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.

FMVS2		99.12 39	99.41 14	98.25 229	99.76 28	95.07 277	99.05 64	99.94 1	97.78 163	99.82 10	99.84 2	98.56 38	99.71 244	99.96 1	99.96 15	99.97 1

FMVS		98.67 100	98.87 55	98.05 246	99.72 37	95.59 257	98.51 110	99.81 9	96.30 259	99.78 12	99.82 4	96.14 196	98.63 374	99.82 2	99.93 32	99.95 2

PS-MVSNAJss		99.46 12	99.49 10	99.35 74	99.90 4	98.15 136	99.20 44	99.65 26	99.48 27	99.92 4	99.71 15	98.07 72	99.96 11	99.53 11	100.00 1	99.93 3

test_djsdf		99.52 9	99.51 9	99.53 37	99.86 14	98.74 87	99.39 16	99.56 48	99.11 63	99.70 18	99.73 13	99.00 15	99.97 4	99.26 23	99.98 9	99.89 4

RRT_MVS		99.09 40	98.94 51	99.55 26	99.87 12	98.82 82	99.48 9	98.16 310	99.49 26	99.59 32	99.65 22	94.79 249	99.95 17	99.45 15	99.96 15	99.88 5

mvs_tets		99.63 5	99.67 5	99.49 52	99.88 9	98.61 98	99.34 19	99.71 15	99.27 50	99.90 5	99.74 11	99.68 2	99.97 4	99.55 10	99.99 5	99.88 5

jajsoiax		99.58 6	99.61 7	99.48 55	99.87 12	98.61 98	99.28 36	99.66 25	99.09 73	99.89 7	99.68 17	99.53 4	99.97 4	99.50 12	99.99 5	99.87 7

EU-MVSNet		97.66 204	98.50 103	95.13 344	99.63 57	85.84 372	98.35 128	98.21 306	98.23 131	99.54 35	99.46 51	95.02 238	99.68 260	98.24 85	99.87 62	99.87 7

UA-Net		99.47 11	99.40 15	99.70 2	99.49 94	99.29 20	99.80 3	99.72 14	99.82 3	99.04 123	99.81 5	98.05 75	99.96 11	98.85 49	99.99 5	99.86 9

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 3	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 3	100.00 1	99.85 10

mvsany_test		98.87 65	98.92 53	98.74 179	99.38 120	96.94 225	98.58 100	99.10 213	96.49 250	99.96 2	99.81 5	98.18 64	99.45 332	98.97 42	99.79 96	99.83 11

anonymousdsp		99.51 10	99.47 12	99.62 6	99.88 9	99.08 66	99.34 19	99.69 18	98.93 90	99.65 26	99.72 14	98.93 19	99.95 17	99.11 32	100.00 1	99.82 12

ANet_high		99.57 7	99.67 5	99.28 88	99.89 6	98.09 140	99.14 52	99.93 2	99.82 3	99.93 3	99.81 5	99.17 12	99.94 26	99.31 20	100.00 1	99.82 12

PS-CasMVS		99.40 18	99.33 21	99.62 6	99.71 39	99.10 63	99.29 32	99.53 61	99.53 24	99.46 48	99.41 61	98.23 57	99.95 17	98.89 47	99.95 19	99.81 14

FC-MVSNet-test		99.27 25	99.25 26	99.34 77	99.77 25	98.37 116	99.30 31	99.57 41	99.61 19	99.40 60	99.50 44	97.12 145	99.85 116	99.02 39	99.94 28	99.80 15

CP-MVSNet		99.21 32	99.09 40	99.56 24	99.65 52	98.96 73	99.13 53	99.34 128	99.42 35	99.33 74	99.26 83	97.01 153	99.94 26	98.74 56	99.93 32	99.79 16

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 18	99.34 17	99.69 4	99.58 34	99.90 2	99.86 8	99.78 8	99.58 3	99.95 17	99.00 40	99.95 19	99.78 17

CVMVSNet		96.25 286	97.21 225	93.38 360	99.10 184	80.56 386	97.20 240	98.19 309	96.94 233	99.00 129	99.02 129	89.50 308	99.80 181	96.36 218	99.59 182	99.78 17

Anonymous20231211		99.27 25	99.27 25	99.26 94	99.29 138	98.18 132	99.49 8	99.51 65	99.70 8	99.80 11	99.68 17	96.84 161	99.83 148	99.21 28	99.91 48	99.77 19

PEN-MVS		99.41 17	99.34 20	99.62 6	99.73 31	99.14 55	99.29 32	99.54 57	99.62 17	99.56 33	99.42 58	98.16 68	99.96 11	98.78 52	99.93 32	99.77 19

WR-MVS_H		99.33 23	99.22 28	99.65 5	99.71 39	99.24 26	99.32 22	99.55 52	99.46 30	99.50 44	99.34 72	97.30 133	99.93 31	98.90 45	99.93 32	99.77 19

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 24	99.85 16	99.11 62	99.90 1	99.78 10	99.63 14	99.78 12	99.67 19	99.48 6	99.81 172	99.30 22	99.97 12	99.77 19
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
patch_mono-2		98.51 128	98.63 85	98.17 235	99.38 120	94.78 282	97.36 227	99.69 18	98.16 141	98.49 210	99.29 78	97.06 148	99.97 4	98.29 84	99.91 48	99.76 23

nrg030		99.40 18	99.35 18	99.54 30	99.58 60	99.13 58	98.98 71	99.48 77	99.68 9	99.46 48	99.26 83	98.62 34	99.73 235	99.17 31	99.92 42	99.76 23

FIs		99.14 35	99.09 40	99.29 85	99.70 45	98.28 122	99.13 53	99.52 64	99.48 27	99.24 94	99.41 61	96.79 167	99.82 158	98.69 60	99.88 59	99.76 23

v7n		99.53 8	99.57 8	99.41 65	99.88 9	98.54 106	99.45 10	99.61 30	99.66 11	99.68 22	99.66 20	98.44 45	99.95 17	99.73 4	99.96 15	99.75 26

APDe-MVS		98.99 48	98.79 64	99.60 13	99.21 153	99.15 50	98.87 77	99.48 77	97.57 178	99.35 71	99.24 88	97.83 88	99.89 64	97.88 108	99.70 139	99.75 26

test_part1		97.91 180	97.46 210	99.27 91	98.80 250	98.18 132	99.07 60	99.36 116	99.75 5	99.63 29	99.49 47	82.20 357	99.89 64	98.87 48	99.95 19	99.74 28

bld_raw_dy_0_64		99.07 43	99.00 47	99.29 85	99.85 16	98.18 132	99.11 56	99.40 103	99.33 44	99.38 64	99.44 56	95.21 233	99.97 4	99.31 20	99.98 9	99.73 29

DTE-MVSNet		99.43 15	99.35 18	99.66 4	99.71 39	99.30 19	99.31 26	99.51 65	99.64 12	99.56 33	99.46 51	98.23 57	99.97 4	98.78 52	99.93 32	99.72 30

MSC_two_6792asdad				99.32 82	98.43 305	98.37 116		98.86 260					99.89 64	97.14 147	99.60 178	99.71 31

No_MVS				99.32 82	98.43 305	98.37 116		98.86 260					99.89 64	97.14 147	99.60 178	99.71 31

PMMVS2		98.07 170	98.08 164	98.04 247	99.41 117	94.59 291	94.59 353	99.40 103	97.50 184	98.82 168	98.83 186	96.83 163	99.84 133	97.50 129	99.81 81	99.71 31

Baseline_NR-MVSNet		98.98 52	98.86 58	99.36 69	99.82 21	98.55 103	97.47 220	99.57 41	99.37 39	99.21 98	99.61 27	96.76 170	99.83 148	98.06 96	99.83 74	99.71 31

XXY-MVS		99.14 35	99.15 35	99.10 118	99.76 28	97.74 183	98.85 80	99.62 28	98.48 115	99.37 67	99.49 47	98.75 26	99.86 101	98.20 88	99.80 91	99.71 31

test_0728_THIRD									98.17 138	99.08 114	99.02 129	97.89 85	99.88 75	97.07 153	99.71 134	99.70 36

MSP-MVS		98.40 140	98.00 170	99.61 9	99.57 64	99.25 25	98.57 101	99.35 122	97.55 181	99.31 82	97.71 306	94.61 252	99.88 75	96.14 231	99.19 265	99.70 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
mvsmamba		99.24 31	99.15 35	99.49 52	99.83 19	98.85 77	99.41 13	99.55 52	99.54 23	99.40 60	99.52 42	95.86 214	99.91 49	99.32 19	99.95 19	99.70 36

dcpmvs_2		98.78 77	99.11 37	97.78 259	99.56 71	93.67 318	99.06 62	99.86 6	99.50 25	99.66 23	99.26 83	97.21 143	99.99 2	98.00 101	99.91 48	99.68 39

test_0728_SECOND				99.60 13	99.50 87	99.23 27	98.02 162	99.32 135					99.88 75	96.99 159	99.63 167	99.68 39

OurMVSNet-221017-0		99.37 21	99.31 23	99.53 37	99.91 3	98.98 68	99.63 6	99.58 34	99.44 32	99.78 12	99.76 9	96.39 188	99.92 39	99.44 16	99.92 42	99.68 39

CHOSEN 1792x2688		97.49 215	97.14 230	98.54 203	99.68 48	96.09 246	96.50 282	99.62 28	91.58 345	98.84 163	98.97 147	92.36 290	99.88 75	96.76 182	99.95 19	99.67 42

IU-MVS					99.49 94	99.15 50		98.87 255	92.97 329	99.41 57				96.76 182	99.62 170	99.66 43

test_241102_TWO								99.30 151	98.03 146	99.26 89	99.02 129	97.51 118	99.88 75	96.91 165	99.60 178	99.66 43

DPE-MVS		98.59 114	98.26 141	99.57 18	99.27 141	99.15 50	97.01 251	99.39 106	97.67 169	99.44 52	98.99 141	97.53 115	99.89 64	95.40 263	99.68 150	99.66 43
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TransMVSNet (Re)		99.44 13	99.47 12	99.36 69	99.80 22	98.58 101	99.27 38	99.57 41	99.39 37	99.75 15	99.62 25	99.17 12	99.83 148	99.06 35	99.62 170	99.66 43

EI-MVSNet-UG-set		98.69 93	98.71 73	98.62 188	99.10 184	96.37 239	97.23 236	98.87 255	99.20 55	99.19 100	98.99 141	97.30 133	99.85 116	98.77 55	99.79 96	99.65 47

pmmvs6		99.67 3	99.70 3	99.60 13	99.90 4	99.27 23	99.53 7	99.76 12	99.64 12	99.84 9	99.83 3	99.50 5	99.87 92	99.36 17	99.92 42	99.64 48

EI-MVSNet-Vis-set		98.68 97	98.70 76	98.63 186	99.09 187	96.40 238	97.23 236	98.86 260	99.20 55	99.18 104	98.97 147	97.29 135	99.85 116	98.72 57	99.78 101	99.64 48

ACMH	96.65 7	99.25 27	99.24 27	99.26 94	99.72 37	98.38 115	99.07 60	99.55 52	98.30 123	99.65 26	99.45 55	99.22 9	99.76 220	98.44 75	99.77 105	99.64 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS		98.93 58	98.81 63	99.28 88	99.21 153	98.45 112	98.46 118	99.33 133	99.63 14	99.48 45	99.15 106	97.23 141	99.75 227	97.17 142	99.66 161	99.63 51

test1111		96.49 278	96.82 248	95.52 338	99.42 115	87.08 369	99.22 41	87.14 382	99.11 63	99.46 48	99.58 31	88.69 312	99.86 101	98.80 51	99.95 19	99.62 52

