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.

MVSFormer		99.17 58	99.12 53	99.29 116	99.51 119	98.94 125	99.88 1	99.46 137	97.55 145	99.80 16	99.65 128	97.39 92	99.28 239	99.03 46	99.85 52	99.65 88

test_djsdf		98.67 122	98.57 121	98.98 143	98.70 279	98.91 130	99.88 1	99.46 137	97.55 145	99.22 143	99.88 14	95.73 139	99.28 239	99.03 46	97.62 200	98.75 199

OurMVSNet-221017-0		97.88 190	97.77 176	98.19 250	98.71 278	96.53 260	99.88 1	99.00 267	97.79 123	98.78 211	99.94 3	91.68 267	99.35 223	97.21 204	96.99 231	98.69 215

v748		97.52 234	97.23 240	98.41 226	98.69 280	97.23 229	99.87 4	99.45 148	95.72 264	98.51 240	99.53 168	94.13 210	99.30 236	96.78 230	92.39 305	98.70 210

v52		97.79 205	97.50 204	98.66 203	98.80 262	98.62 171	99.87 4	99.44 156	95.87 262	99.01 179	99.46 194	94.44 200	99.33 227	96.65 239	93.96 290	98.05 293

V4		97.80 203	97.51 202	98.67 202	98.79 264	98.63 169	99.87 4	99.44 156	95.87 262	99.01 179	99.46 194	94.52 196	99.33 227	96.64 240	93.97 289	98.05 293

K. test v3		97.10 252	96.79 250	98.01 259	98.72 276	96.33 267	99.87 4	97.05 333	97.59 140	96.16 292	99.80 64	88.71 297	99.04 273	96.69 235	96.55 237	98.65 245

FC-MVSNet-test		98.75 116	98.62 115	99.15 129	99.08 210	99.45 66	99.86 8	99.60 35	98.23 75	98.70 223	99.82 44	96.80 107	99.22 254	99.07 44	96.38 240	98.79 192

v7n		97.87 191	97.52 200	98.92 157	98.76 272	98.58 175	99.84 9	99.46 137	96.20 247	98.91 195	99.70 106	94.89 172	99.44 207	96.03 250	93.89 291	98.75 199

DTE-MVSNet		97.51 237	97.19 242	98.46 220	98.63 286	98.13 199	99.84 9	99.48 113	96.68 208	97.97 267	99.67 121	92.92 234	98.56 295	96.88 227	92.60 304	98.70 210

3Dnovator	97.25 9	99.24 53	99.05 58	99.81 27	99.12 202	99.66 34	99.84 9	99.74 10	99.09 8	98.92 194	99.90 7	95.94 131	99.98 5	98.95 53	99.92 12	99.79 43

FIs		98.78 113	98.63 112	99.23 123	99.18 189	99.54 52	99.83 12	99.59 38	98.28 70	98.79 210	99.81 53	96.75 111	99.37 216	99.08 43	96.38 240	98.78 193

jajsoiax		98.43 132	98.28 136	98.88 174	98.60 288	98.43 188	99.82 13	99.53 72	98.19 76	98.63 234	99.80 64	93.22 230	99.44 207	99.22 31	97.50 210	98.77 196

OpenMVS	96.50 16	98.47 129	98.12 143	99.52 83	99.04 217	99.53 55	99.82 13	99.72 11	94.56 282	98.08 261	99.88 14	94.73 186	99.98 5	97.47 191	99.76 76	99.06 168

nrg030		98.64 125	98.42 127	99.28 118	99.05 216	99.69 29	99.81 15	99.46 137	98.04 99	99.01 179	99.82 44	96.69 113	99.38 213	99.34 22	94.59 278	98.78 193

HPM-MVS		99.42 29	99.28 38	99.83 22	99.90 3	99.72 25	99.81 15	99.54 62	97.59 140	99.68 34	99.63 139	98.91 26	99.94 40	98.58 95	99.91 17	99.84 12

EPP-MVSNet		99.13 62	98.99 68	99.53 79	99.65 92	99.06 104	99.81 15	99.33 212	97.43 155	99.60 56	99.88 14	97.14 99	99.84 113	99.13 39	98.94 139	99.69 77

3Dnovator+	97.12 13	99.18 57	98.97 71	99.82 24	99.17 194	99.68 30	99.81 15	99.51 85	99.20 4	98.72 216	99.89 10	95.68 140	99.97 11	98.86 64	99.86 48	99.81 34

canonicalmvs		99.02 85	98.86 87	99.51 85	99.42 137	99.32 77	99.80 19	99.48 113	98.63 48	99.31 112	98.81 281	97.09 100	99.75 147	99.27 29	97.90 193	99.47 130

v8		97.95 183	97.63 194	98.93 152	98.95 235	98.81 149	99.80 19	99.41 168	96.03 260	99.10 164	99.42 201	94.92 169	99.30 236	96.94 223	94.08 287	98.66 242

Vis-MVSNet (Re-imp)		98.87 97	98.72 101	99.31 110	99.71 69	98.88 132	99.80 19	99.44 156	97.91 111	99.36 103	99.78 77	95.49 144	99.43 211	97.91 149	99.11 124	99.62 100

PS-MVSNAJss		98.92 95	98.92 77	98.90 165	98.78 268	98.53 178	99.78 22	99.54 62	98.07 93	99.00 186	99.76 85	99.01 11	99.37 216	99.13 39	97.23 225	98.81 190

PEN-MVS		97.76 208	97.44 215	98.72 197	98.77 271	98.54 177	99.78 22	99.51 85	97.06 189	98.29 254	99.64 135	92.63 246	98.89 289	98.09 134	93.16 297	98.72 204

anonymousdsp		98.44 131	98.28 136	98.94 149	98.50 293	98.96 121	99.77 24	99.50 99	97.07 187	98.87 200	99.77 82	94.76 184	99.28 239	98.66 85	97.60 201	98.57 271

SixPastTwentyTwo		97.50 238	97.33 232	98.03 256	98.65 284	96.23 270	99.77 24	98.68 307	97.14 179	97.90 268	99.93 4	90.45 280	99.18 260	97.00 217	96.43 239	98.67 231

QAPM		98.67 122	98.30 135	99.80 29	99.20 184	99.67 32	99.77 24	99.72 11	94.74 276	98.73 215	99.90 7	95.78 137	99.98 5	96.96 221	99.88 34	99.76 52

v18		96.42 262	95.80 269	98.26 238	98.95 235	98.82 147	99.76 27	99.28 232	94.58 279	94.12 302	97.70 306	95.22 154	98.16 299	94.83 273	87.80 316	97.79 311

v17		96.42 262	95.81 267	98.25 242	98.94 238	98.80 154	99.76 27	99.28 232	94.57 280	94.18 301	97.71 305	95.23 153	98.16 299	94.86 271	87.73 318	97.80 306

v16		96.39 264	95.76 270	98.26 238	98.96 233	98.81 149	99.76 27	99.28 232	94.57 280	94.10 303	97.70 306	95.04 160	98.16 299	94.70 275	87.77 317	97.80 306

v15		96.28 266	95.62 272	98.25 242	98.94 238	98.83 140	99.76 27	99.29 225	94.52 284	94.02 306	97.61 313	95.02 161	98.13 303	94.53 277	86.92 321	97.80 306

v12		96.24 269	95.58 274	98.23 245	98.96 233	98.81 149	99.76 27	99.29 225	94.42 288	93.85 312	97.60 314	95.12 157	98.09 306	94.32 286	86.85 325	97.80 306

V14		96.26 267	95.60 273	98.26 238	98.94 238	98.83 140	99.76 27	99.29 225	94.49 285	93.96 308	97.66 309	94.99 164	98.13 303	94.41 280	86.90 322	97.80 306

HPM-MVS_fast		99.51 12	99.40 14	99.85 17	99.91 1	99.79 16	99.76 27	99.56 48	97.72 131	99.76 26	99.75 90	99.13 6	99.92 63	99.07 44	99.92 12	99.85 8

v13		96.24 269	95.58 274	98.25 242	98.98 227	98.83 140	99.75 34	99.29 225	94.35 289	93.89 311	97.60 314	95.17 156	98.11 305	94.27 289	86.86 324	97.81 304

v10		97.85 193	97.52 200	98.86 182	98.99 223	98.67 164	99.75 34	99.41 168	95.70 265	98.98 188	99.41 204	94.75 185	99.23 251	96.01 251	94.63 277	98.67 231

V9		96.25 268	95.58 274	98.26 238	98.94 238	98.83 140	99.75 34	99.29 225	94.45 287	93.96 308	97.62 312	94.94 166	98.14 302	94.40 281	86.87 323	97.81 304

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 38	99.89 1	99.75 34	99.56 48	99.02 10	99.88 3	99.85 26	99.18 5	99.96 19	99.22 31	99.92 12	99.90 1

IS-MVSNet		99.05 81	98.87 84	99.57 72	99.73 62	99.32 77	99.75 34	99.20 245	98.02 102	99.56 64	99.86 22	96.54 116	99.67 177	98.09 134	99.13 123	99.73 63

v11		96.23 271	95.57 277	98.21 248	98.93 243	98.83 140	99.72 39	99.29 225	94.29 290	94.05 305	97.64 311	94.88 173	98.04 307	92.89 302	88.43 314	97.77 312

RPSCF		98.22 143	98.62 115	96.99 290	99.82 29	91.58 317	99.72 39	99.44 156	96.61 213	99.66 45	99.89 10	95.92 132	99.82 128	97.46 192	99.10 126	99.57 109

CSCG		99.32 42	99.32 26	99.32 109	99.85 23	98.29 192	99.71 41	99.66 25	98.11 86	99.41 90	99.80 64	98.37 67	99.96 19	98.99 50	99.96 5	99.72 69

WR-MVS_H		98.13 151	97.87 165	98.90 165	99.02 220	98.84 137	99.70 42	99.59 38	97.27 168	98.40 246	99.19 250	95.53 142	99.23 251	98.34 120	93.78 292	98.61 264

