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.

SMA-MVS		99.71 32	99.62 44	99.98 16	99.99 45	99.95 19	100.00 1	99.42 121	97.61 123	100.00 1	100.00 1	99.55 24	100.00 1	100.00 1	100.00 1	100.00 1

tfpn111		99.24 98	99.00 101	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.54 122	97.77 186	99.97 96

conf0.01		99.32 80	99.16 86	99.80 95	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.97 96

test_part3							100.00 1		98.23 64		100.00 1		100.00 1	100.00 1

conf0.002		99.32 80	99.16 86	99.80 95	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.97 96

thresconf0.02		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpn_n400		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpnconf		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpnview11		99.32 80	99.16 86	99.79 99	99.79 116	99.73 88	100.00 1	99.71 63	97.66 112	99.71 146	100.00 1	99.78 6	99.70 166	98.15 193	97.67 195	99.94 116

tfpn1000		99.47 69	99.34 72	99.87 81	99.89 96	99.80 80	100.00 1	99.72 61	97.83 98	99.96 88	99.98 141	99.94 4	99.82 152	98.87 161	97.87 176	100.00 1

tfpn_ndepth		99.52 62	99.39 64	99.89 75	99.90 94	99.83 72	100.00 1	99.72 61	97.95 89	99.96 88	99.98 141	99.94 4	99.85 147	99.17 149	97.91 173	100.00 1

conf200view11		99.25 95	99.01 99	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.59 113	97.85 178	99.97 96

thres100view900		99.25 95	99.01 99	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.59 113	97.85 178	99.98 91

tfpn200view9		99.26 93	99.03 97	99.96 36	99.81 100	99.89 49	100.00 1	99.94 18	97.23 146	99.83 125	99.96 160	97.04 139	100.00 1	99.59 113	97.85 178	99.98 91

view600		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

view800		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

conf0.05thres1000		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

tfpn		99.18 102	98.93 112	99.93 62	99.81 100	99.83 72	100.00 1	99.94 18	96.96 163	99.54 157	99.96 160	96.99 151	100.00 1	99.43 129	97.75 188	99.97 96

ESAPD		99.76 15	99.69 18	100.00 1	100.00 1	99.99 1	100.00 1	99.42 121	98.23 64	100.00 1	100.00 1	99.53 26	100.00 1	100.00 1	100.00 1	100.00 1

CANet		99.40 72	99.24 77	99.89 75	99.99 45	99.76 83	100.00 1	99.73 55	98.40 52	99.78 138	100.00 1	95.28 183	99.96 114	100.00 1	99.99 88	99.96 107

Fast-Effi-MVS+-dtu		98.38 163	98.56 147	97.82 241	99.58 158	94.44 297	100.00 1	99.16 254	96.75 173	99.51 162	99.63 229	95.03 189	99.60 174	97.71 211	99.67 117	99.42 203

Effi-MVS+-dtu		98.51 157	98.86 122	97.47 252	99.77 124	94.21 300	100.00 1	98.94 303	97.61 123	99.91 116	98.75 293	95.89 171	99.51 198	99.36 137	99.48 121	98.68 208

CANet_DTU		99.02 119	98.90 120	99.41 140	99.88 97	98.71 165	100.00 1	99.29 211	98.84 30	100.00 1	100.00 1	94.02 199	100.00 1	98.08 200	99.96 100	99.52 201

MVS_0304		98.98 125	98.66 136	99.94 59	99.95 82	99.86 67	100.00 1	99.29 211	98.38 55	99.81 136	100.00 1	90.60 251	100.00 1	100.00 1	99.91 105	99.95 112

MP-MVS-pluss		99.61 55	99.50 57	99.97 24	99.98 72	99.92 36	100.00 1	99.42 121	97.53 131	99.77 139	100.00 1	98.77 105	100.00 1	99.99 45	100.00 1	99.99 89
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HSP-MVS		99.81 9	99.77 8	99.94 59	100.00 1	99.86 67	100.00 1	99.42 121	98.87 29	100.00 1	100.00 1	99.65 18	99.96 114	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 7	99.77 8	99.99 5	100.00 1	99.96 10	100.00 1	99.43 115	99.05 12	100.00 1	100.00 1	99.45 43	99.99 82	100.00 1	100.00 1	100.00 1

OPM-MVS		97.21 194	97.18 192	97.32 257	98.08 273	94.66 291	100.00 1	99.28 219	98.65 42	98.92 193	99.98 141	86.03 300	99.56 183	98.28 189	95.41 219	97.72 261

ACMMP_Plus		99.67 46	99.57 51	99.97 24	99.98 72	99.92 36	100.00 1	99.42 121	97.83 98	100.00 1	100.00 1	98.89 99	100.00 1	99.98 59	100.00 1	100.00 1

zzz-MVS		99.68 43	99.59 47	99.97 24	99.99 45	99.91 39	100.00 1	99.42 121	98.32 61	99.94 109	100.00 1	98.65 109	100.00 1	99.96 66	100.00 1	100.00 1

mvs-test1		98.89 133	98.99 104	98.60 188	99.77 124	95.96 265	100.00 1	98.94 303	97.61 123	99.93 114	99.92 179	95.89 171	99.93 133	99.36 137	99.50 120	99.90 136

pmmvs5		95.94 259	95.61 252	96.95 271	97.42 309	94.66 291	100.00 1	98.08 329	93.60 275	97.05 275	99.43 247	87.02 291	98.46 279	95.76 246	92.12 263	97.72 261

Fast-Effi-MVS+		98.40 162	98.02 171	99.55 131	99.63 145	99.06 147	100.00 1	99.15 255	95.07 233	99.42 167	99.95 172	93.26 208	99.73 162	97.44 220	98.24 158	99.87 160

MTAPA		99.68 43	99.59 47	99.97 24	99.99 45	99.91 39	100.00 1	99.42 121	98.32 61	99.94 109	100.00 1	98.65 109	100.00 1	99.96 66	100.00 1	100.00 1

TEST9					100.00 1	99.95 19	100.00 1	99.42 121	97.65 119	100.00 1	100.00 1	99.53 26	99.97 99

train_agg		99.71 32	99.63 39	99.97 24	100.00 1	99.95 19	100.00 1	99.42 121	97.70 109	100.00 1	100.00 1	99.51 31	99.97 99	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 39	100.00 1	99.42 121	97.70 109	100.00 1	100.00 1	99.51 31	99.98 95

agg_prior3		99.71 32	99.63 39	99.97 24	100.00 1	99.95 19	100.00 1	99.42 121	97.71 108	100.00 1	100.00 1	99.51 31	99.97 99	100.00 1	100.00 1	100.00 1

agg_prior1		99.71 32	99.63 39	99.95 47	100.00 1	99.88 59	100.00 1	99.42 121	97.72 106	100.00 1	100.00 1	99.51 31	99.97 99	100.00 1	100.00 1	100.00 1

canonicalmvs		99.03 115	98.73 132	99.94 59	99.75 128	99.95 19	100.00 1	99.30 207	97.64 121	100.00 1	100.00 1	95.22 185	99.97 99	99.76 97	96.90 210	99.91 128

alignmvs		99.38 74	99.21 80	99.91 69	99.73 129	99.92 36	100.00 1	99.51 80	97.61 123	100.00 1	100.00 1	99.06 81	99.93 133	99.83 86	97.12 206	99.90 136

nrg030		97.64 182	97.27 189	98.75 183	98.34 249	99.53 110	100.00 1	99.22 239	96.21 205	98.27 237	99.95 172	94.40 195	98.98 227	99.23 146	89.78 293	97.75 222

FIs		97.95 176	97.73 180	98.62 187	98.53 244	99.24 137	100.00 1	99.43 115	96.74 174	97.87 253	99.82 194	95.27 184	98.89 234	98.78 165	93.07 253	97.74 246

FC-MVSNet-test		97.84 177	97.63 182	98.45 192	98.30 253	99.05 148	100.00 1	99.43 115	96.63 185	97.61 262	99.82 194	95.19 186	98.57 271	98.64 173	93.05 254	97.73 256

HFP-MVS		99.74 23	99.67 27	99.96 36	100.00 1	99.89 49	100.00 1	99.76 46	97.95 89	100.00 1	100.00 1	99.31 55	100.00 1	99.99 45	100.00 1	100.00 1

v148		96.29 242	95.84 241	97.63 243	97.74 290	96.53 262	100.00 1	99.07 285	93.52 276	98.01 248	99.42 248	91.22 239	98.60 266	96.37 243	87.22 313	97.75 222

AllTest		98.55 152	98.40 154	98.99 168	99.93 88	97.35 240	100.00 1	99.40 155	97.08 159	99.09 184	99.98 141	93.37 205	99.95 120	96.94 231	99.84 111	99.68 193

v1141		96.66 220	96.12 228	98.26 208	98.05 276	98.28 188	100.00 1	99.09 275	93.71 264	98.59 217	99.04 268	91.57 230	98.73 253	95.05 262	90.00 287	97.75 222

region2R		99.72 29	99.64 35	99.97 24	100.00 1	99.90 45	100.00 1	99.74 53	97.86 97	100.00 1	100.00 1	99.19 73	100.00 1	99.99 45	100.00 1	100.00 1

test_normal		96.24 244	95.12 269	99.60 121	96.00 322	99.16 143	100.00 1	99.29 211	96.43 194	88.96 321	98.40 308	80.06 326	99.90 137	98.53 178	98.39 147	99.75 182

v1neww		96.77 211	96.27 219	98.26 208	98.16 263	98.23 195	100.00 1	99.09 275	93.94 260	98.73 207	99.05 265	91.65 226	98.82 242	95.75 247	90.04 284	97.74 246

#test#		99.75 18	99.68 22	99.96 36	100.00 1	99.89 49	100.00 1	99.76 46	98.07 77	100.00 1	100.00 1	99.31 55	100.00 1	99.99 45	100.00 1	100.00 1

Regformer-3		99.73 26	99.65 31	99.96 36	100.00 1	99.89 49	100.00 1	99.44 106	98.40 52	100.00 1	100.00 1	99.03 86	99.97 99	99.99 45	100.00 1	100.00 1

Regformer-4		99.73 26	99.65 31	99.95 47	100.00 1	99.89 49	100.00 1	99.44 106	98.40 52	100.00 1	100.00 1	99.03 86	99.97 99	99.99 45	100.00 1	100.00 1

