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				99.59 122	99.54 161	99.49 112	99.17 315	99.52 75		99.96 87	99.68 215	100.00 1	99.33 211	99.71 100	99.99 87	99.96 105

gg-mvs-nofinetune		96.95 206	96.10 227	99.50 132	99.41 183	99.36 125	99.07 324	99.52 75	83.69 332	99.96 87	83.60 349	100.00 1	99.20 214	99.68 104	99.99 87	99.96 105

CHOSEN 280x420		99.85 3	99.87 1	99.80 93	99.99 45	99.97 8	99.97 196	99.98 13	98.96 21	100.00 1	100.00 1	99.96 3	99.42 205	100.00 1	100.00 1	100.00 1

tfpn1000		99.47 68	99.34 71	99.87 79	99.89 95	99.80 78	100.00 1	99.72 60	97.83 98	99.96 87	99.98 140	99.94 4	99.82 150	98.87 159	97.87 175	100.00 1

tfpn_ndepth		99.52 61	99.39 63	99.89 73	99.90 93	99.83 70	100.00 1	99.72 60	97.95 89	99.96 87	99.98 140	99.94 4	99.85 145	99.17 147	97.91 172	100.00 1

conf0.01		99.32 79	99.16 85	99.80 93	99.79 114	99.73 86	100.00 1	99.71 62	97.66 112	99.71 144	100.00 1	99.78 6	99.70 164	98.15 191	97.67 193	99.97 95

conf0.002		99.32 79	99.16 85	99.80 93	99.79 114	99.73 86	100.00 1	99.71 62	97.66 112	99.71 144	100.00 1	99.78 6	99.70 164	98.15 191	97.67 193	99.97 95

thresconf0.02		99.32 79	99.16 85	99.79 97	99.79 114	99.73 86	100.00 1	99.71 62	97.66 112	99.71 144	100.00 1	99.78 6	99.70 164	98.15 191	97.67 193	99.94 114

tfpn_n400		99.32 79	99.16 85	99.79 97	99.79 114	99.73 86	100.00 1	99.71 62	97.66 112	99.71 144	100.00 1	99.78 6	99.70 164	98.15 191	97.67 193	99.94 114

tfpnconf		99.32 79	99.16 85	99.79 97	99.79 114	99.73 86	100.00 1	99.71 62	97.66 112	99.71 144	100.00 1	99.78 6	99.70 164	98.15 191	97.67 193	99.94 114

tfpnview11		99.32 79	99.16 85	99.79 97	99.79 114	99.73 86	100.00 1	99.71 62	97.66 112	99.71 144	100.00 1	99.78 6	99.70 164	98.15 191	97.67 193	99.94 114

MVSTER		98.58 147	98.52 147	98.77 180	99.65 138	99.68 100	100.00 1	99.29 209	95.63 216	98.65 210	99.80 198	99.78 6	98.88 235	98.59 175	95.31 220	97.73 252

CDS-MVSNet		98.96 125	98.95 108	99.01 165	99.48 178	98.36 179	99.93 211	99.37 174	96.79 170	99.31 171	99.83 189	99.77 13	98.91 229	98.07 199	97.98 169	99.77 177
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 83	99.01 214	99.95 19	100.00 1	99.75 49	99.37 3	99.99 75	100.00 1	99.76 14	99.60 172	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 75	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		99.08 108	98.96 104	99.44 134	99.63 143	99.38 122	100.00 1	99.45 96	95.53 220	99.48 162	100.00 1	99.71 16	99.02 221	96.84 233	99.99 87	99.91 126

test-mter		98.96 125	98.82 122	99.40 139	99.40 187	99.28 129	100.00 1	99.45 96	95.44 229	99.42 165	99.12 255	99.70 17	99.01 222	96.82 234	99.99 87	99.91 126

HSP-MVS		99.81 9	99.77 8	99.94 57	100.00 1	99.86 65	100.00 1	99.42 120	98.87 29	100.00 1	100.00 1	99.65 18	99.96 112	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 82	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 62	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	99.58 126	100.00 1	98.79 160	100.00 1	99.54 74	98.58 45	99.96 87	100.00 1	99.59 21	100.00 1	100.00 1	100.00 1	99.94 114
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	99.44 105	99.06 10	100.00 1	100.00 1	99.56 23	99.99 80	100.00 1	100.00 1	100.00 1

segment_acmp												99.55 24

test_part1								99.42 120				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	99.42 120	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	99.42 120	97.65 119	100.00 1	100.00 1	99.53 25	99.97 97

DWT-MVSNet_test		99.22 98	99.22 78	99.22 155	99.56 160	97.98 213	99.89 219	99.39 167	97.20 149	99.96 87	100.00 1	99.52 28	99.82 150	99.11 151	98.34 151	99.87 158

SteuartSystems-ACMMP		99.78 13	99.71 16	99.98 16	99.76 124	99.95 19	100.00 1	99.42 120	98.69 38	100.00 1	100.00 1	99.52 28	99.99 80	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	99.42 120	97.70 109	100.00 1	100.00 1	99.51 30	99.97 97	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 38	100.00 1	99.42 120	97.70 109	100.00 1	100.00 1	99.51 30	99.98 93

agg_prior3		99.71 32	99.63 39	99.97 23	100.00 1	99.95 19	100.00 1	99.42 120	97.71 108	100.00 1	100.00 1	99.51 30	99.97 97	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	99.42 120	97.72 106	100.00 1	100.00 1	99.51 30	99.97 97	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 82	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		99.10 107	99.00 100	99.40 139	99.51 172	98.68 165	99.92 213	99.43 114	95.47 226	99.65 152	100.00 1	99.51 30	99.76 157	99.53 123	98.00 168	99.75 180

test_post											89.05 342	99.49 36	99.59 174

HyFIR lowres test		99.32 79	99.24 76	99.58 126	99.95 81	99.26 131	100.00 1	99.99 10	96.72 175	99.29 172	99.91 178	99.49 36	99.47 200	99.74 98	98.08 166	100.00 1

PatchmatchNet		99.03 113	98.96 104	99.26 152	99.49 177	98.33 181	99.38 291	99.45 96	96.64 181	99.96 87	99.58 232	99.49 36	99.50 197	97.63 211	99.00 127	99.93 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		97.83 176	97.42 182	99.06 160	99.08 205	97.66 229	98.66 331	99.21 240	93.65 269	98.25 237	99.58 232	99.47 39	99.57 177	90.25 307	98.59 133	99.95 110

test_prior3		99.81 9	99.78 6	99.90 70	100.00 1	99.75 83	100.00 1	99.73 54	98.82 33	100.00 1	100.00 1	99.47 39	99.97 97	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	99.43 114	99.05 12	100.00 1	100.00 1	99.45 42	99.99 80	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 68	100.00 1	99.58 72	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 60	100.00 1	99.80 78		99.66 70	98.18 66	100.00 1	100.00 1	99.43 44	100.00 1	99.50 125	100.00 1	100.00 1

testdata				99.66 114	99.99 45	98.97 155		99.73 54	97.96 88	100.00 1	100.00 1	99.42 45	100.00 1	99.28 142	100.00 1	100.00 1

patchmatchnet-post											97.79 309	99.41 46	99.54 189

Patchmatch-RL test		93.49 280	93.63 276	93.05 312	91.78 329	83.41 329	98.21 336	96.95 340	91.58 295	91.05 310	97.64 310	99.40 47	95.83 327	94.11 275	81.95 322	99.91 126

1121		99.63 51	99.51 55	99.99 5	99.99 45	99.96 10	99.24 301	99.74 52	97.89 93	100.00 1	100.00 1	99.39 48	100.00 1	99.33 138	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 44	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							99.32 295				88.24 344	99.33 50	99.59 174	98.31 185

SD-MVS		99.81 9	99.75 12	99.99 5	99.99 45	99.96 10	100.00 1	99.42 120	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	99.42 120	98.02 80	100.00 1	100.00 1	99.32 53	99.99 80	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 45	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 45	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		99.03 113	98.91 115	99.40 139	99.40 187	99.28 129	100.00 1	99.45 96	96.70 176	99.42 165	99.12 255	99.31 54	99.01 222	96.82 234	99.99 87	99.91 126

test0.0.03 1		98.12 169	98.03 168	98.39 193	99.11 202	98.07 208	100.00 1	99.93 31	96.70 176	96.91 277	99.95 170	99.31 54	98.19 284	91.93 293	98.44 141	98.91 204

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 1	100.00 1	99.73 54	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	97.64 180	97.37 185	98.45 190	99.94 85	95.70 268	100.00 1	99.40 153	97.65 119	99.53 159	100.00 1	99.31 54	99.66 171	80.48 330	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 69	97.66 112	100.00 1	100.00 1	99.30 60			100.00 1	100.00 1

sam_mvs1												99.29 61				99.91 126

ACMMPR		99.74 23	99.67 27	99.96 35	100.00 1	99.89 48	100.00 1	99.76 45	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		99.14 104	99.12 94	99.19 156	99.54 161	97.86 223	99.66 269	99.40 153	96.94 165	99.96 87	100.00 1	99.29 61	99.84 146	98.96 154	98.29 154	99.88 151

CostFormer		98.84 133	98.77 127	99.04 163	99.41 183	97.58 231	99.67 267	99.35 188	94.66 239	99.96 87	99.36 246	99.28 64	99.74 159	99.41 133	97.81 182	99.81 169

