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.

HPM-MVS		98.36 45	98.10 48	99.13 54	99.74 7	97.82 72	99.53 1	98.80 87	94.63 152	98.61 64	98.97 87	95.13 70	99.77 101	97.65 60	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		97.57 83	97.49 75	97.84 135	98.07 183	95.76 161	99.47 2	98.40 183	94.98 135	98.79 49	98.83 107	92.34 113	98.41 265	96.91 93	99.59 71	99.34 111

test_djsdf		96.00 148	95.69 148	96.93 192	95.72 313	95.49 170	99.47 2	98.40 183	94.98 135	94.58 205	97.86 200	89.16 181	98.41 265	96.91 93	94.12 227	96.88 245

HPM-MVS_fast		98.38 43	98.13 46	99.12 57	99.75 3	97.86 68	99.44 4	98.82 70	94.46 159	98.94 39	99.20 52	95.16 69	99.74 107	97.58 66	99.85 3	99.77 20

nrg030		96.28 139	95.72 143	97.96 131	96.90 265	98.15 56	99.39 5	98.31 197	95.47 107	94.42 215	98.35 156	92.09 123	98.69 230	97.50 73	89.05 299	97.04 228

APDe-MVS		99.02 3	98.84 2	99.55 6	99.57 33	98.96 12	99.39 5	98.93 37	97.38 25	99.41 11	99.54 1	96.66 13	99.84 53	98.86 1	99.85 3	99.87 1

3Dnovator+	94.38 6	97.43 92	96.78 107	99.38 17	97.83 198	98.52 27	99.37 7	98.71 114	97.09 46	92.99 273	99.13 64	89.36 175	99.89 35	96.97 89	99.57 75	99.71 44

FIs		96.51 130	96.12 133	97.67 152	97.13 251	97.54 82	99.36 8	99.22 14	95.89 87	94.03 235	98.35 156	91.98 126	98.44 256	96.40 121	92.76 253	97.01 229

FC-MVSNet-test		96.42 133	96.05 134	97.53 162	96.95 260	97.27 91	99.36 8	99.23 12	95.83 90	93.93 237	98.37 154	92.00 125	98.32 274	96.02 133	92.72 254	97.00 230

3Dnovator	94.51 5	97.46 87	96.93 100	99.07 60	97.78 200	97.64 77	99.35 10	99.06 22	97.02 48	93.75 247	99.16 61	89.25 178	99.92 21	97.22 81	99.75 38	99.64 70

canonicalmvs		97.67 74	97.23 87	98.98 65	98.70 135	98.38 35	99.34 11	98.39 185	96.76 55	97.67 117	97.40 240	92.26 116	99.49 146	98.28 27	96.28 202	99.08 148

CP-MVS		98.57 26	98.36 22	99.19 43	99.66 27	97.86 68	99.34 11	98.87 55	95.96 86	98.60 65	99.13 64	96.05 32	99.94 3	97.77 50	99.86 1	99.77 20

EPP-MVSNet		97.46 87	97.28 85	97.99 128	98.64 141	95.38 173	99.33 13	98.31 197	93.61 200	97.19 133	99.07 77	94.05 95	99.23 168	96.89 96	98.43 143	99.37 110

XVS		98.70 9	98.49 16	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 22	98.46 69	99.20 52	95.90 40	99.89 35	97.85 45	99.74 41	99.78 13

X-MVStestdata		94.06 263	92.30 283	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 22	98.46 69	43.50 359	95.90 40	99.89 35	97.85 45	99.74 41	99.78 13

tttt0517		96.07 144	95.51 154	97.78 141	98.41 155	94.84 198	99.28 16	94.33 348	94.26 164	97.64 121	98.64 127	84.05 284	99.47 151	95.34 155	97.60 170	99.03 151

mPP-MVS		98.51 35	98.26 37	99.25 39	99.75 3	98.04 60	99.28 16	98.81 76	96.24 74	98.35 78	99.23 45	95.46 51	99.94 3	97.42 75	99.81 10	99.77 20

MSP-MVS		98.74 8	98.55 10	99.29 31	99.75 3	98.23 49	99.26 18	98.88 49	97.52 15	99.41 11	98.78 112	96.00 34	99.79 92	97.79 49	99.59 71	99.85 2
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
v7n		94.19 252	93.43 263	96.47 230	95.90 308	94.38 221	99.26 18	98.34 193	91.99 257	92.76 278	97.13 255	88.31 203	98.52 248	89.48 298	87.70 314	96.52 292

WR-MVS_H		95.05 198	94.46 202	96.81 199	96.86 267	95.82 159	99.24 20	99.24 10	93.87 180	92.53 286	96.84 288	90.37 160	98.24 285	93.24 220	87.93 312	96.38 304

HFP-MVS		98.63 16	98.40 18	99.32 28	99.72 12	98.29 46	99.23 21	98.96 32	96.10 83	98.94 39	99.17 56	96.06 30	99.92 21	97.62 62	99.78 23	99.75 28

region2R		98.61 17	98.38 20	99.29 31	99.74 7	98.16 55	99.23 21	98.93 37	96.15 78	98.94 39	99.17 56	95.91 39	99.94 3	97.55 70	99.79 19	99.78 13

ACMMPR		98.59 20	98.36 22	99.29 31	99.74 7	98.15 56	99.23 21	98.95 34	96.10 83	98.93 43	99.19 55	95.70 44	99.94 3	97.62 62	99.79 19	99.78 13

QAPM		96.29 137	95.40 155	98.96 67	97.85 197	97.60 80	99.23 21	98.93 37	89.76 310	93.11 270	99.02 80	89.11 183	99.93 15	91.99 256	99.62 66	99.34 111

MP-MVS		98.33 50	98.01 52	99.28 35	99.75 3	98.18 53	99.22 25	98.79 92	96.13 80	97.92 104	99.23 45	94.54 84	99.94 3	96.74 110	99.78 23	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Vis-MVSNet		97.42 93	97.11 91	98.34 106	98.66 139	96.23 136	99.22 25	99.00 27	96.63 61	98.04 89	99.21 48	88.05 212	99.35 159	96.01 134	99.21 106	99.45 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		97.85 66	97.74 62	98.20 115	99.67 26	95.16 181	99.22 25	99.32 7	93.04 220	97.02 142	98.92 99	95.36 58	99.91 30	97.43 74	99.64 62	99.52 85

OpenMVS	93.04 13	95.83 156	95.00 177	98.32 107	97.18 248	97.32 88	99.21 28	98.97 30	89.96 306	91.14 307	99.05 79	86.64 239	99.92 21	93.38 215	99.47 90	97.73 210

DTE-MVSNet		93.98 265	93.26 268	96.14 249	96.06 303	94.39 220	99.20 29	98.86 61	93.06 219	91.78 301	97.81 208	85.87 253	97.58 320	90.53 278	86.17 329	96.46 301

Vis-MVSNet (Re-imp)		96.87 118	96.55 119	97.83 136	98.73 130	95.46 171	99.20 29	98.30 203	94.96 137	96.60 161	98.87 103	90.05 165	98.59 242	93.67 209	98.60 132	99.46 102

ZNCC-MVS		98.49 36	98.20 44	99.35 22	99.73 11	98.39 34	99.19 31	98.86 61	95.77 92	98.31 81	99.10 69	95.46 51	99.93 15	97.57 69	99.81 10	99.74 33

IS-MVSNet		97.22 102	96.88 102	98.25 112	98.85 123	96.36 131	99.19 31	97.97 253	95.39 111	97.23 132	98.99 86	91.11 147	98.93 207	94.60 177	98.59 133	99.47 98

PEN-MVS		94.42 239	93.73 250	96.49 228	96.28 294	94.84 198	99.17 33	99.00 27	93.51 202	92.23 295	97.83 206	86.10 249	97.90 309	92.55 242	86.92 324	96.74 260

PS-MVSNAJss		96.43 132	96.26 129	96.92 194	95.84 311	95.08 187	99.16 34	98.50 167	95.87 89	93.84 243	98.34 160	94.51 85	98.61 238	96.88 99	93.45 243	97.06 227

APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 53	99.50 41	97.92 67	99.15 35	98.81 76	96.24 74	99.20 22	99.37 22	95.30 62	99.80 80	97.73 52	99.67 54	99.72 40

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 40	99.69 25	98.28 48	99.14 36	98.66 132	96.84 52	99.56 5	99.31 35	96.34 19	99.70 115	98.32 25	99.73 43	99.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
anonymousdsp		95.42 174	94.91 182	96.94 191	95.10 326	95.90 157	99.14 36	98.41 181	93.75 185	93.16 266	97.46 234	87.50 225	98.41 265	95.63 150	94.03 229	96.50 297

jajsoiax		95.45 172	95.03 176	96.73 203	95.42 324	94.63 207	99.14 36	98.52 159	95.74 93	93.22 264	98.36 155	83.87 289	98.65 236	96.95 92	94.04 228	96.91 241

PS-CasMVS		94.67 222	93.99 231	96.71 204	96.68 277	95.26 179	99.13 39	99.03 25	93.68 195	92.33 293	97.95 191	85.35 261	98.10 293	93.59 211	88.16 311	96.79 254

abl_6		98.30 53	98.03 51	99.13 54	99.56 34	97.76 75	99.13 39	98.82 70	96.14 79	99.26 18	99.37 22	93.33 102	99.93 15	96.96 91	99.67 54	99.69 51

CPTT-MVS		97.72 72	97.32 84	98.92 69	99.64 28	97.10 100	99.12 41	98.81 76	92.34 245	98.09 85	99.08 76	93.01 106	99.92 21	96.06 131	99.77 26	99.75 28

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 54	99.49 45	97.86 68	99.11 42	98.80 87	96.49 66	99.17 24	99.35 28	95.34 59	99.82 64	97.72 53	99.65 58	99.71 44

RE-MVS-def			98.34 28		99.49 45	97.86 68	99.11 42	98.80 87	96.49 66	99.17 24	99.35 28	95.29 63		97.72 53	99.65 58	99.71 44

CP-MVSNet		94.94 207	94.30 211	96.83 198	96.72 275	95.56 166	99.11 42	98.95 34	93.89 178	92.42 292	97.90 195	87.19 229	98.12 292	94.32 188	88.21 309	96.82 253

SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 21	99.22 94	98.43 33	99.10 45	98.87 55	97.38 25	99.35 14	99.40 13	97.78 3	99.87 44	97.77 50	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
test1172		98.56 28	98.35 24	99.16 50	99.53 36	97.94 66	99.09 46	98.83 68	96.52 65	99.05 32	99.34 31	95.34 59	99.82 64	97.86 44	99.64 62	99.73 36

CS-MVS		97.81 67	97.61 65	98.41 102	98.52 150	97.15 99	99.09 46	98.55 152	96.18 77	97.61 123	97.20 252	94.59 83	99.39 156	97.62 62	99.10 111	98.70 173

