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.

GG-mvs-BLEND				99.59 123	99.54 162	99.49 113	99.17 316	99.52 76		99.96 87	99.68 216	100.00 1	99.33 212	99.71 100	99.99 87	99.96 106

gg-mvs-nofinetune		96.95 207	96.10 228	99.50 133	99.41 184	99.36 126	99.07 325	99.52 76	83.69 333	99.96 87	83.60 350	100.00 1	99.20 215	99.68 104	99.99 87	99.96 106

CHOSEN 280x420		99.85 3	99.87 1	99.80 94	99.99 45	99.97 8	99.97 197	99.98 13	98.96 21	100.00 1	100.00 1	99.96 3	99.42 206	100.00 1	100.00 1	100.00 1

tfpn1000		99.47 68	99.34 71	99.87 80	99.89 95	99.80 79	100.00 1	99.72 61	97.83 98	99.96 87	99.98 140	99.94 4	99.82 151	98.87 160	97.87 175	100.00 1

tfpn_ndepth		99.52 61	99.39 63	99.89 74	99.90 93	99.83 71	100.00 1	99.72 61	97.95 89	99.96 87	99.98 140	99.94 4	99.85 146	99.17 148	97.91 172	100.00 1

conf0.01		99.32 79	99.16 85	99.80 94	99.79 115	99.73 87	100.00 1	99.71 63	97.66 112	99.71 145	100.00 1	99.78 6	99.70 165	98.15 192	97.67 194	99.97 95

conf0.002		99.32 79	99.16 85	99.80 94	99.79 115	99.73 87	100.00 1	99.71 63	97.66 112	99.71 145	100.00 1	99.78 6	99.70 165	98.15 192	97.67 194	99.97 95

thresconf0.02		99.32 79	99.16 85	99.79 98	99.79 115	99.73 87	100.00 1	99.71 63	97.66 112	99.71 145	100.00 1	99.78 6	99.70 165	98.15 192	97.67 194	99.94 115

tfpn_n400		99.32 79	99.16 85	99.79 98	99.79 115	99.73 87	100.00 1	99.71 63	97.66 112	99.71 145	100.00 1	99.78 6	99.70 165	98.15 192	97.67 194	99.94 115

tfpnconf		99.32 79	99.16 85	99.79 98	99.79 115	99.73 87	100.00 1	99.71 63	97.66 112	99.71 145	100.00 1	99.78 6	99.70 165	98.15 192	97.67 194	99.94 115

tfpnview11		99.32 79	99.16 85	99.79 98	99.79 115	99.73 87	100.00 1	99.71 63	97.66 112	99.71 145	100.00 1	99.78 6	99.70 165	98.15 192	97.67 194	99.94 115

MVSTER		98.58 148	98.52 148	98.77 181	99.65 139	99.68 101	100.00 1	99.29 210	95.63 217	98.65 211	99.80 199	99.78 6	98.88 236	98.59 176	95.31 221	97.73 253

CDS-MVSNet		98.96 126	98.95 109	99.01 166	99.48 179	98.36 180	99.93 212	99.37 175	96.79 171	99.31 172	99.83 190	99.77 13	98.91 230	98.07 200	97.98 169	99.77 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		99.78 13	99.76 10	99.85 84	99.01 215	99.95 19	100.00 1	99.75 50	99.37 3	99.99 75	100.00 1	99.76 14	99.60 173	100.00 1	100.00 1	100.00 1

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 75	100.00 1	99.72 15	100.00 1	99.96 65	100.00 1	100.00 1

TESTMET0.1,1		99.08 109	98.96 105	99.44 135	99.63 144	99.38 123	100.00 1	99.45 97	95.53 221	99.48 163	100.00 1	99.71 16	99.02 222	96.84 234	99.99 87	99.91 127

test-mter		98.96 126	98.82 123	99.40 140	99.40 188	99.28 130	100.00 1	99.45 97	95.44 230	99.42 166	99.12 256	99.70 17	99.01 223	96.82 235	99.99 87	99.91 127

HSP-MVS		99.81 9	99.77 8	99.94 58	100.00 1	99.86 66	100.00 1	99.42 121	98.87 29	100.00 1	100.00 1	99.65 18	99.96 113	100.00 1	100.00 1	100.00 1

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

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

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

PLC	98.56 2	99.70 36	99.74 13	99.58 127	100.00 1	98.79 161	100.00 1	99.54 75	98.58 45	99.96 87	100.00 1	99.59 21	100.00 1	100.00 1	100.00 1	99.94 115
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
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 81	100.00 1	100.00 1	100.00 1

segment_acmp												99.55 24

test_part1								99.42 121				99.53 25			100.00 1	100.00 1

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 25	100.00 1	100.00 1	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 25	99.97 98

DWT-MVSNet_test		99.22 99	99.22 78	99.22 156	99.56 161	97.98 214	99.89 220	99.39 168	97.20 149	99.96 87	100.00 1	99.52 28	99.82 151	99.11 152	98.34 151	99.87 159

SteuartSystems-ACMMP		99.78 13	99.71 16	99.98 16	99.76 125	99.95 19	100.00 1	99.42 121	98.69 38	100.00 1	100.00 1	99.52 28	99.99 81	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
train_agg		99.71 32	99.63 39	99.97 23	100.00 1	99.95 19	100.00 1	99.42 121	97.70 109	100.00 1	100.00 1	99.51 30	99.97 98	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 38	100.00 1	99.42 121	97.70 109	100.00 1	100.00 1	99.51 30	99.98 94

agg_prior3		99.71 32	99.63 39	99.97 23	100.00 1	99.95 19	100.00 1	99.42 121	97.71 108	100.00 1	100.00 1	99.51 30	99.97 98	100.00 1	100.00 1	100.00 1

agg_prior1		99.71 32	99.63 39	99.95 46	100.00 1	99.88 58	100.00 1	99.42 121	97.72 106	100.00 1	100.00 1	99.51 30	99.97 98	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 30	100.00 1	100.00 1	100.00 1	100.00 1

EPP-MVSNet		99.10 108	99.00 100	99.40 140	99.51 173	98.68 166	99.92 214	99.43 115	95.47 227	99.65 153	100.00 1	99.51 30	99.76 158	99.53 124	98.00 168	99.75 181

test_post											89.05 343	99.49 36	99.59 175

HyFIR lowres test		99.32 79	99.24 76	99.58 127	99.95 81	99.26 132	100.00 1	99.99 10	96.72 176	99.29 173	99.91 179	99.49 36	99.47 201	99.74 98	98.08 166	100.00 1

PatchmatchNet		99.03 114	98.96 105	99.26 153	99.49 178	98.33 182	99.38 292	99.45 97	96.64 182	99.96 87	99.58 233	99.49 36	99.50 198	97.63 212	99.00 127	99.93 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		97.83 177	97.42 183	99.06 161	99.08 206	97.66 230	98.66 332	99.21 241	93.65 270	98.25 238	99.58 233	99.47 39	99.57 178	90.25 308	98.59 133	99.95 111

test_prior3		99.81 9	99.78 6	99.90 71	100.00 1	99.75 84	100.00 1	99.73 55	98.82 33	100.00 1	100.00 1	99.47 39	99.97 98	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 39		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 42	99.99 81	100.00 1	100.00 1	100.00 1

PGM-MVS		99.69 39	99.61 44	99.95 46	99.99 45	99.85 69	100.00 1	99.58 73	97.69 111	100.00 1	100.00 1	99.44 43	100.00 1	99.79 90	100.00 1	100.00 1

原ACMM1				99.93 61	100.00 1	99.80 79		99.66 71	98.18 66	100.00 1	100.00 1	99.43 44	100.00 1	99.50 126	100.00 1	100.00 1

testdata				99.66 115	99.99 45	98.97 156		99.73 55	97.96 88	100.00 1	100.00 1	99.42 45	100.00 1	99.28 143	100.00 1	100.00 1

patchmatchnet-post											97.79 310	99.41 46	99.54 190

Patchmatch-RL test		93.49 281	93.63 277	93.05 313	91.78 330	83.41 330	98.21 337	96.95 341	91.58 296	91.05 311	97.64 311	99.40 47	95.83 328	94.11 276	81.95 323	99.91 127

1121		99.63 51	99.51 55	99.99 5	99.99 45	99.96 10	99.24 302	99.74 53	97.89 93	100.00 1	100.00 1	99.39 48	100.00 1	99.33 139	100.00 1	100.00 1

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 48	100.00 1	100.00 1	100.00 1	100.00 1

sam_mvs												99.33 50

test_post1							99.32 296				88.24 345	99.33 50	99.59 175	98.31 186

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 50	100.00 1	100.00 1	100.00 1	100.00 1

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 53	99.99 81	100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 23	99.67 27	99.96 35	100.00 1	99.89 48	100.00 1	99.76 46	97.95 89	100.00 1	100.00 1	99.31 54	100.00 1	99.99 44	100.00 1	100.00 1

#test#		99.75 18	99.68 22	99.96 35	100.00 1	99.89 48	100.00 1	99.76 46	98.07 77	100.00 1	100.00 1	99.31 54	100.00 1	99.99 44	100.00 1	100.00 1

test-LLR		99.03 114	98.91 116	99.40 140	99.40 188	99.28 130	100.00 1	99.45 97	96.70 177	99.42 166	99.12 256	99.31 54	99.01 223	96.82 235	99.99 87	99.91 127

test0.0.03 1		98.12 170	98.03 169	98.39 194	99.11 203	98.07 209	100.00 1	99.93 32	96.70 177	96.91 278	99.95 171	99.31 54	98.19 285	91.93 294	98.44 141	98.91 205

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 54	100.00 1	100.00 1	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 181	97.37 186	98.45 191	99.94 85	95.70 269	100.00 1	99.40 154	97.65 119	99.53 160	100.00 1	99.31 54	99.66 172	80.48 331	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					99.99 45	99.90 44	100.00 1	99.69 70	97.66 112	100.00 1	100.00 1	99.30 60			100.00 1	100.00 1

sam_mvs1												99.29 61				99.91 127

ACMMPR		99.74 23	99.67 27	99.96 35	100.00 1	99.89 48	100.00 1	99.76 46	97.95 89	100.00 1	100.00 1	99.29 61	100.00 1	99.99 44	100.00 1	100.00 1

