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 63
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 58

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 58

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	88.42 130	91.97 141	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	88.53 127	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	89.61 109	91.81 148	77.72 143	94.18 89	75.00 157	98.53 16	96.99 23

Baseline_NR-MVSNet		84.00 144	85.90 107	78.29 253	91.47 128	53.44 307	82.29 201	87.00 218	79.06 98	89.55 111	95.72 27	77.20 149	86.14 275	72.30 184	98.51 17	95.28 55

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	92.41 155
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 61
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	92.55 151

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	91.82 147	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	91.97 141	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	92.30 159

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	92.30 159

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	93.20 126
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 62
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 72
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 53
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	92.98 134

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	88.52 128	94.37 69	86.74 53	95.41 48	86.32 34	98.21 30	93.19 127
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 82
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	93.12 129

WR-MVS		83.56 152	84.40 139	81.06 210	93.43 70	54.88 299	78.67 256	85.02 242	81.24 72	90.74 83	91.56 154	72.85 199	91.08 190	68.00 221	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	93.64 113

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	89.71 103	94.82 48	85.09 67	95.77 27	84.17 61	98.03 37	93.26 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
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	73.57 188	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	93.71 109

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 97

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	92.98 134

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	90.55 208

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	92.02 171

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 92

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	92.29 161

IU-MVS					94.18 49	72.64 135		90.82 137	56.98 302	89.67 105				85.78 45	97.92 46	93.28 122

CLD-MVS		83.18 160	82.64 164	84.79 135	89.05 172	67.82 185	77.93 264	92.52 95	68.33 220	85.07 185	81.54 309	82.06 100	92.96 139	69.35 206	97.91 48	93.57 116
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	88.64 186	86.30 29	89.60 110	92.59 124	69.22 221	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	93.27 123

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	92.04 170

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	91.70 182

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	72.94 198	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	93.88 100
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 95

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	92.85 138

#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	92.85 138

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	93.83 102

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 98

UniMVSNet_ETH3D		89.12 64	90.72 46	84.31 147	97.00 2	64.33 209	89.67 62	88.38 190	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 94	94.03 82	86.57 56	95.80 22	87.35 20	97.62 62	94.20 87

X-MVStestdata		85.04 118	82.70 162	92.08 10	95.64 23	86.25 19	92.64 18	93.33 62	85.07 33	89.99 94	16.05 358	86.57 56	95.80 22	87.35 20	97.62 62	94.20 87

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	92.73 143

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	92.73 143

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 89

9.14			89.29 62		91.84 116		88.80 81	95.32 9	75.14 145	91.07 76	92.89 115	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 93

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	92.54 152

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 85

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 85

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	92.69 122	84.48 72	93.83 105	83.38 68	97.48 73	94.47 77

MIMVSNet1		83.63 151	84.59 133	80.74 215	94.06 56	62.77 225	82.72 190	84.53 247	77.57 115	90.34 88	95.92 24	76.88 161	85.83 279	61.88 260	97.42 74	93.62 114

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	92.19 167

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	89.15 179	83.54 47	89.85 99	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	92.29 135	86.11 62	90.22 216	86.24 38	97.24 79	91.36 191
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	87.50 143	92.38 130	81.42 112	93.28 128	83.07 71	97.24 79	91.67 183

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	93.19 105	86.22 61	95.97 11	82.23 80	97.18 81	90.45 210
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 251	79.30 208	62.63 327	75.56 334	75.18 119	80.89 224	73.10 315	75.06 146	94.76 12	95.32 34	87.73 42	52.85 355	34.16 354	97.11 82	59.85 351

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 91	95.12 41	90.98 8	88.92 238	77.55 130	97.07 83	83.13 302
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	88.64 125	91.22 159	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	84.58 194	91.57 152	81.92 106	94.17 91	79.54 108	96.97 85	92.80 140

test_prior2							83.37 173		75.43 140	84.58 194	91.57 152	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	87.98 199	81.51 70	87.05 152	91.83 146	66.18 236	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	88.89 119	92.31 134	83.86 75	94.24 85	81.59 87	96.87 88	92.97 137

VDDNet		84.35 133	85.39 117	81.25 205	95.13 32	59.32 262	85.42 131	81.11 265	86.41 28	87.41 144	96.21 20	73.61 187	90.61 207	66.33 231	96.85 89	93.81 107

VPNet		80.25 203	81.68 176	75.94 278	92.46 94	47.98 338	76.70 281	81.67 263	73.45 162	84.87 189	92.82 117	74.66 178	86.51 269	61.66 263	96.85 89	93.33 120

SixPastTwentyTwo		87.20 85	87.45 83	86.45 102	92.52 92	69.19 175	87.84 94	88.05 196	81.66 68	94.64 14	96.53 15	65.94 237	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	88.69 185	76.29 124	87.58 142	94.44 61	80.60 120	87.20 257	66.60 230	96.82 92	94.34 84

Gipuma		84.44 130	86.33 98	78.78 242	84.20 259	73.57 127	89.55 65	90.44 146	84.24 37	84.38 198	94.89 45	76.35 164	80.40 312	76.14 145	96.80 93	82.36 310
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	92.98 110	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	65.91 243	86.19 167	91.75 150	83.77 78	94.98 64	77.43 133	96.71 95	93.73 108

DIV-MVS_2432*1600		81.93 178	83.14 157	78.30 252	84.75 247	52.75 311	80.37 230	89.42 176	70.24 205	90.26 90	93.39 103	74.55 180	86.77 265	68.61 217	96.64 96	95.38 51

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	89.62 107	92.87 116	84.56 70	93.89 101	77.65 128	96.62 97	90.70 202

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	92.55 127	84.99 68	96.15 5	81.26 88	96.61 98	91.83 178

TransMVSNet (Re)		84.02 143	85.74 110	78.85 241	91.00 140	55.20 298	82.29 201	87.26 206	79.65 90	88.38 132	95.52 32	83.00 85	86.88 262	67.97 222	96.60 99	94.45 80

ambc				82.98 176	90.55 152	64.86 203	88.20 88	89.15 179		89.40 114	93.96 88	71.67 213	91.38 183	78.83 115	96.55 100	92.71 146

train_agg		85.98 104	85.28 118	88.07 83	92.34 97	79.70 73	83.94 154	90.32 150	65.79 244	84.49 196	90.97 169	81.93 104	93.63 111	81.21 89	96.54 101	90.88 198

VDD-MVS		84.23 138	84.58 134	83.20 172	91.17 137	65.16 202	83.25 177	84.97 244	79.79 87	87.18 146	94.27 70	74.77 176	90.89 197	69.24 207	96.54 101	93.55 119

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	86.69 158	92.28 136	80.36 123	95.06 62	86.17 39	96.49 103	90.22 213

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	96.49 103	97.09 20

test9_res														80.83 95	96.45 105	90.57 206

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 97	95.24 38	82.13 98	90.40 211	69.19 210	96.40 106	95.31 54

agg_prior1		85.72 107	85.20 119	87.28 92	91.58 123	77.69 92	83.69 164	90.30 154	66.29 240	84.32 200	91.07 166	82.13 98	93.18 131	81.02 91	96.36 107	90.98 194

anonymousdsp		89.73 53	88.88 70	92.27 9	89.82 164	86.67 15	90.51 46	90.20 160	69.87 208	95.06 11	96.14 22	84.28 73	93.07 138	87.68 13	96.34 108	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	84.76 191	87.70 227	78.87 134	94.18 89	80.67 98	96.29 109	92.73 143

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	96.28 110	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	74.05 183	94.06 96	83.88 63	96.28 110	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	87.07 151	91.47 156	82.94 86	94.71 71	84.67 56	96.27 112	92.62 150

RRT_MVS		83.25 158	81.08 185	89.74 53	80.55 299	79.32 77	86.41 118	86.69 219	72.33 184	87.00 153	91.08 164	44.98 333	95.55 37	84.47 59	96.24 113	94.36 83