VPA-MVSNet		99.30 24	99.30 24	99.28 88	99.49 94	98.36 119	99.00 68	99.45 88	99.63 14	99.52 40	99.44 56	98.25 55	99.88 75	99.09 33	99.84 68	99.62 52

LPG-MVS_test		98.71 88	98.46 112	99.47 58	99.57 64	98.97 69	98.23 136	99.48 77	96.60 246	99.10 111	99.06 116	98.71 29	99.83 148	95.58 259	99.78 101	99.62 52

LGP-MVS_train				99.47 58	99.57 64	98.97 69		99.48 77	96.60 246	99.10 111	99.06 116	98.71 29	99.83 148	95.58 259	99.78 101	99.62 52

Test_1112_low_res		96.99 257	96.55 267	98.31 225	99.35 131	95.47 263	95.84 315	99.53 61	91.51 347	96.80 317	98.48 249	91.36 297	99.83 148	96.58 196	99.53 204	99.62 52

v10		98.97 53	99.11 37	98.55 200	99.44 110	96.21 243	98.90 75	99.55 52	98.73 100	99.48 45	99.60 29	96.63 177	99.83 148	99.70 5	99.99 5	99.61 57

Regformer-4		98.73 86	98.68 79	98.89 153	99.02 204	97.22 210	97.17 244	99.06 219	99.21 52	99.17 105	98.85 180	97.45 125	99.86 101	98.48 73	99.70 139	99.60 58

v8		99.01 46	99.16 31	98.57 195	99.47 104	96.31 241	98.90 75	99.47 83	99.03 79	99.52 40	99.57 32	96.93 157	99.81 172	99.60 6	99.98 9	99.60 58

EI-MVSNet		98.40 140	98.51 101	98.04 247	99.10 184	94.73 285	97.20 240	98.87 255	98.97 85	99.06 116	99.02 129	96.00 203	99.80 181	98.58 64	99.82 77	99.60 58

SixPastTwentyTwo		98.75 83	98.62 87	99.16 109	99.83 19	97.96 161	99.28 36	98.20 307	99.37 39	99.70 18	99.65 22	92.65 288	99.93 31	99.04 37	99.84 68	99.60 58

IterMVS-LS		98.55 120	98.70 76	98.09 239	99.48 102	94.73 285	97.22 239	99.39 106	98.97 85	99.38 64	99.31 77	96.00 203	99.93 31	98.58 64	99.97 12	99.60 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		97.19 240	96.60 265	98.96 143	99.62 59	97.28 206	95.17 335	99.50 67	94.21 311	99.01 127	98.32 266	86.61 324	99.99 2	97.10 151	99.84 68	99.60 58

ACMMP_NAP		98.75 83	98.48 108	99.57 18	99.58 60	99.29 20	97.82 182	99.25 170	96.94 233	98.78 171	99.12 110	98.02 76	99.84 133	97.13 149	99.67 156	99.59 64

VPNet		98.87 65	98.83 60	99.01 139	99.70 45	97.62 191	98.43 121	99.35 122	99.47 29	99.28 83	99.05 123	96.72 173	99.82 158	98.09 94	99.36 236	99.59 64

WR-MVS		98.40 140	98.19 149	99.03 135	99.00 207	97.65 188	96.85 263	98.94 242	98.57 112	98.89 151	98.50 244	95.60 221	99.85 116	97.54 126	99.85 64	99.59 64

HPM-MVS		98.79 74	98.53 98	99.59 17	99.65 52	99.29 20	99.16 50	99.43 97	96.74 241	98.61 192	98.38 258	98.62 34	99.87 92	96.47 209	99.67 156	99.59 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EG-PatchMatch MVS		98.99 48	99.01 46	98.94 146	99.50 87	97.47 196	98.04 159	99.59 32	98.15 142	99.40 60	99.36 67	98.58 37	99.76 220	98.78 52	99.68 150	99.59 64

Vis-MVSNet		99.34 22	99.36 17	99.27 91	99.73 31	98.26 123	99.17 49	99.78 10	99.11 63	99.27 85	99.48 49	98.82 23	99.95 17	98.94 43	99.93 32	99.59 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MP-MVS-pluss		98.57 115	98.23 145	99.60 13	99.69 47	99.35 14	97.16 246	99.38 108	94.87 297	98.97 135	98.99 141	98.01 77	99.88 75	97.29 137	99.70 139	99.58 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		98.69 93	98.40 122	99.54 30	99.53 80	99.17 41	98.52 106	99.31 141	97.46 192	98.44 214	98.51 240	97.83 88	99.88 75	96.46 210	99.58 188	99.58 70

ACMMPR		98.70 91	98.42 120	99.54 30	99.52 82	99.14 55	98.52 106	99.31 141	97.47 187	98.56 202	98.54 236	97.75 95	99.88 75	96.57 198	99.59 182	99.58 70

PGM-MVS		98.66 101	98.37 128	99.55 26	99.53 80	99.18 40	98.23 136	99.49 75	97.01 231	98.69 181	98.88 173	98.00 78	99.89 64	95.87 243	99.59 182	99.58 70

SteuartSystems-ACMMP		98.79 74	98.54 97	99.54 30	99.73 31	99.16 45	98.23 136	99.31 141	97.92 153	98.90 148	98.90 164	98.00 78	99.88 75	96.15 230	99.72 129	99.58 70
Skip Steuart: Steuart Systems R&D Blog.
Regformer-3		98.61 109	98.61 90	98.63 186	99.02 204	96.53 236	97.17 244	98.84 264	99.13 62	99.10 111	98.85 180	97.24 140	99.79 194	98.41 78	99.70 139	99.57 75

TranMVSNet+NR-MVSNet		99.17 33	99.07 43	99.46 60	99.37 126	98.87 76	98.39 124	99.42 100	99.42 35	99.36 69	99.06 116	98.38 48	99.95 17	98.34 81	99.90 55	99.57 75

mPP-MVS		98.64 104	98.34 132	99.54 30	99.54 78	99.17 41	98.63 93	99.24 175	97.47 187	98.09 241	98.68 211	97.62 106	99.89 64	96.22 225	99.62 170	99.57 75

PVSNet_Blended_VisFu		98.17 165	98.15 156	98.22 232	99.73 31	95.15 273	97.36 227	99.68 22	94.45 306	98.99 130	99.27 81	96.87 160	99.94 26	97.13 149	99.91 48	99.57 75

1112_ss		97.29 232	96.86 244	98.58 193	99.34 133	96.32 240	96.75 270	99.58 34	93.14 327	96.89 312	97.48 321	92.11 293	99.86 101	96.91 165	99.54 200	99.57 75

zzz-MVS		98.79 74	98.52 99	99.61 9	99.67 49	99.36 12	97.33 229	99.20 181	98.83 98	98.89 151	98.90 164	96.98 155	99.92 39	97.16 143	99.70 139	99.56 80

MTAPA		98.88 64	98.64 84	99.61 9	99.67 49	99.36 12	98.43 121	99.20 181	98.83 98	98.89 151	98.90 164	96.98 155	99.92 39	97.16 143	99.70 139	99.56 80

XVS		98.72 87	98.45 114	99.53 37	99.46 105	99.21 29	98.65 91	99.34 128	98.62 106	97.54 278	98.63 225	97.50 119	99.83 148	96.79 178	99.53 204	99.56 80

pm-mvs1		99.44 13	99.48 11	99.33 80	99.80 22	98.63 95	99.29 32	99.63 27	99.30 48	99.65 26	99.60 29	99.16 14	99.82 158	99.07 34	99.83 74	99.56 80

X-MVStestdata		94.32 319	92.59 337	99.53 37	99.46 105	99.21 29	98.65 91	99.34 128	98.62 106	97.54 278	45.85 382	97.50 119	99.83 148	96.79 178	99.53 204	99.56 80

HPM-MVS_fast		99.01 46	98.82 61	99.57 18	99.71 39	99.35 14	99.00 68	99.50 67	97.33 204	98.94 144	98.86 177	98.75 26	99.82 158	97.53 127	99.71 134	99.56 80

K. test v3		98.00 175	97.66 194	99.03 135	99.79 24	97.56 192	99.19 48	92.47 372	99.62 17	99.52 40	99.66 20	89.61 306	99.96 11	99.25 25	99.81 81	99.56 80

CP-MVS		98.70 91	98.42 120	99.52 42	99.36 127	99.12 60	98.72 86	99.36 116	97.54 182	98.30 224	98.40 254	97.86 87	99.89 64	96.53 206	99.72 129	99.56 80

ZNCC-MVS		98.68 97	98.40 122	99.54 30	99.57 64	99.21 29	98.46 118	99.29 158	97.28 210	98.11 239	98.39 256	98.00 78	99.87 92	96.86 175	99.64 164	99.55 88

v1192		98.60 111	98.66 82	98.41 216	99.27 141	95.88 251	97.52 214	99.36 116	97.41 197	99.33 74	99.20 93	96.37 191	99.82 158	99.57 8	99.92 42	99.55 88

v1240		98.55 120	98.62 87	98.32 223	99.22 151	95.58 258	97.51 216	99.45 88	97.16 224	99.45 51	99.24 88	96.12 198	99.85 116	99.60 6	99.88 59	99.55 88

UGNet		98.53 125	98.45 114	98.79 167	97.94 334	96.96 223	99.08 57	98.54 292	99.10 70	96.82 316	99.47 50	96.55 180	99.84 133	98.56 69	99.94 28	99.55 88
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		92.39 341	91.89 344	93.89 354	99.38 120	82.28 383	99.32 22	66.03 390	99.08 75	98.77 174	99.57 32	66.26 387	99.84 133	98.71 58	99.95 19	99.54 92

ECVR-MVS		96.42 281	96.61 263	95.85 330	99.38 120	88.18 365	99.22 41	86.00 384	99.08 75	99.36 69	99.57 32	88.47 317	99.82 158	98.52 70	99.95 19	99.54 92

testtj		97.79 197	97.25 221	99.42 62	99.03 202	98.85 77	97.78 184	99.18 190	95.83 275	98.12 237	98.50 244	95.50 226	99.86 101	92.23 338	99.07 281	99.54 92

v144192		98.54 123	98.57 95	98.45 212	99.21 153	95.98 248	97.63 201	99.36 116	97.15 226	99.32 80	99.18 96	95.84 215	99.84 133	99.50 12	99.91 48	99.54 92

v1921920		98.54 123	98.60 92	98.38 219	99.20 157	95.76 256	97.56 210	99.36 116	97.23 219	99.38 64	99.17 100	96.02 201	99.84 133	99.57 8	99.90 55	99.54 92

MP-MVS		98.46 133	98.09 161	99.54 30	99.57 64	99.22 28	98.50 112	99.19 186	97.61 175	97.58 274	98.66 216	97.40 128	99.88 75	94.72 276	99.60 178	99.54 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MIMVSNet1		99.38 20	99.32 22	99.55 26	99.86 14	99.19 39	99.41 13	99.59 32	99.59 20	99.71 17	99.57 32	97.12 145	99.90 54	99.21 28	99.87 62	99.54 92

ACMMP		98.75 83	98.50 103	99.52 42	99.56 71	99.16 45	98.87 77	99.37 112	97.16 224	98.82 168	99.01 138	97.71 97	99.87 92	96.29 222	99.69 145	99.54 92
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SMA-MVS		98.40 140	98.03 168	99.51 46	99.16 171	99.21 29	98.05 157	99.22 178	94.16 313	98.98 132	99.10 113	97.52 117	99.79 194	96.45 211	99.64 164	99.53 100
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HFP-MVS		98.71 88	98.44 116	99.51 46	99.49 94	99.16 45	98.52 106	99.31 141	97.47 187	98.58 198	98.50 244	97.97 82	99.85 116	96.57 198	99.59 182	99.53 100

