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	89.57 164	88.51 15	90.11 84	95.12 48	90.98 8	88.92 224	77.55 127	97.07 69	83.13 293
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 92	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 92	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	58.88 252	88.95 65	93.19 48	91.62 4	96.01 6	96.16 22	87.02 38	95.60 23	78.69 117	98.72 10	98.97 3

PS-CasMVS		90.06 35	91.92 9	84.47 134	96.56 6	58.83 253	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	84.21 143	96.51 7	57.84 256	88.93 67	92.84 64	91.92 2	96.16 3	96.23 20	86.95 39	95.99 7	79.05 114	98.57 16	98.80 6

CP-MVSNet		89.27 54	90.91 34	84.37 138	96.34 8	58.61 255	88.66 72	92.06 84	90.78 6	95.67 9	95.17 46	81.80 90	95.54 27	79.00 115	98.69 11	98.95 4

WR-MVS_H		89.91 44	91.31 25	85.71 110	96.32 9	62.39 215	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	75.79 133	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	77.65 105	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	91.01 125	78.43 96	89.16 115	92.25 129	72.03 208	96.36 2	88.21 9	90.93 231	90.55 200

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	91.19 121	67.85 228	86.63 153	94.84 55	79.58 109	95.96 10	87.62 15	94.50 155	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 71	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	16.05 358	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	77.00 120	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	75.21 142	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	81.25 201	95.13 29	59.32 249	85.42 122	81.11 247	86.41 22	87.41 141	96.21 21	73.61 178	90.61 193	66.33 214	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 76	87.50 140	92.38 123	81.42 94	93.28 119	83.07 63	97.24 65	91.67 170

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 84	93.83 28	93.60 97	90.81 9	92.96 132	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	91.27 118	78.20 99	86.69 152	92.28 128	80.36 103	95.06 46	86.17 32	96.49 87	90.22 206

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 77	92.09 58	93.89 93	83.80 62	93.10 127	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	86.76 102	92.78 66	78.78 91	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	64.47 182	90.13 39	92.97 58	91.68 3	95.35 12	98.79 2	93.19 3	91.76 162	71.67 175	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 98	93.91 27	80.07 74	86.75 151	93.26 101	93.64 2	90.93 181	84.60 45	90.75 236	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 88	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	85.91 114	93.60 34	80.16 73	89.13 116	93.44 98	83.82 61	90.98 179	83.86 55	95.30 129	93.60 116

MIMVSNet1		83.63 155	84.59 132	80.74 206	94.06 43	62.77 206	82.72 184	84.53 229	77.57 107	90.34 82	95.92 27	76.88 140	85.83 271	61.88 241	97.42 61	93.62 115

TranMVSNet+NR-MVSNet		87.86 69	88.76 62	85.18 116	94.02 44	64.13 184	84.38 136	91.29 117	84.88 27	92.06 59	93.84 94	86.45 45	93.73 84	73.22 161	98.66 12	97.69 12

新几何1				82.95 174	93.96 45	78.56 76		80.24 251	55.45 301	83.93 198	91.08 161	71.19 213	88.33 234	65.84 219	93.07 188	81.95 308

1121		80.86 190	79.81 201	84.02 146	93.93 46	78.70 75	81.64 213	80.18 252	55.43 302	83.67 200	91.15 156	71.29 212	91.41 171	67.95 205	93.06 189	81.96 307

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	91.48 176

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	93.16 124	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	73.53 157	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	90.63 130	70.00 209	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 81	86.55 154	92.95 110	74.84 156	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	81.06 204	93.43 56	54.88 279	78.67 263	85.02 225	81.24 62	90.74 80	91.56 145	72.85 194	91.08 177	68.00 203	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	90.29 146	69.27 213	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 87	70.73 202	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	79.38 251	77.79 261	52.59 315	82.36 215	90.84 171	66.83 228			91.69 210	81.25 320

DU-MVS		86.80 84	86.99 84	86.21 99	93.24 60	67.02 166	83.16 173	92.21 80	81.73 58	90.92 76	91.97 132	77.20 128	93.99 74	74.16 150	98.35 24	97.61 13

NR-MVSNet		86.00 100	86.22 98	85.34 114	93.24 60	64.56 181	82.21 199	90.46 134	80.99 65	88.42 128	91.97 132	77.56 124	93.85 81	72.46 169	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 64	95.32 13	97.24 6	72.94 193	94.85 51	85.07 37	97.78 47	97.26 23

pcd1.5k->3k		38.83 334	41.11 335	32.01 345	93.13 63	0.00 366	0.00 357	91.38 115	0.00 361	0.00 362	0.00 363	89.24 16	0.00 364	0.00 361	96.24 96	96.02 49

UniMVSNet (Re)		86.87 81	86.98 85	86.55 89	93.11 64	68.48 157	83.80 151	92.87 61	80.37 69	89.61 107	91.81 139	77.72 122	94.18 67	75.00 147	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	78.35 119	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	86.65 107	90.62 131	54.66 305	81.46 228	90.81 172	76.98 133	94.38 61	72.62 168	96.18 97	90.82 192

testdata				79.54 221	92.87 69	72.34 123		80.14 253	59.91 284	85.47 173	91.75 141	67.96 224	85.24 275	68.57 201	92.18 207	81.06 325

CNVR-MVS		87.81 71	87.68 74	88.21 67	92.87 69	77.30 89	85.25 123	91.23 119	77.31 115	87.07 147	91.47 147	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	67.02 166	82.55 188	91.56 98	83.08 41	90.92 76	91.82 138	78.25 118	93.99 74	74.16 150	98.35 24	97.49 16

plane_prior1					92.83 72

原ACMM1				84.60 130	92.81 73	74.01 111		91.50 100	62.59 265	82.73 214	90.67 177	76.53 141	94.25 64	69.24 191	95.69 119	85.55 256

plane_prior6					92.61 74	76.54 95						74.84 156

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 66	91.13 73	93.19 102	86.22 49	95.97 9	82.23 74	97.18 67	90.45 202
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	84.01 142	89.44 166	88.63 14	94.38 21	95.77 29	86.38 47	93.59 94	79.84 105	95.21 130	91.82 167

SixPastTwentyTwo		87.20 77	87.45 77	86.45 91	92.52 77	69.19 154	87.84 83	88.05 183	81.66 59	94.64 17	96.53 14	65.94 232	94.75 53	83.02 65	96.83 76	95.41 68

ACMH	76.49 14	89.34 53	91.14 28	83.96 149	92.50 78	70.36 144	89.55 54	93.84 29	81.89 57	94.70 16	95.44 40	90.69 10	88.31 235	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		80.25 198	81.68 177	75.94 268	92.46 79	47.98 333	76.70 278	81.67 245	73.45 158	84.87 178	92.82 113	74.66 161	86.51 261	61.66 244	96.85 74	93.33 121

agg_prior3		85.76 106	84.95 119	88.16 68	92.43 80	79.92 62	83.98 143	90.03 154	65.11 250	83.66 201	90.64 180	81.00 97	93.67 86	81.21 83	96.54 84	90.88 189

F-COLMAP		84.97 118	83.42 153	89.63 50	92.39 81	83.40 45	88.83 68	91.92 89	73.19 167	80.18 253	89.15 203	77.04 132	93.28 119	65.82 220	92.28 202	92.21 158

test_djsdf		89.62 48	89.01 55	91.45 21	92.36 82	82.98 49	91.98 25	90.08 152	71.54 196	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	83.94 144	90.32 139	65.41 248	84.49 188	90.97 165	82.03 84	93.63 89

train_agg		85.98 103	85.28 113	88.07 70	92.34 83	79.70 66	83.94 144	90.32 139	65.79 241	84.49 188	90.97 165	81.93 86	93.63 89	81.21 83	96.54 84	90.88 189

NCCC		87.36 74	86.87 88	88.83 59	92.32 85	78.84 74	86.58 109	91.09 123	78.77 92	84.85 179	90.89 169	80.85 98	95.29 36	81.14 85	95.32 126	92.34 152

FC-MVSNet-test		85.93 104	87.05 83	82.58 180	92.25 86	56.44 268	85.75 117	93.09 51	77.33 114	91.94 63	94.65 61	74.78 158	93.41 113	75.11 145	98.58 15	97.88 10

CDPH-MVS		86.17 99	85.54 109	88.05 73	92.25 86	75.45 103	83.85 148	92.01 85	65.91 240	86.19 161	91.75 141	83.77 63	94.98 48	77.43 130	96.71 79	93.73 111

pmmvs6		86.52 90	88.06 67	81.90 189	92.22 88	62.28 221	84.66 131	89.15 169	83.54 36	89.85 95	97.32 4	88.08 28	86.80 256	70.43 184	97.30 64	96.62 38

EG-PatchMatch MVS		84.08 145	84.11 145	83.98 148	92.22 88	72.61 120	82.20 201	87.02 203	72.63 174	88.86 118	91.02 163	78.52 114	91.11 176	73.41 160	91.09 222	88.21 228

test_8					92.09 90	78.87 73	83.82 149	90.31 141	65.79 241	84.36 191	90.96 167	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 89	92.09 83	78.87 90	84.27 196	94.05 83	78.35 117	93.65 87	80.54 95	91.58 212	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	66.87 168	91.21 33	88.64 174	86.30 23	89.60 108	92.59 118	69.22 218	94.91 50	73.89 154	97.89 44	96.72 36

