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.

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 68	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 10	85.07 36	99.27 3	99.54 1

Effi-MVS+-dtu		85.82 103	83.38 152	93.14 3	87.13 205	91.15 3	87.70 81	88.42 173	74.57 144	83.56 200	85.65 245	78.49 114	94.21 65	72.04 169	92.88 190	94.05 98

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 43	87.54 18	98.02 39	93.71 110

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 71	83.09 40	91.54 66	94.25 75	87.67 33	95.51 29	87.21 23	98.11 35	93.12 125

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 50	95.13 45	90.65 11	95.34 34	88.06 10	98.15 34	95.95 51

anonymousdsp		89.73 46	88.88 57	92.27 7	89.82 140	86.67 12	90.51 35	90.20 148	69.87 205	95.06 14	96.14 23	84.28 58	93.07 129	87.68 13	96.34 89	97.09 30

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 86	94.03 82	86.57 43	95.80 15	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 113	82.70 160	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 86	16.05 350	86.57 43	95.80 15	87.35 19	97.62 52	94.20 92

COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 50	86.15 18	93.37 8	95.10 7	90.28 9	92.11 55	95.03 47	89.75 15	94.93 48	79.95 103	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 47	92.39 54	94.14 79	89.15 17	95.62 21	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
CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 39	91.06 72	94.00 83	88.26 23	95.71 20	87.28 22	98.39 23	92.55 143

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 28	80.32 69	91.74 64	94.41 68	88.17 25	95.98 7	86.37 25	97.99 41	93.96 101

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 53	91.40 69	94.17 78	87.51 34	95.87 12	87.74 11	97.76 47	93.99 99

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 43	91.77 63	93.94 90	90.55 13	95.73 19	88.50 8	98.23 31	95.33 69

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 55	90.88 77	94.21 76	87.75 30	95.87 12	87.60 16	97.71 50	93.83 103

mvs-test1		84.55 124	82.12 169	91.84 17	87.13 205	89.54 5	85.05 123	88.42 173	74.57 144	80.60 239	82.98 282	78.49 114	93.98 75	72.04 169	89.77 241	92.00 159

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 55	82.59 46	88.52 124	94.37 72	86.74 40	95.41 32	86.32 26	98.21 32	93.19 124

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 49	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 54	93.16 122	91.10 1	97.53 58	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
APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 64	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 44	86.67 24	97.60 56	94.18 94

test_djsdf		89.62 47	89.01 54	91.45 21	92.36 81	82.98 49	91.98 25	90.08 149	71.54 191	94.28 23	96.54 13	81.57 91	94.27 61	86.26 27	96.49 85	97.09 30

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 64	88.22 16	88.53 123	97.64 3	83.45 65	94.55 59	86.02 33	98.60 14	96.67 36

LS3D		90.60 28	90.34 41	91.38 23	89.03 149	84.23 42	93.58 4	94.68 10	90.65 7	90.33 81	93.95 89	84.50 57	95.37 33	80.87 88	95.50 120	94.53 85

mvs_tets		89.78 45	89.27 52	91.30 24	93.51 52	84.79 36	89.89 44	90.63 127	70.00 204	94.55 18	96.67 11	87.94 29	93.59 93	84.27 48	95.97 105	95.52 65

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 64	91.81 90	84.07 32	92.00 58	94.40 69	86.63 41	95.28 37	88.59 4	98.31 26	92.30 151

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 90	84.07 32	92.00 58	94.40 69	86.63 41	95.28 37	88.59 4	98.31 26	92.30 151

RPSCF		88.00 66	86.93 85	91.22 27	90.08 134	89.30 6	89.68 48	91.11 119	79.26 81	89.68 98	94.81 57	82.44 76	87.74 236	76.54 135	88.74 252	96.61 38

jajsoiax		89.41 50	88.81 59	91.19 28	93.38 56	84.72 37	89.70 46	90.29 143	69.27 208	94.39 20	96.38 15	86.02 50	93.52 103	83.96 51	95.92 108	95.34 68

HSP-MVS		88.63 60	87.84 68	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 94	89.16 113	92.25 126	72.03 205	96.36 2	88.21 9	90.93 225	90.55 194

HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 53	91.47 67	93.96 86	88.35 21	95.56 24	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 42	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 118	91.47 67	93.96 86	88.35 21	95.56 24	84.88 39	97.74 48	92.85 129

3Dnovator+	83.92 2	89.97 40	89.66 48	90.92 32	91.27 112	81.66 54	91.25 32	94.13 22	88.89 12	88.83 118	94.26 74	77.55 124	95.86 14	84.88 39	95.87 110	95.24 71

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 61	80.37 59	91.91 27	93.11 49	81.10 62	95.32 13	97.24 6	72.94 190	94.85 50	85.07 36	97.78 46	97.26 23

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 48	93.67 30	94.82 54	91.18 6	95.52 27	85.36 34	98.73 8	95.23 72

LGP-MVS_train				90.82 34	94.75 35	81.69 51		94.27 13	82.35 48	93.67 30	94.82 54	91.18 6	95.52 27	85.36 34	98.73 8	95.23 72

CPTT-MVS		89.39 51	88.98 56	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 74	87.50 138	92.38 120	81.42 93	93.28 117	83.07 62	97.24 63	91.67 166

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 44	92.60 49	93.97 84	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
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DeepPCF-MVS	81.24 5	87.28 74	86.21 97	90.49 38	91.48 107	84.90 34	83.41 162	92.38 76	70.25 202	89.35 111	90.68 172	82.85 71	94.57 57	79.55 107	95.95 106	92.00 159

XVG-ACMP-BASELINE		89.98 38	89.84 45	90.41 39	94.91 33	84.50 41	89.49 57	93.98 24	79.68 75	92.09 56	93.89 91	83.80 61	93.10 125	82.67 69	98.04 36	93.64 112

HPM-MVS++		88.93 57	88.45 63	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 97	86.69 150	92.28 125	80.36 102	95.06 45	86.17 31	96.49 85	90.22 200

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 61	94.05 23	79.03 86	92.87 40	93.74 93	90.60 12	95.21 41	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		89.59 48	89.37 51	90.28 42	94.47 39	85.95 21	86.84 95	93.91 27	80.07 72	86.75 149	93.26 99	93.64 2	90.93 178	84.60 44	90.75 230	93.97 100

XVG-OURS		89.18 54	88.83 58	90.23 43	94.28 41	86.11 20	85.91 111	93.60 34	80.16 71	89.13 114	93.44 96	83.82 60	90.98 176	83.86 54	95.30 127	93.60 114

OMC-MVS		88.19 64	87.52 73	90.19 44	91.94 95	81.68 53	87.49 85	93.17 48	76.02 127	88.64 121	91.22 147	84.24 59	93.37 112	77.97 123	97.03 68	95.52 65

ITE_SJBPF				90.11 45	90.72 125	84.97 33		90.30 140	81.56 59	90.02 85	91.20 150	82.40 77	90.81 183	73.58 154	94.66 149	94.56 81

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 75	94.51 11	75.79 131	92.94 38	94.96 49	88.36 20	95.01 46	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
AllTest		87.97 67	87.40 77	89.68 47	91.59 100	83.40 45	89.50 56	95.44 5	79.47 77	88.00 131	93.03 103	82.66 73	91.47 164	70.81 174	96.14 98	94.16 95

TestCases				89.68 47	91.59 100	83.40 45		95.44 5	79.47 77	88.00 131	93.03 103	82.66 73	91.47 164	70.81 174	96.14 98	94.16 95

F-COLMAP		84.97 116	83.42 151	89.63 49	92.39 80	83.40 45	88.83 66	91.92 87	73.19 163	80.18 247	89.15 199	77.04 130	93.28 117	65.82 217	92.28 199	92.21 156

ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 52	93.35 38	79.20 82	93.83 28	93.60 95	90.81 9	92.96 130	85.02 38	98.45 20	92.41 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-MVSNAJss		88.31 63	87.90 67	89.56 51	93.31 57	77.96 78	87.94 77	91.97 85	70.73 197	94.19 24	96.67 11	76.94 132	94.57 57	83.07 62	96.28 91	96.15 42

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 31	77.65 103	91.97 60	94.89 51	88.38 19	95.45 30	89.27 3	97.87 45	93.27 120

DeepC-MVS	82.31 4	89.15 55	89.08 53	89.37 53	93.64 51	79.07 70	88.54 71	94.20 17	73.53 153	89.71 97	94.82 54	85.09 52	95.77 17	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
OPM-MVS		89.80 44	89.97 43	89.27 54	94.76 34	79.86 64	86.76 99	92.78 65	78.78 89	92.51 50	93.64 94	88.13 27	93.84 82	84.83 41	97.55 57	94.10 97

APD-MVS		89.54 49	89.63 49	89.26 55	92.57 74	81.34 56	90.19 37	93.08 51	80.87 64	91.13 71	93.19 100	86.22 48	95.97 8	82.23 73	97.18 65	90.45 196
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 66	78.04 77	92.84 12	94.14 21	83.33 37	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 128

TSAR-MVS + MP.		88.14 65	87.82 69	89.09 57	95.72 19	76.74 93	92.49 20	91.19 118	67.85 223	86.63 151	94.84 53	79.58 108	95.96 9	87.62 14	94.50 152	94.56 81

NCCC		87.36 72	86.87 86	88.83 58	92.32 84	78.84 73	86.58 106	91.09 120	78.77 90	84.85 177	90.89 165	80.85 97	95.29 35	81.14 84	95.32 124	92.34 150

Regformer-2		86.74 84	86.08 99	88.73 59	84.18 258	79.20 69	83.52 157	89.33 164	83.33 37	89.92 92	85.07 258	83.23 68	93.16 122	83.39 57	92.72 193	93.83 103

HQP_MVS		87.75 71	87.43 76	88.70 60	93.45 53	76.42 97	89.45 58	93.61 32	79.44 79	86.55 152	92.95 107	74.84 153	95.22 39	80.78 90	95.83 112	94.46 86

