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 35	99.59 12	98.44 262	99.65 70	95.35 326	99.82 3	99.94 2	99.83 7	99.42 111	99.94 2	98.13 117	99.96 14	99.63 36	99.96 28	100.00 1

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 75	99.87 13	98.13 143	98.08 188	99.95 1	99.45 52	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 54	99.30 46	98.80 184	99.75 35	96.59 270	97.97 218	99.86 16	98.22 190	99.88 21	99.71 23	98.59 63	99.84 174	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n_2		99.20 52	99.38 30	98.65 217	99.69 60	96.08 295	97.49 290	99.90 11	99.53 42	99.88 21	99.64 38	98.51 72	99.90 81	99.83 10	99.98 12	99.97 4

mmtdpeth		99.30 35	99.42 26	98.92 168	99.58 89	96.89 257	99.48 13	99.92 7	99.92 2	98.26 305	99.80 11	98.33 90	99.91 74	99.56 41	99.95 38	99.97 4

fmvsm_s_conf0.1_n		99.16 58	99.33 39	98.64 219	99.71 48	96.10 290	97.87 231	99.85 18	98.56 166	99.90 14	99.68 26	98.69 53	99.85 156	99.72 30	99.98 12	99.97 4

test_fmvs3		99.12 71	99.41 27	98.25 284	99.76 31	95.07 338	99.05 68	99.94 2	97.78 236	99.82 34	99.84 3	98.56 69	99.71 297	99.96 1	99.96 28	99.97 4

test_fmvsmconf0.1_n		99.49 16	99.54 14	99.34 84	99.78 25	98.11 144	97.77 245	99.90 11	99.33 67	99.97 3	99.66 33	99.71 3	99.96 14	99.79 19	99.99 5	99.96 8

test_f		98.67 153	98.87 104	98.05 305	99.72 44	95.59 310	98.51 134	99.81 31	96.30 350	99.78 40	99.82 5	96.14 261	98.63 464	99.82 12	99.93 56	99.95 9

test_fmvs2		98.70 140	98.97 93	97.89 313	99.54 117	94.05 368	98.55 125	99.92 7	96.78 326	99.72 48	99.78 13	96.60 242	99.67 321	99.91 2	99.90 87	99.94 10

PS-MVSNAJss		99.46 18	99.49 17	99.35 81	99.90 4	98.15 140	99.20 49	99.65 70	99.48 45	99.92 8	99.71 23	98.07 120	99.96 14	99.53 48	100.00 1	99.93 11

test_vis3_rt		99.14 64	99.17 62	99.07 136	99.78 25	98.38 120	98.92 83	99.94 2	97.80 233	99.91 12	99.67 31	97.15 204	98.91 457	99.76 23	99.56 263	99.92 12

fmvsm_s_conf0.5_n_2		99.14 64	99.31 43	98.63 223	99.49 139	96.08 295	97.38 304	99.81 31	99.48 45	99.84 30	99.57 50	98.46 77	99.89 97	99.82 12	99.97 21	99.91 13

MVStest1		95.86 372	95.60 368	96.63 397	95.87 475	91.70 423	97.93 220	98.94 308	98.03 214	99.56 74	99.66 33	71.83 461	98.26 468	99.35 59	99.24 330	99.91 13

fmvsm_s_conf0.5_n_a		99.10 73	99.20 60	98.78 191	99.55 112	96.59 270	97.79 241	99.82 30	98.21 192	99.81 37	99.53 66	98.46 77	99.84 174	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n_9		99.17 54	99.38 30	98.53 250	99.51 125	95.82 305	97.62 270	99.78 37	99.72 15	99.90 14	99.48 77	98.66 55	99.89 97	99.85 6	99.93 56	99.89 16

fmvsm_s_conf0.5_n		99.09 74	99.26 52	98.61 229	99.55 112	96.09 293	97.74 252	99.81 31	98.55 167	99.85 27	99.55 58	98.60 62	99.84 174	99.69 35	99.98 12	99.89 16

test_fmvsmconf_n		99.44 20	99.48 19	99.31 95	99.64 76	98.10 146	97.68 259	99.84 22	99.29 73	99.92 8	99.57 50	99.60 5	99.96 14	99.74 27	99.98 12	99.89 16

test_djsdf		99.52 13	99.51 16	99.53 39	99.86 15	98.74 93	99.39 20	99.56 105	99.11 99	99.70 52	99.73 21	99.00 28	99.97 7	99.26 66	99.98 12	99.89 16

fmvsm_s_conf0.5_n_11		99.21 49	99.34 37	98.80 184	99.48 147	96.56 275	97.97 218	99.69 55	99.63 29	99.84 30	99.54 64	98.21 107	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 10	98.61 103	99.34 23	99.71 48	99.27 75	99.90 14	99.74 19	99.68 4	99.97 7	99.55 43	99.99 5	99.88 20

fmvsm_s_conf0.5_n_8		99.13 68	99.26 52	98.74 204	99.51 125	96.44 282	97.65 265	99.65 70	99.66 24	99.78 40	99.48 77	97.92 134	99.93 54	99.72 30	99.95 38	99.87 22

fmvsm_s_conf0.5_n_7		98.83 115	99.04 82	98.20 291	99.30 197	94.83 343	97.23 321	99.36 193	98.64 151	99.84 30	99.43 90	98.10 119	99.91 74	99.56 41	99.96 28	99.87 22

fmvsm_l_conf0.5_n_3		99.45 19	99.48 19	99.34 84	99.59 87	98.21 137	97.82 236	99.84 22	99.41 59	99.92 8	99.41 95	99.51 8	99.95 26	99.84 9	99.97 21	99.87 22

ttmdpeth		97.91 256	98.02 241	97.58 346	98.69 346	94.10 367	98.13 178	98.90 317	97.95 220	97.32 377	99.58 48	95.95 277	98.75 462	96.41 303	99.22 334	99.87 22

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 13	98.61 103	99.28 41	99.66 66	99.09 109	99.89 18	99.68 26	99.53 7	99.97 7	99.50 51	99.99 5	99.87 22

EU-MVSNet		97.66 281	98.50 166	95.13 434	99.63 82	85.84 465	98.35 156	98.21 377	98.23 189	99.54 79	99.46 82	95.02 303	99.68 317	98.24 140	99.87 99	99.87 22

fmvsm_s_conf0.5_n_3		99.22 48	99.37 33	98.78 191	99.46 153	96.58 273	97.65 265	99.72 46	99.47 48	99.86 24	99.50 70	98.94 31	99.89 97	99.75 26	99.97 21	99.86 28

UA-Net		99.47 17	99.40 28	99.70 2	99.49 139	99.29 25	99.80 4	99.72 46	99.82 8	99.04 187	99.81 8	98.05 123	99.96 14	98.85 100	99.99 5	99.86 28

fmvsm_l_conf0.5_n_9		99.32 34	99.43 25	98.98 156	99.59 87	97.18 235	97.44 299	99.83 25	99.56 40	99.91 12	99.34 110	99.36 13	99.93 54	99.83 10	99.98 12	99.85 30

MM		98.22 226	97.99 244	98.91 169	98.66 356	96.97 249	97.89 227	94.44 452	99.54 41	98.95 207	99.14 167	93.50 339	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 53	99.27 49	98.94 162	99.65 70	97.05 244	97.80 240	99.76 40	98.70 149	99.78 40	99.11 173	98.79 43	99.95 26	99.85 6	99.96 28	99.83 33

fmvsm_l_conf0.5_n		99.21 49	99.28 48	99.02 149	99.64 76	97.28 224	97.82 236	99.76 40	98.73 146	99.82 34	99.09 181	98.81 39	99.95 26	99.86 4	99.96 28	99.83 33

mvsany_test3		98.87 106	98.92 97	98.74 204	99.38 175	96.94 253	98.58 122	99.10 283	96.49 338	99.96 4	99.81 8	98.18 110	99.45 413	98.97 91	99.79 147	99.83 33

fmvsm_s_conf0.5_n_10		99.15 59	99.27 49	98.78 191	99.47 150	96.56 275	97.75 251	99.71 48	99.60 36	99.74 47	99.44 87	97.96 131	99.95 26	99.86 4	99.94 50	99.82 36

SSC-MVS		98.71 135	98.74 119	98.62 225	99.72 44	96.08 295	98.74 98	98.64 358	99.74 13	99.67 60	99.24 138	94.57 317	99.95 26	99.11 79	99.24 330	99.82 36

anonymousdsp		99.51 14	99.47 22	99.62 10	99.88 10	99.08 70	99.34 23	99.69 55	98.93 130	99.65 64	99.72 22	98.93 33	99.95 26	99.11 79	100.00 1	99.82 36

ANet_high		99.57 10	99.67 6	99.28 97	99.89 7	98.09 147	99.14 58	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 84	99.22 56	98.38 269	99.31 193	95.48 319	97.56 280	99.73 45	98.87 137	99.75 45	99.27 125	98.80 41	99.86 143	99.80 17	99.90 87	99.81 40

PS-CasMVS		99.40 27	99.33 39	99.62 10	99.71 48	99.10 66	99.29 37	99.53 118	99.53 42	99.46 102	99.41 95	98.23 102	99.95 26	98.89 98	99.95 38	99.81 40

VortexMVS		97.98 254	98.31 202	97.02 379	98.88 307	91.45 427	98.03 199	99.47 146	98.65 150	99.55 77	99.47 80	91.49 370	99.81 223	99.32 61	99.91 78	99.80 42

FC-MVSNet-test		99.27 39	99.25 54	99.34 84	99.77 28	98.37 122	99.30 36	99.57 96	99.61 35	99.40 116	99.50 70	97.12 205	99.85 156	99.02 88	99.94 50	99.80 42

test_cas_vis1_n_1920		98.33 211	98.68 134	97.27 368	99.69 60	92.29 417	98.03 199	99.85 18	97.62 246	99.96 4	99.62 41	93.98 332	99.74 281	99.52 50	99.86 106	99.79 44

test_vis1_n_1920		98.40 197	98.92 97	96.81 392	99.74 37	90.76 443	98.15 176	99.91 9	98.33 178	99.89 18	99.55 58	95.07 302	99.88 115	99.76 23	99.93 56	99.79 44

CP-MVSNet		99.21 49	99.09 77	99.56 27	99.65 70	98.96 78	99.13 59	99.34 205	99.42 57	99.33 131	99.26 131	97.01 213	99.94 42	98.74 109	99.93 56	99.79 44

fmvsm_s_conf0.5_n_5		99.07 80	99.10 75	98.99 152	99.47 150	97.22 229	97.40 301	99.83 25	97.61 249	99.85 27	99.30 119	98.80 41	99.95 26	99.71 32	99.90 87	99.78 47

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 18	99.34 20	99.69 5	99.58 89	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 89	99.95 38	99.78 47

CVMVSNet		96.25 361	97.21 303	93.38 455	99.10 255	80.56 483	97.20 326	98.19 380	96.94 315	99.00 192	99.02 196	89.50 389	99.80 232	96.36 307	99.59 251	99.78 47

TestfortrainingZip a		98.95 94	98.72 123	99.64 9	99.58 89	99.32 22	98.68 108	99.60 80	96.46 341	99.53 83	98.77 270	97.87 141	99.83 193	98.39 133	99.64 230	99.77 50

reproduce_monomvs		95.00 394	95.25 383	94.22 443	97.51 443	83.34 475	97.86 232	98.44 367	98.51 168	99.29 141	99.30 119	67.68 469	99.56 377	98.89 98	99.81 130	99.77 50

Anonymous20231211		99.27 39	99.27 49	99.26 102	99.29 200	98.18 138	99.49 12	99.51 124	99.70 16	99.80 38	99.68 26	96.84 222	99.83 193	99.21 71	99.91 78	99.77 50

PEN-MVS		99.41 26	99.34 37	99.62 10	99.73 38	99.14 58	99.29 37	99.54 114	99.62 33	99.56 74	99.42 91	98.16 114	99.96 14	98.78 104	99.93 56	99.77 50

WR-MVS_H		99.33 32	99.22 56	99.65 8	99.71 48	99.24 31	99.32 27	99.55 109	99.46 51	99.50 95	99.34 110	97.30 193	99.93 54	98.90 96	99.93 56	99.77 50

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 27	99.85 17	99.11 65	99.90 1	99.78 37	99.63 29	99.78 40	99.67 31	99.48 10	99.81 223	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		98.52 184	98.55 157	98.43 263	99.65 70	95.59 310	98.52 129	98.77 343	99.65 26	99.52 88	99.00 211	94.34 323	99.93 54	98.65 116	98.83 378	99.76 56

patch_mono-2		98.51 185	98.63 144	98.17 294	99.38 175	94.78 345	97.36 309	99.69 55	98.16 202	98.49 286	99.29 122	97.06 208	99.97 7	98.29 139	99.91 78	99.76 56

nrg030		99.40 27	99.35 35	99.54 32	99.58 89	99.13 61	98.98 76	99.48 137	99.68 20	99.46 102	99.26 131	98.62 60	99.73 288	99.17 75	99.92 69	99.76 56

FIs		99.14 64	99.09 77	99.29 96	99.70 56	98.28 128	99.13 59	99.52 123	99.48 45	99.24 155	99.41 95	96.79 229	99.82 206	98.69 114	99.88 95	99.76 56

v7n		99.53 12	99.57 13	99.41 71	99.88 10	98.54 111	99.45 14	99.61 79	99.66 24	99.68 58	99.66 33	98.44 79	99.95 26	99.73 28	99.96 28	99.75 60

APDe-MVS		98.99 87	98.79 115	99.60 16	99.21 225	99.15 53	98.87 89	99.48 137	97.57 253	99.35 126	99.24 138	97.83 144	99.89 97	97.88 172	99.70 205	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 24	99.35 35	99.66 7	99.71 48	99.30 23	99.31 31	99.51 124	99.64 27	99.56 74	99.46 82	98.23 102	99.97 7	98.78 104	99.93 56	99.72 62

MSC_two_6792asdad				99.32 92	98.43 385	98.37 122		98.86 328					99.89 97	97.14 230	99.60 247	99.71 63

No_MVS				99.32 92	98.43 385	98.37 122		98.86 328					99.89 97	97.14 230	99.60 247	99.71 63

