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.

APDe-MVS		95.46 2	95.64 2	94.91 13	98.26 21	86.29 39	97.46 2	97.40 10	89.03 48	96.20 6	98.10 1	89.39 6	99.34 23	95.88 1	99.03 3	99.10 1

CNVR-MVS		95.40 3	95.37 4	95.50 4	98.11 27	88.51 3	95.29 67	96.96 39	92.09 3	95.32 11	97.08 26	89.49 5	99.33 26	95.10 2	98.85 8	98.66 6

HSP-MVS		95.30 4	95.48 3	94.76 25	98.49 10	86.52 29	96.91 15	96.73 56	91.73 9	96.10 7	96.69 39	89.90 2	99.30 29	94.70 3	98.04 49	98.45 18

SMA-MVS		95.20 5	95.07 7	95.59 2	98.14 26	88.48 4	96.26 28	97.28 20	85.90 118	97.67 1	98.10 1	88.41 10	99.56 2	94.66 4	99.19 1	98.71 5

ESAPD		95.57 1	95.67 1	95.25 6	98.36 18	87.28 11	95.56 59	97.51 4	89.13 45	97.14 2	97.91 3	91.64 1	99.62 1	94.61 5	99.17 2	98.86 2

TSAR-MVS + MP.		94.85 9	94.94 8	94.58 32	98.25 22	86.33 35	96.11 34	96.62 67	88.14 70	96.10 7	96.96 29	89.09 8	98.94 65	94.48 6	98.68 24	98.48 13

SD-MVS		94.96 8	95.33 5	93.88 50	97.25 54	86.69 22	96.19 30	97.11 30	90.42 24	96.95 3	97.27 14	89.53 4	96.91 224	94.38 7	98.85 8	98.03 50

MP-MVS-pluss		94.21 25	94.00 27	94.85 17	98.17 25	86.65 25	94.82 103	97.17 26	86.26 113	92.83 41	97.87 4	85.57 36	99.56 2	94.37 8	98.92 6	98.34 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 5	95.32 6	94.85 17	96.99 57	86.33 35	97.33 3	97.30 18	91.38 12	95.39 10	97.46 10	88.98 9	99.40 21	94.12 9	98.89 7	98.82 3
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	89.96 1	94.20 26	94.77 10	92.49 93	96.52 70	80.00 179	94.00 175	97.08 31	90.05 26	95.65 9	97.29 13	89.66 3	98.97 61	93.95 10	98.71 19	98.50 11

ACMMP_Plus		94.74 11	94.56 12	95.28 5	98.02 32	87.70 5	95.68 51	97.34 12	88.28 66	95.30 12	97.67 5	85.90 33	99.54 10	93.91 11	98.95 4	98.60 8

MVS_0304		93.25 47	92.62 51	95.14 9	95.72 101	87.58 8	94.71 114	96.59 69	91.78 7	91.46 73	96.18 64	75.45 149	99.55 7	93.53 12	98.19 44	98.28 28

Regformer-2		94.33 20	94.22 18	94.68 28	95.54 107	86.75 21	94.57 123	96.70 60	91.84 6	94.41 14	96.56 48	87.19 20	99.13 40	93.50 13	97.65 57	98.16 39

MCST-MVS		94.45 14	94.20 21	95.19 7	98.46 12	87.50 9	95.00 91	97.12 28	87.13 92	92.51 53	96.30 55	89.24 7	99.34 23	93.46 14	98.62 32	98.73 4

zzz-MVS		94.47 13	94.30 15	95.00 10	98.42 14	86.95 13	95.06 87	96.97 36	91.07 14	93.14 37	97.56 7	84.30 49	99.56 2	93.43 15	98.75 16	98.47 14

MTAPA		94.42 18	94.22 18	95.00 10	98.42 14	86.95 13	94.36 144	96.97 36	91.07 14	93.14 37	97.56 7	84.30 49	99.56 2	93.43 15	98.75 16	98.47 14

HPM-MVS++		95.14 7	94.91 9	95.83 1	98.25 22	89.65 1	95.92 42	96.96 39	91.75 8	94.02 21	96.83 33	88.12 11	99.55 7	93.41 17	98.94 5	98.28 28

Regformer-1		94.22 24	94.13 23	94.51 35	95.54 107	86.36 34	94.57 123	96.44 74	91.69 10	94.32 16	96.56 48	87.05 22	99.03 50	93.35 18	97.65 57	98.15 40

Regformer-4		93.91 31	93.81 29	94.19 45	95.36 113	85.47 52	94.68 115	96.41 77	91.60 11	93.75 24	96.71 37	85.95 32	99.10 43	93.21 19	96.65 72	98.01 52

CANet		93.54 38	93.20 41	94.55 33	95.65 104	85.73 51	94.94 94	96.69 62	91.89 5	90.69 82	95.88 73	81.99 73	99.54 10	93.14 20	97.95 51	98.39 21

Regformer-3		93.68 35	93.64 35	93.81 54	95.36 113	84.61 61	94.68 115	95.83 120	91.27 13	93.60 28	96.71 37	85.75 34	98.86 70	92.87 21	96.65 72	97.96 54

NCCC		94.81 10	94.69 11	95.17 8	97.83 34	87.46 10	95.66 53	96.93 42	92.34 2	93.94 22	96.58 46	87.74 14	99.44 20	92.83 22	98.40 39	98.62 7

TSAR-MVS + GP.		93.66 36	93.41 37	94.41 39	96.59 66	86.78 19	94.40 134	93.93 226	89.77 32	94.21 17	95.59 82	87.35 18	98.61 86	92.72 23	96.15 80	97.83 63

APD-MVS_3200maxsize		93.78 33	93.77 32	93.80 55	97.92 33	84.19 76	96.30 26	96.87 47	86.96 99	93.92 23	97.47 9	83.88 53	98.96 64	92.71 24	97.87 52	98.26 33

PHI-MVS		93.89 32	93.65 34	94.62 31	96.84 60	86.43 32	96.69 21	97.49 5	85.15 136	93.56 31	96.28 56	85.60 35	99.31 28	92.45 25	98.79 11	98.12 43

HPM-MVS		94.02 28	93.88 28	94.43 38	98.39 16	85.78 50	97.25 5	97.07 32	86.90 103	92.62 50	96.80 36	84.85 46	99.17 35	92.43 26	98.65 30	98.33 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
alignmvs		93.08 51	92.50 54	94.81 22	95.62 106	87.61 7	95.99 38	96.07 100	89.77 32	94.12 18	94.87 99	80.56 83	98.66 81	92.42 27	93.10 133	98.15 40

canonicalmvs		93.27 45	92.75 50	94.85 17	95.70 103	87.66 6	96.33 25	96.41 77	90.00 28	94.09 19	94.60 110	82.33 63	98.62 85	92.40 28	92.86 137	98.27 31

HFP-MVS		94.52 12	94.40 13	94.86 15	98.61 3	86.81 17	96.94 10	97.34 12	88.63 57	93.65 25	97.21 19	86.10 29	99.49 16	92.35 29	98.77 14	98.30 26

ACMMPR		94.43 16	94.28 16	94.91 13	98.63 2	86.69 22	96.94 10	97.32 17	88.63 57	93.53 32	97.26 16	85.04 42	99.54 10	92.35 29	98.78 13	98.50 11

region2R		94.43 16	94.27 17	94.92 12	98.65 1	86.67 24	96.92 14	97.23 23	88.60 59	93.58 29	97.27 14	85.22 39	99.54 10	92.21 31	98.74 18	98.56 10

DeepC-MVS	88.79 3	93.31 43	92.99 45	94.26 43	96.07 90	85.83 49	94.89 97	96.99 34	89.02 49	89.56 93	97.37 11	82.51 61	99.38 22	92.20 32	98.30 41	97.57 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		93.72 34	94.08 24	92.65 86	97.31 48	83.43 93	95.79 46	97.33 15	90.03 27	93.58 29	96.96 29	84.87 45	97.76 144	92.19 33	98.66 28	96.76 100

casdiffmvs1		92.43 57	92.18 57	93.17 65	95.33 116	83.03 103	95.08 84	96.41 77	83.18 183	93.20 35	94.49 112	83.84 54	98.29 104	92.16 34	95.96 81	98.20 36

CP-MVS		94.34 19	94.21 20	94.74 27	98.39 16	86.64 26	97.60 1	97.24 21	88.53 61	92.73 46	97.23 17	85.20 40	99.32 27	92.15 35	98.83 10	98.25 34

train_agg		93.44 40	93.08 42	94.52 34	97.53 38	86.49 30	94.07 167	96.78 52	81.86 228	92.77 43	96.20 60	87.63 16	99.12 41	92.14 36	98.69 21	97.94 55

agg_prior3		93.27 45	92.89 48	94.40 40	97.49 41	86.12 42	94.07 167	96.73 56	81.46 236	92.46 55	96.05 68	86.90 23	99.15 38	92.14 36	98.69 21	97.94 55

MP-MVS		94.25 22	94.07 25	94.77 24	98.47 11	86.31 37	96.71 20	96.98 35	89.04 47	91.98 63	97.19 21	85.43 37	99.56 2	92.06 38	98.79 11	98.44 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-Vis-set		93.01 52	92.92 47	93.29 60	95.01 130	83.51 92	94.48 126	95.77 124	90.87 16	92.52 52	96.67 41	84.50 48	99.00 58	91.99 39	94.44 109	97.36 77

XVS		94.45 14	94.32 14	94.85 17	98.54 7	86.60 27	96.93 12	97.19 24	90.66 22	92.85 39	97.16 24	85.02 43	99.49 16	91.99 39	98.56 35	98.47 14

X-MVStestdata		88.31 141	86.13 189	94.85 17	98.54 7	86.60 27	96.93 12	97.19 24	90.66 22	92.85 39	23.41 361	85.02 43	99.49 16	91.99 39	98.56 35	98.47 14

agg_prior1		93.29 44	92.97 46	94.26 43	97.38 45	85.92 45	93.92 178	96.72 58	81.96 215	92.16 59	96.23 58	87.85 12	98.97 61	91.95 42	98.55 37	97.90 60

test9_res														91.91 43	98.71 19	98.07 46

abl_6		93.18 50	93.05 43	93.57 59	97.52 40	84.27 75	95.53 60	96.67 63	87.85 76	93.20 35	97.22 18	80.35 85	99.18 34	91.91 43	97.21 62	97.26 80

MVS_111021_HR		93.45 39	93.31 38	93.84 51	96.99 57	84.84 57	93.24 215	97.24 21	88.76 54	91.60 71	95.85 74	86.07 31	98.66 81	91.91 43	98.16 45	98.03 50

APD-MVS		94.24 23	94.07 25	94.75 26	98.06 30	86.90 16	95.88 43	96.94 41	85.68 124	95.05 13	97.18 22	87.31 19	99.07 44	91.90 46	98.61 33	98.28 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		92.47 56	92.29 56	92.98 75	95.99 93	84.43 72	93.08 220	96.09 98	88.20 69	91.12 78	95.72 79	81.33 79	97.76 144	91.74 47	97.37 61	96.75 101