Regformer-4		86.41 89	85.71 104	88.52 61	84.27 254	77.57 83	84.07 137	88.00 182	82.82 44	89.84 94	85.48 248	82.06 81	92.77 136	83.83 55	91.04 218	95.22 74

ESAPD		90.05 35	90.56 40	88.50 62	93.86 47	77.77 79	89.63 50	93.93 25	84.39 28	92.84 42	93.43 97	87.19 36	96.26 4	82.18 74	97.61 54	91.48 172

ACMH+	77.89 11	90.73 25	91.50 19	88.44 63	93.00 65	76.26 99	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 109	78.35 117	98.76 5	95.61 64

TAPA-MVS	77.73 12	85.71 106	84.83 119	88.37 64	88.78 154	79.72 65	87.15 91	93.50 35	69.17 210	85.80 165	89.56 194	80.76 98	92.13 148	73.21 162	95.51 119	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PMVS	80.48 6	90.08 33	90.66 36	88.34 65	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 82	95.12 46	90.98 8	88.92 220	77.55 125	97.07 67	83.13 285
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CNVR-MVS		87.81 70	87.68 72	88.21 66	92.87 68	77.30 88	85.25 120	91.23 116	77.31 113	87.07 145	91.47 143	82.94 70	94.71 53	84.67 43	96.27 93	92.62 142

agg_prior3		85.76 104	84.95 117	88.16 67	92.43 79	79.92 62	83.98 140	90.03 151	65.11 245	83.66 198	90.64 176	81.00 96	93.67 85	81.21 82	96.54 82	90.88 183

PHI-MVS		86.38 90	85.81 102	88.08 68	88.44 163	77.34 86	89.35 60	93.05 52	73.15 164	84.76 178	87.70 223	78.87 112	94.18 66	80.67 92	96.29 90	92.73 133

train_agg		85.98 101	85.28 111	88.07 69	92.34 82	79.70 66	83.94 141	90.32 136	65.79 236	84.49 186	90.97 161	81.93 85	93.63 88	81.21 82	96.54 82	90.88 183

v52		89.97 40	90.60 37	88.07 69	88.69 155	72.01 127	91.35 30	92.64 69	82.22 50	95.97 8	96.31 16	84.82 53	93.98 75	88.59 4	94.83 142	98.23 8

V4		89.97 40	90.60 37	88.07 69	88.69 155	72.01 127	91.35 30	92.64 69	82.22 50	95.98 7	96.31 16	84.80 55	93.98 75	88.59 4	94.83 142	98.23 8

CDPH-MVS		86.17 97	85.54 107	88.05 72	92.25 85	75.45 102	83.85 145	92.01 83	65.91 235	86.19 159	91.75 137	83.77 62	94.98 47	77.43 128	96.71 77	93.73 109

DeepC-MVS_fast	80.27 8	86.23 95	85.65 106	87.96 73	91.30 110	76.92 90	87.19 89	91.99 84	70.56 198	84.96 173	90.69 171	80.01 105	95.14 42	78.37 116	95.78 114	91.82 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		83.95 147	82.69 161	87.72 74	89.27 145	81.45 55	83.72 151	81.58 242	74.73 142	85.66 166	86.06 243	72.56 199	92.69 138	75.44 140	95.21 128	89.01 217

Vis-MVSNet		86.86 80	86.58 90	87.72 74	92.09 89	77.43 85	87.35 86	92.09 81	78.87 88	84.27 193	94.05 81	78.35 116	93.65 86	80.54 94	91.58 208	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v7n		90.13 32	90.96 32	87.65 76	91.95 93	71.06 139	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 72	94.08 71	86.25 29	97.63 51	97.82 11

MVS_0304		84.88 117	83.96 147	87.64 77	87.43 195	74.83 105	84.18 135	93.30 42	77.48 106	77.39 264	88.46 208	74.53 161	95.74 18	78.09 122	94.75 148	92.36 149

Regformer-1		86.00 98	85.50 108	87.49 78	84.18 258	76.90 91	83.52 157	87.94 184	82.18 52	89.19 112	85.07 258	82.28 79	91.89 154	82.40 71	92.72 193	93.69 111

PLC	73.85 16	82.09 173	80.31 190	87.45 79	90.86 123	80.29 60	85.88 112	90.65 126	68.17 217	76.32 270	86.33 239	73.12 188	92.61 141	61.40 241	90.02 240	89.44 208
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
wuykxyi23d		88.46 62	88.80 60	87.44 80	90.96 120	93.03 1	85.85 113	81.96 237	74.58 143	98.58 2	97.29 5	87.73 31	87.31 239	82.84 67	99.41 1	81.99 298

DP-MVS		88.60 61	89.01 54	87.36 81	91.30 110	77.50 84	87.55 83	92.97 57	87.95 17	89.62 103	92.87 109	84.56 56	93.89 79	77.65 124	96.62 79	90.70 188

agg_prior1		85.72 105	85.20 112	87.28 82	91.58 103	77.69 81	83.69 152	90.30 140	66.29 231	84.32 190	91.07 158	82.13 80	93.18 120	81.02 85	96.36 88	90.98 178

EI-MVSNet-Vis-set		85.12 111	84.53 132	86.88 83	84.01 260	72.76 117	83.91 144	85.18 218	80.44 66	88.75 119	85.49 247	80.08 104	91.92 152	82.02 76	90.85 228	95.97 49

EI-MVSNet-UG-set		85.04 113	84.44 134	86.85 84	83.87 263	72.52 120	83.82 146	85.15 219	80.27 70	88.75 119	85.45 251	79.95 106	91.90 153	81.92 78	90.80 229	96.13 43

EPP-MVSNet		85.47 108	85.04 114	86.77 85	91.52 106	69.37 148	91.63 28	87.98 183	81.51 60	87.05 146	91.83 133	66.18 227	95.29 35	70.75 176	96.89 71	95.64 59

CANet		83.79 150	82.85 159	86.63 86	86.17 227	72.21 126	83.76 150	91.43 106	77.24 114	74.39 287	87.45 227	75.36 145	95.42 31	77.03 132	92.83 191	92.25 155

test12				86.57 87	90.74 124	72.63 118		90.69 125		82.76 209		79.20 109	94.80 51		95.32 124	92.27 153

UniMVSNet (Re)		86.87 79	86.98 83	86.55 88	93.11 63	68.48 156	83.80 148	92.87 60	80.37 67	89.61 105	91.81 135	77.72 121	94.18 66	75.00 144	98.53 17	96.99 34

DP-MVS Recon		84.05 144	83.22 154	86.52 89	91.73 98	75.27 103	83.23 169	92.40 74	72.04 180	82.04 215	88.33 213	77.91 120	93.95 78	66.17 212	95.12 131	90.34 199

SixPastTwentyTwo		87.20 75	87.45 75	86.45 90	92.52 76	69.19 153	87.84 80	88.05 180	81.66 58	94.64 17	96.53 14	65.94 228	94.75 52	83.02 64	96.83 74	95.41 67

K. test v3		85.14 110	84.73 120	86.37 91	91.13 117	69.63 147	85.45 118	76.68 264	84.06 34	92.44 52	96.99 8	62.03 240	94.65 54	80.58 93	93.24 183	94.83 80

test_prior3		86.31 92	86.31 94	86.32 92	90.59 127	71.99 129	83.37 163	92.85 61	75.43 136	84.58 184	91.57 139	81.92 87	94.17 68	79.54 108	96.97 69	92.80 131

test_prior				86.32 92	90.59 127	71.99 129		92.85 61					94.17 68			92.80 131

HQP-MVS		84.61 122	84.06 144	86.27 94	91.19 113	70.66 141	84.77 124	92.68 67	73.30 159	80.55 242	90.17 185	72.10 201	94.61 55	77.30 129	94.47 153	93.56 116

EPNet		80.37 192	78.41 204	86.23 95	76.75 314	73.28 114	87.18 90	77.45 258	76.24 124	68.14 315	88.93 202	65.41 230	93.85 80	69.47 186	96.12 100	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Regformer-3		85.06 112	84.67 125	86.22 96	84.27 254	73.43 113	84.07 137	85.26 216	80.77 65	88.62 122	85.48 248	80.56 101	90.39 194	81.99 77	91.04 218	94.85 79

SD-MVS		88.96 56	89.88 44	86.22 96	91.63 99	77.07 89	89.82 45	93.77 30	78.90 87	92.88 39	92.29 124	86.11 49	90.22 198	86.24 30	97.24 63	91.36 175

DU-MVS		86.80 82	86.99 82	86.21 98	93.24 59	67.02 164	83.16 170	92.21 78	81.73 57	90.92 74	91.97 128	77.20 126	93.99 73	74.16 147	98.35 24	97.61 13

UGNet		82.78 163	81.64 175	86.21 98	86.20 226	76.24 100	86.86 94	85.68 212	77.07 117	73.76 290	92.82 110	69.64 213	91.82 157	69.04 192	93.69 169	90.56 193
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
v748		88.91 58	89.82 46	86.19 100	90.06 136	68.53 155	88.81 67	91.48 99	84.36 30	94.19 24	95.98 25	82.52 75	92.67 139	84.30 47	96.67 78	97.37 20

UniMVSNet_NR-MVSNet		86.84 81	87.06 80	86.17 101	92.86 70	67.02 164	82.55 185	91.56 95	83.08 41	90.92 74	91.82 134	78.25 117	93.99 73	74.16 147	98.35 24	97.49 16

IS-MVSNet		86.66 85	86.82 89	86.17 101	92.05 91	66.87 166	91.21 33	88.64 171	86.30 23	89.60 106	92.59 115	69.22 215	94.91 49	73.89 151	97.89 44	96.72 35

lessismore_v0				85.95 103	91.10 118	70.99 140		70.91 306		91.79 62	94.42 67	61.76 241	92.93 132	79.52 110	93.03 187	93.93 102

nrg030		87.85 69	88.49 62	85.91 104	90.07 135	69.73 145	87.86 78	94.20 17	74.04 148	92.70 47	94.66 58	85.88 51	91.50 163	79.72 105	97.32 61	96.50 40

