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.

LCM-MVSNet		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

v7n		98.73 11	98.99 5	97.95 102	99.64 14	94.20 163	98.67 15	99.14 31	99.08 10	99.42 15	99.23 25	96.53 87	99.91 13	99.27 2	99.93 10	99.73 17

FMVS2		97.38 107	97.56 90	96.84 185	98.63 141	92.81 204	97.60 87	99.61 7	90.87 281	98.76 55	99.66 3	94.03 177	97.90 363	99.24 3	99.68 69	99.81 8

mvs_tets		98.90 5	98.94 6	98.75 35	99.69 10	96.48 64	98.54 22	99.22 18	96.23 120	99.71 4	99.48 9	98.77 6	99.93 3	98.89 4	99.95 5	99.84 5

PS-MVSNAJss		98.53 21	98.63 19	98.21 83	99.68 11	94.82 135	98.10 55	99.21 19	96.91 92	99.75 2	99.45 12	95.82 112	99.92 5	98.80 5	99.96 4	99.89 1

jajsoiax		98.77 9	98.79 12	98.74 37	99.66 13	96.48 64	98.45 30	99.12 33	95.83 147	99.67 6	99.37 15	98.25 10	99.92 5	98.77 6	99.94 8	99.82 6

v10		97.55 94	97.97 46	96.31 217	98.60 146	89.64 258	97.44 100	99.02 57	96.60 101	98.72 58	99.16 34	93.48 190	99.72 90	98.76 7	99.92 14	99.58 32

MVSFormer		96.14 177	96.36 163	95.49 254	97.68 259	87.81 296	98.67 15	99.02 57	96.50 108	94.48 296	96.15 291	86.90 291	99.92 5	98.73 8	99.13 211	98.74 221

test_djsdf		98.73 11	98.74 16	98.69 42	99.63 15	96.30 70	98.67 15	99.02 57	96.50 108	99.32 21	99.44 13	97.43 33	99.92 5	98.73 8	99.95 5	99.86 2

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 47	99.77 5	96.35 67	99.17 6	99.05 48	98.05 43	99.61 11	99.52 7	93.72 186	99.88 20	98.72 10	99.88 26	99.65 25

RRT_MVS		97.95 54	97.79 61	98.43 60	99.67 12	95.56 98	98.86 10	96.73 299	97.99 45	99.15 31	99.35 19	89.84 261	99.90 14	98.64 11	99.90 23	99.82 6

v8		97.60 91	98.06 40	96.23 219	98.71 130	89.44 262	97.43 102	98.82 120	97.29 84	98.74 56	99.10 39	93.86 181	99.68 130	98.61 12	99.94 8	99.56 40

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 16	97.29 41	98.65 18	99.19 23	95.62 155	99.35 20	99.37 15	97.38 35	99.90 14	98.59 13	99.91 17	99.77 10

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 40	99.71 9	96.99 48	99.69 2	99.57 8	99.02 15	99.62 10	99.36 17	98.53 7	99.52 186	98.58 14	99.95 5	99.66 23
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
mvsmamba		98.16 35	98.06 40	98.44 58	99.53 27	95.87 84	98.70 13	98.94 79	97.71 59	98.85 46	99.10 39	91.35 238	99.83 33	98.47 15	99.90 23	99.64 27

v1240		96.74 146	97.02 127	95.91 235	98.18 194	88.52 277	95.39 212	98.88 91	93.15 241	98.46 77	98.40 95	92.80 204	99.71 106	98.45 16	99.49 129	99.49 58

bld_raw_dy_0_64		97.69 84	97.61 85	97.91 105	99.54 24	94.27 160	98.06 58	98.60 163	96.60 101	98.79 51	98.95 51	89.62 262	99.84 30	98.43 17	99.91 17	99.62 28

v1192		96.83 140	97.06 124	96.15 224	98.28 180	89.29 264	95.36 214	98.77 127	93.73 220	98.11 118	98.34 98	93.02 201	99.67 135	98.35 18	99.58 93	99.50 50

v1921920		96.72 149	96.96 130	95.99 228	98.21 189	88.79 274	95.42 208	98.79 122	93.22 235	98.19 110	98.26 115	92.68 207	99.70 115	98.34 19	99.55 105	99.49 58

Anonymous20231211		98.55 19	98.76 13	97.94 103	98.79 119	94.37 153	98.84 11	99.15 29	99.37 3	99.67 6	99.43 14	95.61 124	99.72 90	98.12 20	99.86 28	99.73 17

v144192		96.69 152	96.90 135	96.03 227	98.25 185	88.92 269	95.49 204	98.77 127	93.05 243	98.09 122	98.29 109	92.51 216	99.70 115	98.11 21	99.56 99	99.47 67

Anonymous20240521		97.07 123	97.51 94	95.76 240	99.35 48	88.18 285	97.78 72	98.40 187	97.11 87	98.34 91	99.04 45	89.58 264	99.79 45	98.09 22	99.93 10	99.30 112

v1144		96.84 137	97.08 122	96.13 225	98.42 170	89.28 265	95.41 210	98.67 153	94.21 206	97.97 137	98.31 101	93.06 197	99.65 143	98.06 23	99.62 79	99.45 74

SixPastTwentyTwo		97.49 99	97.57 89	97.26 162	99.56 20	92.33 212	98.28 41	96.97 288	98.30 34	99.45 14	99.35 19	88.43 277	99.89 18	98.01 24	99.76 47	99.54 43

WR-MVS_H		98.65 15	98.62 21	98.75 35	99.51 29	96.61 60	98.55 21	99.17 24	99.05 13	99.17 30	98.79 61	95.47 130	99.89 18	97.95 25	99.91 17	99.75 15

UA-Net		98.88 7	98.76 13	99.22 2	99.11 89	97.89 16	99.47 3	99.32 14	99.08 10	97.87 149	99.67 2	96.47 92	99.92 5	97.88 26	99.98 2	99.85 3

FC-MVSNet-test		98.16 35	98.37 29	97.56 131	99.49 33	93.10 198	98.35 34	99.21 19	98.43 29	98.89 44	98.83 60	94.30 169	99.81 38	97.87 27	99.91 17	99.77 10

Vis-MVSNet		98.27 31	98.34 30	98.07 93	99.33 50	95.21 125	98.04 59	99.46 10	97.32 82	97.82 154	99.11 38	96.75 76	99.86 24	97.84 28	99.36 168	99.15 147
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
K. test v3		96.44 165	96.28 166	96.95 177	99.41 42	91.53 232	97.65 84	90.31 368	98.89 19	98.93 41	99.36 17	84.57 306	99.92 5	97.81 29	99.56 99	99.39 91

v2v482		96.78 144	97.06 124	95.95 232	98.57 150	88.77 275	95.36 214	98.26 203	95.18 173	97.85 151	98.23 118	92.58 211	99.63 148	97.80 30	99.69 65	99.45 74

PS-CasMVS		98.73 11	98.85 10	98.39 64	99.55 22	95.47 107	98.49 27	99.13 32	99.22 8	99.22 28	98.96 50	97.35 36	99.92 5	97.79 31	99.93 10	99.79 9

nrg030		98.54 20	98.62 21	98.32 69	99.22 64	95.66 95	97.90 67	99.08 42	98.31 33	99.02 37	98.74 67	97.68 26	99.61 162	97.77 32	99.85 31	99.70 20

pmmvs6		99.07 4	99.24 4	98.56 51	99.81 2	96.38 66	98.87 9	99.30 15	99.01 16	99.63 9	99.66 3	99.27 2	99.68 130	97.75 33	99.89 25	99.62 28

ACMH	93.61 9	98.44 24	98.76 13	97.51 136	99.43 39	93.54 188	98.23 45	99.05 48	97.40 79	99.37 18	99.08 42	98.79 5	99.47 200	97.74 34	99.71 61	99.50 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVS		95.82 191	95.88 186	95.66 245	97.61 266	93.21 196	95.61 201	98.17 217	86.98 323	98.42 80	99.47 10	90.46 249	94.74 378	97.71 35	98.45 278	99.03 173

DTE-MVSNet		98.79 8	98.86 8	98.59 49	99.55 22	96.12 75	98.48 29	99.10 36	99.36 4	99.29 24	99.06 44	97.27 40	99.93 3	97.71 35	99.91 17	99.70 20

DROMVSNet		97.90 66	97.94 49	97.79 114	98.66 136	95.14 126	98.31 38	99.66 4	97.57 67	95.95 254	97.01 242	96.99 57	99.82 35	97.66 37	99.64 76	98.39 252

PEN-MVS		98.75 10	98.85 10	98.44 58	99.58 18	95.67 94	98.45 30	99.15 29	99.33 5	99.30 22	99.00 46	97.27 40	99.92 5	97.64 38	99.92 14	99.75 15

test_part1		96.77 145	96.53 155	97.47 144	98.04 208	92.92 202	97.93 64	98.85 100	98.83 21	99.30 22	99.07 43	79.25 328	99.79 45	97.59 39	99.93 10	99.69 22

CP-MVSNet		98.42 25	98.46 24	98.30 73	99.46 35	95.22 123	98.27 43	98.84 105	99.05 13	99.01 38	98.65 75	95.37 133	99.90 14	97.57 40	99.91 17	99.77 10

EI-MVSNet-UG-set		97.32 113	97.40 100	97.09 170	97.34 290	92.01 224	95.33 217	97.65 262	97.74 54	98.30 100	98.14 127	95.04 144	99.69 123	97.55 41	99.52 116	99.58 32

ANet_high		98.31 30	98.94 6	96.41 213	99.33 50	89.64 258	97.92 66	99.56 9	99.27 6	99.66 8	99.50 8	97.67 27	99.83 33	97.55 41	99.98 2	99.77 10

CS-MVS		98.09 42	98.01 44	98.32 69	98.45 168	96.69 56	98.52 25	99.69 2	98.07 42	96.07 249	97.19 228	96.88 68	99.86 24	97.50 43	99.73 54	98.41 249

EI-MVSNet-Vis-set		97.32 113	97.39 101	97.11 168	97.36 285	92.08 222	95.34 216	97.65 262	97.74 54	98.29 101	98.11 133	95.05 142	99.68 130	97.50 43	99.50 125	99.56 40

EU-MVSNet		94.25 257	94.47 244	93.60 314	98.14 201	82.60 355	97.24 111	92.72 350	85.08 341	98.48 74	98.94 52	82.59 315	98.76 320	97.47 45	99.53 111	99.44 84

Regformer-4		97.53 97	97.47 99	97.71 120	97.35 286	93.91 171	95.26 223	98.14 224	97.97 46	98.34 91	97.89 162	95.49 128	99.71 106	97.41 46	99.42 154	99.51 49

V42		97.04 124	97.16 117	96.68 196	98.59 148	91.05 237	96.33 155	98.36 192	94.60 193	97.99 133	98.30 105	93.32 192	99.62 156	97.40 47	99.53 111	99.38 93

KD-MVS_self_test		97.86 71	98.07 38	97.25 163	99.22 64	92.81 204	97.55 92	98.94 79	97.10 88	98.85 46	98.88 57	95.03 145	99.67 135	97.39 48	99.65 74	99.26 125

lessismore_v0				97.05 172	99.36 47	92.12 220		84.07 380		98.77 54	98.98 48	85.36 300	99.74 80	97.34 49	99.37 165	99.30 112

FIs		97.93 60	98.07 38	97.48 143	99.38 45	92.95 201	98.03 61	99.11 34	98.04 44	98.62 60	98.66 73	93.75 185	99.78 49	97.23 50	99.84 32	99.73 17

