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.

0.3-1-1-0.015		94.22 271	93.13 291	97.49 218	95.50 372	94.17 267	100.00 1	98.22 213	88.44 376	97.14 202	97.04 325	92.73 141	98.59 266	96.45 217	72.65 452	99.70 125

0.4-1-1-0.1		94.07 277	92.95 294	97.42 225	95.24 377	94.00 274	100.00 1	98.22 213	88.27 380	96.81 217	96.93 329	92.27 159	98.56 270	96.21 223	72.63 454	99.70 125

0.4-1-1-0.2		94.14 272	93.02 293	97.51 214	95.45 373	94.25 263	100.00 1	98.22 213	88.53 373	96.83 215	96.95 328	92.25 160	98.57 269	96.34 218	72.65 452	99.70 125

testing3-2		97.72 106	97.43 109	98.60 118	98.55 177	97.11 131	100.00 1	99.23 31	93.78 186	97.90 173	98.73 241	95.50 53	99.69 164	98.53 121	94.63 288	98.99 258

test_fmvsm_n_1920		98.44 49	98.61 30	97.92 174	99.27 115	95.18 227	100.00 1	98.90 50	98.05 20	99.80 28	99.73 92	92.64 145	99.99 39	99.58 57	99.51 117	98.59 281

DELS-MVS		98.54 41	98.22 52	99.50 35	99.15 123	98.65 58	100.00 1	98.58 105	97.70 32	98.21 164	99.24 170	92.58 148	99.94 94	98.63 116	99.94 58	99.92 93
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
PVSNet_Blended		97.94 82	97.64 96	98.83 100	99.59 92	96.99 136	100.00 1	99.10 34	95.38 117	98.27 159	99.08 185	89.00 218	99.95 85	99.12 79	99.25 140	99.57 162

fmvsm_s_conf0.5_n_11		98.03 79	97.89 82	98.46 137	99.35 109	97.76 98	99.99 8	98.04 240	98.20 9	99.90 7	99.78 67	86.21 261	99.95 85	99.89 21	99.68 93	97.65 309

fmvsm_s_conf0.5_n_10		98.24 69	97.90 80	99.26 55	99.24 116	97.88 92	99.99 8	98.76 73	98.20 9	99.92 5	99.74 88	85.97 265	99.94 94	99.72 46	99.53 113	99.96 75

fmvsm_l_conf0.5_n_9		98.55 40	98.23 51	99.49 37	99.10 125	98.50 65	99.99 8	98.70 80	98.14 16	99.94 2	99.68 112	89.02 217	99.98 51	99.89 21	99.61 104	99.99 26

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 124	99.28 113	95.84 188	99.99 8	98.57 107	98.17 13	99.93 3	99.74 88	87.04 246	99.97 64	99.86 27	99.59 108	99.83 105

fmvsm_s_conf0.5_n_5		98.08 77	97.71 92	99.17 66	98.67 166	97.69 104	99.99 8	98.57 107	97.40 40	99.89 11	99.69 105	85.99 264	99.96 76	99.80 32	99.40 132	99.85 103

MM		98.83 24	98.53 33	99.76 11	99.59 92	99.33 9	99.99 8	99.76 6	98.39 4	99.39 91	99.80 59	90.49 195	99.96 76	99.89 21	99.43 129	99.98 57

testing3		93.92 279	94.23 249	92.99 397	97.54 262	90.23 381	99.99 8	99.16 33	90.57 329	91.33 318	98.63 253	92.99 132	92.52 474	82.46 426	95.39 279	96.22 331

test_fmvsmconf_n		98.43 51	98.32 47	98.78 103	98.12 215	96.41 163	99.99 8	98.83 66	98.22 7	99.67 53	99.64 119	91.11 181	99.94 94	99.67 52	99.62 99	99.98 57

test_cas_vis1_n_1920		96.59 169	96.23 163	97.65 197	98.22 205	94.23 264	99.99 8	97.25 346	97.77 29	99.58 70	99.08 185	77.10 376	99.97 64	97.64 172	99.45 127	98.74 275

ET-MVSNet_ETH3D		94.37 265	93.28 286	97.64 198	98.30 198	97.99 85	99.99 8	97.61 290	94.35 156	71.57 479	99.45 141	96.23 39	95.34 444	96.91 200	85.14 371	99.59 154

CS-MVS		97.79 99	97.91 79	97.43 224	99.10 125	94.42 254	99.99 8	97.10 373	95.07 123	99.68 52	99.75 81	92.95 134	98.34 296	98.38 128	99.14 145	99.54 168

MGCNet		99.06 13	98.84 19	99.72 14	99.76 73	99.21 22	99.99 8	99.34 25	98.70 2	99.44 82	99.75 81	93.24 126	99.99 39	99.94 14	99.41 131	99.95 83

alignmvs		97.81 96	97.33 113	99.25 56	98.77 160	98.66 56	99.99 8	98.44 148	94.40 155	98.41 152	99.47 138	93.65 113	99.42 190	98.57 117	94.26 296	99.67 133

lupinMVS		97.85 90	97.60 98	98.62 116	97.28 287	97.70 102	99.99 8	97.55 297	95.50 116	99.43 84	99.67 114	90.92 185	98.71 252	98.40 127	99.62 99	99.45 190

EC-MVSNet		97.38 124	97.24 117	97.80 182	97.41 271	95.64 200	99.99 8	97.06 386	94.59 141	99.63 59	99.32 154	89.20 215	98.14 312	98.76 106	99.23 142	99.62 147

IB-MVS	92.85 6	94.99 239	93.94 260	98.16 155	97.72 243	95.69 198	99.99 8	98.81 67	94.28 162	92.70 304	96.90 330	95.08 62	99.17 205	96.07 224	73.88 447	99.60 153
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
fmvsm_s_conf0.5_n_8		98.38 57	98.05 66	99.35 50	99.20 118	98.12 77	99.98 24	98.81 67	98.22 7	99.80 28	99.71 98	87.37 241	99.97 64	99.91 19	99.48 121	99.97 67

fmvsm_s_conf0.5_n_6		98.27 63	97.96 75	99.23 58	97.66 251	98.11 78	99.98 24	98.64 91	97.85 27	99.87 14	99.72 95	88.86 220	99.93 104	99.64 54	99.36 135	99.63 146

fmvsm_l_conf0.5_n_3		98.41 53	98.08 64	99.39 46	99.12 124	98.29 70	99.98 24	98.64 91	98.14 16	99.86 16	99.76 73	87.99 229	99.97 64	99.72 46	99.54 111	99.91 95

fmvsm_s_conf0.5_n_3		97.95 81	97.66 94	98.81 101	98.99 136	98.07 80	99.98 24	98.81 67	98.18 12	99.89 11	99.70 101	84.15 299	99.97 64	99.76 40	99.50 119	98.39 288

fmvsm_s_conf0.5_n_2		97.59 112	97.28 115	98.53 130	99.01 131	98.15 72	99.98 24	98.59 103	98.17 13	99.75 42	99.63 122	81.83 325	99.94 94	99.78 35	98.79 162	97.51 318

fmvsm_l_conf0.5_n_a		99.00 18	98.91 15	99.28 53	99.21 117	97.91 91	99.98 24	98.85 62	98.25 5	99.92 5	99.75 81	94.72 74	99.97 64	99.87 25	99.64 97	99.95 83

fmvsm_l_conf0.5_n		98.94 19	98.84 19	99.25 56	99.17 121	97.81 96	99.98 24	98.86 59	98.25 5	99.90 7	99.76 73	94.21 97	99.97 64	99.87 25	99.52 114	99.98 57

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 195	99.06 128	94.41 255	99.98 24	98.97 43	97.34 42	99.63 59	99.69 105	87.27 242	99.97 64	99.62 55	99.06 151	98.62 280

test_vis1_n_1920		95.44 225	95.31 213	95.82 292	98.50 184	88.74 405	99.98 24	97.30 333	97.84 28	99.85 20	99.19 176	66.82 443	99.97 64	98.82 101	99.46 126	98.76 273

EIA-MVS		97.53 114	97.46 104	97.76 190	98.04 219	94.84 237	99.98 24	97.61 290	94.41 154	97.90 173	99.59 125	92.40 155	98.87 225	98.04 151	99.13 146	99.59 154

ETV-MVS		97.92 84	97.80 88	98.25 151	98.14 213	96.48 160	99.98 24	97.63 284	95.61 111	99.29 98	99.46 140	92.55 149	98.82 231	99.02 89	98.54 170	99.46 185

CANet		98.27 63	97.82 87	99.63 19	99.72 82	99.10 24	99.98 24	98.51 131	97.00 59	98.52 144	99.71 98	87.80 230	99.95 85	99.75 41	99.38 133	99.83 105

SPE-MVS-test		97.88 86	97.94 77	97.70 194	99.28 113	95.20 226	99.98 24	97.15 361	95.53 114	99.62 62	99.79 63	92.08 166	98.38 292	98.75 107	99.28 139	99.52 173

MSLP-MVS++		99.13 9	99.01 12	99.49 37	99.94 17	98.46 67	99.98 24	98.86 59	97.10 53	99.80 28	99.94 5	95.92 44	100.00 1	99.51 59	100.00 1	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 7	99.98 2	99.51 7	99.98 24	98.69 82	98.20 9	99.93 3	99.98 2	96.82 26	100.00 1	99.75 41	100.00 1	99.99 26

SteuartSystems-ACMMP		99.02 15	98.97 14	99.18 63	98.72 163	97.71 100	99.98 24	98.44 148	96.85 64	99.80 28	99.91 19	97.57 9	99.85 130	99.44 66	99.99 21	99.99 26
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.41 53	98.21 53	99.03 85	99.86 58	97.10 132	99.98 24	98.80 71	90.78 324	99.62 62	99.78 67	95.30 57	100.00 1	99.80 32	99.93 64	99.99 26

CLD-MVS		94.06 278	93.90 261	94.55 336	96.02 347	90.69 370	99.98 24	97.72 276	96.62 77	91.05 321	98.85 232	77.21 375	98.47 276	98.11 146	89.51 325	94.48 340
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MED-MVS test				99.60 24	99.96 9	98.79 42	99.97 42	98.88 55	96.36 90	99.07 112	99.93 12		100.00 1	99.98 9	99.96 46	99.99 26

MED-MVS		99.24 8	99.11 7	99.60 24	99.96 9	98.79 42	99.97 42	98.88 55	96.91 62	99.07 112	99.92 16	97.36 18	100.00 1	99.98 9	99.96 46	100.00 1

TestfortrainingZip a		99.01 16	98.78 21	99.69 17	99.96 9	99.09 25	99.97 42	98.74 76	96.91 62	99.86 16	99.92 16	96.29 37	99.99 39	98.32 133	99.09 149	100.00 1

TestfortrainingZip				99.90 5	99.97 3	99.70 5	99.97 42	98.89 52	96.02 99	99.99 1	99.96 3	97.97 5	100.00 1		99.65 96	100.00 1

lecture		98.67 33	98.46 36	99.28 53	99.86 58	97.88 92	99.97 42	99.25 30	96.07 97	99.79 37	99.70 101	92.53 150	99.98 51	99.51 59	99.48 121	99.97 67

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 207	98.44 188	95.16 229	99.97 42	98.65 88	97.95 24	99.62 62	99.78 67	86.09 262	99.94 94	99.69 50	99.50 119	97.66 308

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 225	99.01 131	94.69 245	99.97 42	98.76 73	97.91 25	99.87 14	99.76 73	86.70 253	99.93 104	99.67 52	99.12 148	97.64 310

thisisatest0515		97.41 122	97.02 128	98.59 121	97.71 245	97.52 109	99.97 42	98.54 123	91.83 283	97.45 190	99.04 191	97.50 10	99.10 209	94.75 253	96.37 247	99.16 236

Fast-Effi-MVS+		95.02 238	94.19 250	97.52 213	97.88 227	94.55 248	99.97 42	97.08 377	88.85 365	94.47 278	97.96 296	84.59 293	98.41 284	89.84 348	97.10 221	99.59 154

MCST-MVS		99.32 3	99.14 4	99.86 6	99.97 3	99.59 6	99.97 42	98.64 91	98.47 3	99.13 107	99.92 16	96.38 36	100.00 1	99.74 43	100.00 1	100.00 1

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 99	99.73 80	96.63 153	99.97 42	97.92 254	98.07 19	98.76 132	99.55 132	95.00 67	99.94 94	99.91 19	97.68 197	99.99 26

jason		97.24 129	96.86 133	98.38 145	95.73 360	97.32 118	99.97 42	97.40 315	95.34 119	98.60 143	99.54 134	87.70 232	98.56 270	97.94 157	99.47 124	99.25 229
jason: jason.
NCCC		99.37 2	99.25 2	99.71 16	99.96 9	99.15 23	99.97 42	98.62 98	98.02 22	99.90 7	99.95 4	97.33 19	100.00 1	99.54 58	100.00 1	100.00 1

CP-MVS		98.45 48	98.32 47	98.87 98	99.96 9	96.62 154	99.97 42	98.39 180	94.43 151	98.90 121	99.87 32	94.30 92	100.00 1	99.04 85	99.99 21	99.99 26

BP-MVS1		98.33 59	98.18 56	98.81 101	97.44 269	97.98 86	99.96 56	98.17 222	94.88 130	98.77 129	99.59 125	97.59 8	99.08 210	98.24 139	98.93 155	99.36 203

fmvsm_s_conf0.5_n_a		97.73 105	97.72 90	97.77 188	98.63 171	94.26 262	99.96 56	98.92 49	97.18 52	99.75 42	99.69 105	87.00 248	99.97 64	99.46 64	98.89 156	99.08 246

test_fmvs1		95.35 228	95.68 196	94.36 347	98.99 136	84.98 439	99.96 56	96.65 423	97.60 34	99.73 47	98.96 208	71.58 422	99.93 104	98.31 134	99.37 134	98.17 293

GeoE		94.36 267	93.48 275	96.99 249	97.29 286	93.54 291	99.96 56	96.72 420	88.35 378	93.43 292	98.94 215	82.05 320	98.05 319	88.12 378	96.48 244	99.37 201

SED-MVS		99.28 5	99.11 7	99.77 9	99.93 28	99.30 13	99.96 56	98.43 156	97.27 47	99.80 28	99.94 5	96.71 29	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 56				99.80 59	97.44 15	100.00 1	100.00 1	99.98 32	100.00 1

save fliter					99.82 65	98.79 42	99.96 56	98.40 177	97.66 33

test0726					99.93 28	99.29 16	99.96 56	98.42 168	97.28 45	99.86 16	99.94 5	97.22 21

DPM-MVS		98.83 24	98.46 36	99.97 1	99.33 110	99.92 1	99.96 56	98.44 148	97.96 23	99.55 71	99.94 5	97.18 23	100.00 1	93.81 277	99.94 58	99.98 57