PMMVS2		98.07 243	98.08 235	98.04 306	99.41 170	94.59 354	94.59 450	99.40 181	97.50 262	98.82 237	98.83 257	96.83 224	99.84 174	97.50 205	99.81 130	99.71 63

Baseline_NR-MVSNet		98.98 90	98.86 108	99.36 75	99.82 20	98.55 108	97.47 295	99.57 96	99.37 62	99.21 161	99.61 44	96.76 232	99.83 193	98.06 155	99.83 120	99.71 63

XXY-MVS		99.14 64	99.15 69	99.10 129	99.76 31	97.74 192	98.85 93	99.62 76	98.48 170	99.37 121	99.49 76	98.75 47	99.86 143	98.20 145	99.80 141	99.71 63

test_0728_THIRD									98.17 199	99.08 175	99.02 196	97.89 139	99.88 115	97.07 236	99.71 198	99.70 68

MSP-MVS		98.40 197	98.00 243	99.61 14	99.57 98	99.25 30	98.57 123	99.35 199	97.55 257	99.31 139	97.71 386	94.61 316	99.88 115	96.14 320	99.19 341	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		98.53 180	98.79 115	97.74 327	99.46 153	93.62 394	96.45 369	99.34 205	99.33 67	98.93 215	98.70 289	97.90 135	99.90 81	99.12 78	99.92 69	99.69 70

NormalMVS		98.26 221	97.97 248	99.15 122	99.64 76	97.83 179	98.28 160	99.43 168	99.24 77	98.80 241	98.85 250	89.76 385	99.94 42	98.04 157	99.67 219	99.68 71

KinetiMVS		99.03 82	99.02 85	99.03 146	99.70 56	97.48 209	98.43 147	99.29 234	99.70 16	99.60 71	99.07 183	96.13 262	99.94 42	99.42 56	99.87 99	99.68 71

dcpmvs_2		98.78 126	99.11 73	97.78 320	99.56 106	93.67 391	99.06 66	99.86 16	99.50 44	99.66 61	99.26 131	97.21 201	99.99 2	98.00 162	99.91 78	99.68 71

test_0728_SECOND				99.60 16	99.50 131	99.23 32	98.02 202	99.32 213					99.88 115	96.99 243	99.63 237	99.68 71

OurMVSNet-221017-0		99.37 30	99.31 43	99.53 39	99.91 3	98.98 72	99.63 7	99.58 89	99.44 54	99.78 40	99.76 15	96.39 250	99.92 65	99.44 55	99.92 69	99.68 71

fmvsm_s_conf0.5_n_6		99.08 78	99.21 59	98.69 212	99.36 182	96.51 277	97.62 270	99.68 61	98.43 172	99.85 27	99.10 176	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

CHOSEN 1792x2688		97.49 293	97.14 308	98.54 248	99.68 63	96.09 293	96.50 367	99.62 76	91.58 443	98.84 233	98.97 220	92.36 358	99.88 115	96.76 266	99.95 38	99.67 76

reproduce_model		99.15 59	98.97 93	99.67 4	99.33 191	99.44 10	98.15 176	99.47 146	99.12 98	99.52 88	99.32 117	98.31 91	99.90 81	97.78 180	99.73 181	99.66 78

IU-MVS					99.49 139	99.15 53		98.87 323	92.97 428	99.41 113				96.76 266	99.62 240	99.66 78

test_241102_TWO								99.30 226	98.03 214	99.26 149	99.02 196	97.51 178	99.88 115	96.91 249	99.60 247	99.66 78

DPE-MVS		98.59 167	98.26 210	99.57 22	99.27 206	99.15 53	97.01 336	99.39 183	97.67 242	99.44 106	98.99 213	97.53 175	99.89 97	95.40 350	99.68 213	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 20	99.47 22	99.36 75	99.80 22	98.58 106	99.27 43	99.57 96	99.39 60	99.75 45	99.62 41	99.17 20	99.83 193	99.06 84	99.62 240	99.66 78

EI-MVSNet-UG-set		98.69 144	98.71 128	98.62 225	99.10 255	96.37 284	97.23 321	98.87 323	99.20 84	99.19 163	98.99 213	97.30 193	99.85 156	98.77 107	99.79 147	99.65 83

Elysia		99.15 59	99.14 70	99.18 114	99.63 82	97.92 170	98.50 136	99.43 168	99.67 21	99.70 52	99.13 169	96.66 238	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 59	99.14 70	99.18 114	99.63 82	97.92 170	98.50 136	99.43 168	99.67 21	99.70 52	99.13 169	96.66 238	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 40	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		98.68 150	98.70 131	98.63 223	99.09 258	96.40 283	97.23 321	98.86 328	99.20 84	99.18 167	98.97 220	97.29 195	99.85 156	98.72 111	99.78 152	99.64 84

ACMH	96.65 7	99.25 42	99.24 55	99.26 102	99.72 44	98.38 120	99.07 65	99.55 109	98.30 182	99.65 64	99.45 86	99.22 17	99.76 268	98.44 130	99.77 158	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 97	98.81 114	99.28 97	99.21 225	98.45 117	98.46 144	99.33 211	99.63 29	99.48 97	99.15 164	97.23 199	99.75 276	97.17 226	99.66 227	99.63 89

reproduce-ours		99.09 74	98.90 99	99.67 4	99.27 206	99.49 6	98.00 206	99.42 174	99.05 116	99.48 97	99.27 125	98.29 93	99.89 97	97.61 195	99.71 198	99.62 90

our_new_method		99.09 74	98.90 99	99.67 4	99.27 206	99.49 6	98.00 206	99.42 174	99.05 116	99.48 97	99.27 125	98.29 93	99.89 97	97.61 195	99.71 198	99.62 90

test_fmvs1_n		98.09 241	98.28 206	97.52 354	99.68 63	93.47 396	98.63 115	99.93 5	95.41 383	99.68 58	99.64 38	91.88 366	99.48 405	99.82 12	99.87 99	99.62 90

test1111		96.49 353	96.82 327	95.52 427	99.42 167	87.08 462	99.22 46	87.14 478	99.11 99	99.46 102	99.58 48	88.69 393	99.86 143	98.80 102	99.95 38	99.62 90

VPA-MVSNet		99.30 35	99.30 46	99.28 97	99.49 139	98.36 125	99.00 73	99.45 154	99.63 29	99.52 88	99.44 87	98.25 100	99.88 115	99.09 81	99.84 113	99.62 90

LPG-MVS_test		98.71 135	98.46 176	99.47 61	99.57 98	98.97 74	98.23 166	99.48 137	96.60 333	99.10 173	99.06 184	98.71 51	99.83 193	95.58 346	99.78 152	99.62 90

LGP-MVS_train				99.47 61	99.57 98	98.97 74		99.48 137	96.60 333	99.10 173	99.06 184	98.71 51	99.83 193	95.58 346	99.78 152	99.62 90

Test_1112_low_res		96.99 334	96.55 345	98.31 278	99.35 187	95.47 322	95.84 410	99.53 118	91.51 445	96.80 402	98.48 328	91.36 371	99.83 193	96.58 285	99.53 273	99.62 90

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 44	98.98 72	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 27	99.90 81	99.54 44	99.95 38	99.61 98

v10		98.97 91	99.11 73	98.55 243	99.44 160	96.21 289	98.90 84	99.55 109	98.73 146	99.48 97	99.60 46	96.63 241	99.83 193	99.70 33	99.99 5	99.61 98

sc_t1		99.62 7	99.66 8	99.53 39	99.82 20	99.09 69	99.50 11	99.63 74	99.88 4	99.86 24	99.80 11	99.03 25	99.89 97	99.48 53	99.93 56	99.60 100

test_vis1_n		98.31 214	98.50 166	97.73 330	99.76 31	94.17 365	98.68 108	99.91 9	96.31 348	99.79 39	99.57 50	92.85 352	99.42 418	99.79 19	99.84 113	99.60 100

v8		99.01 84	99.16 64	98.57 236	99.47 150	96.31 287	98.90 84	99.47 146	99.03 119	99.52 88	99.57 50	96.93 218	99.81 223	99.60 37	99.98 12	99.60 100

EI-MVSNet		98.40 197	98.51 163	98.04 306	99.10 255	94.73 348	97.20 326	98.87 323	98.97 125	99.06 177	99.02 196	96.00 269	99.80 232	98.58 119	99.82 124	99.60 100

SixPastTwentyTwo		98.75 131	98.62 146	99.16 119	99.83 19	97.96 167	99.28 41	98.20 378	99.37 62	99.70 52	99.65 37	92.65 356	99.93 54	99.04 86	99.84 113	99.60 100

IterMVS-LS		98.55 175	98.70 131	98.09 298	99.48 147	94.73 348	97.22 325	99.39 183	98.97 125	99.38 119	99.31 118	96.00 269	99.93 54	98.58 119	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		97.19 319	96.60 343	98.96 159	99.62 86	97.28 224	95.17 432	99.50 127	94.21 410	99.01 191	98.32 346	86.61 405	99.99 2	97.10 234	99.84 113	99.60 100

FE-MVSNET1		99.51 14	99.54 14	99.43 68	99.90 4	98.85 86	99.33 26	99.79 36	99.47 48	99.51 93	99.75 16	99.10 24	99.84 174	99.14 76	99.91 78	99.59 107

lecture		99.25 42	99.12 72	99.62 10	99.64 76	99.40 12	98.89 88	99.51 124	99.19 89	99.37 121	99.25 136	98.36 84	99.88 115	98.23 142	99.67 219	99.59 107

tt0320		99.61 8	99.65 9	99.48 57	99.71 48	98.94 79	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 47	99.90 81	99.54 44	99.95 38	99.59 107

ACMMP_NAP		98.75 131	98.48 172	99.57 22	99.58 89	99.29 25	97.82 236	99.25 247	96.94 315	98.78 243	99.12 172	98.02 124	99.84 174	97.13 232	99.67 219	99.59 107

VPNet		98.87 106	98.83 111	99.01 150	99.70 56	97.62 201	98.43 147	99.35 199	99.47 48	99.28 143	99.05 191	96.72 235	99.82 206	98.09 152	99.36 309	99.59 107

WR-MVS		98.40 197	98.19 221	99.03 146	99.00 282	97.65 198	96.85 346	98.94 308	98.57 163	98.89 222	98.50 325	95.60 287	99.85 156	97.54 201	99.85 108	99.59 107

HPM-MVS		98.79 124	98.53 161	99.59 20	99.65 70	99.29 25	99.16 55	99.43 168	96.74 328	98.61 267	98.38 338	98.62 60	99.87 134	96.47 299	99.67 219	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 87	99.01 87	98.94 162	99.50 131	97.47 210	98.04 197	99.59 87	98.15 207	99.40 116	99.36 105	98.58 68	99.76 268	98.78 104	99.68 213	99.59 107

Vis-MVSNet		99.34 31	99.36 34	99.27 100	99.73 38	98.26 129	99.17 54	99.78 37	99.11 99	99.27 145	99.48 77	98.82 38	99.95 26	98.94 93	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 89	98.93 80	98.68 108	99.60 80	96.46 341	99.53 83	98.77 270		99.83 193	96.67 276	99.64 230	99.58 116

MED-MVS		98.90 101	98.72 123	99.45 64	99.58 89	98.93 80	98.68 108	99.60 80	98.14 208	99.53 83	98.77 270	97.87 141	99.83 193	96.67 276	99.64 230	99.58 116

ME-MVS		98.61 163	98.33 200	99.44 66	99.24 217	98.93 80	97.45 297	99.06 288	98.14 208	99.06 177	98.77 270	96.97 216	99.82 206	96.67 276	99.64 230	99.58 116

MP-MVS-pluss		98.57 170	98.23 215	99.60 16	99.69 60	99.35 17	97.16 331	99.38 185	94.87 395	98.97 201	98.99 213	98.01 125	99.88 115	97.29 219	99.70 205	99.58 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 144	98.40 184	99.54 32	99.53 120	99.17 45	98.52 129	99.31 218	97.46 270	98.44 290	98.51 321	97.83 144	99.88 115	96.46 300	99.58 256	99.58 116

ACMMPR		98.70 140	98.42 182	99.54 32	99.52 123	99.14 58	98.52 129	99.31 218	97.47 265	98.56 277	98.54 316	97.75 153	99.88 115	96.57 287	99.59 251	99.58 116

PGM-MVS		98.66 154	98.37 191	99.55 29	99.53 120	99.18 44	98.23 166	99.49 135	97.01 312	98.69 254	98.88 244	98.00 126	99.89 97	95.87 332	99.59 251	99.58 116

SteuartSystems-ACMMP		98.79 124	98.54 159	99.54 32	99.73 38	99.16 49	98.23 166	99.31 218	97.92 224	98.90 219	98.90 237	98.00 126	99.88 115	96.15 319	99.72 189	99.58 116
Skip Steuart: Steuart Systems R&D Blog.
SDMVSNet		99.23 47	99.32 41	98.96 159	99.68 63	97.35 217	98.84 95	99.48 137	99.69 18	99.63 67	99.68 26	99.03 25	99.96 14	97.97 165	99.92 69	99.57 124

sd_testset		99.28 38	99.31 43	99.19 113	99.68 63	98.06 156	99.41 17	99.30 226	99.69 18	99.63 67	99.68 26	99.25 16	99.96 14	97.25 222	99.92 69	99.57 124

TranMVSNet+NR-MVSNet		99.17 54	99.07 80	99.46 63	99.37 181	98.87 85	98.39 152	99.42 174	99.42 57	99.36 124	99.06 184	98.38 83	99.95 26	98.34 136	99.90 87	99.57 124

mPP-MVS		98.64 157	98.34 195	99.54 32	99.54 117	99.17 45	98.63 115	99.24 252	97.47 265	98.09 319	98.68 293	97.62 164	99.89 97	96.22 314	99.62 240	99.57 124

PVSNet_Blended_VisFu		98.17 235	98.15 227	98.22 290	99.73 38	95.15 334	97.36 309	99.68 61	94.45 405	98.99 196	99.27 125	96.87 221	99.94 42	97.13 232	99.91 78	99.57 124

1112_ss		97.29 311	96.86 323	98.58 233	99.34 190	96.32 286	96.75 352	99.58 89	93.14 426	96.89 397	97.48 400	92.11 363	99.86 143	96.91 249	99.54 269	99.57 124