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 45	99.77 5	96.34 68	99.18 5	99.20 21	99.67 2	99.73 3	99.65 5	99.15 3	99.86 24	97.22 51	99.92 14	99.77 10

MVS_Test		96.27 170	96.79 141	94.73 287	96.94 310	86.63 317	96.18 164	98.33 197	94.94 183	96.07 249	98.28 110	95.25 138	99.26 262	97.21 52	97.90 299	98.30 265

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 24	98.06 10	99.34 4	99.44 11	98.85 20	99.00 39	99.20 27	97.42 34	99.59 164	97.21 52	99.76 47	99.40 89

EG-PatchMatch MVS		97.69 84	97.79 61	97.40 154	99.06 95	93.52 189	95.96 178	98.97 75	94.55 197	98.82 49	98.76 66	97.31 38	99.29 256	97.20 54	99.44 143	99.38 93

VPA-MVSNet		98.27 31	98.46 24	97.70 122	99.06 95	93.80 177	97.76 75	99.00 65	98.40 30	99.07 36	98.98 48	96.89 66	99.75 70	97.19 55	99.79 41	99.55 42

Regformer-3		97.25 117	97.29 107	97.11 168	97.35 286	92.32 213	95.26 223	97.62 267	97.67 65	98.17 111	97.89 162	95.05 142	99.56 173	97.16 56	99.42 154	99.46 69

UniMVSNet (Re)		97.83 73	97.65 76	98.35 68	98.80 118	95.86 86	95.92 183	99.04 54	97.51 72	98.22 106	97.81 174	94.68 156	99.78 49	97.14 57	99.75 52	99.41 88

pm-mvs1		98.47 23	98.67 17	97.86 110	99.52 28	94.58 145	98.28 41	99.00 65	97.57 67	99.27 25	99.22 26	98.32 9	99.50 191	97.09 58	99.75 52	99.50 50

baseline		97.44 103	97.78 65	96.43 209	98.52 155	90.75 245	96.84 129	99.03 55	96.51 107	97.86 150	98.02 146	96.67 78	99.36 237	97.09 58	99.47 135	99.19 139

IterMVS-SCA-FT		95.86 189	96.19 169	94.85 281	97.68 259	85.53 328	92.42 325	97.63 266	96.99 89	98.36 88	98.54 83	87.94 281	99.75 70	97.07 60	99.08 219	99.27 124

UniMVSNet_NR-MVSNet		97.83 73	97.65 76	98.37 65	98.72 127	95.78 87	95.66 195	99.02 57	98.11 40	98.31 98	97.69 186	94.65 158	99.85 27	97.02 61	99.71 61	99.48 64

DU-MVS		97.79 77	97.60 86	98.36 66	98.73 125	95.78 87	95.65 198	98.87 93	97.57 67	98.31 98	97.83 170	94.69 154	99.85 27	97.02 61	99.71 61	99.46 69

EI-MVSNet		96.63 156	96.93 131	95.74 241	97.26 295	88.13 288	95.29 221	97.65 262	96.99 89	97.94 140	98.19 123	92.55 212	99.58 166	96.91 63	99.56 99	99.50 50

IterMVS-LS		96.92 132	97.29 107	95.79 239	98.51 157	88.13 288	95.10 230	98.66 155	96.99 89	98.46 77	98.68 72	92.55 212	99.74 80	96.91 63	99.79 41	99.50 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CS-MVS-test		97.91 64	97.84 56	98.14 88	98.52 155	96.03 80	98.38 33	99.67 3	98.11 40	95.50 271	96.92 248	96.81 74	99.87 22	96.87 65	99.76 47	98.51 242

iter_conf_final		94.54 249	93.91 263	96.43 209	97.23 297	90.41 251	96.81 131	98.10 228	93.87 217	96.80 208	97.89 162	68.02 374	99.72 90	96.73 66	99.77 46	99.18 142

test1111		94.53 250	94.81 225	93.72 311	99.06 95	81.94 360	98.31 38	83.87 381	96.37 113	98.49 73	99.17 33	81.49 317	99.73 85	96.64 67	99.86 28	99.49 58

APDe-MVS		98.14 37	98.03 43	98.47 57	98.72 127	96.04 78	98.07 57	99.10 36	95.96 136	98.59 65	98.69 71	96.94 60	99.81 38	96.64 67	99.58 93	99.57 37

MP-MVS-pluss		97.69 84	97.36 103	98.70 41	99.50 32	96.84 51	95.38 213	98.99 68	92.45 258	98.11 118	98.31 101	97.25 43	99.77 58	96.60 69	99.62 79	99.48 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_anonymous		95.36 209	96.07 176	93.21 323	96.29 323	81.56 361	94.60 256	97.66 260	93.30 232	96.95 202	98.91 55	93.03 200	99.38 232	96.60 69	97.30 326	98.69 227

casdiffmvs		97.50 98	97.81 60	96.56 203	98.51 157	91.04 238	95.83 187	99.09 41	97.23 85	98.33 95	98.30 105	97.03 54	99.37 235	96.58 71	99.38 164	99.28 120

Regformer-2		97.41 105	97.24 112	97.93 104	97.21 298	94.72 138	94.85 247	98.27 201	97.74 54	98.11 118	97.50 200	95.58 126	99.69 123	96.57 72	99.31 187	99.37 100

Regformer-1		97.27 115	97.16 117	97.61 129	97.21 298	93.86 174	94.85 247	98.04 239	97.62 66	98.03 130	97.50 200	95.34 134	99.63 148	96.52 73	99.31 187	99.35 103

TransMVSNet (Re)		98.38 27	98.67 17	97.51 136	99.51 29	93.39 192	98.20 50	98.87 93	98.23 36	99.48 12	99.27 23	98.47 8	99.55 177	96.52 73	99.53 111	99.60 30

HPM-MVS_fast		98.32 29	98.13 35	98.88 26	99.54 24	97.48 34	98.35 34	99.03 55	95.88 142	97.88 146	98.22 121	98.15 12	99.74 80	96.50 75	99.62 79	99.42 86

MIMVSNet1		98.51 22	98.45 26	98.67 43	99.72 8	96.71 54	98.76 12	98.89 85	98.49 28	99.38 17	99.14 37	95.44 132	99.84 30	96.47 76	99.80 40	99.47 67

TranMVSNet+NR-MVSNet		98.33 28	98.30 33	98.43 60	99.07 94	95.87 84	96.73 140	99.05 48	98.67 24	98.84 48	98.45 89	97.58 30	99.88 20	96.45 77	99.86 28	99.54 43

test2506		89.86 329	89.16 334	91.97 343	98.95 105	76.83 375	98.54 22	61.07 389	96.20 121	97.07 191	99.16 34	55.19 388	99.69 123	96.43 78	99.83 34	99.38 93

Gipuma		98.07 44	98.31 31	97.36 156	99.76 7	96.28 71	98.51 26	99.10 36	98.76 23	96.79 209	99.34 21	96.61 82	98.82 313	96.38 79	99.50 125	96.98 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVSTER		94.21 260	93.93 262	95.05 271	95.83 341	86.46 318	95.18 228	97.65 262	92.41 259	97.94 140	98.00 150	72.39 363	99.58 166	96.36 80	99.56 99	99.12 157

GeoE		97.75 80	97.70 69	97.89 107	98.88 112	94.53 146	97.10 118	98.98 71	95.75 151	97.62 157	97.59 192	97.61 29	99.77 58	96.34 81	99.44 143	99.36 101

canonicalmvs		97.23 119	97.21 115	97.30 159	97.65 263	94.39 151	97.84 70	99.05 48	97.42 75	96.68 216	93.85 342	97.63 28	99.33 245	96.29 82	98.47 277	98.18 277

FMVS1		98.57 17	98.45 26	98.93 21	99.79 3	98.78 2	97.69 81	99.42 12	97.69 61	98.92 42	98.77 64	97.80 22	99.25 264	96.27 83	99.69 65	98.76 219

APD_test		98.57 17	98.45 26	98.93 21	99.79 3	98.78 2	97.69 81	99.42 12	97.69 61	98.92 42	98.77 64	97.80 22	99.25 264	96.27 83	99.69 65	98.76 219

alignmvs		96.01 183	95.52 198	97.50 139	97.77 249	94.71 139	96.07 169	96.84 291	97.48 73	96.78 213	94.28 339	85.50 299	99.40 224	96.22 85	98.73 259	98.40 250

tttt0517		93.31 285	92.56 292	95.57 248	98.71 130	87.86 293	97.44 100	87.17 376	95.79 148	97.47 169	96.84 252	64.12 378	99.81 38	96.20 86	99.32 185	99.02 176

iter_conf05		93.65 277	93.05 276	95.46 256	96.13 335	87.45 303	95.95 181	98.22 207	92.66 254	97.04 193	97.89 162	63.52 380	99.72 90	96.19 87	99.82 36	99.21 134

DeepC-MVS	95.41 4	97.82 75	97.70 69	98.16 84	98.78 121	95.72 89	96.23 162	99.02 57	93.92 216	98.62 60	98.99 47	97.69 25	99.62 156	96.18 88	99.87 27	99.15 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		98.01 48	97.66 74	99.06 4	99.44 37	97.90 14	95.66 195	98.73 135	97.69 61	97.90 143	97.96 152	95.81 116	99.82 35	96.13 89	99.61 85	99.45 74

MTAPA		98.14 37	97.84 56	99.06 4	99.44 37	97.90 14	97.25 109	98.73 135	97.69 61	97.90 143	97.96 152	95.81 116	99.82 35	96.13 89	99.61 85	99.45 74

ZNCC-MVS		97.92 61	97.62 83	98.83 28	99.32 52	97.24 43	97.45 99	98.84 105	95.76 149	96.93 203	97.43 206	97.26 42	99.79 45	96.06 91	99.53 111	99.45 74

Patchmatch-RL test		94.66 242	94.49 242	95.19 265	98.54 153	88.91 270	92.57 321	98.74 133	91.46 273	98.32 96	97.75 179	77.31 341	98.81 315	96.06 91	99.61 85	97.85 299

ACMMP_NAP		97.89 67	97.63 81	98.67 43	99.35 48	96.84 51	96.36 153	98.79 122	95.07 178	97.88 146	98.35 97	97.24 44	99.72 90	96.05 93	99.58 93	99.45 74

v148		96.58 159	96.97 128	95.42 258	98.63 141	87.57 300	95.09 231	97.90 243	95.91 141	98.24 104	97.96 152	93.42 191	99.39 229	96.04 94	99.52 116	99.29 119

ACMM	93.33 11	98.05 45	97.79 61	98.85 27	99.15 79	97.55 29	96.68 142	98.83 112	95.21 170	98.36 88	98.13 129	98.13 14	99.62 156	96.04 94	99.54 108	99.39 91
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDD-MVS		97.37 109	97.25 110	97.74 118	98.69 134	94.50 149	97.04 122	95.61 318	98.59 26	98.51 70	98.72 68	92.54 214	99.58 166	96.02 96	99.49 129	99.12 157

IterMVS		95.42 207	95.83 187	94.20 305	97.52 272	83.78 350	92.41 326	97.47 272	95.49 161	98.06 126	98.49 86	87.94 281	99.58 166	96.02 96	99.02 226	99.23 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs		96.04 181	96.23 167	95.46 256	97.35 286	88.03 291	93.42 302	99.08 42	94.09 212	96.66 218	96.93 246	93.85 182	99.29 256	96.01 98	98.67 261	99.06 169

PM-MVS		97.36 111	97.10 120	98.14 88	98.91 110	96.77 53	96.20 163	98.63 161	93.82 218	98.54 68	98.33 99	93.98 178	99.05 292	95.99 99	99.45 142	98.61 235

