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.

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 2	99.99 1	100.00 1	99.98 10	99.78 6	100.00 1	99.92 1	100.00 1	99.87 9

v1921920		99.56 45	99.57 43	99.55 151	99.75 98	99.11 212	99.05 191	99.61 149	99.15 157	99.88 33	99.71 108	99.08 67	99.87 181	99.90 2	99.97 33	99.66 80

v1240		99.56 45	99.58 40	99.51 162	99.80 58	99.00 224	99.00 201	99.65 131	99.15 157	99.90 23	99.75 88	99.09 63	99.88 168	99.90 2	99.96 45	99.67 70

v10		99.69 21	99.69 19	99.66 99	99.81 53	99.39 157	99.66 46	99.75 77	99.60 86	99.92 19	99.87 33	98.75 112	99.86 201	99.90 2	99.99 12	99.73 46

v1192		99.57 42	99.57 43	99.57 144	99.77 83	99.22 198	99.04 193	99.60 161	99.18 147	99.87 40	99.72 101	99.08 67	99.85 219	99.89 5	99.98 24	99.66 80

v144192		99.55 48	99.54 48	99.58 139	99.78 75	99.20 204	99.11 180	99.62 142	99.18 147	99.89 27	99.72 101	98.66 123	99.87 181	99.88 6	99.97 33	99.66 80

v8		99.68 24	99.69 19	99.65 104	99.80 58	99.40 155	99.66 46	99.76 70	99.64 72	99.93 15	99.85 42	98.66 123	99.84 236	99.88 6	99.99 12	99.71 50

v1144		99.54 50	99.53 52	99.59 134	99.79 68	99.28 181	99.10 181	99.61 149	99.20 145	99.84 46	99.73 95	98.67 121	99.84 236	99.86 8	99.98 24	99.64 97

v7n		99.82 10	99.80 10	99.88 11	99.96 4	99.84 21	99.82 8	99.82 41	99.84 30	99.94 12	99.91 21	99.13 60	99.96 37	99.83 9	99.99 12	99.83 18

v2v482		99.50 54	99.47 56	99.58 139	99.78 75	99.25 189	99.14 168	99.58 177	99.25 136	99.81 60	99.62 168	98.24 177	99.84 236	99.83 9	99.97 33	99.64 97

V42		99.56 45	99.54 48	99.63 117	99.79 68	99.46 136	99.39 93	99.59 168	99.24 138	99.86 41	99.70 115	98.55 137	99.82 257	99.79 11	99.95 54	99.60 128

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 39	99.72 24	99.88 19	99.92 7	99.98 3	99.93 15	99.94 1	99.98 8	99.77 12	100.00 1	99.92 3

PS-MVSNAJss		99.84 8	99.82 8	99.89 7	99.96 4	99.77 45	99.68 38	99.85 28	99.95 3	99.98 3	99.92 18	99.28 42	99.98 8	99.75 13	100.00 1	99.94 2

jajsoiax		99.89 3	99.89 3	99.89 7	99.96 4	99.78 42	99.70 29	99.86 24	99.89 14	99.98 3	99.90 23	99.94 1	99.98 8	99.75 13	100.00 1	99.90 4

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 8	100.00 1	99.97 11	99.61 17	99.97 19	99.75 13	100.00 1	99.84 14

CS-MVS-test		99.59 40	99.59 36	99.60 131	99.55 184	99.86 13	99.60 64	99.94 8	99.90 8	99.59 150	98.89 343	99.24 47	99.95 47	99.66 16	99.90 89	98.98 309

CS-MVS		99.67 26	99.70 17	99.59 134	99.54 186	99.86 13	99.80 10	99.96 5	99.90 8	99.59 150	99.41 249	99.51 23	99.95 47	99.65 17	99.90 89	98.97 310

pmmvs6		99.86 6	99.86 6	99.83 21	99.94 10	99.90 5	99.83 6	99.91 12	99.85 27	99.94 12	99.95 13	99.73 8	99.90 137	99.65 17	99.97 33	99.69 57

MIMVSNet1		99.66 27	99.62 29	99.80 29	99.94 10	99.87 10	99.69 35	99.77 65	99.78 42	99.93 15	99.89 27	97.94 203	99.92 95	99.65 17	99.98 24	99.62 113

DROMVSNet		99.69 21	99.69 19	99.68 89	99.71 114	99.91 2	99.76 14	99.96 5	99.86 22	99.51 183	99.39 257	99.57 20	99.93 75	99.64 20	99.86 123	99.20 266

K. test v3		98.87 205	98.60 215	99.69 88	99.93 13	99.46 136	99.74 18	94.97 369	99.78 42	99.88 33	99.88 30	93.66 307	99.97 19	99.61 21	99.95 54	99.64 97

KD-MVS_self_test		99.63 33	99.59 36	99.76 47	99.84 35	99.90 5	99.37 99	99.79 57	99.83 33	99.88 33	99.85 42	98.42 158	99.90 137	99.60 22	99.73 199	99.49 190

Anonymous20240521		99.44 69	99.42 68	99.49 168	99.89 21	98.96 230	99.62 54	99.76 70	99.85 27	99.82 53	99.88 30	96.39 273	99.97 19	99.59 23	99.98 24	99.55 154

TransMVSNet (Re)		99.78 13	99.77 12	99.81 26	99.91 15	99.85 16	99.75 16	99.86 24	99.70 55	99.91 21	99.89 27	99.60 19	99.87 181	99.59 23	99.74 192	99.71 50

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 19	99.96 4	99.83 25	99.83 6	99.85 28	99.80 39	99.93 15	99.93 15	98.54 139	99.93 75	99.59 23	99.98 24	99.76 41

EU-MVSNet		99.39 84	99.62 29	98.72 293	99.88 25	96.44 334	99.56 72	99.85 28	99.90 8	99.90 23	99.85 42	98.09 191	99.83 247	99.58 26	99.95 54	99.90 4

mvs_anonymous		99.28 111	99.39 71	98.94 267	99.19 299	97.81 303	99.02 197	99.55 190	99.78 42	99.85 43	99.80 60	98.24 177	99.86 201	99.57 27	99.50 272	99.15 277

test1111		97.74 289	98.16 263	96.49 350	99.60 154	89.86 379	99.71 28	91.21 376	99.89 14	99.88 33	99.87 33	93.73 306	99.90 137	99.56 28	99.99 12	99.70 53

lessismore_v0				99.64 111	99.86 31	99.38 160		90.66 377		99.89 27	99.83 48	94.56 297	99.97 19	99.56 28	99.92 79	99.57 148

pm-mvs1		99.79 12	99.79 11	99.78 37	99.91 15	99.83 25	99.76 14	99.87 21	99.73 46	99.89 27	99.87 33	99.63 14	99.87 181	99.54 30	99.92 79	99.63 102

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 9	99.90 8	99.97 6	99.87 33	99.81 5	99.95 47	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
RRT_test8_iter05		97.35 305	97.25 302	97.63 329	98.81 346	93.13 361	99.26 129	99.89 16	99.51 94	99.83 51	99.68 132	79.03 379	99.88 168	99.53 32	99.72 205	99.89 8

DSMNet-mixed		99.48 58	99.65 25	98.95 266	99.71 114	97.27 318	99.50 77	99.82 41	99.59 88	99.41 209	99.85 42	99.62 16	100.00 1	99.53 32	99.89 99	99.59 137

test2506		94.73 340	94.59 342	95.15 356	99.59 158	85.90 381	99.75 16	74.01 382	99.89 14	99.71 104	99.86 39	79.00 380	99.90 137	99.52 34	99.99 12	99.65 88

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 4	99.89 21	99.91 2	99.89 4	99.71 97	99.93 5	99.95 11	99.89 27	99.71 9	99.96 37	99.51 35	99.97 33	99.84 14

FC-MVSNet-test		99.70 19	99.65 25	99.86 16	99.88 25	99.86 13	99.72 24	99.78 62	99.90 8	99.82 53	99.83 48	98.45 154	99.87 181	99.51 35	99.97 33	99.86 11

UA-Net		99.78 13	99.76 14	99.86 16	99.72 111	99.71 71	99.91 3	99.95 7	99.96 2	99.71 104	99.91 21	99.15 55	99.97 19	99.50 37	100.00 1	99.90 4

PMMVS2		99.48 58	99.45 61	99.57 144	99.76 87	98.99 225	98.09 302	99.90 15	98.95 179	99.78 71	99.58 194	99.57 20	99.93 75	99.48 38	99.95 54	99.79 31

VPA-MVSNet		99.66 27	99.62 29	99.79 34	99.68 135	99.75 56	99.62 54	99.69 108	99.85 27	99.80 63	99.81 58	98.81 97	99.91 117	99.47 39	99.88 107	99.70 53

ECVR-MVS		97.73 290	98.04 269	96.78 343	99.59 158	90.81 375	99.72 24	90.43 378	99.89 14	99.86 41	99.86 39	93.60 308	99.89 153	99.46 40	99.99 12	99.65 88

nrg030		99.70 19	99.66 23	99.82 23	99.76 87	99.84 21	99.61 59	99.70 102	99.93 5	99.78 71	99.68 132	99.10 61	99.78 284	99.45 41	99.96 45	99.83 18

TAMVS		99.49 56	99.45 61	99.63 117	99.48 219	99.42 150	99.45 84	99.57 179	99.66 68	99.78 71	99.83 48	97.85 212	99.86 201	99.44 42	99.96 45	99.61 124

GeoE		99.69 21	99.66 23	99.78 37	99.76 87	99.76 52	99.60 64	99.82 41	99.46 106	99.75 84	99.56 205	99.63 14	99.95 47	99.43 43	99.88 107	99.62 113

new-patchmatchnet		99.35 94	99.57 43	98.71 295	99.82 46	96.62 332	98.55 261	99.75 77	99.50 95	99.88 33	99.87 33	99.31 38	99.88 168	99.43 43	100.00 1	99.62 113

test20.03		99.55 48	99.54 48	99.58 139	99.79 68	99.37 163	99.02 197	99.89 16	99.60 86	99.82 53	99.62 168	98.81 97	99.89 153	99.43 43	99.86 123	99.47 200

MVSFormer		99.41 77	99.44 63	99.31 223	99.57 173	98.40 272	99.77 12	99.80 51	99.73 46	99.63 131	99.30 279	98.02 197	99.98 8	99.43 43	99.69 214	99.55 154

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 10	99.84 21	99.77 12	99.80 51	99.73 46	99.97 6	99.92 18	99.77 7	99.98 8	99.43 43	100.00 1	99.90 4

Anonymous20231211		99.62 36	99.57 43	99.76 47	99.61 152	99.60 110	99.81 9	99.73 85	99.82 35	99.90 23	99.90 23	97.97 202	99.86 201	99.42 48	99.96 45	99.80 25

SixPastTwentyTwo		99.42 73	99.30 92	99.76 47	99.92 14	99.67 87	99.70 29	99.14 309	99.65 70	99.89 27	99.90 23	96.20 278	99.94 61	99.42 48	99.92 79	99.67 70

bset_n11_16_dypcd		98.69 225	98.45 232	99.42 189	99.69 126	98.52 264	96.06 367	96.80 362	99.71 50	99.73 97	99.54 214	95.14 290	99.96 37	99.39 50	99.95 54	99.79 31

patch_mono-2		99.51 53	99.46 60	99.64 111	99.70 122	99.11 212	99.04 193	99.87 21	99.71 50	99.47 189	99.79 66	98.24 177	99.98 8	99.38 51	99.96 45	99.83 18

