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		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 1	100.00 1	99.85 7

HyFIR lowres test		97.19 230	96.60 253	98.96 136	99.62 52	97.28 200	95.17 324	99.50 60	94.21 301	99.01 120	98.32 253	86.61 314	99.99 2	97.10 142	99.84 59	99.60 52

jajsoiax		99.58 6	99.61 7	99.48 51	99.87 10	98.61 92	99.28 30	99.66 19	99.09 67	99.89 6	99.68 14	99.53 4	99.97 3	99.50 10	99.99 5	99.87 4

mvs_tets		99.63 5	99.67 5	99.49 49	99.88 7	98.61 92	99.34 15	99.71 11	99.27 44	99.90 4	99.74 8	99.68 2	99.97 3	99.55 8	99.99 5	99.88 3

DTE-MVSNet		99.43 15	99.35 17	99.66 4	99.71 32	99.30 17	99.31 21	99.51 58	99.64 12	99.56 28	99.46 46	98.23 52	99.97 3	98.78 46	99.93 28	99.72 25

MVSFormer		98.26 144	98.43 106	97.77 250	98.88 222	93.89 301	99.39 13	99.56 42	99.11 57	98.16 221	98.13 264	93.81 259	99.97 3	99.26 18	99.57 181	99.43 142

test_djsdf		99.52 9	99.51 9	99.53 36	99.86 11	98.74 81	99.39 13	99.56 42	99.11 57	99.70 15	99.73 10	99.00 15	99.97 3	99.26 18	99.98 9	99.89 2

h-mvs33		97.77 188	97.33 209	99.10 111	99.21 143	97.84 165	98.35 116	98.57 282	99.11 57	98.58 190	99.02 119	88.65 306	99.96 8	98.11 84	96.34 352	99.49 109

IterMVS-SCA-FT		97.85 182	98.18 140	96.87 297	99.27 131	91.16 343	95.53 314	99.25 163	99.10 64	99.41 50	99.35 62	93.10 269	99.96 8	98.65 56	99.94 24	99.49 109

UA-Net		99.47 11	99.40 14	99.70 2	99.49 86	99.29 18	99.80 3	99.72 10	99.82 3	99.04 116	99.81 3	98.05 69	99.96 8	98.85 42	99.99 5	99.86 6

RRT_MVS		97.07 238	96.57 255	98.58 186	95.89 370	96.33 234	97.36 216	98.77 266	97.85 152	99.08 106	99.12 98	82.30 345	99.96 8	98.82 44	99.90 47	99.45 133

PS-MVSNAJss		99.46 12	99.49 10	99.35 70	99.90 4	98.15 130	99.20 38	99.65 20	99.48 24	99.92 3	99.71 12	98.07 66	99.96 8	99.53 9	100.00 1	99.93 1

PEN-MVS		99.41 17	99.34 19	99.62 6	99.73 25	99.14 52	99.29 26	99.54 50	99.62 17	99.56 28	99.42 52	98.16 62	99.96 8	98.78 46	99.93 28	99.77 16

K. test v3		98.00 165	97.66 184	99.03 128	99.79 19	97.56 186	99.19 42	92.47 362	99.62 17	99.52 35	99.66 17	89.61 296	99.96 8	99.25 20	99.81 72	99.56 74

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 14	99.34 15	99.69 4	99.58 28	99.90 2	99.86 7	99.78 5	99.58 3	99.95 15	99.00 34	99.95 16	99.78 14

Fast-Effi-MVS+-dtu		98.27 142	98.09 151	98.81 156	98.43 293	98.11 133	97.61 193	99.50 60	98.64 95	97.39 277	97.52 305	98.12 65	99.95 15	96.90 161	98.71 299	98.38 307

Effi-MVS+-dtu		98.26 144	97.90 168	99.35 70	98.02 318	99.49 3	98.02 150	99.16 192	98.29 119	97.64 256	97.99 276	96.44 180	99.95 15	96.66 183	98.93 289	98.60 296

anonymousdsp		99.51 10	99.47 12	99.62 6	99.88 7	99.08 63	99.34 15	99.69 15	98.93 84	99.65 22	99.72 11	98.93 19	99.95 15	99.11 27	100.00 1	99.82 9

v7n		99.53 8	99.57 8	99.41 61	99.88 7	98.54 100	99.45 9	99.61 24	99.66 11	99.68 19	99.66 17	98.44 40	99.95 15	99.73 2	99.96 14	99.75 22

PS-CasMVS		99.40 18	99.33 20	99.62 6	99.71 32	99.10 60	99.29 26	99.53 54	99.53 23	99.46 43	99.41 55	98.23 52	99.95 15	98.89 40	99.95 16	99.81 11

TranMVSNet+NR-MVSNet		99.17 30	99.07 36	99.46 56	99.37 116	98.87 72	98.39 112	99.42 93	99.42 31	99.36 60	99.06 105	98.38 43	99.95 15	98.34 75	99.90 47	99.57 69

Vis-MVSNet		99.34 22	99.36 16	99.27 86	99.73 25	98.26 118	99.17 43	99.78 6	99.11 57	99.27 76	99.48 44	98.82 21	99.95 15	98.94 36	99.93 28	99.59 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous20240521		98.69 83	98.87 45	98.16 229	99.77 20	95.11 269	99.08 50	99.44 84	99.34 38	99.33 65	99.55 32	94.10 256	99.94 23	99.25 20	99.96 14	99.42 145

CP-MVSNet		99.21 29	99.09 34	99.56 24	99.65 45	98.96 69	99.13 47	99.34 121	99.42 31	99.33 65	99.26 73	97.01 147	99.94 23	98.74 51	99.93 28	99.79 13

PVSNet_Blended_VisFu		98.17 154	98.15 146	98.22 225	99.73 25	95.15 266	97.36 216	99.68 16	94.45 296	98.99 124	99.27 71	96.87 154	99.94 23	97.13 140	99.91 43	99.57 69

IterMVS		97.73 189	98.11 150	96.57 304	99.24 136	90.28 344	95.52 316	99.21 172	98.86 87	99.33 65	99.33 66	93.11 268	99.94 23	98.49 65	99.94 24	99.48 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ANet_high		99.57 7	99.67 5	99.28 83	99.89 6	98.09 134	99.14 46	99.93 1	99.82 3	99.93 2	99.81 3	99.17 12	99.94 23	99.31 16	100.00 1	99.82 9

CHOSEN 280x420		95.51 292	95.47 283	95.65 324	98.25 305	88.27 352	93.25 359	98.88 243	93.53 312	94.65 350	97.15 324	86.17 318	99.93 28	97.41 123	99.93 28	98.73 288

bset_n11_16_dypcd		96.99 247	96.56 256	98.27 222	99.00 195	95.25 261	92.18 365	94.05 357	98.75 92	99.01 120	98.38 244	88.98 301	99.93 28	98.77 49	99.92 37	99.64 41

UniMVSNet_NR-MVSNet		98.86 59	98.68 68	99.40 63	99.17 159	98.74 81	97.68 185	99.40 97	99.14 55	99.06 109	98.59 220	96.71 168	99.93 28	98.57 60	99.77 93	99.53 94

DU-MVS		98.82 61	98.63 74	99.39 64	99.16 161	98.74 81	97.54 201	99.25 163	98.84 89	99.06 109	98.76 187	96.76 164	99.93 28	98.57 60	99.77 93	99.50 105

WR-MVS_H		99.33 23	99.22 27	99.65 5	99.71 32	99.24 24	99.32 17	99.55 46	99.46 27	99.50 39	99.34 64	97.30 129	99.93 28	98.90 38	99.93 28	99.77 16

SixPastTwentyTwo		98.75 73	98.62 75	99.16 102	99.83 15	97.96 155	99.28 30	98.20 298	99.37 35	99.70 15	99.65 19	92.65 278	99.93 28	99.04 32	99.84 59	99.60 52

IterMVS-LS		98.55 109	98.70 65	98.09 231	99.48 94	94.73 275	97.22 228	99.39 99	98.97 79	99.38 56	99.31 68	96.00 196	99.93 28	98.58 58	99.97 11	99.60 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tttt0517		95.64 288	94.98 299	97.64 259	99.36 117	93.81 303	98.72 77	90.47 368	98.08 136	98.67 175	98.34 250	73.88 367	99.92 35	97.77 106	99.51 199	99.20 214

xiu_mvs_v1_base_debu		97.86 177	98.17 141	96.92 294	98.98 200	93.91 298	96.45 273	99.17 189	97.85 152	98.41 208	97.14 325	98.47 37	99.92 35	98.02 91	99.05 273	96.92 350

zzz-MVS		98.79 65	98.52 87	99.61 9	99.67 42	99.36 10	97.33 218	99.20 174	98.83 90	98.89 145	98.90 152	96.98 149	99.92 35	97.16 134	99.70 127	99.56 74

mvs-test1		97.83 185	97.48 198	98.89 146	98.02 318	99.20 33	97.20 229	99.16 192	98.29 119	96.46 320	97.17 322	96.44 180	99.92 35	96.66 183	97.90 328	97.54 343

xiu_mvs_v1_base		97.86 177	98.17 141	96.92 294	98.98 200	93.91 298	96.45 273	99.17 189	97.85 152	98.41 208	97.14 325	98.47 37	99.92 35	98.02 91	99.05 273	96.92 350

xiu_mvs_v1_base_debi		97.86 177	98.17 141	96.92 294	98.98 200	93.91 298	96.45 273	99.17 189	97.85 152	98.41 208	97.14 325	98.47 37	99.92 35	98.02 91	99.05 273	96.92 350

MTAPA		98.88 56	98.64 73	99.61 9	99.67 42	99.36 10	98.43 109	99.20 174	98.83 90	98.89 145	98.90 152	96.98 149	99.92 35	97.16 134	99.70 127	99.56 74

LCM-MVSNet-Re		98.64 93	98.48 96	99.11 109	98.85 227	98.51 102	98.49 101	99.83 4	98.37 111	99.69 17	99.46 46	98.21 57	99.92 35	94.13 287	99.30 238	98.91 264

lessismore_v0				98.97 135	99.73 25	97.53 188		86.71 373		99.37 58	99.52 38	89.93 294	99.92 35	98.99 35	99.72 117	99.44 138

OurMVSNet-221017-0		99.37 21	99.31 22	99.53 36	99.91 3	98.98 65	99.63 6	99.58 28	99.44 29	99.78 10	99.76 6	96.39 182	99.92 35	99.44 13	99.92 37	99.68 33

GeoE		99.05 36	98.99 42	99.25 91	99.44 102	98.35 115	98.73 76	99.56 42	98.42 110	98.91 141	98.81 179	98.94 18	99.91 45	98.35 74	99.73 110	99.49 109

Fast-Effi-MVS+		97.67 193	97.38 203	98.57 189	98.71 252	97.43 193	97.23 225	99.45 81	94.82 288	96.13 324	96.51 333	98.52 36	99.91 45	96.19 218	98.83 292	98.37 309

jason		97.45 210	97.35 206	97.76 251	99.24 136	93.93 297	95.86 301	98.42 289	94.24 300	98.50 201	98.13 264	94.82 235	99.91 45	97.22 131	99.73 110	99.43 142
jason: jason.
lupinMVS		97.06 239	96.86 234	97.65 257	98.88 222	93.89 301	95.48 317	97.97 307	93.53 312	98.16 221	97.58 301	93.81 259	99.91 45	96.77 172	99.57 181	99.17 225

thisisatest0530		95.27 295	94.45 305	97.74 253	99.19 150	94.37 283	97.86 167	90.20 369	97.17 216	98.22 218	97.65 297	73.53 368	99.90 49	96.90 161	99.35 229	98.95 255

xiu_mvs_v2_base		97.16 233	97.49 195	96.17 313	98.54 283	92.46 323	95.45 318	98.84 254	97.25 206	97.48 271	96.49 334	98.31 49	99.90 49	96.34 210	98.68 301	96.15 361

