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.

tmp_tt		95.75 336	95.42 334	96.76 344	89.90 381	94.42 355	98.86 224	97.87 353	78.01 372	99.30 236	99.69 122	97.70 220	95.89 376	99.29 69	98.14 354	99.95 1

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 46	99.68 40	99.85 28	99.95 4	99.98 3	99.92 18	99.28 43	99.98 8	99.75 13	100.00 1	99.94 2

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 40	99.72 26	99.88 19	99.92 8	99.98 3	99.93 15	99.94 1	99.98 8	99.77 12	100.00 1	99.92 3

UA-Net		99.78 14	99.76 15	99.86 16	99.72 112	99.71 72	99.91 3	99.95 8	99.96 3	99.71 104	99.91 21	99.15 56	99.97 19	99.50 37	100.00 1	99.90 4

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 43	99.70 31	99.86 24	99.89 15	99.98 3	99.90 23	99.94 1	99.98 8	99.75 13	100.00 1	99.90 4

EU-MVSNet		99.39 85	99.62 31	98.72 294	99.88 26	96.44 335	99.56 73	99.85 28	99.90 10	99.90 24	99.85 43	98.09 192	99.83 247	99.58 27	99.95 56	99.90 4

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 20	99.77 13	99.80 51	99.73 48	99.97 6	99.92 18	99.77 7	99.98 8	99.43 43	100.00 1	99.90 4

CVMVSNet		98.61 231	98.88 191	97.80 325	99.58 163	93.60 360	99.26 130	99.64 137	99.66 69	99.72 99	99.67 138	93.26 310	99.93 75	99.30 66	99.81 160	99.87 8

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 10	99.78 6	100.00 1	99.92 1	100.00 1	99.87 8

FC-MVSNet-test		99.70 20	99.65 27	99.86 16	99.88 26	99.86 13	99.72 26	99.78 62	99.90 10	99.82 53	99.83 49	98.45 155	99.87 181	99.51 35	99.97 34	99.86 10

PS-CasMVS		99.66 29	99.58 41	99.89 7	99.80 59	99.85 14	99.66 48	99.73 85	99.62 77	99.84 47	99.71 109	98.62 128	99.96 37	99.30 66	99.96 47	99.86 10

anonymousdsp		99.80 12	99.77 13	99.90 4	99.96 4	99.88 9	99.73 23	99.85 28	99.70 56	99.92 19	99.93 15	99.45 25	99.97 19	99.36 54	100.00 1	99.85 12

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 22	99.91 2	99.89 4	99.71 97	99.93 6	99.95 11	99.89 27	99.71 9	99.96 37	99.51 35	99.97 34	99.84 13

CP-MVSNet		99.54 51	99.43 67	99.87 14	99.76 88	99.82 30	99.57 71	99.61 149	99.54 91	99.80 63	99.64 149	97.79 217	99.95 46	99.21 76	99.94 68	99.84 13

Test_1112_low_res		98.95 195	98.73 205	99.63 119	99.68 135	99.15 210	98.09 303	99.80 51	97.14 318	99.46 193	99.40 253	96.11 281	99.89 154	99.01 105	99.84 133	99.84 13

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 53	100.00 1	99.90 10	100.00 1	99.97 11	99.61 17	99.97 19	99.75 13	100.00 1	99.84 13

patch_mono-2		99.51 54	99.46 61	99.64 112	99.70 123	99.11 213	99.04 194	99.87 21	99.71 52	99.47 189	99.79 67	98.24 178	99.98 8	99.38 50	99.96 47	99.83 17

nrg030		99.70 20	99.66 25	99.82 24	99.76 88	99.84 20	99.61 61	99.70 102	99.93 6	99.78 71	99.68 133	99.10 62	99.78 284	99.45 41	99.96 47	99.83 17

FIs		99.65 34	99.58 41	99.84 20	99.84 36	99.85 14	99.66 48	99.75 77	99.86 23	99.74 94	99.79 67	98.27 176	99.85 219	99.37 53	99.93 76	99.83 17

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 20	99.82 8	99.82 41	99.84 31	99.94 12	99.91 21	99.13 61	99.96 37	99.83 9	99.99 12	99.83 17

PEN-MVS		99.66 29	99.59 38	99.89 7	99.83 40	99.87 10	99.66 48	99.73 85	99.70 56	99.84 47	99.73 96	98.56 137	99.96 37	99.29 69	99.94 68	99.83 17

WR-MVS_H		99.61 40	99.53 53	99.87 14	99.80 59	99.83 25	99.67 44	99.75 77	99.58 90	99.85 44	99.69 122	98.18 188	99.94 60	99.28 71	99.95 56	99.83 17

test_part1		98.63 229	98.26 252	99.75 58	99.40 245	99.49 130	99.67 44	99.68 111	99.86 23	99.88 34	99.86 39	86.73 363	99.93 75	99.34 56	99.97 34	99.81 23

Anonymous20231211		99.62 38	99.57 44	99.76 48	99.61 152	99.60 111	99.81 9	99.73 85	99.82 36	99.90 24	99.90 23	97.97 203	99.86 201	99.42 48	99.96 47	99.80 24

APDe-MVS		99.48 59	99.36 80	99.85 19	99.55 185	99.81 33	99.50 78	99.69 108	98.99 173	99.75 85	99.71 109	98.79 105	99.93 75	98.46 148	99.85 128	99.80 24

DTE-MVSNet		99.68 25	99.61 35	99.88 11	99.80 59	99.87 10	99.67 44	99.71 97	99.72 51	99.84 47	99.78 74	98.67 122	99.97 19	99.30 66	99.95 56	99.80 24

XXY-MVS		99.71 19	99.67 24	99.81 27	99.89 22	99.72 70	99.59 67	99.82 41	99.39 117	99.82 53	99.84 48	99.38 31	99.91 118	99.38 50	99.93 76	99.80 24

1112_ss		99.05 173	98.84 196	99.67 93	99.66 141	99.29 180	98.52 267	99.82 41	97.65 291	99.43 199	99.16 306	96.42 271	99.91 118	99.07 101	99.84 133	99.80 24

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 16	99.90 5	99.96 1	99.92 9	99.90 10	99.97 6	99.87 33	99.81 5	99.95 46	99.54 31	99.99 12	99.80 24
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
PMMVS2		99.48 59	99.45 62	99.57 145	99.76 88	98.99 226	98.09 303	99.90 15	98.95 179	99.78 71	99.58 194	99.57 20	99.93 75	99.48 38	99.95 56	99.79 30

MSC_two_6792asdad				99.74 64	99.03 324	99.53 125		99.23 297					99.92 95	97.77 206	99.69 214	99.78 31

No_MVS				99.74 64	99.03 324	99.53 125		99.23 297					99.92 95	97.77 206	99.69 214	99.78 31

dcpmvs_2		99.61 40	99.64 29	99.53 158	99.79 69	98.82 245	99.58 69	99.97 2	99.95 4	99.96 8	99.76 84	98.44 156	99.99 5	99.34 56	99.96 47	99.78 31

bld_raw_conf005		99.81 11	99.79 11	99.86 16	99.94 10	99.85 14	99.77 13	99.90 15	99.97 2	99.92 19	99.86 39	99.21 50	99.94 60	99.59 23	99.98 24	99.78 31

CHOSEN 1792x2688		99.39 85	99.30 93	99.65 105	99.88 26	99.25 190	98.78 241	99.88 19	98.66 212	99.96 8	99.79 67	97.45 236	99.93 75	99.34 56	99.99 12	99.78 31

IU-MVS					99.69 127	99.77 46		99.22 300	97.50 300	99.69 110				97.75 210	99.70 211	99.77 36

test_0728_THIRD									99.18 147	99.62 139	99.61 177	98.58 134	99.91 118	97.72 212	99.80 165	99.77 36

test_0728_SECOND				99.83 22	99.70 123	99.79 40	99.14 168	99.61 149					99.92 95	97.88 195	99.72 206	99.77 36

MSP-MVS		99.04 176	98.79 203	99.81 27	99.78 76	99.73 66	99.35 104	99.57 180	98.54 226	99.54 170	98.99 329	96.81 262	99.93 75	96.97 267	99.53 267	99.77 36
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
DPE-MVS		99.14 155	98.92 185	99.82 24	99.57 173	99.77 46	98.74 245	99.60 162	98.55 223	99.76 78	99.69 122	98.23 182	99.92 95	96.39 300	99.75 185	99.76 40
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
Baseline_NR-MVSNet		99.49 57	99.37 77	99.82 24	99.91 16	99.84 20	98.83 229	99.86 24	99.68 61	99.65 125	99.88 30	97.67 225	99.87 181	99.03 103	99.86 124	99.76 40

OurMVSNet-221017-0		99.75 16	99.71 17	99.84 20	99.96 4	99.83 25	99.83 6	99.85 28	99.80 40	99.93 15	99.93 15	98.54 140	99.93 75	99.59 23	99.98 24	99.76 40

test_241102_TWO								99.54 197	99.13 159	99.76 78	99.63 159	98.32 173	99.92 95	97.85 201	99.69 214	99.75 43

DP-MVS		99.48 59	99.39 72	99.74 64	99.57 173	99.62 103	99.29 124	99.61 149	99.87 21	99.74 94	99.76 84	98.69 118	99.87 181	98.20 168	99.80 165	99.75 43

v10		99.69 22	99.69 21	99.66 100	99.81 54	99.39 158	99.66 48	99.75 77	99.60 87	99.92 19	99.87 33	98.75 113	99.86 201	99.90 2	99.99 12	99.73 45

EI-MVSNet-UG-set		99.48 59	99.50 55	99.42 190	99.57 173	98.65 259	99.24 137	99.46 237	99.68 61	99.80 63	99.66 142	98.99 78	99.89 154	99.19 81	99.90 90	99.72 46

Vis-MVSNet		99.75 16	99.74 16	99.79 35	99.88 26	99.66 90	99.69 37	99.92 9	99.67 65	99.77 76	99.75 89	99.61 17	99.98 8	99.35 55	99.98 24	99.72 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HyFIR lowres test		98.91 198	98.64 213	99.73 74	99.85 35	99.47 133	98.07 306	99.83 36	98.64 214	99.89 28	99.60 186	92.57 316	100.00 1	99.33 60	99.97 34	99.72 46

EI-MVSNet-Vis-set		99.47 65	99.49 56	99.42 190	99.57 173	98.66 256	99.24 137	99.46 237	99.67 65	99.79 68	99.65 147	98.97 81	99.89 154	99.15 90	99.89 99	99.71 49

v8		99.68 25	99.69 21	99.65 105	99.80 59	99.40 156	99.66 48	99.76 70	99.64 73	99.93 15	99.85 43	98.66 124	99.84 236	99.88 6	99.99 12	99.71 49

TransMVSNet (Re)		99.78 14	99.77 13	99.81 27	99.91 16	99.85 14	99.75 18	99.86 24	99.70 56	99.91 22	99.89 27	99.60 19	99.87 181	99.59 23	99.74 193	99.71 49

test1111		97.74 290	98.16 264	96.49 350	99.60 154	89.86 379	99.71 30	91.21 376	99.89 15	99.88 34	99.87 33	93.73 306	99.90 138	99.56 29	99.99 12	99.70 52