MTAPA		98.88 105	98.64 142	99.61 14	99.67 67	99.36 16	98.43 147	99.20 258	98.83 144	98.89 222	98.90 237	96.98 215	99.92 65	97.16 227	99.70 205	99.56 130

XVS		98.72 134	98.45 177	99.53 39	99.46 153	99.21 34	98.65 113	99.34 205	98.62 156	97.54 360	98.63 305	97.50 179	99.83 193	96.79 262	99.53 273	99.56 130

pm-mvs1		99.44 20	99.48 19	99.33 90	99.80 22	98.63 100	99.29 37	99.63 74	99.30 72	99.65 64	99.60 46	99.16 22	99.82 206	99.07 82	99.83 120	99.56 130

X-MVStestdata		94.32 401	92.59 420	99.53 39	99.46 153	99.21 34	98.65 113	99.34 205	98.62 156	97.54 360	45.85 480	97.50 179	99.83 193	96.79 262	99.53 273	99.56 130

HPM-MVS_fast		99.01 84	98.82 112	99.57 22	99.71 48	99.35 17	99.00 73	99.50 127	97.33 282	98.94 214	98.86 247	98.75 47	99.82 206	97.53 202	99.71 198	99.56 130

K. test v3		98.00 250	97.66 275	99.03 146	99.79 24	97.56 203	99.19 53	92.47 464	99.62 33	99.52 88	99.66 33	89.61 387	99.96 14	99.25 68	99.81 130	99.56 130

CP-MVS		98.70 140	98.42 182	99.52 45	99.36 182	99.12 63	98.72 103	99.36 193	97.54 259	98.30 299	98.40 335	97.86 143	99.89 97	96.53 296	99.72 189	99.56 130

viewmacassd2359aftdt		98.86 109	98.87 104	98.83 178	99.53 120	97.32 221	97.70 257	99.64 72	98.22 190	99.25 153	99.27 125	98.40 81	99.61 358	97.98 164	99.87 99	99.55 137

FE-MVSNET		98.59 167	98.50 166	98.87 173	99.58 89	97.30 222	98.08 188	99.74 44	96.94 315	98.97 201	99.10 176	96.94 217	99.74 281	97.33 217	99.86 106	99.55 137

ZNCC-MVS		98.68 150	98.40 184	99.54 32	99.57 98	99.21 34	98.46 144	99.29 234	97.28 288	98.11 317	98.39 336	98.00 126	99.87 134	96.86 259	99.64 230	99.55 137

v1192		98.60 165	98.66 139	98.41 265	99.27 206	95.88 301	97.52 285	99.36 193	97.41 274	99.33 131	99.20 147	96.37 253	99.82 206	99.57 39	99.92 69	99.55 137

v1240		98.55 175	98.62 146	98.32 276	99.22 223	95.58 312	97.51 287	99.45 154	97.16 303	99.45 105	99.24 138	96.12 264	99.85 156	99.60 37	99.88 95	99.55 137

UGNet		98.53 180	98.45 177	98.79 188	97.94 414	96.96 251	99.08 62	98.54 362	99.10 106	96.82 401	99.47 80	96.55 244	99.84 174	98.56 124	99.94 50	99.55 137
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
AstraMVS		98.16 237	98.07 237	98.41 265	99.51 125	95.86 302	98.00 206	95.14 447	98.97 125	99.43 107	99.24 138	93.25 340	99.84 174	99.21 71	99.87 99	99.54 143

WBMVS		95.18 389	94.78 395	96.37 403	97.68 431	89.74 450	95.80 411	98.73 351	97.54 259	98.30 299	98.44 332	70.06 463	99.82 206	96.62 282	99.87 99	99.54 143

test2506		92.39 432	91.89 434	93.89 448	99.38 175	82.28 479	99.32 27	66.03 486	99.08 113	98.77 246	99.57 50	66.26 473	99.84 174	98.71 112	99.95 38	99.54 143

ECVR-MVS		96.42 355	96.61 341	95.85 419	99.38 175	88.18 457	99.22 46	86.00 480	99.08 113	99.36 124	99.57 50	88.47 398	99.82 206	98.52 127	99.95 38	99.54 143

v144192		98.54 178	98.57 155	98.45 260	99.21 225	95.98 298	97.63 269	99.36 193	97.15 305	99.32 137	99.18 154	95.84 281	99.84 174	99.50 51	99.91 78	99.54 143

v1921920		98.54 178	98.60 151	98.38 269	99.20 229	95.76 308	97.56 280	99.36 193	97.23 297	99.38 119	99.17 158	96.02 267	99.84 174	99.57 39	99.90 87	99.54 143

MP-MVS		98.46 190	98.09 232	99.54 32	99.57 98	99.22 33	98.50 136	99.19 262	97.61 249	97.58 356	98.66 298	97.40 187	99.88 115	94.72 365	99.60 247	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MIMVSNet1		99.38 29	99.32 41	99.55 29	99.86 15	99.19 43	99.41 17	99.59 87	99.59 37	99.71 50	99.57 50	97.12 205	99.90 81	99.21 71	99.87 99	99.54 143

ACMMP		98.75 131	98.50 166	99.52 45	99.56 106	99.16 49	98.87 89	99.37 189	97.16 303	98.82 237	99.01 207	97.71 155	99.87 134	96.29 311	99.69 208	99.54 143
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		98.40 197	98.03 240	99.51 49	99.16 244	99.21 34	98.05 195	99.22 255	94.16 411	98.98 197	99.10 176	97.52 177	99.79 245	96.45 301	99.64 230	99.53 152
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		98.71 135	98.44 179	99.51 49	99.49 139	99.16 49	98.52 129	99.31 218	97.47 265	98.58 273	98.50 325	97.97 130	99.85 156	96.57 287	99.59 251	99.53 152

UniMVSNet_NR-MVSNet		98.86 109	98.68 134	99.40 73	99.17 242	98.74 93	97.68 259	99.40 181	99.14 97	99.06 177	98.59 312	96.71 236	99.93 54	98.57 121	99.77 158	99.53 152

GST-MVS		98.61 163	98.30 203	99.52 45	99.51 125	99.20 40	98.26 164	99.25 247	97.44 273	98.67 257	98.39 336	97.68 156	99.85 156	96.00 324	99.51 279	99.52 155

MGCNet		97.44 298	97.01 314	98.72 208	96.42 468	96.74 265	97.20 326	91.97 468	98.46 171	98.30 299	98.79 266	92.74 354	99.91 74	99.30 63	99.94 50	99.52 155

TDRefinement		99.42 25	99.38 30	99.55 29	99.76 31	99.33 21	99.68 6	99.71 48	99.38 61	99.53 83	99.61 44	98.64 57	99.80 232	98.24 140	99.84 113	99.52 155

FE-MVSNET2		99.15 59	99.22 56	98.94 162	99.70 56	97.49 206	98.62 117	99.67 65	98.85 142	99.34 128	99.54 64	98.47 73	99.81 223	98.93 94	99.91 78	99.51 158

v1144		98.60 165	98.66 139	98.41 265	99.36 182	95.90 300	97.58 278	99.34 205	97.51 261	99.27 145	99.15 164	96.34 255	99.80 232	99.47 54	99.93 56	99.51 158

v2v482		98.56 171	98.62 146	98.37 272	99.42 167	95.81 306	97.58 278	99.16 273	97.90 226	99.28 143	99.01 207	95.98 274	99.79 245	99.33 60	99.90 87	99.51 158

CPTT-MVS		97.84 270	97.36 294	99.27 100	99.31 193	98.46 116	98.29 159	99.27 241	94.90 394	97.83 340	98.37 339	94.90 305	99.84 174	93.85 393	99.54 269	99.51 158

viewdifsd2359ckpt11		98.84 112	99.04 82	98.24 286	99.56 106	95.51 315	97.38 304	99.70 53	99.16 94	99.57 72	99.40 98	98.26 98	99.71 297	98.55 125	99.82 124	99.50 162

viewmsd2359difaftdt		98.84 112	99.04 82	98.24 286	99.56 106	95.51 315	97.38 304	99.70 53	99.16 94	99.57 72	99.40 98	98.26 98	99.71 297	98.55 125	99.82 124	99.50 162

LuminaMVS		98.39 203	98.20 217	98.98 156	99.50 131	97.49 206	97.78 242	97.69 393	98.75 145	99.49 96	99.25 136	92.30 360	99.94 42	99.14 76	99.88 95	99.50 162

DU-MVS		98.82 118	98.63 144	99.39 74	99.16 244	98.74 93	97.54 283	99.25 247	98.84 143	99.06 177	98.76 276	96.76 232	99.93 54	98.57 121	99.77 158	99.50 162

NR-MVSNet		98.95 94	98.82 112	99.36 75	99.16 244	98.72 98	99.22 46	99.20 258	99.10 106	99.72 48	98.76 276	96.38 252	99.86 143	98.00 162	99.82 124	99.50 162

casdiffmvs_mvg		99.12 71	99.16 64	98.99 152	99.43 165	97.73 194	98.00 206	99.62 76	99.22 80	99.55 77	99.22 144	98.93 33	99.75 276	98.66 115	99.81 130	99.50 162
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 78	99.00 89	99.33 90	99.71 48	98.83 88	98.60 120	99.58 89	99.11 99	99.53 83	99.18 154	98.81 39	99.67 321	96.71 273	99.77 158	99.50 162

SymmetryMVS		98.05 245	97.71 270	99.09 133	99.29 200	97.83 179	98.28 160	97.64 398	99.24 77	98.80 241	98.85 250	89.76 385	99.94 42	98.04 157	99.50 287	99.49 169

DVP-MVS++		98.90 101	98.70 131	99.51 49	98.43 385	99.15 53	99.43 15	99.32 213	98.17 199	99.26 149	99.02 196	98.18 110	99.88 115	97.07 236	99.45 294	99.49 169

PC_three_1452									93.27 424	99.40 116	98.54 316	98.22 105	97.00 475	95.17 353	99.45 294	99.49 169

GeoE		99.05 81	98.99 91	99.25 105	99.44 160	98.35 126	98.73 102	99.56 105	98.42 173	98.91 218	98.81 263	98.94 31	99.91 74	98.35 135	99.73 181	99.49 169

h-mvs33		97.77 273	97.33 297	99.10 129	99.21 225	97.84 178	98.35 156	98.57 361	99.11 99	98.58 273	99.02 196	88.65 396	99.96 14	98.11 150	96.34 456	99.49 169

IterMVS-SCA-FT		97.85 269	98.18 222	96.87 388	99.27 206	91.16 437	95.53 420	99.25 247	99.10 106	99.41 113	99.35 106	93.10 345	99.96 14	98.65 116	99.94 50	99.49 169

new-patchmatchnet		98.35 206	98.74 119	97.18 371	99.24 217	92.23 419	96.42 373	99.48 137	98.30 182	99.69 56	99.53 66	97.44 185	99.82 206	98.84 101	99.77 158	99.49 169

APD-MVS		98.10 239	97.67 272	99.42 69	99.11 253	98.93 80	97.76 248	99.28 238	94.97 392	98.72 252	98.77 270	97.04 209	99.85 156	93.79 394	99.54 269	99.49 169
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 215	98.04 239	99.07 136	99.56 106	97.83 179	99.29 37	98.07 384	99.03 119	98.59 271	99.13 169	92.16 362	99.90 81	96.87 257	99.68 213	99.49 169

DeepC-MVS	97.60 4	98.97 91	98.93 96	99.10 129	99.35 187	97.98 163	98.01 205	99.46 150	97.56 255	99.54 79	99.50 70	98.97 29	99.84 174	98.06 155	99.92 69	99.49 169
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 99	98.73 121	99.48 57	99.55 112	99.14 58	98.07 192	99.37 189	97.62 246	99.04 187	98.96 223	98.84 37	99.79 245	97.43 211	99.65 228	99.49 169
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
guyue		98.01 249	97.93 253	98.26 282	99.45 158	95.48 319	98.08 188	96.24 430	98.89 136	99.34 128	99.14 167	91.32 372	99.82 206	99.07 82	99.83 120	99.48 180

DVP-MVS		98.77 129	98.52 162	99.52 45	99.50 131	99.21 34	98.02 202	98.84 332	97.97 218	99.08 175	99.02 196	97.61 166	99.88 115	96.99 243	99.63 237	99.48 180
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SR-MVS		98.71 135	98.43 180	99.57 22	99.18 240	99.35 17	98.36 155	99.29 234	98.29 185	98.88 226	98.85 250	97.53 175	99.87 134	96.14 320	99.31 318	99.48 180

TSAR-MVS + MP.		98.63 159	98.49 171	99.06 142	99.64 76	97.90 173	98.51 134	98.94 308	96.96 313	99.24 155	98.89 243	97.83 144	99.81 223	96.88 256	99.49 289	99.48 180
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 228	97.95 249	99.01 150	99.58 89	97.74 192	99.01 71	97.29 406	99.67 21	98.97 201	99.50 70	90.45 380	99.80 232	97.88 172	99.20 338	99.48 180

IterMVS		97.73 275	98.11 231	96.57 398	99.24 217	90.28 446	95.52 422	99.21 256	98.86 139	99.33 131	99.33 113	93.11 344	99.94 42	98.49 128	99.94 50	99.48 180
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 231	97.90 257	99.08 134	99.57 98	97.97 164	99.31 31	98.32 373	99.01 121	98.98 197	99.03 195	91.59 368	99.79 245	95.49 348	99.80 141	99.48 180

ACMP	95.32 15	98.41 194	98.09 232	99.36 75	99.51 125	98.79 91	97.68 259	99.38 185	95.76 370	98.81 239	98.82 260	98.36 84	99.82 206	94.75 362	99.77 158	99.48 180
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 250	97.63 278	99.10 129	99.24 217	98.17 139	96.89 345	98.73 351	95.66 371	97.92 331	97.70 388	97.17 203	99.66 334	96.18 318	99.23 333	99.47 188

3Dnovator+	97.89 3	98.69 144	98.51 163	99.24 107	98.81 322	98.40 118	99.02 70	99.19 262	98.99 122	98.07 321	99.28 123	97.11 207	99.84 174	96.84 260	99.32 316	99.47 188

diffmvs_AUTHOR		98.50 186	98.59 153	98.23 289	99.35 187	95.48 319	96.61 360	99.60 80	98.37 174	98.90 219	99.00 211	97.37 189	99.76 268	98.22 143	99.85 108	99.46 190