PatchFormer-LS_test		99.14 105	99.12 94	99.19 157	99.54 162	97.86 224	99.66 270	99.40 154	96.94 166	99.96 87	100.00 1	99.29 61	99.84 147	98.96 155	98.29 154	99.88 152

CostFormer		98.84 134	98.77 128	99.04 164	99.41 184	97.58 232	99.67 268	99.35 189	94.66 240	99.96 87	99.36 247	99.28 64	99.74 160	99.41 134	97.81 182	99.81 170

旧先验1					99.99 45	99.88 58		99.82 41			100.00 1	99.27 65			100.00 1	100.00 1

APD-MVS		99.68 42	99.58 48	99.97 23	99.99 45	99.96 10	100.00 1	99.42 121	97.53 130	100.00 1	100.00 1	99.27 65	99.97 98	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
test12				99.95 46	99.99 45	99.89 48		99.42 121		100.00 1		99.24 67	99.97 98		100.00 1	100.00 1

Regformer-1		99.75 18	99.68 22	99.98 16	100.00 1	99.94 27	100.00 1	99.44 106	98.43 49	100.00 1	100.00 1	99.23 68	99.99 81	99.99 44	100.00 1	100.00 1

Regformer-2		99.75 18	99.68 22	99.97 23	100.00 1	99.94 27	100.00 1	99.44 106	98.42 51	100.00 1	100.00 1	99.22 69	99.99 81	99.99 44	100.00 1	100.00 1

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 87	100.00 1	99.21 70	100.00 1	100.00 1	100.00 1	99.99 88

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 71	100.00 1	97.91 206	100.00 1	100.00 1

region2R		99.72 29	99.64 35	99.97 23	100.00 1	99.90 44	100.00 1	99.74 53	97.86 97	100.00 1	100.00 1	99.19 72	100.00 1	99.99 44	100.00 1	100.00 1

F-COLMAP		99.64 49	99.64 35	99.67 114	99.99 45	99.07 145	100.00 1	99.44 106	98.30 63	99.90 116	100.00 1	99.18 73	99.99 81	99.91 74	100.00 1	99.94 115

tpmp4_e23		98.71 141	98.66 135	98.84 177	99.60 150	96.98 253	99.83 229	99.35 189	94.29 250	99.90 116	99.50 241	99.17 74	99.73 161	98.22 189	98.13 163	99.81 170

XVS		99.79 12	99.73 14	99.98 16	100.00 1	99.94 27	100.00 1	99.75 50	98.67 40	100.00 1	100.00 1	99.16 75	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 203	96.06 230	99.98 16	100.00 1	99.94 27	100.00 1	99.75 50	98.67 40	100.00 1	66.97 355	99.16 75	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 29	99.65 31	99.91 68	99.97 75	99.72 94	100.00 1	99.47 83	98.43 49	99.88 120	100.00 1	99.14 77	100.00 1	99.97 63	100.00 1	100.00 1

Test By Simon												99.10 78

CP-MVS		99.67 45	99.58 48	99.95 46	100.00 1	99.84 70	100.00 1	99.42 121	97.77 103	100.00 1	100.00 1	99.07 79	100.00 1	100.00 1	100.00 1	100.00 1

alignmvs		99.38 73	99.21 79	99.91 68	99.73 128	99.92 35	100.00 1	99.51 80	97.61 123	100.00 1	100.00 1	99.06 80	99.93 132	99.83 85	97.12 205	99.90 135

EPMVS		99.25 94	99.13 92	99.60 120	99.60 150	99.20 139	99.60 275	100.00 1	96.93 167	99.92 114	99.36 247	99.05 81	99.71 164	98.77 165	98.94 128	99.90 135

EI-MVSNet-Vis-set		99.70 36	99.64 35	99.87 80	100.00 1	99.64 104	99.98 191	99.44 106	98.35 59	99.99 75	100.00 1	99.04 82	99.96 113	99.98 58	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 45	99.97 23	100.00 1	99.91 38	100.00 1	99.42 121	97.91 92	100.00 1	100.00 1	99.04 82	100.00 1	100.00 1	100.00 1	100.00 1

tpmrst		98.98 124	98.93 111	99.14 160	99.61 148	97.74 227	99.52 282	99.36 181	96.05 206	99.98 80	99.64 224	99.04 82	99.86 142	98.94 156	98.19 161	99.82 168

Regformer-3		99.73 26	99.65 31	99.96 35	100.00 1	99.89 48	100.00 1	99.44 106	98.40 52	100.00 1	100.00 1	99.03 85	99.97 98	99.99 44	100.00 1	100.00 1

Regformer-4		99.73 26	99.65 31	99.95 46	100.00 1	99.89 48	100.00 1	99.44 106	98.40 52	100.00 1	100.00 1	99.03 85	99.97 98	99.99 44	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 39	99.63 39	99.87 80	99.99 45	99.64 104	99.95 206	99.44 106	98.35 59	100.00 1	100.00 1	98.98 87	99.97 98	99.98 58	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 47	99.55 54	99.97 23	99.99 45	99.91 38	100.00 1	99.48 82	97.54 129	100.00 1	100.00 1	98.97 88	99.99 81	99.98 58	100.00 1	100.00 1

EPNet		99.62 52	99.69 18	99.42 138	99.99 45	98.37 179	100.00 1	99.89 38	98.83 31	100.00 1	100.00 1	98.97 88	100.00 1	99.90 75	99.61 118	99.89 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		98.64 145	98.58 145	98.81 180	99.42 183	97.12 248	99.69 265	99.37 175	93.63 271	99.94 108	99.67 217	98.96 90	99.47 201	98.62 175	97.95 170	99.83 165

TSAR-MVS + GP.		99.61 54	99.69 18	99.35 145	99.99 45	98.06 210	100.00 1	99.36 181	99.83 2	100.00 1	100.00 1	98.95 91	99.99 81	100.00 1	99.11 123	100.00 1

HPM-MVS_fast		99.60 57	99.49 58	99.91 68	99.99 45	99.78 81	100.00 1	99.42 121	97.09 156	100.00 1	100.00 1	98.95 91	99.96 113	99.98 58	100.00 1	100.00 1

WTY-MVS		99.54 59	99.40 61	99.95 46	99.81 99	99.93 32	100.00 1	100.00 1	97.98 84	99.84 122	100.00 1	98.94 93	99.98 94	99.86 80	98.21 159	99.94 115

HY-MVS	96.53 9	99.50 63	99.35 69	99.96 35	99.81 99	99.93 32	99.64 272	100.00 1	97.97 86	99.84 122	99.85 187	98.94 93	99.99 81	99.86 80	98.23 158	99.95 111

MDTV_nov1_ep13			98.94 110		99.53 165	98.36 180	99.39 291	99.46 95	96.54 188	99.99 75	99.63 228	98.92 95	99.86 142	98.30 187	98.71 132

API-MVS		99.72 29	99.70 17	99.79 98	99.97 75	99.37 125	99.96 200	99.94 18	98.48 47	100.00 1	100.00 1	98.92 95	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 47	99.57 50	99.89 74	99.99 45	99.66 102	99.75 250	99.73 55	98.16 67	99.75 141	100.00 1	98.90 97	100.00 1	99.96 65	99.88 107	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
ACMMP_Plus		99.67 45	99.57 50	99.97 23	99.98 71	99.92 35	100.00 1	99.42 121	97.83 98	100.00 1	100.00 1	98.89 98	100.00 1	99.98 58	100.00 1	100.00 1

MDTV_nov1_ep13_2view						99.24 136	99.56 278		96.31 201	99.96 87		98.86 99		98.92 157		99.89 140

dp		98.72 140	98.61 142	99.03 165	99.53 165	97.39 236	99.45 286	99.39 168	95.62 218	99.94 108	99.52 239	98.83 100	99.82 151	96.77 239	98.42 143	99.89 140

TAMVS		98.76 138	98.73 131	98.86 176	99.44 182	97.69 228	99.57 277	99.34 194	96.57 186	99.12 182	99.81 196	98.83 100	99.16 216	97.97 205	97.91 172	99.73 189

IB-MVS	96.24 12	97.54 186	96.95 193	99.33 147	99.67 135	98.10 207	100.00 1	99.47 83	97.42 136	99.26 174	99.69 211	98.83 100	99.89 138	99.43 128	78.77 330	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
DeepC-MVS_fast	98.92 1	99.75 18	99.67 27	99.99 5	99.99 45	99.96 10	99.73 255	99.52 76	99.06 10	100.00 1	100.00 1	98.80 103	100.00 1	99.95 70	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
MP-MVS-pluss		99.61 54	99.50 56	99.97 23	99.98 71	99.92 35	100.00 1	99.42 121	97.53 130	99.77 138	100.00 1	98.77 104	100.00 1	99.99 44	100.00 1	99.99 88
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pcd_1.5k_mvsjas		8.24 334	10.99 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.14 357	98.75 105	0.00 358	0.00 355	0.00 356	0.00 356

PS-MVSNAJss		98.03 172	98.06 168	97.94 235	97.63 294	97.33 242	99.89 220	99.23 235	96.27 202	98.03 244	99.59 232	98.75 105	98.78 245	98.52 178	94.61 241	97.70 266

PS-MVSNAJ		99.64 49	99.57 50	99.85 84	99.78 121	99.81 77	99.95 206	99.42 121	98.38 55	100.00 1	100.00 1	98.75 105	100.00 1	99.88 78	99.99 87	99.74 185

MPTG		99.68 42	99.59 46	99.97 23	99.99 45	99.91 38	100.00 1	99.42 121	98.32 61	99.94 108	100.00 1	98.65 108	100.00 1	99.96 65	100.00 1	100.00 1

MTAPA		99.68 42	99.59 46	99.97 23	99.99 45	99.91 38	100.00 1	99.42 121	98.32 61	99.94 108	100.00 1	98.65 108	100.00 1	99.96 65	100.00 1	100.00 1

HPM-MVS		99.59 58	99.50 56	99.89 74	100.00 1	99.70 99	100.00 1	99.42 121	97.46 132	100.00 1	100.00 1	98.60 110	99.96 113	99.99 44	100.00 1	100.00 1

MG-MVS		99.75 18	99.68 22	99.97 23	100.00 1	99.91 38	99.98 191	99.47 83	99.09 7	100.00 1	100.00 1	98.59 111	100.00 1	99.95 70	100.00 1	100.00 1