VPA-MVSNet		99.66 29	99.62 31	99.79 35	99.68 135	99.75 57	99.62 56	99.69 108	99.85 28	99.80 63	99.81 59	98.81 98	99.91 118	99.47 39	99.88 108	99.70 52

WR-MVS		99.11 163	98.93 181	99.66 100	99.30 278	99.42 151	98.42 277	99.37 266	99.04 171	99.57 156	99.20 303	96.89 260	99.86 201	98.66 140	99.87 117	99.70 52

ACMH	98.42 6	99.59 42	99.54 49	99.72 80	99.86 32	99.62 103	99.56 73	99.79 57	98.77 204	99.80 63	99.85 43	99.64 13	99.85 219	98.70 136	99.89 99	99.70 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		99.86 6	99.86 6	99.83 22	99.94 10	99.90 5	99.83 6	99.91 12	99.85 28	99.94 12	99.95 13	99.73 8	99.90 138	99.65 18	99.97 34	99.69 56

HPM-MVS_fast		99.43 71	99.30 93	99.80 30	99.83 40	99.81 33	99.52 76	99.70 102	98.35 248	99.51 182	99.50 226	99.31 39	99.88 169	98.18 172	99.84 133	99.69 56

LPG-MVS_test		99.22 132	99.05 152	99.74 64	99.82 47	99.63 101	99.16 164	99.73 85	97.56 294	99.64 127	99.69 122	99.37 33	99.89 154	96.66 286	99.87 117	99.69 56

LGP-MVS_train				99.74 64	99.82 47	99.63 101		99.73 85	97.56 294	99.64 127	99.69 122	99.37 33	99.89 154	96.66 286	99.87 117	99.69 56

SteuartSystems-ACMMP		99.30 109	99.14 121	99.76 48	99.87 30	99.66 90	99.18 153	99.60 162	98.55 223	99.57 156	99.67 138	99.03 75	99.94 60	97.01 265	99.80 165	99.69 56
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		98.52 245	98.39 239	98.94 268	99.15 304	97.39 317	98.18 292	99.21 304	98.89 190	99.23 244	99.63 159	97.37 242	99.74 300	94.22 350	99.61 245	99.69 56

ACMMP_NAP		99.28 112	99.11 131	99.79 35	99.75 99	99.81 33	98.95 215	99.53 206	98.27 257	99.53 175	99.73 96	98.75 113	99.87 181	97.70 217	99.83 143	99.68 62

HFP-MVS		99.25 119	99.08 142	99.76 48	99.73 108	99.70 79	99.31 114	99.59 169	98.36 243	99.36 218	99.37 261	98.80 102	99.91 118	97.43 238	99.75 185	99.68 62

#test#		99.12 159	98.90 189	99.76 48	99.73 108	99.70 79	99.10 181	99.59 169	97.60 293	99.36 218	99.37 261	98.80 102	99.91 118	96.84 276	99.75 185	99.68 62

EI-MVSNet		99.38 87	99.44 64	99.21 240	99.58 163	98.09 291	99.26 130	99.46 237	99.62 77	99.75 85	99.67 138	98.54 140	99.85 219	99.15 90	99.92 80	99.68 62

TranMVSNet+NR-MVSNet		99.54 51	99.47 57	99.76 48	99.58 163	99.64 97	99.30 117	99.63 139	99.61 81	99.71 104	99.56 206	98.76 111	99.96 37	99.14 96	99.92 80	99.68 62

PVSNet_Blended_VisFu		99.40 81	99.38 74	99.44 184	99.90 20	98.66 256	98.94 217	99.91 12	97.97 274	99.79 68	99.73 96	99.05 73	99.97 19	99.15 90	99.99 12	99.68 62

IterMVS-LS		99.41 78	99.47 57	99.25 235	99.81 54	98.09 291	98.85 226	99.76 70	99.62 77	99.83 52	99.64 149	98.54 140	99.97 19	99.15 90	99.99 12	99.68 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MP-MVS-pluss		99.14 155	98.92 185	99.80 30	99.83 40	99.83 25	98.61 252	99.63 139	96.84 326	99.44 195	99.58 194	98.81 98	99.91 118	97.70 217	99.82 152	99.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		99.23 123	99.05 152	99.77 41	99.76 88	99.70 79	99.31 114	99.59 169	98.41 237	99.32 227	99.36 266	98.73 116	99.93 75	97.29 245	99.74 193	99.67 69

Regformer-4		99.45 68	99.44 64	99.50 166	99.52 197	98.94 233	99.17 158	99.53 206	99.64 73	99.76 78	99.60 186	98.96 84	99.90 138	98.91 119	99.84 133	99.67 69

XVS		99.27 116	99.11 131	99.75 58	99.71 115	99.71 72	99.37 100	99.61 149	99.29 128	98.76 303	99.47 239	98.47 151	99.88 169	97.62 225	99.73 200	99.67 69

v1240		99.56 46	99.58 41	99.51 163	99.80 59	99.00 225	99.00 202	99.65 131	99.15 157	99.90 24	99.75 89	99.09 64	99.88 169	99.90 2	99.96 47	99.67 69

X-MVStestdata		96.09 329	94.87 339	99.75 58	99.71 115	99.71 72	99.37 100	99.61 149	99.29 128	98.76 303	61.30 383	98.47 151	99.88 169	97.62 225	99.73 200	99.67 69

VPNet		99.46 66	99.37 77	99.71 84	99.82 47	99.59 114	99.48 82	99.70 102	99.81 37	99.69 110	99.58 194	97.66 229	99.86 201	99.17 86	99.44 280	99.67 69

ACMMPR		99.23 123	99.06 148	99.76 48	99.74 105	99.69 83	99.31 114	99.59 169	98.36 243	99.35 220	99.38 259	98.61 130	99.93 75	97.43 238	99.75 185	99.67 69

SixPastTwentyTwo		99.42 74	99.30 93	99.76 48	99.92 15	99.67 88	99.70 31	99.14 310	99.65 71	99.89 28	99.90 23	96.20 279	99.94 60	99.42 48	99.92 80	99.67 69

HPM-MVS		99.25 119	99.07 146	99.78 38	99.81 54	99.75 57	99.61 61	99.67 116	97.72 288	99.35 220	99.25 292	99.23 48	99.92 95	97.21 256	99.82 152	99.67 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v144192		99.55 49	99.54 49	99.58 139	99.78 76	99.20 205	99.11 180	99.62 142	99.18 147	99.89 28	99.72 102	98.66 124	99.87 181	99.88 6	99.97 34	99.66 79

v1921920		99.56 46	99.57 44	99.55 152	99.75 99	99.11 213	99.05 192	99.61 149	99.15 157	99.88 34	99.71 109	99.08 68	99.87 181	99.90 2	99.97 34	99.66 79

v1192		99.57 43	99.57 44	99.57 145	99.77 84	99.22 199	99.04 194	99.60 162	99.18 147	99.87 41	99.72 102	99.08 68	99.85 219	99.89 5	99.98 24	99.66 79

PGM-MVS		99.20 139	99.01 164	99.77 41	99.75 99	99.71 72	99.16 164	99.72 94	97.99 272	99.42 201	99.60 186	98.81 98	99.93 75	96.91 270	99.74 193	99.66 79

mPP-MVS		99.19 142	99.00 167	99.76 48	99.76 88	99.68 86	99.38 96	99.54 197	98.34 252	99.01 274	99.50 226	98.53 144	99.93 75	97.18 258	99.78 176	99.66 79

CP-MVS		99.23 123	99.05 152	99.75 58	99.66 141	99.66 90	99.38 96	99.62 142	98.38 241	99.06 272	99.27 287	98.79 105	99.94 60	97.51 234	99.82 152	99.66 79

EG-PatchMatch MVS		99.57 43	99.56 48	99.62 128	99.77 84	99.33 174	99.26 130	99.76 70	99.32 126	99.80 63	99.78 74	99.29 41	99.87 181	99.15 90	99.91 89	99.66 79

UGNet		99.38 87	99.34 82	99.49 169	98.90 334	98.90 241	99.70 31	99.35 270	99.86 23	98.57 317	99.81 59	98.50 150	99.93 75	99.38 50	99.98 24	99.66 79
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
test2506		94.73 340	94.59 342	95.15 356	99.59 158	85.90 381	99.75 18	74.01 382	99.89 15	99.71 104	99.86 39	79.00 380	99.90 138	99.52 34	99.99 12	99.65 87

ECVR-MVS		97.73 291	98.04 270	96.78 343	99.59 158	90.81 375	99.72 26	90.43 378	99.89 15	99.86 42	99.86 39	93.60 308	99.89 154	99.46 40	99.99 12	99.65 87

h-mvs33		98.61 231	98.34 245	99.44 184	99.60 154	98.67 254	99.27 128	99.44 242	99.68 61	99.32 227	99.49 231	92.50 319	100.00 1	99.24 73	96.51 369	99.65 87

TSAR-MVS + MP.		99.34 100	99.24 109	99.63 119	99.82 47	99.37 164	99.26 130	99.35 270	98.77 204	99.57 156	99.70 116	99.27 46	99.88 169	97.71 214	99.75 185	99.65 87
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		99.30 109	99.14 121	99.80 30	99.81 54	99.81 33	98.73 247	99.53 206	99.27 132	99.42 201	99.63 159	98.21 183	99.95 46	97.83 204	99.79 170	99.65 87

MTAPA		99.35 95	99.20 113	99.80 30	99.81 54	99.81 33	99.33 107	99.53 206	99.27 132	99.42 201	99.63 159	98.21 183	99.95 46	97.83 204	99.79 170	99.65 87

Regformer-3		99.41 78	99.41 70	99.40 199	99.52 197	98.70 252	99.17 158	99.44 242	99.62 77	99.75 85	99.60 186	98.90 91	99.85 219	98.89 120	99.84 133	99.65 87

MCST-MVS		99.02 179	98.81 200	99.65 105	99.58 163	99.49 130	98.58 256	99.07 313	98.40 239	99.04 273	99.25 292	98.51 149	99.80 278	97.31 244	99.51 270	99.65 87

UniMVSNet_NR-MVSNet		99.37 90	99.25 107	99.72 80	99.47 224	99.56 120	98.97 213	99.61 149	99.43 114	99.67 117	99.28 285	97.85 213	99.95 46	99.17 86	99.81 160	99.65 87

ZNCC-MVS		99.22 132	99.04 158	99.77 41	99.76 88	99.73 66	99.28 125	99.56 185	98.19 262	99.14 261	99.29 283	98.84 97	99.92 95	97.53 233	99.80 165	99.64 96

v1144		99.54 51	99.53 53	99.59 135	99.79 69	99.28 182	99.10 181	99.61 149	99.20 145	99.84 47	99.73 96	98.67 122	99.84 236	99.86 8	99.98 24	99.64 96

v2v482		99.50 55	99.47 57	99.58 139	99.78 76	99.25 190	99.14 168	99.58 178	99.25 136	99.81 60	99.62 168	98.24 178	99.84 236	99.83 9	99.97 34	99.64 96

K. test v3		98.87 206	98.60 216	99.69 89	99.93 14	99.46 137	99.74 20	94.97 369	99.78 44	99.88 34	99.88 30	93.66 307	99.97 19	99.61 21	99.95 56	99.64 96

