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 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 61	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 46	97.23 2	95.32 2	99.01 2	97.26 6	80.16 135	98.99 1	95.15 1	99.14 2	96.47 30

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 22	93.73 64	85.72 34	96.79 1	95.51 9	88.86 16	95.63 10	96.99 10	84.81 72	93.16 137	91.10 2	97.53 72	96.58 28
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 3	93.39 4	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 50	96.29 19	88.16 35	94.17 97	86.07 49	98.48 18	97.22 17

reproduce-ours		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 209

our_new_method		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 209

reproduce_model		92.89 5	93.18 7	92.01 13	94.20 49	88.23 9	92.87 13	94.32 21	90.25 11	95.65 9	95.74 30	87.75 41	95.72 36	89.60 4	98.27 26	92.08 198

RE-MVS-def			92.61 8		94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	90.64 10		87.16 33	97.60 66	92.73 162

HPM-MVS_fast		92.50 8	92.54 9	92.37 6	95.93 16	85.81 33	92.99 12	94.23 27	85.21 40	92.51 58	95.13 48	90.65 9	95.34 55	88.06 13	98.15 37	95.95 41

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 5	94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	88.83 26	95.51 47	87.16 33	97.60 66	92.73 162

SR-MVS		92.23 10	92.34 11	91.91 17	94.89 38	87.85 10	92.51 24	93.87 51	88.20 23	93.24 42	94.02 94	90.15 16	95.67 38	86.82 37	97.34 76	92.19 194

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 25	93.02 80	85.17 39	92.47 26	95.05 14	87.65 27	93.21 43	94.39 77	90.09 17	95.08 66	86.67 39	97.60 66	94.18 99

HPM-MVS		92.13 11	92.20 13	91.91 17	95.58 26	84.67 46	93.51 8	94.85 15	82.88 64	91.77 70	93.94 102	90.55 12	95.73 35	88.50 11	98.23 31	95.33 56
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 13	91.95 14	92.04 11	93.68 65	86.15 24	93.37 10	95.10 13	90.28 10	92.11 63	95.03 50	89.75 20	94.93 70	79.95 118	98.27 26	95.04 67
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 25	91.92 15	89.14 66	92.97 82	78.04 93	92.84 16	94.14 36	83.33 58	93.90 28	95.73 31	88.77 27	96.41 3	87.60 22	97.98 45	92.98 156
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PS-CasMVS		90.06 43	91.92 15	84.47 154	96.56 6	58.83 315	89.04 88	92.74 97	91.40 6	96.12 5	96.06 26	87.23 48	95.57 41	79.42 127	98.74 6	99.00 2

DTE-MVSNet		89.98 47	91.91 17	84.21 163	96.51 7	57.84 323	88.93 90	92.84 94	91.92 4	96.16 4	96.23 21	86.95 51	95.99 12	79.05 130	98.57 15	98.80 6

PEN-MVS		90.03 45	91.88 18	84.48 153	96.57 5	58.88 312	88.95 89	93.19 75	91.62 5	96.01 7	96.16 24	87.02 50	95.60 40	78.69 133	98.72 9	98.97 3

ACMMP		91.91 14	91.87 19	92.03 12	95.53 27	85.91 28	93.35 11	94.16 32	82.52 67	92.39 61	94.14 89	89.15 25	95.62 39	87.35 28	98.24 30	94.56 80
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 21	91.68 20	90.82 37	94.75 41	81.69 63	90.00 62	94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 58	98.73 7	95.23 61

SED-MVS		90.46 37	91.64 21	86.93 99	94.18 50	72.65 146	90.47 55	93.69 56	83.77 52	94.11 26	94.27 79	90.28 14	95.84 24	86.03 50	97.92 49	92.29 188

MTAPA		91.52 18	91.60 22	91.29 30	96.59 4	86.29 21	92.02 33	91.81 127	84.07 49	92.00 66	94.40 76	86.63 54	95.28 58	88.59 10	98.31 24	92.30 187

CP-MVS		91.67 16	91.58 23	91.96 14	95.29 31	87.62 13	93.38 9	93.36 65	83.16 60	91.06 82	94.00 95	88.26 32	95.71 37	87.28 31	98.39 21	92.55 173

UA-Net		91.49 19	91.53 24	91.39 27	94.98 35	82.95 58	93.52 7	92.79 95	88.22 22	88.53 133	97.64 3	83.45 86	94.55 83	86.02 53	98.60 13	96.67 25

ACMH+	77.89 11	90.73 31	91.50 25	88.44 78	93.00 81	76.26 119	89.65 75	95.55 8	87.72 26	93.89 30	94.94 52	91.62 3	93.44 128	78.35 137	98.76 4	95.61 50

mPP-MVS		91.69 15	91.47 26	92.37 6	96.04 13	88.48 8	92.72 18	92.60 102	83.09 61	91.54 72	94.25 83	87.67 44	95.51 47	87.21 32	98.11 38	93.12 150

HFP-MVS		91.30 23	91.39 27	91.02 33	95.43 29	84.66 47	92.58 22	93.29 72	81.99 70	91.47 73	93.96 99	88.35 31	95.56 42	87.74 17	97.74 59	92.85 159

XVS		91.54 17	91.36 28	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	94.03 93	86.57 55	95.80 28	87.35 28	97.62 64	94.20 96

SteuartSystems-ACMMP		91.16 27	91.36 28	90.55 41	93.91 60	80.97 70	91.49 40	93.48 63	82.82 65	92.60 57	93.97 96	88.19 33	96.29 6	87.61 21	98.20 34	94.39 91
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR		91.49 19	91.35 30	91.92 16	95.74 20	85.88 30	92.58 22	93.25 73	81.99 70	91.40 74	94.17 88	87.51 45	95.87 20	87.74 17	97.76 57	93.99 106

ZNCC-MVS		91.26 24	91.34 31	91.01 34	95.73 21	83.05 56	92.18 31	94.22 29	80.14 92	91.29 78	93.97 96	87.93 40	95.87 20	88.65 9	97.96 48	94.12 103

DVP-MVS		90.06 43	91.32 32	86.29 111	94.16 53	72.56 152	90.54 52	91.01 148	83.61 55	93.75 34	94.65 61	89.76 18	95.78 32	86.42 40	97.97 46	90.55 245
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 50	91.31 33	85.71 128	96.32 9	62.39 268	89.54 79	93.31 70	90.21 12	95.57 11	95.66 33	81.42 121	95.90 17	80.94 107	98.80 3	98.84 5

region2R		91.44 22	91.30 34	91.87 19	95.75 19	85.90 29	92.63 21	93.30 71	81.91 72	90.88 88	94.21 84	87.75 41	95.87 20	87.60 22	97.71 60	93.83 115

ACMH	76.49 14	89.34 59	91.14 35	83.96 168	92.50 94	70.36 181	89.55 77	93.84 52	81.89 73	94.70 17	95.44 40	90.69 8	88.31 266	83.33 78	98.30 25	93.20 145
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		90.07 42	91.09 36	87.00 97	91.55 129	72.64 148	96.19 2	94.10 39	85.33 38	93.49 39	94.64 64	81.12 124	95.88 18	87.41 26	95.94 128	92.48 176

DPE-MVS		90.53 36	91.08 37	88.88 69	93.38 71	78.65 87	89.15 87	94.05 41	84.68 45	93.90 28	94.11 91	88.13 36	96.30 5	84.51 69	97.81 55	91.70 213
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 30	91.08 37	89.99 50	95.97 14	79.88 75	88.13 102	94.51 18	75.79 147	92.94 47	94.96 51	88.36 30	95.01 68	90.70 3	98.40 20	95.09 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		90.65 32	91.07 39	89.42 61	95.93 16	79.54 80	89.95 66	93.68 58	77.65 126	91.97 67	94.89 53	88.38 29	95.45 51	89.27 5	97.87 53	93.27 142

GST-MVS		90.96 29	91.01 40	90.82 37	95.45 28	82.73 59	91.75 38	93.74 54	80.98 83	91.38 75	93.80 106	87.20 49	95.80 28	87.10 35	97.69 61	93.93 109

ACMM	79.39 9	90.65 32	90.99 41	89.63 57	95.03 34	83.53 51	89.62 76	93.35 66	79.20 105	93.83 31	93.60 116	90.81 7	92.96 144	85.02 63	98.45 19	92.41 180
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		90.13 40	90.96 42	87.65 91	91.95 112	71.06 174	89.99 64	93.05 83	86.53 31	94.29 22	96.27 20	82.69 93	94.08 100	86.25 46	97.63 63	97.82 8

PGM-MVS		91.20 26	90.95 43	91.93 15	95.67 23	85.85 31	90.00 62	93.90 48	80.32 89	91.74 71	94.41 75	88.17 34	95.98 13	86.37 42	97.99 43	93.96 108

MP-MVS		91.14 28	90.91 44	91.83 20	96.18 11	86.88 17	92.20 30	93.03 86	82.59 66	88.52 134	94.37 78	86.74 53	95.41 53	86.32 43	98.21 32	93.19 146
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVSNet		89.27 62	90.91 44	84.37 155	96.34 8	58.61 318	88.66 97	92.06 116	90.78 7	95.67 8	95.17 47	81.80 117	95.54 44	79.00 131	98.69 10	98.95 4

SF-MVS		90.27 39	90.80 46	88.68 76	92.86 86	77.09 108	91.19 44	95.74 6	81.38 78	92.28 62	93.80 106	86.89 52	94.64 78	85.52 57	97.51 73	94.30 95

UniMVSNet_ETH3D		89.12 65	90.72 47	84.31 161	97.00 2	64.33 243	89.67 74	88.38 208	88.84 17	94.29 22	97.57 4	90.48 13	91.26 189	72.57 215	97.65 62	97.34 14

PMVS	80.48 6	90.08 41	90.66 48	88.34 81	96.71 3	92.97 2	90.31 59	89.57 194	88.51 21	90.11 96	95.12 49	90.98 6	88.92 254	77.55 151	97.07 83	83.13 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMP	79.16 10	90.54 35	90.60 49	90.35 45	94.36 46	80.98 69	89.16 86	94.05 41	79.03 108	92.87 49	93.74 111	90.60 11	95.21 61	82.87 86	98.76 4	94.87 70
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		90.31 38	90.48 50	89.83 54	95.31 30	79.52 81	90.98 47	93.24 74	75.37 156	92.84 51	95.28 44	85.58 67	96.09 8	87.92 15	97.76 57	93.88 112
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 34	90.34 51	91.38 28	89.03 185	84.23 49	93.58 6	94.68 17	90.65 8	90.33 94	93.95 101	84.50 74	95.37 54	80.87 108	95.50 145	94.53 83

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 76	86.76 127	92.78 96	78.78 111	92.51 58	93.64 115	88.13 36	93.84 109	84.83 66	97.55 69	94.10 104
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SD-MVS		88.96 67	89.88 53	86.22 115	91.63 123	77.07 109	89.82 69	93.77 53	78.90 109	92.88 48	92.29 159	86.11 63	90.22 222	86.24 47	97.24 79	91.36 221
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 47	89.84 54	90.41 43	94.91 37	84.50 48	89.49 81	93.98 43	79.68 97	92.09 64	93.89 104	83.80 81	93.10 140	82.67 90	98.04 39	93.64 127

tt0805		88.09 77	89.79 55	82.98 198	93.26 75	63.94 247	91.10 45	89.64 191	85.07 41	90.91 86	91.09 194	89.16 24	91.87 175	82.03 97	95.87 132	93.13 148

OurMVSNet-221017-0		90.01 46	89.74 56	90.83 36	93.16 78	80.37 72	91.91 36	93.11 79	81.10 81	95.32 14	97.24 7	72.94 216	94.85 72	85.07 61	97.78 56	97.26 15

3Dnovator+	83.92 2	89.97 49	89.66 57	90.92 35	91.27 138	81.66 66	91.25 42	94.13 37	88.89 15	88.83 126	94.26 82	77.55 158	95.86 23	84.88 64	95.87 132	95.24 60