旧先验1					99.99 45	99.88 58		99.82 40			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	99.42 120	97.53 130	100.00 1	100.00 1	99.27 65	99.97 97	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		99.42 120		100.00 1		99.24 67	99.97 97		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	99.44 105	98.43 49	100.00 1	100.00 1	99.23 68	99.99 80	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	99.44 105	98.42 51	100.00 1	100.00 1	99.22 69	99.99 80	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 39	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 41	97.89 93	100.00 1	100.00 1	99.20 71	100.00 1	97.91 205	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 52	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	99.67 113	99.99 45	99.07 144	100.00 1	99.44 105	98.30 63	99.90 116	100.00 1	99.18 73	99.99 80	99.91 74	100.00 1	99.94 114

tpmp4_e23		98.71 140	98.66 134	98.84 176	99.60 149	96.98 252	99.83 228	99.35 188	94.29 249	99.90 116	99.50 240	99.17 74	99.73 160	98.22 188	98.13 163	99.81 169

XVS		99.79 12	99.73 14	99.98 16	100.00 1	99.94 27	100.00 1	99.75 49	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		97.04 202	96.06 229	99.98 16	100.00 1	99.94 27	100.00 1	99.75 49	98.67 40	100.00 1	66.97 354	99.16 75	100.00 1	100.00 1	100.00 1	100.00 1

CNLPA		99.72 29	99.65 31	99.91 67	99.97 75	99.72 93	100.00 1	99.47 82	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 69	100.00 1	99.42 120	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 67	99.73 127	99.92 35	100.00 1	99.51 79	97.61 123	100.00 1	100.00 1	99.06 80	99.93 131	99.83 85	97.12 204	99.90 134

EPMVS		99.25 94	99.13 92	99.60 119	99.60 149	99.20 138	99.60 274	100.00 1	96.93 166	99.92 114	99.36 246	99.05 81	99.71 163	98.77 164	98.94 128	99.90 134

EI-MVSNet-Vis-set		99.70 36	99.64 35	99.87 79	100.00 1	99.64 103	99.98 190	99.44 105	98.35 59	99.99 75	100.00 1	99.04 82	99.96 112	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	99.42 120	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		98.98 123	98.93 110	99.14 159	99.61 147	97.74 226	99.52 281	99.36 180	96.05 205	99.98 80	99.64 223	99.04 82	99.86 141	98.94 155	98.19 161	99.82 167

Regformer-3		99.73 26	99.65 31	99.96 35	100.00 1	99.89 48	100.00 1	99.44 105	98.40 52	100.00 1	100.00 1	99.03 85	99.97 97	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	99.44 105	98.40 52	100.00 1	100.00 1	99.03 85	99.97 97	99.99 44	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 39	99.63 39	99.87 79	99.99 45	99.64 103	99.95 205	99.44 105	98.35 59	100.00 1	100.00 1	98.98 87	99.97 97	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 81	97.54 129	100.00 1	100.00 1	98.97 88	99.99 80	99.98 58	100.00 1	100.00 1

EPNet		99.62 52	99.69 18	99.42 137	99.99 45	98.37 178	100.00 1	99.89 37	98.83 31	100.00 1	100.00 1	98.97 88	100.00 1	99.90 75	99.61 118	99.89 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		98.64 144	98.58 144	98.81 179	99.42 182	97.12 247	99.69 264	99.37 174	93.63 270	99.94 108	99.67 216	98.96 90	99.47 200	98.62 174	97.95 170	99.83 164

TSAR-MVS + GP.		99.61 54	99.69 18	99.35 144	99.99 45	98.06 209	100.00 1	99.36 180	99.83 2	100.00 1	100.00 1	98.95 91	99.99 80	100.00 1	99.11 123	100.00 1

HPM-MVS_fast		99.60 57	99.49 58	99.91 67	99.99 45	99.78 80	100.00 1	99.42 120	97.09 155	100.00 1	100.00 1	98.95 91	99.96 112	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 93	99.86 80	98.21 159	99.94 114

HY-MVS	96.53 9	99.50 63	99.35 69	99.96 35	99.81 99	99.93 32	99.64 271	100.00 1	97.97 86	99.84 122	99.85 186	98.94 93	99.99 80	99.86 80	98.23 158	99.95 110

MDTV_nov1_ep13			98.94 109		99.53 164	98.36 179	99.39 290	99.46 94	96.54 187	99.99 75	99.63 227	98.92 95	99.86 141	98.30 186	98.71 132

API-MVS		99.72 29	99.70 17	99.79 97	99.97 75	99.37 124	99.96 199	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 73	99.99 45	99.66 101	99.75 249	99.73 54	98.16 67	99.75 140	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	99.42 120	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						99.24 135	99.56 277		96.31 200	99.96 87		98.86 99		98.92 156		99.89 139

dp		98.72 139	98.61 141	99.03 164	99.53 164	97.39 235	99.45 285	99.39 167	95.62 217	99.94 108	99.52 238	98.83 100	99.82 150	96.77 238	98.42 143	99.89 139

TAMVS		98.76 137	98.73 130	98.86 175	99.44 181	97.69 227	99.57 276	99.34 193	96.57 185	99.12 181	99.81 195	98.83 100	99.16 215	97.97 204	97.91 172	99.73 188

IB-MVS	96.24 12	97.54 185	96.95 192	99.33 146	99.67 134	98.10 206	100.00 1	99.47 82	97.42 136	99.26 173	99.69 210	98.83 100	99.89 137	99.43 127	78.77 329	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	99.73 254	99.52 75	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	99.42 120	97.53 130	99.77 137	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		8.24 333	10.99 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.14 356	98.75 105	0.00 357	0.00 354	0.00 355	0.00 355

PS-MVSNAJss		98.03 171	98.06 167	97.94 234	97.63 293	97.33 241	99.89 219	99.23 234	96.27 201	98.03 243	99.59 231	98.75 105	98.78 244	98.52 177	94.61 240	97.70 265

PS-MVSNAJ		99.64 49	99.57 50	99.85 83	99.78 120	99.81 76	99.95 205	99.42 120	98.38 55	100.00 1	100.00 1	98.75 105	100.00 1	99.88 78	99.99 87	99.74 184

MPTG		99.68 42	99.59 46	99.97 23	99.99 45	99.91 38	100.00 1	99.42 120	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	99.42 120	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 73	100.00 1	99.70 98	100.00 1	99.42 120	97.46 132	100.00 1	100.00 1	98.60 110	99.96 112	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	99.98 190	99.47 82	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		98.53 152	98.23 160	99.43 136	99.92 90	99.01 151	99.96 199	99.47 82	98.80 35	99.96 87	99.96 159	98.56 112	99.30 212	87.78 318	99.68 115	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
JIA-IIPM		97.09 198	96.34 213	99.36 143	98.88 229	98.59 168	99.81 235	99.43 114	84.81 329	99.96 87	90.34 341	98.55 113	99.52 194	97.00 228	98.28 156	99.98 90

xiu_mvs_v2_base		99.51 62	99.41 60	99.82 87	99.70 129	99.73 86	99.92 213	99.40 153	98.15 69	100.00 1	100.00 1	98.50 114	100.00 1	99.85 82	99.13 122	99.74 184

sss		99.45 69	99.34 71	99.80 93	99.76 124	99.50 109	100.00 1	99.91 36	97.72 106	99.98 80	99.94 174	98.45 115	100.00 1	99.53 123	98.75 131	99.89 139

IS-MVSNet		99.08 108	98.91 115	99.59 122	99.65 138	99.38 122	99.78 240	99.24 231	96.70 176	99.51 160	100.00 1	98.44 116	99.52 194	98.47 179	98.39 146	99.88 151

114514_t		99.39 72	99.25 74	99.81 90	99.97 75	99.48 116	100.00 1	99.42 120	95.53 220	100.00 1	100.00 1	98.37 117	99.95 118	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	99.42 120	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 87	100.00 1	99.45 118	100.00 1	99.94 18	96.38 197	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 66	100.00 1	99.76 81	100.00 1	99.42 120	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)		98.99 121	98.89 119	99.29 149	99.64 142	98.89 157	99.98 190	99.31 203	96.74 172	99.48 162	100.00 1	98.11 121	99.10 217	98.39 181	98.34 151	99.89 139

DeepPCF-MVS	98.03 4	98.54 151	99.72 15	94.98 301	99.99 45	84.94 327	100.00 1	99.42 120	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		95.90 256	94.95 269	98.75 181	99.03 212	98.39 175	99.08 322	99.32 198	85.52 325	99.96 87	94.99 334	97.94 123	98.05 299	80.20 331	98.47 140	99.81 169

MVS_111021_LR		99.70 36	99.65 31	99.88 78	99.96 80	99.70 98	100.00 1	99.97 14	98.96 21	100.00 1	100.00 1	97.93 124	99.95 118	99.99 44	100.00 1	100.00 1

MVS_111021_HR		99.71 32	99.63 39	99.93 60	99.95 81	99.83 70	100.00 1	100.00 1	98.89 26	100.00 1	100.00 1	97.85 125	99.95 118	100.00 1	100.00 1	100.00 1

