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 1	95.64 1	94.91 11	98.26 19	86.29 37	97.46 2	97.40 9	89.03 47	96.20 3	98.10 1	89.39 6	99.34 21	95.88 1	99.03 1	99.10 1

CNVR-MVS		95.40 2	95.37 3	95.50 3	98.11 24	88.51 3	95.29 61	96.96 37	92.09 3	95.32 8	97.08 23	89.49 5	99.33 24	95.10 2	98.85 7	98.66 5

HSP-MVS		95.30 3	95.48 2	94.76 23	98.49 9	86.52 27	96.91 15	96.73 54	91.73 9	96.10 4	96.69 36	89.90 2	99.30 27	94.70 3	98.04 48	98.45 17

TSAR-MVS + MP.		94.85 7	94.94 6	94.58 30	98.25 20	86.33 33	96.11 31	96.62 65	88.14 68	96.10 4	96.96 26	89.09 8	98.94 63	94.48 4	98.68 23	98.48 12

SD-MVS		94.96 6	95.33 4	93.88 48	97.25 51	86.69 20	96.19 29	97.11 28	90.42 24	96.95 1	97.27 11	89.53 4	96.91 213	94.38 5	98.85 7	98.03 48

MP-MVS-pluss		94.21 23	94.00 25	94.85 15	98.17 23	86.65 23	94.82 94	97.17 24	86.26 108	92.83 37	97.87 2	85.57 35	99.56 1	94.37 6	98.92 5	98.34 22
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 4	95.32 5	94.85 15	96.99 54	86.33 33	97.33 3	97.30 17	91.38 12	95.39 7	97.46 7	88.98 9	99.40 19	94.12 7	98.89 6	98.82 2
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	89.96 1	94.20 24	94.77 8	92.49 87	96.52 65	80.00 171	94.00 160	97.08 29	90.05 26	95.65 6	97.29 10	89.66 3	98.97 59	93.95 8	98.71 18	98.50 10

ACMMP_Plus		94.74 9	94.56 10	95.28 4	98.02 29	87.70 4	95.68 47	97.34 11	88.28 65	95.30 9	97.67 3	85.90 32	99.54 8	93.91 9	98.95 3	98.60 7

MVS_0304		93.25 45	92.62 49	95.14 7	95.72 94	87.58 7	94.71 104	96.59 67	91.78 7	91.46 68	96.18 61	75.45 145	99.55 5	93.53 10	98.19 43	98.28 27

Regformer-2		94.33 18	94.22 16	94.68 26	95.54 99	86.75 19	94.57 113	96.70 58	91.84 6	94.41 11	96.56 45	87.19 19	99.13 38	93.50 11	97.65 56	98.16 37

MCST-MVS		94.45 12	94.20 19	95.19 5	98.46 11	87.50 8	95.00 83	97.12 26	87.13 87	92.51 49	96.30 52	89.24 7	99.34 21	93.46 12	98.62 31	98.73 3

MPTG		94.47 11	94.30 13	95.00 8	98.42 13	86.95 11	95.06 80	96.97 34	91.07 14	93.14 33	97.56 4	84.30 48	99.56 1	93.43 13	98.75 15	98.47 13

MTAPA		94.42 16	94.22 16	95.00 8	98.42 13	86.95 11	94.36 134	96.97 34	91.07 14	93.14 33	97.56 4	84.30 48	99.56 1	93.43 13	98.75 15	98.47 13

HPM-MVS++		95.14 5	94.91 7	95.83 1	98.25 20	89.65 1	95.92 38	96.96 37	91.75 8	94.02 18	96.83 30	88.12 10	99.55 5	93.41 15	98.94 4	98.28 27

Regformer-1		94.22 22	94.13 21	94.51 33	95.54 99	86.36 32	94.57 113	96.44 72	91.69 10	94.32 13	96.56 45	87.05 21	99.03 48	93.35 16	97.65 56	98.15 38

Regformer-4		93.91 29	93.81 27	94.19 43	95.36 105	85.47 50	94.68 105	96.41 75	91.60 11	93.75 21	96.71 34	85.95 31	99.10 41	93.21 17	96.65 71	98.01 50

CANet		93.54 36	93.20 39	94.55 31	95.65 96	85.73 49	94.94 86	96.69 60	91.89 5	90.69 76	95.88 70	81.99 70	99.54 8	93.14 18	97.95 50	98.39 20

Regformer-3		93.68 33	93.64 33	93.81 52	95.36 105	84.61 59	94.68 105	95.83 113	91.27 13	93.60 25	96.71 34	85.75 33	98.86 68	92.87 19	96.65 71	97.96 51

NCCC		94.81 8	94.69 9	95.17 6	97.83 31	87.46 9	95.66 49	96.93 40	92.34 2	93.94 19	96.58 43	87.74 13	99.44 18	92.83 20	98.40 38	98.62 6

TSAR-MVS + GP.		93.66 34	93.41 35	94.41 37	96.59 62	86.78 17	94.40 124	93.93 217	89.77 32	94.21 14	95.59 79	87.35 17	98.61 83	92.72 21	96.15 79	97.83 60

APD-MVS_3200maxsize		93.78 31	93.77 30	93.80 53	97.92 30	84.19 74	96.30 26	96.87 45	86.96 94	93.92 20	97.47 6	83.88 52	98.96 62	92.71 22	97.87 51	98.26 32

PHI-MVS		93.89 30	93.65 32	94.62 29	96.84 57	86.43 30	96.69 21	97.49 4	85.15 129	93.56 28	96.28 53	85.60 34	99.31 26	92.45 23	98.79 10	98.12 41

HPM-MVS		94.02 26	93.88 26	94.43 36	98.39 15	85.78 48	97.25 5	97.07 30	86.90 98	92.62 46	96.80 33	84.85 45	99.17 33	92.43 24	98.65 29	98.33 23

alignmvs		93.08 49	92.50 52	94.81 20	95.62 98	87.61 6	95.99 35	96.07 96	89.77 32	94.12 15	94.87 94	80.56 79	98.66 78	92.42 25	93.10 128	98.15 38

canonicalmvs		93.27 43	92.75 48	94.85 15	95.70 95	87.66 5	96.33 25	96.41 75	90.00 28	94.09 16	94.60 104	82.33 61	98.62 82	92.40 26	92.86 133	98.27 30

HFP-MVS		94.52 10	94.40 11	94.86 13	98.61 3	86.81 15	96.94 10	97.34 11	88.63 56	93.65 22	97.21 16	86.10 28	99.49 14	92.35 27	98.77 13	98.30 25

ACMMPR		94.43 14	94.28 14	94.91 11	98.63 2	86.69 20	96.94 10	97.32 16	88.63 56	93.53 29	97.26 13	85.04 41	99.54 8	92.35 27	98.78 12	98.50 10

region2R		94.43 14	94.27 15	94.92 10	98.65 1	86.67 22	96.92 14	97.23 21	88.60 58	93.58 26	97.27 11	85.22 38	99.54 8	92.21 29	98.74 17	98.56 9

DeepC-MVS	88.79 3	93.31 41	92.99 43	94.26 41	96.07 83	85.83 47	94.89 89	96.99 32	89.02 48	89.56 86	97.37 8	82.51 59	99.38 20	92.20 30	98.30 40	97.57 68
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 32	94.08 22	92.65 81	97.31 45	83.43 90	95.79 42	97.33 14	90.03 27	93.58 26	96.96 26	84.87 44	97.76 136	92.19 31	98.66 27	96.76 93

CP-MVS		94.34 17	94.21 18	94.74 25	98.39 15	86.64 24	97.60 1	97.24 19	88.53 60	92.73 42	97.23 14	85.20 39	99.32 25	92.15 32	98.83 9	98.25 33

train_agg		93.44 38	93.08 40	94.52 32	97.53 35	86.49 28	94.07 152	96.78 50	81.86 214	92.77 39	96.20 57	87.63 15	99.12 39	92.14 33	98.69 20	97.94 52

agg_prior3		93.27 43	92.89 46	94.40 38	97.49 38	86.12 40	94.07 152	96.73 54	81.46 222	92.46 51	96.05 65	86.90 22	99.15 36	92.14 33	98.69 20	97.94 52

MP-MVS		94.25 20	94.07 23	94.77 22	98.47 10	86.31 35	96.71 20	96.98 33	89.04 46	91.98 59	97.19 18	85.43 36	99.56 1	92.06 35	98.79 10	98.44 18

EI-MVSNet-Vis-set		93.01 50	92.92 45	93.29 58	95.01 117	83.51 89	94.48 116	95.77 117	90.87 16	92.52 48	96.67 38	84.50 47	99.00 56	91.99 36	94.44 106	97.36 73

XVS		94.45 12	94.32 12	94.85 15	98.54 6	86.60 25	96.93 12	97.19 22	90.66 22	92.85 35	97.16 21	85.02 42	99.49 14	91.99 36	98.56 34	98.47 13

X-MVStestdata		88.31 135	86.13 181	94.85 15	98.54 6	86.60 25	96.93 12	97.19 22	90.66 22	92.85 35	23.41 344	85.02 42	99.49 14	91.99 36	98.56 34	98.47 13

agg_prior1		93.29 42	92.97 44	94.26 41	97.38 42	85.92 43	93.92 163	96.72 56	81.96 205	92.16 55	96.23 55	87.85 11	98.97 59	91.95 39	98.55 36	97.90 57

test9_res														91.91 40	98.71 18	98.07 44

abl_6		93.18 48	93.05 41	93.57 57	97.52 37	84.27 73	95.53 54	96.67 61	87.85 74	93.20 32	97.22 15	80.35 80	99.18 32	91.91 40	97.21 61	97.26 74

MVS_111021_HR		93.45 37	93.31 36	93.84 49	96.99 54	84.84 55	93.24 200	97.24 19	88.76 53	91.60 67	95.85 71	86.07 30	98.66 78	91.91 40	98.16 44	98.03 48

APD-MVS		94.24 21	94.07 23	94.75 24	98.06 27	86.90 14	95.88 39	96.94 39	85.68 118	95.05 10	97.18 19	87.31 18	99.07 42	91.90 43	98.61 32	98.28 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		92.47 54	92.29 54	92.98 71	95.99 86	84.43 70	93.08 205	96.09 94	88.20 67	91.12 73	95.72 76	81.33 75	97.76 136	91.74 44	97.37 60	96.75 94

