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.

test_fmvsmconf0.1_n		99.87 8	99.86 12	99.91 2	99.97 6	99.74 73	99.01 228	99.99 10	99.99 2	99.98 13	99.88 42	99.97 2	99.99 7	99.96 9	100.00 1	99.98 3

test_fmvsmconf0.01_n		99.89 3	99.88 6	99.91 2	99.98 3	99.76 61	99.12 197	100.00 1	100.00 1	99.99 7	99.91 24	99.98 1	100.00 1	99.97 4	100.00 1	99.99 1

test_fmvsm_n_1920		99.84 15	99.85 16	99.83 33	99.82 72	99.70 90	99.17 177	99.97 18	99.99 2	99.96 23	99.82 73	99.94 4	100.00 1	99.95 12	100.00 1	99.80 47

test_vis3_rt		99.89 3	99.90 3	99.87 21	99.98 3	99.75 67	99.70 35	100.00 1	99.73 74	100.00 1	99.89 34	99.79 16	99.88 189	99.98 1	100.00 1	99.98 3

IterMVS-SCA-FT		99.00 212	99.16 145	98.51 328	99.75 128	95.90 372	98.07 336	99.84 60	99.84 53	99.89 53	99.73 123	96.01 305	99.99 7	99.33 91	100.00 1	99.63 127

new-patchmatchnet		99.35 125	99.57 71	98.71 321	99.82 72	96.62 360	98.55 292	99.75 105	99.50 123	99.88 61	99.87 47	99.31 62	99.88 189	99.43 72	100.00 1	99.62 138

anonymousdsp		99.80 23	99.77 33	99.90 8	99.96 7	99.88 12	99.73 27	99.85 54	99.70 85	99.92 41	99.93 17	99.45 47	99.97 33	99.36 84	100.00 1	99.85 35

UA-Net		99.78 27	99.76 36	99.86 25	99.72 140	99.71 83	99.91 3	99.95 28	99.96 18	99.71 132	99.91 24	99.15 81	99.97 33	99.50 66	100.00 1	99.90 20

PS-MVSNAJss		99.84 15	99.82 22	99.89 11	99.96 7	99.77 54	99.68 45	99.85 54	99.95 20	99.98 13	99.92 21	99.28 66	99.98 20	99.75 39	100.00 1	99.94 13

jajsoiax		99.89 3	99.89 5	99.89 11	99.96 7	99.78 49	99.70 35	99.86 49	99.89 35	99.98 13	99.90 29	99.94 4	99.98 20	99.75 39	100.00 1	99.90 20

mvs_tets		99.90 2	99.90 3	99.90 8	99.96 7	99.79 46	99.72 30	99.88 44	99.92 27	99.98 13	99.93 17	99.94 4	99.98 20	99.77 38	100.00 1	99.92 18

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 18	99.99 2	100.00 1	99.98 10	99.78 17	100.00 1	99.92 22	100.00 1	99.87 30

test_djsdf		99.84 15	99.81 23	99.91 2	99.94 18	99.84 24	99.77 15	99.80 80	99.73 74	99.97 19	99.92 21	99.77 19	99.98 20	99.43 72	100.00 1	99.90 20

IterMVS		98.97 216	99.16 145	98.42 332	99.74 134	95.64 375	98.06 338	99.83 62	99.83 56	99.85 73	99.74 119	96.10 304	99.99 7	99.27 103	100.00 1	99.63 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ANet_high		99.88 6	99.87 10	99.91 2	99.99 1	99.91 4	99.65 58	100.00 1	99.90 29	100.00 1	99.97 11	99.61 32	99.97 33	99.75 39	100.00 1	99.84 36

fmvsm_l_conf0.5_n_a		99.80 23	99.79 27	99.84 30	99.88 44	99.64 110	99.12 197	99.91 33	99.98 14	99.95 31	99.67 166	99.67 27	99.99 7	99.94 16	99.99 16	99.88 25

fmvsm_l_conf0.5_n		99.80 23	99.78 31	99.85 27	99.88 44	99.66 101	99.11 201	99.91 33	99.98 14	99.96 23	99.64 178	99.60 34	99.99 7	99.95 12	99.99 16	99.88 25

fmvsm_s_conf0.1_n_a		99.85 11	99.83 20	99.91 2	99.95 15	99.82 35	99.10 204	99.98 11	99.99 2	99.98 13	99.91 24	99.68 26	99.93 94	99.93 20	99.99 16	99.99 1

fmvsm_s_conf0.1_n		99.86 9	99.85 16	99.89 11	99.93 25	99.78 49	99.07 215	99.98 11	99.99 2	99.98 13	99.90 29	99.88 8	99.92 116	99.93 20	99.99 16	99.98 3

fmvsm_s_conf0.5_n		99.83 19	99.81 23	99.87 21	99.85 58	99.78 49	99.03 223	99.96 23	99.99 2	99.97 19	99.84 62	99.78 17	99.92 116	99.92 22	99.99 16	99.92 18

test_fmvsmconf_n		99.85 11	99.84 19	99.88 17	99.91 31	99.73 76	98.97 240	99.98 11	99.99 2	99.96 23	99.85 56	99.93 7	99.99 7	99.94 16	99.99 16	99.93 15

test_fmvsmvis_n_1920		99.84 15	99.86 12	99.81 40	99.88 44	99.55 138	99.17 177	99.98 11	99.99 2	99.96 23	99.84 62	99.96 3	99.99 7	99.96 9	99.99 16	99.88 25

test_vis1_n_1920		99.72 36	99.88 6	99.27 245	99.93 25	97.84 325	99.34 122	100.00 1	99.99 2	99.99 7	99.82 73	99.87 9	99.99 7	99.97 4	99.99 16	99.97 7

test_fmvs2		99.72 36	99.85 16	99.34 226	99.91 31	98.08 312	99.48 96	100.00 1	99.90 29	99.99 7	99.91 24	99.50 46	99.98 20	99.98 1	99.99 16	99.96 10

test_fmvs3		99.83 19	99.93 2	99.53 174	99.96 7	98.62 275	99.67 49	100.00 1	99.95 20	100.00 1	99.95 13	99.85 10	99.99 7	99.98 1	99.99 16	99.98 3

test_f		99.75 32	99.88 6	99.37 219	99.96 7	98.21 299	99.51 90	100.00 1	99.94 23	100.00 1	99.93 17	99.58 36	99.94 77	99.97 4	99.99 16	99.97 7

bld_raw_dy_0_64		98.97 216	98.90 217	99.17 262	99.07 344	99.24 207	99.24 156	99.93 29	99.23 167	99.87 69	99.03 345	95.48 311	99.81 294	98.29 189	99.99 16	98.47 373

test2506		94.73 371	94.59 372	95.15 387	99.59 186	85.90 413	99.75 22	74.01 413	99.89 35	99.71 132	99.86 54	79.00 404	99.90 157	99.52 63	99.99 16	99.65 112

test1111		97.74 313	98.16 287	96.49 381	99.60 182	89.86 411	99.71 34	91.21 407	99.89 35	99.88 61	99.87 47	93.73 330	99.90 157	99.56 57	99.99 16	99.70 79

ECVR-MVS		97.73 314	98.04 293	96.78 375	99.59 186	90.81 407	99.72 30	90.43 409	99.89 35	99.86 71	99.86 54	93.60 332	99.89 175	99.46 69	99.99 16	99.65 112

pmmvs-eth3d		99.48 87	99.47 85	99.51 178	99.77 113	99.41 170	98.81 261	99.66 148	99.42 144	99.75 114	99.66 171	99.20 76	99.76 318	98.98 138	99.99 16	99.36 249

v7n		99.82 21	99.80 26	99.88 17	99.96 7	99.84 24	99.82 8	99.82 67	99.84 53	99.94 34	99.91 24	99.13 86	99.96 54	99.83 32	99.99 16	99.83 40

RRT_MVS		99.67 51	99.59 64	99.91 2	99.94 18	99.88 12	99.78 12	99.27 302	99.87 41	99.91 44	99.87 47	98.04 219	99.96 54	99.68 44	99.99 16	99.90 20

v8		99.68 45	99.69 43	99.65 121	99.80 86	99.40 171	99.66 53	99.76 100	99.64 102	99.93 37	99.85 56	98.66 146	99.84 255	99.88 29	99.99 16	99.71 76

v10		99.69 42	99.69 43	99.66 116	99.81 80	99.39 173	99.66 53	99.75 105	99.60 115	99.92 41	99.87 47	98.75 133	99.86 222	99.90 25	99.99 16	99.73 71

CHOSEN 1792x2688		99.39 115	99.30 123	99.65 121	99.88 44	99.25 203	98.78 268	99.88 44	98.66 247	99.96 23	99.79 93	97.45 256	99.93 94	99.34 88	99.99 16	99.78 56

PVSNet_Blended_VisFu		99.40 111	99.38 103	99.44 195	99.90 37	98.66 269	98.94 245	99.91 33	97.97 310	99.79 97	99.73 123	99.05 97	99.97 33	99.15 119	99.99 16	99.68 89

IterMVS-LS		99.41 109	99.47 85	99.25 251	99.81 80	98.09 309	98.85 253	99.76 100	99.62 106	99.83 80	99.64 178	98.54 163	99.97 33	99.15 119	99.99 16	99.68 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS	98.90 4	99.62 65	99.61 59	99.67 109	99.72 140	99.44 158	99.24 156	99.71 126	99.27 159	99.93 37	99.90 29	99.70 24	99.93 94	98.99 136	99.99 16	99.64 122
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LTVRE_ROB	99.19 1	99.88 6	99.87 10	99.88 17	99.91 31	99.90 7	99.96 1	99.92 30	99.90 29	99.97 19	99.87 47	99.81 14	99.95 63	99.54 60	99.99 16	99.80 47
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
fmvsm_s_conf0.5_n_a		99.82 21	99.79 27	99.89 11	99.85 58	99.82 35	99.03 223	99.96 23	99.99 2	99.97 19	99.84 62	99.58 36	99.93 94	99.92 22	99.98 41	99.93 15

MM		99.18 172	99.05 179	99.55 168	99.35 281	98.81 255	99.05 216	97.79 380	99.99 2	99.48 216	99.59 218	96.29 300	99.95 63	99.94 16	99.98 41	99.88 25

test_fmvs1_n		99.68 45	99.81 23	99.28 242	99.95 15	97.93 322	99.49 95	100.00 1	99.82 58	99.99 7	99.89 34	99.21 75	99.98 20	99.97 4	99.98 41	99.93 15

mvsany_test3		99.85 11	99.88 6	99.75 74	99.95 15	99.37 178	99.53 85	99.98 11	99.77 72	99.99 7	99.95 13	99.85 10	99.94 77	99.95 12	99.98 41	99.94 13

Anonymous20240521		99.44 99	99.42 98	99.49 181	99.89 39	98.96 242	99.62 62	99.76 100	99.85 50	99.82 81	99.88 42	96.39 296	99.97 33	99.59 51	99.98 41	99.55 174

D2MVS		99.22 158	99.19 142	99.29 240	99.69 155	98.74 262	98.81 261	99.41 266	98.55 258	99.68 142	99.69 151	98.13 213	99.87 203	98.82 153	99.98 41	99.24 273

CHOSEN 280x420		98.41 280	98.41 262	98.40 333	99.34 290	95.89 373	96.94 393	99.44 260	98.80 231	99.25 268	99.52 246	93.51 333	99.98 20	98.94 147	99.98 41	99.32 259

MVS_0304		99.17 177	99.03 187	99.59 152	99.44 259	98.90 249	99.04 219	95.32 398	99.99 2	99.68 142	99.57 229	98.30 197	99.97 33	99.94 16	99.98 41	99.88 25

v1192		99.57 70	99.57 71	99.57 162	99.77 113	99.22 211	99.04 219	99.60 188	99.18 175	99.87 69	99.72 130	99.08 92	99.85 240	99.89 28	99.98 41	99.66 104

v1144		99.54 78	99.53 81	99.59 152	99.79 98	99.28 196	99.10 204	99.61 176	99.20 173	99.84 76	99.73 123	98.67 144	99.84 255	99.86 31	99.98 41	99.64 122

mvsmamba		99.74 35	99.70 39	99.85 27	99.93 25	99.83 29	99.76 19	99.81 76	99.96 18	99.91 44	99.81 79	98.60 154	99.94 77	99.58 54	99.98 41	99.77 60

