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 10	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 38	89.57 167	88.51 14	90.11 84	95.12 48	90.98 7	88.92 228	77.55 129	97.07 67	83.13 298
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
zzz-MVS		91.27 19	91.26 26	91.29 25	96.59 3	86.29 14	88.94 64	91.81 94	84.07 33	92.00 60	94.40 71	86.63 39	95.28 37	88.59 4	98.31 25	92.30 155

MTAPA		91.52 13	91.60 15	91.29 25	96.59 3	86.29 14	92.02 24	91.81 94	84.07 33	92.00 60	94.40 71	86.63 39	95.28 37	88.59 4	98.31 25	92.30 155

PEN-MVS		90.03 36	91.88 12	84.48 133	96.57 5	58.88 256	88.95 63	93.19 49	91.62 3	96.01 6	96.16 22	87.02 36	95.60 22	78.69 119	98.72 10	98.97 3

PS-CasMVS		90.06 35	91.92 9	84.47 134	96.56 6	58.83 259	89.04 62	92.74 68	91.40 4	96.12 4	96.06 24	87.23 35	95.57 23	79.42 113	98.74 7	99.00 2

DTE-MVSNet		89.98 38	91.91 11	84.21 143	96.51 7	57.84 262	88.93 65	92.84 65	91.92 2	96.16 3	96.23 20	86.95 37	95.99 6	79.05 116	98.57 16	98.80 6

CP-MVSNet		89.27 53	90.91 34	84.37 138	96.34 8	58.61 261	88.66 71	92.06 85	90.78 5	95.67 9	95.17 46	81.80 90	95.54 26	79.00 117	98.69 11	98.95 4

WR-MVS_H		89.91 43	91.31 24	85.71 110	96.32 9	62.39 217	89.54 54	93.31 42	90.21 9	95.57 11	95.66 31	81.42 94	95.90 11	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 57	82.59 47	88.52 127	94.37 74	86.74 38	95.41 32	86.32 27	98.21 31	93.19 127
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 19	92.37 5	96.04 11	88.48 8	92.72 14	92.60 73	83.09 41	91.54 68	94.25 77	87.67 33	95.51 29	87.21 24	98.11 34	93.12 128

MP-MVS-pluss		90.81 24	91.08 28	89.99 46	95.97 12	79.88 63	88.13 77	94.51 11	75.79 135	92.94 39	94.96 51	88.36 19	95.01 46	90.70 2	98.40 21	95.09 77
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 7	92.86 42	96.29 18	88.16 25	94.17 68	86.07 33	98.48 19	97.22 25

ACMMP_Plus		90.65 26	91.07 30	89.42 53	95.93 14	79.54 68	89.95 43	93.68 32	77.65 105	91.97 62	94.89 53	88.38 18	95.45 30	89.27 3	97.87 45	93.27 123

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 25	92.51 52	95.13 47	90.65 10	95.34 34	88.06 10	98.15 33	95.95 51

HSP-MVS		88.63 60	87.84 69	91.02 29	95.76 16	86.14 19	92.75 13	91.01 126	78.43 96	89.16 116	92.25 131	72.03 209	96.36 2	88.21 9	90.93 236	90.55 202

region2R		91.44 17	91.30 25	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 12	87.60 17	97.71 52	93.83 106

ACMMPR		91.49 14	91.35 23	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 12	87.74 12	97.76 48	93.99 101

TSAR-MVS + MP.		88.14 66	87.82 70	89.09 58	95.72 19	76.74 94	92.49 20	91.19 122	67.85 233	86.63 155	94.84 55	79.58 109	95.96 9	87.62 15	94.50 155	94.56 83

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 40	93.90 29	80.32 71	91.74 66	94.41 70	88.17 24	95.98 7	86.37 26	97.99 40	93.96 103

XVS		91.54 12	91.36 21	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 26	89.99 88	94.03 85	86.57 41	95.80 15	87.35 20	97.62 54	94.20 94

X-MVStestdata		85.04 115	82.70 164	92.08 8	95.64 21	86.25 16	92.64 15	93.33 40	85.07 26	89.99 88	16.05 364	86.57 41	95.80 15	87.35 20	97.62 54	94.20 94

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 93	90.55 12	95.73 19	88.50 8	98.23 30	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 13	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 16	95.62 21	87.35 20	98.24 29	94.56 83
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 20	91.02 29	95.43 25	84.66 39	92.58 18	93.29 45	81.99 54	91.47 69	93.96 89	88.35 20	95.56 24	87.74 12	97.74 50	92.85 133

#test#		90.49 31	90.31 42	91.02 29	95.43 25	84.66 39	90.65 36	93.29 45	77.00 120	91.47 69	93.96 89	88.35 20	95.56 24	84.88 40	97.74 50	92.85 133

SMA-MVS		90.31 32	90.48 40	89.83 47	95.31 27	79.52 69	90.98 34	93.24 48	75.37 144	92.84 43	95.28 41	85.58 50	96.09 5	87.92 11	97.76 48	93.88 105

CP-MVS		91.67 11	91.58 16	91.96 12	95.29 28	87.62 9	93.38 7	93.36 38	83.16 40	91.06 74	94.00 86	88.26 22	95.71 20	87.28 23	98.39 22	92.55 148

VDDNet		84.35 135	85.39 112	81.25 203	95.13 29	59.32 254	85.42 123	81.11 252	86.41 22	87.41 143	96.21 21	73.61 180	90.61 194	66.33 216	96.85 72	93.81 111

CPTT-MVS		89.39 51	88.98 57	90.63 36	95.09 30	86.95 10	92.09 23	92.30 79	79.74 76	87.50 142	92.38 125	81.42 94	93.28 119	83.07 64	97.24 63	91.67 174

ACMM	79.39 9	90.65 26	90.99 31	89.63 50	95.03 31	83.53 44	89.62 51	93.35 39	79.20 84	93.83 28	93.60 99	90.81 8	92.96 133	85.02 39	98.45 20	92.41 151
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		91.49 14	91.53 17	91.39 22	94.98 32	82.95 50	93.52 5	92.79 66	88.22 15	88.53 126	97.64 2	83.45 64	94.55 59	86.02 34	98.60 14	96.67 36

HPM-MVS++		88.93 57	88.45 64	90.38 40	94.92 33	85.85 25	89.70 47	91.27 119	78.20 99	86.69 154	92.28 130	80.36 103	95.06 45	86.17 32	96.49 85	90.22 208

XVG-ACMP-BASELINE		89.98 38	89.84 45	90.41 39	94.91 34	84.50 41	89.49 56	93.98 25	79.68 77	92.09 58	93.89 94	83.80 60	93.10 127	82.67 71	98.04 35	93.64 115

OPM-MVS		89.80 44	89.97 43	89.27 55	94.76 35	79.86 64	86.76 103	92.78 67	78.78 91	92.51 52	93.64 98	88.13 26	93.84 83	84.83 42	97.55 57	94.10 99

LPG-MVS_test		91.47 16	91.68 14	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 5	95.52 27	85.36 35	98.73 8	95.23 74

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 5	95.52 27	85.36 35	98.73 8	95.23 74

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 10	94.43 18	95.27 42	91.86 3	95.09 43	87.54 19	98.02 38	93.71 113

XVG-OURS-SEG-HR		89.59 48	89.37 52	90.28 42	94.47 39	85.95 21	86.84 99	93.91 28	80.07 74	86.75 153	93.26 102	93.64 2	90.93 182	84.60 45	90.75 241	93.97 102

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 60	94.05 23	79.03 88	92.87 41	93.74 97	90.60 11	95.21 41	82.87 67	98.76 5	94.87 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS		89.18 54	88.83 59	90.23 43	94.28 41	86.11 20	85.91 115	93.60 35	80.16 73	89.13 117	93.44 100	83.82 59	90.98 180	83.86 56	95.30 128	93.60 117

MIMVSNet1		83.63 155	84.59 132	80.74 209	94.06 42	62.77 208	82.72 188	84.53 234	77.57 107	90.34 82	95.92 26	76.88 140	85.83 276	61.88 246	97.42 59	93.62 116

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 117	94.02 43	64.13 186	84.38 138	91.29 118	84.88 28	92.06 59	93.84 95	86.45 43	93.73 84	73.22 163	98.66 12	97.69 11

新几何1				82.95 176	93.96 44	78.56 77		80.24 256	55.45 307	83.93 200	91.08 164	71.19 214	88.33 240	65.84 221	93.07 188	81.95 313

1121		80.86 194	79.81 204	84.02 147	93.93 45	78.70 75	81.64 216	80.18 257	55.43 308	83.67 202	91.15 159	71.29 213	91.41 172	67.95 207	93.06 189	81.96 312

SteuartSystems-ACMMP		91.16 22	91.36 21	90.55 37	93.91 46	80.97 58	91.49 29	93.48 37	82.82 45	92.60 51	93.97 87	88.19 23	96.29 4	87.61 16	98.20 32	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
test_part2					93.86 47	77.77 81				92.84 43

v1.0		38.20 341	50.94 340	0.00 357	93.86 47	0.00 372	0.00 363	93.93 26	84.39 30	92.84 43	93.43 101	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

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 12	95.63 10	96.99 8	84.81 53	93.16 124	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
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 8	92.11 57	95.03 49	89.75 14	94.93 48	79.95 105	98.27 28	95.04 78
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 55	89.08 54	89.37 54	93.64 51	79.07 71	88.54 73	94.20 17	73.53 161	89.71 100	94.82 56	85.09 51	95.77 17	84.17 52	98.03 37	93.26 124
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 45	89.27 53	91.30 24	93.51 52	84.79 36	89.89 45	90.63 132	70.00 214	94.55 17	96.67 11	87.94 29	93.59 94	84.27 50	95.97 106	95.52 66

HQP_MVS		87.75 72	87.43 77	88.70 62	93.45 53	76.42 98	89.45 57	93.61 33	79.44 81	86.55 156	92.95 111	74.84 158	95.22 39	80.78 91	95.83 113	94.46 88

plane_prior7					93.45 53	77.31 88

WR-MVS		83.56 157	84.40 138	81.06 206	93.43 55	54.88 285	78.67 266	85.02 230	81.24 62	90.74 80	91.56 148	72.85 195	91.08 178	68.00 205	98.04 35	97.23 24

ESAPD		90.53 30	91.08 28	88.88 59	93.38 56	78.65 76	89.15 61	94.05 23	84.68 29	93.90 25	94.11 83	88.13 26	96.30 3	84.51 46	97.81 46	91.70 173

jajsoiax		89.41 50	88.81 60	91.19 28	93.38 56	84.72 37	89.70 47	90.29 148	69.27 218	94.39 19	96.38 15	86.02 48	93.52 104	83.96 53	95.92 109	95.34 69