Fast-Effi-MVS+-dtu		82.54 167	81.41 178	85.90 105	85.60 235	76.53 96	83.07 171	89.62 160	73.02 166	79.11 254	83.51 275	80.74 99	90.24 197	68.76 194	89.29 244	90.94 180

test_0402		88.65 59	89.58 50	85.88 106	92.55 75	72.22 125	84.01 139	89.44 163	88.63 14	94.38 21	95.77 28	86.38 47	93.59 93	79.84 104	95.21 128	91.82 163

PCF-MVS	74.62 15	82.15 172	80.92 185	85.84 107	89.43 142	72.30 123	80.53 226	91.82 89	57.36 285	87.81 134	89.92 188	77.67 122	93.63 88	58.69 260	95.08 132	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_LR		84.28 135	83.76 149	85.83 108	89.23 146	83.07 48	80.99 222	83.56 228	72.71 169	86.07 160	89.07 200	81.75 90	86.19 260	77.11 131	93.36 176	88.24 220

WR-MVS_H		89.91 43	91.31 25	85.71 109	96.32 9	62.39 212	89.54 55	93.31 41	90.21 10	95.57 11	95.66 31	81.42 93	95.90 11	80.94 87	98.80 4	98.84 5

MCST-MVS		84.36 131	83.93 148	85.63 110	91.59 100	71.58 136	83.52 157	92.13 80	61.82 266	83.96 194	89.75 191	79.93 107	93.46 108	78.33 118	94.34 155	91.87 162

CSCG		86.26 93	86.47 92	85.60 111	90.87 122	74.26 109	87.98 76	91.85 88	80.35 68	89.54 109	88.01 217	79.09 110	92.13 148	75.51 139	95.06 133	90.41 197

MVS_111021_HR		84.63 121	84.34 141	85.49 112	90.18 133	75.86 101	79.23 252	87.13 196	73.35 156	85.56 169	89.34 197	83.60 64	90.50 192	76.64 134	94.05 159	90.09 205

NR-MVSNet		86.00 98	86.22 96	85.34 113	93.24 59	64.56 178	82.21 196	90.46 131	80.99 63	88.42 126	91.97 128	77.56 123	93.85 80	72.46 166	98.65 13	97.61 13

LF4IMVS		82.75 164	81.93 173	85.19 114	82.08 274	80.15 61	85.53 117	88.76 169	68.01 218	85.58 168	87.75 222	71.80 206	86.85 245	74.02 149	93.87 163	88.58 219

TranMVSNet+NR-MVSNet		87.86 68	88.76 61	85.18 115	94.02 43	64.13 181	84.38 133	91.29 114	84.88 27	92.06 57	93.84 92	86.45 45	93.73 83	73.22 158	98.66 12	97.69 12

3Dnovator	80.37 7	84.80 119	84.71 123	85.06 116	86.36 220	74.71 106	88.77 68	90.00 152	75.65 134	84.96 173	93.17 101	74.06 164	91.19 171	78.28 119	91.09 216	89.29 212

v13		87.31 73	88.10 64	84.94 117	88.84 152	63.75 185	87.85 79	91.47 102	79.12 83	93.72 29	95.82 27	75.20 147	93.58 96	84.76 42	96.16 96	97.48 17

Anonymous20231211		90.14 31	91.88 12	84.92 118	94.75 35	64.47 179	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 159	71.67 172	98.40 21	98.52 7

CNLPA		83.55 155	83.10 157	84.90 119	89.34 144	83.87 43	84.54 131	88.77 168	79.09 84	83.54 201	88.66 206	74.87 152	81.73 292	66.84 208	92.29 198	89.11 213

v12		87.15 76	87.91 66	84.84 120	88.69 155	63.52 188	87.58 82	91.46 103	78.74 91	93.57 32	95.66 31	74.94 151	93.57 97	84.50 45	96.08 101	97.43 18

v10		86.54 87	87.10 79	84.84 120	88.16 170	63.28 193	86.64 105	92.20 79	75.42 138	92.81 44	94.50 64	74.05 165	94.06 72	83.88 53	96.28 91	97.17 28

CLD-MVS		83.18 160	82.64 162	84.79 122	89.05 148	67.82 161	77.93 263	92.52 72	68.33 216	85.07 172	81.54 304	82.06 81	92.96 130	69.35 187	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
114514_t		83.10 162	82.54 165	84.77 123	92.90 67	69.10 154	86.65 104	90.62 128	54.66 297	81.46 224	90.81 168	76.98 131	94.38 60	72.62 165	96.18 95	90.82 186

V9		86.96 77	87.70 71	84.74 124	88.52 160	63.27 194	87.31 87	91.45 105	78.28 96	93.43 33	95.45 38	74.59 159	93.57 97	84.23 49	96.01 104	97.38 19

MAR-MVS		80.24 195	78.74 202	84.73 125	86.87 213	78.18 76	85.75 114	87.81 185	65.67 239	77.84 259	78.50 318	73.79 170	90.53 191	61.59 240	90.87 227	85.49 251
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
PVSNet_Blended_VisFu		81.55 178	80.49 189	84.70 126	91.58 103	73.24 115	84.21 134	91.67 93	62.86 258	80.94 229	87.16 229	67.27 222	92.87 135	69.82 184	88.94 249	87.99 225

V14		86.75 83	87.46 74	84.62 127	88.35 164	63.00 199	87.02 93	91.42 108	77.78 102	93.27 35	95.23 43	74.22 162	93.56 100	83.95 52	95.93 107	97.31 22

原ACMM1				84.60 128	92.81 72	74.01 110		91.50 97	62.59 260	82.73 210	90.67 173	76.53 139	94.25 63	69.24 188	95.69 117	85.55 249

v15		86.56 86	87.25 78	84.51 129	88.15 171	62.72 204	86.72 103	91.40 110	77.38 107	93.11 37	95.00 48	73.93 167	93.55 101	83.67 56	95.86 111	97.26 23

v11		86.96 77	87.78 70	84.51 129	88.50 161	62.60 208	87.21 88	91.63 94	78.08 100	93.40 34	95.56 36	75.07 148	93.57 97	84.46 46	96.08 101	97.36 21

PEN-MVS		90.03 36	91.88 12	84.48 131	96.57 5	58.88 249	88.95 63	93.19 47	91.62 4	96.01 6	96.16 22	87.02 38	95.60 22	78.69 115	98.72 10	98.97 3

PS-CasMVS		90.06 34	91.92 9	84.47 132	96.56 6	58.83 250	89.04 62	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 23	79.42 111	98.74 7	99.00 2

v17		86.32 91	86.95 84	84.44 133	88.00 173	62.62 207	86.74 101	91.48 99	77.17 115	92.74 45	94.56 60	73.74 171	93.53 102	83.27 59	94.87 141	97.18 27

v16		86.24 94	86.85 87	84.43 134	87.96 175	62.59 209	86.73 102	91.48 99	77.17 115	92.67 48	94.55 61	73.63 172	93.52 103	83.26 60	94.16 156	97.17 28

CP-MVSNet		89.27 53	90.91 34	84.37 135	96.34 8	58.61 252	88.66 70	92.06 82	90.78 6	95.67 9	95.17 44	81.80 89	95.54 26	79.00 113	98.69 11	98.95 4

v8		86.22 96	86.83 88	84.36 136	87.82 181	62.35 213	86.42 108	91.33 113	76.78 120	92.73 46	94.48 65	73.41 179	93.72 84	83.10 61	95.41 121	97.01 33

semantic-postprocess				84.34 137	83.93 261	69.66 146		81.09 244	72.43 171	86.47 158	90.19 183	57.56 264	93.15 124	77.45 127	86.39 277	90.22 200

v18		85.99 100	86.55 91	84.30 138	87.73 187	62.29 217	86.40 109	91.49 98	76.64 121	92.40 53	94.20 77	73.28 183	93.52 103	82.87 65	93.99 160	97.09 30

v7		84.81 118	85.00 115	84.23 139	88.15 171	63.27 194	83.79 149	91.39 111	71.10 195	90.07 83	91.28 145	74.04 166	93.63 88	81.48 81	93.67 170	95.79 52

v1192		84.57 123	84.69 124	84.21 140	87.75 186	62.88 201	83.02 172	91.43 106	69.08 212	89.98 88	90.89 165	72.70 195	93.62 92	82.41 70	94.97 136	96.13 43

DTE-MVSNet		89.98 38	91.91 11	84.21 140	96.51 7	57.84 253	88.93 65	92.84 63	91.92 2	96.16 3	96.23 20	86.95 39	95.99 6	79.05 112	98.57 16	98.80 6

MVSFormer		82.23 171	81.57 177	84.19 142	85.54 237	69.26 150	91.98 25	90.08 149	71.54 191	76.23 271	85.07 258	58.69 258	94.27 61	86.26 27	88.77 250	89.03 215

1121		80.86 187	79.81 197	84.02 143	93.93 45	78.70 74	81.64 210	80.18 248	55.43 294	83.67 197	91.15 152	71.29 209	91.41 168	67.95 202	93.06 186	81.96 299

v1144		84.54 126	84.72 122	84.00 144	87.67 189	62.55 210	82.97 173	90.93 123	70.32 201	89.80 95	90.99 160	73.50 177	93.48 107	81.69 80	94.65 150	95.97 49

EG-PatchMatch MVS		84.08 143	84.11 143	83.98 145	92.22 87	72.61 119	82.20 198	87.02 200	72.63 170	88.86 116	91.02 159	78.52 113	91.11 173	73.41 157	91.09 216	88.21 221