TEST9					99.92 36	98.92 31	99.96 56	98.43 156	93.90 182	99.71 49	99.86 34	95.88 45	99.85 130

train_agg		98.88 23	98.65 27	99.59 27	99.92 36	98.92 31	99.96 56	98.43 156	94.35 156	99.71 49	99.86 34	95.94 42	99.85 130	99.69 50	99.98 32	99.99 26

test_8					99.92 36	98.88 34	99.96 56	98.43 156	94.35 156	99.69 51	99.85 38	95.94 42	99.85 130

region2R		98.54 41	98.37 43	99.05 83	99.96 9	97.18 125	99.96 56	98.55 119	94.87 131	99.45 81	99.85 38	94.07 101	100.00 1	98.67 111	100.00 1	99.98 57

test-LLR		96.47 174	96.04 171	97.78 186	97.02 303	95.44 207	99.96 56	98.21 217	94.07 170	95.55 261	96.38 347	93.90 106	98.27 305	90.42 339	98.83 160	99.64 139

TESTMET0.1,1		96.74 161	96.26 162	98.16 155	97.36 279	96.48 160	99.96 56	98.29 203	91.93 279	95.77 255	98.07 290	95.54 50	98.29 301	90.55 336	98.89 156	99.70 125

test-mter		96.39 180	95.93 184	97.78 186	97.02 303	95.44 207	99.96 56	98.21 217	91.81 285	95.55 261	96.38 347	95.17 59	98.27 305	90.42 339	98.83 160	99.64 139

CPTT-MVS		97.64 110	97.32 114	98.58 122	99.97 3	95.77 191	99.96 56	98.35 190	89.90 345	98.36 155	99.79 63	91.18 180	99.99 39	98.37 130	99.99 21	99.99 26

cascas		94.64 253	93.61 267	97.74 192	97.82 232	96.26 170	99.96 56	97.78 270	85.76 412	94.00 288	97.54 307	76.95 382	99.21 199	97.23 185	95.43 278	97.76 307

DeepPCF-MVS	95.94 2	97.71 107	98.98 13	93.92 370	99.63 90	81.76 463	99.96 56	98.56 113	99.47 1	99.19 104	99.99 1	94.16 99	100.00 1	99.92 16	99.93 64	100.00 1

ME-MVS		99.07 11	98.89 17	99.59 27	99.93 28	98.79 42	99.95 75	98.80 71	95.89 103	99.28 99	99.93 12	96.28 38	99.98 51	99.98 9	99.96 46	99.99 26

GDP-MVS		97.88 86	97.59 100	98.75 106	97.59 258	97.81 96	99.95 75	97.37 319	94.44 150	99.08 110	99.58 128	97.13 25	99.08 210	94.99 243	98.17 181	99.37 201

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 176	97.02 303	95.34 215	99.95 75	98.45 143	97.87 26	97.02 206	99.59 125	89.64 205	99.98 51	99.41 68	99.34 137	98.42 287

patch_mono-2		98.24 69	99.12 5	95.59 297	99.67 88	86.91 428	99.95 75	98.89 52	97.60 34	99.90 7	99.76 73	96.54 34	99.98 51	99.94 14	99.82 84	99.88 98

DVP-MVS++		99.26 6	99.09 10	99.77 9	99.91 44	99.31 11	99.95 75	98.43 156	96.48 80	99.80 28	99.93 12	97.44 15	100.00 1	99.92 16	99.98 32	100.00 1

FOURS1					99.92 36	97.66 105	99.95 75	98.36 188	95.58 112	99.52 76

DVP-MVS		99.30 4	99.16 3	99.73 13	99.93 28	99.29 16	99.95 75	98.32 197	97.28 45	99.83 24	99.91 19	97.22 21	100.00 1	99.99 5	100.00 1	99.89 97
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.82 8	99.94 17	99.47 8	99.95 75	98.43 156					100.00 1	99.99 5	100.00 1	100.00 1

MSP-MVS		99.09 10	99.12 5	98.98 92	99.93 28	97.24 122	99.95 75	98.42 168	97.50 38	99.52 76	99.88 29	97.43 17	99.71 160	99.50 61	99.98 32	100.00 1
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
HFP-MVS		98.56 39	98.37 43	99.14 73	99.96 9	97.43 115	99.95 75	98.61 99	94.77 134	99.31 95	99.85 38	94.22 95	100.00 1	98.70 109	99.98 32	99.98 57

HPM-MVS++		99.07 11	98.88 18	99.63 19	99.90 47	99.02 27	99.95 75	98.56 113	97.56 37	99.44 82	99.85 38	95.38 56	100.00 1	99.31 71	99.99 21	99.87 100

test_prior2							99.95 75		95.78 105	99.73 47	99.76 73	96.00 41		99.78 35	100.00 1

ACMMPR		98.50 44	98.32 47	99.05 83	99.96 9	97.18 125	99.95 75	98.60 101	94.77 134	99.31 95	99.84 49	93.73 111	100.00 1	98.70 109	99.98 32	99.98 57

MP-MVS		98.23 71	97.97 72	99.03 85	99.94 17	97.17 128	99.95 75	98.39 180	94.70 138	98.26 161	99.81 58	91.84 171	100.00 1	98.85 100	99.97 42	99.93 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		98.39 56	98.20 54	98.97 93	99.97 3	96.92 139	99.95 75	98.38 184	95.04 124	98.61 140	99.80 59	93.39 117	100.00 1	98.64 114	100.00 1	99.98 57

PVSNet_BlendedMVS		96.05 196	95.82 189	96.72 260	99.59 92	96.99 136	99.95 75	99.10 34	94.06 172	98.27 159	95.80 365	89.00 218	99.95 85	99.12 79	87.53 354	93.24 428

PAPR		98.52 43	98.16 58	99.58 29	99.97 3	98.77 47	99.95 75	98.43 156	95.35 118	98.03 169	99.75 81	94.03 102	99.98 51	98.11 146	99.83 80	99.99 26

PVSNet	91.05 13	97.13 135	96.69 144	98.45 138	99.52 99	95.81 189	99.95 75	99.65 12	94.73 136	99.04 115	99.21 174	84.48 296	99.95 85	94.92 246	98.74 164	99.58 160

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 115	95.76 357	96.20 176	99.94 93	98.05 239	98.17 13	98.89 122	99.42 142	87.65 233	99.90 113	99.50 61	99.60 107	99.82 107

ZNCC-MVS		98.31 60	98.03 67	99.17 66	99.88 54	97.59 106	99.94 93	98.44 148	94.31 159	98.50 147	99.82 54	93.06 131	99.99 39	98.30 135	99.99 21	99.93 88

test_prior4						98.05 82	99.94 93

XVS		98.70 32	98.55 31	99.15 71	99.94 17	97.50 111	99.94 93	98.42 168	96.22 93	99.41 87	99.78 67	94.34 89	99.96 76	98.92 94	99.95 53	99.99 26

X-MVStestdata		93.83 281	92.06 316	99.15 71	99.94 17	97.50 111	99.94 93	98.42 168	96.22 93	99.41 87	41.37 502	94.34 89	99.96 76	98.92 94	99.95 53	99.99 26

SD-MVS		98.92 21	98.70 23	99.56 30	99.70 85	98.73 51	99.94 93	98.34 194	96.38 86	99.81 26	99.76 73	94.59 77	99.98 51	99.84 29	99.96 46	99.97 67
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
PVSNet_0	88.03 19	91.80 336	90.27 350	96.38 273	98.27 202	90.46 377	99.94 93	99.61 13	93.99 175	86.26 416	97.39 312	71.13 426	99.89 118	98.77 105	67.05 471	98.79 272

GST-MVS		98.27 63	97.97 72	99.17 66	99.92 36	97.57 107	99.93 100	98.39 180	94.04 174	98.80 126	99.74 88	92.98 133	100.00 1	98.16 143	99.76 88	99.93 88

test0.0.03 1		93.86 280	93.61 267	94.64 330	95.02 382	92.18 326	99.93 100	98.58 105	94.07 170	87.96 390	98.50 267	93.90 106	94.96 449	81.33 433	93.17 309	96.78 323

MVS_111021_HR		98.72 31	98.62 29	99.01 89	99.36 108	97.18 125	99.93 100	99.90 1	96.81 69	98.67 136	99.77 71	93.92 104	99.89 118	99.27 74	99.94 58	99.96 75

myMVS_eth3d28		97.86 88	97.59 100	98.68 110	98.50 184	97.26 121	99.92 103	98.55 119	93.79 185	98.26 161	98.75 239	95.20 58	99.48 186	98.93 92	96.40 245	99.29 221

fmvsm_s_conf0.1_n_2		97.25 128	96.85 134	98.43 140	98.08 216	98.08 79	99.92 103	97.76 274	98.05 20	99.65 55	99.58 128	80.88 338	99.93 104	99.59 56	98.17 181	97.29 319

WBMVS		94.52 258	94.03 256	95.98 282	98.38 191	96.68 151	99.92 103	97.63 284	90.75 325	89.64 347	95.25 398	96.77 27	96.90 380	94.35 263	83.57 384	94.35 352

testing11		97.48 116	97.27 116	98.10 161	98.36 194	96.02 183	99.92 103	98.45 143	93.45 201	98.15 166	98.70 244	95.48 54	99.22 198	97.85 162	95.05 285	99.07 247

thisisatest0530		97.10 136	96.72 142	98.22 152	97.60 257	96.70 148	99.92 103	98.54 123	91.11 309	97.07 205	98.97 206	97.47 13	99.03 212	93.73 282	96.09 252	98.92 264

PVSNet_Blended_VisFu		97.27 127	96.81 137	98.66 113	98.81 157	96.67 152	99.92 103	98.64 91	94.51 144	96.38 239	98.49 268	89.05 216	99.88 124	97.10 189	98.34 174	99.43 194

DP-MVS Recon		98.41 53	98.02 68	99.56 30	99.97 3	98.70 53	99.92 103	98.44 148	92.06 276	98.40 154	99.84 49	95.68 48	100.00 1	98.19 141	99.71 91	99.97 67

PLC	95.54 3	97.93 83	97.89 82	98.05 165	99.82 65	94.77 242	99.92 103	98.46 142	93.93 179	97.20 199	99.27 163	95.44 55	99.97 64	97.41 177	99.51 117	99.41 197
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testing91		97.16 133	96.90 131	97.97 168	98.35 196	95.67 199	99.91 111	98.42 168	92.91 225	97.33 195	98.72 242	94.81 72	99.21 199	96.98 194	94.63 288	99.03 255

testing99		97.17 132	96.91 130	97.95 170	98.35 196	95.70 196	99.91 111	98.43 156	92.94 223	97.36 193	98.72 242	94.83 71	99.21 199	97.00 192	94.64 287	98.95 260

9.14			98.38 41		99.87 56		99.91 111	98.33 195	93.22 208	99.78 39	99.89 27	94.57 80	99.85 130	99.84 29	99.97 42

APDe-MVS		99.06 13	98.91 15	99.51 34	99.94 17	98.76 50	99.91 111	98.39 180	97.20 51	99.46 80	99.85 38	95.53 52	99.79 145	99.86 27	100.00 1	99.99 26
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVSTER		95.53 223	95.22 217	96.45 269	98.56 174	97.72 99	99.91 111	97.67 280	92.38 263	91.39 316	97.14 317	97.24 20	97.30 352	94.80 251	87.85 347	94.34 354

PMMVS		96.76 157	96.76 139	96.76 258	98.28 201	92.10 327	99.91 111	97.98 246	94.12 167	99.53 74	99.39 149	86.93 249	98.73 249	96.95 197	97.73 194	99.45 190

AstraMVS		96.57 171	96.46 155	96.91 251	96.79 327	92.50 319	99.90 117	97.38 316	96.02 99	97.79 181	99.32 154	86.36 258	98.99 214	98.26 138	96.33 248	99.23 232

UBG		97.84 91	97.69 93	98.29 149	98.38 191	96.59 158	99.90 117	98.53 126	93.91 181	98.52 144	98.42 275	96.77 27	99.17 205	98.54 119	96.20 249	99.11 243

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 204	97.38 275	94.40 257	99.90 117	98.64 91	96.47 82	99.51 78	99.65 118	84.99 283	99.93 104	99.22 76	99.09 149	98.46 284

test_fmvs1_n		94.25 270	94.36 244	93.92 370	97.68 248	83.70 446	99.90 117	96.57 426	97.40 40	99.67 53	98.88 220	61.82 462	99.92 110	98.23 140	99.13 146	98.14 296

SF-MVS		98.67 33	98.40 39	99.50 35	99.77 72	98.67 54	99.90 117	98.21 217	93.53 194	99.81 26	99.89 27	94.70 76	99.86 129	99.84 29	99.93 64	99.96 75

原ACMM2							99.90 117

HPM-MVS		97.96 80	97.72 90	98.68 110	99.84 63	96.39 166	99.90 117	98.17 222	92.61 245	98.62 139	99.57 131	91.87 170	99.67 168	98.87 99	99.99 21	99.99 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPNet		98.49 45	98.40 39	98.77 105	99.62 91	96.80 147	99.90 117	99.51 16	97.60 34	99.20 102	99.36 152	93.71 112	99.91 111	97.99 154	98.71 165	99.61 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CSCG		97.10 136	97.04 126	97.27 237	99.89 50	91.92 332	99.90 117	99.07 37	88.67 369	95.26 269	99.82 54	93.17 129	99.98 51	98.15 144	99.47 124	99.90 96

PAPM		98.60 37	98.42 38	99.14 73	96.05 346	98.96 28	99.90 117	99.35 24	96.68 73	98.35 156	99.66 116	96.45 35	98.51 275	99.45 65	99.89 73	99.96 75

ETVMVS		97.03 142	96.64 145	98.20 153	98.67 166	97.12 129	99.89 127	98.57 107	91.10 310	98.17 165	98.59 257	93.86 108	98.19 310	95.64 233	95.24 283	99.28 223

114514_t		97.41 122	96.83 135	99.14 73	99.51 101	97.83 94	99.89 127	98.27 206	88.48 374	99.06 114	99.66 116	90.30 198	99.64 173	96.32 220	99.97 42	99.96 75

WTY-MVS		98.10 76	97.60 98	99.60 24	98.92 146	99.28 18	99.89 127	99.52 14	95.58 112	98.24 163	99.39 149	93.33 120	99.74 156	97.98 156	95.58 275	99.78 115

GA-MVS		93.83 281	92.84 296	96.80 256	95.73 360	93.57 289	99.88 130	97.24 349	92.57 251	92.92 300	96.66 339	78.73 363	97.67 336	87.75 381	94.06 299	99.17 235

UniMVSNet (Re)		93.07 305	92.13 313	95.88 288	94.84 383	96.24 175	99.88 130	98.98 41	92.49 258	89.25 357	95.40 386	87.09 245	97.14 361	93.13 294	78.16 425	94.26 357

