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		95.75 335	95.42 333	96.76 343	89.90 380	94.42 353	98.86 222	97.87 351	78.01 371	99.30 235	99.69 119	97.70 218	95.89 375	99.29 68	98.14 353	99.95 1

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 45	99.68 38	99.85 27	99.95 3	99.98 3	99.92 17	99.28 42	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 39	99.72 24	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 6	99.96 2	99.71 103	99.91 20	99.15 55	99.97 18	99.50 37	100.00 1	99.90 4

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 42	99.70 29	99.86 23	99.89 13	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 83	99.62 28	98.72 292	99.88 25	96.44 333	99.56 71	99.85 27	99.90 7	99.90 22	99.85 41	98.09 190	99.83 246	99.58 26	99.95 53	99.90 4

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 21	99.77 12	99.80 50	99.73 45	99.97 6	99.92 17	99.77 7	99.98 7	99.43 43	100.00 1	99.90 4

RRT_test8_iter05		97.35 304	97.25 301	97.63 328	98.81 345	93.13 360	99.26 128	99.89 15	99.51 93	99.83 50	99.68 130	79.03 378	99.88 167	99.53 32	99.72 204	99.89 8

CVMVSNet		98.61 230	98.88 189	97.80 323	99.58 162	93.60 358	99.26 128	99.64 136	99.66 67	99.72 98	99.67 136	93.26 309	99.93 74	99.30 65	99.81 158	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 25	99.86 16	99.88 25	99.86 13	99.72 24	99.78 61	99.90 7	99.82 52	99.83 47	98.45 154	99.87 180	99.51 35	99.97 33	99.86 11

PS-CasMVS		99.66 27	99.58 39	99.89 7	99.80 58	99.85 16	99.66 46	99.73 84	99.62 75	99.84 45	99.71 106	98.62 127	99.96 36	99.30 65	99.96 45	99.86 11

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 9	99.73 21	99.85 27	99.70 54	99.92 18	99.93 14	99.45 24	99.97 18	99.36 55	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 96	99.93 4	99.95 10	99.89 26	99.71 9	99.96 36	99.51 35	99.97 33	99.84 14

CP-MVSNet		99.54 49	99.43 65	99.87 14	99.76 86	99.82 29	99.57 69	99.61 148	99.54 89	99.80 62	99.64 147	97.79 215	99.95 46	99.21 75	99.94 66	99.84 14

Test_1112_low_res		98.95 193	98.73 203	99.63 117	99.68 134	99.15 209	98.09 301	99.80 50	97.14 317	99.46 192	99.40 251	96.11 279	99.89 152	99.01 104	99.84 131	99.84 14

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 51	100.00 1	99.90 7	100.00 1	99.97 10	99.61 17	99.97 18	99.75 13	100.00 1	99.84 14

patch_mono-2		99.51 52	99.46 59	99.64 111	99.70 121	99.11 212	99.04 192	99.87 20	99.71 49	99.47 188	99.79 65	98.24 176	99.98 7	99.38 51	99.96 45	99.83 18

nrg030		99.70 19	99.66 23	99.82 23	99.76 86	99.84 21	99.61 59	99.70 101	99.93 4	99.78 70	99.68 130	99.10 61	99.78 283	99.45 41	99.96 45	99.83 18

FIs		99.65 32	99.58 39	99.84 19	99.84 35	99.85 16	99.66 46	99.75 76	99.86 21	99.74 92	99.79 65	98.27 174	99.85 218	99.37 54	99.93 74	99.83 18

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 21	99.82 8	99.82 40	99.84 29	99.94 11	99.91 20	99.13 60	99.96 36	99.83 9	99.99 12	99.83 18

PEN-MVS		99.66 27	99.59 35	99.89 7	99.83 39	99.87 10	99.66 46	99.73 84	99.70 54	99.84 45	99.73 93	98.56 136	99.96 36	99.29 68	99.94 66	99.83 18

WR-MVS_H		99.61 38	99.53 51	99.87 14	99.80 58	99.83 25	99.67 42	99.75 76	99.58 88	99.85 42	99.69 119	98.18 186	99.94 60	99.28 70	99.95 53	99.83 18

test_part1		98.63 228	98.26 251	99.75 57	99.40 244	99.49 129	99.67 42	99.68 110	99.86 21	99.88 32	99.86 38	86.73 361	99.93 74	99.34 57	99.97 33	99.81 24

Anonymous20231211		99.62 36	99.57 42	99.76 47	99.61 151	99.60 110	99.81 9	99.73 84	99.82 34	99.90 22	99.90 22	97.97 201	99.86 200	99.42 48	99.96 45	99.80 25

APDe-MVS		99.48 57	99.36 78	99.85 18	99.55 183	99.81 32	99.50 76	99.69 107	98.99 172	99.75 83	99.71 106	98.79 104	99.93 74	98.46 147	99.85 126	99.80 25

DTE-MVSNet		99.68 24	99.61 32	99.88 11	99.80 58	99.87 10	99.67 42	99.71 96	99.72 48	99.84 45	99.78 72	98.67 121	99.97 18	99.30 65	99.95 53	99.80 25

XXY-MVS		99.71 18	99.67 22	99.81 26	99.89 21	99.72 69	99.59 66	99.82 40	99.39 116	99.82 52	99.84 46	99.38 30	99.91 116	99.38 51	99.93 74	99.80 25

1112_ss		99.05 171	98.84 194	99.67 92	99.66 140	99.29 179	98.52 265	99.82 40	97.65 290	99.43 198	99.16 303	96.42 269	99.91 116	99.07 100	99.84 131	99.80 25

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 8	99.90 7	99.97 6	99.87 32	99.81 5	99.95 46	99.54 30	99.99 12	99.80 25
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		98.69 224	98.45 231	99.42 188	99.69 125	98.52 263	96.06 366	96.80 361	99.71 49	99.73 96	99.54 212	95.14 289	99.96 36	99.39 50	99.95 53	99.79 31

PMMVS2		99.48 57	99.45 60	99.57 144	99.76 86	98.99 225	98.09 301	99.90 14	98.95 178	99.78 70	99.58 192	99.57 20	99.93 74	99.48 38	99.95 53	99.79 31

MSC_two_6792asdad				99.74 63	99.03 322	99.53 124		99.23 295					99.92 94	97.77 205	99.69 213	99.78 33

No_MVS				99.74 63	99.03 322	99.53 124		99.23 295					99.92 94	97.77 205	99.69 213	99.78 33

CHOSEN 1792x2688		99.39 83	99.30 91	99.65 104	99.88 25	99.25 189	98.78 239	99.88 18	98.66 211	99.96 8	99.79 65	97.45 234	99.93 74	99.34 57	99.99 12	99.78 33

IU-MVS					99.69 125	99.77 45		99.22 298	97.50 299	99.69 109				97.75 209	99.70 210	99.77 36

test_0728_THIRD									99.18 146	99.62 138	99.61 175	98.58 133	99.91 116	97.72 211	99.80 163	99.77 36

test_0728_SECOND				99.83 21	99.70 121	99.79 39	99.14 167	99.61 148					99.92 94	97.88 194	99.72 204	99.77 36

MSP-MVS		99.04 174	98.79 201	99.81 26	99.78 74	99.73 65	99.35 102	99.57 178	98.54 225	99.54 170	98.99 326	96.81 260	99.93 74	96.97 266	99.53 266	99.77 36
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		99.14 153	98.92 183	99.82 23	99.57 172	99.77 45	98.74 243	99.60 160	98.55 222	99.76 77	99.69 119	98.23 180	99.92 94	96.39 299	99.75 183	99.76 40
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
Baseline_NR-MVSNet		99.49 55	99.37 75	99.82 23	99.91 15	99.84 21	98.83 227	99.86 23	99.68 59	99.65 124	99.88 29	97.67 223	99.87 180	99.03 102	99.86 122	99.76 40

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 25	99.83 6	99.85 27	99.80 38	99.93 14	99.93 14	98.54 139	99.93 74	99.59 23	99.98 24	99.76 40

test_241102_TWO								99.54 195	99.13 158	99.76 77	99.63 157	98.32 171	99.92 94	97.85 200	99.69 213	99.75 43

DP-MVS		99.48 57	99.39 70	99.74 63	99.57 172	99.62 102	99.29 122	99.61 148	99.87 19	99.74 92	99.76 82	98.69 117	99.87 180	98.20 167	99.80 163	99.75 43

v10		99.69 21	99.69 19	99.66 99	99.81 53	99.39 157	99.66 46	99.75 76	99.60 85	99.92 18	99.87 32	98.75 112	99.86 200	99.90 2	99.99 12	99.73 45

EI-MVSNet-UG-set		99.48 57	99.50 53	99.42 188	99.57 172	98.65 257	99.24 136	99.46 235	99.68 59	99.80 62	99.66 140	98.99 77	99.89 152	99.19 80	99.90 88	99.72 46

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 25	99.66 89	99.69 35	99.92 8	99.67 63	99.77 75	99.75 86	99.61 17	99.98 7	99.35 56	99.98 24	99.72 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HyFIR lowres test		98.91 196	98.64 211	99.73 73	99.85 34	99.47 132	98.07 304	99.83 35	98.64 213	99.89 26	99.60 184	92.57 315	100.00 1	99.33 60	99.97 33	99.72 46

EI-MVSNet-Vis-set		99.47 63	99.49 54	99.42 188	99.57 172	98.66 254	99.24 136	99.46 235	99.67 63	99.79 67	99.65 145	98.97 80	99.89 152	99.15 89	99.89 98	99.71 49

v8		99.68 24	99.69 19	99.65 104	99.80 58	99.40 155	99.66 46	99.76 69	99.64 71	99.93 14	99.85 41	98.66 123	99.84 235	99.88 6	99.99 12	99.71 49

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 16	99.75 16	99.86 23	99.70 54	99.91 20	99.89 26	99.60 19	99.87 180	99.59 23	99.74 191	99.71 49

test1111		97.74 288	98.16 262	96.49 349	99.60 153	89.86 378	99.71 28	91.21 375	99.89 13	99.88 32	99.87 32	93.73 305	99.90 136	99.56 28	99.99 12	99.70 52

VPA-MVSNet		99.66 27	99.62 28	99.79 34	99.68 134	99.75 56	99.62 54	99.69 107	99.85 26	99.80 62	99.81 57	98.81 97	99.91 116	99.47 39	99.88 106	99.70 52

WR-MVS		99.11 161	98.93 179	99.66 99	99.30 277	99.42 150	98.42 275	99.37 264	99.04 170	99.57 156	99.20 300	96.89 258	99.86 200	98.66 139	99.87 115	99.70 52

ACMH	98.42 6	99.59 39	99.54 47	99.72 79	99.86 31	99.62 102	99.56 71	99.79 56	98.77 203	99.80 62	99.85 41	99.64 13	99.85 218	98.70 135	99.89 98	99.70 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 5	99.83 6	99.91 11	99.85 26	99.94 11	99.95 12	99.73 8	99.90 136	99.65 17	99.97 33	99.69 56