UGNet		99.38 86	99.34 81	99.49 168	98.90 333	98.90 240	99.70 29	99.35 269	99.86 22	98.57 317	99.81 58	98.50 149	99.93 75	99.38 51	99.98 24	99.66 80
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
XXY-MVS		99.71 18	99.67 22	99.81 26	99.89 21	99.72 69	99.59 66	99.82 41	99.39 117	99.82 53	99.84 47	99.38 30	99.91 117	99.38 51	99.93 75	99.80 25

FIs		99.65 32	99.58 40	99.84 19	99.84 35	99.85 16	99.66 46	99.75 77	99.86 22	99.74 93	99.79 66	98.27 175	99.85 219	99.37 54	99.93 75	99.83 18

anonymousdsp		99.80 11	99.77 12	99.90 4	99.96 4	99.88 9	99.73 21	99.85 28	99.70 55	99.92 19	99.93 15	99.45 24	99.97 19	99.36 55	100.00 1	99.85 13

Vis-MVSNet		99.75 15	99.74 15	99.79 34	99.88 25	99.66 89	99.69 35	99.92 9	99.67 64	99.77 76	99.75 88	99.61 17	99.98 8	99.35 56	99.98 24	99.72 47
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 38	99.64 27	99.53 157	99.79 68	98.82 244	99.58 68	99.97 2	99.95 3	99.96 8	99.76 83	98.44 155	99.99 5	99.34 57	99.96 45	99.78 33

test_part1		98.63 229	98.26 252	99.75 57	99.40 245	99.49 129	99.67 42	99.68 111	99.86 22	99.88 33	99.86 39	86.73 362	99.93 75	99.34 57	99.97 33	99.81 24

CHOSEN 1792x2688		99.39 84	99.30 92	99.65 104	99.88 25	99.25 189	98.78 240	99.88 19	98.66 212	99.96 8	99.79 66	97.45 235	99.93 75	99.34 57	99.99 12	99.78 33

CDS-MVSNet		99.22 131	99.13 123	99.50 165	99.35 257	99.11 212	98.96 213	99.54 196	99.46 106	99.61 145	99.70 115	96.31 275	99.83 247	99.34 57	99.88 107	99.55 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 184	99.16 116	98.51 300	99.75 98	95.90 342	98.07 305	99.84 34	99.84 30	99.89 27	99.73 95	96.01 282	99.99 5	99.33 61	100.00 1	99.63 102

HyFIR lowres test		98.91 197	98.64 212	99.73 73	99.85 34	99.47 132	98.07 305	99.83 36	98.64 214	99.89 27	99.60 186	92.57 316	100.00 1	99.33 61	99.97 33	99.72 47

pmmvs5		99.19 141	99.11 130	99.42 189	99.76 87	98.88 241	98.55 261	99.73 85	98.82 197	99.72 99	99.62 168	96.56 264	99.82 257	99.32 63	99.95 54	99.56 151

v148		99.40 80	99.41 69	99.39 202	99.76 87	98.94 232	99.09 185	99.59 168	99.17 151	99.81 60	99.61 177	98.41 159	99.69 317	99.32 63	99.94 67	99.53 167

baseline		99.63 33	99.62 29	99.66 99	99.80 58	99.62 102	99.44 87	99.80 51	99.71 50	99.72 99	99.69 121	99.15 55	99.83 247	99.32 63	99.94 67	99.53 167

CVMVSNet		98.61 231	98.88 190	97.80 324	99.58 163	93.60 359	99.26 129	99.64 137	99.66 68	99.72 99	99.67 138	93.26 310	99.93 75	99.30 66	99.81 159	99.87 9

PS-CasMVS		99.66 27	99.58 40	99.89 7	99.80 58	99.85 16	99.66 46	99.73 85	99.62 76	99.84 46	99.71 108	98.62 127	99.96 37	99.30 66	99.96 45	99.86 11

DTE-MVSNet		99.68 24	99.61 33	99.88 11	99.80 58	99.87 10	99.67 42	99.71 97	99.72 49	99.84 46	99.78 73	98.67 121	99.97 19	99.30 66	99.95 54	99.80 25

tmp_tt		95.75 336	95.42 334	96.76 344	89.90 381	94.42 354	98.86 223	97.87 352	78.01 372	99.30 236	99.69 121	97.70 219	95.89 376	99.29 69	98.14 354	99.95 1

PEN-MVS		99.66 27	99.59 36	99.89 7	99.83 39	99.87 10	99.66 46	99.73 85	99.70 55	99.84 46	99.73 95	98.56 136	99.96 37	99.29 69	99.94 67	99.83 18

WR-MVS_H		99.61 38	99.53 52	99.87 14	99.80 58	99.83 25	99.67 42	99.75 77	99.58 89	99.85 43	99.69 121	98.18 187	99.94 61	99.28 71	99.95 54	99.83 18

IterMVS		98.97 188	99.16 116	98.42 304	99.74 104	95.64 345	98.06 307	99.83 36	99.83 33	99.85 43	99.74 91	96.10 281	99.99 5	99.27 72	100.00 1	99.63 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs33		98.61 231	98.34 245	99.44 183	99.60 154	98.67 253	99.27 127	99.44 241	99.68 60	99.32 227	99.49 231	92.50 319	100.00 1	99.24 73	96.51 369	99.65 88

hse-mvs2		98.52 245	98.30 249	99.16 246	99.29 280	98.60 260	98.77 241	99.02 316	99.68 60	99.32 227	99.04 321	92.50 319	99.85 219	99.24 73	97.87 360	99.03 302

FMVSNet1		99.66 27	99.63 28	99.73 73	99.78 75	99.77 45	99.68 38	99.70 102	99.67 64	99.82 53	99.83 48	98.98 78	99.90 137	99.24 73	99.97 33	99.53 167

casdiffmvs		99.63 33	99.61 33	99.67 92	99.79 68	99.59 113	99.13 174	99.85 28	99.79 41	99.76 78	99.72 101	99.33 37	99.82 257	99.21 76	99.94 67	99.59 137

CP-MVSNet		99.54 50	99.43 66	99.87 14	99.76 87	99.82 29	99.57 70	99.61 149	99.54 90	99.80 63	99.64 149	97.79 216	99.95 47	99.21 76	99.94 67	99.84 14

DELS-MVS		99.34 99	99.30 92	99.48 172	99.51 202	99.36 166	98.12 298	99.53 205	99.36 121	99.41 209	99.61 177	99.22 49	99.87 181	99.21 76	99.68 219	99.20 266
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
RRT_MVS		98.75 217	98.54 225	99.41 197	98.14 371	98.61 259	98.98 210	99.66 120	99.31 127	99.84 46	99.75 88	91.98 322	99.98 8	99.20 79	99.95 54	99.62 113

UniMVSNet (Re)		99.37 89	99.26 104	99.68 89	99.51 202	99.58 116	98.98 210	99.60 161	99.43 114	99.70 107	99.36 265	97.70 219	99.88 168	99.20 79	99.87 116	99.59 137

CANet		99.11 162	99.05 151	99.28 228	98.83 342	98.56 261	98.71 249	99.41 248	99.25 136	99.23 244	99.22 298	97.66 228	99.94 61	99.19 81	99.97 33	99.33 240

EI-MVSNet-UG-set		99.48 58	99.50 54	99.42 189	99.57 173	98.65 258	99.24 137	99.46 236	99.68 60	99.80 63	99.66 142	98.99 77	99.89 153	99.19 81	99.90 89	99.72 47

xiu_mvs_v1_base_debu		99.23 122	99.34 81	98.91 273	99.59 158	98.23 280	98.47 270	99.66 120	99.61 80	99.68 112	98.94 338	99.39 26	99.97 19	99.18 83	99.55 259	98.51 338

xiu_mvs_v1_base		99.23 122	99.34 81	98.91 273	99.59 158	98.23 280	98.47 270	99.66 120	99.61 80	99.68 112	98.94 338	99.39 26	99.97 19	99.18 83	99.55 259	98.51 338

xiu_mvs_v1_base_debi		99.23 122	99.34 81	98.91 273	99.59 158	98.23 280	98.47 270	99.66 120	99.61 80	99.68 112	98.94 338	99.39 26	99.97 19	99.18 83	99.55 259	98.51 338

MVS_0304		98.88 203	98.71 206	99.39 202	98.85 340	98.91 239	99.45 84	99.30 281	98.56 221	97.26 361	99.68 132	96.18 279	99.96 37	99.17 86	99.94 67	99.29 249

VPNet		99.46 65	99.37 76	99.71 83	99.82 46	99.59 113	99.48 81	99.70 102	99.81 36	99.69 110	99.58 194	97.66 228	99.86 201	99.17 86	99.44 280	99.67 70

UniMVSNet_NR-MVSNet		99.37 89	99.25 106	99.72 79	99.47 224	99.56 119	98.97 212	99.61 149	99.43 114	99.67 117	99.28 284	97.85 212	99.95 47	99.17 86	99.81 159	99.65 88

DU-MVS		99.33 103	99.21 111	99.71 83	99.43 236	99.56 119	98.83 228	99.53 205	99.38 118	99.67 117	99.36 265	97.67 224	99.95 47	99.17 86	99.81 159	99.63 102

EI-MVSNet-Vis-set		99.47 64	99.49 55	99.42 189	99.57 173	98.66 255	99.24 137	99.46 236	99.67 64	99.79 68	99.65 147	98.97 80	99.89 153	99.15 90	99.89 99	99.71 50

EI-MVSNet		99.38 86	99.44 63	99.21 240	99.58 163	98.09 291	99.26 129	99.46 236	99.62 76	99.75 84	99.67 138	98.54 139	99.85 219	99.15 90	99.92 79	99.68 63

VNet		99.18 145	99.06 147	99.56 148	99.24 290	99.36 166	99.33 106	99.31 278	99.67 64	99.47 189	99.57 202	96.48 267	99.84 236	99.15 90	99.30 301	99.47 200

EG-PatchMatch MVS		99.57 42	99.56 47	99.62 126	99.77 83	99.33 173	99.26 129	99.76 70	99.32 126	99.80 63	99.78 73	99.29 40	99.87 181	99.15 90	99.91 88	99.66 80

PVSNet_Blended_VisFu		99.40 80	99.38 73	99.44 183	99.90 19	98.66 255	98.94 216	99.91 12	97.97 274	99.79 68	99.73 95	99.05 72	99.97 19	99.15 90	99.99 12	99.68 63

IterMVS-LS		99.41 77	99.47 56	99.25 235	99.81 53	98.09 291	98.85 225	99.76 70	99.62 76	99.83 51	99.64 149	98.54 139	99.97 19	99.15 90	99.99 12	99.68 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 50	99.47 56	99.76 47	99.58 163	99.64 96	99.30 116	99.63 139	99.61 80	99.71 104	99.56 205	98.76 110	99.96 37	99.14 96	99.92 79	99.68 63

MVSTER		98.47 252	98.22 255	99.24 237	99.06 319	98.35 277	99.08 188	99.46 236	99.27 132	99.75 84	99.66 142	88.61 352	99.85 219	99.14 96	99.92 79	99.52 177

