87 336	99.59 253	99.58 115

SteuartSystems-ACMMP		98.79 126	98.54 161	99.54 32	99.73 37	99.16 49	98.23 167	99.31 220	97.92 226	98.90 221	98.90 239	98.00 128	99.88 115	96.15 323	99.72 191	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	99.48 139	99.69 18	99.63 67	99.68 25	99.03 24	99.96 14	97.97 167	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	99.30 228	99.69 18	99.63 67	99.68 25	99.25 16	99.96 14	97.25 225	99.92 69	99.57 123

TranMVSNet+NR-MVSNet		99.17 53	99.07 81	99.46 63	99.37 183	98.87 85	98.39 153	99.42 176	99.42 56	99.36 123	99.06 186	98.38 84	99.95 26	98.34 137	99.90 86	99.57 123

mPP-MVS		98.64 159	98.34 197	99.54 32	99.54 118	99.17 45	98.63 114	99.24 254	97.47 267	98.09 321	98.68 295	97.62 166	99.89 97	96.22 318	99.62 242	99.57 123

PVSNet_Blended_VisFu		98.17 237	98.15 229	98.22 292	99.73 37	95.15 337	97.36 311	99.68 60	94.45 409	98.99 198	99.27 127	96.87 223	99.94 42	97.13 235	99.91 78	99.57 123

1112_ss		97.29 314	96.86 326	98.58 235	99.34 192	96.32 288	96.75 354	99.58 91	93.14 430	96.89 400	97.48 403	92.11 366	99.86 143	96.91 252	99.54 271	99.57 123

MTAPA		98.88 106	98.64 144	99.61 14	99.67 66	99.36 16	98.43 146	99.20 260	98.83 143	98.89 224	98.90 239	96.98 217	99.92 65	97.16 230	99.70 207	99.56 129

XVS		98.72 136	98.45 179	99.53 39	99.46 155	99.21 34	98.65 112	99.34 207	98.62 157	97.54 363	98.63 307	97.50 181	99.83 192	96.79 265	99.53 275	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	99.83 121	99.56 129

X-MVStestdata		94.32 404	92.59 423	99.53 39	99.46 155	99.21 34	98.65 112	99.34 207	98.62 157	97.54 363	45.85 484	97.50 181	99.83 192	96.79 265	99.53 275	99.56 129

HPM-MVS_fast		99.01 85	98.82 114	99.57 22	99.71 47	99.35 17	99.00 72	99.50 129	97.33 284	98.94 216	98.86 249	98.75 46	99.82 205	97.53 204	99.71 200	99.56 129

K. test v3		98.00 252	97.66 277	99.03 145	99.79 23	97.56 202	99.19 52	92.47 468	99.62 33	99.52 88	99.66 32	89.61 390	99.96 14	99.25 68	99.81 132	99.56 129

CP-MVS		98.70 142	98.42 184	99.52 45	99.36 184	99.12 63	98.72 102	99.36 195	97.54 261	98.30 301	98.40 337	97.86 145	99.89 97	96.53 300	99.72 191	99.56 129

viewmacassd2359aftdt		98.86 111	98.87 106	98.83 179	99.53 121	97.32 220	97.70 258	99.64 71	98.22 192	99.25 154	99.27 127	98.40 82	99.61 362	97.98 166	99.87 98	99.55 136

FE-MVSNET		98.59 169	98.50 168	98.87 172	99.58 90	97.30 221	98.08 189	99.74 43	96.94 317	98.97 203	99.10 178	96.94 219	99.74 281	97.33 220	99.86 105	99.55 136

ZNCC-MVS		98.68 152	98.40 186	99.54 32	99.57 99	99.21 34	98.46 143	99.29 236	97.28 290	98.11 319	98.39 338	98.00 128	99.87 134	96.86 262	99.64 232	99.55 136

v1192		98.60 167	98.66 141	98.41 267	99.27 208	95.88 303	97.52 287	99.36 195	97.41 276	99.33 130	99.20 149	96.37 255	99.82 205	99.57 39	99.92 69	99.55 136

v1240		98.55 177	98.62 148	98.32 278	99.22 225	95.58 314	97.51 289	99.45 156	97.16 305	99.45 104	99.24 140	96.12 266	99.85 156	99.60 37	99.88 94	99.55 136

UGNet		98.53 182	98.45 179	98.79 190	97.94 417	96.96 253	99.08 61	98.54 366	99.10 105	96.82 404	99.47 79	96.55 246	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
E6		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 149	99.63 73	98.73 145	99.26 148	99.39 100	98.71 50	99.70 305	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	98.42 149	99.63 73	98.73 145	99.26 148	99.39 100	98.71 50	99.70 305	98.43 131	99.84 112	99.54 142

AstraMVS		98.16 239	98.07 239	98.41 267	99.51 127	95.86 304	98.00 207	95.14 451	98.97 124	99.43 106	99.24 140	93.25 342	99.84 174	99.21 71	99.87 98	99.54 142

WBMVS		95.18 392	94.78 398	96.37 407	97.68 434	89.74 454	95.80 413	98.73 354	97.54 261	98.30 301	98.44 334	70.06 466	99.82 205	96.62 286	99.87 98	99.54 142

test2506		92.39 435	91.89 437	93.89 452	99.38 177	82.28 483	99.32 26	66.03 490	99.08 112	98.77 248	99.57 49	66.26 476	99.84 174	98.71 111	99.95 38	99.54 142

ECVR-MVS		96.42 358	96.61 344	95.85 423	99.38 177	88.18 461	99.22 45	86.00 484	99.08 112	99.36 123	99.57 49	88.47 401	99.82 205	98.52 126	99.95 38	99.54 142

v144192		98.54 180	98.57 157	98.45 262	99.21 227	95.98 300	97.63 270	99.36 195	97.15 307	99.32 136	99.18 156	95.84 283	99.84 174	99.50 51	99.91 78	99.54 142

v1921920		98.54 180	98.60 153	98.38 271	99.20 231	95.76 310	97.56 282	99.36 195	97.23 299	99.38 118	99.17 160	96.02 269	99.84 174	99.57 39	99.90 86	99.54 142

MP-MVS		98.46 192	98.09 234	99.54 32	99.57 99	99.22 33	98.50 135	99.19 264	97.61 251	97.58 359	98.66 300	97.40 189	99.88 115	94.72 369	99.60 249	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 89	99.59 37	99.71 50	99.57 49	97.12 207	99.90 81	99.21 71	99.87 98	99.54 142

ACMMP		98.75 133	98.50 168	99.52 45	99.56 107	99.16 49	98.87 88	99.37 191	97.16 305	98.82 239	99.01 209	97.71 157	99.87 134	96.29 315	99.69 210	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		98.40 199	98.03 242	99.51 49	99.16 246	99.21 34	98.05 196	99.22 257	94.16 415	98.98 199	99.10 178	97.52 179	99.79 244	96.45 305	99.64 232	99.53 153
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		98.71 137	98.44 181	99.51 49	99.49 141	99.16 49	98.52 128	99.31 220	97.47 267	98.58 275	98.50 327	97.97 132	99.85 156	96.57 291	99.59 253	99.53 153

UniMVSNet_NR-MVSNet		98.86 111	98.68 136	99.40 72	99.17 244	98.74 92	97.68 260	99.40 183	99.14 96	99.06 179	98.59 314	96.71 238	99.93 54	98.57 120	99.77 160	99.53 153

E4		98.87 107	98.88 103	98.81 183	99.52 124	97.23 228	97.62 271	99.61 80	98.58 163	99.18 168	99.33 114	98.29 94	99.69 311	97.99 165	99.83 121	99.52 156

GST-MVS		98.61 165	98.30 205	99.52 45	99.51 127	99.20 40	98.26 165	99.25 249	97.44 275	98.67 259	98.39 338	97.68 158	99.85 156	96.00 328	99.51 281	99.52 156

MGCNet		97.44 300	97.01 317	98.72 210	96.42 472	96.74 267	97.20 328	91.97 472	98.46 173	98.30 301	98.79 268	92.74 356	99.91 74	99.30 63	99.94 50	99.52 156

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 58	99.80 231	98.24 141	99.84 112	99.52 156

FE-MVSNET2		99.15 58	99.22 55	98.94 161	99.70 55	97.49 205	98.62 116	99.67 64	98.85 141	99.34 127	99.54 63	98.47 74	99.81 222	98.93 93	99.91 78	99.51 160

v1144		98.60 167	98.66 141	98.41 267	99.36 184	95.90 302	97.58 280	99.34 207	97.51 263	99.27 144	99.15 166	96.34 257	99.80 231	99.47 54	99.93 56	99.51 160

v2v482		98.56 173	98.62 148	98.37 274	99.42 169	95.81 308	97.58 280	99.16 275	97.90 228	99.28 142	99.01 209	95.98 276	99.79 244	99.33 60	99.90 86	99.51 160

CPTT-MVS		97.84 272	97.36 296	99.27 99	99.31 195	98.46 115	98.29 160	99.27 243	94.90 398	97.83 343	98.37 341	94.90 307	99.84 174	93.85 397	99.54 271	99.51 160

viewdifsd2359ckpt11		98.84 114	99.04 83	98.24 288	99.56 107	95.51 317	97.38 306	99.70 52	99.16 93	99.57 72	99.40 97	98.26 100	99.71 298	98.55 124	99.82 126	99.50 164

viewmsd2359difaftdt		98.84 114	99.04 83	98.24 288	99.56 107	95.51 317	97.38 306	99.70 52	99.16 93	99.57 72	99.40 97	98.26 100	99.71 298	98.55 124	99.82 126	99.50 164

LuminaMVS		98.39 205	98.20 219	98.98 155	99.50 133	97.49 205	97.78 243	97.69 397	98.75 144	99.49 95	99.25 138	92.30 362	99.94 42	99.14 76	99.88 94	99.50 164

DU-MVS		98.82 120	98.63 146	99.39 73	99.16 246	98.74 92	97.54 285	99.25 249	98.84 142	99.06 179	98.76 278	96.76 234	99.93 54	98.57 120	99.77 160	99.50 164

NR-MVSNet		98.95 95	98.82 114	99.36 74	99.16 246	98.72 97	99.22 45	99.20 260	99.10 105	99.72 48	98.76 278	96.38 254	99.86 143	98.00 163	99.82 126	99.50 164

casdiffmvs_mvg		99.12 70	99.16 63	98.99 151	99.43 167	97.73 193	98.00 207	99.62 77	99.22 79	99.55 77	99.22 146	98.93 32	99.75 275	98.66 114	99.81 132	99.50 164
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 90	99.33 89	99.71 47	98.83 87	98.60 119	99.58 91	99.11 98	99.53 83	99.18 156	98.81 38	99.67 325	96.71 276	99.77 160	99.50 164

SymmetryMVS		98.05 247	97.71 272	99.09 132	99.29 202	97.83 178	98.28 161	97.64 402	99.24 76	98.80 243	98.85 252	89.76 388	99.94 42	98.04 158	99.50 289	99.49 171

