High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 54	99.27 1	99.54 1

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 20	93.73 60	85.72 30	96.79 1	95.51 8	88.86 12	95.63 8	96.99 9	84.81 69	93.16 134	91.10 1	97.53 69	96.58 30
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
TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 17	88.16 33	94.17 92	86.07 45	98.48 17	97.22 19

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 14	94.16 30	88.75 14	93.79 29	94.43 67	90.64 10		87.16 29	97.60 63	92.73 157

HPM-MVS_fast		92.50 4	92.54 5	92.37 5	95.93 15	85.81 29	92.99 12	94.23 24	85.21 36	92.51 55	95.13 43	90.65 9	95.34 52	88.06 8	98.15 33	95.95 41

SR-MVS-dyc-post		92.41 5	92.41 6	92.39 4	94.13 51	88.95 5	92.87 14	94.16 30	88.75 14	93.79 29	94.43 67	88.83 24	95.51 44	87.16 29	97.60 63	92.73 157

SR-MVS		92.23 6	92.34 7	91.91 15	94.89 37	87.85 8	92.51 24	93.87 49	88.20 19	93.24 39	94.02 90	90.15 16	95.67 34	86.82 33	97.34 73	92.19 188

APD-MVS_3200maxsize		92.05 8	92.24 8	91.48 21	93.02 76	85.17 35	92.47 26	95.05 13	87.65 23	93.21 40	94.39 72	90.09 17	95.08 61	86.67 35	97.60 63	94.18 95

HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 14	82.88 60	91.77 68	93.94 98	90.55 12	95.73 31	88.50 6	98.23 27	95.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	83.01 3	91.97 9	91.95 10	92.04 10	93.68 61	86.15 20	93.37 10	95.10 12	90.28 9	92.11 61	95.03 45	89.75 20	94.93 66	79.95 111	98.27 25	95.04 65
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APDe-MVS		91.22 21	91.92 11	89.14 63	92.97 78	78.04 89	92.84 16	94.14 34	83.33 54	93.90 25	95.73 27	88.77 25	96.41 2	87.60 18	97.98 41	92.98 151
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PS-CasMVS		90.06 39	91.92 11	84.47 147	96.56 6	58.83 306	89.04 84	92.74 95	91.40 5	96.12 4	96.06 23	87.23 45	95.57 38	79.42 119	98.74 5	99.00 2

DTE-MVSNet		89.98 43	91.91 13	84.21 156	96.51 7	57.84 313	88.93 86	92.84 92	91.92 3	96.16 3	96.23 19	86.95 48	95.99 10	79.05 121	98.57 14	98.80 6

PEN-MVS		90.03 41	91.88 14	84.48 146	96.57 5	58.88 303	88.95 85	93.19 74	91.62 4	96.01 6	96.16 21	87.02 47	95.60 36	78.69 124	98.72 8	98.97 3

ACMMP		91.91 10	91.87 15	92.03 11	95.53 26	85.91 24	93.35 11	94.16 30	82.52 63	92.39 58	94.14 84	89.15 23	95.62 35	87.35 24	98.24 26	94.56 77
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
LPG-MVS_test		91.47 17	91.68 16	90.82 33	94.75 40	81.69 59	90.00 58	94.27 21	82.35 64	93.67 34	94.82 51	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

SED-MVS		90.46 33	91.64 17	86.93 96	94.18 46	72.65 141	90.47 52	93.69 54	83.77 48	94.11 23	94.27 74	90.28 14	95.84 23	86.03 46	97.92 45	92.29 181

MTAPA		91.52 14	91.60 18	91.29 26	96.59 4	86.29 17	92.02 30	91.81 125	84.07 45	92.00 64	94.40 71	86.63 51	95.28 55	88.59 5	98.31 23	92.30 180

CP-MVS		91.67 12	91.58 19	91.96 12	95.29 30	87.62 9	93.38 9	93.36 63	83.16 56	91.06 80	94.00 91	88.26 30	95.71 32	87.28 27	98.39 20	92.55 167

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 93	88.22 18	88.53 128	97.64 2	83.45 83	94.55 79	86.02 48	98.60 12	96.67 27

ACMH+	77.89 11	90.73 27	91.50 21	88.44 75	93.00 77	76.26 116	89.65 71	95.55 7	87.72 22	93.89 27	94.94 47	91.62 3	93.44 124	78.35 127	98.76 3	95.61 48

mPP-MVS		91.69 11	91.47 22	92.37 5	96.04 12	88.48 7	92.72 18	92.60 99	83.09 57	91.54 70	94.25 78	87.67 41	95.51 44	87.21 28	98.11 34	93.12 145

HFP-MVS		91.30 19	91.39 23	91.02 29	95.43 28	84.66 43	92.58 22	93.29 70	81.99 66	91.47 71	93.96 95	88.35 29	95.56 39	87.74 13	97.74 56	92.85 154

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 19	93.33 65	85.07 37	89.99 98	94.03 89	86.57 52	95.80 25	87.35 24	97.62 61	94.20 92

SteuartSystems-ACMMP		91.16 23	91.36 24	90.55 37	93.91 56	80.97 66	91.49 37	93.48 61	82.82 61	92.60 54	93.97 92	88.19 31	96.29 5	87.61 17	98.20 30	94.39 88
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 22	93.25 72	81.99 66	91.40 72	94.17 83	87.51 42	95.87 18	87.74 13	97.76 54	93.99 103

ZNCC-MVS		91.26 20	91.34 27	91.01 30	95.73 20	83.05 52	92.18 28	94.22 26	80.14 89	91.29 76	93.97 92	87.93 38	95.87 18	88.65 4	97.96 44	94.12 99

DVP-MVS		90.06 39	91.32 28	86.29 108	94.16 49	72.56 147	90.54 49	91.01 146	83.61 51	93.75 31	94.65 56	89.76 18	95.78 28	86.42 36	97.97 42	90.55 234
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
WR-MVS_H		89.91 46	91.31 29	85.71 123	96.32 9	62.39 257	89.54 75	93.31 68	90.21 10	95.57 9	95.66 29	81.42 116	95.90 15	80.94 100	98.80 2	98.84 5

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 21	93.30 69	81.91 68	90.88 86	94.21 79	87.75 39	95.87 18	87.60 18	97.71 57	93.83 112

ACMH	76.49 14	89.34 55	91.14 31	83.96 161	92.50 91	70.36 175	89.55 73	93.84 50	81.89 69	94.70 14	95.44 34	90.69 8	88.31 260	83.33 70	98.30 24	93.20 140
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		90.07 38	91.09 32	87.00 94	91.55 126	72.64 143	96.19 2	94.10 37	85.33 34	93.49 36	94.64 59	81.12 119	95.88 16	87.41 22	95.94 126	92.48 170

DPE-MVS		90.53 32	91.08 33	88.88 66	93.38 67	78.65 83	89.15 83	94.05 39	84.68 41	93.90 25	94.11 87	88.13 34	96.30 4	84.51 61	97.81 51	91.70 204
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		90.81 26	91.08 33	89.99 46	95.97 13	79.88 71	88.13 99	94.51 18	75.79 140	92.94 44	94.96 46	88.36 28	95.01 64	90.70 2	98.40 19	95.09 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		90.65 28	91.07 35	89.42 58	95.93 15	79.54 76	89.95 62	93.68 56	77.65 120	91.97 65	94.89 48	88.38 27	95.45 48	89.27 3	97.87 49	93.27 137

GST-MVS		90.96 25	91.01 36	90.82 33	95.45 27	82.73 55	91.75 35	93.74 52	80.98 80	91.38 73	93.80 102	87.20 46	95.80 25	87.10 31	97.69 58	93.93 107

ACMM	79.39 9	90.65 28	90.99 37	89.63 54	95.03 33	83.53 47	89.62 72	93.35 64	79.20 101	93.83 28	93.60 110	90.81 7	92.96 140	85.02 56	98.45 18	92.41 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		90.13 36	90.96 38	87.65 88	91.95 109	71.06 169	89.99 60	93.05 82	86.53 27	94.29 19	96.27 18	82.69 90	94.08 96	86.25 42	97.63 60	97.82 9

PGM-MVS		91.20 22	90.95 39	91.93 13	95.67 22	85.85 27	90.00 58	93.90 46	80.32 86	91.74 69	94.41 70	88.17 32	95.98 11	86.37 38	97.99 39	93.96 106

MP-MVS		91.14 24	90.91 40	91.83 18	96.18 10	86.88 13	92.20 27	93.03 85	82.59 62	88.52 129	94.37 73	86.74 50	95.41 50	86.32 39	98.21 28	93.19 141
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVSNet		89.27 58	90.91 40	84.37 148	96.34 8	58.61 309	88.66 93	92.06 113	90.78 6	95.67 7	95.17 42	81.80 112	95.54 41	79.00 122	98.69 9	98.95 4

SF-MVS		90.27 35	90.80 42	88.68 73	92.86 83	77.09 104	91.19 41	95.74 5	81.38 74	92.28 59	93.80 102	86.89 49	94.64 74	85.52 51	97.51 70	94.30 91

UniMVSNet_ETH3D		89.12 61	90.72 43	84.31 154	97.00 2	64.33 232	89.67 70	88.38 202	88.84 13	94.29 19	97.57 3	90.48 13	91.26 185	72.57 205	97.65 59	97.34 15

PMVS	80.48 6	90.08 37	90.66 44	88.34 78	96.71 3	92.97 1	90.31 55	89.57 188	88.51 17	90.11 94	95.12 44	90.98 6	88.92 250	77.55 141	97.07 80	83.13 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMP	79.16 10	90.54 31	90.60 45	90.35 41	94.36 43	80.98 65	89.16 82	94.05 39	79.03 104	92.87 46	93.74 106	90.60 11	95.21 58	82.87 78	98.76 3	94.87 68
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 44	93.24 73	75.37 147	92.84 48	95.28 38	85.58 64	96.09 7	87.92 10	97.76 54	93.88 110
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
LS3D		90.60 30	90.34 47	91.38 24	89.03 184	84.23 45	93.58 6	94.68 17	90.65 7	90.33 92	93.95 97	84.50 71	95.37 51	80.87 101	95.50 142	94.53 80

bld_raw_dy_0_64		89.10 62	90.28 48	85.56 127	92.90 79	62.28 260	92.93 13	94.80 15	88.13 20	94.98 12	97.01 7	71.37 227	95.87 18	84.15 65	96.25 111	98.52 7

OPM-MVS		89.80 47	89.97 49	89.27 60	94.76 39	79.86 72	86.76 122	92.78 94	78.78 107	92.51 55	93.64 109	88.13 34	93.84 105	84.83 58	97.55 66	94.10 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SD-MVS		88.96 64	89.88 50	86.22 111	91.63 120	77.07 105	89.82 65	93.77 51	78.90 105	92.88 45	92.29 148	86.11 60	90.22 218	86.24 43	97.24 76	91.36 212
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
XVG-ACMP-BASELINE		89.98 43	89.84 51	90.41 39	94.91 36	84.50 44	89.49 77	93.98 41	79.68 93	92.09 62	93.89 100	83.80 78	93.10 137	82.67 82	98.04 35	93.64 124

tt0805		88.09 74	89.79 52	82.98 189	93.26 71	63.94 236	91.10 42	89.64 185	85.07 37	90.91 84	91.09 182	89.16 22	91.87 171	82.03 89	95.87 130	93.13 143

OurMVSNet-221017-0		90.01 42	89.74 53	90.83 32	93.16 74	80.37 68	91.91 33	93.11 78	81.10 78	95.32 10	97.24 5	72.94 209	94.85 68	85.07 54	97.78 52	97.26 16

3Dnovator+	83.92 2	89.97 45	89.66 54	90.92 31	91.27 135	81.66 62	91.25 39	94.13 35	88.89 11	88.83 123	94.26 77	77.55 151	95.86 22	84.88 57	95.87 130	95.24 58

APD-MVS		89.54 52	89.63 55	89.26 61	92.57 88	81.34 64	90.19 57	93.08 81	80.87 82	91.13 78	93.19 115	86.22 59	95.97 12	82.23 88	97.18 78	90.45 236
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		88.40 68	89.62 56	84.73 141	90.46 154	65.27 222	88.86 87	93.02 86	87.15 24	93.05 43	97.10 6	82.28 102	92.02 166	76.70 151	97.99 39	96.88 25

test_0402		88.65 66	89.58 57	85.88 119	92.55 89	72.22 155	84.01 168	89.44 190	88.63 16	94.38 18	95.77 26	86.38 58	93.59 116	79.84 112	95.21 152	91.82 200

