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		95.75 332	95.42 330	96.76 340	89.90 375	94.42 351	98.86 217	97.87 349	78.01 367	99.30 229	99.69 113	97.70 216	95.89 370	99.29 63	98.14 348	99.95 1

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 43	99.68 34	99.85 26	99.95 3	99.98 3	99.92 16	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 22	99.88 18	99.92 6	99.98 3	99.93 13	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	99.72 109	99.71 70	99.91 3	99.95 5	99.96 2	99.71 101	99.91 19	99.15 55	99.97 17	99.50 33	100.00 1	99.90 4

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 41	99.70 25	99.86 22	99.89 11	99.98 3	99.90 21	99.94 1	99.98 7	99.75 13	100.00 1	99.90 4

EU-MVSNet		99.39 82	99.62 27	98.72 290	99.88 24	96.44 331	99.56 67	99.85 26	99.90 7	99.90 22	99.85 37	98.09 188	99.83 241	99.58 24	99.95 49	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 40	99.97 6	99.92 16	99.77 7	99.98 7	99.43 38	100.00 1	99.90 4

RRT_test8_iter05		97.35 301	97.25 298	97.63 326	98.81 340	93.13 358	99.26 124	99.89 15	99.51 89	99.83 48	99.68 124	79.03 374	99.88 161	99.53 29	99.72 198	99.89 8

CVMVSNet		98.61 229	98.88 188	97.80 321	99.58 157	93.60 356	99.26 124	99.64 135	99.66 62	99.72 96	99.67 130	93.26 305	99.93 71	99.30 60	99.81 151	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 8	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 24	99.86 13	99.72 22	99.78 60	99.90 7	99.82 50	99.83 43	98.45 153	99.87 174	99.51 31	99.97 30	99.86 11

PS-CasMVS		99.66 26	99.58 37	99.89 7	99.80 57	99.85 14	99.66 42	99.73 83	99.62 70	99.84 43	99.71 100	98.62 126	99.96 35	99.30 60	99.96 42	99.86 11

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 9	99.73 19	99.85 26	99.70 49	99.92 18	99.93 13	99.45 23	99.97 17	99.36 50	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 25	99.71 9	99.96 35	99.51 31	99.97 30	99.84 14

CP-MVSNet		99.54 47	99.43 62	99.87 14	99.76 85	99.82 28	99.57 65	99.61 147	99.54 84	99.80 60	99.64 141	97.79 213	99.95 45	99.21 70	99.94 62	99.84 14

Test_1112_low_res		98.95 192	98.73 202	99.63 115	99.68 132	99.15 208	98.09 296	99.80 49	97.14 313	99.46 186	99.40 244	96.11 277	99.89 147	99.01 100	99.84 124	99.84 14

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 47	100.00 1	99.90 7	100.00 1	99.97 9	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 85	99.84 19	99.61 55	99.70 100	99.93 4	99.78 68	99.68 124	99.10 60	99.78 278	99.45 36	99.96 42	99.83 18

FIs		99.65 31	99.58 37	99.84 19	99.84 34	99.85 14	99.66 42	99.75 75	99.86 16	99.74 90	99.79 60	98.27 173	99.85 213	99.37 49	99.93 70	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 24	99.94 11	99.91 19	99.13 59	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 38	99.87 10	99.66 42	99.73 83	99.70 49	99.84 43	99.73 87	98.56 135	99.96 35	99.29 63	99.94 62	99.83 18

WR-MVS_H		99.61 37	99.53 49	99.87 14	99.80 57	99.83 24	99.67 38	99.75 75	99.58 83	99.85 40	99.69 113	98.18 184	99.94 57	99.28 65	99.95 49	99.83 18

test_part1		98.63 227	98.26 250	99.75 57	99.40 239	99.49 128	99.67 38	99.68 109	99.86 16	99.88 32	99.86 36	86.73 357	99.93 71	99.34 52	99.97 30	99.81 23

Anonymous20231211		99.62 35	99.57 40	99.76 47	99.61 149	99.60 109	99.81 9	99.73 83	99.82 29	99.90 22	99.90 21	97.97 199	99.86 194	99.42 43	99.96 42	99.80 24

APDe-MVS		99.48 54	99.36 77	99.85 18	99.55 178	99.81 31	99.50 71	99.69 106	98.99 168	99.75 81	99.71 100	98.79 103	99.93 71	98.46 143	99.85 120	99.80 24

DTE-MVSNet		99.68 24	99.61 31	99.88 11	99.80 57	99.87 10	99.67 38	99.71 95	99.72 43	99.84 43	99.78 66	98.67 120	99.97 17	99.30 60	99.95 49	99.80 24

XXY-MVS		99.71 18	99.67 21	99.81 26	99.89 21	99.72 67	99.59 62	99.82 39	99.39 112	99.82 50	99.84 42	99.38 29	99.91 112	99.38 47	99.93 70	99.80 24

1112_ss		99.05 170	98.84 193	99.67 92	99.66 138	99.29 178	98.52 260	99.82 39	97.65 286	99.43 192	99.16 296	96.42 267	99.91 112	99.07 96	99.84 124	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 31	99.81 5	99.95 45	99.54 27	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	99.42 185	99.69 123	98.52 261	96.06 361	96.80 359	99.71 44	99.73 94	99.54 206	95.14 287	99.96 35	99.39 46	99.95 49	99.79 30

PMMVS2		99.48 54	99.45 57	99.57 140	99.76 85	98.99 223	98.09 296	99.90 14	98.95 174	99.78 68	99.58 186	99.57 20	99.93 71	99.48 34	99.95 49	99.79 30

MSC_two_6792asdad				99.74 63	99.03 317	99.53 123		99.23 293					99.92 91	97.77 201	99.69 207	99.78 32

No_MVS				99.74 63	99.03 317	99.53 123		99.23 293					99.92 91	97.77 201	99.69 207	99.78 32

CHOSEN 1792x2688		99.39 82	99.30 90	99.65 104	99.88 24	99.25 188	98.78 234	99.88 18	98.66 207	99.96 8	99.79 60	97.45 232	99.93 71	99.34 52	99.99 12	99.78 32

IU-MVS					99.69 123	99.77 43		99.22 296	97.50 295	99.69 106				97.75 205	99.70 204	99.77 35

test_0728_THIRD									99.18 142	99.62 135	99.61 169	98.58 132	99.91 112	97.72 207	99.80 156	99.77 35

test_0728_SECOND				99.83 21	99.70 120	99.79 38	99.14 163	99.61 147					99.92 91	97.88 190	99.72 198	99.77 35

MSP-MVS		99.04 173	98.79 200	99.81 26	99.78 73	99.73 63	99.35 98	99.57 177	98.54 221	99.54 165	98.99 319	96.81 258	99.93 71	96.97 261	99.53 261	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	99.57 167	99.77 43	98.74 238	99.60 159	98.55 218	99.76 75	99.69 113	98.23 178	99.92 91	96.39 294	99.75 177	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	98.83 222	99.86 22	99.68 54	99.65 121	99.88 28	97.67 221	99.87 174	99.03 98	99.86 116	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 33	99.93 14	99.93 13	98.54 138	99.93 71	99.59 21	99.98 21	99.76 39

test_241102_TWO								99.54 194	99.13 154	99.76 75	99.63 151	98.32 170	99.92 91	97.85 196	99.69 207	99.75 42

DP-MVS		99.48 54	99.39 69	99.74 63	99.57 167	99.62 101	99.29 118	99.61 147	99.87 14	99.74 90	99.76 76	98.69 116	99.87 174	98.20 163	99.80 156	99.75 42

v10		99.69 21	99.69 18	99.66 99	99.81 52	99.39 156	99.66 42	99.75 75	99.60 80	99.92 18	99.87 31	98.75 111	99.86 194	99.90 2	99.99 12	99.73 44

EI-MVSNet-UG-set		99.48 54	99.50 51	99.42 185	99.57 167	98.65 255	99.24 132	99.46 233	99.68 54	99.80 60	99.66 134	98.99 76	99.89 147	99.19 75	99.90 84	99.72 45

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 24	99.66 88	99.69 31	99.92 7	99.67 58	99.77 73	99.75 80	99.61 17	99.98 7	99.35 51	99.98 21	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 33	99.47 131	98.07 299	99.83 34	98.64 209	99.89 26	99.60 178	92.57 311	100.00 1	99.33 55	99.97 30	99.72 45

EI-MVSNet-Vis-set		99.47 60	99.49 52	99.42 185	99.57 167	98.66 252	99.24 132	99.46 233	99.67 58	99.79 65	99.65 139	98.97 79	99.89 147	99.15 84	99.89 92	99.71 48

v8		99.68 24	99.69 18	99.65 104	99.80 57	99.40 154	99.66 42	99.76 68	99.64 66	99.93 14	99.85 37	98.66 122	99.84 230	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 49	99.91 20	99.89 25	99.60 19	99.87 174	99.59 21	99.74 185	99.71 48

VPA-MVSNet		99.66 26	99.62 27	99.79 34	99.68 132	99.75 54	99.62 50	99.69 106	99.85 21	99.80 60	99.81 52	98.81 96	99.91 112	99.47 35	99.88 100	99.70 51

WR-MVS		99.11 160	98.93 178	99.66 99	99.30 272	99.42 149	98.42 270	99.37 262	99.04 166	99.57 151	99.20 293	96.89 256	99.86 194	98.66 135	99.87 109	99.70 51

ACMH	98.42 6	99.59 38	99.54 45	99.72 79	99.86 30	99.62 101	99.56 67	99.79 55	98.77 199	99.80 60	99.85 37	99.64 13	99.85 213	98.70 131	99.89 92	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 21	99.94 11	99.95 11	99.73 8	99.90 132	99.65 16	99.97 30	99.69 54

HPM-MVS_fast		99.43 66	99.30 90	99.80 29	99.83 38	99.81 31	99.52 69	99.70 100	98.35 243	99.51 177	99.50 218	99.31 37	99.88 161	98.18 167	99.84 124	99.69 54

LPG-MVS_test		99.22 129	99.05 149	99.74 63	99.82 45	99.63 99	99.16 159	99.73 83	97.56 289	99.64 123	99.69 113	99.37 31	99.89 147	96.66 280	99.87 109	99.69 54

LGP-MVS_train				99.74 63	99.82 45	99.63 99		99.73 83	97.56 289	99.64 123	99.69 113	99.37 31	99.89 147	96.66 280	99.87 109	99.69 54

SteuartSystems-ACMMP		99.30 106	99.14 118	99.76 47	99.87 28	99.66 88	99.18 148	99.60 159	98.55 218	99.57 151	99.67 130	99.03 73	99.94 57	97.01 259	99.80 156	99.69 54
Skip Steuart: Steuart Systems R&D Blog.
MG-MVS		98.52 243	98.39 237	98.94 264	99.15 298	97.39 313	98.18 285	99.21 300	98.89 185	99.23 238	99.63 151	97.37 238	99.74 294	94.22 344	99.61 239	99.69 54