LTVRE_ROB	97.16 12	98.02 170	97.90 160	98.40 227	99.23 179	96.80 253	99.70 42	99.60 35	97.12 182	98.18 257	99.70 106	91.73 266	99.72 160	98.39 114	97.45 215	98.68 220
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
view600		97.97 178	97.66 187	98.89 167	99.75 47	97.81 212	99.69 44	98.80 289	98.02 102	99.25 131	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

view800		97.97 178	97.66 187	98.89 167	99.75 47	97.81 212	99.69 44	98.80 289	98.02 102	99.25 131	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

conf0.05thres1000		97.97 178	97.66 187	98.89 167	99.75 47	97.81 212	99.69 44	98.80 289	98.02 102	99.25 131	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

tfpn		97.97 178	97.66 187	98.89 167	99.75 47	97.81 212	99.69 44	98.80 289	98.02 102	99.25 131	98.88 273	91.95 258	99.89 92	94.36 282	98.29 172	98.96 179

XVS		99.53 9	99.42 11	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	99.74 95	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

X-MVStestdata		96.55 258	95.45 279	99.87 6	99.85 23	99.83 7	99.69 44	99.68 19	98.98 19	99.37 99	64.01 343	98.81 33	99.94 40	98.79 72	99.86 48	99.84 12

V42		98.06 159	97.79 170	98.86 182	98.98 227	98.84 137	99.69 44	99.34 204	96.53 219	99.30 113	99.37 216	94.67 189	99.32 230	97.57 180	94.66 275	98.42 280

mPP-MVS		99.44 25	99.30 33	99.86 12	99.88 11	99.79 16	99.69 44	99.48 113	98.12 84	99.50 73	99.75 90	98.78 36	99.97 11	98.57 97	99.89 32	99.83 23

CP-MVS		99.45 22	99.32 26	99.85 17	99.83 28	99.75 21	99.69 44	99.52 76	98.07 93	99.53 68	99.63 139	98.93 25	99.97 11	98.74 75	99.91 17	99.83 23

PS-CasMVS		97.93 184	97.59 197	98.95 148	98.99 223	99.06 104	99.68 53	99.52 76	97.13 180	98.31 252	99.68 117	92.44 255	99.05 272	98.51 106	94.08 287	98.75 199

Vis-MVSNet		99.12 67	98.97 71	99.56 74	99.78 34	99.10 100	99.68 53	99.66 25	98.49 56	99.86 7	99.87 19	94.77 183	99.84 113	99.19 33	99.41 107	99.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HSP-MVS		99.41 32	99.26 43	99.85 17	99.89 8	99.80 12	99.67 55	99.37 191	98.70 45	99.77 23	99.49 180	98.21 73	99.95 33	98.46 111	99.77 74	99.81 34

MVS_Test		99.10 74	98.97 71	99.48 88	99.49 126	99.14 97	99.67 55	99.34 204	97.31 165	99.58 60	99.76 85	97.65 88	99.82 128	98.87 61	99.07 129	99.46 133

CP-MVSNet		98.09 157	97.78 172	99.01 139	98.97 230	99.24 87	99.67 55	99.46 137	97.25 170	98.48 243	99.64 135	93.79 221	99.06 271	98.63 88	94.10 286	98.74 202

MTAPA		99.52 11	99.39 15	99.89 2	99.90 3	99.86 3	99.66 58	99.47 127	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

HFP-MVS		99.49 13	99.37 17	99.86 12	99.87 15	99.80 12	99.66 58	99.67 22	98.15 80	99.68 34	99.69 112	99.06 8	99.96 19	98.69 82	99.87 38	99.84 12

v1neww		98.12 153	97.84 166	98.93 152	98.97 230	98.81 149	99.66 58	99.35 196	96.49 220	99.29 117	99.37 216	95.02 161	99.32 230	97.73 166	94.73 270	98.67 231

mvs_tets		98.40 135	98.23 138	98.91 161	98.67 283	98.51 183	99.66 58	99.53 72	98.19 76	98.65 232	99.81 53	92.75 238	99.44 207	99.31 25	97.48 214	98.77 196

v7new		98.12 153	97.84 166	98.93 152	98.97 230	98.81 149	99.66 58	99.35 196	96.49 220	99.29 117	99.37 216	95.02 161	99.32 230	97.73 166	94.73 270	98.67 231

v6		98.12 153	97.84 166	98.94 149	98.94 238	98.83 140	99.66 58	99.34 204	96.49 220	99.30 113	99.37 216	94.95 165	99.34 226	97.77 161	94.74 269	98.67 231

EU-MVSNet		97.98 175	98.03 151	97.81 274	98.72 276	96.65 258	99.66 58	99.66 25	98.09 89	98.35 250	99.82 44	95.25 152	98.01 309	97.41 196	95.30 258	98.78 193

ACMMPR		99.49 13	99.36 19	99.86 12	99.87 15	99.79 16	99.66 58	99.67 22	98.15 80	99.67 40	99.69 112	98.95 23	99.96 19	98.69 82	99.87 38	99.84 12

MP-MVS		99.33 41	99.15 50	99.87 6	99.88 11	99.82 10	99.66 58	99.46 137	98.09 89	99.48 77	99.74 95	98.29 70	99.96 19	97.93 148	99.87 38	99.82 30

abl_6		99.44 25	99.31 31	99.83 22	99.85 23	99.75 21	99.66 58	99.59 38	98.13 82	99.82 14	99.81 53	98.60 54	99.96 19	98.46 111	99.88 34	99.79 43

region2R		99.48 17	99.35 22	99.87 6	99.88 11	99.80 12	99.65 68	99.66 25	98.13 82	99.66 45	99.68 117	98.96 20	99.96 19	98.62 90	99.87 38	99.84 12

TranMVSNet+NR-MVSNet		97.93 184	97.66 187	98.76 195	98.78 268	98.62 171	99.65 68	99.49 104	97.76 126	98.49 242	99.60 150	94.23 205	98.97 287	98.00 143	92.90 299	98.70 210

tfpnnormal		97.84 195	97.47 208	98.98 143	99.20 184	99.22 89	99.64 70	99.61 32	96.32 236	98.27 255	99.70 106	93.35 228	99.44 207	95.69 257	95.40 256	98.27 287

v7		98.05 165	97.78 172	98.87 178	98.99 223	98.67 164	99.64 70	99.34 204	96.31 238	99.29 117	99.51 175	94.78 179	99.27 242	97.03 215	95.15 262	98.66 242

TSAR-MVS + MP.		99.58 3	99.50 7	99.81 27	99.91 1	99.66 34	99.63 72	99.39 178	98.91 29	99.78 22	99.85 26	99.36 2	99.94 40	98.84 66	99.88 34	99.82 30

Anonymous20231206		96.22 272	96.03 261	96.79 296	97.31 312	94.14 301	99.63 72	99.08 257	96.17 250	97.04 283	99.06 261	93.94 216	97.76 316	86.96 322	95.06 264	98.47 277

APD-MVS_3200maxsize		99.48 17	99.35 22	99.85 17	99.76 41	99.83 7	99.63 72	99.54 62	98.36 65	99.79 18	99.82 44	98.86 29	99.95 33	98.62 90	99.81 66	99.78 47

diffmvs		98.72 118	98.49 124	99.43 100	99.48 129	99.19 90	99.62 75	99.42 165	95.58 267	99.37 99	99.67 121	96.14 126	99.74 148	98.14 131	98.96 137	99.37 143

EPNet		98.86 100	98.71 103	99.30 113	97.20 314	98.18 196	99.62 75	98.91 279	99.28 2	98.63 234	99.81 53	95.96 128	99.99 1	99.24 30	99.72 83	99.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		98.93 94	98.67 107	99.72 47	99.85 23	99.53 55	99.62 75	99.59 38	92.65 307	99.71 29	99.78 77	98.06 78	99.90 84	98.84 66	99.91 17	99.74 58

HY-MVS	97.30 7	98.85 106	98.64 111	99.47 91	99.42 137	99.08 102	99.62 75	99.36 192	97.39 160	99.28 121	99.68 117	96.44 118	99.92 63	98.37 117	98.22 176	99.40 141

ACMMP		99.45 22	99.32 26	99.82 24	99.89 8	99.67 32	99.62 75	99.69 18	98.12 84	99.63 50	99.84 35	98.73 46	99.96 19	98.55 103	99.83 61	99.81 34
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
DeepC-MVS	98.35 2	99.30 44	99.19 47	99.64 62	99.82 29	99.23 88	99.62 75	99.55 55	98.94 26	99.63 50	99.95 2	95.82 136	99.94 40	99.37 17	99.97 3	99.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		99.58 3	99.56 3	99.64 62	99.78 34	99.15 96	99.61 81	99.45 148	99.01 13	99.89 2	99.82 44	99.01 11	99.92 63	99.56 5	99.95 6	99.85 8

EI-MVSNet-UG-set		99.58 3	99.57 1	99.64 62	99.78 34	99.14 97	99.60 82	99.45 148	99.01 13	99.90 1	99.83 37	98.98 18	99.93 55	99.59 2	99.95 6	99.86 5

ACMH	97.28 8	98.10 156	97.99 154	98.44 224	99.41 140	96.96 247	99.60 82	99.56 48	98.09 89	98.15 258	99.91 5	90.87 278	99.70 172	98.88 57	97.45 215	98.67 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		97.76 208	97.45 211	98.69 199	99.72 65	97.86 210	99.59 84	98.74 297	97.93 110	99.26 129	98.62 288	91.75 264	99.83 120	93.22 298	98.18 180	98.37 284

thres600view7		97.86 192	97.51 202	98.92 157	99.72 65	97.95 207	99.59 84	98.74 297	97.94 109	99.27 125	98.62 288	91.75 264	99.86 104	93.73 294	98.19 179	98.96 179

LCM-MVSNet-Re		97.83 197	98.15 140	96.87 294	99.30 166	92.25 315	99.59 84	98.26 318	97.43 155	96.20 291	99.13 254	96.27 123	98.73 293	98.17 129	98.99 134	99.64 94

SteuartSystems-ACMMP		99.54 7	99.42 11	99.87 6	99.82 29	99.81 11	99.59 84	99.51 85	98.62 49	99.79 18	99.83 37	99.28 3	99.97 11	98.48 108	99.90 24	99.84 12
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS		99.11 71	98.90 80	99.74 43	99.80 32	99.46 65	99.59 84	99.49 104	97.03 191	99.63 50	99.69 112	97.27 97	99.96 19	97.82 156	99.84 57	99.81 34

Regformer-3		99.57 6	99.53 5	99.68 50	99.76 41	99.29 81	99.58 89	99.44 156	99.01 13	99.87 6	99.80 64	98.97 19	99.91 72	99.44 16	99.92 12	99.83 23

