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 91	98.31 40	80.10 154	97.42 72	96.46 94	92.20 13	97.11 3	98.29 14	93.46 1	99.10 80	96.01 12	99.30 2	98.77 21

GG-mvs-BLEND				93.49 57	94.94 128	86.26 25	81.62 336	97.00 40		88.32 99	94.30 146	91.23 2	96.21 205	88.49 89	97.43 57	98.00 70

gg-mvs-nofinetune		85.48 181	82.90 197	93.24 66	94.51 146	85.82 31	79.22 340	96.97 43	61.19 336	87.33 108	53.01 354	90.58 3	96.07 209	86.07 108	97.23 62	97.81 83

CHOSEN 280x420		91.71 62	91.85 53	91.29 137	94.94 128	82.69 93	87.89 312	96.17 121	85.94 79	87.27 109	94.31 145	90.27 4	95.65 243	94.04 31	95.86 82	95.53 166

MVSTER		89.25 105	88.92 100	90.24 163	95.98 96	84.66 54	96.79 119	95.36 163	87.19 65	80.33 186	90.61 202	90.02 5	95.97 216	85.38 113	78.64 231	90.09 231

tttt0517		88.57 122	88.19 108	89.71 188	93.00 178	75.99 259	95.67 185	96.67 69	80.78 190	81.82 172	94.40 144	88.97 6	97.58 146	76.05 198	86.31 163	95.57 165

thisisatest0530		89.65 98	89.02 97	91.53 132	93.46 170	80.78 134	96.52 136	96.67 69	81.69 177	83.79 142	94.90 136	88.85 7	97.68 137	77.80 172	87.49 156	96.14 152

thisisatest0515		90.95 77	90.26 74	93.01 77	94.03 158	84.27 64	97.91 33	96.67 69	83.18 149	86.87 113	95.51 112	88.66 8	97.85 131	80.46 151	89.01 139	96.92 129

ESAPD		95.32 5	95.55 6	94.64 21	98.79 12	84.87 52	97.77 43	96.74 60	86.11 75	96.54 6	98.89 2	88.39 9	99.74 20	97.67 2	99.05 8	99.31 5

MCST-MVS		96.17 2	96.12 4	96.32 3	99.42 2	89.36 5	98.94 9	97.10 34	95.17 2	92.11 50	98.46 11	87.33 10	99.97 1	97.21 5	99.31 1	99.63 2

TSAR-MVS + GP.		94.35 15	94.50 13	93.89 37	97.38 73	83.04 87	98.10 26	95.29 168	91.57 15	93.81 35	97.45 63	86.64 11	99.43 52	96.28 10	94.01 98	99.20 9

DWT-MVSNet_test		90.52 86	89.80 87	92.70 90	95.73 107	82.20 101	93.69 238	96.55 86	88.34 45	87.04 112	95.34 115	86.53 12	97.55 148	76.32 195	88.66 144	98.34 41

TSAR-MVS + MP.		94.79 10	95.17 8	93.64 47	97.66 58	84.10 66	95.85 180	96.42 98	91.26 17	97.49 2	96.80 90	86.50 13	98.49 108	95.54 19	99.03 9	98.33 42

CNVR-MVS		96.30 1	96.54 1	95.55 10	99.31 5	87.69 17	99.06 5	97.12 27	94.66 3	96.79 4	98.78 4	86.42 14	99.95 2	97.59 3	99.18 3	99.00 13

DeepPCF-MVS	89.82 1	94.61 12	96.17 3	89.91 180	97.09 79	70.21 305	98.99 8	96.69 67	95.57 1	95.08 19	99.23 1	86.40 15	99.87 8	97.84 1	98.66 22	99.65 1

HPM-MVS++		95.32 5	95.48 7	94.85 16	98.62 25	86.04 27	97.81 40	96.93 47	92.45 11	95.69 12	98.50 9	85.38 16	99.85 10	94.75 23	99.18 3	98.65 29

NCCC		95.63 3	95.94 5	94.69 20	99.21 6	85.15 45	99.16 3	96.96 44	94.11 6	95.59 13	98.64 7	85.07 17	99.91 3	95.61 18	99.10 5	99.00 13

EPP-MVSNet		89.76 96	89.72 88	89.87 181	93.78 161	76.02 258	97.22 79	96.51 89	79.35 224	85.11 123	95.01 135	84.82 18	97.10 173	87.46 100	88.21 150	96.50 142

tfpn1000		86.43 162	85.10 160	90.41 159	95.56 110	80.51 144	95.90 176	97.09 35	75.91 261	80.02 190	94.82 137	84.78 19	95.47 254	57.36 307	84.46 187	95.26 173

tfpn_ndepth		87.25 148	86.00 145	91.01 147	95.86 102	81.46 121	96.53 135	97.09 35	77.35 247	81.36 174	95.07 133	84.74 20	95.86 225	60.88 297	85.14 184	95.72 161

agg_prior1		94.10 21	94.31 19	93.48 58	98.59 27	83.13 84	97.77 43	96.56 84	84.38 123	94.19 31	98.13 24	84.66 21	99.16 75	95.74 17	98.74 19	98.15 56

PatchFormer-LS_test		90.14 91	89.30 94	92.65 93	95.43 113	82.46 96	93.46 245	96.35 108	88.56 41	84.82 126	95.22 122	84.63 22	97.55 148	78.40 171	86.81 159	97.94 75

TEST9					98.64 22	83.71 73	97.82 38	96.65 73	84.29 127	95.16 16	98.09 29	84.39 23	99.36 56

train_agg		94.28 16	94.45 15	93.74 42	98.64 22	83.71 73	97.82 38	96.65 73	84.50 118	95.16 16	98.09 29	84.33 24	99.36 56	95.91 14	98.96 12	98.16 54

test_8					98.63 24	83.64 76	97.81 40	96.63 79	84.50 118	95.10 18	98.11 28	84.33 24	99.23 62

SD-MVS		94.84 9	95.02 9	94.29 26	97.87 56	84.61 55	97.76 47	96.19 120	89.59 32	96.66 5	98.17 22	84.33 24	99.60 37	96.09 11	98.50 26	98.66 28

APDe-MVS		94.56 13	94.75 10	93.96 36	98.84 11	83.40 81	98.04 30	96.41 99	85.79 82	95.00 21	98.28 15	84.32 27	99.18 73	97.35 4	98.77 17	99.28 6

旧先验1					97.39 70	79.58 173		96.54 87			98.08 32	84.00 28			97.42 58	97.62 97

CSCG		92.02 56	91.65 58	93.12 71	98.53 29	80.59 138	97.47 64	97.18 25	77.06 252	84.64 131	97.98 39	83.98 29	99.52 44	90.72 64	97.33 61	99.23 8

IB-MVS	85.34 4	88.67 118	87.14 133	93.26 65	93.12 177	84.32 61	98.76 10	97.27 22	87.19 65	79.36 200	90.45 204	83.92 30	98.53 106	84.41 120	69.79 279	96.93 127
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		89.08 106	88.39 106	91.15 142	93.13 176	79.15 184	88.61 307	96.11 124	83.14 150	89.58 85	86.93 247	83.83 31	96.87 183	88.22 93	85.92 170	97.42 109

SteuartSystems-ACMMP		94.13 20	94.44 16	93.20 67	95.41 115	81.35 123	99.02 7	96.59 82	89.50 33	94.18 33	98.36 13	83.68 32	99.45 51	94.77 22	98.45 28	98.81 19
Skip Steuart: Steuart Systems R&D Blog.
agg_prior3		94.10 21	94.29 21	93.53 55	98.62 25	83.03 88	97.80 42	96.64 76	84.28 128	95.01 20	98.03 33	83.40 33	99.41 53	95.91 14	98.96 12	98.16 54

DELS-MVS		94.98 7	94.49 14	96.44 2	96.42 85	90.59 3	99.21 2	97.02 39	94.40 5	91.46 58	97.08 80	83.32 34	99.69 28	92.83 43	98.70 21	99.04 11
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 73	98.68 16	81.91 106	98.37 18	96.40 101	86.08 77	94.57 27	98.02 34	83.14 35	99.06 82	95.05 20	98.79 15	98.29 46

test_prior2							98.37 18		86.08 77	94.57 27	98.02 34	83.14 35		95.05 20	98.79 15

SMA-MVS		94.70 11	94.68 11	94.76 18	98.02 50	85.94 29	97.47 64	96.77 55	85.32 92	97.92 1	98.70 5	83.09 37	99.84 12	95.79 16	99.08 6	98.49 37

segment_acmp												82.69 38

Regformer-1		94.00 25	94.04 23	93.87 38	98.41 35	84.29 62	97.43 70	97.04 38	89.50 33	92.75 46	98.13 24	82.60 39	99.26 61	93.55 33	96.99 65	98.06 62

Regformer-2		93.92 26	94.01 24	93.67 46	98.41 35	83.75 72	97.43 70	97.00 40	89.43 35	92.69 47	98.13 24	82.48 40	99.22 64	93.51 34	96.99 65	98.04 63

PAPM		92.87 41	92.40 46	94.30 25	92.25 196	87.85 14	96.40 150	96.38 105	91.07 18	88.72 94	96.90 84	82.11 41	97.37 158	90.05 72	97.70 51	97.67 92

APD-MVS		93.61 30	93.59 27	93.69 45	98.76 13	83.26 82	97.21 80	96.09 125	82.41 164	94.65 26	98.21 17	81.96 42	98.81 98	94.65 25	98.36 35	99.01 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
conf0.01		85.70 177	84.35 173	89.77 185	94.53 138	79.70 164	95.17 200	97.11 28	75.97 255	79.44 193	95.31 116	81.90 43	95.73 236	56.78 312	82.91 202	93.89 194

conf0.002		85.70 177	84.35 173	89.77 185	94.53 138	79.70 164	95.17 200	97.11 28	75.97 255	79.44 193	95.31 116	81.90 43	95.73 236	56.78 312	82.91 202	93.89 194

thresconf0.02		85.80 170	84.35 173	90.17 166	94.53 138	79.70 164	95.17 200	97.11 28	75.97 255	79.44 193	95.31 116	81.90 43	95.73 236	56.78 312	82.91 202	95.09 174

tfpn_n400		85.80 170	84.35 173	90.17 166	94.53 138	79.70 164	95.17 200	97.11 28	75.97 255	79.44 193	95.31 116	81.90 43	95.73 236	56.78 312	82.91 202	95.09 174

tfpnconf		85.80 170	84.35 173	90.17 166	94.53 138	79.70 164	95.17 200	97.11 28	75.97 255	79.44 193	95.31 116	81.90 43	95.73 236	56.78 312	82.91 202	95.09 174

tfpnview11		85.80 170	84.35 173	90.17 166	94.53 138	79.70 164	95.17 200	97.11 28	75.97 255	79.44 193	95.31 116	81.90 43	95.73 236	56.78 312	82.91 202	95.09 174

CDPH-MVS		93.12 36	92.91 38	93.74 42	98.65 21	83.88 68	97.67 52	96.26 114	83.00 154	93.22 41	98.24 16	81.31 49	99.21 66	89.12 83	98.74 19	98.14 57

