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.

wuykxyi23d		88.46 61	88.80 59	87.44 79	90.96 119	93.03 1	85.85 111	81.96 237	74.58 141	98.58 2	97.29 5	87.73 31	87.31 237	82.84 67	99.41 1	81.99 294

no-one		71.52 268	70.43 273	74.81 264	78.45 302	63.41 188	57.73 335	77.03 260	51.46 315	77.17 263	90.33 178	54.96 276	80.35 294	47.41 314	99.29 2	80.68 315

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

WR-MVS_H		89.91 42	91.31 25	85.71 108	96.32 9	62.39 211	89.54 53	93.31 41	90.21 10	95.57 11	95.66 31	81.42 92	95.90 9	80.94 85	98.80 4	98.84 5

ACMP	79.16 10	90.54 29	90.60 37	90.35 41	94.36 40	80.98 57	89.16 59	94.05 23	79.03 84	92.87 40	93.74 93	90.60 12	95.21 39	82.87 65	98.76 5	94.87 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	77.89 11	90.73 25	91.50 19	88.44 62	93.00 64	76.26 98	89.65 49	95.55 3	87.72 18	93.89 27	94.94 50	91.62 5	93.44 107	78.35 115	98.76 5	95.61 64

PS-CasMVS		90.06 34	91.92 9	84.47 131	96.56 6	58.83 249	89.04 60	92.74 66	91.40 5	96.12 4	96.06 24	87.23 35	95.57 21	79.42 109	98.74 7	99.00 2

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

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

PEN-MVS		90.03 35	91.88 12	84.48 130	96.57 5	58.88 248	88.95 61	93.19 47	91.62 4	96.01 6	96.16 22	87.02 37	95.60 20	78.69 113	98.72 10	98.97 3

CP-MVSNet		89.27 52	90.91 34	84.37 134	96.34 8	58.61 251	88.66 68	92.06 82	90.78 6	95.67 9	95.17 44	81.80 88	95.54 24	79.00 111	98.69 11	98.95 4

TranMVSNet+NR-MVSNet		87.86 67	88.76 60	85.18 114	94.02 43	64.13 180	84.38 131	91.29 114	84.88 27	92.06 56	93.84 92	86.45 44	93.73 81	73.22 156	98.66 12	97.69 12

NR-MVSNet		86.00 97	86.22 95	85.34 112	93.24 58	64.56 177	82.21 194	90.46 131	80.99 61	88.42 125	91.97 126	77.56 122	93.85 78	72.46 164	98.65 13	97.61 13

UA-Net		91.49 14	91.53 18	91.39 22	94.98 31	82.95 50	93.52 5	92.79 64	88.22 16	88.53 122	97.64 3	83.45 64	94.55 57	86.02 33	98.60 14	96.67 36

FC-MVSNet-test		85.93 101	87.05 80	82.58 176	92.25 84	56.44 262	85.75 112	93.09 50	77.33 110	91.94 60	94.65 59	74.78 154	93.41 109	75.11 140	98.58 15	97.88 10

DTE-MVSNet		89.98 37	91.91 11	84.21 139	96.51 7	57.84 252	88.93 63	92.84 63	91.92 2	96.16 3	96.23 20	86.95 38	95.99 4	79.05 110	98.57 16	98.80 6

UniMVSNet (Re)		86.87 78	86.98 82	86.55 87	93.11 62	68.48 155	83.80 146	92.87 60	80.37 65	89.61 104	91.81 133	77.72 120	94.18 64	75.00 142	98.53 17	96.99 34

Baseline_NR-MVSNet		84.00 145	85.90 100	78.29 230	91.47 107	53.44 281	82.29 190	87.00 201	79.06 83	89.55 106	95.72 30	77.20 125	86.14 258	72.30 165	98.51 18	95.28 70

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 13	90.26 4	95.70 2	96.46 2	90.58 8	92.86 41	96.29 18	88.16 26	94.17 66	86.07 32	98.48 19	97.22 26

ACMM	79.39 9	90.65 26	90.99 31	89.63 49	95.03 30	83.53 44	89.62 50	93.35 38	79.20 80	93.83 28	93.60 95	90.81 9	92.96 128	85.02 38	98.45 20	92.41 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS-pluss		90.81 24	91.08 29	89.99 46	95.97 12	79.88 63	88.13 73	94.51 11	75.79 129	92.94 38	94.96 49	88.36 20	95.01 44	90.70 2	98.40 21	95.09 75
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
Anonymous20231211		90.14 31	91.88 12	84.92 117	94.75 35	64.47 178	90.13 38	92.97 57	91.68 3	95.35 12	98.79 2	93.19 3	91.76 157	71.67 170	98.40 21	98.52 7

CP-MVS		91.67 11	91.58 17	91.96 12	95.29 27	87.62 9	93.38 7	93.36 37	83.16 37	91.06 71	94.00 83	88.26 23	95.71 18	87.28 22	98.39 23	92.55 143

UniMVSNet_NR-MVSNet		86.84 80	87.06 79	86.17 100	92.86 69	67.02 163	82.55 183	91.56 95	83.08 39	90.92 73	91.82 132	78.25 116	93.99 71	74.16 145	98.35 24	97.49 16

DU-MVS		86.80 81	86.99 81	86.21 97	93.24 58	67.02 163	83.16 168	92.21 78	81.73 55	90.92 73	91.97 126	77.20 125	93.99 71	74.16 145	98.35 24	97.61 13

MPTG		91.27 19	91.26 27	91.29 25	96.59 3	86.29 14	88.94 62	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

MTAPA		91.52 13	91.60 16	91.29 25	96.59 3	86.29 14	92.02 24	91.81 90	84.07 30	92.00 57	94.40 69	86.63 40	95.28 35	88.59 4	98.31 26	92.30 151

ACMH	76.49 14	89.34 51	91.14 28	83.96 145	92.50 76	70.36 142	89.55 51	93.84 28	81.89 54	94.70 16	95.44 39	90.69 10	88.31 229	83.33 58	98.30 28	93.20 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	83.01 3	91.97 8	91.95 8	92.04 10	93.68 49	86.15 18	93.37 8	95.10 7	90.28 9	92.11 54	95.03 47	89.75 15	94.93 46	79.95 101	98.27 29	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 9	91.87 14	92.03 11	95.53 24	85.91 22	93.35 9	94.16 20	82.52 45	92.39 53	94.14 79	89.15 17	95.62 19	87.35 19	98.24 30	94.56 81
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
HPM-MVS		92.13 6	92.20 7	91.91 15	95.58 23	84.67 38	93.51 6	94.85 9	82.88 41	91.77 62	93.94 90	90.55 13	95.73 17	88.50 8	98.23 31	95.33 69

MP-MVS		91.14 23	90.91 34	91.83 18	96.18 10	86.88 11	92.20 22	93.03 55	82.59 44	88.52 123	94.37 72	86.74 39	95.41 30	86.32 26	98.21 32	93.19 124

SteuartSystems-ACMMP		91.16 22	91.36 22	90.55 37	93.91 46	80.97 58	91.49 29	93.48 36	82.82 42	92.60 48	93.97 84	88.19 24	96.29 3	87.61 15	98.20 33	94.39 90
Skip Steuart: Steuart Systems R&D Blog.
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 49	95.13 45	90.65 11	95.34 32	88.06 10	98.15 34	95.95 51

mPP-MVS		91.69 10	91.47 20	92.37 5	96.04 11	88.48 8	92.72 14	92.60 71	83.09 38	91.54 65	94.25 75	87.67 33	95.51 27	87.21 23	98.11 35	93.12 125

WR-MVS		83.56 153	84.40 135	81.06 200	93.43 54	54.88 273	78.67 255	85.02 222	81.24 59	90.74 77	91.56 139	72.85 190	91.08 172	68.00 198	98.04 36	97.23 25

XVG-ACMP-BASELINE		89.98 37	89.84 44	90.41 39	94.91 33	84.50 41	89.49 55	93.98 24	79.68 73	92.09 55	93.89 91	83.80 60	93.10 123	82.67 69	98.04 36	93.64 112

DeepC-MVS	82.31 4	89.15 54	89.08 52	89.37 53	93.64 50	79.07 70	88.54 69	94.20 17	73.53 151	89.71 96	94.82 54	85.09 51	95.77 15	84.17 50	98.03 38	93.26 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		85.35 108	86.27 94	82.60 175	91.86 95	57.31 256	85.10 120	93.05 52	75.83 128	91.02 72	93.97 84	73.57 175	92.91 132	73.97 148	98.02 39	97.58 15

abl_6		93.02 4	93.16 4	92.60 4	94.73 38	88.99 7	93.26 10	94.19 19	89.11 11	94.43 19	95.27 41	91.86 4	95.09 41	87.54 18	98.02 39	93.71 110

PGM-MVS		91.20 21	90.95 33	91.93 13	95.67 20	85.85 25	90.00 39	93.90 27	80.32 67	91.74 63	94.41 68	88.17 25	95.98 5	86.37 25	97.99 41	93.96 101

APDe-MVS		91.22 20	91.92 9	89.14 56	92.97 65	78.04 77	92.84 12	94.14 21	83.33 35	93.90 26	95.73 29	88.77 18	96.41 1	87.60 16	97.98 42	92.98 128

CLD-MVS		83.18 159	82.64 161	84.79 121	89.05 147	67.82 160	77.93 260	92.52 72	68.33 213	85.07 171	81.54 301	82.06 80	92.96 128	69.35 185	97.91 43	93.57 115
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS-MVSNet		86.66 84	86.82 88	86.17 100	92.05 90	66.87 165	91.21 33	88.64 171	86.30 23	89.60 105	92.59 113	69.22 214	94.91 47	73.89 149	97.89 44	96.72 35

ACMMP_Plus		90.65 26	91.07 30	89.42 52	95.93 14	79.54 68	89.95 42	93.68 31	77.65 101	91.97 59	94.89 51	88.38 19	95.45 28	89.27 3	97.87 45	93.27 120

OurMVSNet-221017-0		90.01 36	89.74 46	90.83 33	93.16 60	80.37 59	91.91 27	93.11 49	81.10 60	95.32 13	97.24 6	72.94 189	94.85 48	85.07 36	97.78 46	97.26 23