CSCG		99.28 89	99.35 69	99.05 162	99.99 45	97.15 246	100.00 1	99.47 82	97.44 134	99.42 165	100.00 1	97.83 126	100.00 1	99.99 44	100.00 1	100.00 1

tpm cat1		98.05 170	97.76 175	98.92 172	99.50 175	97.10 249	99.77 244	99.30 205	90.20 304	99.72 142	98.71 291	97.71 127	99.86 141	96.75 239	98.20 160	99.81 169

DELS-MVS		99.62 52	99.56 53	99.82 87	99.92 90	99.45 118	100.00 1	99.78 43	98.92 25	99.73 141	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		98.59 146	98.38 153	99.23 154	99.69 130	97.90 218	99.31 296	99.47 82	94.52 243	99.68 151	99.28 251	97.64 129	99.89 137	97.71 209	98.17 162	99.89 139

tpm		98.24 166	98.22 161	98.32 200	99.13 201	95.79 267	99.53 280	99.12 267	95.20 230	99.96 87	99.36 246	97.58 130	99.28 213	97.41 220	96.67 209	99.88 151

ADS-MVSNet2		98.28 164	98.51 148	97.62 243	99.51 172	95.03 273	99.24 301	99.41 151	95.52 222	99.96 87	99.70 208	97.57 131	97.94 301	97.11 226	98.54 134	99.88 151

ADS-MVSNet		98.70 142	98.51 148	99.28 150	99.51 172	98.39 175	99.24 301	99.44 105	95.52 222	99.96 87	99.70 208	97.57 131	99.58 176	97.11 226	98.54 134	99.88 151

Test_1112_low_res		98.83 134	98.60 142	99.51 130	99.69 130	98.75 161	99.99 174	99.14 256	96.81 169	98.84 198	99.06 259	97.45 133	99.89 137	98.66 168	97.75 186	99.89 139

1112_ss		98.91 129	98.71 132	99.51 130	99.69 130	98.75 161	99.99 174	99.15 252	96.82 168	98.84 198	100.00 1	97.45 133	99.89 137	98.66 168	97.75 186	99.89 139

CPTT-MVS		99.49 65	99.38 65	99.85 83	100.00 1	99.54 107	100.00 1	99.42 120	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		97.98 173	97.93 171	98.16 216	99.11 202	97.84 224	99.74 250	99.29 209	94.39 247	98.65 210	100.00 1	97.21 136	98.88 235	97.62 212	95.31 220	97.75 220

IterMVS-LS		97.56 183	97.44 181	97.92 237	99.38 191	97.90 218	99.89 219	99.10 269	94.41 246	98.32 231	99.54 237	97.21 136	98.11 290	97.50 217	91.62 264	97.75 220
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	98.88 229	99.89 48	99.24 301	99.93 31	98.88 27	98.79 201	100.00 1	97.02 141	100.00 1	100.00 1	100.00 1	100.00 1

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	98.96 169	99.95 81	97.06 250	100.00 1	99.40 153	98.83 31	99.88 120	100.00 1	97.01 142	99.86 141	99.47 126	99.84 110	99.97 95

xiu_mvs_v1_base_debu		99.35 75	99.21 79	99.79 97	99.67 134	99.71 94	99.78 240	99.36 180	98.13 71	100.00 1	100.00 1	97.00 146	100.00 1	99.83 85	99.07 124	99.66 193

xiu_mvs_v1_base		99.35 75	99.21 79	99.79 97	99.67 134	99.71 94	99.78 240	99.36 180	98.13 71	100.00 1	100.00 1	97.00 146	100.00 1	99.83 85	99.07 124	99.66 193

xiu_mvs_v1_base_debi		99.35 75	99.21 79	99.79 97	99.67 134	99.71 94	99.78 240	99.36 180	98.13 71	100.00 1	100.00 1	97.00 146	100.00 1	99.83 85	99.07 124	99.66 193

view600		99.18 100	98.93 110	99.93 60	99.81 99	99.83 70	100.00 1	99.94 18	96.96 161	99.54 155	99.96 159	96.99 149	100.00 1	99.43 127	97.75 186	99.97 95

view800		99.18 100	98.93 110	99.93 60	99.81 99	99.83 70	100.00 1	99.94 18	96.96 161	99.54 155	99.96 159	96.99 149	100.00 1	99.43 127	97.75 186	99.97 95

conf0.05thres1000		99.18 100	98.93 110	99.93 60	99.81 99	99.83 70	100.00 1	99.94 18	96.96 161	99.54 155	99.96 159	96.99 149	100.00 1	99.43 127	97.75 186	99.97 95

tfpn		99.18 100	98.93 110	99.93 60	99.81 99	99.83 70	100.00 1	99.94 18	96.96 161	99.54 155	99.96 159	96.99 149	100.00 1	99.43 127	97.75 186	99.97 95

CR-MVSNet		98.02 172	97.71 179	98.93 170	99.31 194	98.86 158	99.13 318	99.00 297	96.53 188	99.96 87	98.98 275	96.94 153	98.10 293	91.18 296	98.40 144	99.84 161

Patchmtry		96.81 208	96.37 212	98.14 217	99.31 194	98.55 170	98.91 327	99.00 297	90.45 301	97.92 249	98.98 275	96.94 153	98.12 288	94.27 271	91.53 265	97.75 220

LS3D		99.31 86	99.13 92	99.87 79	99.99 45	99.71 94	99.55 278	99.46 94	97.32 142	99.82 132	100.00 1	96.85 155	99.97 97	99.14 148	100.00 1	99.92 125

MVSFormer		98.94 127	98.82 122	99.28 150	99.45 179	99.49 112	100.00 1	99.13 261	95.46 227	99.97 84	100.00 1	96.76 156	98.59 266	98.63 172	100.00 1	99.74 184

lupinMVS		99.29 88	99.16 85	99.69 112	99.45 179	99.49 112	100.00 1	99.15 252	97.45 133	99.97 84	100.00 1	96.76 156	99.76 157	99.67 106	100.00 1	99.81 169

MAR-MVS		99.49 65	99.36 67	99.89 73	99.97 75	99.66 101	99.74 250	99.95 15	97.89 93	100.00 1	100.00 1	96.71 158	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		98.90 130	98.63 139	99.70 111	99.92 90	99.25 133	100.00 1	99.37 174	95.71 214	99.40 169	100.00 1	96.58 159	99.95 118	96.80 236	99.94 102	99.91 126

PVSNet_BlendedMVS		98.71 140	98.62 140	98.98 168	99.98 71	99.60 105	100.00 1	100.00 1	97.23 145	100.00 1	99.03 269	96.57 160	99.99 80	100.00 1	94.75 236	97.35 303

PVSNet_Blended		99.48 67	99.36 67	99.83 86	99.98 71	99.60 105	100.00 1	100.00 1	97.79 102	100.00 1	100.00 1	96.57 160	99.99 80	100.00 1	99.88 107	99.90 134

MVS_Test		98.93 128	98.65 137	99.77 107	99.62 145	99.50 109	99.99 174	99.19 243	95.52 222	99.96 87	99.86 184	96.54 162	99.98 93	98.65 170	98.48 138	99.82 167

PMMVS		99.12 105	98.97 103	99.58 126	99.57 158	98.98 153	100.00 1	99.30 205	97.14 151	99.96 87	100.00 1	96.53 163	99.82 150	99.70 102	98.49 137	99.94 114

PVSNet_Blended_VisFu		99.33 78	99.18 84	99.78 105	99.82 98	99.49 112	100.00 1	99.95 15	97.36 138	99.63 153	100.00 1	96.45 164	99.95 118	99.79 90	99.65 117	99.89 139

mvs_anonymous		98.80 136	98.60 142	99.38 142	99.57 158	99.24 135	100.00 1	99.21 240	95.87 208	98.92 191	99.82 192	96.39 165	99.03 220	99.13 149	98.50 136	99.88 151

testpf		97.02 204	96.87 195	97.44 251	99.50 175	95.18 271	95.70 343	99.33 195	91.35 296	98.00 247	99.22 253	96.29 166	98.49 275	94.42 268	98.07 167	98.67 207

DP-MVS		98.86 132	98.54 146	99.81 90	99.97 75	99.45 118	99.52 281	99.40 153	94.35 248	98.36 228	100.00 1	96.13 167	99.97 97	99.12 150	100.00 1	100.00 1

PVSNet	94.91 18	99.30 87	99.25 74	99.44 134	100.00 1	98.32 184	100.00 1	99.86 38	98.04 79	100.00 1	100.00 1	96.10 168	100.00 1	99.55 118	99.73 114	100.00 1

Effi-MVS+-dtu		98.51 155	98.86 120	97.47 250	99.77 122	94.21 296	100.00 1	98.94 299	97.61 123	99.91 115	98.75 289	95.89 169	99.51 196	99.36 135	99.48 120	98.68 206

mvs-test1		98.89 131	98.99 102	98.60 186	99.77 122	95.96 263	100.00 1	98.94 299	97.61 123	99.93 113	99.92 177	95.89 169	99.93 131	99.36 135	99.50 119	99.90 134

UA-Net		99.06 110	98.83 121	99.74 108	99.52 169	99.40 121	99.08 322	99.45 96	97.64 121	99.83 124	100.00 1	95.80 171	99.94 129	98.35 183	99.80 113	99.88 151

FMVSNet5		95.32 268	95.43 256	94.99 300	99.39 190	92.99 307	99.25 300	99.24 231	90.45 301	97.44 265	98.45 300	95.78 172	94.39 332	87.02 319	91.88 262	97.59 292