#test#		98.50 129	98.16 154	99.51 46	99.49 94	99.16 45	98.03 160	99.31 141	96.30 259	98.58 198	98.50 244	97.97 82	99.85 116	95.68 253	99.59 182	99.53 100

UniMVSNet_NR-MVSNet		98.86 68	98.68 79	99.40 67	99.17 169	98.74 87	97.68 196	99.40 103	99.14 61	99.06 116	98.59 232	96.71 174	99.93 31	98.57 66	99.77 105	99.53 100

GST-MVS		98.61 109	98.30 137	99.52 42	99.51 84	99.20 35	98.26 134	99.25 170	97.44 195	98.67 183	98.39 256	97.68 98	99.85 116	96.00 235	99.51 210	99.52 104

Regformer-2		98.60 111	98.46 112	99.02 138	98.85 238	97.71 185	96.91 260	99.09 215	98.98 84	99.01 127	98.64 221	97.37 130	99.84 133	97.75 120	99.57 192	99.52 104

TDRefinement		99.42 16	99.38 16	99.55 26	99.76 28	99.33 18	99.68 5	99.71 15	99.38 38	99.53 38	99.61 27	98.64 32	99.80 181	98.24 85	99.84 68	99.52 104

v1144		98.60 111	98.66 82	98.41 216	99.36 127	95.90 250	97.58 208	99.34 128	97.51 183	99.27 85	99.15 106	96.34 193	99.80 181	99.47 14	99.93 32	99.51 107

Regformer-1		98.55 120	98.44 116	98.87 155	98.85 238	97.29 204	96.91 260	98.99 239	98.97 85	98.99 130	98.64 221	97.26 139	99.81 172	97.79 113	99.57 192	99.51 107

v2v482		98.56 116	98.62 87	98.37 220	99.42 115	95.81 254	97.58 208	99.16 199	97.90 155	99.28 83	99.01 138	95.98 207	99.79 194	99.33 18	99.90 55	99.51 107

CPTT-MVS		97.84 193	97.36 215	99.27 91	99.31 134	98.46 111	98.29 131	99.27 164	94.90 296	97.83 257	98.37 259	94.90 240	99.84 133	93.85 306	99.54 200	99.51 107

DU-MVS		98.82 70	98.63 85	99.39 68	99.16 171	98.74 87	97.54 212	99.25 170	98.84 97	99.06 116	98.76 199	96.76 170	99.93 31	98.57 66	99.77 105	99.50 111

NR-MVSNet		98.95 56	98.82 61	99.36 69	99.16 171	98.72 92	99.22 41	99.20 181	99.10 70	99.72 16	98.76 199	96.38 190	99.86 101	98.00 101	99.82 77	99.50 111

abl_6		98.99 48	98.78 65	99.61 9	99.45 108	99.46 6	98.60 96	99.50 67	98.59 108	99.24 94	99.04 125	98.54 40	99.89 64	96.45 211	99.62 170	99.50 111

ACMH+	96.62 9	99.08 42	99.00 47	99.33 80	99.71 39	98.83 80	98.60 96	99.58 34	99.11 63	99.53 38	99.18 96	98.81 24	99.67 263	96.71 189	99.77 105	99.50 111

DVP-MVS++		98.90 62	98.70 76	99.51 46	98.43 305	99.15 50	99.43 11	99.32 135	98.17 138	99.26 89	99.02 129	98.18 64	99.88 75	97.07 153	99.45 223	99.49 115

PC_three_1452									93.27 325	99.40 60	98.54 236	98.22 60	97.00 380	95.17 265	99.45 223	99.49 115

GeoE		99.05 44	98.99 50	99.25 97	99.44 110	98.35 120	98.73 85	99.56 48	98.42 117	98.91 147	98.81 191	98.94 18	99.91 49	98.35 80	99.73 122	99.49 115

h-mvs33		97.77 198	97.33 219	99.10 118	99.21 153	97.84 171	98.35 128	98.57 291	99.11 63	98.58 198	99.02 129	88.65 315	99.96 11	98.11 91	96.34 363	99.49 115

IterMVS-SCA-FT		97.85 192	98.18 150	96.87 309	99.27 141	91.16 355	95.53 325	99.25 170	99.10 70	99.41 57	99.35 68	93.10 279	99.96 11	98.65 62	99.94 28	99.49 115

new-patchmatchnet		98.35 145	98.74 68	97.18 295	99.24 146	92.23 340	96.42 287	99.48 77	98.30 123	99.69 20	99.53 40	97.44 126	99.82 158	98.84 50	99.77 105	99.49 115

APD-MVS		98.10 167	97.67 191	99.42 62	99.11 180	98.93 74	97.76 189	99.28 161	94.97 294	98.72 180	98.77 197	97.04 149	99.85 116	93.79 307	99.54 200	99.49 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet		98.30 149	98.04 167	99.07 125	99.56 71	97.83 172	99.29 32	98.07 314	99.03 79	98.59 196	99.13 109	92.16 292	99.90 54	96.87 173	99.68 150	99.49 115

DeepC-MVS	97.60 4	98.97 53	98.93 52	99.10 118	99.35 131	97.98 156	98.01 165	99.46 85	97.56 180	99.54 35	99.50 44	98.97 16	99.84 133	98.06 96	99.92 42	99.49 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	96.08 12	98.91 60	98.73 69	99.48 55	99.55 75	99.14 55	98.07 153	99.37 112	97.62 173	99.04 123	98.96 150	98.84 22	99.79 194	97.43 131	99.65 162	99.49 115
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1172		98.76 81	98.49 106	99.57 18	99.18 167	99.37 11	98.39 124	99.31 141	98.43 116	98.90 148	98.88 173	97.49 122	99.86 101	96.43 213	99.37 235	99.48 125

DVP-MVS		98.77 80	98.52 99	99.52 42	99.50 87	99.21 29	98.02 162	98.84 264	97.97 149	99.08 114	99.02 129	97.61 107	99.88 75	96.99 159	99.63 167	99.48 125
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
SR-MVS		98.71 88	98.43 118	99.57 18	99.18 167	99.35 14	98.36 127	99.29 158	98.29 126	98.88 156	98.85 180	97.53 115	99.87 92	96.14 231	99.31 244	99.48 125

TSAR-MVS + MP.		98.63 106	98.49 106	99.06 130	99.64 55	97.90 166	98.51 110	98.94 242	96.96 232	99.24 94	98.89 172	97.83 88	99.81 172	96.88 172	99.49 218	99.48 125
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet		98.21 160	97.95 173	99.01 139	99.58 60	97.74 183	99.01 66	97.29 334	99.67 10	98.97 135	99.50 44	90.45 301	99.80 181	97.88 108	99.20 261	99.48 125

IterMVS		97.73 199	98.11 160	96.57 316	99.24 146	90.28 356	95.52 327	99.21 179	98.86 95	99.33 74	99.33 74	93.11 278	99.94 26	98.49 72	99.94 28	99.48 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet		98.19 162	97.90 178	99.08 122	99.57 64	97.97 157	99.31 26	98.32 302	99.01 81	98.98 132	99.03 128	91.59 296	99.79 194	95.49 261	99.80 91	99.48 125

ACMP	95.32 15	98.41 138	98.09 161	99.36 69	99.51 84	98.79 85	97.68 196	99.38 108	95.76 277	98.81 170	98.82 189	98.36 49	99.82 158	94.75 273	99.77 105	99.48 125
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MCST-MVS		98.00 175	97.63 197	99.10 118	99.24 146	98.17 135	96.89 262	98.73 282	95.66 278	97.92 250	97.70 308	97.17 144	99.66 273	96.18 229	99.23 257	99.47 133

3Dnovator+	97.89 3	98.69 93	98.51 101	99.24 99	98.81 248	98.40 113	99.02 65	99.19 186	98.99 82	98.07 242	99.28 79	97.11 147	99.84 133	96.84 176	99.32 242	99.47 133

HPM-MVS++		98.10 167	97.64 196	99.48 55	99.09 187	99.13 58	97.52 214	98.75 279	97.46 192	96.90 311	97.83 300	96.01 202	99.84 133	95.82 247	99.35 238	99.46 135

V42		98.78 77	98.78 65	98.76 173	99.44 110	97.04 220	98.27 133	99.19 186	97.87 157	99.25 93	99.16 102	96.84 161	99.78 206	99.21 28	99.84 68	99.46 135

APD-MVS_3200maxsize		98.84 69	98.61 90	99.53 37	99.19 160	99.27 23	98.49 113	99.33 133	98.64 102	99.03 126	98.98 145	97.89 85	99.85 116	96.54 205	99.42 227	99.46 135

UniMVSNet (Re)		98.87 65	98.71 73	99.35 74	99.24 146	98.73 90	97.73 192	99.38 108	98.93 90	99.12 107	98.73 202	96.77 168	99.86 101	98.63 63	99.80 91	99.46 135

SR-MVS-dyc-post		98.81 72	98.55 96	99.57 18	99.20 157	99.38 8	98.48 116	99.30 151	98.64 102	98.95 138	98.96 150	97.49 122	99.86 101	96.56 201	99.39 231	99.45 139

RE-MVS-def			98.58 94		99.20 157	99.38 8	98.48 116	99.30 151	98.64 102	98.95 138	98.96 150	97.75 95		96.56 201	99.39 231	99.45 139

HQP_MVS		97.99 178	97.67 191	98.93 147	99.19 160	97.65 188	97.77 187	99.27 164	98.20 135	97.79 260	97.98 290	94.90 240	99.70 247	94.42 285	99.51 210	99.45 139

plane_prior5								99.27 164					99.70 247	94.42 285	99.51 210	99.45 139

lessismore_v0				98.97 142	99.73 31	97.53 194		86.71 383		99.37 67	99.52 42	89.93 304	99.92 39	98.99 41	99.72 129	99.44 143

TAMVS		98.24 158	98.05 166	98.80 165	99.07 191	97.18 215	97.88 175	98.81 270	96.66 245	99.17 105	99.21 91	94.81 246	99.77 212	96.96 163	99.88 59	99.44 143

DeepPCF-MVS	96.93 5	98.32 147	98.01 169	99.23 101	98.39 310	98.97 69	95.03 339	99.18 190	96.88 236	99.33 74	98.78 195	98.16 68	99.28 353	96.74 184	99.62 170	99.44 143

3Dnovator	98.27 2	98.81 72	98.73 69	99.05 132	98.76 253	97.81 177	99.25 39	99.30 151	98.57 112	98.55 204	99.33 74	97.95 84	99.90 54	97.16 143	99.67 156	99.44 143

MVSFormer		98.26 155	98.43 118	97.77 260	98.88 233	93.89 312	99.39 16	99.56 48	99.11 63	98.16 232	98.13 277	93.81 269	99.97 4	99.26 23	99.57 192	99.43 147

jason		97.45 220	97.35 216	97.76 262	99.24 146	93.93 308	95.86 312	98.42 298	94.24 310	98.50 209	98.13 277	94.82 244	99.91 49	97.22 140	99.73 122	99.43 147
jason: jason.
NCCC		97.86 187	97.47 209	99.05 132	98.61 283	98.07 146	96.98 253	98.90 250	97.63 172	97.04 302	97.93 295	95.99 206	99.66 273	95.31 264	98.82 303	99.43 147

Anonymous20240521		98.69 93	98.87 55	98.16 237	99.77 25	95.11 276	99.08 57	99.44 91	99.34 43	99.33 74	99.55 36	94.10 266	99.94 26	99.25 25	99.96 15	99.42 150