DVP-MVS++		98.90 102	98.70 133	99.51 49	98.43 388	99.15 53	99.43 15	99.32 215	98.17 201	99.26 148	99.02 198	98.18 112	99.88 115	97.07 239	99.45 296	99.49 171

PC_three_1452									93.27 428	99.40 115	98.54 318	98.22 107	97.00 479	95.17 357	99.45 296	99.49 171

GeoE		99.05 80	98.99 92	99.25 104	99.44 162	98.35 125	98.73 101	99.56 107	98.42 175	98.91 220	98.81 265	98.94 30	99.91 74	98.35 136	99.73 183	99.49 171

h-mvs33		97.77 275	97.33 299	99.10 128	99.21 227	97.84 177	98.35 157	98.57 365	99.11 98	98.58 275	99.02 198	88.65 399	99.96 14	98.11 151	96.34 459	99.49 171

IterMVS-SCA-FT		97.85 271	98.18 224	96.87 392	99.27 208	91.16 441	95.53 422	99.25 249	99.10 105	99.41 112	99.35 107	93.10 347	99.96 14	98.65 115	99.94 50	99.49 171

new-patchmatchnet		98.35 208	98.74 121	97.18 375	99.24 219	92.23 423	96.42 375	99.48 139	98.30 184	99.69 56	99.53 65	97.44 187	99.82 205	98.84 100	99.77 160	99.49 171

APD-MVS		98.10 241	97.67 274	99.42 68	99.11 255	98.93 80	97.76 249	99.28 240	94.97 396	98.72 254	98.77 272	97.04 211	99.85 156	93.79 398	99.54 271	99.49 171
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 217	98.04 241	99.07 135	99.56 107	97.83 178	99.29 36	98.07 388	99.03 118	98.59 273	99.13 171	92.16 364	99.90 81	96.87 260	99.68 215	99.49 171

DeepC-MVS	97.60 4	98.97 92	98.93 97	99.10 128	99.35 189	97.98 162	98.01 206	99.46 152	97.56 257	99.54 79	99.50 69	98.97 28	99.84 174	98.06 156	99.92 69	99.49 171
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	98.91 100	98.73 123	99.48 57	99.55 113	99.14 58	98.07 193	99.37 191	97.62 248	99.04 189	98.96 225	98.84 36	99.79 244	97.43 214	99.65 230	99.49 171
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
guyue		98.01 251	97.93 255	98.26 284	99.45 160	95.48 321	98.08 189	96.24 434	98.89 135	99.34 127	99.14 169	91.32 375	99.82 205	99.07 81	99.83 121	99.48 182

DVP-MVS		98.77 131	98.52 164	99.52 45	99.50 133	99.21 34	98.02 203	98.84 335	97.97 220	99.08 177	99.02 198	97.61 168	99.88 115	96.99 246	99.63 239	99.48 182
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		98.71 137	98.43 182	99.57 22	99.18 242	99.35 17	98.36 156	99.29 236	98.29 187	98.88 228	98.85 252	97.53 177	99.87 134	96.14 324	99.31 321	99.48 182

TSAR-MVS + MP.		98.63 161	98.49 173	99.06 141	99.64 75	97.90 172	98.51 133	98.94 311	96.96 315	99.24 156	98.89 245	97.83 146	99.81 222	96.88 259	99.49 291	99.48 182
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 230	97.95 251	99.01 149	99.58 90	97.74 191	99.01 70	97.29 410	99.67 21	98.97 203	99.50 69	90.45 383	99.80 231	97.88 174	99.20 341	99.48 182

IterMVS		97.73 277	98.11 233	96.57 402	99.24 219	90.28 450	95.52 424	99.21 258	98.86 138	99.33 130	99.33 114	93.11 346	99.94 42	98.49 127	99.94 50	99.48 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 233	97.90 259	99.08 133	99.57 99	97.97 163	99.31 30	98.32 377	99.01 120	98.98 199	99.03 197	91.59 371	99.79 244	95.49 352	99.80 143	99.48 182

ACMP	95.32 15	98.41 196	98.09 234	99.36 74	99.51 127	98.79 90	97.68 260	99.38 187	95.76 374	98.81 241	98.82 262	98.36 85	99.82 205	94.75 366	99.77 160	99.48 182
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 252	97.63 280	99.10 128	99.24 219	98.17 138	96.89 347	98.73 354	95.66 375	97.92 334	97.70 391	97.17 205	99.66 338	96.18 322	99.23 336	99.47 190

3Dnovator+	97.89 3	98.69 146	98.51 165	99.24 106	98.81 325	98.40 117	99.02 69	99.19 264	98.99 121	98.07 323	99.28 125	97.11 209	99.84 174	96.84 263	99.32 319	99.47 190

diffmvs_AUTHOR		98.50 188	98.59 155	98.23 291	99.35 189	95.48 321	96.61 362	99.60 82	98.37 176	98.90 221	99.00 213	97.37 191	99.76 267	98.22 144	99.85 107	99.46 192

HPM-MVS++		98.10 241	97.64 279	99.48 57	99.09 260	99.13 61	97.52 287	98.75 351	97.46 272	96.90 399	97.83 383	96.01 270	99.84 174	95.82 340	99.35 314	99.46 192

V42		98.78 128	98.78 119	98.76 200	99.44 162	97.04 247	98.27 164	99.19 264	97.87 230	99.25 154	99.16 162	96.84 224	99.78 255	99.21 71	99.84 112	99.46 192

APD-MVS_3200maxsize		98.84 114	98.61 152	99.53 39	99.19 234	99.27 28	98.49 138	99.33 213	98.64 152	99.03 192	98.98 220	97.89 141	99.85 156	96.54 299	99.42 305	99.46 192

UniMVSNet (Re)		98.87 107	98.71 130	99.35 80	99.24 219	98.73 95	97.73 255	99.38 187	98.93 129	99.12 171	98.73 281	96.77 232	99.86 143	98.63 117	99.80 143	99.46 192

SR-MVS-dyc-post		98.81 122	98.55 159	99.57 22	99.20 231	99.38 13	98.48 141	99.30 228	98.64 152	98.95 209	98.96 225	97.49 184	99.86 143	96.56 295	99.39 308	99.45 197

RE-MVS-def			98.58 156		99.20 231	99.38 13	98.48 141	99.30 228	98.64 152	98.95 209	98.96 225	97.75 155		96.56 295	99.39 308	99.45 197

HQP_MVS		97.99 255	97.67 274	98.93 164	99.19 234	97.65 197	97.77 246	99.27 243	98.20 198	97.79 346	97.98 373	94.90 307	99.70 305	94.42 378	99.51 281	99.45 197

plane_prior5								99.27 243					99.70 305	94.42 378	99.51 281	99.45 197

lessismore_v0				98.97 157	99.73 37	97.53 204		86.71 483		99.37 120	99.52 68	89.93 386	99.92 65	98.99 89	99.72 191	99.44 201

TAMVS		98.24 227	98.05 240	98.80 186	99.07 264	97.18 237	97.88 229	98.81 340	96.66 335	99.17 170	99.21 147	94.81 313	99.77 261	96.96 250	99.88 94	99.44 201

DeepPCF-MVS	96.93 5	98.32 214	98.01 244	99.23 108	98.39 393	98.97 74	95.03 439	99.18 268	96.88 322	99.33 130	98.78 270	98.16 116	99.28 443	96.74 271	99.62 242	99.44 201

3Dnovator	98.27 2	98.81 122	98.73 123	99.05 142	98.76 330	97.81 186	99.25 43	99.30 228	98.57 165	98.55 281	99.33 114	97.95 134	99.90 81	97.16 230	99.67 221	99.44 201

E2		98.70 142	98.68 136	98.73 208	99.40 174	97.10 244	97.48 293	99.57 98	98.09 213	99.00 194	99.20 149	97.90 137	99.67 325	97.73 190	99.77 160	99.43 205

E3		98.69 146	98.68 136	98.73 208	99.40 174	97.10 244	97.48 293	99.57 98	98.09 213	99.00 194	99.20 149	97.90 137	99.67 325	97.73 190	99.77 160	99.43 205

MVSFormer		98.26 223	98.43 182	97.77 325	98.88 310	93.89 388	99.39 20	99.56 107	99.11 98	98.16 313	98.13 359	93.81 337	99.97 7	99.26 66	99.57 262	99.43 205

jason		97.45 299	97.35 297	97.76 328	99.24 219	93.93 384	95.86 409	98.42 373	94.24 413	98.50 287	98.13 359	94.82 311	99.91 74	97.22 226	99.73 183	99.43 205
jason: jason.
NCCC		97.86 266	97.47 291	99.05 142	98.61 364	98.07 152	96.98 340	98.90 320	97.63 247	97.04 389	97.93 378	95.99 275	99.66 338	95.31 355	98.82 383	99.43 205

Anonymous20240521		98.69 146	98.87 106	98.16 298	99.77 27	95.11 340	99.08 61	99.44 164	99.34 65	99.33 130	99.55 57	94.10 333	99.94 42	99.25 68	99.96 28	99.42 210

MVS_111021_HR		98.25 226	98.08 237	98.75 202	99.09 260	97.46 210	95.97 400	99.27 243	97.60 253	97.99 331	98.25 351	98.15 118	99.38 428	96.87 260	99.57 262	99.42 210

COLMAP_ROB	96.50 10	98.99 88	98.85 112	99.41 70	99.58 90	99.10 66	98.74 97	99.56 107	99.09 108	99.33 130	99.19 152	98.40 82	99.72 297	95.98 330	99.76 175	99.42 210
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SED-MVS		98.91 100	98.72 125	99.49 55	99.49 141	99.17 45	98.10 186	99.31 220	98.03 216	99.66 61	99.02 198	98.36 85	99.88 115	96.91 252	99.62 242	99.41 213

OPU-MVS				98.82 181	98.59 369	98.30 126	98.10 186				98.52 322	98.18 112	98.75 466	94.62 370	99.48 292	99.41 213

our_test_3		97.39 305	97.73 270	96.34 408	98.70 344	89.78 453	94.61 452	98.97 310	96.50 340	99.04 189	98.85 252	95.98 276	99.84 174	97.26 224	99.67 221	99.41 213

casdiffmvs		98.95 95	99.00 90	98.81 183	99.38 177	97.33 218	97.82 237	99.57 98	99.17 92	99.35 125	99.17 160	98.35 89	99.69 311	98.46 128	99.73 183	99.41 213
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		97.60 286	97.67 274	97.39 368	99.04 273	93.04 407	95.27 431	98.38 376	97.25 293	98.92 219	98.95 229	95.48 295	99.73 288	96.99 246	98.74 385	99.41 213

MDA-MVSNet_test_wron		97.60 286	97.66 277	97.41 367	99.04 273	93.09 403	95.27 431	98.42 373	97.26 292	98.88 228	98.95 229	95.43 296	99.73 288	97.02 242	98.72 387	99.41 213