IterMVS-LS		84.73 120	84.98 116	83.96 146	87.35 196	63.66 186	83.25 167	89.88 154	76.06 125	89.62 103	92.37 123	73.40 181	92.52 142	78.16 120	94.77 146	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	76.49 14	89.34 52	91.14 28	83.96 146	92.50 77	70.36 143	89.55 53	93.84 29	81.89 56	94.70 16	95.44 39	90.69 10	88.31 231	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
alignmvs		83.94 148	83.98 146	83.80 148	87.80 185	67.88 160	84.54 131	91.42 108	73.27 162	88.41 127	87.96 218	72.33 200	90.83 182	76.02 137	94.11 157	92.69 135

v1921920		84.23 137	84.37 140	83.79 149	87.64 191	61.71 221	82.91 175	91.20 117	67.94 221	90.06 84	90.34 179	72.04 204	93.59 93	82.32 72	94.91 137	96.07 45

PM-MVS		80.20 196	79.00 200	83.78 150	88.17 169	86.66 13	81.31 215	66.81 329	69.64 206	88.33 129	90.19 183	64.58 231	83.63 284	71.99 171	90.03 239	81.06 317

v1neww		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.27 144	72.33 175	89.93 90	91.22 147	73.28 183	93.29 114	80.25 99	93.25 181	95.62 60

v7new		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.27 144	72.33 175	89.93 90	91.22 147	73.28 183	93.29 114	80.25 99	93.25 181	95.62 60

v6		84.43 128	84.66 126	83.75 151	87.81 182	62.34 214	83.59 153	90.26 146	72.33 175	89.94 89	91.19 151	73.30 182	93.29 114	80.26 98	93.26 180	95.62 60

V42		83.47 157	83.37 153	83.75 151	83.16 269	63.33 192	81.31 215	90.23 147	69.51 207	90.91 76	90.81 168	74.16 163	92.29 146	80.06 101	90.22 238	95.62 60

v144192		84.24 136	84.41 135	83.71 155	87.59 192	61.57 226	82.95 174	91.03 121	67.82 224	89.80 95	90.49 177	73.28 183	93.51 106	81.88 79	94.89 138	96.04 47

v1240		84.30 134	84.51 133	83.65 156	87.65 190	61.26 230	82.85 176	91.54 96	67.94 221	90.68 79	90.65 174	71.71 207	93.64 87	82.84 67	94.78 144	96.07 45

v2v482		84.09 142	84.24 142	83.62 157	87.13 205	61.40 227	82.71 182	89.71 158	72.19 179	89.55 107	91.41 144	70.70 212	93.20 119	81.02 85	93.76 167	96.25 41

canonicalmvs		85.50 107	86.14 98	83.58 158	87.97 174	67.13 163	87.55 83	94.32 12	73.44 155	88.47 125	87.54 226	86.45 45	91.06 175	75.76 138	93.76 167	92.54 144

Effi-MVS+		83.90 149	84.01 145	83.57 159	87.22 203	65.61 173	86.55 107	92.40 74	78.64 92	81.34 227	84.18 269	83.65 63	92.93 132	74.22 146	87.87 262	92.17 157

AdaColmap		83.66 152	83.69 150	83.57 159	90.05 137	72.26 124	86.29 110	90.00 152	78.19 98	81.65 222	87.16 229	83.40 66	94.24 64	61.69 237	94.76 147	84.21 268

v1141		84.16 139	84.38 137	83.52 161	87.32 198	61.70 223	82.79 178	89.74 155	71.90 188	89.64 100	91.12 154	72.68 196	93.10 125	80.39 97	93.80 165	95.75 54

divwei89l23v2f112		84.16 139	84.38 137	83.52 161	87.32 198	61.70 223	82.79 178	89.74 155	71.90 188	89.64 100	91.12 154	72.68 196	93.10 125	80.40 95	93.81 164	95.75 54

v1		84.16 139	84.38 137	83.52 161	87.33 197	61.71 221	82.79 178	89.73 157	71.89 190	89.64 100	91.11 156	72.72 193	93.10 125	80.40 95	93.79 166	95.75 54

testing_2		84.36 131	84.64 129	83.50 164	86.74 214	63.97 184	84.56 130	90.31 138	66.22 232	91.62 65	94.55 61	75.88 142	91.95 151	77.02 133	94.89 138	94.56 81

PAPM_NR		83.23 159	83.19 156	83.33 165	90.90 121	65.98 170	88.19 74	90.78 124	78.13 99	80.87 231	87.92 221	73.49 178	92.42 143	70.07 182	88.40 253	91.60 168

TAMVS		78.08 207	76.36 220	83.23 166	90.62 126	72.87 116	79.08 253	80.01 250	61.72 268	81.35 226	86.92 232	63.96 234	88.78 225	50.61 303	93.01 188	88.04 224

VDD-MVS		84.23 137	84.58 131	83.20 167	91.17 116	65.16 175	83.25 167	84.97 224	79.79 73	87.18 141	94.27 73	74.77 156	90.89 181	69.24 188	96.54 82	93.55 118

EI-MVSNet		82.61 165	82.42 167	83.20 167	83.25 267	63.66 186	83.50 160	85.07 220	76.06 125	86.55 152	85.10 256	73.41 179	90.25 195	78.15 121	90.67 232	95.68 58

CDS-MVSNet		77.32 213	75.40 228	83.06 169	89.00 150	72.48 121	77.90 264	82.17 236	60.81 273	78.94 255	83.49 276	59.30 255	88.76 226	54.64 289	92.37 197	87.93 227
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ambc				82.98 170	90.55 129	64.86 176	88.20 73	89.15 166		89.40 110	93.96 86	71.67 208	91.38 170	78.83 114	96.55 81	92.71 134

新几何1				82.95 171	93.96 44	78.56 75		80.24 247	55.45 293	83.93 195	91.08 157	71.19 210	88.33 230	65.84 216	93.07 185	81.95 300

xiu_mvs_v1_base_debu		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 237	87.17 193	65.43 240	79.59 249	82.73 288	76.94 132	90.14 201	73.22 158	88.33 254	86.90 237

xiu_mvs_v1_base		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 237	87.17 193	65.43 240	79.59 249	82.73 288	76.94 132	90.14 201	73.22 158	88.33 254	86.90 237

xiu_mvs_v1_base_debi		80.84 188	80.14 193	82.93 172	88.31 165	71.73 132	79.53 237	87.17 193	65.43 240	79.59 249	82.73 288	76.94 132	90.14 201	73.22 158	88.33 254	86.90 237

MVP-Stereo		75.81 232	73.51 250	82.71 175	89.35 143	73.62 111	80.06 229	85.20 217	60.30 276	73.96 289	87.94 219	57.89 262	89.45 210	52.02 297	74.87 330	85.06 254
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FIs		85.35 109	86.27 95	82.60 176	91.86 96	57.31 257	85.10 122	93.05 52	75.83 130	91.02 73	93.97 84	73.57 176	92.91 134	73.97 150	98.02 39	97.58 15

FC-MVSNet-test		85.93 102	87.05 81	82.58 177	92.25 85	56.44 263	85.75 114	93.09 50	77.33 112	91.94 61	94.65 59	74.78 155	93.41 111	75.11 142	98.58 15	97.88 10

QAPM		82.59 166	82.59 164	82.58 177	86.44 215	66.69 167	89.94 43	90.36 135	67.97 220	84.94 175	92.58 117	72.71 194	92.18 147	70.63 179	87.73 264	88.85 218

pmmvs-eth3d		78.42 205	77.04 211	82.57 179	87.44 194	74.41 108	80.86 224	79.67 251	55.68 292	84.69 179	90.31 182	60.91 244	85.42 268	62.20 233	91.59 207	87.88 228

HyFIR lowres test		75.12 237	72.66 257	82.50 180	91.44 109	65.19 174	72.47 301	87.31 190	46.79 333	80.29 245	84.30 268	52.70 281	92.10 150	51.88 302	86.73 272	90.22 200

Fast-Effi-MVS+		81.04 185	80.57 186	82.46 181	87.50 193	63.22 196	78.37 259	89.63 159	68.01 218	81.87 217	82.08 299	82.31 78	92.65 140	67.10 204	88.30 258	91.51 171

jason		77.42 212	75.75 226	82.43 182	87.10 208	69.27 149	77.99 262	81.94 239	51.47 317	77.84 259	85.07 258	60.32 247	89.00 218	70.74 177	89.27 246	89.03 215
jason: jason.
lupinMVS		76.37 227	74.46 237	82.09 183	85.54 237	69.26 150	76.79 271	80.77 246	50.68 324	76.23 271	82.82 286	58.69 258	88.94 219	69.85 183	88.77 250	88.07 222

DELS-MVS		81.44 179	81.25 180	82.03 184	84.27 254	62.87 202	76.47 277	92.49 73	70.97 196	81.64 223	83.83 272	75.03 149	92.70 137	74.29 145	92.22 202	90.51 195
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
OpenMVS	76.72 13	81.98 176	82.00 172	81.93 185	84.42 252	68.22 158	88.50 72	89.48 162	66.92 227	81.80 221	91.86 130	72.59 198	90.16 200	71.19 173	91.25 214	87.40 232

pmmvs6		86.52 88	88.06 65	81.90 186	92.22 87	62.28 218	84.66 128	89.15 166	83.54 36	89.85 93	97.32 4	88.08 28	86.80 251	70.43 181	97.30 62	96.62 37

MSLP-MVS++		85.00 115	86.03 100	81.90 186	91.84 97	71.56 137	86.75 100	93.02 56	75.95 128	87.12 142	89.39 196	77.98 118	89.40 212	77.46 126	94.78 144	84.75 261

Test4		81.31 180	81.13 182	81.88 188	84.89 244	63.05 198	82.37 189	90.50 130	62.75 259	89.00 115	88.29 214	67.55 221	91.68 160	73.55 155	91.24 215	90.89 182