114514_t		83.10 162	82.54 167	84.77 136	92.90 82	69.10 177	86.65 113	90.62 143	54.66 311	81.46 242	90.81 176	76.98 154	94.38 81	72.62 181	96.18 114	90.82 200

agg_prior2														79.68 106	96.16 115	90.22 213

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	88.00 137	93.03 108	82.66 89	91.47 176	70.81 191	96.14 116	94.16 90

TestCases				89.68 54	91.59 120	83.40 48		95.44 7	79.47 91	88.00 137	93.03 108	82.66 89	91.47 176	70.81 191	96.14 116	94.16 90

EPNet		80.37 200	78.41 217	86.23 108	76.75 325	73.28 129	87.18 102	77.45 285	76.24 126	68.14 327	88.93 210	65.41 239	93.85 102	69.47 205	96.12 118	91.55 188
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	87.44 203	75.52 139	87.85 139	95.26 37	81.25 114	85.65 281	68.74 215	96.04 119	94.42 81

mvs_tets		89.78 52	89.27 63	91.30 28	93.51 67	84.79 39	89.89 57	90.63 142	70.00 207	94.55 15	96.67 12	87.94 39	93.59 115	84.27 60	95.97 120	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	89.35 115	90.68 180	82.85 87	94.57 78	79.55 107	95.95 121	92.00 172

jajsoiax		89.41 58	88.81 72	91.19 32	93.38 71	84.72 40	89.70 59	90.29 157	69.27 211	94.39 17	96.38 16	86.02 64	93.52 119	83.96 62	95.92 122	95.34 52

ANet_high		83.17 161	85.68 112	75.65 279	81.24 286	45.26 346	79.94 235	92.91 84	83.83 41	91.33 73	96.88 11	80.25 124	85.92 277	68.89 213	95.89 123	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	88.83 122	94.26 73	77.55 146	95.86 19	84.88 53	95.87 124	95.24 57

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	86.55 160	92.95 113	74.84 173	95.22 55	80.78 96	95.83 125	94.46 78

plane_prior5								93.61 53					95.22 55	80.78 96	95.83 125	94.46 78

cl-mvsnet_		80.42 198	80.23 196	81.02 211	79.99 302	59.25 263	77.07 277	87.02 215	67.37 232	86.18 169	89.21 204	63.08 251	90.16 218	76.31 143	95.80 127	93.65 112

cl-mvsnet1		80.43 197	80.23 196	81.02 211	79.99 302	59.25 263	77.07 277	87.02 215	67.38 231	86.19 167	89.22 203	63.09 250	90.16 218	76.32 142	95.80 127	93.66 111

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	84.96 186	90.69 179	80.01 126	95.14 58	78.37 118	95.78 129	91.82 179
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 206	80.56 190	78.89 240	89.19 171	55.93 290	85.22 133	73.78 309	82.96 54	84.28 204	92.72 121	57.38 283	90.07 225	63.80 247	95.75 130	90.68 203

ACMMP++_ref															95.74 131

原ACMM1				84.60 140	92.81 88	74.01 125		91.50 120	62.59 265	82.73 223	90.67 181	76.53 162	94.25 84	69.24 207	95.69 132	85.55 270

tfpnnormal		81.79 179	82.95 160	78.31 251	88.93 176	55.40 294	80.83 226	82.85 254	76.81 121	85.90 176	94.14 78	74.58 179	86.51 269	66.82 229	95.68 133	93.01 133

ETH3 D test6400		85.09 116	84.87 124	85.75 121	90.80 145	69.34 170	85.90 123	93.31 65	65.43 250	86.11 170	89.95 194	80.92 116	94.86 67	75.90 148	95.57 134	93.05 131

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	69.17 212	85.80 177	89.56 199	80.76 118	92.13 158	73.21 177	95.51 135	93.25 125
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	95.50 136	94.53 76

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	73.41 192	93.72 108	83.10 70	95.41 137	97.01 22

Vis-MVSNet (Re-imp)		77.82 225	77.79 222	77.92 259	88.82 177	51.29 325	83.28 175	71.97 322	74.04 155	82.23 229	89.78 197	57.38 283	89.41 233	57.22 287	95.41 137	93.05 131

OPU-MVS				88.27 80	91.89 114	77.83 90	90.47 47				91.22 159	81.12 115	94.68 72	74.48 158	95.35 139	92.29 161

FMVSNet1		84.55 127	85.45 116	81.85 197	90.27 156	61.05 245	86.83 108	88.27 193	78.57 106	89.66 106	95.64 29	75.43 167	90.68 204	69.09 211	95.33 140	93.82 104

test12				86.57 99	90.74 146	72.63 136		90.69 140		82.76 222		79.20 131	94.80 69		95.32 141	92.27 163

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	84.85 190	90.89 173	80.85 117	95.29 51	81.14 90	95.32 141	92.34 157

Patchmtry		76.56 240	77.46 224	73.83 288	79.37 310	46.60 343	82.41 198	76.90 287	73.81 158	85.56 181	92.38 130	48.07 311	83.98 295	63.36 250	95.31 143	90.92 197

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	89.13 118	93.44 102	83.82 76	90.98 192	83.86 64	95.30 144	93.60 115

TSAR-MVS + GP.		83.95 145	82.69 163	87.72 86	89.27 169	81.45 61	83.72 163	81.58 264	74.73 148	85.66 178	86.06 252	72.56 204	92.69 147	75.44 152	95.21 145	89.01 235

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	95.21 145	91.82 179

TinyColmap		81.25 184	82.34 170	77.99 258	85.33 242	60.68 251	82.32 200	88.33 191	71.26 192	86.97 154	92.22 139	77.10 152	86.98 261	62.37 255	95.17 147	86.31 263

Anonymous202405211		80.51 196	81.19 184	78.49 248	88.48 182	57.26 282	76.63 282	82.49 256	81.21 73	84.30 203	92.24 138	67.99 227	86.24 273	62.22 256	95.13 148	91.98 175

tttt0517		81.07 186	79.58 206	85.52 125	88.99 175	66.45 194	87.03 105	75.51 298	73.76 159	88.32 134	90.20 189	37.96 349	94.16 94	79.36 112	95.13 148	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	82.04 232	88.33 217	77.91 142	93.95 100	66.17 232	95.12 150	90.34 212

PCF-MVS	74.62 15	82.15 173	80.92 188	85.84 119	89.43 166	72.30 144	80.53 228	91.82 113	57.36 300	87.81 140	89.92 196	77.67 144	93.63 111	58.69 279	95.08 151	91.58 187
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	89.54 113	88.01 221	79.09 132	92.13 158	75.51 150	95.06 152	90.41 211

plane_prior						76.42 112	87.15 103		75.94 134						95.03 153

new-patchmatchnet		70.10 289	73.37 263	60.29 334	81.23 287	16.95 362	59.54 345	74.62 301	62.93 263	80.97 247	87.93 224	62.83 253	71.90 331	55.24 300	95.01 154	92.00 172

v1192		84.57 126	84.69 130	84.21 149	87.75 197	62.88 223	83.02 184	91.43 122	69.08 214	89.98 96	90.89 173	72.70 202	93.62 114	82.41 77	94.97 155	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	67.94 226	90.06 92	90.34 186	72.04 209	93.59 115	82.32 79	94.91 156	96.07 35

CL-MVSNet_2432*1600		76.81 236	77.38 226	75.12 282	86.90 218	51.34 323	73.20 311	80.63 270	68.30 221	81.80 238	88.40 216	66.92 232	80.90 309	55.35 299	94.90 157	93.12 129

v144192		84.24 137	84.41 138	83.71 161	87.59 202	61.57 238	82.95 186	91.03 133	67.82 229	89.80 101	90.49 184	73.28 195	93.51 120	81.88 85	94.89 158	96.04 37

testing_2		84.36 131	84.64 132	83.50 167	86.74 221	63.97 213	84.56 142	90.31 152	66.22 241	91.62 67	94.55 56	75.88 165	91.95 164	77.02 138	94.89 158	94.56 72