PS-MVSNAJ		97.08 237	97.39 202	96.16 315	98.56 280	92.46 323	95.24 323	98.85 253	97.25 206	97.49 270	95.99 343	98.07 66	99.90 49	96.37 207	98.67 302	96.12 362

DSMNet-mixed		97.42 212	97.60 190	96.87 297	99.15 165	91.46 334	98.54 93	99.12 201	92.87 321	97.58 261	99.63 20	96.21 189	99.90 49	95.74 239	99.54 189	99.27 201

DROMVSNet		99.09 34	99.05 37	99.20 96	99.28 129	98.93 70	99.24 34	99.84 3	99.08 69	98.12 225	98.37 246	98.72 26	99.90 49	99.05 31	99.77 93	98.77 283

MIMVSNet1		99.38 20	99.32 21	99.55 26	99.86 11	99.19 37	99.41 12	99.59 26	99.59 20	99.71 14	99.57 28	97.12 140	99.90 49	99.21 23	99.87 55	99.54 86

QAPM		97.31 219	96.81 239	98.82 154	98.80 240	97.49 189	99.06 55	99.19 179	90.22 347	97.69 253	99.16 90	96.91 152	99.90 49	90.89 346	99.41 219	99.07 234

EPP-MVSNet		98.30 138	98.04 157	99.07 118	99.56 64	97.83 166	99.29 26	98.07 304	99.03 73	98.59 188	99.13 97	92.16 282	99.90 49	96.87 164	99.68 138	99.49 109

3Dnovator	98.27 2	98.81 63	98.73 58	99.05 125	98.76 243	97.81 171	99.25 33	99.30 144	98.57 105	98.55 196	99.33 66	97.95 78	99.90 49	97.16 134	99.67 144	99.44 138

OpenMVS	96.65 7	97.09 236	96.68 246	98.32 216	98.32 301	97.16 211	98.86 70	99.37 105	89.48 351	96.29 323	99.15 94	96.56 173	99.90 49	92.90 314	99.20 252	97.89 324

MSC_two_6792asdad				99.32 78	98.43 293	98.37 111		98.86 250					99.89 59	97.14 138	99.60 167	99.71 26

No_MVS				99.32 78	98.43 293	98.37 111		98.86 250					99.89 59	97.14 138	99.60 167	99.71 26

DPE-MVS		98.59 103	98.26 130	99.57 18	99.27 131	99.15 48	97.01 240	99.39 99	97.67 161	99.44 47	98.99 131	97.53 111	99.89 59	95.40 253	99.68 138	99.66 36
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part1		97.91 170	97.46 200	99.27 86	98.80 240	98.18 127	99.07 53	99.36 109	99.75 5	99.63 25	99.49 42	82.20 348	99.89 59	98.87 41	99.95 16	99.74 24

CANet		97.87 176	97.76 175	98.19 227	97.75 330	95.51 254	96.76 258	99.05 213	97.74 157	96.93 293	98.21 260	95.59 214	99.89 59	97.86 102	99.93 28	99.19 219

APDe-MVS		98.99 40	98.79 53	99.60 13	99.21 143	99.15 48	98.87 68	99.48 70	97.57 170	99.35 62	99.24 76	97.83 83	99.89 59	97.88 100	99.70 127	99.75 22

PGM-MVS		98.66 90	98.37 116	99.55 26	99.53 72	99.18 38	98.23 123	99.49 68	97.01 224	98.69 173	98.88 161	98.00 72	99.89 59	95.87 233	99.59 171	99.58 64

abl_6		98.99 40	98.78 54	99.61 9	99.45 100	99.46 4	98.60 86	99.50 60	98.59 101	99.24 85	99.04 115	98.54 35	99.89 59	96.45 202	99.62 158	99.50 105

mPP-MVS		98.64 93	98.34 120	99.54 29	99.54 70	99.17 39	98.63 83	99.24 168	97.47 179	98.09 229	98.68 199	97.62 102	99.89 59	96.22 216	99.62 158	99.57 69

CP-MVS		98.70 81	98.42 108	99.52 41	99.36 117	99.12 57	98.72 77	99.36 109	97.54 174	98.30 214	98.40 240	97.86 81	99.89 59	96.53 197	99.72 117	99.56 74

IB-MVS	91.63 19	92.24 334	90.90 338	96.27 310	97.22 351	91.24 341	94.36 347	93.33 360	92.37 326	92.24 365	94.58 364	66.20 378	99.89 59	93.16 312	94.63 364	97.66 338
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
DVP-MVS++		98.90 54	98.70 65	99.51 45	98.43 293	99.15 48	99.43 10	99.32 128	98.17 131	99.26 80	99.02 119	98.18 59	99.88 70	97.07 144	99.45 214	99.49 109

SED-MVS		98.91 52	98.72 60	99.49 49	99.49 86	99.17 39	98.10 137	99.31 134	98.03 138	99.66 20	99.02 119	98.36 44	99.88 70	96.91 156	99.62 158	99.41 148

test_241102_TWO								99.30 144	98.03 138	99.26 80	99.02 119	97.51 114	99.88 70	96.91 156	99.60 167	99.66 36

ETV-MVS		98.03 161	97.86 171	98.56 193	98.69 260	98.07 140	97.51 205	99.50 60	98.10 135	97.50 269	95.51 351	98.41 41	99.88 70	96.27 214	99.24 247	97.71 337

DVP-MVS		98.77 70	98.52 87	99.52 41	99.50 79	99.21 27	98.02 150	98.84 254	97.97 142	99.08 106	99.02 119	97.61 103	99.88 70	96.99 150	99.63 155	99.48 119
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
test_0728_THIRD									98.17 131	99.08 106	99.02 119	97.89 79	99.88 70	97.07 144	99.71 122	99.70 31

test_0728_SECOND				99.60 13	99.50 79	99.23 25	98.02 150	99.32 128					99.88 70	96.99 150	99.63 155	99.68 33

MVS_0304		97.64 195	97.35 206	98.52 198	97.87 326	96.69 228	98.59 88	98.05 306	97.44 188	93.74 361	98.85 168	93.69 263	99.88 70	98.11 84	99.81 72	98.98 249

MP-MVS-pluss		98.57 104	98.23 134	99.60 13	99.69 40	99.35 12	97.16 235	99.38 101	94.87 287	98.97 129	98.99 131	98.01 71	99.88 70	97.29 128	99.70 127	99.58 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		98.40 129	98.00 160	99.61 9	99.57 57	99.25 23	98.57 90	99.35 115	97.55 173	99.31 73	97.71 293	94.61 242	99.88 70	96.14 222	99.19 256	99.70 31
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
region2R		98.69 83	98.40 110	99.54 29	99.53 72	99.17 39	98.52 95	99.31 134	97.46 184	98.44 204	98.51 228	97.83 83	99.88 70	96.46 201	99.58 177	99.58 64

VPA-MVSNet		99.30 24	99.30 23	99.28 83	99.49 86	98.36 114	99.00 59	99.45 81	99.63 14	99.52 35	99.44 51	98.25 50	99.88 70	99.09 28	99.84 59	99.62 46

ACMMPR		98.70 81	98.42 108	99.54 29	99.52 74	99.14 52	98.52 95	99.31 134	97.47 179	98.56 194	98.54 224	97.75 90	99.88 70	96.57 189	99.59 171	99.58 64

MP-MVS		98.46 121	98.09 151	99.54 29	99.57 57	99.22 26	98.50 100	99.19 179	97.61 167	97.58 261	98.66 204	97.40 124	99.88 70	94.72 267	99.60 167	99.54 86
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CHOSEN 1792x2688		97.49 205	97.14 220	98.54 197	99.68 41	96.09 241	96.50 271	99.62 22	91.58 335	98.84 155	98.97 137	92.36 280	99.88 70	96.76 173	99.95 16	99.67 35

SteuartSystems-ACMMP		98.79 65	98.54 85	99.54 29	99.73 25	99.16 43	98.23 123	99.31 134	97.92 146	98.90 142	98.90 152	98.00 72	99.88 70	96.15 221	99.72 117	99.58 64
Skip Steuart: Steuart Systems R&D Blog.
FMVSNet5		96.01 279	95.20 294	98.41 209	97.53 340	96.10 239	98.74 74	99.50 60	97.22 215	98.03 235	99.04 115	69.80 370	99.88 70	97.27 129	99.71 122	99.25 205

ZNCC-MVS		98.68 87	98.40 110	99.54 29	99.57 57	99.21 27	98.46 106	99.29 151	97.28 203	98.11 227	98.39 242	98.00 72	99.87 87	96.86 166	99.64 152	99.55 82

SR-MVS		98.71 78	98.43 106	99.57 18	99.18 157	99.35 12	98.36 115	99.29 151	98.29 119	98.88 149	98.85 168	97.53 111	99.87 87	96.14 222	99.31 235	99.48 119

pmmvs6		99.67 3	99.70 3	99.60 13	99.90 4	99.27 21	99.53 7	99.76 8	99.64 12	99.84 8	99.83 2	99.50 5	99.87 87	99.36 14	99.92 37	99.64 41

HPM-MVS		98.79 65	98.53 86	99.59 17	99.65 45	99.29 18	99.16 44	99.43 90	96.74 234	98.61 184	98.38 244	98.62 30	99.87 87	96.47 200	99.67 144	99.59 58
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPNet		96.14 277	95.44 286	98.25 223	90.76 377	95.50 255	97.92 160	94.65 349	98.97 79	92.98 362	98.85 168	89.12 300	99.87 87	95.99 226	99.68 138	99.39 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPMNet		97.02 243	96.93 228	97.30 279	97.71 332	94.22 285	98.11 135	99.30 144	99.37 35	96.91 296	99.34 64	86.72 313	99.87 87	97.53 118	97.36 339	97.81 330

ACMMP		98.75 73	98.50 91	99.52 41	99.56 64	99.16 43	98.87 68	99.37 105	97.16 217	98.82 160	99.01 128	97.71 93	99.87 87	96.29 213	99.69 133	99.54 86
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
test1111		96.49 267	96.82 238	95.52 327	99.42 107	87.08 358	99.22 35	87.14 372	99.11 57	99.46 43	99.58 27	88.69 303	99.86 94	98.80 45	99.95 16	99.62 46

KD-MVS_self_test		99.25 27	99.18 28	99.44 57	99.63 50	99.06 64	98.69 80	99.54 50	99.31 40	99.62 27	99.53 36	97.36 127	99.86 94	99.24 22	99.71 122	99.39 157

ZD-MVS					99.01 194	98.84 74		99.07 208	94.10 304	98.05 233	98.12 267	96.36 186	99.86 94	92.70 322	99.19 256

test1172		98.76 71	98.49 94	99.57 18	99.18 157	99.37 9	98.39 112	99.31 134	98.43 109	98.90 142	98.88 161	97.49 118	99.86 94	96.43 204	99.37 226	99.48 119