GBi-Net		98.65 157	98.47 176	99.17 115	98.90 304	98.24 130	99.20 48	99.44 164	98.59 160	98.95 209	99.55 57	94.14 329	99.86 143	97.77 183	99.69 210	99.41 213

test1		98.65 157	98.47 176	99.17 115	98.90 304	98.24 130	99.20 48	99.44 164	98.59 160	98.95 209	99.55 57	94.14 329	99.86 143	97.77 183	99.69 210	99.41 213

FMVSNet1		99.17 53	99.17 61	99.17 115	99.55 113	98.24 130	99.20 48	99.44 164	99.21 81	99.43 106	99.55 57	97.82 149	99.86 143	98.42 133	99.89 92	99.41 213

test_fmvs1		97.72 278	97.94 253	97.07 382	98.66 359	92.39 418	97.68 260	99.81 31	95.20 392	99.54 79	99.44 86	91.56 372	99.41 423	99.78 21	99.77 160	99.40 222

viewdifsd2359ckpt07		98.71 137	98.86 110	98.26 284	99.43 167	95.65 311	97.20 328	99.66 65	99.20 83	99.29 140	99.01 209	98.29 94	99.73 288	97.92 170	99.75 179	99.39 223

viewmanbaseed2359cas		98.58 171	98.54 161	98.70 212	99.28 205	97.13 243	97.47 297	99.55 111	97.55 259	98.96 208	98.92 233	97.77 153	99.59 369	97.59 200	99.77 160	99.39 223

KD-MVS_self_test		99.25 41	99.18 60	99.44 66	99.63 81	99.06 71	98.69 106	99.54 116	99.31 69	99.62 70	99.53 65	97.36 192	99.86 143	99.24 70	99.71 200	99.39 223

v148		98.45 193	98.60 153	98.00 311	99.44 162	94.98 343	97.44 301	99.06 290	98.30 184	99.32 136	98.97 222	96.65 242	99.62 355	98.37 135	99.85 107	99.39 223

test20.03		98.78 128	98.77 120	98.78 193	99.46 155	97.20 234	97.78 243	99.24 254	99.04 117	99.41 112	98.90 239	97.65 161	99.76 267	97.70 192	99.79 149	99.39 223

CDPH-MVS		97.26 315	96.66 342	99.07 135	99.00 285	98.15 139	96.03 398	99.01 305	91.21 453	97.79 346	97.85 382	96.89 222	99.69 311	92.75 421	99.38 311	99.39 223

EPNet		96.14 367	95.44 379	98.25 286	90.76 488	95.50 320	97.92 224	94.65 454	98.97 124	92.98 470	98.85 252	89.12 394	99.87 134	95.99 329	99.68 215	99.39 223
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 237	97.87 261	99.07 135	98.67 354	98.24 130	97.01 338	98.93 314	97.25 293	97.62 355	98.34 345	97.27 198	99.57 378	96.42 306	99.33 317	99.39 223

DeepC-MVS_fast	96.85 6	98.30 217	98.15 229	98.75 202	98.61 364	97.23 228	97.76 249	99.09 287	97.31 287	98.75 251	98.66 300	97.56 172	99.64 349	96.10 327	99.55 269	99.39 223
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		98.53 182	98.27 211	99.32 91	99.31 195	98.75 91	98.19 171	99.41 180	96.77 330	98.83 236	98.90 239	97.80 151	99.82 205	95.68 346	99.52 278	99.38 232

test9_res														93.28 410	99.15 349	99.38 232

BP-MVS1		97.40 304	96.97 318	98.71 211	99.07 264	96.81 262	98.34 159	97.18 412	98.58 163	98.17 310	98.61 311	84.01 431	99.94 42	98.97 90	99.78 154	99.37 234

OPM-MVS		98.56 173	98.32 203	99.25 104	99.41 172	98.73 95	97.13 335	99.18 268	97.10 308	98.75 251	98.92 233	98.18 112	99.65 345	96.68 279	99.56 265	99.37 234
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 426	99.16 347	99.37 234

AllTest		98.44 194	98.20 219	99.16 118	99.50 133	98.55 107	98.25 166	99.58 91	96.80 327	98.88 228	99.06 186	97.65 161	99.57 378	94.45 376	99.61 247	99.37 234

TestCases				99.16 118	99.50 133	98.55 107		99.58 91	96.80 327	98.88 228	99.06 186	97.65 161	99.57 378	94.45 376	99.61 247	99.37 234

MDA-MVSNet-bldmvs		97.94 257	97.91 258	98.06 306	99.44 162	94.96 344	96.63 361	99.15 280	98.35 178	98.83 236	99.11 175	94.31 326	99.85 156	96.60 288	98.72 387	99.37 234

MVSTER		96.86 341	96.55 348	97.79 323	97.91 419	94.21 367	97.56 282	98.87 326	97.49 266	99.06 179	99.05 193	80.72 444	99.80 231	98.44 129	99.82 126	99.37 234

viewcassd2359sk11		98.55 177	98.51 165	98.67 217	99.29 202	96.99 250	97.39 304	99.54 116	97.73 240	98.81 241	99.08 184	97.55 173	99.66 338	97.52 206	99.67 221	99.36 241

pmmvs5		97.64 284	97.49 288	98.08 304	99.14 251	95.12 339	96.70 357	99.05 294	93.77 422	98.62 267	98.83 259	93.23 343	99.75 275	98.33 139	99.76 175	99.36 241

Anonymous20231206		98.21 230	98.21 218	98.20 293	99.51 127	95.43 326	98.13 179	99.32 215	96.16 358	98.93 217	98.82 262	96.00 271	99.83 192	97.32 221	99.73 183	99.36 241

train_agg		97.10 327	96.45 352	99.07 135	98.71 340	98.08 150	95.96 402	99.03 299	91.64 445	95.85 433	97.53 399	96.47 249	99.76 267	93.67 400	99.16 347	99.36 241

PVSNet_BlendedMVS		97.55 291	97.53 285	97.60 348	98.92 300	93.77 392	96.64 360	99.43 170	94.49 405	97.62 355	99.18 156	96.82 227	99.67 325	94.73 367	99.93 56	99.36 241

Anonymous20240529		98.93 98	98.87 106	99.12 124	99.19 234	98.22 135	99.01 70	98.99 308	99.25 75	99.54 79	99.37 102	97.04 211	99.80 231	97.89 171	99.52 278	99.35 246

F-COLMAP		97.30 312	96.68 339	99.14 122	99.19 234	98.39 118	97.27 322	99.30 228	92.93 433	96.62 411	98.00 371	95.73 286	99.68 321	92.62 424	98.46 404	99.35 246

viewdifsd2359ckpt13		98.39 205	98.29 207	98.70 212	99.26 217	97.19 235	97.51 289	99.48 139	96.94 317	98.58 275	98.82 262	97.47 186	99.55 385	97.21 227	99.33 317	99.34 248

ppachtmachnet_test		97.50 292	97.74 268	96.78 398	98.70 344	91.23 440	94.55 454	99.05 294	96.36 348	99.21 162	98.79 268	96.39 252	99.78 255	96.74 271	99.82 126	99.34 248

VDD-MVS		98.56 173	98.39 189	99.07 135	99.13 253	98.07 152	98.59 120	97.01 417	99.59 37	99.11 172	99.27 127	94.82 311	99.79 244	98.34 137	99.63 239	99.34 248

testgi		98.32 214	98.39 189	98.13 299	99.57 99	95.54 315	97.78 243	99.49 137	97.37 281	99.19 164	97.65 393	98.96 29	99.49 406	96.50 302	98.99 369	99.34 248

diffmvs		98.22 228	98.24 216	98.17 296	99.00 285	95.44 325	96.38 377	99.58 91	97.79 237	98.53 284	98.50 327	96.76 234	99.74 281	97.95 169	99.64 232	99.34 248
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		97.89 261	97.60 282	98.75 202	99.31 195	97.17 239	97.62 271	99.35 201	98.72 149	98.76 250	98.68 295	92.57 359	99.74 281	97.76 187	95.60 467	99.34 248

viewmambaseed2359dif		98.19 233	98.26 212	97.99 312	99.02 282	95.03 342	96.59 364	99.53 120	96.21 355	99.00 194	98.99 215	97.62 166	99.61 362	97.62 196	99.72 191	99.33 254

baseline		98.96 94	99.02 86	98.76 200	99.38 177	97.26 227	98.49 138	99.50 129	98.86 138	99.19 164	99.06 186	98.23 104	99.69 311	98.71 111	99.76 175	99.33 254

MG-MVS		96.77 345	96.61 344	97.26 373	98.31 397	93.06 404	95.93 405	98.12 387	96.45 346	97.92 334	98.73 281	93.77 339	99.39 426	91.19 445	99.04 361	99.33 254

HQP4-MVS										95.56 438			99.54 391			99.32 257

CDS-MVSNet		97.69 280	97.35 297	98.69 214	98.73 334	97.02 249	96.92 346	98.75 351	95.89 370	98.59 273	98.67 297	92.08 367	99.74 281	96.72 274	99.81 132	99.32 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 336	96.49 351	98.55 245	98.67 354	96.79 263	96.29 383	99.04 297	96.05 361	95.55 439	96.84 420	93.84 335	99.54 391	92.82 418	99.26 331	99.32 257

RPSCF		98.62 164	98.36 194	99.42 68	99.65 69	99.42 11	98.55 124	99.57 98	97.72 242	98.90 221	99.26 133	96.12 266	99.52 397	95.72 343	99.71 200	99.32 257

E3new		98.41 196	98.34 197	98.62 227	99.19 234	96.90 258	97.32 314	99.50 129	97.40 278	98.63 264	98.92 233	97.21 203	99.65 345	97.34 218	99.52 278	99.31 261

MVP-Stereo		98.08 244	97.92 256	98.57 238	98.96 292	96.79 263	97.90 227	99.18 268	96.41 347	98.46 290	98.95 229	95.93 280	99.60 365	96.51 301	98.98 372	99.31 261
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 199	98.68 136	97.54 356	98.96 292	97.99 159	97.88 229	99.36 195	98.20 198	99.63 67	99.04 195	98.76 45	95.33 483	96.56 295	99.74 180	99.31 261
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		98.42 195	98.30 205	98.79 190	98.79 329	97.29 224	98.23 167	98.66 358	99.31 69	98.85 233	98.80 266	94.80 314	99.78 255	98.13 150	99.13 352	99.31 261

test_prior				98.95 160	98.69 349	97.95 167		99.03 299					99.59 369			99.30 265

USDC		97.41 303	97.40 292	97.44 365	98.94 294	93.67 395	95.17 435	99.53 120	94.03 419	98.97 203	99.10 178	95.29 298	99.34 433	95.84 339	99.73 183	99.30 265

viewdifsd2359ckpt09		98.13 240	97.92 256	98.77 198	99.18 242	97.35 216	97.29 318	99.53 120	95.81 372	98.09 321	98.47 331	96.34 257	99.66 338	97.02 242	99.51 281	99.29 267

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 151	99.57 99	97.73 193	97.93 221	99.83 25	99.22 79	99.93 6	99.30 121	99.42 11	99.96 14	99.85 6	99.99 5	99.29 267