#test#		94.32 19	94.14 20	94.86 13	98.61 3	86.81 15	96.43 23	97.34 11	87.51 82	93.65 22	97.21 16	86.10 28	99.49 14	91.68 45	98.77 13	98.30 25

EI-MVSNet-UG-set		92.74 53	92.62 49	93.12 64	94.86 125	83.20 95	94.40 124	95.74 120	90.71 21	92.05 58	96.60 42	84.00 50	98.99 57	91.55 46	93.63 115	97.17 80

test_prior3		93.60 35	93.53 34	93.82 50	97.29 47	84.49 63	94.12 144	96.88 43	87.67 79	92.63 44	96.39 50	86.62 24	98.87 65	91.50 47	98.67 25	98.11 42

test_prior2							94.12 144		87.67 79	92.63 44	96.39 50	86.62 24		91.50 47	98.67 25

mPP-MVS		93.99 27	93.78 29	94.63 28	98.50 8	85.90 46	96.87 16	96.91 41	88.70 54	91.83 63	97.17 20	83.96 51	99.55 5	91.44 49	98.64 30	98.43 19

DELS-MVS		93.43 39	93.25 37	93.97 45	95.42 104	85.04 54	93.06 207	97.13 25	90.74 20	91.84 61	95.09 90	86.32 27	99.21 30	91.22 50	98.45 37	97.65 64
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 73	90.39 73	93.17 63	93.07 184	86.91 13	96.41 24	96.26 82	88.30 64	88.37 99	94.85 97	82.19 65	97.64 143	91.09 51	82.95 240	94.96 147

xiu_mvs_v1_base_debu		90.64 79	90.05 81	92.40 90	93.97 159	84.46 66	93.32 191	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

xiu_mvs_v1_base		90.64 79	90.05 81	92.40 90	93.97 159	84.46 66	93.32 191	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

xiu_mvs_v1_base_debi		90.64 79	90.05 81	92.40 90	93.97 159	84.46 66	93.32 191	95.46 141	85.17 126	92.25 52	94.03 117	70.59 206	98.57 85	90.97 52	94.67 96	94.18 185

VDD-MVS		90.74 76	89.92 84	93.20 61	96.27 70	83.02 101	95.73 44	93.86 218	88.42 62	92.53 47	96.84 29	62.09 279	98.64 80	90.95 55	92.62 135	97.93 55

DeepC-MVS_fast	89.43 2	94.04 25	93.79 28	94.80 21	97.48 40	86.78 17	95.65 51	96.89 42	89.40 38	92.81 38	96.97 25	85.37 37	99.24 29	90.87 56	98.69 20	98.38 21
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 46	92.88 47	94.30 40	98.09 26	85.33 52	96.86 17	97.45 7	88.33 63	90.15 82	97.03 24	81.44 73	99.51 12	90.85 57	95.74 82	98.04 47
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 28	93.72 31	94.68 26	98.43 12	86.22 38	95.30 59	97.78 1	87.45 83	93.26 30	97.33 9	84.62 46	99.51 12	90.75 58	98.57 33	98.32 24

agg_prior2														90.54 59	98.68 23	98.27 30

HPM-MVS_fast		93.40 40	93.22 38	93.94 47	98.36 17	84.83 56	97.15 7	96.80 49	85.77 115	92.47 50	97.13 22	82.38 60	99.07 42	90.51 60	98.40 38	97.92 56

lupinMVS		90.92 74	90.21 76	93.03 69	93.86 162	83.88 79	92.81 214	93.86 218	79.84 235	91.76 64	94.29 111	77.92 108	98.04 123	90.48 61	97.11 62	97.17 80

jason		90.80 75	90.10 79	92.90 74	93.04 186	83.53 88	93.08 205	94.15 204	80.22 231	91.41 69	94.91 92	76.87 114	97.93 130	90.28 62	96.90 65	97.24 75
jason: jason.
CSCG		93.23 47	93.05 41	93.76 54	98.04 28	84.07 76	96.22 28	97.37 10	84.15 149	90.05 83	95.66 77	87.77 12	99.15 36	89.91 63	98.27 41	98.07 44

CPTT-MVS		91.99 57	91.80 56	92.55 84	98.24 22	81.98 125	96.76 19	96.49 71	81.89 210	90.24 80	96.44 49	78.59 100	98.61 83	89.68 64	97.85 52	97.06 85

MVSFormer		91.68 64	91.30 60	92.80 77	93.86 162	83.88 79	95.96 36	95.90 108	84.66 138	91.76 64	94.91 92	77.92 108	97.30 180	89.64 65	97.11 62	97.24 75

test_djsdf		89.03 120	88.64 108	90.21 172	90.74 267	79.28 201	95.96 36	95.90 108	84.66 138	85.33 179	92.94 159	74.02 163	97.30 180	89.64 65	88.53 187	94.05 194

Effi-MVS+		91.59 65	91.11 63	93.01 70	94.35 146	83.39 92	94.60 110	95.10 169	87.10 88	90.57 77	93.10 151	81.43 74	98.07 121	89.29 67	94.48 103	97.59 67

PS-MVSNAJ		91.18 71	90.92 67	91.96 107	95.26 111	82.60 117	92.09 238	95.70 122	86.27 107	91.84 61	92.46 171	79.70 89	98.99 57	89.08 68	95.86 81	94.29 183

xiu_mvs_v2_base		91.13 72	90.89 69	91.86 112	94.97 120	82.42 118	92.24 232	95.64 128	86.11 112	91.74 66	93.14 149	79.67 92	98.89 64	89.06 69	95.46 88	94.28 184

VNet		92.24 56	91.91 55	93.24 60	96.59 62	83.43 90	94.84 93	96.44 72	89.19 43	94.08 17	95.90 69	77.85 111	98.17 103	88.90 70	93.38 122	98.13 40

PS-MVSNAJss		89.97 92	89.62 86	91.02 141	91.90 206	80.85 152	95.26 68	95.98 101	86.26 108	86.21 141	94.29 111	79.70 89	97.65 141	88.87 71	88.10 195	94.57 170

XVG-OURS-SEG-HR		89.95 93	89.45 89	91.47 125	94.00 157	81.21 141	91.87 240	96.06 98	85.78 114	88.55 96	95.73 75	74.67 153	97.27 184	88.71 72	89.64 170	95.91 116

jajsoiax		88.24 137	87.50 132	90.48 161	90.89 262	80.14 165	95.31 57	95.65 127	84.97 132	84.24 204	94.02 120	65.31 266	97.42 166	88.56 73	88.52 188	93.89 200

mvs_tets		88.06 143	87.28 139	90.38 167	90.94 258	79.88 173	95.22 70	95.66 125	85.10 130	84.21 205	93.94 124	63.53 274	97.40 173	88.50 74	88.40 193	93.87 203

VDDNet		89.56 102	88.49 113	92.76 79	95.07 116	82.09 122	96.30 26	93.19 228	81.05 227	91.88 60	96.86 28	61.16 288	98.33 97	88.43 75	92.49 136	97.84 59

test_normal		88.13 141	86.78 159	92.18 99	90.55 275	81.19 142	92.74 216	94.64 190	83.84 153	77.49 278	90.51 243	68.49 242	98.16 104	88.22 76	94.55 101	97.21 78

HQP_MVS		90.60 82	90.19 77	91.82 115	94.70 131	82.73 111	95.85 40	96.22 86	90.81 18	86.91 127	94.86 95	74.23 157	98.12 106	88.15 77	89.99 163	94.63 164

plane_prior5								96.22 86					98.12 106	88.15 77	89.99 163	94.63 164

EPNet		91.79 59	91.02 66	94.10 44	90.10 283	85.25 53	96.03 34	92.05 250	92.83 1	87.39 120	95.78 73	79.39 94	99.01 53	88.13 79	97.48 58	98.05 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		90.12 88	89.56 87	91.82 115	93.14 182	83.90 78	94.16 143	95.74 120	88.96 49	87.86 106	95.43 81	72.48 185	97.91 131	88.10 80	90.18 162	93.65 220

DI_MVS_plusplus_test		88.15 140	86.82 155	92.14 101	90.67 270	81.07 144	93.01 208	94.59 191	83.83 155	77.78 275	90.63 237	68.51 241	98.16 104	88.02 81	94.37 107	97.17 80

MVSTER		88.84 124	88.29 120	90.51 159	92.95 190	80.44 162	93.73 175	95.01 172	84.66 138	87.15 122	93.12 150	72.79 179	97.21 192	87.86 82	87.36 203	93.87 203

3Dnovator+	87.14 4	92.42 55	91.37 59	95.55 2	95.63 97	88.73 2	97.07 8	96.77 52	90.84 17	84.02 206	96.62 41	75.95 134	99.34 21	87.77 83	97.68 54	98.59 8

LPG-MVS_test		89.45 106	88.90 104	91.12 134	94.47 139	81.49 131	95.30 59	96.14 90	86.73 100	85.45 167	95.16 87	69.89 215	98.10 112	87.70 84	89.23 177	93.77 212

LGP-MVS_train				91.12 134	94.47 139	81.49 131		96.14 90	86.73 100	85.45 167	95.16 87	69.89 215	98.10 112	87.70 84	89.23 177	93.77 212

MVS_Test		91.31 68	91.11 63	91.93 109	94.37 143	80.14 165	93.46 189	95.80 115	86.46 104	91.35 70	93.77 133	82.21 64	98.09 119	87.57 86	94.95 94	97.55 70

PVSNet_Blended_VisFu		91.38 67	90.91 68	92.80 77	96.39 67	83.17 96	94.87 92	96.66 62	83.29 171	89.27 89	94.46 106	80.29 82	99.17 33	87.57 86	95.37 89	96.05 113

CDPH-MVS		92.83 51	92.30 53	94.44 34	97.79 32	86.11 41	94.06 155	96.66 62	80.09 233	92.77 39	96.63 40	86.62 24	99.04 47	87.40 88	98.66 27	98.17 36

XVG-OURS		89.40 111	88.70 107	91.52 123	94.06 151	81.46 133	91.27 253	96.07 96	86.14 111	88.89 94	95.77 74	68.73 238	97.26 186	87.39 89	89.96 165	95.83 121

EPP-MVSNet		91.70 63	91.56 58	92.13 102	95.88 89	80.50 161	97.33 3	95.25 160	86.15 110	89.76 85	95.60 78	83.42 53	98.32 98	87.37 90	93.25 125	97.56 69

