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.

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 47	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 52	93.16 120	91.10 1	97.53 56	96.58 39
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
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 66	86.07 32	98.48 19	97.22 26

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 29	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 8	85.07 36	99.27 3	99.54 1

LS3D		90.60 28	90.34 40	91.38 23	89.03 147	84.23 42	93.58 4	94.68 10	90.65 7	90.33 79	93.95 88	84.50 55	95.37 31	80.87 86	95.50 117	94.53 85

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 63	88.22 16	88.53 121	97.64 3	83.45 63	94.55 57	86.02 33	98.60 14	96.67 36

HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 41	91.77 61	93.94 89	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 36	83.16 37	91.06 70	94.00 82	88.26 23	95.71 18	87.28 22	98.39 23	92.55 142

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 48	86.15 18	93.37 8	95.10 7	90.28 9	92.11 53	95.03 47	89.75 15	94.93 46	79.95 101	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 45	92.39 52	94.14 79	89.15 17	95.62 19	87.35 19	98.24 30	94.56 81
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
abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 41	87.54 18	98.02 39	93.71 110

HPM-MVS_fast		92.50 5	92.54 5	92.37 5	95.93 14	85.81 27	92.99 11	94.23 16	85.21 24	92.51 48	95.13 45	90.65 11	95.34 32	88.06 10	98.15 34	95.95 51

APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 64	78.04 77	92.84 12	94.14 21	83.33 35	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 127

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 121	78.43 92	89.16 111	92.25 123	72.03 202	96.36 2	88.21 9	90.93 214	90.55 189

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 70	83.09 38	91.54 64	94.25 75	87.67 33	95.51 27	87.21 23	98.11 35	93.12 125

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 38	85.07 25	89.99 84	94.03 81	86.57 41	95.80 13	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 112	82.70 158	92.08 8	95.64 21	86.25 16	92.64 15	93.33 38	85.07 25	89.99 84	16.05 338	86.57 41	95.80 13	87.35 19	97.62 52	94.20 92

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 41	81.91 53	90.88 75	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 43	81.99 51	91.47 65	93.96 85	88.35 21	95.56 22	87.74 11	97.74 48	92.85 128

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 45	81.99 51	91.40 67	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 91	92.49 20	91.19 117	67.85 211	86.63 149	94.84 53	79.58 106	95.96 7	87.62 14	94.50 149	94.56 81

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 62	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 54	94.18 94

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 54	82.59 44	88.52 122	94.37 72	86.74 38	95.41 30	86.32 26	98.21 32	93.19 124

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 76	79.74 72	87.50 136	92.38 117	81.42 91	93.28 115	83.07 62	97.24 61	91.67 165

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 89	84.07 30	92.00 56	94.40 69	86.63 39	95.28 35	88.59 4	98.31 26	92.30 150

MVSFormer		82.23 170	81.57 175	84.19 141	85.54 227	69.26 148	91.98 25	90.08 148	71.54 180	76.23 259	85.07 253	58.69 255	94.27 59	86.26 27	88.77 238	89.03 209

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 79	82.98 49	91.98 25	90.08 148	71.54 180	94.28 23	96.54 13	81.57 89	94.27 59	86.26 27	96.49 83	97.09 30

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 59	80.37 59	91.91 27	93.11 48	81.10 60	95.32 13	97.24 6	72.94 187	94.85 48	85.07 36	97.78 46	97.26 23

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 104	69.37 146	91.63 28	87.98 182	81.51 58	87.05 144	91.83 130	66.18 224	95.29 33	70.75 174	96.89 69	95.64 59

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 35	82.82 42	92.60 47	93.97 83	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
v52		89.97 39	90.60 37	88.07 68	88.69 152	72.01 125	91.35 30	92.64 68	82.22 48	95.97 8	96.31 16	84.82 51	93.98 73	88.59 4	94.83 139	98.23 8

V4		89.97 39	90.60 37	88.07 68	88.69 152	72.01 125	91.35 30	92.64 68	82.22 48	95.98 7	96.31 16	84.80 53	93.98 73	88.59 4	94.83 139	98.23 8

3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 110	81.66 54	91.25 32	94.13 22	88.89 12	88.83 116	94.26 74	77.55 122	95.86 12	84.88 39	95.87 108	95.24 71

IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 89	66.87 164	91.21 33	88.64 170	86.30 23	89.60 104	92.59 112	69.22 212	94.91 47	73.89 149	97.89 44	96.72 35

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 43	77.00 116	91.47 65	93.96 85	88.35 21	95.56 22	84.88 39	97.74 48	92.85 128

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 138	86.67 12	90.51 35	90.20 147	69.87 194	95.06 14	96.14 23	84.28 56	93.07 127	87.68 13	96.34 87	97.09 30

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 160	88.51 15	90.11 80	95.12 46	90.98 8	88.92 218	77.55 123	97.07 65	83.13 272
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
APD-MVS		89.54 48	89.63 48	89.26 55	92.57 72	81.34 56	90.19 37	93.08 50	80.87 62	91.13 69	93.19 97	86.22 46	95.97 6	82.23 73	97.18 63	90.45 191
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 177	90.13 38	92.97 56	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	98.40 21	98.52 7

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 26	80.32 67	91.74 62	94.41 68	88.17 25	95.98 5	86.37 25	97.99 41	93.96 101

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 35	81.69 51	90.00 39	94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

v7n		90.13 32	90.96 32	87.65 75	91.95 91	71.06 137	89.99 41	93.05 51	86.53 21	94.29 22	96.27 19	82.69 70	94.08 69	86.25 29	97.63 51	97.82 11

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 30	77.65 101	91.97 58	94.89 51	88.38 19	95.45 28	89.27 3	97.87 45	93.27 120

QAPM		82.59 165	82.59 162	82.58 176	86.44 210	66.69 165	89.94 43	90.36 134	67.97 208	84.94 172	92.58 114	72.71 191	92.18 145	70.63 177	87.73 252	88.85 212

mvs_tets		89.78 44	89.27 51	91.30 24	93.51 50	84.79 36	89.89 44	90.63 126	70.00 193	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 103	95.52 65

SD-MVS		88.96 55	89.88 43	86.22 95	91.63 97	77.07 87	89.82 45	93.77 29	78.90 85	92.88 39	92.29 121	86.11 47	90.22 196	86.24 30	97.24 61	91.36 172

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 54	84.72 37	89.70 46	90.29 142	69.27 197	94.39 20	96.38 15	86.02 48	93.52 101	83.96 51	95.92 106	95.34 68

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 114	78.20 95	86.69 148	92.28 122	80.36 100	95.06 43	86.17 31	96.49 83	90.22 195

RPSCF		88.00 65	86.93 84	91.22 27	90.08 132	89.30 6	89.68 48	91.11 118	79.26 79	89.68 96	94.81 57	82.44 74	87.74 232	76.54 133	88.74 240	96.61 38

ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 63	76.26 97	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	98.76 5	95.61 64

ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 50	93.35 37	79.20 80	93.83 28	93.60 94	90.81 9	92.96 128	85.02 38	98.45 20	92.41 145
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 51	91.14 28	83.96 145	92.50 75	70.36 141	89.55 51	93.84 27	81.89 54	94.70 16	95.44 39	90.69 10	88.31 227	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Gipuma		84.44 126	86.33 92	78.78 222	84.20 245	73.57 110	89.55 51	90.44 131	84.24 29	84.38 185	94.89 51	76.35 139	80.40 284	76.14 134	96.80 74	82.36 280
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 210	89.54 53	93.31 40	90.21 10	95.57 11	95.66 31	81.42 91	95.90 9	80.94 85	98.80 4	98.84 5

AllTest		87.97 66	87.40 76	89.68 47	91.59 98	83.40 45	89.50 54	95.44 5	79.47 75	88.00 129	93.03 100	82.66 71	91.47 162	70.81 172	96.14 96	94.16 95

XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 54	93.89 90	83.80 59	93.10 123	82.67 69	98.04 36	93.64 112

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 51	76.42 95	89.45 56	93.61 31	79.44 77	86.55 150	92.95 104	74.84 151	95.22 37	80.78 88	95.83 110	94.46 86

plane_prior2							89.45 56		79.44 77

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 160	77.34 84	89.35 58	93.05 51	73.15 159	84.76 175	87.70 220	78.87 110	94.18 64	80.67 90	96.29 88	92.73 132

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 59	94.05 23	79.03 84	92.87 40	93.74 92	90.60 12	95.21 39	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 248	89.04 60	92.74 65	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 247	88.95 61	93.19 46	91.62 4	96.01 6	96.16 22	87.02 36	95.60 20	78.69 113	98.72 10	98.97 3

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 62	91.81 89	84.07 30	92.00 56	94.40 69	86.63 39	95.28 35	88.59 4	98.31 26	92.30 150

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 251	88.93 63	92.84 62	91.92 2	96.16 3	96.23 20	86.95 37	95.99 4	79.05 110	98.57 16	98.80 6

