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	98.44 259	99.65 68	95.35 323	99.82 3	99.94 2	99.83 7	99.42 110	99.94 2	98.13 115	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	98.08 186	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	98.80 182	99.75 34	96.59 267	97.97 216	99.86 16	98.22 188	99.88 21	99.71 22	98.59 62	99.84 174	99.73 28	99.98 12	99.98 3

fmvsm_s_conf0.1_n_2		99.20 51	99.38 29	98.65 215	99.69 58	96.08 292	97.49 288	99.90 11	99.53 42	99.88 21	99.64 37	98.51 71	99.90 81	99.83 10	99.98 12	99.97 4

mmtdpeth		99.30 34	99.42 25	98.92 166	99.58 87	96.89 254	99.48 13	99.92 7	99.92 2	98.26 302	99.80 11	98.33 88	99.91 74	99.56 41	99.95 38	99.97 4

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 217	99.71 47	96.10 287	97.87 229	99.85 18	98.56 164	99.90 14	99.68 25	98.69 52	99.85 156	99.72 30	99.98 12	99.97 4

test_fmvs3		99.12 69	99.41 26	98.25 281	99.76 30	95.07 335	99.05 67	99.94 2	97.78 234	99.82 34	99.84 3	98.56 68	99.71 295	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	97.77 243	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		98.67 151	98.87 102	98.05 302	99.72 43	95.59 307	98.51 132	99.81 31	96.30 347	99.78 40	99.82 5	96.14 258	98.63 461	99.82 12	99.93 56	99.95 9

test_fmvs2		98.70 138	98.97 91	97.89 310	99.54 115	94.05 365	98.55 123	99.92 7	96.78 323	99.72 48	99.78 13	96.60 239	99.67 319	99.91 2	99.90 85	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 68	99.48 45	99.92 8	99.71 22	98.07 118	99.96 14	99.53 48	100.00 1	99.93 11

test_vis3_rt		99.14 62	99.17 60	99.07 135	99.78 24	98.38 119	98.92 82	99.94 2	97.80 231	99.91 12	99.67 30	97.15 201	98.91 454	99.76 23	99.56 261	99.92 12

fmvsm_s_conf0.5_n_2		99.14 62	99.31 42	98.63 221	99.49 137	96.08 292	97.38 302	99.81 31	99.48 45	99.84 30	99.57 49	98.46 75	99.89 97	99.82 12	99.97 21	99.91 13

MVStest1		95.86 369	95.60 365	96.63 394	95.87 472	91.70 420	97.93 218	98.94 305	98.03 212	99.56 74	99.66 32	71.83 458	98.26 465	99.35 59	99.24 327	99.91 13

fmvsm_s_conf0.5_n_a		99.10 71	99.20 58	98.78 189	99.55 110	96.59 267	97.79 239	99.82 30	98.21 190	99.81 37	99.53 64	98.46 75	99.84 174	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n_9		99.17 53	99.38 29	98.53 247	99.51 123	95.82 302	97.62 268	99.78 36	99.72 15	99.90 14	99.48 75	98.66 54	99.89 97	99.85 6	99.93 56	99.89 16

fmvsm_s_conf0.5_n		99.09 72	99.26 51	98.61 226	99.55 110	96.09 290	97.74 250	99.81 31	98.55 165	99.85 27	99.55 57	98.60 61	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 74	98.10 145	97.68 257	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	99.56 103	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	98.80 182	99.48 145	96.56 272	97.97 216	99.69 54	99.63 29	99.84 30	99.54 63	98.21 105	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 66	99.26 51	98.74 202	99.51 123	96.44 279	97.65 263	99.65 68	99.66 24	99.78 40	99.48 75	97.92 132	99.93 54	99.72 30	99.95 38	99.87 22

fmvsm_s_conf0.5_n_7		98.83 113	99.04 80	98.20 288	99.30 195	94.83 340	97.23 318	99.36 190	98.64 149	99.84 30	99.43 88	98.10 117	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 85	98.21 136	97.82 234	99.84 22	99.41 58	99.92 8	99.41 93	99.51 8	99.95 26	99.84 9	99.97 21	99.87 22

ttmdpeth		97.91 253	98.02 238	97.58 343	98.69 343	94.10 364	98.13 176	98.90 314	97.95 218	97.32 374	99.58 47	95.95 274	98.75 459	96.41 300	99.22 331	99.87 22

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 102	99.28 40	99.66 64	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		97.66 278	98.50 164	95.13 431	99.63 80	85.84 462	98.35 154	98.21 374	98.23 187	99.54 79	99.46 80	95.02 300	99.68 315	98.24 138	99.87 97	99.87 22

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 189	99.46 151	96.58 270	97.65 263	99.72 45	99.47 48	99.86 24	99.50 68	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	99.49 137	99.29 25	99.80 4	99.72 45	99.82 8	99.04 185	99.81 8	98.05 121	99.96 14	98.85 98	99.99 5	99.86 28

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 155	99.59 85	97.18 233	97.44 297	99.83 25	99.56 40	99.91 12	99.34 108	99.36 13	99.93 54	99.83 10	99.98 12	99.85 30

MM		98.22 223	97.99 241	98.91 167	98.66 353	96.97 247	97.89 225	94.44 449	99.54 41	98.95 205	99.14 165	93.50 336	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 68	97.05 242	97.80 238	99.76 39	98.70 147	99.78 40	99.11 171	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 74	97.28 222	97.82 234	99.76 39	98.73 144	99.82 34	99.09 179	98.81 38	99.95 26	99.86 4	99.96 28	99.83 33

mvsany_test3		98.87 104	98.92 95	98.74 202	99.38 173	96.94 251	98.58 120	99.10 280	96.49 335	99.96 4	99.81 8	98.18 108	99.45 410	98.97 90	99.79 145	99.83 33

fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 189	99.47 148	96.56 272	97.75 249	99.71 47	99.60 36	99.74 47	99.44 85	97.96 129	99.95 26	99.86 4	99.94 50	99.82 36

SSC-MVS		98.71 133	98.74 117	98.62 223	99.72 43	96.08 292	98.74 97	98.64 355	99.74 13	99.67 60	99.24 136	94.57 314	99.95 26	99.11 78	99.24 327	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 82	99.22 55	98.38 266	99.31 191	95.48 316	97.56 278	99.73 44	98.87 136	99.75 45	99.27 123	98.80 40	99.86 143	99.80 17	99.90 85	99.81 40

PS-CasMVS		99.40 26	99.33 38	99.62 10	99.71 47	99.10 66	99.29 36	99.53 116	99.53 42	99.46 101	99.41 93	98.23 100	99.95 26	98.89 96	99.95 38	99.81 40

VortexMVS		97.98 251	98.31 199	97.02 376	98.88 304	91.45 424	98.03 197	99.47 143	98.65 148	99.55 77	99.47 78	91.49 367	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 94	99.61 35	99.40 115	99.50 68	97.12 202	99.85 156	99.02 87	99.94 50	99.80 42

test_cas_vis1_n_1920		98.33 208	98.68 132	97.27 365	99.69 58	92.29 414	98.03 197	99.85 18	97.62 244	99.96 4	99.62 40	93.98 329	99.74 279	99.52 50	99.86 104	99.79 44

test_vis1_n_1920		98.40 194	98.92 95	96.81 389	99.74 36	90.76 440	98.15 174	99.91 9	98.33 176	99.89 18	99.55 57	95.07 299	99.88 115	99.76 23	99.93 56	99.79 44

CP-MVSNet		99.21 48	99.09 75	99.56 27	99.65 68	98.96 78	99.13 58	99.34 202	99.42 56	99.33 129	99.26 129	97.01 210	99.94 42	98.74 107	99.93 56	99.79 44

fmvsm_s_conf0.5_n_5		99.07 78	99.10 73	98.99 151	99.47 148	97.22 227	97.40 299	99.83 25	97.61 247	99.85 27	99.30 117	98.80 40	99.95 26	99.71 32	99.90 85	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 87	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		96.25 358	97.21 300	93.38 452	99.10 252	80.56 480	97.20 323	98.19 377	96.94 312	99.00 190	99.02 194	89.50 386	99.80 230	96.36 304	99.59 249	99.78 47

TestfortrainingZip a		98.95 92	98.72 121	99.64 9	99.58 87	99.32 22	98.68 107	99.60 78	96.46 338	99.53 83	98.77 267	97.87 139	99.83 192	98.39 131	99.64 228	99.77 50

reproduce_monomvs		95.00 391	95.25 380	94.22 440	97.51 440	83.34 472	97.86 230	98.44 364	98.51 166	99.29 139	99.30 117	67.68 466	99.56 374	98.89 96	99.81 128	99.77 50

Anonymous20231211		99.27 38	99.27 48	99.26 101	99.29 198	98.18 137	99.49 12	99.51 122	99.70 16	99.80 38	99.68 25	96.84 219	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	99.54 112	99.62 33	99.56 74	99.42 89	98.16 112	99.96 14	98.78 102	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	99.55 107	99.46 50	99.50 94	99.34 108	97.30 191	99.93 54	98.90 94	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		98.52 182	98.55 155	98.43 260	99.65 68	95.59 307	98.52 127	98.77 340	99.65 26	99.52 88	99.00 209	94.34 320	99.93 54	98.65 114	98.83 375	99.76 56

patch_mono-2		98.51 183	98.63 142	98.17 291	99.38 173	94.78 342	97.36 307	99.69 54	98.16 200	98.49 283	99.29 120	97.06 205	99.97 7	98.29 137	99.91 78	99.76 56

nrg030		99.40 26	99.35 34	99.54 32	99.58 87	99.13 61	98.98 75	99.48 134	99.68 20	99.46 101	99.26 129	98.62 59	99.73 286	99.17 75	99.92 69	99.76 56

FIs		99.14 62	99.09 75	99.29 95	99.70 55	98.28 127	99.13 58	99.52 121	99.48 45	99.24 153	99.41 93	96.79 226	99.82 205	98.69 112	99.88 93	99.76 56

v7n		99.53 12	99.57 13	99.41 70	99.88 9	98.54 110	99.45 14	99.61 77	99.66 24	99.68 58	99.66 32	98.44 77	99.95 26	99.73 28	99.96 28	99.75 60

APDe-MVS		98.99 85	98.79 113	99.60 16	99.21 223	99.15 53	98.87 88	99.48 134	97.57 251	99.35 125	99.24 136	97.83 142	99.89 97	97.88 170	99.70 203	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	99.51 122	99.64 27	99.56 74	99.46 80	98.23 100	99.97 7	98.78 102	99.93 56	99.72 62

MSC_two_6792asdad				99.32 91	98.43 382	98.37 121		98.86 325					99.89 97	97.14 227	99.60 245	99.71 63

No_MVS				99.32 91	98.43 382	98.37 121		98.86 325					99.89 97	97.14 227	99.60 245	99.71 63

PMMVS2		98.07 240	98.08 232	98.04 303	99.41 168	94.59 351	94.59 447	99.40 178	97.50 260	98.82 235	98.83 254	96.83 221	99.84 174	97.50 203	99.81 128	99.71 63

Baseline_NR-MVSNet		98.98 88	98.86 106	99.36 74	99.82 19	98.55 107	97.47 293	99.57 94	99.37 61	99.21 159	99.61 43	96.76 229	99.83 192	98.06 153	99.83 118	99.71 63

XXY-MVS		99.14 62	99.15 67	99.10 128	99.76 30	97.74 191	98.85 92	99.62 74	98.48 168	99.37 120	99.49 74	98.75 46	99.86 143	98.20 143	99.80 139	99.71 63

test_0728_THIRD									98.17 197	99.08 173	99.02 194	97.89 137	99.88 115	97.07 233	99.71 196	99.70 68

MSP-MVS		98.40 194	98.00 240	99.61 14	99.57 96	99.25 30	98.57 121	99.35 196	97.55 255	99.31 137	97.71 383	94.61 313	99.88 115	96.14 317	99.19 338	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		98.53 178	98.79 113	97.74 324	99.46 151	93.62 391	96.45 366	99.34 202	99.33 66	98.93 213	98.70 286	97.90 133	99.90 81	99.12 77	99.92 69	99.69 70

NormalMVS		98.26 218	97.97 245	99.15 121	99.64 74	97.83 178	98.28 158	99.43 165	99.24 76	98.80 239	98.85 247	89.76 382	99.94 42	98.04 155	99.67 217	99.68 71

KinetiMVS		99.03 80	99.02 83	99.03 145	99.70 55	97.48 207	98.43 145	99.29 231	99.70 16	99.60 71	99.07 181	96.13 259	99.94 42	99.42 56	99.87 97	99.68 71

dcpmvs_2		98.78 124	99.11 71	97.78 317	99.56 104	93.67 388	99.06 65	99.86 16	99.50 44	99.66 61	99.26 129	97.21 199	99.99 2	98.00 160	99.91 78	99.68 71

test_0728_SECOND				99.60 16	99.50 129	99.23 32	98.02 200	99.32 210					99.88 115	96.99 240	99.63 235	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 87	99.44 53	99.78 40	99.76 15	96.39 247	99.92 65	99.44 55	99.92 69	99.68 71

fmvsm_s_conf0.5_n_6		99.08 76	99.21 57	98.69 210	99.36 180	96.51 274	97.62 268	99.68 60	98.43 170	99.85 27	99.10 174	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

CHOSEN 1792x2688		97.49 290	97.14 305	98.54 245	99.68 61	96.09 290	96.50 364	99.62 74	91.58 440	98.84 231	98.97 218	92.36 355	99.88 115	96.76 263	99.95 38	99.67 76

reproduce_model		99.15 58	98.97 91	99.67 4	99.33 189	99.44 10	98.15 174	99.47 143	99.12 97	99.52 88	99.32 115	98.31 89	99.90 81	97.78 178	99.73 179	99.66 78