Anonymous20231206		99.35 94	99.31 87	99.47 174	99.74 104	99.06 223	99.28 124	99.74 82	99.23 140	99.72 99	99.53 217	97.63 230	99.88 168	99.11 98	99.84 132	99.48 195

MVS_Test		99.28 111	99.31 87	99.19 243	99.35 257	98.79 247	99.36 102	99.49 226	99.17 151	99.21 250	99.67 138	98.78 106	99.66 337	99.09 99	99.66 230	99.10 287

testgi		99.29 110	99.26 104	99.37 209	99.75 98	98.81 245	98.84 226	99.89 16	98.38 241	99.75 84	99.04 321	99.36 35	99.86 201	99.08 100	99.25 307	99.45 206

1112_ss		99.05 172	98.84 195	99.67 92	99.66 141	99.29 179	98.52 266	99.82 41	97.65 291	99.43 199	99.16 305	96.42 270	99.91 117	99.07 101	99.84 132	99.80 25

CANet_DTU		98.91 197	98.85 193	99.09 254	98.79 348	98.13 286	98.18 291	99.31 278	99.48 97	98.86 291	99.51 223	96.56 264	99.95 47	99.05 102	99.95 54	99.19 269

Baseline_NR-MVSNet		99.49 56	99.37 76	99.82 23	99.91 15	99.84 21	98.83 228	99.86 24	99.68 60	99.65 125	99.88 30	97.67 224	99.87 181	99.03 103	99.86 123	99.76 41

FMVSNet2		99.35 94	99.28 99	99.55 151	99.49 213	99.35 170	99.45 84	99.57 179	99.44 109	99.70 107	99.74 91	97.21 247	99.87 181	99.03 103	99.94 67	99.44 211

Test_1112_low_res		98.95 194	98.73 204	99.63 117	99.68 135	99.15 209	98.09 302	99.80 51	97.14 318	99.46 193	99.40 253	96.11 280	99.89 153	99.01 105	99.84 132	99.84 14

VDD-MVS		99.20 138	99.11 130	99.44 183	99.43 236	98.98 226	99.50 77	98.32 345	99.80 39	99.56 164	99.69 121	96.99 257	99.85 219	98.99 106	99.73 199	99.50 185

DeepC-MVS	98.90 4	99.62 36	99.61 33	99.67 92	99.72 111	99.44 143	99.24 137	99.71 97	99.27 132	99.93 15	99.90 23	99.70 11	99.93 75	98.99 106	99.99 12	99.64 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 58	99.47 56	99.51 162	99.77 83	99.41 154	98.81 233	99.66 120	99.42 116	99.75 84	99.66 142	99.20 50	99.76 294	98.98 108	99.99 12	99.36 234

EPNet_dtu		97.62 295	97.79 289	97.11 342	96.67 376	92.31 365	98.51 267	98.04 347	99.24 138	95.77 370	99.47 239	93.78 305	99.66 337	98.98 108	99.62 238	99.37 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 99	99.32 86	99.39 202	99.67 140	98.77 248	98.57 259	99.81 50	99.61 80	99.48 187	99.41 249	98.47 150	99.86 201	98.97 110	99.90 89	99.53 167

NR-MVSNet		99.40 80	99.31 87	99.68 89	99.43 236	99.55 122	99.73 21	99.50 221	99.46 106	99.88 33	99.36 265	97.54 232	99.87 181	98.97 110	99.87 116	99.63 102

GBi-Net		99.42 73	99.31 87	99.73 73	99.49 213	99.77 45	99.68 38	99.70 102	99.44 109	99.62 139	99.83 48	97.21 247	99.90 137	98.96 112	99.90 89	99.53 167

FMVSNet5		97.80 286	97.25 302	99.42 189	98.83 342	98.97 228	99.38 95	99.80 51	98.87 191	99.25 240	99.69 121	80.60 374	99.91 117	98.96 112	99.90 89	99.38 228

test1		99.42 73	99.31 87	99.73 73	99.49 213	99.77 45	99.68 38	99.70 102	99.44 109	99.62 139	99.83 48	97.21 247	99.90 137	98.96 112	99.90 89	99.53 167

FMVSNet3		98.80 212	98.63 214	99.32 220	99.13 307	98.72 250	99.10 181	99.48 228	99.23 140	99.62 139	99.64 149	92.57 316	99.86 201	98.96 112	99.90 89	99.39 226

UnsupCasMVSNet_eth		98.83 208	98.57 221	99.59 134	99.68 135	99.45 141	98.99 206	99.67 116	99.48 97	99.55 169	99.36 265	94.92 291	99.86 201	98.95 116	96.57 368	99.45 206

CHOSEN 280x420		98.41 257	98.41 237	98.40 305	99.34 267	95.89 343	96.94 361	99.44 241	98.80 200	99.25 240	99.52 219	93.51 309	99.98 8	98.94 117	99.98 24	99.32 243

TDRefinement		99.72 17	99.70 17	99.77 40	99.90 19	99.85 16	99.86 5	99.92 9	99.69 58	99.78 71	99.92 18	99.37 32	99.88 168	98.93 118	99.95 54	99.60 128

Regformer-4		99.45 67	99.44 63	99.50 165	99.52 197	98.94 232	99.17 158	99.53 205	99.64 72	99.76 78	99.60 186	98.96 83	99.90 137	98.91 119	99.84 132	99.67 70

Regformer-3		99.41 77	99.41 69	99.40 199	99.52 197	98.70 251	99.17 158	99.44 241	99.62 76	99.75 84	99.60 186	98.90 90	99.85 219	98.89 120	99.84 132	99.65 88

alignmvs		98.28 267	97.96 275	99.25 235	99.12 309	98.93 236	99.03 196	98.42 341	99.64 72	98.72 306	97.85 371	90.86 338	99.62 348	98.88 121	99.13 312	99.19 269

sss		98.90 199	98.77 203	99.27 230	99.48 219	98.44 269	98.72 247	99.32 274	97.94 278	99.37 217	99.35 270	96.31 275	99.91 117	98.85 122	99.63 237	99.47 200

xiu_mvs_v2_base		99.02 178	99.11 130	98.77 290	99.37 253	98.09 291	98.13 297	99.51 217	99.47 102	99.42 201	98.54 360	99.38 30	99.97 19	98.83 123	99.33 298	98.24 350

PS-MVSNAJ		99.00 184	99.08 141	98.76 291	99.37 253	98.10 290	98.00 312	99.51 217	99.47 102	99.41 209	98.50 362	99.28 42	99.97 19	98.83 123	99.34 296	98.20 354

D2MVS		99.22 131	99.19 113	99.29 226	99.69 126	98.74 249	98.81 233	99.41 248	98.55 223	99.68 112	99.69 121	98.13 189	99.87 181	98.82 125	99.98 24	99.24 255

PatchT		98.45 254	98.32 248	98.83 285	98.94 331	98.29 278	99.24 137	98.82 324	99.84 30	99.08 268	99.76 83	91.37 328	99.94 61	98.82 125	99.00 320	98.26 349

Effi-MVS+		99.06 169	98.97 175	99.34 214	99.31 274	98.98 226	98.31 283	99.91 12	98.81 198	98.79 299	98.94 338	99.14 58	99.84 236	98.79 127	98.74 335	99.20 266

canonicalmvs		99.02 178	99.00 166	99.09 254	99.10 315	98.70 251	99.61 59	99.66 120	99.63 75	98.64 311	97.65 373	99.04 73	99.54 357	98.79 127	98.92 324	99.04 301

VDDNet		98.97 188	98.82 198	99.42 189	99.71 114	98.81 245	99.62 54	98.68 329	99.81 36	99.38 216	99.80 60	94.25 299	99.85 219	98.79 127	99.32 299	99.59 137

CR-MVSNet		98.35 264	98.20 257	98.83 285	99.05 320	98.12 287	99.30 116	99.67 116	97.39 306	99.16 257	99.79 66	91.87 325	99.91 117	98.78 130	98.77 331	98.44 343

test_method		91.72 341	92.32 344	89.91 358	93.49 380	70.18 382	90.28 371	99.56 184	61.71 375	95.39 372	99.52 219	93.90 301	99.94 61	98.76 131	98.27 349	99.62 113

RPMNet		98.60 233	98.53 227	98.83 285	99.05 320	98.12 287	99.30 116	99.62 142	99.86 22	99.16 257	99.74 91	92.53 318	99.92 95	98.75 132	98.77 331	98.44 343

pmmvs4		99.13 156	99.06 147	99.36 212	99.57 173	99.10 217	98.01 310	99.25 292	98.78 203	99.58 154	99.44 246	98.24 177	99.76 294	98.74 133	99.93 75	99.22 260

tttt0517		97.62 295	97.20 304	98.90 279	99.76 87	97.40 315	99.48 81	94.36 371	99.06 170	99.70 107	99.49 231	84.55 368	99.94 61	98.73 134	99.65 233	99.36 234

EPP-MVSNet		99.17 149	99.00 166	99.66 99	99.80 58	99.43 147	99.70 29	99.24 295	99.48 97	99.56 164	99.77 80	94.89 292	99.93 75	98.72 135	99.89 99	99.63 102

Anonymous20240529		99.42 73	99.34 81	99.65 104	99.53 192	99.60 110	99.63 53	99.39 258	99.47 102	99.76 78	99.78 73	98.13 189	99.86 201	98.70 136	99.68 219	99.49 190

ACMH	98.42 6	99.59 40	99.54 48	99.72 79	99.86 31	99.62 102	99.56 72	99.79 57	98.77 204	99.80 63	99.85 42	99.64 13	99.85 219	98.70 136	99.89 99	99.70 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 103	99.28 99	99.47 174	99.57 173	99.39 157	99.78 11	99.43 245	98.87 191	99.57 157	99.82 55	98.06 194	99.87 181	98.69 138	99.73 199	99.15 277

LFMVS		98.46 253	98.19 260	99.26 232	99.24 290	98.52 264	99.62 54	96.94 361	99.87 20	99.31 231	99.58 194	91.04 333	99.81 273	98.68 139	99.42 285	99.45 206

WR-MVS		99.11 162	98.93 180	99.66 99	99.30 278	99.42 150	98.42 276	99.37 265	99.04 171	99.57 157	99.20 302	96.89 259	99.86 201	98.66 140	99.87 116	99.70 53

Anonymous202405211		98.75 217	98.46 231	99.63 117	99.34 267	99.66 89	99.47 83	97.65 354	99.28 131	99.56 164	99.50 226	93.15 311	99.84 236	98.62 141	99.58 253	99.40 223

EPNet		98.13 275	97.77 290	99.18 245	94.57 379	97.99 295	99.24 137	97.96 349	99.74 45	97.29 360	99.62 168	93.13 312	99.97 19	98.59 142	99.83 142	99.58 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 172	99.09 139	98.91 273	99.21 294	98.36 276	98.82 232	99.47 232	98.85 193	98.90 286	99.56 205	98.78 106	99.09 370	98.57 143	99.68 219	99.26 252

Patchmatch-RL test		98.60 233	98.36 242	99.33 216	99.77 83	99.07 221	98.27 286	99.87 21	98.91 186	99.74 93	99.72 101	90.57 342	99.79 281	98.55 144	99.85 127	99.11 285

pmmvs3		98.08 278	97.80 287	98.91 273	99.41 242	97.69 308	97.87 326	99.66 120	95.87 340	99.50 185	99.51 223	90.35 344	99.97 19	98.55 144	99.47 277	99.08 293