F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 78	83.40 45	88.83 64	91.92 86	73.19 158	80.18 236	89.15 196	77.04 128	93.28 115	65.82 214	92.28 196	92.21 155

v748		88.91 57	89.82 45	86.19 99	90.06 134	68.53 153	88.81 65	91.48 98	84.36 28	94.19 24	95.98 25	82.52 73	92.67 137	84.30 47	96.67 76	97.37 20

3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 215	74.71 104	88.77 66	90.00 151	75.65 129	84.96 170	93.17 98	74.06 161	91.19 169	78.28 117	91.09 207	89.29 206

API-MVS		82.28 169	82.61 161	81.30 195	86.29 217	69.79 142	88.71 67	87.67 185	78.42 93	82.15 209	84.15 266	77.98 116	91.59 160	65.39 215	92.75 189	82.51 279

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 250	88.66 68	92.06 81	90.78 6	95.67 9	95.17 44	81.80 87	95.54 24	79.00 111	98.69 11	98.95 4

DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 49	79.07 70	88.54 69	94.20 17	73.53 148	89.71 95	94.82 54	85.09 50	95.77 15	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	76.72 13	81.98 175	82.00 170	81.93 184	84.42 240	68.22 156	88.50 70	89.48 161	66.92 215	81.80 216	91.86 127	72.59 195	90.16 198	71.19 171	91.25 205	87.40 226

ambc				82.98 169	90.55 127	64.86 174	88.20 71	89.15 165		89.40 108	93.96 85	71.67 205	91.38 168	78.83 112	96.55 79	92.71 133

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 119	65.98 168	88.19 72	90.78 123	78.13 97	80.87 226	87.92 218	73.49 175	92.42 141	70.07 180	88.40 241	91.60 167

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 73	94.51 11	75.79 128	92.94 38	94.96 49	88.36 20	95.01 44	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CSCG		86.26 92	86.47 91	85.60 110	90.87 120	74.26 107	87.98 74	91.85 87	80.35 66	89.54 107	88.01 214	79.09 108	92.13 146	75.51 137	95.06 130	90.41 192

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 55	77.96 78	87.94 75	91.97 84	70.73 186	94.19 24	96.67 11	76.94 130	94.57 55	83.07 62	96.28 89	96.15 42

nrg030		87.85 68	88.49 61	85.91 103	90.07 133	69.73 143	87.86 76	94.20 17	74.04 143	92.70 45	94.66 58	85.88 49	91.50 161	79.72 103	97.32 59	96.50 40

v13		87.31 72	88.10 63	84.94 116	88.84 149	63.75 183	87.85 77	91.47 101	79.12 81	93.72 29	95.82 27	75.20 145	93.58 94	84.76 42	96.16 94	97.48 17

SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 74	69.19 151	87.84 78	88.05 179	81.66 56	94.64 17	96.53 14	65.94 225	94.75 50	83.02 64	96.83 72	95.41 67

Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 200	91.15 3	87.70 79	88.42 172	74.57 139	83.56 197	85.65 240	78.49 112	94.21 63	72.04 167	92.88 187	94.05 98

v12		87.15 75	87.91 65	84.84 119	88.69 152	63.52 186	87.58 80	91.46 102	78.74 89	93.57 32	95.66 31	74.94 149	93.57 95	84.50 45	96.08 99	97.43 18

canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 171	67.13 161	87.55 81	94.32 12	73.44 150	88.47 123	87.54 223	86.45 43	91.06 173	75.76 136	93.76 164	92.54 143

DP-MVS		88.60 60	89.01 53	87.36 80	91.30 108	77.50 82	87.55 81	92.97 56	87.95 17	89.62 101	92.87 106	84.56 54	93.89 77	77.65 122	96.62 77	90.70 183

OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 93	81.68 53	87.49 83	93.17 47	76.02 124	88.64 119	91.22 144	84.24 57	93.37 110	77.97 121	97.03 66	95.52 65

Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 87	77.43 83	87.35 84	92.09 80	78.87 86	84.27 190	94.05 80	78.35 114	93.65 84	80.54 92	91.58 204	92.08 157
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V9		86.96 76	87.70 70	84.74 123	88.52 157	63.27 192	87.31 85	91.45 104	78.28 94	93.43 33	95.45 38	74.59 157	93.57 95	84.23 49	96.01 102	97.38 19

v11		86.96 76	87.78 69	84.51 128	88.50 158	62.60 206	87.21 86	91.63 93	78.08 98	93.40 34	95.56 36	75.07 146	93.57 95	84.46 46	96.08 99	97.36 21

DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 108	76.92 88	87.19 87	91.99 83	70.56 187	84.96 170	90.69 168	80.01 103	95.14 40	78.37 114	95.78 112	91.82 162
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPNet		80.37 190	78.41 202	86.23 94	76.75 302	73.28 112	87.18 88	77.45 256	76.24 121	68.14 303	88.93 199	65.41 227	93.85 78	69.47 184	96.12 98	91.55 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
plane_prior						76.42 95	87.15 89		75.94 126						95.03 131

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 151	79.72 65	87.15 89	93.50 34	69.17 198	85.80 162	89.56 191	80.76 96	92.13 146	73.21 160	95.51 116	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
V14		86.75 82	87.46 73	84.62 126	88.35 161	63.00 197	87.02 91	91.42 107	77.78 100	93.27 35	95.23 43	74.22 159	93.56 98	83.95 52	95.93 105	97.31 22

UGNet		82.78 162	81.64 173	86.21 97	86.20 221	76.24 98	86.86 92	85.68 211	77.07 115	73.76 278	92.82 107	69.64 210	91.82 155	69.04 190	93.69 166	90.56 188
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
XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 25	80.07 70	86.75 147	93.26 96	93.64 2	90.93 176	84.60 44	90.75 219	93.97 100

GBi-Net		82.02 173	82.07 168	81.85 188	86.38 212	61.05 231	86.83 94	88.27 176	72.43 166	86.00 159	95.64 33	63.78 232	90.68 185	65.95 210	93.34 174	93.82 105

test1		82.02 173	82.07 168	81.85 188	86.38 212	61.05 231	86.83 94	88.27 176	72.43 166	86.00 159	95.64 33	63.78 232	90.68 185	65.95 210	93.34 174	93.82 105

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 130	61.05 231	86.83 94	88.27 176	78.57 91	89.66 97	95.64 33	75.43 142	90.68 185	69.09 189	95.33 120	93.82 105

OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 64	78.78 87	92.51 48	93.64 93	88.13 27	93.84 80	84.83 41	97.55 55	94.10 97

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 95	71.56 135	86.75 98	93.02 55	75.95 125	87.12 140	89.39 193	77.98 116	89.40 210	77.46 124	94.78 141	84.75 251

v17		86.32 90	86.95 83	84.44 132	88.00 170	62.62 205	86.74 99	91.48 98	77.17 113	92.74 43	94.56 60	73.74 168	93.53 100	83.27 59	94.87 138	97.18 27

v16		86.24 93	86.85 86	84.43 133	87.96 172	62.59 207	86.73 100	91.48 98	77.17 113	92.67 46	94.55 61	73.63 169	93.52 101	83.26 60	94.16 153	97.17 28

v15		86.56 85	87.25 77	84.51 128	88.15 168	62.72 202	86.72 101	91.40 109	77.38 105	93.11 37	95.00 48	73.93 164	93.55 99	83.67 56	95.86 109	97.26 23

114514_t		83.10 161	82.54 163	84.77 122	92.90 65	69.10 152	86.65 102	90.62 127	54.66 285	81.46 219	90.81 165	76.98 129	94.38 58	72.62 163	96.18 93	90.82 181

v10		86.54 86	87.10 78	84.84 119	88.16 167	63.28 191	86.64 103	92.20 78	75.42 133	92.81 42	94.50 64	74.05 162	94.06 70	83.88 53	96.28 89	97.17 28

NCCC		87.36 71	86.87 85	88.83 58	92.32 82	78.84 73	86.58 104	91.09 119	78.77 88	84.85 174	90.89 162	80.85 95	95.29 33	81.14 82	95.32 121	92.34 149

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 198	65.61 171	86.55 105	92.40 73	78.64 90	81.34 222	84.18 264	83.65 61	92.93 130	74.22 144	87.87 250	92.17 156

v8		86.22 95	86.83 87	84.36 135	87.82 178	62.35 211	86.42 106	91.33 112	76.78 117	92.73 44	94.48 65	73.41 176	93.72 82	83.10 61	95.41 118	97.01 33

v18		85.99 99	86.55 90	84.30 137	87.73 184	62.29 215	86.40 107	91.49 97	76.64 118	92.40 51	94.20 77	73.28 180	93.52 101	82.87 65	93.99 157	97.09 30

AdaColmap		83.66 151	83.69 149	83.57 158	90.05 135	72.26 122	86.29 108	90.00 151	78.19 96	81.65 217	87.16 226	83.40 64	94.24 62	61.69 234	94.76 144	84.21 257

XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 33	80.16 69	89.13 112	93.44 95	83.82 58	90.98 174	83.86 54	95.30 124	93.60 114