#test#		94.32 21	94.14 22	94.86 15	98.61 3	86.81 17	96.43 23	97.34 12	87.51 85	93.65 25	97.21 19	86.10 29	99.49 16	91.68 48	98.77 14	98.30 26

EI-MVSNet-UG-set		92.74 55	92.62 51	93.12 67	94.86 138	83.20 98	94.40 134	95.74 127	90.71 21	92.05 62	96.60 45	84.00 51	98.99 59	91.55 49	93.63 120	97.17 86

test_prior3		93.60 37	93.53 36	93.82 52	97.29 50	84.49 65	94.12 158	96.88 45	87.67 82	92.63 48	96.39 53	86.62 25	98.87 67	91.50 50	98.67 26	98.11 44

test_prior2							94.12 158		87.67 82	92.63 48	96.39 53	86.62 25		91.50 50	98.67 26

casdiffmvs		91.72 66	91.26 65	93.10 68	94.66 146	83.75 84	94.77 107	96.00 106	83.98 159	90.74 81	93.96 131	82.08 69	98.19 109	91.47 52	93.68 118	97.36 77

mPP-MVS		93.99 29	93.78 31	94.63 30	98.50 9	85.90 48	96.87 16	96.91 43	88.70 55	91.83 67	97.17 23	83.96 52	99.55 7	91.44 53	98.64 31	98.43 20

DELS-MVS		93.43 41	93.25 39	93.97 47	95.42 112	85.04 56	93.06 222	97.13 27	90.74 20	91.84 65	95.09 95	86.32 28	99.21 32	91.22 54	98.45 38	97.65 68
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
nrg030		91.08 78	90.39 78	93.17 65	93.07 198	86.91 15	96.41 24	96.26 85	88.30 65	88.37 108	94.85 102	82.19 67	97.64 151	91.09 55	82.95 252	94.96 156

xiu_mvs_v1_base_debu		90.64 85	90.05 87	92.40 96	93.97 173	84.46 68	93.32 206	95.46 149	85.17 133	92.25 56	94.03 124	70.59 212	98.57 88	90.97 56	94.67 99	94.18 197

xiu_mvs_v1_base		90.64 85	90.05 87	92.40 96	93.97 173	84.46 68	93.32 206	95.46 149	85.17 133	92.25 56	94.03 124	70.59 212	98.57 88	90.97 56	94.67 99	94.18 197

xiu_mvs_v1_base_debi		90.64 85	90.05 87	92.40 96	93.97 173	84.46 68	93.32 206	95.46 149	85.17 133	92.25 56	94.03 124	70.59 212	98.57 88	90.97 56	94.67 99	94.18 197

VDD-MVS		90.74 81	89.92 91	93.20 63	96.27 76	83.02 105	95.73 48	93.86 227	88.42 63	92.53 51	96.84 32	62.09 293	98.64 83	90.95 59	92.62 139	97.93 58

DeepC-MVS_fast	89.43 2	94.04 27	93.79 30	94.80 23	97.48 43	86.78 19	95.65 56	96.89 44	89.40 38	92.81 42	96.97 28	85.37 38	99.24 31	90.87 60	98.69 21	98.38 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		93.24 48	92.88 49	94.30 42	98.09 29	85.33 54	96.86 17	97.45 7	88.33 64	90.15 89	97.03 27	81.44 77	99.51 14	90.85 61	95.74 84	98.04 49
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
PGM-MVS		93.96 30	93.72 33	94.68 28	98.43 13	86.22 40	95.30 65	97.78 1	87.45 86	93.26 33	97.33 12	84.62 47	99.51 14	90.75 62	98.57 34	98.32 25

agg_prior2														90.54 63	98.68 24	98.27 31

HPM-MVS_fast		93.40 42	93.22 40	93.94 49	98.36 18	84.83 58	97.15 7	96.80 51	85.77 121	92.47 54	97.13 25	82.38 62	99.07 44	90.51 64	98.40 39	97.92 59

lupinMVS		90.92 79	90.21 82	93.03 73	93.86 176	83.88 81	92.81 231	93.86 227	79.84 249	91.76 68	94.29 118	77.92 112	98.04 131	90.48 65	97.11 63	97.17 86

jason		90.80 80	90.10 85	92.90 78	93.04 200	83.53 91	93.08 220	94.15 213	80.22 245	91.41 74	94.91 97	76.87 118	97.93 138	90.28 66	96.90 66	97.24 81
jason: jason.
CSCG		93.23 49	93.05 43	93.76 56	98.04 31	84.07 78	96.22 29	97.37 11	84.15 157	90.05 90	95.66 80	87.77 13	99.15 38	89.91 67	98.27 42	98.07 46

diffmvs1		91.33 72	91.22 67	91.68 127	93.43 189	79.77 184	93.02 223	95.50 146	87.72 79	90.47 84	93.87 139	81.76 76	97.52 157	89.84 68	95.36 93	97.74 67

CPTT-MVS		91.99 60	91.80 59	92.55 90	98.24 24	81.98 130	96.76 19	96.49 73	81.89 220	90.24 87	96.44 52	78.59 105	98.61 86	89.68 69	97.85 53	97.06 92

MVSFormer		91.68 68	91.30 63	92.80 81	93.86 176	83.88 81	95.96 40	95.90 114	84.66 146	91.76 68	94.91 97	77.92 112	97.30 190	89.64 70	97.11 63	97.24 81

test_djsdf		89.03 126	88.64 115	90.21 182	90.74 283	79.28 209	95.96 40	95.90 114	84.66 146	85.33 189	92.94 169	74.02 168	97.30 190	89.64 70	88.53 198	94.05 207

Effi-MVS+		91.59 69	91.11 68	93.01 74	94.35 160	83.39 95	94.60 120	95.10 178	87.10 93	90.57 83	93.10 161	81.43 78	98.07 129	89.29 72	94.48 106	97.59 71

PS-MVSNAJ		91.18 76	90.92 72	91.96 114	95.26 120	82.60 121	92.09 253	95.70 129	86.27 112	91.84 65	92.46 181	79.70 94	98.99 59	89.08 73	95.86 83	94.29 195

xiu_mvs_v2_base		91.13 77	90.89 74	91.86 119	94.97 133	82.42 122	92.24 247	95.64 135	86.11 117	91.74 70	93.14 159	79.67 97	98.89 66	89.06 74	95.46 90	94.28 196

VNet		92.24 59	91.91 58	93.24 62	96.59 66	83.43 93	94.84 102	96.44 74	89.19 43	94.08 20	95.90 72	77.85 115	98.17 110	88.90 75	93.38 127	98.13 42

PS-MVSNAJss		89.97 99	89.62 93	91.02 148	91.90 222	80.85 159	95.26 74	95.98 107	86.26 113	86.21 151	94.29 118	79.70 94	97.65 149	88.87 76	88.10 206	94.57 182

XVG-OURS-SEG-HR		89.95 100	89.45 96	91.47 133	94.00 171	81.21 148	91.87 255	96.06 102	85.78 120	88.55 105	95.73 78	74.67 158	97.27 194	88.71 77	89.64 177	95.91 125

jajsoiax		88.24 143	87.50 138	90.48 170	90.89 278	80.14 173	95.31 63	95.65 134	84.97 139	84.24 218	94.02 127	65.31 279	97.42 175	88.56 78	88.52 199	93.89 213

mvs_tets		88.06 150	87.28 145	90.38 177	90.94 274	79.88 181	95.22 76	95.66 132	85.10 137	84.21 219	93.94 132	63.53 287	97.40 182	88.50 79	88.40 204	93.87 216

diffmvs		90.50 90	90.33 80	91.02 148	93.04 200	78.59 225	92.85 230	95.07 181	87.32 88	88.32 109	93.34 147	80.46 84	97.40 182	88.50 79	94.06 113	97.07 91

VDDNet		89.56 109	88.49 120	92.76 83	95.07 129	82.09 127	96.30 26	93.19 237	81.05 241	91.88 64	96.86 31	61.16 303	98.33 101	88.43 81	92.49 141	97.84 62

test_normal		88.13 147	86.78 165	92.18 106	90.55 291	81.19 149	92.74 233	94.64 199	83.84 162	77.49 295	90.51 259	68.49 249	98.16 111	88.22 82	94.55 104	97.21 84

HQP_MVS		90.60 88	90.19 83	91.82 122	94.70 144	82.73 115	95.85 44	96.22 89	90.81 18	86.91 137	94.86 100	74.23 162	98.12 114	88.15 83	89.99 169	94.63 176

plane_prior5								96.22 89					98.12 114	88.15 83	89.99 169	94.63 176

EPNet		91.79 62	91.02 71	94.10 46	90.10 299	85.25 55	96.03 37	92.05 260	92.83 1	87.39 131	95.78 76	79.39 99	99.01 55	88.13 85	97.48 59	98.05 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		90.12 95	89.56 94	91.82 122	93.14 196	83.90 80	94.16 157	95.74 127	88.96 50	87.86 117	95.43 85	72.48 190	97.91 139	88.10 86	90.18 168	93.65 235

DI_MVS_plusplus_test		88.15 146	86.82 161	92.14 108	90.67 286	81.07 151	93.01 224	94.59 200	83.83 164	77.78 291	90.63 253	68.51 248	98.16 111	88.02 87	94.37 110	97.17 86

MVSTER		88.84 130	88.29 126	90.51 168	92.95 205	80.44 170	93.73 190	95.01 182	84.66 146	87.15 132	93.12 160	72.79 184	97.21 202	87.86 88	87.36 215	93.87 216

3Dnovator+	87.14 4	92.42 58	91.37 62	95.55 3	95.63 105	88.73 2	97.07 8	96.77 54	90.84 17	84.02 220	96.62 44	75.95 138	99.34 23	87.77 89	97.68 55	98.59 9

LPG-MVS_test		89.45 113	88.90 111	91.12 141	94.47 153	81.49 138	95.30 65	96.14 94	86.73 105	85.45 177	95.16 92	69.89 221	98.10 120	87.70 90	89.23 184	93.77 225

LGP-MVS_train				91.12 141	94.47 153	81.49 138		96.14 94	86.73 105	85.45 177	95.16 92	69.89 221	98.10 120	87.70 90	89.23 184	93.77 225

MVS_Test		91.31 73	91.11 68	91.93 116	94.37 157	80.14 173	93.46 204	95.80 122	86.46 109	91.35 75	93.77 142	82.21 66	98.09 127	87.57 92	94.95 97	97.55 74

PVSNet_Blended_VisFu		91.38 71	90.91 73	92.80 81	96.39 73	83.17 99	94.87 100	96.66 64	83.29 180	89.27 97	94.46 113	80.29 87	99.17 35	87.57 92	95.37 91	96.05 122

CDPH-MVS		92.83 53	92.30 55	94.44 36	97.79 35	86.11 43	94.06 170	96.66 64	80.09 247	92.77 43	96.63 43	86.62 25	99.04 49	87.40 94	98.66 28	98.17 38