LCM-MVSNet-Re		83.48 154	85.06 120	78.75 243	85.94 237	55.75 293	80.05 233	94.27 20	76.47 123	96.09 5	94.54 57	83.31 83	89.75 229	59.95 274	94.89 158	90.75 201

v1240		84.30 135	84.51 136	83.65 162	87.65 200	61.26 242	82.85 188	91.54 119	67.94 226	90.68 84	90.65 182	71.71 212	93.64 110	82.84 75	94.78 161	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	87.12 147	89.39 200	77.98 140	89.40 234	77.46 131	94.78 161	84.75 279

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	89.62 107	92.37 133	73.40 194	92.52 150	78.16 122	94.77 163	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	81.65 240	87.16 237	83.40 82	94.24 85	61.69 262	94.76 164	84.21 284

ITE_SJBPF				90.11 50	90.72 147	84.97 36		90.30 154	81.56 69	90.02 93	91.20 161	82.40 92	90.81 200	73.58 169	94.66 165	94.56 72

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	89.80 101	90.99 168	73.50 189	93.48 121	81.69 86	94.65 166	95.97 38

test20.03		73.75 264	74.59 252	71.22 301	81.11 288	51.12 327	70.15 322	72.10 321	70.42 200	80.28 260	91.50 155	64.21 243	74.72 328	46.96 337	94.58 167	87.82 249

TSAR-MVS + MP.		88.14 75	87.82 79	89.09 66	95.72 21	76.74 108	92.49 24	91.19 130	67.85 228	86.63 159	94.84 47	79.58 130	95.96 12	87.62 14	94.50 168	94.56 72
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							94.47 169

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	80.55 255	90.17 192	72.10 206	94.61 76	77.30 134	94.47 169	93.56 117

cl_fuxian		81.64 180	81.59 179	81.79 201	80.86 292	59.15 266	78.61 257	90.18 161	68.36 219	87.20 145	87.11 239	69.39 219	91.62 173	78.16 122	94.43 171	94.60 71

MCST-MVS		84.36 131	83.93 147	85.63 123	91.59 120	71.58 155	83.52 167	92.13 103	61.82 271	83.96 207	89.75 198	79.93 129	93.46 122	78.33 120	94.34 172	91.87 177

thisisatest0530		79.07 211	77.33 227	84.26 148	87.13 210	64.58 205	83.66 165	75.95 293	68.86 217	85.22 184	87.36 234	38.10 347	93.57 118	75.47 151	94.28 173	94.62 69

baseline		85.20 113	85.93 106	83.02 175	86.30 229	62.37 232	84.55 143	93.96 38	74.48 152	87.12 147	92.03 140	82.30 94	91.94 165	78.39 117	94.21 174	94.74 68

RRT_test8_iter05		78.08 222	77.52 223	79.75 230	80.84 293	52.54 315	80.61 227	88.96 181	67.77 230	84.62 193	89.29 202	33.89 354	92.10 161	77.59 129	94.15 175	94.62 69

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	88.41 131	87.96 222	72.33 205	90.83 199	76.02 147	94.11 176	92.69 147

USDC		76.63 238	76.73 234	76.34 275	83.46 268	57.20 283	80.02 234	88.04 197	52.14 326	83.65 211	91.25 158	63.24 249	86.65 268	54.66 304	94.11 176	85.17 274

MVS_111021_HR		84.63 124	84.34 141	85.49 127	90.18 158	75.86 116	79.23 249	87.13 210	73.35 164	85.56 181	89.34 201	83.60 80	90.50 209	76.64 140	94.05 178	90.09 218

VNet		79.31 210	80.27 195	76.44 273	87.92 193	53.95 303	75.58 294	84.35 248	74.39 153	82.23 229	90.72 178	72.84 200	84.39 292	60.38 273	93.98 179	90.97 195

FMVSNet2		81.31 183	81.61 178	80.41 221	86.38 224	58.75 273	83.93 156	86.58 221	72.43 180	87.65 141	92.98 110	63.78 246	90.22 216	66.86 226	93.92 180	92.27 163

LF4IMVS		82.75 164	81.93 175	85.19 129	82.08 277	80.15 69	85.53 129	88.76 184	68.01 223	85.58 180	87.75 226	71.80 211	86.85 263	74.02 163	93.87 181	88.58 238

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	88.47 129	87.54 230	86.45 58	91.06 191	75.76 149	93.76 182	92.54 152

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	89.55 111	91.41 157	70.70 217	93.20 130	81.02 91	93.76 182	96.25 31

casdiffmvs		85.21 112	85.85 108	83.31 170	86.17 234	62.77 225	83.03 183	93.93 39	74.69 149	88.21 135	92.68 123	82.29 95	91.89 168	77.87 127	93.75 184	95.27 56

UGNet		82.78 163	81.64 177	86.21 111	86.20 233	76.24 115	86.86 106	85.68 229	77.07 119	73.76 307	92.82 117	69.64 218	91.82 171	69.04 212	93.69 185	90.56 207
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		81.78 262			91.84 145	73.92 184			93.65 186	83.61 292

AUN-MVS		81.18 185	78.78 211	88.39 77	90.93 141	82.14 56	82.51 196	83.67 249	64.69 258	80.29 258	85.91 255	51.07 303	92.38 152	76.29 144	93.63 187	90.65 205

MVS_111021_LR		84.28 136	83.76 149	85.83 120	89.23 170	83.07 51	80.99 223	83.56 250	72.71 178	86.07 171	89.07 208	81.75 109	86.19 274	77.11 136	93.36 188	88.24 239

GBi-Net		82.02 175	82.07 172	81.85 197	86.38 224	61.05 245	86.83 108	88.27 193	72.43 180	86.00 172	95.64 29	63.78 246	90.68 204	65.95 233	93.34 189	93.82 104

test1		82.02 175	82.07 172	81.85 197	86.38 224	61.05 245	86.83 108	88.27 193	72.43 180	86.00 172	95.64 29	63.78 246	90.68 204	65.95 233	93.34 189	93.82 104

FMVSNet3		78.80 215	78.55 214	79.57 234	82.89 275	56.89 286	81.76 209	85.77 228	69.04 215	86.00 172	90.44 185	51.75 301	90.09 224	65.95 233	93.34 189	91.72 181

CS-MVS		83.43 157	83.04 159	84.59 141	87.87 194	66.61 192	85.57 128	94.90 14	73.02 174	81.12 246	78.56 329	80.00 127	95.52 39	73.04 179	93.29 192	91.62 185

K. test v3		85.14 114	84.73 126	86.37 103	91.13 138	69.63 168	85.45 130	76.68 290	84.06 40	92.44 53	96.99 9	62.03 254	94.65 73	80.58 99	93.24 193	94.83 67

Anonymous20231206		71.38 282	71.88 277	69.88 304	86.31 228	54.37 300	70.39 321	74.62 301	52.57 322	76.73 283	88.76 211	59.94 265	72.06 330	44.35 342	93.23 194	83.23 300

D2MVS		76.84 235	75.67 244	80.34 222	80.48 300	62.16 236	73.50 308	84.80 246	57.61 298	82.24 228	87.54 230	51.31 302	87.65 253	70.40 200	93.19 195	91.23 192

miper_lstm_enhance		76.45 242	76.10 239	77.51 263	76.72 326	60.97 249	64.69 337	85.04 241	63.98 260	83.20 217	88.22 218	56.67 286	78.79 317	73.22 172	93.12 196	92.78 142

新几何1				82.95 177	93.96 58	78.56 85		80.24 271	55.45 307	83.93 208	91.08 164	71.19 215	88.33 247	65.84 236	93.07 197	81.95 315

1121		80.86 189	79.81 205	84.02 152	93.93 59	78.70 83	81.64 212	80.18 272	55.43 308	83.67 210	91.15 162	71.29 214	91.41 181	67.95 223	93.06 198	81.96 314

lessismore_v0				85.95 115	91.10 139	70.99 159		70.91 328		91.79 64	94.42 64	61.76 255	92.93 141	79.52 110	93.03 199	93.93 98

