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		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 2

fmvsm_s_conf0.5_n_3		95.20 102	95.95 68	92.94 231	96.60 219	82.18 309	93.13 208	98.39 32	91.44 151	97.16 77	97.68 84	93.03 128	97.82 316	97.54 2	98.63 208	98.81 102

test_fmvsmconf0.01_n		95.90 65	96.09 58	95.31 102	97.30 155	89.21 133	94.24 155	98.76 12	86.25 305	97.56 49	98.66 23	95.73 23	98.44 236	97.35 3	98.99 133	98.27 183

fmvsm_s_conf0.5_n_9		95.58 80	95.91 73	94.59 146	97.25 156	86.26 216	92.96 217	97.86 126	91.88 123	97.52 53	98.13 45	91.45 173	98.54 215	97.17 4	98.99 133	98.98 70

fmvsm_s_conf0.5_n_10		94.63 130	95.11 121	93.18 221	96.28 258	83.51 271	93.00 214	98.25 46	88.37 243	97.43 58	97.70 82	88.90 232	98.63 195	97.15 5	98.90 154	97.41 291

fmvsm_s_conf0.1_n_2		94.38 148	94.78 136	93.19 220	97.07 171	81.72 316	91.97 275	97.51 171	87.05 287	97.31 67	97.92 67	88.29 246	98.15 273	97.10 6	98.81 172	99.70 5

Elysia		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 30	97.28 132	88.82 234	99.51 20	97.08 7	99.38 63	99.26 37

StellarMVS		96.00 60	96.36 43	94.91 122	98.01 100	85.96 227	95.29 110	97.90 118	95.31 45	98.14 30	97.28 132	88.82 234	99.51 20	97.08 7	99.38 63	99.26 37

fmvsm_s_conf0.5_n_2		94.25 160	94.63 148	93.10 223	96.65 209	81.75 315	91.72 293	97.25 196	86.93 291	97.20 76	97.67 86	88.44 244	98.14 276	97.06 9	98.77 182	99.42 24

fmvsm_s_conf0.5_n_4		94.26 156	94.58 150	93.31 213	96.40 241	82.73 299	92.59 242	97.41 178	86.60 292	96.33 130	97.07 157	89.91 221	98.07 286	96.88 10	98.01 293	99.13 50

test_fmvsmconf0.1_n		95.61 77	95.72 85	95.26 104	96.85 190	89.20 134	93.51 193	98.60 16	85.68 326	97.42 61	98.30 40	95.34 39	98.39 237	96.85 11	98.98 135	98.19 193

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 29	98.81 32	93.86 40	99.07 2	98.98 8	97.01 17	98.92 5	98.78 19	95.22 47	98.61 197	96.85 11	99.77 9	99.31 33
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
anonymousdsp		96.74 24	96.42 38	97.68 7	98.00 102	94.03 29	96.97 19	97.61 156	87.68 266	98.45 21	98.77 20	94.20 90	99.50 23	96.70 13	99.40 61	99.53 17

test_fmvsmconf_n		95.43 87	95.50 93	95.22 109	96.48 234	89.19 135	93.23 205	98.36 35	85.61 329	96.92 93	98.02 54	95.23 46	98.38 241	96.69 14	98.95 145	98.09 203

fmvsm_s_conf0.5_n_5		94.50 138	94.80 133	93.60 194	96.80 195	84.93 248	92.81 229	97.59 160	85.27 338	96.85 98	97.29 130	91.48 172	98.05 289	96.67 15	98.47 231	97.83 247

fmvsm_s_conf0.5_n_8		94.70 125	95.34 105	92.78 244	96.77 199	81.50 321	92.64 240	98.50 21	91.51 148	97.22 75	97.93 62	88.07 251	98.45 234	96.62 16	98.80 176	98.39 169

MM		94.41 147	94.14 173	95.22 109	95.84 300	87.21 185	94.31 152	90.92 437	94.48 58	92.80 331	97.52 100	85.27 303	99.49 29	96.58 17	99.57 35	98.97 73

MVSFormer		92.18 261	92.23 252	92.04 287	94.74 363	80.06 350	97.15 15	97.37 180	88.98 219	88.83 445	92.79 409	77.02 409	99.60 9	96.41 18	96.75 379	96.46 354

test_djsdf		96.62 30	96.49 35	97.01 35	98.55 53	91.77 85	97.15 15	97.37 180	88.98 219	98.26 26	98.86 15	93.35 112	99.60 9	96.41 18	99.45 48	99.66 9

test_fmvsmvis_n_1920		95.08 108	95.40 101	94.13 167	96.66 208	87.75 176	93.44 197	98.49 23	85.57 330	98.27 23	97.11 153	94.11 93	97.75 328	96.26 20	98.72 196	96.89 328

v7n		96.82 16	97.31 14	95.33 99	98.54 55	86.81 198	96.83 24	98.07 86	96.59 25	98.46 20	98.43 37	92.91 131	99.52 19	96.25 21	99.76 10	99.65 11

mvs_tets		96.83 15	96.71 26	97.17 30	98.83 29	92.51 70	96.58 38	97.61 156	87.57 269	98.80 10	98.90 14	96.50 12	99.59 13	96.15 22	99.47 44	99.40 27

fmvsm_s_conf0.5_n_6		94.14 166	94.54 153	92.95 229	96.51 230	82.74 298	92.71 235	98.13 73	86.56 294	96.44 121	96.85 177	88.51 241	98.05 289	96.03 23	99.09 117	98.06 204

lecture		97.32 6	97.64 6	96.33 54	99.01 15	90.77 107	96.90 21	98.60 16	96.30 33	97.74 41	98.00 55	96.87 8	99.39 54	95.95 24	99.42 54	98.84 98

jajsoiax		96.59 34	96.42 38	97.12 32	98.76 35	92.49 71	96.44 48	97.42 177	86.96 288	98.71 13	98.72 22	95.36 38	99.56 17	95.92 25	99.45 48	99.32 32

fmvsm_l_conf0.5_n_3		95.19 103	95.36 103	94.68 137	96.79 197	87.49 179	93.05 211	98.38 33	87.21 278	96.59 115	97.76 80	94.20 90	98.11 277	95.90 26	98.40 238	98.42 161

OurMVSNet-221017-0		96.80 19	96.75 25	96.96 38	99.03 12	91.85 82	97.98 7	98.01 102	94.15 64	98.93 4	99.07 10	88.07 251	99.57 14	95.86 27	99.69 17	99.46 22

fmvsm_l_conf0.5_n_9		94.51 137	95.11 121	92.72 246	96.70 205	83.14 284	91.91 281	97.89 122	88.44 239	97.30 68	97.57 93	91.60 164	97.54 344	95.82 28	98.74 190	97.47 285

KinetiMVS		95.09 107	95.40 101	94.15 164	97.42 148	84.35 256	93.91 175	96.69 250	94.41 60	96.67 109	97.25 135	87.67 260	99.14 102	95.78 29	98.81 172	98.97 73

test_fmvsm_n_1920		94.72 123	94.74 139	94.67 139	96.30 257	88.62 148	93.19 206	98.07 86	85.63 328	97.08 82	97.35 124	90.86 193	97.66 335	95.70 30	98.48 230	97.74 263

fmvsm_s_conf0.5_n_11		94.91 113	95.44 98	93.33 212	96.45 235	83.11 286	93.56 191	98.64 14	89.76 200	95.70 179	97.97 59	92.32 146	98.08 282	95.62 31	98.95 145	98.79 106

fmvsm_s_conf0.1_n		94.19 165	94.41 157	93.52 203	97.22 160	84.37 254	93.73 182	95.26 324	84.45 361	95.76 170	98.00 55	91.85 158	97.21 374	95.62 31	97.82 310	98.98 70

fmvsm_s_conf0.5_n		94.00 173	94.20 171	93.42 208	96.69 206	84.37 254	93.38 199	95.13 330	84.50 360	95.40 196	97.55 99	91.77 160	97.20 375	95.59 33	97.79 311	98.69 128

fmvsm_l_conf0.5_n		93.79 180	93.81 184	93.73 188	96.16 272	86.26 216	92.46 249	96.72 247	81.69 411	95.77 167	97.11 153	90.83 195	97.82 316	95.58 34	97.99 297	97.11 309

reproduce_model		97.35 4	97.24 15	97.70 4	98.44 67	95.08 12	95.88 82	98.50 21	96.62 24	98.27 23	97.93 62	94.57 79	99.50 23	95.57 35	99.35 67	98.52 151

fmvsm_s_conf0.1_n_a		94.26 156	94.37 160	93.95 176	97.36 151	85.72 235	94.15 161	95.44 315	83.25 381	95.51 189	98.05 50	92.54 140	97.19 377	95.55 36	97.46 339	98.94 81

fmvsm_s_conf0.5_n_a		94.02 172	94.08 176	93.84 182	96.72 204	85.73 234	93.65 188	95.23 326	83.30 379	95.13 223	97.56 95	92.22 150	97.17 378	95.51 37	97.41 342	98.64 138

MP-MVS-pluss		96.08 57	95.92 72	96.57 47	99.06 10	91.21 94	93.25 203	98.32 38	87.89 258	96.86 95	97.38 115	95.55 30	99.39 54	95.47 38	99.47 44	99.11 54
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvs3		92.42 248	92.40 246	92.46 268	93.80 397	87.28 183	93.86 177	97.05 213	76.86 465	96.25 139	98.66 23	82.87 327	91.26 496	95.44 39	96.83 375	98.82 99

MVSMamba_PlusPlus		94.82 119	95.89 74	91.62 308	97.82 115	78.88 394	96.52 40	97.60 158	97.14 16	94.23 259	98.48 34	87.01 277	99.71 2	95.43 40	98.80 176	96.28 366

PS-MVSNAJss		96.01 59	96.04 63	95.89 71	98.82 30	88.51 154	95.57 97	97.88 123	88.72 227	98.81 9	98.86 15	90.77 196	99.60 9	95.43 40	99.53 39	99.57 16

TestfortrainingZip a		96.50 36	96.80 23	95.62 84	98.69 37	88.28 158	96.32 56	98.06 90	94.10 65	97.65 43	97.37 116	94.54 82	99.28 86	95.41 42	99.04 127	99.30 34

tt0805		95.42 90	95.93 71	93.86 181	98.75 36	88.47 155	97.68 9	94.29 357	96.48 26	95.38 197	93.63 381	94.89 66	97.94 304	95.38 43	96.92 371	95.17 415

fmvsm_l_conf0.5_n_a		93.59 188	93.63 195	93.49 205	96.10 279	85.66 237	92.32 260	96.57 261	81.32 419	95.63 184	97.14 149	90.19 211	97.73 331	95.37 44	98.03 290	97.07 314

UA-Net		97.35 4	97.24 15	97.69 5	98.22 83	93.87 39	98.42 6	98.19 61	96.95 18	95.46 194	99.23 9	93.45 107	99.57 14	95.34 45	99.89 2	99.63 12

reproduce-ours		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

our_new_method		97.28 7	97.19 17	97.57 11	98.37 72	94.84 13	95.57 97	98.40 30	96.36 31	98.18 27	97.78 75	95.47 32	99.50 23	95.26 46	99.33 73	98.36 171

MGCNet		92.88 223	92.27 251	94.69 136	92.35 433	86.03 224	92.88 226	89.68 445	90.53 180	91.52 378	96.43 212	82.52 334	99.32 78	95.01 48	99.54 38	98.71 124

BP-MVS1		91.77 271	91.10 290	93.75 186	96.42 239	83.40 273	94.10 165	91.89 424	91.27 155	93.36 297	94.85 324	64.43 490	99.29 82	94.88 49	98.74 190	98.56 148

ACMH	88.36 12	96.59 34	97.43 9	94.07 169	98.56 49	85.33 243	96.33 54	98.30 41	94.66 54	98.72 11	98.30 40	97.51 5	98.00 298	94.87 50	99.59 29	98.86 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v10		94.68 127	95.27 111	92.90 234	96.57 222	80.15 346	94.65 138	97.57 162	90.68 173	97.43 58	98.00 55	88.18 248	99.15 100	94.84 51	99.55 37	99.41 26

SixPastTwentyTwo		94.91 113	95.21 112	93.98 172	98.52 57	83.19 282	95.93 79	94.84 339	94.86 53	98.49 18	98.74 21	81.45 345	99.60 9	94.69 52	99.39 62	99.15 48

TDRefinement		97.68 3	97.60 8	97.93 2	99.02 13	95.95 8	98.61 3	98.81 10	97.41 13	97.28 71	98.46 35	94.62 77	98.84 150	94.64 53	99.53 39	98.99 66

v1240		93.29 202	93.71 192	92.06 286	96.01 289	77.89 415	91.81 289	97.37 180	85.12 345	96.69 108	96.40 216	86.67 285	99.07 118	94.51 54	98.76 184	99.22 42

mmtdpeth		95.82 69	96.02 65	95.23 107	96.91 185	88.62 148	96.49 44	99.26 3	95.07 49	93.41 293	99.29 7	90.25 210	97.27 367	94.49 55	99.01 131	99.80 3

fmvsm_s_conf0.5_n_7		93.61 186	93.94 181	92.63 253	96.11 278	82.76 297	90.81 325	97.55 164	86.57 293	93.14 316	97.69 83	90.17 213	96.83 399	94.46 56	98.93 148	98.31 178

APDe-MVS		96.46 39	96.64 29	95.93 66	97.68 129	89.38 131	96.90 21	98.41 29	92.52 98	97.43 58	97.92 67	95.11 52	99.50 23	94.45 57	99.30 80	98.92 87
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMP_NAP		96.21 53	96.12 57	96.49 51	98.90 22	91.42 92	94.57 142	98.03 99	90.42 184	96.37 127	97.35 124	95.68 25	99.25 90	94.44 58	99.34 71	98.80 104

ZNCC-MVS		96.42 43	96.20 52	97.07 33	98.80 34	92.79 64	96.08 73	98.16 70	91.74 136	95.34 201	96.36 224	95.68 25	99.44 33	94.41 59	99.28 88	98.97 73

v8		94.65 128	95.29 109	92.74 245	96.65 209	79.77 363	94.59 139	97.17 202	91.86 124	97.47 57	97.93 62	88.16 249	99.08 112	94.32 60	99.47 44	99.38 28

HPM-MVS_fast		97.01 11	96.89 21	97.39 24	99.12 8	93.92 36	97.16 14	98.17 67	93.11 89	96.48 118	97.36 121	96.92 6	99.34 70	94.31 61	99.38 63	98.92 87

MTAPA		96.65 29	96.38 42	97.47 18	98.95 21	94.05 27	95.88 82	97.62 154	94.46 59	96.29 136	96.94 168	93.56 102	99.37 65	94.29 62	99.42 54	98.99 66

WR-MVS_H		96.60 32	97.05 20	95.24 106	99.02 13	86.44 210	96.78 28	98.08 83	97.42 12	98.48 19	97.86 73	91.76 162	99.63 7	94.23 63	99.84 3	99.66 9

v1921920		93.26 204	93.61 197	92.19 278	96.04 288	78.31 408	91.88 284	97.24 198	85.17 342	96.19 147	96.19 240	86.76 284	99.05 119	94.18 64	98.84 164	99.22 42

v1192		93.49 192	93.78 187	92.62 255	96.16 272	79.62 367	91.83 288	97.22 200	86.07 311	96.10 151	96.38 222	87.22 270	99.02 124	94.14 65	98.88 159	99.22 42

test-260524					97.94 107	87.97 171		97.94 115		96.37 127		93.24 116	99.34 70	94.10 66	99.19 102

mvs5depth		95.28 98	95.82 81	93.66 191	96.42 239	83.08 287	97.35 12	99.28 2	96.44 28	96.20 144	99.65 2	84.10 313	98.01 296	94.06 67	98.93 148	99.87 1

MSC_two_6792asdad				95.90 69	96.54 225	89.57 124		96.87 233					99.41 43	94.06 67	99.30 80	98.72 121

No_MVS				95.90 69	96.54 225	89.57 124		96.87 233					99.41 43	94.06 67	99.30 80	98.72 121

HPM-MVS		96.81 18	96.62 31	97.36 26	98.89 23	93.53 51	97.51 10	98.44 26	92.35 104	95.95 157	96.41 215	96.71 11	99.42 37	93.99 70	99.36 66	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SP-LightGlue		90.98 294	90.67 305	91.92 293	91.04 478	91.02 96	90.68 333	94.22 361	89.56 206	90.35 413	92.90 404	77.08 405	89.38 511	93.92 71	96.27 399	95.35 412