OurMVSNet-221017-0		99.75 32	99.71 38	99.84 30	99.96 7	99.83 29	99.83 6	99.85 54	99.80 64	99.93 37	99.93 17	98.54 163	99.93 94	99.59 51	99.98 41	99.76 66

UGNet		99.38 117	99.34 111	99.49 181	98.90 360	98.90 249	99.70 35	99.35 285	99.86 45	98.57 345	99.81 79	98.50 173	99.93 94	99.38 80	99.98 41	99.66 104
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
MIMVSNet1		99.66 53	99.62 55	99.80 45	99.94 18	99.87 15	99.69 42	99.77 95	99.78 68	99.93 37	99.89 34	97.94 227	99.92 116	99.65 46	99.98 41	99.62 138

Vis-MVSNet		99.75 32	99.74 37	99.79 51	99.88 44	99.66 101	99.69 42	99.92 30	99.67 94	99.77 106	99.75 116	99.61 32	99.98 20	99.35 87	99.98 41	99.72 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_cas_vis1_n_1920		99.76 31	99.86 12	99.45 192	99.93 25	98.40 287	99.30 135	99.98 11	99.94 23	99.99 7	99.89 34	99.80 15	99.97 33	99.96 9	99.97 56	99.97 7

test_fmvs1		99.48 87	99.65 50	98.97 288	99.54 215	97.16 348	99.11 201	99.98 11	99.78 68	99.96 23	99.81 79	98.72 138	99.97 33	99.95 12	99.97 56	99.79 54

UniMVSNet_ETH3D		99.85 11	99.83 20	99.90 8	99.89 39	99.91 4	99.89 4	99.71 126	99.93 25	99.95 31	99.89 34	99.71 22	99.96 54	99.51 64	99.97 56	99.84 36

CANet		99.11 190	99.05 179	99.28 242	98.83 367	98.56 277	98.71 275	99.41 266	99.25 163	99.23 272	99.22 319	97.66 250	99.94 77	99.19 111	99.97 56	99.33 256

pmmvs6		99.86 9	99.86 12	99.83 33	99.94 18	99.90 7	99.83 6	99.91 33	99.85 50	99.94 34	99.95 13	99.73 21	99.90 157	99.65 46	99.97 56	99.69 83

v144192		99.55 76	99.54 77	99.58 156	99.78 105	99.20 216	99.11 201	99.62 169	99.18 175	99.89 53	99.72 130	98.66 146	99.87 203	99.88 29	99.97 56	99.66 104

v1921920		99.56 73	99.57 71	99.55 168	99.75 128	99.11 225	99.05 216	99.61 176	99.15 187	99.88 61	99.71 138	99.08 92	99.87 203	99.90 25	99.97 56	99.66 104

FC-MVSNet-test		99.70 40	99.65 50	99.86 25	99.88 44	99.86 18	99.72 30	99.78 92	99.90 29	99.82 81	99.83 66	98.45 178	99.87 203	99.51 64	99.97 56	99.86 32

iter_conf05		98.46 275	98.23 278	99.15 265	99.04 349	97.99 315	99.10 204	99.61 176	99.79 66	99.76 108	99.58 221	87.88 379	99.92 116	99.31 96	99.97 56	99.53 187

v2v482		99.50 83	99.47 85	99.58 156	99.78 105	99.25 203	99.14 187	99.58 203	99.25 163	99.81 88	99.62 196	98.24 202	99.84 255	99.83 32	99.97 56	99.64 122

Patchmtry		98.78 240	98.54 252	99.49 181	98.89 363	99.19 218	99.32 127	99.67 144	99.65 100	99.72 127	99.79 93	91.87 349	99.95 63	98.00 216	99.97 56	99.33 256

PVSNet_BlendedMVS		99.03 203	99.01 192	99.09 275	99.54 215	97.99 315	98.58 286	99.82 67	97.62 329	99.34 249	99.71 138	98.52 170	99.77 316	97.98 217	99.97 56	99.52 198

FMVSNet1		99.66 53	99.63 54	99.73 88	99.78 105	99.77 54	99.68 45	99.70 131	99.67 94	99.82 81	99.83 66	98.98 105	99.90 157	99.24 104	99.97 56	99.53 187

HyFIR lowres test		98.91 226	98.64 239	99.73 88	99.85 58	99.47 147	98.07 336	99.83 62	98.64 249	99.89 53	99.60 213	92.57 341	100.00 1	99.33 91	99.97 56	99.72 73

SDMVSNet		99.77 30	99.77 33	99.76 64	99.80 86	99.65 107	99.63 60	99.86 49	99.97 16	99.89 53	99.89 34	99.52 44	99.99 7	99.42 77	99.96 70	99.65 112

sd_testset		99.78 27	99.78 31	99.80 45	99.80 86	99.76 61	99.80 10	99.79 86	99.97 16	99.89 53	99.89 34	99.53 43	99.99 7	99.36 84	99.96 70	99.65 112

test_vis1_n		99.68 45	99.79 27	99.36 223	99.94 18	98.18 302	99.52 86	100.00 1	99.86 45	100.00 1	99.88 42	98.99 103	99.96 54	99.97 4	99.96 70	99.95 11

patch_mono-2		99.51 82	99.46 89	99.64 128	99.70 151	99.11 225	99.04 219	99.87 46	99.71 80	99.47 218	99.79 93	98.24 202	99.98 20	99.38 80	99.96 70	99.83 40

dcpmvs_2		99.61 67	99.64 53	99.53 174	99.79 98	98.82 254	99.58 76	99.97 18	99.95 20	99.96 23	99.76 111	98.44 179	99.99 7	99.34 88	99.96 70	99.78 56

ppachtmachnet_test		98.89 231	99.12 155	98.20 343	99.66 169	95.24 381	97.63 367	99.68 140	99.08 194	99.78 101	99.62 196	98.65 148	99.88 189	98.02 212	99.96 70	99.48 214

Anonymous20231211		99.62 65	99.57 71	99.76 64	99.61 180	99.60 126	99.81 9	99.73 114	99.82 58	99.90 49	99.90 29	97.97 226	99.86 222	99.42 77	99.96 70	99.80 47

nrg030		99.70 40	99.66 48	99.82 37	99.76 117	99.84 24	99.61 67	99.70 131	99.93 25	99.78 101	99.68 162	99.10 87	99.78 308	99.45 70	99.96 70	99.83 40

v1240		99.56 73	99.58 68	99.51 178	99.80 86	99.00 236	99.00 231	99.65 158	99.15 187	99.90 49	99.75 116	99.09 89	99.88 189	99.90 25	99.96 70	99.67 95

PS-CasMVS		99.66 53	99.58 68	99.89 11	99.80 86	99.85 19	99.66 53	99.73 114	99.62 106	99.84 76	99.71 138	98.62 150	99.96 54	99.30 97	99.96 70	99.86 32

casdiffmvs_mvg		99.68 45	99.68 46	99.69 104	99.81 80	99.59 128	99.29 142	99.90 38	99.71 80	99.79 97	99.73 123	99.54 41	99.84 255	99.36 84	99.96 70	99.65 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TAMVS		99.49 85	99.45 91	99.63 135	99.48 244	99.42 165	99.45 103	99.57 205	99.66 98	99.78 101	99.83 66	97.85 234	99.86 222	99.44 71	99.96 70	99.61 148

test_0402		99.22 158	99.14 149	99.45 192	99.79 98	99.43 162	99.28 144	99.68 140	99.54 119	99.40 241	99.56 233	99.07 94	99.82 279	96.01 348	99.96 70	99.11 304

our_test_3		98.85 235	99.09 167	98.13 345	99.66 169	94.90 385	97.72 363	99.58 203	99.07 196	99.64 155	99.62 196	98.19 209	99.93 94	98.41 181	99.95 83	99.55 174

CANet_DTU		98.91 226	98.85 222	99.09 275	98.79 373	98.13 304	98.18 322	99.31 294	99.48 125	98.86 317	99.51 248	96.56 287	99.95 63	99.05 132	99.95 83	99.19 287

pmmvs5		99.19 168	99.11 158	99.42 201	99.76 117	98.88 251	98.55 292	99.73 114	98.82 227	99.72 127	99.62 196	96.56 287	99.82 279	99.32 93	99.95 83	99.56 171

V42		99.56 73	99.54 77	99.63 135	99.79 98	99.46 151	99.39 111	99.59 194	99.24 165	99.86 71	99.70 145	98.55 161	99.82 279	99.79 37	99.95 83	99.60 152

EU-MVSNet		99.39 115	99.62 55	98.72 319	99.88 44	96.44 362	99.56 81	99.85 54	99.90 29	99.90 49	99.85 56	98.09 215	99.83 270	99.58 54	99.95 83	99.90 20

PMMVS2		99.48 87	99.45 91	99.57 162	99.76 117	98.99 237	98.09 333	99.90 38	98.95 208	99.78 101	99.58 221	99.57 38	99.93 94	99.48 67	99.95 83	99.79 54

DTE-MVSNet		99.68 45	99.61 59	99.88 17	99.80 86	99.87 15	99.67 49	99.71 126	99.72 78	99.84 76	99.78 101	98.67 144	99.97 33	99.30 97	99.95 83	99.80 47

WR-MVS_H		99.61 67	99.53 81	99.87 21	99.80 86	99.83 29	99.67 49	99.75 105	99.58 118	99.85 73	99.69 151	98.18 211	99.94 77	99.28 102	99.95 83	99.83 40

K. test v3		98.87 233	98.60 242	99.69 104	99.93 25	99.46 151	99.74 24	94.97 399	99.78 68	99.88 61	99.88 42	93.66 331	99.97 33	99.61 49	99.95 83	99.64 122

TDRefinement		99.72 36	99.70 39	99.77 57	99.90 37	99.85 19	99.86 5	99.92 30	99.69 88	99.78 101	99.92 21	99.37 56	99.88 189	98.93 148	99.95 83	99.60 152

Gipuma		99.57 70	99.59 64	99.49 181	99.98 3	99.71 83	99.72 30	99.84 60	99.81 61	99.94 34	99.78 101	98.91 113	99.71 334	98.41 181	99.95 83	99.05 323
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v148		99.40 111	99.41 100	99.39 213	99.76 117	98.94 243	99.09 209	99.59 194	99.17 180	99.81 88	99.61 205	98.41 183	99.69 343	99.32 93	99.94 94	99.53 187

casdiffmvs		99.63 59	99.61 59	99.67 109	99.79 98	99.59 128	99.13 193	99.85 54	99.79 66	99.76 108	99.72 130	99.33 61	99.82 279	99.21 107	99.94 94	99.59 159
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PEN-MVS		99.66 53	99.59 64	99.89 11	99.83 65	99.87 15	99.66 53	99.73 114	99.70 85	99.84 76	99.73 123	98.56 160	99.96 54	99.29 100	99.94 94	99.83 40

CP-MVSNet		99.54 78	99.43 96	99.87 21	99.76 117	99.82 35	99.57 79	99.61 176	99.54 119	99.80 92	99.64 178	97.79 238	99.95 63	99.21 107	99.94 94	99.84 36

baseline		99.63 59	99.62 55	99.66 116	99.80 86	99.62 117	99.44 105	99.80 80	99.71 80	99.72 127	99.69 151	99.15 81	99.83 270	99.32 93	99.94 94	99.53 187

FMVSNet2		99.35 125	99.28 130	99.55 168	99.49 239	99.35 185	99.45 103	99.57 205	99.44 135	99.70 136	99.74 119	97.21 267	99.87 203	99.03 133	99.94 94	99.44 228

ACMMP++_ref															99.94 94

eth_miper_zixun_eth		98.68 251	98.71 235	98.60 324	99.10 339	96.84 357	97.52 375	99.54 222	98.94 209	99.58 182	99.48 258	96.25 301	99.76 318	98.01 215	99.93 101	99.21 280

FIs		99.65 58	99.58 68	99.84 30	99.84 61	99.85 19	99.66 53	99.75 105	99.86 45	99.74 122	99.79 93	98.27 200	99.85 240	99.37 83	99.93 101	99.83 40

pmmvs4		99.13 185	99.06 175	99.36 223	99.57 201	99.10 230	98.01 342	99.25 308	98.78 234	99.58 182	99.44 269	98.24 202	99.76 318	98.74 164	99.93 101	99.22 278