SR-MVS-dyc-post		98.81 63	98.55 84	99.57 18	99.20 147	99.38 6	98.48 104	99.30 144	98.64 95	98.95 132	98.96 140	97.49 118	99.86 94	96.56 192	99.39 222	99.45 133

testtj		97.79 187	97.25 211	99.42 58	99.03 190	98.85 73	97.78 173	99.18 183	95.83 266	98.12 225	98.50 231	95.50 218	99.86 94	92.23 328	99.07 272	99.54 86

tfpnnormal		98.90 54	98.90 44	98.91 143	99.67 42	97.82 169	99.00 59	99.44 84	99.45 28	99.51 38	99.24 76	98.20 58	99.86 94	95.92 229	99.69 133	99.04 240

Regformer-4		98.73 76	98.68 68	98.89 146	99.02 192	97.22 204	97.17 233	99.06 209	99.21 46	99.17 96	98.85 168	97.45 121	99.86 94	98.48 66	99.70 127	99.60 52

UniMVSNet (Re)		98.87 57	98.71 62	99.35 70	99.24 136	98.73 84	97.73 181	99.38 101	98.93 84	99.12 98	98.73 190	96.77 162	99.86 94	98.63 57	99.80 80	99.46 129

NR-MVSNet		98.95 48	98.82 50	99.36 65	99.16 161	98.72 86	99.22 35	99.20 174	99.10 64	99.72 13	98.76 187	96.38 184	99.86 94	98.00 94	99.82 68	99.50 105

GBi-Net		98.65 91	98.47 98	99.17 99	98.90 216	98.24 120	99.20 38	99.44 84	98.59 101	98.95 132	99.55 32	94.14 252	99.86 94	97.77 106	99.69 133	99.41 148

test1		98.65 91	98.47 98	99.17 99	98.90 216	98.24 120	99.20 38	99.44 84	98.59 101	98.95 132	99.55 32	94.14 252	99.86 94	97.77 106	99.69 133	99.41 148

FMVSNet1		99.17 30	99.17 29	99.17 99	99.55 67	98.24 120	99.20 38	99.44 84	99.21 46	99.43 48	99.55 32	97.82 86	99.86 94	98.42 70	99.89 51	99.41 148

XXY-MVS		99.14 32	99.15 32	99.10 111	99.76 23	97.74 177	98.85 71	99.62 22	98.48 108	99.37 58	99.49 42	98.75 24	99.86 94	98.20 81	99.80 80	99.71 26

1112_ss		97.29 222	96.86 234	98.58 186	99.34 123	96.32 235	96.75 259	99.58 28	93.14 317	96.89 300	97.48 308	92.11 283	99.86 94	96.91 156	99.54 189	99.57 69

EGC-MVSNET		85.24 338	80.54 341	99.34 73	99.77 20	99.20 33	99.08 50	99.29 151	12.08 374	20.84 375	99.42 52	97.55 108	99.85 109	97.08 143	99.72 117	98.96 254

GST-MVS		98.61 98	98.30 125	99.52 41	99.51 76	99.20 33	98.26 121	99.25 163	97.44 188	98.67 175	98.39 242	97.68 94	99.85 109	96.00 225	99.51 199	99.52 98

patchmatchnet-post											98.77 185	84.37 333	99.85 109

SCA		96.41 271	96.66 249	95.67 322	98.24 306	88.35 351	95.85 303	96.88 333	96.11 255	97.67 254	98.67 201	93.10 269	99.85 109	94.16 282	99.22 249	98.81 275

FC-MVSNet-test		99.27 25	99.25 25	99.34 73	99.77 20	98.37 111	99.30 25	99.57 35	99.61 19	99.40 53	99.50 39	97.12 140	99.85 109	99.02 33	99.94 24	99.80 12

HFP-MVS		98.71 78	98.44 104	99.51 45	99.49 86	99.16 43	98.52 95	99.31 134	97.47 179	98.58 190	98.50 231	97.97 76	99.85 109	96.57 189	99.59 171	99.53 94

#test#		98.50 117	98.16 144	99.51 45	99.49 86	99.16 43	98.03 148	99.31 134	96.30 251	98.58 190	98.50 231	97.97 76	99.85 109	95.68 243	99.59 171	99.53 94

EI-MVSNet-UG-set		98.69 83	98.71 62	98.62 181	99.10 173	96.37 233	97.23 225	98.87 245	99.20 49	99.19 91	98.99 131	97.30 129	99.85 109	98.77 49	99.79 85	99.65 40

EI-MVSNet-Vis-set		98.68 87	98.70 65	98.63 179	99.09 176	96.40 232	97.23 225	98.86 250	99.20 49	99.18 95	98.97 137	97.29 131	99.85 109	98.72 52	99.78 89	99.64 41

v1240		98.55 109	98.62 75	98.32 216	99.22 141	95.58 251	97.51 205	99.45 81	97.16 217	99.45 46	99.24 76	96.12 191	99.85 109	99.60 4	99.88 52	99.55 82

APD-MVS_3200maxsize		98.84 60	98.61 78	99.53 36	99.19 150	99.27 21	98.49 101	99.33 126	98.64 95	99.03 119	98.98 135	97.89 79	99.85 109	96.54 196	99.42 218	99.46 129

ADS-MVSNet2		95.43 293	94.98 299	96.76 303	98.14 312	91.74 331	97.92 160	97.76 311	90.23 345	96.51 316	98.91 149	85.61 323	99.85 109	92.88 315	96.90 345	98.69 292

MDA-MVSNet-bldmvs		97.94 169	97.91 167	98.06 236	99.44 102	94.96 271	96.63 265	99.15 198	98.35 112	98.83 156	99.11 100	94.31 249	99.85 109	96.60 186	98.72 297	99.37 167

WR-MVS		98.40 129	98.19 139	99.03 128	99.00 195	97.65 182	96.85 252	98.94 232	98.57 105	98.89 145	98.50 231	95.60 213	99.85 109	97.54 117	99.85 57	99.59 58

RRT_test8_iter05		95.24 296	95.13 296	95.57 325	97.32 348	87.02 359	97.99 154	99.41 94	98.06 137	99.12 98	99.05 112	66.85 375	99.85 109	98.93 37	99.47 210	99.84 8

APD-MVS		98.10 157	97.67 181	99.42 58	99.11 169	98.93 70	97.76 178	99.28 154	94.97 284	98.72 172	98.77 185	97.04 143	99.85 109	93.79 298	99.54 189	99.49 109
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmtry		97.35 216	96.97 227	98.50 202	97.31 349	96.47 231	98.18 128	98.92 237	98.95 83	98.78 163	99.37 58	85.44 326	99.85 109	95.96 228	99.83 65	99.17 225

N_pmnet		97.63 197	97.17 216	98.99 134	99.27 131	97.86 163	95.98 292	93.41 359	95.25 280	99.47 42	98.90 152	95.63 212	99.85 109	96.91 156	99.73 110	99.27 201

test2506		92.39 331	91.89 334	93.89 344	99.38 112	82.28 373	99.32 17	66.03 380	99.08 69	98.77 166	99.57 28	66.26 377	99.84 127	98.71 53	99.95 16	99.54 86

our_test_3		97.39 214	97.73 179	96.34 308	98.70 256	89.78 346	94.61 341	98.97 231	96.50 242	99.04 116	98.85 168	95.98 200	99.84 127	97.26 130	99.67 144	99.41 148

CANet_DTU		97.26 223	97.06 222	97.84 246	97.57 337	94.65 279	96.19 288	98.79 263	97.23 212	95.14 347	98.24 257	93.22 266	99.84 127	97.34 126	99.84 59	99.04 240

ACMMP_NAP		98.75 73	98.48 96	99.57 18	99.58 53	99.29 18	97.82 171	99.25 163	96.94 226	98.78 163	99.12 98	98.02 70	99.84 127	97.13 140	99.67 144	99.59 58

v144192		98.54 112	98.57 83	98.45 206	99.21 143	95.98 242	97.63 190	99.36 109	97.15 219	99.32 71	99.18 84	95.84 207	99.84 127	99.50 10	99.91 43	99.54 86

v1921920		98.54 112	98.60 80	98.38 212	99.20 147	95.76 250	97.56 199	99.36 109	97.23 212	99.38 56	99.17 88	96.02 194	99.84 127	99.57 6	99.90 47	99.54 86

Regformer-2		98.60 100	98.46 100	99.02 131	98.85 227	97.71 179	96.91 249	99.09 205	98.98 78	99.01 120	98.64 209	97.37 126	99.84 127	97.75 111	99.57 181	99.52 98

HPM-MVS++		98.10 157	97.64 186	99.48 51	99.09 176	99.13 55	97.52 203	98.75 270	97.46 184	96.90 299	97.83 287	96.01 195	99.84 127	95.82 237	99.35 229	99.46 129

PMMVS2		98.07 160	98.08 154	98.04 238	99.41 109	94.59 281	94.59 342	99.40 97	97.50 176	98.82 160	98.83 174	96.83 157	99.84 127	97.50 120	99.81 72	99.71 26

XVG-ACMP-BASELINE		98.56 105	98.34 120	99.22 95	99.54 70	98.59 94	97.71 182	99.46 78	97.25 206	98.98 126	98.99 131	97.54 109	99.84 127	95.88 230	99.74 107	99.23 209

CPTT-MVS		97.84 183	97.36 205	99.27 86	99.31 124	98.46 105	98.29 118	99.27 157	94.90 286	97.83 244	98.37 246	94.90 231	99.84 127	93.85 297	99.54 189	99.51 101

UGNet		98.53 114	98.45 102	98.79 160	97.94 322	96.96 218	99.08 50	98.54 283	99.10 64	96.82 305	99.47 45	96.55 174	99.84 127	98.56 63	99.94 24	99.55 82
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
CSCG		98.68 87	98.50 91	99.20 96	99.45 100	98.63 89	98.56 91	99.57 35	97.87 150	98.85 153	98.04 274	97.66 96	99.84 127	96.72 178	99.81 72	99.13 229

DeepC-MVS	97.60 4	98.97 45	98.93 43	99.10 111	99.35 121	97.98 150	98.01 153	99.46 78	97.56 172	99.54 30	99.50 39	98.97 16	99.84 127	98.06 89	99.92 37	99.49 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	97.89 3	98.69 83	98.51 89	99.24 93	98.81 238	98.40 107	99.02 56	99.19 179	98.99 76	98.07 230	99.28 69	97.11 142	99.84 127	96.84 167	99.32 233	99.47 127

CS-MVS-test		98.41 126	98.30 125	98.73 172	98.84 230	98.39 108	98.71 79	99.79 5	97.98 140	96.86 302	97.38 314	97.86 81	99.83 142	97.81 103	99.46 211	97.97 322

Anonymous20231211		99.27 25	99.27 24	99.26 89	99.29 128	98.18 127	99.49 8	99.51 58	99.70 8	99.80 9	99.68 14	96.84 155	99.83 142	99.21 23	99.91 43	99.77 16

Anonymous20231206		98.21 149	98.21 136	98.20 226	99.51 76	95.43 258	98.13 132	99.32 128	96.16 254	98.93 139	98.82 177	96.00 196	99.83 142	97.32 127	99.73 110	99.36 173