GBi-Net		82.02 174	82.07 170	81.85 189	86.38 217	61.05 233	86.83 96	88.27 177	72.43 171	86.00 161	95.64 33	63.78 235	90.68 187	65.95 213	93.34 177	93.82 105

test1		82.02 174	82.07 170	81.85 189	86.38 217	61.05 233	86.83 96	88.27 177	72.43 171	86.00 161	95.64 33	63.78 235	90.68 187	65.95 213	93.34 177	93.82 105

FMVSNet1		84.55 124	85.45 109	81.85 189	90.27 132	61.05 233	86.83 96	88.27 177	78.57 93	89.66 99	95.64 33	75.43 144	90.68 187	69.09 191	95.33 123	93.82 105

v148		82.31 169	82.48 166	81.81 192	85.59 236	59.66 243	81.47 213	86.02 209	72.85 167	88.05 130	90.65 174	70.73 211	90.91 180	75.15 141	91.79 204	94.87 77

PVSNet_BlendedMVS		78.80 203	77.84 206	81.65 193	84.43 250	63.41 189	79.49 240	90.44 132	61.70 269	75.43 278	87.07 231	69.11 216	91.44 166	60.68 246	92.24 200	90.11 204

BH-RMVSNet		80.53 191	80.22 192	81.49 194	87.19 204	66.21 169	77.79 265	86.23 206	74.21 147	83.69 196	88.50 207	73.25 187	90.75 184	63.18 231	87.90 261	87.52 230

test_normal		81.23 184	81.16 181	81.43 195	84.77 247	61.99 220	81.46 214	86.95 202	63.16 256	87.22 140	89.63 192	73.62 173	91.65 161	72.92 163	90.70 231	90.65 191

API-MVS		82.28 170	82.61 163	81.30 196	86.29 222	69.79 144	88.71 69	87.67 186	78.42 95	82.15 214	84.15 271	77.98 118	91.59 162	65.39 218	92.75 192	82.51 292

DI_MVS_plusplus_test		81.27 182	81.26 179	81.29 197	84.98 242	61.65 225	81.98 201	87.25 192	63.56 251	87.56 137	89.60 193	73.62 173	91.83 156	72.20 168	90.59 236	90.38 198

VDDNet		84.35 133	85.39 110	81.25 198	95.13 28	59.32 246	85.42 119	81.11 243	86.41 22	87.41 139	96.21 21	73.61 175	90.61 190	66.33 211	96.85 72	93.81 108

MVSTER		77.09 215	75.70 227	81.25 198	75.27 328	61.08 232	77.49 269	85.07 220	60.78 274	86.55 152	88.68 205	43.14 324	90.25 195	73.69 153	90.67 232	92.42 145

PAPR		78.84 202	78.10 205	81.07 200	85.17 241	60.22 240	82.21 196	90.57 129	62.51 261	75.32 280	84.61 265	74.99 150	92.30 145	59.48 258	88.04 260	90.68 189

WR-MVS		83.56 154	84.40 136	81.06 201	93.43 55	54.88 274	78.67 258	85.02 222	81.24 61	90.74 78	91.56 141	72.85 191	91.08 174	68.00 200	98.04 36	97.23 25

BH-untuned		80.96 186	80.99 183	80.84 202	88.55 159	68.23 157	80.33 228	88.46 172	72.79 168	86.55 152	86.76 233	74.72 157	91.77 158	61.79 236	88.99 248	82.52 291

MIMVSNet1		83.63 153	84.59 130	80.74 203	94.06 42	62.77 203	82.72 181	84.53 226	77.57 105	90.34 80	95.92 26	76.88 138	85.83 265	61.88 235	97.42 59	93.62 113

pmmvs4		74.92 240	72.98 255	80.73 204	84.95 243	71.71 135	76.23 279	77.59 257	52.83 306	77.73 262	86.38 236	56.35 268	84.97 272	57.72 271	87.05 270	85.51 250

cascas		76.29 228	74.81 233	80.72 205	84.47 249	62.94 200	73.89 296	87.34 189	55.94 291	75.16 282	76.53 326	63.97 233	91.16 172	65.00 219	90.97 224	88.06 223

FMVSNet2		81.31 180	81.61 176	80.41 206	86.38 217	58.75 251	83.93 143	86.58 204	72.43 171	87.65 135	92.98 105	63.78 235	90.22 198	66.86 206	93.92 162	92.27 153

MSDG		80.06 198	79.99 196	80.25 207	83.91 262	68.04 159	77.51 268	89.19 165	77.65 103	81.94 216	83.45 277	76.37 140	86.31 259	63.31 230	86.59 274	86.41 240

MVS_Test		82.47 168	83.22 154	80.22 208	82.62 273	57.75 255	82.54 186	91.96 86	71.16 194	82.89 208	92.52 119	77.41 125	90.50 192	80.04 102	87.84 263	92.40 147

CANet_DTU		77.81 209	77.05 210	80.09 209	81.37 280	59.90 242	83.26 166	88.29 176	69.16 211	67.83 318	83.72 273	60.93 243	89.47 208	69.22 190	89.70 242	90.88 183

pm-mvs1		83.69 151	84.95 117	79.91 210	90.04 138	59.66 243	82.43 187	87.44 188	75.52 135	87.85 133	95.26 42	81.25 95	85.65 267	68.74 195	96.04 103	94.42 89

CMPMVS	59.41 20	75.12 237	73.57 244	79.77 211	75.84 320	67.22 162	81.21 218	82.18 235	50.78 322	76.50 267	87.66 224	55.20 275	82.99 286	62.17 234	90.64 235	89.09 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_Blended		76.49 226	75.40 228	79.76 212	84.43 250	63.41 189	75.14 287	90.44 132	57.36 285	75.43 278	78.30 319	69.11 216	91.44 166	60.68 246	87.70 265	84.42 264

TR-MVS		76.77 222	75.79 224	79.72 213	86.10 233	65.79 172	77.14 270	83.02 230	65.20 244	81.40 225	82.10 298	66.30 225	90.73 186	55.57 281	85.27 285	82.65 287

VPA-MVSNet		83.47 157	84.73 120	79.69 214	90.29 131	57.52 256	81.30 217	88.69 170	76.29 123	87.58 136	94.44 66	80.60 100	87.20 240	66.60 210	96.82 75	94.34 91

diffmvs		79.20 201	79.04 199	79.69 214	78.64 302	58.90 248	81.79 206	87.61 187	65.07 246	73.65 292	89.80 189	73.10 189	87.79 235	75.02 143	86.63 273	92.38 148

IB-MVS	62.13 19	71.64 268	68.97 282	79.66 216	80.80 288	62.26 219	73.94 295	76.90 261	63.27 255	68.63 314	76.79 324	33.83 344	91.84 155	59.28 259	87.26 268	84.88 259
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
FMVSNet3		78.80 203	78.55 203	79.57 217	82.89 271	56.89 261	81.76 207	85.77 211	69.04 213	86.00 161	90.44 178	51.75 283	90.09 204	65.95 213	93.34 177	91.72 165

testdata				79.54 218	92.87 68	72.34 122		80.14 249	59.91 278	85.47 171	91.75 137	67.96 220	85.24 269	68.57 198	92.18 203	81.06 317

GA-MVS		75.83 231	74.61 234	79.48 219	81.87 276	59.25 247	73.42 299	82.88 231	68.68 215	79.75 248	81.80 301	50.62 285	89.46 209	66.85 207	85.64 282	89.72 206

MDA-MVSNet-bldmvs		77.47 211	76.90 212	79.16 220	79.03 298	64.59 177	66.58 321	75.67 268	73.15 164	88.86 116	88.99 201	66.94 223	81.23 293	64.71 220	88.22 259	91.64 167

LFMVS		80.15 197	80.56 187	78.89 221	89.19 147	55.93 265	85.22 121	73.78 280	82.96 42	84.28 192	92.72 114	57.38 265	90.07 205	63.80 226	95.75 115	90.68 189

TransMVSNet (Re)		84.02 145	85.74 103	78.85 222	91.00 119	55.20 273	82.29 192	87.26 191	79.65 76	88.38 128	95.52 37	83.00 69	86.88 244	67.97 201	96.60 80	94.45 88

Gipuma		84.44 127	86.33 93	78.78 223	84.20 257	73.57 112	89.55 53	90.44 132	84.24 31	84.38 188	94.89 51	76.35 141	80.40 296	76.14 136	96.80 76	82.36 293
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet-Re		83.48 156	85.06 113	78.75 224	85.94 234	55.75 268	80.05 230	94.27 13	76.47 122	96.09 5	94.54 63	83.31 67	89.75 207	59.95 249	94.89 138	90.75 187

OpenMVS_ROB	70.19 17	77.77 210	77.46 208	78.71 225	84.39 253	61.15 231	81.18 219	82.52 233	62.45 263	83.34 202	87.37 228	66.20 226	88.66 228	64.69 221	85.02 289	86.32 241

IterMVS		76.91 217	76.34 221	78.64 226	80.91 285	64.03 182	76.30 278	79.03 252	64.88 248	83.11 205	89.16 198	59.90 251	84.46 277	68.61 197	85.15 288	87.42 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-MVSNAJ		77.04 216	76.53 219	78.56 227	87.09 209	61.40 227	75.26 286	87.13 196	61.25 271	74.38 288	77.22 323	76.94 132	90.94 177	64.63 222	84.83 292	83.35 280

xiu_mvs_v2_base		77.19 214	76.75 213	78.52 228	87.01 210	61.30 229	75.55 285	87.12 198	61.24 272	74.45 286	78.79 317	77.20 126	90.93 178	64.62 223	84.80 293	83.32 281

MG-MVS		80.32 193	80.94 184	78.47 229	88.18 168	52.62 289	82.29 192	85.01 223	72.01 181	79.24 253	92.54 118	69.36 214	93.36 113	70.65 178	89.19 247	89.45 207

tfpnnormal		81.79 177	82.95 158	78.31 230	88.93 151	55.40 269	80.83 225	82.85 232	76.81 119	85.90 164	94.14 79	74.58 160	86.51 256	66.82 209	95.68 118	93.01 127