DVP-MVS++		95.93 63	96.34 45	94.70 135	96.54 225	86.66 204	98.45 4	98.22 58	93.26 87	97.54 50	97.36 121	93.12 121	99.38 63	93.88 72	98.68 203	98.04 208

test_0728_THIRD									93.26 87	97.40 63	97.35 124	94.69 74	99.34 70	93.88 72	99.42 54	98.89 91

nrg030		96.32 49	96.55 34	95.62 84	97.83 114	88.55 153	95.77 86	98.29 44	92.68 94	98.03 34	97.91 70	95.13 50	98.95 136	93.85 74	99.49 43	99.36 30

v144192		93.20 211	93.54 201	92.16 282	96.05 284	78.26 409	91.95 276	97.14 204	84.98 351	95.96 156	96.11 249	87.08 276	99.04 122	93.79 75	98.84 164	99.17 46

HFP-MVS		96.39 46	96.17 55	97.04 34	98.51 58	93.37 52	96.30 65	97.98 105	92.35 104	95.63 184	96.47 209	95.37 36	99.27 89	93.78 76	99.14 112	98.48 156

EI-MVSNet-UG-set		94.35 152	94.27 169	94.59 146	92.46 430	85.87 231	92.42 253	94.69 347	93.67 80	96.13 148	95.84 264	91.20 182	98.86 147	93.78 76	98.23 264	99.03 62

ACMMPR		96.46 39	96.14 56	97.41 23	98.60 46	93.82 42	96.30 65	97.96 109	92.35 104	95.57 187	96.61 200	94.93 64	99.41 43	93.78 76	99.15 111	99.00 64

EI-MVSNet-Vis-set		94.36 151	94.28 167	94.61 142	92.55 427	85.98 226	92.44 251	94.69 347	93.70 77	96.12 149	95.81 266	91.24 179	98.86 147	93.76 79	98.22 268	98.98 70

region2R		96.41 44	96.09 58	97.38 25	98.62 43	93.81 44	96.32 56	97.96 109	92.26 107	95.28 207	96.57 203	95.02 58	99.41 43	93.63 80	99.11 114	98.94 81

EC-MVSNet		95.44 86	95.62 89	94.89 124	96.93 184	87.69 177	96.48 45	99.14 6	93.93 72	92.77 333	94.52 342	93.95 97	99.49 29	93.62 81	99.22 98	97.51 282

XVS		96.49 37	96.18 53	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 256	96.49 208	94.56 80	99.39 54	93.57 82	99.05 122	98.93 83

X-MVStestdata		90.70 301	88.45 361	97.44 19	98.56 49	93.99 32	96.50 42	97.95 112	94.58 55	94.38 256	26.89 553	94.56 80	99.39 54	93.57 82	99.05 122	98.93 83

SMA-MVS		95.77 71	95.54 92	96.47 52	98.27 79	91.19 95	95.09 119	97.79 139	86.48 296	97.42 61	97.51 104	94.47 86	99.29 82	93.55 84	99.29 83	98.93 83
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
LuminaMVS		93.43 196	93.18 213	94.16 163	97.32 154	85.29 244	93.36 200	93.94 371	88.09 252	97.12 81	96.43 212	80.11 357	98.98 128	93.53 85	98.76 184	98.21 189

v1144		93.50 191	93.81 184	92.57 258	96.28 258	79.61 368	91.86 287	96.96 219	86.95 289	95.91 160	96.32 226	87.65 262	98.96 134	93.51 86	98.88 159	99.13 50

SR-MVS-dyc-post		96.84 14	96.60 33	97.56 13	98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 88	97.03 161	94.85 69	99.42 37	93.49 87	98.84 164	98.00 213

RE-MVS-def			96.66 27		98.07 92	95.27 9	96.37 51	98.12 76	95.66 42	97.00 88	97.03 161	95.40 35		93.49 87	98.84 164	98.00 213

SteuartSystems-ACMMP		96.40 45	96.30 47	96.71 43	98.63 42	91.96 80	95.70 88	98.01 102	93.34 86	96.64 112	96.57 203	94.99 60	99.36 66	93.48 89	99.34 71	98.82 99
Skip Steuart: Steuart Systems R&D Blog.
CS-MVS		95.77 71	95.58 91	96.37 53	96.84 191	91.72 87	96.73 30	99.06 7	94.23 62	92.48 343	94.79 329	93.56 102	99.49 29	93.47 90	99.05 122	97.89 238

ACMMP		96.61 31	96.34 45	97.43 21	98.61 45	93.88 37	96.95 20	98.18 63	92.26 107	96.33 130	96.84 180	95.10 54	99.40 51	93.47 90	99.33 73	99.02 63
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
TSAR-MVS + MP.		94.96 112	94.75 137	95.57 87	98.86 27	88.69 145	96.37 51	96.81 239	85.23 339	94.75 243	97.12 152	91.85 158	99.40 51	93.45 92	98.33 250	98.62 142
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_fmvs2		90.62 307	90.40 315	91.29 330	91.93 451	85.46 241	92.70 236	96.48 268	74.44 482	94.91 237	97.59 92	75.52 424	90.57 499	93.44 93	96.56 388	97.84 246

DVP-MVS		95.82 69	96.18 53	94.72 134	98.51 58	86.69 202	95.20 116	97.00 216	91.85 125	97.40 63	97.35 124	95.58 28	99.34 70	93.44 93	99.31 78	98.13 201
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				94.88 125	98.55 53	86.72 201	95.20 116	98.22 58					99.38 63	93.44 93	99.31 78	98.53 150

MSP-MVS		95.34 93	94.63 148	97.48 17	98.67 40	94.05 27	96.41 50	98.18 63	91.26 156	95.12 224	95.15 307	86.60 287	99.50 23	93.43 96	96.81 376	98.89 91
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
PS-CasMVS		96.69 27	97.43 9	94.49 153	99.13 6	84.09 264	96.61 37	97.97 107	97.91 8	98.64 16	98.13 45	95.24 45	99.65 4	93.39 97	99.84 3	99.72 4

Vis-MVSNet		95.50 83	95.48 94	95.56 88	98.11 89	89.40 130	95.35 104	98.22 58	92.36 103	94.11 263	98.07 49	92.02 154	99.44 33	93.38 98	97.67 323	97.85 245
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
APD-MVS_3200maxsize		96.82 16	96.65 28	97.32 28	97.95 106	93.82 42	96.31 61	98.25 46	95.51 44	96.99 90	97.05 160	95.63 27	99.39 54	93.31 99	98.88 159	98.75 115

SP-SuperGlue		91.30 287	91.15 289	91.75 300	91.06 477	90.99 99	90.32 351	93.55 385	90.63 176	91.17 389	93.82 375	79.84 361	88.92 515	93.30 100	96.63 385	95.34 413

SED-MVS		96.00 60	96.41 41	94.76 132	98.51 58	86.97 192	95.21 114	98.10 80	91.95 118	97.63 45	97.25 135	96.48 13	99.35 67	93.29 101	99.29 83	97.95 223

test_241102_TWO								98.10 80	91.95 118	97.54 50	97.25 135	95.37 36	99.35 67	93.29 101	99.25 91	98.49 155

DTE-MVSNet		96.74 24	97.43 9	94.67 139	99.13 6	84.68 251	96.51 41	97.94 115	98.14 6	98.67 15	98.32 39	95.04 56	99.69 3	93.27 103	99.82 7	99.62 13

3Dnovator+	92.74 2	95.86 68	95.77 83	96.13 57	96.81 194	90.79 106	96.30 65	97.82 134	96.13 35	94.74 244	97.23 138	91.33 176	99.16 99	93.25 104	98.30 256	98.46 157

K. test v3		93.37 198	93.27 211	93.66 191	98.05 94	82.62 301	94.35 149	86.62 475	96.05 38	97.51 54	98.85 17	76.59 419	99.65 4	93.21 105	98.20 271	98.73 120

Anonymous20231211		96.60 32	97.13 19	95.00 116	97.46 145	86.35 214	97.11 18	98.24 54	97.58 11	98.72 11	98.97 12	93.15 120	99.15 100	93.18 106	99.74 13	99.50 19

GST-MVS		96.24 52	95.99 66	97.00 36	98.65 41	92.71 66	95.69 90	98.01 102	92.08 116	95.74 175	96.28 230	95.22 47	99.42 37	93.17 107	99.06 119	98.88 93

CP-MVS		96.44 42	96.08 60	97.54 14	98.29 77	94.62 18	96.80 26	98.08 83	92.67 96	95.08 229	96.39 221	94.77 73	99.42 37	93.17 107	99.44 51	98.58 146

mPP-MVS		96.46 39	96.05 62	97.69 5	98.62 43	94.65 17	96.45 46	97.74 143	92.59 97	95.47 192	96.68 194	94.50 83	99.42 37	93.10 109	99.26 90	98.99 66

ACMM	88.83 9	96.30 51	96.07 61	96.97 37	98.39 69	92.95 61	94.74 131	98.03 99	90.82 168	97.15 78	96.85 177	96.25 18	99.00 126	93.10 109	99.33 73	98.95 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		96.19 54	96.80 23	94.38 158	98.99 19	83.82 267	96.31 61	97.53 168	97.60 10	98.34 22	97.52 100	91.98 156	99.63 7	93.08 111	99.81 8	99.70 5

v2v482		93.29 202	93.63 195	92.29 270	96.35 250	78.82 396	91.77 292	96.28 277	88.45 238	95.70 179	96.26 233	86.02 294	98.90 140	93.02 112	98.81 172	99.14 49

IU-MVS					98.51 58	86.66 204		96.83 238	72.74 498	95.83 165				93.00 113	99.29 83	98.64 138

SR-MVS		96.70 26	96.42 38	97.54 14	98.05 94	94.69 15	96.13 71	98.07 86	95.17 48	96.82 99	96.73 190	95.09 55	99.43 36	92.99 114	98.71 198	98.50 153

PEN-MVS		96.69 27	97.39 12	94.61 142	99.16 4	84.50 253	96.54 39	98.05 92	98.06 7	98.64 16	98.25 42	95.01 59	99.65 4	92.95 115	99.83 5	99.68 7

FC-MVSNet-test		95.32 94	95.88 75	93.62 193	98.49 65	81.77 313	95.90 81	98.32 38	93.93 72	97.53 52	97.56 95	88.48 242	99.40 51	92.91 116	99.83 5	99.68 7

aaatest				95.52 89	98.69 37	88.21 161	96.32 56	98.58 18	88.79 225	97.38 65	96.22 236		99.39 54	92.89 117	99.10 115	98.96 77

MED-MVS		96.38 47	96.63 30	95.63 83	98.69 37	88.21 161	96.32 56	98.58 18	94.10 65	97.38 65	97.37 116	95.11 52	99.39 54	92.89 117	99.19 102	99.30 34

aaEdge-Enhanced		95.61 77	95.65 88	95.49 91	97.62 133	88.21 161	94.21 158	97.87 125	92.48 99	96.38 125	96.22 236	94.06 94	99.32 78	92.89 117	99.10 115	98.96 77

OPM-MVS		95.61 77	95.45 95	96.08 58	98.49 65	91.00 98	92.65 239	97.33 188	90.05 194	96.77 103	96.85 177	95.04 56	98.56 212	92.77 120	99.06 119	98.70 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PGM-MVS		96.32 49	95.94 69	97.43 21	98.59 48	93.84 41	95.33 106	98.30 41	91.40 153	95.76 170	96.87 176	95.26 44	99.45 32	92.77 120	99.21 99	99.00 64

CNVR-MVS		94.58 133	94.29 165	95.46 93	96.94 181	89.35 132	91.81 289	96.80 240	89.66 203	93.90 275	95.44 291	92.80 135	98.72 175	92.74 122	98.52 225	98.32 176

DeepC-MVS	91.39 4	95.43 87	95.33 107	95.71 78	97.67 130	90.17 117	93.86 177	98.02 101	87.35 273	96.22 142	97.99 58	94.48 85	99.05 119	92.73 123	99.68 20	97.93 228
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		95.19 103	95.73 84	93.55 197	96.62 218	88.88 144	94.67 136	98.05 92	91.26 156	97.25 74	96.40 216	95.42 34	94.36 467	92.72 124	99.19 102	97.40 295
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
EU-MVSNet		87.39 410	86.71 414	89.44 406	93.40 406	76.11 452	94.93 127	90.00 444	57.17 545	95.71 178	97.37 116	64.77 489	97.68 334	92.67 125	94.37 467	94.52 442

lessismore_v0				93.87 180	98.05 94	83.77 268		80.32 537		97.13 79	97.91 70	77.49 396	99.11 110	92.62 126	98.08 283	98.74 119

GDP-MVS		91.56 278	90.83 299	93.77 185	96.34 251	83.65 269	93.66 186	98.12 76	87.32 275	92.98 325	94.71 332	63.58 496	99.30 81	92.61 127	98.14 276	98.35 174

Anonymous20240521		92.86 227	93.57 199	90.74 363	96.57 222	75.50 460	94.15 161	95.60 304	89.38 209	95.90 161	97.90 72	80.39 356	97.96 302	92.60 128	99.68 20	98.75 115

sc_t1		97.21 9	97.71 4	95.71 78	99.06 10	88.89 142	96.72 31	97.79 139	98.34 2	98.97 2	99.40 5	96.81 9	98.79 161	92.58 129	99.72 15	99.45 23

MVS_Test		92.57 243	93.29 208	90.40 376	93.53 402	75.85 455	92.52 245	96.96 219	88.73 226	92.35 352	96.70 193	90.77 196	98.37 245	92.53 130	95.49 425	96.99 320

BridgeMVS		93.45 194	94.17 172	91.28 331	95.81 304	78.40 402	96.20 69	97.48 174	88.56 237	95.29 205	97.20 143	85.56 302	99.21 93	92.52 131	98.91 153	96.24 369

3Dnovator	92.54 3	94.80 121	94.90 128	94.47 154	95.47 330	87.06 189	96.63 36	97.28 195	91.82 131	94.34 258	97.41 112	90.60 203	98.65 192	92.47 132	98.11 279	97.70 265

AstraMVS		92.75 232	92.73 230	92.79 243	97.02 176	81.48 322	92.88 226	90.62 441	87.99 255	96.48 118	96.71 192	82.02 340	98.48 230	92.44 133	98.46 232	98.40 168

SF-MVS		95.88 67	95.88 75	95.87 72	98.12 88	89.65 123	95.58 96	98.56 20	91.84 128	96.36 129	96.68 194	94.37 87	99.32 78	92.41 134	99.05 122	98.64 138

V42		93.43 196	93.58 198	92.97 227	95.34 336	81.22 328	92.67 237	96.49 267	87.25 276	96.20 144	96.37 223	87.32 268	98.85 149	92.39 135	98.21 269	98.85 97

Casviewmamba		95.48 85	95.97 67	94.04 170	96.94 181	84.57 252	93.96 172	98.29 44	93.94 71	96.76 104	97.14 149	95.27 43	98.72 175	92.37 136	99.02 130	98.82 99

casdiffmvs_mvg		95.10 106	95.62 89	93.53 201	96.25 264	83.23 279	92.66 238	98.19 61	93.06 90	97.49 55	97.15 148	94.78 72	98.71 182	92.27 137	98.72 196	98.65 132
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVStest1		84.79 447	84.06 451	86.98 465	77.73 553	74.76 463	91.08 315	85.63 487	77.70 457	96.86 95	97.97 59	41.05 549	88.24 519	92.22 138	96.28 398	97.94 225

HPM-MVS++		95.02 109	94.39 158	96.91 40	97.88 111	93.58 50	94.09 166	96.99 218	91.05 161	92.40 348	95.22 305	91.03 190	99.25 90	92.11 139	98.69 202	97.90 236

UniMVSNet (Re)		95.32 94	95.15 114	95.80 74	97.79 118	88.91 141	92.91 224	98.07 86	93.46 83	96.31 133	95.97 259	90.14 214	99.34 70	92.11 139	99.64 25	99.16 47

XVG-OURS-SEG-HR		95.38 91	95.00 127	96.51 49	98.10 90	94.07 24	92.46 249	98.13 73	90.69 172	93.75 279	96.25 234	98.03 2	97.02 388	92.08 141	95.55 423	98.45 158

LPG-MVS_test		96.38 47	96.23 50	96.84 41	98.36 75	92.13 77	95.33 106	98.25 46	91.78 132	97.07 83	97.22 140	96.38 16	99.28 86	92.07 142	99.59 29	99.11 54

LGP-MVS_train				96.84 41	98.36 75	92.13 77		98.25 46	91.78 132	97.07 83	97.22 140	96.38 16	99.28 86	92.07 142	99.59 29	99.11 54

guyue		92.60 239	92.62 236	92.52 265	96.73 202	81.00 331	93.00 214	91.83 426	88.28 245	96.38 125	96.23 235	80.71 353	98.37 245	92.06 144	98.37 248	98.20 191

tttt0517		89.81 339	88.90 350	92.55 259	97.00 178	79.73 366	95.03 123	83.65 509	89.88 197	95.30 203	94.79 329	53.64 520	99.39 54	91.99 145	98.79 179	98.54 149

