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		95.75 334	95.42 332	96.76 342	89.90 379	94.42 352	98.86 221	97.87 350	78.01 370	99.30 232	99.69 118	97.70 217	95.89 374	99.29 66	98.14 352	99.95 1

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 44	99.68 37	99.85 26	99.95 3	99.98 3	99.92 17	99.28 41	99.98 7	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 38	99.72 23	99.88 18	99.92 6	99.98 3	99.93 14	99.94 1	99.98 7	99.77 12	100.00 1	99.92 3

UA-Net		99.78 13	99.76 14	99.86 16	99.72 110	99.71 71	99.91 3	99.95 5	99.96 2	99.71 103	99.91 20	99.15 55	99.97 17	99.50 35	100.00 1	99.90 4

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 41	99.70 28	99.86 22	99.89 11	99.98 3	99.90 22	99.94 1	99.98 7	99.75 13	100.00 1	99.90 4

EU-MVSNet		99.39 82	99.62 27	98.72 291	99.88 25	96.44 332	99.56 70	99.85 26	99.90 7	99.90 22	99.85 41	98.09 189	99.83 245	99.58 24	99.95 52	99.90 4

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 19	99.77 11	99.80 49	99.73 43	99.97 6	99.92 17	99.77 7	99.98 7	99.43 41	100.00 1	99.90 4

RRT_test8_iter05		97.35 303	97.25 300	97.63 327	98.81 344	93.13 359	99.26 128	99.89 15	99.51 92	99.83 50	99.68 129	79.03 377	99.88 165	99.53 30	99.72 202	99.89 8

CVMVSNet		98.61 229	98.88 188	97.80 322	99.58 161	93.60 357	99.26 128	99.64 135	99.66 65	99.72 98	99.67 135	93.26 308	99.93 71	99.30 63	99.81 155	99.87 9

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 9	99.78 6	100.00 1	99.92 1	100.00 1	99.87 9

FC-MVSNet-test		99.70 19	99.65 24	99.86 16	99.88 25	99.86 13	99.72 23	99.78 60	99.90 7	99.82 52	99.83 47	98.45 154	99.87 178	99.51 33	99.97 33	99.86 11

PS-CasMVS		99.66 26	99.58 37	99.89 7	99.80 58	99.85 14	99.66 45	99.73 83	99.62 73	99.84 45	99.71 105	98.62 127	99.96 35	99.30 63	99.96 45	99.86 11

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 9	99.73 20	99.85 26	99.70 52	99.92 18	99.93 14	99.45 23	99.97 17	99.36 53	100.00 1	99.85 13

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 95	99.93 4	99.95 10	99.89 26	99.71 9	99.96 35	99.51 33	99.97 33	99.84 14

CP-MVSNet		99.54 47	99.43 62	99.87 14	99.76 86	99.82 28	99.57 68	99.61 147	99.54 87	99.80 62	99.64 146	97.79 214	99.95 45	99.21 73	99.94 65	99.84 14

Test_1112_low_res		98.95 192	98.73 202	99.63 116	99.68 133	99.15 209	98.09 300	99.80 49	97.14 316	99.46 189	99.40 249	96.11 278	99.89 150	99.01 103	99.84 128	99.84 14

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 50	100.00 1	99.90 7	100.00 1	99.97 10	99.61 17	99.97 17	99.75 13	100.00 1	99.84 14

nrg030		99.70 19	99.66 22	99.82 23	99.76 86	99.84 19	99.61 58	99.70 100	99.93 4	99.78 70	99.68 129	99.10 61	99.78 282	99.45 39	99.96 45	99.83 18

FIs		99.65 31	99.58 37	99.84 19	99.84 35	99.85 14	99.66 45	99.75 75	99.86 19	99.74 92	99.79 65	98.27 174	99.85 217	99.37 52	99.93 73	99.83 18

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 19	99.82 8	99.82 39	99.84 27	99.94 11	99.91 20	99.13 60	99.96 35	99.83 9	99.99 12	99.83 18

PEN-MVS		99.66 26	99.59 34	99.89 7	99.83 39	99.87 10	99.66 45	99.73 83	99.70 52	99.84 45	99.73 92	98.56 136	99.96 35	99.29 66	99.94 65	99.83 18

WR-MVS_H		99.61 37	99.53 49	99.87 14	99.80 58	99.83 24	99.67 41	99.75 75	99.58 86	99.85 42	99.69 118	98.18 185	99.94 57	99.28 68	99.95 52	99.83 18

test_part1		98.63 227	98.26 250	99.75 57	99.40 243	99.49 129	99.67 41	99.68 109	99.86 19	99.88 32	99.86 38	86.73 360	99.93 71	99.34 55	99.97 33	99.81 23

Anonymous20231211		99.62 35	99.57 40	99.76 47	99.61 150	99.60 110	99.81 9	99.73 83	99.82 32	99.90 22	99.90 22	97.97 200	99.86 198	99.42 46	99.96 45	99.80 24

APDe-MVS		99.48 54	99.36 77	99.85 18	99.55 182	99.81 31	99.50 75	99.69 106	98.99 171	99.75 83	99.71 105	98.79 104	99.93 71	98.46 146	99.85 123	99.80 24

DTE-MVSNet		99.68 24	99.61 31	99.88 11	99.80 58	99.87 10	99.67 41	99.71 95	99.72 46	99.84 45	99.78 71	98.67 121	99.97 17	99.30 63	99.95 52	99.80 24

XXY-MVS		99.71 18	99.67 21	99.81 26	99.89 21	99.72 68	99.59 65	99.82 39	99.39 115	99.82 52	99.84 46	99.38 29	99.91 113	99.38 50	99.93 73	99.80 24

1112_ss		99.05 170	98.84 193	99.67 92	99.66 139	99.29 179	98.52 264	99.82 39	97.65 289	99.43 195	99.16 301	96.42 268	99.91 113	99.07 99	99.84 128	99.80 24

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 15	99.90 5	99.96 1	99.92 7	99.90 7	99.97 6	99.87 32	99.81 5	99.95 45	99.54 28	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
bset_n11_16_dypcd		98.69 223	98.45 230	99.42 186	99.69 124	98.52 262	96.06 365	96.80 360	99.71 47	99.73 96	99.54 211	95.14 288	99.96 35	99.39 49	99.95 52	99.79 30

PMMVS2		99.48 54	99.45 57	99.57 141	99.76 86	98.99 224	98.09 300	99.90 14	98.95 177	99.78 70	99.58 191	99.57 20	99.93 71	99.48 36	99.95 52	99.79 30

MSC_two_6792asdad				99.74 63	99.03 321	99.53 124		99.23 294					99.92 91	97.77 204	99.69 211	99.78 32

No_MVS				99.74 63	99.03 321	99.53 124		99.23 294					99.92 91	97.77 204	99.69 211	99.78 32

CHOSEN 1792x2688		99.39 82	99.30 90	99.65 104	99.88 25	99.25 189	98.78 238	99.88 18	98.66 210	99.96 8	99.79 65	97.45 233	99.93 71	99.34 55	99.99 12	99.78 32

IU-MVS					99.69 124	99.77 44		99.22 297	97.50 298	99.69 109				97.75 208	99.70 208	99.77 35

test_0728_THIRD									99.18 145	99.62 138	99.61 174	98.58 133	99.91 113	97.72 210	99.80 160	99.77 35

test_0728_SECOND				99.83 21	99.70 121	99.79 38	99.14 167	99.61 147					99.92 91	97.88 193	99.72 202	99.77 35

MSP-MVS		99.04 173	98.79 200	99.81 26	99.78 74	99.73 64	99.35 102	99.57 177	98.54 224	99.54 168	98.99 324	96.81 259	99.93 71	96.97 265	99.53 265	99.77 35
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		99.14 152	98.92 182	99.82 23	99.57 171	99.77 44	98.74 242	99.60 159	98.55 221	99.76 77	99.69 118	98.23 179	99.92 91	96.39 298	99.75 181	99.76 39
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 52	99.37 74	99.82 23	99.91 15	99.84 19	98.83 226	99.86 22	99.68 57	99.65 124	99.88 29	97.67 222	99.87 178	99.03 101	99.86 119	99.76 39

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 24	99.83 6	99.85 26	99.80 36	99.93 14	99.93 14	98.54 139	99.93 71	99.59 21	99.98 24	99.76 39

test_241102_TWO								99.54 194	99.13 157	99.76 77	99.63 156	98.32 171	99.92 91	97.85 199	99.69 211	99.75 42

DP-MVS		99.48 54	99.39 69	99.74 63	99.57 171	99.62 102	99.29 122	99.61 147	99.87 17	99.74 92	99.76 81	98.69 117	99.87 178	98.20 166	99.80 160	99.75 42

v10		99.69 21	99.69 18	99.66 99	99.81 53	99.39 157	99.66 45	99.75 75	99.60 83	99.92 18	99.87 32	98.75 112	99.86 198	99.90 2	99.99 12	99.73 44

EI-MVSNet-UG-set		99.48 54	99.50 51	99.42 186	99.57 171	98.65 256	99.24 136	99.46 234	99.68 57	99.80 62	99.66 139	98.99 77	99.89 150	99.19 78	99.90 87	99.72 45

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 25	99.66 89	99.69 34	99.92 7	99.67 61	99.77 75	99.75 85	99.61 17	99.98 7	99.35 54	99.98 24	99.72 45
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HyFIR lowres test		98.91 195	98.64 210	99.73 73	99.85 34	99.47 132	98.07 303	99.83 34	98.64 212	99.89 26	99.60 183	92.57 314	100.00 1	99.33 58	99.97 33	99.72 45

EI-MVSNet-Vis-set		99.47 60	99.49 52	99.42 186	99.57 171	98.66 253	99.24 136	99.46 234	99.67 61	99.79 67	99.65 144	98.97 80	99.89 150	99.15 87	99.89 95	99.71 48

v8		99.68 24	99.69 18	99.65 104	99.80 58	99.40 155	99.66 45	99.76 68	99.64 69	99.93 14	99.85 41	98.66 123	99.84 234	99.88 6	99.99 12	99.71 48

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 14	99.75 15	99.86 22	99.70 52	99.91 20	99.89 26	99.60 19	99.87 178	99.59 21	99.74 189	99.71 48

test1111		97.74 287	98.16 261	96.49 348	99.60 152	89.86 377	99.71 27	91.21 374	99.89 11	99.88 32	99.87 32	93.73 304	99.90 133	99.56 26	99.99 12	99.70 51

VPA-MVSNet		99.66 26	99.62 27	99.79 34	99.68 133	99.75 55	99.62 53	99.69 106	99.85 24	99.80 62	99.81 57	98.81 97	99.91 113	99.47 37	99.88 103	99.70 51

WR-MVS		99.11 160	98.93 178	99.66 99	99.30 276	99.42 150	98.42 274	99.37 263	99.04 169	99.57 154	99.20 298	96.89 257	99.86 198	98.66 138	99.87 112	99.70 51

ACMH	98.42 6	99.59 38	99.54 45	99.72 79	99.86 31	99.62 102	99.56 70	99.79 55	98.77 202	99.80 62	99.85 41	99.64 13	99.85 217	98.70 134	99.89 95	99.70 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 5	99.83 6	99.91 10	99.85 24	99.94 11	99.95 12	99.73 8	99.90 133	99.65 16	99.97 33	99.69 55