VPA-MVSNet		89.62 99	88.96 101	91.60 122	93.86 162	82.89 106	95.46 55	97.33 14	87.91 71	88.43 98	93.31 141	74.17 160	97.40 173	87.32 91	82.86 242	94.52 173

LFMVS		90.08 89	89.13 98	92.95 72	96.71 59	82.32 120	96.08 32	89.91 303	86.79 99	92.15 57	96.81 31	62.60 276	98.34 96	87.18 92	93.90 111	98.19 35

anonymousdsp		87.84 150	87.09 145	90.12 180	89.13 294	80.54 159	94.67 107	95.55 132	82.05 202	83.82 210	92.12 184	71.47 195	97.15 194	87.15 93	87.80 200	92.67 255

CLD-MVS		89.47 105	88.90 104	91.18 133	94.22 147	82.07 123	92.13 236	96.09 94	87.90 72	85.37 177	92.45 172	74.38 155	97.56 146	87.15 93	90.43 156	93.93 198
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 95

HQP-MVS		89.80 97	89.28 95	91.34 128	94.17 148	81.56 128	94.39 126	96.04 99	88.81 50	85.43 170	93.97 123	73.83 166	97.96 127	87.11 95	89.77 168	94.50 175

ACMP	84.23 8	89.01 122	88.35 115	90.99 143	94.73 128	81.27 137	95.07 78	95.89 110	86.48 103	83.67 214	94.30 110	69.33 222	97.99 126	87.10 97	88.55 186	93.72 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_2		83.40 255	81.02 260	90.56 151	85.06 317	80.51 160	91.37 251	95.57 130	82.92 187	67.06 322	85.54 304	49.47 321	97.24 188	86.74 98	85.44 216	93.93 198

旧先验2							93.36 190		71.25 307	94.37 12			97.13 197	86.74 98

3Dnovator	86.66 5	91.73 62	90.82 70	94.44 34	94.59 135	86.37 31	97.18 6	97.02 31	89.20 42	84.31 202	96.66 39	73.74 168	99.17 33	86.74 98	97.96 49	97.79 62

PVSNet_BlendedMVS		89.98 91	89.70 85	90.82 146	96.12 77	81.25 138	93.92 163	96.83 46	83.49 165	89.10 91	92.26 181	81.04 77	98.85 71	86.72 101	87.86 199	92.35 266

PVSNet_Blended		90.73 77	90.32 75	91.98 106	96.12 77	81.25 138	92.55 223	96.83 46	82.04 204	89.10 91	92.56 170	81.04 77	98.85 71	86.72 101	95.91 80	95.84 120

Test4		85.75 214	83.72 230	91.83 114	88.08 306	81.03 146	92.48 224	95.54 134	83.38 169	73.40 306	88.57 269	50.99 318	97.37 177	86.61 103	94.47 104	97.09 84

mvs_anonymous		89.37 112	89.32 93	89.51 209	93.47 173	74.22 271	91.65 247	94.83 185	82.91 188	85.45 167	93.79 132	81.23 76	96.36 240	86.47 104	94.09 109	97.94 52

OMC-MVS		91.23 69	90.62 72	93.08 66	96.27 70	84.07 76	93.52 186	95.93 104	86.95 95	89.51 87	96.13 63	78.50 102	98.35 95	85.84 105	92.90 132	96.83 92

ACMM	84.12 9	89.14 115	88.48 114	91.12 134	94.65 134	81.22 140	95.31 57	96.12 93	85.31 125	85.92 145	94.34 107	70.19 214	98.06 122	85.65 106	88.86 184	94.08 193
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		88.65 128	88.35 115	89.54 206	93.33 176	76.39 260	94.47 119	94.36 197	87.70 77	85.43 170	89.56 258	73.45 171	97.26 186	85.57 107	91.28 142	94.97 144

mvs-test1		89.45 106	89.14 97	90.38 167	93.33 176	77.63 249	94.95 85	94.36 197	87.70 77	87.10 124	92.81 164	73.45 171	98.03 124	85.57 107	93.04 129	95.48 131

FIs		90.51 83	90.35 74	90.99 143	93.99 158	80.98 147	95.73 44	97.54 3	89.15 44	86.72 131	94.68 100	81.83 72	97.24 188	85.18 109	88.31 194	94.76 161

MG-MVS		91.77 60	91.70 57	92.00 105	97.08 53	80.03 170	93.60 184	95.18 167	87.85 74	90.89 75	96.47 48	82.06 68	98.36 93	85.07 110	97.04 64	97.62 65

CANet_DTU		90.26 87	89.41 91	92.81 76	93.46 174	83.01 102	93.48 187	94.47 194	89.43 37	87.76 115	94.23 115	70.54 210	99.03 48	84.97 111	96.39 77	96.38 101

UniMVSNet_NR-MVSNet		89.92 95	89.29 94	91.81 117	93.39 175	83.72 82	94.43 122	97.12 26	89.80 31	86.46 134	93.32 140	83.16 54	97.23 190	84.92 112	81.02 268	94.49 177

DU-MVS		89.34 113	88.50 111	91.85 113	93.04 186	83.72 82	94.47 119	96.59 67	89.50 36	86.46 134	93.29 143	77.25 112	97.23 190	84.92 112	81.02 268	94.59 168

cascas		86.43 200	84.98 205	90.80 147	92.10 204	80.92 150	90.24 261	95.91 107	73.10 293	83.57 217	88.39 272	65.15 267	97.46 153	84.90 114	91.43 141	94.03 195

UniMVSNet (Re)		89.80 97	89.07 99	92.01 103	93.60 171	84.52 62	94.78 97	97.47 5	89.26 41	86.44 137	92.32 177	82.10 66	97.39 176	84.81 115	80.84 272	94.12 189

Vis-MVSNet		91.75 61	91.23 62	93.29 58	95.32 108	83.78 81	96.14 30	95.98 101	89.89 29	90.45 78	96.58 43	75.09 149	98.31 99	84.75 116	96.90 65	97.78 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v2v482		87.84 150	87.06 148	90.17 173	90.99 254	79.23 207	94.00 160	95.13 168	84.87 133	85.53 161	92.07 190	74.45 154	97.45 155	84.71 117	81.75 258	93.85 206

DP-MVS Recon		91.95 58	91.28 61	93.96 46	98.33 18	85.92 43	94.66 108	96.66 62	82.69 194	90.03 84	95.82 72	82.30 62	99.03 48	84.57 118	96.48 76	96.91 89

UA-Net		92.83 51	92.54 51	93.68 55	96.10 81	84.71 58	95.66 49	96.39 77	91.92 4	93.22 31	96.49 47	83.16 54	98.87 65	84.47 119	95.47 87	97.45 72

v6		87.98 144	87.25 141	90.16 174	91.36 230	79.39 194	94.37 130	95.27 159	84.48 141	85.78 147	91.51 212	76.15 122	97.46 153	84.46 120	81.88 255	93.62 224

v1neww		87.98 144	87.25 141	90.16 174	91.38 227	79.41 189	94.37 130	95.28 156	84.48 141	85.77 148	91.53 210	76.12 123	97.45 155	84.45 121	81.89 253	93.61 225

v7new		87.98 144	87.25 141	90.16 174	91.38 227	79.41 189	94.37 130	95.28 156	84.48 141	85.77 148	91.53 210	76.12 123	97.45 155	84.45 121	81.89 253	93.61 225

V42		87.68 157	86.86 153	90.15 178	90.58 272	80.14 165	94.24 138	95.28 156	83.66 158	85.67 156	91.33 216	74.73 152	97.41 171	84.43 123	81.83 256	92.89 249

FC-MVSNet-test		90.27 86	90.18 78	90.53 152	93.71 168	79.85 175	95.77 43	97.59 2	89.31 40	86.27 140	94.67 101	81.93 71	97.01 205	84.26 124	88.09 197	94.71 162

v1		87.85 149	87.10 144	90.11 185	91.21 244	79.24 205	94.09 148	95.24 161	84.44 145	85.70 153	91.31 219	75.96 133	97.45 155	84.18 125	81.73 260	93.64 221

v1141		87.84 150	87.09 145	90.11 185	91.23 242	79.25 203	94.08 150	95.24 161	84.44 145	85.69 155	91.31 219	75.91 135	97.44 162	84.17 126	81.74 259	93.63 223

divwei89l23v2f112		87.84 150	87.09 145	90.10 187	91.23 242	79.24 205	94.09 148	95.24 161	84.44 145	85.70 153	91.31 219	75.91 135	97.44 162	84.17 126	81.73 260	93.64 221

VPNet		88.20 138	87.47 134	90.39 165	93.56 172	79.46 185	94.04 156	95.54 134	88.67 55	86.96 125	94.58 105	69.33 222	97.15 194	84.05 128	80.53 277	94.56 171

UGNet		89.95 93	88.95 102	92.95 72	94.51 138	83.31 93	95.70 46	95.23 164	89.37 39	87.58 117	93.94 124	64.00 272	98.78 75	83.92 129	96.31 78	96.74 95
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		88.36 134	87.91 129	89.70 202	93.80 165	78.29 230	93.73 175	95.08 171	85.73 116	84.75 188	91.90 196	79.88 85	96.92 212	83.83 130	82.51 244	93.89 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.10 116	88.86 106	89.80 199	91.84 208	78.30 229	93.70 179	95.01 172	85.73 116	87.15 122	95.28 83	79.87 86	97.21 192	83.81 131	87.36 203	93.88 202

v7		87.75 155	86.96 151	90.12 180	91.20 245	79.50 180	94.28 136	95.46 141	83.45 166	85.75 150	91.56 209	75.13 147	97.43 164	83.60 132	82.18 248	93.42 234

v1144		87.61 169	86.79 158	90.06 188	91.01 253	79.34 197	93.95 162	95.42 150	83.36 170	85.66 157	91.31 219	74.98 151	97.42 166	83.37 133	82.06 249	93.42 234

diffmvs		89.07 117	88.32 118	91.34 128	93.24 179	79.79 176	92.29 231	94.98 175	80.24 230	87.38 121	92.45 172	78.02 106	97.33 178	83.29 134	92.93 131	96.91 89

testdata				90.49 160	96.40 66	77.89 240		95.37 153	72.51 299	93.63 24	96.69 36	82.08 67	97.65 141	83.08 135	97.39 59	95.94 115

LCM-MVSNet-Re		88.30 136	88.32 118	88.27 248	94.71 130	72.41 291	93.15 201	90.98 282	87.77 76	79.25 268	91.96 193	78.35 104	95.75 263	83.04 136	95.62 83	96.65 96

