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.

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 11	85.07 37	99.27 3	99.54 1

PMVS	80.48 6	90.08 34	90.66 36	88.34 66	96.71 2	92.97 2	90.31 37	89.57 163	88.51 15	90.11 84	95.12 48	90.98 8	88.92 223	77.55 126	97.07 69	83.13 291
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 66	91.81 91	84.07 32	92.00 60	94.40 71	86.63 41	95.28 38	88.59 4	98.31 26	92.30 153

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 91	84.07 32	92.00 60	94.40 71	86.63 41	95.28 38	88.59 4	98.31 26	92.30 153

PEN-MVS		90.03 37	91.88 12	84.48 133	96.57 5	58.88 251	88.95 65	93.19 48	91.62 4	96.01 6	96.16 22	87.02 38	95.60 23	78.69 116	98.72 10	98.97 3

PS-CasMVS		90.06 35	91.92 9	84.47 134	96.56 6	58.83 252	89.04 64	92.74 67	91.40 5	96.12 4	96.06 25	87.23 35	95.57 24	79.42 112	98.74 7	99.00 2

DTE-MVSNet		89.98 39	91.91 11	84.21 142	96.51 7	57.84 255	88.93 67	92.84 64	91.92 2	96.16 3	96.23 20	86.95 39	95.99 7	79.05 113	98.57 16	98.80 6

CP-MVSNet		89.27 54	90.91 34	84.37 137	96.34 8	58.61 254	88.66 72	92.06 83	90.78 6	95.67 9	95.17 46	81.80 90	95.54 27	79.00 114	98.69 11	98.95 4

WR-MVS_H		89.91 44	91.31 25	85.71 110	96.32 9	62.39 214	89.54 56	93.31 42	90.21 10	95.57 11	95.66 32	81.42 94	95.90 12	80.94 88	98.80 4	98.84 5

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 56	82.59 47	88.52 126	94.37 74	86.74 40	95.41 33	86.32 27	98.21 32	93.19 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 72	83.09 40	91.54 68	94.25 77	87.67 33	95.51 30	87.21 24	98.11 35	93.12 127

MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 77	94.51 11	75.79 132	92.94 39	94.96 51	88.36 20	95.01 47	90.70 2	98.40 21	95.09 76
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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 43	96.29 18	88.16 26	94.17 69	86.07 33	98.48 19	97.22 26

ACMMP_Plus		90.65 26	91.07 30	89.42 53	95.93 14	79.54 69	89.95 43	93.68 31	77.65 104	91.97 62	94.89 53	88.38 19	95.45 31	89.27 3	97.87 46	93.27 122

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 52	95.13 47	90.65 11	95.34 35	88.06 10	98.15 34	95.95 52

HSP-MVS		88.63 61	87.84 70	91.02 29	95.76 16	86.14 19	92.75 13	91.01 124	78.43 95	89.16 115	92.25 128	72.03 207	96.36 2	88.21 9	90.93 230	90.55 198

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 43	81.91 56	90.88 79	94.21 78	87.75 30	95.87 13	87.60 17	97.71 52	93.83 105

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 47	81.99 54	91.40 71	94.17 80	87.51 34	95.87 13	87.74 12	97.76 48	93.99 100

TSAR-MVS + MP.		88.14 66	87.82 71	89.09 58	95.72 19	76.74 94	92.49 20	91.19 120	67.85 227	86.63 153	94.84 55	79.58 109	95.96 10	87.62 15	94.50 154	94.56 82

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 40	93.90 28	80.32 70	91.74 66	94.41 70	88.17 25	95.98 8	86.37 26	97.99 41	93.96 102

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 25	89.99 88	94.03 84	86.57 43	95.80 16	87.35 20	97.62 54	94.20 93

X-MVStestdata		85.04 115	82.70 162	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 25	89.99 88	16.05 356	86.57 43	95.80 16	87.35 20	97.62 54	94.20 93

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 44	91.77 65	93.94 92	90.55 13	95.73 20	88.50 8	98.23 31	95.33 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 48	92.39 56	94.14 81	89.15 17	95.62 22	87.35 20	98.24 30	94.56 82
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
HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 45	81.99 54	91.47 69	93.96 88	88.35 21	95.56 25	87.74 12	97.74 50	92.85 131

#test#		90.49 30	90.31 43	91.02 29	95.43 25	84.66 39	90.65 35	93.29 45	77.00 119	91.47 69	93.96 88	88.35 21	95.56 25	84.88 40	97.74 50	92.85 131

SMA-MVS		90.40 31	90.57 40	89.87 47	95.31 27	79.64 68	90.98 34	93.36 37	75.21 141	92.90 40	95.28 42	86.29 48	96.09 6	87.92 11	97.89 44	93.88 104

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 28	87.62 9	93.38 7	93.36 37	83.16 39	91.06 74	94.00 85	88.26 23	95.71 21	87.28 23	98.39 23	92.55 145

VDDNet		84.35 135	85.39 112	81.25 200	95.13 29	59.32 248	85.42 121	81.11 245	86.41 22	87.41 141	96.21 21	73.61 177	90.61 192	66.33 213	96.85 74	93.81 110

CPTT-MVS		89.39 52	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 78	79.74 75	87.50 140	92.38 122	81.42 94	93.28 118	83.07 63	97.24 65	91.67 168

ACMM	79.39 9	90.65 26	90.99 31	89.63 50	95.03 31	83.53 44	89.62 53	93.35 39	79.20 83	93.83 28	93.60 97	90.81 9	92.96 131	85.02 39	98.45 20	92.41 148
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 14	91.53 18	91.39 22	94.98 32	82.95 50	93.52 5	92.79 65	88.22 16	88.53 125	97.64 3	83.45 66	94.55 60	86.02 34	98.60 14	96.67 37

HPM-MVS++		88.93 58	88.45 65	90.38 40	94.92 33	85.85 25	89.70 47	91.27 117	78.20 98	86.69 152	92.28 127	80.36 103	95.06 46	86.17 32	96.49 87	90.22 204

XVG-ACMP-BASELINE		89.98 39	89.84 46	90.41 39	94.91 34	84.50 41	89.49 58	93.98 24	79.68 76	92.09 58	93.89 93	83.80 62	93.10 126	82.67 70	98.04 36	93.64 114

OPM-MVS		89.80 45	89.97 44	89.27 55	94.76 35	79.86 64	86.76 101	92.78 66	78.78 90	92.51 52	93.64 96	88.13 27	93.84 83	84.83 42	97.55 59	94.10 98

Anonymous20231211		90.14 32	91.88 12	84.92 119	94.75 36	64.47 181	90.13 39	92.97 58	91.68 3	95.35 12	98.79 2	93.19 3	91.76 161	71.67 174	98.40 21	98.52 7

LPG-MVS_test		91.47 16	91.68 15	90.82 34	94.75 36	81.69 51	90.00 40	94.27 13	82.35 49	93.67 30	94.82 56	91.18 6	95.52 28	85.36 35	98.73 8	95.23 73

LGP-MVS_train				90.82 34	94.75 36	81.69 51		94.27 13	82.35 49	93.67 30	94.82 56	91.18 6	95.52 28	85.36 35	98.73 8	95.23 73

abl_6		93.02 4	93.16 4	92.60 4	94.73 39	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 43	91.86 4	95.09 44	87.54 19	98.02 39	93.71 112

XVG-OURS-SEG-HR		89.59 49	89.37 52	90.28 42	94.47 40	85.95 21	86.84 97	93.91 27	80.07 73	86.75 151	93.26 101	93.64 2	90.93 180	84.60 45	90.75 235	93.97 101

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 41	80.98 57	89.16 63	94.05 23	79.03 87	92.87 42	93.74 95	90.60 12	95.21 42	82.87 66	98.76 5	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 55	88.83 59	90.23 43	94.28 42	86.11 20	85.91 113	93.60 34	80.16 72	89.13 116	93.44 98	83.82 61	90.98 178	83.86 55	95.30 129	93.60 116

MIMVSNet1		83.63 155	84.59 132	80.74 205	94.06 43	62.77 205	82.72 183	84.53 228	77.57 106	90.34 82	95.92 27	76.88 140	85.83 269	61.88 239	97.42 61	93.62 115

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 44	64.13 183	84.38 135	91.29 116	84.88 27	92.06 59	93.84 94	86.45 45	93.73 84	73.22 160	98.66 12	97.69 12

新几何1				82.95 173	93.96 45	78.56 76		80.24 249	55.45 299	83.93 197	91.08 159	71.19 212	88.33 233	65.84 218	93.07 187	81.95 306

1121		80.86 189	79.81 199	84.02 145	93.93 46	78.70 75	81.64 212	80.18 250	55.43 300	83.67 199	91.15 154	71.29 211	91.41 170	67.95 204	93.06 188	81.96 305

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 47	80.97 58	91.49 29	93.48 36	82.82 45	92.60 51	93.97 86	88.19 24	96.29 3	87.61 16	98.20 33	94.39 91
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 48	77.77 80				92.84 44

ESAPD		90.05 36	90.56 41	88.50 63	93.86 48	77.77 80	89.63 51	93.93 25	84.39 28	92.84 44	93.43 99	87.19 36	96.26 4	82.18 75	97.61 56	91.48 174

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 50	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 55	93.16 123	91.10 1	97.53 60	96.58 40
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
COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 51	86.15 18	93.37 8	95.10 7	90.28 9	92.11 57	95.03 49	89.75 15	94.93 49	79.95 104	98.27 29	95.04 77
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	82.31 4	89.15 56	89.08 54	89.37 54	93.64 52	79.07 71	88.54 73	94.20 17	73.53 156	89.71 99	94.82 56	85.09 53	95.77 18	84.17 51	98.03 38	93.26 123
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mvs_tets		89.78 46	89.27 53	91.30 24	93.51 53	84.79 36	89.89 45	90.63 129	70.00 208	94.55 18	96.67 11	87.94 29	93.59 94	84.27 49	95.97 107	95.52 66

HQP_MVS		87.75 72	87.43 78	88.70 61	93.45 54	76.42 98	89.45 59	93.61 32	79.44 80	86.55 154	92.95 109	74.84 155	95.22 40	80.78 91	95.83 114	94.46 87

plane_prior7					93.45 54	77.31 88

WR-MVS		83.56 156	84.40 138	81.06 203	93.43 56	54.88 277	78.67 262	85.02 224	81.24 62	90.74 80	91.56 143	72.85 193	91.08 176	68.00 202	98.04 36	97.23 25

jajsoiax		89.41 51	88.81 60	91.19 28	93.38 57	84.72 37	89.70 47	90.29 145	69.27 212	94.39 20	96.38 15	86.02 51	93.52 104	83.96 52	95.92 110	95.34 69

