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.

test_part1		93.54 2	94.85 2	89.62 58	94.83 39	74.49 122	94.02 4	97.02 2	92.06 2	96.94 2	98.50 2	95.09 2	96.03 8	85.83 44	99.33 1	98.33 7

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 32	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 13	85.07 50	99.27 2	99.54 1

WR-MVS_H		89.91 50	91.31 32	85.71 122	96.32 10	62.39 231	89.54 67	93.31 65	90.21 10	95.57 9	95.66 28	81.42 112	95.90 14	80.94 93	98.80 3	98.84 5

ACMP	79.16 10	90.54 35	90.60 48	90.35 46	94.36 46	80.98 63	89.16 73	94.05 35	79.03 99	92.87 43	93.74 99	90.60 13	95.21 57	82.87 74	98.76 4	94.87 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	77.89 11	90.73 31	91.50 24	88.44 76	93.00 80	76.26 114	89.65 63	95.55 5	87.72 23	93.89 27	94.94 44	91.62 5	93.44 123	78.35 119	98.76 4	95.61 47

PS-CasMVS		90.06 43	91.92 14	84.47 143	96.56 7	58.83 272	89.04 75	92.74 91	91.40 5	96.12 4	96.06 23	87.23 47	95.57 33	79.42 111	98.74 6	99.00 2

LPG-MVS_test		91.47 20	91.68 19	90.82 38	94.75 41	81.69 57	90.00 52	94.27 20	82.35 60	93.67 34	94.82 48	91.18 6	95.52 39	85.36 48	98.73 7	95.23 57

LGP-MVS_train				90.82 38	94.75 41	81.69 57		94.27 20	82.35 60	93.67 34	94.82 48	91.18 6	95.52 39	85.36 48	98.73 7	95.23 57

PEN-MVS		90.03 45	91.88 17	84.48 142	96.57 6	58.88 269	88.95 76	93.19 72	91.62 4	96.01 6	96.16 21	87.02 49	95.60 32	78.69 116	98.72 9	98.97 3

CP-MVSNet		89.27 61	90.91 43	84.37 144	96.34 9	58.61 274	88.66 85	92.06 105	90.78 6	95.67 7	95.17 39	81.80 108	95.54 38	79.00 114	98.69 10	98.95 4

TranMVSNet+NR-MVSNet		87.86 78	88.76 73	85.18 130	94.02 57	64.13 210	84.38 148	91.29 126	84.88 35	92.06 59	93.84 94	86.45 58	93.73 107	73.22 172	98.66 11	97.69 10

NR-MVSNet		86.00 102	86.22 101	85.34 128	93.24 75	64.56 206	82.21 205	90.46 145	80.99 75	88.42 129	91.97 140	77.56 145	93.85 102	72.46 183	98.65 12	97.61 11

UA-Net		91.49 18	91.53 23	91.39 26	94.98 35	82.95 54	93.52 6	92.79 89	88.22 20	88.53 126	97.64 3	83.45 81	94.55 80	86.02 43	98.60 13	96.67 26

FC-MVSNet-test		85.93 105	87.05 89	82.58 186	92.25 101	56.44 288	85.75 125	93.09 75	77.33 116	91.94 63	94.65 53	74.78 175	93.41 125	75.11 156	98.58 14	97.88 8

DTE-MVSNet		89.98 47	91.91 16	84.21 149	96.51 8	57.84 277	88.93 78	92.84 88	91.92 3	96.16 3	96.23 19	86.95 50	95.99 9	79.05 113	98.57 15	98.80 6

UniMVSNet (Re)		86.87 86	86.98 91	86.55 100	93.11 78	68.48 179	83.80 161	92.87 85	80.37 80	89.61 108	91.81 147	77.72 143	94.18 89	75.00 157	98.53 16	96.99 23

Baseline_NR-MVSNet		84.00 144	85.90 107	78.29 252	91.47 128	53.44 307	82.29 201	87.00 217	79.06 98	89.55 110	95.72 27	77.20 149	86.14 274	72.30 184	98.51 17	95.28 54

TDRefinement		93.52 3	93.39 4	93.88 1	95.94 14	90.26 3	95.70 2	96.46 3	90.58 8	92.86 44	96.29 17	88.16 35	94.17 91	86.07 40	98.48 18	97.22 18

ACMM	79.39 9	90.65 32	90.99 40	89.63 56	95.03 34	83.53 47	89.62 64	93.35 61	79.20 96	93.83 28	93.60 101	90.81 9	92.96 139	85.02 52	98.45 19	92.41 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS-pluss		90.81 30	91.08 36	89.99 51	95.97 13	79.88 70	88.13 90	94.51 18	75.79 136	92.94 41	94.96 43	88.36 29	95.01 63	90.70 2	98.40 20	95.09 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		91.67 15	91.58 22	91.96 14	95.29 31	87.62 12	93.38 8	93.36 60	83.16 51	91.06 77	94.00 84	88.26 32	95.71 29	87.28 23	98.39 21	92.55 149

UniMVSNet_NR-MVSNet		86.84 88	87.06 88	86.17 113	92.86 85	67.02 188	82.55 194	91.56 118	83.08 53	90.92 79	91.82 146	78.25 139	93.99 97	74.16 161	98.35 22	97.49 14

DU-MVS		86.80 89	86.99 90	86.21 111	93.24 75	67.02 188	83.16 181	92.21 101	81.73 67	90.92 79	91.97 140	77.20 149	93.99 97	74.16 161	98.35 22	97.61 11

zzz-MVS		91.27 23	91.26 34	91.29 29	96.59 4	86.29 17	88.94 77	91.81 114	84.07 38	92.00 60	94.40 66	86.63 54	95.28 53	88.59 5	98.31 24	92.30 157

MTAPA		91.52 17	91.60 21	91.29 29	96.59 4	86.29 17	92.02 29	91.81 114	84.07 38	92.00 60	94.40 66	86.63 54	95.28 53	88.59 5	98.31 24	92.30 157

ACMH	76.49 14	89.34 60	91.14 35	83.96 155	92.50 93	70.36 163	89.55 65	93.84 45	81.89 66	94.70 13	95.44 33	90.69 10	88.31 248	83.33 69	98.30 26	93.20 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	83.01 3	91.97 12	91.95 13	92.04 12	93.68 65	86.15 21	93.37 9	95.10 12	90.28 9	92.11 57	95.03 42	89.75 22	94.93 65	79.95 103	98.27 27	95.04 61
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 13	91.87 18	92.03 13	95.53 26	85.91 25	93.35 10	94.16 28	82.52 59	92.39 54	94.14 78	89.15 24	95.62 31	87.35 20	98.24 28	94.56 71
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 10	92.20 12	91.91 17	95.58 25	84.67 41	93.51 7	94.85 15	82.88 55	91.77 65	93.94 92	90.55 14	95.73 28	88.50 7	98.23 29	95.33 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_0728_THIRD									85.33 31	93.75 31	94.65 53	87.44 45	95.78 25	87.41 19	98.21 30	92.98 132

MP-MVS		91.14 28	90.91 43	91.83 21	96.18 11	86.88 14	92.20 26	93.03 80	82.59 58	88.52 127	94.37 69	86.74 53	95.41 48	86.32 34	98.21 30	93.19 126
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		91.16 27	91.36 27	90.55 42	93.91 60	80.97 64	91.49 36	93.48 58	82.82 56	92.60 50	93.97 85	88.19 33	96.29 4	87.61 15	98.20 32	94.39 81
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS_fast		92.50 6	92.54 7	92.37 6	95.93 15	85.81 30	92.99 12	94.23 23	85.21 32	92.51 51	95.13 40	90.65 11	95.34 50	88.06 9	98.15 33	95.95 40

mPP-MVS		91.69 14	91.47 25	92.37 6	96.04 12	88.48 10	92.72 17	92.60 94	83.09 52	91.54 68	94.25 74	87.67 43	95.51 42	87.21 24	98.11 34	93.12 128

WR-MVS		83.56 152	84.40 139	81.06 210	93.43 70	54.88 299	78.67 255	85.02 241	81.24 72	90.74 83	91.56 153	72.85 198	91.08 190	68.00 220	98.04 35	97.23 17

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 44	94.91 37	84.50 44	89.49 69	93.98 37	79.68 89	92.09 58	93.89 93	83.80 77	93.10 137	82.67 76	98.04 35	93.64 112

DeepC-MVS	82.31 4	89.15 63	89.08 65	89.37 62	93.64 66	79.07 79	88.54 86	94.20 25	73.53 161	89.71 102	94.82 48	85.09 67	95.77 27	84.17 61	98.03 37	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
FIs		85.35 111	86.27 100	82.60 185	91.86 115	57.31 281	85.10 134	93.05 77	75.83 135	91.02 78	93.97 85	73.57 187	92.91 143	73.97 164	98.02 38	97.58 13

abl_6		93.02 5	93.16 5	92.60 4	94.73 43	88.99 7	93.26 11	94.19 27	89.11 12	94.43 16	95.27 36	91.86 4	95.09 60	87.54 18	98.02 38	93.71 108

Anonymous20231211		88.40 72	89.62 58	84.73 137	90.46 153	65.27 200	88.86 79	93.02 81	87.15 25	93.05 40	97.10 7	82.28 96	92.02 163	76.70 139	97.99 40	96.88 24

PGM-MVS		91.20 26	90.95 42	91.93 15	95.67 22	85.85 28	90.00 52	93.90 41	80.32 82	91.74 66	94.41 65	88.17 34	95.98 10	86.37 33	97.99 40	93.96 96

APDe-MVS		91.22 25	91.92 14	89.14 65	92.97 81	78.04 88	92.84 15	94.14 32	83.33 49	93.90 25	95.73 26	88.77 27	96.41 1	87.60 16	97.98 42	92.98 132

DVP-MVS		90.06 43	91.32 31	86.29 106	94.16 52	72.56 138	90.54 44	91.01 134	83.61 45	93.75 31	94.65 53	89.76 20	95.78 25	86.42 31	97.97 43	90.55 206

test_0728_SECOND				86.79 96	94.25 48	72.45 142	90.54 44	94.10 34					95.88 15	86.42 31	97.97 43	92.02 169

ZNCC-MVS		91.26 24	91.34 30	91.01 35	95.73 20	83.05 52	92.18 27	94.22 24	80.14 85	91.29 74	93.97 85	87.93 40	95.87 16	88.65 4	97.96 45	94.12 91

SED-MVS		90.46 38	91.64 20	86.93 93	94.18 49	72.65 133	90.47 47	93.69 49	83.77 42	94.11 23	94.27 70	90.28 16	95.84 20	86.03 41	97.92 46	92.29 159