HPM-MVS++		98.10 239	97.64 277	99.48 57	99.09 258	99.13 61	97.52 285	98.75 348	97.46 270	96.90 396	97.83 381	96.01 268	99.84 174	95.82 336	99.35 311	99.46 190

V42		98.78 126	98.78 117	98.76 198	99.44 160	97.04 245	98.27 163	99.19 262	97.87 228	99.25 153	99.16 160	96.84 222	99.78 256	99.21 71	99.84 113	99.46 190

APD-MVS_3200maxsize		98.84 112	98.61 150	99.53 39	99.19 232	99.27 28	98.49 139	99.33 211	98.64 151	99.03 190	98.98 218	97.89 139	99.85 156	96.54 295	99.42 302	99.46 190

UniMVSNet (Re)		98.87 106	98.71 128	99.35 81	99.24 217	98.73 96	97.73 254	99.38 185	98.93 130	99.12 169	98.73 279	96.77 230	99.86 143	98.63 118	99.80 141	99.46 190

SR-MVS-dyc-post		98.81 120	98.55 157	99.57 22	99.20 229	99.38 13	98.48 142	99.30 226	98.64 151	98.95 207	98.96 223	97.49 182	99.86 143	96.56 291	99.39 305	99.45 195

RE-MVS-def			98.58 154		99.20 229	99.38 13	98.48 142	99.30 226	98.64 151	98.95 207	98.96 223	97.75 153		96.56 291	99.39 305	99.45 195

HQP_MVS		97.99 253	97.67 272	98.93 165	99.19 232	97.65 198	97.77 245	99.27 241	98.20 196	97.79 343	97.98 371	94.90 305	99.70 304	94.42 374	99.51 279	99.45 195

plane_prior5								99.27 241					99.70 304	94.42 374	99.51 279	99.45 195

lessismore_v0				98.97 158	99.73 38	97.53 205		86.71 479		99.37 121	99.52 69	89.93 383	99.92 65	98.99 90	99.72 189	99.44 199

TAMVS		98.24 225	98.05 238	98.80 184	99.07 262	97.18 235	97.88 228	98.81 337	96.66 332	99.17 168	99.21 145	94.81 311	99.77 262	96.96 247	99.88 95	99.44 199

DeepPCF-MVS	96.93 5	98.32 212	98.01 242	99.23 109	98.39 390	98.97 74	95.03 436	99.18 266	96.88 320	99.33 131	98.78 268	98.16 114	99.28 439	96.74 268	99.62 240	99.44 199

3Dnovator	98.27 2	98.81 120	98.73 121	99.05 143	98.76 327	97.81 187	99.25 44	99.30 226	98.57 163	98.55 279	99.33 113	97.95 132	99.90 81	97.16 227	99.67 219	99.44 199

E2		98.70 140	98.68 134	98.73 206	99.40 172	97.10 242	97.48 291	99.57 96	98.09 211	99.00 192	99.20 147	97.90 135	99.67 321	97.73 188	99.77 158	99.43 203

E3		98.69 144	98.68 134	98.73 206	99.40 172	97.10 242	97.48 291	99.57 96	98.09 211	99.00 192	99.20 147	97.90 135	99.67 321	97.73 188	99.77 158	99.43 203

MVSFormer		98.26 221	98.43 180	97.77 321	98.88 307	93.89 384	99.39 20	99.56 105	99.11 99	98.16 311	98.13 357	93.81 335	99.97 7	99.26 66	99.57 260	99.43 203

jason		97.45 297	97.35 295	97.76 324	99.24 217	93.93 380	95.86 407	98.42 369	94.24 409	98.50 285	98.13 357	94.82 309	99.91 74	97.22 223	99.73 181	99.43 203
jason: jason.
NCCC		97.86 264	97.47 289	99.05 143	98.61 361	98.07 153	96.98 338	98.90 317	97.63 245	97.04 386	97.93 376	95.99 273	99.66 334	95.31 351	98.82 380	99.43 203

Anonymous20240521		98.69 144	98.87 104	98.16 296	99.77 28	95.11 337	99.08 62	99.44 162	99.34 66	99.33 131	99.55 58	94.10 331	99.94 42	99.25 68	99.96 28	99.42 208

MVS_111021_HR		98.25 224	98.08 235	98.75 200	99.09 258	97.46 211	95.97 398	99.27 241	97.60 251	97.99 329	98.25 349	98.15 116	99.38 424	96.87 257	99.57 260	99.42 208

COLMAP_ROB	96.50 10	98.99 87	98.85 110	99.41 71	99.58 89	99.10 66	98.74 98	99.56 105	99.09 109	99.33 131	99.19 150	98.40 81	99.72 296	95.98 326	99.76 173	99.42 208
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 99	98.72 123	99.49 55	99.49 139	99.17 45	98.10 185	99.31 218	98.03 214	99.66 61	99.02 196	98.36 84	99.88 115	96.91 249	99.62 240	99.41 211

OPU-MVS				98.82 180	98.59 366	98.30 127	98.10 185				98.52 320	98.18 110	98.75 462	94.62 366	99.48 290	99.41 211

our_test_3		97.39 303	97.73 268	96.34 404	98.70 341	89.78 449	94.61 449	98.97 307	96.50 337	99.04 187	98.85 250	95.98 274	99.84 174	97.26 221	99.67 219	99.41 211

casdiffmvs		98.95 94	99.00 89	98.81 182	99.38 175	97.33 219	97.82 236	99.57 96	99.17 93	99.35 126	99.17 158	98.35 88	99.69 308	98.46 129	99.73 181	99.41 211
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		97.60 284	97.67 272	97.39 364	99.04 271	93.04 403	95.27 429	98.38 372	97.25 291	98.92 217	98.95 227	95.48 293	99.73 288	96.99 243	98.74 382	99.41 211

MDA-MVSNet_test_wron		97.60 284	97.66 275	97.41 363	99.04 271	93.09 399	95.27 429	98.42 369	97.26 290	98.88 226	98.95 227	95.43 294	99.73 288	97.02 239	98.72 384	99.41 211

GBi-Net		98.65 155	98.47 174	99.17 116	98.90 301	98.24 131	99.20 49	99.44 162	98.59 159	98.95 207	99.55 58	94.14 327	99.86 143	97.77 181	99.69 208	99.41 211

test1		98.65 155	98.47 174	99.17 116	98.90 301	98.24 131	99.20 49	99.44 162	98.59 159	98.95 207	99.55 58	94.14 327	99.86 143	97.77 181	99.69 208	99.41 211

FMVSNet1		99.17 54	99.17 62	99.17 116	99.55 112	98.24 131	99.20 49	99.44 162	99.21 82	99.43 107	99.55 58	97.82 147	99.86 143	98.42 132	99.89 93	99.41 211

test_fmvs1		97.72 276	97.94 251	97.07 378	98.66 356	92.39 414	97.68 259	99.81 31	95.20 388	99.54 79	99.44 87	91.56 369	99.41 419	99.78 21	99.77 158	99.40 220

viewdifsd2359ckpt07		98.71 135	98.86 108	98.26 282	99.43 165	95.65 309	97.20 326	99.66 66	99.20 84	99.29 141	99.01 207	98.29 93	99.73 288	97.92 168	99.75 177	99.39 221

viewmanbaseed2359cas		98.58 169	98.54 159	98.70 210	99.28 203	97.13 241	97.47 295	99.55 109	97.55 257	98.96 206	98.92 231	97.77 151	99.59 365	97.59 198	99.77 158	99.39 221

KD-MVS_self_test		99.25 42	99.18 61	99.44 66	99.63 82	99.06 71	98.69 107	99.54 114	99.31 70	99.62 70	99.53 66	97.36 190	99.86 143	99.24 70	99.71 198	99.39 221

v148		98.45 191	98.60 151	98.00 308	99.44 160	94.98 340	97.44 299	99.06 288	98.30 182	99.32 137	98.97 220	96.65 240	99.62 351	98.37 134	99.85 108	99.39 221

test20.03		98.78 126	98.77 118	98.78 191	99.46 153	97.20 232	97.78 242	99.24 252	99.04 118	99.41 113	98.90 237	97.65 159	99.76 268	97.70 190	99.79 147	99.39 221

CDPH-MVS		97.26 312	96.66 339	99.07 136	99.00 282	98.15 140	96.03 396	99.01 303	91.21 449	97.79 343	97.85 380	96.89 220	99.69 308	92.75 417	99.38 308	99.39 221

EPNet		96.14 364	95.44 376	98.25 284	90.76 484	95.50 318	97.92 223	94.65 450	98.97 125	92.98 466	98.85 250	89.12 391	99.87 134	95.99 325	99.68 213	99.39 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 235	97.87 259	99.07 136	98.67 351	98.24 131	97.01 336	98.93 311	97.25 291	97.62 352	98.34 343	97.27 196	99.57 374	96.42 302	99.33 314	99.39 221

DeepC-MVS_fast	96.85 6	98.30 215	98.15 227	98.75 200	98.61 361	97.23 227	97.76 248	99.09 285	97.31 285	98.75 249	98.66 298	97.56 170	99.64 345	96.10 323	99.55 267	99.39 221
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		98.53 180	98.27 209	99.32 92	99.31 193	98.75 92	98.19 170	99.41 178	96.77 327	98.83 234	98.90 237	97.80 149	99.82 206	95.68 342	99.52 276	99.38 230

test9_res														93.28 406	99.15 346	99.38 230

BP-MVS1		97.40 302	96.97 315	98.71 209	99.07 262	96.81 260	98.34 158	97.18 408	98.58 162	98.17 308	98.61 309	84.01 428	99.94 42	98.97 91	99.78 152	99.37 232

OPM-MVS		98.56 171	98.32 201	99.25 105	99.41 170	98.73 96	97.13 333	99.18 266	97.10 306	98.75 249	98.92 231	98.18 110	99.65 341	96.68 275	99.56 263	99.37 232
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 422	99.16 344	99.37 232

AllTest		98.44 192	98.20 217	99.16 119	99.50 131	98.55 108	98.25 165	99.58 89	96.80 324	98.88 226	99.06 184	97.65 159	99.57 374	94.45 372	99.61 245	99.37 232

TestCases				99.16 119	99.50 131	98.55 108		99.58 89	96.80 324	98.88 226	99.06 184	97.65 159	99.57 374	94.45 372	99.61 245	99.37 232

MDA-MVSNet-bldmvs		97.94 255	97.91 256	98.06 303	99.44 160	94.96 341	96.63 359	99.15 278	98.35 176	98.83 234	99.11 173	94.31 324	99.85 156	96.60 284	98.72 384	99.37 232

MVSTER		96.86 338	96.55 345	97.79 319	97.91 416	94.21 363	97.56 280	98.87 323	97.49 264	99.06 177	99.05 191	80.72 441	99.80 232	98.44 130	99.82 124	99.37 232

viewcassd2359sk11		98.55 175	98.51 163	98.67 215	99.29 200	96.99 248	97.39 302	99.54 114	97.73 238	98.81 239	99.08 182	97.55 171	99.66 334	97.52 204	99.67 219	99.36 239

pmmvs5		97.64 282	97.49 286	98.08 301	99.14 249	95.12 336	96.70 355	99.05 292	93.77 418	98.62 265	98.83 257	93.23 341	99.75 276	98.33 138	99.76 173	99.36 239

Anonymous20231206		98.21 228	98.21 216	98.20 291	99.51 125	95.43 324	98.13 178	99.32 213	96.16 354	98.93 215	98.82 260	96.00 269	99.83 193	97.32 218	99.73 181	99.36 239

train_agg		97.10 324	96.45 349	99.07 136	98.71 337	98.08 151	95.96 400	99.03 297	91.64 441	95.85 429	97.53 396	96.47 247	99.76 268	93.67 396	99.16 344	99.36 239

PVSNet_BlendedMVS		97.55 289	97.53 283	97.60 344	98.92 297	93.77 388	96.64 358	99.43 168	94.49 401	97.62 352	99.18 154	96.82 225	99.67 321	94.73 363	99.93 56	99.36 239

Anonymous20240529		98.93 97	98.87 104	99.12 125	99.19 232	98.22 136	99.01 71	98.99 306	99.25 76	99.54 79	99.37 101	97.04 209	99.80 232	97.89 169	99.52 276	99.35 244

F-COLMAP		97.30 309	96.68 336	99.14 123	99.19 232	98.39 119	97.27 320	99.30 226	92.93 429	96.62 408	98.00 369	95.73 284	99.68 317	92.62 420	98.46 401	99.35 244

viewdifsd2359ckpt13		98.39 203	98.29 205	98.70 210	99.26 215	97.19 233	97.51 287	99.48 137	96.94 315	98.58 273	98.82 260	97.47 184	99.55 381	97.21 224	99.33 314	99.34 246

ppachtmachnet_test		97.50 290	97.74 266	96.78 394	98.70 341	91.23 436	94.55 451	99.05 292	96.36 345	99.21 161	98.79 266	96.39 250	99.78 256	96.74 268	99.82 124	99.34 246

VDD-MVS		98.56 171	98.39 187	99.07 136	99.13 251	98.07 153	98.59 121	97.01 413	99.59 37	99.11 170	99.27 125	94.82 309	99.79 245	98.34 136	99.63 237	99.34 246

testgi		98.32 212	98.39 187	98.13 297	99.57 98	95.54 313	97.78 242	99.49 135	97.37 279	99.19 163	97.65 390	98.96 30	99.49 402	96.50 298	98.99 366	99.34 246

diffmvs		98.22 226	98.24 214	98.17 294	99.00 282	95.44 323	96.38 375	99.58 89	97.79 235	98.53 282	98.50 325	96.76 232	99.74 281	97.95 167	99.64 230	99.34 246
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		97.89 259	97.60 280	98.75 200	99.31 193	97.17 237	97.62 270	99.35 199	98.72 148	98.76 248	98.68 293	92.57 357	99.74 281	97.76 185	95.60 464	99.34 246

viewmambaseed2359dif		98.19 231	98.26 210	97.99 309	99.02 279	95.03 339	96.59 362	99.53 118	96.21 351	99.00 192	98.99 213	97.62 164	99.61 358	97.62 194	99.72 189	99.33 252

baseline		98.96 93	99.02 85	98.76 198	99.38 175	97.26 226	98.49 139	99.50 127	98.86 139	99.19 163	99.06 184	98.23 102	99.69 308	98.71 112	99.76 173	99.33 252