Regformer-4		99.59 2	99.54 4	99.73 45	99.76 41	99.41 70	99.58 89	99.49 104	99.02 10	99.88 3	99.80 64	99.00 17	99.94 40	99.45 15	99.92 12	99.84 12

PGM-MVS		99.45 22	99.31 31	99.86 12	99.87 15	99.78 20	99.58 89	99.65 30	97.84 117	99.71 29	99.80 64	99.12 7	99.97 11	98.33 121	99.87 38	99.83 23

LPG-MVS_test		98.22 143	98.13 142	98.49 215	99.33 157	97.05 238	99.58 89	99.55 55	97.46 151	99.24 136	99.83 37	92.58 247	99.72 160	98.09 134	97.51 208	98.68 220

tpmp4_e23		97.34 245	97.29 236	97.52 282	99.25 178	93.73 304	99.58 89	99.19 248	94.00 293	98.20 256	99.41 204	90.74 279	99.74 148	97.13 210	98.07 188	99.07 167

PHI-MVS		99.30 44	99.17 49	99.70 49	99.56 114	99.52 58	99.58 89	99.80 8	97.12 182	99.62 53	99.73 98	98.58 55	99.90 84	98.61 92	99.91 17	99.68 81

Effi-MVS+-dtu		98.78 113	98.89 82	98.47 219	99.33 157	96.91 249	99.57 95	99.30 221	98.47 57	99.41 90	98.99 266	96.78 108	99.74 148	98.73 77	99.38 108	98.74 202

v1141		98.05 165	97.76 179	98.91 161	98.91 247	98.78 158	99.57 95	99.35 196	96.41 232	99.23 141	99.36 223	94.93 168	99.27 242	97.38 197	94.72 272	98.68 220

divwei89l23v2f112		98.06 159	97.78 172	98.91 161	98.90 248	98.77 159	99.57 95	99.35 196	96.45 227	99.24 136	99.37 216	94.92 169	99.27 242	97.50 187	94.71 274	98.68 220

v2v482		98.06 159	97.77 176	98.92 157	98.90 248	98.82 147	99.57 95	99.36 192	96.65 210	99.19 151	99.35 227	94.20 206	99.25 248	97.72 170	94.97 266	98.69 215

v1		98.05 165	97.76 179	98.93 152	98.92 245	98.80 154	99.57 95	99.35 196	96.39 234	99.28 121	99.36 223	94.86 174	99.32 230	97.38 197	94.72 272	98.68 220

DWT-MVSNet_test		97.53 233	97.40 221	97.93 264	99.03 219	94.86 293	99.57 95	98.63 309	96.59 217	98.36 249	98.79 282	89.32 291	99.74 148	98.14 131	98.16 185	99.20 154

DSMNet-mixed		97.25 248	97.35 227	96.95 292	97.84 303	93.61 308	99.57 95	96.63 334	96.13 255	98.87 200	98.61 290	94.59 192	97.70 317	95.08 269	98.86 147	99.55 110

AllTest		98.87 97	98.72 101	99.31 110	99.86 20	98.48 186	99.56 102	99.61 32	97.85 115	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

XXY-MVS		98.38 136	98.09 146	99.24 121	99.26 176	99.32 77	99.56 102	99.55 55	97.45 154	98.71 217	99.83 37	93.23 229	99.63 188	98.88 57	96.32 242	98.76 198

ACMH+	97.24 10	97.92 187	97.78 172	98.32 232	99.46 131	96.68 257	99.56 102	99.54 62	98.41 63	97.79 273	99.87 19	90.18 285	99.66 179	98.05 142	97.18 228	98.62 255

ACMM	97.58 5	98.37 137	98.34 131	98.48 217	99.41 140	97.10 232	99.56 102	99.45 148	98.53 54	99.04 176	99.85 26	93.00 232	99.71 166	98.74 75	97.45 215	98.64 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.27 49	99.12 53	99.74 43	99.18 189	99.75 21	99.56 102	99.57 44	98.45 59	99.49 76	99.85 26	97.77 85	99.94 40	98.33 121	99.84 57	99.52 117

v144192		97.92 187	97.60 196	98.87 178	98.83 261	98.65 167	99.55 107	99.34 204	96.20 247	99.32 111	99.40 208	94.36 201	99.26 247	96.37 246	95.03 265	98.70 210

#test#		99.43 27	99.29 36	99.86 12	99.87 15	99.80 12	99.55 107	99.67 22	97.83 118	99.68 34	99.69 112	99.06 8	99.96 19	98.39 114	99.87 38	99.84 12

API-MVS		99.04 82	99.03 63	99.06 134	99.40 145	99.31 80	99.55 107	99.56 48	98.54 53	99.33 110	99.39 212	98.76 41	99.78 141	96.98 219	99.78 72	98.07 292

v1144		97.98 175	97.69 186	98.85 185	98.87 255	98.66 166	99.54 110	99.35 196	96.27 241	99.23 141	99.35 227	94.67 189	99.23 251	96.73 232	95.16 261	98.68 220

v148		97.79 205	97.55 198	98.50 214	98.74 273	97.72 220	99.54 110	99.33 212	96.26 242	98.90 197	99.51 175	94.68 188	99.14 261	97.83 155	93.15 298	98.63 253

CostFormer		97.72 217	97.73 183	97.71 279	99.15 199	94.02 302	99.54 110	99.02 266	94.67 277	99.04 176	99.35 227	92.35 256	99.77 143	98.50 107	97.94 192	99.34 146

MVSTER		98.49 128	98.32 133	99.00 141	99.35 153	99.02 108	99.54 110	99.38 184	97.41 158	99.20 148	99.73 98	93.86 220	99.36 220	98.87 61	97.56 205	98.62 255

Fast-Effi-MVS+-dtu		98.77 115	98.83 93	98.60 205	99.41 140	96.99 243	99.52 114	99.49 104	98.11 86	99.24 136	99.34 230	96.96 104	99.79 139	97.95 147	99.45 104	99.02 172

Fast-Effi-MVS+		98.70 119	98.43 126	99.51 85	99.51 119	99.28 82	99.52 114	99.47 127	96.11 256	99.01 179	99.34 230	96.20 125	99.84 113	97.88 151	98.82 149	99.39 142

v1921920		97.80 203	97.45 211	98.84 186	98.80 262	98.53 178	99.52 114	99.34 204	96.15 253	99.24 136	99.47 190	93.98 215	99.29 238	95.40 264	95.13 263	98.69 215

MIMVSNet1		95.51 281	95.04 284	96.92 293	97.38 309	95.60 277	99.52 114	99.50 99	93.65 297	96.97 286	99.17 251	85.28 319	96.56 324	88.36 317	95.55 255	98.60 266

alignmvs		98.81 109	98.56 122	99.58 71	99.43 136	99.42 69	99.51 118	98.96 272	98.61 50	99.35 106	98.92 272	94.78 179	99.77 143	99.35 18	98.11 187	99.54 112

v1192		97.81 201	97.44 215	98.91 161	98.88 252	98.68 163	99.51 118	99.34 204	96.18 249	99.20 148	99.34 230	94.03 214	99.36 220	95.32 266	95.18 260	98.69 215

test20.03		96.12 275	95.96 264	96.63 297	97.44 308	95.45 284	99.51 118	99.38 184	96.55 218	96.16 292	99.25 245	93.76 223	96.17 325	87.35 321	94.22 284	98.27 287

mvs_anonymous		99.03 84	98.99 68	99.16 127	99.38 148	98.52 181	99.51 118	99.38 184	97.79 123	99.38 97	99.81 53	97.30 96	99.45 202	99.35 18	98.99 134	99.51 120

TAMVS		99.12 67	99.08 56	99.24 121	99.46 131	98.55 176	99.51 118	99.46 137	98.09 89	99.45 81	99.82 44	98.34 68	99.51 198	98.70 80	98.93 140	99.67 84

tfpn200view9		97.72 217	97.38 223	98.72 197	99.69 76	97.96 205	99.50 123	98.73 304	97.83 118	99.17 154	98.45 295	91.67 268	99.83 120	93.22 298	98.18 180	98.37 284

UA-Net		99.42 29	99.29 36	99.80 29	99.62 100	99.55 51	99.50 123	99.70 15	98.79 40	99.77 23	99.96 1	97.45 91	99.96 19	98.92 55	99.90 24	99.89 2

pm-mvs1		97.68 223	97.28 237	98.88 174	99.06 213	98.62 171	99.50 123	99.45 148	96.32 236	97.87 269	99.79 72	92.47 251	99.35 223	97.54 183	93.54 294	98.67 231

EI-MVSNet		98.67 122	98.67 107	98.68 200	99.35 153	97.97 204	99.50 123	99.38 184	96.93 197	99.20 148	99.83 37	97.87 81	99.36 220	98.38 116	97.56 205	98.71 206

CVMVSNet		98.57 127	98.67 107	98.30 234	99.35 153	95.59 278	99.50 123	99.55 55	98.60 51	99.39 95	99.83 37	94.48 197	99.45 202	98.75 74	98.56 161	99.85 8

VPA-MVSNet		98.29 140	97.95 157	99.30 113	99.16 196	99.54 52	99.50 123	99.58 43	98.27 71	99.35 106	99.37 216	92.53 249	99.65 181	99.35 18	94.46 279	98.72 204

thres400		97.77 207	97.38 223	98.92 157	99.69 76	97.96 205	99.50 123	98.73 304	97.83 118	99.17 154	98.45 295	91.67 268	99.83 120	93.22 298	98.18 180	98.96 179

APD-MVS		99.27 49	99.08 56	99.84 21	99.75 47	99.79 16	99.50 123	99.50 99	97.16 178	99.77 23	99.82 44	98.78 36	99.94 40	97.56 181	99.86 48	99.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-1		99.53 9	99.47 8	99.72 47	99.71 69	99.44 67	99.49 131	99.46 137	98.95 24	99.83 11	99.76 85	99.01 11	99.93 55	99.17 36	99.87 38	99.80 39

Regformer-2		99.54 7	99.47 8	99.75 38	99.71 69	99.52 58	99.49 131	99.49 104	98.94 26	99.83 11	99.76 85	99.01 11	99.94 40	99.15 38	99.87 38	99.80 39