XXY-MVS		99.71 39	99.67 47	99.81 40	99.89 39	99.72 81	99.59 74	99.82 67	99.39 145	99.82 81	99.84 62	99.38 54	99.91 139	99.38 80	99.93 101	99.80 47

pm-mvs1		99.79 26	99.79 27	99.78 54	99.91 31	99.83 29	99.76 19	99.87 46	99.73 74	99.89 53	99.87 47	99.63 29	99.87 203	99.54 60	99.92 105	99.63 127

EI-MVSNet		99.38 117	99.44 94	99.21 255	99.58 191	98.09 309	99.26 149	99.46 255	99.62 106	99.75 114	99.67 166	98.54 163	99.85 240	99.15 119	99.92 105	99.68 89

TranMVSNet+NR-MVSNet		99.54 78	99.47 85	99.76 64	99.58 191	99.64 110	99.30 135	99.63 166	99.61 109	99.71 132	99.56 233	98.76 131	99.96 54	99.14 125	99.92 105	99.68 89

lessismore_v0				99.64 128	99.86 54	99.38 175		90.66 408		99.89 53	99.83 66	94.56 321	99.97 33	99.56 57	99.92 105	99.57 169

SixPastTwentyTwo		99.42 105	99.30 123	99.76 64	99.92 30	99.67 99	99.70 35	99.14 326	99.65 100	99.89 53	99.90 29	96.20 302	99.94 77	99.42 77	99.92 105	99.67 95

MVSTER		98.47 274	98.22 280	99.24 253	99.06 346	98.35 293	99.08 212	99.46 255	99.27 159	99.75 114	99.66 171	88.61 377	99.85 240	99.14 125	99.92 105	99.52 198

N_pmnet		98.73 246	98.53 253	99.35 225	99.72 140	98.67 266	98.34 311	94.65 400	98.35 284	99.79 97	99.68 162	98.03 220	99.93 94	98.28 192	99.92 105	99.44 228

CSCG		99.37 120	99.29 128	99.60 150	99.71 143	99.46 151	99.43 107	99.85 54	98.79 232	99.41 236	99.60 213	98.92 111	99.92 116	98.02 212	99.92 105	99.43 234

CMPMVS	77.52 23	98.50 270	98.19 285	99.41 208	98.33 395	99.56 135	99.01 228	99.59 194	95.44 379	99.57 185	99.80 83	95.64 308	99.46 395	96.47 330	99.92 105	99.21 280
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EG-PatchMatch MVS		99.57 70	99.56 76	99.62 144	99.77 113	99.33 188	99.26 149	99.76 100	99.32 153	99.80 92	99.78 101	99.29 64	99.87 203	99.15 119	99.91 114	99.66 104

miper_lstm_enhance		98.65 253	98.60 242	98.82 314	99.20 321	97.33 344	97.78 361	99.66 148	99.01 201	99.59 180	99.50 251	94.62 320	99.85 240	98.12 208	99.90 115	99.26 270

CS-MVS-test		99.68 45	99.70 39	99.64 128	99.57 201	99.83 29	99.78 12	99.97 18	99.92 27	99.50 213	99.38 282	99.57 38	99.95 63	99.69 43	99.90 115	99.15 295

EI-MVSNet-UG-set		99.48 87	99.50 83	99.42 201	99.57 201	98.65 272	99.24 156	99.46 255	99.68 90	99.80 92	99.66 171	98.99 103	99.89 175	99.19 111	99.90 115	99.72 73

diffmvs		99.34 130	99.32 116	99.39 213	99.67 168	98.77 260	98.57 290	99.81 76	99.61 109	99.48 216	99.41 272	98.47 174	99.86 222	98.97 140	99.90 115	99.53 187
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		98.95 223	98.99 201	98.84 309	99.64 173	97.14 350	98.22 321	99.32 290	98.92 214	99.59 180	99.66 171	97.40 258	99.83 270	98.27 193	99.90 115	99.55 174

GBi-Net		99.42 105	99.31 118	99.73 88	99.49 239	99.77 54	99.68 45	99.70 131	99.44 135	99.62 168	99.83 66	97.21 267	99.90 157	98.96 142	99.90 115	99.53 187

FMVSNet5		97.80 311	97.25 327	99.42 201	98.83 367	98.97 240	99.38 113	99.80 80	98.87 220	99.25 268	99.69 151	80.60 398	99.91 139	98.96 142	99.90 115	99.38 243

test1		99.42 105	99.31 118	99.73 88	99.49 239	99.77 54	99.68 45	99.70 131	99.44 135	99.62 168	99.83 66	97.21 267	99.90 157	98.96 142	99.90 115	99.53 187

FMVSNet3		98.80 239	98.63 241	99.32 233	99.13 331	98.72 263	99.10 204	99.48 249	99.23 167	99.62 168	99.64 178	92.57 341	99.86 222	98.96 142	99.90 115	99.39 241

iter_conf05_11		98.54 264	98.33 272	99.18 260	99.07 344	99.20 216	97.94 351	97.59 382	99.17 180	99.30 263	98.92 362	94.79 317	99.86 222	98.29 189	99.89 124	98.47 373

cl____		98.54 264	98.41 262	98.92 296	99.03 350	97.80 329	97.46 377	99.59 194	98.90 216	99.60 177	99.46 265	93.85 327	99.78 308	97.97 219	99.89 124	99.17 291

DIV-MVS_self_test		98.54 264	98.42 261	98.92 296	99.03 350	97.80 329	97.46 377	99.59 194	98.90 216	99.60 177	99.46 265	93.87 326	99.78 308	97.97 219	99.89 124	99.18 289

CS-MVS		99.67 51	99.70 39	99.58 156	99.53 221	99.84 24	99.79 11	99.96 23	99.90 29	99.61 174	99.41 272	99.51 45	99.95 63	99.66 45	99.89 124	98.96 334

EI-MVSNet-Vis-set		99.47 94	99.49 84	99.42 201	99.57 201	98.66 269	99.24 156	99.46 255	99.67 94	99.79 97	99.65 176	98.97 107	99.89 175	99.15 119	99.89 124	99.71 76

DSMNet-mixed		99.48 87	99.65 50	98.95 291	99.71 143	97.27 345	99.50 91	99.82 67	99.59 117	99.41 236	99.85 56	99.62 31	100.00 1	99.53 62	99.89 124	99.59 159

Vis-MVSNet (Re-imp)		98.77 241	98.58 247	99.34 226	99.78 105	98.88 251	99.61 67	99.56 210	99.11 193	99.24 271	99.56 233	93.00 339	99.78 308	97.43 269	99.89 124	99.35 252

EPP-MVSNet		99.17 177	99.00 195	99.66 116	99.80 86	99.43 162	99.70 35	99.24 311	99.48 125	99.56 192	99.77 108	94.89 315	99.93 94	98.72 166	99.89 124	99.63 127

CLD-MVS		98.76 242	98.57 248	99.33 229	99.57 201	98.97 240	97.53 373	99.55 216	96.41 366	99.27 266	99.13 327	99.07 94	99.78 308	96.73 313	99.89 124	99.23 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMH	98.42 6	99.59 69	99.54 77	99.72 94	99.86 54	99.62 117	99.56 81	99.79 86	98.77 236	99.80 92	99.85 56	99.64 28	99.85 240	98.70 167	99.89 124	99.70 79
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		99.63 59	99.60 62	99.72 94	99.94 18	99.95 2	99.47 99	99.89 40	99.43 140	99.88 61	99.80 83	99.26 70	99.90 157	98.81 155	99.88 134	99.32 259

APD_test2		99.63 59	99.60 62	99.72 94	99.94 18	99.95 2	99.47 99	99.89 40	99.43 140	99.88 61	99.80 83	99.26 70	99.90 157	98.81 155	99.88 134	99.32 259

GeoE		99.69 42	99.66 48	99.78 54	99.76 117	99.76 61	99.60 73	99.82 67	99.46 132	99.75 114	99.56 233	99.63 29	99.95 63	99.43 72	99.88 134	99.62 138

c3_l		98.72 247	98.71 235	98.72 319	99.12 333	97.22 347	97.68 366	99.56 210	98.90 216	99.54 199	99.48 258	96.37 297	99.73 328	97.88 226	99.88 134	99.21 280

VPA-MVSNet		99.66 53	99.62 55	99.79 51	99.68 163	99.75 67	99.62 62	99.69 137	99.85 50	99.80 92	99.81 79	98.81 121	99.91 139	99.47 68	99.88 134	99.70 79

MDA-MVSNet_test_wron		98.95 223	98.99 201	98.85 307	99.64 173	97.16 348	98.23 320	99.33 288	98.93 212	99.56 192	99.66 171	97.39 260	99.83 270	98.29 189	99.88 134	99.55 174

XVG-OURS		99.21 163	99.06 175	99.65 121	99.82 72	99.62 117	97.87 358	99.74 110	98.36 279	99.66 152	99.68 162	99.71 22	99.90 157	96.84 308	99.88 134	99.43 234

CDS-MVSNet		99.22 158	99.13 151	99.50 180	99.35 281	99.11 225	98.96 242	99.54 222	99.46 132	99.61 174	99.70 145	96.31 298	99.83 270	99.34 88	99.88 134	99.55 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS-MVSNet		99.03 203	98.85 222	99.55 168	99.80 86	99.25 203	99.73 27	99.15 325	99.37 147	99.61 174	99.71 138	94.73 319	99.81 294	97.70 248	99.88 134	99.58 164

USDC		98.96 220	98.93 209	99.05 282	99.54 215	97.99 315	97.07 391	99.80 80	98.21 296	99.75 114	99.77 108	98.43 180	99.64 372	97.90 224	99.88 134	99.51 200

ACMH+	98.40 8	99.50 83	99.43 96	99.71 99	99.86 54	99.76 61	99.32 127	99.77 95	99.53 121	99.77 106	99.76 111	99.26 70	99.78 308	97.77 237	99.88 134	99.60 152

mvsany_test1		99.44 99	99.45 91	99.40 210	99.37 276	98.64 273	97.90 357	99.59 194	99.27 159	99.92 41	99.82 73	99.74 20	99.93 94	99.55 59	99.87 145	99.63 127

SD-MVS		99.01 210	99.30 123	98.15 344	99.50 234	99.40 171	98.94 245	99.61 176	99.22 172	99.75 114	99.82 73	99.54 41	95.51 408	97.48 266	99.87 145	99.54 182
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
UniMVSNet (Re)		99.37 120	99.26 134	99.68 106	99.51 228	99.58 132	98.98 239	99.60 188	99.43 140	99.70 136	99.36 288	97.70 242	99.88 189	99.20 110	99.87 145	99.59 159

WR-MVS		99.11 190	98.93 209	99.66 116	99.30 301	99.42 165	98.42 307	99.37 281	99.04 199	99.57 185	99.20 323	96.89 279	99.86 222	98.66 171	99.87 145	99.70 79

NR-MVSNet		99.40 111	99.31 118	99.68 106	99.43 263	99.55 138	99.73 27	99.50 244	99.46 132	99.88 61	99.36 288	97.54 253	99.87 203	98.97 140	99.87 145	99.63 127

LPG-MVS_test		99.22 158	99.05 179	99.74 79	99.82 72	99.63 115	99.16 183	99.73 114	97.56 330	99.64 155	99.69 151	99.37 56	99.89 175	96.66 317	99.87 145	99.69 83

LGP-MVS_train				99.74 79	99.82 72	99.63 115		99.73 114	97.56 330	99.64 155	99.69 151	99.37 56	99.89 175	96.66 317	99.87 145	99.69 83

COLMAP_ROB	98.06 12	99.45 97	99.37 106	99.70 103	99.83 65	99.70 90	99.38 113	99.78 92	99.53 121	99.67 148	99.78 101	99.19 77	99.86 222	97.32 275	99.87 145	99.55 174
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test20.03		99.55 76	99.54 77	99.58 156	99.79 98	99.37 178	99.02 226	99.89 40	99.60 115	99.82 81	99.62 196	98.81 121	99.89 175	99.43 72	99.86 153	99.47 218

Baseline_NR-MVSNet		99.49 85	99.37 106	99.82 37	99.91 31	99.84 24	98.83 256	99.86 49	99.68 90	99.65 154	99.88 42	97.67 246	99.87 203	99.03 133	99.86 153	99.76 66

EC-MVSNet		99.69 42	99.69 43	99.68 106	99.71 143	99.91 4	99.76 19	99.96 23	99.86 45	99.51 211	99.39 280	99.57 38	99.93 94	99.64 48	99.86 153	99.20 284