PS-MVSNAJss		88.31 64	87.90 68	89.56 52	93.31 58	77.96 80	87.94 79	91.97 89	70.73 207	94.19 23	96.67 11	76.94 134	94.57 57	83.07 64	96.28 92	96.15 42

test222					93.31 58	76.54 95	79.38 254	77.79 266	52.59 321	82.36 221	90.84 174	66.83 229			91.69 212	81.25 325

DU-MVS		86.80 83	86.99 84	86.21 99	93.24 60	67.02 166	83.16 176	92.21 81	81.73 58	90.92 76	91.97 135	77.20 128	93.99 74	74.16 152	98.35 23	97.61 12

NR-MVSNet		86.00 100	86.22 98	85.34 115	93.24 60	64.56 183	82.21 203	90.46 136	80.99 65	88.42 129	91.97 135	77.56 125	93.85 81	72.46 171	98.65 13	97.61 12

OurMVSNet-221017-0		90.01 37	89.74 47	90.83 33	93.16 62	80.37 59	91.91 27	93.11 51	81.10 64	95.32 12	97.24 5	72.94 194	94.85 50	85.07 37	97.78 47	97.26 22

pcd1.5k->3k		38.83 340	41.11 342	32.01 351	93.13 63	0.00 372	0.00 363	91.38 116	0.00 367	0.00 369	0.00 369	89.24 15	0.00 369	0.00 366	96.24 95	96.02 48

UniMVSNet (Re)		86.87 80	86.98 85	86.55 89	93.11 64	68.48 157	83.80 153	92.87 62	80.37 69	89.61 108	91.81 142	77.72 123	94.18 66	75.00 149	98.53 17	96.99 33

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 18	93.21 36	94.39 73	90.09 13	95.08 44	86.67 25	97.60 56	94.18 96

ACMH+	77.89 11	90.73 25	91.50 18	88.44 64	93.00 66	76.26 100	89.65 50	95.55 3	87.72 17	93.89 27	94.94 52	91.62 4	93.44 110	78.35 121	98.76 5	95.61 65

APDe-MVS		91.22 20	91.92 9	89.14 57	92.97 67	78.04 79	92.84 12	94.14 21	83.33 38	93.90 25	95.73 29	88.77 17	96.41 1	87.60 17	97.98 42	92.98 131

114514_t		83.10 165	82.54 169	84.77 124	92.90 68	69.10 155	86.65 108	90.62 133	54.66 311	81.46 234	90.81 175	76.98 133	94.38 60	72.62 170	96.18 96	90.82 194

testdata				79.54 225	92.87 69	72.34 123		80.14 258	59.91 288	85.47 175	91.75 144	67.96 225	85.24 280	68.57 203	92.18 209	81.06 330

CNVR-MVS		87.81 71	87.68 73	88.21 67	92.87 69	77.30 89	85.25 124	91.23 120	77.31 115	87.07 149	91.47 150	82.94 69	94.71 53	84.67 44	96.27 94	92.62 147

UniMVSNet_NR-MVSNet		86.84 82	87.06 82	86.17 102	92.86 71	67.02 166	82.55 192	91.56 99	83.08 42	90.92 76	91.82 141	78.25 119	93.99 74	74.16 152	98.35 23	97.49 15

plane_prior1					92.83 72

原ACMM1				84.60 130	92.81 73	74.01 111		91.50 101	62.59 269	82.73 217	90.67 180	76.53 141	94.25 63	69.24 192	95.69 118	85.55 261

plane_prior6					92.61 74	76.54 95						74.84 158

APD-MVS		89.54 49	89.63 49	89.26 56	92.57 75	81.34 56	90.19 39	93.08 53	80.87 66	91.13 73	93.19 103	86.22 46	95.97 8	82.23 75	97.18 65	90.45 204
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_0402		88.65 59	89.58 51	85.88 107	92.55 76	72.22 126	84.01 144	89.44 169	88.63 13	94.38 20	95.77 28	86.38 45	93.59 94	79.84 106	95.21 129	91.82 169

SixPastTwentyTwo		87.20 76	87.45 76	86.45 91	92.52 77	69.19 154	87.84 83	88.05 187	81.66 59	94.64 16	96.53 14	65.94 233	94.75 52	83.02 66	96.83 74	95.41 68

ACMH	76.49 14	89.34 52	91.14 27	83.96 150	92.50 78	70.36 144	89.55 52	93.84 30	81.89 57	94.70 15	95.44 39	90.69 9	88.31 241	83.33 60	98.30 27	93.20 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		80.25 202	81.68 180	75.94 274	92.46 79	47.98 339	76.70 283	81.67 250	73.45 163	84.87 180	92.82 115	74.66 163	86.51 266	61.66 249	96.85 72	93.33 122

agg_prior3		85.76 106	84.95 119	88.16 68	92.43 80	79.92 62	83.98 145	90.03 156	65.11 255	83.66 203	90.64 183	81.00 97	93.67 86	81.21 83	96.54 82	90.88 191

F-COLMAP		84.97 118	83.42 155	89.63 50	92.39 81	83.40 45	88.83 67	91.92 91	73.19 172	80.18 259	89.15 206	77.04 132	93.28 119	65.82 222	92.28 204	92.21 160

test_djsdf		89.62 47	89.01 55	91.45 21	92.36 82	82.98 49	91.98 25	90.08 154	71.54 201	94.28 22	96.54 13	81.57 92	94.27 61	86.26 28	96.49 85	97.09 29

TEST9					92.34 83	79.70 66	83.94 146	90.32 141	65.41 253	84.49 190	90.97 168	82.03 84	93.63 89

train_agg		85.98 103	85.28 113	88.07 70	92.34 83	79.70 66	83.94 146	90.32 141	65.79 246	84.49 190	90.97 168	81.93 86	93.63 89	81.21 83	96.54 82	90.88 191

NCCC		87.36 73	86.87 88	88.83 60	92.32 85	78.84 74	86.58 110	91.09 124	78.77 92	84.85 181	90.89 172	80.85 98	95.29 35	81.14 85	95.32 125	92.34 154

FC-MVSNet-test		85.93 104	87.05 83	82.58 182	92.25 86	56.44 274	85.75 118	93.09 52	77.33 114	91.94 63	94.65 61	74.78 160	93.41 113	75.11 148	98.58 15	97.88 9

CDPH-MVS		86.17 99	85.54 109	88.05 73	92.25 86	75.45 103	83.85 150	92.01 87	65.91 245	86.19 163	91.75 144	83.77 61	94.98 47	77.43 132	96.71 77	93.73 112

pmmvs6		86.52 89	88.06 66	81.90 191	92.22 88	62.28 223	84.66 132	89.15 172	83.54 37	89.85 96	97.32 3	88.08 28	86.80 261	70.43 185	97.30 62	96.62 37

EG-PatchMatch MVS		84.08 145	84.11 145	83.98 149	92.22 88	72.61 120	82.20 205	87.02 206	72.63 179	88.86 119	91.02 166	78.52 115	91.11 177	73.41 162	91.09 227	88.21 233

test_8					92.09 90	78.87 73	83.82 151	90.31 143	65.79 246	84.36 193	90.96 170	81.93 86	93.44 110

Vis-MVSNet		86.86 81	86.58 92	87.72 75	92.09 90	77.43 86	87.35 89	92.09 84	78.87 90	84.27 198	94.05 84	78.35 118	93.65 87	80.54 96	91.58 214	92.08 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		86.66 86	86.82 91	86.17 102	92.05 92	66.87 168	91.21 33	88.64 177	86.30 23	89.60 109	92.59 120	69.22 219	94.91 49	73.89 156	97.89 44	96.72 35

旧先验1					91.97 93	71.77 132		81.78 249			91.84 139	73.92 173			93.65 174	83.61 288

v7n		90.13 33	90.96 32	87.65 77	91.95 94	71.06 140	89.99 42	93.05 54	86.53 21	94.29 21	96.27 19	82.69 71	94.08 72	86.25 30	97.63 53	97.82 10

NP-MVS					91.95 94	74.55 108					90.17 193

OMC-MVS		88.19 65	87.52 74	90.19 44	91.94 96	81.68 53	87.49 88	93.17 50	76.02 129	88.64 124	91.22 154	84.24 58	93.37 114	77.97 127	97.03 68	95.52 66

FIs		85.35 111	86.27 97	82.60 181	91.86 97	57.31 266	85.10 126	93.05 54	75.83 134	91.02 75	93.97 87	73.57 181	92.91 137	73.97 155	98.02 38	97.58 14

MSLP-MVS++		85.00 117	86.03 102	81.90 191	91.84 98	71.56 138	86.75 104	93.02 58	75.95 132	87.12 146	89.39 203	77.98 120	89.40 219	77.46 130	94.78 147	84.75 274

DP-MVS Recon		84.05 146	83.22 158	86.52 90	91.73 99	75.27 104	83.23 175	92.40 76	72.04 189	82.04 225	88.33 220	77.91 122	93.95 79	66.17 217	95.12 134	90.34 207

SD-MVS		88.96 56	89.88 44	86.22 97	91.63 100	77.07 90	89.82 46	93.77 31	78.90 89	92.88 40	92.29 129	86.11 47	90.22 203	86.24 31	97.24 63	91.36 181

AllTest		87.97 68	87.40 78	89.68 48	91.59 101	83.40 45	89.50 55	95.44 5	79.47 79	88.00 135	93.03 107	82.66 72	91.47 168	70.81 178	96.14 99	94.16 97

TestCases				89.68 48	91.59 101	83.40 45		95.44 5	79.47 79	88.00 135	93.03 107	82.66 72	91.47 168	70.81 178	96.14 99	94.16 97

MCST-MVS		84.36 133	83.93 150	85.63 111	91.59 101	71.58 137	83.52 162	92.13 83	61.82 275	83.96 199	89.75 198	79.93 108	93.46 109	78.33 122	94.34 158	91.87 168

agg_prior1		85.72 107	85.20 114	87.28 83	91.58 104	77.69 82	83.69 157	90.30 145	66.29 241	84.32 194	91.07 165	82.13 80	93.18 122	81.02 86	96.36 89	90.98 186

agg_prior					91.58 104	77.69 82		90.30 145		84.32 194			93.18 122

PVSNet_Blended_VisFu		81.55 184	80.49 196	84.70 128	91.58 104	73.24 116	84.21 139	91.67 97	62.86 267	80.94 240	87.16 236	67.27 227	92.87 138	69.82 188	88.94 261	87.99 237

EPP-MVSNet		85.47 110	85.04 116	86.77 86	91.52 107	69.37 149	91.63 28	87.98 190	81.51 61	87.05 150	91.83 140	66.18 232	95.29 35	70.75 180	96.89 71	95.64 60