IU-MVS					94.18 49	72.64 135		90.82 137	56.98 298	89.67 104				85.78 45	97.92 46	93.28 121

CLD-MVS		83.18 160	82.64 163	84.79 135	89.05 172	67.82 185	77.93 263	92.52 95	68.33 219	85.07 184	81.54 307	82.06 100	92.96 139	69.35 206	97.91 48	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 91	86.82 95	86.17 113	92.05 108	66.87 190	91.21 38	88.64 185	86.30 29	89.60 109	92.59 123	69.22 220	94.91 66	73.89 165	97.89 49	96.72 25

ACMMP_NAP		90.65 32	91.07 38	89.42 61	95.93 15	79.54 75	89.95 55	93.68 51	77.65 113	91.97 62	94.89 45	88.38 28	95.45 46	89.27 3	97.87 50	93.27 122

test_241102_TWO								93.71 48	83.77 42	93.49 37	94.27 70	89.27 23	95.84 20	86.03 41	97.82 51	92.04 168

DPE-MVS		90.53 36	91.08 36	88.88 67	93.38 71	78.65 84	89.15 74	94.05 35	84.68 36	93.90 25	94.11 80	88.13 36	96.30 3	84.51 58	97.81 52	91.70 180

OurMVSNet-221017-0		90.01 46	89.74 55	90.83 37	93.16 77	80.37 67	91.91 33	93.11 74	81.10 74	95.32 10	97.24 6	72.94 197	94.85 68	85.07 50	97.78 53	97.26 16

SMA-MVS		90.31 39	90.48 49	89.83 52	95.31 30	79.52 76	90.98 40	93.24 71	75.37 143	92.84 45	95.28 35	85.58 66	96.09 7	87.92 10	97.76 54	93.88 99
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMMPR		91.49 18	91.35 29	91.92 16	95.74 19	85.88 27	92.58 21	93.25 70	81.99 63	91.40 71	94.17 76	87.51 44	95.87 16	87.74 11	97.76 54	93.99 94

HFP-MVS		91.30 22	91.39 26	91.02 33	95.43 28	84.66 42	92.58 21	93.29 68	81.99 63	91.47 69	93.96 88	88.35 30	95.56 34	87.74 11	97.74 56	92.85 136

#test#		90.49 37	90.31 51	91.02 33	95.43 28	84.66 42	90.65 42	93.29 68	77.00 120	91.47 69	93.96 88	88.35 30	95.56 34	84.88 53	97.74 56	92.85 136

region2R		91.44 21	91.30 33	91.87 19	95.75 18	85.90 26	92.63 20	93.30 67	81.91 65	90.88 82	94.21 75	87.75 41	95.87 16	87.60 16	97.71 58	93.83 101

GST-MVS		90.96 29	91.01 39	90.82 38	95.45 27	82.73 55	91.75 34	93.74 47	80.98 76	91.38 72	93.80 95	87.20 48	95.80 22	87.10 28	97.69 59	93.93 97

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 147	97.00 2	64.33 209	89.67 62	88.38 189	88.84 15	94.29 19	97.57 4	90.48 15	91.26 184	72.57 182	97.65 60	97.34 15

v7n		90.13 41	90.96 41	87.65 88	91.95 110	71.06 158	89.99 54	93.05 77	86.53 27	94.29 19	96.27 18	82.69 88	94.08 95	86.25 37	97.63 61	97.82 9

XVS		91.54 16	91.36 27	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 93	94.03 82	86.57 56	95.80 22	87.35 20	97.62 62	94.20 86

X-MVStestdata		85.04 118	82.70 161	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 93	16.05 354	86.57 56	95.80 22	87.35 20	97.62 62	94.20 86

SR-MVS-dyc-post		92.41 7	92.41 8	92.39 5	94.13 54	88.95 8	92.87 13	94.16 28	88.75 16	93.79 29	94.43 62	88.83 25	95.51 42	87.16 25	97.60 64	92.73 141

RE-MVS-def			92.61 6		94.13 54	88.95 8	92.87 13	94.16 28	88.75 16	93.79 29	94.43 62	90.64 12		87.16 25	97.60 64	92.73 141

APD-MVS_3200maxsize		92.05 11	92.24 11	91.48 24	93.02 79	85.17 34	92.47 25	95.05 13	87.65 24	93.21 39	94.39 68	90.09 19	95.08 61	86.67 30	97.60 64	94.18 88

9.14			89.29 62		91.84 116		88.80 81	95.32 9	75.14 145	91.07 76	92.89 114	87.27 46	93.78 106	83.69 66	97.55 67

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 71	86.76 111	92.78 90	78.78 102	92.51 51	93.64 100	88.13 36	93.84 104	84.83 55	97.55 67	94.10 92

test1172		92.40 8	92.41 8	92.37 6	94.68 44	89.04 6	91.98 30	93.62 52	90.14 11	93.63 36	94.16 77	88.83 25	95.51 42	87.11 27	97.54 69	92.54 150

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 23	93.73 64	85.72 31	96.79 1	95.51 6	88.86 14	95.63 8	96.99 9	84.81 69	93.16 133	91.10 1	97.53 70	96.58 29
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
xxxxxxxxxxxxxcwj		89.04 66	89.13 64	88.79 69	93.75 62	77.44 96	86.31 119	95.27 10	70.80 196	92.28 55	93.80 95	86.89 51	94.64 74	85.52 46	97.51 71	94.30 84

SF-MVS		90.27 40	90.80 45	88.68 72	92.86 85	77.09 103	91.19 39	95.74 4	81.38 71	92.28 55	93.80 95	86.89 51	94.64 74	85.52 46	97.51 71	94.30 84

ETH3D-3000-0.1		88.85 69	88.96 69	88.52 73	91.94 112	77.27 102	88.71 83	95.26 11	76.08 127	90.66 85	92.69 121	84.48 72	93.83 105	83.38 68	97.48 73	94.47 76

MIMVSNet1		83.63 151	84.59 133	80.74 215	94.06 56	62.77 225	82.72 190	84.53 246	77.57 115	90.34 88	95.92 24	76.88 161	85.83 278	61.88 259	97.42 74	93.62 113

ACMMP++															97.35 75

SR-MVS		92.23 9	92.34 10	91.91 17	94.89 38	87.85 11	92.51 23	93.87 44	88.20 21	93.24 38	94.02 83	90.15 18	95.67 30	86.82 29	97.34 76	92.19 165

nrg030		87.85 79	88.49 74	85.91 116	90.07 160	69.73 166	87.86 93	94.20 25	74.04 155	92.70 49	94.66 52	85.88 65	91.50 175	79.72 105	97.32 77	96.50 30

pmmvs6		86.52 93	88.06 77	81.90 195	92.22 103	62.28 234	84.66 140	89.15 178	83.54 47	89.85 98	97.32 5	88.08 38	86.80 264	70.43 199	97.30 78	96.62 27

SD-MVS		88.96 67	89.88 53	86.22 109	91.63 119	77.07 104	89.82 58	93.77 46	78.90 100	92.88 42	92.29 134	86.11 62	90.22 216	86.24 38	97.24 79	91.36 189
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CPTT-MVS		89.39 59	88.98 68	90.63 41	95.09 33	86.95 13	92.09 28	92.30 100	79.74 88	87.50 142	92.38 129	81.42 112	93.28 128	83.07 71	97.24 79	91.67 181

APD-MVS		89.54 56	89.63 57	89.26 64	92.57 90	81.34 62	90.19 50	93.08 76	80.87 77	91.13 75	93.19 104	86.22 61	95.97 11	82.23 80	97.18 81	90.45 208
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 249	79.30 207	62.63 323	75.56 332	75.18 119	80.89 224	73.10 311	75.06 146	94.76 12	95.32 34	87.73 42	52.85 351	34.16 350	97.11 82	59.85 347

PMVS	80.48 6	90.08 42	90.66 47	88.34 79	96.71 3	92.97 1	90.31 49	89.57 173	88.51 19	90.11 90	95.12 41	90.98 8	88.92 238	77.55 130	97.07 83	83.13 300
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OMC-MVS		88.19 74	87.52 82	90.19 49	91.94 112	81.68 59	87.49 98	93.17 73	76.02 130	88.64 124	91.22 158	84.24 74	93.37 126	77.97 126	97.03 84	95.52 48

test_prior3		86.31 96	86.31 99	86.32 104	90.59 150	71.99 148	83.37 173	92.85 86	75.43 140	84.58 193	91.57 151	81.92 106	94.17 91	79.54 108	96.97 85	92.80 138

test_prior2							83.37 173		75.43 140	84.58 193	91.57 151	81.92 106		79.54 108	96.97 85

EPP-MVSNet		85.47 110	85.04 121	86.77 97	91.52 126	69.37 169	91.63 35	87.98 198	81.51 70	87.05 151	91.83 145	66.18 234	95.29 51	70.75 194	96.89 87	95.64 45

ETH3D cwj APD-0.16		87.83 80	87.62 81	88.47 75	91.21 133	78.20 86	87.26 100	94.54 17	72.05 186	88.89 118	92.31 133	83.86 75	94.24 85	81.59 87	96.87 88	92.97 135

VDDNet		84.35 133	85.39 117	81.25 205	95.13 32	59.32 262	85.42 131	81.11 264	86.41 28	87.41 143	96.21 20	73.61 186	90.61 207	66.33 230	96.85 89	93.81 106

VPNet		80.25 202	81.68 175	75.94 277	92.46 94	47.98 336	76.70 280	81.67 262	73.45 162	84.87 188	92.82 116	74.66 178	86.51 268	61.66 262	96.85 89	93.33 119

SixPastTwentyTwo		87.20 85	87.45 83	86.45 102	92.52 92	69.19 175	87.84 94	88.05 195	81.66 68	94.64 14	96.53 15	65.94 235	94.75 70	83.02 73	96.83 91	95.41 50

VPA-MVSNet		83.47 155	84.73 126	79.69 232	90.29 155	57.52 280	81.30 219	88.69 184	76.29 124	87.58 141	94.44 61	80.60 120	87.20 257	66.60 229	96.82 92	94.34 83

Gipuma		84.44 130	86.33 98	78.78 242	84.20 257	73.57 127	89.55 65	90.44 146	84.24 37	84.38 197	94.89 45	76.35 164	80.40 308	76.14 145	96.80 93	82.36 308
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ZD-MVS					92.22 103	80.48 66		91.85 111	71.22 193	90.38 87	92.98 109	86.06 63	96.11 6	81.99 82	96.75 94

CDPH-MVS		86.17 101	85.54 114	88.05 84	92.25 101	75.45 117	83.85 158	92.01 106	65.91 241	86.19 166	91.75 149	83.77 78	94.98 64	77.43 133	96.71 95	93.73 107