Regformer-1		99.75 18	99.68 22	99.98 16	100.00 1	99.94 28	100.00 1	99.44 106	98.43 49	100.00 1	100.00 1	99.23 69	99.99 82	99.99 45	100.00 1	100.00 1

Regformer-2		99.75 18	99.68 22	99.97 24	100.00 1	99.94 28	100.00 1	99.44 106	98.42 51	100.00 1	100.00 1	99.22 70	99.99 82	99.99 45	100.00 1	100.00 1

v7new		96.77 211	96.27 219	98.26 208	98.16 263	98.23 195	100.00 1	99.09 275	93.94 260	98.73 207	99.05 265	91.65 226	98.82 242	95.75 247	90.04 284	97.74 246

HPM-MVS++		99.82 7	99.76 10	99.99 5	99.99 45	99.98 6	100.00 1	99.83 40	98.88 27	99.96 88	100.00 1	99.21 71	100.00 1	100.00 1	100.00 1	99.99 89

test_prior4						99.93 33	100.00 1

XVS		99.79 12	99.73 14	99.98 16	100.00 1	99.94 28	100.00 1	99.75 50	98.67 40	100.00 1	100.00 1	99.16 76	100.00 1	100.00 1	100.00 1	100.00 1

test_prior3		99.81 9	99.78 6	99.90 72	100.00 1	99.75 85	100.00 1	99.73 55	98.82 33	100.00 1	100.00 1	99.47 40	99.97 99	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 33	100.00 1	100.00 1	99.47 40		100.00 1	100.00 1

X-MVStestdata		97.04 204	96.06 232	99.98 16	100.00 1	99.94 28	100.00 1	99.75 50	98.67 40	100.00 1	66.97 358	99.16 76	100.00 1	100.00 1	100.00 1	100.00 1

divwei89l23v2f112		96.68 219	96.15 226	98.25 212	98.03 279	98.27 190	100.00 1	99.09 275	93.79 263	98.59 217	99.04 268	91.54 232	98.73 253	95.29 260	89.99 288	97.75 222

旧先验2							100.00 1		98.11 75	100.00 1			100.00 1	99.67 107

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 84				100.00 1	99.33 140		100.00 1

原ACMM2							100.00 1

test222					99.99 45	99.90 45	100.00 1	99.69 70	97.66 112	100.00 1	100.00 1	99.30 61			100.00 1	100.00 1

testdata1							100.00 1		98.77 37

v6		96.77 211	96.26 221	98.29 204	98.10 269	98.27 190	100.00 1	99.09 275	93.95 259	98.73 207	99.06 262	91.51 233	98.86 239	95.83 245	90.04 284	97.74 246

v2v482		96.70 218	96.18 223	98.27 206	98.04 278	98.39 177	100.00 1	99.13 264	94.19 256	98.58 219	99.08 261	90.48 254	98.67 261	95.69 250	90.44 282	97.75 222

v1		96.66 220	96.12 228	98.30 203	98.09 270	98.33 183	100.00 1	99.09 275	93.69 269	98.68 210	99.04 268	91.42 237	98.83 241	95.05 262	89.99 288	97.75 222

SD-MVS		99.81 9	99.75 12	99.99 5	99.99 45	99.96 10	100.00 1	99.42 121	99.01 18	100.00 1	100.00 1	99.33 51	100.00 1	100.00 1	100.00 1	100.00 1

GA-MVS		97.72 180	97.27 189	99.06 162	99.24 201	97.93 219	100.00 1	99.24 233	95.80 215	98.99 191	99.64 225	89.77 264	99.36 211	95.12 261	97.62 202	99.89 141

MSLP-MVS++		99.89 1	99.85 2	99.99 5	100.00 1	99.96 10	100.00 1	99.95 15	99.11 6	100.00 1	100.00 1	99.60 19	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 6	99.99 5	100.00 1	99.98 6	100.00 1	99.44 106	99.06 10	100.00 1	100.00 1	99.56 23	99.99 82	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 48	99.55 55	99.97 24	99.99 45	99.91 39	100.00 1	99.48 82	97.54 130	100.00 1	100.00 1	98.97 89	99.99 82	99.98 59	100.00 1	100.00 1

pmmvs4		97.17 196	96.80 199	98.27 206	97.68 292	98.64 169	100.00 1	99.18 250	94.22 254	98.55 220	99.71 207	93.67 202	98.47 278	95.66 251	92.57 258	97.71 268

test-LLR		99.03 115	98.91 117	99.40 141	99.40 189	99.28 131	100.00 1	99.45 97	96.70 178	99.42 167	99.12 258	99.31 55	99.01 224	96.82 236	99.99 88	99.91 128

TESTMET0.1,1		99.08 110	98.96 106	99.44 136	99.63 145	99.38 124	100.00 1	99.45 97	95.53 222	99.48 164	100.00 1	99.71 16	99.02 223	96.84 235	99.99 88	99.91 128

test-mter		98.96 127	98.82 124	99.40 141	99.40 189	99.28 131	100.00 1	99.45 97	95.44 231	99.42 167	99.12 258	99.70 17	99.01 224	96.82 236	99.99 88	99.91 128

ACMMPR		99.74 23	99.67 27	99.96 36	100.00 1	99.89 49	100.00 1	99.76 46	97.95 89	100.00 1	100.00 1	99.29 62	100.00 1	99.99 45	100.00 1	100.00 1

testgi		96.18 247	95.93 238	96.93 273	98.98 224	94.20 301	100.00 1	99.07 285	97.16 151	96.06 291	99.86 186	84.08 312	97.79 307	90.38 307	97.80 184	98.81 207

test20.03		93.11 293	92.85 287	93.88 315	95.19 327	91.83 318	100.00 1	98.87 307	93.68 270	92.76 310	98.88 288	89.20 272	92.71 341	77.88 340	89.19 298	97.09 313

thres600view7		99.24 98	99.00 101	99.95 47	99.81 100	99.87 62	100.00 1	99.94 18	97.13 153	99.83 125	99.96 160	97.01 143	100.00 1	99.54 122	97.77 186	99.97 96

MP-MVS		99.61 55	99.49 59	99.98 16	99.99 45	99.94 28	100.00 1	99.42 121	97.82 100	99.99 76	100.00 1	98.20 119	100.00 1	99.99 45	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		80.17 318	81.95 319	74.80 339	58.54 359	59.58 357	100.00 1	87.14 359	76.09 342	99.61 156	100.00 1	67.06 345	74.19 358	98.84 162	50.30 349	90.64 345

thres400		99.26 93	99.03 97	99.95 47	99.81 100	99.89 49	100.00 1	99.94 18	97.23 146	99.83 125	99.96 160	97.04 139	100.00 1	99.59 113	97.85 178	99.97 96

test123		79.44 320	79.23 322	80.05 335	80.03 350	71.72 344	100.00 1	77.93 362	62.52 347	94.81 299	99.69 212	78.21 334	74.53 357	92.57 290	27.33 357	93.90 338

thres200		99.27 91	99.04 96	99.96 36	99.81 100	99.90 45	100.00 1	99.94 18	97.31 145	99.83 125	99.96 160	97.04 139	100.00 1	99.62 112	97.88 175	99.98 91

test0.0.03 1		98.12 171	98.03 170	98.39 195	99.11 204	98.07 210	100.00 1	99.93 32	96.70 178	96.91 279	99.95 172	99.31 55	98.19 286	91.93 295	98.44 142	98.91 206

testus		91.61 305	92.92 285	87.70 324	93.91 330	74.98 341	100.00 1	98.19 324	92.59 291	93.90 306	98.68 296	81.43 322	90.77 344	79.82 336	97.71 194	97.34 308

pmmvs3		90.62 308	89.36 309	94.40 309	90.53 337	91.49 321	100.00 1	96.73 346	84.21 335	93.65 308	96.65 317	82.56 318	94.83 335	82.28 331	77.62 337	96.89 314

test2356		92.55 297	93.94 275	88.39 323	93.49 331	78.42 337	100.00 1	99.40 155	91.64 297	93.93 305	98.74 294	91.11 244	88.67 346	79.37 337	97.79 185	98.08 214

test1235678		88.02 313	88.73 311	85.90 329	87.57 342	71.70 345	100.00 1	97.42 341	87.52 327	86.19 334	96.39 319	81.60 320	87.93 347	70.72 345	91.18 273	98.08 214

PGM-MVS		99.69 40	99.61 45	99.95 47	99.99 45	99.85 70	100.00 1	99.58 73	97.69 111	100.00 1	100.00 1	99.44 44	100.00 1	99.79 91	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 1	100.00 1	99.73 55	99.19 5	100.00 1	100.00 1	99.31 55	100.00 1	100.00 1	100.00 1	100.00 1

mvs_anonymous		98.80 138	98.60 144	99.38 144	99.57 160	99.24 137	100.00 1	99.21 243	95.87 210	98.92 193	99.82 194	96.39 167	99.03 222	99.13 151	98.50 137	99.88 153

CDPH-MVS		99.73 26	99.64 35	99.99 5	100.00 1	99.97 8	100.00 1	99.42 121	98.02 80	100.00 1	100.00 1	99.32 54	99.99 82	100.00 1	100.00 1	100.00 1

diffmvs		98.52 155	98.11 165	99.73 112	99.59 154	99.47 119	100.00 1	99.19 246	93.68 270	99.70 152	99.75 203	95.07 187	99.84 148	97.57 215	98.48 139	99.89 141

MDA-MVSNet_test_wron		92.61 296	91.09 302	97.19 263	96.71 316	97.26 245	100.00 1	99.14 259	88.61 322	67.90 349	98.32 311	89.03 273	96.57 313	90.47 306	89.59 294	97.74 246

HQP_MVS		97.71 181	97.82 176	97.37 255	99.00 220	94.80 284	100.00 1	99.40 155	99.00 19	99.08 186	99.97 151	88.58 280	99.55 188	99.79 91	95.57 217	97.76 218

plane_prior2							100.00 1		99.00 19

plane_prior						94.80 284	100.00 1		99.03 16						95.58 213

UniMVSNet_NR-MVSNet		97.16 197	96.80 199	98.22 213	98.38 248	98.41 175	100.00 1	99.45 97	96.14 206	97.76 255	99.64 225	95.05 188	98.50 275	97.98 203	86.84 314	97.75 222