ACMMP_NAP		99.28 109	99.11 128	99.79 34	99.75 96	99.81 31	98.95 208	99.53 203	98.27 252	99.53 170	99.73 87	98.75 111	99.87 174	97.70 211	99.83 134	99.68 60

HFP-MVS		99.25 116	99.08 139	99.76 47	99.73 105	99.70 77	99.31 108	99.59 166	98.36 238	99.36 211	99.37 251	98.80 100	99.91 112	97.43 232	99.75 177	99.68 60

#test#		99.12 156	98.90 186	99.76 47	99.73 105	99.70 77	99.10 176	99.59 166	97.60 288	99.36 211	99.37 251	98.80 100	99.91 112	96.84 270	99.75 177	99.68 60

EI-MVSNet		99.38 84	99.44 59	99.21 237	99.58 157	98.09 288	99.26 124	99.46 233	99.62 70	99.75 81	99.67 130	98.54 138	99.85 213	99.15 84	99.92 74	99.68 60

TranMVSNet+NR-MVSNet		99.54 47	99.47 53	99.76 47	99.58 157	99.64 95	99.30 111	99.63 137	99.61 74	99.71 101	99.56 197	98.76 109	99.96 35	99.14 90	99.92 74	99.68 60

PVSNet_Blended_VisFu		99.40 77	99.38 71	99.44 179	99.90 19	98.66 252	98.94 210	99.91 10	97.97 269	99.79 65	99.73 87	99.05 71	99.97 17	99.15 84	99.99 12	99.68 60

IterMVS-LS		99.41 74	99.47 53	99.25 232	99.81 52	98.09 288	98.85 219	99.76 68	99.62 70	99.83 48	99.64 141	98.54 138	99.97 17	99.15 84	99.99 12	99.68 60
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 38	99.83 24	98.61 245	99.63 137	96.84 321	99.44 188	99.58 186	98.81 96	99.91 112	97.70 211	99.82 143	99.67 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R		99.23 120	99.05 149	99.77 40	99.76 85	99.70 77	99.31 108	99.59 166	98.41 232	99.32 220	99.36 256	98.73 114	99.93 71	97.29 239	99.74 185	99.67 67

Regformer-4		99.45 63	99.44 59	99.50 161	99.52 189	98.94 230	99.17 153	99.53 203	99.64 66	99.76 75	99.60 178	98.96 82	99.90 132	98.91 114	99.84 124	99.67 67

XVS		99.27 113	99.11 128	99.75 57	99.71 112	99.71 70	99.37 93	99.61 147	99.29 123	98.76 298	99.47 231	98.47 149	99.88 161	97.62 219	99.73 192	99.67 67

v1240		99.56 42	99.58 37	99.51 158	99.80 57	99.00 222	99.00 195	99.65 129	99.15 152	99.90 22	99.75 80	99.09 62	99.88 161	99.90 2	99.96 42	99.67 67

X-MVStestdata		96.09 325	94.87 335	99.75 57	99.71 112	99.71 70	99.37 93	99.61 147	99.29 123	98.76 298	61.30 375	98.47 149	99.88 161	97.62 219	99.73 192	99.67 67

VPNet		99.46 61	99.37 74	99.71 83	99.82 45	99.59 112	99.48 75	99.70 100	99.81 30	99.69 106	99.58 186	97.66 225	99.86 194	99.17 80	99.44 274	99.67 67

ACMMPR		99.23 120	99.06 145	99.76 47	99.74 102	99.69 81	99.31 108	99.59 166	98.36 238	99.35 213	99.38 250	98.61 128	99.93 71	97.43 232	99.75 177	99.67 67

SixPastTwentyTwo		99.42 70	99.30 90	99.76 47	99.92 14	99.67 86	99.70 25	99.14 306	99.65 64	99.89 26	99.90 21	96.20 275	99.94 57	99.42 43	99.92 74	99.67 67

HPM-MVS		99.25 116	99.07 143	99.78 37	99.81 52	99.75 54	99.61 55	99.67 114	97.72 283	99.35 213	99.25 282	99.23 47	99.92 91	97.21 250	99.82 143	99.67 67
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	99.58 135	99.78 73	99.20 203	99.11 175	99.62 140	99.18 142	99.89 26	99.72 93	98.66 122	99.87 174	99.88 6	99.97 30	99.66 77

v1921920		99.56 42	99.57 40	99.55 147	99.75 96	99.11 211	99.05 186	99.61 147	99.15 152	99.88 32	99.71 100	99.08 66	99.87 174	99.90 2	99.97 30	99.66 77

v1192		99.57 39	99.57 40	99.57 140	99.77 81	99.22 197	99.04 188	99.60 159	99.18 142	99.87 38	99.72 93	99.08 66	99.85 213	99.89 5	99.98 21	99.66 77

PGM-MVS		99.20 136	99.01 161	99.77 40	99.75 96	99.71 70	99.16 159	99.72 92	97.99 267	99.42 194	99.60 178	98.81 96	99.93 71	96.91 264	99.74 185	99.66 77

mPP-MVS		99.19 139	99.00 164	99.76 47	99.76 85	99.68 84	99.38 89	99.54 194	98.34 247	99.01 269	99.50 218	98.53 142	99.93 71	97.18 252	99.78 167	99.66 77

CP-MVS		99.23 120	99.05 149	99.75 57	99.66 138	99.66 88	99.38 89	99.62 140	98.38 236	99.06 267	99.27 277	98.79 103	99.94 57	97.51 228	99.82 143	99.66 77

EG-PatchMatch MVS		99.57 39	99.56 44	99.62 124	99.77 81	99.33 172	99.26 124	99.76 68	99.32 121	99.80 60	99.78 66	99.29 39	99.87 174	99.15 84	99.91 83	99.66 77

UGNet		99.38 84	99.34 79	99.49 164	98.90 327	98.90 238	99.70 25	99.35 266	99.86 16	98.57 312	99.81 52	98.50 148	99.93 71	99.38 47	99.98 21	99.66 77
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
hse-mvs3		98.61 229	98.34 243	99.44 179	99.60 151	98.67 250	99.27 122	99.44 238	99.68 54	99.32 220	99.49 223	92.50 314	100.00 1	99.24 67	96.51 363	99.65 85

TSAR-MVS + MP.		99.34 97	99.24 106	99.63 115	99.82 45	99.37 162	99.26 124	99.35 266	98.77 199	99.57 151	99.70 107	99.27 44	99.88 161	97.71 209	99.75 177	99.65 85
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 52	99.81 31	98.73 240	99.53 203	99.27 127	99.42 194	99.63 151	98.21 179	99.95 45	97.83 199	99.79 161	99.65 85

MTAPA		99.35 92	99.20 110	99.80 29	99.81 52	99.81 31	99.33 101	99.53 203	99.27 127	99.42 194	99.63 151	98.21 179	99.95 45	97.83 199	99.79 161	99.65 85

Regformer-3		99.41 74	99.41 66	99.40 195	99.52 189	98.70 248	99.17 153	99.44 238	99.62 70	99.75 81	99.60 178	98.90 89	99.85 213	98.89 115	99.84 124	99.65 85

MCST-MVS		99.02 176	98.81 197	99.65 104	99.58 157	99.49 128	98.58 249	99.07 309	98.40 234	99.04 268	99.25 282	98.51 147	99.80 272	97.31 238	99.51 264	99.65 85

UniMVSNet_NR-MVSNet		99.37 87	99.25 104	99.72 79	99.47 217	99.56 118	98.97 206	99.61 147	99.43 109	99.67 113	99.28 275	97.85 209	99.95 45	99.17 80	99.81 151	99.65 85

ZNCC-MVS		99.22 129	99.04 155	99.77 40	99.76 85	99.73 63	99.28 119	99.56 182	98.19 257	99.14 255	99.29 273	98.84 95	99.92 91	97.53 227	99.80 156	99.64 92

v1144		99.54 47	99.53 49	99.59 131	99.79 67	99.28 180	99.10 176	99.61 147	99.20 140	99.84 43	99.73 87	98.67 120	99.84 230	99.86 8	99.98 21	99.64 92

v2v482		99.50 50	99.47 53	99.58 135	99.78 73	99.25 188	99.14 163	99.58 175	99.25 131	99.81 57	99.62 160	98.24 175	99.84 230	99.83 9	99.97 30	99.64 92

K. test v3		98.87 203	98.60 213	99.69 88	99.93 13	99.46 135	99.74 16	94.97 366	99.78 36	99.88 32	99.88 28	93.66 303	99.97 17	99.61 19	99.95 49	99.64 92

DeepC-MVS	98.90 4	99.62 35	99.61 31	99.67 92	99.72 109	99.44 142	99.24 132	99.71 95	99.27 127	99.93 14	99.90 21	99.70 11	99.93 71	98.99 101	99.99 12	99.64 92
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	99.45 225	99.60 109	98.88 213	99.50 218	96.88 318	99.18 250	99.48 226	97.08 251	99.92 91	93.69 351	99.38 283	99.63 97

SMA-MVS		99.19 139	99.00 164	99.73 73	99.46 222	99.73 63	99.13 169	99.52 211	97.40 300	99.57 151	99.64 141	98.93 83	99.83 241	97.61 221	99.79 161	99.63 97
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	98.51 297	99.75 96	95.90 339	98.07 299	99.84 32	99.84 24	99.89 26	99.73 87	96.01 279	99.99 5	99.33 55	100.00 1	99.63 97

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 40	99.89 26	99.87 31	99.63 14	99.87 174	99.54 27	99.92 74	99.63 97

MP-MVS		99.06 167	98.83 195	99.76 47	99.76 85	99.71 70	99.32 104	99.50 218	98.35 243	98.97 271	99.48 226	98.37 163	99.92 91	95.95 314	99.75 177	99.63 97
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	99.43 230	99.56 118	98.83 222	99.53 203	99.38 113	99.67 113	99.36 256	97.67 221	99.95 45	99.17 80	99.81 151	99.63 97

NR-MVSNet		99.40 77	99.31 85	99.68 89	99.43 230	99.55 121	99.73 19	99.50 218	99.46 101	99.88 32	99.36 256	97.54 229	99.87 174	98.97 105	99.87 109	99.63 97

IterMVS		98.97 186	99.16 114	98.42 301	99.74 102	95.64 342	98.06 301	99.83 34	99.83 27	99.85 40	99.74 83	96.10 278	99.99 5	99.27 66	100.00 1	99.63 97
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 57	99.43 146	99.70 25	99.24 292	99.48 92	99.56 158	99.77 73	94.89 289	99.93 71	98.72 130	99.89 92	99.63 97