XVG-OURS		89.40 118	88.70 114	91.52 131	94.06 165	81.46 140	91.27 268	96.07 100	86.14 116	88.89 103	95.77 77	68.73 245	97.26 196	87.39 95	89.96 171	95.83 130

EPP-MVSNet		91.70 67	91.56 61	92.13 109	95.88 96	80.50 169	97.33 3	95.25 168	86.15 115	89.76 92	95.60 81	83.42 55	98.32 102	87.37 96	93.25 130	97.56 73

VPA-MVSNet		89.62 106	88.96 108	91.60 130	93.86 176	82.89 110	95.46 61	97.33 15	87.91 73	88.43 107	93.31 151	74.17 165	97.40 182	87.32 97	82.86 254	94.52 185

LFMVS		90.08 96	89.13 105	92.95 76	96.71 62	82.32 125	96.08 35	89.91 316	86.79 104	92.15 61	96.81 34	62.60 290	98.34 100	87.18 98	93.90 115	98.19 37

anonymousdsp		87.84 157	87.09 151	90.12 190	89.13 310	80.54 167	94.67 117	95.55 139	82.05 212	83.82 224	92.12 196	71.47 201	97.15 204	87.15 99	87.80 211	92.67 271

CLD-MVS		89.47 112	88.90 111	91.18 140	94.22 161	82.07 128	92.13 251	96.09 98	87.90 74	85.37 187	92.45 182	74.38 160	97.56 154	87.15 99	90.43 162	93.93 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BP-MVS														87.11 101

HQP-MVS		89.80 104	89.28 102	91.34 136	94.17 162	81.56 134	94.39 136	96.04 104	88.81 51	85.43 180	93.97 130	73.83 171	97.96 135	87.11 101	89.77 175	94.50 187

ACMP	84.23 8	89.01 128	88.35 122	90.99 151	94.73 141	81.27 144	95.07 85	95.89 116	86.48 108	83.67 228	94.30 117	69.33 228	97.99 134	87.10 103	88.55 197	93.72 229
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_2		83.40 270	81.02 275	90.56 160	85.06 335	80.51 168	91.37 266	95.57 137	82.92 197	67.06 339	85.54 322	49.47 337	97.24 198	86.74 104	85.44 228	93.93 211

旧先验2							93.36 205		71.25 325	94.37 15			97.13 207	86.74 104

3Dnovator	86.66 5	91.73 65	90.82 75	94.44 36	94.59 149	86.37 33	97.18 6	97.02 33	89.20 42	84.31 216	96.66 42	73.74 173	99.17 35	86.74 104	97.96 50	97.79 65

PVSNet_BlendedMVS		89.98 98	89.70 92	90.82 155	96.12 83	81.25 145	93.92 178	96.83 48	83.49 174	89.10 99	92.26 192	81.04 81	98.85 73	86.72 107	87.86 210	92.35 282

PVSNet_Blended		90.73 82	90.32 81	91.98 113	96.12 83	81.25 145	92.55 239	96.83 48	82.04 214	89.10 99	92.56 180	81.04 81	98.85 73	86.72 107	95.91 82	95.84 129

Test4		85.75 227	83.72 245	91.83 121	88.08 324	81.03 153	92.48 240	95.54 141	83.38 178	73.40 324	88.57 286	50.99 334	97.37 187	86.61 109	94.47 107	97.09 90

mvs_anonymous		89.37 119	89.32 100	89.51 222	93.47 187	74.22 287	91.65 262	94.83 194	82.91 198	85.45 177	93.79 141	81.23 80	96.36 255	86.47 110	94.09 112	97.94 55

0601test		90.69 83	90.02 90	92.71 84	95.72 101	82.41 124	94.11 160	95.12 177	85.63 125	91.49 72	94.70 105	74.75 156	98.42 96	86.13 111	92.53 140	97.31 79

OMC-MVS		91.23 74	90.62 77	93.08 70	96.27 76	84.07 78	93.52 201	95.93 110	86.95 100	89.51 94	96.13 66	78.50 107	98.35 99	85.84 112	92.90 136	96.83 99

ACMM	84.12 9	89.14 122	88.48 121	91.12 141	94.65 148	81.22 147	95.31 63	96.12 97	85.31 132	85.92 155	94.34 114	70.19 220	98.06 130	85.65 113	88.86 195	94.08 205
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		88.65 134	88.35 122	89.54 219	93.33 191	76.39 275	94.47 129	94.36 206	87.70 80	85.43 180	89.56 274	73.45 176	97.26 196	85.57 114	91.28 147	94.97 153

mvs-test1		89.45 113	89.14 104	90.38 177	93.33 191	77.63 260	94.95 93	94.36 206	87.70 80	87.10 134	92.81 174	73.45 176	98.03 132	85.57 114	93.04 134	95.48 140

FIs		90.51 89	90.35 79	90.99 151	93.99 172	80.98 154	95.73 48	97.54 3	89.15 44	86.72 141	94.68 106	81.83 75	97.24 198	85.18 116	88.31 205	94.76 170

MG-MVS		91.77 63	91.70 60	92.00 112	97.08 56	80.03 178	93.60 199	95.18 175	87.85 76	90.89 80	96.47 51	82.06 71	98.36 97	85.07 117	97.04 65	97.62 69

CANet_DTU		90.26 94	89.41 98	92.81 80	93.46 188	83.01 106	93.48 202	94.47 203	89.43 37	87.76 126	94.23 122	70.54 216	99.03 50	84.97 118	96.39 78	96.38 108

UniMVSNet_NR-MVSNet		89.92 102	89.29 101	91.81 124	93.39 190	83.72 85	94.43 132	97.12 28	89.80 31	86.46 144	93.32 150	83.16 56	97.23 200	84.92 119	81.02 282	94.49 189

DU-MVS		89.34 120	88.50 118	91.85 120	93.04 200	83.72 85	94.47 129	96.59 69	89.50 36	86.46 144	93.29 153	77.25 116	97.23 200	84.92 119	81.02 282	94.59 180

cascas		86.43 211	84.98 216	90.80 156	92.10 220	80.92 157	90.24 276	95.91 113	73.10 311	83.57 232	88.39 289	65.15 280	97.46 162	84.90 121	91.43 146	94.03 208

UniMVSNet (Re)		89.80 104	89.07 106	92.01 110	93.60 185	84.52 64	94.78 106	97.47 6	89.26 41	86.44 147	92.32 187	82.10 68	97.39 186	84.81 122	80.84 286	94.12 201

Vis-MVSNet		91.75 64	91.23 66	93.29 60	95.32 117	83.78 83	96.14 32	95.98 107	89.89 29	90.45 85	96.58 46	75.09 153	98.31 103	84.75 123	96.90 66	97.78 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v2v482		87.84 157	87.06 154	90.17 183	90.99 270	79.23 215	94.00 175	95.13 176	84.87 140	85.53 171	92.07 202	74.45 159	97.45 164	84.71 124	81.75 271	93.85 219

DP-MVS Recon		91.95 61	91.28 64	93.96 48	98.33 20	85.92 45	94.66 118	96.66 64	82.69 204	90.03 91	95.82 75	82.30 64	99.03 50	84.57 125	96.48 77	96.91 97

UA-Net		92.83 53	92.54 53	93.68 57	96.10 88	84.71 60	95.66 53	96.39 80	91.92 4	93.22 34	96.49 50	83.16 56	98.87 67	84.47 126	95.47 89	97.45 76

v6		87.98 151	87.25 147	90.16 184	91.36 246	79.39 202	94.37 140	95.27 167	84.48 149	85.78 157	91.51 224	76.15 126	97.46 162	84.46 127	81.88 267	93.62 239

v1neww		87.98 151	87.25 147	90.16 184	91.38 243	79.41 197	94.37 140	95.28 164	84.48 149	85.77 158	91.53 222	76.12 127	97.45 164	84.45 128	81.89 265	93.61 240

v7new		87.98 151	87.25 147	90.16 184	91.38 243	79.41 197	94.37 140	95.28 164	84.48 149	85.77 158	91.53 222	76.12 127	97.45 164	84.45 128	81.89 265	93.61 240

V42		87.68 164	86.86 159	90.15 188	90.58 288	80.14 173	94.24 148	95.28 164	83.66 167	85.67 166	91.33 232	74.73 157	97.41 180	84.43 130	81.83 269	92.89 265

FC-MVSNet-test		90.27 93	90.18 84	90.53 161	93.71 182	79.85 183	95.77 47	97.59 2	89.31 40	86.27 150	94.67 107	81.93 74	97.01 215	84.26 131	88.09 208	94.71 171

v1		87.85 156	87.10 150	90.11 195	91.21 260	79.24 213	94.09 163	95.24 169	84.44 153	85.70 163	91.31 235	75.96 137	97.45 164	84.18 132	81.73 274	93.64 236

v1141		87.84 157	87.09 151	90.11 195	91.23 258	79.25 211	94.08 165	95.24 169	84.44 153	85.69 165	91.31 235	75.91 139	97.44 171	84.17 133	81.74 272	93.63 238

divwei89l23v2f112		87.84 157	87.09 151	90.10 197	91.23 258	79.24 213	94.09 163	95.24 169	84.44 153	85.70 163	91.31 235	75.91 139	97.44 171	84.17 133	81.73 274	93.64 236

VPNet		88.20 144	87.47 140	90.39 175	93.56 186	79.46 193	94.04 171	95.54 141	88.67 56	86.96 135	94.58 111	69.33 228	97.15 204	84.05 135	80.53 291	94.56 183

UGNet		89.95 100	88.95 109	92.95 76	94.51 152	83.31 96	95.70 50	95.23 172	89.37 39	87.58 128	93.94 132	64.00 285	98.78 78	83.92 136	96.31 79	96.74 102
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
IterMVS-LS		88.36 140	87.91 135	89.70 214	93.80 179	78.29 241	93.73 190	95.08 180	85.73 122	84.75 202	91.90 208	79.88 90	96.92 223	83.83 137	82.51 256	93.89 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.10 123	88.86 113	89.80 209	91.84 224	78.30 240	93.70 194	95.01 182	85.73 122	87.15 132	95.28 87	79.87 91	97.21 202	83.81 138	87.36 215	93.88 215

Anonymous20240529		88.09 148	86.59 179	92.58 89	96.53 69	81.92 131	95.99 38	95.84 119	74.11 303	89.06 101	95.21 91	61.44 298	98.81 76	83.67 139	87.47 212	97.01 93

v7		87.75 162	86.96 157	90.12 190	91.20 261	79.50 188	94.28 146	95.46 149	83.45 175	85.75 160	91.56 221	75.13 151	97.43 173	83.60 140	82.18 260	93.42 249

v1144		87.61 178	86.79 164	90.06 198	91.01 269	79.34 205	93.95 177	95.42 158	83.36 179	85.66 167	91.31 235	74.98 155	97.42 175	83.37 141	82.06 261	93.42 249

testdata				90.49 169	96.40 72	77.89 251		95.37 161	72.51 317	93.63 27	96.69 39	82.08 69	97.65 149	83.08 142	97.39 60	95.94 124