PS-MVSNAJss		88.31 64	87.90 69	89.56 52	93.31 58	77.96 79	87.94 79	91.97 86	70.73 201	94.19 24	96.67 11	76.94 134	94.57 58	83.07 63	96.28 93	96.15 43

test222					93.31 58	76.54 95	79.38 250	77.79 259	52.59 313	82.36 214	90.84 169	66.83 226			91.69 209	81.25 318

DU-MVS		86.80 84	86.99 84	86.21 99	93.24 60	67.02 165	83.16 172	92.21 79	81.73 58	90.92 76	91.97 130	77.20 128	93.99 74	74.16 149	98.35 24	97.61 13

NR-MVSNet		86.00 100	86.22 98	85.34 114	93.24 60	64.56 180	82.21 198	90.46 133	80.99 64	88.42 128	91.97 130	77.56 124	93.85 81	72.46 168	98.65 13	97.61 13

OurMVSNet-221017-0		90.01 38	89.74 48	90.83 33	93.16 62	80.37 59	91.91 27	93.11 50	81.10 63	95.32 13	97.24 6	72.94 192	94.85 51	85.07 37	97.78 47	97.26 23

pcd1.5k->3k		38.83 332	41.11 333	32.01 343	93.13 63	0.00 364	0.00 355	91.38 114	0.00 359	0.00 360	0.00 361	89.24 16	0.00 362	0.00 359	96.24 96	96.02 49

UniMVSNet (Re)		86.87 81	86.98 85	86.55 89	93.11 64	68.48 157	83.80 150	92.87 61	80.37 68	89.61 107	91.81 137	77.72 122	94.18 67	75.00 146	98.53 17	96.99 34

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 65	85.17 31	92.47 21	95.05 8	87.65 19	93.21 37	94.39 73	90.09 14	95.08 45	86.67 25	97.60 58	94.18 95

ACMH+	77.89 11	90.73 25	91.50 19	88.44 64	93.00 66	76.26 100	89.65 50	95.55 3	87.72 18	93.89 27	94.94 52	91.62 5	93.44 110	78.35 118	98.76 5	95.61 65

APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 67	78.04 78	92.84 12	94.14 21	83.33 37	93.90 26	95.73 30	88.77 18	96.41 1	87.60 17	97.98 42	92.98 130

114514_t		83.10 164	82.54 167	84.77 125	92.90 68	69.10 155	86.65 106	90.62 130	54.66 303	81.46 227	90.81 170	76.98 133	94.38 61	72.62 167	96.18 97	90.82 190

testdata				79.54 220	92.87 69	72.34 123		80.14 251	59.91 282	85.47 173	91.75 139	67.96 222	85.24 273	68.57 200	92.18 206	81.06 323

CNVR-MVS		87.81 71	87.68 74	88.21 67	92.87 69	77.30 89	85.25 122	91.23 118	77.31 114	87.07 147	91.47 145	82.94 71	94.71 54	84.67 44	96.27 95	92.62 144

UniMVSNet_NR-MVSNet		86.84 83	87.06 82	86.17 102	92.86 71	67.02 165	82.55 187	91.56 97	83.08 41	90.92 76	91.82 136	78.25 118	93.99 74	74.16 149	98.35 24	97.49 16

plane_prior1					92.83 72

原ACMM1				84.60 130	92.81 73	74.01 111		91.50 99	62.59 264	82.73 213	90.67 175	76.53 141	94.25 64	69.24 190	95.69 119	85.55 254

plane_prior6					92.61 74	76.54 95						74.84 155

APD-MVS		89.54 50	89.63 50	89.26 56	92.57 75	81.34 56	90.19 38	93.08 52	80.87 65	91.13 73	93.19 102	86.22 49	95.97 9	82.23 74	97.18 67	90.45 200
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 60	89.58 51	85.88 107	92.55 76	72.22 126	84.01 141	89.44 165	88.63 14	94.38 21	95.77 29	86.38 47	93.59 94	79.84 105	95.21 130	91.82 165

SixPastTwentyTwo		87.20 77	87.45 77	86.45 91	92.52 77	69.19 154	87.84 82	88.05 182	81.66 59	94.64 17	96.53 14	65.94 230	94.75 53	83.02 65	96.83 76	95.41 68

ACMH	76.49 14	89.34 53	91.14 28	83.96 148	92.50 78	70.36 144	89.55 54	93.84 29	81.89 57	94.70 16	95.44 40	90.69 10	88.31 234	83.33 59	98.30 28	93.20 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 196	81.68 176	75.94 266	92.46 79	47.98 331	76.70 277	81.67 243	73.45 157	84.87 178	92.82 112	74.66 160	86.51 260	61.66 242	96.85 74	93.33 121

agg_prior3		85.76 106	84.95 119	88.16 68	92.43 80	79.92 62	83.98 142	90.03 153	65.11 249	83.66 200	90.64 178	81.00 97	93.67 86	81.21 83	96.54 84	90.88 187

F-COLMAP		84.97 118	83.42 153	89.63 50	92.39 81	83.40 45	88.83 68	91.92 88	73.19 166	80.18 251	89.15 201	77.04 132	93.28 118	65.82 219	92.28 201	92.21 158

test_djsdf		89.62 48	89.01 55	91.45 21	92.36 82	82.98 49	91.98 25	90.08 151	71.54 195	94.28 23	96.54 13	81.57 92	94.27 62	86.26 28	96.49 87	97.09 30

TEST9					92.34 83	79.70 66	83.94 143	90.32 138	65.41 247	84.49 188	90.97 163	82.03 84	93.63 89

train_agg		85.98 103	85.28 113	88.07 70	92.34 83	79.70 66	83.94 143	90.32 138	65.79 240	84.49 188	90.97 163	81.93 86	93.63 89	81.21 83	96.54 84	90.88 187

NCCC		87.36 74	86.87 88	88.83 59	92.32 85	78.84 74	86.58 108	91.09 122	78.77 91	84.85 179	90.89 167	80.85 98	95.29 36	81.14 85	95.32 126	92.34 152

FC-MVSNet-test		85.93 104	87.05 83	82.58 179	92.25 86	56.44 266	85.75 116	93.09 51	77.33 113	91.94 63	94.65 61	74.78 157	93.41 112	75.11 144	98.58 15	97.88 10

CDPH-MVS		86.17 99	85.54 109	88.05 73	92.25 86	75.45 103	83.85 147	92.01 84	65.91 239	86.19 161	91.75 139	83.77 63	94.98 48	77.43 129	96.71 79	93.73 111

pmmvs6		86.52 90	88.06 67	81.90 188	92.22 88	62.28 220	84.66 130	89.15 168	83.54 36	89.85 95	97.32 4	88.08 28	86.80 255	70.43 183	97.30 64	96.62 38

EG-PatchMatch MVS		84.08 145	84.11 145	83.98 147	92.22 88	72.61 120	82.20 200	87.02 202	72.63 173	88.86 118	91.02 161	78.52 114	91.11 175	73.41 159	91.09 221	88.21 226

test_8					92.09 90	78.87 73	83.82 148	90.31 140	65.79 240	84.36 191	90.96 165	81.93 86	93.44 110

Vis-MVSNet		86.86 82	86.58 92	87.72 75	92.09 90	77.43 86	87.35 88	92.09 82	78.87 89	84.27 195	94.05 83	78.35 117	93.65 87	80.54 95	91.58 211	92.08 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 87	86.82 91	86.17 102	92.05 92	66.87 167	91.21 33	88.64 173	86.30 23	89.60 108	92.59 117	69.22 217	94.91 50	73.89 153	97.89 44	96.72 36

旧先验1					91.97 93	71.77 132		81.78 242			91.84 134	73.92 170			93.65 173	83.61 281

v7n		90.13 33	90.96 32	87.65 77	91.95 94	71.06 140	89.99 42	93.05 53	86.53 21	94.29 22	96.27 19	82.69 73	94.08 72	86.25 30	97.63 53	97.82 11

NP-MVS					91.95 94	74.55 108					90.17 187

OMC-MVS		88.19 65	87.52 75	90.19 44	91.94 96	81.68 53	87.49 87	93.17 49	76.02 128	88.64 123	91.22 149	84.24 60	93.37 113	77.97 124	97.03 70	95.52 66

FIs		85.35 111	86.27 97	82.60 178	91.86 97	57.31 259	85.10 124	93.05 53	75.83 131	91.02 75	93.97 86	73.57 178	92.91 135	73.97 152	98.02 39	97.58 15

MSLP-MVS++		85.00 117	86.03 102	81.90 188	91.84 98	71.56 138	86.75 102	93.02 57	75.95 129	87.12 144	89.39 198	77.98 119	89.40 214	77.46 127	94.78 146	84.75 267

Anonymous20240521		87.68 73	88.61 63	84.87 121	91.76 99	64.76 178	89.28 62	91.66 95	83.02 42	93.29 35	96.10 24	77.37 127	92.89 136	77.27 132	97.75 49	96.97 35

DP-MVS Recon		84.05 146	83.22 156	86.52 90	91.73 100	75.27 104	83.23 171	92.40 75	72.04 183	82.04 218	88.33 215	77.91 121	93.95 79	66.17 214	95.12 133	90.34 203

SD-MVS		88.96 57	89.88 45	86.22 97	91.63 101	77.07 90	89.82 46	93.77 30	78.90 88	92.88 41	92.29 126	86.11 50	90.22 200	86.24 31	97.24 65	91.36 177

AllTest		87.97 68	87.40 79	89.68 48	91.59 102	83.40 45	89.50 57	95.44 5	79.47 78	88.00 133	93.03 105	82.66 74	91.47 166	70.81 176	96.14 100	94.16 96

TestCases				89.68 48	91.59 102	83.40 45		95.44 5	79.47 78	88.00 133	93.03 105	82.66 74	91.47 166	70.81 176	96.14 100	94.16 96

MCST-MVS		84.36 133	83.93 150	85.63 111	91.59 102	71.58 137	83.52 159	92.13 81	61.82 270	83.96 196	89.75 193	79.93 108	93.46 109	78.33 119	94.34 157	91.87 164

agg_prior1		85.72 107	85.20 114	87.28 83	91.58 105	77.69 82	83.69 154	90.30 142	66.29 235	84.32 192	91.07 160	82.13 81	93.18 121	81.02 86	96.36 90	90.98 182

agg_prior					91.58 105	77.69 82		90.30 142		84.32 192			93.18 121

PVSNet_Blended_VisFu		81.55 180	80.49 191	84.70 128	91.58 105	73.24 116	84.21 136	91.67 94	62.86 262	80.94 232	87.16 231	67.27 224	92.87 137	69.82 186	88.94 254	87.99 230