1111		88.62 307	89.13 306	87.08 322	87.71 336	77.22 334	99.96 199	97.71 334	85.51 326	83.47 335	96.26 317	95.76 173	90.96 338	66.63 344	89.17 295	93.07 337

.test1245		85.18 311	84.81 312	86.28 324	87.71 336	77.22 334	99.96 199	97.71 334	85.51 326	83.47 335	96.26 317	95.76 173	90.96 338	66.63 344	50.30 345	90.64 341

CVMVSNet		98.56 149	98.47 150	98.82 177	99.11 202	97.67 228	99.74 250	99.47 82	97.57 128	99.06 186	100.00 1	95.72 175	98.97 226	98.21 189	97.33 203	99.83 164

RPMNet		95.10 270	93.82 273	98.93 170	99.31 194	98.86 158	99.13 318	99.38 171	79.82 337	99.96 87	95.13 332	95.69 176	98.10 293	77.54 337	98.40 144	99.84 161

MVS		99.22 98	98.96 104	99.98 16	99.00 218	99.95 19	99.24 301	99.94 18	98.14 70	98.88 194	100.00 1	95.63 177	100.00 1	99.85 82	100.00 1	100.00 1

jason		99.11 106	98.96 104	99.59 122	99.17 200	99.31 127	100.00 1	99.13 261	97.38 137	99.83 124	100.00 1	95.54 178	99.72 162	99.57 117	99.97 97	99.74 184
jason: jason.
AdaColmap		99.44 70	99.26 73	99.95 46	100.00 1	99.86 65	99.70 260	99.99 10	98.53 46	99.90 116	100.00 1	95.34 179	100.00 1	99.92 73	100.00 1	100.00 1

CANet		99.40 71	99.24 76	99.89 73	99.99 45	99.76 81	100.00 1	99.73 54	98.40 52	99.78 136	100.00 1	95.28 180	99.96 112	100.00 1	99.99 87	99.96 105

FIs		97.95 174	97.73 178	98.62 185	98.53 242	99.24 135	100.00 1	99.43 114	96.74 172	97.87 251	99.82 192	95.27 181	98.89 232	98.78 163	93.07 251	97.74 242

canonicalmvs		99.03 113	98.73 130	99.94 57	99.75 126	99.95 19	100.00 1	99.30 205	97.64 121	100.00 1	100.00 1	95.22 182	99.97 97	99.76 96	96.90 208	99.91 126

FC-MVSNet-test		97.84 175	97.63 180	98.45 190	98.30 251	99.05 146	100.00 1	99.43 114	96.63 183	97.61 260	99.82 192	95.19 183	98.57 269	98.64 171	93.05 252	97.73 252

diffmvs		98.52 153	98.11 163	99.73 110	99.59 152	99.47 117	100.00 1	99.19 243	93.68 267	99.70 150	99.75 201	95.07 184	99.84 146	97.57 213	98.48 138	99.89 139

UniMVSNet_NR-MVSNet		97.16 195	96.80 197	98.22 211	98.38 246	98.41 173	100.00 1	99.45 96	96.14 204	97.76 253	99.64 223	95.05 185	98.50 273	97.98 201	86.84 310	97.75 220

Fast-Effi-MVS+-dtu		98.38 161	98.56 145	97.82 239	99.58 156	94.44 293	100.00 1	99.16 251	96.75 171	99.51 160	99.63 227	95.03 186	99.60 172	97.71 209	99.67 116	99.42 201

UniMVSNet (Re)		97.29 191	96.85 196	98.59 188	98.49 243	99.13 142	100.00 1	99.42 120	96.52 189	98.24 238	98.90 283	94.93 187	98.89 232	97.54 216	87.61 307	97.75 220

BH-w/o		98.82 135	98.81 124	98.88 174	99.62 145	96.71 257	100.00 1	99.28 217	97.09 155	98.81 200	100.00 1	94.91 188	99.96 112	99.54 121	100.00 1	99.96 105

PCF-MVS	98.23 3	98.69 143	98.37 154	99.62 115	99.78 120	99.02 148	99.23 307	99.06 290	96.43 192	98.08 240	100.00 1	94.72 189	99.95 118	98.16 190	99.91 104	99.90 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		99.04 112	98.75 129	99.90 70	99.81 99	99.75 83	99.50 283	99.47 82	98.36 58	100.00 1	99.99 139	94.66 190	100.00 1	99.90 75	97.09 205	99.96 105

BH-untuned		98.64 144	98.65 137	98.60 186	99.59 152	96.17 262	100.00 1	99.28 217	96.67 180	98.41 227	100.00 1	94.52 191	99.83 148	99.41 133	100.00 1	99.81 169

nrg030		97.64 180	97.27 187	98.75 181	98.34 247	99.53 108	100.00 1	99.22 236	96.21 203	98.27 235	99.95 170	94.40 192	98.98 225	99.23 144	89.78 289	97.75 220

LP		95.89 258	95.35 259	97.51 247	98.92 226	95.15 272	98.66 331	99.39 167	89.43 315	97.22 270	99.03 269	94.37 193	97.74 304	83.71 325	97.44 201	99.63 196

RPSCF		97.37 187	98.24 158	94.76 303	99.80 112	84.57 328	99.99 174	99.05 292	94.95 234	99.82 132	100.00 1	94.03 194	100.00 1	98.15 191	98.38 149	99.70 189

CANet_DTU		99.02 117	98.90 118	99.41 138	99.88 96	98.71 163	100.00 1	99.29 209	98.84 30	100.00 1	100.00 1	94.02 195	100.00 1	98.08 198	99.96 99	99.52 199

LCM-MVSNet-Re		96.52 226	97.21 189	94.44 304	99.27 197	85.80 326	99.85 226	96.61 344	95.98 206	92.75 307	98.48 299	93.97 196	97.55 306	99.58 116	98.43 142	99.98 90

Effi-MVS+		98.58 147	98.24 158	99.61 116	99.60 149	99.26 131	97.85 338	99.10 269	96.22 202	99.97 84	99.89 180	93.75 197	99.77 156	99.43 127	98.34 151	99.81 169

pmmvs4		97.17 194	96.80 197	98.27 204	97.68 290	98.64 167	100.00 1	99.18 247	94.22 251	98.55 218	99.71 205	93.67 198	98.47 276	95.66 249	92.57 256	97.71 264

CHOSEN 1792x2688		99.00 119	98.91 115	99.25 153	99.90 93	97.79 225	100.00 1	99.99 10	98.79 36	98.28 234	100.00 1	93.63 199	99.95 118	99.66 108	99.95 101	100.00 1

cdsmvs_eth3d_5k		24.41 331	32.55 331	0.00 344	0.00 358	0.00 359	0.00 350	99.39 167	0.00 354	0.00 355	100.00 1	93.55 200	0.00 357	0.00 354	0.00 355	0.00 355

AllTest		98.55 150	98.40 152	98.99 166	99.93 87	97.35 238	100.00 1	99.40 153	97.08 157	99.09 182	99.98 140	93.37 201	99.95 118	96.94 229	99.84 110	99.68 191

TestCases				98.99 166	99.93 87	97.35 238		99.40 153	97.08 157	99.09 182	99.98 140	93.37 201	99.95 118	96.94 229	99.84 110	99.68 191

FMVSNet3		97.30 190	96.95 192	98.37 195	99.65 138	99.25 133	99.71 258	99.28 217	94.23 250	98.53 220	98.91 282	93.30 203	98.11 290	95.31 255	93.60 245	97.73 252

Fast-Effi-MVS+		98.40 160	98.02 169	99.55 129	99.63 143	99.06 145	100.00 1	99.15 252	95.07 231	99.42 165	99.95 170	93.26 204	99.73 160	97.44 218	98.24 157	99.87 158

QAPM		98.99 121	98.66 134	99.96 35	99.01 214	99.87 61	99.88 222	99.93 31	97.99 82	98.68 208	100.00 1	93.17 205	100.00 1	99.32 140	100.00 1	100.00 1

PatchMatch-RL		99.02 117	98.78 126	99.74 108	99.99 45	99.29 128	100.00 1	100.00 1	98.38 55	99.89 119	99.81 195	93.14 206	99.99 80	97.85 206	99.98 94	99.95 110

WR-MVS_H		96.73 214	96.32 215	97.95 233	98.26 253	97.88 220	99.72 256	99.43 114	95.06 232	96.99 274	98.68 292	93.02 207	98.53 271	97.43 219	88.33 303	97.43 299

3Dnovator	95.63 14	99.06 110	98.76 128	99.96 35	98.86 232	99.90 44	99.98 190	99.93 31	98.95 24	98.49 224	100.00 1	92.91 208	100.00 1	99.71 100	100.00 1	100.00 1

3Dnovator+	95.58 15	99.03 113	98.71 132	99.96 35	98.99 221	99.89 48	100.00 1	99.51 79	98.96 21	98.32 231	100.00 1	92.78 209	100.00 1	99.87 79	100.00 1	100.00 1

NR-MVSNet		96.63 222	96.04 230	98.38 194	98.31 249	98.98 153	99.22 309	99.35 188	95.87 208	94.43 299	99.65 219	92.73 210	98.40 279	96.78 237	88.05 304	97.75 220