DeepC-MVS	98.90 4	99.62 38	99.61 35	99.67 93	99.72 112	99.44 144	99.24 137	99.71 97	99.27 132	99.93 15	99.90 23	99.70 11	99.93 75	98.99 106	99.99 12	99.64 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		98.56 239	98.17 263	99.72 80	99.45 232	99.60 111	98.88 220	99.50 222	96.88 323	99.18 256	99.48 234	97.08 255	99.92 95	93.69 357	99.38 289	99.63 101

SMA-MVS		99.19 142	99.00 167	99.73 74	99.46 229	99.73 66	99.13 174	99.52 214	97.40 305	99.57 156	99.64 149	98.93 85	99.83 247	97.61 227	99.79 170	99.63 101
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
IterMVS-SCA-FT		99.00 185	99.16 117	98.51 301	99.75 99	95.90 343	98.07 306	99.84 34	99.84 31	99.89 28	99.73 96	96.01 283	99.99 5	99.33 60	100.00 1	99.63 101

pm-mvs1		99.79 13	99.79 11	99.78 38	99.91 16	99.83 25	99.76 16	99.87 21	99.73 48	99.89 28	99.87 33	99.63 14	99.87 181	99.54 31	99.92 80	99.63 101

MP-MVS		99.06 170	98.83 198	99.76 48	99.76 88	99.71 72	99.32 110	99.50 222	98.35 248	98.97 276	99.48 234	98.37 166	99.92 95	95.95 320	99.75 185	99.63 101
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DU-MVS		99.33 104	99.21 112	99.71 84	99.43 236	99.56 120	98.83 229	99.53 206	99.38 118	99.67 117	99.36 266	97.67 225	99.95 46	99.17 86	99.81 160	99.63 101

NR-MVSNet		99.40 81	99.31 88	99.68 90	99.43 236	99.55 123	99.73 23	99.50 222	99.46 106	99.88 34	99.36 266	97.54 233	99.87 181	98.97 110	99.87 117	99.63 101

IterMVS		98.97 189	99.16 117	98.42 305	99.74 105	95.64 346	98.06 308	99.83 36	99.83 34	99.85 44	99.74 92	96.10 282	99.99 5	99.27 72	100.00 1	99.63 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPP-MVSNet		99.17 150	99.00 167	99.66 100	99.80 59	99.43 148	99.70 31	99.24 296	99.48 97	99.56 163	99.77 81	94.89 292	99.93 75	98.72 135	99.89 99	99.63 101

ACMMP		99.25 119	99.08 142	99.74 64	99.79 69	99.68 86	99.50 78	99.65 131	98.07 268	99.52 177	99.69 122	98.57 135	99.92 95	97.18 258	99.79 170	99.63 101
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
DeepC-MVS_fast	98.47 5	99.23 123	99.12 128	99.56 149	99.28 283	99.22 199	98.99 207	99.40 256	99.08 164	99.58 153	99.64 149	98.90 91	99.83 247	97.44 237	99.75 185	99.63 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS++		99.38 87	99.25 107	99.77 41	99.03 324	99.77 46	99.74 20	99.61 149	99.18 147	99.76 78	99.61 177	99.00 76	99.92 95	97.72 212	99.60 248	99.62 112

PC_three_1452									97.56 294	99.68 112	99.41 249	99.09 64	97.09 375	96.66 286	99.60 248	99.62 112

GeoE		99.69 22	99.66 25	99.78 38	99.76 88	99.76 53	99.60 66	99.82 41	99.46 106	99.75 85	99.56 206	99.63 14	99.95 46	99.43 43	99.88 108	99.62 112

test_method		91.72 341	92.32 344	89.91 358	93.49 380	70.18 382	90.28 371	99.56 185	61.71 375	95.39 372	99.52 219	93.90 301	99.94 60	98.76 131	98.27 349	99.62 112

GST-MVS		99.16 151	98.96 178	99.75 58	99.73 108	99.73 66	99.20 147	99.55 191	98.22 259	99.32 227	99.35 271	98.65 126	99.91 118	96.86 273	99.74 193	99.62 112

new-patchmatchnet		99.35 95	99.57 44	98.71 296	99.82 47	96.62 333	98.55 262	99.75 77	99.50 95	99.88 34	99.87 33	99.31 39	99.88 169	99.43 43	100.00 1	99.62 112

RRT_MVS		98.75 218	98.54 226	99.41 197	98.14 371	98.61 260	98.98 211	99.66 120	99.31 127	99.84 47	99.75 89	91.98 322	99.98 8	99.20 79	99.95 56	99.62 112

CPTT-MVS		98.74 220	98.44 234	99.64 112	99.61 152	99.38 161	99.18 153	99.55 191	96.49 331	99.27 238	99.37 261	97.11 254	99.92 95	95.74 327	99.67 226	99.62 112

MIMVSNet1		99.66 29	99.62 31	99.80 30	99.94 10	99.87 10	99.69 37	99.77 65	99.78 44	99.93 15	99.89 27	97.94 204	99.92 95	99.65 18	99.98 24	99.62 112

DeepPCF-MVS	98.42 6	99.18 146	99.02 161	99.67 93	99.22 292	99.75 57	97.25 354	99.47 233	98.72 209	99.66 121	99.70 116	99.29 41	99.63 347	98.07 181	99.81 160	99.62 112

3Dnovator+	98.92 3	99.35 95	99.24 109	99.67 93	99.35 257	99.47 133	99.62 56	99.50 222	99.44 109	99.12 264	99.78 74	98.77 110	99.94 60	97.87 198	99.72 206	99.62 112

DVP-MVS		99.32 106	99.17 116	99.77 41	99.69 127	99.80 38	99.14 168	99.31 279	99.16 153	99.62 139	99.61 177	98.35 168	99.91 118	97.88 195	99.72 206	99.61 123
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
APD-MVS		98.87 206	98.59 218	99.71 84	99.50 208	99.62 103	99.01 200	99.57 180	96.80 328	99.54 170	99.63 159	98.29 174	99.91 118	95.24 337	99.71 209	99.61 123
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.82 211	98.57 222	99.58 139	99.21 294	99.31 177	98.61 252	99.25 293	98.65 213	98.43 325	99.26 290	97.86 211	99.81 273	96.55 291	99.27 306	99.61 123

TAMVS		99.49 57	99.45 62	99.63 119	99.48 219	99.42 151	99.45 85	99.57 180	99.66 69	99.78 71	99.83 49	97.85 213	99.86 201	99.44 42	99.96 47	99.61 123

Regformer-1		99.32 106	99.27 103	99.47 175	99.41 242	98.95 232	98.99 207	99.48 229	99.48 97	99.66 121	99.52 219	98.78 107	99.87 181	98.36 153	99.74 193	99.60 127

Regformer-2		99.34 100	99.27 103	99.53 158	99.41 242	99.10 218	98.99 207	99.53 206	99.47 102	99.66 121	99.52 219	98.80 102	99.89 154	98.31 159	99.74 193	99.60 127

HPM-MVS++		98.96 192	98.70 210	99.74 64	99.52 197	99.71 72	98.86 224	99.19 305	98.47 233	98.59 315	99.06 318	98.08 194	99.91 118	96.94 268	99.60 248	99.60 127

V42		99.56 46	99.54 49	99.63 119	99.79 69	99.46 137	99.39 94	99.59 169	99.24 138	99.86 42	99.70 116	98.55 138	99.82 257	99.79 11	99.95 56	99.60 127

HQP_MVS		98.90 200	98.68 212	99.55 152	99.58 163	99.24 195	98.80 237	99.54 197	98.94 180	99.14 261	99.25 292	97.24 246	99.82 257	95.84 323	99.78 176	99.60 127

plane_prior5								99.54 197					99.82 257	95.84 323	99.78 176	99.60 127

TDRefinement		99.72 18	99.70 18	99.77 41	99.90 20	99.85 14	99.86 5	99.92 9	99.69 59	99.78 71	99.92 18	99.37 33	99.88 169	98.93 118	99.95 56	99.60 127

ACMH+	98.40 8	99.50 55	99.43 67	99.71 84	99.86 32	99.76 53	99.32 110	99.77 65	99.53 93	99.77 76	99.76 84	99.26 47	99.78 284	97.77 206	99.88 108	99.60 127

ACMM	98.09 11	99.46 66	99.38 74	99.72 80	99.80 59	99.69 83	99.13 174	99.65 131	98.99 173	99.64 127	99.72 102	99.39 27	99.86 201	98.23 165	99.81 160	99.60 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		98.97 189	98.82 199	99.42 190	99.71 115	98.81 246	99.62 56	98.68 330	99.81 37	99.38 216	99.80 61	94.25 299	99.85 219	98.79 127	99.32 299	99.59 136

casdiffmvs		99.63 35	99.61 35	99.67 93	99.79 69	99.59 114	99.13 174	99.85 28	99.79 42	99.76 78	99.72 102	99.33 38	99.82 257	99.21 76	99.94 68	99.59 136

UniMVSNet (Re)		99.37 90	99.26 105	99.68 90	99.51 202	99.58 117	98.98 211	99.60 162	99.43 114	99.70 107	99.36 266	97.70 220	99.88 169	99.20 79	99.87 117	99.59 136

DSMNet-mixed		99.48 59	99.65 27	98.95 267	99.71 115	97.27 319	99.50 78	99.82 41	99.59 89	99.41 209	99.85 43	99.62 16	100.00 1	99.53 33	99.89 99	99.59 136

3Dnovator	99.15 2	99.43 71	99.36 80	99.65 105	99.39 247	99.42 151	99.70 31	99.56 185	99.23 140	99.35 220	99.80 61	99.17 54	99.95 46	98.21 167	99.84 133	99.59 136

SED-MVS		99.40 81	99.28 100	99.77 41	99.69 127	99.82 30	99.20 147	99.54 197	99.13 159	99.82 53	99.63 159	98.91 88	99.92 95	97.85 201	99.70 211	99.58 141

OPU-MVS				99.29 226	99.12 309	99.44 144	99.20 147				99.40 253	99.00 76	98.84 372	96.54 292	99.60 248	99.58 141

abl_6		99.36 93	99.23 111	99.75 58	99.71 115	99.74 63	99.33 107	99.76 70	99.07 166	99.65 125	99.63 159	99.09 64	99.92 95	97.13 261	99.76 182	99.58 141

EPNet		98.13 276	97.77 291	99.18 245	94.57 379	97.99 295	99.24 137	97.96 350	99.74 47	97.29 360	99.62 168	93.13 312	99.97 19	98.59 142	99.83 143	99.58 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.03 177	98.85 194	99.55 152	99.80 59	99.25 190	99.73 23	99.15 309	99.37 119	99.61 145	99.71 109	94.73 295	99.81 273	97.70 217	99.88 108	99.58 141

ACMP	97.51 14	99.05 173	98.84 196	99.67 93	99.78 76	99.55 123	98.88 220	99.66 120	97.11 320	99.47 189	99.60 186	99.07 70	99.89 154	96.18 309	99.85 128	99.58 141
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		99.23 123	99.05 152	99.74 64	99.52 197	99.75 57	99.20 147	99.61 149	98.97 175	99.48 187	99.58 194	98.41 160	99.91 118	97.15 260	99.55 259	99.57 147