HPM-MVS_fast		99.43 66	99.30 90	99.80 29	99.83 39	99.81 31	99.52 73	99.70 100	98.35 246	99.51 180	99.50 223	99.31 37	99.88 165	98.18 170	99.84 128	99.69 55

LPG-MVS_test		99.22 129	99.05 149	99.74 63	99.82 46	99.63 100	99.16 163	99.73 83	97.56 292	99.64 126	99.69 118	99.37 31	99.89 150	96.66 284	99.87 112	99.69 55

LGP-MVS_train				99.74 63	99.82 46	99.63 100		99.73 83	97.56 292	99.64 126	99.69 118	99.37 31	99.89 150	96.66 284	99.87 112	99.69 55

SteuartSystems-ACMMP		99.30 106	99.14 118	99.76 47	99.87 29	99.66 89	99.18 152	99.60 159	98.55 221	99.57 154	99.67 135	99.03 74	99.94 57	97.01 263	99.80 160	99.69 55
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		98.52 243	98.39 237	98.94 265	99.15 302	97.39 314	98.18 289	99.21 301	98.89 188	99.23 241	99.63 156	97.37 239	99.74 298	94.22 348	99.61 243	99.69 55

ACMMP_NAP		99.28 109	99.11 128	99.79 34	99.75 97	99.81 31	98.95 212	99.53 203	98.27 255	99.53 173	99.73 92	98.75 112	99.87 178	97.70 215	99.83 138	99.68 61

HFP-MVS		99.25 116	99.08 139	99.76 47	99.73 106	99.70 78	99.31 112	99.59 166	98.36 241	99.36 214	99.37 256	98.80 101	99.91 113	97.43 236	99.75 181	99.68 61

#test#		99.12 156	98.90 186	99.76 47	99.73 106	99.70 78	99.10 180	99.59 166	97.60 291	99.36 214	99.37 256	98.80 101	99.91 113	96.84 274	99.75 181	99.68 61

EI-MVSNet		99.38 84	99.44 59	99.21 238	99.58 161	98.09 289	99.26 128	99.46 234	99.62 73	99.75 83	99.67 135	98.54 139	99.85 217	99.15 87	99.92 77	99.68 61

TranMVSNet+NR-MVSNet		99.54 47	99.47 53	99.76 47	99.58 161	99.64 96	99.30 115	99.63 137	99.61 77	99.71 103	99.56 202	98.76 110	99.96 35	99.14 93	99.92 77	99.68 61

PVSNet_Blended_VisFu		99.40 77	99.38 71	99.44 180	99.90 19	98.66 253	98.94 214	99.91 10	97.97 272	99.79 67	99.73 92	99.05 72	99.97 17	99.15 87	99.99 12	99.68 61

IterMVS-LS		99.41 74	99.47 53	99.25 233	99.81 53	98.09 289	98.85 223	99.76 68	99.62 73	99.83 50	99.64 146	98.54 139	99.97 17	99.15 87	99.99 12	99.68 61
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MP-MVS-pluss		99.14 152	98.92 182	99.80 29	99.83 39	99.83 24	98.61 249	99.63 137	96.84 324	99.44 191	99.58 191	98.81 97	99.91 113	97.70 215	99.82 147	99.67 68
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		99.23 120	99.05 149	99.77 40	99.76 86	99.70 78	99.31 112	99.59 166	98.41 235	99.32 223	99.36 261	98.73 115	99.93 71	97.29 243	99.74 189	99.67 68

Regformer-4		99.45 63	99.44 59	99.50 162	99.52 193	98.94 231	99.17 157	99.53 203	99.64 69	99.76 77	99.60 183	98.96 83	99.90 133	98.91 117	99.84 128	99.67 68

XVS		99.27 113	99.11 128	99.75 57	99.71 113	99.71 71	99.37 97	99.61 147	99.29 126	98.76 301	99.47 236	98.47 150	99.88 165	97.62 223	99.73 196	99.67 68

v1240		99.56 42	99.58 37	99.51 159	99.80 58	99.00 223	99.00 199	99.65 129	99.15 155	99.90 22	99.75 85	99.09 63	99.88 165	99.90 2	99.96 45	99.67 68

X-MVStestdata		96.09 327	94.87 337	99.75 57	99.71 113	99.71 71	99.37 97	99.61 147	99.29 126	98.76 301	61.30 380	98.47 150	99.88 165	97.62 223	99.73 196	99.67 68

VPNet		99.46 61	99.37 74	99.71 83	99.82 46	99.59 113	99.48 79	99.70 100	99.81 33	99.69 109	99.58 191	97.66 226	99.86 198	99.17 83	99.44 278	99.67 68

ACMMPR		99.23 120	99.06 145	99.76 47	99.74 103	99.69 82	99.31 112	99.59 166	98.36 241	99.35 216	99.38 255	98.61 129	99.93 71	97.43 236	99.75 181	99.67 68

SixPastTwentyTwo		99.42 70	99.30 90	99.76 47	99.92 14	99.67 87	99.70 28	99.14 307	99.65 67	99.89 26	99.90 22	96.20 276	99.94 57	99.42 46	99.92 77	99.67 68

HPM-MVS		99.25 116	99.07 143	99.78 37	99.81 53	99.75 55	99.61 58	99.67 114	97.72 286	99.35 216	99.25 287	99.23 47	99.92 91	97.21 254	99.82 147	99.67 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v144192		99.55 45	99.54 45	99.58 136	99.78 74	99.20 204	99.11 179	99.62 140	99.18 145	99.89 26	99.72 98	98.66 123	99.87 178	99.88 6	99.97 33	99.66 78

v1921920		99.56 42	99.57 40	99.55 148	99.75 97	99.11 212	99.05 190	99.61 147	99.15 155	99.88 32	99.71 105	99.08 67	99.87 178	99.90 2	99.97 33	99.66 78

v1192		99.57 39	99.57 40	99.57 141	99.77 82	99.22 198	99.04 192	99.60 159	99.18 145	99.87 39	99.72 98	99.08 67	99.85 217	99.89 5	99.98 24	99.66 78

PGM-MVS		99.20 136	99.01 161	99.77 40	99.75 97	99.71 71	99.16 163	99.72 92	97.99 270	99.42 197	99.60 183	98.81 97	99.93 71	96.91 268	99.74 189	99.66 78

mPP-MVS		99.19 139	99.00 164	99.76 47	99.76 86	99.68 85	99.38 93	99.54 194	98.34 250	99.01 272	99.50 223	98.53 143	99.93 71	97.18 256	99.78 171	99.66 78

CP-MVS		99.23 120	99.05 149	99.75 57	99.66 139	99.66 89	99.38 93	99.62 140	98.38 239	99.06 270	99.27 282	98.79 104	99.94 57	97.51 232	99.82 147	99.66 78

EG-PatchMatch MVS		99.57 39	99.56 44	99.62 125	99.77 82	99.33 173	99.26 128	99.76 68	99.32 124	99.80 62	99.78 71	99.29 39	99.87 178	99.15 87	99.91 86	99.66 78

UGNet		99.38 84	99.34 79	99.49 165	98.90 331	98.90 239	99.70 28	99.35 267	99.86 19	98.57 315	99.81 57	98.50 149	99.93 71	99.38 50	99.98 24	99.66 78
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		94.73 338	94.59 340	95.15 354	99.59 156	85.90 379	99.75 15	74.01 380	99.89 11	99.71 103	99.86 38	79.00 378	99.90 133	99.52 32	99.99 12	99.65 86

ECVR-MVS		97.73 288	98.04 267	96.78 341	99.59 156	90.81 373	99.72 23	90.43 376	99.89 11	99.86 40	99.86 38	93.60 306	99.89 150	99.46 38	99.99 12	99.65 86

h-mvs33		98.61 229	98.34 243	99.44 180	99.60 152	98.67 251	99.27 126	99.44 239	99.68 57	99.32 223	99.49 228	92.50 317	100.00 1	99.24 70	96.51 367	99.65 86

TSAR-MVS + MP.		99.34 97	99.24 106	99.63 116	99.82 46	99.37 163	99.26 128	99.35 267	98.77 202	99.57 154	99.70 112	99.27 44	99.88 165	97.71 212	99.75 181	99.65 86
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		99.30 106	99.14 118	99.80 29	99.81 53	99.81 31	98.73 244	99.53 203	99.27 130	99.42 197	99.63 156	98.21 180	99.95 45	97.83 202	99.79 165	99.65 86

MTAPA		99.35 92	99.20 110	99.80 29	99.81 53	99.81 31	99.33 105	99.53 203	99.27 130	99.42 197	99.63 156	98.21 180	99.95 45	97.83 202	99.79 165	99.65 86

Regformer-3		99.41 74	99.41 66	99.40 196	99.52 193	98.70 249	99.17 157	99.44 239	99.62 73	99.75 83	99.60 183	98.90 90	99.85 217	98.89 118	99.84 128	99.65 86

MCST-MVS		99.02 176	98.81 197	99.65 104	99.58 161	99.49 129	98.58 253	99.07 310	98.40 237	99.04 271	99.25 287	98.51 148	99.80 276	97.31 242	99.51 268	99.65 86

UniMVSNet_NR-MVSNet		99.37 87	99.25 104	99.72 79	99.47 221	99.56 119	98.97 210	99.61 147	99.43 112	99.67 116	99.28 280	97.85 210	99.95 45	99.17 83	99.81 155	99.65 86

ZNCC-MVS		99.22 129	99.04 155	99.77 40	99.76 86	99.73 64	99.28 123	99.56 182	98.19 260	99.14 258	99.29 278	98.84 96	99.92 91	97.53 231	99.80 160	99.64 95

v1144		99.54 47	99.53 49	99.59 132	99.79 68	99.28 181	99.10 180	99.61 147	99.20 143	99.84 45	99.73 92	98.67 121	99.84 234	99.86 8	99.98 24	99.64 95

v2v482		99.50 50	99.47 53	99.58 136	99.78 74	99.25 189	99.14 167	99.58 175	99.25 134	99.81 59	99.62 165	98.24 176	99.84 234	99.83 9	99.97 33	99.64 95

K. test v3		98.87 203	98.60 213	99.69 88	99.93 13	99.46 136	99.74 17	94.97 367	99.78 39	99.88 32	99.88 29	93.66 305	99.97 17	99.61 19	99.95 52	99.64 95

DeepC-MVS	98.90 4	99.62 35	99.61 31	99.67 92	99.72 110	99.44 143	99.24 136	99.71 95	99.27 130	99.93 14	99.90 22	99.70 11	99.93 71	98.99 104	99.99 12	99.64 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		98.56 237	98.17 260	99.72 79	99.45 229	99.60 110	98.88 217	99.50 219	96.88 321	99.18 253	99.48 231	97.08 252	99.92 91	93.69 355	99.38 287	99.63 100