EPNet_dtu		98.53 153	98.23 161	99.43 137	99.92 90	99.01 152	99.96 200	99.47 83	98.80 35	99.96 87	99.96 159	98.56 112	99.30 213	87.78 319	99.68 115	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
JIA-IIPM		97.09 199	96.34 214	99.36 144	98.88 230	98.59 169	99.81 236	99.43 115	84.81 330	99.96 87	90.34 342	98.55 113	99.52 195	97.00 229	98.28 156	99.98 90

xiu_mvs_v2_base		99.51 62	99.41 60	99.82 88	99.70 130	99.73 87	99.92 214	99.40 154	98.15 69	100.00 1	100.00 1	98.50 114	100.00 1	99.85 82	99.13 122	99.74 185

sss		99.45 69	99.34 71	99.80 94	99.76 125	99.50 110	100.00 1	99.91 37	97.72 106	99.98 80	99.94 175	98.45 115	100.00 1	99.53 124	98.75 131	99.89 140

IS-MVSNet		99.08 109	98.91 116	99.59 123	99.65 139	99.38 123	99.78 241	99.24 232	96.70 177	99.51 161	100.00 1	98.44 116	99.52 195	98.47 180	98.39 146	99.88 152

114514_t		99.39 72	99.25 74	99.81 91	99.97 75	99.48 117	100.00 1	99.42 121	95.53 221	100.00 1	100.00 1	98.37 117	99.95 119	99.97 63	100.00 1	100.00 1

MP-MVS		99.61 54	99.49 58	99.98 16	99.99 45	99.94 27	100.00 1	99.42 121	97.82 100	99.99 75	100.00 1	98.20 118	100.00 1	99.99 44	100.00 1	100.00 1

PHI-MVS		99.50 63	99.39 63	99.82 88	100.00 1	99.45 119	100.00 1	99.94 18	96.38 198	100.00 1	100.00 1	98.18 119	100.00 1	100.00 1	100.00 1	100.00 1

abl_6		99.53 60	99.40 61	99.92 67	100.00 1	99.76 82	100.00 1	99.42 121	97.21 148	100.00 1	100.00 1	98.12 120	100.00 1	99.82 89	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 122	98.89 120	99.29 150	99.64 143	98.89 158	99.98 191	99.31 204	96.74 173	99.48 163	100.00 1	98.11 121	99.10 218	98.39 182	98.34 151	99.89 140

DeepPCF-MVS	98.03 4	98.54 152	99.72 15	94.98 302	99.99 45	84.94 328	100.00 1	99.42 121	99.98 1	100.00 1	100.00 1	98.11 121	100.00 1	100.00 1	100.00 1	100.00 1

PatchT		95.90 257	94.95 270	98.75 182	99.03 213	98.39 176	99.08 323	99.32 199	85.52 326	99.96 87	94.99 335	97.94 123	98.05 300	80.20 332	98.47 140	99.81 170

MVS_111021_LR		99.70 36	99.65 31	99.88 79	99.96 80	99.70 99	100.00 1	99.97 14	98.96 21	100.00 1	100.00 1	97.93 124	99.95 119	99.99 44	100.00 1	100.00 1

MVS_111021_HR		99.71 32	99.63 39	99.93 61	99.95 81	99.83 71	100.00 1	100.00 1	98.89 26	100.00 1	100.00 1	97.85 125	99.95 119	100.00 1	100.00 1	100.00 1

CSCG		99.28 89	99.35 69	99.05 163	99.99 45	97.15 247	100.00 1	99.47 83	97.44 134	99.42 166	100.00 1	97.83 126	100.00 1	99.99 44	100.00 1	100.00 1

tpm cat1		98.05 171	97.76 176	98.92 173	99.50 176	97.10 250	99.77 245	99.30 206	90.20 305	99.72 143	98.71 292	97.71 127	99.86 142	96.75 240	98.20 160	99.81 170

DELS-MVS		99.62 52	99.56 53	99.82 88	99.92 90	99.45 119	100.00 1	99.78 44	98.92 25	99.73 142	100.00 1	97.70 128	100.00 1	99.93 72	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
tpmvs		98.59 147	98.38 154	99.23 155	99.69 131	97.90 219	99.31 297	99.47 83	94.52 244	99.68 152	99.28 252	97.64 129	99.89 138	97.71 210	98.17 162	99.89 140

tpm		98.24 167	98.22 162	98.32 201	99.13 202	95.79 268	99.53 281	99.12 268	95.20 231	99.96 87	99.36 247	97.58 130	99.28 214	97.41 221	96.67 210	99.88 152

ADS-MVSNet2		98.28 165	98.51 149	97.62 244	99.51 173	95.03 274	99.24 302	99.41 152	95.52 223	99.96 87	99.70 209	97.57 131	97.94 302	97.11 227	98.54 134	99.88 152

ADS-MVSNet		98.70 143	98.51 149	99.28 151	99.51 173	98.39 176	99.24 302	99.44 106	95.52 223	99.96 87	99.70 209	97.57 131	99.58 177	97.11 227	98.54 134	99.88 152

Test_1112_low_res		98.83 135	98.60 143	99.51 131	99.69 131	98.75 162	99.99 175	99.14 257	96.81 170	98.84 199	99.06 260	97.45 133	99.89 138	98.66 169	97.75 187	99.89 140

1112_ss		98.91 130	98.71 133	99.51 131	99.69 131	98.75 162	99.99 175	99.15 253	96.82 169	98.84 199	100.00 1	97.45 133	99.89 138	98.66 169	97.75 187	99.89 140

CPTT-MVS		99.49 65	99.38 65	99.85 84	100.00 1	99.54 108	100.00 1	99.42 121	97.58 127	99.98 80	100.00 1	97.43 135	100.00 1	99.99 44	100.00 1	100.00 1

EI-MVSNet		97.98 174	97.93 172	98.16 217	99.11 203	97.84 225	99.74 251	99.29 210	94.39 248	98.65 211	100.00 1	97.21 136	98.88 236	97.62 213	95.31 221	97.75 221

IterMVS-LS		97.56 184	97.44 182	97.92 238	99.38 192	97.90 219	99.89 220	99.10 270	94.41 247	98.32 232	99.54 238	97.21 136	98.11 291	97.50 218	91.62 265	97.75 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		99.26 92	99.03 96	99.96 35	99.81 99	99.89 48	100.00 1	99.94 18	97.23 145	99.83 124	99.96 159	97.04 138	100.00 1	99.59 112	97.85 177	99.98 90

thres400		99.26 92	99.03 96	99.95 46	99.81 99	99.89 48	100.00 1	99.94 18	97.23 145	99.83 124	99.96 159	97.04 138	100.00 1	99.59 112	97.85 177	99.97 95

thres200		99.27 90	99.04 95	99.96 35	99.81 99	99.90 44	100.00 1	99.94 18	97.31 144	99.83 124	99.96 159	97.04 138	100.00 1	99.62 111	97.88 174	99.98 90

1314		99.38 73	99.19 83	99.96 35	98.88 230	99.89 48	99.24 302	99.93 32	98.88 27	98.79 202	100.00 1	97.02 141	100.00 1	100.00 1	100.00 1	100.00 1

tfpn111		99.24 97	99.00 100	99.95 46	99.81 99	99.87 61	100.00 1	99.94 18	97.13 152	99.83 124	99.96 159	97.01 142	100.00 1	99.54 121	97.77 185	99.97 95

conf200view11		99.25 94	99.01 98	99.95 46	99.81 99	99.87 61	100.00 1	99.94 18	97.13 152	99.83 124	99.96 159	97.01 142	100.00 1	99.59 112	97.85 177	99.97 95

thres100view900		99.25 94	99.01 98	99.95 46	99.81 99	99.87 61	100.00 1	99.94 18	97.13 152	99.83 124	99.96 159	97.01 142	100.00 1	99.59 112	97.85 177	99.98 90

thres600view7		99.24 97	99.00 100	99.95 46	99.81 99	99.87 61	100.00 1	99.94 18	97.13 152	99.83 124	99.96 159	97.01 142	100.00 1	99.54 121	97.77 185	99.97 95

OMC-MVS		99.27 90	99.38 65	98.96 170	99.95 81	97.06 251	100.00 1	99.40 154	98.83 31	99.88 120	100.00 1	97.01 142	99.86 142	99.47 127	99.84 110	99.97 95

xiu_mvs_v1_base_debu		99.35 75	99.21 79	99.79 98	99.67 135	99.71 95	99.78 241	99.36 181	98.13 71	100.00 1	100.00 1	97.00 147	100.00 1	99.83 85	99.07 124	99.66 194

xiu_mvs_v1_base		99.35 75	99.21 79	99.79 98	99.67 135	99.71 95	99.78 241	99.36 181	98.13 71	100.00 1	100.00 1	97.00 147	100.00 1	99.83 85	99.07 124	99.66 194

xiu_mvs_v1_base_debi		99.35 75	99.21 79	99.79 98	99.67 135	99.71 95	99.78 241	99.36 181	98.13 71	100.00 1	100.00 1	97.00 147	100.00 1	99.83 85	99.07 124	99.66 194

view600		99.18 101	98.93 111	99.93 61	99.81 99	99.83 71	100.00 1	99.94 18	96.96 162	99.54 156	99.96 159	96.99 150	100.00 1	99.43 128	97.75 187	99.97 95

view800		99.18 101	98.93 111	99.93 61	99.81 99	99.83 71	100.00 1	99.94 18	96.96 162	99.54 156	99.96 159	96.99 150	100.00 1	99.43 128	97.75 187	99.97 95

conf0.05thres1000		99.18 101	98.93 111	99.93 61	99.81 99	99.83 71	100.00 1	99.94 18	96.96 162	99.54 156	99.96 159	96.99 150	100.00 1	99.43 128	97.75 187	99.97 95

tfpn		99.18 101	98.93 111	99.93 61	99.81 99	99.83 71	100.00 1	99.94 18	96.96 162	99.54 156	99.96 159	96.99 150	100.00 1	99.43 128	97.75 187	99.97 95

CR-MVSNet		98.02 173	97.71 180	98.93 171	99.31 195	98.86 159	99.13 319	99.00 298	96.53 189	99.96 87	98.98 276	96.94 154	98.10 294	91.18 297	98.40 144	99.84 162

