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.

HSP-MVS		95.55 4	96.51 2	92.66 85	98.31 39	80.10 147	97.42 68	96.46 88	92.20 13	97.11 3	98.29 14	93.46 1	99.10 80	96.01 13	99.30 2	98.77 22

GG-mvs-BLEND				93.49 55	94.94 120	86.26 25	81.62 324	97.00 38		88.32 93	94.30 140	91.23 2	96.21 195	88.49 85	97.43 57	98.00 66

gg-mvs-nofinetune		85.48 172	82.90 188	93.24 63	94.51 137	85.82 30	79.22 328	96.97 41	61.19 325	87.33 101	53.01 342	90.58 3	96.07 199	86.07 103	97.23 62	97.81 79

CHOSEN 280x420		91.71 60	91.85 52	91.29 128	94.94 120	82.69 89	87.89 299	96.17 114	85.94 77	87.27 102	94.31 139	90.27 4	95.65 232	94.04 32	95.86 82	95.53 157

MVSTER		89.25 97	88.92 92	90.24 153	95.98 91	84.66 52	96.79 113	95.36 155	87.19 65	80.33 174	90.61 191	90.02 5	95.97 206	85.38 108	78.64 222	90.09 221

test_part1								96.77 53				89.33 6			98.95 12	99.18 10

ESAPD		95.32 5	95.52 6	94.70 19	98.90 7	85.14 44	98.15 25	96.77 53	84.95 102	96.07 8	98.83 2	89.33 6	99.80 14	97.78 2	98.95 12	99.18 10

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 32	95.17 2	92.11 48	98.46 11	87.33 8	99.97 1	97.21 6	99.31 1	99.63 2

TSAR-MVS + GP.		94.35 15	94.50 13	93.89 35	97.38 69	83.04 82	98.10 28	95.29 160	91.57 15	93.81 33	97.45 63	86.64 9	99.43 52	96.28 11	94.01 97	99.20 8

DWT-MVSNet_test		90.52 80	89.80 81	92.70 84	95.73 101	82.20 97	93.69 227	96.55 80	88.34 45	87.04 105	95.34 111	86.53 10	97.55 140	76.32 186	88.66 140	98.34 39

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 45	97.66 56	84.10 62	95.85 173	96.42 92	91.26 17	97.49 2	96.80 89	86.50 11	98.49 104	95.54 20	99.03 7	98.33 40

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 25	94.66 3	96.79 4	98.78 4	86.42 12	99.95 2	97.59 4	99.18 3	99.00 14

DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 170	97.09 75	70.21 292	98.99 8	96.69 64	95.57 1	95.08 18	99.23 1	86.40 13	99.87 8	97.84 1	98.66 23	99.65 1

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 24	86.04 27	97.81 39	96.93 45	92.45 11	95.69 11	98.50 9	85.38 14	99.85 10	94.75 24	99.18 3	98.65 28

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 43	99.16 3	96.96 42	94.11 6	95.59 12	98.64 7	85.07 15	99.91 3	95.61 19	99.10 5	99.00 14

EPP-MVSNet		89.76 89	89.72 82	89.87 171	93.78 150	76.02 246	97.22 74	96.51 83	79.35 214	85.11 115	95.01 131	84.82 16	97.10 163	87.46 96	88.21 144	96.50 135

tfpn1000		86.43 153	85.10 151	90.41 149	95.56 103	80.51 137	95.90 169	97.09 33	75.91 252	80.02 178	94.82 132	84.78 17	95.47 243	57.36 295	84.46 179	95.26 163

tfpn_ndepth		87.25 139	86.00 136	91.01 138	95.86 97	81.46 116	96.53 129	97.09 33	77.35 238	81.36 162	95.07 129	84.74 18	95.86 215	60.88 286	85.14 176	95.72 153

agg_prior1		94.10 21	94.31 19	93.48 56	98.59 26	83.13 79	97.77 42	96.56 78	84.38 118	94.19 29	98.13 24	84.66 19	99.16 75	95.74 18	98.74 20	98.15 52

PatchFormer-LS_test		90.14 85	89.30 88	92.65 87	95.43 106	82.46 92	93.46 232	96.35 101	88.56 41	84.82 118	95.22 118	84.63 20	97.55 140	78.40 162	86.81 152	97.94 71

TEST9					98.64 21	83.71 69	97.82 37	96.65 67	84.29 122	95.16 15	98.09 29	84.39 21	99.36 56

train_agg		94.28 16	94.45 15	93.74 40	98.64 21	83.71 69	97.82 37	96.65 67	84.50 114	95.16 15	98.09 29	84.33 22	99.36 56	95.91 15	98.96 10	98.16 50

test_8					98.63 23	83.64 72	97.81 39	96.63 73	84.50 114	95.10 17	98.11 28	84.33 22	99.23 62

SD-MVS		94.84 9	95.02 9	94.29 26	97.87 54	84.61 53	97.76 45	96.19 113	89.59 32	96.66 5	98.17 22	84.33 22	99.60 37	96.09 12	98.50 27	98.66 27

APDe-MVS		94.56 13	94.75 10	93.96 34	98.84 11	83.40 76	98.04 30	96.41 93	85.79 80	95.00 20	98.28 15	84.32 25	99.18 73	97.35 5	98.77 18	99.28 5

旧先验1					97.39 66	79.58 165		96.54 81			98.08 32	84.00 26			97.42 58	97.62 91

CSCG		92.02 55	91.65 57	93.12 67	98.53 28	80.59 132	97.47 61	97.18 23	77.06 243	84.64 123	97.98 39	83.98 27	99.52 44	90.72 64	97.33 61	99.23 7

IB-MVS	85.34 4	88.67 109	87.14 123	93.26 62	93.12 165	84.32 59	98.76 10	97.27 20	87.19 65	79.36 188	90.45 193	83.92 28	98.53 102	84.41 114	69.79 269	96.93 121
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
CostFormer		89.08 98	88.39 98	91.15 133	93.13 164	79.15 176	88.61 294	96.11 117	83.14 144	89.58 78	86.93 236	83.83 29	96.87 173	88.22 89	85.92 162	97.42 102

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 64	95.41 108	81.35 118	99.02 7	96.59 76	89.50 33	94.18 31	98.36 13	83.68 30	99.45 51	94.77 23	98.45 29	98.81 20
Skip Steuart: Steuart Systems R&D Blog.
agg_prior3		94.10 21	94.29 21	93.53 53	98.62 24	83.03 83	97.80 41	96.64 70	84.28 123	95.01 19	98.03 33	83.40 31	99.41 53	95.91 15	98.96 10	98.16 50

DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 80	90.59 3	99.21 2	97.02 37	94.40 5	91.46 55	97.08 79	83.32 32	99.69 28	92.83 44	98.70 22	99.04 12
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
test_prior3		94.03 24	94.34 18	93.09 69	98.68 15	81.91 102	98.37 18	96.40 95	86.08 75	94.57 26	98.02 34	83.14 33	99.06 82	95.05 21	98.79 16	98.29 44

test_prior2							98.37 18		86.08 75	94.57 26	98.02 34	83.14 33		95.05 21	98.79 16

SMA-MVS		94.64 11	94.66 11	94.58 21	98.02 48	85.42 38	97.47 61	96.74 57	85.49 88	98.01 1	98.70 5	82.85 35	99.84 12	95.79 17	98.92 14	98.49 35

segment_acmp												82.69 36

Regformer-1		94.00 25	94.04 23	93.87 36	98.41 34	84.29 60	97.43 66	97.04 36	89.50 33	92.75 44	98.13 24	82.60 37	99.26 61	93.55 34	96.99 65	98.06 58

Regformer-2		93.92 26	94.01 24	93.67 44	98.41 34	83.75 68	97.43 66	97.00 38	89.43 35	92.69 45	98.13 24	82.48 38	99.22 64	93.51 35	96.99 65	98.04 59

PAPM		92.87 41	92.40 46	94.30 25	92.25 183	87.85 14	96.40 143	96.38 99	91.07 18	88.72 88	96.90 83	82.11 39	97.37 148	90.05 70	97.70 51	97.67 86

APD-MVS		93.61 30	93.59 27	93.69 43	98.76 12	83.26 77	97.21 75	96.09 118	82.41 158	94.65 25	98.21 17	81.96 40	98.81 96	94.65 26	98.36 36	99.01 13
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
conf0.01		85.70 168	84.35 164	89.77 175	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	93.89 184

conf0.002		85.70 168	84.35 164	89.77 175	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	93.89 184

thresconf0.02		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

tfpn_n400		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

tfpnconf		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

tfpnview11		85.80 161	84.35 164	90.17 156	94.53 129	79.70 156	95.17 190	97.11 26	75.97 246	79.44 181	95.31 112	81.90 41	95.73 226	56.78 300	82.91 194	95.09 164

CDPH-MVS		93.12 36	92.91 38	93.74 40	98.65 20	83.88 64	97.67 50	96.26 107	83.00 148	93.22 39	98.24 16	81.31 47	99.21 66	89.12 79	98.74 20	98.14 53

MG-MVS		94.25 18	93.72 25	95.85 7	99.38 3	89.35 6	97.98 32	98.09 14	89.99 29	92.34 47	96.97 82	81.30 48	98.99 86	88.54 83	98.88 15	99.20 8

test12				94.25 27	98.34 37	85.55 35		96.35 101		92.36 46		80.84 49	99.22 64		98.31 37	97.98 68

Regformer-3		93.19 34	93.19 33	93.19 65	98.10 45	83.01 84	97.08 96	96.98 40	88.98 37	91.35 60	97.89 44	80.80 50	99.23 62	92.30 50	95.20 87	97.32 106

tpmrst		88.36 117	87.38 117	91.31 126	94.36 139	79.92 149	87.32 303	95.26 162	85.32 91	88.34 92	86.13 256	80.60 51	96.70 180	83.78 119	85.34 175	97.30 109

Regformer-4		93.06 37	93.12 34	92.89 75	98.10 45	82.20 97	97.08 96	96.92 47	88.87 39	91.23 62	97.89 44	80.57 52	99.19 71	92.21 52	95.20 87	97.29 111

PHI-MVS		93.59 31	93.63 26	93.48 56	98.05 47	81.76 108	98.64 13	97.13 24	82.60 156	94.09 32	98.49 10	80.35 53	99.85 10	94.74 25	98.62 24	98.83 19