TransMVSNet (Re)		97.15 250	96.58 253	98.86 182	99.12 202	98.85 136	99.49 131	98.91 279	95.48 268	97.16 281	99.80 64	93.38 227	99.11 267	94.16 292	91.73 306	98.62 255

UniMVSNet (Re)		98.29 140	98.00 153	99.13 130	99.00 222	99.36 74	99.49 131	99.51 85	97.95 108	98.97 189	99.13 254	96.30 122	99.38 213	98.36 119	93.34 295	98.66 242

EPMVS		97.82 200	97.65 192	98.35 230	98.88 252	95.98 273	99.49 131	94.71 338	97.57 143	99.26 129	99.48 186	92.46 254	99.71 166	97.87 152	99.08 128	99.35 145

v1240		97.69 221	97.32 233	98.79 192	98.85 259	98.43 188	99.48 136	99.36 192	96.11 256	99.27 125	99.36 223	93.76 223	99.24 250	94.46 279	95.23 259	98.70 210

VPNet		97.84 195	97.44 215	99.01 139	99.21 182	98.94 125	99.48 136	99.57 44	98.38 64	99.28 121	99.73 98	88.89 295	99.39 212	99.19 33	93.27 296	98.71 206

UniMVSNet_NR-MVSNet		98.22 143	97.97 155	98.96 146	98.92 245	98.98 114	99.48 136	99.53 72	97.76 126	98.71 217	99.46 194	96.43 119	99.22 254	98.57 97	92.87 301	98.69 215

TDRefinement		95.42 283	94.57 288	97.97 262	89.83 333	96.11 272	99.48 136	98.75 294	96.74 204	96.68 287	99.88 14	88.65 300	99.71 166	98.37 117	82.74 329	98.09 291

ACMMP_Plus		99.47 20	99.34 24	99.88 4	99.87 15	99.86 3	99.47 140	99.48 113	98.05 98	99.76 26	99.86 22	98.82 32	99.93 55	98.82 71	99.91 17	99.84 12

NR-MVSNet		97.97 178	97.61 195	99.02 138	98.87 255	99.26 85	99.47 140	99.42 165	97.63 139	97.08 282	99.50 177	95.07 159	99.13 264	97.86 153	93.59 293	98.68 220

PVSNet_Blended_VisFu		99.36 38	99.28 38	99.61 66	99.86 20	99.07 103	99.47 140	99.93 2	97.66 138	99.71 29	99.86 22	97.73 86	99.96 19	99.47 13	99.82 65	99.79 43

SD-MVS		99.41 32	99.52 6	99.05 136	99.74 57	99.68 30	99.46 143	99.52 76	99.11 7	99.88 3	99.91 5	99.43 1	97.70 317	98.72 79	99.93 11	99.77 49

tpm2		97.44 242	97.34 230	97.74 278	99.15 199	94.36 299	99.45 144	98.94 273	93.45 302	98.90 197	99.44 198	91.35 273	99.59 193	97.31 200	98.07 188	99.29 149

FMVSNet2		97.72 217	97.36 225	98.80 191	99.51 119	98.84 137	99.45 144	99.42 165	96.49 220	98.86 205	99.29 240	90.26 282	98.98 280	96.44 243	96.56 236	98.58 270

CDS-MVSNet		99.09 75	99.03 63	99.25 119	99.42 137	98.73 160	99.45 144	99.46 137	98.11 86	99.46 80	99.77 82	98.01 79	99.37 216	98.70 80	98.92 142	99.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS		98.86 100	98.63 112	99.54 75	99.37 150	99.66 34	99.45 144	99.54 62	96.61 213	99.01 179	99.40 208	97.09 100	99.86 104	97.68 174	99.53 103	99.10 158
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
mvs-test1		98.86 100	98.84 90	98.89 167	99.33 157	97.77 217	99.44 148	99.30 221	98.47 57	99.10 164	99.43 199	96.78 108	99.95 33	98.73 77	99.02 132	98.96 179

UGNet		98.87 97	98.69 105	99.40 102	99.22 181	98.72 161	99.44 148	99.68 19	99.24 3	99.18 153	99.42 201	92.74 240	99.96 19	99.34 22	99.94 10	99.53 116
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
ab-mvs		98.86 100	98.63 112	99.54 75	99.64 93	99.19 90	99.44 148	99.54 62	97.77 125	99.30 113	99.81 53	94.20 206	99.93 55	99.17 36	98.82 149	99.49 124

test_0402		96.64 256	96.24 257	97.85 270	98.85 259	96.43 264	99.44 148	99.26 238	93.52 299	96.98 285	99.52 172	88.52 302	99.20 259	92.58 306	97.50 210	97.93 301

ACMP	97.20 11	98.06 159	97.94 158	98.45 221	99.37 150	97.01 241	99.44 148	99.49 104	97.54 148	98.45 244	99.79 72	91.95 258	99.72 160	97.91 149	97.49 213	98.62 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND				98.45 221	98.55 291	98.16 197	99.43 153	93.68 340		97.23 279	98.46 294	89.30 292	99.22 254	95.43 263	98.22 176	97.98 298

HPM-MVS++		99.39 36	99.23 45	99.87 6	99.75 47	99.84 6	99.43 153	99.51 85	98.68 47	99.27 125	99.53 168	98.64 52	99.96 19	98.44 113	99.80 68	99.79 43

tpm cat1		97.39 244	97.36 225	97.50 284	99.17 194	93.73 304	99.43 153	99.31 219	91.27 313	98.71 217	99.08 258	94.31 204	99.77 143	96.41 245	98.50 164	99.00 173

tpm		97.67 226	97.55 198	98.03 256	99.02 220	95.01 292	99.43 153	98.54 314	96.44 228	99.12 159	99.34 230	91.83 263	99.60 191	97.75 164	96.46 238	99.48 126

GBi-Net		97.68 223	97.48 206	98.29 235	99.51 119	97.26 226	99.43 153	99.48 113	96.49 220	99.07 170	99.32 235	90.26 282	98.98 280	97.10 211	96.65 233	98.62 255

test1		97.68 223	97.48 206	98.29 235	99.51 119	97.26 226	99.43 153	99.48 113	96.49 220	99.07 170	99.32 235	90.26 282	98.98 280	97.10 211	96.65 233	98.62 255

FMVSNet1		96.84 255	96.36 256	98.29 235	99.32 164	97.26 226	99.43 153	99.48 113	95.11 271	98.55 239	99.32 235	83.95 324	98.98 280	95.81 254	96.26 243	98.62 255

testing_2		94.44 291	92.93 297	98.98 143	94.16 324	99.00 112	99.42 160	99.28 232	96.60 215	84.86 327	96.84 322	70.91 330	99.27 242	98.23 126	96.08 246	98.68 220

testgi		97.65 228	97.50 204	98.13 253	99.36 152	96.45 263	99.42 160	99.48 113	97.76 126	97.87 269	99.45 197	91.09 275	98.81 291	94.53 277	98.52 163	99.13 157

PatchFormer-LS_test		98.01 173	98.05 150	97.87 268	99.15 199	94.76 295	99.42 160	98.93 274	97.12 182	98.84 206	98.59 291	93.74 225	99.80 136	98.55 103	98.17 184	99.06 168

F-COLMAP		99.19 55	99.04 61	99.64 62	99.78 34	99.27 84	99.42 160	99.54 62	97.29 167	99.41 90	99.59 152	98.42 64	99.93 55	98.19 127	99.69 90	99.73 63

MSLP-MVS++		99.46 21	99.47 8	99.44 97	99.60 106	99.16 93	99.41 164	99.71 13	98.98 19	99.45 81	99.78 77	99.19 4	99.54 197	99.28 27	99.84 57	99.63 98

VNet		99.11 71	98.90 80	99.73 45	99.52 117	99.56 49	99.41 164	99.39 178	99.01 13	99.74 28	99.78 77	95.56 141	99.92 63	99.52 7	98.18 180	99.72 69

DU-MVS		98.08 158	97.79 170	98.96 146	98.87 255	98.98 114	99.41 164	99.45 148	97.87 112	98.71 217	99.50 177	94.82 176	99.22 254	98.57 97	92.87 301	98.68 220

Baseline_NR-MVSNet		97.76 208	97.45 211	98.68 200	99.09 209	98.29 192	99.41 164	98.85 285	95.65 266	98.63 234	99.67 121	94.82 176	99.10 269	98.07 140	92.89 300	98.64 247

XVG-ACMP-BASELINE		97.83 197	97.71 185	98.20 249	99.11 204	96.33 267	99.41 164	99.52 76	98.06 97	99.05 175	99.50 177	89.64 289	99.73 156	97.73 166	97.38 221	98.53 273

DP-MVS		99.16 60	98.95 75	99.78 33	99.77 38	99.53 55	99.41 164	99.50 99	97.03 191	99.04 176	99.88 14	97.39 92	99.92 63	98.66 85	99.90 24	99.87 4

FMVSNet3		98.03 168	97.76 179	98.84 186	99.39 147	98.98 114	99.40 170	99.38 184	96.67 209	99.07 170	99.28 241	92.93 233	98.98 280	97.10 211	96.65 233	98.56 272

LFMVS		97.90 189	97.35 227	99.54 75	99.52 117	99.01 110	99.39 171	98.24 319	97.10 186	99.65 48	99.79 72	84.79 321	99.91 72	99.28 27	98.38 169	99.69 77

HQP_MVS		98.27 142	98.22 139	98.44 224	99.29 169	96.97 245	99.39 171	99.47 127	98.97 22	99.11 161	99.61 147	92.71 242	99.69 175	97.78 159	97.63 198	98.67 231

plane_prior2							99.39 171		98.97 22

CHOSEN 1792x2688		99.19 55	99.10 55	99.45 94	99.89 8	98.52 181	99.39 171	99.94 1	98.73 44	99.11 161	99.89 10	95.50 143	99.94 40	99.50 8	99.97 3	99.89 2

PAPM_NR		99.04 82	98.84 90	99.66 53	99.74 57	99.44 67	99.39 171	99.38 184	97.70 134	99.28 121	99.28 241	98.34 68	99.85 108	96.96 221	99.45 104	99.69 77

gg-mvs-nofinetune		96.17 274	95.32 281	98.73 196	98.79 264	98.14 198	99.38 176	94.09 339	91.07 316	98.07 264	91.04 334	89.62 290	99.35 223	96.75 231	99.09 127	98.68 220