Patchmtry		96.81 209	96.37 213	98.14 218	99.31 195	98.55 171	98.91 328	99.00 298	90.45 302	97.92 250	98.98 276	96.94 154	98.12 289	94.27 272	91.53 266	97.75 221

LS3D		99.31 86	99.13 92	99.87 80	99.99 45	99.71 95	99.55 279	99.46 95	97.32 142	99.82 133	100.00 1	96.85 156	99.97 98	99.14 149	100.00 1	99.92 126

MVSFormer		98.94 128	98.82 123	99.28 151	99.45 180	99.49 113	100.00 1	99.13 262	95.46 228	99.97 84	100.00 1	96.76 157	98.59 267	98.63 173	100.00 1	99.74 185

lupinMVS		99.29 88	99.16 85	99.69 113	99.45 180	99.49 113	100.00 1	99.15 253	97.45 133	99.97 84	100.00 1	96.76 157	99.76 158	99.67 106	100.00 1	99.81 170

MAR-MVS		99.49 65	99.36 67	99.89 74	99.97 75	99.66 102	99.74 251	99.95 15	97.89 93	100.00 1	100.00 1	96.71 159	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
MSDG		98.90 131	98.63 140	99.70 112	99.92 90	99.25 134	100.00 1	99.37 175	95.71 215	99.40 170	100.00 1	96.58 160	99.95 119	96.80 237	99.94 102	99.91 127

PVSNet_BlendedMVS		98.71 141	98.62 141	98.98 169	99.98 71	99.60 106	100.00 1	100.00 1	97.23 145	100.00 1	99.03 270	96.57 161	99.99 81	100.00 1	94.75 237	97.35 304

PVSNet_Blended		99.48 67	99.36 67	99.83 87	99.98 71	99.60 106	100.00 1	100.00 1	97.79 102	100.00 1	100.00 1	96.57 161	99.99 81	100.00 1	99.88 107	99.90 135

MVS_Test		98.93 129	98.65 138	99.77 108	99.62 146	99.50 110	99.99 175	99.19 244	95.52 223	99.96 87	99.86 185	96.54 163	99.98 94	98.65 171	98.48 138	99.82 168

PMMVS		99.12 106	98.97 104	99.58 127	99.57 159	98.98 154	100.00 1	99.30 206	97.14 151	99.96 87	100.00 1	96.53 164	99.82 151	99.70 102	98.49 137	99.94 115

PVSNet_Blended_VisFu		99.33 78	99.18 84	99.78 106	99.82 98	99.49 113	100.00 1	99.95 15	97.36 138	99.63 154	100.00 1	96.45 165	99.95 119	99.79 90	99.65 117	99.89 140

mvs_anonymous		98.80 137	98.60 143	99.38 143	99.57 159	99.24 136	100.00 1	99.21 241	95.87 209	98.92 192	99.82 193	96.39 166	99.03 221	99.13 150	98.50 136	99.88 152

testpf		97.02 205	96.87 196	97.44 252	99.50 176	95.18 272	95.70 344	99.33 196	91.35 297	98.00 248	99.22 254	96.29 167	98.49 276	94.42 269	98.07 167	98.67 208

DP-MVS		98.86 133	98.54 147	99.81 91	99.97 75	99.45 119	99.52 282	99.40 154	94.35 249	98.36 229	100.00 1	96.13 168	99.97 98	99.12 151	100.00 1	100.00 1

PVSNet	94.91 18	99.30 87	99.25 74	99.44 135	100.00 1	98.32 185	100.00 1	99.86 39	98.04 79	100.00 1	100.00 1	96.10 169	100.00 1	99.55 118	99.73 114	100.00 1

Effi-MVS+-dtu		98.51 156	98.86 121	97.47 251	99.77 123	94.21 297	100.00 1	98.94 300	97.61 123	99.91 115	98.75 290	95.89 170	99.51 197	99.36 136	99.48 120	98.68 207

mvs-test1		98.89 132	98.99 103	98.60 187	99.77 123	95.96 264	100.00 1	98.94 300	97.61 123	99.93 113	99.92 178	95.89 170	99.93 132	99.36 136	99.50 119	99.90 135

UA-Net		99.06 111	98.83 122	99.74 109	99.52 170	99.40 122	99.08 323	99.45 97	97.64 121	99.83 124	100.00 1	95.80 172	99.94 130	98.35 184	99.80 113	99.88 152

FMVSNet5		95.32 269	95.43 257	94.99 301	99.39 191	92.99 308	99.25 301	99.24 232	90.45 302	97.44 266	98.45 301	95.78 173	94.39 333	87.02 320	91.88 263	97.59 293

1111		88.62 308	89.13 307	87.08 323	87.71 337	77.22 335	99.96 200	97.71 335	85.51 327	83.47 336	96.26 318	95.76 174	90.96 339	66.63 345	89.17 296	93.07 338

.test1245		85.18 312	84.81 313	86.28 325	87.71 337	77.22 335	99.96 200	97.71 335	85.51 327	83.47 336	96.26 318	95.76 174	90.96 339	66.63 345	50.30 346	90.64 342

CVMVSNet		98.56 150	98.47 151	98.82 178	99.11 203	97.67 229	99.74 251	99.47 83	97.57 128	99.06 187	100.00 1	95.72 176	98.97 227	98.21 190	97.33 204	99.83 165

RPMNet		95.10 271	93.82 274	98.93 171	99.31 195	98.86 159	99.13 319	99.38 172	79.82 338	99.96 87	95.13 333	95.69 177	98.10 294	77.54 338	98.40 144	99.84 162

MVS		99.22 99	98.96 105	99.98 16	99.00 219	99.95 19	99.24 302	99.94 18	98.14 70	98.88 195	100.00 1	95.63 178	100.00 1	99.85 82	100.00 1	100.00 1

jason		99.11 107	98.96 105	99.59 123	99.17 201	99.31 128	100.00 1	99.13 262	97.38 137	99.83 124	100.00 1	95.54 179	99.72 163	99.57 117	99.97 97	99.74 185
jason: jason.
AdaColmap		99.44 70	99.26 73	99.95 46	100.00 1	99.86 66	99.70 261	99.99 10	98.53 46	99.90 116	100.00 1	95.34 180	100.00 1	99.92 73	100.00 1	100.00 1

CANet		99.40 71	99.24 76	99.89 74	99.99 45	99.76 82	100.00 1	99.73 55	98.40 52	99.78 137	100.00 1	95.28 181	99.96 113	100.00 1	99.99 87	99.96 106

FIs		97.95 175	97.73 179	98.62 186	98.53 243	99.24 136	100.00 1	99.43 115	96.74 173	97.87 252	99.82 193	95.27 182	98.89 233	98.78 164	93.07 252	97.74 243

canonicalmvs		99.03 114	98.73 131	99.94 58	99.75 127	99.95 19	100.00 1	99.30 206	97.64 121	100.00 1	100.00 1	95.22 183	99.97 98	99.76 96	96.90 209	99.91 127

FC-MVSNet-test		97.84 176	97.63 181	98.45 191	98.30 252	99.05 147	100.00 1	99.43 115	96.63 184	97.61 261	99.82 193	95.19 184	98.57 270	98.64 172	93.05 253	97.73 253

diffmvs		98.52 154	98.11 164	99.73 111	99.59 153	99.47 118	100.00 1	99.19 244	93.68 268	99.70 151	99.75 202	95.07 185	99.84 147	97.57 214	98.48 138	99.89 140

UniMVSNet_NR-MVSNet		97.16 196	96.80 198	98.22 212	98.38 247	98.41 174	100.00 1	99.45 97	96.14 205	97.76 254	99.64 224	95.05 186	98.50 274	97.98 202	86.84 311	97.75 221

Fast-Effi-MVS+-dtu		98.38 162	98.56 146	97.82 240	99.58 157	94.44 294	100.00 1	99.16 252	96.75 172	99.51 161	99.63 228	95.03 187	99.60 173	97.71 210	99.67 116	99.42 202

UniMVSNet (Re)		97.29 192	96.85 197	98.59 189	98.49 244	99.13 143	100.00 1	99.42 121	96.52 190	98.24 239	98.90 284	94.93 188	98.89 233	97.54 217	87.61 308	97.75 221

BH-w/o		98.82 136	98.81 125	98.88 175	99.62 146	96.71 258	100.00 1	99.28 218	97.09 156	98.81 201	100.00 1	94.91 189	99.96 113	99.54 121	100.00 1	99.96 106

PCF-MVS	98.23 3	98.69 144	98.37 155	99.62 116	99.78 121	99.02 149	99.23 308	99.06 291	96.43 193	98.08 241	100.00 1	94.72 190	99.95 119	98.16 191	99.91 104	99.90 135
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		99.04 113	98.75 130	99.90 71	99.81 99	99.75 84	99.50 284	99.47 83	98.36 58	100.00 1	99.99 139	94.66 191	100.00 1	99.90 75	97.09 206	99.96 106

BH-untuned		98.64 145	98.65 138	98.60 187	99.59 153	96.17 263	100.00 1	99.28 218	96.67 181	98.41 228	100.00 1	94.52 192	99.83 149	99.41 134	100.00 1	99.81 170

nrg030		97.64 181	97.27 188	98.75 182	98.34 248	99.53 109	100.00 1	99.22 237	96.21 204	98.27 236	99.95 171	94.40 193	98.98 226	99.23 145	89.78 290	97.75 221

LP		95.89 259	95.35 260	97.51 248	98.92 227	95.15 273	98.66 332	99.39 168	89.43 316	97.22 271	99.03 270	94.37 194	97.74 305	83.71 326	97.44 202	99.63 197

RPSCF		97.37 188	98.24 159	94.76 304	99.80 113	84.57 329	99.99 175	99.05 293	94.95 235	99.82 133	100.00 1	94.03 195	100.00 1	98.15 192	98.38 149	99.70 190

CANet_DTU		99.02 118	98.90 119	99.41 139	99.88 96	98.71 164	100.00 1	99.29 210	98.84 30	100.00 1	100.00 1	94.02 196	100.00 1	98.08 199	99.96 99	99.52 200

LCM-MVSNet-Re		96.52 227	97.21 190	94.44 305	99.27 198	85.80 327	99.85 227	96.61 345	95.98 207	92.75 308	98.48 300	93.97 197	97.55 307	99.58 116	98.43 142	99.98 90