COLMAP_ROB	97.10 7	98.29 163	98.17 162	98.65 184	99.94 85	97.39 235	99.30 297	99.40 153	95.64 215	97.75 256	100.00 1	92.69 211	99.95 118	98.89 157	99.92 103	98.62 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EU-MVSNet		96.63 222	96.53 206	96.94 268	97.59 295	96.87 254	99.76 247	99.47 82	96.35 198	96.85 279	99.78 199	92.57 212	96.27 323	95.33 254	91.08 270	97.68 272

MVP-Stereo		96.51 227	96.48 209	96.60 287	95.65 320	94.25 295	98.84 329	98.16 321	95.85 212	95.23 293	99.04 264	92.54 213	99.13 216	92.98 286	99.98 94	96.43 315
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DeepC-MVS	97.84 5	99.00 119	98.80 125	99.60 119	99.93 87	99.03 147	100.00 1	99.40 153	98.61 44	99.33 170	100.00 1	92.23 214	99.95 118	99.74 98	99.96 99	99.83 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS		96.93 207	96.49 208	98.22 211	98.31 249	98.41 173	100.00 1	99.37 174	96.41 195	97.76 253	99.65 219	92.14 215	98.50 273	97.98 201	86.84 310	97.75 220

Baseline_NR-MVSNet		96.16 246	95.70 245	97.56 246	98.28 252	96.79 256	100.00 1	97.86 332	91.93 292	97.63 258	99.47 243	92.14 215	98.35 281	97.13 225	86.83 312	97.54 295

cascas		98.43 157	98.07 166	99.50 132	99.65 138	99.02 148	100.00 1	99.22 236	94.21 252	99.72 142	99.98 140	92.03 217	99.93 131	99.68 104	98.12 164	99.54 198

v18		93.55 278	92.42 286	96.91 270	98.12 264	98.26 191	99.83 228	98.74 306	89.99 306	88.94 318	95.64 321	92.02 218	96.54 310	90.27 306	78.26 330	95.94 324

test_djsdf		97.55 184	97.38 184	98.07 224	97.50 299	97.99 212	100.00 1	99.13 261	95.46 227	98.47 225	99.85 186	92.01 219	98.59 266	98.63 172	95.36 218	97.62 288

v17		93.53 279	92.42 286	96.88 275	98.09 268	98.16 201	99.83 228	98.74 306	89.95 307	89.05 315	95.65 320	91.99 220	96.54 310	90.31 305	78.15 332	95.95 322

v13		93.18 286	91.97 293	96.81 281	98.12 264	98.12 204	99.74 250	98.73 312	89.47 314	88.52 324	95.40 330	91.72 221	96.41 317	89.23 317	76.77 337	95.92 326

v1neww		96.77 209	96.27 216	98.26 206	98.16 261	98.23 193	100.00 1	99.09 272	93.94 257	98.73 205	99.05 261	91.65 222	98.82 240	95.75 245	90.04 280	97.74 242

v7new		96.77 209	96.27 216	98.26 206	98.16 261	98.23 193	100.00 1	99.09 272	93.94 257	98.73 205	99.05 261	91.65 222	98.82 240	95.75 245	90.04 280	97.74 242

v16		93.48 281	92.34 288	96.89 271	98.13 263	98.19 199	99.82 233	98.74 306	89.94 308	88.90 319	95.62 322	91.65 222	96.54 310	90.03 308	78.22 331	95.95 322

v12		93.18 286	91.97 293	96.80 282	98.09 268	98.10 206	99.76 247	98.73 312	89.61 313	88.45 325	95.41 329	91.63 225	96.39 319	89.32 314	76.76 338	95.91 329

v1141		96.66 218	96.12 225	98.26 206	98.05 274	98.28 186	100.00 1	99.09 272	93.71 261	98.59 215	99.04 264	91.57 226	98.73 251	95.05 260	90.00 283	97.75 220

v15		93.28 283	92.08 289	96.87 276	98.07 272	98.22 197	99.82 233	98.74 306	89.83 309	88.74 321	95.44 326	91.55 227	96.47 314	89.70 309	76.87 334	95.93 325

divwei89l23v2f112		96.68 217	96.15 223	98.25 210	98.03 277	98.27 188	100.00 1	99.09 272	93.79 260	98.59 215	99.04 264	91.54 228	98.73 251	95.29 258	89.99 284	97.75 220

v6		96.77 209	96.26 218	98.29 202	98.10 267	98.27 188	100.00 1	99.09 272	93.95 256	98.73 205	99.06 259	91.51 229	98.86 237	95.83 243	90.04 280	97.74 242

V14		93.24 284	92.04 290	96.87 276	98.07 272	98.20 198	99.80 238	98.74 306	89.76 311	88.64 322	95.53 323	91.47 230	96.47 314	89.49 311	76.84 335	95.92 326

v8		96.35 236	95.73 244	98.21 213	98.11 266	98.23 193	99.94 209	99.07 282	92.66 286	98.29 233	99.00 274	91.46 231	98.77 247	94.17 272	88.83 299	97.62 288

OpenMVS	95.20 17	98.76 137	98.41 151	99.78 105	98.89 228	99.81 76	99.99 174	99.76 45	98.02 80	98.02 245	100.00 1	91.44 232	100.00 1	99.63 110	99.97 97	99.55 197

v1		96.66 218	96.12 225	98.30 201	98.09 268	98.33 181	100.00 1	99.09 272	93.69 266	98.68 208	99.04 264	91.42 233	98.83 239	95.05 260	89.99 284	97.75 220

V9		93.20 285	91.99 292	96.85 278	98.05 274	98.16 201	99.77 244	98.73 312	89.67 312	88.64 322	95.44 326	91.34 234	96.47 314	89.46 312	76.79 336	95.92 326

v148		96.29 239	95.84 237	97.63 241	97.74 288	96.53 260	100.00 1	99.07 282	93.52 273	98.01 246	99.42 245	91.22 235	98.60 264	96.37 241	87.22 309	97.75 220

XVG-OURS		98.30 162	98.36 155	98.13 220	99.58 156	95.91 266	100.00 1	99.36 180	98.69 38	99.23 174	100.00 1	91.20 236	99.92 134	99.34 137	97.82 181	98.56 210

WR-MVS		97.09 198	96.64 202	98.46 189	98.43 244	99.09 143	99.97 196	99.33 195	95.62 217	97.76 253	99.67 216	91.17 237	98.56 270	98.49 178	89.28 293	97.74 242

v11		93.14 288	91.93 295	96.77 284	98.01 278	98.17 200	99.72 256	98.74 306	89.39 316	88.81 320	95.49 325	91.15 238	96.29 321	86.78 320	79.25 327	95.86 331

v7		96.64 221	96.14 224	98.13 220	98.18 258	97.97 214	99.99 174	99.09 272	93.62 271	98.76 202	99.30 249	91.13 239	98.70 256	94.37 269	90.80 273	97.74 242

test2356		92.55 293	93.94 271	88.39 319	93.49 327	78.42 333	100.00 1	99.40 153	91.64 293	93.93 301	98.74 290	91.11 240	88.67 342	79.37 333	97.79 184	98.08 212

pcd1.5k->3k		40.07 329	42.58 330	32.57 342	97.97 282	0.00 359	0.00 350	99.25 228	0.00 354	0.00 355	0.14 356	91.10 241	0.00 357	0.00 354	94.75 236	97.70 265

V42		96.65 220	96.16 222	98.11 223	98.17 260	98.23 193	99.99 174	99.09 272	93.97 254	98.74 204	99.05 261	91.09 242	98.82 240	95.46 253	89.90 287	97.27 306

v10		96.14 248	95.50 251	98.07 224	98.19 257	97.96 215	99.83 228	99.07 282	92.10 290	98.07 242	98.94 280	91.07 243	98.61 262	92.41 292	89.82 288	97.63 286

v7n		96.06 252	95.42 257	97.99 232	97.58 296	97.35 238	99.86 225	99.11 268	92.81 285	97.91 250	99.49 241	90.99 244	98.92 228	92.51 289	88.49 302	97.70 265

v1144		96.51 227	95.97 234	98.13 220	97.98 281	98.04 211	99.99 174	99.08 280	93.51 274	98.62 213	98.98 275	90.98 245	98.62 261	93.79 279	90.79 274	97.74 242

ab-mvs		98.42 158	98.02 169	99.61 116	99.71 128	99.00 152	99.10 320	99.64 71	96.70 176	99.04 187	99.81 195	90.64 246	99.98 93	99.64 109	97.93 171	99.84 161

MVS_0304		98.98 123	98.66 134	99.94 57	99.95 81	99.86 65	100.00 1	99.29 209	98.38 55	99.81 134	100.00 1	90.60 247	100.00 1	100.00 1	99.91 104	99.95 110

Anonymous20231206		93.45 282	93.17 280	94.30 306	95.00 324	89.69 320	99.98 190	98.43 316	93.30 279	94.50 298	98.59 296	90.52 248	95.73 328	77.46 338	90.73 276	97.48 298

anonymousdsp		97.16 195	96.88 194	98.00 230	97.08 310	98.06 209	99.81 235	99.15 252	94.58 241	97.84 252	99.62 229	90.49 249	98.60 264	97.98 201	95.32 219	97.33 305

v2v482		96.70 216	96.18 220	98.27 204	98.04 276	98.39 175	100.00 1	99.13 261	94.19 253	98.58 217	99.08 258	90.48 250	98.67 259	95.69 248	90.44 278	97.75 220

v144192		96.40 233	95.81 238	98.17 215	97.89 285	98.11 205	99.99 174	99.06 290	93.39 276	98.75 203	99.09 257	90.43 251	98.66 260	93.10 285	90.55 277	97.75 220