ACMMP		99.25 116	99.08 139	99.74 63	99.79 67	99.68 84	99.50 71	99.65 129	98.07 263	99.52 172	99.69 113	98.57 133	99.92 91	97.18 252	99.79 161	99.63 97
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	99.56 144	99.28 277	99.22 197	98.99 200	99.40 252	99.08 159	99.58 148	99.64 141	98.90 89	99.83 241	97.44 231	99.75 177	99.63 97
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	99.03 317	99.77 43	99.74 16	99.61 147	99.18 142	99.76 75	99.61 169	99.00 74	99.92 91	97.72 207	99.60 242	99.62 108

PC_three_1452									97.56 289	99.68 108	99.41 241	99.09 62	97.09 369	96.66 280	99.60 242	99.62 108

GeoE		99.69 21	99.66 22	99.78 37	99.76 85	99.76 50	99.60 60	99.82 39	99.46 101	99.75 81	99.56 197	99.63 14	99.95 45	99.43 38	99.88 100	99.62 108

test_method		91.72 336	92.32 339	89.91 352	93.49 374	70.18 376	90.28 365	99.56 182	61.71 370	95.39 367	99.52 211	93.90 298	99.94 57	98.76 126	98.27 343	99.62 108

GST-MVS		99.16 148	98.96 175	99.75 57	99.73 105	99.73 63	99.20 142	99.55 188	98.22 254	99.32 220	99.35 261	98.65 124	99.91 112	96.86 267	99.74 185	99.62 108

new-patchmatchnet		99.35 92	99.57 40	98.71 292	99.82 45	96.62 329	98.55 255	99.75 75	99.50 90	99.88 32	99.87 31	99.31 37	99.88 161	99.43 38	100.00 1	99.62 108

RRT_MVS		98.75 215	98.54 223	99.41 193	98.14 365	98.61 256	98.98 204	99.66 118	99.31 122	99.84 43	99.75 80	91.98 317	99.98 7	99.20 73	99.95 49	99.62 108

CPTT-MVS		98.74 217	98.44 232	99.64 111	99.61 149	99.38 159	99.18 148	99.55 188	96.49 326	99.27 232	99.37 251	97.11 250	99.92 91	95.74 321	99.67 219	99.62 108

MIMVSNet1		99.66 26	99.62 27	99.80 29	99.94 10	99.87 10	99.69 31	99.77 63	99.78 36	99.93 14	99.89 25	97.94 200	99.92 91	99.65 16	99.98 21	99.62 108

DeepPCF-MVS	98.42 6	99.18 143	99.02 158	99.67 92	99.22 286	99.75 54	97.25 347	99.47 229	98.72 204	99.66 117	99.70 107	99.29 39	99.63 341	98.07 176	99.81 151	99.62 108

3Dnovator+	98.92 3	99.35 92	99.24 106	99.67 92	99.35 251	99.47 131	99.62 50	99.50 218	99.44 104	99.12 258	99.78 66	98.77 108	99.94 57	97.87 193	99.72 198	99.62 108

DVP-MVS		99.32 103	99.17 113	99.77 40	99.69 123	99.80 36	99.14 163	99.31 275	99.16 148	99.62 135	99.61 169	98.35 165	99.91 112	97.88 190	99.72 198	99.61 119
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	99.50 201	99.62 101	99.01 193	99.57 177	96.80 323	99.54 165	99.63 151	98.29 171	99.91 112	95.24 331	99.71 202	99.61 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		98.82 208	98.57 219	99.58 135	99.21 288	99.31 175	98.61 245	99.25 289	98.65 208	98.43 320	99.26 280	97.86 207	99.81 267	96.55 285	99.27 300	99.61 119

TAMVS		99.49 52	99.45 57	99.63 115	99.48 212	99.42 149	99.45 78	99.57 177	99.66 62	99.78 68	99.83 43	97.85 209	99.86 194	99.44 37	99.96 42	99.61 119

Regformer-1		99.32 103	99.27 100	99.47 170	99.41 236	98.95 229	98.99 200	99.48 225	99.48 92	99.66 117	99.52 211	98.78 105	99.87 174	98.36 148	99.74 185	99.60 123

Regformer-2		99.34 97	99.27 100	99.53 153	99.41 236	99.10 215	98.99 200	99.53 203	99.47 97	99.66 117	99.52 211	98.80 100	99.89 147	98.31 154	99.74 185	99.60 123

HPM-MVS++		98.96 189	98.70 207	99.74 63	99.52 189	99.71 70	98.86 217	99.19 301	98.47 228	98.59 310	99.06 308	98.08 190	99.91 112	96.94 262	99.60 242	99.60 123

V42		99.56 42	99.54 45	99.63 115	99.79 67	99.46 135	99.39 87	99.59 166	99.24 133	99.86 39	99.70 107	98.55 136	99.82 251	99.79 11	99.95 49	99.60 123

HQP_MVS		98.90 197	98.68 209	99.55 147	99.58 157	99.24 193	98.80 230	99.54 194	98.94 175	99.14 255	99.25 282	97.24 242	99.82 251	95.84 317	99.78 167	99.60 123

plane_prior5								99.54 194					99.82 251	95.84 317	99.78 167	99.60 123

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 14	99.86 5	99.92 7	99.69 52	99.78 68	99.92 16	99.37 31	99.88 161	98.93 113	99.95 49	99.60 123

ACMH+	98.40 8	99.50 50	99.43 62	99.71 83	99.86 30	99.76 50	99.32 104	99.77 63	99.53 86	99.77 73	99.76 76	99.26 45	99.78 278	97.77 201	99.88 100	99.60 123

ACMM	98.09 11	99.46 61	99.38 71	99.72 79	99.80 57	99.69 81	99.13 169	99.65 129	98.99 168	99.64 123	99.72 93	99.39 25	99.86 194	98.23 160	99.81 151	99.60 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VDDNet		98.97 186	98.82 196	99.42 185	99.71 112	98.81 242	99.62 50	98.68 326	99.81 30	99.38 209	99.80 54	94.25 296	99.85 213	98.79 122	99.32 293	99.59 132

casdiffmvs		99.63 32	99.61 31	99.67 92	99.79 67	99.59 112	99.13 169	99.85 26	99.79 35	99.76 75	99.72 93	99.33 36	99.82 251	99.21 70	99.94 62	99.59 132

UniMVSNet (Re)		99.37 87	99.26 102	99.68 89	99.51 194	99.58 115	98.98 204	99.60 159	99.43 109	99.70 103	99.36 256	97.70 216	99.88 161	99.20 73	99.87 109	99.59 132

DSMNet-mixed		99.48 54	99.65 24	98.95 263	99.71 112	97.27 315	99.50 71	99.82 39	99.59 82	99.41 202	99.85 37	99.62 16	100.00 1	99.53 29	99.89 92	99.59 132

3Dnovator	99.15 2	99.43 66	99.36 77	99.65 104	99.39 241	99.42 149	99.70 25	99.56 182	99.23 135	99.35 213	99.80 54	99.17 53	99.95 45	98.21 162	99.84 124	99.59 132

SED-MVS		99.40 77	99.28 97	99.77 40	99.69 123	99.82 28	99.20 142	99.54 194	99.13 154	99.82 50	99.63 151	98.91 86	99.92 91	97.85 196	99.70 204	99.58 137

OPU-MVS				99.29 222	99.12 303	99.44 142	99.20 142				99.40 244	99.00 74	98.84 366	96.54 286	99.60 242	99.58 137

abl_6		99.36 90	99.23 108	99.75 57	99.71 112	99.74 60	99.33 101	99.76 68	99.07 161	99.65 121	99.63 151	99.09 62	99.92 91	97.13 255	99.76 174	99.58 137

EPNet		98.13 273	97.77 286	99.18 242	94.57 373	97.99 292	99.24 132	97.96 346	99.74 39	97.29 355	99.62 160	93.13 307	99.97 17	98.59 137	99.83 134	99.58 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		99.03 174	98.85 191	99.55 147	99.80 57	99.25 188	99.73 19	99.15 305	99.37 114	99.61 141	99.71 100	94.73 292	99.81 267	97.70 211	99.88 100	99.58 137

ACMP	97.51 14	99.05 170	98.84 193	99.67 92	99.78 73	99.55 121	98.88 213	99.66 118	97.11 315	99.47 183	99.60 178	99.07 68	99.89 147	96.18 303	99.85 120	99.58 137
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		99.23 120	99.05 149	99.74 63	99.52 189	99.75 54	99.20 142	99.61 147	98.97 170	99.48 181	99.58 186	98.41 157	99.91 112	97.15 254	99.55 253	99.57 143

SR-MVS		99.19 139	99.00 164	99.74 63	99.51 194	99.72 67	99.18 148	99.60 159	98.85 188	99.47 183	99.58 186	98.38 162	99.92 91	96.92 263	99.54 259	99.57 143

lessismore_v0				99.64 111	99.86 30	99.38 159		90.66 373		99.89 26	99.83 43	94.56 294	99.97 17	99.56 26	99.92 74	99.57 143

pmmvs5		99.19 139	99.11 128	99.42 185	99.76 85	98.88 239	98.55 255	99.73 83	98.82 192	99.72 96	99.62 160	96.56 261	99.82 251	99.32 57	99.95 49	99.56 146

APD-MVS_3200maxsize		99.31 105	99.16 114	99.74 63	99.53 184	99.75 54	99.27 122	99.61 147	99.19 141	99.57 151	99.64 141	98.76 109	99.90 132	97.29 239	99.62 232	99.56 146

CDPH-MVS		98.56 237	98.20 255	99.61 127	99.50 201	99.46 135	98.32 276	99.41 245	95.22 344	99.21 244	99.10 305	98.34 167	99.82 251	95.09 334	99.66 223	99.56 146

Anonymous20240521		99.44 65	99.42 65	99.49 164	99.89 21	98.96 228	99.62 50	99.76 68	99.85 21	99.82 50	99.88 28	96.39 270	99.97 17	99.59 21	99.98 21	99.55 149

our_test_3		98.85 205	99.09 137	98.13 313	99.66 138	94.90 349	97.72 325	99.58 175	99.07 161	99.64 123	99.62 160	98.19 182	99.93 71	98.41 145	99.95 49	99.55 149

YYNet1		98.95 192	98.99 169	98.84 280	99.64 142	97.14 319	98.22 284	99.32 271	98.92 180	99.59 146	99.66 134	97.40 234	99.83 241	98.27 157	99.90 84	99.55 149

MDA-MVSNet_test_wron		98.95 192	98.99 169	98.85 278	99.64 142	97.16 318	98.23 283	99.33 269	98.93 178	99.56 158	99.66 134	97.39 236	99.83 241	98.29 155	99.88 100	99.55 149