APD-MVS		89.54 56	89.63 58	89.26 64	92.57 91	81.34 68	90.19 61	93.08 82	80.87 85	91.13 80	93.19 122	86.22 62	95.97 14	82.23 96	97.18 81	90.45 247
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Anonymous20231211		88.40 71	89.62 59	84.73 146	90.46 157	65.27 233	88.86 91	93.02 87	87.15 28	93.05 46	97.10 8	82.28 106	92.02 170	76.70 161	97.99 43	96.88 23

test_0402		88.65 69	89.58 60	85.88 124	92.55 92	72.22 160	84.01 180	89.44 196	88.63 20	94.38 21	95.77 29	86.38 61	93.59 120	79.84 119	95.21 154	91.82 207

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 46	94.47 43	85.95 27	86.84 123	93.91 47	80.07 93	86.75 175	93.26 121	93.64 2	90.93 200	84.60 68	90.75 275	93.97 107

9.14			89.29 62		91.84 119		88.80 93	95.32 12	75.14 158	91.07 81	92.89 136	87.27 47	93.78 110	83.69 77	97.55 69

mvs_tets		89.78 52	89.27 63	91.30 29	93.51 67	84.79 44	89.89 68	90.63 158	70.00 229	94.55 19	96.67 14	87.94 39	93.59 120	84.27 71	95.97 124	95.52 51

testf1		89.30 60	89.12 64	89.84 52	88.67 195	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 246	74.12 190	96.10 119	94.45 86

APD_test2		89.30 60	89.12 64	89.84 52	88.67 195	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 246	74.12 190	96.10 119	94.45 86

DeepC-MVS	82.31 4	89.15 64	89.08 66	89.37 62	93.64 66	79.07 83	88.54 98	94.20 30	73.53 175	89.71 107	94.82 56	85.09 68	95.77 34	84.17 72	98.03 41	93.26 143
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 54	89.01 67	91.45 26	92.36 97	82.98 57	91.98 34	90.08 181	71.54 209	94.28 24	96.54 16	81.57 119	94.27 89	86.26 44	96.49 100	97.09 19

DP-MVS		88.60 70	89.01 67	87.36 93	91.30 136	77.50 101	87.55 109	92.97 90	87.95 25	89.62 111	92.87 137	84.56 73	93.89 106	77.65 149	96.62 95	90.70 239

CPTT-MVS		89.39 58	88.98 69	90.63 40	95.09 33	86.95 16	92.09 32	92.30 110	79.74 96	87.50 160	92.38 153	81.42 121	93.28 133	83.07 82	97.24 79	91.67 214

anonymousdsp		89.73 53	88.88 70	92.27 8	89.82 171	86.67 18	90.51 54	90.20 178	69.87 230	95.06 15	96.14 25	84.28 77	93.07 141	87.68 19	96.34 106	97.09 19

MVSMamba_PlusPlus		87.53 86	88.86 71	83.54 184	92.03 110	62.26 272	91.49 40	92.62 100	88.07 24	88.07 146	96.17 23	72.24 225	95.79 31	84.85 65	94.16 193	92.58 171

XVG-OURS		89.18 63	88.83 72	90.23 47	94.28 47	86.11 26	85.91 141	93.60 61	80.16 91	89.13 123	93.44 118	83.82 80	90.98 198	83.86 75	95.30 153	93.60 130

jajsoiax		89.41 57	88.81 73	91.19 32	93.38 71	84.72 45	89.70 71	90.29 175	69.27 233	94.39 20	96.38 18	86.02 65	93.52 124	83.96 73	95.92 130	95.34 55

TranMVSNet+NR-MVSNet		87.86 81	88.76 74	85.18 137	94.02 58	64.13 244	84.38 174	91.29 140	84.88 44	92.06 65	93.84 105	86.45 58	93.73 111	73.22 206	98.66 11	97.69 9

nrg030		87.85 82	88.49 75	85.91 122	90.07 166	69.73 187	87.86 106	94.20 30	74.04 167	92.70 56	94.66 60	85.88 66	91.50 181	79.72 121	97.32 77	96.50 29

HPM-MVS++		88.93 68	88.45 76	90.38 44	94.92 36	85.85 31	89.70 71	91.27 141	78.20 118	86.69 178	92.28 160	80.36 133	95.06 67	86.17 48	96.49 100	90.22 251

EC-MVSNet		88.01 78	88.32 77	87.09 95	89.28 180	72.03 162	90.31 59	96.31 4	80.88 84	85.12 210	89.67 237	84.47 75	95.46 50	82.56 91	96.26 111	93.77 121

MSP-MVS		89.08 66	88.16 78	91.83 20	95.76 18	86.14 25	92.75 17	93.90 48	78.43 116	89.16 121	92.25 161	72.03 230	96.36 4	88.21 12	90.93 268	92.98 156
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 99	88.06 79	81.90 220	92.22 103	62.28 271	84.66 167	89.15 199	83.54 57	89.85 104	97.32 5	88.08 38	86.80 287	70.43 232	97.30 78	96.62 26

APD_test1		88.40 71	87.91 80	89.88 51	89.50 175	86.65 20	89.98 65	91.91 122	84.26 47	90.87 89	93.92 103	82.18 108	89.29 250	73.75 198	94.81 173	93.70 123

PS-MVSNAJss		88.31 73	87.90 81	89.56 59	93.31 73	77.96 96	87.94 105	91.97 119	70.73 220	94.19 25	96.67 14	76.94 168	94.57 81	83.07 82	96.28 108	96.15 33

TSAR-MVS + MP.		88.14 75	87.82 82	89.09 67	95.72 22	76.74 112	92.49 25	91.19 144	67.85 254	86.63 179	94.84 55	79.58 140	95.96 15	87.62 20	94.50 182	94.56 80
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 83	87.68 83	88.21 83	92.87 84	77.30 107	85.25 156	91.23 142	77.31 131	87.07 169	91.47 183	82.94 91	94.71 75	84.67 67	96.27 110	92.62 169

CS-MVS		88.14 75	87.67 84	89.54 60	89.56 173	79.18 82	90.47 55	94.77 16	79.37 103	84.32 230	89.33 242	83.87 79	94.53 84	82.45 92	94.89 169	94.90 68

OMC-MVS		88.19 74	87.52 85	90.19 48	91.94 114	81.68 65	87.49 112	93.17 76	76.02 141	88.64 130	91.22 189	84.24 78	93.37 131	77.97 147	97.03 84	95.52 51

casdiffmvs_mvg		86.72 95	87.51 86	84.36 157	87.09 239	65.22 234	84.16 176	94.23 27	77.89 122	91.28 79	93.66 114	84.35 76	92.71 150	80.07 115	94.87 172	95.16 64
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 89	87.45 87	86.45 108	92.52 93	69.19 196	87.84 107	88.05 215	81.66 75	94.64 18	96.53 17	65.94 262	94.75 74	83.02 84	96.83 89	95.41 53

HQP_MVS		87.75 84	87.43 88	88.70 75	93.45 68	76.42 116	89.45 82	93.61 59	79.44 101	86.55 180	92.95 134	74.84 189	95.22 59	80.78 110	95.83 134	94.46 84

AllTest		87.97 80	87.40 89	89.68 55	91.59 124	83.40 52	89.50 80	95.44 10	79.47 99	88.00 149	93.03 129	82.66 94	91.47 182	70.81 224	96.14 116	94.16 100

fmvsm_s_conf0.5_n_3		86.19 105	87.27 90	82.95 200	86.91 243	70.38 180	85.31 155	92.61 101	75.59 151	88.32 141	92.87 137	82.22 107	88.63 261	88.80 8	92.82 227	89.83 261

MM		87.64 85	87.15 91	89.09 67	89.51 174	76.39 118	88.68 96	86.76 238	84.54 46	83.58 248	93.78 108	73.36 212	96.48 2	87.98 14	96.21 112	94.41 90

Anonymous20240529		86.20 104	87.13 92	83.42 186	90.19 162	64.55 241	84.55 169	90.71 155	85.85 36	89.94 103	95.24 46	82.13 109	90.40 218	69.19 245	96.40 105	95.31 57

v10		86.54 98	87.10 93	84.84 141	88.16 210	63.28 254	86.64 130	92.20 112	75.42 155	92.81 53	94.50 68	74.05 200	94.06 101	83.88 74	96.28 108	97.17 18

UniMVSNet_NR-MVSNet		86.84 93	87.06 94	86.17 118	92.86 86	67.02 217	82.55 224	91.56 130	83.08 62	90.92 84	91.82 173	78.25 149	93.99 102	74.16 188	98.35 22	97.49 13

FC-MVSNet-test		85.93 110	87.05 95	82.58 210	92.25 101	56.44 334	85.75 146	93.09 81	77.33 130	91.94 68	94.65 61	74.78 191	93.41 130	75.11 182	98.58 14	97.88 7

DU-MVS		86.80 94	86.99 96	86.21 116	93.24 76	67.02 217	83.16 207	92.21 111	81.73 74	90.92 84	91.97 166	77.20 162	93.99 102	74.16 188	98.35 22	97.61 10

UniMVSNet (Re)		86.87 91	86.98 97	86.55 106	93.11 79	68.48 203	83.80 189	92.87 92	80.37 87	89.61 113	91.81 174	77.72 155	94.18 95	75.00 183	98.53 16	96.99 22

RPSCF		88.00 79	86.93 98	91.22 31	90.08 164	89.30 5	89.68 73	91.11 145	79.26 104	89.68 108	94.81 59	82.44 97	87.74 272	76.54 163	88.74 304	96.61 27

NCCC		87.36 87	86.87 99	88.83 70	92.32 100	78.84 86	86.58 131	91.09 146	78.77 112	84.85 219	90.89 203	80.85 127	95.29 56	81.14 105	95.32 150	92.34 185

v8		86.22 103	86.83 100	84.36 157	87.82 217	62.35 270	86.42 134	91.33 139	76.78 135	92.73 55	94.48 70	73.41 209	93.72 112	83.10 81	95.41 146	97.01 21

IS-MVSNet		86.66 97	86.82 101	86.17 118	92.05 109	66.87 220	91.21 43	88.64 205	86.30 33	89.60 114	92.59 146	69.22 246	94.91 71	73.89 195	97.89 52	96.72 24

Vis-MVSNet		86.86 92	86.58 102	87.72 89	92.09 107	77.43 104	87.35 113	92.09 115	78.87 110	84.27 235	94.05 92	78.35 148	93.65 113	80.54 114	91.58 255	92.08 198
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		86.68 96	86.52 103	87.18 94	85.94 268	78.30 89	86.93 120	92.20 112	65.94 270	89.16 121	93.16 124	83.10 89	89.89 235	87.81 16	94.43 185	93.35 137

CSCG		86.26 101	86.47 104	85.60 130	90.87 149	74.26 132	87.98 104	91.85 123	80.35 88	89.54 117	88.01 261	79.09 142	92.13 166	75.51 176	95.06 161	90.41 248

SPE-MVS-test		87.00 90	86.43 105	88.71 74	89.46 176	77.46 102	89.42 84	95.73 7	77.87 124	81.64 284	87.25 279	82.43 98	94.53 84	77.65 149	96.46 102	94.14 102

Gipuma		84.44 140	86.33 106	78.78 268	84.20 298	73.57 136	89.55 77	90.44 164	84.24 48	84.38 227	94.89 53	76.35 179	80.40 351	76.14 170	96.80 91	82.36 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FIs		85.35 119	86.27 107	82.60 209	91.86 116	57.31 327	85.10 160	93.05 83	75.83 146	91.02 83	93.97 96	73.57 205	92.91 148	73.97 194	98.02 42	97.58 12

NR-MVSNet		86.00 108	86.22 108	85.34 135	93.24 76	64.56 240	82.21 236	90.46 163	80.99 82	88.42 137	91.97 166	77.56 157	93.85 107	72.46 216	98.65 12	97.61 10