DTE-MVSNet		95.52 268	94.99 272	97.08 264	97.49 305	96.45 263	100.00 1	99.25 230	93.82 262	96.17 289	99.57 237	87.81 284	97.18 311	94.57 267	86.26 319	97.62 292

DU-MVS		96.93 209	96.49 210	98.22 213	98.31 251	98.41 175	100.00 1	99.37 176	96.41 197	97.76 255	99.65 221	92.14 219	98.50 275	97.98 203	86.84 314	97.75 222

UniMVSNet (Re)		97.29 193	96.85 198	98.59 190	98.49 245	99.13 144	100.00 1	99.42 121	96.52 191	98.24 240	98.90 287	94.93 190	98.89 234	97.54 218	87.61 311	97.75 222

Baseline_NR-MVSNet		96.16 250	95.70 249	97.56 248	98.28 254	96.79 258	100.00 1	97.86 336	91.93 296	97.63 260	99.47 246	92.14 219	98.35 283	97.13 227	86.83 316	97.54 299

TranMVSNet+NR-MVSNet		96.45 233	96.01 234	97.79 242	98.00 282	97.62 232	100.00 1	99.35 190	95.98 208	97.31 269	99.64 225	90.09 259	98.00 304	96.89 234	86.80 317	97.75 222

TSAR-MVS + GP.		99.61 55	99.69 18	99.35 146	99.99 45	98.06 211	100.00 1	99.36 182	99.83 2	100.00 1	100.00 1	98.95 92	99.99 82	100.00 1	99.11 124	100.00 1

abl_6		99.53 61	99.40 62	99.92 68	100.00 1	99.76 83	100.00 1	99.42 121	97.21 149	100.00 1	100.00 1	98.12 121	100.00 1	99.82 90	100.00 1	100.00 1

mPP-MVS		99.69 40	99.60 46	99.97 24	100.00 1	99.91 39	100.00 1	99.42 121	97.91 92	100.00 1	100.00 1	99.04 83	100.00 1	100.00 1	100.00 1	100.00 1

DI_MVS_plusplus_test		96.28 243	95.16 267	99.61 118	96.01 321	99.18 142	100.00 1	99.29 211	96.49 193	89.08 318	98.42 307	80.06 326	99.90 137	98.74 168	98.39 147	99.75 182

XVG-OURS-SEG-HR		98.27 167	98.31 158	98.14 219	99.59 154	95.92 267	100.00 1	99.36 182	98.48 47	99.21 177	100.00 1	89.27 271	99.94 131	99.76 97	99.17 122	98.56 212

MVSFormer		98.94 129	98.82 124	99.28 152	99.45 181	99.49 114	100.00 1	99.13 264	95.46 229	99.97 85	100.00 1	96.76 158	98.59 268	98.63 174	100.00 1	99.74 186

jason		99.11 108	98.96 106	99.59 124	99.17 202	99.31 129	100.00 1	99.13 264	97.38 138	99.83 125	100.00 1	95.54 181	99.72 164	99.57 118	99.97 98	99.74 186
jason: jason.
lupinMVS		99.29 89	99.16 86	99.69 114	99.45 181	99.49 114	100.00 1	99.15 255	97.45 134	99.97 85	100.00 1	96.76 158	99.76 159	99.67 107	100.00 1	99.81 171

test_djsdf		97.55 186	97.38 186	98.07 226	97.50 303	97.99 214	100.00 1	99.13 264	95.46 229	98.47 227	99.85 188	92.01 223	98.59 268	98.63 174	95.36 220	97.62 292

HPM-MVS_fast		99.60 58	99.49 59	99.91 69	99.99 45	99.78 82	100.00 1	99.42 121	97.09 157	100.00 1	100.00 1	98.95 92	99.96 114	99.98 59	100.00 1	100.00 1

HPM-MVS		99.59 59	99.50 57	99.89 75	100.00 1	99.70 100	100.00 1	99.42 121	97.46 133	100.00 1	100.00 1	98.60 111	99.96 114	99.99 45	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.30 164	98.36 157	98.13 222	99.58 158	95.91 268	100.00 1	99.36 182	98.69 38	99.23 176	100.00 1	91.20 240	99.92 136	99.34 139	97.82 182	98.56 212

LPG-MVS_test		97.31 191	97.32 188	97.28 259	98.85 235	94.60 294	100.00 1	99.37 176	97.35 140	98.85 198	99.98 141	86.66 294	99.56 183	99.55 119	95.26 224	97.70 269

CHOSEN 1792x2688		99.00 121	98.91 117	99.25 155	99.90 94	97.79 227	100.00 1	99.99 10	98.79 36	98.28 236	100.00 1	93.63 203	99.95 120	99.66 109	99.95 102	100.00 1

EPNet		99.62 53	99.69 18	99.42 139	99.99 45	98.37 180	100.00 1	99.89 38	98.83 31	100.00 1	100.00 1	98.97 89	100.00 1	99.90 76	99.61 119	99.89 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP-NCC					99.07 208		100.00 1		99.04 13	99.17 178

ACMP_Plane					99.07 208		100.00 1		99.04 13	99.17 178

APD-MVS		99.68 43	99.58 49	99.97 24	99.99 45	99.96 10	100.00 1	99.42 121	97.53 131	100.00 1	100.00 1	99.27 66	99.97 99	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 1	100.00 1	99.77 45	99.07 8	100.00 1	100.00 1	99.39 49	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 1	100.00 1	99.71 63	99.07 8	100.00 1	100.00 1	99.59 21	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 73	99.25 75	99.81 92	99.97 76	99.48 118	100.00 1	99.42 121	95.53 222	100.00 1	100.00 1	98.37 118	99.95 120	99.97 64	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 47	100.00 1	99.84 71	100.00 1	99.42 121	97.77 103	100.00 1	100.00 1	99.07 80	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 13	99.71 16	99.98 16	99.76 126	99.95 19	100.00 1	99.42 121	98.69 38	100.00 1	100.00 1	99.52 29	99.99 82	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
BH-w/o		98.82 137	98.81 126	98.88 176	99.62 147	96.71 259	100.00 1	99.28 219	97.09 157	98.81 202	100.00 1	94.91 191	99.96 114	99.54 122	100.00 1	99.96 107

DELS-MVS		99.62 53	99.56 54	99.82 89	99.92 91	99.45 120	100.00 1	99.78 44	98.92 25	99.73 143	100.00 1	97.70 129	100.00 1	99.93 73	100.00 1	100.00 1
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
BH-untuned		98.64 146	98.65 139	98.60 188	99.59 154	96.17 264	100.00 1	99.28 219	96.67 182	98.41 229	100.00 1	94.52 194	99.83 150	99.41 135	100.00 1	99.81 171

MVSTER		98.58 149	98.52 149	98.77 182	99.65 140	99.68 102	100.00 1	99.29 211	95.63 218	98.65 212	99.80 200	99.78 6	98.88 237	98.59 177	95.31 222	97.73 256

CPTT-MVS		99.49 66	99.38 66	99.85 85	100.00 1	99.54 109	100.00 1	99.42 121	97.58 128	99.98 81	100.00 1	97.43 136	100.00 1	99.99 45	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 79	99.18 85	99.78 107	99.82 99	99.49 114	100.00 1	99.95 15	97.36 139	99.63 155	100.00 1	96.45 166	99.95 120	99.79 91	99.65 118	99.89 141

PVSNet_BlendedMVS		98.71 142	98.62 142	98.98 170	99.98 72	99.60 107	100.00 1	100.00 1	97.23 146	100.00 1	99.03 273	96.57 162	99.99 82	100.00 1	94.75 238	97.35 307

PVSNet_Blended		99.48 68	99.36 68	99.83 88	99.98 72	99.60 107	100.00 1	100.00 1	97.79 102	100.00 1	100.00 1	96.57 162	99.99 82	100.00 1	99.88 108	99.90 136

cascas		98.43 159	98.07 168	99.50 134	99.65 140	99.02 150	100.00 1	99.22 239	94.21 255	99.72 144	99.98 141	92.03 221	99.93 133	99.68 105	98.12 165	99.54 200

BH-RMVSNet		98.46 158	98.08 167	99.59 124	99.61 149	99.19 141	100.00 1	99.28 219	97.06 161	98.95 192	100.00 1	88.99 274	99.82 152	98.83 164	100.00 1	99.77 179

WTY-MVS		99.54 60	99.40 62	99.95 47	99.81 100	99.93 33	100.00 1	100.00 1	97.98 84	99.84 123	100.00 1	98.94 94	99.98 95	99.86 81	98.21 160	99.94 116

sss		99.45 70	99.34 72	99.80 95	99.76 126	99.50 111	100.00 1	99.91 37	97.72 106	99.98 81	99.94 176	98.45 116	100.00 1	99.53 125	98.75 132	99.89 141

DP-MVS Recon		99.76 15	99.69 18	99.98 16	100.00 1	99.95 19	100.00 1	99.52 76	97.99 82	99.99 76	100.00 1	99.72 15	100.00 1	99.96 66	100.00 1	100.00 1

MVS_111021_LR		99.70 37	99.65 31	99.88 80	99.96 81	99.70 100	100.00 1	99.97 14	98.96 21	100.00 1	100.00 1	97.93 125	99.95 120	99.99 45	100.00 1	100.00 1

HQP-MVS		97.73 179	97.85 175	97.39 254	99.07 208	94.82 281	100.00 1	99.40 155	99.04 13	99.17 178	99.97 151	88.61 278	99.57 179	99.79 91	95.58 213	97.77 216

HyFIR lowres test		99.32 80	99.24 77	99.58 128	99.95 82	99.26 133	100.00 1	99.99 10	96.72 177	99.29 174	99.91 180	99.49 37	99.47 202	99.74 99	98.08 167	100.00 1

PAPM_NR		99.74 23	99.66 30	99.99 5	100.00 1	99.96 10	100.00 1	99.47 83	97.87 96	100.00 1	100.00 1	99.60 19	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 15	99.68 22	99.99 5	100.00 1	99.96 10	100.00 1	99.47 83	98.16 67	100.00 1	100.00 1	99.51 31	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 187	96.95 194	99.33 148	99.67 136	98.10 208	100.00 1	99.47 83	97.42 137	99.26 175	99.69 212	98.83 101	99.89 139	99.43 129	78.77 333	100.00 1
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
MVS_111021_HR		99.71 32	99.63 39	99.93 62	99.95 82	99.83 72	100.00 1	100.00 1	98.89 26	100.00 1	100.00 1	97.85 126	99.95 120	100.00 1	100.00 1	100.00 1