Baseline_NR-MVSNet		97.72 82	97.79 61	97.50 139	99.56 20	93.29 193	95.44 206	98.86 96	98.20 38	98.37 85	99.24 24	94.69 154	99.55 177	95.98 100	99.79 41	99.65 25

ECVR-MVS		94.37 255	94.48 243	94.05 308	98.95 105	83.10 352	98.31 38	82.48 382	96.20 121	98.23 105	99.16 34	81.18 320	99.66 141	95.95 101	99.83 34	99.38 93

3Dnovator	96.53 2	97.61 90	97.64 79	97.50 139	97.74 255	93.65 186	98.49 27	98.88 91	96.86 94	97.11 185	98.55 82	95.82 112	99.73 85	95.94 102	99.42 154	99.13 152

PatchT		93.75 272	93.57 268	94.29 304	95.05 355	87.32 307	96.05 170	92.98 346	97.54 71	94.25 299	98.72 68	75.79 349	99.24 267	95.92 103	95.81 348	96.32 348

NR-MVSNet		97.96 50	97.86 55	98.26 75	98.73 125	95.54 100	98.14 53	98.73 135	97.79 49	99.42 15	97.83 170	94.40 167	99.78 49	95.91 104	99.76 47	99.46 69

h-mvs33		96.29 169	95.63 194	98.26 75	98.50 160	96.11 76	96.90 127	97.09 283	96.58 104	97.21 178	98.19 123	84.14 307	99.78 49	95.89 105	96.17 346	98.89 199

hse-mvs2		95.77 192	95.09 208	97.79 114	97.84 230	95.51 102	95.66 195	95.43 323	96.58 104	97.21 178	96.16 290	84.14 307	99.54 180	95.89 105	96.92 329	98.32 261

MSC_two_6792asdad				98.22 80	97.75 252	95.34 115		98.16 221					99.75 70	95.87 107	99.51 121	99.57 37

No_MVS				98.22 80	97.75 252	95.34 115		98.16 221					99.75 70	95.87 107	99.51 121	99.57 37

new-patchmatchnet		95.67 195	96.58 149	92.94 331	97.48 275	80.21 366	92.96 313	98.19 216	94.83 186	98.82 49	98.79 61	93.31 193	99.51 190	95.83 109	99.04 225	99.12 157

FMVSNet1		97.95 54	98.08 37	97.56 131	99.14 87	93.67 182	98.23 45	98.66 155	97.41 78	99.00 39	99.19 28	95.47 130	99.73 85	95.83 109	99.76 47	99.30 112

patch_mono-2		96.59 157	96.93 131	95.55 251	98.88 112	87.12 310	94.47 261	99.30 15	94.12 210	96.65 220	98.41 92	94.98 148	99.87 22	95.81 111	99.78 44	99.66 23

DVP-MVS++		97.96 50	97.90 50	98.12 90	97.75 252	95.40 108	99.03 7	98.89 85	96.62 99	98.62 60	98.30 105	96.97 58	99.75 70	95.70 112	99.25 196	99.21 134

test_0728_THIRD									96.62 99	98.40 82	98.28 110	97.10 47	99.71 106	95.70 112	99.62 79	99.58 32

EGC-MVSNET		83.08 348	77.93 351	98.53 53	99.57 19	97.55 29	98.33 37	98.57 168	4.71 384	10.38 385	98.90 56	95.60 125	99.50 191	95.69 114	99.61 85	98.55 240

RPMNet		94.68 241	94.60 236	94.90 278	95.44 350	88.15 286	96.18 164	98.86 96	97.43 74	94.10 303	98.49 86	79.40 327	99.76 63	95.69 114	95.81 348	96.81 338

TSAR-MVS + MP.		97.42 104	97.23 113	98.00 100	99.38 45	95.00 130	97.63 86	98.20 211	93.00 245	98.16 112	98.06 142	95.89 107	99.72 90	95.67 116	99.10 217	99.28 120
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
abl_6		98.42 25	98.19 34	99.09 3	99.16 76	98.10 8	97.73 80	99.11 34	97.76 53	98.62 60	98.27 114	97.88 19	99.80 44	95.67 116	99.50 125	99.38 93

XVS		97.96 50	97.63 81	98.94 18	99.15 79	97.66 22	97.77 73	98.83 112	97.42 75	96.32 235	97.64 188	96.49 90	99.72 90	95.66 118	99.37 165	99.45 74

X-MVStestdata		92.86 292	90.83 316	98.94 18	99.15 79	97.66 22	97.77 73	98.83 112	97.42 75	96.32 235	36.50 382	96.49 90	99.72 90	95.66 118	99.37 165	99.45 74

3Dnovator+	96.13 3	97.73 81	97.59 87	98.15 87	98.11 206	95.60 97	98.04 59	98.70 145	98.13 39	96.93 203	98.45 89	95.30 137	99.62 156	95.64 120	98.96 230	99.24 131

DELS-MVS		96.17 176	96.23 167	95.99 228	97.55 271	90.04 253	92.38 327	98.52 171	94.13 209	96.55 226	97.06 237	94.99 147	99.58 166	95.62 121	99.28 192	98.37 254
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
HFP-MVS		97.94 57	97.64 79	98.83 28	99.15 79	97.50 32	97.59 89	98.84 105	96.05 129	97.49 164	97.54 195	97.07 50	99.70 115	95.61 122	99.46 138	99.30 112

ACMMPR		97.95 54	97.62 83	98.94 18	99.20 72	97.56 28	97.59 89	98.83 112	96.05 129	97.46 170	97.63 189	96.77 75	99.76 63	95.61 122	99.46 138	99.49 58

UGNet		96.81 142	96.56 151	97.58 130	96.64 315	93.84 176	97.75 76	97.12 282	96.47 111	93.62 320	98.88 57	93.22 195	99.53 182	95.61 122	99.69 65	99.36 101
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
HPM-MVS		98.11 41	97.83 59	98.92 24	99.42 41	97.46 35	98.57 19	99.05 48	95.43 164	97.41 172	97.50 200	97.98 15	99.79 45	95.58 125	99.57 96	99.50 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
dcpmvs_2		97.12 121	97.99 45	94.51 297	99.11 89	84.00 348	97.75 76	99.65 5	97.38 80	99.14 32	98.42 91	95.16 140	99.96 2	95.52 126	99.78 44	99.58 32

SR-MVS-dyc-post		98.14 37	97.84 56	99.02 9	98.81 116	98.05 11	97.55 92	98.86 96	97.77 50	98.20 107	98.07 137	96.60 84	99.76 63	95.49 127	99.20 201	99.26 125

RE-MVS-def			97.88 54		98.81 116	98.05 11	97.55 92	98.86 96	97.77 50	98.20 107	98.07 137	96.94 60		95.49 127	99.20 201	99.26 125

Anonymous20240529		97.96 50	98.04 42	97.71 120	98.69 134	94.28 159	97.86 69	98.31 200	98.79 22	99.23 27	98.86 59	95.76 119	99.61 162	95.49 127	99.36 168	99.23 132

DVP-MVS		97.78 78	97.65 76	98.16 84	99.24 59	95.51 102	96.74 136	98.23 206	95.92 139	98.40 82	98.28 110	97.06 52	99.71 106	95.48 130	99.52 116	99.26 125
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				98.25 78	99.23 61	95.49 106	96.74 136	98.89 85					99.75 70	95.48 130	99.52 116	99.53 46

region2R		97.92 61	97.59 87	98.92 24	99.22 64	97.55 29	97.60 87	98.84 105	96.00 134	97.22 176	97.62 190	96.87 70	99.76 63	95.48 130	99.43 151	99.46 69

pmmvs-eth3d		96.49 162	96.18 170	97.42 152	98.25 185	94.29 156	94.77 251	98.07 236	89.81 293	97.97 137	98.33 99	93.11 196	99.08 289	95.46 133	99.84 32	98.89 199

SED-MVS		97.94 57	97.90 50	98.07 93	99.22 64	95.35 113	96.79 133	98.83 112	96.11 126	99.08 34	98.24 116	97.87 20	99.72 90	95.44 134	99.51 121	99.14 150

test_241102_TWO								98.83 112	96.11 126	98.62 60	98.24 116	96.92 64	99.72 90	95.44 134	99.49 129	99.49 58

APD-MVS_3200maxsize		98.13 40	97.90 50	98.79 33	98.79 119	97.31 40	97.55 92	98.92 82	97.72 57	98.25 103	98.13 129	97.10 47	99.75 70	95.44 134	99.24 199	99.32 106

xiu_mvs_v1_base_debu		95.62 196	95.96 181	94.60 291	98.01 212	88.42 278	93.99 283	98.21 208	92.98 246	95.91 256	94.53 332	96.39 96	99.72 90	95.43 137	98.19 287	95.64 356

xiu_mvs_v1_base		95.62 196	95.96 181	94.60 291	98.01 212	88.42 278	93.99 283	98.21 208	92.98 246	95.91 256	94.53 332	96.39 96	99.72 90	95.43 137	98.19 287	95.64 356

xiu_mvs_v1_base_debi		95.62 196	95.96 181	94.60 291	98.01 212	88.42 278	93.99 283	98.21 208	92.98 246	95.91 256	94.53 332	96.39 96	99.72 90	95.43 137	98.19 287	95.64 356

c3_l		95.20 215	95.32 201	94.83 283	96.19 329	86.43 320	91.83 335	98.35 196	93.47 226	97.36 173	97.26 224	88.69 274	99.28 258	95.41 140	99.36 168	98.78 214

mvsany_test		96.21 173	95.93 184	97.05 172	97.40 283	94.33 155	95.76 189	94.20 334	89.10 298	99.36 19	99.60 6	93.97 179	97.85 364	95.40 141	98.63 267	98.99 180

ACMMP		98.05 45	97.75 68	98.93 21	99.23 61	97.60 25	98.09 56	98.96 76	95.75 151	97.91 142	98.06 142	96.89 66	99.76 63	95.32 142	99.57 96	99.43 85
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
test1172		98.08 43	97.76 66	99.05 6	98.78 121	98.07 9	97.41 104	98.85 100	97.57 67	98.15 114	97.96 152	96.60 84	99.76 63	95.30 143	99.18 205	99.33 105

miper_lstm_enhance		94.81 232	94.80 226	94.85 281	96.16 331	86.45 319	91.14 348	98.20 211	93.49 225	97.03 195	97.37 216	84.97 303	99.26 262	95.28 144	99.56 99	98.83 208

MSLP-MVS++		96.42 167	96.71 143	95.57 248	97.82 233	90.56 249	95.71 190	98.84 105	94.72 189	96.71 215	97.39 212	94.91 151	98.10 361	95.28 144	99.02 226	98.05 289

SteuartSystems-ACMMP		98.02 47	97.76 66	98.79 33	99.43 39	97.21 45	97.15 114	98.90 84	96.58 104	98.08 124	97.87 168	97.02 55	99.76 63	95.25 146	99.59 91	99.40 89
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		97.37 109	97.70 69	96.35 214	98.14 201	95.13 127	96.54 145	98.92 82	95.94 138	99.19 29	98.08 135	97.74 24	95.06 377	95.24 147	99.54 108	98.87 205
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
IU-MVS					99.22 64	95.40 108		98.14 224	85.77 334	98.36 88				95.23 148	99.51 121	99.49 58

CP-MVS		97.92 61	97.56 90	98.99 13	98.99 103	97.82 18	97.93 64	98.96 76	96.11 126	96.89 206	97.45 204	96.85 71	99.78 49	95.19 149	99.63 78	99.38 93