HPM-MVS_fast		97.80 97	97.50 103	98.68 110	99.79 69	96.42 162	99.88 130	98.16 227	91.75 287	98.94 119	99.54 134	91.82 172	99.65 172	97.62 174	99.99 21	99.99 26

test_vis1_n		93.61 292	93.03 292	95.35 306	95.86 352	86.94 426	99.87 133	96.36 432	96.85 64	99.54 73	98.79 237	52.41 478	99.83 140	98.64 114	98.97 154	99.29 221

test_vis1_rt		86.87 409	86.05 406	89.34 439	96.12 343	78.07 474	99.87 133	83.54 500	92.03 277	78.21 461	89.51 468	45.80 484	99.91 111	96.25 221	93.11 311	90.03 468

DPE-MVS		99.26 6	99.10 9	99.74 12	99.89 50	99.24 20	99.87 133	98.44 148	97.48 39	99.64 58	99.94 5	96.68 31	99.99 39	99.99 5	100.00 1	99.99 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MTMP							99.87 133	96.49 429

CDPH-MVS		98.65 35	98.36 45	99.49 37	99.94 17	98.73 51	99.87 133	98.33 195	93.97 176	99.76 41	99.87 32	94.99 68	99.75 154	98.55 118	100.00 1	99.98 57

HQP-NCC					95.78 353		99.87 133		96.82 66	93.37 293

ACMP_Plane					95.78 353		99.87 133		96.82 66	93.37 293

APD-MVS		98.62 36	98.35 46	99.41 44	99.90 47	98.51 64	99.87 133	98.36 188	94.08 169	99.74 45	99.73 92	94.08 100	99.74 156	99.42 67	99.99 21	99.99 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.42 52	98.38 41	98.53 130	99.39 106	95.79 190	99.87 133	99.86 2	96.70 72	98.78 127	99.79 63	92.03 167	99.90 113	99.17 78	99.86 78	99.88 98

HQP-MVS		94.61 254	94.50 241	94.92 320	95.78 353	91.85 335	99.87 133	97.89 256	96.82 66	93.37 293	98.65 249	80.65 343	98.39 288	97.92 158	89.60 320	94.53 336

CNLPA		97.76 101	97.38 110	98.92 97	99.53 98	96.84 141	99.87 133	98.14 231	93.78 186	96.55 227	99.69 105	92.28 158	99.98 51	97.13 187	99.44 128	99.93 88

SMA-MVS		98.76 29	98.48 35	99.62 22	99.87 56	98.87 35	99.86 144	98.38 184	93.19 210	99.77 40	99.94 5	95.54 50	100.00 1	99.74 43	99.99 21	100.00 1
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
plane_prior						91.74 342	99.86 144		96.76 70						89.59 322

casdiffmvs_mvg		96.43 177	95.94 183	97.89 178	97.44 269	95.47 205	99.86 144	97.29 341	93.35 203	96.03 248	99.19 176	85.39 277	98.72 251	97.89 161	97.04 224	99.49 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		97.08 141	96.75 140	98.06 164	98.56 174	96.82 142	99.85 147	98.61 99	92.53 255	98.84 123	98.84 233	93.36 118	98.30 300	95.84 229	94.30 295	99.05 250

tttt0517		96.85 151	96.49 152	97.92 174	97.48 268	95.89 187	99.85 147	98.54 123	90.72 326	96.63 221	98.93 218	97.47 13	99.02 213	93.03 296	95.76 265	98.85 268

ACMMP_NAP		98.49 45	98.14 59	99.54 32	99.66 89	98.62 60	99.85 147	98.37 187	94.68 139	99.53 74	99.83 51	92.87 136	100.00 1	98.66 113	99.84 79	99.99 26

thres200		96.96 145	96.21 166	99.22 59	98.97 139	98.84 38	99.85 147	99.71 7	93.17 212	96.26 241	98.88 220	89.87 203	99.51 178	94.26 265	94.91 286	99.31 216

F-COLMAP		96.93 148	96.95 129	96.87 254	99.71 83	91.74 342	99.85 147	97.95 249	93.11 217	95.72 258	99.16 181	92.35 156	99.94 94	95.32 236	99.35 136	98.92 264

test_fmvsmconf0.01_n		96.39 180	95.74 192	98.32 147	91.47 447	95.56 203	99.84 152	97.30 333	97.74 30	97.89 175	99.35 153	79.62 353	99.85 130	99.25 75	99.24 141	99.55 164

SR-MVS		98.46 47	98.30 50	98.93 96	99.88 54	97.04 134	99.84 152	98.35 190	94.92 128	99.32 94	99.80 59	93.35 119	99.78 147	99.30 72	99.95 53	99.96 75

CANet_DTU		96.76 157	96.15 168	98.60 118	98.78 159	97.53 108	99.84 152	97.63 284	97.25 50	99.20 102	99.64 119	81.36 331	99.98 51	92.77 299	98.89 156	98.28 292

casdiffmvs		96.42 179	95.97 178	97.77 188	97.30 285	94.98 231	99.84 152	97.09 376	93.75 189	96.58 224	99.26 167	85.07 281	98.78 242	97.77 169	97.04 224	99.54 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HQP_MVS		94.49 261	94.36 244	94.87 321	95.71 363	91.74 342	99.84 152	97.87 258	96.38 86	93.01 298	98.59 257	80.47 347	98.37 294	97.79 167	89.55 323	94.52 338

plane_prior2							99.84 152		96.38 86

BH-w/o		95.71 216	95.38 211	96.68 261	98.49 186	92.28 323	99.84 152	97.50 305	92.12 273	92.06 312	98.79 237	84.69 292	98.67 259	95.29 237	99.66 95	99.09 244

usedtu_dtu_shiyan1		92.78 311	91.73 322	95.92 286	93.03 419	96.82 142	99.83 159	97.79 266	90.58 327	90.09 330	95.04 405	84.75 287	96.72 393	88.19 374	86.23 361	94.23 361

FE-MVSNET3		92.78 311	91.73 322	95.92 286	93.03 419	96.82 142	99.83 159	97.79 266	90.58 327	90.09 330	95.04 405	84.75 287	96.72 393	88.20 373	86.23 361	94.23 361

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 201	95.65 367	94.21 266	99.83 159	98.50 137	96.27 92	99.65 55	99.64 119	84.72 291	99.93 104	99.04 85	98.84 159	98.74 275

test_fmvs2		89.47 386	89.70 361	88.77 446	94.54 389	75.74 476	99.83 159	94.70 470	94.71 137	91.08 319	96.82 338	54.46 474	97.78 333	92.87 297	88.27 342	92.80 438

UniMVSNet_NR-MVSNet		92.95 307	92.11 314	95.49 298	94.61 388	95.28 221	99.83 159	99.08 36	91.49 292	89.21 360	96.86 333	87.14 244	96.73 391	93.20 290	77.52 430	94.46 341

APD-MVS_3200maxsize		98.25 68	98.08 64	98.78 103	99.81 67	96.60 156	99.82 164	98.30 202	93.95 178	99.37 92	99.77 71	92.84 137	99.76 153	98.95 90	99.92 67	99.97 67

PAPM_NR		98.12 75	97.93 78	98.70 109	99.94 17	96.13 180	99.82 164	98.43 156	94.56 142	97.52 186	99.70 101	94.40 84	99.98 51	97.00 192	99.98 32	99.99 26

gbinet_0.2-2-1-0.02		87.63 406	85.51 412	93.99 367	87.22 467	91.56 356	99.81 166	97.36 320	79.54 457	88.60 374	93.29 442	73.76 412	96.34 416	89.27 356	60.78 487	94.06 390

nrg030		93.51 294	92.53 308	96.45 269	94.36 392	97.20 124	99.81 166	97.16 360	91.60 289	89.86 339	97.46 308	86.37 257	97.68 335	95.88 228	80.31 414	94.46 341

diffmvs		97.00 143	96.64 145	98.09 162	97.64 253	96.17 179	99.81 166	97.19 354	94.67 140	98.95 118	99.28 160	86.43 256	98.76 245	98.37 130	97.42 203	99.33 210
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DU-MVS		92.46 322	91.45 331	95.49 298	94.05 398	95.28 221	99.81 166	98.74 76	92.25 271	89.21 360	96.64 341	81.66 327	96.73 391	93.20 290	77.52 430	94.46 341

ACMP	92.05 9	92.74 314	92.42 311	93.73 375	95.91 351	88.72 406	99.81 166	97.53 301	94.13 166	87.00 404	98.23 285	74.07 410	98.47 276	96.22 222	88.86 332	93.99 397
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
E3new		96.75 159	96.43 156	97.71 193	97.79 234	94.83 238	99.80 171	97.33 325	93.52 197	97.49 189	99.31 157	87.73 231	98.83 228	97.52 175	97.40 205	99.48 182

mvsany_test1		97.82 95	97.90 80	97.55 209	98.77 160	93.04 304	99.80 171	97.93 251	96.95 61	99.61 69	99.68 112	90.92 185	99.83 140	99.18 77	98.29 179	99.80 111

Fast-Effi-MVS+-dtu		93.72 289	93.86 263	93.29 388	97.06 298	86.16 430	99.80 171	96.83 412	92.66 242	92.58 305	97.83 303	81.39 330	97.67 336	89.75 349	96.87 231	96.05 333

BH-untuned		95.18 232	94.83 232	96.22 277	98.36 194	91.22 360	99.80 171	97.32 331	90.91 314	91.08 319	98.67 246	83.51 304	98.54 274	94.23 266	99.61 104	98.92 264

viewdifsd2359ckpt09		96.21 192	95.77 190	97.53 211	97.69 247	94.50 251	99.78 175	97.23 351	92.88 226	96.58 224	99.26 167	84.85 285	98.66 262	96.61 211	97.02 227	99.43 194

viewmambaseed2359dif		95.92 203	95.55 200	97.04 247	97.38 275	93.41 295	99.78 175	96.97 398	91.14 308	96.58 224	99.27 163	84.85 285	98.75 247	96.87 201	97.12 220	98.97 259

tfpn200view9		96.79 154	95.99 173	99.19 62	98.94 141	98.82 39	99.78 175	99.71 7	92.86 227	96.02 249	98.87 227	89.33 210	99.50 180	93.84 274	94.57 290	99.27 225

thres400		96.78 156	95.99 173	99.16 69	98.94 141	98.82 39	99.78 175	99.71 7	92.86 227	96.02 249	98.87 227	89.33 210	99.50 180	93.84 274	94.57 290	99.16 236

TAPA-MVS	92.12 8	94.42 263	93.60 269	96.90 253	99.33 110	91.78 341	99.78 175	98.00 243	89.89 346	94.52 276	99.47 138	91.97 168	99.18 204	69.90 472	99.52 114	99.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
viewcassd2359sk11		96.59 169	96.23 163	97.66 196	97.63 254	94.70 243	99.77 180	97.33 325	93.41 202	97.34 194	99.17 178	86.72 250	98.83 228	97.40 178	97.32 209	99.46 185

TSAR-MVS + MP.		98.93 20	98.77 22	99.41 44	99.74 77	98.67 54	99.77 180	98.38 184	96.73 71	99.88 13	99.74 88	94.89 70	99.59 174	99.80 32	99.98 32	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		93.21 299	92.80 298	94.44 343	93.12 415	90.85 368	99.77 180	97.61 290	96.19 95	91.56 315	98.65 249	75.16 404	98.47 276	93.78 280	89.39 326	93.99 397
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v2v482		91.30 343	90.07 357	95.01 316	93.13 413	93.79 278	99.77 180	97.02 390	88.05 382	89.25 357	95.37 390	80.73 341	97.15 360	87.28 387	80.04 417	94.09 387

Baseline_NR-MVSNet		90.33 367	89.51 367	92.81 401	92.84 425	89.95 389	99.77 180	93.94 477	84.69 427	89.04 364	95.66 372	81.66 327	96.52 402	90.99 326	76.98 436	91.97 450

ACMM	91.95 10	92.88 309	92.52 309	93.98 369	95.75 359	89.08 401	99.77 180	97.52 303	93.00 221	89.95 336	97.99 294	76.17 393	98.46 279	93.63 285	88.87 331	94.39 348
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wanda-best-256-512		87.82 402	85.71 408	94.15 355	86.66 471	91.88 333	99.76 186	97.08 377	79.46 458	88.37 383	92.36 452	78.01 369	96.43 409	88.39 369	61.26 483	94.14 379

FE-blended-shiyan7		87.82 402	85.71 408	94.15 355	86.66 471	91.88 333	99.76 186	97.08 377	79.46 458	88.37 383	92.36 452	78.01 369	96.43 409	88.39 369	61.26 483	94.14 379

reproduce_monomvs		95.38 227	95.07 224	96.32 275	99.32 112	96.60 156	99.76 186	98.85 62	96.65 74	87.83 392	96.05 362	99.52 1	98.11 314	96.58 213	81.07 406	94.25 359

SR-MVS-dyc-post		98.31 60	98.17 57	98.71 108	99.79 69	96.37 167	99.76 186	98.31 199	94.43 151	99.40 89	99.75 81	93.28 124	99.78 147	98.90 97	99.92 67	99.97 67

RE-MVS-def			98.13 60		99.79 69	96.37 167	99.76 186	98.31 199	94.43 151	99.40 89	99.75 81	92.95 134		98.90 97	99.92 67	99.97 67

BH-RMVSNet		95.18 232	94.31 247	97.80 182	98.17 210	95.23 224	99.76 186	97.53 301	92.52 256	94.27 285	99.25 169	76.84 383	98.80 239	90.89 330	99.54 111	99.35 207

blend_shiyan4		90.13 375	88.79 380	94.17 352	87.12 468	91.83 337	99.75 192	97.08 377	79.27 462	88.69 370	92.53 447	92.25 160	96.50 403	89.35 353	73.04 450	94.18 368

v148		90.70 357	89.63 362	93.92 370	92.97 421	90.97 362	99.75 192	96.89 408	87.51 388	88.27 386	95.01 408	81.67 326	97.04 371	87.40 385	77.17 435	93.75 413

PGM-MVS		98.34 58	98.13 60	98.99 90	99.92 36	97.00 135	99.75 192	99.50 17	93.90 182	99.37 92	99.76 73	93.24 126	100.00 1	97.75 171	99.96 46	99.98 57

LPG-MVS_test		92.96 306	92.71 301	93.71 377	95.43 374	88.67 407	99.75 192	97.62 287	92.81 230	90.05 332	98.49 268	75.24 400	98.40 286	95.84 229	89.12 327	94.07 388

reproduce-ours		98.78 27	98.67 24	99.09 80	99.70 85	97.30 119	99.74 196	98.25 208	97.10 53	99.10 108	99.90 23	94.59 77	99.99 39	99.77 37	99.91 70	99.99 26

our_new_method		98.78 27	98.67 24	99.09 80	99.70 85	97.30 119	99.74 196	98.25 208	97.10 53	99.10 108	99.90 23	94.59 77	99.99 39	99.77 37	99.91 70	99.99 26

thres100view900		96.74 161	95.92 185	99.18 63	98.90 151	98.77 47	99.74 196	99.71 7	92.59 247	95.84 252	98.86 229	89.25 212	99.50 180	93.84 274	94.57 290	99.27 225