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 49	96.97 83	81.30 50	98.99 86	88.54 87	98.88 14	99.20 9

test12				94.25 27	98.34 38	85.55 36		96.35 108		92.36 48		80.84 51	99.22 64		98.31 36	97.98 72

Regformer-3		93.19 34	93.19 33	93.19 68	98.10 47	83.01 89	97.08 101	96.98 42	88.98 37	91.35 63	97.89 44	80.80 52	99.23 62	92.30 49	95.20 87	97.32 113

tpmrst		88.36 127	87.38 127	91.31 135	94.36 148	79.92 156	87.32 315	95.26 170	85.32 92	88.34 98	86.13 268	80.60 53	96.70 190	83.78 125	85.34 183	97.30 116

Regformer-4		93.06 37	93.12 34	92.89 81	98.10 47	82.20 101	97.08 101	96.92 49	88.87 39	91.23 65	97.89 44	80.57 54	99.19 71	92.21 51	95.20 87	97.29 118

PHI-MVS		93.59 31	93.63 26	93.48 58	98.05 49	81.76 112	98.64 13	97.13 26	82.60 162	94.09 34	98.49 10	80.35 55	99.85 10	94.74 24	98.62 23	98.83 18

CDS-MVSNet		89.50 100	88.96 99	91.14 143	91.94 209	80.93 130	97.09 99	95.81 140	84.26 129	84.72 129	94.20 148	80.31 56	95.64 244	83.37 136	88.96 140	96.85 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		87.35 147	86.26 143	90.62 155	92.93 181	78.67 202	88.06 311	95.99 130	79.33 225	87.40 106	86.43 263	80.28 57	96.40 196	80.23 154	85.73 175	96.79 133

1112_ss		88.60 121	87.47 125	92.00 115	93.21 173	80.97 129	96.47 139	92.46 279	83.64 143	80.86 179	97.30 72	80.24 58	97.62 143	77.60 180	85.49 176	97.40 110

Test_1112_low_res		88.03 134	86.73 139	91.94 117	93.15 175	80.88 131	96.44 144	92.41 280	83.59 145	80.74 181	91.16 191	80.18 59	97.59 145	77.48 182	85.40 177	97.36 112

DeepC-MVS_fast	89.06 2	94.48 14	94.30 20	95.02 14	98.86 10	85.68 34	98.06 29	96.64 76	93.64 8	91.74 55	98.54 8	80.17 60	99.90 4	92.28 50	98.75 18	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		86.46 160	84.95 163	90.98 148	93.74 164	78.60 206	88.13 310	95.90 136	79.65 220	85.42 122	85.67 270	80.08 61	97.06 174	71.71 228	84.26 190	97.28 120

MSLP-MVS++		94.28 16	94.39 17	93.97 35	98.30 41	84.06 67	98.64 13	96.93 47	90.71 22	93.08 42	98.70 5	79.98 62	99.21 66	94.12 30	99.07 7	98.63 30

EPNet		94.06 23	94.15 22	93.76 41	97.27 76	84.35 60	98.29 20	97.64 19	94.57 4	95.36 14	96.88 86	79.96 63	99.12 79	91.30 57	96.11 77	97.82 82
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 61	97.34 74	82.83 92	97.56 58	98.27 12	89.16 36	89.71 81	97.14 77	79.77 64	99.56 42	93.65 32	97.94 46	98.02 65

TESTMET0.1,1		89.83 94	89.34 93	91.31 135	92.54 187	80.19 152	97.11 95	96.57 83	86.15 74	86.85 114	91.83 183	79.32 65	96.95 178	81.30 147	92.35 117	96.77 135

WTY-MVS		92.65 48	91.68 57	95.56 9	96.00 95	88.90 8	98.23 22	97.65 18	88.57 40	89.82 80	97.22 75	79.29 66	99.06 82	89.57 79	88.73 143	98.73 26

1121		90.66 80	89.82 86	93.16 70	97.39 70	81.71 116	93.33 249	96.66 72	74.45 285	91.38 59	97.55 61	79.27 67	99.52 44	79.95 157	98.43 29	98.26 49

HY-MVS	84.06 6	91.63 63	90.37 73	95.39 12	96.12 90	88.25 10	90.22 294	97.58 20	88.33 46	90.50 72	91.96 179	79.26 68	99.06 82	90.29 70	89.07 138	98.88 17

PAPM_NR		91.46 67	90.82 68	93.37 62	98.50 32	81.81 111	95.03 210	96.13 122	84.65 114	86.10 119	97.65 55	79.24 69	99.75 18	83.20 137	96.88 70	98.56 34

alignmvs		92.97 39	92.26 49	95.12 13	95.54 111	87.77 15	98.67 11	96.38 105	88.04 50	93.01 43	97.45 63	79.20 70	98.60 102	93.25 40	88.76 142	98.99 15

新几何1				93.12 71	97.44 66	81.60 119		96.71 64	74.54 284	91.22 66	97.57 57	79.13 71	99.51 47	77.40 183	98.46 27	98.26 49

casdiffmvs1		91.94 57	91.49 61	93.28 64	95.02 127	83.53 77	95.37 194	95.49 155	86.52 71	94.24 30	91.65 184	79.04 72	97.74 136	91.67 54	94.45 93	98.57 32

JIA-IIPM		79.00 261	77.20 257	84.40 283	89.74 240	64.06 327	75.30 348	95.44 161	62.15 331	81.90 170	59.08 352	78.92 73	95.59 248	66.51 270	85.78 174	93.54 200

MVSFormer		91.36 70	90.57 70	93.73 44	93.00 178	88.08 12	94.80 216	94.48 205	80.74 194	94.90 22	97.13 78	78.84 74	95.10 268	83.77 126	97.46 54	98.02 65

lupinMVS		93.87 28	93.58 28	94.75 19	93.00 178	88.08 12	99.15 4	95.50 154	91.03 19	94.90 22	97.66 51	78.84 74	97.56 147	94.64 26	97.46 54	98.62 31

testdata				90.13 170	95.92 97	74.17 272		96.49 93	73.49 292	94.82 24	97.99 37	78.80 76	97.93 122	83.53 134	97.52 53	98.29 46

PAPR		92.74 42	92.17 52	94.45 22	98.89 9	84.87 52	97.20 82	96.20 118	87.73 57	88.40 97	98.12 27	78.71 77	99.76 14	87.99 95	96.28 75	98.74 22

EI-MVSNet-Vis-set		91.84 60	91.77 56	92.04 114	97.60 60	81.17 125	96.61 132	96.87 51	88.20 48	89.19 90	97.55 61	78.69 78	99.14 77	90.29 70	90.94 130	95.80 158

HFP-MVS		92.89 40	92.86 39	92.98 78	98.71 14	81.12 126	97.58 56	96.70 65	85.20 97	91.75 53	97.97 41	78.47 79	99.71 24	90.95 60	98.41 30	98.12 59

#test#		92.99 38	92.99 36	92.98 78	98.71 14	81.12 126	97.77 43	96.70 65	85.75 83	91.75 53	97.97 41	78.47 79	99.71 24	91.36 56	98.41 30	98.12 59

Patchmatch-test		78.25 267	74.72 283	88.83 199	91.20 217	74.10 273	73.91 352	88.70 327	59.89 342	66.82 292	85.12 284	78.38 81	94.54 280	48.84 339	79.58 223	97.86 79

Vis-MVSNet (Re-imp)		88.88 113	88.87 101	88.91 197	93.89 159	74.43 270	96.93 113	94.19 219	84.39 122	83.22 150	95.67 109	78.24 82	94.70 277	78.88 168	94.40 95	97.61 98

tpm		85.55 179	84.47 171	88.80 200	90.19 232	75.39 264	88.79 305	94.69 193	84.83 108	83.96 139	85.21 279	78.22 83	94.68 278	76.32 195	78.02 237	96.34 147

MP-MVS		92.61 49	92.67 43	92.42 99	98.13 46	79.73 162	97.33 77	96.20 118	85.63 85	90.53 71	97.66 51	78.14 84	99.70 27	92.12 52	98.30 37	97.85 80
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HyFIR lowres test		89.36 102	88.60 103	91.63 130	94.91 130	80.76 135	95.60 188	95.53 151	82.56 163	84.03 136	91.24 190	78.03 85	96.81 186	87.07 103	88.41 148	97.32 113

ACMMP_Plus		93.46 32	93.23 32	94.17 31	97.16 77	84.28 63	96.82 117	96.65 73	86.24 73	94.27 29	97.99 37	77.94 86	99.83 13	93.39 35	98.57 24	98.39 40

原ACMM1				91.22 141	97.77 57	78.10 225		96.61 80	81.05 183	91.28 64	97.42 67	77.92 87	98.98 87	79.85 159	98.51 25	96.59 140

EI-MVSNet-UG-set		91.35 71	91.22 63	91.73 127	97.39 70	80.68 136	96.47 139	96.83 53	87.92 52	88.30 101	97.36 69	77.84 88	99.13 78	89.43 82	89.45 136	95.37 169

patchmatchnet-post											77.09 329	77.78 89	95.39 256

sam_mvs1												77.59 90				97.54 100

GST-MVS		92.43 53	92.22 51	93.04 76	98.17 44	81.64 118	97.40 74	96.38 105	84.71 111	90.90 69	97.40 68	77.55 91	99.76 14	89.75 77	97.74 50	97.72 88

MP-MVS-pluss		92.58 50	92.35 47	93.29 63	97.30 75	82.53 95	96.44 144	96.04 129	84.68 113	89.12 91	98.37 12	77.48 92	99.74 20	93.31 39	98.38 33	97.59 99
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 51	92.60 45	92.34 101	98.50 32	79.90 157	98.40 17	96.40 101	84.75 109	90.48 73	98.09 29	77.40 93	99.21 66	91.15 59	98.23 39	97.92 76

region2R		92.72 45	92.70 42	92.79 85	98.68 16	80.53 142	97.53 60	96.51 89	85.22 95	91.94 51	97.98 39	77.26 94	99.67 32	90.83 63	98.37 34	98.18 51

PatchmatchNet		86.83 154	85.12 159	91.95 116	94.12 153	82.27 99	86.55 323	95.64 147	84.59 116	82.98 153	84.99 285	77.26 94	95.96 220	68.61 256	91.34 128	97.64 95
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 94	98.52 30	80.29 147	97.37 75	96.44 96	87.04 67	91.38 59	97.83 47	77.24 96	99.59 38	90.46 66	98.07 42	98.02 65

X-MVStestdata		86.26 164	84.14 181	92.63 94	98.52 30	80.29 147	97.37 75	96.44 96	87.04 67	91.38 59	20.73 367	77.24 96	99.59 38	90.46 66	98.07 42	98.02 65

ACMMPR		92.69 46	92.67 43	92.75 87	98.66 19	80.57 139	97.58 56	96.69 67	85.20 97	91.57 56	97.92 43	77.01 98	99.67 32	90.95 60	98.41 30	98.00 70

UniMVSNet_NR-MVSNet		85.49 180	84.59 167	88.21 213	89.44 246	79.36 174	96.71 125	96.41 99	85.22 95	78.11 209	90.98 197	76.97 99	95.14 266	79.14 165	68.30 292	90.12 229