DeepPCF-MVS	81.24 5	87.28 88	86.21 109	90.49 42	91.48 133	84.90 42	83.41 198	92.38 107	70.25 226	89.35 119	90.68 212	82.85 92	94.57 81	79.55 124	95.95 127	92.00 202

sasdasda		85.50 114	86.14 110	83.58 180	87.97 212	67.13 214	87.55 109	94.32 21	73.44 178	88.47 135	87.54 272	86.45 58	91.06 196	75.76 174	93.76 203	92.54 174

canonicalmvs		85.50 114	86.14 110	83.58 180	87.97 212	67.13 214	87.55 109	94.32 21	73.44 178	88.47 135	87.54 272	86.45 58	91.06 196	75.76 174	93.76 203	92.54 174

MSLP-MVS++		85.00 130	86.03 112	81.90 220	91.84 119	71.56 171	86.75 128	93.02 87	75.95 144	87.12 164	89.39 240	77.98 150	89.40 249	77.46 152	94.78 174	84.75 330

MGCFI-Net		85.04 127	85.95 113	82.31 216	87.52 226	63.59 250	86.23 138	93.96 44	73.46 176	88.07 146	87.83 267	86.46 57	90.87 205	76.17 169	93.89 200	92.47 178

baseline		85.20 122	85.93 114	83.02 196	86.30 257	62.37 269	84.55 169	93.96 44	74.48 164	87.12 164	92.03 165	82.30 103	91.94 171	78.39 135	94.21 190	94.74 77

Baseline_NR-MVSNet		84.00 155	85.90 115	78.29 279	91.47 134	53.44 357	82.29 232	87.00 237	79.06 107	89.55 115	95.72 32	77.20 162	86.14 301	72.30 217	98.51 17	95.28 58

test_fmvsmconf0.1_n		86.18 106	85.88 116	87.08 96	85.26 278	78.25 90	85.82 145	91.82 125	65.33 284	88.55 132	92.35 158	82.62 96	89.80 237	86.87 36	94.32 188	93.18 147

casdiffmvs		85.21 121	85.85 117	83.31 189	86.17 262	62.77 261	83.03 209	93.93 46	74.69 162	88.21 143	92.68 145	82.29 105	91.89 174	77.87 148	93.75 206	95.27 59
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		85.45 117	85.81 118	84.37 155	90.08 164	67.07 216	85.86 144	91.39 137	72.33 202	87.59 158	90.25 224	84.85 71	92.37 160	78.00 145	91.94 246	93.66 124

PHI-MVS		86.38 100	85.81 118	88.08 84	88.44 204	77.34 105	89.35 85	93.05 83	73.15 188	84.76 220	87.70 269	78.87 144	94.18 95	80.67 112	96.29 107	92.73 162

mmtdpeth		85.13 124	85.78 120	83.17 194	84.65 288	74.71 128	85.87 143	90.35 169	77.94 121	83.82 242	96.96 12	77.75 153	80.03 354	78.44 134	96.21 112	94.79 76

TransMVSNet (Re)		84.02 154	85.74 121	78.85 267	91.00 146	55.20 346	82.29 232	87.26 224	79.65 98	88.38 139	95.52 37	83.00 90	86.88 285	67.97 260	96.60 96	94.45 86

ANet_high		83.17 175	85.68 122	75.65 314	81.24 338	45.26 400	79.94 265	92.91 91	83.83 51	91.33 76	96.88 13	80.25 134	85.92 304	68.89 249	95.89 131	95.76 43

DeepC-MVS_fast	80.27 8	86.23 102	85.65 123	87.96 87	91.30 136	76.92 110	87.19 115	91.99 118	70.56 221	84.96 214	90.69 211	80.01 137	95.14 64	78.37 136	95.78 138	91.82 207
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		86.17 107	85.54 124	88.05 86	92.25 101	75.45 125	83.85 186	92.01 117	65.91 272	86.19 189	91.75 177	83.77 82	94.98 69	77.43 154	96.71 93	93.73 122

test_fmvsmconf_n		85.88 111	85.51 125	86.99 98	84.77 286	78.21 91	85.40 154	91.39 137	65.32 285	87.72 156	91.81 174	82.33 101	89.78 238	86.68 38	94.20 191	92.99 155

FMVSNet1		84.55 138	85.45 126	81.85 222	90.27 161	61.05 286	86.83 124	88.27 212	78.57 115	89.66 110	95.64 34	75.43 182	90.68 211	69.09 246	95.33 149	93.82 116

balanced_conf03		84.80 132	85.40 127	83.00 197	88.95 188	61.44 279	90.42 58	92.37 108	71.48 211	88.72 129	93.13 125	70.16 242	95.15 63	79.26 129	94.11 194	92.41 180

VDDNet		84.35 142	85.39 128	81.25 233	95.13 32	59.32 305	85.42 153	81.11 304	86.41 32	87.41 161	96.21 22	73.61 204	90.61 214	66.33 270	96.85 87	93.81 119

test_fmvsmvis_n_1920		85.22 120	85.36 129	84.81 143	85.80 270	76.13 122	85.15 159	92.32 109	61.40 314	91.33 76	90.85 206	83.76 83	86.16 300	84.31 70	93.28 215	92.15 196

train_agg		85.98 109	85.28 130	88.07 85	92.34 98	79.70 78	83.94 182	90.32 170	65.79 274	84.49 224	90.97 198	81.93 113	93.63 115	81.21 104	96.54 98	90.88 233

dcpmvs_2		84.23 148	85.14 131	81.50 230	88.61 199	61.98 276	82.90 215	93.11 79	68.66 242	92.77 54	92.39 152	78.50 146	87.63 274	76.99 160	92.30 234	94.90 68

LCM-MVSNet-Re		83.48 169	85.06 132	78.75 269	85.94 268	55.75 340	80.05 263	94.27 24	76.47 136	96.09 6	94.54 67	83.31 88	89.75 241	59.95 323	94.89 169	90.75 236

EPP-MVSNet		85.47 116	85.04 133	86.77 103	91.52 132	69.37 191	91.63 39	87.98 217	81.51 77	87.05 170	91.83 172	66.18 261	95.29 56	70.75 227	96.89 86	95.64 48

IterMVS-LS		84.73 134	84.98 134	83.96 168	87.35 230	63.66 248	83.25 203	89.88 186	76.06 139	89.62 111	92.37 156	73.40 211	92.52 155	78.16 142	94.77 176	95.69 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_3		85.11 126	84.96 135	85.56 131	87.49 228	75.69 124	84.71 165	90.61 160	67.64 256	84.88 217	92.05 164	82.30 103	88.36 264	83.84 76	91.10 261	92.62 169

pm-mvs1		83.69 162	84.95 136	79.91 254	90.04 168	59.66 302	82.43 228	87.44 221	75.52 153	87.85 152	95.26 45	81.25 123	85.65 311	68.74 252	96.04 121	94.42 89

TAPA-MVS	77.73 12	85.71 113	84.83 137	88.37 80	88.78 194	79.72 77	87.15 117	93.50 62	69.17 234	85.80 198	89.56 238	80.76 128	92.13 166	73.21 211	95.51 144	93.25 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		83.47 170	84.73 138	79.69 258	90.29 160	57.52 326	81.30 248	88.69 204	76.29 137	87.58 159	94.44 71	80.60 131	87.20 279	66.60 268	96.82 90	94.34 93

K. test v3		85.14 123	84.73 138	86.37 109	91.13 143	69.63 189	85.45 152	76.68 333	84.06 50	92.44 60	96.99 10	62.03 284	94.65 77	80.58 113	93.24 216	94.83 75

v1144		84.54 139	84.72 140	84.00 166	87.67 222	62.55 265	82.97 212	90.93 151	70.32 225	89.80 105	90.99 197	73.50 206	93.48 126	81.69 103	94.65 180	95.97 39

3Dnovator	80.37 7	84.80 132	84.71 141	85.06 139	86.36 255	74.71 128	88.77 94	90.00 183	75.65 149	84.96 214	93.17 123	74.06 199	91.19 191	78.28 139	91.09 262	89.29 271

v1192		84.57 137	84.69 142	84.21 163	87.75 219	62.88 258	83.02 210	91.43 134	69.08 236	89.98 102	90.89 203	72.70 220	93.62 118	82.41 93	94.97 166	96.13 34

MIMVSNet1		83.63 164	84.59 143	80.74 242	94.06 57	62.77 261	82.72 218	84.53 276	77.57 128	90.34 93	95.92 28	76.88 174	85.83 309	61.88 310	97.42 74	93.62 128

MVS_0304		85.37 118	84.58 144	87.75 88	85.28 277	73.36 137	86.54 133	85.71 253	77.56 129	81.78 282	92.47 151	70.29 240	96.02 11	85.59 56	95.96 125	93.87 113

VDD-MVS		84.23 148	84.58 144	83.20 192	91.17 142	65.16 236	83.25 203	84.97 270	79.79 95	87.18 163	94.27 79	74.77 192	90.89 203	69.24 242	96.54 98	93.55 135

mvs5depth		83.82 159	84.54 146	81.68 227	82.23 326	68.65 201	86.89 121	89.90 185	80.02 94	87.74 155	97.86 2	64.19 271	82.02 339	76.37 165	95.63 143	94.35 92

EI-MVSNet-Vis-set		85.12 125	84.53 147	86.88 100	84.01 300	72.76 145	83.91 185	85.18 262	80.44 86	88.75 127	85.49 306	80.08 136	91.92 172	82.02 98	90.85 273	95.97 39

v1240		84.30 144	84.51 148	83.65 177	87.65 223	61.26 283	82.85 216	91.54 131	67.94 252	90.68 91	90.65 215	71.71 233	93.64 114	82.84 87	94.78 174	96.07 36

EI-MVSNet-UG-set		85.04 127	84.44 149	86.85 101	83.87 304	72.52 154	83.82 187	85.15 263	80.27 90	88.75 127	85.45 308	79.95 138	91.90 173	81.92 101	90.80 274	96.13 34

v144192		84.24 147	84.41 150	83.71 176	87.59 225	61.57 278	82.95 213	91.03 147	67.82 255	89.80 105	90.49 218	73.28 213	93.51 125	81.88 102	94.89 169	96.04 38

WR-MVS		83.56 167	84.40 151	81.06 238	93.43 70	54.88 347	78.67 287	85.02 267	81.24 79	90.74 90	91.56 181	72.85 217	91.08 195	68.00 259	98.04 39	97.23 16

v1921920		84.23 148	84.37 152	83.79 172	87.64 224	61.71 277	82.91 214	91.20 143	67.94 252	90.06 97	90.34 221	72.04 229	93.59 120	82.32 94	94.91 167	96.07 36

MVS_111021_HR		84.63 135	84.34 153	85.49 134	90.18 163	75.86 123	79.23 279	87.13 229	73.35 180	85.56 203	89.34 241	83.60 85	90.50 216	76.64 162	94.05 197	90.09 257

v2v482		84.09 151	84.24 154	83.62 178	87.13 235	61.40 280	82.71 219	89.71 189	72.19 205	89.55 115	91.41 184	70.70 239	93.20 135	81.02 106	93.76 203	96.25 32

EG-PatchMatch MVS		84.08 152	84.11 155	83.98 167	92.22 103	72.61 151	82.20 238	87.02 234	72.63 196	88.86 124	91.02 196	78.52 145	91.11 194	73.41 203	91.09 262	88.21 286

HQP-MVS		84.61 136	84.06 156	86.27 112	91.19 139	70.66 176	84.77 161	92.68 98	73.30 183	80.55 298	90.17 229	72.10 226	94.61 79	77.30 156	94.47 183	93.56 133