FMVSNet2		98.49 189	98.40 186	98.75 202	98.90 304	97.14 242	98.61 118	99.13 281	98.59 160	99.19 164	99.28 125	94.14 329	99.82 205	97.97 167	99.80 143	99.29 267

XVG-OURS-SEG-HR		98.49 189	98.28 208	99.14 122	99.49 141	98.83 87	96.54 365	99.48 139	97.32 286	99.11 172	98.61 311	99.33 15	99.30 439	96.23 317	98.38 405	99.28 270

mamba_0408		98.80 124	98.88 103	98.55 245	99.27 208	96.50 280	98.00 207	99.60 82	98.93 129	99.22 159	98.84 257	98.59 64	99.89 97	97.74 188	99.72 191	99.27 271

SSM_04072		98.80 124	98.88 103	98.56 243	99.27 208	96.50 280	98.00 207	99.60 82	98.93 129	99.22 159	98.84 257	98.59 64	99.90 81	97.74 188	99.72 191	99.27 271

SSM_0407		98.86 111	98.96 96	98.55 245	99.27 208	96.50 280	98.04 198	99.66 65	99.09 108	99.22 159	99.02 198	98.79 42	99.87 134	97.87 176	99.72 191	99.27 271

test12				98.93 164	98.58 371	97.83 178		98.66 358		96.53 415		95.51 293	99.69 311		99.13 352	99.27 271

DSMNet-mixed		97.42 302	97.60 282	96.87 392	99.15 250	91.46 430	98.54 126	99.12 282	92.87 435	97.58 359	99.63 39	96.21 261	99.90 81	95.74 342	99.54 271	99.27 271

N_pmnet		97.63 285	97.17 306	98.99 151	99.27 208	97.86 175	95.98 399	93.41 465	95.25 389	99.47 100	98.90 239	95.63 288	99.85 156	96.91 252	99.73 183	99.27 271

ambc				98.24 288	98.82 322	95.97 301	98.62 116	99.00 307		99.27 144	99.21 147	96.99 216	99.50 403	96.55 298	99.50 289	99.26 277

LFMVS		97.20 321	96.72 336	98.64 221	98.72 336	96.95 254	98.93 81	94.14 462	99.74 13	98.78 245	99.01 209	84.45 426	99.73 288	97.44 213	99.27 328	99.25 278

FMVSNet5		96.01 370	95.20 389	98.41 267	97.53 441	96.10 292	98.74 97	99.50 129	97.22 302	98.03 328	99.04 195	69.80 467	99.88 115	97.27 223	99.71 200	99.25 278

BH-RMVSNet		96.83 342	96.58 347	97.58 350	98.47 382	94.05 372	96.67 358	97.36 406	96.70 334	97.87 339	97.98 373	95.14 302	99.44 419	90.47 453	98.58 401	99.25 278

testf1		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 104	99.69 54	98.90 133	99.43 106	99.35 107	98.86 34	99.67 325	97.81 179	99.81 132	99.24 281

APD_test2		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 104	99.69 54	98.90 133	99.43 106	99.35 107	98.86 34	99.67 325	97.81 179	99.81 132	99.24 281

SSM_0404		98.90 102	99.01 88	98.57 238	99.42 169	96.59 272	98.13 179	99.66 65	99.09 108	99.30 139	99.02 198	98.79 42	99.89 97	97.87 176	99.80 143	99.23 283

旧先验1					98.82 322	97.45 211		98.76 348			98.34 345	95.50 294			99.01 366	99.23 283

test222					98.92 300	96.93 256	95.54 421	98.78 345	85.72 473	96.86 402	98.11 362	94.43 321			99.10 357	99.23 283

XVG-ACMP-BASELINE		98.56 173	98.34 197	99.22 109	99.54 118	98.59 104	97.71 256	99.46 152	97.25 293	98.98 199	98.99 215	97.54 175	99.84 174	95.88 333	99.74 180	99.23 283

FMVSNet3		97.50 292	97.24 303	98.29 282	98.08 412	95.83 306	97.86 233	98.91 319	97.89 229	98.95 209	98.95 229	87.06 405	99.81 222	97.77 183	99.69 210	99.23 283

icg_test_0407_2		98.20 232	98.38 191	97.65 341	99.03 276	94.03 375	95.78 414	99.45 156	98.16 204	99.06 179	98.71 284	98.27 98	99.68 321	97.50 207	99.45 296	99.22 288

IMVS_0407		98.39 205	98.64 144	97.66 339	99.03 276	94.03 375	98.10 186	99.45 156	98.16 204	99.06 179	98.71 284	98.27 98	99.71 298	97.50 207	99.45 296	99.22 288

IMVS_0404		98.07 245	98.20 219	97.69 336	99.03 276	94.03 375	96.67 358	99.45 156	98.16 204	98.03 328	98.71 284	96.80 230	99.82 205	97.50 207	99.45 296	99.22 288

IMVS_0403		98.34 209	98.56 158	97.66 339	99.03 276	94.03 375	97.98 215	99.45 156	98.16 204	98.89 224	98.71 284	97.90 137	99.74 281	97.50 207	99.45 296	99.22 288

无先验							95.74 416	98.74 353	89.38 464				99.73 288	92.38 428		99.22 288

tttt0517		95.64 383	94.98 393	97.64 344	99.36 184	93.81 390	98.72 102	90.47 476	98.08 215	98.67 259	98.34 345	73.88 462	99.92 65	97.77 183	99.51 281	99.20 293

pmmvs-eth3d		98.47 191	98.34 197	98.86 174	99.30 199	97.76 189	97.16 333	99.28 240	95.54 380	99.42 110	99.19 152	97.27 198	99.63 352	97.89 171	99.97 21	99.20 293

MS-PatchMatch		97.68 281	97.75 267	97.45 364	98.23 403	93.78 391	97.29 318	98.84 335	96.10 360	98.64 263	98.65 302	96.04 268	99.36 429	96.84 263	99.14 350	99.20 293

新几何1				98.91 168	98.94 294	97.76 189		98.76 348	87.58 470	96.75 407	98.10 363	94.80 314	99.78 255	92.73 422	99.00 367	99.20 293

PHI-MVS		98.29 220	97.95 251	99.34 83	98.44 387	99.16 49	98.12 183	99.38 187	96.01 365	98.06 324	98.43 335	97.80 151	99.67 325	95.69 345	99.58 258	99.20 293

GDP-MVS		97.50 292	97.11 312	98.67 217	99.02 282	96.85 260	98.16 176	99.71 47	98.32 182	98.52 286	98.54 318	83.39 435	99.95 26	98.79 102	99.56 265	99.19 298

Anonymous202405211		97.90 259	97.50 287	99.08 133	98.90 304	98.25 129	98.53 127	96.16 435	98.87 136	99.11 172	98.86 249	90.40 384	99.78 255	97.36 217	99.31 321	99.19 298

CANet		97.87 265	97.76 266	98.19 295	97.75 425	95.51 317	96.76 353	99.05 294	97.74 239	96.93 393	98.21 355	95.59 290	99.89 97	97.86 178	99.93 56	99.19 298

XVG-OURS		98.53 182	98.34 197	99.11 126	99.50 133	98.82 89	95.97 400	99.50 129	97.30 288	99.05 187	98.98 220	99.35 14	99.32 436	95.72 343	99.68 215	99.18 301

WTY-MVS		96.67 348	96.27 358	97.87 318	98.81 325	94.61 357	96.77 352	97.92 392	94.94 397	97.12 384	97.74 388	91.11 377	99.82 205	93.89 394	98.15 417	99.18 301

Vis-MVSNet (Re-imp)		97.46 297	97.16 307	98.34 277	99.55 113	96.10 292	98.94 80	98.44 371	98.32 182	98.16 313	98.62 309	88.76 395	99.73 288	93.88 395	99.79 149	99.18 301

TinyColmap		97.89 261	97.98 247	97.60 348	98.86 313	94.35 363	96.21 387	99.44 164	97.45 274	99.06 179	98.88 246	97.99 131	99.28 443	94.38 382	99.58 258	99.18 301

testdata				98.09 301	98.93 296	95.40 327		98.80 342	90.08 461	97.45 372	98.37 341	95.26 299	99.70 305	93.58 403	98.95 375	99.17 305

lupinMVS		97.06 330	96.86 326	97.65 341	98.88 310	93.89 388	95.48 425	97.97 390	93.53 425	98.16 313	97.58 397	93.81 337	99.91 74	96.77 268	99.57 262	99.17 305

Patchmtry		97.35 308	96.97 318	98.50 258	97.31 452	96.47 283	98.18 172	98.92 317	98.95 128	98.78 245	99.37 102	85.44 420	99.85 156	95.96 331	99.83 121	99.17 305

FE-MVSNET3		97.37 306	97.13 311	98.11 300	99.03 276	95.40 327	94.47 456	98.99 308	96.87 323	97.97 332	97.81 384	92.12 365	99.75 275	97.49 212	99.43 304	99.16 308

SD_0403		96.28 362	95.83 363	97.64 344	98.72 336	94.30 364	98.87 88	98.77 346	97.80 235	96.53 415	98.02 370	97.34 193	99.47 412	76.93 481	99.48 292	99.16 308

RRT-MVS		97.88 263	97.98 247	97.61 347	98.15 407	93.77 392	98.97 76	99.64 71	99.16 93	98.69 256	99.42 90	91.60 370	99.89 97	97.63 195	98.52 403	99.16 308

sss		97.21 320	96.93 320	98.06 306	98.83 319	95.22 335	96.75 354	98.48 370	94.49 405	97.27 381	97.90 379	92.77 355	99.80 231	96.57 291	99.32 319	99.16 308

CSCG		98.68 152	98.50 168	99.20 110	99.45 160	98.63 99	98.56 123	99.57 98	97.87 230	98.85 233	98.04 369	97.66 160	99.84 174	96.72 274	99.81 132	99.13 312

MVS_111021_LR		98.30 217	98.12 232	98.83 179	99.16 246	98.03 157	96.09 396	99.30 228	97.58 254	98.10 320	98.24 352	98.25 102	99.34 433	96.69 278	99.65 230	99.12 313

miper_lstm_enhance		97.18 323	97.16 307	97.25 374	98.16 406	92.85 409	95.15 437	99.31 220	97.25 293	98.74 253	98.78 270	90.07 385	99.78 255	97.19 228	99.80 143	99.11 314

testing3		93.51 419	92.09 430	97.75 329	98.60 366	94.40 361	97.32 314	95.26 450	97.56 257	96.79 406	95.50 448	53.57 488	99.77 261	95.26 356	98.97 373	99.08 315

原ACMM1				98.35 276	98.90 304	96.25 290		98.83 339	92.48 439	96.07 429	98.10 363	95.39 297	99.71 298	92.61 425	98.99 369	99.08 315

QAPM		97.31 311	96.81 332	98.82 181	98.80 328	97.49 205	99.06 65	99.19 264	90.22 459	97.69 352	99.16 162	96.91 221	99.90 81	90.89 450	99.41 306	99.07 317

