High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

HSP-MVS		89.28 1	89.76 1	87.85 18	94.28 16	73.46 14	92.90 8	92.73 39	80.27 13	91.35 2	94.16 20	78.35 3	96.77 9	89.59 1	94.22 43	93.33 53

APDe-MVS		89.15 2	89.63 2	87.73 20	94.49 9	71.69 42	93.83 2	93.96 4	75.70 72	91.06 3	96.03 1	76.84 4	97.03 5	89.09 2	95.65 14	94.47 11

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 27	72.04 39	89.80 55	93.50 11	75.17 83	86.34 17	95.29 2	70.86 38	96.00 33	88.78 3	96.04 4	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 3	73.82 6	90.51 39	93.00 26	80.90 10	88.06 10	94.06 24	76.43 5	96.84 7	88.48 4	95.99 5	94.34 15

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 26	72.13 38	91.41 26	92.35 50	74.62 89	88.90 6	93.85 27	75.75 9	96.00 33	87.80 5	94.63 32	95.04 2

ACMMP_Plus		88.05 9	88.08 9	87.94 11	93.70 24	73.05 18	90.86 33	93.59 9	76.27 66	88.14 8	95.09 3	71.06 37	96.67 13	87.67 6	96.37 3	94.09 22

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 49	72.71 24	91.81 23	93.19 20	77.87 32	90.32 4	94.00 25	74.83 11	93.78 112	87.63 7	94.27 41	93.65 42

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 53	72.96 19	93.73 3	93.67 8	80.19 15	88.10 9	94.80 4	73.76 21	97.11 3	87.51 8	95.82 9	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		89.02 3	89.15 3	88.63 1	95.01 2	76.03 1	92.38 14	92.85 34	80.26 14	87.78 12	94.27 16	75.89 8	96.81 8	87.45 9	96.44 1	93.05 63

MPTG		87.53 14	87.41 15	87.90 15	94.18 20	74.25 2	90.23 47	92.02 60	79.45 19	85.88 19	94.80 4	68.07 58	96.21 27	86.69 10	95.34 18	93.23 55

MTAPA		87.23 20	87.00 20	87.90 15	94.18 20	74.25 2	86.58 160	92.02 60	79.45 19	85.88 19	94.80 4	68.07 58	96.21 27	86.69 10	95.34 18	93.23 55

DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 51	69.03 80	89.57 62	93.39 15	77.53 39	89.79 5	94.12 22	78.98 2	96.58 20	85.66 12	95.72 10	94.58 7

MP-MVS		87.71 11	87.64 12	87.93 14	94.36 15	73.88 4	92.71 13	92.65 42	77.57 35	83.84 48	94.40 15	72.24 31	96.28 25	85.65 13	95.30 22	93.62 44

HPM-MVS		87.11 22	86.98 21	87.50 28	93.88 23	72.16 37	92.19 18	93.33 16	76.07 69	83.81 49	93.95 26	69.77 49	96.01 32	85.15 14	94.66 31	94.32 17

train_agg		86.43 31	86.20 31	87.13 33	93.26 34	72.96 19	88.75 84	91.89 69	68.69 196	85.00 28	93.10 39	74.43 14	95.41 48	84.97 15	95.71 11	93.02 64

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 31	72.86 23	88.77 82	91.68 79	68.29 206	84.26 43	92.83 47	72.83 26	95.42 47	84.97 15	95.71 11	93.02 64

test9_res														84.90 17	95.70 13	92.87 69

NCCC		88.06 7	88.01 10	88.24 5	94.41 13	73.62 7	91.22 30	92.83 35	81.50 7	85.79 22	93.47 33	73.02 25	97.00 6	84.90 17	94.94 25	94.10 21

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 8	72.46 32	89.82 53	93.82 6	73.07 124	84.86 35	92.89 45	76.22 6	96.33 23	84.89 19	95.13 23	94.40 12

DeepC-MVS	79.81 2	87.08 24	86.88 24	87.69 24	91.16 63	72.32 36	90.31 45	93.94 5	77.12 44	82.82 60	94.23 18	72.13 32	97.09 4	84.83 20	95.37 17	93.65 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		86.37 35	85.81 39	88.02 8	90.13 76	72.39 33	89.66 60	92.75 38	81.64 6	82.66 64	92.04 54	64.44 87	97.35 1	84.76 21	94.25 42	94.33 16

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 42	71.94 40	88.59 89	91.78 75	68.96 193	84.41 40	93.18 38	74.94 10	94.93 65	84.75 22	95.33 20	93.01 66

HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 6	73.54 11	93.04 5	93.24 17	76.78 52	84.91 30	94.44 12	70.78 39	96.61 16	84.53 23	94.89 27	93.66 37

ACMMPR		87.44 15	87.23 17	88.08 7	94.64 4	73.59 8	93.04 5	93.20 19	76.78 52	84.66 36	94.52 7	68.81 56	96.65 14	84.53 23	94.90 26	94.00 28

Regformer-2		86.63 29	86.53 27	86.95 36	89.33 100	71.24 45	88.43 92	92.05 59	82.50 1	86.88 15	90.09 94	74.45 13	95.61 39	84.38 25	90.63 69	94.01 27

region2R		87.42 17	87.20 18	88.09 6	94.63 5	73.55 9	93.03 7	93.12 22	76.73 55	84.45 39	94.52 7	69.09 54	96.70 12	84.37 26	94.83 29	94.03 25

CANet		86.45 30	86.10 34	87.51 27	90.09 78	70.94 50	89.70 59	92.59 43	81.78 4	81.32 74	91.43 71	70.34 42	97.23 2	84.26 27	93.36 47	94.37 13

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 17	72.39 33	91.86 22	92.83 35	73.01 125	88.58 7	94.52 7	73.36 22	96.49 21	84.26 27	95.01 24	92.70 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		87.11 22	86.92 22	87.68 25	94.20 19	73.86 5	93.98 1	92.82 37	76.62 57	83.68 50	94.46 11	67.93 60	95.95 35	84.20 29	94.39 37	93.23 55

Regformer-1		86.41 33	86.33 28	86.64 41	89.33 100	70.93 51	88.43 92	91.39 89	82.14 3	86.65 16	90.09 94	74.39 16	95.01 64	83.97 30	90.63 69	93.97 29

#test#		87.33 19	87.13 19	87.94 11	94.58 6	73.54 11	92.34 15	93.24 17	75.23 80	84.91 30	94.44 12	70.78 39	96.61 16	83.75 31	94.89 27	93.66 37

test_prior3		86.73 26	86.86 25	86.33 46	92.61 47	69.59 72	88.85 79	92.97 31	75.41 76	84.91 30	93.54 29	74.28 18	95.48 43	83.31 32	95.86 7	93.91 30

test_prior2							88.85 79		75.41 76	84.91 30	93.54 29	74.28 18		83.31 32	95.86 7

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 115	69.67 71	88.29 101	91.29 91	81.73 5	85.36 24	90.01 96	72.62 28	95.35 53	83.28 34	87.57 102	94.03 25

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 68	70.96 48	92.27 17	94.07 3	72.45 136	85.22 26	91.90 58	69.47 51	96.42 22	83.28 34	95.94 6	94.35 14

XVS		87.18 21	86.91 23	88.00 9	94.42 11	73.33 16	92.78 9	92.99 28	79.14 21	83.67 51	94.17 19	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 11	73.33 16	92.78 9	92.99 28	79.14 21	83.67 51	12.47 346	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 45	69.53 74	91.93 21	92.99 28	73.54 111	85.94 18	94.51 10	65.80 79	95.61 39	83.04 38	92.51 54	93.53 48

agg_prior2														82.91 39	95.45 15	92.70 70

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 13	73.55 9	92.74 11	92.22 53	76.87 50	82.81 61	94.25 17	66.44 72	96.24 26	82.88 40	94.28 40	93.38 50

PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 18	73.38 15	90.22 48	93.04 23	75.53 74	83.86 47	94.42 14	67.87 62	96.64 15	82.70 41	94.57 33	93.66 37

ACMMP		85.89 40	85.39 42	87.38 29	93.59 28	72.63 27	92.74 11	93.18 21	76.78 52	80.73 83	93.82 28	64.33 88	96.29 24	82.67 42	90.69 68	93.23 55
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
Regformer-3		85.23 49	85.07 47	85.70 56	88.95 115	69.01 82	88.29 101	89.91 136	80.95 9	85.01 27	90.01 96	72.45 29	94.19 90	82.50 43	87.57 102	93.90 32

abl_6		85.23 49	84.95 49	86.07 52	92.23 52	70.48 58	90.80 35	92.08 58	73.51 112	85.26 25	94.16 20	62.75 114	95.92 36	82.46 44	91.30 63	91.81 97

TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 61	72.50 30	89.07 74	87.28 204	76.41 59	85.80 21	90.22 92	74.15 20	95.37 52	81.82 45	91.88 56	92.65 73

alignmvs		85.48 44	85.32 44	85.96 54	89.51 95	69.47 76	89.74 57	92.47 44	76.17 67	87.73 13	91.46 70	70.32 43	93.78 112	81.51 46	88.95 84	94.63 6

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 84	69.44 78	90.45 43	93.00 26	76.70 56	88.01 11	91.23 73	73.28 23	93.91 103	81.50 47	88.80 87	94.77 5

MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 59	72.70 25	85.98 175	90.33 118	76.11 68	82.08 67	91.61 65	71.36 36	94.17 92	81.02 48	92.58 53	92.08 90