XVG-OURS-SEG-HR		89.59 51	89.37 58	90.28 42	94.47 42	85.95 23	86.84 118	93.91 45	80.07 90	86.75 166	93.26 114	93.64 2	90.93 196	84.60 60	90.75 266	93.97 105

9.14			89.29 59		91.84 116		88.80 89	95.32 11	75.14 149	91.07 79	92.89 128	87.27 44	93.78 106	83.69 69	97.55 66

mvs_tets		89.78 48	89.27 60	91.30 25	93.51 63	84.79 40	89.89 64	90.63 156	70.00 220	94.55 16	96.67 12	87.94 37	93.59 116	84.27 63	95.97 123	95.52 49

testf1		89.30 56	89.12 61	89.84 48	88.67 194	85.64 31	90.61 47	93.17 75	86.02 30	93.12 41	95.30 36	84.94 66	89.44 242	74.12 181	96.10 118	94.45 83

APD_test2		89.30 56	89.12 61	89.84 48	88.67 194	85.64 31	90.61 47	93.17 75	86.02 30	93.12 41	95.30 36	84.94 66	89.44 242	74.12 181	96.10 118	94.45 83

DeepC-MVS	82.31 4	89.15 60	89.08 63	89.37 59	93.64 62	79.07 79	88.54 94	94.20 27	73.53 166	89.71 105	94.82 51	85.09 65	95.77 30	84.17 64	98.03 37	93.26 138
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_djsdf		89.62 50	89.01 64	91.45 22	92.36 94	82.98 53	91.98 31	90.08 176	71.54 202	94.28 21	96.54 14	81.57 114	94.27 84	86.26 40	96.49 99	97.09 21

DP-MVS		88.60 67	89.01 64	87.36 90	91.30 133	77.50 97	87.55 106	92.97 88	87.95 21	89.62 109	92.87 129	84.56 70	93.89 102	77.65 139	96.62 93	90.70 228

CPTT-MVS		89.39 54	88.98 66	90.63 36	95.09 32	86.95 12	92.09 29	92.30 107	79.74 92	87.50 151	92.38 143	81.42 116	93.28 129	83.07 74	97.24 76	91.67 205

anonymousdsp		89.73 49	88.88 67	92.27 7	89.82 168	86.67 14	90.51 51	90.20 173	69.87 221	95.06 11	96.14 22	84.28 74	93.07 138	87.68 15	96.34 105	97.09 21

XVG-OURS		89.18 59	88.83 68	90.23 43	94.28 44	86.11 22	85.91 133	93.60 59	80.16 88	89.13 120	93.44 112	83.82 77	90.98 194	83.86 68	95.30 151	93.60 126

jajsoiax		89.41 53	88.81 69	91.19 28	93.38 67	84.72 41	89.70 67	90.29 170	69.27 224	94.39 17	96.38 16	86.02 62	93.52 120	83.96 66	95.92 128	95.34 53

TranMVSNet+NR-MVSNet		87.86 79	88.76 70	85.18 133	94.02 54	64.13 233	84.38 162	91.29 138	84.88 40	92.06 63	93.84 101	86.45 55	93.73 107	73.22 196	98.66 10	97.69 10

nrg030		87.85 80	88.49 71	85.91 117	90.07 163	69.73 179	87.86 103	94.20 27	74.04 158	92.70 53	94.66 55	85.88 63	91.50 177	79.72 114	97.32 74	96.50 31

HPM-MVS++		88.93 65	88.45 72	90.38 40	94.92 35	85.85 27	89.70 67	91.27 139	78.20 114	86.69 169	92.28 149	80.36 128	95.06 62	86.17 44	96.49 99	90.22 240

EC-MVSNet		88.01 75	88.32 73	87.09 92	89.28 177	72.03 157	90.31 55	96.31 3	80.88 81	85.12 198	89.67 226	84.47 72	95.46 47	82.56 83	96.26 110	93.77 118

MSP-MVS		89.08 63	88.16 74	91.83 18	95.76 17	86.14 21	92.75 17	93.90 46	78.43 112	89.16 118	92.25 150	72.03 222	96.36 3	88.21 7	90.93 260	92.98 151
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
pmmvs6		86.52 96	88.06 75	81.90 212	92.22 101	62.28 260	84.66 155	89.15 193	83.54 53	89.85 102	97.32 4	88.08 36	86.80 279	70.43 222	97.30 75	96.62 28

APD_test1		88.40 68	87.91 76	89.88 47	89.50 172	86.65 16	89.98 61	91.91 119	84.26 43	90.87 87	93.92 99	82.18 103	89.29 246	73.75 188	94.81 171	93.70 120

PS-MVSNAJss		88.31 70	87.90 77	89.56 56	93.31 69	77.96 92	87.94 102	91.97 116	70.73 211	94.19 22	96.67 12	76.94 161	94.57 77	83.07 74	96.28 107	96.15 33

TSAR-MVS + MP.		88.14 72	87.82 78	89.09 64	95.72 21	76.74 108	92.49 25	91.19 142	67.85 244	86.63 170	94.84 50	79.58 134	95.96 13	87.62 16	94.50 179	94.56 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		87.81 81	87.68 79	88.21 80	92.87 81	77.30 103	85.25 145	91.23 140	77.31 124	87.07 160	91.47 171	82.94 88	94.71 71	84.67 59	96.27 109	92.62 164

CS-MVS		88.14 72	87.67 80	89.54 57	89.56 170	79.18 78	90.47 52	94.77 16	79.37 99	84.32 218	89.33 230	83.87 76	94.53 80	82.45 84	94.89 167	94.90 66

OMC-MVS		88.19 71	87.52 81	90.19 44	91.94 111	81.68 61	87.49 109	93.17 75	76.02 134	88.64 126	91.22 177	84.24 75	93.37 127	77.97 137	97.03 81	95.52 49

casdiffmvs_mvg		86.72 92	87.51 82	84.36 150	87.09 234	65.22 223	84.16 164	94.23 24	77.89 117	91.28 77	93.66 108	84.35 73	92.71 146	80.07 108	94.87 170	95.16 61
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SixPastTwentyTwo		87.20 86	87.45 83	86.45 105	92.52 90	69.19 188	87.84 104	88.05 209	81.66 71	94.64 15	96.53 15	65.94 257	94.75 70	83.02 76	96.83 86	95.41 51

HQP_MVS		87.75 82	87.43 84	88.70 72	93.45 64	76.42 113	89.45 78	93.61 57	79.44 97	86.55 171	92.95 126	74.84 182	95.22 56	80.78 103	95.83 132	94.46 81

AllTest		87.97 77	87.40 85	89.68 52	91.59 121	83.40 48	89.50 76	95.44 9	79.47 95	88.00 143	93.03 121	82.66 91	91.47 178	70.81 214	96.14 115	94.16 96

mvsmamba		87.87 78	87.23 86	89.78 51	92.31 98	76.51 112	91.09 43	91.87 120	72.61 188	92.16 60	95.23 41	66.01 256	95.59 37	86.02 48	97.78 52	97.24 17

MM		87.64 83	87.15 87	89.09 64	89.51 171	76.39 115	88.68 92	86.76 230	84.54 42	83.58 234	93.78 104	73.36 205	96.48 1	87.98 9	96.21 112	94.41 87

Anonymous20240529		86.20 102	87.13 88	83.42 179	90.19 159	64.55 230	84.55 157	90.71 153	85.85 32	89.94 101	95.24 40	82.13 104	90.40 214	69.19 234	96.40 104	95.31 55

v10		86.54 95	87.10 89	84.84 137	88.16 208	63.28 243	86.64 125	92.20 109	75.42 146	92.81 50	94.50 63	74.05 193	94.06 97	83.88 67	96.28 107	97.17 20

UniMVSNet_NR-MVSNet		86.84 90	87.06 90	86.17 114	92.86 83	67.02 206	82.55 214	91.56 128	83.08 58	90.92 82	91.82 161	78.25 143	93.99 98	74.16 178	98.35 21	97.49 14

FC-MVSNet-test		85.93 107	87.05 91	82.58 202	92.25 99	56.44 324	85.75 137	93.09 80	77.33 123	91.94 66	94.65 56	74.78 184	93.41 126	75.11 171	98.58 13	97.88 8

DU-MVS		86.80 91	86.99 92	86.21 112	93.24 72	67.02 206	83.16 195	92.21 108	81.73 70	90.92 82	91.97 155	77.20 155	93.99 98	74.16 178	98.35 21	97.61 11

UniMVSNet (Re)		86.87 88	86.98 93	86.55 103	93.11 75	68.48 192	83.80 177	92.87 90	80.37 84	89.61 111	91.81 162	77.72 148	94.18 90	75.00 172	98.53 15	96.99 24

RPSCF		88.00 76	86.93 94	91.22 27	90.08 161	89.30 4	89.68 69	91.11 143	79.26 100	89.68 106	94.81 54	82.44 94	87.74 264	76.54 154	88.74 291	96.61 29

NCCC		87.36 84	86.87 95	88.83 67	92.32 97	78.84 82	86.58 126	91.09 144	78.77 108	84.85 207	90.89 190	80.85 122	95.29 53	81.14 98	95.32 148	92.34 178

v8		86.22 101	86.83 96	84.36 150	87.82 213	62.35 259	86.42 128	91.33 137	76.78 128	92.73 52	94.48 65	73.41 202	93.72 108	83.10 73	95.41 143	97.01 23

IS-MVSNet		86.66 94	86.82 97	86.17 114	92.05 107	66.87 209	91.21 40	88.64 199	86.30 29	89.60 112	92.59 137	69.22 238	94.91 67	73.89 185	97.89 48	96.72 26

Vis-MVSNet		86.86 89	86.58 98	87.72 85	92.09 105	77.43 100	87.35 110	92.09 112	78.87 106	84.27 223	94.05 88	78.35 142	93.65 109	80.54 107	91.58 248	92.08 192
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		86.68 93	86.52 99	87.18 91	85.94 261	78.30 85	86.93 116	92.20 109	65.94 256	89.16 118	93.16 117	83.10 86	89.89 231	87.81 11	94.43 183	93.35 133

CSCG		86.26 99	86.47 100	85.60 125	90.87 146	74.26 127	87.98 101	91.85 121	80.35 85	89.54 115	88.01 248	79.09 136	92.13 162	75.51 165	95.06 159	90.41 237

CS-MVS-test		87.00 87	86.43 101	88.71 71	89.46 173	77.46 98	89.42 80	95.73 6	77.87 118	81.64 268	87.25 266	82.43 95	94.53 80	77.65 139	96.46 101	94.14 98

Gipuma		84.44 134	86.33 102	78.78 258	84.20 289	73.57 131	89.55 73	90.44 161	84.24 44	84.38 215	94.89 48	76.35 172	80.40 339	76.14 159	96.80 88	82.36 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FIs		85.35 116	86.27 103	82.60 201	91.86 113	57.31 317	85.10 149	93.05 82	75.83 139	91.02 81	93.97 92	73.57 198	92.91 144	73.97 184	98.02 38	97.58 13

NR-MVSNet		86.00 105	86.22 104	85.34 131	93.24 72	64.56 229	82.21 227	90.46 160	80.99 79	88.42 132	91.97 155	77.56 150	93.85 103	72.46 206	98.65 11	97.61 11

DeepPCF-MVS	81.24 5	87.28 85	86.21 105	90.49 38	91.48 130	84.90 38	83.41 186	92.38 105	70.25 217	89.35 117	90.68 199	82.85 89	94.57 77	79.55 116	95.95 125	92.00 195

sasdasda		85.50 112	86.14 106	83.58 174	87.97 209	67.13 203	87.55 106	94.32 19	73.44 169	88.47 130	87.54 259	86.45 55	91.06 192	75.76 163	93.76 199	92.54 168

canonicalmvs		85.50 112	86.14 106	83.58 174	87.97 209	67.13 203	87.55 106	94.32 19	73.44 169	88.47 130	87.54 259	86.45 55	91.06 192	75.76 163	93.76 199	92.54 168