LS3D		97.77 79	97.50 96	98.57 50	96.24 325	97.58 27	98.45 30	98.85 100	98.58 27	97.51 162	97.94 157	95.74 120	99.63 148	95.19 149	98.97 229	98.51 242

SMA-MVS		97.48 100	97.11 119	98.60 48	98.83 115	96.67 57	96.74 136	98.73 135	91.61 270	98.48 74	98.36 96	96.53 87	99.68 130	95.17 151	99.54 108	99.45 74
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
CR-MVSNet		93.29 286	92.79 284	94.78 285	95.44 350	88.15 286	96.18 164	97.20 277	84.94 345	94.10 303	98.57 79	77.67 336	99.39 229	95.17 151	95.81 348	96.81 338

OPM-MVS		97.54 95	97.25 110	98.41 62	99.11 89	96.61 60	95.24 225	98.46 176	94.58 196	98.10 121	98.07 137	97.09 49	99.39 229	95.16 153	99.44 143	99.21 134
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mPP-MVS		97.91 64	97.53 92	99.04 7	99.22 64	97.87 17	97.74 78	98.78 126	96.04 131	97.10 186	97.73 182	96.53 87	99.78 49	95.16 153	99.50 125	99.46 69

DIV-MVS_self_test		94.73 234	94.64 232	95.01 272	95.86 339	87.00 312	91.33 342	98.08 232	93.34 230	97.10 186	97.34 218	84.02 309	99.31 249	95.15 155	99.55 105	98.72 224

cl____		94.73 234	94.64 232	95.01 272	95.85 340	87.00 312	91.33 342	98.08 232	93.34 230	97.10 186	97.33 219	84.01 310	99.30 252	95.14 156	99.56 99	98.71 226

MSP-MVS		97.45 102	96.92 133	99.03 8	99.26 55	97.70 21	97.66 83	98.89 85	95.65 153	98.51 70	96.46 276	92.15 221	99.81 38	95.14 156	98.58 272	99.58 32
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
VDDNet		96.98 129	96.84 136	97.41 153	99.40 43	93.26 194	97.94 63	95.31 324	99.26 7	98.39 84	99.18 31	87.85 286	99.62 156	95.13 158	99.09 218	99.35 103

CANet		95.86 189	95.65 193	96.49 206	96.41 321	90.82 242	94.36 263	98.41 185	94.94 183	92.62 346	96.73 261	92.68 207	99.71 106	95.12 159	99.60 89	98.94 186

CNVR-MVS		96.92 132	96.55 152	98.03 99	98.00 216	95.54 100	94.87 245	98.17 217	94.60 193	96.38 232	97.05 238	95.67 122	99.36 237	95.12 159	99.08 219	99.19 139

eth_miper_zixun_eth		94.89 228	94.93 216	94.75 286	95.99 337	86.12 323	91.35 341	98.49 174	93.40 227	97.12 184	97.25 225	86.87 293	99.35 240	95.08 161	98.82 249	98.78 214

GST-MVS		97.82 75	97.49 97	98.81 31	99.23 61	97.25 42	97.16 113	98.79 122	95.96 136	97.53 160	97.40 208	96.93 62	99.77 58	95.04 162	99.35 173	99.42 86

DP-MVS		97.87 69	97.89 53	97.81 113	98.62 143	94.82 135	97.13 117	98.79 122	98.98 17	98.74 56	98.49 86	95.80 118	99.49 194	95.04 162	99.44 143	99.11 160

D2MVS		95.18 216	95.17 205	95.21 264	97.76 250	87.76 298	94.15 275	97.94 241	89.77 294	96.99 198	97.68 187	87.45 288	99.14 280	95.03 164	99.81 37	98.74 221

SR-MVS		98.00 49	97.66 74	99.01 11	98.77 123	97.93 13	97.38 105	98.83 112	97.32 82	98.06 126	97.85 169	96.65 79	99.77 58	95.00 165	99.11 215	99.32 106

FMVSNet2		96.72 149	96.67 146	96.87 183	97.96 218	91.88 226	97.15 114	98.06 237	95.59 157	98.50 72	98.62 76	89.51 268	99.65 143	94.99 166	99.60 89	99.07 167

miper_ehance_all_eth		94.69 239	94.70 229	94.64 288	95.77 343	86.22 322	91.32 344	98.24 205	91.67 269	97.05 192	96.65 266	88.39 278	99.22 271	94.88 167	98.34 281	98.49 245

XVG-OURS-SEG-HR		97.38 107	97.07 123	98.30 73	99.01 102	97.41 38	94.66 254	99.02 57	95.20 171	98.15 114	97.52 198	98.83 4	98.43 346	94.87 168	96.41 342	99.07 167

MVS_111021_HR		96.73 148	96.54 154	97.27 160	98.35 175	93.66 185	93.42 302	98.36 192	94.74 188	96.58 222	96.76 260	96.54 86	98.99 299	94.87 168	99.27 194	99.15 147

test_0402		97.84 72	97.97 46	97.47 144	99.19 74	94.07 166	96.71 141	98.73 135	98.66 25	98.56 67	98.41 92	96.84 72	99.69 123	94.82 170	99.81 37	98.64 230

MVS_111021_LR		96.82 141	96.55 152	97.62 128	98.27 182	95.34 115	93.81 292	98.33 197	94.59 195	96.56 224	96.63 267	96.61 82	98.73 322	94.80 171	99.34 176	98.78 214

WR-MVS		96.90 134	96.81 138	97.16 165	98.56 151	92.20 218	94.33 264	98.12 227	97.34 81	98.20 107	97.33 219	92.81 203	99.75 70	94.79 172	99.81 37	99.54 43

ACMH+	93.58 10	98.23 34	98.31 31	97.98 101	99.39 44	95.22 123	97.55 92	99.20 21	98.21 37	99.25 26	98.51 85	98.21 11	99.40 224	94.79 172	99.72 58	99.32 106

thisisatest0530		92.71 295	91.76 302	95.56 250	98.42 170	88.23 283	96.03 172	87.35 375	94.04 213	96.56 224	95.47 316	64.03 379	99.77 58	94.78 174	99.11 215	98.68 229

PGM-MVS		97.88 68	97.52 93	98.96 16	99.20 72	97.62 24	97.09 119	99.06 46	95.45 162	97.55 159	97.94 157	97.11 46	99.78 49	94.77 175	99.46 138	99.48 64

TSAR-MVS + GP.		96.47 164	96.12 172	97.49 142	97.74 255	95.23 120	94.15 275	96.90 290	93.26 233	98.04 129	96.70 263	94.41 166	98.89 308	94.77 175	99.14 208	98.37 254

VNet		96.84 137	96.83 137	96.88 182	98.06 207	92.02 223	96.35 154	97.57 269	97.70 60	97.88 146	97.80 175	92.40 218	99.54 180	94.73 177	98.96 230	99.08 165

VPNet		97.26 116	97.49 97	96.59 199	99.47 34	90.58 247	96.27 157	98.53 170	97.77 50	98.46 77	98.41 92	94.59 160	99.68 130	94.61 178	99.29 191	99.52 47

GBi-Net		96.99 126	96.80 139	97.56 131	97.96 218	93.67 182	98.23 45	98.66 155	95.59 157	97.99 133	99.19 28	89.51 268	99.73 85	94.60 179	99.44 143	99.30 112

test1		96.99 126	96.80 139	97.56 131	97.96 218	93.67 182	98.23 45	98.66 155	95.59 157	97.99 133	99.19 28	89.51 268	99.73 85	94.60 179	99.44 143	99.30 112

FMVSNet3		95.26 214	94.94 214	96.22 221	96.53 318	90.06 252	95.99 175	97.66 260	94.11 211	97.99 133	97.91 161	80.22 326	99.63 148	94.60 179	99.44 143	98.96 183

xxxxxxxxxxxxxcwj		97.24 118	97.03 126	97.89 107	98.48 163	94.71 139	94.53 259	99.07 45	95.02 181	97.83 152	97.88 166	96.44 94	99.72 90	94.59 182	99.39 162	99.25 129

SF-MVS		97.60 91	97.39 101	98.22 80	98.93 108	95.69 91	97.05 121	99.10 36	95.32 167	97.83 152	97.88 166	96.44 94	99.72 90	94.59 182	99.39 162	99.25 129

MVS_0304		95.50 200	95.05 212	96.84 185	96.28 324	93.12 197	97.00 124	96.16 304	95.03 180	89.22 368	97.70 184	90.16 257	99.48 197	94.51 184	99.34 176	97.93 296

XXY-MVS		97.54 95	97.70 69	97.07 171	99.46 35	92.21 216	97.22 112	99.00 65	94.93 185	98.58 66	98.92 54	97.31 38	99.41 222	94.44 185	99.43 151	99.59 31

UnsupCasMVSNet_eth		95.91 186	95.73 191	96.44 208	98.48 163	91.52 233	95.31 219	98.45 177	95.76 149	97.48 167	97.54 195	89.53 267	98.69 326	94.43 186	94.61 361	99.13 152

LPG-MVS_test		97.94 57	97.67 73	98.74 37	99.15 79	97.02 46	97.09 119	99.02 57	95.15 174	98.34 91	98.23 118	97.91 17	99.70 115	94.41 187	99.73 54	99.50 50

LGP-MVS_train				98.74 37	99.15 79	97.02 46		99.02 57	95.15 174	98.34 91	98.23 118	97.91 17	99.70 115	94.41 187	99.73 54	99.50 50

DeepPCF-MVS	94.58 5	96.90 134	96.43 161	98.31 72	97.48 275	97.23 44	92.56 322	98.60 163	92.84 252	98.54 68	97.40 208	96.64 81	98.78 317	94.40 189	99.41 160	98.93 190

#test#		97.62 89	97.22 114	98.83 28	99.15 79	97.50 32	96.81 131	98.84 105	94.25 205	97.49 164	97.54 195	97.07 50	99.70 115	94.37 190	99.46 138	99.30 112

XVG-ACMP-BASELINE		97.58 93	97.28 109	98.49 55	99.16 76	96.90 50	96.39 150	98.98 71	95.05 179	98.06 126	98.02 146	95.86 108	99.56 173	94.37 190	99.64 76	99.00 177

RPSCF		97.87 69	97.51 94	98.95 17	99.15 79	98.43 5	97.56 91	99.06 46	96.19 123	98.48 74	98.70 70	94.72 153	99.24 267	94.37 190	99.33 183	99.17 143

CSCG		97.40 106	97.30 106	97.69 124	98.95 105	94.83 134	97.28 108	98.99 68	96.35 116	98.13 117	95.95 303	95.99 105	99.66 141	94.36 193	99.73 54	98.59 236

HPM-MVS++		96.99 126	96.38 162	98.81 31	98.64 137	97.59 26	95.97 177	98.20 211	95.51 160	95.06 279	96.53 272	94.10 174	99.70 115	94.29 194	99.15 207	99.13 152

XVG-OURS		97.12 121	96.74 142	98.26 75	98.99 103	97.45 36	93.82 290	99.05 48	95.19 172	98.32 96	97.70 184	95.22 139	98.41 347	94.27 195	98.13 290	98.93 190

jason		94.39 254	94.04 258	95.41 260	98.29 178	87.85 295	92.74 319	96.75 296	85.38 340	95.29 275	96.15 291	88.21 280	99.65 143	94.24 196	99.34 176	98.74 221
jason: jason.
CVMVSNet		92.33 301	92.79 284	90.95 348	97.26 295	75.84 378	95.29 221	92.33 353	81.86 355	96.27 239	98.19 123	81.44 318	98.46 345	94.23 197	98.29 284	98.55 240