Effi-MVS+		83.90 158	84.01 157	83.57 182	87.22 233	65.61 232	86.55 132	92.40 105	78.64 114	81.34 289	84.18 327	83.65 84	92.93 146	74.22 187	87.87 318	92.17 195

alignmvs		83.94 157	83.98 158	83.80 171	87.80 218	67.88 210	84.54 171	91.42 136	73.27 186	88.41 138	87.96 262	72.33 223	90.83 206	76.02 172	94.11 194	92.69 166

MCST-MVS		84.36 141	83.93 159	85.63 129	91.59 124	71.58 169	83.52 195	92.13 114	61.82 307	83.96 240	89.75 236	79.93 139	93.46 127	78.33 138	94.34 187	91.87 206

ETV-MVS		84.31 143	83.91 160	85.52 132	88.58 200	70.40 179	84.50 173	93.37 64	78.76 113	84.07 238	78.72 381	80.39 132	95.13 65	73.82 197	92.98 223	91.04 227

MVS_111021_LR		84.28 145	83.76 161	85.83 126	89.23 182	83.07 55	80.99 252	83.56 284	72.71 195	86.07 192	89.07 247	81.75 118	86.19 299	77.11 158	93.36 211	88.24 285

AdaColmap		83.66 163	83.69 162	83.57 182	90.05 167	72.26 159	86.29 136	90.00 183	78.19 119	81.65 283	87.16 281	83.40 87	94.24 92	61.69 312	94.76 177	84.21 340

fmvsm_s_conf0.1_n_2		83.82 159	83.49 163	84.84 141	85.99 267	70.19 183	80.93 253	87.58 220	67.26 262	87.94 151	92.37 156	71.40 235	88.01 268	86.03 50	91.87 247	96.31 31

RRT-MVS		82.97 178	83.44 164	81.57 229	85.06 281	58.04 321	87.20 114	90.37 167	77.88 123	88.59 131	93.70 113	63.17 278	93.05 142	76.49 164	88.47 306	93.62 128

F-COLMAP		84.97 131	83.42 165	89.63 57	92.39 96	83.40 52	88.83 92	91.92 121	73.19 187	80.18 306	89.15 246	77.04 166	93.28 133	65.82 277	92.28 237	92.21 193

Effi-MVS+-dtu		85.82 112	83.38 166	93.14 4	87.13 235	91.15 3	87.70 108	88.42 207	74.57 163	83.56 249	85.65 303	78.49 147	94.21 93	72.04 218	92.88 225	94.05 105

V42		83.47 170	83.37 167	83.75 174	83.16 320	63.33 253	81.31 246	90.23 177	69.51 232	90.91 86	90.81 208	74.16 198	92.29 164	80.06 116	90.22 283	95.62 49

fmvsm_s_conf0.5_n_2		83.62 165	83.29 168	84.62 149	85.43 275	70.18 184	80.61 257	87.24 225	67.14 263	87.79 154	91.87 168	71.79 232	87.98 269	86.00 54	91.77 250	95.71 45

MVS_Test		82.47 186	83.22 169	80.22 251	82.62 325	57.75 325	82.54 225	91.96 120	71.16 216	82.89 260	92.52 150	77.41 159	90.50 216	80.04 117	87.84 319	92.40 182

DP-MVS Recon		84.05 153	83.22 169	86.52 107	91.73 122	75.27 126	83.23 205	92.40 105	72.04 206	82.04 273	88.33 257	77.91 152	93.95 104	66.17 271	95.12 159	90.34 250

PAPM_NR		83.23 173	83.19 171	83.33 188	90.90 148	65.98 228	88.19 101	90.78 154	78.13 120	80.87 294	87.92 265	73.49 208	92.42 157	70.07 235	88.40 307	91.60 216

SDMVSNet		81.90 202	83.17 172	78.10 282	88.81 192	62.45 267	76.08 328	86.05 248	73.67 172	83.41 251	93.04 127	82.35 100	80.65 348	70.06 236	95.03 162	91.21 223

KD-MVS_self_test		81.93 200	83.14 173	78.30 278	84.75 287	52.75 361	80.37 260	89.42 197	70.24 227	90.26 95	93.39 119	74.55 196	86.77 288	68.61 254	96.64 94	95.38 54

CNLPA		83.55 168	83.10 174	84.90 140	89.34 179	83.87 50	84.54 171	88.77 202	79.09 106	83.54 250	88.66 254	74.87 188	81.73 341	66.84 265	92.29 236	89.11 273

FA-MVS(test-final)		83.13 176	83.02 175	83.43 185	86.16 264	66.08 227	88.00 103	88.36 209	75.55 152	85.02 212	92.75 143	65.12 266	92.50 156	74.94 184	91.30 259	91.72 211

tfpnnormal		81.79 203	82.95 176	78.31 277	88.93 189	55.40 342	80.83 256	82.85 290	76.81 134	85.90 197	94.14 89	74.58 195	86.51 292	66.82 266	95.68 142	93.01 154

test_fmvsm_n_1920		83.60 166	82.89 177	85.74 127	85.22 279	77.74 99	84.12 178	90.48 162	59.87 333	86.45 188	91.12 193	75.65 180	85.89 307	82.28 95	90.87 271	93.58 131

CANet		83.79 161	82.85 178	86.63 104	86.17 262	72.21 161	83.76 190	91.43 134	77.24 132	74.39 357	87.45 275	75.36 183	95.42 52	77.03 159	92.83 226	92.25 192

h-mvs33		84.25 146	82.76 179	88.72 73	91.82 121	82.60 60	84.00 181	84.98 269	71.27 212	86.70 176	90.55 217	63.04 281	93.92 105	78.26 140	94.20 191	89.63 263

X-MVStestdata		85.04 127	82.70 180	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	16.05 427	86.57 55	95.80 28	87.35 28	97.62 64	94.20 96

TSAR-MVS + GP.		83.95 156	82.69 181	87.72 89	89.27 181	81.45 67	83.72 191	81.58 302	74.73 161	85.66 199	86.06 298	72.56 222	92.69 152	75.44 178	95.21 154	89.01 279

CLD-MVS		83.18 174	82.64 182	84.79 144	89.05 184	67.82 211	77.93 295	92.52 103	68.33 245	85.07 211	81.54 356	82.06 110	92.96 144	69.35 241	97.91 51	93.57 132
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		82.28 188	82.61 183	81.30 232	86.29 258	69.79 185	88.71 95	87.67 219	78.42 117	82.15 272	84.15 328	77.98 150	91.59 180	65.39 280	92.75 228	82.51 367

QAPM		82.59 183	82.59 184	82.58 210	86.44 250	66.69 221	89.94 67	90.36 168	67.97 251	84.94 216	92.58 148	72.71 219	92.18 165	70.63 230	87.73 320	88.85 280

114514_t		83.10 177	82.54 185	84.77 145	92.90 83	69.10 198	86.65 129	90.62 159	54.66 365	81.46 286	90.81 208	76.98 167	94.38 87	72.62 214	96.18 114	90.82 235

v148		82.31 187	82.48 186	81.81 225	85.59 272	59.66 302	81.47 245	86.02 249	72.85 191	88.05 148	90.65 215	70.73 238	90.91 202	75.15 181	91.79 248	94.87 70

EI-MVSNet		82.61 182	82.42 187	83.20 192	83.25 317	63.66 248	83.50 196	85.07 264	76.06 139	86.55 180	85.10 314	73.41 209	90.25 219	78.15 144	90.67 277	95.68 47

TinyColmap		81.25 209	82.34 188	77.99 285	85.33 276	60.68 293	82.32 231	88.33 210	71.26 214	86.97 171	92.22 163	77.10 165	86.98 283	62.37 304	95.17 156	86.31 313

GBi-Net		82.02 197	82.07 189	81.85 222	86.38 252	61.05 286	86.83 124	88.27 212	72.43 197	86.00 193	95.64 34	63.78 274	90.68 211	65.95 273	93.34 212	93.82 116

test1		82.02 197	82.07 189	81.85 222	86.38 252	61.05 286	86.83 124	88.27 212	72.43 197	86.00 193	95.64 34	63.78 274	90.68 211	65.95 273	93.34 212	93.82 116

OpenMVS	76.72 13	81.98 199	82.00 191	81.93 219	84.42 293	68.22 205	88.50 99	89.48 195	66.92 265	81.80 280	91.86 169	72.59 221	90.16 224	71.19 223	91.25 260	87.40 301

fmvsm_s_conf0.1_n_a		82.58 184	81.93 192	84.50 152	87.68 221	73.35 138	86.14 139	77.70 322	61.64 312	85.02 212	91.62 179	77.75 153	86.24 296	82.79 88	87.07 327	93.91 111

LF4IMVS		82.75 181	81.93 192	85.19 136	82.08 327	80.15 74	85.53 150	88.76 203	68.01 249	85.58 202	87.75 268	71.80 231	86.85 286	74.02 193	93.87 201	88.58 282

hse-mvs2		83.47 170	81.81 194	88.47 77	91.03 145	82.27 61	82.61 220	83.69 282	71.27 212	86.70 176	86.05 299	63.04 281	92.41 158	78.26 140	93.62 210	90.71 238

VPNet		80.25 228	81.68 195	75.94 312	92.46 95	47.98 387	76.70 315	81.67 300	73.45 177	84.87 218	92.82 139	74.66 194	86.51 292	61.66 313	96.85 87	93.33 138

BP-MVS1		82.81 179	81.67 196	86.23 113	87.88 216	68.53 202	86.06 140	84.36 277	75.65 149	85.14 209	90.19 226	45.84 370	94.42 86	85.18 60	94.72 178	95.75 44

SSC-MVS		77.55 257	81.64 197	65.29 384	90.46 157	20.33 431	73.56 352	68.28 386	85.44 37	88.18 145	94.64 64	70.93 237	81.33 343	71.25 221	92.03 242	94.20 96

UGNet		82.78 180	81.64 197	86.21 116	86.20 261	76.24 120	86.86 122	85.68 254	77.07 133	73.76 361	92.82 139	69.64 243	91.82 177	69.04 248	93.69 207	90.56 244
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		81.31 208	81.61 199	80.41 248	86.38 252	58.75 316	83.93 184	86.58 240	72.43 197	87.65 157	92.98 131	63.78 274	90.22 222	66.86 263	93.92 199	92.27 190

fmvsm_s_conf0.1_n		82.17 192	81.59 200	83.94 170	86.87 246	71.57 170	85.19 158	77.42 325	62.27 306	84.47 226	91.33 186	76.43 176	85.91 305	83.14 79	87.14 325	94.33 94

c3_l		81.64 204	81.59 200	81.79 226	80.86 344	59.15 309	78.61 288	90.18 179	68.36 244	87.20 162	87.11 283	69.39 244	91.62 179	78.16 142	94.43 185	94.60 79

MVSFormer		82.23 189	81.57 202	84.19 165	85.54 273	69.26 193	91.98 34	90.08 181	71.54 209	76.23 338	85.07 317	58.69 306	94.27 89	86.26 44	88.77 302	89.03 277

fmvsm_l_conf0.5_n		82.06 196	81.54 203	83.60 179	83.94 301	73.90 134	83.35 200	86.10 245	58.97 335	83.80 243	90.36 220	74.23 197	86.94 284	82.90 85	90.22 283	89.94 259

fmvsm_s_conf0.5_n_a		82.21 190	81.51 204	84.32 160	86.56 248	73.35 138	85.46 151	77.30 326	61.81 308	84.51 223	90.88 205	77.36 160	86.21 298	82.72 89	86.97 332	93.38 136

Fast-Effi-MVS+-dtu		82.54 185	81.41 205	85.90 123	85.60 271	76.53 115	83.07 208	89.62 193	73.02 190	79.11 316	83.51 332	80.74 129	90.24 221	68.76 251	89.29 294	90.94 230

sd_testset		79.95 236	81.39 206	75.64 315	88.81 192	58.07 320	76.16 327	82.81 291	73.67 172	83.41 251	93.04 127	80.96 126	77.65 364	58.62 329	95.03 162	91.21 223

fmvsm_s_conf0.5_n		81.91 201	81.30 207	83.75 174	86.02 266	71.56 171	84.73 164	77.11 329	62.44 303	84.00 239	90.68 212	76.42 177	85.89 307	83.14 79	87.11 326	93.81 119