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 25	70.58 57	92.15 19	91.62 80	73.89 100	82.67 63	94.09 23	62.60 121	95.54 42	80.93 49	92.93 49	93.57 45

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 33	70.86 53	92.09 20	90.38 113	68.75 195	79.57 88	92.83 47	60.60 157	93.04 150	80.92 50	91.56 60	90.86 118

DeepC-MVS_fast	79.65 3	86.91 25	86.62 26	87.76 19	93.52 29	72.37 35	91.26 27	93.04 23	76.62 57	84.22 44	93.36 35	71.44 35	96.76 10	80.82 51	95.33 20	94.16 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		83.88 54	83.53 54	84.96 69	86.77 180	69.28 79	90.46 42	92.67 40	74.79 87	82.95 57	91.33 72	72.70 27	93.09 146	80.79 52	79.28 203	92.50 76

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 140	67.85 109	87.66 116	89.73 140	80.05 17	82.95 57	89.59 103	70.74 41	94.82 72	80.66 53	84.72 133	93.28 54

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 56	70.24 59	90.71 36	92.86 33	77.46 41	84.22 44	92.81 50	67.16 68	92.94 152	80.36 54	94.35 39	90.16 148

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 120	71.58 43	85.15 201	86.16 217	74.69 88	80.47 84	91.04 77	62.29 129	90.55 222	80.33 55	90.08 75	90.20 147

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 131	67.93 108	85.52 196	93.44 13	78.70 28	83.63 53	89.03 117	74.57 12	95.71 38	80.26 56	94.04 44	93.66 37
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
EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 147	67.53 113	87.44 127	89.66 141	79.74 18	82.23 66	89.41 112	70.24 44	94.74 74	79.95 57	83.92 139	92.99 67

CSCG		86.41 33	86.19 32	87.07 35	92.91 41	72.48 31	90.81 34	93.56 10	73.95 97	83.16 56	91.07 76	75.94 7	95.19 55	79.94 58	94.38 38	93.55 46

OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 123	70.95 49	89.13 73	91.52 84	77.55 38	80.96 81	91.75 60	60.71 153	94.50 80	79.67 59	86.51 118	89.97 166

CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 30	71.08 46	88.58 90	92.42 48	68.32 205	84.61 37	93.48 31	72.32 30	96.15 30	79.00 60	95.43 16	94.28 18

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 167	70.21 60	90.50 40	90.38 113	68.55 198	81.32 74	89.47 106	61.68 135	93.46 128	78.98 61	90.26 72	92.05 91

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 229	65.37 150	90.50 40	90.38 113	68.55 198	76.19 155	88.70 121	56.44 184	93.46 128	78.98 61	80.14 192	90.97 115

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 79	68.71 92	91.25 28	92.44 45	79.12 23	78.92 95	91.00 80	60.42 159	95.38 50	78.71 63	86.32 120	91.33 106

plane_prior5								92.44 45					95.38 50	78.71 63	86.32 120	91.33 106

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 108	68.76 88	90.22 48	91.94 67	75.37 78	76.64 144	91.51 67	54.29 200	94.91 67	78.44 65	83.78 140	89.83 170

LGP-MVS_train				84.50 79	89.23 108	68.76 88		91.94 67	75.37 78	76.64 144	91.51 67	54.29 200	94.91 67	78.44 65	83.78 140	89.83 170

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 167	70.21 60	85.55 192	86.41 212	62.85 256	81.32 74	88.61 125	61.68 135	92.24 173	78.41 67	90.26 72	91.83 95

jason		81.39 91	80.29 95	84.70 76	86.63 181	69.90 67	85.95 176	86.77 208	63.24 250	81.07 80	89.47 106	61.08 149	92.15 174	78.33 68	90.07 76	92.05 91
jason: jason.
xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 167	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 167	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 167	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 136	67.45 114	88.89 77	89.15 157	75.50 75	82.27 65	88.28 135	69.61 50	94.45 81	77.81 72	87.84 100	93.84 34

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 182	67.27 119	89.27 66	91.51 85	71.75 146	79.37 90	90.22 92	63.15 102	94.27 85	77.69 73	82.36 166	91.49 104

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 97	68.69 95	89.97 51	91.50 87	74.46 90	75.04 184	90.41 88	53.82 205	94.54 77	77.56 74	82.91 158	89.86 169
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS														77.47 75

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 100	66.98 123	89.17 68	92.19 55	76.41 59	77.23 135	90.23 91	60.17 162	95.11 58	77.47 75	85.99 124	91.03 112

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 177	63.21 203	86.11 173	92.00 63	74.31 92	82.87 59	89.44 111	70.03 45	93.21 137	77.39 77	88.50 96	93.81 35

3Dnovator+	77.84 4	85.48 44	84.47 52	88.51 2	91.08 64	73.49 13	93.18 4	93.78 7	80.79 11	76.66 143	93.37 34	60.40 161	96.75 11	77.20 78	93.73 46	95.29 1

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 289	67.08 121	87.24 135	89.53 144	65.66 231	75.16 180	87.19 165	52.52 210	92.25 172	77.17 79	79.34 202	89.61 177

test_normal		79.81 130	78.45 137	83.89 103	82.70 261	65.40 147	85.82 182	89.48 146	69.39 179	70.12 240	85.66 217	57.15 181	93.71 121	77.08 80	88.62 91	92.56 75

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 65	66.40 130	88.91 76	88.11 187	77.57 35	84.39 42	93.29 36	52.19 217	93.91 103	77.05 81	88.70 89	94.57 9

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 192	68.78 87	83.54 235	90.50 110	70.66 163	76.71 142	91.66 61	60.69 154	91.26 205	76.94 82	81.58 173	91.83 95

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 257	65.32 151	86.12 172	89.55 143	69.64 178	70.55 231	85.82 214	57.24 179	93.81 110	76.85 83	88.55 93	92.41 79

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 205	66.57 129	89.25 67	90.16 126	69.20 185	75.46 169	89.49 105	45.75 279	93.13 144	76.84 84	80.80 181	90.11 151

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 204	66.37 131	89.17 68	90.19 125	69.38 181	75.40 172	89.46 108	44.17 285	93.15 142	76.78 85	80.70 183	90.14 149

testing_2		75.73 214	73.34 222	82.89 140	77.37 309	65.22 154	84.10 227	90.54 109	69.09 187	60.46 305	81.15 277	40.48 303	92.84 157	76.36 86	80.54 187	90.60 130

v2v482		80.23 120	79.29 121	83.05 128	83.62 237	64.14 183	87.04 144	89.97 133	73.61 108	78.18 117	87.22 163	61.10 148	93.82 109	76.11 87	76.78 233	91.18 110

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 132	67.73 112	85.81 183	92.35 50	75.78 70	78.33 109	86.58 189	64.01 91	94.35 82	76.05 88	87.48 107	90.79 119
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		83.72 57	82.92 63	86.14 51	84.22 212	69.48 75	91.05 32	85.27 224	81.30 8	76.83 140	91.65 62	66.09 75	95.56 41	76.00 89	93.85 45	93.38 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test4		77.83 172	75.90 189	83.62 106	80.24 291	65.25 153	85.27 198	90.67 103	69.03 191	66.48 281	83.75 245	43.07 290	93.00 151	75.93 90	88.66 90	92.62 74

v6		80.40 113	79.54 108	82.98 132	84.09 222	64.50 176	87.57 119	90.22 122	73.25 117	78.47 103	86.63 186	62.84 109	93.86 106	75.73 91	77.02 222	90.58 133

v1neww		80.40 113	79.54 108	82.98 132	84.10 220	64.51 172	87.57 119	90.22 122	73.25 117	78.47 103	86.65 184	62.83 110	93.86 106	75.72 92	77.02 222	90.58 133

v7new		80.40 113	79.54 108	82.98 132	84.10 220	64.51 172	87.57 119	90.22 122	73.25 117	78.47 103	86.65 184	62.83 110	93.86 106	75.72 92	77.02 222	90.58 133

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 191	69.02 81	83.03 243	90.39 112	71.09 156	77.63 127	91.49 69	54.62 199	91.35 203	75.71 94	83.47 148	91.54 101

V42		79.38 141	78.24 145	82.83 144	81.10 283	65.50 146	85.55 192	89.82 137	71.57 151	78.21 115	86.12 204	60.66 155	93.18 141	75.64 95	75.46 249	89.81 172

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 95	68.21 104	84.28 223	90.09 128	70.79 159	81.26 78	85.62 219	63.15 102	94.29 83	75.62 96	88.87 86	88.59 210

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 111	68.03 107	84.46 216	90.02 132	70.67 162	81.30 77	86.53 192	63.17 101	94.19 90	75.60 97	88.54 94	88.57 212

v1		80.19 122	79.31 118	82.85 141	83.83 234	64.12 185	87.14 137	90.07 131	73.13 120	78.27 111	86.38 199	62.72 116	93.75 116	75.41 98	76.82 231	90.68 124

v1141		80.19 122	79.31 118	82.85 141	83.84 232	64.12 185	87.14 137	90.08 129	73.13 120	78.27 111	86.39 195	62.67 119	93.75 116	75.40 99	76.83 230	90.68 124

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 232	64.11 187	87.13 140	90.08 129	73.13 120	78.27 111	86.39 195	62.69 117	93.75 116	75.40 99	76.82 231	90.68 124