ETV-MVS		99.18 145	99.18 114	99.16 246	99.34 267	99.28 181	99.12 178	99.79 57	99.48 97	98.93 280	98.55 359	99.40 25	99.93 75	98.51 146	99.52 269	98.28 348

jason		99.16 150	99.11 130	99.32 220	99.75 98	98.44 269	98.26 287	99.39 258	98.70 210	99.74 93	99.30 279	98.54 139	99.97 19	98.48 147	99.82 151	99.55 154
jason: jason.
APDe-MVS		99.48 58	99.36 79	99.85 18	99.55 184	99.81 32	99.50 77	99.69 108	98.99 173	99.75 84	99.71 108	98.79 104	99.93 75	98.46 148	99.85 127	99.80 25

CL-MVSNet_self_test		98.71 223	98.56 224	99.15 248	99.22 292	98.66 255	97.14 356	99.51 217	98.09 267	99.54 171	99.27 286	96.87 260	99.74 300	98.43 149	98.96 321	99.03 302

our_test_3		98.85 207	99.09 139	98.13 316	99.66 141	94.90 352	97.72 331	99.58 177	99.07 166	99.64 127	99.62 168	98.19 185	99.93 75	98.41 150	99.95 54	99.55 154

Gipuma		99.57 42	99.59 36	99.49 168	99.98 3	99.71 71	99.72 24	99.84 34	99.81 36	99.94 12	99.78 73	98.91 87	99.71 309	98.41 150	99.95 54	99.05 300
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 303	96.91 314	98.74 292	97.72 372	97.57 310	97.60 337	97.36 360	98.00 270	99.21 250	98.02 369	90.04 347	99.79 281	98.37 152	95.89 372	98.86 320

Regformer-1		99.32 105	99.27 102	99.47 174	99.41 242	98.95 231	98.99 206	99.48 228	99.48 97	99.66 121	99.52 219	98.78 106	99.87 181	98.36 153	99.74 192	99.60 128

PM-MVS		99.36 92	99.29 97	99.58 139	99.83 39	99.66 89	98.95 214	99.86 24	98.85 193	99.81 60	99.73 95	98.40 163	99.92 95	98.36 153	99.83 142	99.17 273

baseline1		97.73 290	97.33 299	98.96 265	99.30 278	97.73 306	99.40 91	98.42 341	99.33 125	99.46 193	99.21 300	91.18 331	99.82 257	98.35 155	91.26 374	99.32 243

MVS-HIRNet		97.86 284	98.22 255	96.76 344	99.28 283	91.53 371	98.38 278	92.60 375	99.13 159	99.31 231	99.96 12	97.18 251	99.68 328	98.34 156	99.83 142	99.07 298

GA-MVS		97.99 283	97.68 293	98.93 270	99.52 197	98.04 294	97.19 355	99.05 315	98.32 254	98.81 296	98.97 334	89.89 349	99.41 367	98.33 157	99.05 316	99.34 239

Fast-Effi-MVS+		99.02 178	98.87 191	99.46 177	99.38 250	99.50 128	99.04 193	99.79 57	97.17 316	98.62 312	98.74 352	99.34 36	99.95 47	98.32 158	99.41 286	98.92 315

Regformer-2		99.34 99	99.27 102	99.53 157	99.41 242	99.10 217	98.99 206	99.53 205	99.47 102	99.66 121	99.52 219	98.80 101	99.89 153	98.31 159	99.74 192	99.60 128

MDA-MVSNet_test_wron		98.95 194	98.99 171	98.85 281	99.64 145	97.16 321	98.23 289	99.33 272	98.93 183	99.56 164	99.66 142	97.39 239	99.83 247	98.29 160	99.88 107	99.55 154

N_pmnet		98.73 221	98.53 227	99.35 213	99.72 111	98.67 253	98.34 279	94.65 370	98.35 248	99.79 68	99.68 132	98.03 195	99.93 75	98.28 161	99.92 79	99.44 211

ET-MVSNet_ETH3D		96.78 315	96.07 324	98.91 273	99.26 287	97.92 301	97.70 333	96.05 366	97.96 277	92.37 375	98.43 363	87.06 356	99.90 137	98.27 162	97.56 363	98.91 316

thisisatest0530		97.45 300	96.95 311	98.94 267	99.68 135	97.73 306	99.09 185	94.19 373	98.61 218	99.56 164	99.30 279	84.30 369	99.93 75	98.27 162	99.54 265	99.16 275

YYNet1		98.95 194	98.99 171	98.84 283	99.64 145	97.14 322	98.22 290	99.32 274	98.92 185	99.59 150	99.66 142	97.40 237	99.83 247	98.27 162	99.90 89	99.55 154

ACMM	98.09 11	99.46 65	99.38 73	99.72 79	99.80 58	99.69 82	99.13 174	99.65 131	98.99 173	99.64 127	99.72 101	99.39 26	99.86 201	98.23 165	99.81 159	99.60 128
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 191	98.87 191	99.24 237	99.57 173	98.40 272	98.12 298	99.18 305	98.28 256	99.63 131	99.13 307	98.02 197	99.97 19	98.22 166	99.69 214	99.35 237

3Dnovator	99.15 2	99.43 70	99.36 79	99.65 104	99.39 247	99.42 150	99.70 29	99.56 184	99.23 140	99.35 220	99.80 60	99.17 53	99.95 47	98.21 167	99.84 132	99.59 137

Fast-Effi-MVS+-dtu		99.20 138	99.12 127	99.43 187	99.25 288	99.69 82	99.05 191	99.82 41	99.50 95	98.97 276	99.05 318	98.98 78	99.98 8	98.20 168	99.24 309	98.62 330

MS-PatchMatch		99.00 184	98.97 175	99.09 254	99.11 314	98.19 283	98.76 243	99.33 272	98.49 231	99.44 195	99.58 194	98.21 182	99.69 317	98.20 168	99.62 238	99.39 226

TSAR-MVS + GP.		99.12 158	99.04 157	99.38 206	99.34 267	99.16 207	98.15 294	99.29 283	98.18 263	99.63 131	99.62 168	99.18 52	99.68 328	98.20 168	99.74 192	99.30 246

DP-MVS		99.48 58	99.39 71	99.74 63	99.57 173	99.62 102	99.29 123	99.61 149	99.87 20	99.74 93	99.76 83	98.69 117	99.87 181	98.20 168	99.80 164	99.75 44

MVP-Stereo		99.16 150	99.08 141	99.43 187	99.48 219	99.07 221	99.08 188	99.55 190	98.63 215	99.31 231	99.68 132	98.19 185	99.78 284	98.18 172	99.58 253	99.45 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 70	99.30 92	99.80 29	99.83 39	99.81 32	99.52 75	99.70 102	98.35 248	99.51 183	99.50 226	99.31 38	99.88 168	98.18 172	99.84 132	99.69 57

MDA-MVSNet-bldmvs		99.06 169	99.05 151	99.07 258	99.80 58	97.83 302	98.89 218	99.72 94	99.29 128	99.63 131	99.70 115	96.47 268	99.89 153	98.17 174	99.82 151	99.50 185

JIA-IIPM		98.06 279	97.92 282	98.50 301	98.59 357	97.02 324	98.80 236	98.51 337	99.88 19	97.89 348	99.87 33	91.89 324	99.90 137	98.16 175	97.68 362	98.59 332

EIA-MVS		99.12 158	99.01 163	99.45 181	99.36 255	99.62 102	99.34 104	99.79 57	98.41 237	98.84 293	98.89 343	98.75 112	99.84 236	98.15 176	99.51 270	98.89 317

miper_lstm_enhance		98.65 228	98.60 215	98.82 288	99.20 297	97.33 317	97.78 329	99.66 120	99.01 172	99.59 150	99.50 226	94.62 296	99.85 219	98.12 177	99.90 89	99.26 252

Effi-MVS+-dtu		99.07 168	98.92 184	99.52 159	98.89 336	99.78 42	99.15 166	99.66 120	99.34 122	98.92 283	99.24 296	97.69 221	99.98 8	98.11 178	99.28 303	98.81 324

mvs-test1		98.83 208	98.70 209	99.22 239	98.89 336	99.65 94	98.88 219	99.66 120	99.34 122	98.29 328	98.94 338	97.69 221	99.96 37	98.11 178	98.54 343	98.04 358

tpm		97.15 307	96.95 311	97.75 326	98.91 332	94.24 355	99.32 109	97.96 349	97.71 289	98.29 328	99.32 275	86.72 363	99.92 95	98.10 180	96.24 371	99.09 290

DeepPCF-MVS	98.42 6	99.18 145	99.02 160	99.67 92	99.22 292	99.75 56	97.25 353	99.47 232	98.72 209	99.66 121	99.70 115	99.29 40	99.63 347	98.07 181	99.81 159	99.62 113

ppachtmachnet_test		98.89 202	99.12 127	98.20 314	99.66 141	95.24 349	97.63 335	99.68 111	99.08 164	99.78 71	99.62 168	98.65 125	99.88 168	98.02 182	99.96 45	99.48 195

tpmrst		97.73 290	98.07 268	96.73 346	98.71 354	92.00 366	99.10 181	98.86 321	98.52 227	98.92 283	99.54 214	91.90 323	99.82 257	98.02 182	99.03 318	98.37 345

CSCG		99.37 89	99.29 97	99.60 131	99.71 114	99.46 136	99.43 89	99.85 28	98.79 201	99.41 209	99.60 186	98.92 85	99.92 95	98.02 182	99.92 79	99.43 217

eth_miper_zixun_eth		98.68 226	98.71 206	98.60 297	99.10 315	96.84 329	97.52 343	99.54 196	98.94 180	99.58 154	99.48 234	96.25 277	99.76 294	98.01 185	99.93 75	99.21 262

Patchmtry		98.78 213	98.54 225	99.49 168	98.89 336	99.19 205	99.32 109	99.67 116	99.65 70	99.72 99	99.79 66	91.87 325	99.95 47	98.00 186	99.97 33	99.33 240

PVSNet_BlendedMVS		99.03 176	99.01 163	99.09 254	99.54 186	97.99 295	98.58 255	99.82 41	97.62 292	99.34 223	99.71 108	98.52 146	99.77 292	97.98 187	99.97 33	99.52 177

PVSNet_Blended		98.70 224	98.59 217	99.02 262	99.54 186	97.99 295	97.58 338	99.82 41	95.70 344	99.34 223	98.98 331	98.52 146	99.77 292	97.98 187	99.83 142	99.30 246

cl____		98.54 243	98.41 237	98.92 271	99.03 323	97.80 304	97.46 345	99.59 168	98.90 187	99.60 147	99.46 242	93.85 303	99.78 284	97.97 189	99.89 99	99.17 273

DIV-MVS_self_test		98.54 243	98.42 236	98.92 271	99.03 323	97.80 304	97.46 345	99.59 168	98.90 187	99.60 147	99.46 242	93.87 302	99.78 284	97.97 189	99.89 99	99.18 271