SMA-MVS		99.19 139	99.00 164	99.73 73	99.46 226	99.73 64	99.13 173	99.52 211	97.40 303	99.57 154	99.64 146	98.93 84	99.83 245	97.61 225	99.79 165	99.63 100
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
IterMVS-SCA-FT		99.00 182	99.16 114	98.51 298	99.75 97	95.90 340	98.07 303	99.84 32	99.84 27	99.89 26	99.73 92	96.01 280	99.99 5	99.33 58	100.00 1	99.63 100

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 24	99.76 13	99.87 20	99.73 43	99.89 26	99.87 32	99.63 14	99.87 178	99.54 28	99.92 77	99.63 100

MP-MVS		99.06 167	98.83 195	99.76 47	99.76 86	99.71 71	99.32 108	99.50 219	98.35 246	98.97 274	99.48 231	98.37 164	99.92 91	95.95 318	99.75 181	99.63 100
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DU-MVS		99.33 101	99.21 109	99.71 83	99.43 234	99.56 119	98.83 226	99.53 203	99.38 116	99.67 116	99.36 261	97.67 222	99.95 45	99.17 83	99.81 155	99.63 100

NR-MVSNet		99.40 77	99.31 85	99.68 89	99.43 234	99.55 122	99.73 20	99.50 219	99.46 104	99.88 32	99.36 261	97.54 230	99.87 178	98.97 108	99.87 112	99.63 100

IterMVS		98.97 186	99.16 114	98.42 302	99.74 103	95.64 343	98.06 305	99.83 34	99.83 30	99.85 42	99.74 88	96.10 279	99.99 5	99.27 69	100.00 1	99.63 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPP-MVSNet		99.17 147	99.00 164	99.66 99	99.80 58	99.43 147	99.70 28	99.24 293	99.48 95	99.56 161	99.77 78	94.89 290	99.93 71	98.72 133	99.89 95	99.63 100

ACMMP		99.25 116	99.08 139	99.74 63	99.79 68	99.68 85	99.50 75	99.65 129	98.07 266	99.52 175	99.69 118	98.57 134	99.92 91	97.18 256	99.79 165	99.63 100
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	99.23 120	99.12 125	99.56 145	99.28 281	99.22 198	98.99 204	99.40 253	99.08 162	99.58 151	99.64 146	98.90 90	99.83 245	97.44 235	99.75 181	99.63 100
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 84	99.25 104	99.77 40	99.03 321	99.77 44	99.74 17	99.61 147	99.18 145	99.76 77	99.61 174	99.00 75	99.92 91	97.72 210	99.60 246	99.62 111

PC_three_1452									97.56 292	99.68 111	99.41 246	99.09 63	97.09 373	96.66 284	99.60 246	99.62 111

GeoE		99.69 21	99.66 22	99.78 37	99.76 86	99.76 51	99.60 63	99.82 39	99.46 104	99.75 83	99.56 202	99.63 14	99.95 45	99.43 41	99.88 103	99.62 111

test_method		91.72 339	92.32 342	89.91 356	93.49 378	70.18 380	90.28 369	99.56 182	61.71 373	95.39 370	99.52 216	93.90 299	99.94 57	98.76 129	98.27 347	99.62 111

GST-MVS		99.16 148	98.96 175	99.75 57	99.73 106	99.73 64	99.20 146	99.55 188	98.22 257	99.32 223	99.35 266	98.65 125	99.91 113	96.86 271	99.74 189	99.62 111

new-patchmatchnet		99.35 92	99.57 40	98.71 293	99.82 46	96.62 330	98.55 259	99.75 75	99.50 93	99.88 32	99.87 32	99.31 37	99.88 165	99.43 41	100.00 1	99.62 111

RRT_MVS		98.75 215	98.54 223	99.41 194	98.14 369	98.61 257	98.98 208	99.66 118	99.31 125	99.84 45	99.75 85	91.98 320	99.98 7	99.20 76	99.95 52	99.62 111

CPTT-MVS		98.74 217	98.44 232	99.64 111	99.61 150	99.38 160	99.18 152	99.55 188	96.49 329	99.27 235	99.37 256	97.11 251	99.92 91	95.74 325	99.67 223	99.62 111

MIMVSNet1		99.66 26	99.62 27	99.80 29	99.94 10	99.87 10	99.69 34	99.77 63	99.78 39	99.93 14	99.89 26	97.94 201	99.92 91	99.65 16	99.98 24	99.62 111

DeepPCF-MVS	98.42 6	99.18 143	99.02 158	99.67 92	99.22 290	99.75 55	97.25 351	99.47 230	98.72 207	99.66 120	99.70 112	99.29 39	99.63 345	98.07 179	99.81 155	99.62 111

3Dnovator+	98.92 3	99.35 92	99.24 106	99.67 92	99.35 255	99.47 132	99.62 53	99.50 219	99.44 107	99.12 261	99.78 71	98.77 109	99.94 57	97.87 196	99.72 202	99.62 111

DVP-MVS		99.32 103	99.17 113	99.77 40	99.69 124	99.80 36	99.14 167	99.31 276	99.16 151	99.62 138	99.61 174	98.35 166	99.91 113	97.88 193	99.72 202	99.61 122
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		98.87 203	98.59 215	99.71 83	99.50 205	99.62 102	99.01 197	99.57 177	96.80 326	99.54 168	99.63 156	98.29 172	99.91 113	95.24 335	99.71 206	99.61 122
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.82 208	98.57 219	99.58 136	99.21 292	99.31 176	98.61 249	99.25 290	98.65 211	98.43 323	99.26 285	97.86 208	99.81 271	96.55 289	99.27 304	99.61 122

TAMVS		99.49 52	99.45 57	99.63 116	99.48 216	99.42 150	99.45 82	99.57 177	99.66 65	99.78 70	99.83 47	97.85 210	99.86 198	99.44 40	99.96 45	99.61 122

Regformer-1		99.32 103	99.27 100	99.47 171	99.41 240	98.95 230	98.99 204	99.48 226	99.48 95	99.66 120	99.52 216	98.78 106	99.87 178	98.36 151	99.74 189	99.60 126

Regformer-2		99.34 97	99.27 100	99.53 154	99.41 240	99.10 216	98.99 204	99.53 203	99.47 100	99.66 120	99.52 216	98.80 101	99.89 150	98.31 157	99.74 189	99.60 126

HPM-MVS++		98.96 189	98.70 207	99.74 63	99.52 193	99.71 71	98.86 221	99.19 302	98.47 231	98.59 313	99.06 313	98.08 191	99.91 113	96.94 266	99.60 246	99.60 126

V42		99.56 42	99.54 45	99.63 116	99.79 68	99.46 136	99.39 91	99.59 166	99.24 136	99.86 40	99.70 112	98.55 137	99.82 255	99.79 11	99.95 52	99.60 126

HQP_MVS		98.90 197	98.68 209	99.55 148	99.58 161	99.24 194	98.80 234	99.54 194	98.94 178	99.14 258	99.25 287	97.24 243	99.82 255	95.84 321	99.78 171	99.60 126

plane_prior5								99.54 194					99.82 255	95.84 321	99.78 171	99.60 126

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 14	99.86 5	99.92 7	99.69 55	99.78 70	99.92 17	99.37 31	99.88 165	98.93 116	99.95 52	99.60 126

ACMH+	98.40 8	99.50 50	99.43 62	99.71 83	99.86 31	99.76 51	99.32 108	99.77 63	99.53 89	99.77 75	99.76 81	99.26 45	99.78 282	97.77 204	99.88 103	99.60 126

ACMM	98.09 11	99.46 61	99.38 71	99.72 79	99.80 58	99.69 82	99.13 173	99.65 129	98.99 171	99.64 126	99.72 98	99.39 25	99.86 198	98.23 163	99.81 155	99.60 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		98.97 186	98.82 196	99.42 186	99.71 113	98.81 243	99.62 53	98.68 327	99.81 33	99.38 212	99.80 59	94.25 297	99.85 217	98.79 125	99.32 297	99.59 135

casdiffmvs		99.63 32	99.61 31	99.67 92	99.79 68	99.59 113	99.13 173	99.85 26	99.79 38	99.76 77	99.72 98	99.33 36	99.82 255	99.21 73	99.94 65	99.59 135

UniMVSNet (Re)		99.37 87	99.26 102	99.68 89	99.51 198	99.58 116	98.98 208	99.60 159	99.43 112	99.70 106	99.36 261	97.70 217	99.88 165	99.20 76	99.87 112	99.59 135

DSMNet-mixed		99.48 54	99.65 24	98.95 264	99.71 113	97.27 316	99.50 75	99.82 39	99.59 85	99.41 205	99.85 41	99.62 16	100.00 1	99.53 30	99.89 95	99.59 135

3Dnovator	99.15 2	99.43 66	99.36 77	99.65 104	99.39 245	99.42 150	99.70 28	99.56 182	99.23 138	99.35 216	99.80 59	99.17 53	99.95 45	98.21 165	99.84 128	99.59 135

SED-MVS		99.40 77	99.28 97	99.77 40	99.69 124	99.82 28	99.20 146	99.54 194	99.13 157	99.82 52	99.63 156	98.91 87	99.92 91	97.85 199	99.70 208	99.58 140

OPU-MVS				99.29 223	99.12 307	99.44 143	99.20 146				99.40 249	99.00 75	98.84 370	96.54 290	99.60 246	99.58 140

abl_6		99.36 90	99.23 108	99.75 57	99.71 113	99.74 61	99.33 105	99.76 68	99.07 164	99.65 124	99.63 156	99.09 63	99.92 91	97.13 259	99.76 178	99.58 140

EPNet		98.13 273	97.77 288	99.18 243	94.57 377	97.99 293	99.24 136	97.96 347	99.74 42	97.29 358	99.62 165	93.13 310	99.97 17	98.59 140	99.83 138	99.58 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.03 174	98.85 191	99.55 148	99.80 58	99.25 189	99.73 20	99.15 306	99.37 117	99.61 144	99.71 105	94.73 293	99.81 271	97.70 215	99.88 103	99.58 140

ACMP	97.51 14	99.05 170	98.84 193	99.67 92	99.78 74	99.55 122	98.88 217	99.66 118	97.11 318	99.47 186	99.60 183	99.07 69	99.89 150	96.18 307	99.85 123	99.58 140
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		99.23 120	99.05 149	99.74 63	99.52 193	99.75 55	99.20 146	99.61 147	98.97 173	99.48 184	99.58 191	98.41 158	99.91 113	97.15 258	99.55 257	99.57 146

SR-MVS		99.19 139	99.00 164	99.74 63	99.51 198	99.72 68	99.18 152	99.60 159	98.85 191	99.47 186	99.58 191	98.38 163	99.92 91	96.92 267	99.54 263	99.57 146

lessismore_v0				99.64 111	99.86 31	99.38 160		90.66 375		99.89 26	99.83 47	94.56 295	99.97 17	99.56 26	99.92 77	99.57 146

pmmvs5		99.19 139	99.11 128	99.42 186	99.76 86	98.88 240	98.55 259	99.73 83	98.82 195	99.72 98	99.62 165	96.56 262	99.82 255	99.32 60	99.95 52	99.56 149