CDS-MVSNet		89.50 92	88.96 91	91.14 134	91.94 196	80.93 125	97.09 94	95.81 133	84.26 124	84.72 121	94.20 141	80.31 54	95.64 233	83.37 130	88.96 136	96.85 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		87.35 138	86.26 134	90.62 145	92.93 168	78.67 194	88.06 298	95.99 123	79.33 215	87.40 99	86.43 252	80.28 55	96.40 186	80.23 147	85.73 167	96.79 126

1112_ss		88.60 112	87.47 115	92.00 109	93.21 161	80.97 124	96.47 132	92.46 267	83.64 138	80.86 167	97.30 71	80.24 56	97.62 136	77.60 171	85.49 168	97.40 103

Test_1112_low_res		88.03 124	86.73 129	91.94 111	93.15 163	80.88 126	96.44 137	92.41 268	83.59 140	80.74 169	91.16 182	80.18 57	97.59 137	77.48 173	85.40 169	97.36 105

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 33	98.06 29	96.64 70	93.64 8	91.74 53	98.54 8	80.17 58	99.90 4	92.28 51	98.75 19	99.49 3
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tpmp4_e23		86.46 151	84.95 154	90.98 139	93.74 153	78.60 198	88.13 297	95.90 129	79.65 210	85.42 114	85.67 258	80.08 59	97.06 164	71.71 217	84.26 182	97.28 113

MSLP-MVS++		94.28 16	94.39 17	93.97 33	98.30 40	84.06 63	98.64 13	96.93 45	90.71 22	93.08 40	98.70 5	79.98 60	99.21 66	94.12 31	99.07 6	98.63 29

EPNet		94.06 23	94.15 22	93.76 39	97.27 72	84.35 58	98.29 20	97.64 17	94.57 4	95.36 13	96.88 85	79.96 61	99.12 79	91.30 57	96.11 77	97.82 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		93.41 33	93.39 30	93.47 59	97.34 70	82.83 87	97.56 56	98.27 12	89.16 36	89.71 74	97.14 76	79.77 62	99.56 42	93.65 33	97.94 47	98.02 61

TESTMET0.1,1		89.83 87	89.34 87	91.31 126	92.54 174	80.19 145	97.11 90	96.57 77	86.15 73	86.85 106	91.83 175	79.32 63	96.95 168	81.30 141	92.35 114	96.77 128

WTY-MVS		92.65 48	91.68 56	95.56 9	96.00 90	88.90 8	98.23 22	97.65 16	88.57 40	89.82 73	97.22 74	79.29 64	99.06 82	89.57 75	88.73 139	98.73 25

1121		90.66 74	89.82 80	93.16 66	97.39 66	81.71 112	93.33 236	96.66 66	74.45 276	91.38 56	97.55 61	79.27 65	99.52 44	79.95 150	98.43 30	98.26 47

HY-MVS	84.06 6	91.63 61	90.37 69	95.39 12	96.12 85	88.25 10	90.22 281	97.58 18	88.33 46	90.50 68	91.96 171	79.26 66	99.06 82	90.29 68	89.07 135	98.88 18

PAPM_NR		91.46 65	90.82 66	93.37 60	98.50 31	81.81 107	95.03 200	96.13 115	84.65 110	86.10 111	97.65 55	79.24 67	99.75 19	83.20 131	96.88 70	98.56 32

alignmvs		92.97 39	92.26 49	95.12 13	95.54 104	87.77 15	98.67 11	96.38 99	88.04 50	93.01 41	97.45 63	79.20 68	98.60 98	93.25 41	88.76 138	98.99 16

新几何1				93.12 67	97.44 64	81.60 114		96.71 61	74.54 275	91.22 63	97.57 57	79.13 69	99.51 47	77.40 174	98.46 28	98.26 47

JIA-IIPM		79.00 249	77.20 246	84.40 270	89.74 227	64.06 314	75.30 336	95.44 153	62.15 320	81.90 160	59.08 340	78.92 70	95.59 237	66.51 259	85.78 166	93.54 190

MVSFormer		91.36 66	90.57 68	93.73 42	93.00 166	88.08 12	94.80 205	94.48 195	80.74 184	94.90 21	97.13 77	78.84 71	95.10 257	83.77 120	97.46 54	98.02 61

lupinMVS		93.87 28	93.58 28	94.75 18	93.00 166	88.08 12	99.15 4	95.50 147	91.03 19	94.90 21	97.66 51	78.84 71	97.56 138	94.64 27	97.46 54	98.62 30

testdata				90.13 160	95.92 92	74.17 259		96.49 87	73.49 283	94.82 23	97.99 37	78.80 73	97.93 118	83.53 128	97.52 53	98.29 44

PAPR		92.74 42	92.17 51	94.45 22	98.89 9	84.87 51	97.20 77	96.20 111	87.73 57	88.40 91	98.12 27	78.71 74	99.76 16	87.99 91	96.28 75	98.74 23

EI-MVSNet-Vis-set		91.84 58	91.77 55	92.04 108	97.60 58	81.17 120	96.61 126	96.87 49	88.20 48	89.19 83	97.55 61	78.69 75	99.14 77	90.29 68	90.94 127	95.80 150

HFP-MVS		92.89 40	92.86 39	92.98 72	98.71 13	81.12 121	97.58 54	96.70 62	85.20 95	91.75 51	97.97 41	78.47 76	99.71 24	90.95 60	98.41 31	98.12 55

#test#		92.99 38	92.99 36	92.98 72	98.71 13	81.12 121	97.77 42	96.70 62	85.75 81	91.75 51	97.97 41	78.47 76	99.71 24	91.36 56	98.41 31	98.12 55

Patchmatch-test		78.25 255	74.72 270	88.83 188	91.20 204	74.10 260	73.91 340	88.70 313	59.89 330	66.82 279	85.12 272	78.38 78	94.54 269	48.84 327	79.58 214	97.86 75

Vis-MVSNet (Re-imp)		88.88 104	88.87 93	88.91 186	93.89 149	74.43 257	96.93 108	94.19 207	84.39 117	83.22 140	95.67 106	78.24 79	94.70 266	78.88 159	94.40 94	97.61 92

tpm		85.55 170	84.47 162	88.80 189	90.19 219	75.39 251	88.79 292	94.69 183	84.83 106	83.96 130	85.21 267	78.22 80	94.68 267	76.32 186	78.02 227	96.34 140

MP-MVS		92.61 49	92.67 43	92.42 93	98.13 44	79.73 155	97.33 72	96.20 111	85.63 83	90.53 67	97.66 51	78.14 81	99.70 27	92.12 53	98.30 38	97.85 76

HyFIR lowres test		89.36 94	88.60 95	91.63 123	94.91 122	80.76 129	95.60 180	95.53 144	82.56 157	84.03 127	91.24 181	78.03 82	96.81 176	87.07 99	88.41 142	97.32 106

ACMMP_Plus		93.46 32	93.23 32	94.17 29	97.16 73	84.28 61	96.82 112	96.65 67	86.24 72	94.27 28	97.99 37	77.94 83	99.83 13	93.39 36	98.57 25	98.39 38

原ACMM1				91.22 132	97.77 55	78.10 215		96.61 74	81.05 176	91.28 61	97.42 67	77.92 84	98.98 87	79.85 152	98.51 26	96.59 133

EI-MVSNet-UG-set		91.35 67	91.22 61	91.73 120	97.39 66	80.68 130	96.47 132	96.83 51	87.92 52	88.30 94	97.36 68	77.84 85	99.13 78	89.43 78	89.45 133	95.37 160

patchmatchnet-post											77.09 316	77.78 86	95.39 245

sam_mvs1												77.59 87				97.54 94

MP-MVS-pluss		92.58 50	92.35 47	93.29 61	97.30 71	82.53 91	96.44 137	96.04 122	84.68 109	89.12 84	98.37 12	77.48 88	99.74 21	93.31 40	98.38 34	97.59 93
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 51	92.60 45	92.34 96	98.50 31	79.90 150	98.40 17	96.40 95	84.75 107	90.48 69	98.09 29	77.40 89	99.21 66	91.15 59	98.23 40	97.92 72

region2R		92.72 45	92.70 42	92.79 79	98.68 15	80.53 136	97.53 58	96.51 83	85.22 93	91.94 49	97.98 39	77.26 90	99.67 32	90.83 63	98.37 35	98.18 49

PatchmatchNet		86.83 145	85.12 150	91.95 110	94.12 144	82.27 95	86.55 311	95.64 140	84.59 112	82.98 143	84.99 273	77.26 90	95.96 210	68.61 245	91.34 125	97.64 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
XVS		92.69 46	92.71 40	92.63 88	98.52 29	80.29 140	97.37 70	96.44 90	87.04 67	91.38 56	97.83 47	77.24 92	99.59 38	90.46 66	98.07 43	98.02 61

X-MVStestdata		86.26 155	84.14 172	92.63 88	98.52 29	80.29 140	97.37 70	96.44 90	87.04 67	91.38 56	20.73 355	77.24 92	99.59 38	90.46 66	98.07 43	98.02 61

ACMMPR		92.69 46	92.67 43	92.75 81	98.66 18	80.57 133	97.58 54	96.69 64	85.20 95	91.57 54	97.92 43	77.01 94	99.67 32	90.95 60	98.41 31	98.00 66

UniMVSNet_NR-MVSNet		85.49 171	84.59 158	88.21 202	89.44 233	79.36 166	96.71 119	96.41 93	85.22 93	78.11 197	90.98 186	76.97 95	95.14 255	79.14 156	68.30 281	90.12 219

DP-MVS Recon		91.72 59	90.85 65	94.34 24	99.50 1	85.00 46	98.51 16	95.96 125	80.57 188	88.08 96	97.63 56	76.84 96	99.89 6	85.67 105	94.88 91	98.13 54

CANet		94.89 8	94.64 12	95.63 8	97.55 61	88.12 11	99.06 5	96.39 98	94.07 7	95.34 14	97.80 48	76.83 97	99.87 8	97.08 7	97.64 52	98.89 17

PVSNet_Blended_VisFu		91.24 68	90.77 67	92.66 85	95.09 116	82.40 93	97.77 42	95.87 131	88.26 47	86.39 107	93.94 146	76.77 98	99.27 59	88.80 82	94.00 98	96.31 143

FIs		86.73 149	86.10 135	88.61 192	90.05 222	80.21 144	96.14 157	96.95 43	85.56 87	78.37 195	92.30 167	76.73 99	95.28 251	79.51 153	79.27 217	90.35 213

zzz-MVS		92.74 42	92.71 40	92.86 76	97.90 50	80.85 127	96.47 132	96.33 103	87.92 52	90.20 71	98.18 18	76.71 100	99.76 16	92.57 48	98.09 41	97.96 69