PLC	73.85 16	82.09 172	80.31 188	87.45 78	90.86 121	80.29 60	85.88 110	90.65 125	68.17 205	76.32 258	86.33 234	73.12 185	92.61 139	61.40 238	90.02 228	89.44 203
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 118	93.03 1	85.85 111	81.96 235	74.58 138	98.58 2	97.29 5	87.73 31	87.31 235	82.84 67	99.41 1	81.99 285

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 83	56.44 261	85.75 112	93.09 49	77.33 110	91.94 59	94.65 59	74.78 153	93.41 109	75.11 140	98.58 15	97.88 10

MAR-MVS		80.24 193	78.74 200	84.73 124	86.87 208	78.18 76	85.75 112	87.81 184	65.67 227	77.84 247	78.50 306	73.79 167	90.53 189	61.59 237	90.87 216	85.49 245
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
EU-MVSNet		75.12 233	74.43 234	77.18 244	83.11 258	59.48 243	85.71 114	82.43 232	39.76 333	85.64 164	88.76 200	44.71 315	87.88 230	73.86 150	85.88 268	84.16 258

LF4IMVS		82.75 163	81.93 171	85.19 113	82.08 262	80.15 61	85.53 115	88.76 168	68.01 206	85.58 165	87.75 219	71.80 203	86.85 241	74.02 147	93.87 160	88.58 213

K. test v3		85.14 109	84.73 119	86.37 90	91.13 115	69.63 145	85.45 116	76.68 262	84.06 32	92.44 50	96.99 8	62.03 237	94.65 52	80.58 91	93.24 180	94.83 80

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 244	85.42 117	81.11 241	86.41 22	87.41 137	96.21 21	73.61 172	90.61 188	66.33 208	96.85 70	93.81 108

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 66	77.30 86	85.25 118	91.23 115	77.31 111	87.07 143	91.47 140	82.94 68	94.71 51	84.67 43	96.27 91	92.62 141

LFMVS		80.15 195	80.56 185	78.89 220	89.19 145	55.93 263	85.22 119	73.78 278	82.96 40	84.28 189	92.72 111	57.38 262	90.07 203	63.80 223	95.75 113	90.68 184

FIs		85.35 108	86.27 94	82.60 175	91.86 94	57.31 255	85.10 120	93.05 51	75.83 127	91.02 71	93.97 83	73.57 173	92.91 132	73.97 148	98.02 39	97.58 15

mvs-test1		84.55 123	82.12 167	91.84 17	87.13 200	89.54 5	85.05 121	88.42 172	74.57 139	80.60 228	82.98 276	78.49 112	93.98 73	72.04 167	89.77 229	92.00 158

HQP-NCC					91.19 111		84.77 122		73.30 154	80.55 231

ACMP_Plane					91.19 111		84.77 122		73.30 154	80.55 231

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 111	70.66 139	84.77 122	92.68 66	73.30 154	80.55 231	90.17 182	72.10 198	94.61 53	77.30 127	94.47 150	93.56 116

ab-mvs		79.67 197	80.56 185	76.99 245	88.48 159	56.93 257	84.70 125	86.06 207	68.95 202	80.78 227	93.08 99	75.30 144	84.62 264	56.78 264	90.90 215	89.43 204

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 85	62.28 216	84.66 126	89.15 165	83.54 34	89.85 91	97.32 4	88.08 28	86.80 242	70.43 179	97.30 60	96.62 37

test_prior4						78.97 71	84.59 127

testing_2		84.36 130	84.64 128	83.50 163	86.74 209	63.97 182	84.56 128	90.31 137	66.22 220	91.62 63	94.55 61	75.88 140	91.95 149	77.02 131	94.89 135	94.56 81

alignmvs		83.94 147	83.98 145	83.80 147	87.80 182	67.88 158	84.54 129	91.42 107	73.27 157	88.41 125	87.96 215	72.33 197	90.83 180	76.02 135	94.11 154	92.69 134

CNLPA		83.55 154	83.10 156	84.90 118	89.34 142	83.87 43	84.54 129	88.77 167	79.09 82	83.54 198	88.66 203	74.87 150	81.73 280	66.84 206	92.29 195	89.11 207

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 179	84.38 131	91.29 113	84.88 27	92.06 55	93.84 91	86.45 43	93.73 81	73.22 156	98.66 12	97.69 12

PVSNet_Blended_VisFu		81.55 176	80.49 187	84.70 125	91.58 101	73.24 113	84.21 132	91.67 92	62.86 246	80.94 224	87.16 226	67.27 219	92.87 133	69.82 182	88.94 237	87.99 219

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 192	74.83 103	84.18 133	93.30 41	77.48 104	77.39 252	88.46 205	74.53 158	95.74 16	78.09 120	94.75 145	92.36 148

GG-mvs-BLEND				67.16 295	73.36 323	46.54 316	84.15 134	55.04 336		58.64 332	61.95 335	29.93 337	83.87 271	38.71 325	76.92 315	71.07 320

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 242	73.43 111	84.07 135	85.26 215	80.77 63	88.62 120	85.48 243	80.56 99	90.39 192	81.99 75	91.04 209	94.85 79

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 242	77.57 81	84.07 135	88.00 181	82.82 42	89.84 92	85.48 243	82.06 79	92.77 134	83.83 55	91.04 209	95.22 74

test_0402		88.65 58	89.58 49	85.88 105	92.55 73	72.22 123	84.01 137	89.44 162	88.63 14	94.38 21	95.77 28	86.38 45	93.59 91	79.84 102	95.21 125	91.82 162

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 77	79.92 62	83.98 138	90.03 150	65.11 233	83.66 195	90.64 173	81.00 94	93.67 83	81.21 80	96.54 80	90.88 178

TEST9					92.34 80	79.70 66	83.94 139	90.32 135	65.41 231	84.49 183	90.97 158	82.03 81	93.63 86

train_agg		85.98 100	85.28 110	88.07 68	92.34 80	79.70 66	83.94 139	90.32 135	65.79 224	84.49 183	90.97 158	81.93 83	93.63 86	81.21 80	96.54 80	90.88 178

FMVSNet2		81.31 178	81.61 174	80.41 205	86.38 212	58.75 249	83.93 141	86.58 203	72.43 166	87.65 133	92.98 102	63.78 232	90.22 196	66.86 204	93.92 159	92.27 152

EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 248	72.76 115	83.91 142	85.18 217	80.44 64	88.75 117	85.49 242	80.08 102	91.92 150	82.02 74	90.85 217	95.97 49

CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 83	75.45 100	83.85 143	92.01 82	65.91 223	86.19 157	91.75 134	83.77 60	94.98 45	77.43 126	96.71 75	93.73 109

test_8					92.09 87	78.87 72	83.82 144	90.31 137	65.79 224	84.36 186	90.96 160	81.93 83	93.44 107

EI-MVSNet-UG-set		85.04 112	84.44 133	86.85 83	83.87 251	72.52 118	83.82 144	85.15 218	80.27 68	88.75 117	85.45 246	79.95 104	91.90 151	81.92 76	90.80 218	96.13 43

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 61	68.48 154	83.80 146	92.87 59	80.37 65	89.61 103	91.81 132	77.72 119	94.18 64	75.00 142	98.53 17	96.99 34

v7		84.81 117	85.00 114	84.23 138	88.15 168	63.27 192	83.79 147	91.39 110	71.10 184	90.07 81	91.28 142	74.04 163	93.63 86	81.48 79	93.67 167	95.79 52

CANet		83.79 149	82.85 157	86.63 85	86.17 222	72.21 124	83.76 148	91.43 105	77.24 112	74.39 275	87.45 224	75.36 143	95.42 29	77.03 130	92.83 188	92.25 154

TSAR-MVS + GP.		83.95 146	82.69 159	87.72 73	89.27 143	81.45 55	83.72 149	81.58 240	74.73 137	85.66 163	86.06 238	72.56 196	92.69 136	75.44 138	95.21 125	89.01 211

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 101	77.69 79	83.69 150	90.30 139	66.29 219	84.32 187	91.07 155	82.13 78	93.18 118	81.02 83	96.36 86	90.98 173

gg-mvs-nofinetune		68.96 277	69.11 270	68.52 292	76.12 307	45.32 317	83.59 151	55.88 335	86.68 20	64.62 319	97.01 7	30.36 336	83.97 270	44.78 313	82.94 290	76.26 311

v1neww		84.43 127	84.66 125	83.75 150	87.81 179	62.34 212	83.59 151	90.27 143	72.33 170	89.93 88	91.22 144	73.28 180	93.29 112	80.25 97	93.25 178	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 179	62.34 212	83.59 151	90.27 143	72.33 170	89.93 88	91.22 144	73.28 180	93.29 112	80.25 97	93.25 178	95.62 60

v6		84.43 127	84.66 125	83.75 150	87.81 179	62.34 212	83.59 151	90.26 145	72.33 170	89.94 87	91.19 148	73.30 179	93.29 112	80.26 96	93.26 177	95.62 60

Regformer-1		86.00 97	85.50 107	87.49 77	84.18 246	76.90 89	83.52 155	87.94 183	82.18 50	89.19 110	85.07 253	82.28 77	91.89 152	82.40 71	92.72 190	93.69 111