旧先验1					91.97 93	71.77 132		81.78 244			91.84 136	73.92 171			93.65 174	83.61 283

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					90.17 189

OMC-MVS		88.19 65	87.52 75	90.19 44	91.94 96	81.68 53	87.49 88	93.17 49	76.02 129	88.64 123	91.22 151	84.24 60	93.37 114	77.97 125	97.03 70	95.52 66

FIs		85.35 111	86.27 97	82.60 179	91.86 97	57.31 260	85.10 125	93.05 53	75.83 132	91.02 75	93.97 86	73.57 179	92.91 136	73.97 153	98.02 39	97.58 15

MSLP-MVS++		85.00 117	86.03 102	81.90 189	91.84 98	71.56 138	86.75 103	93.02 57	75.95 130	87.12 144	89.39 200	77.98 119	89.40 215	77.46 128	94.78 147	84.75 269

Anonymous20240521		87.68 73	88.61 63	84.87 121	91.76 99	64.76 179	89.28 62	91.66 96	83.02 42	93.29 35	96.10 24	77.37 127	92.89 137	77.27 133	97.75 49	96.97 35

DP-MVS Recon		84.05 146	83.22 156	86.52 90	91.73 100	75.27 104	83.23 172	92.40 75	72.04 184	82.04 219	88.33 217	77.91 121	93.95 79	66.17 215	95.12 134	90.34 205

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 89	92.88 41	92.29 127	86.11 50	90.22 201	86.24 31	97.24 65	91.36 179

AllTest		87.97 68	87.40 79	89.68 48	91.59 102	83.40 45	89.50 57	95.44 5	79.47 79	88.00 133	93.03 106	82.66 74	91.47 167	70.81 177	96.14 100	94.16 96

TestCases				89.68 48	91.59 102	83.40 45		95.44 5	79.47 79	88.00 133	93.03 106	82.66 74	91.47 167	70.81 177	96.14 100	94.16 96

MCST-MVS		84.36 133	83.93 150	85.63 111	91.59 102	71.58 137	83.52 160	92.13 82	61.82 271	83.96 197	89.75 195	79.93 108	93.46 109	78.33 120	94.34 158	91.87 166

agg_prior1		85.72 107	85.20 114	87.28 83	91.58 105	77.69 82	83.69 155	90.30 143	66.29 236	84.32 192	91.07 162	82.13 81	93.18 122	81.02 86	96.36 90	90.98 184

agg_prior					91.58 105	77.69 82		90.30 143		84.32 192			93.18 122

PVSNet_Blended_VisFu		81.55 181	80.49 193	84.70 128	91.58 105	73.24 116	84.21 137	91.67 95	62.86 263	80.94 234	87.16 233	67.27 226	92.87 138	69.82 187	88.94 255	87.99 232

EPP-MVSNet		85.47 110	85.04 116	86.77 86	91.52 108	69.37 149	91.63 28	87.98 186	81.51 61	87.05 148	91.83 137	66.18 231	95.29 36	70.75 179	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	83.41 165	92.38 77	70.25 207	89.35 113	90.68 176	82.85 72	94.57 58	79.55 108	95.95 108	92.00 161

Baseline_NR-MVSNet		84.00 148	85.90 103	78.29 235	91.47 110	53.44 288	82.29 195	87.00 204	79.06 87	89.55 109	95.72 31	77.20 128	86.14 267	72.30 170	98.51 18	95.28 71

HyFIR lowres test		75.12 242	72.66 264	82.50 183	91.44 111	65.19 176	72.47 307	87.31 193	46.79 341	80.29 251	84.30 273	52.70 287	92.10 153	51.88 310	86.73 278	90.22 206

DP-MVS		88.60 62	89.01 55	87.36 82	91.30 112	77.50 85	87.55 86	92.97 58	87.95 17	89.62 105	92.87 112	84.56 57	93.89 80	77.65 126	96.62 81	90.70 194

DeepC-MVS_fast	80.27 8	86.23 97	85.65 108	87.96 74	91.30 112	76.92 91	87.19 92	91.99 86	70.56 203	84.96 175	90.69 175	80.01 106	95.14 43	78.37 118	95.78 116	91.82 167
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		84.77 127		73.30 163	80.55 248

ACMP_Plane					91.19 115		84.77 127		73.30 163	80.55 248

HQP-MVS		84.61 124	84.06 146	86.27 95	91.19 115	70.66 142	84.77 127	92.68 68	73.30 163	80.55 248	90.17 189	72.10 204	94.61 56	77.30 131	94.47 156	93.56 118

VDD-MVS		84.23 139	84.58 133	83.20 170	91.17 118	65.16 177	83.25 170	84.97 227	79.79 75	87.18 143	94.27 75	74.77 159	90.89 184	69.24 191	96.54 84	93.55 120

K. test v3		85.14 112	84.73 122	86.37 92	91.13 119	69.63 148	85.45 121	76.68 269	84.06 34	92.44 54	96.99 8	62.03 244	94.65 55	80.58 94	93.24 186	94.83 81

lessismore_v0				85.95 104	91.10 120	70.99 141		70.91 313		91.79 64	94.42 69	61.76 245	92.93 134	79.52 111	93.03 190	93.93 103

TransMVSNet (Re)		84.02 147	85.74 105	78.85 225	91.00 121	55.20 278	82.29 195	87.26 194	79.65 78	88.38 130	95.52 38	83.00 70	86.88 248	67.97 204	96.60 82	94.45 89

wuykxyi23d		88.46 63	88.80 61	87.44 81	90.96 122	93.03 1	85.85 116	81.96 241	74.58 147	98.58 2	97.29 5	87.73 31	87.31 243	82.84 68	99.41 1	81.99 306

PAPM_NR		83.23 161	83.19 158	83.33 168	90.90 123	65.98 172	88.19 76	90.78 127	78.13 101	80.87 236	87.92 225	73.49 181	92.42 146	70.07 185	88.40 259	91.60 172

CSCG		86.26 95	86.47 94	85.60 112	90.87 124	74.26 110	87.98 78	91.85 90	80.35 70	89.54 111	88.01 221	79.09 111	92.13 151	75.51 142	95.06 136	90.41 203

PLC	73.85 16	82.09 176	80.31 194	87.45 80	90.86 125	80.29 60	85.88 115	90.65 129	68.17 222	76.32 278	86.33 244	73.12 191	92.61 144	61.40 247	90.02 246	89.44 215
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		90.69 128		82.76 213		79.20 110	94.80 52		95.32 126	92.27 155

ITE_SJBPF				90.11 45	90.72 127	84.97 33		90.30 143	81.56 60	90.02 87	91.20 154	82.40 78	90.81 186	73.58 157	94.66 152	94.56 82

TAMVS		78.08 211	76.36 224	83.23 169	90.62 128	72.87 117	79.08 258	80.01 254	61.72 273	81.35 230	86.92 236	63.96 238	88.78 229	50.61 311	93.01 191	88.04 231

test_prior3		86.31 94	86.31 96	86.32 93	90.59 129	71.99 130	83.37 166	92.85 62	75.43 139	84.58 186	91.57 143	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				82.98 173	90.55 131	64.86 178	88.20 75	89.15 169		89.40 112	93.96 88	71.67 211	91.38 173	78.83 116	96.55 83	92.71 136

Test_1112_low_res		73.90 255	73.08 260	76.35 262	90.35 132	55.95 269	73.40 306	86.17 210	50.70 331	73.14 301	85.94 249	58.31 264	85.90 270	56.51 282	83.22 308	87.20 241

VPA-MVSNet		83.47 159	84.73 122	79.69 217	90.29 133	57.52 259	81.30 220	88.69 173	76.29 125	87.58 138	94.44 68	80.60 101	87.20 244	66.60 213	96.82 77	94.34 92

FMVSNet1		84.55 126	85.45 111	81.85 192	90.27 134	61.05 236	86.83 99	88.27 180	78.57 95	89.66 101	95.64 34	75.43 147	90.68 190	69.09 194	95.33 125	93.82 107

MVS_111021_HR		84.63 123	84.34 143	85.49 113	90.18 135	75.86 102	79.23 257	87.13 199	73.35 160	85.56 171	89.34 201	83.60 65	90.50 195	76.64 137	94.05 162	90.09 211

RPSCF		88.00 67	86.93 87	91.22 27	90.08 136	89.30 6	89.68 49	91.11 122	79.26 83	89.68 100	94.81 59	82.44 77	87.74 240	76.54 138	88.74 258	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	74.04 152	92.70 49	94.66 60	85.88 52	91.50 166	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	91.48 102	84.36 30	94.19 24	95.98 26	82.52 76	92.67 142	84.30 48	96.67 80	97.37 20

AdaColmap		83.66 154	83.69 152	83.57 162	90.05 139	72.26 125	86.29 113	90.00 155	78.19 100	81.65 226	87.16 233	83.40 67	94.24 65	61.69 243	94.76 150	84.21 276

pm-mvs1		83.69 153	84.95 119	79.91 213	90.04 140	59.66 246	82.43 190	87.44 191	75.52 138	87.85 135	95.26 44	81.25 96	85.65 273	68.74 198	96.04 105	94.42 90