CSCG		99.28 90	99.35 70	99.05 164	99.99 45	97.15 248	100.00 1	99.47 83	97.44 135	99.42 167	100.00 1	97.83 127	100.00 1	99.99 45	100.00 1	100.00 1

PatchMatch-RL		99.02 119	98.78 128	99.74 110	99.99 45	99.29 130	100.00 1	100.00 1	98.38 55	99.89 120	99.81 197	93.14 210	99.99 82	97.85 208	99.98 95	99.95 112

USDC		95.90 260	95.70 249	96.50 292	98.60 241	92.56 315	100.00 1	98.30 321	97.77 103	96.92 277	99.94 176	81.25 325	99.45 204	93.54 285	94.96 237	97.49 300

PMMVS		99.12 107	98.97 105	99.58 128	99.57 160	98.98 155	100.00 1	99.30 207	97.14 152	99.96 88	100.00 1	96.53 165	99.82 152	99.70 103	98.49 138	99.94 116

PAPM		99.78 13	99.76 10	99.85 85	99.01 216	99.95 19	100.00 1	99.75 50	99.37 3	99.99 76	100.00 1	99.76 14	99.60 174	100.00 1	100.00 1	100.00 1

CNLPA		99.72 29	99.65 31	99.91 69	99.97 76	99.72 95	100.00 1	99.47 83	98.43 49	99.88 121	100.00 1	99.14 78	100.00 1	99.97 64	100.00 1	100.00 1

PHI-MVS		99.50 64	99.39 64	99.82 89	100.00 1	99.45 120	100.00 1	99.94 18	96.38 199	100.00 1	100.00 1	98.18 120	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 88	99.25 75	99.44 136	100.00 1	98.32 186	100.00 1	99.86 39	98.04 79	100.00 1	100.00 1	96.10 170	100.00 1	99.55 119	99.73 115	100.00 1

PVSNet_0	93.57 19	96.41 234	95.74 247	98.41 194	99.84 98	95.22 273	100.00 1	100.00 1	98.08 76	97.55 264	99.78 201	84.40 307	100.00 1	100.00 1	81.99 325	100.00 1

F-COLMAP		99.64 50	99.64 35	99.67 115	99.99 45	99.07 146	100.00 1	99.44 106	98.30 63	99.90 117	100.00 1	99.18 74	99.99 82	99.91 75	100.00 1	99.94 116

DeepPCF-MVS	98.03 4	98.54 153	99.72 15	94.98 305	99.99 45	84.94 331	100.00 1	99.42 121	99.98 1	100.00 1	100.00 1	98.11 122	100.00 1	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 91	99.38 66	98.96 171	99.95 82	97.06 252	100.00 1	99.40 155	98.83 31	99.88 121	100.00 1	97.01 143	99.86 143	99.47 128	99.84 111	99.97 96

DeepC-MVS	97.84 5	99.00 121	98.80 127	99.60 121	99.93 88	99.03 149	100.00 1	99.40 155	98.61 44	99.33 172	100.00 1	92.23 218	99.95 120	99.74 99	99.96 100	99.83 166
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PLC	98.56 2	99.70 37	99.74 13	99.58 128	100.00 1	98.79 162	100.00 1	99.54 75	98.58 45	99.96 88	100.00 1	99.59 21	100.00 1	100.00 1	100.00 1	99.94 116
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMP	97.00 8	97.19 195	97.16 193	97.27 261	98.97 225	94.58 296	100.00 1	99.32 200	97.97 86	97.45 266	99.98 141	85.79 302	99.56 183	99.70 103	95.24 227	97.67 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+	95.58 15	99.03 115	98.71 134	99.96 36	98.99 223	99.89 49	100.00 1	99.51 80	98.96 21	98.32 233	100.00 1	92.78 213	100.00 1	99.87 80	100.00 1	100.00 1

LF4IMVS		96.19 246	96.18 223	96.23 297	98.26 255	92.09 317	100.00 1	97.89 335	97.82 100	97.94 250	99.87 184	82.71 316	99.38 209	97.41 222	93.71 246	97.20 311

TAPA-MVS	96.40 10	97.64 182	97.37 187	98.45 192	99.94 86	95.70 270	100.00 1	99.40 155	97.65 119	99.53 161	100.00 1	99.31 55	99.66 173	80.48 334	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		98.90 132	98.63 141	99.70 113	99.92 91	99.25 135	100.00 1	99.37 176	95.71 216	99.40 171	100.00 1	96.58 161	99.95 120	96.80 238	99.94 103	99.91 128

ACMM	97.17 6	97.37 189	97.40 185	97.29 258	99.01 216	94.64 293	100.00 1	99.25 230	98.07 77	98.44 228	99.98 141	87.38 288	99.55 188	99.25 145	95.19 229	97.69 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 182	97.74 178	97.36 256	99.01 216	94.76 289	100.00 1	99.34 195	99.30 4	99.00 190	99.97 151	87.49 287	99.57 179	99.96 66	95.58 213	97.75 222
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ppachtmachnet_test		96.17 249	95.89 239	97.02 267	97.61 297	95.24 272	99.99 176	99.24 233	93.31 282	96.71 285	99.62 231	94.34 197	98.07 299	89.87 312	92.30 262	97.75 222

Patchmatch-test1		98.23 169	97.91 174	99.17 159	99.41 185	98.25 194	99.99 176	99.45 97	96.91 169	99.76 141	99.69 212	89.65 266	99.37 210	97.55 216	98.79 131	99.89 141

v144192		96.40 236	95.81 242	98.17 217	97.89 287	98.11 207	99.99 176	99.06 293	93.39 279	98.75 205	99.09 260	90.43 255	98.66 262	93.10 287	90.55 281	97.75 222

v1921920		96.16 250	95.50 255	98.14 219	97.88 288	97.96 217	99.99 176	99.07 285	93.33 281	98.60 216	99.24 255	89.37 270	98.71 257	91.28 298	90.74 279	97.75 222

v1192		96.18 247	95.49 257	98.26 208	98.01 280	98.15 205	99.99 176	99.08 283	93.36 280	98.54 221	98.97 282	89.47 269	98.89 234	91.15 300	90.82 276	97.75 222

v1144		96.51 229	95.97 237	98.13 222	97.98 283	98.04 213	99.99 176	99.08 283	93.51 277	98.62 215	98.98 279	90.98 249	98.62 263	93.79 281	90.79 278	97.74 246

v7		96.64 223	96.14 227	98.13 222	98.18 260	97.97 216	99.99 176	99.09 275	93.62 274	98.76 204	99.30 252	91.13 243	98.70 258	94.37 271	90.80 277	97.74 246

V42		96.65 222	96.16 225	98.11 225	98.17 262	98.23 195	99.99 176	99.09 275	93.97 257	98.74 206	99.05 265	91.09 246	98.82 242	95.46 255	89.90 291	97.27 310

MVS_Test		98.93 130	98.65 139	99.77 109	99.62 147	99.50 111	99.99 176	99.19 246	95.52 224	99.96 88	99.86 186	96.54 164	99.98 95	98.65 172	98.48 139	99.82 169

YYNet1		92.44 298	90.92 303	97.03 266	96.20 318	97.06 252	99.99 176	99.14 259	88.21 324	67.93 348	98.43 306	88.63 277	96.28 326	90.64 302	89.08 300	97.74 246

K. test v3		95.46 271	95.14 268	96.40 293	97.53 302	93.40 307	99.99 176	99.23 237	95.49 227	92.70 312	99.73 204	84.26 308	98.12 291	93.94 280	93.38 251	97.68 276

XVG-ACMP-BASELINE		96.60 226	96.52 209	96.84 283	98.41 247	93.29 308	99.99 176	99.32 200	97.76 105	98.51 225	99.29 253	81.95 319	99.54 191	98.40 182	95.03 235	97.68 276

Test_1112_low_res		98.83 136	98.60 144	99.51 132	99.69 132	98.75 163	99.99 176	99.14 259	96.81 171	98.84 200	99.06 262	97.45 134	99.89 139	98.66 170	97.75 188	99.89 141

1112_ss		98.91 131	98.71 134	99.51 132	99.69 132	98.75 163	99.99 176	99.15 255	96.82 170	98.84 200	100.00 1	97.45 134	99.89 139	98.66 170	97.75 188	99.89 141

IterMVS		96.76 215	96.46 212	97.63 243	99.41 185	96.89 255	99.99 176	99.13 264	94.74 240	97.59 263	99.66 220	89.63 268	98.28 285	95.71 249	92.31 261	97.72 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		97.37 189	98.24 160	94.76 307	99.80 114	84.57 332	99.99 176	99.05 295	94.95 236	99.82 134	100.00 1	94.03 198	100.00 1	98.15 193	98.38 150	99.70 191

OpenMVS	95.20 17	98.76 139	98.41 153	99.78 107	98.89 230	99.81 78	99.99 176	99.76 46	98.02 80	98.02 247	100.00 1	91.44 236	100.00 1	99.63 111	99.97 98	99.55 199

our_test_3		96.51 229	96.35 215	96.98 270	97.61 297	95.05 276	99.98 193	99.01 300	94.68 241	96.77 284	99.06 262	95.87 173	98.14 289	91.81 296	92.37 260	97.75 222

Anonymous20231206		93.45 286	93.17 284	94.30 310	95.00 328	89.69 324	99.98 193	98.43 320	93.30 283	94.50 302	98.59 300	90.52 252	95.73 332	77.46 342	90.73 280	97.48 302

EI-MVSNet-Vis-set		99.70 37	99.64 35	99.87 81	100.00 1	99.64 105	99.98 193	99.44 106	98.35 59	99.99 76	100.00 1	99.04 83	99.96 114	99.98 59	100.00 1	100.00 1

Test4		92.63 295	90.45 306	99.17 159	92.81 332	99.02 150	99.98 193	99.13 264	96.60 186	82.04 341	96.27 320	51.35 350	98.75 252	97.55 216	98.36 151	99.75 182

Vis-MVSNet (Re-imp)		98.99 123	98.89 121	99.29 151	99.64 144	98.89 159	99.98 193	99.31 205	96.74 174	99.48 164	100.00 1	98.11 122	99.10 219	98.39 183	98.34 152	99.89 141