MVS_111021_HR		98.25 157	98.08 164	98.75 175	99.09 187	97.46 197	95.97 304	99.27 164	97.60 176	97.99 248	98.25 269	98.15 70	99.38 341	96.87 173	99.57 192	99.42 150

COLMAP_ROB	96.50 10	98.99 48	98.85 59	99.41 65	99.58 60	99.10 63	98.74 83	99.56 48	99.09 73	99.33 74	99.19 94	98.40 47	99.72 243	95.98 237	99.76 115	99.42 150
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SED-MVS		98.91 60	98.72 71	99.49 52	99.49 94	99.17 41	98.10 150	99.31 141	98.03 146	99.66 23	99.02 129	98.36 49	99.88 75	96.91 165	99.62 170	99.41 153

OPU-MVS				98.82 161	98.59 287	98.30 121	98.10 150				98.52 239	98.18 64	98.75 373	94.62 277	99.48 220	99.41 153

our_test_3		97.39 224	97.73 189	96.34 320	98.70 266	89.78 358	94.61 352	98.97 241	96.50 249	99.04 123	98.85 180	95.98 207	99.84 133	97.26 139	99.67 156	99.41 153

casdiffmvs		98.95 56	99.00 47	98.81 163	99.38 120	97.33 202	97.82 182	99.57 41	99.17 60	99.35 71	99.17 100	98.35 52	99.69 251	98.46 74	99.73 122	99.41 153

YYNet1		97.60 208	97.67 191	97.39 289	99.04 199	93.04 327	95.27 332	98.38 301	97.25 213	98.92 146	98.95 154	95.48 228	99.73 235	96.99 159	98.74 305	99.41 153

MDA-MVSNet_test_wron		97.60 208	97.66 194	97.41 288	99.04 199	93.09 323	95.27 332	98.42 298	97.26 212	98.88 156	98.95 154	95.43 229	99.73 235	97.02 156	98.72 307	99.41 153

GBi-Net		98.65 102	98.47 110	99.17 106	98.90 227	98.24 125	99.20 44	99.44 91	98.59 108	98.95 138	99.55 36	94.14 262	99.86 101	97.77 115	99.69 145	99.41 153

test1		98.65 102	98.47 110	99.17 106	98.90 227	98.24 125	99.20 44	99.44 91	98.59 108	98.95 138	99.55 36	94.14 262	99.86 101	97.77 115	99.69 145	99.41 153

FMVSNet1		99.17 33	99.17 30	99.17 106	99.55 75	98.24 125	99.20 44	99.44 91	99.21 52	99.43 53	99.55 36	97.82 91	99.86 101	98.42 77	99.89 58	99.41 153

iter_conf_final		97.10 245	96.65 262	98.45 212	98.53 295	96.08 247	98.30 130	99.11 211	98.10 143	98.85 160	98.95 154	79.38 367	99.87 92	98.68 61	99.91 48	99.40 162

iter_conf05		96.54 274	96.07 279	97.92 251	97.90 337	94.50 292	97.87 178	99.14 206	97.73 165	98.89 151	98.95 154	75.75 377	99.87 92	98.50 71	99.92 42	99.40 162

KD-MVS_self_test		99.25 27	99.18 29	99.44 61	99.63 57	99.06 67	98.69 90	99.54 57	99.31 46	99.62 31	99.53 40	97.36 131	99.86 101	99.24 27	99.71 134	99.39 164

v148		98.45 134	98.60 92	98.00 249	99.44 110	94.98 278	97.44 223	99.06 219	98.30 123	99.32 80	98.97 147	96.65 176	99.62 286	98.37 79	99.85 64	99.39 164

test20.03		98.78 77	98.77 67	98.78 170	99.46 105	97.20 213	97.78 184	99.24 175	99.04 78	99.41 57	98.90 164	97.65 101	99.76 220	97.70 121	99.79 96	99.39 164

CDPH-MVS		97.26 233	96.66 260	99.07 125	99.00 207	98.15 136	96.03 302	99.01 235	91.21 351	97.79 260	97.85 299	96.89 159	99.69 251	92.75 329	99.38 234	99.39 164

EPNet		96.14 288	95.44 297	98.25 229	90.76 387	95.50 262	97.92 171	94.65 360	98.97 85	92.98 373	98.85 180	89.12 310	99.87 92	95.99 236	99.68 150	99.39 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		98.17 165	97.87 180	99.07 125	98.67 276	98.24 125	97.01 251	98.93 244	97.25 213	97.62 270	98.34 263	97.27 136	99.57 303	96.42 214	99.33 241	99.39 164

DeepC-MVS_fast	96.85 6	98.30 149	98.15 156	98.75 175	98.61 283	97.23 208	97.76 189	99.09 215	97.31 207	98.75 177	98.66 216	97.56 111	99.64 281	96.10 234	99.55 199	99.39 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xxxxxxxxxxxxxcwj		98.44 135	98.24 143	99.06 130	99.11 180	97.97 157	96.53 279	99.54 57	98.24 129	98.83 164	98.90 164	97.80 92	99.82 158	95.68 253	99.52 207	99.38 171

SF-MVS		98.53 125	98.27 140	99.32 82	99.31 134	98.75 86	98.19 140	99.41 101	96.77 240	98.83 164	98.90 164	97.80 92	99.82 158	95.68 253	99.52 207	99.38 171

test9_res														93.28 320	99.15 271	99.38 171

OPM-MVS		98.56 116	98.32 136	99.25 97	99.41 117	98.73 90	97.13 248	99.18 190	97.10 227	98.75 177	98.92 160	98.18 64	99.65 278	96.68 191	99.56 197	99.37 174
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior2														92.50 334	99.16 268	99.37 174

AllTest		98.44 135	98.20 147	99.16 109	99.50 87	98.55 103	98.25 135	99.58 34	96.80 238	98.88 156	99.06 116	97.65 101	99.57 303	94.45 283	99.61 176	99.37 174

TestCases				99.16 109	99.50 87	98.55 103		99.58 34	96.80 238	98.88 156	99.06 116	97.65 101	99.57 303	94.45 283	99.61 176	99.37 174

MDA-MVSNet-bldmvs		97.94 179	97.91 177	98.06 244	99.44 110	94.96 279	96.63 276	99.15 205	98.35 119	98.83 164	99.11 111	94.31 259	99.85 116	96.60 195	98.72 307	99.37 174

MVSTER		96.86 261	96.55 267	97.79 258	97.91 336	94.21 298	97.56 210	98.87 255	97.49 186	99.06 116	99.05 123	80.72 359	99.80 181	98.44 75	99.82 77	99.37 174

pmmvs5		97.64 205	97.49 205	98.08 242	99.14 176	95.12 275	96.70 273	99.05 223	93.77 319	98.62 190	98.83 186	93.23 275	99.75 227	98.33 83	99.76 115	99.36 180

Anonymous20231206		98.21 160	98.21 146	98.20 233	99.51 84	95.43 265	98.13 145	99.32 135	96.16 263	98.93 145	98.82 189	96.00 203	99.83 148	97.32 136	99.73 122	99.36 180

train_agg		97.10 245	96.45 270	99.07 125	98.71 262	98.08 144	95.96 306	99.03 228	91.64 343	95.85 342	97.53 316	96.47 184	99.76 220	93.67 309	99.16 268	99.36 180

PVSNet_BlendedMVS		97.55 211	97.53 202	97.60 272	98.92 223	93.77 316	96.64 275	99.43 97	94.49 302	97.62 270	99.18 96	96.82 164	99.67 263	94.73 274	99.93 32	99.36 180

Anonymous20240529		98.93 58	98.87 55	99.12 114	99.19 160	98.22 130	99.01 66	98.99 239	99.25 51	99.54 35	99.37 64	97.04 149	99.80 181	97.89 105	99.52 207	99.35 184

F-COLMAP		97.30 230	96.68 257	99.14 112	99.19 160	98.39 114	97.27 235	99.30 151	92.93 330	96.62 322	98.00 288	95.73 218	99.68 260	92.62 332	98.46 320	99.35 184

ppachtmachnet_test		97.50 213	97.74 187	96.78 314	98.70 266	91.23 354	94.55 354	99.05 223	96.36 255	99.21 98	98.79 194	96.39 188	99.78 206	96.74 184	99.82 77	99.34 186

agg_prior1		97.06 249	96.40 271	99.03 135	98.68 274	97.99 152	95.76 316	99.01 235	91.73 342	95.59 345	97.50 319	96.49 183	99.77 212	93.71 308	99.14 272	99.34 186

VDD-MVS		98.56 116	98.39 125	99.07 125	99.13 178	98.07 146	98.59 98	97.01 338	99.59 20	99.11 108	99.27 81	94.82 244	99.79 194	98.34 81	99.63 167	99.34 186

testgi		98.32 147	98.39 125	98.13 238	99.57 64	95.54 259	97.78 184	99.49 75	97.37 201	99.19 100	97.65 310	98.96 17	99.49 324	96.50 208	98.99 293	99.34 186

diffmvs		98.22 159	98.24 143	98.17 235	99.00 207	95.44 264	96.38 289	99.58 34	97.79 162	98.53 207	98.50 244	96.76 170	99.74 231	97.95 104	99.64 164	99.34 186

UnsupCasMVSNet_eth		97.89 183	97.60 200	98.75 175	99.31 134	97.17 216	97.62 202	99.35 122	98.72 101	98.76 176	98.68 211	92.57 289	99.74 231	97.76 119	95.60 370	99.34 186

baseline		98.96 55	99.02 45	98.76 173	99.38 120	97.26 207	98.49 113	99.50 67	98.86 95	99.19 100	99.06 116	98.23 57	99.69 251	98.71 58	99.76 115	99.33 192

MG-MVS		96.77 265	96.61 263	97.26 293	98.31 314	93.06 324	95.93 309	98.12 313	96.45 253	97.92 250	98.73 202	93.77 271	99.39 339	91.19 352	99.04 285	99.33 192

HQP4-MVS										95.56 348			99.54 312			99.32 194

CDS-MVSNet		97.69 201	97.35 216	98.69 180	98.73 257	97.02 222	96.92 259	98.75 279	95.89 273	98.59 196	98.67 213	92.08 294	99.74 231	96.72 187	99.81 81	99.32 194
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS		97.00 256	96.49 269	98.55 200	98.67 276	96.79 229	96.29 293	99.04 226	96.05 266	95.55 349	96.84 339	93.84 267	99.54 312	92.82 326	99.26 254	99.32 194

RPSCF		98.62 108	98.36 129	99.42 62	99.65 52	99.42 7	98.55 103	99.57 41	97.72 167	98.90 148	99.26 83	96.12 198	99.52 318	95.72 250	99.71 134	99.32 194

MVP-Stereo		98.08 169	97.92 176	98.57 195	98.96 214	96.79 229	97.90 174	99.18 190	96.41 254	98.46 212	98.95 154	95.93 210	99.60 293	96.51 207	98.98 295	99.31 198
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 140	98.68 79	97.54 279	98.96 214	97.99 152	97.88 175	99.36 116	98.20 135	99.63 29	99.04 125	98.76 25	95.33 383	96.56 201	99.74 119	99.31 198
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
VNet		98.42 137	98.30 137	98.79 167	98.79 252	97.29 204	98.23 136	98.66 286	99.31 46	98.85 160	98.80 192	94.80 247	99.78 206	98.13 90	99.13 275	99.31 198