EI-MVSNet		92.99 218	93.26 212	92.19 278	92.12 444	79.21 387	92.32 260	94.67 349	91.77 134	95.24 214	95.85 262	87.14 274	98.49 225	91.99 145	98.26 259	98.86 94

MP-MVS		96.14 55	95.68 86	97.51 16	98.81 32	94.06 25	96.10 72	97.78 141	92.73 93	93.48 291	96.72 191	94.23 89	99.42 37	91.99 145	99.29 83	99.05 61
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
IterMVS-LS		93.78 181	94.28 167	92.27 271	96.27 261	79.21 387	91.87 285	96.78 241	91.77 134	96.57 117	97.07 157	87.15 273	98.74 172	91.99 145	99.03 129	98.86 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		91.65 275	91.55 273	91.94 292	93.89 392	79.22 386	87.56 433	93.51 386	91.53 145	95.37 199	96.62 199	78.65 375	98.90 140	91.89 149	94.95 451	97.70 265

EGC-MVSNET		80.97 487	75.73 507	96.67 45	98.85 28	94.55 19	96.83 24	96.60 258	2.44 555	5.32 558	98.25 42	92.24 149	98.02 295	91.85 150	99.21 99	97.45 287

SPE-MVS-test		95.32 94	95.10 123	95.96 62	96.86 189	90.75 108	96.33 54	99.20 4	93.99 68	91.03 394	93.73 378	93.52 104	99.55 18	91.81 151	99.45 48	97.58 276

tt0320-xc		97.00 12	97.67 5	94.98 117	98.89 23	86.94 195	96.72 31	98.46 24	98.28 4	98.86 7	99.43 4	96.80 10	98.51 223	91.79 152	99.76 10	99.50 19

LS3D		96.11 56	95.83 79	96.95 39	94.75 360	94.20 23	97.34 13	97.98 105	97.31 14	95.32 202	96.77 183	93.08 123	99.20 96	91.79 152	98.16 273	97.44 289

DPE-MVS		95.89 66	95.88 75	95.92 68	97.93 108	89.83 121	93.46 195	98.30 41	92.37 102	97.75 39	96.95 167	95.14 49	99.51 20	91.74 154	99.28 88	98.41 164
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
tt0320		96.97 13	97.64 6	94.96 120	98.89 23	86.86 197	96.85 23	98.45 25	98.29 3	98.88 6	99.45 3	96.48 13	98.54 215	91.73 155	99.72 15	99.47 21

FIs		94.90 115	95.35 104	93.55 197	98.28 78	81.76 314	95.33 106	98.14 72	93.05 91	97.07 83	97.18 144	87.65 262	99.29 82	91.72 156	99.69 17	99.61 14

Gipuma		95.31 97	95.80 82	93.81 184	97.99 105	90.91 101	96.42 49	97.95 112	96.69 21	91.78 372	98.85 17	91.77 160	95.49 440	91.72 156	99.08 118	95.02 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
baseline		94.26 156	94.80 133	92.64 250	96.08 281	80.99 332	93.69 184	98.04 98	90.80 169	94.89 238	96.32 226	93.19 118	98.48 230	91.68 158	98.51 227	98.43 160

alignmvs		93.26 204	92.85 224	94.50 151	95.70 311	87.45 180	93.45 196	95.76 299	91.58 141	95.25 213	92.42 426	81.96 342	98.72 175	91.61 159	97.87 308	97.33 300

UniMVSNet_NR-MVSNet		95.35 92	95.21 112	95.76 75	97.69 128	88.59 151	92.26 266	97.84 130	94.91 52	96.80 100	95.78 271	90.42 206	99.41 43	91.60 160	99.58 33	99.29 36

DU-MVS		95.28 98	95.12 120	95.75 76	97.75 120	88.59 151	92.58 243	97.81 135	93.99 68	96.80 100	95.90 260	90.10 217	99.41 43	91.60 160	99.58 33	99.26 37

EG-PatchMatch MVS		94.54 136	94.67 146	94.14 166	97.87 113	86.50 206	92.00 274	96.74 246	88.16 251	96.93 92	97.61 91	93.04 127	97.90 305	91.60 160	98.12 278	98.03 211

MGCFI-Net		94.44 145	94.67 146	93.75 186	95.56 324	85.47 240	95.25 113	98.24 54	91.53 145	95.04 231	92.21 433	94.94 63	98.54 215	91.56 163	97.66 324	97.24 304

test_0402		95.73 73	96.22 51	94.26 161	98.19 85	85.77 233	93.24 204	97.24 198	96.88 20	97.69 42	97.77 79	94.12 92	99.13 105	91.54 164	99.29 83	97.88 239

sasdasda		94.59 131	94.69 141	94.30 159	95.60 321	87.03 190	95.59 93	98.24 54	91.56 143	95.21 216	92.04 438	94.95 61	98.66 189	91.45 165	97.57 331	97.20 306

canonicalmvs		94.59 131	94.69 141	94.30 159	95.60 321	87.03 190	95.59 93	98.24 54	91.56 143	95.21 216	92.04 438	94.95 61	98.66 189	91.45 165	97.57 331	97.20 306

XVG-OURS		94.72 123	94.12 174	96.50 50	98.00 102	94.23 22	91.48 300	98.17 67	90.72 171	95.30 203	96.47 209	87.94 256	96.98 389	91.41 167	97.61 328	98.30 180

pmmvs6		96.80 19	97.36 13	95.15 112	99.12 8	87.82 175	96.68 33	97.86 126	96.10 36	98.14 30	99.28 8	97.94 3	98.21 263	91.38 168	99.69 17	99.42 24

RoMa-HiRes		94.64 129	94.29 165	95.68 81	97.47 144	93.88 37	93.83 179	96.23 281	88.05 253	97.75 39	96.20 239	88.58 240	94.93 458	91.33 169	99.17 109	98.22 188

diffmvs_AUTHOR		92.34 253	92.70 233	91.26 332	94.20 380	78.42 401	89.12 399	97.60 158	87.16 281	93.17 315	95.50 287	88.66 237	97.57 343	91.30 170	97.61 328	97.79 253

VortexMVS		92.13 262	92.56 239	90.85 357	94.54 371	76.17 451	92.30 263	96.63 257	86.20 307	96.66 111	96.79 182	79.87 360	98.16 271	91.27 171	98.76 184	98.24 185

XVG-ACMP-BASELINE		95.68 75	95.34 105	96.69 44	98.40 68	93.04 58	94.54 146	98.05 92	90.45 183	96.31 133	96.76 185	92.91 131	98.72 175	91.19 172	99.42 54	98.32 176

hybridcas		94.81 120	95.45 95	92.88 237	96.74 201	81.36 324	93.32 202	98.13 73	92.16 113	96.79 102	96.98 166	94.91 65	98.53 219	91.16 173	98.90 154	98.75 115

E5new		94.50 138	95.15 114	92.55 259	97.04 172	80.27 342	92.96 217	98.25 46	90.18 188	95.77 167	97.45 108	94.85 69	98.59 202	91.16 173	98.73 192	98.79 106

E6new		94.50 138	95.15 114	92.55 259	97.04 172	80.28 340	92.96 217	98.25 46	90.18 188	95.76 170	97.45 108	94.86 67	98.59 202	91.16 173	98.73 192	98.79 106

E6		94.50 138	95.15 114	92.55 259	97.04 172	80.28 340	92.96 217	98.25 46	90.18 188	95.76 170	97.45 108	94.86 67	98.59 202	91.16 173	98.73 192	98.79 106

E5		94.50 138	95.15 114	92.55 259	97.04 172	80.27 342	92.96 217	98.25 46	90.18 188	95.77 167	97.45 108	94.85 69	98.59 202	91.16 173	98.73 192	98.79 106

test_fmvs1_n		88.73 371	88.38 363	89.76 397	92.06 446	82.53 302	92.30 263	96.59 260	71.14 508	92.58 340	95.41 296	68.55 466	89.57 508	91.12 178	95.66 420	97.18 308

RPSCF		95.58 80	94.89 130	97.62 8	97.58 136	96.30 7	95.97 78	97.53 168	92.42 100	93.41 293	97.78 75	91.21 181	97.77 324	91.06 179	97.06 362	98.80 104

h-mvs33		92.89 222	91.99 261	95.58 86	96.97 179	90.55 110	93.94 174	94.01 369	89.23 212	93.95 272	96.19 240	76.88 414	99.14 102	91.02 180	95.71 418	97.04 318

hse-mvs2		92.24 259	91.20 285	95.38 96	96.16 272	90.65 109	92.52 245	92.01 423	89.23 212	93.95 272	92.99 398	76.88 414	98.69 185	91.02 180	96.03 407	96.81 333

casdiffmvs		94.32 154	94.80 133	92.85 239	96.05 284	81.44 323	92.35 257	98.05 92	91.53 145	95.75 174	96.80 181	93.35 112	98.49 225	91.01 182	98.32 252	98.64 138
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		94.55 135	94.68 145	94.15 164	97.23 158	85.11 246	94.14 163	97.34 187	88.71 228	95.26 210	95.50 287	94.65 76	99.12 106	90.94 183	98.40 238	98.23 186

c3_l		91.32 286	91.42 278	91.00 347	92.29 435	76.79 439	87.52 436	96.42 271	85.76 324	94.72 246	93.89 371	82.73 330	98.16 271	90.93 184	98.55 218	98.04 208

DKM-HiRes		92.87 225	91.94 263	95.65 82	97.16 163	93.66 47	90.90 321	94.27 359	87.11 285	95.29 205	95.39 298	77.59 395	95.36 444	90.86 185	98.92 152	97.94 225

viewmamba		92.69 235	93.03 217	91.69 305	93.92 391	79.50 375	89.92 367	97.33 188	88.86 224	93.13 318	95.79 267	90.97 191	97.65 337	90.86 185	96.45 393	97.94 225

TranMVSNet+NR-MVSNet		96.07 58	96.26 49	95.50 90	98.26 80	87.69 177	93.75 181	97.86 126	95.96 41	97.48 56	97.14 149	95.33 40	99.44 33	90.79 187	99.76 10	99.38 28

test_vis1_n		89.01 361	89.01 346	89.03 418	92.57 426	82.46 304	92.62 241	96.06 290	73.02 495	90.40 409	95.77 272	74.86 428	89.68 506	90.78 188	94.98 449	94.95 427

UniMVSNet_ETH3D		97.13 10	97.72 3	95.35 97	99.51 2	87.38 181	97.70 8	97.54 165	98.16 5	98.94 3	99.33 6	97.84 4	99.08 112	90.73 189	99.73 14	99.59 15

9.14			94.81 132		97.49 141		94.11 164	98.37 34	87.56 270	95.38 197	96.03 253	94.66 75	99.08 112	90.70 190	98.97 141

diffmvs		91.74 273	91.93 264	91.15 340	93.06 415	78.17 410	88.77 413	97.51 171	86.28 304	92.42 347	93.96 368	88.04 253	97.46 351	90.69 191	96.67 383	97.82 250
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvs1		87.59 402	87.27 394	88.54 434	88.32 522	81.26 326	90.43 345	95.72 301	70.55 515	91.70 373	94.63 336	68.13 467	89.42 510	90.59 192	95.34 433	94.94 429

dcpmvs_2		93.96 175	95.01 126	90.82 360	97.60 134	74.04 476	93.68 185	98.85 9	89.80 199	97.82 36	97.01 164	91.14 186	99.21 93	90.56 193	98.59 214	99.19 45

SP-NN		88.21 383	87.96 379	88.97 421	89.33 515	87.99 168	88.06 426	90.93 436	85.48 336	84.50 500	91.11 457	77.25 403	84.79 539	90.55 194	94.42 463	94.14 452

RRT-MVS		92.28 255	93.01 218	90.07 386	94.06 386	73.01 484	95.36 103	97.88 123	92.24 109	95.16 221	97.52 100	78.51 379	99.29 82	90.55 194	95.83 415	97.92 233

balanced_ft_v1		92.65 238	93.17 214	91.10 341	94.47 373	77.32 428	96.67 34	96.70 249	88.23 247	93.70 283	97.16 145	83.33 319	99.41 43	90.51 196	97.76 313	96.57 342

MVSTER		89.32 349	88.75 353	91.03 344	90.10 503	76.62 446	90.85 323	94.67 349	82.27 401	95.24 214	95.79 267	61.09 506	98.49 225	90.49 197	98.26 259	97.97 221

DP-MVS		95.62 76	95.84 78	94.97 118	97.16 163	88.62 148	94.54 146	97.64 152	96.94 19	96.58 116	97.32 128	93.07 125	98.72 175	90.45 198	98.84 164	97.57 277

ACMP	88.15 13	95.71 74	95.43 99	96.54 48	98.17 86	91.73 86	94.24 155	98.08 83	89.46 207	96.61 114	96.47 209	95.85 22	99.12 106	90.45 198	99.56 36	98.77 114
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS_111021_LR		93.66 183	93.28 210	94.80 130	96.25 264	90.95 100	90.21 355	95.43 317	87.91 256	93.74 281	94.40 348	92.88 133	96.38 418	90.39 200	98.28 257	97.07 314

NormalMVS		94.10 167	93.36 207	96.31 55	99.01 15	90.84 104	94.70 134	97.90 118	90.98 162	93.22 310	95.73 274	78.94 369	99.12 106	90.38 201	99.42 54	98.97 73

SymmetryMVS		93.26 204	92.36 248	95.97 61	97.13 167	90.84 104	94.70 134	91.61 430	90.98 162	93.22 310	95.73 274	78.94 369	99.12 106	90.38 201	98.53 222	97.97 221

ANet_high		94.83 118	96.28 48	90.47 373	96.65 209	73.16 482	94.33 150	98.74 13	96.39 30	98.09 33	98.93 13	93.37 111	98.70 183	90.38 201	99.68 20	99.53 17

DeepPCF-MVS	90.46 6	94.20 163	93.56 200	96.14 56	95.96 291	92.96 60	89.48 387	97.46 175	85.14 344	96.23 141	95.42 293	93.19 118	98.08 282	90.37 204	98.76 184	97.38 298

SP-MNN		89.68 342	89.55 338	90.06 389	90.43 497	88.06 166	89.60 381	92.13 419	86.42 301	89.57 433	92.55 418	78.14 387	87.91 521	90.35 205	96.74 381	94.22 451

MSLP-MVS++		93.25 207	93.88 183	91.37 324	96.34 251	82.81 293	93.11 209	97.74 143	89.37 210	94.08 265	95.29 303	90.40 208	96.35 420	90.35 205	98.25 261	94.96 426

PM-MVS		93.33 201	92.67 235	95.33 99	96.58 221	94.06 25	92.26 266	92.18 415	85.92 316	96.22 142	96.61 200	85.64 300	95.99 430	90.35 205	98.23 264	95.93 386

test_vis1_n_1920		89.45 346	89.85 329	88.28 441	93.59 401	76.71 445	90.67 334	97.78 141	79.67 437	90.30 414	96.11 249	76.62 418	92.17 488	90.31 208	93.57 486	95.96 384

ACMH+	88.43 11	96.48 38	96.82 22	95.47 92	98.54 55	89.06 138	95.65 91	98.61 15	96.10 36	98.16 29	97.52 100	96.90 7	98.62 196	90.30 209	99.60 27	98.72 121

DIV-MVS_self_test		90.65 304	90.56 311	90.91 355	91.85 453	76.99 435	86.75 454	95.36 320	85.52 334	94.06 267	94.89 320	77.37 401	97.99 300	90.28 210	98.97 141	97.76 258

cl____		90.65 304	90.56 311	90.91 355	91.85 453	76.98 436	86.75 454	95.36 320	85.53 331	94.06 267	94.89 320	77.36 402	97.98 301	90.27 211	98.98 135	97.76 258

PHI-MVS		94.34 153	93.80 186	95.95 63	95.65 316	91.67 88	94.82 129	97.86 126	87.86 259	93.04 322	94.16 360	91.58 165	98.78 165	90.27 211	98.96 143	97.41 291

patch_mono-2		92.46 247	92.72 232	91.71 303	96.65 209	78.91 393	88.85 407	97.17 202	83.89 372	92.45 345	96.76 185	89.86 223	97.09 383	90.24 213	98.59 214	99.12 53

MVS_111021_HR		93.63 184	93.42 206	94.26 161	96.65 209	86.96 194	89.30 394	96.23 281	88.36 244	93.57 287	94.60 338	93.45 107	97.77 324	90.23 214	98.38 243	98.03 211