Vis-MVSNet		98.52 153	98.25 157	99.34 145	99.68 133	98.55 170	99.68 266	99.41 151	97.34 141	99.94 108	100.00 1	90.38 252	99.70 164	99.03 153	98.84 129	99.76 179
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V4		95.89 258	95.36 258	97.49 248	97.65 292	97.40 234	99.88 222	99.07 282	92.19 288	98.08 240	99.01 272	90.20 253	98.72 253	93.57 282	88.63 300	96.34 316

CP-MVSNet		96.73 214	96.25 219	98.18 214	98.21 256	98.67 166	99.77 244	99.32 198	95.06 232	97.20 271	99.65 219	90.10 254	98.19 284	98.06 200	88.90 297	97.66 278

TranMVSNet+NR-MVSNet		96.45 230	96.01 231	97.79 240	98.00 280	97.62 230	100.00 1	99.35 188	95.98 206	97.31 267	99.64 223	90.09 255	98.00 300	96.89 232	86.80 313	97.75 220

v52		95.88 260	95.35 259	97.48 249	97.66 291	97.38 237	99.88 222	99.07 282	92.19 288	98.09 239	99.02 271	90.06 256	98.72 253	93.58 281	88.61 301	96.34 316

DSMNet-mixed		95.18 269	95.21 262	95.08 298	96.03 316	90.21 319	99.65 270	93.64 349	92.91 284	98.34 230	97.40 311	90.05 257	95.51 330	91.02 298	97.86 176	99.51 200

N_pmnet		91.88 298	93.37 279	87.40 321	97.24 309	66.33 346	99.90 216	91.05 351	89.77 310	95.65 291	98.58 297	90.05 257	98.11 290	85.39 322	92.72 255	97.75 220

v748		95.47 266	94.95 269	97.04 263	97.46 304	94.76 285	99.93 211	99.10 269	91.95 291	97.11 272	99.18 254	89.94 259	98.68 257	94.01 276	85.88 316	97.67 276

GA-MVS		97.72 178	97.27 187	99.06 160	99.24 199	97.93 217	100.00 1	99.24 231	95.80 213	98.99 189	99.64 223	89.77 260	99.36 209	95.12 259	97.62 200	99.89 139

MIMVSNet		97.06 201	96.73 200	98.05 228	99.38 191	96.64 259	98.47 334	99.35 188	93.41 275	99.48 162	98.53 298	89.66 261	97.70 305	94.16 274	98.11 165	99.80 176

Patchmatch-test1		98.23 167	97.91 172	99.17 157	99.41 183	98.25 192	99.99 174	99.45 96	96.91 167	99.76 139	99.69 210	89.65 262	99.37 208	97.55 214	98.79 130	99.89 139

semantic-postprocess				97.62 243	99.40 187	96.83 255		99.14 256	94.65 240	97.55 262	99.72 203	89.64 263	98.31 282	95.62 251	92.05 260	97.73 252

IterMVS		96.76 213	96.46 210	97.63 241	99.41 183	96.89 253	99.99 174	99.13 261	94.74 238	97.59 261	99.66 218	89.63 264	98.28 283	95.71 247	92.31 258	97.72 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		96.18 244	95.49 253	98.26 206	98.01 278	98.15 203	99.99 174	99.08 280	93.36 277	98.54 219	98.97 278	89.47 265	98.89 232	91.15 297	90.82 272	97.75 220

v1921920		96.16 246	95.50 251	98.14 217	97.88 286	97.96 215	99.99 174	99.07 282	93.33 278	98.60 214	99.24 252	89.37 266	98.71 255	91.28 295	90.74 275	97.75 220

XVG-OURS-SEG-HR		98.27 165	98.31 156	98.14 217	99.59 152	95.92 265	100.00 1	99.36 180	98.48 47	99.21 175	100.00 1	89.27 267	99.94 129	99.76 96	99.17 121	98.56 210

test20.03		93.11 289	92.85 283	93.88 311	95.19 323	91.83 314	100.00 1	98.87 303	93.68 267	92.76 306	98.88 284	89.20 268	92.71 337	77.88 336	89.19 294	97.09 309

MDA-MVSNet_test_wron		92.61 292	91.09 298	97.19 261	96.71 312	97.26 243	100.00 1	99.14 256	88.61 318	67.90 345	98.32 307	89.03 269	96.57 309	90.47 303	89.59 290	97.74 242

BH-RMVSNet		98.46 156	98.08 165	99.59 122	99.61 147	99.19 139	100.00 1	99.28 217	97.06 159	98.95 190	100.00 1	88.99 270	99.82 150	98.83 162	100.00 1	99.77 177

v1240		95.96 254	95.25 261	98.07 224	97.91 284	97.87 222	99.96 199	99.07 282	93.24 280	98.64 212	98.96 279	88.98 271	98.61 262	89.58 310	90.92 271	97.75 220

TR-MVS		98.14 168	97.74 176	99.33 146	99.59 152	98.28 186	99.27 298	99.21 240	96.42 194	99.15 180	99.94 174	88.87 272	99.79 155	98.88 158	98.29 154	99.93 123

YYNet1		92.44 294	90.92 299	97.03 264	96.20 314	97.06 250	99.99 174	99.14 256	88.21 320	67.93 344	98.43 302	88.63 273	96.28 322	90.64 299	89.08 296	97.74 242

HQP2-MVS												88.61 274

HQP-MVS		97.73 177	97.85 173	97.39 252	99.07 206	94.82 277	100.00 1	99.40 153	99.04 13	99.17 176	99.97 150	88.61 274	99.57 177	99.79 90	95.58 211	97.77 214

HQP_MVS		97.71 179	97.82 174	97.37 253	99.00 218	94.80 280	100.00 1	99.40 153	99.00 19	99.08 184	99.97 150	88.58 276	99.55 186	99.79 90	95.57 215	97.76 216

plane_prior6					99.06 210	94.80 280						88.58 276

tfpnnormal		96.36 235	95.69 247	98.37 195	98.55 240	98.71 163	99.69 264	99.45 96	93.16 282	96.69 282	99.71 205	88.44 278	98.99 224	94.17 272	91.38 267	97.41 300

TransMVSNet (Re)		94.78 271	93.72 274	97.93 236	98.34 247	97.88 220	99.23 307	97.98 329	91.60 294	94.55 297	99.71 205	87.89 279	98.36 280	89.30 315	84.92 317	97.56 294

DTE-MVSNet		95.52 264	94.99 268	97.08 262	97.49 301	96.45 261	100.00 1	99.25 228	93.82 259	96.17 285	99.57 234	87.81 280	97.18 307	94.57 265	86.26 315	97.62 288

XXY-MVS		97.14 197	96.63 203	98.67 183	98.65 236	98.92 156	99.54 279	99.29 209	95.57 219	97.63 258	99.83 189	87.79 281	99.35 210	98.39 181	92.95 253	97.75 220

UGNet		98.41 159	98.11 163	99.31 148	99.54 161	98.55 170	99.18 310	100.00 1	98.64 43	99.79 135	99.04 264	87.61 282	100.00 1	99.30 141	99.89 106	99.40 202
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		97.64 180	97.74 176	97.36 254	99.01 214	94.76 285	100.00 1	99.34 193	99.30 4	99.00 188	99.97 150	87.49 283	99.57 177	99.96 65	95.58 211	97.75 220
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PEN-MVS		96.01 253	95.48 255	97.58 245	97.74 288	97.26 243	99.90 216	99.29 209	94.55 242	96.79 281	99.55 235	87.38 284	97.84 302	96.92 231	87.24 308	97.65 282

ACMM	97.17 6	97.37 187	97.40 183	97.29 256	99.01 214	94.64 289	100.00 1	99.25 228	98.07 77	98.44 226	99.98 140	87.38 284	99.55 186	99.25 143	95.19 227	97.69 270
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		97.07 200	96.79 199	97.89 238	97.28 308	97.12 247	99.95 205	99.19 243	96.55 186	97.31 267	99.69 210	87.35 286	98.91 229	98.70 167	95.12 231	97.66 278

pmmvs5		95.94 255	95.61 248	96.95 267	97.42 305	94.66 287	100.00 1	98.08 325	93.60 272	97.05 273	99.43 244	87.02 287	98.46 277	95.76 244	92.12 259	97.72 257

VPA-MVSNet		97.03 203	96.43 211	98.82 177	98.64 237	99.32 126	99.38 291	99.47 82	96.73 174	98.91 193	98.94 280	87.00 288	99.40 206	99.23 144	89.59 290	97.76 216

PS-CasMVS		96.34 237	95.78 241	98.03 229	98.18 258	98.27 188	99.71 258	99.32 198	94.75 236	96.82 280	99.65 219	86.98 289	98.15 286	97.74 208	88.85 298	97.66 278

LPG-MVS_test		97.31 189	97.32 186	97.28 257	98.85 233	94.60 290	100.00 1	99.37 174	97.35 139	98.85 196	99.98 140	86.66 290	99.56 181	99.55 118	95.26 222	97.70 265