MSLP-MVS++		85.00 125	86.03 108	81.90 212	91.84 116	71.56 166	86.75 123	93.02 86	75.95 137	87.12 155	89.39 228	77.98 144	89.40 245	77.46 142	94.78 172	84.75 315

MGCFI-Net		85.04 122	85.95 109	82.31 208	87.52 222	63.59 239	86.23 131	93.96 42	73.46 167	88.07 140	87.83 254	86.46 54	90.87 201	76.17 158	93.89 197	92.47 172

baseline		85.20 119	85.93 110	83.02 188	86.30 251	62.37 258	84.55 157	93.96 42	74.48 155	87.12 155	92.03 154	82.30 100	91.94 167	78.39 125	94.21 188	94.74 74

MVS_0304		86.35 98	85.92 111	87.66 87	89.21 180	73.16 138	88.40 96	83.63 272	81.27 75	80.87 278	94.12 86	71.49 226	95.71 32	87.79 12	96.50 98	94.11 100

Baseline_NR-MVSNet		84.00 149	85.90 112	78.29 269	91.47 131	53.44 343	82.29 223	87.00 229	79.06 103	89.55 113	95.72 28	77.20 155	86.14 293	72.30 207	98.51 16	95.28 56

test_fmvsmconf0.1_n		86.18 103	85.88 113	87.08 93	85.26 270	78.25 86	85.82 136	91.82 123	65.33 271	88.55 127	92.35 147	82.62 93	89.80 233	86.87 32	94.32 186	93.18 142

casdiffmvs		85.21 118	85.85 114	83.31 182	86.17 256	62.77 250	83.03 197	93.93 44	74.69 153	88.21 137	92.68 136	82.29 101	91.89 170	77.87 138	93.75 202	95.27 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		85.45 115	85.81 115	84.37 148	90.08 161	67.07 205	85.86 135	91.39 135	72.33 194	87.59 149	90.25 212	84.85 68	92.37 156	78.00 135	91.94 241	93.66 121

PHI-MVS		86.38 97	85.81 115	88.08 81	88.44 202	77.34 101	89.35 81	93.05 82	73.15 179	84.76 208	87.70 256	78.87 138	94.18 90	80.67 105	96.29 106	92.73 157

iter_conf05_11		85.73 110	85.77 117	85.60 125	88.77 193	67.74 201	91.49 37	94.17 29	71.86 201	88.07 140	92.18 153	68.84 242	95.06 62	81.20 97	95.33 146	93.99 103

TransMVSNet (Re)		84.02 148	85.74 118	78.85 257	91.00 143	55.20 334	82.29 223	87.26 217	79.65 94	88.38 134	95.52 33	83.00 87	86.88 277	67.97 249	96.60 94	94.45 83

ANet_high		83.17 167	85.68 119	75.65 303	81.24 325	45.26 386	79.94 254	92.91 89	83.83 47	91.33 74	96.88 11	80.25 129	85.92 295	68.89 238	95.89 129	95.76 43

DeepC-MVS_fast	80.27 8	86.23 100	85.65 120	87.96 84	91.30 133	76.92 106	87.19 111	91.99 115	70.56 212	84.96 203	90.69 198	80.01 131	95.14 59	78.37 126	95.78 136	91.82 200
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		86.17 104	85.54 121	88.05 83	92.25 99	75.45 121	83.85 174	92.01 114	65.91 258	86.19 180	91.75 165	83.77 79	94.98 65	77.43 144	96.71 90	93.73 119

test_fmvsmconf_n		85.88 108	85.51 122	86.99 95	84.77 278	78.21 87	85.40 144	91.39 135	65.32 272	87.72 147	91.81 162	82.33 98	89.78 234	86.68 34	94.20 189	92.99 150

FMVSNet1		84.55 132	85.45 123	81.85 214	90.27 158	61.05 277	86.83 119	88.27 206	78.57 111	89.66 108	95.64 30	75.43 175	90.68 207	69.09 235	95.33 146	93.82 113

VDDNet		84.35 136	85.39 124	81.25 223	95.13 31	59.32 296	85.42 143	81.11 292	86.41 28	87.41 152	96.21 20	73.61 197	90.61 210	66.33 258	96.85 84	93.81 116

test_fmvsmvis_n_1920		85.22 117	85.36 125	84.81 138	85.80 263	76.13 119	85.15 148	92.32 106	61.40 301	91.33 74	90.85 193	83.76 80	86.16 292	84.31 62	93.28 211	92.15 190

train_agg		85.98 106	85.28 126	88.07 82	92.34 95	79.70 74	83.94 170	90.32 165	65.79 259	84.49 212	90.97 186	81.93 108	93.63 111	81.21 96	96.54 96	90.88 222

dcpmvs_2		84.23 142	85.14 127	81.50 220	88.61 197	61.98 267	82.90 203	93.11 78	68.66 233	92.77 51	92.39 142	78.50 140	87.63 266	76.99 150	92.30 229	94.90 66

LCM-MVSNet-Re		83.48 160	85.06 128	78.75 259	85.94 261	55.75 329	80.05 252	94.27 21	76.47 129	96.09 5	94.54 62	83.31 85	89.75 237	59.95 309	94.89 167	90.75 225

EPP-MVSNet		85.47 114	85.04 129	86.77 100	91.52 129	69.37 183	91.63 36	87.98 211	81.51 73	87.05 161	91.83 160	66.18 255	95.29 53	70.75 217	96.89 83	95.64 46

IterMVS-LS		84.73 128	84.98 130	83.96 161	87.35 225	63.66 237	83.25 191	89.88 180	76.06 132	89.62 109	92.37 146	73.40 204	92.52 151	78.16 132	94.77 174	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pm-mvs1		83.69 154	84.95 131	79.91 244	90.04 165	59.66 293	82.43 218	87.44 214	75.52 144	87.85 145	95.26 39	81.25 118	85.65 302	68.74 241	96.04 120	94.42 86

TAPA-MVS	77.73 12	85.71 111	84.83 132	88.37 77	88.78 192	79.72 73	87.15 113	93.50 60	69.17 225	85.80 189	89.56 227	80.76 123	92.13 162	73.21 201	95.51 141	93.25 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		83.47 161	84.73 133	79.69 248	90.29 157	57.52 316	81.30 239	88.69 198	76.29 130	87.58 150	94.44 66	80.60 126	87.20 271	66.60 257	96.82 87	94.34 89

K. test v3		85.14 120	84.73 133	86.37 106	91.13 140	69.63 181	85.45 142	76.68 321	84.06 46	92.44 57	96.99 9	62.03 277	94.65 73	80.58 106	93.24 212	94.83 73

v1144		84.54 133	84.72 135	84.00 159	87.67 218	62.55 254	82.97 200	90.93 149	70.32 216	89.80 103	90.99 185	73.50 199	93.48 122	81.69 95	94.65 177	95.97 39

3Dnovator	80.37 7	84.80 127	84.71 136	85.06 135	86.36 249	74.71 124	88.77 90	90.00 178	75.65 142	84.96 203	93.17 116	74.06 192	91.19 187	78.28 129	91.09 254	89.29 258

v1192		84.57 131	84.69 137	84.21 156	87.75 215	62.88 247	83.02 198	91.43 132	69.08 227	89.98 100	90.89 190	72.70 213	93.62 114	82.41 85	94.97 164	96.13 34

MIMVSNet1		83.63 156	84.59 138	80.74 232	94.06 53	62.77 250	82.72 207	84.53 265	77.57 122	90.34 91	95.92 25	76.88 167	85.83 300	61.88 297	97.42 71	93.62 125

VDD-MVS		84.23 142	84.58 139	83.20 185	91.17 139	65.16 225	83.25 191	84.97 260	79.79 91	87.18 154	94.27 74	74.77 185	90.89 199	69.24 231	96.54 96	93.55 131

EI-MVSNet-Vis-set		85.12 121	84.53 140	86.88 97	84.01 291	72.76 140	83.91 173	85.18 252	80.44 83	88.75 124	85.49 291	80.08 130	91.92 168	82.02 90	90.85 264	95.97 39

v1240		84.30 138	84.51 141	83.65 171	87.65 219	61.26 274	82.85 205	91.54 129	67.94 242	90.68 89	90.65 202	71.71 224	93.64 110	82.84 79	94.78 172	96.07 36

EI-MVSNet-UG-set		85.04 122	84.44 142	86.85 98	83.87 295	72.52 149	83.82 175	85.15 253	80.27 87	88.75 124	85.45 293	79.95 132	91.90 169	81.92 93	90.80 265	96.13 34

v144192		84.24 141	84.41 143	83.71 170	87.59 221	61.57 270	82.95 201	91.03 145	67.82 245	89.80 103	90.49 206	73.28 206	93.51 121	81.88 94	94.89 167	96.04 38

WR-MVS		83.56 158	84.40 144	81.06 228	93.43 66	54.88 335	78.67 276	85.02 257	81.24 76	90.74 88	91.56 169	72.85 210	91.08 191	68.00 248	98.04 35	97.23 18

v1921920		84.23 142	84.37 145	83.79 166	87.64 220	61.71 269	82.91 202	91.20 141	67.94 242	90.06 95	90.34 209	72.04 221	93.59 116	82.32 86	94.91 165	96.07 36

MVS_111021_HR		84.63 129	84.34 146	85.49 130	90.18 160	75.86 120	79.23 268	87.13 221	73.35 171	85.56 193	89.34 229	83.60 82	90.50 212	76.64 153	94.05 194	90.09 245

v2v482		84.09 145	84.24 147	83.62 172	87.13 230	61.40 271	82.71 208	89.71 183	72.19 197	89.55 113	91.41 172	70.70 231	93.20 132	81.02 99	93.76 199	96.25 32

EG-PatchMatch MVS		84.08 146	84.11 148	83.98 160	92.22 101	72.61 146	82.20 229	87.02 226	72.63 187	88.86 121	91.02 184	78.52 139	91.11 190	73.41 193	91.09 254	88.21 272

HQP-MVS		84.61 130	84.06 149	86.27 109	91.19 136	70.66 171	84.77 150	92.68 96	73.30 174	80.55 283	90.17 217	72.10 218	94.61 75	77.30 146	94.47 180	93.56 129

Effi-MVS+		83.90 152	84.01 150	83.57 176	87.22 228	65.61 221	86.55 127	92.40 103	78.64 110	81.34 273	84.18 312	83.65 81	92.93 142	74.22 177	87.87 303	92.17 189

alignmvs		83.94 151	83.98 151	83.80 165	87.80 214	67.88 199	84.54 159	91.42 134	73.27 177	88.41 133	87.96 249	72.33 216	90.83 202	76.02 161	94.11 192	92.69 161

MCST-MVS		84.36 135	83.93 152	85.63 124	91.59 121	71.58 164	83.52 183	92.13 111	61.82 294	83.96 228	89.75 225	79.93 133	93.46 123	78.33 128	94.34 185	91.87 199

ETV-MVS		84.31 137	83.91 153	85.52 128	88.58 198	70.40 174	84.50 161	93.37 62	78.76 109	84.07 226	78.72 365	80.39 127	95.13 60	73.82 187	92.98 219	91.04 218

MVS_111021_LR		84.28 139	83.76 154	85.83 121	89.23 179	83.07 51	80.99 243	83.56 273	72.71 186	86.07 183	89.07 235	81.75 113	86.19 291	77.11 148	93.36 207	88.24 271

AdaColmap		83.66 155	83.69 155	83.57 176	90.05 164	72.26 154	86.29 130	90.00 178	78.19 115	81.65 267	87.16 268	83.40 84	94.24 87	61.69 299	94.76 175	84.21 324

F-COLMAP		84.97 126	83.42 156	89.63 54	92.39 93	83.40 48	88.83 88	91.92 118	73.19 178	80.18 291	89.15 234	77.04 159	93.28 129	65.82 265	92.28 232	92.21 186

Effi-MVS+-dtu		85.82 109	83.38 157	93.14 3	87.13 230	91.15 2	87.70 105	88.42 201	74.57 154	83.56 235	85.65 289	78.49 141	94.21 88	72.04 208	92.88 221	94.05 102

V42		83.47 161	83.37 158	83.75 168	83.16 308	63.33 242	81.31 237	90.23 172	69.51 223	90.91 84	90.81 195	74.16 191	92.29 160	80.06 109	90.22 273	95.62 47