NCCC		94.08 169	93.54 201	95.70 80	96.49 232	89.90 120	92.39 255	96.91 226	90.64 174	92.33 355	94.60 338	90.58 204	98.96 134	90.21 215	97.70 321	98.23 186

viewdifsd2359ckpt11		93.36 199	93.99 177	91.48 316	95.50 328	78.39 404	90.47 340	96.69 250	88.59 232	96.03 154	96.88 174	93.48 105	97.63 339	90.20 216	98.07 285	98.41 164

viewmsd2359difaftdt		93.36 199	93.99 177	91.48 316	95.50 328	78.39 404	90.47 340	96.69 250	88.59 232	96.03 154	96.88 174	93.48 105	97.63 339	90.20 216	98.07 285	98.41 164

pm-mvs1		95.43 87	95.94 69	93.93 177	98.38 70	85.08 247	95.46 102	97.12 209	91.84 128	97.28 71	98.46 35	95.30 42	97.71 332	90.17 218	99.42 54	98.99 66

RPMNet		90.31 321	90.14 323	90.81 361	91.01 480	78.93 390	92.52 245	98.12 76	91.91 121	89.10 440	96.89 173	68.84 465	99.41 43	90.17 218	92.70 503	94.08 453

NR-MVSNet		95.28 98	95.28 110	95.26 104	97.75 120	87.21 185	95.08 120	97.37 180	93.92 74	97.65 43	95.90 260	90.10 217	99.33 77	90.11 220	99.66 23	99.26 37

COLMAP_ROB	91.06 5	96.75 23	96.62 31	97.13 31	98.38 70	94.31 21	96.79 27	98.32 38	96.69 21	96.86 95	97.56 95	95.48 31	98.77 168	90.11 220	99.44 51	98.31 178
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		94.47 144	95.09 124	92.60 257	98.50 64	80.82 336	92.08 270	96.68 253	93.82 75	96.29 136	98.56 29	90.10 217	97.75 328	90.10 222	99.66 23	99.24 41

v148		92.87 225	93.29 208	91.62 308	96.25 264	77.72 421	91.28 306	95.05 331	89.69 201	95.93 159	96.04 252	87.34 267	98.38 241	90.05 223	97.99 297	98.78 111

E4		94.00 173	94.53 154	92.42 269	96.78 198	79.99 354	91.33 305	98.16 70	89.69 201	95.27 208	97.16 145	93.94 98	98.64 193	89.99 224	98.42 237	98.61 143

MCST-MVS		92.91 221	92.51 240	94.10 168	97.52 139	85.72 235	91.36 304	97.13 206	80.33 429	92.91 329	94.24 355	91.23 180	98.72 175	89.99 224	97.93 304	97.86 243

miper_lstm_enhance		89.90 336	89.80 330	90.19 384	91.37 469	77.50 423	83.82 513	95.00 333	84.84 354	93.05 321	94.96 317	76.53 420	95.20 451	89.96 226	98.67 205	97.86 243

ambc				92.98 226	96.88 187	83.01 289	95.92 80	96.38 273		96.41 124	97.48 106	88.26 247	97.80 319	89.96 226	98.93 148	98.12 202

CPTT-MVS		94.74 122	94.12 174	96.60 46	98.15 87	93.01 59	95.84 84	97.66 151	89.21 215	93.28 303	95.46 289	88.89 233	98.98 128	89.80 228	98.82 170	97.80 252

viewmacassd2359aftdt		93.83 179	94.36 162	92.24 274	96.45 235	79.58 372	91.60 295	97.96 109	89.14 216	95.05 230	97.09 156	93.69 100	98.48 230	89.79 229	98.43 235	98.65 132

miper_ehance_all_eth		90.48 309	90.42 314	90.69 365	91.62 463	76.57 447	86.83 452	96.18 286	83.38 378	94.06 267	92.66 415	82.20 336	98.04 291	89.79 229	97.02 364	97.45 287

eth_miper_zixun_eth		90.72 300	90.61 307	91.05 342	92.04 447	76.84 438	86.91 449	96.67 254	85.21 340	94.41 254	93.92 369	79.53 364	98.26 256	89.76 231	97.02 364	98.06 204

VPA-MVSNet		95.14 105	95.67 87	93.58 196	97.76 119	83.15 283	94.58 141	97.58 161	93.39 84	97.05 86	98.04 52	93.25 115	98.51 223	89.75 232	99.59 29	99.08 58

PMatch-Up-SfM		92.38 250	91.36 280	95.46 93	96.22 267	92.32 73	89.61 380	95.31 322	85.08 347	96.71 106	96.12 247	75.90 422	97.27 367	89.73 233	97.54 333	96.78 335

SP-DiffGlue		90.34 318	90.20 319	90.76 362	90.52 492	90.29 114	90.37 347	94.02 367	87.19 279	93.85 277	92.55 418	78.24 383	87.50 522	89.68 234	95.41 428	94.49 443

DELS-MVS		92.05 265	92.16 254	91.72 302	94.44 374	80.13 348	87.62 430	97.25 196	87.34 274	92.22 358	93.18 395	89.54 227	98.73 174	89.67 235	98.20 271	96.30 364
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
DKM		92.97 220	92.35 249	94.81 129	96.53 228	93.72 46	90.94 319	94.88 337	85.21 340	96.42 123	95.18 306	83.11 322	93.06 481	89.66 236	99.24 93	97.64 270

thisisatest0530		88.69 372	87.52 385	92.20 277	96.33 253	79.36 381	92.81 229	84.01 506	86.44 299	93.67 284	92.68 414	53.62 521	99.25 90	89.65 237	98.45 233	98.00 213

DeepC-MVS_fast	89.96 7	93.73 182	93.44 204	94.60 145	96.14 275	87.90 172	93.36 200	97.14 204	85.53 331	93.90 275	95.45 290	91.30 178	98.59 202	89.51 238	98.62 210	97.31 301
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		92.38 250	91.99 261	93.52 203	93.82 396	83.46 272	91.14 311	97.00 216	89.81 198	86.47 482	94.04 363	87.90 257	99.21 93	89.50 239	98.27 258	97.90 236

reproduce_monomvs		87.13 419	86.90 407	87.84 454	90.92 483	68.15 511	91.19 309	93.75 376	85.84 321	94.21 261	95.83 265	42.99 542	97.10 382	89.46 240	97.88 307	98.26 184

TSAR-MVS + GP.		93.07 217	92.41 245	95.06 114	95.82 302	90.87 103	90.97 318	92.61 408	88.04 254	94.61 249	93.79 376	88.08 250	97.81 318	89.41 241	98.39 242	96.50 350

testf1		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 68	97.93 62	96.05 20	97.90 305	89.32 242	99.23 95	98.19 193

APD_test2		96.77 21	96.49 35	97.60 9	99.01 15	96.70 3	96.31 61	98.33 36	94.96 50	97.30 68	97.93 62	96.05 20	97.90 305	89.32 242	99.23 95	98.19 193

APD-MVS		95.00 110	94.69 141	95.93 66	97.38 149	90.88 102	94.59 139	97.81 135	89.22 214	95.46 194	96.17 244	93.42 110	99.34 70	89.30 244	98.87 162	97.56 279
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
xiu_mvs_v1_base_debu		91.47 282	91.52 274	91.33 327	95.69 312	81.56 318	89.92 367	96.05 292	83.22 382	91.26 384	90.74 463	91.55 166	98.82 152	89.29 245	95.91 411	93.62 468

xiu_mvs_v1_base		91.47 282	91.52 274	91.33 327	95.69 312	81.56 318	89.92 367	96.05 292	83.22 382	91.26 384	90.74 463	91.55 166	98.82 152	89.29 245	95.91 411	93.62 468

xiu_mvs_v1_base_debi		91.47 282	91.52 274	91.33 327	95.69 312	81.56 318	89.92 367	96.05 292	83.22 382	91.26 384	90.74 463	91.55 166	98.82 152	89.29 245	95.91 411	93.62 468

HQP_MVS		94.26 156	93.93 182	95.23 107	97.71 125	88.12 164	94.56 143	97.81 135	91.74 136	93.31 300	95.59 281	86.93 280	98.95 136	89.26 248	98.51 227	98.60 144

plane_prior5								97.81 135					98.95 136	89.26 248	98.51 227	98.60 144

Patchmatch-RL test		88.81 367	88.52 359	89.69 401	95.33 337	79.94 356	86.22 471	92.71 403	78.46 453	95.80 166	94.18 359	66.25 480	95.33 447	89.22 250	98.53 222	93.78 462

PatchT		87.51 406	88.17 375	85.55 486	90.64 488	66.91 516	92.02 273	86.09 481	92.20 110	89.05 444	97.16 145	64.15 492	96.37 419	89.21 251	92.98 501	93.37 473

E2		93.53 189	93.96 179	92.25 272	96.39 242	79.76 364	91.06 316	98.05 92	88.58 234	94.71 247	96.64 196	93.08 123	98.57 208	89.16 252	97.97 299	98.42 161

E3		93.53 189	93.96 179	92.25 272	96.39 242	79.76 364	91.06 316	98.05 92	88.58 234	94.71 247	96.64 196	93.07 125	98.57 208	89.16 252	97.97 299	98.42 161

test_f		86.65 428	87.13 401	85.19 490	90.28 499	86.11 222	86.52 464	91.66 428	69.76 520	95.73 177	97.21 142	69.51 462	81.28 545	89.15 254	94.40 464	88.17 517

PMatch-SfM		91.76 272	90.58 310	95.30 103	95.64 318	91.67 88	89.49 386	94.79 344	84.45 361	96.31 133	96.02 254	71.68 452	97.26 369	89.13 255	97.75 314	96.98 321

CSCG		94.69 126	94.75 137	94.52 150	97.55 138	87.87 173	95.01 124	97.57 162	92.68 94	96.20 144	93.44 387	91.92 157	98.78 165	89.11 256	99.24 93	96.92 325

KD-MVS_self_test		94.10 167	94.73 140	92.19 278	97.66 131	79.49 376	94.86 128	97.12 209	89.59 205	96.87 94	97.65 88	90.40 208	98.34 248	89.08 257	99.35 67	98.75 115

test_vis3_rt		90.40 312	90.03 325	91.52 314	92.58 425	88.95 140	90.38 346	97.72 146	73.30 492	97.79 37	97.51 104	77.05 406	87.10 529	89.03 258	94.89 452	98.50 153

hybridnocas07		91.51 281	91.66 271	91.04 343	93.14 413	78.03 411	88.75 415	96.92 223	85.97 314	91.63 377	95.31 302	87.67 260	97.31 362	88.97 259	96.61 387	97.79 253

cl22		89.02 359	88.50 360	90.59 371	89.76 507	76.45 448	86.62 460	94.03 365	82.98 390	92.65 337	92.49 421	72.05 450	97.53 345	88.93 260	97.02 364	97.78 256

VDD-MVS		94.37 150	94.37 160	94.40 157	97.49 141	86.07 223	93.97 171	93.28 391	94.49 57	96.24 140	97.78 75	87.99 255	98.79 161	88.92 261	99.14 112	98.34 175

AUN-MVS		90.05 332	88.30 366	95.32 101	96.09 280	90.52 112	92.42 253	92.05 422	82.08 404	88.45 458	92.86 405	65.76 482	98.69 185	88.91 262	96.07 406	96.75 338

TransMVSNet (Re)		95.27 101	96.04 63	92.97 227	98.37 72	81.92 312	95.07 121	96.76 245	93.97 70	97.77 38	98.57 28	95.72 24	97.90 305	88.89 263	99.23 95	99.08 58

SSM_0407		94.23 161	94.56 152	93.24 218	96.65 209	82.79 294	93.66 186	97.84 130	91.46 149	95.19 218	96.56 205	92.50 144	98.99 127	88.83 264	98.32 252	97.93 228

SSM_0404		94.38 148	94.69 141	93.43 207	97.16 163	83.23 279	93.95 173	97.84 130	91.46 149	95.70 179	96.56 205	92.50 144	99.08 112	88.83 264	98.23 264	97.98 217

viewdifsd2359ckpt07		93.63 184	94.33 164	91.55 311	96.19 270	77.86 416	90.11 362	97.74 143	90.76 170	96.11 150	96.61 200	94.37 87	98.27 255	88.82 266	98.23 264	98.51 152

CR-MVSNet		87.89 392	87.12 402	90.22 381	91.01 480	78.93 390	92.52 245	92.81 399	73.08 494	89.10 440	96.93 170	67.11 472	97.64 338	88.80 267	92.70 503	94.08 453

CVMVSNet		85.16 443	84.72 441	86.48 474	92.12 444	70.19 501	92.32 260	88.17 460	56.15 546	90.64 404	95.85 262	67.97 470	96.69 405	88.78 268	90.52 520	92.56 485

FMVSNet1		94.84 117	95.13 119	93.97 173	97.60 134	84.29 257	95.99 75	96.56 262	92.38 101	97.03 87	98.53 30	90.12 215	98.98 128	88.78 268	99.16 110	98.65 132

ZD-MVS					97.23 158	90.32 113		97.54 165	84.40 363	94.78 242	95.79 267	92.76 136	99.39 54	88.72 270	98.40 238

casdiffseed414692147		94.56 134	94.90 128	93.54 199	96.60 219	83.33 275	93.57 190	98.06 90	91.57 142	95.26 210	97.31 129	94.06 94	98.39 237	88.67 271	98.95 145	98.91 89

train_agg		92.71 234	91.83 268	95.35 97	96.45 235	89.46 126	90.60 336	96.92 223	79.37 441	90.49 405	94.39 349	91.20 182	98.88 143	88.66 272	98.43 235	97.72 264

mamba_0408		93.60 187	93.72 189	93.27 216	96.65 209	82.79 294	88.81 410	97.68 148	90.62 177	95.19 218	96.01 255	91.54 170	99.08 112	88.63 273	98.32 252	97.93 228

SSM_04072		93.25 207	93.72 189	91.84 295	96.65 209	82.79 294	88.81 410	97.68 148	90.62 177	95.19 218	96.01 255	91.54 170	94.81 459	88.63 273	98.32 252	97.93 228

Anonymous20240529		95.50 83	95.83 79	94.50 151	97.33 153	85.93 229	95.19 118	96.77 244	96.64 23	97.61 48	98.05 50	93.23 117	98.79 161	88.60 275	99.04 127	98.78 111

viewcassd2359sk11		93.16 212	93.51 203	92.13 284	96.07 282	79.59 369	90.88 322	97.97 107	87.82 260	94.23 259	96.19 240	92.31 147	98.53 219	88.58 276	97.51 334	98.28 181

viewmanbaseed2359cas		93.08 214	93.43 205	92.01 290	95.69 312	79.29 383	91.15 310	97.70 147	87.45 272	94.18 262	96.12 247	92.31 147	98.37 245	88.58 276	97.73 316	98.38 170

RoMa-SfM		93.45 194	92.92 223	95.03 115	96.77 199	94.01 31	93.01 212	95.19 328	83.99 369	97.28 71	95.33 301	87.17 272	93.66 474	88.55 278	99.00 132	97.42 290

PRO-TEST		92.55 245	92.43 244	92.90 234	95.14 347	82.69 300	94.18 159	97.13 206	86.47 298	93.36 297	97.39 114	82.07 338	99.34 70	88.52 279	97.64 325	96.68 339

test1111		90.39 314	90.61 307	89.74 399	98.04 97	71.50 496	95.59 93	79.72 539	89.41 208	95.94 158	98.14 44	70.79 456	98.81 157	88.52 279	99.32 77	98.90 90

icg_test_0407_2		91.18 290	91.92 265	88.94 423	95.19 342	76.72 441	84.66 499	96.89 227	85.92 316	93.55 288	94.50 343	91.06 187	92.99 482	88.49 281	97.07 358	97.10 310

IMVS_0407		92.28 255	92.83 225	90.63 369	95.19 342	76.72 441	92.79 232	96.89 227	85.92 316	93.55 288	94.50 343	91.06 187	98.07 286	88.49 281	97.07 358	97.10 310

IMVS_0404		90.67 303	91.06 291	89.50 403	95.19 342	76.72 441	86.58 462	96.89 227	85.92 316	89.17 439	94.50 343	85.77 295	94.67 460	88.49 281	97.07 358	97.10 310

IMVS_0403		92.20 260	92.70 233	90.69 365	95.19 342	76.72 441	92.39 255	96.89 227	85.92 316	93.66 285	94.50 343	90.18 212	98.24 259	88.49 281	97.07 358	97.10 310

onestephybrid01		92.06 264	92.07 258	92.04 287	93.45 405	80.93 334	89.82 373	96.78 241	87.60 268	91.68 374	95.43 292	88.73 236	97.43 354	88.32 285	96.85 374	97.76 258

test_prior2							90.21 355		89.33 211	90.77 400	94.81 326	90.41 207		88.21 286	98.55 218