DP-MVS		88.60 71	89.01 66	87.36 91	91.30 130	77.50 95	87.55 96	92.97 83	87.95 22	89.62 106	92.87 115	84.56 70	93.89 101	77.65 128	96.62 96	90.70 200

testtj		89.51 57	89.48 60	89.59 59	92.26 100	80.80 65	90.14 51	93.54 56	83.37 48	90.57 86	92.55 126	84.99 68	96.15 5	81.26 88	96.61 97	91.83 176

TransMVSNet (Re)		84.02 143	85.74 110	78.85 241	91.00 140	55.20 298	82.29 201	87.26 205	79.65 90	88.38 131	95.52 32	83.00 85	86.88 262	67.97 221	96.60 98	94.45 79

ambc				82.98 176	90.55 152	64.86 203	88.20 88	89.15 178		89.40 113	93.96 88	71.67 212	91.38 183	78.83 115	96.55 99	92.71 144

train_agg		85.98 104	85.28 118	88.07 83	92.34 97	79.70 73	83.94 154	90.32 150	65.79 242	84.49 195	90.97 168	81.93 104	93.63 111	81.21 89	96.54 100	90.88 196

VDD-MVS		84.23 138	84.58 134	83.20 172	91.17 137	65.16 202	83.25 177	84.97 243	79.79 87	87.18 145	94.27 70	74.77 176	90.89 197	69.24 207	96.54 100	93.55 118

HPM-MVS++		88.93 68	88.45 75	90.38 45	94.92 36	85.85 28	89.70 59	91.27 127	78.20 109	86.69 157	92.28 135	80.36 123	95.06 62	86.17 39	96.49 102	90.22 211

test_djsdf		89.62 54	89.01 66	91.45 25	92.36 96	82.98 53	91.98 30	90.08 163	71.54 190	94.28 21	96.54 14	81.57 110	94.27 82	86.26 35	96.49 102	97.09 20

test9_res														80.83 95	96.45 104	90.57 204

Anonymous20240529		86.20 100	87.13 86	83.42 168	90.19 157	64.55 207	84.55 143	90.71 139	85.85 30	89.94 96	95.24 38	82.13 98	90.40 211	69.19 210	96.40 105	95.31 53

agg_prior1		85.72 107	85.20 119	87.28 92	91.58 123	77.69 92	83.69 164	90.30 154	66.29 238	84.32 199	91.07 165	82.13 98	93.18 131	81.02 91	96.36 106	90.98 192

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 164	86.67 15	90.51 46	90.20 160	69.87 207	95.06 11	96.14 22	84.28 73	93.07 138	87.68 13	96.34 107	97.09 20

PHI-MVS		86.38 95	85.81 109	88.08 82	88.44 184	77.34 99	89.35 72	93.05 77	73.15 172	84.76 190	87.70 225	78.87 134	94.18 89	80.67 98	96.29 108	92.73 141

PS-MVSNAJss		88.31 73	87.90 78	89.56 60	93.31 73	77.96 89	87.94 92	91.97 108	70.73 198	94.19 22	96.67 12	76.94 155	94.57 78	83.07 71	96.28 109	96.15 32

v10		86.54 92	87.10 87	84.84 134	88.16 190	63.28 219	86.64 114	92.20 102	75.42 142	92.81 47	94.50 58	74.05 182	94.06 96	83.88 63	96.28 109	97.17 19

CNVR-MVS		87.81 81	87.68 80	88.21 81	92.87 83	77.30 101	85.25 132	91.23 128	77.31 117	87.07 150	91.47 155	82.94 86	94.71 71	84.67 56	96.27 111	92.62 148

RRT_MVS		83.25 158	81.08 184	89.74 53	80.55 297	79.32 77	86.41 118	86.69 218	72.33 184	87.00 152	91.08 163	44.98 331	95.55 37	84.47 59	96.24 112	94.36 82

114514_t		83.10 162	82.54 166	84.77 136	92.90 82	69.10 177	86.65 113	90.62 143	54.66 307	81.46 240	90.81 175	76.98 154	94.38 81	72.62 181	96.18 113	90.82 198

agg_prior2														79.68 106	96.16 114	90.22 211

AllTest		87.97 77	87.40 85	89.68 54	91.59 120	83.40 48	89.50 68	95.44 7	79.47 91	88.00 136	93.03 107	82.66 89	91.47 176	70.81 191	96.14 115	94.16 89

TestCases				89.68 54	91.59 120	83.40 48		95.44 7	79.47 91	88.00 136	93.03 107	82.66 89	91.47 176	70.81 191	96.14 115	94.16 89

EPNet		80.37 199	78.41 216	86.23 108	76.75 323	73.28 129	87.18 102	77.45 281	76.24 126	68.14 323	88.93 209	65.41 237	93.85 102	69.47 205	96.12 117	91.55 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		83.69 149	84.95 123	79.91 227	90.04 162	59.66 259	82.43 197	87.44 202	75.52 139	87.85 138	95.26 37	81.25 114	85.65 280	68.74 215	96.04 118	94.42 80

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 67	84.79 39	89.89 57	90.63 142	70.00 206	94.55 15	96.67 12	87.94 39	93.59 115	84.27 60	95.97 119	95.52 48

DeepPCF-MVS	81.24 5	87.28 84	86.21 102	90.49 43	91.48 127	84.90 37	83.41 172	92.38 99	70.25 204	89.35 114	90.68 179	82.85 87	94.57 78	79.55 107	95.95 120	92.00 170

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 71	84.72 40	89.70 59	90.29 157	69.27 210	94.39 17	96.38 16	86.02 64	93.52 119	83.96 62	95.92 121	95.34 51

ANet_high		83.17 161	85.68 112	75.65 278	81.24 284	45.26 342	79.94 234	92.91 84	83.83 41	91.33 73	96.88 11	80.25 124	85.92 276	68.89 213	95.89 122	95.76 42

3Dnovator+	83.92 2	89.97 49	89.66 56	90.92 36	91.27 132	81.66 60	91.25 37	94.13 33	88.89 13	88.83 121	94.26 73	77.55 146	95.86 19	84.88 53	95.87 123	95.24 56

HQP_MVS		87.75 82	87.43 84	88.70 71	93.45 68	76.42 112	89.45 70	93.61 53	79.44 93	86.55 159	92.95 112	74.84 173	95.22 55	80.78 96	95.83 124	94.46 77

plane_prior5								93.61 53					95.22 55	80.78 96	95.83 124	94.46 77

cl-mvsnet_		80.42 197	80.23 195	81.02 211	79.99 300	59.25 263	77.07 276	87.02 214	67.37 230	86.18 168	89.21 203	63.08 249	90.16 218	76.31 143	95.80 126	93.65 111

cl-mvsnet1		80.43 196	80.23 195	81.02 211	79.99 300	59.25 263	77.07 276	87.02 214	67.38 229	86.19 166	89.22 202	63.09 248	90.16 218	76.32 142	95.80 126	93.66 110

DeepC-MVS_fast	80.27 8	86.23 98	85.65 113	87.96 85	91.30 130	76.92 105	87.19 101	91.99 107	70.56 199	84.96 185	90.69 178	80.01 126	95.14 58	78.37 118	95.78 128	91.82 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 205	80.56 189	78.89 240	89.19 171	55.93 290	85.22 133	73.78 305	82.96 54	84.28 203	92.72 120	57.38 281	90.07 225	63.80 246	95.75 129	90.68 201

ACMMP++_ref															95.74 130

原ACMM1				84.60 140	92.81 88	74.01 125		91.50 120	62.59 263	82.73 222	90.67 180	76.53 162	94.25 84	69.24 207	95.69 131	85.55 268

tfpnnormal		81.79 178	82.95 159	78.31 251	88.93 176	55.40 294	80.83 226	82.85 253	76.81 121	85.90 175	94.14 78	74.58 179	86.51 268	66.82 228	95.68 132	93.01 131

ETH3 D test6400		85.09 116	84.87 124	85.75 121	90.80 145	69.34 170	85.90 123	93.31 65	65.43 248	86.11 169	89.95 193	80.92 116	94.86 67	75.90 148	95.57 133	93.05 129

TAPA-MVS	77.73 12	85.71 108	84.83 125	88.37 78	88.78 178	79.72 72	87.15 103	93.50 57	69.17 211	85.80 176	89.56 198	80.76 118	92.13 158	73.21 177	95.51 134	93.25 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		90.60 34	90.34 50	91.38 27	89.03 173	84.23 45	93.58 5	94.68 16	90.65 7	90.33 89	93.95 91	84.50 71	95.37 49	80.87 94	95.50 135	94.53 75

v8		86.22 99	86.83 94	84.36 145	87.82 195	62.35 233	86.42 117	91.33 125	76.78 122	92.73 48	94.48 60	73.41 191	93.72 108	83.10 70	95.41 136	97.01 22

Vis-MVSNet (Re-imp)		77.82 224	77.79 221	77.92 258	88.82 177	51.29 323	83.28 175	71.97 318	74.04 155	82.23 228	89.78 196	57.38 281	89.41 233	57.22 286	95.41 136	93.05 129

OPU-MVS				88.27 80	91.89 114	77.83 90	90.47 47				91.22 158	81.12 115	94.68 72	74.48 158	95.35 138	92.29 159

FMVSNet1		84.55 127	85.45 116	81.85 197	90.27 156	61.05 245	86.83 108	88.27 192	78.57 106	89.66 105	95.64 29	75.43 167	90.68 204	69.09 211	95.33 139	93.82 103

test12				86.57 99	90.74 146	72.63 136		90.69 140		82.76 221		79.20 131	94.80 69		95.32 140	92.27 161

NCCC		87.36 83	86.87 93	88.83 68	92.32 99	78.84 82	86.58 115	91.09 132	78.77 103	84.85 189	90.89 172	80.85 117	95.29 51	81.14 90	95.32 140	92.34 155

Patchmtry		76.56 238	77.46 223	73.83 286	79.37 308	46.60 339	82.41 198	76.90 283	73.81 158	85.56 180	92.38 129	48.07 309	83.98 294	63.36 249	95.31 142	90.92 195

XVG-OURS		89.18 62	88.83 71	90.23 48	94.28 47	86.11 23	85.91 122	93.60 55	80.16 84	89.13 117	93.44 102	83.82 76	90.98 192	83.86 64	95.30 143	93.60 114

TSAR-MVS + GP.		83.95 145	82.69 162	87.72 86	89.27 169	81.45 61	83.72 163	81.58 263	74.73 148	85.66 177	86.06 250	72.56 203	92.69 147	75.44 152	95.21 144	89.01 233

test_0402		88.65 70	89.58 59	85.88 118	92.55 91	72.22 146	84.01 153	89.44 175	88.63 18	94.38 18	95.77 25	86.38 60	93.59 115	79.84 104	95.21 144	91.82 177