LCM-MVSNet-Re		88.30 142	88.32 125	88.27 263	94.71 143	72.41 308	93.15 216	90.98 295	87.77 78	79.25 284	91.96 205	78.35 109	95.75 278	83.04 143	95.62 85	96.65 103

IS-MVSNet		91.43 70	91.09 70	92.46 94	95.87 98	81.38 143	96.95 9	93.69 231	89.72 34	89.50 95	95.98 69	78.57 106	97.77 143	83.02 144	96.50 76	98.22 35

XVG-ACMP-BASELINE		86.00 218	84.84 223	89.45 224	91.20 261	78.00 247	91.70 260	95.55 139	85.05 138	82.97 239	92.25 193	54.49 327	97.48 160	82.93 145	87.45 214	92.89 265

v144192		87.19 193	86.35 183	89.74 210	90.64 287	78.24 243	93.92 178	95.43 156	81.93 217	85.51 173	91.05 247	74.21 164	97.45 164	82.86 146	81.56 276	93.53 244

v8		87.50 182	86.71 167	89.89 204	91.37 245	79.40 201	94.50 125	95.38 159	84.81 142	83.60 231	91.33 232	76.05 131	97.42 175	82.84 147	80.51 293	92.84 267

Anonymous20231211		86.59 207	85.13 213	90.98 153	96.52 70	81.50 136	96.14 32	96.16 93	73.78 305	83.65 229	92.15 194	63.26 288	97.37 187	82.82 148	81.74 272	94.06 206

PAPM_NR		91.22 75	90.78 76	92.52 92	97.60 37	81.46 140	94.37 140	96.24 88	86.39 111	87.41 129	94.80 104	82.06 71	98.48 92	82.80 149	95.37 91	97.61 70

Patchmatch-RL test		81.67 283	79.96 287	86.81 297	85.42 333	71.23 314	82.17 341	87.50 342	78.47 264	77.19 297	82.50 332	70.81 209	93.48 319	82.66 150	72.89 321	95.71 135

v52		86.50 208	85.53 206	89.39 226	89.17 309	78.99 218	94.72 112	95.54 141	83.59 168	82.10 249	90.60 255	71.59 197	97.45 164	82.52 151	79.99 298	91.73 292

V4		86.50 208	85.54 203	89.39 226	89.13 310	78.99 218	94.73 109	95.54 141	83.59 168	82.10 249	90.61 254	71.60 196	97.45 164	82.52 151	80.01 297	91.74 291

tpmrst		85.35 236	84.99 215	86.43 299	90.88 279	67.88 331	88.71 299	91.43 282	80.13 246	86.08 154	88.80 282	73.05 181	96.02 266	82.48 153	83.40 251	95.40 143

sss		88.93 129	88.26 128	90.94 154	94.05 166	80.78 161	91.71 259	95.38 159	81.55 234	88.63 104	93.91 136	75.04 154	95.47 289	82.47 154	91.61 145	96.57 105

ab-mvs		89.41 116	88.35 122	92.60 87	95.15 128	82.65 119	92.20 249	95.60 136	83.97 160	88.55 105	93.70 145	74.16 166	98.21 108	82.46 155	89.37 180	96.94 96

CostFormer		85.77 226	84.94 219	88.26 264	91.16 266	72.58 307	89.47 289	91.04 294	76.26 284	86.45 146	89.97 267	70.74 210	96.86 227	82.35 156	87.07 220	95.34 146

v1192		87.25 189	86.33 184	90.00 202	90.76 282	79.04 217	93.80 184	95.48 148	82.57 205	85.48 175	91.18 241	73.38 179	97.42 175	82.30 157	82.06 261	93.53 244

Baseline_NR-MVSNet		87.07 195	86.63 178	88.40 260	91.44 236	77.87 252	94.23 149	92.57 249	84.12 158	85.74 162	92.08 200	77.25 116	96.04 264	82.29 158	79.94 299	91.30 301

Anonymous202405211		87.68 164	86.13 189	92.31 101	96.66 63	80.74 162	94.87 100	91.49 280	80.47 244	89.46 96	95.44 83	54.72 326	98.23 105	82.19 159	89.89 172	97.97 53

v148		87.04 196	86.32 185	89.21 235	90.94 274	77.26 268	93.71 193	94.43 204	84.84 141	84.36 214	90.80 250	76.04 133	97.05 213	82.12 160	79.60 302	93.31 251

114514_t		89.51 110	88.50 118	92.54 91	98.11 27	81.99 129	95.16 80	96.36 82	70.19 330	85.81 156	95.25 89	76.70 121	98.63 84	82.07 161	96.86 68	97.00 94

v1921920		86.97 197	86.06 194	89.69 215	90.53 292	78.11 246	93.80 184	95.43 156	81.90 219	85.33 189	91.05 247	72.66 186	97.41 180	82.05 162	81.80 270	93.53 244

OurMVSNet-221017-0		85.35 236	84.64 228	87.49 280	90.77 281	72.59 306	94.01 174	94.40 205	84.72 145	79.62 282	93.17 157	61.91 295	96.72 235	81.99 163	81.16 277	93.16 256

v10		87.25 189	86.38 182	89.85 205	91.19 263	79.50 188	94.48 126	95.45 153	83.79 165	83.62 230	91.19 240	75.13 151	97.42 175	81.94 164	80.60 288	92.63 273

TranMVSNet+NR-MVSNet		88.84 130	87.95 133	91.49 132	92.68 211	83.01 106	94.92 96	96.31 83	89.88 30	85.53 171	93.85 140	76.63 123	96.96 219	81.91 165	79.87 301	94.50 187

test-LLR		85.87 220	85.41 208	87.25 285	90.95 272	71.67 311	89.55 285	89.88 317	83.41 176	84.54 206	87.95 296	67.25 262	95.11 303	81.82 166	93.37 128	94.97 153

test-mter		84.54 259	83.64 249	87.25 285	90.95 272	71.67 311	89.55 285	89.88 317	79.17 254	84.54 206	87.95 296	55.56 322	95.11 303	81.82 166	93.37 128	94.97 153

PMMVS		85.71 232	84.96 218	87.95 271	88.90 314	77.09 269	88.68 300	90.06 312	72.32 318	86.47 143	90.76 251	72.15 193	94.40 310	81.78 168	93.49 123	92.36 281

NR-MVSNet		88.58 136	87.47 140	91.93 116	93.04 200	84.16 77	94.77 107	96.25 87	89.05 46	80.04 278	93.29 153	79.02 100	97.05 213	81.71 169	80.05 296	94.59 180

WTY-MVS		89.60 107	88.92 110	91.67 128	95.47 111	81.15 150	92.38 244	94.78 196	83.11 184	89.06 101	94.32 116	78.67 104	96.61 242	81.57 170	90.89 160	97.24 81

v1240		86.78 201	85.85 199	89.56 218	90.45 293	77.79 254	93.61 198	95.37 161	81.65 230	85.43 180	91.15 243	71.50 200	97.43 173	81.47 171	82.05 263	93.47 248

WR-MVS		88.38 138	87.67 137	90.52 167	93.30 193	80.18 171	93.26 213	95.96 109	88.57 60	85.47 176	92.81 174	76.12 127	96.91 224	81.24 172	82.29 258	94.47 192

1314		87.51 181	86.57 180	90.34 180	92.42 214	79.74 186	92.63 235	95.35 163	78.35 266	80.14 276	91.62 217	74.05 167	97.15 204	81.05 173	93.53 122	94.12 201

semantic-postprocess				88.18 267	91.71 229	76.87 272		92.65 248	85.40 130	81.44 259	90.54 256	66.21 272	95.00 306	81.04 174	81.05 280	92.66 272

PatchFormer-LS_test		86.02 217	85.13 213	88.70 245	91.52 233	74.12 290	91.19 270	92.09 258	82.71 203	84.30 217	87.24 305	70.87 207	96.98 217	81.04 174	85.17 232	95.00 152

XXY-MVS		87.65 166	86.85 160	90.03 199	92.14 218	80.60 166	93.76 187	95.23 172	82.94 196	84.60 204	94.02 127	74.27 161	95.49 288	81.04 174	83.68 245	94.01 210

GA-MVS		86.61 205	85.27 212	90.66 157	91.33 251	78.71 222	90.40 274	93.81 230	85.34 131	85.12 191	89.57 273	61.25 300	97.11 208	80.99 177	89.59 178	96.15 113

IB-MVS	80.51 15	85.24 239	83.26 258	91.19 139	92.13 219	79.86 182	91.75 257	91.29 285	83.28 181	80.66 269	88.49 288	61.28 299	98.46 93	80.99 177	79.46 303	95.25 147
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
CVMVSNet		84.69 257	84.79 224	84.37 315	91.84 224	64.92 338	93.70 194	91.47 281	66.19 340	86.16 153	95.28 87	67.18 264	93.33 321	80.89 179	90.42 163	94.88 165

HyFIR lowres test		88.09 148	86.81 162	91.93 116	96.00 92	80.63 164	90.01 280	95.79 123	73.42 308	87.68 127	92.10 199	73.86 170	97.96 135	80.75 180	91.70 144	97.19 85

AdaColmap		89.89 103	89.07 106	92.37 99	97.41 44	83.03 103	94.42 133	95.92 111	82.81 200	86.34 149	94.65 108	73.89 169	99.02 53	80.69 181	95.51 87	95.05 150

原ACMM1				92.01 110	97.34 47	81.05 152		96.81 50	78.89 257	90.45 85	95.92 71	82.65 60	98.84 75	80.68 182	98.26 43	96.14 114

TESTMET0.1,1		83.74 267	82.85 264	86.42 300	89.96 303	71.21 315	89.55 285	87.88 337	77.41 273	83.37 236	87.31 304	56.71 319	93.65 318	80.62 183	92.85 138	94.40 193

无先验							93.28 212	96.26 85	73.95 304				99.05 46	80.56 184		96.59 104

1121		90.42 91	89.49 95	93.20 63	97.27 52	84.46 68	92.63 235	95.51 145	71.01 328	91.20 77	96.21 59	82.92 58	99.05 46	80.56 184	98.07 48	96.10 118

Fast-Effi-MVS+		89.41 116	88.64 115	91.71 126	94.74 140	80.81 160	93.54 200	95.10 178	83.11 184	86.82 140	90.67 252	79.74 93	97.75 147	80.51 186	93.55 121	96.57 105

v18		84.97 244	83.76 242	88.60 250	91.36 246	79.41 197	93.82 183	94.04 216	83.00 194	76.61 300	86.60 308	76.19 125	95.43 290	80.39 187	71.79 325	90.96 307

CHOSEN 1792x2688		88.84 130	87.69 136	92.30 102	96.14 82	81.42 142	90.01 280	95.86 118	74.52 300	87.41 129	93.94 132	75.46 148	98.36 97	80.36 188	95.53 86	97.12 89