ACMMPR		91.49 14	91.35 24	91.92 14	95.74 18	85.88 24	92.58 18	93.25 46	81.99 51	91.40 68	94.17 78	87.51 34	95.87 10	87.74 11	97.76 47	93.99 99

HFP-MVS		91.30 18	91.39 21	91.02 29	95.43 25	84.66 39	92.58 18	93.29 44	81.99 51	91.47 66	93.96 86	88.35 21	95.56 22	87.74 11	97.74 48	92.85 129

#test#		90.49 30	90.31 41	91.02 29	95.43 25	84.66 39	90.65 34	93.29 44	77.00 116	91.47 66	93.96 86	88.35 21	95.56 22	84.88 39	97.74 48	92.85 129

region2R		91.44 17	91.30 26	91.87 16	95.75 17	85.90 23	92.63 17	93.30 42	81.91 53	90.88 76	94.21 76	87.75 30	95.87 10	87.60 16	97.71 50	93.83 103

v7n		90.13 32	90.96 32	87.65 75	91.95 92	71.06 138	89.99 41	93.05 52	86.53 21	94.29 22	96.27 19	82.69 71	94.08 69	86.25 29	97.63 51	97.82 11

XVS		91.54 12	91.36 22	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	94.03 82	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

X-MVStestdata		85.04 112	82.70 159	92.08 8	95.64 21	86.25 16	92.64 15	93.33 39	85.07 25	89.99 85	16.05 347	86.57 42	95.80 13	87.35 19	97.62 52	94.20 92

test_part1								93.93 25				87.19 36			97.61 54	91.48 172

APD-MVS_3200maxsize		92.05 7	92.24 6	91.48 20	93.02 63	85.17 31	92.47 21	95.05 8	87.65 19	93.21 36	94.39 71	90.09 14	95.08 42	86.67 24	97.60 55	94.18 94

OPM-MVS		89.80 43	89.97 42	89.27 54	94.76 34	79.86 64	86.76 97	92.78 65	78.78 87	92.51 49	93.64 94	88.13 27	93.84 80	84.83 41	97.55 56	94.10 97

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 19	93.73 48	85.72 28	96.79 1	95.51 4	88.86 13	95.63 10	96.99 8	84.81 53	93.16 120	91.10 1	97.53 57	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
MIMVSNet1		83.63 152	84.59 129	80.74 202	94.06 42	62.77 202	82.72 179	84.53 226	77.57 103	90.34 79	95.92 26	76.88 137	85.83 262	61.88 233	97.42 58	93.62 113

ACMMP++															97.35 59

nrg030		87.85 68	88.49 61	85.91 103	90.07 134	69.73 144	87.86 76	94.20 17	74.04 146	92.70 46	94.66 58	85.88 50	91.50 161	79.72 103	97.32 60	96.50 40

pmmvs6		86.52 87	88.06 64	81.90 185	92.22 86	62.28 217	84.66 126	89.15 166	83.54 34	89.85 92	97.32 4	88.08 28	86.80 248	70.43 179	97.30 61	96.62 37

SD-MVS		88.96 55	89.88 43	86.22 95	91.63 98	77.07 88	89.82 45	93.77 30	78.90 85	92.88 39	92.29 122	86.11 48	90.22 196	86.24 30	97.24 62	91.36 174

CPTT-MVS		89.39 50	88.98 55	90.63 36	95.09 29	86.95 10	92.09 23	92.30 77	79.74 72	87.50 137	92.38 118	81.42 92	93.28 115	83.07 62	97.24 62	91.67 166

APD-MVS		89.54 48	89.63 48	89.26 55	92.57 73	81.34 56	90.19 37	93.08 51	80.87 62	91.13 70	93.19 98	86.22 47	95.97 6	82.23 73	97.18 64	90.45 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 235	79.30 197	62.63 315	75.56 320	75.18 103	80.89 221	73.10 288	75.06 138	94.76 15	95.32 40	87.73 31	52.85 345	34.16 339	97.11 65	59.85 339

PMVS	80.48 6	90.08 33	90.66 36	88.34 64	96.71 2	92.97 2	90.31 36	89.57 161	88.51 15	90.11 81	95.12 46	90.98 8	88.92 218	77.55 123	97.07 66	83.13 281
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OMC-MVS		88.19 63	87.52 72	90.19 44	91.94 94	81.68 53	87.49 83	93.17 48	76.02 125	88.64 120	91.22 145	84.24 58	93.37 110	77.97 121	97.03 67	95.52 65

test_prior3		86.31 91	86.31 93	86.32 91	90.59 126	71.99 128	83.37 161	92.85 61	75.43 134	84.58 183	91.57 137	81.92 86	94.17 66	79.54 106	96.97 68	92.80 131

test_prior2							83.37 161		75.43 134	84.58 183	91.57 137	81.92 86		79.54 106	96.97 68

EPP-MVSNet		85.47 107	85.04 113	86.77 84	91.52 105	69.37 147	91.63 28	87.98 183	81.51 58	87.05 145	91.83 131	66.18 226	95.29 33	70.75 174	96.89 70	95.64 59

VDDNet		84.35 132	85.39 109	81.25 197	95.13 28	59.32 245	85.42 117	81.11 243	86.41 22	87.41 138	96.21 21	73.61 174	90.61 188	66.33 209	96.85 71	93.81 108

VPNet		80.25 193	81.68 173	75.94 259	92.46 77	47.98 323	76.70 270	81.67 241	73.45 152	84.87 175	92.82 108	74.66 157	86.51 253	61.66 236	96.85 71	93.33 119

SixPastTwentyTwo		87.20 74	87.45 74	86.45 89	92.52 75	69.19 152	87.84 78	88.05 180	81.66 56	94.64 17	96.53 14	65.94 227	94.75 50	83.02 64	96.83 73	95.41 67

VPA-MVSNet		83.47 156	84.73 119	79.69 213	90.29 130	57.52 255	81.30 215	88.69 170	76.29 121	87.58 135	94.44 66	80.60 99	87.20 238	66.60 208	96.82 74	94.34 91

Gipuma		84.44 126	86.33 92	78.78 222	84.20 255	73.57 111	89.55 51	90.44 132	84.24 29	84.38 187	94.89 51	76.35 140	80.40 293	76.14 134	96.80 75	82.36 289
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CDPH-MVS		86.17 96	85.54 106	88.05 71	92.25 84	75.45 101	83.85 143	92.01 83	65.91 232	86.19 158	91.75 135	83.77 61	94.98 45	77.43 126	96.71 76	93.73 109

v748		88.91 57	89.82 45	86.19 99	90.06 135	68.53 154	88.81 65	91.48 99	84.36 28	94.19 24	95.98 25	82.52 74	92.67 137	84.30 47	96.67 77	97.37 20

DP-MVS		88.60 60	89.01 53	87.36 80	91.30 109	77.50 83	87.55 81	92.97 57	87.95 17	89.62 102	92.87 107	84.56 55	93.89 77	77.65 122	96.62 78	90.70 186

TransMVSNet (Re)		84.02 144	85.74 102	78.85 221	91.00 118	55.20 272	82.29 190	87.26 191	79.65 74	88.38 127	95.52 37	83.00 68	86.88 242	67.97 199	96.60 79	94.45 88

ambc				82.98 169	90.55 128	64.86 175	88.20 71	89.15 166		89.40 109	93.96 86	71.67 207	91.38 168	78.83 112	96.55 80	92.71 134

train_agg		85.98 100	85.28 110	88.07 68	92.34 81	79.70 66	83.94 139	90.32 136	65.79 233	84.49 185	90.97 159	81.93 84	93.63 86	81.21 80	96.54 81	90.88 181

agg_prior3		85.76 103	84.95 116	88.16 66	92.43 78	79.92 62	83.98 138	90.03 151	65.11 242	83.66 197	90.64 174	81.00 95	93.67 83	81.21 80	96.54 81	90.88 181

VDD-MVS		84.23 136	84.58 130	83.20 166	91.17 115	65.16 174	83.25 165	84.97 224	79.79 71	87.18 140	94.27 73	74.77 155	90.89 179	69.24 186	96.54 81	93.55 118

HPM-MVS++		88.93 56	88.45 62	90.38 40	94.92 32	85.85 25	89.70 46	91.27 115	78.20 95	86.69 149	92.28 123	80.36 101	95.06 43	86.17 31	96.49 84	90.22 198

test_djsdf		89.62 46	89.01 53	91.45 21	92.36 80	82.98 49	91.98 25	90.08 149	71.54 188	94.28 23	96.54 13	81.57 90	94.27 59	86.26 27	96.49 84	97.09 30

test9_res														80.83 87	96.45 86	90.57 190

agg_prior1		85.72 104	85.20 111	87.28 81	91.58 102	77.69 80	83.69 150	90.30 140	66.29 228	84.32 189	91.07 156	82.13 79	93.18 118	81.02 83	96.36 87	90.98 176

anonymousdsp		89.73 45	88.88 56	92.27 7	89.82 139	86.67 12	90.51 35	90.20 148	69.87 202	95.06 14	96.14 23	84.28 57	93.07 127	87.68 13	96.34 88	97.09 30

PHI-MVS		86.38 89	85.81 101	88.08 67	88.44 162	77.34 85	89.35 58	93.05 52	73.15 162	84.76 177	87.70 221	78.87 111	94.18 64	80.67 90	96.29 89	92.73 133

PS-MVSNAJss		88.31 62	87.90 66	89.56 51	93.31 56	77.96 78	87.94 75	91.97 85	70.73 194	94.19 24	96.67 11	76.94 131	94.57 55	83.07 62	96.28 90	96.15 42

v10		86.54 86	87.10 78	84.84 119	88.16 169	63.28 192	86.64 103	92.20 79	75.42 136	92.81 43	94.50 64	74.05 164	94.06 70	83.88 53	96.28 90	97.17 28

CNVR-MVS		87.81 69	87.68 71	88.21 65	92.87 67	77.30 87	85.25 118	91.23 116	77.31 111	87.07 144	91.47 141	82.94 69	94.71 51	84.67 43	96.27 92	92.62 142

pcd1.5k->3k		38.83 324	41.11 325	32.01 335	93.13 61	0.00 356	0.00 346	91.38 112	0.00 350	0.00 352	0.00 352	89.24 16	0.00 353	0.00 350	96.24 93	96.02 48