MVSFormer		99.41 74	99.44 59	99.31 219	99.57 167	98.40 269	99.77 11	99.80 49	99.73 40	99.63 127	99.30 270	98.02 194	99.98 7	99.43 38	99.69 207	99.55 149

jason		99.16 148	99.11 128	99.32 216	99.75 96	98.44 266	98.26 281	99.39 255	98.70 205	99.74 90	99.30 270	98.54 138	99.97 17	98.48 142	99.82 143	99.55 149
jason: jason.
CDS-MVSNet		99.22 129	99.13 121	99.50 161	99.35 251	99.11 211	98.96 207	99.54 194	99.46 101	99.61 141	99.70 107	96.31 272	99.83 241	99.34 52	99.88 100	99.55 149
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 38	99.70 77	99.38 89	99.78 60	99.53 86	99.67 113	99.78 66	99.19 51	99.86 194	97.32 237	99.87 109	99.55 149
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	99.54 179	99.74 60	99.26 124	99.62 140	99.16 148	99.52 172	99.64 141	98.41 157	99.91 112	97.27 242	99.61 239	99.54 157

RE-MVS-def			99.13 121		99.54 179	99.74 60	99.26 124	99.62 140	99.16 148	99.52 172	99.64 141	98.57 133		97.27 242	99.61 239	99.54 157

SD-MVS		99.01 180	99.30 90	98.15 312	99.50 201	99.40 154	98.94 210	99.61 147	99.22 139	99.75 81	99.82 49	99.54 22	95.51 371	97.48 229	99.87 109	99.54 157
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	99.56 144	99.25 282	99.43 146	98.54 258	99.27 284	98.58 215	98.80 293	99.43 239	98.53 142	99.70 305	97.22 249	99.59 246	99.54 157

MVS_111021_HR		99.12 156	99.02 158	99.40 195	99.50 201	99.11 211	97.92 317	99.71 95	98.76 202	99.08 263	99.47 231	99.17 53	99.54 351	97.85 196	99.76 174	99.54 157

v148		99.40 77	99.41 66	99.39 198	99.76 85	98.94 230	99.09 180	99.59 166	99.17 146	99.81 57	99.61 169	98.41 157	99.69 311	99.32 57	99.94 62	99.53 162

diffmvs		99.34 97	99.32 84	99.39 198	99.67 137	98.77 245	98.57 253	99.81 48	99.61 74	99.48 181	99.41 241	98.47 149	99.86 194	98.97 105	99.90 84	99.53 162

baseline		99.63 32	99.62 27	99.66 99	99.80 57	99.62 101	99.44 81	99.80 49	99.71 44	99.72 96	99.69 113	99.15 55	99.83 241	99.32 57	99.94 62	99.53 162

HQP4-MVS										98.15 330			99.70 305			99.53 162

GBi-Net		99.42 70	99.31 85	99.73 73	99.49 206	99.77 43	99.68 34	99.70 100	99.44 104	99.62 135	99.83 43	97.21 244	99.90 132	98.96 107	99.90 84	99.53 162

test1		99.42 70	99.31 85	99.73 73	99.49 206	99.77 43	99.68 34	99.70 100	99.44 104	99.62 135	99.83 43	97.21 244	99.90 132	98.96 107	99.90 84	99.53 162

FMVSNet1		99.66 26	99.63 26	99.73 73	99.78 73	99.77 43	99.68 34	99.70 100	99.67 58	99.82 50	99.83 43	98.98 77	99.90 132	99.24 67	99.97 30	99.53 162

HQP-MVS		98.36 259	98.02 267	99.39 198	99.31 268	98.94 230	97.98 309	99.37 262	97.45 297	98.15 330	98.83 338	96.67 259	99.70 305	94.73 337	99.67 219	99.53 162

QAPM		98.40 257	97.99 268	99.65 104	99.39 241	99.47 131	99.67 38	99.52 211	91.70 360	98.78 296	99.80 54	98.55 136	99.95 45	94.71 339	99.75 177	99.53 162

F-COLMAP		98.74 217	98.45 230	99.62 124	99.57 167	99.47 131	98.84 220	99.65 129	96.31 330	98.93 275	99.19 295	97.68 220	99.87 174	96.52 287	99.37 287	99.53 162

MVSTER		98.47 250	98.22 253	99.24 234	99.06 313	98.35 274	99.08 183	99.46 233	99.27 127	99.75 81	99.66 134	88.61 347	99.85 213	99.14 90	99.92 74	99.52 172

PVSNet_BlendedMVS		99.03 174	99.01 161	99.09 251	99.54 179	97.99 292	98.58 249	99.82 39	97.62 287	99.34 216	99.71 100	98.52 145	99.77 286	97.98 182	99.97 30	99.52 172

OPM-MVS		99.26 115	99.13 121	99.63 115	99.70 120	99.61 107	98.58 249	99.48 225	98.50 224	99.52 172	99.63 151	99.14 57	99.76 288	97.89 189	99.77 171	99.51 174
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior1		98.33 264	97.92 278	99.57 140	99.35 251	99.36 165	97.99 308	99.39 255	94.85 351	97.76 350	98.98 322	98.03 192	99.85 213	95.49 325	99.44 274	99.51 174

AllTest		99.21 134	99.07 143	99.63 115	99.78 73	99.64 95	99.12 173	99.83 34	98.63 210	99.63 127	99.72 93	98.68 117	99.75 292	96.38 295	99.83 134	99.51 174

TestCases				99.63 115	99.78 73	99.64 95		99.83 34	98.63 210	99.63 127	99.72 93	98.68 117	99.75 292	96.38 295	99.83 134	99.51 174

BH-RMVSNet		98.41 255	98.14 262	99.21 237	99.21 288	98.47 263	98.60 247	98.26 343	98.35 243	98.93 275	99.31 268	97.20 247	99.66 331	94.32 342	99.10 308	99.51 174

USDC		98.96 189	98.93 178	99.05 257	99.54 179	97.99 292	97.07 353	99.80 49	98.21 255	99.75 81	99.77 73	98.43 154	99.64 340	97.90 188	99.88 100	99.51 174

test9_res														95.10 333	99.44 274	99.50 180

train_agg		98.35 262	97.95 272	99.57 140	99.35 251	99.35 169	98.11 294	99.41 245	94.90 348	97.92 341	98.99 319	98.02 194	99.85 213	95.38 329	99.44 274	99.50 180

agg_prior2														94.58 341	99.46 273	99.50 180

VDD-MVS		99.20 136	99.11 128	99.44 179	99.43 230	98.98 224	99.50 71	98.32 342	99.80 33	99.56 158	99.69 113	96.99 254	99.85 213	98.99 101	99.73 192	99.50 180

MDA-MVSNet-bldmvs		99.06 167	99.05 149	99.07 255	99.80 57	97.83 299	98.89 212	99.72 92	99.29 123	99.63 127	99.70 107	96.47 265	99.89 147	98.17 169	99.82 143	99.50 180

DIV-MVS_2432*1600		99.63 32	99.59 34	99.76 47	99.84 34	99.90 5	99.37 93	99.79 55	99.83 27	99.88 32	99.85 37	98.42 156	99.90 132	99.60 20	99.73 192	99.49 185

xxxxxxxxxxxxxcwj		99.11 160	98.96 175	99.54 151	99.53 184	99.25 188	98.29 278	99.76 68	99.07 161	99.42 194	99.61 169	98.86 92	99.87 174	96.45 292	99.68 212	99.49 185

SF-MVS		99.10 164	98.93 178	99.62 124	99.58 157	99.51 126	99.13 169	99.65 129	97.97 269	99.42 194	99.61 169	98.86 92	99.87 174	96.45 292	99.68 212	99.49 185

Anonymous20240529		99.42 70	99.34 79	99.65 104	99.53 184	99.60 109	99.63 49	99.39 255	99.47 97	99.76 75	99.78 66	98.13 186	99.86 194	98.70 131	99.68 212	99.49 185

WTY-MVS		98.59 234	98.37 239	99.26 229	99.43 230	98.40 269	98.74 238	99.13 308	98.10 260	99.21 244	99.24 287	94.82 290	99.90 132	97.86 194	98.77 325	99.49 185

ppachtmachnet_test		98.89 200	99.12 125	98.20 311	99.66 138	95.24 346	97.63 329	99.68 109	99.08 159	99.78 68	99.62 160	98.65 124	99.88 161	98.02 177	99.96 42	99.48 190

Anonymous20231206		99.35 92	99.31 85	99.47 170	99.74 102	99.06 221	99.28 119	99.74 80	99.23 135	99.72 96	99.53 209	97.63 227	99.88 161	99.11 92	99.84 124	99.48 190

test_prior3		98.62 228	98.34 243	99.46 173	99.35 251	99.22 197	97.95 313	99.39 255	97.87 276	98.05 336	99.05 309	97.90 203	99.69 311	95.99 310	99.49 268	99.48 190

test_prior				99.46 173	99.35 251	99.22 197		99.39 255					99.69 311			99.48 190

test12				99.54 151	99.29 274	99.33 172		99.16 304		98.43 320		97.54 229	99.82 251		99.47 271	99.48 190

VNet		99.18 143	99.06 145	99.56 144	99.24 284	99.36 165	99.33 101	99.31 275	99.67 58	99.47 183	99.57 194	96.48 264	99.84 230	99.15 84	99.30 295	99.47 195

test20.03		99.55 45	99.54 45	99.58 135	99.79 67	99.37 162	99.02 191	99.89 15	99.60 80	99.82 50	99.62 160	98.81 96	99.89 147	99.43 38	99.86 116	99.47 195

114514_t		98.49 248	98.11 263	99.64 111	99.73 105	99.58 115	99.24 132	99.76 68	89.94 363	99.42 194	99.56 197	97.76 215	99.86 194	97.74 206	99.82 143	99.47 195

sss		98.90 197	98.77 201	99.27 227	99.48 212	98.44 266	98.72 241	99.32 271	97.94 273	99.37 210	99.35 261	96.31 272	99.91 112	98.85 117	99.63 231	99.47 195

旧先验1					99.49 206	99.29 178		99.26 286			99.39 248	97.67 221			99.36 288	99.46 199

1121		98.56 237	98.24 251	99.52 155	99.49 206	99.24 193	99.30 111	99.22 296	95.77 337	98.52 315	99.29 273	97.39 236	99.85 213	95.79 319	99.34 290	99.46 199

MVP-Stereo		99.16 148	99.08 139	99.43 183	99.48 212	99.07 219	99.08 183	99.55 188	98.63 210	99.31 224	99.68 124	98.19 182	99.78 278	98.18 167	99.58 247	99.45 201
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
新几何1				99.52 155	99.50 201	99.22 197		99.26 286	95.66 340	98.60 309	99.28 275	97.67 221	99.89 147	95.95 314	99.32 293	99.45 201