CHOSEN 1792x2688		72.45 269	70.56 280	78.13 237	90.02 141	63.08 200	68.72 320	83.16 232	42.99 350	75.92 282	85.46 255	57.22 272	85.18 277	49.87 315	81.67 317	86.14 250

anonymousdsp		89.73 47	88.88 58	92.27 7	89.82 142	86.67 12	90.51 36	90.20 151	69.87 210	95.06 14	96.14 23	84.28 59	93.07 131	87.68 14	96.34 91	97.09 30

1112_ss		74.82 247	73.74 247	78.04 239	89.57 143	60.04 244	76.49 282	87.09 202	54.31 306	73.66 299	79.80 321	60.25 252	86.76 258	58.37 272	84.15 304	87.32 240

PCF-MVS	74.62 15	82.15 175	80.92 189	85.84 108	89.43 144	72.30 124	80.53 230	91.82 91	57.36 293	87.81 136	89.92 192	77.67 123	93.63 89	58.69 267	95.08 135	91.58 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		75.81 237	73.51 257	82.71 178	89.35 145	73.62 112	80.06 233	85.20 220	60.30 282	73.96 297	87.94 223	57.89 267	89.45 213	52.02 305	74.87 338	85.06 262
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	84.54 134	88.77 171	79.09 86	83.54 204	88.66 210	74.87 155	81.73 299	66.84 211	92.29 201	89.11 220

TSAR-MVS + GP.		83.95 149	82.69 163	87.72 75	89.27 147	81.45 55	83.72 154	81.58 246	74.73 146	85.66 168	86.06 248	72.56 202	92.69 141	75.44 143	95.21 130	89.01 224

MVS_111021_LR		84.28 137	83.76 151	85.83 109	89.23 148	83.07 48	80.99 226	83.56 231	72.71 173	86.07 162	89.07 204	81.75 91	86.19 266	77.11 134	93.36 179	88.24 227

LFMVS		80.15 201	80.56 191	78.89 224	89.19 149	55.93 270	85.22 124	73.78 285	82.96 43	84.28 195	92.72 117	57.38 270	90.07 208	63.80 230	95.75 117	90.68 195

CLD-MVS		83.18 162	82.64 164	84.79 124	89.05 150	67.82 163	77.93 268	92.52 73	68.33 221	85.07 174	81.54 311	82.06 82	92.96 132	69.35 190	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		77.32 217	75.40 233	83.06 172	89.00 152	72.48 122	77.90 269	82.17 240	60.81 279	78.94 262	83.49 281	59.30 259	88.76 230	54.64 297	92.37 200	87.93 234
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpnnormal		81.79 180	82.95 160	78.31 234	88.93 153	55.40 274	80.83 229	82.85 235	76.81 121	85.90 166	94.14 81	74.58 163	86.51 261	66.82 212	95.68 120	93.01 129

v13		87.31 75	88.10 66	84.94 118	88.84 154	63.75 188	87.85 82	91.47 105	79.12 85	93.72 29	95.82 28	75.20 150	93.58 97	84.76 43	96.16 98	97.48 17

Vis-MVSNet (Re-imp)		77.82 212	77.79 211	77.92 241	88.82 155	51.29 308	83.28 168	71.97 301	74.04 152	82.23 216	89.78 194	57.38 270	89.41 214	57.22 279	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 94	93.50 35	69.17 215	85.80 167	89.56 198	80.76 99	92.13 151	73.21 165	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	63.52 191	87.58 85	91.46 106	78.74 93	93.57 32	95.66 32	74.94 154	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	94.83 145	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	94.83 145	98.23 8

FPMVS		72.29 272	72.00 271	73.14 288	88.63 160	85.00 32	74.65 297	67.39 331	71.94 192	77.80 269	87.66 228	50.48 292	75.83 316	49.95 313	79.51 325	58.58 353

BH-untuned		80.96 189	80.99 187	80.84 205	88.55 161	68.23 158	80.33 232	88.46 175	72.79 172	86.55 154	86.76 237	74.72 160	91.77 161	61.79 242	88.99 254	82.52 299

V9		86.96 79	87.70 73	84.74 126	88.52 162	63.27 197	87.31 90	91.45 108	78.28 98	93.43 33	95.45 39	74.59 162	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	62.60 211	87.21 91	91.63 97	78.08 102	93.40 34	95.56 37	75.07 151	93.57 98	84.46 47	96.08 103	97.36 21

Anonymous202405211		80.51 195	81.19 184	78.49 232	88.48 164	57.26 261	76.63 279	82.49 237	81.21 63	84.30 194	92.24 130	67.99 223	86.24 265	62.22 237	95.13 133	91.98 164

ab-mvs		79.67 203	80.56 191	76.99 253	88.48 164	56.93 263	84.70 130	86.06 211	68.95 219	80.78 237	93.08 104	75.30 149	84.62 282	56.78 281	90.90 232	89.43 216

PHI-MVS		86.38 92	85.81 104	88.08 69	88.44 166	77.34 87	89.35 61	93.05 53	73.15 168	84.76 180	87.70 227	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	88.35 167	63.00 202	87.02 96	91.42 111	77.78 104	93.27 36	95.23 45	74.22 165	93.56 101	83.95 53	95.93 109	97.31 22

xiu_mvs_v1_base_debu		80.84 191	80.14 197	82.93 175	88.31 168	71.73 133	79.53 241	87.17 196	65.43 245	79.59 255	82.73 294	76.94 134	90.14 204	73.22 161	88.33 260	86.90 244

xiu_mvs_v1_base		80.84 191	80.14 197	82.93 175	88.31 168	71.73 133	79.53 241	87.17 196	65.43 245	79.59 255	82.73 294	76.94 134	90.14 204	73.22 161	88.33 260	86.90 244

xiu_mvs_v1_base_debi		80.84 191	80.14 197	82.93 175	88.31 168	71.73 133	79.53 241	87.17 196	65.43 245	79.59 255	82.73 294	76.94 134	90.14 204	73.22 161	88.33 260	86.90 244

MG-MVS		80.32 197	80.94 188	78.47 233	88.18 171	52.62 295	82.29 195	85.01 226	72.01 185	79.24 260	92.54 121	69.36 217	93.36 115	70.65 181	89.19 253	89.45 214

PM-MVS		80.20 200	79.00 204	83.78 153	88.17 172	86.66 13	81.31 218	66.81 337	69.64 211	88.33 131	90.19 187	64.58 235	83.63 291	71.99 174	90.03 245	81.06 325

v10		86.54 89	87.10 81	84.84 122	88.16 173	63.28 196	86.64 108	92.20 81	75.42 141	92.81 46	94.50 66	74.05 168	94.06 73	83.88 54	96.28 93	97.17 28

v15		86.56 88	87.25 80	84.51 131	88.15 174	62.72 207	86.72 106	91.40 113	77.38 109	93.11 38	95.00 50	73.93 170	93.55 102	83.67 57	95.86 113	97.26 23

v7		84.81 120	85.00 117	84.23 142	88.15 174	63.27 197	83.79 152	91.39 114	71.10 200	90.07 85	91.28 149	74.04 169	93.63 89	81.48 82	93.67 173	95.79 53

v17		86.32 93	86.95 86	84.44 135	88.00 176	62.62 210	86.74 104	91.48 102	77.17 117	92.74 47	94.56 62	73.74 174	93.53 103	83.27 60	94.87 144	97.18 27

canonicalmvs		85.50 109	86.14 100	83.58 161	87.97 177	67.13 165	87.55 86	94.32 12	73.44 159	88.47 127	87.54 230	86.45 45	91.06 178	75.76 141	93.76 170	92.54 146

v16		86.24 96	86.85 89	84.43 136	87.96 178	62.59 212	86.73 105	91.48 102	77.17 117	92.67 50	94.55 63	73.63 175	93.52 104	83.26 61	94.16 159	97.17 28