EPP-MVSNet		85.47 110	85.04 116	86.77 86	91.52 108	69.37 149	91.63 28	87.98 185	81.51 61	87.05 148	91.83 135	66.18 229	95.29 36	70.75 178	96.89 73	95.64 60

DeepPCF-MVS	81.24 5	87.28 76	86.21 99	90.49 38	91.48 109	84.90 34	83.41 164	92.38 77	70.25 206	89.35 113	90.68 174	82.85 72	94.57 58	79.55 108	95.95 108	92.00 161

Baseline_NR-MVSNet		84.00 148	85.90 103	78.29 233	91.47 110	53.44 286	82.29 194	87.00 203	79.06 86	89.55 109	95.72 31	77.20 128	86.14 265	72.30 169	98.51 18	95.28 71

HyFIR lowres test		75.12 240	72.66 262	82.50 182	91.44 111	65.19 175	72.47 305	87.31 192	46.79 339	80.29 249	84.30 271	52.70 285	92.10 152	51.88 308	86.73 277	90.22 204

DP-MVS		88.60 62	89.01 55	87.36 82	91.30 112	77.50 85	87.55 85	92.97 58	87.95 17	89.62 105	92.87 111	84.56 57	93.89 80	77.65 125	96.62 81	90.70 192

DeepC-MVS_fast	80.27 8	86.23 97	85.65 108	87.96 74	91.30 112	76.92 91	87.19 91	91.99 85	70.56 202	84.96 175	90.69 173	80.01 106	95.14 43	78.37 117	95.78 116	91.82 165
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	83.92 2	89.97 41	89.66 49	90.92 32	91.27 114	81.66 54	91.25 32	94.13 22	88.89 12	88.83 120	94.26 76	77.55 125	95.86 15	84.88 40	95.87 112	95.24 72

HQP-NCC					91.19 115		84.77 126		73.30 162	80.55 246

ACMP_Plane					91.19 115		84.77 126		73.30 162	80.55 246

HQP-MVS		84.61 124	84.06 146	86.27 95	91.19 115	70.66 142	84.77 126	92.68 68	73.30 162	80.55 246	90.17 187	72.10 203	94.61 56	77.30 130	94.47 155	93.56 118

VDD-MVS		84.23 139	84.58 133	83.20 169	91.17 118	65.16 176	83.25 169	84.97 226	79.79 74	87.18 143	94.27 75	74.77 158	90.89 183	69.24 190	96.54 84	93.55 120

K. test v3		85.14 112	84.73 122	86.37 92	91.13 119	69.63 148	85.45 120	76.68 267	84.06 34	92.44 54	96.99 8	62.03 242	94.65 55	80.58 94	93.24 185	94.83 81

lessismore_v0				85.95 104	91.10 120	70.99 141		70.91 311		91.79 64	94.42 69	61.76 243	92.93 133	79.52 111	93.03 189	93.93 103

TransMVSNet (Re)		84.02 147	85.74 105	78.85 224	91.00 121	55.20 276	82.29 194	87.26 193	79.65 77	88.38 130	95.52 38	83.00 70	86.88 247	67.97 203	96.60 82	94.45 89

wuykxyi23d		88.46 63	88.80 61	87.44 81	90.96 122	93.03 1	85.85 115	81.96 239	74.58 146	98.58 2	97.29 5	87.73 31	87.31 242	82.84 68	99.41 1	81.99 304

PAPM_NR		83.23 161	83.19 158	83.33 167	90.90 123	65.98 171	88.19 76	90.78 126	78.13 100	80.87 234	87.92 223	73.49 180	92.42 145	70.07 184	88.40 258	91.60 170

CSCG		86.26 95	86.47 94	85.60 112	90.87 124	74.26 110	87.98 78	91.85 89	80.35 69	89.54 111	88.01 219	79.09 111	92.13 150	75.51 141	95.06 135	90.41 201

PLC	73.85 16	82.09 175	80.31 192	87.45 80	90.86 125	80.29 60	85.88 114	90.65 128	68.17 221	76.32 276	86.33 242	73.12 190	92.61 143	61.40 245	90.02 245	89.44 213
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test12				86.57 88	90.74 126	72.63 119		90.69 127		82.76 212		79.20 110	94.80 52		95.32 126	92.27 155

ITE_SJBPF				90.11 45	90.72 127	84.97 33		90.30 142	81.56 60	90.02 87	91.20 152	82.40 78	90.81 185	73.58 156	94.66 151	94.56 82

TAMVS		78.08 209	76.36 222	83.23 168	90.62 128	72.87 117	79.08 257	80.01 252	61.72 272	81.35 229	86.92 234	63.96 236	88.78 228	50.61 309	93.01 190	88.04 229

test_prior3		86.31 94	86.31 96	86.32 93	90.59 129	71.99 130	83.37 165	92.85 62	75.43 138	84.58 186	91.57 141	81.92 88	94.17 69	79.54 109	96.97 71	92.80 133

test_prior				86.32 93	90.59 129	71.99 130		92.85 62					94.17 69			92.80 133

ambc				82.98 172	90.55 131	64.86 177	88.20 75	89.15 168		89.40 112	93.96 88	71.67 210	91.38 172	78.83 115	96.55 83	92.71 136

Test_1112_low_res		73.90 253	73.08 258	76.35 260	90.35 132	55.95 267	73.40 304	86.17 209	50.70 329	73.14 299	85.94 247	58.31 262	85.90 268	56.51 280	83.22 306	87.20 239

VPA-MVSNet		83.47 159	84.73 122	79.69 216	90.29 133	57.52 258	81.30 219	88.69 172	76.29 124	87.58 138	94.44 68	80.60 101	87.20 243	66.60 212	96.82 77	94.34 92

FMVSNet1		84.55 126	85.45 111	81.85 191	90.27 134	61.05 235	86.83 98	88.27 179	78.57 94	89.66 101	95.64 34	75.43 146	90.68 189	69.09 193	95.33 125	93.82 107

MVS_111021_HR		84.63 123	84.34 143	85.49 113	90.18 135	75.86 102	79.23 256	87.13 198	73.35 159	85.56 171	89.34 199	83.60 65	90.50 194	76.64 136	94.05 161	90.09 209

RPSCF		88.00 67	86.93 87	91.22 27	90.08 136	89.30 6	89.68 49	91.11 121	79.26 82	89.68 100	94.81 59	82.44 77	87.74 239	76.54 137	88.74 257	96.61 39

nrg030		87.85 70	88.49 64	85.91 105	90.07 137	69.73 146	87.86 80	94.20 17	74.04 151	92.70 49	94.66 60	85.88 52	91.50 165	79.72 106	97.32 63	96.50 41

v748		88.91 59	89.82 47	86.19 101	90.06 138	68.53 156	88.81 69	91.48 101	84.36 30	94.19 24	95.98 26	82.52 76	92.67 141	84.30 48	96.67 80	97.37 20

AdaColmap		83.66 154	83.69 152	83.57 161	90.05 139	72.26 125	86.29 112	90.00 154	78.19 99	81.65 225	87.16 231	83.40 67	94.24 65	61.69 241	94.76 149	84.21 274

pm-mvs1		83.69 153	84.95 119	79.91 212	90.04 140	59.66 245	82.43 189	87.44 190	75.52 137	87.85 135	95.26 44	81.25 96	85.65 271	68.74 197	96.04 105	94.42 90

CHOSEN 1792x2688		72.45 267	70.56 278	78.13 235	90.02 141	63.08 199	68.72 318	83.16 231	42.99 348	75.92 280	85.46 253	57.22 270	85.18 275	49.87 313	81.67 315	86.14 248

anonymousdsp		89.73 47	88.88 58	92.27 7	89.82 142	86.67 12	90.51 36	90.20 150	69.87 209	95.06 14	96.14 23	84.28 59	93.07 130	87.68 14	96.34 91	97.09 30

1112_ss		74.82 245	73.74 245	78.04 237	89.57 143	60.04 243	76.49 280	87.09 201	54.31 304	73.66 297	79.80 319	60.25 250	86.76 257	58.37 270	84.15 302	87.32 238

PCF-MVS	74.62 15	82.15 174	80.92 187	85.84 108	89.43 144	72.30 124	80.53 229	91.82 90	57.36 291	87.81 136	89.92 190	77.67 123	93.63 89	58.69 265	95.08 134	91.58 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 235	73.51 255	82.71 177	89.35 145	73.62 112	80.06 232	85.20 219	60.30 280	73.96 295	87.94 221	57.89 265	89.45 212	52.02 303	74.87 336	85.06 260
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 157	83.10 159	84.90 120	89.34 146	83.87 43	84.54 133	88.77 170	79.09 85	83.54 203	88.66 208	74.87 154	81.73 297	66.84 210	92.29 200	89.11 218

TSAR-MVS + GP.		83.95 149	82.69 163	87.72 75	89.27 147	81.45 55	83.72 153	81.58 244	74.73 145	85.66 168	86.06 246	72.56 201	92.69 140	75.44 142	95.21 130	89.01 222

MVS_111021_LR		84.28 137	83.76 151	85.83 109	89.23 148	83.07 48	80.99 225	83.56 230	72.71 172	86.07 162	89.07 202	81.75 91	86.19 264	77.11 133	93.36 178	88.24 225

LFMVS		80.15 199	80.56 189	78.89 223	89.19 149	55.93 268	85.22 123	73.78 283	82.96 43	84.28 194	92.72 116	57.38 268	90.07 207	63.80 229	95.75 117	90.68 193

CLD-MVS		83.18 162	82.64 164	84.79 124	89.05 150	67.82 162	77.93 267	92.52 73	68.33 220	85.07 174	81.54 309	82.06 82	92.96 131	69.35 189	97.91 43	93.57 117
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LS3D		90.60 28	90.34 42	91.38 23	89.03 151	84.23 42	93.58 4	94.68 10	90.65 7	90.33 83	93.95 91	84.50 58	95.37 34	80.87 89	95.50 122	94.53 86