SR-MVS		99.19 142	99.00 167	99.74 64	99.51 202	99.72 70	99.18 153	99.60 162	98.85 193	99.47 189	99.58 194	98.38 165	99.92 95	96.92 269	99.54 265	99.57 147

lessismore_v0				99.64 112	99.86 32	99.38 161		90.66 377		99.89 28	99.83 49	94.56 297	99.97 19	99.56 29	99.92 80	99.57 147

pmmvs5		99.19 142	99.11 131	99.42 190	99.76 88	98.88 242	98.55 262	99.73 85	98.82 197	99.72 99	99.62 168	96.56 265	99.82 257	99.32 62	99.95 56	99.56 150

APD-MVS_3200maxsize		99.31 108	99.16 117	99.74 64	99.53 191	99.75 57	99.27 128	99.61 149	99.19 146	99.57 156	99.64 149	98.76 111	99.90 138	97.29 245	99.62 238	99.56 150

CDPH-MVS		98.56 239	98.20 258	99.61 131	99.50 208	99.46 137	98.32 283	99.41 249	95.22 349	99.21 250	99.10 315	98.34 170	99.82 257	95.09 340	99.66 230	99.56 150

Anonymous20240521		99.44 70	99.42 69	99.49 169	99.89 22	98.96 231	99.62 56	99.76 70	99.85 28	99.82 53	99.88 30	96.39 274	99.97 19	99.59 23	99.98 24	99.55 153

our_test_3		98.85 208	99.09 140	98.13 317	99.66 141	94.90 353	97.72 332	99.58 178	99.07 166	99.64 127	99.62 168	98.19 186	99.93 75	98.41 150	99.95 56	99.55 153

YYNet1		98.95 195	98.99 172	98.84 284	99.64 145	97.14 323	98.22 291	99.32 275	98.92 185	99.59 151	99.66 142	97.40 238	99.83 247	98.27 162	99.90 90	99.55 153

MDA-MVSNet_test_wron		98.95 195	98.99 172	98.85 282	99.64 145	97.16 322	98.23 290	99.33 273	98.93 183	99.56 163	99.66 142	97.39 240	99.83 247	98.29 160	99.88 108	99.55 153

MVSFormer		99.41 78	99.44 64	99.31 223	99.57 173	98.40 272	99.77 13	99.80 51	99.73 48	99.63 131	99.30 280	98.02 198	99.98 8	99.43 43	99.69 214	99.55 153

jason		99.16 151	99.11 131	99.32 220	99.75 99	98.44 269	98.26 288	99.39 259	98.70 210	99.74 94	99.30 280	98.54 140	99.97 19	98.48 147	99.82 152	99.55 153
jason: jason.
CDS-MVSNet		99.22 132	99.13 124	99.50 166	99.35 257	99.11 213	98.96 214	99.54 197	99.46 106	99.61 145	99.70 116	96.31 276	99.83 247	99.34 56	99.88 108	99.55 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
COLMAP_ROB	98.06 12	99.45 68	99.37 77	99.70 88	99.83 40	99.70 79	99.38 96	99.78 62	99.53 93	99.67 117	99.78 74	99.19 52	99.86 201	97.32 243	99.87 117	99.55 153
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SR-MVS-dyc-post		99.27 116	99.11 131	99.73 74	99.54 186	99.74 63	99.26 130	99.62 142	99.16 153	99.52 177	99.64 149	98.41 160	99.91 118	97.27 248	99.61 245	99.54 161

RE-MVS-def			99.13 124		99.54 186	99.74 63	99.26 130	99.62 142	99.16 153	99.52 177	99.64 149	98.57 135		97.27 248	99.61 245	99.54 161

SD-MVS		99.01 183	99.30 93	98.15 316	99.50 208	99.40 156	98.94 217	99.61 149	99.22 144	99.75 85	99.82 56	99.54 23	95.51 377	97.48 235	99.87 117	99.54 161
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
CNVR-MVS		98.99 188	98.80 202	99.56 149	99.25 288	99.43 148	98.54 265	99.27 288	98.58 220	98.80 298	99.43 247	98.53 144	99.70 311	97.22 255	99.59 252	99.54 161

MVS_111021_HR		99.12 159	99.02 161	99.40 199	99.50 208	99.11 213	97.92 324	99.71 97	98.76 207	99.08 268	99.47 239	99.17 54	99.54 357	97.85 201	99.76 182	99.54 161

v148		99.40 81	99.41 70	99.39 202	99.76 88	98.94 233	99.09 186	99.59 169	99.17 151	99.81 60	99.61 177	98.41 160	99.69 317	99.32 62	99.94 68	99.53 166

iter_conf05		98.46 253	98.23 254	99.15 248	99.04 322	97.99 295	99.10 181	99.61 149	99.79 42	99.76 78	99.58 194	87.88 354	99.92 95	99.31 65	99.97 34	99.53 166

diffmvs		99.34 100	99.32 87	99.39 202	99.67 140	98.77 249	98.57 260	99.81 50	99.61 81	99.48 187	99.41 249	98.47 151	99.86 201	98.97 110	99.90 90	99.53 166

baseline		99.63 35	99.62 31	99.66 100	99.80 59	99.62 103	99.44 88	99.80 51	99.71 52	99.72 99	99.69 122	99.15 56	99.83 247	99.32 62	99.94 68	99.53 166

HQP4-MVS										98.15 335			99.70 311			99.53 166

GBi-Net		99.42 74	99.31 88	99.73 74	99.49 213	99.77 46	99.68 40	99.70 102	99.44 109	99.62 139	99.83 49	97.21 248	99.90 138	98.96 112	99.90 90	99.53 166

test1		99.42 74	99.31 88	99.73 74	99.49 213	99.77 46	99.68 40	99.70 102	99.44 109	99.62 139	99.83 49	97.21 248	99.90 138	98.96 112	99.90 90	99.53 166

FMVSNet1		99.66 29	99.63 30	99.73 74	99.78 76	99.77 46	99.68 40	99.70 102	99.67 65	99.82 53	99.83 49	98.98 79	99.90 138	99.24 73	99.97 34	99.53 166

HQP-MVS		98.36 262	98.02 272	99.39 202	99.31 274	98.94 233	97.98 316	99.37 266	97.45 302	98.15 335	98.83 347	96.67 263	99.70 311	94.73 343	99.67 226	99.53 166

QAPM		98.40 260	97.99 273	99.65 105	99.39 247	99.47 133	99.67 44	99.52 214	91.70 365	98.78 301	99.80 61	98.55 138	99.95 46	94.71 345	99.75 185	99.53 166

F-COLMAP		98.74 220	98.45 233	99.62 128	99.57 173	99.47 133	98.84 227	99.65 131	96.31 335	98.93 280	99.19 305	97.68 224	99.87 181	96.52 293	99.37 293	99.53 166

MVSTER		98.47 252	98.22 256	99.24 237	99.06 319	98.35 277	99.08 189	99.46 237	99.27 132	99.75 85	99.66 142	88.61 352	99.85 219	99.14 96	99.92 80	99.52 177

PVSNet_BlendedMVS		99.03 177	99.01 164	99.09 255	99.54 186	97.99 295	98.58 256	99.82 41	97.62 292	99.34 223	99.71 109	98.52 147	99.77 292	97.98 187	99.97 34	99.52 177

OPM-MVS		99.26 118	99.13 124	99.63 119	99.70 123	99.61 109	98.58 256	99.48 229	98.50 229	99.52 177	99.63 159	99.14 59	99.76 294	97.89 194	99.77 180	99.51 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior1		98.33 267	97.92 283	99.57 145	99.35 257	99.36 167	97.99 315	99.39 259	94.85 356	97.76 355	98.98 332	98.03 196	99.85 219	95.49 331	99.44 280	99.51 179

AllTest		99.21 137	99.07 146	99.63 119	99.78 76	99.64 97	99.12 178	99.83 36	98.63 215	99.63 131	99.72 102	98.68 119	99.75 298	96.38 301	99.83 143	99.51 179

TestCases				99.63 119	99.78 76	99.64 97		99.83 36	98.63 215	99.63 131	99.72 102	98.68 119	99.75 298	96.38 301	99.83 143	99.51 179

BH-RMVSNet		98.41 258	98.14 266	99.21 240	99.21 294	98.47 266	98.60 254	98.26 347	98.35 248	98.93 280	99.31 278	97.20 251	99.66 337	94.32 348	99.10 314	99.51 179

USDC		98.96 192	98.93 181	99.05 261	99.54 186	97.99 295	97.07 360	99.80 51	98.21 260	99.75 85	99.77 81	98.43 157	99.64 346	97.90 193	99.88 108	99.51 179

test9_res														95.10 339	99.44 280	99.50 185

train_agg		98.35 265	97.95 277	99.57 145	99.35 257	99.35 171	98.11 301	99.41 249	94.90 353	97.92 346	98.99 329	98.02 198	99.85 219	95.38 335	99.44 280	99.50 185

agg_prior2														94.58 347	99.46 279	99.50 185

VDD-MVS		99.20 139	99.11 131	99.44 184	99.43 236	98.98 227	99.50 78	98.32 346	99.80 40	99.56 163	99.69 122	96.99 258	99.85 219	98.99 106	99.73 200	99.50 185

MDA-MVSNet-bldmvs		99.06 170	99.05 152	99.07 259	99.80 59	97.83 303	98.89 219	99.72 94	99.29 128	99.63 131	99.70 116	96.47 269	99.89 154	98.17 174	99.82 152	99.50 185

KD-MVS_self_test		99.63 35	99.59 38	99.76 48	99.84 36	99.90 5	99.37 100	99.79 57	99.83 34	99.88 34	99.85 43	98.42 159	99.90 138	99.60 22	99.73 200	99.49 190

xxxxxxxxxxxxxcwj		99.11 163	98.96 178	99.54 156	99.53 191	99.25 190	98.29 285	99.76 70	99.07 166	99.42 201	99.61 177	98.86 94	99.87 181	96.45 298	99.68 219	99.49 190

SF-MVS		99.10 167	98.93 181	99.62 128	99.58 163	99.51 128	99.13 174	99.65 131	97.97 274	99.42 201	99.61 177	98.86 94	99.87 181	96.45 298	99.68 219	99.49 190

Anonymous20240529		99.42 74	99.34 82	99.65 105	99.53 191	99.60 111	99.63 55	99.39 259	99.47 102	99.76 78	99.78 74	98.13 190	99.86 201	98.70 136	99.68 219	99.49 190

WTY-MVS		98.59 236	98.37 241	99.26 232	99.43 236	98.40 272	98.74 245	99.13 312	98.10 265	99.21 250	99.24 297	94.82 293	99.90 138	97.86 199	98.77 331	99.49 190

ppachtmachnet_test		98.89 203	99.12 128	98.20 315	99.66 141	95.24 350	97.63 336	99.68 111	99.08 164	99.78 71	99.62 168	98.65 126	99.88 169	98.02 182	99.96 47	99.48 195

Anonymous20231206		99.35 95	99.31 88	99.47 175	99.74 105	99.06 224	99.28 125	99.74 82	99.23 140	99.72 99	99.53 217	97.63 231	99.88 169	99.11 98	99.84 133	99.48 195

test_prior3		98.62 230	98.34 245	99.46 178	99.35 257	99.22 199	97.95 320	99.39 259	97.87 281	98.05 341	99.05 319	97.90 207	99.69 317	95.99 316	99.49 274	99.48 195

test_prior				99.46 178	99.35 257	99.22 199		99.39 259					99.69 317			99.48 195

test12				99.54 156	99.29 280	99.33 174		99.16 308		98.43 325		97.54 233	99.82 257		99.47 277	99.48 195