TinyColmap		81.25 183	82.34 169	77.99 257	85.33 241	60.68 251	82.32 200	88.33 190	71.26 192	86.97 153	92.22 138	77.10 152	86.98 261	62.37 254	95.17 146	86.31 261

Anonymous202405211		80.51 195	81.19 183	78.49 248	88.48 182	57.26 282	76.63 281	82.49 255	81.21 73	84.30 202	92.24 137	67.99 226	86.24 272	62.22 255	95.13 147	91.98 173

tttt0517		81.07 185	79.58 205	85.52 125	88.99 175	66.45 194	87.03 105	75.51 294	73.76 159	88.32 133	90.20 188	37.96 347	94.16 94	79.36 112	95.13 147	95.93 41

DP-MVS Recon		84.05 142	83.22 154	86.52 101	91.73 118	75.27 118	83.23 179	92.40 97	72.04 187	82.04 231	88.33 215	77.91 142	93.95 100	66.17 231	95.12 149	90.34 210

PCF-MVS	74.62 15	82.15 173	80.92 187	85.84 119	89.43 166	72.30 144	80.53 228	91.82 113	57.36 296	87.81 139	89.92 195	77.67 144	93.63 111	58.69 278	95.08 150	91.58 185
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CSCG		86.26 97	86.47 97	85.60 124	90.87 143	74.26 124	87.98 91	91.85 111	80.35 81	89.54 112	88.01 219	79.09 132	92.13 158	75.51 150	95.06 151	90.41 209

plane_prior						76.42 112	87.15 103		75.94 134						95.03 152

new-patchmatchnet		70.10 287	73.37 261	60.29 330	81.23 285	16.95 358	59.54 341	74.62 297	62.93 261	80.97 245	87.93 222	62.83 251	71.90 327	55.24 298	95.01 153	92.00 170

v1192		84.57 126	84.69 130	84.21 149	87.75 197	62.88 223	83.02 184	91.43 122	69.08 213	89.98 95	90.89 172	72.70 201	93.62 114	82.41 77	94.97 154	96.13 33

v1921920		84.23 138	84.37 140	83.79 158	87.64 201	61.71 237	82.91 187	91.20 129	67.94 224	90.06 91	90.34 185	72.04 208	93.59 115	82.32 79	94.91 155	96.07 35

v144192		84.24 137	84.41 138	83.71 161	87.59 202	61.57 238	82.95 186	91.03 133	67.82 227	89.80 100	90.49 183	73.28 194	93.51 120	81.88 85	94.89 156	96.04 37

testing_2		84.36 131	84.64 132	83.50 167	86.74 220	63.97 213	84.56 142	90.31 152	66.22 239	91.62 67	94.55 56	75.88 165	91.95 164	77.02 138	94.89 156	94.56 71

LCM-MVSNet-Re		83.48 154	85.06 120	78.75 243	85.94 236	55.75 293	80.05 232	94.27 20	76.47 123	96.09 5	94.54 57	83.31 83	89.75 229	59.95 273	94.89 156	90.75 199

v1240		84.30 135	84.51 136	83.65 162	87.65 200	61.26 242	82.85 188	91.54 119	67.94 224	90.68 84	90.65 181	71.71 211	93.64 110	82.84 75	94.78 159	96.07 35

MSLP-MVS++		85.00 120	86.03 105	81.90 195	91.84 116	71.56 156	86.75 112	93.02 81	75.95 133	87.12 146	89.39 199	77.98 140	89.40 234	77.46 131	94.78 159	84.75 277

IterMVS-LS		84.73 123	84.98 122	83.96 155	87.35 205	63.66 214	83.25 177	89.88 167	76.06 128	89.62 106	92.37 132	73.40 193	92.52 150	78.16 122	94.77 161	95.69 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 150	83.69 150	83.57 165	90.05 161	72.26 145	86.29 121	90.00 165	78.19 110	81.65 238	87.16 235	83.40 82	94.24 85	61.69 261	94.76 162	84.21 282

ITE_SJBPF				90.11 50	90.72 147	84.97 36		90.30 154	81.56 69	90.02 92	91.20 160	82.40 92	90.81 200	73.58 169	94.66 163	94.56 71

v1144		84.54 129	84.72 128	84.00 153	87.67 199	62.55 229	82.97 185	90.93 135	70.32 203	89.80 100	90.99 167	73.50 188	93.48 121	81.69 86	94.65 164	95.97 38

test20.03		73.75 262	74.59 250	71.22 299	81.11 286	51.12 325	70.15 320	72.10 317	70.42 200	80.28 258	91.50 154	64.21 241	74.72 324	46.96 333	94.58 165	87.82 247

TSAR-MVS + MP.		88.14 75	87.82 79	89.09 66	95.72 21	76.74 108	92.49 24	91.19 130	67.85 226	86.63 158	94.84 47	79.58 130	95.96 12	87.62 14	94.50 166	94.56 71
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HQP3-MVS								92.68 92							94.47 167

HQP-MVS		84.61 125	84.06 144	86.27 107	91.19 134	70.66 160	84.77 136	92.68 92	73.30 167	80.55 253	90.17 191	72.10 205	94.61 76	77.30 134	94.47 167	93.56 116

cl_fuxian		81.64 179	81.59 178	81.79 201	80.86 290	59.15 266	78.61 256	90.18 161	68.36 218	87.20 144	87.11 237	69.39 218	91.62 173	78.16 122	94.43 169	94.60 70

MCST-MVS		84.36 131	83.93 147	85.63 123	91.59 120	71.58 155	83.52 167	92.13 103	61.82 269	83.96 206	89.75 197	79.93 129	93.46 122	78.33 120	94.34 170	91.87 175

thisisatest0530		79.07 210	77.33 225	84.26 148	87.13 210	64.58 205	83.66 165	75.95 289	68.86 216	85.22 183	87.36 232	38.10 345	93.57 118	75.47 151	94.28 171	94.62 68

baseline		85.20 113	85.93 106	83.02 175	86.30 228	62.37 232	84.55 143	93.96 38	74.48 152	87.12 146	92.03 139	82.30 94	91.94 165	78.39 117	94.21 172	94.74 67

RRT_test8_iter05		78.08 221	77.52 222	79.75 230	80.84 291	52.54 314	80.61 227	88.96 180	67.77 228	84.62 192	89.29 201	33.89 352	92.10 161	77.59 129	94.15 173	94.62 68

alignmvs		83.94 146	83.98 146	83.80 157	87.80 196	67.88 184	84.54 145	91.42 124	73.27 170	88.41 130	87.96 220	72.33 204	90.83 199	76.02 147	94.11 174	92.69 145

USDC		76.63 236	76.73 232	76.34 274	83.46 266	57.20 283	80.02 233	88.04 196	52.14 322	83.65 210	91.25 157	63.24 247	86.65 267	54.66 302	94.11 174	85.17 272

MVS_111021_HR		84.63 124	84.34 141	85.49 127	90.18 158	75.86 116	79.23 248	87.13 209	73.35 164	85.56 180	89.34 200	83.60 80	90.50 209	76.64 140	94.05 176	90.09 216

VNet		79.31 209	80.27 194	76.44 272	87.92 193	53.95 303	75.58 293	84.35 247	74.39 153	82.23 228	90.72 177	72.84 199	84.39 291	60.38 272	93.98 177	90.97 193

FMVSNet2		81.31 182	81.61 177	80.41 221	86.38 223	58.75 273	83.93 156	86.58 220	72.43 180	87.65 140	92.98 109	63.78 244	90.22 216	66.86 225	93.92 178	92.27 161

LF4IMVS		82.75 164	81.93 174	85.19 129	82.08 275	80.15 69	85.53 129	88.76 183	68.01 221	85.58 179	87.75 224	71.80 210	86.85 263	74.02 163	93.87 179	88.58 236

canonicalmvs		85.50 109	86.14 103	83.58 164	87.97 191	67.13 187	87.55 96	94.32 19	73.44 163	88.47 128	87.54 228	86.45 58	91.06 191	75.76 149	93.76 180	92.54 150

v2v482		84.09 140	84.24 142	83.62 163	87.13 210	61.40 239	82.71 191	89.71 169	72.19 185	89.55 110	91.41 156	70.70 216	93.20 130	81.02 91	93.76 180	96.25 31

casdiffmvs		85.21 112	85.85 108	83.31 170	86.17 233	62.77 225	83.03 183	93.93 39	74.69 149	88.21 134	92.68 122	82.29 95	91.89 168	77.87 127	93.75 182	95.27 55

UGNet		82.78 163	81.64 176	86.21 111	86.20 232	76.24 115	86.86 106	85.68 228	77.07 119	73.76 303	92.82 116	69.64 217	91.82 171	69.04 212	93.69 183	90.56 205
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
旧先验1					91.97 109	71.77 150		81.78 261			91.84 144	73.92 183			93.65 184	83.61 290

AUN-MVS		81.18 184	78.78 210	88.39 77	90.93 141	82.14 56	82.51 196	83.67 248	64.69 256	80.29 256	85.91 253	51.07 301	92.38 152	76.29 144	93.63 185	90.65 203

MVS_111021_LR		84.28 136	83.76 149	85.83 120	89.23 170	83.07 51	80.99 223	83.56 249	72.71 178	86.07 170	89.07 207	81.75 109	86.19 273	77.11 136	93.36 186	88.24 237

GBi-Net		82.02 175	82.07 171	81.85 197	86.38 223	61.05 245	86.83 108	88.27 192	72.43 180	86.00 171	95.64 29	63.78 244	90.68 204	65.95 232	93.34 187	93.82 103

test1		82.02 175	82.07 171	81.85 197	86.38 223	61.05 245	86.83 108	88.27 192	72.43 180	86.00 171	95.64 29	63.78 244	90.68 204	65.95 232	93.34 187	93.82 103

FMVSNet3		78.80 214	78.55 213	79.57 234	82.89 273	56.89 286	81.76 209	85.77 227	69.04 214	86.00 171	90.44 184	51.75 299	90.09 224	65.95 232	93.34 187	91.72 179

CS-MVS		83.43 157	83.04 158	84.59 141	87.87 194	66.61 192	85.57 128	94.90 14	73.02 174	81.12 244	78.56 325	80.00 127	95.52 39	73.04 179	93.29 190	91.62 183

K. test v3		85.14 114	84.73 126	86.37 103	91.13 138	69.63 168	85.45 130	76.68 286	84.06 40	92.44 53	96.99 9	62.03 252	94.65 73	80.58 99	93.24 191	94.83 66