IS-MVSNet		91.43 66	91.09 65	92.46 88	95.87 91	81.38 136	96.95 9	93.69 222	89.72 34	89.50 88	95.98 66	78.57 101	97.77 135	83.02 137	96.50 75	98.22 34

XVG-ACMP-BASELINE		86.00 207	84.84 212	89.45 211	91.20 245	78.00 236	91.70 245	95.55 132	85.05 131	82.97 224	92.25 182	54.49 311	97.48 151	82.93 138	87.45 202	92.89 249

v144192		87.19 184	86.35 175	89.74 200	90.64 271	78.24 232	93.92 163	95.43 148	81.93 207	85.51 163	91.05 231	74.21 159	97.45 155	82.86 139	81.56 262	93.53 229

v8		87.50 173	86.71 161	89.89 194	91.37 229	79.40 193	94.50 115	95.38 151	84.81 135	83.60 216	91.33 216	76.05 127	97.42 166	82.84 140	80.51 279	92.84 251

PAPM_NR		91.22 70	90.78 71	92.52 86	97.60 34	81.46 133	94.37 130	96.24 85	86.39 106	87.41 118	94.80 99	82.06 68	98.48 89	82.80 141	95.37 89	97.61 66

Patchmatch-RL test		81.67 266	79.96 270	86.81 281	85.42 315	71.23 297	82.17 324	87.50 326	78.47 250	77.19 280	82.50 315	70.81 203	93.48 303	82.66 142	72.89 305	95.71 126

v52		86.50 197	85.53 196	89.39 213	89.17 293	78.99 210	94.72 102	95.54 134	83.59 159	82.10 234	90.60 239	71.59 192	97.45 155	82.52 143	79.99 284	91.73 276

V4		86.50 197	85.54 193	89.39 213	89.13 294	78.99 210	94.73 99	95.54 134	83.59 159	82.10 234	90.61 238	71.60 191	97.45 155	82.52 143	80.01 283	91.74 275

tpmrst		85.35 221	84.99 204	86.43 283	90.88 263	67.88 314	88.71 283	91.43 270	80.13 232	86.08 144	88.80 265	73.05 176	96.02 251	82.48 145	83.40 239	95.40 134

sss		88.93 123	88.26 122	90.94 145	94.05 152	80.78 154	91.71 244	95.38 151	81.55 220	88.63 95	93.91 128	75.04 150	95.47 274	82.47 146	91.61 140	96.57 98

ab-mvs		89.41 109	88.35 115	92.60 82	95.15 115	82.65 115	92.20 234	95.60 129	83.97 151	88.55 96	93.70 136	74.16 161	98.21 102	82.46 147	89.37 173	96.94 88

CostFormer		85.77 213	84.94 208	88.26 249	91.16 250	72.58 290	89.47 274	91.04 281	76.26 269	86.45 136	89.97 251	70.74 204	96.86 216	82.35 148	87.07 208	95.34 137

v1192		87.25 180	86.33 176	90.00 192	90.76 266	79.04 209	93.80 169	95.48 140	82.57 195	85.48 165	91.18 225	73.38 174	97.42 166	82.30 149	82.06 249	93.53 229

Baseline_NR-MVSNet		87.07 186	86.63 171	88.40 245	91.44 220	77.87 241	94.23 139	92.57 240	84.12 150	85.74 152	92.08 188	77.25 112	96.04 249	82.29 150	79.94 285	91.30 284

v148		87.04 187	86.32 177	89.21 220	90.94 258	77.26 253	93.71 178	94.43 195	84.84 134	84.36 200	90.80 234	76.04 129	97.05 203	82.12 151	79.60 288	93.31 236

114514_t		89.51 103	88.50 111	92.54 85	98.11 24	81.99 124	95.16 74	96.36 79	70.19 312	85.81 146	95.25 85	76.70 117	98.63 81	82.07 152	96.86 67	97.00 86

v1921920		86.97 188	86.06 185	89.69 203	90.53 276	78.11 235	93.80 169	95.43 148	81.90 209	85.33 179	91.05 231	72.66 181	97.41 171	82.05 153	81.80 257	93.53 229

OurMVSNet-221017-0		85.35 221	84.64 217	87.49 265	90.77 265	72.59 289	94.01 159	94.40 196	84.72 137	79.62 266	93.17 147	61.91 281	96.72 220	81.99 154	81.16 263	93.16 241

v10		87.25 180	86.38 174	89.85 195	91.19 247	79.50 180	94.48 116	95.45 145	83.79 156	83.62 215	91.19 224	75.13 147	97.42 166	81.94 155	80.60 274	92.63 257

TranMVSNet+NR-MVSNet		88.84 124	87.95 127	91.49 124	92.68 195	83.01 102	94.92 88	96.31 80	89.88 30	85.53 161	93.85 131	76.63 119	96.96 209	81.91 156	79.87 287	94.50 175

test-LLR		85.87 209	85.41 198	87.25 270	90.95 256	71.67 294	89.55 270	89.88 304	83.41 167	84.54 192	87.95 278	67.25 251	95.11 287	81.82 157	93.37 123	94.97 144

test-mter		84.54 244	83.64 234	87.25 270	90.95 256	71.67 294	89.55 270	89.88 304	79.17 240	84.54 192	87.95 278	55.56 307	95.11 287	81.82 157	93.37 123	94.97 144

PMMVS		85.71 217	84.96 207	87.95 256	88.90 298	77.09 254	88.68 284	90.06 299	72.32 300	86.47 133	90.76 235	72.15 188	94.40 294	81.78 159	93.49 118	92.36 265

NR-MVSNet		88.58 130	87.47 134	91.93 109	93.04 186	84.16 75	94.77 98	96.25 84	89.05 45	80.04 262	93.29 143	79.02 95	97.05 203	81.71 160	80.05 282	94.59 168

WTY-MVS		89.60 100	88.92 103	91.67 120	95.47 103	81.15 143	92.38 228	94.78 187	83.11 174	89.06 93	94.32 109	78.67 99	96.61 227	81.57 161	90.89 154	97.24 75

v1240		86.78 191	85.85 189	89.56 205	90.45 277	77.79 243	93.61 183	95.37 153	81.65 216	85.43 170	91.15 227	71.50 194	97.43 164	81.47 162	82.05 251	93.47 233

WR-MVS		88.38 132	87.67 131	90.52 158	93.30 178	80.18 163	93.26 198	95.96 103	88.57 59	85.47 166	92.81 164	76.12 123	96.91 213	81.24 163	82.29 246	94.47 180

1314		87.51 172	86.57 172	90.34 170	92.42 198	79.74 178	92.63 219	95.35 155	78.35 252	80.14 260	91.62 205	74.05 162	97.15 194	81.05 164	93.53 117	94.12 189

semantic-postprocess				88.18 252	91.71 213	76.87 257		92.65 239	85.40 123	81.44 243	90.54 240	66.21 260	95.00 290	81.04 165	81.05 266	92.66 256

PatchFormer-LS_test		86.02 206	85.13 203	88.70 230	91.52 217	74.12 274	91.19 255	92.09 248	82.71 193	84.30 203	87.24 287	70.87 201	96.98 207	81.04 165	85.17 220	95.00 143

XXY-MVS		87.65 158	86.85 154	90.03 189	92.14 202	80.60 158	93.76 172	95.23 164	82.94 186	84.60 190	94.02 120	74.27 156	95.49 273	81.04 165	83.68 233	94.01 197

GA-MVS		86.61 195	85.27 202	90.66 148	91.33 235	78.71 213	90.40 259	93.81 221	85.34 124	85.12 181	89.57 257	61.25 285	97.11 198	80.99 168	89.59 171	96.15 106

IB-MVS	80.51 15	85.24 224	83.26 243	91.19 132	92.13 203	79.86 174	91.75 242	91.29 273	83.28 172	80.66 253	88.49 271	61.28 284	98.46 90	80.99 168	79.46 289	95.25 138
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		84.69 242	84.79 213	84.37 298	91.84 208	64.92 322	93.70 179	91.47 269	66.19 322	86.16 143	95.28 83	67.18 253	93.33 305	80.89 170	90.42 157	94.88 156

HyFIR lowres test		88.09 142	86.81 156	91.93 109	96.00 85	80.63 156	90.01 265	95.79 116	73.42 290	87.68 116	92.10 187	73.86 165	97.96 127	80.75 171	91.70 139	97.19 79

AdaColmap		89.89 96	89.07 99	92.37 93	97.41 41	83.03 100	94.42 123	95.92 105	82.81 190	86.34 139	94.65 102	73.89 164	99.02 51	80.69 172	95.51 85	95.05 141

原ACMM1				92.01 103	97.34 44	81.05 145		96.81 48	78.89 243	90.45 78	95.92 68	82.65 58	98.84 73	80.68 173	98.26 42	96.14 107

TESTMET0.1,1		83.74 252	82.85 249	86.42 284	89.96 287	71.21 298	89.55 270	87.88 321	77.41 259	83.37 221	87.31 286	56.71 304	93.65 301	80.62 174	92.85 134	94.40 181

无先验							93.28 197	96.26 82	73.95 287				99.05 44	80.56 175		96.59 97

1121		90.42 84	89.49 88	93.20 61	97.27 49	84.46 66	92.63 219	95.51 138	71.01 310	91.20 72	96.21 56	82.92 56	99.05 44	80.56 175	98.07 47	96.10 109

Fast-Effi-MVS+		89.41 109	88.64 108	91.71 119	94.74 127	80.81 153	93.54 185	95.10 169	83.11 174	86.82 130	90.67 236	79.74 88	97.75 139	80.51 177	93.55 116	96.57 98

v18		84.97 229	83.76 227	88.60 235	91.36 230	79.41 189	93.82 168	94.04 207	83.00 184	76.61 282	86.60 290	76.19 121	95.43 275	80.39 178	71.79 309	90.96 289

CHOSEN 1792x2688		88.84 124	87.69 130	92.30 95	96.14 76	81.42 135	90.01 265	95.86 112	74.52 284	87.41 118	93.94 124	75.46 144	98.36 93	80.36 179	95.53 84	97.12 83