DeepPCF-MVS	81.24 5	87.28 75	86.21 99	90.49 38	91.48 108	84.90 34	83.41 167	92.38 78	70.25 212	89.35 114	90.68 179	82.85 70	94.57 57	79.55 109	95.95 107	92.00 163

Baseline_NR-MVSNet		84.00 148	85.90 103	78.29 241	91.47 109	53.44 294	82.29 199	87.00 207	79.06 87	89.55 110	95.72 30	77.20 128	86.14 272	72.30 172	98.51 18	95.28 72

HyFIR lowres test		75.12 247	72.66 269	82.50 185	91.44 110	65.19 179	72.47 313	87.31 196	46.79 347	80.29 257	84.30 278	52.70 290	92.10 154	51.88 315	86.73 284	90.22 208

DP-MVS		88.60 61	89.01 55	87.36 82	91.30 111	77.50 85	87.55 86	92.97 60	87.95 16	89.62 106	92.87 114	84.56 55	93.89 80	77.65 128	96.62 79	90.70 196

DeepC-MVS_fast	80.27 8	86.23 96	85.65 108	87.96 74	91.30 111	76.92 91	87.19 92	91.99 88	70.56 208	84.96 177	90.69 178	80.01 106	95.14 42	78.37 120	95.78 115	91.82 169
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 40	89.66 48	90.92 32	91.27 113	81.66 54	91.25 32	94.13 22	88.89 11	88.83 121	94.26 76	77.55 126	95.86 14	84.88 40	95.87 111	95.24 73

HQP-NCC					91.19 114		84.77 128		73.30 168	80.55 254

ACMP_Plane					91.19 114		84.77 128		73.30 168	80.55 254

HQP-MVS		84.61 124	84.06 146	86.27 95	91.19 114	70.66 142	84.77 128	92.68 69	73.30 168	80.55 254	90.17 193	72.10 205	94.61 55	77.30 133	94.47 156	93.56 119

VDD-MVS		84.23 139	84.58 133	83.20 172	91.17 117	65.16 180	83.25 173	84.97 232	79.79 75	87.18 145	94.27 75	74.77 161	90.89 185	69.24 192	96.54 82	93.55 121

K. test v3		85.14 112	84.73 122	86.37 92	91.13 118	69.63 148	85.45 122	76.68 274	84.06 35	92.44 54	96.99 8	62.03 245	94.65 54	80.58 95	93.24 186	94.83 82

lessismore_v0				85.95 104	91.10 119	70.99 141		70.91 320		91.79 64	94.42 69	61.76 246	92.93 135	79.52 112	93.03 190	93.93 104

TransMVSNet (Re)		84.02 147	85.74 105	78.85 231	91.00 120	55.20 284	82.29 199	87.26 197	79.65 78	88.38 131	95.52 37	83.00 68	86.88 253	67.97 206	96.60 80	94.45 90

wuykxyi23d		88.46 62	88.80 61	87.44 81	90.96 121	93.03 1	85.85 117	81.96 246	74.58 150	98.58 2	97.29 4	87.73 31	87.31 248	82.84 69	99.41 1	81.99 311

PAPM_NR		83.23 162	83.19 160	83.33 170	90.90 122	65.98 174	88.19 76	90.78 128	78.13 101	80.87 242	87.92 228	73.49 183	92.42 146	70.07 186	88.40 265	91.60 176

CSCG		86.26 94	86.47 94	85.60 112	90.87 123	74.26 110	87.98 78	91.85 92	80.35 70	89.54 112	88.01 224	79.09 111	92.13 151	75.51 145	95.06 136	90.41 205

PLC	73.85 16	82.09 178	80.31 197	87.45 80	90.86 124	80.29 60	85.88 116	90.65 131	68.17 227	76.32 285	86.33 249	73.12 193	92.61 144	61.40 252	90.02 251	89.44 217
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test12				86.57 88	90.74 125	72.63 119		90.69 130		82.76 216		79.20 110	94.80 51		95.32 125	92.27 157

ITE_SJBPF				90.11 45	90.72 126	84.97 33		90.30 145	81.56 60	90.02 87	91.20 157	82.40 76	90.81 187	73.58 159	94.66 152	94.56 83

TAMVS		78.08 216	76.36 229	83.23 171	90.62 127	72.87 117	79.08 261	80.01 259	61.72 277	81.35 236	86.92 239	63.96 239	88.78 234	50.61 316	93.01 191	88.04 236

test_prior3		86.31 93	86.31 96	86.32 93	90.59 128	71.99 130	83.37 168	92.85 63	75.43 141	84.58 188	91.57 146	81.92 88	94.17 68	79.54 110	96.97 69	92.80 136

test_prior				86.32 93	90.59 128	71.99 130		92.85 63					94.17 68			92.80 136

ambc				82.98 175	90.55 130	64.86 181	88.20 75	89.15 172		89.40 113	93.96 89	71.67 212	91.38 174	78.83 118	96.55 81	92.71 139

Anonymous20231211		88.40 63	89.62 50	84.73 126	90.46 131	65.27 178	88.86 66	93.02 58	87.15 19	93.05 38	97.10 6	82.28 78	92.02 155	76.70 139	97.99 40	96.88 34

Test_1112_low_res		73.90 260	73.08 265	76.35 268	90.35 132	55.95 275	73.40 311	86.17 213	50.70 337	73.14 308	85.94 254	58.31 265	85.90 275	56.51 287	83.22 313	87.20 246

VPA-MVSNet		83.47 160	84.73 122	79.69 222	90.29 133	57.52 265	81.30 223	88.69 176	76.29 125	87.58 140	94.44 68	80.60 101	87.20 249	66.60 215	96.82 75	94.34 93

FMVSNet1		84.55 126	85.45 111	81.85 194	90.27 134	61.05 238	86.83 100	88.27 184	78.57 95	89.66 102	95.64 33	75.43 149	90.68 191	69.09 196	95.33 124	93.82 108

Anonymous20240529		86.20 98	87.13 80	83.42 169	90.19 135	64.55 184	84.55 135	90.71 129	85.85 24	89.94 91	95.24 44	82.13 80	90.40 198	69.19 195	96.40 88	95.31 71

MVS_111021_HR		84.63 123	84.34 143	85.49 114	90.18 136	75.86 102	79.23 260	87.13 202	73.35 165	85.56 173	89.34 204	83.60 63	90.50 196	76.64 140	94.05 162	90.09 213

RPSCF		88.00 67	86.93 87	91.22 27	90.08 137	89.30 6	89.68 49	91.11 123	79.26 83	89.68 101	94.81 59	82.44 75	87.74 245	76.54 141	88.74 264	96.61 38

nrg030		87.85 70	88.49 63	85.91 105	90.07 138	69.73 146	87.86 80	94.20 17	74.04 155	92.70 49	94.66 60	85.88 49	91.50 167	79.72 107	97.32 61	96.50 40

v748		88.91 58	89.82 46	86.19 101	90.06 139	68.53 156	88.81 68	91.48 103	84.36 31	94.19 23	95.98 25	82.52 74	92.67 142	84.30 49	96.67 78	97.37 19

AdaColmap		83.66 154	83.69 152	83.57 163	90.05 140	72.26 125	86.29 114	90.00 157	78.19 100	81.65 232	87.16 236	83.40 65	94.24 64	61.69 248	94.76 150	84.21 281

pm-mvs1		83.69 153	84.95 119	79.91 218	90.04 141	59.66 251	82.43 194	87.44 194	75.52 140	87.85 137	95.26 43	81.25 96	85.65 278	68.74 200	96.04 104	94.42 91

CHOSEN 1792x2688		72.45 275	70.56 285	78.13 243	90.02 142	63.08 202	68.72 326	83.16 237	42.99 356	75.92 290	85.46 260	57.22 275	85.18 282	49.87 320	81.67 322	86.14 255

anonymousdsp		89.73 46	88.88 58	92.27 7	89.82 143	86.67 12	90.51 37	90.20 153	69.87 215	95.06 13	96.14 23	84.28 57	93.07 132	87.68 14	96.34 90	97.09 29

1112_ss		74.82 252	73.74 252	78.04 245	89.57 144	60.04 247	76.49 287	87.09 205	54.31 312	73.66 307	79.80 326	60.25 253	86.76 263	58.37 277	84.15 309	87.32 245

PCF-MVS	74.62 15	82.15 177	80.92 192	85.84 108	89.43 145	72.30 124	80.53 233	91.82 93	57.36 299	87.81 138	89.92 196	77.67 124	93.63 89	58.69 272	95.08 135	91.58 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 242	73.51 262	82.71 180	89.35 146	73.62 112	80.06 236	85.20 225	60.30 286	73.96 305	87.94 226	57.89 270	89.45 217	52.02 310	74.87 343	85.06 267
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA		83.55 158	83.10 161	84.90 120	89.34 147	83.87 43	84.54 136	88.77 174	79.09 86	83.54 206	88.66 213	74.87 157	81.73 304	66.84 213	92.29 203	89.11 223

TSAR-MVS + GP.		83.95 149	82.69 165	87.72 75	89.27 148	81.45 55	83.72 156	81.58 251	74.73 148	85.66 170	86.06 253	72.56 203	92.69 141	75.44 146	95.21 129	89.01 229

MVS_111021_LR		84.28 137	83.76 151	85.83 109	89.23 149	83.07 48	80.99 229	83.56 236	72.71 178	86.07 164	89.07 207	81.75 91	86.19 271	77.11 135	93.36 179	88.24 232

LFMVS		80.15 205	80.56 194	78.89 230	89.19 150	55.93 276	85.22 125	73.78 292	82.96 43	84.28 197	92.72 119	57.38 273	90.07 210	63.80 233	95.75 116	90.68 197

CLD-MVS		83.18 163	82.64 166	84.79 123	89.05 151	67.82 163	77.93 273	92.52 74	68.33 226	85.07 176	81.54 316	82.06 82	92.96 133	69.35 191	97.91 43	93.57 118
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 41	91.38 23	89.03 152	84.23 42	93.58 4	94.68 10	90.65 6	90.33 83	93.95 92	84.50 56	95.37 33	80.87 89	95.50 121	94.53 87

CDS-MVSNet		77.32 222	75.40 238	83.06 174	89.00 153	72.48 122	77.90 274	82.17 245	60.81 283	78.94 268	83.49 286	59.30 260	88.76 235	54.64 302	92.37 202	87.93 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tttt0517		81.07 191	79.58 205	85.52 113	88.99 154	66.45 172	87.03 96	75.51 280	73.76 159	88.32 133	90.20 190	37.96 352	94.16 71	79.36 115	95.13 132	95.93 52

tfpnnormal		81.79 182	82.95 162	78.31 240	88.93 155	55.40 280	80.83 232	82.85 240	76.81 121	85.90 168	94.14 81	74.58 165	86.51 266	66.82 214	95.68 119	93.01 130

v13		87.31 74	88.10 65	84.94 119	88.84 156	63.75 190	87.85 82	91.47 106	79.12 85	93.72 29	95.82 27	75.20 152	93.58 97	84.76 43	96.16 97	97.48 16