HPM-MVS_fast		99.43 69	99.30 91	99.80 29	99.83 39	99.81 32	99.52 74	99.70 101	98.35 247	99.51 182	99.50 224	99.31 38	99.88 167	98.18 171	99.84 131	99.69 56

LPG-MVS_test		99.22 130	99.05 150	99.74 63	99.82 46	99.63 100	99.16 163	99.73 84	97.56 293	99.64 126	99.69 119	99.37 32	99.89 152	96.66 285	99.87 115	99.69 56

LGP-MVS_train				99.74 63	99.82 46	99.63 100		99.73 84	97.56 293	99.64 126	99.69 119	99.37 32	99.89 152	96.66 285	99.87 115	99.69 56

SteuartSystems-ACMMP		99.30 107	99.14 119	99.76 47	99.87 29	99.66 89	99.18 152	99.60 160	98.55 222	99.57 156	99.67 136	99.03 74	99.94 60	97.01 264	99.80 163	99.69 56
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		98.52 244	98.39 238	98.94 266	99.15 303	97.39 315	98.18 290	99.21 302	98.89 189	99.23 243	99.63 157	97.37 240	99.74 299	94.22 349	99.61 244	99.69 56

ACMMP_NAP		99.28 110	99.11 129	99.79 34	99.75 97	99.81 32	98.95 213	99.53 204	98.27 256	99.53 175	99.73 93	98.75 112	99.87 180	97.70 216	99.83 141	99.68 62

HFP-MVS		99.25 117	99.08 140	99.76 47	99.73 106	99.70 78	99.31 112	99.59 167	98.36 242	99.36 217	99.37 258	98.80 101	99.91 116	97.43 237	99.75 183	99.68 62

#test#		99.12 157	98.90 187	99.76 47	99.73 106	99.70 78	99.10 180	99.59 167	97.60 292	99.36 217	99.37 258	98.80 101	99.91 116	96.84 275	99.75 183	99.68 62

EI-MVSNet		99.38 85	99.44 62	99.21 239	99.58 162	98.09 290	99.26 128	99.46 235	99.62 75	99.75 83	99.67 136	98.54 139	99.85 218	99.15 89	99.92 78	99.68 62

TranMVSNet+NR-MVSNet		99.54 49	99.47 55	99.76 47	99.58 162	99.64 96	99.30 115	99.63 138	99.61 79	99.71 103	99.56 203	98.76 110	99.96 36	99.14 95	99.92 78	99.68 62

PVSNet_Blended_VisFu		99.40 79	99.38 72	99.44 182	99.90 19	98.66 254	98.94 215	99.91 11	97.97 273	99.79 67	99.73 93	99.05 72	99.97 18	99.15 89	99.99 12	99.68 62

IterMVS-LS		99.41 76	99.47 55	99.25 234	99.81 53	98.09 290	98.85 224	99.76 69	99.62 75	99.83 50	99.64 147	98.54 139	99.97 18	99.15 89	99.99 12	99.68 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MP-MVS-pluss		99.14 153	98.92 183	99.80 29	99.83 39	99.83 25	98.61 250	99.63 138	96.84 325	99.44 194	99.58 192	98.81 97	99.91 116	97.70 216	99.82 150	99.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		99.23 121	99.05 150	99.77 40	99.76 86	99.70 78	99.31 112	99.59 167	98.41 236	99.32 226	99.36 263	98.73 115	99.93 74	97.29 244	99.74 191	99.67 69

Regformer-4		99.45 66	99.44 62	99.50 164	99.52 196	98.94 232	99.17 157	99.53 204	99.64 71	99.76 77	99.60 184	98.96 83	99.90 136	98.91 118	99.84 131	99.67 69

XVS		99.27 114	99.11 129	99.75 57	99.71 113	99.71 71	99.37 98	99.61 148	99.29 127	98.76 302	99.47 237	98.47 150	99.88 167	97.62 224	99.73 198	99.67 69

v1240		99.56 44	99.58 39	99.51 161	99.80 58	99.00 224	99.00 200	99.65 130	99.15 156	99.90 22	99.75 86	99.09 63	99.88 167	99.90 2	99.96 45	99.67 69

X-MVStestdata		96.09 328	94.87 338	99.75 57	99.71 113	99.71 71	99.37 98	99.61 148	99.29 127	98.76 302	61.30 381	98.47 150	99.88 167	97.62 224	99.73 198	99.67 69

VPNet		99.46 64	99.37 75	99.71 83	99.82 46	99.59 113	99.48 80	99.70 101	99.81 35	99.69 109	99.58 192	97.66 227	99.86 200	99.17 85	99.44 279	99.67 69

ACMMPR		99.23 121	99.06 146	99.76 47	99.74 103	99.69 82	99.31 112	99.59 167	98.36 242	99.35 219	99.38 257	98.61 129	99.93 74	97.43 237	99.75 183	99.67 69

SixPastTwentyTwo		99.42 72	99.30 91	99.76 47	99.92 14	99.67 87	99.70 29	99.14 308	99.65 69	99.89 26	99.90 22	96.20 277	99.94 60	99.42 48	99.92 78	99.67 69

HPM-MVS		99.25 117	99.07 144	99.78 37	99.81 53	99.75 56	99.61 59	99.67 115	97.72 287	99.35 219	99.25 289	99.23 48	99.92 94	97.21 255	99.82 150	99.67 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v144192		99.55 47	99.54 47	99.58 139	99.78 74	99.20 204	99.11 179	99.62 141	99.18 146	99.89 26	99.72 99	98.66 123	99.87 180	99.88 6	99.97 33	99.66 79

v1921920		99.56 44	99.57 42	99.55 151	99.75 97	99.11 212	99.05 190	99.61 148	99.15 156	99.88 32	99.71 106	99.08 67	99.87 180	99.90 2	99.97 33	99.66 79

v1192		99.57 41	99.57 42	99.57 144	99.77 82	99.22 198	99.04 192	99.60 160	99.18 146	99.87 39	99.72 99	99.08 67	99.85 218	99.89 5	99.98 24	99.66 79

PGM-MVS		99.20 137	99.01 162	99.77 40	99.75 97	99.71 71	99.16 163	99.72 93	97.99 271	99.42 200	99.60 184	98.81 97	99.93 74	96.91 269	99.74 191	99.66 79

mPP-MVS		99.19 140	99.00 165	99.76 47	99.76 86	99.68 85	99.38 94	99.54 195	98.34 251	99.01 273	99.50 224	98.53 143	99.93 74	97.18 257	99.78 174	99.66 79

CP-MVS		99.23 121	99.05 150	99.75 57	99.66 140	99.66 89	99.38 94	99.62 141	98.38 240	99.06 271	99.27 284	98.79 104	99.94 60	97.51 233	99.82 150	99.66 79

EG-PatchMatch MVS		99.57 41	99.56 46	99.62 126	99.77 82	99.33 173	99.26 128	99.76 69	99.32 125	99.80 62	99.78 72	99.29 40	99.87 180	99.15 89	99.91 87	99.66 79

UGNet		99.38 85	99.34 80	99.49 167	98.90 332	98.90 240	99.70 29	99.35 268	99.86 21	98.57 316	99.81 57	98.50 149	99.93 74	99.38 51	99.98 24	99.66 79
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test2506		94.73 339	94.59 341	95.15 355	99.59 157	85.90 380	99.75 16	74.01 381	99.89 13	99.71 103	99.86 38	79.00 379	99.90 136	99.52 34	99.99 12	99.65 87

ECVR-MVS		97.73 289	98.04 268	96.78 342	99.59 157	90.81 374	99.72 24	90.43 377	99.89 13	99.86 40	99.86 38	93.60 307	99.89 152	99.46 40	99.99 12	99.65 87

h-mvs33		98.61 230	98.34 244	99.44 182	99.60 153	98.67 252	99.27 126	99.44 240	99.68 59	99.32 226	99.49 229	92.50 318	100.00 1	99.24 72	96.51 368	99.65 87

TSAR-MVS + MP.		99.34 98	99.24 107	99.63 117	99.82 46	99.37 163	99.26 128	99.35 268	98.77 203	99.57 156	99.70 113	99.27 45	99.88 167	97.71 213	99.75 183	99.65 87
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		99.30 107	99.14 119	99.80 29	99.81 53	99.81 32	98.73 245	99.53 204	99.27 131	99.42 200	99.63 157	98.21 181	99.95 46	97.83 203	99.79 168	99.65 87

MTAPA		99.35 93	99.20 111	99.80 29	99.81 53	99.81 32	99.33 105	99.53 204	99.27 131	99.42 200	99.63 157	98.21 181	99.95 46	97.83 203	99.79 168	99.65 87

Regformer-3		99.41 76	99.41 68	99.40 198	99.52 196	98.70 250	99.17 157	99.44 240	99.62 75	99.75 83	99.60 184	98.90 90	99.85 218	98.89 119	99.84 131	99.65 87

MCST-MVS		99.02 177	98.81 198	99.65 104	99.58 162	99.49 129	98.58 254	99.07 311	98.40 238	99.04 272	99.25 289	98.51 148	99.80 277	97.31 243	99.51 269	99.65 87

UniMVSNet_NR-MVSNet		99.37 88	99.25 105	99.72 79	99.47 223	99.56 119	98.97 211	99.61 148	99.43 113	99.67 116	99.28 282	97.85 211	99.95 46	99.17 85	99.81 158	99.65 87

ZNCC-MVS		99.22 130	99.04 156	99.77 40	99.76 86	99.73 65	99.28 123	99.56 183	98.19 261	99.14 260	99.29 280	98.84 96	99.92 94	97.53 232	99.80 163	99.64 96

v1144		99.54 49	99.53 51	99.59 134	99.79 68	99.28 181	99.10 180	99.61 148	99.20 144	99.84 45	99.73 93	98.67 121	99.84 235	99.86 8	99.98 24	99.64 96

v2v482		99.50 53	99.47 55	99.58 139	99.78 74	99.25 189	99.14 167	99.58 176	99.25 135	99.81 59	99.62 166	98.24 176	99.84 235	99.83 9	99.97 33	99.64 96

K. test v3		98.87 204	98.60 214	99.69 88	99.93 13	99.46 136	99.74 18	94.97 368	99.78 41	99.88 32	99.88 29	93.66 306	99.97 18	99.61 21	99.95 53	99.64 96

DeepC-MVS	98.90 4	99.62 36	99.61 32	99.67 92	99.72 110	99.44 143	99.24 136	99.71 96	99.27 131	99.93 14	99.90 22	99.70 11	99.93 74	98.99 105	99.99 12	99.64 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		98.56 238	98.17 261	99.72 79	99.45 231	99.60 110	98.88 218	99.50 220	96.88 322	99.18 255	99.48 232	97.08 253	99.92 94	93.69 356	99.38 288	99.63 101

SMA-MVS		99.19 140	99.00 165	99.73 73	99.46 228	99.73 65	99.13 173	99.52 212	97.40 304	99.57 156	99.64 147	98.93 84	99.83 246	97.61 226	99.79 168	99.63 101
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
IterMVS-SCA-FT		99.00 183	99.16 115	98.51 299	99.75 97	95.90 341	98.07 304	99.84 33	99.84 29	99.89 26	99.73 93	96.01 281	99.99 5	99.33 60	100.00 1	99.63 101