PAPM_NR		96.82 344	96.32 355	98.30 281	99.07 264	96.69 270	97.48 293	98.76 348	95.81 372	96.61 412	96.47 429	94.12 332	99.17 450	90.82 451	97.78 430	99.06 318

eth_miper_zixun_eth		97.23 319	97.25 302	97.17 377	98.00 415	92.77 411	94.71 446	99.18 268	97.27 291	98.56 279	98.74 280	91.89 368	99.69 311	97.06 241	99.81 132	99.05 319

D2MVS		97.84 272	97.84 263	97.83 320	99.14 251	94.74 351	96.94 342	98.88 324	95.84 371	98.89 224	98.96 225	94.40 323	99.69 311	97.55 201	99.95 38	99.05 319

c3_l		97.36 307	97.37 295	97.31 369	98.09 411	93.25 402	95.01 440	99.16 275	97.05 310	98.77 248	98.72 283	92.88 352	99.64 349	96.93 251	99.76 175	99.05 319

PLC	94.65 16	96.51 353	95.73 366	98.85 175	98.75 332	97.91 171	96.42 375	99.06 290	90.94 456	95.59 436	97.38 409	94.41 322	99.59 369	90.93 448	98.04 426	99.05 319
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 102	98.90 100	98.91 168	99.67 66	97.82 183	99.00 72	99.44 164	99.45 51	99.51 93	99.24 140	98.20 111	99.86 143	95.92 332	99.69 210	99.04 323

CANet_DTU		97.26 315	97.06 314	97.84 319	97.57 436	94.65 356	96.19 389	98.79 343	97.23 299	95.14 448	98.24 352	93.22 344	99.84 174	97.34 218	99.84 112	99.04 323

PM-MVS		98.82 120	98.72 125	99.12 124	99.64 75	98.54 110	97.98 215	99.68 60	97.62 248	99.34 127	99.18 156	97.54 175	99.77 261	97.79 181	99.74 180	99.04 323

TSAR-MVS + GP.		98.18 235	97.98 247	98.77 198	98.71 340	97.88 173	96.32 381	98.66 358	96.33 349	99.23 158	98.51 323	97.48 185	99.40 424	97.16 230	99.46 294	99.02 326

DIV-MVS_self_test		97.02 333	96.84 328	97.58 350	97.82 423	94.03 375	94.66 449	99.16 275	97.04 311	98.63 264	98.71 284	88.69 396	99.69 311	97.00 244	99.81 132	99.01 327

mamv4		99.44 19	99.39 28	99.58 21	99.30 199	99.74 2	99.04 68	99.81 31	99.77 10	99.82 34	99.57 49	97.82 149	99.98 4	99.53 48	99.89 92	99.01 327

GA-MVS		95.86 375	95.32 385	97.49 361	98.60 366	94.15 370	93.83 467	97.93 391	95.49 382	96.68 408	97.42 407	83.21 436	99.30 439	96.22 318	98.55 402	99.01 327

OMC-MVS		97.88 263	97.49 288	99.04 144	98.89 309	98.63 99	96.94 342	99.25 249	95.02 394	98.53 284	98.51 323	97.27 198	99.47 412	93.50 406	99.51 281	99.01 327

cl____		97.02 333	96.83 329	97.58 350	97.82 423	94.04 374	94.66 449	99.16 275	97.04 311	98.63 264	98.71 284	88.68 398	99.69 311	97.00 244	99.81 132	99.00 331

pmmvs4		97.58 289	97.28 300	98.51 254	98.84 317	96.93 256	95.40 429	98.52 368	93.60 424	98.61 269	98.65 302	95.10 303	99.60 365	96.97 249	99.79 149	98.99 332

blend_shiyan4		92.09 441	90.16 448	97.88 317	96.78 464	94.93 345	95.24 433	98.58 364	96.22 354	96.07 429	91.42 481	63.46 484	99.73 288	96.70 277	76.98 483	98.98 333

EPNet_dtu		94.93 398	94.78 398	95.38 436	93.58 484	87.68 463	96.78 351	95.69 447	97.35 283	89.14 481	98.09 365	88.15 403	99.49 406	94.95 363	99.30 324	98.98 333
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 355	95.77 364	98.69 214	99.48 149	97.43 213	97.84 236	99.55 111	81.42 479	96.51 418	98.58 315	95.53 291	99.67 325	93.41 408	99.58 258	98.98 333

PVSNet_Blended		96.88 340	96.68 339	97.47 363	98.92 300	93.77 392	94.71 446	99.43 170	90.98 455	97.62 355	97.36 411	96.82 227	99.67 325	94.73 367	99.56 265	98.98 333

APD_test1		98.83 117	98.66 141	99.34 83	99.78 24	99.47 9	98.42 149	99.45 156	98.28 189	98.98 199	99.19 152	97.76 154	99.58 376	96.57 291	99.55 269	98.97 337

PAPR		95.29 389	94.47 400	97.75 329	97.50 447	95.14 338	94.89 443	98.71 356	91.39 451	95.35 446	95.48 450	94.57 319	99.14 453	84.95 469	97.37 443	98.97 337

EGC-MVSNET		85.24 447	80.54 450	99.34 83	99.77 27	99.20 40	99.08 61	99.29 236	12.08 486	20.84 487	99.42 90	97.55 173	99.85 156	97.08 238	99.72 191	98.96 339

thisisatest0530		95.27 390	94.45 401	97.74 331	99.19 234	94.37 362	97.86 233	90.20 477	97.17 304	98.22 308	97.65 393	73.53 463	99.90 81	96.90 257	99.35 314	98.95 340

mvs_anonymous		97.83 274	98.16 228	96.87 392	98.18 405	91.89 425	97.31 316	98.90 320	97.37 281	98.83 236	99.46 81	96.28 259	99.79 244	98.90 95	98.16 416	98.95 340

baseline1		95.96 373	95.44 379	97.52 358	98.51 380	93.99 382	98.39 153	96.09 438	98.21 194	98.40 299	97.76 387	86.88 406	99.63 352	95.42 353	89.27 480	98.95 340

CLD-MVS		97.49 295	97.16 307	98.48 259	99.07 264	97.03 248	94.71 446	99.21 258	94.46 407	98.06 324	97.16 415	97.57 171	99.48 409	94.46 375	99.78 154	98.95 340
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 249	98.14 231	97.64 344	98.58 371	95.19 336	97.48 293	99.23 256	97.47 267	97.90 336	98.62 309	97.04 211	98.81 464	97.55 201	99.41 306	98.94 344

DELS-MVS		98.27 221	98.20 219	98.48 259	98.86 313	96.70 269	95.60 420	99.20 260	97.73 240	98.45 291	98.71 284	97.50 181	99.82 205	98.21 145	99.59 253	98.93 345
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		95.79 378	95.39 382	96.98 386	96.77 465	92.79 410	94.40 458	98.53 367	94.59 404	97.89 337	98.17 358	82.82 440	99.24 445	96.37 309	99.03 362	98.92 346

LS3D		98.63 161	98.38 191	99.36 74	97.25 453	99.38 13	99.12 60	99.32 215	99.21 81	98.44 292	98.88 246	97.31 194	99.80 231	96.58 289	99.34 316	98.92 346

CMPMVS	75.91 23	96.29 361	95.44 379	98.84 178	96.25 475	98.69 98	97.02 337	99.12 282	88.90 466	97.83 343	98.86 249	89.51 391	98.90 462	91.92 429	99.51 281	98.92 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 159	98.48 174	99.11 126	98.85 316	98.51 112	98.49 138	99.83 25	98.37 176	99.69 56	99.46 81	98.21 109	99.92 65	94.13 388	99.30 324	98.91 349

mvsmamba		97.57 290	97.26 301	98.51 254	98.69 349	96.73 268	98.74 97	97.25 411	97.03 313	97.88 338	99.23 145	90.95 378	99.87 134	96.61 287	99.00 367	98.91 349

DPM-MVS		96.32 360	95.59 373	98.51 254	98.76 330	97.21 233	94.54 455	98.26 379	91.94 444	96.37 422	97.25 413	93.06 349	99.43 420	91.42 440	98.74 385	98.89 351

test_yl		96.69 346	96.29 356	97.90 314	98.28 398	95.24 333	97.29 318	97.36 406	98.21 194	98.17 310	97.86 380	86.27 410	99.55 385	94.87 364	98.32 406	98.89 351

DCV-MVSNet		96.69 346	96.29 356	97.90 314	98.28 398	95.24 333	97.29 318	97.36 406	98.21 194	98.17 310	97.86 380	86.27 410	99.55 385	94.87 364	98.32 406	98.89 351

SPE-MVS-test		99.13 67	99.09 78	99.26 101	99.13 253	98.97 74	99.31 30	99.88 14	99.44 53	98.16 313	98.51 323	98.64 58	99.93 54	98.91 94	99.85 107	98.88 354

UnsupCasMVSNet_bld		97.30 312	96.92 322	98.45 262	99.28 205	96.78 266	96.20 388	99.27 243	95.42 384	98.28 305	98.30 349	93.16 345	99.71 298	94.99 360	97.37 443	98.87 355

Effi-MVS+		98.02 249	97.82 264	98.62 227	98.53 378	97.19 235	97.33 313	99.68 60	97.30 288	96.68 408	97.46 405	98.56 70	99.80 231	96.63 285	98.20 412	98.86 356

test_0402		98.76 132	98.71 130	98.93 164	99.56 107	98.14 141	98.45 145	99.34 207	99.28 73	98.95 209	98.91 236	98.34 90	99.79 244	95.63 347	99.91 78	98.86 356

PatchmatchNet		95.58 384	95.67 369	95.30 437	97.34 451	87.32 465	97.65 266	96.65 427	95.30 388	97.07 387	98.69 293	84.77 423	99.75 275	94.97 362	98.64 396	98.83 358
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		93.78 415	93.91 407	93.39 458	98.82 322	81.72 485	97.76 249	95.28 449	98.60 159	96.54 414	96.66 424	65.85 479	99.62 355	96.65 284	98.99 369	98.82 359

test_vis1_rt		97.75 276	97.72 271	97.83 320	98.81 325	96.35 287	97.30 317	99.69 54	94.61 403	97.87 339	98.05 368	96.26 260	98.32 471	98.74 108	98.18 413	98.82 359

CL-MVSNet_self_test		97.44 300	97.22 304	98.08 304	98.57 373	95.78 309	94.30 460	98.79 343	96.58 338	98.60 271	98.19 357	94.74 317	99.64 349	96.41 307	98.84 380	98.82 359

miper_ehance_all_eth		97.06 330	97.03 315	97.16 379	97.83 422	93.06 404	94.66 449	99.09 287	95.99 366	98.69 256	98.45 333	92.73 357	99.61 362	96.79 265	99.03 362	98.82 359