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 246	79.20 69	83.52 155	89.33 163	83.33 35	89.92 90	85.07 253	83.23 66	93.16 120	83.39 57	92.72 190	93.83 103

MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 98	71.58 134	83.52 155	92.13 79	61.82 254	83.96 191	89.75 188	79.93 105	93.46 106	78.33 116	94.34 152	91.87 161

EI-MVSNet		82.61 164	82.42 165	83.20 166	83.25 255	63.66 184	83.50 158	85.07 219	76.06 122	86.55 150	85.10 251	73.41 176	90.25 193	78.15 119	90.67 221	95.68 58

CVMVSNet		72.62 250	71.41 258	76.28 254	83.25 255	60.34 237	83.50 158	79.02 251	37.77 334	76.33 257	85.10 251	49.60 285	87.41 234	70.54 178	77.54 314	81.08 301

DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 105	84.90 34	83.41 160	92.38 75	70.25 191	89.35 109	90.68 169	82.85 69	94.57 55	79.55 105	95.95 104	92.00 158

test_prior3		86.31 91	86.31 93	86.32 91	90.59 125	71.99 127	83.37 161	92.85 60	75.43 131	84.58 181	91.57 136	81.92 85	94.17 66	79.54 106	96.97 67	92.80 130

test_prior2							83.37 161		75.43 131	84.58 181	91.57 136	81.92 85		79.54 106	96.97 67

Vis-MVSNet (Re-imp)		77.82 206	77.79 205	77.92 235	88.82 150	51.29 295	83.28 163	71.97 291	74.04 143	82.23 207	89.78 187	57.38 262	89.41 209	57.22 262	95.41 118	93.05 126

CANet_DTU		77.81 207	77.05 208	80.09 208	81.37 268	59.90 240	83.26 164	88.29 175	69.16 199	67.83 306	83.72 268	60.93 240	89.47 206	69.22 188	89.70 230	90.88 178

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 114	65.16 173	83.25 165	84.97 223	79.79 71	87.18 139	94.27 73	74.77 154	90.89 179	69.24 186	96.54 80	93.55 118

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 193	63.66 184	83.25 165	89.88 153	76.06 122	89.62 101	92.37 120	73.40 178	92.52 140	78.16 118	94.77 143	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 96	75.27 101	83.23 167	92.40 73	72.04 174	82.04 210	88.33 210	77.91 118	93.95 76	66.17 209	95.12 128	90.34 194

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 57	67.02 162	83.16 168	92.21 77	81.73 55	90.92 72	91.97 125	77.20 124	93.99 71	74.16 145	98.35 24	97.61 13

Fast-Effi-MVS+-dtu		82.54 166	81.41 176	85.90 104	85.60 225	76.53 94	83.07 169	89.62 159	73.02 161	79.11 243	83.51 270	80.74 97	90.24 195	68.76 192	89.29 232	90.94 175

v1192		84.57 122	84.69 123	84.21 139	87.75 183	62.88 199	83.02 170	91.43 105	69.08 200	89.98 86	90.89 162	72.70 192	93.62 90	82.41 70	94.97 133	96.13 43

v1144		84.54 125	84.72 121	84.00 143	87.67 186	62.55 208	82.97 171	90.93 122	70.32 190	89.80 93	90.99 157	73.50 174	93.48 105	81.69 78	94.65 147	95.97 49

v144192		84.24 135	84.41 134	83.71 154	87.59 189	61.57 224	82.95 172	91.03 120	67.82 212	89.80 93	90.49 174	73.28 180	93.51 104	81.88 77	94.89 135	96.04 47

v1921920		84.23 136	84.37 139	83.79 148	87.64 188	61.71 219	82.91 173	91.20 116	67.94 209	90.06 82	90.34 176	72.04 201	93.59 91	82.32 72	94.91 134	96.07 45

v1240		84.30 133	84.51 132	83.65 155	87.65 187	61.26 228	82.85 174	91.54 95	67.94 209	90.68 77	90.65 171	71.71 204	93.64 85	82.84 67	94.78 141	96.07 45

无先验							82.81 175	85.62 212	58.09 270				91.41 166	67.95 200		84.48 252

v1141		84.16 138	84.38 136	83.52 160	87.32 195	61.70 221	82.79 176	89.74 154	71.90 177	89.64 98	91.12 151	72.68 193	93.10 123	80.39 95	93.80 162	95.75 54

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 195	61.70 221	82.79 176	89.74 154	71.90 177	89.64 98	91.12 151	72.68 193	93.10 123	80.40 93	93.81 161	95.75 54

v1		84.16 138	84.38 136	83.52 160	87.33 194	61.71 219	82.79 176	89.73 156	71.89 179	89.64 98	91.11 153	72.72 190	93.10 123	80.40 93	93.79 163	95.75 54

MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 201	82.72 179	84.53 225	77.57 103	90.34 78	95.92 26	76.88 136	85.83 253	61.88 232	97.42 57	93.62 113

v2v482		84.09 141	84.24 141	83.62 156	87.13 200	61.40 225	82.71 180	89.71 157	72.19 173	89.55 105	91.41 141	70.70 209	93.20 117	81.02 83	93.76 164	96.25 41

CR-MVSNet		74.00 242	73.04 244	76.85 248	79.58 279	62.64 203	82.58 181	76.90 259	50.50 313	75.72 264	92.38 117	48.07 289	84.07 267	68.72 194	82.91 291	83.85 259

RPMNet		76.06 227	75.79 222	76.85 248	79.58 279	62.64 203	82.58 181	71.75 295	74.80 136	75.72 264	92.59 112	48.69 287	84.07 267	73.48 154	82.91 291	83.85 259

UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 100	92.86 68	67.02 162	82.55 183	91.56 94	83.08 39	90.92 72	91.82 131	78.25 115	93.99 71	74.16 145	98.35 24	97.49 16

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 261	57.75 253	82.54 184	91.96 85	71.16 183	82.89 203	92.52 116	77.41 123	90.50 190	80.04 100	87.84 251	92.40 146

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 136	59.66 241	82.43 185	87.44 187	75.52 130	87.85 131	95.26 42	81.25 93	85.65 255	68.74 193	96.04 101	94.42 89

Patchmtry		76.56 223	77.46 206	73.83 266	79.37 283	46.60 315	82.41 186	76.90 259	73.81 146	85.56 166	92.38 117	48.07 289	83.98 269	63.36 226	95.31 123	90.92 176

Test4		81.31 178	81.13 180	81.88 187	84.89 233	63.05 196	82.37 187	90.50 129	62.75 247	89.00 113	88.29 211	67.55 218	91.68 158	73.55 153	91.24 206	90.89 177

EPNet_dtu		72.87 248	71.33 259	77.49 243	77.72 296	60.55 236	82.35 188	75.79 264	66.49 218	58.39 333	81.06 296	53.68 276	85.98 250	53.55 282	92.97 186	85.95 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		81.25 181	82.34 166	77.99 234	85.33 229	60.68 235	82.32 189	88.33 174	71.26 182	86.97 145	92.22 124	77.10 127	86.98 239	62.37 229	95.17 127	86.31 236

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 117	55.20 270	82.29 190	87.26 190	79.65 74	88.38 126	95.52 37	83.00 67	86.88 240	67.97 199	96.60 78	94.45 88

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 229	91.47 106	53.44 279	82.29 190	87.00 200	79.06 83	89.55 105	95.72 30	77.20 124	86.14 249	72.30 165	98.51 18	95.28 70

MG-MVS		80.32 191	80.94 182	78.47 228	88.18 165	52.62 286	82.29 190	85.01 222	72.01 175	79.24 242	92.54 115	69.36 211	93.36 111	70.65 176	89.19 235	89.45 202

原ACMM2							82.26 193

NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 57	64.56 176	82.21 194	90.46 130	80.99 61	88.42 124	91.97 125	77.56 121	93.85 78	72.46 164	98.65 13	97.61 13

PAPR		78.84 200	78.10 203	81.07 199	85.17 230	60.22 238	82.21 194	90.57 128	62.51 249	75.32 268	84.61 260	74.99 148	92.30 143	59.48 249	88.04 248	90.68 184

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 85	72.61 117	82.20 196	87.02 199	72.63 165	88.86 114	91.02 156	78.52 111	91.11 171	73.41 155	91.09 207	88.21 215

HY-MVS	64.64 18	73.03 247	72.47 251	74.71 262	83.36 254	54.19 273	82.14 197	81.96 235	56.76 278	69.57 299	86.21 237	60.03 246	84.83 263	49.58 297	82.65 293	85.11 247

tpmp4_e23		69.43 273	67.33 280	75.72 257	78.53 291	52.75 283	82.13 198	74.91 269	49.23 318	66.37 309	84.17 265	41.28 323	88.67 223	49.73 296	79.63 304	85.75 242