Anonymous20240521		80.18 231	81.25 208	76.95 298	83.15 321	60.84 291	82.46 227	85.99 250	68.76 240	86.78 173	93.73 112	59.13 303	77.44 365	73.71 199	97.55 69	92.56 172

DELS-MVS		81.44 207	81.25 208	82.03 218	84.27 297	62.87 259	76.47 322	92.49 104	70.97 218	81.64 284	83.83 329	75.03 186	92.70 151	74.29 186	92.22 240	90.51 246
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		82.19 191	81.23 210	85.10 138	87.95 214	69.17 197	83.22 206	93.33 67	70.42 222	78.58 320	79.77 372	77.29 161	94.20 94	71.51 220	88.96 300	91.93 205

Anonymous202405211		80.51 220	81.19 211	78.49 274	88.48 202	57.26 328	76.63 317	82.49 293	81.21 80	84.30 233	92.24 162	67.99 252	86.24 296	62.22 305	95.13 157	91.98 204

BH-untuned		80.96 213	80.99 212	80.84 241	88.55 201	68.23 204	80.33 261	88.46 206	72.79 194	86.55 180	86.76 287	74.72 193	91.77 178	61.79 311	88.99 299	82.52 366

MG-MVS		80.32 226	80.94 213	78.47 275	88.18 208	52.62 364	82.29 232	85.01 268	72.01 207	79.24 315	92.54 149	69.36 245	93.36 132	70.65 229	89.19 297	89.45 265

PCF-MVS	74.62 15	82.15 194	80.92 214	85.84 125	89.43 177	72.30 158	80.53 258	91.82 125	57.36 349	87.81 153	89.92 233	77.67 156	93.63 115	58.69 328	95.08 160	91.58 217
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_l_conf0.5_n_a		81.46 206	80.87 215	83.25 190	83.73 306	73.21 143	83.00 211	85.59 256	58.22 341	82.96 259	90.09 231	72.30 224	86.65 290	81.97 100	89.95 287	89.88 260

GDP-MVS		82.17 192	80.85 216	86.15 120	88.65 197	68.95 199	85.65 149	93.02 87	68.42 243	83.73 244	89.54 239	45.07 381	94.31 88	79.66 123	93.87 201	95.19 63

Fast-Effi-MVS+		81.04 212	80.57 217	82.46 214	87.50 227	63.22 255	78.37 291	89.63 192	68.01 249	81.87 276	82.08 350	82.31 102	92.65 153	67.10 262	88.30 313	91.51 219

LFMVS		80.15 232	80.56 218	78.89 266	89.19 183	55.93 336	85.22 157	73.78 353	82.96 63	84.28 234	92.72 144	57.38 315	90.07 231	63.80 295	95.75 139	90.68 240

ab-mvs		79.67 237	80.56 218	76.99 297	88.48 202	56.93 330	84.70 166	86.06 247	68.95 238	80.78 295	93.08 126	75.30 184	84.62 319	56.78 338	90.90 269	89.43 267

PVSNet_Blended_VisFu		81.55 205	80.49 220	84.70 148	91.58 127	73.24 142	84.21 175	91.67 129	62.86 297	80.94 292	87.16 281	67.27 255	92.87 149	69.82 238	88.94 301	87.99 292

diffmvs		80.40 223	80.48 221	80.17 252	79.02 365	60.04 297	77.54 302	90.28 176	66.65 268	82.40 267	87.33 278	73.50 206	87.35 277	77.98 146	89.62 291	93.13 148
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	82.09 195	80.31 222	87.45 92	90.86 150	80.29 73	85.88 142	90.65 157	68.17 248	76.32 337	86.33 293	73.12 215	92.61 154	61.40 315	90.02 286	89.44 266
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		79.31 238	80.27 223	76.44 306	87.92 215	53.95 353	75.58 334	84.35 278	74.39 165	82.23 270	90.72 210	72.84 218	84.39 323	60.38 321	93.98 198	90.97 229

cl____		80.42 222	80.23 224	81.02 239	79.99 352	59.25 306	77.07 310	87.02 234	67.37 259	86.18 191	89.21 244	63.08 280	90.16 224	76.31 167	95.80 136	93.65 126

DIV-MVS_self_test		80.43 221	80.23 224	81.02 239	79.99 352	59.25 306	77.07 310	87.02 234	67.38 258	86.19 189	89.22 243	63.09 279	90.16 224	76.32 166	95.80 136	93.66 124

eth_miper_zixun_eth		80.84 214	80.22 226	82.71 207	81.41 336	60.98 289	77.81 297	90.14 180	67.31 261	86.95 172	87.24 280	64.26 269	92.31 162	75.23 180	91.61 253	94.85 74

BH-RMVSNet		80.53 219	80.22 226	81.49 231	87.19 234	66.21 226	77.79 298	86.23 243	74.21 166	83.69 245	88.50 255	73.25 214	90.75 208	63.18 301	87.90 317	87.52 299

xiu_mvs_v1_base_debu		80.84 214	80.14 228	82.93 202	88.31 205	71.73 165	79.53 270	87.17 226	65.43 280	79.59 308	82.73 344	76.94 168	90.14 227	73.22 206	88.33 309	86.90 307

xiu_mvs_v1_base		80.84 214	80.14 228	82.93 202	88.31 205	71.73 165	79.53 270	87.17 226	65.43 280	79.59 308	82.73 344	76.94 168	90.14 227	73.22 206	88.33 309	86.90 307

xiu_mvs_v1_base_debi		80.84 214	80.14 228	82.93 202	88.31 205	71.73 165	79.53 270	87.17 226	65.43 280	79.59 308	82.73 344	76.94 168	90.14 227	73.22 206	88.33 309	86.90 307

miper_ehance_all_eth		80.34 225	80.04 231	81.24 235	79.82 355	58.95 311	77.66 299	89.66 190	65.75 277	85.99 196	85.11 313	68.29 251	91.42 186	76.03 171	92.03 242	93.33 138

WB-MVS		76.06 276	80.01 232	64.19 387	89.96 170	20.58 430	72.18 361	68.19 387	83.21 59	86.46 187	93.49 117	70.19 241	78.97 359	65.96 272	90.46 282	93.02 153

MSDG		80.06 234	79.99 233	80.25 250	83.91 303	68.04 209	77.51 303	89.19 198	77.65 126	81.94 274	83.45 334	76.37 178	86.31 295	63.31 300	86.59 335	86.41 311

tttt0517		81.07 211	79.58 234	85.52 132	88.99 187	66.45 224	87.03 119	75.51 341	73.76 171	88.32 141	90.20 225	37.96 402	94.16 99	79.36 128	95.13 157	95.93 42

IterMVS-SCA-FT		80.64 218	79.41 235	84.34 159	83.93 302	69.66 188	76.28 324	81.09 305	72.43 197	86.47 186	90.19 226	60.46 291	93.15 138	77.45 153	86.39 338	90.22 251

patch_mono-2		78.89 241	79.39 236	77.41 294	84.78 285	68.11 207	75.60 332	83.11 287	60.96 322	79.36 312	89.89 234	75.18 185	72.97 378	73.32 205	92.30 234	91.15 225

wuyk23d		75.13 284	79.30 237	62.63 390	75.56 390	75.18 127	80.89 254	73.10 360	75.06 159	94.76 16	95.32 41	87.73 43	52.85 422	34.16 420	97.11 82	59.85 418

DPM-MVS		80.10 233	79.18 238	82.88 205	90.71 153	69.74 186	78.87 284	90.84 152	60.29 329	75.64 347	85.92 301	67.28 254	93.11 139	71.24 222	91.79 248	85.77 319

PM-MVS		80.20 230	79.00 239	83.78 173	88.17 209	86.66 19	81.31 246	66.81 396	69.64 231	88.33 140	90.19 226	64.58 267	83.63 331	71.99 219	90.03 285	81.06 386

mvsmamba		80.30 227	78.87 240	84.58 151	88.12 211	67.55 212	92.35 29	84.88 271	63.15 295	85.33 206	90.91 202	50.71 347	95.20 62	66.36 269	87.98 316	90.99 228

FE-MVS		79.98 235	78.86 241	83.36 187	86.47 249	66.45 224	89.73 70	84.74 275	72.80 193	84.22 237	91.38 185	44.95 382	93.60 119	63.93 293	91.50 256	90.04 258

test1111		78.53 248	78.85 242	77.56 291	92.22 103	47.49 389	82.61 220	69.24 384	72.43 197	85.28 207	94.20 85	51.91 341	90.07 231	65.36 281	96.45 103	95.11 65

AUN-MVS		81.18 210	78.78 243	88.39 79	90.93 147	82.14 62	82.51 226	83.67 283	64.69 289	80.29 302	85.91 302	51.07 345	92.38 159	76.29 168	93.63 209	90.65 242

mvs_anonymous		78.13 251	78.76 244	76.23 311	79.24 362	50.31 380	78.69 286	84.82 273	61.60 313	83.09 258	92.82 139	73.89 202	87.01 280	68.33 258	86.41 337	91.37 220

MAR-MVS		80.24 229	78.74 245	84.73 146	86.87 246	78.18 92	85.75 146	87.81 218	65.67 279	77.84 325	78.50 382	73.79 203	90.53 215	61.59 314	90.87 271	85.49 323
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		78.44 249	78.63 246	77.88 287	91.85 117	48.95 383	83.68 192	69.91 380	72.30 203	84.26 236	94.20 85	51.89 342	89.82 236	63.58 296	96.02 122	94.87 70

FMVSNet3		78.80 244	78.55 247	79.57 260	82.89 324	56.89 332	81.76 240	85.77 252	69.04 237	86.00 193	90.44 219	51.75 343	90.09 230	65.95 273	93.34 212	91.72 211

test_yl		78.71 246	78.51 248	79.32 263	84.32 295	58.84 313	78.38 289	85.33 259	75.99 142	82.49 265	86.57 289	58.01 309	90.02 233	62.74 302	92.73 229	89.10 274

DCV-MVSNet		78.71 246	78.51 248	79.32 263	84.32 295	58.84 313	78.38 289	85.33 259	75.99 142	82.49 265	86.57 289	58.01 309	90.02 233	62.74 302	92.73 229	89.10 274

EPNet		80.37 224	78.41 250	86.23 113	76.75 379	73.28 140	87.18 116	77.45 324	76.24 138	68.14 390	88.93 249	65.41 265	93.85 107	69.47 240	96.12 118	91.55 218
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPMNet		78.88 242	78.28 251	80.68 245	79.58 356	62.64 263	82.58 222	94.16 32	74.80 160	75.72 345	92.59 146	48.69 354	95.56 42	73.48 202	82.91 374	83.85 345

cl22		78.97 240	78.21 252	81.24 235	77.74 369	59.01 310	77.46 306	87.13 229	65.79 274	84.32 230	85.10 314	58.96 305	90.88 204	75.36 179	92.03 242	93.84 114

PAPR		78.84 243	78.10 253	81.07 237	85.17 280	60.22 296	82.21 236	90.57 161	62.51 299	75.32 351	84.61 322	74.99 187	92.30 163	59.48 326	88.04 315	90.68 240

PVSNet_BlendedMVS		78.80 244	77.84 254	81.65 228	84.43 291	63.41 251	79.49 273	90.44 164	61.70 311	75.43 348	87.07 284	69.11 247	91.44 184	60.68 319	92.24 238	90.11 256

Vis-MVSNet (Re-imp)		77.82 254	77.79 255	77.92 286	88.82 191	51.29 374	83.28 201	71.97 368	74.04 167	82.23 270	89.78 235	57.38 315	89.41 248	57.22 337	95.41 146	93.05 152