Vis-MVSNet (Re-imp)		77.82 217	77.79 216	77.92 247	88.82 157	51.29 314	83.28 171	71.97 308	74.04 155	82.23 222	89.78 197	57.38 273	89.41 218	57.22 284	95.41 122	93.05 129

TAPA-MVS	77.73 12	85.71 108	84.83 121	88.37 65	88.78 158	79.72 65	87.15 94	93.50 36	69.17 220	85.80 169	89.56 201	80.76 99	92.13 151	73.21 167	95.51 120	93.25 125
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v12		87.15 77	87.91 67	84.84 121	88.69 159	63.52 193	87.58 85	91.46 107	78.74 93	93.57 32	95.66 31	74.94 156	93.57 98	84.50 47	96.08 102	97.43 17

v52		89.97 40	90.60 37	88.07 70	88.69 159	72.01 128	91.35 30	92.64 71	82.22 51	95.97 8	96.31 16	84.82 52	93.98 76	88.59 4	94.83 145	98.23 7

V4		89.97 40	90.60 37	88.07 70	88.69 159	72.01 128	91.35 30	92.64 71	82.22 51	95.98 7	96.31 16	84.80 54	93.98 76	88.59 4	94.83 145	98.23 7

FPMVS		72.29 278	72.00 276	73.14 294	88.63 162	85.00 32	74.65 302	67.39 338	71.94 197	77.80 276	87.66 231	50.48 295	75.83 321	49.95 318	79.51 330	58.58 358

BH-untuned		80.96 193	80.99 190	80.84 208	88.55 163	68.23 158	80.33 235	88.46 178	72.79 177	86.55 156	86.76 240	74.72 162	91.77 163	61.79 247	88.99 260	82.52 304

V9		86.96 78	87.70 72	84.74 125	88.52 164	63.27 199	87.31 90	91.45 109	78.28 98	93.43 33	95.45 38	74.59 164	93.57 98	84.23 51	96.01 105	97.38 18

v11		86.96 78	87.78 71	84.51 131	88.50 165	62.60 213	87.21 91	91.63 98	78.08 102	93.40 34	95.56 36	75.07 153	93.57 98	84.46 48	96.08 102	97.36 20

Anonymous202405211		80.51 199	81.19 187	78.49 238	88.48 166	57.26 267	76.63 284	82.49 242	81.21 63	84.30 196	92.24 132	67.99 224	86.24 270	62.22 242	95.13 132	91.98 166

ab-mvs		79.67 207	80.56 194	76.99 259	88.48 166	56.93 269	84.70 131	86.06 214	68.95 224	80.78 243	93.08 105	75.30 151	84.62 287	56.78 286	90.90 237	89.43 218

PHI-MVS		86.38 91	85.81 104	88.08 69	88.44 168	77.34 87	89.35 59	93.05 54	73.15 173	84.76 182	87.70 230	78.87 113	94.18 66	80.67 94	96.29 91	92.73 138

V14		86.75 84	87.46 75	84.62 129	88.35 169	63.00 204	87.02 97	91.42 112	77.78 104	93.27 35	95.23 45	74.22 167	93.56 101	83.95 54	95.93 108	97.31 21

xiu_mvs_v1_base_debu		80.84 195	80.14 200	82.93 177	88.31 170	71.73 133	79.53 244	87.17 199	65.43 250	79.59 261	82.73 299	76.94 134	90.14 206	73.22 163	88.33 266	86.90 249

xiu_mvs_v1_base		80.84 195	80.14 200	82.93 177	88.31 170	71.73 133	79.53 244	87.17 199	65.43 250	79.59 261	82.73 299	76.94 134	90.14 206	73.22 163	88.33 266	86.90 249

xiu_mvs_v1_base_debi		80.84 195	80.14 200	82.93 177	88.31 170	71.73 133	79.53 244	87.17 199	65.43 250	79.59 261	82.73 299	76.94 134	90.14 206	73.22 163	88.33 266	86.90 249

MG-MVS		80.32 201	80.94 191	78.47 239	88.18 173	52.62 301	82.29 199	85.01 231	72.01 190	79.24 266	92.54 123	69.36 218	93.36 115	70.65 182	89.19 259	89.45 216

PM-MVS		80.20 204	79.00 207	83.78 154	88.17 174	86.66 13	81.31 221	66.81 344	69.64 216	88.33 132	90.19 191	64.58 236	83.63 296	71.99 176	90.03 250	81.06 330

v10		86.54 88	87.10 81	84.84 121	88.16 175	63.28 198	86.64 109	92.20 82	75.42 143	92.81 46	94.50 66	74.05 170	94.06 73	83.88 55	96.28 92	97.17 27

v15		86.56 87	87.25 79	84.51 131	88.15 176	62.72 209	86.72 107	91.40 114	77.38 109	93.11 37	95.00 50	73.93 172	93.55 102	83.67 58	95.86 112	97.26 22

v7		84.81 120	85.00 117	84.23 142	88.15 176	63.27 199	83.79 154	91.39 115	71.10 205	90.07 85	91.28 152	74.04 171	93.63 89	81.48 82	93.67 173	95.79 53

casdiffmvs1		83.63 155	83.44 154	84.20 145	88.08 178	66.53 171	88.62 72	92.02 86	58.66 291	82.94 214	93.84 95	78.76 114	93.10 127	76.73 138	91.29 224	92.96 132

v17		86.32 92	86.95 86	84.44 135	88.00 179	62.62 212	86.74 105	91.48 103	77.17 117	92.74 47	94.56 62	73.74 176	93.53 103	83.27 61	94.87 144	97.18 26

canonicalmvs		85.50 109	86.14 100	83.58 162	87.97 180	67.13 165	87.55 86	94.32 12	73.44 164	88.47 128	87.54 233	86.45 43	91.06 179	75.76 144	93.76 170	92.54 149

v16		86.24 95	86.85 89	84.43 136	87.96 181	62.59 214	86.73 106	91.48 103	77.17 117	92.67 50	94.55 63	73.63 177	93.52 104	83.26 62	94.16 159	97.17 27

VNet		79.31 208	80.27 198	76.44 267	87.92 182	53.95 289	75.58 295	84.35 235	74.39 153	82.23 222	90.72 177	72.84 196	84.39 289	60.38 259	93.98 164	90.97 187

view600		76.79 227	76.54 224	77.56 251	87.91 183	50.77 320	81.92 209	71.35 316	77.38 109	84.62 184	88.40 216	45.18 324	89.26 221	58.58 273	93.49 175	92.66 141

view800		76.79 227	76.54 224	77.56 251	87.91 183	50.77 320	81.92 209	71.35 316	77.38 109	84.62 184	88.40 216	45.18 324	89.26 221	58.58 273	93.49 175	92.66 141

conf0.05thres1000		76.79 227	76.54 224	77.56 251	87.91 183	50.77 320	81.92 209	71.35 316	77.38 109	84.62 184	88.40 216	45.18 324	89.26 221	58.58 273	93.49 175	92.66 141

tfpn		76.79 227	76.54 224	77.56 251	87.91 183	50.77 320	81.92 209	71.35 316	77.38 109	84.62 184	88.40 216	45.18 324	89.26 221	58.58 273	93.49 175	92.66 141

v8		86.22 97	86.83 90	84.36 139	87.82 187	62.35 218	86.42 112	91.33 117	76.78 122	92.73 48	94.48 67	73.41 184	93.72 85	83.10 63	95.41 122	97.01 32

v1neww		84.43 130	84.66 128	83.75 155	87.81 188	62.34 219	83.59 158	90.27 149	72.33 184	89.93 93	91.22 154	73.28 188	93.29 116	80.25 101	93.25 184	95.62 61

v7new		84.43 130	84.66 128	83.75 155	87.81 188	62.34 219	83.59 158	90.27 149	72.33 184	89.93 93	91.22 154	73.28 188	93.29 116	80.25 101	93.25 184	95.62 61

v6		84.43 130	84.66 128	83.75 155	87.81 188	62.34 219	83.59 158	90.26 151	72.33 184	89.94 91	91.19 158	73.30 187	93.29 116	80.26 100	93.26 183	95.62 61

alignmvs		83.94 150	83.98 148	83.80 152	87.80 191	67.88 162	84.54 136	91.42 112	73.27 171	88.41 130	87.96 225	72.33 204	90.83 186	76.02 143	94.11 160	92.69 140

v1192		84.57 125	84.69 126	84.21 143	87.75 192	62.88 206	83.02 178	91.43 110	69.08 222	89.98 90	90.89 172	72.70 199	93.62 93	82.41 72	94.97 139	96.13 43

v18		85.99 102	86.55 93	84.30 141	87.73 193	62.29 222	86.40 113	91.49 102	76.64 123	92.40 55	94.20 79	73.28 188	93.52 104	82.87 67	93.99 163	97.09 29

PatchMatch-RL		74.48 254	73.22 264	78.27 242	87.70 194	85.26 30	75.92 291	70.09 323	64.34 259	76.09 288	81.25 318	65.87 234	78.07 314	53.86 304	83.82 310	71.48 346

v1144		84.54 128	84.72 124	84.00 148	87.67 195	62.55 215	82.97 180	90.93 127	70.32 211	89.80 98	90.99 167	73.50 182	93.48 108	81.69 81	94.65 153	95.97 49

v1240		84.30 136	84.51 135	83.65 160	87.65 196	61.26 235	82.85 183	91.54 100	67.94 231	90.68 81	90.65 181	71.71 211	93.64 88	82.84 69	94.78 147	96.07 45

v1921920		84.23 139	84.37 142	83.79 153	87.64 197	61.71 226	82.91 182	91.20 121	67.94 231	90.06 86	90.34 186	72.04 208	93.59 94	82.32 74	94.91 140	96.07 45

v144192		84.24 138	84.41 137	83.71 159	87.59 198	61.57 231	82.95 181	91.03 125	67.82 234	89.80 98	90.49 184	73.28 188	93.51 107	81.88 79	94.89 141	96.04 47

Fast-Effi-MVS+		81.04 192	80.57 193	82.46 186	87.50 199	63.22 201	78.37 269	89.63 165	68.01 228	81.87 227	82.08 311	82.31 77	92.65 143	67.10 209	88.30 270	91.51 179

pmmvs-eth3d		78.42 214	77.04 220	82.57 184	87.44 200	74.41 109	80.86 231	79.67 260	55.68 306	84.69 183	90.31 189	60.91 249	85.42 279	62.20 243	91.59 213	87.88 240