TAMVS		78.08 222	76.36 236	83.23 171	90.62 149	72.87 131	79.08 250	80.01 274	61.72 273	81.35 244	86.92 241	63.96 245	88.78 242	50.61 321	93.01 200	88.04 243

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	84.07 206	78.72 328	80.39 122	95.13 59	73.82 167	92.98 201	91.04 193

EPNet_dtu		72.87 271	71.33 283	77.49 264	77.72 319	60.55 252	82.35 199	75.79 294	66.49 239	58.39 355	81.06 312	53.68 298	85.98 276	53.55 308	92.97 202	85.95 266
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	88.42 188	74.57 150	83.56 213	85.65 256	78.49 136	94.21 87	72.04 185	92.88 203	94.05 94

CANet		83.79 148	82.85 161	86.63 98	86.17 234	72.21 147	83.76 162	91.43 122	77.24 118	74.39 304	87.45 232	75.36 168	95.42 47	77.03 137	92.83 204	92.25 165

API-MVS		82.28 170	82.61 165	81.30 204	86.29 230	69.79 164	88.71 83	87.67 202	78.42 108	82.15 231	84.15 283	77.98 140	91.59 174	65.39 239	92.75 205	82.51 309

test_yl		78.71 217	78.51 215	79.32 237	84.32 254	58.84 270	78.38 258	85.33 233	75.99 131	82.49 224	86.57 243	58.01 277	90.02 226	62.74 253	92.73 206	89.10 230

DCV-MVSNet		78.71 217	78.51 215	79.32 237	84.32 254	58.84 270	78.38 258	85.33 233	75.99 131	82.49 224	86.57 243	58.01 277	90.02 226	62.74 253	92.73 206	89.10 230

Regformer-1		86.00 102	85.50 115	87.49 89	84.18 260	76.90 106	83.52 167	87.94 200	82.18 62	89.19 116	85.07 271	82.28 96	91.89 168	82.40 78	92.72 208	93.69 110

Regformer-2		86.74 90	86.08 104	88.73 70	84.18 260	79.20 78	83.52 167	89.33 177	83.33 49	89.92 98	85.07 271	83.23 84	93.16 133	83.39 67	92.72 208	93.83 102

testgi		72.36 274	74.61 250	65.59 321	80.56 298	42.82 352	68.29 327	73.35 312	66.87 236	81.84 235	89.93 195	72.08 208	66.92 344	46.05 339	92.54 210	87.01 257

FMVSNet5		72.10 277	71.69 278	73.32 289	81.57 282	53.02 310	76.77 280	78.37 281	63.31 261	76.37 285	91.85 144	36.68 351	78.98 315	47.87 333	92.45 211	87.95 245

CDS-MVSNet		77.32 230	75.40 245	83.06 174	89.00 174	72.48 141	77.90 265	82.17 259	60.81 280	78.94 270	83.49 288	59.30 270	88.76 243	54.64 305	92.37 212	87.93 246
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		83.55 153	83.10 158	84.90 133	89.34 168	83.87 46	84.54 145	88.77 183	79.09 97	83.54 214	88.66 214	74.87 172	81.73 306	66.84 228	92.29 213	89.11 229

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	80.18 261	89.15 206	77.04 153	93.28 128	65.82 237	92.28 214	92.21 166

thres600view7		75.97 245	75.35 247	77.85 261	87.01 216	51.84 321	80.45 229	73.26 313	75.20 144	83.10 219	86.31 249	45.54 324	89.05 235	55.03 302	92.24 215	92.66 148

PVSNet_BlendedMVS		78.80 215	77.84 221	81.65 202	84.43 250	63.41 216	79.49 243	90.44 146	61.70 274	75.43 297	87.07 240	69.11 222	91.44 178	60.68 271	92.24 215	90.11 217

DELS-MVS		81.44 182	81.25 182	82.03 193	84.27 256	62.87 224	76.47 286	92.49 96	70.97 195	81.64 241	83.83 284	75.03 170	92.70 146	74.29 159	92.22 217	90.51 209
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		80.14 273	59.91 287	85.47 183	91.75 150	67.96 228	85.24 283	68.57 219	92.18 218	81.06 328

cl-mvsnet2		78.97 212	78.21 219	81.24 207	77.74 318	59.01 267	77.46 274	87.13 210	65.79 244	84.32 200	85.10 268	58.96 273	90.88 198	75.36 153	92.03 219	93.84 101

miper_ehance_all_eth		80.34 201	80.04 203	81.24 207	79.82 304	58.95 268	77.66 268	89.66 170	65.75 247	85.99 175	85.11 267	68.29 226	91.42 180	76.03 146	92.03 219	93.33 120

miper_enhance_ethall		77.83 224	76.93 231	80.51 219	76.15 331	58.01 276	75.47 296	88.82 182	58.05 294	83.59 212	80.69 313	64.41 241	91.20 185	73.16 178	92.03 219	92.33 158

MVS_0304		78.17 220	77.23 228	80.99 213	84.13 262	69.07 178	81.39 216	80.81 268	76.28 125	67.53 332	89.11 207	62.87 252	86.77 265	60.90 270	92.01 222	87.13 255

DPM-MVS		80.10 207	79.18 209	82.88 181	90.71 148	69.74 165	78.87 253	90.84 136	60.29 285	75.64 296	85.92 254	67.28 229	93.11 136	71.24 189	91.79 223	85.77 269

v148		82.31 169	82.48 168	81.81 200	85.59 239	59.66 259	81.47 215	86.02 226	72.85 176	88.05 136	90.65 182	70.73 216	90.91 196	75.15 155	91.79 223	94.87 63

test222					93.31 73	76.54 109	79.38 244	77.79 283	52.59 321	82.36 227	90.84 175	66.83 233			91.69 225	81.25 323

eth_miper_zixun_eth		80.84 190	80.22 198	82.71 183	81.41 284	60.98 248	77.81 266	90.14 162	67.31 233	86.95 155	87.24 236	64.26 242	92.31 154	75.23 154	91.61 226	94.85 65

pmmvs-eth3d		78.42 219	77.04 230	82.57 188	87.44 204	74.41 123	80.86 225	79.67 275	55.68 306	84.69 192	90.31 188	60.91 258	85.42 282	62.20 257	91.59 227	87.88 247

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	84.27 205	94.05 81	78.35 138	93.65 109	80.54 100	91.58 228	92.08 169
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0515		73.00 270	70.52 285	80.46 220	81.45 283	59.90 257	73.16 312	74.31 305	57.86 295	76.08 291	77.78 332	37.60 350	92.12 160	65.00 240	91.45 229	89.35 224

ppachtmachnet_test		74.73 258	74.00 257	76.90 268	80.71 296	56.89 286	71.53 317	78.42 280	58.24 292	79.32 267	82.92 296	57.91 280	84.26 293	65.60 238	91.36 230	89.56 220

OpenMVS	76.72 13	81.98 177	82.00 174	81.93 194	84.42 252	68.22 181	88.50 87	89.48 174	66.92 235	81.80 238	91.86 143	72.59 203	90.16 218	71.19 190	91.25 231	87.40 252

EG-PatchMatch MVS		84.08 141	84.11 143	83.98 154	92.22 103	72.61 137	82.20 207	87.02 215	72.63 179	88.86 120	91.02 167	78.52 135	91.11 189	73.41 171	91.09 232	88.21 240

3Dnovator	80.37 7	84.80 122	84.71 129	85.06 132	86.36 227	74.71 120	88.77 82	90.00 165	75.65 138	84.96 186	93.17 106	74.06 182	91.19 186	78.28 121	91.09 232	89.29 227

Regformer-3		85.06 117	84.67 131	86.22 109	84.27 256	73.43 128	84.07 151	85.26 235	80.77 78	88.62 126	85.48 259	80.56 121	90.39 212	81.99 82	91.04 234	94.85 65