APD-MVS_3200maxsize		99.31 105	99.16 114	99.74 63	99.53 188	99.75 55	99.27 126	99.61 147	99.19 144	99.57 154	99.64 146	98.76 110	99.90 133	97.29 243	99.62 236	99.56 149

CDPH-MVS		98.56 237	98.20 255	99.61 128	99.50 205	99.46 136	98.32 280	99.41 246	95.22 347	99.21 247	99.10 310	98.34 168	99.82 255	95.09 338	99.66 227	99.56 149

Anonymous20240521		99.44 65	99.42 65	99.49 165	99.89 21	98.96 229	99.62 53	99.76 68	99.85 24	99.82 52	99.88 29	96.39 271	99.97 17	99.59 21	99.98 24	99.55 152

our_test_3		98.85 205	99.09 137	98.13 314	99.66 139	94.90 350	97.72 329	99.58 175	99.07 164	99.64 126	99.62 165	98.19 183	99.93 71	98.41 148	99.95 52	99.55 152

YYNet1		98.95 192	98.99 169	98.84 281	99.64 143	97.14 320	98.22 288	99.32 272	98.92 183	99.59 149	99.66 139	97.40 235	99.83 245	98.27 160	99.90 87	99.55 152

MDA-MVSNet_test_wron		98.95 192	98.99 169	98.85 279	99.64 143	97.16 319	98.23 287	99.33 270	98.93 181	99.56 161	99.66 139	97.39 237	99.83 245	98.29 158	99.88 103	99.55 152

MVSFormer		99.41 74	99.44 59	99.31 220	99.57 171	98.40 270	99.77 11	99.80 49	99.73 43	99.63 130	99.30 275	98.02 195	99.98 7	99.43 41	99.69 211	99.55 152

jason		99.16 148	99.11 128	99.32 217	99.75 97	98.44 267	98.26 285	99.39 256	98.70 208	99.74 92	99.30 275	98.54 139	99.97 17	98.48 145	99.82 147	99.55 152
jason: jason.
CDS-MVSNet		99.22 129	99.13 121	99.50 162	99.35 255	99.11 212	98.96 211	99.54 194	99.46 104	99.61 144	99.70 112	96.31 273	99.83 245	99.34 55	99.88 103	99.55 152
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 63	99.37 74	99.70 87	99.83 39	99.70 78	99.38 93	99.78 60	99.53 89	99.67 116	99.78 71	99.19 51	99.86 198	97.32 241	99.87 112	99.55 152
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		99.27 113	99.11 128	99.73 73	99.54 183	99.74 61	99.26 128	99.62 140	99.16 151	99.52 175	99.64 146	98.41 158	99.91 113	97.27 246	99.61 243	99.54 160

RE-MVS-def			99.13 121		99.54 183	99.74 61	99.26 128	99.62 140	99.16 151	99.52 175	99.64 146	98.57 134		97.27 246	99.61 243	99.54 160

SD-MVS		99.01 180	99.30 90	98.15 313	99.50 205	99.40 155	98.94 214	99.61 147	99.22 142	99.75 83	99.82 54	99.54 22	95.51 375	97.48 233	99.87 112	99.54 160
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		98.99 185	98.80 199	99.56 145	99.25 286	99.43 147	98.54 262	99.27 285	98.58 218	98.80 296	99.43 244	98.53 143	99.70 309	97.22 253	99.59 250	99.54 160

MVS_111021_HR		99.12 156	99.02 158	99.40 196	99.50 205	99.11 212	97.92 321	99.71 95	98.76 205	99.08 266	99.47 236	99.17 53	99.54 355	97.85 199	99.76 178	99.54 160

v148		99.40 77	99.41 66	99.39 199	99.76 86	98.94 231	99.09 184	99.59 166	99.17 149	99.81 59	99.61 174	98.41 158	99.69 315	99.32 60	99.94 65	99.53 165

diffmvs		99.34 97	99.32 84	99.39 199	99.67 138	98.77 246	98.57 257	99.81 48	99.61 77	99.48 184	99.41 246	98.47 150	99.86 198	98.97 108	99.90 87	99.53 165

baseline		99.63 32	99.62 27	99.66 99	99.80 58	99.62 102	99.44 85	99.80 49	99.71 47	99.72 98	99.69 118	99.15 55	99.83 245	99.32 60	99.94 65	99.53 165

HQP4-MVS										98.15 333			99.70 309			99.53 165

GBi-Net		99.42 70	99.31 85	99.73 73	99.49 210	99.77 44	99.68 37	99.70 100	99.44 107	99.62 138	99.83 47	97.21 245	99.90 133	98.96 110	99.90 87	99.53 165

test1		99.42 70	99.31 85	99.73 73	99.49 210	99.77 44	99.68 37	99.70 100	99.44 107	99.62 138	99.83 47	97.21 245	99.90 133	98.96 110	99.90 87	99.53 165

FMVSNet1		99.66 26	99.63 26	99.73 73	99.78 74	99.77 44	99.68 37	99.70 100	99.67 61	99.82 52	99.83 47	98.98 78	99.90 133	99.24 70	99.97 33	99.53 165

HQP-MVS		98.36 259	98.02 269	99.39 199	99.31 272	98.94 231	97.98 313	99.37 263	97.45 300	98.15 333	98.83 343	96.67 260	99.70 309	94.73 341	99.67 223	99.53 165

QAPM		98.40 257	97.99 270	99.65 104	99.39 245	99.47 132	99.67 41	99.52 211	91.70 363	98.78 299	99.80 59	98.55 137	99.95 45	94.71 343	99.75 181	99.53 165

F-COLMAP		98.74 217	98.45 230	99.62 125	99.57 171	99.47 132	98.84 224	99.65 129	96.31 333	98.93 278	99.19 300	97.68 221	99.87 178	96.52 291	99.37 291	99.53 165

MVSTER		98.47 250	98.22 253	99.24 235	99.06 317	98.35 275	99.08 187	99.46 234	99.27 130	99.75 83	99.66 139	88.61 350	99.85 217	99.14 93	99.92 77	99.52 175

PVSNet_BlendedMVS		99.03 174	99.01 161	99.09 252	99.54 183	97.99 293	98.58 253	99.82 39	97.62 290	99.34 219	99.71 105	98.52 146	99.77 290	97.98 185	99.97 33	99.52 175

OPM-MVS		99.26 115	99.13 121	99.63 116	99.70 121	99.61 108	98.58 253	99.48 226	98.50 227	99.52 175	99.63 156	99.14 58	99.76 292	97.89 192	99.77 175	99.51 177
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior1		98.33 264	97.92 280	99.57 141	99.35 255	99.36 166	97.99 312	99.39 256	94.85 354	97.76 353	98.98 327	98.03 193	99.85 217	95.49 329	99.44 278	99.51 177

AllTest		99.21 134	99.07 143	99.63 116	99.78 74	99.64 96	99.12 177	99.83 34	98.63 213	99.63 130	99.72 98	98.68 118	99.75 296	96.38 299	99.83 138	99.51 177

TestCases				99.63 116	99.78 74	99.64 96		99.83 34	98.63 213	99.63 130	99.72 98	98.68 118	99.75 296	96.38 299	99.83 138	99.51 177

BH-RMVSNet		98.41 255	98.14 263	99.21 238	99.21 292	98.47 264	98.60 251	98.26 344	98.35 246	98.93 278	99.31 273	97.20 248	99.66 335	94.32 346	99.10 312	99.51 177

USDC		98.96 189	98.93 178	99.05 258	99.54 183	97.99 293	97.07 357	99.80 49	98.21 258	99.75 83	99.77 78	98.43 155	99.64 344	97.90 191	99.88 103	99.51 177

test9_res														95.10 337	99.44 278	99.50 183

train_agg		98.35 262	97.95 274	99.57 141	99.35 255	99.35 170	98.11 298	99.41 246	94.90 351	97.92 344	98.99 324	98.02 195	99.85 217	95.38 333	99.44 278	99.50 183

agg_prior2														94.58 345	99.46 277	99.50 183

VDD-MVS		99.20 136	99.11 128	99.44 180	99.43 234	98.98 225	99.50 75	98.32 343	99.80 36	99.56 161	99.69 118	96.99 255	99.85 217	98.99 104	99.73 196	99.50 183

MDA-MVSNet-bldmvs		99.06 167	99.05 149	99.07 256	99.80 58	97.83 300	98.89 216	99.72 92	99.29 126	99.63 130	99.70 112	96.47 266	99.89 150	98.17 172	99.82 147	99.50 183

KD-MVS_self_test		99.63 32	99.59 34	99.76 47	99.84 35	99.90 5	99.37 97	99.79 55	99.83 30	99.88 32	99.85 41	98.42 157	99.90 133	99.60 20	99.73 196	99.49 188

xxxxxxxxxxxxxcwj		99.11 160	98.96 175	99.54 152	99.53 188	99.25 189	98.29 282	99.76 68	99.07 164	99.42 197	99.61 174	98.86 93	99.87 178	96.45 296	99.68 216	99.49 188

SF-MVS		99.10 164	98.93 178	99.62 125	99.58 161	99.51 127	99.13 173	99.65 129	97.97 272	99.42 197	99.61 174	98.86 93	99.87 178	96.45 296	99.68 216	99.49 188

Anonymous20240529		99.42 70	99.34 79	99.65 104	99.53 188	99.60 110	99.63 52	99.39 256	99.47 100	99.76 77	99.78 71	98.13 187	99.86 198	98.70 134	99.68 216	99.49 188

WTY-MVS		98.59 234	98.37 239	99.26 230	99.43 234	98.40 270	98.74 242	99.13 309	98.10 263	99.21 247	99.24 292	94.82 291	99.90 133	97.86 197	98.77 329	99.49 188

ppachtmachnet_test		98.89 200	99.12 125	98.20 312	99.66 139	95.24 347	97.63 333	99.68 109	99.08 162	99.78 70	99.62 165	98.65 125	99.88 165	98.02 180	99.96 45	99.48 193

Anonymous20231206		99.35 92	99.31 85	99.47 171	99.74 103	99.06 222	99.28 123	99.74 80	99.23 138	99.72 98	99.53 214	97.63 228	99.88 165	99.11 95	99.84 128	99.48 193

test_prior3		98.62 228	98.34 243	99.46 174	99.35 255	99.22 198	97.95 317	99.39 256	97.87 279	98.05 339	99.05 314	97.90 204	99.69 315	95.99 314	99.49 272	99.48 193

test_prior				99.46 174	99.35 255	99.22 198		99.39 256					99.69 315			99.48 193

test12				99.54 152	99.29 278	99.33 173		99.16 305		98.43 323		97.54 230	99.82 255		99.47 275	99.48 193

VNet		99.18 143	99.06 145	99.56 145	99.24 288	99.36 166	99.33 105	99.31 276	99.67 61	99.47 186	99.57 199	96.48 265	99.84 234	99.15 87	99.30 299	99.47 198

test20.03		99.55 45	99.54 45	99.58 136	99.79 68	99.37 163	99.02 195	99.89 15	99.60 83	99.82 52	99.62 165	98.81 97	99.89 150	99.43 41	99.86 119	99.47 198