MSDG		99.08 194	98.98 204	99.37 219	99.60 182	99.13 223	97.54 371	99.74 110	98.84 226	99.53 204	99.55 240	99.10 87	99.79 305	97.07 295	99.86 153	99.18 289

EGC-MVSNET		89.05 373	85.52 376	99.64 128	99.89 39	99.78 49	99.56 81	99.52 236	24.19 407	49.96 408	99.83 66	99.15 81	99.92 116	97.71 245	99.85 157	99.21 280

Patchmatch-RL test		98.60 256	98.36 267	99.33 229	99.77 113	99.07 233	98.27 316	99.87 46	98.91 215	99.74 122	99.72 130	90.57 366	99.79 305	98.55 175	99.85 157	99.11 304

APDe-MVS		99.48 87	99.36 109	99.85 27	99.55 213	99.81 40	99.50 91	99.69 137	98.99 202	99.75 114	99.71 138	98.79 126	99.93 94	98.46 179	99.85 157	99.80 47
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMP	97.51 14	99.05 199	98.84 224	99.67 109	99.78 105	99.55 138	98.88 249	99.66 148	97.11 356	99.47 218	99.60 213	99.07 94	99.89 175	96.18 343	99.85 157	99.58 164
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PMVS	92.94 21	98.82 237	98.81 228	98.85 307	99.84 61	97.99 315	99.20 167	99.47 252	99.71 80	99.42 230	99.82 73	98.09 215	99.47 393	93.88 389	99.85 157	99.07 321
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Anonymous20231206		99.35 125	99.31 118	99.47 187	99.74 134	99.06 235	99.28 144	99.74 110	99.23 167	99.72 127	99.53 244	97.63 252	99.88 189	99.11 127	99.84 162	99.48 214

HPM-MVS_fast		99.43 102	99.30 123	99.80 45	99.83 65	99.81 40	99.52 86	99.70 131	98.35 284	99.51 211	99.50 251	99.31 62	99.88 189	98.18 203	99.84 162	99.69 83

XVG-ACMP-BASELINE		99.23 150	99.10 166	99.63 135	99.82 72	99.58 132	98.83 256	99.72 123	98.36 279	99.60 177	99.71 138	98.92 111	99.91 139	97.08 294	99.84 162	99.40 239

new_pmnet		98.88 232	98.89 218	98.84 309	99.70 151	97.62 334	98.15 325	99.50 244	97.98 309	99.62 168	99.54 242	98.15 212	99.94 77	97.55 261	99.84 162	98.95 336

Test_1112_low_res		98.95 223	98.73 233	99.63 135	99.68 163	99.15 222	98.09 333	99.80 80	97.14 354	99.46 222	99.40 276	96.11 303	99.89 175	99.01 135	99.84 162	99.84 36

1112_ss		99.05 199	98.84 224	99.67 109	99.66 169	99.29 194	98.52 298	99.82 67	97.65 328	99.43 228	99.16 325	96.42 293	99.91 139	99.07 131	99.84 162	99.80 47

3Dnovator	99.15 2	99.43 102	99.36 109	99.65 121	99.39 271	99.42 165	99.70 35	99.56 210	99.23 167	99.35 246	99.80 83	99.17 79	99.95 63	98.21 198	99.84 162	99.59 159

LF4IMVS		99.01 210	98.92 213	99.27 245	99.71 143	99.28 196	98.59 284	99.77 95	98.32 290	99.39 242	99.41 272	98.62 150	99.84 255	96.62 322	99.84 162	98.69 358

ACMMP_NAP		99.28 139	99.11 158	99.79 51	99.75 128	99.81 40	98.95 243	99.53 231	98.27 293	99.53 204	99.73 123	98.75 133	99.87 203	97.70 248	99.83 170	99.68 89

AllTest		99.21 163	99.07 173	99.63 135	99.78 105	99.64 110	99.12 197	99.83 62	98.63 250	99.63 159	99.72 130	98.68 141	99.75 322	96.38 335	99.83 170	99.51 200

TestCases				99.63 135	99.78 105	99.64 110		99.83 62	98.63 250	99.63 159	99.72 130	98.68 141	99.75 322	96.38 335	99.83 170	99.51 200

PM-MVS		99.36 123	99.29 128	99.58 156	99.83 65	99.66 101	98.95 243	99.86 49	98.85 223	99.81 88	99.73 123	98.40 187	99.92 116	98.36 184	99.83 170	99.17 291

EPNet		98.13 299	97.77 314	99.18 260	94.57 410	97.99 315	99.24 156	97.96 375	99.74 73	97.29 385	99.62 196	93.13 336	99.97 33	98.59 173	99.83 170	99.58 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended		98.70 249	98.59 244	99.02 284	99.54 215	97.99 315	97.58 370	99.82 67	95.70 377	99.34 249	98.98 352	98.52 170	99.77 316	97.98 217	99.83 170	99.30 265

MVS-HIRNet		97.86 308	98.22 280	96.76 376	99.28 306	91.53 403	98.38 309	92.60 406	99.13 189	99.31 258	99.96 12	97.18 271	99.68 355	98.34 186	99.83 170	99.07 321

RPSCF		99.18 172	99.02 189	99.64 128	99.83 65	99.85 19	99.44 105	99.82 67	98.33 289	99.50 213	99.78 101	97.90 229	99.65 370	96.78 310	99.83 170	99.44 228

TinyColmap		98.97 216	98.93 209	99.07 280	99.46 254	98.19 300	97.75 362	99.75 105	98.79 232	99.54 199	99.70 145	98.97 107	99.62 374	96.63 321	99.83 170	99.41 238

test_vis1_rt		99.45 97	99.46 89	99.41 208	99.71 143	98.63 274	98.99 236	99.96 23	99.03 200	99.95 31	99.12 331	98.75 133	99.84 255	99.82 35	99.82 179	99.77 60

MP-MVS-pluss		99.14 183	98.92 213	99.80 45	99.83 65	99.83 29	98.61 279	99.63 166	96.84 361	99.44 224	99.58 221	98.81 121	99.91 139	97.70 248	99.82 179	99.67 95
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MDA-MVSNet-bldmvs		99.06 196	99.05 179	99.07 280	99.80 86	97.83 326	98.89 248	99.72 123	99.29 155	99.63 159	99.70 145	96.47 291	99.89 175	98.17 205	99.82 179	99.50 205

jason		99.16 179	99.11 158	99.32 233	99.75 128	98.44 284	98.26 318	99.39 276	98.70 244	99.74 122	99.30 301	98.54 163	99.97 33	98.48 178	99.82 179	99.55 174
jason: jason.
HPM-MVS		99.25 146	99.07 173	99.78 54	99.81 80	99.75 67	99.61 67	99.67 144	97.72 325	99.35 246	99.25 312	99.23 73	99.92 116	97.21 289	99.82 179	99.67 95
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		98.49 272	98.11 290	99.64 128	99.73 137	99.58 132	99.24 156	99.76 100	89.94 399	99.42 230	99.56 233	97.76 241	99.86 222	97.74 242	99.82 179	99.47 218

CP-MVS		99.23 150	99.05 179	99.75 74	99.66 169	99.66 101	99.38 113	99.62 169	98.38 277	99.06 298	99.27 307	98.79 126	99.94 77	97.51 265	99.82 179	99.66 104

PHI-MVS		99.11 190	98.95 208	99.59 152	99.13 331	99.59 128	99.17 177	99.65 158	97.88 318	99.25 268	99.46 265	98.97 107	99.80 302	97.26 282	99.82 179	99.37 246

wuyk23d		97.58 321	99.13 151	92.93 388	99.69 155	99.49 145	99.52 86	99.77 95	97.97 310	99.96 23	99.79 93	99.84 12	99.94 77	95.85 357	99.82 179	79.36 404

CVMVSNet		98.61 254	98.88 219	97.80 356	99.58 191	93.60 393	99.26 149	99.64 164	99.66 98	99.72 127	99.67 166	93.26 334	99.93 94	99.30 97	99.81 188	99.87 30

UniMVSNet_NR-MVSNet		99.37 120	99.25 136	99.72 94	99.47 250	99.56 135	98.97 240	99.61 176	99.43 140	99.67 148	99.28 305	97.85 234	99.95 63	99.17 116	99.81 188	99.65 112

DU-MVS		99.33 133	99.21 140	99.71 99	99.43 263	99.56 135	98.83 256	99.53 231	99.38 146	99.67 148	99.36 288	97.67 246	99.95 63	99.17 116	99.81 188	99.63 127

DeepPCF-MVS	98.42 6	99.18 172	99.02 189	99.67 109	99.22 316	99.75 67	97.25 385	99.47 252	98.72 241	99.66 152	99.70 145	99.29 64	99.63 373	98.07 211	99.81 188	99.62 138

ACMM	98.09 11	99.46 95	99.38 103	99.72 94	99.80 86	99.69 94	99.13 193	99.65 158	98.99 202	99.64 155	99.72 130	99.39 50	99.86 222	98.23 196	99.81 188	99.60 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		99.22 158	99.04 185	99.77 57	99.76 117	99.73 76	99.28 144	99.56 210	98.19 298	99.14 287	99.29 304	98.84 120	99.92 116	97.53 264	99.80 193	99.64 122

test_0728_THIRD									99.18 175	99.62 168	99.61 205	98.58 157	99.91 139	97.72 243	99.80 193	99.77 60

SteuartSystems-ACMMP		99.30 137	99.14 149	99.76 64	99.87 51	99.66 101	99.18 172	99.60 188	98.55 258	99.57 185	99.67 166	99.03 100	99.94 77	97.01 296	99.80 193	99.69 83
Skip Steuart: Steuart Systems R&D Blog.
DP-MVS		99.48 87	99.39 101	99.74 79	99.57 201	99.62 117	99.29 142	99.61 176	99.87 41	99.74 122	99.76 111	98.69 140	99.87 203	98.20 199	99.80 193	99.75 69

PCF-MVS	96.03 18	96.73 341	95.86 352	99.33 229	99.44 259	99.16 220	96.87 394	99.44 260	86.58 401	98.95 304	99.40 276	94.38 322	99.88 189	87.93 400	99.80 193	98.95 336
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SMA-MVS		99.19 168	99.00 195	99.73 88	99.46 254	99.73 76	99.13 193	99.52 236	97.40 341	99.57 185	99.64 178	98.93 110	99.83 270	97.61 258	99.79 198	99.63 127
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
MTAPA		99.35 125	99.20 141	99.80 45	99.81 80	99.81 40	99.33 125	99.53 231	99.27 159	99.42 230	99.63 189	98.21 207	99.95 63	97.83 236	99.79 198	99.65 112

ACMMP++															99.79 198

ACMMP		99.25 146	99.08 169	99.74 79	99.79 98	99.68 97	99.50 91	99.65 158	98.07 304	99.52 206	99.69 151	98.57 158	99.92 116	97.18 291	99.79 198	99.63 127
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
OMC-MVS		98.90 228	98.72 234	99.44 195	99.39 271	99.42 165	98.58 286	99.64 164	97.31 346	99.44 224	99.62 196	98.59 155	99.69 343	96.17 344	99.79 198	99.22 278

tfpnnormal		99.43 102	99.38 103	99.60 150	99.87 51	99.75 67	99.59 74	99.78 92	99.71 80	99.90 49	99.69 151	98.85 119	99.90 157	97.25 286	99.78 203	99.15 295

HQP_MVS		98.90 228	98.68 238	99.55 168	99.58 191	99.24 207	98.80 264	99.54 222	98.94 209	99.14 287	99.25 312	97.24 265	99.82 279	95.84 358	99.78 203	99.60 152

plane_prior5								99.54 222					99.82 279	95.84 358	99.78 203	99.60 152

mPP-MVS		99.19 168	99.00 195	99.76 64	99.76 117	99.68 97	99.38 113	99.54 222	98.34 288	99.01 300	99.50 251	98.53 167	99.93 94	97.18 291	99.78 203	99.66 104

OPM-MVS		99.26 145	99.13 151	99.63 135	99.70 151	99.61 123	98.58 286	99.48 249	98.50 265	99.52 206	99.63 189	99.14 84	99.76 318	97.89 225	99.77 207	99.51 200
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_111021_LR		99.13 185	99.03 187	99.42 201	99.58 191	99.32 190	97.91 356	99.73 114	98.68 245	99.31 258	99.48 258	99.09 89	99.66 364	97.70 248	99.77 207	99.29 268