VDDNet		97.55 231	97.02 246	99.16 127	99.49 126	98.12 200	99.38 176	99.30 221	95.35 269	99.68 34	99.90 7	82.62 327	99.93 55	99.31 25	98.13 186	99.42 139

pmmvs6		96.53 259	96.09 260	97.82 273	98.69 280	95.47 283	99.37 178	99.47 127	93.46 301	97.41 276	99.78 77	87.06 313	99.33 227	96.92 225	92.70 303	98.65 245

PM-MVS		92.96 297	92.23 299	95.14 303	95.61 317	89.98 320	99.37 178	98.21 320	94.80 275	95.04 299	97.69 308	65.06 334	97.90 312	94.30 287	89.98 311	97.54 318

WTY-MVS		99.06 79	98.88 83	99.61 66	99.62 100	99.16 93	99.37 178	99.56 48	98.04 99	99.53 68	99.62 144	96.84 106	99.94 40	98.85 65	98.49 165	99.72 69

IterMVS-LS		98.46 130	98.42 127	98.58 207	99.59 108	98.00 202	99.37 178	99.43 164	96.94 196	99.07 170	99.59 152	97.87 81	99.03 275	98.32 123	95.62 253	98.71 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MPTG		99.49 13	99.36 19	99.89 2	99.90 3	99.86 3	99.36 182	99.47 127	98.79 40	99.68 34	99.81 53	98.43 61	99.97 11	98.88 57	99.90 24	99.83 23

UnsupCasMVSNet_eth		96.44 260	96.12 259	97.40 286	98.65 284	95.65 276	99.36 182	99.51 85	97.13 180	96.04 295	98.99 266	88.40 304	98.17 298	96.71 233	90.27 309	98.40 282

sss		99.17 58	99.05 58	99.53 79	99.62 100	98.97 117	99.36 182	99.62 31	97.83 118	99.67 40	99.65 128	97.37 95	99.95 33	99.19 33	99.19 120	99.68 81

DeepC-MVS_fast	98.69 1	99.49 13	99.39 15	99.77 35	99.63 96	99.59 46	99.36 182	99.46 137	99.07 9	99.79 18	99.82 44	98.85 30	99.92 63	98.68 84	99.87 38	99.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		99.25 52	99.14 51	99.59 68	99.41 140	99.16 93	99.35 186	99.57 44	98.82 35	99.51 72	99.61 147	96.46 117	99.95 33	99.59 2	99.98 2	99.65 88

pmmvs-eth3d		95.34 285	94.73 286	97.15 287	95.53 319	95.94 274	99.35 186	99.10 255	95.13 270	93.55 313	97.54 317	88.15 308	97.91 311	94.58 276	89.69 312	97.61 315

MDTV_nov1_ep13_2view						95.18 290	99.35 186		96.84 201	99.58 60		95.19 155		97.82 156		99.46 133

VDD-MVS		97.73 215	97.35 227	98.88 174	99.47 130	97.12 231	99.34 189	98.85 285	98.19 76	99.67 40	99.85 26	82.98 325	99.92 63	99.49 12	98.32 171	99.60 102

COLMAP_ROB	97.56 6	98.86 100	98.75 100	99.17 126	99.88 11	98.53 178	99.34 189	99.59 38	97.55 145	98.70 223	99.89 10	95.83 135	99.90 84	98.10 133	99.90 24	99.08 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
no-one		83.04 308	80.12 310	91.79 314	89.44 334	85.65 325	99.32 191	98.32 316	89.06 320	79.79 335	89.16 336	44.86 342	96.67 323	84.33 327	46.78 338	93.05 327

FMVSNet5		96.43 261	96.19 258	97.15 287	99.11 204	95.89 275	99.32 191	99.52 76	94.47 286	98.34 251	99.07 259	87.54 310	97.07 320	92.61 305	95.72 251	98.47 277

dp		97.75 212	97.80 169	97.59 281	99.10 207	93.71 306	99.32 191	98.88 283	96.48 226	99.08 169	99.55 164	92.67 245	99.82 128	96.52 241	98.58 158	99.24 152

tpmvs		97.98 175	98.02 152	97.84 271	99.04 217	94.73 296	99.31 194	99.20 245	96.10 259	98.76 213	99.42 201	94.94 166	99.81 132	96.97 220	98.45 166	98.97 177

tpmrst		98.33 138	98.48 125	97.90 267	99.16 196	94.78 294	99.31 194	99.11 254	97.27 168	99.45 81	99.59 152	95.33 146	99.84 113	98.48 108	98.61 155	99.09 162

MP-MVS-pluss		99.37 37	99.20 46	99.88 4	99.90 3	99.87 2	99.30 196	99.52 76	97.18 176	99.60 56	99.79 72	98.79 35	99.95 33	98.83 68	99.91 17	99.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC		99.34 40	99.19 47	99.79 32	99.61 104	99.65 37	99.30 196	99.48 113	98.86 31	99.21 145	99.63 139	98.72 47	99.90 84	98.25 125	99.63 100	99.80 39

JIA-IIPM		97.50 238	97.02 246	98.93 152	98.73 274	97.80 216	99.30 196	98.97 270	91.73 312	98.91 195	94.86 328	95.10 158	99.71 166	97.58 178	97.98 191	99.28 150

BH-RMVSNet		98.41 134	98.08 147	99.40 102	99.41 140	98.83 140	99.30 196	98.77 293	97.70 134	98.94 192	99.65 128	92.91 236	99.74 148	96.52 241	99.55 102	99.64 94

Anonymous20231211		90.69 302	89.39 303	94.58 304	94.25 323	88.18 321	99.29 200	99.07 260	82.45 329	92.95 316	97.65 310	63.96 336	97.79 314	89.27 314	85.63 327	97.77 312

MCST-MVS		99.43 27	99.30 33	99.82 24	99.79 33	99.74 24	99.29 200	99.40 175	98.79 40	99.52 70	99.62 144	98.91 26	99.90 84	98.64 87	99.75 77	99.82 30

LF4IMVS		97.52 234	97.46 210	97.70 280	98.98 227	95.55 279	99.29 200	98.82 288	98.07 93	98.66 226	99.64 135	89.97 286	99.61 190	97.01 216	96.68 232	97.94 300

OPM-MVS		98.19 147	98.10 144	98.45 221	98.88 252	97.07 236	99.28 203	99.38 184	98.57 52	99.22 143	99.81 53	92.12 257	99.66 179	98.08 138	97.54 207	98.61 264

PVSNet_BlendedMVS		98.86 100	98.80 94	99.03 137	99.76 41	98.79 156	99.28 203	99.91 3	97.42 157	99.67 40	99.37 216	97.53 89	99.88 99	98.98 51	97.29 224	98.42 280

OMC-MVS		99.08 77	99.04 61	99.20 125	99.67 80	98.22 195	99.28 203	99.52 76	98.07 93	99.66 45	99.81 53	97.79 84	99.78 141	97.79 158	99.81 66	99.60 102

pmmvs5		97.52 234	97.30 235	98.16 252	98.57 290	96.73 254	99.27 206	98.90 281	96.14 254	98.37 248	99.53 168	91.54 272	99.14 261	97.51 186	95.87 248	98.63 253

1314		98.68 121	98.54 123	99.11 131	98.89 251	98.65 167	99.27 206	99.49 104	96.89 198	97.99 266	99.56 161	97.72 87	99.83 120	97.74 165	99.27 116	98.84 189

1121		99.09 75	98.87 84	99.75 38	99.74 57	99.60 44	99.27 206	99.48 113	96.82 202	99.25 131	99.65 128	98.38 65	99.93 55	97.53 184	99.67 94	99.73 63

MVS		97.28 247	96.55 254	99.48 88	98.78 268	98.95 122	99.27 206	99.39 178	83.53 327	98.08 261	99.54 167	96.97 103	99.87 101	94.23 290	99.16 121	99.63 98

BH-untuned		98.42 133	98.36 129	98.59 206	99.49 126	96.70 255	99.27 206	99.13 253	97.24 172	98.80 209	99.38 213	95.75 138	99.74 148	97.07 214	99.16 121	99.33 147

MDTV_nov1_ep13			98.32 133		99.11 204	94.44 298	99.27 206	98.74 297	97.51 149	99.40 94	99.62 144	94.78 179	99.76 146	97.59 177	98.81 151

DP-MVS Recon		99.12 67	98.95 75	99.65 57	99.74 57	99.70 28	99.27 206	99.57 44	96.40 233	99.42 88	99.68 117	98.75 44	99.80 136	97.98 144	99.72 83	99.44 136

PatchmatchNet		98.31 139	98.36 129	98.19 250	99.16 196	95.32 286	99.27 206	98.92 276	97.37 161	99.37 99	99.58 155	94.90 171	99.70 172	97.43 195	99.21 118	99.54 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		97.61 229	97.28 237	98.62 204	99.64 93	98.03 201	99.26 214	98.74 297	97.68 136	99.09 168	98.32 297	91.66 270	99.81 132	92.88 303	98.22 176	98.03 296

CNVR-MVS		99.42 29	99.30 33	99.78 33	99.62 100	99.71 26	99.26 214	99.52 76	98.82 35	99.39 95	99.71 103	98.96 20	99.85 108	98.59 94	99.80 68	99.77 49

1112_ss		98.98 90	98.77 97	99.59 68	99.68 79	99.02 108	99.25 216	99.48 113	97.23 173	99.13 157	99.58 155	96.93 105	99.90 84	98.87 61	98.78 152	99.84 12

TAPA-MVS	97.07 15	97.74 214	97.34 230	98.94 149	99.70 74	97.53 221	99.25 216	99.51 85	91.90 311	99.30 113	99.63 139	98.78 36	99.64 183	88.09 318	99.87 38	99.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	97.94 4	99.02 85	98.85 89	99.53 79	99.66 90	99.01 110	99.24 218	99.52 76	96.85 200	99.27 125	99.48 186	98.25 72	99.91 72	97.76 162	99.62 101	99.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post1							99.23 219				65.14 342	94.18 209	99.71 166	97.58 178

ADS-MVSNet2		98.02 170	98.07 149	97.87 268	99.33 157	95.19 289	99.23 219	99.08 257	96.24 244	99.10 164	99.67 121	94.11 211	98.93 288	96.81 228	99.05 130	99.48 126