EIA-MVS		96.04 181	95.77 190	96.85 184	97.80 238	92.98 200	96.12 167	99.16 25	94.65 191	93.77 314	91.69 367	95.68 121	99.67 135	94.18 198	98.85 246	97.91 297

ET-MVSNet_ETH3D		91.12 315	89.67 327	95.47 255	96.41 321	89.15 268	91.54 338	90.23 369	89.07 299	86.78 377	92.84 353	69.39 372	99.44 210	94.16 199	96.61 339	97.82 301

cl22		93.25 287	92.84 283	94.46 298	94.30 363	86.00 324	91.09 350	96.64 301	90.74 282	95.79 261	96.31 284	78.24 333	98.77 318	94.15 200	98.34 281	98.62 233

MCST-MVS		96.24 171	95.80 188	97.56 131	98.75 124	94.13 165	94.66 254	98.17 217	90.17 289	96.21 243	96.10 296	95.14 141	99.43 212	94.13 201	98.85 246	99.13 152

COLMAP_ROB	94.48 6	98.25 33	98.11 36	98.64 46	99.21 71	97.35 39	97.96 62	99.16 25	98.34 32	98.78 52	98.52 84	97.32 37	99.45 207	94.08 202	99.67 71	99.13 152
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous202405211		96.34 168	95.98 180	97.43 151	98.25 185	93.85 175	96.74 136	94.41 332	97.72 57	98.37 85	98.03 145	87.15 290	99.53 182	94.06 203	99.07 221	98.92 194

Effi-MVS+-dtu		96.81 142	96.09 174	98.99 13	96.90 312	98.69 4	96.42 149	98.09 230	95.86 144	95.15 278	95.54 314	94.26 170	99.81 38	94.06 203	98.51 276	98.47 246

mvs-test1		96.20 174	95.50 199	98.32 69	96.90 312	98.16 7	95.07 234	98.09 230	95.86 144	93.63 319	94.32 338	94.26 170	99.71 106	94.06 203	97.27 327	97.07 324

ambc				96.56 203	98.23 188	91.68 231	97.88 68	98.13 226		98.42 80	98.56 81	94.22 172	99.04 293	94.05 206	99.35 173	98.95 184

our_test_3		94.20 262	94.58 239	93.07 325	96.16 331	81.20 363	90.42 356	96.84 291	90.72 283	97.14 182	97.13 230	90.47 248	99.11 285	94.04 207	98.25 285	98.91 195

pmmvs5		94.63 244	94.34 249	95.50 253	97.63 265	88.34 281	94.02 281	97.13 281	87.15 320	95.22 277	97.15 229	87.50 287	99.27 261	93.99 208	99.26 195	98.88 203

DPE-MVS		97.64 87	97.35 104	98.50 54	98.85 114	96.18 72	95.21 227	98.99 68	95.84 146	98.78 52	98.08 135	96.84 72	99.81 38	93.98 209	99.57 96	99.52 47
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ppachtmachnet_test		94.49 251	94.84 222	93.46 317	96.16 331	82.10 357	90.59 354	97.48 271	90.53 285	97.01 197	97.59 192	91.01 241	99.36 237	93.97 210	99.18 205	98.94 186

tfpnnormal		97.72 82	97.97 46	96.94 178	99.26 55	92.23 215	97.83 71	98.45 177	98.25 35	99.13 33	98.66 73	96.65 79	99.69 123	93.92 211	99.62 79	98.91 195

LFMVS		95.32 211	94.88 220	96.62 197	98.03 209	91.47 234	97.65 84	90.72 365	99.11 9	97.89 145	98.31 101	79.20 329	99.48 197	93.91 212	99.12 214	98.93 190

EPP-MVSNet		96.84 137	96.58 149	97.65 126	99.18 75	93.78 179	98.68 14	96.34 302	97.91 48	97.30 174	98.06 142	88.46 276	99.85 27	93.85 213	99.40 161	99.32 106

Fast-Effi-MVS+-dtu		96.44 165	96.12 172	97.39 155	97.18 301	94.39 151	95.46 205	98.73 135	96.03 133	94.72 287	94.92 326	96.28 102	99.69 123	93.81 214	97.98 295	98.09 279

PHI-MVS		96.96 130	96.53 155	98.25 78	97.48 275	96.50 63	96.76 135	98.85 100	93.52 224	96.19 245	96.85 251	95.94 106	99.42 213	93.79 215	99.43 151	98.83 208

miper_enhance_ethall		93.14 289	92.78 286	94.20 305	93.65 371	85.29 332	89.97 360	97.85 246	85.05 342	96.15 248	94.56 331	85.74 297	99.14 280	93.74 216	98.34 281	98.17 278

DeepC-MVS_fast	94.34 7	96.74 146	96.51 158	97.44 150	97.69 258	94.15 164	96.02 173	98.43 180	93.17 240	97.30 174	97.38 214	95.48 129	99.28 258	93.74 216	99.34 176	98.88 203
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AUN-MVS		93.95 270	92.69 288	97.74 118	97.80 238	95.38 110	95.57 203	95.46 322	91.26 277	92.64 344	96.10 296	74.67 352	99.55 177	93.72 218	96.97 328	98.30 265

MP-MVS		97.64 87	97.18 116	99.00 12	99.32 52	97.77 20	97.49 98	98.73 135	96.27 117	95.59 269	97.75 179	96.30 100	99.78 49	93.70 219	99.48 133	99.45 74
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu		95.95 185	95.80 188	96.42 211	99.28 54	90.62 246	95.31 219	99.08 42	88.40 308	96.97 201	98.17 126	92.11 223	99.78 49	93.64 220	99.21 200	98.86 206

lupinMVS		93.77 271	93.28 272	95.24 263	97.68 259	87.81 296	92.12 330	96.05 306	84.52 347	94.48 296	95.06 322	86.90 291	99.63 148	93.62 221	99.13 211	98.27 269

NCCC		96.52 161	95.99 179	98.10 91	97.81 234	95.68 93	95.00 240	98.20 211	95.39 165	95.40 274	96.36 282	93.81 183	99.45 207	93.55 222	98.42 279	99.17 143

ETV-MVS		96.13 178	95.90 185	96.82 187	97.76 250	93.89 172	95.40 211	98.95 78	95.87 143	95.58 270	91.00 373	96.36 99	99.72 90	93.36 223	98.83 248	96.85 334

FA-MVS(test-final)		94.91 227	94.89 219	94.99 274	97.51 273	88.11 290	98.27 43	95.20 325	92.40 260	96.68 216	98.60 77	83.44 312	99.28 258	93.34 224	98.53 273	97.59 312

MDA-MVSNet_test_wron		94.73 234	94.83 224	94.42 299	97.48 275	85.15 335	90.28 358	95.87 312	92.52 255	97.48 167	97.76 176	91.92 231	99.17 277	93.32 225	96.80 335	98.94 186

YYNet1		94.73 234	94.84 222	94.41 300	97.47 279	85.09 337	90.29 357	95.85 313	92.52 255	97.53 160	97.76 176	91.97 227	99.18 273	93.31 226	96.86 332	98.95 184

pmmvs4		94.82 231	94.19 254	96.70 194	97.42 282	92.75 207	92.09 332	96.76 295	86.80 325	95.73 266	97.22 226	89.28 271	98.89 308	93.28 227	99.14 208	98.46 248

CANet_DTU		94.65 243	94.21 253	95.96 230	95.90 338	89.68 257	93.92 287	97.83 250	93.19 236	90.12 363	95.64 311	88.52 275	99.57 172	93.27 228	99.47 135	98.62 233

ACMP	92.54 13	97.47 101	97.10 120	98.55 52	99.04 100	96.70 55	96.24 161	98.89 85	93.71 221	97.97 137	97.75 179	97.44 32	99.63 148	93.22 229	99.70 64	99.32 106
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Effi-MVS+		96.19 175	96.01 177	96.71 193	97.43 281	92.19 219	96.12 167	99.10 36	95.45 162	93.33 332	94.71 329	97.23 45	99.56 173	93.21 230	97.54 316	98.37 254

MDA-MVSNet-bldmvs		95.69 193	95.67 192	95.74 241	98.48 163	88.76 276	92.84 314	97.25 275	96.00 134	97.59 158	97.95 156	91.38 237	99.46 203	93.16 231	96.35 343	98.99 180

IS-MVSNet		96.93 131	96.68 145	97.70 122	99.25 58	94.00 169	98.57 19	96.74 297	98.36 31	98.14 116	97.98 151	88.23 279	99.71 106	93.10 232	99.72 58	99.38 93

9.14			96.69 144		98.53 154		96.02 173	98.98 71	93.23 234	97.18 180	97.46 203	96.47 92	99.62 156	92.99 233	99.32 185

MS-PatchMatch		94.83 230	94.91 218	94.57 294	96.81 314	87.10 311	94.23 270	97.34 274	88.74 305	97.14 182	97.11 233	91.94 229	98.23 357	92.99 233	97.92 297	98.37 254

Patchmtry		95.03 224	94.59 238	96.33 215	94.83 357	90.82 242	96.38 152	97.20 277	96.59 103	97.49 164	98.57 79	77.67 336	99.38 232	92.95 235	99.62 79	98.80 211

ETH3D-3000-0.1		96.89 136	96.46 160	98.16 84	98.62 143	95.69 91	95.96 178	98.98 71	93.36 229	97.04 193	97.31 221	94.93 150	99.63 148	92.60 236	99.34 176	99.17 143

Fast-Effi-MVS+		95.49 201	95.07 209	96.75 191	97.67 262	92.82 203	94.22 271	98.60 163	91.61 270	93.42 330	92.90 352	96.73 77	99.70 115	92.60 236	97.89 300	97.74 304

HQP_MVS		96.66 155	96.33 165	97.68 125	98.70 132	94.29 156	96.50 146	98.75 131	96.36 114	96.16 246	96.77 258	91.91 232	99.46 203	92.59 238	99.20 201	99.28 120

plane_prior5								98.75 131					99.46 203	92.59 238	99.20 201	99.28 120

GA-MVS		92.83 293	92.15 297	94.87 280	96.97 307	87.27 308	90.03 359	96.12 305	91.83 268	94.05 306	94.57 330	76.01 348	98.97 305	92.46 240	97.34 324	98.36 259

CPTT-MVS		96.69 152	96.08 175	98.49 55	98.89 111	96.64 59	97.25 109	98.77 127	92.89 251	96.01 253	97.13 230	92.23 220	99.67 135	92.24 241	99.34 176	99.17 143

EPNet		93.72 273	92.62 291	97.03 175	87.61 387	92.25 214	96.27 157	91.28 359	96.74 97	87.65 373	97.39 212	85.00 302	99.64 146	92.14 242	99.48 133	99.20 138
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PC_three_1452									87.24 318	98.37 85	97.44 205	97.00 56	96.78 374	92.01 243	99.25 196	99.21 134

APD-MVS		97.00 125	96.53 155	98.41 62	98.55 152	96.31 69	96.32 156	98.77 127	92.96 250	97.44 171	97.58 194	95.84 109	99.74 80	91.96 244	99.35 173	99.19 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CL-MVSNet_self_test		95.04 222	94.79 227	95.82 238	97.51 273	89.79 256	91.14 348	96.82 293	93.05 243	96.72 214	96.40 280	90.82 244	99.16 278	91.95 245	98.66 263	98.50 244

test_prior3		95.91 186	95.39 200	97.46 147	97.79 244	94.26 161	93.33 307	98.42 183	94.21 206	94.02 307	96.25 286	93.64 187	99.34 242	91.90 246	98.96 230	98.79 212

test_prior2							93.33 307		94.21 206	94.02 307	96.25 286	93.64 187		91.90 246	98.96 230

test-LLR		89.97 327	89.90 325	90.16 352	94.24 365	74.98 379	89.89 361	89.06 371	92.02 263	89.97 364	90.77 374	73.92 355	98.57 337	91.88 248	97.36 322	96.92 329

test-mter		87.92 343	87.17 344	90.16 352	94.24 365	74.98 379	89.89 361	89.06 371	86.44 327	89.97 364	90.77 374	54.96 389	98.57 337	91.88 248	97.36 322	96.92 329