MVS_Test		82.47 176	83.22 159	80.22 241	82.62 313	57.75 315	82.54 215	91.96 117	71.16 208	82.89 246	92.52 141	77.41 152	90.50 212	80.04 110	87.84 304	92.40 175

DP-MVS Recon		84.05 147	83.22 159	86.52 104	91.73 119	75.27 122	83.23 193	92.40 103	72.04 198	82.04 258	88.33 244	77.91 146	93.95 100	66.17 259	95.12 157	90.34 239

PAPM_NR		83.23 164	83.19 161	83.33 181	90.90 145	65.98 217	88.19 98	90.78 152	78.13 116	80.87 278	87.92 252	73.49 201	92.42 153	70.07 224	88.40 293	91.60 207

SDMVSNet		81.90 191	83.17 162	78.10 272	88.81 190	62.45 256	76.08 314	86.05 239	73.67 163	83.41 237	93.04 119	82.35 97	80.65 337	70.06 225	95.03 160	91.21 214

KD-MVS_self_test		81.93 189	83.14 163	78.30 268	84.75 279	52.75 347	80.37 249	89.42 191	70.24 218	90.26 93	93.39 113	74.55 189	86.77 280	68.61 243	96.64 91	95.38 52

CNLPA		83.55 159	83.10 164	84.90 136	89.34 176	83.87 46	84.54 159	88.77 196	79.09 102	83.54 236	88.66 241	74.87 181	81.73 330	66.84 254	92.29 231	89.11 260

FA-MVS(test-final)		83.13 168	83.02 165	83.43 178	86.16 258	66.08 216	88.00 100	88.36 203	75.55 143	85.02 200	92.75 134	65.12 261	92.50 152	74.94 173	91.30 252	91.72 202

iter_conf05		83.19 165	82.97 166	83.85 164	89.06 182	61.92 268	82.41 219	93.28 71	65.43 265	84.98 202	89.78 223	68.44 244	94.48 82	76.66 152	96.64 91	95.15 62

tfpnnormal		81.79 193	82.95 167	78.31 267	88.93 187	55.40 330	80.83 246	82.85 279	76.81 127	85.90 188	94.14 84	74.58 188	86.51 284	66.82 255	95.68 140	93.01 149

test_fmvsm_n_1920		83.60 157	82.89 168	85.74 122	85.22 272	77.74 95	84.12 166	90.48 159	59.87 320	86.45 179	91.12 181	75.65 173	85.89 298	82.28 87	90.87 262	93.58 127

CANet		83.79 153	82.85 169	86.63 101	86.17 256	72.21 156	83.76 178	91.43 132	77.24 125	74.39 342	87.45 262	75.36 176	95.42 49	77.03 149	92.83 222	92.25 185

h-mvs33		84.25 140	82.76 170	88.72 70	91.82 118	82.60 56	84.00 169	84.98 259	71.27 204	86.70 167	90.55 205	63.04 274	93.92 101	78.26 130	94.20 189	89.63 250

X-MVStestdata		85.04 122	82.70 171	92.08 8	95.64 23	86.25 18	92.64 19	93.33 65	85.07 37	89.99 98	16.05 409	86.57 52	95.80 25	87.35 24	97.62 61	94.20 92

TSAR-MVS + GP.		83.95 150	82.69 172	87.72 85	89.27 178	81.45 63	83.72 179	81.58 291	74.73 152	85.66 190	86.06 284	72.56 215	92.69 148	75.44 167	95.21 152	89.01 266

CLD-MVS		83.18 166	82.64 173	84.79 139	89.05 183	67.82 200	77.93 284	92.52 101	68.33 235	85.07 199	81.54 341	82.06 105	92.96 140	69.35 230	97.91 47	93.57 128
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		82.28 178	82.61 174	81.30 222	86.29 252	69.79 177	88.71 91	87.67 213	78.42 113	82.15 257	84.15 313	77.98 144	91.59 176	65.39 268	92.75 223	82.51 350

QAPM		82.59 173	82.59 175	82.58 202	86.44 244	66.69 210	89.94 63	90.36 164	67.97 241	84.94 205	92.58 139	72.71 212	92.18 161	70.63 220	87.73 305	88.85 267

114514_t		83.10 169	82.54 176	84.77 140	92.90 79	69.10 190	86.65 124	90.62 157	54.66 349	81.46 270	90.81 195	76.98 160	94.38 83	72.62 204	96.18 113	90.82 224

v148		82.31 177	82.48 177	81.81 217	85.59 265	59.66 293	81.47 236	86.02 240	72.85 182	88.05 142	90.65 202	70.73 230	90.91 198	75.15 170	91.79 242	94.87 68

EI-MVSNet		82.61 172	82.42 178	83.20 185	83.25 305	63.66 237	83.50 184	85.07 254	76.06 132	86.55 171	85.10 299	73.41 202	90.25 215	78.15 134	90.67 268	95.68 45

TinyColmap		81.25 200	82.34 179	77.99 275	85.33 269	60.68 284	82.32 222	88.33 204	71.26 206	86.97 162	92.22 152	77.10 158	86.98 275	62.37 291	95.17 154	86.31 298

GBi-Net		82.02 186	82.07 180	81.85 214	86.38 246	61.05 277	86.83 119	88.27 206	72.43 189	86.00 184	95.64 30	63.78 268	90.68 207	65.95 261	93.34 208	93.82 113

test1		82.02 186	82.07 180	81.85 214	86.38 246	61.05 277	86.83 119	88.27 206	72.43 189	86.00 184	95.64 30	63.78 268	90.68 207	65.95 261	93.34 208	93.82 113

OpenMVS	76.72 13	81.98 188	82.00 182	81.93 211	84.42 284	68.22 194	88.50 95	89.48 189	66.92 251	81.80 265	91.86 157	72.59 214	90.16 220	71.19 213	91.25 253	87.40 287

fmvsm_s_conf0.1_n_a		82.58 174	81.93 183	84.50 145	87.68 217	73.35 132	86.14 132	77.70 310	61.64 299	85.02 200	91.62 167	77.75 147	86.24 288	82.79 80	87.07 312	93.91 109

LF4IMVS		82.75 171	81.93 183	85.19 132	82.08 314	80.15 70	85.53 140	88.76 197	68.01 239	85.58 192	87.75 255	71.80 223	86.85 278	74.02 183	93.87 198	88.58 269

hse-mvs2		83.47 161	81.81 185	88.47 74	91.03 142	82.27 57	82.61 210	83.69 270	71.27 204	86.70 167	86.05 285	63.04 274	92.41 154	78.26 130	93.62 206	90.71 227

VPNet		80.25 218	81.68 186	75.94 301	92.46 92	47.98 373	76.70 302	81.67 289	73.45 168	84.87 206	92.82 130	74.66 187	86.51 284	61.66 300	96.85 84	93.33 134

SSC-MVS		77.55 247	81.64 187	65.29 367	90.46 154	20.33 413	73.56 338	68.28 372	85.44 33	88.18 139	94.64 59	70.93 229	81.33 332	71.25 211	92.03 237	94.20 92

UGNet		82.78 170	81.64 187	86.21 112	86.20 255	76.24 117	86.86 117	85.68 244	77.07 126	73.76 346	92.82 130	69.64 234	91.82 173	69.04 237	93.69 203	90.56 233
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
FMVSNet2		81.31 199	81.61 189	80.41 238	86.38 246	58.75 307	83.93 172	86.58 232	72.43 189	87.65 148	92.98 123	63.78 268	90.22 218	66.86 252	93.92 196	92.27 183

fmvsm_s_conf0.1_n		82.17 182	81.59 190	83.94 163	86.87 240	71.57 165	85.19 147	77.42 313	62.27 293	84.47 214	91.33 174	76.43 169	85.91 296	83.14 71	87.14 310	94.33 90

c3_l		81.64 195	81.59 190	81.79 218	80.86 331	59.15 300	78.61 277	90.18 174	68.36 234	87.20 153	87.11 270	69.39 235	91.62 175	78.16 132	94.43 183	94.60 76

MVSFormer		82.23 179	81.57 192	84.19 158	85.54 266	69.26 185	91.98 31	90.08 176	71.54 202	76.23 323	85.07 302	58.69 299	94.27 84	86.26 40	88.77 289	89.03 264

fmvsm_l_conf0.5_n		82.06 185	81.54 193	83.60 173	83.94 292	73.90 129	83.35 188	86.10 237	58.97 322	83.80 230	90.36 208	74.23 190	86.94 276	82.90 77	90.22 273	89.94 247

mamv4		81.86 192	81.52 194	82.87 196	85.42 268	62.26 262	82.66 209	92.62 98	65.43 265	79.34 300	90.22 213	69.65 233	94.15 95	74.14 180	94.16 191	92.21 186

fmvsm_s_conf0.5_n_a		82.21 180	81.51 195	84.32 153	86.56 242	73.35 132	85.46 141	77.30 314	61.81 295	84.51 211	90.88 192	77.36 153	86.21 290	82.72 81	86.97 317	93.38 132

Fast-Effi-MVS+-dtu		82.54 175	81.41 196	85.90 118	85.60 264	76.53 111	83.07 196	89.62 187	73.02 181	79.11 303	83.51 317	80.74 124	90.24 217	68.76 240	89.29 282	90.94 220

sd_testset		79.95 226	81.39 197	75.64 304	88.81 190	58.07 311	76.16 313	82.81 280	73.67 163	83.41 237	93.04 119	80.96 121	77.65 350	58.62 315	95.03 160	91.21 214

MVSMamba_pp		81.67 194	81.33 198	82.70 200	85.24 271	62.25 264	82.88 204	92.53 100	62.64 284	79.42 296	90.65 202	69.37 236	93.26 131	74.78 174	94.44 182	92.58 165

fmvsm_s_conf0.5_n		81.91 190	81.30 199	83.75 168	86.02 260	71.56 166	84.73 153	77.11 317	62.44 290	84.00 227	90.68 199	76.42 170	85.89 298	83.14 71	87.11 311	93.81 116

Anonymous20240521		80.18 221	81.25 200	76.95 288	83.15 309	60.84 282	82.46 217	85.99 241	68.76 231	86.78 164	93.73 107	59.13 296	77.44 351	73.71 189	97.55 66	92.56 166

DELS-MVS		81.44 198	81.25 200	82.03 210	84.27 288	62.87 248	76.47 308	92.49 102	70.97 209	81.64 268	83.83 314	75.03 179	92.70 147	74.29 176	92.22 235	90.51 235
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
EIA-MVS		82.19 181	81.23 202	85.10 134	87.95 211	69.17 189	83.22 194	93.33 65	70.42 213	78.58 306	79.77 357	77.29 154	94.20 89	71.51 210	88.96 287	91.93 198

Anonymous202405211		80.51 211	81.19 203	78.49 264	88.48 200	57.26 318	76.63 304	82.49 282	81.21 77	84.30 221	92.24 151	67.99 246	86.24 288	62.22 292	95.13 155	91.98 197

BH-untuned		80.96 204	80.99 204	80.84 231	88.55 199	68.23 193	80.33 250	88.46 200	72.79 185	86.55 171	86.76 274	74.72 186	91.77 174	61.79 298	88.99 286	82.52 349

MG-MVS		80.32 217	80.94 205	78.47 265	88.18 206	52.62 350	82.29 223	85.01 258	72.01 199	79.24 302	92.54 140	69.36 237	93.36 128	70.65 219	89.19 285	89.45 252

PCF-MVS	74.62 15	82.15 183	80.92 206	85.84 120	89.43 174	72.30 153	80.53 247	91.82 123	57.36 336	87.81 146	89.92 221	77.67 149	93.63 111	58.69 314	95.08 158	91.58 208
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_l_conf0.5_n_a		81.46 197	80.87 207	83.25 183	83.73 297	73.21 137	83.00 199	85.59 246	58.22 328	82.96 245	90.09 219	72.30 217	86.65 282	81.97 92	89.95 277	89.88 248

Fast-Effi-MVS+		81.04 203	80.57 208	82.46 206	87.50 223	63.22 244	78.37 280	89.63 186	68.01 239	81.87 261	82.08 335	82.31 99	92.65 149	67.10 251	88.30 299	91.51 210