DI_MVS_plusplus_test		81.27 180	81.26 177	81.29 196	84.98 231	61.65 223	81.98 199	87.25 191	63.56 239	87.56 135	89.60 190	73.62 170	91.83 154	72.20 166	90.59 225	90.38 193

view600		76.79 216	76.54 213	77.56 239	87.91 174	50.77 301	81.92 200	71.35 298	77.38 105	84.62 177	88.40 206	45.18 309	89.26 211	58.58 252	93.49 169	92.66 135

view800		76.79 216	76.54 213	77.56 239	87.91 174	50.77 301	81.92 200	71.35 298	77.38 105	84.62 177	88.40 206	45.18 309	89.26 211	58.58 252	93.49 169	92.66 135

conf0.05thres1000		76.79 216	76.54 213	77.56 239	87.91 174	50.77 301	81.92 200	71.35 298	77.38 105	84.62 177	88.40 206	45.18 309	89.26 211	58.58 252	93.49 169	92.66 135

tfpn		76.79 216	76.54 213	77.56 239	87.91 174	50.77 301	81.92 200	71.35 298	77.38 105	84.62 177	88.40 206	45.18 309	89.26 211	58.58 252	93.49 169	92.66 135

diffmvs		79.20 199	79.04 197	79.69 213	78.64 290	58.90 246	81.79 204	87.61 186	65.07 234	73.65 280	89.80 186	73.10 186	87.79 231	75.02 141	86.63 261	92.38 147

FMVSNet3		78.80 201	78.55 201	79.57 216	82.89 259	56.89 259	81.76 205	85.77 210	69.04 201	86.00 159	90.44 175	51.75 280	90.09 202	65.95 210	93.34 174	91.72 164

旧先验2							81.73 206		56.88 277	86.54 155			84.90 261	72.81 162

新几何2							81.72 207

1314		73.22 245	72.56 250	75.20 259	80.41 278	57.84 251	81.64 208	85.36 214	51.68 303	73.10 282	76.65 313	61.45 239	85.19 258	63.54 224	79.21 309	82.59 275

1121		80.86 185	79.81 195	84.02 142	93.93 45	78.70 74	81.64 208	80.18 246	55.43 282	83.67 194	91.15 149	71.29 206	91.41 166	67.95 200	93.06 183	81.96 286

MVS		73.21 246	72.59 249	75.06 260	80.97 272	60.81 234	81.64 208	85.92 209	46.03 324	71.68 289	77.54 308	68.47 216	89.77 204	55.70 270	85.39 271	74.60 314

v148		82.31 168	82.48 164	81.81 191	85.59 226	59.66 241	81.47 211	86.02 208	72.85 162	88.05 128	90.65 171	70.73 208	90.91 178	75.15 139	91.79 201	94.87 77

test_normal		81.23 182	81.16 179	81.43 194	84.77 235	61.99 218	81.46 212	86.95 201	63.16 244	87.22 138	89.63 189	73.62 170	91.65 159	72.92 161	90.70 220	90.65 186

V42		83.47 156	83.37 152	83.75 150	83.16 257	63.33 190	81.31 213	90.23 146	69.51 196	90.91 74	90.81 165	74.16 160	92.29 144	80.06 99	90.22 226	95.62 60

PM-MVS		80.20 194	79.00 198	83.78 149	88.17 166	86.66 13	81.31 213	66.81 318	69.64 195	88.33 127	90.19 180	64.58 228	83.63 272	71.99 169	90.03 227	81.06 303

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 129	57.52 254	81.30 215	88.69 169	76.29 120	87.58 134	94.44 66	80.60 98	87.20 236	66.60 207	96.82 73	94.34 91

CMPMVS	59.41 20	75.12 233	73.57 239	79.77 210	75.84 308	67.22 160	81.21 216	82.18 233	50.78 310	76.50 255	87.66 221	55.20 272	82.99 274	62.17 231	90.64 224	89.09 208
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	70.19 17	77.77 208	77.46 206	78.71 224	84.39 241	61.15 229	81.18 217	82.52 231	62.45 251	83.34 199	87.37 225	66.20 223	88.66 224	64.69 218	85.02 277	86.32 235

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 144	83.07 48	80.99 218	83.56 227	72.71 164	86.07 158	89.07 197	81.75 88	86.19 248	77.11 129	93.36 173	88.24 214

wuyk23d		75.13 232	79.30 196	62.63 306	75.56 310	75.18 102	80.89 219	73.10 284	75.06 135	94.76 15	95.32 40	87.73 31	52.85 336	34.16 330	97.11 64	59.85 329

pmmvs-eth3d		78.42 203	77.04 209	82.57 178	87.44 191	74.41 106	80.86 220	79.67 249	55.68 280	84.69 176	90.31 179	60.91 241	85.42 256	62.20 230	91.59 203	87.88 222

PCF-MVS	74.62 15	82.15 171	80.92 183	85.84 106	89.43 140	72.30 121	80.53 221	91.82 88	57.36 273	87.81 132	89.92 185	77.67 120	93.63 86	58.69 251	95.08 129	91.58 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres600view7		75.97 228	75.35 228	77.85 237	87.01 205	51.84 292	80.45 222	73.26 283	75.20 134	83.10 201	86.31 236	45.54 302	89.05 215	55.03 276	92.24 197	92.66 135

BH-untuned		80.96 184	80.99 181	80.84 201	88.55 156	68.23 155	80.33 223	88.46 171	72.79 163	86.55 150	86.76 230	74.72 155	91.77 156	61.79 233	88.99 236	82.52 278

MVP-Stereo		75.81 230	73.51 240	82.71 174	89.35 141	73.62 109	80.06 224	85.20 216	60.30 264	73.96 277	87.94 216	57.89 259	89.45 208	52.02 287	74.87 318	85.06 248
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 224	55.75 266	80.05 225	94.27 13	76.47 119	96.09 5	94.54 63	83.31 65	89.75 205	59.95 245	94.89 135	90.75 182

USDC		76.63 221	76.73 212	76.34 253	83.46 253	57.20 256	80.02 226	88.04 180	52.14 300	83.65 196	91.25 143	63.24 235	86.65 244	54.66 278	94.11 154	85.17 246

ANet_high		83.17 160	85.68 104	75.65 258	81.24 269	45.26 318	79.94 227	92.91 58	83.83 33	91.33 68	96.88 10	80.25 101	85.92 251	68.89 191	95.89 107	95.76 53

tpm2		68.45 278	66.83 282	73.30 268	78.93 287	48.50 310	79.76 228	71.76 294	47.50 320	69.92 298	83.60 269	42.07 322	88.40 225	48.44 302	79.51 305	83.01 273

tpmvs		70.16 267	69.56 269	71.96 278	74.71 320	48.13 311	79.63 229	75.45 268	65.02 235	70.26 296	81.88 289	45.34 306	85.68 254	58.34 257	75.39 317	82.08 284

testdata1							79.62 230		73.95 145

xiu_mvs_v1_base_debu		80.84 186	80.14 191	82.93 171	88.31 162	71.73 130	79.53 231	87.17 192	65.43 228	79.59 238	82.73 282	76.94 130	90.14 199	73.22 156	88.33 242	86.90 231

xiu_mvs_v1_base		80.84 186	80.14 191	82.93 171	88.31 162	71.73 130	79.53 231	87.17 192	65.43 228	79.59 238	82.73 282	76.94 130	90.14 199	73.22 156	88.33 242	86.90 231

xiu_mvs_v1_base_debi		80.84 186	80.14 191	82.93 171	88.31 162	71.73 130	79.53 231	87.17 192	65.43 228	79.59 238	82.73 282	76.94 130	90.14 199	73.22 156	88.33 242	86.90 231

PVSNet_BlendedMVS		78.80 201	77.84 204	81.65 192	84.43 238	63.41 187	79.49 234	90.44 131	61.70 257	75.43 266	87.07 228	69.11 213	91.44 164	60.68 242	92.24 197	90.11 199

test222					93.31 55	76.54 92	79.38 235	77.79 254	52.59 295	82.36 206	90.84 164	66.83 221			91.69 202	81.25 298

PatchmatchNet		69.71 272	68.83 272	72.33 276	77.66 297	53.60 277	79.29 236	69.99 305	57.66 272	72.53 284	82.93 279	46.45 293	80.08 287	60.91 241	72.09 323	83.31 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		69.98 271	68.68 274	73.87 265	77.14 299	50.72 305	79.26 237	74.51 274	51.94 302	70.97 294	84.75 258	45.16 313	87.49 233	55.16 275	79.23 308	83.40 266

tfpn200view9		74.86 237	74.23 235	76.74 250	86.24 218	52.12 288	79.24 238	73.87 276	73.34 152	81.82 214	84.60 261	46.02 296	88.80 219	51.98 288	90.99 211	89.31 205

thres400		75.14 231	74.23 235	77.86 236	86.24 218	52.12 288	79.24 238	73.87 276	73.34 152	81.82 214	84.60 261	46.02 296	88.80 219	51.98 288	90.99 211	92.66 135

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 131	75.86 99	79.23 240	87.13 195	73.35 151	85.56 166	89.34 194	83.60 62	90.50 190	76.64 132	94.05 156	90.09 200