ADS-MVSNet		98.20 146	98.08 147	98.56 210	99.33 157	96.48 262	99.23 219	99.15 250	96.24 244	99.10 164	99.67 121	94.11 211	99.71 166	96.81 228	99.05 130	99.48 126

EPNet_dtu		98.03 168	97.96 156	98.23 245	98.27 298	95.54 281	99.23 219	98.75 294	99.02 10	97.82 271	99.71 103	96.11 127	99.48 199	93.04 301	99.65 97	99.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		99.06 79	98.86 87	99.66 53	99.51 119	99.36 74	99.22 223	99.51 85	98.95 24	99.58 60	99.65 128	93.74 225	99.98 5	99.66 1	99.95 6	99.64 94

CR-MVSNet		98.17 148	97.93 159	98.87 178	99.18 189	98.49 184	99.22 223	99.33 212	96.96 194	99.56 64	99.38 213	94.33 202	99.00 278	94.83 273	98.58 158	99.14 155

RPMNet		96.61 257	95.85 265	98.87 178	99.18 189	98.49 184	99.22 223	99.08 257	88.72 323	99.56 64	97.38 319	94.08 213	99.00 278	86.87 323	98.58 158	99.14 155

plane_prior						96.97 245	99.21 226		98.45 59						97.60 201

DI_MVS_plusplus_test		97.45 241	96.79 250	99.44 97	97.76 305	99.04 106	99.21 226	98.61 311	97.74 129	94.01 307	98.83 279	87.38 312	99.83 120	98.63 88	98.90 144	99.44 136

Test4		95.05 286	93.67 294	99.22 124	96.07 316	98.94 125	99.20 228	99.27 237	97.71 132	89.96 325	97.59 316	66.18 333	99.25 248	98.06 141	98.96 137	99.47 130

WR-MVS		98.06 159	97.73 183	99.06 134	98.86 258	99.25 86	99.19 229	99.35 196	97.30 166	98.66 226	99.43 199	93.94 216	99.21 258	98.58 95	94.28 282	98.71 206

new-patchmatchnet		94.48 290	94.08 291	95.67 302	95.08 321	92.41 313	99.18 230	99.28 232	94.55 283	93.49 314	97.37 320	87.86 309	97.01 321	91.57 307	88.36 315	97.61 315

AdaColmap		99.01 88	98.80 94	99.66 53	99.56 114	99.54 52	99.18 230	99.70 15	98.18 79	99.35 106	99.63 139	96.32 121	99.90 84	97.48 189	99.77 74	99.55 110

EG-PatchMatch MVS		95.97 277	95.69 271	96.81 295	97.78 304	92.79 312	99.16 232	98.93 274	96.16 251	94.08 304	99.22 248	82.72 326	99.47 200	95.67 259	97.50 210	98.17 290

PatchT		97.03 254	96.44 255	98.79 192	98.99 223	98.34 191	99.16 232	99.07 260	92.13 308	99.52 70	97.31 321	94.54 195	98.98 280	88.54 316	98.73 154	99.03 170

CNLPA		99.14 61	98.99 68	99.59 68	99.58 109	99.41 70	99.16 232	99.44 156	98.45 59	99.19 151	99.49 180	98.08 77	99.89 92	97.73 166	99.75 77	99.48 126

1111		92.30 299	92.21 300	92.55 310	93.30 325	86.27 322	99.15 235	98.74 297	91.94 309	90.85 322	97.82 303	84.18 322	95.21 327	79.65 330	94.27 283	96.19 322

.test1245		83.42 307	86.17 305	75.15 329	93.30 325	86.27 322	99.15 235	98.74 297	91.94 309	90.85 322	97.82 303	84.18 322	95.21 327	79.65 330	39.90 340	43.98 339

MDA-MVSNet-bldmvs		94.96 287	93.98 292	97.92 265	98.24 299	97.27 225	99.15 235	99.33 212	93.80 296	80.09 333	99.03 264	88.31 305	97.86 313	93.49 296	94.36 281	98.62 255

CDPH-MVS		99.13 62	98.91 79	99.80 29	99.75 47	99.71 26	99.15 235	99.41 168	96.60 215	99.60 56	99.55 164	98.83 31	99.90 84	97.48 189	99.83 61	99.78 47

xiu_mvs_v1_base_debu		99.29 46	99.27 40	99.34 105	99.63 96	98.97 117	99.12 239	99.51 85	98.86 31	99.84 8	99.47 190	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base		99.29 46	99.27 40	99.34 105	99.63 96	98.97 117	99.12 239	99.51 85	98.86 31	99.84 8	99.47 190	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

xiu_mvs_v1_base_debi		99.29 46	99.27 40	99.34 105	99.63 96	98.97 117	99.12 239	99.51 85	98.86 31	99.84 8	99.47 190	98.18 74	99.99 1	99.50 8	99.31 113	99.08 163

XVG-OURS-SEG-HR		98.69 120	98.62 115	98.89 167	99.71 69	97.74 218	99.12 239	99.54 62	98.44 62	99.42 88	99.71 103	94.20 206	99.92 63	98.54 105	98.90 144	99.00 173

jason		99.13 62	99.03 63	99.45 94	99.46 131	98.87 133	99.12 239	99.26 238	98.03 101	99.79 18	99.65 128	97.02 102	99.85 108	99.02 48	99.90 24	99.65 88
jason: jason.
N_pmnet		94.95 288	95.83 266	92.31 312	98.47 294	79.33 334	99.12 239	92.81 344	93.87 295	97.68 274	99.13 254	93.87 219	99.01 277	91.38 308	96.19 244	98.59 267

MDA-MVSNet_test_wron		95.45 282	94.60 287	98.01 259	98.16 300	97.21 230	99.11 245	99.24 241	93.49 300	80.73 332	98.98 269	93.02 231	98.18 297	94.22 291	94.45 280	98.64 247

Patchmtry		97.75 212	97.40 221	98.81 189	99.10 207	98.87 133	99.11 245	99.33 212	94.83 274	98.81 208	99.38 213	94.33 202	99.02 276	96.10 248	95.57 254	98.53 273

test_normal		97.44 242	96.77 252	99.44 97	97.75 306	99.00 112	99.10 247	98.64 308	97.71 132	93.93 310	98.82 280	87.39 311	99.83 120	98.61 92	98.97 136	99.49 124

YYNet1		95.36 284	94.51 289	97.92 265	97.89 302	97.10 232	99.10 247	99.23 242	93.26 303	80.77 331	99.04 263	92.81 237	98.02 308	94.30 287	94.18 285	98.64 247

CANet_DTU		98.97 92	98.87 84	99.25 119	99.33 157	98.42 190	99.08 249	99.30 221	99.16 5	99.43 85	99.75 90	95.27 149	99.97 11	98.56 100	99.95 6	99.36 144

Patchmatch-test1		98.16 149	98.14 141	98.22 247	99.30 166	95.55 279	99.07 250	98.97 270	97.57 143	99.43 85	99.60 150	92.72 241	99.60 191	97.38 197	99.20 119	99.50 123

testmv		87.91 303	87.80 304	88.24 319	87.68 336	77.50 336	99.07 250	97.66 330	89.27 319	86.47 326	96.22 325	68.35 332	92.49 335	76.63 334	88.82 313	94.72 326

TSAR-MVS + GP.		99.36 38	99.36 19	99.36 104	99.67 80	98.61 174	99.07 250	99.33 212	99.00 17	99.82 14	99.81 53	99.06 8	99.84 113	99.09 42	99.42 106	99.65 88

MG-MVS		99.13 62	99.02 66	99.45 94	99.57 111	98.63 169	99.07 250	99.34 204	98.99 18	99.61 55	99.82 44	97.98 80	99.87 101	97.00 217	99.80 68	99.85 8

PatchMatch-RL		98.84 108	98.62 115	99.52 83	99.71 69	99.28 82	99.06 254	99.77 9	97.74 129	99.50 73	99.53 168	95.41 145	99.84 113	97.17 209	99.64 98	99.44 136

OpenMVS_ROB	92.34 20	94.38 292	93.70 293	96.41 300	97.38 309	93.17 310	99.06 254	98.75 294	86.58 324	94.84 300	98.26 299	81.53 328	99.32 230	89.01 315	97.87 194	96.76 319

TEST9					99.67 80	99.65 37	99.05 256	99.41 168	96.22 246	98.95 190	99.49 180	98.77 39	99.91 72

train_agg		99.02 85	98.77 97	99.77 35	99.67 80	99.65 37	99.05 256	99.41 168	96.28 239	98.95 190	99.49 180	98.76 41	99.91 72	97.63 175	99.72 83	99.75 53

lupinMVS		99.13 62	99.01 67	99.46 93	99.51 119	98.94 125	99.05 256	99.16 249	97.86 113	99.80 16	99.56 161	97.39 92	99.86 104	98.94 54	99.85 52	99.58 108

DELS-MVS		99.48 17	99.42 11	99.65 57	99.72 65	99.40 72	99.05 256	99.66 25	99.14 6	99.57 63	99.80 64	98.46 59	99.94 40	99.57 4	99.84 57	99.60 102
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
new_pmnet		96.38 265	96.03 261	97.41 285	98.13 301	95.16 291	99.05 256	99.20 245	93.94 294	97.39 277	98.79 282	91.61 271	99.04 273	90.43 311	95.77 250	98.05 293

agg_prior3		98.97 92	98.71 103	99.75 38	99.67 80	99.60 44	99.04 261	99.41 168	95.93 261	98.87 200	99.48 186	98.61 53	99.91 72	97.63 175	99.72 83	99.75 53

Patchmatch-test		97.93 184	97.65 192	98.77 194	99.18 189	97.07 236	99.03 262	99.14 252	96.16 251	98.74 214	99.57 159	94.56 193	99.72 160	93.36 297	99.11 124	99.52 117

test_8					99.67 80	99.61 42	99.03 262	99.41 168	96.28 239	98.93 193	99.48 186	98.76 41	99.91 72

Test_1112_low_res		98.89 96	98.66 110	99.57 72	99.69 76	98.95 122	99.03 262	99.47 127	96.98 193	99.15 156	99.23 247	96.77 110	99.89 92	98.83 68	98.78 152	99.86 5

xiu_mvs_v2_base		99.26 51	99.25 44	99.29 116	99.53 116	98.91 130	99.02 265	99.45 148	98.80 39	99.71 29	99.26 244	98.94 24	99.98 5	99.34 22	99.23 117	98.98 176