VNet		99.18 146	99.06 148	99.56 149	99.24 290	99.36 167	99.33 107	99.31 279	99.67 65	99.47 189	99.57 203	96.48 268	99.84 236	99.15 90	99.30 301	99.47 200

test20.03		99.55 49	99.54 49	99.58 139	99.79 69	99.37 164	99.02 198	99.89 17	99.60 87	99.82 53	99.62 168	98.81 98	99.89 154	99.43 43	99.86 124	99.47 200

114514_t		98.49 250	98.11 267	99.64 112	99.73 108	99.58 117	99.24 137	99.76 70	89.94 368	99.42 201	99.56 206	97.76 219	99.86 201	97.74 211	99.82 152	99.47 200

sss		98.90 200	98.77 204	99.27 230	99.48 219	98.44 269	98.72 248	99.32 275	97.94 278	99.37 217	99.35 271	96.31 276	99.91 118	98.85 122	99.63 237	99.47 200

旧先验1					99.49 213	99.29 180		99.26 290			99.39 257	97.67 225			99.36 294	99.46 204

1121		98.56 239	98.24 253	99.52 160	99.49 213	99.24 195	99.30 117	99.22 300	95.77 342	98.52 320	99.29 283	97.39 240	99.85 219	95.79 325	99.34 296	99.46 204

MVP-Stereo		99.16 151	99.08 142	99.43 188	99.48 219	99.07 222	99.08 189	99.55 191	98.63 215	99.31 231	99.68 133	98.19 186	99.78 284	98.18 172	99.58 253	99.45 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
新几何1				99.52 160	99.50 208	99.22 199		99.26 290	95.66 345	98.60 314	99.28 285	97.67 225	99.89 154	95.95 320	99.32 299	99.45 206

LFMVS		98.46 253	98.19 261	99.26 232	99.24 290	98.52 265	99.62 56	96.94 362	99.87 21	99.31 231	99.58 194	91.04 333	99.81 273	98.68 139	99.42 285	99.45 206

testgi		99.29 111	99.26 105	99.37 209	99.75 99	98.81 246	98.84 227	99.89 17	98.38 241	99.75 85	99.04 322	99.36 36	99.86 201	99.08 100	99.25 307	99.45 206

UnsupCasMVSNet_eth		98.83 209	98.57 222	99.59 135	99.68 135	99.45 142	98.99 207	99.67 116	99.48 97	99.55 168	99.36 266	94.92 291	99.86 201	98.95 116	96.57 368	99.45 206

无先验							98.01 311	99.23 297	95.83 341				99.85 219	95.79 325		99.44 211

testdata				99.42 190	99.51 202	98.93 237		99.30 282	96.20 336	98.87 290	99.40 253	98.33 172	99.89 154	96.29 304	99.28 303	99.44 211

XVG-OURS-SEG-HR		99.16 151	98.99 172	99.66 100	99.84 36	99.64 97	98.25 289	99.73 85	98.39 240	99.63 131	99.43 247	99.70 11	99.90 138	97.34 242	98.64 339	99.44 211

FMVSNet2		99.35 95	99.28 100	99.55 152	99.49 213	99.35 171	99.45 85	99.57 180	99.44 109	99.70 107	99.74 92	97.21 248	99.87 181	99.03 103	99.94 68	99.44 211

N_pmnet		98.73 222	98.53 228	99.35 213	99.72 112	98.67 254	98.34 280	94.65 370	98.35 248	99.79 68	99.68 133	98.03 196	99.93 75	98.28 161	99.92 80	99.44 211

RPSCF		99.18 146	99.02 161	99.64 112	99.83 40	99.85 14	99.44 88	99.82 41	98.33 253	99.50 184	99.78 74	97.90 207	99.65 344	96.78 279	99.83 143	99.44 211

原ACMM1				99.37 209	99.47 224	98.87 244		99.27 288	96.74 329	98.26 330	99.32 276	97.93 205	99.82 257	95.96 319	99.38 289	99.43 217

test222					99.51 202	99.08 221	97.83 329	99.29 284	95.21 350	98.68 309	99.31 278	97.28 245			99.38 289	99.43 217

XVG-OURS		99.21 137	99.06 148	99.65 105	99.82 47	99.62 103	97.87 327	99.74 82	98.36 243	99.66 121	99.68 133	99.71 9	99.90 138	96.84 276	99.88 108	99.43 217

CSCG		99.37 90	99.29 98	99.60 133	99.71 115	99.46 137	99.43 90	99.85 28	98.79 201	99.41 209	99.60 186	98.92 86	99.92 95	98.02 182	99.92 80	99.43 217

ETH3D-3000-0.1		98.77 215	98.50 230	99.59 135	99.47 224	99.53 125	98.77 242	99.60 162	97.33 309	99.23 244	99.50 226	97.91 206	99.83 247	95.02 341	99.67 226	99.41 221

TinyColmap		98.97 189	98.93 181	99.07 259	99.46 229	98.19 283	97.75 331	99.75 77	98.79 201	99.54 170	99.70 116	98.97 81	99.62 348	96.63 289	99.83 143	99.41 221

ETH3 D test6400		97.76 289	97.19 305	99.50 166	99.38 250	99.26 186	98.34 280	99.49 227	92.99 362	98.54 319	99.20 303	95.92 285	99.82 257	91.14 364	99.66 230	99.40 223

Anonymous202405211		98.75 218	98.46 232	99.63 119	99.34 267	99.66 90	99.47 84	97.65 355	99.28 131	99.56 163	99.50 226	93.15 311	99.84 236	98.62 141	99.58 253	99.40 223

XVG-ACMP-BASELINE		99.23 123	99.10 139	99.63 119	99.82 47	99.58 117	98.83 229	99.72 94	98.36 243	99.60 148	99.71 109	98.92 86	99.91 118	97.08 263	99.84 133	99.40 223

MS-PatchMatch		99.00 185	98.97 176	99.09 255	99.11 314	98.19 283	98.76 244	99.33 273	98.49 231	99.44 195	99.58 194	98.21 183	99.69 317	98.20 168	99.62 238	99.39 226

FMVSNet3		98.80 213	98.63 215	99.32 220	99.13 307	98.72 251	99.10 181	99.48 229	99.23 140	99.62 139	99.64 149	92.57 316	99.86 201	98.96 112	99.90 90	99.39 226

ambc				99.20 242	99.35 257	98.53 263	99.17 158	99.46 237		99.67 117	99.80 61	98.46 154	99.70 311	97.92 192	99.70 211	99.38 228

FMVSNet5		97.80 287	97.25 303	99.42 190	98.83 343	98.97 229	99.38 96	99.80 51	98.87 191	99.25 240	99.69 122	80.60 375	99.91 118	98.96 112	99.90 90	99.38 228

PAPM_NR		98.36 262	98.04 270	99.33 216	99.48 219	98.93 237	98.79 240	99.28 287	97.54 297	98.56 318	98.57 357	97.12 253	99.69 317	94.09 352	98.90 326	99.38 228

EPNet_dtu		97.62 296	97.79 290	97.11 342	96.67 376	92.31 365	98.51 268	98.04 348	99.24 138	95.77 370	99.47 239	93.78 305	99.66 337	98.98 108	99.62 238	99.37 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		99.11 163	98.95 180	99.59 135	99.13 307	99.59 114	99.17 158	99.65 131	97.88 280	99.25 240	99.46 242	98.97 81	99.80 278	97.26 250	99.82 152	99.37 231

PLC	97.35 16	98.36 262	97.99 273	99.48 173	99.32 273	99.24 195	98.50 269	99.51 218	95.19 351	98.58 316	98.96 337	96.95 259	99.83 247	95.63 328	99.25 307	99.37 231
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		97.62 296	97.20 304	98.90 280	99.76 88	97.40 316	99.48 82	94.36 371	99.06 170	99.70 107	99.49 231	84.55 369	99.94 60	98.73 134	99.65 233	99.36 234

pmmvs-eth3d		99.48 59	99.47 57	99.51 163	99.77 84	99.41 155	98.81 234	99.66 120	99.42 116	99.75 85	99.66 142	99.20 51	99.76 294	98.98 108	99.99 12	99.36 234

PVSNet_0	95.53 19	95.85 335	95.31 337	97.47 332	98.78 350	93.48 361	95.72 368	99.40 256	96.18 337	97.37 358	97.73 372	95.73 286	99.58 355	95.49 331	81.40 375	99.36 234

lupinMVS		98.96 192	98.87 192	99.24 237	99.57 173	98.40 272	98.12 299	99.18 306	98.28 256	99.63 131	99.13 308	98.02 198	99.97 19	98.22 166	99.69 214	99.35 237

Vis-MVSNet (Re-imp)		98.77 215	98.58 221	99.34 214	99.78 76	98.88 242	99.61 61	99.56 185	99.11 163	99.24 243	99.56 206	93.00 314	99.78 284	97.43 238	99.89 99	99.35 237

GA-MVS		97.99 284	97.68 294	98.93 271	99.52 197	98.04 294	97.19 356	99.05 316	98.32 254	98.81 296	98.97 335	89.89 349	99.41 367	98.33 157	99.05 316	99.34 239

CANet		99.11 163	99.05 152	99.28 228	98.83 343	98.56 262	98.71 250	99.41 249	99.25 136	99.23 244	99.22 299	97.66 229	99.94 60	99.19 81	99.97 34	99.33 240

Patchmtry		98.78 214	98.54 226	99.49 169	98.89 337	99.19 206	99.32 110	99.67 116	99.65 71	99.72 99	99.79 67	91.87 325	99.95 46	98.00 186	99.97 34	99.33 240

PAPR		97.56 299	97.07 307	99.04 262	98.80 347	98.11 289	97.63 336	99.25 293	94.56 359	98.02 344	98.25 367	97.43 237	99.68 328	90.90 365	98.74 335	99.33 240

CHOSEN 280x420		98.41 258	98.41 237	98.40 306	99.34 267	95.89 344	96.94 362	99.44 242	98.80 200	99.25 240	99.52 219	93.51 309	99.98 8	98.94 117	99.98 24	99.32 243

baseline1		97.73 291	97.33 300	98.96 266	99.30 278	97.73 307	99.40 92	98.42 342	99.33 125	99.46 193	99.21 301	91.18 331	99.82 257	98.35 155	91.26 374	99.32 243

TAPA-MVS	97.92 13	98.03 281	97.55 297	99.46 178	99.47 224	99.44 144	98.50 269	99.62 142	86.79 369	99.07 271	99.26 290	98.26 177	99.62 348	97.28 247	99.73 200	99.31 245
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet-Re		99.28 112	99.15 120	99.67 93	99.33 272	99.76 53	99.34 105	99.97 2	98.93 183	99.91 22	99.79 67	98.68 119	99.93 75	96.80 278	99.56 255	99.30 246

TSAR-MVS + GP.		99.12 159	99.04 158	99.38 206	99.34 267	99.16 208	98.15 295	99.29 284	98.18 263	99.63 131	99.62 168	99.18 53	99.68 328	98.20 168	99.74 193	99.30 246