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 25	99.76 14	99.87 20	99.73 45	99.89 26	99.87 32	99.63 14	99.87 180	99.54 30	99.92 78	99.63 101

MP-MVS		99.06 168	98.83 196	99.76 47	99.76 86	99.71 71	99.32 108	99.50 220	98.35 247	98.97 275	99.48 232	98.37 164	99.92 94	95.95 319	99.75 183	99.63 101
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DU-MVS		99.33 102	99.21 110	99.71 83	99.43 235	99.56 119	98.83 227	99.53 204	99.38 117	99.67 116	99.36 263	97.67 223	99.95 46	99.17 85	99.81 158	99.63 101

NR-MVSNet		99.40 79	99.31 86	99.68 89	99.43 235	99.55 122	99.73 21	99.50 220	99.46 105	99.88 32	99.36 263	97.54 231	99.87 180	98.97 109	99.87 115	99.63 101

IterMVS		98.97 187	99.16 115	98.42 303	99.74 103	95.64 344	98.06 306	99.83 35	99.83 32	99.85 42	99.74 89	96.10 280	99.99 5	99.27 71	100.00 1	99.63 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPP-MVSNet		99.17 148	99.00 165	99.66 99	99.80 58	99.43 147	99.70 29	99.24 294	99.48 96	99.56 163	99.77 79	94.89 291	99.93 74	98.72 134	99.89 98	99.63 101

ACMMP		99.25 117	99.08 140	99.74 63	99.79 68	99.68 85	99.50 76	99.65 130	98.07 267	99.52 177	99.69 119	98.57 134	99.92 94	97.18 257	99.79 168	99.63 101
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
DeepC-MVS_fast	98.47 5	99.23 121	99.12 126	99.56 148	99.28 282	99.22 198	98.99 205	99.40 254	99.08 163	99.58 153	99.64 147	98.90 90	99.83 246	97.44 236	99.75 183	99.63 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS++		99.38 85	99.25 105	99.77 40	99.03 322	99.77 45	99.74 18	99.61 148	99.18 146	99.76 77	99.61 175	99.00 75	99.92 94	97.72 211	99.60 247	99.62 112

PC_three_1452									97.56 293	99.68 111	99.41 247	99.09 63	97.09 374	96.66 285	99.60 247	99.62 112

GeoE		99.69 21	99.66 23	99.78 37	99.76 86	99.76 52	99.60 64	99.82 40	99.46 105	99.75 83	99.56 203	99.63 14	99.95 46	99.43 43	99.88 106	99.62 112

test_method		91.72 340	92.32 343	89.91 357	93.49 379	70.18 381	90.28 370	99.56 183	61.71 374	95.39 371	99.52 217	93.90 300	99.94 60	98.76 130	98.27 348	99.62 112

GST-MVS		99.16 149	98.96 176	99.75 57	99.73 106	99.73 65	99.20 146	99.55 189	98.22 258	99.32 226	99.35 268	98.65 125	99.91 116	96.86 272	99.74 191	99.62 112

new-patchmatchnet		99.35 93	99.57 42	98.71 294	99.82 46	96.62 331	98.55 260	99.75 76	99.50 94	99.88 32	99.87 32	99.31 38	99.88 167	99.43 43	100.00 1	99.62 112

RRT_MVS		98.75 216	98.54 224	99.41 196	98.14 370	98.61 258	98.98 209	99.66 119	99.31 126	99.84 45	99.75 86	91.98 321	99.98 7	99.20 78	99.95 53	99.62 112

CPTT-MVS		98.74 218	98.44 233	99.64 111	99.61 151	99.38 160	99.18 152	99.55 189	96.49 330	99.27 237	99.37 258	97.11 252	99.92 94	95.74 326	99.67 225	99.62 112

MIMVSNet1		99.66 27	99.62 28	99.80 29	99.94 10	99.87 10	99.69 35	99.77 64	99.78 41	99.93 14	99.89 26	97.94 202	99.92 94	99.65 17	99.98 24	99.62 112

DeepPCF-MVS	98.42 6	99.18 144	99.02 159	99.67 92	99.22 291	99.75 56	97.25 352	99.47 231	98.72 208	99.66 120	99.70 113	99.29 40	99.63 346	98.07 180	99.81 158	99.62 112

3Dnovator+	98.92 3	99.35 93	99.24 107	99.67 92	99.35 256	99.47 132	99.62 54	99.50 220	99.44 108	99.12 263	99.78 72	98.77 109	99.94 60	97.87 197	99.72 204	99.62 112

DVP-MVS		99.32 104	99.17 114	99.77 40	99.69 125	99.80 37	99.14 167	99.31 277	99.16 152	99.62 138	99.61 175	98.35 166	99.91 116	97.88 194	99.72 204	99.61 123
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
APD-MVS		98.87 204	98.59 216	99.71 83	99.50 207	99.62 102	99.01 198	99.57 178	96.80 327	99.54 170	99.63 157	98.29 172	99.91 116	95.24 336	99.71 208	99.61 123
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.82 209	98.57 220	99.58 139	99.21 293	99.31 176	98.61 250	99.25 291	98.65 212	98.43 324	99.26 287	97.86 209	99.81 272	96.55 290	99.27 305	99.61 123

TAMVS		99.49 55	99.45 60	99.63 117	99.48 218	99.42 150	99.45 83	99.57 178	99.66 67	99.78 70	99.83 47	97.85 211	99.86 200	99.44 42	99.96 45	99.61 123

Regformer-1		99.32 104	99.27 101	99.47 173	99.41 241	98.95 231	98.99 205	99.48 227	99.48 96	99.66 120	99.52 217	98.78 106	99.87 180	98.36 152	99.74 191	99.60 127

Regformer-2		99.34 98	99.27 101	99.53 157	99.41 241	99.10 217	98.99 205	99.53 204	99.47 101	99.66 120	99.52 217	98.80 101	99.89 152	98.31 158	99.74 191	99.60 127

HPM-MVS++		98.96 190	98.70 208	99.74 63	99.52 196	99.71 71	98.86 222	99.19 303	98.47 232	98.59 314	99.06 315	98.08 192	99.91 116	96.94 267	99.60 247	99.60 127

V42		99.56 44	99.54 47	99.63 117	99.79 68	99.46 136	99.39 92	99.59 167	99.24 137	99.86 40	99.70 113	98.55 137	99.82 256	99.79 11	99.95 53	99.60 127

HQP_MVS		98.90 198	98.68 210	99.55 151	99.58 162	99.24 194	98.80 235	99.54 195	98.94 179	99.14 260	99.25 289	97.24 244	99.82 256	95.84 322	99.78 174	99.60 127

plane_prior5								99.54 195					99.82 256	95.84 322	99.78 174	99.60 127

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 16	99.86 5	99.92 8	99.69 57	99.78 70	99.92 17	99.37 32	99.88 167	98.93 117	99.95 53	99.60 127

ACMH+	98.40 8	99.50 53	99.43 65	99.71 83	99.86 31	99.76 52	99.32 108	99.77 64	99.53 91	99.77 75	99.76 82	99.26 46	99.78 283	97.77 205	99.88 106	99.60 127

ACMM	98.09 11	99.46 64	99.38 72	99.72 79	99.80 58	99.69 82	99.13 173	99.65 130	98.99 172	99.64 126	99.72 99	99.39 26	99.86 200	98.23 164	99.81 158	99.60 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		98.97 187	98.82 197	99.42 188	99.71 113	98.81 244	99.62 54	98.68 328	99.81 35	99.38 215	99.80 59	94.25 298	99.85 218	98.79 126	99.32 298	99.59 136

casdiffmvs		99.63 33	99.61 32	99.67 92	99.79 68	99.59 113	99.13 173	99.85 27	99.79 40	99.76 77	99.72 99	99.33 37	99.82 256	99.21 75	99.94 66	99.59 136

UniMVSNet (Re)		99.37 88	99.26 103	99.68 89	99.51 201	99.58 116	98.98 209	99.60 160	99.43 113	99.70 106	99.36 263	97.70 218	99.88 167	99.20 78	99.87 115	99.59 136

DSMNet-mixed		99.48 57	99.65 25	98.95 265	99.71 113	97.27 317	99.50 76	99.82 40	99.59 87	99.41 208	99.85 41	99.62 16	100.00 1	99.53 32	99.89 98	99.59 136

3Dnovator	99.15 2	99.43 69	99.36 78	99.65 104	99.39 246	99.42 150	99.70 29	99.56 183	99.23 139	99.35 219	99.80 59	99.17 53	99.95 46	98.21 166	99.84 131	99.59 136

SED-MVS		99.40 79	99.28 98	99.77 40	99.69 125	99.82 29	99.20 146	99.54 195	99.13 158	99.82 52	99.63 157	98.91 87	99.92 94	97.85 200	99.70 210	99.58 141

OPU-MVS				99.29 225	99.12 308	99.44 143	99.20 146				99.40 251	99.00 75	98.84 371	96.54 291	99.60 247	99.58 141

abl_6		99.36 91	99.23 109	99.75 57	99.71 113	99.74 62	99.33 105	99.76 69	99.07 165	99.65 124	99.63 157	99.09 63	99.92 94	97.13 260	99.76 180	99.58 141

EPNet		98.13 274	97.77 289	99.18 244	94.57 378	97.99 294	99.24 136	97.96 348	99.74 44	97.29 359	99.62 166	93.13 311	99.97 18	98.59 141	99.83 141	99.58 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.03 175	98.85 192	99.55 151	99.80 58	99.25 189	99.73 21	99.15 307	99.37 118	99.61 144	99.71 106	94.73 294	99.81 272	97.70 216	99.88 106	99.58 141

ACMP	97.51 14	99.05 171	98.84 194	99.67 92	99.78 74	99.55 122	98.88 218	99.66 119	97.11 319	99.47 188	99.60 184	99.07 69	99.89 152	96.18 308	99.85 126	99.58 141
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		99.23 121	99.05 150	99.74 63	99.52 196	99.75 56	99.20 146	99.61 148	98.97 174	99.48 186	99.58 192	98.41 158	99.91 116	97.15 259	99.55 258	99.57 147

SR-MVS		99.19 140	99.00 165	99.74 63	99.51 201	99.72 69	99.18 152	99.60 160	98.85 192	99.47 188	99.58 192	98.38 163	99.92 94	96.92 268	99.54 264	99.57 147

lessismore_v0				99.64 111	99.86 31	99.38 160		90.66 376		99.89 26	99.83 47	94.56 296	99.97 18	99.56 28	99.92 78	99.57 147

pmmvs5		99.19 140	99.11 129	99.42 188	99.76 86	98.88 241	98.55 260	99.73 84	98.82 196	99.72 98	99.62 166	96.56 263	99.82 256	99.32 62	99.95 53	99.56 150

APD-MVS_3200maxsize		99.31 106	99.16 115	99.74 63	99.53 191	99.75 56	99.27 126	99.61 148	99.19 145	99.57 156	99.64 147	98.76 110	99.90 136	97.29 244	99.62 237	99.56 150