MIMVSNet		98.43 278	98.20 282	99.11 272	99.53 221	98.38 291	99.58 76	98.61 353	98.96 206	99.33 251	99.76 111	90.92 359	99.81 294	97.38 272	99.76 209	99.15 295

MVS_111021_HR		99.12 187	99.02 189	99.40 210	99.50 234	99.11 225	97.92 354	99.71 126	98.76 239	99.08 294	99.47 262	99.17 79	99.54 385	97.85 232	99.76 209	99.54 182

DPE-MVS		99.14 183	98.92 213	99.82 37	99.57 201	99.77 54	98.74 271	99.60 188	98.55 258	99.76 108	99.69 151	98.23 206	99.92 116	96.39 334	99.75 211	99.76 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + MP.		99.34 130	99.24 138	99.63 135	99.82 72	99.37 178	99.26 149	99.35 285	98.77 236	99.57 185	99.70 145	99.27 69	99.88 189	97.71 245	99.75 211	99.65 112
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HFP-MVS		99.25 146	99.08 169	99.76 64	99.73 137	99.70 90	99.31 132	99.59 194	98.36 279	99.36 245	99.37 284	98.80 125	99.91 139	97.43 269	99.75 211	99.68 89

ACMMPR		99.23 150	99.06 175	99.76 64	99.74 134	99.69 94	99.31 132	99.59 194	98.36 279	99.35 246	99.38 282	98.61 152	99.93 94	97.43 269	99.75 211	99.67 95

MP-MVS		99.06 196	98.83 226	99.76 64	99.76 117	99.71 83	99.32 127	99.50 244	98.35 284	98.97 302	99.48 258	98.37 189	99.92 116	95.95 354	99.75 211	99.63 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
QAPM		98.40 282	97.99 296	99.65 121	99.39 271	99.47 147	99.67 49	99.52 236	91.70 396	98.78 328	99.80 83	98.55 161	99.95 63	94.71 378	99.75 211	99.53 187

DeepC-MVS_fast	98.47 5	99.23 150	99.12 155	99.56 165	99.28 306	99.22 211	98.99 236	99.40 273	99.08 194	99.58 182	99.64 178	98.90 116	99.83 270	97.44 268	99.75 211	99.63 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		99.16 179	98.96 207	99.75 74	99.73 137	99.73 76	99.20 167	99.55 216	98.22 295	99.32 254	99.35 293	98.65 148	99.91 139	96.86 305	99.74 218	99.62 138

region2R		99.23 150	99.05 179	99.77 57	99.76 117	99.70 90	99.31 132	99.59 194	98.41 273	99.32 254	99.36 288	98.73 137	99.93 94	97.29 277	99.74 218	99.67 95

PGM-MVS		99.20 165	99.01 192	99.77 57	99.75 128	99.71 83	99.16 183	99.72 123	97.99 308	99.42 230	99.60 213	98.81 121	99.93 94	96.91 302	99.74 218	99.66 104

TransMVSNet (Re)		99.78 27	99.77 33	99.81 40	99.91 31	99.85 19	99.75 22	99.86 49	99.70 85	99.91 44	99.89 34	99.60 34	99.87 203	99.59 51	99.74 218	99.71 76

TSAR-MVS + GP.		99.12 187	99.04 185	99.38 216	99.34 290	99.16 220	98.15 325	99.29 298	98.18 299	99.63 159	99.62 196	99.18 78	99.68 355	98.20 199	99.74 218	99.30 265

KD-MVS_self_test		99.63 59	99.59 64	99.76 64	99.84 61	99.90 7	99.37 117	99.79 86	99.83 56	99.88 61	99.85 56	98.42 182	99.90 157	99.60 50	99.73 223	99.49 210

XVS		99.27 143	99.11 158	99.75 74	99.71 143	99.71 83	99.37 117	99.61 176	99.29 155	98.76 329	99.47 262	98.47 174	99.88 189	97.62 256	99.73 223	99.67 95

X-MVStestdata		96.09 356	94.87 368	99.75 74	99.71 143	99.71 83	99.37 117	99.61 176	99.29 155	98.76 329	61.30 414	98.47 174	99.88 189	97.62 256	99.73 223	99.67 95

VDD-MVS		99.20 165	99.11 158	99.44 195	99.43 263	98.98 238	99.50 91	98.32 369	99.80 64	99.56 192	99.69 151	96.99 277	99.85 240	98.99 136	99.73 223	99.50 205

ab-mvs		99.33 133	99.28 130	99.47 187	99.57 201	99.39 173	99.78 12	99.43 263	98.87 220	99.57 185	99.82 73	98.06 218	99.87 203	98.69 169	99.73 223	99.15 295

TAPA-MVS	97.92 13	98.03 304	97.55 320	99.46 189	99.47 250	99.44 158	98.50 300	99.62 169	86.79 400	99.07 297	99.26 310	98.26 201	99.62 374	97.28 279	99.73 223	99.31 263
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DVP-MVS		99.32 135	99.17 144	99.77 57	99.69 155	99.80 44	99.14 187	99.31 294	99.16 183	99.62 168	99.61 205	98.35 191	99.91 139	97.88 226	99.72 229	99.61 148
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.83 33	99.70 151	99.79 46	99.14 187	99.61 176					99.92 116	97.88 226	99.72 229	99.77 60

3Dnovator+	98.92 3	99.35 125	99.24 138	99.67 109	99.35 281	99.47 147	99.62 62	99.50 244	99.44 135	99.12 290	99.78 101	98.77 130	99.94 77	97.87 229	99.72 229	99.62 138

plane_prior						99.24 207	98.42 307		97.87 319						99.71 232

APD-MVS		98.87 233	98.59 244	99.71 99	99.50 234	99.62 117	99.01 228	99.57 205	96.80 363	99.54 199	99.63 189	98.29 198	99.91 139	95.24 370	99.71 232	99.61 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD_test1		99.36 123	99.28 130	99.61 147	99.89 39	99.89 10	99.32 127	99.74 110	99.18 175	99.69 139	99.75 116	98.41 183	99.84 255	97.85 232	99.70 234	99.10 306

SED-MVS		99.40 111	99.28 130	99.77 57	99.69 155	99.82 35	99.20 167	99.54 222	99.13 189	99.82 81	99.63 189	98.91 113	99.92 116	97.85 232	99.70 234	99.58 164

IU-MVS					99.69 155	99.77 54		99.22 315	97.50 336	99.69 139				97.75 241	99.70 234	99.77 60

ambc				99.20 257	99.35 281	98.53 278	99.17 177	99.46 255		99.67 148	99.80 83	98.46 177	99.70 337	97.92 222	99.70 234	99.38 243

MSC_two_6792asdad				99.74 79	99.03 350	99.53 141		99.23 312					99.92 116	97.77 237	99.69 238	99.78 56

No_MVS				99.74 79	99.03 350	99.53 141		99.23 312					99.92 116	97.77 237	99.69 238	99.78 56

test_241102_TWO								99.54 222	99.13 189	99.76 108	99.63 189	98.32 196	99.92 116	97.85 232	99.69 238	99.75 69

MVSFormer		99.41 109	99.44 94	99.31 236	99.57 201	98.40 287	99.77 15	99.80 80	99.73 74	99.63 159	99.30 301	98.02 221	99.98 20	99.43 72	99.69 238	99.55 174

lupinMVS		98.96 220	98.87 220	99.24 253	99.57 201	98.40 287	98.12 329	99.18 322	98.28 292	99.63 159	99.13 327	98.02 221	99.97 33	98.22 197	99.69 238	99.35 252

SF-MVS		99.10 193	98.93 209	99.62 144	99.58 191	99.51 143	99.13 193	99.65 158	97.97 310	99.42 230	99.61 205	98.86 118	99.87 203	96.45 332	99.68 243	99.49 210

Anonymous20240529		99.42 105	99.34 111	99.65 121	99.53 221	99.60 126	99.63 60	99.39 276	99.47 129	99.76 108	99.78 101	98.13 213	99.86 222	98.70 167	99.68 243	99.49 210

MSLP-MVS++		99.05 199	99.09 167	98.91 298	99.21 318	98.36 292	98.82 260	99.47 252	98.85 223	98.90 312	99.56 233	98.78 128	99.09 400	98.57 174	99.68 243	99.26 270

DELS-MVS		99.34 130	99.30 123	99.48 185	99.51 228	99.36 182	98.12 329	99.53 231	99.36 149	99.41 236	99.61 205	99.22 74	99.87 203	99.21 107	99.68 243	99.20 284
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
PVSNet	97.47 15	98.42 279	98.44 259	98.35 335	99.46 254	96.26 366	96.70 396	99.34 287	97.68 327	99.00 301	99.13 327	97.40 258	99.72 330	97.59 260	99.68 243	99.08 316

LS3D		99.24 149	99.11 158	99.61 147	98.38 393	99.79 46	99.57 79	99.68 140	99.61 109	99.15 285	99.71 138	98.70 139	99.91 139	97.54 262	99.68 243	99.13 303

HQP3-MVS								99.37 281							99.67 249

CPTT-MVS		98.74 244	98.44 259	99.64 128	99.61 180	99.38 175	99.18 172	99.55 216	96.49 365	99.27 266	99.37 284	97.11 273	99.92 116	95.74 361	99.67 249	99.62 138

HQP-MVS		98.36 284	98.02 295	99.39 213	99.31 297	98.94 243	97.98 346	99.37 281	97.45 338	98.15 361	98.83 366	96.67 284	99.70 337	94.73 376	99.67 249	99.53 187

MVS_Test		99.28 139	99.31 118	99.19 258	99.35 281	98.79 258	99.36 120	99.49 248	99.17 180	99.21 277	99.67 166	98.78 128	99.66 364	99.09 129	99.66 252	99.10 306

CDPH-MVS		98.56 262	98.20 282	99.61 147	99.50 234	99.46 151	98.32 313	99.41 266	95.22 382	99.21 277	99.10 335	98.34 193	99.82 279	95.09 374	99.66 252	99.56 171

tttt0517		97.62 319	97.20 328	98.90 304	99.76 117	97.40 342	99.48 96	94.36 401	99.06 198	99.70 136	99.49 255	84.55 393	99.94 77	98.73 165	99.65 254	99.36 249

ITE_SJBPF				99.38 216	99.63 175	99.44 158		99.73 114	98.56 257	99.33 251	99.53 244	98.88 117	99.68 355	96.01 348	99.65 254	99.02 330

9.14			98.64 239		99.45 258		98.81 261	99.60 188	97.52 335	99.28 265	99.56 233	98.53 167	99.83 270	95.36 369	99.64 256

Patchmatch-test		98.10 301	97.98 298	98.48 330	99.27 308	96.48 361	99.40 109	99.07 330	98.81 229	99.23 272	99.57 229	90.11 370	99.87 203	96.69 314	99.64 256	99.09 310

sss		98.90 228	98.77 232	99.27 245	99.48 244	98.44 284	98.72 273	99.32 290	97.94 314	99.37 244	99.35 293	96.31 298	99.91 139	98.85 150	99.63 258	99.47 218

cl22		97.56 322	97.28 325	98.40 333	98.37 394	96.75 358	97.24 386	99.37 281	97.31 346	99.41 236	99.22 319	87.30 380	99.37 397	97.70 248	99.62 259	99.08 316

miper_ehance_all_eth		98.59 259	98.59 244	98.59 325	98.98 356	97.07 351	97.49 376	99.52 236	98.50 265	99.52 206	99.37 284	96.41 295	99.71 334	97.86 230	99.62 259	99.00 332

miper_enhance_ethall		98.03 304	97.94 304	98.32 338	98.27 396	96.43 363	96.95 392	99.41 266	96.37 368	99.43 228	98.96 356	94.74 318	99.69 343	97.71 245	99.62 259	98.83 350

SCA		98.11 300	98.36 267	97.36 367	99.20 321	92.99 395	98.17 324	98.49 360	98.24 294	99.10 293	99.57 229	96.01 305	99.94 77	96.86 305	99.62 259	99.14 300

MS-PatchMatch		99.00 212	98.97 205	99.09 275	99.11 338	98.19 300	98.76 270	99.33 288	98.49 267	99.44 224	99.58 221	98.21 207	99.69 343	98.20 199	99.62 259	99.39 241