MP-MVS-pluss		98.07 78	97.64 96	99.38 49	99.74 77	98.41 69	99.74 196	98.18 221	93.35 203	96.45 231	99.85 38	92.64 145	99.97 64	98.91 96	99.89 73	99.77 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs5		90.17 373	89.09 374	93.40 385	92.10 439	89.77 392	99.74 196	95.58 451	85.88 411	87.24 403	95.74 367	73.41 416	96.48 406	88.54 364	83.56 385	93.95 400

thres600view7		96.69 164	95.87 188	99.14 73	98.90 151	98.78 46	99.74 196	99.71 7	92.59 247	95.84 252	98.86 229	89.25 212	99.50 180	93.44 287	94.50 293	99.16 236

baseline2		96.71 163	96.49 152	97.37 230	95.63 369	95.96 185	99.74 196	98.88 55	92.94 223	91.61 314	98.97 206	97.72 7	98.62 265	94.83 250	98.08 189	97.53 317

reproduce_model		98.75 30	98.66 26	99.03 85	99.71 83	97.10 132	99.73 203	98.23 212	97.02 58	99.18 105	99.90 23	94.54 81	99.99 39	99.77 37	99.90 72	99.99 26

miper_enhance_ethall		94.36 267	93.98 258	95.49 298	98.68 165	95.24 223	99.73 203	97.29 341	93.28 207	89.86 339	95.97 363	94.37 88	97.05 368	92.20 303	84.45 377	94.19 367

testgi		89.01 391	88.04 392	91.90 412	93.49 408	84.89 440	99.73 203	95.66 449	93.89 184	85.14 424	98.17 286	59.68 467	94.66 455	77.73 454	88.88 330	96.16 332

sss		97.57 113	97.03 127	99.18 63	98.37 193	98.04 83	99.73 203	99.38 22	93.46 199	98.76 132	99.06 189	91.21 176	99.89 118	96.33 219	97.01 228	99.62 147

blended_shiyan8		87.82 402	85.71 408	94.16 353	86.54 474	91.79 339	99.72 207	97.08 377	79.32 460	88.44 377	92.35 455	77.88 373	96.56 400	88.53 365	61.51 482	94.15 375

blended_shiyan6		87.74 405	85.62 411	94.09 360	86.53 475	91.73 345	99.72 207	97.08 377	79.32 460	88.22 387	92.31 457	77.82 374	96.43 409	88.31 371	61.26 483	94.13 384

sasdasda		97.09 138	96.32 160	99.39 46	98.93 143	98.95 29	99.72 207	97.35 321	94.45 147	97.88 176	99.42 142	86.71 251	99.52 176	98.48 123	93.97 300	99.72 122

canonicalmvs		97.09 138	96.32 160	99.39 46	98.93 143	98.95 29	99.72 207	97.35 321	94.45 147	97.88 176	99.42 142	86.71 251	99.52 176	98.48 123	93.97 300	99.72 122

3Dnovator+	91.53 11	96.31 186	95.24 216	99.52 33	96.88 320	98.64 59	99.72 207	98.24 210	95.27 121	88.42 382	98.98 204	82.76 316	99.94 94	97.10 189	99.83 80	99.96 75

E2		96.36 182	95.95 181	97.60 204	97.41 271	94.52 249	99.71 212	97.33 325	93.20 209	97.02 206	99.07 187	85.37 278	98.82 231	97.27 181	97.14 218	99.46 185

E3		96.36 182	95.95 181	97.60 204	97.37 277	94.52 249	99.71 212	97.33 325	93.18 211	97.02 206	99.07 187	85.45 276	98.82 231	97.27 181	97.14 218	99.46 185

guyue		97.15 134	96.82 136	98.15 158	97.56 260	96.25 174	99.71 212	97.84 263	95.75 107	98.13 167	98.65 249	87.58 235	98.82 231	98.29 136	97.91 193	99.36 203

UWE-MVS		96.79 154	96.72 142	97.00 248	98.51 182	93.70 282	99.71 212	98.60 101	92.96 222	97.09 203	98.34 279	96.67 33	98.85 227	92.11 309	96.50 242	98.44 286

WB-MVSnew		92.90 308	92.77 300	93.26 390	96.95 314	93.63 284	99.71 212	98.16 227	91.49 292	94.28 284	98.14 287	81.33 332	96.48 406	79.47 443	95.46 276	89.68 472

Syy-MVS		90.00 377	90.63 342	88.11 450	97.68 248	74.66 480	99.71 212	98.35 190	90.79 322	92.10 310	98.67 246	79.10 360	93.09 470	63.35 485	95.95 258	96.59 326

myMVS_eth3d		94.46 262	94.76 237	93.55 383	97.68 248	90.97 362	99.71 212	98.35 190	90.79 322	92.10 310	98.67 246	92.46 154	93.09 470	87.13 389	95.95 258	96.59 326

viewdifsd2359ckpt13		96.19 193	95.77 190	97.45 220	97.62 255	94.40 257	99.70 219	97.23 351	92.76 235	96.63 221	99.05 190	84.96 284	98.64 263	96.65 210	97.35 207	99.31 216

viewmanbaseed2359cas		96.45 176	96.07 169	97.59 207	97.55 261	94.59 246	99.70 219	97.33 325	93.62 193	97.00 209	99.32 154	85.57 272	98.71 252	97.26 184	97.33 208	99.47 183

HyFIR lowres test		96.66 166	96.43 156	97.36 232	99.05 129	93.91 277	99.70 219	99.80 3	90.54 330	96.26 241	98.08 289	92.15 164	98.23 308	96.84 202	95.46 276	99.93 88

diffmvs_AUTHOR		96.75 159	96.41 158	97.79 184	97.20 290	95.46 206	99.69 222	97.15 361	94.46 146	98.78 127	99.21 174	85.64 270	98.77 243	98.27 137	97.31 210	99.13 240

D2MVS		92.76 313	92.59 307	93.27 389	95.13 378	89.54 395	99.69 222	99.38 22	92.26 270	87.59 395	94.61 422	85.05 282	97.79 331	91.59 316	88.01 345	92.47 444

TranMVSNet+NR-MVSNet		91.68 340	90.61 343	94.87 321	93.69 405	93.98 275	99.69 222	98.65 88	91.03 312	88.44 377	96.83 337	80.05 351	96.18 424	90.26 343	76.89 438	94.45 346

V42		91.28 345	90.12 356	94.74 326	93.42 410	93.46 293	99.68 225	97.02 390	87.36 391	89.85 341	95.05 404	81.31 333	97.34 347	87.34 386	80.07 416	93.40 423

testmvs		40.60 465	44.45 468	29.05 484	19.49 508	14.11 510	99.68 225	18.47 507	20.74 500	64.59 485	98.48 271	10.95 504	17.09 504	56.66 492	11.01 500	55.94 497

MGCFI-Net		97.00 143	96.22 165	99.34 51	98.86 154	98.80 41	99.67 227	97.30 333	94.31 159	97.77 182	99.41 146	86.36 258	99.50 180	98.38 128	93.90 302	99.72 122

DeepC-MVS	94.51 4	96.92 149	96.40 159	98.45 138	99.16 122	95.90 186	99.66 228	98.06 237	96.37 89	94.37 282	99.49 137	83.29 312	99.90 113	97.63 173	99.61 104	99.55 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 1792x2688		96.81 153	96.53 150	97.64 198	98.91 150	93.07 301	99.65 229	99.80 3	95.64 110	95.39 265	98.86 229	84.35 298	99.90 113	96.98 194	99.16 144	99.95 83

Test_1112_low_res		95.72 214	94.83 232	98.42 142	97.79 234	96.41 163	99.65 229	96.65 423	92.70 239	92.86 303	96.13 358	92.15 164	99.30 194	91.88 313	93.64 304	99.55 164

1112_ss		96.01 198	95.20 218	98.42 142	97.80 233	96.41 163	99.65 229	96.66 422	92.71 238	92.88 302	99.40 147	92.16 163	99.30 194	91.92 312	93.66 303	99.55 164

OMC-MVS		97.28 126	97.23 118	97.41 227	99.76 73	93.36 299	99.65 229	97.95 249	96.03 98	97.41 192	99.70 101	89.61 206	99.51 178	96.73 209	98.25 180	99.38 199

test_yl		97.83 92	97.37 111	99.21 60	99.18 119	97.98 86	99.64 233	99.27 27	91.43 297	97.88 176	98.99 202	95.84 46	99.84 138	98.82 101	95.32 281	99.79 112

DCV-MVSNet		97.83 92	97.37 111	99.21 60	99.18 119	97.98 86	99.64 233	99.27 27	91.43 297	97.88 176	98.99 202	95.84 46	99.84 138	98.82 101	95.32 281	99.79 112

MG-MVS		98.91 22	98.65 27	99.68 18	99.94 17	99.07 26	99.64 233	99.44 19	97.33 44	99.00 117	99.72 95	94.03 102	99.98 51	98.73 108	100.00 1	100.00 1

viewdifsd2359ckpt11		94.09 275	93.63 266	95.46 302	96.68 332	88.92 402	99.62 236	97.12 366	93.07 218	95.73 256	99.22 171	77.05 377	98.88 224	96.52 215	87.69 352	98.58 282

viewmsd2359difaftdt		94.09 275	93.64 265	95.46 302	96.68 332	88.92 402	99.62 236	97.13 365	93.07 218	95.73 256	99.22 171	77.05 377	98.89 223	96.52 215	87.70 351	98.58 282

v1144		91.09 349	89.83 358	94.87 321	93.25 412	93.69 283	99.62 236	96.98 396	86.83 401	89.64 347	94.99 411	80.94 336	97.05 368	85.08 409	81.16 402	93.87 407

mvsmamba		96.94 146	96.73 141	97.55 209	97.99 221	94.37 259	99.62 236	97.70 277	93.13 215	98.42 151	97.92 297	88.02 228	98.75 247	98.78 104	99.01 153	99.52 173

NormalMVS		97.90 85	97.85 85	98.04 166	99.86 58	95.39 212	99.61 240	97.78 270	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 207	99.48 121	99.06 248

SymmetryMVS		97.64 110	97.46 104	98.17 154	98.74 162	95.39 212	99.61 240	99.26 29	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 207	95.63 273	99.45 190

cl22		93.77 286	93.25 287	95.33 308	99.49 102	94.43 253	99.61 240	98.09 234	90.38 334	89.16 363	95.61 373	90.56 193	97.34 347	91.93 311	84.45 377	94.21 366

viewdifsd2359ckpt07		95.83 207	95.42 204	97.07 246	97.40 273	93.04 304	99.60 243	97.24 349	92.39 262	96.09 247	99.14 182	83.07 315	98.93 221	97.02 191	96.87 231	99.23 232

WR-MVS		92.31 325	91.25 333	95.48 301	94.45 391	95.29 220	99.60 243	98.68 84	90.10 341	88.07 389	96.89 331	80.68 342	96.80 389	93.14 293	79.67 418	94.36 349

SDMVSNet		94.80 244	93.96 259	97.33 235	98.92 146	95.42 209	99.59 245	98.99 40	92.41 260	92.55 306	97.85 301	75.81 396	98.93 221	97.90 160	91.62 315	97.64 310

Effi-MVS+-dtu		94.53 257	95.30 214	92.22 408	97.77 236	82.54 456	99.59 245	97.06 386	94.92 128	95.29 267	95.37 390	85.81 266	97.89 328	94.80 251	97.07 222	96.23 330

casdiffseed414692147		95.07 235	94.26 248	97.50 216	97.01 306	94.70 243	99.58 247	97.02 390	91.27 303	94.66 274	98.82 236	80.79 340	98.55 273	93.39 288	95.79 263	99.27 225

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 90	98.60 172	98.15 72	99.58 247	97.74 275	90.34 337	99.26 101	98.32 280	94.29 93	99.23 197	99.03 88	99.89 73	99.58 160

DIV-MVS_self_test		92.32 324	91.60 325	94.47 341	97.31 284	92.74 310	99.58 247	96.75 418	86.99 398	87.64 394	95.54 377	89.55 207	96.50 403	88.58 363	82.44 392	94.17 369

FIs		94.10 274	93.43 276	96.11 279	94.70 386	96.82 142	99.58 247	98.93 48	92.54 254	89.34 355	97.31 313	87.62 234	97.10 365	94.22 267	86.58 358	94.40 347

E4		96.01 198	95.53 201	97.44 223	97.05 299	94.23 264	99.57 251	97.30 333	92.72 236	96.47 230	99.03 192	83.98 302	98.83 228	96.92 198	96.77 234	99.27 225

viewmacassd2359aftdt		95.93 202	95.45 202	97.36 232	97.09 295	94.12 270	99.57 251	97.26 345	93.05 220	96.50 228	99.17 178	82.76 316	98.68 257	96.61 211	97.04 224	99.28 223

cl____		92.31 325	91.58 326	94.52 337	97.33 282	92.77 308	99.57 251	96.78 417	86.97 399	87.56 396	95.51 380	89.43 208	96.62 397	88.60 362	82.44 392	94.16 374

EPNet_dtu		95.71 216	95.39 206	96.66 262	98.92 146	93.41 295	99.57 251	98.90 50	96.19 95	97.52 186	98.56 262	92.65 144	97.36 345	77.89 453	98.33 175	99.20 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KinetiMVS		96.10 194	95.29 215	98.53 130	97.08 296	97.12 129	99.56 255	98.12 233	94.78 133	98.44 149	98.94 215	80.30 349	99.39 191	91.56 317	98.79 162	99.06 248

v144192		90.79 356	89.52 366	94.59 333	93.11 416	92.77 308	99.56 255	96.99 394	86.38 405	89.82 342	94.95 413	80.50 346	97.10 365	83.98 416	80.41 412	93.90 404

OpenMVS	90.15 15	94.77 247	93.59 270	98.33 146	96.07 345	97.48 113	99.56 255	98.57 107	90.46 333	86.51 410	98.95 213	78.57 365	99.94 94	93.86 273	99.74 89	97.57 315

MVSFormer		96.94 146	96.60 147	97.95 170	97.28 287	97.70 102	99.55 258	97.27 343	91.17 305	99.43 84	99.54 134	90.92 185	96.89 381	94.67 256	99.62 99	99.25 229

test_djsdf		92.83 310	92.29 312	94.47 341	91.90 441	92.46 320	99.55 258	97.27 343	91.17 305	89.96 335	96.07 361	81.10 334	96.89 381	94.67 256	88.91 329	94.05 391

PS-MVSNAJ		98.44 49	98.20 54	99.16 69	98.80 158	98.92 31	99.54 260	98.17 222	97.34 42	99.85 20	99.85 38	91.20 177	99.89 118	99.41 68	99.67 94	98.69 278