LFMVS		80.15 222	80.56 209	78.89 256	89.19 181	55.93 326	85.22 146	73.78 341	82.96 59	84.28 222	92.72 135	57.38 308	90.07 227	63.80 282	95.75 137	90.68 229

ab-mvs		79.67 227	80.56 209	76.99 287	88.48 200	56.93 320	84.70 154	86.06 238	68.95 229	80.78 280	93.08 118	75.30 177	84.62 310	56.78 324	90.90 261	89.43 254

PVSNet_Blended_VisFu		81.55 196	80.49 211	84.70 143	91.58 124	73.24 136	84.21 163	91.67 127	62.86 283	80.94 276	87.16 268	67.27 249	92.87 145	69.82 227	88.94 288	87.99 278

diffmvs		80.40 214	80.48 212	80.17 242	79.02 351	60.04 288	77.54 291	90.28 171	66.65 254	82.40 252	87.33 265	73.50 199	87.35 269	77.98 136	89.62 280	93.13 143
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PLC	73.85 16	82.09 184	80.31 213	87.45 89	90.86 147	80.29 69	85.88 134	90.65 155	68.17 238	76.32 322	86.33 279	73.12 208	92.61 150	61.40 302	90.02 276	89.44 253
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		79.31 228	80.27 214	76.44 295	87.92 212	53.95 339	75.58 320	84.35 266	74.39 156	82.23 255	90.72 197	72.84 211	84.39 313	60.38 308	93.98 195	90.97 219

cl____		80.42 213	80.23 215	81.02 229	79.99 339	59.25 297	77.07 297	87.02 226	67.37 247	86.18 182	89.21 232	63.08 273	90.16 220	76.31 156	95.80 134	93.65 123

DIV-MVS_self_test		80.43 212	80.23 215	81.02 229	79.99 339	59.25 297	77.07 297	87.02 226	67.38 246	86.19 180	89.22 231	63.09 272	90.16 220	76.32 155	95.80 134	93.66 121

eth_miper_zixun_eth		80.84 205	80.22 217	82.71 198	81.41 323	60.98 280	77.81 286	90.14 175	67.31 249	86.95 163	87.24 267	64.26 264	92.31 158	75.23 169	91.61 246	94.85 72

BH-RMVSNet		80.53 210	80.22 217	81.49 221	87.19 229	66.21 215	77.79 287	86.23 235	74.21 157	83.69 231	88.50 242	73.25 207	90.75 204	63.18 288	87.90 302	87.52 285

xiu_mvs_v1_base_debu		80.84 205	80.14 219	82.93 192	88.31 203	71.73 160	79.53 259	87.17 218	65.43 265	79.59 293	82.73 329	76.94 161	90.14 223	73.22 196	88.33 295	86.90 292

xiu_mvs_v1_base		80.84 205	80.14 219	82.93 192	88.31 203	71.73 160	79.53 259	87.17 218	65.43 265	79.59 293	82.73 329	76.94 161	90.14 223	73.22 196	88.33 295	86.90 292

xiu_mvs_v1_base_debi		80.84 205	80.14 219	82.93 192	88.31 203	71.73 160	79.53 259	87.17 218	65.43 265	79.59 293	82.73 329	76.94 161	90.14 223	73.22 196	88.33 295	86.90 292

miper_ehance_all_eth		80.34 216	80.04 222	81.24 225	79.82 341	58.95 302	77.66 288	89.66 184	65.75 262	85.99 187	85.11 298	68.29 245	91.42 182	76.03 160	92.03 237	93.33 134

WB-MVS		76.06 265	80.01 223	64.19 370	89.96 167	20.58 412	72.18 347	68.19 373	83.21 55	86.46 178	93.49 111	70.19 232	78.97 346	65.96 260	90.46 272	93.02 148

MSDG		80.06 224	79.99 224	80.25 240	83.91 294	68.04 198	77.51 292	89.19 192	77.65 120	81.94 259	83.45 319	76.37 171	86.31 287	63.31 287	86.59 320	86.41 296

tttt0517		81.07 202	79.58 225	85.52 128	88.99 186	66.45 213	87.03 115	75.51 329	73.76 162	88.32 136	90.20 214	37.96 390	94.16 94	79.36 120	95.13 155	95.93 42

IterMVS-SCA-FT		80.64 209	79.41 226	84.34 152	83.93 293	69.66 180	76.28 310	81.09 293	72.43 189	86.47 177	90.19 215	60.46 284	93.15 135	77.45 143	86.39 323	90.22 240

patch_mono-2		78.89 231	79.39 227	77.41 284	84.78 277	68.11 196	75.60 318	83.11 276	60.96 309	79.36 298	89.89 222	75.18 178	72.97 362	73.32 195	92.30 229	91.15 216

wuyk23d		75.13 273	79.30 228	62.63 373	75.56 375	75.18 123	80.89 244	73.10 348	75.06 150	94.76 13	95.32 35	87.73 40	52.85 404	34.16 403	97.11 79	59.85 400

DPM-MVS		80.10 223	79.18 229	82.88 195	90.71 150	69.74 178	78.87 273	90.84 150	60.29 316	75.64 332	85.92 287	67.28 248	93.11 136	71.24 212	91.79 242	85.77 304

PM-MVS		80.20 220	79.00 230	83.78 167	88.17 207	86.66 15	81.31 237	66.81 381	69.64 222	88.33 135	90.19 215	64.58 262	83.63 321	71.99 209	90.03 275	81.06 368

FE-MVS		79.98 225	78.86 231	83.36 180	86.47 243	66.45 213	89.73 66	84.74 264	72.80 184	84.22 225	91.38 173	44.95 371	93.60 115	63.93 281	91.50 249	90.04 246

test1111		78.53 238	78.85 232	77.56 281	92.22 101	47.49 375	82.61 210	69.24 370	72.43 189	85.28 196	94.20 80	51.91 333	90.07 227	65.36 269	96.45 102	95.11 63

AUN-MVS		81.18 201	78.78 233	88.39 76	90.93 144	82.14 58	82.51 216	83.67 271	64.69 276	80.29 287	85.91 288	51.07 337	92.38 155	76.29 157	93.63 205	90.65 231

mvs_anonymous		78.13 241	78.76 234	76.23 300	79.24 348	50.31 366	78.69 275	84.82 262	61.60 300	83.09 244	92.82 130	73.89 195	87.01 272	68.33 247	86.41 322	91.37 211

MAR-MVS		80.24 219	78.74 235	84.73 141	86.87 240	78.18 88	85.75 137	87.81 212	65.67 264	77.84 311	78.50 366	73.79 196	90.53 211	61.59 301	90.87 262	85.49 308
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
ECVR-MVS		78.44 239	78.63 236	77.88 277	91.85 114	48.95 369	83.68 180	69.91 367	72.30 195	84.26 224	94.20 80	51.89 334	89.82 232	63.58 283	96.02 121	94.87 68

FMVSNet3		78.80 234	78.55 237	79.57 250	82.89 312	56.89 322	81.76 231	85.77 243	69.04 228	86.00 184	90.44 207	51.75 335	90.09 226	65.95 261	93.34 208	91.72 202

test_yl		78.71 236	78.51 238	79.32 253	84.32 286	58.84 304	78.38 278	85.33 249	75.99 135	82.49 250	86.57 275	58.01 302	90.02 229	62.74 289	92.73 224	89.10 261

DCV-MVSNet		78.71 236	78.51 238	79.32 253	84.32 286	58.84 304	78.38 278	85.33 249	75.99 135	82.49 250	86.57 275	58.01 302	90.02 229	62.74 289	92.73 224	89.10 261

EPNet		80.37 215	78.41 240	86.23 110	76.75 365	73.28 134	87.18 112	77.45 312	76.24 131	68.14 374	88.93 237	65.41 260	93.85 103	69.47 229	96.12 117	91.55 209
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPMNet		78.88 232	78.28 241	80.68 235	79.58 342	62.64 252	82.58 212	94.16 30	74.80 151	75.72 330	92.59 137	48.69 345	95.56 39	73.48 192	82.91 358	83.85 329

cl22		78.97 230	78.21 242	81.24 225	77.74 355	59.01 301	77.46 294	87.13 221	65.79 259	84.32 218	85.10 299	58.96 298	90.88 200	75.36 168	92.03 237	93.84 111

PAPR		78.84 233	78.10 243	81.07 227	85.17 273	60.22 287	82.21 227	90.57 158	62.51 286	75.32 336	84.61 307	74.99 180	92.30 159	59.48 312	88.04 301	90.68 229

PVSNet_BlendedMVS		78.80 234	77.84 244	81.65 219	84.43 282	63.41 240	79.49 262	90.44 161	61.70 298	75.43 333	87.07 271	69.11 239	91.44 180	60.68 306	92.24 233	90.11 244

Vis-MVSNet (Re-imp)		77.82 244	77.79 245	77.92 276	88.82 189	51.29 360	83.28 189	71.97 355	74.04 158	82.23 255	89.78 223	57.38 308	89.41 244	57.22 323	95.41 143	93.05 147

Patchmtry		76.56 260	77.46 246	73.83 313	79.37 347	46.60 379	82.41 219	76.90 318	73.81 161	85.56 193	92.38 143	48.07 348	83.98 318	63.36 286	95.31 150	90.92 221

OpenMVS_ROB	70.19 17	77.77 246	77.46 246	78.71 260	84.39 285	61.15 275	81.18 241	82.52 281	62.45 289	83.34 239	87.37 263	66.20 254	88.66 256	64.69 276	85.02 339	86.32 297

CL-MVSNet_self_test		76.81 256	77.38 248	75.12 307	86.90 238	51.34 358	73.20 342	80.63 297	68.30 236	81.80 265	88.40 243	66.92 251	80.90 334	55.35 336	94.90 166	93.12 145

thisisatest0530		79.07 229	77.33 249	84.26 155	87.13 230	64.58 228	83.66 181	75.95 324	68.86 230	85.22 197	87.36 264	38.10 388	93.57 119	75.47 166	94.28 187	94.62 75

CANet_DTU		77.81 245	77.05 250	80.09 243	81.37 324	59.90 291	83.26 190	88.29 205	69.16 226	67.83 377	83.72 315	60.93 281	89.47 239	69.22 233	89.70 279	90.88 222

pmmvs-eth3d		78.42 240	77.04 251	82.57 204	87.44 224	74.41 126	80.86 245	79.67 301	55.68 343	84.69 209	90.31 211	60.91 282	85.42 303	62.20 293	91.59 247	87.88 281

miper_enhance_ethall		77.83 243	76.93 252	80.51 236	76.15 371	58.01 312	75.47 322	88.82 195	58.05 330	83.59 233	80.69 345	64.41 263	91.20 186	73.16 202	92.03 237	92.33 179

MDA-MVSNet-bldmvs		77.47 248	76.90 253	79.16 255	79.03 350	64.59 227	66.58 377	75.67 327	73.15 179	88.86 121	88.99 236	66.94 250	81.23 333	64.71 275	88.22 300	91.64 206

xiu_mvs_v2_base		77.19 251	76.75 254	78.52 263	87.01 236	61.30 273	75.55 321	87.12 224	61.24 306	74.45 341	78.79 364	77.20 155	90.93 196	64.62 278	84.80 346	83.32 338

USDC		76.63 258	76.73 255	76.34 297	83.46 299	57.20 319	80.02 253	88.04 210	52.14 363	83.65 232	91.25 176	63.24 271	86.65 282	54.66 341	94.11 192	85.17 310

PS-MVSNAJ		77.04 253	76.53 256	78.56 262	87.09 234	61.40 271	75.26 323	87.13 221	61.25 305	74.38 343	77.22 377	76.94 161	90.94 195	64.63 277	84.83 345	83.35 337

TAMVS		78.08 242	76.36 257	83.23 184	90.62 151	72.87 139	79.08 269	80.01 300	61.72 297	81.35 272	86.92 273	63.96 267	88.78 254	50.61 362	93.01 218	88.04 277

IterMVS		76.91 254	76.34 258	78.64 261	80.91 329	64.03 234	76.30 309	79.03 304	64.88 275	83.11 242	89.16 233	59.90 290	84.46 311	68.61 243	85.15 337	87.42 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		74.44 284	76.19 259	69.21 345	84.61 280	52.43 351	71.70 350	77.18 316	60.73 312	80.60 281	90.96 188	75.44 174	69.35 373	56.13 329	88.33 295	85.86 303

miper_lstm_enhance		76.45 262	76.10 260	77.51 282	76.72 366	60.97 281	64.69 381	85.04 256	63.98 279	83.20 241	88.22 245	56.67 312	78.79 348	73.22 196	93.12 215	92.78 156