114514_t		98.49 248	98.11 264	99.64 111	99.73 106	99.58 116	99.24 136	99.76 68	89.94 366	99.42 197	99.56 202	97.76 216	99.86 198	97.74 209	99.82 147	99.47 198

sss		98.90 197	98.77 201	99.27 228	99.48 216	98.44 267	98.72 245	99.32 272	97.94 276	99.37 213	99.35 266	96.31 273	99.91 113	98.85 120	99.63 235	99.47 198

旧先验1					99.49 210	99.29 179		99.26 287			99.39 253	97.67 222			99.36 292	99.46 202

1121		98.56 237	98.24 251	99.52 156	99.49 210	99.24 194	99.30 115	99.22 297	95.77 340	98.52 318	99.29 278	97.39 237	99.85 217	95.79 323	99.34 294	99.46 202

MVP-Stereo		99.16 148	99.08 139	99.43 184	99.48 216	99.07 220	99.08 187	99.55 188	98.63 213	99.31 227	99.68 129	98.19 183	99.78 282	98.18 170	99.58 251	99.45 204
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
新几何1				99.52 156	99.50 205	99.22 198		99.26 287	95.66 343	98.60 312	99.28 280	97.67 222	99.89 150	95.95 318	99.32 297	99.45 204

LFMVS		98.46 251	98.19 258	99.26 230	99.24 288	98.52 262	99.62 53	96.94 359	99.87 17	99.31 227	99.58 191	91.04 331	99.81 271	98.68 137	99.42 283	99.45 204

testgi		99.29 108	99.26 102	99.37 206	99.75 97	98.81 243	98.84 224	99.89 15	98.38 239	99.75 83	99.04 317	99.36 34	99.86 198	99.08 98	99.25 305	99.45 204

UnsupCasMVSNet_eth		98.83 206	98.57 219	99.59 132	99.68 133	99.45 141	98.99 204	99.67 114	99.48 95	99.55 166	99.36 261	94.92 289	99.86 198	98.95 114	96.57 366	99.45 204

无先验							98.01 308	99.23 294	95.83 339				99.85 217	95.79 323		99.44 209

testdata				99.42 186	99.51 198	98.93 235		99.30 279	96.20 334	98.87 288	99.40 249	98.33 170	99.89 150	96.29 302	99.28 301	99.44 209

XVG-OURS-SEG-HR		99.16 148	98.99 169	99.66 99	99.84 35	99.64 96	98.25 286	99.73 83	98.39 238	99.63 130	99.43 244	99.70 11	99.90 133	97.34 240	98.64 337	99.44 209

FMVSNet2		99.35 92	99.28 97	99.55 148	99.49 210	99.35 170	99.45 82	99.57 177	99.44 107	99.70 106	99.74 88	97.21 245	99.87 178	99.03 101	99.94 65	99.44 209

N_pmnet		98.73 219	98.53 225	99.35 210	99.72 110	98.67 251	98.34 277	94.65 368	98.35 246	99.79 67	99.68 129	98.03 193	99.93 71	98.28 159	99.92 77	99.44 209

RPSCF		99.18 143	99.02 158	99.64 111	99.83 39	99.85 14	99.44 85	99.82 39	98.33 251	99.50 182	99.78 71	97.90 204	99.65 342	96.78 277	99.83 138	99.44 209

原ACMM1				99.37 206	99.47 221	98.87 242		99.27 285	96.74 327	98.26 328	99.32 271	97.93 202	99.82 255	95.96 317	99.38 287	99.43 215

test222					99.51 198	99.08 219	97.83 326	99.29 281	95.21 348	98.68 307	99.31 273	97.28 242			99.38 287	99.43 215

XVG-OURS		99.21 134	99.06 145	99.65 104	99.82 46	99.62 102	97.87 324	99.74 80	98.36 241	99.66 120	99.68 129	99.71 9	99.90 133	96.84 274	99.88 103	99.43 215

CSCG		99.37 87	99.29 95	99.60 130	99.71 113	99.46 136	99.43 87	99.85 26	98.79 199	99.41 205	99.60 183	98.92 85	99.92 91	98.02 180	99.92 77	99.43 215

ETH3D-3000-0.1		98.77 212	98.50 227	99.59 132	99.47 221	99.53 124	98.77 239	99.60 159	97.33 307	99.23 241	99.50 223	97.91 203	99.83 245	95.02 339	99.67 223	99.41 219

TinyColmap		98.97 186	98.93 178	99.07 256	99.46 226	98.19 281	97.75 328	99.75 75	98.79 199	99.54 168	99.70 112	98.97 80	99.62 346	96.63 287	99.83 138	99.41 219

ETH3 D test6400		97.76 286	97.19 303	99.50 162	99.38 248	99.26 185	98.34 277	99.49 224	92.99 360	98.54 317	99.20 298	95.92 282	99.82 255	91.14 362	99.66 227	99.40 221

Anonymous202405211		98.75 215	98.46 229	99.63 116	99.34 265	99.66 89	99.47 81	97.65 352	99.28 129	99.56 161	99.50 223	93.15 309	99.84 234	98.62 139	99.58 251	99.40 221

XVG-ACMP-BASELINE		99.23 120	99.10 136	99.63 116	99.82 46	99.58 116	98.83 226	99.72 92	98.36 241	99.60 146	99.71 105	98.92 85	99.91 113	97.08 261	99.84 128	99.40 221

MS-PatchMatch		99.00 182	98.97 173	99.09 252	99.11 312	98.19 281	98.76 241	99.33 270	98.49 229	99.44 191	99.58 191	98.21 180	99.69 315	98.20 166	99.62 236	99.39 224

FMVSNet3		98.80 210	98.63 212	99.32 217	99.13 305	98.72 248	99.10 180	99.48 226	99.23 138	99.62 138	99.64 146	92.57 314	99.86 198	98.96 110	99.90 87	99.39 224

ambc				99.20 240	99.35 255	98.53 260	99.17 157	99.46 234		99.67 116	99.80 59	98.46 153	99.70 309	97.92 190	99.70 208	99.38 226

FMVSNet5		97.80 284	97.25 300	99.42 186	98.83 340	98.97 227	99.38 93	99.80 49	98.87 189	99.25 237	99.69 118	80.60 372	99.91 113	98.96 110	99.90 87	99.38 226

PAPM_NR		98.36 259	98.04 267	99.33 213	99.48 216	98.93 235	98.79 237	99.28 284	97.54 295	98.56 316	98.57 354	97.12 250	99.69 315	94.09 350	98.90 324	99.38 226

EPNet_dtu		97.62 293	97.79 287	97.11 340	96.67 374	92.31 363	98.51 265	98.04 345	99.24 136	95.77 368	99.47 236	93.78 303	99.66 335	98.98 106	99.62 236	99.37 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		99.11 160	98.95 177	99.59 132	99.13 305	99.59 113	99.17 157	99.65 129	97.88 278	99.25 237	99.46 239	98.97 80	99.80 276	97.26 248	99.82 147	99.37 229

PLC	97.35 16	98.36 259	97.99 270	99.48 169	99.32 271	99.24 194	98.50 266	99.51 215	95.19 349	98.58 314	98.96 332	96.95 256	99.83 245	95.63 326	99.25 305	99.37 229
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		97.62 293	97.20 302	98.90 277	99.76 86	97.40 313	99.48 79	94.36 369	99.06 168	99.70 106	99.49 228	84.55 366	99.94 57	98.73 132	99.65 231	99.36 232

pmmvs-eth3d		99.48 54	99.47 53	99.51 159	99.77 82	99.41 154	98.81 231	99.66 118	99.42 114	99.75 83	99.66 139	99.20 50	99.76 292	98.98 106	99.99 12	99.36 232

PVSNet_0	95.53 19	95.85 333	95.31 335	97.47 330	98.78 348	93.48 358	95.72 366	99.40 253	96.18 335	97.37 356	97.73 369	95.73 283	99.58 353	95.49 329	81.40 373	99.36 232

lupinMVS		98.96 189	98.87 189	99.24 235	99.57 171	98.40 270	98.12 296	99.18 303	98.28 254	99.63 130	99.13 303	98.02 195	99.97 17	98.22 164	99.69 211	99.35 235

Vis-MVSNet (Re-imp)		98.77 212	98.58 218	99.34 211	99.78 74	98.88 240	99.61 58	99.56 182	99.11 161	99.24 240	99.56 202	93.00 312	99.78 282	97.43 236	99.89 95	99.35 235

GA-MVS		97.99 281	97.68 291	98.93 268	99.52 193	98.04 292	97.19 353	99.05 313	98.32 252	98.81 294	98.97 330	89.89 347	99.41 365	98.33 155	99.05 314	99.34 237

CANet		99.11 160	99.05 149	99.28 226	98.83 340	98.56 259	98.71 247	99.41 246	99.25 134	99.23 241	99.22 294	97.66 226	99.94 57	99.19 78	99.97 33	99.33 238

Patchmtry		98.78 211	98.54 223	99.49 165	98.89 334	99.19 205	99.32 108	99.67 114	99.65 67	99.72 98	99.79 65	91.87 323	99.95 45	98.00 184	99.97 33	99.33 238

PAPR		97.56 296	97.07 305	99.04 259	98.80 345	98.11 287	97.63 333	99.25 290	94.56 357	98.02 342	98.25 364	97.43 234	99.68 326	90.90 363	98.74 333	99.33 238

CHOSEN 280x420		98.41 255	98.41 235	98.40 303	99.34 265	95.89 341	96.94 359	99.44 239	98.80 198	99.25 237	99.52 216	93.51 307	99.98 7	98.94 115	99.98 24	99.32 241

baseline1		97.73 288	97.33 297	98.96 263	99.30 276	97.73 304	99.40 89	98.42 339	99.33 123	99.46 189	99.21 296	91.18 329	99.82 255	98.35 153	91.26 372	99.32 241

TAPA-MVS	97.92 13	98.03 278	97.55 294	99.46 174	99.47 221	99.44 143	98.50 266	99.62 140	86.79 367	99.07 269	99.26 285	98.26 175	99.62 346	97.28 245	99.73 196	99.31 243
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet-Re		99.28 109	99.15 117	99.67 92	99.33 270	99.76 51	99.34 103	99.97 2	98.93 181	99.91 20	99.79 65	98.68 118	99.93 71	96.80 276	99.56 253	99.30 244

TSAR-MVS + GP.		99.12 156	99.04 155	99.38 203	99.34 265	99.16 207	98.15 292	99.29 281	98.18 261	99.63 130	99.62 165	99.18 52	99.68 326	98.20 166	99.74 189	99.30 244

PVSNet_Blended		98.70 222	98.59 215	99.02 260	99.54 183	97.99 293	97.58 336	99.82 39	95.70 342	99.34 219	98.98 327	98.52 146	99.77 290	97.98 185	99.83 138	99.30 244

MVS_0304		98.88 201	98.71 204	99.39 199	98.85 338	98.91 238	99.45 82	99.30 279	98.56 219	97.26 359	99.68 129	96.18 277	99.96 35	99.17 83	99.94 65	99.29 247

MVS_111021_LR		99.13 154	99.03 157	99.42 186	99.58 161	99.32 175	97.91 323	99.73 83	98.68 209	99.31 227	99.48 231	99.09 63	99.66 335	97.70 215	99.77 175	99.29 247