IU-MVS					99.49 137	99.15 53		98.87 320	92.97 425	99.41 112				96.76 263	99.62 238	99.66 78

test_241102_TWO								99.30 223	98.03 212	99.26 147	99.02 194	97.51 176	99.88 115	96.91 246	99.60 245	99.66 78

DPE-MVS		98.59 165	98.26 207	99.57 22	99.27 204	99.15 53	97.01 333	99.39 180	97.67 240	99.44 105	98.99 211	97.53 173	99.89 97	95.40 347	99.68 211	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 94	99.39 59	99.75 45	99.62 40	99.17 20	99.83 192	99.06 83	99.62 238	99.66 78

EI-MVSNet-UG-set		98.69 142	98.71 126	98.62 223	99.10 252	96.37 281	97.23 318	98.87 320	99.20 83	99.19 161	98.99 211	97.30 191	99.85 156	98.77 105	99.79 145	99.65 83

Elysia		99.15 58	99.14 68	99.18 113	99.63 80	97.92 169	98.50 134	99.43 165	99.67 21	99.70 52	99.13 167	96.66 235	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 58	99.14 68	99.18 113	99.63 80	97.92 169	98.50 134	99.43 165	99.67 21	99.70 52	99.13 167	96.66 235	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		98.68 148	98.70 129	98.63 221	99.09 255	96.40 280	97.23 318	98.86 325	99.20 83	99.18 165	98.97 218	97.29 193	99.85 156	98.72 109	99.78 150	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	99.55 107	98.30 180	99.65 64	99.45 84	99.22 17	99.76 266	98.44 128	99.77 156	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 95	98.81 112	99.28 96	99.21 223	98.45 116	98.46 142	99.33 208	99.63 29	99.48 96	99.15 162	97.23 197	99.75 274	97.17 223	99.66 225	99.63 89

reproduce-ours		99.09 72	98.90 97	99.67 4	99.27 204	99.49 6	98.00 204	99.42 171	99.05 115	99.48 96	99.27 123	98.29 91	99.89 97	97.61 193	99.71 196	99.62 90

our_new_method		99.09 72	98.90 97	99.67 4	99.27 204	99.49 6	98.00 204	99.42 171	99.05 115	99.48 96	99.27 123	98.29 91	99.89 97	97.61 193	99.71 196	99.62 90

test_fmvs1_n		98.09 238	98.28 203	97.52 351	99.68 61	93.47 393	98.63 114	99.93 5	95.41 380	99.68 58	99.64 37	91.88 363	99.48 402	99.82 12	99.87 97	99.62 90

test1111		96.49 350	96.82 324	95.52 424	99.42 165	87.08 459	99.22 45	87.14 475	99.11 98	99.46 101	99.58 47	88.69 390	99.86 143	98.80 100	99.95 38	99.62 90

VPA-MVSNet		99.30 34	99.30 45	99.28 96	99.49 137	98.36 124	99.00 72	99.45 151	99.63 29	99.52 88	99.44 85	98.25 98	99.88 115	99.09 80	99.84 111	99.62 90

LPG-MVS_test		98.71 133	98.46 174	99.47 61	99.57 96	98.97 74	98.23 164	99.48 134	96.60 330	99.10 171	99.06 182	98.71 50	99.83 192	95.58 343	99.78 150	99.62 90

LGP-MVS_train				99.47 61	99.57 96	98.97 74		99.48 134	96.60 330	99.10 171	99.06 182	98.71 50	99.83 192	95.58 343	99.78 150	99.62 90

Test_1112_low_res		96.99 331	96.55 342	98.31 275	99.35 185	95.47 319	95.84 407	99.53 116	91.51 442	96.80 399	98.48 325	91.36 368	99.83 192	96.58 282	99.53 271	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 89	99.11 71	98.55 240	99.44 158	96.21 286	98.90 83	99.55 107	98.73 144	99.48 96	99.60 45	96.63 238	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 72	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		98.31 211	98.50 164	97.73 327	99.76 30	94.17 362	98.68 107	99.91 9	96.31 345	99.79 39	99.57 49	92.85 349	99.42 415	99.79 19	99.84 111	99.60 100

v8		99.01 82	99.16 62	98.57 233	99.47 148	96.31 284	98.90 83	99.47 143	99.03 118	99.52 88	99.57 49	96.93 215	99.81 222	99.60 37	99.98 12	99.60 100

EI-MVSNet		98.40 194	98.51 161	98.04 303	99.10 252	94.73 345	97.20 323	98.87 320	98.97 124	99.06 175	99.02 194	96.00 266	99.80 230	98.58 117	99.82 122	99.60 100

SixPastTwentyTwo		98.75 129	98.62 144	99.16 118	99.83 18	97.96 166	99.28 40	98.20 375	99.37 61	99.70 52	99.65 36	92.65 353	99.93 54	99.04 85	99.84 111	99.60 100

IterMVS-LS		98.55 173	98.70 129	98.09 295	99.48 145	94.73 345	97.22 322	99.39 180	98.97 124	99.38 118	99.31 116	96.00 266	99.93 54	98.58 117	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		97.19 316	96.60 340	98.96 158	99.62 84	97.28 222	95.17 429	99.50 125	94.21 407	99.01 189	98.32 343	86.61 402	99.99 2	97.10 231	99.84 111	99.60 100

lecture		99.25 41	99.12 70	99.62 10	99.64 74	99.40 12	98.89 87	99.51 122	99.19 88	99.37 120	99.25 134	98.36 82	99.88 115	98.23 140	99.67 217	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		98.75 129	98.48 170	99.57 22	99.58 87	99.29 25	97.82 234	99.25 244	96.94 312	98.78 241	99.12 170	98.02 122	99.84 174	97.13 229	99.67 217	99.59 107

VPNet		98.87 104	98.83 109	99.01 149	99.70 55	97.62 200	98.43 145	99.35 196	99.47 48	99.28 141	99.05 189	96.72 232	99.82 205	98.09 150	99.36 306	99.59 107

WR-MVS		98.40 194	98.19 218	99.03 145	99.00 279	97.65 197	96.85 343	98.94 305	98.57 161	98.89 220	98.50 322	95.60 284	99.85 156	97.54 199	99.85 106	99.59 107

HPM-MVS		98.79 122	98.53 159	99.59 20	99.65 68	99.29 25	99.16 54	99.43 165	96.74 325	98.61 264	98.38 335	98.62 59	99.87 134	96.47 296	99.67 217	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 85	99.01 85	98.94 161	99.50 129	97.47 208	98.04 195	99.59 85	98.15 205	99.40 115	99.36 103	98.58 67	99.76 266	98.78 102	99.68 211	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	99.27 143	99.48 75	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 87	98.93 80	98.68 107	99.60 78	96.46 338	99.53 83	98.77 267		99.83 192	96.67 273	99.64 228	99.58 115

MED-MVS		98.90 99	98.72 121	99.45 64	99.58 87	98.93 80	98.68 107	99.60 78	98.14 206	99.53 83	98.77 267	97.87 139	99.83 192	96.67 273	99.64 228	99.58 115

ME-MVS		98.61 161	98.33 197	99.44 66	99.24 215	98.93 80	97.45 295	99.06 285	98.14 206	99.06 175	98.77 267	96.97 213	99.82 205	96.67 273	99.64 228	99.58 115

MP-MVS-pluss		98.57 168	98.23 212	99.60 16	99.69 58	99.35 17	97.16 328	99.38 182	94.87 392	98.97 199	98.99 211	98.01 123	99.88 115	97.29 216	99.70 203	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 142	98.40 182	99.54 32	99.53 118	99.17 45	98.52 127	99.31 215	97.46 268	98.44 287	98.51 318	97.83 142	99.88 115	96.46 297	99.58 254	99.58 115

ACMMPR		98.70 138	98.42 180	99.54 32	99.52 121	99.14 58	98.52 127	99.31 215	97.47 263	98.56 274	98.54 313	97.75 151	99.88 115	96.57 284	99.59 249	99.58 115

PGM-MVS		98.66 152	98.37 189	99.55 29	99.53 118	99.18 44	98.23 164	99.49 132	97.01 309	98.69 252	98.88 241	98.00 124	99.89 97	95.87 329	99.59 249	99.58 115

SteuartSystems-ACMMP		98.79 122	98.54 157	99.54 32	99.73 37	99.16 49	98.23 164	99.31 215	97.92 222	98.90 217	98.90 234	98.00 124	99.88 115	96.15 316	99.72 187	99.58 115
Skip Steuart: Steuart Systems R&D Blog.
SDMVSNet		99.23 46	99.32 40	98.96 158	99.68 61	97.35 215	98.84 94	99.48 134	99.69 18	99.63 67	99.68 25	99.03 24	99.96 14	97.97 163	99.92 69	99.57 123

sd_testset		99.28 37	99.31 42	99.19 112	99.68 61	98.06 155	99.41 17	99.30 223	99.69 18	99.63 67	99.68 25	99.25 16	99.96 14	97.25 219	99.92 69	99.57 123

TranMVSNet+NR-MVSNet		99.17 53	99.07 78	99.46 63	99.37 179	98.87 85	98.39 150	99.42 171	99.42 56	99.36 123	99.06 182	98.38 81	99.95 26	98.34 134	99.90 85	99.57 123

mPP-MVS		98.64 155	98.34 193	99.54 32	99.54 115	99.17 45	98.63 114	99.24 249	97.47 263	98.09 316	98.68 290	97.62 162	99.89 97	96.22 311	99.62 238	99.57 123

PVSNet_Blended_VisFu		98.17 232	98.15 224	98.22 287	99.73 37	95.15 331	97.36 307	99.68 60	94.45 402	98.99 194	99.27 123	96.87 218	99.94 42	97.13 229	99.91 78	99.57 123

1112_ss		97.29 308	96.86 320	98.58 230	99.34 188	96.32 283	96.75 349	99.58 87	93.14 423	96.89 394	97.48 397	92.11 360	99.86 143	96.91 246	99.54 267	99.57 123

MTAPA		98.88 103	98.64 140	99.61 14	99.67 65	99.36 16	98.43 145	99.20 255	98.83 142	98.89 220	98.90 234	96.98 212	99.92 65	97.16 224	99.70 203	99.56 129

XVS		98.72 132	98.45 175	99.53 39	99.46 151	99.21 34	98.65 112	99.34 202	98.62 154	97.54 357	98.63 302	97.50 177	99.83 192	96.79 259	99.53 271	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 72	99.30 71	99.65 64	99.60 45	99.16 22	99.82 205	99.07 81	99.83 118	99.56 129

X-MVStestdata		94.32 398	92.59 417	99.53 39	99.46 151	99.21 34	98.65 112	99.34 202	98.62 154	97.54 357	45.85 477	97.50 177	99.83 192	96.79 259	99.53 271	99.56 129

HPM-MVS_fast		99.01 82	98.82 110	99.57 22	99.71 47	99.35 17	99.00 72	99.50 125	97.33 279	98.94 212	98.86 244	98.75 46	99.82 205	97.53 200	99.71 196	99.56 129

K. test v3		98.00 247	97.66 272	99.03 145	99.79 23	97.56 202	99.19 52	92.47 461	99.62 33	99.52 88	99.66 32	89.61 384	99.96 14	99.25 68	99.81 128	99.56 129

CP-MVS		98.70 138	98.42 180	99.52 45	99.36 180	99.12 63	98.72 102	99.36 190	97.54 257	98.30 296	98.40 332	97.86 141	99.89 97	96.53 293	99.72 187	99.56 129

viewmacassd2359aftdt		98.86 107	98.87 102	98.83 176	99.53 118	97.32 219	97.70 255	99.64 70	98.22 188	99.25 151	99.27 123	98.40 79	99.61 355	97.98 162	99.87 97	99.55 136

FE-MVSNET		98.59 165	98.50 164	98.87 171	99.58 87	97.30 220	98.08 186	99.74 43	96.94 312	98.97 199	99.10 174	96.94 214	99.74 279	97.33 214	99.86 104	99.55 136

ZNCC-MVS		98.68 148	98.40 182	99.54 32	99.57 96	99.21 34	98.46 142	99.29 231	97.28 285	98.11 314	98.39 333	98.00 124	99.87 134	96.86 256	99.64 228	99.55 136

v1192		98.60 163	98.66 137	98.41 262	99.27 204	95.88 298	97.52 283	99.36 190	97.41 272	99.33 129	99.20 145	96.37 250	99.82 205	99.57 39	99.92 69	99.55 136

v1240		98.55 173	98.62 144	98.32 273	99.22 221	95.58 309	97.51 285	99.45 151	97.16 300	99.45 104	99.24 136	96.12 261	99.85 156	99.60 37	99.88 93	99.55 136

UGNet		98.53 178	98.45 175	98.79 186	97.94 411	96.96 249	99.08 61	98.54 359	99.10 105	96.82 398	99.47 78	96.55 241	99.84 174	98.56 122	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
AstraMVS		98.16 234	98.07 234	98.41 262	99.51 123	95.86 299	98.00 204	95.14 444	98.97 124	99.43 106	99.24 136	93.25 337	99.84 174	99.21 71	99.87 97	99.54 142

WBMVS		95.18 386	94.78 392	96.37 400	97.68 428	89.74 447	95.80 408	98.73 348	97.54 257	98.30 296	98.44 329	70.06 460	99.82 205	96.62 279	99.87 97	99.54 142

test2506		92.39 429	91.89 431	93.89 445	99.38 173	82.28 476	99.32 26	66.03 483	99.08 112	98.77 244	99.57 49	66.26 470	99.84 174	98.71 110	99.95 38	99.54 142