TAMVS		78.08 205	76.36 218	83.23 165	90.62 124	72.87 114	79.08 241	80.01 248	61.72 256	81.35 221	86.92 229	63.96 231	88.78 221	50.61 291	93.01 185	88.04 218

MIMVSNet		71.09 261	71.59 256	69.57 285	87.23 197	50.07 308	78.91 242	71.83 293	60.20 265	71.26 291	91.76 133	55.08 273	76.09 294	41.06 319	87.02 259	82.54 277

Patchmatch-test1		72.75 249	72.61 248	73.19 269	81.62 266	55.86 264	78.89 243	71.37 297	61.73 255	74.93 271	82.15 286	60.46 243	81.80 278	59.68 247	82.63 295	81.92 288

test_post1							78.85 244				3.13 339	45.19 308	80.13 286	58.11 260

mvs_anonymous		78.13 204	78.76 199	76.23 255	79.24 284	50.31 307	78.69 245	84.82 224	61.60 258	83.09 202	92.82 107	73.89 166	87.01 237	68.33 197	86.41 264	91.37 171

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 53	54.88 271	78.67 246	85.02 221	81.24 59	90.74 76	91.56 138	72.85 188	91.08 172	68.00 198	98.04 36	97.23 25

Fast-Effi-MVS+		81.04 183	80.57 184	82.46 180	87.50 190	63.22 194	78.37 247	89.63 158	68.01 206	81.87 212	82.08 288	82.31 76	92.65 138	67.10 202	88.30 246	91.51 170

tpmrst		66.28 287	66.69 284	65.05 303	72.82 329	39.33 329	78.20 248	70.69 303	53.16 293	67.88 305	80.36 298	48.18 288	74.75 299	58.13 259	70.79 325	81.08 301

tpm cat1		66.76 284	65.21 289	71.42 279	77.09 300	50.62 306	78.01 249	73.68 280	44.89 326	68.64 300	79.00 304	45.51 303	82.42 277	49.91 294	70.15 327	81.23 300

jason		77.42 210	75.75 224	82.43 181	87.10 203	69.27 147	77.99 250	81.94 237	51.47 305	77.84 247	85.07 253	60.32 244	89.00 216	70.74 175	89.27 234	89.03 209
jason: jason.
CLD-MVS		83.18 159	82.64 160	84.79 121	89.05 146	67.82 159	77.93 251	92.52 71	68.33 204	85.07 169	81.54 293	82.06 79	92.96 128	69.35 185	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet		77.32 211	75.40 226	83.06 168	89.00 148	72.48 119	77.90 252	82.17 234	60.81 261	78.94 244	83.49 271	59.30 252	88.76 222	54.64 279	92.37 194	87.93 221
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
BH-RMVSNet		80.53 189	80.22 190	81.49 193	87.19 199	66.21 167	77.79 253	86.23 205	74.21 142	83.69 193	88.50 204	73.25 184	90.75 182	63.18 228	87.90 249	87.52 224

PatchT		70.52 264	72.76 246	63.79 305	79.38 282	33.53 334	77.63 254	65.37 320	73.61 147	71.77 288	92.79 110	44.38 316	75.65 297	64.53 221	85.37 272	82.18 283

BH-w/o		76.57 222	76.07 221	78.10 232	86.88 207	65.92 169	77.63 254	86.33 204	65.69 226	80.89 225	79.95 301	68.97 215	90.74 183	53.01 284	85.25 274	77.62 308

MSDG		80.06 196	79.99 194	80.25 206	83.91 250	68.04 157	77.51 256	89.19 164	77.65 101	81.94 211	83.45 272	76.37 138	86.31 247	63.31 227	86.59 262	86.41 234

MVSTER		77.09 213	75.70 225	81.25 197	75.27 316	61.08 230	77.49 257	85.07 219	60.78 262	86.55 150	88.68 202	43.14 319	90.25 193	73.69 151	90.67 221	92.42 144

TR-MVS		76.77 220	75.79 222	79.72 212	86.10 223	65.79 170	77.14 258	83.02 229	65.20 232	81.40 220	82.10 287	66.30 222	90.73 184	55.57 271	85.27 273	82.65 274

lupinMVS		76.37 225	74.46 233	82.09 182	85.54 227	69.26 148	76.79 259	80.77 244	50.68 312	76.23 259	82.82 280	58.69 255	88.94 217	69.85 181	88.77 238	88.07 216

FMVSNet5		72.10 255	71.69 255	73.32 267	81.57 267	53.02 282	76.77 260	78.37 252	63.31 242	76.37 256	91.85 128	36.68 329	78.98 288	47.87 304	92.45 193	87.95 220

VPNet		80.25 192	81.68 172	75.94 256	92.46 76	47.98 313	76.70 261	81.67 239	73.45 149	84.87 173	92.82 107	74.66 156	86.51 245	61.66 235	96.85 70	93.33 119

PAPM		71.77 256	70.06 267	76.92 246	86.39 211	53.97 274	76.62 262	86.62 202	53.44 291	63.97 320	84.73 259	57.79 260	92.34 142	39.65 321	81.33 300	84.45 253

DWT-MVSNet_test		66.43 285	64.37 290	72.63 274	74.86 319	50.86 300	76.52 263	72.74 285	54.06 288	65.50 313	68.30 329	32.13 334	84.84 262	61.63 236	73.59 319	82.19 282

1112_ss		74.82 238	73.74 237	78.04 233	89.57 139	60.04 239	76.49 264	87.09 198	54.31 286	73.66 279	79.80 302	60.25 245	86.76 243	58.37 256	84.15 284	87.32 227

DELS-MVS		81.44 177	81.25 178	82.03 183	84.27 242	62.87 200	76.47 265	92.49 72	70.97 185	81.64 218	83.83 267	75.03 147	92.70 135	74.29 143	92.22 199	90.51 190
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
IterMVS		76.91 215	76.34 219	78.64 225	80.91 273	64.03 180	76.30 266	79.03 250	64.88 236	83.11 200	89.16 195	59.90 248	84.46 265	68.61 195	85.15 276	87.42 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		74.92 236	72.98 245	80.73 203	84.95 232	71.71 133	76.23 267	77.59 255	52.83 294	77.73 250	86.38 233	56.35 265	84.97 260	57.72 261	87.05 258	85.51 244

PatchMatch-RL		74.48 239	73.22 242	78.27 230	87.70 185	85.26 30	75.92 268	70.09 304	64.34 238	76.09 261	81.25 295	65.87 226	78.07 289	53.86 281	83.82 285	71.48 319

PatchFormer-LS_test		67.91 280	66.49 286	72.17 277	75.29 315	51.85 291	75.68 269	73.62 281	57.23 275	68.64 300	68.13 330	42.19 321	82.76 275	64.06 222	73.51 320	81.89 289

JIA-IIPM		69.41 275	66.64 285	77.70 238	73.19 325	71.24 136	75.67 270	65.56 319	70.42 188	65.18 315	92.97 103	33.64 333	83.06 273	53.52 283	69.61 330	78.79 307

tpm		67.95 279	68.08 278	67.55 294	78.74 289	43.53 324	75.60 271	67.10 317	54.92 284	72.23 286	88.10 213	42.87 320	75.97 295	52.21 286	80.95 303	83.15 271

VNet		79.31 198	80.27 189	76.44 251	87.92 173	53.95 275	75.58 272	84.35 226	74.39 141	82.23 207	90.72 167	72.84 189	84.39 266	60.38 244	93.98 158	90.97 174

xiu_mvs_v2_base		77.19 212	76.75 211	78.52 227	87.01 205	61.30 227	75.55 273	87.12 197	61.24 260	74.45 274	78.79 305	77.20 124	90.93 176	64.62 220	84.80 281	83.32 268

PS-MVSNAJ		77.04 214	76.53 217	78.56 226	87.09 204	61.40 225	75.26 274	87.13 195	61.25 259	74.38 276	77.22 311	76.94 130	90.94 175	64.63 219	84.83 280	83.35 267

PVSNet_Blended		76.49 224	75.40 226	79.76 211	84.43 238	63.41 187	75.14 275	90.44 131	57.36 273	75.43 266	78.30 307	69.11 213	91.44 164	60.68 242	87.70 253	84.42 254

thres200		72.34 253	71.55 257	74.70 263	83.48 252	51.60 293	75.02 276	73.71 279	70.14 192	78.56 245	80.57 297	46.20 294	88.20 228	46.99 308	89.29 232	84.32 256

EPMVS		62.47 293	62.63 297	62.01 307	70.63 334	38.74 330	74.76 277	52.86 337	53.91 289	67.71 307	80.01 300	39.40 327	66.60 320	55.54 272	68.81 332	80.68 305

DSMNet-mixed		60.98 303	61.61 300	59.09 317	72.88 328	45.05 320	74.70 278	46.61 341	26.20 336	65.34 314	90.32 178	55.46 270	63.12 330	41.72 318	81.30 301	69.09 323

