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.

CHOSEN 280x420		99.01 10	99.03 5	98.95 75	99.38 83	98.87 20	98.46 262	99.42 25	97.03 17	99.02 59	99.09 112	99.35 1	98.21 198	99.73 16	99.78 69	99.77 85

GG-mvs-BLEND				98.54 100	98.21 140	98.01 65	93.87 327	98.52 91		97.92 99	97.92 179	99.02 2	97.94 211	98.17 72	99.58 83	99.67 97

gg-mvs-nofinetune		93.51 183	91.86 198	98.47 108	97.72 171	97.96 66	92.62 332	98.51 97	74.70 330	97.33 109	69.59 346	98.91 3	97.79 214	97.77 90	99.56 84	99.67 97

SteuartSystems-ACMMP		99.02 9	98.97 9	99.18 43	98.72 117	97.71 72	99.98 6	98.44 107	96.85 20	99.80 9	99.91 7	97.57 4	99.85 79	99.44 26	99.99 13	99.99 12
Skip Steuart: Steuart Systems R&D Blog.
DWT-MVSNet_test		97.31 77	97.19 70	97.66 140	98.24 138	94.67 169	98.86 236	98.20 151	93.60 115	98.09 95	98.89 126	97.51 5	98.78 150	94.04 151	97.28 134	99.55 117

HSP-MVS		99.07 6	99.11 4	98.95 75	99.93 24	97.24 95	99.95 31	98.32 137	97.50 10	99.52 32	99.88 12	97.43 6	99.71 105	99.50 23	99.98 26	99.89 72

tfpn_ndepth		97.21 82	96.63 87	98.92 77	99.06 88	98.28 56	99.95 31	98.91 42	92.96 127	96.49 125	98.67 152	97.40 7	99.07 139	91.87 186	94.38 179	99.41 134

tfpn1000		96.90 92	96.29 95	98.74 85	99.00 93	98.09 62	99.92 52	98.91 42	92.08 167	95.85 138	98.65 154	97.39 8	98.83 147	90.56 201	94.23 187	99.31 149

NCCC		99.37 2	99.25 2	99.71 5	99.96 8	99.15 9	99.97 12	98.62 74	98.02 6	99.90 2	99.95 3	97.33 9	100.00 1	99.54 21	100.00 1	100.00 1

MVSTER		95.53 141	95.22 139	96.45 173	98.56 125	97.72 71	99.91 56	97.67 196	92.38 158	91.39 193	97.14 192	97.24 10	97.30 233	94.80 134	87.85 233	94.34 230

test_part1								98.41 122				97.20 11			99.99 13	99.99 12

ESAPD		99.18 4	98.99 7	99.75 3	99.89 36	99.25 6	99.88 66	98.41 122	96.14 43	99.49 33	99.91 7	97.20 11	100.00 1	99.99 1	99.99 13	99.99 12

CNVR-MVS		99.40 1	99.26 1	99.84 2	99.98 2	99.51 2	99.98 6	98.69 63	98.20 3	99.93 1	99.98 2	96.82 13	100.00 1	99.75 11	100.00 1	99.99 12

segment_acmp												96.68 14

conf0.01		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

conf0.002		96.52 113	95.88 113	98.41 116	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.20 160

thresconf0.02		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpn_n400		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnconf		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

tfpnview11		96.53 108	95.88 113	98.48 104	98.59 119	97.38 88	99.87 71	98.91 42	91.32 188	95.22 157	98.83 138	96.57 15	98.66 159	89.55 215	94.09 189	99.40 137

PAPM		98.60 26	98.42 26	99.14 52	96.05 211	98.96 13	99.90 59	99.35 27	96.68 28	98.35 87	99.66 78	96.45 21	98.51 169	99.45 25	99.89 55	99.96 58

MCST-MVS		99.32 3	99.14 3	99.86 1	99.97 3	99.59 1	99.97 12	98.64 70	98.47 2	99.13 55	99.92 6	96.38 22	100.00 1	99.74 13	100.00 1	100.00 1

SMA-MVS		98.82 18	98.55 22	99.65 8	99.87 39	98.95 14	99.86 86	98.35 133	93.19 122	99.83 7	99.94 4	96.17 23	100.00 1	99.74 13	99.99 13	100.00 1

agg_prior3		98.84 17	98.62 18	99.47 26	99.92 27	98.56 46	99.96 19	98.43 112	94.07 95	99.67 19	99.85 21	96.05 24	99.85 79	99.69 19	99.98 26	99.99 12

EPP-MVSNet		96.69 102	96.60 88	96.96 159	97.74 167	93.05 209	99.37 182	98.56 84	88.75 233	95.83 143	99.01 117	96.01 25	98.56 166	96.92 109	97.20 139	99.25 156

test_prior3		98.99 11	98.84 12	99.43 27	99.94 14	98.49 50	99.95 31	98.65 67	95.78 50	99.73 14	99.76 56	96.00 26	99.80 88	99.78 9	100.00 1	99.99 12

test_prior2							99.95 31		95.78 50	99.73 14	99.76 56	96.00 26		99.78 9	100.00 1

train_agg		98.88 15	98.65 16	99.59 14	99.92 27	98.92 16	99.96 19	98.43 112	94.35 85	99.71 16	99.86 17	95.94 28	99.85 79	99.69 19	99.98 26	99.99 12

test_8					99.92 27	98.88 19	99.96 19	98.43 112	94.35 85	99.69 18	99.85 21	95.94 28	99.85 79

MSLP-MVS++		99.13 5	99.01 6	99.49 23	99.94 14	98.46 52	99.98 6	98.86 53	97.10 15	99.80 9	99.94 4	95.92 30	100.00 1	99.51 22	100.00 1	100.00 1

TEST9					99.92 27	98.92 16	99.96 19	98.43 112	93.90 105	99.71 16	99.86 17	95.88 31	99.85 79

agg_prior1		98.88 15	98.66 15	99.54 18	99.93 24	98.77 26	99.96 19	98.43 112	94.63 78	99.63 21	99.85 21	95.79 32	99.85 79	99.72 17	99.99 13	99.99 12

DP-MVS Recon		98.41 41	98.02 47	99.56 16	99.97 3	98.70 35	99.92 52	98.44 107	92.06 170	98.40 85	99.84 35	95.68 33	100.00 1	98.19 71	99.71 73	99.97 54

旧先验1					99.76 54	97.52 78		98.64 70			99.85 21	95.63 34			99.94 44	99.99 12

TESTMET0.1,1		96.74 99	96.26 96	98.16 123	97.36 180	96.48 117	99.96 19	98.29 140	91.93 172	95.77 144	98.07 175	95.54 35	98.29 192	90.55 202	98.89 101	99.70 93

PatchFormer-LS_test		97.01 87	96.79 83	97.69 139	98.26 137	94.80 165	98.66 251	98.13 161	93.70 112	97.86 101	98.80 144	95.54 35	98.67 157	94.12 150	96.00 155	99.60 109

APDe-MVS		99.06 8	98.91 10	99.51 21	99.94 14	98.76 32	99.91 56	98.39 126	97.20 14	99.46 35	99.85 21	95.53 37	99.79 90	99.86 5	100.00 1	99.99 12

PLC	95.54 3	97.93 59	97.89 54	98.05 130	99.82 50	94.77 168	99.92 52	98.46 105	93.93 104	97.20 111	99.27 102	95.44 38	99.97 41	97.41 95	99.51 88	99.41 134
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HPM-MVS++		99.07 6	98.88 11	99.63 9	99.90 33	99.02 12	99.95 31	98.56 84	97.56 9	99.44 37	99.85 21	95.38 39	100.00 1	99.31 30	99.99 13	99.87 75

PHI-MVS		98.41 41	98.21 39	99.03 68	99.86 40	97.10 102	99.98 6	98.80 58	90.78 204	99.62 23	99.78 50	95.30 40	100.00 1	99.80 7	99.93 49	99.99 12

test-mter		96.39 121	95.93 109	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	91.81 176	95.55 146	96.38 218	95.17 41	98.27 195	90.42 204	98.83 103	99.64 103

patchmatchnet-post											91.70 309	95.12 42	97.95 210

MDTV_nov1_ep13			95.69 124		97.90 154	94.15 176	95.98 317	98.44 107	93.12 124	97.98 98	95.74 232	95.10 43	98.58 165	90.02 211	96.92 145

1121		98.03 56	97.57 62	99.40 33	99.74 57	98.21 58	98.31 272	98.62 74	92.78 135	99.53 29	99.83 37	95.08 44	100.00 1	94.36 143	99.92 51	99.99 12

IB-MVS	92.85 6	94.99 153	93.94 161	98.16 123	97.72 171	95.69 147	99.99 3	98.81 56	94.28 88	92.70 187	96.90 202	95.08 44	99.17 138	96.07 116	73.88 318	99.60 109
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
CDS-MVSNet		96.34 122	96.07 99	97.13 156	97.37 179	94.96 161	99.53 164	97.91 178	91.55 181	95.37 150	98.32 170	95.05 46	97.13 249	93.80 158	95.75 163	99.30 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmatch-test		92.65 198	91.50 202	96.10 182	96.85 194	90.49 261	91.50 337	97.19 238	82.76 299	90.23 202	95.59 237	95.02 47	98.00 206	77.41 312	96.98 144	99.82 79

CostFormer		96.10 129	95.88 113	96.78 164	97.03 186	92.55 222	97.08 301	97.83 187	90.04 214	98.72 71	94.89 271	95.01 48	98.29 192	96.54 112	95.77 162	99.50 126

TSAR-MVS + GP.		98.60 26	98.51 25	98.86 80	99.73 61	96.63 112	99.97 12	97.92 177	98.07 5	98.76 69	99.55 85	95.00 49	99.94 59	99.91 4	97.68 125	99.99 12

CDPH-MVS		98.65 24	98.36 34	99.49 23	99.94 14	98.73 33	99.87 71	98.33 136	93.97 101	99.76 12	99.87 15	94.99 50	99.75 97	98.55 64	100.00 1	99.98 44

原ACMM1				98.96 74	99.73 61	96.99 104		98.51 97	94.06 98	99.62 23	99.85 21	94.97 51	99.96 43	95.11 128	99.95 40	99.92 69

Regformer-1		98.79 20	98.60 20	99.36 36	99.85 41	98.34 54	99.87 71	98.52 91	96.05 45	99.41 40	99.79 45	94.93 52	99.76 94	99.07 35	99.90 53	99.99 12