MVS_0304		84.88 119	83.96 149	87.64 78	87.43 201	74.83 106	84.18 140	93.30 43	77.48 108	77.39 279	88.46 215	74.53 166	95.74 18	78.09 126	94.75 151	92.36 153

IterMVS-LS		84.73 122	84.98 118	83.96 150	87.35 202	63.66 191	83.25 173	89.88 160	76.06 127	89.62 106	92.37 128	73.40 186	92.52 145	78.16 124	94.77 149	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	83.52 165	87.33 203	61.71 226	82.79 185	89.73 163	71.89 200	89.64 103	91.11 163	72.72 197	93.10 127	80.40 97	93.79 169	95.75 55

v1141		84.16 141	84.38 139	83.52 165	87.32 204	61.70 228	82.79 185	89.74 161	71.90 198	89.64 103	91.12 161	72.68 200	93.10 127	80.39 99	93.80 168	95.75 55

divwei89l23v2f112		84.16 141	84.38 139	83.52 165	87.32 204	61.70 228	82.79 185	89.74 161	71.90 198	89.64 103	91.12 161	72.68 200	93.10 127	80.40 97	93.81 167	95.75 55

tfpn111		76.03 239	75.53 237	77.53 255	87.27 206	51.88 306	81.07 226	73.26 297	75.68 136	83.25 208	86.37 246	45.54 314	89.38 220	55.07 298	92.26 205	91.34 182

conf200view11		75.62 243	75.05 241	77.34 257	87.27 206	51.88 306	81.07 226	73.26 297	75.68 136	83.25 208	86.37 246	45.54 314	88.80 229	51.98 311	90.99 231	91.34 182

thres100view900		75.45 244	75.05 241	76.66 266	87.27 206	51.88 306	81.07 226	73.26 297	75.68 136	83.25 208	86.37 246	45.54 314	88.80 229	51.98 311	90.99 231	89.31 220

MIMVSNet		71.09 286	71.59 279	69.57 310	87.23 209	50.07 328	78.91 262	71.83 310	60.20 287	71.26 318	91.76 143	55.08 285	76.09 319	41.06 344	87.02 283	82.54 303

Effi-MVS+		83.90 151	84.01 147	83.57 163	87.22 210	65.61 177	86.55 111	92.40 76	78.64 94	81.34 237	84.18 279	83.65 62	92.93 135	74.22 151	87.87 274	92.17 161

BH-RMVSNet		80.53 198	80.22 199	81.49 199	87.19 211	66.21 173	77.79 275	86.23 212	74.21 154	83.69 201	88.50 214	73.25 192	90.75 188	63.18 238	87.90 273	87.52 242

Effi-MVS+-dtu		85.82 105	83.38 156	93.14 3	87.13 212	91.15 3	87.70 84	88.42 179	74.57 151	83.56 205	85.65 255	78.49 116	94.21 65	72.04 174	92.88 193	94.05 100

mvs-test1		84.55 126	82.12 175	91.84 17	87.13 212	89.54 5	85.05 127	88.42 179	74.57 151	80.60 251	82.98 292	78.49 116	93.98 76	72.04 174	89.77 253	92.00 163

v2v482		84.09 144	84.24 144	83.62 161	87.13 212	61.40 232	82.71 189	89.71 164	72.19 188	89.55 110	91.41 151	70.70 216	93.20 121	81.02 86	93.76 170	96.25 41

jason		77.42 221	75.75 235	82.43 187	87.10 215	69.27 150	77.99 272	81.94 248	51.47 331	77.84 274	85.07 268	60.32 252	89.00 226	70.74 181	89.27 258	89.03 227
jason: jason.
PS-MVSNAJ		77.04 225	76.53 228	78.56 236	87.09 216	61.40 232	75.26 297	87.13 202	61.25 280	74.38 304	77.22 337	76.94 134	90.94 181	64.63 229	84.83 305	83.35 293

xiu_mvs_v2_base		77.19 223	76.75 222	78.52 237	87.01 217	61.30 234	75.55 296	87.12 204	61.24 281	74.45 302	78.79 330	77.20 128	90.93 182	64.62 230	84.80 306	83.32 294

thres600view7		75.97 240	75.35 240	77.85 249	87.01 217	51.84 310	80.45 234	73.26 297	75.20 145	83.10 212	86.31 251	45.54 314	89.05 225	55.03 299	92.24 206	92.66 141

BH-w/o		76.57 233	76.07 232	78.10 244	86.88 219	65.92 175	77.63 276	86.33 211	65.69 248	80.89 241	79.95 325	68.97 222	90.74 189	53.01 307	85.25 299	77.62 335

MAR-MVS		80.24 203	78.74 209	84.73 126	86.87 220	78.18 78	85.75 118	87.81 192	65.67 249	77.84 274	78.50 331	73.79 175	90.53 195	61.59 251	90.87 238	85.49 263
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	83.50 168	86.74 221	63.97 189	84.56 134	90.31 143	66.22 242	91.62 67	94.55 63	75.88 146	91.95 156	77.02 137	94.89 141	94.56 83

QAPM		82.59 171	82.59 168	82.58 182	86.44 222	66.69 169	89.94 44	90.36 140	67.97 230	84.94 179	92.58 122	72.71 198	92.18 150	70.63 183	87.73 276	88.85 230

PAPM		71.77 281	70.06 292	76.92 260	86.39 223	53.97 288	76.62 285	86.62 209	53.44 317	63.97 347	84.73 274	57.79 271	92.34 147	39.65 346	81.33 325	84.45 276

GBi-Net		82.02 179	82.07 176	81.85 194	86.38 224	61.05 238	86.83 100	88.27 184	72.43 180	86.00 165	95.64 33	63.78 240	90.68 191	65.95 218	93.34 180	93.82 108

test1		82.02 179	82.07 176	81.85 194	86.38 224	61.05 238	86.83 100	88.27 184	72.43 180	86.00 165	95.64 33	63.78 240	90.68 191	65.95 218	93.34 180	93.82 108

FMVSNet2		81.31 186	81.61 182	80.41 213	86.38 224	58.75 260	83.93 148	86.58 210	72.43 180	87.65 139	92.98 109	63.78 240	90.22 203	66.86 211	93.92 165	92.27 157

3Dnovator	80.37 7	84.80 121	84.71 125	85.06 118	86.36 227	74.71 107	88.77 69	90.00 157	75.65 139	84.96 177	93.17 104	74.06 169	91.19 175	78.28 123	91.09 227	89.29 222

casdiffmvs		82.99 166	82.51 170	84.42 137	86.34 228	67.92 161	87.86 80	92.28 80	60.95 282	81.12 238	93.08 105	76.07 145	93.43 112	79.41 114	85.45 295	91.93 167

Anonymous20231206		71.38 285	71.88 277	69.88 306	86.31 229	54.37 286	70.39 321	74.62 285	52.57 322	76.73 281	88.76 210	59.94 255	72.06 328	44.35 339	93.23 187	83.23 296

API-MVS		82.28 175	82.61 167	81.30 201	86.29 230	69.79 145	88.71 70	87.67 193	78.42 97	82.15 224	84.15 281	77.98 120	91.59 166	65.39 224	92.75 195	82.51 305

tfpn200view9		74.86 251	74.23 249	76.74 265	86.24 231	52.12 303	79.24 257	73.87 290	73.34 166	81.82 229	84.60 276	46.02 308	88.80 229	51.98 311	90.99 231	89.31 220

thres400		75.14 245	74.23 249	77.86 248	86.24 231	52.12 303	79.24 257	73.87 290	73.34 166	81.82 229	84.60 276	46.02 308	88.80 229	51.98 311	90.99 231	92.66 141

testmv		70.47 290	70.70 284	69.77 308	86.22 233	53.89 290	67.32 332	71.91 309	63.32 262	78.16 272	89.47 202	56.12 279	73.10 326	36.43 352	87.33 279	82.33 307

UGNet		82.78 168	81.64 181	86.21 99	86.20 234	76.24 101	86.86 98	85.68 218	77.07 119	73.76 306	92.82 115	69.64 217	91.82 162	69.04 197	93.69 172	90.56 201
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	82.85 163	86.63 87	86.17 235	72.21 127	83.76 155	91.43 110	77.24 116	74.39 303	87.45 234	75.36 150	95.42 31	77.03 136	92.83 194	92.25 159

conf0.01		74.17 257	73.53 256	76.08 272	86.13 236	50.06 329	79.45 248	68.54 329	72.01 190	80.76 244	82.50 302	41.39 338	86.83 255	59.66 263	91.36 217	91.34 182

conf0.002		74.17 257	73.53 256	76.08 272	86.13 236	50.06 329	79.45 248	68.54 329	72.01 190	80.76 244	82.50 302	41.39 338	86.83 255	59.66 263	91.36 217	91.34 182

thresconf0.02		73.65 262	73.53 256	73.98 283	86.13 236	50.06 329	79.45 248	68.54 329	72.01 190	80.76 244	82.50 302	41.39 338	86.83 255	59.66 263	91.36 217	85.06 267

tfpn_n400		73.65 262	73.53 256	73.98 283	86.13 236	50.06 329	79.45 248	68.54 329	72.01 190	80.76 244	82.50 302	41.39 338	86.83 255	59.66 263	91.36 217	85.06 267

tfpnconf		73.65 262	73.53 256	73.98 283	86.13 236	50.06 329	79.45 248	68.54 329	72.01 190	80.76 244	82.50 302	41.39 338	86.83 255	59.66 263	91.36 217	85.06 267

tfpnview11		73.65 262	73.53 256	73.98 283	86.13 236	50.06 329	79.45 248	68.54 329	72.01 190	80.76 244	82.50 302	41.39 338	86.83 255	59.66 263	91.36 217	85.06 267

TR-MVS		76.77 231	75.79 233	79.72 221	86.10 242	65.79 176	77.14 280	83.02 238	65.20 254	81.40 235	82.10 310	66.30 230	90.73 190	55.57 293	85.27 298	82.65 300

LCM-MVSNet-Re		83.48 159	85.06 115	78.75 233	85.94 243	55.75 279	80.05 237	94.27 13	76.47 124	96.09 5	94.54 65	83.31 66	89.75 214	59.95 260	94.89 141	90.75 195

Fast-Effi-MVS+-dtu		82.54 172	81.41 184	85.90 106	85.60 244	76.53 97	83.07 177	89.62 166	73.02 175	79.11 267	83.51 285	80.74 100	90.24 202	68.76 199	89.29 256	90.94 188

v148		82.31 174	82.48 171	81.81 197	85.59 245	59.66 251	81.47 219	86.02 215	72.85 176	88.05 134	90.65 181	70.73 215	90.91 184	75.15 147	91.79 210	94.87 79