MIMVSNet		97.73 215	97.45 211	98.57 208	99.45 135	97.50 222	99.02 265	98.98 269	96.11 256	99.41 90	99.14 253	90.28 281	98.74 292	95.74 255	98.93 140	99.47 130

IterMVS		97.83 197	97.77 176	98.02 258	99.58 109	96.27 269	99.02 265	99.48 113	97.22 174	98.71 217	99.70 106	92.75 238	99.13 264	97.46 192	96.00 247	98.67 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		99.11 71	98.92 77	99.65 57	99.90 3	99.37 73	99.02 265	99.91 3	97.67 137	99.59 59	99.75 90	95.90 133	99.73 156	99.53 6	99.02 132	99.86 5

新几何2							99.01 269

BH-w/o		98.00 174	97.89 164	98.32 232	99.35 153	96.20 271	99.01 269	98.90 281	96.42 230	98.38 247	99.00 265	95.26 151	99.72 160	96.06 249	98.61 155	99.03 170

agg_prior1		99.01 88	98.76 99	99.76 37	99.67 80	99.62 40	98.99 271	99.40 175	96.26 242	98.87 200	99.49 180	98.77 39	99.91 72	97.69 172	99.72 83	99.75 53

test_prior4						99.56 49	98.99 271

无先验							98.99 271	99.51 85	96.89 198				99.93 55	97.53 184		99.72 69

pmmvs4		98.13 151	97.90 160	98.81 189	98.61 287	98.87 133	98.99 271	99.21 244	96.44 228	99.06 174	99.58 155	95.90 133	99.11 267	97.18 208	96.11 245	98.46 279

HQP-NCC					99.19 186		98.98 275		98.24 72	98.66 226

ACMP_Plane					99.19 186		98.98 275		98.24 72	98.66 226

HQP-MVS		98.02 170	97.90 160	98.37 229	99.19 186	96.83 250	98.98 275	99.39 178	98.24 72	98.66 226	99.40 208	92.47 251	99.64 183	97.19 206	97.58 203	98.64 247

PS-MVSNAJ		99.32 42	99.32 26	99.30 113	99.57 111	98.94 125	98.97 278	99.46 137	98.92 28	99.71 29	99.24 246	99.01 11	99.98 5	99.35 18	99.66 95	98.97 177

LP		97.04 253	96.80 249	97.77 276	98.90 248	95.23 287	98.97 278	99.06 262	94.02 292	98.09 260	99.41 204	93.88 218	98.82 290	90.46 310	98.42 168	99.26 151

MVP-Stereo		97.81 201	97.75 182	97.99 261	97.53 307	96.60 259	98.96 280	98.85 285	97.22 174	97.23 279	99.36 223	95.28 148	99.46 201	95.51 261	99.78 72	97.92 302
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior3		99.21 54	99.05 58	99.68 50	99.67 80	99.48 62	98.96 280	99.56 48	98.34 66	99.01 179	99.52 172	98.68 49	99.83 120	97.96 145	99.74 79	99.74 58

test_prior2							98.96 280		98.34 66	99.01 179	99.52 172	98.68 49		97.96 145	99.74 79

旧先验2							98.96 280		96.70 207	99.47 78			99.94 40	98.19 127

原ACMM2							98.95 284

MVS_111021_HR		99.41 32	99.32 26	99.66 53	99.72 65	99.47 64	98.95 284	99.85 6	98.82 35	99.54 67	99.73 98	98.51 56	99.74 148	98.91 56	99.88 34	99.77 49

MVS_111021_LR		99.41 32	99.33 25	99.65 57	99.77 38	99.51 60	98.94 286	99.85 6	98.82 35	99.65 48	99.74 95	98.51 56	99.80 136	98.83 68	99.89 32	99.64 94

pmmvs3		94.09 294	93.25 296	96.60 298	94.76 322	94.49 297	98.92 287	98.18 322	89.66 318	96.48 289	98.06 300	86.28 314	97.33 319	89.68 313	87.20 320	97.97 299

XVG-OURS		98.73 117	98.68 106	98.88 174	99.70 74	97.73 219	98.92 287	99.55 55	98.52 55	99.45 81	99.84 35	95.27 149	99.91 72	98.08 138	98.84 148	99.00 173

test222					99.75 47	99.49 61	98.91 289	99.49 104	96.42 230	99.34 109	99.65 128	98.28 71			99.69 90	99.72 69

PMMVS2		86.87 304	85.37 307	91.35 317	90.21 332	83.80 327	98.89 290	97.45 332	83.13 328	91.67 321	95.03 326	48.49 340	94.70 330	85.86 325	77.62 331	95.54 324

MVS-HIRNet		95.75 279	95.16 283	97.51 283	99.30 166	93.69 307	98.88 291	95.78 335	85.09 326	98.78 211	92.65 330	91.29 274	99.37 216	94.85 272	99.85 52	99.46 133

TR-MVS		97.76 208	97.41 220	98.82 188	99.06 213	97.87 209	98.87 292	98.56 313	96.63 212	98.68 225	99.22 248	92.49 250	99.65 181	95.40 264	97.79 195	98.95 186

testdata1							98.85 293		98.32 69

MS-PatchMatch		97.24 249	97.32 233	96.99 290	98.45 295	93.51 309	98.82 294	99.32 218	97.41 158	98.13 259	99.30 238	88.99 294	99.56 194	95.68 258	99.80 68	97.90 303

PAPR		98.63 126	98.34 131	99.51 85	99.40 145	99.03 107	98.80 295	99.36 192	96.33 235	99.00 186	99.12 257	98.46 59	99.84 113	95.23 267	99.37 112	99.66 85

test0.0.03 1		97.71 220	97.42 219	98.56 210	98.41 296	97.82 211	98.78 296	98.63 309	97.34 162	98.05 265	98.98 269	94.45 198	98.98 280	95.04 270	97.15 229	98.89 187

PVSNet_Blended		99.08 77	98.97 71	99.42 101	99.76 41	98.79 156	98.78 296	99.91 3	96.74 204	99.67 40	99.49 180	97.53 89	99.88 99	98.98 51	99.85 52	99.60 102

PMMVS		98.80 112	98.62 115	99.34 105	99.27 174	98.70 162	98.76 298	99.31 219	97.34 162	99.21 145	99.07 259	97.20 98	99.82 128	98.56 100	98.87 146	99.52 117

test123		39.01 320	42.50 320	28.53 332	39.17 345	20.91 346	98.75 299	19.17 349	19.83 342	38.57 341	66.67 340	33.16 344	15.42 344	37.50 342	29.66 342	49.26 338

test1235678		92.91 298	93.30 295	91.71 315	93.14 327	83.01 328	98.75 299	98.58 312	92.80 306	92.45 317	97.91 302	88.51 303	93.54 332	82.26 328	95.35 257	98.59 267

MSDG		98.98 90	98.80 94	99.53 79	99.76 41	99.19 90	98.75 299	99.55 55	97.25 170	99.47 78	99.77 82	97.82 83	99.87 101	96.93 224	99.90 24	99.54 112

CLD-MVS		98.16 149	98.10 144	98.33 231	99.29 169	96.82 252	98.75 299	99.44 156	97.83 118	99.13 157	99.55 164	92.92 234	99.67 177	98.32 123	97.69 197	98.48 276
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test-LLR		98.06 159	97.90 160	98.55 212	98.79 264	97.10 232	98.67 303	97.75 327	97.34 162	98.61 237	98.85 277	94.45 198	99.45 202	97.25 202	99.38 108	99.10 158

TESTMET0.1,1		97.55 231	97.27 239	98.40 227	98.93 243	96.53 260	98.67 303	97.61 331	96.96 194	98.64 233	99.28 241	88.63 301	99.45 202	97.30 201	99.38 108	99.21 153

test-mter		97.49 240	97.13 243	98.55 212	98.79 264	97.10 232	98.67 303	97.75 327	96.65 210	98.61 237	98.85 277	88.23 306	99.45 202	97.25 202	99.38 108	99.10 158

IB-MVS	95.67 18	96.22 272	95.44 280	98.57 208	99.21 182	96.70 255	98.65 306	97.74 329	96.71 206	97.27 278	98.54 293	86.03 315	99.92 63	98.47 110	86.30 326	99.10 158
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
test12356		91.74 300	92.19 301	90.37 318	91.22 329	82.41 329	98.61 307	98.28 317	90.66 317	91.82 320	97.92 301	84.90 320	92.61 333	81.64 329	94.66 275	96.09 323

DeepPCF-MVS	98.18 3	98.81 109	99.37 17	97.12 289	99.60 106	91.75 316	98.61 307	99.44 156	99.35 1	99.83 11	99.85 26	98.70 48	99.81 132	99.02 48	99.91 17	99.81 34

GA-MVS		97.85 193	97.47 208	99.00 141	99.38 148	97.99 203	98.57 309	99.15 250	97.04 190	98.90 197	99.30 238	89.83 287	99.38 213	96.70 234	98.33 170	99.62 100

TinyColmap		97.12 251	96.89 248	97.83 272	99.07 211	95.52 282	98.57 309	98.74 297	97.58 142	97.81 272	99.79 72	88.16 307	99.56 194	95.10 268	97.21 226	98.39 283

CMPMVS	69.68 23	94.13 293	94.90 285	91.84 313	97.24 313	80.01 333	98.52 311	99.48 113	89.01 321	91.99 319	99.67 121	85.67 317	99.13 264	95.44 262	97.03 230	96.39 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		97.34 245	97.20 241	97.75 277	99.07 211	95.20 288	98.51 312	99.04 264	97.99 107	98.31 252	99.86 22	89.02 293	99.55 196	95.67 259	97.36 222	98.49 275

ambc				93.06 308	92.68 328	82.36 330	98.47 313	98.73 304		95.09 298	97.41 318	55.55 338	99.10 269	96.42 244	91.32 307	97.71 314