Regformer-4		86.41 94	85.71 111	88.52 73	84.27 256	77.57 94	84.07 151	88.00 198	82.82 56	89.84 100	85.48 259	82.06 100	92.77 145	83.83 65	91.04 234	95.22 60

thres100view900		75.45 248	75.05 248	76.66 272	87.27 206	51.88 320	81.07 222	73.26 313	75.68 137	83.25 216	86.37 246	45.54 324	88.80 239	51.98 317	90.99 236	89.31 225

tfpn200view9		74.86 256	74.23 255	76.74 271	86.24 231	52.12 317	79.24 247	73.87 307	73.34 165	81.82 236	84.60 279	46.02 318	88.80 239	51.98 317	90.99 236	89.31 225

thres400		75.14 250	74.23 255	77.86 260	86.24 231	52.12 317	79.24 247	73.87 307	73.34 165	81.82 236	84.60 279	46.02 318	88.80 239	51.98 317	90.99 236	92.66 148

cascas		76.29 244	74.81 249	80.72 217	84.47 249	62.94 222	73.89 306	87.34 204	55.94 305	75.16 301	76.53 340	63.97 244	91.16 187	65.00 240	90.97 239	88.06 242

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	89.16 117	92.25 137	72.03 210	96.36 2	88.21 8	90.93 240	92.98 134

ab-mvs		79.67 209	80.56 190	76.99 266	88.48 182	56.93 284	84.70 139	86.06 225	68.95 216	80.78 251	93.08 107	75.30 169	84.62 290	56.78 288	90.90 241	89.43 223

MAR-MVS		80.24 204	78.74 213	84.73 137	86.87 220	78.18 87	85.75 125	87.81 201	65.67 249	77.84 277	78.50 330	73.79 186	90.53 208	61.59 265	90.87 242	85.49 272
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	84.01 263	72.76 132	83.91 157	85.18 237	80.44 79	88.75 123	85.49 258	80.08 125	91.92 166	82.02 81	90.85 243	95.97 38

EI-MVSNet-UG-set		85.04 118	84.44 137	86.85 95	83.87 266	72.52 140	83.82 159	85.15 238	80.27 83	88.75 123	85.45 262	79.95 128	91.90 167	81.92 84	90.80 244	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	86.75 157	93.26 104	93.64 3	90.93 194	84.60 57	90.75 245	93.97 96

ET-MVSNet_ETH3D		75.28 249	72.77 268	82.81 182	83.03 274	68.11 182	77.09 276	76.51 291	60.67 283	77.60 281	80.52 317	38.04 348	91.15 188	70.78 193	90.68 246	89.17 228

EI-MVSNet		82.61 165	82.42 169	83.20 172	83.25 270	63.66 214	83.50 170	85.07 239	76.06 128	86.55 160	85.10 268	73.41 192	90.25 213	78.15 124	90.67 247	95.68 44

MVSTER		77.09 232	75.70 243	81.25 205	75.27 338	61.08 244	77.49 273	85.07 239	60.78 281	86.55 160	88.68 213	43.14 339	90.25 213	73.69 168	90.67 247	92.42 154

Patchmatch-RL test		74.48 259	73.68 259	76.89 269	84.83 246	66.54 193	72.29 314	69.16 333	57.70 296	86.76 156	86.33 247	45.79 323	82.59 301	69.63 204	90.65 249	81.54 319

CMPMVS	59.41 20	75.12 252	73.57 260	79.77 228	75.84 333	67.22 186	81.21 220	82.18 258	50.78 334	76.50 284	87.66 228	55.20 295	82.99 300	62.17 259	90.64 250	89.09 232
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
V42		83.47 155	83.37 153	83.75 160	83.16 272	63.33 218	81.31 217	90.23 159	69.51 210	90.91 81	90.81 176	74.16 181	92.29 156	80.06 101	90.22 251	95.62 46

PM-MVS		80.20 205	79.00 210	83.78 159	88.17 189	86.66 16	81.31 217	66.81 340	69.64 209	88.33 133	90.19 190	64.58 240	83.63 298	71.99 187	90.03 252	81.06 328

PLC	73.85 16	82.09 174	80.31 194	87.45 90	90.86 144	80.29 68	85.88 124	90.65 141	68.17 222	76.32 287	86.33 247	73.12 197	92.61 149	61.40 266	90.02 253	89.44 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
mvs-test1		84.55 127	82.12 171	91.84 20	87.13 210	89.54 4	85.05 135	88.42 188	74.57 150	80.60 252	82.98 292	78.49 136	93.98 99	72.04 185	89.77 254	92.00 172

CANet_DTU		77.81 226	77.05 229	80.09 226	81.37 285	59.90 257	83.26 176	88.29 192	69.16 213	67.83 330	83.72 285	60.93 257	89.47 230	69.22 209	89.70 255	90.88 198

diffmvs		80.40 199	80.48 193	80.17 225	79.02 314	60.04 255	77.54 271	90.28 158	66.65 238	82.40 226	87.33 235	73.50 189	87.35 256	77.98 125	89.62 256	93.13 128

PMMVS2		55.64 326	59.27 326	44.74 340	64.30 360	12.32 363	40.60 354	49.79 359	53.19 318	65.06 342	84.81 275	53.60 299	49.76 356	32.68 356	89.41 257	72.15 341

Fast-Effi-MVS+-dtu		82.54 167	81.41 181	85.90 117	85.60 238	76.53 111	83.07 182	89.62 172	73.02 174	79.11 269	83.51 287	80.74 119	90.24 215	68.76 214	89.29 258	90.94 196

thres200		72.34 275	71.55 281	74.70 285	83.48 267	51.60 322	75.02 299	73.71 310	70.14 206	78.56 273	80.57 316	46.20 316	88.20 249	46.99 336	89.29 258	84.32 283

jason		77.42 229	75.75 242	82.43 191	87.10 214	69.27 171	77.99 263	81.94 261	51.47 330	77.84 277	85.07 271	60.32 262	89.00 236	70.74 195	89.27 260	89.03 233
jason: jason.
MG-MVS		80.32 202	80.94 187	78.47 249	88.18 188	52.62 314	82.29 201	85.01 243	72.01 188	79.24 268	92.54 128	69.36 220	93.36 127	70.65 196	89.19 261	89.45 221

BH-untuned		80.96 188	80.99 186	80.84 214	88.55 181	68.23 180	80.33 231	88.46 187	72.79 177	86.55 160	86.76 242	74.72 177	91.77 172	61.79 261	88.99 262	82.52 308

EIA-MVS		82.19 172	81.23 183	85.10 131	87.95 192	69.17 176	83.22 180	93.33 62	70.42 200	78.58 272	79.77 325	77.29 148	94.20 88	71.51 188	88.96 263	91.93 176

PVSNet_Blended_VisFu		81.55 181	80.49 192	84.70 139	91.58 123	73.24 130	84.21 149	91.67 117	62.86 264	80.94 248	87.16 237	67.27 230	92.87 144	69.82 203	88.94 264	87.99 244

MVSFormer		82.23 171	81.57 180	84.19 151	85.54 240	69.26 172	91.98 30	90.08 163	71.54 190	76.23 288	85.07 271	58.69 274	94.27 82	86.26 35	88.77 265	89.03 233

lupinMVS		76.37 243	74.46 253	82.09 192	85.54 240	69.26 172	76.79 279	80.77 269	50.68 336	76.23 288	82.82 297	58.69 274	88.94 237	69.85 202	88.77 265	88.07 241

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	89.68 104	94.81 51	82.44 91	87.74 252	76.54 141	88.74 267	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	80.87 250	87.92 225	73.49 191	92.42 151	70.07 201	88.40 268	91.60 186

xiu_mvs_v1_base_debu		80.84 190	80.14 200	82.93 178	88.31 185	71.73 151	79.53 240	87.17 207	65.43 250	79.59 263	82.73 299	76.94 155	90.14 221	73.22 172	88.33 269	86.90 258

xiu_mvs_v1_base		80.84 190	80.14 200	82.93 178	88.31 185	71.73 151	79.53 240	87.17 207	65.43 250	79.59 263	82.73 299	76.94 155	90.14 221	73.22 172	88.33 269	86.90 258

xiu_mvs_v1_base_debi		80.84 190	80.14 200	82.93 178	88.31 185	71.73 151	79.53 240	87.17 207	65.43 250	79.59 263	82.73 299	76.94 155	90.14 221	73.22 172	88.33 269	86.90 258