LFMVS		98.46 251	98.19 258	99.26 229	99.24 284	98.52 261	99.62 50	96.94 358	99.87 14	99.31 224	99.58 186	91.04 328	99.81 267	98.68 134	99.42 279	99.45 201

testgi		99.29 108	99.26 102	99.37 205	99.75 96	98.81 242	98.84 220	99.89 15	98.38 236	99.75 81	99.04 312	99.36 34	99.86 194	99.08 95	99.25 301	99.45 201

UnsupCasMVSNet_eth		98.83 206	98.57 219	99.59 131	99.68 132	99.45 140	98.99 200	99.67 114	99.48 92	99.55 163	99.36 256	94.92 288	99.86 194	98.95 111	96.57 362	99.45 201

无先验							98.01 304	99.23 293	95.83 336				99.85 213	95.79 319		99.44 206

testdata				99.42 185	99.51 194	98.93 234		99.30 278	96.20 331	98.87 285	99.40 244	98.33 169	99.89 147	96.29 298	99.28 297	99.44 206

XVG-OURS-SEG-HR		99.16 148	98.99 169	99.66 99	99.84 34	99.64 95	98.25 282	99.73 83	98.39 235	99.63 127	99.43 239	99.70 11	99.90 132	97.34 236	98.64 333	99.44 206

FMVSNet2		99.35 92	99.28 97	99.55 147	99.49 206	99.35 169	99.45 78	99.57 177	99.44 104	99.70 103	99.74 83	97.21 244	99.87 174	99.03 98	99.94 62	99.44 206

N_pmnet		98.73 219	98.53 225	99.35 209	99.72 109	98.67 250	98.34 273	94.65 367	98.35 243	99.79 65	99.68 124	98.03 192	99.93 71	98.28 156	99.92 74	99.44 206

RPSCF		99.18 143	99.02 158	99.64 111	99.83 38	99.85 14	99.44 81	99.82 39	98.33 248	99.50 179	99.78 66	97.90 203	99.65 338	96.78 273	99.83 134	99.44 206

原ACMM1				99.37 205	99.47 217	98.87 241		99.27 284	96.74 324	98.26 325	99.32 266	97.93 201	99.82 251	95.96 313	99.38 283	99.43 212

test222					99.51 194	99.08 218	97.83 322	99.29 280	95.21 345	98.68 304	99.31 268	97.28 241			99.38 283	99.43 212

XVG-OURS		99.21 134	99.06 145	99.65 104	99.82 45	99.62 101	97.87 320	99.74 80	98.36 238	99.66 117	99.68 124	99.71 9	99.90 132	96.84 270	99.88 100	99.43 212

CSCG		99.37 87	99.29 95	99.60 129	99.71 112	99.46 135	99.43 83	99.85 26	98.79 196	99.41 202	99.60 178	98.92 84	99.92 91	98.02 177	99.92 74	99.43 212

ETH3D-3000-0.1		98.77 212	98.50 227	99.59 131	99.47 217	99.53 123	98.77 235	99.60 159	97.33 304	99.23 238	99.50 218	97.91 202	99.83 241	95.02 335	99.67 219	99.41 216

TinyColmap		98.97 186	98.93 178	99.07 255	99.46 222	98.19 280	97.75 324	99.75 75	98.79 196	99.54 165	99.70 107	98.97 79	99.62 342	96.63 283	99.83 134	99.41 216

ETH3 D test6400		97.76 286	97.19 301	99.50 161	99.38 244	99.26 184	98.34 273	99.49 223	92.99 357	98.54 314	99.20 293	95.92 281	99.82 251	91.14 358	99.66 223	99.40 218

Anonymous202405211		98.75 215	98.46 229	99.63 115	99.34 261	99.66 88	99.47 77	97.65 351	99.28 126	99.56 158	99.50 218	93.15 306	99.84 230	98.62 136	99.58 247	99.40 218

XVG-ACMP-BASELINE		99.23 120	99.10 136	99.63 115	99.82 45	99.58 115	98.83 222	99.72 92	98.36 238	99.60 143	99.71 100	98.92 84	99.91 112	97.08 257	99.84 124	99.40 218

MS-PatchMatch		99.00 182	98.97 173	99.09 251	99.11 308	98.19 280	98.76 237	99.33 269	98.49 226	99.44 188	99.58 186	98.21 179	99.69 311	98.20 163	99.62 232	99.39 221

FMVSNet3		98.80 210	98.63 212	99.32 216	99.13 301	98.72 247	99.10 176	99.48 225	99.23 135	99.62 135	99.64 141	92.57 311	99.86 194	98.96 107	99.90 84	99.39 221

ambc				99.20 239	99.35 251	98.53 259	99.17 153	99.46 233		99.67 113	99.80 54	98.46 152	99.70 305	97.92 187	99.70 204	99.38 223

FMVSNet5		97.80 284	97.25 298	99.42 185	98.83 336	98.97 226	99.38 89	99.80 49	98.87 186	99.25 234	99.69 113	80.60 369	99.91 112	98.96 107	99.90 84	99.38 223

PAPM_NR		98.36 259	98.04 266	99.33 212	99.48 212	98.93 234	98.79 233	99.28 283	97.54 292	98.56 313	98.57 349	97.12 249	99.69 311	94.09 346	98.90 320	99.38 223

EPNet_dtu		97.62 291	97.79 285	97.11 339	96.67 370	92.31 362	98.51 261	98.04 344	99.24 133	95.77 365	99.47 231	93.78 302	99.66 331	98.98 103	99.62 232	99.37 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		99.11 160	98.95 177	99.59 131	99.13 301	99.59 112	99.17 153	99.65 129	97.88 275	99.25 234	99.46 234	98.97 79	99.80 272	97.26 244	99.82 143	99.37 226

PLC	97.35 16	98.36 259	97.99 268	99.48 168	99.32 267	99.24 193	98.50 262	99.51 214	95.19 346	98.58 311	98.96 327	96.95 255	99.83 241	95.63 322	99.25 301	99.37 226
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		97.62 291	97.20 300	98.90 276	99.76 85	97.40 312	99.48 75	94.36 368	99.06 165	99.70 103	99.49 223	84.55 363	99.94 57	98.73 129	99.65 227	99.36 229

pmmvs-eth3d		99.48 54	99.47 53	99.51 158	99.77 81	99.41 153	98.81 227	99.66 118	99.42 111	99.75 81	99.66 134	99.20 50	99.76 288	98.98 103	99.99 12	99.36 229

PVSNet_0	95.53 19	95.85 331	95.31 333	97.47 329	98.78 344	93.48 357	95.72 362	99.40 252	96.18 332	97.37 353	97.73 364	95.73 282	99.58 349	95.49 325	81.40 369	99.36 229

lupinMVS		98.96 189	98.87 189	99.24 234	99.57 167	98.40 269	98.12 292	99.18 302	98.28 251	99.63 127	99.13 298	98.02 194	99.97 17	98.22 161	99.69 207	99.35 232

Vis-MVSNet (Re-imp)		98.77 212	98.58 218	99.34 210	99.78 73	98.88 239	99.61 55	99.56 182	99.11 158	99.24 237	99.56 197	93.00 309	99.78 278	97.43 232	99.89 92	99.35 232

GA-MVS		97.99 281	97.68 289	98.93 267	99.52 189	98.04 291	97.19 349	99.05 312	98.32 249	98.81 291	98.97 325	89.89 344	99.41 361	98.33 152	99.05 310	99.34 234

CANet		99.11 160	99.05 149	99.28 225	98.83 336	98.56 258	98.71 243	99.41 245	99.25 131	99.23 238	99.22 289	97.66 225	99.94 57	99.19 75	99.97 30	99.33 235

Patchmtry		98.78 211	98.54 223	99.49 164	98.89 330	99.19 204	99.32 104	99.67 114	99.65 64	99.72 96	99.79 60	91.87 320	99.95 45	98.00 181	99.97 30	99.33 235

PAPR		97.56 294	97.07 303	99.04 258	98.80 341	98.11 286	97.63 329	99.25 289	94.56 354	98.02 339	98.25 359	97.43 233	99.68 322	90.90 359	98.74 329	99.33 235

CHOSEN 280x420		98.41 255	98.41 235	98.40 302	99.34 261	95.89 340	96.94 355	99.44 238	98.80 195	99.25 234	99.52 211	93.51 304	99.98 7	98.94 112	99.98 21	99.32 238

baseline1		97.73 287	97.33 295	98.96 262	99.30 272	97.73 303	99.40 85	98.42 338	99.33 120	99.46 186	99.21 291	91.18 326	99.82 251	98.35 150	91.26 368	99.32 238

TAPA-MVS	97.92 13	98.03 278	97.55 292	99.46 173	99.47 217	99.44 142	98.50 262	99.62 140	86.79 364	99.07 266	99.26 280	98.26 174	99.62 342	97.28 241	99.73 192	99.31 240
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	99.33 266	99.76 50	99.34 99	99.97 2	98.93 178	99.91 20	99.79 60	98.68 117	99.93 71	96.80 272	99.56 249	99.30 241

TSAR-MVS + GP.		99.12 156	99.04 155	99.38 202	99.34 261	99.16 206	98.15 288	99.29 280	98.18 258	99.63 127	99.62 160	99.18 52	99.68 322	98.20 163	99.74 185	99.30 241

PVSNet_Blended		98.70 222	98.59 215	99.02 259	99.54 179	97.99 292	97.58 332	99.82 39	95.70 339	99.34 216	98.98 322	98.52 145	99.77 286	97.98 182	99.83 134	99.30 241

MVS_0304		98.88 201	98.71 204	99.39 198	98.85 334	98.91 237	99.45 78	99.30 278	98.56 216	97.26 356	99.68 124	96.18 276	99.96 35	99.17 80	99.94 62	99.29 244

MVS_111021_LR		99.13 154	99.03 157	99.42 185	99.58 157	99.32 174	97.91 319	99.73 83	98.68 206	99.31 224	99.48 226	99.09 62	99.66 331	97.70 211	99.77 171	99.29 244

ETH3D cwj APD-0.16		98.50 245	98.16 261	99.51 158	99.04 316	99.39 156	98.47 264	99.47 229	96.70 325	98.78 296	99.33 265	97.62 228	99.86 194	94.69 340	99.38 283	99.28 246

miper_lstm_enhance		98.65 226	98.60 213	98.82 285	99.20 291	97.33 314	97.78 323	99.66 118	99.01 167	99.59 146	99.50 218	94.62 293	99.85 213	98.12 172	99.90 84	99.26 247

MVS		95.72 333	94.63 337	98.99 260	98.56 352	97.98 297	99.30 111	98.86 318	72.71 369	97.30 354	99.08 306	98.34 167	99.74 294	89.21 360	98.33 341	99.26 247