Anonymous20231206		71.38 280	71.88 275	69.88 300	86.31 227	54.37 300	70.39 319	74.62 297	52.57 318	76.73 279	88.76 210	59.94 263	72.06 326	44.35 338	93.23 192	83.23 298

D2MVS		76.84 234	75.67 242	80.34 222	80.48 298	62.16 236	73.50 307	84.80 245	57.61 294	82.24 227	87.54 228	51.31 300	87.65 253	70.40 200	93.19 193	91.23 190

miper_lstm_enhance		76.45 240	76.10 237	77.51 262	76.72 324	60.97 249	64.69 335	85.04 240	63.98 258	83.20 216	88.22 216	56.67 284	78.79 313	73.22 172	93.12 194	92.78 140

新几何1				82.95 177	93.96 58	78.56 85		80.24 269	55.45 303	83.93 207	91.08 163	71.19 214	88.33 247	65.84 235	93.07 195	81.95 313

1121		80.86 188	79.81 204	84.02 152	93.93 59	78.70 83	81.64 212	80.18 270	55.43 304	83.67 209	91.15 161	71.29 213	91.41 181	67.95 222	93.06 196	81.96 312

lessismore_v0				85.95 115	91.10 139	70.99 159		70.91 324		91.79 64	94.42 64	61.76 253	92.93 141	79.52 110	93.03 197	93.93 97

TAMVS		78.08 221	76.36 234	83.23 171	90.62 149	72.87 131	79.08 249	80.01 272	61.72 271	81.35 242	86.92 239	63.96 243	88.78 242	50.61 317	93.01 198	88.04 241

ETV-MVS		84.31 134	83.91 148	85.52 125	88.58 180	70.40 162	84.50 147	93.37 59	78.76 104	84.07 205	78.72 324	80.39 122	95.13 59	73.82 167	92.98 199	91.04 191

EPNet_dtu		72.87 269	71.33 281	77.49 263	77.72 317	60.55 252	82.35 199	75.79 290	66.49 237	58.39 351	81.06 310	53.68 296	85.98 275	53.55 306	92.97 200	85.95 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		85.82 106	83.38 152	93.14 3	87.13 210	91.15 2	87.70 95	88.42 187	74.57 150	83.56 212	85.65 254	78.49 136	94.21 87	72.04 185	92.88 201	94.05 93

CANet		83.79 148	82.85 160	86.63 98	86.17 233	72.21 147	83.76 162	91.43 122	77.24 118	74.39 300	87.45 230	75.36 168	95.42 47	77.03 137	92.83 202	92.25 163

API-MVS		82.28 170	82.61 164	81.30 204	86.29 229	69.79 164	88.71 83	87.67 201	78.42 108	82.15 230	84.15 281	77.98 140	91.59 174	65.39 238	92.75 203	82.51 307

test_yl		78.71 216	78.51 214	79.32 237	84.32 252	58.84 270	78.38 257	85.33 232	75.99 131	82.49 223	86.57 241	58.01 275	90.02 226	62.74 252	92.73 204	89.10 228

DCV-MVSNet		78.71 216	78.51 214	79.32 237	84.32 252	58.84 270	78.38 257	85.33 232	75.99 131	82.49 223	86.57 241	58.01 275	90.02 226	62.74 252	92.73 204	89.10 228

Regformer-1		86.00 102	85.50 115	87.49 89	84.18 258	76.90 106	83.52 167	87.94 199	82.18 62	89.19 115	85.07 269	82.28 96	91.89 168	82.40 78	92.72 206	93.69 109

Regformer-2		86.74 90	86.08 104	88.73 70	84.18 258	79.20 78	83.52 167	89.33 176	83.33 49	89.92 97	85.07 269	83.23 84	93.16 133	83.39 67	92.72 206	93.83 101

testgi		72.36 272	74.61 248	65.59 317	80.56 296	42.82 348	68.29 325	73.35 308	66.87 234	81.84 234	89.93 194	72.08 207	66.92 340	46.05 335	92.54 208	87.01 255

FMVSNet5		72.10 275	71.69 276	73.32 287	81.57 280	53.02 310	76.77 279	78.37 277	63.31 259	76.37 281	91.85 143	36.68 349	78.98 311	47.87 329	92.45 209	87.95 243

CDS-MVSNet		77.32 229	75.40 243	83.06 174	89.00 174	72.48 141	77.90 264	82.17 258	60.81 276	78.94 268	83.49 286	59.30 268	88.76 243	54.64 303	92.37 210	87.93 244
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		83.55 153	83.10 157	84.90 133	89.34 168	83.87 46	84.54 145	88.77 182	79.09 97	83.54 213	88.66 213	74.87 172	81.73 303	66.84 227	92.29 211	89.11 227

F-COLMAP		84.97 121	83.42 151	89.63 56	92.39 95	83.40 48	88.83 80	91.92 110	73.19 171	80.18 259	89.15 205	77.04 153	93.28 128	65.82 236	92.28 212	92.21 164

thres600view7		75.97 243	75.35 245	77.85 260	87.01 216	51.84 320	80.45 229	73.26 309	75.20 144	83.10 218	86.31 247	45.54 322	89.05 235	55.03 300	92.24 213	92.66 146

PVSNet_BlendedMVS		78.80 214	77.84 220	81.65 202	84.43 248	63.41 216	79.49 242	90.44 146	61.70 272	75.43 293	87.07 238	69.11 221	91.44 178	60.68 270	92.24 213	90.11 215

DELS-MVS		81.44 181	81.25 181	82.03 193	84.27 254	62.87 224	76.47 285	92.49 96	70.97 195	81.64 239	83.83 282	75.03 170	92.70 146	74.29 159	92.22 215	90.51 207
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				79.54 235	92.87 83	72.34 143		80.14 271	59.91 283	85.47 182	91.75 149	67.96 227	85.24 282	68.57 218	92.18 216	81.06 326

cl-mvsnet2		78.97 211	78.21 218	81.24 207	77.74 316	59.01 267	77.46 273	87.13 209	65.79 242	84.32 199	85.10 266	58.96 271	90.88 198	75.36 153	92.03 217	93.84 100

miper_ehance_all_eth		80.34 200	80.04 202	81.24 207	79.82 302	58.95 268	77.66 267	89.66 170	65.75 245	85.99 174	85.11 265	68.29 225	91.42 180	76.03 146	92.03 217	93.33 119

miper_enhance_ethall		77.83 223	76.93 229	80.51 219	76.15 329	58.01 276	75.47 295	88.82 181	58.05 290	83.59 211	80.69 311	64.41 239	91.20 185	73.16 178	92.03 217	92.33 156

MVS_0304		78.17 219	77.23 226	80.99 213	84.13 260	69.07 178	81.39 216	80.81 267	76.28 125	67.53 328	89.11 206	62.87 250	86.77 265	60.90 269	92.01 220	87.13 253

DPM-MVS		80.10 206	79.18 208	82.88 181	90.71 148	69.74 165	78.87 252	90.84 136	60.29 281	75.64 292	85.92 252	67.28 228	93.11 136	71.24 189	91.79 221	85.77 267

v148		82.31 169	82.48 167	81.81 200	85.59 238	59.66 259	81.47 215	86.02 225	72.85 176	88.05 135	90.65 181	70.73 215	90.91 196	75.15 155	91.79 221	94.87 62

test222					93.31 73	76.54 109	79.38 243	77.79 279	52.59 317	82.36 226	90.84 174	66.83 231			91.69 223	81.25 321

eth_miper_zixun_eth		80.84 189	80.22 197	82.71 183	81.41 282	60.98 248	77.81 265	90.14 162	67.31 231	86.95 154	87.24 234	64.26 240	92.31 154	75.23 154	91.61 224	94.85 64

pmmvs-eth3d		78.42 218	77.04 228	82.57 188	87.44 204	74.41 123	80.86 225	79.67 273	55.68 302	84.69 191	90.31 187	60.91 256	85.42 281	62.20 256	91.59 225	87.88 245

Vis-MVSNet		86.86 87	86.58 96	87.72 86	92.09 106	77.43 98	87.35 99	92.09 104	78.87 101	84.27 204	94.05 81	78.35 138	93.65 109	80.54 100	91.58 226	92.08 167
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		73.00 268	70.52 283	80.46 220	81.45 281	59.90 257	73.16 310	74.31 301	57.86 291	76.08 287	77.78 328	37.60 348	92.12 160	65.00 239	91.45 227	89.35 222

ppachtmachnet_test		74.73 256	74.00 255	76.90 267	80.71 294	56.89 286	71.53 315	78.42 276	58.24 288	79.32 265	82.92 294	57.91 278	84.26 292	65.60 237	91.36 228	89.56 218

OpenMVS	76.72 13	81.98 177	82.00 173	81.93 194	84.42 250	68.22 181	88.50 87	89.48 174	66.92 233	81.80 237	91.86 142	72.59 202	90.16 218	71.19 190	91.25 229	87.40 250

EG-PatchMatch MVS		84.08 141	84.11 143	83.98 154	92.22 103	72.61 137	82.20 207	87.02 214	72.63 179	88.86 119	91.02 166	78.52 135	91.11 189	73.41 171	91.09 230	88.21 238

3Dnovator	80.37 7	84.80 122	84.71 129	85.06 132	86.36 226	74.71 120	88.77 82	90.00 165	75.65 138	84.96 185	93.17 105	74.06 181	91.19 186	78.28 121	91.09 230	89.29 225

Regformer-3		85.06 117	84.67 131	86.22 109	84.27 254	73.43 128	84.07 151	85.26 234	80.77 78	88.62 125	85.48 257	80.56 121	90.39 212	81.99 82	91.04 232	94.85 64

Regformer-4		86.41 94	85.71 111	88.52 73	84.27 254	77.57 94	84.07 151	88.00 197	82.82 56	89.84 99	85.48 257	82.06 100	92.77 145	83.83 65	91.04 232	95.22 59

thres100view900		75.45 246	75.05 246	76.66 271	87.27 206	51.88 319	81.07 222	73.26 309	75.68 137	83.25 215	86.37 244	45.54 322	88.80 239	51.98 313	90.99 234	89.31 223

tfpn200view9		74.86 254	74.23 253	76.74 270	86.24 230	52.12 316	79.24 246	73.87 303	73.34 165	81.82 235	84.60 277	46.02 316	88.80 239	51.98 313	90.99 234	89.31 223

thres400		75.14 248	74.23 253	77.86 259	86.24 230	52.12 316	79.24 246	73.87 303	73.34 165	81.82 235	84.60 277	46.02 316	88.80 239	51.98 313	90.99 234	92.66 146

cascas		76.29 242	74.81 247	80.72 217	84.47 247	62.94 222	73.89 305	87.34 203	55.94 301	75.16 297	76.53 336	63.97 242	91.16 187	65.00 239	90.97 237	88.06 240