VNet		79.31 204	80.27 195	76.44 261	87.92 179	53.95 283	75.58 290	84.35 230	74.39 150	82.23 216	90.72 174	72.84 195	84.39 284	60.38 254	93.98 164	90.97 185

view600		76.79 222	76.54 219	77.56 245	87.91 180	50.77 314	81.92 205	71.35 309	77.38 109	84.62 182	88.40 213	45.18 321	89.26 217	58.58 268	93.49 175	92.66 138

view800		76.79 222	76.54 219	77.56 245	87.91 180	50.77 314	81.92 205	71.35 309	77.38 109	84.62 182	88.40 213	45.18 321	89.26 217	58.58 268	93.49 175	92.66 138

conf0.05thres1000		76.79 222	76.54 219	77.56 245	87.91 180	50.77 314	81.92 205	71.35 309	77.38 109	84.62 182	88.40 213	45.18 321	89.26 217	58.58 268	93.49 175	92.66 138

tfpn		76.79 222	76.54 219	77.56 245	87.91 180	50.77 314	81.92 205	71.35 309	77.38 109	84.62 182	88.40 213	45.18 321	89.26 217	58.58 268	93.49 175	92.66 138

v8		86.22 98	86.83 90	84.36 139	87.82 184	62.35 216	86.42 111	91.33 116	76.78 122	92.73 48	94.48 67	73.41 182	93.72 85	83.10 62	95.41 123	97.01 33

v1neww		84.43 130	84.66 128	83.75 154	87.81 185	62.34 217	83.59 156	90.27 147	72.33 179	89.93 92	91.22 151	73.28 186	93.29 116	80.25 100	93.25 184	95.62 61

v7new		84.43 130	84.66 128	83.75 154	87.81 185	62.34 217	83.59 156	90.27 147	72.33 179	89.93 92	91.22 151	73.28 186	93.29 116	80.25 100	93.25 184	95.62 61

v6		84.43 130	84.66 128	83.75 154	87.81 185	62.34 217	83.59 156	90.26 149	72.33 179	89.94 91	91.19 155	73.30 185	93.29 116	80.26 99	93.26 183	95.62 61

alignmvs		83.94 150	83.98 148	83.80 151	87.80 188	67.88 162	84.54 134	91.42 111	73.27 166	88.41 129	87.96 222	72.33 203	90.83 185	76.02 140	94.11 160	92.69 137

v1192		84.57 125	84.69 126	84.21 143	87.75 189	62.88 204	83.02 175	91.43 109	69.08 217	89.98 90	90.89 169	72.70 198	93.62 93	82.41 71	94.97 139	96.13 44

v18		85.99 102	86.55 93	84.30 141	87.73 190	62.29 220	86.40 112	91.49 101	76.64 123	92.40 55	94.20 79	73.28 186	93.52 104	82.87 66	93.99 163	97.09 30

PatchMatch-RL		74.48 249	73.22 259	78.27 236	87.70 191	85.26 30	75.92 286	70.09 316	64.34 255	76.09 281	81.25 313	65.87 233	78.07 309	53.86 299	83.82 305	71.48 341

v1144		84.54 128	84.72 124	84.00 147	87.67 192	62.55 213	82.97 176	90.93 126	70.32 206	89.80 97	90.99 164	73.50 180	93.48 108	81.69 81	94.65 153	95.97 50

v1240		84.30 136	84.51 135	83.65 159	87.65 193	61.26 233	82.85 179	91.54 99	67.94 226	90.68 81	90.65 178	71.71 210	93.64 88	82.84 68	94.78 147	96.07 46

v1921920		84.23 139	84.37 142	83.79 152	87.64 194	61.71 224	82.91 178	91.20 120	67.94 226	90.06 86	90.34 183	72.04 207	93.59 94	82.32 73	94.91 140	96.07 46

v144192		84.24 138	84.41 137	83.71 158	87.59 195	61.57 229	82.95 177	91.03 124	67.82 229	89.80 97	90.49 181	73.28 186	93.51 107	81.88 80	94.89 141	96.04 48

Fast-Effi-MVS+		81.04 188	80.57 190	82.46 184	87.50 196	63.22 199	78.37 264	89.63 162	68.01 223	81.87 221	82.08 306	82.31 79	92.65 143	67.10 207	88.30 264	91.51 175

pmmvs-eth3d		78.42 209	77.04 215	82.57 182	87.44 197	74.41 109	80.86 228	79.67 255	55.68 300	84.69 181	90.31 186	60.91 248	85.42 274	62.20 238	91.59 211	87.88 235

MVS_0304		84.88 119	83.96 149	87.64 78	87.43 198	74.83 106	84.18 138	93.30 43	77.48 108	77.39 272	88.46 212	74.53 164	95.74 19	78.09 124	94.75 151	92.36 151

IterMVS-LS		84.73 122	84.98 118	83.96 149	87.35 199	63.66 189	83.25 170	89.88 157	76.06 127	89.62 105	92.37 126	73.40 184	92.52 145	78.16 122	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	83.52 164	87.33 200	61.71 224	82.79 181	89.73 160	71.89 195	89.64 102	91.11 160	72.72 196	93.10 127	80.40 96	93.79 169	95.75 55

v1141		84.16 141	84.38 139	83.52 164	87.32 201	61.70 226	82.79 181	89.74 158	71.90 193	89.64 102	91.12 158	72.68 199	93.10 127	80.39 98	93.80 168	95.75 55

divwei89l23v2f112		84.16 141	84.38 139	83.52 164	87.32 201	61.70 226	82.79 181	89.74 158	71.90 193	89.64 102	91.12 158	72.68 199	93.10 127	80.40 96	93.81 167	95.75 55

tfpn111		76.03 234	75.53 232	77.53 249	87.27 203	51.88 300	81.07 223	73.26 290	75.68 134	83.25 206	86.37 241	45.54 311	89.38 216	55.07 293	92.26 203	91.34 180

conf200view11		75.62 238	75.05 236	77.34 251	87.27 203	51.88 300	81.07 223	73.26 290	75.68 134	83.25 206	86.37 241	45.54 311	88.80 225	51.98 306	90.99 226	91.34 180

thres100view900		75.45 239	75.05 236	76.66 260	87.27 203	51.88 300	81.07 223	73.26 290	75.68 134	83.25 206	86.37 241	45.54 311	88.80 225	51.98 306	90.99 226	89.31 217

MIMVSNet		71.09 280	71.59 274	69.57 304	87.23 206	50.07 322	78.91 259	71.83 303	60.20 283	71.26 311	91.76 140	55.08 282	76.09 314	41.06 339	87.02 277	82.54 298

Effi-MVS+		83.90 151	84.01 147	83.57 162	87.22 207	65.61 175	86.55 110	92.40 75	78.64 94	81.34 231	84.18 274	83.65 64	92.93 134	74.22 149	87.87 268	92.17 159

BH-RMVSNet		80.53 194	80.22 196	81.49 197	87.19 208	66.21 171	77.79 270	86.23 209	74.21 151	83.69 199	88.50 211	73.25 190	90.75 187	63.18 235	87.90 267	87.52 237

Effi-MVS+-dtu		85.82 105	83.38 154	93.14 3	87.13 209	91.15 3	87.70 84	88.42 176	74.57 148	83.56 203	85.65 250	78.49 115	94.21 66	72.04 172	92.88 193	94.05 99

mvs-test1		84.55 126	82.12 172	91.84 17	87.13 209	89.54 5	85.05 126	88.42 176	74.57 148	80.60 245	82.98 287	78.49 115	93.98 76	72.04 172	89.77 247	92.00 161

v2v482		84.09 144	84.24 144	83.62 160	87.13 209	61.40 230	82.71 185	89.71 161	72.19 183	89.55 109	91.41 148	70.70 215	93.20 121	81.02 86	93.76 170	96.25 42

jason		77.42 216	75.75 230	82.43 185	87.10 212	69.27 150	77.99 267	81.94 243	51.47 325	77.84 267	85.07 263	60.32 251	89.00 222	70.74 180	89.27 252	89.03 222
jason: jason.
PS-MVSNAJ		77.04 220	76.53 223	78.56 230	87.09 213	61.40 230	75.26 292	87.13 199	61.25 276	74.38 296	77.22 331	76.94 134	90.94 180	64.63 226	84.83 300	83.35 288

xiu_mvs_v2_base		77.19 218	76.75 217	78.52 231	87.01 214	61.30 232	75.55 291	87.12 201	61.24 277	74.45 294	78.79 325	77.20 128	90.93 181	64.62 227	84.80 301	83.32 289

thres600view7		75.97 235	75.35 235	77.85 243	87.01 214	51.84 304	80.45 231	73.26 290	75.20 143	83.10 210	86.31 246	45.54 311	89.05 221	55.03 294	92.24 204	92.66 138

BH-w/o		76.57 228	76.07 227	78.10 238	86.88 216	65.92 173	77.63 271	86.33 208	65.69 243	80.89 235	79.95 320	68.97 221	90.74 188	53.01 302	85.25 294	77.62 330

MAR-MVS		80.24 199	78.74 206	84.73 127	86.87 217	78.18 77	85.75 117	87.81 188	65.67 244	77.84 267	78.50 326	73.79 173	90.53 194	61.59 246	90.87 233	85.49 258
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	83.50 167	86.74 218	63.97 187	84.56 133	90.31 141	66.22 237	91.62 67	94.55 63	75.88 145	91.95 154	77.02 136	94.89 141	94.56 82

QAPM		82.59 169	82.59 166	82.58 180	86.44 219	66.69 169	89.94 44	90.36 138	67.97 225	84.94 177	92.58 120	72.71 197	92.18 150	70.63 182	87.73 270	88.85 225

PAPM		71.77 275	70.06 286	76.92 254	86.39 220	53.97 282	76.62 280	86.62 206	53.44 311	63.97 340	84.73 269	57.79 268	92.34 147	39.65 341	81.33 320	84.45 271

GBi-Net		82.02 177	82.07 173	81.85 192	86.38 221	61.05 236	86.83 99	88.27 180	72.43 175	86.00 163	95.64 34	63.78 239	90.68 190	65.95 216	93.34 180	93.82 107

test1		82.02 177	82.07 173	81.85 192	86.38 221	61.05 236	86.83 99	88.27 180	72.43 175	86.00 163	95.64 34	63.78 239	90.68 190	65.95 216	93.34 180	93.82 107