CDPH-MVS		98.56 238	98.20 256	99.61 129	99.50 207	99.46 136	98.32 281	99.41 247	95.22 348	99.21 249	99.10 312	98.34 168	99.82 256	95.09 339	99.66 229	99.56 150

Anonymous20240521		99.44 68	99.42 67	99.49 167	99.89 21	98.96 230	99.62 54	99.76 69	99.85 26	99.82 52	99.88 29	96.39 272	99.97 18	99.59 23	99.98 24	99.55 153

our_test_3		98.85 206	99.09 138	98.13 315	99.66 140	94.90 351	97.72 330	99.58 176	99.07 165	99.64 126	99.62 166	98.19 184	99.93 74	98.41 149	99.95 53	99.55 153

YYNet1		98.95 193	98.99 170	98.84 282	99.64 144	97.14 321	98.22 289	99.32 273	98.92 184	99.59 149	99.66 140	97.40 236	99.83 246	98.27 161	99.90 88	99.55 153

MDA-MVSNet_test_wron		98.95 193	98.99 170	98.85 280	99.64 144	97.16 320	98.23 288	99.33 271	98.93 182	99.56 163	99.66 140	97.39 238	99.83 246	98.29 159	99.88 106	99.55 153

MVSFormer		99.41 76	99.44 62	99.31 222	99.57 172	98.40 271	99.77 12	99.80 50	99.73 45	99.63 130	99.30 277	98.02 196	99.98 7	99.43 43	99.69 213	99.55 153

jason		99.16 149	99.11 129	99.32 219	99.75 97	98.44 268	98.26 286	99.39 257	98.70 209	99.74 92	99.30 277	98.54 139	99.97 18	98.48 146	99.82 150	99.55 153
jason: jason.
CDS-MVSNet		99.22 130	99.13 122	99.50 164	99.35 256	99.11 212	98.96 212	99.54 195	99.46 105	99.61 144	99.70 113	96.31 274	99.83 246	99.34 57	99.88 106	99.55 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
COLMAP_ROB	98.06 12	99.45 66	99.37 75	99.70 87	99.83 39	99.70 78	99.38 94	99.78 61	99.53 91	99.67 116	99.78 72	99.19 51	99.86 200	97.32 242	99.87 115	99.55 153
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SR-MVS-dyc-post		99.27 114	99.11 129	99.73 73	99.54 185	99.74 62	99.26 128	99.62 141	99.16 152	99.52 177	99.64 147	98.41 158	99.91 116	97.27 247	99.61 244	99.54 161

RE-MVS-def			99.13 122		99.54 185	99.74 62	99.26 128	99.62 141	99.16 152	99.52 177	99.64 147	98.57 134		97.27 247	99.61 244	99.54 161

SD-MVS		99.01 181	99.30 91	98.15 314	99.50 207	99.40 155	98.94 215	99.61 148	99.22 143	99.75 83	99.82 54	99.54 22	95.51 376	97.48 234	99.87 115	99.54 161
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CNVR-MVS		98.99 186	98.80 200	99.56 148	99.25 287	99.43 147	98.54 263	99.27 286	98.58 219	98.80 297	99.43 245	98.53 143	99.70 310	97.22 254	99.59 251	99.54 161

MVS_111021_HR		99.12 157	99.02 159	99.40 198	99.50 207	99.11 212	97.92 322	99.71 96	98.76 206	99.08 267	99.47 237	99.17 53	99.54 356	97.85 200	99.76 180	99.54 161

v148		99.40 79	99.41 68	99.39 201	99.76 86	98.94 232	99.09 184	99.59 167	99.17 150	99.81 59	99.61 175	98.41 158	99.69 316	99.32 62	99.94 66	99.53 166

diffmvs		99.34 98	99.32 85	99.39 201	99.67 139	98.77 247	98.57 258	99.81 49	99.61 79	99.48 186	99.41 247	98.47 150	99.86 200	98.97 109	99.90 88	99.53 166

baseline		99.63 33	99.62 28	99.66 99	99.80 58	99.62 102	99.44 86	99.80 50	99.71 49	99.72 98	99.69 119	99.15 55	99.83 246	99.32 62	99.94 66	99.53 166

HQP4-MVS										98.15 334			99.70 310			99.53 166

GBi-Net		99.42 72	99.31 86	99.73 73	99.49 212	99.77 45	99.68 38	99.70 101	99.44 108	99.62 138	99.83 47	97.21 246	99.90 136	98.96 111	99.90 88	99.53 166

test1		99.42 72	99.31 86	99.73 73	99.49 212	99.77 45	99.68 38	99.70 101	99.44 108	99.62 138	99.83 47	97.21 246	99.90 136	98.96 111	99.90 88	99.53 166

FMVSNet1		99.66 27	99.63 27	99.73 73	99.78 74	99.77 45	99.68 38	99.70 101	99.67 63	99.82 52	99.83 47	98.98 78	99.90 136	99.24 72	99.97 33	99.53 166

HQP-MVS		98.36 260	98.02 270	99.39 201	99.31 273	98.94 232	97.98 314	99.37 264	97.45 301	98.15 334	98.83 345	96.67 261	99.70 310	94.73 342	99.67 225	99.53 166

QAPM		98.40 258	97.99 271	99.65 104	99.39 246	99.47 132	99.67 42	99.52 212	91.70 364	98.78 300	99.80 59	98.55 137	99.95 46	94.71 344	99.75 183	99.53 166

F-COLMAP		98.74 218	98.45 231	99.62 126	99.57 172	99.47 132	98.84 225	99.65 130	96.31 334	98.93 279	99.19 302	97.68 222	99.87 180	96.52 292	99.37 292	99.53 166

MVSTER		98.47 251	98.22 254	99.24 236	99.06 318	98.35 276	99.08 187	99.46 235	99.27 131	99.75 83	99.66 140	88.61 351	99.85 218	99.14 95	99.92 78	99.52 176

PVSNet_BlendedMVS		99.03 175	99.01 162	99.09 253	99.54 185	97.99 294	98.58 254	99.82 40	97.62 291	99.34 222	99.71 106	98.52 146	99.77 291	97.98 186	99.97 33	99.52 176

OPM-MVS		99.26 116	99.13 122	99.63 117	99.70 121	99.61 108	98.58 254	99.48 227	98.50 228	99.52 177	99.63 157	99.14 58	99.76 293	97.89 193	99.77 178	99.51 178
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior1		98.33 265	97.92 281	99.57 144	99.35 256	99.36 166	97.99 313	99.39 257	94.85 355	97.76 354	98.98 329	98.03 194	99.85 218	95.49 330	99.44 279	99.51 178

AllTest		99.21 135	99.07 144	99.63 117	99.78 74	99.64 96	99.12 177	99.83 35	98.63 214	99.63 130	99.72 99	98.68 118	99.75 297	96.38 300	99.83 141	99.51 178

TestCases				99.63 117	99.78 74	99.64 96		99.83 35	98.63 214	99.63 130	99.72 99	98.68 118	99.75 297	96.38 300	99.83 141	99.51 178

BH-RMVSNet		98.41 256	98.14 264	99.21 239	99.21 293	98.47 265	98.60 252	98.26 345	98.35 247	98.93 279	99.31 275	97.20 249	99.66 336	94.32 347	99.10 313	99.51 178

USDC		98.96 190	98.93 179	99.05 259	99.54 185	97.99 294	97.07 358	99.80 50	98.21 259	99.75 83	99.77 79	98.43 155	99.64 345	97.90 192	99.88 106	99.51 178

test9_res														95.10 338	99.44 279	99.50 184

train_agg		98.35 263	97.95 275	99.57 144	99.35 256	99.35 170	98.11 299	99.41 247	94.90 352	97.92 345	98.99 326	98.02 196	99.85 218	95.38 334	99.44 279	99.50 184

agg_prior2														94.58 346	99.46 278	99.50 184

VDD-MVS		99.20 137	99.11 129	99.44 182	99.43 235	98.98 226	99.50 76	98.32 344	99.80 38	99.56 163	99.69 119	96.99 256	99.85 218	98.99 105	99.73 198	99.50 184

MDA-MVSNet-bldmvs		99.06 168	99.05 150	99.07 257	99.80 58	97.83 301	98.89 217	99.72 93	99.29 127	99.63 130	99.70 113	96.47 267	99.89 152	98.17 173	99.82 150	99.50 184

KD-MVS_self_test		99.63 33	99.59 35	99.76 47	99.84 35	99.90 5	99.37 98	99.79 56	99.83 32	99.88 32	99.85 41	98.42 157	99.90 136	99.60 22	99.73 198	99.49 189

xxxxxxxxxxxxxcwj		99.11 161	98.96 176	99.54 155	99.53 191	99.25 189	98.29 283	99.76 69	99.07 165	99.42 200	99.61 175	98.86 93	99.87 180	96.45 297	99.68 218	99.49 189

SF-MVS		99.10 165	98.93 179	99.62 126	99.58 162	99.51 127	99.13 173	99.65 130	97.97 273	99.42 200	99.61 175	98.86 93	99.87 180	96.45 297	99.68 218	99.49 189

Anonymous20240529		99.42 72	99.34 80	99.65 104	99.53 191	99.60 110	99.63 53	99.39 257	99.47 101	99.76 77	99.78 72	98.13 188	99.86 200	98.70 135	99.68 218	99.49 189

WTY-MVS		98.59 235	98.37 240	99.26 231	99.43 235	98.40 271	98.74 243	99.13 310	98.10 264	99.21 249	99.24 294	94.82 292	99.90 136	97.86 198	98.77 330	99.49 189

ppachtmachnet_test		98.89 201	99.12 126	98.20 313	99.66 140	95.24 348	97.63 334	99.68 110	99.08 163	99.78 70	99.62 166	98.65 125	99.88 167	98.02 181	99.96 45	99.48 194

Anonymous20231206		99.35 93	99.31 86	99.47 173	99.74 103	99.06 223	99.28 123	99.74 81	99.23 139	99.72 98	99.53 215	97.63 229	99.88 167	99.11 97	99.84 131	99.48 194

test_prior3		98.62 229	98.34 244	99.46 176	99.35 256	99.22 198	97.95 318	99.39 257	97.87 280	98.05 340	99.05 316	97.90 205	99.69 316	95.99 315	99.49 273	99.48 194

test_prior				99.46 176	99.35 256	99.22 198		99.39 257					99.69 316			99.48 194

test12				99.54 155	99.29 279	99.33 173		99.16 306		98.43 324		97.54 231	99.82 256		99.47 276	99.48 194

VNet		99.18 144	99.06 146	99.56 148	99.24 289	99.36 166	99.33 105	99.31 277	99.67 63	99.47 188	99.57 200	96.48 266	99.84 235	99.15 89	99.30 300	99.47 199

test20.03		99.55 47	99.54 47	99.58 139	99.79 68	99.37 163	99.02 196	99.89 15	99.60 85	99.82 52	99.62 166	98.81 97	99.89 152	99.43 43	99.86 122	99.47 199