MIMVSNet		96.62 351	96.25 359	97.71 335	99.04 273	94.66 355	99.16 54	96.92 423	97.23 299	97.87 339	99.10 178	86.11 414	99.65 345	91.65 435	99.21 340	98.82 359

hse-mvs2		97.46 297	97.07 313	98.64 221	98.73 334	97.33 218	97.45 299	97.64 402	99.11 98	98.58 275	97.98 373	88.65 399	99.79 244	98.11 151	97.39 442	98.81 364

GSMVS																98.81 364

sam_mvs1												84.74 424				98.81 364

SCA		96.41 359	96.66 342	95.67 427	98.24 401	88.35 459	95.85 411	96.88 424	96.11 359	97.67 353	98.67 297	93.10 347	99.85 156	94.16 384	99.22 337	98.81 364

Patchmatch-RL test		97.26 315	97.02 316	97.99 312	99.52 124	95.53 316	96.13 394	99.71 47	97.47 267	99.27 144	99.16 162	84.30 429	99.62 355	97.89 171	99.77 160	98.81 364

AUN-MVS		96.24 366	95.45 378	98.60 233	98.70 344	97.22 231	97.38 306	97.65 400	95.95 368	95.53 443	97.96 377	82.11 443	99.79 244	96.31 313	97.44 439	98.80 369

ITE_SJBPF				98.87 172	99.22 225	98.48 114		99.35 201	97.50 264	98.28 305	98.60 313	97.64 164	99.35 432	93.86 396	99.27 328	98.79 370

tpm		94.67 400	94.34 404	95.66 428	97.68 434	88.42 458	97.88 229	94.90 452	94.46 407	96.03 432	98.56 317	78.66 454	99.79 244	95.88 333	95.01 470	98.78 371

Patchmatch-test		96.55 352	96.34 354	97.17 377	98.35 394	93.06 404	98.40 152	97.79 393	97.33 284	98.41 295	98.67 297	83.68 434	99.69 311	95.16 358	99.31 321	98.77 372

EC-MVSNet		99.09 73	99.05 82	99.20 110	99.28 205	98.93 80	99.24 44	99.84 22	99.08 112	98.12 318	98.37 341	98.72 49	99.90 81	99.05 84	99.77 160	98.77 372

PMMVS		96.51 353	95.98 360	98.09 301	97.53 441	95.84 305	94.92 442	98.84 335	91.58 447	96.05 431	95.58 445	95.68 287	99.66 338	95.59 349	98.09 420	98.76 374

test_method		79.78 448	79.50 451	80.62 465	80.21 490	45.76 493	70.82 482	98.41 375	31.08 485	80.89 485	97.71 389	84.85 422	97.37 478	91.51 439	80.03 481	98.75 375

ab-mvs		98.41 196	98.36 194	98.59 234	99.19 234	97.23 228	99.32 26	98.81 340	97.66 245	98.62 267	99.40 97	96.82 227	99.80 231	95.88 333	99.51 281	98.75 375

CHOSEN 280x420		95.51 387	95.47 376	95.65 429	98.25 400	88.27 460	93.25 471	98.88 324	93.53 425	94.65 454	97.15 416	86.17 412	99.93 54	97.41 215	99.93 56	98.73 377

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 250	99.66 68	96.97 251	98.00 207	99.85 18	99.24 76	99.92 8	99.50 69	99.39 12	99.95 26	99.89 3	99.98 12	98.71 378

MVS_Test		98.18 235	98.36 194	97.67 337	98.48 381	94.73 352	98.18 172	99.02 302	97.69 243	98.04 327	99.11 175	97.22 202	99.56 381	98.57 120	98.90 379	98.71 378

PVSNet	93.40 17	95.67 381	95.70 367	95.57 430	98.83 319	88.57 457	92.50 474	97.72 395	92.69 437	96.49 421	96.44 430	93.72 340	99.43 420	93.61 401	99.28 327	98.71 378

alignmvs		97.35 308	96.88 325	98.78 193	98.54 376	98.09 146	97.71 256	97.69 397	99.20 83	97.59 358	95.90 440	88.12 404	99.55 385	98.18 147	98.96 374	98.70 381

ADS-MVSNet2		95.43 388	94.98 393	96.76 399	98.14 408	91.74 426	97.92 224	97.76 394	90.23 457	96.51 418	98.91 236	85.61 417	99.85 156	92.88 416	96.90 452	98.69 382

ADS-MVSNet		95.24 391	94.93 396	96.18 416	98.14 408	90.10 452	97.92 224	97.32 409	90.23 457	96.51 418	98.91 236	85.61 417	99.74 281	92.88 416	96.90 452	98.69 382

MDTV_nov1_ep13_2view						74.92 489	97.69 259		90.06 462	97.75 349		85.78 416		93.52 404		98.69 382

MSDG		97.71 279	97.52 286	98.28 283	98.91 303	96.82 261	94.42 457	99.37 191	97.65 246	98.37 300	98.29 350	97.40 189	99.33 435	94.09 389	99.22 337	98.68 385

mvsany_test1		97.60 286	97.54 284	97.77 325	97.72 426	95.35 329	95.36 430	97.13 415	94.13 416	99.71 50	99.33 114	97.93 135	99.30 439	97.60 199	98.94 376	98.67 386

CS-MVS		99.13 67	99.10 76	99.24 106	99.06 269	99.15 53	99.36 22	99.88 14	99.36 64	98.21 309	98.46 332	98.68 55	99.93 54	99.03 86	99.85 107	98.64 387

Syy-MVS		96.04 369	95.56 375	97.49 361	97.10 457	94.48 359	96.18 391	96.58 429	95.65 376	94.77 451	92.29 479	91.27 376	99.36 429	98.17 149	98.05 424	98.63 388

myMVS_eth3d		91.92 443	90.45 444	96.30 409	97.10 457	90.90 444	96.18 391	96.58 429	95.65 376	94.77 451	92.29 479	53.88 487	99.36 429	89.59 457	98.05 424	98.63 388

balanced_conf03		98.63 161	98.72 125	98.38 271	98.66 359	96.68 271	98.90 83	99.42 176	98.99 121	98.97 203	99.19 152	95.81 284	99.85 156	98.77 106	99.77 160	98.60 390

miper_enhance_ethall		96.01 370	95.74 365	96.81 396	96.41 473	92.27 422	93.69 469	98.89 323	91.14 454	98.30 301	97.35 412	90.58 382	99.58 376	96.31 313	99.03 362	98.60 390

Effi-MVS+-dtu		98.26 223	97.90 259	99.35 80	98.02 414	99.49 6	98.02 203	99.16 275	98.29 187	97.64 354	97.99 372	96.44 251	99.95 26	96.66 283	98.93 377	98.60 390

new_pmnet		96.99 337	96.76 334	97.67 337	98.72 336	94.89 346	95.95 404	98.20 382	92.62 438	98.55 281	98.54 318	94.88 310	99.52 397	93.96 392	99.44 303	98.59 393

MVSMamba_PlusPlus		98.83 117	98.98 93	98.36 275	99.32 194	96.58 275	98.90 83	99.41 180	99.75 11	98.72 254	99.50 69	96.17 262	99.94 42	99.27 65	99.78 154	98.57 394

testing91		93.32 422	92.27 427	96.47 405	97.54 439	91.25 438	96.17 393	96.76 426	97.18 303	93.65 468	93.50 471	65.11 481	99.63 352	93.04 413	97.45 438	98.53 395

EIA-MVS		98.00 252	97.74 268	98.80 186	98.72 336	98.09 146	98.05 196	99.60 82	97.39 279	96.63 410	95.55 446	97.68 158	99.80 231	96.73 273	99.27 328	98.52 396

PatchMatch-RL		97.24 318	96.78 333	98.61 231	99.03 276	97.83 178	96.36 378	99.06 290	93.49 427	97.36 379	97.78 385	95.75 285	99.49 406	93.44 407	98.77 384	98.52 396

sasdasda		98.34 209	98.26 212	98.58 235	98.46 384	97.82 183	98.96 77	99.46 152	99.19 88	97.46 370	95.46 451	98.59 64	99.46 415	98.08 154	98.71 389	98.46 398

ET-MVSNet_ETH3D		94.30 406	93.21 417	97.58 350	98.14 408	94.47 360	94.78 445	93.24 467	94.72 401	89.56 479	95.87 441	78.57 456	99.81 222	96.91 252	97.11 451	98.46 398

canonicalmvs		98.34 209	98.26 212	98.58 235	98.46 384	97.82 183	98.96 77	99.46 152	99.19 88	97.46 370	95.46 451	98.59 64	99.46 415	98.08 154	98.71 389	98.46 398

UBG		93.25 424	92.32 425	96.04 421	97.72 426	90.16 451	95.92 407	95.91 442	96.03 364	93.95 465	93.04 475	69.60 468	99.52 397	90.72 452	97.98 427	98.45 401

tt0805		98.69 146	98.62 148	98.90 171	99.75 34	99.30 23	99.15 56	96.97 419	98.86 138	98.87 232	97.62 396	98.63 60	98.96 458	99.41 57	98.29 409	98.45 401

TAPA-MVS	96.21 11	96.63 350	95.95 361	98.65 219	98.93 296	98.09 146	96.93 344	99.28 240	83.58 476	98.13 317	97.78 385	96.13 264	99.40 424	93.52 404	99.29 326	98.45 401
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net		98.34 209	98.28 208	98.51 254	98.47 382	97.59 201	98.96 77	99.48 139	99.18 91	97.40 375	95.50 448	98.66 56	99.50 403	98.18 147	98.71 389	98.44 404

BH-untuned		96.83 342	96.75 335	97.08 380	98.74 333	93.33 401	96.71 356	98.26 379	96.72 332	98.44 292	97.37 410	95.20 300	99.47 412	91.89 430	97.43 440	98.44 404

WB-MVSnew		95.73 380	95.57 374	96.23 414	96.70 466	90.70 448	96.07 397	93.86 463	95.60 378	97.04 389	95.45 454	96.00 271	99.55 385	91.04 446	98.31 408	98.43 406

pmmvs3		95.03 395	94.40 402	96.93 388	97.70 431	92.53 415	95.08 438	97.71 396	88.57 467	97.71 350	98.08 366	79.39 451	99.82 205	96.19 320	99.11 356	98.43 406

DP-MVS Recon		97.33 310	96.92 322	98.57 238	99.09 260	97.99 159	96.79 350	99.35 201	93.18 429	97.71 350	98.07 367	95.00 306	99.31 437	93.97 391	99.13 352	98.42 408

testing99		93.04 428	91.98 435	96.23 414	97.53 441	90.70 448	96.35 379	95.94 441	96.87 323	93.41 469	93.43 473	63.84 483	99.59 369	93.24 411	97.19 448	98.40 409

ETVMVS		92.60 433	91.08 442	97.18 375	97.70 431	93.65 397	96.54 365	95.70 445	96.51 339	94.68 453	92.39 478	61.80 485	99.50 403	86.97 464	97.41 441	98.40 409

Fast-Effi-MVS+-dtu		98.27 221	98.09 234	98.81 183	98.43 388	98.11 143	97.61 276	99.50 129	98.64 152	97.39 377	97.52 401	98.12 120	99.95 26	96.90 257	98.71 389	98.38 411