114514_t		83.10 161	82.54 164	84.77 122	92.90 66	69.10 153	86.65 102	90.62 128	54.66 294	81.46 223	90.81 166	76.98 130	94.38 58	72.62 163	96.18 94	90.82 184

agg_prior2														79.68 104	96.16 95	90.22 198

v13		87.31 72	88.10 63	84.94 116	88.84 151	63.75 184	87.85 77	91.47 102	79.12 81	93.72 29	95.82 27	75.20 146	93.58 94	84.76 42	96.16 95	97.48 17

AllTest		87.97 66	87.40 76	89.68 47	91.59 99	83.40 45	89.50 54	95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

TestCases				89.68 47	91.59 99	83.40 45		95.44 5	79.47 75	88.00 130	93.03 101	82.66 72	91.47 162	70.81 172	96.14 97	94.16 95

EPNet		80.37 191	78.41 203	86.23 94	76.75 312	73.28 113	87.18 88	77.45 258	76.24 122	68.14 313	88.93 200	65.41 229	93.85 78	69.47 184	96.12 99	91.55 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v12		87.15 75	87.91 65	84.84 119	88.69 154	63.52 187	87.58 80	91.46 103	78.74 89	93.57 32	95.66 31	74.94 150	93.57 95	84.50 45	96.08 100	97.43 18

v11		86.96 76	87.78 69	84.51 128	88.50 160	62.60 207	87.21 86	91.63 94	78.08 98	93.40 34	95.56 36	75.07 147	93.57 95	84.46 46	96.08 100	97.36 21

pm-mvs1		83.69 150	84.95 116	79.91 209	90.04 137	59.66 242	82.43 185	87.44 188	75.52 133	87.85 132	95.26 42	81.25 94	85.65 264	68.74 193	96.04 102	94.42 89

V9		86.96 76	87.70 70	84.74 123	88.52 159	63.27 193	87.31 85	91.45 105	78.28 94	93.43 33	95.45 38	74.59 158	93.57 95	84.23 49	96.01 103	97.38 19

mvs_tets		89.78 44	89.27 51	91.30 24	93.51 51	84.79 36	89.89 44	90.63 127	70.00 201	94.55 18	96.67 11	87.94 29	93.59 91	84.27 48	95.97 104	95.52 65

DeepPCF-MVS	81.24 5	87.28 73	86.21 96	90.49 38	91.48 106	84.90 34	83.41 160	92.38 76	70.25 199	89.35 110	90.68 170	82.85 70	94.57 55	79.55 105	95.95 105	92.00 159

V14		86.75 82	87.46 73	84.62 126	88.35 163	63.00 198	87.02 91	91.42 108	77.78 100	93.27 35	95.23 43	74.22 161	93.56 98	83.95 52	95.93 106	97.31 22

jajsoiax		89.41 49	88.81 58	91.19 28	93.38 55	84.72 37	89.70 46	90.29 143	69.27 205	94.39 20	96.38 15	86.02 49	93.52 101	83.96 51	95.92 107	95.34 68

ANet_high		83.17 160	85.68 104	75.65 261	81.24 279	45.26 328	79.94 231	92.91 59	83.83 33	91.33 69	96.88 10	80.25 102	85.92 260	68.89 191	95.89 108	95.76 53

3Dnovator+	83.92 2	89.97 39	89.66 47	90.92 32	91.27 111	81.66 54	91.25 32	94.13 22	88.89 12	88.83 117	94.26 74	77.55 123	95.86 12	84.88 39	95.87 109	95.24 71

v15		86.56 85	87.25 77	84.51 128	88.15 170	62.72 203	86.72 101	91.40 110	77.38 105	93.11 37	95.00 48	73.93 166	93.55 99	83.67 56	95.86 110	97.26 23

HQP_MVS		87.75 70	87.43 75	88.70 60	93.45 52	76.42 96	89.45 56	93.61 32	79.44 77	86.55 151	92.95 105	74.84 152	95.22 37	80.78 88	95.83 111	94.46 86

plane_prior5								93.61 32					95.22 37	80.78 88	95.83 111	94.46 86

DeepC-MVS_fast	80.27 8	86.23 94	85.65 105	87.96 72	91.30 109	76.92 89	87.19 87	91.99 84	70.56 195	84.96 172	90.69 169	80.01 104	95.14 40	78.37 114	95.78 113	91.82 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 196	80.56 186	78.89 220	89.19 146	55.93 264	85.22 119	73.78 280	82.96 40	84.28 191	92.72 112	57.38 264	90.07 203	63.80 224	95.75 114	90.68 187

ACMMP++_ref															95.74 115

原ACMM1				84.60 127	92.81 71	74.01 109		91.50 97	62.59 257	82.73 209	90.67 171	76.53 138	94.25 61	69.24 186	95.69 116	85.55 247

tfpnnormal		81.79 176	82.95 157	78.31 229	88.93 150	55.40 268	80.83 223	82.85 232	76.81 117	85.90 163	94.14 79	74.58 159	86.51 253	66.82 207	95.68 117	93.01 127

TAPA-MVS	77.73 12	85.71 105	84.83 118	88.37 63	88.78 153	79.72 65	87.15 89	93.50 35	69.17 207	85.80 164	89.56 192	80.76 97	92.13 146	73.21 160	95.51 118	93.25 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		90.60 28	90.34 40	91.38 23	89.03 148	84.23 42	93.58 4	94.68 10	90.65 7	90.33 80	93.95 89	84.50 56	95.37 31	80.87 86	95.50 119	94.53 85

v8		86.22 95	86.83 87	84.36 135	87.82 180	62.35 212	86.42 106	91.33 113	76.78 118	92.73 45	94.48 65	73.41 178	93.72 82	83.10 61	95.41 120	97.01 33

Vis-MVSNet (Re-imp)		77.82 207	77.79 206	77.92 236	88.82 152	51.29 300	83.28 163	71.97 295	74.04 146	82.23 211	89.78 188	57.38 264	89.41 209	57.22 269	95.41 120	93.05 126

FMVSNet1		84.55 123	85.45 108	81.85 188	90.27 131	61.05 232	86.83 94	88.27 177	78.57 91	89.66 98	95.64 33	75.43 143	90.68 185	69.09 189	95.33 122	93.82 105

test12				86.57 86	90.74 123	72.63 117		90.69 125		82.76 208		79.20 108	94.80 49		95.32 123	92.27 153

NCCC		87.36 71	86.87 85	88.83 58	92.32 83	78.84 73	86.58 104	91.09 120	78.77 88	84.85 176	90.89 163	80.85 96	95.29 33	81.14 82	95.32 123	92.34 150

Patchmtry		76.56 224	77.46 207	73.83 273	79.37 293	46.60 325	82.41 186	76.90 261	73.81 149	85.56 168	92.38 118	48.07 291	83.98 278	63.36 227	95.31 125	90.92 179

XVG-OURS		89.18 53	88.83 57	90.23 43	94.28 41	86.11 20	85.91 109	93.60 34	80.16 69	89.13 113	93.44 96	83.82 59	90.98 174	83.86 54	95.30 126	93.60 114

TSAR-MVS + GP.		83.95 146	82.69 160	87.72 73	89.27 144	81.45 55	83.72 149	81.58 242	74.73 140	85.66 165	86.06 241	72.56 198	92.69 136	75.44 138	95.21 127	89.01 215

test_0402		88.65 58	89.58 49	85.88 105	92.55 74	72.22 124	84.01 137	89.44 163	88.63 14	94.38 21	95.77 28	86.38 46	93.59 91	79.84 102	95.21 127	91.82 163

TinyColmap		81.25 182	82.34 167	77.99 235	85.33 238	60.68 236	82.32 189	88.33 175	71.26 190	86.97 146	92.22 125	77.10 128	86.98 241	62.37 230	95.17 129	86.31 240

DP-MVS Recon		84.05 143	83.22 153	86.52 88	91.73 97	75.27 102	83.23 167	92.40 74	72.04 178	82.04 214	88.33 211	77.91 119	93.95 76	66.17 210	95.12 130	90.34 197

PCF-MVS	74.62 15	82.15 171	80.92 184	85.84 106	89.43 141	72.30 122	80.53 224	91.82 89	57.36 282	87.81 133	89.92 186	77.67 121	93.63 86	58.69 257	95.08 131	91.58 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CSCG		86.26 92	86.47 91	85.60 110	90.87 121	74.26 108	87.98 74	91.85 88	80.35 66	89.54 108	88.01 215	79.09 109	92.13 146	75.51 137	95.06 132	90.41 195

plane_prior						76.42 96	87.15 89		75.94 127						95.03 133

new-patchmatchnet		70.10 277	73.37 249	60.29 323	81.23 280	16.95 351	59.54 329	74.62 274	62.93 254	80.97 227	87.93 218	62.83 238	71.90 313	55.24 281	95.01 134	92.00 159

v1192		84.57 122	84.69 123	84.21 139	87.75 185	62.88 200	83.02 170	91.43 106	69.08 209	89.98 87	90.89 163	72.70 194	93.62 90	82.41 70	94.97 135	96.13 43

v1921920		84.23 136	84.37 139	83.79 148	87.64 190	61.71 220	82.91 173	91.20 117	67.94 218	90.06 83	90.34 177	72.04 203	93.59 91	82.32 72	94.91 136	96.07 45

v144192		84.24 135	84.41 134	83.71 154	87.59 191	61.57 225	82.95 172	91.03 121	67.82 221	89.80 94	90.49 175	73.28 182	93.51 104	81.88 77	94.89 137	96.04 47

testing_2		84.36 130	84.64 128	83.50 163	86.74 213	63.97 183	84.56 128	90.31 138	66.22 229	91.62 64	94.55 61	75.88 141	91.95 149	77.02 131	94.89 137	94.56 81