XVS		98.72 77	98.45 102	99.53 36	99.46 97	99.21 27	98.65 81	99.34 121	98.62 99	97.54 265	98.63 213	97.50 115	99.83 142	96.79 169	99.53 193	99.56 74

X-MVStestdata		94.32 308	92.59 326	99.53 36	99.46 97	99.21 27	98.65 81	99.34 121	98.62 99	97.54 265	45.85 372	97.50 115	99.83 142	96.79 169	99.53 193	99.56 74

v10		98.97 45	99.11 33	98.55 194	99.44 102	96.21 238	98.90 66	99.55 46	98.73 93	99.48 40	99.60 25	96.63 171	99.83 142	99.70 3	99.99 5	99.61 51

TransMVSNet (Re)		99.44 13	99.47 12	99.36 65	99.80 17	98.58 95	99.27 32	99.57 35	99.39 33	99.75 12	99.62 21	99.17 12	99.83 142	99.06 30	99.62 158	99.66 36

Baseline_NR-MVSNet		98.98 44	98.86 47	99.36 65	99.82 16	98.55 97	97.47 209	99.57 35	99.37 35	99.21 89	99.61 23	96.76 164	99.83 142	98.06 89	99.83 65	99.71 26

LPG-MVS_test		98.71 78	98.46 100	99.47 54	99.57 57	98.97 66	98.23 123	99.48 70	96.60 239	99.10 103	99.06 105	98.71 27	99.83 142	95.58 249	99.78 89	99.62 46

LGP-MVS_train				99.47 54	99.57 57	98.97 66		99.48 70	96.60 239	99.10 103	99.06 105	98.71 27	99.83 142	95.58 249	99.78 89	99.62 46

Test_1112_low_res		96.99 247	96.55 257	98.31 218	99.35 121	95.47 256	95.84 304	99.53 54	91.51 337	96.80 306	98.48 236	91.36 287	99.83 142	96.58 187	99.53 193	99.62 46

ECVR-MVS		96.42 270	96.61 251	95.85 318	99.38 112	88.18 353	99.22 35	86.00 374	99.08 69	99.36 60	99.57 28	88.47 308	99.82 153	98.52 64	99.95 16	99.54 86

xxxxxxxxxxxxxcwj		98.44 123	98.24 132	99.06 123	99.11 169	97.97 151	96.53 268	99.54 50	98.24 122	98.83 156	98.90 152	97.80 87	99.82 153	95.68 243	99.52 196	99.38 164

SF-MVS		98.53 114	98.27 129	99.32 78	99.31 124	98.75 80	98.19 127	99.41 94	96.77 233	98.83 156	98.90 152	97.80 87	99.82 153	95.68 243	99.52 196	99.38 164

new-patchmatchnet		98.35 134	98.74 57	97.18 283	99.24 136	92.23 328	96.42 276	99.48 70	98.30 116	99.69 17	99.53 36	97.44 122	99.82 153	98.84 43	99.77 93	99.49 109

FIs		99.14 32	99.09 34	99.29 81	99.70 38	98.28 117	99.13 47	99.52 57	99.48 24	99.24 85	99.41 55	96.79 161	99.82 153	98.69 55	99.88 52	99.76 20

v1192		98.60 100	98.66 71	98.41 209	99.27 131	95.88 245	97.52 203	99.36 109	97.41 190	99.33 65	99.20 81	96.37 185	99.82 153	99.57 6	99.92 37	99.55 82

pm-mvs1		99.44 13	99.48 11	99.33 76	99.80 17	98.63 89	99.29 26	99.63 21	99.30 42	99.65 22	99.60 25	99.16 14	99.82 153	99.07 29	99.83 65	99.56 74

VPNet		98.87 57	98.83 49	99.01 132	99.70 38	97.62 185	98.43 109	99.35 115	99.47 26	99.28 74	99.05 112	96.72 167	99.82 153	98.09 87	99.36 227	99.59 58

pmmvs3		95.03 300	94.40 306	96.93 293	97.70 334	92.53 322	95.08 327	97.71 313	88.57 356	97.71 251	98.08 272	79.39 357	99.82 153	96.19 218	99.11 270	98.43 305

HPM-MVS_fast		99.01 38	98.82 50	99.57 18	99.71 32	99.35 12	99.00 59	99.50 60	97.33 197	98.94 138	98.86 165	98.75 24	99.82 153	97.53 118	99.71 122	99.56 74

DELS-MVS		98.27 142	98.20 137	98.48 203	98.86 225	96.70 227	95.60 312	99.20 174	97.73 158	98.45 203	98.71 193	97.50 115	99.82 153	98.21 80	99.59 171	98.93 260
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
FMVSNet2		98.49 118	98.40 110	98.75 168	98.90 216	97.14 213	98.61 85	99.13 199	98.59 101	99.19 91	99.28 69	94.14 252	99.82 153	97.97 95	99.80 80	99.29 198

WTY-MVS		96.67 259	96.27 267	97.87 245	98.81 238	94.61 280	96.77 257	97.92 309	94.94 285	97.12 284	97.74 292	91.11 288	99.82 153	93.89 294	98.15 319	99.18 221

ACMP	95.32 15	98.41 126	98.09 151	99.36 65	99.51 76	98.79 79	97.68 185	99.38 101	95.76 268	98.81 162	98.82 177	98.36 44	99.82 153	94.75 264	99.77 93	99.48 119
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETH3D cwj APD-0.16		97.55 201	97.00 225	99.19 98	98.51 286	98.64 88	96.85 252	99.13 199	94.19 302	97.65 255	98.40 240	95.78 208	99.81 167	93.37 309	99.16 259	99.12 230

ET-MVSNet_ETH3D		94.30 310	93.21 320	97.58 263	98.14 312	94.47 282	94.78 334	93.24 361	94.72 289	89.56 369	95.87 346	78.57 361	99.81 167	96.91 156	97.11 344	98.46 301

TSAR-MVS + MP.		98.63 95	98.49 94	99.06 123	99.64 48	97.90 160	98.51 99	98.94 232	96.96 225	99.24 85	98.89 160	97.83 83	99.81 167	96.88 163	99.49 207	99.48 119
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Regformer-1		98.55 109	98.44 104	98.87 148	98.85 227	97.29 198	96.91 249	98.99 229	98.97 79	98.99 124	98.64 209	97.26 135	99.81 167	97.79 104	99.57 181	99.51 101

v8		99.01 38	99.16 30	98.57 189	99.47 96	96.31 236	98.90 66	99.47 76	99.03 73	99.52 35	99.57 28	96.93 151	99.81 167	99.60 4	99.98 9	99.60 52

CR-MVSNet		96.28 274	95.95 271	97.28 280	97.71 332	94.22 285	98.11 135	98.92 237	92.31 327	96.91 296	99.37 58	85.44 326	99.81 167	97.39 124	97.36 339	97.81 330

PatchT		96.65 260	96.35 262	97.54 268	97.40 345	95.32 260	97.98 156	96.64 336	99.33 39	96.89 300	99.42 52	84.32 334	99.81 167	97.69 114	97.49 332	97.48 344

FMVSNet3		97.50 203	97.24 213	98.29 220	98.08 316	95.83 247	97.86 167	98.91 239	97.89 149	98.95 132	98.95 144	87.06 311	99.81 167	97.77 106	99.69 133	99.23 209

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 24	99.85 13	99.11 59	99.90 1	99.78 6	99.63 14	99.78 10	99.67 16	99.48 6	99.81 167	99.30 17	99.97 11	99.77 16
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
EIA-MVS		98.00 165	97.74 177	98.80 158	98.72 249	98.09 134	98.05 145	99.60 25	97.39 192	96.63 310	95.55 350	97.68 94	99.80 176	96.73 177	99.27 242	98.52 299

CS-MVS		98.16 156	98.22 135	97.97 242	98.56 280	97.01 217	98.10 137	99.70 14	97.45 186	97.29 280	97.19 320	97.72 92	99.80 176	98.37 72	99.62 158	97.11 349

Anonymous20240529		98.93 50	98.87 45	99.12 107	99.19 150	98.22 125	99.01 57	98.99 229	99.25 45	99.54 30	99.37 58	97.04 143	99.80 176	97.89 97	99.52 196	99.35 177

thisisatest0515		94.12 314	93.16 321	96.97 292	98.60 274	92.90 316	93.77 356	90.61 367	94.10 304	96.91 296	95.87 346	74.99 366	99.80 176	94.52 271	99.12 269	98.20 312

Effi-MVS+		98.02 163	97.82 173	98.62 181	98.53 285	97.19 208	97.33 218	99.68 16	97.30 201	96.68 308	97.46 310	98.56 34	99.80 176	96.63 185	98.20 315	98.86 269

v1144		98.60 100	98.66 71	98.41 209	99.36 117	95.90 244	97.58 197	99.34 121	97.51 175	99.27 76	99.15 94	96.34 187	99.80 176	99.47 12	99.93 28	99.51 101

VDDNet		98.21 149	97.95 163	99.01 132	99.58 53	97.74 177	99.01 57	97.29 324	99.67 10	98.97 129	99.50 39	90.45 291	99.80 176	97.88 100	99.20 252	99.48 119

EI-MVSNet		98.40 129	98.51 89	98.04 238	99.10 173	94.73 275	97.20 229	98.87 245	98.97 79	99.06 109	99.02 119	96.00 196	99.80 176	98.58 58	99.82 68	99.60 52

CVMVSNet		96.25 275	97.21 215	93.38 350	99.10 173	80.56 376	97.20 229	98.19 300	96.94 226	99.00 123	99.02 119	89.50 298	99.80 176	96.36 209	99.59 171	99.78 14

MVSTER		96.86 251	96.55 257	97.79 249	97.91 324	94.21 287	97.56 199	98.87 245	97.49 178	99.06 109	99.05 112	80.72 350	99.80 176	98.44 68	99.82 68	99.37 167

sss		97.21 228	96.93 228	98.06 236	98.83 233	95.22 264	96.75 259	98.48 287	94.49 292	97.27 281	97.90 283	92.77 276	99.80 176	96.57 189	99.32 233	99.16 228

ab-mvs		98.41 126	98.36 117	98.59 185	99.19 150	97.23 202	99.32 17	98.81 260	97.66 162	98.62 182	99.40 57	96.82 158	99.80 176	95.88 230	99.51 199	98.75 286

TDRefinement		99.42 16	99.38 15	99.55 26	99.76 23	99.33 16	99.68 5	99.71 11	99.38 34	99.53 33	99.61 23	98.64 29	99.80 176	98.24 78	99.84 59	99.52 98

LS3D		98.63 95	98.38 115	99.36 65	97.25 350	99.38 6	99.12 49	99.32 128	99.21 46	98.44 204	98.88 161	97.31 128	99.80 176	96.58 187	99.34 231	98.92 261

hse-mvs2		97.46 208	97.07 221	98.64 176	98.73 247	97.33 196	97.45 211	97.64 317	99.11 57	98.58 190	97.98 277	88.65 306	99.79 190	98.11 84	97.39 336	98.81 275

AUN-MVS		96.24 276	95.45 285	98.60 184	98.70 256	97.22 204	97.38 214	97.65 315	95.95 262	95.53 342	97.96 281	82.11 349	99.79 190	96.31 211	97.44 334	98.80 280