PVSNet_Blended		98.70 225	98.59 218	99.02 263	99.54 186	97.99 295	97.58 339	99.82 41	95.70 344	99.34 223	98.98 332	98.52 147	99.77 292	97.98 187	99.83 143	99.30 246

MVS_0304		98.88 204	98.71 207	99.39 202	98.85 341	98.91 240	99.45 85	99.30 282	98.56 221	97.26 361	99.68 133	96.18 280	99.96 37	99.17 86	99.94 68	99.29 249

MVS_111021_LR		99.13 157	99.03 160	99.42 190	99.58 163	99.32 176	97.91 326	99.73 85	98.68 211	99.31 231	99.48 234	99.09 64	99.66 337	97.70 217	99.77 180	99.29 249

ETH3D cwj APD-0.16		98.50 247	98.16 264	99.51 163	99.04 322	99.39 158	98.47 271	99.47 233	96.70 330	98.78 301	99.33 275	97.62 232	99.86 201	94.69 346	99.38 289	99.28 251

miper_lstm_enhance		98.65 228	98.60 216	98.82 289	99.20 297	97.33 318	97.78 330	99.66 120	99.01 172	99.59 151	99.50 226	94.62 296	99.85 219	98.12 177	99.90 90	99.26 252

MVS		95.72 337	94.63 341	98.99 264	98.56 358	97.98 301	99.30 117	98.86 322	72.71 374	97.30 359	99.08 316	98.34 170	99.74 300	89.21 366	98.33 347	99.26 252

MSLP-MVS++		99.05 173	99.09 140	98.91 274	99.21 294	98.36 276	98.82 233	99.47 233	98.85 193	98.90 286	99.56 206	98.78 107	99.09 370	98.57 143	99.68 219	99.26 252

D2MVS		99.22 132	99.19 114	99.29 226	99.69 127	98.74 250	98.81 234	99.41 249	98.55 223	99.68 112	99.69 122	98.13 190	99.87 181	98.82 125	99.98 24	99.24 255

test_yl		98.25 270	97.95 277	99.13 251	99.17 302	98.47 266	99.00 202	98.67 332	98.97 175	99.22 248	99.02 327	91.31 329	99.69 317	97.26 250	98.93 322	99.24 255

DCV-MVSNet		98.25 270	97.95 277	99.13 251	99.17 302	98.47 266	99.00 202	98.67 332	98.97 175	99.22 248	99.02 327	91.31 329	99.69 317	97.26 250	98.93 322	99.24 255

DPM-MVS		98.28 268	97.94 281	99.32 220	99.36 255	99.11 213	97.31 352	98.78 327	96.88 323	98.84 293	99.11 314	97.77 218	99.61 352	94.03 354	99.36 294	99.23 258

CLD-MVS		98.76 217	98.57 222	99.33 216	99.57 173	98.97 229	97.53 342	99.55 191	96.41 332	99.27 238	99.13 308	99.07 70	99.78 284	96.73 282	99.89 99	99.23 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		99.13 157	99.06 148	99.36 212	99.57 173	99.10 218	98.01 311	99.25 293	98.78 203	99.58 153	99.44 246	98.24 178	99.76 294	98.74 133	99.93 76	99.22 260

OMC-MVS		98.90 200	98.72 206	99.44 184	99.39 247	99.42 151	98.58 256	99.64 137	97.31 310	99.44 195	99.62 168	98.59 132	99.69 317	96.17 310	99.79 170	99.22 260

EGC-MVSNET		89.05 342	85.52 345	99.64 112	99.89 22	99.78 43	99.56 73	99.52 214	24.19 376	49.96 377	99.83 49	99.15 56	99.92 95	97.71 214	99.85 128	99.21 262

eth_miper_zixun_eth		98.68 226	98.71 207	98.60 298	99.10 315	96.84 330	97.52 344	99.54 197	98.94 180	99.58 153	99.48 234	96.25 278	99.76 294	98.01 185	99.93 76	99.21 262

c3_l		98.72 223	98.71 207	98.72 294	99.12 309	97.22 321	97.68 335	99.56 185	98.90 187	99.54 170	99.48 234	96.37 275	99.73 303	97.88 195	99.88 108	99.21 262

CMPMVS	77.52 23	98.50 247	98.19 261	99.41 197	98.33 364	99.56 120	99.01 200	99.59 169	95.44 346	99.57 156	99.80 61	95.64 287	99.46 366	96.47 297	99.92 80	99.21 262
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		99.06 170	98.97 176	99.34 214	99.31 274	98.98 227	98.31 284	99.91 12	98.81 198	98.79 299	98.94 339	99.14 59	99.84 236	98.79 127	98.74 335	99.20 266

DELS-MVS		99.34 100	99.30 93	99.48 173	99.51 202	99.36 167	98.12 299	99.53 206	99.36 121	99.41 209	99.61 177	99.22 49	99.87 181	99.21 76	99.68 219	99.20 266
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
DROMVSNet		99.69 22	99.69 21	99.68 90	99.71 115	99.91 2	99.76 16	99.96 6	99.86 23	99.51 182	99.39 257	99.57 20	99.93 75	99.64 20	99.86 124	99.20 266

CANet_DTU		98.91 198	98.85 194	99.09 255	98.79 348	98.13 286	98.18 292	99.31 279	99.48 97	98.86 291	99.51 223	96.56 265	99.95 46	99.05 102	99.95 56	99.19 269

alignmvs		98.28 268	97.96 276	99.25 235	99.12 309	98.93 237	99.03 197	98.42 342	99.64 73	98.72 306	97.85 371	90.86 338	99.62 348	98.88 121	99.13 312	99.19 269

DIV-MVS_self_test		98.54 243	98.42 236	98.92 272	99.03 324	97.80 305	97.46 346	99.59 169	98.90 187	99.60 148	99.46 242	93.87 302	99.78 284	97.97 189	99.89 99	99.18 271

MSDG		99.08 168	98.98 175	99.37 209	99.60 154	99.13 211	97.54 340	99.74 82	98.84 196	99.53 175	99.55 213	99.10 62	99.79 281	97.07 264	99.86 124	99.18 271

cl____		98.54 243	98.41 237	98.92 272	99.03 324	97.80 305	97.46 346	99.59 169	98.90 187	99.60 148	99.46 242	93.85 303	99.78 284	97.97 189	99.89 99	99.17 273

PM-MVS		99.36 93	99.29 98	99.58 139	99.83 40	99.66 90	98.95 215	99.86 24	98.85 193	99.81 60	99.73 96	98.40 164	99.92 95	98.36 153	99.83 143	99.17 273

thisisatest0530		97.45 301	96.95 311	98.94 268	99.68 135	97.73 307	99.09 186	94.19 373	98.61 218	99.56 163	99.30 280	84.30 370	99.93 75	98.27 162	99.54 265	99.16 275

PatchmatchNet		97.65 295	97.80 288	97.18 340	98.82 346	92.49 364	99.17 158	98.39 344	98.12 264	98.79 299	99.58 194	90.71 340	99.89 154	97.23 254	99.41 286	99.16 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpnnormal		99.43 71	99.38 74	99.60 133	99.87 30	99.75 57	99.59 67	99.78 62	99.71 52	99.90 24	99.69 122	98.85 96	99.90 138	97.25 253	99.78 176	99.15 277

CS-MVS-test		99.68 25	99.70 18	99.64 112	99.57 173	99.83 25	99.78 11	99.97 2	99.92 8	99.50 184	99.38 259	99.57 20	99.95 46	99.69 16	99.90 90	99.15 277

mvs_anonymous		99.28 112	99.39 72	98.94 268	99.19 299	97.81 304	99.02 198	99.55 191	99.78 44	99.85 44	99.80 61	98.24 178	99.86 201	99.57 28	99.50 272	99.15 277

ab-mvs		99.33 104	99.28 100	99.47 175	99.57 173	99.39 158	99.78 11	99.43 246	98.87 191	99.57 156	99.82 56	98.06 195	99.87 181	98.69 138	99.73 200	99.15 277

MIMVSNet		98.43 256	98.20 258	99.11 253	99.53 191	98.38 275	99.58 69	98.61 334	98.96 178	99.33 225	99.76 84	90.92 335	99.81 273	97.38 241	99.76 182	99.15 277

GSMVS																99.14 282

sam_mvs1												90.81 339				99.14 282

SCA		98.11 277	98.36 242	97.36 335	99.20 297	92.99 362	98.17 294	98.49 340	98.24 258	99.10 267	99.57 203	96.01 283	99.94 60	96.86 273	99.62 238	99.14 282

LS3D		99.24 122	99.11 131	99.61 131	98.38 362	99.79 40	99.57 71	99.68 111	99.61 81	99.15 259	99.71 109	98.70 117	99.91 118	97.54 231	99.68 219	99.13 285

Patchmatch-RL test		98.60 233	98.36 242	99.33 216	99.77 84	99.07 222	98.27 287	99.87 21	98.91 186	99.74 94	99.72 102	90.57 342	99.79 281	98.55 144	99.85 128	99.11 286

test_0402		99.22 132	99.14 121	99.45 182	99.79 69	99.43 148	99.28 125	99.68 111	99.54 91	99.40 214	99.56 206	99.07 70	99.82 257	96.01 314	99.96 47	99.11 286

MVS_Test		99.28 112	99.31 88	99.19 243	99.35 257	98.79 248	99.36 103	99.49 227	99.17 151	99.21 250	99.67 138	98.78 107	99.66 337	99.09 99	99.66 230	99.10 288

AdaColmap		98.60 233	98.35 244	99.38 206	99.12 309	99.22 199	98.67 251	99.42 248	97.84 285	98.81 296	99.27 287	97.32 244	99.81 273	95.14 338	99.53 267	99.10 288

FPMVS		96.32 325	95.50 333	98.79 290	99.60 154	98.17 285	98.46 276	98.80 326	97.16 317	96.28 366	99.63 159	82.19 371	99.09 370	88.45 368	98.89 327	99.10 288

Patchmatch-test		98.10 278	97.98 275	98.48 303	99.27 285	96.48 334	99.40 92	99.07 313	98.81 198	99.23 244	99.57 203	90.11 346	99.87 181	96.69 283	99.64 235	99.09 291

tpm		97.15 307	96.95 311	97.75 327	98.91 333	94.24 356	99.32 110	97.96 350	97.71 289	98.29 328	99.32 276	86.72 364	99.92 95	98.10 180	96.24 371	99.09 291

PMMVS		98.49 250	98.29 250	99.11 253	98.96 331	98.42 271	97.54 340	99.32 275	97.53 298	98.47 324	98.15 368	97.88 210	99.82 257	97.46 236	99.24 309	99.09 291

cl22		97.56 299	97.28 301	98.40 306	98.37 363	96.75 331	97.24 355	99.37 266	97.31 310	99.41 209	99.22 299	87.30 355	99.37 368	97.70 217	99.62 238	99.08 294

ADS-MVSNet2		97.78 288	97.66 296	98.12 318	99.14 305	95.36 348	99.22 144	98.75 328	96.97 321	98.25 331	99.64 149	90.90 336	99.94 60	96.51 294	99.56 255	99.08 294

ADS-MVSNet		97.72 294	97.67 295	97.86 323	99.14 305	94.65 354	99.22 144	98.86 322	96.97 321	98.25 331	99.64 149	90.90 336	99.84 236	96.51 294	99.56 255	99.08 294

pmmvs3		98.08 279	97.80 288	98.91 274	99.41 242	97.69 309	97.87 327	99.66 120	95.87 340	99.50 184	99.51 223	90.35 344	99.97 19	98.55 144	99.47 277	99.08 294