SR-MVS		98.57 26	98.35 24	99.24 40	99.53 36	98.18 53	99.09 46	98.82 70	96.58 62	99.10 29	99.32 33	95.39 55	99.82 64	97.70 58	99.63 64	99.72 40

GST-MVS		98.43 40	98.12 47	99.34 23	99.72 12	98.38 35	99.09 46	98.82 70	95.71 95	98.73 55	99.06 78	95.27 64	99.93 15	97.07 86	99.63 64	99.72 40

K. test v3		92.55 287	91.91 289	94.48 304	95.64 315	89.24 317	99.07 50	94.88 342	94.04 169	86.78 335	97.59 225	77.64 330	97.64 318	92.08 251	89.43 294	96.57 282

test0726					99.72 12	99.25 2	99.06 51	98.88 49	97.62 11	99.56 5	99.50 4	97.42 6

v8		94.47 237	93.77 246	96.57 220	96.36 291	94.83 200	99.05 52	98.19 216	91.92 259	93.16 266	96.97 276	88.82 194	98.48 250	91.69 263	87.79 313	96.39 303

SF-MVS		98.59 20	98.32 33	99.41 16	99.54 35	98.71 18	99.04 53	98.81 76	95.12 128	99.32 15	99.39 14	96.22 20	99.84 53	97.72 53	99.73 43	99.67 61

PHI-MVS		98.34 48	98.06 49	99.18 47	99.15 101	98.12 58	99.04 53	99.09 20	93.32 210	98.83 48	99.10 69	96.54 16	99.83 56	97.70 58	99.76 32	99.59 80

test_part1		94.82 211	93.82 241	97.82 138	98.84 124	97.82 72	99.03 55	98.81 76	92.31 249	92.51 288	97.89 197	81.96 298	98.67 234	94.80 172	88.24 308	96.98 231

TranMVSNet+NR-MVSNet		95.14 193	94.48 200	97.11 181	96.45 288	96.36 131	99.03 55	99.03 25	95.04 133	93.58 250	97.93 193	88.27 204	98.03 300	94.13 194	86.90 325	96.95 235

ACMMP		98.23 54	97.95 55	99.09 59	99.74 7	97.62 79	99.03 55	99.41 6	95.98 85	97.60 125	99.36 26	94.45 89	99.93 15	97.14 83	98.85 122	99.70 48
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
SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 58	98.87 55	97.65 9	99.73 1	99.48 6	97.53 4	99.94 3	98.43 18	99.81 10	99.70 48

OPU-MVS				99.37 20	99.24 92	99.05 10	99.02 58				99.16 61	97.81 2	99.37 158	97.24 80	99.73 43	99.70 48

EIA-MVS		97.75 70	97.58 67	98.27 109	98.38 156	96.44 127	99.01 60	98.60 140	95.88 88	97.26 131	97.53 230	94.97 74	99.33 161	97.38 77	99.20 107	99.05 150

Anonymous20231211		94.10 259	93.26 268	96.61 214	99.11 104	94.28 223	99.01 60	98.88 49	86.43 331	92.81 276	97.57 227	81.66 301	98.68 233	94.83 169	89.02 301	96.88 245

mvs_tets		95.41 176	95.00 177	96.65 209	95.58 317	94.42 218	99.00 62	98.55 152	95.73 94	93.21 265	98.38 153	83.45 293	98.63 237	97.09 85	94.00 230	96.91 241

baseline		97.64 76	97.44 79	98.25 112	98.35 158	96.20 137	99.00 62	98.32 195	96.33 73	98.03 90	99.17 56	91.35 141	99.16 174	98.10 31	98.29 149	99.39 108

v10		94.29 246	93.55 258	96.51 227	96.39 290	94.80 202	98.99 64	98.19 216	91.35 277	93.02 272	96.99 274	88.09 210	98.41 265	90.50 279	88.41 307	96.33 307

PGM-MVS		98.49 36	98.23 42	99.27 38	99.72 12	98.08 59	98.99 64	99.49 5	95.43 109	99.03 33	99.32 33	95.56 47	99.94 3	96.80 106	99.77 26	99.78 13

LPG-MVS_test		95.62 166	95.34 161	96.47 230	97.46 225	93.54 246	98.99 64	98.54 155	94.67 149	94.36 217	98.77 114	85.39 259	99.11 183	95.71 146	94.15 225	96.76 258

#test#		98.54 32	98.27 36	99.32 28	99.72 12	98.29 46	98.98 67	98.96 32	95.65 99	98.94 39	99.17 56	96.06 30	99.92 21	97.21 82	99.78 23	99.75 28

DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 68	98.58 147	97.62 11	99.45 9	99.46 9	97.42 6	99.94 3	98.47 15	99.81 10	99.69 51
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				99.71 1	99.72 12	99.35 1	98.97 68	98.88 49					99.94 3	98.47 15	99.81 10	99.84 4

tfpnnormal		93.66 268	92.70 277	96.55 224	96.94 261	95.94 151	98.97 68	99.19 15	91.04 289	91.38 305	97.34 241	84.94 267	98.61 238	85.45 326	89.02 301	95.11 331

V42		94.78 215	94.14 221	96.70 206	96.33 293	95.22 180	98.97 68	98.09 239	92.32 247	94.31 220	97.06 266	88.39 202	98.55 245	92.90 231	88.87 303	96.34 305

SMA-MVS		98.58 23	98.25 38	99.56 5	99.51 39	99.04 11	98.95 72	98.80 87	93.67 197	99.37 13	99.52 3	96.52 17	99.89 35	98.06 33	99.81 10	99.76 26
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
pm-mvs1		93.94 266	93.06 270	96.59 217	96.49 286	95.16 181	98.95 72	98.03 250	92.32 247	91.08 308	97.84 203	84.54 275	98.41 265	92.16 249	86.13 331	96.19 312

Anonymous20240521		91.18 297	90.44 298	93.42 315	93.70 341	88.47 329	98.94 74	97.56 275	88.46 322	89.56 322	95.08 330	77.15 333	96.97 330	83.92 333	89.55 291	94.82 336

VPA-MVSNet		95.75 159	95.11 173	97.69 150	97.24 240	97.27 91	98.94 74	99.23 12	95.13 127	95.51 186	97.32 243	85.73 254	98.91 209	97.33 79	89.55 291	96.89 244

RRT_test8_iter05		94.56 229	94.19 216	95.67 268	97.60 212	91.34 287	98.93 76	98.42 180	94.75 144	93.39 259	97.87 199	79.00 318	98.61 238	96.78 108	90.99 274	97.07 226

LS3D		97.16 107	96.66 116	98.68 79	98.53 149	97.19 97	98.93 76	98.90 44	92.83 230	95.99 182	99.37 22	92.12 122	99.87 44	93.67 209	99.57 75	98.97 157

ACMM	93.85 9	95.69 163	95.38 159	96.61 214	97.61 211	93.84 235	98.91 78	98.44 176	95.25 121	94.28 221	98.47 143	86.04 252	99.12 180	95.50 153	93.95 232	96.87 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MTAPA		98.58 23	98.29 35	99.46 12	99.76 1	98.64 22	98.90 79	98.74 104	97.27 34	98.02 91	99.39 14	94.81 77	99.96 1	97.91 39	99.79 19	99.77 20

SD-MVS		98.64 14	98.68 5	98.53 91	99.33 65	98.36 42	98.90 79	98.85 64	97.28 30	99.72 3	99.39 14	96.63 15	97.60 319	98.17 28	99.85 3	99.64 70
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
TransMVSNet (Re)		92.67 286	91.51 291	96.15 248	96.58 281	94.65 205	98.90 79	96.73 322	90.86 291	89.46 323	97.86 200	85.62 256	98.09 295	86.45 318	81.12 340	95.71 321

EPNet		97.28 100	96.87 103	98.51 92	94.98 327	96.14 140	98.90 79	97.02 310	98.28 1	95.99 182	99.11 67	91.36 140	99.89 35	96.98 88	99.19 108	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MTMP							98.89 83	94.14 351

UA-Net		97.96 58	97.62 64	98.98 65	98.86 121	97.47 84	98.89 83	99.08 21	96.67 59	98.72 56	99.54 1	93.15 105	99.81 71	94.87 167	98.83 123	99.65 67

OurMVSNet-221017-0		94.21 250	94.00 229	94.85 292	95.60 316	89.22 318	98.89 83	97.43 291	95.29 118	92.18 296	98.52 140	82.86 294	98.59 242	93.46 214	91.76 262	96.74 260

thisisatest0530		96.01 147	95.36 160	97.97 129	98.38 156	95.52 169	98.88 86	94.19 350	94.04 169	97.64 121	98.31 163	83.82 291	99.46 152	95.29 159	97.70 167	98.93 161

UGNet		96.78 121	96.30 127	98.19 117	98.24 168	95.89 158	98.88 86	98.93 37	97.39 24	96.81 153	97.84 203	82.60 295	99.90 33	96.53 115	99.49 88	98.79 168
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
Anonymous20240529		95.10 195	94.22 214	97.75 144	99.01 109	94.26 225	98.87 88	98.83 68	85.79 337	96.64 158	98.97 87	78.73 319	99.85 50	96.27 123	94.89 216	99.12 142

thres100view900		95.38 177	94.70 190	97.41 166	98.98 113	94.92 196	98.87 88	96.90 316	95.38 112	96.61 160	96.88 284	84.29 277	99.56 137	88.11 307	96.29 199	97.76 207

XXY-MVS		95.20 190	94.45 204	97.46 163	96.75 273	96.56 122	98.86 90	98.65 136	93.30 212	93.27 263	98.27 168	84.85 269	98.87 216	94.82 170	91.26 270	96.96 233

VDDNet		95.36 180	94.53 197	97.86 134	98.10 182	95.13 185	98.85 91	97.75 265	90.46 296	98.36 77	99.39 14	73.27 346	99.64 126	97.98 36	96.58 189	98.81 167

thres600view7		95.49 169	94.77 186	97.67 152	98.98 113	95.02 188	98.85 91	96.90 316	95.38 112	96.63 159	96.90 283	84.29 277	99.59 133	88.65 306	96.33 197	98.40 190

114514_t		96.93 115	96.27 128	98.92 69	99.50 41	97.63 78	98.85 91	98.90 44	84.80 340	97.77 109	99.11 67	92.84 107	99.66 123	94.85 168	99.77 26	99.47 98

LFMVS		95.86 154	94.98 179	98.47 96	98.87 120	96.32 133	98.84 94	96.02 329	93.40 207	98.62 63	99.20 52	74.99 340	99.63 129	97.72 53	97.20 176	99.46 102

testtj		98.33 50	97.95 55	99.47 11	99.49 45	98.70 19	98.83 95	98.86 61	95.48 106	98.91 45	99.17 56	95.48 50	99.93 15	95.80 141	99.53 85	99.76 26

alignmvs		97.56 84	97.07 94	99.01 62	98.66 139	98.37 41	98.83 95	98.06 248	96.74 56	98.00 97	97.65 219	90.80 153	99.48 150	98.37 23	96.56 190	99.19 132