MSP-MVS		89.08 65	88.16 76	91.83 21	95.76 17	86.14 22	92.75 16	93.90 41	78.43 107	89.16 116	92.25 136	72.03 209	96.36 2	88.21 8	90.93 238	92.98 132

ab-mvs		79.67 208	80.56 189	76.99 265	88.48 182	56.93 284	84.70 139	86.06 224	68.95 215	80.78 249	93.08 106	75.30 169	84.62 289	56.78 287	90.90 239	89.43 221

MAR-MVS		80.24 203	78.74 212	84.73 137	86.87 219	78.18 87	85.75 125	87.81 200	65.67 247	77.84 273	78.50 326	73.79 185	90.53 208	61.59 264	90.87 240	85.49 270
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		85.12 115	84.53 135	86.88 94	84.01 261	72.76 132	83.91 157	85.18 236	80.44 79	88.75 122	85.49 256	80.08 125	91.92 166	82.02 81	90.85 241	95.97 38

EI-MVSNet-UG-set		85.04 118	84.44 137	86.85 95	83.87 264	72.52 140	83.82 159	85.15 237	80.27 83	88.75 122	85.45 260	79.95 128	91.90 167	81.92 84	90.80 242	96.13 33

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 47	94.47 45	85.95 24	86.84 107	93.91 40	80.07 86	86.75 156	93.26 103	93.64 3	90.93 194	84.60 57	90.75 243	93.97 95

ET-MVSNet_ETH3D		75.28 247	72.77 266	82.81 182	83.03 272	68.11 182	77.09 275	76.51 287	60.67 279	77.60 277	80.52 313	38.04 346	91.15 188	70.78 193	90.68 244	89.17 226

EI-MVSNet		82.61 165	82.42 168	83.20 172	83.25 268	63.66 214	83.50 170	85.07 238	76.06 128	86.55 159	85.10 266	73.41 191	90.25 213	78.15 124	90.67 245	95.68 44

MVSTER		77.09 231	75.70 241	81.25 205	75.27 336	61.08 244	77.49 272	85.07 238	60.78 277	86.55 159	88.68 212	43.14 337	90.25 213	73.69 168	90.67 245	92.42 152

Patchmatch-RL test		74.48 257	73.68 257	76.89 268	84.83 245	66.54 193	72.29 312	69.16 329	57.70 292	86.76 155	86.33 245	45.79 321	82.59 300	69.63 204	90.65 247	81.54 317

CMPMVS	59.41 20	75.12 250	73.57 258	79.77 228	75.84 331	67.22 186	81.21 220	82.18 257	50.78 330	76.50 280	87.66 226	55.20 293	82.99 299	62.17 258	90.64 248	89.09 230
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
V42		83.47 155	83.37 153	83.75 160	83.16 270	63.33 218	81.31 217	90.23 159	69.51 209	90.91 81	90.81 175	74.16 180	92.29 156	80.06 101	90.22 249	95.62 46

PM-MVS		80.20 204	79.00 209	83.78 159	88.17 189	86.66 16	81.31 217	66.81 336	69.64 208	88.33 132	90.19 189	64.58 238	83.63 297	71.99 187	90.03 250	81.06 326

PLC	73.85 16	82.09 174	80.31 193	87.45 90	90.86 144	80.29 68	85.88 124	90.65 141	68.17 220	76.32 283	86.33 245	73.12 196	92.61 149	61.40 265	90.02 251	89.44 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		84.55 127	82.12 170	91.84 20	87.13 210	89.54 4	85.05 135	88.42 187	74.57 150	80.60 250	82.98 290	78.49 136	93.98 99	72.04 185	89.77 252	92.00 170

CANet_DTU		77.81 225	77.05 227	80.09 226	81.37 283	59.90 257	83.26 176	88.29 191	69.16 212	67.83 326	83.72 283	60.93 255	89.47 230	69.22 209	89.70 253	90.88 196

diffmvs		80.40 198	80.48 192	80.17 225	79.02 312	60.04 255	77.54 270	90.28 158	66.65 236	82.40 225	87.33 233	73.50 188	87.35 256	77.98 125	89.62 254	93.13 127

PMMVS2		55.64 322	59.27 322	44.74 336	64.30 356	12.32 359	40.60 350	49.79 355	53.19 314	65.06 338	84.81 273	53.60 297	49.76 352	32.68 352	89.41 255	72.15 337

Fast-Effi-MVS+-dtu		82.54 167	81.41 180	85.90 117	85.60 237	76.53 111	83.07 182	89.62 172	73.02 174	79.11 267	83.51 285	80.74 119	90.24 215	68.76 214	89.29 256	90.94 194

thres200		72.34 273	71.55 279	74.70 283	83.48 265	51.60 321	75.02 298	73.71 306	70.14 205	78.56 271	80.57 312	46.20 314	88.20 249	46.99 332	89.29 256	84.32 281

jason		77.42 228	75.75 240	82.43 191	87.10 214	69.27 171	77.99 262	81.94 260	51.47 326	77.84 273	85.07 269	60.32 260	89.00 236	70.74 195	89.27 258	89.03 231
jason: jason.
MG-MVS		80.32 201	80.94 186	78.47 249	88.18 188	52.62 313	82.29 201	85.01 242	72.01 188	79.24 266	92.54 127	69.36 219	93.36 127	70.65 196	89.19 259	89.45 219

BH-untuned		80.96 187	80.99 185	80.84 214	88.55 181	68.23 180	80.33 230	88.46 186	72.79 177	86.55 159	86.76 240	74.72 177	91.77 172	61.79 260	88.99 260	82.52 306

EIA-MVS		82.19 172	81.23 182	85.10 131	87.95 192	69.17 176	83.22 180	93.33 62	70.42 200	78.58 270	79.77 321	77.29 148	94.20 88	71.51 188	88.96 261	91.93 174

PVSNet_Blended_VisFu		81.55 180	80.49 191	84.70 139	91.58 123	73.24 130	84.21 149	91.67 117	62.86 262	80.94 246	87.16 235	67.27 229	92.87 144	69.82 203	88.94 262	87.99 242

MVSFormer		82.23 171	81.57 179	84.19 151	85.54 239	69.26 172	91.98 30	90.08 163	71.54 190	76.23 284	85.07 269	58.69 272	94.27 82	86.26 35	88.77 263	89.03 231

lupinMVS		76.37 241	74.46 251	82.09 192	85.54 239	69.26 172	76.79 278	80.77 268	50.68 332	76.23 284	82.82 295	58.69 272	88.94 237	69.85 202	88.77 263	88.07 239

RPSCF		88.00 76	86.93 92	91.22 31	90.08 159	89.30 5	89.68 61	91.11 131	79.26 95	89.68 103	94.81 51	82.44 91	87.74 252	76.54 141	88.74 265	96.61 28

PAPM_NR		83.23 159	83.19 156	83.33 169	90.90 142	65.98 196	88.19 89	90.78 138	78.13 111	80.87 248	87.92 223	73.49 190	92.42 151	70.07 201	88.40 266	91.60 184

xiu_mvs_v1_base_debu		80.84 189	80.14 199	82.93 178	88.31 185	71.73 151	79.53 239	87.17 206	65.43 248	79.59 261	82.73 297	76.94 155	90.14 221	73.22 172	88.33 267	86.90 256

xiu_mvs_v1_base		80.84 189	80.14 199	82.93 178	88.31 185	71.73 151	79.53 239	87.17 206	65.43 248	79.59 261	82.73 297	76.94 155	90.14 221	73.22 172	88.33 267	86.90 256

xiu_mvs_v1_base_debi		80.84 189	80.14 199	82.93 178	88.31 185	71.73 151	79.53 239	87.17 206	65.43 248	79.59 261	82.73 297	76.94 155	90.14 221	73.22 172	88.33 267	86.90 256

XXY-MVS		74.44 259	76.19 236	69.21 304	84.61 246	52.43 315	71.70 314	77.18 282	60.73 278	80.60 250	90.96 170	75.44 166	69.35 332	56.13 291	88.33 267	85.86 266

Fast-Effi-MVS+		81.04 186	80.57 188	82.46 190	87.50 203	63.22 220	78.37 259	89.63 171	68.01 221	81.87 233	82.08 302	82.31 93	92.65 148	67.10 224	88.30 271	91.51 187

MDA-MVSNet-bldmvs		77.47 227	76.90 230	79.16 239	79.03 311	64.59 204	66.58 332	75.67 292	73.15 172	88.86 119	88.99 208	66.94 230	81.23 305	64.71 241	88.22 272	91.64 182

PAPR		78.84 213	78.10 219	81.07 209	85.17 242	60.22 254	82.21 205	90.57 144	62.51 264	75.32 295	84.61 276	74.99 171	92.30 155	59.48 276	88.04 273	90.68 201

BH-RMVSNet		80.53 194	80.22 197	81.49 203	87.19 209	66.21 195	77.79 266	86.23 222	74.21 154	83.69 208	88.50 214	73.25 195	90.75 201	63.18 251	87.90 274	87.52 248

Effi-MVS+		83.90 147	84.01 145	83.57 165	87.22 208	65.61 199	86.55 116	92.40 97	78.64 105	81.34 243	84.18 280	83.65 79	92.93 141	74.22 160	87.87 275	92.17 166

MVS_Test		82.47 168	83.22 154	80.22 224	82.62 274	57.75 279	82.54 195	91.96 109	71.16 194	82.89 220	92.52 128	77.41 147	90.50 209	80.04 102	87.84 276	92.40 154

QAPM		82.59 166	82.59 165	82.58 186	86.44 221	66.69 191	89.94 56	90.36 149	67.97 223	84.94 187	92.58 125	72.71 200	92.18 157	70.63 197	87.73 277	88.85 234

PVSNet_Blended		76.49 239	75.40 243	79.76 229	84.43 248	63.41 216	75.14 297	90.44 146	57.36 296	75.43 293	78.30 327	69.11 221	91.44 178	60.68 270	87.70 278	84.42 280

pmmvs5		70.73 283	70.07 287	72.72 291	77.03 322	52.73 311	74.14 302	75.65 293	50.36 334	72.17 310	85.37 263	55.42 292	80.67 307	52.86 310	87.59 279	84.77 276