DP-MVS Recon		91.72 61	90.85 67	94.34 24	99.50 1	85.00 47	98.51 16	95.96 132	80.57 198	88.08 103	97.63 56	76.84 100	99.89 6	85.67 110	94.88 91	98.13 58

CANet		94.89 8	94.64 12	95.63 8	97.55 63	88.12 11	99.06 5	96.39 104	94.07 7	95.34 15	97.80 48	76.83 101	99.87 8	97.08 6	97.64 52	98.89 16

PVSNet_Blended_VisFu		91.24 72	90.77 69	92.66 91	95.09 123	82.40 97	97.77 43	95.87 138	88.26 47	86.39 115	93.94 153	76.77 102	99.27 59	88.80 86	94.00 99	96.31 150

FIs		86.73 158	86.10 144	88.61 203	90.05 235	80.21 151	96.14 164	96.95 45	85.56 89	78.37 207	92.30 175	76.73 103	95.28 262	79.51 160	79.27 226	90.35 223

casdiffmvs		90.98 75	90.24 75	93.19 68	94.60 135	84.15 65	95.01 211	94.98 178	84.98 104	91.53 57	91.14 192	76.72 104	97.62 143	89.78 76	93.42 110	97.81 83

zzz-MVS		92.74 42	92.71 40	92.86 82	97.90 52	80.85 132	96.47 139	96.33 110	87.92 52	90.20 75	98.18 18	76.71 105	99.76 14	92.57 47	98.09 40	97.96 73

MTAPA		92.45 52	92.31 48	92.86 82	97.90 52	80.85 132	92.88 263	96.33 110	87.92 52	90.20 75	98.18 18	76.71 105	99.76 14	92.57 47	98.09 40	97.96 73

PVSNet_BlendedMVS		90.05 92	89.96 81	90.33 161	97.47 64	83.86 69	98.02 31	96.73 61	87.98 51	89.53 86	89.61 215	76.42 107	99.57 40	94.29 28	79.59 222	87.57 285

PVSNet_Blended		93.13 35	92.98 37	93.57 51	97.47 64	83.86 69	99.32 1	96.73 61	91.02 20	89.53 86	96.21 98	76.42 107	99.57 40	94.29 28	95.81 84	97.29 118

test-mter		88.95 109	88.60 103	89.98 176	92.26 194	77.23 244	97.11 95	95.96 132	85.32 92	86.30 117	91.38 187	76.37 109	96.78 188	80.82 149	91.92 123	95.94 155

test222					96.15 89	78.41 213	95.87 178	96.46 94	71.97 302	89.66 83	97.45 63	76.33 110			98.24 38	98.30 45

FC-MVSNet-test		85.96 166	85.39 154	87.66 227	89.38 247	78.02 226	95.65 187	96.87 51	85.12 99	77.34 219	91.94 181	76.28 111	94.74 276	77.09 187	78.82 229	90.21 226

test_post											33.80 362	76.17 112	95.97 216

PGM-MVS		91.93 58	91.80 55	92.32 103	98.27 42	79.74 161	95.28 195	97.27 22	83.83 138	90.89 70	97.78 49	76.12 113	99.56 42	88.82 85	97.93 48	97.66 93

Patchmatch-RL test		76.65 289	74.01 293	84.55 278	77.37 341	64.23 325	78.49 344	82.84 354	78.48 237	64.63 305	73.40 341	76.05 114	91.70 322	76.99 188	57.84 326	97.72 88

TAMVS		88.48 123	87.79 115	90.56 156	91.09 219	79.18 182	96.45 142	95.88 137	83.64 143	83.12 151	93.33 166	75.94 115	95.74 235	82.40 142	88.27 149	96.75 137

EPNet_dtu		87.65 140	87.89 112	86.93 242	94.57 136	71.37 296	96.72 123	96.50 91	88.56 41	87.12 110	95.02 134	75.91 116	94.01 290	66.62 267	90.00 134	95.42 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs_anonymous		88.68 117	87.62 120	91.86 123	94.80 131	81.69 117	93.53 244	94.92 180	82.03 169	78.87 204	90.43 205	75.77 117	95.34 259	85.04 115	93.16 111	98.55 36

0601test		91.46 67	90.53 71	94.24 28	97.41 68	85.18 42	98.08 27	97.72 16	80.94 184	89.85 78	96.14 99	75.61 118	98.81 98	90.42 68	88.56 146	98.74 22

Anonymous20240521		91.46 67	90.53 71	94.24 28	97.41 68	85.18 42	98.08 27	97.72 16	80.94 184	89.85 78	96.14 99	75.61 118	98.81 98	90.42 68	88.56 146	98.74 22

HPM-MVS		91.62 64	91.53 60	91.89 118	97.88 55	79.22 181	96.99 105	95.73 143	82.07 168	89.50 88	97.19 76	75.59 120	98.93 94	90.91 62	97.94 46	97.54 100
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		91.88 59	91.82 54	92.07 112	98.38 37	78.63 204	97.29 78	96.09 125	85.12 99	88.45 96	97.66 51	75.53 121	99.68 30	89.83 74	98.02 45	97.88 77

PatchT		79.75 252	76.85 263	88.42 205	89.55 243	75.49 263	77.37 346	94.61 202	63.07 326	82.46 156	73.32 342	75.52 122	93.41 299	51.36 331	84.43 188	96.36 145

CR-MVSNet		83.53 205	81.36 222	90.06 172	90.16 233	79.75 159	79.02 342	91.12 296	84.24 130	82.27 166	80.35 315	75.45 123	93.67 296	63.37 290	86.25 164	96.75 137

Patchmtry		77.36 279	74.59 286	85.67 256	89.75 238	75.75 261	77.85 345	91.12 296	60.28 339	71.23 270	80.35 315	75.45 123	93.56 298	57.94 305	67.34 303	87.68 282

view600		87.45 143	85.98 146	91.88 119	95.90 98	84.52 56	96.68 128	98.85 2	81.85 171	82.30 158	93.39 162	75.44 125	97.66 138	64.02 282	85.36 178	93.45 203

view800		87.45 143	85.98 146	91.88 119	95.90 98	84.52 56	96.68 128	98.85 2	81.85 171	82.30 158	93.39 162	75.44 125	97.66 138	64.02 282	85.36 178	93.45 203

conf0.05thres1000		87.45 143	85.98 146	91.88 119	95.90 98	84.52 56	96.68 128	98.85 2	81.85 171	82.30 158	93.39 162	75.44 125	97.66 138	64.02 282	85.36 178	93.45 203

tfpn		87.45 143	85.98 146	91.88 119	95.90 98	84.52 56	96.68 128	98.85 2	81.85 171	82.30 158	93.39 162	75.44 125	97.66 138	64.02 282	85.36 178	93.45 203

tfpn111		88.08 132	86.70 140	92.20 107	96.10 91	84.90 49	97.14 90	98.85 2	82.69 159	83.41 145	93.66 158	75.43 129	97.82 132	67.13 263	85.88 171	93.89 194

conf200view11		88.27 130	86.95 136	92.24 104	96.10 91	84.90 49	97.14 90	98.85 2	82.69 159	83.41 145	93.66 158	75.43 129	97.93 122	69.04 248	86.24 166	93.89 194

thres100view900		88.30 128	86.95 136	92.33 102	96.10 91	84.90 49	97.14 90	98.85 2	82.69 159	83.41 145	93.66 158	75.43 129	97.93 122	69.04 248	86.24 166	94.17 187

thres600view7		88.06 133	86.70 140	92.15 109	96.10 91	85.17 44	97.14 90	98.85 2	82.70 158	83.41 145	93.66 158	75.43 129	97.82 132	67.13 263	85.88 171	93.45 203

UniMVSNet (Re)		85.31 183	84.23 180	88.55 204	89.75 238	80.55 140	96.72 123	96.89 50	85.42 90	78.40 206	88.93 221	75.38 133	95.52 251	78.58 169	68.02 295	89.57 238

tfpn200view9		88.48 123	87.15 131	92.47 97	96.21 87	85.30 40	97.44 66	98.85 2	83.37 146	83.99 137	93.82 155	75.36 134	97.93 122	69.04 248	86.24 166	94.17 187

thres400		88.42 126	87.15 131	92.23 105	96.21 87	85.30 40	97.44 66	98.85 2	83.37 146	83.99 137	93.82 155	75.36 134	97.93 122	69.04 248	86.24 166	93.45 203

sam_mvs												75.35 136

jason		92.73 44	92.23 50	94.21 30	90.50 227	87.30 21	98.65 12	95.09 172	90.61 23	92.76 45	97.13 78	75.28 137	97.30 161	93.32 38	96.75 72	98.02 65
jason: jason.
MVS_Test		90.29 89	89.18 96	93.62 49	95.23 119	84.93 48	94.41 222	94.66 197	84.31 125	90.37 74	91.02 194	75.13 138	97.82 132	83.11 139	94.42 94	98.12 59

thres200		88.92 111	87.65 117	92.73 88	96.30 86	85.62 35	97.85 36	98.86 1	84.38 123	84.82 126	93.99 152	75.12 139	98.01 120	70.86 238	86.67 160	94.56 186

EPMVS		87.47 142	85.90 151	92.18 108	95.41 115	82.26 100	87.00 319	96.28 113	85.88 81	84.23 134	85.57 275	75.07 140	96.26 202	71.14 236	92.50 114	98.03 64

UA-Net		88.92 111	88.48 105	90.24 163	94.06 155	77.18 246	93.04 260	94.66 197	87.39 60	91.09 67	93.89 154	74.92 141	98.18 119	75.83 200	91.43 127	95.35 170

diffmvs1		89.92 93	89.20 95	92.09 110	94.14 152	80.52 143	93.69 238	94.25 215	84.71 111	89.91 77	91.00 196	74.90 142	97.55 148	86.71 106	93.48 105	98.18 51

tpm cat1		83.63 204	81.38 221	90.39 160	93.53 169	78.19 224	85.56 329	95.09 172	70.78 309	78.51 205	83.28 303	74.80 143	97.03 175	66.77 266	84.05 191	95.95 154

APD-MVS_3200maxsize		91.23 73	91.35 62	90.89 150	97.89 54	76.35 255	96.30 158	95.52 153	79.82 217	91.03 68	97.88 46	74.70 144	98.54 105	92.11 53	96.89 69	97.77 86

IS-MVSNet		88.67 118	88.16 109	90.20 165	93.61 166	76.86 249	96.77 122	93.07 273	84.02 133	83.62 144	95.60 111	74.69 145	96.24 204	78.43 170	93.66 104	97.49 106

MDTV_nov1_ep13			83.69 184		94.09 154	81.01 128	86.78 321	96.09 125	83.81 139	84.75 128	84.32 290	74.44 146	96.54 192	63.88 286	85.07 185