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 125	67.42 115	87.98 108	90.87 100	74.92 86	79.72 87	91.65 62	62.19 132	93.96 98	75.26 101	86.42 119	93.16 60

v7		80.24 119	79.26 122	83.15 122	84.07 226	64.94 161	87.56 122	90.67 103	72.26 141	78.28 110	86.51 193	61.45 140	94.03 97	75.14 102	77.41 216	90.49 138

v1144		80.03 126	79.03 126	83.01 130	83.78 235	64.51 172	87.11 142	90.57 108	71.96 145	78.08 120	86.20 203	61.41 141	93.94 100	74.93 103	77.23 218	90.60 130

MVSTER		79.01 147	77.88 150	82.38 157	83.07 251	64.80 164	84.08 228	88.95 169	69.01 192	78.69 97	87.17 166	54.70 197	92.43 165	74.69 104	80.57 185	89.89 168

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 70	69.76 69	88.74 86	91.70 78	69.39 179	78.96 94	88.46 130	65.47 80	94.87 71	74.42 105	88.57 92	90.24 146

v8		79.97 128	79.02 127	82.80 146	84.09 222	64.50 176	87.96 109	90.29 121	74.13 96	75.24 179	86.81 173	62.88 107	93.89 105	74.39 106	75.40 250	90.00 159

v144192		79.47 138	78.37 141	82.78 149	83.35 242	63.96 189	86.96 146	90.36 116	69.99 171	77.50 128	85.67 216	60.66 155	93.77 114	74.27 107	76.58 234	90.62 128

ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 105	68.37 99	89.99 50	91.60 81	70.28 168	77.25 133	89.66 101	53.37 208	93.53 126	74.24 108	82.85 159	88.85 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 161		58.10 290	87.04 14			88.98 251	74.07 109

v1192		79.59 134	78.43 140	83.07 127	83.55 239	64.52 170	86.93 148	90.58 107	70.83 158	77.78 124	85.90 210	59.15 165	93.94 100	73.96 110	77.19 220	90.76 120

v10		79.74 132	78.67 130	82.97 136	84.06 227	64.95 160	87.88 114	90.62 106	73.11 123	75.11 182	86.56 190	61.46 139	94.05 96	73.68 111	75.55 247	89.90 167

v52		77.94 170	76.37 175	82.67 151	79.39 301	65.52 143	86.43 163	89.94 134	72.28 139	72.15 216	84.94 233	55.70 188	93.44 130	73.64 112	72.84 273	89.06 185

V4		77.95 168	76.37 175	82.67 151	79.40 300	65.52 143	86.43 163	89.94 134	72.28 139	72.14 217	84.95 232	55.72 187	93.44 130	73.64 112	72.86 272	89.05 189

v1921920		79.22 143	78.03 147	82.80 146	83.30 245	63.94 190	86.80 152	90.33 118	69.91 172	77.48 129	85.53 221	58.44 169	93.75 116	73.60 114	76.85 228	90.71 123

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 199	68.74 90	88.77 82	88.10 189	74.99 84	74.97 185	83.49 249	57.27 177	93.36 133	73.53 115	80.88 179	91.18 110

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 199	69.75 70	89.28 65	88.10 189	74.99 84	76.44 149	86.72 176	57.27 177	94.26 88	73.53 115	83.18 156	91.87 94

VDDNet		81.52 88	80.67 89	84.05 94	90.44 72	64.13 184	89.73 58	85.91 220	71.11 155	83.18 55	93.48 31	50.54 249	93.49 127	73.40 117	88.25 98	94.54 10

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 192	63.17 206	87.36 128	88.65 180	76.37 63	75.88 160	88.44 131	53.51 207	93.07 147	73.30 118	89.74 79	92.25 84

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 146	72.94 22	90.64 37	92.14 57	77.21 42	75.47 168	92.83 47	58.56 168	94.72 75	73.24 119	92.71 52	92.13 89

v1240		78.99 148	77.78 153	82.64 153	83.21 246	63.54 193	86.62 159	90.30 120	69.74 177	77.33 131	85.68 215	57.04 182	93.76 115	73.13 120	76.92 225	90.62 128

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 44	62.28 217	88.17 105	89.50 145	75.22 81	81.49 73	92.74 51	66.75 69	95.11 58	72.85 121	91.58 59	92.45 77

EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 76	64.47 178	92.32 16	90.73 102	74.45 91	79.35 91	91.10 74	69.05 55	95.12 57	72.78 122	87.22 109	94.13 20

v18		77.67 177	76.35 179	81.64 176	84.09 222	64.47 178	87.27 133	89.01 162	72.59 135	69.39 251	82.04 264	62.85 108	91.80 182	72.72 123	67.20 298	88.63 204

v16		77.69 174	76.36 178	81.68 174	84.15 217	64.63 169	87.33 130	88.99 164	72.69 134	69.31 254	82.08 262	62.80 113	91.79 183	72.70 124	67.23 297	88.63 204

v17		77.68 175	76.35 179	81.69 173	84.15 217	64.65 167	87.33 130	88.99 164	72.70 133	69.25 255	82.07 263	62.82 112	91.79 183	72.69 125	67.15 299	88.63 204

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 187	63.20 204	86.79 153	89.34 149	74.19 93	75.45 170	86.72 176	66.62 70	92.39 167	72.58 126	76.86 227	90.75 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 209	63.19 205	86.41 165	88.95 169	74.18 94	78.69 97	87.54 154	66.62 70	92.43 165	72.57 127	80.57 185	90.74 122

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 75	68.74 90	90.30 46	90.13 127	76.33 65	80.87 82	92.89 45	61.00 150	94.20 89	72.45 128	90.97 65	93.35 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v15		77.51 182	76.12 182	81.66 175	84.09 222	64.65 167	87.14 137	88.96 168	72.76 131	68.90 256	81.91 271	62.74 115	91.73 187	72.32 129	66.29 304	88.61 207

V14		77.52 180	76.12 182	81.70 172	84.15 217	64.77 165	87.21 136	88.95 169	72.80 130	68.79 257	81.94 270	62.69 117	91.72 189	72.31 130	66.27 305	88.60 208

V9		77.52 180	76.11 185	81.73 171	84.19 216	64.89 162	87.26 134	88.94 172	72.87 129	68.65 260	81.96 269	62.65 120	91.72 189	72.27 131	66.24 306	88.60 208

v12		77.51 182	76.09 186	81.76 170	84.22 212	64.99 159	87.30 132	88.93 173	72.92 126	68.48 264	81.97 267	62.54 124	91.70 192	72.24 132	66.21 308	88.58 211

v13		77.50 184	76.07 187	81.77 168	84.23 211	65.07 158	87.34 129	88.91 174	72.92 126	68.35 265	81.97 267	62.53 125	91.69 193	72.20 133	66.22 307	88.56 213

LFMVS		81.82 83	81.23 82	83.57 109	91.89 57	63.43 198	89.84 52	81.85 266	77.04 47	83.21 54	93.10 39	52.26 216	93.43 132	71.98 134	89.95 77	93.85 33

v11		77.45 185	76.06 188	81.59 179	84.22 212	64.52 170	87.11 142	89.02 160	72.76 131	68.76 258	81.90 272	62.09 133	91.71 191	71.98 134	66.73 300	88.56 213

v148		78.72 151	77.80 152	81.47 181	82.73 260	61.96 220	86.30 169	88.08 191	73.26 116	76.18 156	85.47 223	62.46 127	92.36 169	71.92 136	73.82 267	90.09 153

diffmvs		79.51 135	78.59 133	82.25 159	83.31 244	62.66 212	84.17 224	88.11 187	67.64 209	76.09 159	87.47 156	64.01 91	91.15 208	71.71 137	84.82 132	92.94 68

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 117	65.40 147	86.16 171	92.00 63	69.34 182	78.11 118	86.09 205	66.02 77	94.27 85	71.52 138	82.06 167	87.39 236

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 117	65.40 147	84.43 218	92.00 63	67.62 211	78.11 118	85.05 231	66.02 77	94.27 85	71.52 138	89.50 80	89.01 192

UA-Net		85.08 52	84.96 48	85.45 57	92.07 54	68.07 106	89.78 56	90.86 101	82.48 2	84.60 38	93.20 37	69.35 52	95.22 54	71.39 140	90.88 67	93.07 62

VNet		82.21 76	82.41 67	81.62 177	90.82 69	60.93 224	84.47 214	89.78 138	76.36 64	84.07 46	91.88 59	64.71 86	90.26 224	70.68 141	88.89 85	93.66 37

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 208	57.97 249	82.59 244	87.62 198	67.40 216	76.17 158	88.56 128	68.47 57	89.59 234	70.65 142	86.05 123	93.47 49

VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 142	60.80 227	86.86 150	91.58 82	75.67 73	80.24 85	89.45 110	63.34 96	90.25 225	70.51 143	79.22 204	91.23 109

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 50	66.37 131	87.93 112	91.80 73	73.82 105	77.32 132	90.66 85	67.90 61	94.90 69	70.37 144	89.48 81	93.19 59

UniMVSNet_NR-MVSNet		81.88 81	81.54 79	82.92 137	88.46 133	63.46 196	87.13 140	92.37 49	80.19 15	78.38 107	89.14 114	71.66 34	93.05 148	70.05 145	76.46 236	92.25 84

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 155	63.46 196	87.02 145	91.87 71	79.01 26	78.38 107	89.07 115	65.02 84	93.05 148	70.05 145	76.46 236	92.20 86