FPMVS		72.29 254	72.00 253	73.14 270	88.63 155	85.00 32	74.65 279	67.39 312	71.94 176	77.80 249	87.66 221	50.48 283	75.83 296	49.95 293	79.51 305	58.58 331

testpf		58.55 306	61.58 301	49.48 323	66.03 336	40.05 328	74.40 280	58.07 333	64.72 237	59.36 328	72.67 321	22.76 342	66.92 318	67.07 203	69.15 331	41.46 334

pmmvs5		70.73 263	70.07 266	72.72 273	77.03 301	52.73 284	74.14 281	75.65 267	50.36 314	72.17 287	85.37 249	55.42 271	80.67 283	52.86 285	87.59 254	84.77 250

MDTV_nov1_ep13			68.29 277		78.03 295	43.87 323	74.12 282	72.22 289	52.17 298	67.02 308	85.54 241	45.36 305	80.85 282	55.73 268	84.42 283

IB-MVS	62.13 19	71.64 257	68.97 271	79.66 215	80.80 276	62.26 217	73.94 283	76.90 259	63.27 243	68.63 302	76.79 312	33.83 332	91.84 153	59.28 250	87.26 256	84.88 249
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
cascas		76.29 226	74.81 229	80.72 204	84.47 237	62.94 198	73.89 284	87.34 188	55.94 279	75.16 270	76.53 314	63.97 230	91.16 170	65.00 216	90.97 213	88.06 217

MS-PatchMatch		70.93 262	70.22 265	73.06 271	81.85 265	62.50 209	73.82 285	77.90 253	52.44 297	75.92 262	81.27 294	55.67 269	81.75 279	55.37 273	77.70 312	74.94 313

LP		69.42 274	68.30 276	72.77 272	71.48 333	56.84 260	73.66 286	74.84 270	63.52 240	70.95 295	83.35 274	49.55 286	77.15 292	57.13 263	70.21 326	84.33 255

GA-MVS		75.83 229	74.61 230	79.48 218	81.87 264	59.25 245	73.42 287	82.88 230	68.68 203	79.75 237	81.80 290	50.62 282	89.46 207	66.85 205	85.64 270	89.72 201

Test_1112_low_res		73.90 243	73.08 243	76.35 252	90.35 128	55.95 262	73.40 288	86.17 206	50.70 311	73.14 281	85.94 239	58.31 257	85.90 252	56.51 265	83.22 288	87.20 228

HyFIR lowres test		75.12 233	72.66 247	82.50 179	91.44 107	65.19 172	72.47 289	87.31 189	46.79 321	80.29 234	84.30 263	52.70 278	92.10 148	51.88 290	86.73 260	90.22 195

Patchmatch-RL test		74.48 239	73.68 238	76.89 247	84.83 234	66.54 166	72.29 290	69.16 309	57.70 271	86.76 146	86.33 234	45.79 301	82.59 276	69.63 183	90.65 223	81.54 294

MVS-HIRNet		61.16 301	62.92 296	55.87 318	79.09 285	35.34 333	71.83 291	57.98 334	46.56 322	59.05 330	91.14 150	49.95 284	76.43 293	38.74 324	71.92 324	55.84 332

XXY-MVS		74.44 241	76.19 220	69.21 286	84.61 236	52.43 287	71.70 292	77.18 257	60.73 263	80.60 228	90.96 160	75.44 141	69.35 310	56.13 267	88.33 242	85.86 241

dp		60.70 304	60.29 305	61.92 309	72.04 331	38.67 331	70.83 293	64.08 321	51.28 307	60.75 324	77.28 310	36.59 330	71.58 305	47.41 305	62.34 334	75.52 312

MDTV_nov1_ep13_2view						27.60 337	70.76 294		46.47 323	61.27 323		45.20 307		49.18 298		83.75 261

pmmvs3		62.47 293	60.02 306	69.80 282	71.58 332	64.00 181	70.52 295	58.44 332	39.77 332	66.05 310	75.84 315	27.10 341	72.28 302	46.15 310	84.77 282	73.11 317

Anonymous20231206		71.38 260	71.88 254	69.88 281	86.31 216	54.37 272	70.39 296	74.62 272	52.57 296	76.73 254	88.76 200	59.94 247	72.06 303	44.35 314	93.23 181	83.23 270

test20.03		73.75 244	74.59 232	71.22 280	81.11 271	51.12 297	70.15 297	72.10 290	70.42 188	80.28 235	91.50 139	64.21 229	74.72 300	46.96 309	94.58 148	87.82 223

UnsupCasMVSNet_eth		71.63 258	72.30 252	69.62 284	76.47 304	52.70 285	70.03 298	80.97 243	59.18 267	79.36 241	88.21 212	60.50 242	69.12 311	58.33 258	77.62 313	87.04 229

Patchmatch-test		65.91 288	67.38 279	61.48 311	75.51 312	43.21 325	68.84 299	63.79 322	62.48 250	72.80 283	83.42 273	44.89 314	59.52 332	48.27 303	86.45 263	81.70 290

CHOSEN 1792x2688		72.45 251	70.56 261	78.13 231	90.02 137	63.08 195	68.72 300	83.16 228	42.99 330	75.92 262	85.46 245	57.22 264	85.18 259	49.87 295	81.67 297	86.14 237

testgi		72.36 252	74.61 230	65.59 299	80.56 277	42.82 326	68.29 301	73.35 282	66.87 216	81.84 213	89.93 184	72.08 200	66.92 318	46.05 311	92.54 192	87.01 230

test-LLR		67.21 282	66.74 283	68.63 290	76.45 305	55.21 268	67.89 302	67.14 315	62.43 252	65.08 316	72.39 322	43.41 317	69.37 308	61.00 239	84.89 278	81.31 296

TESTMET0.1,1		61.29 300	60.32 304	64.19 304	72.06 330	51.30 294	67.89 302	62.09 324	45.27 325	60.65 325	69.01 325	27.93 340	64.74 326	56.31 266	81.65 299	76.53 309

test-mter		65.00 291	63.79 292	68.63 290	76.45 305	55.21 268	67.89 302	67.14 315	50.98 309	65.08 316	72.39 322	28.27 339	69.37 308	61.00 239	84.89 278	81.31 296

UnsupCasMVSNet_bld		69.21 276	69.68 268	67.82 293	79.42 281	51.15 296	67.82 305	75.79 264	54.15 287	77.47 251	85.36 250	59.26 253	70.64 306	48.46 301	79.35 307	81.66 292

testmv		70.47 265	70.70 260	69.77 283	86.22 220	53.89 276	67.32 306	71.91 292	63.32 241	78.16 246	89.47 192	56.12 267	73.10 301	36.43 327	87.33 255	82.33 281

ADS-MVSNet2		65.87 289	63.64 294	72.55 275	73.16 326	56.92 258	67.10 307	74.81 271	49.74 315	66.04 311	82.97 277	46.71 291	77.26 290	42.29 316	69.96 328	83.46 264

ADS-MVSNet		61.90 296	62.19 298	61.03 313	73.16 326	36.42 332	67.10 307	61.75 326	49.74 315	66.04 311	82.97 277	46.71 291	63.21 329	42.29 316	69.96 328	83.46 264

MDA-MVSNet-bldmvs		77.47 209	76.90 210	79.16 219	79.03 286	64.59 175	66.58 309	75.67 266	73.15 159	88.86 114	88.99 198	66.94 220	81.23 281	64.71 217	88.22 247	91.64 166

WTY-MVS		67.91 280	68.35 275	66.58 297	80.82 275	48.12 312	65.96 310	72.60 286	53.67 290	71.20 292	81.68 292	58.97 254	69.06 312	48.57 300	81.67 297	82.55 276

sss		66.92 283	67.26 281	65.90 298	77.23 298	51.10 298	64.79 311	71.72 296	52.12 301	70.13 297	80.18 299	57.96 258	65.36 325	50.21 292	81.01 302	81.25 298

test0.0.03 1		64.66 292	64.36 291	65.57 300	75.03 318	46.89 314	64.69 312	61.58 328	62.43 252	71.18 293	77.54 308	43.41 317	68.47 313	40.75 320	82.65 293	81.35 295

PMMVS		61.65 298	60.38 303	65.47 301	65.40 338	69.26 148	63.97 313	61.73 327	36.80 335	60.11 326	68.43 326	59.42 251	66.35 321	48.97 299	78.57 310	60.81 328

test123		6.27 320	8.08 321	0.84 330	1.11 344	0.57 344	62.90 314	0.82 346	0.54 339	1.07 341	2.75 342	1.26 347	0.30 342	1.04 339	1.26 341	1.66 337

test1235678		65.57 290	65.73 287	65.06 302	82.84 260	50.90 299	62.90 314	69.26 307	57.17 276	72.36 285	83.04 275	46.02 296	70.10 307	32.79 332	85.24 275	74.19 315

PVSNet	58.17 21	66.41 286	65.63 288	68.75 289	81.96 263	49.88 309	62.19 316	72.51 288	51.03 308	68.04 304	75.34 318	50.84 281	74.77 298	45.82 312	82.96 289	81.60 293