AUN-MVS		97.82 285	97.38 298	99.14 249	99.27 285	98.53 262	98.72 247	99.02 316	98.10 265	97.18 363	99.03 325	89.26 351	99.85 219	97.94 191	97.91 358	99.03 302

ambc				99.20 242	99.35 257	98.53 262	99.17 158	99.46 236		99.67 117	99.80 60	98.46 153	99.70 311	97.92 192	99.70 211	99.38 228

USDC		98.96 191	98.93 180	99.05 260	99.54 186	97.99 295	97.07 359	99.80 51	98.21 260	99.75 84	99.77 80	98.43 156	99.64 346	97.90 193	99.88 107	99.51 179

OPM-MVS		99.26 117	99.13 123	99.63 117	99.70 122	99.61 108	98.58 255	99.48 228	98.50 229	99.52 178	99.63 159	99.14 58	99.76 294	97.89 194	99.77 179	99.51 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 105	99.17 115	99.77 40	99.69 126	99.80 37	99.14 168	99.31 278	99.16 153	99.62 139	99.61 177	98.35 167	99.91 117	97.88 195	99.72 205	99.61 124
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
test_0728_SECOND				99.83 21	99.70 122	99.79 39	99.14 168	99.61 149					99.92 95	97.88 195	99.72 205	99.77 37

c3_l		98.72 222	98.71 206	98.72 293	99.12 309	97.22 320	97.68 334	99.56 184	98.90 187	99.54 171	99.48 234	96.37 274	99.73 303	97.88 195	99.88 107	99.21 262

3Dnovator+	98.92 3	99.35 94	99.24 108	99.67 92	99.35 257	99.47 132	99.62 54	99.50 221	99.44 109	99.12 264	99.78 73	98.77 109	99.94 61	97.87 198	99.72 205	99.62 113

miper_ehance_all_eth		98.59 236	98.59 217	98.59 298	98.98 329	97.07 323	97.49 344	99.52 213	98.50 229	99.52 178	99.37 260	96.41 272	99.71 309	97.86 199	99.62 238	99.00 308

WTY-MVS		98.59 236	98.37 241	99.26 232	99.43 236	98.40 272	98.74 244	99.13 311	98.10 265	99.21 250	99.24 296	94.82 293	99.90 137	97.86 199	98.77 331	99.49 190

SED-MVS		99.40 80	99.28 99	99.77 40	99.69 126	99.82 29	99.20 147	99.54 196	99.13 159	99.82 53	99.63 159	98.91 87	99.92 95	97.85 201	99.70 211	99.58 142

test_241102_TWO								99.54 196	99.13 159	99.76 78	99.63 159	98.32 172	99.92 95	97.85 201	99.69 214	99.75 44

MVS_111021_HR		99.12 158	99.02 160	99.40 199	99.50 208	99.11 212	97.92 323	99.71 97	98.76 207	99.08 268	99.47 239	99.17 53	99.54 357	97.85 201	99.76 181	99.54 162

zzz-MVS		99.30 108	99.14 120	99.80 29	99.81 53	99.81 32	98.73 246	99.53 205	99.27 132	99.42 201	99.63 159	98.21 182	99.95 47	97.83 204	99.79 169	99.65 88

MTAPA		99.35 94	99.20 112	99.80 29	99.81 53	99.81 32	99.33 106	99.53 205	99.27 132	99.42 201	99.63 159	98.21 182	99.95 47	97.83 204	99.79 169	99.65 88

MSC_two_6792asdad				99.74 63	99.03 323	99.53 124		99.23 296					99.92 95	97.77 206	99.69 214	99.78 33

No_MVS				99.74 63	99.03 323	99.53 124		99.23 296					99.92 95	97.77 206	99.69 214	99.78 33

TESTMET0.1,1		96.24 327	95.84 329	97.41 334	98.24 366	93.84 358	97.38 347	95.84 367	98.43 234	97.81 352	98.56 358	79.77 375	99.89 153	97.77 206	98.77 331	98.52 337

ACMH+	98.40 8	99.50 54	99.43 66	99.71 83	99.86 31	99.76 52	99.32 109	99.77 65	99.53 92	99.77 76	99.76 83	99.26 46	99.78 284	97.77 206	99.88 107	99.60 128

IU-MVS					99.69 126	99.77 45		99.22 299	97.50 300	99.69 110				97.75 210	99.70 211	99.77 37

114514_t		98.49 250	98.11 266	99.64 111	99.73 107	99.58 116	99.24 137	99.76 70	89.94 368	99.42 201	99.56 205	97.76 218	99.86 201	97.74 211	99.82 151	99.47 200

DVP-MVS++		99.38 86	99.25 106	99.77 40	99.03 323	99.77 45	99.74 18	99.61 149	99.18 147	99.76 78	99.61 177	99.00 75	99.92 95	97.72 212	99.60 248	99.62 113

test_0728_THIRD									99.18 147	99.62 139	99.61 177	98.58 133	99.91 117	97.72 212	99.80 164	99.77 37

EGC-MVSNET		89.05 342	85.52 345	99.64 111	99.89 21	99.78 42	99.56 72	99.52 213	24.19 376	49.96 377	99.83 48	99.15 55	99.92 95	97.71 214	99.85 127	99.21 262

miper_enhance_ethall		98.03 280	97.94 280	98.32 309	98.27 365	96.43 335	96.95 360	99.41 248	96.37 334	99.43 199	98.96 336	94.74 294	99.69 317	97.71 214	99.62 238	98.83 323

TSAR-MVS + MP.		99.34 99	99.24 108	99.63 117	99.82 46	99.37 163	99.26 129	99.35 269	98.77 204	99.57 157	99.70 115	99.27 45	99.88 168	97.71 214	99.75 184	99.65 88
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 298	97.28 300	98.40 305	98.37 363	96.75 330	97.24 354	99.37 265	97.31 310	99.41 209	99.22 298	87.30 354	99.37 368	97.70 217	99.62 238	99.08 293

MP-MVS-pluss		99.14 154	98.92 184	99.80 29	99.83 39	99.83 25	98.61 251	99.63 139	96.84 326	99.44 195	99.58 194	98.81 97	99.91 117	97.70 217	99.82 151	99.67 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 111	99.11 130	99.79 34	99.75 98	99.81 32	98.95 214	99.53 205	98.27 257	99.53 176	99.73 95	98.75 112	99.87 181	97.70 217	99.83 142	99.68 63

UnsupCasMVSNet_bld		98.55 242	98.27 251	99.40 199	99.56 183	99.37 163	97.97 318	99.68 111	97.49 301	99.08 268	99.35 270	95.41 289	99.82 257	97.70 217	98.19 352	99.01 307

MVS_111021_LR		99.13 156	99.03 159	99.42 189	99.58 163	99.32 175	97.91 325	99.73 85	98.68 211	99.31 231	99.48 234	99.09 63	99.66 337	97.70 217	99.77 179	99.29 249

IS-MVSNet		99.03 176	98.85 193	99.55 151	99.80 58	99.25 189	99.73 21	99.15 308	99.37 119	99.61 145	99.71 108	94.73 295	99.81 273	97.70 217	99.88 107	99.58 142

test-LLR		97.15 307	96.95 311	97.74 327	98.18 368	95.02 350	97.38 347	96.10 363	98.00 270	97.81 352	98.58 355	90.04 347	99.91 117	97.69 223	98.78 329	98.31 346

test-mter		96.23 328	95.73 331	97.74 327	98.18 368	95.02 350	97.38 347	96.10 363	97.90 279	97.81 352	98.58 355	79.12 378	99.91 117	97.69 223	98.78 329	98.31 346

XVS		99.27 115	99.11 130	99.75 57	99.71 114	99.71 71	99.37 99	99.61 149	99.29 128	98.76 303	99.47 239	98.47 150	99.88 168	97.62 225	99.73 199	99.67 70

X-MVStestdata		96.09 329	94.87 339	99.75 57	99.71 114	99.71 71	99.37 99	99.61 149	99.29 128	98.76 303	61.30 383	98.47 150	99.88 168	97.62 225	99.73 199	99.67 70

SMA-MVS		99.19 141	99.00 166	99.73 73	99.46 229	99.73 65	99.13 174	99.52 213	97.40 305	99.57 157	99.64 149	98.93 84	99.83 247	97.61 227	99.79 169	99.63 102
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
CostFormer		96.71 318	96.79 317	96.46 351	98.90 333	90.71 376	99.41 90	98.68 329	94.69 358	98.14 339	99.34 273	86.32 365	99.80 278	97.60 228	98.07 356	98.88 318

PVSNet	97.47 15	98.42 256	98.44 234	98.35 307	99.46 229	96.26 336	96.70 364	99.34 271	97.68 290	99.00 275	99.13 307	97.40 237	99.72 305	97.59 229	99.68 219	99.08 293

new_pmnet		98.88 203	98.89 189	98.84 283	99.70 122	97.62 309	98.15 294	99.50 221	97.98 273	99.62 139	99.54 214	98.15 188	99.94 61	97.55 230	99.84 132	98.95 312

IB-MVS	95.41 20	95.30 339	94.46 343	97.84 323	98.76 352	95.33 348	97.33 350	96.07 365	96.02 338	95.37 373	97.41 376	76.17 381	99.96 37	97.54 231	95.44 373	98.22 351
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
LS3D		99.24 121	99.11 130	99.61 129	98.38 362	99.79 39	99.57 70	99.68 111	99.61 80	99.15 259	99.71 108	98.70 116	99.91 117	97.54 231	99.68 219	99.13 284

ZNCC-MVS		99.22 131	99.04 157	99.77 40	99.76 87	99.73 65	99.28 124	99.56 184	98.19 262	99.14 261	99.29 282	98.84 96	99.92 95	97.53 233	99.80 164	99.64 97

CP-MVS		99.23 122	99.05 151	99.75 57	99.66 141	99.66 89	99.38 95	99.62 142	98.38 241	99.06 272	99.27 286	98.79 104	99.94 61	97.51 234	99.82 151	99.66 80

SD-MVS		99.01 182	99.30 92	98.15 315	99.50 208	99.40 155	98.94 216	99.61 149	99.22 144	99.75 84	99.82 55	99.54 22	95.51 377	97.48 235	99.87 116	99.54 162
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
PMMVS		98.49 250	98.29 250	99.11 252	98.96 330	98.42 271	97.54 339	99.32 274	97.53 298	98.47 324	98.15 368	97.88 209	99.82 257	97.46 236	99.24 309	99.09 290

DeepC-MVS_fast	98.47 5	99.23 122	99.12 127	99.56 148	99.28 283	99.22 198	98.99 206	99.40 255	99.08 164	99.58 154	99.64 149	98.90 90	99.83 247	97.44 237	99.75 184	99.63 102
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 118	99.08 141	99.76 47	99.73 107	99.70 78	99.31 113	99.59 168	98.36 243	99.36 218	99.37 260	98.80 101	99.91 117	97.43 238	99.75 184	99.68 63

ACMMPR		99.23 122	99.06 147	99.76 47	99.74 104	99.69 82	99.31 113	99.59 168	98.36 243	99.35 220	99.38 259	98.61 129	99.93 75	97.43 238	99.75 184	99.67 70

Vis-MVSNet (Re-imp)		98.77 214	98.58 220	99.34 214	99.78 75	98.88 241	99.61 59	99.56 184	99.11 163	99.24 243	99.56 205	93.00 314	99.78 284	97.43 238	99.89 99	99.35 237