Effi-MVS+		98.58 148	98.24 159	99.61 117	99.60 150	99.26 132	97.85 339	99.10 270	96.22 203	99.97 84	99.89 181	93.75 198	99.77 157	99.43 128	98.34 151	99.81 170

pmmvs4		97.17 195	96.80 198	98.27 205	97.68 291	98.64 168	100.00 1	99.18 248	94.22 252	98.55 219	99.71 206	93.67 199	98.47 277	95.66 250	92.57 257	97.71 265

CHOSEN 1792x2688		99.00 120	98.91 116	99.25 154	99.90 93	97.79 226	100.00 1	99.99 10	98.79 36	98.28 235	100.00 1	93.63 200	99.95 119	99.66 108	99.95 101	100.00 1

cdsmvs_eth3d_5k		24.41 332	32.55 332	0.00 345	0.00 359	0.00 360	0.00 351	99.39 168	0.00 355	0.00 356	100.00 1	93.55 201	0.00 358	0.00 355	0.00 356	0.00 356

AllTest		98.55 151	98.40 153	98.99 167	99.93 87	97.35 239	100.00 1	99.40 154	97.08 158	99.09 183	99.98 140	93.37 202	99.95 119	96.94 230	99.84 110	99.68 192

TestCases				98.99 167	99.93 87	97.35 239		99.40 154	97.08 158	99.09 183	99.98 140	93.37 202	99.95 119	96.94 230	99.84 110	99.68 192

FMVSNet3		97.30 191	96.95 193	98.37 196	99.65 139	99.25 134	99.71 259	99.28 218	94.23 251	98.53 221	98.91 283	93.30 204	98.11 291	95.31 256	93.60 246	97.73 253

Fast-Effi-MVS+		98.40 161	98.02 170	99.55 130	99.63 144	99.06 146	100.00 1	99.15 253	95.07 232	99.42 166	99.95 171	93.26 205	99.73 161	97.44 219	98.24 157	99.87 159

QAPM		98.99 122	98.66 135	99.96 35	99.01 215	99.87 61	99.88 223	99.93 32	97.99 82	98.68 209	100.00 1	93.17 206	100.00 1	99.32 141	100.00 1	100.00 1

PatchMatch-RL		99.02 118	98.78 127	99.74 109	99.99 45	99.29 129	100.00 1	100.00 1	98.38 55	99.89 119	99.81 196	93.14 207	99.99 81	97.85 207	99.98 94	99.95 111

WR-MVS_H		96.73 215	96.32 216	97.95 234	98.26 254	97.88 221	99.72 257	99.43 115	95.06 233	96.99 275	98.68 293	93.02 208	98.53 272	97.43 220	88.33 304	97.43 300

3Dnovator	95.63 14	99.06 111	98.76 129	99.96 35	98.86 233	99.90 44	99.98 191	99.93 32	98.95 24	98.49 225	100.00 1	92.91 209	100.00 1	99.71 100	100.00 1	100.00 1

3Dnovator+	95.58 15	99.03 114	98.71 133	99.96 35	98.99 222	99.89 48	100.00 1	99.51 80	98.96 21	98.32 232	100.00 1	92.78 210	100.00 1	99.87 79	100.00 1	100.00 1

NR-MVSNet		96.63 223	96.04 231	98.38 195	98.31 250	98.98 154	99.22 310	99.35 189	95.87 209	94.43 300	99.65 220	92.73 211	98.40 280	96.78 238	88.05 305	97.75 221

COLMAP_ROB	97.10 7	98.29 164	98.17 163	98.65 185	99.94 85	97.39 236	99.30 298	99.40 154	95.64 216	97.75 257	100.00 1	92.69 212	99.95 119	98.89 158	99.92 103	98.62 210
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EU-MVSNet		96.63 223	96.53 207	96.94 269	97.59 296	96.87 255	99.76 248	99.47 83	96.35 199	96.85 280	99.78 200	92.57 213	96.27 324	95.33 255	91.08 271	97.68 273

MVP-Stereo		96.51 228	96.48 210	96.60 288	95.65 321	94.25 296	98.84 330	98.16 322	95.85 213	95.23 294	99.04 265	92.54 214	99.13 217	92.98 287	99.98 94	96.43 316
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DeepC-MVS	97.84 5	99.00 120	98.80 126	99.60 120	99.93 87	99.03 148	100.00 1	99.40 154	98.61 44	99.33 171	100.00 1	92.23 215	99.95 119	99.74 98	99.96 99	99.83 165
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS		96.93 208	96.49 209	98.22 212	98.31 250	98.41 174	100.00 1	99.37 175	96.41 196	97.76 254	99.65 220	92.14 216	98.50 274	97.98 202	86.84 311	97.75 221

Baseline_NR-MVSNet		96.16 247	95.70 246	97.56 247	98.28 253	96.79 257	100.00 1	97.86 333	91.93 293	97.63 259	99.47 244	92.14 216	98.35 282	97.13 226	86.83 313	97.54 296

cascas		98.43 158	98.07 167	99.50 133	99.65 139	99.02 149	100.00 1	99.22 237	94.21 253	99.72 143	99.98 140	92.03 218	99.93 132	99.68 104	98.12 164	99.54 199

v18		93.55 279	92.42 287	96.91 271	98.12 265	98.26 192	99.83 229	98.74 307	89.99 307	88.94 319	95.64 322	92.02 219	96.54 311	90.27 307	78.26 331	95.94 325

test_djsdf		97.55 185	97.38 185	98.07 225	97.50 300	97.99 213	100.00 1	99.13 262	95.46 228	98.47 226	99.85 187	92.01 220	98.59 267	98.63 173	95.36 219	97.62 289

v17		93.53 280	92.42 287	96.88 276	98.09 269	98.16 202	99.83 229	98.74 307	89.95 308	89.05 316	95.65 321	91.99 221	96.54 311	90.31 306	78.15 333	95.95 323

v13		93.18 287	91.97 294	96.81 282	98.12 265	98.12 205	99.74 251	98.73 313	89.47 315	88.52 325	95.40 331	91.72 222	96.41 318	89.23 318	76.77 338	95.92 327

v1neww		96.77 210	96.27 217	98.26 207	98.16 262	98.23 194	100.00 1	99.09 273	93.94 258	98.73 206	99.05 262	91.65 223	98.82 241	95.75 246	90.04 281	97.74 243

v7new		96.77 210	96.27 217	98.26 207	98.16 262	98.23 194	100.00 1	99.09 273	93.94 258	98.73 206	99.05 262	91.65 223	98.82 241	95.75 246	90.04 281	97.74 243

v16		93.48 282	92.34 289	96.89 272	98.13 264	98.19 200	99.82 234	98.74 307	89.94 309	88.90 320	95.62 323	91.65 223	96.54 311	90.03 309	78.22 332	95.95 323

v12		93.18 287	91.97 294	96.80 283	98.09 269	98.10 207	99.76 248	98.73 313	89.61 314	88.45 326	95.41 330	91.63 226	96.39 320	89.32 315	76.76 339	95.91 330

v1141		96.66 219	96.12 226	98.26 207	98.05 275	98.28 187	100.00 1	99.09 273	93.71 262	98.59 216	99.04 265	91.57 227	98.73 252	95.05 261	90.00 284	97.75 221

v15		93.28 284	92.08 290	96.87 277	98.07 273	98.22 198	99.82 234	98.74 307	89.83 310	88.74 322	95.44 327	91.55 228	96.47 315	89.70 310	76.87 335	95.93 326

divwei89l23v2f112		96.68 218	96.15 224	98.25 211	98.03 278	98.27 189	100.00 1	99.09 273	93.79 261	98.59 216	99.04 265	91.54 229	98.73 252	95.29 259	89.99 285	97.75 221

v6		96.77 210	96.26 219	98.29 203	98.10 268	98.27 189	100.00 1	99.09 273	93.95 257	98.73 206	99.06 260	91.51 230	98.86 238	95.83 244	90.04 281	97.74 243

V14		93.24 285	92.04 291	96.87 277	98.07 273	98.20 199	99.80 239	98.74 307	89.76 312	88.64 323	95.53 324	91.47 231	96.47 315	89.49 312	76.84 336	95.92 327

v8		96.35 237	95.73 245	98.21 214	98.11 267	98.23 194	99.94 210	99.07 283	92.66 287	98.29 234	99.00 275	91.46 232	98.77 248	94.17 273	88.83 300	97.62 289

OpenMVS	95.20 17	98.76 138	98.41 152	99.78 106	98.89 229	99.81 77	99.99 175	99.76 46	98.02 80	98.02 246	100.00 1	91.44 233	100.00 1	99.63 110	99.97 97	99.55 198

v1		96.66 219	96.12 226	98.30 202	98.09 269	98.33 182	100.00 1	99.09 273	93.69 267	98.68 209	99.04 265	91.42 234	98.83 240	95.05 261	89.99 285	97.75 221

V9		93.20 286	91.99 293	96.85 279	98.05 275	98.16 202	99.77 245	98.73 313	89.67 313	88.64 323	95.44 327	91.34 235	96.47 315	89.46 313	76.79 337	95.92 327

v148		96.29 240	95.84 238	97.63 242	97.74 289	96.53 261	100.00 1	99.07 283	93.52 274	98.01 247	99.42 246	91.22 236	98.60 265	96.37 242	87.22 310	97.75 221

XVG-OURS		98.30 163	98.36 156	98.13 221	99.58 157	95.91 267	100.00 1	99.36 181	98.69 38	99.23 175	100.00 1	91.20 237	99.92 135	99.34 138	97.82 181	98.56 211

WR-MVS		97.09 199	96.64 203	98.46 190	98.43 245	99.09 144	99.97 197	99.33 196	95.62 218	97.76 254	99.67 217	91.17 238	98.56 271	98.49 179	89.28 294	97.74 243

v11		93.14 289	91.93 296	96.77 285	98.01 279	98.17 201	99.72 257	98.74 307	89.39 317	88.81 321	95.49 326	91.15 239	96.29 322	86.78 321	79.25 328	95.86 332

v7		96.64 222	96.14 225	98.13 221	98.18 259	97.97 215	99.99 175	99.09 273	93.62 272	98.76 203	99.30 250	91.13 240	98.70 257	94.37 270	90.80 274	97.74 243

test2356		92.55 294	93.94 272	88.39 320	93.49 328	78.42 334	100.00 1	99.40 154	91.64 294	93.93 302	98.74 291	91.11 241	88.67 343	79.37 334	97.79 184	98.08 213

pcd1.5k->3k		40.07 330	42.58 331	32.57 343	97.97 283	0.00 360	0.00 351	99.25 229	0.00 355	0.00 356	0.14 357	91.10 242	0.00 358	0.00 355	94.75 237	97.70 266