MVP-Stereo		95.69 193	95.28 202	96.92 179	98.15 200	93.03 199	95.64 200	98.20 211	90.39 286	96.63 221	97.73 182	91.63 235	99.10 287	91.84 250	97.31 325	98.63 232
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1112_ss		94.12 263	93.42 270	96.23 219	98.59 148	90.85 241	94.24 269	98.85 100	85.49 335	92.97 336	94.94 324	86.01 296	99.64 146	91.78 251	97.92 297	98.20 275

train_agg		95.46 205	94.66 230	97.88 109	97.84 230	95.23 120	93.62 296	98.39 188	87.04 321	93.78 312	95.99 298	94.58 161	99.52 186	91.76 252	98.90 238	98.89 199

LF4IMVS		96.07 179	95.63 194	97.36 156	98.19 191	95.55 99	95.44 206	98.82 120	92.29 261	95.70 267	96.55 270	92.63 210	98.69 326	91.75 253	99.33 183	97.85 299

agg_prior1		95.39 208	94.60 236	97.75 117	97.80 238	94.96 131	93.39 304	98.36 192	87.20 319	93.49 325	95.97 301	94.65 158	99.53 182	91.69 254	98.86 244	98.77 217

N_pmnet		95.18 216	94.23 251	98.06 95	97.85 226	96.55 62	92.49 323	91.63 358	89.34 296	98.09 122	97.41 207	90.33 251	99.06 291	91.58 255	99.31 187	98.56 238

ETH3D cwj APD-0.16		96.23 172	95.61 196	98.09 92	97.91 222	95.65 96	94.94 242	98.74 133	91.31 276	96.02 252	97.08 235	94.05 176	99.69 123	91.51 256	98.94 234	98.93 190

AllTest		97.20 120	96.92 133	98.06 95	99.08 92	96.16 73	97.14 116	99.16 25	94.35 201	97.78 155	98.07 137	95.84 109	99.12 282	91.41 257	99.42 154	98.91 195

TestCases				98.06 95	99.08 92	96.16 73		99.16 25	94.35 201	97.78 155	98.07 137	95.84 109	99.12 282	91.41 257	99.42 154	98.91 195

test9_res														91.29 259	98.89 241	99.00 177

xiu_mvs_v2_base		94.22 258	94.63 234	92.99 329	97.32 293	84.84 340	92.12 330	97.84 248	91.96 265	94.17 301	93.43 343	96.07 104	99.71 106	91.27 260	97.48 319	94.42 365

PS-MVSNAJ		94.10 264	94.47 244	93.00 328	97.35 286	84.88 339	91.86 334	97.84 248	91.96 265	94.17 301	92.50 359	95.82 112	99.71 106	91.27 260	97.48 319	94.40 366

tpm		91.08 317	90.85 315	91.75 344	95.33 353	78.09 369	95.03 239	91.27 360	88.75 304	93.53 324	97.40 208	71.24 365	99.30 252	91.25 262	93.87 364	97.87 298

OPU-MVS				97.64 127	98.01 212	95.27 118	96.79 133				97.35 217	96.97 58	98.51 343	91.21 263	99.25 196	99.14 150

ZD-MVS					98.43 169	95.94 83		98.56 169	90.72 283	96.66 218	97.07 236	95.02 146	99.74 80	91.08 264	98.93 236

tpmrst		90.31 322	90.61 320	89.41 355	94.06 368	72.37 384	95.06 236	93.69 336	88.01 312	92.32 349	96.86 250	77.45 338	98.82 313	91.04 265	87.01 377	97.04 326

sss		94.22 258	93.72 266	95.74 241	97.71 257	89.95 255	93.84 289	96.98 287	88.38 309	93.75 315	95.74 307	87.94 281	98.89 308	91.02 266	98.10 291	98.37 254

ITE_SJBPF				97.85 111	98.64 137	96.66 58		98.51 173	95.63 154	97.22 176	97.30 222	95.52 127	98.55 340	90.97 267	98.90 238	98.34 260

Test_1112_low_res		93.53 281	92.86 281	95.54 252	98.60 146	88.86 272	92.75 317	98.69 148	82.66 354	92.65 343	96.92 248	84.75 304	99.56 173	90.94 268	97.76 303	98.19 276

TESTMET0.1,1		87.20 345	86.57 347	89.07 356	93.62 372	72.84 383	89.89 361	87.01 377	85.46 337	89.12 369	90.20 376	56.00 387	97.72 367	90.91 269	96.92 329	96.64 342

FMVSNet5		93.39 283	92.35 293	96.50 205	95.83 341	90.81 244	97.31 106	98.27 201	92.74 253	96.27 239	98.28 110	62.23 381	99.67 135	90.86 270	99.36 168	99.03 173

PatchmatchNet		91.98 307	91.87 299	92.30 341	94.60 360	79.71 367	95.12 229	93.59 341	89.52 295	93.61 321	97.02 240	77.94 334	99.18 273	90.84 271	94.57 363	98.01 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CLD-MVS		95.47 204	95.07 209	96.69 195	98.27 182	92.53 209	91.36 340	98.67 153	91.22 278	95.78 263	94.12 340	95.65 123	98.98 301	90.81 272	99.72 58	98.57 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cascas		91.89 308	91.35 306	93.51 316	94.27 364	85.60 327	88.86 369	98.61 162	79.32 367	92.16 350	91.44 369	89.22 272	98.12 360	90.80 273	97.47 321	96.82 337

test20.03		96.58 159	96.61 147	96.48 207	98.49 161	91.72 230	95.68 194	97.69 257	96.81 95	98.27 102	97.92 160	94.18 173	98.71 324	90.78 274	99.66 73	99.00 177

test_yl		94.40 252	94.00 259	95.59 246	96.95 308	89.52 260	94.75 252	95.55 320	96.18 124	96.79 209	96.14 293	81.09 321	99.18 273	90.75 275	97.77 301	98.07 282

DCV-MVSNet		94.40 252	94.00 259	95.59 246	96.95 308	89.52 260	94.75 252	95.55 320	96.18 124	96.79 209	96.14 293	81.09 321	99.18 273	90.75 275	97.77 301	98.07 282

EPMVS		89.26 333	88.55 337	91.39 346	92.36 380	79.11 368	95.65 198	79.86 383	88.60 306	93.12 334	96.53 272	70.73 369	98.10 361	90.75 275	89.32 375	96.98 327

旧先验2							93.35 306		77.95 373	95.77 265			98.67 330	90.74 278

USDC		94.56 247	94.57 241	94.55 295	97.78 248	86.43 320	92.75 317	98.65 160	85.96 330	96.91 205	97.93 159	90.82 244	98.74 321	90.71 279	99.59 91	98.47 246

OpenMVS	94.22 8	95.48 203	95.20 203	96.32 216	97.16 302	91.96 225	97.74 78	98.84 105	87.26 317	94.36 298	98.01 148	93.95 180	99.67 135	90.70 280	98.75 255	97.35 321

Patchmatch-test		93.60 279	93.25 274	94.63 289	96.14 334	87.47 302	96.04 171	94.50 331	93.57 223	96.47 228	96.97 243	76.50 344	98.61 334	90.67 281	98.41 280	97.81 303

thisisatest0515		90.43 321	89.18 333	94.17 307	97.07 305	85.44 329	89.75 365	87.58 374	88.28 310	93.69 318	91.72 366	65.27 377	99.58 166	90.59 282	98.67 261	97.50 316

DP-MVS Recon		95.55 199	95.13 206	96.80 188	98.51 157	93.99 170	94.60 256	98.69 148	90.20 288	95.78 263	96.21 289	92.73 206	98.98 301	90.58 283	98.86 244	97.42 318

testtj		96.69 152	96.13 171	98.36 66	98.46 167	96.02 81	96.44 148	98.70 145	94.26 204	96.79 209	97.13 230	94.07 175	99.75 70	90.53 284	98.80 250	99.31 111

TinyColmap		96.00 184	96.34 164	94.96 275	97.90 224	87.91 292	94.13 278	98.49 174	94.41 199	98.16 112	97.76 176	96.29 101	98.68 329	90.52 285	99.42 154	98.30 265

BP-MVS														90.51 286

HQP-MVS		95.17 218	94.58 239	96.92 179	97.85 226	92.47 210	94.26 265	98.43 180	93.18 237	92.86 338	95.08 320	90.33 251	99.23 269	90.51 286	98.74 256	99.05 171

OMC-MVS		96.48 163	96.00 178	97.91 105	98.30 177	96.01 82	94.86 246	98.60 163	91.88 267	97.18 180	97.21 227	96.11 103	99.04 293	90.49 288	99.34 176	98.69 227

ab-mvs		96.59 157	96.59 148	96.60 198	98.64 137	92.21 216	98.35 34	97.67 258	94.45 198	96.99 198	98.79 61	94.96 149	99.49 194	90.39 289	99.07 221	98.08 280

HyFIR lowres test		93.72 273	92.65 289	96.91 181	98.93 108	91.81 229	91.23 346	98.52 171	82.69 353	96.46 229	96.52 274	80.38 325	99.90 14	90.36 290	98.79 251	99.03 173

agg_prior2														90.34 291	98.90 238	99.10 164

LCM-MVSNet-Re		97.33 112	97.33 105	97.32 158	98.13 204	93.79 178	96.99 125	99.65 5	96.74 97	99.47 13	98.93 53	96.91 65	99.84 30	90.11 292	99.06 224	98.32 261

CDS-MVSNet		94.88 229	94.12 256	97.14 167	97.64 264	93.57 187	93.96 286	97.06 285	90.05 290	96.30 238	96.55 270	86.10 295	99.47 200	90.10 293	99.31 187	98.40 250
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CDPH-MVS		95.45 206	94.65 231	97.84 112	98.28 180	94.96 131	93.73 294	98.33 197	85.03 343	95.44 272	96.60 268	95.31 136	99.44 210	90.01 294	99.13 211	99.11 160

baseline1		93.14 289	92.64 290	94.62 290	97.34 290	87.20 309	96.67 143	93.02 345	94.71 190	96.51 227	95.83 306	81.64 316	98.60 336	90.00 295	88.06 376	98.07 282

TAPA-MVS	93.32 12	94.93 226	94.23 251	97.04 174	98.18 194	94.51 147	95.22 226	98.73 135	81.22 360	96.25 241	95.95 303	93.80 184	98.98 301	89.89 296	98.87 242	97.62 309
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PMMVS		92.39 298	91.08 310	96.30 218	93.12 375	92.81 204	90.58 355	95.96 310	79.17 368	91.85 353	92.27 360	90.29 255	98.66 331	89.85 297	96.68 338	97.43 317

PVSNet_BlendedMVS		95.02 225	94.93 216	95.27 262	97.79 244	87.40 305	94.14 277	98.68 150	88.94 302	94.51 294	98.01 148	93.04 198	99.30 252	89.77 298	99.49 129	99.11 160

PVSNet_Blended		93.96 268	93.65 267	94.91 276	97.79 244	87.40 305	91.43 339	98.68 150	84.50 348	94.51 294	94.48 335	93.04 198	99.30 252	89.77 298	98.61 269	98.02 292