DeepC-MVS	95.98 3	97.88 64	97.58 67	98.77 75	99.25 86	96.93 105	98.83 95	98.75 102	96.96 50	96.89 149	99.50 4	90.46 159	99.87 44	97.84 47	99.76 32	99.52 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		98.61 17	98.30 34	99.55 6	99.62 30	98.95 13	98.82 98	98.81 76	95.80 91	99.16 26	99.47 8	95.37 57	99.92 21	97.89 42	99.75 38	99.79 10

casdiffmvs		97.63 77	97.41 80	98.28 108	98.33 163	96.14 140	98.82 98	98.32 195	96.38 71	97.95 99	99.21 48	91.23 145	99.23 168	98.12 30	98.37 144	99.48 96

GBi-Net		94.49 235	93.80 243	96.56 221	98.21 171	95.00 189	98.82 98	98.18 219	92.46 238	94.09 231	97.07 263	81.16 302	97.95 305	92.08 251	92.14 257	96.72 263

test1		94.49 235	93.80 243	96.56 221	98.21 171	95.00 189	98.82 98	98.18 219	92.46 238	94.09 231	97.07 263	81.16 302	97.95 305	92.08 251	92.14 257	96.72 263

FMVSNet1		93.19 280	92.07 285	96.56 221	97.54 219	95.00 189	98.82 98	98.18 219	90.38 299	92.27 294	97.07 263	73.68 345	97.95 305	89.36 300	91.30 268	96.72 263

API-MVS		97.41 94	97.25 86	97.91 132	98.70 135	96.80 110	98.82 98	98.69 118	94.53 154	98.11 84	98.28 165	94.50 88	99.57 135	94.12 195	99.49 88	97.37 220

ACMH	92.88 16	94.55 230	93.95 233	96.34 241	97.63 210	93.26 258	98.81 104	98.49 171	93.43 206	89.74 319	98.53 137	81.91 299	99.08 188	93.69 206	93.30 247	96.70 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		96.29 137	96.56 118	95.51 271	97.89 195	90.22 306	98.80 105	98.10 235	96.57 63	96.45 172	96.66 294	90.81 151	98.91 209	95.72 144	97.99 155	97.40 217

HQP_MVS		96.14 143	95.90 139	96.85 197	97.42 230	94.60 212	98.80 105	98.56 150	97.28 30	95.34 187	98.28 165	87.09 231	99.03 194	96.07 128	94.27 219	96.92 236

plane_prior2							98.80 105		97.28 30

APD-MVS		98.35 46	98.00 53	99.42 15	99.51 39	98.72 17	98.80 105	98.82 70	94.52 156	99.23 20	99.25 43	95.54 49	99.80 80	96.52 116	99.77 26	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet (Re)		95.78 158	95.19 169	97.58 158	96.99 259	97.47 84	98.79 109	99.18 16	95.60 100	93.92 238	97.04 269	91.68 131	98.48 250	95.80 141	87.66 315	96.79 254

FMVSNet2		94.47 237	93.61 256	97.04 184	98.21 171	96.43 128	98.79 109	98.27 206	92.46 238	93.50 256	97.09 260	81.16 302	98.00 303	91.09 268	91.93 260	96.70 267

testgi		93.06 282	92.45 281	94.88 291	96.43 289	89.90 307	98.75 111	97.54 281	95.60 100	91.63 304	97.91 194	74.46 343	97.02 329	86.10 320	93.67 236	97.72 211

LCM-MVSNet-Re		95.22 188	95.32 164	94.91 289	98.18 176	87.85 337	98.75 111	95.66 335	95.11 129	88.96 325	96.85 287	90.26 164	97.65 317	95.65 149	98.44 141	99.22 128

SixPastTwentyTwo		93.34 274	92.86 273	94.75 296	95.67 314	89.41 316	98.75 111	96.67 326	93.89 178	90.15 317	98.25 170	80.87 306	98.27 284	90.90 273	90.64 277	96.57 282

UniMVSNet_ETH3D		94.24 249	93.33 265	96.97 189	97.19 247	93.38 254	98.74 114	98.57 148	91.21 286	93.81 244	98.58 133	72.85 347	98.77 227	95.05 165	93.93 233	98.77 170

MVS_Test		97.28 100	97.00 97	98.13 120	98.33 163	95.97 148	98.74 114	98.07 243	94.27 163	98.44 73	98.07 181	92.48 111	99.26 164	96.43 120	98.19 150	99.16 137

UniMVSNet_NR-MVSNet		95.71 161	95.15 170	97.40 168	96.84 268	96.97 103	98.74 114	99.24 10	95.16 125	93.88 240	97.72 214	91.68 131	98.31 276	95.81 139	87.25 320	96.92 236

NR-MVSNet		94.98 203	94.16 219	97.44 164	96.53 283	97.22 96	98.74 114	98.95 34	94.96 137	89.25 324	97.69 215	89.32 176	98.18 287	94.59 179	87.40 318	96.92 236

ETV-MVS		97.96 58	97.81 59	98.40 103	98.42 154	97.27 91	98.73 118	98.55 152	96.84 52	98.38 76	97.44 237	95.39 55	99.35 159	97.62 62	98.89 118	98.58 185

baseline1		95.84 155	95.12 172	98.01 127	98.49 152	95.98 143	98.73 118	97.03 308	95.37 114	96.22 176	98.19 174	89.96 167	99.16 174	94.60 177	87.48 316	98.90 163

MVSTER		96.06 145	95.72 143	97.08 183	98.23 169	95.93 154	98.73 118	98.27 206	94.86 141	95.07 191	98.09 180	88.21 205	98.54 246	96.59 112	93.46 241	96.79 254

ACMP	93.49 10	95.34 182	94.98 179	96.43 235	97.67 207	93.48 249	98.73 118	98.44 176	94.94 140	92.53 286	98.53 137	84.50 276	99.14 178	95.48 154	94.00 230	96.66 273
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS++		98.58 23	98.25 38	99.55 6	99.50 41	99.08 9	98.72 122	98.66 132	97.51 16	98.15 82	98.83 107	95.70 44	99.92 21	97.53 72	99.67 54	99.66 65

9.14			98.06 49		99.47 48		98.71 123	98.82 70	94.36 161	99.16 26	99.29 39	96.05 32	99.81 71	97.00 87	99.71 50

VPNet		94.99 201	94.19 216	97.40 168	97.16 249	96.57 121	98.71 123	98.97 30	95.67 97	94.84 197	98.24 171	80.36 310	98.67 234	96.46 117	87.32 319	96.96 233

MSLP-MVS++		98.56 28	98.57 8	98.55 87	99.26 85	96.80 110	98.71 123	99.05 24	97.28 30	98.84 46	99.28 40	96.47 18	99.40 155	98.52 13	99.70 51	99.47 98

ACMH+	92.99 14	94.30 245	93.77 246	95.88 261	97.81 199	92.04 275	98.71 123	98.37 188	93.99 174	90.60 313	98.47 143	80.86 307	99.05 190	92.75 235	92.40 256	96.55 286

Anonymous202405211		95.28 185	94.49 199	97.67 152	99.00 110	93.75 239	98.70 127	97.04 307	90.66 292	96.49 169	98.80 110	78.13 324	99.83 56	96.21 126	95.36 215	99.44 105

DP-MVS		96.59 127	95.93 138	98.57 85	99.34 62	96.19 139	98.70 127	98.39 185	89.45 315	94.52 207	99.35 28	91.85 128	99.85 50	92.89 233	98.88 119	99.68 57

Fast-Effi-MVS+-dtu		95.87 153	95.85 140	95.91 258	97.74 204	91.74 281	98.69 129	98.15 227	95.56 102	94.92 195	97.68 218	88.98 189	98.79 225	93.19 222	97.78 163	97.20 224

tfpn200view9		95.32 184	94.62 193	97.43 165	98.94 115	94.98 192	98.68 130	96.93 314	95.33 115	96.55 164	96.53 300	84.23 280	99.56 137	88.11 307	96.29 199	97.76 207

VDD-MVS		95.82 157	95.23 167	97.61 157	98.84 124	93.98 231	98.68 130	97.40 293	95.02 134	97.95 99	99.34 31	74.37 344	99.78 96	98.64 3	96.80 182	99.08 148

thres400		95.38 177	94.62 193	97.65 155	98.94 115	94.98 192	98.68 130	96.93 314	95.33 115	96.55 164	96.53 300	84.23 280	99.56 137	88.11 307	96.29 199	98.40 190

ETH3D-3000-0.1		98.35 46	98.00 53	99.38 17	99.47 48	98.68 21	98.67 133	98.84 65	94.66 151	99.11 28	99.25 43	95.46 51	99.81 71	96.80 106	99.73 43	99.63 73

pmmvs6		91.77 292	90.63 296	95.17 282	94.69 333	91.24 292	98.67 133	97.92 258	86.14 333	89.62 320	97.56 229	75.79 337	98.34 272	90.75 276	84.56 333	95.94 318

v2v482		94.69 217	94.03 225	96.65 209	96.17 298	94.79 203	98.67 133	98.08 241	92.72 231	94.00 236	97.16 254	87.69 222	98.45 254	92.91 230	88.87 303	96.72 263

DU-MVS		95.42 174	94.76 187	97.40 168	96.53 283	96.97 103	98.66 136	98.99 29	95.43 109	93.88 240	97.69 215	88.57 197	98.31 276	95.81 139	87.25 320	96.92 236

MAR-MVS		96.91 116	96.40 124	98.45 97	98.69 137	96.90 107	98.66 136	98.68 121	92.40 244	97.07 139	97.96 190	91.54 137	99.75 105	93.68 207	98.92 116	98.69 175
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
hse-mvs3		96.17 142	95.62 151	97.81 139	99.03 108	94.45 216	98.64 138	98.75 102	97.48 18	98.67 58	98.72 119	89.76 169	99.86 49	97.95 37	81.59 339	99.11 143

VNet		97.79 69	97.40 81	98.96 67	98.88 119	97.55 81	98.63 139	98.93 37	96.74 56	99.02 34	98.84 106	90.33 162	99.83 56	98.53 9	96.66 186	99.50 91

PVSNet_Blended_VisFu		97.70 73	97.46 77	98.44 98	99.27 83	95.91 156	98.63 139	99.16 17	94.48 158	97.67 117	98.88 102	92.80 108	99.91 30	97.11 84	99.12 110	99.50 91

PAPM_NR		97.46 87	97.11 91	98.50 93	99.50 41	96.41 129	98.63 139	98.60 140	95.18 124	97.06 140	98.06 182	94.26 93	99.57 135	93.80 205	98.87 121	99.52 85