CDS-MVSNet		89.45 113	88.51 117	92.29 103	93.62 184	83.61 90	93.01 224	94.68 198	81.95 216	87.82 124	93.24 155	78.69 103	96.99 216	80.34 189	93.23 131	96.28 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		87.44 183	86.72 166	89.63 217	92.04 221	77.68 259	94.03 172	93.94 225	85.81 119	82.42 244	91.32 234	70.33 218	97.06 212	80.33 190	90.23 167	94.14 200

v16		84.96 245	83.74 244	88.62 248	91.40 241	79.48 191	93.83 181	94.04 216	83.03 191	76.54 301	86.59 309	76.11 130	95.42 291	80.33 190	71.80 324	90.95 309

v17		84.93 247	83.70 246	88.62 248	91.36 246	79.48 191	93.83 181	94.03 218	83.04 190	76.51 302	86.57 310	76.05 131	95.42 291	80.31 192	71.65 326	90.96 307

API-MVS		90.66 84	90.07 86	92.45 95	96.36 74	84.57 63	96.06 36	95.22 174	82.39 206	89.13 98	94.27 121	80.32 86	98.46 93	80.16 193	96.71 70	94.33 194

v15		84.79 250	83.53 251	88.57 254	91.30 257	79.41 197	93.70 194	94.01 219	83.06 187	76.27 303	86.42 314	76.03 134	95.38 293	80.01 194	71.00 329	90.92 310

MAR-MVS		90.30 92	89.37 99	93.07 72	96.61 65	84.48 67	95.68 51	95.67 130	82.36 208	87.85 118	92.85 170	76.63 123	98.80 77	80.01 194	96.68 71	95.91 125
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
V14		84.79 250	83.52 252	88.57 254	91.32 253	79.43 196	93.72 192	94.01 219	83.06 187	76.22 304	86.43 311	76.01 135	95.37 294	79.96 196	70.99 330	90.91 311

HY-MVS	83.01 12	89.03 126	87.94 134	92.29 103	94.86 138	82.77 111	92.08 254	94.49 202	81.52 235	86.93 136	92.79 176	78.32 110	98.23 105	79.93 197	90.55 161	95.88 127

CHOSEN 280x420		85.15 240	83.99 239	88.65 246	92.47 213	78.40 238	79.68 346	92.76 244	74.90 297	81.41 260	89.59 272	69.85 223	95.51 285	79.92 198	95.29 94	92.03 287

V9		84.77 252	83.50 253	88.58 251	91.33 251	79.46 193	93.75 188	94.00 222	83.07 186	76.07 309	86.43 311	75.97 136	95.37 294	79.91 199	70.93 332	90.91 311

v12		84.74 253	83.46 254	88.58 251	91.32 253	79.50 188	93.75 188	94.01 219	83.06 187	75.98 311	86.41 315	75.82 142	95.36 297	79.87 200	70.89 333	90.89 313

v13		84.72 255	83.44 256	88.58 251	91.31 256	79.52 187	93.77 186	94.00 222	83.03 191	75.85 312	86.38 316	75.84 141	95.35 298	79.83 201	70.95 331	90.87 314

v748		86.27 212	85.28 211	89.25 234	90.26 296	77.58 267	94.89 97	95.50 146	84.28 156	81.41 260	90.46 260	72.57 189	97.32 189	79.81 202	78.36 306	92.84 267

MVS		87.44 183	86.10 192	91.44 134	92.61 212	83.62 89	92.63 235	95.66 132	67.26 338	81.47 258	92.15 194	77.95 111	98.22 107	79.71 203	95.48 88	92.47 277

pm-mvs1		86.61 205	85.54 203	89.82 206	91.44 236	80.18 171	95.28 73	94.85 192	83.84 162	81.66 257	92.62 179	72.45 192	96.48 248	79.67 204	78.06 307	92.82 269

v11		84.67 258	83.41 257	88.44 259	91.32 253	79.13 216	93.69 197	93.99 224	82.81 200	76.20 305	86.24 318	75.48 147	95.35 298	79.53 205	71.48 328	90.85 315

IterMVS		84.88 248	83.98 240	87.60 276	91.44 236	76.03 279	90.18 278	92.41 251	83.24 182	81.06 265	90.42 261	66.60 267	94.28 312	79.46 206	80.98 285	92.48 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1112_ss		88.42 137	87.33 143	91.72 125	94.92 135	80.98 154	92.97 227	94.54 201	78.16 270	83.82 224	93.88 137	78.78 102	97.91 139	79.45 207	89.41 179	96.26 111

gm-plane-assit					89.60 308	68.00 330			77.28 276		88.99 278		97.57 153	79.44 208

PM-MVS		78.11 307	76.12 309	84.09 318	83.54 340	70.08 324	88.97 297	85.27 347	79.93 248	74.73 317	86.43 311	34.70 352	93.48 319	79.43 209	72.06 323	88.72 332

v7n		86.81 199	85.76 201	89.95 203	90.72 284	79.25 211	95.07 85	95.92 111	84.45 152	82.29 245	90.86 249	72.60 188	97.53 156	79.42 210	80.52 292	93.08 261

PAPR		90.02 97	89.27 103	92.29 103	95.78 99	80.95 156	92.68 234	96.22 89	81.91 218	86.66 142	93.75 144	82.23 65	98.44 95	79.40 211	94.79 98	97.48 75

新几何1				93.10 68	97.30 49	84.35 74		95.56 138	71.09 327	91.26 76	96.24 57	82.87 59	98.86 70	79.19 212	98.10 47	96.07 120

CP-MVSNet		87.63 170	87.26 146	88.74 244	93.12 197	76.59 274	95.29 67	96.58 71	88.43 62	83.49 234	92.98 168	75.28 150	95.83 274	78.97 213	81.15 279	93.79 221

pmmvs4		85.43 234	83.86 241	90.16 184	90.02 302	82.97 108	90.27 275	92.67 247	75.93 287	80.73 267	91.74 212	71.05 204	95.73 279	78.85 214	83.46 249	91.78 290

DWT-MVSNet_test		84.95 246	83.68 247	88.77 242	91.43 239	73.75 293	91.74 258	90.98 295	80.66 243	83.84 223	87.36 303	62.44 291	97.11 208	78.84 215	85.81 225	95.46 141

Test_1112_low_res		87.65 166	86.51 181	91.08 144	94.94 134	79.28 209	91.77 256	94.30 209	76.04 286	83.51 233	92.37 185	77.86 114	97.73 148	78.69 216	89.13 192	96.22 112

Vis-MVSNet (Re-imp)		89.59 108	89.44 97	90.03 199	95.74 100	75.85 280	95.61 57	90.80 300	87.66 84	87.83 123	95.40 86	76.79 120	96.46 250	78.37 217	96.73 69	97.80 64

PS-CasMVS		87.32 186	86.88 158	88.63 247	92.99 204	76.33 277	95.33 62	96.61 68	88.22 68	83.30 237	93.07 162	73.03 182	95.79 277	78.36 218	81.00 284	93.75 227

testdata2													98.75 79	78.30 219

GBi-Net		87.26 187	85.98 195	91.08 144	94.01 168	83.10 100	95.14 81	94.94 185	83.57 170	84.37 211	91.64 213	66.59 268	96.34 256	78.23 220	85.36 229	93.79 221

test1		87.26 187	85.98 195	91.08 144	94.01 168	83.10 100	95.14 81	94.94 185	83.57 170	84.37 211	91.64 213	66.59 268	96.34 256	78.23 220	85.36 229	93.79 221

FMVSNet3		87.40 185	86.11 191	91.30 137	93.79 181	83.64 88	94.20 156	94.81 195	83.89 161	84.37 211	91.87 209	68.45 251	96.56 243	78.23 220	85.36 229	93.70 230

OpenMVS	83.78 11	88.74 133	87.29 144	93.08 70	92.70 210	85.39 53	96.57 22	96.43 76	78.74 262	80.85 266	96.07 67	69.64 225	99.01 55	78.01 223	96.65 72	94.83 167

tpm		84.73 254	84.02 238	86.87 296	90.33 294	68.90 328	89.06 295	89.94 315	80.85 242	85.75 160	89.86 269	68.54 247	95.97 268	77.76 224	84.05 241	95.75 134

TAMVS		89.21 121	88.29 126	91.96 114	93.71 182	82.62 120	93.30 210	94.19 211	82.22 209	87.78 125	93.94 132	78.83 101	96.95 221	77.70 225	92.98 135	96.32 109

BH-untuned		88.60 135	88.13 130	90.01 201	95.24 127	78.50 235	93.29 211	94.15 213	84.75 144	84.46 208	93.40 146	75.76 143	97.40 182	77.59 226	94.52 105	94.12 201

FMVSNet2		87.19 193	85.82 200	91.30 137	94.01 168	83.67 87	94.79 105	94.94 185	83.57 170	83.88 222	92.05 203	66.59 268	96.51 246	77.56 227	85.01 233	93.73 228

RPSCF		85.07 241	84.27 235	87.48 281	92.91 206	70.62 321	91.69 261	92.46 250	76.20 285	82.67 243	95.22 90	63.94 286	97.29 193	77.51 228	85.80 226	94.53 184

PLC	84.53 7	89.06 125	88.03 131	92.15 107	97.27 52	82.69 118	94.29 145	95.44 155	79.71 251	84.01 221	94.18 123	76.68 122	98.75 79	77.28 229	93.41 126	95.02 151
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		89.07 124	87.98 132	92.34 100	96.87 59	84.78 59	94.08 165	93.24 236	81.41 237	84.46 208	95.13 94	75.57 146	96.62 240	77.21 230	93.84 117	95.61 138

K. test v3		81.59 285	80.15 285	85.91 304	89.89 305	69.42 327	92.57 238	87.71 339	85.56 126	73.44 323	89.71 271	55.58 321	95.52 284	77.17 231	69.76 336	92.78 270

QAPM		89.51 110	88.15 129	93.59 58	94.92 135	84.58 62	96.82 18	96.70 60	78.43 265	83.41 235	96.19 63	73.18 180	99.30 29	77.11 232	96.54 75	96.89 98

pmmvs5		84.21 261	82.84 265	88.34 262	88.95 313	76.94 271	92.41 242	91.91 268	75.63 289	80.28 273	91.18 241	64.59 283	95.57 282	77.09 233	83.47 248	92.53 275

pmmvs6		83.42 268	81.60 271	88.87 241	88.01 325	77.87 252	94.96 92	94.24 210	74.67 299	78.80 285	91.09 246	60.17 308	96.49 247	77.06 234	75.40 316	92.23 285

test_post1							88.00 307				9.81 363	69.31 230	95.53 283	76.65 235

Anonymous20240521		86.87 198	85.95 197	89.64 216	92.89 207	78.88 220	95.66 53	96.05 103	84.77 143	81.92 254	92.39 184	71.54 198	96.96 219	76.46 236	81.87 268	93.08 261

WR-MVS_H		87.80 161	87.37 142	89.10 239	93.23 194	78.12 245	95.61 57	97.30 18	87.90 74	83.72 226	92.01 204	79.65 98	96.01 267	76.36 237	80.54 290	93.16 256