CDS-MVSNet		96.34 184	96.07 169	97.13 243	97.37 277	94.96 232	99.53 261	97.91 255	91.55 291	95.37 266	98.32 280	95.05 64	97.13 362	93.80 278	95.75 266	99.30 219
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		98.23 71	97.97 72	99.02 88	98.69 164	98.66 56	99.52 262	98.08 236	97.05 56	99.86 16	99.86 34	90.65 190	99.71 160	99.39 70	98.63 166	98.69 278

PatchMatch-RL		96.04 197	95.40 205	97.95 170	99.59 92	95.22 225	99.52 262	99.07 37	93.96 177	96.49 229	98.35 277	82.28 319	99.82 142	90.15 344	99.22 143	98.81 271

test_method		80.79 441	79.70 444	84.08 459	92.83 426	67.06 485	99.51 264	95.42 454	54.34 491	81.07 448	93.53 437	44.48 485	92.22 476	78.90 449	77.23 434	92.94 435

baseline		96.43 177	95.98 175	97.76 190	97.34 280	95.17 228	99.51 264	97.17 358	93.92 180	96.90 212	99.28 160	85.37 278	98.64 263	97.50 176	96.86 233	99.46 185

miper_ehance_all_eth		93.16 302	92.60 303	94.82 325	97.57 259	93.56 290	99.50 266	97.07 385	88.75 367	88.85 367	95.52 379	90.97 184	96.74 390	90.77 332	84.45 377	94.17 369

v1192		90.62 361	89.25 371	94.72 328	93.13 413	93.07 301	99.50 266	97.02 390	86.33 406	89.56 351	95.01 408	79.22 357	97.09 367	82.34 428	81.16 402	94.01 394

SSC-MVS3.2		89.59 384	88.66 384	92.38 405	94.29 395	86.12 431	99.49 268	97.66 283	90.28 340	88.63 373	95.18 400	64.46 452	96.88 383	85.30 407	82.66 389	94.14 379

v1921920		90.46 363	89.12 373	94.50 339	92.96 422	92.46 320	99.49 268	96.98 396	86.10 408	89.61 349	95.30 393	78.55 366	97.03 373	82.17 429	80.89 410	94.01 394

无先验							99.49 268	98.71 79	93.46 199				100.00 1	94.36 261		99.99 26

pmmvs4		92.10 329	91.07 337	95.18 312	92.82 427	94.96 232	99.48 271	96.83 412	87.45 390	88.66 372	96.56 345	83.78 303	96.83 387	89.29 355	84.77 375	93.75 413

dongtai		91.55 342	91.13 335	92.82 400	98.16 211	86.35 429	99.47 272	98.51 131	83.24 436	85.07 426	97.56 306	90.33 197	94.94 450	76.09 461	91.73 313	97.18 321

BridgeMVS		98.27 63	97.99 70	99.11 78	98.64 170	98.43 68	99.47 272	97.79 266	94.56 142	99.74 45	98.35 277	94.33 91	99.25 196	99.12 79	99.96 46	99.64 139

Vis-MVSNet (Re-imp)		96.32 185	95.98 175	97.35 234	97.93 225	94.82 239	99.47 272	98.15 230	91.83 283	95.09 270	99.11 183	91.37 175	97.47 343	93.47 286	97.43 201	99.74 119

API-MVS		97.86 88	97.66 94	98.47 135	99.52 99	95.41 210	99.47 272	98.87 58	91.68 288	98.84 123	99.85 38	92.34 157	99.99 39	98.44 126	99.96 46	100.00 1

旧先验2							99.46 276		94.21 165	99.85 20			99.95 85	96.96 196

IterMVS-LS		92.69 316	92.11 314	94.43 345	96.80 324	92.74 310	99.45 277	96.89 408	88.98 358	89.65 346	95.38 389	88.77 221	96.34 416	90.98 327	82.04 395	94.22 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator	91.47 12	96.28 189	95.34 212	99.08 82	96.82 323	97.47 114	99.45 277	98.81 67	95.52 115	89.39 353	99.00 199	81.97 322	99.95 85	97.27 181	99.83 80	99.84 104

FC-MVSNet-test		93.81 284	93.15 289	95.80 293	94.30 394	96.20 176	99.42 279	98.89 52	92.33 265	89.03 365	97.27 315	87.39 240	96.83 387	93.20 290	86.48 359	94.36 349

balanced_ft_v1		96.88 150	96.52 151	97.96 169	98.60 172	94.94 234	99.41 280	97.56 296	93.53 194	99.42 86	97.89 300	83.33 311	99.31 193	99.29 73	99.62 99	99.64 139

c3_l		92.53 320	91.87 320	94.52 337	97.40 273	92.99 306	99.40 281	96.93 405	87.86 385	88.69 370	95.44 384	89.95 202	96.44 408	90.45 338	80.69 411	94.14 379

EI-MVSNet-Vis-set		98.27 63	98.11 62	98.75 106	99.83 64	96.59 158	99.40 281	98.51 131	95.29 120	98.51 146	99.76 73	93.60 115	99.71 160	98.53 121	99.52 114	99.95 83

新几何2							99.40 281

QAPM		95.40 226	94.17 251	99.10 79	96.92 315	97.71 100	99.40 281	98.68 84	89.31 351	88.94 366	98.89 219	82.48 318	99.96 76	93.12 295	99.83 80	99.62 147

UWE-MVS-28		95.95 200	96.49 152	94.34 348	98.51 182	89.99 387	99.39 285	98.57 107	93.14 214	97.33 195	98.31 282	93.44 116	94.68 454	93.69 284	95.98 255	98.34 291

MTAPA		98.29 62	97.96 75	99.30 52	99.85 61	97.93 90	99.39 285	98.28 204	95.76 106	97.18 201	99.88 29	92.74 140	100.00 1	98.67 111	99.88 76	99.99 26

miper_lstm_enhance		91.81 333	91.39 332	93.06 396	97.34 280	89.18 399	99.38 287	96.79 416	86.70 402	87.47 398	95.22 399	90.00 201	95.86 435	88.26 372	81.37 400	94.15 375

v1240		90.20 371	88.79 380	94.44 343	93.05 418	92.27 324	99.38 287	96.92 406	85.89 410	89.36 354	94.87 415	77.89 372	97.03 373	80.66 437	81.08 405	94.01 394

EPP-MVSNet		96.69 164	96.60 147	96.96 250	97.74 238	93.05 303	99.37 289	98.56 113	88.75 367	95.83 254	99.01 195	96.01 40	98.56 270	96.92 198	97.20 214	99.25 229

MSDG		94.37 265	93.36 284	97.40 228	98.88 153	93.95 276	99.37 289	97.38 316	85.75 414	90.80 325	99.17 178	84.11 301	99.88 124	86.35 397	98.43 173	98.36 290

EI-MVSNet-UG-set		98.14 74	97.99 70	98.60 118	99.80 68	96.27 169	99.36 291	98.50 137	95.21 122	98.30 158	99.75 81	93.29 123	99.73 159	98.37 130	99.30 138	99.81 109

VortexMVS		94.11 273	93.50 274	95.94 284	97.70 246	96.61 155	99.35 292	97.18 356	93.52 197	89.57 350	95.74 367	87.55 236	96.97 376	95.76 232	85.13 372	94.23 361

test222					99.55 97	97.41 117	99.34 293	98.55 119	91.86 282	99.27 100	99.83 51	93.84 109			99.95 53	99.99 26

our_test_3		90.39 364	89.48 369	93.12 393	92.40 434	89.57 394	99.33 294	96.35 433	87.84 386	85.30 423	94.99 411	84.14 300	96.09 429	80.38 439	84.56 376	93.71 418

ppachtmachnet_test		89.58 385	88.35 388	93.25 391	92.40 434	90.44 378	99.33 294	96.73 419	85.49 417	85.90 420	95.77 366	81.09 335	96.00 433	76.00 462	82.49 391	93.30 426

mvs_anonymous		95.65 220	95.03 226	97.53 211	98.19 208	95.74 193	99.33 294	97.49 306	90.87 315	90.47 328	97.10 319	88.23 226	97.16 359	95.92 227	97.66 198	99.68 131

E5new		95.83 207	95.39 206	97.15 239	97.03 300	93.59 285	99.32 297	97.30 333	92.58 249	96.45 231	99.00 199	83.37 308	98.81 235	96.81 203	96.65 237	99.04 251

E5		95.83 207	95.39 206	97.15 239	97.03 300	93.59 285	99.32 297	97.30 333	92.58 249	96.45 231	99.00 199	83.37 308	98.81 235	96.81 203	96.65 237	99.04 251

E6new		95.83 207	95.39 206	97.14 241	97.00 307	93.58 287	99.31 299	97.30 333	92.57 251	96.45 231	99.01 195	83.44 306	98.81 235	96.80 205	96.66 235	99.04 251

E6		95.83 207	95.39 206	97.14 241	97.00 307	93.58 287	99.31 299	97.30 333	92.57 251	96.45 231	99.01 195	83.44 306	98.81 235	96.80 205	96.66 235	99.04 251

AUN-MVS		93.28 298	92.60 303	95.34 307	98.29 199	90.09 385	99.31 299	98.56 113	91.80 286	96.35 240	98.00 292	89.38 209	98.28 303	92.46 300	69.22 464	97.64 310

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 299	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 303

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 299	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 303

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 299	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 303

MVS_Test		96.46 175	95.74 192	98.61 117	98.18 209	97.23 123	99.31 299	97.15 361	91.07 311	98.84 123	97.05 323	88.17 227	98.97 217	94.39 260	97.50 200	99.61 151

hse-mvs2		94.38 264	94.08 255	95.31 309	98.27 202	90.02 386	99.29 306	98.56 113	95.90 101	98.77 129	98.00 292	90.89 188	98.26 307	97.80 164	69.20 465	97.64 310

testdata1							99.28 307		96.35 91

Vis-MVSNet		95.72 214	95.15 221	97.45 220	97.62 255	94.28 261	99.28 307	98.24 210	94.27 164	96.84 214	98.94 215	79.39 355	98.76 245	93.25 289	98.49 171	99.30 219
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
RRT-MVS		96.24 191	95.68 196	97.94 173	97.65 252	94.92 235	99.27 309	97.10 373	92.79 233	97.43 191	97.99 294	81.85 324	99.37 192	98.46 125	98.57 167	99.53 172

FMVSNet3		92.69 316	91.58 326	95.99 281	98.29 199	97.42 116	99.26 310	97.62 287	89.80 347	89.68 343	95.32 392	81.62 329	96.27 420	87.01 393	85.65 365	94.29 356

DeepC-MVS_fast	96.59 1	98.81 26	98.54 32	99.62 22	99.90 47	98.85 37	99.24 311	98.47 140	98.14 16	99.08 110	99.91 19	93.09 130	100.00 1	99.04 85	99.99 21	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
dcpmvs_2		97.42 121	98.09 63	95.42 304	99.58 96	87.24 424	99.23 312	96.95 400	94.28 162	98.93 120	99.73 92	94.39 87	99.16 207	99.89 21	99.82 84	99.86 102

YYNet1		85.50 417	83.33 423	92.00 410	90.89 452	88.38 414	99.22 313	96.55 427	79.60 456	57.26 491	92.72 444	79.09 361	93.78 464	77.25 456	77.37 433	93.84 409

v8		90.54 362	89.17 372	94.66 329	93.43 409	93.40 297	99.20 314	96.94 404	85.76 412	87.56 396	94.51 423	81.96 323	97.19 358	84.94 410	78.25 424	93.38 425

MDA-MVSNet_test_wron		85.51 416	83.32 424	92.10 409	90.96 451	88.58 410	99.20 314	96.52 428	79.70 455	57.12 492	92.69 445	79.11 359	93.86 462	77.10 457	77.46 432	93.86 408

ACMMP		97.74 103	97.44 107	98.66 113	99.92 36	96.13 180	99.18 316	99.45 18	94.84 132	96.41 238	99.71 98	91.40 174	99.99 39	97.99 154	98.03 190	99.87 100
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
WR-MVS_H		91.30 343	90.35 347	94.15 355	94.17 397	92.62 317	99.17 317	98.94 44	88.87 364	86.48 412	94.46 427	84.36 297	96.61 398	88.19 374	78.51 423	93.21 429

TAMVS		95.85 205	95.58 198	96.65 263	97.07 297	93.50 292	99.17 317	97.82 265	91.39 301	95.02 271	98.01 291	92.20 162	97.30 352	93.75 281	95.83 262	99.14 239

LuminaMVS		96.63 167	96.21 166	97.87 179	95.58 371	96.82 142	99.12 319	97.67 280	94.47 145	97.88 176	98.31 282	87.50 237	98.71 252	98.07 150	97.29 211	98.10 297

PS-MVSNAJss		93.64 291	93.31 285	94.61 331	92.11 438	92.19 325	99.12 319	97.38 316	92.51 257	88.45 376	96.99 327	91.20 177	97.29 355	94.36 261	87.71 349	94.36 349

SSM_0404		95.75 213	95.16 220	97.50 216	97.53 263	95.39 212	99.11 321	97.25 346	90.81 318	95.27 268	98.83 234	84.74 289	98.67 259	95.24 238	97.69 195	98.45 285

DTE-MVSNet		89.40 387	88.24 390	92.88 399	92.66 430	89.95 389	99.10 322	98.22 213	87.29 392	85.12 425	96.22 353	76.27 392	95.30 446	83.56 420	75.74 442	93.41 422

CP-MVSNet		91.23 347	90.22 351	94.26 350	93.96 400	92.39 322	99.09 323	98.57 107	88.95 361	86.42 413	96.57 344	79.19 358	96.37 414	90.29 342	78.95 420	94.02 392

AdaColmap		97.23 130	96.80 138	98.51 133	99.99 1	95.60 202	99.09 323	98.84 65	93.32 205	96.74 219	99.72 95	86.04 263	100.00 1	98.01 152	99.43 129	99.94 87

v10		90.25 370	88.82 379	94.57 335	93.53 407	93.43 294	99.08 325	96.87 410	85.00 422	87.34 402	94.51 423	80.93 337	97.02 375	82.85 424	79.23 419	93.26 427

XVG-OURS-SEG-HR		94.79 245	94.70 239	95.08 314	98.05 218	89.19 397	99.08 325	97.54 299	93.66 191	94.87 272	99.58 128	78.78 362	99.79 145	97.31 180	93.40 307	96.25 328

XVG-OURS		94.82 242	94.74 238	95.06 315	98.00 220	89.19 397	99.08 325	97.55 297	94.10 168	94.71 273	99.62 123	80.51 345	99.74 156	96.04 225	93.06 312	96.25 328

IS-MVSNet		96.29 188	95.90 186	97.45 220	98.13 214	94.80 240	99.08 325	97.61 290	92.02 278	95.54 263	98.96 208	90.64 191	98.08 316	93.73 282	97.41 204	99.47 183

v7n		89.65 383	88.29 389	93.72 376	92.22 436	90.56 375	99.07 329	97.10 373	85.42 419	86.73 406	94.72 416	80.06 350	97.13 362	81.14 434	78.12 426	93.49 421