Baseline_NR-MVSNet		94.35 242	93.81 242	95.96 256	96.20 296	94.05 230	98.61 142	96.67 326	91.44 273	93.85 242	97.60 224	88.57 197	98.14 290	94.39 184	86.93 323	95.68 322

v1144		94.59 227	93.92 234	96.60 216	96.21 295	94.78 204	98.59 143	98.14 229	91.86 262	94.21 226	97.02 271	87.97 213	98.41 265	91.72 262	89.57 289	96.61 277

AllTest		95.24 187	94.65 192	96.99 186	99.25 86	93.21 260	98.59 143	98.18 219	91.36 275	93.52 253	98.77 114	84.67 272	99.72 109	89.70 293	97.87 159	98.02 202

Fast-Effi-MVS+		96.28 139	95.70 147	98.03 126	98.29 167	95.97 148	98.58 145	98.25 211	91.74 263	95.29 190	97.23 249	91.03 150	99.15 177	92.90 231	97.96 156	98.97 157

Anonymous20231206		91.66 293	91.10 293	93.33 318	94.02 340	87.35 339	98.58 145	97.26 300	90.48 295	90.16 316	96.31 306	83.83 290	96.53 340	79.36 345	89.90 285	96.12 313

v144192		94.39 241	93.70 252	96.48 229	96.06 303	94.35 222	98.58 145	98.16 226	91.45 272	94.33 219	97.02 271	87.50 225	98.45 254	91.08 269	89.11 298	96.63 275

v148		94.29 246	93.76 248	95.91 258	96.10 301	92.93 265	98.58 145	97.97 253	92.59 236	93.47 257	96.95 280	88.53 200	98.32 274	92.56 241	87.06 322	96.49 298

COLMAP_ROB	93.27 12	95.33 183	94.87 184	96.71 204	99.29 78	93.24 259	98.58 145	98.11 233	89.92 307	93.57 251	99.10 69	86.37 245	99.79 92	90.78 275	98.10 153	97.09 225
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RRT_MVS		96.04 146	95.53 152	97.56 160	97.07 255	97.32 88	98.57 150	98.09 239	95.15 126	95.02 193	98.44 145	88.20 206	98.58 244	96.17 127	93.09 250	96.79 254

mvs-test1		96.60 125	96.68 115	96.37 238	97.89 195	91.81 277	98.56 151	98.10 235	96.57 63	96.52 168	97.94 192	90.81 151	99.45 153	95.72 144	98.01 154	97.86 206

FMVSNet3		94.97 204	94.26 213	97.11 181	98.18 176	96.62 116	98.56 151	98.26 210	93.67 197	94.09 231	97.10 256	84.25 279	98.01 301	92.08 251	92.14 257	96.70 267

zzz-MVS		98.55 30	98.25 38	99.46 12	99.76 1	98.64 22	98.55 153	98.74 104	97.27 34	98.02 91	99.39 14	94.81 77	99.96 1	97.91 39	99.79 19	99.77 20

F-COLMAP		97.09 111	96.80 104	97.97 129	99.45 55	94.95 195	98.55 153	98.62 139	93.02 221	96.17 178	98.58 133	94.01 96	99.81 71	93.95 200	98.90 117	99.14 140

v1921920		94.20 251	93.47 262	96.40 237	95.98 306	94.08 229	98.52 155	98.15 227	91.33 278	94.25 223	97.20 252	86.41 244	98.42 258	90.04 287	89.39 295	96.69 272

EU-MVSNet		93.66 268	94.14 221	92.25 326	95.96 307	83.38 347	98.52 155	98.12 231	94.69 147	92.61 283	98.13 178	87.36 228	96.39 342	91.82 259	90.00 284	96.98 231

TAMVS		97.02 112	96.79 106	97.70 149	98.06 185	95.31 178	98.52 155	98.31 197	93.95 176	97.05 141	98.61 128	93.49 101	98.52 248	95.33 156	97.81 161	99.29 122

LTVRE_ROB	92.95 15	94.60 225	93.90 236	96.68 208	97.41 233	94.42 218	98.52 155	98.59 142	91.69 266	91.21 306	98.35 156	84.87 268	99.04 193	91.06 270	93.44 244	96.60 278
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
TDRefinement		91.06 299	89.68 304	95.21 280	85.35 355	91.49 286	98.51 159	97.07 305	91.47 271	88.83 328	97.84 203	77.31 331	99.09 187	92.79 234	77.98 344	95.04 333

v1192		94.32 244	93.58 257	96.53 225	96.10 301	94.45 216	98.50 160	98.17 224	91.54 270	94.19 227	97.06 266	86.95 235	98.43 257	90.14 282	89.57 289	96.70 267

test_0402		91.32 295	90.27 300	94.48 304	96.60 280	91.12 293	98.50 160	97.22 301	86.10 334	88.30 330	96.98 275	77.65 329	97.99 304	78.13 349	92.94 252	94.34 338

DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 47	99.25 86	98.04 60	98.50 160	98.78 95	97.72 6	98.92 44	99.28 40	95.27 64	99.82 64	97.55 70	99.77 26	99.69 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNVR-MVS		98.78 6	98.56 9	99.45 14	99.32 68	98.87 15	98.47 163	98.81 76	97.72 6	98.76 52	99.16 61	97.05 10	99.78 96	98.06 33	99.66 57	99.69 51

test_yl		97.22 102	96.78 107	98.54 89	98.73 130	96.60 119	98.45 164	98.31 197	94.70 145	98.02 91	98.42 148	90.80 153	99.70 115	96.81 104	96.79 183	99.34 111

DCV-MVSNet		97.22 102	96.78 107	98.54 89	98.73 130	96.60 119	98.45 164	98.31 197	94.70 145	98.02 91	98.42 148	90.80 153	99.70 115	96.81 104	96.79 183	99.34 111

NCCC		98.61 17	98.35 24	99.38 17	99.28 82	98.61 24	98.45 164	98.76 99	97.82 5	98.45 72	98.93 97	96.65 14	99.83 56	97.38 77	99.41 97	99.71 44

v1240		94.06 263	93.29 267	96.34 241	96.03 305	93.90 233	98.44 167	98.17 224	91.18 287	94.13 230	97.01 273	86.05 250	98.42 258	89.13 303	89.50 293	96.70 267

plane_prior						94.60 212	98.44 167		96.74 56						94.22 221

MP-MVS-pluss		98.31 52	97.92 57	99.49 9	99.72 12	98.88 14	98.43 169	98.78 95	94.10 167	97.69 116	99.42 12	95.25 66	99.92 21	98.09 32	99.80 17	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		95.69 163	95.33 163	96.76 201	96.16 300	94.63 207	98.43 169	98.39 185	96.64 60	95.02 193	98.78 112	85.15 264	99.05 190	95.21 163	94.20 222	96.60 278

DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 171	98.91 43	97.58 14	99.54 7	99.46 9	97.10 9	99.94 3	97.64 61	99.84 8	99.83 5

MCST-MVS		98.65 13	98.37 21	99.48 10	99.60 31	98.87 15	98.41 172	98.68 121	97.04 47	98.52 68	98.80 110	96.78 12	99.83 56	97.93 38	99.61 67	99.74 33

Regformer-3		98.59 20	98.50 14	98.86 73	99.43 57	97.05 101	98.40 173	98.68 121	97.43 21	99.06 31	99.31 35	95.80 43	99.77 101	98.62 5	99.76 32	99.78 13

Regformer-4		98.64 14	98.53 11	98.99 63	99.43 57	97.37 87	98.40 173	98.79 92	97.46 20	99.09 30	99.31 35	95.86 42	99.80 80	98.64 3	99.76 32	99.79 10

Regformer-1		98.66 12	98.51 13	99.12 57	99.35 60	97.81 74	98.37 175	98.76 99	97.49 17	99.20 22	99.21 48	96.08 29	99.79 92	98.42 20	99.73 43	99.75 28

Regformer-2		98.69 11	98.52 12	99.19 43	99.35 60	98.01 62	98.37 175	98.81 76	97.48 18	99.21 21	99.21 48	96.13 27	99.80 80	98.40 22	99.73 43	99.75 28

CANet		98.05 56	97.76 61	98.90 71	98.73 130	97.27 91	98.35 177	98.78 95	97.37 27	97.72 114	98.96 93	91.53 138	99.92 21	98.79 2	99.65 58	99.51 89

AUN-MVS		94.53 232	93.73 250	96.92 194	98.50 151	93.52 248	98.34 178	98.10 235	93.83 183	95.94 184	97.98 189	85.59 257	99.03 194	94.35 186	80.94 342	98.22 197

DWT-MVSNet_test		94.82 211	94.36 209	96.20 247	97.35 235	90.79 298	98.34 178	96.57 328	92.91 226	95.33 189	96.44 304	82.00 297	99.12 180	94.52 181	95.78 213	98.70 173

ETH3D cwj APD-0.16		97.96 58	97.52 72	99.29 31	99.05 105	98.52 27	98.33 180	98.68 121	93.18 215	98.68 57	99.13 64	94.62 81	99.83 56	96.45 118	99.55 83	99.52 85

test20.03		90.89 301	90.38 299	92.43 324	93.48 342	88.14 334	98.33 180	97.56 275	93.40 207	87.96 331	96.71 293	80.69 309	94.13 351	79.15 346	86.17 329	95.01 335

DP-MVS Recon		97.86 65	97.46 77	99.06 61	99.53 36	98.35 43	98.33 180	98.89 46	92.62 234	98.05 87	98.94 96	95.34 59	99.65 124	96.04 132	99.42 96	99.19 132

RPSCF		94.87 210	95.40 155	93.26 320	98.89 118	82.06 351	98.33 180	98.06 248	90.30 301	96.56 162	99.26 42	87.09 231	99.49 146	93.82 204	96.32 198	98.24 196

TAPA-MVS	93.98 7	95.35 181	94.56 196	97.74 145	99.13 102	94.83 200	98.33 180	98.64 137	86.62 329	96.29 175	98.61 128	94.00 97	99.29 163	80.00 343	99.41 97	99.09 145
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IterMVS-LS		95.46 170	95.21 168	96.22 246	98.12 180	93.72 242	98.32 185	98.13 230	93.71 190	94.26 222	97.31 244	92.24 117	98.10 293	94.63 174	90.12 282	96.84 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_anonymous		96.70 123	96.53 121	97.18 176	98.19 174	93.78 236	98.31 186	98.19 216	94.01 172	94.47 209	98.27 168	92.08 124	98.46 253	97.39 76	97.91 157	99.31 117

WTY-MVS		97.37 97	96.92 101	98.72 77	98.86 121	96.89 109	98.31 186	98.71 114	95.26 120	97.67 117	98.56 136	92.21 119	99.78 96	95.89 136	96.85 181	99.48 96