BH-w/o		76.57 259	76.07 261	78.10 272	86.88 239	65.92 218	77.63 289	86.33 233	65.69 263	80.89 277	79.95 354	68.97 241	90.74 205	53.01 352	85.25 334	77.62 379

TR-MVS		76.77 257	75.79 262	79.72 247	86.10 259	65.79 219	77.14 295	83.02 277	65.20 273	81.40 271	82.10 333	66.30 253	90.73 206	55.57 333	85.27 333	82.65 344

jason		77.42 249	75.75 263	82.43 207	87.10 233	69.27 184	77.99 283	81.94 287	51.47 367	77.84 311	85.07 302	60.32 286	89.00 248	70.74 218	89.27 284	89.03 264
jason: jason.
MVSTER		77.09 252	75.70 264	81.25 223	75.27 379	61.08 276	77.49 293	85.07 254	60.78 311	86.55 171	88.68 240	43.14 380	90.25 215	73.69 190	90.67 268	92.42 173

D2MVS		76.84 255	75.67 265	80.34 239	80.48 337	62.16 266	73.50 339	84.80 263	57.61 334	82.24 254	87.54 259	51.31 336	87.65 265	70.40 223	93.19 214	91.23 213

PVSNet_Blended		76.49 261	75.40 266	79.76 246	84.43 282	63.41 240	75.14 324	90.44 161	57.36 336	75.43 333	78.30 367	69.11 239	91.44 180	60.68 306	87.70 306	84.42 320

CDS-MVSNet		77.32 250	75.40 266	83.06 187	89.00 185	72.48 150	77.90 285	82.17 285	60.81 310	78.94 304	83.49 318	59.30 294	88.76 255	54.64 342	92.37 228	87.93 280
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres600view7		75.97 266	75.35 268	77.85 279	87.01 236	51.84 356	80.45 248	73.26 346	75.20 148	83.10 243	86.31 281	45.54 362	89.05 247	55.03 339	92.24 233	92.66 162

test_fmvs3		75.72 269	75.20 269	77.27 285	75.01 382	69.47 182	78.93 270	84.88 261	46.67 381	87.08 159	87.84 253	50.44 341	71.62 367	77.42 145	88.53 292	90.72 226

thres100view900		75.45 270	75.05 270	76.66 294	87.27 226	51.88 355	81.07 242	73.26 346	75.68 141	83.25 240	86.37 278	45.54 362	88.80 251	51.98 357	90.99 256	89.31 256

cascas		76.29 264	74.81 271	80.72 234	84.47 281	62.94 246	73.89 336	87.34 215	55.94 342	75.16 338	76.53 382	63.97 266	91.16 188	65.00 272	90.97 259	88.06 276

GA-MVS		75.83 267	74.61 272	79.48 252	81.87 316	59.25 297	73.42 340	82.88 278	68.68 232	79.75 292	81.80 338	50.62 339	89.46 240	66.85 253	85.64 330	89.72 249

testgi		72.36 299	74.61 272	65.59 364	80.56 336	42.82 394	68.29 369	73.35 345	66.87 252	81.84 262	89.93 220	72.08 220	66.92 386	46.05 383	92.54 226	87.01 291

test20.03		73.75 288	74.59 274	71.22 333	81.11 327	51.12 362	70.15 363	72.10 354	70.42 213	80.28 289	91.50 170	64.21 265	74.72 361	46.96 380	94.58 178	87.82 283

lupinMVS		76.37 263	74.46 275	82.09 209	85.54 266	69.26 185	76.79 300	80.77 296	50.68 374	76.23 323	82.82 327	58.69 299	88.94 249	69.85 226	88.77 289	88.07 274

EU-MVSNet		75.12 274	74.43 276	77.18 286	83.11 310	59.48 295	85.71 139	82.43 283	39.76 401	85.64 191	88.76 238	44.71 373	87.88 263	73.86 186	85.88 329	84.16 325

tfpn200view9		74.86 278	74.23 277	76.74 293	86.24 253	52.12 352	79.24 266	73.87 339	73.34 172	81.82 263	84.60 308	46.02 356	88.80 251	51.98 357	90.99 256	89.31 256

thres400		75.14 272	74.23 277	77.86 278	86.24 253	52.12 352	79.24 266	73.87 339	73.34 172	81.82 263	84.60 308	46.02 356	88.80 251	51.98 357	90.99 256	92.66 162

ppachtmachnet_test		74.73 281	74.00 279	76.90 290	80.71 334	56.89 322	71.53 353	78.42 306	58.24 327	79.32 301	82.92 326	57.91 305	84.26 315	65.60 267	91.36 251	89.56 251

1112_ss		74.82 279	73.74 280	78.04 274	89.57 169	60.04 288	76.49 307	87.09 225	54.31 350	73.66 347	79.80 355	60.25 287	86.76 281	58.37 316	84.15 350	87.32 288

Patchmatch-RL test		74.48 282	73.68 281	76.89 291	84.83 276	66.54 211	72.29 346	69.16 371	57.70 332	86.76 165	86.33 279	45.79 361	82.59 325	69.63 228	90.65 270	81.54 359

CMPMVS	59.41 20	75.12 274	73.57 282	79.77 245	75.84 374	67.22 202	81.21 240	82.18 284	50.78 372	76.50 319	87.66 257	55.20 322	82.99 324	62.17 295	90.64 271	89.09 263
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		73.26 291	73.54 283	72.43 327	84.92 275	47.79 374	79.89 255	74.00 337	65.93 257	78.81 305	86.28 282	56.36 314	81.63 331	56.63 325	79.04 379	87.87 282

MVP-Stereo		75.81 268	73.51 284	82.71 198	89.35 175	73.62 130	80.06 251	85.20 251	60.30 315	73.96 344	87.94 250	57.89 306	89.45 241	52.02 356	74.87 390	85.06 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test2506		74.12 285	73.39 285	76.28 298	91.85 114	44.20 389	84.06 167	48.20 408	72.30 195	81.90 260	94.20 80	27.22 409	89.77 235	64.81 274	96.02 121	94.87 68

new-patchmatchnet		70.10 319	73.37 286	60.29 380	81.23 326	16.95 415	59.54 391	74.62 332	62.93 282	80.97 274	87.93 251	62.83 276	71.90 365	55.24 337	95.01 163	92.00 195

PatchMatch-RL		74.48 282	73.22 287	78.27 270	87.70 216	85.26 34	75.92 316	70.09 365	64.34 277	76.09 326	81.25 343	65.87 258	78.07 349	53.86 344	83.82 352	71.48 388

Test_1112_low_res		73.90 287	73.08 288	76.35 296	90.35 156	55.95 325	73.40 341	86.17 236	50.70 373	73.14 348	85.94 286	58.31 301	85.90 297	56.51 326	83.22 355	87.20 289

CR-MVSNet		74.00 286	73.04 289	76.85 292	79.58 342	62.64 252	82.58 212	76.90 318	50.50 375	75.72 330	92.38 143	48.07 348	84.07 317	68.72 242	82.91 358	83.85 329

pmmvs4		74.92 277	72.98 290	80.73 233	84.95 274	71.71 163	76.23 311	77.59 311	52.83 357	77.73 315	86.38 277	56.35 315	84.97 307	57.72 322	87.05 313	85.51 307

test_fmvs2		73.57 289	72.80 291	75.90 302	72.74 394	68.84 191	77.07 297	84.32 267	45.14 387	82.89 246	84.22 311	48.37 346	70.36 370	73.40 194	87.03 314	88.52 270

ET-MVSNet_ETH3D		75.28 271	72.77 292	82.81 197	83.03 311	68.11 196	77.09 296	76.51 322	60.67 313	77.60 316	80.52 349	38.04 389	91.15 189	70.78 216	90.68 267	89.17 259

PatchT		70.52 315	72.76 293	63.79 372	79.38 346	33.53 406	77.63 289	65.37 384	73.61 165	71.77 355	92.79 133	44.38 374	75.65 358	64.53 279	85.37 332	82.18 352

HyFIR lowres test		75.12 274	72.66 294	82.50 205	91.44 132	65.19 224	72.47 345	87.31 216	46.79 380	80.29 287	84.30 310	52.70 330	92.10 165	51.88 361	86.73 318	90.22 240

MVS		73.21 293	72.59 295	75.06 308	80.97 328	60.81 283	81.64 234	85.92 242	46.03 385	71.68 356	77.54 372	68.47 243	89.77 235	55.70 332	85.39 331	74.60 385

SCA		73.32 290	72.57 296	75.58 305	81.62 320	55.86 327	78.89 272	71.37 360	61.73 296	74.93 339	83.42 320	60.46 284	87.01 272	58.11 320	82.63 363	83.88 326

1314		73.22 292	72.56 297	75.20 306	80.41 338	57.84 313	81.64 234	85.36 248	51.68 366	73.10 349	76.65 381	61.45 279	85.19 305	63.54 284	79.21 377	82.59 345

HY-MVS	64.64 18	73.03 294	72.47 298	74.71 309	83.36 303	54.19 337	82.14 230	81.96 286	56.76 341	69.57 369	86.21 283	60.03 288	84.83 309	49.58 368	82.65 361	85.11 311

UnsupCasMVSNet_eth		71.63 306	72.30 299	69.62 342	76.47 368	52.70 349	70.03 364	80.97 294	59.18 321	79.36 298	88.21 246	60.50 283	69.12 374	58.33 318	77.62 384	87.04 290

FPMVS		72.29 301	72.00 300	73.14 318	88.63 196	85.00 36	74.65 329	67.39 375	71.94 200	77.80 313	87.66 257	50.48 340	75.83 357	49.95 364	79.51 373	58.58 402

Anonymous20231206		71.38 309	71.88 301	69.88 340	86.31 250	54.37 336	70.39 361	74.62 332	52.57 359	76.73 318	88.76 238	59.94 289	72.06 364	44.35 387	93.23 213	83.23 340

FMVSNet5		72.10 302	71.69 302	73.32 316	81.57 321	53.02 346	76.77 301	78.37 307	63.31 280	76.37 320	91.85 158	36.68 392	78.98 345	47.87 376	92.45 227	87.95 279

our_test_3		71.85 303	71.59 303	72.62 324	80.71 334	53.78 340	69.72 365	71.71 359	58.80 324	78.03 308	80.51 350	56.61 313	78.84 347	62.20 293	86.04 328	85.23 309

MIMVSNet		71.09 311	71.59 303	69.57 343	87.23 227	50.07 367	78.91 271	71.83 356	60.20 318	71.26 357	91.76 164	55.08 324	76.09 355	41.06 392	87.02 315	82.54 348

test_vis1_n_1920		71.30 310	71.58 305	70.47 336	77.58 358	59.99 290	74.25 330	84.22 268	51.06 369	74.85 340	79.10 361	55.10 323	68.83 376	68.86 239	79.20 378	82.58 346

thres200		72.34 300	71.55 306	74.70 310	83.48 298	51.60 357	75.02 325	73.71 342	70.14 219	78.56 307	80.57 348	46.20 354	88.20 261	46.99 379	89.29 282	84.32 321

CVMVSNet		72.62 297	71.41 307	76.28 298	83.25 305	60.34 286	83.50 184	79.02 305	37.77 405	76.33 321	85.10 299	49.60 344	87.41 268	70.54 221	77.54 385	81.08 366

EPNet_dtu		72.87 296	71.33 308	77.49 283	77.72 356	60.55 285	82.35 221	75.79 325	66.49 255	58.39 403	81.06 344	53.68 326	85.98 294	53.55 347	92.97 220	85.95 301
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing3		71.53 307	70.79 309	73.77 314	88.89 188	41.86 396	76.60 306	59.12 398	72.83 183	80.97 274	82.08 335	19.80 414	87.33 270	65.12 271	91.68 245	92.13 191

test_vis3_rt		71.42 308	70.67 310	73.64 315	69.66 401	70.46 173	66.97 376	89.73 181	42.68 397	88.20 138	83.04 322	43.77 375	60.07 398	65.35 270	86.66 319	90.39 238