LCM-MVSNet-Re		83.48 155	85.06 112	78.75 223	85.94 232	55.75 267	80.05 228	94.27 13	76.47 120	96.09 5	94.54 63	83.31 66	89.75 205	59.95 247	94.89 137	90.75 185

v17		86.32 90	86.95 83	84.44 132	88.00 172	62.62 206	86.74 99	91.48 99	77.17 113	92.74 44	94.56 60	73.74 170	93.53 100	83.27 59	94.87 140	97.18 27

v52		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.97 8	96.31 16	84.82 52	93.98 73	88.59 4	94.83 141	98.23 8

V4		89.97 39	90.60 37	88.07 68	88.69 154	72.01 126	91.35 30	92.64 69	82.22 48	95.98 7	96.31 16	84.80 54	93.98 73	88.59 4	94.83 141	98.23 8

v1240		84.30 133	84.51 132	83.65 155	87.65 189	61.26 229	82.85 174	91.54 96	67.94 218	90.68 78	90.65 172	71.71 206	93.64 85	82.84 67	94.78 143	96.07 45

MSLP-MVS++		85.00 114	86.03 99	81.90 185	91.84 96	71.56 136	86.75 98	93.02 56	75.95 126	87.12 141	89.39 194	77.98 117	89.40 210	77.46 124	94.78 143	84.75 259

IterMVS-LS		84.73 119	84.98 115	83.96 145	87.35 195	63.66 185	83.25 165	89.88 154	76.06 123	89.62 102	92.37 121	73.40 180	92.52 140	78.16 118	94.77 145	95.69 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 151	83.69 149	83.57 158	90.05 136	72.26 123	86.29 108	90.00 152	78.19 96	81.65 221	87.16 227	83.40 65	94.24 62	61.69 235	94.76 146	84.21 266

MVS_0304		84.88 116	83.96 146	87.64 76	87.43 194	74.83 104	84.18 133	93.30 42	77.48 104	77.39 262	88.46 206	74.53 160	95.74 16	78.09 120	94.75 147	92.36 149

ITE_SJBPF				90.11 45	90.72 124	84.97 33		90.30 140	81.56 57	90.02 84	91.20 148	82.40 76	90.81 181	73.58 152	94.66 148	94.56 81

v1144		84.54 125	84.72 121	84.00 143	87.67 188	62.55 209	82.97 171	90.93 123	70.32 198	89.80 94	90.99 158	73.50 176	93.48 105	81.69 78	94.65 149	95.97 49

test20.03		73.75 247	74.59 235	71.22 289	81.11 281	51.12 302	70.15 306	72.10 294	70.42 196	80.28 244	91.50 140	64.21 231	74.72 309	46.96 318	94.58 150	87.82 227

TSAR-MVS + MP.		88.14 64	87.82 68	89.09 57	95.72 19	76.74 92	92.49 20	91.19 118	67.85 220	86.63 150	94.84 53	79.58 107	95.96 7	87.62 14	94.50 151	94.56 81

HQP3-MVS								92.68 67							94.47 152

HQP-MVS		84.61 121	84.06 143	86.27 93	91.19 112	70.66 140	84.77 122	92.68 67	73.30 157	80.55 240	90.17 183	72.10 200	94.61 53	77.30 127	94.47 152	93.56 116

MCST-MVS		84.36 130	83.93 147	85.63 109	91.59 99	71.58 135	83.52 155	92.13 80	61.82 263	83.96 193	89.75 189	79.93 106	93.46 106	78.33 116	94.34 154	91.87 162

v16		86.24 93	86.85 86	84.43 133	87.96 174	62.59 208	86.73 100	91.48 99	77.17 113	92.67 47	94.55 61	73.63 171	93.52 101	83.26 60	94.16 155	97.17 28

alignmvs		83.94 147	83.98 145	83.80 147	87.80 184	67.88 159	84.54 129	91.42 108	73.27 160	88.41 126	87.96 216	72.33 199	90.83 180	76.02 135	94.11 156	92.69 135

USDC		76.63 222	76.73 213	76.34 256	83.46 263	57.20 257	80.02 229	88.04 181	52.14 309	83.65 198	91.25 144	63.24 237	86.65 252	54.66 285	94.11 156	85.17 250

MVS_111021_HR		84.63 120	84.34 140	85.49 111	90.18 132	75.86 100	79.23 249	87.13 196	73.35 154	85.56 168	89.34 195	83.60 63	90.50 190	76.64 132	94.05 158	90.09 203

v18		85.99 99	86.55 90	84.30 137	87.73 186	62.29 216	86.40 107	91.49 98	76.64 119	92.40 52	94.20 77	73.28 182	93.52 101	82.87 65	93.99 159	97.09 30

VNet		79.31 199	80.27 190	76.44 254	87.92 175	53.95 277	75.58 281	84.35 227	74.39 144	82.23 211	90.72 168	72.84 191	84.39 275	60.38 246	93.98 160	90.97 177

FMVSNet2		81.31 179	81.61 175	80.41 205	86.38 216	58.75 250	83.93 141	86.58 204	72.43 169	87.65 134	92.98 103	63.78 234	90.22 196	66.86 204	93.92 161	92.27 153

LF4IMVS		82.75 163	81.93 172	85.19 113	82.08 272	80.15 61	85.53 115	88.76 169	68.01 215	85.58 167	87.75 220	71.80 205	86.85 243	74.02 147	93.87 162	88.58 217

divwei89l23v2f112		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 185	89.64 99	91.12 152	72.68 195	93.10 123	80.40 93	93.81 163	95.75 54

v1141		84.16 138	84.38 136	83.52 160	87.32 197	61.70 222	82.79 176	89.74 155	71.90 185	89.64 99	91.12 152	72.68 195	93.10 123	80.39 95	93.80 164	95.75 54

v1		84.16 138	84.38 136	83.52 160	87.33 196	61.71 220	82.79 176	89.73 157	71.89 187	89.64 99	91.11 154	72.72 192	93.10 123	80.40 93	93.79 165	95.75 54

canonicalmvs		85.50 106	86.14 97	83.58 157	87.97 173	67.13 162	87.55 81	94.32 12	73.44 153	88.47 124	87.54 224	86.45 44	91.06 173	75.76 136	93.76 166	92.54 144

v2v482		84.09 141	84.24 141	83.62 156	87.13 204	61.40 226	82.71 180	89.71 158	72.19 177	89.55 106	91.41 142	70.70 211	93.20 117	81.02 83	93.76 166	96.25 41

UGNet		82.78 162	81.64 174	86.21 97	86.20 225	76.24 99	86.86 92	85.68 212	77.07 115	73.76 288	92.82 108	69.64 212	91.82 155	69.04 190	93.69 168	90.56 191
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
v7		84.81 117	85.00 114	84.23 138	88.15 170	63.27 193	83.79 147	91.39 111	71.10 192	90.07 82	91.28 143	74.04 165	93.63 86	81.48 79	93.67 169	95.79 52

旧先验1					91.97 91	71.77 130		81.78 240			91.84 130	73.92 167			93.65 170	83.61 271

view600		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 258	93.49 171	92.66 136

view800		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 258	93.49 171	92.66 136

conf0.05thres1000		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 258	93.49 171	92.66 136

tfpn		76.79 217	76.54 214	77.56 240	87.91 176	50.77 306	81.92 200	71.35 302	77.38 105	84.62 179	88.40 207	45.18 313	89.26 211	58.58 258	93.49 171	92.66 136

MVS_111021_LR		84.28 134	83.76 148	85.83 107	89.23 145	83.07 48	80.99 220	83.56 228	72.71 167	86.07 159	89.07 198	81.75 89	86.19 257	77.11 129	93.36 175	88.24 218

GBi-Net		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

test1		82.02 173	82.07 169	81.85 188	86.38 216	61.05 232	86.83 94	88.27 177	72.43 169	86.00 160	95.64 33	63.78 234	90.68 185	65.95 211	93.34 176	93.82 105

FMVSNet3		78.80 202	78.55 202	79.57 216	82.89 269	56.89 260	81.76 205	85.77 211	69.04 210	86.00 160	90.44 176	51.75 282	90.09 202	65.95 211	93.34 176	91.72 165

v6		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.26 146	72.33 173	89.94 88	91.19 149	73.30 181	93.29 112	80.26 96	93.26 179	95.62 60

v1neww		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

v7new		84.43 127	84.66 125	83.75 150	87.81 181	62.34 213	83.59 151	90.27 144	72.33 173	89.93 89	91.22 145	73.28 182	93.29 112	80.25 97	93.25 180	95.62 60

K. test v3		85.14 109	84.73 119	86.37 90	91.13 116	69.63 146	85.45 116	76.68 264	84.06 32	92.44 51	96.99 8	62.03 239	94.65 52	80.58 91	93.24 182	94.83 80

Anonymous20231206		71.38 269	71.88 263	69.88 290	86.31 220	54.37 274	70.39 305	74.62 274	52.57 305	76.73 264	88.76 201	59.94 249	72.06 312	44.35 323	93.23 183	83.23 279

新几何1				82.95 170	93.96 44	78.56 75		80.24 247	55.45 290	83.93 194	91.08 155	71.19 209	88.33 228	65.84 214	93.07 184	81.95 296

1121		80.86 186	79.81 196	84.02 142	93.93 45	78.70 74	81.64 208	80.18 248	55.43 291	83.67 196	91.15 150	71.29 208	91.41 166	67.95 200	93.06 185	81.96 295

lessismore_v0				85.95 102	91.10 117	70.99 139		70.91 306		91.79 61	94.42 67	61.76 240	92.93 130	79.52 108	93.03 186	93.93 102

TAMVS		78.08 206	76.36 219	83.23 165	90.62 125	72.87 115	79.08 250	80.01 250	61.72 265	81.35 225	86.92 230	63.96 233	88.78 223	50.61 300	93.01 187	88.04 222

EPNet_dtu		72.87 256	71.33 268	77.49 244	77.72 306	60.55 237	82.35 188	75.79 266	66.49 227	58.39 343	81.06 304	53.68 278	85.98 259	53.55 289	92.97 188	85.95 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		85.82 102	83.38 151	93.14 3	87.13 204	91.15 3	87.70 79	88.42 173	74.57 142	83.56 199	85.65 243	78.49 113	94.21 63	72.04 167	92.88 189	94.05 98