APD_test1		95.91 64	95.42 100	97.36 26	98.82 30	96.62 6	95.64 92	97.64 152	93.38 85	95.89 162	97.23 138	93.35 112	97.66 335	88.20 287	98.66 207	97.79 253

D2MVS		89.93 335	89.60 336	90.92 353	94.03 387	78.40 402	88.69 417	94.85 338	78.96 449	93.08 319	95.09 312	74.57 429	96.94 392	88.19 288	98.96 143	97.41 291

IS-MVSNet		94.49 143	94.35 163	94.92 121	98.25 82	86.46 209	97.13 17	94.31 356	96.24 34	96.28 138	96.36 224	82.88 326	99.35 67	88.19 288	99.52 41	98.96 77

E3new		92.83 228	93.10 216	92.04 287	95.78 306	79.45 377	90.76 327	97.90 118	87.23 277	93.79 278	95.70 277	91.55 166	98.49 225	88.17 290	96.99 369	98.16 196

test9_res														88.16 291	98.40 238	97.83 247

FE-MVSNET2		94.07 170	94.47 156	92.90 234	97.45 147	81.26 326	93.58 189	97.54 165	88.28 245	96.46 120	97.92 67	91.41 174	98.74 172	88.12 292	99.44 51	98.69 128

UGNet		93.08 214	92.50 241	94.79 131	93.87 393	87.99 168	95.07 121	94.26 360	90.64 174	87.33 478	97.67 86	86.89 282	98.49 225	88.10 293	98.71 198	97.91 235
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test2506		85.42 441	84.57 444	87.96 448	97.81 116	66.53 519	96.14 70	56.35 554	89.04 217	93.55 288	98.10 47	42.88 545	98.68 187	88.09 294	99.18 106	98.67 130

hybrid		91.14 291	91.24 284	90.83 359	93.15 411	77.49 424	88.76 414	96.87 233	84.51 359	91.25 387	95.23 304	87.14 274	97.25 370	88.05 295	96.24 402	97.76 258

test_cas_vis1_n_1920		88.25 382	88.27 369	88.20 444	92.19 440	78.92 392	89.45 388	95.44 315	75.29 479	93.23 309	95.65 280	71.58 453	90.23 503	88.05 295	93.55 488	95.44 409

FA-MVS(test-final)		91.81 270	91.85 267	91.68 306	94.95 351	79.99 354	96.00 74	93.44 389	87.80 261	94.02 270	97.29 130	77.60 394	98.45 234	88.04 297	97.49 336	96.61 341

ETV-MVS		92.99 218	92.74 228	93.72 189	95.86 299	86.30 215	92.33 259	97.84 130	91.70 139	92.81 330	86.17 509	92.22 150	99.19 97	88.03 298	97.73 316	95.66 401

EIA-MVS		92.35 252	92.03 259	93.30 215	95.81 304	83.97 265	92.80 231	98.17 67	87.71 264	89.79 428	87.56 497	91.17 185	99.18 98	87.97 299	97.27 348	96.77 336

mvs_anonymous		90.37 316	91.30 283	87.58 456	92.17 442	68.00 512	89.84 372	94.73 346	83.82 373	93.22 310	97.40 113	87.54 264	97.40 358	87.94 300	95.05 448	97.34 299

IterMVS		90.18 323	90.16 320	90.21 382	93.15 411	75.98 454	87.56 433	92.97 397	86.43 300	94.09 264	96.40 216	78.32 381	97.43 354	87.87 301	94.69 459	97.23 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_enhance_ethall		88.42 377	87.87 380	90.07 386	88.67 521	75.52 459	85.10 489	95.59 308	75.68 471	92.49 342	89.45 481	78.96 368	97.88 309	87.86 302	97.02 364	96.81 333

ET-MVSNet_ETH3D		86.15 434	84.27 447	91.79 298	93.04 416	81.28 325	87.17 444	86.14 479	79.57 438	83.65 511	88.66 487	57.10 513	98.18 267	87.74 303	95.40 429	95.90 389

Effi-MVS+-dtu		93.90 178	92.60 238	97.77 3	94.74 363	96.67 5	94.00 169	95.41 318	89.94 195	91.93 369	92.13 436	90.12 215	98.97 133	87.68 304	97.48 337	97.67 268

SDMVSNet		94.43 146	95.02 125	92.69 248	97.93 108	82.88 291	91.92 280	95.99 295	93.65 81	95.51 189	98.63 25	94.60 78	96.48 412	87.57 305	99.35 67	98.70 125

WR-MVS		93.49 192	93.72 189	92.80 242	97.57 137	80.03 352	90.14 359	95.68 302	93.70 77	96.62 113	95.39 298	87.21 271	99.04 122	87.50 306	99.64 25	99.33 31

tfpnnormal		94.27 155	94.87 131	92.48 266	97.71 125	80.88 335	94.55 145	95.41 318	93.70 77	96.67 109	97.72 81	91.40 175	98.18 267	87.45 307	99.18 106	98.36 171

jason		89.17 353	88.32 365	91.70 304	95.73 310	80.07 349	88.10 424	93.22 392	71.98 502	90.09 416	92.79 409	78.53 378	98.56 212	87.43 308	97.06 362	96.46 354
jason: jason.
Effi-MVS+		92.79 229	92.74 228	92.94 231	95.10 348	83.30 277	94.00 169	97.53 168	91.36 154	89.35 437	90.65 468	94.01 96	98.66 189	87.40 309	95.30 438	96.88 330

FMVSNet2		92.78 230	92.73 230	92.95 229	95.40 332	81.98 311	94.18 159	95.53 313	88.63 229	96.05 152	97.37 116	81.31 347	98.81 157	87.38 310	98.67 205	98.06 204

EPP-MVSNet		93.91 177	93.68 194	94.59 146	98.08 91	85.55 239	97.44 11	94.03 365	94.22 63	94.94 235	96.19 240	82.07 338	99.57 14	87.28 311	98.89 157	98.65 132

PC_three_1452									75.31 478	95.87 163	95.75 273	92.93 130	96.34 422	87.18 312	98.68 203	98.04 208

ECVR-MVS		90.12 326	90.16 320	90.00 391	97.81 116	72.68 488	95.76 87	78.54 543	89.04 217	95.36 200	98.10 47	70.51 458	98.64 193	87.10 313	99.18 106	98.67 130

VDDNet		94.03 171	94.27 169	93.31 213	98.87 26	82.36 305	95.51 101	91.78 427	97.19 15	96.32 132	98.60 27	84.24 311	98.75 169	87.09 314	98.83 169	98.81 102

agg_prior2														87.06 315	98.36 249	97.98 217

LF4IMVS		92.72 233	92.02 260	94.84 128	95.65 316	91.99 79	92.92 223	96.60 258	85.08 347	92.44 346	93.62 382	86.80 283	96.35 420	86.81 316	98.25 261	96.18 373

GBi-Net		93.21 209	92.96 219	93.97 173	95.40 332	84.29 257	95.99 75	96.56 262	88.63 229	95.10 226	98.53 30	81.31 347	98.98 128	86.74 317	98.38 243	98.65 132

test1		93.21 209	92.96 219	93.97 173	95.40 332	84.29 257	95.99 75	96.56 262	88.63 229	95.10 226	98.53 30	81.31 347	98.98 128	86.74 317	98.38 243	98.65 132

FMVSNet3		90.78 298	90.32 318	92.16 282	93.03 417	79.92 357	92.54 244	94.95 335	86.17 310	95.10 226	96.01 255	69.97 461	98.75 169	86.74 317	98.38 243	97.82 250

viewdifsd2359ckpt13		92.57 243	92.48 243	92.83 240	95.60 321	82.35 307	91.80 291	97.49 173	85.04 349	93.14 316	95.41 296	90.94 192	98.25 257	86.68 320	96.24 402	97.87 242

lupinMVS		88.34 381	87.31 392	91.45 318	94.74 363	80.06 350	87.23 441	92.27 414	71.10 509	88.83 445	91.15 455	77.02 409	98.53 219	86.67 321	96.75 379	95.76 395

OMC-MVS		94.22 162	93.69 193	95.81 73	97.25 156	91.27 93	92.27 265	97.40 179	87.10 286	94.56 250	95.42 293	93.74 99	98.11 277	86.62 322	98.85 163	98.06 204

mvsany_test3		89.11 355	88.21 374	91.83 296	91.30 470	90.25 115	88.09 425	78.76 541	76.37 469	96.43 122	98.39 38	83.79 316	90.43 502	86.57 323	94.20 472	94.80 434

pmmvs-eth3d		91.54 279	90.73 304	93.99 171	95.76 309	87.86 174	90.83 324	93.98 370	78.23 455	94.02 270	96.22 236	82.62 333	96.83 399	86.57 323	98.33 250	97.29 302

BP-MVS														86.55 325

HQP-MVS		92.09 263	91.49 277	93.88 179	96.36 247	84.89 249	91.37 301	97.31 190	87.16 281	88.81 447	93.40 388	84.76 308	98.60 200	86.55 325	97.73 316	98.14 200

viewdifsd2359ckpt09		92.60 239	92.34 250	93.36 210	95.94 294	83.36 274	92.35 257	97.93 117	83.17 385	92.92 328	94.66 335	89.87 222	98.57 208	86.51 327	97.71 320	98.15 198

ppachtmachnet_test		88.61 373	88.64 355	88.50 437	91.76 456	70.99 499	84.59 501	92.98 396	79.30 445	92.38 349	93.53 386	79.57 363	97.45 352	86.50 328	97.17 355	97.07 314

MIMVSNet1		95.52 82	95.45 95	95.72 77	99.14 5	89.02 139	96.23 68	96.87 233	93.73 76	97.87 35	98.49 33	90.73 200	99.05 119	86.43 329	99.60 27	99.10 57

PVSNet_Blended_VisFu		91.63 276	91.20 285	92.94 231	97.73 123	83.95 266	92.14 269	97.46 175	78.85 451	92.35 352	94.98 316	84.16 312	99.08 112	86.36 330	96.77 378	95.79 394

Fast-Effi-MVS+-dtu		92.77 231	92.16 254	94.58 149	94.66 368	88.25 159	92.05 271	96.65 255	89.62 204	90.08 420	91.23 454	92.56 139	98.60 200	86.30 331	96.27 399	96.90 326

OPU-MVS				95.15 112	96.84 191	89.43 128	95.21 114				95.66 279	93.12 121	98.06 288	86.28 332	98.61 211	97.95 223

PMVS	87.21 14	94.97 111	95.33 107	93.91 178	98.97 20	97.16 2	95.54 100	95.85 298	96.47 27	93.40 296	97.46 107	95.31 41	95.47 441	86.18 333	98.78 181	89.11 511
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		91.28 288	90.08 324	94.88 125	95.22 340	92.66 68	89.81 374	94.51 353	79.15 446	95.27 208	93.71 379	78.33 380	95.52 437	86.11 334	98.63 208	96.46 354

DenseAffine		91.92 268	90.90 294	94.97 118	96.37 244	93.07 56	90.35 348	93.65 379	84.62 358	95.66 183	94.39 349	78.19 385	94.97 457	86.02 335	98.90 154	96.87 331

OpenMVS	89.45 8	92.27 258	92.13 257	92.68 249	94.53 372	84.10 263	95.70 88	97.03 214	82.44 400	91.14 392	96.42 214	88.47 243	98.38 241	85.95 336	97.47 338	95.55 406

Syy-MVS		84.81 446	84.93 440	84.42 498	91.71 459	63.36 535	85.89 474	81.49 525	81.03 420	85.13 493	81.64 536	77.44 397	95.00 453	85.94 337	94.12 475	94.91 430

CDPH-MVS		92.67 236	91.83 268	95.18 111	96.94 181	88.46 156	90.70 332	97.07 212	77.38 459	92.34 354	95.08 313	92.67 138	98.88 143	85.74 338	98.57 216	98.20 191

SSC-MVS		90.16 324	92.96 219	81.78 516	97.88 111	48.48 553	90.75 328	87.69 466	96.02 40	96.70 107	97.63 90	85.60 301	97.80 319	85.73 339	98.60 213	99.06 60

CANet_DTU		89.85 338	89.17 342	91.87 294	92.20 439	80.02 353	90.79 326	95.87 297	86.02 312	82.53 522	91.77 445	80.01 358	98.57 208	85.66 340	97.70 321	97.01 319

ITE_SJBPF				95.95 63	97.34 152	93.36 54		96.55 265	91.93 120	94.82 240	95.39 298	91.99 155	97.08 384	85.53 341	97.96 302	97.41 291

FE-MVSNET		92.02 266	92.22 253	91.41 321	96.63 217	79.08 389	91.53 297	96.84 237	85.52 334	95.16 221	96.14 245	83.97 314	97.50 347	85.48 342	98.75 188	97.64 270

gbinet_0.2-2-1-0.02		88.14 386	86.86 409	91.99 291	90.70 487	80.51 338	87.36 440	93.01 395	83.45 377	90.38 410	82.42 534	72.73 441	98.54 215	85.40 343	96.27 399	96.90 326

new-patchmatchnet		88.97 363	90.79 302	83.50 508	94.28 379	55.83 548	85.34 488	93.56 384	86.18 309	95.47 192	95.73 274	83.10 323	96.51 411	85.40 343	98.06 287	98.16 196

viewmambaseed2359dif		90.77 299	90.81 300	90.64 368	93.46 404	77.04 432	88.83 408	96.29 276	80.79 427	92.21 360	95.11 310	88.99 231	97.28 364	85.39 345	96.20 405	97.59 275

EPNet		89.80 340	88.25 370	94.45 155	83.91 542	86.18 220	93.87 176	87.07 473	91.16 160	80.64 534	94.72 331	78.83 371	98.89 142	85.17 346	98.89 157	98.28 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		90.11 327	89.92 327	90.66 367	90.35 498	77.00 434	92.96 217	92.81 399	90.25 187	94.74 244	96.93 170	67.11 472	97.52 346	85.17 346	98.98 135	97.46 286

旧先验2							90.00 365		68.65 524	92.71 336			96.52 410	85.15 348

MDA-MVSNet-bldmvs		91.04 292	90.88 296	91.55 311	94.68 367	80.16 345	85.49 485	92.14 418	90.41 185	94.93 236	95.79 267	85.10 305	96.93 394	85.15 348	94.19 474	97.57 277

Anonymous202405211		92.58 241	92.50 241	92.83 240	96.55 224	83.22 281	92.43 252	91.64 429	94.10 65	95.59 186	96.64 196	81.88 344	97.50 347	85.12 350	98.52 225	97.77 257

AllTest		94.88 116	94.51 155	96.00 59	98.02 98	92.17 74	95.26 112	98.43 27	90.48 181	95.04 231	96.74 188	92.54 140	97.86 313	85.11 351	98.98 135	97.98 217

TestCases				96.00 59	98.02 98	92.17 74		98.43 27	90.48 181	95.04 231	96.74 188	92.54 140	97.86 313	85.11 351	98.98 135	97.98 217

VPNet		93.08 214	93.76 188	91.03 344	98.60 46	75.83 458	91.51 298	95.62 303	91.84 128	95.74 175	97.10 155	89.31 228	98.32 249	85.07 353	99.06 119	98.93 83

ArgMatch-Sym		90.98 294	89.75 333	94.68 137	95.17 346	92.64 69	89.09 400	93.46 388	78.60 452	95.11 225	92.37 427	80.44 354	95.24 450	85.04 354	98.44 234	96.18 373

LFMVS		91.33 285	91.16 288	91.82 297	96.27 261	79.36 381	95.01 124	85.61 490	96.04 39	94.82 240	97.06 159	72.03 451	98.46 233	84.96 355	98.70 201	97.65 269

VNet		92.67 236	92.96 219	91.79 298	96.27 261	80.15 346	91.95 276	94.98 334	92.19 111	94.52 252	96.07 251	87.43 266	97.39 359	84.83 356	98.38 243	97.83 247

our_test_3		87.55 403	87.59 384	87.44 458	91.76 456	70.48 500	83.83 512	90.55 442	79.79 434	92.06 367	92.17 435	78.63 377	95.63 435	84.77 357	94.73 457	96.22 371

TAPA-MVS	88.58 10	92.49 246	91.75 270	94.73 133	96.50 231	89.69 122	92.91 224	97.68 148	78.02 456	92.79 332	94.10 361	90.85 194	97.96 302	84.76 358	98.16 273	96.54 343
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		91.28 288	90.86 297	92.53 264	95.45 331	82.53 302	89.25 397	96.52 266	85.00 350	89.91 424	88.55 490	92.94 129	98.84 150	84.72 359	95.44 427	96.22 371

GA-MVS		87.70 397	86.82 410	90.31 377	93.27 409	77.22 431	84.72 497	92.79 401	85.11 346	89.82 426	90.07 469	66.80 475	97.76 327	84.56 360	94.27 470	95.96 384