PVSNet	97.47 15	98.42 257	98.44 234	98.35 308	99.46 229	96.26 337	96.70 365	99.34 272	97.68 290	99.00 275	99.13 308	97.40 238	99.72 305	97.59 229	99.68 219	99.08 294

MVS-HIRNet		97.86 285	98.22 256	96.76 344	99.28 283	91.53 371	98.38 279	92.60 375	99.13 159	99.31 231	99.96 12	97.18 252	99.68 328	98.34 156	99.83 143	99.07 299

PMVS	92.94 21	98.82 211	98.81 200	98.85 282	99.84 36	97.99 295	99.20 147	99.47 233	99.71 52	99.42 201	99.82 56	98.09 192	99.47 364	93.88 356	99.85 128	99.07 299
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		99.57 43	99.59 38	99.49 169	99.98 3	99.71 72	99.72 26	99.84 34	99.81 37	99.94 12	99.78 74	98.91 88	99.71 309	98.41 150	99.95 56	99.05 301
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
canonicalmvs		99.02 179	99.00 167	99.09 255	99.10 315	98.70 252	99.61 61	99.66 120	99.63 76	98.64 311	97.65 373	99.04 74	99.54 357	98.79 127	98.92 324	99.04 302

hse-mvs2		98.52 245	98.30 249	99.16 246	99.29 280	98.60 261	98.77 242	99.02 317	99.68 61	99.32 227	99.04 322	92.50 319	99.85 219	99.24 73	97.87 360	99.03 303

CL-MVSNet_self_test		98.71 224	98.56 225	99.15 248	99.22 292	98.66 256	97.14 357	99.51 218	98.09 267	99.54 170	99.27 287	96.87 261	99.74 300	98.43 149	98.96 321	99.03 303

AUN-MVS		97.82 286	97.38 299	99.14 250	99.27 285	98.53 263	98.72 248	99.02 317	98.10 265	97.18 363	99.03 326	89.26 351	99.85 219	97.94 191	97.91 358	99.03 303

MDTV_nov1_ep13_2view						91.44 372	99.14 168		97.37 307	99.21 250		91.78 327		96.75 280		99.03 303

ITE_SJBPF				99.38 206	99.63 147	99.44 144		99.73 85	98.56 221	99.33 225	99.53 217	98.88 93	99.68 328	96.01 314	99.65 233	99.02 307

UnsupCasMVSNet_bld		98.55 242	98.27 251	99.40 199	99.56 184	99.37 164	97.97 319	99.68 111	97.49 301	99.08 268	99.35 271	95.41 290	99.82 257	97.70 217	98.19 352	99.01 308

miper_ehance_all_eth		98.59 236	98.59 218	98.59 299	98.98 330	97.07 324	97.49 345	99.52 214	98.50 229	99.52 177	99.37 261	96.41 273	99.71 309	97.86 199	99.62 238	99.00 309

CS-MVS		99.67 28	99.70 18	99.58 139	99.53 191	99.84 20	99.79 10	99.96 6	99.90 10	99.61 145	99.41 249	99.51 24	99.95 46	99.66 17	99.89 99	98.96 310

CNLPA		98.57 238	98.34 245	99.28 228	99.18 301	99.10 218	98.34 280	99.41 249	98.48 232	98.52 320	98.98 332	97.05 256	99.78 284	95.59 329	99.50 272	98.96 310

new_pmnet		98.88 204	98.89 190	98.84 284	99.70 123	97.62 310	98.15 295	99.50 222	97.98 273	99.62 139	99.54 215	98.15 189	99.94 60	97.55 230	99.84 133	98.95 312

PCF-MVS	96.03 18	96.73 317	95.86 328	99.33 216	99.44 234	99.16 208	96.87 363	99.44 242	86.58 370	98.95 278	99.40 253	94.38 298	99.88 169	87.93 369	99.80 165	98.95 312
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		98.68 226	98.47 231	99.30 225	99.44 234	99.28 182	98.14 297	99.54 197	97.12 319	99.11 265	99.25 292	97.80 216	99.70 311	96.51 294	99.30 301	98.93 314

Fast-Effi-MVS+		99.02 179	98.87 192	99.46 178	99.38 250	99.50 129	99.04 194	99.79 57	97.17 316	98.62 312	98.74 352	99.34 37	99.95 46	98.32 158	99.41 286	98.92 315

ET-MVSNet_ETH3D		96.78 315	96.07 324	98.91 274	99.26 287	97.92 302	97.70 334	96.05 366	97.96 277	92.37 375	98.43 363	87.06 357	99.90 138	98.27 162	97.56 363	98.91 316

EIA-MVS		99.12 159	99.01 164	99.45 182	99.36 255	99.62 103	99.34 105	99.79 57	98.41 237	98.84 293	98.89 344	98.75 113	99.84 236	98.15 176	99.51 270	98.89 317

CostFormer		96.71 318	96.79 317	96.46 351	98.90 334	90.71 376	99.41 91	98.68 330	94.69 358	98.14 339	99.34 274	86.32 366	99.80 278	97.60 228	98.07 356	98.88 318

DP-MVS Recon		98.50 247	98.23 254	99.31 223	99.49 213	99.46 137	98.56 261	99.63 139	94.86 355	98.85 292	99.37 261	97.81 215	99.59 354	96.08 311	99.44 280	98.88 318

test0.0.03 1		97.37 304	96.91 314	98.74 293	97.72 372	97.57 311	97.60 338	97.36 361	98.00 270	99.21 250	98.02 369	90.04 347	99.79 281	98.37 152	95.89 372	98.86 320

BH-untuned		98.22 274	98.09 268	98.58 300	99.38 250	97.24 320	98.55 262	98.98 320	97.81 286	99.20 255	98.76 351	97.01 257	99.65 344	94.83 342	98.33 347	98.86 320

HY-MVS	98.23 9	98.21 275	97.95 277	98.99 264	99.03 324	98.24 279	99.61 61	98.72 329	96.81 327	98.73 305	99.51 223	94.06 300	99.86 201	96.91 270	98.20 350	98.86 320

miper_enhance_ethall		98.03 281	97.94 281	98.32 310	98.27 365	96.43 336	96.95 361	99.41 249	96.37 334	99.43 199	98.96 337	94.74 294	99.69 317	97.71 214	99.62 238	98.83 323

Effi-MVS+-dtu		99.07 169	98.92 185	99.52 160	98.89 337	99.78 43	99.15 166	99.66 120	99.34 122	98.92 283	99.24 297	97.69 222	99.98 8	98.11 178	99.28 303	98.81 324

EPMVS		96.53 321	96.32 319	97.17 341	98.18 368	92.97 363	99.39 94	89.95 379	98.21 260	98.61 313	99.59 192	86.69 365	99.72 305	96.99 266	99.23 311	98.81 324

MVE	92.54 22	96.66 319	96.11 323	98.31 312	99.68 135	97.55 312	97.94 322	95.60 368	99.37 119	90.68 376	98.70 353	96.56 265	98.61 374	86.94 374	99.55 259	98.77 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tpm2		96.35 324	96.22 321	96.73 346	98.88 340	91.75 369	99.21 146	98.51 338	93.27 361	97.89 348	99.21 301	84.83 368	99.70 311	96.04 313	98.18 353	98.75 327

LF4IMVS		99.01 183	98.92 185	99.27 230	99.71 115	99.28 182	98.59 255	99.77 65	98.32 254	99.39 215	99.41 249	98.62 128	99.84 236	96.62 290	99.84 133	98.69 328

thisisatest0515		96.98 311	96.42 318	98.66 297	99.42 241	97.47 313	97.27 353	94.30 372	97.24 312	99.15 259	98.86 346	85.01 367	99.87 181	97.10 262	99.39 288	98.63 329

Fast-Effi-MVS+-dtu		99.20 139	99.12 128	99.43 188	99.25 288	99.69 83	99.05 192	99.82 41	99.50 95	98.97 276	99.05 319	98.98 79	99.98 8	98.20 168	99.24 309	98.62 330

PAPM		95.61 338	94.71 340	98.31 312	99.12 309	96.63 332	96.66 366	98.46 341	90.77 367	96.25 367	98.68 354	93.01 313	99.69 317	81.60 375	97.86 361	98.62 330

JIA-IIPM		98.06 280	97.92 283	98.50 302	98.59 357	97.02 325	98.80 237	98.51 338	99.88 20	97.89 348	99.87 33	91.89 324	99.90 138	98.16 175	97.68 362	98.59 332

dp		96.86 313	97.07 307	96.24 353	98.68 356	90.30 378	99.19 152	98.38 345	97.35 308	98.23 333	99.59 192	87.23 356	99.82 257	96.27 305	98.73 337	98.59 332

OpenMVS	98.12 10	98.23 273	97.89 287	99.26 232	99.19 299	99.26 186	99.65 53	99.69 108	91.33 366	98.14 339	99.77 81	98.28 175	99.96 37	95.41 334	99.55 259	98.58 334

baseline2		96.83 314	96.28 320	98.46 304	99.09 317	96.91 328	98.83 229	93.87 374	97.23 313	96.23 369	98.36 364	88.12 353	99.90 138	96.68 284	98.14 354	98.57 335

DWT-MVSNet_test		96.03 331	95.80 330	96.71 348	98.50 360	91.93 367	99.25 136	97.87 353	95.99 339	96.81 365	97.61 374	81.02 373	99.66 337	97.20 257	97.98 357	98.54 336

TESTMET0.1,1		96.24 327	95.84 329	97.41 334	98.24 366	93.84 359	97.38 348	95.84 367	98.43 234	97.81 352	98.56 358	79.77 376	99.89 154	97.77 206	98.77 331	98.52 337

xiu_mvs_v1_base_debu		99.23 123	99.34 82	98.91 274	99.59 158	98.23 280	98.47 271	99.66 120	99.61 81	99.68 112	98.94 339	99.39 27	99.97 19	99.18 83	99.55 259	98.51 338

xiu_mvs_v1_base		99.23 123	99.34 82	98.91 274	99.59 158	98.23 280	98.47 271	99.66 120	99.61 81	99.68 112	98.94 339	99.39 27	99.97 19	99.18 83	99.55 259	98.51 338

xiu_mvs_v1_base_debi		99.23 123	99.34 82	98.91 274	99.59 158	98.23 280	98.47 271	99.66 120	99.61 81	99.68 112	98.94 339	99.39 27	99.97 19	99.18 83	99.55 259	98.51 338

KD-MVS_2432*1600		95.89 332	95.41 335	97.31 338	94.96 377	93.89 357	97.09 358	99.22 300	97.23 313	98.88 287	99.04 322	79.23 377	99.54 357	96.24 307	96.81 366	98.50 341

miper_refine_blended		95.89 332	95.41 335	97.31 338	94.96 377	93.89 357	97.09 358	99.22 300	97.23 313	98.88 287	99.04 322	79.23 377	99.54 357	96.24 307	96.81 366	98.50 341

CR-MVSNet		98.35 265	98.20 258	98.83 286	99.05 320	98.12 287	99.30 117	99.67 116	97.39 306	99.16 257	99.79 67	91.87 325	99.91 118	98.78 130	98.77 331	98.44 343