MG-MVS		99.75 18	99.68 22	99.97 24	100.00 1	99.91 39	99.98 193	99.47 83	99.09 7	100.00 1	100.00 1	98.59 112	100.00 1	99.95 71	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 112	98.76 130	99.96 36	98.86 234	99.90 45	99.98 193	99.93 32	98.95 24	98.49 226	100.00 1	92.91 212	100.00 1	99.71 101	100.00 1	100.00 1

CHOSEN 280x420		99.85 3	99.87 1	99.80 95	99.99 45	99.97 8	99.97 200	99.98 13	98.96 21	100.00 1	100.00 1	99.96 3	99.42 207	100.00 1	100.00 1	100.00 1

WR-MVS		97.09 200	96.64 204	98.46 191	98.43 246	99.09 145	99.97 200	99.33 197	95.62 219	97.76 255	99.67 218	91.17 241	98.56 272	98.49 180	89.28 297	97.74 246

new_pmnet		94.11 280	93.47 282	96.04 299	96.60 317	92.82 312	99.97 200	98.91 306	90.21 307	95.26 296	98.05 312	85.89 301	98.14 289	84.28 328	92.01 265	97.16 312

v1240		95.96 258	95.25 265	98.07 226	97.91 286	97.87 224	99.96 203	99.07 285	93.24 284	98.64 214	98.96 283	88.98 275	98.61 264	89.58 314	90.92 275	97.75 222

1111		88.62 311	89.13 310	87.08 326	87.71 340	77.22 338	99.96 203	97.71 338	85.51 330	83.47 339	96.26 321	95.76 176	90.96 342	66.63 348	89.17 299	93.07 341

.test1245		85.18 315	84.81 316	86.28 328	87.71 340	77.22 338	99.96 203	97.71 338	85.51 330	83.47 339	96.26 321	95.76 176	90.96 342	66.63 348	50.30 349	90.64 345

EMVS		69.88 328	69.09 328	72.24 344	84.70 344	65.82 351	99.96 203	87.08 360	49.82 355	71.51 347	84.74 351	49.30 351	75.32 356	50.97 356	43.71 352	75.59 356

EPNet_dtu		98.53 154	98.23 162	99.43 138	99.92 91	99.01 153	99.96 203	99.47 83	98.80 35	99.96 88	99.96 160	98.56 113	99.30 214	87.78 322	99.68 116	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
API-MVS		99.72 29	99.70 17	99.79 99	99.97 76	99.37 126	99.96 203	99.94 18	98.48 47	100.00 1	100.00 1	98.92 96	100.00 1	100.00 1	100.00 1	100.00 1

PS-MVSNAJ		99.64 50	99.57 51	99.85 85	99.78 122	99.81 78	99.95 209	99.42 121	98.38 55	100.00 1	100.00 1	98.75 106	100.00 1	99.88 79	99.99 88	99.74 186

jajsoiax		97.07 202	96.79 201	97.89 240	97.28 312	97.12 249	99.95 209	99.19 246	96.55 188	97.31 269	99.69 212	87.35 290	98.91 231	98.70 169	95.12 233	97.66 282

EI-MVSNet-UG-set		99.69 40	99.63 39	99.87 81	99.99 45	99.64 105	99.95 209	99.44 106	98.35 59	100.00 1	100.00 1	98.98 88	99.97 99	99.98 59	100.00 1	100.00 1

E-PMN		70.72 326	70.06 327	72.69 343	83.92 346	65.48 352	99.95 209	92.72 354	49.88 354	72.30 346	86.26 350	47.17 353	77.43 355	53.83 355	44.49 351	75.17 357

v8		96.35 239	95.73 248	98.21 215	98.11 268	98.23 195	99.94 213	99.07 285	92.66 290	98.29 235	99.00 278	91.46 235	98.77 249	94.17 274	88.83 303	97.62 292

test12356		86.30 314	87.15 315	83.77 333	84.24 345	72.00 343	99.94 213	96.80 345	84.63 334	85.16 337	96.51 318	77.02 337	86.31 350	70.06 346	89.93 290	93.00 342

v748		95.47 270	94.95 273	97.04 265	97.46 308	94.76 289	99.93 215	99.10 272	91.95 295	97.11 274	99.18 257	89.94 263	98.68 259	94.01 278	85.88 320	97.67 280

CDS-MVSNet		98.96 127	98.95 110	99.01 167	99.48 180	98.36 181	99.93 215	99.37 176	96.79 172	99.31 173	99.83 191	99.77 13	98.91 231	98.07 201	97.98 170	99.77 179
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		99.51 63	99.41 61	99.82 89	99.70 131	99.73 88	99.92 217	99.40 155	98.15 69	100.00 1	100.00 1	98.50 115	100.00 1	99.85 83	99.13 123	99.74 186

OurMVSNet-221017-0		96.14 252	95.98 236	96.62 290	97.49 305	93.44 306	99.92 217	98.16 325	95.86 212	97.65 259	99.95 172	85.71 303	98.78 246	94.93 265	94.18 245	97.64 289

EPP-MVSNet		99.10 109	99.00 101	99.40 141	99.51 174	98.68 167	99.92 217	99.43 115	95.47 228	99.65 154	100.00 1	99.51 31	99.76 159	99.53 125	98.00 169	99.75 182

pmmvs-eth3d		91.73 303	90.67 304	94.92 306	91.63 335	92.71 313	99.90 220	98.54 319	91.19 302	88.08 330	95.50 328	79.31 332	96.13 328	90.55 305	81.32 328	95.91 333

PEN-MVS		96.01 257	95.48 259	97.58 247	97.74 290	97.26 245	99.90 220	99.29 211	94.55 245	96.79 283	99.55 238	87.38 288	97.84 306	96.92 233	87.24 312	97.65 286

N_pmnet		91.88 302	93.37 283	87.40 325	97.24 313	66.33 350	99.90 220	91.05 355	89.77 314	95.65 295	98.58 301	90.05 261	98.11 293	85.39 326	92.72 257	97.75 222

PS-MVSNAJss		98.03 173	98.06 169	97.94 236	97.63 295	97.33 243	99.89 223	99.23 237	96.27 203	98.03 245	99.59 234	98.75 106	98.78 246	98.52 179	94.61 242	97.70 269

DWT-MVSNet_test		99.22 100	99.22 79	99.22 157	99.56 162	97.98 215	99.89 223	99.39 169	97.20 150	99.96 88	100.00 1	99.52 29	99.82 152	99.11 153	98.34 152	99.87 160

IterMVS-LS		97.56 185	97.44 183	97.92 239	99.38 193	97.90 220	99.89 223	99.10 272	94.41 249	98.32 233	99.54 240	97.21 137	98.11 293	97.50 219	91.62 268	97.75 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v52		95.88 264	95.35 263	97.48 251	97.66 293	97.38 239	99.88 226	99.07 285	92.19 292	98.09 241	99.02 275	90.06 260	98.72 255	93.58 283	88.61 305	96.34 320

V4		95.89 262	95.36 262	97.49 250	97.65 294	97.40 236	99.88 226	99.07 285	92.19 292	98.08 242	99.01 276	90.20 257	98.72 255	93.57 284	88.63 304	96.34 320

QAPM		98.99 123	98.66 136	99.96 36	99.01 216	99.87 62	99.88 226	99.93 32	97.99 82	98.68 210	100.00 1	93.17 209	100.00 1	99.32 142	100.00 1	100.00 1

v7n		96.06 256	95.42 261	97.99 234	97.58 300	97.35 240	99.86 229	99.11 271	92.81 289	97.91 252	99.49 244	90.99 248	98.92 230	92.51 291	88.49 306	97.70 269

LCM-MVSNet-Re		96.52 228	97.21 191	94.44 308	99.27 199	85.80 330	99.85 230	96.61 348	95.98 208	92.75 311	98.48 303	93.97 200	97.55 310	99.58 117	98.43 143	99.98 91

SixPastTwentyTwo		95.71 266	95.49 257	96.38 294	97.42 309	93.01 309	99.84 231	98.23 322	94.75 238	95.98 292	99.97 151	85.35 304	98.43 280	94.71 266	93.17 252	97.69 274

new-patchmatchnet		90.30 309	89.46 308	92.84 317	90.77 336	88.55 327	99.83 232	98.80 309	90.07 309	87.86 331	95.00 337	78.77 333	94.30 337	84.86 327	79.15 332	95.68 337

v18		93.55 282	92.42 290	96.91 274	98.12 266	98.26 193	99.83 232	98.74 310	89.99 310	88.94 322	95.64 325	92.02 222	96.54 314	90.27 309	78.26 334	95.94 328

v17		93.53 283	92.42 290	96.88 279	98.09 270	98.16 203	99.83 232	98.74 310	89.95 311	89.05 319	95.65 324	91.99 224	96.54 314	90.31 308	78.15 336	95.95 326

v10		96.14 252	95.50 255	98.07 226	98.19 259	97.96 217	99.83 232	99.07 285	92.10 294	98.07 244	98.94 284	91.07 247	98.61 264	92.41 294	89.82 292	97.63 290

tpmp4_e23		98.71 142	98.66 136	98.84 178	99.60 151	96.98 254	99.83 232	99.35 190	94.29 252	99.90 117	99.50 243	99.17 75	99.73 162	98.22 190	98.13 164	99.81 171

v16		93.48 285	92.34 292	96.89 275	98.13 265	98.19 201	99.82 237	98.74 310	89.94 312	88.90 323	95.62 326	91.65 226	96.54 314	90.03 311	78.22 335	95.95 326

v15		93.28 287	92.08 293	96.87 280	98.07 274	98.22 199	99.82 237	98.74 310	89.83 313	88.74 325	95.44 330	91.55 231	96.47 318	89.70 313	76.87 338	95.93 329

anonymousdsp		97.16 197	96.88 196	98.00 232	97.08 314	98.06 211	99.81 239	99.15 255	94.58 244	97.84 254	99.62 231	90.49 253	98.60 266	97.98 203	95.32 221	97.33 309

JIA-IIPM		97.09 200	96.34 216	99.36 145	98.88 231	98.59 170	99.81 239	99.43 115	84.81 333	99.96 88	90.34 345	98.55 114	99.52 196	97.00 230	98.28 157	99.98 91

ACMH+	96.20 13	96.49 232	96.33 217	97.00 268	99.06 212	93.80 303	99.81 239	99.31 205	97.32 143	95.89 294	99.97 151	82.62 317	99.54 191	98.34 186	94.63 241	97.65 286