IB-MVS	62.13 19	71.64 278	68.97 292	79.66 233	80.80 293	62.26 235	73.94 304	76.90 283	63.27 260	68.63 322	76.79 334	33.83 353	91.84 170	59.28 277	87.26 280	84.88 275
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		70.20 285	68.80 294	74.38 284	80.91 288	84.81 38	59.12 343	76.45 288	55.06 305	75.31 296	82.36 300	55.74 289	54.82 350	47.02 331	87.24 281	83.52 291

pmmvs4		74.92 253	72.98 265	80.73 216	84.95 243	71.71 154	76.23 288	77.59 280	52.83 316	77.73 276	86.38 243	56.35 287	84.97 285	57.72 285	87.05 282	85.51 269

MIMVSNet		71.09 281	71.59 277	69.57 303	87.23 207	50.07 331	78.91 250	71.83 319	60.20 282	71.26 313	91.76 148	55.08 294	76.09 318	41.06 343	87.02 283	82.54 305

HyFIR lowres test		75.12 250	72.66 268	82.50 189	91.44 129	65.19 201	72.47 311	87.31 204	46.79 338	80.29 256	84.30 279	52.70 298	92.10 161	51.88 316	86.73 284	90.22 211

MSDG		80.06 207	79.99 203	80.25 223	83.91 263	68.04 183	77.51 271	89.19 177	77.65 113	81.94 232	83.45 287	76.37 163	86.31 271	63.31 250	86.59 285	86.41 259

Patchmatch-test		65.91 305	67.38 299	61.48 328	75.51 333	43.21 347	68.84 323	63.79 340	62.48 265	72.80 307	83.42 288	44.89 332	59.52 349	48.27 328	86.45 286	81.70 314

mvs_anonymous		78.13 220	78.76 211	76.23 276	79.24 309	50.31 330	78.69 254	84.82 244	61.60 273	83.09 219	92.82 116	73.89 184	87.01 258	68.33 219	86.41 287	91.37 188

IterMVS-SCA-FT		80.64 193	79.41 206	84.34 146	83.93 262	69.66 167	76.28 287	81.09 265	72.43 180	86.47 165	90.19 189	60.46 258	93.15 135	77.45 132	86.39 288	90.22 211

E-PMN		61.59 313	61.62 315	61.49 327	66.81 353	55.40 294	53.77 347	60.34 346	66.80 235	58.90 349	65.50 348	40.48 342	66.12 343	55.72 293	86.25 289	62.95 345

EMVS		61.10 316	60.81 317	61.99 325	65.96 354	55.86 291	53.10 348	58.97 348	67.06 232	56.89 352	63.33 349	40.98 340	67.03 339	54.79 301	86.18 290	63.08 344

our_test_3		71.85 276	71.59 277	72.62 293	80.71 294	53.78 304	69.72 322	71.71 322	58.80 285	78.03 272	80.51 314	56.61 285	78.84 312	62.20 256	86.04 291	85.23 271

EU-MVSNet		75.12 250	74.43 252	77.18 264	83.11 271	59.48 261	85.71 127	82.43 256	39.76 350	85.64 178	88.76 210	44.71 333	87.88 251	73.86 166	85.88 292	84.16 283

GA-MVS		75.83 244	74.61 248	79.48 236	81.87 277	59.25 263	73.42 308	82.88 252	68.68 217	79.75 260	81.80 304	50.62 303	89.46 231	66.85 226	85.64 293	89.72 217

MVS		73.21 266	72.59 269	75.06 281	80.97 287	60.81 250	81.64 212	85.92 226	46.03 341	71.68 312	77.54 329	68.47 224	89.77 228	55.70 294	85.39 294	74.60 335

PatchT		70.52 284	72.76 267	63.79 322	79.38 307	33.53 354	77.63 268	65.37 338	73.61 160	71.77 311	92.79 119	44.38 334	75.65 321	64.53 245	85.37 295	82.18 310

TR-MVS		76.77 235	75.79 239	79.72 231	86.10 235	65.79 198	77.14 274	83.02 251	65.20 253	81.40 241	82.10 301	66.30 232	90.73 203	55.57 295	85.27 296	82.65 302

BH-w/o		76.57 237	76.07 238	78.10 255	86.88 218	65.92 197	77.63 268	86.33 221	65.69 246	80.89 247	79.95 318	68.97 223	90.74 202	53.01 309	85.25 297	77.62 330

IterMVS		76.91 233	76.34 235	78.64 245	80.91 288	64.03 211	76.30 286	79.03 274	64.88 255	83.11 217	89.16 204	59.90 264	84.46 290	68.61 217	85.15 298	87.42 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	70.19 17	77.77 226	77.46 223	78.71 244	84.39 251	61.15 243	81.18 221	82.52 254	62.45 266	83.34 214	87.37 231	66.20 233	88.66 244	64.69 242	85.02 299	86.32 260

test-LLR		67.21 299	66.74 303	68.63 308	76.45 327	55.21 296	67.89 326	67.14 333	62.43 267	65.08 336	72.39 341	43.41 335	69.37 330	61.00 266	84.89 300	81.31 319

test-mter		65.00 307	63.79 310	68.63 308	76.45 327	55.21 296	67.89 326	67.14 333	50.98 329	65.08 336	72.39 341	28.27 359	69.37 330	61.00 266	84.89 300	81.31 319

PS-MVSNAJ		77.04 232	76.53 233	78.56 246	87.09 215	61.40 239	75.26 296	87.13 209	61.25 274	74.38 301	77.22 333	76.94 155	90.94 193	64.63 243	84.83 302	83.35 295

xiu_mvs_v2_base		77.19 230	76.75 231	78.52 247	87.01 216	61.30 241	75.55 294	87.12 212	61.24 275	74.45 299	78.79 323	77.20 149	90.93 194	64.62 244	84.80 303	83.32 296

pmmvs3		62.47 309	60.02 321	69.80 301	71.58 351	64.00 212	70.52 318	58.44 349	39.77 349	66.05 330	75.84 337	27.10 361	72.28 325	46.15 334	84.77 304	73.11 336

MDTV_nov1_ep13			68.29 297		78.03 315	43.87 345	74.12 303	72.22 316	52.17 320	67.02 329	85.54 255	45.36 326	80.85 306	55.73 292	84.42 305

1112_ss		74.82 255	73.74 256	78.04 256	89.57 165	60.04 255	76.49 284	87.09 213	54.31 308	73.66 304	79.80 319	60.25 261	86.76 266	58.37 279	84.15 306	87.32 251

PatchMatch-RL		74.48 257	73.22 262	78.27 253	87.70 198	85.26 33	75.92 290	70.09 326	64.34 257	76.09 286	81.25 309	65.87 236	78.07 314	53.86 305	83.82 307	71.48 338

MDA-MVSNet_test_wron		70.05 289	70.44 284	68.88 306	73.84 341	53.47 306	58.93 345	67.28 331	58.43 286	87.09 149	85.40 261	59.80 266	67.25 338	59.66 275	83.54 308	85.92 265

YYNet1		70.06 288	70.44 284	68.90 305	73.76 342	53.42 308	58.99 344	67.20 332	58.42 287	87.10 148	85.39 262	59.82 265	67.32 337	59.79 274	83.50 309	85.96 263

Test_1112_low_res		73.90 261	73.08 263	76.35 273	90.35 154	55.95 289	73.40 309	86.17 223	50.70 331	73.14 305	85.94 251	58.31 274	85.90 277	56.51 289	83.22 310	87.20 252

PVSNet	58.17 21	66.41 303	65.63 306	68.75 307	81.96 276	49.88 332	62.19 339	72.51 315	51.03 328	68.04 324	75.34 339	50.84 302	74.77 322	45.82 336	82.96 311	81.60 316

gg-mvs-nofinetune		68.96 295	69.11 291	68.52 310	76.12 330	45.32 341	83.59 166	55.88 351	86.68 26	64.62 340	97.01 8	30.36 357	83.97 295	44.78 337	82.94 312	76.26 332

CR-MVSNet		74.00 260	73.04 264	76.85 269	79.58 303	62.64 227	82.58 192	76.90 283	50.50 333	75.72 290	92.38 129	48.07 309	84.07 293	68.72 216	82.91 313	83.85 287

RPMNet		78.88 212	78.28 217	80.68 218	79.58 303	62.64 227	82.58 192	94.16 28	74.80 147	75.72 290	92.59 123	48.69 307	95.56 34	73.48 170	82.91 313	83.85 287

test0.0.03 1		64.66 308	64.36 309	65.57 318	75.03 338	46.89 338	64.69 335	61.58 345	62.43 267	71.18 315	77.54 329	43.41 335	68.47 335	40.75 344	82.65 315	81.35 318

HY-MVS	64.64 18	73.03 267	72.47 272	74.71 282	83.36 267	54.19 301	82.14 208	81.96 259	56.76 300	69.57 320	86.21 249	60.03 262	84.83 288	49.58 322	82.65 315	85.11 273

SCA		73.32 263	72.57 270	75.58 279	81.62 279	55.86 291	78.89 251	71.37 323	61.73 270	74.93 298	83.42 288	60.46 258	87.01 258	58.11 283	82.63 317	83.88 284

CHOSEN 1792x2688		72.45 271	70.56 282	78.13 254	90.02 163	63.08 221	68.72 324	83.16 250	42.99 347	75.92 288	85.46 259	57.22 283	85.18 284	49.87 321	81.67 318	86.14 262

WTY-MVS		67.91 298	68.35 296	66.58 315	80.82 292	48.12 335	65.96 333	72.60 313	53.67 312	71.20 314	81.68 306	58.97 270	69.06 334	48.57 325	81.67 318	82.55 304

TESTMET0.1,1		61.29 314	60.32 319	64.19 321	72.06 349	51.30 322	67.89 326	62.09 341	45.27 342	60.65 345	69.01 344	27.93 360	64.74 346	56.31 290	81.65 320	76.53 331

PAPM		71.77 277	70.06 288	76.92 266	86.39 222	53.97 302	76.62 282	86.62 219	53.44 313	63.97 341	84.73 275	57.79 280	92.34 153	39.65 345	81.33 321	84.45 279

DSMNet-mixed		60.98 317	61.61 316	59.09 332	72.88 347	45.05 343	74.70 300	46.61 356	26.20 353	65.34 334	90.32 186	55.46 291	63.12 348	41.72 342	81.30 322	69.09 342

sss		66.92 300	67.26 300	65.90 316	77.23 319	51.10 326	64.79 334	71.72 321	52.12 323	70.13 318	80.18 316	57.96 277	65.36 345	50.21 318	81.01 323	81.25 321

tpm		67.95 297	68.08 298	67.55 312	78.74 314	43.53 346	75.60 292	67.10 335	54.92 306	72.23 309	88.10 218	42.87 338	75.97 319	52.21 311	80.95 324	83.15 299

tpm2		68.45 296	66.83 302	73.30 288	78.93 313	48.50 333	79.76 236	71.76 320	47.50 337	69.92 319	83.60 284	42.07 339	88.40 246	48.44 327	79.51 325	83.01 301