CHOSEN 280x420		99.12 67	99.13 52	99.08 132	99.66 90	97.89 208	98.43 314	99.71 13	98.88 30	99.62 53	99.76 85	96.63 114	99.70 172	99.46 14	99.99 1	99.66 85

testmvs		39.17 319	43.78 318	25.37 333	36.04 346	16.84 347	98.36 315	26.56 347	20.06 341	38.51 342	67.32 339	29.64 346	15.30 345	37.59 341	39.90 340	43.98 339

testus		94.61 289	95.30 282	92.54 311	96.44 315	84.18 326	98.36 315	99.03 265	94.18 291	96.49 288	98.57 292	88.74 296	95.09 329	87.41 320	98.45 166	98.36 286

FPMVS		84.93 306	85.65 306	82.75 326	86.77 337	63.39 343	98.35 317	98.92 276	74.11 332	83.39 329	98.98 269	50.85 339	92.40 336	84.54 326	94.97 266	92.46 329

PVSNet	96.02 17	98.85 106	98.84 90	98.89 167	99.73 62	97.28 224	98.32 318	99.60 35	97.86 113	99.50 73	99.57 159	96.75 111	99.86 104	98.56 100	99.70 89	99.54 112

PAPM		97.59 230	97.09 244	99.07 133	99.06 213	98.26 194	98.30 319	99.10 255	94.88 273	98.08 261	99.34 230	96.27 123	99.64 183	89.87 312	98.92 142	99.31 148

Patchmatch-RL test		95.84 278	95.81 267	95.95 301	95.61 317	90.57 318	98.24 320	98.39 315	95.10 272	95.20 297	98.67 287	94.78 179	97.77 315	96.28 247	90.02 310	99.51 120

UnsupCasMVSNet_bld		93.53 296	92.51 298	96.58 299	97.38 309	93.82 303	98.24 320	99.48 113	91.10 315	93.10 315	96.66 323	74.89 329	98.37 296	94.03 293	87.71 319	97.56 317

LCM-MVSNet		86.80 305	85.22 308	91.53 316	87.81 335	80.96 332	98.23 322	98.99 268	71.05 333	90.13 324	96.51 324	48.45 341	96.88 322	90.51 309	85.30 328	96.76 319

cascas		97.69 221	97.43 218	98.48 217	98.60 288	97.30 223	98.18 323	99.39 178	92.96 304	98.41 245	98.78 284	93.77 222	99.27 242	98.16 130	98.61 155	98.86 188

Effi-MVS+		98.81 109	98.59 120	99.48 88	99.46 131	99.12 99	98.08 324	99.50 99	97.50 150	99.38 97	99.41 204	96.37 120	99.81 132	99.11 41	98.54 162	99.51 120

PCF-MVS	97.08 14	97.66 227	97.06 245	99.47 91	99.61 104	99.09 101	98.04 325	99.25 240	91.24 314	98.51 240	99.70 106	94.55 194	99.91 72	92.76 304	99.85 52	99.42 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_0	94.43 19	96.09 276	95.47 278	97.94 263	99.31 165	94.34 300	97.81 326	99.70 15	97.12 182	97.46 275	98.75 285	89.71 288	99.79 139	97.69 172	81.69 330	99.68 81

E-PMN		80.61 310	79.88 311	82.81 325	90.75 331	76.38 338	97.69 327	95.76 336	66.44 337	83.52 328	92.25 331	62.54 337	87.16 340	68.53 338	61.40 334	84.89 337

ANet_high		77.30 313	74.86 315	84.62 323	75.88 342	77.61 335	97.63 328	93.15 343	88.81 322	64.27 338	89.29 335	36.51 343	83.93 342	75.89 335	52.31 337	92.33 331

test2356		94.07 295	94.46 290	92.89 309	95.18 320	86.13 324	97.60 329	99.06 262	93.61 298	96.15 294	98.28 298	85.60 318	93.95 331	86.68 324	98.00 190	98.59 267

EMVS		80.02 311	79.22 312	82.43 327	91.19 330	76.40 337	97.55 330	92.49 346	66.36 338	83.01 330	91.27 332	64.63 335	85.79 341	65.82 339	60.65 335	85.08 336

testpf		95.66 280	96.02 263	94.58 304	98.35 297	92.32 314	97.25 331	97.91 326	92.83 305	97.03 284	98.99 266	88.69 298	98.61 294	95.72 256	97.40 219	92.80 328

MVE	76.82 21	76.91 314	74.31 316	84.70 321	85.38 340	76.05 339	96.88 332	93.17 342	67.39 336	71.28 337	89.01 337	21.66 350	87.69 339	71.74 337	72.29 333	90.35 333
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		79.43 312	77.68 313	84.67 322	86.18 338	71.69 341	96.50 333	93.68 340	75.17 331	71.33 336	91.18 333	32.18 345	90.62 337	78.57 333	74.34 332	91.71 332

wuykxyi23d		74.42 316	71.19 317	84.14 324	76.16 341	74.29 340	96.00 334	92.57 345	69.57 334	63.84 339	87.49 338	21.98 347	88.86 338	75.56 336	57.50 336	89.26 335

Gipuma		90.99 301	90.15 302	93.51 306	98.73 274	90.12 319	93.98 335	99.45 148	79.32 330	92.28 318	94.91 327	69.61 331	97.98 310	87.42 319	95.67 252	92.45 330
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	70.75 22	75.98 315	74.97 314	79.01 328	70.98 343	55.18 344	93.37 336	98.21 320	65.08 339	61.78 340	93.83 329	21.74 349	92.53 334	78.59 332	91.12 308	89.34 334
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		82.80 309	81.52 309	86.66 320	66.61 344	68.44 342	92.79 337	97.92 324	68.96 335	80.04 334	99.85 26	85.77 316	96.15 326	97.86 153	43.89 339	95.39 325

wuyk23d		40.18 318	41.29 321	36.84 330	86.18 338	49.12 345	79.73 338	22.81 348	27.64 340	25.46 343	28.45 344	21.98 347	48.89 343	55.80 340	23.56 343	12.51 341

cdsmvs_eth3d_5k		24.64 321	32.85 322	0.00 334	0.00 347	0.00 348	0.00 339	99.51 85	0.00 343	0.00 344	99.56 161	96.58 115	0.00 346	0.00 343	0.00 344	0.00 342

pcd_1.5k_mvsjas		8.27 323	11.03 324	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.27 345	99.01 11	0.00 346	0.00 343	0.00 344	0.00 342

pcd1.5k->3k		40.85 317	43.49 319	32.93 331	98.95 235	0.00 348	0.00 339	99.53 72	0.00 343	0.00 344	0.27 345	95.32 147	0.00 346	0.00 343	97.30 223	98.80 191

sosnet-low-res		0.02 324	0.03 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.27 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

sosnet		0.02 324	0.03 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.27 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uncertanet		0.02 324	0.03 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.27 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

Regformer		0.02 324	0.03 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.27 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ab-mvs-re		8.30 322	11.06 323	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	99.58 155	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uanet		0.02 324	0.03 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.27 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ESAPD								99.47 127

sam_mvs1												94.86 174

sam_mvs												94.72 187

semantic-postprocess				98.06 255	99.57 111	96.36 266		99.49 104	97.18 176	98.71 217	99.72 102	92.70 244	99.14 261	97.44 194	95.86 249	98.67 231

MTGPA								99.47 127

test_post											65.99 341	94.65 191	99.73 156

patchmatchnet-post											98.70 286	94.79 178	99.74 148

MTMP								98.88 283

gm-plane-assit					98.54 292	92.96 311			94.65 278		99.15 252		99.64 183	97.56 181

test9_res														97.49 188	99.72 83	99.75 53

agg_prior2														97.21 204	99.73 82	99.75 53

agg_prior					99.67 80	99.62 40		99.40 175		98.87 200			99.91 72

TestCases				99.31 110	99.86 20	98.48 186		99.61 32	97.85 115	99.36 103	99.85 26	95.95 129	99.85 108	96.66 237	99.83 61	99.59 106

test_prior				99.68 50	99.67 80	99.48 62		99.56 48					99.83 120			99.74 58

新几何1				99.75 38	99.75 47	99.59 46		99.54 62	96.76 203	99.29 117	99.64 135	98.43 61	99.94 40	96.92 225	99.66 95	99.72 69

旧先验1					99.74 57	99.59 46		99.54 62			99.69 112	98.47 58			99.68 93	99.73 63

原ACMM1				99.65 57	99.73 62	99.33 76		99.47 127	97.46 151	99.12 159	99.66 127	98.67 51	99.91 72	97.70 171	99.69 90	99.71 76

testdata2													99.95 33	96.67 236

segment_acmp												98.96 20

testdata				99.54 75	99.75 47	98.95 122		99.51 85	97.07 187	99.43 85	99.70 106	98.87 28	99.94 40	97.76 162	99.64 98	99.72 69

test12				99.75 38	99.64 93	99.61 42		99.29 225		99.21 145		98.38 65	99.89 92		99.74 79	99.74 58

plane_prior7					99.29 169	97.03 240

plane_prior6					99.27 174	96.98 244						92.71 242

plane_prior5								99.47 127					99.69 175	97.78 159	97.63 198	98.67 231

plane_prior4											99.61 147

plane_prior3						97.00 242			98.69 46	99.11 161

plane_prior1					99.26 176

n2								0.00 350

nn								0.00 350

door-mid								98.05 323

lessismore_v0				97.79 275	98.69 280	95.44 285		94.75 337		95.71 296	99.87 19	88.69 298	99.32 230	95.89 252	94.93 268	98.62 255

LGP-MVS_train				98.49 215	99.33 157	97.05 238		99.55 55	97.46 151	99.24 136	99.83 37	92.58 247	99.72 160	98.09 134	97.51 208	98.68 220

test11								99.35 196

door								97.92 324

HQP5-MVS						96.83 250

BP-MVS														97.19 206

HQP4-MVS										98.66 226			99.64 183			98.64 247

HQP3-MVS								99.39 178							97.58 203

HQP2-MVS												92.47 251

NP-MVS					99.23 179	96.92 248					99.40 208

ACMMP++_ref															97.19 227

ACMMP++															97.43 218

Test By Simon												98.75 44

ITE_SJBPF				98.08 254	99.29 169	96.37 265		98.92 276	98.34 66	98.83 207	99.75 90	91.09 275	99.62 189	95.82 253	97.40 219	98.25 289

DeepMVS_CX				93.34 307	99.29 169	82.27 331		99.22 243	85.15 325	96.33 290	99.05 262	90.97 277	99.73 156	93.57 295	97.77 196	98.01 297

High-resolution multi-view benchmark