MVSFormer		82.23 176	81.57 183	84.19 146	85.54 246	69.26 151	91.98 25	90.08 154	71.54 201	76.23 286	85.07 268	58.69 263	94.27 61	86.26 28	88.77 262	89.03 227

lupinMVS		76.37 236	74.46 247	82.09 188	85.54 246	69.26 151	76.79 281	80.77 255	50.68 338	76.23 286	82.82 297	58.69 263	88.94 227	69.85 187	88.77 262	88.07 234

tfpn1000		73.63 266	73.58 254	73.79 289	85.46 248	50.31 326	79.99 239	68.18 335	72.33 184	80.66 250	83.05 290	39.80 349	86.74 264	60.96 255	91.78 211	84.32 279

TinyColmap		81.25 189	82.34 174	77.99 246	85.33 249	60.68 243	82.32 198	88.33 182	71.26 203	86.97 151	92.22 133	77.10 131	86.98 252	62.37 241	95.17 131	86.31 254

PAPR		78.84 209	78.10 214	81.07 205	85.17 250	60.22 246	82.21 203	90.57 134	62.51 270	75.32 296	84.61 275	74.99 155	92.30 148	59.48 270	88.04 272	90.68 197

DI_MVS_plusplus_test		81.27 188	81.26 185	81.29 202	84.98 251	61.65 230	81.98 208	87.25 198	63.56 260	87.56 141	89.60 200	73.62 178	91.83 161	72.20 173	90.59 247	90.38 206

pmmvs4		74.92 250	72.98 267	80.73 210	84.95 252	71.71 136	76.23 290	77.59 267	52.83 320	77.73 277	86.38 245	56.35 277	84.97 283	57.72 283	87.05 282	85.51 262

Test4		81.31 186	81.13 189	81.88 193	84.89 253	63.05 203	82.37 196	90.50 135	62.75 268	89.00 118	88.29 221	67.55 226	91.68 164	73.55 160	91.24 226	90.89 190

Patchmatch-RL test		74.48 254	73.68 253	76.89 262	84.83 254	66.54 170	72.29 314	69.16 328	57.70 297	86.76 152	86.33 249	45.79 313	82.59 300	69.63 189	90.65 245	81.54 320

tfpn_ndepth		72.54 274	72.30 274	73.24 292	84.81 255	51.42 312	79.24 257	70.49 322	69.26 219	78.48 270	79.80 326	40.16 348	86.77 262	58.08 282	90.43 248	81.53 321

test_normal		81.23 190	81.16 188	81.43 200	84.77 256	61.99 225	81.46 220	86.95 208	63.16 265	87.22 144	89.63 199	73.62 178	91.65 165	72.92 168	90.70 242	90.65 199

XXY-MVS		74.44 256	76.19 231	69.21 311	84.61 257	52.43 302	71.70 316	77.18 269	60.73 285	80.60 251	90.96 170	75.44 148	69.35 335	56.13 289	88.33 266	85.86 259

cascas		76.29 237	74.81 243	80.72 211	84.47 258	62.94 205	73.89 307	87.34 195	55.94 305	75.16 298	76.53 340	63.97 238	91.16 176	65.00 225	90.97 235	88.06 235

PVSNet_BlendedMVS		78.80 210	77.84 215	81.65 198	84.43 259	63.41 194	79.49 247	90.44 137	61.70 278	75.43 294	87.07 238	69.11 220	91.44 170	60.68 257	92.24 206	90.11 212

PVSNet_Blended		76.49 235	75.40 238	79.76 220	84.43 259	63.41 194	75.14 298	90.44 137	57.36 299	75.43 294	78.30 332	69.11 220	91.44 170	60.68 257	87.70 277	84.42 277

OpenMVS	76.72 13	81.98 181	82.00 178	81.93 190	84.42 261	68.22 159	88.50 74	89.48 168	66.92 237	81.80 231	91.86 137	72.59 202	90.16 205	71.19 177	91.25 225	87.40 244

OpenMVS_ROB	70.19 17	77.77 219	77.46 217	78.71 234	84.39 262	61.15 236	81.18 225	82.52 241	62.45 272	83.34 207	87.37 235	66.20 231	88.66 237	64.69 228	85.02 302	86.32 253

0601test		78.71 212	78.51 211	79.32 227	84.32 263	58.84 257	78.38 267	85.33 222	75.99 130	82.49 218	86.57 241	58.01 266	90.02 211	62.74 239	92.73 196	89.10 224

Anonymous20240521		78.71 212	78.51 211	79.32 227	84.32 263	58.84 257	78.38 267	85.33 222	75.99 130	82.49 218	86.57 241	58.01 266	90.02 211	62.74 239	92.73 196	89.10 224

Regformer-3		85.06 114	84.67 127	86.22 97	84.27 265	73.43 114	84.07 142	85.26 224	80.77 67	88.62 125	85.48 258	80.56 102	90.39 199	81.99 77	91.04 229	94.85 81

Regformer-4		86.41 90	85.71 106	88.52 63	84.27 265	77.57 84	84.07 142	88.00 189	82.82 45	89.84 97	85.48 258	82.06 82	92.77 139	83.83 57	91.04 229	95.22 76

DELS-MVS		81.44 185	81.25 186	82.03 189	84.27 265	62.87 207	76.47 288	92.49 75	70.97 206	81.64 233	83.83 282	75.03 154	92.70 140	74.29 150	92.22 208	90.51 203
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	78.78 232	84.20 268	73.57 113	89.55 52	90.44 137	84.24 32	84.38 192	94.89 53	76.35 144	80.40 308	76.14 142	96.80 76	82.36 306
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	84.18 269	76.90 92	83.52 162	87.94 191	82.18 53	89.19 115	85.07 268	82.28 78	91.89 159	82.40 73	92.72 198	93.69 114

Regformer-2		86.74 85	86.08 101	88.73 61	84.18 269	79.20 70	83.52 162	89.33 170	83.33 38	89.92 95	85.07 268	83.23 67	93.16 124	83.39 59	92.72 198	93.83 106

EI-MVSNet-Vis-set		85.12 113	84.53 134	86.88 84	84.01 271	72.76 118	83.91 149	85.18 226	80.44 68	88.75 122	85.49 257	80.08 105	91.92 157	82.02 76	90.85 239	95.97 49

semantic-postprocess				84.34 140	83.93 272	69.66 147		81.09 253	72.43 180	86.47 162	90.19 191	57.56 272	93.15 126	77.45 131	86.39 288	90.22 208

MSDG		80.06 206	79.99 203	80.25 214	83.91 273	68.04 160	77.51 278	89.19 171	77.65 105	81.94 226	83.45 287	76.37 143	86.31 269	63.31 237	86.59 285	86.41 252

EI-MVSNet-UG-set		85.04 115	84.44 136	86.85 85	83.87 274	72.52 121	83.82 151	85.15 227	80.27 72	88.75 122	85.45 261	79.95 107	91.90 158	81.92 78	90.80 240	96.13 43

thres200		72.34 277	71.55 281	74.70 281	83.48 275	51.60 311	75.02 299	73.71 293	70.14 213	78.56 269	80.57 320	46.20 306	88.20 242	46.99 333	89.29 256	84.32 279

USDC		76.63 232	76.73 223	76.34 269	83.46 276	57.20 268	80.02 238	88.04 188	52.14 326	83.65 204	91.25 153	63.24 243	86.65 265	54.66 301	94.11 160	85.17 265

HY-MVS	64.64 18	73.03 269	72.47 273	74.71 280	83.36 277	54.19 287	82.14 206	81.96 246	56.76 304	69.57 326	86.21 252	60.03 254	84.83 286	49.58 322	82.65 318	85.11 266

EI-MVSNet		82.61 170	82.42 172	83.20 172	83.25 278	63.66 191	83.50 165	85.07 228	76.06 127	86.55 156	85.10 266	73.41 184	90.25 200	78.15 125	90.67 243	95.68 59

CVMVSNet		72.62 273	71.41 282	76.28 270	83.25 278	60.34 245	83.50 165	79.02 262	37.77 360	76.33 284	85.10 266	49.60 297	87.41 247	70.54 184	77.54 339	81.08 328

V42		83.47 160	83.37 157	83.75 155	83.16 280	63.33 197	81.31 221	90.23 152	69.51 217	90.91 78	90.81 175	74.16 168	92.29 149	80.06 103	90.22 249	95.62 61

EU-MVSNet		75.12 247	74.43 248	77.18 258	83.11 281	59.48 253	85.71 120	82.43 243	39.76 359	85.64 171	88.76 210	44.71 330	87.88 244	73.86 157	85.88 292	84.16 282

FMVSNet3		78.80 210	78.55 210	79.57 224	82.89 282	56.89 271	81.76 213	85.77 217	69.04 223	86.00 165	90.44 185	51.75 292	90.09 209	65.95 218	93.34 180	91.72 172

test1235678		65.57 315	65.73 312	65.06 327	82.84 283	50.90 318	62.90 340	69.26 326	57.17 302	72.36 312	83.04 291	46.02 308	70.10 332	32.79 357	85.24 300	74.19 342

MVS_Test		82.47 173	83.22 158	80.22 215	82.62 284	57.75 264	82.54 193	91.96 90	71.16 204	82.89 215	92.52 124	77.41 127	90.50 196	80.04 104	87.84 275	92.40 152

LF4IMVS		82.75 169	81.93 179	85.19 116	82.08 285	80.15 61	85.53 121	88.76 175	68.01 228	85.58 172	87.75 229	71.80 210	86.85 254	74.02 154	93.87 166	88.58 231

PVSNet	58.17 21	66.41 311	65.63 313	68.75 314	81.96 286	49.88 335	62.19 342	72.51 305	51.03 334	68.04 331	75.34 344	50.84 293	74.77 323	45.82 337	82.96 314	81.60 319

GA-MVS		75.83 241	74.61 244	79.48 226	81.87 287	59.25 255	73.42 310	82.88 239	68.68 225	79.75 260	81.80 313	50.62 294	89.46 216	66.85 212	85.64 294	89.72 214

MS-PatchMatch		70.93 287	70.22 290	73.06 295	81.85 288	62.50 216	73.82 308	77.90 265	52.44 323	75.92 290	81.27 317	55.67 281	81.75 303	55.37 295	77.70 337	74.94 340

Patchmatch-test1		72.75 272	72.61 270	73.19 293	81.62 289	55.86 277	78.89 263	71.37 315	61.73 276	74.93 299	82.15 309	60.46 251	81.80 302	59.68 262	82.63 320	81.92 314

FMVSNet5		72.10 279	71.69 278	73.32 290	81.57 290	53.02 297	76.77 282	78.37 264	63.31 263	76.37 283	91.85 138	36.68 354	78.98 312	47.87 329	92.45 201	87.95 238

thisisatest0515		73.00 270	70.52 286	80.46 212	81.45 291	59.90 249	73.16 312	74.31 289	57.86 296	76.08 289	77.78 333	37.60 353	92.12 153	65.00 225	91.45 216	89.35 219