ETH3D-3000-0.1		98.03 171	97.62 198	99.29 85	99.11 180	98.80 84	97.47 220	99.32 135	95.54 280	98.43 217	98.62 227	96.61 178	99.77 212	93.95 301	99.49 218	99.30 201

test_prior3		97.48 217	97.00 235	98.95 144	98.69 271	97.95 162	95.74 318	99.03 228	96.48 251	96.11 336	97.63 312	95.92 211	99.59 297	94.16 291	99.20 261	99.30 201

test_prior				98.95 144	98.69 271	97.95 162		99.03 228					99.59 297			99.30 201

USDC		97.41 223	97.40 211	97.44 286	98.94 217	93.67 318	95.17 335	99.53 61	94.03 316	98.97 135	99.10 113	95.29 231	99.34 344	95.84 246	99.73 122	99.30 201

FMVSNet2		98.49 130	98.40 122	98.75 175	98.90 227	97.14 219	98.61 95	99.13 207	98.59 108	99.19 100	99.28 79	94.14 262	99.82 158	97.97 103	99.80 91	99.29 205

ETH3 D test6400		96.46 280	95.59 292	99.08 122	98.88 233	98.21 131	96.53 279	99.18 190	88.87 365	97.08 299	97.79 301	93.64 274	99.77 212	88.92 363	99.40 230	99.28 206

XVG-OURS-SEG-HR		98.49 130	98.28 139	99.14 112	99.49 94	98.83 80	96.54 278	99.48 77	97.32 206	99.11 108	98.61 230	99.33 8	99.30 350	96.23 224	98.38 321	99.28 206

test12				98.93 147	98.58 288	97.83 172		98.66 286		96.53 325		95.51 225	99.69 251		99.13 275	99.27 208

DSMNet-mixed		97.42 222	97.60 200	96.87 309	99.15 175	91.46 346	98.54 104	99.12 209	92.87 332	97.58 274	99.63 24	96.21 195	99.90 54	95.74 249	99.54 200	99.27 208

N_pmnet		97.63 207	97.17 226	98.99 141	99.27 141	97.86 169	95.98 303	93.41 369	95.25 289	99.47 47	98.90 164	95.63 220	99.85 116	96.91 165	99.73 122	99.27 208

ambc				98.24 231	98.82 246	95.97 249	98.62 94	99.00 238		99.27 85	99.21 91	96.99 154	99.50 323	96.55 204	99.50 217	99.26 211

LFMVS		97.20 239	96.72 254	98.64 183	98.72 259	96.95 224	98.93 74	94.14 367	99.74 7	98.78 171	99.01 138	84.45 342	99.73 235	97.44 130	99.27 251	99.25 212

FMVSNet5		96.01 290	95.20 305	98.41 216	97.53 353	96.10 244	98.74 83	99.50 67	97.22 222	98.03 247	99.04 125	69.80 381	99.88 75	97.27 138	99.71 134	99.25 212

BH-RMVSNet		96.83 262	96.58 266	97.58 274	98.47 301	94.05 301	96.67 274	97.36 330	96.70 244	97.87 254	97.98 290	95.14 236	99.44 334	90.47 358	98.58 318	99.25 212

FMVS1		99.25 27	99.16 31	99.51 46	99.89 6	99.63 3	98.71 88	99.69 18	98.90 92	99.43 53	99.35 68	98.86 20	99.67 263	97.81 111	99.81 81	99.24 215

APD_test		99.25 27	99.16 31	99.51 46	99.89 6	99.63 3	98.71 88	99.69 18	98.90 92	99.43 53	99.35 68	98.86 20	99.67 263	97.81 111	99.81 81	99.24 215

1121		96.73 266	96.00 280	98.91 150	98.95 216	97.76 180	98.07 153	98.73 282	87.65 369	96.54 324	98.13 277	94.52 254	99.73 235	92.38 336	99.02 289	99.24 215

旧先验1					98.82 246	97.45 198		98.76 276			98.34 263	95.50 226			99.01 291	99.23 218

test222					98.92 223	96.93 226	95.54 324	98.78 275	85.72 373	96.86 314	98.11 281	94.43 255			99.10 280	99.23 218

XVG-ACMP-BASELINE		98.56 116	98.34 132	99.22 102	99.54 78	98.59 100	97.71 193	99.46 85	97.25 213	98.98 132	98.99 141	97.54 113	99.84 133	95.88 240	99.74 119	99.23 218

FMVSNet3		97.50 213	97.24 223	98.29 227	98.08 328	95.83 253	97.86 179	98.91 249	97.89 156	98.95 138	98.95 154	87.06 321	99.81 172	97.77 115	99.69 145	99.23 218

无先验							95.74 318	98.74 281	89.38 362				99.73 235	92.38 336		99.22 222

tttt0517		95.64 300	94.98 309	97.64 270	99.36 127	93.81 314	98.72 86	90.47 378	98.08 145	98.67 183	98.34 263	73.88 379	99.92 39	97.77 115	99.51 210	99.20 223

pmmvs-eth3d		98.47 132	98.34 132	98.86 157	99.30 137	97.76 180	97.16 246	99.28 161	95.54 280	99.42 56	99.19 94	97.27 136	99.63 284	97.89 105	99.97 12	99.20 223

MS-PatchMatch		97.68 202	97.75 186	97.45 285	98.23 320	93.78 315	97.29 232	98.84 264	96.10 265	98.64 187	98.65 218	96.04 200	99.36 342	96.84 176	99.14 272	99.20 223

新几何1				98.91 150	98.94 217	97.76 180		98.76 276	87.58 370	96.75 318	98.10 282	94.80 247	99.78 206	92.73 330	99.00 292	99.20 223

PHI-MVS		98.29 152	97.95 173	99.34 77	98.44 304	99.16 45	98.12 147	99.38 108	96.01 269	98.06 243	98.43 252	97.80 92	99.67 263	95.69 252	99.58 188	99.20 223

Anonymous202405211		97.90 181	97.50 204	99.08 122	98.90 227	98.25 124	98.53 105	96.16 351	98.87 94	99.11 108	98.86 177	90.40 302	99.78 206	97.36 134	99.31 244	99.19 228

CANet		97.87 186	97.76 185	98.19 234	97.75 343	95.51 261	96.76 269	99.05 223	97.74 164	96.93 305	98.21 273	95.59 222	99.89 64	97.86 110	99.93 32	99.19 228

XVG-OURS		98.53 125	98.34 132	99.11 116	99.50 87	98.82 82	95.97 304	99.50 67	97.30 208	99.05 121	98.98 145	99.35 7	99.32 347	95.72 250	99.68 150	99.18 230

WTY-MVS		96.67 269	96.27 277	97.87 254	98.81 248	94.61 290	96.77 268	97.92 319	94.94 295	97.12 296	97.74 305	91.11 298	99.82 158	93.89 303	98.15 330	99.18 230

Vis-MVSNet (Re-imp)		97.46 218	97.16 227	98.34 222	99.55 75	96.10 244	98.94 73	98.44 297	98.32 122	98.16 232	98.62 227	88.76 311	99.73 235	93.88 304	99.79 96	99.18 230

TinyColmap		97.89 183	97.98 171	97.60 272	98.86 236	94.35 295	96.21 297	99.44 91	97.45 194	99.06 116	98.88 173	97.99 81	99.28 353	94.38 289	99.58 188	99.18 230

testdata				98.09 239	98.93 219	95.40 266		98.80 272	90.08 359	97.45 286	98.37 259	95.26 232	99.70 247	93.58 312	98.95 297	99.17 234

lupinMVS		97.06 249	96.86 244	97.65 268	98.88 233	93.89 312	95.48 328	97.97 317	93.53 322	98.16 232	97.58 314	93.81 269	99.91 49	96.77 181	99.57 192	99.17 234

Patchmtry		97.35 226	96.97 237	98.50 208	97.31 361	96.47 237	98.18 141	98.92 247	98.95 89	98.78 171	99.37 64	85.44 336	99.85 116	95.96 238	99.83 74	99.17 234

sss		97.21 238	96.93 238	98.06 244	98.83 243	95.22 271	96.75 270	98.48 296	94.49 302	97.27 293	97.90 296	92.77 286	99.80 181	96.57 198	99.32 242	99.16 237

CSCG		98.68 97	98.50 103	99.20 103	99.45 108	98.63 95	98.56 102	99.57 41	97.87 157	98.85 160	98.04 287	97.66 100	99.84 133	96.72 187	99.81 81	99.13 238

ETH3D cwj APD-0.16		97.55 211	97.00 235	99.19 105	98.51 298	98.64 94	96.85 263	99.13 207	94.19 312	97.65 268	98.40 254	95.78 216	99.81 172	93.37 318	99.16 268	99.12 239

MVS_111021_LR		98.30 149	98.12 159	98.83 160	99.16 171	98.03 150	96.09 301	99.30 151	97.58 177	98.10 240	98.24 270	98.25 55	99.34 344	96.69 190	99.65 162	99.12 239

miper_lstm_enhance		97.18 241	97.16 227	97.25 294	98.16 323	92.85 329	95.15 337	99.31 141	97.25 213	98.74 179	98.78 195	90.07 303	99.78 206	97.19 141	99.80 91	99.11 241

原ACMM1				98.35 221	98.90 227	96.25 242		98.83 269	92.48 336	96.07 339	98.10 282	95.39 230	99.71 244	92.61 333	98.99 293	99.08 242

QAPM		97.31 229	96.81 250	98.82 161	98.80 250	97.49 195	99.06 62	99.19 186	90.22 357	97.69 266	99.16 102	96.91 158	99.90 54	90.89 356	99.41 228	99.07 243

PAPM_NR		96.82 264	96.32 274	98.30 226	99.07 191	96.69 234	97.48 218	98.76 276	95.81 276	96.61 323	96.47 347	94.12 265	99.17 360	90.82 357	97.78 340	99.06 244

eth_miper_zixun_eth		97.23 237	97.25 221	97.17 296	98.00 332	92.77 331	94.71 346	99.18 190	97.27 211	98.56 202	98.74 201	91.89 295	99.69 251	97.06 155	99.81 81	99.05 245

D2MVS		97.84 193	97.84 182	97.83 256	99.14 176	94.74 284	96.94 255	98.88 253	95.84 274	98.89 151	98.96 150	94.40 257	99.69 251	97.55 124	99.95 19	99.05 245

c3_l		97.36 225	97.37 214	97.31 290	98.09 327	93.25 322	95.01 340	99.16 199	97.05 228	98.77 174	98.72 204	92.88 284	99.64 281	96.93 164	99.76 115	99.05 245

PLC	94.65 16	96.51 275	95.73 286	98.85 158	98.75 255	97.91 165	96.42 287	99.06 219	90.94 354	95.59 345	97.38 327	94.41 256	99.59 297	90.93 354	98.04 337	99.05 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal		98.90 62	98.90 54	98.91 150	99.67 49	97.82 175	99.00 68	99.44 91	99.45 31	99.51 43	99.24 88	98.20 63	99.86 101	95.92 239	99.69 145	99.04 249

CANet_DTU		97.26 233	97.06 232	97.84 255	97.57 350	94.65 289	96.19 299	98.79 273	97.23 219	95.14 358	98.24 270	93.22 276	99.84 133	97.34 135	99.84 68	99.04 249

PM-MVS		98.82 70	98.72 71	99.12 114	99.64 55	98.54 106	97.98 167	99.68 22	97.62 173	99.34 73	99.18 96	97.54 113	99.77 212	97.79 113	99.74 119	99.04 249

TSAR-MVS + GP.		98.18 163	97.98 171	98.77 172	98.71 262	97.88 167	96.32 292	98.66 286	96.33 256	99.23 97	98.51 240	97.48 124	99.40 337	97.16 143	99.46 221	99.02 252