MTAPA		92.45 52	92.31 48	92.86 76	97.90 50	80.85 127	92.88 249	96.33 103	87.92 52	90.20 71	98.18 18	76.71 100	99.76 16	92.57 48	98.09 41	97.96 69

PVSNet_BlendedMVS		90.05 86	89.96 75	90.33 151	97.47 62	83.86 65	98.02 31	96.73 58	87.98 51	89.53 79	89.61 204	76.42 102	99.57 40	94.29 29	79.59 213	87.57 274

PVSNet_Blended		93.13 35	92.98 37	93.57 49	97.47 62	83.86 65	99.32 1	96.73 58	91.02 20	89.53 79	96.21 97	76.42 102	99.57 40	94.29 29	95.81 84	97.29 111

test-mter		88.95 100	88.60 95	89.98 166	92.26 181	77.23 234	97.11 90	95.96 125	85.32 91	86.30 109	91.38 178	76.37 104	96.78 178	80.82 143	91.92 120	95.94 147

test222					96.15 84	78.41 205	95.87 171	96.46 88	71.97 292	89.66 76	97.45 63	76.33 105			98.24 39	98.30 43

FC-MVSNet-test		85.96 157	85.39 145	87.66 216	89.38 234	78.02 216	95.65 179	96.87 49	85.12 97	77.34 207	91.94 173	76.28 106	94.74 265	77.09 178	78.82 220	90.21 216

test_post											33.80 350	76.17 107	95.97 206

PGM-MVS		91.93 56	91.80 54	92.32 98	98.27 41	79.74 154	95.28 186	97.27 20	83.83 133	90.89 66	97.78 49	76.12 108	99.56 42	88.82 81	97.93 49	97.66 87

Patchmatch-RL test		76.65 276	74.01 280	84.55 265	77.37 327	64.23 312	78.49 332	82.84 340	78.48 227	64.63 291	73.40 328	76.05 109	91.70 310	76.99 179	57.84 314	97.72 83

TAMVS		88.48 113	87.79 105	90.56 146	91.09 206	79.18 174	96.45 135	95.88 130	83.64 138	83.12 141	93.33 159	75.94 110	95.74 225	82.40 136	88.27 143	96.75 130

EPNet_dtu		87.65 131	87.89 102	86.93 230	94.57 127	71.37 283	96.72 117	96.50 85	88.56 41	87.12 103	95.02 130	75.91 111	94.01 279	66.62 256	90.00 131	95.42 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs_anonymous		88.68 108	87.62 110	91.86 117	94.80 123	81.69 113	93.53 231	94.92 171	82.03 163	78.87 192	90.43 194	75.77 112	95.34 248	85.04 110	93.16 108	98.55 34

HPM-MVS		91.62 62	91.53 59	91.89 112	97.88 53	79.22 173	96.99 100	95.73 136	82.07 162	89.50 81	97.19 75	75.59 113	98.93 93	90.91 62	97.94 47	97.54 94
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		91.88 57	91.82 53	92.07 106	98.38 36	78.63 196	97.29 73	96.09 118	85.12 97	88.45 90	97.66 51	75.53 114	99.68 30	89.83 72	98.02 46	97.88 73

PatchT		79.75 241	76.85 251	88.42 194	89.55 230	75.49 250	77.37 334	94.61 192	63.07 315	82.46 146	73.32 329	75.52 115	93.41 288	51.36 319	84.43 180	96.36 138

CR-MVSNet		83.53 195	81.36 212	90.06 162	90.16 220	79.75 152	79.02 330	91.12 282	84.24 125	82.27 156	80.35 302	75.45 116	93.67 285	63.37 279	86.25 156	96.75 130

Patchmtry		77.36 266	74.59 273	85.67 244	89.75 225	75.75 248	77.85 333	91.12 282	60.28 328	71.23 257	80.35 302	75.45 116	93.56 287	57.94 294	67.34 291	87.68 271

view600		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

view800		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

conf0.05thres1000		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

tfpn		87.45 134	85.98 137	91.88 113	95.90 93	84.52 54	96.68 122	98.85 2	81.85 165	82.30 148	93.39 155	75.44 118	97.66 131	64.02 271	85.36 170	93.45 193

tfpn111		88.08 122	86.70 130	92.20 102	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.82 127	67.13 252	85.88 163	93.89 184

conf200view11		88.27 120	86.95 126	92.24 99	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.93 118	69.04 237	86.24 158	93.89 184

thres100view900		88.30 118	86.95 126	92.33 97	96.10 86	84.90 48	97.14 85	98.85 2	82.69 153	83.41 135	93.66 151	75.43 122	97.93 118	69.04 237	86.24 158	94.17 177

thres600view7		88.06 123	86.70 130	92.15 104	96.10 86	85.17 42	97.14 85	98.85 2	82.70 152	83.41 135	93.66 151	75.43 122	97.82 127	67.13 252	85.88 163	93.45 193

UniMVSNet (Re)		85.31 174	84.23 171	88.55 193	89.75 225	80.55 134	96.72 117	96.89 48	85.42 89	78.40 194	88.93 210	75.38 126	95.52 240	78.58 160	68.02 284	89.57 228

tfpn200view9		88.48 113	87.15 121	92.47 91	96.21 82	85.30 40	97.44 63	98.85 2	83.37 141	83.99 128	93.82 148	75.36 127	97.93 118	69.04 237	86.24 158	94.17 177

thres400		88.42 116	87.15 121	92.23 100	96.21 82	85.30 40	97.44 63	98.85 2	83.37 141	83.99 128	93.82 148	75.36 127	97.93 118	69.04 237	86.24 158	93.45 193

sam_mvs												75.35 129

jason		92.73 44	92.23 50	94.21 28	90.50 214	87.30 21	98.65 12	95.09 164	90.61 23	92.76 43	97.13 77	75.28 130	97.30 151	93.32 39	96.75 72	98.02 61
jason: jason.
MVS_Test		90.29 83	89.18 89	93.62 47	95.23 112	84.93 47	94.41 211	94.66 187	84.31 120	90.37 70	91.02 184	75.13 131	97.82 127	83.11 133	94.42 93	98.12 55

thres200		88.92 102	87.65 107	92.73 82	96.30 81	85.62 34	97.85 35	98.86 1	84.38 118	84.82 118	93.99 145	75.12 132	98.01 116	70.86 227	86.67 153	94.56 176

EPMVS		87.47 133	85.90 142	92.18 103	95.41 108	82.26 96	87.00 307	96.28 106	85.88 79	84.23 126	85.57 263	75.07 133	96.26 192	71.14 225	92.50 111	98.03 60

UA-Net		88.92 102	88.48 97	90.24 153	94.06 146	77.18 236	93.04 246	94.66 187	87.39 60	91.09 64	93.89 147	74.92 134	98.18 115	75.83 190	91.43 124	95.35 161

tpm cat1		83.63 194	81.38 211	90.39 150	93.53 158	78.19 214	85.56 317	95.09 164	70.78 299	78.51 193	83.28 290	74.80 135	97.03 165	66.77 255	84.05 183	95.95 146

APD-MVS_3200maxsize		91.23 69	91.35 60	90.89 141	97.89 52	76.35 243	96.30 151	95.52 146	79.82 207	91.03 65	97.88 46	74.70 136	98.54 101	92.11 54	96.89 69	97.77 81

IS-MVSNet		88.67 109	88.16 99	90.20 155	93.61 155	76.86 238	96.77 116	93.07 261	84.02 128	83.62 134	95.60 108	74.69 137	96.24 194	78.43 161	93.66 103	97.49 99

MDTV_nov1_ep13			83.69 175		94.09 145	81.01 123	86.78 309	96.09 118	83.81 134	84.75 120	84.32 277	74.44 138	96.54 182	63.88 275	85.07 177

MDTV_nov1_ep13_2view						81.74 109	86.80 308		80.65 186	85.65 112		74.26 139		76.52 184		96.98 118

tpmvs		83.04 207	80.77 217	89.84 172	95.43 106	77.96 218	85.59 316	95.32 158	75.31 261	76.27 223	83.70 286	73.89 140	97.41 146	59.53 288	81.93 205	94.14 179

test_post1							85.88 315				30.24 354	73.77 141	95.07 259	73.89 205

EI-MVSNet		85.80 161	85.20 148	87.59 218	91.55 200	77.41 230	95.13 196	95.36 155	80.43 192	80.33 174	94.71 134	73.72 142	95.97 206	76.96 181	78.64 222	89.39 230

IterMVS-LS		83.93 190	82.80 190	87.31 225	91.46 203	77.39 231	95.66 178	93.43 247	80.44 190	75.51 232	87.26 230	73.72 142	95.16 254	76.99 179	70.72 256	89.39 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS	86.58 3	91.53 64	91.06 64	92.94 74	94.52 135	81.89 104	95.95 164	95.98 124	90.76 21	83.76 133	96.76 90	73.24 144	99.71 24	91.67 55	96.96 67	97.22 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPMNet		79.32 246	75.75 258	90.06 162	90.16 220	79.75 152	79.02 330	93.92 225	58.43 332	82.27 156	72.55 330	73.03 145	93.67 285	46.10 331	86.25 156	96.75 130

CHOSEN 1792x2688		91.07 70	90.21 71	93.64 45	95.18 114	83.53 73	96.26 153	96.13 115	88.92 38	84.90 117	93.10 163	72.86 146	99.62 36	88.86 80	95.67 85	97.79 80

canonicalmvs		92.27 53	91.22 61	95.41 11	95.80 99	88.31 9	97.09 94	94.64 190	88.49 43	92.99 42	97.31 69	72.68 147	98.57 100	93.38 38	88.58 141	99.36 4

API-MVS		90.18 84	88.97 90	93.80 38	98.66 18	82.95 86	97.50 60	95.63 141	75.16 263	86.31 108	97.69 50	72.49 148	99.90 4	81.26 142	96.07 78	98.56 32

nrg030		86.79 147	85.43 144	90.87 142	88.76 238	85.34 39	97.06 98	94.33 201	84.31 120	80.45 172	91.98 170	72.36 149	96.36 188	88.48 86	71.13 252	90.93 208