ECVR-MVS		96.42 352	96.61 338	95.85 416	99.38 173	88.18 454	99.22 45	86.00 477	99.08 112	99.36 123	99.57 49	88.47 395	99.82 205	98.52 125	99.95 38	99.54 142

v144192		98.54 176	98.57 153	98.45 257	99.21 223	95.98 295	97.63 267	99.36 190	97.15 302	99.32 135	99.18 152	95.84 278	99.84 174	99.50 51	99.91 78	99.54 142

v1921920		98.54 176	98.60 149	98.38 266	99.20 227	95.76 305	97.56 278	99.36 190	97.23 294	99.38 118	99.17 156	96.02 264	99.84 174	99.57 39	99.90 85	99.54 142

MP-MVS		98.46 188	98.09 229	99.54 32	99.57 96	99.22 33	98.50 134	99.19 259	97.61 247	97.58 353	98.66 295	97.40 185	99.88 115	94.72 362	99.60 245	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 85	99.59 37	99.71 50	99.57 49	97.12 202	99.90 81	99.21 71	99.87 97	99.54 142

ACMMP		98.75 129	98.50 164	99.52 45	99.56 104	99.16 49	98.87 88	99.37 186	97.16 300	98.82 235	99.01 205	97.71 153	99.87 134	96.29 308	99.69 206	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		98.40 194	98.03 237	99.51 49	99.16 241	99.21 34	98.05 193	99.22 252	94.16 408	98.98 195	99.10 174	97.52 175	99.79 243	96.45 298	99.64 228	99.53 151
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		98.71 133	98.44 177	99.51 49	99.49 137	99.16 49	98.52 127	99.31 215	97.47 263	98.58 270	98.50 322	97.97 128	99.85 156	96.57 284	99.59 249	99.53 151

UniMVSNet_NR-MVSNet		98.86 107	98.68 132	99.40 72	99.17 239	98.74 92	97.68 257	99.40 178	99.14 96	99.06 175	98.59 309	96.71 233	99.93 54	98.57 119	99.77 156	99.53 151

GST-MVS		98.61 161	98.30 200	99.52 45	99.51 123	99.20 40	98.26 162	99.25 244	97.44 271	98.67 255	98.39 333	97.68 154	99.85 156	96.00 321	99.51 276	99.52 154

MGCNet		97.44 295	97.01 311	98.72 206	96.42 465	96.74 262	97.20 323	91.97 465	98.46 169	98.30 296	98.79 263	92.74 351	99.91 74	99.30 63	99.94 50	99.52 154

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 56	99.80 230	98.24 138	99.84 111	99.52 154

v1144		98.60 163	98.66 137	98.41 262	99.36 180	95.90 297	97.58 276	99.34 202	97.51 259	99.27 143	99.15 162	96.34 252	99.80 230	99.47 54	99.93 56	99.51 157

v2v482		98.56 169	98.62 144	98.37 269	99.42 165	95.81 303	97.58 276	99.16 270	97.90 224	99.28 141	99.01 205	95.98 271	99.79 243	99.33 60	99.90 85	99.51 157

CPTT-MVS		97.84 267	97.36 291	99.27 99	99.31 191	98.46 115	98.29 157	99.27 238	94.90 391	97.83 337	98.37 336	94.90 302	99.84 174	93.85 390	99.54 267	99.51 157

viewdifsd2359ckpt11		98.84 110	99.04 80	98.24 283	99.56 104	95.51 312	97.38 302	99.70 52	99.16 93	99.57 72	99.40 96	98.26 96	99.71 295	98.55 123	99.82 122	99.50 160

viewmsd2359difaftdt		98.84 110	99.04 80	98.24 283	99.56 104	95.51 312	97.38 302	99.70 52	99.16 93	99.57 72	99.40 96	98.26 96	99.71 295	98.55 123	99.82 122	99.50 160

LuminaMVS		98.39 200	98.20 214	98.98 155	99.50 129	97.49 205	97.78 240	97.69 390	98.75 143	99.49 95	99.25 134	92.30 357	99.94 42	99.14 76	99.88 93	99.50 160

DU-MVS		98.82 116	98.63 142	99.39 73	99.16 241	98.74 92	97.54 281	99.25 244	98.84 141	99.06 175	98.76 273	96.76 229	99.93 54	98.57 119	99.77 156	99.50 160

NR-MVSNet		98.95 92	98.82 110	99.36 74	99.16 241	98.72 97	99.22 45	99.20 255	99.10 105	99.72 48	98.76 273	96.38 249	99.86 143	98.00 160	99.82 122	99.50 160

casdiffmvs_mvg		99.12 69	99.16 62	98.99 151	99.43 163	97.73 193	98.00 204	99.62 74	99.22 79	99.55 77	99.22 142	98.93 32	99.75 274	98.66 113	99.81 128	99.50 160
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 76	99.00 87	99.33 89	99.71 47	98.83 87	98.60 118	99.58 87	99.11 98	99.53 83	99.18 152	98.81 38	99.67 319	96.71 270	99.77 156	99.50 160

SymmetryMVS		98.05 242	97.71 267	99.09 132	99.29 198	97.83 178	98.28 158	97.64 395	99.24 76	98.80 239	98.85 247	89.76 382	99.94 42	98.04 155	99.50 284	99.49 167

DVP-MVS++		98.90 99	98.70 129	99.51 49	98.43 382	99.15 53	99.43 15	99.32 210	98.17 197	99.26 147	99.02 194	98.18 108	99.88 115	97.07 233	99.45 291	99.49 167

PC_three_1452									93.27 421	99.40 115	98.54 313	98.22 103	97.00 472	95.17 350	99.45 291	99.49 167

GeoE		99.05 79	98.99 89	99.25 104	99.44 158	98.35 125	98.73 101	99.56 103	98.42 171	98.91 216	98.81 260	98.94 30	99.91 74	98.35 133	99.73 179	99.49 167

h-mvs33		97.77 270	97.33 294	99.10 128	99.21 223	97.84 177	98.35 154	98.57 358	99.11 98	98.58 270	99.02 194	88.65 393	99.96 14	98.11 148	96.34 453	99.49 167

IterMVS-SCA-FT		97.85 266	98.18 219	96.87 385	99.27 204	91.16 434	95.53 417	99.25 244	99.10 105	99.41 112	99.35 104	93.10 342	99.96 14	98.65 114	99.94 50	99.49 167

new-patchmatchnet		98.35 203	98.74 117	97.18 368	99.24 215	92.23 416	96.42 370	99.48 134	98.30 180	99.69 56	99.53 64	97.44 183	99.82 205	98.84 99	99.77 156	99.49 167

APD-MVS		98.10 236	97.67 269	99.42 68	99.11 250	98.93 80	97.76 246	99.28 235	94.97 389	98.72 250	98.77 267	97.04 206	99.85 156	93.79 391	99.54 267	99.49 167
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 212	98.04 236	99.07 135	99.56 104	97.83 178	99.29 36	98.07 381	99.03 118	98.59 268	99.13 167	92.16 359	99.90 81	96.87 254	99.68 211	99.49 167

DeepC-MVS	97.60 4	98.97 89	98.93 94	99.10 128	99.35 185	97.98 162	98.01 203	99.46 147	97.56 253	99.54 79	99.50 68	98.97 28	99.84 174	98.06 153	99.92 69	99.49 167
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 97	98.73 119	99.48 57	99.55 110	99.14 58	98.07 190	99.37 186	97.62 244	99.04 185	98.96 221	98.84 36	99.79 243	97.43 209	99.65 226	99.49 167
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
guyue		98.01 246	97.93 250	98.26 279	99.45 156	95.48 316	98.08 186	96.24 427	98.89 135	99.34 127	99.14 165	91.32 369	99.82 205	99.07 81	99.83 118	99.48 178

DVP-MVS		98.77 127	98.52 160	99.52 45	99.50 129	99.21 34	98.02 200	98.84 329	97.97 216	99.08 173	99.02 194	97.61 164	99.88 115	96.99 240	99.63 235	99.48 178
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		98.71 133	98.43 178	99.57 22	99.18 237	99.35 17	98.36 153	99.29 231	98.29 183	98.88 224	98.85 247	97.53 173	99.87 134	96.14 317	99.31 315	99.48 178

TSAR-MVS + MP.		98.63 157	98.49 169	99.06 141	99.64 74	97.90 172	98.51 132	98.94 305	96.96 310	99.24 153	98.89 240	97.83 142	99.81 222	96.88 253	99.49 286	99.48 178
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 225	97.95 246	99.01 149	99.58 87	97.74 191	99.01 70	97.29 403	99.67 21	98.97 199	99.50 68	90.45 377	99.80 230	97.88 170	99.20 335	99.48 178

IterMVS		97.73 272	98.11 228	96.57 395	99.24 215	90.28 443	95.52 419	99.21 253	98.86 138	99.33 129	99.33 111	93.11 341	99.94 42	98.49 126	99.94 50	99.48 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 228	97.90 254	99.08 133	99.57 96	97.97 163	99.31 30	98.32 370	99.01 120	98.98 195	99.03 193	91.59 365	99.79 243	95.49 345	99.80 139	99.48 178

ACMP	95.32 15	98.41 192	98.09 229	99.36 74	99.51 123	98.79 90	97.68 257	99.38 182	95.76 367	98.81 237	98.82 257	98.36 82	99.82 205	94.75 359	99.77 156	99.48 178
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 247	97.63 275	99.10 128	99.24 215	98.17 138	96.89 342	98.73 348	95.66 368	97.92 328	97.70 385	97.17 200	99.66 332	96.18 315	99.23 330	99.47 186

3Dnovator+	97.89 3	98.69 142	98.51 161	99.24 106	98.81 319	98.40 117	99.02 69	99.19 259	98.99 121	98.07 318	99.28 121	97.11 204	99.84 174	96.84 257	99.32 313	99.47 186

diffmvs_AUTHOR		98.50 184	98.59 151	98.23 286	99.35 185	95.48 316	96.61 357	99.60 78	98.37 172	98.90 217	99.00 209	97.37 187	99.76 266	98.22 141	99.85 106	99.46 188

HPM-MVS++		98.10 236	97.64 274	99.48 57	99.09 255	99.13 61	97.52 283	98.75 345	97.46 268	96.90 393	97.83 378	96.01 265	99.84 174	95.82 333	99.35 308	99.46 188

V42		98.78 124	98.78 115	98.76 196	99.44 158	97.04 243	98.27 161	99.19 259	97.87 226	99.25 151	99.16 158	96.84 219	99.78 254	99.21 71	99.84 111	99.46 188

APD-MVS_3200maxsize		98.84 110	98.61 148	99.53 39	99.19 230	99.27 28	98.49 137	99.33 208	98.64 149	99.03 188	98.98 216	97.89 137	99.85 156	96.54 292	99.42 299	99.46 188

UniMVSNet (Re)		98.87 104	98.71 126	99.35 80	99.24 215	98.73 95	97.73 252	99.38 182	98.93 129	99.12 167	98.73 276	96.77 227	99.86 143	98.63 116	99.80 139	99.46 188

SR-MVS-dyc-post		98.81 118	98.55 155	99.57 22	99.20 227	99.38 13	98.48 140	99.30 223	98.64 149	98.95 205	98.96 221	97.49 180	99.86 143	96.56 288	99.39 302	99.45 193

RE-MVS-def			98.58 152		99.20 227	99.38 13	98.48 140	99.30 223	98.64 149	98.95 205	98.96 221	97.75 151		96.56 288	99.39 302	99.45 193

HQP_MVS		97.99 250	97.67 269	98.93 163	99.19 230	97.65 197	97.77 243	99.27 238	98.20 194	97.79 340	97.98 368	94.90 302	99.70 302	94.42 371	99.51 276	99.45 193

plane_prior5								99.27 238					99.70 302	94.42 371	99.51 276	99.45 193

lessismore_v0				98.97 157	99.73 37	97.53 204		86.71 476		99.37 120	99.52 67	89.93 380	99.92 65	98.99 89	99.72 187	99.44 197

TAMVS		98.24 222	98.05 235	98.80 182	99.07 259	97.18 233	97.88 226	98.81 334	96.66 329	99.17 166	99.21 143	94.81 308	99.77 260	96.96 244	99.88 93	99.44 197

DeepPCF-MVS	96.93 5	98.32 209	98.01 239	99.23 108	98.39 387	98.97 74	95.03 433	99.18 263	96.88 317	99.33 129	98.78 265	98.16 112	99.28 436	96.74 265	99.62 238	99.44 197

3Dnovator	98.27 2	98.81 118	98.73 119	99.05 142	98.76 324	97.81 186	99.25 43	99.30 223	98.57 161	98.55 276	99.33 111	97.95 130	99.90 81	97.16 224	99.67 217	99.44 197

E2		98.70 138	98.68 132	98.73 204	99.40 170	97.10 240	97.48 289	99.57 94	98.09 209	99.00 190	99.20 145	97.90 133	99.67 319	97.73 186	99.77 156	99.43 201

E3		98.69 142	98.68 132	98.73 204	99.40 170	97.10 240	97.48 289	99.57 94	98.09 209	99.00 190	99.20 145	97.90 133	99.67 319	97.73 186	99.77 156	99.43 201