V42		96.65 221	96.16 223	98.11 224	98.17 261	98.23 194	99.99 175	99.09 273	93.97 255	98.74 205	99.05 262	91.09 243	98.82 241	95.46 254	89.90 288	97.27 307

v10		96.14 249	95.50 252	98.07 225	98.19 258	97.96 216	99.83 229	99.07 283	92.10 291	98.07 243	98.94 281	91.07 244	98.61 263	92.41 293	89.82 289	97.63 287

v7n		96.06 253	95.42 258	97.99 233	97.58 297	97.35 239	99.86 226	99.11 269	92.81 286	97.91 251	99.49 242	90.99 245	98.92 229	92.51 290	88.49 303	97.70 266

v1144		96.51 228	95.97 235	98.13 221	97.98 282	98.04 212	99.99 175	99.08 281	93.51 275	98.62 214	98.98 276	90.98 246	98.62 262	93.79 280	90.79 275	97.74 243

ab-mvs		98.42 159	98.02 170	99.61 117	99.71 129	99.00 153	99.10 321	99.64 72	96.70 177	99.04 188	99.81 196	90.64 247	99.98 94	99.64 109	97.93 171	99.84 162

MVS_0304		98.98 124	98.66 135	99.94 58	99.95 81	99.86 66	100.00 1	99.29 210	98.38 55	99.81 135	100.00 1	90.60 248	100.00 1	100.00 1	99.91 104	99.95 111

Anonymous20231206		93.45 283	93.17 281	94.30 307	95.00 325	89.69 321	99.98 191	98.43 317	93.30 280	94.50 299	98.59 297	90.52 249	95.73 329	77.46 339	90.73 277	97.48 299

anonymousdsp		97.16 196	96.88 195	98.00 231	97.08 311	98.06 210	99.81 236	99.15 253	94.58 242	97.84 253	99.62 230	90.49 250	98.60 265	97.98 202	95.32 220	97.33 306

v2v482		96.70 217	96.18 221	98.27 205	98.04 277	98.39 176	100.00 1	99.13 262	94.19 254	98.58 218	99.08 259	90.48 251	98.67 260	95.69 249	90.44 279	97.75 221

v144192		96.40 234	95.81 239	98.17 216	97.89 286	98.11 206	99.99 175	99.06 291	93.39 277	98.75 204	99.09 258	90.43 252	98.66 261	93.10 286	90.55 278	97.75 221

Vis-MVSNet		98.52 154	98.25 158	99.34 146	99.68 134	98.55 171	99.68 267	99.41 152	97.34 141	99.94 108	100.00 1	90.38 253	99.70 165	99.03 154	98.84 129	99.76 180
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V4		95.89 259	95.36 259	97.49 249	97.65 293	97.40 235	99.88 223	99.07 283	92.19 289	98.08 241	99.01 273	90.20 254	98.72 254	93.57 283	88.63 301	96.34 317

CP-MVSNet		96.73 215	96.25 220	98.18 215	98.21 257	98.67 167	99.77 245	99.32 199	95.06 233	97.20 272	99.65 220	90.10 255	98.19 285	98.06 201	88.90 298	97.66 279

TranMVSNet+NR-MVSNet		96.45 231	96.01 232	97.79 241	98.00 281	97.62 231	100.00 1	99.35 189	95.98 207	97.31 268	99.64 224	90.09 256	98.00 301	96.89 233	86.80 314	97.75 221

v52		95.88 261	95.35 260	97.48 250	97.66 292	97.38 238	99.88 223	99.07 283	92.19 289	98.09 240	99.02 272	90.06 257	98.72 254	93.58 282	88.61 302	96.34 317

DSMNet-mixed		95.18 270	95.21 263	95.08 299	96.03 317	90.21 320	99.65 271	93.64 350	92.91 285	98.34 231	97.40 312	90.05 258	95.51 331	91.02 299	97.86 176	99.51 201

N_pmnet		91.88 299	93.37 280	87.40 322	97.24 310	66.33 347	99.90 217	91.05 352	89.77 311	95.65 292	98.58 298	90.05 258	98.11 291	85.39 323	92.72 256	97.75 221

v748		95.47 267	94.95 270	97.04 264	97.46 305	94.76 286	99.93 212	99.10 270	91.95 292	97.11 273	99.18 255	89.94 260	98.68 258	94.01 277	85.88 317	97.67 277

GA-MVS		97.72 179	97.27 188	99.06 161	99.24 200	97.93 218	100.00 1	99.24 232	95.80 214	98.99 190	99.64 224	89.77 261	99.36 210	95.12 260	97.62 201	99.89 140

MIMVSNet		97.06 202	96.73 201	98.05 229	99.38 192	96.64 260	98.47 335	99.35 189	93.41 276	99.48 163	98.53 299	89.66 262	97.70 306	94.16 275	98.11 165	99.80 177

Patchmatch-test1		98.23 168	97.91 173	99.17 158	99.41 184	98.25 193	99.99 175	99.45 97	96.91 168	99.76 140	99.69 211	89.65 263	99.37 209	97.55 215	98.79 130	99.89 140

semantic-postprocess				97.62 244	99.40 188	96.83 256		99.14 257	94.65 241	97.55 263	99.72 204	89.64 264	98.31 283	95.62 252	92.05 261	97.73 253

IterMVS		96.76 214	96.46 211	97.63 242	99.41 184	96.89 254	99.99 175	99.13 262	94.74 239	97.59 262	99.66 219	89.63 265	98.28 284	95.71 248	92.31 259	97.72 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		96.18 245	95.49 254	98.26 207	98.01 279	98.15 204	99.99 175	99.08 281	93.36 278	98.54 220	98.97 279	89.47 266	98.89 233	91.15 298	90.82 273	97.75 221

v1921920		96.16 247	95.50 252	98.14 218	97.88 287	97.96 216	99.99 175	99.07 283	93.33 279	98.60 215	99.24 253	89.37 267	98.71 256	91.28 296	90.74 276	97.75 221

XVG-OURS-SEG-HR		98.27 166	98.31 157	98.14 218	99.59 153	95.92 266	100.00 1	99.36 181	98.48 47	99.21 176	100.00 1	89.27 268	99.94 130	99.76 96	99.17 121	98.56 211

test20.03		93.11 290	92.85 284	93.88 312	95.19 324	91.83 315	100.00 1	98.87 304	93.68 268	92.76 307	98.88 285	89.20 269	92.71 338	77.88 337	89.19 295	97.09 310

MDA-MVSNet_test_wron		92.61 293	91.09 299	97.19 262	96.71 313	97.26 244	100.00 1	99.14 257	88.61 319	67.90 346	98.32 308	89.03 270	96.57 310	90.47 304	89.59 291	97.74 243

BH-RMVSNet		98.46 157	98.08 166	99.59 123	99.61 148	99.19 140	100.00 1	99.28 218	97.06 160	98.95 191	100.00 1	88.99 271	99.82 151	98.83 163	100.00 1	99.77 178

v1240		95.96 255	95.25 262	98.07 225	97.91 285	97.87 223	99.96 200	99.07 283	93.24 281	98.64 213	98.96 280	88.98 272	98.61 263	89.58 311	90.92 272	97.75 221

TR-MVS		98.14 169	97.74 177	99.33 147	99.59 153	98.28 187	99.27 299	99.21 241	96.42 195	99.15 181	99.94 175	88.87 273	99.79 156	98.88 159	98.29 154	99.93 124

YYNet1		92.44 295	90.92 300	97.03 265	96.20 315	97.06 251	99.99 175	99.14 257	88.21 321	67.93 345	98.43 303	88.63 274	96.28 323	90.64 300	89.08 297	97.74 243

HQP2-MVS												88.61 275

HQP-MVS		97.73 178	97.85 174	97.39 253	99.07 207	94.82 278	100.00 1	99.40 154	99.04 13	99.17 177	99.97 150	88.61 275	99.57 178	99.79 90	95.58 212	97.77 215

HQP_MVS		97.71 180	97.82 175	97.37 254	99.00 219	94.80 281	100.00 1	99.40 154	99.00 19	99.08 185	99.97 150	88.58 277	99.55 187	99.79 90	95.57 216	97.76 217

plane_prior6					99.06 211	94.80 281						88.58 277

tfpnnormal		96.36 236	95.69 248	98.37 196	98.55 241	98.71 164	99.69 265	99.45 97	93.16 283	96.69 283	99.71 206	88.44 279	98.99 225	94.17 273	91.38 268	97.41 301

TransMVSNet (Re)		94.78 272	93.72 275	97.93 237	98.34 248	97.88 221	99.23 308	97.98 330	91.60 295	94.55 298	99.71 206	87.89 280	98.36 281	89.30 316	84.92 318	97.56 295

DTE-MVSNet		95.52 265	94.99 269	97.08 263	97.49 302	96.45 262	100.00 1	99.25 229	93.82 260	96.17 286	99.57 235	87.81 281	97.18 308	94.57 266	86.26 316	97.62 289

XXY-MVS		97.14 198	96.63 204	98.67 184	98.65 237	98.92 157	99.54 280	99.29 210	95.57 220	97.63 259	99.83 190	87.79 282	99.35 211	98.39 182	92.95 254	97.75 221

UGNet		98.41 160	98.11 164	99.31 149	99.54 162	98.55 171	99.18 311	100.00 1	98.64 43	99.79 136	99.04 265	87.61 283	100.00 1	99.30 142	99.89 106	99.40 203
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
CLD-MVS		97.64 181	97.74 177	97.36 255	99.01 215	94.76 286	100.00 1	99.34 194	99.30 4	99.00 189	99.97 150	87.49 284	99.57 178	99.96 65	95.58 212	97.75 221
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PEN-MVS		96.01 254	95.48 256	97.58 246	97.74 289	97.26 244	99.90 217	99.29 210	94.55 243	96.79 282	99.55 236	87.38 285	97.84 303	96.92 232	87.24 309	97.65 283