RPMNet		98.60 233	98.53 228	98.83 286	99.05 320	98.12 287	99.30 117	99.62 142	99.86 23	99.16 257	99.74 92	92.53 318	99.92 95	98.75 132	98.77 331	98.44 343

tpmrst		97.73 291	98.07 269	96.73 346	98.71 354	92.00 366	99.10 181	98.86 322	98.52 227	98.92 283	99.54 215	91.90 323	99.82 257	98.02 182	99.03 318	98.37 345

test-LLR		97.15 307	96.95 311	97.74 328	98.18 368	95.02 351	97.38 348	96.10 363	98.00 270	97.81 352	98.58 355	90.04 347	99.91 118	97.69 223	98.78 329	98.31 346

test-mter		96.23 328	95.73 331	97.74 328	98.18 368	95.02 351	97.38 348	96.10 363	97.90 279	97.81 352	98.58 355	79.12 379	99.91 118	97.69 223	98.78 329	98.31 346

ETV-MVS		99.18 146	99.18 115	99.16 246	99.34 267	99.28 182	99.12 178	99.79 57	99.48 97	98.93 280	98.55 359	99.40 26	99.93 75	98.51 146	99.52 269	98.28 348

PatchT		98.45 255	98.32 248	98.83 286	98.94 332	98.29 278	99.24 137	98.82 325	99.84 31	99.08 268	99.76 84	91.37 328	99.94 60	98.82 125	99.00 320	98.26 349

xiu_mvs_v2_base		99.02 179	99.11 131	98.77 291	99.37 253	98.09 291	98.13 298	99.51 218	99.47 102	99.42 201	98.54 360	99.38 31	99.97 19	98.83 123	99.33 298	98.24 350

IB-MVS	95.41 20	95.30 339	94.46 343	97.84 324	98.76 352	95.33 349	97.33 351	96.07 365	96.02 338	95.37 373	97.41 376	76.17 381	99.96 37	97.54 231	95.44 373	98.22 351
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
tpm cat1		96.78 315	96.98 310	96.16 354	98.85 341	90.59 377	99.08 189	99.32 275	92.37 363	97.73 357	99.46 242	91.15 332	99.69 317	96.07 312	98.80 328	98.21 352

MAR-MVS		98.24 272	97.92 283	99.19 243	98.78 350	99.65 95	99.17 158	99.14 310	95.36 347	98.04 343	98.81 349	97.47 235	99.72 305	95.47 333	99.06 315	98.21 352
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
PS-MVSNAJ		99.00 185	99.08 142	98.76 292	99.37 253	98.10 290	98.00 313	99.51 218	99.47 102	99.41 209	98.50 362	99.28 43	99.97 19	98.83 123	99.34 296	98.20 354

cascas		96.99 310	96.82 316	97.48 331	97.57 375	95.64 346	96.43 367	99.56 185	91.75 364	97.13 364	97.61 374	95.58 289	98.63 373	96.68 284	99.11 313	98.18 355

BH-w/o		97.20 306	97.01 309	97.76 326	99.08 318	95.69 345	98.03 310	98.52 337	95.76 343	97.96 345	98.02 369	95.62 288	99.47 364	92.82 359	97.25 365	98.12 356

tpmvs		97.39 303	97.69 293	96.52 349	98.41 361	91.76 368	99.30 117	98.94 321	97.74 287	97.85 351	99.55 213	92.40 321	99.73 303	96.25 306	98.73 337	98.06 357

mvs-test1		98.83 209	98.70 210	99.22 239	98.89 337	99.65 95	98.88 220	99.66 120	99.34 122	98.29 328	98.94 339	97.69 222	99.96 37	98.11 178	98.54 343	98.04 358

thres600view7		96.60 320	96.16 322	97.93 321	99.63 147	96.09 341	99.18 153	97.57 356	98.77 204	98.72 306	97.32 377	87.04 358	99.72 305	88.57 367	98.62 340	97.98 359

thres400		96.40 322	95.89 326	97.92 322	99.58 163	96.11 339	99.00 202	97.54 359	98.43 234	98.52 320	96.98 380	86.85 360	99.67 333	87.62 370	98.51 344	97.98 359

TR-MVS		97.44 302	97.15 306	98.32 310	98.53 359	97.46 314	98.47 271	97.91 352	96.85 325	98.21 334	98.51 361	96.42 271	99.51 362	92.16 360	97.29 364	97.98 359

1314		98.00 283	97.90 286	98.27 314	98.90 334	97.45 315	99.30 117	99.06 315	94.98 352	97.21 362	99.12 312	98.43 157	99.67 333	95.58 330	98.56 342	97.71 362

E-PMN		97.14 309	97.43 298	96.27 352	98.79 348	91.62 370	95.54 369	99.01 319	99.44 109	98.88 287	99.12 312	92.78 315	99.68 328	94.30 349	99.03 318	97.50 363

gg-mvs-nofinetune		95.87 334	95.17 338	97.97 320	98.19 367	96.95 326	99.69 37	89.23 380	99.89 15	96.24 368	99.94 14	81.19 372	99.51 362	93.99 355	98.20 350	97.44 364

DeepMVS_CX				97.98 319	99.69 127	96.95 326		99.26 290	75.51 373	95.74 371	98.28 366	96.47 269	99.62 348	91.23 363	97.89 359	97.38 365

OpenMVS_ROB	97.31 17	97.36 305	96.84 315	98.89 281	99.29 280	99.45 142	98.87 223	99.48 229	86.54 371	99.44 195	99.74 92	97.34 243	99.86 201	91.61 361	99.28 303	97.37 366

EMVS		96.96 312	97.28 301	95.99 355	98.76 352	91.03 373	95.26 370	98.61 334	99.34 122	98.92 283	98.88 345	93.79 304	99.66 337	92.87 358	99.05 316	97.30 367

thres100view900		96.39 323	96.03 325	97.47 332	99.63 147	95.93 342	99.18 153	97.57 356	98.75 208	98.70 308	97.31 378	87.04 358	99.67 333	87.62 370	98.51 344	96.81 368

tfpn200view9		96.30 326	95.89 326	97.53 330	99.58 163	96.11 339	99.00 202	97.54 359	98.43 234	98.52 320	96.98 380	86.85 360	99.67 333	87.62 370	98.51 344	96.81 368

API-MVS		98.38 261	98.39 239	98.35 308	98.83 343	99.26 186	99.14 168	99.18 306	98.59 219	98.66 310	98.78 350	98.61 130	99.57 356	94.14 351	99.56 255	96.21 370

thres200		96.09 329	95.68 332	97.33 337	99.48 219	96.22 338	98.53 266	97.57 356	98.06 269	98.37 327	96.73 382	86.84 362	99.61 352	86.99 373	98.57 341	96.16 371

GG-mvs-BLEND				97.36 335	97.59 373	96.87 329	99.70 31	88.49 381		94.64 374	97.26 379	80.66 374	99.12 369	91.50 362	96.50 370	96.08 372

wuyk23d		97.58 298	99.13 124	92.93 357	99.69 127	99.49 130	99.52 76	99.77 65	97.97 274	99.96 8	99.79 67	99.84 3	99.94 60	95.85 322	99.82 152	79.36 373

test123		29.31 343	33.05 348	18.08 359	25.93 383	12.24 383	97.53 342	10.93 384	11.78 377	24.21 378	50.08 387	21.04 382	8.60 378	23.51 376	32.43 377	33.39 374

testmvs		28.94 344	33.33 346	15.79 360	26.03 382	9.81 384	96.77 364	15.67 383	11.55 378	23.87 379	50.74 386	19.03 383	8.53 379	23.21 377	33.07 376	29.03 375

test_blank		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		24.88 345	33.17 347	0.00 361	0.00 384	0.00 385	0.00 372	99.62 142	0.00 379	0.00 380	99.13 308	99.82 4	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		16.61 346	22.14 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	99.28 43	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		8.26 355	11.02 358	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	99.16 306	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		8.33 347	11.11 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					99.83 40	99.89 8	99.74 20	99.71 97	99.69 59	99.63 131

test_one_0601					99.63 147	99.76 53		99.55 191	99.23 140	99.31 231	99.61 177	98.59 132

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					99.43 236	99.61 109		99.43 246	96.38 333	99.11 265	99.07 317	97.86 211	99.92 95	94.04 353	99.49 274

test_241102_ONE					99.69 127	99.82 30		99.54 197	99.12 162	99.82 53	99.49 231	98.91 88	99.52 361

9.14			98.64 213		99.45 232		98.81 234	99.60 162	97.52 299	99.28 237	99.56 206	98.53 144	99.83 247	95.36 336	99.64 235

save fliter					99.53 191	99.25 190	98.29 285	99.38 265	99.07 166

test0726					99.69 127	99.80 38	99.24 137	99.57 180	99.16 153	99.73 98	99.65 147	98.35 168

test_part2					99.62 151	99.67 88				99.55 168

sam_mvs												90.52 343

MTGPA								99.53 206

test_post1							99.14 168				51.63 385	89.54 350	99.82 257	96.86 273

test_post											52.41 384	90.25 345	99.86 201

patchmatchnet-post											99.62 168	90.58 341	99.94 60

MTMP							99.09 186	98.59 336

gm-plane-assit					97.59 373	89.02 380			93.47 360		98.30 365		99.84 236	96.38 301

TEST9					99.35 257	99.35 171	98.11 301	99.41 249	94.83 357	97.92 346	98.99 329	98.02 198	99.85 219

test_8					99.34 267	99.31 177	98.08 305	99.40 256	94.90 353	97.87 350	98.97 335	98.02 198	99.84 236

agg_prior					99.35 257	99.36 167		99.39 259		97.76 355			99.85 219

test_prior4						99.19 206	98.00 313

test_prior2							97.95 320		97.87 281	98.05 341	99.05 319	97.90 207		95.99 316	99.49 274

旧先验2							97.94 322		95.33 348	98.94 279			99.88 169	96.75 280

新几何2							98.04 309

原ACMM2							97.92 324

testdata2													99.89 154	95.99 316

segment_acmp												98.37 166

testdata1							97.72 332		97.86 284

plane_prior7					99.58 163	99.38 161

plane_prior6					99.47 224	99.26 186						97.24 246

plane_prior4											99.25 292

plane_prior3						99.31 177			98.36 243	99.14 261

plane_prior2							98.80 237		98.94 180

plane_prior1					99.51 202

plane_prior						99.24 195	98.42 277		97.87 281						99.71 209

n2								0.00 385

nn								0.00 385

door-mid								99.83 36

test11								99.29 284

door								99.77 65

HQP5-MVS						98.94 233

HQP-NCC					99.31 274		97.98 316		97.45 302	98.15 335

ACMP_Plane					99.31 274		97.98 316		97.45 302	98.15 335

BP-MVS														94.73 343

HQP3-MVS								99.37 266							99.67 226

HQP2-MVS												96.67 263

NP-MVS					99.40 245	99.13 211					98.83 347

MDTV_nov1_ep13			97.73 292		98.70 355	90.83 374	99.15 166	98.02 349	98.51 228	98.82 295	99.61 177	90.98 334	99.66 337	96.89 272	98.92 324

ACMMP++_ref															99.94 68

ACMMP++															99.79 170

Test By Simon												98.41 160

High-resolution multi-view benchmark