XVG-ACMP-BASELINE		76.11 210	74.27 215	81.62 177	83.20 247	64.67 166	83.60 234	89.75 139	69.75 175	71.85 220	87.09 169	32.78 322	92.11 175	69.99 147	80.43 188	88.09 222

FIs		82.07 78	82.42 66	81.04 190	88.80 122	58.34 243	88.26 103	93.49 12	76.93 49	78.47 103	91.04 77	69.92 47	92.34 170	69.87 148	84.97 129	92.44 78

114514_t		80.68 106	79.51 111	84.20 89	94.09 22	67.27 119	89.64 61	91.11 96	58.75 288	74.08 191	90.72 84	58.10 171	95.04 63	69.70 149	89.42 82	90.30 145

Patchmatch-RL test		70.24 265	67.78 274	77.61 248	77.43 308	59.57 234	71.16 307	70.33 328	62.94 255	68.65 260	72.77 318	50.62 244	85.49 281	69.58 150	66.58 302	87.77 229

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 136	64.41 180	87.60 117	93.02 25	78.42 31	78.56 100	88.16 137	69.78 48	93.26 136	69.58 150	76.49 235	91.60 99

semantic-postprocess				80.11 204	82.69 262	64.85 163		83.47 242	69.16 186	70.49 234	84.15 241	50.83 243	88.15 262	69.23 152	72.14 278	87.34 238

v7n		78.97 149	77.58 157	83.14 123	83.45 241	65.51 145	88.32 99	91.21 93	73.69 107	72.41 211	86.32 200	57.93 172	93.81 110	69.18 153	75.65 245	90.11 151

testdata				79.97 206	90.90 67	64.21 182		84.71 228	59.27 284	85.40 23	92.91 44	62.02 134	89.08 248	68.95 154	91.37 62	86.63 256

GA-MVS		76.87 193	75.17 205	81.97 164	82.75 259	62.58 213	81.44 256	86.35 215	72.16 144	74.74 187	82.89 252	46.20 274	92.02 176	68.85 155	81.09 177	91.30 108

v748		77.97 167	76.65 171	81.92 166	82.29 267	63.28 201	87.53 123	90.35 117	73.50 113	70.76 230	85.55 220	58.28 170	92.81 158	68.81 156	72.76 274	89.67 176

UGNet		80.83 99	79.59 107	84.54 78	88.04 143	68.09 105	89.42 63	88.16 186	76.95 48	76.22 154	89.46 108	49.30 259	93.94 100	68.48 157	90.31 71	91.60 99
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
FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 135	55.97 280	87.95 110	93.42 14	77.10 45	77.38 130	90.98 82	69.96 46	91.79 183	68.46 158	84.50 134	92.33 80

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 10	70.92 52	88.79 81	92.20 54	70.53 164	79.17 92	91.03 79	64.12 90	96.03 31	68.39 159	90.14 74	91.50 103

IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 82	63.30 200	91.59 24	88.46 184	79.04 25	79.49 89	92.16 52	65.10 83	94.28 84	67.71 160	91.86 57	94.95 3

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 250	66.96 125	86.94 147	87.45 203	72.45 136	71.49 225	84.17 240	54.79 196	91.58 200	67.61 161	80.31 189	89.30 181

PAPR		81.66 86	80.89 87	83.99 99	90.27 74	64.00 188	86.76 156	91.77 77	68.84 194	77.13 139	89.50 104	67.63 63	94.88 70	67.55 162	88.52 95	93.09 61

cascas		76.72 195	74.64 208	82.99 131	85.78 189	65.88 139	82.33 246	89.21 156	60.85 272	72.74 201	81.02 279	47.28 268	93.75 116	67.48 163	85.02 128	89.34 180

1314		76.53 197	75.30 200	80.21 203	83.93 230	62.32 216	84.66 208	88.81 175	60.23 276	70.16 239	84.07 242	55.30 191	90.73 220	67.37 164	83.21 155	87.59 233

无先验							87.48 125	88.98 166	60.00 278				94.12 93	67.28 165		88.97 195

1121		80.84 97	79.77 102	84.05 94	93.11 38	70.78 54	84.66 208	85.42 223	57.37 298	81.76 72	92.02 55	63.41 95	94.12 93	67.28 165	92.93 49	87.26 241

原ACMM1				84.35 85	93.01 40	68.79 86		92.44 45	63.96 248	81.09 79	91.57 66	66.06 76	95.45 45	67.19 167	94.82 30	88.81 198

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 248	85.79 188	56.21 278	86.78 154	85.76 221	73.60 109	77.93 122	87.57 152	65.02 84	88.99 250	67.14 168	75.33 251	87.63 231

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 148	62.33 215	87.74 115	91.33 90	80.55 12	77.99 121	89.86 98	65.23 82	92.62 160	67.05 169	75.24 254	92.30 82

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 117	64.51 172	85.53 194	89.39 148	70.79 159	78.49 102	85.06 230	67.54 64	93.58 123	67.03 170	86.58 116	92.32 81

VPNet		78.69 152	78.66 131	78.76 231	88.31 138	55.72 286	84.45 217	86.63 210	76.79 51	78.26 114	90.55 87	59.30 164	89.70 233	66.63 171	77.05 221	90.88 117

PM-MVS		66.41 285	64.14 285	73.20 287	73.92 320	56.45 272	78.97 275	64.96 342	63.88 249	64.72 291	80.24 285	19.84 338	83.44 291	66.24 172	64.52 312	79.71 314

test-LLR		72.94 247	72.43 230	74.48 279	81.35 279	58.04 247	78.38 279	77.46 301	66.66 218	69.95 244	79.00 294	48.06 265	79.24 305	66.13 173	84.83 130	86.15 265

test-mter		71.41 255	70.39 253	74.48 279	81.35 279	58.04 247	78.38 279	77.46 301	60.32 275	69.95 244	79.00 294	36.08 319	79.24 305	66.13 173	84.83 130	86.15 265

MVS		78.19 161	76.99 165	81.78 167	85.66 190	66.99 122	84.66 208	90.47 111	55.08 308	72.02 219	85.27 226	63.83 93	94.11 95	66.10 175	89.80 78	84.24 287

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 155	63.34 199	86.31 168	91.09 97	79.01 26	72.17 214	89.07 115	67.20 67	92.81 158	66.08 176	75.65 245	92.20 86

CVMVSNet		72.99 246	72.58 228	74.25 282	84.28 209	50.85 314	86.41 165	83.45 243	44.56 329	73.23 197	87.54 154	49.38 257	85.70 279	65.90 177	78.44 208	86.19 264

IterMVS		74.29 224	72.94 225	78.35 239	81.53 275	63.49 195	81.58 254	82.49 254	68.06 207	69.99 243	83.69 247	51.66 233	85.54 280	65.85 178	71.64 281	86.01 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 225	72.42 231	79.80 209	83.76 236	59.59 233	85.92 178	86.64 209	66.39 223	66.96 276	87.58 151	39.46 306	91.60 199	65.76 179	69.27 290	88.22 219

tpmrst		72.39 249	72.13 236	73.18 288	80.54 288	49.91 318	79.91 267	79.08 290	63.11 251	71.69 222	79.95 287	55.32 190	82.77 294	65.66 180	73.89 265	86.87 249

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 62	71.53 44	89.82 53	90.92 99	69.77 174	78.50 101	86.21 202	62.36 128	94.52 79	65.36 181	92.05 55	89.77 174
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
ab-mvs		79.51 135	78.97 128	81.14 188	88.46 133	60.91 225	83.84 230	89.24 155	70.36 166	79.03 93	88.87 119	63.23 100	90.21 226	65.12 182	82.57 164	92.28 83

IB-MVS	68.01 15	75.85 213	73.36 221	83.31 116	84.76 203	66.03 134	83.38 236	85.06 226	70.21 170	69.40 250	81.05 278	45.76 278	94.66 76	65.10 183	75.49 248	89.25 182
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
PatchFormer-LS_test		74.50 222	73.05 224	78.86 229	82.95 255	59.55 236	81.65 253	82.30 257	67.44 215	71.62 223	78.15 298	52.34 214	88.92 255	65.05 184	75.90 242	88.12 221

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 123	58.35 242	85.06 202	88.61 182	78.56 29	77.65 126	88.34 133	63.81 94	90.66 221	64.98 185	77.22 219	91.80 98

CostFormer		75.24 220	73.90 218	79.27 219	82.65 263	58.27 244	80.80 257	82.73 253	61.57 267	75.33 177	83.13 251	55.52 189	91.07 215	64.98 185	78.34 210	88.45 216

API-MVS		81.99 80	81.23 82	84.26 88	90.94 66	70.18 65	91.10 31	89.32 150	71.51 152	78.66 99	88.28 135	65.26 81	95.10 61	64.74 187	91.23 64	87.51 234

新几何1				83.42 112	93.13 36	70.71 55		85.48 222	57.43 297	81.80 71	91.98 56	63.28 97	92.27 171	64.60 188	92.99 48	87.27 240

pm-mvs1		77.25 189	76.68 170	78.93 228	84.22 212	58.62 240	86.41 165	88.36 185	71.37 153	73.31 195	88.01 141	61.22 146	89.15 247	64.24 189	73.01 271	89.03 191

TESTMET0.1,1		69.89 268	69.00 259	72.55 289	79.27 303	56.85 265	78.38 279	74.71 317	57.64 295	68.09 266	77.19 305	37.75 313	76.70 316	63.92 190	84.09 138	84.10 290