XXY-MVS		74.44 261	76.19 238	69.21 308	84.61 248	52.43 316	71.70 316	77.18 286	60.73 282	80.60 252	90.96 171	75.44 166	69.35 336	56.13 292	88.33 269	85.86 268

Fast-Effi-MVS+		81.04 187	80.57 189	82.46 190	87.50 203	63.22 220	78.37 260	89.63 171	68.01 223	81.87 234	82.08 304	82.31 93	92.65 148	67.10 225	88.30 273	91.51 189

MDA-MVSNet-bldmvs		77.47 228	76.90 232	79.16 239	79.03 313	64.59 204	66.58 334	75.67 296	73.15 172	88.86 120	88.99 209	66.94 231	81.23 308	64.71 242	88.22 274	91.64 184

PAPR		78.84 214	78.10 220	81.07 209	85.17 243	60.22 254	82.21 205	90.57 144	62.51 266	75.32 299	84.61 278	74.99 171	92.30 155	59.48 277	88.04 275	90.68 203

BH-RMVSNet		80.53 195	80.22 198	81.49 203	87.19 209	66.21 195	77.79 267	86.23 223	74.21 154	83.69 209	88.50 215	73.25 196	90.75 201	63.18 252	87.90 276	87.52 250

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	81.34 245	84.18 282	83.65 79	92.93 141	74.22 160	87.87 277	92.17 168

MVS_Test		82.47 168	83.22 154	80.22 224	82.62 276	57.75 279	82.54 195	91.96 109	71.16 194	82.89 221	92.52 129	77.41 147	90.50 209	80.04 102	87.84 278	92.40 156

QAPM		82.59 166	82.59 166	82.58 186	86.44 222	66.69 191	89.94 56	90.36 149	67.97 225	84.94 188	92.58 126	72.71 201	92.18 157	70.63 197	87.73 279	88.85 236

PVSNet_Blended		76.49 241	75.40 245	79.76 229	84.43 250	63.41 216	75.14 298	90.44 146	57.36 300	75.43 297	78.30 331	69.11 222	91.44 178	60.68 271	87.70 280	84.42 282

pmmvs5		70.73 285	70.07 289	72.72 293	77.03 324	52.73 312	74.14 303	75.65 297	50.36 338	72.17 314	85.37 265	55.42 294	80.67 311	52.86 314	87.59 281	84.77 278

IB-MVS	62.13 19	71.64 280	68.97 294	79.66 233	80.80 295	62.26 235	73.94 305	76.90 287	63.27 262	68.63 326	76.79 338	33.83 355	91.84 170	59.28 278	87.26 282	84.88 277
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		70.20 287	68.80 296	74.38 286	80.91 290	84.81 38	59.12 347	76.45 292	55.06 309	75.31 300	82.36 302	55.74 291	54.82 354	47.02 335	87.24 283	83.52 293

pmmvs4		74.92 255	72.98 267	80.73 216	84.95 244	71.71 154	76.23 289	77.59 284	52.83 320	77.73 280	86.38 245	56.35 289	84.97 286	57.72 286	87.05 284	85.51 271

MIMVSNet		71.09 283	71.59 279	69.57 307	87.23 207	50.07 333	78.91 251	71.83 323	60.20 286	71.26 317	91.76 149	55.08 296	76.09 322	41.06 347	87.02 285	82.54 307

HyFIR lowres test		75.12 252	72.66 270	82.50 189	91.44 129	65.19 201	72.47 313	87.31 205	46.79 342	80.29 258	84.30 281	52.70 300	92.10 161	51.88 320	86.73 286	90.22 213

MSDG		80.06 208	79.99 204	80.25 223	83.91 265	68.04 183	77.51 272	89.19 178	77.65 113	81.94 233	83.45 289	76.37 163	86.31 272	63.31 251	86.59 287	86.41 261

Patchmatch-test		65.91 309	67.38 301	61.48 332	75.51 335	43.21 351	68.84 325	63.79 344	62.48 267	72.80 311	83.42 290	44.89 334	59.52 353	48.27 332	86.45 288	81.70 316

mvs_anonymous		78.13 221	78.76 212	76.23 277	79.24 311	50.31 332	78.69 255	84.82 245	61.60 275	83.09 220	92.82 117	73.89 185	87.01 258	68.33 220	86.41 289	91.37 190

IterMVS-SCA-FT		80.64 194	79.41 207	84.34 146	83.93 264	69.66 167	76.28 288	81.09 266	72.43 180	86.47 166	90.19 190	60.46 260	93.15 135	77.45 132	86.39 290	90.22 213

E-PMN		61.59 317	61.62 319	61.49 331	66.81 357	55.40 294	53.77 351	60.34 350	66.80 237	58.90 353	65.50 352	40.48 344	66.12 347	55.72 294	86.25 291	62.95 349

EMVS		61.10 320	60.81 321	61.99 329	65.96 358	55.86 291	53.10 352	58.97 352	67.06 234	56.89 356	63.33 353	40.98 342	67.03 343	54.79 303	86.18 292	63.08 348

our_test_3		71.85 278	71.59 279	72.62 295	80.71 296	53.78 304	69.72 324	71.71 326	58.80 289	78.03 274	80.51 318	56.61 287	78.84 316	62.20 257	86.04 293	85.23 273

EU-MVSNet		75.12 252	74.43 254	77.18 265	83.11 273	59.48 261	85.71 127	82.43 257	39.76 354	85.64 179	88.76 211	44.71 335	87.88 251	73.86 166	85.88 294	84.16 285

GA-MVS		75.83 246	74.61 250	79.48 236	81.87 279	59.25 263	73.42 309	82.88 253	68.68 218	79.75 262	81.80 306	50.62 305	89.46 231	66.85 227	85.64 295	89.72 219

MVS		73.21 268	72.59 271	75.06 283	80.97 289	60.81 250	81.64 212	85.92 227	46.03 345	71.68 316	77.54 333	68.47 225	89.77 228	55.70 295	85.39 296	74.60 339

PatchT		70.52 286	72.76 269	63.79 326	79.38 309	33.53 358	77.63 269	65.37 342	73.61 160	71.77 315	92.79 120	44.38 336	75.65 325	64.53 246	85.37 297	82.18 312

TR-MVS		76.77 237	75.79 241	79.72 231	86.10 236	65.79 198	77.14 275	83.02 252	65.20 255	81.40 243	82.10 303	66.30 234	90.73 203	55.57 296	85.27 298	82.65 304

BH-w/o		76.57 239	76.07 240	78.10 256	86.88 219	65.92 197	77.63 269	86.33 222	65.69 248	80.89 249	79.95 322	68.97 224	90.74 202	53.01 313	85.25 299	77.62 334

IterMVS		76.91 234	76.34 237	78.64 245	80.91 290	64.03 211	76.30 287	79.03 278	64.88 257	83.11 218	89.16 205	59.90 266	84.46 291	68.61 217	85.15 300	87.42 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OpenMVS_ROB	70.19 17	77.77 227	77.46 224	78.71 244	84.39 253	61.15 243	81.18 221	82.52 255	62.45 268	83.34 215	87.37 233	66.20 235	88.66 244	64.69 243	85.02 301	86.32 262

KD-MVS_2432*1600		66.87 303	65.81 308	70.04 302	67.50 355	47.49 340	62.56 341	79.16 276	61.21 278	77.98 275	80.61 314	25.29 364	82.48 302	53.02 311	84.92 302	80.16 331

miper_refine_blended		66.87 303	65.81 308	70.04 302	67.50 355	47.49 340	62.56 341	79.16 276	61.21 278	77.98 275	80.61 314	25.29 364	82.48 302	53.02 311	84.92 302	80.16 331

test-LLR		67.21 301	66.74 305	68.63 312	76.45 329	55.21 296	67.89 328	67.14 337	62.43 269	65.08 340	72.39 345	43.41 337	69.37 334	61.00 267	84.89 304	81.31 321

test-mter		65.00 311	63.79 314	68.63 312	76.45 329	55.21 296	67.89 328	67.14 337	50.98 333	65.08 340	72.39 345	28.27 361	69.37 334	61.00 267	84.89 304	81.31 321