MVSFormer		98.26 218	98.43 178	97.77 318	98.88 304	93.89 381	99.39 20	99.56 103	99.11 98	98.16 308	98.13 354	93.81 332	99.97 7	99.26 66	99.57 258	99.43 201

jason		97.45 294	97.35 292	97.76 321	99.24 215	93.93 377	95.86 404	98.42 366	94.24 406	98.50 282	98.13 354	94.82 306	99.91 74	97.22 220	99.73 179	99.43 201
jason: jason.
NCCC		97.86 261	97.47 286	99.05 142	98.61 358	98.07 152	96.98 335	98.90 314	97.63 243	97.04 383	97.93 373	95.99 270	99.66 332	95.31 348	98.82 377	99.43 201

Anonymous20240521		98.69 142	98.87 102	98.16 293	99.77 27	95.11 334	99.08 61	99.44 159	99.34 65	99.33 129	99.55 57	94.10 328	99.94 42	99.25 68	99.96 28	99.42 206

MVS_111021_HR		98.25 221	98.08 232	98.75 198	99.09 255	97.46 209	95.97 395	99.27 238	97.60 249	97.99 326	98.25 346	98.15 114	99.38 421	96.87 254	99.57 258	99.42 206

COLMAP_ROB	96.50 10	98.99 85	98.85 108	99.41 70	99.58 87	99.10 66	98.74 97	99.56 103	99.09 108	99.33 129	99.19 148	98.40 79	99.72 294	95.98 323	99.76 171	99.42 206
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 97	98.72 121	99.49 55	99.49 137	99.17 45	98.10 183	99.31 215	98.03 212	99.66 61	99.02 194	98.36 82	99.88 115	96.91 246	99.62 238	99.41 209

OPU-MVS				98.82 178	98.59 363	98.30 126	98.10 183				98.52 317	98.18 108	98.75 459	94.62 363	99.48 287	99.41 209

our_test_3		97.39 300	97.73 265	96.34 401	98.70 338	89.78 446	94.61 446	98.97 304	96.50 334	99.04 185	98.85 247	95.98 271	99.84 174	97.26 218	99.67 217	99.41 209

casdiffmvs		98.95 92	99.00 87	98.81 180	99.38 173	97.33 217	97.82 234	99.57 94	99.17 92	99.35 125	99.17 156	98.35 86	99.69 306	98.46 127	99.73 179	99.41 209
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		97.60 281	97.67 269	97.39 361	99.04 268	93.04 400	95.27 426	98.38 369	97.25 288	98.92 215	98.95 225	95.48 290	99.73 286	96.99 240	98.74 379	99.41 209

MDA-MVSNet_test_wron		97.60 281	97.66 272	97.41 360	99.04 268	93.09 396	95.27 426	98.42 366	97.26 287	98.88 224	98.95 225	95.43 291	99.73 286	97.02 236	98.72 381	99.41 209

GBi-Net		98.65 153	98.47 172	99.17 115	98.90 298	98.24 130	99.20 48	99.44 159	98.59 157	98.95 205	99.55 57	94.14 324	99.86 143	97.77 179	99.69 206	99.41 209

test1		98.65 153	98.47 172	99.17 115	98.90 298	98.24 130	99.20 48	99.44 159	98.59 157	98.95 205	99.55 57	94.14 324	99.86 143	97.77 179	99.69 206	99.41 209

FMVSNet1		99.17 53	99.17 60	99.17 115	99.55 110	98.24 130	99.20 48	99.44 159	99.21 81	99.43 106	99.55 57	97.82 145	99.86 143	98.42 130	99.89 91	99.41 209

test_fmvs1		97.72 273	97.94 248	97.07 375	98.66 353	92.39 411	97.68 257	99.81 31	95.20 385	99.54 79	99.44 85	91.56 366	99.41 416	99.78 21	99.77 156	99.40 218

viewdifsd2359ckpt07		98.71 133	98.86 106	98.26 279	99.43 163	95.65 306	97.20 323	99.66 64	99.20 83	99.29 139	99.01 205	98.29 91	99.73 286	97.92 166	99.75 175	99.39 219

viewmanbaseed2359cas		98.58 167	98.54 157	98.70 208	99.28 201	97.13 239	97.47 293	99.55 107	97.55 255	98.96 204	98.92 229	97.77 149	99.59 362	97.59 196	99.77 156	99.39 219

KD-MVS_self_test		99.25 41	99.18 59	99.44 66	99.63 80	99.06 71	98.69 106	99.54 112	99.31 69	99.62 70	99.53 64	97.36 188	99.86 143	99.24 70	99.71 196	99.39 219

v148		98.45 189	98.60 149	98.00 305	99.44 158	94.98 337	97.44 297	99.06 285	98.30 180	99.32 135	98.97 218	96.65 237	99.62 348	98.37 132	99.85 106	99.39 219

test20.03		98.78 124	98.77 116	98.78 189	99.46 151	97.20 230	97.78 240	99.24 249	99.04 117	99.41 112	98.90 234	97.65 157	99.76 266	97.70 188	99.79 145	99.39 219

CDPH-MVS		97.26 309	96.66 336	99.07 135	99.00 279	98.15 139	96.03 393	99.01 300	91.21 446	97.79 340	97.85 377	96.89 217	99.69 306	92.75 414	99.38 305	99.39 219

EPNet		96.14 361	95.44 373	98.25 281	90.76 481	95.50 315	97.92 221	94.65 447	98.97 124	92.98 463	98.85 247	89.12 388	99.87 134	95.99 322	99.68 211	99.39 219
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 232	97.87 256	99.07 135	98.67 348	98.24 130	97.01 333	98.93 308	97.25 288	97.62 349	98.34 340	97.27 194	99.57 371	96.42 299	99.33 311	99.39 219

DeepC-MVS_fast	96.85 6	98.30 212	98.15 224	98.75 198	98.61 358	97.23 225	97.76 246	99.09 282	97.31 282	98.75 247	98.66 295	97.56 168	99.64 342	96.10 320	99.55 265	99.39 219
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		98.53 178	98.27 206	99.32 91	99.31 191	98.75 91	98.19 168	99.41 175	96.77 324	98.83 232	98.90 234	97.80 147	99.82 205	95.68 339	99.52 274	99.38 228

test9_res														93.28 403	99.15 343	99.38 228

BP-MVS1		97.40 299	96.97 312	98.71 207	99.07 259	96.81 257	98.34 156	97.18 405	98.58 160	98.17 305	98.61 306	84.01 425	99.94 42	98.97 90	99.78 150	99.37 230

OPM-MVS		98.56 169	98.32 198	99.25 104	99.41 168	98.73 95	97.13 330	99.18 263	97.10 303	98.75 247	98.92 229	98.18 108	99.65 339	96.68 272	99.56 261	99.37 230
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 419	99.16 341	99.37 230

AllTest		98.44 190	98.20 214	99.16 118	99.50 129	98.55 107	98.25 163	99.58 87	96.80 321	98.88 224	99.06 182	97.65 157	99.57 371	94.45 369	99.61 243	99.37 230

TestCases				99.16 118	99.50 129	98.55 107		99.58 87	96.80 321	98.88 224	99.06 182	97.65 157	99.57 371	94.45 369	99.61 243	99.37 230

MDA-MVSNet-bldmvs		97.94 252	97.91 253	98.06 300	99.44 158	94.96 338	96.63 356	99.15 275	98.35 174	98.83 232	99.11 171	94.31 321	99.85 156	96.60 281	98.72 381	99.37 230

MVSTER		96.86 335	96.55 342	97.79 316	97.91 413	94.21 360	97.56 278	98.87 320	97.49 262	99.06 175	99.05 189	80.72 438	99.80 230	98.44 128	99.82 122	99.37 230

viewcassd2359sk11		98.55 173	98.51 161	98.67 213	99.29 198	96.99 246	97.39 300	99.54 112	97.73 236	98.81 237	99.08 180	97.55 169	99.66 332	97.52 202	99.67 217	99.36 237

pmmvs5		97.64 279	97.49 283	98.08 298	99.14 246	95.12 333	96.70 352	99.05 289	93.77 415	98.62 262	98.83 254	93.23 338	99.75 274	98.33 136	99.76 171	99.36 237

Anonymous20231206		98.21 225	98.21 213	98.20 288	99.51 123	95.43 321	98.13 176	99.32 210	96.16 351	98.93 213	98.82 257	96.00 266	99.83 192	97.32 215	99.73 179	99.36 237

train_agg		97.10 321	96.45 346	99.07 135	98.71 334	98.08 150	95.96 397	99.03 294	91.64 438	95.85 426	97.53 393	96.47 244	99.76 266	93.67 393	99.16 341	99.36 237

PVSNet_BlendedMVS		97.55 286	97.53 280	97.60 341	98.92 294	93.77 385	96.64 355	99.43 165	94.49 398	97.62 349	99.18 152	96.82 222	99.67 319	94.73 360	99.93 56	99.36 237

Anonymous20240529		98.93 95	98.87 102	99.12 124	99.19 230	98.22 135	99.01 70	98.99 303	99.25 75	99.54 79	99.37 99	97.04 206	99.80 230	97.89 167	99.52 274	99.35 242

F-COLMAP		97.30 306	96.68 333	99.14 122	99.19 230	98.39 118	97.27 317	99.30 223	92.93 426	96.62 405	98.00 366	95.73 281	99.68 315	92.62 417	98.46 398	99.35 242

viewdifsd2359ckpt13		98.39 200	98.29 202	98.70 208	99.26 213	97.19 231	97.51 285	99.48 134	96.94 312	98.58 270	98.82 257	97.47 182	99.55 378	97.21 221	99.33 311	99.34 244

ppachtmachnet_test		97.50 287	97.74 263	96.78 391	98.70 338	91.23 433	94.55 448	99.05 289	96.36 342	99.21 159	98.79 263	96.39 247	99.78 254	96.74 265	99.82 122	99.34 244

VDD-MVS		98.56 169	98.39 185	99.07 135	99.13 248	98.07 152	98.59 119	97.01 410	99.59 37	99.11 168	99.27 123	94.82 306	99.79 243	98.34 134	99.63 235	99.34 244

testgi		98.32 209	98.39 185	98.13 294	99.57 96	95.54 310	97.78 240	99.49 132	97.37 276	99.19 161	97.65 387	98.96 29	99.49 399	96.50 295	98.99 363	99.34 244

diffmvs		98.22 223	98.24 211	98.17 291	99.00 279	95.44 320	96.38 372	99.58 87	97.79 233	98.53 279	98.50 322	96.76 229	99.74 279	97.95 165	99.64 228	99.34 244
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		97.89 256	97.60 277	98.75 198	99.31 191	97.17 235	97.62 268	99.35 196	98.72 146	98.76 246	98.68 290	92.57 354	99.74 279	97.76 183	95.60 461	99.34 244

viewmambaseed2359dif		98.19 228	98.26 207	97.99 306	99.02 276	95.03 336	96.59 359	99.53 116	96.21 348	99.00 190	98.99 211	97.62 162	99.61 355	97.62 192	99.72 187	99.33 250

baseline		98.96 91	99.02 83	98.76 196	99.38 173	97.26 224	98.49 137	99.50 125	98.86 138	99.19 161	99.06 182	98.23 100	99.69 306	98.71 110	99.76 171	99.33 250

MG-MVS		96.77 339	96.61 338	97.26 366	98.31 391	93.06 397	95.93 400	98.12 380	96.45 340	97.92 328	98.73 276	93.77 334	99.39 419	91.19 438	99.04 355	99.33 250

HQP4-MVS										95.56 431			99.54 384			99.32 253

CDS-MVSNet		97.69 275	97.35 292	98.69 210	98.73 328	97.02 245	96.92 341	98.75 345	95.89 363	98.59 268	98.67 292	92.08 361	99.74 279	96.72 268	99.81 128	99.32 253
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 330	96.49 345	98.55 240	98.67 348	96.79 258	96.29 378	99.04 292	96.05 354	95.55 432	96.84 414	93.84 330	99.54 384	92.82 411	99.26 325	99.32 253

RPSCF		98.62 160	98.36 190	99.42 68	99.65 68	99.42 11	98.55 123	99.57 94	97.72 238	98.90 217	99.26 129	96.12 261	99.52 390	95.72 336	99.71 196	99.32 253

MVP-Stereo		98.08 239	97.92 251	98.57 233	98.96 286	96.79 258	97.90 224	99.18 263	96.41 341	98.46 285	98.95 225	95.93 275	99.60 358	96.51 294	98.98 366	99.31 257
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 194	98.68 132	97.54 349	98.96 286	97.99 159	97.88 226	99.36 190	98.20 194	99.63 67	99.04 191	98.76 45	95.33 476	96.56 288	99.74 176	99.31 257
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		98.42 191	98.30 200	98.79 186	98.79 323	97.29 221	98.23 164	98.66 352	99.31 69	98.85 229	98.80 261	94.80 309	99.78 254	98.13 147	99.13 346	99.31 257

test_prior				98.95 160	98.69 343	97.95 167		99.03 294					99.59 362			99.30 260

USDC		97.41 298	97.40 287	97.44 358	98.94 288	93.67 388	95.17 429	99.53 116	94.03 412	98.97 199	99.10 174	95.29 293	99.34 426	95.84 332	99.73 179	99.30 260

viewdifsd2359ckpt09		98.13 235	97.92 251	98.77 194	99.18 237	97.35 215	97.29 313	99.53 116	95.81 365	98.09 316	98.47 326	96.34 252	99.66 332	97.02 236	99.51 276	99.29 262

test_fmvsm_n_1920		99.33 31	99.45 23	98.99 151	99.57 96	97.73 193	97.93 218	99.83 25	99.22 79	99.93 6	99.30 117	99.42 11	99.96 14	99.85 6	99.99 5	99.29 262