MSDG		95.33 210	95.13 206	95.94 234	97.40 283	91.85 227	91.02 351	98.37 191	95.30 168	96.31 237	95.99 298	94.51 164	98.38 350	89.59 300	97.65 313	97.60 311

PMVS	89.60 17	96.71 151	96.97 128	95.95 232	99.51 29	97.81 19	97.42 103	97.49 270	97.93 47	95.95 254	98.58 78	96.88 68	96.91 371	89.59 300	99.36 168	93.12 372
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_post1							94.98 241				10.37 386	76.21 347	99.04 293	89.47 302

SCA		93.38 284	93.52 269	92.96 330	96.24 325	81.40 362	93.24 309	94.00 335	91.58 272	94.57 291	96.97 243	87.94 281	99.42 213	89.47 302	97.66 312	98.06 286

tpmvs		90.79 320	90.87 314	90.57 351	92.75 379	76.30 376	95.79 188	93.64 340	91.04 280	91.91 352	96.26 285	77.19 342	98.86 312	89.38 304	89.85 374	96.56 345

Anonymous20231206		95.27 213	95.06 211	95.88 236	98.72 127	89.37 263	95.70 191	97.85 246	88.00 313	96.98 200	97.62 190	91.95 228	99.34 242	89.21 305	99.53 111	98.94 186

CHOSEN 1792x2688		94.10 264	93.41 271	96.18 223	99.16 76	90.04 253	92.15 329	98.68 150	79.90 365	96.22 242	97.83 170	87.92 285	99.42 213	89.18 306	99.65 74	99.08 165

114514_t		93.96 268	93.22 275	96.19 222	99.06 95	90.97 240	95.99 175	98.94 79	73.88 378	93.43 329	96.93 246	92.38 219	99.37 235	89.09 307	99.28 192	98.25 271

pmmvs3		90.00 325	88.90 335	93.32 318	94.20 367	85.34 330	91.25 345	92.56 352	78.59 369	93.82 311	95.17 319	67.36 376	98.69 326	89.08 308	98.03 294	95.92 351

testdata				95.70 244	98.16 198	90.58 247		97.72 255	80.38 363	95.62 268	97.02 240	92.06 226	98.98 301	89.06 309	98.52 274	97.54 313

MDTV_nov1_ep13			91.28 307		94.31 362	73.51 382	94.80 249	93.16 344	86.75 326	93.45 328	97.40 208	76.37 345	98.55 340	88.85 310	96.43 341

PMMVS2		93.66 276	94.07 257	92.45 339	97.57 268	80.67 365	86.46 372	96.00 308	93.99 214	97.10 186	97.38 214	89.90 259	97.82 365	88.76 311	99.47 135	98.86 206

QAPM		95.88 188	95.57 197	96.80 188	97.90 224	91.84 228	98.18 52	98.73 135	88.41 307	96.42 230	98.13 129	94.73 152	99.75 70	88.72 312	98.94 234	98.81 210

CHOSEN 280x420		89.98 326	89.19 332	92.37 340	95.60 347	81.13 364	86.22 373	97.09 283	81.44 359	87.44 374	93.15 344	73.99 353	99.47 200	88.69 313	99.07 221	96.52 346

testgi		96.07 179	96.50 159	94.80 284	99.26 55	87.69 299	95.96 178	98.58 167	95.08 177	98.02 132	96.25 286	97.92 16	97.60 368	88.68 314	98.74 256	99.11 160

CostFormer		89.75 330	89.25 328	91.26 347	94.69 359	78.00 371	95.32 218	91.98 355	81.50 358	90.55 359	96.96 245	71.06 367	98.89 308	88.59 315	92.63 368	96.87 332

UnsupCasMVSNet_bld		94.72 238	94.26 250	96.08 226	98.62 143	90.54 250	93.38 305	98.05 238	90.30 287	97.02 196	96.80 257	89.54 265	99.16 278	88.44 316	96.18 345	98.56 238

TAMVS		95.49 201	94.94 214	97.16 165	98.31 176	93.41 191	95.07 234	96.82 293	91.09 279	97.51 162	97.82 173	89.96 258	99.42 213	88.42 317	99.44 143	98.64 230

Vis-MVSNet (Re-imp)		95.11 219	94.85 221	95.87 237	99.12 88	89.17 266	97.54 97	94.92 327	96.50 108	96.58 222	97.27 223	83.64 311	99.48 197	88.42 317	99.67 71	98.97 182

EPNet_dtu		91.39 314	90.75 317	93.31 319	90.48 384	82.61 354	94.80 249	92.88 347	93.39 228	81.74 381	94.90 327	81.36 319	99.11 285	88.28 319	98.87 242	98.21 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
JIA-IIPM		91.79 309	90.69 318	95.11 267	93.80 370	90.98 239	94.16 274	91.78 357	96.38 112	90.30 362	99.30 22	72.02 364	98.90 307	88.28 319	90.17 373	95.45 360

新几何1				97.25 163	98.29 178	94.70 142		97.73 254	77.98 371	94.83 286	96.67 265	92.08 225	99.45 207	88.17 321	98.65 265	97.61 310

testdata2													99.46 203	87.84 322

FE-MVS		92.95 291	92.22 295	95.11 267	97.21 298	88.33 282	98.54 22	93.66 339	89.91 292	96.21 243	98.14 127	70.33 370	99.50 191	87.79 323	98.24 286	97.51 314

无先验							93.20 310	97.91 242	80.78 361				99.40 224	87.71 324		97.94 295

1121		94.26 256	93.26 273	97.27 160	98.26 184	94.73 137	95.86 184	97.71 256	77.96 372	94.53 293	96.71 262	91.93 230	99.40 224	87.71 324	98.64 266	97.69 307

WTY-MVS		93.55 280	93.00 279	95.19 265	97.81 234	87.86 293	93.89 288	96.00 308	89.02 300	94.07 305	95.44 317	86.27 294	99.33 245	87.69 326	96.82 333	98.39 252

原ACMM1				96.58 200	98.16 198	92.12 220		98.15 223	85.90 332	93.49 325	96.43 277	92.47 217	99.38 232	87.66 327	98.62 268	98.23 272

BH-untuned		94.69 239	94.75 228	94.52 296	97.95 221	87.53 301	94.07 280	97.01 286	93.99 214	97.10 186	95.65 310	92.65 209	98.95 306	87.60 328	96.74 336	97.09 323

PAPM_NR		94.61 245	94.17 255	95.96 230	98.36 174	91.23 235	95.93 182	97.95 240	92.98 246	93.42 330	94.43 336	90.53 247	98.38 350	87.60 328	96.29 344	98.27 269

DPM-MVS		93.68 275	92.77 287	96.42 211	97.91 222	92.54 208	91.17 347	97.47 272	84.99 344	93.08 335	94.74 328	89.90 259	99.00 297	87.54 330	98.09 292	97.72 305

MG-MVS		94.08 266	94.00 259	94.32 302	97.09 304	85.89 325	93.19 311	95.96 310	92.52 255	94.93 285	97.51 199	89.54 265	98.77 318	87.52 331	97.71 307	98.31 263

F-COLMAP		95.30 212	94.38 248	98.05 98	98.64 137	96.04 78	95.61 201	98.66 155	89.00 301	93.22 333	96.40 280	92.90 202	99.35 240	87.45 332	97.53 317	98.77 217

PatchMatch-RL		94.61 245	93.81 265	97.02 176	98.19 191	95.72 89	93.66 295	97.23 276	88.17 311	94.94 284	95.62 312	91.43 236	98.57 337	87.36 333	97.68 310	96.76 340

IB-MVS	85.98 20	88.63 337	86.95 346	93.68 313	95.12 354	84.82 341	90.85 352	90.17 370	87.55 316	88.48 371	91.34 370	58.01 382	99.59 164	87.24 334	93.80 365	96.63 344
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
dp		88.08 341	88.05 339	88.16 361	92.85 377	68.81 386	94.17 273	92.88 347	85.47 336	91.38 355	96.14 293	68.87 373	98.81 315	86.88 335	83.80 380	96.87 332

1314		92.38 299	92.30 294	92.64 335	95.42 352	85.15 335	95.86 184	96.97 288	85.40 339	90.62 357	93.06 350	91.12 240	97.80 366	86.74 336	95.49 355	94.97 363

CNLPA		95.04 222	94.47 244	96.75 191	97.81 234	95.25 119	94.12 279	97.89 244	94.41 199	94.57 291	95.69 308	90.30 254	98.35 353	86.72 337	98.76 254	96.64 342

ETH3 D test6400		94.77 233	93.87 264	97.47 144	98.12 205	93.73 180	94.56 258	98.70 145	85.45 338	94.70 289	95.93 305	91.77 234	99.63 148	86.45 338	99.14 208	99.05 171

baseline2		89.65 331	88.44 338	93.25 321	95.62 346	82.71 353	93.82 290	85.94 378	88.89 303	87.35 375	92.54 358	71.23 366	99.33 245	86.01 339	94.60 362	97.72 305

BH-RMVSNet		94.56 247	94.44 247	94.91 276	97.57 268	87.44 304	93.78 293	96.26 303	93.69 222	96.41 231	96.50 275	92.10 224	99.00 297	85.96 340	97.71 307	98.31 263

E-PMN		89.52 332	89.78 326	88.73 357	93.14 374	77.61 372	83.26 376	92.02 354	94.82 187	93.71 316	93.11 345	75.31 350	96.81 372	85.81 341	96.81 334	91.77 375

API-MVS		95.09 221	95.01 213	95.31 261	96.61 316	94.02 168	96.83 130	97.18 279	95.60 156	95.79 261	94.33 337	94.54 163	98.37 352	85.70 342	98.52 274	93.52 369

AdaColmap		95.11 219	94.62 235	96.58 200	97.33 292	94.45 150	94.92 243	98.08 232	93.15 241	93.98 310	95.53 315	94.34 168	99.10 287	85.69 343	98.61 269	96.20 350

ADS-MVSNet2		91.47 313	90.51 321	94.36 301	95.51 348	85.63 326	95.05 237	95.70 314	83.46 351	92.69 341	96.84 252	79.15 330	99.41 222	85.66 344	90.52 371	98.04 290

ADS-MVSNet		90.95 319	90.26 323	93.04 326	95.51 348	82.37 356	95.05 237	93.41 342	83.46 351	92.69 341	96.84 252	79.15 330	98.70 325	85.66 344	90.52 371	98.04 290

MDTV_nov1_ep13_2view						57.28 388	94.89 244		80.59 362	94.02 307		78.66 332		85.50 346		97.82 301

OpenMVS_ROB	91.80 14	93.64 278	93.05 276	95.42 258	97.31 294	91.21 236	95.08 233	96.68 300	81.56 357	96.88 207	96.41 278	90.44 250	99.25 264	85.39 347	97.67 311	95.80 354

KD-MVS_2432*1600		88.93 335	87.74 340	92.49 336	88.04 385	81.99 358	89.63 366	95.62 316	91.35 274	95.06 279	93.11 345	56.58 384	98.63 332	85.19 348	95.07 356	96.85 334

miper_refine_blended		88.93 335	87.74 340	92.49 336	88.04 385	81.99 358	89.63 366	95.62 316	91.35 274	95.06 279	93.11 345	56.58 384	98.63 332	85.19 348	95.07 356	96.85 334

PVSNet	86.72 19	91.10 316	90.97 313	91.49 345	97.56 270	78.04 370	87.17 371	94.60 330	84.65 346	92.34 348	92.20 361	87.37 289	98.47 344	85.17 350	97.69 309	97.96 294