DIV-MVS_self_test		97.02 253	96.84 246	97.58 274	97.82 341	94.03 303	94.66 349	99.16 199	97.04 229	98.63 188	98.71 205	88.69 312	99.69 251	97.00 157	99.81 81	99.01 253

GA-MVS		95.86 294	95.32 302	97.49 283	98.60 285	94.15 300	93.83 366	97.93 318	95.49 283	96.68 319	97.42 325	83.21 350	99.30 350	96.22 225	98.55 319	99.01 253

OMC-MVS		97.88 185	97.49 205	99.04 134	98.89 232	98.63 95	96.94 255	99.25 170	95.02 292	98.53 207	98.51 240	97.27 136	99.47 329	93.50 315	99.51 210	99.01 253

cl____		97.02 253	96.83 247	97.58 274	97.82 341	94.04 302	94.66 349	99.16 199	97.04 229	98.63 188	98.71 205	88.68 314	99.69 251	97.00 157	99.81 81	99.00 256

pmmvs4		97.58 210	97.28 220	98.51 206	98.84 241	96.93 226	95.40 331	98.52 294	93.60 321	98.61 192	98.65 218	95.10 237	99.60 293	96.97 162	99.79 96	98.99 257

MVS_0304		97.64 205	97.35 216	98.52 204	97.87 339	96.69 234	98.59 98	98.05 316	97.44 195	93.74 372	98.85 180	93.69 273	99.88 75	98.11 91	99.81 81	98.98 258

EPNet_dtu		94.93 313	94.78 314	95.38 342	93.58 384	87.68 367	96.78 267	95.69 358	97.35 203	89.14 380	98.09 284	88.15 319	99.49 324	94.95 270	99.30 247	98.98 258
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		96.50 277	95.77 284	98.69 180	99.48 102	97.43 199	97.84 181	99.55 52	81.42 378	96.51 327	98.58 233	95.53 223	99.67 263	93.41 317	99.58 188	98.98 258

PVSNet_Blended		96.88 260	96.68 257	97.47 284	98.92 223	93.77 316	94.71 346	99.43 97	90.98 353	97.62 270	97.36 329	96.82 164	99.67 263	94.73 274	99.56 197	98.98 258

PAPR		95.29 306	94.47 315	97.75 263	97.50 357	95.14 274	94.89 343	98.71 284	91.39 349	95.35 356	95.48 363	94.57 253	99.14 363	84.95 371	97.37 348	98.97 262

EGC-MVSNET		85.24 348	80.54 351	99.34 77	99.77 25	99.20 35	99.08 57	99.29 158	12.08 384	20.84 385	99.42 58	97.55 112	99.85 116	97.08 152	99.72 129	98.96 263

thisisatest0530		95.27 307	94.45 316	97.74 264	99.19 160	94.37 294	97.86 179	90.20 379	97.17 223	98.22 228	97.65 310	73.53 380	99.90 54	96.90 170	99.35 238	98.95 264

mvs_anonymous		97.83 195	98.16 154	96.87 309	98.18 322	91.89 342	97.31 231	98.90 250	97.37 201	98.83 164	99.46 51	96.28 194	99.79 194	98.90 45	98.16 329	98.95 264

baseline1		95.96 292	95.44 297	97.52 281	98.51 298	93.99 306	98.39 124	96.09 353	98.21 132	98.40 222	97.76 304	86.88 322	99.63 284	95.42 262	89.27 381	98.95 264

CLD-MVS		97.49 215	97.16 227	98.48 209	99.07 191	97.03 221	94.71 346	99.21 179	94.46 304	98.06 243	97.16 334	97.57 110	99.48 327	94.46 282	99.78 101	98.95 264
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		98.02 173	98.14 158	97.64 270	98.58 288	95.19 272	97.48 218	99.23 177	97.47 187	97.90 252	98.62 227	97.04 149	98.81 372	97.55 124	99.41 228	98.94 268

DELS-MVS		98.27 153	98.20 147	98.48 209	98.86 236	96.70 233	95.60 323	99.20 181	97.73 165	98.45 213	98.71 205	97.50 119	99.82 158	98.21 87	99.59 182	98.93 269
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
cl22		95.79 296	95.39 300	96.98 303	96.77 370	92.79 330	94.40 357	98.53 293	94.59 301	97.89 253	98.17 276	82.82 354	99.24 355	96.37 216	99.03 286	98.92 270

LS3D		98.63 106	98.38 127	99.36 69	97.25 362	99.38 8	99.12 55	99.32 135	99.21 52	98.44 214	98.88 173	97.31 132	99.80 181	96.58 196	99.34 240	98.92 270

CMPMVS	75.91 23	96.29 284	95.44 297	98.84 159	96.25 377	98.69 93	97.02 250	99.12 209	88.90 364	97.83 257	98.86 177	89.51 307	98.90 370	91.92 339	99.51 210	98.92 270
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		98.64 104	98.48 108	99.11 116	98.85 238	98.51 108	98.49 113	99.83 8	98.37 118	99.69 20	99.46 51	98.21 62	99.92 39	94.13 296	99.30 247	98.91 273

DPM-MVS		96.32 283	95.59 292	98.51 206	98.76 253	97.21 212	94.54 355	98.26 304	91.94 341	96.37 332	97.25 331	93.06 281	99.43 335	91.42 348	98.74 305	98.89 274

test_yl		96.69 267	96.29 275	97.90 252	98.28 315	95.24 269	97.29 232	97.36 330	98.21 132	98.17 230	97.86 297	86.27 326	99.55 309	94.87 271	98.32 322	98.89 274

DCV-MVSNet		96.69 267	96.29 275	97.90 252	98.28 315	95.24 269	97.29 232	97.36 330	98.21 132	98.17 230	97.86 297	86.27 326	99.55 309	94.87 271	98.32 322	98.89 274

CS-MVS-test		99.13 37	99.09 40	99.26 94	99.13 178	98.97 69	99.31 26	99.88 4	99.44 32	98.16 232	98.51 240	98.64 32	99.93 31	98.91 44	99.85 64	98.88 277

UnsupCasMVSNet_bld		97.30 230	96.92 240	98.45 212	99.28 139	96.78 232	96.20 298	99.27 164	95.42 285	98.28 226	98.30 267	93.16 277	99.71 244	94.99 268	97.37 348	98.87 278

Effi-MVS+		98.02 173	97.82 183	98.62 188	98.53 295	97.19 214	97.33 229	99.68 22	97.30 208	96.68 319	97.46 323	98.56 38	99.80 181	96.63 194	98.20 326	98.86 279

test_0402		98.76 81	98.71 73	98.93 147	99.56 71	98.14 138	98.45 120	99.34 128	99.28 49	98.95 138	98.91 161	98.34 53	99.79 194	95.63 256	99.91 48	98.86 279

PatchmatchNet		95.58 301	95.67 289	95.30 343	97.34 360	87.32 368	97.65 200	96.65 346	95.30 288	97.07 300	98.69 209	84.77 339	99.75 227	94.97 269	98.64 314	98.83 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CL-MVSNet_self_test		97.44 221	97.22 224	98.08 242	98.57 290	95.78 255	94.30 359	98.79 273	96.58 248	98.60 194	98.19 275	94.74 251	99.64 281	96.41 215	98.84 301	98.82 282

miper_ehance_all_eth		97.06 249	97.03 233	97.16 298	97.83 340	93.06 324	94.66 349	99.09 215	95.99 270	98.69 181	98.45 251	92.73 287	99.61 292	96.79 178	99.03 286	98.82 282

MIMVSNet		96.62 272	96.25 278	97.71 265	99.04 199	94.66 288	99.16 50	96.92 343	97.23 219	97.87 254	99.10 113	86.11 330	99.65 278	91.65 343	99.21 260	98.82 282

hse-mvs2		97.46 218	97.07 231	98.64 183	98.73 257	97.33 202	97.45 222	97.64 327	99.11 63	98.58 198	97.98 290	88.65 315	99.79 194	98.11 91	97.39 347	98.81 285

GSMVS																98.81 285

sam_mvs1												84.74 340				98.81 285

SCA		96.41 282	96.66 260	95.67 334	98.24 318	88.35 363	95.85 314	96.88 344	96.11 264	97.67 267	98.67 213	93.10 279	99.85 116	94.16 291	99.22 258	98.81 285

Patchmatch-RL test		97.26 233	97.02 234	97.99 250	99.52 82	95.53 260	96.13 300	99.71 15	97.47 187	99.27 85	99.16 102	84.30 345	99.62 286	97.89 105	99.77 105	98.81 285

AUN-MVS		96.24 287	95.45 296	98.60 191	98.70 266	97.22 210	97.38 225	97.65 325	95.95 271	95.53 353	97.96 294	82.11 358	99.79 194	96.31 220	97.44 345	98.80 290

ITE_SJBPF				98.87 155	99.22 151	98.48 110		99.35 122	97.50 184	98.28 226	98.60 231	97.64 104	99.35 343	93.86 305	99.27 251	98.79 291

tpm		94.67 315	94.34 319	95.66 335	97.68 349	88.42 362	97.88 175	94.90 359	94.46 304	96.03 341	98.56 235	78.66 370	99.79 194	95.88 240	95.01 373	98.78 292

Patchmatch-test		96.55 273	96.34 273	97.17 296	98.35 311	93.06 324	98.40 123	97.79 320	97.33 204	98.41 218	98.67 213	83.68 349	99.69 251	95.16 266	99.31 244	98.77 293

DROMVSNet		99.09 40	99.05 44	99.20 103	99.28 139	98.93 74	99.24 40	99.84 7	99.08 75	98.12 237	98.37 259	98.72 28	99.90 54	99.05 36	99.77 105	98.77 293

PMMVS		96.51 275	95.98 281	98.09 239	97.53 353	95.84 252	94.92 342	98.84 264	91.58 345	96.05 340	95.58 360	95.68 219	99.66 273	95.59 258	98.09 333	98.76 295

test_method		79.78 349	79.50 352	80.62 365	80.21 388	45.76 390	70.82 379	98.41 300	31.08 383	80.89 384	97.71 306	84.85 338	97.37 379	91.51 347	80.03 382	98.75 296

ab-mvs		98.41 138	98.36 129	98.59 192	99.19 160	97.23 208	99.32 22	98.81 270	97.66 170	98.62 190	99.40 63	96.82 164	99.80 181	95.88 240	99.51 210	98.75 296

CHOSEN 280x420		95.51 304	95.47 294	95.65 336	98.25 317	88.27 364	93.25 370	98.88 253	93.53 322	94.65 361	97.15 335	86.17 328	99.93 31	97.41 132	99.93 32	98.73 298

MVS_Test		98.18 163	98.36 129	97.67 266	98.48 300	94.73 285	98.18 141	99.02 232	97.69 168	98.04 246	99.11 111	97.22 142	99.56 306	98.57 66	98.90 300	98.71 299

PVSNet	93.40 17	95.67 298	95.70 287	95.57 337	98.83 243	88.57 361	92.50 373	97.72 322	92.69 334	96.49 330	96.44 348	93.72 272	99.43 335	93.61 310	99.28 250	98.71 299

alignmvs		97.35 226	96.88 243	98.78 170	98.54 293	98.09 140	97.71 193	97.69 324	99.20 55	97.59 273	95.90 356	88.12 320	99.55 309	98.18 89	98.96 296	98.70 301

ADS-MVSNet2		95.43 305	94.98 309	96.76 315	98.14 324	91.74 343	97.92 171	97.76 321	90.23 355	96.51 327	98.91 161	85.61 333	99.85 116	92.88 324	96.90 356	98.69 302