MG-MVS		96.77 342	96.61 341	97.26 369	98.31 394	93.06 400	95.93 403	98.12 383	96.45 343	97.92 331	98.73 279	93.77 337	99.39 422	91.19 441	99.04 358	99.33 252

HQP4-MVS										95.56 434			99.54 387			99.32 255

CDS-MVSNet		97.69 278	97.35 295	98.69 212	98.73 331	97.02 247	96.92 344	98.75 348	95.89 366	98.59 271	98.67 295	92.08 364	99.74 281	96.72 271	99.81 130	99.32 255
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 333	96.49 348	98.55 243	98.67 351	96.79 261	96.29 381	99.04 295	96.05 357	95.55 435	96.84 417	93.84 333	99.54 387	92.82 414	99.26 328	99.32 255

RPSCF		98.62 162	98.36 192	99.42 69	99.65 70	99.42 11	98.55 125	99.57 96	97.72 240	98.90 219	99.26 131	96.12 264	99.52 393	95.72 339	99.71 198	99.32 255

E3new		98.41 194	98.34 195	98.62 225	99.19 232	96.90 256	97.32 312	99.50 127	97.40 276	98.63 262	98.92 231	97.21 201	99.65 341	97.34 215	99.52 276	99.31 259

MVP-Stereo		98.08 242	97.92 254	98.57 236	98.96 289	96.79 261	97.90 226	99.18 266	96.41 344	98.46 288	98.95 227	95.93 278	99.60 361	96.51 297	98.98 369	99.31 259
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 197	98.68 134	97.54 352	98.96 289	97.99 160	97.88 228	99.36 193	98.20 196	99.63 67	99.04 193	98.76 46	95.33 479	96.56 291	99.74 178	99.31 259
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		98.42 193	98.30 203	98.79 188	98.79 326	97.29 223	98.23 166	98.66 355	99.31 70	98.85 231	98.80 264	94.80 312	99.78 256	98.13 149	99.13 349	99.31 259

test_prior				98.95 161	98.69 346	97.95 168		99.03 297					99.59 365			99.30 263

USDC		97.41 301	97.40 290	97.44 361	98.94 291	93.67 391	95.17 432	99.53 118	94.03 415	98.97 201	99.10 176	95.29 296	99.34 429	95.84 335	99.73 181	99.30 263

viewdifsd2359ckpt09		98.13 238	97.92 254	98.77 196	99.18 240	97.35 217	97.29 316	99.53 118	95.81 368	98.09 319	98.47 329	96.34 255	99.66 334	97.02 239	99.51 279	99.29 265

test_fmvsm_n_1920		99.33 32	99.45 24	98.99 152	99.57 98	97.73 194	97.93 220	99.83 25	99.22 80	99.93 6	99.30 119	99.42 11	99.96 14	99.85 6	99.99 5	99.29 265

FMVSNet2		98.49 187	98.40 184	98.75 200	98.90 301	97.14 240	98.61 119	99.13 279	98.59 159	99.19 163	99.28 123	94.14 327	99.82 206	97.97 165	99.80 141	99.29 265

XVG-OURS-SEG-HR		98.49 187	98.28 206	99.14 123	99.49 139	98.83 88	96.54 363	99.48 137	97.32 284	99.11 170	98.61 309	99.33 15	99.30 435	96.23 313	98.38 402	99.28 268

mamba_0408		98.80 122	98.88 102	98.55 243	99.27 206	96.50 278	98.00 206	99.60 80	98.93 130	99.22 158	98.84 255	98.59 63	99.89 97	97.74 186	99.72 189	99.27 269

SSM_04072		98.80 122	98.88 102	98.56 241	99.27 206	96.50 278	98.00 206	99.60 80	98.93 130	99.22 158	98.84 255	98.59 63	99.90 81	97.74 186	99.72 189	99.27 269

SSM_0407		98.86 109	98.96 95	98.55 243	99.27 206	96.50 278	98.04 197	99.66 66	99.09 109	99.22 158	99.02 196	98.79 43	99.87 134	97.87 174	99.72 189	99.27 269

test12				98.93 165	98.58 368	97.83 179		98.66 355		96.53 412		95.51 291	99.69 308		99.13 349	99.27 269

DSMNet-mixed		97.42 300	97.60 280	96.87 388	99.15 248	91.46 426	98.54 127	99.12 280	92.87 431	97.58 356	99.63 40	96.21 259	99.90 81	95.74 338	99.54 269	99.27 269

N_pmnet		97.63 283	97.17 304	98.99 152	99.27 206	97.86 176	95.98 397	93.41 461	95.25 385	99.47 101	98.90 237	95.63 286	99.85 156	96.91 249	99.73 181	99.27 269

ambc				98.24 286	98.82 319	95.97 299	98.62 117	99.00 305		99.27 145	99.21 145	96.99 214	99.50 399	96.55 294	99.50 287	99.26 275

LFMVS		97.20 318	96.72 333	98.64 219	98.72 333	96.95 252	98.93 82	94.14 458	99.74 13	98.78 243	99.01 207	84.45 423	99.73 288	97.44 210	99.27 325	99.25 276

FMVSNet5		96.01 367	95.20 386	98.41 265	97.53 438	96.10 290	98.74 98	99.50 127	97.22 300	98.03 326	99.04 193	69.80 464	99.88 115	97.27 220	99.71 198	99.25 276

BH-RMVSNet		96.83 339	96.58 344	97.58 346	98.47 379	94.05 368	96.67 356	97.36 402	96.70 331	97.87 336	97.98 371	95.14 300	99.44 415	90.47 449	98.58 398	99.25 276

testf1		99.25 42	99.16 64	99.51 49	99.89 7	99.63 4	98.71 105	99.69 55	98.90 134	99.43 107	99.35 106	98.86 35	99.67 321	97.81 177	99.81 130	99.24 279

APD_test2		99.25 42	99.16 64	99.51 49	99.89 7	99.63 4	98.71 105	99.69 55	98.90 134	99.43 107	99.35 106	98.86 35	99.67 321	97.81 177	99.81 130	99.24 279

SSM_0404		98.90 101	99.01 87	98.57 236	99.42 167	96.59 270	98.13 178	99.66 66	99.09 109	99.30 140	99.02 196	98.79 43	99.89 97	97.87 174	99.80 141	99.23 281

旧先验1					98.82 319	97.45 212		98.76 345			98.34 343	95.50 292			99.01 363	99.23 281

test222					98.92 297	96.93 254	95.54 419	98.78 342	85.72 469	96.86 399	98.11 360	94.43 319			99.10 354	99.23 281

XVG-ACMP-BASELINE		98.56 171	98.34 195	99.22 110	99.54 117	98.59 105	97.71 255	99.46 150	97.25 291	98.98 197	98.99 213	97.54 173	99.84 174	95.88 329	99.74 178	99.23 281

FMVSNet3		97.50 290	97.24 301	98.29 280	98.08 409	95.83 304	97.86 232	98.91 316	97.89 227	98.95 207	98.95 227	87.06 402	99.81 223	97.77 181	99.69 208	99.23 281

icg_test_0407_2		98.20 230	98.38 189	97.65 337	99.03 274	94.03 371	95.78 412	99.45 154	98.16 202	99.06 177	98.71 282	98.27 96	99.68 317	97.50 205	99.45 294	99.22 286

IMVS_0407		98.39 203	98.64 142	97.66 335	99.03 274	94.03 371	98.10 185	99.45 154	98.16 202	99.06 177	98.71 282	98.27 96	99.71 297	97.50 205	99.45 294	99.22 286

IMVS_0404		98.07 243	98.20 217	97.69 332	99.03 274	94.03 371	96.67 356	99.45 154	98.16 202	98.03 326	98.71 282	96.80 228	99.82 206	97.50 205	99.45 294	99.22 286

IMVS_0403		98.34 207	98.56 156	97.66 335	99.03 274	94.03 371	97.98 214	99.45 154	98.16 202	98.89 222	98.71 282	97.90 135	99.74 281	97.50 205	99.45 294	99.22 286

无先验							95.74 414	98.74 350	89.38 460				99.73 288	92.38 424		99.22 286

tttt0517		95.64 380	94.98 390	97.64 340	99.36 182	93.81 386	98.72 103	90.47 472	98.08 213	98.67 257	98.34 343	73.88 459	99.92 65	97.77 181	99.51 279	99.20 291

pmmvs-eth3d		98.47 189	98.34 195	98.86 175	99.30 197	97.76 190	97.16 331	99.28 238	95.54 376	99.42 111	99.19 150	97.27 196	99.63 348	97.89 169	99.97 21	99.20 291

MS-PatchMatch		97.68 279	97.75 265	97.45 360	98.23 400	93.78 387	97.29 316	98.84 332	96.10 356	98.64 261	98.65 300	96.04 266	99.36 425	96.84 260	99.14 347	99.20 291

新几何1				98.91 169	98.94 291	97.76 190		98.76 345	87.58 466	96.75 404	98.10 361	94.80 312	99.78 256	92.73 418	99.00 364	99.20 291

PHI-MVS		98.29 218	97.95 249	99.34 84	98.44 384	99.16 49	98.12 182	99.38 185	96.01 361	98.06 322	98.43 333	97.80 149	99.67 321	95.69 341	99.58 256	99.20 291

GDP-MVS		97.50 290	97.11 309	98.67 215	99.02 279	96.85 258	98.16 175	99.71 48	98.32 180	98.52 284	98.54 316	83.39 432	99.95 26	98.79 103	99.56 263	99.19 296

Anonymous202405211		97.90 257	97.50 285	99.08 134	98.90 301	98.25 130	98.53 128	96.16 431	98.87 137	99.11 170	98.86 247	90.40 381	99.78 256	97.36 214	99.31 318	99.19 296

CANet		97.87 263	97.76 264	98.19 293	97.75 422	95.51 315	96.76 351	99.05 292	97.74 237	96.93 390	98.21 353	95.59 288	99.89 97	97.86 176	99.93 56	99.19 296

XVG-OURS		98.53 180	98.34 195	99.11 127	99.50 131	98.82 90	95.97 398	99.50 127	97.30 286	99.05 185	98.98 218	99.35 14	99.32 432	95.72 339	99.68 213	99.18 299

WTY-MVS		96.67 345	96.27 355	97.87 314	98.81 322	94.61 353	96.77 350	97.92 388	94.94 393	97.12 381	97.74 385	91.11 374	99.82 206	93.89 390	98.15 414	99.18 299

Vis-MVSNet (Re-imp)		97.46 295	97.16 305	98.34 275	99.55 112	96.10 290	98.94 81	98.44 367	98.32 180	98.16 311	98.62 307	88.76 392	99.73 288	93.88 391	99.79 147	99.18 299

TinyColmap		97.89 259	97.98 245	97.60 344	98.86 310	94.35 359	96.21 385	99.44 162	97.45 272	99.06 177	98.88 244	97.99 129	99.28 439	94.38 378	99.58 256	99.18 299

testdata				98.09 298	98.93 293	95.40 325		98.80 339	90.08 457	97.45 369	98.37 339	95.26 297	99.70 304	93.58 399	98.95 372	99.17 303

lupinMVS		97.06 327	96.86 323	97.65 337	98.88 307	93.89 384	95.48 423	97.97 386	93.53 421	98.16 311	97.58 394	93.81 335	99.91 74	96.77 265	99.57 260	99.17 303

Patchmtry		97.35 305	96.97 315	98.50 256	97.31 449	96.47 281	98.18 171	98.92 314	98.95 129	98.78 243	99.37 101	85.44 417	99.85 156	95.96 327	99.83 120	99.17 303

SD_0403		96.28 359	95.83 360	97.64 340	98.72 333	94.30 360	98.87 89	98.77 343	97.80 233	96.53 412	98.02 368	97.34 191	99.47 408	76.93 477	99.48 290	99.16 306

RRT-MVS		97.88 261	97.98 245	97.61 343	98.15 404	93.77 388	98.97 77	99.64 72	99.16 94	98.69 254	99.42 91	91.60 367	99.89 97	97.63 193	98.52 400	99.16 306

sss		97.21 317	96.93 317	98.06 303	98.83 316	95.22 332	96.75 352	98.48 366	94.49 401	97.27 378	97.90 377	92.77 353	99.80 232	96.57 287	99.32 316	99.16 306

CSCG		98.68 150	98.50 166	99.20 111	99.45 158	98.63 100	98.56 124	99.57 96	97.87 228	98.85 231	98.04 367	97.66 158	99.84 174	96.72 271	99.81 130	99.13 309

MVS_111021_LR		98.30 215	98.12 230	98.83 178	99.16 244	98.03 158	96.09 394	99.30 226	97.58 252	98.10 318	98.24 350	98.25 100	99.34 429	96.69 274	99.65 228	99.12 310

miper_lstm_enhance		97.18 320	97.16 305	97.25 370	98.16 403	92.85 405	95.15 434	99.31 218	97.25 291	98.74 251	98.78 268	90.07 382	99.78 256	97.19 225	99.80 141	99.11 311

testing3		93.51 416	92.09 427	97.75 325	98.60 363	94.40 357	97.32 312	95.26 446	97.56 255	96.79 403	95.50 445	53.57 484	99.77 262	95.26 352	98.97 370	99.08 312

原ACMM1				98.35 274	98.90 301	96.25 288		98.83 336	92.48 435	96.07 426	98.10 361	95.39 295	99.71 297	92.61 421	98.99 366	99.08 312

QAPM		97.31 308	96.81 329	98.82 180	98.80 325	97.49 206	99.06 66	99.19 262	90.22 455	97.69 349	99.16 160	96.91 219	99.90 81	90.89 446	99.41 303	99.07 314

PAPM_NR		96.82 341	96.32 352	98.30 279	99.07 262	96.69 268	97.48 291	98.76 345	95.81 368	96.61 409	96.47 426	94.12 330	99.17 446	90.82 447	97.78 427	99.06 315

eth_miper_zixun_eth		97.23 316	97.25 300	97.17 373	98.00 412	92.77 407	94.71 443	99.18 266	97.27 289	98.56 277	98.74 278	91.89 365	99.69 308	97.06 238	99.81 130	99.05 316