Regformer-2		98.78 21	98.59 21	99.36 36	99.85 41	98.32 55	99.87 71	98.52 91	96.04 46	99.41 40	99.79 45	94.92 53	99.76 94	99.05 36	99.90 53	99.98 44

TSAR-MVS + MP.		98.93 12	98.77 13	99.41 31	99.74 57	98.67 36	99.77 110	98.38 129	96.73 26	99.88 3	99.74 63	94.89 54	99.59 116	99.80 7	99.98 26	99.97 54

test12				99.43 27	99.74 57	98.56 46		98.40 124		99.65 20		94.76 55	99.75 97		99.98 26	99.99 12

sam_mvs1												94.72 56				99.59 111

SD-MVS		98.92 13	98.70 14	99.56 16	99.70 65	98.73 33	99.94 45	98.34 135	96.38 34	99.81 8	99.76 56	94.59 57	99.98 32	99.84 6	99.96 37	99.97 54

test_post											63.35 352	94.43 58	98.13 200

EPMVS		96.53 108	96.01 101	98.09 129	98.43 131	96.12 134	96.36 310	99.43 24	93.53 116	97.64 103	95.04 262	94.41 59	98.38 186	91.13 192	98.11 117	99.75 87

Regformer-3		98.58 29	98.41 27	99.10 58	99.84 46	97.57 76	99.66 144	98.52 91	95.79 49	99.01 60	99.77 52	94.40 60	99.75 97	98.82 50	99.83 62	99.98 44

新几何1				99.42 30	99.75 56	98.27 57		98.63 73	92.69 140	99.55 28	99.82 40	94.40 60	100.00 1	91.21 190	99.94 44	99.99 12

MDTV_nov1_ep13_2view						96.26 123	96.11 315		91.89 173	98.06 96		94.40 60		94.30 146		99.67 97

PAPM_NR		98.12 53	97.93 53	98.70 86	99.94 14	96.13 131	99.82 96	98.43 112	94.56 79	97.52 106	99.70 69	94.40 60	99.98 32	97.00 105	99.98 26	99.99 12

XVS		98.70 23	98.55 22	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	99.78 50	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

X-MVStestdata		93.83 174	92.06 196	99.15 50	99.94 14	97.50 80	99.94 45	98.42 120	96.22 39	99.41 40	41.37 357	94.34 64	99.96 43	98.92 45	99.95 40	99.99 12

Regformer-4		98.56 30	98.39 30	99.08 60	99.84 46	97.52 78	99.66 144	98.52 91	95.76 52	99.01 60	99.77 52	94.33 66	99.75 97	98.80 51	99.83 62	99.98 44

CP-MVS		98.45 38	98.32 36	98.87 79	99.96 8	96.62 113	99.97 12	98.39 126	94.43 83	98.90 64	99.87 15	94.30 67	100.00 1	99.04 40	99.99 13	99.99 12

sam_mvs												94.25 68

Patchmatch-RL test		86.90 276	85.98 276	89.67 306	84.45 332	75.59 330	89.71 341	92.43 340	86.89 262	77.83 307	90.94 312	94.22 69	93.63 326	87.75 236	69.61 322	99.79 83

HFP-MVS		98.56 30	98.37 32	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	94.77 73	99.31 46	99.85 21	94.22 69	100.00 1	98.70 55	99.98 26	99.98 44

#test#		98.59 28	98.41 27	99.14 52	99.96 8	97.43 84	99.95 31	98.61 76	95.00 68	99.31 46	99.85 21	94.22 69	100.00 1	98.78 52	99.98 26	99.98 44

PatchmatchNet		95.94 133	95.45 132	97.39 151	97.83 160	94.41 172	96.05 316	98.40 124	92.86 128	97.09 115	95.28 253	94.21 72	98.07 204	89.26 223	98.11 117	99.70 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DeepPCF-MVS	95.94 2	97.71 67	98.98 8	93.92 247	99.63 68	81.76 321	99.96 19	98.56 84	99.47 1	99.19 53	99.99 1	94.16 73	100.00 1	99.92 3	99.93 49	100.00 1

APD-MVS		98.62 25	98.35 35	99.41 31	99.90 33	98.51 49	99.87 71	98.36 132	94.08 94	99.74 13	99.73 64	94.08 74	99.74 101	99.42 27	99.99 13	99.99 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
region2R		98.54 32	98.37 32	99.05 66	99.96 8	97.18 98	99.96 19	98.55 88	94.87 71	99.45 36	99.85 21	94.07 75	100.00 1	98.67 57	100.00 1	99.98 44

PAPR		98.52 34	98.16 42	99.58 15	99.97 3	98.77 26	99.95 31	98.43 112	95.35 62	98.03 97	99.75 61	94.03 76	99.98 32	98.11 75	99.83 62	99.99 12

MG-MVS		98.91 14	98.65 16	99.68 7	99.94 14	99.07 11	99.64 151	99.44 23	97.33 12	99.00 62	99.72 65	94.03 76	99.98 32	98.73 54	100.00 1	100.00 1

tpmp4_e23		95.15 150	94.69 150	96.55 171	97.84 159	91.77 240	97.10 300	97.91 178	88.33 240	97.19 112	95.06 260	93.92 78	98.51 169	89.64 214	95.19 171	99.37 143

MVS_111021_HR		98.72 22	98.62 18	99.01 71	99.36 84	97.18 98	99.93 50	99.90 1	96.81 24	98.67 73	99.77 52	93.92 78	99.89 69	99.27 31	99.94 44	99.96 58

tpmrst		96.27 128	95.98 104	97.13 156	97.96 151	93.15 206	96.34 311	98.17 153	92.07 168	98.71 72	95.12 256	93.91 80	98.73 153	94.91 132	96.62 147	99.50 126

test-LLR		96.47 115	96.04 100	97.78 136	97.02 187	95.44 150	99.96 19	98.21 148	94.07 95	95.55 146	96.38 218	93.90 81	98.27 195	90.42 204	98.83 103	99.64 103

test0.0.03 1		93.86 173	93.61 165	94.64 221	95.02 238	92.18 229	99.93 50	98.58 80	94.07 95	87.96 251	98.50 162	93.90 81	94.96 307	81.33 291	93.17 204	96.78 202

test222					99.55 74	97.41 87	99.34 185	98.55 88	91.86 174	99.27 49	99.83 37	93.84 83			99.95 40	99.99 12

dp		95.05 151	94.43 153	96.91 160	97.99 150	92.73 216	96.29 312	97.98 171	89.70 218	95.93 137	94.67 279	93.83 84	98.45 175	86.91 253	96.53 149	99.54 121

ACMMPR		98.50 35	98.32 36	99.05 66	99.96 8	97.18 98	99.95 31	98.60 78	94.77 73	99.31 46	99.84 35	93.73 85	100.00 1	98.70 55	99.98 26	99.98 44

EPNet		98.49 36	98.40 29	98.77 83	99.62 69	96.80 110	99.90 59	99.51 20	97.60 8	99.20 51	99.36 100	93.71 86	99.91 65	97.99 81	98.71 106	99.61 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
alignmvs		97.81 62	97.33 67	99.25 40	98.77 116	98.66 37	99.99 3	98.44 107	94.40 84	98.41 83	99.47 91	93.65 87	99.42 133	98.57 63	94.26 186	99.67 97

testdata				98.42 113	99.47 79	95.33 154		98.56 84	93.78 109	99.79 11	99.85 21	93.64 88	99.94 59	94.97 129	99.94 44	100.00 1

EI-MVSNet-Vis-set		98.27 47	98.11 45	98.75 84	99.83 49	96.59 115	99.40 177	98.51 97	95.29 64	98.51 80	99.76 56	93.60 89	99.71 105	98.53 65	99.52 86	99.95 63

mPP-MVS		98.39 43	98.20 40	98.97 73	99.97 3	96.92 107	99.95 31	98.38 129	95.04 67	98.61 77	99.80 44	93.39 90	100.00 1	98.64 61	100.00 1	99.98 44

WTY-MVS		98.10 54	97.60 60	99.60 13	98.92 101	99.28 5	99.89 64	99.52 18	95.58 58	98.24 93	99.39 97	93.33 91	99.74 101	97.98 83	95.58 166	99.78 84

tpm2		95.47 143	95.18 141	96.35 177	96.91 191	91.70 245	96.96 304	97.93 176	88.04 244	98.44 82	95.40 241	93.32 92	97.97 207	94.00 152	95.61 165	99.38 141

HY-MVS	92.50 7	97.79 64	97.17 72	99.63 9	98.98 95	99.32 3	97.49 293	99.52 18	95.69 56	98.32 88	97.41 186	93.32 92	99.77 92	98.08 78	95.75 163	99.81 80

EI-MVSNet-UG-set		98.14 52	97.99 49	98.60 94	99.80 52	96.27 122	99.36 184	98.50 101	95.21 66	98.30 89	99.75 61	93.29 94	99.73 104	98.37 69	99.30 96	99.81 80

PGM-MVS		98.34 44	98.13 44	98.99 72	99.92 27	97.00 103	99.75 117	99.50 21	93.90 105	99.37 44	99.76 56	93.24 95	100.00 1	97.75 91	99.96 37	99.98 44

test_post1							95.78 320				59.23 355	93.20 96	97.74 215	91.06 194

CSCG		97.10 85	97.04 76	97.27 154	99.89 36	91.92 235	99.90 59	99.07 33	88.67 234	95.26 152	99.82 40	93.17 97	99.98 32	98.15 73	99.47 89	99.90 71

DeepC-MVS_fast	96.59 1	98.81 19	98.54 24	99.62 12	99.90 33	98.85 21	99.24 196	98.47 103	98.14 4	99.08 56	99.91 7	93.09 98	100.00 1	99.04 40	99.99 13	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
ACMMP_Plus		98.49 36	98.14 43	99.54 18	99.66 67	98.62 41	99.85 88	98.37 131	94.68 77	99.53 29	99.83 37	92.87 99	100.00 1	98.66 60	99.84 61	99.99 12

APD-MVS_3200maxsize		98.25 49	98.08 46	98.78 82	99.81 51	96.60 114	99.82 96	98.30 139	93.95 103	99.37 44	99.77 52	92.84 100	99.76 94	98.95 42	99.92 51	99.97 54

JIA-IIPM		91.76 214	90.70 211	94.94 208	96.11 209	87.51 292	93.16 330	98.13 161	75.79 327	97.58 105	77.68 342	92.84 100	97.97 207	88.47 229	96.54 148	99.33 148