D2MVS		95.18 191	95.08 174	95.48 272	97.10 253	92.07 273	98.30 188	99.13 19	94.02 171	92.90 274	96.73 291	89.48 172	98.73 229	94.48 183	93.60 240	95.65 323

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 78	99.46 51	96.49 125	98.30 188	98.69 118	97.21 37	98.84 46	99.36 26	95.41 54	99.78 96	98.62 5	99.65 58	99.80 9

DSMNet-mixed		92.52 288	92.58 279	92.33 325	94.15 336	82.65 349	98.30 188	94.26 349	89.08 319	92.65 282	95.73 320	85.01 266	95.76 344	86.24 319	97.76 164	98.59 183

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 83	99.45 55	96.32 133	98.28 191	98.68 121	97.17 40	98.74 53	99.37 22	95.25 66	99.79 92	98.57 7	99.54 84	99.73 36

OMC-MVS		97.55 85	97.34 83	98.20 115	99.33 65	95.92 155	98.28 191	98.59 142	95.52 105	97.97 98	99.10 69	93.28 104	99.49 146	95.09 164	98.88 119	99.19 132

baseline2		95.11 194	94.52 198	96.87 196	96.65 279	93.56 245	98.27 193	94.10 352	93.45 205	92.02 300	97.43 238	87.45 227	99.19 172	93.88 202	97.41 174	97.87 205

PVSNet_BlendedMVS		96.73 122	96.60 117	97.12 180	99.25 86	95.35 176	98.26 194	99.26 8	94.28 162	97.94 101	97.46 234	92.74 109	99.81 71	96.88 99	93.32 246	96.20 311

BH-untuned		95.95 150	95.72 143	96.65 209	98.55 148	92.26 270	98.23 195	97.79 263	93.73 188	94.62 204	98.01 186	88.97 190	99.00 198	93.04 227	98.51 137	98.68 176

sss		97.39 95	96.98 99	98.61 83	98.60 145	96.61 118	98.22 196	98.93 37	93.97 175	98.01 95	98.48 142	91.98 126	99.85 50	96.45 118	98.15 151	99.39 108

xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 30	99.46 51	98.38 35	98.21 197	98.52 159	97.95 3	99.32 15	99.39 14	96.22 20	99.84 53	97.72 53	99.73 43	99.67 61

save fliter					99.46 51	98.38 35	98.21 197	98.71 114	97.95 3

WR-MVS		95.15 192	94.46 202	97.22 173	96.67 278	96.45 126	98.21 197	98.81 76	94.15 165	93.16 266	97.69 215	87.51 223	98.30 278	95.29 159	88.62 305	96.90 243

ETH3 D test6400		97.59 81	97.01 96	99.34 23	99.40 59	98.56 25	98.20 200	98.81 76	91.63 268	98.44 73	98.85 104	93.98 98	99.82 64	94.11 196	99.69 52	99.64 70

pmmvs5		93.65 270	92.97 272	95.68 267	95.49 320	92.37 269	98.20 200	97.28 298	89.66 312	92.58 284	97.26 246	82.14 296	98.09 295	93.18 223	90.95 275	96.58 280

thres200		95.25 186	94.57 195	97.28 171	98.81 126	94.92 196	98.20 200	97.11 303	95.24 123	96.54 166	96.22 312	84.58 274	99.53 143	87.93 311	96.50 193	97.39 218

CDS-MVSNet		96.99 113	96.69 113	97.90 133	98.05 186	95.98 143	98.20 200	98.33 194	93.67 197	96.95 143	98.49 141	93.54 100	98.42 258	95.24 162	97.74 165	99.31 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		96.25 141	95.73 142	97.79 140	97.13 251	95.55 168	98.19 204	98.59 142	93.47 204	92.03 299	97.82 207	91.33 142	99.49 146	94.62 176	98.44 141	98.32 195

1121		97.37 97	96.77 111	99.16 50	99.34 62	97.99 65	98.19 204	98.68 121	90.14 304	98.01 95	98.97 87	94.80 79	99.87 44	93.36 217	99.46 93	99.61 75

MVS		94.67 222	93.54 259	98.08 123	96.88 266	96.56 122	98.19 204	98.50 167	78.05 349	92.69 281	98.02 184	91.07 149	99.63 129	90.09 283	98.36 146	98.04 201

BH-RMVSNet		95.92 152	95.32 164	97.69 150	98.32 165	94.64 206	98.19 204	97.45 289	94.56 153	96.03 180	98.61 128	85.02 265	99.12 180	90.68 277	99.06 112	99.30 120

1112_ss		96.63 124	96.00 137	98.50 93	98.56 146	96.37 130	98.18 208	98.10 235	92.92 225	94.84 197	98.43 146	92.14 121	99.58 134	94.35 186	96.51 192	99.56 84

MVS_0304		92.81 284	92.01 286	95.23 279	97.46 225	91.33 289	98.17 209	98.81 76	91.13 288	93.80 245	95.68 325	66.08 353	98.06 298	90.79 274	96.13 208	96.32 308

EPNet_dtu		95.21 189	94.95 181	95.99 253	96.17 298	90.45 304	98.16 210	97.27 299	96.77 54	93.14 269	98.33 161	90.34 161	98.42 258	85.57 324	98.81 125	99.09 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HY-MVS	93.96 8	96.82 120	96.23 131	98.57 85	98.46 153	97.00 102	98.14 211	98.21 213	93.95 176	96.72 156	97.99 188	91.58 133	99.76 103	94.51 182	96.54 191	98.95 160

PLC	95.07 4	97.20 105	96.78 107	98.44 98	99.29 78	96.31 135	98.14 211	98.76 99	92.41 243	96.39 173	98.31 163	94.92 76	99.78 96	94.06 198	98.77 126	99.23 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EG-PatchMatch MVS		91.13 298	90.12 301	94.17 311	94.73 332	89.00 322	98.13 213	97.81 262	89.22 318	85.32 342	96.46 302	67.71 350	98.42 258	87.89 312	93.82 235	95.08 332

EI-MVSNet		95.96 149	95.83 141	96.36 239	97.93 192	93.70 243	98.12 214	98.27 206	93.70 192	95.07 191	99.02 80	92.23 118	98.54 246	94.68 173	93.46 241	96.84 250

CVMVSNet		95.43 173	96.04 135	93.57 314	97.93 192	83.62 346	98.12 214	98.59 142	95.68 96	96.56 162	99.02 80	87.51 223	97.51 323	93.56 213	97.44 172	99.60 78

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 74	99.22 94	97.25 95	98.11 216	98.29 205	97.19 39	98.99 38	99.02 80	96.22 20	99.67 122	98.52 13	98.56 135	99.51 89

XVG-ACMP-BASELINE		94.54 231	94.14 221	95.75 266	96.55 282	91.65 283	98.11 216	98.44 176	94.96 137	94.22 225	97.90 195	79.18 317	99.11 183	94.05 199	93.85 234	96.48 299

CNLPA		97.45 90	97.03 95	98.73 76	99.05 105	97.44 86	98.07 218	98.53 157	95.32 117	96.80 154	98.53 137	93.32 103	99.72 109	94.31 189	99.31 104	99.02 152

diffmvs		97.58 82	97.40 81	98.13 120	98.32 165	95.81 160	98.06 219	98.37 188	96.20 76	98.74 53	98.89 101	91.31 143	99.25 165	98.16 29	98.52 136	99.34 111

CHOSEN 1792x2688		97.12 109	96.80 104	98.08 123	99.30 75	94.56 214	98.05 220	99.71 1	93.57 201	97.09 136	98.91 100	88.17 207	99.89 35	96.87 102	99.56 80	99.81 8

HQP-NCC					97.20 244		98.05 220		96.43 68	94.45 210

ACMP_Plane					97.20 244		98.05 220		96.43 68	94.45 210

HQP-MVS		95.72 160	95.40 155	96.69 207	97.20 244	94.25 226	98.05 220	98.46 172	96.43 68	94.45 210	97.73 212	86.75 237	98.96 202	95.30 157	94.18 223	96.86 249

MIMVSNet1		89.67 310	88.28 314	93.82 312	92.81 346	91.08 294	98.01 224	97.45 289	87.95 324	87.90 332	95.87 318	67.63 351	94.56 350	78.73 348	88.18 310	95.83 320

AdaColmap		97.15 108	96.70 112	98.48 95	99.16 99	96.69 115	98.01 224	98.89 46	94.44 160	96.83 150	98.68 122	90.69 156	99.76 103	94.36 185	99.29 105	98.98 156

FMVSNet5		91.81 291	90.92 294	94.49 303	97.21 243	92.09 272	98.00 226	97.55 280	89.31 317	90.86 310	95.61 326	74.48 342	95.32 347	85.57 324	89.70 287	96.07 315

CANet_DTU		96.96 114	96.55 119	98.21 114	98.17 178	96.07 142	97.98 227	98.21 213	97.24 36	97.13 135	98.93 97	86.88 236	99.91 30	95.00 166	99.37 101	98.66 179

MVP-Stereo		94.28 248	93.92 234	95.35 277	94.95 328	92.60 268	97.97 228	97.65 269	91.61 269	90.68 312	97.09 260	86.32 246	98.42 258	89.70 293	99.34 102	95.02 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DIV-MVS_2432*1600		90.38 304	89.38 307	93.40 317	92.85 345	88.94 323	97.95 229	97.94 256	90.35 300	90.25 315	93.96 338	79.82 312	95.94 343	84.62 332	76.69 346	95.33 326

MVS_111021_LR		98.34 48	98.23 42	98.67 80	99.27 83	96.90 107	97.95 229	99.58 3	97.14 42	98.44 73	99.01 84	95.03 73	99.62 131	97.91 39	99.75 38	99.50 91

TEST9					99.31 70	98.50 29	97.92 231	98.73 108	92.63 233	97.74 112	98.68 122	96.20 23	99.80 80

train_agg		97.97 57	97.52 72	99.33 27	99.31 70	98.50 29	97.92 231	98.73 108	92.98 222	97.74 112	98.68 122	96.20 23	99.80 80	96.59 112	99.57 75	99.68 57

CDPH-MVS		97.94 62	97.49 75	99.28 35	99.47 48	98.44 31	97.91 233	98.67 129	92.57 237	98.77 51	98.85 104	95.93 38	99.72 109	95.56 151	99.69 52	99.68 57

MVS_111021_HR		98.47 38	98.34 28	98.88 72	99.22 94	97.32 88	97.91 233	99.58 3	97.20 38	98.33 79	99.00 85	95.99 35	99.64 126	98.05 35	99.76 32	99.69 51

PatchMatch-RL		96.59 127	96.03 136	98.27 109	99.31 70	96.51 124	97.91 233	99.06 22	93.72 189	96.92 147	98.06 182	88.50 201	99.65 124	91.77 261	99.00 114	98.66 179