CDS-MVSNet		89.45 106	88.51 110	92.29 96	93.62 170	83.61 87	93.01 208	94.68 189	81.95 206	87.82 113	93.24 145	78.69 98	96.99 206	80.34 180	93.23 126	96.28 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		87.44 174	86.72 160	89.63 204	92.04 205	77.68 248	94.03 157	93.94 216	85.81 113	82.42 229	91.32 218	70.33 212	97.06 202	80.33 181	90.23 161	94.14 188

v16		84.96 230	83.74 229	88.62 233	91.40 225	79.48 183	93.83 166	94.04 207	83.03 181	76.54 283	86.59 291	76.11 126	95.42 276	80.33 181	71.80 308	90.95 291

v17		84.93 232	83.70 231	88.62 233	91.36 230	79.48 183	93.83 166	94.03 209	83.04 180	76.51 284	86.57 292	76.05 127	95.42 276	80.31 183	71.65 310	90.96 289

API-MVS		90.66 78	90.07 80	92.45 89	96.36 68	84.57 61	96.06 33	95.22 166	82.39 196	89.13 90	94.27 114	80.32 81	98.46 90	80.16 184	96.71 69	94.33 182

v15		84.79 235	83.53 236	88.57 239	91.30 241	79.41 189	93.70 179	94.01 210	83.06 177	76.27 285	86.42 296	76.03 130	95.38 278	80.01 185	71.00 313	90.92 292

MAR-MVS		90.30 85	89.37 92	93.07 68	96.61 61	84.48 65	95.68 47	95.67 123	82.36 198	87.85 107	92.85 160	76.63 119	98.80 74	80.01 185	96.68 70	95.91 116
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		84.79 235	83.52 237	88.57 239	91.32 237	79.43 188	93.72 177	94.01 210	83.06 177	76.22 286	86.43 293	76.01 131	95.37 279	79.96 187	70.99 314	90.91 293

HY-MVS	83.01 12	89.03 120	87.94 128	92.29 96	94.86 125	82.77 107	92.08 239	94.49 193	81.52 221	86.93 126	92.79 166	78.32 105	98.23 100	79.93 188	90.55 155	95.88 118

CHOSEN 280x420		85.15 225	83.99 224	88.65 231	92.47 197	78.40 227	79.68 329	92.76 235	74.90 281	81.41 244	89.59 256	69.85 217	95.51 270	79.92 189	95.29 91	92.03 271

V9		84.77 237	83.50 238	88.58 236	91.33 235	79.46 185	93.75 173	94.00 213	83.07 176	76.07 291	86.43 293	75.97 132	95.37 279	79.91 190	70.93 316	90.91 293

v12		84.74 238	83.46 239	88.58 236	91.32 237	79.50 180	93.75 173	94.01 210	83.06 177	75.98 293	86.41 297	75.82 138	95.36 281	79.87 191	70.89 317	90.89 295

v13		84.72 240	83.44 241	88.58 236	91.31 240	79.52 179	93.77 171	94.00 213	83.03 181	75.85 294	86.38 298	75.84 137	95.35 282	79.83 192	70.95 315	90.87 296

v748		86.27 201	85.28 201	89.25 219	90.26 280	77.58 252	94.89 89	95.50 139	84.28 148	81.41 244	90.46 244	72.57 184	97.32 179	79.81 193	78.36 291	92.84 251

MVS		87.44 174	86.10 183	91.44 126	92.61 196	83.62 86	92.63 219	95.66 125	67.26 320	81.47 242	92.15 183	77.95 107	98.22 101	79.71 194	95.48 86	92.47 261

pm-mvs1		86.61 195	85.54 193	89.82 196	91.44 220	80.18 163	95.28 67	94.85 183	83.84 153	81.66 241	92.62 169	72.45 187	96.48 233	79.67 195	78.06 292	92.82 253

v11		84.67 243	83.41 242	88.44 244	91.32 237	79.13 208	93.69 182	93.99 215	82.81 190	76.20 287	86.24 300	75.48 143	95.35 282	79.53 196	71.48 312	90.85 297

IterMVS		84.88 233	83.98 225	87.60 261	91.44 220	76.03 264	90.18 263	92.41 242	83.24 173	81.06 249	90.42 245	66.60 255	94.28 295	79.46 197	80.98 271	92.48 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1112_ss		88.42 131	87.33 137	91.72 118	94.92 122	80.98 147	92.97 211	94.54 192	78.16 256	83.82 210	93.88 129	78.78 97	97.91 131	79.45 198	89.41 172	96.26 104

gm-plane-assit					89.60 292	68.00 313			77.28 262		88.99 262		97.57 145	79.44 199

PM-MVS		78.11 290	76.12 292	84.09 301	83.54 322	70.08 307	88.97 281	85.27 331	79.93 234	74.73 299	86.43 293	34.70 336	93.48 303	79.43 200	72.06 307	88.72 314

v7n		86.81 189	85.76 191	89.95 193	90.72 268	79.25 203	95.07 78	95.92 105	84.45 144	82.29 230	90.86 233	72.60 183	97.53 148	79.42 201	80.52 278	93.08 246

PAPR		90.02 90	89.27 96	92.29 96	95.78 92	80.95 149	92.68 218	96.22 86	81.91 208	86.66 132	93.75 135	82.23 63	98.44 92	79.40 202	94.79 95	97.48 71

新几何1				93.10 65	97.30 46	84.35 72		95.56 131	71.09 309	91.26 71	96.24 54	82.87 57	98.86 68	79.19 203	98.10 46	96.07 111

CP-MVSNet		87.63 162	87.26 140	88.74 229	93.12 183	76.59 259	95.29 61	96.58 69	88.43 61	83.49 219	92.98 158	75.28 146	95.83 259	78.97 204	81.15 265	93.79 208

pmmvs4		85.43 219	83.86 226	90.16 174	90.02 286	82.97 104	90.27 260	92.67 238	75.93 272	80.73 251	91.74 200	71.05 198	95.73 264	78.85 205	83.46 237	91.78 274

DWT-MVSNet_test		84.95 231	83.68 232	88.77 227	91.43 223	73.75 277	91.74 243	90.98 282	80.66 229	83.84 209	87.36 285	62.44 277	97.11 198	78.84 206	85.81 213	95.46 132

Test_1112_low_res		87.65 158	86.51 173	91.08 137	94.94 121	79.28 201	91.77 241	94.30 200	76.04 271	83.51 218	92.37 175	77.86 110	97.73 140	78.69 207	89.13 181	96.22 105

Vis-MVSNet (Re-imp)		89.59 101	89.44 90	90.03 189	95.74 93	75.85 265	95.61 52	90.80 287	87.66 81	87.83 112	95.40 82	76.79 116	96.46 235	78.37 208	96.73 68	97.80 61

PS-CasMVS		87.32 177	86.88 152	88.63 232	92.99 189	76.33 262	95.33 56	96.61 66	88.22 66	83.30 222	93.07 152	73.03 177	95.79 262	78.36 209	81.00 270	93.75 214

testdata2													98.75 76	78.30 210

GBi-Net		87.26 178	85.98 186	91.08 137	94.01 154	83.10 97	95.14 75	94.94 176	83.57 161	84.37 197	91.64 201	66.59 256	96.34 241	78.23 211	85.36 217	93.79 208

test1		87.26 178	85.98 186	91.08 137	94.01 154	83.10 97	95.14 75	94.94 176	83.57 161	84.37 197	91.64 201	66.59 256	96.34 241	78.23 211	85.36 217	93.79 208

FMVSNet3		87.40 176	86.11 182	91.30 130	93.79 167	83.64 85	94.20 142	94.81 186	83.89 152	84.37 197	91.87 197	68.45 244	96.56 228	78.23 211	85.36 217	93.70 217

OpenMVS	83.78 11	88.74 127	87.29 138	93.08 66	92.70 194	85.39 51	96.57 22	96.43 74	78.74 248	80.85 250	96.07 64	69.64 219	99.01 53	78.01 214	96.65 71	94.83 158

tpm		84.73 239	84.02 223	86.87 280	90.33 278	68.90 311	89.06 280	89.94 302	80.85 228	85.75 150	89.86 253	68.54 240	95.97 253	77.76 215	84.05 229	95.75 125

TAMVS		89.21 114	88.29 120	91.96 107	93.71 168	82.62 116	93.30 195	94.19 202	82.22 199	87.78 114	93.94 124	78.83 96	96.95 210	77.70 216	92.98 130	96.32 102

BH-untuned		88.60 129	88.13 124	90.01 191	95.24 114	78.50 224	93.29 196	94.15 204	84.75 136	84.46 194	93.40 137	75.76 139	97.40 173	77.59 217	94.52 102	94.12 189

FMVSNet2		87.19 184	85.82 190	91.30 130	94.01 154	83.67 84	94.79 96	94.94 176	83.57 161	83.88 208	92.05 191	66.59 256	96.51 231	77.56 218	85.01 221	93.73 215

RPSCF		85.07 226	84.27 220	87.48 266	92.91 191	70.62 304	91.69 246	92.46 241	76.20 270	82.67 228	95.22 86	63.94 273	97.29 183	77.51 219	85.80 214	94.53 172

PLC	84.53 7	89.06 119	88.03 125	92.15 100	97.27 49	82.69 114	94.29 135	95.44 147	79.71 237	84.01 207	94.18 116	76.68 118	98.75 76	77.28 220	93.41 121	95.02 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		89.07 117	87.98 126	92.34 94	96.87 56	84.78 57	94.08 150	93.24 227	81.41 223	84.46 194	95.13 89	75.57 142	96.62 225	77.21 221	93.84 113	95.61 129

K. test v3		81.59 268	80.15 269	85.91 287	89.89 289	69.42 310	92.57 222	87.71 323	85.56 119	73.44 305	89.71 255	55.58 306	95.52 269	77.17 222	69.76 320	92.78 254

QAPM		89.51 103	88.15 123	93.59 56	94.92 122	84.58 60	96.82 18	96.70 58	78.43 251	83.41 220	96.19 60	73.18 175	99.30 27	77.11 223	96.54 74	96.89 91

pmmvs5		84.21 246	82.84 250	88.34 247	88.95 297	76.94 256	92.41 226	91.91 258	75.63 274	80.28 257	91.18 225	64.59 270	95.57 267	77.09 224	83.47 236	92.53 259

pmmvs6		83.42 253	81.60 256	88.87 226	88.01 307	77.87 241	94.96 84	94.24 201	74.67 283	78.80 269	91.09 230	60.17 293	96.49 232	77.06 225	75.40 300	92.23 269

test_post1							88.00 290				9.81 346	69.31 224	95.53 268	76.65 226

WR-MVS_H		87.80 154	87.37 136	89.10 224	93.23 180	78.12 234	95.61 52	97.30 17	87.90 72	83.72 212	92.01 192	79.65 93	96.01 252	76.36 227	80.54 276	93.16 241