Test By Simon												92.82 102

zzz-MVS		98.33 45	98.00 48	99.30 38	99.85 41	97.93 67	99.80 101	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

MTAPA		98.29 46	97.96 52	99.30 38	99.85 41	97.93 67	99.39 180	98.28 141	95.76 52	97.18 113	99.88 12	92.74 103	100.00 1	98.67 57	99.88 57	99.99 12

EPNet_dtu		95.71 138	95.39 134	96.66 169	98.92 101	93.41 197	99.57 157	98.90 50	96.19 41	97.52 106	98.56 161	92.65 105	97.36 225	77.89 309	98.33 112	99.20 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MP-MVS-pluss		98.07 55	97.64 58	99.38 35	99.74 57	98.41 53	99.74 120	98.18 152	93.35 119	96.45 127	99.85 21	92.64 106	99.97 41	98.91 47	99.89 55	99.77 85
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DELS-MVS		98.54 32	98.22 38	99.50 22	99.15 87	98.65 39	100.00 1	98.58 80	97.70 7	98.21 94	99.24 106	92.58 107	99.94 59	98.63 62	99.94 44	99.92 69
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
F-COLMAP		96.93 90	96.95 78	96.87 162	99.71 64	91.74 241	99.85 88	97.95 174	93.11 125	95.72 145	99.16 110	92.35 108	99.94 59	95.32 126	99.35 94	98.92 181

API-MVS		97.86 60	97.66 57	98.47 108	99.52 76	95.41 152	99.47 171	98.87 52	91.68 178	98.84 65	99.85 21	92.34 109	99.99 28	98.44 67	99.96 37	100.00 1

CNLPA		97.76 65	97.38 65	98.92 77	99.53 75	96.84 108	99.87 71	98.14 159	93.78 109	96.55 124	99.69 72	92.28 110	99.98 32	97.13 101	99.44 91	99.93 66

TAMVS		95.85 134	95.58 130	96.65 170	97.07 184	93.50 190	99.17 201	97.82 188	91.39 187	95.02 164	98.01 176	92.20 111	97.30 233	93.75 160	95.83 161	99.14 172

1112_ss		96.01 132	95.20 140	98.42 113	97.80 162	96.41 119	99.65 147	96.66 286	92.71 138	92.88 185	99.40 95	92.16 112	99.30 134	91.92 184	93.66 198	99.55 117

Test_1112_low_res		95.72 136	94.83 146	98.42 113	97.79 163	96.41 119	99.65 147	96.65 287	92.70 139	92.86 186	96.13 226	92.15 113	99.30 134	91.88 185	93.64 199	99.55 117

HyFIR lowres test		96.66 105	96.43 92	97.36 152	99.05 89	93.91 181	99.70 133	99.80 3	90.54 205	96.26 132	98.08 174	92.15 113	98.23 197	96.84 110	95.46 167	99.93 66

MVS_111021_LR		98.42 40	98.38 31	98.53 101	99.39 82	95.79 139	99.87 71	99.86 2	96.70 27	98.78 68	99.79 45	92.03 115	99.90 66	99.17 32	99.86 60	99.88 74

TAPA-MVS	92.12 8	94.42 167	93.60 167	96.90 161	99.33 85	91.78 239	99.78 105	98.00 169	89.89 216	94.52 168	99.47 91	91.97 116	99.18 137	69.90 324	99.52 86	99.73 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchT		90.38 243	88.75 255	95.25 196	95.99 213	90.16 267	91.22 339	97.54 208	76.80 324	97.26 110	86.01 337	91.88 117	96.07 291	66.16 332	95.91 159	99.51 124

HPM-MVS		97.96 57	97.72 56	98.68 87	99.84 46	96.39 121	99.90 59	98.17 153	92.61 146	98.62 76	99.57 84	91.87 118	99.67 112	98.87 48	99.99 13	99.99 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS		98.23 50	97.97 50	99.03 68	99.94 14	97.17 101	99.95 31	98.39 126	94.70 76	98.26 92	99.81 43	91.84 119	100.00 1	98.85 49	99.97 35	99.93 66

HPM-MVS_fast		97.80 63	97.50 63	98.68 87	99.79 53	96.42 118	99.88 66	98.16 156	91.75 177	98.94 63	99.54 87	91.82 120	99.65 114	97.62 93	99.99 13	99.99 12

tpmvs		94.28 170	93.57 169	96.40 175	98.55 126	91.50 250	95.70 321	98.55 88	87.47 253	92.15 189	94.26 287	91.42 121	98.95 144	88.15 231	95.85 160	98.76 186

ACMMP		97.74 66	97.44 64	98.66 89	99.92 27	96.13 131	99.18 200	99.45 22	94.84 72	96.41 130	99.71 67	91.40 122	99.99 28	97.99 81	98.03 121	99.87 75
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
Vis-MVSNet (Re-imp)		96.32 123	95.98 104	97.35 153	97.93 153	94.82 164	99.47 171	98.15 158	91.83 175	95.09 163	99.11 111	91.37 123	97.47 221	93.47 163	97.43 130	99.74 88

sss		97.57 70	97.03 77	99.18 43	98.37 132	98.04 64	99.73 126	99.38 26	93.46 117	98.76 69	99.06 114	91.21 124	99.89 69	96.33 113	97.01 143	99.62 105

pcd_1.5k_mvsjas		7.60 336	10.13 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	91.20 125	0.00 360	0.00 357	0.00 358	0.00 358

PS-MVSNAJss		93.64 182	93.31 179	94.61 222	92.11 299	92.19 228	99.12 203	97.38 227	92.51 155	88.45 243	96.99 201	91.20 125	97.29 236	94.36 143	87.71 235	94.36 226

PS-MVSNAJ		98.44 39	98.20 40	99.16 46	98.80 114	98.92 16	99.54 163	98.17 153	97.34 11	99.85 5	99.85 21	91.20 125	99.89 69	99.41 28	99.67 75	98.69 187

CPTT-MVS		97.64 69	97.32 68	98.58 96	99.97 3	95.77 140	99.96 19	98.35 133	89.90 215	98.36 86	99.79 45	91.18 128	99.99 28	98.37 69	99.99 13	99.99 12

CR-MVSNet		93.45 186	92.62 186	95.94 184	96.29 206	92.66 218	92.01 335	96.23 294	92.62 145	96.94 116	93.31 301	91.04 129	96.03 292	79.23 302	95.96 157	99.13 174

Patchmtry		89.70 255	88.49 258	93.33 258	96.24 208	89.94 274	91.37 338	96.23 294	78.22 321	87.69 253	93.31 301	91.04 129	96.03 292	80.18 296	82.10 263	94.02 248

MVSFormer		96.94 89	96.60 88	97.95 132	97.28 181	97.70 74	99.55 161	97.27 234	91.17 194	99.43 38	99.54 87	90.92 131	96.89 265	94.67 138	99.62 78	99.25 156

lupinMVS		97.85 61	97.60 60	98.62 92	97.28 181	97.70 74	99.99 3	97.55 206	95.50 60	99.43 38	99.67 76	90.92 131	98.71 155	98.40 68	99.62 78	99.45 129

xiu_mvs_v2_base		98.23 50	97.97 50	99.02 70	98.69 118	98.66 37	99.52 165	98.08 164	97.05 16	99.86 4	99.86 17	90.65 133	99.71 105	99.39 29	98.63 107	98.69 187

IS-MVSNet		96.29 126	95.90 112	97.45 147	98.13 145	94.80 165	99.08 208	97.61 203	92.02 171	95.54 148	98.96 122	90.64 134	98.08 202	93.73 161	97.41 132	99.47 128

tpm		93.70 181	93.41 176	94.58 224	95.36 233	87.41 294	97.01 302	96.90 274	90.85 202	96.72 122	94.14 290	90.40 135	96.84 268	90.75 200	88.54 226	99.51 124

114514_t		97.41 76	96.83 80	99.14 52	99.51 78	97.83 69	99.89 64	98.27 144	88.48 237	99.06 57	99.66 78	90.30 136	99.64 115	96.32 114	99.97 35	99.96 58

ADS-MVSNet2		93.80 177	93.88 163	93.55 256	97.87 156	85.94 299	94.24 323	96.84 279	90.07 212	96.43 128	94.48 283	90.29 137	95.37 301	87.44 239	97.23 137	99.36 144

ADS-MVSNet		94.79 155	94.02 160	97.11 158	97.87 156	93.79 183	94.24 323	98.16 156	90.07 212	96.43 128	94.48 283	90.29 137	98.19 199	87.44 239	97.23 137	99.36 144

thres200		96.96 88	96.21 97	99.22 41	98.97 96	98.84 22	99.85 88	99.71 5	93.17 123	96.26 132	98.88 128	89.87 139	99.51 119	94.26 147	94.91 172	99.31 149

tpm cat1		93.51 183	92.52 189	96.47 172	97.77 164	91.47 251	96.13 314	98.06 165	80.98 314	92.91 184	93.78 295	89.66 140	98.87 145	87.03 249	96.39 151	99.09 177

OMC-MVS		97.28 78	97.23 69	97.41 149	99.76 54	93.36 201	99.65 147	97.95 174	96.03 47	97.41 108	99.70 69	89.61 141	99.51 119	96.73 111	98.25 116	99.38 141

tfpn200view9		96.79 95	95.99 102	99.19 42	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.27 154

thres400		96.78 96	95.99 102	99.16 46	98.94 98	98.82 23	99.78 105	99.71 5	92.86 128	96.02 135	98.87 130	89.33 142	99.50 121	93.84 154	94.57 173	99.16 166

tfpn111		96.69 102	95.87 120	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.20 160

conf200view11		96.73 101	95.92 110	99.16 46	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.20 160

thres100view900		96.74 99	95.92 110	99.18 43	98.90 104	98.77 26	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.84 154	94.57 173	99.27 154

thres600view7		96.69 102	95.87 120	99.14 52	98.90 104	98.78 25	99.74 120	99.71 5	92.59 148	95.84 139	98.86 132	89.25 144	99.50 121	93.44 164	94.50 177	99.16 166

view600		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

view800		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

conf0.05thres1000		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

tfpn		96.46 116	95.59 126	99.06 62	98.87 109	98.60 42	99.69 134	99.71 5	92.20 161	95.23 153	98.80 144	89.17 148	99.43 129	92.29 177	94.37 180	99.16 166