OpenMVS_ROB	86.42 20	89.00 314	87.43 319	93.69 313	93.08 344	89.42 315	97.91 233	96.89 318	78.58 348	85.86 339	94.69 332	69.48 349	98.29 281	77.13 350	93.29 248	93.36 347

test_8					99.29 78	98.44 31	97.89 237	98.72 110	92.98 222	97.70 115	98.66 125	96.20 23	99.80 80

ab-mvs		96.42 133	95.71 146	98.55 87	98.63 142	96.75 113	97.88 238	98.74 104	93.84 181	96.54 166	98.18 175	85.34 262	99.75 105	95.93 135	96.35 196	99.15 138

jason		97.32 99	97.08 93	98.06 125	97.45 229	95.59 164	97.87 239	97.91 259	94.79 143	98.55 67	98.83 107	91.12 146	99.23 168	97.58 66	99.60 68	99.34 111
jason: jason.
xiu_mvs_v1_base_debu		97.60 78	97.56 69	97.72 146	98.35 158	95.98 143	97.86 240	98.51 162	97.13 43	99.01 35	98.40 150	91.56 134	99.80 80	98.53 9	98.68 127	97.37 220

xiu_mvs_v1_base		97.60 78	97.56 69	97.72 146	98.35 158	95.98 143	97.86 240	98.51 162	97.13 43	99.01 35	98.40 150	91.56 134	99.80 80	98.53 9	98.68 127	97.37 220

xiu_mvs_v1_base_debi		97.60 78	97.56 69	97.72 146	98.35 158	95.98 143	97.86 240	98.51 162	97.13 43	99.01 35	98.40 150	91.56 134	99.80 80	98.53 9	98.68 127	97.37 220

test_prior4						98.01 62	97.86 240

agg_prior1		97.95 61	97.51 74	99.28 35	99.30 75	98.38 35	97.81 244	98.72 110	93.16 217	97.57 126	98.66 125	96.14 26	99.81 71	96.63 111	99.56 80	99.66 65

test_prior3		98.22 55	97.90 58	99.19 43	99.31 70	98.22 50	97.80 245	98.84 65	96.12 81	97.89 106	98.69 120	95.96 36	99.70 115	96.89 96	99.60 68	99.65 67

test_prior2							97.80 245		96.12 81	97.89 106	98.69 120	95.96 36		96.89 96	99.60 68

XVG-OURS-SEG-HR		96.51 130	96.34 125	97.02 185	98.77 128	93.76 237	97.79 247	98.50 167	95.45 108	96.94 144	99.09 74	87.87 217	99.55 142	96.76 109	95.83 212	97.74 209

MS-PatchMatch		93.84 267	93.63 255	94.46 306	96.18 297	89.45 314	97.76 248	98.27 206	92.23 251	92.13 297	97.49 232	79.50 314	98.69 230	89.75 291	99.38 100	95.25 327

DELS-MVS		98.40 42	98.20 44	98.99 63	99.00 110	97.66 76	97.75 249	98.89 46	97.71 8	98.33 79	98.97 87	94.97 74	99.88 43	98.42 20	99.76 32	99.42 107
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
MG-MVS		97.81 67	97.60 66	98.44 98	99.12 103	95.97 148	97.75 249	98.78 95	96.89 51	98.46 69	99.22 47	93.90 99	99.68 121	94.81 171	99.52 87	99.67 61

Test_1112_low_res		96.34 136	95.66 150	98.36 105	98.56 146	95.94 151	97.71 251	98.07 243	92.10 255	94.79 201	97.29 245	91.75 130	99.56 137	94.17 193	96.50 193	99.58 82

BH-w/o		95.38 177	95.08 174	96.26 245	98.34 162	91.79 278	97.70 252	97.43 291	92.87 228	94.24 224	97.22 250	88.66 195	98.84 219	91.55 265	97.70 167	98.16 199

lupinMVS		97.44 91	97.22 88	98.12 122	98.07 183	95.76 161	97.68 253	97.76 264	94.50 157	98.79 49	98.61 128	92.34 113	99.30 162	97.58 66	99.59 71	99.31 117

原ACMM2							97.67 254

LF4IMVS		93.14 281	92.79 275	94.20 309	95.88 309	88.67 326	97.66 255	97.07 305	93.81 184	91.71 302	97.65 219	77.96 326	98.81 223	91.47 266	91.92 261	95.12 330

新几何2							97.64 256

MDA-MVSNet-bldmvs		89.97 308	88.35 313	94.83 294	95.21 325	91.34 287	97.64 256	97.51 283	88.36 323	71.17 354	96.13 314	79.22 316	96.63 339	83.65 334	86.27 328	96.52 292

pmmvs-eth3d		90.36 305	89.05 310	94.32 308	91.10 350	92.12 271	97.63 258	96.95 313	88.86 320	84.91 343	93.13 341	78.32 321	96.74 334	88.70 305	81.81 338	94.09 342

TR-MVS		94.94 207	94.20 215	97.17 177	97.75 201	94.14 228	97.59 259	97.02 310	92.28 250	95.75 185	97.64 221	83.88 288	98.96 202	89.77 290	96.15 207	98.40 190

无先验							97.58 260	98.72 110	91.38 274				99.87 44	93.36 217		99.60 78

旧先验2							97.57 261		91.30 280	98.67 58			99.80 80	95.70 148

CostFormer		94.95 205	94.73 189	95.60 270	97.28 238	89.06 320	97.53 262	96.89 318	89.66 312	96.82 152	96.72 292	86.05 250	98.95 206	95.53 152	96.13 208	98.79 168

XVG-OURS		96.55 129	96.41 123	96.99 186	98.75 129	93.76 237	97.50 263	98.52 159	95.67 97	96.83 150	99.30 38	88.95 191	99.53 143	95.88 137	96.26 203	97.69 212

xiu_mvs_v2_base		97.66 75	97.70 63	97.56 160	98.61 144	95.46 171	97.44 264	98.46 172	97.15 41	98.65 62	98.15 176	94.33 91	99.80 80	97.84 47	98.66 131	97.41 216

tpm		94.13 256	93.80 243	95.12 283	96.50 285	87.91 336	97.44 264	95.89 334	92.62 234	96.37 174	96.30 307	84.13 283	98.30 278	93.24 220	91.66 264	99.14 140

DeepPCF-MVS	96.37 2	97.93 63	98.48 17	96.30 243	99.00 110	89.54 313	97.43 266	98.87 55	98.16 2	99.26 18	99.38 21	96.12 28	99.64 126	98.30 26	99.77 26	99.72 40

test222					99.23 93	97.17 98	97.40 267	98.66 132	88.68 321	98.05 87	98.96 93	94.14 94			99.53 85	99.61 75

pmmvs4		94.69 217	93.99 231	96.81 199	95.74 312	95.94 151	97.40 267	97.67 268	90.42 298	93.37 260	97.59 225	89.08 184	98.20 286	92.97 229	91.67 263	96.30 309

test0.0.03 1		94.08 261	93.51 260	95.80 263	95.53 319	92.89 266	97.38 269	95.97 331	95.11 129	92.51 288	96.66 294	87.71 219	96.94 331	87.03 315	93.67 236	97.57 214

HyFIR lowres test		96.90 117	96.49 122	98.14 118	99.33 65	95.56 166	97.38 269	99.65 2	92.34 245	97.61 123	98.20 173	89.29 177	99.10 186	96.97 89	97.60 170	99.77 20

Effi-MVS+		97.12 109	96.69 113	98.39 104	98.19 174	96.72 114	97.37 271	98.43 179	93.71 190	97.65 120	98.02 184	92.20 120	99.25 165	96.87 102	97.79 162	99.19 132

N_pmnet		87.12 318	87.77 317	85.17 335	95.46 321	61.92 360	97.37 271	70.66 365	85.83 336	88.73 329	96.04 316	85.33 263	97.76 316	80.02 342	90.48 278	95.84 319

PAPR		96.84 119	96.24 130	98.65 81	98.72 134	96.92 106	97.36 273	98.57 148	93.33 209	96.67 157	97.57 227	94.30 92	99.56 137	91.05 272	98.59 133	99.47 98

PMMVS		96.60 125	96.33 126	97.41 166	97.90 194	93.93 232	97.35 274	98.41 181	92.84 229	97.76 110	97.45 236	91.10 148	99.20 171	96.26 124	97.91 157	99.11 143

PS-MVSNAJ		97.73 71	97.77 60	97.62 156	98.68 138	95.58 165	97.34 275	98.51 162	97.29 29	98.66 61	97.88 198	94.51 85	99.90 33	97.87 43	99.17 109	97.39 218

SCA		95.46 170	95.13 171	96.46 233	97.67 207	91.29 291	97.33 276	97.60 273	94.68 148	96.92 147	97.10 256	83.97 286	98.89 213	92.59 239	98.32 148	99.20 129

testdata1							97.32 277		96.34 72

ET-MVSNet_ETH3D		94.13 256	92.98 271	97.58 158	98.22 170	96.20 137	97.31 278	95.37 337	94.53 154	79.56 348	97.63 223	86.51 240	97.53 322	96.91 93	90.74 276	99.02 152

tpm2		94.19 252	93.76 248	95.46 274	97.23 241	89.04 321	97.31 278	96.85 321	87.08 328	96.21 177	96.79 290	83.75 292	98.74 228	92.43 247	96.23 205	98.59 183

PVSNet_Blended		97.38 96	97.12 90	98.14 118	99.25 86	95.35 176	97.28 280	99.26 8	93.13 218	97.94 101	98.21 172	92.74 109	99.81 71	96.88 99	99.40 99	99.27 124

CLD-MVS		95.62 166	95.34 161	96.46 233	97.52 222	93.75 239	97.27 281	98.46 172	95.53 103	94.42 215	98.00 187	86.21 247	98.97 199	96.25 125	94.37 217	96.66 273
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPMVS		94.99 201	94.48 200	96.52 226	97.22 242	91.75 280	97.23 282	91.66 356	94.11 166	97.28 130	96.81 289	85.70 255	98.84 219	93.04 227	97.28 175	98.97 157

miper_lstm_enhance		94.33 243	94.07 224	95.11 284	97.75 201	90.97 295	97.22 283	98.03 250	91.67 267	92.76 278	96.97 276	90.03 166	97.78 315	92.51 244	89.64 288	96.56 284

YYNet1		90.70 303	89.39 306	94.62 300	94.79 331	90.65 301	97.20 284	97.46 287	87.54 326	72.54 352	95.74 319	86.51 240	96.66 338	86.00 321	86.76 327	96.54 287

MDA-MVSNet_test_wron		90.71 302	89.38 307	94.68 298	94.83 330	90.78 299	97.19 285	97.46 287	87.60 325	72.41 353	95.72 322	86.51 240	96.71 337	85.92 322	86.80 326	96.56 284