MDTV_nov1_ep13_2view						81.74 113	86.80 320		80.65 196	85.65 120		74.26 147		76.52 193		96.98 125

tpmvs		83.04 218	80.77 227	89.84 182	95.43 113	77.96 228	85.59 328	95.32 166	75.31 270	76.27 235	83.70 299	73.89 148	97.41 155	59.53 299	81.93 214	94.14 189

test_post1							85.88 327				30.24 366	73.77 149	95.07 270	73.89 216

EI-MVSNet		85.80 170	85.20 157	87.59 229	91.55 213	77.41 240	95.13 206	95.36 163	80.43 202	80.33 186	94.71 139	73.72 150	95.97 216	76.96 190	78.64 231	89.39 240

IterMVS-LS		83.93 200	82.80 199	87.31 237	91.46 216	77.39 241	95.66 186	93.43 259	80.44 200	75.51 244	87.26 241	73.72 150	95.16 265	76.99 188	70.72 266	89.39 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS	86.58 3	91.53 66	91.06 66	92.94 80	94.52 144	81.89 108	95.95 171	95.98 131	90.76 21	83.76 143	96.76 91	73.24 152	99.71 24	91.67 54	96.96 67	97.22 122
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPMNet		79.32 258	75.75 270	90.06 172	90.16 233	79.75 159	79.02 342	93.92 237	58.43 345	82.27 166	72.55 343	73.03 153	93.67 296	46.10 343	86.25 164	96.75 137

diffmvs		89.05 107	88.22 107	91.55 131	93.88 160	79.73 162	93.18 259	94.40 210	84.43 121	88.32 99	90.40 206	72.91 154	97.41 155	84.71 118	91.74 125	97.51 104

CHOSEN 1792x2688		91.07 74	90.21 77	93.64 47	95.18 121	83.53 77	96.26 160	96.13 122	88.92 38	84.90 125	93.10 170	72.86 155	99.62 36	88.86 84	95.67 85	97.79 85

canonicalmvs		92.27 54	91.22 63	95.41 11	95.80 104	88.31 9	97.09 99	94.64 200	88.49 43	92.99 44	97.31 70	72.68 156	98.57 104	93.38 37	88.58 145	99.36 4

API-MVS		90.18 90	88.97 98	93.80 40	98.66 19	82.95 91	97.50 63	95.63 148	75.16 272	86.31 116	97.69 50	72.49 157	99.90 4	81.26 148	96.07 78	98.56 34

nrg030		86.79 156	85.43 153	90.87 151	88.76 251	85.34 39	97.06 103	94.33 212	84.31 125	80.45 184	91.98 178	72.36 158	96.36 198	88.48 90	71.13 262	90.93 218

MVS_111021_LR		91.60 65	91.64 59	91.47 134	95.74 105	78.79 201	96.15 163	96.77 55	88.49 43	88.64 95	97.07 81	72.33 159	99.19 71	93.13 41	96.48 74	96.43 144

test-LLR		88.48 123	87.98 111	89.98 176	92.26 194	77.23 244	97.11 95	95.96 132	83.76 140	86.30 117	91.38 187	72.30 160	96.78 188	80.82 149	91.92 123	95.94 155

test0.0.03 1		82.79 223	82.48 203	83.74 293	86.81 269	72.22 283	96.52 136	95.03 176	83.76 140	73.00 260	93.20 167	72.30 160	88.88 336	64.15 281	77.52 239	90.12 229

Effi-MVS+		90.70 79	89.90 84	93.09 73	93.61 166	83.48 79	95.20 198	92.79 276	83.22 148	91.82 52	95.70 107	71.82 162	97.48 154	91.25 58	93.67 103	98.32 43

sss		90.87 78	89.96 81	93.60 50	94.15 151	83.84 71	97.14 90	98.13 13	85.93 80	89.68 82	96.09 101	71.67 163	99.30 58	87.69 97	89.16 137	97.66 93

Test By Simon												71.65 164

HPM-MVS_fast		90.38 88	90.17 78	91.03 145	97.61 59	77.35 242	97.15 89	95.48 156	79.51 222	88.79 93	96.90 84	71.64 165	98.81 98	87.01 104	97.44 56	96.94 126

MVS		90.60 82	88.64 102	96.50 1	94.25 149	90.53 4	93.33 249	97.21 24	77.59 243	78.88 203	97.31 70	71.52 166	99.69 28	89.60 78	98.03 44	99.27 7

dp		84.30 197	82.31 205	90.28 162	94.24 150	77.97 227	86.57 322	95.53 151	79.94 215	80.75 180	85.16 282	71.49 167	96.39 197	63.73 287	83.36 196	96.48 143

ACMMP		90.39 87	89.97 80	91.64 129	97.58 62	78.21 222	96.78 120	96.72 63	84.73 110	84.72 129	97.23 74	71.22 168	99.63 35	88.37 92	92.41 116	97.08 124
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	86.77 157	85.00 162	92.08 111	92.06 203	83.07 86	92.14 278	94.47 207	79.63 221	76.90 226	94.78 138	71.15 169	99.20 70	72.87 219	91.05 129	93.98 192
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	81.61 12	85.02 185	83.67 185	89.06 193	96.79 81	73.27 278	95.92 173	94.79 190	74.81 279	80.47 183	96.83 88	71.07 170	98.19 118	49.82 337	92.57 113	95.71 162
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		5.92 349	7.89 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	71.04 171	0.00 371	0.00 368	0.00 369	0.00 369

PS-MVSNAJss		84.91 187	84.30 179	86.74 243	85.89 300	74.40 271	94.95 212	94.16 224	83.93 135	76.45 231	90.11 212	71.04 171	95.77 230	83.16 138	79.02 228	90.06 233

PS-MVSNAJ		94.17 19	93.52 29	96.10 4	95.65 108	92.35 1	98.21 23	95.79 141	92.42 12	96.24 7	98.18 18	71.04 171	99.17 74	96.77 8	97.39 59	96.79 133

xiu_mvs_v2_base		93.92 26	93.26 31	95.91 6	95.07 125	92.02 2	98.19 24	95.68 145	92.06 14	96.01 11	98.14 23	70.83 174	98.96 88	96.74 9	96.57 73	96.76 136

CPTT-MVS		89.72 97	89.87 85	89.29 191	98.33 39	73.30 276	97.70 50	95.35 165	75.68 264	87.40 106	97.44 66	70.43 175	98.25 115	89.56 80	96.90 68	96.33 149

WR-MVS_H		81.02 245	80.09 236	83.79 291	88.08 260	71.26 299	94.46 220	96.54 87	80.08 211	72.81 263	86.82 252	70.36 176	92.65 303	64.18 280	67.50 301	87.46 289

NR-MVSNet		83.35 213	81.52 219	88.84 198	88.76 251	81.31 124	94.45 221	95.16 171	84.65 114	67.81 288	90.82 198	70.36 176	94.87 273	74.75 209	66.89 305	90.33 224

VNet		92.11 55	91.22 63	94.79 17	96.91 80	86.98 22	97.91 33	97.96 15	86.38 72	93.65 37	95.74 105	70.16 178	98.95 91	93.39 35	88.87 141	98.43 38

Fast-Effi-MVS+		87.93 137	86.94 138	90.92 149	94.04 156	79.16 183	98.26 21	93.72 249	81.29 180	83.94 140	92.90 171	69.83 179	96.68 191	76.70 191	91.74 125	96.93 127

PLC	83.97 7	88.00 135	87.38 127	89.83 183	98.02 50	76.46 253	97.16 88	94.43 209	79.26 228	81.98 169	96.28 97	69.36 180	99.27 59	77.71 179	92.25 120	93.77 198
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-w/o		88.24 131	87.47 125	90.54 157	95.03 126	78.54 207	97.41 73	93.82 241	84.08 131	78.23 208	94.51 143	69.34 181	97.21 166	80.21 155	94.58 92	95.87 157

abl_6		89.80 95	89.71 89	90.07 171	96.53 84	75.52 262	94.48 219	95.04 175	81.12 182	89.22 89	97.00 82	68.83 182	98.96 88	89.86 73	95.27 86	95.73 160

MAR-MVS		90.63 81	90.22 76	91.86 123	98.47 34	78.20 223	97.18 84	96.61 80	83.87 137	88.18 102	98.18 18	68.71 183	99.75 18	83.66 131	97.15 63	97.63 96
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		88.79 116	87.57 122	92.45 98	98.21 43	81.74 113	96.99 105	95.45 160	75.16 272	82.48 155	95.69 108	68.59 184	98.50 107	80.33 152	95.18 89	97.10 123

DU-MVS		84.57 193	83.33 193	88.28 211	88.76 251	79.36 174	96.43 148	95.41 162	85.42 90	78.11 209	90.82 198	67.61 185	95.14 266	79.14 165	68.30 292	90.33 224

Baseline_NR-MVSNet		81.22 244	80.07 238	84.68 273	85.32 308	75.12 266	96.48 138	88.80 324	76.24 254	77.28 221	86.40 264	67.61 185	94.39 283	75.73 202	66.73 308	84.54 316

WR-MVS		84.32 196	82.96 195	88.41 206	89.38 247	80.32 146	96.59 133	96.25 115	83.97 134	76.63 228	90.36 207	67.53 187	94.86 274	75.82 201	70.09 274	90.06 233

OMC-MVS		88.80 115	88.16 109	90.72 153	95.30 118	77.92 231	94.81 215	94.51 204	86.80 69	84.97 124	96.85 87	67.53 187	98.60 102	85.08 114	87.62 153	95.63 163

LCM-MVSNet-Re		83.75 202	83.54 189	84.39 284	93.54 168	64.14 326	92.51 269	84.03 349	83.90 136	66.14 296	86.59 257	67.36 189	92.68 302	84.89 117	92.87 112	96.35 146

v148		82.41 230	80.89 225	86.99 241	86.18 293	76.81 250	96.27 159	93.82 241	80.49 199	75.28 247	86.11 269	67.32 190	95.75 232	75.48 204	67.03 304	88.42 268

CNLPA		86.96 150	85.37 155	91.72 128	97.59 61	79.34 176	97.21 80	91.05 299	74.22 286	78.90 202	96.75 92	67.21 191	98.95 91	74.68 210	90.77 131	96.88 131

v1		83.37 210	81.82 212	88.01 216	86.58 279	79.24 178	96.45 142	94.13 225	80.88 186	77.48 216	86.88 248	67.15 192	96.04 210	77.15 184	69.67 283	88.76 257

v18		77.96 271	75.61 272	84.98 264	86.66 271	79.01 193	93.02 261	90.94 301	75.69 263	63.19 310	77.62 324	67.11 193	92.07 311	70.05 242	56.24 330	83.87 323

v1141		83.36 211	81.81 214	88.01 216	86.61 277	79.26 177	96.44 144	94.12 228	80.88 186	77.48 216	86.87 249	67.08 194	96.03 211	77.14 185	69.69 282	88.75 259

divwei89l23v2f112		83.36 211	81.81 214	88.01 216	86.60 278	79.23 180	96.44 144	94.12 228	80.88 186	77.49 214	86.87 249	67.08 194	96.03 211	77.14 185	69.67 283	88.76 257

v6		83.45 207	81.94 209	87.95 219	86.62 275	79.03 192	96.32 154	94.17 221	80.76 191	77.57 211	87.23 244	67.03 196	96.09 206	77.73 175	70.06 277	88.75 259

v1neww		83.45 207	81.95 207	87.95 219	86.66 271	79.04 189	96.32 154	94.17 221	80.76 191	77.56 212	87.25 242	67.02 197	96.08 207	77.73 175	70.07 275	88.74 261

v7new		83.45 207	81.95 207	87.95 219	86.66 271	79.04 189	96.32 154	94.17 221	80.76 191	77.56 212	87.25 242	67.02 197	96.08 207	77.73 175	70.07 275	88.74 261