CANet_DTU		77.81 218	77.05 219	80.09 216	81.37 292	59.90 249	83.26 172	88.29 183	69.16 221	67.83 333	83.72 283	60.93 248	89.47 215	69.22 194	89.70 254	90.88 191

ANet_high		83.17 164	85.68 107	75.65 276	81.24 293	45.26 344	79.94 240	92.91 61	83.83 36	91.33 72	96.88 10	80.25 104	85.92 274	68.89 198	95.89 110	95.76 54

new-patchmatchnet		70.10 293	73.37 263	60.29 339	81.23 294	16.95 367	59.54 346	74.62 285	62.93 266	80.97 239	87.93 227	62.83 244	71.90 329	55.24 296	95.01 138	92.00 163

test20.03		73.75 261	74.59 246	71.22 305	81.11 295	51.12 316	70.15 322	72.10 307	70.42 209	80.28 258	91.50 149	64.21 237	74.72 325	46.96 334	94.58 154	87.82 241

MVS		73.21 268	72.59 271	75.06 278	80.97 296	60.81 242	81.64 216	85.92 216	46.03 350	71.68 316	77.54 334	68.47 223	89.77 213	55.70 292	85.39 296	74.60 341

N_pmnet		70.20 291	68.80 298	74.38 282	80.91 297	84.81 35	59.12 349	76.45 275	55.06 309	75.31 297	82.36 308	55.74 280	54.82 360	47.02 332	87.24 281	83.52 289

IterMVS		76.91 226	76.34 230	78.64 235	80.91 297	64.03 187	76.30 289	79.03 261	64.88 257	83.11 211	89.16 205	59.90 256	84.46 288	68.61 202	85.15 301	87.42 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WTY-MVS		67.91 305	68.35 300	66.58 322	80.82 299	48.12 338	65.96 336	72.60 303	53.67 316	71.20 319	81.68 315	58.97 262	69.06 337	48.57 325	81.67 322	82.55 302

IB-MVS	62.13 19	71.64 282	68.97 296	79.66 223	80.80 300	62.26 224	73.94 306	76.90 271	63.27 264	68.63 329	76.79 338	33.83 357	91.84 160	59.28 271	87.26 280	84.88 272
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		71.85 280	71.59 279	72.62 299	80.71 301	53.78 291	69.72 324	71.71 314	58.80 290	78.03 273	80.51 321	56.61 276	78.84 313	62.20 243	86.04 291	85.23 264

ppachtmachnet_test		74.73 253	74.00 251	76.90 261	80.71 301	56.89 271	71.53 317	78.42 263	58.24 294	79.32 265	82.92 296	57.91 269	84.26 290	65.60 223	91.36 217	89.56 215

testgi		72.36 276	74.61 244	65.59 324	80.56 303	42.82 352	68.29 327	73.35 296	66.87 238	81.84 228	89.93 195	72.08 207	66.92 343	46.05 336	92.54 200	87.01 248

1314		73.22 267	72.56 272	75.20 277	80.41 304	57.84 262	81.64 216	85.36 221	51.68 329	73.10 309	76.65 339	61.45 247	85.19 281	63.54 234	79.21 334	82.59 301

diffmvs1		82.95 167	83.63 153	80.90 207	80.05 305	61.05 238	82.98 179	89.93 159	74.72 149	82.37 220	92.93 113	76.47 142	88.80 229	81.73 80	91.54 215	92.85 133

CR-MVSNet		74.00 259	73.04 266	76.85 263	79.58 306	62.64 210	82.58 190	76.90 271	50.50 339	75.72 292	92.38 125	48.07 301	84.07 291	68.72 201	82.91 316	83.85 285

RPMNet		76.06 238	75.79 233	76.85 263	79.58 306	62.64 210	82.58 190	71.75 312	74.80 147	75.72 292	92.59 120	48.69 299	84.07 291	73.48 161	82.91 316	83.85 285

UnsupCasMVSNet_bld		69.21 301	69.68 293	67.82 318	79.42 308	51.15 315	67.82 331	75.79 276	54.15 313	77.47 278	85.36 265	59.26 261	70.64 331	48.46 326	79.35 332	81.66 318

PatchT		70.52 289	72.76 268	63.79 330	79.38 309	33.53 360	77.63 276	65.37 346	73.61 160	71.77 315	92.79 118	44.38 331	75.65 322	64.53 231	85.37 297	82.18 309

Patchmtry		76.56 234	77.46 217	73.83 288	79.37 310	46.60 341	82.41 195	76.90 271	73.81 158	85.56 173	92.38 125	48.07 301	83.98 293	63.36 236	95.31 127	90.92 189

mvs_anonymous		78.13 215	78.76 208	76.23 271	79.24 311	50.31 326	78.69 265	84.82 233	61.60 279	83.09 213	92.82 115	73.89 174	87.01 250	68.33 204	86.41 287	91.37 180

MVS-HIRNet		61.16 326	62.92 321	55.87 343	79.09 312	35.34 359	71.83 315	57.98 360	46.56 348	59.05 357	91.14 160	49.95 296	76.43 318	38.74 349	71.92 349	55.84 359

diffmvs		81.78 183	82.36 173	80.02 217	79.06 313	59.93 248	83.30 170	88.41 181	73.47 162	78.38 271	92.05 134	75.85 147	88.38 239	80.73 93	89.98 252	91.76 171

MDA-MVSNet-bldmvs		77.47 220	76.90 221	79.16 229	79.03 314	64.59 182	66.58 335	75.67 278	73.15 173	88.86 119	88.99 208	66.94 228	81.23 305	64.71 227	88.22 271	91.64 175

tpm2		68.45 303	66.83 307	73.30 291	78.93 315	48.50 336	79.76 241	71.76 311	47.50 346	69.92 325	83.60 284	42.07 337	88.40 238	48.44 327	79.51 330	83.01 299

testus		62.33 320	63.03 320	60.20 340	78.78 316	40.74 353	59.14 347	69.80 325	49.26 343	71.41 317	74.72 346	52.33 291	63.52 352	29.84 359	82.01 321	76.36 337

tpm		67.95 304	68.08 303	67.55 319	78.74 317	43.53 350	75.60 294	67.10 343	54.92 310	72.23 313	88.10 223	42.87 335	75.97 320	52.21 309	80.95 328	83.15 297

tpmp4_e23		69.43 298	67.33 305	75.72 275	78.53 318	52.75 298	82.13 207	74.91 282	49.23 344	66.37 336	84.17 280	41.28 344	88.67 236	49.73 321	79.63 329	85.75 260

no-one		71.52 284	70.43 289	74.81 279	78.45 319	63.41 194	57.73 352	77.03 270	51.46 332	77.17 280	90.33 187	54.96 286	80.35 309	47.41 330	99.29 2	80.68 332

1111		61.71 322	63.77 318	55.55 345	78.05 320	25.74 364	60.62 343	67.52 336	66.09 243	74.68 300	86.50 243	16.00 369	59.22 358	38.79 347	85.65 293	81.70 316

.test1245		48.02 339	54.41 338	28.84 352	78.05 320	25.74 364	60.62 343	67.52 336	66.09 243	74.68 300	86.50 243	16.00 369	59.22 358	38.79 347	1.47 364	1.55 365

MDTV_nov1_ep13			68.29 302		78.03 322	43.87 349	74.12 305	72.22 306	52.17 324	67.02 335	85.54 256	45.36 320	80.85 306	55.73 290	84.42 308

EPNet_dtu		72.87 271	71.33 283	77.49 256	77.72 323	60.55 244	82.35 197	75.79 276	66.49 240	58.39 360	81.06 319	53.68 288	85.98 273	53.55 305	92.97 192	85.95 257
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 297	68.83 297	72.33 301	77.66 324	53.60 292	79.29 255	69.99 324	57.66 298	72.53 311	82.93 295	46.45 305	80.08 311	60.91 256	72.09 348	83.31 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sss		66.92 308	67.26 306	65.90 323	77.23 325	51.10 317	64.79 337	71.72 313	52.12 327	70.13 324	80.18 323	57.96 268	65.36 350	50.21 317	81.01 327	81.25 325

CostFormer		69.98 296	68.68 299	73.87 287	77.14 326	50.72 324	79.26 256	74.51 287	51.94 328	70.97 321	84.75 273	45.16 328	87.49 246	55.16 297	79.23 333	83.40 292

tpm cat1		66.76 309	65.21 314	71.42 304	77.09 327	50.62 325	78.01 271	73.68 294	44.89 352	68.64 327	79.00 329	45.51 318	82.42 301	49.91 319	70.15 352	81.23 327

pmmvs5		70.73 288	70.07 291	72.72 297	77.03 328	52.73 299	74.14 304	75.65 279	50.36 340	72.17 314	85.37 264	55.42 283	80.67 307	52.86 308	87.59 278	84.77 273

EPNet		80.37 200	78.41 213	86.23 96	76.75 329	73.28 115	87.18 93	77.45 268	76.24 126	68.14 330	88.93 209	65.41 235	93.85 81	69.47 190	96.12 101	91.55 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		59.08 330	56.52 335	66.76 321	76.51 330	64.39 185	49.62 359	59.00 356	43.86 354	55.66 363	68.41 354	35.55 356	68.21 339	43.25 340	76.78 341	67.69 351

UnsupCasMVSNet_eth		71.63 283	72.30 274	69.62 309	76.47 331	52.70 300	70.03 323	80.97 254	59.18 289	79.36 264	88.21 222	60.50 250	69.12 336	58.33 279	77.62 338	87.04 247

test-LLR		67.21 307	66.74 308	68.63 315	76.45 332	55.21 282	67.89 328	67.14 341	62.43 273	65.08 343	72.39 348	43.41 332	69.37 333	61.00 253	84.89 303	81.31 323

test-mter		65.00 316	63.79 317	68.63 315	76.45 332	55.21 282	67.89 328	67.14 341	50.98 335	65.08 343	72.39 348	28.27 364	69.37 333	61.00 253	84.89 303	81.31 323

gg-mvs-nofinetune		68.96 302	69.11 295	68.52 317	76.12 334	45.32 343	83.59 158	55.88 361	86.68 20	64.62 346	97.01 7	30.36 361	83.97 294	44.78 338	82.94 315	76.26 338

CMPMVS	59.41 20	75.12 247	73.57 255	79.77 219	75.84 335	67.22 164	81.21 224	82.18 244	50.78 336	76.50 282	87.66 231	55.20 284	82.99 298	62.17 245	90.64 246	89.09 226
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		54.91 336	57.14 334	48.22 349	75.83 336	17.47 366	52.31 358	69.20 327	51.66 330	60.11 353	75.40 343	29.77 363	62.62 356	27.64 360	72.37 347	64.59 352