CHOSEN 1792x2688		72.45 298	70.56 311	78.13 271	90.02 166	63.08 245	68.72 368	83.16 275	42.99 395	75.92 328	85.46 292	57.22 310	85.18 306	49.87 366	81.67 365	86.14 299

thisisatest0515		73.00 295	70.52 312	80.46 237	81.45 322	59.90 291	73.16 343	74.31 336	57.86 331	76.08 327	77.78 370	37.60 391	92.12 164	65.00 272	91.45 250	89.35 255

YYNet1		70.06 320	70.44 313	68.90 347	73.76 386	53.42 344	58.99 394	67.20 377	58.42 326	87.10 157	85.39 295	59.82 291	67.32 383	59.79 310	83.50 354	85.96 300

MDA-MVSNet_test_wron		70.05 321	70.44 313	68.88 348	73.84 385	53.47 342	58.93 395	67.28 376	58.43 325	87.09 158	85.40 294	59.80 292	67.25 384	59.66 311	83.54 353	85.92 302

test_fmvs1_n		70.94 312	70.41 315	72.53 326	73.92 384	66.93 208	75.99 315	84.21 269	43.31 394	79.40 297	79.39 359	43.47 376	68.55 378	69.05 236	84.91 342	82.10 353

MS-PatchMatch		70.93 313	70.22 316	73.06 319	81.85 317	62.50 255	73.82 337	77.90 308	52.44 360	75.92 328	81.27 342	55.67 319	81.75 329	55.37 335	77.70 383	74.94 384

pmmvs5		70.73 314	70.07 317	72.72 322	77.03 363	52.73 348	74.14 331	75.65 328	50.36 376	72.17 354	85.37 296	55.42 321	80.67 336	52.86 353	87.59 307	84.77 314

PAPM		71.77 304	70.06 318	76.92 289	86.39 245	53.97 338	76.62 305	86.62 231	53.44 354	63.97 393	84.73 306	57.79 307	92.34 157	39.65 394	81.33 369	84.45 319

Syy-MVS		69.40 328	70.03 319	67.49 357	81.72 318	38.94 399	71.00 355	61.99 389	61.38 302	70.81 361	72.36 392	61.37 280	79.30 343	64.50 280	85.18 335	84.22 322

test_vis1_n		70.29 316	69.99 320	71.20 334	75.97 373	66.50 212	76.69 303	80.81 295	44.22 390	75.43 333	77.23 376	50.00 342	68.59 377	66.71 256	82.85 360	78.52 378

EGC-MVSNET		74.79 280	69.99 320	89.19 62	94.89 37	87.00 11	91.89 34	86.28 234	1.09 410	2.23 412	95.98 24	81.87 111	89.48 238	79.76 113	95.96 124	91.10 217

UnsupCasMVSNet_bld		69.21 330	69.68 322	67.82 355	79.42 345	51.15 361	67.82 373	75.79 325	54.15 351	77.47 317	85.36 297	59.26 295	70.64 369	48.46 373	79.35 375	81.66 357

tpmvs		70.16 318	69.56 323	71.96 329	74.71 383	48.13 371	79.63 257	75.45 330	65.02 274	70.26 365	81.88 337	45.34 367	85.68 301	58.34 317	75.39 389	82.08 354

test_cas_vis1_n_1920		69.20 331	69.12 324	69.43 344	73.68 387	62.82 249	70.38 362	77.21 315	46.18 384	80.46 286	78.95 363	52.03 332	65.53 391	65.77 266	77.45 386	79.95 374

gg-mvs-nofinetune		68.96 332	69.11 325	68.52 353	76.12 372	45.32 385	83.59 182	55.88 403	86.68 25	64.62 392	97.01 7	30.36 401	83.97 319	44.78 386	82.94 357	76.26 381

test_fmvs1		69.57 326	69.05 326	71.14 335	69.15 402	65.77 220	73.98 334	83.32 274	42.83 396	77.77 314	78.27 368	43.39 379	68.50 379	68.39 246	84.38 349	79.15 376

WB-MVSnew		68.72 333	69.01 327	67.85 354	83.22 307	43.98 390	74.93 326	65.98 382	55.09 345	73.83 345	79.11 360	65.63 259	71.89 366	38.21 399	85.04 338	87.69 284

testing91		69.94 323	68.99 328	72.80 321	83.81 296	45.89 382	71.57 352	73.64 344	68.24 237	70.77 363	77.82 369	34.37 395	84.44 312	53.64 346	87.00 316	88.07 274

IB-MVS	62.13 19	71.64 305	68.97 329	79.66 249	80.80 333	62.26 262	73.94 335	76.90 318	63.27 281	68.63 373	76.79 379	33.83 396	91.84 172	59.28 313	87.26 308	84.88 313
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
PatchmatchNet		69.71 325	68.83 330	72.33 328	77.66 357	53.60 341	79.29 264	69.99 366	57.66 333	72.53 352	82.93 325	46.45 353	80.08 341	60.91 305	72.09 393	83.31 339
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		70.20 317	68.80 331	74.38 311	80.91 329	84.81 39	59.12 393	76.45 323	55.06 346	75.31 337	82.36 332	55.74 318	54.82 403	47.02 378	87.24 309	83.52 333

CostFormer		69.98 322	68.68 332	73.87 312	77.14 361	50.72 364	79.26 265	74.51 334	51.94 365	70.97 360	84.75 305	45.16 370	87.49 267	55.16 338	79.23 376	83.40 336

WTY-MVS		67.91 336	68.35 333	66.58 361	80.82 332	48.12 372	65.96 378	72.60 349	53.67 353	71.20 358	81.68 340	58.97 297	69.06 375	48.57 372	81.67 365	82.55 347

MDTV_nov1_ep13			68.29 334		78.03 354	43.87 391	74.12 332	72.22 353	52.17 361	67.02 379	85.54 290	45.36 366	80.85 335	55.73 330	84.42 348

testing99		69.27 329	68.15 335	72.63 323	83.29 304	45.45 384	71.15 354	71.08 361	67.34 248	70.43 364	77.77 371	32.24 398	84.35 314	53.72 345	86.33 324	88.10 273

tpm		67.95 335	68.08 336	67.55 356	78.74 353	43.53 392	75.60 318	67.10 380	54.92 347	72.23 353	88.10 247	42.87 381	75.97 356	52.21 355	80.95 372	83.15 341

Patchmatch-test		65.91 348	67.38 337	61.48 378	75.51 376	43.21 393	68.84 367	63.79 387	62.48 287	72.80 351	83.42 320	44.89 372	59.52 400	48.27 375	86.45 321	81.70 356

sss		66.92 340	67.26 338	65.90 363	77.23 360	51.10 363	64.79 380	71.72 358	52.12 364	70.13 366	80.18 352	57.96 304	65.36 392	50.21 363	81.01 371	81.25 363

dmvs_re		66.81 343	66.98 339	66.28 362	76.87 364	58.68 308	71.66 351	72.24 352	60.29 316	69.52 370	73.53 389	52.38 331	64.40 394	44.90 385	81.44 368	75.76 382

baseline2		69.77 324	66.89 340	78.41 266	79.51 344	58.09 310	76.23 311	69.57 368	57.50 335	64.82 391	77.45 374	46.02 356	88.44 257	53.08 349	77.83 381	88.70 268

tpm2		68.45 334	66.83 341	73.30 317	78.93 352	48.50 370	79.76 256	71.76 357	47.50 379	69.92 367	83.60 316	42.07 382	88.40 258	48.44 374	79.51 373	83.01 343

test-LLR		67.21 338	66.74 342	68.63 351	76.45 369	55.21 332	67.89 370	67.14 378	62.43 291	65.08 388	72.39 390	43.41 377	69.37 371	61.00 303	84.89 343	81.31 361

tpmrst		66.28 347	66.69 343	65.05 368	72.82 393	39.33 398	78.20 281	70.69 364	53.16 356	67.88 376	80.36 351	48.18 347	74.75 360	58.13 319	70.79 395	81.08 366

JIA-IIPM		69.41 327	66.64 344	77.70 280	73.19 389	71.24 168	75.67 317	65.56 383	70.42 213	65.18 387	92.97 125	33.64 397	83.06 322	53.52 348	69.61 399	78.79 377

testing11		67.38 337	65.93 345	71.73 331	83.37 302	46.60 379	70.95 357	69.40 369	62.47 288	66.14 380	76.66 380	31.22 399	84.10 316	49.10 370	84.10 351	84.49 317

test_f		64.31 356	65.85 346	59.67 381	66.54 406	62.24 265	57.76 397	70.96 362	40.13 399	84.36 216	82.09 334	46.93 350	51.67 405	61.99 296	81.89 364	65.12 396

KD-MVS_2432*1600		66.87 341	65.81 347	70.04 338	67.50 403	47.49 375	62.56 385	79.16 302	61.21 307	77.98 309	80.61 346	25.29 411	82.48 326	53.02 350	84.92 340	80.16 372

miper_refine_blended		66.87 341	65.81 347	70.04 338	67.50 403	47.49 375	62.56 385	79.16 302	61.21 307	77.98 309	80.61 346	25.29 411	82.48 326	53.02 350	84.92 340	80.16 372

PVSNet	58.17 21	66.41 346	65.63 349	68.75 349	81.96 315	49.88 368	62.19 387	72.51 351	51.03 370	68.04 375	75.34 387	50.84 338	74.77 359	45.82 384	82.96 356	81.60 358

UWE-MVS		66.43 345	65.56 350	69.05 346	84.15 290	40.98 397	73.06 344	64.71 385	54.84 348	76.18 325	79.62 358	29.21 403	80.50 338	38.54 398	89.75 278	85.66 305

testing222		66.93 339	65.30 351	71.81 330	83.38 301	45.83 383	72.06 348	67.50 374	64.12 278	69.68 368	76.37 383	27.34 408	83.00 323	38.88 395	88.38 294	86.62 295

tpm cat1		66.76 344	65.21 352	71.42 332	77.09 362	50.62 365	78.01 282	73.68 343	44.89 388	68.64 372	79.00 362	45.51 364	82.42 328	49.91 365	70.15 396	81.23 365

test0.0.03 1		64.66 354	64.36 353	65.57 365	75.03 381	46.89 378	64.69 381	61.58 395	62.43 291	71.18 359	77.54 372	43.41 377	68.47 380	40.75 393	82.65 361	81.35 360

test_vis1_rt		65.64 350	64.09 354	70.31 337	66.09 407	70.20 176	61.16 388	81.60 290	38.65 402	72.87 350	69.66 395	52.84 328	60.04 399	56.16 328	77.77 382	80.68 370

myMVS_eth3d		64.66 354	63.89 355	66.97 359	81.72 318	37.39 402	71.00 355	61.99 389	61.38 302	70.81 361	72.36 392	20.96 413	79.30 343	49.59 367	85.18 335	84.22 322

test-mter		65.00 352	63.79 356	68.63 351	76.45 369	55.21 332	67.89 370	67.14 378	50.98 371	65.08 388	72.39 390	28.27 406	69.37 371	61.00 303	84.89 343	81.31 361

ADS-MVSNet2		65.87 349	63.64 357	72.55 325	73.16 390	56.92 321	67.10 374	74.81 331	49.74 377	66.04 382	82.97 323	46.71 351	77.26 352	42.29 389	69.96 397	83.46 334

ETVMVS		64.67 353	63.34 358	68.64 350	83.44 300	41.89 395	69.56 366	61.70 394	61.33 304	68.74 371	75.76 385	28.76 404	79.35 342	34.65 402	86.16 327	84.67 316

mvsany_test3		65.48 351	62.97 359	73.03 320	69.99 400	76.17 118	64.83 379	43.71 410	43.68 392	80.25 290	87.05 272	52.83 329	63.09 397	51.92 360	72.44 392	79.84 375

MVS-HIRNet		61.16 363	62.92 360	55.87 384	79.09 349	35.34 405	71.83 349	57.98 402	46.56 382	59.05 400	91.14 180	49.95 343	76.43 354	38.74 396	71.92 394	55.84 403

EPMVS		62.47 357	62.63 361	62.01 374	70.63 399	38.74 400	74.76 327	52.86 405	53.91 352	67.71 378	80.01 353	39.40 386	66.60 387	55.54 334	68.81 401	80.68 370

dmvs_testset		60.59 367	62.54 362	54.72 386	77.26 359	27.74 409	74.05 333	61.00 396	60.48 314	65.62 385	67.03 399	55.93 317	68.23 381	32.07 406	69.46 400	68.17 393