MSLP-MVS++		99.05 170	99.09 137	98.91 270	99.21 288	98.36 273	98.82 226	99.47 229	98.85 188	98.90 281	99.56 197	98.78 105	99.09 364	98.57 138	99.68 212	99.26 247

D2MVS		99.22 129	99.19 111	99.29 222	99.69 123	98.74 246	98.81 227	99.41 245	98.55 218	99.68 108	99.69 113	98.13 186	99.87 174	98.82 120	99.98 21	99.24 250

test_yl		98.25 267	97.95 272	99.13 247	99.17 296	98.47 263	99.00 195	98.67 328	98.97 170	99.22 242	99.02 317	91.31 324	99.69 311	97.26 244	98.93 316	99.24 250

DCV-MVSNet		98.25 267	97.95 272	99.13 247	99.17 296	98.47 263	99.00 195	98.67 328	98.97 170	99.22 242	99.02 317	91.31 324	99.69 311	97.26 244	98.93 316	99.24 250

DPM-MVS		98.28 265	97.94 276	99.32 216	99.36 249	99.11 211	97.31 345	98.78 323	96.88 318	98.84 288	99.11 304	97.77 214	99.61 346	94.03 348	99.36 288	99.23 253

CLD-MVS		98.76 214	98.57 219	99.33 212	99.57 167	98.97 226	97.53 335	99.55 188	96.41 327	99.27 232	99.13 298	99.07 68	99.78 278	96.73 276	99.89 92	99.23 253
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	99.36 208	99.57 167	99.10 215	98.01 304	99.25 289	98.78 198	99.58 148	99.44 238	98.24 175	99.76 288	98.74 128	99.93 70	99.22 255

OMC-MVS		98.90 197	98.72 203	99.44 179	99.39 241	99.42 149	98.58 249	99.64 135	97.31 305	99.44 188	99.62 160	98.59 130	99.69 311	96.17 304	99.79 161	99.22 255

eth_miper_zixun_eth		98.68 224	98.71 204	98.60 294	99.10 309	96.84 326	97.52 337	99.54 194	98.94 175	99.58 148	99.48 226	96.25 274	99.76 288	98.01 180	99.93 70	99.21 257

cl_fuxian		98.72 220	98.71 204	98.72 290	99.12 303	97.22 317	97.68 328	99.56 182	98.90 182	99.54 165	99.48 226	96.37 271	99.73 297	97.88 190	99.88 100	99.21 257

CMPMVS	77.52 23	98.50 245	98.19 258	99.41 193	98.33 358	99.56 118	99.01 193	99.59 166	95.44 341	99.57 151	99.80 54	95.64 283	99.46 360	96.47 291	99.92 74	99.21 257
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		99.06 167	98.97 173	99.34 210	99.31 268	98.98 224	98.31 277	99.91 10	98.81 193	98.79 294	98.94 329	99.14 57	99.84 230	98.79 122	98.74 329	99.20 260

DELS-MVS		99.34 97	99.30 90	99.48 168	99.51 194	99.36 165	98.12 292	99.53 203	99.36 116	99.41 202	99.61 169	99.22 48	99.87 174	99.21 70	99.68 212	99.20 260
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	99.71 112	99.91 2	99.76 13	99.96 4	99.86 16	99.51 177	99.39 248	99.57 20	99.93 71	99.64 18	99.86 116	99.20 260

CANet_DTU		98.91 195	98.85 191	99.09 251	98.79 342	98.13 283	98.18 285	99.31 275	99.48 92	98.86 286	99.51 215	96.56 261	99.95 45	99.05 97	99.95 49	99.19 263

alignmvs		98.28 265	97.96 271	99.25 232	99.12 303	98.93 234	99.03 190	98.42 338	99.64 66	98.72 301	97.85 363	90.86 333	99.62 342	98.88 116	99.13 306	99.19 263

cl-mvsnet1		98.54 241	98.42 234	98.92 268	99.03 317	97.80 301	97.46 339	99.59 166	98.90 182	99.60 143	99.46 234	93.87 299	99.78 278	97.97 184	99.89 92	99.18 265

MSDG		99.08 165	98.98 172	99.37 205	99.60 151	99.13 209	97.54 333	99.74 80	98.84 191	99.53 170	99.55 204	99.10 60	99.79 275	97.07 258	99.86 116	99.18 265

cl-mvsnet____		98.54 241	98.41 235	98.92 268	99.03 317	97.80 301	97.46 339	99.59 166	98.90 182	99.60 143	99.46 234	93.85 300	99.78 278	97.97 184	99.89 92	99.17 267

PM-MVS		99.36 90	99.29 95	99.58 135	99.83 38	99.66 88	98.95 208	99.86 22	98.85 188	99.81 57	99.73 87	98.40 161	99.92 91	98.36 148	99.83 134	99.17 267

thisisatest0530		97.45 296	96.95 307	98.94 264	99.68 132	97.73 303	99.09 180	94.19 370	98.61 213	99.56 158	99.30 270	84.30 364	99.93 71	98.27 157	99.54 259	99.16 269

PatchmatchNet		97.65 290	97.80 283	97.18 337	98.82 339	92.49 361	99.17 153	98.39 340	98.12 259	98.79 294	99.58 186	90.71 335	99.89 147	97.23 248	99.41 280	99.16 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpnnormal		99.43 66	99.38 71	99.60 129	99.87 28	99.75 54	99.59 62	99.78 60	99.71 44	99.90 22	99.69 113	98.85 94	99.90 132	97.25 247	99.78 167	99.15 271

mvs_anonymous		99.28 109	99.39 69	98.94 264	99.19 293	97.81 300	99.02 191	99.55 188	99.78 36	99.85 40	99.80 54	98.24 175	99.86 194	99.57 25	99.50 266	99.15 271

ab-mvs		99.33 101	99.28 97	99.47 170	99.57 167	99.39 156	99.78 10	99.43 242	98.87 186	99.57 151	99.82 49	98.06 191	99.87 174	98.69 133	99.73 192	99.15 271

MIMVSNet		98.43 253	98.20 255	99.11 249	99.53 184	98.38 272	99.58 64	98.61 330	98.96 173	99.33 218	99.76 76	90.92 330	99.81 267	97.38 235	99.76 174	99.15 271

GSMVS																99.14 275

sam_mvs1												90.81 334				99.14 275

SCA		98.11 274	98.36 240	97.36 332	99.20 291	92.99 359	98.17 287	98.49 336	98.24 253	99.10 261	99.57 194	96.01 279	99.94 57	96.86 267	99.62 232	99.14 275

LS3D		99.24 119	99.11 128	99.61 127	98.38 356	99.79 38	99.57 65	99.68 109	99.61 74	99.15 253	99.71 100	98.70 115	99.91 112	97.54 225	99.68 212	99.13 278

Patchmatch-RL test		98.60 231	98.36 240	99.33 212	99.77 81	99.07 219	98.27 280	99.87 20	98.91 181	99.74 90	99.72 93	90.57 337	99.79 275	98.55 139	99.85 120	99.11 279

test_0402		99.22 129	99.14 118	99.45 177	99.79 67	99.43 146	99.28 119	99.68 109	99.54 84	99.40 207	99.56 197	99.07 68	99.82 251	96.01 308	99.96 42	99.11 279

MVS_Test		99.28 109	99.31 85	99.19 240	99.35 251	98.79 244	99.36 96	99.49 223	99.17 146	99.21 244	99.67 130	98.78 105	99.66 331	99.09 94	99.66 223	99.10 281

AdaColmap		98.60 231	98.35 242	99.38 202	99.12 303	99.22 197	98.67 244	99.42 244	97.84 280	98.81 291	99.27 277	97.32 240	99.81 267	95.14 332	99.53 261	99.10 281

FPMVS		96.32 321	95.50 329	98.79 286	99.60 151	98.17 282	98.46 269	98.80 322	97.16 312	96.28 361	99.63 151	82.19 365	99.09 364	88.45 362	98.89 321	99.10 281

Patchmatch-test		98.10 275	97.98 270	98.48 299	99.27 279	96.48 330	99.40 85	99.07 309	98.81 193	99.23 238	99.57 194	90.11 341	99.87 174	96.69 277	99.64 229	99.09 284

tpm		97.15 303	96.95 307	97.75 323	98.91 326	94.24 352	99.32 104	97.96 346	97.71 284	98.29 323	99.32 266	86.72 358	99.92 91	98.10 175	96.24 365	99.09 284

PMMVS		98.49 248	98.29 248	99.11 249	98.96 324	98.42 268	97.54 333	99.32 271	97.53 293	98.47 319	98.15 360	97.88 206	99.82 251	97.46 230	99.24 303	99.09 284

cl-mvsnet2		97.56 294	97.28 296	98.40 302	98.37 357	96.75 327	97.24 348	99.37 262	97.31 305	99.41 202	99.22 289	87.30 349	99.37 362	97.70 211	99.62 232	99.08 287

ADS-MVSNet2		97.78 285	97.66 291	98.12 314	99.14 299	95.36 344	99.22 139	98.75 324	96.97 316	98.25 326	99.64 141	90.90 331	99.94 57	96.51 288	99.56 249	99.08 287

ADS-MVSNet		97.72 289	97.67 290	97.86 319	99.14 299	94.65 350	99.22 139	98.86 318	96.97 316	98.25 326	99.64 141	90.90 331	99.84 230	96.51 288	99.56 249	99.08 287

pmmvs3		98.08 276	97.80 283	98.91 270	99.41 236	97.69 305	97.87 320	99.66 118	95.87 335	99.50 179	99.51 215	90.35 339	99.97 17	98.55 139	99.47 271	99.08 287

PVSNet	97.47 15	98.42 254	98.44 232	98.35 304	99.46 222	96.26 333	96.70 358	99.34 268	97.68 285	99.00 270	99.13 298	97.40 234	99.72 299	97.59 223	99.68 212	99.08 287

MVS-HIRNet		97.86 282	98.22 253	96.76 340	99.28 277	91.53 368	98.38 272	92.60 372	99.13 154	99.31 224	99.96 10	97.18 248	99.68 322	98.34 151	99.83 134	99.07 292

PMVS	92.94 21	98.82 208	98.81 197	98.85 278	99.84 34	97.99 292	99.20 142	99.47 229	99.71 44	99.42 194	99.82 49	98.09 188	99.47 358	93.88 350	99.85 120	99.07 292
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		99.57 39	99.59 34	99.49 164	99.98 3	99.71 70	99.72 22	99.84 32	99.81 30	99.94 11	99.78 66	98.91 86	99.71 303	98.41 145	99.95 49	99.05 294
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
canonicalmvs		99.02 176	99.00 164	99.09 251	99.10 309	98.70 248	99.61 55	99.66 118	99.63 69	98.64 306	97.65 365	99.04 72	99.54 351	98.79 122	98.92 318	99.04 295

hse-mvs2		98.52 243	98.30 247	99.16 243	99.29 274	98.60 257	98.77 235	99.02 313	99.68 54	99.32 220	99.04 312	92.50 314	99.85 213	99.24 67	97.87 354	99.03 296