MVS_111021_LR		91.60 63	91.64 58	91.47 125	95.74 100	78.79 193	96.15 156	96.77 53	88.49 43	88.64 89	97.07 80	72.33 150	99.19 71	93.13 42	96.48 74	96.43 137

test-LLR		88.48 113	87.98 101	89.98 166	92.26 181	77.23 234	97.11 90	95.96 125	83.76 135	86.30 109	91.38 178	72.30 151	96.78 178	80.82 143	91.92 120	95.94 147

test0.0.03 1		82.79 212	82.48 194	83.74 280	86.81 256	72.22 270	96.52 130	95.03 168	83.76 135	73.00 248	93.20 160	72.30 151	88.88 324	64.15 270	77.52 229	90.12 219

Effi-MVS+		90.70 73	89.90 78	93.09 69	93.61 155	83.48 74	95.20 189	92.79 264	83.22 143	91.82 50	95.70 104	71.82 153	97.48 145	91.25 58	93.67 102	98.32 41

sss		90.87 72	89.96 75	93.60 48	94.15 143	83.84 67	97.14 85	98.13 13	85.93 78	89.68 75	96.09 98	71.67 154	99.30 58	87.69 93	89.16 134	97.66 87

Test By Simon												71.65 155

HPM-MVS_fast		90.38 82	90.17 72	91.03 136	97.61 57	77.35 232	97.15 84	95.48 148	79.51 212	88.79 87	96.90 83	71.64 156	98.81 96	87.01 100	97.44 56	96.94 120

MVS		90.60 76	88.64 94	96.50 1	94.25 141	90.53 4	93.33 236	97.21 22	77.59 234	78.88 191	97.31 69	71.52 157	99.69 28	89.60 74	98.03 45	99.27 6

dp		84.30 187	82.31 196	90.28 152	94.24 142	77.97 217	86.57 310	95.53 144	79.94 205	80.75 168	85.16 270	71.49 158	96.39 187	63.73 276	83.36 188	96.48 136

ACMMP		90.39 81	89.97 74	91.64 122	97.58 60	78.21 212	96.78 114	96.72 60	84.73 108	84.72 121	97.23 73	71.22 159	99.63 35	88.37 88	92.41 113	97.08 117
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
PCF-MVS	84.09 5	86.77 148	85.00 153	92.08 105	92.06 190	83.07 81	92.14 265	94.47 197	79.63 211	76.90 214	94.78 133	71.15 160	99.20 70	72.87 208	91.05 126	93.98 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	81.61 12	85.02 176	83.67 176	89.06 182	96.79 77	73.27 265	95.92 166	94.79 180	74.81 270	80.47 171	96.83 87	71.07 161	98.19 114	49.82 325	92.57 110	95.71 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		5.92 336	7.89 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	71.04 162	0.00 360	0.00 357	0.00 358	0.00 358

PS-MVSNAJss		84.91 178	84.30 170	86.74 231	85.89 287	74.40 258	94.95 201	94.16 211	83.93 130	76.45 219	90.11 200	71.04 162	95.77 220	83.16 132	79.02 219	90.06 223

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 102	92.35 1	98.21 23	95.79 134	92.42 12	96.24 6	98.18 18	71.04 162	99.17 74	96.77 9	97.39 59	96.79 126

xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 118	92.02 2	98.19 24	95.68 138	92.06 14	96.01 10	98.14 23	70.83 165	98.96 88	96.74 10	96.57 73	96.76 129

diffmvs		87.96 127	86.47 133	92.42 93	94.26 140	82.70 88	92.79 253	94.03 220	77.94 229	88.99 86	89.98 201	70.72 166	97.56 138	77.75 164	91.80 122	96.98 118

CPTT-MVS		89.72 90	89.87 79	89.29 180	98.33 38	73.30 263	97.70 48	95.35 157	75.68 255	87.40 99	97.44 66	70.43 167	98.25 111	89.56 76	96.90 68	96.33 142

WR-MVS_H		81.02 234	80.09 225	83.79 278	88.08 247	71.26 286	94.46 209	96.54 81	80.08 201	72.81 251	86.82 241	70.36 168	92.65 291	64.18 269	67.50 289	87.46 278

NR-MVSNet		83.35 203	81.52 209	88.84 187	88.76 238	81.31 119	94.45 210	95.16 163	84.65 110	67.81 275	90.82 187	70.36 168	94.87 262	74.75 199	66.89 293	90.33 214

VNet		92.11 54	91.22 61	94.79 17	96.91 76	86.98 22	97.91 33	97.96 15	86.38 71	93.65 35	95.74 102	70.16 170	98.95 90	93.39 36	88.87 137	98.43 36

Fast-Effi-MVS+		87.93 128	86.94 128	90.92 140	94.04 147	79.16 175	98.26 21	93.72 237	81.29 173	83.94 131	92.90 164	69.83 171	96.68 181	76.70 182	91.74 123	96.93 121

PLC	83.97 7	88.00 125	87.38 117	89.83 173	98.02 48	76.46 241	97.16 83	94.43 199	79.26 218	81.98 159	96.28 96	69.36 172	99.27 59	77.71 170	92.25 117	93.77 188
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-w/o		88.24 121	87.47 115	90.54 147	95.03 119	78.54 199	97.41 69	93.82 229	84.08 126	78.23 196	94.51 138	69.34 173	97.21 156	80.21 148	94.58 92	95.87 149

abl_6		89.80 88	89.71 83	90.07 161	96.53 79	75.52 249	94.48 208	95.04 167	81.12 175	89.22 82	97.00 81	68.83 174	98.96 88	89.86 71	95.27 86	95.73 152

MAR-MVS		90.63 75	90.22 70	91.86 117	98.47 33	78.20 213	97.18 79	96.61 74	83.87 132	88.18 95	98.18 18	68.71 175	99.75 19	83.66 125	97.15 63	97.63 90
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
114514_t		88.79 107	87.57 112	92.45 92	98.21 42	81.74 109	96.99 100	95.45 152	75.16 263	82.48 145	95.69 105	68.59 176	98.50 103	80.33 145	95.18 89	97.10 116

DU-MVS		84.57 184	83.33 184	88.28 200	88.76 238	79.36 166	96.43 141	95.41 154	85.42 89	78.11 197	90.82 187	67.61 177	95.14 255	79.14 156	68.30 281	90.33 214

Baseline_NR-MVSNet		81.22 233	80.07 227	84.68 260	85.32 294	75.12 253	96.48 131	88.80 310	76.24 245	77.28 209	86.40 253	67.61 177	94.39 272	75.73 192	66.73 296	84.54 304

WR-MVS		84.32 186	82.96 186	88.41 195	89.38 234	80.32 139	96.59 127	96.25 108	83.97 129	76.63 216	90.36 195	67.53 179	94.86 263	75.82 191	70.09 264	90.06 223

OMC-MVS		88.80 106	88.16 99	90.72 143	95.30 111	77.92 221	94.81 204	94.51 194	86.80 69	84.97 116	96.85 86	67.53 179	98.60 98	85.08 109	87.62 147	95.63 155

LCM-MVSNet-Re		83.75 192	83.54 180	84.39 271	93.54 157	64.14 313	92.51 256	84.03 335	83.90 131	66.14 283	86.59 246	67.36 181	92.68 290	84.89 112	92.87 109	96.35 139

v148		82.41 219	80.89 215	86.99 229	86.18 280	76.81 239	96.27 152	93.82 229	80.49 189	75.28 235	86.11 257	67.32 182	95.75 222	75.48 194	67.03 292	88.42 257

CNLPA		86.96 141	85.37 146	91.72 121	97.59 59	79.34 168	97.21 75	91.05 285	74.22 277	78.90 190	96.75 91	67.21 183	98.95 90	74.68 200	90.77 128	96.88 124

v1		83.37 200	81.82 202	88.01 205	86.58 266	79.24 170	96.45 135	94.13 212	80.88 177	77.48 204	86.88 237	67.15 184	96.04 200	77.15 175	69.67 273	88.76 246

v18		77.96 259	75.61 260	84.98 251	86.66 258	79.01 185	93.02 247	90.94 287	75.69 254	63.19 296	77.62 311	67.11 185	92.07 299	70.05 231	56.24 318	83.87 311

v1141		83.36 201	81.81 204	88.01 205	86.61 264	79.26 169	96.44 137	94.12 215	80.88 177	77.48 204	86.87 238	67.08 186	96.03 201	77.14 176	69.69 272	88.75 248

divwei89l23v2f112		83.36 201	81.81 204	88.01 205	86.60 265	79.23 172	96.44 137	94.12 215	80.88 177	77.49 202	86.87 238	67.08 186	96.03 201	77.14 176	69.67 273	88.76 246

v6		83.45 197	81.94 200	87.95 208	86.62 262	79.03 184	96.32 147	94.17 208	80.76 181	77.57 199	87.23 233	67.03 188	96.09 196	77.73 166	70.06 267	88.75 248

v1neww		83.45 197	81.95 198	87.95 208	86.66 258	79.04 181	96.32 147	94.17 208	80.76 181	77.56 200	87.25 231	67.02 189	96.08 197	77.73 166	70.07 265	88.74 250

v7new		83.45 197	81.95 198	87.95 208	86.66 258	79.04 181	96.32 147	94.17 208	80.76 181	77.56 200	87.25 231	67.02 189	96.08 197	77.73 166	70.07 265	88.74 250

FMVSNet3		84.71 181	82.71 192	90.70 144	94.55 128	87.71 16	95.92 166	94.67 186	81.73 170	75.82 229	88.08 222	66.99 191	94.47 270	71.23 222	75.38 235	89.91 225

v8		81.88 225	80.06 228	87.32 224	86.63 261	79.04 181	94.41 211	93.65 240	78.77 225	73.19 247	85.57 263	66.87 192	95.81 218	73.84 206	67.61 288	87.11 281

1314		88.94 101	87.20 119	94.17 29	93.21 161	85.73 31	93.33 236	96.64 70	82.89 149	75.98 226	96.36 95	66.83 193	99.39 54	83.52 129	96.02 80	97.39 104

v16		77.84 260	75.47 261	84.93 253	86.62 262	78.93 187	92.84 251	90.89 288	75.50 257	63.03 300	77.54 312	66.82 194	92.04 300	69.82 232	56.22 319	83.82 313

v17		77.79 261	75.41 263	84.94 252	86.53 267	78.94 186	92.83 252	90.88 289	75.51 256	62.97 301	77.50 313	66.69 195	92.03 301	69.80 233	56.01 320	83.83 312