FMVSNet2		98.49 185	98.40 182	98.75 198	98.90 298	97.14 238	98.61 117	99.13 276	98.59 157	99.19 161	99.28 121	94.14 324	99.82 205	97.97 163	99.80 139	99.29 262

XVG-OURS-SEG-HR		98.49 185	98.28 203	99.14 122	99.49 137	98.83 87	96.54 360	99.48 134	97.32 281	99.11 168	98.61 306	99.33 15	99.30 432	96.23 310	98.38 399	99.28 265

mamba_0408		98.80 120	98.88 100	98.55 240	99.27 204	96.50 275	98.00 204	99.60 78	98.93 129	99.22 156	98.84 252	98.59 62	99.89 97	97.74 184	99.72 187	99.27 266

SSM_04072		98.80 120	98.88 100	98.56 238	99.27 204	96.50 275	98.00 204	99.60 78	98.93 129	99.22 156	98.84 252	98.59 62	99.90 81	97.74 184	99.72 187	99.27 266

SSM_0407		98.86 107	98.96 93	98.55 240	99.27 204	96.50 275	98.04 195	99.66 64	99.09 108	99.22 156	99.02 194	98.79 42	99.87 134	97.87 172	99.72 187	99.27 266

test12				98.93 163	98.58 365	97.83 178		98.66 352		96.53 409		95.51 288	99.69 306		99.13 346	99.27 266

DSMNet-mixed		97.42 297	97.60 277	96.87 385	99.15 245	91.46 423	98.54 125	99.12 277	92.87 428	97.58 353	99.63 39	96.21 256	99.90 81	95.74 335	99.54 267	99.27 266

N_pmnet		97.63 280	97.17 301	98.99 151	99.27 204	97.86 175	95.98 394	93.41 458	95.25 382	99.47 100	98.90 234	95.63 283	99.85 156	96.91 246	99.73 179	99.27 266

ambc				98.24 283	98.82 316	95.97 296	98.62 116	99.00 302		99.27 143	99.21 143	96.99 211	99.50 396	96.55 291	99.50 284	99.26 272

LFMVS		97.20 315	96.72 330	98.64 217	98.72 330	96.95 250	98.93 81	94.14 455	99.74 13	98.78 241	99.01 205	84.45 420	99.73 286	97.44 208	99.27 322	99.25 273

FMVSNet5		96.01 364	95.20 383	98.41 262	97.53 435	96.10 287	98.74 97	99.50 125	97.22 297	98.03 323	99.04 191	69.80 461	99.88 115	97.27 217	99.71 196	99.25 273

BH-RMVSNet		96.83 336	96.58 341	97.58 343	98.47 376	94.05 365	96.67 353	97.36 399	96.70 328	97.87 333	97.98 368	95.14 297	99.44 412	90.47 446	98.58 395	99.25 273

testf1		99.25 41	99.16 62	99.51 49	99.89 6	99.63 4	98.71 104	99.69 54	98.90 133	99.43 106	99.35 104	98.86 34	99.67 319	97.81 175	99.81 128	99.24 276

APD_test2		99.25 41	99.16 62	99.51 49	99.89 6	99.63 4	98.71 104	99.69 54	98.90 133	99.43 106	99.35 104	98.86 34	99.67 319	97.81 175	99.81 128	99.24 276

SSM_0404		98.90 99	99.01 85	98.57 233	99.42 165	96.59 267	98.13 176	99.66 64	99.09 108	99.30 138	99.02 194	98.79 42	99.89 97	97.87 172	99.80 139	99.23 278

旧先验1					98.82 316	97.45 210		98.76 342			98.34 340	95.50 289			99.01 360	99.23 278

test222					98.92 294	96.93 252	95.54 416	98.78 339	85.72 466	96.86 396	98.11 357	94.43 316			99.10 351	99.23 278

XVG-ACMP-BASELINE		98.56 169	98.34 193	99.22 109	99.54 115	98.59 104	97.71 253	99.46 147	97.25 288	98.98 195	98.99 211	97.54 171	99.84 174	95.88 326	99.74 176	99.23 278

FMVSNet3		97.50 287	97.24 298	98.29 277	98.08 406	95.83 301	97.86 230	98.91 313	97.89 225	98.95 205	98.95 225	87.06 399	99.81 222	97.77 179	99.69 206	99.23 278

icg_test_0407_2		98.20 227	98.38 187	97.65 334	99.03 271	94.03 368	95.78 409	99.45 151	98.16 200	99.06 175	98.71 279	98.27 94	99.68 315	97.50 203	99.45 291	99.22 283

IMVS_0407		98.39 200	98.64 140	97.66 332	99.03 271	94.03 368	98.10 183	99.45 151	98.16 200	99.06 175	98.71 279	98.27 94	99.71 295	97.50 203	99.45 291	99.22 283

IMVS_0404		98.07 240	98.20 214	97.69 329	99.03 271	94.03 368	96.67 353	99.45 151	98.16 200	98.03 323	98.71 279	96.80 225	99.82 205	97.50 203	99.45 291	99.22 283

IMVS_0403		98.34 204	98.56 154	97.66 332	99.03 271	94.03 368	97.98 212	99.45 151	98.16 200	98.89 220	98.71 279	97.90 133	99.74 279	97.50 203	99.45 291	99.22 283

无先验							95.74 411	98.74 347	89.38 457				99.73 286	92.38 421		99.22 283

tttt0517		95.64 377	94.98 387	97.64 337	99.36 180	93.81 383	98.72 102	90.47 469	98.08 211	98.67 255	98.34 340	73.88 456	99.92 65	97.77 179	99.51 276	99.20 288

pmmvs-eth3d		98.47 187	98.34 193	98.86 173	99.30 195	97.76 189	97.16 328	99.28 235	95.54 373	99.42 110	99.19 148	97.27 194	99.63 345	97.89 167	99.97 21	99.20 288

MS-PatchMatch		97.68 276	97.75 262	97.45 357	98.23 397	93.78 384	97.29 313	98.84 329	96.10 353	98.64 259	98.65 297	96.04 263	99.36 422	96.84 257	99.14 344	99.20 288

新几何1				98.91 167	98.94 288	97.76 189		98.76 342	87.58 463	96.75 401	98.10 358	94.80 309	99.78 254	92.73 415	99.00 361	99.20 288

PHI-MVS		98.29 215	97.95 246	99.34 83	98.44 381	99.16 49	98.12 180	99.38 182	96.01 358	98.06 319	98.43 330	97.80 147	99.67 319	95.69 338	99.58 254	99.20 288

GDP-MVS		97.50 287	97.11 306	98.67 213	99.02 276	96.85 255	98.16 173	99.71 47	98.32 178	98.52 281	98.54 313	83.39 429	99.95 26	98.79 101	99.56 261	99.19 293

Anonymous202405211		97.90 254	97.50 282	99.08 133	98.90 298	98.25 129	98.53 126	96.16 428	98.87 136	99.11 168	98.86 244	90.40 378	99.78 254	97.36 212	99.31 315	99.19 293

CANet		97.87 260	97.76 261	98.19 290	97.75 419	95.51 312	96.76 348	99.05 289	97.74 235	96.93 387	98.21 350	95.59 285	99.89 97	97.86 174	99.93 56	99.19 293

XVG-OURS		98.53 178	98.34 193	99.11 126	99.50 129	98.82 89	95.97 395	99.50 125	97.30 283	99.05 183	98.98 216	99.35 14	99.32 429	95.72 336	99.68 211	99.18 296

WTY-MVS		96.67 342	96.27 352	97.87 311	98.81 319	94.61 350	96.77 347	97.92 385	94.94 390	97.12 378	97.74 382	91.11 371	99.82 205	93.89 387	98.15 411	99.18 296

Vis-MVSNet (Re-imp)		97.46 292	97.16 302	98.34 272	99.55 110	96.10 287	98.94 80	98.44 364	98.32 178	98.16 308	98.62 304	88.76 389	99.73 286	93.88 388	99.79 145	99.18 296

TinyColmap		97.89 256	97.98 242	97.60 341	98.86 307	94.35 356	96.21 382	99.44 159	97.45 270	99.06 175	98.88 241	97.99 127	99.28 436	94.38 375	99.58 254	99.18 296

testdata				98.09 295	98.93 290	95.40 322		98.80 336	90.08 454	97.45 366	98.37 336	95.26 294	99.70 302	93.58 396	98.95 369	99.17 300

lupinMVS		97.06 324	96.86 320	97.65 334	98.88 304	93.89 381	95.48 420	97.97 383	93.53 418	98.16 308	97.58 391	93.81 332	99.91 74	96.77 262	99.57 258	99.17 300

Patchmtry		97.35 302	96.97 312	98.50 253	97.31 446	96.47 278	98.18 169	98.92 311	98.95 128	98.78 241	99.37 99	85.44 414	99.85 156	95.96 324	99.83 118	99.17 300

SD_0403		96.28 356	95.83 357	97.64 337	98.72 330	94.30 357	98.87 88	98.77 340	97.80 231	96.53 409	98.02 365	97.34 189	99.47 405	76.93 474	99.48 287	99.16 303

RRT-MVS		97.88 258	97.98 242	97.61 340	98.15 401	93.77 385	98.97 76	99.64 70	99.16 93	98.69 252	99.42 89	91.60 364	99.89 97	97.63 191	98.52 397	99.16 303

sss		97.21 314	96.93 314	98.06 300	98.83 313	95.22 329	96.75 349	98.48 363	94.49 398	97.27 375	97.90 374	92.77 350	99.80 230	96.57 284	99.32 313	99.16 303

CSCG		98.68 148	98.50 164	99.20 110	99.45 156	98.63 99	98.56 122	99.57 94	97.87 226	98.85 229	98.04 364	97.66 156	99.84 174	96.72 268	99.81 128	99.13 306

MVS_111021_LR		98.30 212	98.12 227	98.83 176	99.16 241	98.03 157	96.09 391	99.30 223	97.58 250	98.10 315	98.24 347	98.25 98	99.34 426	96.69 271	99.65 226	99.12 307

miper_lstm_enhance		97.18 317	97.16 302	97.25 367	98.16 400	92.85 402	95.15 431	99.31 215	97.25 288	98.74 249	98.78 265	90.07 379	99.78 254	97.19 222	99.80 139	99.11 308

testing3		93.51 413	92.09 424	97.75 322	98.60 360	94.40 354	97.32 310	95.26 443	97.56 253	96.79 400	95.50 442	53.57 481	99.77 260	95.26 349	98.97 367	99.08 309

原ACMM1				98.35 271	98.90 298	96.25 285		98.83 333	92.48 432	96.07 423	98.10 358	95.39 292	99.71 295	92.61 418	98.99 363	99.08 309

QAPM		97.31 305	96.81 326	98.82 178	98.80 322	97.49 205	99.06 65	99.19 259	90.22 452	97.69 346	99.16 158	96.91 216	99.90 81	90.89 443	99.41 300	99.07 311

PAPM_NR		96.82 338	96.32 349	98.30 276	99.07 259	96.69 265	97.48 289	98.76 342	95.81 365	96.61 406	96.47 423	94.12 327	99.17 443	90.82 444	97.78 424	99.06 312

eth_miper_zixun_eth		97.23 313	97.25 297	97.17 370	98.00 409	92.77 404	94.71 440	99.18 263	97.27 286	98.56 274	98.74 275	91.89 362	99.69 306	97.06 235	99.81 128	99.05 313

D2MVS		97.84 267	97.84 258	97.83 313	99.14 246	94.74 344	96.94 337	98.88 318	95.84 364	98.89 220	98.96 221	94.40 318	99.69 306	97.55 197	99.95 38	99.05 313

c3_l		97.36 301	97.37 290	97.31 362	98.09 405	93.25 395	95.01 434	99.16 270	97.05 305	98.77 244	98.72 278	92.88 347	99.64 342	96.93 245	99.76 171	99.05 313

PLC	94.65 16	96.51 347	95.73 360	98.85 174	98.75 326	97.91 171	96.42 370	99.06 285	90.94 449	95.59 429	97.38 403	94.41 317	99.59 362	90.93 441	98.04 420	99.05 313
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 99	98.90 97	98.91 167	99.67 65	97.82 183	99.00 72	99.44 159	99.45 51	99.51 93	99.24 136	98.20 107	99.86 143	95.92 325	99.69 206	99.04 317

CANet_DTU		97.26 309	97.06 308	97.84 312	97.57 430	94.65 349	96.19 384	98.79 337	97.23 294	95.14 441	98.24 347	93.22 339	99.84 174	97.34 213	99.84 111	99.04 317

PM-MVS		98.82 116	98.72 121	99.12 124	99.64 74	98.54 110	97.98 212	99.68 60	97.62 244	99.34 127	99.18 152	97.54 171	99.77 260	97.79 177	99.74 176	99.04 317

TSAR-MVS + GP.		98.18 230	97.98 242	98.77 194	98.71 334	97.88 173	96.32 376	98.66 352	96.33 343	99.23 155	98.51 318	97.48 181	99.40 417	97.16 224	99.46 289	99.02 320

DIV-MVS_self_test		97.02 327	96.84 322	97.58 343	97.82 417	94.03 368	94.66 443	99.16 270	97.04 306	98.63 260	98.71 279	88.69 390	99.69 306	97.00 238	99.81 128	99.01 321

mamv4		99.44 19	99.39 28	99.58 21	99.30 195	99.74 2	99.04 68	99.81 31	99.77 10	99.82 34	99.57 49	97.82 145	99.98 4	99.53 48	99.89 91	99.01 321

GA-MVS		95.86 369	95.32 379	97.49 354	98.60 360	94.15 363	93.83 460	97.93 384	95.49 375	96.68 402	97.42 401	83.21 430	99.30 432	96.22 311	98.55 396	99.01 321