CL-MVSNet_2432*1600		98.71 221	98.56 222	99.15 245	99.22 286	98.66 252	97.14 350	99.51 214	98.09 262	99.54 165	99.27 277	96.87 257	99.74 294	98.43 144	98.96 315	99.03 296

AUN-MVS		97.82 283	97.38 294	99.14 246	99.27 279	98.53 259	98.72 241	99.02 313	98.10 260	97.18 358	99.03 316	89.26 346	99.85 213	97.94 186	97.91 352	99.03 296

MDTV_nov1_ep13_2view						91.44 369	99.14 163		97.37 302	99.21 244		91.78 322		96.75 274		99.03 296

ITE_SJBPF				99.38 202	99.63 144	99.44 142		99.73 83	98.56 216	99.33 218	99.53 209	98.88 91	99.68 322	96.01 308	99.65 227	99.02 300

UnsupCasMVSNet_bld		98.55 240	98.27 249	99.40 195	99.56 177	99.37 162	97.97 312	99.68 109	97.49 296	99.08 263	99.35 261	95.41 286	99.82 251	97.70 211	98.19 346	99.01 301

miper_ehance_all_eth		98.59 234	98.59 215	98.59 295	98.98 323	97.07 320	97.49 338	99.52 211	98.50 224	99.52 172	99.37 251	96.41 269	99.71 303	97.86 194	99.62 232	99.00 302

CNLPA		98.57 236	98.34 243	99.28 225	99.18 295	99.10 215	98.34 273	99.41 245	98.48 227	98.52 315	98.98 322	97.05 252	99.78 278	95.59 323	99.50 266	98.96 303

new_pmnet		98.88 201	98.89 187	98.84 280	99.70 120	97.62 306	98.15 288	99.50 218	97.98 268	99.62 135	99.54 206	98.15 185	99.94 57	97.55 224	99.84 124	98.95 304

PCF-MVS	96.03 18	96.73 313	95.86 324	99.33 212	99.44 227	99.16 206	96.87 356	99.44 238	86.58 365	98.95 273	99.40 244	94.38 295	99.88 161	87.93 363	99.80 156	98.95 304
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	99.30 221	99.44 227	99.28 180	98.14 290	99.54 194	97.12 314	99.11 259	99.25 282	97.80 212	99.70 305	96.51 288	99.30 295	98.93 306

Fast-Effi-MVS+		99.02 176	98.87 189	99.46 173	99.38 244	99.50 127	99.04 188	99.79 55	97.17 311	98.62 307	98.74 344	99.34 35	99.95 45	98.32 153	99.41 280	98.92 307

ET-MVSNet_ETH3D		96.78 311	96.07 320	98.91 270	99.26 281	97.92 298	97.70 327	96.05 363	97.96 272	92.37 370	98.43 355	87.06 351	99.90 132	98.27 157	97.56 357	98.91 308

EIA-MVS		99.12 156	99.01 161	99.45 177	99.36 249	99.62 101	99.34 99	99.79 55	98.41 232	98.84 288	98.89 334	98.75 111	99.84 230	98.15 171	99.51 264	98.89 309

CostFormer		96.71 314	96.79 313	96.46 346	98.90 327	90.71 372	99.41 84	98.68 326	94.69 353	98.14 334	99.34 264	86.32 360	99.80 272	97.60 222	98.07 350	98.88 310

DP-MVS Recon		98.50 245	98.23 252	99.31 219	99.49 206	99.46 135	98.56 254	99.63 137	94.86 350	98.85 287	99.37 251	97.81 211	99.59 348	96.08 305	99.44 274	98.88 310

test0.0.03 1		97.37 299	96.91 310	98.74 289	97.72 366	97.57 307	97.60 331	97.36 357	98.00 265	99.21 244	98.02 361	90.04 342	99.79 275	98.37 147	95.89 366	98.86 312

BH-untuned		98.22 271	98.09 264	98.58 296	99.38 244	97.24 316	98.55 255	98.98 316	97.81 281	99.20 249	98.76 343	97.01 253	99.65 338	94.83 336	98.33 341	98.86 312

HY-MVS	98.23 9	98.21 272	97.95 272	98.99 260	99.03 317	98.24 276	99.61 55	98.72 325	96.81 322	98.73 300	99.51 215	94.06 297	99.86 194	96.91 264	98.20 344	98.86 312

CS-MVS-test		99.43 66	99.40 68	99.53 153	99.51 194	99.84 19	99.60 60	99.94 6	99.52 88	99.10 261	98.89 334	99.24 46	99.90 132	99.11 92	99.66 223	98.84 315

miper_enhance_ethall		98.03 278	97.94 276	98.32 306	98.27 359	96.43 332	96.95 354	99.41 245	96.37 329	99.43 192	98.96 327	94.74 291	99.69 311	97.71 209	99.62 232	98.83 316

Effi-MVS+-dtu		99.07 166	98.92 182	99.52 155	98.89 330	99.78 41	99.15 161	99.66 118	99.34 117	98.92 278	99.24 287	97.69 218	99.98 7	98.11 173	99.28 297	98.81 317

EPMVS		96.53 317	96.32 315	97.17 338	98.18 362	92.97 360	99.39 87	89.95 374	98.21 255	98.61 308	99.59 184	86.69 359	99.72 299	96.99 260	99.23 305	98.81 317

MVE	92.54 22	96.66 315	96.11 319	98.31 308	99.68 132	97.55 308	97.94 315	95.60 365	99.37 114	90.68 371	98.70 345	96.56 261	98.61 368	86.94 368	99.55 253	98.77 319
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tpm2		96.35 320	96.22 317	96.73 342	98.88 333	91.75 366	99.21 141	98.51 334	93.27 356	97.89 343	99.21 291	84.83 362	99.70 305	96.04 307	98.18 347	98.75 320

LF4IMVS		99.01 180	98.92 182	99.27 227	99.71 112	99.28 180	98.59 248	99.77 63	98.32 249	99.39 208	99.41 241	98.62 126	99.84 230	96.62 284	99.84 124	98.69 321

thisisatest0515		96.98 307	96.42 314	98.66 293	99.42 235	97.47 309	97.27 346	94.30 369	97.24 307	99.15 253	98.86 337	85.01 361	99.87 174	97.10 256	99.39 282	98.63 322

Fast-Effi-MVS+-dtu		99.20 136	99.12 125	99.43 183	99.25 282	99.69 81	99.05 186	99.82 39	99.50 90	98.97 271	99.05 309	98.98 77	99.98 7	98.20 163	99.24 303	98.62 323

PAPM		95.61 334	94.71 336	98.31 308	99.12 303	96.63 328	96.66 359	98.46 337	90.77 362	96.25 362	98.68 346	93.01 308	99.69 311	81.60 369	97.86 355	98.62 323

JIA-IIPM		98.06 277	97.92 278	98.50 298	98.59 351	97.02 321	98.80 230	98.51 334	99.88 13	97.89 343	99.87 31	91.89 319	99.90 132	98.16 170	97.68 356	98.59 325

dp		96.86 309	97.07 303	96.24 348	98.68 350	90.30 374	99.19 147	98.38 341	97.35 303	98.23 328	99.59 184	87.23 350	99.82 251	96.27 299	98.73 331	98.59 325

OpenMVS	98.12 10	98.23 270	97.89 282	99.26 229	99.19 293	99.26 184	99.65 47	99.69 106	91.33 361	98.14 334	99.77 73	98.28 172	99.96 35	95.41 328	99.55 253	98.58 327

baseline2		96.83 310	96.28 316	98.46 300	99.09 311	96.91 324	98.83 222	93.87 371	97.23 308	96.23 364	98.36 356	88.12 348	99.90 132	96.68 278	98.14 348	98.57 328

DWT-MVSNet_test		96.03 327	95.80 326	96.71 344	98.50 354	91.93 364	99.25 131	97.87 349	95.99 334	96.81 360	97.61 366	81.02 367	99.66 331	97.20 251	97.98 351	98.54 329

TESTMET0.1,1		96.24 323	95.84 325	97.41 331	98.24 360	93.84 355	97.38 341	95.84 364	98.43 229	97.81 347	98.56 350	79.77 370	99.89 147	97.77 201	98.77 325	98.52 330

xiu_mvs_v1_base_debu		99.23 120	99.34 79	98.91 270	99.59 154	98.23 277	98.47 264	99.66 118	99.61 74	99.68 108	98.94 329	99.39 25	99.97 17	99.18 77	99.55 253	98.51 331

xiu_mvs_v1_base		99.23 120	99.34 79	98.91 270	99.59 154	98.23 277	98.47 264	99.66 118	99.61 74	99.68 108	98.94 329	99.39 25	99.97 17	99.18 77	99.55 253	98.51 331

xiu_mvs_v1_base_debi		99.23 120	99.34 79	98.91 270	99.59 154	98.23 277	98.47 264	99.66 118	99.61 74	99.68 108	98.94 329	99.39 25	99.97 17	99.18 77	99.55 253	98.51 331

KD-MVS_2432*1600		95.89 328	95.41 331	97.31 335	94.96 371	93.89 353	97.09 351	99.22 296	97.23 308	98.88 282	99.04 312	79.23 371	99.54 351	96.24 301	96.81 360	98.50 334

miper_refine_blended		95.89 328	95.41 331	97.31 335	94.96 371	93.89 353	97.09 351	99.22 296	97.23 308	98.88 282	99.04 312	79.23 371	99.54 351	96.24 301	96.81 360	98.50 334

CR-MVSNet		98.35 262	98.20 255	98.83 282	99.05 314	98.12 284	99.30 111	99.67 114	97.39 301	99.16 251	99.79 60	91.87 320	99.91 112	98.78 125	98.77 325	98.44 336

RPMNet		98.60 231	98.53 225	98.83 282	99.05 314	98.12 284	99.30 111	99.62 140	99.86 16	99.16 251	99.74 83	92.53 313	99.92 91	98.75 127	98.77 325	98.44 336

tpmrst		97.73 287	98.07 265	96.73 342	98.71 348	92.00 363	99.10 176	98.86 318	98.52 222	98.92 278	99.54 206	91.90 318	99.82 251	98.02 177	99.03 312	98.37 338

test-LLR		97.15 303	96.95 307	97.74 324	98.18 362	95.02 347	97.38 341	96.10 360	98.00 265	97.81 347	98.58 347	90.04 342	99.91 112	97.69 217	98.78 323	98.31 339

test-mter		96.23 324	95.73 327	97.74 324	98.18 362	95.02 347	97.38 341	96.10 360	97.90 274	97.81 347	98.58 347	79.12 373	99.91 112	97.69 217	98.78 323	98.31 339