Baseline_NR-MVSNet		84.00 146	85.90 101	78.29 231	91.47 108	53.44 282	82.29 192	87.00 201	79.06 85	89.55 107	95.72 30	77.20 126	86.14 261	72.30 167	98.51 18	95.28 70

PatchMatch-RL		74.48 243	73.22 252	78.27 232	87.70 188	85.26 30	75.92 280	70.09 309	64.34 250	76.09 273	81.25 306	65.87 229	78.07 301	53.86 291	83.82 297	71.48 333

CHOSEN 1792x2688		72.45 262	70.56 272	78.13 233	90.02 139	63.08 197	68.72 312	83.16 229	42.99 342	75.92 274	85.46 250	57.22 267	85.18 271	49.87 307	81.67 309	86.14 243

BH-w/o		76.57 224	76.07 223	78.10 234	86.88 212	65.92 171	77.63 266	86.33 205	65.69 238	80.89 230	79.95 312	68.97 218	90.74 185	53.01 294	85.25 286	77.62 322

1112_ss		74.82 242	73.74 241	78.04 235	89.57 141	60.04 241	76.49 276	87.09 199	54.31 298	73.66 291	79.80 313	60.25 248	86.76 253	58.37 265	84.15 296	87.32 233

TinyColmap		81.25 183	82.34 168	77.99 236	85.33 240	60.68 237	82.32 191	88.33 175	71.26 193	86.97 147	92.22 127	77.10 129	86.98 243	62.37 232	95.17 130	86.31 242

Vis-MVSNet (Re-imp)		77.82 208	77.79 207	77.92 237	88.82 153	51.29 301	83.28 165	71.97 295	74.04 148	82.23 212	89.78 190	57.38 265	89.41 211	57.22 272	95.41 121	93.05 126

thres400		75.14 235	74.23 239	77.86 238	86.24 223	52.12 291	79.24 249	73.87 278	73.34 157	81.82 219	84.60 266	46.02 299	88.80 221	51.98 298	90.99 220	92.66 136

thres600view7		75.97 230	75.35 230	77.85 239	87.01 210	51.84 297	80.45 227	73.26 285	75.20 139	83.10 206	86.31 241	45.54 305	89.05 217	55.03 286	92.24 200	92.66 136

JIA-IIPM		69.41 286	66.64 296	77.70 240	73.19 337	71.24 138	75.67 282	65.56 330	70.42 199	65.18 327	92.97 106	33.64 345	83.06 285	53.52 293	69.61 342	78.79 321

view600		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 261	93.49 172	92.66 136

view800		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 261	93.49 172	92.66 136

conf0.05thres1000		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 261	93.49 172	92.66 136

tfpn		76.79 218	76.54 215	77.56 241	87.91 177	50.77 307	81.92 202	71.35 302	77.38 107	84.62 180	88.40 209	45.18 314	89.26 213	58.58 261	93.49 172	92.66 136

EPNet_dtu		72.87 258	71.33 270	77.49 245	77.72 308	60.55 238	82.35 190	75.79 266	66.49 230	58.39 345	81.06 307	53.68 279	85.98 262	53.55 292	92.97 189	85.95 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
conf200view11		75.62 233	75.05 231	77.34 246	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	88.80 221	51.98 298	90.99 220	91.34 176

EU-MVSNet		75.12 237	74.43 238	77.18 247	83.11 270	59.48 245	85.71 116	82.43 234	39.76 345	85.64 167	88.76 203	44.71 320	87.88 234	73.86 152	85.88 280	84.16 269

ab-mvs		79.67 199	80.56 187	76.99 248	88.48 162	56.93 259	84.70 127	86.06 208	68.95 214	80.78 232	93.08 102	75.30 146	84.62 276	56.78 274	90.90 226	89.43 209

PAPM		71.77 267	70.06 278	76.92 249	86.39 216	53.97 277	76.62 274	86.62 203	53.44 303	63.97 332	84.73 264	57.79 263	92.34 144	39.65 333	81.33 312	84.45 263

Patchmatch-RL test		74.48 243	73.68 242	76.89 250	84.83 245	66.54 168	72.29 302	69.16 314	57.70 283	86.76 148	86.33 239	45.79 304	82.59 288	69.63 185	90.65 234	81.54 307

CR-MVSNet		74.00 247	73.04 254	76.85 251	79.58 291	62.64 205	82.58 183	76.90 261	50.50 325	75.72 276	92.38 120	48.07 292	84.07 279	68.72 196	82.91 303	83.85 272

RPMNet		76.06 229	75.79 224	76.85 251	79.58 291	62.64 205	82.58 183	71.75 299	74.80 141	75.72 276	92.59 115	48.69 290	84.07 279	73.48 156	82.91 303	83.85 272

tfpn200view9		74.86 241	74.23 239	76.74 253	86.24 223	52.12 291	79.24 249	73.87 278	73.34 157	81.82 219	84.60 266	46.02 299	88.80 221	51.98 298	90.99 220	89.31 210

thres100view900		75.45 234	75.05 231	76.66 254	87.27 200	51.88 294	81.07 220	73.26 285	75.68 132	83.25 203	86.37 237	45.54 305	88.80 221	51.98 298	90.99 220	89.31 210

VNet		79.31 200	80.27 191	76.44 255	87.92 176	53.95 278	75.58 284	84.35 227	74.39 146	82.23 212	90.72 170	72.84 192	84.39 278	60.38 248	93.98 161	90.97 179

Test_1112_low_res		73.90 248	73.08 253	76.35 256	90.35 130	55.95 264	73.40 300	86.17 207	50.70 323	73.14 293	85.94 244	58.31 260	85.90 264	56.51 275	83.22 300	87.20 234

USDC		76.63 223	76.73 214	76.34 257	83.46 265	57.20 258	80.02 231	88.04 181	52.14 312	83.65 199	91.25 146	63.24 238	86.65 255	54.66 288	94.11 157	85.17 252

CVMVSNet		72.62 260	71.41 269	76.28 258	83.25 267	60.34 239	83.50 160	79.02 253	37.77 346	76.33 269	85.10 256	49.60 288	87.41 238	70.54 180	77.54 326	81.08 315

mvs_anonymous		78.13 206	78.76 201	76.23 259	79.24 296	50.31 313	78.69 257	84.82 225	61.60 270	83.09 207	92.82 110	73.89 169	87.01 241	68.33 199	86.41 276	91.37 174

conf0.002		74.17 246	73.53 245	76.08 260	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	91.34 176

VPNet		80.25 194	81.68 174	75.94 261	92.46 78	47.98 325	76.70 273	81.67 241	73.45 154	84.87 176	92.82 110	74.66 158	86.51 256	61.66 238	96.85 72	93.33 119

tpmp4_e23		69.43 284	67.33 291	75.72 262	78.53 303	52.75 286	82.13 200	74.91 271	49.23 330	66.37 321	84.17 270	41.28 333	88.67 227	49.73 308	79.63 316	85.75 248

ANet_high		83.17 161	85.68 105	75.65 263	81.24 281	45.26 330	79.94 233	92.91 59	83.83 35	91.33 70	96.88 10	80.25 103	85.92 263	68.89 193	95.89 109	95.76 53

1314		73.22 255	72.56 260	75.20 264	80.41 290	57.84 253	81.64 210	85.36 215	51.68 315	73.10 294	76.65 325	61.45 242	85.19 270	63.54 227	79.21 321	82.59 288

MVS		73.21 256	72.59 259	75.06 265	80.97 284	60.81 236	81.64 210	85.92 210	46.03 336	71.68 301	77.54 320	68.47 219	89.77 206	55.70 280	85.39 283	74.60 328

no-one		71.52 270	70.43 275	74.81 266	78.45 304	63.41 189	57.73 338	77.03 260	51.46 318	77.17 265	90.33 180	54.96 277	80.35 297	47.41 317	99.29 2	80.68 319

HY-MVS	64.64 18	73.03 257	72.47 261	74.71 267	83.36 266	54.19 276	82.14 199	81.96 237	56.76 290	69.57 311	86.21 242	60.03 249	84.83 275	49.58 309	82.65 305	85.11 253

thres200		72.34 264	71.55 268	74.70 268	83.48 264	51.60 298	75.02 288	73.71 281	70.14 203	78.56 256	80.57 308	46.20 297	88.20 232	46.99 320	89.29 244	84.32 266

N_pmnet		70.20 277	68.80 284	74.38 269	80.91 285	84.81 35	59.12 335	76.45 265	55.06 295	75.31 281	82.36 296	55.74 271	54.82 347	47.02 319	87.24 269	83.52 276

thresconf0.02		73.65 250	73.53 245	73.98 270	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 254

tfpn_n400		73.65 250	73.53 245	73.98 270	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 254

tfpnconf		73.65 250	73.53 245	73.98 270	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 254

tfpnview11		73.65 250	73.53 245	73.98 270	86.13 228	50.06 316	79.45 241	68.54 315	72.01 181	80.76 233	82.50 291	41.39 328	86.83 246	59.66 252	91.36 209	85.06 254

CostFormer		69.98 282	68.68 285	73.87 274	77.14 311	50.72 311	79.26 248	74.51 276	51.94 314	70.97 306	84.75 263	45.16 318	87.49 237	55.16 285	79.23 320	83.40 279

Patchmtry		76.56 225	77.46 208	73.83 275	79.37 295	46.60 327	82.41 188	76.90 261	73.81 151	85.56 169	92.38 120	48.07 292	83.98 281	63.36 229	95.31 126	90.92 181

tfpn1000		73.63 254	73.58 243	73.79 276	85.46 239	50.31 313	79.99 232	68.18 320	72.33 175	80.66 238	83.05 280	39.80 338	86.74 254	60.96 244	91.78 205	84.32 266