OMC-MVS		97.88 258	97.49 283	99.04 144	98.89 303	98.63 99	96.94 337	99.25 244	95.02 387	98.53 279	98.51 318	97.27 194	99.47 405	93.50 399	99.51 276	99.01 321

cl____		97.02 327	96.83 323	97.58 343	97.82 417	94.04 367	94.66 443	99.16 270	97.04 306	98.63 260	98.71 279	88.68 392	99.69 306	97.00 238	99.81 128	99.00 325

pmmvs4		97.58 284	97.28 295	98.51 249	98.84 311	96.93 252	95.40 424	98.52 361	93.60 417	98.61 264	98.65 297	95.10 298	99.60 358	96.97 243	99.79 145	98.99 326

EPNet_dtu		94.93 392	94.78 392	95.38 429	93.58 477	87.68 456	96.78 346	95.69 440	97.35 278	89.14 474	98.09 360	88.15 397	99.49 399	94.95 356	99.30 318	98.98 327
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 349	95.77 358	98.69 210	99.48 145	97.43 212	97.84 233	99.55 107	81.42 472	96.51 412	98.58 310	95.53 286	99.67 319	93.41 401	99.58 254	98.98 327

PVSNet_Blended		96.88 334	96.68 333	97.47 356	98.92 294	93.77 385	94.71 440	99.43 165	90.98 448	97.62 349	97.36 405	96.82 222	99.67 319	94.73 360	99.56 261	98.98 327

APD_test1		98.83 113	98.66 137	99.34 83	99.78 24	99.47 9	98.42 148	99.45 151	98.28 185	98.98 195	99.19 148	97.76 150	99.58 369	96.57 284	99.55 265	98.97 330

PAPR		95.29 383	94.47 394	97.75 322	97.50 441	95.14 332	94.89 437	98.71 350	91.39 444	95.35 439	95.48 444	94.57 314	99.14 446	84.95 462	97.37 437	98.97 330

EGC-MVSNET		85.24 440	80.54 443	99.34 83	99.77 27	99.20 40	99.08 61	99.29 231	12.08 479	20.84 480	99.42 89	97.55 169	99.85 156	97.08 232	99.72 187	98.96 332

thisisatest0530		95.27 384	94.45 395	97.74 324	99.19 230	94.37 355	97.86 230	90.20 470	97.17 299	98.22 303	97.65 387	73.53 457	99.90 81	96.90 251	99.35 308	98.95 333

mvs_anonymous		97.83 269	98.16 223	96.87 385	98.18 399	91.89 418	97.31 311	98.90 314	97.37 276	98.83 232	99.46 80	96.28 254	99.79 243	98.90 94	98.16 410	98.95 333

baseline1		95.96 367	95.44 373	97.52 351	98.51 374	93.99 375	98.39 150	96.09 431	98.21 190	98.40 294	97.76 381	86.88 400	99.63 345	95.42 346	89.27 474	98.95 333

CLD-MVS		97.49 290	97.16 302	98.48 254	99.07 259	97.03 244	94.71 440	99.21 253	94.46 400	98.06 319	97.16 409	97.57 167	99.48 402	94.46 368	99.78 150	98.95 333
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 244	98.14 226	97.64 337	98.58 365	95.19 330	97.48 289	99.23 251	97.47 263	97.90 330	98.62 304	97.04 206	98.81 457	97.55 197	99.41 300	98.94 337

DELS-MVS		98.27 216	98.20 214	98.48 254	98.86 307	96.70 264	95.60 415	99.20 255	97.73 236	98.45 286	98.71 279	97.50 177	99.82 205	98.21 142	99.59 249	98.93 338
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		95.79 372	95.39 376	96.98 379	96.77 458	92.79 403	94.40 451	98.53 360	94.59 397	97.89 331	98.17 353	82.82 434	99.24 438	96.37 302	99.03 356	98.92 339

LS3D		98.63 157	98.38 187	99.36 74	97.25 447	99.38 13	99.12 60	99.32 210	99.21 81	98.44 287	98.88 241	97.31 190	99.80 230	96.58 282	99.34 310	98.92 339

CMPMVS	75.91 23	96.29 355	95.44 373	98.84 175	96.25 468	98.69 98	97.02 332	99.12 277	88.90 459	97.83 337	98.86 244	89.51 385	98.90 455	91.92 422	99.51 276	98.92 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 155	98.48 170	99.11 126	98.85 310	98.51 112	98.49 137	99.83 25	98.37 172	99.69 56	99.46 80	98.21 105	99.92 65	94.13 381	99.30 318	98.91 342

mvsmamba		97.57 285	97.26 296	98.51 249	98.69 343	96.73 263	98.74 97	97.25 404	97.03 308	97.88 332	99.23 141	90.95 372	99.87 134	96.61 280	99.00 361	98.91 342

DPM-MVS		96.32 354	95.59 367	98.51 249	98.76 324	97.21 229	94.54 449	98.26 372	91.94 437	96.37 416	97.25 407	93.06 344	99.43 413	91.42 433	98.74 379	98.89 344

test_yl		96.69 340	96.29 350	97.90 308	98.28 392	95.24 327	97.29 313	97.36 399	98.21 190	98.17 305	97.86 375	86.27 404	99.55 378	94.87 357	98.32 400	98.89 344

DCV-MVSNet		96.69 340	96.29 350	97.90 308	98.28 392	95.24 327	97.29 313	97.36 399	98.21 190	98.17 305	97.86 375	86.27 404	99.55 378	94.87 357	98.32 400	98.89 344

SPE-MVS-test		99.13 66	99.09 75	99.26 101	99.13 248	98.97 74	99.31 30	99.88 14	99.44 53	98.16 308	98.51 318	98.64 56	99.93 54	98.91 93	99.85 106	98.88 347

UnsupCasMVSNet_bld		97.30 306	96.92 316	98.45 257	99.28 201	96.78 261	96.20 383	99.27 238	95.42 377	98.28 300	98.30 344	93.16 340	99.71 295	94.99 353	97.37 437	98.87 348

Effi-MVS+		98.02 244	97.82 259	98.62 223	98.53 372	97.19 231	97.33 309	99.68 60	97.30 283	96.68 402	97.46 399	98.56 68	99.80 230	96.63 278	98.20 406	98.86 349

test_0402		98.76 128	98.71 126	98.93 163	99.56 104	98.14 141	98.45 144	99.34 202	99.28 73	98.95 205	98.91 231	98.34 87	99.79 243	95.63 340	99.91 78	98.86 349

PatchmatchNet		95.58 378	95.67 363	95.30 430	97.34 445	87.32 458	97.65 263	96.65 420	95.30 381	97.07 381	98.69 288	84.77 417	99.75 274	94.97 355	98.64 390	98.83 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		93.78 409	93.91 401	93.39 451	98.82 316	81.72 478	97.76 246	95.28 442	98.60 156	96.54 408	96.66 418	65.85 473	99.62 348	96.65 277	98.99 363	98.82 352

test_vis1_rt		97.75 271	97.72 266	97.83 313	98.81 319	96.35 282	97.30 312	99.69 54	94.61 396	97.87 333	98.05 363	96.26 255	98.32 464	98.74 107	98.18 407	98.82 352

CL-MVSNet_self_test		97.44 295	97.22 299	98.08 298	98.57 367	95.78 304	94.30 453	98.79 337	96.58 332	98.60 266	98.19 352	94.74 312	99.64 342	96.41 300	98.84 374	98.82 352

miper_ehance_all_eth		97.06 324	97.03 309	97.16 372	97.83 416	93.06 397	94.66 443	99.09 282	95.99 359	98.69 252	98.45 328	92.73 352	99.61 355	96.79 259	99.03 356	98.82 352

MIMVSNet		96.62 345	96.25 353	97.71 328	99.04 268	94.66 348	99.16 54	96.92 416	97.23 294	97.87 333	99.10 174	86.11 408	99.65 339	91.65 428	99.21 334	98.82 352

hse-mvs2		97.46 292	97.07 307	98.64 217	98.73 328	97.33 217	97.45 295	97.64 395	99.11 98	98.58 270	97.98 368	88.65 393	99.79 243	98.11 148	97.39 436	98.81 357

GSMVS																98.81 357

sam_mvs1												84.74 418				98.81 357

SCA		96.41 353	96.66 336	95.67 420	98.24 395	88.35 452	95.85 406	96.88 417	96.11 352	97.67 347	98.67 292	93.10 342	99.85 156	94.16 377	99.22 331	98.81 357

Patchmatch-RL test		97.26 309	97.02 310	97.99 306	99.52 121	95.53 311	96.13 389	99.71 47	97.47 263	99.27 143	99.16 158	84.30 423	99.62 348	97.89 167	99.77 156	98.81 357

AUN-MVS		96.24 360	95.45 372	98.60 228	98.70 338	97.22 227	97.38 302	97.65 393	95.95 361	95.53 436	97.96 372	82.11 437	99.79 243	96.31 306	97.44 433	98.80 362

ITE_SJBPF				98.87 171	99.22 221	98.48 114		99.35 196	97.50 260	98.28 300	98.60 308	97.64 160	99.35 425	93.86 389	99.27 322	98.79 363

tpm		94.67 394	94.34 398	95.66 421	97.68 428	88.42 451	97.88 226	94.90 445	94.46 400	96.03 425	98.56 312	78.66 448	99.79 243	95.88 326	95.01 464	98.78 364

Patchmatch-test		96.55 346	96.34 348	97.17 370	98.35 388	93.06 397	98.40 149	97.79 386	97.33 279	98.41 290	98.67 292	83.68 428	99.69 306	95.16 351	99.31 315	98.77 365

EC-MVSNet		99.09 72	99.05 79	99.20 110	99.28 201	98.93 80	99.24 44	99.84 22	99.08 112	98.12 313	98.37 336	98.72 49	99.90 81	99.05 84	99.77 156	98.77 365

PMMVS		96.51 347	95.98 354	98.09 295	97.53 435	95.84 300	94.92 436	98.84 329	91.58 440	96.05 424	95.58 439	95.68 282	99.66 332	95.59 342	98.09 414	98.76 367

test_method		79.78 441	79.50 444	80.62 458	80.21 483	45.76 486	70.82 475	98.41 368	31.08 478	80.89 478	97.71 383	84.85 416	97.37 471	91.51 432	80.03 475	98.75 368

ab-mvs		98.41 192	98.36 190	98.59 229	99.19 230	97.23 225	99.32 26	98.81 334	97.66 241	98.62 262	99.40 96	96.82 222	99.80 230	95.88 326	99.51 276	98.75 368

CHOSEN 280x420		95.51 381	95.47 370	95.65 422	98.25 394	88.27 453	93.25 464	98.88 318	93.53 418	94.65 447	97.15 410	86.17 406	99.93 54	97.41 210	99.93 56	98.73 370

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 245	99.66 67	96.97 247	98.00 204	99.85 18	99.24 76	99.92 8	99.50 68	99.39 12	99.95 26	99.89 3	99.98 12	98.71 371

MVS_Test		98.18 230	98.36 190	97.67 330	98.48 375	94.73 345	98.18 169	99.02 297	97.69 239	98.04 322	99.11 171	97.22 198	99.56 374	98.57 119	98.90 373	98.71 371

PVSNet	93.40 17	95.67 375	95.70 361	95.57 423	98.83 313	88.57 450	92.50 467	97.72 388	92.69 430	96.49 415	96.44 424	93.72 335	99.43 413	93.61 394	99.28 321	98.71 371

alignmvs		97.35 302	96.88 319	98.78 189	98.54 370	98.09 146	97.71 253	97.69 390	99.20 83	97.59 352	95.90 434	88.12 398	99.55 378	98.18 144	98.96 368	98.70 374

ADS-MVSNet2		95.43 382	94.98 387	96.76 392	98.14 402	91.74 419	97.92 221	97.76 387	90.23 450	96.51 412	98.91 231	85.61 411	99.85 156	92.88 409	96.90 446	98.69 375

ADS-MVSNet		95.24 385	94.93 390	96.18 409	98.14 402	90.10 445	97.92 221	97.32 402	90.23 450	96.51 412	98.91 231	85.61 411	99.74 279	92.88 409	96.90 446	98.69 375

MDTV_nov1_ep13_2view						74.92 482	97.69 256		90.06 455	97.75 343		85.78 410		93.52 397		98.69 375

MSDG		97.71 274	97.52 281	98.28 278	98.91 297	96.82 256	94.42 450	99.37 186	97.65 242	98.37 295	98.29 345	97.40 185	99.33 428	94.09 382	99.22 331	98.68 378

mvsany_test1		97.60 281	97.54 279	97.77 318	97.72 420	95.35 323	95.36 425	97.13 408	94.13 409	99.71 50	99.33 111	97.93 131	99.30 432	97.60 195	98.94 370	98.67 379

CS-MVS		99.13 66	99.10 73	99.24 106	99.06 264	99.15 53	99.36 22	99.88 14	99.36 64	98.21 304	98.46 327	98.68 53	99.93 54	99.03 86	99.85 106	98.64 380

Syy-MVS		96.04 363	95.56 369	97.49 354	97.10 451	94.48 352	96.18 386	96.58 422	95.65 369	94.77 444	92.29 473	91.27 370	99.36 422	98.17 146	98.05 418	98.63 381

myMVS_eth3d		91.92 436	90.45 438	96.30 402	97.10 451	90.90 437	96.18 386	96.58 422	95.65 369	94.77 444	92.29 473	53.88 480	99.36 422	89.59 450	98.05 418	98.63 381

balanced_conf03		98.63 157	98.72 121	98.38 266	98.66 353	96.68 266	98.90 83	99.42 171	98.99 121	98.97 199	99.19 148	95.81 279	99.85 156	98.77 105	99.77 156	98.60 383

miper_enhance_ethall		96.01 364	95.74 359	96.81 389	96.41 466	92.27 415	93.69 462	98.89 317	91.14 447	98.30 296	97.35 406	90.58 376	99.58 369	96.31 306	99.03 356	98.60 383