V14		93.24 288	92.04 294	96.87 280	98.07 274	98.20 200	99.80 242	98.74 310	89.76 315	88.64 326	95.53 327	91.47 234	96.47 318	89.49 315	76.84 339	95.92 330

PM-MVS		88.39 312	87.41 314	91.31 318	91.73 334	82.02 335	99.79 243	96.62 347	91.06 303	90.71 316	95.73 323	48.60 352	95.96 329	90.56 304	81.91 327	95.97 325

xiu_mvs_v1_base_debu		99.35 76	99.21 80	99.79 99	99.67 136	99.71 96	99.78 244	99.36 182	98.13 71	100.00 1	100.00 1	97.00 148	100.00 1	99.83 86	99.07 125	99.66 195

xiu_mvs_v1_base		99.35 76	99.21 80	99.79 99	99.67 136	99.71 96	99.78 244	99.36 182	98.13 71	100.00 1	100.00 1	97.00 148	100.00 1	99.83 86	99.07 125	99.66 195

xiu_mvs_v1_base_debi		99.35 76	99.21 80	99.79 99	99.67 136	99.71 96	99.78 244	99.36 182	98.13 71	100.00 1	100.00 1	97.00 148	100.00 1	99.83 86	99.07 125	99.66 195

IS-MVSNet		99.08 110	98.91 117	99.59 124	99.65 140	99.38 124	99.78 244	99.24 233	96.70 178	99.51 162	100.00 1	98.44 117	99.52 196	98.47 181	98.39 147	99.88 153

V9		93.20 289	91.99 296	96.85 282	98.05 276	98.16 203	99.77 248	98.73 316	89.67 316	88.64 326	95.44 330	91.34 238	96.47 318	89.46 316	76.79 340	95.92 330

CP-MVSNet		96.73 216	96.25 222	98.18 216	98.21 258	98.67 168	99.77 248	99.32 200	95.06 234	97.20 273	99.65 221	90.10 258	98.19 286	98.06 202	88.90 301	97.66 282

tpm cat1		98.05 172	97.76 177	98.92 174	99.50 177	97.10 251	99.77 248	99.30 207	90.20 308	99.72 144	98.71 295	97.71 128	99.86 143	96.75 241	98.20 161	99.81 171

v12		93.18 290	91.97 297	96.80 286	98.09 270	98.10 208	99.76 251	98.73 316	89.61 317	88.45 329	95.41 333	91.63 229	96.39 323	89.32 318	76.76 342	95.91 333

EU-MVSNet		96.63 224	96.53 208	96.94 272	97.59 299	96.87 256	99.76 251	99.47 83	96.35 200	96.85 281	99.78 201	92.57 216	96.27 327	95.33 256	91.08 274	97.68 276

ACMMP		99.65 48	99.57 51	99.89 75	99.99 45	99.66 103	99.75 253	99.73 55	98.16 67	99.75 142	100.00 1	98.90 98	100.00 1	99.96 66	99.88 108	100.00 1
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
v13		93.18 290	91.97 297	96.81 285	98.12 266	98.12 206	99.74 254	98.73 316	89.47 318	88.52 328	95.40 334	91.72 225	96.41 321	89.23 321	76.77 341	95.92 330

EI-MVSNet		97.98 175	97.93 173	98.16 218	99.11 204	97.84 226	99.74 254	99.29 211	94.39 250	98.65 212	100.00 1	97.21 137	98.88 237	97.62 214	95.31 222	97.75 222

CVMVSNet		98.56 151	98.47 152	98.82 179	99.11 204	97.67 230	99.74 254	99.47 83	97.57 129	99.06 188	100.00 1	95.72 178	98.97 228	98.21 191	97.33 205	99.83 166

MAR-MVS		99.49 66	99.36 68	99.89 75	99.97 76	99.66 103	99.74 254	99.95 15	97.89 93	100.00 1	100.00 1	96.71 160	100.00 1	100.00 1	100.00 1	100.00 1
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
UnsupCasMVSNet_eth		94.25 278	93.89 276	95.34 301	97.63 295	92.13 316	99.73 258	99.36 182	94.88 237	92.78 309	98.63 299	82.72 315	96.53 317	94.57 267	84.73 322	97.36 306

DeepC-MVS_fast	98.92 1	99.75 18	99.67 27	99.99 5	99.99 45	99.96 10	99.73 258	99.52 76	99.06 10	100.00 1	100.00 1	98.80 104	100.00 1	99.95 71	100.00 1	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
v11		93.14 292	91.93 299	96.77 288	98.01 280	98.17 202	99.72 260	98.74 310	89.39 320	88.81 324	95.49 329	91.15 242	96.29 325	86.78 324	79.25 331	95.86 335

WR-MVS_H		96.73 216	96.32 218	97.95 235	98.26 255	97.88 222	99.72 260	99.43 115	95.06 234	96.99 276	98.68 296	93.02 211	98.53 273	97.43 221	88.33 307	97.43 303

PS-CasMVS		96.34 240	95.78 245	98.03 231	98.18 260	98.27 190	99.71 262	99.32 200	94.75 238	96.82 282	99.65 221	86.98 293	98.15 288	97.74 210	88.85 302	97.66 282

FMVSNet3		97.30 192	96.95 194	98.37 197	99.65 140	99.25 135	99.71 262	99.28 219	94.23 253	98.53 222	98.91 286	93.30 207	98.11 293	95.31 257	93.60 247	97.73 256

testing_2		90.79 306	88.26 312	98.35 199	89.11 339	98.56 171	99.70 264	99.14 259	93.70 268	73.80 344	94.06 340	55.19 347	98.76 251	97.17 226	92.50 259	97.74 246

PMMVS2		79.15 321	77.28 323	84.76 332	82.34 347	72.66 342	99.70 264	95.11 352	71.68 344	84.78 338	90.87 343	32.05 357	89.99 345	75.53 343	63.45 346	91.64 343

no-one		74.65 325	71.03 326	85.51 330	80.99 349	75.42 340	99.70 264	97.15 342	81.02 338	66.48 352	80.55 356	29.10 359	93.65 339	73.74 344	30.53 355	87.47 348

AdaColmap		99.44 71	99.26 74	99.95 47	100.00 1	99.86 67	99.70 264	99.99 10	98.53 46	99.90 117	100.00 1	95.34 182	100.00 1	99.92 74	100.00 1	100.00 1

tfpnnormal		96.36 238	95.69 251	98.37 197	98.55 242	98.71 165	99.69 268	99.45 97	93.16 286	96.69 286	99.71 207	88.44 282	98.99 226	94.17 274	91.38 271	97.41 304

tpm2		98.64 146	98.58 146	98.81 181	99.42 184	97.12 249	99.69 268	99.37 176	93.63 273	99.94 109	99.67 218	98.96 91	99.47 202	98.62 176	97.95 171	99.83 166

Vis-MVSNet		98.52 155	98.25 159	99.34 147	99.68 135	98.55 172	99.68 270	99.41 153	97.34 142	99.94 109	100.00 1	90.38 256	99.70 166	99.03 155	98.84 130	99.76 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testmv		79.83 319	79.77 320	79.99 336	78.22 352	63.55 353	99.67 271	95.51 351	80.89 339	76.24 343	92.90 342	52.16 349	86.10 351	62.45 351	79.90 329	90.33 347

CostFormer		98.84 135	98.77 129	99.04 165	99.41 185	97.58 233	99.67 271	99.35 190	94.66 242	99.96 88	99.36 249	99.28 65	99.74 161	99.41 135	97.81 183	99.81 171

PatchFormer-LS_test		99.14 106	99.12 95	99.19 158	99.54 163	97.86 225	99.66 273	99.40 155	96.94 167	99.96 88	100.00 1	99.29 62	99.84 148	98.96 156	98.29 155	99.88 153

DSMNet-mixed		95.18 273	95.21 266	95.08 302	96.03 320	90.21 323	99.65 274	93.64 353	92.91 288	98.34 232	97.40 315	90.05 261	95.51 334	91.02 301	97.86 177	99.51 202

HY-MVS	96.53 9	99.50 64	99.35 70	99.96 36	99.81 100	99.93 33	99.64 275	100.00 1	97.97 86	99.84 123	99.85 188	98.94 94	99.99 82	99.86 81	98.23 159	99.95 112

ACMH	96.25 11	96.77 211	96.62 206	97.21 262	98.96 226	94.43 298	99.64 275	99.33 197	97.43 136	96.55 287	99.97 151	83.52 313	99.54 191	99.07 154	95.13 232	97.66 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_0402		94.35 277	93.70 279	96.32 295	97.92 285	93.60 304	99.61 277	98.85 308	88.19 325	94.68 300	99.48 245	80.01 329	98.58 270	89.39 317	95.15 231	96.77 315

mvs_tets		97.00 207	96.69 203	97.94 236	97.41 311	97.27 244	99.60 278	99.18 250	96.51 192	97.35 268	99.69 212	86.53 296	98.91 231	98.84 162	95.09 234	97.65 286

EPMVS		99.25 95	99.13 93	99.60 121	99.60 151	99.20 140	99.60 278	100.00 1	96.93 168	99.92 115	99.36 249	99.05 82	99.71 165	98.77 166	98.94 129	99.90 136

TAMVS		98.76 139	98.73 132	98.86 177	99.44 183	97.69 229	99.57 280	99.34 195	96.57 187	99.12 183	99.81 197	98.83 101	99.16 217	97.97 206	97.91 173	99.73 190

MDTV_nov1_ep13_2view						99.24 137	99.56 281		96.31 202	99.96 88		98.86 100		98.92 158		99.89 141

LS3D		99.31 87	99.13 93	99.87 81	99.99 45	99.71 96	99.55 282	99.46 95	97.32 143	99.82 134	100.00 1	96.85 157	99.97 99	99.14 150	100.00 1	99.92 127

XXY-MVS		97.14 199	96.63 205	98.67 185	98.65 238	98.92 158	99.54 283	99.29 211	95.57 221	97.63 260	99.83 191	87.79 285	99.35 212	98.39 183	92.95 255	97.75 222

tpm		98.24 168	98.22 163	98.32 202	99.13 203	95.79 269	99.53 284	99.12 270	95.20 232	99.96 88	99.36 249	97.58 131	99.28 215	97.41 222	96.67 211	99.88 153

tpmrst		98.98 125	98.93 112	99.14 161	99.61 149	97.74 228	99.52 285	99.36 182	96.05 207	99.98 81	99.64 225	99.04 83	99.86 143	98.94 157	98.19 162	99.82 169