QAPM		80.88 95	79.50 112	85.03 67	88.01 145	68.97 84	91.59 24	92.00 63	66.63 221	75.15 181	92.16 52	57.70 173	95.45 45	63.52 191	88.76 88	90.66 127

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 251	87.20 173	51.60 308	80.06 264	80.46 278	75.20 82	67.69 269	86.72 176	62.48 126	88.98 251	63.44 192	89.25 83	91.51 102

gm-plane-assit					81.40 277	53.83 296			62.72 259		80.94 281		92.39 167	63.40 193

DWT-MVSNet_test		73.70 229	71.86 238	79.21 221	82.91 256	58.94 238	82.34 245	82.17 258	65.21 233	71.05 229	78.31 296	44.21 284	90.17 227	63.29 194	77.28 217	88.53 215

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 39	68.91 85	89.36 64	88.97 167	69.27 183	75.70 167	89.69 100	57.20 180	95.77 37	63.06 195	88.41 97	87.50 235

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 162	63.01 207	88.39 96	89.28 151	71.63 148	75.34 174	87.28 159	54.80 193	91.11 209	62.72 196	79.57 198	90.09 153

test1		78.40 155	77.40 159	81.40 183	87.60 162	63.01 207	88.39 96	89.28 151	71.63 148	75.34 174	87.28 159	54.80 193	91.11 209	62.72 196	79.57 198	90.09 153

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 178	62.36 214	86.52 162	88.77 176	71.13 154	75.34 174	86.66 183	54.07 203	91.10 212	62.72 196	79.57 198	89.45 179

CMPMVS	51.72 21	70.19 266	68.16 266	76.28 265	73.15 325	57.55 257	79.47 270	83.92 235	48.02 327	56.48 320	84.81 235	43.13 289	86.42 275	62.67 199	81.81 171	84.89 281
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 162	62.89 211	87.47 126	89.02 160	71.63 148	75.29 178	87.28 159	54.80 193	91.10 212	62.38 200	79.38 201	89.61 177

testdata2													91.01 216	62.37 201

CP-MVSNet		78.22 158	78.34 142	77.84 244	87.83 153	54.54 291	87.94 111	91.17 95	77.65 33	73.48 194	88.49 129	62.24 131	88.43 259	62.19 202	74.07 262	90.55 136

XXY-MVS		75.41 218	75.56 192	74.96 275	83.59 238	57.82 253	80.59 261	83.87 236	66.54 222	74.93 186	88.31 134	63.24 99	80.09 303	62.16 203	76.85 228	86.97 248

pmmvs6		74.69 221	73.39 220	78.61 233	81.38 278	57.48 258	86.64 158	87.95 193	64.99 237	70.18 237	86.61 187	50.43 250	89.52 235	62.12 204	70.18 288	88.83 197

1112_ss		77.40 188	76.43 173	80.32 200	89.11 114	60.41 230	83.65 232	87.72 197	62.13 264	73.05 199	86.72 176	62.58 123	89.97 228	62.11 205	80.80 181	90.59 132

PS-CasMVS		78.01 166	78.09 146	77.77 246	87.71 160	54.39 293	88.02 107	91.22 92	77.50 40	73.26 196	88.64 124	60.73 152	88.41 260	61.88 206	73.88 266	90.53 137

CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 162	68.23 103	84.40 220	86.20 216	67.49 214	76.36 150	86.54 191	61.54 138	90.79 219	61.86 207	87.33 108	90.49 138
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 196	69.91 66	90.57 38	90.97 98	66.70 217	72.17 214	91.91 57	54.70 197	93.96 98	61.81 208	90.95 66	88.41 218

K. test v3		71.19 256	68.51 262	79.21 221	83.04 253	57.78 254	84.35 221	76.91 305	72.90 128	62.99 300	82.86 253	39.27 307	91.09 214	61.65 209	52.66 331	88.75 200

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 81	68.58 97	78.70 278	87.50 201	56.38 303	75.80 162	86.84 172	58.67 167	91.40 202	61.58 210	85.75 127	90.34 144

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 160	68.99 83	83.65 232	91.46 88	63.00 253	77.77 125	90.28 89	66.10 74	95.09 62	61.40 211	88.22 99	90.94 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 166	60.21 231	83.37 241	87.78 196	66.11 225	75.37 173	87.06 171	63.27 98	90.48 223	61.38 212	82.43 165	90.40 143

HyFIR lowres test		77.53 179	75.40 197	83.94 102	89.59 91	66.62 127	80.36 262	88.64 181	56.29 304	76.45 146	85.17 227	57.64 174	93.28 135	61.34 213	83.10 157	91.91 93

PMMVS		69.34 270	68.67 261	71.35 296	75.67 316	62.03 219	75.17 296	73.46 322	50.00 325	68.68 259	79.05 292	52.07 221	78.13 310	61.16 214	82.77 160	73.90 327

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 179	63.01 207	88.39 96	89.28 151	70.49 165	74.39 190	87.28 159	49.06 262	91.11 209	60.91 215	78.52 206	90.09 153

sss		73.60 231	73.64 219	73.51 286	82.80 258	55.01 288	76.12 290	81.69 267	62.47 261	74.68 188	85.85 213	57.32 176	78.11 311	60.86 216	80.93 178	87.39 236

Test_1112_low_res		76.40 202	75.44 195	79.27 219	89.28 106	58.09 245	81.69 252	87.07 206	59.53 282	72.48 206	86.67 182	61.30 143	89.33 239	60.81 217	80.15 191	90.41 142

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 110	65.91 138	86.07 174	88.52 183	72.18 142	75.42 171	87.69 149	61.15 147	93.54 125	60.38 218	86.83 113	86.70 254

WTY-MVS		75.65 215	75.68 191	75.57 271	86.40 183	56.82 266	77.92 284	82.40 255	65.10 235	76.18 156	87.72 147	63.13 105	80.90 299	60.31 219	81.96 168	89.00 194

pmmvs4		74.03 227	71.91 237	80.39 197	81.96 270	68.32 100	81.45 255	82.14 259	59.32 283	69.87 246	85.13 228	52.40 213	88.13 263	60.21 220	74.74 258	84.73 284

PEN-MVS		77.73 173	77.69 156	77.84 244	87.07 175	53.91 295	87.91 113	91.18 94	77.56 37	73.14 198	88.82 120	61.23 145	89.17 246	59.95 221	72.37 275	90.43 141

CR-MVSNet		73.37 239	71.27 245	79.67 212	81.32 281	65.19 155	75.92 292	80.30 280	59.92 279	72.73 202	81.19 275	52.50 211	86.69 271	59.84 222	77.71 212	87.11 246

lessismore_v0				78.97 227	81.01 284	57.15 261		65.99 339		61.16 303	82.82 254	39.12 308	91.34 204	59.67 223	46.92 335	88.43 217

CNLPA		78.08 163	76.79 169	81.97 164	90.40 73	71.07 47	87.59 118	84.55 230	66.03 228	72.38 212	89.64 102	57.56 175	86.04 277	59.61 224	83.35 153	88.79 199

testpf		56.51 305	57.58 302	53.30 325	71.99 328	41.19 334	46.89 341	69.32 334	58.06 291	52.87 328	69.45 326	27.99 327	72.73 330	59.59 225	62.07 315	45.98 339

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 99	65.93 137	84.95 204	87.15 205	73.56 110	78.19 116	89.79 99	56.67 183	93.36 133	59.53 226	86.74 114	90.13 150

MS-PatchMatch		73.83 228	72.67 227	77.30 253	83.87 231	66.02 135	81.82 249	84.66 229	61.37 270	68.61 262	82.82 254	47.29 267	88.21 261	59.27 227	84.32 137	77.68 319

test_post1							78.90 277				5.43 348	48.81 264	85.44 282	59.25 228

SixPastTwentyTwo		73.37 239	71.26 246	79.70 210	85.08 201	57.89 251	85.57 188	83.56 240	71.03 157	65.66 285	85.88 211	42.10 297	92.57 162	59.11 229	63.34 313	88.65 203

WR-MVS_H		78.51 154	78.49 136	78.56 234	88.02 144	56.38 275	88.43 92	92.67 40	77.14 43	73.89 192	87.55 153	66.25 73	89.24 241	58.92 230	73.55 269	90.06 157

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 58	67.80 110	88.19 104	89.46 147	64.33 243	69.87 246	88.38 132	53.66 206	93.58 123	58.86 231	82.73 161	87.86 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 243	71.46 242	78.54 235	82.50 265	59.85 232	82.18 247	82.84 252	58.96 285	71.15 228	89.41 112	45.48 281	84.77 286	58.82 232	71.83 280	91.02 114

EU-MVSNet		68.53 274	67.61 276	71.31 297	78.51 305	47.01 324	84.47 214	84.27 233	42.27 330	66.44 282	84.79 236	40.44 304	83.76 288	58.76 233	68.54 296	83.17 295

pmmvs-eth3d		70.50 263	67.83 272	78.52 236	77.37 309	66.18 133	81.82 249	81.51 269	58.90 286	63.90 296	80.42 284	42.69 293	86.28 276	58.56 234	65.30 310	83.11 297

TAMVS		78.89 150	77.51 158	83.03 129	87.80 155	67.79 111	84.72 207	85.05 227	67.63 210	76.75 141	87.70 148	62.25 130	90.82 218	58.53 235	87.13 110	90.49 138