SMA-MVS		98.40 129	98.03 158	99.51 45	99.16 161	99.21 27	98.05 145	99.22 171	94.16 303	98.98 126	99.10 102	97.52 113	99.79 190	96.45 202	99.64 152	99.53 94
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
Regformer-3		98.61 98	98.61 78	98.63 179	99.02 192	96.53 230	97.17 233	98.84 254	99.13 56	99.10 103	98.85 168	97.24 136	99.79 190	98.41 71	99.70 127	99.57 69

testdata2													99.79 190	92.80 319

VDD-MVS		98.56 105	98.39 113	99.07 118	99.13 168	98.07 140	98.59 88	97.01 328	99.59 20	99.11 100	99.27 71	94.82 235	99.79 190	98.34 75	99.63 155	99.34 179

v2v482		98.56 105	98.62 75	98.37 213	99.42 107	95.81 248	97.58 197	99.16 192	97.90 148	99.28 74	99.01 128	95.98 200	99.79 190	99.33 15	99.90 47	99.51 101

mvs_anonymous		97.83 185	98.16 144	96.87 297	98.18 310	91.89 330	97.31 220	98.90 240	97.37 194	98.83 156	99.46 46	96.28 188	99.79 190	98.90 38	98.16 318	98.95 255

tpm		94.67 304	94.34 308	95.66 323	97.68 336	88.42 350	97.88 164	94.90 348	94.46 294	96.03 330	98.56 223	78.66 359	99.79 190	95.88 230	95.01 362	98.78 282

IS-MVSNet		98.19 151	97.90 168	99.08 115	99.57 57	97.97 151	99.31 21	98.32 293	99.01 75	98.98 126	99.03 118	91.59 286	99.79 190	95.49 251	99.80 80	99.48 119

test_0402		98.76 71	98.71 62	98.93 140	99.56 64	98.14 132	98.45 108	99.34 121	99.28 43	98.95 132	98.91 149	98.34 48	99.79 190	95.63 246	99.91 43	98.86 269

ACMM	96.08 12	98.91 52	98.73 58	99.48 51	99.55 67	99.14 52	98.07 141	99.37 105	97.62 165	99.04 116	98.96 140	98.84 20	99.79 190	97.43 122	99.65 150	99.49 109
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_lstm_enhance		97.18 231	97.16 217	97.25 282	98.16 311	92.85 317	95.15 326	99.31 134	97.25 206	98.74 171	98.78 183	90.07 293	99.78 202	97.19 132	99.80 80	99.11 232

Anonymous202405211		97.90 171	97.50 194	99.08 115	98.90 216	98.25 119	98.53 94	96.16 340	98.87 86	99.11 100	98.86 165	90.40 292	99.78 202	97.36 125	99.31 235	99.19 219

ppachtmachnet_test		97.50 203	97.74 177	96.78 302	98.70 256	91.23 342	94.55 343	99.05 213	96.36 247	99.21 89	98.79 182	96.39 182	99.78 202	96.74 175	99.82 68	99.34 179

新几何1				98.91 143	98.94 206	97.76 174		98.76 267	87.58 360	96.75 307	98.10 269	94.80 238	99.78 202	92.73 321	99.00 283	99.20 214

V42		98.78 68	98.78 54	98.76 166	99.44 102	97.04 214	98.27 120	99.19 179	97.87 150	99.25 84	99.16 90	96.84 155	99.78 202	99.21 23	99.84 59	99.46 129

VNet		98.42 125	98.30 125	98.79 160	98.79 242	97.29 198	98.23 123	98.66 277	99.31 40	98.85 153	98.80 180	94.80 238	99.78 202	98.13 83	99.13 266	99.31 191

ETH3 D test6400		96.46 269	95.59 281	99.08 115	98.88 222	98.21 126	96.53 268	99.18 183	88.87 355	97.08 287	97.79 288	93.64 264	99.77 208	88.92 353	99.40 221	99.28 199

ETH3D-3000-0.1		98.03 161	97.62 188	99.29 81	99.11 169	98.80 78	97.47 209	99.32 128	95.54 271	98.43 207	98.62 215	96.61 172	99.77 208	93.95 292	99.49 207	99.30 194

agg_prior1		97.06 239	96.40 261	99.03 128	98.68 263	97.99 146	95.76 305	99.01 225	91.73 332	95.59 334	97.50 306	96.49 177	99.77 208	93.71 299	99.14 263	99.34 179

agg_prior					98.68 263	97.99 146		99.01 225		95.59 334			99.77 208

baseline2		93.73 319	92.83 325	96.42 307	97.70 334	91.28 340	96.84 254	89.77 370	93.96 308	92.44 364	95.93 344	79.14 358	99.77 208	92.94 313	96.76 349	98.21 311

PM-MVS		98.82 61	98.72 60	99.12 107	99.64 48	98.54 100	97.98 156	99.68 16	97.62 165	99.34 64	99.18 84	97.54 109	99.77 208	97.79 104	99.74 107	99.04 240

TAMVS		98.24 147	98.05 156	98.80 158	99.07 180	97.18 209	97.88 164	98.81 260	96.66 238	99.17 96	99.21 79	94.81 237	99.77 208	96.96 154	99.88 52	99.44 138

9.14			97.78 174		99.07 180		97.53 202	99.32 128	95.53 273	98.54 198	98.70 196	97.58 105	99.76 215	94.32 281	99.46 211

TEST9					98.71 252	98.08 138	95.96 295	99.03 218	91.40 338	95.85 331	97.53 303	96.52 175	99.76 215

train_agg		97.10 235	96.45 260	99.07 118	98.71 252	98.08 138	95.96 295	99.03 218	91.64 333	95.85 331	97.53 303	96.47 178	99.76 215	93.67 300	99.16 259	99.36 173

test_8					98.67 265	98.01 145	95.91 300	99.02 222	91.64 333	95.79 333	97.50 306	96.47 178	99.76 215

test20.03		98.78 68	98.77 56	98.78 163	99.46 97	97.20 207	97.78 173	99.24 168	99.04 72	99.41 50	98.90 152	97.65 97	99.76 215	97.70 112	99.79 85	99.39 157

EG-PatchMatch MVS		98.99 40	99.01 39	98.94 139	99.50 79	97.47 190	98.04 147	99.59 26	98.15 134	99.40 53	99.36 61	98.58 33	99.76 215	98.78 46	99.68 138	99.59 58

ACMH	96.65 7	99.25 27	99.24 26	99.26 89	99.72 31	98.38 110	99.07 53	99.55 46	98.30 116	99.65 22	99.45 50	99.22 9	99.76 215	98.44 68	99.77 93	99.64 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs5		97.64 195	97.49 195	98.08 234	99.14 166	95.12 268	96.70 262	99.05 213	93.77 309	98.62 182	98.83 174	93.23 265	99.75 222	98.33 77	99.76 103	99.36 173

HY-MVS	95.94 13	95.90 282	95.35 290	97.55 267	97.95 321	94.79 273	98.81 73	96.94 331	92.28 328	95.17 346	98.57 222	89.90 295	99.75 222	91.20 341	97.33 341	98.10 316

DP-MVS		98.93 50	98.81 52	99.28 83	99.21 143	98.45 106	98.46 106	99.33 126	99.63 14	99.48 40	99.15 94	97.23 137	99.75 222	97.17 133	99.66 149	99.63 45

PatchmatchNet		95.58 289	95.67 278	95.30 332	97.34 347	87.32 356	97.65 189	96.65 335	95.30 279	97.07 288	98.69 197	84.77 329	99.75 222	94.97 260	98.64 303	98.83 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		95.24 296	94.93 301	96.18 312	98.14 312	90.10 345	97.92 160	97.32 323	90.23 345	96.51 316	98.91 149	85.61 323	99.74 226	92.88 315	96.90 345	98.69 292

diffmvs		98.22 148	98.24 132	98.17 228	99.00 195	95.44 257	96.38 278	99.58 28	97.79 156	98.53 199	98.50 231	96.76 164	99.74 226	97.95 96	99.64 152	99.34 179

UnsupCasMVSNet_eth		97.89 173	97.60 190	98.75 168	99.31 124	97.17 210	97.62 191	99.35 115	98.72 94	98.76 168	98.68 199	92.57 279	99.74 226	97.76 110	95.60 359	99.34 179

CDS-MVSNet		97.69 191	97.35 206	98.69 173	98.73 247	97.02 216	96.92 248	98.75 270	95.89 264	98.59 188	98.67 201	92.08 284	99.74 226	96.72 178	99.81 72	99.32 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
nrg030		99.40 18	99.35 17	99.54 29	99.58 53	99.13 55	98.98 62	99.48 70	99.68 9	99.46 43	99.26 73	98.62 30	99.73 230	99.17 26	99.92 37	99.76 20

无先验							95.74 307	98.74 272	89.38 352				99.73 230	92.38 326		99.22 213

1121		96.73 256	96.00 269	98.91 143	98.95 205	97.76 174	98.07 141	98.73 273	87.65 359	96.54 313	98.13 264	94.52 244	99.73 230	92.38 326	99.02 280	99.24 208

LFMVS		97.20 229	96.72 243	98.64 176	98.72 249	96.95 219	98.93 65	94.14 356	99.74 7	98.78 163	99.01 128	84.45 332	99.73 230	97.44 121	99.27 242	99.25 205

YYNet1		97.60 198	97.67 181	97.39 277	99.04 187	93.04 315	95.27 321	98.38 292	97.25 206	98.92 140	98.95 144	95.48 220	99.73 230	96.99 150	98.74 295	99.41 148

MDA-MVSNet_test_wron		97.60 198	97.66 184	97.41 276	99.04 187	93.09 311	95.27 321	98.42 289	97.26 205	98.88 149	98.95 144	95.43 221	99.73 230	97.02 147	98.72 297	99.41 148

Vis-MVSNet (Re-imp)		97.46 208	97.16 217	98.34 215	99.55 67	96.10 239	98.94 64	98.44 288	98.32 115	98.16 221	98.62 215	88.76 302	99.73 230	93.88 295	99.79 85	99.18 221

PCF-MVS	92.86 18	94.36 307	93.00 324	98.42 208	98.70 256	97.56 186	93.16 360	99.11 203	79.59 369	97.55 264	97.43 311	92.19 281	99.73 230	79.85 370	99.45 214	97.97 322
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	96.50 10	98.99 40	98.85 48	99.41 61	99.58 53	99.10 60	98.74 74	99.56 42	99.09 67	99.33 65	99.19 82	98.40 42	99.72 238	95.98 227	99.76 103	99.42 145
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
原ACMM1				98.35 214	98.90 216	96.25 237		98.83 259	92.48 325	96.07 328	98.10 269	95.39 222	99.71 239	92.61 324	98.99 284	99.08 233

UnsupCasMVSNet_bld		97.30 220	96.92 230	98.45 206	99.28 129	96.78 226	96.20 287	99.27 157	95.42 276	98.28 216	98.30 254	93.16 267	99.71 239	94.99 259	97.37 337	98.87 268

test_post											21.25 375	83.86 338	99.70 241

testdata				98.09 231	98.93 208	95.40 259		98.80 262	90.08 349	97.45 273	98.37 246	95.26 224	99.70 241	93.58 303	98.95 288	99.17 225