LGP-MVS_train				97.28 257	98.85 233	94.60 290		99.37 174	97.35 139	98.85 196	99.98 140	86.66 290	99.56 181	99.55 118	95.26 222	97.70 265

mvs_tets		97.00 205	96.69 201	97.94 234	97.41 307	97.27 242	99.60 274	99.18 247	96.51 190	97.35 266	99.69 210	86.53 292	98.91 229	98.84 160	95.09 232	97.65 282

pm-mvs1		95.76 261	95.01 267	98.00 230	98.23 255	97.45 232	99.24 301	99.04 295	93.13 283	95.93 289	99.72 203	86.28 293	98.84 238	95.62 251	87.92 305	97.72 257

MVS-HIRNet		94.12 275	92.73 284	98.29 202	99.33 193	95.95 264	99.38 291	99.19 243	74.54 339	98.26 236	86.34 345	86.07 294	99.06 219	91.60 294	99.87 109	99.85 160

DeepMVS_CX				89.98 316	98.90 227	71.46 342		99.18 247	97.61 123	96.92 275	99.83 189	86.07 294	99.83 148	96.02 242	97.65 199	98.65 208

OPM-MVS		97.21 192	97.18 190	97.32 255	98.08 271	94.66 287	100.00 1	99.28 217	98.65 42	98.92 191	99.98 140	86.03 296	99.56 181	98.28 187	95.41 217	97.72 257

new_pmnet		94.11 276	93.47 278	96.04 295	96.60 313	92.82 308	99.97 196	98.91 302	90.21 303	95.26 292	98.05 308	85.89 297	98.14 287	84.28 324	92.01 261	97.16 308

ACMP	97.00 8	97.19 193	97.16 191	97.27 259	98.97 223	94.58 292	100.00 1	99.32 198	97.97 86	97.45 264	99.98 140	85.79 298	99.56 181	99.70 102	95.24 225	97.67 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OurMVSNet-221017-0		96.14 248	95.98 233	96.62 286	97.49 301	93.44 302	99.92 213	98.16 321	95.86 210	97.65 257	99.95 170	85.71 299	98.78 244	94.93 263	94.18 243	97.64 285

SixPastTwentyTwo		95.71 262	95.49 253	96.38 290	97.42 305	93.01 305	99.84 227	98.23 318	94.75 236	95.98 288	99.97 150	85.35 300	98.43 278	94.71 264	93.17 250	97.69 270

LTVRE_ROB	95.29 16	96.32 238	96.10 227	96.99 266	98.55 240	93.88 298	99.45 285	99.28 217	94.50 244	96.46 284	99.52 238	84.86 301	99.48 199	97.26 223	95.03 233	97.59 292
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				96.84 279	98.96 224	93.49 301		98.12 323	98.12 74	98.35 229	99.97 150	84.45 302	99.56 181	95.63 250	95.25 224	97.49 296

PVSNet_0	93.57 19	96.41 231	95.74 243	98.41 192	99.84 97	95.22 270	100.00 1	100.00 1	98.08 76	97.55 262	99.78 199	84.40 303	100.00 1	100.00 1	81.99 321	100.00 1

K. test v3		95.46 267	95.14 264	96.40 289	97.53 298	93.40 303	99.99 174	99.23 234	95.49 225	92.70 308	99.73 202	84.26 304	98.12 288	93.94 278	93.38 249	97.68 272

GBi-Net		96.07 250	95.80 239	96.89 271	99.53 164	94.87 274	99.18 310	99.27 224	93.71 261	98.53 220	98.81 286	84.23 305	98.07 296	95.31 255	93.60 245	97.72 257

test1		96.07 250	95.80 239	96.89 271	99.53 164	94.87 274	99.18 310	99.27 224	93.71 261	98.53 220	98.81 286	84.23 305	98.07 296	95.31 255	93.60 245	97.72 257

FMVSNet2		96.22 242	95.60 249	98.06 227	99.53 164	98.33 181	99.45 285	99.27 224	93.71 261	98.03 243	98.84 285	84.23 305	98.10 293	93.97 277	93.40 248	97.73 252

testgi		96.18 244	95.93 235	96.93 269	98.98 222	94.20 297	100.00 1	99.07 282	97.16 150	96.06 287	99.86 184	84.08 308	97.79 303	90.38 304	97.80 183	98.81 205

ACMH	96.25 11	96.77 209	96.62 204	97.21 260	98.96 224	94.43 294	99.64 271	99.33 195	97.43 135	96.55 283	99.97 150	83.52 309	99.54 189	99.07 152	95.13 230	97.66 278
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				96.05 294	97.55 297	91.80 315		99.22 236		91.87 309	99.91 178	83.50 310	98.68 257	92.48 290	90.42 279	97.68 272

UnsupCasMVSNet_eth		94.25 274	93.89 272	95.34 297	97.63 293	92.13 312	99.73 254	99.36 180	94.88 235	92.78 305	98.63 295	82.72 311	96.53 313	94.57 265	84.73 318	97.36 302

LF4IMVS		96.19 243	96.18 220	96.23 293	98.26 253	92.09 313	100.00 1	97.89 331	97.82 100	97.94 248	99.87 182	82.71 312	99.38 207	97.41 220	93.71 244	97.20 307

ACMH+	96.20 13	96.49 229	96.33 214	97.00 265	99.06 210	93.80 299	99.81 235	99.31 203	97.32 142	95.89 290	99.97 150	82.62 313	99.54 189	98.34 184	94.63 239	97.65 282

pmmvs3		90.62 304	89.36 305	94.40 305	90.53 333	91.49 317	100.00 1	96.73 342	84.21 331	93.65 304	96.65 313	82.56 314	94.83 331	82.28 327	77.62 333	96.89 310

XVG-ACMP-BASELINE		96.60 224	96.52 207	96.84 279	98.41 245	93.29 304	99.99 174	99.32 198	97.76 105	98.51 223	99.29 250	81.95 315	99.54 189	98.40 180	95.03 233	97.68 272

test1235678		88.02 309	88.73 307	85.90 325	87.57 338	71.70 341	100.00 1	97.42 337	87.52 323	86.19 330	96.39 315	81.60 316	87.93 343	70.72 341	91.18 269	98.08 212

VPNet		96.41 231	95.76 242	98.33 199	98.61 238	98.30 185	99.48 284	99.45 96	96.98 160	98.87 195	99.88 181	81.57 317	98.93 227	99.22 146	87.82 306	97.76 216

testus		91.61 301	92.92 281	87.70 320	93.91 326	74.98 337	100.00 1	98.19 320	92.59 287	93.90 302	98.68 292	81.43 318	90.77 340	79.82 332	97.71 192	97.34 304

MDA-MVSNet-bldmvs		91.65 300	89.94 303	96.79 283	96.72 311	96.70 258	99.42 288	98.94 299	88.89 317	66.97 347	98.37 305	81.43 318	95.91 326	89.24 316	89.46 292	97.75 220

MS-PatchMatch		95.66 263	95.87 236	95.05 299	97.80 287	89.25 321	98.88 328	99.30 205	96.35 198	96.86 278	99.01 272	81.35 320	99.43 203	93.30 284	99.98 94	96.46 314

USDC		95.90 256	95.70 245	96.50 288	98.60 239	92.56 311	100.00 1	98.30 317	97.77 103	96.92 275	99.94 174	81.25 321	99.45 202	93.54 283	94.96 235	97.49 296

test_normal		96.24 241	95.12 265	99.60 119	96.00 318	99.16 141	100.00 1	99.29 209	96.43 192	88.96 317	98.40 304	80.06 322	99.90 135	98.53 176	98.39 146	99.75 180

DI_MVS_plusplus_test		96.28 240	95.16 263	99.61 116	96.01 317	99.18 140	100.00 1	99.29 209	96.49 191	89.08 314	98.42 303	80.06 322	99.90 135	98.74 166	98.39 146	99.75 180

TDRefinement		91.93 296	90.48 301	96.27 292	81.60 344	92.65 310	99.10 320	97.61 336	93.96 255	93.77 303	99.85 186	80.03 324	99.53 193	97.82 207	70.59 341	96.63 313

test_0402		94.35 273	93.70 275	96.32 291	97.92 283	93.60 300	99.61 273	98.85 304	88.19 321	94.68 296	99.48 242	80.01 325	98.58 268	89.39 313	95.15 229	96.77 311

TinyColmap		95.50 265	95.12 265	96.64 285	98.69 235	93.00 306	99.40 289	97.75 333	96.40 196	96.14 286	99.87 182	79.47 326	99.50 197	93.62 280	94.72 238	97.40 301

LFMVS		97.42 186	96.62 204	99.81 90	99.80 112	99.50 109	99.16 316	99.56 73	94.48 245	100.00 1	100.00 1	79.35 327	100.00 1	99.89 77	97.37 202	99.94 114

pmmvs-eth3d		91.73 299	90.67 300	94.92 302	91.63 331	92.71 309	99.90 216	98.54 315	91.19 298	88.08 326	95.50 324	79.31 328	96.13 324	90.55 302	81.32 324	95.91 329

new-patchmatchnet		90.30 305	89.46 304	92.84 313	90.77 332	88.55 323	99.83 228	98.80 305	90.07 305	87.86 327	95.00 333	78.77 329	94.30 333	84.86 323	79.15 328	95.68 333

test123		79.44 316	79.23 318	80.05 331	80.03 346	71.72 340	100.00 1	77.93 358	62.52 343	94.81 295	99.69 210	78.21 330	74.53 353	92.57 288	27.33 353	93.90 334