CANet		83.79 149	82.85 158	86.63 85	86.17 226	72.21 125	83.76 148	91.43 106	77.24 112	74.39 285	87.45 225	75.36 144	95.42 29	77.03 130	92.83 190	92.25 155

API-MVS		82.28 169	82.61 162	81.30 195	86.29 221	69.79 143	88.71 67	87.67 186	78.42 93	82.15 213	84.15 269	77.98 117	91.59 160	65.39 216	92.75 191	82.51 288

Regformer-1		86.00 97	85.50 107	87.49 77	84.18 256	76.90 90	83.52 155	87.94 184	82.18 50	89.19 111	85.07 256	82.28 78	91.89 152	82.40 71	92.72 192	93.69 111

Regformer-2		86.74 83	86.08 98	88.73 59	84.18 256	79.20 69	83.52 155	89.33 164	83.33 35	89.92 91	85.07 256	83.23 67	93.16 120	83.39 57	92.72 192	93.83 103

testgi		72.36 261	74.61 233	65.59 308	80.56 287	42.82 336	68.29 310	73.35 284	66.87 225	81.84 217	89.93 185	72.08 202	66.92 327	46.05 320	92.54 194	87.01 234

FMVSNet5		72.10 264	71.69 264	73.32 275	81.57 277	53.02 284	76.77 269	78.37 254	63.31 251	76.37 266	91.85 129	36.68 339	78.98 297	47.87 313	92.45 195	87.95 224

CDS-MVSNet		77.32 212	75.40 227	83.06 168	89.00 149	72.48 120	77.90 261	82.17 236	60.81 270	78.94 253	83.49 274	59.30 254	88.76 224	54.64 286	92.37 196	87.93 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		83.55 154	83.10 156	84.90 118	89.34 143	83.87 43	84.54 129	88.77 168	79.09 82	83.54 200	88.66 204	74.87 151	81.73 289	66.84 206	92.29 197	89.11 211

F-COLMAP		84.97 115	83.42 150	89.63 49	92.39 79	83.40 45	88.83 64	91.92 87	73.19 161	80.18 245	89.15 197	77.04 129	93.28 115	65.82 215	92.28 198	92.21 156

thres600view7		75.97 229	75.35 229	77.85 238	87.01 209	51.84 296	80.45 225	73.26 285	75.20 137	83.10 205	86.31 239	45.54 304	89.05 215	55.03 283	92.24 199	92.66 136

PVSNet_BlendedMVS		78.80 202	77.84 205	81.65 192	84.43 248	63.41 188	79.49 238	90.44 132	61.70 266	75.43 276	87.07 229	69.11 215	91.44 164	60.68 244	92.24 199	90.11 202

DELS-MVS		81.44 178	81.25 179	82.03 183	84.27 252	62.87 201	76.47 274	92.49 73	70.97 193	81.64 222	83.83 270	75.03 148	92.70 135	74.29 143	92.22 201	90.51 193
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
testdata				79.54 217	92.87 67	72.34 121		80.14 249	59.91 275	85.47 170	91.75 135	67.96 219	85.24 266	68.57 196	92.18 202	81.06 313

v148		82.31 168	82.48 165	81.81 191	85.59 234	59.66 242	81.47 211	86.02 209	72.85 165	88.05 129	90.65 172	70.73 210	90.91 178	75.15 139	91.79 203	94.87 77

tfpn1000		73.63 252	73.58 242	73.79 274	85.46 237	50.31 312	79.99 230	68.18 319	72.33 173	80.66 236	83.05 278	39.80 336	86.74 251	60.96 242	91.78 204	84.32 264

test222					93.31 56	76.54 93	79.38 243	77.79 256	52.59 304	82.36 210	90.84 165	66.83 223			91.69 205	81.25 308

pmmvs-eth3d		78.42 204	77.04 210	82.57 178	87.44 193	74.41 107	80.86 222	79.67 251	55.68 289	84.69 178	90.31 180	60.91 243	85.42 265	62.20 231	91.59 206	87.88 226

Vis-MVSNet		86.86 79	86.58 89	87.72 73	92.09 88	77.43 84	87.35 84	92.09 81	78.87 86	84.27 192	94.05 81	78.35 115	93.65 84	80.54 92	91.58 207	92.08 158
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thresconf0.02		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

tfpn_n400		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

tfpnconf		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

tfpnview11		73.65 248	73.53 244	73.98 268	86.13 227	50.06 315	79.45 239	68.54 315	72.01 179	80.76 232	82.50 289	41.39 327	86.83 244	59.66 250	91.36 208	85.06 252

OpenMVS	76.72 13	81.98 175	82.00 171	81.93 184	84.42 250	68.22 157	88.50 70	89.48 162	66.92 224	81.80 220	91.86 128	72.59 197	90.16 198	71.19 171	91.25 212	87.40 230

Test4		81.31 179	81.13 181	81.88 187	84.89 242	63.05 197	82.37 187	90.50 130	62.75 256	89.00 114	88.29 212	67.55 220	91.68 158	73.55 153	91.24 213	90.89 180

EG-PatchMatch MVS		84.08 142	84.11 142	83.98 144	92.22 86	72.61 118	82.20 196	87.02 200	72.63 168	88.86 115	91.02 157	78.52 112	91.11 171	73.41 155	91.09 214	88.21 219

3Dnovator	80.37 7	84.80 118	84.71 122	85.06 115	86.36 219	74.71 105	88.77 66	90.00 152	75.65 132	84.96 172	93.17 99	74.06 163	91.19 169	78.28 117	91.09 214	89.29 210

Regformer-3		85.06 111	84.67 124	86.22 95	84.27 252	73.43 112	84.07 135	85.26 216	80.77 63	88.62 121	85.48 246	80.56 100	90.39 192	81.99 75	91.04 216	94.85 79

Regformer-4		86.41 88	85.71 103	88.52 61	84.27 252	77.57 82	84.07 135	88.00 182	82.82 42	89.84 93	85.48 246	82.06 80	92.77 134	83.83 55	91.04 216	95.22 74

conf200view11		75.62 232	75.05 230	77.34 245	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 295	90.99 218	91.34 175

thres100view900		75.45 233	75.05 230	76.66 253	87.27 199	51.88 293	81.07 218	73.26 285	75.68 130	83.25 202	86.37 235	45.54 304	88.80 219	51.98 295	90.99 218	89.31 208

tfpn200view9		74.86 240	74.23 238	76.74 252	86.24 222	52.12 290	79.24 246	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 295	90.99 218	89.31 208

thres400		75.14 234	74.23 238	77.86 237	86.24 222	52.12 290	79.24 246	73.87 278	73.34 155	81.82 218	84.60 264	46.02 298	88.80 219	51.98 295	90.99 218	92.66 136

cascas		76.29 227	74.81 232	80.72 204	84.47 247	62.94 199	73.89 293	87.34 189	55.94 288	75.16 280	76.53 323	63.97 232	91.16 170	65.00 217	90.97 222	88.06 221

HSP-MVS		88.63 59	87.84 67	91.02 29	95.76 16	86.14 19	92.75 13	91.01 122	78.43 92	89.16 112	92.25 124	72.03 204	96.36 2	88.21 9	90.93 223	90.55 192

ab-mvs		79.67 198	80.56 186	76.99 247	88.48 161	56.93 258	84.70 125	86.06 208	68.95 211	80.78 231	93.08 100	75.30 145	84.62 273	56.78 271	90.90 224	89.43 207

MAR-MVS		80.24 194	78.74 201	84.73 124	86.87 212	78.18 76	85.75 112	87.81 185	65.67 236	77.84 257	78.50 315	73.79 169	90.53 189	61.59 238	90.87 225	85.49 249
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
EI-MVSNet-Vis-set		85.12 110	84.53 131	86.88 82	84.01 258	72.76 116	83.91 142	85.18 218	80.44 64	88.75 118	85.49 245	80.08 103	91.92 150	82.02 74	90.85 226	95.97 49

EI-MVSNet-UG-set		85.04 112	84.44 133	86.85 83	83.87 261	72.52 119	83.82 144	85.15 219	80.27 68	88.75 118	85.45 249	79.95 105	91.90 151	81.92 76	90.80 227	96.13 43

XVG-OURS-SEG-HR		89.59 47	89.37 50	90.28 42	94.47 39	85.95 21	86.84 93	93.91 26	80.07 70	86.75 148	93.26 97	93.64 2	90.93 176	84.60 44	90.75 228	93.97 100

test_normal		81.23 183	81.16 180	81.43 194	84.77 245	61.99 219	81.46 212	86.95 202	63.16 253	87.22 139	89.63 190	73.62 172	91.65 159	72.92 161	90.70 229	90.65 189

EI-MVSNet		82.61 164	82.42 166	83.20 166	83.25 265	63.66 185	83.50 158	85.07 220	76.06 123	86.55 151	85.10 254	73.41 178	90.25 193	78.15 119	90.67 230	95.68 58

MVSTER		77.09 214	75.70 226	81.25 197	75.27 326	61.08 231	77.49 266	85.07 220	60.78 271	86.55 151	88.68 203	43.14 323	90.25 193	73.69 151	90.67 230	92.42 145

Patchmatch-RL test		74.48 242	73.68 241	76.89 249	84.83 243	66.54 167	72.29 299	69.16 314	57.70 280	86.76 147	86.33 237	45.79 303	82.59 285	69.63 183	90.65 232	81.54 303

CMPMVS	59.41 20	75.12 236	73.57 243	79.77 210	75.84 318	67.22 161	81.21 216	82.18 235	50.78 319	76.50 265	87.66 222	55.20 274	82.99 283	62.17 232	90.64 233	89.09 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DI_MVS_plusplus_test		81.27 181	81.26 178	81.29 196	84.98 240	61.65 224	81.98 199	87.25 192	63.56 248	87.56 136	89.60 191	73.62 172	91.83 154	72.20 166	90.59 234	90.38 196

tfpn_ndepth		72.54 259	72.30 260	73.24 277	84.81 244	51.42 298	79.24 246	70.49 308	69.26 206	78.48 255	79.80 310	40.16 335	86.77 249	58.08 267	90.43 235	81.53 304