ETH3D cwj APD-0.16		98.50 245	98.16 261	99.51 159	99.04 320	99.39 157	98.47 268	99.47 230	96.70 328	98.78 299	99.33 270	97.62 229	99.86 198	94.69 344	99.38 287	99.28 249

miper_lstm_enhance		98.65 226	98.60 213	98.82 286	99.20 295	97.33 315	97.78 327	99.66 118	99.01 170	99.59 149	99.50 223	94.62 294	99.85 217	98.12 175	99.90 87	99.26 250

MVS		95.72 335	94.63 339	98.99 261	98.56 356	97.98 298	99.30 115	98.86 319	72.71 372	97.30 357	99.08 311	98.34 168	99.74 298	89.21 364	98.33 345	99.26 250

MSLP-MVS++		99.05 170	99.09 137	98.91 271	99.21 292	98.36 274	98.82 230	99.47 230	98.85 191	98.90 284	99.56 202	98.78 106	99.09 368	98.57 141	99.68 216	99.26 250

D2MVS		99.22 129	99.19 111	99.29 223	99.69 124	98.74 247	98.81 231	99.41 246	98.55 221	99.68 111	99.69 118	98.13 187	99.87 178	98.82 123	99.98 24	99.24 253

test_yl		98.25 267	97.95 274	99.13 248	99.17 300	98.47 264	99.00 199	98.67 329	98.97 173	99.22 245	99.02 322	91.31 327	99.69 315	97.26 248	98.93 320	99.24 253

DCV-MVSNet		98.25 267	97.95 274	99.13 248	99.17 300	98.47 264	99.00 199	98.67 329	98.97 173	99.22 245	99.02 322	91.31 327	99.69 315	97.26 248	98.93 320	99.24 253

DPM-MVS		98.28 265	97.94 278	99.32 217	99.36 253	99.11 212	97.31 349	98.78 324	96.88 321	98.84 291	99.11 309	97.77 215	99.61 350	94.03 352	99.36 292	99.23 256

CLD-MVS		98.76 214	98.57 219	99.33 213	99.57 171	98.97 227	97.53 339	99.55 188	96.41 330	99.27 235	99.13 303	99.07 69	99.78 282	96.73 280	99.89 95	99.23 256
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		99.13 154	99.06 145	99.36 209	99.57 171	99.10 216	98.01 308	99.25 290	98.78 201	99.58 151	99.44 243	98.24 176	99.76 292	98.74 131	99.93 73	99.22 258

OMC-MVS		98.90 197	98.72 203	99.44 180	99.39 245	99.42 150	98.58 253	99.64 135	97.31 308	99.44 191	99.62 165	98.59 131	99.69 315	96.17 308	99.79 165	99.22 258

EGC-MVSNET		89.05 340	85.52 343	99.64 111	99.89 21	99.78 41	99.56 70	99.52 211	24.19 374	49.96 375	99.83 47	99.15 55	99.92 91	97.71 212	99.85 123	99.21 260

eth_miper_zixun_eth		98.68 224	98.71 204	98.60 295	99.10 313	96.84 327	97.52 341	99.54 194	98.94 178	99.58 151	99.48 231	96.25 275	99.76 292	98.01 183	99.93 73	99.21 260

c3_l		98.72 220	98.71 204	98.72 291	99.12 307	97.22 318	97.68 332	99.56 182	98.90 185	99.54 168	99.48 231	96.37 272	99.73 301	97.88 193	99.88 103	99.21 260

CMPMVS	77.52 23	98.50 245	98.19 258	99.41 194	98.33 362	99.56 119	99.01 197	99.59 166	95.44 344	99.57 154	99.80 59	95.64 284	99.46 364	96.47 295	99.92 77	99.21 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		99.06 167	98.97 173	99.34 211	99.31 272	98.98 225	98.31 281	99.91 10	98.81 196	98.79 297	98.94 334	99.14 58	99.84 234	98.79 125	98.74 333	99.20 264

DELS-MVS		99.34 97	99.30 90	99.48 169	99.51 198	99.36 166	98.12 296	99.53 203	99.36 119	99.41 205	99.61 174	99.22 48	99.87 178	99.21 73	99.68 216	99.20 264
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 21	99.69 18	99.68 89	99.71 113	99.91 2	99.76 13	99.96 4	99.86 19	99.51 180	99.39 253	99.57 20	99.93 71	99.64 18	99.86 119	99.20 264

CANet_DTU		98.91 195	98.85 191	99.09 252	98.79 346	98.13 284	98.18 289	99.31 276	99.48 95	98.86 289	99.51 220	96.56 262	99.95 45	99.05 100	99.95 52	99.19 267

alignmvs		98.28 265	97.96 273	99.25 233	99.12 307	98.93 235	99.03 194	98.42 339	99.64 69	98.72 304	97.85 368	90.86 336	99.62 346	98.88 119	99.13 310	99.19 267

DIV-MVS_self_test		98.54 241	98.42 234	98.92 269	99.03 321	97.80 302	97.46 343	99.59 166	98.90 185	99.60 146	99.46 239	93.87 300	99.78 282	97.97 187	99.89 95	99.18 269

MSDG		99.08 165	98.98 172	99.37 206	99.60 152	99.13 210	97.54 337	99.74 80	98.84 194	99.53 173	99.55 209	99.10 61	99.79 279	97.07 262	99.86 119	99.18 269

cl____		98.54 241	98.41 235	98.92 269	99.03 321	97.80 302	97.46 343	99.59 166	98.90 185	99.60 146	99.46 239	93.85 301	99.78 282	97.97 187	99.89 95	99.17 271

PM-MVS		99.36 90	99.29 95	99.58 136	99.83 39	99.66 89	98.95 212	99.86 22	98.85 191	99.81 59	99.73 92	98.40 162	99.92 91	98.36 151	99.83 138	99.17 271

thisisatest0530		97.45 298	96.95 309	98.94 265	99.68 133	97.73 304	99.09 184	94.19 371	98.61 216	99.56 161	99.30 275	84.30 367	99.93 71	98.27 160	99.54 263	99.16 273

PatchmatchNet		97.65 292	97.80 285	97.18 338	98.82 343	92.49 362	99.17 157	98.39 341	98.12 262	98.79 297	99.58 191	90.71 338	99.89 150	97.23 252	99.41 284	99.16 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpnnormal		99.43 66	99.38 71	99.60 130	99.87 29	99.75 55	99.59 65	99.78 60	99.71 47	99.90 22	99.69 118	98.85 95	99.90 133	97.25 251	99.78 171	99.15 275

mvs_anonymous		99.28 109	99.39 69	98.94 265	99.19 297	97.81 301	99.02 195	99.55 188	99.78 39	99.85 42	99.80 59	98.24 176	99.86 198	99.57 25	99.50 270	99.15 275

ab-mvs		99.33 101	99.28 97	99.47 171	99.57 171	99.39 157	99.78 10	99.43 243	98.87 189	99.57 154	99.82 54	98.06 192	99.87 178	98.69 136	99.73 196	99.15 275

MIMVSNet		98.43 253	98.20 255	99.11 250	99.53 188	98.38 273	99.58 67	98.61 331	98.96 176	99.33 221	99.76 81	90.92 333	99.81 271	97.38 239	99.76 178	99.15 275

GSMVS																99.14 279

sam_mvs1												90.81 337				99.14 279

SCA		98.11 274	98.36 240	97.36 333	99.20 295	92.99 360	98.17 291	98.49 337	98.24 256	99.10 264	99.57 199	96.01 280	99.94 57	96.86 271	99.62 236	99.14 279

LS3D		99.24 119	99.11 128	99.61 128	98.38 360	99.79 38	99.57 68	99.68 109	99.61 77	99.15 256	99.71 105	98.70 116	99.91 113	97.54 229	99.68 216	99.13 282

Patchmatch-RL test		98.60 231	98.36 240	99.33 213	99.77 82	99.07 220	98.27 284	99.87 20	98.91 184	99.74 92	99.72 98	90.57 340	99.79 279	98.55 142	99.85 123	99.11 283

test_0402		99.22 129	99.14 118	99.45 178	99.79 68	99.43 147	99.28 123	99.68 109	99.54 87	99.40 210	99.56 202	99.07 69	99.82 255	96.01 312	99.96 45	99.11 283

MVS_Test		99.28 109	99.31 85	99.19 241	99.35 255	98.79 245	99.36 100	99.49 224	99.17 149	99.21 247	99.67 135	98.78 106	99.66 335	99.09 97	99.66 227	99.10 285

AdaColmap		98.60 231	98.35 242	99.38 203	99.12 307	99.22 198	98.67 248	99.42 245	97.84 283	98.81 294	99.27 282	97.32 241	99.81 271	95.14 336	99.53 265	99.10 285

FPMVS		96.32 323	95.50 331	98.79 287	99.60 152	98.17 283	98.46 273	98.80 323	97.16 315	96.28 364	99.63 156	82.19 368	99.09 368	88.45 366	98.89 325	99.10 285

Patchmatch-test		98.10 275	97.98 272	98.48 300	99.27 283	96.48 331	99.40 89	99.07 310	98.81 196	99.23 241	99.57 199	90.11 344	99.87 178	96.69 281	99.64 233	99.09 288

tpm		97.15 305	96.95 309	97.75 324	98.91 330	94.24 353	99.32 108	97.96 347	97.71 287	98.29 326	99.32 271	86.72 361	99.92 91	98.10 178	96.24 369	99.09 288

PMMVS		98.49 248	98.29 248	99.11 250	98.96 328	98.42 269	97.54 337	99.32 272	97.53 296	98.47 322	98.15 365	97.88 207	99.82 255	97.46 234	99.24 307	99.09 288

cl22		97.56 296	97.28 298	98.40 303	98.37 361	96.75 328	97.24 352	99.37 263	97.31 308	99.41 205	99.22 294	87.30 352	99.37 366	97.70 215	99.62 236	99.08 291

ADS-MVSNet2		97.78 285	97.66 293	98.12 315	99.14 303	95.36 345	99.22 143	98.75 325	96.97 319	98.25 329	99.64 146	90.90 334	99.94 57	96.51 292	99.56 253	99.08 291

ADS-MVSNet		97.72 291	97.67 292	97.86 320	99.14 303	94.65 351	99.22 143	98.86 319	96.97 319	98.25 329	99.64 146	90.90 334	99.84 234	96.51 292	99.56 253	99.08 291

pmmvs3		98.08 276	97.80 285	98.91 271	99.41 240	97.69 306	97.87 324	99.66 118	95.87 338	99.50 182	99.51 220	90.35 342	99.97 17	98.55 142	99.47 275	99.08 291

PVSNet	97.47 15	98.42 254	98.44 232	98.35 305	99.46 226	96.26 334	96.70 362	99.34 269	97.68 288	99.00 273	99.13 303	97.40 235	99.72 303	97.59 227	99.68 216	99.08 291

MVS-HIRNet		97.86 282	98.22 253	96.76 342	99.28 281	91.53 369	98.38 276	92.60 373	99.13 157	99.31 227	99.96 11	97.18 249	99.68 326	98.34 154	99.83 138	99.07 296