Patchmtry		76.56 271	77.46 256	73.83 326	79.37 361	46.60 393	82.41 229	76.90 330	73.81 170	85.56 203	92.38 153	48.07 357	83.98 328	63.36 299	95.31 152	90.92 231

OpenMVS_ROB	70.19 17	77.77 256	77.46 256	78.71 270	84.39 294	61.15 284	81.18 250	82.52 292	62.45 302	83.34 253	87.37 276	66.20 260	88.66 260	64.69 288	85.02 354	86.32 312

CL-MVSNet_self_test		76.81 266	77.38 258	75.12 318	86.90 244	51.34 372	73.20 356	80.63 309	68.30 246	81.80 280	88.40 256	66.92 257	80.90 345	55.35 350	94.90 168	93.12 150

thisisatest0530		79.07 239	77.33 259	84.26 162	87.13 235	64.58 239	83.66 193	75.95 336	68.86 239	85.22 208	87.36 277	38.10 399	93.57 123	75.47 177	94.28 189	94.62 78

MonoMVSNet		76.66 268	77.26 260	74.86 320	79.86 354	54.34 350	86.26 137	86.08 246	71.08 217	85.59 201	88.68 252	53.95 333	85.93 303	63.86 294	80.02 390	84.32 336

CANet_DTU		77.81 255	77.05 261	80.09 253	81.37 337	59.90 300	83.26 202	88.29 211	69.16 235	67.83 393	83.72 330	60.93 288	89.47 243	69.22 244	89.70 290	90.88 233

pmmvs-eth3d		78.42 250	77.04 262	82.57 212	87.44 229	74.41 131	80.86 255	79.67 313	55.68 358	84.69 221	90.31 223	60.91 289	85.42 312	62.20 306	91.59 254	87.88 295

miper_enhance_ethall		77.83 253	76.93 263	80.51 246	76.15 386	58.01 322	75.47 336	88.82 201	58.05 343	83.59 247	80.69 360	64.41 268	91.20 190	73.16 212	92.03 242	92.33 186

MDA-MVSNet-bldmvs		77.47 258	76.90 264	79.16 265	79.03 364	64.59 238	66.58 395	75.67 339	73.15 188	88.86 124	88.99 248	66.94 256	81.23 344	64.71 287	88.22 314	91.64 215

xiu_mvs_v2_base		77.19 261	76.75 265	78.52 273	87.01 241	61.30 282	75.55 335	87.12 232	61.24 319	74.45 356	78.79 380	77.20 162	90.93 200	64.62 290	84.80 361	83.32 354

USDC		76.63 269	76.73 266	76.34 308	83.46 309	57.20 329	80.02 264	88.04 216	52.14 381	83.65 246	91.25 188	63.24 277	86.65 290	54.66 355	94.11 194	85.17 325

PS-MVSNAJ		77.04 263	76.53 267	78.56 272	87.09 239	61.40 280	75.26 337	87.13 229	61.25 318	74.38 358	77.22 394	76.94 168	90.94 199	64.63 289	84.83 360	83.35 353

TAMVS		78.08 252	76.36 268	83.23 191	90.62 154	72.87 144	79.08 280	80.01 312	61.72 310	81.35 288	86.92 286	63.96 273	88.78 258	50.61 376	93.01 222	88.04 291

IterMVS		76.91 264	76.34 269	78.64 271	80.91 342	64.03 245	76.30 323	79.03 316	64.88 288	83.11 256	89.16 245	59.90 297	84.46 321	68.61 254	85.15 352	87.42 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		74.44 295	76.19 270	69.21 360	84.61 289	52.43 365	71.70 364	77.18 328	60.73 325	80.60 296	90.96 200	75.44 181	69.35 390	56.13 343	88.33 309	85.86 318

miper_lstm_enhance		76.45 273	76.10 271	77.51 292	76.72 380	60.97 290	64.69 399	85.04 266	63.98 292	83.20 255	88.22 258	56.67 319	78.79 361	73.22 206	93.12 219	92.78 161

BH-w/o		76.57 270	76.07 272	78.10 282	86.88 245	65.92 229	77.63 300	86.33 241	65.69 278	80.89 293	79.95 369	68.97 249	90.74 209	53.01 366	85.25 349	77.62 397

TR-MVS		76.77 267	75.79 273	79.72 257	86.10 265	65.79 230	77.14 308	83.02 288	65.20 286	81.40 287	82.10 348	66.30 259	90.73 210	55.57 347	85.27 348	82.65 361

jason		77.42 259	75.75 274	82.43 215	87.10 238	69.27 192	77.99 294	81.94 298	51.47 385	77.84 325	85.07 317	60.32 293	89.00 252	70.74 228	89.27 296	89.03 277
jason: jason.
MVSTER		77.09 262	75.70 275	81.25 233	75.27 394	61.08 285	77.49 305	85.07 264	60.78 324	86.55 180	88.68 252	43.14 391	90.25 219	73.69 200	90.67 277	92.42 179

D2MVS		76.84 265	75.67 276	80.34 249	80.48 350	62.16 275	73.50 353	84.80 274	57.61 347	82.24 269	87.54 272	51.31 344	87.65 273	70.40 233	93.19 218	91.23 222

PVSNet_Blended		76.49 272	75.40 277	79.76 256	84.43 291	63.41 251	75.14 338	90.44 164	57.36 349	75.43 348	78.30 383	69.11 247	91.44 184	60.68 319	87.70 321	84.42 335

CDS-MVSNet		77.32 260	75.40 277	83.06 195	89.00 186	72.48 155	77.90 296	82.17 296	60.81 323	78.94 317	83.49 333	59.30 301	88.76 259	54.64 356	92.37 233	87.93 294
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres600view7		75.97 277	75.35 279	77.85 289	87.01 241	51.84 370	80.45 259	73.26 358	75.20 157	83.10 257	86.31 295	45.54 372	89.05 251	55.03 353	92.24 238	92.66 167

test_fmvs3		75.72 280	75.20 280	77.27 295	75.01 397	69.47 190	78.93 281	84.88 271	46.67 399	87.08 168	87.84 266	50.44 350	71.62 383	77.42 155	88.53 305	90.72 237

thres100view900		75.45 281	75.05 281	76.66 304	87.27 231	51.88 369	81.07 251	73.26 358	75.68 148	83.25 254	86.37 292	45.54 372	88.80 255	51.98 371	90.99 264	89.31 269

cascas		76.29 275	74.81 282	80.72 244	84.47 290	62.94 257	73.89 350	87.34 222	55.94 356	75.16 353	76.53 399	63.97 272	91.16 192	65.00 284	90.97 267	88.06 290

GA-MVS		75.83 278	74.61 283	79.48 262	81.87 329	59.25 306	73.42 354	82.88 289	68.68 241	79.75 307	81.80 353	50.62 348	89.46 244	66.85 264	85.64 345	89.72 262

testgi		72.36 311	74.61 283	65.59 381	80.56 349	42.82 408	68.29 384	73.35 357	66.87 266	81.84 277	89.93 232	72.08 228	66.92 404	46.05 398	92.54 231	87.01 306

test20.03		73.75 300	74.59 285	71.22 347	81.11 340	51.12 376	70.15 377	72.10 367	70.42 222	80.28 304	91.50 182	64.21 270	74.72 376	46.96 395	94.58 181	87.82 297

lupinMVS		76.37 274	74.46 286	82.09 217	85.54 273	69.26 193	76.79 313	80.77 308	50.68 392	76.23 338	82.82 342	58.69 306	88.94 253	69.85 237	88.77 302	88.07 288

EU-MVSNet		75.12 285	74.43 287	77.18 296	83.11 322	59.48 304	85.71 148	82.43 294	39.76 419	85.64 200	88.76 250	44.71 384	87.88 271	73.86 196	85.88 344	84.16 341

tfpn200view9		74.86 289	74.23 288	76.74 303	86.24 259	52.12 366	79.24 277	73.87 351	73.34 181	81.82 278	84.60 323	46.02 365	88.80 255	51.98 371	90.99 264	89.31 269

thres400		75.14 283	74.23 288	77.86 288	86.24 259	52.12 366	79.24 277	73.87 351	73.34 181	81.82 278	84.60 323	46.02 365	88.80 255	51.98 371	90.99 264	92.66 167

ppachtmachnet_test		74.73 292	74.00 290	76.90 300	80.71 347	56.89 332	71.53 367	78.42 318	58.24 340	79.32 314	82.92 341	57.91 312	84.26 325	65.60 279	91.36 258	89.56 264

1112_ss		74.82 290	73.74 291	78.04 284	89.57 172	60.04 297	76.49 321	87.09 233	54.31 366	73.66 362	79.80 370	60.25 294	86.76 289	58.37 330	84.15 365	87.32 302

Patchmatch-RL test		74.48 293	73.68 292	76.89 301	84.83 284	66.54 222	72.29 360	69.16 385	57.70 345	86.76 174	86.33 293	45.79 371	82.59 335	69.63 239	90.65 280	81.54 377

CMPMVS	59.41 20	75.12 285	73.57 293	79.77 255	75.84 389	67.22 213	81.21 249	82.18 295	50.78 390	76.50 334	87.66 270	55.20 329	82.99 334	62.17 308	90.64 281	89.09 276
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		73.26 303	73.54 294	72.43 340	84.92 283	47.79 388	79.89 266	74.00 349	65.93 271	78.81 318	86.28 296	56.36 321	81.63 342	56.63 339	79.04 397	87.87 296

MVP-Stereo		75.81 279	73.51 295	82.71 207	89.35 178	73.62 135	80.06 262	85.20 261	60.30 328	73.96 359	87.94 263	57.89 313	89.45 245	52.02 370	74.87 408	85.06 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test2506		74.12 296	73.39 296	76.28 309	91.85 117	44.20 403	84.06 179	48.20 426	72.30 203	81.90 275	94.20 85	27.22 426	89.77 239	64.81 286	96.02 122	94.87 70

new-patchmatchnet		70.10 332	73.37 297	60.29 398	81.23 339	16.95 433	59.54 409	74.62 344	62.93 296	80.97 290	87.93 264	62.83 283	71.90 381	55.24 351	95.01 165	92.00 202

reproduce_monomvs		74.09 297	73.23 298	76.65 305	76.52 381	54.54 348	77.50 304	81.40 303	65.85 273	82.86 262	86.67 288	27.38 424	84.53 320	70.24 234	90.66 279	90.89 232

PatchMatch-RL		74.48 293	73.22 299	78.27 280	87.70 220	85.26 38	75.92 330	70.09 378	64.34 290	76.09 341	81.25 358	65.87 263	78.07 363	53.86 358	83.82 367	71.48 406

Test_1112_low_res		73.90 299	73.08 300	76.35 307	90.35 159	55.95 335	73.40 355	86.17 244	50.70 391	73.14 363	85.94 300	58.31 308	85.90 306	56.51 340	83.22 371	87.20 304

CR-MVSNet		74.00 298	73.04 301	76.85 302	79.58 356	62.64 263	82.58 222	76.90 330	50.50 393	75.72 345	92.38 153	48.07 357	84.07 327	68.72 253	82.91 374	83.85 345

pmmvs4		74.92 288	72.98 302	80.73 243	84.95 282	71.71 168	76.23 325	77.59 323	52.83 375	77.73 329	86.38 291	56.35 322	84.97 316	57.72 336	87.05 328	85.51 322

test_fmvs2		73.57 301	72.80 303	75.90 313	72.74 411	68.84 200	77.07 310	84.32 279	45.14 405	82.89 260	84.22 326	48.37 355	70.36 387	73.40 204	87.03 329	88.52 283

ET-MVSNet_ETH3D		75.28 282	72.77 304	82.81 206	83.03 323	68.11 207	77.09 309	76.51 334	60.67 326	77.60 330	80.52 364	38.04 400	91.15 193	70.78 226	90.68 276	89.17 272

PatchT		70.52 328	72.76 305	63.79 389	79.38 360	33.53 423	77.63 300	65.37 400	73.61 174	71.77 370	92.79 142	44.38 385	75.65 372	64.53 291	85.37 347	82.18 370