QAPM		92.88 223	92.77 226	93.22 219	95.82 302	83.31 276	96.45 46	97.35 186	83.91 371	93.75 279	96.77 183	89.25 229	98.88 143	84.56 360	97.02 364	97.49 284

mvsmamba		90.24 322	89.43 339	92.64 250	95.52 326	82.36 305	96.64 35	92.29 413	81.77 408	92.14 363	96.28 230	70.59 457	99.10 111	84.44 362	95.22 443	96.47 353

blended_shiyan8		88.43 376	87.44 387	91.40 322	92.37 431	79.45 377	87.43 437	93.92 373	82.51 397	91.24 388	85.42 515	74.35 430	98.23 261	84.43 363	95.28 439	96.52 346

blended_shiyan6		88.42 377	87.43 388	91.40 322	92.37 431	79.43 379	87.41 438	93.91 374	82.51 397	91.17 389	85.44 514	74.34 431	98.24 259	84.38 364	95.32 434	96.53 345

SSC-MVS3.2		89.88 337	91.06 291	86.31 480	95.90 296	63.76 533	82.68 518	92.43 412	91.42 152	92.37 351	94.58 340	86.34 289	96.60 408	84.35 365	99.50 42	98.57 147

UnsupCasMVSNet_eth		90.33 319	90.34 317	90.28 378	94.64 369	80.24 344	89.69 379	95.88 296	85.77 323	93.94 274	95.69 278	81.99 341	92.98 483	84.21 366	91.30 515	97.62 272

LoFTR		90.05 332	89.57 337	91.50 315	93.73 398	91.47 90	90.72 330	89.37 449	81.71 410	97.13 79	96.40 216	74.09 433	92.38 486	84.18 367	98.79 179	90.63 505

testing3		83.66 461	82.52 465	87.08 462	95.84 300	65.84 524	89.80 376	77.17 547	88.17 250	90.84 399	88.63 488	30.95 554	98.11 277	84.05 368	97.19 354	97.28 303

wanda-best-256-512		87.53 404	86.39 424	90.97 349	91.29 471	78.39 404	85.63 482	93.75 376	81.91 406	90.09 416	83.30 528	72.25 446	98.18 267	83.96 369	95.32 434	96.33 360

FE-blended-shiyan7		87.53 404	86.39 424	90.97 349	91.29 471	78.39 404	85.63 482	93.75 376	81.91 406	90.09 416	83.30 528	72.25 446	98.18 267	83.96 369	95.32 434	96.33 360

CLD-MVS		91.82 269	91.41 279	93.04 224	96.37 244	83.65 269	86.82 453	97.29 193	84.65 357	92.27 356	89.67 478	92.20 152	97.85 315	83.95 371	99.47 44	97.62 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
114514_t		90.51 308	89.80 330	92.63 253	98.00 102	82.24 308	93.40 198	97.29 193	65.84 534	89.40 436	94.80 328	86.99 278	98.75 169	83.88 372	98.61 211	96.89 328

ELoFTR		89.04 358	88.72 354	89.99 392	94.38 377	89.08 137	90.15 358	89.10 450	75.60 473	95.85 164	96.52 207	75.00 427	89.26 512	83.82 373	98.08 283	91.61 495

DP-MVS Recon		92.31 254	91.88 266	93.60 194	97.18 162	86.87 196	91.10 313	97.37 180	84.92 352	92.08 366	94.08 362	88.59 238	98.20 264	83.50 374	98.14 276	95.73 396

YYNet1		88.17 384	88.24 371	87.93 450	92.21 438	73.62 479	80.75 527	88.77 452	82.51 397	94.99 234	95.11 310	82.70 331	93.70 473	83.33 375	93.83 481	96.48 352

MDA-MVSNet_test_wron		88.16 385	88.23 372	87.93 450	92.22 437	73.71 478	80.71 528	88.84 451	82.52 396	94.88 239	95.14 308	82.70 331	93.61 475	83.28 376	93.80 482	96.46 354

XXY-MVS		92.58 241	93.16 215	90.84 358	97.75 120	79.84 358	91.87 285	96.22 284	85.94 315	95.53 188	97.68 84	92.69 137	94.48 463	83.21 377	97.51 334	98.21 189

cascas		87.02 423	86.28 427	89.25 415	91.56 465	76.45 448	84.33 505	96.78 241	71.01 511	86.89 481	85.91 510	81.35 346	96.94 392	83.09 378	95.60 422	94.35 448

test-LLR		83.58 462	83.17 459	84.79 494	89.68 509	66.86 517	83.08 515	84.52 501	83.07 388	82.85 518	84.78 520	62.86 500	93.49 476	82.85 379	94.86 453	94.03 456

test-mter		81.21 485	80.01 493	84.79 494	89.68 509	66.86 517	83.08 515	84.52 501	73.85 488	82.85 518	84.78 520	43.66 540	93.49 476	82.85 379	94.86 453	94.03 456

pmmvs4		88.95 364	87.70 383	92.70 247	94.30 378	85.60 238	87.22 442	92.16 417	74.62 481	89.75 430	94.19 358	77.97 391	96.41 416	82.71 381	96.36 395	96.09 377

testdata				91.03 344	96.87 188	82.01 310		94.28 358	71.55 505	92.46 344	95.42 293	85.65 299	97.38 361	82.64 382	97.27 348	93.70 465

usedtu_dtu_shiyan1		89.18 350	88.59 356	90.95 351	94.75 360	77.79 418	86.25 468	94.63 351	81.61 412	90.88 396	92.24 431	77.03 407	98.08 282	82.62 383	97.27 348	96.97 322

FE-MVSNET3		89.18 350	88.59 356	90.95 351	94.75 360	77.79 418	86.25 468	94.63 351	81.61 412	90.88 396	92.25 430	77.03 407	98.08 282	82.62 383	97.27 348	96.97 322

MonoMVSNet		88.46 375	89.28 340	85.98 482	90.52 492	70.07 505	95.31 109	94.81 342	88.38 241	93.47 292	96.13 246	73.21 438	95.07 452	82.61 385	89.12 524	92.81 482

thisisatest0515		84.72 448	82.99 462	89.90 393	92.96 419	75.33 461	84.36 504	83.42 511	77.37 460	88.27 461	86.65 504	53.94 519	98.72 175	82.56 386	97.40 343	95.67 400

PS-MVSNAJ		88.86 366	88.99 347	88.48 438	94.88 352	74.71 464	86.69 457	95.60 304	80.88 424	87.83 469	87.37 501	90.77 196	98.82 152	82.52 387	94.37 467	91.93 491

xiu_mvs_v2_base		89.00 362	89.19 341	88.46 439	94.86 354	74.63 466	86.97 447	95.60 304	80.88 424	87.83 469	88.62 489	91.04 189	98.81 157	82.51 388	94.38 466	91.93 491

WB-MVS		89.44 347	92.15 256	81.32 517	97.73 123	48.22 554	89.73 377	87.98 463	95.24 47	96.05 152	96.99 165	85.18 304	96.95 391	82.45 389	97.97 299	98.78 111

PAPM_NR		91.03 293	90.81 300	91.68 306	96.73 202	81.10 330	93.72 183	96.35 275	88.19 249	88.77 451	92.12 437	85.09 306	97.25 370	82.40 390	93.90 480	96.68 339

test_yl		90.11 327	89.73 334	91.26 332	94.09 384	79.82 359	90.44 342	92.65 405	90.90 164	93.19 313	93.30 390	73.90 434	98.03 292	82.23 391	96.87 372	95.93 386

DCV-MVSNet		90.11 327	89.73 334	91.26 332	94.09 384	79.82 359	90.44 342	92.65 405	90.90 164	93.19 313	93.30 390	73.90 434	98.03 292	82.23 391	96.87 372	95.93 386

DPM-MVS		89.35 348	88.40 362	92.18 281	96.13 277	84.20 261	86.96 448	96.15 289	75.40 476	87.36 477	91.55 452	83.30 320	98.01 296	82.17 393	96.62 386	94.32 449

dtuplus		90.63 306	90.59 309	90.74 363	93.85 395	77.43 426	89.01 402	96.16 288	81.42 416	92.77 333	95.54 286	88.59 238	97.28 364	81.99 394	96.00 408	97.50 283

MG-MVS		89.54 344	89.80 330	88.76 427	94.88 352	72.47 492	89.60 381	92.44 411	85.82 322	89.48 434	95.98 258	82.85 328	97.74 330	81.87 395	95.27 440	96.08 378

PatchmatchNet		85.22 442	84.64 442	86.98 465	89.51 513	69.83 507	90.52 338	87.34 470	78.87 450	87.22 479	92.74 411	66.91 474	96.53 409	81.77 396	86.88 531	94.58 441
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		92.00 267	92.76 227	89.71 400	95.62 320	77.02 433	90.72 330	96.17 287	87.70 265	95.26 210	96.29 228	92.54 140	96.45 415	81.77 396	98.77 182	95.66 401

sd_testset		93.94 176	94.39 158	92.61 256	97.93 108	83.24 278	93.17 207	95.04 332	93.65 81	95.51 189	98.63 25	94.49 84	95.89 432	81.72 398	99.35 67	98.70 125

test_vis1_rt		85.58 440	84.58 443	88.60 433	87.97 523	86.76 199	85.45 487	93.59 382	66.43 531	87.64 472	89.20 484	79.33 365	85.38 538	81.59 399	89.98 523	93.66 466

ttmdpeth		86.91 426	86.57 417	87.91 452	89.68 509	74.24 473	91.49 299	87.09 471	79.84 431	89.46 435	97.86 73	65.42 484	91.04 497	81.57 400	96.74 381	98.44 159

原ACMM1				92.87 238	96.91 185	84.22 260		97.01 215	76.84 466	89.64 431	94.46 347	88.00 254	98.70 183	81.53 401	98.01 293	95.70 399

usedtu_blend_shiyan5		89.08 356	88.33 364	91.34 326	91.29 471	79.59 369	94.02 167	97.13 206	90.07 193	90.09 416	83.30 528	72.25 446	98.10 280	81.45 402	95.32 434	96.33 360

blend_shiyan4		83.29 466	80.66 485	91.19 338	91.86 452	79.59 369	87.05 446	93.91 374	82.66 393	89.60 432	83.36 527	42.82 547	98.10 280	81.45 402	73.26 547	95.87 391

1112_ss		88.42 377	87.41 390	91.45 318	96.69 206	80.99 332	89.72 378	96.72 247	73.37 491	87.00 480	90.69 466	77.38 400	98.20 264	81.38 404	93.72 483	95.15 417

MS-PatchMatch		88.05 387	87.75 381	88.95 422	93.28 408	77.93 413	87.88 428	92.49 410	75.42 475	92.57 341	93.59 384	80.44 354	94.24 470	81.28 405	92.75 502	94.69 440

LCM-MVSNet-Re		94.20 163	94.58 150	93.04 224	95.91 295	83.13 285	93.79 180	99.19 5	92.00 117	98.84 8	98.04 52	93.64 101	99.02 124	81.28 405	98.54 221	96.96 324

tpmrst		82.85 472	82.93 463	82.64 511	87.65 524	58.99 544	90.14 359	87.90 464	75.54 474	83.93 508	91.63 449	66.79 477	95.36 444	81.21 407	81.54 541	93.57 472

无先验							89.94 366	95.75 300	70.81 513				98.59 202	81.17 408		94.81 433

新几何1				93.17 222	97.16 163	87.29 182		94.43 354	67.95 526	91.29 383	94.94 318	86.97 279	98.23 261	81.06 409	97.75 314	93.98 458

MSDG		90.82 296	90.67 305	91.26 332	94.16 381	83.08 287	86.63 459	96.19 285	90.60 179	91.94 368	91.89 442	89.16 230	95.75 434	80.96 410	94.51 462	94.95 427

mvsany_test1		83.91 459	82.93 463	86.84 470	86.18 534	85.93 229	81.11 526	75.03 548	70.80 514	88.57 457	94.63 336	83.08 324	87.38 524	80.39 411	86.57 532	87.21 526

pmmvs5		87.87 393	87.14 400	90.07 386	93.26 410	76.97 437	88.89 405	92.18 415	73.71 489	88.36 459	93.89 371	76.86 416	96.73 404	80.32 412	96.81 376	96.51 347

PVSNet_BlendedMVS		90.35 317	89.96 326	91.54 313	94.81 356	78.80 398	90.14 359	96.93 221	79.43 440	88.68 455	95.06 314	86.27 291	98.15 273	80.27 413	98.04 289	97.68 267

PVSNet_Blended		88.74 370	88.16 376	90.46 375	94.81 356	78.80 398	86.64 458	96.93 221	74.67 480	88.68 455	89.18 485	86.27 291	98.15 273	80.27 413	96.00 408	94.44 446

testdata2													98.03 292	80.24 415

FE-MVS		89.06 357	88.29 367	91.36 325	94.78 358	79.57 373	96.77 29	90.99 434	84.87 353	92.96 326	96.29 228	60.69 508	98.80 160	80.18 416	97.11 357	95.71 397

F-COLMAP		92.28 255	91.06 291	95.95 63	97.52 139	91.90 81	93.53 192	97.18 201	83.98 370	88.70 453	94.04 363	88.41 245	98.55 214	80.17 417	95.99 410	97.39 296

EPMVS		81.17 486	80.37 489	83.58 507	85.58 536	65.08 528	90.31 353	71.34 549	77.31 462	85.80 488	91.30 453	59.38 509	92.70 484	79.99 418	82.34 540	92.96 480

TESTMET0.1,1		79.09 502	78.04 504	82.25 513	87.52 526	64.03 532	83.08 515	80.62 534	70.28 517	80.16 535	83.22 531	44.13 538	90.56 500	79.95 419	93.36 490	92.15 489

Test_1112_low_res		87.50 408	86.58 416	90.25 380	96.80 195	77.75 420	87.53 435	96.25 279	69.73 521	86.47 482	93.61 383	75.67 423	97.88 309	79.95 419	93.20 494	95.11 421

CL-MVSNet_self_test		90.04 334	89.90 328	90.47 373	95.24 339	77.81 417	86.60 461	92.62 407	85.64 327	93.25 308	93.92 369	83.84 315	96.06 427	79.93 421	98.03 290	97.53 281

OpenMVS_ROB	85.12 16	89.52 345	89.05 344	90.92 353	94.58 370	81.21 329	91.10 313	93.41 390	77.03 464	93.41 293	93.99 367	83.23 321	97.80 319	79.93 421	94.80 456	93.74 464

CNLPA		91.72 274	91.20 285	93.26 217	96.17 271	91.02 96	91.14 311	95.55 312	90.16 192	90.87 398	93.56 385	86.31 290	94.40 466	79.92 423	97.12 356	94.37 447

SIFT-UMatch		87.96 389	87.52 385	89.29 411	91.48 466	92.84 63	85.46 486	83.94 507	87.47 271	91.86 370	92.92 402	76.78 417	87.35 525	79.73 424	98.00 296	87.69 520

MASt3R-SfM		82.76 473	82.17 470	84.53 496	83.29 545	86.01 225	82.08 522	80.49 535	63.10 541	92.22 358	94.20 357	69.18 464	77.62 546	79.63 425	95.37 432	89.94 509

ab-mvs		92.40 249	92.62 236	91.74 301	97.02 176	81.65 317	95.84 84	95.50 314	86.95 289	92.95 327	97.56 95	90.70 201	97.50 347	79.63 425	97.43 341	96.06 379

test_post1							90.21 355				5.85 558	65.36 485	96.00 429	79.61 427

SCA		87.43 409	87.21 396	88.10 446	92.01 448	71.98 494	89.43 389	88.11 461	82.26 402	88.71 452	92.83 406	78.65 375	97.59 341	79.61 427	93.30 492	94.75 437

tpmvs		84.22 453	83.97 452	84.94 492	87.09 530	65.18 526	91.21 307	88.35 455	82.87 391	85.21 491	90.96 461	65.24 487	96.75 403	79.60 429	85.25 534	92.90 481

baseline1		87.62 401	87.31 392	88.54 434	94.71 366	74.27 472	93.10 210	88.20 459	86.20 307	92.18 361	93.04 396	73.21 438	95.52 437	79.32 430	85.82 533	95.83 392

tpm		84.38 451	84.08 450	85.30 489	90.47 495	63.43 534	89.34 392	85.63 487	77.24 463	87.62 473	95.03 315	61.00 507	97.30 363	79.26 431	91.09 518	95.16 416