ADS-MVSNet		95.24 308	94.93 312	96.18 324	98.14 324	90.10 357	97.92 171	97.32 333	90.23 355	96.51 327	98.91 161	85.61 333	99.74 231	92.88 324	96.90 356	98.69 302

MDTV_nov1_ep13_2view						74.92 388	97.69 195		90.06 360	97.75 263		85.78 332		93.52 313		98.69 302

MSDG		97.71 200	97.52 203	98.28 228	98.91 226	96.82 228	94.42 356	99.37 112	97.65 171	98.37 223	98.29 268	97.40 128	99.33 346	94.09 297	99.22 258	98.68 305

CS-MVS		99.13 37	99.10 39	99.24 99	99.06 195	99.15 50	99.36 18	99.88 4	99.36 42	98.21 229	98.46 250	98.68 31	99.93 31	99.03 38	99.85 64	98.64 306

miper_enhance_ethall		96.01 290	95.74 285	96.81 313	96.41 375	92.27 339	93.69 368	98.89 252	91.14 352	98.30 224	97.35 330	90.58 300	99.58 302	96.31 220	99.03 286	98.60 307

Effi-MVS+-dtu		98.26 155	97.90 178	99.35 74	98.02 330	99.49 5	98.02 162	99.16 199	98.29 126	97.64 269	97.99 289	96.44 186	99.95 17	96.66 192	98.93 298	98.60 307

new_pmnet		96.99 257	96.76 252	97.67 266	98.72 259	94.89 280	95.95 308	98.20 307	92.62 335	98.55 204	98.54 236	94.88 243	99.52 318	93.96 300	99.44 226	98.59 309

EIA-MVS		98.00 175	97.74 187	98.80 165	98.72 259	98.09 140	98.05 157	99.60 31	97.39 199	96.63 321	95.55 361	97.68 98	99.80 181	96.73 186	99.27 251	98.52 310

PatchMatch-RL		97.24 236	96.78 251	98.61 190	99.03 202	97.83 172	96.36 290	99.06 219	93.49 324	97.36 292	97.78 302	95.75 217	99.49 324	93.44 316	98.77 304	98.52 310

ET-MVSNet_ETH3D		94.30 321	93.21 331	97.58 274	98.14 324	94.47 293	94.78 345	93.24 371	94.72 299	89.56 379	95.87 357	78.57 372	99.81 172	96.91 165	97.11 355	98.46 312

canonicalmvs		98.34 146	98.26 141	98.58 193	98.46 302	97.82 175	98.96 72	99.46 85	99.19 59	97.46 285	95.46 364	98.59 36	99.46 331	98.08 95	98.71 309	98.46 312

TAPA-MVS	96.21 11	96.63 271	95.95 282	98.65 182	98.93 219	98.09 140	96.93 257	99.28 161	83.58 376	98.13 236	97.78 302	96.13 197	99.40 337	93.52 313	99.29 249	98.45 314
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
BH-untuned		96.83 262	96.75 253	97.08 299	98.74 256	93.33 321	96.71 272	98.26 304	96.72 242	98.44 214	97.37 328	95.20 234	99.47 329	91.89 340	97.43 346	98.44 315

pmmvs3		95.03 311	94.40 317	96.93 305	97.70 347	92.53 334	95.08 338	97.71 323	88.57 366	97.71 264	98.08 285	79.39 366	99.82 158	96.19 227	99.11 279	98.43 316

DP-MVS Recon		97.33 228	96.92 240	98.57 195	99.09 187	97.99 152	96.79 266	99.35 122	93.18 326	97.71 264	98.07 286	95.00 239	99.31 348	93.97 299	99.13 275	98.42 317

Fast-Effi-MVS+-dtu		98.27 153	98.09 161	98.81 163	98.43 305	98.11 139	97.61 204	99.50 67	98.64 102	97.39 290	97.52 318	98.12 71	99.95 17	96.90 170	98.71 309	98.38 318

LF4IMVS		97.90 181	97.69 190	98.52 204	99.17 169	97.66 187	97.19 243	99.47 83	96.31 258	97.85 256	98.20 274	96.71 174	99.52 318	94.62 277	99.72 129	98.38 318

Fast-Effi-MVS+		97.67 203	97.38 213	98.57 195	98.71 262	97.43 199	97.23 236	99.45 88	94.82 298	96.13 335	96.51 344	98.52 41	99.91 49	96.19 227	98.83 302	98.37 320

test0.0.03 1		94.51 316	93.69 325	96.99 302	96.05 378	93.61 320	94.97 341	93.49 368	96.17 261	97.57 276	94.88 372	82.30 355	99.01 367	93.60 311	94.17 377	98.37 320

FE-MVS		95.66 299	94.95 311	97.77 260	98.53 295	95.28 268	99.40 15	96.09 353	93.11 328	97.96 249	99.26 83	79.10 369	99.77 212	92.40 335	98.71 309	98.27 322

baseline2		93.73 330	92.83 336	96.42 319	97.70 347	91.28 352	96.84 265	89.77 380	93.96 318	92.44 374	95.93 355	79.14 368	99.77 212	92.94 322	96.76 360	98.21 323

thisisatest0515		94.12 325	93.16 332	96.97 304	98.60 285	92.90 328	93.77 367	90.61 377	94.10 314	96.91 308	95.87 357	74.99 378	99.80 181	94.52 280	99.12 278	98.20 324

EPMVS		93.72 331	93.27 330	95.09 345	96.04 379	87.76 366	98.13 145	85.01 385	94.69 300	96.92 306	98.64 221	78.47 374	99.31 348	95.04 267	96.46 362	98.20 324

dp		93.47 333	93.59 327	93.13 362	96.64 371	81.62 385	97.66 198	96.42 349	92.80 333	96.11 336	98.64 221	78.55 373	99.59 297	93.31 319	92.18 380	98.16 326

CNLPA		97.17 242	96.71 255	98.55 200	98.56 291	98.05 149	96.33 291	98.93 244	96.91 235	97.06 301	97.39 326	94.38 258	99.45 332	91.66 342	99.18 267	98.14 327

HY-MVS	95.94 13	95.90 293	95.35 301	97.55 278	97.95 333	94.79 281	98.81 82	96.94 342	92.28 339	95.17 357	98.57 234	89.90 305	99.75 227	91.20 351	97.33 352	98.10 328

CostFormer		93.97 327	93.78 324	94.51 348	97.53 353	85.83 373	97.98 167	95.96 355	89.29 363	94.99 360	98.63 225	78.63 371	99.62 286	94.54 279	96.50 361	98.09 329

FA-MVS(test-final)		96.99 257	96.82 248	97.50 282	98.70 266	94.78 282	99.34 19	96.99 339	95.07 291	98.48 211	99.33 74	88.41 318	99.65 278	96.13 233	98.92 299	98.07 330

AdaColmap		97.14 244	96.71 255	98.46 211	98.34 312	97.80 178	96.95 254	98.93 244	95.58 279	96.92 306	97.66 309	95.87 213	99.53 314	90.97 353	99.14 272	98.04 331

KD-MVS_2432*1600		92.87 338	91.99 341	95.51 339	91.37 385	89.27 359	94.07 361	98.14 311	95.42 285	97.25 294	96.44 348	67.86 383	99.24 355	91.28 349	96.08 367	98.02 332

miper_refine_blended		92.87 338	91.99 341	95.51 339	91.37 385	89.27 359	94.07 361	98.14 311	95.42 285	97.25 294	96.44 348	67.86 383	99.24 355	91.28 349	96.08 367	98.02 332

TESTMET0.1,1		92.19 345	91.77 345	93.46 358	96.48 374	82.80 382	94.05 363	91.52 376	94.45 306	94.00 369	94.88 372	66.65 386	99.56 306	95.78 248	98.11 332	98.02 332

PCF-MVS	92.86 18	94.36 318	93.00 335	98.42 215	98.70 266	97.56 192	93.16 371	99.11 211	79.59 379	97.55 277	97.43 324	92.19 291	99.73 235	79.85 380	99.45 223	97.97 335
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OpenMVS	96.65 7	97.09 247	96.68 257	98.32 223	98.32 313	97.16 217	98.86 79	99.37 112	89.48 361	96.29 334	99.15 106	96.56 179	99.90 54	92.90 323	99.20 261	97.89 336

Gipuma		99.03 45	99.16 31	98.64 183	99.94 2	98.51 108	99.32 22	99.75 13	99.58 22	98.60 194	99.62 25	98.22 60	99.51 322	97.70 121	99.73 122	97.89 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_0	89.98 21	91.15 347	90.30 350	93.70 356	97.72 344	84.34 380	90.24 376	97.42 328	90.20 358	93.79 370	93.09 379	90.90 299	98.89 371	86.57 369	72.76 383	97.87 338

test-LLR		93.90 328	93.85 322	94.04 351	96.53 372	84.62 377	94.05 363	92.39 373	96.17 261	94.12 366	95.07 366	82.30 355	99.67 263	95.87 243	98.18 327	97.82 339

test-mter		92.33 343	91.76 346	94.04 351	96.53 372	84.62 377	94.05 363	92.39 373	94.00 317	94.12 366	95.07 366	65.63 389	99.67 263	95.87 243	98.18 327	97.82 339

tpm2		93.09 337	92.58 338	94.62 347	97.56 351	86.53 370	97.66 198	95.79 357	86.15 372	94.07 368	98.23 272	75.95 375	99.53 314	90.91 355	96.86 359	97.81 341

CR-MVSNet		96.28 285	95.95 282	97.28 292	97.71 345	94.22 296	98.11 148	98.92 247	92.31 338	96.91 308	99.37 64	85.44 336	99.81 172	97.39 133	97.36 350	97.81 341

RPMNet		97.02 253	96.93 238	97.30 291	97.71 345	94.22 296	98.11 148	99.30 151	99.37 39	96.91 308	99.34 72	86.72 323	99.87 92	97.53 127	97.36 350	97.81 341

tpmrst		95.07 310	95.46 295	93.91 353	97.11 364	84.36 379	97.62 202	96.96 340	94.98 293	96.35 333	98.80 192	85.46 335	99.59 297	95.60 257	96.23 365	97.79 344

PAPM		91.88 346	90.34 349	96.51 317	98.06 329	92.56 333	92.44 374	97.17 335	86.35 371	90.38 378	96.01 353	86.61 324	99.21 358	70.65 383	95.43 371	97.75 345

FPMVS		93.44 334	92.23 339	97.08 299	99.25 145	97.86 169	95.61 322	97.16 336	92.90 331	93.76 371	98.65 218	75.94 376	95.66 381	79.30 381	97.49 343	97.73 346

MAR-MVS		96.47 279	95.70 287	98.79 167	97.92 335	99.12 60	98.28 132	98.60 290	92.16 340	95.54 352	96.17 352	94.77 250	99.52 318	89.62 361	98.23 324	97.72 347
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
ETV-MVS		98.03 171	97.86 181	98.56 199	98.69 271	98.07 146	97.51 216	99.50 67	98.10 143	97.50 282	95.51 362	98.41 46	99.88 75	96.27 223	99.24 256	97.71 348

thres600view7		94.45 317	93.83 323	96.29 321	99.06 195	91.53 345	97.99 166	94.24 365	98.34 120	97.44 287	95.01 368	79.84 362	99.67 263	84.33 372	98.23 324	97.66 349

thres400		94.14 324	93.44 328	96.24 323	98.93 219	91.44 347	97.60 205	94.29 363	97.94 151	97.10 297	94.31 376	79.67 364	99.62 286	83.05 374	98.08 334	97.66 349