114514_t		98.49 249	98.11 265	99.64 111	99.73 106	99.58 116	99.24 136	99.76 69	89.94 367	99.42 200	99.56 203	97.76 217	99.86 200	97.74 210	99.82 150	99.47 199

sss		98.90 198	98.77 202	99.27 229	99.48 218	98.44 268	98.72 246	99.32 273	97.94 277	99.37 216	99.35 268	96.31 274	99.91 116	98.85 121	99.63 236	99.47 199

旧先验1					99.49 212	99.29 179		99.26 288			99.39 255	97.67 223			99.36 293	99.46 203

1121		98.56 238	98.24 252	99.52 158	99.49 212	99.24 194	99.30 115	99.22 298	95.77 341	98.52 319	99.29 280	97.39 238	99.85 218	95.79 324	99.34 295	99.46 203

MVP-Stereo		99.16 149	99.08 140	99.43 186	99.48 218	99.07 221	99.08 187	99.55 189	98.63 214	99.31 230	99.68 130	98.19 184	99.78 283	98.18 171	99.58 252	99.45 205
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
新几何1				99.52 158	99.50 207	99.22 198		99.26 288	95.66 344	98.60 313	99.28 282	97.67 223	99.89 152	95.95 319	99.32 298	99.45 205

LFMVS		98.46 252	98.19 259	99.26 231	99.24 289	98.52 263	99.62 54	96.94 360	99.87 19	99.31 230	99.58 192	91.04 332	99.81 272	98.68 138	99.42 284	99.45 205

testgi		99.29 109	99.26 103	99.37 208	99.75 97	98.81 244	98.84 225	99.89 15	98.38 240	99.75 83	99.04 319	99.36 35	99.86 200	99.08 99	99.25 306	99.45 205

UnsupCasMVSNet_eth		98.83 207	98.57 220	99.59 134	99.68 134	99.45 141	98.99 205	99.67 115	99.48 96	99.55 168	99.36 263	94.92 290	99.86 200	98.95 115	96.57 367	99.45 205

无先验							98.01 309	99.23 295	95.83 340				99.85 218	95.79 324		99.44 210

testdata				99.42 188	99.51 201	98.93 236		99.30 280	96.20 335	98.87 289	99.40 251	98.33 170	99.89 152	96.29 303	99.28 302	99.44 210

XVG-OURS-SEG-HR		99.16 149	98.99 170	99.66 99	99.84 35	99.64 96	98.25 287	99.73 84	98.39 239	99.63 130	99.43 245	99.70 11	99.90 136	97.34 241	98.64 338	99.44 210

FMVSNet2		99.35 93	99.28 98	99.55 151	99.49 212	99.35 170	99.45 83	99.57 178	99.44 108	99.70 106	99.74 89	97.21 246	99.87 180	99.03 102	99.94 66	99.44 210

N_pmnet		98.73 220	98.53 226	99.35 212	99.72 110	98.67 252	98.34 278	94.65 369	98.35 247	99.79 67	99.68 130	98.03 194	99.93 74	98.28 160	99.92 78	99.44 210

RPSCF		99.18 144	99.02 159	99.64 111	99.83 39	99.85 16	99.44 86	99.82 40	98.33 252	99.50 184	99.78 72	97.90 205	99.65 343	96.78 278	99.83 141	99.44 210

原ACMM1				99.37 208	99.47 223	98.87 243		99.27 286	96.74 328	98.26 329	99.32 273	97.93 203	99.82 256	95.96 318	99.38 288	99.43 216

test222					99.51 201	99.08 220	97.83 327	99.29 282	95.21 349	98.68 308	99.31 275	97.28 243			99.38 288	99.43 216

XVG-OURS		99.21 135	99.06 146	99.65 104	99.82 46	99.62 102	97.87 325	99.74 81	98.36 242	99.66 120	99.68 130	99.71 9	99.90 136	96.84 275	99.88 106	99.43 216

CSCG		99.37 88	99.29 96	99.60 131	99.71 113	99.46 136	99.43 88	99.85 27	98.79 200	99.41 208	99.60 184	98.92 85	99.92 94	98.02 181	99.92 78	99.43 216

ETH3D-3000-0.1		98.77 213	98.50 228	99.59 134	99.47 223	99.53 124	98.77 240	99.60 160	97.33 308	99.23 243	99.50 224	97.91 204	99.83 246	95.02 340	99.67 225	99.41 220

TinyColmap		98.97 187	98.93 179	99.07 257	99.46 228	98.19 282	97.75 329	99.75 76	98.79 200	99.54 170	99.70 113	98.97 80	99.62 347	96.63 288	99.83 141	99.41 220

ETH3 D test6400		97.76 287	97.19 304	99.50 164	99.38 249	99.26 185	98.34 278	99.49 225	92.99 361	98.54 318	99.20 300	95.92 283	99.82 256	91.14 363	99.66 229	99.40 222

Anonymous202405211		98.75 216	98.46 230	99.63 117	99.34 266	99.66 89	99.47 82	97.65 353	99.28 130	99.56 163	99.50 224	93.15 310	99.84 235	98.62 140	99.58 252	99.40 222

XVG-ACMP-BASELINE		99.23 121	99.10 137	99.63 117	99.82 46	99.58 116	98.83 227	99.72 93	98.36 242	99.60 146	99.71 106	98.92 85	99.91 116	97.08 262	99.84 131	99.40 222

MS-PatchMatch		99.00 183	98.97 174	99.09 253	99.11 313	98.19 282	98.76 242	99.33 271	98.49 230	99.44 194	99.58 192	98.21 181	99.69 316	98.20 167	99.62 237	99.39 225

FMVSNet3		98.80 211	98.63 213	99.32 219	99.13 306	98.72 249	99.10 180	99.48 227	99.23 139	99.62 138	99.64 147	92.57 315	99.86 200	98.96 111	99.90 88	99.39 225

ambc				99.20 241	99.35 256	98.53 261	99.17 157	99.46 235		99.67 116	99.80 59	98.46 153	99.70 310	97.92 191	99.70 210	99.38 227

FMVSNet5		97.80 285	97.25 301	99.42 188	98.83 341	98.97 228	99.38 94	99.80 50	98.87 190	99.25 239	99.69 119	80.60 373	99.91 116	98.96 111	99.90 88	99.38 227

PAPM_NR		98.36 260	98.04 268	99.33 215	99.48 218	98.93 236	98.79 238	99.28 285	97.54 296	98.56 317	98.57 355	97.12 251	99.69 316	94.09 351	98.90 325	99.38 227

EPNet_dtu		97.62 294	97.79 288	97.11 341	96.67 375	92.31 364	98.51 266	98.04 346	99.24 137	95.77 369	99.47 237	93.78 304	99.66 336	98.98 107	99.62 237	99.37 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		99.11 161	98.95 178	99.59 134	99.13 306	99.59 113	99.17 157	99.65 130	97.88 279	99.25 239	99.46 240	98.97 80	99.80 277	97.26 249	99.82 150	99.37 230

PLC	97.35 16	98.36 260	97.99 271	99.48 171	99.32 272	99.24 194	98.50 267	99.51 216	95.19 350	98.58 315	98.96 334	96.95 257	99.83 246	95.63 327	99.25 306	99.37 230
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		97.62 294	97.20 303	98.90 278	99.76 86	97.40 314	99.48 80	94.36 370	99.06 169	99.70 106	99.49 229	84.55 367	99.94 60	98.73 133	99.65 232	99.36 233

pmmvs-eth3d		99.48 57	99.47 55	99.51 161	99.77 82	99.41 154	98.81 232	99.66 119	99.42 115	99.75 83	99.66 140	99.20 50	99.76 293	98.98 107	99.99 12	99.36 233

PVSNet_0	95.53 19	95.85 334	95.31 336	97.47 331	98.78 349	93.48 359	95.72 367	99.40 254	96.18 336	97.37 357	97.73 370	95.73 284	99.58 354	95.49 330	81.40 374	99.36 233

lupinMVS		98.96 190	98.87 190	99.24 236	99.57 172	98.40 271	98.12 297	99.18 304	98.28 255	99.63 130	99.13 305	98.02 196	99.97 18	98.22 165	99.69 213	99.35 236

Vis-MVSNet (Re-imp)		98.77 213	98.58 219	99.34 213	99.78 74	98.88 241	99.61 59	99.56 183	99.11 162	99.24 242	99.56 203	93.00 313	99.78 283	97.43 237	99.89 98	99.35 236

GA-MVS		97.99 282	97.68 292	98.93 269	99.52 196	98.04 293	97.19 354	99.05 314	98.32 253	98.81 295	98.97 332	89.89 348	99.41 366	98.33 156	99.05 315	99.34 238

CANet		99.11 161	99.05 150	99.28 227	98.83 341	98.56 260	98.71 248	99.41 247	99.25 135	99.23 243	99.22 296	97.66 227	99.94 60	99.19 80	99.97 33	99.33 239

Patchmtry		98.78 212	98.54 224	99.49 167	98.89 335	99.19 205	99.32 108	99.67 115	99.65 69	99.72 98	99.79 65	91.87 324	99.95 46	98.00 185	99.97 33	99.33 239

PAPR		97.56 297	97.07 306	99.04 260	98.80 346	98.11 288	97.63 334	99.25 291	94.56 358	98.02 343	98.25 365	97.43 235	99.68 327	90.90 364	98.74 334	99.33 239

CHOSEN 280x420		98.41 256	98.41 236	98.40 304	99.34 266	95.89 342	96.94 360	99.44 240	98.80 199	99.25 239	99.52 217	93.51 308	99.98 7	98.94 116	99.98 24	99.32 242

baseline1		97.73 289	97.33 298	98.96 264	99.30 277	97.73 305	99.40 90	98.42 340	99.33 124	99.46 192	99.21 298	91.18 330	99.82 256	98.35 154	91.26 373	99.32 242

TAPA-MVS	97.92 13	98.03 279	97.55 295	99.46 176	99.47 223	99.44 143	98.50 267	99.62 141	86.79 368	99.07 270	99.26 287	98.26 175	99.62 347	97.28 246	99.73 198	99.31 244
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet-Re		99.28 110	99.15 118	99.67 92	99.33 271	99.76 52	99.34 103	99.97 2	98.93 182	99.91 20	99.79 65	98.68 118	99.93 74	96.80 277	99.56 254	99.30 245

TSAR-MVS + GP.		99.12 157	99.04 156	99.38 205	99.34 266	99.16 207	98.15 293	99.29 282	98.18 262	99.63 130	99.62 166	99.18 52	99.68 327	98.20 167	99.74 191	99.30 245

PVSNet_Blended		98.70 223	98.59 216	99.02 261	99.54 185	97.99 294	97.58 337	99.82 40	95.70 343	99.34 222	98.98 329	98.52 146	99.77 291	97.98 186	99.83 141	99.30 245

MVS_0304		98.88 202	98.71 205	99.39 201	98.85 339	98.91 239	99.45 83	99.30 280	98.56 220	97.26 360	99.68 130	96.18 278	99.96 36	99.17 85	99.94 66	99.29 248

MVS_111021_LR		99.13 155	99.03 158	99.42 188	99.58 162	99.32 175	97.91 324	99.73 84	98.68 210	99.31 230	99.48 232	99.09 63	99.66 336	97.70 216	99.77 178	99.29 248