BH-untuned		90.68 302	90.90 294	90.05 390	95.98 290	79.57 373	90.04 363	94.94 336	87.91 256	94.07 266	93.00 397	87.76 258	97.78 323	79.19 432	95.17 444	92.80 483

dtuonly		84.38 451	85.24 438	81.80 515	87.13 529	58.46 545	81.58 525	92.71 403	74.41 483	85.68 489	92.62 416	78.17 386	92.13 489	79.15 433	95.73 417	94.82 432

API-MVS		91.52 280	91.61 272	91.26 332	94.16 381	86.26 216	94.66 137	94.82 340	91.17 159	92.13 364	91.08 458	90.03 220	97.06 387	79.09 434	97.35 345	90.45 506

dtuonlycased		90.11 327	90.39 316	89.28 413	97.09 170	72.61 489	85.75 478	95.27 323	81.57 414	94.42 253	94.89 320	90.47 205	96.81 401	78.74 435	95.27 440	98.41 164

1314		86.46 432	86.33 426	86.87 469	91.65 462	74.54 467	91.94 278	94.10 364	74.28 485	84.78 499	87.33 502	83.03 325	95.00 453	78.72 436	91.16 517	91.06 500

BH-RMVSNet		90.47 310	90.44 313	90.56 372	95.21 341	78.65 400	89.15 398	93.94 371	88.21 248	92.74 335	94.22 356	86.38 288	97.88 309	78.67 437	95.39 430	95.14 418

SIFT-MNN		87.81 396	87.11 403	89.90 393	92.19 440	93.62 48	86.73 456	84.68 500	87.19 279	90.95 395	92.80 408	73.54 437	87.09 531	78.62 438	97.32 346	88.98 512

MVP-Stereo		90.07 331	88.92 348	93.54 199	96.31 255	86.49 207	90.93 320	95.59 308	79.80 433	91.48 379	95.59 281	80.79 351	97.39 359	78.57 439	91.19 516	96.76 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDTV_nov1_ep13			83.88 455		89.42 514	61.52 537	88.74 416	87.41 468	73.99 487	84.96 497	94.01 366	65.25 486	95.53 436	78.02 440	93.16 495

Vis-MVSNet (Re-imp)		90.42 311	90.16 320	91.20 337	97.66 131	77.32 428	94.33 150	87.66 467	91.20 158	92.99 323	95.13 309	75.40 425	98.28 251	77.86 441	99.19 102	97.99 216

sss		87.23 414	86.82 410	88.46 439	93.96 389	77.94 412	86.84 451	92.78 402	77.59 458	87.61 475	91.83 444	78.75 373	91.92 490	77.84 442	94.20 472	95.52 408

IB-MVS	77.21 19	83.11 467	81.05 479	89.29 411	91.15 475	75.85 455	85.66 480	86.00 482	79.70 436	82.02 527	86.61 505	48.26 527	98.39 237	77.84 442	92.22 508	93.63 467
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
Patchmatch-test		86.10 435	86.01 428	86.38 478	90.63 489	74.22 474	89.57 383	86.69 474	85.73 325	89.81 427	92.83 406	65.24 487	91.04 497	77.82 444	95.78 416	93.88 461

USDC		89.02 359	89.08 343	88.84 426	95.07 349	74.50 469	88.97 403	96.39 272	73.21 493	93.27 304	96.28 230	82.16 337	96.39 417	77.55 445	98.80 176	95.62 404

CDS-MVSNet		89.55 343	88.22 373	93.53 201	95.37 335	86.49 207	89.26 395	93.59 382	79.76 435	91.15 391	92.31 429	77.12 404	98.38 241	77.51 446	97.92 305	95.71 397
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
N_pmnet		88.90 365	87.25 395	93.83 183	94.40 376	93.81 44	84.73 494	87.09 471	79.36 443	93.26 306	92.43 425	79.29 366	91.68 492	77.50 447	97.22 353	96.00 381

PatchmatchNet1														77.38 448	97.25 352	96.00 381
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
AdaColmap		91.63 276	91.36 280	92.47 267	95.56 324	86.36 213	92.24 268	96.27 278	88.88 223	89.90 425	92.69 413	91.65 163	98.32 249	77.38 448	97.64 325	92.72 484

SIFT-NCM-Cal		87.99 388	87.39 391	89.77 396	92.16 443	93.98 34	86.51 465	82.96 518	85.99 313	91.10 393	92.99 398	80.00 359	87.11 528	77.21 450	97.60 330	88.22 516

CostFormer		83.09 468	82.21 468	85.73 483	89.27 516	67.01 515	90.35 348	86.47 476	70.42 516	83.52 514	93.23 393	61.18 505	96.85 398	77.21 450	88.26 528	93.34 474

E-PMN		80.72 491	80.86 482	80.29 520	85.11 539	68.77 509	72.96 540	81.97 523	87.76 263	83.25 517	83.01 532	62.22 503	89.17 513	77.15 452	94.31 469	82.93 539

SIFT-PointCN		87.02 423	86.47 423	88.65 432	90.27 500	91.47 90	83.91 509	84.08 504	84.84 354	91.35 382	92.24 431	75.25 426	87.29 527	77.11 453	99.20 101	87.20 527

PLC	85.34 15	90.40 312	88.92 348	94.85 127	96.53 228	90.02 118	91.58 296	96.48 268	80.16 430	86.14 485	92.18 434	85.73 297	98.25 257	76.87 454	94.61 461	96.30 364
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SIFT-UM-Cal		87.93 391	87.42 389	89.44 406	90.95 482	92.71 66	84.33 505	88.32 456	86.32 302	90.41 408	92.73 412	78.78 372	88.31 518	76.83 455	98.16 273	87.31 524

MAR-MVS		90.32 320	88.87 352	94.66 141	94.82 355	91.85 82	94.22 157	94.75 345	80.91 423	87.52 476	88.07 495	86.63 286	97.87 312	76.67 456	96.21 404	94.25 450
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
SIFT-ConvMatch		87.94 390	87.21 396	90.11 385	91.67 461	93.60 49	85.55 484	83.12 516	86.48 296	92.15 362	92.98 400	78.11 388	88.58 517	76.60 457	98.25 261	88.14 518

EPNet_dtu		85.63 439	84.37 445	89.40 409	86.30 533	74.33 471	91.64 294	88.26 457	84.84 354	72.96 545	89.85 470	71.27 455	97.69 333	76.60 457	97.62 327	96.18 373
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing99		82.94 470	81.72 472	86.59 471	92.55 427	66.53 519	86.08 473	85.70 485	85.47 337	83.95 507	85.70 512	45.87 533	97.07 386	76.58 459	93.56 487	96.17 376

JIA-IIPM		85.08 444	83.04 460	91.19 338	87.56 525	86.14 221	89.40 391	84.44 503	88.98 219	82.20 523	97.95 61	56.82 515	96.15 423	76.55 460	83.45 537	91.30 498

PatchMatch-RL		89.18 350	88.02 378	92.64 250	95.90 296	92.87 62	88.67 419	91.06 433	80.34 428	90.03 422	91.67 448	83.34 318	94.42 465	76.35 461	94.84 455	90.64 504

SIFT-NN-UMatch		86.43 433	85.66 434	88.76 427	90.73 486	92.76 65	84.99 491	81.25 529	84.13 367	88.17 463	92.04 438	76.90 413	86.62 533	76.34 462	96.36 395	86.91 530

testing91		83.56 463	82.45 466	86.91 468	92.92 420	67.29 513	86.33 467	88.07 462	86.22 306	84.26 504	85.76 511	48.15 529	97.17 378	76.27 463	94.08 478	96.27 367

FMVSNet5		87.82 395	86.56 418	91.62 308	92.31 434	79.81 361	93.49 194	94.81 342	83.26 380	91.36 381	96.93 170	52.77 523	97.49 350	76.07 464	98.03 290	97.55 280

PMMVS		83.00 469	81.11 478	88.66 431	83.81 543	86.44 210	82.24 521	85.65 486	61.75 543	82.07 525	85.64 513	79.75 362	91.59 494	75.99 465	93.09 498	87.94 519

CMPMVS	68.83 22	87.28 413	85.67 433	92.09 285	88.77 520	85.42 242	90.31 353	94.38 355	70.02 518	88.00 465	93.30 390	73.78 436	94.03 472	75.96 466	96.54 389	96.83 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EMVS		80.35 494	80.28 491	80.54 519	84.73 541	69.07 508	72.54 542	80.73 533	87.80 261	81.66 529	81.73 535	62.89 499	89.84 505	75.79 467	94.65 460	82.71 540

WBMVS		84.00 457	83.48 456	85.56 485	92.71 423	61.52 537	83.82 513	89.38 448	79.56 439	90.74 401	93.20 394	48.21 528	97.28 364	75.63 468	98.10 281	97.88 239

PDCNetPlus		79.66 499	78.21 503	84.01 503	79.49 550	73.91 477	75.29 538	96.44 270	66.51 530	89.20 438	91.98 441	30.56 555	84.51 542	75.48 469	98.93 148	93.62 468

SIFT-CM-Cal		87.51 406	86.76 413	89.76 397	91.48 466	93.30 55	84.73 494	84.04 505	85.53 331	91.66 375	92.58 417	77.01 411	88.75 516	75.29 470	98.56 217	87.24 525

HyFIR lowres test		87.19 417	85.51 437	92.24 274	97.12 169	80.51 338	85.03 490	96.06 290	66.11 533	91.66 375	92.98 400	70.12 459	99.14 102	75.29 470	95.23 442	97.07 314

SIFT-NN-CMatch		86.64 429	85.79 431	89.18 417	91.21 474	93.07 56	84.60 500	80.33 536	84.07 368	89.10 440	91.58 451	78.69 374	87.33 526	75.28 472	97.28 347	87.13 528

0.4-1-1-0.1		77.15 505	73.55 509	87.95 449	85.49 537	75.84 457	80.59 530	82.87 519	73.51 490	73.61 544	68.65 544	42.84 546	97.22 373	75.20 473	79.18 543	90.80 502

UnsupCasMVSNet_bld		88.50 374	88.03 377	89.90 393	95.52 326	78.88 394	87.39 439	94.02 367	79.32 444	93.06 320	94.02 365	80.72 352	94.27 468	75.16 474	93.08 499	96.54 343

ALIKED-LG		89.78 341	88.57 358	93.39 209	93.97 388	95.11 11	94.30 153	95.57 311	79.81 432	93.27 304	94.93 319	72.44 443	92.52 485	75.11 475	97.77 312	92.53 487

SIFT-NN-PointCN		86.59 430	85.79 431	88.99 419	90.15 501	92.46 72	84.96 492	82.76 520	83.11 386	88.70 453	92.34 428	77.62 393	87.10 529	75.03 476	97.44 340	87.42 523

WTY-MVS		86.93 425	86.50 422	88.24 442	94.96 350	74.64 465	87.19 443	92.07 421	78.29 454	88.32 460	91.59 450	78.06 389	94.27 468	74.88 477	93.15 496	95.80 393

SIFT-PCN-Cal		87.04 422	86.65 415	88.22 443	90.09 504	90.20 116	83.84 511	85.36 492	85.16 343	91.83 371	91.84 443	78.22 384	87.02 532	74.79 478	98.71 198	87.44 522

MatchFormer		85.84 438	85.60 435	86.56 473	90.63 489	87.98 170	89.85 371	83.79 508	72.98 496	95.69 182	94.88 323	69.40 463	87.92 520	74.60 479	98.55 218	83.77 537

0.3-1-1-0.015		75.73 508	71.83 514	87.44 458	83.47 544	74.98 462	78.69 532	83.38 513	72.24 501	70.43 547	65.81 545	39.55 550	97.08 384	74.57 480	78.30 545	90.28 507

WAC-MVS						61.25 539								74.55 481

nomal-1		83.48 464	81.65 473	88.98 420	91.07 476	80.73 337	85.66 480	86.34 477	80.98 422	83.93 508	86.95 503	51.44 524	91.71 491	74.53 482	93.93 479	94.49 443

ALIKED-MNN		88.42 377	87.16 399	92.21 276	93.47 403	93.93 35	92.87 228	95.20 327	71.10 509	87.62 473	93.76 377	77.41 398	91.34 495	74.50 483	98.53 222	91.36 496

KD-MVS_2432*1600		82.17 477	80.75 483	86.42 476	82.04 547	70.09 503	81.75 523	90.80 438	82.56 394	90.37 411	89.30 482	42.90 543	96.11 425	74.47 484	92.55 505	93.06 476

miper_refine_blended		82.17 477	80.75 483	86.42 476	82.04 547	70.09 503	81.75 523	90.80 438	82.56 394	90.37 411	89.30 482	42.90 543	96.11 425	74.47 484	92.55 505	93.06 476

testing3-2		83.95 458	84.22 448	83.13 510	96.28 258	54.34 552	88.51 421	83.01 517	92.19 111	89.09 443	90.98 459	45.51 534	97.44 353	74.38 486	98.01 293	97.60 274

0.4-1-1-0.2		75.80 507	72.05 513	87.04 463	82.70 546	74.17 475	77.51 534	83.48 510	71.80 503	71.57 546	65.16 546	43.07 541	96.96 390	74.34 487	78.78 544	90.00 508

baseline2		83.38 465	81.54 476	88.90 424	91.38 468	72.84 487	88.78 412	81.22 530	78.97 448	79.82 536	87.56 497	61.73 504	97.80 319	74.30 488	90.05 522	96.05 380

XFeat-MNN		80.76 490	79.73 494	83.85 505	79.29 551	82.86 292	76.90 536	83.32 514	69.86 519	92.27 356	87.53 499	57.82 512	84.65 540	74.17 489	96.44 394	84.03 536

testing11		81.98 480	80.52 487	86.38 478	92.69 424	67.13 514	85.79 476	84.80 499	82.16 403	81.19 533	85.41 516	45.24 535	96.88 397	74.14 490	93.24 493	95.14 418

SIFT-NN-NCMNet		86.55 431	85.56 436	89.51 402	91.84 455	94.02 30	85.72 479	81.31 528	84.33 365	86.13 486	91.77 445	79.22 367	87.46 523	74.06 491	95.70 419	87.07 529

gm-plane-assit					87.08 531	59.33 543			71.22 507		83.58 526		97.20 375	73.95 492

test20.03		90.80 297	90.85 298	90.63 369	95.63 319	79.24 385	89.81 374	92.87 398	89.90 196	94.39 255	96.40 216	85.77 295	95.27 449	73.86 493	99.05 122	97.39 296

TAMVS		90.16 324	89.05 344	93.49 205	96.49 232	86.37 212	90.34 350	92.55 409	80.84 426	92.99 323	94.57 341	81.94 343	98.20 264	73.51 494	98.21 269	95.90 389

CHOSEN 1792x2688		87.19 417	85.92 430	91.00 347	97.13 167	79.41 380	84.51 502	95.60 304	64.14 538	90.07 421	94.81 326	78.26 382	97.14 381	73.34 495	95.38 431	96.46 354

thres600view7		87.66 399	87.10 404	89.36 410	96.05 284	73.17 481	92.72 233	85.31 494	91.89 122	93.29 302	90.97 460	63.42 497	98.39 237	73.23 496	96.99 369	96.51 347

dp		79.28 501	78.62 500	81.24 518	85.97 535	56.45 547	86.91 449	85.26 496	72.97 497	81.45 531	89.17 486	56.01 517	95.45 442	73.19 497	76.68 546	91.82 494

pmmvs3		80.83 489	78.96 498	86.45 475	87.23 528	77.48 425	84.87 493	82.31 522	63.83 539	85.03 495	89.50 480	49.66 525	93.10 479	73.12 498	95.10 445	88.78 515

usedtu_dtu_shiyan2		93.15 213	92.40 246	95.41 95	98.56 49	90.53 111	94.71 133	94.14 363	92.10 115	93.73 282	96.94 168	89.66 225	97.77 324	72.97 499	98.81 172	97.92 233

MDTV_nov1_ep13_2view						42.48 557	88.45 422		67.22 529	83.56 513		66.80 475		72.86 500		94.06 455

TR-MVS		87.70 397	87.17 398	89.27 414	94.11 383	79.26 384	88.69 417	91.86 425	81.94 405	90.69 403	89.79 474	82.82 329	97.42 356	72.65 501	91.98 511	91.14 499

PAPR		87.65 400	86.77 412	90.27 379	92.85 422	77.38 427	88.56 420	96.23 281	76.82 467	84.98 496	89.75 476	86.08 293	97.16 380	72.33 502	93.35 491	96.26 368

Anonymous20231206		88.77 369	88.29 367	90.20 383	96.31 255	78.81 397	89.56 384	93.49 387	74.26 486	92.38 349	95.58 284	82.21 335	95.43 443	72.07 503	98.75 188	96.34 359

SIFT-NCMNet		87.31 412	87.07 405	88.02 447	90.01 505	91.85 82	82.65 519	89.57 447	86.52 295	93.34 299	92.51 420	78.05 390	86.22 536	71.95 504	98.98 135	86.01 533