IterMVS-SCA-FT		94.11 258	93.87 238	94.85 292	97.98 191	90.56 303	97.18 286	98.11 233	93.75 185	92.58 284	97.48 233	83.97 286	97.41 324	92.48 246	91.30 268	96.58 280

IterMVS		94.09 260	93.85 240	94.80 295	97.99 189	90.35 305	97.18 286	98.12 231	93.68 195	92.46 291	97.34 241	84.05 284	97.41 324	92.51 244	91.33 267	96.62 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DPM-MVS		97.55 85	96.99 98	99.23 42	99.04 107	98.55 26	97.17 288	98.35 191	94.85 142	97.93 103	98.58 133	95.07 72	99.71 114	92.60 237	99.34 102	99.43 106

cl_fuxian		94.79 214	94.43 206	95.89 260	97.75 201	93.12 263	97.16 289	98.03 250	92.23 251	93.46 258	97.05 268	91.39 139	98.01 301	93.58 212	89.21 297	96.53 289

new-patchmatchnet		88.50 315	87.45 318	91.67 328	90.31 352	85.89 343	97.16 289	97.33 295	89.47 314	83.63 345	92.77 342	76.38 334	95.06 349	82.70 336	77.29 345	94.06 343

UnsupCasMVSNet_eth		90.99 300	89.92 303	94.19 310	94.08 337	89.83 308	97.13 291	98.67 129	93.69 193	85.83 340	96.19 313	75.15 339	96.74 334	89.14 302	79.41 343	96.00 316

IB-MVS	91.98 17	93.27 276	91.97 287	97.19 175	97.47 224	93.41 252	97.09 292	95.99 330	93.32 210	92.47 290	95.73 320	78.06 325	99.53 143	94.59 179	82.98 334	98.62 182
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
cl-mvsnet_		94.51 234	94.01 228	96.02 252	97.58 214	93.40 253	97.05 293	97.96 255	91.73 265	92.76 278	97.08 262	89.06 185	98.13 291	92.61 236	90.29 281	96.52 292

cl-mvsnet1		94.52 233	94.03 225	95.99 253	97.57 218	93.38 254	97.05 293	97.94 256	91.74 263	92.81 276	97.10 256	89.12 182	98.07 297	92.60 237	90.30 280	96.53 289

miper_ehance_all_eth		95.01 199	94.69 191	95.97 255	97.70 206	93.31 256	97.02 295	98.07 243	92.23 251	93.51 255	96.96 278	91.85 128	98.15 289	93.68 207	91.16 271	96.44 302

CMPMVS	66.06 21	89.70 309	89.67 305	89.78 330	93.19 343	76.56 353	97.00 296	98.35 191	80.97 346	81.57 347	97.75 211	74.75 341	98.61 238	89.85 289	93.63 238	94.17 340
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmrst		95.63 165	95.69 148	95.44 275	97.54 219	88.54 328	96.97 297	97.56 275	93.50 203	97.52 128	96.93 282	89.49 171	99.16 174	95.25 161	96.42 195	98.64 181

dp		94.15 255	93.90 236	94.90 290	97.31 237	86.82 342	96.97 297	97.19 302	91.22 285	96.02 181	96.61 299	85.51 258	99.02 197	90.00 288	94.30 218	98.85 164

cl-mvsnet2		94.68 219	94.19 216	96.13 250	98.11 181	93.60 244	96.94 299	98.31 197	92.43 242	93.32 262	96.87 286	86.51 240	98.28 283	94.10 197	91.16 271	96.51 295

PM-MVS		87.77 316	86.55 320	91.40 329	91.03 351	83.36 348	96.92 300	95.18 340	91.28 282	86.48 338	93.42 340	53.27 356	96.74 334	89.43 299	81.97 337	94.11 341

TinyColmap		92.31 289	91.53 290	94.65 299	96.92 262	89.75 309	96.92 300	96.68 325	90.45 297	89.62 320	97.85 202	76.06 336	98.81 223	86.74 316	92.51 255	95.41 325

our_test_3		93.65 270	93.30 266	94.69 297	95.45 322	89.68 312	96.91 302	97.65 269	91.97 258	91.66 303	96.88 284	89.67 170	97.93 308	88.02 310	91.49 265	96.48 299

test-LLR		95.10 195	94.87 184	95.80 263	96.77 270	89.70 310	96.91 302	95.21 338	95.11 129	94.83 199	95.72 322	87.71 219	98.97 199	93.06 225	98.50 138	98.72 171

TESTMET0.1,1		94.18 254	93.69 253	95.63 269	96.92 262	89.12 319	96.91 302	94.78 343	93.17 216	94.88 196	96.45 303	78.52 320	98.92 208	93.09 224	98.50 138	98.85 164

test-mter		94.08 261	93.51 260	95.80 263	96.77 270	89.70 310	96.91 302	95.21 338	92.89 227	94.83 199	95.72 322	77.69 327	98.97 199	93.06 225	98.50 138	98.72 171

USDC		93.33 275	92.71 276	95.21 280	96.83 269	90.83 297	96.91 302	97.50 284	93.84 181	90.72 311	98.14 177	77.69 327	98.82 222	89.51 297	93.21 249	95.97 317

MDTV_nov1_ep13_2view						84.26 345	96.89 307		90.97 290	97.90 105		89.89 168		93.91 201		99.18 136

ppachtmachnet_test		93.22 278	92.63 278	94.97 288	95.45 322	90.84 296	96.88 308	97.88 260	90.60 293	92.08 298	97.26 246	88.08 211	97.86 314	85.12 328	90.33 279	96.22 310

tpmvs		94.60 225	94.36 209	95.33 278	97.46 225	88.60 327	96.88 308	97.68 267	91.29 281	93.80 245	96.42 305	88.58 196	99.24 167	91.06 270	96.04 210	98.17 198

MDTV_nov1_ep13			95.40 155		97.48 223	88.34 331	96.85 310	97.29 297	93.74 187	97.48 129	97.26 246	89.18 180	99.05 190	91.92 258	97.43 173

PatchmatchNet		95.71 161	95.52 153	96.29 244	97.58 214	90.72 300	96.84 311	97.52 282	94.06 168	97.08 137	96.96 278	89.24 179	98.90 212	92.03 255	98.37 144	99.26 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MSDG		95.93 151	95.30 166	97.83 136	98.90 117	95.36 174	96.83 312	98.37 188	91.32 279	94.43 214	98.73 118	90.27 163	99.60 132	90.05 286	98.82 124	98.52 186

thisisatest0515		95.61 168	94.89 183	97.76 143	98.15 179	95.15 183	96.77 313	94.41 346	92.95 224	97.18 134	97.43 238	84.78 270	99.45 153	94.63 174	97.73 166	98.68 176

GA-MVS		94.81 213	94.03 225	97.14 178	97.15 250	93.86 234	96.76 314	97.58 274	94.00 173	94.76 202	97.04 269	80.91 305	98.48 250	91.79 260	96.25 204	99.09 145

tpm cat1		93.36 272	92.80 274	95.07 286	97.58 214	87.97 335	96.76 314	97.86 261	82.17 345	93.53 252	96.04 316	86.13 248	99.13 179	89.24 301	95.87 211	98.10 200

eth_miper_zixun_eth		94.68 219	94.41 207	95.47 273	97.64 209	91.71 282	96.73 316	98.07 243	92.71 232	93.64 248	97.21 251	90.54 158	98.17 288	93.38 215	89.76 286	96.54 287

test_post1							96.68 317				30.43 363	87.85 218	98.69 230	92.59 239

pmmvs3		86.67 319	84.86 322	92.11 327	88.16 353	87.19 341	96.63 318	94.75 344	79.88 347	87.22 334	92.75 343	66.56 352	95.20 348	81.24 340	76.56 347	93.96 344

miper_enhance_ethall		95.10 195	94.75 188	96.12 251	97.53 221	93.73 241	96.61 319	98.08 241	92.20 254	93.89 239	96.65 296	92.44 112	98.30 278	94.21 192	91.16 271	96.34 305

testmvs		21.48 332	24.95 335	11.09 346	14.89 366	6.47 368	96.56 320	9.87 367	7.55 362	17.93 362	39.02 360	9.43 369	5.90 364	16.56 362	12.72 361	20.91 359

test123		20.95 333	23.72 336	12.64 345	13.54 367	8.19 367	96.55 321	6.13 368	7.48 363	16.74 363	37.98 361	12.97 366	6.05 363	16.69 361	5.43 362	23.68 358

CL-MVSNet_2432*1600		90.11 306	89.14 309	93.02 322	91.86 348	88.23 333	96.51 322	98.07 243	90.49 294	90.49 314	94.41 333	84.75 271	95.34 346	80.79 341	74.95 348	95.50 324

GG-mvs-BLEND				96.59 217	96.34 292	94.98 192	96.51 322	88.58 360		93.10 271	94.34 337	80.34 311	98.05 299	89.53 296	96.99 179	96.74 260

new_pmnet		90.06 307	89.00 311	93.22 321	94.18 335	88.32 332	96.42 324	96.89 318	86.19 332	85.67 341	93.62 339	77.18 332	97.10 328	81.61 339	89.29 296	94.23 339

PVSNet	91.96 18	96.35 135	96.15 132	96.96 190	99.17 98	92.05 274	96.08 325	98.68 121	93.69 193	97.75 111	97.80 209	88.86 192	99.69 120	94.26 191	99.01 113	99.15 138

ADS-MVSNet2		94.58 228	94.40 208	95.11 284	98.00 187	88.74 325	96.04 326	97.30 296	90.15 302	96.47 170	96.64 297	87.89 215	97.56 321	90.08 284	97.06 177	99.02 152

ADS-MVSNet		95.00 200	94.45 204	96.63 212	98.00 187	91.91 276	96.04 326	97.74 266	90.15 302	96.47 170	96.64 297	87.89 215	98.96 202	90.08 284	97.06 177	99.02 152

PAPM		94.95 205	94.00 229	97.78 141	97.04 256	95.65 163	96.03 328	98.25 211	91.23 284	94.19 227	97.80 209	91.27 144	98.86 218	82.61 337	97.61 169	98.84 166

cascas		94.63 224	93.86 239	96.93 192	96.91 264	94.27 224	96.00 329	98.51 162	85.55 338	94.54 206	96.23 310	84.20 282	98.87 216	95.80 141	96.98 180	97.66 213

gg-mvs-nofinetune		92.21 290	90.58 297	97.13 179	96.75 273	95.09 186	95.85 330	89.40 359	85.43 339	94.50 208	81.98 353	80.80 308	98.40 271	92.16 249	98.33 147	97.88 204

FPMVS		77.62 323	77.14 323	79.05 338	79.25 359	60.97 361	95.79 331	95.94 332	65.96 353	67.93 355	94.40 334	37.73 361	88.88 356	68.83 353	88.46 306	87.29 350