V42		83.47 156	83.37 152	83.75 150	83.16 267	63.33 191	81.31 213	90.23 147	69.51 204	90.91 75	90.81 166	74.16 162	92.29 144	80.06 99	90.22 236	95.62 60

PM-MVS		80.20 195	79.00 199	83.78 149	88.17 168	86.66 13	81.31 213	66.81 328	69.64 203	88.33 128	90.19 181	64.58 230	83.63 281	71.99 169	90.03 237	81.06 313

PLC	73.85 16	82.09 172	80.31 189	87.45 78	90.86 122	80.29 60	85.88 110	90.65 126	68.17 214	76.32 268	86.33 237	73.12 187	92.61 139	61.40 239	90.02 238	89.44 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		84.55 123	82.12 168	91.84 17	87.13 204	89.54 5	85.05 121	88.42 173	74.57 142	80.60 237	82.98 280	78.49 113	93.98 73	72.04 167	89.77 239	92.00 159

CANet_DTU		77.81 208	77.05 209	80.09 208	81.37 278	59.90 241	83.26 164	88.29 176	69.16 208	67.83 316	83.72 271	60.93 242	89.47 206	69.22 188	89.70 240	90.88 181

PMMVS2		55.64 319	59.27 316	44.74 334	64.30 349	12.32 352	40.60 343	49.79 350	53.19 301	65.06 328	84.81 260	53.60 279	49.76 346	32.68 342	89.41 241	72.15 328

Fast-Effi-MVS+-dtu		82.54 166	81.41 177	85.90 104	85.60 233	76.53 95	83.07 169	89.62 160	73.02 164	79.11 252	83.51 273	80.74 98	90.24 195	68.76 192	89.29 242	90.94 178

thres200		72.34 262	71.55 266	74.70 266	83.48 262	51.60 297	75.02 285	73.71 281	70.14 200	78.56 254	80.57 305	46.20 296	88.20 230	46.99 317	89.29 242	84.32 264

jason		77.42 211	75.75 225	82.43 181	87.10 207	69.27 148	77.99 259	81.94 239	51.47 314	77.84 257	85.07 256	60.32 246	89.00 216	70.74 175	89.27 244	89.03 213
jason: jason.
MG-MVS		80.32 192	80.94 183	78.47 228	88.18 167	52.62 288	82.29 190	85.01 223	72.01 179	79.24 251	92.54 116	69.36 213	93.36 111	70.65 176	89.19 245	89.45 205

BH-untuned		80.96 185	80.99 182	80.84 201	88.55 158	68.23 156	80.33 226	88.46 172	72.79 166	86.55 151	86.76 231	74.72 156	91.77 156	61.79 234	88.99 246	82.52 287

PVSNet_Blended_VisFu		81.55 177	80.49 188	84.70 125	91.58 102	73.24 114	84.21 132	91.67 93	62.86 255	80.94 228	87.16 227	67.27 221	92.87 133	69.82 182	88.94 247	87.99 223

MVSFormer		82.23 170	81.57 176	84.19 141	85.54 235	69.26 149	91.98 25	90.08 149	71.54 188	76.23 269	85.07 256	58.69 257	94.27 59	86.26 27	88.77 248	89.03 213

lupinMVS		76.37 226	74.46 236	82.09 182	85.54 235	69.26 149	76.79 268	80.77 246	50.68 321	76.23 269	82.82 284	58.69 257	88.94 217	69.85 181	88.77 248	88.07 220

RPSCF		88.00 65	86.93 84	91.22 27	90.08 133	89.30 6	89.68 48	91.11 119	79.26 79	89.68 97	94.81 57	82.44 75	87.74 234	76.54 133	88.74 250	96.61 38

PAPM_NR		83.23 158	83.19 155	83.33 164	90.90 120	65.98 169	88.19 72	90.78 124	78.13 97	80.87 230	87.92 219	73.49 177	92.42 141	70.07 180	88.40 251	91.60 168

xiu_mvs_v1_base_debu		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 237	79.59 247	82.73 286	76.94 131	90.14 199	73.22 156	88.33 252	86.90 235

xiu_mvs_v1_base		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 237	79.59 247	82.73 286	76.94 131	90.14 199	73.22 156	88.33 252	86.90 235

xiu_mvs_v1_base_debi		80.84 187	80.14 192	82.93 171	88.31 164	71.73 131	79.53 235	87.17 193	65.43 237	79.59 247	82.73 286	76.94 131	90.14 199	73.22 156	88.33 252	86.90 235

XXY-MVS		74.44 244	76.19 221	69.21 295	84.61 246	52.43 289	71.70 301	77.18 259	60.73 272	80.60 237	90.96 161	75.44 142	69.35 319	56.13 274	88.33 252	85.86 245

Fast-Effi-MVS+		81.04 184	80.57 185	82.46 180	87.50 192	63.22 195	78.37 256	89.63 159	68.01 215	81.87 216	82.08 296	82.31 77	92.65 138	67.10 202	88.30 256	91.51 171

MDA-MVSNet-bldmvs		77.47 210	76.90 211	79.16 219	79.03 296	64.59 176	66.58 318	75.67 268	73.15 162	88.86 115	88.99 199	66.94 222	81.23 290	64.71 218	88.22 257	91.64 167

PAPR		78.84 201	78.10 204	81.07 199	85.17 239	60.22 239	82.21 194	90.57 129	62.51 258	75.32 278	84.61 263	74.99 149	92.30 143	59.48 255	88.04 258	90.68 187

BH-RMVSNet		80.53 190	80.22 191	81.49 193	87.19 203	66.21 168	77.79 262	86.23 206	74.21 145	83.69 195	88.50 205	73.25 186	90.75 182	63.18 229	87.90 259	87.52 228

Effi-MVS+		83.90 148	84.01 144	83.57 158	87.22 202	65.61 172	86.55 105	92.40 74	78.64 90	81.34 226	84.18 267	83.65 62	92.93 130	74.22 144	87.87 260	92.17 157

MVS_Test		82.47 167	83.22 153	80.22 207	82.62 271	57.75 254	82.54 184	91.96 86	71.16 191	82.89 207	92.52 117	77.41 124	90.50 190	80.04 100	87.84 261	92.40 147

QAPM		82.59 165	82.59 163	82.58 176	86.44 214	66.69 166	89.94 43	90.36 135	67.97 217	84.94 174	92.58 115	72.71 193	92.18 145	70.63 177	87.73 262	88.85 216

PVSNet_Blended		76.49 225	75.40 227	79.76 211	84.43 248	63.41 188	75.14 284	90.44 132	57.36 282	75.43 276	78.30 316	69.11 215	91.44 164	60.68 244	87.70 263	84.42 262

pmmvs5		70.73 272	70.07 275	72.72 282	77.03 311	52.73 286	74.14 290	75.65 269	50.36 323	72.17 297	85.37 252	55.42 273	80.67 292	52.86 292	87.59 264	84.77 258

testmv		70.47 274	70.70 269	69.77 292	86.22 224	53.89 278	67.32 315	71.91 296	63.32 250	78.16 256	89.47 193	56.12 269	73.10 310	36.43 336	87.33 265	82.33 290

IB-MVS	62.13 19	71.64 266	68.97 280	79.66 215	80.80 286	62.26 218	73.94 292	76.90 261	63.27 252	68.63 312	76.79 321	33.83 342	91.84 153	59.28 256	87.26 266	84.88 257
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
N_pmnet		70.20 275	68.80 282	74.38 267	80.91 283	84.81 35	59.12 332	76.45 265	55.06 292	75.31 279	82.36 293	55.74 270	54.82 344	47.02 316	87.24 267	83.52 272

pmmvs4		74.92 239	72.98 253	80.73 203	84.95 241	71.71 134	76.23 276	77.59 257	52.83 303	77.73 260	86.38 234	56.35 267	84.97 269	57.72 268	87.05 268	85.51 248

MIMVSNet		71.09 270	71.59 265	69.57 294	87.23 201	50.07 314	78.91 251	71.83 297	60.20 274	71.26 301	91.76 134	55.08 275	76.09 303	41.06 328	87.02 269	82.54 286

HyFIR lowres test		75.12 236	72.66 255	82.50 179	91.44 108	65.19 173	72.47 298	87.31 190	46.79 330	80.29 243	84.30 266	52.70 280	92.10 148	51.88 299	86.73 270	90.22 198

diffmvs		79.20 200	79.04 198	79.69 213	78.64 300	58.90 247	81.79 204	87.61 187	65.07 243	73.65 290	89.80 187	73.10 188	87.79 233	75.02 141	86.63 271	92.38 148

MSDG		80.06 197	79.99 195	80.25 206	83.91 260	68.04 158	77.51 265	89.19 165	77.65 101	81.94 215	83.45 275	76.37 139	86.31 256	63.31 228	86.59 272	86.41 238

Patchmatch-test		65.91 297	67.38 288	61.48 320	75.51 322	43.21 335	68.84 308	63.79 332	62.48 259	72.80 293	83.42 276	44.89 318	59.52 341	48.27 312	86.45 273	81.70 299

mvs_anonymous		78.13 205	78.76 200	76.23 258	79.24 294	50.31 312	78.69 254	84.82 225	61.60 267	83.09 206	92.82 108	73.89 168	87.01 239	68.33 197	86.41 274	91.37 173

semantic-postprocess				84.34 136	83.93 259	69.66 145		81.09 244	72.43 169	86.47 157	90.19 181	57.56 263	93.15 122	77.45 125	86.39 275	90.22 198

E-PMN		61.59 308	61.62 308	61.49 319	66.81 345	55.40 268	53.77 339	60.34 339	66.80 226	58.90 341	65.50 341	40.48 334	66.12 331	55.72 276	86.25 276	62.95 337

EMVS		61.10 311	60.81 311	61.99 317	65.96 347	55.86 265	53.10 340	58.97 341	67.06 222	56.89 345	63.33 342	40.98 332	67.03 326	54.79 284	86.18 277	63.08 336

EU-MVSNet		75.12 236	74.43 237	77.18 246	83.11 268	59.48 244	85.71 114	82.43 234	39.76 342	85.64 166	88.76 201	44.71 319	87.88 232	73.86 150	85.88 278	84.16 267