ETH3D cwj APD-0.16		98.50 246	98.16 262	99.51 161	99.04 321	99.39 157	98.47 269	99.47 231	96.70 329	98.78 300	99.33 272	97.62 230	99.86 200	94.69 345	99.38 288	99.28 250

miper_lstm_enhance		98.65 227	98.60 214	98.82 287	99.20 296	97.33 316	97.78 328	99.66 119	99.01 171	99.59 149	99.50 224	94.62 295	99.85 218	98.12 176	99.90 88	99.26 251

MVS		95.72 336	94.63 340	98.99 262	98.56 357	97.98 299	99.30 115	98.86 320	72.71 373	97.30 358	99.08 313	98.34 168	99.74 299	89.21 365	98.33 346	99.26 251

MSLP-MVS++		99.05 171	99.09 138	98.91 272	99.21 293	98.36 275	98.82 231	99.47 231	98.85 192	98.90 285	99.56 203	98.78 106	99.09 369	98.57 142	99.68 218	99.26 251

D2MVS		99.22 130	99.19 112	99.29 225	99.69 125	98.74 248	98.81 232	99.41 247	98.55 222	99.68 111	99.69 119	98.13 188	99.87 180	98.82 124	99.98 24	99.24 254

test_yl		98.25 268	97.95 275	99.13 249	99.17 301	98.47 265	99.00 200	98.67 330	98.97 174	99.22 247	99.02 324	91.31 328	99.69 316	97.26 249	98.93 321	99.24 254

DCV-MVSNet		98.25 268	97.95 275	99.13 249	99.17 301	98.47 265	99.00 200	98.67 330	98.97 174	99.22 247	99.02 324	91.31 328	99.69 316	97.26 249	98.93 321	99.24 254

DPM-MVS		98.28 266	97.94 279	99.32 219	99.36 254	99.11 212	97.31 350	98.78 325	96.88 322	98.84 292	99.11 311	97.77 216	99.61 351	94.03 353	99.36 293	99.23 257

CLD-MVS		98.76 215	98.57 220	99.33 215	99.57 172	98.97 228	97.53 340	99.55 189	96.41 331	99.27 237	99.13 305	99.07 69	99.78 283	96.73 281	99.89 98	99.23 257
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		99.13 155	99.06 146	99.36 211	99.57 172	99.10 217	98.01 309	99.25 291	98.78 202	99.58 153	99.44 244	98.24 176	99.76 293	98.74 132	99.93 74	99.22 259

OMC-MVS		98.90 198	98.72 204	99.44 182	99.39 246	99.42 150	98.58 254	99.64 136	97.31 309	99.44 194	99.62 166	98.59 131	99.69 316	96.17 309	99.79 168	99.22 259

EGC-MVSNET		89.05 341	85.52 344	99.64 111	99.89 21	99.78 42	99.56 71	99.52 212	24.19 375	49.96 376	99.83 47	99.15 55	99.92 94	97.71 213	99.85 126	99.21 261

eth_miper_zixun_eth		98.68 225	98.71 205	98.60 296	99.10 314	96.84 328	97.52 342	99.54 195	98.94 179	99.58 153	99.48 232	96.25 276	99.76 293	98.01 184	99.93 74	99.21 261

c3_l		98.72 221	98.71 205	98.72 292	99.12 308	97.22 319	97.68 333	99.56 183	98.90 186	99.54 170	99.48 232	96.37 273	99.73 302	97.88 194	99.88 106	99.21 261

CMPMVS	77.52 23	98.50 246	98.19 259	99.41 196	98.33 363	99.56 119	99.01 198	99.59 167	95.44 345	99.57 156	99.80 59	95.64 285	99.46 365	96.47 296	99.92 78	99.21 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		99.06 168	98.97 174	99.34 213	99.31 273	98.98 226	98.31 282	99.91 11	98.81 197	98.79 298	98.94 336	99.14 58	99.84 235	98.79 126	98.74 334	99.20 265

DELS-MVS		99.34 98	99.30 91	99.48 171	99.51 201	99.36 166	98.12 297	99.53 204	99.36 120	99.41 208	99.61 175	99.22 49	99.87 180	99.21 75	99.68 218	99.20 265
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 19	99.68 89	99.71 113	99.91 2	99.76 14	99.96 4	99.86 21	99.51 182	99.39 255	99.57 20	99.93 74	99.64 20	99.86 122	99.20 265

CANet_DTU		98.91 196	98.85 192	99.09 253	98.79 347	98.13 285	98.18 290	99.31 277	99.48 96	98.86 290	99.51 221	96.56 263	99.95 46	99.05 101	99.95 53	99.19 268

alignmvs		98.28 266	97.96 274	99.25 234	99.12 308	98.93 236	99.03 195	98.42 340	99.64 71	98.72 305	97.85 369	90.86 337	99.62 347	98.88 120	99.13 311	99.19 268

DIV-MVS_self_test		98.54 242	98.42 235	98.92 270	99.03 322	97.80 303	97.46 344	99.59 167	98.90 186	99.60 146	99.46 240	93.87 301	99.78 283	97.97 188	99.89 98	99.18 270

MSDG		99.08 166	98.98 173	99.37 208	99.60 153	99.13 210	97.54 338	99.74 81	98.84 195	99.53 175	99.55 210	99.10 61	99.79 280	97.07 263	99.86 122	99.18 270

cl____		98.54 242	98.41 236	98.92 270	99.03 322	97.80 303	97.46 344	99.59 167	98.90 186	99.60 146	99.46 240	93.85 302	99.78 283	97.97 188	99.89 98	99.17 272

PM-MVS		99.36 91	99.29 96	99.58 139	99.83 39	99.66 89	98.95 213	99.86 23	98.85 192	99.81 59	99.73 93	98.40 162	99.92 94	98.36 152	99.83 141	99.17 272

thisisatest0530		97.45 299	96.95 310	98.94 266	99.68 134	97.73 305	99.09 184	94.19 372	98.61 217	99.56 163	99.30 277	84.30 368	99.93 74	98.27 161	99.54 264	99.16 274

PatchmatchNet		97.65 293	97.80 286	97.18 339	98.82 344	92.49 363	99.17 157	98.39 342	98.12 263	98.79 298	99.58 192	90.71 339	99.89 152	97.23 253	99.41 285	99.16 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpnnormal		99.43 69	99.38 72	99.60 131	99.87 29	99.75 56	99.59 66	99.78 61	99.71 49	99.90 22	99.69 119	98.85 95	99.90 136	97.25 252	99.78 174	99.15 276

mvs_anonymous		99.28 110	99.39 70	98.94 266	99.19 298	97.81 302	99.02 196	99.55 189	99.78 41	99.85 42	99.80 59	98.24 176	99.86 200	99.57 27	99.50 271	99.15 276

ab-mvs		99.33 102	99.28 98	99.47 173	99.57 172	99.39 157	99.78 11	99.43 244	98.87 190	99.57 156	99.82 54	98.06 193	99.87 180	98.69 137	99.73 198	99.15 276

MIMVSNet		98.43 254	98.20 256	99.11 251	99.53 191	98.38 274	99.58 68	98.61 332	98.96 177	99.33 224	99.76 82	90.92 334	99.81 272	97.38 240	99.76 180	99.15 276

GSMVS																99.14 280

sam_mvs1												90.81 338				99.14 280

SCA		98.11 275	98.36 241	97.36 334	99.20 296	92.99 361	98.17 292	98.49 338	98.24 257	99.10 266	99.57 200	96.01 281	99.94 60	96.86 272	99.62 237	99.14 280

LS3D		99.24 120	99.11 129	99.61 129	98.38 361	99.79 39	99.57 69	99.68 110	99.61 79	99.15 258	99.71 106	98.70 116	99.91 116	97.54 230	99.68 218	99.13 283

Patchmatch-RL test		98.60 232	98.36 241	99.33 215	99.77 82	99.07 221	98.27 285	99.87 20	98.91 185	99.74 92	99.72 99	90.57 341	99.79 280	98.55 143	99.85 126	99.11 284

test_0402		99.22 130	99.14 119	99.45 180	99.79 68	99.43 147	99.28 123	99.68 110	99.54 89	99.40 213	99.56 203	99.07 69	99.82 256	96.01 313	99.96 45	99.11 284

MVS_Test		99.28 110	99.31 86	99.19 242	99.35 256	98.79 246	99.36 101	99.49 225	99.17 150	99.21 249	99.67 136	98.78 106	99.66 336	99.09 98	99.66 229	99.10 286

AdaColmap		98.60 232	98.35 243	99.38 205	99.12 308	99.22 198	98.67 249	99.42 246	97.84 284	98.81 295	99.27 284	97.32 242	99.81 272	95.14 337	99.53 266	99.10 286

FPMVS		96.32 324	95.50 332	98.79 288	99.60 153	98.17 284	98.46 274	98.80 324	97.16 316	96.28 365	99.63 157	82.19 369	99.09 369	88.45 367	98.89 326	99.10 286

Patchmatch-test		98.10 276	97.98 273	98.48 301	99.27 284	96.48 332	99.40 90	99.07 311	98.81 197	99.23 243	99.57 200	90.11 345	99.87 180	96.69 282	99.64 234	99.09 289

tpm		97.15 306	96.95 310	97.75 325	98.91 331	94.24 354	99.32 108	97.96 348	97.71 288	98.29 327	99.32 273	86.72 362	99.92 94	98.10 179	96.24 370	99.09 289

PMMVS		98.49 249	98.29 249	99.11 251	98.96 329	98.42 270	97.54 338	99.32 273	97.53 297	98.47 323	98.15 366	97.88 208	99.82 256	97.46 235	99.24 308	99.09 289

cl22		97.56 297	97.28 299	98.40 304	98.37 362	96.75 329	97.24 353	99.37 264	97.31 309	99.41 208	99.22 296	87.30 353	99.37 367	97.70 216	99.62 237	99.08 292

ADS-MVSNet2		97.78 286	97.66 294	98.12 316	99.14 304	95.36 346	99.22 143	98.75 326	96.97 320	98.25 330	99.64 147	90.90 335	99.94 60	96.51 293	99.56 254	99.08 292

ADS-MVSNet		97.72 292	97.67 293	97.86 321	99.14 304	94.65 352	99.22 143	98.86 320	96.97 320	98.25 330	99.64 147	90.90 335	99.84 235	96.51 293	99.56 254	99.08 292

pmmvs3		98.08 277	97.80 286	98.91 272	99.41 241	97.69 307	97.87 325	99.66 119	95.87 339	99.50 184	99.51 221	90.35 343	99.97 18	98.55 143	99.47 276	99.08 292

PVSNet	97.47 15	98.42 255	98.44 233	98.35 306	99.46 228	96.26 335	96.70 363	99.34 270	97.68 289	99.00 274	99.13 305	97.40 236	99.72 304	97.59 228	99.68 218	99.08 292

MVS-HIRNet		97.86 283	98.22 254	96.76 343	99.28 282	91.53 370	98.38 277	92.60 374	99.13 158	99.31 230	99.96 11	97.18 250	99.68 327	98.34 155	99.83 141	99.07 297