FMVSNet3		84.71 190	82.71 201	90.70 154	94.55 137	87.71 16	95.92 173	94.67 196	81.73 176	75.82 241	88.08 233	66.99 199	94.47 281	71.23 233	75.38 245	89.91 235

v8		81.88 236	80.06 239	87.32 236	86.63 274	79.04 189	94.41 222	93.65 252	78.77 235	73.19 259	85.57 275	66.87 200	95.81 228	73.84 217	67.61 300	87.11 292

1314		88.94 110	87.20 129	94.17 31	93.21 173	85.73 32	93.33 249	96.64 76	82.89 155	75.98 238	96.36 96	66.83 201	99.39 54	83.52 135	96.02 80	97.39 111

v16		77.84 273	75.47 273	84.93 266	86.62 275	78.93 195	92.84 265	90.89 302	75.50 266	63.03 314	77.54 325	66.82 202	92.04 312	69.82 243	56.22 331	83.82 325

v17		77.79 274	75.41 275	84.94 265	86.53 280	78.94 194	92.83 266	90.88 303	75.51 265	62.97 315	77.50 326	66.69 203	92.03 313	69.80 244	56.01 332	83.83 324

BH-untuned		86.95 151	85.94 150	89.99 175	94.52 144	77.46 239	96.78 120	93.37 265	81.80 175	76.62 229	93.81 157	66.64 204	97.02 176	76.06 197	93.88 101	95.48 167

MVS_0304		93.82 29	93.11 35	95.95 5	96.79 81	89.15 7	98.56 15	95.30 167	93.61 9	94.82 24	98.02 34	66.60 205	99.88 7	96.94 7	97.39 59	98.81 19

v15		77.52 276	75.09 277	84.82 268	86.37 284	78.82 198	92.58 268	90.78 305	75.47 267	62.53 317	77.17 327	66.58 206	91.92 314	69.18 247	55.16 334	83.73 326

V14		77.43 278	74.99 278	84.75 269	86.32 287	78.67 202	92.44 272	90.77 306	75.28 271	62.42 318	77.13 328	66.40 207	91.88 315	69.01 252	55.14 335	83.70 327

CVMVSNet		84.83 189	85.57 152	82.63 304	91.55 213	60.38 336	95.13 206	95.03 176	80.60 197	82.10 168	94.71 139	66.40 207	90.19 333	74.30 213	90.32 133	97.31 115

V9		77.32 280	74.87 281	84.69 272	86.26 291	78.52 208	92.33 275	90.72 307	75.11 274	62.21 320	77.08 330	66.19 209	91.87 316	68.84 253	55.06 337	83.69 328

PMMVS		89.46 101	89.92 83	88.06 214	94.64 133	69.57 311	96.22 161	94.95 179	87.27 61	91.37 62	96.54 95	65.88 210	97.39 157	88.54 87	93.89 100	97.23 121

v12		77.20 282	74.73 282	84.63 276	86.15 294	78.41 213	92.17 277	90.71 308	74.92 277	62.05 322	77.00 331	65.83 211	91.83 317	68.69 255	55.01 338	83.64 329

v13		77.11 285	74.63 285	84.55 278	86.08 297	78.27 217	92.06 279	90.68 310	74.73 280	61.86 325	76.93 332	65.73 212	91.81 320	68.55 258	55.07 336	83.59 330

v2v482		83.46 206	81.86 211	88.25 212	86.19 292	79.65 170	96.34 153	94.02 233	81.56 178	77.32 220	88.23 230	65.62 213	96.03 211	77.77 173	69.72 281	89.09 247

v1144		82.90 222	81.27 223	87.78 224	86.29 288	79.07 188	96.14 164	93.93 236	80.05 212	77.38 218	86.80 253	65.50 214	95.93 222	75.21 206	70.13 271	88.33 271

v11		77.21 281	74.72 283	84.68 273	86.29 288	78.62 205	92.30 276	90.63 311	74.86 278	62.32 319	76.73 333	65.49 215	91.83 317	68.17 259	55.72 333	83.59 330

v7		82.99 221	81.41 220	87.73 225	86.41 282	78.86 197	96.10 167	93.98 234	79.88 216	77.49 214	87.11 246	65.44 216	95.97 216	75.69 203	70.59 268	88.36 269

v10		81.43 241	79.53 244	87.11 239	86.38 283	78.87 196	94.31 225	93.43 259	77.88 240	73.24 258	85.26 278	65.44 216	95.75 232	72.14 224	67.71 299	86.72 297

HQP2-MVS												65.40 218

HQP-MVS		87.91 138	87.55 123	88.98 196	92.08 200	78.48 209	97.63 53	94.80 188	90.52 24	82.30 158	94.56 141	65.40 218	97.32 159	87.67 98	83.01 199	91.13 214

V42		83.04 218	81.53 218	87.57 231	86.27 290	79.09 187	95.87 178	94.11 230	80.35 204	77.22 222	86.79 254	65.32 220	96.02 214	77.74 174	70.14 270	87.61 284

pmmvs4		82.54 226	80.79 226	87.79 223	86.11 295	80.49 145	93.55 243	93.18 269	77.29 248	73.35 256	89.40 217	65.26 221	95.05 271	75.32 205	73.61 252	87.83 279

3Dnovator+	82.88 8	89.63 99	87.85 113	94.99 15	94.49 147	86.76 23	97.84 37	95.74 142	86.10 76	75.47 245	96.02 102	65.00 222	99.51 47	82.91 141	97.07 64	98.72 27

HQP_MVS		87.50 141	87.09 134	88.74 201	91.86 210	77.96 228	97.18 84	94.69 193	89.89 30	81.33 175	94.15 149	64.77 223	97.30 161	87.08 101	82.82 209	90.96 216

plane_prior6					91.98 205	77.92 231						64.77 223

v144192		82.43 227	80.73 228	87.54 232	85.81 301	78.22 219	95.98 169	93.78 246	79.09 230	77.11 223	86.49 259	64.66 225	95.91 223	74.20 214	69.42 285	88.49 264

TranMVSNet+NR-MVSNet		83.24 215	81.71 216	87.83 222	87.71 263	78.81 200	96.13 166	94.82 187	84.52 117	76.18 237	90.78 200	64.07 226	94.60 279	74.60 211	66.59 309	90.09 231

CP-MVSNet		81.01 246	80.08 237	83.79 291	87.91 262	70.51 302	94.29 229	95.65 146	80.83 189	72.54 266	88.84 222	63.71 227	92.32 306	68.58 257	68.36 291	88.55 263

cdsmvs_eth3d_5k		21.43 344	28.57 345	0.00 359	0.00 374	0.00 374	0.00 365	95.93 135	0.00 369	0.00 371	97.66 51	63.57 228	0.00 371	0.00 368	0.00 369	0.00 369

1111		65.60 323	64.33 321	69.41 336	68.26 351	45.11 356	87.06 317	87.32 333	54.99 349	51.20 346	73.45 339	63.57 228	85.70 346	36.53 351	56.59 329	77.42 347

.test1245		54.61 328	58.07 327	44.24 351	68.26 351	45.11 356	87.06 317	87.32 333	54.99 349	51.20 346	73.45 339	63.57 228	85.70 346	36.53 351	0.21 366	1.91 366

Vis-MVSNet		88.67 118	87.82 114	91.24 140	92.68 182	78.82 198	96.95 111	93.85 240	87.55 58	87.07 111	95.13 130	63.43 231	97.21 166	77.58 181	96.15 76	97.70 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		82.31 231	80.55 231	87.60 228	85.94 298	78.47 212	95.85 180	93.80 244	79.33 225	76.97 225	86.51 258	63.33 232	95.87 224	73.11 218	70.13 271	88.46 266

CANet_DTU		90.98 75	90.04 79	93.83 39	94.76 132	86.23 26	96.32 154	93.12 272	93.11 10	93.71 36	96.82 89	63.08 233	99.48 49	84.29 121	95.12 90	95.77 159

ab-mvs		87.08 149	84.94 164	93.48 58	93.34 172	83.67 75	88.82 304	95.70 144	81.18 181	84.55 132	90.14 211	62.72 234	98.94 93	85.49 112	82.54 213	97.85 80

v1921920		82.02 235	80.23 235	87.41 234	85.62 302	77.92 231	95.79 182	93.69 250	78.86 234	76.67 227	86.44 261	62.50 235	95.83 227	72.69 220	69.77 280	88.47 265

CLD-MVS		87.97 136	87.48 124	89.44 189	92.16 199	80.54 141	98.14 25	94.92 180	91.41 16	79.43 199	95.40 114	62.34 236	97.27 164	90.60 65	82.90 208	90.50 221
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	89.33 103	87.64 118	94.42 23	93.73 165	85.70 33	97.73 49	96.75 59	86.73 70	76.21 236	95.93 103	62.17 237	99.68 30	81.67 146	97.81 49	97.88 77

ADS-MVSNet2		79.57 254	77.53 255	85.71 255	93.78 161	72.13 285	79.48 338	86.11 340	73.09 295	80.14 188	79.99 317	62.15 238	90.14 334	59.49 300	83.52 193	94.85 180

ADS-MVSNet		81.26 243	78.36 249	89.96 178	93.78 161	79.78 158	79.48 338	93.60 254	73.09 295	80.14 188	79.99 317	62.15 238	95.24 264	59.49 300	83.52 193	94.85 180

QAPM		86.88 152	84.51 168	93.98 34	94.04 156	85.89 30	97.19 83	96.05 128	73.62 290	75.12 248	95.62 110	62.02 240	99.74 20	70.88 237	96.06 79	96.30 151

Effi-MVS+-dtu		84.61 192	84.90 166	83.72 294	91.96 206	63.14 330	94.95 212	93.34 266	85.57 86	79.79 191	87.12 245	61.99 241	95.61 247	83.55 132	85.83 173	92.41 210

mvs-test1		86.83 154	87.17 130	85.81 254	91.96 206	65.24 323	97.90 35	93.34 266	85.57 86	84.51 133	95.14 129	61.99 241	97.19 168	83.55 132	90.55 132	95.00 178

XXY-MVS		83.84 201	82.00 206	89.35 190	87.13 267	81.38 122	95.72 183	94.26 214	80.15 210	75.92 240	90.63 201	61.96 243	96.52 193	78.98 167	73.28 256	90.14 227

AdaColmap		88.81 114	87.61 121	92.39 100	99.33 4	79.95 155	96.70 127	95.58 149	77.51 244	83.05 152	96.69 93	61.90 244	99.72 23	84.29 121	93.47 106	97.50 105