MIMVSNet		98.43 255	98.20 257	99.11 252	99.53 192	98.38 275	99.58 68	98.61 333	98.96 178	99.33 225	99.76 83	90.92 335	99.81 273	97.38 241	99.76 181	99.15 277

XVG-OURS-SEG-HR		99.16 150	98.99 171	99.66 99	99.84 35	99.64 96	98.25 288	99.73 85	98.39 240	99.63 131	99.43 247	99.70 11	99.90 137	97.34 242	98.64 339	99.44 211

COLMAP_ROB	98.06 12	99.45 67	99.37 76	99.70 87	99.83 39	99.70 78	99.38 95	99.78 62	99.53 92	99.67 117	99.78 73	99.19 51	99.86 201	97.32 243	99.87 116	99.55 154
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 178	98.81 199	99.65 104	99.58 163	99.49 129	98.58 255	99.07 312	98.40 239	99.04 273	99.25 291	98.51 148	99.80 278	97.31 244	99.51 270	99.65 88

region2R		99.23 122	99.05 151	99.77 40	99.76 87	99.70 78	99.31 113	99.59 168	98.41 237	99.32 227	99.36 265	98.73 115	99.93 75	97.29 245	99.74 192	99.67 70

APD-MVS_3200maxsize		99.31 107	99.16 116	99.74 63	99.53 192	99.75 56	99.27 127	99.61 149	99.19 146	99.57 157	99.64 149	98.76 110	99.90 137	97.29 245	99.62 238	99.56 151

TAPA-MVS	97.92 13	98.03 280	97.55 296	99.46 177	99.47 224	99.44 143	98.50 268	99.62 142	86.79 369	99.07 271	99.26 289	98.26 176	99.62 348	97.28 247	99.73 199	99.31 245
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 115	99.11 130	99.73 73	99.54 186	99.74 62	99.26 129	99.62 142	99.16 153	99.52 178	99.64 149	98.41 159	99.91 117	97.27 248	99.61 245	99.54 162

RE-MVS-def			99.13 123		99.54 186	99.74 62	99.26 129	99.62 142	99.16 153	99.52 178	99.64 149	98.57 134		97.27 248	99.61 245	99.54 162

test_yl		98.25 269	97.95 276	99.13 250	99.17 302	98.47 266	99.00 201	98.67 331	98.97 175	99.22 248	99.02 326	91.31 329	99.69 317	97.26 250	98.93 322	99.24 255

DCV-MVSNet		98.25 269	97.95 276	99.13 250	99.17 302	98.47 266	99.00 201	98.67 331	98.97 175	99.22 248	99.02 326	91.31 329	99.69 317	97.26 250	98.93 322	99.24 255

PHI-MVS		99.11 162	98.95 179	99.59 134	99.13 307	99.59 113	99.17 158	99.65 131	97.88 280	99.25 240	99.46 242	98.97 80	99.80 278	97.26 250	99.82 151	99.37 231

tfpnnormal		99.43 70	99.38 73	99.60 131	99.87 29	99.75 56	99.59 66	99.78 62	99.71 50	99.90 23	99.69 121	98.85 95	99.90 137	97.25 253	99.78 175	99.15 277

PatchmatchNet		97.65 294	97.80 287	97.18 340	98.82 345	92.49 364	99.17 158	98.39 343	98.12 264	98.79 299	99.58 194	90.71 340	99.89 153	97.23 254	99.41 286	99.16 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 187	98.80 201	99.56 148	99.25 288	99.43 147	98.54 264	99.27 287	98.58 220	98.80 298	99.43 247	98.53 143	99.70 311	97.22 255	99.59 252	99.54 162

HPM-MVS		99.25 118	99.07 145	99.78 37	99.81 53	99.75 56	99.61 59	99.67 116	97.72 288	99.35 220	99.25 291	99.23 48	99.92 95	97.21 256	99.82 151	99.67 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DWT-MVSNet_test		96.03 331	95.80 330	96.71 348	98.50 360	91.93 367	99.25 136	97.87 352	95.99 339	96.81 365	97.61 374	81.02 372	99.66 337	97.20 257	97.98 357	98.54 336

mPP-MVS		99.19 141	99.00 166	99.76 47	99.76 87	99.68 85	99.38 95	99.54 196	98.34 252	99.01 274	99.50 226	98.53 143	99.93 75	97.18 258	99.78 175	99.66 80

ACMMP		99.25 118	99.08 141	99.74 63	99.79 68	99.68 85	99.50 77	99.65 131	98.07 268	99.52 178	99.69 121	98.57 134	99.92 95	97.18 258	99.79 169	99.63 102
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
test1172		99.23 122	99.05 151	99.74 63	99.52 197	99.75 56	99.20 147	99.61 149	98.97 175	99.48 187	99.58 194	98.41 159	99.91 117	97.15 260	99.55 259	99.57 148

abl_6		99.36 92	99.23 110	99.75 57	99.71 114	99.74 62	99.33 106	99.76 70	99.07 166	99.65 125	99.63 159	99.09 63	99.92 95	97.13 261	99.76 181	99.58 142

thisisatest0515		96.98 311	96.42 318	98.66 296	99.42 241	97.47 312	97.27 352	94.30 372	97.24 312	99.15 259	98.86 346	85.01 366	99.87 181	97.10 262	99.39 288	98.63 329

XVG-ACMP-BASELINE		99.23 122	99.10 138	99.63 117	99.82 46	99.58 116	98.83 228	99.72 94	98.36 243	99.60 147	99.71 108	98.92 85	99.91 117	97.08 263	99.84 132	99.40 223

MSDG		99.08 167	98.98 174	99.37 209	99.60 154	99.13 210	97.54 339	99.74 82	98.84 196	99.53 176	99.55 212	99.10 61	99.79 281	97.07 264	99.86 123	99.18 271

SteuartSystems-ACMMP		99.30 108	99.14 120	99.76 47	99.87 29	99.66 89	99.18 153	99.60 161	98.55 223	99.57 157	99.67 138	99.03 74	99.94 61	97.01 265	99.80 164	99.69 57
Skip Steuart: Steuart Systems R&D Blog.
EPMVS		96.53 321	96.32 319	97.17 341	98.18 368	92.97 363	99.39 93	89.95 379	98.21 260	98.61 313	99.59 192	86.69 364	99.72 305	96.99 266	99.23 311	98.81 324

MSP-MVS		99.04 175	98.79 202	99.81 26	99.78 75	99.73 65	99.35 103	99.57 179	98.54 226	99.54 171	98.99 328	96.81 261	99.93 75	96.97 267	99.53 267	99.77 37
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
HPM-MVS++		98.96 191	98.70 209	99.74 63	99.52 197	99.71 71	98.86 223	99.19 304	98.47 233	98.59 315	99.06 317	98.08 193	99.91 117	96.94 268	99.60 248	99.60 128

SR-MVS		99.19 141	99.00 166	99.74 63	99.51 202	99.72 69	99.18 153	99.60 161	98.85 193	99.47 189	99.58 194	98.38 164	99.92 95	96.92 269	99.54 265	99.57 148

PGM-MVS		99.20 138	99.01 163	99.77 40	99.75 98	99.71 71	99.16 164	99.72 94	97.99 272	99.42 201	99.60 186	98.81 97	99.93 75	96.91 270	99.74 192	99.66 80

HY-MVS	98.23 9	98.21 274	97.95 276	98.99 263	99.03 323	98.24 279	99.61 59	98.72 328	96.81 327	98.73 305	99.51 223	94.06 300	99.86 201	96.91 270	98.20 350	98.86 320

MDTV_nov1_ep13			97.73 291		98.70 355	90.83 374	99.15 166	98.02 348	98.51 228	98.82 295	99.61 177	90.98 334	99.66 337	96.89 272	98.92 324

GST-MVS		99.16 150	98.96 177	99.75 57	99.73 107	99.73 65	99.20 147	99.55 190	98.22 259	99.32 227	99.35 270	98.65 125	99.91 117	96.86 273	99.74 192	99.62 113

test_post1							99.14 168				51.63 385	89.54 350	99.82 257	96.86 273

SCA		98.11 276	98.36 242	97.36 335	99.20 297	92.99 362	98.17 293	98.49 339	98.24 258	99.10 267	99.57 202	96.01 282	99.94 61	96.86 273	99.62 238	99.14 281

#test#		99.12 158	98.90 188	99.76 47	99.73 107	99.70 78	99.10 181	99.59 168	97.60 293	99.36 218	99.37 260	98.80 101	99.91 117	96.84 276	99.75 184	99.68 63

XVG-OURS		99.21 136	99.06 147	99.65 104	99.82 46	99.62 102	97.87 326	99.74 82	98.36 243	99.66 121	99.68 132	99.71 9	99.90 137	96.84 276	99.88 107	99.43 217

LCM-MVSNet-Re		99.28 111	99.15 119	99.67 92	99.33 272	99.76 52	99.34 104	99.97 2	98.93 183	99.91 21	99.79 66	98.68 118	99.93 75	96.80 278	99.56 255	99.30 246

RPSCF		99.18 145	99.02 160	99.64 111	99.83 39	99.85 16	99.44 87	99.82 41	98.33 253	99.50 185	99.78 73	97.90 206	99.65 344	96.78 279	99.83 142	99.44 211

旧先验2							97.94 321		95.33 348	98.94 279			99.88 168	96.75 280

MDTV_nov1_ep13_2view						91.44 372	99.14 168		97.37 307	99.21 250		91.78 327		96.75 280		99.03 302

CLD-MVS		98.76 216	98.57 221	99.33 216	99.57 173	98.97 228	97.53 341	99.55 190	96.41 332	99.27 238	99.13 307	99.07 69	99.78 284	96.73 282	99.89 99	99.23 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 277	97.98 274	98.48 302	99.27 285	96.48 333	99.40 91	99.07 312	98.81 198	99.23 244	99.57 202	90.11 346	99.87 181	96.69 283	99.64 235	99.09 290

baseline2		96.83 314	96.28 320	98.46 303	99.09 317	96.91 327	98.83 228	93.87 374	97.23 313	96.23 369	98.36 364	88.12 353	99.90 137	96.68 284	98.14 354	98.57 335

cascas		96.99 310	96.82 316	97.48 331	97.57 375	95.64 345	96.43 366	99.56 184	91.75 364	97.13 364	97.61 374	95.58 288	98.63 373	96.68 284	99.11 313	98.18 355

PC_three_1452									97.56 294	99.68 112	99.41 249	99.09 63	97.09 375	96.66 286	99.60 248	99.62 113

LPG-MVS_test		99.22 131	99.05 151	99.74 63	99.82 46	99.63 100	99.16 164	99.73 85	97.56 294	99.64 127	99.69 121	99.37 32	99.89 153	96.66 286	99.87 116	99.69 57

LGP-MVS_train				99.74 63	99.82 46	99.63 100		99.73 85	97.56 294	99.64 127	99.69 121	99.37 32	99.89 153	96.66 286	99.87 116	99.69 57

TinyColmap		98.97 188	98.93 180	99.07 258	99.46 229	98.19 283	97.75 330	99.75 77	98.79 201	99.54 171	99.70 115	98.97 80	99.62 348	96.63 289	99.83 142	99.41 221

LF4IMVS		99.01 182	98.92 184	99.27 230	99.71 114	99.28 181	98.59 254	99.77 65	98.32 254	99.39 215	99.41 249	98.62 127	99.84 236	96.62 290	99.84 132	98.69 328