LF4IMVS		97.90 259	97.69 273	98.52 253	99.17 244	97.66 196	97.19 332	99.47 148	96.31 351	97.85 342	98.20 356	96.71 238	99.52 397	94.62 370	99.72 191	98.38 411

testing11		93.08 427	92.02 432	96.26 412	97.56 437	90.83 446	96.32 381	95.70 445	96.47 343	92.66 472	93.73 468	64.36 482	99.59 369	93.77 399	97.57 434	98.37 413

Fast-Effi-MVS+		97.67 282	97.38 294	98.57 238	98.71 340	97.43 213	97.23 323	99.45 156	94.82 400	96.13 426	96.51 426	98.52 72	99.91 74	96.19 320	98.83 381	98.37 413

test0.0.03 1		94.51 401	93.69 411	96.99 385	96.05 476	93.61 399	94.97 441	93.49 464	96.17 356	97.57 361	94.88 461	82.30 441	99.01 457	93.60 402	94.17 474	98.37 413

UWE-MVS		92.38 436	91.76 439	94.21 448	97.16 455	84.65 474	95.42 428	88.45 480	95.96 367	96.17 425	95.84 443	66.36 475	99.71 298	91.87 431	98.64 396	98.28 416

FE-MVS		95.66 382	94.95 395	97.77 325	98.53 378	95.28 332	99.40 19	96.09 438	93.11 431	97.96 333	99.26 133	79.10 453	99.77 261	92.40 427	98.71 389	98.27 417

baseline2		93.73 416	92.83 422	96.42 406	97.70 431	91.28 437	96.84 349	89.77 478	93.96 421	92.44 473	95.93 439	79.14 452	99.77 261	92.94 414	96.76 456	98.21 418

thisisatest0515		94.12 410	93.16 418	96.97 387	98.60 366	92.90 408	93.77 468	90.61 475	94.10 417	96.91 396	95.87 441	74.99 461	99.80 231	94.52 373	99.12 355	98.20 419

EPMVS		93.72 417	93.27 416	95.09 440	96.04 477	87.76 462	98.13 179	85.01 485	94.69 402	96.92 394	98.64 305	78.47 458	99.31 437	95.04 359	96.46 458	98.20 419

dp		93.47 420	93.59 413	93.13 461	96.64 467	81.62 486	97.66 264	96.42 432	92.80 436	96.11 427	98.64 305	78.55 457	99.59 369	93.31 409	92.18 479	98.16 421

CNLPA		97.17 324	96.71 337	98.55 245	98.56 374	98.05 156	96.33 380	98.93 314	96.91 321	97.06 388	97.39 408	94.38 324	99.45 417	91.66 434	99.18 346	98.14 422

dmvs_re		95.98 372	95.39 382	97.74 331	98.86 313	97.45 211	98.37 155	95.69 447	97.95 222	96.56 413	95.95 438	90.70 381	97.68 477	88.32 460	96.13 463	98.11 423

HY-MVS	95.94 13	95.90 374	95.35 384	97.55 355	97.95 416	94.79 348	98.81 96	96.94 422	92.28 442	95.17 447	98.57 316	89.90 387	99.75 275	91.20 444	97.33 447	98.10 424

CostFormer		93.97 412	93.78 410	94.51 444	97.53 441	85.83 470	97.98 215	95.96 440	89.29 465	94.99 450	98.63 307	78.63 455	99.62 355	94.54 372	96.50 457	98.09 425

FA-MVS(test-final)		96.99 337	96.82 330	97.50 360	98.70 344	94.78 349	99.34 23	96.99 418	95.07 393	98.48 289	99.33 114	88.41 402	99.65 345	96.13 326	98.92 378	98.07 426

AdaColmap		97.14 326	96.71 337	98.46 261	98.34 395	97.80 187	96.95 341	98.93 314	95.58 379	96.92 394	97.66 392	95.87 282	99.53 393	90.97 447	99.14 350	98.04 427

KD-MVS_2432*1600		92.87 431	91.99 433	95.51 432	91.37 486	89.27 455	94.07 462	98.14 385	95.42 384	97.25 382	96.44 430	67.86 470	99.24 445	91.28 442	96.08 464	98.02 428

miper_refine_blended		92.87 431	91.99 433	95.51 432	91.37 486	89.27 455	94.07 462	98.14 385	95.42 384	97.25 382	96.44 430	67.86 470	99.24 445	91.28 442	96.08 464	98.02 428

TESTMET0.1,1		92.19 440	91.77 438	93.46 456	96.48 471	82.80 482	94.05 464	91.52 474	94.45 409	94.00 463	94.88 461	66.65 474	99.56 381	95.78 341	98.11 419	98.02 428

testing222		91.96 442	90.37 445	96.72 400	97.47 448	92.59 413	96.11 395	94.76 453	96.83 326	92.90 471	92.87 476	57.92 486	99.55 385	86.93 465	97.52 435	98.00 431

PCF-MVS	92.86 18	94.36 403	93.00 421	98.42 266	98.70 344	97.56 202	93.16 472	99.11 284	79.59 480	97.55 362	97.43 406	92.19 363	99.73 288	79.85 478	99.45 296	97.97 432
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		90.22 446	89.28 449	93.02 462	94.50 483	82.87 481	96.52 368	87.51 481	95.21 391	92.36 474	96.04 435	71.57 465	98.25 473	72.04 483	97.77 431	97.94 433

myMVS_eth3d28		92.92 430	92.31 426	94.77 441	97.84 421	87.59 464	96.19 389	96.11 437	97.08 309	94.27 457	93.49 472	66.07 478	98.78 465	91.78 432	97.93 429	97.92 434

OpenMVS	96.65 7	97.09 328	96.68 339	98.32 278	98.32 396	97.16 240	98.86 91	99.37 191	89.48 463	96.29 424	99.15 166	96.56 245	99.90 81	92.90 415	99.20 341	97.89 435

Gipuma		99.03 83	99.16 63	98.64 221	99.94 2	98.51 112	99.32 26	99.75 42	99.58 39	98.60 271	99.62 40	98.22 107	99.51 402	97.70 192	99.73 183	97.89 435
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 445	90.30 447	93.70 454	97.72 426	84.34 478	90.24 478	97.42 404	90.20 460	93.79 466	93.09 474	90.90 380	98.89 463	86.57 467	72.76 484	97.87 437

test-LLR		93.90 413	93.85 408	94.04 449	96.53 469	84.62 475	94.05 464	92.39 469	96.17 356	94.12 460	95.07 455	82.30 441	99.67 325	95.87 336	98.18 413	97.82 438

test-mter		92.33 438	91.76 439	94.04 449	96.53 469	84.62 475	94.05 464	92.39 469	94.00 420	94.12 460	95.07 455	65.63 480	99.67 325	95.87 336	98.18 413	97.82 438

tpm2		93.09 426	92.58 424	94.62 443	97.56 437	86.53 467	97.66 264	95.79 444	86.15 472	94.07 462	98.23 354	75.95 459	99.53 393	90.91 449	96.86 455	97.81 440

CR-MVSNet		96.28 362	95.95 361	97.28 371	97.71 429	94.22 365	98.11 184	98.92 317	92.31 441	96.91 396	99.37 102	85.44 420	99.81 222	97.39 216	97.36 445	97.81 440

RPMNet		97.02 333	96.93 320	97.30 370	97.71 429	94.22 365	98.11 184	99.30 228	99.37 61	96.91 396	99.34 111	86.72 407	99.87 134	97.53 204	97.36 445	97.81 440

tpmrst		95.07 394	95.46 377	93.91 451	97.11 456	84.36 477	97.62 271	96.96 420	94.98 395	96.35 423	98.80 266	85.46 419	99.59 369	95.60 348	96.23 461	97.79 443

PAPM		91.88 444	90.34 446	96.51 403	98.06 413	92.56 414	92.44 475	97.17 413	86.35 471	90.38 478	96.01 436	86.61 408	99.21 448	70.65 484	95.43 468	97.75 444

FPMVS		93.44 421	92.23 428	97.08 380	99.25 218	97.86 175	95.61 419	97.16 414	92.90 434	93.76 467	98.65 302	75.94 460	95.66 481	79.30 479	97.49 436	97.73 445

MAR-MVS		96.47 357	95.70 367	98.79 190	97.92 418	99.12 63	98.28 161	98.60 363	92.16 443	95.54 442	96.17 434	94.77 316	99.52 397	89.62 456	98.23 410	97.72 446
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		98.03 248	97.86 262	98.56 243	98.69 349	98.07 152	97.51 289	99.50 129	98.10 212	97.50 367	95.51 447	98.41 81	99.88 115	96.27 316	99.24 333	97.71 447

thres600view7		94.45 402	93.83 409	96.29 410	99.06 269	91.53 429	97.99 214	94.24 460	98.34 179	97.44 373	95.01 457	79.84 447	99.67 325	84.33 470	98.23 410	97.66 448

thres400		94.14 409	93.44 414	96.24 413	98.93 296	91.44 432	97.60 277	94.29 458	97.94 224	97.10 385	94.31 466	79.67 449	99.62 355	83.05 472	98.08 421	97.66 448

IB-MVS	91.63 19	92.24 439	90.90 443	96.27 411	97.22 454	91.24 439	94.36 459	93.33 466	92.37 440	92.24 475	94.58 465	66.20 477	99.89 97	93.16 412	94.63 472	97.66 448
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		95.02 396	95.25 386	94.33 445	96.39 474	85.87 468	98.08 189	96.83 425	95.46 383	95.51 444	98.69 293	85.91 415	99.53 393	94.16 384	96.23 461	97.58 451

cascas		94.79 399	94.33 405	96.15 420	96.02 478	92.36 420	92.34 476	99.26 248	85.34 474	95.08 449	94.96 460	92.96 351	98.53 469	94.41 381	98.59 400	97.56 452

PatchT		96.65 349	96.35 353	97.54 356	97.40 449	95.32 331	97.98 215	96.64 428	99.33 66	96.89 400	99.42 90	84.32 428	99.81 222	97.69 194	97.49 436	97.48 453

TR-MVS		95.55 385	95.12 391	96.86 395	97.54 439	93.94 383	96.49 370	96.53 431	94.36 412	97.03 391	96.61 425	94.26 328	99.16 451	86.91 466	96.31 460	97.47 454

dmvs_testset		92.94 429	92.21 429	95.13 438	98.59 369	90.99 443	97.65 266	92.09 471	96.95 316	94.00 463	93.55 470	92.34 361	96.97 480	72.20 482	92.52 477	97.43 455

MonoMVSNet		96.25 364	96.53 350	95.39 435	96.57 468	91.01 442	98.82 95	97.68 399	98.57 165	98.03 328	99.37 102	90.92 379	97.78 476	94.99 360	93.88 475	97.38 456