ACMH+	68.96 14	76.01 211	74.01 216	82.03 163	88.60 128	65.31 152	88.86 78	87.55 199	70.25 169	67.75 268	87.47 156	41.27 300	93.19 140	58.37 236	75.94 241	87.60 232

tpm		72.37 251	71.71 241	74.35 281	82.19 268	52.00 305	79.22 273	77.29 303	64.56 240	72.95 200	83.68 248	51.35 234	83.26 293	58.33 237	75.80 243	87.81 228

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 121	65.13 157	84.87 205	87.85 195	69.75 175	74.52 189	84.74 237	61.34 142	93.11 145	58.24 238	85.84 126	84.27 286

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 242	88.64 127	51.78 307	86.70 157	79.63 287	74.14 95	75.11 182	90.83 83	61.29 144	89.75 231	58.10 239	91.60 58	92.69 72

MVP-Stereo		76.12 209	74.46 213	81.13 189	85.37 195	69.79 68	84.42 219	87.95 193	65.03 236	67.46 271	85.33 225	53.28 209	91.73 187	58.01 240	83.27 154	81.85 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 274	73.16 324	50.51 316	63.05 332	87.47 202		64.28 293	77.81 302	17.80 341	89.73 232	57.88 241	60.64 320	85.49 273

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 139	63.24 202	84.61 212	86.40 213	67.55 213	77.81 123	86.48 194	54.10 202	93.15 142	57.75 242	82.72 162	87.20 242

F-COLMAP		76.38 203	74.33 214	82.50 155	89.28 106	66.95 126	88.41 95	89.03 159	64.05 245	66.83 277	88.61 125	46.78 271	92.89 153	57.48 243	78.55 205	87.67 230

EG-PatchMatch MVS		74.04 226	71.82 240	80.71 195	84.92 202	67.42 115	85.86 179	88.08 191	66.04 227	64.22 294	83.85 243	35.10 321	92.56 163	57.44 244	80.83 180	82.16 305

PatchmatchNet		73.12 244	71.33 244	78.49 237	83.18 248	60.85 226	79.63 268	78.57 296	64.13 244	71.73 221	79.81 290	51.20 236	85.97 278	57.40 245	76.36 238	88.66 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		76.99 191	76.80 168	77.54 250	86.24 184	53.06 304	87.52 124	90.66 105	77.08 46	72.50 204	88.67 123	60.48 158	89.52 235	57.33 246	70.74 286	90.05 158

UnsupCasMVSNet_eth		67.33 279	65.99 280	71.37 294	73.48 322	51.47 310	75.16 297	85.19 225	65.20 234	60.78 304	80.93 282	42.35 294	77.20 315	57.12 247	53.69 330	85.44 274

pmmvs5		71.55 254	70.20 254	75.61 270	77.83 306	56.39 274	81.74 251	80.89 272	57.76 294	67.46 271	84.49 238	49.26 260	85.32 283	57.08 248	75.29 252	85.11 280

TransMVSNet (Re)		75.39 219	74.56 210	77.86 243	85.50 194	57.10 262	86.78 154	86.09 219	72.17 143	71.53 224	87.34 158	63.01 106	89.31 240	56.84 249	61.83 316	87.17 243

EPMVS		69.02 271	68.16 266	71.59 292	79.61 297	49.80 320	77.40 286	66.93 338	62.82 257	70.01 241	79.05 292	45.79 277	77.86 313	56.58 250	75.26 253	87.13 245

tpm2		73.26 242	71.46 242	78.63 232	83.34 243	56.71 269	80.65 260	80.40 279	56.63 302	73.55 193	82.02 265	51.80 231	91.24 206	56.35 251	78.42 209	87.95 224

LTVRE_ROB	69.57 13	76.25 204	74.54 211	81.41 182	88.60 128	64.38 181	79.24 272	89.12 158	70.76 161	69.79 248	87.86 142	49.09 261	93.20 139	56.21 252	80.16 190	86.65 255
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
ACMH	67.68 16	75.89 212	73.93 217	81.77 168	88.71 126	66.61 128	88.62 88	89.01 162	69.81 173	66.78 278	86.70 181	41.95 299	91.51 201	55.64 253	78.14 211	87.17 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmatch-test1		73.49 232	71.85 239	78.41 238	84.05 228	62.17 218	79.96 266	79.29 289	66.30 224	72.38 212	79.58 291	51.95 223	85.08 284	55.46 254	77.67 214	87.99 223

CHOSEN 280x420		66.51 284	64.71 283	71.90 291	81.45 276	63.52 194	57.98 336	68.95 336	53.57 315	62.59 301	76.70 306	46.22 273	75.29 323	55.25 255	79.68 193	76.88 325

EPNet_dtu		75.46 217	74.86 206	77.23 254	82.57 264	54.60 290	86.89 149	83.09 249	71.64 147	66.25 283	85.86 212	55.99 186	88.04 264	54.92 256	86.55 117	89.05 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	64.34 18	72.08 252	70.87 250	75.69 269	86.21 185	56.44 273	74.37 302	80.73 275	62.06 265	70.17 238	82.23 260	42.86 292	83.31 292	54.77 257	84.45 136	87.32 239

ITE_SJBPF				78.22 240	81.77 272	60.57 228		83.30 244	69.25 184	67.54 270	87.20 164	36.33 318	87.28 269	54.34 258	74.62 259	86.80 251

MDTV_nov1_ep13_2view						37.79 338	75.16 297		55.10 307	66.53 280		49.34 258		53.98 259		87.94 225

tpmp4_e23		73.45 233	71.17 247	80.31 201	83.55 239	59.56 235	81.88 248	82.33 256	57.94 293	70.51 233	81.62 273	51.19 237	91.63 198	53.96 260	77.51 215	89.75 175

gg-mvs-nofinetune		69.95 267	67.96 269	75.94 267	83.07 251	54.51 292	77.23 287	70.29 329	63.11 251	70.32 235	62.33 330	43.62 287	88.69 257	53.88 261	87.76 101	84.62 285

PatchMatch-RL		72.38 250	70.90 249	76.80 257	88.60 128	67.38 117	79.53 269	76.17 307	62.75 258	69.36 252	82.00 266	45.51 280	84.89 285	53.62 262	80.58 184	78.12 317

Patchmtry		70.74 259	69.16 258	75.49 272	80.72 285	54.07 294	74.94 301	80.30 280	58.34 289	70.01 241	81.19 275	52.50 211	86.54 273	53.37 263	71.09 284	85.87 272

USDC		70.33 264	68.37 263	76.21 266	80.60 287	56.23 277	79.19 274	86.49 211	60.89 271	61.29 302	85.47 223	31.78 325	89.47 237	53.37 263	76.21 239	82.94 302

LF4IMVS		64.02 293	62.19 293	69.50 303	70.90 330	53.29 299	76.13 289	77.18 304	52.65 319	58.59 310	80.98 280	23.55 333	76.52 317	53.06 265	66.66 301	78.68 316

PAPM		77.68 175	76.40 174	81.51 180	87.29 172	61.85 221	83.78 231	89.59 142	64.74 238	71.23 226	88.70 121	62.59 122	93.66 122	52.66 266	87.03 112	89.01 192

tpm cat1		70.57 261	68.31 264	77.35 252	82.41 266	57.95 250	78.08 283	80.22 283	52.04 320	68.54 263	77.66 303	52.00 222	87.84 266	51.77 267	72.07 279	86.25 263

MDTV_nov1_ep13			69.97 255		83.18 248	53.48 298	77.10 288	80.18 284	60.45 273	69.33 253	80.44 283	48.89 263	86.90 270	51.60 268	78.51 207

JIA-IIPM		66.32 286	62.82 292	76.82 256	77.09 312	61.72 222	65.34 328	75.38 309	58.04 292	64.51 292	62.32 331	42.05 298	86.51 274	51.45 269	69.22 291	82.21 304

MSDG		73.36 241	70.99 248	80.49 196	84.51 207	65.80 140	80.71 259	86.13 218	65.70 230	65.46 286	83.74 246	44.60 282	90.91 217	51.13 270	76.89 226	84.74 283

PatchT		68.46 275	67.85 271	70.29 300	80.70 286	43.93 328	72.47 305	74.88 313	60.15 277	70.55 231	76.57 307	49.94 255	81.59 297	50.58 271	74.83 257	85.34 275

GG-mvs-BLEND				75.38 273	81.59 274	55.80 285	79.32 271	69.63 331		67.19 274	73.67 317	43.24 288	88.90 256	50.41 272	84.50 134	81.45 308

AllTest		70.96 258	68.09 268	79.58 214	85.15 197	63.62 191	84.58 213	79.83 285	62.31 262	60.32 306	86.73 174	32.02 323	88.96 253	50.28 273	71.57 282	86.15 265

TestCases				79.58 214	85.15 197	63.62 191		79.83 285	62.31 262	60.32 306	86.73 174	32.02 323	88.96 253	50.28 273	71.57 282	86.15 265

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 91	62.99 210	88.16 106	91.51 85	65.77 229	77.14 138	91.09 75	60.91 151	93.21 137	50.26 275	87.05 111	92.17 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 288	62.91 290	71.38 293	75.85 315	56.60 271	69.12 318	74.66 319	57.28 299	54.12 323	77.87 301	45.85 276	74.48 325	49.95 276	61.52 318	83.05 298

MDA-MVSNet_test_wron		65.03 288	62.92 289	71.37 294	75.93 314	56.73 267	69.09 319	74.73 316	57.28 299	54.03 324	77.89 300	45.88 275	74.39 326	49.89 277	61.55 317	82.99 300

tpmvs		71.09 257	69.29 257	76.49 259	82.04 269	56.04 279	78.92 276	81.37 271	64.05 245	67.18 275	78.28 297	49.74 256	89.77 230	49.67 278	72.37 275	83.67 291