CDS-MVSNet		77.32 215	75.40 231	83.06 171	89.00 152	72.48 122	77.90 268	82.17 238	60.81 277	78.94 260	83.49 279	59.30 257	88.76 229	54.64 295	92.37 199	87.93 232
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpnnormal		81.79 179	82.95 160	78.31 232	88.93 153	55.40 272	80.83 228	82.85 234	76.81 120	85.90 166	94.14 81	74.58 162	86.51 260	66.82 211	95.68 120	93.01 129

v13		87.31 75	88.10 66	84.94 118	88.84 154	63.75 187	87.85 81	91.47 104	79.12 84	93.72 29	95.82 28	75.20 149	93.58 97	84.76 43	96.16 98	97.48 17

Vis-MVSNet (Re-imp)		77.82 210	77.79 209	77.92 239	88.82 155	51.29 306	83.28 167	71.97 299	74.04 151	82.23 215	89.78 192	57.38 268	89.41 213	57.22 277	95.41 123	93.05 128

TAPA-MVS	77.73 12	85.71 108	84.83 121	88.37 65	88.78 156	79.72 65	87.15 93	93.50 35	69.17 214	85.80 167	89.56 196	80.76 99	92.13 150	73.21 164	95.51 121	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v12		87.15 78	87.91 68	84.84 122	88.69 157	63.52 190	87.58 84	91.46 105	78.74 92	93.57 32	95.66 32	74.94 153	93.57 98	84.50 46	96.08 103	97.43 18

v52		89.97 41	90.60 37	88.07 70	88.69 157	72.01 128	91.35 30	92.64 70	82.22 51	95.97 8	96.31 16	84.82 54	93.98 76	88.59 4	94.83 144	98.23 8

V4		89.97 41	90.60 37	88.07 70	88.69 157	72.01 128	91.35 30	92.64 70	82.22 51	95.98 7	96.31 16	84.80 56	93.98 76	88.59 4	94.83 144	98.23 8

FPMVS		72.29 270	72.00 269	73.14 286	88.63 160	85.00 32	74.65 295	67.39 329	71.94 191	77.80 267	87.66 226	50.48 290	75.83 314	49.95 311	79.51 323	58.58 351

BH-untuned		80.96 188	80.99 185	80.84 204	88.55 161	68.23 158	80.33 231	88.46 174	72.79 171	86.55 154	86.76 235	74.72 159	91.77 160	61.79 240	88.99 253	82.52 297

V9		86.96 79	87.70 73	84.74 126	88.52 162	63.27 196	87.31 89	91.45 107	78.28 97	93.43 33	95.45 39	74.59 161	93.57 98	84.23 50	96.01 106	97.38 19

v11		86.96 79	87.78 72	84.51 131	88.50 163	62.60 210	87.21 90	91.63 96	78.08 101	93.40 34	95.56 37	75.07 150	93.57 98	84.46 47	96.08 103	97.36 21

ab-mvs		79.67 201	80.56 189	76.99 251	88.48 164	56.93 261	84.70 129	86.06 210	68.95 218	80.78 235	93.08 104	75.30 148	84.62 280	56.78 279	90.90 231	89.43 214

PHI-MVS		86.38 92	85.81 104	88.08 69	88.44 165	77.34 87	89.35 61	93.05 53	73.15 167	84.76 180	87.70 225	78.87 113	94.18 67	80.67 93	96.29 92	92.73 135

V14		86.75 85	87.46 76	84.62 129	88.35 166	63.00 201	87.02 95	91.42 110	77.78 103	93.27 36	95.23 45	74.22 164	93.56 101	83.95 53	95.93 109	97.31 22

xiu_mvs_v1_base_debu		80.84 190	80.14 195	82.93 174	88.31 167	71.73 133	79.53 240	87.17 195	65.43 244	79.59 253	82.73 292	76.94 134	90.14 203	73.22 160	88.33 259	86.90 242

xiu_mvs_v1_base		80.84 190	80.14 195	82.93 174	88.31 167	71.73 133	79.53 240	87.17 195	65.43 244	79.59 253	82.73 292	76.94 134	90.14 203	73.22 160	88.33 259	86.90 242

xiu_mvs_v1_base_debi		80.84 190	80.14 195	82.93 174	88.31 167	71.73 133	79.53 240	87.17 195	65.43 244	79.59 253	82.73 292	76.94 134	90.14 203	73.22 160	88.33 259	86.90 242

MG-MVS		80.32 195	80.94 186	78.47 231	88.18 170	52.62 293	82.29 194	85.01 225	72.01 184	79.24 258	92.54 120	69.36 216	93.36 114	70.65 180	89.19 252	89.45 212

PM-MVS		80.20 198	79.00 202	83.78 152	88.17 171	86.66 13	81.31 217	66.81 335	69.64 210	88.33 131	90.19 185	64.58 233	83.63 289	71.99 173	90.03 244	81.06 323

v10		86.54 89	87.10 81	84.84 122	88.16 172	63.28 195	86.64 107	92.20 80	75.42 140	92.81 46	94.50 66	74.05 167	94.06 73	83.88 54	96.28 93	97.17 28

v15		86.56 88	87.25 80	84.51 131	88.15 173	62.72 206	86.72 105	91.40 112	77.38 108	93.11 38	95.00 50	73.93 169	93.55 102	83.67 57	95.86 113	97.26 23

v7		84.81 120	85.00 117	84.23 141	88.15 173	63.27 196	83.79 151	91.39 113	71.10 199	90.07 85	91.28 147	74.04 168	93.63 89	81.48 82	93.67 172	95.79 53

v17		86.32 93	86.95 86	84.44 135	88.00 175	62.62 209	86.74 103	91.48 101	77.17 116	92.74 47	94.56 62	73.74 173	93.53 103	83.27 60	94.87 143	97.18 27

canonicalmvs		85.50 109	86.14 100	83.58 160	87.97 176	67.13 164	87.55 85	94.32 12	73.44 158	88.47 127	87.54 228	86.45 45	91.06 177	75.76 140	93.76 169	92.54 146

v16		86.24 96	86.85 89	84.43 136	87.96 177	62.59 211	86.73 104	91.48 101	77.17 116	92.67 50	94.55 63	73.63 174	93.52 104	83.26 61	94.16 158	97.17 28

VNet		79.31 202	80.27 193	76.44 259	87.92 178	53.95 281	75.58 288	84.35 229	74.39 149	82.23 215	90.72 172	72.84 194	84.39 282	60.38 252	93.98 163	90.97 183

view600		76.79 220	76.54 217	77.56 243	87.91 179	50.77 312	81.92 204	71.35 307	77.38 108	84.62 182	88.40 211	45.18 319	89.26 216	58.58 266	93.49 174	92.66 138

view800		76.79 220	76.54 217	77.56 243	87.91 179	50.77 312	81.92 204	71.35 307	77.38 108	84.62 182	88.40 211	45.18 319	89.26 216	58.58 266	93.49 174	92.66 138

conf0.05thres1000		76.79 220	76.54 217	77.56 243	87.91 179	50.77 312	81.92 204	71.35 307	77.38 108	84.62 182	88.40 211	45.18 319	89.26 216	58.58 266	93.49 174	92.66 138

tfpn		76.79 220	76.54 217	77.56 243	87.91 179	50.77 312	81.92 204	71.35 307	77.38 108	84.62 182	88.40 211	45.18 319	89.26 216	58.58 266	93.49 174	92.66 138

v8		86.22 98	86.83 90	84.36 138	87.82 183	62.35 215	86.42 110	91.33 115	76.78 121	92.73 48	94.48 67	73.41 181	93.72 85	83.10 62	95.41 123	97.01 33

v1neww		84.43 130	84.66 128	83.75 153	87.81 184	62.34 216	83.59 155	90.27 146	72.33 178	89.93 92	91.22 149	73.28 185	93.29 115	80.25 100	93.25 183	95.62 61

v7new		84.43 130	84.66 128	83.75 153	87.81 184	62.34 216	83.59 155	90.27 146	72.33 178	89.93 92	91.22 149	73.28 185	93.29 115	80.25 100	93.25 183	95.62 61

v6		84.43 130	84.66 128	83.75 153	87.81 184	62.34 216	83.59 155	90.26 148	72.33 178	89.94 91	91.19 153	73.30 184	93.29 115	80.26 99	93.26 182	95.62 61

alignmvs		83.94 150	83.98 148	83.80 150	87.80 187	67.88 161	84.54 133	91.42 110	73.27 165	88.41 129	87.96 220	72.33 202	90.83 184	76.02 139	94.11 159	92.69 137

v1192		84.57 125	84.69 126	84.21 142	87.75 188	62.88 203	83.02 174	91.43 108	69.08 216	89.98 90	90.89 167	72.70 197	93.62 93	82.41 71	94.97 138	96.13 44

v18		85.99 102	86.55 93	84.30 140	87.73 189	62.29 219	86.40 111	91.49 100	76.64 122	92.40 55	94.20 79	73.28 185	93.52 104	82.87 66	93.99 162	97.09 30

PatchMatch-RL		74.48 247	73.22 257	78.27 234	87.70 190	85.26 30	75.92 284	70.09 314	64.34 254	76.09 279	81.25 311	65.87 231	78.07 307	53.86 297	83.82 303	71.48 339

v1144		84.54 128	84.72 124	84.00 146	87.67 191	62.55 212	82.97 175	90.93 125	70.32 205	89.80 97	90.99 162	73.50 179	93.48 108	81.69 81	94.65 152	95.97 50

v1240		84.30 136	84.51 135	83.65 158	87.65 192	61.26 232	82.85 178	91.54 98	67.94 225	90.68 81	90.65 176	71.71 209	93.64 88	82.84 68	94.78 146	96.07 46

v1921920		84.23 139	84.37 142	83.79 151	87.64 193	61.71 223	82.91 177	91.20 119	67.94 225	90.06 86	90.34 181	72.04 206	93.59 94	82.32 73	94.91 139	96.07 46

v144192		84.24 138	84.41 137	83.71 157	87.59 194	61.57 228	82.95 176	91.03 123	67.82 228	89.80 97	90.49 179	73.28 185	93.51 107	81.88 80	94.89 140	96.04 48

Fast-Effi-MVS+		81.04 187	80.57 188	82.46 183	87.50 195	63.22 198	78.37 263	89.63 161	68.01 222	81.87 220	82.08 304	82.31 79	92.65 142	67.10 206	88.30 263	91.51 173

pmmvs-eth3d		78.42 207	77.04 213	82.57 181	87.44 196	74.41 109	80.86 227	79.67 253	55.68 298	84.69 181	90.31 184	60.91 246	85.42 272	62.20 236	91.59 210	87.88 233