PLC	91.02 16	94.05 267	92.90 280	97.51 136	98.00 216	95.12 128	94.25 268	98.25 204	86.17 328	91.48 354	95.25 318	91.01 241	99.19 272	85.02 351	96.69 337	98.22 273
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
gm-plane-assit					91.79 381	71.40 385			81.67 356		90.11 377		98.99 299	84.86 352

CMPMVS	73.10 23	92.74 294	91.39 305	96.77 190	93.57 373	94.67 143	94.21 272	97.67 258	80.36 364	93.61 321	96.60 268	82.85 314	97.35 369	84.86 352	98.78 252	98.29 268
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		92.34 300	91.69 303	94.32 302	96.23 327	89.16 267	92.27 328	92.88 347	84.39 350	95.29 275	96.35 283	85.66 298	96.74 375	84.53 354	97.56 315	97.05 325

tpm cat1		88.01 342	87.33 343	90.05 354	94.48 361	76.28 377	94.47 261	94.35 333	73.84 379	89.26 367	95.61 313	73.64 357	98.30 355	84.13 355	86.20 378	95.57 359

MAR-MVS		94.21 260	93.03 278	97.76 116	96.94 310	97.44 37	96.97 126	97.15 280	87.89 315	92.00 351	92.73 356	92.14 222	99.12 282	83.92 356	97.51 318	96.73 341
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
DSMNet-mixed		92.19 303	91.83 300	93.25 321	96.18 330	83.68 351	96.27 157	93.68 338	76.97 375	92.54 347	99.18 31	89.20 273	98.55 340	83.88 357	98.60 271	97.51 314

EMVS		89.06 334	89.22 329	88.61 358	93.00 376	77.34 373	82.91 377	90.92 362	94.64 192	92.63 345	91.81 365	76.30 346	97.02 370	83.83 358	96.90 331	91.48 376

HY-MVS	91.43 15	92.58 296	91.81 301	94.90 278	96.49 319	88.87 271	97.31 106	94.62 329	85.92 331	90.50 360	96.84 252	85.05 301	99.40 224	83.77 359	95.78 351	96.43 347

test0.0.03 1		90.11 323	89.21 330	92.83 332	93.89 369	86.87 315	91.74 336	88.74 373	92.02 263	94.71 288	91.14 372	73.92 355	94.48 379	83.75 360	92.94 366	97.16 322

tpm2		88.47 338	87.69 342	90.79 349	94.98 356	77.34 373	95.09 231	91.83 356	77.51 374	89.40 366	96.41 278	67.83 375	98.73 322	83.58 361	92.60 369	96.29 349

MVS-HIRNet		88.40 339	90.20 324	82.99 363	97.01 306	60.04 387	93.11 312	85.61 379	84.45 349	88.72 370	99.09 41	84.72 305	98.23 357	82.52 362	96.59 340	90.69 378

BH-w/o		92.14 304	91.94 298	92.73 334	97.13 303	85.30 331	92.46 324	95.64 315	89.33 297	94.21 300	92.74 355	89.60 263	98.24 356	81.68 363	94.66 360	94.66 364

MIMVSNet		93.42 282	92.86 281	95.10 269	98.17 196	88.19 284	98.13 54	93.69 336	92.07 262	95.04 282	98.21 122	80.95 323	99.03 296	81.42 364	98.06 293	98.07 282

TR-MVS		92.54 297	92.20 296	93.57 315	96.49 319	86.66 316	93.51 300	94.73 328	89.96 291	94.95 283	93.87 341	90.24 256	98.61 334	81.18 365	94.88 358	95.45 360

thres600view7		92.03 306	91.43 304	93.82 309	98.19 191	84.61 342	96.27 157	90.39 366	96.81 95	96.37 233	93.11 345	73.44 361	99.49 194	80.32 366	97.95 296	97.36 319

PAPR		92.22 302	91.27 308	95.07 270	95.73 345	88.81 273	91.97 333	97.87 245	85.80 333	90.91 356	92.73 356	91.16 239	98.33 354	79.48 367	95.76 352	98.08 280

MVS		90.02 324	89.20 331	92.47 338	94.71 358	86.90 314	95.86 184	96.74 297	64.72 380	90.62 357	92.77 354	92.54 214	98.39 349	79.30 368	95.56 354	92.12 373

gg-mvs-nofinetune		88.28 340	86.96 345	92.23 342	92.84 378	84.44 344	98.19 51	74.60 385	99.08 10	87.01 376	99.47 10	56.93 383	98.23 357	78.91 369	95.61 353	94.01 367

thres100view900		91.76 310	91.26 309	93.26 320	98.21 189	84.50 343	96.39 150	90.39 366	96.87 93	96.33 234	93.08 349	73.44 361	99.42 213	78.85 370	97.74 304	95.85 352

tfpn200view9		91.55 312	91.00 311	93.21 323	98.02 210	84.35 345	95.70 191	90.79 363	96.26 118	95.90 259	92.13 362	73.62 358	99.42 213	78.85 370	97.74 304	95.85 352

thres400		91.68 311	91.00 311	93.71 312	98.02 210	84.35 345	95.70 191	90.79 363	96.26 118	95.90 259	92.13 362	73.62 358	99.42 213	78.85 370	97.74 304	97.36 319

thres200		91.00 318	90.42 322	92.77 333	97.47 279	83.98 349	94.01 282	91.18 361	95.12 176	95.44 272	91.21 371	73.93 354	99.31 249	77.76 373	97.63 314	95.01 362

wuyk23d		93.25 287	95.20 203	87.40 362	96.07 336	95.38 110	97.04 122	94.97 326	95.33 166	99.70 5	98.11 133	98.14 13	91.94 380	77.76 373	99.68 69	74.89 380

test_method		66.88 349	66.13 352	69.11 365	62.68 388	25.73 390	49.76 379	96.04 307	14.32 383	64.27 384	91.69 367	73.45 360	88.05 382	76.06 375	66.94 382	93.54 368

PCF-MVS	89.43 18	92.12 305	90.64 319	96.57 202	97.80 238	93.48 190	89.88 364	98.45 177	74.46 377	96.04 251	95.68 309	90.71 246	99.31 249	73.73 376	99.01 228	96.91 331
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	81.89 21	84.49 347	83.21 350	88.34 359	95.76 344	74.97 381	83.49 375	92.70 351	78.47 370	87.94 372	86.90 379	83.38 313	96.63 376	73.44 377	66.86 383	93.40 370

GG-mvs-BLEND				90.60 350	91.00 382	84.21 347	98.23 45	72.63 388		82.76 379	84.11 380	56.14 386	96.79 373	72.20 378	92.09 370	90.78 377

FPMVS		89.92 328	88.63 336	93.82 309	98.37 173	96.94 49	91.58 337	93.34 343	88.00 313	90.32 361	97.10 234	70.87 368	91.13 381	71.91 379	96.16 347	93.39 371

MVE	73.61 22	86.48 346	85.92 348	88.18 360	96.23 327	85.28 333	81.78 378	75.79 384	86.01 329	82.53 380	91.88 364	92.74 205	87.47 383	71.42 380	94.86 359	91.78 374
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		57.23 350	62.50 353	41.44 366	34.77 389	49.21 389	83.93 374	60.22 390	15.31 382	71.11 383	79.37 381	70.09 371	44.86 385	64.76 381	82.93 381	30.25 381

PAPM		87.64 344	85.84 349	93.04 326	96.54 317	84.99 338	88.42 370	95.57 319	79.52 366	83.82 378	93.05 351	80.57 324	98.41 347	62.29 382	92.79 367	95.71 355

DeepMVS_CX				77.17 364	90.94 383	85.28 333		74.08 387	52.51 381	80.87 382	88.03 378	75.25 351	70.63 384	59.23 383	84.94 379	75.62 379

test123		12.59 352	15.49 355	3.87 367	6.07 390	2.55 391	90.75 353	2.59 392	2.52 385	5.20 387	13.02 384	4.96 390	1.85 387	5.20 384	9.09 384	7.23 382

testmvs		12.33 353	15.23 356	3.64 368	5.77 391	2.23 392	88.99 368	3.62 391	2.30 386	5.29 386	13.09 383	4.52 391	1.95 386	5.16 385	8.32 385	6.75 383

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

cdsmvs_eth3d_5k		24.22 351	32.30 354	0.00 369	0.00 392	0.00 393	0.00 380	98.10 228	0.00 387	0.00 388	95.06 322	97.54 31	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		7.98 354	10.65 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	95.82 112	0.00 388	0.00 386	0.00 386	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

ab-mvs-re		7.91 355	10.55 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	94.94 324	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 386	0.00 384

FOURS1					99.59 17	98.20 6	99.03 7	99.25 17	98.96 18	98.87 45

test_one_0601					99.05 99	95.50 105		98.87 93	97.21 86	98.03 130	98.30 105	96.93 62

eth-test2					0.00 392

eth-test					0.00 392

test_241102_ONE					99.22 64	95.35 113		98.83 112	96.04 131	99.08 34	98.13 129	97.87 20	99.33 245

save fliter					98.48 163	94.71 139	94.53 259	98.41 185	95.02 181

test0726					99.24 59	95.51 102	96.89 128	98.89 85	95.92 139	98.64 59	98.31 101	97.06 52

GSMVS																98.06 286

test_part2					99.03 101	96.07 77				98.08 124

sam_mvs1												77.80 335				98.06 286

sam_mvs												77.38 339

MTGPA								98.73 135

test_post											10.87 385	76.83 343	99.07 290

patchmatchnet-post											96.84 252	77.36 340	99.42 213

MTMP							96.55 144	74.60 385

TEST9					97.84 230	95.23 120	93.62 296	98.39 188	86.81 324	93.78 312	95.99 298	94.68 156	99.52 186

test_8					97.81 234	95.07 129	93.54 299	98.38 190	87.04 321	93.71 316	95.96 302	94.58 161	99.52 186

agg_prior					97.80 238	94.96 131		98.36 192		93.49 325			99.53 182

test_prior4						95.38 110	93.61 298

test_prior				97.46 147	97.79 244	94.26 161		98.42 183					99.34 242			98.79 212

新几何2							93.43 301

旧先验1					97.80 238	93.87 173		97.75 253			97.04 239	93.57 189			98.68 260	98.72 224

原ACMM2							92.82 315

test222					98.17 196	93.24 195	92.74 319	97.61 268	75.17 376	94.65 290	96.69 264	90.96 243			98.66 263	97.66 308

segment_acmp												95.34 134

testdata1							92.77 316		93.78 219

test12				97.46 147	97.61 266	94.07 166		97.78 252		93.57 323		93.31 193	99.42 213		98.78 252	98.89 199

plane_prior7					98.70 132	94.67 143

plane_prior6					98.38 172	94.37 153						91.91 232

plane_prior4											96.77 258

plane_prior3						94.51 147			95.29 169	96.16 246

plane_prior2							96.50 146		96.36 114

plane_prior1					98.49 161

plane_prior						94.29 156	95.42 208		94.31 203						98.93 236

n2								0.00 393

nn								0.00 393

door-mid								98.17 217

test11								98.08 232

door								97.81 251

HQP5-MVS						92.47 210

HQP-NCC					97.85 226		94.26 265		93.18 237	92.86 338

ACMP_Plane					97.85 226		94.26 265		93.18 237	92.86 338

HQP4-MVS										92.87 337			99.23 269			99.06 169

HQP3-MVS								98.43 180							98.74 256

HQP2-MVS												90.33 251

NP-MVS					98.14 201	93.72 181					95.08 320

ACMMP++_ref															99.52 116

ACMMP++															99.55 105

Test By Simon												94.51 164

High-resolution multi-view benchmark