PNet_i23d		52.13 337	51.24 339	54.79 346	75.56 337	45.26 344	54.54 355	52.55 364	66.95 236	57.19 361	65.82 357	13.15 371	63.40 353	36.39 353	39.04 362	55.71 360

wuyk23d		75.13 246	79.30 206	62.63 331	75.56 337	75.18 105	80.89 230	73.10 301	75.06 146	94.76 14	95.32 40	87.73 31	52.85 361	34.16 355	97.11 66	59.85 356

Patchmatch-test		65.91 313	67.38 304	61.48 336	75.51 339	43.21 351	68.84 325	63.79 348	62.48 271	72.80 310	83.42 288	44.89 329	59.52 357	48.27 328	86.45 286	81.70 316

new_pmnet		55.69 334	57.66 333	49.76 347	75.47 340	30.59 361	59.56 345	51.45 365	43.62 355	62.49 348	75.48 342	40.96 346	49.15 363	37.39 351	72.52 346	69.55 349

gm-plane-assit					75.42 341	44.97 347			52.17 324		72.36 350		87.90 243	54.10 303

PatchFormer-LS_test		67.91 305	66.49 311	72.17 302	75.29 342	51.85 309	75.68 292	73.62 295	57.23 301	68.64 327	68.13 356	42.19 336	82.76 299	64.06 232	73.51 345	81.89 315

MVSTER		77.09 224	75.70 236	81.25 203	75.27 343	61.08 237	77.49 279	85.07 228	60.78 284	86.55 156	88.68 212	43.14 334	90.25 200	73.69 158	90.67 243	92.42 150

PVSNet_0	51.08 22	56.10 333	54.97 337	59.48 341	75.12 344	53.28 296	55.16 353	61.89 351	44.30 353	59.16 356	62.48 360	54.22 287	65.91 348	35.40 354	47.01 360	59.25 357

test0.0.03 1		64.66 317	64.36 316	65.57 325	75.03 345	46.89 340	64.69 338	61.58 354	62.43 273	71.18 320	77.54 334	43.41 332	68.47 338	40.75 345	82.65 318	81.35 322

DWT-MVSNet_test		66.43 310	64.37 315	72.63 298	74.86 346	50.86 319	76.52 286	72.74 302	54.06 314	65.50 340	68.30 355	32.13 359	84.84 285	61.63 250	73.59 344	82.19 308

tpmvs		70.16 292	69.56 294	71.96 303	74.71 347	48.13 337	79.63 242	75.45 281	65.02 256	70.26 323	81.88 312	45.34 321	85.68 277	58.34 278	75.39 342	82.08 310

MDA-MVSNet_test_wron		70.05 295	70.44 287	68.88 313	73.84 348	53.47 293	58.93 351	67.28 339	58.43 292	87.09 148	85.40 262	59.80 258	67.25 341	59.66 263	83.54 311	85.92 258

YYNet1		70.06 294	70.44 287	68.90 312	73.76 349	53.42 295	58.99 350	67.20 340	58.42 293	87.10 147	85.39 263	59.82 257	67.32 340	59.79 261	83.50 312	85.96 256

GG-mvs-BLEND				67.16 320	73.36 350	46.54 342	84.15 141	55.04 362		58.64 359	61.95 361	29.93 362	83.87 295	38.71 350	76.92 340	71.07 347

test2356		56.69 332	55.15 336	61.32 337	73.20 351	44.11 348	54.95 354	62.52 349	48.75 345	62.45 349	68.42 353	21.10 368	65.67 349	26.86 361	78.08 336	74.19 342

JIA-IIPM		69.41 300	66.64 310	77.70 250	73.19 352	71.24 139	75.67 293	65.56 345	70.42 209	65.18 342	92.97 110	33.64 358	83.06 297	53.52 306	69.61 355	78.79 334

ADS-MVSNet2		65.87 314	63.64 319	72.55 300	73.16 353	56.92 270	67.10 333	74.81 284	49.74 341	66.04 338	82.97 293	46.71 303	77.26 315	42.29 341	69.96 353	83.46 290

ADS-MVSNet		61.90 321	62.19 323	61.03 338	73.16 353	36.42 358	67.10 333	61.75 352	49.74 341	66.04 338	82.97 293	46.71 303	63.21 354	42.29 341	69.96 353	83.46 290

DSMNet-mixed		60.98 328	61.61 325	59.09 342	72.88 355	45.05 346	74.70 301	46.61 367	26.20 362	65.34 341	90.32 188	55.46 282	63.12 355	41.72 343	81.30 326	69.09 350

tpmrst		66.28 312	66.69 309	65.05 328	72.82 356	39.33 355	78.20 270	70.69 321	53.16 319	67.88 332	80.36 322	48.18 300	74.75 324	58.13 280	70.79 350	81.08 328

TESTMET0.1,1		61.29 325	60.32 329	64.19 329	72.06 357	51.30 313	67.89 328	62.09 350	45.27 351	60.65 352	69.01 351	27.93 365	64.74 351	56.31 288	81.65 324	76.53 336

dp		60.70 329	60.29 330	61.92 334	72.04 358	38.67 357	70.83 318	64.08 347	51.28 333	60.75 351	77.28 336	36.59 355	71.58 330	47.41 330	62.34 359	75.52 339

pmmvs3		62.47 318	60.02 331	69.80 307	71.58 359	64.00 188	70.52 320	58.44 358	39.77 358	66.05 337	75.84 341	27.10 366	72.28 327	46.15 335	84.77 307	73.11 344

LP		69.42 299	68.30 301	72.77 296	71.48 360	56.84 273	73.66 309	74.84 283	63.52 261	70.95 322	83.35 289	49.55 298	77.15 317	57.13 285	70.21 351	84.33 278

EPMVS		62.47 318	62.63 322	62.01 332	70.63 361	38.74 356	74.76 300	52.86 363	53.91 315	67.71 334	80.01 324	39.40 350	66.60 345	55.54 294	68.81 357	80.68 332

E-PMN		61.59 324	61.62 324	61.49 335	66.81 362	55.40 280	53.77 356	60.34 355	66.80 239	58.90 358	65.50 358	40.48 347	66.12 347	55.72 291	86.25 289	62.95 354

testpf		58.55 331	61.58 326	49.48 348	66.03 363	40.05 354	74.40 303	58.07 359	64.72 258	59.36 355	72.67 347	22.76 367	66.92 343	67.07 210	69.15 356	41.46 361

EMVS		61.10 327	60.81 327	61.99 333	65.96 364	55.86 277	53.10 357	58.97 357	67.06 235	56.89 362	63.33 359	40.98 345	67.03 342	54.79 300	86.18 290	63.08 353

PMMVS		61.65 323	60.38 328	65.47 326	65.40 365	69.26 151	63.97 339	61.73 353	36.80 361	60.11 353	68.43 352	59.42 259	66.35 346	48.97 324	78.57 335	60.81 355

PMMVS2		55.64 335	59.27 332	44.74 350	64.30 366	12.32 368	40.60 360	49.79 366	53.19 318	65.06 345	84.81 272	53.60 289	49.76 362	32.68 358	89.41 255	72.15 345

MVE	40.22 23	51.82 338	50.47 341	55.87 343	62.66 367	51.91 305	31.61 362	39.28 368	40.65 357	50.76 364	74.98 345	56.24 278	44.67 364	33.94 356	64.11 358	71.04 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 353	32.95 368	29.49 362		21.63 371	12.07 363	37.95 365	45.07 362	30.84 360	19.21 365	17.94 363	33.06 363	23.69 362

tmp_tt		20.25 343	24.50 344	7.49 354	4.47 369	8.70 369	34.17 361	25.16 370	1.00 364	32.43 366	18.49 363	39.37 351	9.21 366	21.64 362	43.75 361	4.57 363

testmvs		5.91 347	7.65 348	0.72 356	1.20 370	0.37 371	59.14 347	0.67 373	0.49 366	1.11 367	2.76 367	0.94 373	0.24 368	1.02 365	1.47 364	1.55 365

test123		6.27 346	8.08 347	0.84 355	1.11 371	0.57 370	62.90 340	0.82 372	0.54 365	1.07 368	2.75 368	1.26 372	0.30 367	1.04 364	1.26 366	1.66 364

cdsmvs_eth3d_5k		20.81 342	27.75 343	0.00 357	0.00 372	0.00 372	0.00 363	85.44 220	0.00 367	0.00 369	82.82 297	81.46 93	0.00 369	0.00 366	0.00 367	0.00 367

pcd_1.5k_mvsjas		6.41 345	8.55 346	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	76.94 134	0.00 369	0.00 366	0.00 367	0.00 367

sosnet-low-res		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sosnet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uncertanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

Regformer		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

ab-mvs-re		6.65 344	8.87 345	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	79.80 326	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

GSMVS																83.88 283

test_part1				0.00 357		0.00 372	0.00 363	93.93 26				0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sam_mvs1												46.11 307				83.88 283

sam_mvs												45.92 312

MTGPA								91.81 94

test_post1							78.85 264				3.13 365	45.19 323	80.13 310	58.11 281

test_post											3.10 366	45.43 319	77.22 316

patchmatchnet-post											81.71 314	45.93 311	87.01 250

MTMP							90.66 35	33.14 369

test9_res														80.83 90	96.45 87	90.57 200

agg_prior2														79.68 108	96.16 97	90.22 208

test_prior4						78.97 72	84.59 133

test_prior2							83.37 168		75.43 141	84.58 188	91.57 146	81.92 88		79.54 110	96.97 69

旧先验2							81.73 214		56.88 303	86.54 161			84.90 284	72.81 169

新几何2							81.72 215

无先验							82.81 184	85.62 219	58.09 295				91.41 172	67.95 207		84.48 275

原ACMM2							82.26 202

testdata2													86.43 268	63.52 235

segment_acmp												81.94 85

testdata1							79.62 243		73.95 157

plane_prior5								93.61 33					95.22 39	80.78 91	95.83 113	94.46 88

plane_prior4											92.95 111

plane_prior3						76.85 93			77.79 103	86.55 156

plane_prior2							89.45 57		79.44 81

plane_prior						76.42 98	87.15 94		75.94 133						95.03 137

n2								0.00 374

nn								0.00 374

door-mid								74.45 288

test11								91.46 107

door								72.57 304

HQP5-MVS						70.66 142

BP-MVS														77.30 133

HQP4-MVS										80.56 253			94.61 55			93.56 119

HQP3-MVS								92.68 69							94.47 156

HQP2-MVS												72.10 205

MDTV_nov1_ep13_2view						27.60 363	70.76 319		46.47 349	61.27 350		45.20 322		49.18 323		83.75 287

ACMMP++_ref															95.74 117

ACMMP++															97.35 60

Test By Simon												79.09 111

High-resolution multi-view benchmark