PMVS	92.94 21	98.82 209	98.81 198	98.85 280	99.84 35	97.99 294	99.20 146	99.47 231	99.71 49	99.42 200	99.82 54	98.09 190	99.47 363	93.88 355	99.85 126	99.07 297
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		99.57 41	99.59 35	99.49 167	99.98 3	99.71 71	99.72 24	99.84 33	99.81 35	99.94 11	99.78 72	98.91 87	99.71 308	98.41 149	99.95 53	99.05 299
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
canonicalmvs		99.02 177	99.00 165	99.09 253	99.10 314	98.70 250	99.61 59	99.66 119	99.63 74	98.64 310	97.65 371	99.04 73	99.54 356	98.79 126	98.92 323	99.04 300

hse-mvs2		98.52 244	98.30 248	99.16 245	99.29 279	98.60 259	98.77 240	99.02 315	99.68 59	99.32 226	99.04 319	92.50 318	99.85 218	99.24 72	97.87 359	99.03 301

CL-MVSNet_self_test		98.71 222	98.56 223	99.15 247	99.22 291	98.66 254	97.14 355	99.51 216	98.09 266	99.54 170	99.27 284	96.87 259	99.74 299	98.43 148	98.96 320	99.03 301

AUN-MVS		97.82 284	97.38 297	99.14 248	99.27 284	98.53 261	98.72 246	99.02 315	98.10 264	97.18 362	99.03 323	89.26 350	99.85 218	97.94 190	97.91 357	99.03 301

MDTV_nov1_ep13_2view						91.44 371	99.14 167		97.37 306	99.21 249		91.78 326		96.75 279		99.03 301

ITE_SJBPF				99.38 205	99.63 146	99.44 143		99.73 84	98.56 220	99.33 224	99.53 215	98.88 92	99.68 327	96.01 313	99.65 232	99.02 305

UnsupCasMVSNet_bld		98.55 241	98.27 250	99.40 198	99.56 182	99.37 163	97.97 317	99.68 110	97.49 300	99.08 267	99.35 268	95.41 288	99.82 256	97.70 216	98.19 351	99.01 306

miper_ehance_all_eth		98.59 235	98.59 216	98.59 297	98.98 328	97.07 322	97.49 343	99.52 212	98.50 228	99.52 177	99.37 258	96.41 271	99.71 308	97.86 198	99.62 237	99.00 307

CS-MVS-test		99.59 39	99.59 35	99.60 131	99.55 183	99.86 13	99.60 64	99.94 7	99.90 7	99.59 149	98.89 341	99.24 47	99.95 46	99.66 16	99.90 88	98.98 308

CS-MVS		99.67 26	99.70 17	99.59 134	99.54 185	99.86 13	99.80 10	99.96 4	99.90 7	99.59 149	99.41 247	99.51 23	99.95 46	99.65 17	99.90 88	98.97 309

CNLPA		98.57 237	98.34 244	99.28 227	99.18 300	99.10 217	98.34 278	99.41 247	98.48 231	98.52 319	98.98 329	97.05 254	99.78 283	95.59 328	99.50 271	98.96 310

new_pmnet		98.88 202	98.89 188	98.84 282	99.70 121	97.62 308	98.15 293	99.50 220	97.98 272	99.62 138	99.54 212	98.15 187	99.94 60	97.55 229	99.84 131	98.95 311

PCF-MVS	96.03 18	96.73 316	95.86 327	99.33 215	99.44 233	99.16 207	96.87 361	99.44 240	86.58 369	98.95 277	99.40 251	94.38 297	99.88 167	87.93 368	99.80 163	98.95 311
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		98.68 225	98.47 229	99.30 224	99.44 233	99.28 181	98.14 295	99.54 195	97.12 318	99.11 264	99.25 289	97.80 214	99.70 310	96.51 293	99.30 300	98.93 313

Fast-Effi-MVS+		99.02 177	98.87 190	99.46 176	99.38 249	99.50 128	99.04 192	99.79 56	97.17 315	98.62 311	98.74 350	99.34 36	99.95 46	98.32 157	99.41 285	98.92 314

ET-MVSNet_ETH3D		96.78 314	96.07 323	98.91 272	99.26 286	97.92 300	97.70 332	96.05 365	97.96 276	92.37 374	98.43 361	87.06 355	99.90 136	98.27 161	97.56 362	98.91 315

EIA-MVS		99.12 157	99.01 162	99.45 180	99.36 254	99.62 102	99.34 103	99.79 56	98.41 236	98.84 292	98.89 341	98.75 112	99.84 235	98.15 175	99.51 269	98.89 316

CostFormer		96.71 317	96.79 316	96.46 350	98.90 332	90.71 375	99.41 89	98.68 328	94.69 357	98.14 338	99.34 271	86.32 364	99.80 277	97.60 227	98.07 355	98.88 317

DP-MVS Recon		98.50 246	98.23 253	99.31 222	99.49 212	99.46 136	98.56 259	99.63 138	94.86 354	98.85 291	99.37 258	97.81 213	99.59 353	96.08 310	99.44 279	98.88 317

test0.0.03 1		97.37 302	96.91 313	98.74 291	97.72 371	97.57 309	97.60 336	97.36 359	98.00 269	99.21 249	98.02 367	90.04 346	99.79 280	98.37 151	95.89 371	98.86 319

BH-untuned		98.22 272	98.09 266	98.58 298	99.38 249	97.24 318	98.55 260	98.98 318	97.81 285	99.20 254	98.76 349	97.01 255	99.65 343	94.83 341	98.33 346	98.86 319

HY-MVS	98.23 9	98.21 273	97.95 275	98.99 262	99.03 322	98.24 278	99.61 59	98.72 327	96.81 326	98.73 304	99.51 221	94.06 299	99.86 200	96.91 269	98.20 349	98.86 319

miper_enhance_ethall		98.03 279	97.94 279	98.32 308	98.27 364	96.43 334	96.95 359	99.41 247	96.37 333	99.43 198	98.96 334	94.74 293	99.69 316	97.71 213	99.62 237	98.83 322

Effi-MVS+-dtu		99.07 167	98.92 183	99.52 158	98.89 335	99.78 42	99.15 165	99.66 119	99.34 121	98.92 282	99.24 294	97.69 220	99.98 7	98.11 177	99.28 302	98.81 323

EPMVS		96.53 320	96.32 318	97.17 340	98.18 367	92.97 362	99.39 92	89.95 378	98.21 259	98.61 312	99.59 190	86.69 363	99.72 304	96.99 265	99.23 310	98.81 323

MVE	92.54 22	96.66 318	96.11 322	98.31 310	99.68 134	97.55 310	97.94 320	95.60 367	99.37 118	90.68 375	98.70 351	96.56 263	98.61 373	86.94 373	99.55 258	98.77 325
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tpm2		96.35 323	96.22 320	96.73 345	98.88 338	91.75 368	99.21 145	98.51 336	93.27 360	97.89 347	99.21 298	84.83 366	99.70 310	96.04 312	98.18 352	98.75 326

LF4IMVS		99.01 181	98.92 183	99.27 229	99.71 113	99.28 181	98.59 253	99.77 64	98.32 253	99.39 214	99.41 247	98.62 127	99.84 235	96.62 289	99.84 131	98.69 327

thisisatest0515		96.98 310	96.42 317	98.66 295	99.42 240	97.47 311	97.27 351	94.30 371	97.24 311	99.15 258	98.86 344	85.01 365	99.87 180	97.10 261	99.39 287	98.63 328

Fast-Effi-MVS+-dtu		99.20 137	99.12 126	99.43 186	99.25 287	99.69 82	99.05 190	99.82 40	99.50 94	98.97 275	99.05 316	98.98 78	99.98 7	98.20 167	99.24 308	98.62 329

PAPM		95.61 337	94.71 339	98.31 310	99.12 308	96.63 330	96.66 364	98.46 339	90.77 366	96.25 366	98.68 352	93.01 312	99.69 316	81.60 374	97.86 360	98.62 329

JIA-IIPM		98.06 278	97.92 281	98.50 300	98.59 356	97.02 323	98.80 235	98.51 336	99.88 18	97.89 347	99.87 32	91.89 323	99.90 136	98.16 174	97.68 361	98.59 331

dp		96.86 312	97.07 306	96.24 352	98.68 355	90.30 377	99.19 151	98.38 343	97.35 307	98.23 332	99.59 190	87.23 354	99.82 256	96.27 304	98.73 336	98.59 331

OpenMVS	98.12 10	98.23 271	97.89 285	99.26 231	99.19 298	99.26 185	99.65 51	99.69 107	91.33 365	98.14 338	99.77 79	98.28 173	99.96 36	95.41 333	99.55 258	98.58 333

baseline2		96.83 313	96.28 319	98.46 302	99.09 316	96.91 326	98.83 227	93.87 373	97.23 312	96.23 368	98.36 362	88.12 352	99.90 136	96.68 283	98.14 353	98.57 334

DWT-MVSNet_test		96.03 330	95.80 329	96.71 347	98.50 359	91.93 366	99.25 135	97.87 351	95.99 338	96.81 364	97.61 372	81.02 371	99.66 336	97.20 256	97.98 356	98.54 335

TESTMET0.1,1		96.24 326	95.84 328	97.41 333	98.24 365	93.84 357	97.38 346	95.84 366	98.43 233	97.81 351	98.56 356	79.77 374	99.89 152	97.77 205	98.77 330	98.52 336

xiu_mvs_v1_base_debu		99.23 121	99.34 80	98.91 272	99.59 157	98.23 279	98.47 269	99.66 119	99.61 79	99.68 111	98.94 336	99.39 26	99.97 18	99.18 82	99.55 258	98.51 337

xiu_mvs_v1_base		99.23 121	99.34 80	98.91 272	99.59 157	98.23 279	98.47 269	99.66 119	99.61 79	99.68 111	98.94 336	99.39 26	99.97 18	99.18 82	99.55 258	98.51 337

xiu_mvs_v1_base_debi		99.23 121	99.34 80	98.91 272	99.59 157	98.23 279	98.47 269	99.66 119	99.61 79	99.68 111	98.94 336	99.39 26	99.97 18	99.18 82	99.55 258	98.51 337

KD-MVS_2432*1600		95.89 331	95.41 334	97.31 337	94.96 376	93.89 355	97.09 356	99.22 298	97.23 312	98.88 286	99.04 319	79.23 375	99.54 356	96.24 306	96.81 365	98.50 340

miper_refine_blended		95.89 331	95.41 334	97.31 337	94.96 376	93.89 355	97.09 356	99.22 298	97.23 312	98.88 286	99.04 319	79.23 375	99.54 356	96.24 306	96.81 365	98.50 340

CR-MVSNet		98.35 263	98.20 256	98.83 284	99.05 319	98.12 286	99.30 115	99.67 115	97.39 305	99.16 256	99.79 65	91.87 324	99.91 116	98.78 129	98.77 330	98.44 342