ACMM	97.17 6	97.37 188	97.40 184	97.29 257	99.01 215	94.64 290	100.00 1	99.25 229	98.07 77	98.44 227	99.98 140	87.38 285	99.55 187	99.25 144	95.19 228	97.69 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		97.07 201	96.79 200	97.89 239	97.28 309	97.12 248	99.95 206	99.19 244	96.55 187	97.31 268	99.69 211	87.35 287	98.91 230	98.70 168	95.12 232	97.66 279

pmmvs5		95.94 256	95.61 249	96.95 268	97.42 306	94.66 288	100.00 1	98.08 326	93.60 273	97.05 274	99.43 245	87.02 288	98.46 278	95.76 245	92.12 260	97.72 258

VPA-MVSNet		97.03 204	96.43 212	98.82 178	98.64 238	99.32 127	99.38 292	99.47 83	96.73 175	98.91 194	98.94 281	87.00 289	99.40 207	99.23 145	89.59 291	97.76 217

PS-CasMVS		96.34 238	95.78 242	98.03 230	98.18 259	98.27 189	99.71 259	99.32 199	94.75 237	96.82 281	99.65 220	86.98 290	98.15 287	97.74 209	88.85 299	97.66 279

LPG-MVS_test		97.31 190	97.32 187	97.28 258	98.85 234	94.60 291	100.00 1	99.37 175	97.35 139	98.85 197	99.98 140	86.66 291	99.56 182	99.55 118	95.26 223	97.70 266

LGP-MVS_train				97.28 258	98.85 234	94.60 291		99.37 175	97.35 139	98.85 197	99.98 140	86.66 291	99.56 182	99.55 118	95.26 223	97.70 266

mvs_tets		97.00 206	96.69 202	97.94 235	97.41 308	97.27 243	99.60 275	99.18 248	96.51 191	97.35 267	99.69 211	86.53 293	98.91 230	98.84 161	95.09 233	97.65 283

pm-mvs1		95.76 262	95.01 268	98.00 231	98.23 256	97.45 233	99.24 302	99.04 296	93.13 284	95.93 290	99.72 204	86.28 294	98.84 239	95.62 252	87.92 306	97.72 258

MVS-HIRNet		94.12 276	92.73 285	98.29 203	99.33 194	95.95 265	99.38 292	99.19 244	74.54 340	98.26 237	86.34 346	86.07 295	99.06 220	91.60 295	99.87 109	99.85 161

DeepMVS_CX				89.98 317	98.90 228	71.46 343		99.18 248	97.61 123	96.92 276	99.83 190	86.07 295	99.83 149	96.02 243	97.65 200	98.65 209

OPM-MVS		97.21 193	97.18 191	97.32 256	98.08 272	94.66 288	100.00 1	99.28 218	98.65 42	98.92 192	99.98 140	86.03 297	99.56 182	98.28 188	95.41 218	97.72 258

new_pmnet		94.11 277	93.47 279	96.04 296	96.60 314	92.82 309	99.97 197	98.91 303	90.21 304	95.26 293	98.05 309	85.89 298	98.14 288	84.28 325	92.01 262	97.16 309

ACMP	97.00 8	97.19 194	97.16 192	97.27 260	98.97 224	94.58 293	100.00 1	99.32 199	97.97 86	97.45 265	99.98 140	85.79 299	99.56 182	99.70 102	95.24 226	97.67 277
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OurMVSNet-221017-0		96.14 249	95.98 234	96.62 287	97.49 302	93.44 303	99.92 214	98.16 322	95.86 211	97.65 258	99.95 171	85.71 300	98.78 245	94.93 264	94.18 244	97.64 286

SixPastTwentyTwo		95.71 263	95.49 254	96.38 291	97.42 306	93.01 306	99.84 228	98.23 319	94.75 237	95.98 289	99.97 150	85.35 301	98.43 279	94.71 265	93.17 251	97.69 271

LTVRE_ROB	95.29 16	96.32 239	96.10 228	96.99 267	98.55 241	93.88 299	99.45 286	99.28 218	94.50 245	96.46 285	99.52 239	84.86 302	99.48 200	97.26 224	95.03 234	97.59 293
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
ITE_SJBPF				96.84 280	98.96 225	93.49 302		98.12 324	98.12 74	98.35 230	99.97 150	84.45 303	99.56 182	95.63 251	95.25 225	97.49 297

PVSNet_0	93.57 19	96.41 232	95.74 244	98.41 193	99.84 97	95.22 271	100.00 1	100.00 1	98.08 76	97.55 263	99.78 200	84.40 304	100.00 1	100.00 1	81.99 322	100.00 1

K. test v3		95.46 268	95.14 265	96.40 290	97.53 299	93.40 304	99.99 175	99.23 235	95.49 226	92.70 309	99.73 203	84.26 305	98.12 289	93.94 279	93.38 250	97.68 273

GBi-Net		96.07 251	95.80 240	96.89 272	99.53 165	94.87 275	99.18 311	99.27 225	93.71 262	98.53 221	98.81 287	84.23 306	98.07 297	95.31 256	93.60 246	97.72 258

test1		96.07 251	95.80 240	96.89 272	99.53 165	94.87 275	99.18 311	99.27 225	93.71 262	98.53 221	98.81 287	84.23 306	98.07 297	95.31 256	93.60 246	97.72 258

FMVSNet2		96.22 243	95.60 250	98.06 228	99.53 165	98.33 182	99.45 286	99.27 225	93.71 262	98.03 244	98.84 286	84.23 306	98.10 294	93.97 278	93.40 249	97.73 253

testgi		96.18 245	95.93 236	96.93 270	98.98 223	94.20 298	100.00 1	99.07 283	97.16 150	96.06 288	99.86 185	84.08 309	97.79 304	90.38 305	97.80 183	98.81 206

ACMH	96.25 11	96.77 210	96.62 205	97.21 261	98.96 225	94.43 295	99.64 272	99.33 196	97.43 135	96.55 284	99.97 150	83.52 310	99.54 190	99.07 153	95.13 231	97.66 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				96.05 295	97.55 298	91.80 316		99.22 237		91.87 310	99.91 179	83.50 311	98.68 258	92.48 291	90.42 280	97.68 273

UnsupCasMVSNet_eth		94.25 275	93.89 273	95.34 298	97.63 294	92.13 313	99.73 255	99.36 181	94.88 236	92.78 306	98.63 296	82.72 312	96.53 314	94.57 266	84.73 319	97.36 303

LF4IMVS		96.19 244	96.18 221	96.23 294	98.26 254	92.09 314	100.00 1	97.89 332	97.82 100	97.94 249	99.87 183	82.71 313	99.38 208	97.41 221	93.71 245	97.20 308

ACMH+	96.20 13	96.49 230	96.33 215	97.00 266	99.06 211	93.80 300	99.81 236	99.31 204	97.32 142	95.89 291	99.97 150	82.62 314	99.54 190	98.34 185	94.63 240	97.65 283

pmmvs3		90.62 305	89.36 306	94.40 306	90.53 334	91.49 318	100.00 1	96.73 343	84.21 332	93.65 305	96.65 314	82.56 315	94.83 332	82.28 328	77.62 334	96.89 311

XVG-ACMP-BASELINE		96.60 225	96.52 208	96.84 280	98.41 246	93.29 305	99.99 175	99.32 199	97.76 105	98.51 224	99.29 251	81.95 316	99.54 190	98.40 181	95.03 234	97.68 273

test1235678		88.02 310	88.73 308	85.90 326	87.57 339	71.70 342	100.00 1	97.42 338	87.52 324	86.19 331	96.39 316	81.60 317	87.93 344	70.72 342	91.18 270	98.08 213

VPNet		96.41 232	95.76 243	98.33 200	98.61 239	98.30 186	99.48 285	99.45 97	96.98 161	98.87 196	99.88 182	81.57 318	98.93 228	99.22 147	87.82 307	97.76 217

testus		91.61 302	92.92 282	87.70 321	93.91 327	74.98 338	100.00 1	98.19 321	92.59 288	93.90 303	98.68 293	81.43 319	90.77 341	79.82 333	97.71 193	97.34 305

MDA-MVSNet-bldmvs		91.65 301	89.94 304	96.79 284	96.72 312	96.70 259	99.42 289	98.94 300	88.89 318	66.97 348	98.37 306	81.43 319	95.91 327	89.24 317	89.46 293	97.75 221

MS-PatchMatch		95.66 264	95.87 237	95.05 300	97.80 288	89.25 322	98.88 329	99.30 206	96.35 199	96.86 279	99.01 273	81.35 321	99.43 204	93.30 285	99.98 94	96.46 315

USDC		95.90 257	95.70 246	96.50 289	98.60 240	92.56 312	100.00 1	98.30 318	97.77 103	96.92 276	99.94 175	81.25 322	99.45 203	93.54 284	94.96 236	97.49 297

test_normal		96.24 242	95.12 266	99.60 120	96.00 319	99.16 142	100.00 1	99.29 210	96.43 193	88.96 318	98.40 305	80.06 323	99.90 136	98.53 177	98.39 146	99.75 181

DI_MVS_plusplus_test		96.28 241	95.16 264	99.61 117	96.01 318	99.18 141	100.00 1	99.29 210	96.49 192	89.08 315	98.42 304	80.06 323	99.90 136	98.74 167	98.39 146	99.75 181

TDRefinement		91.93 297	90.48 302	96.27 293	81.60 345	92.65 311	99.10 321	97.61 337	93.96 256	93.77 304	99.85 187	80.03 325	99.53 194	97.82 208	70.59 342	96.63 314

test_0402		94.35 274	93.70 276	96.32 292	97.92 284	93.60 301	99.61 274	98.85 305	88.19 322	94.68 297	99.48 243	80.01 326	98.58 269	89.39 314	95.15 230	96.77 312

TinyColmap		95.50 266	95.12 266	96.64 286	98.69 236	93.00 307	99.40 290	97.75 334	96.40 197	96.14 287	99.87 183	79.47 327	99.50 198	93.62 281	94.72 239	97.40 302