PMVS	92.94 21	98.82 208	98.81 197	98.85 279	99.84 35	97.99 293	99.20 146	99.47 230	99.71 47	99.42 197	99.82 54	98.09 189	99.47 362	93.88 354	99.85 123	99.07 296
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		99.57 39	99.59 34	99.49 165	99.98 3	99.71 71	99.72 23	99.84 32	99.81 33	99.94 11	99.78 71	98.91 87	99.71 307	98.41 148	99.95 52	99.05 298
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
canonicalmvs		99.02 176	99.00 164	99.09 252	99.10 313	98.70 249	99.61 58	99.66 118	99.63 72	98.64 309	97.65 370	99.04 73	99.54 355	98.79 125	98.92 322	99.04 299

hse-mvs2		98.52 243	98.30 247	99.16 244	99.29 278	98.60 258	98.77 239	99.02 314	99.68 57	99.32 223	99.04 317	92.50 317	99.85 217	99.24 70	97.87 358	99.03 300

CL-MVSNet_self_test		98.71 221	98.56 222	99.15 246	99.22 290	98.66 253	97.14 354	99.51 215	98.09 265	99.54 168	99.27 282	96.87 258	99.74 298	98.43 147	98.96 319	99.03 300

AUN-MVS		97.82 283	97.38 296	99.14 247	99.27 283	98.53 260	98.72 245	99.02 314	98.10 263	97.18 361	99.03 321	89.26 349	99.85 217	97.94 189	97.91 356	99.03 300

MDTV_nov1_ep13_2view						91.44 370	99.14 167		97.37 305	99.21 247		91.78 325		96.75 278		99.03 300

ITE_SJBPF				99.38 203	99.63 145	99.44 143		99.73 83	98.56 219	99.33 221	99.53 214	98.88 92	99.68 326	96.01 312	99.65 231	99.02 304

UnsupCasMVSNet_bld		98.55 240	98.27 249	99.40 196	99.56 181	99.37 163	97.97 316	99.68 109	97.49 299	99.08 266	99.35 266	95.41 287	99.82 255	97.70 215	98.19 350	99.01 305

miper_ehance_all_eth		98.59 234	98.59 215	98.59 296	98.98 327	97.07 321	97.49 342	99.52 211	98.50 227	99.52 175	99.37 256	96.41 270	99.71 307	97.86 197	99.62 236	99.00 306

CNLPA		98.57 236	98.34 243	99.28 226	99.18 299	99.10 216	98.34 277	99.41 246	98.48 230	98.52 318	98.98 327	97.05 253	99.78 282	95.59 327	99.50 270	98.96 307

new_pmnet		98.88 201	98.89 187	98.84 281	99.70 121	97.62 307	98.15 292	99.50 219	97.98 271	99.62 138	99.54 211	98.15 186	99.94 57	97.55 228	99.84 128	98.95 308

PCF-MVS	96.03 18	96.73 315	95.86 326	99.33 213	99.44 231	99.16 207	96.87 360	99.44 239	86.58 368	98.95 276	99.40 249	94.38 296	99.88 165	87.93 367	99.80 160	98.95 308
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		98.68 224	98.47 228	99.30 222	99.44 231	99.28 181	98.14 294	99.54 194	97.12 317	99.11 262	99.25 287	97.80 213	99.70 309	96.51 292	99.30 299	98.93 310

Fast-Effi-MVS+		99.02 176	98.87 189	99.46 174	99.38 248	99.50 128	99.04 192	99.79 55	97.17 314	98.62 310	98.74 349	99.34 35	99.95 45	98.32 156	99.41 284	98.92 311

ET-MVSNet_ETH3D		96.78 313	96.07 322	98.91 271	99.26 285	97.92 299	97.70 331	96.05 364	97.96 275	92.37 373	98.43 360	87.06 354	99.90 133	98.27 160	97.56 361	98.91 312

EIA-MVS		99.12 156	99.01 161	99.45 178	99.36 253	99.62 102	99.34 103	99.79 55	98.41 235	98.84 291	98.89 339	98.75 112	99.84 234	98.15 174	99.51 268	98.89 313

CostFormer		96.71 316	96.79 315	96.46 349	98.90 331	90.71 374	99.41 88	98.68 327	94.69 356	98.14 337	99.34 269	86.32 363	99.80 276	97.60 226	98.07 354	98.88 314

DP-MVS Recon		98.50 245	98.23 252	99.31 220	99.49 210	99.46 136	98.56 258	99.63 137	94.86 353	98.85 290	99.37 256	97.81 212	99.59 352	96.08 309	99.44 278	98.88 314

test0.0.03 1		97.37 301	96.91 312	98.74 290	97.72 370	97.57 308	97.60 335	97.36 358	98.00 268	99.21 247	98.02 366	90.04 345	99.79 279	98.37 150	95.89 370	98.86 316

BH-untuned		98.22 271	98.09 265	98.58 297	99.38 248	97.24 317	98.55 259	98.98 317	97.81 284	99.20 252	98.76 348	97.01 254	99.65 342	94.83 340	98.33 345	98.86 316

HY-MVS	98.23 9	98.21 272	97.95 274	98.99 261	99.03 321	98.24 277	99.61 58	98.72 326	96.81 325	98.73 303	99.51 220	94.06 298	99.86 198	96.91 268	98.20 348	98.86 316

CS-MVS-test		99.43 66	99.40 68	99.53 154	99.51 198	99.84 19	99.60 63	99.94 6	99.52 91	99.10 264	98.89 339	99.24 46	99.90 133	99.11 95	99.66 227	98.84 319

miper_enhance_ethall		98.03 278	97.94 278	98.32 307	98.27 363	96.43 333	96.95 358	99.41 246	96.37 332	99.43 195	98.96 332	94.74 292	99.69 315	97.71 212	99.62 236	98.83 320

Effi-MVS+-dtu		99.07 166	98.92 182	99.52 156	98.89 334	99.78 41	99.15 165	99.66 118	99.34 120	98.92 281	99.24 292	97.69 219	99.98 7	98.11 176	99.28 301	98.81 321

EPMVS		96.53 319	96.32 317	97.17 339	98.18 366	92.97 361	99.39 91	89.95 377	98.21 258	98.61 311	99.59 189	86.69 362	99.72 303	96.99 264	99.23 309	98.81 321

MVE	92.54 22	96.66 317	96.11 321	98.31 309	99.68 133	97.55 309	97.94 319	95.60 366	99.37 117	90.68 374	98.70 350	96.56 262	98.61 372	86.94 372	99.55 257	98.77 323
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tpm2		96.35 322	96.22 319	96.73 344	98.88 337	91.75 367	99.21 145	98.51 335	93.27 359	97.89 346	99.21 296	84.83 365	99.70 309	96.04 311	98.18 351	98.75 324

LF4IMVS		99.01 180	98.92 182	99.27 228	99.71 113	99.28 181	98.59 252	99.77 63	98.32 252	99.39 211	99.41 246	98.62 127	99.84 234	96.62 288	99.84 128	98.69 325

thisisatest0515		96.98 309	96.42 316	98.66 294	99.42 239	97.47 310	97.27 350	94.30 370	97.24 310	99.15 256	98.86 342	85.01 364	99.87 178	97.10 260	99.39 286	98.63 326

Fast-Effi-MVS+-dtu		99.20 136	99.12 125	99.43 184	99.25 286	99.69 82	99.05 190	99.82 39	99.50 93	98.97 274	99.05 314	98.98 78	99.98 7	98.20 166	99.24 307	98.62 327

PAPM		95.61 336	94.71 338	98.31 309	99.12 307	96.63 329	96.66 363	98.46 338	90.77 365	96.25 365	98.68 351	93.01 311	99.69 315	81.60 373	97.86 359	98.62 327

JIA-IIPM		98.06 277	97.92 280	98.50 299	98.59 355	97.02 322	98.80 234	98.51 335	99.88 16	97.89 346	99.87 32	91.89 322	99.90 133	98.16 173	97.68 360	98.59 329

dp		96.86 311	97.07 305	96.24 351	98.68 354	90.30 376	99.19 151	98.38 342	97.35 306	98.23 331	99.59 189	87.23 353	99.82 255	96.27 303	98.73 335	98.59 329

OpenMVS	98.12 10	98.23 270	97.89 284	99.26 230	99.19 297	99.26 185	99.65 50	99.69 106	91.33 364	98.14 337	99.77 78	98.28 173	99.96 35	95.41 332	99.55 257	98.58 331

baseline2		96.83 312	96.28 318	98.46 301	99.09 315	96.91 325	98.83 226	93.87 372	97.23 311	96.23 367	98.36 361	88.12 351	99.90 133	96.68 282	98.14 352	98.57 332

DWT-MVSNet_test		96.03 329	95.80 328	96.71 346	98.50 358	91.93 365	99.25 135	97.87 350	95.99 337	96.81 363	97.61 371	81.02 370	99.66 335	97.20 255	97.98 355	98.54 333

TESTMET0.1,1		96.24 325	95.84 327	97.41 332	98.24 364	93.84 356	97.38 345	95.84 365	98.43 232	97.81 350	98.56 355	79.77 373	99.89 150	97.77 204	98.77 329	98.52 334

xiu_mvs_v1_base_debu		99.23 120	99.34 79	98.91 271	99.59 156	98.23 278	98.47 268	99.66 118	99.61 77	99.68 111	98.94 334	99.39 25	99.97 17	99.18 80	99.55 257	98.51 335

xiu_mvs_v1_base		99.23 120	99.34 79	98.91 271	99.59 156	98.23 278	98.47 268	99.66 118	99.61 77	99.68 111	98.94 334	99.39 25	99.97 17	99.18 80	99.55 257	98.51 335

xiu_mvs_v1_base_debi		99.23 120	99.34 79	98.91 271	99.59 156	98.23 278	98.47 268	99.66 118	99.61 77	99.68 111	98.94 334	99.39 25	99.97 17	99.18 80	99.55 257	98.51 335

KD-MVS_2432*1600		95.89 330	95.41 333	97.31 336	94.96 375	93.89 354	97.09 355	99.22 297	97.23 311	98.88 285	99.04 317	79.23 374	99.54 355	96.24 305	96.81 364	98.50 338

miper_refine_blended		95.89 330	95.41 333	97.31 336	94.96 375	93.89 354	97.09 355	99.22 297	97.23 311	98.88 285	99.04 317	79.23 374	99.54 355	96.24 305	96.81 364	98.50 338

CR-MVSNet		98.35 262	98.20 255	98.83 283	99.05 318	98.12 285	99.30 115	99.67 114	97.39 304	99.16 254	99.79 65	91.87 323	99.91 113	98.78 128	98.77 329	98.44 340

RPMNet		98.60 231	98.53 225	98.83 283	99.05 318	98.12 285	99.30 115	99.62 140	99.86 19	99.16 254	99.74 88	92.53 316	99.92 91	98.75 130	98.77 329	98.44 340

tpmrst		97.73 288	98.07 266	96.73 344	98.71 352	92.00 364	99.10 180	98.86 319	98.52 225	98.92 281	99.54 211	91.90 321	99.82 255	98.02 180	99.03 316	98.37 342

test-LLR		97.15 305	96.95 309	97.74 325	98.18 366	95.02 348	97.38 345	96.10 361	98.00 268	97.81 350	98.58 352	90.04 345	99.91 113	97.69 221	98.78 327	98.31 343

test-mter		96.23 326	95.73 329	97.74 325	98.18 366	95.02 348	97.38 345	96.10 361	97.90 277	97.81 350	98.58 352	79.12 376	99.91 113	97.69 221	98.78 327	98.31 343