FMVSNet2		81.31 183	81.61 179	80.41 209	86.38 221	58.75 254	83.93 146	86.58 207	72.43 175	87.65 137	92.98 108	63.78 239	90.22 201	66.86 209	93.92 165	92.27 155

3Dnovator	80.37 7	84.80 121	84.71 125	85.06 117	86.36 224	74.71 107	88.77 70	90.00 155	75.65 137	84.96 175	93.17 103	74.06 167	91.19 174	78.28 121	91.09 222	89.29 219

casdiffmvs		82.99 165	82.51 168	84.42 137	86.34 225	67.92 161	87.86 80	92.28 79	60.95 278	81.12 232	93.08 104	76.07 144	93.43 112	79.41 113	85.45 290	91.93 165

Anonymous20231206		71.38 279	71.88 272	69.88 300	86.31 226	54.37 280	70.39 315	74.62 279	52.57 316	76.73 274	88.76 207	59.94 254	72.06 323	44.35 334	93.23 187	83.23 291

API-MVS		82.28 173	82.61 165	81.30 199	86.29 227	69.79 145	88.71 71	87.67 189	78.42 97	82.15 218	84.15 276	77.98 119	91.59 165	65.39 222	92.75 195	82.51 300

tfpn200view9		74.86 246	74.23 244	76.74 259	86.24 228	52.12 297	79.24 254	73.87 283	73.34 161	81.82 223	84.60 271	46.02 305	88.80 225	51.98 306	90.99 226	89.31 217

thres400		75.14 240	74.23 244	77.86 242	86.24 228	52.12 297	79.24 254	73.87 283	73.34 161	81.82 223	84.60 271	46.02 305	88.80 225	51.98 306	90.99 226	92.66 138

testmv		70.47 284	70.70 279	69.77 302	86.22 230	53.89 284	67.32 326	71.91 302	63.32 258	78.16 265	89.47 199	56.12 276	73.10 321	36.43 347	87.33 273	82.33 302

UGNet		82.78 166	81.64 178	86.21 99	86.20 231	76.24 101	86.86 97	85.68 215	77.07 119	73.76 298	92.82 113	69.64 216	91.82 160	69.04 195	93.69 172	90.56 199
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	86.17 232	72.21 127	83.76 153	91.43 109	77.24 116	74.39 295	87.45 231	75.36 148	95.42 32	77.03 135	92.83 194	92.25 157

conf0.01		74.17 252	73.53 251	76.08 266	86.13 233	50.06 323	79.45 245	68.54 322	72.01 185	80.76 238	82.50 297	41.39 335	86.83 250	59.66 258	91.36 213	91.34 180

conf0.002		74.17 252	73.53 251	76.08 266	86.13 233	50.06 323	79.45 245	68.54 322	72.01 185	80.76 238	82.50 297	41.39 335	86.83 250	59.66 258	91.36 213	91.34 180

thresconf0.02		73.65 257	73.53 251	73.98 277	86.13 233	50.06 323	79.45 245	68.54 322	72.01 185	80.76 238	82.50 297	41.39 335	86.83 250	59.66 258	91.36 213	85.06 262

tfpn_n400		73.65 257	73.53 251	73.98 277	86.13 233	50.06 323	79.45 245	68.54 322	72.01 185	80.76 238	82.50 297	41.39 335	86.83 250	59.66 258	91.36 213	85.06 262

tfpnconf		73.65 257	73.53 251	73.98 277	86.13 233	50.06 323	79.45 245	68.54 322	72.01 185	80.76 238	82.50 297	41.39 335	86.83 250	59.66 258	91.36 213	85.06 262

tfpnview11		73.65 257	73.53 251	73.98 277	86.13 233	50.06 323	79.45 245	68.54 322	72.01 185	80.76 238	82.50 297	41.39 335	86.83 250	59.66 258	91.36 213	85.06 262

TR-MVS		76.77 226	75.79 228	79.72 216	86.10 239	65.79 174	77.14 275	83.02 233	65.20 249	81.40 229	82.10 305	66.30 229	90.73 189	55.57 288	85.27 293	82.65 295

LCM-MVSNet-Re		83.48 158	85.06 115	78.75 227	85.94 240	55.75 273	80.05 234	94.27 13	76.47 124	96.09 5	94.54 65	83.31 68	89.75 210	59.95 255	94.89 141	90.75 193

Fast-Effi-MVS+-dtu		82.54 170	81.41 181	85.90 106	85.60 241	76.53 97	83.07 174	89.62 163	73.02 170	79.11 261	83.51 280	80.74 100	90.24 200	68.76 197	89.29 250	90.94 186

v148		82.31 172	82.48 169	81.81 195	85.59 242	59.66 246	81.47 216	86.02 212	72.85 171	88.05 132	90.65 178	70.73 214	90.91 183	75.15 144	91.79 208	94.87 78

MVSFormer		82.23 174	81.57 180	84.19 145	85.54 243	69.26 151	91.98 25	90.08 152	71.54 196	76.23 279	85.07 263	58.69 262	94.27 62	86.26 28	88.77 256	89.03 222

lupinMVS		76.37 231	74.46 242	82.09 186	85.54 243	69.26 151	76.79 276	80.77 250	50.68 332	76.23 279	82.82 292	58.69 262	88.94 223	69.85 186	88.77 256	88.07 229

tfpn1000		73.63 261	73.58 249	73.79 283	85.46 245	50.31 320	79.99 236	68.18 328	72.33 179	80.66 244	83.05 285	39.80 346	86.74 259	60.96 250	91.78 209	84.32 274

TinyColmap		81.25 186	82.34 171	77.99 240	85.33 246	60.68 240	82.32 194	88.33 178	71.26 198	86.97 149	92.22 131	77.10 131	86.98 247	62.37 236	95.17 132	86.31 249

PAPR		78.84 206	78.10 209	81.07 203	85.17 247	60.22 243	82.21 199	90.57 132	62.51 266	75.32 288	84.61 270	74.99 153	92.30 148	59.48 265	88.04 266	90.68 195

DI_MVS_plusplus_test		81.27 185	81.26 182	81.29 200	84.98 248	61.65 228	81.98 204	87.25 195	63.56 256	87.56 139	89.60 197	73.62 176	91.83 159	72.20 171	90.59 242	90.38 204

pmmvs4		74.92 245	72.98 262	80.73 207	84.95 249	71.71 136	76.23 285	77.59 262	52.83 314	77.73 270	86.38 240	56.35 274	84.97 278	57.72 278	87.05 276	85.51 257

Test4		81.31 183	81.13 186	81.88 191	84.89 250	63.05 201	82.37 192	90.50 133	62.75 264	89.00 117	88.29 218	67.55 225	91.68 163	73.55 158	91.24 221	90.89 188

Patchmatch-RL test		74.48 249	73.68 248	76.89 256	84.83 251	66.54 170	72.29 308	69.16 321	57.70 291	86.76 150	86.33 244	45.79 310	82.59 295	69.63 188	90.65 240	81.54 315

tfpn_ndepth		72.54 268	72.30 269	73.24 286	84.81 252	51.42 306	79.24 254	70.49 315	69.26 214	78.48 264	79.80 321	40.16 345	86.77 257	58.08 277	90.43 243	81.53 316

test_normal		81.23 187	81.16 185	81.43 198	84.77 253	61.99 223	81.46 217	86.95 205	63.16 261	87.22 142	89.63 196	73.62 176	91.65 164	72.92 166	90.70 237	90.65 197

XXY-MVS		74.44 251	76.19 226	69.21 305	84.61 254	52.43 296	71.70 310	77.18 264	60.73 281	80.60 245	90.96 167	75.44 146	69.35 330	56.13 284	88.33 260	85.86 254

cascas		76.29 232	74.81 238	80.72 208	84.47 255	62.94 203	73.89 302	87.34 192	55.94 299	75.16 290	76.53 334	63.97 237	91.16 175	65.00 223	90.97 230	88.06 230

PVSNet_BlendedMVS		78.80 207	77.84 210	81.65 196	84.43 256	63.41 192	79.49 244	90.44 135	61.70 274	75.43 286	87.07 235	69.11 219	91.44 169	60.68 252	92.24 204	90.11 210

PVSNet_Blended		76.49 230	75.40 233	79.76 215	84.43 256	63.41 192	75.14 293	90.44 135	57.36 293	75.43 286	78.30 327	69.11 219	91.44 169	60.68 252	87.70 271	84.42 272

OpenMVS	76.72 13	81.98 179	82.00 175	81.93 188	84.42 258	68.22 159	88.50 74	89.48 165	66.92 232	81.80 225	91.86 134	72.59 201	90.16 203	71.19 176	91.25 220	87.40 239

OpenMVS_ROB	70.19 17	77.77 214	77.46 212	78.71 228	84.39 259	61.15 234	81.18 222	82.52 236	62.45 268	83.34 205	87.37 232	66.20 230	88.66 232	64.69 225	85.02 297	86.32 248