D2MVS		97.84 270	97.84 261	97.83 316	99.14 249	94.74 347	96.94 340	98.88 321	95.84 367	98.89 222	98.96 223	94.40 321	99.69 308	97.55 199	99.95 38	99.05 316

c3_l		97.36 304	97.37 293	97.31 365	98.09 408	93.25 398	95.01 437	99.16 273	97.05 308	98.77 246	98.72 281	92.88 350	99.64 345	96.93 248	99.76 173	99.05 316

PLC	94.65 16	96.51 350	95.73 363	98.85 176	98.75 329	97.91 172	96.42 373	99.06 288	90.94 452	95.59 432	97.38 406	94.41 320	99.59 365	90.93 444	98.04 423	99.05 316
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 101	98.90 99	98.91 169	99.67 67	97.82 184	99.00 73	99.44 162	99.45 52	99.51 93	99.24 138	98.20 109	99.86 143	95.92 328	99.69 208	99.04 320

CANet_DTU		97.26 312	97.06 311	97.84 315	97.57 433	94.65 352	96.19 387	98.79 340	97.23 297	95.14 444	98.24 350	93.22 342	99.84 174	97.34 215	99.84 113	99.04 320

PM-MVS		98.82 118	98.72 123	99.12 125	99.64 76	98.54 111	97.98 214	99.68 61	97.62 246	99.34 128	99.18 154	97.54 173	99.77 262	97.79 179	99.74 178	99.04 320

TSAR-MVS + GP.		98.18 233	97.98 245	98.77 196	98.71 337	97.88 174	96.32 379	98.66 355	96.33 346	99.23 157	98.51 321	97.48 183	99.40 420	97.16 227	99.46 292	99.02 323

DIV-MVS_self_test		97.02 330	96.84 325	97.58 346	97.82 420	94.03 371	94.66 446	99.16 273	97.04 309	98.63 262	98.71 282	88.69 393	99.69 308	97.00 241	99.81 130	99.01 324

mamv4		99.44 20	99.39 29	99.58 21	99.30 197	99.74 2	99.04 69	99.81 31	99.77 10	99.82 34	99.57 50	97.82 147	99.98 4	99.53 48	99.89 93	99.01 324

GA-MVS		95.86 372	95.32 382	97.49 357	98.60 363	94.15 366	93.83 463	97.93 387	95.49 378	96.68 405	97.42 404	83.21 433	99.30 435	96.22 314	98.55 399	99.01 324

OMC-MVS		97.88 261	97.49 286	99.04 145	98.89 306	98.63 100	96.94 340	99.25 247	95.02 390	98.53 282	98.51 321	97.27 196	99.47 408	93.50 402	99.51 279	99.01 324

cl____		97.02 330	96.83 326	97.58 346	97.82 420	94.04 370	94.66 446	99.16 273	97.04 309	98.63 262	98.71 282	88.68 395	99.69 308	97.00 241	99.81 130	99.00 328

pmmvs4		97.58 287	97.28 298	98.51 252	98.84 314	96.93 254	95.40 427	98.52 364	93.60 420	98.61 267	98.65 300	95.10 301	99.60 361	96.97 246	99.79 147	98.99 329

EPNet_dtu		94.93 395	94.78 395	95.38 432	93.58 480	87.68 459	96.78 349	95.69 443	97.35 281	89.14 477	98.09 363	88.15 400	99.49 402	94.95 359	99.30 321	98.98 330
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 352	95.77 361	98.69 212	99.48 147	97.43 214	97.84 235	99.55 109	81.42 475	96.51 415	98.58 313	95.53 289	99.67 321	93.41 404	99.58 256	98.98 330

PVSNet_Blended		96.88 337	96.68 336	97.47 359	98.92 297	93.77 388	94.71 443	99.43 168	90.98 451	97.62 352	97.36 408	96.82 225	99.67 321	94.73 363	99.56 263	98.98 330

APD_test1		98.83 115	98.66 139	99.34 84	99.78 25	99.47 9	98.42 150	99.45 154	98.28 187	98.98 197	99.19 150	97.76 152	99.58 372	96.57 287	99.55 267	98.97 333

PAPR		95.29 386	94.47 397	97.75 325	97.50 444	95.14 335	94.89 440	98.71 353	91.39 447	95.35 442	95.48 447	94.57 317	99.14 449	84.95 465	97.37 440	98.97 333

EGC-MVSNET		85.24 443	80.54 446	99.34 84	99.77 28	99.20 40	99.08 62	99.29 234	12.08 482	20.84 483	99.42 91	97.55 171	99.85 156	97.08 235	99.72 189	98.96 335

thisisatest0530		95.27 387	94.45 398	97.74 327	99.19 232	94.37 358	97.86 232	90.20 473	97.17 302	98.22 306	97.65 390	73.53 460	99.90 81	96.90 254	99.35 311	98.95 336

mvs_anonymous		97.83 272	98.16 226	96.87 388	98.18 402	91.89 421	97.31 314	98.90 317	97.37 279	98.83 234	99.46 82	96.28 257	99.79 245	98.90 96	98.16 413	98.95 336

baseline1		95.96 370	95.44 376	97.52 354	98.51 377	93.99 378	98.39 152	96.09 434	98.21 192	98.40 297	97.76 384	86.88 403	99.63 348	95.42 349	89.27 477	98.95 336

CLD-MVS		97.49 293	97.16 305	98.48 257	99.07 262	97.03 246	94.71 443	99.21 256	94.46 403	98.06 322	97.16 412	97.57 169	99.48 405	94.46 371	99.78 152	98.95 336
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 247	98.14 229	97.64 340	98.58 368	95.19 333	97.48 291	99.23 254	97.47 265	97.90 333	98.62 307	97.04 209	98.81 460	97.55 199	99.41 303	98.94 340

DELS-MVS		98.27 219	98.20 217	98.48 257	98.86 310	96.70 267	95.60 418	99.20 258	97.73 238	98.45 289	98.71 282	97.50 179	99.82 206	98.21 144	99.59 251	98.93 341
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		95.79 375	95.39 379	96.98 382	96.77 461	92.79 406	94.40 454	98.53 363	94.59 400	97.89 334	98.17 356	82.82 437	99.24 441	96.37 305	99.03 359	98.92 342

LS3D		98.63 159	98.38 189	99.36 75	97.25 450	99.38 13	99.12 61	99.32 213	99.21 82	98.44 290	98.88 244	97.31 192	99.80 232	96.58 285	99.34 313	98.92 342

CMPMVS	75.91 23	96.29 358	95.44 376	98.84 177	96.25 471	98.69 99	97.02 335	99.12 280	88.90 462	97.83 340	98.86 247	89.51 388	98.90 458	91.92 425	99.51 279	98.92 342
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 157	98.48 172	99.11 127	98.85 313	98.51 113	98.49 139	99.83 25	98.37 174	99.69 56	99.46 82	98.21 107	99.92 65	94.13 384	99.30 321	98.91 345

mvsmamba		97.57 288	97.26 299	98.51 252	98.69 346	96.73 266	98.74 98	97.25 407	97.03 311	97.88 335	99.23 143	90.95 375	99.87 134	96.61 283	99.00 364	98.91 345

DPM-MVS		96.32 357	95.59 370	98.51 252	98.76 327	97.21 231	94.54 452	98.26 375	91.94 440	96.37 419	97.25 410	93.06 347	99.43 416	91.42 436	98.74 382	98.89 347

test_yl		96.69 343	96.29 353	97.90 311	98.28 395	95.24 330	97.29 316	97.36 402	98.21 192	98.17 308	97.86 378	86.27 407	99.55 381	94.87 360	98.32 403	98.89 347

DCV-MVSNet		96.69 343	96.29 353	97.90 311	98.28 395	95.24 330	97.29 316	97.36 402	98.21 192	98.17 308	97.86 378	86.27 407	99.55 381	94.87 360	98.32 403	98.89 347

SPE-MVS-test		99.13 68	99.09 77	99.26 102	99.13 251	98.97 74	99.31 31	99.88 14	99.44 54	98.16 311	98.51 321	98.64 57	99.93 54	98.91 95	99.85 108	98.88 350

UnsupCasMVSNet_bld		97.30 309	96.92 319	98.45 260	99.28 203	96.78 264	96.20 386	99.27 241	95.42 380	98.28 303	98.30 347	93.16 343	99.71 297	94.99 356	97.37 440	98.87 351

Effi-MVS+		98.02 247	97.82 262	98.62 225	98.53 375	97.19 233	97.33 311	99.68 61	97.30 286	96.68 405	97.46 402	98.56 69	99.80 232	96.63 281	98.20 409	98.86 352

test_0402		98.76 130	98.71 128	98.93 165	99.56 106	98.14 142	98.45 146	99.34 205	99.28 74	98.95 207	98.91 234	98.34 89	99.79 245	95.63 343	99.91 78	98.86 352

PatchmatchNet		95.58 381	95.67 366	95.30 433	97.34 448	87.32 461	97.65 265	96.65 423	95.30 384	97.07 384	98.69 291	84.77 420	99.75 276	94.97 358	98.64 393	98.83 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		93.78 412	93.91 404	93.39 454	98.82 319	81.72 481	97.76 248	95.28 445	98.60 158	96.54 411	96.66 421	65.85 476	99.62 351	96.65 280	98.99 366	98.82 355

test_vis1_rt		97.75 274	97.72 269	97.83 316	98.81 322	96.35 285	97.30 315	99.69 55	94.61 399	97.87 336	98.05 366	96.26 258	98.32 467	98.74 109	98.18 410	98.82 355

CL-MVSNet_self_test		97.44 298	97.22 302	98.08 301	98.57 370	95.78 307	94.30 456	98.79 340	96.58 335	98.60 269	98.19 355	94.74 315	99.64 345	96.41 303	98.84 377	98.82 355

miper_ehance_all_eth		97.06 327	97.03 312	97.16 375	97.83 419	93.06 400	94.66 446	99.09 285	95.99 362	98.69 254	98.45 331	92.73 355	99.61 358	96.79 262	99.03 359	98.82 355

MIMVSNet		96.62 348	96.25 356	97.71 331	99.04 271	94.66 351	99.16 55	96.92 419	97.23 297	97.87 336	99.10 176	86.11 411	99.65 341	91.65 431	99.21 337	98.82 355

hse-mvs2		97.46 295	97.07 310	98.64 219	98.73 331	97.33 219	97.45 297	97.64 398	99.11 99	98.58 273	97.98 371	88.65 396	99.79 245	98.11 150	97.39 439	98.81 360

GSMVS																98.81 360

sam_mvs1												84.74 421				98.81 360

SCA		96.41 356	96.66 339	95.67 423	98.24 398	88.35 455	95.85 409	96.88 420	96.11 355	97.67 350	98.67 295	93.10 345	99.85 156	94.16 380	99.22 334	98.81 360

Patchmatch-RL test		97.26 312	97.02 313	97.99 309	99.52 123	95.53 314	96.13 392	99.71 48	97.47 265	99.27 145	99.16 160	84.30 426	99.62 351	97.89 169	99.77 158	98.81 360

AUN-MVS		96.24 363	95.45 375	98.60 231	98.70 341	97.22 229	97.38 304	97.65 396	95.95 364	95.53 439	97.96 375	82.11 440	99.79 245	96.31 309	97.44 436	98.80 365

ITE_SJBPF				98.87 173	99.22 223	98.48 115		99.35 199	97.50 262	98.28 303	98.60 311	97.64 162	99.35 428	93.86 392	99.27 325	98.79 366

tpm		94.67 397	94.34 401	95.66 424	97.68 431	88.42 454	97.88 228	94.90 448	94.46 403	96.03 428	98.56 315	78.66 451	99.79 245	95.88 329	95.01 467	98.78 367

Patchmatch-test		96.55 349	96.34 351	97.17 373	98.35 391	93.06 400	98.40 151	97.79 389	97.33 282	98.41 293	98.67 295	83.68 431	99.69 308	95.16 354	99.31 318	98.77 368

EC-MVSNet		99.09 74	99.05 81	99.20 111	99.28 203	98.93 80	99.24 45	99.84 22	99.08 113	98.12 316	98.37 339	98.72 50	99.90 81	99.05 85	99.77 158	98.77 368

PMMVS		96.51 350	95.98 357	98.09 298	97.53 438	95.84 303	94.92 439	98.84 332	91.58 443	96.05 427	95.58 442	95.68 285	99.66 334	95.59 345	98.09 417	98.76 370

test_method		79.78 444	79.50 447	80.62 461	80.21 486	45.76 489	70.82 478	98.41 371	31.08 481	80.89 481	97.71 386	84.85 419	97.37 474	91.51 435	80.03 478	98.75 371

ab-mvs		98.41 194	98.36 192	98.59 232	99.19 232	97.23 227	99.32 27	98.81 337	97.66 243	98.62 265	99.40 98	96.82 225	99.80 232	95.88 329	99.51 279	98.75 371

CHOSEN 280x420		95.51 384	95.47 373	95.65 425	98.25 397	88.27 456	93.25 467	98.88 321	93.53 421	94.65 450	97.15 413	86.17 409	99.93 54	97.41 212	99.93 56	98.73 373

test_fmvsmvis_n_1920		99.26 41	99.49 17	98.54 248	99.66 69	96.97 249	98.00 206	99.85 18	99.24 77	99.92 8	99.50 70	99.39 12	99.95 26	99.89 3	99.98 12	98.71 374

MVS_Test		98.18 233	98.36 192	97.67 333	98.48 378	94.73 348	98.18 171	99.02 300	97.69 241	98.04 325	99.11 173	97.22 200	99.56 377	98.57 121	98.90 376	98.71 374

PVSNet	93.40 17	95.67 378	95.70 364	95.57 426	98.83 316	88.57 453	92.50 470	97.72 391	92.69 433	96.49 418	96.44 427	93.72 338	99.43 416	93.61 397	99.28 324	98.71 374

alignmvs		97.35 305	96.88 322	98.78 191	98.54 373	98.09 147	97.71 255	97.69 393	99.20 84	97.59 355	95.90 437	88.12 401	99.55 381	98.18 146	98.96 371	98.70 377