APD-MVS_3200maxsize		99.31 136	99.16 145	99.74 79	99.53 221	99.75 67	99.27 147	99.61 176	99.19 174	99.57 185	99.64 178	98.76 131	99.90 157	97.29 277	99.62 259	99.56 171

EPNet_dtu		97.62 319	97.79 313	97.11 374	96.67 407	92.31 398	98.51 299	98.04 373	99.24 165	95.77 401	99.47 262	93.78 329	99.66 364	98.98 138	99.62 259	99.37 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SR-MVS-dyc-post		99.27 143	99.11 158	99.73 88	99.54 215	99.74 73	99.26 149	99.62 169	99.16 183	99.52 206	99.64 178	98.41 183	99.91 139	97.27 280	99.61 266	99.54 182

RE-MVS-def			99.13 151		99.54 215	99.74 73	99.26 149	99.62 169	99.16 183	99.52 206	99.64 178	98.57 158		97.27 280	99.61 266	99.54 182

MG-MVS		98.52 267	98.39 264	98.94 292	99.15 328	97.39 343	98.18 322	99.21 318	98.89 219	99.23 272	99.63 189	97.37 261	99.74 325	94.22 383	99.61 266	99.69 83

DVP-MVS++		99.38 117	99.25 136	99.77 57	99.03 350	99.77 54	99.74 24	99.61 176	99.18 175	99.76 108	99.61 205	99.00 101	99.92 116	97.72 243	99.60 269	99.62 138

PC_three_1452									97.56 330	99.68 142	99.41 272	99.09 89	97.09 406	96.66 317	99.60 269	99.62 138

OPU-MVS				99.29 240	99.12 333	99.44 158	99.20 167				99.40 276	99.00 101	98.84 403	96.54 324	99.60 269	99.58 164

HPM-MVS++		98.96 220	98.70 237	99.74 79	99.52 226	99.71 83	98.86 251	99.19 321	98.47 269	98.59 343	99.06 338	98.08 217	99.91 139	96.94 300	99.60 269	99.60 152

CNVR-MVS		98.99 215	98.80 230	99.56 165	99.25 311	99.43 162	98.54 295	99.27 302	98.58 256	98.80 324	99.43 270	98.53 167	99.70 337	97.22 288	99.59 273	99.54 182

Anonymous202405211		98.75 243	98.46 257	99.63 135	99.34 290	99.66 101	99.47 99	97.65 381	99.28 158	99.56 192	99.50 251	93.15 335	99.84 255	98.62 172	99.58 274	99.40 239

MVP-Stereo		99.16 179	99.08 169	99.43 199	99.48 244	99.07 233	99.08 212	99.55 216	98.63 250	99.31 258	99.68 162	98.19 209	99.78 308	98.18 203	99.58 274	99.45 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ADS-MVSNet2		97.78 312	97.66 319	98.12 346	99.14 329	95.36 378	99.22 164	98.75 345	96.97 357	98.25 357	99.64 178	90.90 360	99.94 77	96.51 326	99.56 276	99.08 316

ADS-MVSNet		97.72 317	97.67 318	97.86 354	99.14 329	94.65 386	99.22 164	98.86 339	96.97 357	98.25 357	99.64 178	90.90 360	99.84 255	96.51 326	99.56 276	99.08 316

LCM-MVSNet-Re		99.28 139	99.15 148	99.67 109	99.33 295	99.76 61	99.34 122	99.97 18	98.93 212	99.91 44	99.79 93	98.68 141	99.93 94	96.80 309	99.56 276	99.30 265

API-MVS		98.38 283	98.39 264	98.35 335	98.83 367	99.26 200	99.14 187	99.18 322	98.59 255	98.66 337	98.78 369	98.61 152	99.57 382	94.14 384	99.56 276	96.21 401

xiu_mvs_v1_base_debu		99.23 150	99.34 111	98.91 298	99.59 186	98.23 296	98.47 302	99.66 148	99.61 109	99.68 142	98.94 358	99.39 50	99.97 33	99.18 113	99.55 280	98.51 368

xiu_mvs_v1_base		99.23 150	99.34 111	98.91 298	99.59 186	98.23 296	98.47 302	99.66 148	99.61 109	99.68 142	98.94 358	99.39 50	99.97 33	99.18 113	99.55 280	98.51 368

xiu_mvs_v1_base_debi		99.23 150	99.34 111	98.91 298	99.59 186	98.23 296	98.47 302	99.66 148	99.61 109	99.68 142	98.94 358	99.39 50	99.97 33	99.18 113	99.55 280	98.51 368

OpenMVS	98.12 10	98.23 295	97.89 309	99.26 248	99.19 323	99.26 200	99.65 58	99.69 137	91.33 397	98.14 365	99.77 108	98.28 199	99.96 54	95.41 367	99.55 280	98.58 364

MVE	92.54 22	96.66 343	96.11 347	98.31 340	99.68 163	97.55 336	97.94 351	95.60 397	99.37 147	90.68 407	98.70 373	96.56 287	98.61 405	86.94 405	99.55 280	98.77 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SR-MVS		99.19 168	99.00 195	99.74 79	99.51 228	99.72 81	99.18 172	99.60 188	98.85 223	99.47 218	99.58 221	98.38 188	99.92 116	96.92 301	99.54 285	99.57 169

thisisatest0530		97.45 324	96.95 334	98.94 292	99.68 163	97.73 331	99.09 209	94.19 403	98.61 254	99.56 192	99.30 301	84.30 394	99.93 94	98.27 193	99.54 285	99.16 293

tt0805		99.63 59	99.57 71	99.81 40	99.87 51	99.88 12	99.58 76	98.70 347	99.72 78	99.91 44	99.60 213	99.43 48	99.81 294	99.81 36	99.53 287	99.73 71

MSP-MVS		99.04 202	98.79 231	99.81 40	99.78 105	99.73 76	99.35 121	99.57 205	98.54 261	99.54 199	98.99 349	96.81 281	99.93 94	96.97 299	99.53 287	99.77 60
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
AdaColmap		98.60 256	98.35 269	99.38 216	99.12 333	99.22 211	98.67 276	99.42 265	97.84 322	98.81 322	99.27 307	97.32 263	99.81 294	95.14 372	99.53 287	99.10 306

ETV-MVS		99.18 172	99.18 143	99.16 263	99.34 290	99.28 196	99.12 197	99.79 86	99.48 125	98.93 306	98.55 379	99.40 49	99.93 94	98.51 177	99.52 290	98.28 380

SSC-MVS		99.52 81	99.42 98	99.83 33	99.86 54	99.65 107	99.52 86	99.81 76	99.87 41	99.81 88	99.79 93	96.78 282	99.99 7	99.83 32	99.51 291	99.86 32

EIA-MVS		99.12 187	99.01 192	99.45 192	99.36 279	99.62 117	99.34 122	99.79 86	98.41 273	98.84 319	98.89 363	98.75 133	99.84 255	98.15 207	99.51 291	98.89 344

MCST-MVS		99.02 205	98.81 228	99.65 121	99.58 191	99.49 145	98.58 286	99.07 330	98.40 275	99.04 299	99.25 312	98.51 172	99.80 302	97.31 276	99.51 291	99.65 112

mvs_anonymous		99.28 139	99.39 101	98.94 292	99.19 323	97.81 327	99.02 226	99.55 216	99.78 68	99.85 73	99.80 83	98.24 202	99.86 222	99.57 56	99.50 294	99.15 295

CNLPA		98.57 261	98.34 270	99.28 242	99.18 325	99.10 230	98.34 311	99.41 266	98.48 268	98.52 347	98.98 352	97.05 275	99.78 308	95.59 363	99.50 294	98.96 334

ZD-MVS					99.43 263	99.61 123		99.43 263	96.38 367	99.11 291	99.07 337	97.86 232	99.92 116	94.04 386	99.49 296

test_prior2							97.95 350		97.87 319	98.05 367	99.05 339	97.90 229		95.99 351	99.49 296

pmmvs3		98.08 302	97.80 311	98.91 298	99.41 269	97.69 333	97.87 358	99.66 148	95.87 373	99.50 213	99.51 248	90.35 368	99.97 33	98.55 175	99.47 298	99.08 316

test12				99.54 173	99.29 303	99.33 188		99.16 324		98.43 352		97.54 253	99.82 279		99.47 298	99.48 214

agg_prior2														94.58 379	99.46 300	99.50 205

test9_res														95.10 373	99.44 301	99.50 205

train_agg		98.35 287	97.95 300	99.57 162	99.35 281	99.35 185	98.11 331	99.41 266	94.90 386	97.92 371	98.99 349	98.02 221	99.85 240	95.38 368	99.44 301	99.50 205

VPNet		99.46 95	99.37 106	99.71 99	99.82 72	99.59 128	99.48 96	99.70 131	99.81 61	99.69 139	99.58 221	97.66 250	99.86 222	99.17 116	99.44 301	99.67 95

DP-MVS Recon		98.50 270	98.23 278	99.31 236	99.49 239	99.46 151	98.56 291	99.63 166	94.86 388	98.85 318	99.37 284	97.81 236	99.59 380	96.08 345	99.44 301	98.88 345

LFMVS		98.46 275	98.19 285	99.26 248	99.24 313	98.52 280	99.62 62	96.94 390	99.87 41	99.31 258	99.58 221	91.04 357	99.81 294	98.68 170	99.42 305	99.45 223

Fast-Effi-MVS+		99.02 205	98.87 220	99.46 189	99.38 274	99.50 144	99.04 219	99.79 86	97.17 352	98.62 340	98.74 371	99.34 60	99.95 63	98.32 188	99.41 306	98.92 340

PatchmatchNet		97.65 318	97.80 311	97.18 372	98.82 370	92.49 397	99.17 177	98.39 365	98.12 300	98.79 326	99.58 221	90.71 364	99.89 175	97.23 287	99.41 306	99.16 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thisisatest0515		96.98 335	96.42 342	98.66 322	99.42 268	97.47 338	97.27 384	94.30 402	97.24 348	99.15 285	98.86 365	85.01 391	99.87 203	97.10 293	99.39 308	98.63 359

原ACMM1				99.37 219	99.47 250	98.87 253		99.27 302	96.74 364	98.26 356	99.32 297	97.93 228	99.82 279	95.96 353	99.38 309	99.43 234

test222					99.51 228	99.08 232	97.83 360	99.29 298	95.21 383	98.68 336	99.31 299	97.28 264			99.38 309	99.43 234

F-COLMAP		98.74 244	98.45 258	99.62 144	99.57 201	99.47 147	98.84 254	99.65 158	96.31 369	98.93 306	99.19 324	97.68 245	99.87 203	96.52 325	99.37 311	99.53 187

DPM-MVS		98.28 290	97.94 304	99.32 233	99.36 279	99.11 225	97.31 383	98.78 344	96.88 359	98.84 319	99.11 334	97.77 239	99.61 378	94.03 387	99.36 312	99.23 276

旧先验1					99.49 239	99.29 194		99.26 305			99.39 280	97.67 246			99.36 312	99.46 222

dmvs_re		98.69 250	98.48 255	99.31 236	99.55 213	99.42 165	99.54 84	98.38 366	99.32 153	98.72 332	98.71 372	96.76 283	99.21 398	96.01 348	99.35 314	99.31 263

PS-MVSNAJ		99.00 212	99.08 169	98.76 317	99.37 276	98.10 308	98.00 344	99.51 240	99.47 129	99.41 236	98.50 382	99.28 66	99.97 33	98.83 151	99.34 315	98.20 386

testing3		96.48 346	95.63 357	99.01 285	99.23 315	97.81 327	98.90 247	99.10 329	98.72 241	97.84 377	97.92 393	72.44 410	99.85 240	97.21 289	99.33 316	99.35 252

xiu_mvs_v2_base		99.02 205	99.11 158	98.77 316	99.37 276	98.09 309	98.13 328	99.51 240	99.47 129	99.42 230	98.54 380	99.38 54	99.97 33	98.83 151	99.33 316	98.24 382

新几何1				99.52 176	99.50 234	99.22 211		99.26 305	95.66 378	98.60 342	99.28 305	97.67 246	99.89 175	95.95 354	99.32 318	99.45 223

VDDNet		98.97 216	98.82 227	99.42 201	99.71 143	98.81 255	99.62 62	98.68 348	99.81 61	99.38 243	99.80 83	94.25 323	99.85 240	98.79 157	99.32 318	99.59 159