Regformer-3		85.06 114	84.67 127	86.22 97	84.27 260	73.43 114	84.07 140	85.26 219	80.77 67	88.62 124	85.48 253	80.56 102	90.39 197	81.99 78	91.04 224	94.85 80

Regformer-4		86.41 91	85.71 106	88.52 62	84.27 260	77.57 84	84.07 140	88.00 185	82.82 45	89.84 96	85.48 253	82.06 82	92.77 139	83.83 56	91.04 224	95.22 75

DELS-MVS		81.44 182	81.25 183	82.03 187	84.27 260	62.87 205	76.47 283	92.49 74	70.97 201	81.64 227	83.83 277	75.03 152	92.70 140	74.29 148	92.22 206	90.51 201
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	78.78 226	84.20 263	73.57 113	89.55 54	90.44 135	84.24 31	84.38 190	94.89 53	76.35 143	80.40 303	76.14 139	96.80 78	82.36 301
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	84.18 264	76.90 92	83.52 160	87.94 187	82.18 53	89.19 114	85.07 263	82.28 80	91.89 157	82.40 72	92.72 196	93.69 113

Regformer-2		86.74 86	86.08 101	88.73 60	84.18 264	79.20 70	83.52 160	89.33 167	83.33 37	89.92 94	85.07 263	83.23 69	93.16 124	83.39 58	92.72 196	93.83 105

EI-MVSNet-Vis-set		85.12 113	84.53 134	86.88 84	84.01 266	72.76 118	83.91 147	85.18 221	80.44 68	88.75 121	85.49 252	80.08 105	91.92 155	82.02 77	90.85 234	95.97 50

semantic-postprocess				84.34 140	83.93 267	69.66 147		81.09 248	72.43 175	86.47 160	90.19 187	57.56 269	93.15 126	77.45 129	86.39 283	90.22 206

MSDG		80.06 202	79.99 200	80.25 210	83.91 268	68.04 160	77.51 273	89.19 168	77.65 105	81.94 220	83.45 282	76.37 142	86.31 264	63.31 234	86.59 280	86.41 247

EI-MVSNet-UG-set		85.04 115	84.44 136	86.85 85	83.87 269	72.52 121	83.82 149	85.15 222	80.27 72	88.75 121	85.45 256	79.95 107	91.90 156	81.92 79	90.80 235	96.13 44

thres200		72.34 271	71.55 276	74.70 275	83.48 270	51.60 305	75.02 294	73.71 286	70.14 208	78.56 263	80.57 315	46.20 303	88.20 236	46.99 328	89.29 250	84.32 274

USDC		76.63 227	76.73 218	76.34 263	83.46 271	57.20 262	80.02 235	88.04 184	52.14 320	83.65 202	91.25 150	63.24 242	86.65 260	54.66 296	94.11 160	85.17 260

HY-MVS	64.64 18	73.03 264	72.47 268	74.71 274	83.36 272	54.19 281	82.14 202	81.96 241	56.76 298	69.57 319	86.21 247	60.03 253	84.83 281	49.58 317	82.65 313	85.11 261

EI-MVSNet		82.61 168	82.42 170	83.20 170	83.25 273	63.66 189	83.50 163	85.07 223	76.06 127	86.55 154	85.10 261	73.41 182	90.25 198	78.15 123	90.67 238	95.68 59

CVMVSNet		72.62 267	71.41 277	76.28 264	83.25 273	60.34 242	83.50 163	79.02 257	37.77 354	76.33 277	85.10 261	49.60 294	87.41 242	70.54 183	77.54 334	81.08 323

V42		83.47 159	83.37 155	83.75 154	83.16 275	63.33 195	81.31 218	90.23 150	69.51 212	90.91 78	90.81 172	74.16 166	92.29 149	80.06 102	90.22 244	95.62 61

EU-MVSNet		75.12 242	74.43 243	77.18 252	83.11 276	59.48 248	85.71 119	82.43 238	39.76 353	85.64 169	88.76 207	44.71 327	87.88 238	73.86 155	85.88 287	84.16 277

FMVSNet3		78.80 207	78.55 207	79.57 220	82.89 277	56.89 265	81.76 210	85.77 214	69.04 218	86.00 163	90.44 182	51.75 289	90.09 207	65.95 216	93.34 180	91.72 169

test1235678		65.57 309	65.73 306	65.06 321	82.84 278	50.90 312	62.90 334	69.26 319	57.17 296	72.36 305	83.04 286	46.02 305	70.10 327	32.79 352	85.24 295	74.19 337

MVS_Test		82.47 171	83.22 156	80.22 211	82.62 279	57.75 258	82.54 189	91.96 88	71.16 199	82.89 212	92.52 122	77.41 126	90.50 195	80.04 103	87.84 269	92.40 149

LF4IMVS		82.75 167	81.93 176	85.19 115	82.08 280	80.15 61	85.53 120	88.76 172	68.01 223	85.58 170	87.75 226	71.80 209	86.85 249	74.02 152	93.87 166	88.58 226

PVSNet	58.17 21	66.41 305	65.63 307	68.75 308	81.96 281	49.88 329	62.19 336	72.51 298	51.03 328	68.04 324	75.34 338	50.84 290	74.77 318	45.82 332	82.96 309	81.60 314

GA-MVS		75.83 236	74.61 239	79.48 222	81.87 282	59.25 250	73.42 305	82.88 234	68.68 220	79.75 254	81.80 308	50.62 291	89.46 212	66.85 210	85.64 289	89.72 212

MS-PatchMatch		70.93 281	70.22 284	73.06 289	81.85 283	62.50 214	73.82 303	77.90 260	52.44 317	75.92 282	81.27 312	55.67 278	81.75 298	55.37 290	77.70 332	74.94 335

Patchmatch-test1		72.75 266	72.61 265	73.19 287	81.62 284	55.86 271	78.89 260	71.37 308	61.73 272	74.93 291	82.15 304	60.46 250	81.80 297	59.68 257	82.63 315	81.92 309

FMVSNet5		72.10 273	71.69 273	73.32 284	81.57 285	53.02 291	76.77 277	78.37 259	63.31 259	76.37 276	91.85 135	36.68 349	78.98 307	47.87 324	92.45 199	87.95 233

CANet_DTU		77.81 213	77.05 214	80.09 212	81.37 286	59.90 245	83.26 169	88.29 179	69.16 216	67.83 326	83.72 278	60.93 247	89.47 211	69.22 193	89.70 248	90.88 189

ANet_high		83.17 163	85.68 107	75.65 270	81.24 287	45.26 338	79.94 237	92.91 60	83.83 35	91.33 72	96.88 10	80.25 104	85.92 269	68.89 196	95.89 111	95.76 54

new-patchmatchnet		70.10 287	73.37 258	60.29 333	81.23 288	16.95 361	59.54 340	74.62 279	62.93 262	80.97 233	87.93 224	62.83 243	71.90 324	55.24 291	95.01 138	92.00 161

test20.03		73.75 256	74.59 241	71.22 299	81.11 289	51.12 310	70.15 316	72.10 300	70.42 204	80.28 252	91.50 146	64.21 236	74.72 320	46.96 329	94.58 154	87.82 236

MVS		73.21 263	72.59 266	75.06 272	80.97 290	60.81 239	81.64 213	85.92 213	46.03 344	71.68 309	77.54 328	68.47 222	89.77 209	55.70 287	85.39 291	74.60 336

N_pmnet		70.20 285	68.80 292	74.38 276	80.91 291	84.81 35	59.12 343	76.45 270	55.06 303	75.31 289	82.36 303	55.74 277	54.82 355	47.02 327	87.24 275	83.52 284

IterMVS		76.91 221	76.34 225	78.64 229	80.91 291	64.03 185	76.30 284	79.03 256	64.88 253	83.11 209	89.16 202	59.90 255	84.46 283	68.61 200	85.15 296	87.42 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WTY-MVS		67.91 299	68.35 294	66.58 316	80.82 293	48.12 332	65.96 330	72.60 296	53.67 310	71.20 312	81.68 310	58.97 261	69.06 332	48.57 320	81.67 317	82.55 297

IB-MVS	62.13 19	71.64 276	68.97 290	79.66 219	80.80 294	62.26 222	73.94 301	76.90 266	63.27 260	68.63 322	76.79 332	33.83 352	91.84 158	59.28 266	87.26 274	84.88 267
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		71.85 274	71.59 274	72.62 293	80.71 295	53.78 285	69.72 318	71.71 307	58.80 286	78.03 266	80.51 316	56.61 273	78.84 308	62.20 238	86.04 286	85.23 259

ppachtmachnet_test		74.73 248	74.00 246	76.90 255	80.71 295	56.89 265	71.53 311	78.42 258	58.24 289	79.32 259	82.92 291	57.91 266	84.26 285	65.60 221	91.36 213	89.56 213

testgi		72.36 270	74.61 239	65.59 318	80.56 297	42.82 346	68.29 321	73.35 289	66.87 233	81.84 222	89.93 191	72.08 206	66.92 338	46.05 331	92.54 198	87.01 243

1314		73.22 262	72.56 267	75.20 271	80.41 298	57.84 256	81.64 213	85.36 218	51.68 323	73.10 302	76.65 333	61.45 246	85.19 276	63.54 231	79.21 329	82.59 296