PS-MVSNAJ		77.04 233	76.53 235	78.56 246	87.09 215	61.40 239	75.26 297	87.13 210	61.25 276	74.38 305	77.22 337	76.94 155	90.94 193	64.63 244	84.83 306	83.35 297

xiu_mvs_v2_base		77.19 231	76.75 233	78.52 247	87.01 216	61.30 241	75.55 295	87.12 213	61.24 277	74.45 303	78.79 327	77.20 149	90.93 194	64.62 245	84.80 307	83.32 298

pmmvs3		62.47 313	60.02 325	69.80 305	71.58 353	64.00 212	70.52 320	58.44 353	39.77 353	66.05 334	75.84 341	27.10 363	72.28 329	46.15 338	84.77 308	73.11 340

MDTV_nov1_ep13			68.29 299		78.03 317	43.87 349	74.12 304	72.22 320	52.17 324	67.02 333	85.54 257	45.36 328	80.85 310	55.73 293	84.42 309

1112_ss		74.82 257	73.74 258	78.04 257	89.57 165	60.04 255	76.49 285	87.09 214	54.31 312	73.66 308	79.80 323	60.25 263	86.76 267	58.37 280	84.15 310	87.32 253

PatchMatch-RL		74.48 259	73.22 264	78.27 254	87.70 198	85.26 33	75.92 291	70.09 330	64.34 259	76.09 290	81.25 311	65.87 238	78.07 318	53.86 307	83.82 311	71.48 342

MDA-MVSNet_test_wron		70.05 291	70.44 286	68.88 310	73.84 343	53.47 306	58.93 349	67.28 335	58.43 290	87.09 150	85.40 263	59.80 268	67.25 342	59.66 276	83.54 312	85.92 267

YYNet1		70.06 290	70.44 286	68.90 309	73.76 344	53.42 308	58.99 348	67.20 336	58.42 291	87.10 149	85.39 264	59.82 267	67.32 341	59.79 275	83.50 313	85.96 265

Test_1112_low_res		73.90 263	73.08 265	76.35 274	90.35 154	55.95 289	73.40 310	86.17 224	50.70 335	73.14 309	85.94 253	58.31 276	85.90 278	56.51 290	83.22 314	87.20 254

PVSNet	58.17 21	66.41 307	65.63 310	68.75 311	81.96 278	49.88 334	62.19 343	72.51 319	51.03 332	68.04 328	75.34 343	50.84 304	74.77 326	45.82 340	82.96 315	81.60 318

gg-mvs-nofinetune		68.96 297	69.11 293	68.52 314	76.12 332	45.32 345	83.59 166	55.88 355	86.68 26	64.62 344	97.01 8	30.36 359	83.97 296	44.78 341	82.94 316	76.26 336

CR-MVSNet		74.00 262	73.04 266	76.85 270	79.58 305	62.64 227	82.58 192	76.90 287	50.50 337	75.72 294	92.38 130	48.07 311	84.07 294	68.72 216	82.91 317	83.85 289

RPMNet		78.88 213	78.28 218	80.68 218	79.58 305	62.64 227	82.58 192	94.16 28	74.80 147	75.72 294	92.59 124	48.69 309	95.56 34	73.48 170	82.91 317	83.85 289

test0.0.03 1		64.66 312	64.36 313	65.57 322	75.03 340	46.89 342	64.69 337	61.58 349	62.43 269	71.18 319	77.54 333	43.41 337	68.47 339	40.75 348	82.65 319	81.35 320

HY-MVS	64.64 18	73.03 269	72.47 274	74.71 284	83.36 269	54.19 301	82.14 208	81.96 260	56.76 304	69.57 324	86.21 251	60.03 264	84.83 289	49.58 326	82.65 319	85.11 275

SCA		73.32 265	72.57 272	75.58 280	81.62 281	55.86 291	78.89 252	71.37 327	61.73 272	74.93 302	83.42 290	60.46 260	87.01 258	58.11 284	82.63 321	83.88 286

CHOSEN 1792x2688		72.45 273	70.56 284	78.13 255	90.02 163	63.08 221	68.72 326	83.16 251	42.99 351	75.92 292	85.46 261	57.22 285	85.18 285	49.87 325	81.67 322	86.14 264

WTY-MVS		67.91 300	68.35 298	66.58 319	80.82 294	48.12 337	65.96 335	72.60 317	53.67 316	71.20 318	81.68 308	58.97 272	69.06 338	48.57 329	81.67 322	82.55 306

TESTMET0.1,1		61.29 318	60.32 323	64.19 325	72.06 351	51.30 324	67.89 328	62.09 345	45.27 346	60.65 349	69.01 348	27.93 362	64.74 350	56.31 291	81.65 324	76.53 335

PAPM		71.77 279	70.06 290	76.92 267	86.39 223	53.97 302	76.62 283	86.62 220	53.44 317	63.97 345	84.73 277	57.79 282	92.34 153	39.65 349	81.33 325	84.45 281

DSMNet-mixed		60.98 321	61.61 320	59.09 336	72.88 349	45.05 347	74.70 301	46.61 360	26.20 357	65.34 338	90.32 187	55.46 293	63.12 352	41.72 346	81.30 326	69.09 346

sss		66.92 302	67.26 302	65.90 320	77.23 321	51.10 328	64.79 336	71.72 325	52.12 327	70.13 322	80.18 320	57.96 279	65.36 349	50.21 322	81.01 327	81.25 323

tpm		67.95 299	68.08 300	67.55 316	78.74 316	43.53 350	75.60 293	67.10 339	54.92 310	72.23 313	88.10 220	42.87 340	75.97 323	52.21 315	80.95 328	83.15 301

tpm2		68.45 298	66.83 304	73.30 290	78.93 315	48.50 335	79.76 237	71.76 324	47.50 341	69.92 323	83.60 286	42.07 341	88.40 246	48.44 331	79.51 329	83.01 303

FPMVS		72.29 276	72.00 276	73.14 291	88.63 179	85.00 35	74.65 302	67.39 334	71.94 189	77.80 279	87.66 228	50.48 306	75.83 324	49.95 323	79.51 329	58.58 353

UnsupCasMVSNet_bld		69.21 296	69.68 291	67.82 315	79.42 308	51.15 326	67.82 331	75.79 294	54.15 313	77.47 282	85.36 266	59.26 271	70.64 333	48.46 330	79.35 331	81.66 317

CostFormer		69.98 292	68.68 297	73.87 287	77.14 322	50.72 330	79.26 246	74.51 303	51.94 328	70.97 320	84.75 276	45.16 332	87.49 254	55.16 301	79.23 332	83.40 296

1314		73.22 267	72.56 273	75.20 281	80.41 301	57.84 277	81.64 212	85.36 232	51.68 329	73.10 310	76.65 339	61.45 256	85.19 284	63.54 248	79.21 333	82.59 305

baseline1		73.26 266	73.54 261	72.43 297	84.92 245	47.79 339	79.89 236	74.00 306	65.93 242	78.81 271	86.28 250	56.36 288	81.63 307	56.63 289	79.04 334	87.87 248

PMMVS		61.65 316	60.38 322	65.47 323	65.40 359	69.26 172	63.97 339	61.73 348	36.80 356	60.11 350	68.43 349	59.42 269	66.35 346	48.97 328	78.57 335	60.81 350

baseline2		69.77 293	66.89 303	78.41 250	79.51 307	58.09 275	76.23 289	69.57 332	57.50 299	64.82 343	77.45 335	46.02 318	88.44 245	53.08 310	77.83 336	88.70 237

MS-PatchMatch		70.93 284	70.22 288	73.06 292	81.85 280	62.50 230	73.82 307	77.90 282	52.44 323	75.92 292	81.27 310	55.67 292	81.75 305	55.37 298	77.70 337	74.94 338