NCCC		98.82 210	98.57 221	99.58 139	99.21 294	99.31 176	98.61 251	99.25 292	98.65 213	98.43 325	99.26 289	97.86 210	99.81 273	96.55 291	99.27 306	99.61 124

OPU-MVS				99.29 226	99.12 309	99.44 143	99.20 147				99.40 253	99.00 75	98.84 372	96.54 292	99.60 248	99.58 142

F-COLMAP		98.74 219	98.45 232	99.62 126	99.57 173	99.47 132	98.84 226	99.65 131	96.31 335	98.93 280	99.19 304	97.68 223	99.87 181	96.52 293	99.37 293	99.53 167

ADS-MVSNet2		97.78 287	97.66 295	98.12 317	99.14 305	95.36 347	99.22 144	98.75 327	96.97 321	98.25 331	99.64 149	90.90 336	99.94 61	96.51 294	99.56 255	99.08 293

ADS-MVSNet		97.72 293	97.67 294	97.86 322	99.14 305	94.65 353	99.22 144	98.86 321	96.97 321	98.25 331	99.64 149	90.90 336	99.84 236	96.51 294	99.56 255	99.08 293

PatchMatch-RL		98.68 226	98.47 230	99.30 225	99.44 234	99.28 181	98.14 296	99.54 196	97.12 319	99.11 265	99.25 291	97.80 215	99.70 311	96.51 294	99.30 301	98.93 314

CMPMVS	77.52 23	98.50 247	98.19 260	99.41 197	98.33 364	99.56 119	99.01 199	99.59 168	95.44 346	99.57 157	99.80 60	95.64 286	99.46 366	96.47 297	99.92 79	99.21 262
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
xxxxxxxxxxxxxcwj		99.11 162	98.96 177	99.54 155	99.53 192	99.25 189	98.29 284	99.76 70	99.07 166	99.42 201	99.61 177	98.86 93	99.87 181	96.45 298	99.68 219	99.49 190

SF-MVS		99.10 166	98.93 180	99.62 126	99.58 163	99.51 127	99.13 174	99.65 131	97.97 274	99.42 201	99.61 177	98.86 93	99.87 181	96.45 298	99.68 219	99.49 190

DPE-MVS		99.14 154	98.92 184	99.82 23	99.57 173	99.77 45	98.74 244	99.60 161	98.55 223	99.76 78	99.69 121	98.23 181	99.92 95	96.39 300	99.75 184	99.76 41
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 373	89.02 380			93.47 360		98.30 365		99.84 236	96.38 301

AllTest		99.21 136	99.07 145	99.63 117	99.78 75	99.64 96	99.12 178	99.83 36	98.63 215	99.63 131	99.72 101	98.68 118	99.75 298	96.38 301	99.83 142	99.51 179

TestCases				99.63 117	99.78 75	99.64 96		99.83 36	98.63 215	99.63 131	99.72 101	98.68 118	99.75 298	96.38 301	99.83 142	99.51 179

testdata				99.42 189	99.51 202	98.93 236		99.30 281	96.20 336	98.87 290	99.40 253	98.33 171	99.89 153	96.29 304	99.28 303	99.44 211

dp		96.86 313	97.07 307	96.24 353	98.68 356	90.30 378	99.19 152	98.38 344	97.35 308	98.23 333	99.59 192	87.23 355	99.82 257	96.27 305	98.73 337	98.59 332

tpmvs		97.39 302	97.69 292	96.52 349	98.41 361	91.76 368	99.30 116	98.94 320	97.74 287	97.85 351	99.55 212	92.40 321	99.73 303	96.25 306	98.73 337	98.06 357

KD-MVS_2432*1600		95.89 332	95.41 335	97.31 338	94.96 377	93.89 356	97.09 357	99.22 299	97.23 313	98.88 287	99.04 321	79.23 376	99.54 357	96.24 307	96.81 366	98.50 341

miper_refine_blended		95.89 332	95.41 335	97.31 338	94.96 377	93.89 356	97.09 357	99.22 299	97.23 313	98.88 287	99.04 321	79.23 376	99.54 357	96.24 307	96.81 366	98.50 341

ACMP	97.51 14	99.05 172	98.84 195	99.67 92	99.78 75	99.55 122	98.88 219	99.66 120	97.11 320	99.47 189	99.60 186	99.07 69	99.89 153	96.18 309	99.85 127	99.58 142
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 199	98.72 205	99.44 183	99.39 247	99.42 150	98.58 255	99.64 137	97.31 310	99.44 195	99.62 168	98.59 131	99.69 317	96.17 310	99.79 169	99.22 260

DP-MVS Recon		98.50 247	98.23 254	99.31 223	99.49 213	99.46 136	98.56 260	99.63 139	94.86 355	98.85 292	99.37 260	97.81 214	99.59 354	96.08 311	99.44 280	98.88 318

tpm cat1		96.78 315	96.98 310	96.16 354	98.85 340	90.59 377	99.08 188	99.32 274	92.37 363	97.73 357	99.46 242	91.15 332	99.69 317	96.07 312	98.80 328	98.21 352

tpm2		96.35 324	96.22 321	96.73 346	98.88 339	91.75 369	99.21 146	98.51 337	93.27 361	97.89 348	99.21 300	84.83 367	99.70 311	96.04 313	98.18 353	98.75 327

test_0402		99.22 131	99.14 120	99.45 181	99.79 68	99.43 147	99.28 124	99.68 111	99.54 90	99.40 214	99.56 205	99.07 69	99.82 257	96.01 314	99.96 45	99.11 285

ITE_SJBPF				99.38 206	99.63 147	99.44 143		99.73 85	98.56 221	99.33 225	99.53 217	98.88 92	99.68 328	96.01 314	99.65 233	99.02 306

test_prior3		98.62 230	98.34 245	99.46 177	99.35 257	99.22 198	97.95 319	99.39 258	97.87 281	98.05 341	99.05 318	97.90 206	99.69 317	95.99 316	99.49 274	99.48 195

test_prior2							97.95 319		97.87 281	98.05 341	99.05 318	97.90 206		95.99 316	99.49 274

testdata2													99.89 153	95.99 316

原ACMM1				99.37 209	99.47 224	98.87 243		99.27 287	96.74 329	98.26 330	99.32 275	97.93 204	99.82 257	95.96 319	99.38 289	99.43 217

新几何1				99.52 159	99.50 208	99.22 198		99.26 289	95.66 345	98.60 314	99.28 284	97.67 224	99.89 153	95.95 320	99.32 299	99.45 206

MP-MVS		99.06 169	98.83 197	99.76 47	99.76 87	99.71 71	99.32 109	99.50 221	98.35 248	98.97 276	99.48 234	98.37 165	99.92 95	95.95 320	99.75 184	99.63 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
wuyk23d		97.58 297	99.13 123	92.93 357	99.69 126	99.49 129	99.52 75	99.77 65	97.97 274	99.96 8	99.79 66	99.84 3	99.94 61	95.85 322	99.82 151	79.36 373

HQP_MVS		98.90 199	98.68 211	99.55 151	99.58 163	99.24 194	98.80 236	99.54 196	98.94 180	99.14 261	99.25 291	97.24 245	99.82 257	95.84 323	99.78 175	99.60 128

plane_prior5								99.54 196					99.82 257	95.84 323	99.78 175	99.60 128

无先验							98.01 310	99.23 296	95.83 341				99.85 219	95.79 325		99.44 211

1121		98.56 239	98.24 253	99.52 159	99.49 213	99.24 194	99.30 116	99.22 299	95.77 342	98.52 320	99.29 282	97.39 239	99.85 219	95.79 325	99.34 296	99.46 204

CPTT-MVS		98.74 219	98.44 234	99.64 111	99.61 152	99.38 160	99.18 153	99.55 190	96.49 331	99.27 238	99.37 260	97.11 253	99.92 95	95.74 327	99.67 226	99.62 113

PLC	97.35 16	98.36 261	97.99 272	99.48 172	99.32 273	99.24 194	98.50 268	99.51 217	95.19 351	98.58 316	98.96 336	96.95 258	99.83 247	95.63 328	99.25 307	99.37 231
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 238	98.34 245	99.28 228	99.18 301	99.10 217	98.34 279	99.41 248	98.48 232	98.52 320	98.98 331	97.05 255	99.78 284	95.59 329	99.50 272	98.96 311

1314		98.00 282	97.90 285	98.27 313	98.90 333	97.45 314	99.30 116	99.06 314	94.98 352	97.21 362	99.12 311	98.43 156	99.67 333	95.58 330	98.56 342	97.71 362

agg_prior1		98.33 266	97.92 282	99.57 144	99.35 257	99.36 166	97.99 314	99.39 258	94.85 356	97.76 355	98.98 331	98.03 195	99.85 219	95.49 331	99.44 280	99.51 179

PVSNet_0	95.53 19	95.85 335	95.31 337	97.47 332	98.78 350	93.48 360	95.72 368	99.40 255	96.18 337	97.37 358	97.73 372	95.73 285	99.58 355	95.49 331	81.40 375	99.36 234

MAR-MVS		98.24 271	97.92 282	99.19 243	98.78 350	99.65 94	99.17 158	99.14 309	95.36 347	98.04 343	98.81 349	97.47 234	99.72 305	95.47 333	99.06 315	98.21 352
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
OpenMVS	98.12 10	98.23 272	97.89 286	99.26 232	99.19 299	99.26 185	99.65 51	99.69 108	91.33 366	98.14 339	99.77 80	98.28 174	99.96 37	95.41 334	99.55 259	98.58 334

train_agg		98.35 264	97.95 276	99.57 144	99.35 257	99.35 170	98.11 300	99.41 248	94.90 353	97.92 346	98.99 328	98.02 197	99.85 219	95.38 335	99.44 280	99.50 185

9.14			98.64 212		99.45 232		98.81 233	99.60 161	97.52 299	99.28 237	99.56 205	98.53 143	99.83 247	95.36 336	99.64 235

APD-MVS		98.87 205	98.59 217	99.71 83	99.50 208	99.62 102	99.01 199	99.57 179	96.80 328	99.54 171	99.63 159	98.29 173	99.91 117	95.24 337	99.71 209	99.61 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
AdaColmap		98.60 233	98.35 244	99.38 206	99.12 309	99.22 198	98.67 250	99.42 247	97.84 285	98.81 296	99.27 286	97.32 243	99.81 273	95.14 338	99.53 267	99.10 287

test9_res														95.10 339	99.44 280	99.50 185

CDPH-MVS		98.56 239	98.20 257	99.61 129	99.50 208	99.46 136	98.32 282	99.41 248	95.22 349	99.21 250	99.10 314	98.34 169	99.82 257	95.09 340	99.66 230	99.56 151

ETH3D-3000-0.1		98.77 214	98.50 229	99.59 134	99.47 224	99.53 124	98.77 241	99.60 161	97.33 309	99.23 244	99.50 226	97.91 205	99.83 247	95.02 341	99.67 226	99.41 221

BH-untuned		98.22 273	98.09 267	98.58 299	99.38 250	97.24 319	98.55 261	98.98 319	97.81 286	99.20 255	98.76 351	97.01 256	99.65 344	94.83 342	98.33 347	98.86 320