ADS-MVSNet		61.90 359	62.19 363	61.03 379	73.16 390	36.42 404	67.10 374	61.75 392	49.74 377	66.04 382	82.97 323	46.71 351	63.21 395	42.29 389	69.96 397	83.46 334

E-PMN		61.59 361	61.62 364	61.49 377	66.81 405	55.40 330	53.77 400	60.34 397	66.80 253	58.90 401	65.50 400	40.48 385	66.12 389	55.72 331	86.25 325	62.95 398

DSMNet-mixed		60.98 365	61.61 365	59.09 383	72.88 392	45.05 387	74.70 328	46.61 409	26.20 407	65.34 386	90.32 210	55.46 320	63.12 396	41.72 391	81.30 370	69.09 392

EMVS		61.10 364	60.81 366	61.99 375	65.96 408	55.86 327	53.10 401	58.97 400	67.06 250	56.89 405	63.33 401	40.98 383	67.03 385	54.79 340	86.18 326	63.08 397

PMMVS		61.65 360	60.38 367	65.47 366	65.40 410	69.26 185	63.97 383	61.73 393	36.80 406	60.11 398	68.43 397	59.42 293	66.35 388	48.97 371	78.57 380	60.81 399

TESTMET0.1,1		61.29 362	60.32 368	64.19 370	72.06 395	51.30 359	67.89 370	62.09 388	45.27 386	60.65 397	69.01 396	27.93 407	64.74 393	56.31 327	81.65 367	76.53 380

dp		60.70 366	60.29 369	61.92 376	72.04 396	38.67 401	70.83 358	64.08 386	51.28 368	60.75 396	77.28 375	36.59 393	71.58 368	47.41 377	62.34 403	75.52 383

pmmvs3		62.47 357	60.02 370	69.80 341	71.58 397	64.00 235	70.52 360	58.44 401	39.77 400	66.05 381	75.84 384	27.10 410	72.28 363	46.15 382	84.77 347	73.11 386

PMMVS2		55.64 372	59.27 371	44.74 388	64.30 411	12.32 416	40.60 403	49.79 407	53.19 355	65.06 390	84.81 304	53.60 327	49.76 406	32.68 405	89.41 281	72.15 387

new_pmnet		55.69 371	57.66 372	49.76 387	75.47 377	30.59 407	59.56 390	51.45 406	43.62 393	62.49 394	75.48 386	40.96 384	49.15 407	37.39 400	72.52 391	69.55 391

CHOSEN 280x420		59.08 368	56.52 373	66.76 360	76.51 367	64.39 231	49.62 402	59.00 399	43.86 391	55.66 406	68.41 398	35.55 394	68.21 382	43.25 388	76.78 388	67.69 394

mvsany_test1		58.48 369	56.47 374	64.50 369	65.90 409	68.21 195	56.95 398	42.11 411	38.30 403	65.69 384	77.19 378	56.96 311	59.35 401	46.16 381	58.96 404	65.93 395

PVSNet_0	51.08 22	56.10 370	54.97 375	59.48 382	75.12 380	53.28 345	55.16 399	61.89 391	44.30 389	59.16 399	62.48 402	54.22 325	65.91 390	35.40 401	47.01 405	59.25 401

MVE	40.22 23	51.82 373	50.47 376	55.87 384	62.66 412	51.91 354	31.61 405	39.28 412	40.65 398	50.76 407	74.98 388	56.24 316	44.67 408	33.94 404	64.11 402	71.04 390
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 374	42.65 377	39.67 389	70.86 398	21.11 411	61.01 389	21.42 416	57.36 336	57.97 404	50.06 405	16.40 415	58.73 402	21.03 409	27.69 409	39.17 405

kuosan		30.83 375	32.17 378	26.83 391	53.36 413	19.02 414	57.90 396	20.44 417	38.29 404	38.01 408	37.82 407	15.18 416	33.45 410	7.74 411	20.76 410	28.03 406

test_method		30.46 376	29.60 379	33.06 390	17.99 415	3.84 418	13.62 406	73.92 338	2.79 409	18.29 411	53.41 404	28.53 405	43.25 409	22.56 407	35.27 407	52.11 404

cdsmvs_eth3d_5k		20.81 377	27.75 380	0.00 396	0.00 419	0.00 421	0.00 407	85.44 247	0.00 414	0.00 415	82.82 327	81.46 115	0.00 415	0.00 414	0.00 413	0.00 411

tmp_tt		20.25 378	24.50 381	7.49 393	4.47 416	8.70 417	34.17 404	25.16 414	1.00 411	32.43 410	18.49 408	39.37 387	9.21 412	21.64 408	43.75 406	4.57 408

ab-mvs-re		6.65 379	8.87 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	79.80 355	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		6.41 380	8.55 383	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	76.94 161	0.00 415	0.00 414	0.00 413	0.00 411

test123		6.27 381	8.08 384	0.84 394	1.11 418	0.57 419	62.90 384	0.82 418	0.54 412	1.07 414	2.75 413	1.26 417	0.30 413	1.04 412	1.26 412	1.66 409

testmvs		5.91 382	7.65 385	0.72 395	1.20 417	0.37 420	59.14 392	0.67 419	0.49 413	1.11 413	2.76 412	0.94 418	0.24 414	1.02 413	1.47 411	1.55 410

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						37.39 402								52.61 354

FOURS1					96.08 11	87.41 10	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 68	91.55 126	77.99 90		91.01 146					96.05 8	87.45 20	98.17 31	92.40 175

PC_three_1452									58.96 323	90.06 95	91.33 174	80.66 125	93.03 139	75.78 162	95.94 126	92.48 170

No_MVS				88.81 68	91.55 126	77.99 90		91.01 146					96.05 8	87.45 20	98.17 31	92.40 175

test_one_0601					93.85 58	73.27 135		94.11 36	86.57 26	93.47 38	94.64 59	88.42 26

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					92.22 101	80.48 67		91.85 121	71.22 207	90.38 90	92.98 123	86.06 61	96.11 6	81.99 91	96.75 89

IU-MVS					94.18 46	72.64 143		90.82 151	56.98 339	89.67 107				85.78 50	97.92 45	93.28 136

OPU-MVS				88.27 79	91.89 112	77.83 93	90.47 52				91.22 177	81.12 119	94.68 72	74.48 175	95.35 145	92.29 181

test_241102_TWO								93.71 53	83.77 48	93.49 36	94.27 74	89.27 21	95.84 23	86.03 46	97.82 50	92.04 193

test_241102_ONE					94.18 46	72.65 141		93.69 54	83.62 50	94.11 23	93.78 104	90.28 14	95.50 46

save fliter					93.75 59	77.44 99	86.31 129	89.72 182	70.80 210

test_0728_THIRD									85.33 34	93.75 31	94.65 56	87.44 43	95.78 28	87.41 22	98.21 28	92.98 151

test_0728_SECOND				86.79 99	94.25 45	72.45 151	90.54 49	94.10 37					95.88 16	86.42 36	97.97 42	92.02 194

test0726					94.16 49	72.56 147	90.63 46	93.90 46	83.61 51	93.75 31	94.49 64	89.76 18

GSMVS																83.88 326

test_part2					93.86 57	77.77 94				92.84 48

sam_mvs1												46.11 355				83.88 326

sam_mvs												45.92 360

ambc				82.98 189	90.55 153	64.86 226	88.20 97	89.15 193		89.40 116	93.96 95	71.67 225	91.38 184	78.83 123	96.55 95	92.71 160

MTGPA								91.81 125

test_post1							78.85 274				3.13 410	45.19 369	80.13 340	58.11 320

test_post											3.10 411	45.43 365	77.22 353

patchmatchnet-post											81.71 339	45.93 359	87.01 272

GG-mvs-BLEND				67.16 358	73.36 388	46.54 381	84.15 165	55.04 404		58.64 402	61.95 403	29.93 402	83.87 320	38.71 397	76.92 387	71.07 389

MTMP							90.66 45	33.14 413

gm-plane-assit					75.42 378	44.97 388			52.17 361		72.36 392		87.90 262	54.10 343

test9_res														80.83 102	96.45 102	90.57 232

TEST9					92.34 95	79.70 74	83.94 170	90.32 165	65.41 270	84.49 212	90.97 186	82.03 106	93.63 111

test_8					92.09 105	78.87 81	83.82 175	90.31 167	65.79 259	84.36 216	90.96 188	81.93 108	93.44 124

agg_prior2														79.68 115	96.16 114	90.22 240

agg_prior					91.58 124	77.69 96		90.30 168		84.32 218			93.18 133

TestCases				89.68 52	91.59 121	83.40 48		95.44 9	79.47 95	88.00 143	93.03 121	82.66 91	91.47 178	70.81 214	96.14 115	94.16 96

test_prior4						78.97 80	84.59 156

test_prior2							83.37 187		75.43 145	84.58 210	91.57 168	81.92 110		79.54 117	96.97 82

test_prior				86.32 107	90.59 152	71.99 158		92.85 91					94.17 92			92.80 155

旧先验2							81.73 232		56.88 340	86.54 176			84.90 308	72.81 203

新几何2							81.72 233

新几何1				82.95 191	93.96 55	78.56 84		80.24 298	55.45 344	83.93 229	91.08 183	71.19 228	88.33 259	65.84 264	93.07 216	81.95 355

旧先验1					91.97 108	71.77 159		81.78 288			91.84 159	73.92 194			93.65 204	83.61 332

无先验							82.81 206	85.62 245	58.09 329				91.41 183	67.95 250		84.48 318

原ACMM2							82.26 226

原ACMM1				84.60 144	92.81 86	74.01 128		91.50 130	62.59 285	82.73 249	90.67 201	76.53 168	94.25 86	69.24 231	95.69 139	85.55 306

test222					93.31 69	76.54 109	79.38 263	77.79 309	52.59 358	82.36 253	90.84 194	66.83 252			91.69 244	81.25 363

testdata2													86.43 286	63.52 285

segment_acmp												81.94 107

testdata				79.54 251	92.87 81	72.34 152		80.14 299	59.91 319	85.47 195	91.75 165	67.96 247	85.24 304	68.57 245	92.18 236	81.06 368

testdata1							79.62 258		73.95 160

test12				86.57 102	90.74 148	72.63 145		90.69 154		82.76 248		79.20 135	94.80 69		95.32 148	92.27 183

plane_prior7					93.45 64	77.31 102

plane_prior6					92.61 87	76.54 109						74.84 182

plane_prior5								93.61 57					95.22 56	80.78 103	95.83 132	94.46 81

plane_prior4											92.95 126

plane_prior3						76.85 107			77.79 119	86.55 171

plane_prior2							89.45 78		79.44 97

plane_prior1					92.83 85

plane_prior						76.42 113	87.15 113		75.94 138						95.03 160

n2								0.00 420

nn								0.00 420

door-mid								74.45 335

lessismore_v0				85.95 116	91.10 141	70.99 170		70.91 363		91.79 67	94.42 69	61.76 278	92.93 142	79.52 118	93.03 217	93.93 107

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 21	82.35 64	93.67 34	94.82 51	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

test11								91.46 131

door								72.57 350

HQP5-MVS						70.66 171

HQP-NCC					91.19 136		84.77 150		73.30 174	80.55 283

ACMP_Plane					91.19 136		84.77 150		73.30 174	80.55 283

BP-MVS														77.30 146

HQP4-MVS										80.56 282			94.61 75			93.56 129

HQP3-MVS								92.68 96							94.47 180

HQP2-MVS												72.10 218

NP-MVS					91.95 109	74.55 125					90.17 217

MDTV_nov1_ep13_2view						27.60 410	70.76 359		46.47 383	61.27 395		45.20 368		49.18 369		83.75 331

ACMMP++_ref															95.74 138

ACMMP++															97.35 72

Test By Simon												79.09 136

ITE_SJBPF				90.11 45	90.72 149	84.97 37		90.30 168	81.56 72	90.02 97	91.20 179	82.40 96	90.81 203	73.58 191	94.66 176	94.56 77

DeepMVS_CX				24.13 392	32.95 414	29.49 408		21.63 415	12.07 408	37.95 409	45.07 406	30.84 400	19.21 411	17.94 410	33.06 408	23.69 407

High-resolution multi-view benchmark