RPMNet		98.60 232	98.53 226	98.83 284	99.05 319	98.12 286	99.30 115	99.62 141	99.86 21	99.16 256	99.74 89	92.53 317	99.92 94	98.75 131	98.77 330	98.44 342

tpmrst		97.73 289	98.07 267	96.73 345	98.71 353	92.00 365	99.10 180	98.86 320	98.52 226	98.92 282	99.54 212	91.90 322	99.82 256	98.02 181	99.03 317	98.37 344

test-LLR		97.15 306	96.95 310	97.74 326	98.18 367	95.02 349	97.38 346	96.10 362	98.00 269	97.81 351	98.58 353	90.04 346	99.91 116	97.69 222	98.78 328	98.31 345

test-mter		96.23 327	95.73 330	97.74 326	98.18 367	95.02 349	97.38 346	96.10 362	97.90 278	97.81 351	98.58 353	79.12 377	99.91 116	97.69 222	98.78 328	98.31 345

ETV-MVS		99.18 144	99.18 113	99.16 245	99.34 266	99.28 181	99.12 177	99.79 56	99.48 96	98.93 279	98.55 357	99.40 25	99.93 74	98.51 145	99.52 268	98.28 347

PatchT		98.45 253	98.32 247	98.83 284	98.94 330	98.29 277	99.24 136	98.82 323	99.84 29	99.08 267	99.76 82	91.37 327	99.94 60	98.82 124	99.00 319	98.26 348

xiu_mvs_v2_base		99.02 177	99.11 129	98.77 289	99.37 252	98.09 290	98.13 296	99.51 216	99.47 101	99.42 200	98.54 358	99.38 30	99.97 18	98.83 122	99.33 297	98.24 349

IB-MVS	95.41 20	95.30 338	94.46 342	97.84 322	98.76 351	95.33 347	97.33 349	96.07 364	96.02 337	95.37 372	97.41 374	76.17 380	99.96 36	97.54 230	95.44 372	98.22 350
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		96.78 314	96.98 309	96.16 353	98.85 339	90.59 376	99.08 187	99.32 273	92.37 362	97.73 356	99.46 240	91.15 331	99.69 316	96.07 311	98.80 327	98.21 351

MAR-MVS		98.24 270	97.92 281	99.19 242	98.78 349	99.65 94	99.17 157	99.14 308	95.36 346	98.04 342	98.81 347	97.47 233	99.72 304	95.47 332	99.06 314	98.21 351
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		99.00 183	99.08 140	98.76 290	99.37 252	98.10 289	98.00 311	99.51 216	99.47 101	99.41 208	98.50 360	99.28 42	99.97 18	98.83 122	99.34 295	98.20 353

cascas		96.99 309	96.82 315	97.48 330	97.57 374	95.64 344	96.43 365	99.56 183	91.75 363	97.13 363	97.61 372	95.58 287	98.63 372	96.68 283	99.11 312	98.18 354

BH-w/o		97.20 305	97.01 308	97.76 324	99.08 317	95.69 343	98.03 308	98.52 335	95.76 342	97.96 344	98.02 367	95.62 286	99.47 363	92.82 358	97.25 364	98.12 355

tpmvs		97.39 301	97.69 291	96.52 348	98.41 360	91.76 367	99.30 115	98.94 319	97.74 286	97.85 350	99.55 210	92.40 320	99.73 302	96.25 305	98.73 336	98.06 356

mvs-test1		98.83 207	98.70 208	99.22 238	98.89 335	99.65 94	98.88 218	99.66 119	99.34 121	98.29 327	98.94 336	97.69 220	99.96 36	98.11 177	98.54 342	98.04 357

thres600view7		96.60 319	96.16 321	97.93 319	99.63 146	96.09 339	99.18 152	97.57 354	98.77 203	98.72 305	97.32 375	87.04 356	99.72 304	88.57 366	98.62 339	97.98 358

thres400		96.40 321	95.89 325	97.92 320	99.58 162	96.11 337	99.00 200	97.54 357	98.43 233	98.52 319	96.98 378	86.85 358	99.67 332	87.62 369	98.51 343	97.98 358

TR-MVS		97.44 300	97.15 305	98.32 308	98.53 358	97.46 312	98.47 269	97.91 350	96.85 324	98.21 333	98.51 359	96.42 269	99.51 361	92.16 359	97.29 363	97.98 358

1314		98.00 281	97.90 284	98.27 312	98.90 332	97.45 313	99.30 115	99.06 313	94.98 351	97.21 361	99.12 309	98.43 155	99.67 332	95.58 329	98.56 341	97.71 361

E-PMN		97.14 308	97.43 296	96.27 351	98.79 347	91.62 369	95.54 368	99.01 317	99.44 108	98.88 286	99.12 309	92.78 314	99.68 327	94.30 348	99.03 317	97.50 362

gg-mvs-nofinetune		95.87 333	95.17 337	97.97 318	98.19 366	96.95 324	99.69 35	89.23 379	99.89 13	96.24 367	99.94 13	81.19 370	99.51 361	93.99 354	98.20 349	97.44 363

DeepMVS_CX				97.98 317	99.69 125	96.95 324		99.26 288	75.51 372	95.74 370	98.28 364	96.47 267	99.62 347	91.23 362	97.89 358	97.38 364

OpenMVS_ROB	97.31 17	97.36 303	96.84 314	98.89 279	99.29 279	99.45 141	98.87 221	99.48 227	86.54 370	99.44 194	99.74 89	97.34 241	99.86 200	91.61 360	99.28 302	97.37 365

EMVS		96.96 311	97.28 299	95.99 354	98.76 351	91.03 372	95.26 369	98.61 332	99.34 121	98.92 282	98.88 343	93.79 303	99.66 336	92.87 357	99.05 315	97.30 366

thres100view900		96.39 322	96.03 324	97.47 331	99.63 146	95.93 340	99.18 152	97.57 354	98.75 207	98.70 307	97.31 376	87.04 356	99.67 332	87.62 369	98.51 343	96.81 367

tfpn200view9		96.30 325	95.89 325	97.53 329	99.58 162	96.11 337	99.00 200	97.54 357	98.43 233	98.52 319	96.98 378	86.85 358	99.67 332	87.62 369	98.51 343	96.81 367

API-MVS		98.38 259	98.39 238	98.35 306	98.83 341	99.26 185	99.14 167	99.18 304	98.59 218	98.66 309	98.78 348	98.61 129	99.57 355	94.14 350	99.56 254	96.21 369

thres200		96.09 328	95.68 331	97.33 336	99.48 218	96.22 336	98.53 264	97.57 354	98.06 268	98.37 326	96.73 380	86.84 360	99.61 351	86.99 372	98.57 340	96.16 370

GG-mvs-BLEND				97.36 334	97.59 372	96.87 327	99.70 29	88.49 380		94.64 373	97.26 377	80.66 372	99.12 368	91.50 361	96.50 369	96.08 371

wuyk23d		97.58 296	99.13 122	92.93 356	99.69 125	99.49 129	99.52 74	99.77 64	97.97 273	99.96 8	99.79 65	99.84 3	99.94 60	95.85 321	99.82 150	79.36 372

test123		29.31 342	33.05 347	18.08 358	25.93 382	12.24 382	97.53 340	10.93 383	11.78 376	24.21 377	50.08 385	21.04 381	8.60 377	23.51 375	32.43 376	33.39 373

testmvs		28.94 343	33.33 345	15.79 359	26.03 381	9.81 383	96.77 362	15.67 382	11.55 377	23.87 378	50.74 384	19.03 382	8.53 378	23.21 376	33.07 375	29.03 374

test_blank		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

cdsmvs_eth3d_5k		24.88 344	33.17 346	0.00 360	0.00 383	0.00 384	0.00 371	99.62 141	0.00 378	0.00 379	99.13 305	99.82 4	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		16.61 345	22.14 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	99.28 42	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		8.26 353	11.02 356	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	99.16 303	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		8.33 346	11.11 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	100.00 1	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					99.83 39	99.89 8	99.74 18	99.71 96	99.69 57	99.63 130

test_one_0601					99.63 146	99.76 52		99.55 189	99.23 139	99.31 230	99.61 175	98.59 131

eth-test2					0.00 383

eth-test					0.00 383

ZD-MVS					99.43 235	99.61 108		99.43 244	96.38 332	99.11 264	99.07 314	97.86 209	99.92 94	94.04 352	99.49 273

test_241102_ONE					99.69 125	99.82 29		99.54 195	99.12 161	99.82 52	99.49 229	98.91 87	99.52 360

9.14			98.64 211		99.45 231		98.81 232	99.60 160	97.52 298	99.28 236	99.56 203	98.53 143	99.83 246	95.36 335	99.64 234

save fliter					99.53 191	99.25 189	98.29 283	99.38 263	99.07 165

test0726					99.69 125	99.80 37	99.24 136	99.57 178	99.16 152	99.73 96	99.65 145	98.35 166

test_part2					99.62 150	99.67 87				99.55 168

sam_mvs												90.52 342

MTGPA								99.53 204

test_post1							99.14 167				51.63 383	89.54 349	99.82 256	96.86 272

test_post											52.41 382	90.25 344	99.86 200

patchmatchnet-post											99.62 166	90.58 340	99.94 60

MTMP							99.09 184	98.59 334

gm-plane-assit					97.59 372	89.02 379			93.47 359		98.30 363		99.84 235	96.38 300

TEST9					99.35 256	99.35 170	98.11 299	99.41 247	94.83 356	97.92 345	98.99 326	98.02 196	99.85 218

test_8					99.34 266	99.31 176	98.08 303	99.40 254	94.90 352	97.87 349	98.97 332	98.02 196	99.84 235

agg_prior					99.35 256	99.36 166		99.39 257		97.76 354			99.85 218

test_prior4						99.19 205	98.00 311

test_prior2							97.95 318		97.87 280	98.05 340	99.05 316	97.90 205		95.99 315	99.49 273

旧先验2							97.94 320		95.33 347	98.94 278			99.88 167	96.75 279

新几何2							98.04 307

原ACMM2							97.92 322

testdata2													99.89 152	95.99 315

segment_acmp												98.37 164

testdata1							97.72 330		97.86 283

plane_prior7					99.58 162	99.38 160

plane_prior6					99.47 223	99.26 185						97.24 244

plane_prior4											99.25 289

plane_prior3						99.31 176			98.36 242	99.14 260

plane_prior2							98.80 235		98.94 179

plane_prior1					99.51 201

plane_prior						99.24 194	98.42 275		97.87 280						99.71 208

n2								0.00 384

nn								0.00 384

door-mid								99.83 35

test11								99.29 282

door								99.77 64

HQP5-MVS						98.94 232

HQP-NCC					99.31 273		97.98 314		97.45 301	98.15 334

ACMP_Plane					99.31 273		97.98 314		97.45 301	98.15 334

BP-MVS														94.73 342

HQP3-MVS								99.37 264							99.67 225

HQP2-MVS												96.67 261

NP-MVS					99.40 244	99.13 210					98.83 345

MDTV_nov1_ep13			97.73 290		98.70 354	90.83 373	99.15 165	98.02 347	98.51 227	98.82 294	99.61 175	90.98 333	99.66 336	96.89 271	98.92 323

ACMMP++_ref															99.94 66

ACMMP++															99.79 168

Test By Simon												98.41 158

High-resolution multi-view benchmark