HyFIR lowres test		75.12 285	72.66 306	82.50 213	91.44 135	65.19 235	72.47 359	87.31 223	46.79 398	80.29 302	84.30 325	52.70 338	92.10 169	51.88 375	86.73 333	90.22 251

MVS		73.21 305	72.59 307	75.06 319	80.97 341	60.81 292	81.64 243	85.92 251	46.03 403	71.68 371	77.54 389	68.47 250	89.77 239	55.70 346	85.39 346	74.60 403

SCA		73.32 302	72.57 308	75.58 316	81.62 333	55.86 338	78.89 283	71.37 373	61.73 309	74.93 354	83.42 335	60.46 291	87.01 280	58.11 334	82.63 379	83.88 342

1314		73.22 304	72.56 309	75.20 317	80.41 351	57.84 323	81.64 243	85.36 258	51.68 384	73.10 364	76.65 398	61.45 286	85.19 314	63.54 297	79.21 395	82.59 362

HY-MVS	64.64 18	73.03 306	72.47 310	74.71 322	83.36 314	54.19 351	82.14 239	81.96 297	56.76 355	69.57 385	86.21 297	60.03 295	84.83 318	49.58 382	82.65 377	85.11 326

UnsupCasMVSNet_eth		71.63 319	72.30 311	69.62 357	76.47 383	52.70 363	70.03 378	80.97 306	59.18 334	79.36 312	88.21 259	60.50 290	69.12 391	58.33 332	77.62 402	87.04 305

FPMVS		72.29 313	72.00 312	73.14 331	88.63 198	85.00 40	74.65 343	67.39 390	71.94 208	77.80 327	87.66 270	50.48 349	75.83 371	49.95 378	79.51 391	58.58 420

Anonymous20231206		71.38 322	71.88 313	69.88 354	86.31 256	54.37 349	70.39 375	74.62 344	52.57 377	76.73 333	88.76 250	59.94 296	72.06 380	44.35 402	93.23 217	83.23 356

FMVSNet5		72.10 314	71.69 314	73.32 329	81.57 334	53.02 360	76.77 314	78.37 319	63.31 293	76.37 335	91.85 170	36.68 404	78.98 358	47.87 391	92.45 232	87.95 293

our_test_3		71.85 315	71.59 315	72.62 337	80.71 347	53.78 354	69.72 379	71.71 372	58.80 337	78.03 322	80.51 365	56.61 320	78.84 360	62.20 306	86.04 343	85.23 324

MIMVSNet		71.09 324	71.59 315	69.57 358	87.23 232	50.07 381	78.91 282	71.83 369	60.20 331	71.26 372	91.76 176	55.08 331	76.09 369	41.06 407	87.02 330	82.54 365

test_vis1_n_1920		71.30 323	71.58 317	70.47 350	77.58 372	59.99 299	74.25 344	84.22 280	51.06 387	74.85 355	79.10 376	55.10 330	68.83 393	68.86 250	79.20 396	82.58 363

thres200		72.34 312	71.55 318	74.70 323	83.48 308	51.60 371	75.02 339	73.71 354	70.14 228	78.56 321	80.57 363	46.20 363	88.20 267	46.99 394	89.29 294	84.32 336

CVMVSNet		72.62 309	71.41 319	76.28 309	83.25 317	60.34 295	83.50 196	79.02 317	37.77 423	76.33 336	85.10 314	49.60 353	87.41 276	70.54 231	77.54 403	81.08 384

EPNet_dtu		72.87 308	71.33 320	77.49 293	77.72 370	60.55 294	82.35 230	75.79 337	66.49 269	58.39 421	81.06 359	53.68 334	85.98 302	53.55 361	92.97 224	85.95 316
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing3		71.53 320	70.79 321	73.77 327	88.89 190	41.86 410	76.60 320	59.12 415	72.83 192	80.97 290	82.08 350	19.80 432	87.33 278	65.12 283	91.68 252	92.13 197

ttmdpeth		71.72 317	70.67 322	74.86 320	73.08 408	55.88 337	77.41 307	69.27 383	55.86 357	78.66 319	93.77 110	38.01 401	75.39 373	60.12 322	89.87 288	93.31 140

test_vis3_rt		71.42 321	70.67 322	73.64 328	69.66 418	70.46 178	66.97 394	89.73 187	42.68 415	88.20 144	83.04 337	43.77 386	60.07 416	65.35 282	86.66 334	90.39 249

CHOSEN 1792x2688		72.45 310	70.56 324	78.13 281	90.02 169	63.08 256	68.72 383	83.16 286	42.99 413	75.92 343	85.46 307	57.22 317	85.18 315	49.87 380	81.67 381	86.14 314

thisisatest0515		73.00 307	70.52 325	80.46 247	81.45 335	59.90 300	73.16 357	74.31 348	57.86 344	76.08 342	77.78 386	37.60 403	92.12 168	65.00 284	91.45 257	89.35 268

YYNet1		70.06 333	70.44 326	68.90 362	73.76 401	53.42 358	58.99 412	67.20 392	58.42 339	87.10 166	85.39 310	59.82 298	67.32 401	59.79 324	83.50 370	85.96 315

MDA-MVSNet_test_wron		70.05 334	70.44 326	68.88 363	73.84 400	53.47 356	58.93 413	67.28 391	58.43 338	87.09 167	85.40 309	59.80 299	67.25 402	59.66 325	83.54 369	85.92 317

test_fmvs1_n		70.94 325	70.41 328	72.53 339	73.92 399	66.93 219	75.99 329	84.21 281	43.31 412	79.40 311	79.39 374	43.47 387	68.55 395	69.05 247	84.91 357	82.10 371

MS-PatchMatch		70.93 326	70.22 329	73.06 332	81.85 330	62.50 266	73.82 351	77.90 320	52.44 378	75.92 343	81.27 357	55.67 326	81.75 340	55.37 349	77.70 401	74.94 402

pmmvs5		70.73 327	70.07 330	72.72 335	77.03 377	52.73 362	74.14 345	75.65 340	50.36 394	72.17 369	85.37 311	55.42 328	80.67 347	52.86 367	87.59 322	84.77 329

PAPM		71.77 316	70.06 331	76.92 299	86.39 251	53.97 352	76.62 318	86.62 239	53.44 370	63.97 410	84.73 321	57.79 314	92.34 161	39.65 410	81.33 385	84.45 334

Syy-MVS		69.40 342	70.03 332	67.49 372	81.72 331	38.94 415	71.00 369	61.99 406	61.38 315	70.81 376	72.36 410	61.37 287	79.30 356	64.50 292	85.18 350	84.22 338

test_vis1_n		70.29 329	69.99 333	71.20 348	75.97 388	66.50 223	76.69 316	80.81 307	44.22 408	75.43 348	77.23 393	50.00 351	68.59 394	66.71 267	82.85 376	78.52 396

EGC-MVSNET		74.79 291	69.99 333	89.19 65	94.89 38	87.00 15	91.89 37	86.28 242	1.09 428	2.23 430	95.98 27	81.87 116	89.48 242	79.76 120	95.96 125	91.10 226

UnsupCasMVSNet_bld		69.21 344	69.68 335	67.82 370	79.42 359	51.15 375	67.82 388	75.79 337	54.15 367	77.47 331	85.36 312	59.26 302	70.64 386	48.46 388	79.35 393	81.66 375

tpmvs		70.16 331	69.56 336	71.96 343	74.71 398	48.13 385	79.63 268	75.45 342	65.02 287	70.26 380	81.88 352	45.34 377	85.68 310	58.34 331	75.39 407	82.08 372

MVStest1		70.05 334	69.26 337	72.41 341	58.62 430	55.59 341	76.61 319	65.58 398	53.44 370	89.28 120	93.32 120	22.91 430	71.44 385	74.08 192	89.52 292	90.21 255

test_cas_vis1_n_1920		69.20 345	69.12 338	69.43 359	73.68 402	62.82 260	70.38 376	77.21 327	46.18 402	80.46 301	78.95 378	52.03 340	65.53 409	65.77 278	77.45 404	79.95 392

gg-mvs-nofinetune		68.96 346	69.11 339	68.52 368	76.12 387	45.32 399	83.59 194	55.88 420	86.68 29	64.62 409	97.01 9	30.36 417	83.97 329	44.78 401	82.94 373	76.26 399

test_fmvs1		69.57 340	69.05 340	71.14 349	69.15 419	65.77 231	73.98 348	83.32 285	42.83 414	77.77 328	78.27 384	43.39 390	68.50 396	68.39 257	84.38 364	79.15 394

WB-MVSnew		68.72 348	69.01 341	67.85 369	83.22 319	43.98 404	74.93 340	65.98 397	55.09 360	73.83 360	79.11 375	65.63 264	71.89 382	38.21 415	85.04 353	87.69 298

testing91		69.94 337	68.99 342	72.80 334	83.81 305	45.89 396	71.57 366	73.64 356	68.24 247	70.77 378	77.82 385	34.37 407	84.44 322	53.64 360	87.00 331	88.07 288

IB-MVS	62.13 19	71.64 318	68.97 343	79.66 259	80.80 346	62.26 272	73.94 349	76.90 330	63.27 294	68.63 389	76.79 396	33.83 408	91.84 176	59.28 327	87.26 323	84.88 328
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		69.71 339	68.83 344	72.33 342	77.66 371	53.60 355	79.29 275	69.99 379	57.66 346	72.53 367	82.93 340	46.45 362	80.08 353	60.91 318	72.09 411	83.31 355
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		70.20 330	68.80 345	74.38 324	80.91 342	84.81 43	59.12 411	76.45 335	55.06 361	75.31 352	82.36 347	55.74 325	54.82 421	47.02 393	87.24 324	83.52 349

CostFormer		69.98 336	68.68 346	73.87 325	77.14 375	50.72 378	79.26 276	74.51 346	51.94 383	70.97 375	84.75 320	45.16 380	87.49 275	55.16 352	79.23 394	83.40 352

WBMVS		68.76 347	68.43 347	69.75 356	83.29 315	40.30 413	67.36 390	72.21 366	57.09 352	77.05 332	85.53 305	33.68 409	80.51 349	48.79 386	90.90 269	88.45 284

WTY-MVS		67.91 351	68.35 348	66.58 377	80.82 345	48.12 386	65.96 396	72.60 361	53.67 369	71.20 373	81.68 355	58.97 304	69.06 392	48.57 387	81.67 381	82.55 364

MDTV_nov1_ep13			68.29 349		78.03 368	43.87 405	74.12 346	72.22 365	52.17 379	67.02 396	85.54 304	45.36 376	80.85 346	55.73 344	84.42 363

testing99		69.27 343	68.15 350	72.63 336	83.29 315	45.45 398	71.15 368	71.08 374	67.34 260	70.43 379	77.77 387	32.24 413	84.35 324	53.72 359	86.33 339	88.10 287

tpm		67.95 350	68.08 351	67.55 371	78.74 367	43.53 406	75.60 332	67.10 395	54.92 362	72.23 368	88.10 260	42.87 392	75.97 370	52.21 369	80.95 389	83.15 357