UnsupCasMVSNet_bld		63.70 294	61.53 296	70.21 301	73.69 321	51.39 311	72.82 304	81.89 265	55.63 306	57.81 314	71.80 320	38.67 309	78.61 308	49.26 279	52.21 332	80.63 310

dp		66.80 281	65.43 281	70.90 299	79.74 296	48.82 321	75.12 299	74.77 315	59.61 281	64.08 295	77.23 304	42.89 291	80.72 300	48.86 280	66.58 302	83.16 296

FMVSNet5		69.50 269	67.96 269	74.15 283	82.97 254	55.35 287	80.01 265	82.12 260	62.56 260	63.02 298	81.53 274	36.92 316	81.92 296	48.42 281	74.06 263	85.17 279

conf200view11		76.55 196	75.55 193	79.57 216	89.52 93	56.99 263	85.83 180	83.23 245	73.94 98	76.32 151	87.12 167	51.89 224	91.95 177	48.33 282	83.75 142	89.78 173

thres100view900		76.50 198	75.55 193	79.33 218	89.52 93	56.99 263	85.83 180	83.23 245	73.94 98	76.32 151	87.12 167	51.89 224	91.95 177	48.33 282	83.75 142	89.07 183

tfpn200view9		76.42 201	75.37 198	79.55 217	89.13 112	57.65 255	85.17 199	83.60 238	73.41 114	76.45 146	86.39 195	52.12 218	91.95 177	48.33 282	83.75 142	89.07 183

thres400		76.50 198	75.37 198	79.86 207	89.13 112	57.65 255	85.17 199	83.60 238	73.41 114	76.45 146	86.39 195	52.12 218	91.95 177	48.33 282	83.75 142	90.00 159

LCM-MVSNet		54.25 306	49.68 312	67.97 309	53.73 344	45.28 325	66.85 327	80.78 274	35.96 336	39.45 336	62.23 332	8.70 350	78.06 312	48.24 286	51.20 333	80.57 311

RPMNet		71.62 253	68.94 260	79.67 212	81.32 281	65.19 155	75.92 292	78.30 298	57.60 296	72.73 202	76.45 308	52.30 215	86.69 271	48.14 287	77.71 212	87.11 246

thres600view7		76.50 198	75.44 195	79.68 211	89.40 98	57.16 260	85.53 194	83.23 245	73.79 106	76.26 153	87.09 169	51.89 224	91.89 181	48.05 288	83.72 146	90.00 159

TDRefinement		67.49 277	64.34 284	76.92 255	73.47 323	61.07 223	84.86 206	82.98 250	59.77 280	58.30 312	85.13 228	26.06 330	87.89 265	47.92 289	60.59 321	81.81 307

thres200		75.55 216	74.47 212	78.82 230	87.78 158	57.85 252	83.07 242	83.51 241	72.44 138	75.84 161	84.42 239	52.08 220	91.75 186	47.41 290	83.64 147	86.86 250

PVSNet_0	57.27 20	61.67 296	59.27 297	68.85 306	79.61 297	57.44 259	68.01 322	73.44 323	55.93 305	58.54 311	70.41 323	44.58 283	77.55 314	47.01 291	35.91 337	71.55 329

view600		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

view800		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

conf0.05thres1000		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

tfpn		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

DP-MVS		76.78 194	74.57 209	83.42 112	93.29 32	69.46 77	88.55 91	83.70 237	63.98 247	70.20 236	88.89 118	54.01 204	94.80 73	46.66 296	81.88 170	86.01 270

COLMAP_ROB	66.92 17	73.01 245	70.41 252	80.81 193	87.13 174	65.63 142	88.30 100	84.19 234	62.96 254	63.80 297	87.69 149	38.04 312	92.56 163	46.66 296	74.91 256	84.24 287
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 260	69.30 256	74.88 276	84.52 206	56.35 276	75.87 294	79.42 288	64.59 239	67.76 267	82.41 257	41.10 301	81.54 298	46.64 298	81.34 175	86.75 253

LS3D		76.95 192	74.82 207	83.37 115	90.45 71	67.36 118	89.15 72	86.94 207	61.87 266	69.52 249	90.61 86	51.71 232	94.53 78	46.38 299	86.71 115	88.21 220

MDA-MVSNet-bldmvs		66.68 282	63.66 286	75.75 268	79.28 302	60.56 229	73.92 303	78.35 297	64.43 241	50.13 331	79.87 289	44.02 286	83.67 289	46.10 300	56.86 325	83.03 299

new-patchmatchnet		61.73 295	61.73 295	61.70 318	72.74 326	24.50 349	69.16 317	78.03 299	61.40 268	56.72 319	75.53 311	38.42 310	76.48 318	45.95 301	57.67 323	84.13 289

tfpn_ndepth		73.70 229	72.75 226	76.52 258	87.78 158	54.92 289	84.32 222	80.28 282	67.57 212	72.50 204	84.82 234	50.12 252	89.44 238	45.73 302	81.66 172	85.20 276

TinyColmap		67.30 280	64.81 282	74.76 278	81.92 271	56.68 270	80.29 263	81.49 270	60.33 274	56.27 321	83.22 250	24.77 332	87.66 268	45.52 303	69.47 289	79.95 313

pmmvs3		57.79 302	54.26 306	68.37 308	64.02 337	56.72 268	75.12 299	65.17 340	40.20 332	52.93 327	69.86 325	20.36 337	75.48 322	45.45 304	55.25 329	72.90 328

OpenMVS_ROB	64.09 19	70.56 262	68.19 265	77.65 247	80.26 290	59.41 237	85.01 203	82.96 251	58.76 287	65.43 287	82.33 258	37.63 315	91.23 207	45.34 305	76.03 240	82.32 303

test0.0.03 1		68.00 276	67.69 275	68.90 305	77.55 307	47.43 322	75.70 295	72.95 324	66.66 218	66.56 279	82.29 259	48.06 265	75.87 320	44.97 306	74.51 260	83.41 293

thresconf0.02		73.39 235	72.42 231	76.31 260	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 257

tfpn_n400		73.39 235	72.42 231	76.31 260	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 257

tfpnconf		73.39 235	72.42 231	76.31 260	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 257

tfpnview11		73.39 235	72.42 231	76.31 260	87.85 148	53.28 300	83.38 236	79.08 290	68.40 201	72.45 207	86.08 206	50.60 245	89.19 242	44.25 307	79.66 194	86.48 257

tfpn1000		73.44 234	72.49 229	76.29 264	87.81 154	53.69 297	84.05 229	78.81 295	67.99 208	72.09 218	86.27 201	49.95 254	89.04 249	44.09 311	81.38 174	86.15 265

testgi		66.67 283	66.53 279	67.08 310	75.62 317	41.69 333	75.93 291	76.50 306	66.11 225	65.20 290	86.59 188	35.72 320	74.71 324	43.71 312	73.38 270	84.84 282

Anonymous20231206		68.60 272	67.80 273	71.02 298	80.23 292	50.75 315	78.30 282	80.47 277	56.79 301	66.11 284	82.63 256	46.35 272	78.95 307	43.62 313	75.70 244	83.36 294

tfpnnormal		74.39 223	73.16 223	78.08 241	86.10 186	58.05 246	84.65 211	87.53 200	70.32 167	71.22 227	85.63 218	54.97 192	89.86 229	43.03 314	75.02 255	86.32 262

MIMVSNet1		68.58 273	66.78 278	73.98 284	80.07 293	51.82 306	80.77 258	84.37 231	64.40 242	59.75 309	82.16 261	36.47 317	83.63 290	42.73 315	70.33 287	86.48 257

test20.03		67.45 278	66.95 277	68.94 304	75.48 319	44.84 326	77.50 285	77.67 300	66.66 218	63.01 299	83.80 244	47.02 269	78.40 309	42.53 316	68.86 294	83.58 292

ADS-MVSNet2		66.20 287	63.33 287	74.82 277	79.92 294	58.75 239	67.55 324	75.19 311	53.37 316	65.25 288	75.86 309	42.32 295	80.53 301	41.57 317	68.91 292	85.18 277

ADS-MVSNet		64.36 292	62.88 291	68.78 307	79.92 294	47.17 323	67.55 324	71.18 327	53.37 316	65.25 288	75.86 309	42.32 295	73.99 328	41.57 317	68.91 292	85.18 277

Patchmatch-test		64.82 290	63.24 288	69.57 302	79.42 299	49.82 319	63.49 331	69.05 335	51.98 321	59.95 308	80.13 286	50.91 239	70.98 334	40.66 319	73.57 268	87.90 226

MVS-HIRNet		59.14 299	57.67 301	63.57 315	81.65 273	43.50 329	71.73 306	65.06 341	39.59 334	51.43 329	57.73 334	38.34 311	82.58 295	39.53 320	73.95 264	64.62 334

DSMNet-mixed		57.77 303	56.90 303	60.38 319	67.70 335	35.61 339	69.18 316	53.97 345	32.30 340	57.49 316	79.88 288	40.39 305	68.57 338	38.78 321	72.37 275	76.97 322

Anonymous20231211		64.82 290	61.79 294	73.91 285	77.11 311	50.92 313	85.29 197	81.53 268	54.19 310	57.98 313	78.03 299	26.90 328	87.83 267	37.92 322	57.12 324	82.99 300