1111		61.71 306	63.77 302	55.55 329	78.05 303	25.74 348	60.62 326	67.52 320	66.09 230	74.68 282	86.50 232	16.00 354	59.22 342	38.79 331	85.65 279	81.70 299

GA-MVS		75.83 230	74.61 233	79.48 218	81.87 274	59.25 246	73.42 296	82.88 231	68.68 212	79.75 246	81.80 298	50.62 284	89.46 207	66.85 205	85.64 280	89.72 204

MVS		73.21 254	72.59 257	75.06 263	80.97 282	60.81 235	81.64 208	85.92 210	46.03 333	71.68 299	77.54 317	68.47 218	89.77 204	55.70 277	85.39 281	74.60 324

PatchT		70.52 273	72.76 254	63.79 314	79.38 292	33.53 344	77.63 263	65.37 330	73.61 150	71.77 298	92.79 111	44.38 320	75.65 306	64.53 222	85.37 282	82.18 292

TR-MVS		76.77 221	75.79 223	79.72 212	86.10 231	65.79 171	77.14 267	83.02 230	65.20 241	81.40 224	82.10 295	66.30 224	90.73 184	55.57 278	85.27 283	82.65 283

BH-w/o		76.57 223	76.07 222	78.10 233	86.88 211	65.92 170	77.63 263	86.33 205	65.69 235	80.89 229	79.95 309	68.97 217	90.74 183	53.01 291	85.25 284	77.62 318

test1235678		65.57 299	65.73 296	65.06 311	82.84 270	50.90 304	62.90 323	69.26 312	57.17 285	72.36 295	83.04 279	46.02 298	70.10 316	32.79 341	85.24 285	74.19 325

IterMVS		76.91 216	76.34 220	78.64 225	80.91 283	64.03 181	76.30 275	79.03 252	64.88 245	83.11 204	89.16 196	59.90 250	84.46 274	68.61 195	85.15 286	87.42 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	70.19 17	77.77 209	77.46 207	78.71 224	84.39 251	61.15 230	81.18 217	82.52 233	62.45 260	83.34 201	87.37 226	66.20 225	88.66 226	64.69 219	85.02 287	86.32 239

test-LLR		67.21 291	66.74 292	68.63 299	76.45 315	55.21 270	67.89 311	67.14 325	62.43 261	65.08 326	72.39 331	43.41 321	69.37 317	61.00 240	84.89 288	81.31 306

test-mter		65.00 300	63.79 301	68.63 299	76.45 315	55.21 270	67.89 311	67.14 325	50.98 318	65.08 326	72.39 331	28.27 349	69.37 317	61.00 240	84.89 288	81.31 306

PS-MVSNAJ		77.04 215	76.53 218	78.56 226	87.09 208	61.40 226	75.26 283	87.13 196	61.25 268	74.38 286	77.22 320	76.94 131	90.94 175	64.63 220	84.83 290	83.35 276

xiu_mvs_v2_base		77.19 213	76.75 212	78.52 227	87.01 209	61.30 228	75.55 282	87.12 198	61.24 269	74.45 284	78.79 314	77.20 125	90.93 176	64.62 221	84.80 291	83.32 277

pmmvs3		62.47 302	60.02 315	69.80 291	71.58 342	64.00 182	70.52 304	58.44 342	39.77 341	66.05 320	75.84 324	27.10 351	72.28 311	46.15 319	84.77 292	73.11 327

MDTV_nov1_ep13			68.29 286		78.03 305	43.87 333	74.12 291	72.22 293	52.17 307	67.02 318	85.54 244	45.36 309	80.85 291	55.73 275	84.42 293

1112_ss		74.82 241	73.74 240	78.04 234	89.57 140	60.04 240	76.49 273	87.09 199	54.31 295	73.66 289	79.80 310	60.25 247	86.76 250	58.37 262	84.15 294	87.32 231

PatchMatch-RL		74.48 242	73.22 250	78.27 231	87.70 187	85.26 30	75.92 277	70.09 309	64.34 247	76.09 271	81.25 303	65.87 228	78.07 298	53.86 288	83.82 295	71.48 329

MDA-MVSNet_test_wron		70.05 279	70.44 271	68.88 297	73.84 331	53.47 280	58.93 334	67.28 323	58.43 277	87.09 143	85.40 250	59.80 252	67.25 325	59.66 250	83.54 296	85.92 244

YYNet1		70.06 278	70.44 271	68.90 296	73.76 332	53.42 282	58.99 333	67.20 324	58.42 278	87.10 142	85.39 251	59.82 251	67.32 324	59.79 248	83.50 297	85.96 242

Test_1112_low_res		73.90 246	73.08 251	76.35 255	90.35 129	55.95 263	73.40 297	86.17 207	50.70 320	73.14 291	85.94 242	58.31 259	85.90 261	56.51 272	83.22 298	87.20 232

PVSNet	58.17 21	66.41 295	65.63 297	68.75 298	81.96 273	49.88 319	62.19 325	72.51 292	51.03 317	68.04 314	75.34 327	50.84 283	74.77 307	45.82 321	82.96 299	81.60 302

gg-mvs-nofinetune		68.96 286	69.11 279	68.52 301	76.12 317	45.32 327	83.59 151	55.88 345	86.68 20	64.62 329	97.01 7	30.36 346	83.97 279	44.78 322	82.94 300	76.26 321

CR-MVSNet		74.00 245	73.04 252	76.85 250	79.58 289	62.64 204	82.58 181	76.90 261	50.50 322	75.72 274	92.38 118	48.07 291	84.07 276	68.72 194	82.91 301	83.85 268

RPMNet		76.06 228	75.79 223	76.85 250	79.58 289	62.64 204	82.58 181	71.75 299	74.80 139	75.72 274	92.59 113	48.69 289	84.07 276	73.48 154	82.91 301	83.85 268

test0.0.03 1		64.66 301	64.36 300	65.57 309	75.03 328	46.89 324	64.69 321	61.58 338	62.43 261	71.18 303	77.54 317	43.41 321	68.47 322	40.75 329	82.65 303	81.35 305

HY-MVS	64.64 18	73.03 255	72.47 259	74.71 265	83.36 264	54.19 275	82.14 197	81.96 237	56.76 287	69.57 309	86.21 240	60.03 248	84.83 272	49.58 306	82.65 303	85.11 251

Patchmatch-test1		72.75 257	72.61 256	73.19 278	81.62 276	55.86 265	78.89 252	71.37 301	61.73 264	74.93 281	82.15 294	60.46 245	81.80 287	59.68 249	82.63 305	81.92 297

testus		62.33 304	63.03 304	60.20 324	78.78 298	40.74 337	59.14 330	69.80 311	49.26 326	71.41 300	74.72 329	52.33 281	63.52 336	29.84 343	82.01 306	76.36 320

CHOSEN 1792x2688		72.45 260	70.56 270	78.13 232	90.02 138	63.08 196	68.72 309	83.16 229	42.99 339	75.92 272	85.46 248	57.22 266	85.18 268	49.87 304	81.67 307	86.14 241

WTY-MVS		67.91 289	68.35 284	66.58 306	80.82 285	48.12 322	65.96 319	72.60 290	53.67 299	71.20 302	81.68 300	58.97 256	69.06 321	48.57 309	81.67 307	82.55 285

TESTMET0.1,1		61.29 309	60.32 313	64.19 313	72.06 340	51.30 299	67.89 311	62.09 334	45.27 334	60.65 335	69.01 334	27.93 350	64.74 335	56.31 273	81.65 309	76.53 319

PAPM		71.77 265	70.06 276	76.92 248	86.39 215	53.97 276	76.62 271	86.62 203	53.44 300	63.97 330	84.73 262	57.79 262	92.34 142	39.65 330	81.33 310	84.45 261

DSMNet-mixed		60.98 312	61.61 309	59.09 326	72.88 338	45.05 330	74.70 287	46.61 351	26.20 345	65.34 324	90.32 179	55.46 272	63.12 339	41.72 327	81.30 311	69.09 333

sss		66.92 292	67.26 290	65.90 307	77.23 308	51.10 303	64.79 320	71.72 300	52.12 310	70.13 307	80.18 307	57.96 260	65.36 334	50.21 301	81.01 312	81.25 308

tpm		67.95 288	68.08 287	67.55 303	78.74 299	43.53 334	75.60 280	67.10 327	54.92 293	72.23 296	88.10 214	42.87 324	75.97 304	52.21 293	80.95 313	83.15 280

tpmp4_e23		69.43 282	67.33 289	75.72 260	78.53 301	52.75 285	82.13 198	74.91 271	49.23 327	66.37 319	84.17 268	41.28 331	88.67 225	49.73 305	79.63 314	85.75 246

tpm2		68.45 287	66.83 291	73.30 276	78.93 297	48.50 320	79.76 232	71.76 298	47.50 329	69.92 308	83.60 272	42.07 326	88.40 227	48.44 311	79.51 315	83.01 282

FPMVS		72.29 263	72.00 262	73.14 279	88.63 157	85.00 32	74.65 288	67.39 322	71.94 184	77.80 259	87.66 222	50.48 285	75.83 305	49.95 302	79.51 315	58.58 341

UnsupCasMVSNet_bld		69.21 285	69.68 277	67.82 302	79.42 291	51.15 301	67.82 314	75.79 266	54.15 296	77.47 261	85.36 253	59.26 255	70.64 315	48.46 310	79.35 317	81.66 301

CostFormer		69.98 280	68.68 283	73.87 272	77.14 309	50.72 310	79.26 245	74.51 276	51.94 311	70.97 304	84.75 261	45.16 317	87.49 235	55.16 282	79.23 318	83.40 275

1314		73.22 253	72.56 258	75.20 262	80.41 288	57.84 252	81.64 208	85.36 215	51.68 312	73.10 292	76.65 322	61.45 241	85.19 267	63.54 225	79.21 319	82.59 284

PMMVS		61.65 307	60.38 312	65.47 310	65.40 348	69.26 149	63.97 322	61.73 337	36.80 344	60.11 336	68.43 335	59.42 253	66.35 330	48.97 308	78.57 320	60.81 338

test2356		56.69 316	55.15 320	61.32 321	73.20 334	44.11 332	54.95 337	62.52 333	48.75 328	62.45 332	68.42 336	21.10 353	65.67 333	26.86 345	78.08 321	74.19 325