VPA-MVSNet		85.32 182	83.83 183	89.77 185	90.25 230	82.63 94	96.36 151	97.07 37	83.03 153	81.21 177	89.02 220	61.58 245	96.31 200	85.02 116	70.95 264	90.36 222

test_djsdf		83.00 220	82.45 204	84.64 275	84.07 319	69.78 308	94.80 216	94.48 205	80.74 194	75.41 246	87.70 236	61.32 246	95.10 268	83.77 126	79.76 219	89.04 249

v1240		81.70 238	79.83 242	87.30 238	85.50 303	77.70 236	95.48 192	93.44 257	78.46 238	76.53 230	86.44 261	60.85 247	95.84 226	71.59 230	70.17 269	88.35 270

XVG-OURS-SEG-HR		85.74 176	85.16 158	87.49 233	90.22 231	71.45 295	91.29 288	94.09 231	81.37 179	83.90 141	95.22 122	60.30 248	97.53 153	85.58 111	84.42 189	93.50 201

PEN-MVS		79.47 256	78.26 251	83.08 300	86.36 285	68.58 314	93.85 235	94.77 191	79.76 218	71.37 269	88.55 225	59.79 249	92.46 304	64.50 279	65.40 310	88.19 273

TransMVSNet (Re)		76.94 287	74.38 289	84.62 277	85.92 299	75.25 265	95.28 195	89.18 321	73.88 289	67.22 289	86.46 260	59.64 250	94.10 288	59.24 303	52.57 344	84.50 317

DP-MVS		81.47 240	78.28 250	91.04 144	98.14 45	78.48 209	95.09 209	86.97 335	61.14 337	71.12 272	92.78 173	59.59 251	99.38 55	53.11 327	86.61 161	95.27 172

v7n		79.32 258	77.34 256	85.28 260	84.05 320	72.89 282	93.38 247	93.87 239	75.02 275	70.68 274	84.37 289	59.58 252	95.62 246	67.60 260	67.50 301	87.32 291

F-COLMAP		84.50 194	83.44 192	87.67 226	95.22 120	72.22 283	95.95 171	93.78 246	75.74 262	76.30 234	95.18 126	59.50 253	98.45 110	72.67 221	86.59 162	92.35 211

v748		78.69 264	76.79 264	84.39 284	83.40 323	70.78 300	93.25 255	93.62 253	74.96 276	69.40 283	83.74 296	59.40 254	95.39 256	68.74 254	64.59 312	86.99 295

LS3D		82.22 233	79.94 241	89.06 193	97.43 67	74.06 274	93.20 257	92.05 283	61.90 332	73.33 257	95.21 124	59.35 255	99.21 66	54.54 323	92.48 115	93.90 193

BH-RMVSNet		86.84 153	85.28 156	91.49 133	95.35 117	80.26 150	96.95 111	92.21 281	82.86 156	81.77 173	95.46 113	59.34 256	97.64 142	69.79 245	93.81 102	96.57 141

MVP-Stereo		82.65 225	81.67 217	85.59 257	86.10 296	78.29 216	93.33 249	92.82 275	77.75 241	69.17 286	87.98 234	59.28 257	95.76 231	71.77 227	96.88 70	82.73 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-CasMVS		80.27 250	79.18 245	83.52 297	87.56 265	69.88 307	94.08 231	95.29 168	80.27 207	72.08 267	88.51 228	59.22 258	92.23 308	67.49 261	68.15 294	88.45 267

DTE-MVSNet		78.37 266	77.06 259	82.32 308	85.22 309	67.17 319	93.40 246	93.66 251	78.71 236	70.53 276	88.29 229	59.06 259	92.23 308	61.38 295	63.28 318	87.56 286

TR-MVS		86.30 163	84.93 165	90.42 158	94.63 134	77.58 237	96.57 134	93.82 241	80.30 205	82.42 157	95.16 127	58.74 260	97.55 148	74.88 208	87.82 152	96.13 153

OPM-MVS		85.84 168	85.10 160	88.06 214	88.34 257	77.83 234	95.72 183	94.20 217	87.89 55	80.45 184	94.05 151	58.57 261	97.26 165	83.88 124	82.76 211	89.09 247

PatchMatch-RL		85.00 186	83.66 186	89.02 195	95.86 102	74.55 269	92.49 270	93.60 254	79.30 227	79.29 201	91.47 185	58.53 262	98.45 110	70.22 241	92.17 121	94.07 191

pm-mvs1		80.05 251	78.02 252	86.15 251	85.42 304	75.81 260	95.11 208	92.69 278	77.13 249	70.36 277	87.43 238	58.44 263	95.27 263	71.36 232	64.25 314	87.36 290

testpf		70.88 313	70.47 307	72.08 334	88.92 250	59.57 339	48.62 362	93.15 271	63.05 327	63.07 312	79.51 320	58.33 264	86.63 342	66.93 265	72.69 258	70.05 353

our_test_3		77.90 272	75.37 276	85.48 259	85.39 305	76.74 251	93.63 240	91.67 288	73.39 293	65.72 300	84.65 288	58.20 265	93.13 301	57.82 306	67.87 296	86.57 299

pcd1.5k->3k		34.11 340	35.46 340	30.05 355	86.70 270	0.00 374	0.00 365	94.74 192	0.00 369	0.00 371	0.00 371	58.13 266	0.00 371	0.00 368	79.56 224	90.14 227

V4		78.70 262	76.95 260	83.95 287	81.66 328	71.34 297	91.94 280	93.44 257	74.69 282	70.35 279	83.73 297	58.07 267	95.50 252	71.84 225	66.86 306	85.02 312

Patchmatch-test1		84.89 188	82.76 200	91.27 138	92.30 192	81.86 109	92.88 263	95.56 150	84.85 107	82.52 154	85.19 280	58.04 268	94.21 286	65.93 273	87.58 155	97.74 87

v52		78.70 262	76.95 260	83.95 287	81.71 327	71.34 297	91.93 281	93.43 259	74.69 282	70.36 277	83.71 298	58.04 268	95.50 252	71.84 225	66.82 307	85.00 313

EU-MVSNet		76.92 288	76.95 260	76.83 325	84.10 318	54.73 346	91.77 284	92.71 277	72.74 298	69.57 282	88.69 223	58.03 270	87.43 340	64.91 278	70.00 278	88.33 271

semantic-postprocess				84.73 271	89.63 242	74.66 267		91.81 287	80.05 212	71.06 273	85.18 281	57.98 271	91.40 323	72.48 223	70.70 267	88.12 275

IterMVS		80.67 248	79.16 246	85.20 261	89.79 237	76.08 257	92.97 262	91.86 285	80.28 206	71.20 271	85.14 283	57.93 272	91.34 324	72.52 222	70.74 265	88.18 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		80.98 247	79.97 240	84.01 286	81.73 326	70.44 303	92.49 270	93.58 256	77.10 251	72.98 261	86.31 265	57.58 273	94.90 272	79.32 162	78.63 233	86.69 298

xiu_mvs_v1_base_debu		90.54 83	89.54 90	93.55 52	92.31 189	87.58 18	96.99 105	94.87 183	87.23 62	93.27 38	97.56 58	57.43 274	98.32 112	92.72 44	93.46 107	94.74 183

xiu_mvs_v1_base		90.54 83	89.54 90	93.55 52	92.31 189	87.58 18	96.99 105	94.87 183	87.23 62	93.27 38	97.56 58	57.43 274	98.32 112	92.72 44	93.46 107	94.74 183

xiu_mvs_v1_base_debi		90.54 83	89.54 90	93.55 52	92.31 189	87.58 18	96.99 105	94.87 183	87.23 62	93.27 38	97.56 58	57.43 274	98.32 112	92.72 44	93.46 107	94.74 183

OpenMVS	79.58 14	86.09 165	83.62 187	93.50 56	90.95 221	86.71 24	97.44 66	95.83 139	75.35 268	72.64 264	95.72 106	57.42 277	99.64 34	71.41 231	95.85 83	94.13 190

test2356		74.41 299	74.53 288	74.07 332	76.13 345	54.45 347	94.74 218	92.08 282	71.96 303	65.51 301	83.05 305	56.96 278	83.71 350	52.74 328	77.58 238	84.06 320

PVSNet	82.34 9	89.02 108	87.79 115	92.71 89	95.49 112	81.50 120	97.70 50	97.29 21	87.76 56	85.47 121	95.12 131	56.90 279	98.90 95	80.33 152	94.02 97	97.71 90

Fast-Effi-MVS+-dtu		83.33 214	82.60 202	85.50 258	89.55 243	69.38 312	96.09 168	91.38 291	82.30 165	75.96 239	91.41 186	56.71 280	95.58 249	75.13 207	84.90 186	91.54 212

DI_MVS_plusplus_test		85.92 167	83.61 188	92.86 82	86.43 281	83.20 83	95.57 189	95.46 157	85.10 102	65.99 297	86.84 251	56.70 281	97.89 129	88.10 94	92.33 118	97.48 107

ppachtmachnet_test		77.19 283	74.22 291	86.13 252	85.39 305	78.22 219	93.98 233	91.36 293	71.74 305	67.11 291	84.87 286	56.67 282	93.37 300	52.21 329	64.59 312	86.80 296

VPNet		84.69 191	82.92 196	90.01 174	89.01 249	83.45 80	96.71 125	95.46 157	85.71 84	79.65 192	92.18 177	56.66 283	96.01 215	83.05 140	67.84 298	90.56 220

test_normal		85.83 169	83.51 190	92.78 86	86.33 286	83.01 89	95.56 191	95.46 157	85.11 101	65.73 299	86.63 256	56.62 284	97.86 130	87.87 96	92.29 119	97.47 108

GA-MVS		85.79 175	84.04 182	91.02 146	89.47 245	80.27 149	96.90 114	94.84 186	85.57 86	80.88 178	89.08 218	56.56 285	96.47 195	77.72 178	85.35 182	96.34 147

XVG-OURS		85.18 184	84.38 172	87.59 229	90.42 229	71.73 292	91.06 291	94.07 232	82.00 170	83.29 149	95.08 132	56.42 286	97.55 148	83.70 130	83.42 195	93.49 202

GBi-Net		82.42 228	80.43 233	88.39 207	92.66 183	81.95 103	94.30 226	93.38 262	79.06 231	75.82 241	85.66 271	56.38 287	93.84 292	71.23 233	75.38 245	89.38 242

test1		82.42 228	80.43 233	88.39 207	92.66 183	81.95 103	94.30 226	93.38 262	79.06 231	75.82 241	85.66 271	56.38 287	93.84 292	71.23 233	75.38 245	89.38 242

FMVSNet2		82.79 223	80.44 232	89.83 183	92.66 183	85.43 38	95.42 193	94.35 211	79.06 231	74.46 249	87.28 239	56.38 287	94.31 284	69.72 246	74.68 249	89.76 236

pmmvs5		81.34 242	79.54 243	86.73 244	85.02 310	76.91 248	96.22 161	91.65 289	77.65 242	73.55 253	88.61 224	55.70 290	94.43 282	74.12 215	73.35 255	88.86 256

tfpnnormal		78.14 268	75.42 274	86.31 249	88.33 258	79.24 178	94.41 222	96.22 117	73.51 291	69.81 281	85.52 277	55.43 291	95.75 232	47.65 341	67.86 297	83.95 322