BH-untuned		86.95 142	85.94 141	89.99 165	94.52 135	77.46 229	96.78 114	93.37 253	81.80 169	76.62 217	93.81 150	66.64 196	97.02 166	76.06 188	93.88 100	95.48 158

MVS_0304		93.82 29	93.11 35	95.95 5	96.79 77	89.15 7	98.56 15	95.30 159	93.61 9	94.82 23	98.02 34	66.60 197	99.88 7	96.94 8	97.39 59	98.81 20

v15		77.52 263	75.09 264	84.82 255	86.37 271	78.82 190	92.58 255	90.78 291	75.47 258	62.53 303	77.17 314	66.58 198	91.92 302	69.18 236	55.16 322	83.73 314

V14		77.43 265	74.99 265	84.75 256	86.32 274	78.67 194	92.44 259	90.77 292	75.28 262	62.42 304	77.13 315	66.40 199	91.88 303	69.01 241	55.14 323	83.70 315

CVMVSNet		84.83 180	85.57 143	82.63 291	91.55 200	60.38 323	95.13 196	95.03 168	80.60 187	82.10 158	94.71 134	66.40 199	90.19 321	74.30 202	90.32 130	97.31 108

V9		77.32 267	74.87 268	84.69 259	86.26 278	78.52 200	92.33 262	90.72 293	75.11 265	62.21 306	77.08 317	66.19 201	91.87 304	68.84 242	55.06 325	83.69 316

PMMVS		89.46 93	89.92 77	88.06 203	94.64 125	69.57 298	96.22 154	94.95 170	87.27 61	91.37 59	96.54 94	65.88 202	97.39 147	88.54 83	93.89 99	97.23 114

v12		77.20 269	74.73 269	84.63 263	86.15 281	78.41 205	92.17 264	90.71 294	74.92 268	62.05 308	77.00 318	65.83 203	91.83 305	68.69 244	55.01 326	83.64 317

v13		77.11 272	74.63 272	84.55 265	86.08 284	78.27 208	92.06 266	90.68 296	74.73 271	61.86 311	76.93 319	65.73 204	91.81 308	68.55 247	55.07 324	83.59 318

v2v482		83.46 196	81.86 201	88.25 201	86.19 279	79.65 162	96.34 146	94.02 221	81.56 171	77.32 208	88.23 219	65.62 205	96.03 201	77.77 163	69.72 271	89.09 236

v1144		82.90 211	81.27 213	87.78 213	86.29 275	79.07 180	96.14 157	93.93 224	80.05 202	77.38 206	86.80 242	65.50 206	95.93 212	75.21 196	70.13 261	88.33 260

v11		77.21 268	74.72 270	84.68 260	86.29 275	78.62 197	92.30 263	90.63 297	74.86 269	62.32 305	76.73 320	65.49 207	91.83 305	68.17 248	55.72 321	83.59 318

v7		82.99 210	81.41 210	87.73 214	86.41 269	78.86 189	96.10 160	93.98 222	79.88 206	77.49 202	87.11 235	65.44 208	95.97 206	75.69 193	70.59 258	88.36 258

v10		81.43 230	79.53 233	87.11 227	86.38 270	78.87 188	94.31 214	93.43 247	77.88 231	73.24 246	85.26 266	65.44 208	95.75 222	72.14 213	67.71 287	86.72 286

HQP2-MVS												65.40 210

HQP-MVS		87.91 129	87.55 113	88.98 185	92.08 187	78.48 201	97.63 51	94.80 178	90.52 24	82.30 148	94.56 136	65.40 210	97.32 149	87.67 94	83.01 191	91.13 204

V42		83.04 207	81.53 208	87.57 220	86.27 277	79.09 179	95.87 171	94.11 217	80.35 194	77.22 210	86.79 243	65.32 212	96.02 204	77.74 165	70.14 260	87.61 273

pmmvs4		82.54 215	80.79 216	87.79 212	86.11 282	80.49 138	93.55 230	93.18 257	77.29 239	73.35 244	89.40 206	65.26 213	95.05 260	75.32 195	73.61 242	87.83 268

3Dnovator+	82.88 8	89.63 91	87.85 103	94.99 15	94.49 138	86.76 23	97.84 36	95.74 135	86.10 74	75.47 233	96.02 99	65.00 214	99.51 47	82.91 135	97.07 64	98.72 26

HQP_MVS		87.50 132	87.09 124	88.74 190	91.86 197	77.96 218	97.18 79	94.69 183	89.89 30	81.33 163	94.15 142	64.77 215	97.30 151	87.08 97	82.82 201	90.96 206

plane_prior6					91.98 192	77.92 221						64.77 215

v144192		82.43 216	80.73 218	87.54 221	85.81 288	78.22 209	95.98 162	93.78 234	79.09 220	77.11 211	86.49 248	64.66 217	95.91 213	74.20 203	69.42 275	88.49 253

TranMVSNet+NR-MVSNet		83.24 205	81.71 206	87.83 211	87.71 250	78.81 192	96.13 159	94.82 177	84.52 113	76.18 225	90.78 189	64.07 218	94.60 268	74.60 201	66.59 297	90.09 221

CP-MVSNet		81.01 235	80.08 226	83.79 278	87.91 249	70.51 289	94.29 218	95.65 139	80.83 180	72.54 253	88.84 211	63.71 219	92.32 294	68.58 246	68.36 280	88.55 252

cdsmvs_eth3d_5k		21.43 331	28.57 332	0.00 347	0.00 361	0.00 362	0.00 353	95.93 128	0.00 357	0.00 358	97.66 51	63.57 220	0.00 360	0.00 357	0.00 358	0.00 358

1111		65.60 310	64.33 308	69.41 324	68.26 338	45.11 344	87.06 305	87.32 319	54.99 336	51.20 332	73.45 326	63.57 220	85.70 335	36.53 339	56.59 317	77.42 336

.test1245		54.61 316	58.07 314	44.24 339	68.26 338	45.11 344	87.06 305	87.32 319	54.99 336	51.20 332	73.45 326	63.57 220	85.70 335	36.53 339	0.21 355	1.91 355

Vis-MVSNet		88.67 109	87.82 104	91.24 131	92.68 169	78.82 190	96.95 106	93.85 228	87.55 58	87.07 104	95.13 126	63.43 223	97.21 156	77.58 172	96.15 76	97.70 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		82.31 220	80.55 220	87.60 217	85.94 285	78.47 204	95.85 173	93.80 232	79.33 215	76.97 213	86.51 247	63.33 224	95.87 214	73.11 207	70.13 261	88.46 255

CANet_DTU		90.98 71	90.04 73	93.83 37	94.76 124	86.23 26	96.32 147	93.12 260	93.11 10	93.71 34	96.82 88	63.08 225	99.48 49	84.29 115	95.12 90	95.77 151

ab-mvs		87.08 140	84.94 155	93.48 56	93.34 160	83.67 71	88.82 291	95.70 137	81.18 174	84.55 124	90.14 199	62.72 226	98.94 92	85.49 107	82.54 204	97.85 76

v1921920		82.02 224	80.23 224	87.41 223	85.62 289	77.92 221	95.79 175	93.69 238	78.86 224	76.67 215	86.44 250	62.50 227	95.83 217	72.69 209	69.77 270	88.47 254

CLD-MVS		87.97 126	87.48 114	89.44 178	92.16 186	80.54 135	98.14 27	94.92 171	91.41 16	79.43 187	95.40 110	62.34 228	97.27 154	90.60 65	82.90 200	90.50 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	82.32 10	89.33 95	87.64 108	94.42 23	93.73 154	85.70 32	97.73 47	96.75 56	86.73 70	76.21 224	95.93 100	62.17 229	99.68 30	81.67 140	97.81 50	97.88 73

ADS-MVSNet2		79.57 242	77.53 244	85.71 243	93.78 150	72.13 272	79.48 326	86.11 326	73.09 285	80.14 176	79.99 304	62.15 230	90.14 322	59.49 289	83.52 185	94.85 170

ADS-MVSNet		81.26 232	78.36 238	89.96 168	93.78 150	79.78 151	79.48 326	93.60 242	73.09 285	80.14 176	79.99 304	62.15 230	95.24 253	59.49 289	83.52 185	94.85 170

QAPM		86.88 143	84.51 159	93.98 32	94.04 147	85.89 29	97.19 78	96.05 121	73.62 281	75.12 236	95.62 107	62.02 232	99.74 21	70.88 226	96.06 79	96.30 144

Effi-MVS+-dtu		84.61 183	84.90 157	83.72 281	91.96 193	63.14 317	94.95 201	93.34 254	85.57 84	79.79 179	87.12 234	61.99 233	95.61 236	83.55 126	85.83 165	92.41 200

mvs-test1		86.83 145	87.17 120	85.81 242	91.96 193	65.24 310	97.90 34	93.34 254	85.57 84	84.51 125	95.14 125	61.99 233	97.19 158	83.55 126	90.55 129	95.00 168

XXY-MVS		83.84 191	82.00 197	89.35 179	87.13 254	81.38 117	95.72 176	94.26 203	80.15 200	75.92 228	90.63 190	61.96 235	96.52 183	78.98 158	73.28 246	90.14 217

AdaColmap		88.81 105	87.61 111	92.39 95	99.33 4	79.95 148	96.70 121	95.58 142	77.51 235	83.05 142	96.69 92	61.90 236	99.72 23	84.29 115	93.47 104	97.50 98

VPA-MVSNet		85.32 173	83.83 174	89.77 175	90.25 217	82.63 90	96.36 144	97.07 35	83.03 147	81.21 165	89.02 209	61.58 237	96.31 190	85.02 111	70.95 254	90.36 212

test_djsdf		83.00 209	82.45 195	84.64 262	84.07 305	69.78 295	94.80 205	94.48 195	80.74 184	75.41 234	87.70 225	61.32 238	95.10 257	83.77 120	79.76 210	89.04 238

v1240		81.70 227	79.83 231	87.30 226	85.50 290	77.70 226	95.48 184	93.44 245	78.46 228	76.53 218	86.44 250	60.85 239	95.84 216	71.59 219	70.17 259	88.35 259

XVG-OURS-SEG-HR		85.74 167	85.16 149	87.49 222	90.22 218	71.45 282	91.29 275	94.09 218	81.37 172	83.90 132	95.22 118	60.30 240	97.53 144	85.58 106	84.42 181	93.50 191