1111		61.71 297	63.77 293	55.55 320	78.05 293	25.74 338	60.62 317	67.52 310	66.09 221	74.68 272	86.50 231	16.00 344	59.22 333	38.79 322	85.65 269	81.70 290

.test1245		48.02 314	54.41 313	28.84 327	78.05 293	25.74 338	60.62 317	67.52 310	66.09 221	74.68 272	86.50 231	16.00 344	59.22 333	38.79 322	1.47 339	1.55 338

new_pmnet		55.69 309	57.66 308	49.76 322	75.47 313	30.59 335	59.56 319	51.45 339	43.62 329	62.49 321	75.48 316	40.96 325	49.15 338	37.39 326	72.52 321	69.55 322

new-patchmatchnet		70.10 268	73.37 241	60.29 314	81.23 270	16.95 341	59.54 320	74.62 272	62.93 245	80.97 223	87.93 217	62.83 236	71.90 304	55.24 274	95.01 132	92.00 158

testmvs		5.91 321	7.65 322	0.72 331	1.20 343	0.37 345	59.14 321	0.67 347	0.49 340	1.11 340	2.76 341	0.94 348	0.24 343	1.02 340	1.47 339	1.55 338

testus		62.33 295	63.03 295	60.20 315	78.78 288	40.74 327	59.14 321	69.80 306	49.26 317	71.41 290	74.72 320	52.33 279	63.52 327	29.84 334	82.01 296	76.36 310

N_pmnet		70.20 266	68.80 273	74.38 264	80.91 273	84.81 35	59.12 323	76.45 263	55.06 283	75.31 269	82.36 285	55.74 268	54.82 335	47.02 307	87.24 257	83.52 263

YYNet1		70.06 269	70.44 262	68.90 287	73.76 322	53.42 280	58.99 324	67.20 314	58.42 269	87.10 141	85.39 248	59.82 249	67.32 315	59.79 246	83.50 287	85.96 238

MDA-MVSNet_test_wron		70.05 270	70.44 262	68.88 288	73.84 321	53.47 278	58.93 325	67.28 313	58.43 268	87.09 142	85.40 247	59.80 250	67.25 316	59.66 248	83.54 286	85.92 240

no-one		71.52 259	70.43 264	74.81 261	78.45 292	63.41 187	57.73 326	77.03 258	51.46 306	77.17 253	90.33 177	54.96 274	80.35 285	47.41 305	99.29 2	80.68 305

PVSNet_0	51.08 22	56.10 308	54.97 312	59.48 316	75.12 317	53.28 281	55.16 327	61.89 325	44.30 327	59.16 329	62.48 334	54.22 275	65.91 323	35.40 329	47.01 335	59.25 330

test2356		56.69 307	55.15 311	61.32 312	73.20 324	44.11 322	54.95 328	62.52 323	48.75 319	62.45 322	68.42 327	21.10 343	65.67 324	26.86 336	78.08 311	74.19 315

PNet_i23d		52.13 312	51.24 314	54.79 321	75.56 310	45.26 318	54.54 329	52.55 338	66.95 214	57.19 334	65.82 331	13.15 346	63.40 328	36.39 328	39.04 337	55.71 333

E-PMN		61.59 299	61.62 299	61.49 310	66.81 335	55.40 267	53.77 330	60.34 329	66.80 217	58.90 331	65.50 332	40.48 326	66.12 322	55.72 269	86.25 266	62.95 327

EMVS		61.10 302	60.81 302	61.99 308	65.96 337	55.86 264	53.10 331	58.97 331	67.06 213	56.89 335	63.33 333	40.98 324	67.03 317	54.79 277	86.18 267	63.08 326

test12356		54.91 311	57.14 309	48.22 324	75.83 309	17.47 340	52.31 332	69.20 308	51.66 304	60.11 326	75.40 317	29.77 338	62.62 331	27.64 335	72.37 322	64.59 325

CHOSEN 280x420		59.08 305	56.52 310	66.76 296	76.51 303	64.39 178	49.62 333	59.00 330	43.86 328	55.66 336	68.41 328	35.55 331	68.21 314	43.25 315	76.78 316	67.69 324

PMMVS2		55.64 310	59.27 307	44.74 325	64.30 339	12.32 342	40.60 334	49.79 340	53.19 292	65.06 318	84.81 257	53.60 277	49.76 337	32.68 333	89.41 231	72.15 318

tmp_tt		20.25 317	24.50 318	7.49 329	4.47 342	8.70 343	34.17 335	25.16 344	1.00 338	32.43 339	18.49 337	39.37 328	9.21 341	21.64 337	43.75 336	4.57 336

MVE	40.22 23	51.82 313	50.47 315	55.87 318	62.66 340	51.91 290	31.61 336	39.28 342	40.65 331	50.76 337	74.98 319	56.24 266	44.67 339	33.94 331	64.11 333	71.04 321
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		20.81 316	27.75 317	0.00 332	0.00 345	0.00 346	0.00 337	85.44 213	0.00 341	0.00 342	82.82 280	81.46 90	0.00 344	0.00 341	0.00 342	0.00 340

pcd_1.5k_mvsjas		6.41 319	8.55 320	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	76.94 130	0.00 344	0.00 341	0.00 342	0.00 340

pcd1.5k->3k		38.83 315	41.11 316	32.01 326	93.13 60	0.00 346	0.00 337	91.38 111	0.00 341	0.00 342	0.00 343	89.24 16	0.00 344	0.00 341	96.24 92	96.02 48

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ab-mvs-re		6.65 318	8.87 319	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	79.80 302	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 342	0.00 340

ESAPD								93.79 28

sam_mvs1												46.11 295

sam_mvs												45.92 300

semantic-postprocess				84.34 136	83.93 249	69.66 144		81.09 242	72.43 166	86.47 156	90.19 180	57.56 261	93.15 122	77.45 125	86.39 265	90.22 195

MTGPA								91.81 89

test_post											3.10 340	45.43 304	77.22 291

patchmatchnet-post											81.71 291	45.93 299	87.01 237

MTMP								33.14 343

gm-plane-assit					75.42 314	44.97 321			52.17 298		72.36 324		87.90 229	54.10 280

test9_res														80.83 87	96.45 85	90.57 187

agg_prior2														79.68 104	96.16 94	90.22 195

agg_prior					91.58 101	77.69 79		90.30 139		84.32 187			93.18 118

TestCases				89.68 47	91.59 98	83.40 45		95.44 5	79.47 75	88.00 129	93.03 100	82.66 71	91.47 162	70.81 172	96.14 96	94.16 95

test_prior				86.32 91	90.59 125	71.99 127		92.85 60					94.17 66			92.80 130

新几何1				82.95 170	93.96 44	78.56 75		80.24 245	55.45 281	83.93 192	91.08 154	71.19 207	88.33 226	65.84 213	93.07 182	81.95 287

旧先验1					91.97 90	71.77 129		81.78 238			91.84 129	73.92 165			93.65 168	83.61 262

原ACMM1				84.60 127	92.81 70	74.01 108		91.50 96	62.59 248	82.73 205	90.67 170	76.53 137	94.25 61	69.24 186	95.69 115	85.55 243

testdata2													86.43 246	63.52 225

segment_acmp												81.94 82

testdata				79.54 217	92.87 66	72.34 120		80.14 247	59.91 266	85.47 168	91.75 134	67.96 217	85.24 257	68.57 196	92.18 200	81.06 303

test12				86.57 86	90.74 122	72.63 116		90.69 124		82.76 204		79.20 107	94.80 49		95.32 121	92.27 152

plane_prior7					93.45 51	77.31 85

plane_prior6					92.61 71	76.54 92						74.84 151

plane_prior5								93.61 31					95.22 37	80.78 88	95.83 110	94.46 86

plane_prior4											92.95 104

plane_prior3						76.85 90			77.79 99	86.55 150

plane_prior1					92.83 69

n2								0.00 348

nn								0.00 348

door-mid								74.45 275

lessismore_v0				85.95 102	91.10 116	70.99 138		70.91 302		91.79 60	94.42 67	61.76 238	92.93 130	79.52 108	93.03 184	93.93 102

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 46	93.67 30	94.82 54	91.18 6	95.52 25	85.36 34	98.73 8	95.23 72

test11								91.46 102

door								72.57 287

HQP5-MVS						70.66 139

BP-MVS														77.30 127

HQP4-MVS										80.56 230			94.61 53			93.56 116

HQP3-MVS								92.68 66							94.47 150

HQP2-MVS												72.10 198

NP-MVS					91.95 91	74.55 105					90.17 182

ACMMP++_ref															95.74 114

ACMMP++															97.35 58

Test By Simon												79.09 108

ITE_SJBPF				90.11 45	90.72 123	84.97 33		90.30 139	81.56 57	90.02 83	91.20 147	82.40 75	90.81 181	73.58 152	94.66 146	94.56 81

DeepMVS_CX				24.13 328	32.95 341	29.49 336		21.63 345	12.07 337	37.95 338	45.07 336	30.84 335	19.21 340	17.94 338	33.06 338	23.69 335

High-resolution multi-view benchmark