EU-MVSNet		81.32 290	80.95 276	82.42 322	88.50 317	63.67 339	93.32 206	91.33 283	64.02 344	80.57 271	92.83 172	61.21 302	92.27 329	76.34 238	80.38 294	91.32 300

CMPMVS	59.16 21	80.52 296	79.20 294	84.48 314	83.98 338	67.63 333	89.95 282	93.84 229	64.79 343	66.81 340	91.14 244	57.93 317	95.17 301	76.25 239	88.10 206	90.65 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
F-COLMAP		87.95 154	86.80 163	91.40 135	96.35 75	80.88 158	94.73 109	95.45 153	79.65 252	82.04 252	94.61 109	71.13 203	98.50 91	76.24 240	91.05 154	94.80 169

PEN-MVS		86.80 200	86.27 187	88.40 260	92.32 216	75.71 281	95.18 78	96.38 81	87.97 71	82.82 241	93.15 158	73.39 178	95.92 270	76.15 241	79.03 305	93.59 242

SixPastTwentyTwo		83.91 264	82.90 263	86.92 293	90.99 270	70.67 320	93.48 202	91.99 263	85.54 127	77.62 294	92.11 198	60.59 305	96.87 226	76.05 242	77.75 308	93.20 254

MS-PatchMatch		85.05 242	84.16 236	87.73 274	91.42 240	78.51 234	91.25 269	93.53 232	77.50 272	80.15 275	91.58 218	61.99 294	95.51 285	75.69 243	94.35 111	89.16 328

BH-w/o		87.57 180	87.05 155	89.12 237	94.90 137	77.90 250	92.41 242	93.51 233	82.89 199	83.70 227	91.34 231	75.75 144	97.07 211	75.49 244	93.49 123	92.39 280

gg-mvs-nofinetune		81.77 282	79.37 292	88.99 240	90.85 280	77.73 258	86.29 318	79.63 357	74.88 298	83.19 238	69.05 349	60.34 306	96.11 263	75.46 245	94.64 102	93.11 259

FMVSNet1		85.85 221	84.11 237	91.08 144	92.81 208	83.10 100	95.14 81	94.94 185	81.64 231	82.68 242	91.64 213	59.01 313	96.34 256	75.37 246	83.78 242	93.79 221

EPMVS		83.90 265	82.70 266	87.51 278	90.23 298	72.67 303	88.62 301	81.96 353	81.37 238	85.01 193	88.34 290	66.31 271	94.45 309	75.30 247	87.12 218	95.43 142

pmmvs-eth3d		80.97 294	78.72 299	87.74 273	84.99 336	79.97 180	90.11 279	91.65 272	75.36 290	73.51 322	86.03 319	59.45 311	93.96 315	75.17 248	72.21 322	89.29 326

tpm2		84.08 262	82.94 262	87.48 281	91.39 242	71.27 313	89.23 293	90.37 305	71.95 321	84.64 203	89.33 275	67.30 261	96.55 245	75.17 248	87.09 219	94.63 176

lessismore_v0				86.04 302	88.46 318	68.78 329		80.59 355		73.01 326	90.11 265	55.39 323	96.43 252	75.06 250	65.06 342	92.90 264

MVP-Stereo		85.97 219	84.86 222	89.32 232	90.92 276	82.19 126	92.11 252	94.19 211	78.76 261	78.77 286	91.63 216	68.38 258	96.56 243	75.01 251	93.95 114	89.20 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet	78.82 18	85.55 233	84.65 227	88.23 266	94.72 142	71.93 309	87.12 314	92.75 245	78.80 260	84.95 194	90.53 258	64.43 284	96.71 237	74.74 252	93.86 116	96.06 121

MDTV_nov1_ep13_2view						55.91 352	87.62 312		73.32 309	84.59 205		70.33 218		74.65 253		95.50 139

PatchmatchNet		85.85 221	84.70 226	89.29 233	91.76 227	75.54 282	88.49 302	91.30 284	81.63 232	85.05 192	88.70 284	71.71 194	96.24 259	74.61 254	89.05 193	96.08 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LF4IMVS		80.37 297	79.07 297	84.27 317	86.64 330	69.87 326	89.39 290	91.05 293	76.38 281	74.97 316	90.00 266	47.85 340	94.25 313	74.55 255	80.82 287	88.69 333

testpf		71.41 319	72.11 317	69.30 339	84.53 337	59.79 343	62.74 356	83.14 350	71.11 326	68.83 337	81.57 337	46.70 342	84.83 352	74.51 256	75.86 315	63.30 352

DTE-MVSNet		86.11 214	85.48 207	87.98 270	91.65 232	74.92 284	94.93 95	95.75 126	87.36 87	82.26 246	93.04 163	72.85 183	95.82 275	74.04 257	77.46 311	93.20 254

BH-RMVSNet		88.37 139	87.48 139	91.02 148	95.28 118	79.45 195	92.89 229	93.07 239	85.45 129	86.91 137	94.84 103	70.35 217	97.76 144	73.97 258	94.59 103	95.85 128

CR-MVSNet		85.35 236	83.76 242	90.12 190	90.58 288	79.34 205	85.24 326	91.96 266	78.27 267	85.55 169	87.87 299	71.03 205	95.61 280	73.96 259	89.36 181	95.40 143

ACMH+	81.04 14	85.05 242	83.46 254	89.82 206	94.66 146	79.37 203	94.44 131	94.12 215	82.19 210	78.04 289	92.82 173	58.23 315	97.54 155	73.77 260	82.90 253	92.54 274

TR-MVS		86.78 201	85.76 201	89.82 206	94.37 157	78.41 237	92.47 241	92.83 242	81.11 240	86.36 148	92.40 183	68.73 245	97.48 160	73.75 261	89.85 174	93.57 243

UnsupCasMVSNet_eth		80.07 298	78.27 300	85.46 307	85.24 334	72.63 305	88.45 304	94.87 191	82.99 195	71.64 332	88.07 295	56.34 320	91.75 333	73.48 262	63.36 346	92.01 288

PatchMatch-RL		86.77 203	85.54 203	90.47 171	95.88 96	82.71 117	90.54 273	92.31 252	79.82 250	84.32 215	91.57 220	68.77 244	96.39 253	73.16 263	93.48 125	92.32 283

ambc				83.06 320	79.99 346	63.51 340	77.47 349	92.86 241		74.34 320	84.45 324	28.74 353	95.06 305	73.06 264	68.89 339	90.61 317

Patchmatch-test1		85.81 225	84.71 225	89.12 237	92.15 217	76.60 273	91.12 271	91.69 271	83.53 173	85.50 174	88.56 287	66.79 266	95.00 306	72.69 265	90.35 164	95.76 133

ITE_SJBPF				88.24 265	91.88 223	77.05 270		92.92 240	85.54 127	80.13 277	93.30 152	57.29 318	96.20 260	72.46 266	84.71 235	91.49 297

ACMH	80.38 17	85.36 235	83.68 247	90.39 175	94.45 155	80.63 164	94.73 109	94.85 192	82.09 211	77.24 296	92.65 178	60.01 309	97.58 152	72.25 267	84.87 234	92.96 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
USDC		82.76 273	81.26 274	87.26 284	91.17 264	74.55 285	89.27 291	93.39 235	78.26 268	75.30 314	92.08 200	54.43 328	96.63 239	71.64 268	85.79 227	90.61 317

tpmp4_e23		83.87 266	82.33 267	88.48 257	91.46 235	72.82 300	89.82 283	91.57 277	73.02 313	81.86 256	89.05 277	66.20 273	96.97 218	71.57 269	86.39 222	95.66 136

EPNet_dtu		86.49 210	85.94 198	88.14 268	90.24 297	72.82 300	94.11 160	92.20 255	86.66 107	79.42 283	92.36 186	73.52 174	95.81 276	71.26 270	93.66 119	95.80 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				87.94 272	89.73 307	77.91 249	87.80 308	78.23 359		80.58 270	83.86 326	59.88 310	95.33 300	71.20 271	92.22 143	90.60 319

LTVRE_ROB	82.13 13	86.26 213	84.90 221	90.34 180	94.44 156	81.50 136	92.31 246	94.89 190	83.03 191	79.63 281	92.67 177	69.69 224	97.79 142	71.20 271	86.26 223	91.72 293
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
JIA-IIPM		81.04 292	78.98 298	87.25 285	88.64 315	73.48 295	81.75 342	89.61 321	73.19 310	82.05 251	73.71 346	66.07 277	95.87 273	71.18 273	84.60 236	92.41 279

TransMVSNet (Re)		84.43 260	83.06 261	88.54 256	91.72 228	78.44 236	95.18 78	92.82 243	82.73 202	79.67 280	92.12 196	73.49 175	95.96 269	71.10 274	68.73 340	91.21 302

PCF-MVS	84.11 10	87.74 163	86.08 193	92.70 85	94.02 167	84.43 72	89.27 291	95.87 117	73.62 307	84.43 210	94.33 115	78.48 108	98.86 70	70.27 275	94.45 108	94.81 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		82.37 278	80.34 281	88.46 258	90.27 295	79.35 204	92.80 232	94.33 208	77.14 277	73.26 325	90.18 264	47.47 341	96.72 235	70.25 276	87.32 217	89.30 325

MDTV_nov1_ep13			83.56 250		91.69 231	69.93 325	87.75 310	91.54 278	78.60 263	84.86 201	88.90 280	69.54 226	96.03 265	70.25 276	88.93 194

TDRefinement		79.81 300	77.34 302	87.22 288	79.24 349	75.48 283	93.12 217	92.03 261	76.45 280	75.01 315	91.58 218	49.19 338	96.44 251	70.22 278	69.18 337	89.75 323

conf200view11		87.65 166	86.71 167	90.46 173	96.12 83	78.55 226	95.03 88	91.58 273	87.15 89	88.06 113	92.29 189	68.91 236	98.10 120	70.13 279	91.10 148	94.71 171

thres100view900		87.63 170	86.71 167	90.38 177	96.12 83	78.55 226	95.03 88	91.58 273	87.15 89	88.06 113	92.29 189	68.91 236	98.10 120	70.13 279	91.10 148	94.48 190

tfpn200view9		87.58 179	86.64 176	90.41 174	95.99 93	78.64 223	94.58 121	91.98 264	86.94 101	88.09 110	91.77 210	69.18 233	98.10 120	70.13 279	91.10 148	94.48 190

thres400		87.62 173	86.64 176	90.57 159	95.99 93	78.64 223	94.58 121	91.98 264	86.94 101	88.09 110	91.77 210	69.18 233	98.10 120	70.13 279	91.10 148	94.96 156

thres600view7		87.65 166	86.67 171	90.59 158	96.08 89	78.72 221	94.88 99	91.58 273	87.06 98	88.08 112	92.30 188	68.91 236	98.10 120	70.05 283	91.10 148	94.96 156

tfpn111		87.63 170	86.68 170	90.47 171	96.12 83	78.55 226	95.03 88	91.58 273	87.15 89	88.06 113	92.29 189	68.91 236	98.15 113	69.88 284	91.10 148	94.71 171

thres200		87.21 192	86.24 188	90.12 190	95.36 113	78.53 229	93.26 213	92.10 257	86.42 110	88.00 116	91.11 245	69.24 232	98.00 133	69.58 285	91.04 155	93.83 220

view600		87.62 173	86.65 172	90.53 161	96.19 78	78.52 230	95.29 67	91.09 287	87.08 94	87.84 119	93.03 164	68.86 240	98.11 116	69.44 286	91.02 156	94.96 156

view800		87.62 173	86.65 172	90.53 161	96.19 78	78.52 230	95.29 67	91.09 287	87.08 94	87.84 119	93.03 164	68.86 240	98.11 116	69.44 286	91.02 156	94.96 156

conf0.05thres1000		87.62 173	86.65 172	90.53 161	96.19 78	78.52 230	95.29 67	91.09 287	87.08 94	87.84 119	93.03 164	68.86 240	98.11 116	69.44 286	91.02 156	94.96 156

tfpn		87.62 173	86.65 172	90.53 161	96.19 78	78.52 230	95.29 67	91.09 287	87.08 94	87.84 119	93.03 164	68.86 240	98.11 116	69.44 286	91.02 156	94.96 156

tpm cat1		81.96 279	80.27 282	87.01 291	91.09 267	71.02 317	87.38 313	91.53 279	66.25 339	80.17 274	86.35 317	68.22 260	96.15 262	69.16 290	82.29 258	93.86 218