PEN-MVS		79.47 244	78.26 240	83.08 287	86.36 272	68.58 301	93.85 224	94.77 181	79.76 208	71.37 256	88.55 214	59.79 241	92.46 292	64.50 268	65.40 298	88.19 262

TransMVSNet (Re)		76.94 274	74.38 276	84.62 264	85.92 286	75.25 252	95.28 186	89.18 307	73.88 280	67.22 276	86.46 249	59.64 242	94.10 277	59.24 292	52.57 332	84.50 305

DP-MVS		81.47 229	78.28 239	91.04 135	98.14 43	78.48 201	95.09 199	86.97 321	61.14 326	71.12 259	92.78 166	59.59 243	99.38 55	53.11 315	86.61 154	95.27 162

v7n		79.32 246	77.34 245	85.28 247	84.05 306	72.89 269	93.38 234	93.87 227	75.02 266	70.68 261	84.37 276	59.58 244	95.62 235	67.60 249	67.50 289	87.32 280

F-COLMAP		84.50 185	83.44 183	87.67 215	95.22 113	72.22 270	95.95 164	93.78 234	75.74 253	76.30 222	95.18 122	59.50 245	98.45 106	72.67 210	86.59 155	92.35 201

v748		78.69 252	76.79 252	84.39 271	83.40 309	70.78 287	93.25 242	93.62 241	74.96 267	69.40 270	83.74 283	59.40 246	95.39 245	68.74 243	64.59 300	86.99 284

LS3D		82.22 222	79.94 230	89.06 182	97.43 65	74.06 261	93.20 244	92.05 271	61.90 321	73.33 245	95.21 120	59.35 247	99.21 66	54.54 311	92.48 112	93.90 183

BH-RMVSNet		86.84 144	85.28 147	91.49 124	95.35 110	80.26 143	96.95 106	92.21 269	82.86 150	81.77 161	95.46 109	59.34 248	97.64 135	69.79 234	93.81 101	96.57 134

MVP-Stereo		82.65 214	81.67 207	85.59 245	86.10 283	78.29 207	93.33 236	92.82 263	77.75 232	69.17 273	87.98 223	59.28 249	95.76 221	71.77 216	96.88 70	82.73 324
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-CasMVS		80.27 239	79.18 234	83.52 284	87.56 252	69.88 294	94.08 220	95.29 160	80.27 197	72.08 254	88.51 217	59.22 250	92.23 296	67.49 250	68.15 283	88.45 256

DTE-MVSNet		78.37 254	77.06 247	82.32 295	85.22 295	67.17 306	93.40 233	93.66 239	78.71 226	70.53 263	88.29 218	59.06 251	92.23 296	61.38 284	63.28 306	87.56 275

TR-MVS		86.30 154	84.93 156	90.42 148	94.63 126	77.58 227	96.57 128	93.82 229	80.30 195	82.42 147	95.16 123	58.74 252	97.55 140	74.88 198	87.82 146	96.13 145

OPM-MVS		85.84 159	85.10 151	88.06 203	88.34 244	77.83 224	95.72 176	94.20 205	87.89 55	80.45 172	94.05 144	58.57 253	97.26 155	83.88 118	82.76 203	89.09 236

PatchMatch-RL		85.00 177	83.66 177	89.02 184	95.86 97	74.55 256	92.49 257	93.60 242	79.30 217	79.29 189	91.47 176	58.53 254	98.45 106	70.22 230	92.17 118	94.07 181

pm-mvs1		80.05 240	78.02 241	86.15 239	85.42 291	75.81 247	95.11 198	92.69 266	77.13 240	70.36 264	87.43 227	58.44 255	95.27 252	71.36 221	64.25 302	87.36 279

testpf		70.88 300	70.47 294	72.08 322	88.92 237	59.57 326	48.62 350	93.15 259	63.05 316	63.07 298	79.51 307	58.33 256	86.63 331	66.93 254	72.69 248	70.05 342

pcd1.5k->3k		34.11 327	35.46 327	30.05 343	86.70 257	0.00 362	0.00 353	94.74 182	0.00 357	0.00 358	0.00 359	58.13 257	0.00 360	0.00 357	79.56 215	90.14 217

V4		78.70 250	76.95 248	83.95 274	81.66 314	71.34 284	91.94 267	93.44 245	74.69 273	70.35 266	83.73 284	58.07 258	95.50 241	71.84 214	66.86 294	85.02 300

Patchmatch-test1		84.89 179	82.76 191	91.27 129	92.30 179	81.86 105	92.88 249	95.56 143	84.85 105	82.52 144	85.19 268	58.04 259	94.21 275	65.93 262	87.58 149	97.74 82

v52		78.70 250	76.95 248	83.95 274	81.71 313	71.34 284	91.93 268	93.43 247	74.69 273	70.36 264	83.71 285	58.04 259	95.50 241	71.84 214	66.82 295	85.00 301

EU-MVSNet		76.92 275	76.95 248	76.83 312	84.10 304	54.73 333	91.77 271	92.71 265	72.74 288	69.57 269	88.69 212	58.03 261	87.43 329	64.91 267	70.00 268	88.33 260

semantic-postprocess				84.73 258	89.63 229	74.66 254		91.81 275	80.05 202	71.06 260	85.18 269	57.98 262	91.40 311	72.48 212	70.70 257	88.12 264

IterMVS		80.67 237	79.16 235	85.20 248	89.79 224	76.08 245	92.97 248	91.86 273	80.28 196	71.20 258	85.14 271	57.93 263	91.34 312	72.52 211	70.74 255	88.18 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		80.98 236	79.97 229	84.01 273	81.73 312	70.44 290	92.49 257	93.58 244	77.10 242	72.98 249	86.31 254	57.58 264	94.90 261	79.32 154	78.63 224	86.69 287

xiu_mvs_v1_base_debu		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 265	98.32 108	92.72 45	93.46 105	94.74 173

xiu_mvs_v1_base		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 265	98.32 108	92.72 45	93.46 105	94.74 173

xiu_mvs_v1_base_debi		90.54 77	89.54 84	93.55 50	92.31 176	87.58 18	96.99 100	94.87 173	87.23 62	93.27 36	97.56 58	57.43 265	98.32 108	92.72 45	93.46 105	94.74 173

OpenMVS	79.58 14	86.09 156	83.62 178	93.50 54	90.95 208	86.71 24	97.44 63	95.83 132	75.35 259	72.64 252	95.72 103	57.42 268	99.64 34	71.41 220	95.85 83	94.13 180

test2356		74.41 286	74.53 275	74.07 319	76.13 331	54.45 334	94.74 207	92.08 270	71.96 293	65.51 287	83.05 292	56.96 269	83.71 339	52.74 316	77.58 228	84.06 308

PVSNet	82.34 9	89.02 99	87.79 105	92.71 83	95.49 105	81.50 115	97.70 48	97.29 19	87.76 56	85.47 113	95.12 127	56.90 270	98.90 94	80.33 145	94.02 96	97.71 84

Fast-Effi-MVS+-dtu		83.33 204	82.60 193	85.50 246	89.55 230	69.38 299	96.09 161	91.38 278	82.30 159	75.96 227	91.41 177	56.71 271	95.58 238	75.13 197	84.90 178	91.54 202

DI_MVS_plusplus_test		85.92 158	83.61 179	92.86 76	86.43 268	83.20 78	95.57 181	95.46 149	85.10 100	65.99 284	86.84 240	56.70 272	97.89 125	88.10 90	92.33 115	97.48 100

ppachtmachnet_test		77.19 270	74.22 278	86.13 240	85.39 292	78.22 209	93.98 222	91.36 280	71.74 295	67.11 278	84.87 274	56.67 273	93.37 289	52.21 317	64.59 300	86.80 285

VPNet		84.69 182	82.92 187	90.01 164	89.01 236	83.45 75	96.71 119	95.46 149	85.71 82	79.65 180	92.18 169	56.66 274	96.01 205	83.05 134	67.84 286	90.56 210

test_normal		85.83 160	83.51 181	92.78 80	86.33 273	83.01 84	95.56 183	95.46 149	85.11 99	65.73 286	86.63 245	56.62 275	97.86 126	87.87 92	92.29 116	97.47 101

GA-MVS		85.79 166	84.04 173	91.02 137	89.47 232	80.27 142	96.90 109	94.84 176	85.57 84	80.88 166	89.08 207	56.56 276	96.47 185	77.72 169	85.35 174	96.34 140

XVG-OURS		85.18 175	84.38 163	87.59 218	90.42 216	71.73 279	91.06 278	94.07 219	82.00 164	83.29 139	95.08 128	56.42 277	97.55 140	83.70 124	83.42 187	93.49 192

GBi-Net		82.42 217	80.43 222	88.39 196	92.66 170	81.95 99	94.30 215	93.38 250	79.06 221	75.82 229	85.66 259	56.38 278	93.84 281	71.23 222	75.38 235	89.38 232

test1		82.42 217	80.43 222	88.39 196	92.66 170	81.95 99	94.30 215	93.38 250	79.06 221	75.82 229	85.66 259	56.38 278	93.84 281	71.23 222	75.38 235	89.38 232

FMVSNet2		82.79 212	80.44 221	89.83 173	92.66 170	85.43 37	95.42 185	94.35 200	79.06 221	74.46 237	87.28 228	56.38 278	94.31 273	69.72 235	74.68 239	89.76 226

pmmvs5		81.34 231	79.54 232	86.73 232	85.02 296	76.91 237	96.22 154	91.65 276	77.65 233	73.55 241	88.61 213	55.70 281	94.43 271	74.12 204	73.35 245	88.86 245

tfpnnormal		78.14 256	75.42 262	86.31 237	88.33 245	79.24 170	94.41 211	96.22 110	73.51 282	69.81 268	85.52 265	55.43 282	95.75 222	47.65 329	67.86 285	83.95 310

LFMVS		89.27 96	87.64 108	94.16 31	97.16 73	85.52 36	97.18 79	94.66 187	79.17 219	89.63 77	96.57 93	55.35 283	98.22 112	89.52 77	89.54 132	98.74 23

ACMM	80.70 13	83.72 193	82.85 189	86.31 237	91.19 205	72.12 273	95.88 170	94.29 202	80.44 190	77.02 212	91.96 171	55.24 284	97.14 162	79.30 155	80.38 209	89.67 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MDA-MVSNet_test_wron		73.54 289	70.43 295	82.86 288	84.55 298	71.85 275	91.74 272	91.32 281	67.63 306	46.73 337	81.09 299	55.11 285	90.42 320	55.91 308	59.76 312	86.31 290