CR-MVSNet		74.00 254	73.04 261	76.85 257	79.58 299	62.64 208	82.58 186	76.90 266	50.50 333	75.72 284	92.38 123	48.07 298	84.07 286	68.72 199	82.91 311	83.85 280

RPMNet		76.06 233	75.79 228	76.85 257	79.58 299	62.64 208	82.58 186	71.75 305	74.80 145	75.72 284	92.59 118	48.69 296	84.07 286	73.48 159	82.91 311	83.85 280

UnsupCasMVSNet_bld		69.21 295	69.68 287	67.82 312	79.42 301	51.15 309	67.82 325	75.79 271	54.15 307	77.47 271	85.36 260	59.26 260	70.64 326	48.46 321	79.35 327	81.66 313

PatchT		70.52 283	72.76 263	63.79 324	79.38 302	33.53 354	77.63 271	65.37 339	73.61 156	71.77 308	92.79 116	44.38 328	75.65 317	64.53 228	85.37 292	82.18 304

Patchmtry		76.56 229	77.46 212	73.83 282	79.37 303	46.60 335	82.41 191	76.90 266	73.81 155	85.56 171	92.38 123	48.07 298	83.98 288	63.36 233	95.31 128	90.92 187

mvs_anonymous		78.13 210	78.76 205	76.23 265	79.24 304	50.31 320	78.69 262	84.82 228	61.60 275	83.09 211	92.82 113	73.89 172	87.01 245	68.33 202	86.41 282	91.37 178

MVS-HIRNet		61.16 320	62.92 315	55.87 337	79.09 305	35.34 353	71.83 309	57.98 353	46.56 342	59.05 350	91.14 157	49.95 293	76.43 313	38.74 344	71.92 344	55.84 354

MDA-MVSNet-bldmvs		77.47 215	76.90 216	79.16 223	79.03 306	64.59 180	66.58 329	75.67 273	73.15 168	88.86 118	88.99 205	66.94 227	81.23 300	64.71 224	88.22 265	91.64 171

tpm2		68.45 297	66.83 301	73.30 285	78.93 307	48.50 330	79.76 238	71.76 304	47.50 340	69.92 318	83.60 279	42.07 334	88.40 233	48.44 322	79.51 325	83.01 294

testus		62.33 314	63.03 314	60.20 334	78.78 308	40.74 347	59.14 341	69.80 318	49.26 337	71.41 310	74.72 340	52.33 288	63.52 347	29.84 354	82.01 316	76.36 332

tpm		67.95 298	68.08 297	67.55 313	78.74 309	43.53 344	75.60 289	67.10 336	54.92 304	72.23 306	88.10 220	42.87 332	75.97 315	52.21 304	80.95 323	83.15 292

diffmvs		79.20 205	79.04 203	79.69 217	78.64 310	58.90 251	81.79 209	87.61 190	65.07 251	73.65 300	89.80 193	73.10 192	87.79 239	75.02 146	86.63 279	92.38 150

tpmp4_e23		69.43 292	67.33 299	75.72 269	78.53 311	52.75 292	82.13 203	74.91 276	49.23 338	66.37 329	84.17 275	41.28 341	88.67 231	49.73 316	79.63 324	85.75 255

no-one		71.52 278	70.43 283	74.81 273	78.45 312	63.41 192	57.73 346	77.03 265	51.46 326	77.17 273	90.33 184	54.96 283	80.35 304	47.41 325	99.29 2	80.68 327

1111		61.71 316	63.77 312	55.55 339	78.05 313	25.74 358	60.62 337	67.52 329	66.09 238	74.68 292	86.50 238	16.00 364	59.22 353	38.79 342	85.65 288	81.70 311

.test1245		48.02 333	54.41 332	28.84 346	78.05 313	25.74 358	60.62 337	67.52 329	66.09 238	74.68 292	86.50 238	16.00 364	59.22 353	38.79 342	1.47 359	1.55 360

MDTV_nov1_ep13			68.29 296		78.03 315	43.87 343	74.12 300	72.22 299	52.17 318	67.02 328	85.54 251	45.36 317	80.85 301	55.73 285	84.42 303

EPNet_dtu		72.87 265	71.33 278	77.49 250	77.72 316	60.55 241	82.35 193	75.79 271	66.49 235	58.39 353	81.06 314	53.68 285	85.98 268	53.55 300	92.97 192	85.95 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		69.71 291	68.83 291	72.33 295	77.66 317	53.60 286	79.29 252	69.99 317	57.66 292	72.53 304	82.93 290	46.45 302	80.08 306	60.91 251	72.09 343	83.31 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sss		66.92 302	67.26 300	65.90 317	77.23 318	51.10 311	64.79 331	71.72 306	52.12 321	70.13 317	80.18 318	57.96 265	65.36 345	50.21 312	81.01 322	81.25 320

CostFormer		69.98 290	68.68 293	73.87 281	77.14 319	50.72 318	79.26 253	74.51 281	51.94 322	70.97 314	84.75 268	45.16 325	87.49 241	55.16 292	79.23 328	83.40 287

tpm cat1		66.76 303	65.21 308	71.42 298	77.09 320	50.62 319	78.01 266	73.68 287	44.89 346	68.64 320	79.00 324	45.51 315	82.42 296	49.91 314	70.15 347	81.23 322

pmmvs5		70.73 282	70.07 285	72.72 291	77.03 321	52.73 293	74.14 299	75.65 274	50.36 334	72.17 307	85.37 259	55.42 280	80.67 302	52.86 303	87.59 272	84.77 268

EPNet		80.37 196	78.41 208	86.23 96	76.75 322	73.28 115	87.18 93	77.45 263	76.24 126	68.14 323	88.93 206	65.41 234	93.85 81	69.47 189	96.12 102	91.55 174
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 280x420		59.08 324	56.52 329	66.76 315	76.51 323	64.39 183	49.62 353	59.00 349	43.86 348	55.66 356	68.41 348	35.55 351	68.21 334	43.25 335	76.78 336	67.69 346

UnsupCasMVSNet_eth		71.63 277	72.30 269	69.62 303	76.47 324	52.70 294	70.03 317	80.97 249	59.18 285	79.36 258	88.21 219	60.50 249	69.12 331	58.33 274	77.62 333	87.04 242

test-LLR		67.21 301	66.74 302	68.63 309	76.45 325	55.21 276	67.89 322	67.14 334	62.43 269	65.08 336	72.39 342	43.41 329	69.37 328	61.00 248	84.89 298	81.31 318

test-mter		65.00 310	63.79 311	68.63 309	76.45 325	55.21 276	67.89 322	67.14 334	50.98 329	65.08 336	72.39 342	28.27 359	69.37 328	61.00 248	84.89 298	81.31 318

gg-mvs-nofinetune		68.96 296	69.11 289	68.52 311	76.12 327	45.32 337	83.59 156	55.88 354	86.68 20	64.62 339	97.01 7	30.36 356	83.97 289	44.78 333	82.94 310	76.26 333

CMPMVS	59.41 20	75.12 242	73.57 250	79.77 214	75.84 328	67.22 164	81.21 221	82.18 239	50.78 330	76.50 275	87.66 228	55.20 281	82.99 293	62.17 240	90.64 241	89.09 221
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test12356		54.91 330	57.14 328	48.22 343	75.83 329	17.47 360	52.31 352	69.20 320	51.66 324	60.11 346	75.40 337	29.77 358	62.62 351	27.64 355	72.37 342	64.59 347

PNet_i23d		52.13 331	51.24 333	54.79 340	75.56 330	45.26 338	54.54 349	52.55 357	66.95 231	57.19 354	65.82 351	13.15 366	63.40 348	36.39 348	39.04 357	55.71 355

wuyk23d		75.13 241	79.30 202	62.63 325	75.56 330	75.18 105	80.89 227	73.10 294	75.06 144	94.76 15	95.32 41	87.73 31	52.85 356	34.16 350	97.11 68	59.85 351

Patchmatch-test		65.91 307	67.38 298	61.48 330	75.51 332	43.21 345	68.84 319	63.79 341	62.48 267	72.80 303	83.42 283	44.89 326	59.52 352	48.27 323	86.45 281	81.70 311

new_pmnet		55.69 328	57.66 327	49.76 341	75.47 333	30.59 355	59.56 339	51.45 358	43.62 349	62.49 341	75.48 336	40.96 343	49.15 358	37.39 346	72.52 341	69.55 344

gm-plane-assit					75.42 334	44.97 341			52.17 318		72.36 344		87.90 237	54.10 298

PatchFormer-LS_test		67.91 299	66.49 305	72.17 296	75.29 335	51.85 303	75.68 287	73.62 288	57.23 295	68.64 320	68.13 350	42.19 333	82.76 294	64.06 229	73.51 340	81.89 310

MVSTER		77.09 219	75.70 231	81.25 201	75.27 336	61.08 235	77.49 274	85.07 223	60.78 280	86.55 154	88.68 209	43.14 331	90.25 198	73.69 156	90.67 238	92.42 147