EI-MVSNet		93.73 288	93.40 280	94.74 326	96.80 324	92.69 313	99.06 330	97.67 280	88.96 360	91.39 316	99.02 193	88.75 222	97.30 352	91.07 323	87.85 347	94.22 364

CVMVSNet		94.68 252	94.94 230	93.89 373	96.80 324	86.92 427	99.06 330	98.98 41	94.45 147	94.23 286	99.02 193	85.60 271	95.31 445	90.91 329	95.39 279	99.43 194

baseline1		95.78 212	94.86 231	98.54 128	98.47 187	98.07 80	99.06 330	97.99 244	92.68 241	94.13 287	98.62 254	93.28 124	98.69 256	93.79 279	85.76 364	98.84 269

PEN-MVS		90.19 372	89.06 375	93.57 382	93.06 417	90.90 366	99.06 330	98.47 140	88.11 381	85.91 419	96.30 351	76.67 385	95.94 434	87.07 390	76.91 437	93.89 405

test_fmvs3		79.99 445	80.17 443	79.45 465	84.02 481	62.83 486	99.05 334	93.49 482	88.29 379	80.06 453	86.65 482	28.09 493	88.00 486	88.63 361	73.27 449	87.54 482

Anonymous20231206		86.32 411	85.42 413	89.02 442	89.11 463	80.53 471	99.05 334	95.28 457	85.43 418	82.82 437	93.92 432	74.40 408	93.44 467	66.99 477	81.83 397	93.08 432

MAR-MVS		97.43 117	97.19 120	98.15 158	99.47 103	94.79 241	99.05 334	98.76 73	92.65 243	98.66 137	99.82 54	88.52 224	99.98 51	98.12 145	99.63 98	99.67 133
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
MonoMVSNet		94.82 242	94.43 242	95.98 282	94.54 389	90.73 369	99.03 337	97.06 386	93.16 213	93.15 297	95.47 383	88.29 225	97.57 339	97.85 162	91.33 317	99.62 147

VNet		97.21 131	96.57 149	99.13 77	98.97 139	97.82 95	99.03 337	99.21 32	94.31 159	99.18 105	98.88 220	86.26 260	99.89 118	98.93 92	94.32 294	99.69 130

LCM-MVSNet-Re		92.31 325	92.60 303	91.43 417	97.53 263	79.27 473	99.02 339	91.83 488	92.07 274	80.31 450	94.38 428	83.50 305	95.48 440	97.22 186	97.58 199	99.54 168

SSM_0407		95.62 221	94.95 229	97.61 203	97.14 291	95.31 217	99.00 340	97.25 346	90.81 318	94.40 279	98.83 234	84.74 289	98.58 267	95.24 238	97.18 215	98.93 261

jajsoiax		91.92 331	91.18 334	94.15 355	91.35 448	90.95 365	99.00 340	97.42 312	92.61 245	87.38 400	97.08 320	72.46 418	97.36 345	94.53 259	88.77 333	94.13 384

Elysia		94.50 259	93.38 281	97.85 180	96.49 336	96.70 148	98.98 342	97.78 270	90.81 318	96.19 244	98.55 264	73.63 414	98.98 215	89.41 350	98.56 168	97.88 301

StellarMVS		94.50 259	93.38 281	97.85 180	96.49 336	96.70 148	98.98 342	97.78 270	90.81 318	96.19 244	98.55 264	73.63 414	98.98 215	89.41 350	98.56 168	97.88 301

VPNet		91.81 333	90.46 344	95.85 290	94.74 385	95.54 204	98.98 342	98.59 103	92.14 272	90.77 326	97.44 309	68.73 434	97.54 341	94.89 249	77.89 427	94.46 341

PS-CasMVS		90.63 360	89.51 367	93.99 367	93.83 402	91.70 347	98.98 342	98.52 128	88.48 374	86.15 417	96.53 346	75.46 398	96.31 419	88.83 360	78.86 422	93.95 400

FMVSNet2		91.02 350	89.56 364	95.41 305	97.53 263	95.74 193	98.98 342	97.41 314	87.05 395	88.43 380	95.00 410	71.34 423	96.24 422	85.12 408	85.21 370	94.25 359

IMVS_0403		95.25 230	94.81 234	96.58 265	96.97 309	91.64 349	98.97 347	97.12 366	92.33 265	95.43 264	98.88 220	85.78 267	98.79 240	92.12 305	95.70 269	99.32 212

K. test v3		88.05 398	87.24 399	90.47 429	91.82 443	82.23 459	98.96 348	97.42 312	89.05 354	76.93 466	95.60 374	68.49 435	95.42 442	85.87 404	81.01 408	93.75 413

tfpnnormal		89.29 389	87.61 396	94.34 348	94.35 393	94.13 269	98.95 349	98.94 44	83.94 430	84.47 429	95.51 380	74.84 405	97.39 344	77.05 458	80.41 412	91.48 454

mmtdpeth		88.52 393	87.75 395	90.85 422	95.71 363	83.47 451	98.94 350	94.85 464	88.78 366	97.19 200	89.58 467	63.29 456	98.97 217	98.54 119	62.86 479	90.10 467

AllTest		92.48 321	91.64 324	95.00 317	99.01 131	88.43 411	98.94 350	96.82 414	86.50 403	88.71 368	98.47 272	74.73 406	99.88 124	85.39 405	96.18 250	96.71 324

h-mvs33		94.92 241	94.36 244	96.59 264	98.85 155	91.29 359	98.93 352	98.94 44	95.90 101	98.77 129	98.42 275	90.89 188	99.77 150	97.80 164	70.76 458	98.72 277

anonymousdsp		91.79 338	90.92 338	94.41 346	90.76 453	92.93 307	98.93 352	97.17 358	89.08 353	87.46 399	95.30 393	78.43 368	96.92 379	92.38 301	88.73 334	93.39 424

DP-MVS		94.54 255	93.42 277	97.91 176	99.46 105	94.04 271	98.93 352	97.48 307	81.15 449	90.04 334	99.55 132	87.02 247	99.95 85	88.97 359	98.11 186	99.73 120

ttmdpeth		88.23 397	87.06 400	91.75 415	89.91 460	87.35 423	98.92 355	95.73 445	87.92 384	84.02 431	96.31 350	68.23 438	96.84 385	86.33 398	76.12 440	91.06 456

IterMVS-SCA-FT		90.85 355	90.16 355	92.93 398	96.72 330	89.96 388	98.89 356	96.99 394	88.95 361	86.63 408	95.67 371	76.48 389	95.00 448	87.04 391	84.04 383	93.84 409

IterMVS		90.91 352	90.17 354	93.12 393	96.78 328	90.42 379	98.89 356	97.05 389	89.03 355	86.49 411	95.42 385	76.59 387	95.02 447	87.22 388	84.09 380	93.93 402
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211		93.10 304	91.99 317	96.40 271	99.10 125	89.65 393	98.88 358	97.93 251	83.71 433	94.00 288	98.75 239	68.79 432	99.88 124	95.08 241	91.71 314	99.68 131

VPA-MVSNet		92.70 315	91.55 328	96.16 278	95.09 379	96.20 176	98.88 358	99.00 39	91.02 313	91.82 313	95.29 396	76.05 395	97.96 324	95.62 234	81.19 401	94.30 355

test20.03		84.72 425	83.99 417	86.91 453	88.19 466	80.62 470	98.88 358	95.94 441	88.36 377	78.87 456	94.62 421	68.75 433	89.11 485	66.52 479	75.82 441	91.00 457

XXY-MVS		91.82 332	90.46 344	95.88 288	93.91 401	95.40 211	98.87 361	97.69 279	88.63 371	87.87 391	97.08 320	74.38 409	97.89 328	91.66 315	84.07 381	94.35 352

test1111		95.57 222	94.98 228	97.37 230	98.56 174	93.37 298	98.86 362	98.45 143	94.95 125	96.63 221	98.95 213	75.21 403	99.11 208	95.02 242	98.14 185	99.64 139

SCA		94.69 250	93.81 264	97.33 235	97.10 294	94.44 252	98.86 362	98.32 197	93.30 206	96.17 246	95.59 375	76.48 389	97.95 325	91.06 324	97.43 201	99.59 154

ECVR-MVS		95.66 219	95.05 225	97.51 214	98.66 168	93.71 281	98.85 364	98.45 143	94.93 126	96.86 213	98.96 208	75.22 402	99.20 202	95.34 235	98.15 183	99.64 139

eth_miper_zixun_eth		92.41 323	91.93 318	93.84 374	97.28 287	90.68 371	98.83 365	96.97 398	88.57 372	89.19 362	95.73 370	89.24 214	96.69 395	89.97 347	81.55 398	94.15 375

CL-MVSNet_self_test		84.50 426	83.15 426	88.53 447	86.00 476	81.79 462	98.82 366	97.35 321	85.12 421	83.62 435	90.91 463	76.66 386	91.40 478	69.53 473	60.36 488	92.40 445

IMVS_0407		95.21 231	94.80 235	96.46 268	96.97 309	91.64 349	98.81 367	97.12 366	92.33 265	95.60 259	98.88 220	85.65 268	98.42 282	92.12 305	95.70 269	99.32 212

test2506		97.53 114	97.19 120	98.58 122	98.66 168	96.90 140	98.81 367	99.77 5	94.93 126	97.95 171	98.96 208	92.51 151	99.20 202	94.93 245	98.15 183	99.64 139

ACMH+	89.98 16	90.35 366	89.54 365	92.78 402	95.99 348	86.12 431	98.81 367	97.18 356	89.38 350	83.14 436	97.76 304	68.42 436	98.43 281	89.11 358	86.05 363	93.78 412

Anonymous20240521		85.15 419	83.81 421	89.16 441	88.32 464	82.69 454	98.80 370	95.74 444	79.72 454	81.53 444	90.99 461	65.38 449	94.16 458	72.69 467	81.11 404	90.63 462

N_pmnet		80.06 444	80.78 440	77.89 466	91.94 440	45.28 504	98.80 370	56.82 506	78.10 465	80.08 452	93.33 438	77.03 379	95.76 437	68.14 476	82.81 387	92.64 439

VDD-MVS		93.77 286	92.94 295	96.27 276	98.55 177	90.22 382	98.77 372	97.79 266	90.85 316	96.82 216	99.42 142	61.18 465	99.77 150	98.95 90	94.13 297	98.82 270

LFMVS		94.75 249	93.56 272	98.30 148	99.03 130	95.70 196	98.74 373	97.98 246	87.81 387	98.47 148	99.39 149	67.43 441	99.53 175	98.01 152	95.20 284	99.67 133

LS3D		95.84 206	95.11 222	98.02 167	99.85 61	95.10 230	98.74 373	98.50 137	87.22 394	93.66 291	99.86 34	87.45 239	99.95 85	90.94 328	99.81 86	99.02 256

Anonymous20240529		92.10 329	90.65 341	96.47 266	98.82 156	90.61 373	98.72 375	98.67 87	75.54 471	93.90 290	98.58 260	66.23 445	99.90 113	94.70 255	90.67 318	98.90 267

dmvs_re		93.20 300	93.15 289	93.34 386	96.54 335	83.81 445	98.71 376	98.51 131	91.39 301	92.37 308	98.56 262	78.66 364	97.83 330	93.89 272	89.74 319	98.38 289

TR-MVS		94.54 255	93.56 272	97.49 218	97.96 223	94.34 260	98.71 376	97.51 304	90.30 339	94.51 277	98.69 245	75.56 397	98.77 243	92.82 298	95.99 254	99.35 207

USDC		90.00 377	88.96 377	93.10 395	94.81 384	88.16 415	98.71 376	95.54 452	93.66 191	83.75 434	97.20 316	65.58 447	98.31 299	83.96 417	87.49 355	92.85 437

VDDNet		93.12 303	91.91 319	96.76 258	96.67 334	92.65 316	98.69 379	98.21 217	82.81 441	97.75 183	99.28 160	61.57 463	99.48 186	98.09 148	94.09 298	98.15 294

EU-MVSNet		90.14 374	90.34 348	89.54 438	92.55 431	81.06 467	98.69 379	98.04 240	91.41 300	86.59 409	96.84 336	80.83 339	93.31 468	86.20 399	81.91 396	94.26 357

mvs_tets		91.81 333	91.08 336	94.00 366	91.63 445	90.58 374	98.67 381	97.43 310	92.43 259	87.37 401	97.05 323	71.76 420	97.32 350	94.75 253	88.68 335	94.11 386

MDA-MVSNet-bldmvs		84.09 428	81.52 435	91.81 414	91.32 449	88.00 418	98.67 381	95.92 442	80.22 453	55.60 493	93.32 439	68.29 437	93.60 466	73.76 465	76.61 439	93.82 411

UGNet		95.33 229	94.57 240	97.62 202	98.55 177	94.85 236	98.67 381	99.32 26	95.75 107	96.80 218	96.27 352	72.18 419	99.96 76	94.58 258	99.05 152	98.04 298
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
icg_test_0407_2		95.04 237	94.78 236	95.84 291	96.97 309	91.64 349	98.63 384	97.12 366	92.33 265	95.60 259	98.88 220	85.65 268	96.56 400	92.12 305	95.70 269	99.32 212

pm-mvs1		89.36 388	87.81 394	94.01 365	93.40 411	91.93 331	98.62 385	96.48 430	86.25 407	83.86 433	96.14 357	73.68 413	97.04 371	86.16 400	75.73 443	93.04 433

MVStest1		85.03 420	82.76 429	91.83 413	92.95 423	89.16 400	98.57 386	94.82 465	71.68 479	68.54 484	95.11 403	83.17 314	95.66 438	74.69 464	65.32 474	90.65 461

test_0402		85.58 414	83.94 419	90.50 428	93.81 403	85.04 438	98.55 387	95.20 461	76.01 468	79.72 455	95.13 401	64.15 454	96.26 421	66.04 481	86.88 357	90.21 465

ACMH	89.72 17	90.64 359	89.63 362	93.66 381	95.64 368	88.64 409	98.55 387	97.45 308	89.03 355	81.62 443	97.61 305	69.75 430	98.41 284	89.37 352	87.62 353	93.92 403
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		89.86 379	88.44 387	94.13 359	98.93 143	90.68 371	98.54 389	98.26 207	76.28 467	86.73 406	95.54 377	70.60 428	97.56 340	90.82 331	80.27 415	94.15 375

TransMVSNet (Re)		87.25 407	85.28 414	93.16 392	93.56 406	91.03 361	98.54 389	94.05 476	83.69 434	81.09 447	96.16 355	75.32 399	96.40 413	76.69 459	68.41 467	92.06 448

XVG-ACMP-BASELINE		91.22 348	90.75 339	92.63 404	93.73 404	85.61 434	98.52 391	97.44 309	92.77 234	89.90 338	96.85 334	66.64 444	98.39 288	92.29 302	88.61 336	93.89 405

CHOSEN 280x420		99.01 16	99.03 11	98.95 95	99.38 107	98.87 35	98.46 392	99.42 21	97.03 57	99.02 116	99.09 184	99.35 2	98.21 309	99.73 45	99.78 87	99.77 116