PVSNet_Blended_VisFu		97.27 79	96.81 81	98.66 89	98.81 113	96.67 111	99.92 52	98.64 70	94.51 80	96.38 131	98.49 163	89.05 152	99.88 75	97.10 103	98.34 111	99.43 132

PVSNet_BlendedMVS		96.05 130	95.82 122	96.72 167	99.59 70	96.99 104	99.95 31	99.10 30	94.06 98	98.27 90	95.80 230	89.00 153	99.95 51	99.12 33	87.53 238	93.24 291

PVSNet_Blended		97.94 58	97.64 58	98.83 81	99.59 70	96.99 104	100.00 1	99.10 30	95.38 61	98.27 90	99.08 113	89.00 153	99.95 51	99.12 33	99.25 97	99.57 115

IterMVS-LS		92.69 196	92.11 194	94.43 231	96.80 197	92.74 215	99.45 174	96.89 275	88.98 226	89.65 223	95.38 244	88.77 155	96.34 282	90.98 196	82.04 264	94.22 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		93.73 179	93.40 177	94.74 217	96.80 197	92.69 217	99.06 214	97.67 196	88.96 228	91.39 193	99.02 115	88.75 156	97.30 233	91.07 193	87.85 233	94.22 237

UA-Net		96.54 107	95.96 107	98.27 121	98.23 139	95.71 145	98.00 287	98.45 106	93.72 111	98.41 83	99.27 102	88.71 157	99.66 113	91.19 191	97.69 124	99.44 131

abl_6		97.67 68	97.34 66	98.66 89	99.68 66	96.11 135	99.68 139	98.14 159	93.80 108	99.27 49	99.70 69	88.65 158	99.98 32	97.46 94	99.72 72	99.89 72

MAR-MVS		97.43 72	97.19 70	98.15 126	99.47 79	94.79 167	99.05 217	98.76 59	92.65 144	98.66 74	99.82 40	88.52 159	99.98 32	98.12 74	99.63 77	99.67 97
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
mvs_anonymous		95.65 140	95.03 144	97.53 143	98.19 141	95.74 142	99.33 186	97.49 216	90.87 201	90.47 201	97.10 194	88.23 160	97.16 243	95.92 119	97.66 126	99.68 96

MVS_Test		96.46 116	95.74 123	98.61 93	98.18 142	97.23 96	99.31 188	97.15 243	91.07 197	98.84 65	97.05 198	88.17 161	98.97 143	94.39 142	97.50 128	99.61 107

CANet		98.27 47	97.82 55	99.63 9	99.72 63	99.10 10	99.98 6	98.51 97	97.00 18	98.52 79	99.71 67	87.80 162	99.95 51	99.75 11	99.38 93	99.83 78

jason		97.24 80	96.86 79	98.38 118	95.73 223	97.32 94	99.97 12	97.40 225	95.34 63	98.60 78	99.54 87	87.70 163	98.56 166	97.94 84	99.47 89	99.25 156
jason: jason.
FIs		94.10 171	93.43 173	96.11 181	94.70 242	96.82 109	99.58 156	98.93 41	92.54 153	89.34 231	97.31 188	87.62 164	97.10 252	94.22 149	86.58 242	94.40 223

1314		96.84 93	95.96 107	99.48 25	96.74 201	98.52 48	98.31 272	98.86 53	95.82 48	89.91 211	98.98 120	87.49 165	99.96 43	97.80 87	99.73 71	99.96 58

LS3D		95.84 135	95.11 143	98.02 131	99.85 41	95.10 160	98.74 241	98.50 101	87.22 258	93.66 176	99.86 17	87.45 166	99.95 51	90.94 197	99.81 68	99.02 179

FC-MVSNet-test		93.81 176	93.15 180	95.80 189	94.30 247	96.20 128	99.42 176	98.89 51	92.33 159	89.03 238	97.27 190	87.39 167	96.83 269	93.20 168	86.48 243	94.36 226

RPMNet		89.39 261	87.20 273	95.94 184	96.29 206	92.66 218	92.01 335	97.63 198	70.19 338	96.94 116	85.87 338	87.25 168	96.03 292	62.69 335	95.96 157	99.13 174

UniMVSNet_NR-MVSNet		92.95 191	92.11 194	95.49 191	94.61 243	95.28 156	99.83 95	99.08 32	91.49 182	89.21 235	96.86 205	87.14 169	96.73 272	93.20 168	77.52 303	94.46 217

UniMVSNet (Re)		93.07 189	92.13 193	95.88 186	94.84 239	96.24 127	99.88 66	98.98 36	92.49 156	89.25 233	95.40 241	87.09 170	97.14 247	93.13 172	78.16 297	94.26 234

DP-MVS		94.54 163	93.42 174	97.91 134	99.46 81	94.04 178	98.93 228	97.48 217	81.15 313	90.04 208	99.55 85	87.02 171	99.95 51	88.97 225	98.11 117	99.73 90

PMMVS		96.76 97	96.76 85	96.76 165	98.28 135	92.10 230	99.91 56	97.98 171	94.12 92	99.53 29	99.39 97	86.93 172	98.73 153	96.95 108	97.73 123	99.45 129

canonicalmvs		97.09 86	96.32 94	99.39 34	98.93 100	98.95 14	99.72 131	97.35 229	94.45 81	97.88 100	99.42 93	86.71 173	99.52 118	98.48 66	93.97 197	99.72 92

MVS		96.60 106	95.56 131	99.72 4	96.85 194	99.22 8	98.31 272	98.94 38	91.57 180	90.90 197	99.61 82	86.66 174	99.96 43	97.36 96	99.88 57	99.99 12

Effi-MVS+		96.30 125	95.69 124	98.16 123	97.85 158	96.26 123	97.41 294	97.21 237	90.37 207	98.65 75	98.58 160	86.61 175	98.70 156	97.11 102	97.37 133	99.52 123

testpf		89.10 265	88.73 256	90.24 301	97.59 174	83.48 312	74.22 350	97.39 226	79.66 318	89.64 224	93.92 291	86.38 176	95.76 297	85.42 264	94.31 185	91.49 312

nrg030		93.51 183	92.53 188	96.45 173	94.36 245	97.20 97	99.81 98	97.16 242	91.60 179	89.86 214	97.46 184	86.37 177	97.68 216	95.88 120	80.31 279	94.46 217

VNet		97.21 82	96.57 90	99.13 57	98.97 96	97.82 70	99.03 219	99.21 29	94.31 87	99.18 54	98.88 128	86.26 178	99.89 69	98.93 44	94.32 184	99.69 95

AdaColmap		97.23 81	96.80 82	98.51 102	99.99 1	95.60 148	99.09 206	98.84 55	93.32 120	96.74 121	99.72 65	86.04 179	100.00 1	98.01 79	99.43 92	99.94 65

diffmvs		95.25 146	94.26 156	98.23 122	98.13 145	96.59 115	99.12 203	97.18 239	85.78 275	97.64 103	96.70 210	85.92 180	98.87 145	90.40 206	97.45 129	99.24 159

Effi-MVS+-dtu		94.53 165	95.30 137	92.22 284	97.77 164	82.54 315	99.59 155	97.06 247	94.92 69	95.29 151	95.37 246	85.81 181	97.89 212	94.80 134	97.07 142	96.23 208

mvs-test1		95.53 141	95.97 106	94.20 236	97.77 164	85.44 304	99.95 31	97.06 247	94.92 69	96.58 123	98.72 150	85.81 181	98.98 142	94.80 134	98.11 117	98.18 191

CVMVSNet		94.68 160	94.94 145	93.89 249	96.80 197	86.92 296	99.06 214	98.98 36	94.45 81	94.23 174	99.02 115	85.60 183	95.31 302	90.91 198	95.39 169	99.43 132

xiu_mvs_v1_base_debu		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 188	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 188	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

xiu_mvs_v1_base_debi		97.43 72	97.06 73	98.55 97	97.74 167	98.14 59	99.31 188	97.86 184	96.43 31	99.62 23	99.69 72	85.56 184	99.68 109	99.05 36	98.31 113	97.83 196

1111		79.11 311	78.74 310	80.23 324	78.33 339	67.13 336	97.31 296	93.65 336	71.34 335	68.35 334	87.87 320	85.42 187	88.46 337	52.93 344	73.46 319	85.11 340

.test1245		71.48 315	71.80 315	70.51 334	78.33 339	67.13 336	97.31 296	93.65 336	71.34 335	68.35 334	87.87 320	85.42 187	88.46 337	52.93 344	11.01 354	55.94 353

PCF-MVS	94.20 5	95.18 147	94.10 159	98.43 112	98.55 126	95.99 136	97.91 289	97.31 233	90.35 208	89.48 228	99.22 107	85.19 189	99.89 69	90.40 206	98.47 109	99.41 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-w/o		95.71 138	95.38 135	96.68 168	98.49 130	92.28 226	99.84 91	97.50 215	92.12 166	92.06 190	98.79 149	84.69 190	98.67 157	95.29 127	99.66 76	99.09 177

Fast-Effi-MVS+		95.02 152	94.19 157	97.52 144	97.88 155	94.55 170	99.97 12	97.08 246	88.85 232	94.47 170	97.96 178	84.59 191	98.41 178	89.84 212	97.10 141	99.59 111

PVSNet	91.05 13	97.13 84	96.69 86	98.45 110	99.52 76	95.81 138	99.95 31	99.65 16	94.73 75	99.04 58	99.21 108	84.48 192	99.95 51	94.92 130	98.74 105	99.58 114

WR-MVS_H		91.30 223	90.35 218	94.15 237	94.17 249	92.62 221	99.17 201	98.94 38	88.87 231	86.48 269	94.46 285	84.36 193	96.61 275	88.19 230	78.51 293	93.21 292

CHOSEN 1792x2688		96.81 94	96.53 91	97.64 141	98.91 103	93.07 207	99.65 147	99.80 3	95.64 57	95.39 149	98.86 132	84.35 194	99.90 66	96.98 106	99.16 99	99.95 63

MSDG		94.37 169	93.36 178	97.40 150	98.88 108	93.95 180	99.37 182	97.38 227	85.75 279	90.80 198	99.17 109	84.11 195	99.88 75	86.35 257	98.43 110	98.36 189

pmmvs4		92.10 206	91.07 208	95.18 198	92.82 290	94.96 161	99.48 170	96.83 280	87.45 254	88.66 242	96.56 216	83.78 196	96.83 269	89.29 222	84.77 254	93.75 276