LFMVS		89.27 104	87.64 118	94.16 33	97.16 77	85.52 37	97.18 84	94.66 197	79.17 229	89.63 84	96.57 94	55.35 292	98.22 116	89.52 81	89.54 135	98.74 22

ACMM	80.70 13	83.72 203	82.85 198	86.31 249	91.19 218	72.12 286	95.88 177	94.29 213	80.44 200	77.02 224	91.96 179	55.24 293	97.14 172	79.30 163	80.38 218	89.67 237
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MDA-MVSNet_test_wron		73.54 302	70.43 308	82.86 301	84.55 312	71.85 288	91.74 285	91.32 295	67.63 317	46.73 350	81.09 312	55.11 294	90.42 332	55.91 320	59.76 324	86.31 302

YYNet1		73.53 303	70.43 308	82.85 302	84.52 314	71.73 292	91.69 286	91.37 292	67.63 317	46.79 349	81.21 311	55.04 295	90.43 331	55.93 319	59.70 325	86.38 301

LTVRE_ROB	73.68 18	77.99 269	75.74 271	84.74 270	90.45 228	72.02 287	86.41 324	91.12 296	72.57 300	66.63 293	87.27 240	54.95 296	96.98 177	56.29 318	75.98 241	85.21 311
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		84.20 198	83.49 191	86.33 246	90.88 222	73.06 279	95.28 195	94.13 225	82.20 166	76.31 232	93.20 167	54.83 297	96.95 178	83.72 128	80.83 216	88.98 251

LGP-MVS_train				86.33 246	90.88 222	73.06 279		94.13 225	82.20 166	76.31 232	93.20 167	54.83 297	96.95 178	83.72 128	80.83 216	88.98 251

cascas		86.50 159	84.48 170	92.55 96	92.64 186	85.95 28	97.04 104	95.07 174	75.32 269	80.50 182	91.02 194	54.33 299	97.98 121	86.79 105	87.62 153	93.71 199

ACMP	81.66 11	84.00 199	83.22 194	86.33 246	91.53 215	72.95 281	95.91 175	93.79 245	83.70 142	73.79 252	92.22 176	54.31 300	96.89 182	83.98 123	79.74 221	89.16 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_0	77.72 15	81.70 238	78.95 248	89.94 179	90.77 224	76.72 252	95.96 170	96.95 45	85.01 103	70.24 280	88.53 227	52.32 301	98.20 117	86.68 107	44.08 353	94.89 179

MSDG		80.62 249	77.77 254	89.14 192	93.43 171	77.24 243	91.89 282	90.18 313	69.86 313	68.02 287	91.94 181	52.21 302	98.84 96	59.32 302	83.12 197	91.35 213

DSMNet-mixed		73.13 305	72.45 299	75.19 330	77.51 340	46.82 353	85.09 330	82.01 355	67.61 321	69.27 285	81.33 310	50.89 303	86.28 343	54.54 323	83.80 192	92.46 209

UGNet		87.73 139	86.55 142	91.27 138	95.16 122	79.11 185	96.35 152	96.23 116	88.14 49	87.83 105	90.48 203	50.65 304	99.09 81	80.13 156	94.03 96	95.60 164
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		76.46 290	74.16 292	83.35 299	90.05 235	76.17 256	89.58 298	89.85 315	71.39 308	65.29 303	80.42 314	50.61 305	87.70 339	61.05 296	69.24 286	86.18 304

MS-PatchMatch		83.05 217	81.82 212	86.72 245	89.64 241	79.10 186	94.88 214	94.59 203	79.70 219	70.67 275	89.65 214	50.43 306	96.82 185	70.82 240	95.99 81	84.25 318

Anonymous20231206		75.29 295	73.64 294	80.22 316	80.75 329	63.38 329	93.36 248	90.71 308	73.09 295	67.12 290	83.70 299	50.33 307	90.85 328	53.63 326	70.10 273	86.44 300

N_pmnet		61.30 325	60.20 326	64.60 341	84.32 315	17.00 371	91.67 287	10.98 372	61.77 333	58.45 335	78.55 321	49.89 308	91.83 317	42.27 346	63.94 315	84.97 314

jajsoiax		82.12 234	81.15 224	85.03 263	84.19 317	70.70 301	94.22 230	93.95 235	83.07 152	73.48 254	89.75 213	49.66 309	95.37 258	82.24 144	79.76 219	89.02 250

LP		68.54 320	63.92 322	82.39 305	87.93 261	71.79 291	72.37 355	86.01 342	55.89 348	58.33 336	71.46 346	49.58 310	90.10 335	32.25 355	61.48 321	85.27 309

RPSCF		77.73 275	76.63 265	81.06 313	88.66 255	55.76 345	87.77 313	87.88 330	64.82 325	74.14 251	92.79 172	49.22 311	96.81 186	67.47 262	76.88 240	90.62 219

SixPastTwentyTwo		76.04 291	74.32 290	81.22 312	84.54 313	61.43 335	91.16 289	89.30 320	77.89 239	64.04 306	86.31 265	48.23 312	94.29 285	63.54 289	63.84 316	87.93 278

test20.03		72.36 309	71.15 303	75.98 329	77.79 338	59.16 340	92.40 273	89.35 319	74.09 287	61.50 326	84.32 290	48.09 313	85.54 348	50.63 335	62.15 320	83.24 332

VDDNet		86.44 161	84.51 168	92.22 106	91.56 212	81.83 110	97.10 98	94.64 200	69.50 314	87.84 104	95.19 125	48.01 314	97.92 128	89.82 75	86.92 157	96.89 130

VDD-MVS		88.28 129	87.02 135	92.06 113	95.09 123	80.18 153	97.55 59	94.45 208	83.09 151	89.10 92	95.92 104	47.97 315	98.49 108	93.08 42	86.91 158	97.52 103

Anonymous20231211		79.72 253	77.19 258	87.33 235	95.59 109	77.16 247	95.18 199	94.18 220	59.31 343	72.57 265	86.20 267	47.89 316	95.66 242	74.53 212	69.24 286	89.18 245

OurMVSNet-221017-0		77.18 284	76.06 268	80.55 315	83.78 321	60.00 337	90.35 293	91.05 299	77.01 253	66.62 294	87.92 235	47.73 317	94.03 289	71.63 229	68.44 290	87.62 283

CMPMVS	54.94 21	75.71 294	74.56 287	79.17 321	79.69 334	55.98 343	89.59 297	93.30 268	60.28 339	53.85 343	89.07 219	47.68 318	96.33 199	76.55 192	81.02 215	85.22 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvs_tets		81.74 237	80.71 229	84.84 267	84.22 316	70.29 304	93.91 234	93.78 246	82.77 157	73.37 255	89.46 216	47.36 319	95.31 261	81.99 145	79.55 225	88.92 255

MDA-MVSNet-bldmvs		71.45 311	67.94 314	81.98 310	85.33 307	68.50 315	92.35 274	88.76 325	70.40 310	42.99 351	81.96 307	46.57 320	91.31 325	48.75 340	54.39 340	86.11 305

pmmvs-eth3d		73.59 301	70.66 305	82.38 306	76.40 343	73.38 275	89.39 302	89.43 318	72.69 299	60.34 330	77.79 323	46.43 321	91.26 326	66.42 271	57.06 327	82.51 337

Anonymous20240529		83.15 216	80.60 230	90.80 152	95.74 105	78.27 217	96.81 118	94.92 180	60.10 341	81.89 171	92.54 174	45.82 322	98.82 97	79.25 164	78.32 235	95.31 171

MVS-HIRNet		71.36 312	67.00 315	84.46 282	90.58 226	69.74 309	79.15 341	87.74 331	46.09 354	61.96 324	50.50 355	45.14 323	95.64 244	53.74 325	88.11 151	88.00 277

FMVSNet1		79.50 255	76.54 266	88.39 207	88.47 256	81.95 103	94.30 226	93.38 262	73.14 294	72.04 268	85.66 271	43.86 324	93.84 292	65.48 275	72.53 259	89.38 242

K. test v3		73.62 300	71.59 302	79.69 318	82.98 324	59.85 338	90.85 292	88.83 323	77.13 249	58.90 332	82.11 306	43.62 325	91.72 321	65.83 274	54.10 341	87.50 288

pmmvs6		74.65 298	71.67 301	83.60 296	79.13 336	69.94 306	93.31 253	90.88 303	61.05 338	65.83 298	84.15 292	43.43 326	94.83 275	66.62 267	60.63 322	86.02 307

ACMH	75.40 17	77.99 269	74.96 279	87.10 240	90.67 225	76.41 254	93.19 258	91.64 290	72.47 301	63.44 309	87.61 237	43.34 327	97.16 169	58.34 304	73.94 251	87.72 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_0402		72.68 307	69.54 311	82.09 309	88.67 254	71.81 290	92.72 267	86.77 337	61.52 334	62.21 320	83.91 293	43.22 328	93.76 295	34.60 353	72.23 260	80.72 344

lessismore_v0				79.98 317	80.59 331	58.34 341		80.87 356		58.49 334	83.46 301	43.10 329	93.89 291	63.11 291	48.68 347	87.72 280

UnsupCasMVSNet_eth		73.25 304	70.57 306	81.30 311	77.53 339	66.33 321	87.24 316	93.89 238	80.38 203	57.90 338	81.59 309	42.91 330	90.56 330	65.18 277	48.51 348	87.01 294

COLMAP_ROB	73.24 19	75.74 293	73.00 298	83.94 289	92.38 188	69.08 313	91.85 283	86.93 336	61.48 335	65.32 302	90.27 208	42.27 331	96.93 181	50.91 334	75.63 244	85.80 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		79.18 260	75.99 269	88.72 202	87.37 266	80.66 137	79.96 337	91.82 286	77.38 246	74.33 250	81.87 308	41.78 332	90.74 329	66.36 272	83.10 198	94.76 182

ACMH+	76.62 16	77.47 277	74.94 280	85.05 262	91.07 220	71.58 294	93.26 254	90.01 314	71.80 304	64.76 304	88.55 225	41.62 333	96.48 194	62.35 293	71.00 263	87.09 293

ITE_SJBPF				82.38 306	87.00 268	65.59 322		89.55 317	79.99 214	69.37 284	91.30 189	41.60 334	95.33 260	62.86 292	74.63 250	86.24 303

Anonymous202405211		84.41 195	81.93 210	91.85 125	96.78 83	78.41 213	97.44 66	91.34 294	70.29 311	84.06 135	94.26 147	41.09 335	98.96 88	79.46 161	82.65 212	98.17 53

new-patchmatchnet		68.85 318	65.93 318	77.61 323	73.57 350	63.94 328	90.11 295	88.73 326	71.62 306	55.08 341	73.60 338	40.84 336	87.22 341	51.35 332	48.49 349	81.67 343

USDC		78.65 265	76.25 267	85.85 253	87.58 264	74.60 268	89.58 298	90.58 312	84.05 132	63.13 311	88.23 230	40.69 337	96.86 184	66.57 269	75.81 243	86.09 306