MS-PatchMatch		70.93 271	70.22 274	73.06 280	81.85 275	62.50 210	73.82 294	77.90 255	52.44 306	75.92 272	81.27 302	55.67 271	81.75 288	55.37 280	77.70 322	74.94 323

UnsupCasMVSNet_eth		71.63 267	72.30 260	69.62 293	76.47 314	52.70 287	70.03 307	80.97 245	59.18 276	79.36 250	88.21 213	60.50 244	69.12 320	58.33 264	77.62 323	87.04 233

CVMVSNet		72.62 258	71.41 267	76.28 257	83.25 265	60.34 238	83.50 158	79.02 253	37.77 343	76.33 267	85.10 254	49.60 287	87.41 236	70.54 178	77.54 324	81.08 311

GG-mvs-BLEND				67.16 304	73.36 333	46.54 326	84.15 134	55.04 346		58.64 342	61.95 344	29.93 347	83.87 280	38.71 334	76.92 325	71.07 330

CHOSEN 280x420		59.08 314	56.52 319	66.76 305	76.51 313	64.39 179	49.62 342	59.00 340	43.86 337	55.66 346	68.41 337	35.55 341	68.21 323	43.25 324	76.78 326	67.69 334

tpmvs		70.16 276	69.56 278	71.96 287	74.71 330	48.13 321	79.63 233	75.45 270	65.02 244	70.26 306	81.88 297	45.34 310	85.68 263	58.34 263	75.39 327	82.08 293

MVP-Stereo		75.81 231	73.51 248	82.71 174	89.35 142	73.62 110	80.06 227	85.20 217	60.30 273	73.96 287	87.94 217	57.89 261	89.45 208	52.02 294	74.87 328	85.06 252
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		66.43 294	64.37 299	72.63 283	74.86 329	50.86 305	76.52 272	72.74 289	54.06 297	65.50 323	68.30 338	32.13 344	84.84 271	61.63 237	73.59 329	82.19 291

PatchFormer-LS_test		67.91 289	66.49 295	72.17 286	75.29 325	51.85 295	75.68 278	73.62 283	57.23 284	68.64 310	68.13 339	42.19 325	82.76 284	64.06 223	73.51 330	81.89 298

new_pmnet		55.69 318	57.66 317	49.76 331	75.47 323	30.59 345	59.56 328	51.45 349	43.62 338	62.49 331	75.48 325	40.96 333	49.15 347	37.39 335	72.52 331	69.55 332

test12356		54.91 320	57.14 318	48.22 333	75.83 319	17.47 350	52.31 341	69.20 313	51.66 313	60.11 336	75.40 326	29.77 348	62.62 340	27.64 344	72.37 332	64.59 335

PatchmatchNet		69.71 281	68.83 281	72.33 285	77.66 307	53.60 279	79.29 244	69.99 310	57.66 281	72.53 294	82.93 283	46.45 295	80.08 296	60.91 243	72.09 333	83.31 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 310	62.92 305	55.87 327	79.09 295	35.34 343	71.83 300	57.98 344	46.56 331	59.05 340	91.14 151	49.95 286	76.43 302	38.74 333	71.92 334	55.84 342

tpmrst		66.28 296	66.69 293	65.05 312	72.82 339	39.33 339	78.20 257	70.69 307	53.16 302	67.88 315	80.36 306	48.18 290	74.75 308	58.13 265	70.79 335	81.08 311

LP		69.42 283	68.30 285	72.77 281	71.48 343	56.84 261	73.66 295	74.84 272	63.52 249	70.95 305	83.35 277	49.55 288	77.15 301	57.13 270	70.21 336	84.33 263

tpm cat1		66.76 293	65.21 298	71.42 288	77.09 310	50.62 311	78.01 258	73.68 282	44.89 335	68.64 310	79.00 313	45.51 307	82.42 286	49.91 303	70.15 337	81.23 310

ADS-MVSNet2		65.87 298	63.64 303	72.55 284	73.16 336	56.92 259	67.10 316	74.81 273	49.74 324	66.04 321	82.97 281	46.71 293	77.26 299	42.29 325	69.96 338	83.46 273

ADS-MVSNet		61.90 305	62.19 307	61.03 322	73.16 336	36.42 342	67.10 316	61.75 336	49.74 324	66.04 321	82.97 281	46.71 293	63.21 338	42.29 325	69.96 338	83.46 273

JIA-IIPM		69.41 284	66.64 294	77.70 239	73.19 335	71.24 137	75.67 279	65.56 329	70.42 196	65.18 325	92.97 104	33.64 343	83.06 282	53.52 290	69.61 340	78.79 317

testpf		58.55 315	61.58 310	49.48 332	66.03 346	40.05 338	74.40 289	58.07 343	64.72 246	59.36 338	72.67 330	22.76 352	66.92 327	67.07 203	69.15 341	41.46 344

EPMVS		62.47 302	62.63 306	62.01 316	70.63 344	38.74 340	74.76 286	52.86 347	53.91 298	67.71 317	80.01 308	39.40 337	66.60 329	55.54 279	68.81 342	80.68 315

MVE	40.22 23	51.82 322	50.47 324	55.87 327	62.66 350	51.91 292	31.61 345	39.28 352	40.65 340	50.76 347	74.98 328	56.24 268	44.67 348	33.94 340	64.11 343	71.04 331
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 313	60.29 314	61.92 318	72.04 341	38.67 341	70.83 302	64.08 331	51.28 316	60.75 334	77.28 319	36.59 340	71.58 314	47.41 314	62.34 344	75.52 322

PVSNet_0	51.08 22	56.10 317	54.97 321	59.48 325	75.12 327	53.28 283	55.16 336	61.89 335	44.30 336	59.16 339	62.48 343	54.22 277	65.91 332	35.40 338	47.01 345	59.25 340

tmp_tt		20.25 326	24.50 327	7.49 338	4.47 352	8.70 353	34.17 344	25.16 354	1.00 347	32.43 349	18.49 346	39.37 338	9.21 350	21.64 346	43.75 346	4.57 346

PNet_i23d		52.13 321	51.24 323	54.79 330	75.56 320	45.26 328	54.54 338	52.55 348	66.95 223	57.19 344	65.82 340	13.15 356	63.40 337	36.39 337	39.04 347	55.71 343

DeepMVS_CX				24.13 337	32.95 351	29.49 346		21.63 355	12.07 346	37.95 348	45.07 345	30.84 345	19.21 349	17.94 347	33.06 348	23.69 345

.test1245		48.02 323	54.41 322	28.84 336	78.05 303	25.74 348	60.62 326	67.52 320	66.09 230	74.68 282	86.50 232	16.00 354	59.22 342	38.79 331	1.47 349	1.55 348

testmvs		5.91 330	7.65 331	0.72 340	1.20 353	0.37 355	59.14 330	0.67 357	0.49 349	1.11 350	2.76 350	0.94 358	0.24 352	1.02 349	1.47 349	1.55 348

test123		6.27 329	8.08 330	0.84 339	1.11 354	0.57 354	62.90 323	0.82 356	0.54 348	1.07 351	2.75 351	1.26 357	0.30 351	1.04 348	1.26 351	1.66 347

cdsmvs_eth3d_5k		20.81 325	27.75 326	0.00 341	0.00 355	0.00 356	0.00 346	85.44 214	0.00 350	0.00 352	82.82 284	81.46 91	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		6.41 328	8.55 329	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	76.94 131	0.00 353	0.00 350	0.00 352	0.00 350

sosnet-low-res		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

sosnet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uncertanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

Regformer		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ab-mvs-re		6.65 327	8.87 328	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	79.80 310	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

test_part2					93.86 47	77.77 79				92.84 42

test_all								93.79 29

sam_mvs1												46.11 297

sam_mvs												45.92 302

MTGPA								91.81 90

test_post1							78.85 253				3.13 348	45.19 312	80.13 295	58.11 266

test_post											3.10 349	45.43 308	77.22 300

patchmatchnet-post											81.71 299	45.93 301	87.01 239

MTMP								33.14 353

gm-plane-assit					75.42 324	44.97 331			52.17 307		72.36 333		87.90 231	54.10 287

TEST9					92.34 81	79.70 66	83.94 139	90.32 136	65.41 240	84.49 185	90.97 159	82.03 82	93.63 86

test_8					92.09 88	78.87 72	83.82 144	90.31 138	65.79 233	84.36 188	90.96 161	81.93 84	93.44 107

agg_prior					91.58 102	77.69 80		90.30 140		84.32 189			93.18 118

test_prior4						78.97 71	84.59 127

test_prior				86.32 91	90.59 126	71.99 128		92.85 61					94.17 66			92.80 131

旧先验2							81.73 206		56.88 286	86.54 156			84.90 270	72.81 162

新几何2							81.72 207

无先验							82.81 175	85.62 213	58.09 279				91.41 166	67.95 200		84.48 260

原ACMM2							82.26 193

testdata2													86.43 255	63.52 226

segment_acmp												81.94 83

testdata1							79.62 234		73.95 148

plane_prior7					93.45 52	77.31 86

plane_prior6					92.61 72	76.54 93						74.84 152

plane_prior4											92.95 105

plane_prior3						76.85 91			77.79 99	86.55 151

plane_prior2							89.45 56		79.44 77

plane_prior1					92.83 70

n2								0.00 358

nn								0.00 358

door-mid								74.45 277

test11								91.46 103

door								72.57 291

HQP5-MVS						70.66 140

HQP-NCC					91.19 112		84.77 122		73.30 157	80.55 240

ACMP_Plane					91.19 112		84.77 122		73.30 157	80.55 240

BP-MVS														77.30 127

HQP4-MVS										80.56 239			94.61 53			93.56 116

HQP2-MVS												72.10 200

NP-MVS					91.95 92	74.55 106					90.17 183

MDTV_nov1_ep13_2view						27.60 347	70.76 303		46.47 332	61.27 333		45.20 311		49.18 307		83.75 270

Test By Simon												79.09 109

High-resolution multi-view benchmark