FA-MVS(test-final)		98.52 267	98.32 273	99.10 274	99.48 244	98.67 266	99.77 15	98.60 355	97.35 344	99.63 159	99.80 83	93.07 337	99.84 255	97.92 222	99.30 320	98.78 354

VNet		99.18 172	99.06 175	99.56 165	99.24 313	99.36 182	99.33 125	99.31 294	99.67 94	99.47 218	99.57 229	96.48 290	99.84 255	99.15 119	99.30 320	99.47 218

PatchMatch-RL		98.68 251	98.47 256	99.30 239	99.44 259	99.28 196	98.14 327	99.54 222	97.12 355	99.11 291	99.25 312	97.80 237	99.70 337	96.51 326	99.30 320	98.93 338

Effi-MVS+-dtu		99.07 195	98.92 213	99.52 176	98.89 363	99.78 49	99.15 185	99.66 148	99.34 150	98.92 309	99.24 317	97.69 244	99.98 20	98.11 209	99.28 323	98.81 351

testdata				99.42 201	99.51 228	98.93 246		99.30 297	96.20 370	98.87 316	99.40 276	98.33 195	99.89 175	96.29 338	99.28 323	99.44 228

OpenMVS_ROB	97.31 17	97.36 328	96.84 338	98.89 305	99.29 303	99.45 156	98.87 250	99.48 249	86.54 402	99.44 224	99.74 119	97.34 262	99.86 222	91.61 393	99.28 323	97.37 397

NCCC		98.82 237	98.57 248	99.58 156	99.21 318	99.31 191	98.61 279	99.25 308	98.65 248	98.43 352	99.26 310	97.86 232	99.81 294	96.55 323	99.27 326	99.61 148

testgi		99.29 138	99.26 134	99.37 219	99.75 128	98.81 255	98.84 254	99.89 40	98.38 277	99.75 114	99.04 341	99.36 59	99.86 222	99.08 130	99.25 327	99.45 223

PLC	97.35 16	98.36 284	97.99 296	99.48 185	99.32 296	99.24 207	98.50 300	99.51 240	95.19 384	98.58 344	98.96 356	96.95 278	99.83 270	95.63 362	99.25 327	99.37 246
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		99.20 165	99.12 155	99.43 199	99.25 311	99.69 94	99.05 216	99.82 67	99.50 123	98.97 302	99.05 339	98.98 105	99.98 20	98.20 199	99.24 329	98.62 360

PMMVS		98.49 272	98.29 276	99.11 272	98.96 357	98.42 286	97.54 371	99.32 290	97.53 334	98.47 350	98.15 389	97.88 231	99.82 279	97.46 267	99.24 329	99.09 310

WB-MVS		99.44 99	99.32 116	99.80 45	99.81 80	99.61 123	99.47 99	99.81 76	99.82 58	99.71 132	99.72 130	96.60 286	99.98 20	99.75 39	99.23 331	99.82 46

EPMVS		96.53 345	96.32 343	97.17 373	98.18 399	92.97 396	99.39 111	89.95 410	98.21 296	98.61 341	99.59 218	86.69 389	99.72 330	96.99 297	99.23 331	98.81 351

alignmvs		98.28 290	97.96 299	99.25 251	99.12 333	98.93 246	99.03 223	98.42 363	99.64 102	98.72 332	97.85 394	90.86 362	99.62 374	98.88 149	99.13 333	99.19 287

FE-MVS		97.85 309	97.42 322	99.15 265	99.44 259	98.75 261	99.77 15	98.20 372	95.85 374	99.33 251	99.80 83	88.86 376	99.88 189	96.40 333	99.12 334	98.81 351

cascas		96.99 334	96.82 340	97.48 363	97.57 406	95.64 375	96.43 398	99.56 210	91.75 395	97.13 390	97.61 399	95.58 310	98.63 404	96.68 315	99.11 335	98.18 387

BH-RMVSNet		98.41 280	98.14 288	99.21 255	99.21 318	98.47 281	98.60 281	98.26 370	98.35 284	98.93 306	99.31 299	97.20 270	99.66 364	94.32 381	99.10 336	99.51 200

UWE-MVS		96.21 354	95.78 354	97.49 362	98.53 388	93.83 392	98.04 339	93.94 404	98.96 206	98.46 351	98.17 388	79.86 399	99.87 203	96.99 297	99.06 337	98.78 354

MAR-MVS		98.24 294	97.92 306	99.19 258	98.78 375	99.65 107	99.17 177	99.14 326	95.36 380	98.04 368	98.81 368	97.47 255	99.72 330	95.47 366	99.06 337	98.21 384
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
GA-MVS		97.99 307	97.68 317	98.93 295	99.52 226	98.04 313	97.19 387	99.05 333	98.32 290	98.81 322	98.97 354	89.89 373	99.41 396	98.33 187	99.05 339	99.34 255

EMVS		96.96 336	97.28 325	95.99 386	98.76 378	91.03 405	95.26 401	98.61 353	99.34 150	98.92 309	98.88 364	93.79 328	99.66 364	92.87 390	99.05 339	97.30 398

E-PMN		97.14 333	97.43 321	96.27 383	98.79 373	91.62 402	95.54 400	99.01 336	99.44 135	98.88 313	99.12 331	92.78 340	99.68 355	94.30 382	99.03 341	97.50 394

tpmrst		97.73 314	98.07 292	96.73 378	98.71 382	92.00 399	99.10 204	98.86 339	98.52 263	98.92 309	99.54 242	91.90 347	99.82 279	98.02 212	99.03 341	98.37 377

PatchT		98.45 277	98.32 273	98.83 311	98.94 358	98.29 294	99.24 156	98.82 342	99.84 53	99.08 294	99.76 111	91.37 352	99.94 77	98.82 153	99.00 343	98.26 381

WB-MVSnew		98.34 289	98.14 288	98.96 289	98.14 402	97.90 324	98.27 316	97.26 389	98.63 250	98.80 324	98.00 392	97.77 239	99.90 157	97.37 273	98.98 344	99.09 310

CL-MVSNet_self_test		98.71 248	98.56 251	99.15 265	99.22 316	98.66 269	97.14 388	99.51 240	98.09 303	99.54 199	99.27 307	96.87 280	99.74 325	98.43 180	98.96 345	99.03 326

test_yl		98.25 292	97.95 300	99.13 270	99.17 326	98.47 281	99.00 231	98.67 350	98.97 204	99.22 275	99.02 347	91.31 353	99.69 343	97.26 282	98.93 346	99.24 273

DCV-MVSNet		98.25 292	97.95 300	99.13 270	99.17 326	98.47 281	99.00 231	98.67 350	98.97 204	99.22 275	99.02 347	91.31 353	99.69 343	97.26 282	98.93 346	99.24 273

MGCFI-Net		99.02 205	99.00 195	99.09 275	99.10 339	98.70 264	99.61 67	99.66 148	99.63 104	98.64 338	97.65 397	99.04 98	99.54 385	98.79 157	98.92 348	99.04 324

canonicalmvs		99.02 205	99.00 195	99.09 275	99.10 339	98.70 264	99.61 67	99.66 148	99.63 104	98.64 338	97.65 397	99.04 98	99.54 385	98.79 157	98.92 348	99.04 324

MDTV_nov1_ep13			97.73 315		98.70 383	90.83 406	99.15 185	98.02 374	98.51 264	98.82 321	99.61 205	90.98 358	99.66 364	96.89 304	98.92 348

PAPM_NR		98.36 284	98.04 293	99.33 229	99.48 244	98.93 246	98.79 267	99.28 301	97.54 333	98.56 346	98.57 377	97.12 272	99.69 343	94.09 385	98.90 351	99.38 243

FPMVS		96.32 350	95.50 358	98.79 315	99.60 182	98.17 303	98.46 306	98.80 343	97.16 353	96.28 397	99.63 189	82.19 395	99.09 400	88.45 399	98.89 352	99.10 306

tpm cat1		96.78 339	96.98 333	96.16 385	98.85 366	90.59 409	99.08 212	99.32 290	92.37 394	97.73 382	99.46 265	91.15 356	99.69 343	96.07 346	98.80 353	98.21 384

test-LLR		97.15 331	96.95 334	97.74 359	98.18 399	95.02 383	97.38 379	96.10 392	98.00 306	97.81 378	98.58 375	90.04 371	99.91 139	97.69 254	98.78 354	98.31 378

test-mter		96.23 353	95.73 355	97.74 359	98.18 399	95.02 383	97.38 379	96.10 392	97.90 315	97.81 378	98.58 375	79.12 403	99.91 139	97.69 254	98.78 354	98.31 378

TESTMET0.1,1		96.24 352	95.84 353	97.41 366	98.24 397	93.84 391	97.38 379	95.84 396	98.43 270	97.81 378	98.56 378	79.77 400	99.89 175	97.77 237	98.77 356	98.52 367

CR-MVSNet		98.35 287	98.20 282	98.83 311	99.05 347	98.12 305	99.30 135	99.67 144	97.39 342	99.16 283	99.79 93	91.87 349	99.91 139	98.78 161	98.77 356	98.44 375

RPMNet		98.60 256	98.53 253	98.83 311	99.05 347	98.12 305	99.30 135	99.62 169	99.86 45	99.16 283	99.74 119	92.53 343	99.92 116	98.75 163	98.77 356	98.44 375

WTY-MVS		98.59 259	98.37 266	99.26 248	99.43 263	98.40 287	98.74 271	99.13 328	98.10 301	99.21 277	99.24 317	94.82 316	99.90 157	97.86 230	98.77 356	99.49 210

Effi-MVS+		99.06 196	98.97 205	99.34 226	99.31 297	98.98 238	98.31 314	99.91 33	98.81 229	98.79 326	98.94 358	99.14 84	99.84 255	98.79 157	98.74 360	99.20 284

PAPR		97.56 322	97.07 330	99.04 283	98.80 371	98.11 307	97.63 367	99.25 308	94.56 391	98.02 369	98.25 387	97.43 257	99.68 355	90.90 396	98.74 360	99.33 256

Syy-MVS		98.17 298	97.85 310	99.15 265	98.50 390	98.79 258	98.60 281	99.21 318	97.89 316	96.76 392	96.37 411	95.47 312	99.57 382	99.10 128	98.73 362	99.09 310

myMVS_eth3d		95.63 367	94.73 369	98.34 337	98.50 390	96.36 364	98.60 281	99.21 318	97.89 316	96.76 392	96.37 411	72.10 411	99.57 382	94.38 380	98.73 362	99.09 310

tpmvs		97.39 326	97.69 316	96.52 380	98.41 392	91.76 400	99.30 135	98.94 338	97.74 324	97.85 376	99.55 240	92.40 346	99.73 328	96.25 340	98.73 362	98.06 389

dp		96.86 337	97.07 330	96.24 384	98.68 384	90.30 410	99.19 171	98.38 366	97.35 344	98.23 359	99.59 218	87.23 381	99.82 279	96.27 339	98.73 362	98.59 362

XVG-OURS-SEG-HR		99.16 179	98.99 201	99.66 116	99.84 61	99.64 110	98.25 319	99.73 114	98.39 276	99.63 159	99.43 270	99.70 24	99.90 157	97.34 274	98.64 366	99.44 228

thres600view7		96.60 344	96.16 346	97.93 351	99.63 175	96.09 370	99.18 172	97.57 383	98.77 236	98.72 332	97.32 401	87.04 383	99.72 330	88.57 398	98.62 367	97.98 390

thres200		96.09 356	95.68 356	97.33 369	99.48 244	96.22 367	98.53 297	97.57 383	98.06 305	98.37 354	96.73 407	86.84 387	99.61 378	86.99 404	98.57 368	96.16 402

1314		98.00 306	97.90 308	98.27 342	98.90 360	97.45 340	99.30 135	99.06 332	94.98 385	97.21 387	99.12 331	98.43 180	99.67 360	95.58 364	98.56 369	97.71 393

dmvs_testset		97.27 329	96.83 339	98.59 325	99.46 254	97.55 336	99.25 155	96.84 391	98.78 234	97.24 386	97.67 396	97.11 273	98.97 402	86.59 406	98.54 370	99.27 269

thres100view900		96.39 348	96.03 349	97.47 364	99.63 175	95.93 371	99.18 172	97.57 383	98.75 240	98.70 335	97.31 402	87.04 383	99.67 360	87.62 401	98.51 371	96.81 399

tfpn200view9		96.30 351	95.89 350	97.53 361	99.58 191	96.11 368	99.00 231	97.54 386	98.43 270	98.52 347	96.98 404	86.85 385	99.67 360	87.62 401	98.51 371	96.81 399

thres400		96.40 347	95.89 350	97.92 352	99.58 191	96.11 368	99.00 231	97.54 386	98.43 270	98.52 347	96.98 404	86.85 385	99.67 360	87.62 401	98.51 371	97.98 390