ADS-MVSNet2		95.43 385	94.98 390	96.76 395	98.14 405	91.74 422	97.92 223	97.76 390	90.23 453	96.51 415	98.91 234	85.61 414	99.85 156	92.88 412	96.90 449	98.69 378

ADS-MVSNet		95.24 388	94.93 393	96.18 412	98.14 405	90.10 448	97.92 223	97.32 405	90.23 453	96.51 415	98.91 234	85.61 414	99.74 281	92.88 412	96.90 449	98.69 378

MDTV_nov1_ep13_2view						74.92 485	97.69 258		90.06 458	97.75 346		85.78 413		93.52 400		98.69 378

MSDG		97.71 277	97.52 284	98.28 281	98.91 300	96.82 259	94.42 453	99.37 189	97.65 244	98.37 298	98.29 348	97.40 187	99.33 431	94.09 385	99.22 334	98.68 381

mvsany_test1		97.60 284	97.54 282	97.77 321	97.72 423	95.35 326	95.36 428	97.13 411	94.13 412	99.71 50	99.33 113	97.93 133	99.30 435	97.60 197	98.94 373	98.67 382

CS-MVS		99.13 68	99.10 75	99.24 107	99.06 267	99.15 53	99.36 22	99.88 14	99.36 65	98.21 307	98.46 330	98.68 54	99.93 54	99.03 87	99.85 108	98.64 383

Syy-MVS		96.04 366	95.56 372	97.49 357	97.10 454	94.48 355	96.18 389	96.58 425	95.65 372	94.77 447	92.29 476	91.27 373	99.36 425	98.17 148	98.05 421	98.63 384

myMVS_eth3d		91.92 439	90.45 441	96.30 405	97.10 454	90.90 440	96.18 389	96.58 425	95.65 372	94.77 447	92.29 476	53.88 483	99.36 425	89.59 453	98.05 421	98.63 384

balanced_conf03		98.63 159	98.72 123	98.38 269	98.66 356	96.68 269	98.90 84	99.42 174	98.99 122	98.97 201	99.19 150	95.81 282	99.85 156	98.77 107	99.77 158	98.60 386

miper_enhance_ethall		96.01 367	95.74 362	96.81 392	96.41 469	92.27 418	93.69 465	98.89 320	91.14 450	98.30 299	97.35 409	90.58 379	99.58 372	96.31 309	99.03 359	98.60 386

Effi-MVS+-dtu		98.26 221	97.90 257	99.35 81	98.02 411	99.49 6	98.02 202	99.16 273	98.29 185	97.64 351	97.99 370	96.44 249	99.95 26	96.66 279	98.93 374	98.60 386

new_pmnet		96.99 334	96.76 331	97.67 333	98.72 333	94.89 342	95.95 402	98.20 378	92.62 434	98.55 279	98.54 316	94.88 308	99.52 393	93.96 388	99.44 301	98.59 389

MVSMamba_PlusPlus		98.83 115	98.98 92	98.36 273	99.32 192	96.58 273	98.90 84	99.41 178	99.75 11	98.72 252	99.50 70	96.17 260	99.94 42	99.27 65	99.78 152	98.57 390

testing91		93.32 419	92.27 424	96.47 401	97.54 436	91.25 434	96.17 391	96.76 422	97.18 301	93.65 464	93.50 468	65.11 478	99.63 348	93.04 409	97.45 435	98.53 391

EIA-MVS		98.00 250	97.74 266	98.80 184	98.72 333	98.09 147	98.05 195	99.60 80	97.39 277	96.63 407	95.55 443	97.68 156	99.80 232	96.73 270	99.27 325	98.52 392

PatchMatch-RL		97.24 315	96.78 330	98.61 229	99.03 274	97.83 179	96.36 376	99.06 288	93.49 423	97.36 376	97.78 382	95.75 283	99.49 402	93.44 403	98.77 381	98.52 392

sasdasda		98.34 207	98.26 210	98.58 233	98.46 381	97.82 184	98.96 78	99.46 150	99.19 89	97.46 367	95.46 448	98.59 63	99.46 411	98.08 153	98.71 386	98.46 394

ET-MVSNet_ETH3D		94.30 403	93.21 414	97.58 346	98.14 405	94.47 356	94.78 442	93.24 463	94.72 397	89.56 475	95.87 438	78.57 453	99.81 223	96.91 249	97.11 448	98.46 394

canonicalmvs		98.34 207	98.26 210	98.58 233	98.46 381	97.82 184	98.96 78	99.46 150	99.19 89	97.46 367	95.46 448	98.59 63	99.46 411	98.08 153	98.71 386	98.46 394

UBG		93.25 421	92.32 422	96.04 417	97.72 423	90.16 447	95.92 405	95.91 438	96.03 360	93.95 461	93.04 472	69.60 465	99.52 393	90.72 448	97.98 424	98.45 397

tt0805		98.69 144	98.62 146	98.90 172	99.75 35	99.30 23	99.15 57	96.97 415	98.86 139	98.87 230	97.62 393	98.63 59	98.96 454	99.41 57	98.29 406	98.45 397

TAPA-MVS	96.21 11	96.63 347	95.95 358	98.65 217	98.93 293	98.09 147	96.93 342	99.28 238	83.58 472	98.13 315	97.78 382	96.13 262	99.40 420	93.52 400	99.29 323	98.45 397
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net		98.34 207	98.28 206	98.51 252	98.47 379	97.59 202	98.96 78	99.48 137	99.18 92	97.40 372	95.50 445	98.66 55	99.50 399	98.18 146	98.71 386	98.44 400

BH-untuned		96.83 339	96.75 332	97.08 376	98.74 330	93.33 397	96.71 354	98.26 375	96.72 329	98.44 290	97.37 407	95.20 298	99.47 408	91.89 426	97.43 437	98.44 400

WB-MVSnew		95.73 377	95.57 371	96.23 410	96.70 462	90.70 444	96.07 395	93.86 459	95.60 374	97.04 386	95.45 451	96.00 269	99.55 381	91.04 442	98.31 405	98.43 402

pmmvs3		95.03 392	94.40 399	96.93 384	97.70 428	92.53 411	95.08 435	97.71 392	88.57 463	97.71 347	98.08 364	79.39 448	99.82 206	96.19 316	99.11 353	98.43 402

DP-MVS Recon		97.33 307	96.92 319	98.57 236	99.09 258	97.99 160	96.79 348	99.35 199	93.18 425	97.71 347	98.07 365	95.00 304	99.31 433	93.97 387	99.13 349	98.42 404

testing99		93.04 425	91.98 432	96.23 410	97.53 438	90.70 444	96.35 377	95.94 437	96.87 321	93.41 465	93.43 470	63.84 480	99.59 365	93.24 407	97.19 445	98.40 405

ETVMVS		92.60 430	91.08 439	97.18 371	97.70 428	93.65 393	96.54 363	95.70 441	96.51 336	94.68 449	92.39 475	61.80 481	99.50 399	86.97 460	97.41 438	98.40 405

Fast-Effi-MVS+-dtu		98.27 219	98.09 232	98.81 182	98.43 385	98.11 144	97.61 274	99.50 127	98.64 151	97.39 374	97.52 398	98.12 118	99.95 26	96.90 254	98.71 386	98.38 407

LF4IMVS		97.90 257	97.69 271	98.52 251	99.17 242	97.66 197	97.19 330	99.47 146	96.31 348	97.85 339	98.20 354	96.71 236	99.52 393	94.62 366	99.72 189	98.38 407

testing11		93.08 424	92.02 429	96.26 408	97.56 434	90.83 442	96.32 379	95.70 441	96.47 340	92.66 468	93.73 465	64.36 479	99.59 365	93.77 395	97.57 431	98.37 409

Fast-Effi-MVS+		97.67 280	97.38 292	98.57 236	98.71 337	97.43 214	97.23 321	99.45 154	94.82 396	96.13 423	96.51 423	98.52 71	99.91 74	96.19 316	98.83 378	98.37 409

test0.0.03 1		94.51 398	93.69 408	96.99 381	96.05 472	93.61 395	94.97 438	93.49 460	96.17 352	97.57 358	94.88 458	82.30 438	99.01 453	93.60 398	94.17 471	98.37 409

UWE-MVS		92.38 433	91.76 436	94.21 444	97.16 452	84.65 470	95.42 426	88.45 476	95.96 363	96.17 422	95.84 440	66.36 472	99.71 297	91.87 427	98.64 393	98.28 412

FE-MVS		95.66 379	94.95 392	97.77 321	98.53 375	95.28 329	99.40 19	96.09 434	93.11 427	97.96 330	99.26 131	79.10 450	99.77 262	92.40 423	98.71 386	98.27 413

baseline2		93.73 413	92.83 419	96.42 402	97.70 428	91.28 433	96.84 347	89.77 474	93.96 417	92.44 469	95.93 436	79.14 449	99.77 262	92.94 410	96.76 453	98.21 414

thisisatest0515		94.12 407	93.16 415	96.97 383	98.60 363	92.90 404	93.77 464	90.61 471	94.10 413	96.91 393	95.87 438	74.99 458	99.80 232	94.52 369	99.12 352	98.20 415

EPMVS		93.72 414	93.27 413	95.09 436	96.04 473	87.76 458	98.13 178	85.01 481	94.69 398	96.92 391	98.64 303	78.47 455	99.31 433	95.04 355	96.46 455	98.20 415

dp		93.47 417	93.59 410	93.13 457	96.64 463	81.62 482	97.66 263	96.42 428	92.80 432	96.11 424	98.64 303	78.55 454	99.59 365	93.31 405	92.18 476	98.16 417

CNLPA		97.17 321	96.71 334	98.55 243	98.56 371	98.05 157	96.33 378	98.93 311	96.91 319	97.06 385	97.39 405	94.38 322	99.45 413	91.66 430	99.18 343	98.14 418

dmvs_re		95.98 369	95.39 379	97.74 327	98.86 310	97.45 212	98.37 154	95.69 443	97.95 220	96.56 410	95.95 435	90.70 378	97.68 473	88.32 456	96.13 460	98.11 419

HY-MVS	95.94 13	95.90 371	95.35 381	97.55 351	97.95 413	94.79 344	98.81 97	96.94 418	92.28 438	95.17 443	98.57 314	89.90 384	99.75 276	91.20 440	97.33 444	98.10 420

CostFormer		93.97 409	93.78 407	94.51 440	97.53 438	85.83 466	97.98 214	95.96 436	89.29 461	94.99 446	98.63 305	78.63 452	99.62 351	94.54 368	96.50 454	98.09 421

FA-MVS(test-final)		96.99 334	96.82 327	97.50 356	98.70 341	94.78 345	99.34 23	96.99 414	95.07 389	98.48 287	99.33 113	88.41 399	99.65 341	96.13 322	98.92 375	98.07 422

AdaColmap		97.14 323	96.71 334	98.46 259	98.34 392	97.80 188	96.95 339	98.93 311	95.58 375	96.92 391	97.66 389	95.87 280	99.53 389	90.97 443	99.14 347	98.04 423

KD-MVS_2432*1600		92.87 428	91.99 430	95.51 428	91.37 482	89.27 451	94.07 458	98.14 381	95.42 380	97.25 379	96.44 427	67.86 467	99.24 441	91.28 438	96.08 461	98.02 424

miper_refine_blended		92.87 428	91.99 430	95.51 428	91.37 482	89.27 451	94.07 458	98.14 381	95.42 380	97.25 379	96.44 427	67.86 467	99.24 441	91.28 438	96.08 461	98.02 424

TESTMET0.1,1		92.19 437	91.77 435	93.46 452	96.48 467	82.80 478	94.05 460	91.52 470	94.45 405	94.00 459	94.88 458	66.65 471	99.56 377	95.78 337	98.11 416	98.02 424

testing222		91.96 438	90.37 442	96.72 396	97.47 445	92.59 409	96.11 393	94.76 449	96.83 323	92.90 467	92.87 473	57.92 482	99.55 381	86.93 461	97.52 432	98.00 427

PCF-MVS	92.86 18	94.36 400	93.00 418	98.42 264	98.70 341	97.56 203	93.16 468	99.11 282	79.59 476	97.55 359	97.43 403	92.19 361	99.73 288	79.85 474	99.45 294	97.97 428
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		90.22 442	89.28 445	93.02 458	94.50 479	82.87 477	96.52 366	87.51 477	95.21 387	92.36 470	96.04 432	71.57 462	98.25 469	72.04 479	97.77 428	97.94 429

myMVS_eth3d28		92.92 427	92.31 423	94.77 437	97.84 418	87.59 460	96.19 387	96.11 433	97.08 307	94.27 453	93.49 469	66.07 475	98.78 461	91.78 428	97.93 426	97.92 430

OpenMVS	96.65 7	97.09 325	96.68 336	98.32 276	98.32 393	97.16 238	98.86 92	99.37 189	89.48 459	96.29 421	99.15 164	96.56 243	99.90 81	92.90 411	99.20 338	97.89 431

Gipuma		99.03 82	99.16 64	98.64 219	99.94 2	98.51 113	99.32 27	99.75 43	99.58 39	98.60 269	99.62 41	98.22 105	99.51 398	97.70 190	99.73 181	97.89 431
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 441	90.30 444	93.70 450	97.72 423	84.34 474	90.24 474	97.42 400	90.20 456	93.79 462	93.09 471	90.90 377	98.89 459	86.57 463	72.76 480	97.87 433

test-LLR		93.90 410	93.85 405	94.04 445	96.53 465	84.62 471	94.05 460	92.39 465	96.17 352	94.12 456	95.07 452	82.30 438	99.67 321	95.87 332	98.18 410	97.82 434

test-mter		92.33 435	91.76 436	94.04 445	96.53 465	84.62 471	94.05 460	92.39 465	94.00 416	94.12 456	95.07 452	65.63 477	99.67 321	95.87 332	98.18 410	97.82 434

tpm2		93.09 423	92.58 421	94.62 439	97.56 434	86.53 463	97.66 263	95.79 440	86.15 468	94.07 458	98.23 352	75.95 456	99.53 389	90.91 445	96.86 452	97.81 436

CR-MVSNet		96.28 359	95.95 358	97.28 367	97.71 426	94.22 361	98.11 183	98.92 314	92.31 437	96.91 393	99.37 101	85.44 417	99.81 223	97.39 213	97.36 442	97.81 436