CMPMVS	66.12 22	90.65 303	92.04 290	86.46 323	96.18 315	66.87 345	98.03 337	99.38 171	83.38 333	85.49 332	99.55 235	77.59 331	98.80 243	94.44 267	94.31 242	93.72 336
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		93.64 277	92.87 282	95.94 296	97.47 303	91.41 318	98.92 326	99.02 296	87.84 322	95.01 294	99.61 230	77.24 332	98.77 247	94.33 270	86.41 314	97.63 286

test12356		86.30 310	87.15 311	83.77 329	84.24 341	72.00 339	99.94 209	96.80 341	84.63 330	85.16 333	96.51 314	77.02 333	86.31 346	70.06 342	89.93 286	93.00 338

tmp_tt		75.80 320	74.26 321	80.43 330	52.91 357	53.67 355	87.42 348	97.98 329	61.80 344	67.04 346	100.00 1	76.43 334	96.40 318	96.47 240	28.26 352	91.23 340

MIMVSNet1		91.96 295	91.20 296	94.23 308	94.94 325	91.69 316	99.34 294	99.22 236	88.23 319	94.18 300	98.45 300	75.52 335	93.41 336	79.37 333	91.49 266	97.60 291

VDDNet		96.39 234	95.55 250	98.90 173	99.27 197	97.45 232	99.15 317	99.92 35	91.28 297	99.98 80	100.00 1	73.55 336	100.00 1	99.85 82	96.98 206	99.24 203

FMVSNet1		94.45 272	93.63 276	96.89 271	98.87 231	94.87 274	99.18 310	99.27 224	90.95 300	97.31 267	98.81 286	72.89 337	98.07 296	92.61 287	92.81 254	97.72 257

VDD-MVS		96.58 225	95.99 232	98.34 198	99.52 169	95.33 269	99.18 310	99.38 171	96.64 181	99.77 137	100.00 1	72.51 338	100.00 1	100.00 1	96.94 207	99.70 189

EG-PatchMatch MVS		92.94 290	92.49 285	94.29 307	95.87 319	87.07 325	99.07 324	98.11 324	93.19 281	88.98 316	98.66 294	70.89 339	99.08 218	92.43 291	95.21 226	96.72 312

OpenMVS_ROB	88.34 20	91.89 297	91.12 297	94.19 309	95.55 321	87.63 324	99.26 299	98.03 326	86.61 324	90.65 313	96.82 312	70.14 340	98.78 244	86.54 321	96.50 210	96.15 318

testmvs		80.17 314	81.95 315	74.80 335	58.54 355	59.58 353	100.00 1	87.14 355	76.09 338	99.61 154	100.00 1	67.06 341	74.19 354	98.84 160	50.30 345	90.64 341

UnsupCasMVSNet_bld		89.50 306	88.00 309	93.99 310	95.30 322	88.86 322	98.52 333	99.28 217	85.50 328	87.80 328	94.11 335	61.63 342	96.96 308	90.63 300	79.26 326	96.15 318

testing_2		90.79 302	88.26 308	98.35 197	89.11 335	98.56 169	99.70 260	99.14 256	93.70 265	73.80 340	94.06 336	55.19 343	98.76 249	97.17 224	92.50 257	97.74 242

Gipuma		84.73 312	83.50 313	88.40 318	97.50 299	82.21 330	88.87 346	99.05 292	65.81 342	85.71 331	90.49 340	53.70 344	96.31 320	78.64 335	91.74 263	86.67 345
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		79.83 315	79.77 316	79.99 332	78.22 348	63.55 349	99.67 267	95.51 347	80.89 335	76.24 339	92.90 338	52.16 345	86.10 347	62.45 347	79.90 325	90.33 343

Test4		92.63 291	90.45 302	99.17 157	92.81 328	99.02 148	99.98 190	99.13 261	96.60 184	82.04 337	96.27 316	51.35 346	98.75 250	97.55 214	98.36 150	99.75 180

EMVS		69.88 324	69.09 324	72.24 340	84.70 340	65.82 347	99.96 199	87.08 356	49.82 351	71.51 343	84.74 347	49.30 347	75.32 352	50.97 352	43.71 348	75.59 352

PM-MVS		88.39 308	87.41 310	91.31 314	91.73 330	82.02 331	99.79 239	96.62 343	91.06 299	90.71 312	95.73 319	48.60 348	95.96 325	90.56 301	81.91 323	95.97 321

E-PMN		70.72 322	70.06 323	72.69 339	83.92 342	65.48 348	99.95 205	92.72 350	49.88 350	72.30 342	86.26 346	47.17 349	77.43 351	53.83 351	44.49 347	75.17 353

Anonymous20231211		84.35 313	82.41 314	90.15 315	89.14 334	78.88 332	98.71 330	98.22 319	70.20 341	86.88 329	95.44 326	47.11 350	95.72 329	81.14 328	74.88 339	95.84 332

ambc				88.45 317	86.84 339	70.76 343	97.79 340	98.02 328		90.91 311	95.14 331	38.69 351	98.51 272	94.97 262	84.23 319	96.09 320

FPMVS		77.92 319	79.45 317	73.34 337	76.87 349	46.81 356	98.24 335	99.05 292	59.89 346	73.55 341	98.34 306	36.81 352	86.55 344	80.96 329	91.35 268	86.65 346

PMMVS2		79.15 317	77.28 319	84.76 328	82.34 343	72.66 338	99.70 260	95.11 348	71.68 340	84.78 334	90.87 339	32.05 353	89.99 341	75.53 339	63.45 342	91.64 339

LCM-MVSNet		79.01 318	76.93 320	85.27 327	78.28 347	68.01 344	96.57 341	98.03 326	55.10 347	82.03 338	93.27 337	31.99 354	93.95 334	82.72 326	74.37 340	93.84 335

no-one		74.65 321	71.03 322	85.51 326	80.99 345	75.42 336	99.70 260	97.15 338	81.02 334	66.48 348	80.55 352	29.10 355	93.65 335	73.74 340	30.53 351	87.47 344

ANet_high		66.05 326	63.44 328	73.88 336	61.14 354	63.45 350	95.68 344	87.18 354	79.93 336	47.35 351	80.68 351	22.35 356	72.33 355	61.24 348	35.42 350	85.88 347

PNet_i23d		69.93 323	67.61 325	76.86 333	75.89 350	62.08 352	97.85 338	88.23 353	61.04 345	55.65 350	84.10 348	19.35 357	83.21 348	66.45 346	59.98 343	85.29 348

wuyk23d		28.28 330	29.73 332	23.92 343	75.89 350	32.61 358	66.50 349	12.88 359	16.09 353	14.59 354	16.59 355	12.35 358	32.36 356	39.36 353	13.36 354	6.79 354

wuykxyi23d		62.75 328	59.23 329	73.34 337	61.53 353	59.39 354	93.28 345	86.17 357	53.70 348	46.89 352	77.89 353	12.35 358	79.96 349	60.86 350	40.35 349	80.68 351

PMVS	60.66 23	65.98 327	65.05 326	68.75 341	55.06 356	38.40 357	88.19 347	96.98 339	48.30 352	44.82 353	88.52 343	12.22 360	86.49 345	67.58 343	83.79 320	81.35 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	68.59 21	67.22 325	64.68 327	74.84 334	74.67 352	62.32 351	95.84 342	90.87 352	50.98 349	58.72 349	81.05 350	12.20 361	78.95 350	61.06 349	56.75 344	83.24 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
sosnet-low-res		0.01 334	0.02 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.14 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.01 334	0.02 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.14 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.01 334	0.02 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.14 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.01 334	0.02 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.14 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		8.33 332	11.11 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	100.00 1	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.01 334	0.02 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.14 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																99.91 126

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								99.42 120

MTMP								99.18 247

gm-plane-assit					99.52 169	97.26 243			95.86 210		100.00 1		99.43 203	98.76 165

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		99.42 120		100.00 1			99.97 97

test_prior4						99.93 32	100.00 1

test_prior				99.90 70	100.00 1	99.75 83		99.73 54					99.97 97			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 42	97.98 84				100.00 1	99.33 138		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 222

testdata1							100.00 1		98.77 37

plane_prior7					99.00 218	94.78 284

plane_prior5								99.40 153					99.55 186	99.79 90	95.57 215	97.76 216

plane_prior4											99.97 150

plane_prior3						94.79 283			99.03 16	99.08 184

plane_prior2							100.00 1		99.00 19

plane_prior1					99.02 213

plane_prior						94.80 280	100.00 1		99.03 16						95.58 211

n2								0.00 360

nn								0.00 360

door-mid								96.32 345

test11								99.42 120

door								96.13 346

HQP5-MVS						94.82 277

HQP-NCC					99.07 206		100.00 1		99.04 13	99.17 176

ACMP_Plane					99.07 206		100.00 1		99.04 13	99.17 176

BP-MVS														99.79 90

HQP4-MVS										99.17 176			99.57 177			97.77 214

HQP3-MVS								99.40 153							95.58 211

NP-MVS					99.07 206	94.81 279					99.97 150

ACMMP++_ref															94.58 241

ACMMP++															95.17 228

High-resolution multi-view benchmark