BH-untuned		95.18 147	94.83 146	96.22 179	98.36 133	91.22 252	99.80 101	97.32 232	90.91 200	91.08 195	98.67 152	83.51 197	98.54 168	94.23 148	99.61 81	98.92 181

LCM-MVSNet-Re		92.31 203	92.60 187	91.43 291	97.53 175	79.27 329	99.02 220	91.83 343	92.07 168	80.31 299	94.38 286	83.50 198	95.48 299	97.22 100	97.58 127	99.54 121

cdsmvs_eth3d_5k		23.43 333	31.24 334	0.00 347	0.00 361	0.00 362	0.00 353	98.09 163	0.00 357	0.00 358	99.67 76	83.37 199	0.00 360	0.00 357	0.00 358	0.00 358

DeepC-MVS	94.51 4	96.92 91	96.40 93	98.45 110	99.16 86	95.90 137	99.66 144	98.06 165	96.37 37	94.37 171	99.49 90	83.29 200	99.90 66	97.63 92	99.61 81	99.55 117
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NR-MVSNet		91.56 216	90.22 225	95.60 190	94.05 250	95.76 141	98.25 276	98.70 62	91.16 196	80.78 298	96.64 213	83.23 201	96.57 276	91.41 189	77.73 301	94.46 217

3Dnovator+	91.53 11	96.31 124	95.24 138	99.52 20	96.88 193	98.64 40	99.72 131	98.24 145	95.27 65	88.42 247	98.98 120	82.76 202	99.94 59	97.10 103	99.83 62	99.96 58

QAPM		95.40 144	94.17 158	99.10 58	96.92 190	97.71 72	99.40 177	98.68 64	89.31 220	88.94 239	98.89 126	82.48 203	99.96 43	93.12 173	99.83 62	99.62 105

PatchMatch-RL		96.04 131	95.40 133	97.95 132	99.59 70	95.22 159	99.52 165	99.07 33	93.96 102	96.49 125	98.35 169	82.28 204	99.82 87	90.15 210	99.22 98	98.81 184

divwei89l23v2f112		91.37 220	90.15 228	95.00 204	93.35 271	93.78 186	99.78 105	97.05 251	87.54 249	89.73 219	94.89 271	82.24 205	97.21 239	86.91 253	79.90 287	94.00 253

v1141		91.36 221	90.14 229	95.00 204	93.33 273	93.79 183	99.78 105	97.05 251	87.52 251	89.75 218	94.89 271	82.13 206	97.21 239	86.84 256	80.00 285	94.00 253

v18		86.59 278	84.57 282	92.65 270	93.41 268	93.43 193	98.69 245	95.55 308	82.44 301	74.71 318	87.68 324	82.11 207	94.21 312	80.14 297	66.37 331	90.32 320

v17		86.51 280	84.49 283	92.57 274	93.38 270	93.29 203	98.61 253	95.54 309	82.32 302	74.69 319	87.63 325	82.03 208	94.17 314	80.02 298	66.17 332	90.26 322

v1		91.36 221	90.14 229	95.04 202	93.35 271	93.80 182	99.77 110	97.05 251	87.53 250	89.77 217	94.91 269	81.99 209	97.33 229	86.90 255	79.98 286	94.00 253

v1neww		91.44 217	90.28 221	94.91 211	93.50 261	93.43 193	99.73 126	97.06 247	87.55 247	90.08 203	95.11 257	81.98 210	97.32 230	87.41 241	80.15 281	93.99 256

v7new		91.44 217	90.28 221	94.91 211	93.50 261	93.43 193	99.73 126	97.06 247	87.55 247	90.08 203	95.11 257	81.98 210	97.32 230	87.41 241	80.15 281	93.99 256

3Dnovator	91.47 12	96.28 127	95.34 136	99.08 60	96.82 196	97.47 83	99.45 174	98.81 56	95.52 59	89.39 229	99.00 119	81.97 212	99.95 51	97.27 98	99.83 62	99.84 77

v8		90.54 241	89.17 246	94.66 220	93.43 266	93.40 200	99.20 198	96.94 271	85.76 276	87.56 254	94.51 281	81.96 213	97.19 241	84.94 269	78.25 296	93.38 287

v16		86.52 279	84.49 283	92.60 273	93.45 264	93.31 202	98.60 254	95.52 311	82.30 303	74.59 320	87.70 323	81.95 214	94.18 313	79.93 299	66.38 330	90.30 321

v6		91.44 217	90.27 223	94.93 209	93.44 265	93.44 192	99.73 126	97.05 251	87.57 246	90.05 205	95.10 259	81.87 215	97.39 223	87.45 238	80.17 280	93.98 260

V14		86.22 284	84.15 287	92.41 279	93.30 274	93.16 205	98.47 261	95.47 312	82.10 306	74.27 322	87.41 326	81.73 216	94.02 317	79.26 301	65.37 335	90.04 329

v15		86.26 283	84.19 286	92.47 276	93.29 275	93.28 204	98.53 258	95.47 312	82.24 305	74.34 321	87.34 327	81.71 217	94.07 315	79.39 300	65.42 333	90.06 328

v148		90.70 236	89.63 237	93.92 247	92.97 287	90.97 254	99.75 117	96.89 275	87.51 252	88.27 248	95.01 263	81.67 218	97.04 256	87.40 243	77.17 307	93.75 276

DU-MVS		92.46 201	91.45 204	95.49 191	94.05 250	95.28 156	99.81 98	98.74 60	92.25 160	89.21 235	96.64 213	81.66 219	96.73 272	93.20 168	77.52 303	94.46 217

Baseline_NR-MVSNet		90.33 245	89.51 242	92.81 268	92.84 289	89.95 272	99.77 110	93.94 334	84.69 289	89.04 237	95.66 235	81.66 219	96.52 277	90.99 195	76.98 308	91.97 307

FMVSNet3		92.69 196	91.58 200	95.99 183	98.29 134	97.42 86	99.26 195	97.62 200	89.80 217	89.68 220	95.32 248	81.62 221	96.27 284	87.01 250	85.65 246	94.29 233

v12		86.10 287	84.01 289	92.37 281	93.23 280	92.96 211	98.33 271	95.45 314	81.87 309	74.05 326	87.15 330	81.60 222	93.98 320	79.09 305	65.28 337	90.18 326

v13		86.06 289	83.97 293	92.34 283	93.25 278	92.85 213	98.26 275	95.44 316	81.70 312	74.02 327	87.11 332	81.58 223	94.00 319	78.94 306	65.41 334	90.18 326

V9		86.16 286	84.07 288	92.43 277	93.27 277	93.04 210	98.40 268	95.45 314	81.98 308	74.18 324	87.31 328	81.58 223	94.06 316	79.12 304	65.33 336	90.20 325

Fast-Effi-MVS+-dtu		93.72 180	93.86 164	93.29 259	97.06 185	86.16 297	99.80 101	96.83 280	92.66 142	92.58 188	97.83 180	81.39 225	97.67 217	89.75 213	96.87 146	96.05 210

CANet_DTU		96.76 97	96.15 98	98.60 94	98.78 115	97.53 77	99.84 91	97.63 198	97.25 13	99.20 51	99.64 80	81.36 226	99.98 32	92.77 175	98.89 101	98.28 190

V42		91.28 225	90.12 231	94.74 217	93.42 267	93.46 191	99.68 139	97.02 257	87.36 255	89.85 215	95.05 261	81.31 227	97.34 227	87.34 244	80.07 283	93.40 285

v11		86.09 288	83.98 292	92.42 278	93.29 275	93.41 197	98.52 259	95.30 319	81.73 311	74.27 322	87.20 329	81.24 228	93.85 324	77.68 311	66.61 329	90.00 330

test_djsdf		92.83 193	92.29 192	94.47 228	91.90 303	92.46 223	99.55 161	97.27 234	91.17 194	89.96 209	96.07 228	81.10 229	96.89 265	94.67 138	88.91 217	94.05 247

ppachtmachnet_test		89.58 257	88.35 261	93.25 260	92.40 295	90.44 263	99.33 186	96.73 284	85.49 282	85.90 277	95.77 231	81.09 230	96.00 295	76.00 318	82.49 262	93.30 288

v7		91.20 228	89.99 233	94.82 216	93.57 258	93.41 197	99.57 157	96.98 263	86.83 263	89.88 213	95.22 254	81.01 231	97.14 247	85.53 263	81.31 268	93.90 266

v1144		91.09 229	89.83 234	94.87 213	93.25 278	93.69 188	99.62 153	96.98 263	86.83 263	89.64 224	94.99 266	80.94 232	97.05 255	85.08 268	81.16 270	93.87 270

v10		90.25 248	88.82 253	94.57 225	93.53 260	93.43 193	99.08 208	96.87 278	85.00 285	87.34 258	94.51 281	80.93 233	97.02 261	82.85 283	79.23 289	93.26 290

EU-MVSNet		90.14 252	90.34 219	89.54 307	92.55 294	81.06 324	98.69 245	98.04 167	91.41 186	86.59 266	96.84 208	80.83 234	93.31 329	86.20 258	81.91 265	94.26 234

LP		86.76 277	84.85 281	92.50 275	95.08 235	85.89 300	89.97 340	96.97 266	75.28 329	84.97 282	90.68 313	80.78 235	95.13 304	61.64 337	88.31 229	96.46 205

v2v482		91.30 223	90.07 232	95.01 203	93.13 281	93.79 183	99.77 110	97.02 257	88.05 243	89.25 233	95.37 246	80.73 236	97.15 245	87.28 245	80.04 284	94.09 244

WR-MVS		92.31 203	91.25 205	95.48 193	94.45 244	95.29 155	99.60 154	98.68 64	90.10 211	88.07 250	96.89 203	80.68 237	96.80 271	93.14 171	79.67 288	94.36 226

HQP2-MVS												80.65 238

HQP-MVS		94.61 162	94.50 152	94.92 210	95.78 217	91.85 236	99.87 71	97.89 180	96.82 21	93.37 177	98.65 154	80.65 238	98.39 182	97.92 85	89.60 208	94.53 212

XVG-OURS		94.82 154	94.74 148	95.06 201	98.00 149	89.19 276	99.08 208	97.55 206	94.10 93	94.71 166	99.62 81	80.51 240	99.74 101	96.04 117	93.06 206	96.25 206

v144192		90.79 235	89.52 241	94.59 223	93.11 284	92.77 214	99.56 159	96.99 261	86.38 268	89.82 216	94.95 268	80.50 241	97.10 252	83.98 275	80.41 277	93.90 266