Effi-MVS+-dtu		98.26 218	97.90 254	99.35 80	98.02 408	99.49 6	98.02 200	99.16 270	98.29 183	97.64 348	97.99 367	96.44 246	99.95 26	96.66 276	98.93 371	98.60 383

new_pmnet		96.99 331	96.76 328	97.67 330	98.72 330	94.89 339	95.95 399	98.20 375	92.62 431	98.55 276	98.54 313	94.88 305	99.52 390	93.96 385	99.44 298	98.59 386

MVSMamba_PlusPlus		98.83 113	98.98 90	98.36 270	99.32 190	96.58 270	98.90 83	99.41 175	99.75 11	98.72 250	99.50 68	96.17 257	99.94 42	99.27 65	99.78 150	98.57 387

testing91		93.32 416	92.27 421	96.47 398	97.54 433	91.25 431	96.17 388	96.76 419	97.18 298	93.65 461	93.50 465	65.11 475	99.63 345	93.04 406	97.45 432	98.53 388

EIA-MVS		98.00 247	97.74 263	98.80 182	98.72 330	98.09 146	98.05 193	99.60 78	97.39 274	96.63 404	95.55 440	97.68 154	99.80 230	96.73 267	99.27 322	98.52 389

PatchMatch-RL		97.24 312	96.78 327	98.61 226	99.03 271	97.83 178	96.36 373	99.06 285	93.49 420	97.36 373	97.78 379	95.75 280	99.49 399	93.44 400	98.77 378	98.52 389

sasdasda		98.34 204	98.26 207	98.58 230	98.46 378	97.82 183	98.96 77	99.46 147	99.19 88	97.46 364	95.46 445	98.59 62	99.46 408	98.08 151	98.71 383	98.46 391

ET-MVSNet_ETH3D		94.30 400	93.21 411	97.58 343	98.14 402	94.47 353	94.78 439	93.24 460	94.72 394	89.56 472	95.87 435	78.57 450	99.81 222	96.91 246	97.11 445	98.46 391

canonicalmvs		98.34 204	98.26 207	98.58 230	98.46 378	97.82 183	98.96 77	99.46 147	99.19 88	97.46 364	95.46 445	98.59 62	99.46 408	98.08 151	98.71 383	98.46 391

UBG		93.25 418	92.32 419	96.04 414	97.72 420	90.16 444	95.92 402	95.91 435	96.03 357	93.95 458	93.04 469	69.60 462	99.52 390	90.72 445	97.98 421	98.45 394

tt0805		98.69 142	98.62 144	98.90 170	99.75 34	99.30 23	99.15 56	96.97 412	98.86 138	98.87 228	97.62 390	98.63 58	98.96 451	99.41 57	98.29 403	98.45 394

TAPA-MVS	96.21 11	96.63 344	95.95 355	98.65 215	98.93 290	98.09 146	96.93 339	99.28 235	83.58 469	98.13 312	97.78 379	96.13 259	99.40 417	93.52 397	99.29 320	98.45 394
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net		98.34 204	98.28 203	98.51 249	98.47 376	97.59 201	98.96 77	99.48 134	99.18 91	97.40 369	95.50 442	98.66 54	99.50 396	98.18 144	98.71 383	98.44 397

BH-untuned		96.83 336	96.75 329	97.08 373	98.74 327	93.33 394	96.71 351	98.26 372	96.72 326	98.44 287	97.37 404	95.20 295	99.47 405	91.89 423	97.43 434	98.44 397

WB-MVSnew		95.73 374	95.57 368	96.23 407	96.70 459	90.70 441	96.07 392	93.86 456	95.60 371	97.04 383	95.45 448	96.00 266	99.55 378	91.04 439	98.31 402	98.43 399

pmmvs3		95.03 389	94.40 396	96.93 381	97.70 425	92.53 408	95.08 432	97.71 389	88.57 460	97.71 344	98.08 361	79.39 445	99.82 205	96.19 313	99.11 350	98.43 399

DP-MVS Recon		97.33 304	96.92 316	98.57 233	99.09 255	97.99 159	96.79 345	99.35 196	93.18 422	97.71 344	98.07 362	95.00 301	99.31 430	93.97 384	99.13 346	98.42 401

testing99		93.04 422	91.98 429	96.23 407	97.53 435	90.70 441	96.35 374	95.94 434	96.87 318	93.41 462	93.43 467	63.84 477	99.59 362	93.24 404	97.19 442	98.40 402

ETVMVS		92.60 427	91.08 436	97.18 368	97.70 425	93.65 390	96.54 360	95.70 438	96.51 333	94.68 446	92.39 472	61.80 478	99.50 396	86.97 457	97.41 435	98.40 402

Fast-Effi-MVS+-dtu		98.27 216	98.09 229	98.81 180	98.43 382	98.11 143	97.61 272	99.50 125	98.64 149	97.39 371	97.52 395	98.12 116	99.95 26	96.90 251	98.71 383	98.38 404

LF4IMVS		97.90 254	97.69 268	98.52 248	99.17 239	97.66 196	97.19 327	99.47 143	96.31 345	97.85 336	98.20 351	96.71 233	99.52 390	94.62 363	99.72 187	98.38 404

testing11		93.08 421	92.02 426	96.26 405	97.56 431	90.83 439	96.32 376	95.70 438	96.47 337	92.66 465	93.73 462	64.36 476	99.59 362	93.77 392	97.57 428	98.37 406

Fast-Effi-MVS+		97.67 277	97.38 289	98.57 233	98.71 334	97.43 212	97.23 318	99.45 151	94.82 393	96.13 420	96.51 420	98.52 70	99.91 74	96.19 313	98.83 375	98.37 406

test0.0.03 1		94.51 395	93.69 405	96.99 378	96.05 469	93.61 392	94.97 435	93.49 457	96.17 349	97.57 355	94.88 455	82.30 435	99.01 450	93.60 395	94.17 468	98.37 406

UWE-MVS		92.38 430	91.76 433	94.21 441	97.16 449	84.65 467	95.42 423	88.45 473	95.96 360	96.17 419	95.84 437	66.36 469	99.71 295	91.87 424	98.64 390	98.28 409

FE-MVS		95.66 376	94.95 389	97.77 318	98.53 372	95.28 326	99.40 19	96.09 431	93.11 424	97.96 327	99.26 129	79.10 447	99.77 260	92.40 420	98.71 383	98.27 410

baseline2		93.73 410	92.83 416	96.42 399	97.70 425	91.28 430	96.84 344	89.77 471	93.96 414	92.44 466	95.93 433	79.14 446	99.77 260	92.94 407	96.76 450	98.21 411

thisisatest0515		94.12 404	93.16 412	96.97 380	98.60 360	92.90 401	93.77 461	90.61 468	94.10 410	96.91 390	95.87 435	74.99 455	99.80 230	94.52 366	99.12 349	98.20 412

EPMVS		93.72 411	93.27 410	95.09 433	96.04 470	87.76 455	98.13 176	85.01 478	94.69 395	96.92 388	98.64 300	78.47 452	99.31 430	95.04 352	96.46 452	98.20 412

dp		93.47 414	93.59 407	93.13 454	96.64 460	81.62 479	97.66 261	96.42 425	92.80 429	96.11 421	98.64 300	78.55 451	99.59 362	93.31 402	92.18 473	98.16 414

CNLPA		97.17 318	96.71 331	98.55 240	98.56 368	98.05 156	96.33 375	98.93 308	96.91 316	97.06 382	97.39 402	94.38 319	99.45 410	91.66 427	99.18 340	98.14 415

dmvs_re		95.98 366	95.39 376	97.74 324	98.86 307	97.45 210	98.37 152	95.69 440	97.95 218	96.56 407	95.95 432	90.70 375	97.68 470	88.32 453	96.13 457	98.11 416

HY-MVS	95.94 13	95.90 368	95.35 378	97.55 348	97.95 410	94.79 341	98.81 96	96.94 415	92.28 435	95.17 440	98.57 311	89.90 381	99.75 274	91.20 437	97.33 441	98.10 417

CostFormer		93.97 406	93.78 404	94.51 437	97.53 435	85.83 463	97.98 212	95.96 433	89.29 458	94.99 443	98.63 302	78.63 449	99.62 348	94.54 365	96.50 451	98.09 418

FA-MVS(test-final)		96.99 331	96.82 324	97.50 353	98.70 338	94.78 342	99.34 23	96.99 411	95.07 386	98.48 284	99.33 111	88.41 396	99.65 339	96.13 319	98.92 372	98.07 419

AdaColmap		97.14 320	96.71 331	98.46 256	98.34 389	97.80 187	96.95 336	98.93 308	95.58 372	96.92 388	97.66 386	95.87 277	99.53 386	90.97 440	99.14 344	98.04 420

KD-MVS_2432*1600		92.87 425	91.99 427	95.51 425	91.37 479	89.27 448	94.07 455	98.14 378	95.42 377	97.25 376	96.44 424	67.86 464	99.24 438	91.28 435	96.08 458	98.02 421

miper_refine_blended		92.87 425	91.99 427	95.51 425	91.37 479	89.27 448	94.07 455	98.14 378	95.42 377	97.25 376	96.44 424	67.86 464	99.24 438	91.28 435	96.08 458	98.02 421

TESTMET0.1,1		92.19 434	91.77 432	93.46 449	96.48 464	82.80 475	94.05 457	91.52 467	94.45 402	94.00 456	94.88 455	66.65 468	99.56 374	95.78 334	98.11 413	98.02 421

testing222		91.96 435	90.37 439	96.72 393	97.47 442	92.59 406	96.11 390	94.76 446	96.83 320	92.90 464	92.87 470	57.92 479	99.55 378	86.93 458	97.52 429	98.00 424

PCF-MVS	92.86 18	94.36 397	93.00 415	98.42 261	98.70 338	97.56 202	93.16 465	99.11 279	79.59 473	97.55 356	97.43 400	92.19 358	99.73 286	79.85 471	99.45 291	97.97 425
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		90.22 439	89.28 442	93.02 455	94.50 476	82.87 474	96.52 363	87.51 474	95.21 384	92.36 467	96.04 429	71.57 459	98.25 466	72.04 476	97.77 425	97.94 426

myMVS_eth3d28		92.92 424	92.31 420	94.77 434	97.84 415	87.59 457	96.19 384	96.11 430	97.08 304	94.27 450	93.49 466	66.07 472	98.78 458	91.78 425	97.93 423	97.92 427

OpenMVS	96.65 7	97.09 322	96.68 333	98.32 273	98.32 390	97.16 236	98.86 91	99.37 186	89.48 456	96.29 418	99.15 162	96.56 240	99.90 81	92.90 408	99.20 335	97.89 428

Gipuma		99.03 80	99.16 62	98.64 217	99.94 2	98.51 112	99.32 26	99.75 42	99.58 39	98.60 266	99.62 40	98.22 103	99.51 395	97.70 188	99.73 179	97.89 428
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 438	90.30 441	93.70 447	97.72 420	84.34 471	90.24 471	97.42 397	90.20 453	93.79 459	93.09 468	90.90 374	98.89 456	86.57 460	72.76 477	97.87 430

test-LLR		93.90 407	93.85 402	94.04 442	96.53 462	84.62 468	94.05 457	92.39 462	96.17 349	94.12 453	95.07 449	82.30 435	99.67 319	95.87 329	98.18 407	97.82 431

test-mter		92.33 432	91.76 433	94.04 442	96.53 462	84.62 468	94.05 457	92.39 462	94.00 413	94.12 453	95.07 449	65.63 474	99.67 319	95.87 329	98.18 407	97.82 431

tpm2		93.09 420	92.58 418	94.62 436	97.56 431	86.53 460	97.66 261	95.79 437	86.15 465	94.07 455	98.23 349	75.95 453	99.53 386	90.91 442	96.86 449	97.81 433

CR-MVSNet		96.28 356	95.95 355	97.28 364	97.71 423	94.22 358	98.11 181	98.92 311	92.31 434	96.91 390	99.37 99	85.44 414	99.81 222	97.39 211	97.36 439	97.81 433

RPMNet		97.02 327	96.93 314	97.30 363	97.71 423	94.22 358	98.11 181	99.30 223	99.37 61	96.91 390	99.34 108	86.72 401	99.87 134	97.53 200	97.36 439	97.81 433

tpmrst		95.07 388	95.46 371	93.91 444	97.11 450	84.36 470	97.62 268	96.96 413	94.98 388	96.35 417	98.80 261	85.46 413	99.59 362	95.60 341	96.23 455	97.79 436

PAPM		91.88 437	90.34 440	96.51 396	98.06 407	92.56 407	92.44 468	97.17 406	86.35 464	90.38 471	96.01 430	86.61 402	99.21 441	70.65 477	95.43 462	97.75 437

FPMVS		93.44 415	92.23 422	97.08 373	99.25 214	97.86 175	95.61 414	97.16 407	92.90 427	93.76 460	98.65 297	75.94 454	95.66 474	79.30 472	97.49 430	97.73 438