FMVSNet5		72.10 266	71.69 266	73.32 277	81.57 279	53.02 285	76.77 272	78.37 254	63.31 254	76.37 268	91.85 131	36.68 341	78.98 300	47.87 316	92.45 196	87.95 226

tpm2		68.45 289	66.83 293	73.30 278	78.93 299	48.50 322	79.76 234	71.76 298	47.50 332	69.92 310	83.60 274	42.07 327	88.40 229	48.44 314	79.51 317	83.01 286

tfpn_ndepth		72.54 261	72.30 262	73.24 279	84.81 246	51.42 299	79.24 249	70.49 308	69.26 209	78.48 257	79.80 313	40.16 337	86.77 252	58.08 270	90.43 237	81.53 308

Patchmatch-test1		72.75 259	72.61 258	73.19 280	81.62 278	55.86 266	78.89 255	71.37 301	61.73 267	74.93 283	82.15 297	60.46 246	81.80 290	59.68 251	82.63 307	81.92 301

FPMVS		72.29 265	72.00 264	73.14 281	88.63 158	85.00 32	74.65 291	67.39 323	71.94 187	77.80 261	87.66 224	50.48 286	75.83 308	49.95 305	79.51 317	58.58 345

MS-PatchMatch		70.93 273	70.22 276	73.06 282	81.85 277	62.50 211	73.82 297	77.90 255	52.44 309	75.92 274	81.27 305	55.67 272	81.75 291	55.37 283	77.70 324	74.94 327

LP		69.42 285	68.30 287	72.77 283	71.48 345	56.84 262	73.66 298	74.84 272	63.52 252	70.95 307	83.35 279	49.55 289	77.15 304	57.13 273	70.21 338	84.33 265

pmmvs5		70.73 274	70.07 277	72.72 284	77.03 313	52.73 287	74.14 293	75.65 269	50.36 326	72.17 299	85.37 254	55.42 274	80.67 295	52.86 295	87.59 266	84.77 260

DWT-MVSNet_test		66.43 296	64.37 301	72.63 285	74.86 331	50.86 306	76.52 275	72.74 289	54.06 300	65.50 325	68.30 341	32.13 346	84.84 274	61.63 239	73.59 331	82.19 295

ADS-MVSNet2		65.87 300	63.64 305	72.55 286	73.16 338	56.92 260	67.10 319	74.81 273	49.74 327	66.04 323	82.97 283	46.71 294	77.26 302	42.29 328	69.96 340	83.46 277

PatchmatchNet		69.71 283	68.83 283	72.33 287	77.66 309	53.60 280	79.29 247	69.99 310	57.66 284	72.53 296	82.93 285	46.45 296	80.08 299	60.91 245	72.09 335	83.31 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchFormer-LS_test		67.91 291	66.49 297	72.17 288	75.29 327	51.85 296	75.68 281	73.62 283	57.23 287	68.64 312	68.13 342	42.19 326	82.76 287	64.06 225	73.51 332	81.89 302

tpmvs		70.16 278	69.56 280	71.96 289	74.71 332	48.13 323	79.63 235	75.45 270	65.02 247	70.26 308	81.88 300	45.34 311	85.68 266	58.34 266	75.39 329	82.08 297

tpm cat1		66.76 295	65.21 300	71.42 290	77.09 312	50.62 312	78.01 261	73.68 282	44.89 338	68.64 312	79.00 316	45.51 308	82.42 289	49.91 306	70.15 339	81.23 314

test20.03		73.75 249	74.59 236	71.22 291	81.11 283	51.12 303	70.15 309	72.10 294	70.42 199	80.28 246	91.50 142	64.21 232	74.72 312	46.96 321	94.58 151	87.82 229

Anonymous20231206		71.38 271	71.88 265	69.88 292	86.31 221	54.37 275	70.39 308	74.62 274	52.57 308	76.73 266	88.76 203	59.94 250	72.06 315	44.35 326	93.23 184	83.23 283

pmmvs3		62.47 304	60.02 317	69.80 293	71.58 344	64.00 183	70.52 307	58.44 343	39.77 344	66.05 322	75.84 327	27.10 353	72.28 314	46.15 322	84.77 294	73.11 331

testmv		70.47 276	70.70 271	69.77 294	86.22 225	53.89 279	67.32 318	71.91 296	63.32 253	78.16 258	89.47 195	56.12 270	73.10 313	36.43 339	87.33 267	82.33 294

UnsupCasMVSNet_eth		71.63 269	72.30 262	69.62 295	76.47 316	52.70 288	70.03 310	80.97 245	59.18 279	79.36 252	88.21 215	60.50 245	69.12 323	58.33 267	77.62 325	87.04 235

MIMVSNet		71.09 272	71.59 267	69.57 296	87.23 202	50.07 315	78.91 254	71.83 297	60.20 277	71.26 303	91.76 136	55.08 276	76.09 306	41.06 331	87.02 271	82.54 290

XXY-MVS		74.44 245	76.19 222	69.21 297	84.61 248	52.43 290	71.70 304	77.18 259	60.73 275	80.60 239	90.96 163	75.44 143	69.35 322	56.13 277	88.33 254	85.86 247

YYNet1		70.06 280	70.44 273	68.90 298	73.76 334	53.42 283	58.99 336	67.20 325	58.42 281	87.10 143	85.39 253	59.82 252	67.32 327	59.79 250	83.50 299	85.96 244

MDA-MVSNet_test_wron		70.05 281	70.44 273	68.88 299	73.84 333	53.47 281	58.93 337	67.28 324	58.43 280	87.09 144	85.40 252	59.80 253	67.25 328	59.66 252	83.54 298	85.92 246

PVSNet	58.17 21	66.41 297	65.63 299	68.75 300	81.96 275	49.88 321	62.19 328	72.51 292	51.03 320	68.04 316	75.34 330	50.84 284	74.77 310	45.82 324	82.96 301	81.60 306

test-LLR		67.21 293	66.74 294	68.63 301	76.45 317	55.21 271	67.89 314	67.14 326	62.43 264	65.08 328	72.39 334	43.41 322	69.37 320	61.00 242	84.89 290	81.31 310

test-mter		65.00 302	63.79 303	68.63 301	76.45 317	55.21 271	67.89 314	67.14 326	50.98 321	65.08 328	72.39 334	28.27 351	69.37 320	61.00 242	84.89 290	81.31 310

gg-mvs-nofinetune		68.96 288	69.11 281	68.52 303	76.12 319	45.32 329	83.59 153	55.88 346	86.68 20	64.62 331	97.01 7	30.36 348	83.97 282	44.78 325	82.94 302	76.26 325

UnsupCasMVSNet_bld		69.21 287	69.68 279	67.82 304	79.42 293	51.15 302	67.82 317	75.79 266	54.15 299	77.47 263	85.36 255	59.26 256	70.64 318	48.46 313	79.35 319	81.66 305

tpm		67.95 290	68.08 289	67.55 305	78.74 301	43.53 336	75.60 283	67.10 328	54.92 296	72.23 298	88.10 216	42.87 325	75.97 307	52.21 296	80.95 315	83.15 284

GG-mvs-BLEND				67.16 306	73.36 335	46.54 328	84.15 136	55.04 347		58.64 344	61.95 347	29.93 349	83.87 283	38.71 337	76.92 327	71.07 334

CHOSEN 280x420		59.08 316	56.52 321	66.76 307	76.51 315	64.39 180	49.62 345	59.00 341	43.86 340	55.66 348	68.41 340	35.55 343	68.21 326	43.25 327	76.78 328	67.69 338

WTY-MVS		67.91 291	68.35 286	66.58 308	80.82 287	48.12 324	65.96 322	72.60 290	53.67 302	71.20 304	81.68 303	58.97 257	69.06 324	48.57 312	81.67 309	82.55 289

sss		66.92 294	67.26 292	65.90 309	77.23 310	51.10 304	64.79 323	71.72 300	52.12 313	70.13 309	80.18 310	57.96 261	65.36 337	50.21 304	81.01 314	81.25 312

testgi		72.36 263	74.61 234	65.59 310	80.56 289	42.82 338	68.29 313	73.35 284	66.87 228	81.84 218	89.93 187	72.08 203	66.92 330	46.05 323	92.54 195	87.01 236

test0.0.03 1		64.66 303	64.36 302	65.57 311	75.03 330	46.89 326	64.69 324	61.58 339	62.43 264	71.18 305	77.54 320	43.41 322	68.47 325	40.75 332	82.65 305	81.35 309

PMMVS		61.65 309	60.38 314	65.47 312	65.40 350	69.26 150	63.97 325	61.73 338	36.80 347	60.11 338	68.43 338	59.42 254	66.35 333	48.97 311	78.57 322	60.81 342

test1235678		65.57 301	65.73 298	65.06 313	82.84 272	50.90 305	62.90 326	69.26 312	57.17 288	72.36 297	83.04 281	46.02 299	70.10 319	32.79 344	85.24 287	74.19 329

tpmrst		66.28 298	66.69 295	65.05 314	72.82 341	39.33 341	78.20 260	70.69 307	53.16 305	67.88 317	80.36 309	48.18 291	74.75 311	58.13 268	70.79 337	81.08 315

TESTMET0.1,1		61.29 311	60.32 315	64.19 315	72.06 342	51.30 300	67.89 314	62.09 335	45.27 337	60.65 337	69.01 337	27.93 352	64.74 338	56.31 276	81.65 311	76.53 323

PatchT		70.52 275	72.76 256	63.79 316	79.38 294	33.53 346	77.63 266	65.37 331	73.61 152	71.77 300	92.79 113	44.38 321	75.65 309	64.53 224	85.37 284	82.18 296

wuyk23d		75.13 236	79.30 198	62.63 317	75.56 322	75.18 104	80.89 223	73.10 288	75.06 140	94.76 15	95.32 40	87.73 31	52.85 348	34.16 342	97.11 66	59.85 343

EPMVS		62.47 304	62.63 308	62.01 318	70.63 346	38.74 342	74.76 289	52.86 348	53.91 301	67.71 319	80.01 311	39.40 339	66.60 332	55.54 282	68.81 344	80.68 319