HQP_MVS		94.49 166	94.36 154	94.87 213	95.71 226	91.74 241	99.84 91	97.87 182	96.38 34	93.01 181	98.59 158	80.47 242	98.37 187	97.79 88	89.55 211	94.52 214

plane_prior6					95.76 221	91.72 244						80.47 242

test2356		86.43 281	87.59 270	82.95 321	85.90 329	69.43 334	99.79 104	96.63 288	85.76 276	83.44 289	94.99 266	80.45 244	86.52 343	68.12 329	93.21 203	92.90 296

v7n		89.65 256	88.29 262	93.72 251	92.22 297	90.56 260	99.07 212	97.10 245	85.42 284	86.73 264	94.72 275	80.06 245	97.13 249	81.14 292	78.12 298	93.49 283

TranMVSNet+NR-MVSNet		91.68 215	90.61 212	94.87 213	93.69 257	93.98 179	99.69 134	98.65 67	91.03 198	88.44 244	96.83 209	80.05 246	96.18 287	90.26 209	76.89 310	94.45 222

FMVSNet5		88.32 271	87.47 271	90.88 294	96.90 192	88.39 287	97.28 298	95.68 304	82.60 300	84.67 283	92.40 307	79.83 247	91.16 332	76.39 317	81.51 267	93.09 293

RPSCF		91.80 211	92.79 184	88.83 310	98.15 144	69.87 333	98.11 283	96.60 289	83.93 294	94.33 172	99.27 102	79.60 248	99.46 128	91.99 183	93.16 205	97.18 201

Vis-MVSNet		95.72 136	95.15 142	97.45 147	97.62 173	94.28 174	99.28 193	98.24 145	94.27 89	96.84 118	98.94 125	79.39 249	98.76 152	93.25 167	98.49 108	99.30 151
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V4		89.55 258	88.41 259	92.98 264	92.21 298	90.03 269	98.87 234	96.91 273	84.51 290	86.84 262	94.21 289	79.37 250	97.15 245	84.45 272	78.28 294	91.76 309

v52		89.55 258	88.41 259	92.98 264	92.32 296	90.01 270	98.88 231	96.89 275	84.51 290	86.89 261	94.22 288	79.23 251	97.16 243	84.46 271	78.27 295	91.76 309

v1192		90.62 240	89.25 245	94.72 219	93.13 281	93.07 207	99.50 167	97.02 257	86.33 269	89.56 227	95.01 263	79.22 252	97.09 254	82.34 286	81.16 270	94.01 250

v748		88.94 267	87.72 268	92.61 272	91.91 302	87.50 293	99.07 212	96.97 266	84.76 287	85.79 278	93.63 298	79.19 253	97.04 256	83.16 281	75.03 317	93.28 289

CP-MVSNet		91.23 226	90.22 225	94.26 234	93.96 252	92.39 225	99.09 206	98.57 82	88.95 229	86.42 270	96.57 215	79.19 253	96.37 280	90.29 208	78.95 290	94.02 248

MDA-MVSNet_test_wron		85.51 293	83.32 298	92.10 285	90.96 314	88.58 284	99.20 198	96.52 291	79.70 317	57.12 343	92.69 305	79.11 255	93.86 323	77.10 314	77.46 305	93.86 271

YYNet1		85.50 294	83.33 297	92.00 286	90.89 315	88.38 288	99.22 197	96.55 290	79.60 319	57.26 342	92.72 304	79.09 256	93.78 325	77.25 313	77.37 306	93.84 272

MVS_0304		97.52 71	96.79 83	99.69 6	99.59 70	99.30 4	99.97 12	98.01 168	96.99 19	98.84 65	99.79 45	78.90 257	99.96 43	99.74 13	99.32 95	99.81 80

XVG-OURS-SEG-HR		94.79 155	94.70 149	95.08 200	98.05 148	89.19 276	99.08 208	97.54 208	93.66 113	94.87 165	99.58 83	78.78 258	99.79 90	97.31 97	93.40 201	96.25 206

GA-MVS		93.83 174	92.84 182	96.80 163	95.73 223	93.57 189	99.88 66	97.24 236	92.57 152	92.92 183	96.66 211	78.73 259	97.67 217	87.75 236	94.06 196	99.17 165

OpenMVS	90.15 15	94.77 157	93.59 168	98.33 119	96.07 210	97.48 82	99.56 159	98.57 82	90.46 206	86.51 267	98.95 124	78.57 260	99.94 59	93.86 153	99.74 70	97.57 200

v1921920		90.46 242	89.12 247	94.50 227	92.96 288	92.46 223	99.49 168	96.98 263	86.10 271	89.61 226	95.30 249	78.55 261	97.03 259	82.17 287	80.89 276	94.01 250

MVP-Stereo		90.93 231	90.45 217	92.37 281	91.25 313	88.76 279	98.05 286	96.17 296	87.27 257	84.04 285	95.30 249	78.46 262	97.27 238	83.78 277	99.70 74	91.09 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
anonymousdsp		91.79 213	90.92 209	94.41 232	90.76 316	92.93 212	98.93 228	97.17 241	89.08 222	87.46 255	95.30 249	78.43 263	96.92 264	92.38 176	88.73 222	93.39 286

pcd1.5k->3k		37.58 332	39.62 332	31.46 344	92.73 292	0.00 362	0.00 353	97.52 212	0.00 357	0.00 358	0.00 359	78.40 264	0.00 360	0.00 357	87.90 232	94.37 225

v1240		90.20 249	88.79 254	94.44 229	93.05 286	92.27 227	99.38 181	96.92 272	85.89 273	89.36 230	94.87 274	77.89 265	97.03 259	80.66 294	81.08 272	94.01 250

CLD-MVS		94.06 172	93.90 162	94.55 226	96.02 212	90.69 258	99.98 6	97.72 193	96.62 30	91.05 196	98.85 137	77.21 266	98.47 171	98.11 75	89.51 213	94.48 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
N_pmnet		80.06 309	80.78 306	77.89 326	91.94 301	45.28 355	98.80 239	56.82 359	78.10 322	80.08 301	93.33 299	77.03 267	95.76 297	68.14 328	82.81 261	92.64 300

COLMAP_ROB	90.47 14	92.18 205	91.49 203	94.25 235	99.00 93	88.04 290	98.42 267	96.70 285	82.30 303	88.43 245	99.01 117	76.97 268	99.85 79	86.11 260	96.50 150	94.86 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
cascas		94.64 161	93.61 165	97.74 138	97.82 161	96.26 123	99.96 19	97.78 190	85.76 276	94.00 175	97.54 183	76.95 269	99.21 136	97.23 99	95.43 168	97.76 199

BH-RMVSNet		95.18 147	94.31 155	97.80 135	98.17 143	95.23 158	99.76 116	97.53 210	92.52 154	94.27 173	99.25 105	76.84 270	98.80 148	90.89 199	99.54 85	99.35 146

PEN-MVS		90.19 250	89.06 249	93.57 255	93.06 285	90.90 256	99.06 214	98.47 103	88.11 242	85.91 276	96.30 221	76.67 271	95.94 296	87.07 247	76.91 309	93.89 268

IterMVS		90.91 232	90.17 227	93.12 262	96.78 200	90.42 264	98.89 230	97.05 251	89.03 224	86.49 268	95.42 240	76.59 272	95.02 305	87.22 246	84.09 255	93.93 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				92.93 266	96.72 202	89.96 271		96.99 261	88.95 229	86.63 265	95.67 234	76.50 273	95.00 306	87.04 248	84.04 258	93.84 272

Patchmatch-test1		94.39 168	93.46 172	97.17 155	97.10 183	94.44 171	98.86 236	98.32 137	93.30 121	96.17 134	95.38 244	76.48 274	97.34 227	88.12 233	97.43 130	99.74 88

ab-mvs		94.69 159	93.42 174	98.51 102	98.07 147	96.26 123	96.49 308	98.68 64	90.31 209	94.54 167	97.00 200	76.30 275	99.71 105	95.98 118	93.38 202	99.56 116

DTE-MVSNet		89.40 260	88.24 263	92.88 267	92.66 293	89.95 272	99.10 205	98.22 147	87.29 256	85.12 281	96.22 223	76.27 276	95.30 303	83.56 279	75.74 313	93.41 284

ACMM	91.95 10	92.88 192	92.52 189	93.98 246	95.75 222	89.08 278	99.77 110	97.52 212	93.00 126	89.95 210	97.99 177	76.17 277	98.46 174	93.63 162	88.87 219	94.39 224
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DSMNet-mixed		88.28 272	88.24 263	88.42 313	89.64 322	75.38 331	98.06 285	89.86 348	85.59 281	88.20 249	92.14 308	76.15 278	91.95 331	78.46 307	96.05 154	97.92 195

VPA-MVSNet		92.70 195	91.55 201	96.16 180	95.09 234	96.20 128	98.88 231	99.00 35	91.02 199	91.82 191	95.29 252	76.05 279	97.96 209	95.62 125	81.19 269	94.30 232

TR-MVS		94.54 163	93.56 170	97.49 145	97.96 151	94.34 173	98.71 243	97.51 214	90.30 210	94.51 169	98.69 151	75.56 280	98.77 151	92.82 174	95.99 156	99.35 146

PS-CasMVS		90.63 239	89.51 242	93.99 245	93.83 254	91.70 245	98.98 222	98.52 91	88.48 237	86.15 274	96.53 217	75.46 281	96.31 283	88.83 226	78.86 292	93.95 262

TransMVSNet (Re)		87.25 275	85.28 279	93.16 261	93.56 259	91.03 253	98.54 257	94.05 333	83.69 295	81.09 297	96.16 224	75.32 282	96.40 279	76.69 316	68.41 325	92.06 305

LPG-MVS_test		92.96 190	92.71 185	93.71 252	95.43 231	88.67 281	99.75 117	97.62 200	92.81 132	90.05 205	98.49 163	75.24 283	98.40 180	95.84 122	89.12 215	94.07 245

LGP-MVS_train				93.71 252	95.43 231	88.67 281		97.62 200	92.81 132	90.05 205	98.49 163	75.24 283	98.40 180	95.84 122	89.12 215	94.07 245

OPM-MVS		93.21 187	92.80 183	94.44 229	93.12 283	90.85 257	99.77 110	97.61 203	96.19 41	91.56 192	98.65 154	75.16 285	98.47 171	93.78 159	89.39 214	93.99 256

tfpnnormal		89.29 263	87.61 269	94.34 233	94.35 246	94.13 177	98.95 226	98.94 38	83.94 293	84.47 284	95.51 238	74.84 286	97.39 223	77.05 315	80.41 277	91.48 313