EU-MVSNet		81.32 273	80.95 261	82.42 306	88.50 301	63.67 323	93.32 191	91.33 271	64.02 326	80.57 255	92.83 162	61.21 287	92.27 313	76.34 228	80.38 280	91.32 283

CMPMVS	59.16 21	80.52 279	79.20 277	84.48 297	83.98 320	67.63 316	89.95 267	93.84 220	64.79 325	66.81 323	91.14 228	57.93 302	95.17 285	76.25 229	88.10 195	90.65 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
F-COLMAP		87.95 147	86.80 157	91.40 127	96.35 69	80.88 151	94.73 99	95.45 145	79.65 238	82.04 237	94.61 103	71.13 197	98.50 88	76.24 230	91.05 148	94.80 160

PEN-MVS		86.80 190	86.27 179	88.40 245	92.32 200	75.71 266	95.18 72	96.38 78	87.97 69	82.82 226	93.15 148	73.39 173	95.92 255	76.15 231	79.03 290	93.59 227

SixPastTwentyTwo		83.91 249	82.90 248	86.92 277	90.99 254	70.67 303	93.48 187	91.99 253	85.54 120	77.62 277	92.11 186	60.59 290	96.87 215	76.05 232	77.75 293	93.20 239

MS-PatchMatch		85.05 227	84.16 221	87.73 259	91.42 224	78.51 223	91.25 254	93.53 223	77.50 258	80.15 259	91.58 206	61.99 280	95.51 270	75.69 233	94.35 108	89.16 310

BH-w/o		87.57 171	87.05 149	89.12 222	94.90 124	77.90 239	92.41 226	93.51 224	82.89 189	83.70 213	91.34 215	75.75 140	97.07 201	75.49 234	93.49 118	92.39 264

gg-mvs-nofinetune		81.77 265	79.37 275	88.99 225	90.85 264	77.73 247	86.29 301	79.63 341	74.88 282	83.19 223	69.05 332	60.34 291	96.11 248	75.46 235	94.64 99	93.11 244

FMVSNet1		85.85 210	84.11 222	91.08 137	92.81 192	83.10 97	95.14 75	94.94 176	81.64 217	82.68 227	91.64 201	59.01 298	96.34 241	75.37 236	83.78 230	93.79 208

EPMVS		83.90 250	82.70 251	87.51 263	90.23 282	72.67 286	88.62 285	81.96 337	81.37 224	85.01 183	88.34 273	66.31 259	94.45 293	75.30 237	87.12 206	95.43 133

pmmvs-eth3d		80.97 277	78.72 282	87.74 258	84.99 318	79.97 172	90.11 264	91.65 262	75.36 275	73.51 304	86.03 301	59.45 296	93.96 298	75.17 238	72.21 306	89.29 308

tpm2		84.08 247	82.94 247	87.48 266	91.39 226	71.27 296	89.23 278	90.37 292	71.95 303	84.64 189	89.33 259	67.30 250	96.55 230	75.17 238	87.09 207	94.63 164

lessismore_v0				86.04 285	88.46 302	68.78 312		80.59 339		73.01 308	90.11 249	55.39 308	96.43 237	75.06 240	65.06 326	92.90 248

MVP-Stereo		85.97 208	84.86 211	89.32 217	90.92 260	82.19 121	92.11 237	94.19 202	78.76 247	78.77 270	91.63 204	68.38 247	96.56 228	75.01 241	93.95 110	89.20 309
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet	78.82 18	85.55 218	84.65 216	88.23 251	94.72 129	71.93 292	87.12 297	92.75 236	78.80 246	84.95 184	90.53 242	64.43 271	96.71 222	74.74 242	93.86 112	96.06 112

MDTV_nov1_ep13_2view						55.91 336	87.62 295		73.32 291	84.59 191		70.33 212		74.65 243		95.50 130

PatchmatchNet		85.85 210	84.70 215	89.29 218	91.76 211	75.54 267	88.49 286	91.30 272	81.63 218	85.05 182	88.70 267	71.71 189	96.24 244	74.61 244	89.05 182	96.08 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LF4IMVS		80.37 280	79.07 280	84.27 300	86.64 312	69.87 309	89.39 275	91.05 280	76.38 266	74.97 298	90.00 250	47.85 324	94.25 296	74.55 245	80.82 273	88.69 315

testpf		71.41 303	72.11 300	69.30 323	84.53 319	59.79 327	62.74 339	83.14 334	71.11 308	68.83 319	81.57 320	46.70 326	84.83 336	74.51 246	75.86 299	63.30 335

DTE-MVSNet		86.11 203	85.48 197	87.98 255	91.65 216	74.92 269	94.93 87	95.75 119	87.36 84	82.26 231	93.04 153	72.85 178	95.82 260	74.04 247	77.46 295	93.20 239

BH-RMVSNet		88.37 133	87.48 133	91.02 141	95.28 109	79.45 187	92.89 213	93.07 230	85.45 122	86.91 127	94.84 98	70.35 211	97.76 136	73.97 248	94.59 100	95.85 119

CR-MVSNet		85.35 221	83.76 227	90.12 180	90.58 272	79.34 197	85.24 309	91.96 256	78.27 253	85.55 159	87.87 281	71.03 199	95.61 265	73.96 249	89.36 174	95.40 134

ACMH+	81.04 14	85.05 227	83.46 239	89.82 196	94.66 133	79.37 195	94.44 121	94.12 206	82.19 200	78.04 273	92.82 163	58.23 300	97.54 147	73.77 250	82.90 241	92.54 258

TR-MVS		86.78 191	85.76 191	89.82 196	94.37 143	78.41 226	92.47 225	92.83 233	81.11 226	86.36 138	92.40 174	68.73 238	97.48 151	73.75 251	89.85 167	93.57 228

UnsupCasMVSNet_eth		80.07 281	78.27 283	85.46 290	85.24 316	72.63 288	88.45 287	94.87 182	82.99 185	71.64 314	88.07 277	56.34 305	91.75 317	73.48 252	63.36 331	92.01 272

PatchMatch-RL		86.77 193	85.54 193	90.47 162	95.88 89	82.71 113	90.54 258	92.31 243	79.82 236	84.32 201	91.57 208	68.77 237	96.39 238	73.16 253	93.48 120	92.32 267

ambc				83.06 303	79.99 329	63.51 324	77.47 332	92.86 232		74.34 302	84.45 306	28.74 338	95.06 289	73.06 254	68.89 323	90.61 299

Patchmatch-test1		85.81 212	84.71 214	89.12 222	92.15 201	76.60 258	91.12 256	91.69 261	83.53 164	85.50 164	88.56 270	66.79 254	95.00 290	72.69 255	90.35 158	95.76 124

ITE_SJBPF				88.24 250	91.88 207	77.05 255		92.92 231	85.54 120	80.13 261	93.30 142	57.29 303	96.20 245	72.46 256	84.71 223	91.49 280

ACMH	80.38 17	85.36 220	83.68 232	90.39 165	94.45 141	80.63 156	94.73 99	94.85 183	82.09 201	77.24 279	92.65 168	60.01 294	97.58 144	72.25 257	84.87 222	92.96 247
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
USDC		82.76 258	81.26 259	87.26 269	91.17 248	74.55 270	89.27 276	93.39 226	78.26 254	75.30 296	92.08 188	54.43 312	96.63 224	71.64 258	85.79 215	90.61 299

tpmp4_e23		83.87 251	82.33 252	88.48 242	91.46 219	72.82 283	89.82 268	91.57 266	73.02 295	81.86 240	89.05 261	66.20 261	96.97 208	71.57 259	86.39 210	95.66 127

EPNet_dtu		86.49 199	85.94 188	88.14 253	90.24 281	72.82 283	94.11 146	92.20 246	86.66 102	79.42 267	92.36 176	73.52 169	95.81 261	71.26 260	93.66 114	95.80 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				87.94 257	89.73 291	77.91 238	87.80 291	78.23 343		80.58 254	83.86 308	59.88 295	95.33 284	71.20 261	92.22 138	90.60 301

LTVRE_ROB	82.13 13	86.26 202	84.90 210	90.34 170	94.44 142	81.50 130	92.31 230	94.89 181	83.03 181	79.63 265	92.67 167	69.69 218	97.79 134	71.20 261	86.26 211	91.72 277
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		81.04 275	78.98 281	87.25 270	88.64 299	73.48 279	81.75 325	89.61 308	73.19 292	82.05 236	73.71 329	66.07 264	95.87 258	71.18 263	84.60 224	92.41 263

TransMVSNet (Re)		84.43 245	83.06 246	88.54 241	91.72 212	78.44 225	95.18 72	92.82 234	82.73 192	79.67 264	92.12 184	73.49 170	95.96 254	71.10 264	68.73 324	91.21 285

PCF-MVS	84.11 10	87.74 156	86.08 184	92.70 80	94.02 153	84.43 70	89.27 276	95.87 111	73.62 289	84.43 196	94.33 108	78.48 103	98.86 68	70.27 265	94.45 105	94.81 159
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		82.37 263	80.34 265	88.46 243	90.27 279	79.35 196	92.80 215	94.33 199	77.14 263	73.26 307	90.18 248	47.47 325	96.72 220	70.25 266	87.32 205	89.30 307

MDTV_nov1_ep13			83.56 235		91.69 215	69.93 308	87.75 293	91.54 267	78.60 249	84.86 187	88.90 263	69.54 220	96.03 250	70.25 266	88.93 183