MVS_0304		84.88 119	83.96 149	87.64 78	87.43 197	74.83 106	84.18 137	93.30 43	77.48 107	77.39 270	88.46 210	74.53 163	95.74 19	78.09 123	94.75 150	92.36 151

IterMVS-LS		84.73 122	84.98 118	83.96 148	87.35 198	63.66 188	83.25 169	89.88 156	76.06 126	89.62 105	92.37 125	73.40 183	92.52 144	78.16 121	94.77 148	95.69 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1		84.16 141	84.38 139	83.52 163	87.33 199	61.71 223	82.79 180	89.73 159	71.89 194	89.64 102	91.11 158	72.72 195	93.10 126	80.40 96	93.79 168	95.75 55

v1141		84.16 141	84.38 139	83.52 163	87.32 200	61.70 225	82.79 180	89.74 157	71.90 192	89.64 102	91.12 156	72.68 198	93.10 126	80.39 98	93.80 167	95.75 55

divwei89l23v2f112		84.16 141	84.38 139	83.52 163	87.32 200	61.70 225	82.79 180	89.74 157	71.90 192	89.64 102	91.12 156	72.68 198	93.10 126	80.40 96	93.81 166	95.75 55

tfpn111		76.03 232	75.53 230	77.53 247	87.27 202	51.88 298	81.07 222	73.26 288	75.68 133	83.25 205	86.37 239	45.54 309	89.38 215	55.07 291	92.26 202	91.34 178

conf200view11		75.62 236	75.05 234	77.34 249	87.27 202	51.88 298	81.07 222	73.26 288	75.68 133	83.25 205	86.37 239	45.54 309	88.80 224	51.98 304	90.99 225	91.34 178

thres100view900		75.45 237	75.05 234	76.66 258	87.27 202	51.88 298	81.07 222	73.26 288	75.68 133	83.25 205	86.37 239	45.54 309	88.80 224	51.98 304	90.99 225	89.31 215

MIMVSNet		71.09 278	71.59 272	69.57 302	87.23 205	50.07 320	78.91 258	71.83 301	60.20 281	71.26 309	91.76 138	55.08 280	76.09 312	41.06 337	87.02 276	82.54 296

Effi-MVS+		83.90 151	84.01 147	83.57 161	87.22 206	65.61 174	86.55 109	92.40 75	78.64 93	81.34 230	84.18 272	83.65 64	92.93 133	74.22 148	87.87 267	92.17 159

BH-RMVSNet		80.53 193	80.22 194	81.49 196	87.19 207	66.21 170	77.79 269	86.23 208	74.21 150	83.69 198	88.50 209	73.25 189	90.75 186	63.18 234	87.90 266	87.52 235

Effi-MVS+-dtu		85.82 105	83.38 154	93.14 3	87.13 208	91.15 3	87.70 83	88.42 175	74.57 147	83.56 202	85.65 248	78.49 115	94.21 66	72.04 171	92.88 192	94.05 99

mvs-test1		84.55 126	82.12 171	91.84 17	87.13 208	89.54 5	85.05 125	88.42 175	74.57 147	80.60 243	82.98 285	78.49 115	93.98 76	72.04 171	89.77 246	92.00 161

v2v482		84.09 144	84.24 144	83.62 159	87.13 208	61.40 229	82.71 184	89.71 160	72.19 182	89.55 109	91.41 146	70.70 214	93.20 120	81.02 86	93.76 169	96.25 42

jason		77.42 214	75.75 228	82.43 184	87.10 211	69.27 150	77.99 266	81.94 241	51.47 323	77.84 265	85.07 261	60.32 249	89.00 221	70.74 179	89.27 251	89.03 220
jason: jason.
PS-MVSNAJ		77.04 218	76.53 221	78.56 229	87.09 212	61.40 229	75.26 290	87.13 198	61.25 275	74.38 294	77.22 329	76.94 134	90.94 179	64.63 225	84.83 298	83.35 286

xiu_mvs_v2_base		77.19 216	76.75 215	78.52 230	87.01 213	61.30 231	75.55 289	87.12 200	61.24 276	74.45 292	78.79 323	77.20 128	90.93 180	64.62 226	84.80 299	83.32 287

thres600view7		75.97 233	75.35 233	77.85 241	87.01 213	51.84 302	80.45 230	73.26 288	75.20 142	83.10 209	86.31 244	45.54 309	89.05 220	55.03 292	92.24 203	92.66 138

BH-w/o		76.57 226	76.07 225	78.10 236	86.88 215	65.92 172	77.63 270	86.33 207	65.69 242	80.89 233	79.95 318	68.97 220	90.74 187	53.01 300	85.25 292	77.62 328

MAR-MVS		80.24 197	78.74 204	84.73 127	86.87 216	78.18 77	85.75 116	87.81 187	65.67 243	77.84 265	78.50 324	73.79 172	90.53 193	61.59 244	90.87 232	85.49 256
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
testing_2		84.36 133	84.64 131	83.50 166	86.74 217	63.97 186	84.56 132	90.31 140	66.22 236	91.62 67	94.55 63	75.88 144	91.95 153	77.02 135	94.89 140	94.56 82

QAPM		82.59 168	82.59 166	82.58 179	86.44 218	66.69 168	89.94 44	90.36 137	67.97 224	84.94 177	92.58 119	72.71 196	92.18 149	70.63 181	87.73 269	88.85 223

PAPM		71.77 273	70.06 284	76.92 252	86.39 219	53.97 280	76.62 278	86.62 205	53.44 309	63.97 338	84.73 267	57.79 266	92.34 146	39.65 339	81.33 318	84.45 269

GBi-Net		82.02 176	82.07 172	81.85 191	86.38 220	61.05 235	86.83 98	88.27 179	72.43 174	86.00 163	95.64 34	63.78 237	90.68 189	65.95 215	93.34 179	93.82 107

test1		82.02 176	82.07 172	81.85 191	86.38 220	61.05 235	86.83 98	88.27 179	72.43 174	86.00 163	95.64 34	63.78 237	90.68 189	65.95 215	93.34 179	93.82 107

FMVSNet2		81.31 182	81.61 178	80.41 208	86.38 220	58.75 253	83.93 145	86.58 206	72.43 174	87.65 137	92.98 107	63.78 237	90.22 200	66.86 208	93.92 164	92.27 155

3Dnovator	80.37 7	84.80 121	84.71 125	85.06 117	86.36 223	74.71 107	88.77 70	90.00 154	75.65 136	84.96 175	93.17 103	74.06 166	91.19 173	78.28 120	91.09 221	89.29 217

Anonymous20231206		71.38 277	71.88 270	69.88 298	86.31 224	54.37 278	70.39 313	74.62 277	52.57 314	76.73 272	88.76 205	59.94 252	72.06 321	44.35 332	93.23 186	83.23 289

API-MVS		82.28 172	82.61 165	81.30 198	86.29 225	69.79 145	88.71 71	87.67 188	78.42 96	82.15 217	84.15 274	77.98 119	91.59 164	65.39 221	92.75 194	82.51 298

tfpn200view9		74.86 244	74.23 242	76.74 257	86.24 226	52.12 295	79.24 253	73.87 281	73.34 160	81.82 222	84.60 269	46.02 303	88.80 224	51.98 304	90.99 225	89.31 215

thres400		75.14 238	74.23 242	77.86 240	86.24 226	52.12 295	79.24 253	73.87 281	73.34 160	81.82 222	84.60 269	46.02 303	88.80 224	51.98 304	90.99 225	92.66 138

testmv		70.47 282	70.70 277	69.77 300	86.22 228	53.89 282	67.32 324	71.91 300	63.32 257	78.16 263	89.47 197	56.12 274	73.10 319	36.43 345	87.33 272	82.33 300

UGNet		82.78 165	81.64 177	86.21 99	86.20 229	76.24 101	86.86 96	85.68 214	77.07 118	73.76 296	92.82 112	69.64 215	91.82 159	69.04 194	93.69 171	90.56 197
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
CANet		83.79 152	82.85 161	86.63 87	86.17 230	72.21 127	83.76 152	91.43 108	77.24 115	74.39 293	87.45 229	75.36 147	95.42 32	77.03 134	92.83 193	92.25 157

conf0.01		74.17 250	73.53 249	76.08 264	86.13 231	50.06 321	79.45 244	68.54 320	72.01 184	80.76 236	82.50 295	41.39 333	86.83 249	59.66 256	91.36 212	91.34 178

conf0.002		74.17 250	73.53 249	76.08 264	86.13 231	50.06 321	79.45 244	68.54 320	72.01 184	80.76 236	82.50 295	41.39 333	86.83 249	59.66 256	91.36 212	91.34 178

thresconf0.02		73.65 255	73.53 249	73.98 275	86.13 231	50.06 321	79.45 244	68.54 320	72.01 184	80.76 236	82.50 295	41.39 333	86.83 249	59.66 256	91.36 212	85.06 260

tfpn_n400		73.65 255	73.53 249	73.98 275	86.13 231	50.06 321	79.45 244	68.54 320	72.01 184	80.76 236	82.50 295	41.39 333	86.83 249	59.66 256	91.36 212	85.06 260

tfpnconf		73.65 255	73.53 249	73.98 275	86.13 231	50.06 321	79.45 244	68.54 320	72.01 184	80.76 236	82.50 295	41.39 333	86.83 249	59.66 256	91.36 212	85.06 260

tfpnview11		73.65 255	73.53 249	73.98 275	86.13 231	50.06 321	79.45 244	68.54 320	72.01 184	80.76 236	82.50 295	41.39 333	86.83 249	59.66 256	91.36 212	85.06 260

TR-MVS		76.77 224	75.79 226	79.72 215	86.10 237	65.79 173	77.14 274	83.02 232	65.20 248	81.40 228	82.10 303	66.30 227	90.73 188	55.57 286	85.27 291	82.65 293

LCM-MVSNet-Re		83.48 158	85.06 115	78.75 226	85.94 238	55.75 271	80.05 233	94.27 13	76.47 123	96.09 5	94.54 65	83.31 68	89.75 209	59.95 253	94.89 140	90.75 191

Fast-Effi-MVS+-dtu		82.54 169	81.41 180	85.90 106	85.60 239	76.53 97	83.07 173	89.62 162	73.02 169	79.11 259	83.51 278	80.74 100	90.24 199	68.76 196	89.29 249	90.94 184

v148		82.31 171	82.48 168	81.81 194	85.59 240	59.66 245	81.47 215	86.02 211	72.85 170	88.05 132	90.65 176	70.73 213	90.91 182	75.15 143	91.79 207	94.87 78