YYNet1		73.53 290	70.43 295	82.85 289	84.52 300	71.73 279	91.69 273	91.37 279	67.63 306	46.79 336	81.21 298	55.04 286	90.43 319	55.93 307	59.70 313	86.38 289

LTVRE_ROB	73.68 18	77.99 257	75.74 259	84.74 257	90.45 215	72.02 274	86.41 312	91.12 282	72.57 290	66.63 280	87.27 229	54.95 287	96.98 167	56.29 306	75.98 231	85.21 299
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
LPG-MVS_test		84.20 188	83.49 182	86.33 234	90.88 209	73.06 266	95.28 186	94.13 212	82.20 160	76.31 220	93.20 160	54.83 288	96.95 168	83.72 122	80.83 207	88.98 240

LGP-MVS_train				86.33 234	90.88 209	73.06 266		94.13 212	82.20 160	76.31 220	93.20 160	54.83 288	96.95 168	83.72 122	80.83 207	88.98 240

cascas		86.50 150	84.48 161	92.55 90	92.64 173	85.95 28	97.04 99	95.07 166	75.32 260	80.50 170	91.02 184	54.33 290	97.98 117	86.79 101	87.62 147	93.71 189

ACMP	81.66 11	84.00 189	83.22 185	86.33 234	91.53 202	72.95 268	95.91 168	93.79 233	83.70 137	73.79 240	92.22 168	54.31 291	96.89 172	83.98 117	79.74 212	89.16 235
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_0	77.72 15	81.70 227	78.95 237	89.94 169	90.77 211	76.72 240	95.96 163	96.95 43	85.01 101	70.24 267	88.53 216	52.32 292	98.20 113	86.68 102	44.08 342	94.89 169

MSDG		80.62 238	77.77 243	89.14 181	93.43 159	77.24 233	91.89 269	90.18 299	69.86 302	68.02 274	91.94 173	52.21 293	98.84 95	59.32 291	83.12 189	91.35 203

DSMNet-mixed		73.13 292	72.45 286	75.19 317	77.51 326	46.82 341	85.09 318	82.01 341	67.61 310	69.27 272	81.33 297	50.89 294	86.28 332	54.54 311	83.80 184	92.46 199

UGNet		87.73 130	86.55 132	91.27 129	95.16 115	79.11 177	96.35 145	96.23 109	88.14 49	87.83 98	90.48 192	50.65 295	99.09 81	80.13 149	94.03 95	95.60 156
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
FMVSNet5		76.46 277	74.16 279	83.35 286	90.05 222	76.17 244	89.58 285	89.85 301	71.39 298	65.29 289	80.42 301	50.61 296	87.70 328	61.05 285	69.24 276	86.18 292

MS-PatchMatch		83.05 206	81.82 202	86.72 233	89.64 228	79.10 178	94.88 203	94.59 193	79.70 209	70.67 262	89.65 203	50.43 297	96.82 175	70.82 229	95.99 81	84.25 306

Anonymous20231206		75.29 282	73.64 281	80.22 303	80.75 315	63.38 316	93.36 235	90.71 294	73.09 285	67.12 277	83.70 286	50.33 298	90.85 316	53.63 314	70.10 263	86.44 288

N_pmnet		61.30 312	60.20 313	64.60 329	84.32 301	17.00 359	91.67 274	10.98 359	61.77 322	58.45 321	78.55 308	49.89 299	91.83 305	42.27 334	63.94 303	84.97 302

jajsoiax		82.12 223	81.15 214	85.03 250	84.19 303	70.70 288	94.22 219	93.95 223	83.07 146	73.48 242	89.75 202	49.66 300	95.37 247	82.24 138	79.76 210	89.02 239

LP		68.54 307	63.92 309	82.39 292	87.93 248	71.79 278	72.37 343	86.01 328	55.89 335	58.33 322	71.46 334	49.58 301	90.10 323	32.25 344	61.48 309	85.27 297

RPSCF		77.73 262	76.63 253	81.06 300	88.66 242	55.76 332	87.77 300	87.88 316	64.82 314	74.14 239	92.79 165	49.22 302	96.81 176	67.47 251	76.88 230	90.62 209

SixPastTwentyTwo		76.04 278	74.32 277	81.22 299	84.54 299	61.43 322	91.16 276	89.30 306	77.89 230	64.04 292	86.31 254	48.23 303	94.29 274	63.54 278	63.84 304	87.93 267

test20.03		72.36 296	71.15 290	75.98 316	77.79 324	59.16 327	92.40 260	89.35 305	74.09 278	61.50 312	84.32 277	48.09 304	85.54 337	50.63 323	62.15 308	83.24 320

VDDNet		86.44 152	84.51 159	92.22 101	91.56 199	81.83 106	97.10 93	94.64 190	69.50 303	87.84 97	95.19 121	48.01 305	97.92 124	89.82 73	86.92 150	96.89 123

VDD-MVS		88.28 119	87.02 125	92.06 107	95.09 116	80.18 146	97.55 57	94.45 198	83.09 145	89.10 85	95.92 101	47.97 306	98.49 104	93.08 43	86.91 151	97.52 97

OurMVSNet-221017-0		77.18 271	76.06 256	80.55 302	83.78 307	60.00 324	90.35 280	91.05 285	77.01 244	66.62 281	87.92 224	47.73 307	94.03 278	71.63 218	68.44 279	87.62 272

CMPMVS	54.94 21	75.71 281	74.56 274	79.17 308	79.69 320	55.98 330	89.59 284	93.30 256	60.28 328	53.85 329	89.07 208	47.68 308	96.33 189	76.55 183	81.02 206	85.22 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvs_tets		81.74 226	80.71 219	84.84 254	84.22 302	70.29 291	93.91 223	93.78 234	82.77 151	73.37 243	89.46 205	47.36 309	95.31 250	81.99 139	79.55 216	88.92 244

MDA-MVSNet-bldmvs		71.45 298	67.94 301	81.98 297	85.33 293	68.50 302	92.35 261	88.76 311	70.40 300	42.99 338	81.96 294	46.57 310	91.31 313	48.75 328	54.39 328	86.11 293

pmmvs-eth3d		73.59 288	70.66 292	82.38 293	76.40 329	73.38 262	89.39 289	89.43 304	72.69 289	60.34 316	77.79 310	46.43 311	91.26 314	66.42 260	57.06 315	82.51 325

MVS-HIRNet		71.36 299	67.00 302	84.46 269	90.58 213	69.74 296	79.15 329	87.74 317	46.09 341	61.96 310	50.50 343	45.14 312	95.64 233	53.74 313	88.11 145	88.00 266

FMVSNet1		79.50 243	76.54 254	88.39 196	88.47 243	81.95 99	94.30 215	93.38 250	73.14 284	72.04 255	85.66 259	43.86 313	93.84 281	65.48 264	72.53 249	89.38 232

K. test v3		73.62 287	71.59 289	79.69 305	82.98 310	59.85 325	90.85 279	88.83 309	77.13 240	58.90 318	82.11 293	43.62 314	91.72 309	65.83 263	54.10 329	87.50 277

pmmvs6		74.65 285	71.67 288	83.60 283	79.13 322	69.94 293	93.31 240	90.88 289	61.05 327	65.83 285	84.15 279	43.43 315	94.83 264	66.62 256	60.63 310	86.02 295

ACMH	75.40 17	77.99 257	74.96 266	87.10 228	90.67 212	76.41 242	93.19 245	91.64 277	72.47 291	63.44 295	87.61 226	43.34 316	97.16 159	58.34 293	73.94 241	87.72 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_0402		72.68 294	69.54 298	82.09 296	88.67 241	71.81 277	92.72 254	86.77 323	61.52 323	62.21 306	83.91 280	43.22 317	93.76 284	34.60 342	72.23 250	80.72 332

lessismore_v0				79.98 304	80.59 317	58.34 328		80.87 343		58.49 320	83.46 288	43.10 318	93.89 280	63.11 280	48.68 335	87.72 269

UnsupCasMVSNet_eth		73.25 291	70.57 293	81.30 298	77.53 325	66.33 308	87.24 304	93.89 226	80.38 193	57.90 324	81.59 296	42.91 319	90.56 318	65.18 266	48.51 336	87.01 283

COLMAP_ROB	73.24 19	75.74 280	73.00 285	83.94 276	92.38 175	69.08 300	91.85 270	86.93 322	61.48 324	65.32 288	90.27 196	42.27 320	96.93 171	50.91 322	75.63 234	85.80 296
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		79.18 248	75.99 257	88.72 191	87.37 253	80.66 131	79.96 325	91.82 274	77.38 237	74.33 238	81.87 295	41.78 321	90.74 317	66.36 261	83.10 190	94.76 172

ACMH+	76.62 16	77.47 264	74.94 267	85.05 249	91.07 207	71.58 281	93.26 241	90.01 300	71.80 294	64.76 290	88.55 214	41.62 322	96.48 184	62.35 282	71.00 253	87.09 282

ITE_SJBPF				82.38 293	87.00 255	65.59 309		89.55 303	79.99 204	69.37 271	91.30 180	41.60 323	95.33 249	62.86 281	74.63 240	86.24 291

new-patchmatchnet		68.85 305	65.93 305	77.61 310	73.57 336	63.94 315	90.11 282	88.73 312	71.62 296	55.08 327	73.60 325	40.84 324	87.22 330	51.35 320	48.49 337	81.67 331

USDC		78.65 253	76.25 255	85.85 241	87.58 251	74.60 255	89.58 285	90.58 298	84.05 127	63.13 297	88.23 219	40.69 325	96.86 174	66.57 258	75.81 233	86.09 294

XVG-ACMP-BASELINE		79.38 245	77.90 242	83.81 277	84.98 297	67.14 307	89.03 290	93.18 257	80.26 198	72.87 250	88.15 221	38.55 326	96.26 192	76.05 189	78.05 226	88.02 265

AllTest		75.92 279	73.06 284	84.47 267	92.18 184	67.29 304	91.07 277	84.43 332	67.63 306	63.48 293	90.18 197	38.20 327	97.16 159	57.04 296	73.37 243	88.97 242

TestCases				84.47 267	92.18 184	67.29 304		84.43 332	67.63 306	63.48 293	90.18 197	38.20 327	97.16 159	57.04 296	73.37 243	88.97 242