FPMVS		72.29 274	72.00 274	73.14 289	88.63 179	85.00 35	74.65 301	67.39 330	71.94 189	77.80 275	87.66 226	50.48 304	75.83 320	49.95 319	79.51 325	58.58 349

UnsupCasMVSNet_bld		69.21 294	69.68 289	67.82 311	79.42 306	51.15 324	67.82 329	75.79 290	54.15 309	77.47 278	85.36 264	59.26 269	70.64 329	48.46 326	79.35 327	81.66 315

CostFormer		69.98 290	68.68 295	73.87 285	77.14 320	50.72 328	79.26 245	74.51 299	51.94 324	70.97 316	84.75 274	45.16 330	87.49 254	55.16 299	79.23 328	83.40 294

1314		73.22 265	72.56 271	75.20 280	80.41 299	57.84 277	81.64 212	85.36 231	51.68 325	73.10 306	76.65 335	61.45 254	85.19 283	63.54 247	79.21 329	82.59 303

baseline1		73.26 264	73.54 259	72.43 295	84.92 244	47.79 337	79.89 235	74.00 302	65.93 240	78.81 269	86.28 248	56.36 286	81.63 304	56.63 288	79.04 330	87.87 246

PMMVS		61.65 312	60.38 318	65.47 319	65.40 355	69.26 172	63.97 337	61.73 344	36.80 352	60.11 346	68.43 345	59.42 267	66.35 342	48.97 324	78.57 331	60.81 346

baseline2		69.77 291	66.89 301	78.41 250	79.51 305	58.09 275	76.23 288	69.57 328	57.50 295	64.82 339	77.45 331	46.02 316	88.44 245	53.08 308	77.83 332	88.70 235

MS-PatchMatch		70.93 282	70.22 286	73.06 290	81.85 278	62.50 230	73.82 306	77.90 278	52.44 319	75.92 288	81.27 308	55.67 290	81.75 302	55.37 297	77.70 333	74.94 334

UnsupCasMVSNet_eth		71.63 279	72.30 273	69.62 302	76.47 326	52.70 312	70.03 321	80.97 266	59.18 284	79.36 264	88.21 217	60.50 257	69.12 333	58.33 281	77.62 334	87.04 254

CVMVSNet		72.62 270	71.41 280	76.28 275	83.25 268	60.34 253	83.50 170	79.02 275	37.77 351	76.33 282	85.10 266	49.60 306	87.41 255	70.54 198	77.54 335	81.08 324

GG-mvs-BLEND				67.16 313	73.36 343	46.54 340	84.15 150	55.04 352		58.64 350	61.95 351	29.93 358	83.87 296	38.71 347	76.92 336	71.07 339

CHOSEN 280x420		59.08 319	56.52 324	66.76 314	76.51 325	64.39 208	49.62 349	59.00 347	43.86 345	55.66 353	68.41 346	35.55 351	68.21 336	43.25 339	76.78 337	67.69 343

tpmvs		70.16 286	69.56 290	71.96 297	74.71 340	48.13 334	79.63 237	75.45 295	65.02 254	70.26 317	81.88 303	45.34 327	85.68 279	58.34 280	75.39 338	82.08 311

MVP-Stereo		75.81 245	73.51 260	82.71 183	89.35 167	73.62 126	80.06 231	85.20 235	60.30 280	73.96 302	87.94 221	57.89 279	89.45 232	52.02 312	74.87 339	85.06 274
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		66.43 302	64.37 308	72.63 292	74.86 339	50.86 327	76.52 283	72.74 312	54.06 310	65.50 333	68.30 347	32.13 355	84.84 287	61.63 263	73.59 340	82.19 309

new_pmnet		55.69 321	57.66 323	49.76 335	75.47 334	30.59 355	59.56 340	51.45 354	43.62 346	62.49 342	75.48 338	40.96 341	49.15 353	37.39 348	72.52 341	69.55 341

PatchmatchNet		69.71 292	68.83 293	72.33 296	77.66 318	53.60 305	79.29 244	69.99 327	57.66 293	72.53 308	82.93 293	46.45 313	80.08 310	60.91 268	72.09 342	83.31 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 315	62.92 312	55.87 333	79.09 310	35.34 353	71.83 313	57.98 350	46.56 339	59.05 348	91.14 162	49.95 305	76.43 317	38.74 346	71.92 343	55.84 350

tpmrst		66.28 304	66.69 304	65.05 320	72.82 348	39.33 349	78.20 260	70.69 325	53.16 315	67.88 325	80.36 315	48.18 308	74.75 323	58.13 282	70.79 344	81.08 324

tpm cat1		66.76 301	65.21 307	71.42 298	77.09 321	50.62 329	78.01 261	73.68 307	44.89 343	68.64 321	79.00 322	45.51 324	82.42 301	49.91 320	70.15 345	81.23 323

ADS-MVSNet2		65.87 306	63.64 311	72.55 294	73.16 345	56.92 285	67.10 330	74.81 296	49.74 335	66.04 331	82.97 291	46.71 311	77.26 315	42.29 340	69.96 346	83.46 292

ADS-MVSNet		61.90 311	62.19 314	61.03 329	73.16 345	36.42 352	67.10 330	61.75 343	49.74 335	66.04 331	82.97 291	46.71 311	63.21 347	42.29 340	69.96 346	83.46 292

JIA-IIPM		69.41 293	66.64 305	77.70 261	73.19 344	71.24 157	75.67 291	65.56 337	70.42 200	65.18 335	92.97 111	33.64 354	83.06 298	53.52 307	69.61 348	78.79 329

EPMVS		62.47 309	62.63 313	62.01 324	70.63 352	38.74 350	74.76 299	52.86 353	53.91 311	67.71 327	80.01 317	39.40 343	66.60 341	55.54 296	68.81 349	80.68 328

MVE	40.22 23	51.82 323	50.47 326	55.87 333	62.66 357	51.91 318	31.61 352	39.28 357	40.65 348	50.76 354	74.98 340	56.24 288	44.67 354	33.94 351	64.11 350	71.04 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 318	60.29 320	61.92 326	72.04 350	38.67 351	70.83 316	64.08 339	51.28 327	60.75 344	77.28 332	36.59 350	71.58 328	47.41 330	62.34 351	75.52 333

PVSNet_0	51.08 22	56.10 320	54.97 325	59.48 331	75.12 337	53.28 309	55.16 346	61.89 342	44.30 344	59.16 347	62.48 350	54.22 295	65.91 344	35.40 349	47.01 352	59.25 348

tmp_tt		20.25 325	24.50 328	7.49 338	4.47 359	8.70 360	34.17 351	25.16 359	1.00 355	32.43 356	18.49 353	39.37 344	9.21 356	21.64 353	43.75 353	4.57 352

DeepMVS_CX				24.13 337	32.95 358	29.49 356		21.63 360	12.07 354	37.95 355	45.07 352	30.84 356	19.21 355	17.94 354	33.06 354	23.69 351

testmvs		5.91 329	7.65 332	0.72 340	1.20 360	0.37 362	59.14 342	0.67 362	0.49 357	1.11 357	2.76 357	0.94 363	0.24 358	1.02 356	1.47 355	1.55 354

test123		6.27 328	8.08 331	0.84 339	1.11 361	0.57 361	62.90 338	0.82 361	0.54 356	1.07 358	2.75 358	1.26 362	0.30 357	1.04 355	1.26 356	1.66 353

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

cdsmvs_eth3d_5k		20.81 324	27.75 327	0.00 341	0.00 362	0.00 363	0.00 353	85.44 230	0.00 358	0.00 359	82.82 295	81.46 111	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		6.41 327	8.55 330	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	76.94 155	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		6.65 326	8.87 329	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	79.80 319	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

test_241102_ONE					94.18 49	72.65 133		93.69 49	83.62 44	94.11 23	93.78 98	90.28 16	95.50 45

save fliter					93.75 62	77.44 96	86.31 119	89.72 168	70.80 196

test0726					94.16 52	72.56 138	90.63 43	93.90 41	83.61 45	93.75 31	94.49 59	89.76 20

GSMVS																83.88 284

test_part2					93.86 61	77.77 91				92.84 45

sam_mvs1												46.11 315				83.88 284

sam_mvs												45.92 320

MTGPA								91.81 114

test_post1							78.85 253				3.13 355	45.19 329	80.13 309	58.11 283

test_post											3.10 356	45.43 325	77.22 316

patchmatchnet-post											81.71 305	45.93 319	87.01 258

MTMP							90.66 41	33.14 358

gm-plane-assit					75.42 335	44.97 344			52.17 320		72.36 343		87.90 250	54.10 304

TEST9					92.34 97	79.70 73	83.94 154	90.32 150	65.41 252	84.49 195	90.97 168	82.03 102	93.63 111

test_8					92.09 106	78.87 81	83.82 159	90.31 152	65.79 242	84.36 198	90.96 170	81.93 104	93.44 123

agg_prior					91.58 123	77.69 92		90.30 154		84.32 199			93.18 131

test_prior4						78.97 80	84.59 141

test_prior				86.32 104	90.59 150	71.99 148		92.85 86					94.17 91			92.80 138

旧先验2							81.73 210		56.88 299	86.54 164			84.90 286	72.81 180

新几何2							81.72 211

无先验							82.81 189	85.62 229	58.09 289				91.41 181	67.95 222		84.48 278

原ACMM2							82.26 204

testdata2													86.43 270	63.52 248

segment_acmp												81.94 103

testdata1							79.62 238		73.95 157

plane_prior7					93.45 68	77.31 100

plane_prior6					92.61 89	76.54 109						74.84 173

plane_prior4											92.95 112

plane_prior3						76.85 107			77.79 112	86.55 159

plane_prior2							89.45 70		79.44 93

plane_prior1					92.83 87

n2								0.00 363

nn								0.00 363

door-mid								74.45 300

test11								91.46 121

door								72.57 314

HQP5-MVS						70.66 160

HQP-NCC					91.19 134		84.77 136		73.30 167	80.55 253

ACMP_Plane					91.19 134		84.77 136		73.30 167	80.55 253

BP-MVS														77.30 134

HQP4-MVS										80.56 252			94.61 76			93.56 116

HQP2-MVS												72.10 205

NP-MVS					91.95 110	74.55 121					90.17 191

MDTV_nov1_ep13_2view						27.60 357	70.76 317		46.47 340	61.27 343		45.20 328		49.18 323		83.75 289

Test By Simon												79.09 132

High-resolution multi-view benchmark