PVSNet_0	51.08 22	56.10 327	54.97 331	59.48 335	75.12 337	53.28 290	55.16 347	61.89 344	44.30 347	59.16 349	62.48 354	54.22 284	65.91 343	35.40 349	47.01 355	59.25 352

test0.0.03 1		64.66 311	64.36 310	65.57 319	75.03 338	46.89 334	64.69 332	61.58 347	62.43 269	71.18 313	77.54 328	43.41 329	68.47 333	40.75 340	82.65 313	81.35 317

DWT-MVSNet_test		66.43 304	64.37 309	72.63 292	74.86 339	50.86 313	76.52 281	72.74 295	54.06 308	65.50 333	68.30 349	32.13 354	84.84 280	61.63 245	73.59 339	82.19 303

tpmvs		70.16 286	69.56 288	71.96 297	74.71 340	48.13 331	79.63 239	75.45 275	65.02 252	70.26 316	81.88 307	45.34 318	85.68 272	58.34 273	75.39 337	82.08 305

MDA-MVSNet_test_wron		70.05 289	70.44 281	68.88 307	73.84 341	53.47 287	58.93 345	67.28 332	58.43 287	87.09 146	85.40 257	59.80 257	67.25 336	59.66 258	83.54 306	85.92 253

YYNet1		70.06 288	70.44 281	68.90 306	73.76 342	53.42 289	58.99 344	67.20 333	58.42 288	87.10 145	85.39 258	59.82 256	67.32 335	59.79 256	83.50 307	85.96 251

GG-mvs-BLEND				67.16 314	73.36 343	46.54 336	84.15 139	55.04 355		58.64 352	61.95 355	29.93 357	83.87 290	38.71 345	76.92 335	71.07 342

test2356		56.69 326	55.15 330	61.32 331	73.20 344	44.11 342	54.95 348	62.52 342	48.75 339	62.45 342	68.42 347	21.10 363	65.67 344	26.86 356	78.08 331	74.19 337

JIA-IIPM		69.41 294	66.64 304	77.70 244	73.19 345	71.24 139	75.67 288	65.56 338	70.42 204	65.18 335	92.97 109	33.64 353	83.06 292	53.52 301	69.61 350	78.79 329

ADS-MVSNet2		65.87 308	63.64 313	72.55 294	73.16 346	56.92 264	67.10 327	74.81 278	49.74 335	66.04 331	82.97 288	46.71 300	77.26 310	42.29 336	69.96 348	83.46 285

ADS-MVSNet		61.90 315	62.19 317	61.03 332	73.16 346	36.42 352	67.10 327	61.75 345	49.74 335	66.04 331	82.97 288	46.71 300	63.21 349	42.29 336	69.96 348	83.46 285

DSMNet-mixed		60.98 322	61.61 319	59.09 336	72.88 348	45.05 340	74.70 296	46.61 360	26.20 356	65.34 334	90.32 185	55.46 279	63.12 350	41.72 338	81.30 321	69.09 345

tpmrst		66.28 306	66.69 303	65.05 322	72.82 349	39.33 349	78.20 265	70.69 314	53.16 313	67.88 325	80.36 317	48.18 297	74.75 319	58.13 275	70.79 345	81.08 323

TESTMET0.1,1		61.29 319	60.32 323	64.19 323	72.06 350	51.30 307	67.89 322	62.09 343	45.27 345	60.65 345	69.01 345	27.93 360	64.74 346	56.31 283	81.65 319	76.53 331

dp		60.70 323	60.29 324	61.92 328	72.04 351	38.67 351	70.83 312	64.08 340	51.28 327	60.75 344	77.28 330	36.59 350	71.58 325	47.41 325	62.34 354	75.52 334

pmmvs3		62.47 312	60.02 325	69.80 301	71.58 352	64.00 186	70.52 314	58.44 351	39.77 352	66.05 330	75.84 335	27.10 361	72.28 322	46.15 330	84.77 302	73.11 339

LP		69.42 293	68.30 295	72.77 290	71.48 353	56.84 267	73.66 304	74.84 277	63.52 257	70.95 315	83.35 284	49.55 295	77.15 312	57.13 280	70.21 346	84.33 273

EPMVS		62.47 312	62.63 316	62.01 326	70.63 354	38.74 350	74.76 295	52.86 356	53.91 309	67.71 327	80.01 319	39.40 347	66.60 340	55.54 289	68.81 352	80.68 327

E-PMN		61.59 318	61.62 318	61.49 329	66.81 355	55.40 274	53.77 350	60.34 348	66.80 234	58.90 351	65.50 352	40.48 344	66.12 342	55.72 286	86.25 284	62.95 349

testpf		58.55 325	61.58 320	49.48 342	66.03 356	40.05 348	74.40 298	58.07 352	64.72 254	59.36 348	72.67 341	22.76 362	66.92 338	67.07 208	69.15 351	41.46 356

EMVS		61.10 321	60.81 321	61.99 327	65.96 357	55.86 271	53.10 351	58.97 350	67.06 230	56.89 355	63.33 353	40.98 342	67.03 337	54.79 295	86.18 285	63.08 348

PMMVS		61.65 317	60.38 322	65.47 320	65.40 358	69.26 151	63.97 333	61.73 346	36.80 355	60.11 346	68.43 346	59.42 258	66.35 341	48.97 319	78.57 330	60.81 350

PMMVS2		55.64 329	59.27 326	44.74 344	64.30 359	12.32 362	40.60 354	49.79 359	53.19 312	65.06 338	84.81 267	53.60 286	49.76 357	32.68 353	89.41 249	72.15 340

MVE	40.22 23	51.82 332	50.47 334	55.87 337	62.66 360	51.91 299	31.61 356	39.28 361	40.65 351	50.76 357	74.98 339	56.24 275	44.67 359	33.94 351	64.11 353	71.04 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				24.13 347	32.95 361	29.49 356		21.63 364	12.07 357	37.95 358	45.07 356	30.84 355	19.21 360	17.94 358	33.06 358	23.69 357

tmp_tt		20.25 336	24.50 337	7.49 348	4.47 362	8.70 363	34.17 355	25.16 363	1.00 358	32.43 359	18.49 357	39.37 348	9.21 361	21.64 357	43.75 356	4.57 358

testmvs		5.91 340	7.65 341	0.72 350	1.20 363	0.37 365	59.14 341	0.67 366	0.49 360	1.11 360	2.76 361	0.94 368	0.24 363	1.02 360	1.47 359	1.55 360

test123		6.27 339	8.08 340	0.84 349	1.11 364	0.57 364	62.90 334	0.82 365	0.54 359	1.07 361	2.75 362	1.26 367	0.30 362	1.04 359	1.26 361	1.66 359

cdsmvs_eth3d_5k		20.81 335	27.75 336	0.00 351	0.00 365	0.00 366	0.00 357	85.44 217	0.00 361	0.00 362	82.82 292	81.46 93	0.00 364	0.00 361	0.00 362	0.00 362

pcd_1.5k_mvsjas		6.41 338	8.55 339	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	76.94 134	0.00 364	0.00 361	0.00 362	0.00 362

sosnet-low-res		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

sosnet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uncertanet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

Regformer		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

ab-mvs-re		6.65 337	8.87 338	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	79.80 321	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uanet		0.00 341	0.00 342	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	0.00 363	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

GSMVS																83.88 278

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	91.48 176

sam_mvs1												46.11 304				83.88 278

sam_mvs												45.92 309

MTGPA								91.81 92

test_post1							78.85 261				3.13 359	45.19 320	80.13 305	58.11 276

test_post											3.10 360	45.43 316	77.22 311

patchmatchnet-post											81.71 309	45.93 308	87.01 245

MTMP								33.14 362

test9_res														80.83 90	96.45 89	90.57 198

agg_prior2														79.68 107	96.16 98	90.22 206

test_prior4						78.97 72	84.59 132

test_prior2							83.37 166		75.43 139	84.58 186	91.57 143	81.92 88		79.54 109	96.97 71

旧先验2							81.73 211		56.88 297	86.54 159			84.90 279	72.81 167

新几何2							81.72 212

无先验							82.81 180	85.62 216	58.09 290				91.41 171	67.95 205		84.48 270

原ACMM2							82.26 198

testdata2													86.43 263	63.52 232

segment_acmp												81.94 85

testdata1							79.62 240		73.95 154

plane_prior5								93.61 32					95.22 40	80.78 91	95.83 114	94.46 87

plane_prior4											92.95 110

plane_prior3						76.85 93			77.79 103	86.55 154

plane_prior2							89.45 59		79.44 81

plane_prior						76.42 98	87.15 94		75.94 131						95.03 137

n2								0.00 367

nn								0.00 367

door-mid								74.45 282

test11								91.46 106

door								72.57 297

HQP5-MVS						70.66 142

BP-MVS														77.30 131

HQP4-MVS										80.56 247			94.61 56			93.56 118

HQP3-MVS								92.68 68							94.47 156

HQP2-MVS												72.10 204

MDTV_nov1_ep13_2view						27.60 357	70.76 313		46.47 343	61.27 343		45.20 319		49.18 318		83.75 282

ACMMP++_ref															95.74 118

ACMMP++															97.35 62

Test By Simon												79.09 111

High-resolution multi-view benchmark