IB-MVS	91.63 19	92.24 344	90.90 348	96.27 322	97.22 363	91.24 353	94.36 358	93.33 370	92.37 337	92.24 375	94.58 375	66.20 388	99.89 64	93.16 321	94.63 375	97.66 349
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpmvs		95.02 312	95.25 303	94.33 349	96.39 376	85.87 371	98.08 152	96.83 345	95.46 284	95.51 354	98.69 209	85.91 331	99.53 314	94.16 291	96.23 365	97.58 352

cascas		94.79 314	94.33 320	96.15 328	96.02 380	92.36 338	92.34 375	99.26 169	85.34 374	95.08 359	94.96 371	92.96 283	98.53 375	94.41 288	98.59 317	97.56 353

mvs-test1		97.83 195	97.48 208	98.89 153	98.02 330	99.20 35	97.20 240	99.16 199	98.29 126	96.46 331	97.17 333	96.44 186	99.92 39	96.66 192	97.90 339	97.54 354

PatchT		96.65 270	96.35 272	97.54 279	97.40 358	95.32 267	97.98 167	96.64 347	99.33 44	96.89 312	99.42 58	84.32 344	99.81 172	97.69 123	97.49 343	97.48 355

TR-MVS		95.55 302	95.12 307	96.86 312	97.54 352	93.94 307	96.49 283	96.53 348	94.36 309	97.03 303	96.61 343	94.26 261	99.16 361	86.91 368	96.31 364	97.47 356

JIA-IIPM		95.52 303	95.03 308	97.00 301	96.85 368	94.03 303	96.93 257	95.82 356	99.20 55	94.63 362	99.71 15	83.09 351	99.60 293	94.42 285	94.64 374	97.36 357

BH-w/o		95.13 309	94.89 313	95.86 329	98.20 321	91.31 350	95.65 321	97.37 329	93.64 320	96.52 326	95.70 359	93.04 282	99.02 365	88.10 365	95.82 369	97.24 358

tpm cat1		93.29 335	93.13 334	93.75 355	97.39 359	84.74 376	97.39 224	97.65 325	83.39 377	94.16 365	98.41 253	82.86 353	99.39 339	91.56 346	95.35 372	97.14 359

xiu_mvs_v1_base_debu		97.86 187	98.17 151	96.92 306	98.98 211	93.91 309	96.45 284	99.17 196	97.85 159	98.41 218	97.14 336	98.47 42	99.92 39	98.02 98	99.05 282	96.92 360

xiu_mvs_v1_base		97.86 187	98.17 151	96.92 306	98.98 211	93.91 309	96.45 284	99.17 196	97.85 159	98.41 218	97.14 336	98.47 42	99.92 39	98.02 98	99.05 282	96.92 360

xiu_mvs_v1_base_debi		97.86 187	98.17 151	96.92 306	98.98 211	93.91 309	96.45 284	99.17 196	97.85 159	98.41 218	97.14 336	98.47 42	99.92 39	98.02 98	99.05 282	96.92 360

PMVS	91.26 20	97.86 187	97.94 175	97.65 268	99.71 39	97.94 164	98.52 106	98.68 285	98.99 82	97.52 280	99.35 68	97.41 127	98.18 377	91.59 345	99.67 156	96.82 363
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1314		95.74 297	95.60 291	96.17 325	97.53 353	92.75 332	98.07 153	98.31 303	91.22 350	94.25 364	96.68 342	95.53 223	99.03 364	91.64 344	97.18 353	96.74 364

MVS-HIRNet		94.32 319	95.62 290	90.42 364	98.46 302	75.36 387	96.29 293	89.13 381	95.25 289	95.38 355	99.75 10	92.88 284	99.19 359	94.07 298	99.39 231	96.72 365

OpenMVS_ROB	95.38 14	95.84 295	95.18 306	97.81 257	98.41 309	97.15 218	97.37 226	98.62 289	83.86 375	98.65 186	98.37 259	94.29 260	99.68 260	88.41 364	98.62 316	96.60 366

thres100view900		94.19 322	93.67 326	95.75 333	99.06 195	91.35 349	98.03 160	94.24 365	98.33 121	97.40 289	94.98 370	79.84 362	99.62 286	83.05 374	98.08 334	96.29 367

tfpn200view9		94.03 326	93.44 328	95.78 332	98.93 219	91.44 347	97.60 205	94.29 363	97.94 151	97.10 297	94.31 376	79.67 364	99.62 286	83.05 374	98.08 334	96.29 367

MVS		93.19 336	92.09 340	96.50 318	96.91 366	94.03 303	98.07 153	98.06 315	68.01 380	94.56 363	96.48 346	95.96 209	99.30 350	83.84 373	96.89 358	96.17 369

gg-mvs-nofinetune		92.37 342	91.20 347	95.85 330	95.80 381	92.38 337	99.31 26	81.84 387	99.75 5	91.83 376	99.74 11	68.29 382	99.02 365	87.15 367	97.12 354	96.16 370

xiu_mvs_v2_base		97.16 243	97.49 205	96.17 325	98.54 293	92.46 335	95.45 329	98.84 264	97.25 213	97.48 284	96.49 345	98.31 54	99.90 54	96.34 219	98.68 312	96.15 371

PS-MVSNAJ		97.08 248	97.39 212	96.16 327	98.56 291	92.46 335	95.24 334	98.85 263	97.25 213	97.49 283	95.99 354	98.07 72	99.90 54	96.37 216	98.67 313	96.12 372

E-PMN		94.17 323	94.37 318	93.58 357	96.86 367	85.71 374	90.11 377	97.07 337	98.17 138	97.82 259	97.19 332	84.62 341	98.94 368	89.77 360	97.68 342	96.09 373

EMVS		93.83 329	94.02 321	93.23 361	96.83 369	84.96 375	89.77 378	96.32 350	97.92 153	97.43 288	96.36 351	86.17 328	98.93 369	87.68 366	97.73 341	95.81 374

MVE	83.40 22	92.50 340	91.92 343	94.25 350	98.83 243	91.64 344	92.71 372	83.52 386	95.92 272	86.46 383	95.46 364	95.20 234	95.40 382	80.51 379	98.64 314	95.73 375
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres200		93.72 331	93.14 333	95.46 341	98.66 281	91.29 351	96.61 277	94.63 361	97.39 199	96.83 315	93.71 378	79.88 361	99.56 306	82.40 377	98.13 331	95.54 376

API-MVS		97.04 252	96.91 242	97.42 287	97.88 338	98.23 129	98.18 141	98.50 295	97.57 178	97.39 290	96.75 341	96.77 168	99.15 362	90.16 359	99.02 289	94.88 377

GG-mvs-BLEND				94.76 346	94.54 383	92.13 341	99.31 26	80.47 388		88.73 381	91.01 381	67.59 385	98.16 378	82.30 378	94.53 376	93.98 378

DeepMVS_CX				93.44 359	98.24 318	94.21 298		94.34 362	64.28 381	91.34 377	94.87 374	89.45 309	92.77 384	77.54 382	93.14 378	93.35 379

tmp_tt		78.77 350	78.73 353	78.90 366	58.45 389	74.76 389	94.20 360	78.26 389	39.16 382	86.71 382	92.82 380	80.50 360	75.19 385	86.16 370	92.29 379	86.74 380

wuyk23d		96.06 289	97.62 198	91.38 363	98.65 282	98.57 102	98.85 80	96.95 341	96.86 237	99.90 5	99.16 102	99.18 11	98.40 376	89.23 362	99.77 105	77.18 381

test123		17.04 353	20.11 356	7.82 367	10.25 391	4.91 391	94.80 344	4.47 392	4.93 385	10.00 387	24.28 384	9.69 390	3.64 386	10.14 384	12.43 385	14.92 382

testmvs		17.12 352	20.53 355	6.87 368	12.05 390	4.20 392	93.62 369	6.73 391	4.62 386	10.41 386	24.33 383	8.28 391	3.56 387	9.69 385	15.07 384	12.86 383

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		24.66 351	32.88 354	0.00 369	0.00 392	0.00 393	0.00 380	99.10 213	0.00 387	0.00 388	97.58 314	99.21 10	0.00 388	0.00 386	0.00 386	0.00 384

pcd_1.5k_mvsjas		8.17 354	10.90 357	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	98.07 72	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		8.12 355	10.83 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	97.48 321	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					99.73 31	99.67 2	99.43 11	99.54 57	99.43 34	99.26 89

test_one_0601					99.39 119	99.20 35		99.31 141	98.49 114	98.66 185	99.02 129	97.64 104

eth-test2					0.00 392

eth-test					0.00 392

ZD-MVS					99.01 206	98.84 79		99.07 218	94.10 314	98.05 245	98.12 280	96.36 192	99.86 101	92.70 331	99.19 265

test_241102_ONE					99.49 94	99.17 41		99.31 141	97.98 148	99.66 23	98.90 164	98.36 49	99.48 327

9.14			97.78 184		99.07 191		97.53 213	99.32 135	95.53 282	98.54 206	98.70 208	97.58 109	99.76 220	94.32 290	99.46 221

save fliter					99.11 180	97.97 157	96.53 279	99.02 232	98.24 129

test0726					99.50 87	99.21 29	98.17 144	99.35 122	97.97 149	99.26 89	99.06 116	97.61 107

test_part2					99.36 127	99.10 63				99.05 121

sam_mvs												84.29 346

MTGPA								99.20 181

test_post1							97.59 207				20.48 386	83.07 352	99.66 273	94.16 291

test_post											21.25 385	83.86 348	99.70 247

patchmatchnet-post											98.77 197	84.37 343	99.85 116

MTMP							97.93 170	91.91 375

gm-plane-assit					94.83 382	81.97 384			88.07 368		94.99 369		99.60 293	91.76 341

TEST9					98.71 262	98.08 144	95.96 306	99.03 228	91.40 348	95.85 342	97.53 316	96.52 181	99.76 220

test_8					98.67 276	98.01 151	95.91 311	99.02 232	91.64 343	95.79 344	97.50 319	96.47 184	99.76 220

agg_prior					98.68 274	97.99 152		99.01 235		95.59 345			99.77 212

test_prior4						97.97 157	95.86 312

test_prior2							95.74 318		96.48 251	96.11 336	97.63 312	95.92 211		94.16 291	99.20 261

旧先验2							95.76 316		88.56 367	97.52 280			99.66 273	94.48 281

新几何2							95.93 309

原ACMM2							95.53 325

testdata2													99.79 194	92.80 328

segment_acmp												97.02 152

testdata1							95.44 330		96.32 257

plane_prior7					99.19 160	97.87 168

plane_prior6					98.99 210	97.70 186						94.90 240

plane_prior4											97.98 290

plane_prior3						97.78 179			97.41 197	97.79 260

plane_prior2							97.77 187		98.20 135

plane_prior1					99.05 198

plane_prior						97.65 188	97.07 249		96.72 242						99.36 236

n2								0.00 393

nn								0.00 393

door-mid								99.57 41

test11								98.87 255

door								99.41 101

HQP5-MVS						96.79 229

HQP-NCC					98.67 276		96.29 293		96.05 266	95.55 349

ACMP_Plane					98.67 276		96.29 293		96.05 266	95.55 349

BP-MVS														92.82 326

HQP3-MVS								99.04 226							99.26 254

HQP2-MVS												93.84 267

NP-MVS					98.84 241	97.39 201					96.84 339

MDTV_nov1_ep13			95.22 304		97.06 365	83.20 381	97.74 191	96.16 351	94.37 308	96.99 304	98.83 186	83.95 347	99.53 314	93.90 302	97.95 338

ACMMP++_ref															99.77 105

ACMMP++															99.68 150

Test By Simon												96.52 181

High-resolution multi-view benchmark