AllTest		92.48 199	91.64 199	95.00 204	99.01 91	88.43 285	98.94 227	96.82 282	86.50 266	88.71 240	98.47 167	74.73 287	99.88 75	85.39 265	96.18 152	96.71 203

TestCases				95.00 204	99.01 91	88.43 285		96.82 282	86.50 266	88.71 240	98.47 167	74.73 287	99.88 75	85.39 265	96.18 152	96.71 203

Anonymous20231206		86.32 282	85.42 278	89.02 309	89.11 324	80.53 327	99.05 217	95.28 320	85.43 283	82.82 291	93.92 291	74.40 289	93.44 328	66.99 330	81.83 266	93.08 294

XXY-MVS		91.82 208	90.46 215	95.88 186	93.91 253	95.40 153	98.87 234	97.69 195	88.63 236	87.87 252	97.08 195	74.38 290	97.89 212	91.66 188	84.07 256	94.35 229

ACMP	92.05 9	92.74 194	92.42 191	93.73 250	95.91 216	88.72 280	99.81 98	97.53 210	94.13 91	87.00 260	98.23 171	74.07 291	98.47 171	96.22 115	88.86 220	93.99 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	88.28 18	90.29 247	89.05 250	94.02 242	95.08 235	90.15 268	97.19 299	97.43 220	84.91 286	83.99 286	97.06 197	74.00 292	98.28 194	84.08 273	87.71 235	93.62 281
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
pm-mvs1		89.36 262	87.81 267	94.01 243	93.40 269	91.93 234	98.62 252	96.48 293	86.25 270	83.86 287	96.14 225	73.68 293	97.04 256	86.16 259	75.73 314	93.04 295

pmmvs5		90.17 251	89.09 248	93.40 257	92.10 300	89.77 275	99.74 120	95.58 307	85.88 274	87.24 259	95.74 232	73.41 294	96.48 278	88.54 227	83.56 259	93.95 262

OurMVSNet-221017-0		89.81 254	89.48 244	90.83 296	91.64 308	81.21 322	98.17 281	95.38 318	91.48 183	85.65 279	97.31 188	72.66 295	97.29 236	88.15 231	84.83 253	93.97 261

jajsoiax		91.92 207	91.18 206	94.15 237	91.35 311	90.95 255	99.00 221	97.42 222	92.61 146	87.38 256	97.08 195	72.46 296	97.36 225	94.53 141	88.77 221	94.13 242

UGNet		95.33 145	94.57 151	97.62 142	98.55 126	94.85 163	98.67 248	99.32 28	95.75 55	96.80 120	96.27 222	72.18 297	99.96 43	94.58 140	99.05 100	98.04 194
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
mvs_tets		91.81 209	91.08 207	94.00 244	91.63 309	90.58 259	98.67 248	97.43 220	92.43 157	87.37 257	97.05 198	71.76 298	97.32 230	94.75 137	88.68 223	94.11 243

SixPastTwentyTwo		88.73 269	88.01 266	90.88 294	91.85 305	82.24 317	98.22 279	95.18 324	88.97 227	82.26 293	96.89 203	71.75 299	96.67 274	84.00 274	82.98 260	93.72 280

GBi-Net		90.88 233	89.82 235	94.08 239	97.53 175	91.97 231	98.43 264	96.95 268	87.05 259	89.68 220	94.72 275	71.34 300	96.11 288	87.01 250	85.65 246	94.17 239

test1		90.88 233	89.82 235	94.08 239	97.53 175	91.97 231	98.43 264	96.95 268	87.05 259	89.68 220	94.72 275	71.34 300	96.11 288	87.01 250	85.65 246	94.17 239

FMVSNet2		91.02 230	89.56 239	95.41 194	97.53 175	95.74 142	98.98 222	97.41 224	87.05 259	88.43 245	95.00 265	71.34 300	96.24 286	85.12 267	85.21 251	94.25 236

PVSNet_0	88.03 19	91.80 211	90.27 223	96.38 176	98.27 136	90.46 262	99.94 45	99.61 17	93.99 100	86.26 273	97.39 187	71.13 303	99.89 69	98.77 53	67.05 328	98.79 185

test_normal		92.44 202	90.54 214	98.12 127	91.85 305	96.18 130	99.68 139	97.73 191	92.66 142	75.76 316	93.74 296	70.49 304	99.04 141	95.71 124	97.27 135	99.13 174

DI_MVS_plusplus_test		92.48 199	90.60 213	98.11 128	91.88 304	96.13 131	99.64 151	97.73 191	92.69 140	76.02 312	93.79 294	70.49 304	99.07 139	95.88 120	97.26 136	99.14 172

ITE_SJBPF				92.38 280	95.69 228	85.14 305		95.71 303	92.81 132	89.33 232	98.11 173	70.23 306	98.42 177	85.91 261	88.16 231	93.59 282

ACMH	89.72 17	90.64 238	89.63 237	93.66 254	95.64 229	88.64 283	98.55 255	97.45 218	89.03 224	81.62 295	97.61 182	69.75 307	98.41 178	89.37 221	87.62 237	93.92 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS-HIRNet		86.22 284	83.19 299	95.31 195	96.71 203	90.29 265	92.12 334	97.33 231	62.85 341	86.82 263	70.37 345	69.37 308	97.49 220	75.12 319	97.99 122	98.15 192

test20.03		84.72 299	83.99 290	86.91 315	88.19 326	80.62 326	98.88 231	95.94 300	88.36 239	78.87 303	94.62 280	68.75 309	89.11 336	66.52 331	75.82 312	91.00 315

VPNet		91.81 209	90.46 215	95.85 188	94.74 241	95.54 149	98.98 222	98.59 79	92.14 165	90.77 199	97.44 185	68.73 310	97.54 219	94.89 133	77.89 299	94.46 217

K. test v3		88.05 273	87.24 272	90.47 299	91.82 307	82.23 318	98.96 225	97.42 222	89.05 223	76.93 309	95.60 236	68.49 311	95.42 300	85.87 262	81.01 274	93.75 276

ACMH+	89.98 16	90.35 244	89.54 240	92.78 269	95.99 213	86.12 298	98.81 238	97.18 239	89.38 219	83.14 290	97.76 181	68.42 312	98.43 176	89.11 224	86.05 245	93.78 275

MDA-MVSNet-bldmvs		84.09 301	81.52 305	91.81 289	91.32 312	88.00 291	98.67 248	95.92 301	80.22 316	55.60 344	93.32 300	68.29 313	93.60 327	73.76 320	76.61 311	93.82 274

MS-PatchMatch		90.65 237	90.30 220	91.71 290	94.22 248	85.50 303	98.24 277	97.70 194	88.67 234	86.42 270	96.37 220	67.82 314	98.03 205	83.62 278	99.62 78	91.60 311

LFMVS		94.75 158	93.56 170	98.30 120	99.03 90	95.70 146	98.74 241	97.98 171	87.81 245	98.47 81	99.39 97	67.43 315	99.53 117	98.01 79	95.20 170	99.67 97

MIMVSNet		90.30 246	88.67 257	95.17 199	96.45 205	91.64 247	92.39 333	97.15 243	85.99 272	90.50 200	93.19 303	66.95 316	94.86 309	82.01 288	93.43 200	99.01 180

XVG-ACMP-BASELINE		91.22 227	90.75 210	92.63 271	93.73 256	85.61 301	98.52 259	97.44 219	92.77 136	89.90 212	96.85 206	66.64 317	98.39 182	92.29 177	88.61 224	93.89 268

lessismore_v0				90.53 297	90.58 317	80.90 325		95.80 302		77.01 308	95.84 229	66.15 318	96.95 262	83.03 282	75.05 316	93.74 279

USDC		90.00 253	88.96 251	93.10 263	94.81 240	88.16 289	98.71 243	95.54 309	93.66 113	83.75 288	97.20 191	65.58 319	98.31 191	83.96 276	87.49 239	92.85 299

pmmvs-eth3d		84.03 302	81.97 302	90.20 302	84.15 333	87.09 295	98.10 284	94.73 328	83.05 296	74.10 325	87.77 322	65.56 320	94.01 318	81.08 293	69.24 324	89.49 335

LF4IMVS		89.25 264	88.85 252	90.45 300	92.81 291	81.19 323	98.12 282	94.79 326	91.44 185	86.29 272	97.11 193	65.30 321	98.11 201	88.53 228	85.25 250	92.07 304

new_pmnet		84.49 300	82.92 300	89.21 308	90.03 320	82.60 314	96.89 305	95.62 306	80.59 315	75.77 315	89.17 315	65.04 322	94.79 310	72.12 321	81.02 273	90.23 323

CMPMVS	61.59 21	84.75 298	85.14 280	83.57 318	90.32 319	62.54 342	96.98 303	97.59 205	74.33 331	69.95 331	96.66 211	64.17 323	98.32 190	87.88 235	88.41 228	89.84 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_0402		85.58 291	83.94 294	90.50 298	93.81 255	85.04 306	98.55 255	95.20 323	76.01 325	79.72 302	95.13 255	64.15 324	96.26 285	66.04 333	86.88 241	90.21 324

TDRefinement		84.76 297	82.56 301	91.38 292	74.58 343	84.80 308	97.36 295	94.56 329	84.73 288	80.21 300	96.12 227	63.56 325	98.39 182	87.92 234	63.97 338	90.95 317

UnsupCasMVSNet_eth		85.52 292	83.99 290	90.10 303	89.36 323	83.51 311	96.65 306	97.99 170	89.14 221	75.89 314	93.83 293	63.25 326	93.92 321	81.92 289	67.90 327	92.88 298

new-patchmatchnet		81.19 306	79.34 308	86.76 316	82.86 335	80.36 328	97.92 288	95.27 321	82.09 307	72.02 328	86.87 333	62.81 327	90.74 334	71.10 322	63.08 339	89.19 337

TinyColmap		87.87 274	86.51 275	91.94 287	95.05 237	85.57 302	97.65 291	94.08 332	84.40 292	81.82 294	96.85 206	62.14 328	98.33 189	80.25 295	86.37 244	91.91 308