Test4		82.30 221	79.15 236	91.78 119	81.84 311	81.74 109	94.04 221	94.20 205	84.86 104	59.75 317	83.88 281	37.14 329	96.28 191	84.60 113	92.00 119	97.30 109

UnsupCasMVSNet_bld		68.60 306	64.50 307	80.92 301	74.63 333	67.80 303	83.97 319	92.94 262	65.12 313	54.63 328	68.23 337	35.97 330	92.17 298	60.13 287	44.83 340	82.78 323

tmp_tt		41.54 323	41.93 323	40.38 340	20.10 358	26.84 355	61.93 346	59.09 355	14.81 353	28.51 345	80.58 300	35.53 331	48.33 356	63.70 277	13.11 352	45.96 350

testgi		74.88 284	73.40 282	79.32 307	80.13 319	61.75 320	93.21 243	86.64 324	79.49 213	66.56 282	91.06 183	35.51 332	88.67 325	56.79 299	71.25 251	87.56 275

OpenMVS_ROB	68.52 20	73.02 293	69.57 297	83.37 285	80.54 318	71.82 276	93.60 229	88.22 314	62.37 319	61.98 309	83.15 291	35.31 333	95.47 243	45.08 332	75.88 232	82.82 322

testing_2		76.96 273	73.18 283	88.30 199	75.87 332	79.64 163	89.92 283	94.21 204	80.16 199	51.23 331	75.94 322	33.94 334	95.81 218	82.28 137	75.12 238	89.46 229

TDRefinement		69.20 304	65.78 306	79.48 306	66.04 343	62.21 319	88.21 296	86.12 325	62.92 317	61.03 314	85.61 262	33.23 335	94.16 276	55.82 309	53.02 330	82.08 329

LF4IMVS		72.36 296	70.82 291	76.95 311	79.18 321	56.33 329	86.12 313	86.11 326	69.30 304	63.06 299	86.66 244	33.03 336	92.25 295	65.33 265	68.64 278	82.28 328

MIMVSNet1		69.44 302	66.65 304	77.84 309	76.48 328	62.84 318	87.42 302	88.97 308	66.96 311	57.75 325	79.72 306	32.77 337	85.83 334	46.32 330	63.42 305	84.85 303

EG-PatchMatch MVS		74.92 283	72.02 287	83.62 282	83.76 308	73.28 264	93.62 228	92.04 272	68.57 305	58.88 319	83.80 282	31.87 338	95.57 239	56.97 298	78.67 221	82.00 330

new_pmnet		66.18 308	63.18 310	75.18 318	76.27 330	61.74 321	83.79 320	84.66 331	56.64 334	51.57 330	71.85 332	31.29 339	87.93 327	49.98 324	62.55 307	75.86 337

TinyColmap		72.41 295	68.99 300	82.68 290	88.11 246	69.59 297	88.41 295	85.20 329	65.55 312	57.91 323	84.82 275	30.80 340	95.94 211	51.38 318	68.70 277	82.49 327

test1235678		64.50 311	62.19 311	71.42 323	66.82 342	48.00 340	89.44 287	87.90 315	62.82 318	49.12 335	71.31 335	30.14 341	82.19 341	41.88 335	60.32 311	84.06 308

pmmvs3		65.75 309	62.18 312	76.45 314	67.12 341	64.54 311	88.68 293	85.05 330	54.77 339	57.54 326	73.79 324	29.40 342	86.21 333	55.49 310	47.77 338	78.62 334

testus		70.06 301	69.09 299	72.98 321	74.54 334	51.28 339	93.78 225	87.34 318	71.49 297	62.69 302	83.46 288	24.44 343	84.77 338	51.22 321	72.85 247	82.90 321

PM-MVS		69.32 303	66.93 303	76.49 313	73.60 335	55.84 331	85.91 314	79.32 347	74.72 272	61.09 313	78.18 309	21.76 344	91.10 315	70.86 227	56.90 316	82.51 325

Anonymous20231211		61.03 313	56.76 315	73.82 320	71.24 337	53.47 335	87.60 301	81.65 342	44.19 342	51.08 334	71.82 333	20.79 345	88.46 326	35.45 341	47.07 339	79.52 333

test12356		58.24 314	56.06 316	64.77 327	60.65 344	39.42 350	82.78 323	84.34 334	57.47 333	42.65 339	69.10 336	19.21 346	81.18 342	38.97 338	49.40 333	73.69 338

DeepMVS_CX				64.06 330	78.53 323	43.26 346		68.11 352	69.94 301	38.55 340	76.14 321	18.53 347	79.34 343	43.72 333	41.62 344	69.57 343

ambc				76.02 315	68.11 340	51.43 337	64.97 345	89.59 302		60.49 315	74.49 323	17.17 348	92.46 292	61.50 283	52.85 331	84.17 307

no-one		51.12 319	45.81 321	67.03 325	53.16 351	52.22 336	75.21 337	80.40 344	54.89 338	28.26 346	48.50 345	15.54 349	82.81 340	39.29 337	17.06 348	66.07 345

testmv		54.58 317	51.53 318	63.74 331	53.58 349	40.82 348	83.26 321	83.92 336	54.07 340	36.35 342	61.26 338	14.80 350	77.07 344	33.00 343	43.53 343	73.33 339

FPMVS		55.09 315	52.93 317	61.57 332	55.98 345	40.51 349	83.11 322	83.41 339	37.61 344	34.95 343	71.95 331	14.40 351	76.95 345	29.81 346	65.16 299	67.25 344

EMVS		31.70 330	31.45 330	32.48 342	50.72 352	23.95 357	74.78 338	52.30 358	20.36 351	16.08 353	31.48 352	12.80 352	53.60 355	11.39 353	13.10 353	19.88 353

ANet_high		46.22 321	41.28 324	61.04 333	39.91 356	46.25 343	70.59 344	76.18 348	58.87 331	23.09 348	48.00 346	12.58 353	66.54 351	28.65 347	13.62 351	70.35 341

E-PMN		32.70 329	32.39 328	33.65 341	53.35 350	25.70 356	74.07 339	53.33 357	21.08 350	17.17 352	33.63 351	11.85 354	54.84 354	12.98 352	14.04 350	20.42 352

Gipuma		45.11 322	42.05 322	54.30 335	80.69 316	51.30 338	35.80 351	83.81 337	28.13 347	27.94 347	34.53 349	11.41 355	76.70 347	21.45 349	54.65 327	34.90 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		50.90 320	46.31 320	64.67 328	55.53 346	46.67 342	77.30 335	71.02 349	40.89 343	34.16 344	59.32 339	9.83 356	76.14 348	40.09 336	28.63 345	71.21 340

LCM-MVSNet		52.52 318	48.24 319	65.35 326	47.63 353	41.45 347	72.55 342	83.62 338	31.75 345	37.66 341	57.92 341	9.19 357	76.76 346	49.26 326	44.60 341	77.84 335

PNet_i23d		41.20 324	38.13 325	50.41 336	55.23 347	36.24 353	73.80 341	65.45 354	29.75 346	21.36 349	47.05 347	3.43 358	63.23 352	28.17 348	18.92 347	51.76 347

PMVS	34.80 23	39.19 325	35.53 326	50.18 337	29.72 357	30.30 354	59.60 348	66.20 353	26.06 348	17.91 351	49.53 344	3.12 359	74.09 349	18.19 351	49.40 333	46.14 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 328	29.49 331	46.92 338	41.86 354	36.28 352	50.45 349	56.52 356	18.75 352	18.28 350	37.84 348	2.41 360	58.41 353	18.71 350	20.62 346	46.06 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		14.10 332	13.89 333	14.72 344	55.23 347	22.91 358	33.83 352	3.56 360	4.94 354	4.11 355	2.28 358	2.06 361	19.66 357	10.23 354	8.74 354	1.59 357

wuykxyi23d		37.75 326	31.85 329	55.46 334	40.00 355	38.01 351	59.81 347	69.47 350	25.46 349	12.42 354	30.55 353	2.06 361	67.08 350	31.81 345	15.03 349	61.29 346

test123		9.07 334	11.73 335	1.11 345	0.50 360	0.77 360	89.44 287	0.20 362	0.34 356	2.15 357	10.72 357	0.34 363	0.32 358	1.79 356	0.08 357	2.23 354

testmvs		9.92 333	12.94 334	0.84 346	0.65 359	0.29 361	93.78 225	0.39 361	0.42 355	2.85 356	15.84 356	0.17 364	0.30 359	2.18 355	0.21 355	1.91 355

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		8.11 335	10.81 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	97.30 71	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																97.54 94

test_part3							98.15 25		84.95 102		98.83 2		99.80 14	97.78 2

test_part2					98.90 7	85.14 44				96.07 8

MTGPA								96.33 103

MTMP								68.16 351

gm-plane-assit					92.27 180	79.64 163			84.47 116		95.15 124		97.93 118	85.81 104

test9_res														96.00 14	99.03 7	98.31 42

agg_prior2														94.30 28	99.00 9	98.57 31

agg_prior					98.59 26	83.13 79		96.56 78		94.19 29			99.16 75

test_prior4						82.34 94	97.75 46

test_prior				93.09 69	98.68 15	81.91 102		96.40 95					99.06 82			98.29 44

旧先验2							96.97 105		74.06 279	96.10 7			97.76 130	88.38 87

新几何2							96.42 142

无先验							96.87 110	96.78 52	77.39 236				99.52 44	79.95 150		98.43 36

原ACMM2							96.84 111

testdata2													99.48 49	76.45 185

testdata1							95.57 181		87.44 59

plane_prior7					91.86 197	77.55 228

plane_prior5								94.69 183					97.30 151	87.08 97	82.82 201	90.96 206

plane_prior4											94.15 142

plane_prior3						77.75 225			90.17 28	81.33 163

plane_prior2							97.18 79		89.89 30

plane_prior1					91.95 195

plane_prior						77.96 218	97.52 59		90.36 27						82.96 193

n2								0.00 363

nn								0.00 363

door-mid								79.75 346

test11								96.50 85

door								80.13 345

HQP5-MVS						78.48 201

HQP-NCC					92.08 187		97.63 51		90.52 24	82.30 148

ACMP_Plane					92.08 187		97.63 51		90.52 24	82.30 148

BP-MVS														87.67 94

HQP4-MVS										82.30 148			97.32 149			91.13 204

HQP3-MVS								94.80 178							83.01 191

NP-MVS					92.04 191	78.22 209					94.56 136

ACMMP++_ref															78.45 225

ACMMP++															79.05 218

High-resolution multi-view benchmark