HQP_MVS		97.99 168	97.67 181	98.93 140	99.19 150	97.65 182	97.77 176	99.27 157	98.20 128	97.79 247	97.98 277	94.90 231	99.70 241	94.42 276	99.51 199	99.45 133

plane_prior5								99.27 157					99.70 241	94.42 276	99.51 199	99.45 133

cl____		97.02 243	96.83 237	97.58 263	97.82 328	94.04 291	94.66 338	99.16 192	97.04 222	98.63 180	98.71 193	88.68 305	99.69 245	97.00 148	99.81 72	99.00 247

DIV-MVS_self_test		97.02 243	96.84 236	97.58 263	97.82 328	94.03 292	94.66 338	99.16 192	97.04 222	98.63 180	98.71 193	88.69 303	99.69 245	97.00 148	99.81 72	99.01 244

eth_miper_zixun_eth		97.23 227	97.25 211	97.17 284	98.00 320	92.77 319	94.71 335	99.18 183	97.27 204	98.56 194	98.74 189	91.89 285	99.69 245	97.06 146	99.81 72	99.05 236

D2MVS		97.84 183	97.84 172	97.83 247	99.14 166	94.74 274	96.94 244	98.88 243	95.84 265	98.89 145	98.96 140	94.40 247	99.69 245	97.55 115	99.95 16	99.05 236

Patchmatch-test		96.55 263	96.34 263	97.17 284	98.35 299	93.06 312	98.40 111	97.79 310	97.33 197	98.41 208	98.67 201	83.68 339	99.69 245	95.16 256	99.31 235	98.77 283

CDPH-MVS		97.26 223	96.66 249	99.07 118	99.00 195	98.15 130	96.03 291	99.01 225	91.21 341	97.79 247	97.85 286	96.89 153	99.69 245	92.75 320	99.38 225	99.39 157

test12				98.93 140	98.58 277	97.83 166		98.66 277		96.53 314		95.51 217	99.69 245		99.13 266	99.27 201

casdiffmvs		98.95 48	99.00 40	98.81 156	99.38 112	97.33 196	97.82 171	99.57 35	99.17 54	99.35 62	99.17 88	98.35 47	99.69 245	98.46 67	99.73 110	99.41 148

baseline		98.96 47	99.02 38	98.76 166	99.38 112	97.26 201	98.49 101	99.50 60	98.86 87	99.19 91	99.06 105	98.23 52	99.69 245	98.71 53	99.76 103	99.33 185

EU-MVSNet		97.66 194	98.50 91	95.13 333	99.63 50	85.84 362	98.35 116	98.21 297	98.23 124	99.54 30	99.46 46	95.02 229	99.68 254	98.24 78	99.87 55	99.87 4

F-COLMAP		97.30 220	96.68 246	99.14 105	99.19 150	98.39 108	97.27 224	99.30 144	92.93 319	96.62 311	98.00 275	95.73 210	99.68 254	92.62 323	98.46 309	99.35 177

OpenMVS_ROB	95.38 14	95.84 284	95.18 295	97.81 248	98.41 297	97.15 212	97.37 215	98.62 280	83.86 365	98.65 178	98.37 246	94.29 250	99.68 254	88.41 354	98.62 305	96.60 356

test-LLR		93.90 317	93.85 311	94.04 341	96.53 360	84.62 367	94.05 352	92.39 363	96.17 252	94.12 355	95.07 355	82.30 345	99.67 257	95.87 233	98.18 316	97.82 328

test-mter		92.33 333	91.76 336	94.04 341	96.53 360	84.62 367	94.05 352	92.39 363	94.00 307	94.12 355	95.07 355	65.63 379	99.67 257	95.87 233	98.18 316	97.82 328

thres600view7		94.45 306	93.83 312	96.29 309	99.06 184	91.53 333	97.99 154	94.24 354	98.34 113	97.44 274	95.01 357	79.84 353	99.67 257	84.33 362	98.23 313	97.66 338

114514_t		96.50 266	95.77 273	98.69 173	99.48 94	97.43 193	97.84 169	99.55 46	81.42 368	96.51 316	98.58 221	95.53 215	99.67 257	93.41 308	99.58 177	98.98 249

PVSNet_BlendedMVS		97.55 201	97.53 192	97.60 261	98.92 212	93.77 305	96.64 264	99.43 90	94.49 292	97.62 257	99.18 84	96.82 158	99.67 257	94.73 265	99.93 28	99.36 173

PVSNet_Blended		96.88 250	96.68 246	97.47 272	98.92 212	93.77 305	94.71 335	99.43 90	90.98 343	97.62 257	97.36 317	96.82 158	99.67 257	94.73 265	99.56 186	98.98 249

PHI-MVS		98.29 141	97.95 163	99.34 73	98.44 292	99.16 43	98.12 134	99.38 101	96.01 260	98.06 231	98.43 238	97.80 87	99.67 257	95.69 242	99.58 177	99.20 214

ACMH+	96.62 9	99.08 35	99.00 40	99.33 76	99.71 32	98.83 75	98.60 86	99.58 28	99.11 57	99.53 33	99.18 84	98.81 22	99.67 257	96.71 180	99.77 93	99.50 105

test_post1							97.59 196				20.48 376	83.07 342	99.66 265	94.16 282

旧先验2							95.76 305		88.56 357	97.52 267			99.66 265	94.48 272

MCST-MVS		98.00 165	97.63 187	99.10 111	99.24 136	98.17 129	96.89 251	98.73 273	95.66 269	97.92 237	97.70 295	97.17 139	99.66 265	96.18 220	99.23 248	99.47 127

NCCC		97.86 177	97.47 199	99.05 125	98.61 272	98.07 140	96.98 242	98.90 240	97.63 164	97.04 290	97.93 282	95.99 199	99.66 265	95.31 254	98.82 293	99.43 142

PMMVS		96.51 264	95.98 270	98.09 231	97.53 340	95.84 246	94.92 331	98.84 254	91.58 335	96.05 329	95.58 349	95.68 211	99.66 265	95.59 248	98.09 322	98.76 285

OPM-MVS		98.56 105	98.32 124	99.25 91	99.41 109	98.73 84	97.13 237	99.18 183	97.10 220	98.75 169	98.92 148	98.18 59	99.65 270	96.68 182	99.56 186	99.37 167
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MIMVSNet		96.62 262	96.25 268	97.71 254	99.04 187	94.66 278	99.16 44	96.92 332	97.23 212	97.87 241	99.10 102	86.11 320	99.65 270	91.65 333	99.21 251	98.82 272

CL-MVSNet_self_test		97.44 211	97.22 214	98.08 234	98.57 279	95.78 249	94.30 348	98.79 263	96.58 241	98.60 186	98.19 262	94.74 241	99.64 272	96.41 206	98.84 291	98.82 272

c3_l		97.36 215	97.37 204	97.31 278	98.09 315	93.25 310	95.01 329	99.16 192	97.05 221	98.77 166	98.72 192	92.88 274	99.64 272	96.93 155	99.76 103	99.05 236

DeepC-MVS_fast	96.85 6	98.30 138	98.15 146	98.75 168	98.61 272	97.23 202	97.76 178	99.09 205	97.31 200	98.75 169	98.66 204	97.56 107	99.64 272	96.10 224	99.55 188	99.39 157
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		98.47 120	98.34 120	98.86 150	99.30 127	97.76 174	97.16 235	99.28 154	95.54 271	99.42 49	99.19 82	97.27 132	99.63 275	97.89 97	99.97 11	99.20 214

baseline1		95.96 281	95.44 286	97.52 270	98.51 286	93.99 295	98.39 112	96.09 342	98.21 125	98.40 212	97.76 291	86.88 312	99.63 275	95.42 252	89.27 371	98.95 255

thres100view900		94.19 311	93.67 315	95.75 321	99.06 184	91.35 337	98.03 148	94.24 354	98.33 114	97.40 276	94.98 359	79.84 353	99.62 277	83.05 364	98.08 323	96.29 357

tfpn200view9		94.03 315	93.44 317	95.78 320	98.93 208	91.44 335	97.60 194	94.29 352	97.94 144	97.10 285	94.31 365	79.67 355	99.62 277	83.05 364	98.08 323	96.29 357

Patchmatch-RL test		97.26 223	97.02 224	97.99 241	99.52 74	95.53 253	96.13 289	99.71 11	97.47 179	99.27 76	99.16 90	84.30 335	99.62 277	97.89 97	99.77 93	98.81 275

v148		98.45 122	98.60 80	98.00 240	99.44 102	94.98 270	97.44 212	99.06 209	98.30 116	99.32 71	98.97 137	96.65 170	99.62 277	98.37 72	99.85 57	99.39 157

thres400		94.14 313	93.44 317	96.24 311	98.93 208	91.44 335	97.60 194	94.29 352	97.94 144	97.10 285	94.31 365	79.67 355	99.62 277	83.05 364	98.08 323	97.66 338

CostFormer		93.97 316	93.78 313	94.51 338	97.53 340	85.83 363	97.98 156	95.96 343	89.29 353	94.99 349	98.63 213	78.63 360	99.62 277	94.54 270	96.50 350	98.09 317

miper_ehance_all_eth		97.06 239	97.03 223	97.16 286	97.83 327	93.06 312	94.66 338	99.09 205	95.99 261	98.69 173	98.45 237	92.73 277	99.61 283	96.79 169	99.03 277	98.82 272

gm-plane-assit					94.83 372	81.97 374			88.07 358		94.99 358		99.60 284	91.76 331

MVP-Stereo		98.08 159	97.92 166	98.57 189	98.96 203	96.79 223	97.90 163	99.18 183	96.41 246	98.46 202	98.95 144	95.93 203	99.60 284	96.51 198	98.98 286	99.31 191
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		97.58 200	97.28 210	98.51 200	98.84 230	96.93 220	95.40 320	98.52 285	93.60 311	98.61 184	98.65 206	95.10 228	99.60 284	96.97 153	99.79 85	98.99 248

JIA-IIPM		95.52 291	95.03 298	97.00 289	96.85 356	94.03 292	96.93 246	95.82 344	99.20 49	94.63 351	99.71 12	83.09 341	99.60 284	94.42 276	94.64 363	97.36 346

test_prior3		97.48 207	97.00 225	98.95 137	98.69 260	97.95 156	95.74 307	99.03 218	96.48 243	96.11 325	97.63 299	95.92 204	99.59 288	94.16 282	99.20 252	99.30 194

test_prior				98.95 137	98.69 260	97.95 156		99.03 218					99.59 288			99.30 194

tpmrst		95.07 299	95.46 284	93.91 343	97.11 352	84.36 369	97.62 191	96.96 329	94.98 283	96.35 322	98.80 180	85.46 325	99.59 288	95.60 247	96.23 354	97.79 333

dp		93.47 322	93.59 316	93.13 352	96.64 359	81.62 375	97.66 187	96.42 338	92.80 322	96.11 325	98.64 209	78.55 362	99.59 288	93.31 310	92.18 370	98.16 314

PLC	94.65 16	96.51 264	95.73 275	98.85 151	98.75 245	97.91 159	96.42 276	99.06 209	90.94 344	95.59 334	97.38 314	94.41 246	99.59 288	90.93 344	98.04 326	99.05 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
miper_enhance_ethall		96.01 279	95.74 274	96.81 301	96.41 364	92.27 327	93.69 357	98.89 242	91.14 342	98.30 214	97.35 318	90.58 290	99.58 293	96.31 211	99.03 277	98.60 296