TDRefinement		79.81 283	77.34 285	87.22 273	79.24 332	75.48 268	93.12 202	92.03 251	76.45 265	75.01 297	91.58 206	49.19 322	96.44 236	70.22 268	69.18 321	89.75 305

conf200view11		87.65 158	86.71 161	90.46 163	96.12 77	78.55 216	95.03 81	91.58 263	87.15 85	88.06 103	92.29 179	68.91 230	98.10 112	70.13 269	91.10 143	94.71 162

thres100view900		87.63 162	86.71 161	90.38 167	96.12 77	78.55 216	95.03 81	91.58 263	87.15 85	88.06 103	92.29 179	68.91 230	98.10 112	70.13 269	91.10 143	94.48 178

tfpn200view9		87.58 170	86.64 169	90.41 164	95.99 86	78.64 214	94.58 111	91.98 254	86.94 96	88.09 100	91.77 198	69.18 227	98.10 112	70.13 269	91.10 143	94.48 178

thres400		87.62 164	86.64 169	90.57 150	95.99 86	78.64 214	94.58 111	91.98 254	86.94 96	88.09 100	91.77 198	69.18 227	98.10 112	70.13 269	91.10 143	94.96 147

thres600view7		87.65 158	86.67 164	90.59 149	96.08 82	78.72 212	94.88 91	91.58 263	87.06 93	88.08 102	92.30 178	68.91 230	98.10 112	70.05 273	91.10 143	94.96 147

thres200		87.21 183	86.24 180	90.12 180	95.36 105	78.53 218	93.26 198	92.10 247	86.42 105	88.00 105	91.11 229	69.24 226	98.00 125	69.58 274	91.04 149	93.83 207

view600		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

view800		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

conf0.05thres1000		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

tfpn		87.62 164	86.65 165	90.53 152	96.19 72	78.52 219	95.29 61	91.09 274	87.08 89	87.84 108	93.03 154	68.86 233	98.11 108	69.44 275	91.02 150	94.96 147

tpm cat1		81.96 264	80.27 266	87.01 275	91.09 251	71.02 300	87.38 296	91.53 268	66.25 321	80.17 258	86.35 299	68.22 249	96.15 247	69.16 279	82.29 246	93.86 205

Patchmtry		82.71 259	80.93 262	88.06 254	90.05 285	76.37 261	84.74 311	91.96 256	72.28 301	81.32 246	87.87 281	71.03 199	95.50 272	68.97 280	80.15 281	92.32 267

PVSNet_0	73.20 20	77.22 291	74.83 294	84.37 298	90.70 269	71.10 299	83.09 322	89.67 307	72.81 298	73.93 303	83.13 313	60.79 289	93.70 300	68.54 281	50.84 336	88.30 321

MSDG		84.86 234	83.09 245	90.14 179	93.80 165	80.05 168	89.18 279	93.09 229	78.89 243	78.19 271	91.91 195	65.86 265	97.27 184	68.47 282	88.45 190	93.11 244

tfpn_ndepth		86.10 204	84.98 205	89.43 212	95.52 102	78.29 230	94.62 109	89.60 309	81.88 213	85.43 170	90.54 240	68.47 243	96.85 217	68.46 283	90.34 159	93.15 243

LS3D		87.89 148	86.32 177	92.59 83	96.07 83	82.92 105	95.23 69	94.92 180	75.66 273	82.89 225	95.98 66	72.48 185	99.21 30	68.43 284	95.23 93	95.64 128

AllTest		83.42 253	81.39 257	89.52 207	95.01 117	77.79 243	93.12 202	90.89 285	77.41 259	76.12 289	93.34 138	54.08 313	97.51 149	68.31 285	84.27 227	93.26 237

TestCases				89.52 207	95.01 117	77.79 243		90.89 285	77.41 259	76.12 289	93.34 138	54.08 313	97.51 149	68.31 285	84.27 227	93.26 237

dp		81.47 271	80.23 267	85.17 293	89.92 288	65.49 321	86.74 298	90.10 298	76.30 268	81.10 247	87.12 289	62.81 275	95.92 255	68.13 287	79.88 286	94.09 192

tpmvs		83.35 256	82.07 253	87.20 274	91.07 252	71.00 301	88.31 288	91.70 260	78.91 242	80.49 256	87.18 288	69.30 225	97.08 200	68.12 288	83.56 235	93.51 232

FMVSNet5		81.52 270	79.60 274	87.27 268	91.17 248	77.95 237	91.49 249	92.26 245	76.87 264	76.16 288	87.91 280	51.67 316	92.34 312	67.74 289	81.16 263	91.52 279

tfpn1000		86.06 205	84.92 209	89.49 210	95.54 99	77.79 243	94.72 102	89.07 314	82.05 202	85.36 178	91.94 194	68.32 248	96.65 223	67.04 290	90.24 160	94.02 196

YYNet1		79.22 287	77.20 287	85.28 292	88.20 305	72.66 287	85.87 304	90.05 301	74.33 286	62.70 328	87.61 283	66.09 263	92.03 314	66.94 291	72.97 304	91.15 286

PAPM		86.68 194	85.39 199	90.53 152	93.05 185	79.33 200	89.79 269	94.77 188	78.82 245	81.95 238	93.24 145	76.81 115	97.30 180	66.94 291	93.16 127	94.95 154

DP-MVS		87.25 180	85.36 200	92.90 74	97.65 33	83.24 94	94.81 95	92.00 252	74.99 279	81.92 239	95.00 91	72.66 181	99.05 44	66.92 293	92.33 137	96.40 100

MDA-MVSNet_test_wron		79.21 288	77.19 288	85.29 291	88.22 304	72.77 285	85.87 304	90.06 299	74.34 285	62.62 329	87.56 284	66.14 262	91.99 315	66.90 294	73.01 303	91.10 288

UnsupCasMVSNet_bld		76.23 294	73.27 296	85.09 294	83.79 321	72.92 281	85.65 308	93.47 225	71.52 304	68.84 318	79.08 325	49.77 319	93.21 306	66.81 295	60.52 333	89.13 312

tfpnconf		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

tfpnview11		85.75 214	84.54 218	89.38 215	95.26 111	77.63 249	94.21 140	89.33 311	81.89 210	84.94 185	91.51 212	68.43 245	96.80 218	66.05 296	89.23 177	93.70 217

MIMVSNet		82.59 261	80.53 264	88.76 228	91.51 218	78.32 228	86.57 300	90.13 297	79.32 239	80.70 252	88.69 268	52.98 315	93.07 310	66.03 298	88.86 184	94.90 155

LCM-MVSNet		66.00 306	62.16 310	77.51 315	64.51 344	58.29 329	83.87 318	90.90 284	48.17 335	54.69 332	73.31 330	16.83 349	86.75 330	65.47 299	61.67 332	87.48 323

PatchT		82.68 260	81.27 258	86.89 279	90.09 284	70.94 302	84.06 316	90.15 296	74.91 280	85.63 158	83.57 310	69.37 221	94.87 292	65.19 300	88.50 189	94.84 157

test0.0.03 1		82.41 262	81.69 255	84.59 296	88.23 303	72.89 282	90.24 261	87.83 322	83.41 167	79.86 263	89.78 254	67.25 251	88.99 323	65.18 301	83.42 238	91.90 273

COLMAP_ROB	80.39 16	83.96 248	82.04 254	89.74 200	95.28 109	79.75 177	94.25 137	92.28 244	75.17 277	78.02 274	93.77 133	58.60 299	97.84 133	65.06 302	85.92 212	91.63 278
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ADS-MVSNet2		81.66 267	79.71 273	87.50 264	91.35 233	74.19 272	83.33 320	88.48 318	72.90 296	82.24 232	85.77 302	64.98 268	93.20 307	64.57 303	83.74 231	95.12 139

ADS-MVSNet		81.56 269	79.78 271	86.90 278	91.35 233	71.82 293	83.33 320	89.16 313	72.90 296	82.24 232	85.77 302	64.98 268	93.76 299	64.57 303	83.74 231	95.12 139

new-patchmatchnet		76.41 293	75.17 293	80.13 308	82.65 326	59.61 328	87.66 294	91.08 278	78.23 255	69.85 316	83.22 312	54.76 310	91.63 319	64.14 305	64.89 327	89.16 310

testgi		80.94 278	80.20 268	83.18 302	87.96 308	66.29 318	91.28 252	90.70 290	83.70 157	78.12 272	92.84 161	51.37 317	90.82 320	63.34 306	82.46 245	92.43 262

TinyColmap		79.76 284	77.69 284	85.97 286	91.71 213	73.12 280	89.55 270	90.36 293	75.03 278	72.03 312	90.19 247	46.22 327	96.19 246	63.11 307	81.03 267	88.59 316

pmmvs3		71.81 302	68.71 305	81.11 307	75.86 334	70.42 305	86.74 298	83.66 333	58.95 331	68.64 320	80.89 321	36.93 335	89.52 322	63.10 308	63.59 330	83.39 326

TAPA-MVS	84.62 6	88.16 139	87.01 150	91.62 121	96.64 60	80.65 155	94.39 126	96.21 89	76.38 266	86.19 142	95.44 80	79.75 87	98.08 120	62.75 309	95.29 91	96.13 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDA-MVSNet-bldmvs		78.85 289	76.31 290	86.46 282	89.76 290	73.88 276	88.79 282	90.42 291	79.16 241	59.18 330	88.33 274	60.20 292	94.04 297	62.00 310	68.96 322	91.48 281

tfpnnormal		84.72 240	83.23 244	89.20 221	92.79 193	80.05 168	94.48 116	95.81 114	82.38 197	81.08 248	91.21 223	69.01 229	96.95 210	61.69 311	80.59 275	90.58 302

Anonymous20231206		81.03 276	79.77 272	84.82 295	87.85 310	70.26 306	91.42 250	92.08 249	73.67 288	77.75 276	89.25 260	62.43 278	93.08 309	61.50 312	82.00 252	91.12 287

RPMNet		83.18 257	80.87 263	90.12 180	90.58 272	79.34 197	85.24 309	90.78 288	71.44 305	85.55 159	82.97 314	70.87 201	95.61 265	61.01 313	89.36 174	95.40 134

MIMVSNet1		79.38 286	77.28 286	85.69 288	86.35 313	73.67 278	91.61 248	92.75 236	78.11 257	72.64 310	88.12 276	48.16 323	91.97 316	60.32 314	77.49 294	91.43 282

test20.03		79.95 282	79.08 279	82.55 305	85.79 314	67.74 315	91.09 257	91.08 278	81.23 225	74.48 301	89.96 252	61.63 282	90.15 321	60.08 315	76.38 297	89.76 304

DSMNet-mixed		76.94 292	76.29 291	78.89 309	83.10 323	56.11 335	87.78 292	79.77 340	60.65 330	75.64 295	88.71 266	61.56 283	88.34 325	60.07 316	89.29 176	92.21 270

Patchmatch-test		81.37 272	79.30 276	87.58 262	90.92 260	74.16 273	80.99 326	87.68 324	70.52 311	76.63 281	88.81 264	71.21 196	92.76 311	60.01 317	86.93 209	95.83 121