N_pmnet		52.79 309	53.26 307	51.40 328	78.99 304	7.68 353	69.52 314	3.89 354	51.63 323	57.01 318	74.98 312	40.83 302	65.96 341	37.78 323	64.67 311	80.56 312

no-one		51.08 310	45.79 315	66.95 311	57.92 342	50.49 317	59.63 335	76.04 308	48.04 326	31.85 337	56.10 337	19.12 339	80.08 304	36.89 324	26.52 339	70.29 330

test_0402		72.79 248	70.44 251	79.84 208	88.13 141	65.99 136	85.93 177	84.29 232	65.57 232	67.40 273	85.49 222	46.92 270	92.61 161	35.88 325	74.38 261	80.94 309

new_pmnet		50.91 311	50.29 310	52.78 326	68.58 334	34.94 342	63.71 330	56.63 344	39.73 333	44.95 332	65.47 329	21.93 336	58.48 343	34.98 326	56.62 326	64.92 333

wuykxyi23d		39.76 318	33.18 321	59.51 321	46.98 348	44.01 327	57.70 337	67.74 337	24.13 342	13.98 348	34.33 342	1.27 355	71.33 333	34.23 327	18.23 342	63.18 335

testus		59.00 300	57.91 299	62.25 317	72.25 327	39.09 336	69.74 312	75.02 312	53.04 318	57.21 317	73.72 316	18.76 340	70.33 335	32.86 328	68.57 295	77.35 320

LP		61.36 297	57.78 300	72.09 290	75.54 318	58.53 241	67.16 326	75.22 310	51.90 322	54.13 322	69.97 324	37.73 314	80.45 302	32.74 329	55.63 327	77.29 321

1111		57.11 304	56.82 305	57.97 322	69.10 332	28.28 344	68.90 320	74.54 320	54.01 312	53.71 325	74.51 313	23.09 334	67.90 339	32.28 330	61.26 319	77.73 318

.test1245		45.55 314	50.02 311	32.14 334	69.10 332	28.28 344	68.90 320	74.54 320	54.01 312	53.71 325	74.51 313	23.09 334	67.90 339	32.28 330	0.02 349	0.25 348

test2356		59.50 298	58.08 298	63.74 314	71.23 329	41.88 331	67.59 323	72.42 326	53.72 314	57.65 315	70.74 322	26.31 329	72.40 331	32.03 332	71.06 285	76.93 323

test1235678		58.74 301	56.89 304	64.30 312	69.70 331	41.87 332	71.05 308	74.87 314	54.06 311	50.63 330	71.53 321	25.30 331	74.10 327	31.80 333	63.10 314	76.93 323

ANet_high		50.57 312	46.10 314	63.99 313	48.67 347	39.13 335	70.99 310	80.85 273	61.39 269	31.18 339	57.70 335	17.02 342	73.65 329	31.22 334	15.89 346	79.18 315

testmv		53.85 307	51.03 309	62.31 316	61.46 339	38.88 337	70.95 311	74.69 318	51.11 324	41.26 333	66.85 327	14.28 344	72.13 332	29.19 335	49.51 334	75.93 326

PMMVS2		40.82 317	38.86 318	46.69 330	53.84 343	16.45 351	48.61 340	49.92 347	37.49 335	31.67 338	60.97 333	8.14 351	56.42 344	28.42 336	30.72 338	67.19 331

test12356		49.28 313	48.51 313	51.59 327	62.06 338	19.11 350	60.40 333	72.45 325	47.60 328	40.64 335	65.68 328	13.84 345	68.72 337	27.29 337	46.67 336	66.94 332

tmp_tt		18.61 325	21.40 326	10.23 338	4.82 352	10.11 352	34.70 343	30.74 352	1.48 347	23.91 343	26.07 345	28.42 326	13.41 350	27.12 338	15.35 347	7.17 345

PNet_i23d		38.26 319	35.42 319	46.79 329	58.74 340	35.48 340	59.65 334	51.25 346	32.45 339	23.44 344	47.53 339	2.04 354	58.96 342	25.60 339	18.09 344	45.92 340

FPMVS		53.68 308	51.64 308	59.81 320	65.08 336	51.03 312	69.48 315	69.58 332	41.46 331	40.67 334	72.32 319	16.46 343	70.00 336	24.24 340	65.42 309	58.40 336

Gipuma		45.18 315	41.86 316	55.16 324	77.03 313	51.52 309	32.50 344	80.52 276	32.46 338	27.12 340	35.02 341	9.52 349	75.50 321	22.31 341	60.21 322	38.45 341
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				27.40 336	40.17 351	26.90 347		24.59 353	17.44 345	23.95 342	48.61 338	9.77 348	26.48 348	18.06 342	24.47 340	28.83 342

PMVS	37.38 22	44.16 316	40.28 317	55.82 323	40.82 350	42.54 330	65.12 329	63.99 343	34.43 337	24.48 341	57.12 336	3.92 352	76.17 319	17.10 343	55.52 328	48.75 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 323	25.89 325	43.81 331	44.55 349	35.46 341	28.87 345	39.07 350	18.20 344	18.58 345	40.18 340	2.68 353	47.37 347	17.07 344	23.78 341	48.60 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 321	30.64 322	35.15 332	52.87 345	27.67 346	57.09 338	47.86 348	24.64 341	16.40 346	33.05 343	11.23 347	54.90 345	14.46 345	18.15 343	22.87 343

EMVS		30.81 322	29.65 323	34.27 333	50.96 346	25.95 348	56.58 339	46.80 349	24.01 343	15.53 347	30.68 344	12.47 346	54.43 346	12.81 346	17.05 345	22.43 344

wuyk23d		16.82 326	15.94 327	19.46 337	58.74 340	31.45 343	39.22 342	3.74 355	6.84 346	6.04 349	2.70 349	1.27 355	24.29 349	10.54 347	14.40 348	2.63 346

testmvs		6.04 329	8.02 330	0.10 340	0.08 353	0.03 355	69.74 312	0.04 356	0.05 348	0.31 350	1.68 350	0.02 358	0.04 351	0.24 348	0.02 349	0.25 348

test123		6.12 328	8.11 329	0.14 339	0.06 354	0.09 354	71.05 308	0.03 357	0.04 349	0.25 351	1.30 351	0.05 357	0.03 352	0.21 349	0.01 351	0.29 347

cdsmvs_eth3d_5k		19.96 324	26.61 324	0.00 341	0.00 355	0.00 356	0.00 346	89.26 154	0.00 350	0.00 352	88.61 125	61.62 137	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		5.26 330	7.02 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	63.15 102	0.00 353	0.00 350	0.00 352	0.00 350

pcd1.5k->3k		34.07 320	35.26 320	30.50 335	86.92 176	0.00 356	0.00 346	91.58 82	0.00 350	0.00 352	0.00 352	56.23 185	0.00 353	0.00 350	82.60 163	91.49 104

sosnet-low-res		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

sosnet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uncertanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

Regformer		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ab-mvs-re		7.23 327	9.64 328	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	86.72 176	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

test_part2					95.06 1	72.65 26				91.80 1

test_part1								94.09 2				81.79 1			96.38 2	93.74 36

test_all								94.22 1

sam_mvs1												51.32 235

sam_mvs												50.01 253

MTGPA								92.02 60

test_post											5.46 347	50.36 251	84.24 287

patchmatchnet-post											74.00 315	51.12 238	88.60 258

MTMP								32.83 351

TEST9					93.26 34	72.96 19	88.75 84	91.89 69	68.44 200	85.00 28	93.10 39	74.36 17	95.41 48

test_8					93.13 36	72.57 29	88.68 87	91.84 72	68.69 196	84.87 34	93.10 39	74.43 14	95.16 56

agg_prior					92.85 42	71.94 40		91.78 75		84.41 40			94.93 65

test_prior4						72.60 28	89.01 75

test_prior				86.33 46	92.61 47	69.59 72		92.97 31					95.48 43			93.91 30

新几何2							86.29 170

旧先验1					91.96 55	65.79 141		86.37 214			93.08 43	69.31 53			92.74 51	88.74 201

原ACMM2							86.86 150

test222					91.50 60	68.26 102	84.16 225	83.20 248	54.63 309	79.74 86	91.63 64	58.97 166			91.42 61	86.77 252

segment_acmp												73.08 24

testdata1							84.14 226		75.71 71

test12				86.80 38	92.63 46	70.70 56		91.79 74		82.71 62		71.67 33	96.16 29		94.50 34	93.54 47

plane_prior7					90.08 79	68.51 98

plane_prior6					89.84 84	68.70 94						60.42 159

plane_prior4											91.00 80

plane_prior3						68.60 96			78.44 30	78.92 95

plane_prior2							91.25 28		79.12 23

plane_prior1					89.90 83

plane_prior						68.71 92	90.38 44		77.62 34						86.16 122

n2								0.00 358

nn								0.00 358

door-mid								69.98 330

test11								92.23 52

door								69.44 333

HQP5-MVS						66.98 123

HQP-NCC					89.33 100		89.17 68		76.41 59	77.23 135

ACMP_Plane					89.33 100		89.17 68		76.41 59	77.23 135

HQP4-MVS										77.24 134			95.11 58			91.03 112

HQP3-MVS								92.19 55							85.99 124

HQP2-MVS												60.17 162

NP-MVS					89.62 90	68.32 100					90.24 90

ACMMP++_ref															81.95 169

ACMMP++															81.25 176

Test By Simon												64.33 88

High-resolution multi-view benchmark