XVG-ACMP-BASELINE		79.38 257	77.90 253	83.81 290	84.98 311	67.14 320	89.03 303	93.18 269	80.26 208	72.87 262	88.15 232	38.55 338	96.26 202	76.05 198	78.05 236	88.02 276

AllTest		75.92 292	73.06 297	84.47 280	92.18 197	67.29 317	91.07 290	84.43 346	67.63 317	63.48 307	90.18 209	38.20 339	97.16 169	57.04 308	73.37 253	88.97 253

TestCases				84.47 280	92.18 197	67.29 317		84.43 346	67.63 317	63.48 307	90.18 209	38.20 339	97.16 169	57.04 308	73.37 253	88.97 253

Test4		82.30 232	79.15 247	91.78 126	81.84 325	81.74 113	94.04 232	94.20 217	84.86 106	59.75 331	83.88 294	37.14 341	96.28 201	84.60 119	92.00 122	97.30 116

UnsupCasMVSNet_bld		68.60 319	64.50 320	80.92 314	74.63 347	67.80 316	83.97 331	92.94 274	65.12 324	54.63 342	68.23 349	35.97 342	92.17 310	60.13 298	44.83 351	82.78 335

tmp_tt		41.54 335	41.93 336	40.38 352	20.10 371	26.84 367	61.93 358	59.09 368	14.81 365	28.51 358	80.58 313	35.53 343	48.33 367	63.70 288	13.11 363	45.96 361

testgi		74.88 297	73.40 295	79.32 320	80.13 333	61.75 333	93.21 256	86.64 338	79.49 223	66.56 295	91.06 193	35.51 344	88.67 337	56.79 311	71.25 261	87.56 286

OpenMVS_ROB	68.52 20	73.02 306	69.57 310	83.37 298	80.54 332	71.82 289	93.60 242	88.22 328	62.37 330	61.98 323	83.15 304	35.31 345	95.47 254	45.08 344	75.88 242	82.82 334

testing_2		76.96 286	73.18 296	88.30 210	75.87 346	79.64 171	89.92 296	94.21 216	80.16 209	51.23 345	75.94 335	33.94 346	95.81 228	82.28 143	75.12 248	89.46 239

TDRefinement		69.20 317	65.78 319	79.48 319	66.04 356	62.21 332	88.21 309	86.12 339	62.92 328	61.03 328	85.61 274	33.23 347	94.16 287	55.82 321	53.02 342	82.08 341

LF4IMVS		72.36 309	70.82 304	76.95 324	79.18 335	56.33 342	86.12 325	86.11 340	69.30 315	63.06 313	86.66 255	33.03 348	92.25 307	65.33 276	68.64 289	82.28 340

MIMVSNet1		69.44 315	66.65 317	77.84 322	76.48 342	62.84 331	87.42 314	88.97 322	66.96 322	57.75 339	79.72 319	32.77 349	85.83 345	46.32 342	63.42 317	84.85 315

EG-PatchMatch MVS		74.92 296	72.02 300	83.62 295	83.76 322	73.28 277	93.62 241	92.04 284	68.57 316	58.88 333	83.80 295	31.87 350	95.57 250	56.97 310	78.67 230	82.00 342

new_pmnet		66.18 321	63.18 323	75.18 331	76.27 344	61.74 334	83.79 332	84.66 345	56.64 347	51.57 344	71.85 345	31.29 351	87.93 338	49.98 336	62.55 319	75.86 348

TinyColmap		72.41 308	68.99 313	82.68 303	88.11 259	69.59 310	88.41 308	85.20 343	65.55 323	57.91 337	84.82 287	30.80 352	95.94 221	51.38 330	68.70 288	82.49 339

test1235678		64.50 324	62.19 324	71.42 335	66.82 355	48.00 352	89.44 300	87.90 329	62.82 329	49.12 348	71.31 347	30.14 353	82.19 352	41.88 347	60.32 323	84.06 320

pmmvs3		65.75 322	62.18 325	76.45 327	67.12 354	64.54 324	88.68 306	85.05 344	54.77 352	57.54 340	73.79 337	29.40 354	86.21 344	55.49 322	47.77 350	78.62 345

testus		70.06 314	69.09 312	72.98 333	74.54 348	51.28 351	93.78 236	87.34 332	71.49 307	62.69 316	83.46 301	24.44 355	84.77 349	51.22 333	72.85 257	82.90 333

PM-MVS		69.32 316	66.93 316	76.49 326	73.60 349	55.84 344	85.91 326	79.32 360	74.72 281	61.09 327	78.18 322	21.76 356	91.10 327	70.86 238	56.90 328	82.51 337

test12356		58.24 326	56.06 328	64.77 339	60.65 357	39.42 362	82.78 335	84.34 348	57.47 346	42.65 352	69.10 348	19.21 357	81.18 353	38.97 350	49.40 345	73.69 349

DeepMVS_CX				64.06 342	78.53 337	43.26 358		68.11 365	69.94 312	38.55 353	76.14 334	18.53 358	79.34 354	43.72 345	41.62 355	69.57 354

ambc				76.02 328	68.11 353	51.43 349	64.97 357	89.59 316		60.49 329	74.49 336	17.17 359	92.46 304	61.50 294	52.85 343	84.17 319

no-one		51.12 331	45.81 334	67.03 337	53.16 364	52.22 348	75.21 349	80.40 357	54.89 351	28.26 359	48.50 357	15.54 360	82.81 351	39.29 349	17.06 359	66.07 356

testmv		54.58 329	51.53 331	63.74 343	53.58 362	40.82 360	83.26 333	83.92 350	54.07 353	36.35 355	61.26 350	14.80 361	77.07 355	33.00 354	43.53 354	73.33 350

FPMVS		55.09 327	52.93 329	61.57 344	55.98 358	40.51 361	83.11 334	83.41 353	37.61 356	34.95 356	71.95 344	14.40 362	76.95 356	29.81 357	65.16 311	67.25 355

EMVS		31.70 343	31.45 343	32.48 354	50.72 365	23.95 369	74.78 350	52.30 371	20.36 363	16.08 366	31.48 364	12.80 363	53.60 366	11.39 364	13.10 364	19.88 364

ANet_high		46.22 333	41.28 337	61.04 345	39.91 369	46.25 355	70.59 356	76.18 361	58.87 344	23.09 361	48.00 358	12.58 364	66.54 362	28.65 358	13.62 362	70.35 352

E-PMN		32.70 342	32.39 341	33.65 353	53.35 363	25.70 368	74.07 351	53.33 370	21.08 362	17.17 365	33.63 363	11.85 365	54.84 365	12.98 363	14.04 361	20.42 363

Gipuma		45.11 334	42.05 335	54.30 347	80.69 330	51.30 350	35.80 363	83.81 351	28.13 359	27.94 360	34.53 361	11.41 366	76.70 358	21.45 360	54.65 339	34.90 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		50.90 332	46.31 333	64.67 340	55.53 359	46.67 354	77.30 347	71.02 362	40.89 355	34.16 357	59.32 351	9.83 367	76.14 359	40.09 348	28.63 356	71.21 351

LCM-MVSNet		52.52 330	48.24 332	65.35 338	47.63 366	41.45 359	72.55 354	83.62 352	31.75 357	37.66 354	57.92 353	9.19 368	76.76 357	49.26 338	44.60 352	77.84 346

PNet_i23d		41.20 336	38.13 338	50.41 348	55.23 360	36.24 365	73.80 353	65.45 367	29.75 358	21.36 362	47.05 359	3.43 369	63.23 363	28.17 359	18.92 358	51.76 358

PMVS	34.80 23	39.19 338	35.53 339	50.18 349	29.72 370	30.30 366	59.60 360	66.20 366	26.06 360	17.91 364	49.53 356	3.12 370	74.09 360	18.19 362	49.40 345	46.14 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 341	29.49 344	46.92 350	41.86 367	36.28 364	50.45 361	56.52 369	18.75 364	18.28 363	37.84 360	2.41 371	58.41 364	18.71 361	20.62 357	46.06 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		14.10 345	13.89 346	14.72 356	55.23 360	22.91 370	33.83 364	3.56 373	4.94 366	4.11 368	2.28 370	2.06 372	19.66 368	10.23 365	8.74 365	1.59 368

wuykxyi23d		37.75 339	31.85 342	55.46 346	40.00 368	38.01 363	59.81 359	69.47 363	25.46 361	12.42 367	30.55 365	2.06 372	67.08 361	31.81 356	15.03 360	61.29 357

test123		9.07 347	11.73 348	1.11 357	0.50 373	0.77 372	89.44 300	0.20 375	0.34 368	2.15 370	10.72 369	0.34 374	0.32 369	1.79 367	0.08 368	2.23 365

testmvs		9.92 346	12.94 347	0.84 358	0.65 372	0.29 373	93.78 236	0.39 374	0.42 367	2.85 369	15.84 368	0.17 375	0.30 370	2.18 366	0.21 366	1.91 366

test_part1				0.00 359		0.00 374	0.00 365	96.77 55				0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

v1.0		39.63 337	52.84 330	0.00 359	98.90 7	0.00 374	0.00 365	96.77 55	84.95 105	96.07 9	98.83 3	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

ab-mvs-re		8.11 348	10.81 349	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	97.30 72	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 369	0.00 369

GSMVS																97.54 100

test_part2					98.90 7	85.14 46				96.07 9

MTGPA								96.33 110

MTMP							97.53 60	68.16 364

gm-plane-assit					92.27 193	79.64 171			84.47 120		95.15 128		97.93 122	85.81 109

test9_res														96.00 13	99.03 9	98.31 44

agg_prior2														94.30 27	99.00 11	98.57 32

agg_prior					98.59 27	83.13 84		96.56 84		94.19 31			99.16 75

test_prior4						82.34 98	97.75 48

test_prior				93.09 73	98.68 16	81.91 106		96.40 101					99.06 82			98.29 46

旧先验2							96.97 110		74.06 288	96.10 8			97.76 135	88.38 91

新几何2							96.42 149

无先验							96.87 115	96.78 54	77.39 245				99.52 44	79.95 157		98.43 38

原ACMM2							96.84 116

testdata2													99.48 49	76.45 194

testdata1							95.57 189		87.44 59

plane_prior7					91.86 210	77.55 238

plane_prior5								94.69 193					97.30 161	87.08 101	82.82 209	90.96 216

plane_prior4											94.15 149

plane_prior3						77.75 235			90.17 28	81.33 175

plane_prior2							97.18 84		89.89 30

plane_prior1					91.95 208

plane_prior						77.96 228	97.52 62		90.36 27						82.96 201

n2								0.00 376

nn								0.00 376

door-mid								79.75 359

test11								96.50 91

door								80.13 358

HQP5-MVS						78.48 209

HQP-NCC					92.08 200		97.63 53		90.52 24	82.30 158

ACMP_Plane					92.08 200		97.63 53		90.52 24	82.30 158

BP-MVS														87.67 98

HQP4-MVS										82.30 158			97.32 159			91.13 214

HQP3-MVS								94.80 188							83.01 199

NP-MVS					92.04 204	78.22 219					94.56 141

ACMMP++_ref															78.45 234

ACMMP++															79.05 227

High-resolution multi-view benchmark