EMVS		61.10 313	60.81 313	61.99 319	65.96 349	55.86 266	53.10 343	58.97 342	67.06 225	56.89 347	63.33 345	40.98 334	67.03 329	54.79 287	86.18 279	63.08 340

dp		60.70 315	60.29 316	61.92 320	72.04 343	38.67 343	70.83 305	64.08 332	51.28 319	60.75 336	77.28 322	36.59 342	71.58 317	47.41 317	62.34 346	75.52 326

E-PMN		61.59 310	61.62 310	61.49 321	66.81 347	55.40 269	53.77 342	60.34 340	66.80 229	58.90 343	65.50 344	40.48 336	66.12 334	55.72 279	86.25 278	62.95 341

Patchmatch-test		65.91 299	67.38 290	61.48 322	75.51 324	43.21 337	68.84 311	63.79 333	62.48 262	72.80 295	83.42 278	44.89 319	59.52 344	48.27 315	86.45 275	81.70 303

test2356		56.69 318	55.15 322	61.32 323	73.20 336	44.11 334	54.95 340	62.52 334	48.75 331	62.45 334	68.42 339	21.10 355	65.67 336	26.86 348	78.08 323	74.19 329

ADS-MVSNet		61.90 307	62.19 309	61.03 324	73.16 338	36.42 344	67.10 319	61.75 337	49.74 327	66.04 323	82.97 283	46.71 294	63.21 341	42.29 328	69.96 340	83.46 277

new-patchmatchnet		70.10 279	73.37 251	60.29 325	81.23 282	16.95 353	59.54 332	74.62 274	62.93 257	80.97 228	87.93 220	62.83 239	71.90 316	55.24 284	95.01 135	92.00 159

testus		62.33 306	63.03 306	60.20 326	78.78 300	40.74 339	59.14 333	69.80 311	49.26 329	71.41 302	74.72 332	52.33 282	63.52 339	29.84 346	82.01 308	76.36 324

PVSNet_0	51.08 22	56.10 319	54.97 323	59.48 327	75.12 329	53.28 284	55.16 339	61.89 336	44.30 339	59.16 341	62.48 346	54.22 278	65.91 335	35.40 341	47.01 347	59.25 344

DSMNet-mixed		60.98 314	61.61 311	59.09 328	72.88 340	45.05 332	74.70 290	46.61 352	26.20 348	65.34 326	90.32 181	55.46 273	63.12 342	41.72 330	81.30 313	69.09 337

MVS-HIRNet		61.16 312	62.92 307	55.87 329	79.09 297	35.34 345	71.83 303	57.98 345	46.56 334	59.05 342	91.14 153	49.95 287	76.43 305	38.74 336	71.92 336	55.84 346

MVE	40.22 23	51.82 324	50.47 326	55.87 329	62.66 352	51.91 293	31.61 348	39.28 353	40.65 343	50.76 349	74.98 331	56.24 269	44.67 351	33.94 343	64.11 345	71.04 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
1111		61.71 308	63.77 304	55.55 331	78.05 305	25.74 350	60.62 329	67.52 321	66.09 233	74.68 284	86.50 234	16.00 356	59.22 345	38.79 334	85.65 281	81.70 303

PNet_i23d		52.13 323	51.24 325	54.79 332	75.56 322	45.26 330	54.54 341	52.55 349	66.95 226	57.19 346	65.82 343	13.15 358	63.40 340	36.39 340	39.04 349	55.71 347

new_pmnet		55.69 320	57.66 319	49.76 333	75.47 325	30.59 347	59.56 331	51.45 350	43.62 341	62.49 333	75.48 328	40.96 335	49.15 350	37.39 338	72.52 333	69.55 336

testpf		58.55 317	61.58 312	49.48 334	66.03 348	40.05 340	74.40 292	58.07 344	64.72 249	59.36 340	72.67 333	22.76 354	66.92 330	67.07 205	69.15 343	41.46 348

test12356		54.91 322	57.14 320	48.22 335	75.83 321	17.47 352	52.31 344	69.20 313	51.66 316	60.11 338	75.40 329	29.77 350	62.62 343	27.64 347	72.37 334	64.59 339

PMMVS2		55.64 321	59.27 318	44.74 336	64.30 351	12.32 354	40.60 346	49.79 351	53.19 304	65.06 330	84.81 262	53.60 280	49.76 349	32.68 345	89.41 243	72.15 332

pcd1.5k->3k		38.83 326	41.11 327	32.01 337	93.13 62	0.00 358	0.00 349	91.38 112	0.00 353	0.00 354	0.00 355	89.24 16	0.00 356	0.00 353	96.24 94	96.02 48

.test1245		48.02 325	54.41 324	28.84 338	78.05 305	25.74 350	60.62 329	67.52 321	66.09 233	74.68 284	86.50 234	16.00 356	59.22 345	38.79 334	1.47 351	1.55 352

DeepMVS_CX				24.13 339	32.95 353	29.49 348		21.63 356	12.07 349	37.95 350	45.07 348	30.84 347	19.21 352	17.94 350	33.06 350	23.69 349

tmp_tt		20.25 328	24.50 329	7.49 340	4.47 354	8.70 355	34.17 347	25.16 355	1.00 350	32.43 351	18.49 349	39.37 340	9.21 353	21.64 349	43.75 348	4.57 350

test123		6.27 331	8.08 332	0.84 341	1.11 356	0.57 356	62.90 326	0.82 357	0.54 351	1.07 353	2.75 354	1.26 359	0.30 354	1.04 351	1.26 353	1.66 351

testmvs		5.91 332	7.65 333	0.72 342	1.20 355	0.37 357	59.14 333	0.67 358	0.49 352	1.11 352	2.76 353	0.94 360	0.24 355	1.02 352	1.47 351	1.55 352

cdsmvs_eth3d_5k		20.81 327	27.75 328	0.00 343	0.00 357	0.00 358	0.00 349	85.44 214	0.00 353	0.00 354	82.82 286	81.46 92	0.00 356	0.00 353	0.00 354	0.00 354

pcd_1.5k_mvsjas		6.41 330	8.55 331	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	76.94 132	0.00 356	0.00 353	0.00 354	0.00 354

sosnet-low-res		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

sosnet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uncertanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

Regformer		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

ab-mvs-re		6.65 329	8.87 330	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	79.80 313	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

uanet		0.00 333	0.00 334	0.00 343	0.00 357	0.00 358	0.00 349	0.00 359	0.00 353	0.00 354	0.00 355	0.00 361	0.00 356	0.00 353	0.00 354	0.00 354

GSMVS																83.88 270

test_part3							89.63 50		84.39 28		93.43 97		96.26 4	82.18 74

test_part2					93.86 47	77.77 79				92.84 42

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

sam_mvs1												46.11 298				83.88 270

sam_mvs												45.92 303

MTGPA								91.81 90

test_post1							78.85 256				3.13 351	45.19 313	80.13 298	58.11 269

test_post											3.10 352	45.43 309	77.22 303

patchmatchnet-post											81.71 302	45.93 302	87.01 241

MTMP								33.14 354

gm-plane-assit					75.42 326	44.97 333			52.17 310		72.36 336		87.90 233	54.10 290

test9_res														80.83 89	96.45 87	90.57 192

TEST9					92.34 82	79.70 66	83.94 141	90.32 136	65.41 243	84.49 186	90.97 161	82.03 83	93.63 88

test_8					92.09 89	78.87 72	83.82 146	90.31 138	65.79 236	84.36 189	90.96 163	81.93 85	93.44 109

agg_prior2														79.68 106	96.16 96	90.22 200

agg_prior					91.58 103	77.69 81		90.30 140		84.32 190			93.18 120

test_prior4						78.97 71	84.59 129

test_prior2							83.37 163		75.43 136	84.58 184	91.57 139	81.92 87		79.54 108	96.97 69

旧先验2							81.73 208		56.88 289	86.54 157			84.90 273	72.81 164

新几何2							81.72 209

旧先验1					91.97 92	71.77 131		81.78 240			91.84 132	73.92 168			93.65 171	83.61 275

无先验							82.81 177	85.62 213	58.09 282				91.41 168	67.95 202		84.48 262

原ACMM2							82.26 195

test222					93.31 57	76.54 94	79.38 246	77.79 256	52.59 307	82.36 211	90.84 167	66.83 224			91.69 206	81.25 312

testdata2													86.43 258	63.52 228

segment_acmp												81.94 84

testdata1							79.62 236		73.95 150

plane_prior7					93.45 53	77.31 87

plane_prior6					92.61 73	76.54 94						74.84 153

plane_prior5								93.61 32					95.22 39	80.78 90	95.83 112	94.46 86

plane_prior4											92.95 107

plane_prior3						76.85 92			77.79 101	86.55 152

plane_prior2							89.45 58		79.44 79

plane_prior1					92.83 71

plane_prior						76.42 97	87.15 91		75.94 129						95.03 134

n2								0.00 359

nn								0.00 359

door-mid								74.45 277

test11								91.46 103

door								72.57 291

HQP5-MVS						70.66 141

HQP-NCC					91.19 113		84.77 124		73.30 159	80.55 242

ACMP_Plane					91.19 113		84.77 124		73.30 159	80.55 242

BP-MVS														77.30 129

HQP4-MVS										80.56 241			94.61 55			93.56 116

HQP3-MVS								92.68 67							94.47 153

HQP2-MVS												72.10 201

NP-MVS					91.95 93	74.55 107					90.17 185

MDTV_nov1_ep13_2view						27.60 349	70.76 306		46.47 335	61.27 335		45.20 312		49.18 310		83.75 274

MDTV_nov1_ep13			68.29 288		78.03 307	43.87 335	74.12 294	72.22 293	52.17 310	67.02 320	85.54 246	45.36 310	80.85 294	55.73 278	84.42 295

ACMMP++_ref															95.74 116

ACMMP++															97.35 60

Test By Simon												79.09 110

High-resolution multi-view benchmark