DP-MVS		98.86 134	98.54 148	99.81 92	99.97 76	99.45 120	99.52 285	99.40 155	94.35 251	98.36 230	100.00 1	96.13 169	99.97 99	99.12 152	100.00 1	100.00 1

VNet		99.04 114	98.75 131	99.90 72	99.81 100	99.75 85	99.50 287	99.47 83	98.36 58	100.00 1	99.99 140	94.66 193	100.00 1	99.90 76	97.09 207	99.96 107

VPNet		96.41 234	95.76 246	98.33 201	98.61 240	98.30 187	99.48 288	99.45 97	96.98 162	98.87 197	99.88 183	81.57 321	98.93 229	99.22 148	87.82 310	97.76 218

dp		98.72 141	98.61 143	99.03 166	99.53 166	97.39 237	99.45 289	99.39 169	95.62 219	99.94 109	99.52 241	98.83 101	99.82 152	96.77 240	98.42 144	99.89 141

FMVSNet2		96.22 245	95.60 253	98.06 229	99.53 166	98.33 183	99.45 289	99.27 226	93.71 264	98.03 245	98.84 289	84.23 309	98.10 296	93.97 279	93.40 250	97.73 256

LTVRE_ROB	95.29 16	96.32 241	96.10 230	96.99 269	98.55 242	93.88 302	99.45 289	99.28 219	94.50 247	96.46 288	99.52 241	84.86 305	99.48 201	97.26 225	95.03 235	97.59 296
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
MDA-MVSNet-bldmvs		91.65 304	89.94 307	96.79 287	96.72 315	96.70 260	99.42 292	98.94 303	88.89 321	66.97 351	98.37 309	81.43 322	95.91 330	89.24 320	89.46 296	97.75 222

TinyColmap		95.50 269	95.12 269	96.64 289	98.69 237	93.00 310	99.40 293	97.75 337	96.40 198	96.14 290	99.87 184	79.47 330	99.50 199	93.62 282	94.72 240	97.40 305

MDTV_nov1_ep13			98.94 111		99.53 166	98.36 181	99.39 294	99.46 95	96.54 189	99.99 76	99.63 229	98.92 96	99.86 143	98.30 188	98.71 133

VPA-MVSNet		97.03 205	96.43 213	98.82 179	98.64 239	99.32 128	99.38 295	99.47 83	96.73 176	98.91 195	98.94 284	87.00 292	99.40 208	99.23 146	89.59 294	97.76 218

MVS-HIRNet		94.12 279	92.73 288	98.29 204	99.33 195	95.95 266	99.38 295	99.19 246	74.54 343	98.26 238	86.34 349	86.07 298	99.06 221	91.60 297	99.87 110	99.85 162

PatchmatchNet		99.03 115	98.96 106	99.26 154	99.49 179	98.33 183	99.38 295	99.45 97	96.64 183	99.96 88	99.58 235	99.49 37	99.50 199	97.63 213	99.00 128	99.93 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet1		91.96 299	91.20 300	94.23 312	94.94 329	91.69 320	99.34 298	99.22 239	88.23 323	94.18 304	98.45 304	75.52 339	93.41 340	79.37 337	91.49 270	97.60 295

test_post1							99.32 299				88.24 348	99.33 51	99.59 176	98.31 187

tpmvs		98.59 148	98.38 155	99.23 156	99.69 132	97.90 220	99.31 300	99.47 83	94.52 246	99.68 153	99.28 254	97.64 130	99.89 139	97.71 211	98.17 163	99.89 141

COLMAP_ROB	97.10 7	98.29 165	98.17 164	98.65 186	99.94 86	97.39 237	99.30 301	99.40 155	95.64 217	97.75 258	100.00 1	92.69 215	99.95 120	98.89 159	99.92 104	98.62 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		98.14 170	97.74 178	99.33 148	99.59 154	98.28 188	99.27 302	99.21 243	96.42 196	99.15 182	99.94 176	88.87 276	99.79 157	98.88 160	98.29 155	99.93 125

OpenMVS_ROB	88.34 20	91.89 301	91.12 301	94.19 313	95.55 325	87.63 328	99.26 303	98.03 330	86.61 328	90.65 317	96.82 316	70.14 344	98.78 246	86.54 325	96.50 212	96.15 322

FMVSNet5		95.32 272	95.43 260	94.99 304	99.39 192	92.99 311	99.25 304	99.24 233	90.45 305	97.44 267	98.45 304	95.78 175	94.39 336	87.02 323	91.88 266	97.59 296

pm-mvs1		95.76 265	95.01 271	98.00 232	98.23 257	97.45 234	99.24 305	99.04 298	93.13 287	95.93 293	99.72 205	86.28 297	98.84 240	95.62 253	87.92 309	97.72 261

1314		99.38 74	99.19 84	99.96 36	98.88 231	99.89 49	99.24 305	99.93 32	98.88 27	98.79 203	100.00 1	97.02 142	100.00 1	100.00 1	100.00 1	100.00 1

1121		99.63 52	99.51 56	99.99 5	99.99 45	99.96 10	99.24 305	99.74 53	97.89 93	100.00 1	100.00 1	99.39 49	100.00 1	99.33 140	100.00 1	100.00 1

MVS		99.22 100	98.96 106	99.98 16	99.00 220	99.95 19	99.24 305	99.94 18	98.14 70	98.88 196	100.00 1	95.63 180	100.00 1	99.85 83	100.00 1	100.00 1

ADS-MVSNet2		98.28 166	98.51 150	97.62 245	99.51 174	95.03 277	99.24 305	99.41 153	95.52 224	99.96 88	99.70 210	97.57 132	97.94 305	97.11 228	98.54 135	99.88 153

ADS-MVSNet		98.70 144	98.51 150	99.28 152	99.51 174	98.39 177	99.24 305	99.44 106	95.52 224	99.96 88	99.70 210	97.57 132	99.58 178	97.11 228	98.54 135	99.88 153

TransMVSNet (Re)		94.78 275	93.72 278	97.93 238	98.34 249	97.88 222	99.23 311	97.98 333	91.60 298	94.55 301	99.71 207	87.89 283	98.36 282	89.30 319	84.92 321	97.56 298

PCF-MVS	98.23 3	98.69 145	98.37 156	99.62 117	99.78 122	99.02 150	99.23 311	99.06 293	96.43 194	98.08 242	100.00 1	94.72 192	99.95 120	98.16 192	99.91 105	99.90 136
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		96.63 224	96.04 233	98.38 196	98.31 251	98.98 155	99.22 313	99.35 190	95.87 210	94.43 303	99.65 221	92.73 214	98.40 281	96.78 239	88.05 308	97.75 222

VDD-MVS		96.58 227	95.99 235	98.34 200	99.52 171	95.33 271	99.18 314	99.38 173	96.64 183	99.77 139	100.00 1	72.51 342	100.00 1	100.00 1	96.94 209	99.70 191

GBi-Net		96.07 254	95.80 243	96.89 275	99.53 166	94.87 278	99.18 314	99.27 226	93.71 264	98.53 222	98.81 290	84.23 309	98.07 299	95.31 257	93.60 247	97.72 261

test1		96.07 254	95.80 243	96.89 275	99.53 166	94.87 278	99.18 314	99.27 226	93.71 264	98.53 222	98.81 290	84.23 309	98.07 299	95.31 257	93.60 247	97.72 261

FMVSNet1		94.45 276	93.63 280	96.89 275	98.87 233	94.87 278	99.18 314	99.27 226	90.95 304	97.31 269	98.81 290	72.89 341	98.07 299	92.61 289	92.81 256	97.72 261

UGNet		98.41 161	98.11 165	99.31 150	99.54 163	98.55 172	99.18 314	100.00 1	98.64 43	99.79 137	99.04 268	87.61 286	100.00 1	99.30 143	99.89 107	99.40 204
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
GG-mvs-BLEND				99.59 124	99.54 163	99.49 114	99.17 319	99.52 76		99.96 88	99.68 217	100.00 1	99.33 213	99.71 101	99.99 88	99.96 107

LFMVS		97.42 188	96.62 206	99.81 92	99.80 114	99.50 111	99.16 320	99.56 74	94.48 248	100.00 1	100.00 1	79.35 331	100.00 1	99.89 78	97.37 204	99.94 116

VDDNet		96.39 237	95.55 254	98.90 175	99.27 199	97.45 234	99.15 321	99.92 36	91.28 301	99.98 81	100.00 1	73.55 340	100.00 1	99.85 83	96.98 208	99.24 205

CR-MVSNet		98.02 174	97.71 181	98.93 172	99.31 196	98.86 160	99.13 322	99.00 301	96.53 190	99.96 88	98.98 279	96.94 155	98.10 296	91.18 299	98.40 145	99.84 163

RPMNet		95.10 274	93.82 277	98.93 172	99.31 196	98.86 160	99.13 322	99.38 173	79.82 341	99.96 88	95.13 336	95.69 179	98.10 296	77.54 341	98.40 145	99.84 163

ab-mvs		98.42 160	98.02 171	99.61 118	99.71 130	99.00 154	99.10 324	99.64 72	96.70 178	99.04 189	99.81 197	90.64 250	99.98 95	99.64 110	97.93 172	99.84 163

TDRefinement		91.93 300	90.48 305	96.27 296	81.60 348	92.65 314	99.10 324	97.61 340	93.96 258	93.77 307	99.85 188	80.03 328	99.53 195	97.82 209	70.59 345	96.63 317

UA-Net		99.06 112	98.83 123	99.74 110	99.52 171	99.40 123	99.08 326	99.45 97	97.64 121	99.83 125	100.00 1	95.80 174	99.94 131	98.35 185	99.80 114	99.88 153

PatchT		95.90 260	94.95 273	98.75 183	99.03 214	98.39 177	99.08 326	99.32 200	85.52 329	99.96 88	94.99 338	97.94 124	98.05 303	80.20 335	98.47 141	99.81 171

gg-mvs-nofinetune		96.95 208	96.10 230	99.50 134	99.41 185	99.36 127	99.07 328	99.52 76	83.69 336	99.96 88	83.60 353	100.00 1	99.20 216	99.68 105	99.99 88	99.96 107