BP-MVS														94.73 343

HQP-MVS		98.36 261	98.02 271	99.39 202	99.31 274	98.94 232	97.98 315	99.37 265	97.45 302	98.15 335	98.83 347	96.67 262	99.70 311	94.73 343	99.67 226	99.53 167

QAPM		98.40 259	97.99 272	99.65 104	99.39 247	99.47 132	99.67 42	99.52 213	91.70 365	98.78 301	99.80 60	98.55 137	99.95 47	94.71 345	99.75 184	99.53 167

ETH3D cwj APD-0.16		98.50 247	98.16 263	99.51 162	99.04 322	99.39 157	98.47 270	99.47 232	96.70 330	98.78 301	99.33 274	97.62 231	99.86 201	94.69 346	99.38 289	99.28 251

agg_prior2														94.58 347	99.46 279	99.50 185

BH-RMVSNet		98.41 257	98.14 265	99.21 240	99.21 294	98.47 266	98.60 253	98.26 346	98.35 248	98.93 280	99.31 277	97.20 250	99.66 337	94.32 348	99.10 314	99.51 179

E-PMN		97.14 309	97.43 297	96.27 352	98.79 348	91.62 370	95.54 369	99.01 318	99.44 109	98.88 287	99.12 311	92.78 315	99.68 328	94.30 349	99.03 318	97.50 363

MG-MVS		98.52 245	98.39 239	98.94 267	99.15 304	97.39 316	98.18 291	99.21 303	98.89 190	99.23 244	99.63 159	97.37 241	99.74 300	94.22 350	99.61 245	99.69 57

API-MVS		98.38 260	98.39 239	98.35 307	98.83 342	99.26 185	99.14 168	99.18 305	98.59 219	98.66 310	98.78 350	98.61 129	99.57 356	94.14 351	99.56 255	96.21 370

PAPM_NR		98.36 261	98.04 269	99.33 216	99.48 219	98.93 236	98.79 239	99.28 286	97.54 297	98.56 318	98.57 357	97.12 252	99.69 317	94.09 352	98.90 326	99.38 228

ZD-MVS					99.43 236	99.61 108		99.43 245	96.38 333	99.11 265	99.07 316	97.86 210	99.92 95	94.04 353	99.49 274

DPM-MVS		98.28 267	97.94 280	99.32 220	99.36 255	99.11 212	97.31 351	98.78 326	96.88 323	98.84 293	99.11 313	97.77 217	99.61 352	94.03 354	99.36 294	99.23 258

gg-mvs-nofinetune		95.87 334	95.17 338	97.97 319	98.19 367	96.95 325	99.69 35	89.23 380	99.89 14	96.24 368	99.94 14	81.19 371	99.51 362	93.99 355	98.20 350	97.44 364

PMVS	92.94 21	98.82 210	98.81 199	98.85 281	99.84 35	97.99 295	99.20 147	99.47 232	99.71 50	99.42 201	99.82 55	98.09 191	99.47 364	93.88 356	99.85 127	99.07 298
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testtj		98.56 239	98.17 262	99.72 79	99.45 232	99.60 110	98.88 219	99.50 221	96.88 323	99.18 256	99.48 234	97.08 254	99.92 95	93.69 357	99.38 289	99.63 102

EMVS		96.96 312	97.28 300	95.99 355	98.76 352	91.03 373	95.26 370	98.61 333	99.34 122	98.92 283	98.88 345	93.79 304	99.66 337	92.87 358	99.05 316	97.30 367

BH-w/o		97.20 306	97.01 309	97.76 325	99.08 318	95.69 344	98.03 309	98.52 336	95.76 343	97.96 345	98.02 369	95.62 287	99.47 364	92.82 359	97.25 365	98.12 356

TR-MVS		97.44 301	97.15 306	98.32 309	98.53 359	97.46 313	98.47 270	97.91 351	96.85 325	98.21 334	98.51 361	96.42 270	99.51 362	92.16 360	97.29 364	97.98 359

OpenMVS_ROB	97.31 17	97.36 304	96.84 315	98.89 280	99.29 280	99.45 141	98.87 222	99.48 228	86.54 371	99.44 195	99.74 91	97.34 242	99.86 201	91.61 361	99.28 303	97.37 366

GG-mvs-BLEND				97.36 335	97.59 373	96.87 328	99.70 29	88.49 381		94.64 374	97.26 379	80.66 373	99.12 369	91.50 362	96.50 370	96.08 372

DeepMVS_CX				97.98 318	99.69 126	96.95 325		99.26 289	75.51 373	95.74 371	98.28 366	96.47 268	99.62 348	91.23 363	97.89 359	97.38 365

ETH3 D test6400		97.76 288	97.19 305	99.50 165	99.38 250	99.26 185	98.34 279	99.49 226	92.99 362	98.54 319	99.20 302	95.92 284	99.82 257	91.14 364	99.66 230	99.40 223

PAPR		97.56 298	97.07 307	99.04 261	98.80 347	98.11 289	97.63 335	99.25 292	94.56 359	98.02 344	98.25 367	97.43 236	99.68 328	90.90 365	98.74 335	99.33 240

MVS		95.72 337	94.63 341	98.99 263	98.56 358	97.98 300	99.30 116	98.86 321	72.71 374	97.30 359	99.08 315	98.34 169	99.74 300	89.21 366	98.33 347	99.26 252

thres600view7		96.60 320	96.16 322	97.93 320	99.63 147	96.09 340	99.18 153	97.57 355	98.77 204	98.72 306	97.32 377	87.04 357	99.72 305	88.57 367	98.62 340	97.98 359

FPMVS		96.32 325	95.50 333	98.79 289	99.60 154	98.17 285	98.46 275	98.80 325	97.16 317	96.28 366	99.63 159	82.19 370	99.09 370	88.45 368	98.89 327	99.10 287

PCF-MVS	96.03 18	96.73 317	95.86 328	99.33 216	99.44 234	99.16 207	96.87 362	99.44 241	86.58 370	98.95 278	99.40 253	94.38 298	99.88 168	87.93 369	99.80 164	98.95 312
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 323	96.03 325	97.47 332	99.63 147	95.93 341	99.18 153	97.57 355	98.75 208	98.70 308	97.31 378	87.04 357	99.67 333	87.62 370	98.51 344	96.81 368

tfpn200view9		96.30 326	95.89 326	97.53 330	99.58 163	96.11 338	99.00 201	97.54 358	98.43 234	98.52 320	96.98 380	86.85 359	99.67 333	87.62 370	98.51 344	96.81 368

thres400		96.40 322	95.89 326	97.92 321	99.58 163	96.11 338	99.00 201	97.54 358	98.43 234	98.52 320	96.98 380	86.85 359	99.67 333	87.62 370	98.51 344	97.98 359

thres200		96.09 329	95.68 332	97.33 337	99.48 219	96.22 337	98.53 265	97.57 355	98.06 269	98.37 327	96.73 382	86.84 361	99.61 352	86.99 373	98.57 341	96.16 371

MVE	92.54 22	96.66 319	96.11 323	98.31 311	99.68 135	97.55 311	97.94 321	95.60 368	99.37 119	90.68 376	98.70 353	96.56 264	98.61 374	86.94 374	99.55 259	98.77 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM		95.61 338	94.71 340	98.31 311	99.12 309	96.63 331	96.66 365	98.46 340	90.77 367	96.25 367	98.68 354	93.01 313	99.69 317	81.60 375	97.86 361	98.62 330

test123		29.31 343	33.05 348	18.08 359	25.93 383	12.24 383	97.53 341	10.93 384	11.78 377	24.21 378	50.08 387	21.04 382	8.60 378	23.51 376	32.43 377	33.39 374

testmvs		28.94 344	33.33 346	15.79 360	26.03 382	9.81 384	96.77 363	15.67 383	11.55 378	23.87 379	50.74 386	19.03 383	8.53 379	23.21 377	33.07 376	29.03 375

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

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

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

cdsmvs_eth3d_5k		24.88 345	33.17 347	0.00 361	0.00 384	0.00 385	0.00 372	99.62 142	0.00 379	0.00 380	99.13 307	99.82 4	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		16.61 346	22.14 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	100.00 1	99.28 42	0.00 380	0.00 378	0.00 378	0.00 376

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

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

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

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

ab-mvs-re		8.26 355	11.02 358	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	99.16 305	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

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

FOURS1					99.83 39	99.89 8	99.74 18	99.71 97	99.69 58	99.63 131

test_one_0601					99.63 147	99.76 52		99.55 190	99.23 140	99.31 231	99.61 177	98.59 131

eth-test2					0.00 384

eth-test					0.00 384

test_241102_ONE					99.69 126	99.82 29		99.54 196	99.12 162	99.82 53	99.49 231	98.91 87	99.52 361

save fliter					99.53 192	99.25 189	98.29 284	99.38 264	99.07 166

test0726					99.69 126	99.80 37	99.24 137	99.57 179	99.16 153	99.73 97	99.65 147	98.35 167

GSMVS																99.14 281

test_part2					99.62 151	99.67 87				99.55 169

sam_mvs1												90.81 339				99.14 281

sam_mvs												90.52 343

MTGPA								99.53 205

test_post											52.41 384	90.25 345	99.86 201

patchmatchnet-post											99.62 168	90.58 341	99.94 61

MTMP							99.09 185	98.59 335

TEST9					99.35 257	99.35 170	98.11 300	99.41 248	94.83 357	97.92 346	98.99 328	98.02 197	99.85 219

test_8					99.34 267	99.31 176	98.08 304	99.40 255	94.90 353	97.87 350	98.97 334	98.02 197	99.84 236

agg_prior					99.35 257	99.36 166		99.39 258		97.76 355			99.85 219

test_prior4						99.19 205	98.00 312

test_prior				99.46 177	99.35 257	99.22 198		99.39 258					99.69 317			99.48 195

新几何2							98.04 308

旧先验1					99.49 213	99.29 179		99.26 289			99.39 257	97.67 224			99.36 294	99.46 204

原ACMM2							97.92 323

test222					99.51 202	99.08 220	97.83 328	99.29 283	95.21 350	98.68 309	99.31 277	97.28 244			99.38 289	99.43 217

segment_acmp												98.37 165

testdata1							97.72 331		97.86 284

test12				99.54 155	99.29 280	99.33 173		99.16 307		98.43 325		97.54 232	99.82 257		99.47 277	99.48 195

plane_prior7					99.58 163	99.38 160

plane_prior6					99.47 224	99.26 185						97.24 245

plane_prior4											99.25 291

plane_prior3						99.31 176			98.36 243	99.14 261

plane_prior2							98.80 236		98.94 180

plane_prior1					99.51 202

plane_prior						99.24 194	98.42 276		97.87 281						99.71 209

n2								0.00 385

nn								0.00 385

door-mid								99.83 36

test11								99.29 283

door								99.77 65

HQP5-MVS						98.94 232

HQP-NCC					99.31 274		97.98 315		97.45 302	98.15 335

ACMP_Plane					99.31 274		97.98 315		97.45 302	98.15 335

HQP4-MVS										98.15 335			99.70 311			99.53 167

HQP3-MVS								99.37 265							99.67 226

HQP2-MVS												96.67 262

NP-MVS					99.40 245	99.13 210					98.83 347

ACMMP++_ref															99.94 67

ACMMP++															99.79 169

Test By Simon												98.41 159

High-resolution multi-view benchmark