CHOSEN 280x420		97.18 106	97.18 89	97.20 174	98.81 126	93.27 257	95.78 332	99.15 18	95.25 121	96.79 155	98.11 179	92.29 115	99.07 189	98.56 8	99.85 3	99.25 126

bset_n11_16_dypcd		94.89 209	94.27 212	96.76 201	94.41 334	95.15 183	95.67 333	95.64 336	95.53 103	94.65 203	97.52 231	87.10 230	98.29 281	96.58 114	91.35 266	96.83 252

MIMVSNet		93.26 277	92.21 284	96.41 236	97.73 205	93.13 262	95.65 334	97.03 308	91.27 283	94.04 234	96.06 315	75.33 338	97.19 327	86.56 317	96.23 205	98.92 162

KD-MVS_2432*1600		89.61 311	87.96 315	94.54 301	94.06 338	91.59 284	95.59 335	97.63 271	89.87 308	88.95 326	94.38 335	78.28 322	96.82 332	84.83 329	68.05 352	95.21 328

miper_refine_blended		89.61 311	87.96 315	94.54 301	94.06 338	91.59 284	95.59 335	97.63 271	89.87 308	88.95 326	94.38 335	78.28 322	96.82 332	84.83 329	68.05 352	95.21 328

PCF-MVS	93.45 11	94.68 219	93.43 263	98.42 101	98.62 143	96.77 112	95.48 337	98.20 215	84.63 341	93.34 261	98.32 162	88.55 199	99.81 71	84.80 331	98.96 115	98.68 176
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
JIA-IIPM		93.35 273	92.49 280	95.92 257	96.48 287	90.65 301	95.01 338	96.96 312	85.93 335	96.08 179	87.33 350	87.70 221	98.78 226	91.35 267	95.58 214	98.34 193

CR-MVSNet		94.76 216	94.15 220	96.59 217	97.00 257	93.43 250	94.96 339	97.56 275	92.46 238	96.93 145	96.24 308	88.15 208	97.88 313	87.38 313	96.65 187	98.46 188

RPMNet		92.81 284	91.34 292	97.24 172	97.00 257	93.43 250	94.96 339	98.80 87	82.27 344	96.93 145	92.12 346	86.98 234	99.82 64	76.32 351	96.65 187	98.46 188

UnsupCasMVSNet_bld		87.17 317	85.12 321	93.31 319	91.94 347	88.77 324	94.92 341	98.30 203	84.30 342	82.30 346	90.04 347	63.96 355	97.25 326	85.85 323	74.47 350	93.93 345

PVSNet_0	88.72 19	91.28 296	90.03 302	95.00 287	97.99 189	87.29 340	94.84 342	98.50 167	92.06 256	89.86 318	95.19 327	79.81 313	99.39 156	92.27 248	69.79 351	98.33 194

Patchmatch-test		94.42 239	93.68 254	96.63 212	97.60 212	91.76 279	94.83 343	97.49 286	89.45 315	94.14 229	97.10 256	88.99 186	98.83 221	85.37 327	98.13 152	99.29 122

Patchmtry		93.22 278	92.35 282	95.84 262	96.77 270	93.09 264	94.66 344	97.56 275	87.37 327	92.90 274	96.24 308	88.15 208	97.90 309	87.37 314	90.10 283	96.53 289

PatchT		93.06 282	91.97 287	96.35 240	96.69 276	92.67 267	94.48 345	97.08 304	86.62 329	97.08 137	92.23 345	87.94 214	97.90 309	78.89 347	96.69 185	98.49 187

LCM-MVSNet		78.70 320	76.24 325	86.08 333	77.26 361	71.99 357	94.34 346	96.72 323	61.62 355	76.53 349	89.33 348	33.91 363	92.78 353	81.85 338	74.60 349	93.46 346

PMMVS2		77.95 322	75.44 326	85.46 334	82.54 356	74.95 355	94.23 347	93.08 354	72.80 352	74.68 350	87.38 349	36.36 362	91.56 354	73.95 352	63.94 354	89.87 349

MVS-HIRNet		89.46 313	88.40 312	92.64 323	97.58 214	82.15 350	94.16 348	93.05 355	75.73 351	90.90 309	82.52 352	79.42 315	98.33 273	83.53 335	98.68 127	97.43 215

Patchmatch-RL test		91.49 294	90.85 295	93.41 316	91.37 349	84.40 344	92.81 349	95.93 333	91.87 261	87.25 333	94.87 331	88.99 186	96.53 340	92.54 243	82.00 336	99.30 120

ambc				89.49 331	86.66 354	75.78 354	92.66 350	96.72 323		86.55 337	92.50 344	46.01 357	97.90 309	90.32 280	82.09 335	94.80 337

EMVS		64.07 328	63.26 331	66.53 343	81.73 358	58.81 364	91.85 351	84.75 362	51.93 359	59.09 358	75.13 357	43.32 359	79.09 360	42.03 359	39.47 357	61.69 356

E-PMN		64.94 327	64.25 329	67.02 342	82.28 357	59.36 363	91.83 352	85.63 361	52.69 357	60.22 357	77.28 356	41.06 360	80.12 359	46.15 358	41.14 356	61.57 357

ANet_high		69.08 324	65.37 328	80.22 337	65.99 363	71.96 358	90.91 353	90.09 358	82.62 343	49.93 360	78.39 355	29.36 364	81.75 357	62.49 355	38.52 358	86.95 352

tmp_tt		68.90 325	66.97 327	74.68 340	50.78 365	59.95 362	87.13 354	83.47 363	38.80 360	62.21 356	96.23 310	64.70 354	76.91 361	88.91 304	30.49 359	87.19 351

MVE	62.14 22	63.28 329	59.38 332	74.99 339	74.33 362	65.47 359	85.55 355	80.50 364	52.02 358	51.10 359	75.00 358	10.91 368	80.50 358	51.60 357	53.40 355	78.99 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 326	63.57 330	73.09 341	57.90 364	51.22 365	85.05 356	93.93 353	54.45 356	44.32 361	83.57 351	13.22 365	89.15 355	58.68 356	81.00 341	78.91 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		78.40 321	76.75 324	83.38 336	95.54 318	80.43 352	79.42 357	97.40 293	64.67 354	73.46 351	80.82 354	45.65 358	93.14 352	66.32 354	87.43 317	76.56 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		30.17 330	30.18 334	30.16 344	78.61 360	43.29 366	66.79 358	14.21 366	17.31 361	14.82 364	11.93 364	11.55 367	41.43 362	37.08 360	19.30 360	5.76 360

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		23.98 331	31.98 333	0.00 347	0.00 368	0.00 369	0.00 359	98.59 142	0.00 364	0.00 365	98.61 128	90.60 157	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		7.88 335	10.50 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	94.51 85	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		8.20 334	10.94 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	98.43 146	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ZD-MVS					99.46 51	98.70 19		98.79 92	93.21 214	98.67 58	98.97 87	95.70 44	99.83 56	96.07 128	99.58 74

IU-MVS					99.71 20	99.23 6		98.64 137	95.28 119	99.63 4				98.35 24	99.81 10	99.83 5

test_241102_TWO								98.87 55	97.65 9	99.53 8	99.48 6	97.34 8	99.94 3	98.43 18	99.80 17	99.83 5

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 4	99.74 107

test_0728_THIRD									97.32 28	99.45 9	99.46 9	97.88 1	99.94 3	98.47 15	99.86 1	99.85 2

GSMVS																99.20 129

test_part2					99.63 29	99.18 8				99.27 17

sam_mvs1												89.45 173				99.20 129

sam_mvs												88.99 186

MTGPA								98.74 104

test_post											31.83 362	88.83 193	98.91 209

patchmatchnet-post											95.10 329	89.42 174	98.89 213

gm-plane-assit					95.88 309	87.47 338			89.74 311		96.94 281		99.19 172	93.32 219

test9_res														96.39 122	99.57 75	99.69 51

agg_prior2														95.87 138	99.57 75	99.68 57

agg_prior					99.30 75	98.38 35		98.72 110		97.57 126			99.81 71

TestCases				96.99 186	99.25 86	93.21 260		98.18 219	91.36 275	93.52 253	98.77 114	84.67 272	99.72 109	89.70 293	97.87 159	98.02 202

test_prior				99.19 43	99.31 70	98.22 50		98.84 65					99.70 115			99.65 67

新几何1				99.16 50	99.34 62	98.01 62		98.69 118	90.06 305	98.13 83	98.95 95	94.60 82	99.89 35	91.97 257	99.47 90	99.59 80

旧先验1					99.29 78	97.48 83		98.70 117			99.09 74	95.56 47			99.47 90	99.61 75

原ACMM1				98.65 81	99.32 68	96.62 116		98.67 129	93.27 213	97.81 108	98.97 87	95.18 68	99.83 56	93.84 203	99.46 93	99.50 91

testdata2													99.89 35	91.65 264

segment_acmp												96.85 11

testdata				98.26 111	99.20 97	95.36 174		98.68 121	91.89 260	98.60 65	99.10 69	94.44 90	99.82 64	94.27 190	99.44 95	99.58 82

test12				99.18 47	99.16 99	98.19 52		98.53 157		98.07 86		95.13 70	99.72 109		99.56 80	99.63 73

plane_prior7					97.42 230	94.63 207

plane_prior6					97.35 235	94.61 210						87.09 231

plane_prior5								98.56 150					99.03 194	96.07 128	94.27 219	96.92 236

plane_prior4											98.28 165

plane_prior3						94.61 210			97.02 48	95.34 187

plane_prior1					97.37 234

n2								0.00 369

nn								0.00 369

door-mid								94.37 347

lessismore_v0				94.45 307	94.93 329	88.44 330		91.03 357		86.77 336	97.64 221	76.23 335	98.42 258	90.31 281	85.64 332	96.51 295

LGP-MVS_train				96.47 230	97.46 225	93.54 246		98.54 155	94.67 149	94.36 217	98.77 114	85.39 259	99.11 183	95.71 146	94.15 225	96.76 258

test11								98.66 132

door								94.64 345

HQP5-MVS						94.25 226

BP-MVS														95.30 157

HQP4-MVS										94.45 210			98.96 202			96.87 247

HQP3-MVS								98.46 172							94.18 223

HQP2-MVS												86.75 237

NP-MVS					97.28 238	94.51 215					97.73 212

ACMMP++_ref															92.97 251

ACMMP++															93.61 239

Test By Simon												94.64 80

ITE_SJBPF				95.44 275	97.42 230	91.32 290		97.50 284	95.09 132	93.59 249	98.35 156	81.70 300	98.88 215	89.71 292	93.39 245	96.12 313

DeepMVS_CX				86.78 332	97.09 254	72.30 356		95.17 341	75.92 350	84.34 344	95.19 327	70.58 348	95.35 345	79.98 344	89.04 300	92.68 348

High-resolution multi-view benchmark