MVSFormer		82.23 173	81.57 179	84.19 144	85.54 241	69.26 151	91.98 25	90.08 151	71.54 195	76.23 277	85.07 261	58.69 260	94.27 62	86.26 28	88.77 255	89.03 220

lupinMVS		76.37 229	74.46 240	82.09 185	85.54 241	69.26 151	76.79 275	80.77 248	50.68 330	76.23 277	82.82 290	58.69 260	88.94 222	69.85 185	88.77 255	88.07 227

tfpn1000		73.63 259	73.58 247	73.79 281	85.46 243	50.31 318	79.99 235	68.18 326	72.33 178	80.66 242	83.05 283	39.80 344	86.74 258	60.96 248	91.78 208	84.32 272

TinyColmap		81.25 185	82.34 170	77.99 238	85.33 244	60.68 239	82.32 193	88.33 177	71.26 197	86.97 149	92.22 129	77.10 131	86.98 246	62.37 235	95.17 132	86.31 247

PAPR		78.84 204	78.10 207	81.07 202	85.17 245	60.22 242	82.21 198	90.57 131	62.51 265	75.32 286	84.61 268	74.99 152	92.30 147	59.48 263	88.04 265	90.68 193

DI_MVS_plusplus_test		81.27 184	81.26 181	81.29 199	84.98 246	61.65 227	81.98 203	87.25 194	63.56 255	87.56 139	89.60 195	73.62 175	91.83 158	72.20 170	90.59 241	90.38 202

pmmvs4		74.92 243	72.98 260	80.73 206	84.95 247	71.71 136	76.23 283	77.59 260	52.83 312	77.73 268	86.38 238	56.35 272	84.97 276	57.72 276	87.05 275	85.51 255

Test4		81.31 182	81.13 184	81.88 190	84.89 248	63.05 200	82.37 191	90.50 132	62.75 263	89.00 117	88.29 216	67.55 223	91.68 162	73.55 157	91.24 220	90.89 186

Patchmatch-RL test		74.48 247	73.68 246	76.89 254	84.83 249	66.54 169	72.29 306	69.16 319	57.70 289	86.76 150	86.33 242	45.79 308	82.59 293	69.63 187	90.65 239	81.54 313

tfpn_ndepth		72.54 266	72.30 267	73.24 284	84.81 250	51.42 304	79.24 253	70.49 313	69.26 213	78.48 262	79.80 319	40.16 343	86.77 256	58.08 275	90.43 242	81.53 314

test_normal		81.23 186	81.16 183	81.43 197	84.77 251	61.99 222	81.46 216	86.95 204	63.16 260	87.22 142	89.63 194	73.62 175	91.65 163	72.92 165	90.70 236	90.65 195

XXY-MVS		74.44 249	76.19 224	69.21 303	84.61 252	52.43 294	71.70 308	77.18 262	60.73 279	80.60 243	90.96 165	75.44 145	69.35 328	56.13 282	88.33 259	85.86 252

cascas		76.29 230	74.81 236	80.72 207	84.47 253	62.94 202	73.89 300	87.34 191	55.94 297	75.16 288	76.53 332	63.97 235	91.16 174	65.00 222	90.97 229	88.06 228

PVSNet_BlendedMVS		78.80 205	77.84 208	81.65 195	84.43 254	63.41 191	79.49 243	90.44 134	61.70 273	75.43 284	87.07 233	69.11 218	91.44 168	60.68 250	92.24 203	90.11 208

PVSNet_Blended		76.49 228	75.40 231	79.76 214	84.43 254	63.41 191	75.14 291	90.44 134	57.36 291	75.43 284	78.30 325	69.11 218	91.44 168	60.68 250	87.70 270	84.42 270

OpenMVS	76.72 13	81.98 178	82.00 174	81.93 187	84.42 256	68.22 159	88.50 74	89.48 164	66.92 231	81.80 224	91.86 132	72.59 200	90.16 202	71.19 175	91.25 219	87.40 237

OpenMVS_ROB	70.19 17	77.77 212	77.46 210	78.71 227	84.39 257	61.15 233	81.18 221	82.52 235	62.45 267	83.34 204	87.37 230	66.20 228	88.66 231	64.69 224	85.02 295	86.32 246

Regformer-3		85.06 114	84.67 127	86.22 97	84.27 258	73.43 114	84.07 139	85.26 218	80.77 66	88.62 124	85.48 251	80.56 102	90.39 196	81.99 78	91.04 223	94.85 80

Regformer-4		86.41 91	85.71 106	88.52 62	84.27 258	77.57 84	84.07 139	88.00 184	82.82 45	89.84 96	85.48 251	82.06 82	92.77 138	83.83 56	91.04 223	95.22 75

DELS-MVS		81.44 181	81.25 182	82.03 186	84.27 258	62.87 204	76.47 281	92.49 74	70.97 200	81.64 226	83.83 275	75.03 151	92.70 139	74.29 147	92.22 205	90.51 199
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
Gipuma		84.44 129	86.33 95	78.78 225	84.20 261	73.57 113	89.55 54	90.44 134	84.24 31	84.38 190	94.89 53	76.35 143	80.40 301	76.14 138	96.80 78	82.36 299
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Regformer-1		86.00 100	85.50 110	87.49 79	84.18 262	76.90 92	83.52 159	87.94 186	82.18 53	89.19 114	85.07 261	82.28 80	91.89 156	82.40 72	92.72 195	93.69 113

Regformer-2		86.74 86	86.08 101	88.73 60	84.18 262	79.20 70	83.52 159	89.33 166	83.33 37	89.92 94	85.07 261	83.23 69	93.16 123	83.39 58	92.72 195	93.83 105

EI-MVSNet-Vis-set		85.12 113	84.53 134	86.88 84	84.01 264	72.76 118	83.91 146	85.18 220	80.44 67	88.75 121	85.49 250	80.08 105	91.92 154	82.02 77	90.85 233	95.97 50

semantic-postprocess				84.34 139	83.93 265	69.66 147		81.09 246	72.43 174	86.47 160	90.19 185	57.56 267	93.15 125	77.45 128	86.39 282	90.22 204

MSDG		80.06 200	79.99 198	80.25 209	83.91 266	68.04 160	77.51 272	89.19 167	77.65 104	81.94 219	83.45 280	76.37 142	86.31 263	63.31 233	86.59 279	86.41 245

EI-MVSNet-UG-set		85.04 115	84.44 136	86.85 85	83.87 267	72.52 121	83.82 148	85.15 221	80.27 71	88.75 121	85.45 254	79.95 107	91.90 155	81.92 79	90.80 234	96.13 44

thres200		72.34 269	71.55 274	74.70 273	83.48 268	51.60 303	75.02 292	73.71 284	70.14 207	78.56 261	80.57 313	46.20 301	88.20 235	46.99 326	89.29 249	84.32 272

USDC		76.63 225	76.73 216	76.34 261	83.46 269	57.20 260	80.02 234	88.04 183	52.14 318	83.65 201	91.25 148	63.24 240	86.65 259	54.66 294	94.11 159	85.17 258

HY-MVS	64.64 18	73.03 262	72.47 266	74.71 272	83.36 270	54.19 279	82.14 201	81.96 239	56.76 296	69.57 317	86.21 245	60.03 251	84.83 279	49.58 315	82.65 311	85.11 259

EI-MVSNet		82.61 167	82.42 169	83.20 169	83.25 271	63.66 188	83.50 162	85.07 222	76.06 126	86.55 154	85.10 259	73.41 181	90.25 197	78.15 122	90.67 237	95.68 59

CVMVSNet		72.62 265	71.41 275	76.28 262	83.25 271	60.34 241	83.50 162	79.02 255	37.77 352	76.33 275	85.10 259	49.60 292	87.41 241	70.54 182	77.54 332	81.08 321

V42		83.47 159	83.37 155	83.75 153	83.16 273	63.33 194	81.31 217	90.23 149	69.51 211	90.91 78	90.81 170	74.16 165	92.29 148	80.06 102	90.22 243	95.62 61

EU-MVSNet		75.12 240	74.43 241	77.18 250	83.11 274	59.48 247	85.71 118	82.43 236	39.76 351	85.64 169	88.76 205	44.71 325	87.88 237	73.86 154	85.88 286	84.16 275

FMVSNet3		78.80 205	78.55 205	79.57 219	82.89 275	56.89 263	81.76 209	85.77 213	69.04 217	86.00 163	90.44 180	51.75 287	90.09 206	65.95 215	93.34 179	91.72 167

test1235678		65.57 307	65.73 304	65.06 319	82.84 276	50.90 310	62.90 332	69.26 317	57.17 294	72.36 303	83.04 284	46.02 303	70.10 325	32.79 350	85.24 293	74.19 335

MVS_Test		82.47 170	83.22 156	80.22 210	82.62 277	57.75 257	82.54 188	91.96 87	71.16 198	82.89 211	92.52 121	77.41 126	90.50 194	80.04 103	87.84 268	92.40 149

LF4IMVS		82.75 166	81.93 175	85.19 115	82.08 278	80.15 61	85.53 119	88.76 171	68.01 222	85.58 170	87.75 224	71.80 208	86.85 248	74.02 151	93.87 165	88.58 224

PVSNet	58.17 21	66.41 303	65.63 305	68.75 306	81.96 279	49.88 327	62.19 334	72.51 296	51.03 326	68.04 322	75.34 336	50.84 288	74.77 316	45.82 330	82.96 307	81.60 312

GA-MVS		75.83 234	74.61 237	79.48 221	81.87 280	59.25 249	73.42 303	82.88 233	68.68 219	79.75 252	81.80 306	50.62 289	89.46 211	66.85 209	85.64 288	89.72 210

MS-PatchMatch		70.93 279	70.22 282	73.06 287	81.85 281	62.50 213	73.82 301	77.90 258	52.44 315	75.92 280	81.27 310	55.67 276	81.75 296	55.37 288	77.70 330	74.94 333

Patchmatch-test1		72.75 264	72.61 263	73.19 285	81.62 282	55.86 269	78.89 259	71.37 306	61.73 271	74.93 289	82.15 302	60.46 248	81.80 295	59.68 255	82.63 313	81.92 307

FMVSNet5		72.10 271	71.69 271	73.32 282	81.57 283	53.02 289	76.77 276	78.37 257	63.31 258	76.37 274	91.85 133	36.68 347	78.98 305	47.87 322	92.45 198	87.95 231