RPMNet		97.02 330	96.93 317	97.30 366	97.71 426	94.22 361	98.11 183	99.30 226	99.37 62	96.91 393	99.34 110	86.72 404	99.87 134	97.53 202	97.36 442	97.81 436

tpmrst		95.07 391	95.46 374	93.91 447	97.11 453	84.36 473	97.62 270	96.96 416	94.98 391	96.35 420	98.80 264	85.46 416	99.59 365	95.60 344	96.23 458	97.79 439

PAPM		91.88 440	90.34 443	96.51 399	98.06 410	92.56 410	92.44 471	97.17 409	86.35 467	90.38 474	96.01 433	86.61 405	99.21 444	70.65 480	95.43 465	97.75 440

FPMVS		93.44 418	92.23 425	97.08 376	99.25 216	97.86 176	95.61 417	97.16 410	92.90 430	93.76 463	98.65 300	75.94 457	95.66 477	79.30 475	97.49 433	97.73 441

MAR-MVS		96.47 354	95.70 364	98.79 188	97.92 415	99.12 63	98.28 160	98.60 360	92.16 439	95.54 438	96.17 431	94.77 314	99.52 393	89.62 452	98.23 407	97.72 442
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		98.03 246	97.86 260	98.56 241	98.69 346	98.07 153	97.51 287	99.50 127	98.10 210	97.50 364	95.51 444	98.41 80	99.88 115	96.27 312	99.24 330	97.71 443

thres600view7		94.45 399	93.83 406	96.29 406	99.06 267	91.53 425	97.99 213	94.24 456	98.34 177	97.44 370	95.01 454	79.84 444	99.67 321	84.33 466	98.23 407	97.66 444

thres400		94.14 406	93.44 411	96.24 409	98.93 293	91.44 428	97.60 275	94.29 454	97.94 222	97.10 382	94.31 463	79.67 446	99.62 351	83.05 468	98.08 418	97.66 444

IB-MVS	91.63 19	92.24 436	90.90 440	96.27 407	97.22 451	91.24 435	94.36 455	93.33 462	92.37 436	92.24 471	94.58 462	66.20 474	99.89 97	93.16 408	94.63 469	97.66 444
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		95.02 393	95.25 383	94.33 441	96.39 470	85.87 464	98.08 188	96.83 421	95.46 379	95.51 440	98.69 291	85.91 412	99.53 389	94.16 380	96.23 458	97.58 447

cascas		94.79 396	94.33 402	96.15 416	96.02 474	92.36 416	92.34 472	99.26 246	85.34 470	95.08 445	94.96 457	92.96 349	98.53 465	94.41 377	98.59 397	97.56 448

PatchT		96.65 346	96.35 350	97.54 352	97.40 446	95.32 328	97.98 214	96.64 424	99.33 67	96.89 397	99.42 91	84.32 425	99.81 223	97.69 192	97.49 433	97.48 449

TR-MVS		95.55 382	95.12 388	96.86 391	97.54 436	93.94 379	96.49 368	96.53 427	94.36 408	97.03 388	96.61 422	94.26 326	99.16 447	86.91 462	96.31 457	97.47 450

dmvs_testset		92.94 426	92.21 426	95.13 434	98.59 366	90.99 439	97.65 265	92.09 467	96.95 314	94.00 459	93.55 467	92.34 359	96.97 476	72.20 478	92.52 474	97.43 451

MonoMVSNet		96.25 361	96.53 347	95.39 431	96.57 464	91.01 438	98.82 96	97.68 395	98.57 163	98.03 326	99.37 101	90.92 376	97.78 472	94.99 356	93.88 472	97.38 452

JIA-IIPM		95.52 383	95.03 389	97.00 380	96.85 459	94.03 371	96.93 342	95.82 439	99.20 84	94.63 451	99.71 23	83.09 434	99.60 361	94.42 374	94.64 468	97.36 453

BH-w/o		95.13 390	94.89 394	95.86 418	98.20 401	91.31 431	95.65 416	97.37 401	93.64 419	96.52 414	95.70 441	93.04 348	99.02 451	88.10 457	95.82 463	97.24 454

tpm cat1		93.29 420	93.13 417	93.75 449	97.39 447	84.74 469	97.39 302	97.65 396	83.39 473	94.16 455	98.41 334	82.86 436	99.39 422	91.56 434	95.35 466	97.14 455

xiu_mvs_v1_base_debu		97.86 264	98.17 223	96.92 385	98.98 286	93.91 381	96.45 369	99.17 270	97.85 230	98.41 293	97.14 414	98.47 73	99.92 65	98.02 159	99.05 355	96.92 456

xiu_mvs_v1_base		97.86 264	98.17 223	96.92 385	98.98 286	93.91 381	96.45 369	99.17 270	97.85 230	98.41 293	97.14 414	98.47 73	99.92 65	98.02 159	99.05 355	96.92 456

xiu_mvs_v1_base_debi		97.86 264	98.17 223	96.92 385	98.98 286	93.91 381	96.45 369	99.17 270	97.85 230	98.41 293	97.14 414	98.47 73	99.92 65	98.02 159	99.05 355	96.92 456

PMVS	91.26 20	97.86 264	97.94 251	97.65 337	99.71 48	97.94 169	98.52 129	98.68 354	98.99 122	97.52 362	99.35 106	97.41 186	98.18 470	91.59 433	99.67 219	96.82 459
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 376	95.60 368	96.17 413	97.53 438	92.75 408	98.07 192	98.31 374	91.22 448	94.25 454	96.68 420	95.53 289	99.03 450	91.64 432	97.18 446	96.74 460

MVS-HIRNet		94.32 401	95.62 367	90.42 460	98.46 381	75.36 484	96.29 381	89.13 475	95.25 385	95.38 441	99.75 16	92.88 350	99.19 445	94.07 386	99.39 305	96.72 461

OpenMVS_ROB	95.38 14	95.84 374	95.18 387	97.81 318	98.41 389	97.15 239	97.37 308	98.62 359	83.86 471	98.65 260	98.37 339	94.29 325	99.68 317	88.41 455	98.62 396	96.60 462

thres100view900		94.19 404	93.67 409	95.75 422	99.06 267	91.35 430	98.03 199	94.24 456	98.33 178	97.40 372	94.98 456	79.84 444	99.62 351	83.05 468	98.08 418	96.29 463

tfpn200view9		94.03 408	93.44 411	95.78 421	98.93 293	91.44 428	97.60 275	94.29 454	97.94 222	97.10 382	94.31 463	79.67 446	99.62 351	83.05 468	98.08 418	96.29 463

MVS		93.19 422	92.09 427	96.50 400	96.91 457	94.03 371	98.07 192	98.06 385	68.01 478	94.56 452	96.48 425	95.96 276	99.30 435	83.84 467	96.89 451	96.17 465

gg-mvs-nofinetune		92.37 434	91.20 438	95.85 419	95.80 476	92.38 415	99.31 31	81.84 483	99.75 11	91.83 472	99.74 19	68.29 466	99.02 451	87.15 459	97.12 447	96.16 466

xiu_mvs_v2_base		97.16 322	97.49 286	96.17 413	98.54 373	92.46 412	95.45 424	98.84 332	97.25 291	97.48 366	96.49 424	98.31 91	99.90 81	96.34 308	98.68 391	96.15 467

PS-MVSNAJ		97.08 326	97.39 291	96.16 415	98.56 371	92.46 412	95.24 431	98.85 331	97.25 291	97.49 365	95.99 434	98.07 120	99.90 81	96.37 305	98.67 392	96.12 468

E-PMN		94.17 405	94.37 400	93.58 451	96.86 458	85.71 467	90.11 476	97.07 412	98.17 199	97.82 342	97.19 411	84.62 422	98.94 455	89.77 451	97.68 430	96.09 469

EMVS		93.83 411	94.02 403	93.23 456	96.83 460	84.96 468	89.77 477	96.32 429	97.92 224	97.43 371	96.36 430	86.17 409	98.93 456	87.68 458	97.73 429	95.81 470

MVE	83.40 22	92.50 431	91.92 433	94.25 442	98.83 316	91.64 424	92.71 469	83.52 482	95.92 365	86.46 480	95.46 448	95.20 298	95.40 478	80.51 473	98.64 393	95.73 471
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 414	93.14 416	95.46 430	98.66 356	91.29 432	96.61 360	94.63 451	97.39 277	96.83 400	93.71 466	79.88 443	99.56 377	82.40 471	98.13 415	95.54 472

API-MVS		97.04 329	96.91 321	97.42 362	97.88 417	98.23 135	98.18 171	98.50 365	97.57 253	97.39 374	96.75 419	96.77 230	99.15 448	90.16 450	99.02 362	94.88 473

GG-mvs-BLEND				94.76 438	94.54 478	92.13 420	99.31 31	80.47 484		88.73 478	91.01 478	67.59 470	98.16 471	82.30 472	94.53 470	93.98 474

DeepMVS_CX				93.44 453	98.24 398	94.21 363		94.34 453	64.28 479	91.34 473	94.87 460	89.45 390	92.77 480	77.54 476	93.14 473	93.35 475

tmp_tt		78.77 445	78.73 448	78.90 462	58.45 487	74.76 486	94.20 457	78.26 485	39.16 480	86.71 479	92.82 474	80.50 442	75.19 482	86.16 464	92.29 475	86.74 476

dongtai		76.24 446	75.95 449	77.12 463	92.39 481	67.91 487	90.16 475	59.44 488	82.04 474	89.42 476	94.67 461	49.68 485	81.74 481	48.06 481	77.66 479	81.72 477

kuosan		69.30 447	68.95 450	70.34 464	87.68 485	65.00 488	91.11 473	59.90 487	69.02 477	74.46 482	88.89 479	48.58 486	68.03 483	28.61 482	72.33 481	77.99 478

wuyk23d		96.06 365	97.62 279	91.38 459	98.65 360	98.57 107	98.85 93	96.95 417	96.86 322	99.90 14	99.16 160	99.18 19	98.40 466	89.23 454	99.77 158	77.18 479

test123		17.04 450	20.11 453	7.82 465	10.25 489	4.91 490	94.80 441	4.47 490	4.93 483	10.00 485	24.28 482	9.69 487	3.64 484	10.14 483	12.43 483	14.92 480

testmvs		17.12 449	20.53 452	6.87 466	12.05 488	4.20 491	93.62 466	6.73 489	4.62 484	10.41 484	24.33 481	8.28 488	3.56 485	9.69 484	15.07 482	12.86 481

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

cdsmvs_eth3d_5k		24.66 448	32.88 451	0.00 467	0.00 490	0.00 492	0.00 479	99.10 283	0.00 485	0.00 486	97.58 394	99.21 18	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		8.17 451	10.90 454	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	98.07 120	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

ab-mvs-re		8.12 452	10.83 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	97.48 400	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

TestfortrainingZip							98.68 108

WAC-MVS						90.90 440								91.37 437

FOURS1					99.73 38	99.67 3	99.43 15	99.54 114	99.43 56	99.26 149

test_one_0601					99.39 174	99.20 40		99.31 218	98.49 169	98.66 259	99.02 196	97.64 162

eth-test2					0.00 490

eth-test					0.00 490

ZD-MVS					99.01 281	98.84 87		99.07 287	94.10 413	98.05 324	98.12 359	96.36 254	99.86 143	92.70 419	99.19 341

test_241102_ONE					99.49 139	99.17 45		99.31 218	97.98 217	99.66 61	98.90 237	98.36 84	99.48 405

9.14			97.78 263		99.07 262		97.53 284	99.32 213	95.53 377	98.54 281	98.70 289	97.58 168	99.76 268	94.32 379	99.46 292

save fliter					99.11 253	97.97 164	96.53 365	99.02 300	98.24 188

test0726					99.50 131	99.21 34	98.17 174	99.35 199	97.97 218	99.26 149	99.06 184	97.61 166

test_part2					99.36 182	99.10 66				99.05 185

sam_mvs												84.29 427

MTGPA								99.20 258

test_post1							97.59 277				20.48 484	83.07 435	99.66 334	94.16 380

test_post											21.25 483	83.86 430	99.70 304

patchmatchnet-post											98.77 270	84.37 424	99.85 156

MTMP							97.93 220	91.91 469

gm-plane-assit					94.83 477	81.97 480			88.07 465		94.99 455		99.60 361	91.76 429

TEST9					98.71 337	98.08 151	95.96 400	99.03 297	91.40 446	95.85 429	97.53 396	96.52 245	99.76 268

test_8					98.67 351	98.01 159	95.91 406	99.02 300	91.64 441	95.79 431	97.50 399	96.47 247	99.76 268

agg_prior					98.68 350	97.99 160		99.01 303		95.59 432			99.77 262

test_prior4						97.97 164	95.86 407

test_prior2							95.74 414		96.48 339	96.11 424	97.63 392	95.92 279		94.16 380	99.20 338

旧先验2							95.76 413		88.56 464	97.52 362			99.66 334	94.48 370

新几何2							95.93 403

原ACMM2							95.53 420

testdata2													99.79 245	92.80 416

segment_acmp												97.02 212

testdata1							95.44 425		96.32 347

plane_prior7					99.19 232	97.87 175

plane_prior6					98.99 285	97.70 196						94.90 305

plane_prior4											97.98 371

plane_prior3						97.78 189			97.41 274	97.79 343

plane_prior2							97.77 245		98.20 196

plane_prior1					99.05 270

plane_prior						97.65 198	97.07 334		96.72 329						99.36 309

n2								0.00 491

nn								0.00 491

door-mid								99.57 96

test11								98.87 323

door								99.41 178

HQP5-MVS						96.79 261

HQP-NCC					98.67 351		96.29 381		96.05 357	95.55 435

ACMP_Plane					98.67 351		96.29 381		96.05 357	95.55 435

BP-MVS														92.82 414

HQP3-MVS								99.04 295							99.26 328

HQP2-MVS												93.84 333

NP-MVS					98.84 314	97.39 216					96.84 417

MDTV_nov1_ep13			95.22 385		97.06 456	83.20 476	97.74 252	96.16 431	94.37 407	96.99 389	98.83 257	83.95 429	99.53 389	93.90 389	97.95 425

ACMMP++_ref															99.77 158

ACMMP++															99.68 213

Test By Simon												96.52 245

High-resolution multi-view benchmark