JIA-IIPM		95.52 386	95.03 392	97.00 384	96.85 462	94.03 375	96.93 344	95.82 443	99.20 83	94.63 455	99.71 22	83.09 437	99.60 365	94.42 378	94.64 471	97.36 457

BH-w/o		95.13 393	94.89 397	95.86 422	98.20 404	91.31 435	95.65 418	97.37 405	93.64 423	96.52 417	95.70 444	93.04 350	99.02 455	88.10 461	95.82 466	97.24 458

tpm cat1		93.29 423	93.13 420	93.75 453	97.39 450	84.74 473	97.39 304	97.65 400	83.39 477	94.16 459	98.41 336	82.86 439	99.39 426	91.56 438	95.35 469	97.14 459

xiu_mvs_v1_base_debu		97.86 266	98.17 225	96.92 389	98.98 289	93.91 385	96.45 371	99.17 272	97.85 232	98.41 295	97.14 417	98.47 74	99.92 65	98.02 160	99.05 358	96.92 460

xiu_mvs_v1_base		97.86 266	98.17 225	96.92 389	98.98 289	93.91 385	96.45 371	99.17 272	97.85 232	98.41 295	97.14 417	98.47 74	99.92 65	98.02 160	99.05 358	96.92 460

xiu_mvs_v1_base_debi		97.86 266	98.17 225	96.92 389	98.98 289	93.91 385	96.45 371	99.17 272	97.85 232	98.41 295	97.14 417	98.47 74	99.92 65	98.02 160	99.05 358	96.92 460

PMVS	91.26 20	97.86 266	97.94 253	97.65 341	99.71 47	97.94 168	98.52 128	98.68 357	98.99 121	97.52 365	99.35 107	97.41 188	98.18 474	91.59 437	99.67 221	96.82 463
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 379	95.60 371	96.17 417	97.53 441	92.75 412	98.07 193	98.31 378	91.22 452	94.25 458	96.68 423	95.53 291	99.03 454	91.64 436	97.18 449	96.74 464

MVS-HIRNet		94.32 404	95.62 370	90.42 464	98.46 384	75.36 488	96.29 383	89.13 479	95.25 389	95.38 445	99.75 16	92.88 352	99.19 449	94.07 390	99.39 308	96.72 465

OpenMVS_ROB	95.38 14	95.84 377	95.18 390	97.81 322	98.41 392	97.15 241	97.37 310	98.62 362	83.86 475	98.65 262	98.37 341	94.29 327	99.68 321	88.41 459	98.62 399	96.60 466

thres100view900		94.19 407	93.67 412	95.75 426	99.06 269	91.35 434	98.03 200	94.24 460	98.33 180	97.40 375	94.98 459	79.84 447	99.62 355	83.05 472	98.08 421	96.29 467

tfpn200view9		94.03 411	93.44 414	95.78 425	98.93 296	91.44 432	97.60 277	94.29 458	97.94 224	97.10 385	94.31 466	79.67 449	99.62 355	83.05 472	98.08 421	96.29 467

MVS		93.19 425	92.09 430	96.50 404	96.91 460	94.03 375	98.07 193	98.06 389	68.01 482	94.56 456	96.48 428	95.96 278	99.30 439	83.84 471	96.89 454	96.17 469

gg-mvs-nofinetune		92.37 437	91.20 441	95.85 423	95.80 480	92.38 419	99.31 30	81.84 487	99.75 11	91.83 476	99.74 18	68.29 469	99.02 455	87.15 463	97.12 450	96.16 470

xiu_mvs_v2_base		97.16 325	97.49 288	96.17 417	98.54 376	92.46 416	95.45 426	98.84 335	97.25 293	97.48 369	96.49 427	98.31 92	99.90 81	96.34 312	98.68 394	96.15 471

PS-MVSNAJ		97.08 329	97.39 293	96.16 419	98.56 374	92.46 416	95.24 433	98.85 334	97.25 293	97.49 368	95.99 437	98.07 122	99.90 81	96.37 309	98.67 395	96.12 472

E-PMN		94.17 408	94.37 403	93.58 455	96.86 461	85.71 471	90.11 480	97.07 416	98.17 201	97.82 345	97.19 414	84.62 425	98.94 459	89.77 455	97.68 433	96.09 473

EMVS		93.83 414	94.02 406	93.23 460	96.83 463	84.96 472	89.77 481	96.32 433	97.92 226	97.43 374	96.36 433	86.17 412	98.93 460	87.68 462	97.73 432	95.81 474

MVE	83.40 22	92.50 434	91.92 436	94.25 446	98.83 319	91.64 428	92.71 473	83.52 486	95.92 369	86.46 484	95.46 451	95.20 300	95.40 482	80.51 477	98.64 396	95.73 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 417	93.14 419	95.46 434	98.66 359	91.29 436	96.61 362	94.63 455	97.39 279	96.83 403	93.71 469	79.88 446	99.56 381	82.40 475	98.13 418	95.54 476

API-MVS		97.04 332	96.91 324	97.42 366	97.88 420	98.23 134	98.18 172	98.50 369	97.57 255	97.39 377	96.75 422	96.77 232	99.15 452	90.16 454	99.02 365	94.88 477

GG-mvs-BLEND				94.76 442	94.54 482	92.13 424	99.31 30	80.47 488		88.73 482	91.01 482	67.59 473	98.16 475	82.30 476	94.53 473	93.98 478

DeepMVS_CX				93.44 457	98.24 401	94.21 367		94.34 457	64.28 483	91.34 477	94.87 463	89.45 393	92.77 484	77.54 480	93.14 476	93.35 479

tmp_tt		78.77 449	78.73 452	78.90 466	58.45 491	74.76 490	94.20 461	78.26 489	39.16 484	86.71 483	92.82 477	80.50 445	75.19 486	86.16 468	92.29 478	86.74 480

dongtai		76.24 450	75.95 453	77.12 467	92.39 485	67.91 491	90.16 479	59.44 492	82.04 478	89.42 480	94.67 464	49.68 489	81.74 485	48.06 485	77.66 482	81.72 481

kuosan		69.30 451	68.95 454	70.34 468	87.68 489	65.00 492	91.11 477	59.90 491	69.02 481	74.46 486	88.89 483	48.58 490	68.03 487	28.61 486	72.33 485	77.99 482

wuyk23d		96.06 368	97.62 281	91.38 463	98.65 363	98.57 106	98.85 92	96.95 421	96.86 325	99.90 14	99.16 162	99.18 19	98.40 470	89.23 458	99.77 160	77.18 483

test123		17.04 454	20.11 457	7.82 469	10.25 493	4.91 494	94.80 444	4.47 494	4.93 487	10.00 489	24.28 486	9.69 491	3.64 488	10.14 487	12.43 487	14.92 484

testmvs		17.12 453	20.53 456	6.87 470	12.05 492	4.20 495	93.62 470	6.73 493	4.62 488	10.41 488	24.33 485	8.28 492	3.56 489	9.69 488	15.07 486	12.86 485

mmdepth		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

monomultidepth		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

test_blank		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet_test		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

DCPMVS		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

cdsmvs_eth3d_5k		24.66 452	32.88 455	0.00 471	0.00 494	0.00 496	0.00 483	99.10 285	0.00 489	0.00 490	97.58 397	99.21 18	0.00 490	0.00 489	0.00 488	0.00 486

pcd_1.5k_mvsjas		8.17 455	10.90 458	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	98.07 122	0.00 490	0.00 489	0.00 488	0.00 486

sosnet-low-res		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

sosnet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uncertanet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

Regformer		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

ab-mvs-re		8.12 456	10.83 459	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	97.48 403	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

TestfortrainingZip							98.68 107

WAC-MVS						90.90 444								91.37 441

FOURS1					99.73 37	99.67 3	99.43 15	99.54 116	99.43 55	99.26 148

test_one_0601					99.39 176	99.20 40		99.31 220	98.49 171	98.66 261	99.02 198	97.64 164

eth-test2					0.00 494

eth-test					0.00 494

ZD-MVS					99.01 284	98.84 86		99.07 289	94.10 417	98.05 326	98.12 361	96.36 256	99.86 143	92.70 423	99.19 344

test_241102_ONE					99.49 141	99.17 45		99.31 220	97.98 219	99.66 61	98.90 239	98.36 85	99.48 409

9.14			97.78 265		99.07 264		97.53 286	99.32 215	95.53 381	98.54 283	98.70 291	97.58 170	99.76 267	94.32 383	99.46 294

save fliter					99.11 255	97.97 163	96.53 367	99.02 302	98.24 190

test0726					99.50 133	99.21 34	98.17 175	99.35 201	97.97 220	99.26 148	99.06 186	97.61 168

test_part2					99.36 184	99.10 66				99.05 187

sam_mvs												84.29 430

MTGPA								99.20 260

test_post1							97.59 279				20.48 488	83.07 438	99.66 338	94.16 384

test_post											21.25 487	83.86 433	99.70 305

patchmatchnet-post											98.77 272	84.37 427	99.85 156

MTMP							97.93 221	91.91 473

gm-plane-assit					94.83 481	81.97 484			88.07 469		94.99 458		99.60 365	91.76 433

TEST9					98.71 340	98.08 150	95.96 402	99.03 299	91.40 450	95.85 433	97.53 399	96.52 247	99.76 267

test_8					98.67 354	98.01 158	95.91 408	99.02 302	91.64 445	95.79 435	97.50 402	96.47 249	99.76 267

agg_prior					98.68 353	97.99 159		99.01 305		95.59 436			99.77 261

test_prior4						97.97 163	95.86 409

test_prior2							95.74 416		96.48 342	96.11 427	97.63 395	95.92 281		94.16 384	99.20 341

旧先验2							95.76 415		88.56 468	97.52 365			99.66 338	94.48 374

新几何2							95.93 405

原ACMM2							95.53 422

testdata2													99.79 244	92.80 420

segment_acmp												97.02 214

testdata1							95.44 427		96.32 350

plane_prior7					99.19 234	97.87 174

plane_prior6					98.99 288	97.70 195						94.90 307

plane_prior4											97.98 373

plane_prior3						97.78 188			97.41 276	97.79 346

plane_prior2							97.77 246		98.20 198

plane_prior1					99.05 272

plane_prior						97.65 197	97.07 336		96.72 332						99.36 312

n2								0.00 495

nn								0.00 495

door-mid								99.57 98

test11								98.87 326

door								99.41 180

HQP5-MVS						96.79 263

HQP-NCC					98.67 354		96.29 383		96.05 361	95.55 439

ACMP_Plane					98.67 354		96.29 383		96.05 361	95.55 439

BP-MVS														92.82 418

HQP3-MVS								99.04 297							99.26 331

HQP2-MVS												93.84 335

NP-MVS					98.84 317	97.39 215					96.84 420

MDTV_nov1_ep13			95.22 388		97.06 459	83.20 480	97.74 253	96.16 435	94.37 411	96.99 392	98.83 259	83.95 432	99.53 393	93.90 393	97.95 428

ACMMP++_ref															99.77 160

ACMMP++															99.68 215

Test By Simon												96.52 247

High-resolution multi-view benchmark