UnsupCasMVSNet_eth		71.63 281	72.30 275	69.62 306	76.47 328	52.70 313	70.03 323	80.97 267	59.18 288	79.36 266	88.21 219	60.50 259	69.12 337	58.33 282	77.62 338	87.04 256

CVMVSNet		72.62 272	71.41 282	76.28 276	83.25 270	60.34 253	83.50 170	79.02 279	37.77 355	76.33 286	85.10 268	49.60 308	87.41 255	70.54 198	77.54 339	81.08 326

GG-mvs-BLEND				67.16 317	73.36 345	46.54 344	84.15 150	55.04 356		58.64 354	61.95 355	29.93 360	83.87 297	38.71 351	76.92 340	71.07 343

CHOSEN 280x420		59.08 323	56.52 328	66.76 318	76.51 327	64.39 208	49.62 353	59.00 351	43.86 349	55.66 357	68.41 350	35.55 353	68.21 340	43.25 343	76.78 341	67.69 347

tpmvs		70.16 288	69.56 292	71.96 299	74.71 342	48.13 336	79.63 238	75.45 299	65.02 256	70.26 321	81.88 305	45.34 329	85.68 280	58.34 281	75.39 342	82.08 313

MVP-Stereo		75.81 247	73.51 262	82.71 183	89.35 167	73.62 126	80.06 232	85.20 236	60.30 284	73.96 306	87.94 223	57.89 281	89.45 232	52.02 316	74.87 343	85.06 276
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DWT-MVSNet_test		66.43 306	64.37 312	72.63 294	74.86 341	50.86 329	76.52 284	72.74 316	54.06 314	65.50 337	68.30 351	32.13 357	84.84 288	61.63 264	73.59 344	82.19 311

new_pmnet		55.69 325	57.66 327	49.76 339	75.47 336	30.59 359	59.56 344	51.45 358	43.62 350	62.49 346	75.48 342	40.96 343	49.15 357	37.39 352	72.52 345	69.55 345

PatchmatchNet		69.71 294	68.83 295	72.33 298	77.66 320	53.60 305	79.29 245	69.99 331	57.66 297	72.53 312	82.93 295	46.45 315	80.08 314	60.91 269	72.09 346	83.31 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 319	62.92 316	55.87 337	79.09 312	35.34 357	71.83 315	57.98 354	46.56 343	59.05 352	91.14 163	49.95 307	76.43 321	38.74 350	71.92 347	55.84 354

tpmrst		66.28 308	66.69 306	65.05 324	72.82 350	39.33 353	78.20 261	70.69 329	53.16 319	67.88 329	80.36 319	48.18 310	74.75 327	58.13 283	70.79 348	81.08 326

tpm cat1		66.76 305	65.21 311	71.42 300	77.09 323	50.62 331	78.01 262	73.68 311	44.89 347	68.64 325	79.00 326	45.51 326	82.42 304	49.91 324	70.15 349	81.23 325

ADS-MVSNet2		65.87 310	63.64 315	72.55 296	73.16 347	56.92 285	67.10 332	74.81 300	49.74 339	66.04 335	82.97 293	46.71 313	77.26 319	42.29 344	69.96 350	83.46 294

ADS-MVSNet		61.90 315	62.19 318	61.03 333	73.16 347	36.42 356	67.10 332	61.75 347	49.74 339	66.04 335	82.97 293	46.71 313	63.21 351	42.29 344	69.96 350	83.46 294

JIA-IIPM		69.41 295	66.64 307	77.70 262	73.19 346	71.24 157	75.67 292	65.56 341	70.42 200	65.18 339	92.97 112	33.64 356	83.06 299	53.52 309	69.61 352	78.79 333

EPMVS		62.47 313	62.63 317	62.01 328	70.63 354	38.74 354	74.76 300	52.86 357	53.91 315	67.71 331	80.01 321	39.40 345	66.60 345	55.54 297	68.81 353	80.68 330

MVE	40.22 23	51.82 327	50.47 330	55.87 337	62.66 361	51.91 319	31.61 356	39.28 361	40.65 352	50.76 358	74.98 344	56.24 290	44.67 358	33.94 355	64.11 354	71.04 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 322	60.29 324	61.92 330	72.04 352	38.67 355	70.83 318	64.08 343	51.28 331	60.75 348	77.28 336	36.59 352	71.58 332	47.41 334	62.34 355	75.52 337

PVSNet_0	51.08 22	56.10 324	54.97 329	59.48 335	75.12 339	53.28 309	55.16 350	61.89 346	44.30 348	59.16 351	62.48 354	54.22 297	65.91 348	35.40 353	47.01 356	59.25 352

tmp_tt		20.25 329	24.50 332	7.49 342	4.47 363	8.70 364	34.17 355	25.16 363	1.00 359	32.43 360	18.49 357	39.37 346	9.21 360	21.64 357	43.75 357	4.57 356

DeepMVS_CX				24.13 341	32.95 362	29.49 360		21.63 364	12.07 358	37.95 359	45.07 356	30.84 358	19.21 359	17.94 358	33.06 358	23.69 355

testmvs		5.91 333	7.65 336	0.72 344	1.20 364	0.37 366	59.14 346	0.67 366	0.49 361	1.11 361	2.76 361	0.94 367	0.24 362	1.02 360	1.47 359	1.55 358

test123		6.27 332	8.08 335	0.84 343	1.11 365	0.57 365	62.90 340	0.82 365	0.54 360	1.07 362	2.75 362	1.26 366	0.30 361	1.04 359	1.26 360	1.66 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		20.81 328	27.75 331	0.00 345	0.00 366	0.00 367	0.00 357	85.44 231	0.00 362	0.00 363	82.82 297	81.46 111	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		6.41 331	8.55 334	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	76.94 155	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		6.65 330	8.87 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	79.80 323	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

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																83.88 286

test_part2					93.86 61	77.77 91				92.84 45

sam_mvs1												46.11 317				83.88 286

sam_mvs												45.92 322

MTGPA								91.81 114

test_post1							78.85 254				3.13 359	45.19 331	80.13 313	58.11 284

test_post											3.10 360	45.43 327	77.22 320

patchmatchnet-post											81.71 307	45.93 321	87.01 258

MTMP							90.66 41	33.14 362

gm-plane-assit					75.42 337	44.97 348			52.17 324		72.36 347		87.90 250	54.10 306

TEST9					92.34 97	79.70 73	83.94 154	90.32 150	65.41 254	84.49 196	90.97 169	82.03 102	93.63 111

test_8					92.09 106	78.87 81	83.82 159	90.31 152	65.79 244	84.36 199	90.96 171	81.93 104	93.44 123

agg_prior					91.58 123	77.69 92		90.30 154		84.32 200			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			92.80 140

旧先验2							81.73 210		56.88 303	86.54 165			84.90 287	72.81 180

新几何2							81.72 211

无先验							82.81 189	85.62 230	58.09 293				91.41 181	67.95 223		84.48 280

原ACMM2							82.26 204

testdata2													86.43 271	63.52 249

segment_acmp												81.94 103

testdata1							79.62 239		73.95 157

plane_prior7					93.45 68	77.31 100

plane_prior6					92.61 89	76.54 109						74.84 173

plane_prior4											92.95 113

plane_prior3						76.85 107			77.79 112	86.55 160

plane_prior2							89.45 70		79.44 93

plane_prior1					92.83 87

n2								0.00 367

nn								0.00 367

door-mid								74.45 304

test11								91.46 121

door								72.57 318

HQP5-MVS						70.66 160

HQP-NCC					91.19 134		84.77 136		73.30 167	80.55 255

ACMP_Plane					91.19 134		84.77 136		73.30 167	80.55 255

BP-MVS														77.30 134

HQP4-MVS										80.56 254			94.61 76			93.56 117

HQP2-MVS												72.10 206

NP-MVS					91.95 110	74.55 121					90.17 192

MDTV_nov1_ep13_2view						27.60 361	70.76 319		46.47 344	61.27 347		45.20 330		49.18 327		83.75 291

Test By Simon												79.09 132

High-resolution multi-view benchmark