VDDNet		93.12 188	91.91 197	96.76 165	96.67 204	92.65 220	98.69 245	98.21 148	82.81 298	97.75 102	99.28 101	61.57 329	99.48 127	98.09 77	94.09 189	98.15 192

pmmvs6		85.69 290	83.84 295	91.26 293	90.00 321	84.41 309	97.82 290	96.15 297	75.86 326	81.29 296	95.39 243	61.21 330	96.87 267	83.52 280	73.29 320	92.50 301

VDD-MVS		93.77 178	92.94 181	96.27 178	98.55 126	90.22 266	98.77 240	97.79 189	90.85 202	96.82 119	99.42 93	61.18 331	99.77 92	98.95 42	94.13 188	98.82 183

test1235678		78.45 312	77.88 311	80.16 325	77.83 341	62.18 343	98.36 269	93.45 339	77.46 323	69.08 333	88.23 317	60.33 332	85.41 344	58.46 340	77.68 302	92.90 296

testgi		89.01 266	88.04 265	91.90 288	93.49 263	84.89 307	99.73 126	95.66 305	93.89 107	85.14 280	98.17 172	59.68 333	94.66 311	77.73 310	88.88 218	96.16 209

FMVSNet1		88.50 270	86.64 274	94.08 239	95.62 230	91.97 231	98.43 264	96.95 268	83.00 297	86.08 275	94.72 275	59.09 334	96.11 288	81.82 290	84.07 256	94.17 239

DeepMVS_CX				82.92 322	95.98 215	58.66 346		96.01 299	92.72 137	78.34 306	95.51 238	58.29 335	98.08 202	82.57 284	85.29 249	92.03 306

pmmvs3		80.27 308	77.77 312	87.76 314	80.32 338	82.43 316	98.23 278	91.97 342	72.74 334	78.75 304	87.97 319	57.30 336	90.99 333	70.31 323	62.37 340	89.87 331

testus		83.91 303	84.49 283	82.17 323	85.68 330	66.11 339	99.68 139	93.53 338	86.55 265	82.60 292	94.91 269	56.70 337	88.19 339	68.46 326	92.31 207	92.21 303

OpenMVS_ROB	79.82 20	83.77 304	81.68 304	90.03 304	88.30 325	82.82 313	98.46 262	95.22 322	73.92 333	76.00 313	91.29 311	55.00 338	96.94 263	68.40 327	88.51 227	90.34 319

tmp_tt		65.23 321	62.94 322	72.13 333	44.90 357	50.03 353	81.05 346	89.42 351	38.45 349	48.51 348	99.90 11	54.09 339	78.70 350	91.84 187	18.26 353	87.64 339

test12356		75.26 313	75.12 314	75.67 330	74.02 344	60.60 345	96.43 309	92.15 341	74.17 332	66.35 336	88.11 318	52.29 340	84.36 346	57.41 341	75.12 315	82.05 341

MIMVSNet1		82.58 305	80.51 307	88.78 311	86.68 328	84.20 310	96.65 306	95.41 317	78.75 320	78.59 305	92.44 306	51.88 341	89.76 335	65.26 334	78.95 290	92.38 302

EG-PatchMatch MVS		85.35 295	83.81 296	89.99 305	90.39 318	81.89 320	98.21 280	96.09 298	81.78 310	74.73 317	93.72 297	51.56 342	97.12 251	79.16 303	88.61 224	90.96 316

UnsupCasMVSNet_bld		79.97 310	77.03 313	88.78 311	85.62 331	81.98 319	93.66 328	97.35 229	75.51 328	70.79 329	83.05 339	48.70 343	94.91 308	78.31 308	60.29 343	89.46 336

testing_2		85.10 296	81.72 303	95.22 197	82.25 336	94.16 175	97.54 292	97.01 260	88.15 241	62.23 338	86.43 335	44.43 344	97.18 242	92.28 182	85.20 252	94.31 231

Test4		88.80 268	85.91 277	97.48 146	87.33 327	95.72 144	99.29 192	97.04 256	92.82 131	70.35 330	91.46 310	44.37 345	97.43 222	93.37 166	97.17 140	99.29 153

PM-MVS		80.47 307	78.88 309	85.26 317	83.79 334	72.22 332	95.89 319	91.08 344	85.71 280	76.56 311	88.30 316	36.64 346	93.90 322	82.39 285	69.57 323	89.66 333

Anonymous20231211		74.17 314	71.17 316	83.17 320	80.58 337	67.02 338	96.27 313	94.45 331	57.31 343	69.60 332	86.25 336	33.67 347	92.96 330	61.86 336	60.50 342	89.54 334

testmv		67.54 318	65.93 318	72.37 332	64.46 352	54.05 349	95.09 322	90.07 346	68.90 340	55.16 345	77.63 343	30.39 348	82.61 348	49.42 347	62.26 341	80.45 343

ambc				83.23 319	77.17 342	62.61 341	87.38 344	94.55 330		76.72 310	86.65 334	30.16 349	96.36 281	84.85 270	69.86 321	90.73 318

Gipuma		66.95 320	65.00 319	72.79 331	91.52 310	67.96 335	66.16 351	95.15 325	47.89 345	58.54 341	67.99 348	29.74 350	87.54 341	50.20 346	77.83 300	62.87 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		51.44 328	51.22 328	52.11 342	70.71 346	44.97 356	94.04 325	75.66 358	35.34 353	42.40 351	61.56 354	28.93 351	65.87 356	27.64 355	24.73 350	45.49 355

E-PMN		52.30 326	52.18 326	52.67 341	71.51 345	45.40 354	93.62 329	76.60 357	36.01 351	43.50 350	64.13 351	27.11 352	67.31 355	31.06 354	26.06 349	45.30 356

FPMVS		68.72 316	68.72 317	68.71 335	65.95 349	44.27 357	95.97 318	94.74 327	51.13 344	53.26 346	90.50 314	25.11 353	83.00 347	60.80 338	80.97 275	78.87 344

PMMVS2		67.15 319	64.15 321	76.14 328	70.56 347	62.07 344	93.89 326	87.52 352	58.09 342	60.02 340	78.32 341	22.38 354	84.54 345	59.56 339	47.03 345	81.80 342

no-one		63.48 322	59.26 323	76.14 328	66.71 348	65.06 340	92.75 331	89.92 347	68.96 339	46.96 349	66.55 349	21.74 355	87.68 340	57.07 342	22.69 352	75.68 346

LCM-MVSNet		67.77 317	64.73 320	76.87 327	62.95 353	56.25 348	89.37 342	93.74 335	44.53 347	61.99 339	80.74 340	20.42 356	86.53 342	69.37 325	59.50 344	87.84 338

test123		37.68 331	39.14 333	33.31 343	19.94 359	24.83 360	98.36 269	9.75 361	15.53 355	51.31 347	87.14 331	19.62 357	17.74 358	47.10 349	3.47 357	57.36 352

ANet_high		56.10 324	52.24 325	67.66 336	49.27 356	56.82 347	83.94 345	82.02 353	70.47 337	33.28 354	64.54 350	17.23 358	69.16 354	45.59 351	23.85 351	77.02 345

testmvs		40.60 330	44.45 331	29.05 345	19.49 360	14.11 361	99.68 139	18.47 360	20.74 354	64.59 337	98.48 166	10.95 359	17.09 359	56.66 343	11.01 354	55.94 353

PNet_i23d		56.44 323	53.54 324	65.14 338	65.34 350	50.33 352	89.06 343	79.57 354	45.77 346	35.75 353	68.95 347	10.75 360	74.40 351	48.48 348	38.20 346	70.70 347

PMVS	49.05 23	53.75 325	51.34 327	60.97 340	40.80 358	34.68 358	74.82 349	89.62 350	37.55 350	28.67 355	72.12 344	7.09 361	81.63 349	43.17 352	68.21 326	66.59 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		20.37 334	20.84 335	18.99 346	65.34 350	27.73 359	50.43 352	7.67 362	9.50 356	8.01 357	6.34 358	6.13 362	26.24 357	23.40 356	10.69 356	2.99 357

wuykxyi23d		50.36 329	45.43 330	65.16 337	51.13 355	51.75 350	77.46 348	78.42 355	41.45 348	26.98 356	54.30 356	6.13 362	74.03 352	46.82 350	26.19 348	69.71 348

MVE	53.74 22	51.54 327	47.86 329	62.60 339	59.56 354	50.93 351	79.41 347	77.69 356	35.69 352	36.27 352	61.76 353	5.79 364	69.63 353	37.97 353	36.61 347	67.24 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		8.28 335	11.04 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	99.40 95	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																99.59 111

test_part3							99.88 66		96.14 43		99.91 7		100.00 1	99.99 1

test_part2					99.89 36	99.25 6				99.49 33

MTGPA								98.28 141

MTMP								96.49 292

gm-plane-assit					96.97 189	93.76 187			91.47 184		98.96 122		98.79 149	94.92 130

test9_res														99.71 18	99.99 13	100.00 1

agg_prior2														99.48 24	100.00 1	100.00 1

agg_prior					99.93 24	98.77 26		98.43 112		99.63 21			99.85 79

test_prior4						98.05 63	99.94 45

test_prior				99.43 27	99.94 14	98.49 50		98.65 67					99.80 88			99.99 12

旧先验2							99.46 173		94.21 90	99.85 5			99.95 51	96.96 107

新几何2							99.40 177

无先验							99.49 168	98.71 61	93.46 117				100.00 1	94.36 143		99.99 12

原ACMM2							99.90 59

testdata2													99.99 28	90.54 203

testdata1							99.28 193		96.35 38

plane_prior7					95.71 226	91.59 249

plane_prior5								97.87 182					98.37 187	97.79 88	89.55 211	94.52 214

plane_prior4											98.59 158

plane_prior3						91.64 247			96.63 29	93.01 181

plane_prior2							99.84 91		96.38 34

plane_prior1					95.73 223

plane_prior						91.74 241	99.86 86		96.76 25						89.59 210

n2								0.00 363

nn								0.00 363

door-mid								89.69 349

test11								98.44 107

door								90.31 345

HQP5-MVS						91.85 236

HQP-NCC					95.78 217		99.87 71		96.82 21	93.37 177

ACMP_Plane					95.78 217		99.87 71		96.82 21	93.37 177

BP-MVS														97.92 85

HQP4-MVS										93.37 177			98.39 182			94.53 212

HQP3-MVS								97.89 180							89.60 208

NP-MVS					95.77 220	91.79 238					98.65 154

ACMMP++_ref															87.04 240

ACMMP++															88.23 230

High-resolution multi-view benchmark