Patchmtry		82.71 274	80.93 277	88.06 269	90.05 301	76.37 276	84.74 328	91.96 266	72.28 319	81.32 262	87.87 299	71.03 205	95.50 287	68.97 291	80.15 295	92.32 283

our_test_3		81.93 280	80.46 280	86.33 301	88.46 318	73.48 295	88.46 303	91.11 286	76.46 279	76.69 298	88.25 292	66.89 265	94.36 311	68.75 292	79.08 304	91.14 304

PVSNet_0	73.20 20	77.22 308	74.83 311	84.37 315	90.70 285	71.10 316	83.09 339	89.67 320	72.81 316	73.93 321	83.13 330	60.79 304	93.70 317	68.54 293	50.84 351	88.30 338

MSDG		84.86 249	83.09 260	90.14 189	93.80 179	80.05 176	89.18 294	93.09 238	78.89 257	78.19 287	91.91 207	65.86 278	97.27 194	68.47 294	88.45 201	93.11 259

tfpn_ndepth		86.10 215	84.98 216	89.43 225	95.52 110	78.29 241	94.62 119	89.60 322	81.88 227	85.43 180	90.54 256	68.47 250	96.85 228	68.46 295	90.34 165	93.15 258

LS3D		87.89 155	86.32 185	92.59 88	96.07 90	82.92 109	95.23 75	94.92 189	75.66 288	82.89 240	95.98 69	72.48 190	99.21 32	68.43 296	95.23 96	95.64 137

AllTest		83.42 268	81.39 272	89.52 220	95.01 130	77.79 254	93.12 217	90.89 298	77.41 273	76.12 307	93.34 147	54.08 329	97.51 158	68.31 297	84.27 239	93.26 252

TestCases				89.52 220	95.01 130	77.79 254		90.89 298	77.41 273	76.12 307	93.34 147	54.08 329	97.51 158	68.31 297	84.27 239	93.26 252

dp		81.47 288	80.23 283	85.17 310	89.92 304	65.49 337	86.74 315	90.10 311	76.30 283	81.10 263	87.12 307	62.81 289	95.92 270	68.13 299	79.88 300	94.09 204

tpmvs		83.35 271	82.07 268	87.20 289	91.07 268	71.00 318	88.31 305	91.70 270	78.91 256	80.49 272	87.18 306	69.30 231	97.08 210	68.12 300	83.56 247	93.51 247

FMVSNet5		81.52 287	79.60 291	87.27 283	91.17 264	77.95 248	91.49 264	92.26 254	76.87 278	76.16 306	87.91 298	51.67 332	92.34 328	67.74 301	81.16 277	91.52 296

tfpn1000		86.06 216	84.92 220	89.49 223	95.54 107	77.79 254	94.72 112	89.07 330	82.05 212	85.36 188	91.94 206	68.32 259	96.65 238	67.04 302	90.24 166	94.02 209

YYNet1		79.22 304	77.20 304	85.28 309	88.20 323	72.66 304	85.87 321	90.05 314	74.33 302	62.70 345	87.61 301	66.09 276	92.03 330	66.94 303	72.97 320	91.15 303

PAPM		86.68 204	85.39 209	90.53 161	93.05 199	79.33 208	89.79 284	94.77 197	78.82 259	81.95 253	93.24 155	76.81 119	97.30 190	66.94 303	93.16 132	94.95 163

DP-MVS		87.25 189	85.36 210	92.90 78	97.65 36	83.24 97	94.81 104	92.00 262	74.99 295	81.92 254	95.00 96	72.66 186	99.05 46	66.92 305	92.33 142	96.40 107

MDA-MVSNet_test_wron		79.21 305	77.19 305	85.29 308	88.22 322	72.77 302	85.87 321	90.06 312	74.34 301	62.62 346	87.56 302	66.14 275	91.99 331	66.90 306	73.01 319	91.10 306

UnsupCasMVSNet_bld		76.23 311	73.27 313	85.09 311	83.79 339	72.92 298	85.65 325	93.47 234	71.52 322	68.84 336	79.08 342	49.77 335	93.21 322	66.81 307	60.52 348	89.13 330

conf0.01		85.83 223	84.54 229	89.71 212	95.26 120	77.63 260	94.21 150	89.33 323	81.89 220	84.94 195	91.51 224	68.43 252	96.80 229	66.05 308	89.23 184	94.71 171

conf0.002		85.83 223	84.54 229	89.71 212	95.26 120	77.63 260	94.21 150	89.33 323	81.89 220	84.94 195	91.51 224	68.43 252	96.80 229	66.05 308	89.23 184	94.71 171

thresconf0.02		85.75 227	84.54 229	89.38 228	95.26 120	77.63 260	94.21 150	89.33 323	81.89 220	84.94 195	91.51 224	68.43 252	96.80 229	66.05 308	89.23 184	93.70 230

tfpn_n400		85.75 227	84.54 229	89.38 228	95.26 120	77.63 260	94.21 150	89.33 323	81.89 220	84.94 195	91.51 224	68.43 252	96.80 229	66.05 308	89.23 184	93.70 230

tfpnconf		85.75 227	84.54 229	89.38 228	95.26 120	77.63 260	94.21 150	89.33 323	81.89 220	84.94 195	91.51 224	68.43 252	96.80 229	66.05 308	89.23 184	93.70 230

tfpnview11		85.75 227	84.54 229	89.38 228	95.26 120	77.63 260	94.21 150	89.33 323	81.89 220	84.94 195	91.51 224	68.43 252	96.80 229	66.05 308	89.23 184	93.70 230

MIMVSNet		82.59 276	80.53 279	88.76 243	91.51 234	78.32 239	86.57 317	90.13 310	79.32 253	80.70 268	88.69 285	52.98 331	93.07 326	66.03 314	88.86 195	94.90 164

LCM-MVSNet		66.00 322	62.16 326	77.51 331	64.51 361	58.29 345	83.87 335	90.90 297	48.17 352	54.69 349	73.31 347	16.83 364	86.75 346	65.47 315	61.67 347	87.48 340

PatchT		82.68 275	81.27 273	86.89 295	90.09 300	70.94 319	84.06 333	90.15 309	74.91 296	85.63 168	83.57 328	69.37 227	94.87 308	65.19 316	88.50 200	94.84 166

test0.0.03 1		82.41 277	81.69 270	84.59 313	88.23 321	72.89 299	90.24 276	87.83 338	83.41 176	79.86 279	89.78 270	67.25 262	88.99 339	65.18 317	83.42 250	91.90 289

ppachtmachnet_test		81.84 281	80.07 286	87.15 290	88.46 318	74.43 286	89.04 296	92.16 256	75.33 291	77.75 292	88.99 278	66.20 273	95.37 294	65.12 318	77.60 309	91.65 294

COLMAP_ROB	80.39 16	83.96 263	82.04 269	89.74 210	95.28 118	79.75 185	94.25 147	92.28 253	75.17 293	78.02 290	93.77 142	58.60 314	97.84 141	65.06 319	85.92 224	91.63 295
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		81.66 284	79.71 290	87.50 279	91.35 249	74.19 288	83.33 337	88.48 334	72.90 314	82.24 247	85.77 320	64.98 281	93.20 323	64.57 320	83.74 243	95.12 148

ADS-MVSNet		81.56 286	79.78 288	86.90 294	91.35 249	71.82 310	83.33 337	89.16 329	72.90 314	82.24 247	85.77 320	64.98 281	93.76 316	64.57 320	83.74 243	95.12 148

new-patchmatchnet		76.41 310	75.17 310	80.13 324	82.65 343	59.61 344	87.66 311	91.08 291	78.23 269	69.85 334	83.22 329	54.76 325	91.63 335	64.14 322	64.89 343	89.16 328

testgi		80.94 295	80.20 284	83.18 319	87.96 326	66.29 334	91.28 267	90.70 303	83.70 166	78.12 288	92.84 171	51.37 333	90.82 336	63.34 323	82.46 257	92.43 278

TinyColmap		79.76 301	77.69 301	85.97 303	91.71 229	73.12 297	89.55 285	90.36 306	75.03 294	72.03 330	90.19 263	46.22 343	96.19 261	63.11 324	81.03 281	88.59 334

pmmvs3		71.81 318	68.71 321	81.11 323	75.86 351	70.42 322	86.74 315	83.66 349	58.95 348	68.64 338	80.89 338	36.93 351	89.52 338	63.10 325	63.59 345	83.39 343

TAPA-MVS	84.62 6	88.16 145	87.01 156	91.62 129	96.64 64	80.65 163	94.39 136	96.21 92	76.38 281	86.19 152	95.44 83	79.75 92	98.08 128	62.75 326	95.29 94	96.13 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet-bldmvs		78.85 306	76.31 307	86.46 298	89.76 306	73.88 292	88.79 298	90.42 304	79.16 255	59.18 347	88.33 291	60.20 307	94.04 314	62.00 327	68.96 338	91.48 298

tfpnnormal		84.72 255	83.23 259	89.20 236	92.79 209	80.05 176	94.48 126	95.81 121	82.38 207	81.08 264	91.21 239	69.01 235	96.95 221	61.69 328	80.59 289	90.58 320

Anonymous20231206		81.03 293	79.77 289	84.82 312	87.85 328	70.26 323	91.42 265	92.08 259	73.67 306	77.75 292	89.25 276	62.43 292	93.08 325	61.50 329	82.00 264	91.12 305

RPMNet		83.18 272	80.87 278	90.12 190	90.58 288	79.34 205	85.24 326	90.78 301	71.44 323	85.55 169	82.97 331	70.87 207	95.61 280	61.01 330	89.36 181	95.40 143