CANet_DTU		77.81 211	77.05 212	80.09 211	81.37 284	59.90 244	83.26 168	88.29 178	69.16 215	67.83 324	83.72 276	60.93 245	89.47 210	69.22 192	89.70 247	90.88 187

ANet_high		83.17 163	85.68 107	75.65 268	81.24 285	45.26 336	79.94 236	92.91 60	83.83 35	91.33 72	96.88 10	80.25 104	85.92 267	68.89 195	95.89 111	95.76 54

new-patchmatchnet		70.10 285	73.37 256	60.29 331	81.23 286	16.95 359	59.54 338	74.62 277	62.93 261	80.97 231	87.93 222	62.83 241	71.90 322	55.24 289	95.01 137	92.00 161

test20.03		73.75 254	74.59 239	71.22 297	81.11 287	51.12 308	70.15 314	72.10 298	70.42 203	80.28 250	91.50 144	64.21 234	74.72 318	46.96 327	94.58 153	87.82 234

MVS		73.21 261	72.59 264	75.06 270	80.97 288	60.81 238	81.64 212	85.92 212	46.03 342	71.68 307	77.54 326	68.47 221	89.77 208	55.70 285	85.39 289	74.60 334

N_pmnet		70.20 283	68.80 290	74.38 274	80.91 289	84.81 35	59.12 341	76.45 268	55.06 301	75.31 287	82.36 301	55.74 275	54.82 353	47.02 325	87.24 274	83.52 282

IterMVS		76.91 219	76.34 223	78.64 228	80.91 289	64.03 184	76.30 282	79.03 254	64.88 252	83.11 208	89.16 200	59.90 253	84.46 281	68.61 199	85.15 294	87.42 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WTY-MVS		67.91 297	68.35 292	66.58 314	80.82 291	48.12 330	65.96 328	72.60 294	53.67 308	71.20 310	81.68 308	58.97 259	69.06 330	48.57 318	81.67 315	82.55 295

IB-MVS	62.13 19	71.64 274	68.97 288	79.66 218	80.80 292	62.26 221	73.94 299	76.90 264	63.27 259	68.63 320	76.79 330	33.83 350	91.84 157	59.28 264	87.26 273	84.88 265
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
our_test_3		71.85 272	71.59 272	72.62 291	80.71 293	53.78 283	69.72 316	71.71 305	58.80 284	78.03 264	80.51 314	56.61 271	78.84 306	62.20 236	86.04 285	85.23 257

ppachtmachnet_test		74.73 246	74.00 244	76.90 253	80.71 293	56.89 263	71.53 309	78.42 256	58.24 287	79.32 257	82.92 289	57.91 264	84.26 283	65.60 220	91.36 212	89.56 211

testgi		72.36 268	74.61 237	65.59 316	80.56 295	42.82 344	68.29 319	73.35 287	66.87 232	81.84 221	89.93 189	72.08 205	66.92 336	46.05 329	92.54 197	87.01 241

1314		73.22 260	72.56 265	75.20 269	80.41 296	57.84 255	81.64 212	85.36 217	51.68 321	73.10 300	76.65 331	61.45 244	85.19 274	63.54 230	79.21 327	82.59 294

CR-MVSNet		74.00 252	73.04 259	76.85 255	79.58 297	62.64 207	82.58 185	76.90 264	50.50 331	75.72 282	92.38 122	48.07 296	84.07 284	68.72 198	82.91 309	83.85 278

RPMNet		76.06 231	75.79 226	76.85 255	79.58 297	62.64 207	82.58 185	71.75 303	74.80 144	75.72 282	92.59 117	48.69 294	84.07 284	73.48 158	82.91 309	83.85 278

UnsupCasMVSNet_bld		69.21 293	69.68 285	67.82 310	79.42 299	51.15 307	67.82 323	75.79 269	54.15 305	77.47 269	85.36 258	59.26 258	70.64 324	48.46 319	79.35 325	81.66 311

PatchT		70.52 281	72.76 261	63.79 322	79.38 300	33.53 352	77.63 270	65.37 337	73.61 155	71.77 306	92.79 115	44.38 326	75.65 315	64.53 227	85.37 290	82.18 302

Patchmtry		76.56 227	77.46 210	73.83 280	79.37 301	46.60 333	82.41 190	76.90 264	73.81 154	85.56 171	92.38 122	48.07 296	83.98 286	63.36 232	95.31 128	90.92 185

mvs_anonymous		78.13 208	78.76 203	76.23 263	79.24 302	50.31 318	78.69 261	84.82 227	61.60 274	83.09 210	92.82 112	73.89 171	87.01 244	68.33 201	86.41 281	91.37 176

MVS-HIRNet		61.16 318	62.92 313	55.87 335	79.09 303	35.34 351	71.83 307	57.98 351	46.56 340	59.05 348	91.14 155	49.95 291	76.43 311	38.74 342	71.92 342	55.84 352

MDA-MVSNet-bldmvs		77.47 213	76.90 214	79.16 222	79.03 304	64.59 179	66.58 327	75.67 271	73.15 167	88.86 118	88.99 203	66.94 225	81.23 298	64.71 223	88.22 264	91.64 169

tpm2		68.45 295	66.83 299	73.30 283	78.93 305	48.50 328	79.76 237	71.76 302	47.50 338	69.92 316	83.60 277	42.07 332	88.40 232	48.44 320	79.51 323	83.01 292

testus		62.33 312	63.03 312	60.20 332	78.78 306	40.74 345	59.14 339	69.80 316	49.26 335	71.41 308	74.72 338	52.33 286	63.52 345	29.84 352	82.01 314	76.36 330

tpm		67.95 296	68.08 295	67.55 311	78.74 307	43.53 342	75.60 287	67.10 334	54.92 302	72.23 304	88.10 218	42.87 330	75.97 313	52.21 302	80.95 321	83.15 290

diffmvs		79.20 203	79.04 201	79.69 216	78.64 308	58.90 250	81.79 208	87.61 189	65.07 250	73.65 298	89.80 191	73.10 191	87.79 238	75.02 145	86.63 278	92.38 150

tpmp4_e23		69.43 290	67.33 297	75.72 267	78.53 309	52.75 290	82.13 202	74.91 274	49.23 336	66.37 327	84.17 273	41.28 339	88.67 230	49.73 314	79.63 322	85.75 253

no-one		71.52 276	70.43 281	74.81 271	78.45 310	63.41 191	57.73 344	77.03 263	51.46 324	77.17 271	90.33 182	54.96 281	80.35 302	47.41 323	99.29 2	80.68 325

1111		61.71 314	63.77 310	55.55 337	78.05 311	25.74 356	60.62 335	67.52 327	66.09 237	74.68 290	86.50 236	16.00 362	59.22 351	38.79 340	85.65 287	81.70 309

.test1245		48.02 331	54.41 330	28.84 344	78.05 311	25.74 356	60.62 335	67.52 327	66.09 237	74.68 290	86.50 236	16.00 362	59.22 351	38.79 340	1.47 357	1.55 358

MDTV_nov1_ep13			68.29 294		78.03 313	43.87 341	74.12 298	72.22 297	52.17 316	67.02 326	85.54 249	45.36 315	80.85 299	55.73 283	84.42 301

EPNet_dtu		72.87 263	71.33 276	77.49 248	77.72 314	60.55 240	82.35 192	75.79 269	66.49 234	58.39 351	81.06 312	53.68 283	85.98 266	53.55 298	92.97 191	85.95 250
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 289	68.83 289	72.33 293	77.66 315	53.60 284	79.29 251	69.99 315	57.66 290	72.53 302	82.93 288	46.45 300	80.08 304	60.91 249	72.09 341	83.31 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sss		66.92 300	67.26 298	65.90 315	77.23 316	51.10 309	64.79 329	71.72 304	52.12 319	70.13 315	80.18 316	57.96 263	65.36 343	50.21 310	81.01 320	81.25 318

CostFormer		69.98 288	68.68 291	73.87 279	77.14 317	50.72 316	79.26 252	74.51 279	51.94 320	70.97 312	84.75 266	45.16 323	87.49 240	55.16 290	79.23 326	83.40 285

tpm cat1		66.76 301	65.21 306	71.42 296	77.09 318	50.62 317	78.01 265	73.68 285	44.89 344	68.64 318	79.00 322	45.51 313	82.42 294	49.91 312	70.15 345	81.23 320

pmmvs5		70.73 280	70.07 283	72.72 289	77.03 319	52.73 291	74.14 297	75.65 272	50.36 332	72.17 305	85.37 257	55.42 278	80.67 300	52.86 301	87.59 271	84.77 266

EPNet		80.37 194	78.41 206	86.23 96	76.75 320	73.28 115	87.18 92	77.45 261	76.24 125	68.14 321	88.93 204	65.41 232	93.85 81	69.47 188	96.12 102	91.55 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		59.08 322	56.52 327	66.76 313	76.51 321	64.39 182	49.62 351	59.00 347	43.86 346	55.66 354	68.41 346	35.55 349	68.21 332	43.25 333	76.78 334	67.69 344

UnsupCasMVSNet_eth		71.63 275	72.30 267	69.62 301	76.47 322	52.70 292	70.03 315	80.97 247	59.18 283	79.36 256	88.21 217	60.50 247	69.12 329	58.33 272	77.62 331	87.04 240

test-LLR		67.21 299	66.74 300	68.63 307	76.45 323	55.21 274	67.89 320	67.14 332	62.43 268	65.08 334	72.39 340	43.41 327	69.37 326	61.00 246	84.89 296	81.31 316

test-mter		65.00 308	63.79 309	68.63 307	76.45 323	55.21 274	67.89 320	67.14 332	50.98 327	65.08 334	72.39 340	28.27 357	69.37 326	61.00 246	84.89 296	81.31 316

gg-mvs-nofinetune		68.96 294	69.11 287	68.52 309	76.12 325	45.32 335	83.59 155	55.88 352	86.68 20	64.62 337	97.01 7	30.36 354	83.97 287	44.78 331	82.94 308	76.26 331

CMPMVS	59.41 20	75.12 240	73.57 248	79.77 213	75.84 326	67.22 163	81.21 220	82.18 237	50.78 328	76.50 273	87.66 226	55.20 279	82.99 291	62.17 238	90.64 240	89.09 219
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		54.91 328	57.14 326	48.22 341	75.83 327	17.47 358	52.31 350	69.20 318	51.66 322	60.11 344	75.40 335	29.77 356	62.62 349	27.64 353	72.37 340	64.59 345