OpenMVS_ROB	79.82 20	83.77 431	81.68 434	90.03 435	88.30 465	82.82 453	98.46 392	95.22 460	73.92 476	76.00 469	91.29 460	55.00 473	96.94 378	68.40 475	88.51 340	90.34 463

kuosan		93.17 301	92.60 303	94.86 324	98.40 190	89.54 395	98.44 394	98.53 126	84.46 428	88.49 375	97.92 297	90.57 192	97.05 368	83.10 422	93.49 305	97.99 299

GBi-Net		90.88 353	89.82 359	94.08 361	97.53 263	91.97 328	98.43 395	96.95 400	87.05 395	89.68 343	94.72 416	71.34 423	96.11 426	87.01 393	85.65 365	94.17 369

test1		90.88 353	89.82 359	94.08 361	97.53 263	91.97 328	98.43 395	96.95 400	87.05 395	89.68 343	94.72 416	71.34 423	96.11 426	87.01 393	85.65 365	94.17 369

FMVSNet1		88.50 394	86.64 401	94.08 361	95.62 370	91.97 328	98.43 395	96.95 400	83.00 439	86.08 418	94.72 416	59.09 469	96.11 426	81.82 432	84.07 381	94.17 369

COLMAP_ROB	90.47 14	92.18 328	91.49 330	94.25 351	99.00 135	88.04 417	98.42 398	96.70 421	82.30 444	88.43 380	99.01 195	76.97 381	99.85 130	86.11 401	96.50 242	94.86 335
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tt0805		91.28 345	90.18 353	94.60 332	96.26 341	87.55 420	98.39 399	98.72 78	89.00 357	89.22 359	98.47 272	62.98 458	98.96 219	90.57 335	88.00 346	97.28 320

test123		37.68 466	39.14 469	33.31 483	19.94 507	24.83 509	98.36 400	9.75 508	15.53 501	51.31 495	87.14 480	19.62 501	17.74 503	47.10 494	3.47 502	57.36 496

1314		96.84 152	95.96 179	99.48 40	96.74 329	98.52 63	98.31 401	98.86 59	95.82 104	89.91 337	98.98 204	87.49 238	99.96 76	97.80 164	99.73 90	99.96 75

MVS		96.60 168	95.56 199	99.72 14	96.85 321	99.22 21	98.31 401	98.94 44	91.57 290	90.90 322	99.61 124	86.66 254	99.96 76	97.36 179	99.88 76	99.99 26

FE-MVSNET2		83.57 433	81.36 436	90.20 432	82.83 484	87.59 419	98.28 403	96.04 439	85.33 420	74.13 475	87.45 478	59.16 468	93.26 469	79.12 448	69.91 459	89.77 471

NR-MVSNet		91.56 341	90.22 351	95.60 296	94.05 398	95.76 192	98.25 404	98.70 80	91.16 307	80.78 449	96.64 341	83.23 313	96.57 399	91.41 318	77.73 429	94.46 341

sd_testset		93.55 293	92.83 297	95.74 295	98.92 146	90.89 367	98.24 405	98.85 62	92.41 260	92.55 306	97.85 301	71.07 427	98.68 257	93.93 271	91.62 315	97.64 310

MS-PatchMatch		90.65 358	90.30 349	91.71 416	94.22 396	85.50 436	98.24 405	97.70 277	88.67 369	86.42 413	96.37 349	67.82 439	98.03 320	83.62 419	99.62 99	91.60 452

FE-MVSNET		81.05 440	78.81 447	87.79 451	81.98 485	83.70 446	98.23 407	91.78 489	81.27 448	74.29 474	87.44 479	60.92 466	90.67 483	64.92 483	68.43 466	89.01 479

pmmvs3		80.27 443	77.77 448	87.76 452	80.32 490	82.43 457	98.23 407	91.97 487	72.74 478	78.75 457	87.97 476	57.30 472	90.99 481	70.31 471	62.37 480	89.87 469

SixPastTwentyTwo		88.73 392	88.01 393	90.88 420	91.85 442	82.24 458	98.22 409	95.18 462	88.97 359	82.26 439	96.89 331	71.75 421	96.67 396	84.00 415	82.98 386	93.72 417

EG-PatchMatch MVS		85.35 418	83.81 421	89.99 436	90.39 455	81.89 461	98.21 410	96.09 438	81.78 446	74.73 472	93.72 436	51.56 480	97.12 364	79.16 447	88.61 336	90.96 458

OurMVSNet-221017-0		89.81 380	89.48 369	90.83 423	91.64 444	81.21 465	98.17 411	95.38 456	91.48 294	85.65 421	97.31 313	72.66 417	97.29 355	88.15 376	84.83 374	93.97 399

LF4IMVS		89.25 390	88.85 378	90.45 430	92.81 428	81.19 466	98.12 412	94.79 466	91.44 296	86.29 415	97.11 318	65.30 450	98.11 314	88.53 365	85.25 369	92.07 447

RPSCF		91.80 336	92.79 299	88.83 443	98.15 212	69.87 483	98.11 413	96.60 425	83.93 431	94.33 283	99.27 163	79.60 354	99.46 189	91.99 310	93.16 310	97.18 321

pmmvs-eth3d		84.03 429	81.97 433	90.20 432	84.15 480	87.09 425	98.10 414	94.73 468	83.05 438	74.10 476	87.77 477	65.56 448	94.01 459	81.08 435	69.24 463	89.49 475

DSMNet-mixed		88.28 396	88.24 390	88.42 448	89.64 461	75.38 479	98.06 415	89.86 493	85.59 416	88.20 388	92.14 458	76.15 394	91.95 477	78.46 451	96.05 253	97.92 300

MVP-Stereo		90.93 351	90.45 346	92.37 407	91.25 450	88.76 404	98.05 416	96.17 436	87.27 393	84.04 430	95.30 393	78.46 367	97.27 357	83.78 418	99.70 92	91.09 455
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UA-Net		96.54 172	95.96 179	98.27 150	98.23 204	95.71 195	98.00 417	98.45 143	93.72 190	98.41 152	99.27 163	88.71 223	99.66 171	91.19 321	97.69 195	99.44 193

new-patchmatchnet		81.19 438	79.34 445	86.76 454	82.86 483	80.36 472	97.92 418	95.27 458	82.09 445	72.02 478	86.87 481	62.81 459	90.74 482	71.10 470	63.08 478	89.19 478

PCF-MVS	94.20 5	95.18 232	94.10 252	98.43 140	98.55 177	95.99 184	97.91 419	97.31 332	90.35 336	89.48 352	99.22 171	85.19 280	99.89 118	90.40 341	98.47 172	99.41 197
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVS		76.28 448	77.28 450	73.29 471	81.18 487	54.68 496	97.87 420	94.19 473	81.30 447	69.43 482	90.70 464	77.02 380	82.06 494	35.71 498	68.11 469	83.13 485

pmmvs6		85.69 413	83.84 420	91.26 419	90.00 459	84.41 443	97.82 421	96.15 437	75.86 469	81.29 446	95.39 388	61.21 464	96.87 384	83.52 421	73.29 448	92.50 443

UniMVSNet_ETH3D		90.06 376	88.58 385	94.49 340	94.67 387	88.09 416	97.81 422	97.57 295	83.91 432	88.44 377	97.41 310	57.44 471	97.62 338	91.41 318	88.59 338	97.77 306

IMVS_0404		93.83 281	93.17 288	95.80 293	96.97 309	91.64 349	97.78 423	97.12 366	92.33 265	90.87 323	98.88 220	76.78 384	96.43 409	92.12 305	95.70 269	99.32 212

SD_0403		92.63 319	93.38 281	90.40 431	97.32 283	77.91 475	97.75 424	98.03 242	91.89 280	90.83 324	98.29 284	82.00 321	93.79 463	88.51 367	95.75 266	99.52 173

TinyColmap		87.87 401	86.51 402	91.94 411	95.05 381	85.57 435	97.65 425	94.08 474	84.40 429	81.82 442	96.85 334	62.14 461	98.33 297	80.25 441	86.37 360	91.91 451

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 19	98.98 138	99.32 10	97.49 426	99.52 14	95.69 109	98.32 157	97.41 310	93.32 121	99.77 150	98.08 149	95.75 266	99.81 109

usedtu_blend_shiyan5		86.75 410	84.29 416	94.16 353	86.66 471	91.83 337	97.42 427	95.23 459	69.94 482	88.37 383	92.36 452	78.01 369	96.50 403	89.35 353	61.26 483	94.14 379

SSC-MVS		75.42 450	76.40 452	72.49 475	80.68 489	53.62 497	97.42 427	94.06 475	80.42 452	68.75 483	90.14 466	76.54 388	81.66 495	33.25 499	66.34 473	82.19 486

Effi-MVS+		96.30 187	95.69 194	98.16 155	97.85 230	96.26 170	97.41 429	97.21 353	90.37 335	98.65 138	98.58 260	86.61 255	98.70 255	97.11 188	97.37 206	99.52 173

sc_t1		85.01 421	82.46 431	92.67 403	92.44 433	83.09 452	97.39 430	95.72 446	65.06 483	85.64 422	96.16 355	49.50 481	97.34 347	84.86 411	75.39 444	97.57 315

TDRefinement		84.76 423	82.56 430	91.38 418	74.58 496	84.80 442	97.36 431	94.56 471	84.73 426	80.21 451	96.12 360	63.56 455	98.39 288	87.92 379	63.97 477	90.95 459

FMVSNet5		88.32 395	87.47 397	90.88 420	96.90 319	88.39 413	97.28 432	95.68 448	82.60 443	84.67 428	92.40 451	79.83 352	91.16 479	76.39 460	81.51 399	93.09 431

KD-MVS_self_test		83.59 432	82.06 432	88.20 449	86.93 469	80.70 469	97.21 433	96.38 431	82.87 440	82.49 438	88.97 470	67.63 440	92.32 475	73.75 466	62.30 481	91.58 453

LTVRE_ROB	88.28 18	90.29 369	89.05 376	94.02 364	95.08 380	90.15 384	97.19 434	97.43 310	84.91 425	83.99 432	97.06 322	74.00 411	98.28 303	84.08 414	87.71 349	93.62 419
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
KD-MVS_2432*1600		88.00 399	86.10 403	93.70 379	96.91 316	94.04 271	97.17 435	97.12 366	84.93 423	81.96 440	92.41 449	92.48 152	94.51 456	79.23 444	52.68 492	92.56 440

miper_refine_blended		88.00 399	86.10 403	93.70 379	96.91 316	94.04 271	97.17 435	97.12 366	84.93 423	81.96 440	92.41 449	92.48 152	94.51 456	79.23 444	52.68 492	92.56 440

mvsany_test3		82.12 437	81.14 438	85.06 458	81.87 486	70.41 482	97.09 437	92.14 486	91.27 303	77.84 462	88.73 471	39.31 487	95.49 439	90.75 333	71.24 457	89.29 477

CostFormer		96.10 194	95.88 187	96.78 257	97.03 300	92.55 318	97.08 438	97.83 264	90.04 344	98.72 134	94.89 414	95.01 66	98.29 301	96.54 214	95.77 264	99.50 179

tpm		93.70 290	93.41 279	94.58 334	95.36 376	87.41 422	97.01 439	96.90 407	90.85 316	96.72 220	94.14 431	90.40 196	96.84 385	90.75 333	88.54 339	99.51 177

CMPMVS	61.59 21	84.75 424	85.14 415	83.57 460	90.32 456	62.54 488	96.98 440	97.59 294	74.33 475	69.95 481	96.66 339	64.17 453	98.32 298	87.88 380	88.41 341	89.84 470
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_f		78.40 447	77.59 449	80.81 464	80.82 488	62.48 489	96.96 441	93.08 484	83.44 435	74.57 473	84.57 486	27.95 494	92.63 473	84.15 413	72.79 451	87.32 483

tpm2		95.47 224	95.18 219	96.35 274	96.91 316	91.70 347	96.96 441	97.93 251	88.04 383	98.44 149	95.40 386	93.32 121	97.97 322	94.00 268	95.61 274	99.38 199

new_pmnet		84.49 427	82.92 427	89.21 440	90.03 458	82.60 455	96.89 443	95.62 450	80.59 451	75.77 471	89.17 469	65.04 451	94.79 453	72.12 469	81.02 407	90.23 464

tt0320-xc		82.94 435	80.35 442	90.72 426	92.90 424	83.54 449	96.85 444	94.73 468	63.12 486	79.85 454	93.77 435	49.43 482	95.46 441	80.98 436	71.54 456	93.16 430

tt0320		83.56 434	81.15 437	90.77 424	92.77 429	83.58 448	96.83 445	95.52 453	63.26 485	81.36 445	92.54 446	53.26 476	95.77 436	80.45 438	74.38 446	92.96 434

dmvs_testset		83.79 430	86.07 405	76.94 467	92.14 437	48.60 502	96.75 446	90.27 492	89.48 349	78.65 458	98.55 264	79.25 356	86.65 490	66.85 478	82.69 388	95.57 334

UnsupCasMVSNet_eth		85.52 415	83.99 417	90.10 434	89.36 462	83.51 450	96.65 447	97.99 244	89.14 352	75.89 470	93.83 433	63.25 457	93.92 460	81.92 431	67.90 470	92.88 436

MIMVSNet1		82.58 436	80.51 441	88.78 444	86.68 470	84.20 444	96.65 447	95.41 455	78.75 463	78.59 459	92.44 448	51.88 479	89.76 484	65.26 482	78.95 420	92.38 446

usedtu_dtu_shiyan2		75.87 449	72.37 453	86.39 455	76.18 495	75.49 478	96.53 449	93.82 479	64.74 484	72.53 477	88.48 472	37.67 488	91.12 480	64.13 484	57.22 491	92.56 440

ab-mvs		94.69 250	93.42 277	98.51 133	98.07 217	96.26 170	96.49 450	98.68 84	90.31 338	94.54 275	97.00 326	76.30 391	99.71 160	95.98 226	93.38 308	99.56 163

test_vis3_rt		68.82 452	66.69 457	75.21 470	76.24 494	60.41 491	96.44 451	68.71 505	75.13 473	50.54 496	69.52 494	16.42 503	96.32 418	80.27 440	66.92 472	68.89 492

EPMVS		96.53 173	96.01 172	98.09 162	98.43 189	96.12 182	96.36 452	99.43 20	93.53 194	97.64 184	95.04 405	94.41 83	98.38 292	91.13 322	98.11 186	99.75 118

tpmrst		96.27 190	95.98 175	97.13 243	97.96 223	93.15 300	96.34 453	98.17 222	92.07 274	98.71 135	95.12 402	93.91 105	98.73 249	94.91 248	96.62 239	99.50 179