MIMVSNet1		79.38 303	77.28 303	85.69 305	86.35 331	73.67 294	91.61 263	92.75 245	78.11 271	72.64 328	88.12 294	48.16 339	91.97 332	60.32 331	77.49 310	91.43 299

test20.03		79.95 299	79.08 296	82.55 321	85.79 332	67.74 332	91.09 272	91.08 291	81.23 239	74.48 319	89.96 268	61.63 296	90.15 337	60.08 332	76.38 313	89.76 322

DSMNet-mixed		76.94 309	76.29 308	78.89 325	83.10 341	56.11 351	87.78 309	79.77 356	60.65 347	75.64 313	88.71 283	61.56 297	88.34 341	60.07 333	89.29 183	92.21 286

Patchmatch-test		81.37 289	79.30 293	87.58 277	90.92 276	74.16 289	80.99 343	87.68 340	70.52 329	76.63 299	88.81 281	71.21 202	92.76 327	60.01 334	86.93 221	95.83 130

MVS-HIRNet		73.70 315	72.20 315	78.18 328	91.81 226	56.42 350	82.94 340	82.58 351	55.24 349	68.88 335	66.48 350	55.32 324	95.13 302	58.12 335	88.42 203	83.01 344

OpenMVS_ROB	74.94 19	79.51 302	77.03 306	86.93 292	87.00 329	76.23 278	92.33 245	90.74 302	68.93 333	74.52 318	88.23 293	49.58 336	96.62 240	57.64 336	84.29 238	87.94 339

new_pmnet		72.15 317	70.13 319	78.20 326	82.95 342	65.68 335	83.91 334	82.40 352	62.94 346	64.47 343	79.82 341	42.85 346	86.26 347	57.41 337	74.44 318	82.65 345

N_pmnet		68.89 321	68.44 322	70.23 337	89.07 312	28.79 366	88.06 306	19.50 368	69.47 332	71.86 331	84.93 323	61.24 301	91.75 333	54.70 338	77.15 312	90.15 321

tmp_tt		35.64 339	39.24 338	24.84 351	14.87 367	23.90 367	62.71 357	51.51 367	6.58 362	36.66 357	62.08 354	44.37 345	30.34 364	52.40 339	22.00 360	20.27 360

testus		74.41 314	73.35 312	77.59 330	82.49 344	57.08 347	86.02 319	90.21 308	72.28 319	72.89 327	84.32 325	37.08 350	86.96 345	52.24 340	82.65 255	88.73 331

test2356		74.50 313	73.27 313	78.20 326	80.81 345	59.84 342	83.76 336	88.33 336	71.43 324	72.37 329	81.84 335	45.60 344	86.26 347	50.97 341	84.32 237	88.50 335

no-one		61.56 326	56.58 328	76.49 332	67.80 359	62.76 341	78.13 348	86.11 343	63.16 345	43.24 354	64.70 352	26.12 356	88.95 340	50.84 342	29.15 354	77.77 349

test_0402		81.30 291	79.17 295	87.67 275	93.19 195	78.17 244	92.98 226	91.71 269	75.25 292	76.02 310	90.31 262	59.23 312	96.37 254	50.22 343	83.63 246	88.47 337

PMMVS2		59.60 327	56.40 329	69.21 340	68.83 355	46.58 357	73.02 354	77.48 360	55.07 350	49.21 352	72.95 348	17.43 363	80.04 355	49.32 344	44.33 352	80.99 348

test1235678		72.22 316	70.31 318	77.93 329	78.04 350	58.04 346	85.76 323	89.80 319	70.15 331	63.43 344	80.20 340	42.24 347	87.24 344	48.68 345	74.50 317	88.50 335

wuykxyi23d		50.55 332	44.13 335	69.81 338	56.77 363	54.58 353	73.22 353	80.78 354	39.79 357	22.08 363	46.69 359	4.03 368	79.71 356	47.65 346	26.13 356	75.14 350

1111		70.54 320	69.71 320	73.04 334	79.30 347	44.83 359	84.23 331	88.96 331	67.33 336	65.42 341	82.28 333	41.11 348	88.11 342	47.12 347	71.60 327	86.19 341

.test1245		57.63 330	61.79 327	45.14 348	79.30 347	44.83 359	84.23 331	88.96 331	67.33 336	65.42 341	82.28 333	41.11 348	88.11 342	47.12 347	0.39 362	2.46 363

LP		75.51 312	72.15 316	85.61 306	87.86 327	73.93 291	80.20 345	88.43 335	67.39 335	70.05 333	80.56 339	58.18 316	93.18 324	46.28 349	70.36 335	89.71 324

test12356		64.99 324	63.78 323	68.61 341	72.69 353	39.14 362	78.46 347	87.61 341	64.91 342	55.77 348	77.48 343	28.10 354	85.59 349	44.69 350	64.35 344	81.12 347

testmv		65.49 323	62.66 324	73.96 333	68.78 356	53.14 354	84.70 329	88.56 333	65.94 341	52.35 350	74.65 345	25.02 357	85.14 350	43.54 351	60.40 349	83.60 342

ANet_high		58.88 328	54.22 331	72.86 335	56.50 365	56.67 349	80.75 344	86.00 344	73.09 312	37.39 356	64.63 353	22.17 359	79.49 357	43.51 352	23.96 358	82.43 346

DeepMVS_CX				56.31 346	74.23 352	51.81 355		56.67 366	44.85 353	48.54 353	75.16 344	27.87 355	58.74 362	40.92 353	52.22 350	58.39 356

FPMVS		64.63 325	62.55 325	70.88 336	70.80 354	56.71 348	84.42 330	84.42 348	51.78 351	49.57 351	81.61 336	23.49 358	81.48 354	40.61 354	76.25 314	74.46 351

PNet_i23d		50.48 333	47.18 334	60.36 344	68.59 357	44.56 361	72.75 355	72.61 361	43.92 354	33.91 358	60.19 355	6.16 365	73.52 358	38.50 355	28.04 355	63.01 353

Gipuma		57.99 329	54.91 330	67.24 342	88.51 316	65.59 336	52.21 359	90.33 307	43.58 355	42.84 355	51.18 357	20.29 361	85.07 351	34.77 356	70.45 334	51.05 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 331	48.46 333	63.48 343	45.72 366	46.20 358	73.41 352	78.31 358	41.03 356	30.06 359	65.68 351	6.05 366	83.43 353	30.04 357	65.86 341	60.80 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 334	38.59 340	57.77 345	56.52 364	48.77 356	55.38 358	58.64 365	29.33 360	28.96 360	52.65 356	4.68 367	64.62 361	28.11 358	33.07 353	59.93 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 335	42.29 336	46.03 347	65.58 360	37.41 363	73.51 351	64.62 362	33.99 358	28.47 361	47.87 358	19.90 362	67.91 359	22.23 359	24.45 357	32.77 358

EMVS		42.07 336	41.12 337	44.92 349	63.45 362	35.56 365	73.65 350	63.48 363	33.05 359	26.88 362	45.45 360	21.27 360	67.14 360	19.80 360	23.02 359	32.06 359

wuyk23d		21.27 341	20.48 342	23.63 352	68.59 357	36.41 364	49.57 360	6.85 369	9.37 361	7.89 364	4.46 366	4.03 368	31.37 363	17.47 361	16.07 361	3.12 361

testmvs		8.92 342	11.52 343	1.12 354	1.06 368	0.46 369	86.02 319	0.65 370	0.62 363	2.74 365	9.52 364	0.31 371	0.45 366	2.38 362	0.39 362	2.46 363

test123		8.76 343	11.22 344	1.39 353	0.85 369	0.97 368	85.76 323	0.35 371	0.54 364	2.45 366	8.14 365	0.60 370	0.48 365	2.16 363	0.17 364	2.71 362

test_part1				0.00 355		0.00 370	0.00 361	97.45 7				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

v1.0		39.85 337	53.14 332	0.00 355	98.55 5	0.00 370	0.00 361	97.45 7	88.25 67	96.40 4	97.60 6	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

cdsmvs_eth3d_5k		22.14 340	29.52 341	0.00 355	0.00 370	0.00 370	0.00 361	95.76 125	0.00 365	0.00 367	94.29 118	75.66 145	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		6.64 345	8.86 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	79.70 94	0.00 367	0.00 364	0.00 365	0.00 365

pcd1.5k->3k		37.02 338	38.84 339	31.53 350	92.33 215	0.00 370	0.00 361	96.13 96	0.00 365	0.00 367	0.00 367	72.70 185	0.00 367	0.00 364	88.43 202	94.60 179

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		7.82 344	10.43 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	93.88 137	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																96.12 116

test_part2					98.55 5	87.22 12				96.40 4

sam_mvs1												71.70 195				96.12 116

sam_mvs												70.60 211

MTGPA								96.97 36

test_post											10.29 362	70.57 215	95.91 272

patchmatchnet-post											83.76 327	71.53 199	96.48 248

MTMP							96.16 31	60.64 364

TEST9					97.53 38	86.49 30	94.07 167	96.78 52	81.61 233	92.77 43	96.20 60	87.71 15	99.12 41

test_8					97.49 41	86.30 38	94.02 173	96.76 55	81.86 228	92.70 47	96.20 60	87.63 16	99.02 53

agg_prior					97.38 45	85.92 45		96.72 58		92.16 59			98.97 61

test_prior4						85.96 44	94.11 160

test_prior				93.82 52	97.29 50	84.49 65		96.88 45					98.87 67			98.11 44

新几何2							93.11 219

旧先验1					96.79 61	81.81 132		95.67 130			96.81 34	86.69 24			97.66 56	96.97 95

原ACMM2							92.94 228

test222					96.55 68	81.70 133	92.22 248	95.01 182	68.36 334	90.20 88	96.14 65	80.26 88			97.80 54	96.05 122

segment_acmp												87.16 21

testdata1							92.15 250		87.94 72

test12				94.34 41	97.13 55	86.15 41		96.29 84		91.04 79		85.08 41	99.01 55		98.13 46	97.86 61

plane_prior7					94.70 144	82.74 114

plane_prior6					94.52 151	82.75 112						74.23 162

plane_prior4											94.86 100

plane_prior3						82.75 112			90.26 25	86.91 137

plane_prior2							95.85 44		90.81 18

plane_prior1					94.59 149

plane_prior						82.73 115	95.21 77		89.66 35						89.88 173

n2								0.00 372

nn								0.00 372

door-mid								85.49 345

test11								96.57 72

door								85.33 346

HQP5-MVS						81.56 134

HQP-NCC					94.17 162		94.39 136		88.81 51	85.43 180

ACMP_Plane					94.17 162		94.39 136		88.81 51	85.43 180

HQP4-MVS										85.43 180			97.96 135			94.51 186

HQP3-MVS								96.04 104							89.77 175

HQP2-MVS												73.83 171

NP-MVS					94.37 157	82.42 122					93.98 129

ACMMP++_ref															87.47 212

ACMMP++															88.01 209

Test By Simon												80.02 89

High-resolution multi-view benchmark