PNet_i23d		52.13 329	51.24 331	54.79 338	75.56 328	45.26 336	54.54 347	52.55 355	66.95 230	57.19 352	65.82 349	13.15 364	63.40 346	36.39 346	39.04 355	55.71 353

wuyk23d		75.13 239	79.30 200	62.63 323	75.56 328	75.18 105	80.89 226	73.10 292	75.06 143	94.76 15	95.32 41	87.73 31	52.85 354	34.16 348	97.11 68	59.85 349

Patchmatch-test		65.91 305	67.38 296	61.48 328	75.51 330	43.21 343	68.84 317	63.79 339	62.48 266	72.80 301	83.42 281	44.89 324	59.52 350	48.27 321	86.45 280	81.70 309

new_pmnet		55.69 326	57.66 325	49.76 339	75.47 331	30.59 353	59.56 337	51.45 356	43.62 347	62.49 339	75.48 334	40.96 341	49.15 356	37.39 344	72.52 339	69.55 342

gm-plane-assit					75.42 332	44.97 339			52.17 316		72.36 342		87.90 236	54.10 296

PatchFormer-LS_test		67.91 297	66.49 303	72.17 294	75.29 333	51.85 301	75.68 285	73.62 286	57.23 293	68.64 318	68.13 348	42.19 331	82.76 292	64.06 228	73.51 338	81.89 308

MVSTER		77.09 217	75.70 229	81.25 200	75.27 334	61.08 234	77.49 273	85.07 222	60.78 278	86.55 154	88.68 207	43.14 329	90.25 197	73.69 155	90.67 237	92.42 147

PVSNet_0	51.08 22	56.10 325	54.97 329	59.48 333	75.12 335	53.28 288	55.16 345	61.89 342	44.30 345	59.16 347	62.48 352	54.22 282	65.91 341	35.40 347	47.01 353	59.25 350

test0.0.03 1		64.66 309	64.36 308	65.57 317	75.03 336	46.89 332	64.69 330	61.58 345	62.43 268	71.18 311	77.54 326	43.41 327	68.47 331	40.75 338	82.65 311	81.35 315

DWT-MVSNet_test		66.43 302	64.37 307	72.63 290	74.86 337	50.86 311	76.52 279	72.74 293	54.06 306	65.50 331	68.30 347	32.13 352	84.84 278	61.63 243	73.59 337	82.19 301

tpmvs		70.16 284	69.56 286	71.96 295	74.71 338	48.13 329	79.63 238	75.45 273	65.02 251	70.26 314	81.88 305	45.34 316	85.68 270	58.34 271	75.39 335	82.08 303

MDA-MVSNet_test_wron		70.05 287	70.44 279	68.88 305	73.84 339	53.47 285	58.93 343	67.28 330	58.43 285	87.09 146	85.40 255	59.80 255	67.25 334	59.66 256	83.54 304	85.92 251

YYNet1		70.06 286	70.44 279	68.90 304	73.76 340	53.42 287	58.99 342	67.20 331	58.42 286	87.10 145	85.39 256	59.82 254	67.32 333	59.79 254	83.50 305	85.96 249

GG-mvs-BLEND				67.16 312	73.36 341	46.54 334	84.15 138	55.04 353		58.64 350	61.95 353	29.93 355	83.87 288	38.71 343	76.92 333	71.07 340

test2356		56.69 324	55.15 328	61.32 329	73.20 342	44.11 340	54.95 346	62.52 340	48.75 337	62.45 340	68.42 345	21.10 361	65.67 342	26.86 354	78.08 329	74.19 335

JIA-IIPM		69.41 292	66.64 302	77.70 242	73.19 343	71.24 139	75.67 286	65.56 336	70.42 203	65.18 333	92.97 108	33.64 351	83.06 290	53.52 299	69.61 348	78.79 327

ADS-MVSNet2		65.87 306	63.64 311	72.55 292	73.16 344	56.92 262	67.10 325	74.81 276	49.74 333	66.04 329	82.97 286	46.71 298	77.26 308	42.29 334	69.96 346	83.46 283

ADS-MVSNet		61.90 313	62.19 315	61.03 330	73.16 344	36.42 350	67.10 325	61.75 343	49.74 333	66.04 329	82.97 286	46.71 298	63.21 347	42.29 334	69.96 346	83.46 283

DSMNet-mixed		60.98 320	61.61 317	59.09 334	72.88 346	45.05 338	74.70 294	46.61 358	26.20 354	65.34 332	90.32 183	55.46 277	63.12 348	41.72 336	81.30 319	69.09 343

tpmrst		66.28 304	66.69 301	65.05 320	72.82 347	39.33 347	78.20 264	70.69 312	53.16 311	67.88 323	80.36 315	48.18 295	74.75 317	58.13 273	70.79 343	81.08 321

TESTMET0.1,1		61.29 317	60.32 321	64.19 321	72.06 348	51.30 305	67.89 320	62.09 341	45.27 343	60.65 343	69.01 343	27.93 358	64.74 344	56.31 281	81.65 317	76.53 329

dp		60.70 321	60.29 322	61.92 326	72.04 349	38.67 349	70.83 310	64.08 338	51.28 325	60.75 342	77.28 328	36.59 348	71.58 323	47.41 323	62.34 352	75.52 332

pmmvs3		62.47 310	60.02 323	69.80 299	71.58 350	64.00 185	70.52 312	58.44 349	39.77 350	66.05 328	75.84 333	27.10 359	72.28 320	46.15 328	84.77 300	73.11 337

LP		69.42 291	68.30 293	72.77 288	71.48 351	56.84 265	73.66 302	74.84 275	63.52 256	70.95 313	83.35 282	49.55 293	77.15 310	57.13 278	70.21 344	84.33 271

EPMVS		62.47 310	62.63 314	62.01 324	70.63 352	38.74 348	74.76 293	52.86 354	53.91 307	67.71 325	80.01 317	39.40 345	66.60 338	55.54 287	68.81 350	80.68 325

E-PMN		61.59 316	61.62 316	61.49 327	66.81 353	55.40 272	53.77 348	60.34 346	66.80 233	58.90 349	65.50 350	40.48 342	66.12 340	55.72 284	86.25 283	62.95 347

testpf		58.55 323	61.58 318	49.48 340	66.03 354	40.05 346	74.40 296	58.07 350	64.72 253	59.36 346	72.67 339	22.76 360	66.92 336	67.07 207	69.15 349	41.46 354

EMVS		61.10 319	60.81 319	61.99 325	65.96 355	55.86 269	53.10 349	58.97 348	67.06 229	56.89 353	63.33 351	40.98 340	67.03 335	54.79 293	86.18 284	63.08 346

PMMVS		61.65 315	60.38 320	65.47 318	65.40 356	69.26 151	63.97 331	61.73 344	36.80 353	60.11 344	68.43 344	59.42 256	66.35 339	48.97 317	78.57 328	60.81 348

PMMVS2		55.64 327	59.27 324	44.74 342	64.30 357	12.32 360	40.60 352	49.79 357	53.19 310	65.06 336	84.81 265	53.60 284	49.76 355	32.68 351	89.41 248	72.15 338

MVE	40.22 23	51.82 330	50.47 332	55.87 335	62.66 358	51.91 297	31.61 354	39.28 359	40.65 349	50.76 355	74.98 337	56.24 273	44.67 357	33.94 349	64.11 351	71.04 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 345	32.95 359	29.49 354		21.63 362	12.07 355	37.95 356	45.07 354	30.84 353	19.21 358	17.94 356	33.06 356	23.69 355

tmp_tt		20.25 334	24.50 335	7.49 346	4.47 360	8.70 361	34.17 353	25.16 361	1.00 356	32.43 357	18.49 355	39.37 346	9.21 359	21.64 355	43.75 354	4.57 356

testmvs		5.91 338	7.65 339	0.72 348	1.20 361	0.37 363	59.14 339	0.67 364	0.49 358	1.11 358	2.76 359	0.94 366	0.24 361	1.02 358	1.47 357	1.55 358

test123		6.27 337	8.08 338	0.84 347	1.11 362	0.57 362	62.90 332	0.82 363	0.54 357	1.07 359	2.75 360	1.26 365	0.30 360	1.04 357	1.26 359	1.66 357

cdsmvs_eth3d_5k		20.81 333	27.75 334	0.00 349	0.00 363	0.00 364	0.00 355	85.44 216	0.00 359	0.00 360	82.82 290	81.46 93	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		6.41 336	8.55 337	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	76.94 134	0.00 362	0.00 359	0.00 360	0.00 360

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		6.65 335	8.87 336	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	79.80 319	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																83.88 276

test_part3							89.63 51		84.39 28		93.43 99		96.26 4	82.18 75

test_part1								93.93 25				87.19 36			97.61 56	91.48 174

sam_mvs1												46.11 302				83.88 276

sam_mvs												45.92 307

MTGPA								91.81 91

test_post1							78.85 260				3.13 357	45.19 318	80.13 303	58.11 274

test_post											3.10 358	45.43 314	77.22 309

patchmatchnet-post											81.71 307	45.93 306	87.01 244

MTMP								33.14 360

test9_res														80.83 90	96.45 89	90.57 196

agg_prior2														79.68 107	96.16 98	90.22 204

test_prior4						78.97 72	84.59 131

test_prior2							83.37 165		75.43 138	84.58 186	91.57 141	81.92 88		79.54 109	96.97 71

旧先验2							81.73 210		56.88 295	86.54 159			84.90 277	72.81 166

新几何2							81.72 211

无先验							82.81 179	85.62 215	58.09 288				91.41 170	67.95 204		84.48 268

原ACMM2							82.26 197

testdata2													86.43 262	63.52 231

segment_acmp												81.94 85

testdata1							79.62 239		73.95 153

plane_prior5								93.61 32					95.22 40	80.78 91	95.83 114	94.46 87

plane_prior4											92.95 109

plane_prior3						76.85 93			77.79 102	86.55 154

plane_prior2							89.45 59		79.44 80

plane_prior						76.42 98	87.15 93		75.94 130						95.03 136

n2								0.00 365

nn								0.00 365

door-mid								74.45 280

test11								91.46 105

door								72.57 295

HQP5-MVS						70.66 142

BP-MVS														77.30 130

HQP4-MVS										80.56 245			94.61 56			93.56 118

HQP3-MVS								92.68 68							94.47 155

HQP2-MVS												72.10 203

MDTV_nov1_ep13_2view						27.60 355	70.76 311		46.47 341	61.27 341		45.20 317		49.18 316		83.75 280

ACMMP++_ref															95.74 118

ACMMP++															97.35 62

Test By Simon												79.09 111

High-resolution multi-view benchmark