CS-MVS		99.40 77	99.43 62	99.29 223	99.44 231	99.72 68	99.36 100	99.91 10	99.71 47	99.28 233	98.83 343	99.22 48	99.86 198	99.40 48	99.77 175	98.29 345

ETV-MVS		99.18 143	99.18 112	99.16 244	99.34 265	99.28 181	99.12 177	99.79 55	99.48 95	98.93 278	98.55 356	99.40 24	99.93 71	98.51 144	99.52 267	98.28 346

PatchT		98.45 252	98.32 246	98.83 283	98.94 329	98.29 276	99.24 136	98.82 322	99.84 27	99.08 266	99.76 81	91.37 326	99.94 57	98.82 123	99.00 318	98.26 347

xiu_mvs_v2_base		99.02 176	99.11 128	98.77 288	99.37 251	98.09 289	98.13 295	99.51 215	99.47 100	99.42 197	98.54 357	99.38 29	99.97 17	98.83 121	99.33 296	98.24 348

IB-MVS	95.41 20	95.30 337	94.46 341	97.84 321	98.76 350	95.33 346	97.33 348	96.07 363	96.02 336	95.37 371	97.41 373	76.17 379	99.96 35	97.54 229	95.44 371	98.22 349
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
tpm cat1		96.78 313	96.98 308	96.16 352	98.85 338	90.59 375	99.08 187	99.32 272	92.37 361	97.73 355	99.46 239	91.15 330	99.69 315	96.07 310	98.80 326	98.21 350

MAR-MVS		98.24 269	97.92 280	99.19 241	98.78 348	99.65 94	99.17 157	99.14 307	95.36 345	98.04 341	98.81 346	97.47 232	99.72 303	95.47 331	99.06 313	98.21 350
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		99.00 182	99.08 139	98.76 289	99.37 251	98.10 288	98.00 310	99.51 215	99.47 100	99.41 205	98.50 359	99.28 41	99.97 17	98.83 121	99.34 294	98.20 352

cascas		96.99 308	96.82 314	97.48 329	97.57 373	95.64 343	96.43 364	99.56 182	91.75 362	97.13 362	97.61 371	95.58 286	98.63 371	96.68 282	99.11 311	98.18 353

BH-w/o		97.20 304	97.01 307	97.76 323	99.08 316	95.69 342	98.03 307	98.52 334	95.76 341	97.96 343	98.02 366	95.62 285	99.47 362	92.82 357	97.25 363	98.12 354

tpmvs		97.39 300	97.69 290	96.52 347	98.41 359	91.76 366	99.30 115	98.94 318	97.74 285	97.85 349	99.55 209	92.40 319	99.73 301	96.25 304	98.73 335	98.06 355

mvs-test1		98.83 206	98.70 207	99.22 237	98.89 334	99.65 94	98.88 217	99.66 118	99.34 120	98.29 326	98.94 334	97.69 219	99.96 35	98.11 176	98.54 341	98.04 356

thres600view7		96.60 318	96.16 320	97.93 318	99.63 145	96.09 338	99.18 152	97.57 353	98.77 202	98.72 304	97.32 374	87.04 355	99.72 303	88.57 365	98.62 338	97.98 357

thres400		96.40 320	95.89 324	97.92 319	99.58 161	96.11 336	99.00 199	97.54 356	98.43 232	98.52 318	96.98 377	86.85 357	99.67 331	87.62 368	98.51 342	97.98 357

TR-MVS		97.44 299	97.15 304	98.32 307	98.53 357	97.46 311	98.47 268	97.91 349	96.85 323	98.21 332	98.51 358	96.42 268	99.51 360	92.16 358	97.29 362	97.98 357

1314		98.00 280	97.90 283	98.27 311	98.90 331	97.45 312	99.30 115	99.06 312	94.98 350	97.21 360	99.12 307	98.43 155	99.67 331	95.58 328	98.56 340	97.71 360

E-PMN		97.14 307	97.43 295	96.27 350	98.79 346	91.62 368	95.54 367	99.01 316	99.44 107	98.88 285	99.12 307	92.78 313	99.68 326	94.30 347	99.03 316	97.50 361

gg-mvs-nofinetune		95.87 332	95.17 336	97.97 317	98.19 365	96.95 323	99.69 34	89.23 378	99.89 11	96.24 366	99.94 13	81.19 369	99.51 360	93.99 353	98.20 348	97.44 362

DeepMVS_CX				97.98 316	99.69 124	96.95 323		99.26 287	75.51 371	95.74 369	98.28 363	96.47 266	99.62 346	91.23 361	97.89 357	97.38 363

OpenMVS_ROB	97.31 17	97.36 302	96.84 313	98.89 278	99.29 278	99.45 141	98.87 220	99.48 226	86.54 369	99.44 191	99.74 88	97.34 240	99.86 198	91.61 359	99.28 301	97.37 364

EMVS		96.96 310	97.28 298	95.99 353	98.76 350	91.03 371	95.26 368	98.61 331	99.34 120	98.92 281	98.88 341	93.79 302	99.66 335	92.87 356	99.05 314	97.30 365

thres100view900		96.39 321	96.03 323	97.47 330	99.63 145	95.93 339	99.18 152	97.57 353	98.75 206	98.70 306	97.31 375	87.04 355	99.67 331	87.62 368	98.51 342	96.81 366

tfpn200view9		96.30 324	95.89 324	97.53 328	99.58 161	96.11 336	99.00 199	97.54 356	98.43 232	98.52 318	96.98 377	86.85 357	99.67 331	87.62 368	98.51 342	96.81 366

API-MVS		98.38 258	98.39 237	98.35 305	98.83 340	99.26 185	99.14 167	99.18 303	98.59 217	98.66 308	98.78 347	98.61 129	99.57 354	94.14 349	99.56 253	96.21 368

thres200		96.09 327	95.68 330	97.33 335	99.48 216	96.22 335	98.53 263	97.57 353	98.06 267	98.37 325	96.73 379	86.84 359	99.61 350	86.99 371	98.57 339	96.16 369

GG-mvs-BLEND				97.36 333	97.59 371	96.87 326	99.70 28	88.49 379		94.64 372	97.26 376	80.66 371	99.12 367	91.50 360	96.50 368	96.08 370

wuyk23d		97.58 295	99.13 121	92.93 355	99.69 124	99.49 129	99.52 73	99.77 63	97.97 272	99.96 8	99.79 65	99.84 3	99.94 57	95.85 320	99.82 147	79.36 371

test123		29.31 341	33.05 346	18.08 357	25.93 381	12.24 381	97.53 339	10.93 382	11.78 375	24.21 376	50.08 384	21.04 380	8.60 376	23.51 374	32.43 375	33.39 372

testmvs		28.94 342	33.33 344	15.79 358	26.03 380	9.81 382	96.77 361	15.67 381	11.55 376	23.87 377	50.74 383	19.03 381	8.53 377	23.21 375	33.07 374	29.03 373

test_blank		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

cdsmvs_eth3d_5k		24.88 343	33.17 345	0.00 359	0.00 382	0.00 383	0.00 370	99.62 140	0.00 377	0.00 378	99.13 303	99.82 4	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		16.61 344	22.14 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	99.28 41	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

ab-mvs-re		8.26 352	11.02 355	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	99.16 301	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		8.33 345	11.11 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	100.00 1	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

FOURS1					99.83 39	99.89 8	99.74 17	99.71 95	99.69 55	99.63 130

test_one_0601					99.63 145	99.76 51		99.55 188	99.23 138	99.31 227	99.61 174	98.59 131

eth-test2					0.00 382

eth-test					0.00 382

ZD-MVS					99.43 234	99.61 108		99.43 243	96.38 331	99.11 262	99.07 312	97.86 208	99.92 91	94.04 351	99.49 272

test_241102_ONE					99.69 124	99.82 28		99.54 194	99.12 160	99.82 52	99.49 228	98.91 87	99.52 359

9.14			98.64 210		99.45 229		98.81 231	99.60 159	97.52 297	99.28 233	99.56 202	98.53 143	99.83 245	95.36 334	99.64 233

save fliter					99.53 188	99.25 189	98.29 282	99.38 262	99.07 164

test0726					99.69 124	99.80 36	99.24 136	99.57 177	99.16 151	99.73 96	99.65 144	98.35 166

test_part2					99.62 149	99.67 87				99.55 166

sam_mvs												90.52 341

MTGPA								99.53 203

test_post1							99.14 167				51.63 382	89.54 348	99.82 255	96.86 271

test_post											52.41 381	90.25 343	99.86 198

patchmatchnet-post											99.62 165	90.58 339	99.94 57

MTMP							99.09 184	98.59 333

gm-plane-assit					97.59 371	89.02 378			93.47 358		98.30 362		99.84 234	96.38 299

TEST9					99.35 255	99.35 170	98.11 298	99.41 246	94.83 355	97.92 344	98.99 324	98.02 195	99.85 217

test_8					99.34 265	99.31 176	98.08 302	99.40 253	94.90 351	97.87 348	98.97 330	98.02 195	99.84 234

agg_prior					99.35 255	99.36 166		99.39 256		97.76 353			99.85 217

test_prior4						99.19 205	98.00 310

test_prior2							97.95 317		97.87 279	98.05 339	99.05 314	97.90 204		95.99 314	99.49 272

旧先验2							97.94 319		95.33 346	98.94 277			99.88 165	96.75 278

新几何2							98.04 306

原ACMM2							97.92 321

testdata2													99.89 150	95.99 314

segment_acmp												98.37 164

testdata1							97.72 329		97.86 282

plane_prior7					99.58 161	99.38 160

plane_prior6					99.47 221	99.26 185						97.24 243

plane_prior4											99.25 287

plane_prior3						99.31 176			98.36 241	99.14 258

plane_prior2							98.80 234		98.94 178

plane_prior1					99.51 198

plane_prior						99.24 194	98.42 274		97.87 279						99.71 206

n2								0.00 383

nn								0.00 383

door-mid								99.83 34

test11								99.29 281

door								99.77 63

HQP5-MVS						98.94 231

HQP-NCC					99.31 272		97.98 313		97.45 300	98.15 333

ACMP_Plane					99.31 272		97.98 313		97.45 300	98.15 333

BP-MVS														94.73 341

HQP3-MVS								99.37 263							99.67 223

HQP2-MVS												96.67 260

NP-MVS					99.40 243	99.13 210					98.83 343

MDTV_nov1_ep13			97.73 289		98.70 353	90.83 372	99.15 165	98.02 346	98.51 226	98.82 293	99.61 174	90.98 332	99.66 335	96.89 270	98.92 322

ACMMP++_ref															99.94 65

ACMMP++															99.79 165

Test By Simon												98.41 158

High-resolution multi-view benchmark