AllTest		98.44 123	98.20 137	99.16 102	99.50 79	98.55 97	98.25 122	99.58 28	96.80 231	98.88 149	99.06 105	97.65 97	99.57 294	94.45 274	99.61 165	99.37 167

TestCases				99.16 102	99.50 79	98.55 97		99.58 28	96.80 231	98.88 149	99.06 105	97.65 97	99.57 294	94.45 274	99.61 165	99.37 167

CNVR-MVS		98.17 154	97.87 170	99.07 118	98.67 265	98.24 120	97.01 240	98.93 234	97.25 206	97.62 257	98.34 250	97.27 132	99.57 294	96.42 205	99.33 232	99.39 157

TESTMET0.1,1		92.19 335	91.77 335	93.46 348	96.48 362	82.80 372	94.05 352	91.52 366	94.45 296	94.00 358	94.88 361	66.65 376	99.56 297	95.78 238	98.11 321	98.02 319

thres200		93.72 320	93.14 322	95.46 330	98.66 270	91.29 339	96.61 266	94.63 350	97.39 192	96.83 304	93.71 368	79.88 352	99.56 297	82.40 367	98.13 320	95.54 366

MVS_Test		98.18 152	98.36 117	97.67 255	98.48 288	94.73 275	98.18 128	99.02 222	97.69 160	98.04 234	99.11 100	97.22 138	99.56 297	98.57 60	98.90 290	98.71 289

test_yl		96.69 257	96.29 265	97.90 243	98.28 303	95.24 262	97.29 221	97.36 320	98.21 125	98.17 219	97.86 284	86.27 316	99.55 300	94.87 262	98.32 311	98.89 265

DCV-MVSNet		96.69 257	96.29 265	97.90 243	98.28 303	95.24 262	97.29 221	97.36 320	98.21 125	98.17 219	97.86 284	86.27 316	99.55 300	94.87 262	98.32 311	98.89 265

alignmvs		97.35 216	96.88 233	98.78 163	98.54 283	98.09 134	97.71 182	97.69 314	99.20 49	97.59 260	95.90 345	88.12 310	99.55 300	98.18 82	98.96 287	98.70 291

HQP4-MVS										95.56 337			99.54 303			99.32 187

HQP-MVS		97.00 246	96.49 259	98.55 194	98.67 265	96.79 223	96.29 282	99.04 216	96.05 257	95.55 338	96.84 328	93.84 257	99.54 303	92.82 317	99.26 245	99.32 187

tpmvs		95.02 301	95.25 292	94.33 339	96.39 365	85.87 361	98.08 140	96.83 334	95.46 275	95.51 343	98.69 197	85.91 321	99.53 305	94.16 282	96.23 354	97.58 341

tpm2		93.09 326	92.58 327	94.62 337	97.56 338	86.53 360	97.66 187	95.79 345	86.15 362	94.07 357	98.23 259	75.95 364	99.53 305	90.91 345	96.86 348	97.81 330

MDTV_nov1_ep13			95.22 293		97.06 353	83.20 371	97.74 180	96.16 340	94.37 298	96.99 292	98.83 174	83.95 337	99.53 305	93.90 293	97.95 327

AdaColmap		97.14 234	96.71 244	98.46 205	98.34 300	97.80 172	96.95 243	98.93 234	95.58 270	96.92 294	97.66 296	95.87 206	99.53 305	90.97 343	99.14 263	98.04 318

new_pmnet		96.99 247	96.76 241	97.67 255	98.72 249	94.89 272	95.95 297	98.20 298	92.62 324	98.55 196	98.54 224	94.88 234	99.52 309	93.96 291	99.44 217	98.59 298

RPSCF		98.62 97	98.36 117	99.42 58	99.65 45	99.42 5	98.55 92	99.57 35	97.72 159	98.90 142	99.26 73	96.12 191	99.52 309	95.72 240	99.71 122	99.32 187

MAR-MVS		96.47 268	95.70 276	98.79 160	97.92 323	99.12 57	98.28 119	98.60 281	92.16 330	95.54 341	96.17 341	94.77 240	99.52 309	89.62 351	98.23 313	97.72 336
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
LF4IMVS		97.90 171	97.69 180	98.52 198	99.17 159	97.66 181	97.19 232	99.47 76	96.31 250	97.85 243	98.20 261	96.71 168	99.52 309	94.62 268	99.72 117	98.38 307

Gipuma		99.03 37	99.16 30	98.64 176	99.94 2	98.51 102	99.32 17	99.75 9	99.58 22	98.60 186	99.62 21	98.22 55	99.51 313	97.70 112	99.73 110	97.89 324
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ambc				98.24 224	98.82 236	95.97 243	98.62 84	99.00 228		99.27 76	99.21 79	96.99 148	99.50 314	96.55 195	99.50 206	99.26 204

testgi		98.32 136	98.39 113	98.13 230	99.57 57	95.54 252	97.78 173	99.49 68	97.37 194	99.19 91	97.65 297	98.96 17	99.49 315	96.50 199	98.99 284	99.34 179

EPNet_dtu		94.93 302	94.78 303	95.38 331	93.58 374	87.68 355	96.78 256	95.69 346	97.35 196	89.14 370	98.09 271	88.15 309	99.49 315	94.95 261	99.30 238	98.98 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchMatch-RL		97.24 226	96.78 240	98.61 183	99.03 190	97.83 166	96.36 279	99.06 209	93.49 314	97.36 279	97.78 289	95.75 209	99.49 315	93.44 307	98.77 294	98.52 299

test_241102_ONE					99.49 86	99.17 39		99.31 134	97.98 140	99.66 20	98.90 152	98.36 44	99.48 318

CLD-MVS		97.49 205	97.16 217	98.48 203	99.07 180	97.03 215	94.71 335	99.21 172	94.46 294	98.06 231	97.16 323	97.57 106	99.48 318	94.46 273	99.78 89	98.95 255
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-untuned		96.83 252	96.75 242	97.08 287	98.74 246	93.33 309	96.71 261	98.26 295	96.72 235	98.44 204	97.37 316	95.20 225	99.47 320	91.89 330	97.43 335	98.44 304

OMC-MVS		97.88 175	97.49 195	99.04 127	98.89 221	98.63 89	96.94 244	99.25 163	95.02 282	98.53 199	98.51 228	97.27 132	99.47 320	93.50 306	99.51 199	99.01 244

canonicalmvs		98.34 135	98.26 130	98.58 186	98.46 290	97.82 169	98.96 63	99.46 78	99.19 53	97.46 272	95.46 353	98.59 32	99.46 322	98.08 88	98.71 299	98.46 301

DWT-MVSNet_test		92.75 329	92.05 330	94.85 335	96.48 362	87.21 357	97.83 170	94.99 347	92.22 329	92.72 363	94.11 367	70.75 369	99.46 322	95.01 258	94.33 366	97.87 326

CNLPA		97.17 232	96.71 244	98.55 194	98.56 280	98.05 143	96.33 280	98.93 234	96.91 228	97.06 289	97.39 313	94.38 248	99.45 324	91.66 332	99.18 258	98.14 315

BH-RMVSNet		96.83 252	96.58 254	97.58 263	98.47 289	94.05 290	96.67 263	97.36 320	96.70 237	97.87 241	97.98 277	95.14 227	99.44 325	90.47 348	98.58 307	99.25 205

DPM-MVS		96.32 272	95.59 281	98.51 200	98.76 243	97.21 206	94.54 344	98.26 295	91.94 331	96.37 321	97.25 319	93.06 271	99.43 326	91.42 338	98.74 295	98.89 265

PVSNet	93.40 17	95.67 287	95.70 276	95.57 325	98.83 233	88.57 349	92.50 362	97.72 312	92.69 323	96.49 319	96.44 337	93.72 262	99.43 326	93.61 301	99.28 241	98.71 289

TSAR-MVS + GP.		98.18 152	97.98 161	98.77 165	98.71 252	97.88 161	96.32 281	98.66 277	96.33 248	99.23 88	98.51 228	97.48 120	99.40 328	97.16 134	99.46 211	99.02 243

TAPA-MVS	96.21 11	96.63 261	95.95 271	98.65 175	98.93 208	98.09 134	96.93 246	99.28 154	83.58 366	98.13 224	97.78 289	96.13 190	99.40 328	93.52 304	99.29 240	98.45 303
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpm cat1		93.29 324	93.13 323	93.75 345	97.39 346	84.74 366	97.39 213	97.65 315	83.39 367	94.16 354	98.41 239	82.86 343	99.39 330	91.56 336	95.35 361	97.14 348

MG-MVS		96.77 255	96.61 251	97.26 281	98.31 302	93.06 312	95.93 298	98.12 303	96.45 245	97.92 237	98.73 190	93.77 261	99.39 330	91.19 342	99.04 276	99.33 185

MVS_111021_HR		98.25 146	98.08 154	98.75 168	99.09 176	97.46 191	95.97 293	99.27 157	97.60 168	97.99 236	98.25 256	98.15 64	99.38 332	96.87 164	99.57 181	99.42 145

MS-PatchMatch		97.68 192	97.75 176	97.45 273	98.23 308	93.78 304	97.29 221	98.84 254	96.10 256	98.64 179	98.65 206	96.04 193	99.36 333	96.84 167	99.14 263	99.20 214

ITE_SJBPF				98.87 148	99.22 141	98.48 104		99.35 115	97.50 176	98.28 216	98.60 219	97.64 100	99.35 334	93.86 296	99.27 242	98.79 281

MVS_111021_LR		98.30 138	98.12 149	98.83 153	99.16 161	98.03 144	96.09 290	99.30 144	97.58 169	98.10 228	98.24 257	98.25 50	99.34 335	96.69 181	99.65 150	99.12 230

USDC		97.41 213	97.40 201	97.44 274	98.94 206	93.67 307	95.17 324	99.53 54	94.03 306	98.97 129	99.10 102	95.29 223	99.34 335	95.84 236	99.73 110	99.30 194

MSDG		97.71 190	97.52 193	98.28 221	98.91 215	96.82 222	94.42 345	99.37 105	97.65 163	98.37 213	98.29 255	97.40 124	99.33 337	94.09 288	99.22 249	98.68 295

XVG-OURS		98.53 114	98.34 120	99.11 109	99.50 79	98.82 77	95.97 293	99.50 60	97.30 201	99.05 114	98.98 135	99.35 7	99.32 338	95.72 240	99.68 138	99.18 221

DP-MVS Recon		97.33 218	96.92 230	98.57 189	99.09 176	97.99 146	96.79 255	99.35 115	93.18 316	97.71 251	98.07 273	95.00 230	99.31 339	93.97 290	99.13 266	98.42 306

EPMVS		93.72 320	93.27 319	95.09 334	96.04 368	87.76 354	98.13 132	85.01 375	94.69 290	96.92 294	98.64 209	78.47 363	99.31 339	95.04 257	96.46 351	98.20 312

MVS		93.19 325	92.09 329	96.50 306	96.91 354	94.03 292	98.07 141	98.06 305	68.01 370	94.56 352	96.48 335	95.96 202	99.30 341	83.84 363	96.89 347	96.17 359

GA-MVS		95.86 283	95.32 291	97.49 271	98.60 274	94.15 289	93.83 355	97.93 308	95.49 274	96.68 308	97.42 312	83.21 340	99.30 341	96.22 216	98.55 308	99.01 244