MVS		95.72 366	94.63 371	98.99 286	98.56 387	97.98 321	99.30 135	98.86 339	72.71 405	97.30 384	99.08 336	98.34 193	99.74 325	89.21 397	98.33 374	99.26 270

BH-untuned		98.22 296	98.09 291	98.58 327	99.38 274	97.24 346	98.55 292	98.98 337	97.81 323	99.20 282	98.76 370	97.01 276	99.65 370	94.83 375	98.33 374	98.86 347

testing11		96.05 358	95.41 360	97.97 349	98.78 375	95.27 380	98.59 284	98.23 371	98.86 222	96.56 395	96.91 406	75.20 406	99.69 343	97.26 282	98.29 376	98.93 338

test_method		91.72 372	92.32 375	89.91 389	93.49 411	70.18 414	90.28 402	99.56 210	61.71 406	95.39 403	99.52 246	93.90 325	99.94 77	98.76 162	98.27 377	99.62 138

testing91		96.00 359	95.32 363	98.02 347	98.76 378	95.39 377	98.38 309	98.65 352	98.82 227	96.84 391	96.71 408	75.06 407	99.71 334	96.46 331	98.23 378	98.98 333

gg-mvs-nofinetune		95.87 362	95.17 367	97.97 349	98.19 398	96.95 353	99.69 42	89.23 411	99.89 35	96.24 399	99.94 16	81.19 396	99.51 391	93.99 388	98.20 379	97.44 395

HY-MVS	98.23 9	98.21 297	97.95 300	98.99 286	99.03 350	98.24 295	99.61 67	98.72 346	96.81 362	98.73 331	99.51 248	94.06 324	99.86 222	96.91 302	98.20 379	98.86 347

UnsupCasMVSNet_bld		98.55 263	98.27 277	99.40 210	99.56 212	99.37 178	97.97 349	99.68 140	97.49 337	99.08 294	99.35 293	95.41 313	99.82 279	97.70 248	98.19 381	99.01 331

tpm2		96.35 349	96.22 345	96.73 378	98.88 365	91.75 401	99.21 166	98.51 358	93.27 393	97.89 373	99.21 321	84.83 392	99.70 337	96.04 347	98.18 382	98.75 357

testing222		95.60 369	94.59 372	98.61 323	98.66 385	97.45 340	98.54 295	97.90 378	98.53 262	96.54 396	96.47 410	70.62 412	99.81 294	95.91 356	98.15 383	98.56 366

tmp_tt		95.75 365	95.42 359	96.76 376	89.90 412	94.42 387	98.86 251	97.87 379	78.01 403	99.30 263	99.69 151	97.70 242	95.89 407	99.29 100	98.14 384	99.95 11

baseline2		96.83 338	96.28 344	98.46 331	99.09 342	96.91 355	98.83 256	93.87 405	97.23 349	96.23 400	98.36 384	88.12 378	99.90 157	96.68 315	98.14 384	98.57 365

ETVMVS		96.14 355	95.22 365	98.89 305	98.80 371	98.01 314	98.66 277	98.35 368	98.71 243	97.18 388	96.31 413	74.23 409	99.75 322	96.64 320	98.13 386	98.90 342

CostFormer		96.71 342	96.79 341	96.46 382	98.90 360	90.71 408	99.41 108	98.68 348	94.69 390	98.14 365	99.34 296	86.32 390	99.80 302	97.60 259	98.07 387	98.88 345

testing99		95.86 363	95.19 366	97.87 353	98.76 378	95.03 382	98.62 278	98.44 362	98.68 245	96.67 394	96.66 409	74.31 408	99.69 343	96.51 326	98.03 388	98.90 342

AUN-MVS		97.82 310	97.38 323	99.14 269	99.27 308	98.53 278	98.72 273	99.02 334	98.10 301	97.18 388	99.03 345	89.26 375	99.85 240	97.94 221	97.91 389	99.03 326

DeepMVS_CX				97.98 348	99.69 155	96.95 353		99.26 305	75.51 404	95.74 402	98.28 386	96.47 291	99.62 374	91.23 395	97.89 390	97.38 396

hse-mvs2		98.52 267	98.30 275	99.16 263	99.29 303	98.60 276	98.77 269	99.02 334	99.68 90	99.32 254	99.04 341	92.50 344	99.85 240	99.24 104	97.87 391	99.03 326

PAPM		95.61 368	94.71 370	98.31 340	99.12 333	96.63 359	96.66 397	98.46 361	90.77 398	96.25 398	98.68 374	93.01 338	99.69 343	81.60 407	97.86 392	98.62 360

JIA-IIPM		98.06 303	97.92 306	98.50 329	98.59 386	97.02 352	98.80 264	98.51 358	99.88 40	97.89 373	99.87 47	91.89 348	99.90 157	98.16 206	97.68 393	98.59 362

ET-MVSNet_ETH3D		96.78 339	96.07 348	98.91 298	99.26 310	97.92 323	97.70 365	96.05 395	97.96 313	92.37 406	98.43 383	87.06 382	99.90 157	98.27 193	97.56 394	98.91 341

TR-MVS		97.44 325	97.15 329	98.32 338	98.53 388	97.46 339	98.47 302	97.91 377	96.85 360	98.21 360	98.51 381	96.42 293	99.51 391	92.16 392	97.29 395	97.98 390

BH-w/o		97.20 330	97.01 332	97.76 357	99.08 343	95.69 374	98.03 341	98.52 357	95.76 376	97.96 370	98.02 390	95.62 309	99.47 393	92.82 391	97.25 396	98.12 388

KD-MVS_2432*1600		95.89 360	95.41 360	97.31 370	94.96 408	93.89 389	97.09 389	99.22 315	97.23 349	98.88 313	99.04 341	79.23 401	99.54 385	96.24 341	96.81 397	98.50 371

miper_refine_blended		95.89 360	95.41 360	97.31 370	94.96 408	93.89 389	97.09 389	99.22 315	97.23 349	98.88 313	99.04 341	79.23 401	99.54 385	96.24 341	96.81 397	98.50 371

UnsupCasMVSNet_eth		98.83 236	98.57 248	99.59 152	99.68 163	99.45 156	98.99 236	99.67 144	99.48 125	99.55 197	99.36 288	94.92 314	99.86 222	98.95 146	96.57 399	99.45 223

h-mvs33		98.61 254	98.34 270	99.44 195	99.60 182	98.67 266	99.27 147	99.44 260	99.68 90	99.32 254	99.49 255	92.50 344	100.00 1	99.24 104	96.51 400	99.65 112

GG-mvs-BLEND				97.36 367	97.59 404	96.87 356	99.70 35	88.49 412		94.64 405	97.26 403	80.66 397	99.12 399	91.50 394	96.50 401	96.08 403

tpm		97.15 331	96.95 334	97.75 358	98.91 359	94.24 388	99.32 127	97.96 375	97.71 326	98.29 355	99.32 297	86.72 388	99.92 116	98.10 210	96.24 402	99.09 310

test0.0.03 1		97.37 327	96.91 337	98.74 318	97.72 403	97.57 335	97.60 369	97.36 388	98.00 306	99.21 277	98.02 390	90.04 371	99.79 305	98.37 183	95.89 403	98.86 347

IB-MVS	95.41 20	95.30 370	94.46 374	97.84 355	98.76 378	95.33 379	97.33 382	96.07 394	96.02 372	95.37 404	97.41 400	76.17 405	99.96 54	97.54 262	95.44 404	98.22 383
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
baseline1		97.73 314	97.33 324	98.96 289	99.30 301	97.73 331	99.40 109	98.42 363	99.33 152	99.46 222	99.21 321	91.18 355	99.82 279	98.35 185	91.26 405	99.32 259

PVSNet_0	95.53 19	95.85 364	95.31 364	97.47 364	98.78 375	93.48 394	95.72 399	99.40 273	96.18 371	97.37 383	97.73 395	95.73 307	99.58 381	95.49 365	81.40 406	99.36 249

testmvs		28.94 375	33.33 377	15.79 391	26.03 413	9.81 416	96.77 395	15.67 414	11.55 409	23.87 410	50.74 417	19.03 414	8.53 410	23.21 409	33.07 407	29.03 406

test123		29.31 374	33.05 379	18.08 390	25.93 414	12.24 415	97.53 373	10.93 415	11.78 408	24.21 409	50.08 418	21.04 413	8.60 409	23.51 408	32.43 408	33.39 405

test_blank		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet_test		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

cdsmvs_eth3d_5k		24.88 376	33.17 378	0.00 392	0.00 415	0.00 417	0.00 403	99.62 169	0.00 410	0.00 411	99.13 327	99.82 13	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		16.61 377	22.14 380	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	99.28 66	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

ab-mvs-re		8.26 386	11.02 389	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	99.16 325	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		8.33 378	11.11 381	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	100.00 1	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						96.36 364								95.20 371

FOURS1					99.83 65	99.89 10	99.74 24	99.71 126	99.69 88	99.63 159

test_one_0601					99.63 175	99.76 61		99.55 216	99.23 167	99.31 258	99.61 205	98.59 155

eth-test2					0.00 415

eth-test					0.00 415

test_241102_ONE					99.69 155	99.82 35		99.54 222	99.12 192	99.82 81	99.49 255	98.91 113	99.52 390

save fliter					99.53 221	99.25 203	98.29 315	99.38 280	99.07 196

test0726					99.69 155	99.80 44	99.24 156	99.57 205	99.16 183	99.73 126	99.65 176	98.35 191

GSMVS																99.14 300

test_part2					99.62 179	99.67 99				99.55 197

sam_mvs1												90.81 363				99.14 300

sam_mvs												90.52 367

MTGPA								99.53 231

test_post1							99.14 187				51.63 416	89.54 374	99.82 279	96.86 305

test_post											52.41 415	90.25 369	99.86 222

patchmatchnet-post											99.62 196	90.58 365	99.94 77

MTMP							99.09 209	98.59 356

gm-plane-assit					97.59 404	89.02 412			93.47 392		98.30 385		99.84 255	96.38 335

TEST9					99.35 281	99.35 185	98.11 331	99.41 266	94.83 389	97.92 371	98.99 349	98.02 221	99.85 240

test_8					99.34 290	99.31 191	98.08 335	99.40 273	94.90 386	97.87 375	98.97 354	98.02 221	99.84 255

agg_prior					99.35 281	99.36 182		99.39 276		97.76 381			99.85 240

test_prior4						99.19 218	98.00 344

test_prior				99.46 189	99.35 281	99.22 211		99.39 276					99.69 343			99.48 214

旧先验2							97.94 351		95.33 381	98.94 305			99.88 189	96.75 311

新几何2							98.04 339

无先验							98.01 342	99.23 312	95.83 375				99.85 240	95.79 360		99.44 228

原ACMM2							97.92 354

testdata2													99.89 175	95.99 351

segment_acmp												98.37 189

testdata1							97.72 363		97.86 321

plane_prior7					99.58 191	99.38 175

plane_prior6					99.47 250	99.26 200						97.24 265

plane_prior4											99.25 312

plane_prior3						99.31 191			98.36 279	99.14 287

plane_prior2							98.80 264		98.94 209

plane_prior1					99.51 228

n2								0.00 416

nn								0.00 416

door-mid								99.83 62

test11								99.29 298

door								99.77 95

HQP5-MVS						98.94 243

HQP-NCC					99.31 297		97.98 346		97.45 338	98.15 361

ACMP_Plane					99.31 297		97.98 346		97.45 338	98.15 361

BP-MVS														94.73 376

HQP4-MVS										98.15 361			99.70 337			99.53 187

HQP2-MVS												96.67 284

NP-MVS					99.40 270	99.13 223					98.83 366

MDTV_nov1_ep13_2view						91.44 404	99.14 187		97.37 343	99.21 277		91.78 351		96.75 311		99.03 326

Test By Simon												98.41 183

High-resolution multi-view benchmark