MAR-MVS		96.47 351	95.70 361	98.79 186	97.92 412	99.12 63	98.28 158	98.60 357	92.16 436	95.54 435	96.17 428	94.77 311	99.52 390	89.62 449	98.23 404	97.72 439
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		98.03 243	97.86 257	98.56 238	98.69 343	98.07 152	97.51 285	99.50 125	98.10 208	97.50 361	95.51 441	98.41 78	99.88 115	96.27 309	99.24 327	97.71 440

thres600view7		94.45 396	93.83 403	96.29 403	99.06 264	91.53 422	97.99 211	94.24 453	98.34 175	97.44 367	95.01 451	79.84 441	99.67 319	84.33 463	98.23 404	97.66 441

thres400		94.14 403	93.44 408	96.24 406	98.93 290	91.44 425	97.60 273	94.29 451	97.94 220	97.10 379	94.31 460	79.67 443	99.62 348	83.05 465	98.08 415	97.66 441

IB-MVS	91.63 19	92.24 433	90.90 437	96.27 404	97.22 448	91.24 432	94.36 452	93.33 459	92.37 433	92.24 468	94.58 459	66.20 471	99.89 97	93.16 405	94.63 466	97.66 441
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		95.02 390	95.25 380	94.33 438	96.39 467	85.87 461	98.08 186	96.83 418	95.46 376	95.51 437	98.69 288	85.91 409	99.53 386	94.16 377	96.23 455	97.58 444

cascas		94.79 393	94.33 399	96.15 413	96.02 471	92.36 413	92.34 469	99.26 243	85.34 467	95.08 442	94.96 454	92.96 346	98.53 462	94.41 374	98.59 394	97.56 445

PatchT		96.65 343	96.35 347	97.54 349	97.40 443	95.32 325	97.98 212	96.64 421	99.33 66	96.89 394	99.42 89	84.32 422	99.81 222	97.69 190	97.49 430	97.48 446

TR-MVS		95.55 379	95.12 385	96.86 388	97.54 433	93.94 376	96.49 365	96.53 424	94.36 405	97.03 385	96.61 419	94.26 323	99.16 444	86.91 459	96.31 454	97.47 447

dmvs_testset		92.94 423	92.21 423	95.13 431	98.59 363	90.99 436	97.65 263	92.09 464	96.95 311	94.00 456	93.55 464	92.34 356	96.97 473	72.20 475	92.52 471	97.43 448

MonoMVSNet		96.25 358	96.53 344	95.39 428	96.57 461	91.01 435	98.82 95	97.68 392	98.57 161	98.03 323	99.37 99	90.92 373	97.78 469	94.99 353	93.88 469	97.38 449

JIA-IIPM		95.52 380	95.03 386	97.00 377	96.85 456	94.03 368	96.93 339	95.82 436	99.20 83	94.63 448	99.71 22	83.09 431	99.60 358	94.42 371	94.64 465	97.36 450

BH-w/o		95.13 387	94.89 391	95.86 415	98.20 398	91.31 428	95.65 413	97.37 398	93.64 416	96.52 411	95.70 438	93.04 345	99.02 448	88.10 454	95.82 460	97.24 451

tpm cat1		93.29 417	93.13 414	93.75 446	97.39 444	84.74 466	97.39 300	97.65 393	83.39 470	94.16 452	98.41 331	82.86 433	99.39 419	91.56 431	95.35 463	97.14 452

xiu_mvs_v1_base_debu		97.86 261	98.17 220	96.92 382	98.98 283	93.91 378	96.45 366	99.17 267	97.85 228	98.41 290	97.14 411	98.47 72	99.92 65	98.02 157	99.05 352	96.92 453

xiu_mvs_v1_base		97.86 261	98.17 220	96.92 382	98.98 283	93.91 378	96.45 366	99.17 267	97.85 228	98.41 290	97.14 411	98.47 72	99.92 65	98.02 157	99.05 352	96.92 453

xiu_mvs_v1_base_debi		97.86 261	98.17 220	96.92 382	98.98 283	93.91 378	96.45 366	99.17 267	97.85 228	98.41 290	97.14 411	98.47 72	99.92 65	98.02 157	99.05 352	96.92 453

PMVS	91.26 20	97.86 261	97.94 248	97.65 334	99.71 47	97.94 168	98.52 127	98.68 351	98.99 121	97.52 359	99.35 104	97.41 184	98.18 467	91.59 430	99.67 217	96.82 456
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 373	95.60 365	96.17 410	97.53 435	92.75 405	98.07 190	98.31 371	91.22 445	94.25 451	96.68 417	95.53 286	99.03 447	91.64 429	97.18 443	96.74 457

MVS-HIRNet		94.32 398	95.62 364	90.42 457	98.46 378	75.36 481	96.29 378	89.13 472	95.25 382	95.38 438	99.75 16	92.88 347	99.19 442	94.07 383	99.39 302	96.72 458

OpenMVS_ROB	95.38 14	95.84 371	95.18 384	97.81 315	98.41 386	97.15 237	97.37 306	98.62 356	83.86 468	98.65 258	98.37 336	94.29 322	99.68 315	88.41 452	98.62 393	96.60 459

thres100view900		94.19 401	93.67 406	95.75 419	99.06 264	91.35 427	98.03 197	94.24 453	98.33 176	97.40 369	94.98 453	79.84 441	99.62 348	83.05 465	98.08 415	96.29 460

tfpn200view9		94.03 405	93.44 408	95.78 418	98.93 290	91.44 425	97.60 273	94.29 451	97.94 220	97.10 379	94.31 460	79.67 443	99.62 348	83.05 465	98.08 415	96.29 460

MVS		93.19 419	92.09 424	96.50 397	96.91 454	94.03 368	98.07 190	98.06 382	68.01 475	94.56 449	96.48 422	95.96 273	99.30 432	83.84 464	96.89 448	96.17 462

gg-mvs-nofinetune		92.37 431	91.20 435	95.85 416	95.80 473	92.38 412	99.31 30	81.84 480	99.75 11	91.83 469	99.74 18	68.29 463	99.02 448	87.15 456	97.12 444	96.16 463

xiu_mvs_v2_base		97.16 319	97.49 283	96.17 410	98.54 370	92.46 409	95.45 421	98.84 329	97.25 288	97.48 363	96.49 421	98.31 89	99.90 81	96.34 305	98.68 388	96.15 464

PS-MVSNAJ		97.08 323	97.39 288	96.16 412	98.56 368	92.46 409	95.24 428	98.85 328	97.25 288	97.49 362	95.99 431	98.07 118	99.90 81	96.37 302	98.67 389	96.12 465

E-PMN		94.17 402	94.37 397	93.58 448	96.86 455	85.71 464	90.11 473	97.07 409	98.17 197	97.82 339	97.19 408	84.62 419	98.94 452	89.77 448	97.68 427	96.09 466

EMVS		93.83 408	94.02 400	93.23 453	96.83 457	84.96 465	89.77 474	96.32 426	97.92 222	97.43 368	96.36 427	86.17 406	98.93 453	87.68 455	97.73 426	95.81 467

MVE	83.40 22	92.50 428	91.92 430	94.25 439	98.83 313	91.64 421	92.71 466	83.52 479	95.92 362	86.46 477	95.46 445	95.20 295	95.40 475	80.51 470	98.64 390	95.73 468
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 411	93.14 413	95.46 427	98.66 353	91.29 429	96.61 357	94.63 448	97.39 274	96.83 397	93.71 463	79.88 440	99.56 374	82.40 468	98.13 412	95.54 469

API-MVS		97.04 326	96.91 318	97.42 359	97.88 414	98.23 134	98.18 169	98.50 362	97.57 251	97.39 371	96.75 416	96.77 227	99.15 445	90.16 447	99.02 359	94.88 470

GG-mvs-BLEND				94.76 435	94.54 475	92.13 417	99.31 30	80.47 481		88.73 475	91.01 475	67.59 467	98.16 468	82.30 469	94.53 467	93.98 471

DeepMVS_CX				93.44 450	98.24 395	94.21 360		94.34 450	64.28 476	91.34 470	94.87 457	89.45 387	92.77 477	77.54 473	93.14 470	93.35 472

tmp_tt		78.77 442	78.73 445	78.90 459	58.45 484	74.76 483	94.20 454	78.26 482	39.16 477	86.71 476	92.82 471	80.50 439	75.19 479	86.16 461	92.29 472	86.74 473

dongtai		76.24 443	75.95 446	77.12 460	92.39 478	67.91 484	90.16 472	59.44 485	82.04 471	89.42 473	94.67 458	49.68 482	81.74 478	48.06 478	77.66 476	81.72 474

kuosan		69.30 444	68.95 447	70.34 461	87.68 482	65.00 485	91.11 470	59.90 484	69.02 474	74.46 479	88.89 476	48.58 483	68.03 480	28.61 479	72.33 478	77.99 475

wuyk23d		96.06 362	97.62 276	91.38 456	98.65 357	98.57 106	98.85 92	96.95 414	96.86 319	99.90 14	99.16 158	99.18 19	98.40 463	89.23 451	99.77 156	77.18 476

test123		17.04 447	20.11 450	7.82 462	10.25 486	4.91 487	94.80 438	4.47 487	4.93 480	10.00 482	24.28 479	9.69 484	3.64 481	10.14 480	12.43 480	14.92 477

testmvs		17.12 446	20.53 449	6.87 463	12.05 485	4.20 488	93.62 463	6.73 486	4.62 481	10.41 481	24.33 478	8.28 485	3.56 482	9.69 481	15.07 479	12.86 478

mmdepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

monomultidepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

test_blank		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet_test		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

DCPMVS		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

cdsmvs_eth3d_5k		24.66 445	32.88 448	0.00 464	0.00 487	0.00 489	0.00 476	99.10 280	0.00 482	0.00 483	97.58 391	99.21 18	0.00 483	0.00 482	0.00 481	0.00 479

pcd_1.5k_mvsjas		8.17 448	10.90 451	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	98.07 118	0.00 483	0.00 482	0.00 481	0.00 479

sosnet-low-res		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

sosnet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uncertanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

Regformer		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

ab-mvs-re		8.12 449	10.83 452	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	97.48 397	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

uanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 489	0.00 476	0.00 488	0.00 482	0.00 483	0.00 482	0.00 486	0.00 483	0.00 482	0.00 481	0.00 479

TestfortrainingZip							98.68 107

WAC-MVS						90.90 437								91.37 434

FOURS1					99.73 37	99.67 3	99.43 15	99.54 112	99.43 55	99.26 147

test_one_0601					99.39 172	99.20 40		99.31 215	98.49 167	98.66 257	99.02 194	97.64 160

eth-test2					0.00 487

eth-test					0.00 487

ZD-MVS					99.01 278	98.84 86		99.07 284	94.10 410	98.05 321	98.12 356	96.36 251	99.86 143	92.70 416	99.19 338

test_241102_ONE					99.49 137	99.17 45		99.31 215	97.98 215	99.66 61	98.90 234	98.36 82	99.48 402

9.14			97.78 260		99.07 259		97.53 282	99.32 210	95.53 374	98.54 278	98.70 286	97.58 166	99.76 266	94.32 376	99.46 289

save fliter					99.11 250	97.97 163	96.53 362	99.02 297	98.24 186

test0726					99.50 129	99.21 34	98.17 172	99.35 196	97.97 216	99.26 147	99.06 182	97.61 164

test_part2					99.36 180	99.10 66				99.05 183

sam_mvs												84.29 424

MTGPA								99.20 255

test_post1							97.59 275				20.48 481	83.07 432	99.66 332	94.16 377

test_post											21.25 480	83.86 427	99.70 302

patchmatchnet-post											98.77 267	84.37 421	99.85 156

MTMP							97.93 218	91.91 466

gm-plane-assit					94.83 474	81.97 477			88.07 462		94.99 452		99.60 358	91.76 426

TEST9					98.71 334	98.08 150	95.96 397	99.03 294	91.40 443	95.85 426	97.53 393	96.52 242	99.76 266

test_8					98.67 348	98.01 158	95.91 403	99.02 297	91.64 438	95.79 428	97.50 396	96.47 244	99.76 266

agg_prior					98.68 347	97.99 159		99.01 300		95.59 429			99.77 260

test_prior4						97.97 163	95.86 404

test_prior2							95.74 411		96.48 336	96.11 421	97.63 389	95.92 276		94.16 377	99.20 335

旧先验2							95.76 410		88.56 461	97.52 359			99.66 332	94.48 367

新几何2							95.93 400

原ACMM2							95.53 417

testdata2													99.79 243	92.80 413

segment_acmp												97.02 209

testdata1							95.44 422		96.32 344

plane_prior7					99.19 230	97.87 174

plane_prior6					98.99 282	97.70 195						94.90 302

plane_prior4											97.98 368

plane_prior3						97.78 188			97.41 272	97.79 340

plane_prior2							97.77 243		98.20 194

plane_prior1					99.05 267

plane_prior						97.65 197	97.07 331		96.72 326						99.36 306

n2								0.00 488

nn								0.00 488

door-mid								99.57 94

test11								98.87 320

door								99.41 175

HQP5-MVS						96.79 258

HQP-NCC					98.67 348		96.29 378		96.05 354	95.55 432

ACMP_Plane					98.67 348		96.29 378		96.05 354	95.55 432

BP-MVS														92.82 411

HQP3-MVS								99.04 292							99.26 325

HQP2-MVS												93.84 330

NP-MVS					98.84 311	97.39 214					96.84 414

MDTV_nov1_ep13			95.22 382		97.06 453	83.20 473	97.74 250	96.16 428	94.37 404	96.99 386	98.83 254	83.95 426	99.53 386	93.90 386	97.95 422

ACMMP++_ref															99.77 156

ACMMP++															99.68 211

Test By Simon												96.52 242

High-resolution multi-view benchmark