XVG-OURS-SEG-HR		98.49 118	98.28 128	99.14 105	99.49 86	98.83 75	96.54 267	99.48 70	97.32 199	99.11 100	98.61 218	99.33 8	99.30 341	96.23 215	98.38 310	99.28 199

DeepPCF-MVS	96.93 5	98.32 136	98.01 159	99.23 94	98.39 298	98.97 66	95.03 328	99.18 183	96.88 229	99.33 65	98.78 183	98.16 62	99.28 344	96.74 175	99.62 158	99.44 138

TinyColmap		97.89 173	97.98 161	97.60 261	98.86 225	94.35 284	96.21 286	99.44 84	97.45 186	99.06 109	98.88 161	97.99 75	99.28 344	94.38 280	99.58 177	99.18 221

KD-MVS_2432*1600		92.87 327	91.99 331	95.51 328	91.37 375	89.27 347	94.07 350	98.14 301	95.42 276	97.25 282	96.44 337	67.86 372	99.24 346	91.28 339	96.08 356	98.02 319

cl22		95.79 285	95.39 289	96.98 291	96.77 358	92.79 318	94.40 346	98.53 284	94.59 291	97.89 240	98.17 263	82.82 344	99.24 346	96.37 207	99.03 277	98.92 261

miper_refine_blended		92.87 327	91.99 331	95.51 328	91.37 375	89.27 347	94.07 350	98.14 301	95.42 276	97.25 282	96.44 337	67.86 372	99.24 346	91.28 339	96.08 356	98.02 319

PAPM		91.88 336	90.34 339	96.51 305	98.06 317	92.56 321	92.44 363	97.17 325	86.35 361	90.38 368	96.01 342	86.61 314	99.21 349	70.65 373	95.43 360	97.75 334

MVS-HIRNet		94.32 308	95.62 279	90.42 354	98.46 290	75.36 377	96.29 282	89.13 371	95.25 280	95.38 344	99.75 7	92.88 274	99.19 350	94.07 289	99.39 222	96.72 355

PAPM_NR		96.82 254	96.32 264	98.30 219	99.07 180	96.69 228	97.48 207	98.76 267	95.81 267	96.61 312	96.47 336	94.12 255	99.17 351	90.82 347	97.78 329	99.06 235

TR-MVS		95.55 290	95.12 297	96.86 300	97.54 339	93.94 296	96.49 272	96.53 337	94.36 299	97.03 291	96.61 332	94.26 251	99.16 352	86.91 358	96.31 353	97.47 345

API-MVS		97.04 242	96.91 232	97.42 275	97.88 325	98.23 124	98.18 128	98.50 286	97.57 170	97.39 277	96.75 330	96.77 162	99.15 353	90.16 349	99.02 280	94.88 367

PAPR		95.29 294	94.47 304	97.75 252	97.50 344	95.14 267	94.89 332	98.71 275	91.39 339	95.35 345	95.48 352	94.57 243	99.14 354	84.95 361	97.37 337	98.97 253

1314		95.74 286	95.60 280	96.17 313	97.53 340	92.75 320	98.07 141	98.31 294	91.22 340	94.25 353	96.68 331	95.53 215	99.03 355	91.64 334	97.18 342	96.74 354

gg-mvs-nofinetune		92.37 332	91.20 337	95.85 318	95.80 371	92.38 325	99.31 21	81.84 377	99.75 5	91.83 366	99.74 8	68.29 371	99.02 356	87.15 357	97.12 343	96.16 360

BH-w/o		95.13 298	94.89 302	95.86 317	98.20 309	91.31 338	95.65 310	97.37 319	93.64 310	96.52 315	95.70 348	93.04 272	99.02 356	88.10 355	95.82 358	97.24 347

test0.0.03 1		94.51 305	93.69 314	96.99 290	96.05 367	93.61 308	94.97 330	93.49 358	96.17 252	97.57 263	94.88 361	82.30 345	99.01 358	93.60 302	94.17 367	98.37 309

E-PMN		94.17 312	94.37 307	93.58 347	96.86 355	85.71 364	90.11 367	97.07 327	98.17 131	97.82 246	97.19 320	84.62 331	98.94 359	89.77 350	97.68 331	96.09 363

EMVS		93.83 318	94.02 310	93.23 351	96.83 357	84.96 365	89.77 368	96.32 339	97.92 146	97.43 275	96.36 340	86.17 318	98.93 360	87.68 356	97.73 330	95.81 364

CMPMVS	75.91 23	96.29 273	95.44 286	98.84 152	96.25 366	98.69 87	97.02 239	99.12 201	88.90 354	97.83 244	98.86 165	89.51 297	98.90 361	91.92 329	99.51 199	98.92 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	89.98 21	91.15 337	90.30 340	93.70 346	97.72 331	84.34 370	90.24 366	97.42 318	90.20 348	93.79 359	93.09 369	90.90 289	98.89 362	86.57 359	72.76 373	97.87 326

MSLP-MVS++		98.02 163	98.14 148	97.64 259	98.58 277	95.19 265	97.48 207	99.23 170	97.47 179	97.90 239	98.62 215	97.04 143	98.81 363	97.55 115	99.41 219	98.94 259

OPU-MVS				98.82 154	98.59 276	98.30 116	98.10 137				98.52 227	98.18 59	98.75 364	94.62 268	99.48 209	99.41 148

cascas		94.79 303	94.33 309	96.15 316	96.02 369	92.36 326	92.34 364	99.26 162	85.34 364	95.08 348	94.96 360	92.96 273	98.53 365	94.41 279	98.59 306	97.56 342

wuyk23d		96.06 278	97.62 188	91.38 353	98.65 271	98.57 96	98.85 71	96.95 330	96.86 230	99.90 4	99.16 90	99.18 11	98.40 366	89.23 352	99.77 93	77.18 371

PMVS	91.26 20	97.86 177	97.94 165	97.65 257	99.71 32	97.94 158	98.52 95	98.68 276	98.99 76	97.52 267	99.35 62	97.41 123	98.18 367	91.59 335	99.67 144	96.82 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GG-mvs-BLEND				94.76 336	94.54 373	92.13 329	99.31 21	80.47 378		88.73 371	91.01 371	67.59 374	98.16 368	82.30 368	94.53 365	93.98 368

test_method		79.78 339	79.50 342	80.62 355	80.21 378	45.76 380	70.82 369	98.41 291	31.08 373	80.89 374	97.71 293	84.85 328	97.37 369	91.51 337	80.03 372	98.75 286

PC_three_1452									93.27 315	99.40 53	98.54 224	98.22 55	97.00 370	95.17 255	99.45 214	99.49 109

FPMVS		93.44 323	92.23 328	97.08 287	99.25 135	97.86 163	95.61 311	97.16 326	92.90 320	93.76 360	98.65 206	75.94 365	95.66 371	79.30 371	97.49 332	97.73 335

MVE	83.40 22	92.50 330	91.92 333	94.25 340	98.83 233	91.64 332	92.71 361	83.52 376	95.92 263	86.46 373	95.46 353	95.20 225	95.40 372	80.51 369	98.64 303	95.73 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SD-MVS		98.40 129	98.68 68	97.54 268	98.96 203	97.99 146	97.88 164	99.36 109	98.20 128	99.63 25	99.04 115	98.76 23	95.33 373	96.56 192	99.74 107	99.31 191
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
DeepMVS_CX				93.44 349	98.24 306	94.21 287		94.34 351	64.28 371	91.34 367	94.87 363	89.45 299	92.77 374	77.54 372	93.14 368	93.35 369

tmp_tt		78.77 340	78.73 343	78.90 356	58.45 379	74.76 379	94.20 349	78.26 379	39.16 372	86.71 372	92.82 370	80.50 351	75.19 375	86.16 360	92.29 369	86.74 370

test123		17.04 343	20.11 346	7.82 357	10.25 381	4.91 381	94.80 333	4.47 382	4.93 375	10.00 377	24.28 374	9.69 380	3.64 376	10.14 374	12.43 375	14.92 372

testmvs		17.12 342	20.53 345	6.87 358	12.05 380	4.20 382	93.62 358	6.73 381	4.62 376	10.41 376	24.33 373	8.28 381	3.56 377	9.69 375	15.07 374	12.86 373

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

cdsmvs_eth3d_5k		24.66 341	32.88 344	0.00 359	0.00 382	0.00 383	0.00 370	99.10 204	0.00 377	0.00 378	97.58 301	99.21 10	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		8.17 344	10.90 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	98.07 66	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

ab-mvs-re		8.12 345	10.83 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	97.48 308	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

FOURS1					99.73 25	99.67 2	99.43 10	99.54 50	99.43 30	99.26 80

test_one_0601					99.39 111	99.20 33		99.31 134	98.49 107	98.66 177	99.02 119	97.64 100

eth-test2					0.00 382

eth-test					0.00 382

RE-MVS-def			98.58 82		99.20 147	99.38 6	98.48 104	99.30 144	98.64 95	98.95 132	98.96 140	97.75 90		96.56 192	99.39 222	99.45 133

IU-MVS					99.49 86	99.15 48		98.87 245	92.97 318	99.41 50				96.76 173	99.62 158	99.66 36

save fliter					99.11 169	97.97 151	96.53 268	99.02 222	98.24 122

test0726					99.50 79	99.21 27	98.17 131	99.35 115	97.97 142	99.26 80	99.06 105	97.61 103

GSMVS																98.81 275

test_part2					99.36 117	99.10 60				99.05 114

sam_mvs1												84.74 330				98.81 275

sam_mvs												84.29 336

MTGPA								99.20 174

MTMP							97.93 159	91.91 365

test9_res														93.28 311	99.15 262	99.38 164

agg_prior2														92.50 325	99.16 259	99.37 167

test_prior4						97.97 151	95.86 301

test_prior2							95.74 307		96.48 243	96.11 325	97.63 299	95.92 204		94.16 282	99.20 252

新几何2							95.93 298

旧先验1					98.82 236	97.45 192		98.76 267			98.34 250	95.50 218			99.01 282	99.23 209

原ACMM2							95.53 314

test222					98.92 212	96.93 220	95.54 313	98.78 265	85.72 363	96.86 302	98.11 268	94.43 245			99.10 271	99.23 209

segment_acmp												97.02 146

testdata1							95.44 319		96.32 249

plane_prior7					99.19 150	97.87 162

plane_prior6					98.99 199	97.70 180						94.90 231

plane_prior4											97.98 277

plane_prior3						97.78 173			97.41 190	97.79 247

plane_prior2							97.77 176		98.20 128

plane_prior1					99.05 186

plane_prior						97.65 182	97.07 238		96.72 235						99.36 227

n2								0.00 383

nn								0.00 383

door-mid								99.57 35

test11								98.87 245

door								99.41 94

HQP5-MVS						96.79 223

HQP-NCC					98.67 265		96.29 282		96.05 257	95.55 338

ACMP_Plane					98.67 265		96.29 282		96.05 257	95.55 338

BP-MVS														92.82 317

HQP3-MVS								99.04 216							99.26 245

HQP2-MVS												93.84 257

NP-MVS					98.84 230	97.39 195					96.84 328

MDTV_nov1_ep13_2view						74.92 378	97.69 184		90.06 350	97.75 250		85.78 322		93.52 304		98.69 292

ACMMP++_ref															99.77 93

ACMMP++															99.68 138

Test By Simon												96.52 175

High-resolution multi-view benchmark