EG-PatchMatch MVS		92.94 294	92.49 289	94.29 311	95.87 323	87.07 329	99.07 328	98.11 328	93.19 285	88.98 320	98.66 298	70.89 343	99.08 220	92.43 293	95.21 228	96.72 316

pmmvs6		93.64 281	92.87 286	95.94 300	97.47 307	91.41 322	98.92 330	99.02 299	87.84 326	95.01 298	99.61 233	77.24 336	98.77 249	94.33 272	86.41 318	97.63 290

Patchmtry		96.81 210	96.37 214	98.14 219	99.31 196	98.55 172	98.91 331	99.00 301	90.45 305	97.92 251	98.98 279	96.94 155	98.12 291	94.27 273	91.53 269	97.75 222

MS-PatchMatch		95.66 267	95.87 240	95.05 303	97.80 289	89.25 325	98.88 332	99.30 207	96.35 200	96.86 280	99.01 276	81.35 324	99.43 205	93.30 286	99.98 95	96.46 318

MVP-Stereo		96.51 229	96.48 211	96.60 291	95.65 324	94.25 299	98.84 333	98.16 325	95.85 214	95.23 297	99.04 268	92.54 217	99.13 218	92.98 288	99.98 95	96.43 319
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		84.35 317	82.41 318	90.15 319	89.14 338	78.88 336	98.71 334	98.22 323	70.20 345	86.88 333	95.44 330	47.11 354	95.72 333	81.14 332	74.88 343	95.84 336

Patchmatch-test		97.83 178	97.42 184	99.06 162	99.08 207	97.66 231	98.66 335	99.21 243	93.65 272	98.25 239	99.58 235	99.47 40	99.57 179	90.25 310	98.59 134	99.95 112

LP		95.89 262	95.35 263	97.51 249	98.92 228	95.15 275	98.66 335	99.39 169	89.43 319	97.22 272	99.03 273	94.37 196	97.74 308	83.71 329	97.44 203	99.63 198

UnsupCasMVSNet_bld		89.50 310	88.00 313	93.99 314	95.30 326	88.86 326	98.52 337	99.28 219	85.50 332	87.80 332	94.11 339	61.63 346	96.96 312	90.63 303	79.26 330	96.15 322

MIMVSNet		97.06 203	96.73 202	98.05 230	99.38 193	96.64 261	98.47 338	99.35 190	93.41 278	99.48 164	98.53 302	89.66 265	97.70 309	94.16 276	98.11 166	99.80 178

FPMVS		77.92 323	79.45 321	73.34 341	76.87 353	46.81 360	98.24 339	99.05 295	59.89 350	73.55 345	98.34 310	36.81 356	86.55 348	80.96 333	91.35 272	86.65 350

Patchmatch-RL test		93.49 284	93.63 280	93.05 316	91.78 333	83.41 333	98.21 340	96.95 344	91.58 299	91.05 314	97.64 314	99.40 48	95.83 331	94.11 277	81.95 326	99.91 128

CMPMVS	66.12 22	90.65 307	92.04 294	86.46 327	96.18 319	66.87 349	98.03 341	99.38 173	83.38 337	85.49 336	99.55 238	77.59 335	98.80 245	94.44 269	94.31 244	93.72 340
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		98.58 149	98.24 160	99.61 118	99.60 151	99.26 133	97.85 342	99.10 272	96.22 204	99.97 85	99.89 182	93.75 201	99.77 158	99.43 129	98.34 152	99.81 171

PNet_i23d		69.93 327	67.61 329	76.86 337	75.89 354	62.08 356	97.85 342	88.23 357	61.04 349	55.65 354	84.10 352	19.35 361	83.21 352	66.45 350	59.98 347	85.29 352

ambc				88.45 321	86.84 343	70.76 347	97.79 344	98.02 332		90.91 315	95.14 335	38.69 355	98.51 274	94.97 264	84.23 323	96.09 324

LCM-MVSNet		79.01 322	76.93 324	85.27 331	78.28 351	68.01 348	96.57 345	98.03 330	55.10 351	82.03 342	93.27 341	31.99 358	93.95 338	82.72 330	74.37 344	93.84 339

MVE	68.59 21	67.22 329	64.68 331	74.84 338	74.67 356	62.32 355	95.84 346	90.87 356	50.98 353	58.72 353	81.05 354	12.20 365	78.95 354	61.06 353	56.75 348	83.24 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testpf		97.02 206	96.87 197	97.44 253	99.50 177	95.18 274	95.70 347	99.33 197	91.35 300	98.00 249	99.22 256	96.29 168	98.49 277	94.42 270	98.07 168	98.67 209

ANet_high		66.05 330	63.44 332	73.88 340	61.14 358	63.45 354	95.68 348	87.18 358	79.93 340	47.35 355	80.68 355	22.35 360	72.33 359	61.24 352	35.42 354	85.88 351

wuykxyi23d		62.75 332	59.23 333	73.34 341	61.53 357	59.39 358	93.28 349	86.17 361	53.70 352	46.89 356	77.89 357	12.35 362	79.96 353	60.86 354	40.35 353	80.68 355

Gipuma		84.73 316	83.50 317	88.40 322	97.50 303	82.21 334	88.87 350	99.05 295	65.81 346	85.71 335	90.49 344	53.70 348	96.31 324	78.64 339	91.74 267	86.67 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	60.66 23	65.98 331	65.05 330	68.75 345	55.06 360	38.40 361	88.19 351	96.98 343	48.30 356	44.82 357	88.52 347	12.22 364	86.49 349	67.58 347	83.79 324	81.35 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 324	74.26 325	80.43 334	52.91 361	53.67 359	87.42 352	97.98 333	61.80 348	67.04 350	100.00 1	76.43 338	96.40 322	96.47 242	28.26 356	91.23 344

wuyk23d		28.28 334	29.73 336	23.92 347	75.89 354	32.61 362	66.50 353	12.88 363	16.09 357	14.59 358	16.59 359	12.35 362	32.36 360	39.36 357	13.36 358	6.79 358

cdsmvs_eth3d_5k		24.41 335	32.55 335	0.00 348	0.00 362	0.00 363	0.00 354	99.39 169	0.00 358	0.00 359	100.00 1	93.55 204	0.00 361	0.00 358	0.00 359	0.00 359

pcd_1.5k_mvsjas		8.24 337	10.99 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.14 360	98.75 106	0.00 361	0.00 358	0.00 359	0.00 359

pcd1.5k->3k		40.07 333	42.58 334	32.57 346	97.97 284	0.00 363	0.00 354	99.25 230	0.00 358	0.00 359	0.14 360	91.10 245	0.00 361	0.00 358	94.75 238	97.70 269

sosnet-low-res		0.01 338	0.02 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.14 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.01 338	0.02 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.14 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.01 338	0.02 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.14 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.01 338	0.02 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.14 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		8.33 336	11.11 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	100.00 1	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.01 338	0.02 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.14 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																99.91 128

test_part2					100.00 1	99.99 1				100.00 1

test_part1								99.42 121				99.53 26			100.00 1	100.00 1

sam_mvs1												99.29 62				99.91 128

sam_mvs												99.33 51

semantic-postprocess				97.62 245	99.40 189	96.83 257		99.14 259	94.65 243	97.55 264	99.72 205	89.64 267	98.31 284	95.62 253	92.05 264	97.73 256

MTGPA								99.42 121

test_post											89.05 346	99.49 37	99.59 176

patchmatchnet-post											97.79 313	99.41 47	99.54 191

MTMP								99.18 250

gm-plane-assit					99.52 171	97.26 245			95.86 212		100.00 1		99.43 205	98.76 167

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 59		99.42 121		100.00 1			99.97 99

TestCases				98.99 168	99.93 88	97.35 240		99.40 155	97.08 159	99.09 184	99.98 141	93.37 205	99.95 120	96.94 231	99.84 111	99.68 193

test_prior				99.90 72	100.00 1	99.75 85		99.73 55					99.97 99			100.00 1

新几何1				99.99 5	100.00 1	99.96 10		99.81 42	97.89 93	100.00 1	100.00 1	99.20 72	100.00 1	97.91 207	100.00 1	100.00 1

旧先验1					99.99 45	99.88 59		99.82 41			100.00 1	99.27 66			100.00 1	100.00 1

原ACMM1				99.93 62	100.00 1	99.80 80		99.66 71	98.18 66	100.00 1	100.00 1	99.43 45	100.00 1	99.50 127	100.00 1	100.00 1

testdata2													100.00 1	97.36 224

segment_acmp												99.55 24

testdata				99.66 116	99.99 45	98.97 157		99.73 55	97.96 88	100.00 1	100.00 1	99.42 46	100.00 1	99.28 144	100.00 1	100.00 1

test12				99.95 47	99.99 45	99.89 49		99.42 121		100.00 1		99.24 68	99.97 99		100.00 1	100.00 1

plane_prior7					99.00 220	94.78 288

plane_prior6					99.06 212	94.80 284						88.58 280

plane_prior5								99.40 155					99.55 188	99.79 91	95.57 217	97.76 218

plane_prior4											99.97 151

plane_prior3						94.79 287			99.03 16	99.08 186

plane_prior1					99.02 215

n2								0.00 364

nn								0.00 364

door-mid								96.32 349

lessismore_v0				96.05 298	97.55 301	91.80 319		99.22 239		91.87 313	99.91 180	83.50 314	98.68 259	92.48 292	90.42 283	97.68 276

LGP-MVS_train				97.28 259	98.85 235	94.60 294		99.37 176	97.35 140	98.85 198	99.98 141	86.66 294	99.56 183	99.55 119	95.26 224	97.70 269

test11								99.42 121

door								96.13 350

HQP5-MVS						94.82 281

BP-MVS														99.79 91

HQP4-MVS										99.17 178			99.57 179			97.77 216

HQP3-MVS								99.40 155							95.58 213

HQP2-MVS												88.61 278

NP-MVS					99.07 208	94.81 283					99.97 151

ACMMP++_ref															94.58 243

ACMMP++															95.17 230

Test By Simon												99.10 79

ITE_SJBPF				96.84 283	98.96 226	93.49 305		98.12 327	98.12 74	98.35 231	99.97 151	84.45 306	99.56 183	95.63 252	95.25 226	97.49 300

DeepMVS_CX				89.98 320	98.90 229	71.46 346		99.18 250	97.61 123	96.92 277	99.83 191	86.07 298	99.83 150	96.02 244	97.65 201	98.65 210

High-resolution multi-view benchmark