FA-MVS(test-final)		95.86 204	95.09 223	98.15 158	97.74 238	95.62 201	96.31 454	98.17 222	91.42 299	96.26 241	96.13 358	90.56 193	99.47 188	92.18 304	97.07 222	99.35 207

dp		95.05 236	94.43 242	96.91 251	97.99 221	92.73 312	96.29 455	97.98 246	89.70 348	95.93 251	94.67 420	93.83 110	98.45 280	86.91 396	96.53 241	99.54 168

EGC-MVSNET		69.38 451	63.76 461	86.26 456	90.32 456	81.66 464	96.24 456	93.85 478	0.99 503	3.22 504	92.33 456	52.44 477	92.92 472	59.53 489	84.90 373	84.21 484

tpm cat1		93.51 294	92.52 309	96.47 266	97.77 236	91.47 358	96.13 457	98.06 237	80.98 450	92.91 301	93.78 434	89.66 204	98.87 225	87.03 392	96.39 246	99.09 244

MDTV_nov1_ep13_2view						96.26 170	96.11 458		91.89 280	98.06 168		94.40 84		94.30 264		99.67 133

PatchmatchNet		95.94 201	95.45 202	97.39 229	97.83 231	94.41 255	96.05 459	98.40 177	92.86 227	97.09 203	95.28 397	94.21 97	98.07 318	89.26 357	98.11 186	99.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
APD_test1		81.15 439	80.92 439	81.86 463	92.45 432	59.76 492	96.04 460	93.61 481	73.29 477	77.06 464	96.64 341	44.28 486	96.16 425	72.35 468	82.52 390	89.67 473

MDTV_nov1_ep13			95.69 194		97.90 226	94.15 268	95.98 461	98.44 148	93.12 216	97.98 170	95.74 367	95.10 61	98.58 267	90.02 345	96.92 230

FPMVS		68.72 453	68.72 454	68.71 477	65.95 500	44.27 506	95.97 462	94.74 467	51.13 492	53.26 494	90.50 465	25.11 496	83.00 493	60.80 487	80.97 409	78.87 490

PM-MVS		80.47 442	78.88 446	85.26 457	83.79 482	72.22 481	95.89 463	91.08 490	85.71 415	76.56 468	88.30 473	36.64 489	93.90 461	82.39 427	69.57 462	89.66 474

test_post1							95.78 464				59.23 501	93.20 128	97.74 334	91.06 324

tpmvs		94.28 269	93.57 271	96.40 271	98.55 177	91.50 357	95.70 465	98.55 119	87.47 389	92.15 309	94.26 430	91.42 173	98.95 220	88.15 376	95.85 261	98.76 273

FE-MVS		95.70 218	95.01 227	97.79 184	98.21 206	94.57 247	95.03 466	98.69 82	88.90 363	97.50 188	96.19 354	92.60 147	99.49 185	89.99 346	97.94 192	99.31 216

ADS-MVSNet2		93.80 285	93.88 262	93.55 383	97.87 228	85.94 433	94.24 467	96.84 411	90.07 342	96.43 236	94.48 425	90.29 199	95.37 443	87.44 383	97.23 212	99.36 203

ADS-MVSNet		94.79 245	94.02 257	97.11 245	97.87 228	93.79 278	94.24 467	98.16 227	90.07 342	96.43 236	94.48 425	90.29 199	98.19 310	87.44 383	97.23 212	99.36 203

EMVS		51.44 464	51.22 466	52.11 482	70.71 498	44.97 505	94.04 469	75.66 504	35.34 499	42.40 499	61.56 500	28.93 492	65.87 501	27.64 501	24.73 497	45.49 498

PMMVS2		67.15 457	64.15 460	76.14 469	70.56 499	62.07 490	93.89 470	87.52 497	58.09 488	60.02 487	78.32 489	22.38 497	84.54 492	59.56 488	47.03 494	81.80 487

GG-mvs-BLEND				98.54 128	98.21 206	98.01 84	93.87 471	98.52 128		97.92 172	97.92 297	99.02 3	97.94 327	98.17 142	99.58 109	99.67 133

UnsupCasMVSNet_bld		79.97 446	77.03 451	88.78 444	85.62 477	81.98 460	93.66 472	97.35 321	75.51 472	70.79 480	83.05 487	48.70 483	94.91 451	78.31 452	60.29 489	89.46 476

E-PMN		52.30 462	52.18 464	52.67 481	71.51 497	45.40 503	93.62 473	76.60 503	36.01 497	43.50 498	64.13 497	27.11 495	67.31 500	31.06 500	26.06 496	45.30 499

mamba_0408		94.98 240	94.09 253	97.64 198	97.14 291	95.31 217	93.48 474	97.08 377	90.48 331	94.40 279	98.62 254	84.49 294	98.67 259	93.99 269	97.18 215	98.93 261

SSM_04072		94.77 247	94.09 253	96.82 255	97.14 291	95.31 217	93.48 474	97.08 377	90.48 331	94.40 279	98.62 254	84.49 294	96.21 423	93.99 269	97.18 215	98.93 261

JIA-IIPM		91.76 339	90.70 340	94.94 319	96.11 344	87.51 421	93.16 476	98.13 232	75.79 470	97.58 185	77.68 490	92.84 137	97.97 322	88.47 368	96.54 240	99.33 210

gg-mvs-nofinetune		93.51 294	91.86 321	98.47 135	97.72 243	97.96 89	92.62 477	98.51 131	74.70 474	97.33 195	69.59 493	98.91 4	97.79 331	97.77 169	99.56 110	99.67 133

MIMVSNet		90.30 368	88.67 383	95.17 313	96.45 338	91.64 349	92.39 478	97.15 361	85.99 409	90.50 327	93.19 443	66.95 442	94.86 452	82.01 430	93.43 306	99.01 257

MVS-HIRNet		86.22 412	83.19 425	95.31 309	96.71 331	90.29 380	92.12 479	97.33 325	62.85 487	86.82 405	70.37 492	69.37 431	97.49 342	75.12 463	97.99 191	98.15 294

CR-MVSNet		93.45 297	92.62 302	95.94 284	96.29 339	92.66 314	92.01 480	96.23 434	92.62 244	96.94 210	93.31 440	91.04 182	96.03 431	79.23 444	95.96 256	99.13 240

RPMNet		89.76 381	87.28 398	97.19 238	96.29 339	92.66 314	92.01 480	98.31 199	70.19 481	96.94 210	85.87 485	87.25 243	99.78 147	62.69 486	95.96 256	99.13 240

Patchmatch-test		92.65 318	91.50 329	96.10 280	96.85 321	90.49 376	91.50 482	97.19 354	82.76 442	90.23 329	95.59 375	95.02 65	98.00 321	77.41 455	96.98 229	99.82 107

Patchmtry		89.70 382	88.49 386	93.33 387	96.24 342	89.94 391	91.37 483	96.23 434	78.22 464	87.69 393	93.31 440	91.04 182	96.03 431	80.18 442	82.10 394	94.02 392

PatchT		90.38 365	88.75 382	95.25 311	95.99 348	90.16 383	91.22 484	97.54 299	76.80 466	97.26 198	86.01 484	91.88 169	96.07 430	66.16 480	95.91 260	99.51 177

mvs5depth		84.87 422	82.90 428	90.77 424	85.59 478	84.84 441	91.10 485	93.29 483	83.14 437	85.07 426	94.33 429	62.17 460	97.32 350	78.83 450	72.59 455	90.14 466

testf1		68.38 454	66.92 455	72.78 473	78.80 491	50.36 499	90.95 486	87.35 498	55.47 489	58.95 488	88.14 474	20.64 498	87.60 487	57.28 490	64.69 475	80.39 488

APD_test2		68.38 454	66.92 455	72.78 473	78.80 491	50.36 499	90.95 486	87.35 498	55.47 489	58.95 488	88.14 474	20.64 498	87.60 487	57.28 490	64.69 475	80.39 488

Patchmatch-RL test		86.90 408	85.98 407	89.67 437	84.45 479	75.59 477	89.71 488	92.43 485	86.89 400	77.83 463	90.94 462	94.22 95	93.63 465	87.75 381	69.61 461	99.79 112

LCM-MVSNet		67.77 456	64.73 459	76.87 468	62.95 502	56.25 495	89.37 489	93.74 480	44.53 494	61.99 486	80.74 488	20.42 500	86.53 491	69.37 474	59.50 490	87.84 480

ambc				83.23 461	77.17 493	62.61 487	87.38 490	94.55 472		76.72 467	86.65 482	30.16 490	96.36 415	84.85 412	69.86 460	90.73 460

ANet_high		56.10 460	52.24 463	67.66 478	49.27 504	56.82 494	83.94 491	82.02 501	70.47 480	33.28 501	64.54 496	17.23 502	69.16 499	45.59 495	23.85 498	77.02 491

tmp_tt		65.23 459	62.94 462	72.13 476	44.90 505	50.03 501	81.05 492	89.42 496	38.45 495	48.51 497	99.90 23	54.09 475	78.70 497	91.84 314	18.26 499	87.64 481

MVE	53.74 22	51.54 463	47.86 467	62.60 479	59.56 503	50.93 498	79.41 493	77.69 502	35.69 498	36.27 500	61.76 499	5.79 507	69.63 498	37.97 497	36.61 495	67.24 493
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	49.05 23	53.75 461	51.34 465	60.97 480	40.80 506	34.68 507	74.82 494	89.62 495	37.55 496	28.67 502	72.12 491	7.09 505	81.63 496	43.17 496	68.21 468	66.59 494
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		66.95 458	65.00 458	72.79 472	91.52 446	67.96 484	66.16 495	95.15 463	47.89 493	58.54 490	67.99 495	29.74 491	87.54 489	50.20 493	77.83 428	62.87 495
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		20.37 468	20.84 471	18.99 485	65.34 501	27.73 508	50.43 496	7.67 509	9.50 502	8.01 503	6.34 503	6.13 506	26.24 502	23.40 502	10.69 501	2.99 500

mmdepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

monomultidepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

test_blank		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.02 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet_test		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

DCPMVS		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

cdsmvs_eth3d_5k		23.43 467	31.24 470	0.00 486	0.00 509	0.00 511	0.00 497	98.09 234	0.00 504	0.00 505	99.67 114	83.37 308	0.00 505	0.00 503	0.00 503	0.00 501

pcd_1.5k_mvsjas		7.60 470	10.13 473	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	91.20 177	0.00 505	0.00 503	0.00 503	0.00 501

sosnet-low-res		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

sosnet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uncertanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

Regformer		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

ab-mvs-re		8.28 469	11.04 472	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	99.40 147	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 505	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

WAC-MVS						90.97 362								86.10 402

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 173					100.00 1	99.96 12	100.00 1	100.00 1

PC_three_1452									96.96 60	99.80 28	99.79 63	97.49 11	100.00 1	99.99 5	99.98 32	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 173					100.00 1	99.96 12	100.00 1	100.00 1

test_one_0601					99.94 17	99.30 13		98.41 173	96.63 75	99.75 42	99.93 12	97.49 11

eth-test2					0.00 509

eth-test					0.00 509

ZD-MVS					99.92 36	98.57 61		98.52 128	92.34 264	99.31 95	99.83 51	95.06 63	99.80 143	99.70 49	99.97 42

IU-MVS					99.93 28	99.31 11		98.41 173	97.71 31	99.84 23				100.00 1	100.00 1	100.00 1

test_241102_TWO								98.43 156	97.27 47	99.80 28	99.94 5	97.18 23	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 28	99.30 13		98.43 156	97.26 49	99.80 28	99.88 29	96.71 29	100.00 1

test_0728_THIRD									96.48 80	99.83 24	99.91 19	97.87 6	100.00 1	99.92 16	100.00 1	100.00 1

GSMVS																99.59 154

test_part2					99.89 50	99.25 19				99.49 79

sam_mvs1												94.72 74				99.59 154

sam_mvs												94.25 94

MTGPA								98.28 204

test_post											63.35 498	94.43 82	98.13 313

patchmatchnet-post											91.70 459	95.12 60	97.95 325

gm-plane-assit					96.97 309	93.76 280			91.47 295		98.96 208		98.79 240	94.92 246

test9_res														99.71 48	99.99 21	100.00 1

agg_prior2														99.48 63	100.00 1	100.00 1

agg_prior					99.93 28	98.77 47		98.43 156		99.63 59			99.85 130

TestCases				95.00 317	99.01 131	88.43 411		96.82 414	86.50 403	88.71 368	98.47 272	74.73 406	99.88 124	85.39 405	96.18 250	96.71 324

test_prior				99.43 41	99.94 17	98.49 66		98.65 88					99.80 143			99.99 26

新几何1				99.42 43	99.75 76	98.27 71		98.63 97	92.69 240	99.55 71	99.82 54	94.40 84	100.00 1	91.21 320	99.94 58	99.99 26

旧先验1					99.76 73	97.52 109		98.64 91			99.85 38	95.63 49			99.94 58	99.99 26

原ACMM1				98.96 94	99.73 80	96.99 136		98.51 131	94.06 172	99.62 62	99.85 38	94.97 69	99.96 76	95.11 240	99.95 53	99.92 93

testdata2													99.99 39	90.54 337

segment_acmp												96.68 31

testdata				98.42 142	99.47 103	95.33 216		98.56 113	93.78 186	99.79 37	99.85 38	93.64 114	99.94 94	94.97 244	99.94 58	100.00 1

test12				99.43 41	99.74 77	98.56 62		98.40 177		99.65 55		94.76 73	99.75 154		99.98 32	99.99 26

plane_prior7					95.71 363	91.59 355

plane_prior6					95.76 357	91.72 346						80.47 347

plane_prior5								97.87 258					98.37 294	97.79 167	89.55 323	94.52 338

plane_prior4											98.59 257

plane_prior3						91.64 349			96.63 75	93.01 298

plane_prior1					95.73 360

n2								0.00 510

nn								0.00 510

door-mid								89.69 494

lessismore_v0				90.53 427	90.58 454	80.90 468		95.80 443		77.01 465	95.84 364	66.15 446	96.95 377	83.03 423	75.05 445	93.74 416

LGP-MVS_train				93.71 377	95.43 374	88.67 407		97.62 287	92.81 230	90.05 332	98.49 268	75.24 400	98.40 286	95.84 229	89.12 327	94.07 388

test11								98.44 148

door								90.31 491

HQP5-MVS						91.85 335

BP-MVS														97.92 158

HQP4-MVS										93.37 293			98.39 288			94.53 336

HQP3-MVS								97.89 256							89.60 320

HQP2-MVS												80.65 343

NP-MVS					95.77 356	91.79 339					98.65 249

ACMMP++_ref															87.04 356

ACMMP++															88.23 343

Test By Simon												92.82 139

ITE_SJBPF				92.38 405	95.69 366	85.14 437		95.71 447	92.81 230	89.33 356	98.11 288	70.23 429	98.42 282	85.91 403	88.16 344	93.59 420

DeepMVS_CX				82.92 462	95.98 350	58.66 493		96.01 440	92.72 236	78.34 460	95.51 380	58.29 470	98.08 316	82.57 425	85.29 368	92.03 449

High-resolution multi-view benchmark