CS-MVS		99.40 77	99.43 62	99.29 222	99.44 227	99.72 67	99.36 96	99.91 10	99.71 44	99.28 230	98.83 338	99.22 48	99.86 194	99.40 45	99.77 171	98.29 341

ETV-MVS		99.18 143	99.18 112	99.16 243	99.34 261	99.28 180	99.12 173	99.79 55	99.48 92	98.93 275	98.55 351	99.40 24	99.93 71	98.51 141	99.52 263	98.28 342

PatchT		98.45 252	98.32 246	98.83 282	98.94 325	98.29 275	99.24 132	98.82 321	99.84 24	99.08 263	99.76 76	91.37 323	99.94 57	98.82 120	99.00 314	98.26 343

xiu_mvs_v2_base		99.02 176	99.11 128	98.77 287	99.37 247	98.09 288	98.13 291	99.51 214	99.47 97	99.42 194	98.54 352	99.38 29	99.97 17	98.83 118	99.33 292	98.24 344

IB-MVS	95.41 20	95.30 335	94.46 338	97.84 320	98.76 346	95.33 345	97.33 344	96.07 362	96.02 333	95.37 368	97.41 368	76.17 375	99.96 35	97.54 225	95.44 367	98.22 345
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		96.78 311	96.98 306	96.16 349	98.85 334	90.59 373	99.08 183	99.32 271	92.37 358	97.73 352	99.46 234	91.15 327	99.69 311	96.07 306	98.80 322	98.21 346

MAR-MVS		98.24 269	97.92 278	99.19 240	98.78 344	99.65 93	99.17 153	99.14 306	95.36 342	98.04 338	98.81 341	97.47 231	99.72 299	95.47 327	99.06 309	98.21 346
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	98.76 288	99.37 247	98.10 287	98.00 306	99.51 214	99.47 97	99.41 202	98.50 354	99.28 41	99.97 17	98.83 118	99.34 290	98.20 348

cascas		96.99 306	96.82 312	97.48 328	97.57 369	95.64 342	96.43 360	99.56 182	91.75 359	97.13 359	97.61 366	95.58 285	98.63 367	96.68 278	99.11 307	98.18 349

BH-w/o		97.20 302	97.01 305	97.76 322	99.08 312	95.69 341	98.03 303	98.52 333	95.76 338	97.96 340	98.02 361	95.62 284	99.47 358	92.82 353	97.25 359	98.12 350

tpmvs		97.39 298	97.69 288	96.52 345	98.41 355	91.76 365	99.30 111	98.94 317	97.74 282	97.85 346	99.55 204	92.40 316	99.73 297	96.25 300	98.73 331	98.06 351

mvs-test1		98.83 206	98.70 207	99.22 236	98.89 330	99.65 93	98.88 213	99.66 118	99.34 117	98.29 323	98.94 329	97.69 218	99.96 35	98.11 173	98.54 337	98.04 352

thres600view7		96.60 316	96.16 318	97.93 317	99.63 144	96.09 337	99.18 148	97.57 352	98.77 199	98.72 301	97.32 369	87.04 352	99.72 299	88.57 361	98.62 334	97.98 353

thres400		96.40 318	95.89 322	97.92 318	99.58 157	96.11 335	99.00 195	97.54 355	98.43 229	98.52 315	96.98 372	86.85 354	99.67 327	87.62 364	98.51 338	97.98 353

TR-MVS		97.44 297	97.15 302	98.32 306	98.53 353	97.46 310	98.47 264	97.91 348	96.85 320	98.21 329	98.51 353	96.42 267	99.51 356	92.16 354	97.29 358	97.98 353

1314		98.00 280	97.90 281	98.27 310	98.90 327	97.45 311	99.30 111	99.06 311	94.98 347	97.21 357	99.12 302	98.43 154	99.67 327	95.58 324	98.56 336	97.71 356

E-PMN		97.14 305	97.43 293	96.27 347	98.79 342	91.62 367	95.54 363	99.01 315	99.44 104	98.88 282	99.12 302	92.78 310	99.68 322	94.30 343	99.03 312	97.50 357

gg-mvs-nofinetune		95.87 330	95.17 334	97.97 316	98.19 361	96.95 322	99.69 31	89.23 375	99.89 11	96.24 363	99.94 12	81.19 366	99.51 356	93.99 349	98.20 344	97.44 358

DeepMVS_CX				97.98 315	99.69 123	96.95 322		99.26 286	75.51 368	95.74 366	98.28 358	96.47 265	99.62 342	91.23 357	97.89 353	97.38 359

OpenMVS_ROB	97.31 17	97.36 300	96.84 311	98.89 277	99.29 274	99.45 140	98.87 216	99.48 225	86.54 366	99.44 188	99.74 83	97.34 239	99.86 194	91.61 355	99.28 297	97.37 360

EMVS		96.96 308	97.28 296	95.99 350	98.76 346	91.03 370	95.26 364	98.61 330	99.34 117	98.92 278	98.88 336	93.79 301	99.66 331	92.87 352	99.05 310	97.30 361

thres100view900		96.39 319	96.03 321	97.47 329	99.63 144	95.93 338	99.18 148	97.57 352	98.75 203	98.70 303	97.31 370	87.04 352	99.67 327	87.62 364	98.51 338	96.81 362

tfpn200view9		96.30 322	95.89 322	97.53 327	99.58 157	96.11 335	99.00 195	97.54 355	98.43 229	98.52 315	96.98 372	86.85 354	99.67 327	87.62 364	98.51 338	96.81 362

API-MVS		98.38 258	98.39 237	98.35 304	98.83 336	99.26 184	99.14 163	99.18 302	98.59 214	98.66 305	98.78 342	98.61 128	99.57 350	94.14 345	99.56 249	96.21 364

thres200		96.09 325	95.68 328	97.33 334	99.48 212	96.22 334	98.53 259	97.57 352	98.06 264	98.37 322	96.73 374	86.84 356	99.61 346	86.99 367	98.57 335	96.16 365

GG-mvs-BLEND				97.36 332	97.59 367	96.87 325	99.70 25	88.49 376		94.64 369	97.26 371	80.66 368	99.12 363	91.50 356	96.50 364	96.08 366

wuyk23d		97.58 293	99.13 121	92.93 351	99.69 123	99.49 128	99.52 69	99.77 63	97.97 269	99.96 8	99.79 60	99.84 3	99.94 57	95.85 316	99.82 143	79.36 367

test123		29.31 337	33.05 342	18.08 353	25.93 377	12.24 377	97.53 335	10.93 378	11.78 371	24.21 372	50.08 379	21.04 376	8.60 372	23.51 370	32.43 371	33.39 368

testmvs		28.94 338	33.33 340	15.79 354	26.03 376	9.81 378	96.77 357	15.67 377	11.55 372	23.87 373	50.74 378	19.03 377	8.53 373	23.21 371	33.07 370	29.03 369

uanet_test		8.33 341	11.11 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		24.88 339	33.17 341	0.00 355	0.00 378	0.00 379	0.00 366	99.62 140	0.00 373	0.00 374	99.13 298	99.82 4	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		16.61 340	22.14 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	99.28 41	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		8.33 341	11.11 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		8.33 341	11.11 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		8.33 341	11.11 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		8.33 341	11.11 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		8.26 347	11.02 350	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	99.16 296	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		8.33 341	11.11 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

FOURS1					99.83 38	99.89 8	99.74 16	99.71 95	99.69 52	99.63 127

test_one_0601					99.63 144	99.76 50		99.55 188	99.23 135	99.31 224	99.61 169	98.59 130

eth-test2					0.00 378

eth-test					0.00 378

ZD-MVS					99.43 230	99.61 107		99.43 242	96.38 328	99.11 259	99.07 307	97.86 207	99.92 91	94.04 347	99.49 268

test_241102_ONE					99.69 123	99.82 28		99.54 194	99.12 157	99.82 50	99.49 223	98.91 86	99.52 355

9.14			98.64 210		99.45 225		98.81 227	99.60 159	97.52 294	99.28 230	99.56 197	98.53 142	99.83 241	95.36 330	99.64 229

save fliter					99.53 184	99.25 188	98.29 278	99.38 261	99.07 161

test0726					99.69 123	99.80 36	99.24 132	99.57 177	99.16 148	99.73 94	99.65 139	98.35 165

test_part2					99.62 148	99.67 86				99.55 163

sam_mvs												90.52 338

MTGPA								99.53 203

test_post1							99.14 163				51.63 377	89.54 345	99.82 251	96.86 267

test_post											52.41 376	90.25 340	99.86 194

patchmatchnet-post											99.62 160	90.58 336	99.94 57

MTMP							99.09 180	98.59 332

gm-plane-assit					97.59 367	89.02 375			93.47 355		98.30 357		99.84 230	96.38 295

TEST9					99.35 251	99.35 169	98.11 294	99.41 245	94.83 352	97.92 341	98.99 319	98.02 194	99.85 213

test_8					99.34 261	99.31 175	98.08 298	99.40 252	94.90 348	97.87 345	98.97 325	98.02 194	99.84 230

agg_prior					99.35 251	99.36 165		99.39 255		97.76 350			99.85 213

test_prior4						99.19 204	98.00 306

test_prior2							97.95 313		97.87 276	98.05 336	99.05 309	97.90 203		95.99 310	99.49 268

旧先验2							97.94 315		95.33 343	98.94 274			99.88 161	96.75 274

新几何2							98.04 302

原ACMM2							97.92 317

testdata2													99.89 147	95.99 310

segment_acmp												98.37 163

testdata1							97.72 325		97.86 279

plane_prior7					99.58 157	99.38 159

plane_prior6					99.47 217	99.26 184						97.24 242

plane_prior4											99.25 282

plane_prior3						99.31 175			98.36 238	99.14 255

plane_prior2							98.80 230		98.94 175

plane_prior1					99.51 194

plane_prior						99.24 193	98.42 270		97.87 276						99.71 202

n2								0.00 379

nn								0.00 379

door-mid								99.83 34

test11								99.29 280

door								99.77 63

HQP5-MVS						98.94 230

HQP-NCC					99.31 268		97.98 309		97.45 297	98.15 330

ACMP_Plane					99.31 268		97.98 309		97.45 297	98.15 330

BP-MVS														94.73 337

HQP3-MVS								99.37 262							99.67 219

HQP2-MVS												96.67 259

NP-MVS					99.40 239	99.13 209					98.83 338

MDTV_nov1_ep13			97.73 287		98.70 349	90.83 371	99.15 161	98.02 345	98.51 223	98.82 290	99.61 169	90.98 329	99.66 331	96.89 266	98.92 318

ACMMP++_ref															99.94 62

ACMMP++															99.79 161

Test By Simon												98.41 157

High-resolution multi-view benchmark