LFMVS		97.42 187	96.62 205	99.81 91	99.80 113	99.50 110	99.16 317	99.56 74	94.48 246	100.00 1	100.00 1	79.35 328	100.00 1	99.89 77	97.37 203	99.94 115

pmmvs-eth3d		91.73 300	90.67 301	94.92 303	91.63 332	92.71 310	99.90 217	98.54 316	91.19 299	88.08 327	95.50 325	79.31 329	96.13 325	90.55 303	81.32 325	95.91 330

new-patchmatchnet		90.30 306	89.46 305	92.84 314	90.77 333	88.55 324	99.83 229	98.80 306	90.07 306	87.86 328	95.00 334	78.77 330	94.30 334	84.86 324	79.15 329	95.68 334

test123		79.44 317	79.23 319	80.05 332	80.03 347	71.72 341	100.00 1	77.93 359	62.52 344	94.81 296	99.69 211	78.21 331	74.53 354	92.57 289	27.33 354	93.90 335

CMPMVS	66.12 22	90.65 304	92.04 291	86.46 324	96.18 316	66.87 346	98.03 338	99.38 172	83.38 334	85.49 333	99.55 236	77.59 332	98.80 244	94.44 268	94.31 243	93.72 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		93.64 278	92.87 283	95.94 297	97.47 304	91.41 319	98.92 327	99.02 297	87.84 323	95.01 295	99.61 231	77.24 333	98.77 248	94.33 271	86.41 315	97.63 287

test12356		86.30 311	87.15 312	83.77 330	84.24 342	72.00 340	99.94 210	96.80 342	84.63 331	85.16 334	96.51 315	77.02 334	86.31 347	70.06 343	89.93 287	93.00 339

tmp_tt		75.80 321	74.26 322	80.43 331	52.91 358	53.67 356	87.42 349	97.98 330	61.80 345	67.04 347	100.00 1	76.43 335	96.40 319	96.47 241	28.26 353	91.23 341

MIMVSNet1		91.96 296	91.20 297	94.23 309	94.94 326	91.69 317	99.34 295	99.22 237	88.23 320	94.18 301	98.45 301	75.52 336	93.41 337	79.37 334	91.49 267	97.60 292

VDDNet		96.39 235	95.55 251	98.90 174	99.27 198	97.45 233	99.15 318	99.92 36	91.28 298	99.98 80	100.00 1	73.55 337	100.00 1	99.85 82	96.98 207	99.24 204

FMVSNet1		94.45 273	93.63 277	96.89 272	98.87 232	94.87 275	99.18 311	99.27 225	90.95 301	97.31 268	98.81 287	72.89 338	98.07 297	92.61 288	92.81 255	97.72 258

VDD-MVS		96.58 226	95.99 233	98.34 199	99.52 170	95.33 270	99.18 311	99.38 172	96.64 182	99.77 138	100.00 1	72.51 339	100.00 1	100.00 1	96.94 208	99.70 190

EG-PatchMatch MVS		92.94 291	92.49 286	94.29 308	95.87 320	87.07 326	99.07 325	98.11 325	93.19 282	88.98 317	98.66 295	70.89 340	99.08 219	92.43 292	95.21 227	96.72 313

OpenMVS_ROB	88.34 20	91.89 298	91.12 298	94.19 310	95.55 322	87.63 325	99.26 300	98.03 327	86.61 325	90.65 314	96.82 313	70.14 341	98.78 245	86.54 322	96.50 211	96.15 319

testmvs		80.17 315	81.95 316	74.80 336	58.54 356	59.58 354	100.00 1	87.14 356	76.09 339	99.61 155	100.00 1	67.06 342	74.19 355	98.84 161	50.30 346	90.64 342

UnsupCasMVSNet_bld		89.50 307	88.00 310	93.99 311	95.30 323	88.86 323	98.52 334	99.28 218	85.50 329	87.80 329	94.11 336	61.63 343	96.96 309	90.63 301	79.26 327	96.15 319

testing_2		90.79 303	88.26 309	98.35 198	89.11 336	98.56 170	99.70 261	99.14 257	93.70 266	73.80 341	94.06 337	55.19 344	98.76 250	97.17 225	92.50 258	97.74 243

Gipuma		84.73 313	83.50 314	88.40 319	97.50 300	82.21 331	88.87 347	99.05 293	65.81 343	85.71 332	90.49 341	53.70 345	96.31 321	78.64 336	91.74 264	86.67 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		79.83 316	79.77 317	79.99 333	78.22 349	63.55 350	99.67 268	95.51 348	80.89 336	76.24 340	92.90 339	52.16 346	86.10 348	62.45 348	79.90 326	90.33 344

Test4		92.63 292	90.45 303	99.17 158	92.81 329	99.02 149	99.98 191	99.13 262	96.60 185	82.04 338	96.27 317	51.35 347	98.75 251	97.55 215	98.36 150	99.75 181

EMVS		69.88 325	69.09 325	72.24 341	84.70 341	65.82 348	99.96 200	87.08 357	49.82 352	71.51 344	84.74 348	49.30 348	75.32 353	50.97 353	43.71 349	75.59 353

PM-MVS		88.39 309	87.41 311	91.31 315	91.73 331	82.02 332	99.79 240	96.62 344	91.06 300	90.71 313	95.73 320	48.60 349	95.96 326	90.56 302	81.91 324	95.97 322

E-PMN		70.72 323	70.06 324	72.69 340	83.92 343	65.48 349	99.95 206	92.72 351	49.88 351	72.30 343	86.26 347	47.17 350	77.43 352	53.83 352	44.49 348	75.17 354

Anonymous20231211		84.35 314	82.41 315	90.15 316	89.14 335	78.88 333	98.71 331	98.22 320	70.20 342	86.88 330	95.44 327	47.11 351	95.72 330	81.14 329	74.88 340	95.84 333

ambc				88.45 318	86.84 340	70.76 344	97.79 341	98.02 329		90.91 312	95.14 332	38.69 352	98.51 273	94.97 263	84.23 320	96.09 321

FPMVS		77.92 320	79.45 318	73.34 338	76.87 350	46.81 357	98.24 336	99.05 293	59.89 347	73.55 342	98.34 307	36.81 353	86.55 345	80.96 330	91.35 269	86.65 347

PMMVS2		79.15 318	77.28 320	84.76 329	82.34 344	72.66 339	99.70 261	95.11 349	71.68 341	84.78 335	90.87 340	32.05 354	89.99 342	75.53 340	63.45 343	91.64 340

LCM-MVSNet		79.01 319	76.93 321	85.27 328	78.28 348	68.01 345	96.57 342	98.03 327	55.10 348	82.03 339	93.27 338	31.99 355	93.95 335	82.72 327	74.37 341	93.84 336

no-one		74.65 322	71.03 323	85.51 327	80.99 346	75.42 337	99.70 261	97.15 339	81.02 335	66.48 349	80.55 353	29.10 356	93.65 336	73.74 341	30.53 352	87.47 345

ANet_high		66.05 327	63.44 329	73.88 337	61.14 355	63.45 351	95.68 345	87.18 355	79.93 337	47.35 352	80.68 352	22.35 357	72.33 356	61.24 349	35.42 351	85.88 348

PNet_i23d		69.93 324	67.61 326	76.86 334	75.89 351	62.08 353	97.85 339	88.23 354	61.04 346	55.65 351	84.10 349	19.35 358	83.21 349	66.45 347	59.98 344	85.29 349

wuyk23d		28.28 331	29.73 333	23.92 344	75.89 351	32.61 359	66.50 350	12.88 360	16.09 354	14.59 355	16.59 356	12.35 359	32.36 357	39.36 354	13.36 355	6.79 355

wuykxyi23d		62.75 329	59.23 330	73.34 338	61.53 354	59.39 355	93.28 346	86.17 358	53.70 349	46.89 353	77.89 354	12.35 359	79.96 350	60.86 351	40.35 350	80.68 352

PMVS	60.66 23	65.98 328	65.05 327	68.75 342	55.06 357	38.40 358	88.19 348	96.98 340	48.30 353	44.82 354	88.52 344	12.22 361	86.49 346	67.58 344	83.79 321	81.35 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	68.59 21	67.22 326	64.68 328	74.84 335	74.67 353	62.32 352	95.84 343	90.87 353	50.98 350	58.72 350	81.05 351	12.20 362	78.95 351	61.06 350	56.75 345	83.24 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
sosnet-low-res		0.01 335	0.02 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.14 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.01 335	0.02 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.14 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.01 335	0.02 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.14 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.01 335	0.02 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.14 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.33 333	11.11 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.01 335	0.02 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.14 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																99.91 127

test_part3							100.00 1		98.23 64		100.00 1		100.00 1	100.00 1

test_part2					100.00 1	99.99 1				100.00 1

MTGPA								99.42 121

MTMP								99.18 248

gm-plane-assit					99.52 170	97.26 244			95.86 211		100.00 1		99.43 204	98.76 166

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 58		99.42 121		100.00 1			99.97 98

test_prior4						99.93 32	100.00 1

test_prior				99.90 71	100.00 1	99.75 84		99.73 55					99.97 98			100.00 1

旧先验2							100.00 1		98.11 75	100.00 1			100.00 1	99.67 106

新几何2							100.00 1

无先验							100.00 1	99.80 43	97.98 84				100.00 1	99.33 139		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 223

testdata1							100.00 1		98.77 37

plane_prior7					99.00 219	94.78 285

plane_prior5								99.40 154					99.55 187	99.79 90	95.57 216	97.76 217

plane_prior4											99.97 150

plane_prior3						94.79 284			99.03 16	99.08 185

plane_prior2							100.00 1		99.00 19

plane_prior1					99.02 214

plane_prior						94.80 281	100.00 1		99.03 16						95.58 212

n2								0.00 361

nn								0.00 361

door-mid								96.32 346

test11								99.42 121

door								96.13 347

HQP5-MVS						94.82 278

HQP-NCC					99.07 207		100.00 1		99.04 13	99.17 177

ACMP_Plane					99.07 207		100.00 1		99.04 13	99.17 177

BP-MVS														99.79 90

HQP4-MVS										99.17 177			99.57 178			97.77 215

HQP3-MVS								99.40 154							95.58 212

NP-MVS					99.07 207	94.81 280					99.97 150

ACMMP++_ref															94.58 242

ACMMP++															95.17 229

High-resolution multi-view benchmark