MVS		84.98 445	84.30 446	87.01 464	91.03 479	77.69 422	91.94 278	94.16 362	59.36 544	84.23 505	87.50 500	85.66 298	96.80 402	71.79 505	93.05 500	86.54 532

tpm cat1		80.61 492	79.46 495	84.07 502	88.78 519	65.06 529	89.26 395	88.23 458	62.27 542	81.90 528	89.66 479	62.70 502	95.29 448	71.72 506	80.60 542	91.86 493

HY-MVS	82.50 18	86.81 427	85.93 429	89.47 404	93.63 399	77.93 413	94.02 167	91.58 431	75.68 471	83.64 512	93.64 380	77.40 399	97.42 356	71.70 507	92.07 510	93.05 478

testgi		90.38 315	91.34 282	87.50 457	97.49 141	71.54 495	89.43 389	95.16 329	88.38 241	94.54 251	94.68 334	92.88 133	93.09 480	71.60 508	97.85 309	97.88 239

BH-w/o		87.21 415	87.02 406	87.79 455	94.77 359	77.27 430	87.90 427	93.21 394	81.74 409	89.99 423	88.39 492	83.47 317	96.93 394	71.29 509	92.43 507	89.15 510

thres100view900		87.35 411	86.89 408	88.72 429	96.14 275	73.09 483	93.00 214	85.31 494	92.13 114	93.26 306	90.96 461	63.42 497	98.28 251	71.27 510	96.54 389	94.79 435

tfpn200view9		87.05 421	86.52 420	88.67 430	95.77 307	72.94 485	91.89 282	86.00 482	90.84 166	92.61 338	89.80 472	63.93 493	98.28 251	71.27 510	96.54 389	94.79 435

thres400		87.20 416	86.52 420	89.24 416	95.77 307	72.94 485	91.89 282	86.00 482	90.84 166	92.61 338	89.80 472	63.93 493	98.28 251	71.27 510	96.54 389	96.51 347

myMVS_eth3d		79.62 500	78.26 502	83.72 506	91.71 459	61.25 539	85.89 474	81.49 525	81.03 420	85.13 493	81.64 536	32.12 553	95.00 453	71.17 513	94.12 475	94.91 430

tpm2		81.46 482	80.35 490	84.80 493	89.90 506	65.14 527	90.44 342	85.36 492	65.82 535	82.05 526	92.44 424	57.94 511	96.69 405	70.71 514	88.49 527	92.56 485

ADS-MVSNet2		84.01 456	82.20 469	89.41 408	89.04 517	76.37 450	87.57 431	90.98 435	72.71 499	84.46 501	92.45 422	68.08 468	96.48 412	70.58 515	83.97 535	95.38 410

ADS-MVSNet		82.25 475	81.55 475	84.34 499	89.04 517	65.30 525	87.57 431	85.13 498	72.71 499	84.46 501	92.45 422	68.08 468	92.33 487	70.58 515	83.97 535	95.38 410

FBQ-MVS		83.72 460	81.80 471	89.47 404	93.62 400	76.73 440	91.20 308	87.89 465	81.52 415	84.88 498	83.74 524	49.19 526	96.66 407	70.51 517	93.70 484	95.00 425

PVSNet	76.22 20	82.89 471	82.37 467	84.48 497	93.96 389	64.38 531	78.60 533	88.61 453	71.50 506	84.43 503	86.36 508	74.27 432	94.60 462	69.87 518	93.69 485	94.46 445

CHOSEN 280x420		80.04 497	77.97 505	86.23 481	90.13 502	74.53 468	72.87 541	89.59 446	66.38 532	76.29 541	85.32 517	56.96 514	95.36 444	69.49 519	94.72 458	88.79 514

ALIKED-NN		85.96 436	84.14 449	91.44 320	91.73 458	93.37 52	90.32 351	93.65 379	67.84 527	82.08 524	92.92 402	72.88 440	90.01 504	69.17 520	96.64 384	90.93 501

SIFT-NN		84.10 455	83.04 460	87.28 461	90.76 485	92.16 76	84.45 503	81.34 527	83.54 376	83.80 510	89.75 476	70.08 460	82.09 544	68.68 521	94.96 450	87.60 521

thres200		85.85 437	85.18 439	87.88 453	94.44 374	72.52 491	89.08 401	86.21 478	88.57 236	91.44 380	88.40 491	64.22 491	98.00 298	68.35 522	95.88 414	93.12 475

dmvs_re		84.69 449	83.94 453	86.95 467	92.24 436	82.93 290	89.51 385	87.37 469	84.38 364	85.37 490	85.08 519	72.44 443	86.59 534	68.05 523	91.03 519	91.33 497

PCF-MVS	84.52 17	89.12 354	87.71 382	93.34 211	96.06 283	85.84 232	86.58 462	97.31 190	68.46 525	93.61 286	93.89 371	87.51 265	98.52 222	67.85 524	98.11 279	95.66 401
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
new_pmnet		81.22 484	81.01 481	81.86 514	90.92 483	70.15 502	84.03 507	80.25 538	70.83 512	85.97 487	89.78 475	67.93 471	84.65 540	67.44 525	91.90 512	90.78 503

gg-mvs-nofinetune		82.10 479	81.02 480	85.34 488	87.46 527	71.04 497	94.74 131	67.56 550	96.44 28	79.43 537	98.99 11	45.24 535	96.15 423	67.18 526	92.17 509	88.85 513

XFeat-NN		75.97 506	74.88 508	79.25 523	77.98 552	79.81 361	70.81 543	79.50 540	64.75 537	86.32 484	82.83 533	53.44 522	76.70 548	66.89 527	91.40 514	81.23 543

DSMNet-mixed		82.21 476	81.56 474	84.16 501	89.57 512	70.00 506	90.65 335	77.66 545	54.99 547	83.30 516	97.57 93	77.89 392	90.50 501	66.86 528	95.54 424	91.97 490

SD_0403		88.79 368	88.88 351	88.51 436	95.89 298	72.58 490	94.27 154	95.24 325	83.77 375	87.92 468	94.38 352	87.70 259	96.47 414	66.36 529	94.40 464	96.49 351

test0.0.03 1		82.48 474	81.47 477	85.48 487	89.70 508	73.57 480	84.73 494	81.64 524	83.07 388	88.13 464	86.61 505	62.86 500	89.10 514	66.24 530	90.29 521	93.77 463

MIMVSNet		87.13 419	86.54 419	88.89 425	96.05 284	76.11 452	94.39 148	88.51 454	81.37 418	88.27 461	96.75 187	72.38 445	95.52 437	65.71 531	95.47 426	95.03 423

UBG		80.28 496	78.94 499	84.31 500	92.86 421	61.77 536	83.87 510	83.31 515	77.33 461	82.78 520	83.72 525	47.60 531	96.06 427	65.47 532	93.48 489	95.11 421

UWE-MVS		80.29 495	79.10 496	83.87 504	91.97 450	59.56 542	86.50 466	77.43 546	75.40 476	87.79 471	88.10 494	44.08 539	96.90 396	64.23 533	96.36 395	95.14 418

PMMVS2		81.31 483	83.44 457	74.92 527	90.52 492	46.49 556	69.19 544	85.23 497	84.30 366	87.95 467	94.71 332	76.95 412	84.36 543	64.07 534	98.09 282	93.89 460

FPMVS		84.50 450	83.28 458	88.16 445	96.32 254	94.49 20	85.76 477	85.47 491	83.09 387	85.20 492	94.26 354	63.79 495	86.58 535	63.72 535	91.88 513	83.40 538

MVS-HIRNet		78.83 503	80.60 486	73.51 528	93.07 414	47.37 555	87.10 445	78.00 544	68.94 523	77.53 539	97.26 134	71.45 454	94.62 461	63.28 536	88.74 526	78.55 544

myMVS_eth3d28		80.97 487	80.42 488	82.62 512	93.35 407	58.25 546	84.70 498	85.62 489	86.31 303	84.04 506	85.20 518	46.00 532	94.07 471	62.93 537	95.65 421	95.53 407

WB-MVSnew		84.20 454	83.89 454	85.16 491	91.62 463	66.15 523	88.44 423	81.00 531	76.23 470	87.98 466	87.77 496	84.98 307	93.35 478	62.85 538	94.10 477	95.98 383

testing222		80.54 493	78.53 501	86.58 472	92.54 429	68.60 510	86.24 470	82.72 521	83.78 374	82.68 521	84.24 522	39.25 551	95.94 431	60.25 539	95.09 446	95.20 414

wuyk23d		87.83 394	90.79 302	78.96 524	90.46 496	88.63 147	92.72 233	90.67 440	91.65 140	98.68 14	97.64 89	96.06 19	77.53 547	59.84 540	99.41 60	70.73 545

GG-mvs-BLEND				83.24 509	85.06 540	71.03 498	94.99 126	65.55 552		74.09 543	75.51 541	44.57 537	94.46 464	59.57 541	87.54 529	84.24 535

PVSNet_0	70.34 21	74.58 510	72.96 511	79.47 521	90.63 489	66.24 521	73.26 539	83.40 512	63.67 540	78.02 538	78.35 540	72.53 442	89.59 507	56.68 542	60.05 550	82.57 541

ETVMVS		79.85 498	77.94 506	85.59 484	92.97 418	66.20 522	86.13 472	80.99 532	81.41 417	83.52 514	83.89 523	41.81 548	94.98 456	56.47 543	94.25 471	95.61 405

MVE	59.87 23	73.86 511	72.65 512	77.47 525	87.00 532	74.35 470	61.37 546	60.93 553	67.27 528	69.69 548	86.49 507	81.24 350	72.33 550	56.45 544	83.45 537	85.74 534
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM		81.91 481	80.11 492	87.31 460	93.87 393	72.32 493	84.02 508	93.22 392	69.47 522	76.13 542	89.84 471	72.15 449	97.23 372	53.27 545	89.02 525	92.37 488

test_method		50.44 514	48.94 517	54.93 530	39.68 557	12.38 563	28.59 548	90.09 443	6.82 553	41.10 554	78.41 539	54.41 518	70.69 551	50.12 546	51.26 551	81.72 542

dmvs_testset		78.23 504	78.99 497	75.94 526	91.99 449	55.34 550	88.86 406	78.70 542	82.69 392	81.64 530	79.46 538	75.93 421	85.74 537	48.78 547	82.85 539	86.76 531

tmp_tt		37.97 516	44.33 518	18.88 535	11.80 560	21.54 561	63.51 545	45.66 558	4.23 554	51.34 552	50.48 550	59.08 510	22.11 556	44.50 548	68.35 549	13.00 551

UWE-MVS-28		74.73 509	73.18 510	79.35 522	85.42 538	55.55 549	87.63 429	65.92 551	74.39 484	77.33 540	88.19 493	47.63 530	89.48 509	39.01 549	93.14 497	93.03 479

DeepMVS_CX				53.83 531	70.38 554	64.56 530		48.52 557	33.01 550	65.50 550	74.21 542	56.19 516	46.64 554	38.45 550	70.07 548	50.30 548

GLUNet-SfM		58.71 512	56.43 515	65.55 529	45.28 556	59.80 541	54.31 547	55.90 555	37.80 549	81.24 532	73.75 543	38.27 552	70.23 552	34.22 551	87.09 530	66.64 546

dongtai		53.72 513	53.79 516	53.51 532	79.69 549	36.70 558	77.18 535	32.53 561	71.69 504	68.63 549	60.79 548	26.65 556	73.11 549	30.67 552	36.29 554	50.73 547

MVS_clip		28.84 517	32.57 520	17.67 536	37.77 558	25.94 560	27.92 549	7.17 562	9.16 552	54.91 551	62.94 547	20.70 558	10.56 557	26.96 553	45.58 552	16.52 550

VLMVS_CLIP		26.72 518	28.23 522	22.16 534	23.46 559	19.29 562	25.04 550	38.45 559	10.30 551	37.65 555	43.37 551	16.55 559	34.48 555	19.59 554	39.68 553	12.71 552

kuosan		43.63 515	44.25 519	41.78 533	66.04 555	34.37 559	75.56 537	32.62 560	53.25 548	50.46 553	51.18 549	25.28 557	49.13 553	13.44 555	30.41 555	41.84 549

MVS_baseline		9.63 520	12.05 523	2.37 538	9.15 561	0.73 567	5.23 552	1.75 565	0.31 559	26.23 556	30.60 552	5.95 561	0.00 561	4.43 556	24.78 556	6.38 554

VLMVS		7.75 523	8.50 528	5.52 537	7.85 562	5.47 564	5.34 551	3.06 563	0.41 558	11.88 557	15.91 554	11.95 560	3.89 558	3.42 557	16.65 557	7.20 553

test123		9.49 521	12.01 524	1.91 539	2.87 563	1.30 565	82.38 520	1.34 566	1.36 556	2.84 559	6.56 556	2.45 562	0.97 559	2.73 558	5.56 558	3.47 555

testmvs		9.02 522	11.42 525	1.81 540	2.77 564	1.13 566	79.44 531	1.90 564	1.18 557	2.65 560	6.80 555	1.95 563	0.87 560	2.62 559	3.45 559	3.44 556

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		23.35 519	31.13 521	0.00 541	0.00 565	0.00 568	0.00 553	95.58 310	0.00 560	0.00 561	91.15 455	93.43 109	0.00 561	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		7.56 524	10.09 526	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	90.77 196	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		7.56 524	10.08 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	90.69 466	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	54.43 551	80.66 529	86.13 480	76.71 468
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													91.63 493
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip				93.68 190	95.25 338	86.20 219	96.32 56	96.38 273	92.81 92	92.13 364	93.87 374	87.28 269	98.61 197		95.07 447	96.23 370

FOURS1					99.21 3	94.68 16	98.45 4	98.81 10	97.73 9	98.27 23

test_one_0601					98.26 80	87.14 187		98.18 63	94.25 61	96.99 90	97.36 121	95.13 50

eth-test2					0.00 565

eth-test					0.00 565

test_241102_ONE					98.51 58	86.97 192		98.10 80	91.85 125	97.63 45	97.03 161	96.48 13	98.95 136

save fliter					97.46 145	88.05 167	92.04 272	97.08 211	87.63 267

test0726					98.51 58	86.69 202	95.34 105	98.18 63	91.85 125	97.63 45	97.37 116	95.58 28

GSMVS																94.75 437

test_part2					98.21 84	89.41 129				96.72 105

sam_mvs1												66.64 478				94.75 437

sam_mvs												66.41 479

MTGPA								97.62 154

test_post											6.07 557	65.74 483	95.84 433

patchmatchnet-post											91.71 447	66.22 481	97.59 341

MTMP							94.82 129	54.62 556

TEST9					96.45 235	89.46 126	90.60 336	96.92 223	79.09 447	90.49 405	94.39 349	91.31 177	98.88 143

test_8					96.37 244	89.14 136	90.51 339	96.89 227	79.37 441	90.42 407	94.36 353	91.20 182	98.82 152

agg_prior					96.20 268	88.89 142		96.88 232		90.21 415			98.78 165

test_prior4						89.91 119	90.74 329

test_prior				94.61 142	95.95 292	87.23 184		97.36 185					98.68 187			97.93 228

新几何2							90.02 364

旧先验1					96.20 268	84.17 262		94.82 340			95.57 285	89.57 226			97.89 306	96.32 363

原ACMM2							89.34 392

test222					96.95 180	85.27 245	88.83 408	93.61 381	65.09 536	90.74 401	94.85 324	84.62 310			97.36 344	93.91 459

segment_acmp												92.14 153

testdata1							88.96 404		88.44 239

test12				94.43 156	95.95 292	86.75 200		96.24 280		89.76 429		89.79 224	98.79 161		97.95 303	97.75 262

plane_prior7					97.71 125	88.68 146

plane_prior6					97.21 161	88.23 160						86.93 280

plane_prior4											95.59 281

plane_prior3						88.43 157			90.35 186	93.31 300

plane_prior2							94.56 143		91.74 136

plane_prior1					97.38 149

plane_prior						88.12 164	93.01 212		88.98 219						98.06 287

n2								0.00 567

nn								0.00 567

door-mid								92.13 419

test11								96.65 255

door								91.26 432

HQP5-MVS						84.89 249

HQP-NCC					96.36 247		91.37 301		87.16 281	88.81 447

ACMP_Plane					96.36 247		91.37 301		87.16 281	88.81 447

HQP4-MVS										88.81 447			98.61 197			98.15 198

HQP3-MVS								97.31 190							97.73 316

HQP2-MVS												84.76 308

NP-MVS					96.82 193	87.10 188					93.40 388

ACMMP++_ref															98.82 170

ACMMP++															99.25 91

Test By Simon												90.61 202

High-resolution multi-view benchmark