MVS-HIRNet		73.70 298	72.20 298	78.18 312	91.81 210	56.42 334	82.94 323	82.58 335	55.24 332	68.88 317	66.48 333	55.32 309	95.13 286	58.12 318	88.42 192	83.01 327

OpenMVS_ROB	74.94 19	79.51 285	77.03 289	86.93 276	87.00 311	76.23 263	92.33 229	90.74 289	68.93 315	74.52 300	88.23 275	49.58 320	96.62 225	57.64 319	84.29 226	87.94 322

new_pmnet		72.15 301	70.13 302	78.20 310	82.95 325	65.68 319	83.91 317	82.40 336	62.94 328	64.47 326	79.82 324	42.85 330	86.26 331	57.41 320	74.44 302	82.65 328

N_pmnet		68.89 305	68.44 306	70.23 321	89.07 296	28.79 350	88.06 289	19.50 352	69.47 314	71.86 313	84.93 305	61.24 286	91.75 317	54.70 321	77.15 296	90.15 303

tmp_tt		35.64 322	39.24 321	24.84 335	14.87 350	23.90 351	62.71 340	51.51 351	6.58 345	36.66 340	62.08 337	44.37 329	30.34 348	52.40 322	22.00 345	20.27 343

testus		74.41 297	73.35 295	77.59 314	82.49 327	57.08 331	86.02 302	90.21 295	72.28 301	72.89 309	84.32 307	37.08 334	86.96 329	52.24 323	82.65 243	88.73 313

Anonymous20231211		72.97 299	69.63 304	83.00 304	83.05 324	66.91 317	92.69 217	89.45 310	61.06 329	67.50 321	83.46 311	34.34 337	93.61 302	51.11 324	63.97 329	88.48 319

test2356		74.50 296	73.27 296	78.20 310	80.81 328	59.84 326	83.76 319	88.33 320	71.43 306	72.37 311	81.84 318	45.60 328	86.26 331	50.97 325	84.32 225	88.50 317

no-one		61.56 310	56.58 312	76.49 316	67.80 342	62.76 325	78.13 331	86.11 327	63.16 327	43.24 337	64.70 335	26.12 341	88.95 324	50.84 326	29.15 339	77.77 332

test_0402		81.30 274	79.17 278	87.67 260	93.19 181	78.17 233	92.98 210	91.71 259	75.25 276	76.02 292	90.31 246	59.23 297	96.37 239	50.22 327	83.63 234	88.47 320

PMMVS2		59.60 311	56.40 313	69.21 324	68.83 338	46.58 341	73.02 337	77.48 344	55.07 333	49.21 335	72.95 331	17.43 348	80.04 339	49.32 328	44.33 337	80.99 331

test1235678		72.22 300	70.31 301	77.93 313	78.04 333	58.04 330	85.76 306	89.80 306	70.15 313	63.43 327	80.20 323	42.24 331	87.24 328	48.68 329	74.50 301	88.50 317

wuykxyi23d		50.55 316	44.13 318	69.81 322	56.77 346	54.58 337	73.22 336	80.78 338	39.79 340	22.08 346	46.69 342	4.03 353	79.71 340	47.65 330	26.13 341	75.14 333

1111		70.54 304	69.71 303	73.04 318	79.30 330	44.83 343	84.23 314	88.96 315	67.33 318	65.42 324	82.28 316	41.11 332	88.11 326	47.12 331	71.60 311	86.19 324

.test1245		57.63 314	61.79 311	45.14 332	79.30 330	44.83 343	84.23 314	88.96 315	67.33 318	65.42 324	82.28 316	41.11 332	88.11 326	47.12 331	0.39 347	2.46 346

LP		75.51 295	72.15 299	85.61 289	87.86 309	73.93 275	80.20 328	88.43 319	67.39 317	70.05 315	80.56 322	58.18 301	93.18 308	46.28 333	70.36 319	89.71 306

test12356		64.99 308	63.78 307	68.61 325	72.69 336	39.14 346	78.46 330	87.61 325	64.91 324	55.77 331	77.48 326	28.10 339	85.59 333	44.69 334	64.35 328	81.12 330

testmv		65.49 307	62.66 308	73.96 317	68.78 339	53.14 338	84.70 312	88.56 317	65.94 323	52.35 333	74.65 328	25.02 342	85.14 334	43.54 335	60.40 334	83.60 325

ANet_high		58.88 312	54.22 315	72.86 319	56.50 348	56.67 333	80.75 327	86.00 328	73.09 294	37.39 339	64.63 336	22.17 344	79.49 341	43.51 336	23.96 343	82.43 329

DeepMVS_CX				56.31 330	74.23 335	51.81 339		56.67 350	44.85 336	48.54 336	75.16 327	27.87 340	58.74 346	40.92 337	52.22 335	58.39 339

FPMVS		64.63 309	62.55 309	70.88 320	70.80 337	56.71 332	84.42 313	84.42 332	51.78 334	49.57 334	81.61 319	23.49 343	81.48 338	40.61 338	76.25 298	74.46 334

PNet_i23d		50.48 317	47.18 317	60.36 328	68.59 340	44.56 345	72.75 338	72.61 345	43.92 337	33.91 341	60.19 338	6.16 350	73.52 342	38.50 339	28.04 340	63.01 336

Gipuma		57.99 313	54.91 314	67.24 326	88.51 300	65.59 320	52.21 342	90.33 294	43.58 338	42.84 338	51.18 340	20.29 346	85.07 335	34.77 340	70.45 318	51.05 340
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 315	48.46 316	63.48 327	45.72 349	46.20 342	73.41 335	78.31 342	41.03 339	30.06 342	65.68 334	6.05 351	83.43 337	30.04 341	65.86 325	60.80 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 318	38.59 323	57.77 329	56.52 347	48.77 340	55.38 341	58.64 349	29.33 343	28.96 343	52.65 339	4.68 352	64.62 345	28.11 342	33.07 338	59.93 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 319	42.29 319	46.03 331	65.58 343	37.41 347	73.51 334	64.62 346	33.99 341	28.47 344	47.87 341	19.90 347	67.91 343	22.23 343	24.45 342	32.77 341

EMVS		42.07 320	41.12 320	44.92 333	63.45 345	35.56 349	73.65 333	63.48 347	33.05 342	26.88 345	45.45 343	21.27 345	67.14 344	19.80 344	23.02 344	32.06 342

wuyk23d		21.27 324	20.48 325	23.63 336	68.59 340	36.41 348	49.57 343	6.85 353	9.37 344	7.89 347	4.46 349	4.03 353	31.37 347	17.47 345	16.07 346	3.12 344

testmvs		8.92 325	11.52 326	1.12 338	1.06 351	0.46 353	86.02 302	0.65 354	0.62 346	2.74 348	9.52 347	0.31 356	0.45 350	2.38 346	0.39 347	2.46 346

test123		8.76 326	11.22 327	1.39 337	0.85 352	0.97 352	85.76 306	0.35 355	0.54 347	2.45 349	8.14 348	0.60 355	0.48 349	2.16 347	0.17 349	2.71 345

cdsmvs_eth3d_5k		22.14 323	29.52 324	0.00 339	0.00 353	0.00 354	0.00 344	95.76 118	0.00 348	0.00 350	94.29 111	75.66 141	0.00 351	0.00 348	0.00 350	0.00 348

pcd_1.5k_mvsjas		6.64 328	8.86 329	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	79.70 89	0.00 351	0.00 348	0.00 350	0.00 348

pcd1.5k->3k		37.02 321	38.84 322	31.53 334	92.33 199	0.00 354	0.00 344	96.13 92	0.00 348	0.00 350	0.00 350	72.70 180	0.00 351	0.00 348	88.43 191	94.60 167

sosnet-low-res		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

sosnet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

uncertanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

Regformer		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

ab-mvs-re		7.82 327	10.43 328	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	93.88 129	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

uanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 354	0.00 344	0.00 356	0.00 348	0.00 350	0.00 350	0.00 357	0.00 351	0.00 348	0.00 350	0.00 348

test_part2					98.55 5	87.22 10				96.40 2

test_part1								97.45 7				91.93 1			99.02 2	98.67 4

test_full								97.46 6

sam_mvs1												71.70 190

sam_mvs												70.60 205

MTGPA								96.97 34

test_post											10.29 345	70.57 209	95.91 257

patchmatchnet-post											83.76 309	71.53 193	96.48 233

MTMP								60.64 348

TEST9					97.53 35	86.49 28	94.07 152	96.78 50	81.61 219	92.77 39	96.20 57	87.71 14	99.12 39

test_8					97.49 38	86.30 36	94.02 158	96.76 53	81.86 214	92.70 43	96.20 57	87.63 15	99.02 51

agg_prior					97.38 42	85.92 43		96.72 56		92.16 55			98.97 59

test_prior4						85.96 42	94.11 146

test_prior				93.82 50	97.29 47	84.49 63		96.88 43					98.87 65			98.11 42

新几何2							93.11 204

旧先验1					96.79 58	81.81 126		95.67 123			96.81 31	86.69 23			97.66 55	96.97 87

原ACMM2							92.94 212

test222					96.55 64	81.70 127	92.22 233	95.01 172	68.36 316	90.20 81	96.14 62	80.26 83			97.80 53	96.05 113

segment_acmp												87.16 20

testdata1							92.15 235		87.94 70

test12				94.34 39	97.13 52	86.15 39		96.29 81		91.04 74		85.08 40	99.01 53		98.13 45	97.86 58

plane_prior7					94.70 131	82.74 110

plane_prior6					94.52 137	82.75 108						74.23 157

plane_prior4											94.86 95

plane_prior3						82.75 108			90.26 25	86.91 127

plane_prior2							95.85 40		90.81 18

plane_prior1					94.59 135

plane_prior						82.73 111	95.21 71		89.66 35						89.88 166

n2								0.00 356

nn								0.00 356

door-mid								85.49 329

test11								96.57 70

door								85.33 330

HQP5-MVS						81.56 128

HQP-NCC					94.17 148		94.39 126		88.81 50	85.43 170

ACMP_Plane					94.17 148		94.39 126		88.81 50	85.43 170

HQP4-MVS										85.43 170			97.96 127			94.51 174

HQP3-MVS								96.04 99							89.77 168

HQP2-MVS												73.83 166

NP-MVS					94.37 143	82.42 118					93.98 122

ACMMP++_ref															87.47 201

ACMMP++															88.01 198

Test By Simon												80.02 84

High-resolution multi-view benchmark