Patchmatch-test		65.91 363	67.38 352	61.48 395	75.51 391	43.21 407	68.84 382	63.79 404	62.48 300	72.80 366	83.42 335	44.89 383	59.52 418	48.27 390	86.45 336	81.70 374

sss		66.92 355	67.26 353	65.90 379	77.23 374	51.10 377	64.79 398	71.72 371	52.12 382	70.13 381	80.18 367	57.96 311	65.36 410	50.21 377	81.01 387	81.25 381

dmvs_re		66.81 358	66.98 354	66.28 378	76.87 378	58.68 317	71.66 365	72.24 364	60.29 329	69.52 386	73.53 407	52.38 339	64.40 412	44.90 400	81.44 384	75.76 400

baseline2		69.77 338	66.89 355	78.41 276	79.51 358	58.09 319	76.23 325	69.57 381	57.50 348	64.82 408	77.45 391	46.02 365	88.44 262	53.08 363	77.83 399	88.70 281

tpm2		68.45 349	66.83 356	73.30 330	78.93 366	48.50 384	79.76 267	71.76 370	47.50 397	69.92 382	83.60 331	42.07 393	88.40 263	48.44 389	79.51 391	83.01 359

test-LLR		67.21 353	66.74 357	68.63 366	76.45 384	55.21 344	67.89 385	67.14 393	62.43 304	65.08 405	72.39 408	43.41 388	69.37 388	61.00 316	84.89 358	81.31 379

tpmrst		66.28 362	66.69 358	65.05 385	72.82 410	39.33 414	78.20 292	70.69 377	53.16 373	67.88 392	80.36 366	48.18 356	74.75 375	58.13 333	70.79 413	81.08 384

JIA-IIPM		69.41 341	66.64 359	77.70 290	73.19 405	71.24 173	75.67 331	65.56 399	70.42 222	65.18 404	92.97 133	33.64 410	83.06 332	53.52 362	69.61 417	78.79 395

testing11		67.38 352	65.93 360	71.73 345	83.37 313	46.60 393	70.95 371	69.40 382	62.47 301	66.14 397	76.66 397	31.22 414	84.10 326	49.10 384	84.10 366	84.49 332

myMVS_eth3d28		65.83 365	65.85 361	65.78 380	83.42 311	35.71 421	67.29 391	68.01 388	67.58 257	69.80 383	77.72 388	32.29 412	74.30 377	37.49 416	89.06 298	87.32 302

test_f		64.31 373	65.85 361	59.67 399	66.54 423	62.24 274	57.76 415	70.96 375	40.13 417	84.36 228	82.09 349	46.93 359	51.67 423	61.99 309	81.89 380	65.12 414

KD-MVS_2432*1600		66.87 356	65.81 363	70.04 352	67.50 420	47.49 389	62.56 403	79.16 314	61.21 320	77.98 323	80.61 361	25.29 428	82.48 336	53.02 364	84.92 355	80.16 390

miper_refine_blended		66.87 356	65.81 363	70.04 352	67.50 420	47.49 389	62.56 403	79.16 314	61.21 320	77.98 323	80.61 361	25.29 428	82.48 336	53.02 364	84.92 355	80.16 390

PVSNet	58.17 21	66.41 361	65.63 365	68.75 364	81.96 328	49.88 382	62.19 405	72.51 363	51.03 388	68.04 391	75.34 404	50.84 346	74.77 374	45.82 399	82.96 372	81.60 376

UWE-MVS		66.43 360	65.56 366	69.05 361	84.15 299	40.98 411	73.06 358	64.71 402	54.84 363	76.18 340	79.62 373	29.21 419	80.50 350	38.54 414	89.75 289	85.66 320

testing222		66.93 354	65.30 367	71.81 344	83.38 312	45.83 397	72.06 362	67.50 389	64.12 291	69.68 384	76.37 400	27.34 425	83.00 333	38.88 411	88.38 308	86.62 310

tpm cat1		66.76 359	65.21 368	71.42 346	77.09 376	50.62 379	78.01 293	73.68 355	44.89 406	68.64 388	79.00 377	45.51 374	82.42 338	49.91 379	70.15 414	81.23 383

test0.0.03 1		64.66 370	64.36 369	65.57 382	75.03 396	46.89 392	64.69 399	61.58 412	62.43 304	71.18 374	77.54 389	43.41 388	68.47 397	40.75 409	82.65 377	81.35 378

test_vis1_rt		65.64 366	64.09 370	70.31 351	66.09 424	70.20 182	61.16 406	81.60 301	38.65 420	72.87 365	69.66 413	52.84 336	60.04 417	56.16 342	77.77 400	80.68 388

myMVS_eth3d		64.66 370	63.89 371	66.97 375	81.72 331	37.39 418	71.00 369	61.99 406	61.38 315	70.81 376	72.36 410	20.96 431	79.30 356	49.59 381	85.18 350	84.22 338

test-mter		65.00 368	63.79 372	68.63 366	76.45 384	55.21 344	67.89 385	67.14 393	50.98 389	65.08 405	72.39 408	28.27 422	69.37 388	61.00 316	84.89 358	81.31 379

ADS-MVSNet2		65.87 364	63.64 373	72.55 338	73.16 406	56.92 331	67.10 392	74.81 343	49.74 395	66.04 399	82.97 338	46.71 360	77.26 366	42.29 404	69.96 415	83.46 350

UBG		64.34 372	63.35 374	67.30 373	83.50 307	40.53 412	67.46 389	65.02 401	54.77 364	67.54 395	74.47 406	32.99 411	78.50 362	40.82 408	83.58 368	82.88 360

ETVMVS		64.67 369	63.34 375	68.64 365	83.44 310	41.89 409	69.56 381	61.70 411	61.33 317	68.74 387	75.76 402	28.76 420	79.35 355	34.65 419	86.16 342	84.67 331

mvsany_test3		65.48 367	62.97 376	73.03 333	69.99 417	76.17 121	64.83 397	43.71 428	43.68 410	80.25 305	87.05 285	52.83 337	63.09 415	51.92 374	72.44 410	79.84 393

MVS-HIRNet		61.16 380	62.92 377	55.87 402	79.09 363	35.34 422	71.83 363	57.98 419	46.56 400	59.05 418	91.14 192	49.95 352	76.43 368	38.74 412	71.92 412	55.84 421

EPMVS		62.47 374	62.63 378	62.01 391	70.63 416	38.74 416	74.76 341	52.86 422	53.91 368	67.71 394	80.01 368	39.40 397	66.60 405	55.54 348	68.81 419	80.68 388

dmvs_testset		60.59 384	62.54 379	54.72 404	77.26 373	27.74 427	74.05 347	61.00 413	60.48 327	65.62 402	67.03 417	55.93 324	68.23 399	32.07 423	69.46 418	68.17 411

ADS-MVSNet		61.90 376	62.19 380	61.03 396	73.16 406	36.42 420	67.10 392	61.75 409	49.74 395	66.04 399	82.97 338	46.71 360	63.21 413	42.29 404	69.96 415	83.46 350

E-PMN		61.59 378	61.62 381	61.49 394	66.81 422	55.40 342	53.77 418	60.34 414	66.80 267	58.90 419	65.50 418	40.48 396	66.12 407	55.72 345	86.25 340	62.95 416

DSMNet-mixed		60.98 382	61.61 382	59.09 401	72.88 409	45.05 401	74.70 342	46.61 427	26.20 425	65.34 403	90.32 222	55.46 327	63.12 414	41.72 406	81.30 386	69.09 410

EMVS		61.10 381	60.81 383	61.99 392	65.96 425	55.86 338	53.10 419	58.97 417	67.06 264	56.89 423	63.33 419	40.98 394	67.03 403	54.79 354	86.18 341	63.08 415

PMMVS		61.65 377	60.38 384	65.47 383	65.40 427	69.26 193	63.97 401	61.73 410	36.80 424	60.11 416	68.43 415	59.42 300	66.35 406	48.97 385	78.57 398	60.81 417

TESTMET0.1,1		61.29 379	60.32 385	64.19 387	72.06 412	51.30 373	67.89 385	62.09 405	45.27 404	60.65 415	69.01 414	27.93 423	64.74 411	56.31 341	81.65 383	76.53 398

dp		60.70 383	60.29 386	61.92 393	72.04 413	38.67 417	70.83 372	64.08 403	51.28 386	60.75 414	77.28 392	36.59 405	71.58 384	47.41 392	62.34 421	75.52 401

pmmvs3		62.47 374	60.02 387	69.80 355	71.58 414	64.00 246	70.52 374	58.44 418	39.77 418	66.05 398	75.84 401	27.10 427	72.28 379	46.15 397	84.77 362	73.11 404

PMMVS2		55.64 390	59.27 388	44.74 406	64.30 428	12.32 434	40.60 421	49.79 424	53.19 372	65.06 407	84.81 319	53.60 335	49.76 424	32.68 422	89.41 293	72.15 405

UWE-MVS-28		58.44 387	57.71 389	60.65 397	73.58 403	31.23 424	69.68 380	48.80 425	53.12 374	61.79 412	78.83 379	30.98 415	68.40 398	21.58 426	80.99 388	82.33 369

new_pmnet		55.69 389	57.66 390	49.76 405	75.47 392	30.59 425	59.56 408	51.45 423	43.62 411	62.49 411	75.48 403	40.96 395	49.15 425	37.39 417	72.52 409	69.55 409

CHOSEN 280x420		59.08 385	56.52 391	66.76 376	76.51 382	64.39 242	49.62 420	59.00 416	43.86 409	55.66 424	68.41 416	35.55 406	68.21 400	43.25 403	76.78 406	67.69 412

mvsany_test1		58.48 386	56.47 392	64.50 386	65.90 426	68.21 206	56.95 416	42.11 429	38.30 421	65.69 401	77.19 395	56.96 318	59.35 419	46.16 396	58.96 422	65.93 413

PVSNet_0	51.08 22	56.10 388	54.97 393	59.48 400	75.12 395	53.28 359	55.16 417	61.89 408	44.30 407	59.16 417	62.48 420	54.22 332	65.91 408	35.40 418	47.01 423	59.25 419

MVE	40.22 23	51.82 391	50.47 394	55.87 402	62.66 429	51.91 368	31.61 423	39.28 430	40.65 416	50.76 425	74.98 405	56.24 323	44.67 426	33.94 421	64.11 420	71.04 408
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 392	42.65 395	39.67 407	70.86 415	21.11 429	61.01 407	21.42 434	57.36 349	57.97 422	50.06 423	16.40 433	58.73 420	21.03 427	27.69 427	39.17 423

kuosan		30.83 393	32.17 396	26.83 409	53.36 431	19.02 432	57.90 414	20.44 435	38.29 422	38.01 426	37.82 425	15.18 434	33.45 428	7.74 429	20.76 428	28.03 424

test_method		30.46 394	29.60 397	33.06 408	17.99 433	3.84 436	13.62 424	73.92 350	2.79 427	18.29 429	53.41 422	28.53 421	43.25 427	22.56 424	35.27 425	52.11 422

cdsmvs_eth3d_5k		20.81 395	27.75 398	0.00 414	0.00 437	0.00 439	0.00 425	85.44 257	0.00 432	0.00 433	82.82 342	81.46 120	0.00 433	0.00 432	0.00 431	0.00 429

tmp_tt		20.25 396	24.50 399	7.49 411	4.47 434	8.70 435	34.17 422	25.16 432	1.00 429	32.43 428	18.49 426	39.37 398	9.21 430	21.64 425	43.75 424	4.57 426

ab-mvs-re		6.65 397	8.87 400	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	79.80 370	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

pcd_1.5k_mvsjas		6.41 398	8.55 401	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	76.94 168	0.00 433	0.00 432	0.00 431	0.00 429

test123		6.27 399	8.08 402	0.84 412	1.11 436	0.57 437	62.90 402	0.82 436	0.54 430	1.07 432	2.75 431	1.26 435	0.30 431	1.04 430	1.26 430	1.66 427

testmvs		5.91 400	7.65 403	0.72 413	1.20 435	0.37 438	59.14 410	0.67 437	0.49 431	1.11 431	2.76 430	0.94 436	0.24 432	1.02 431	1.47 429	1.55 428

mmdepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

monomultidepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

test_blank		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet_test		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

DCPMVS		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet-low-res		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uncertanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

Regformer		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

WAC-MVS						37.39 418								52.61 368

FOURS1					96.08 12	87.41 14	96.19 2	95.83 5	92.95 3	96.57 3

MSC_two_6792asdad				88.81 71	91.55 129	77.99 94		91.01 148					96.05 9	87.45 24	98.17 35	92.40 182

PC_three_1452									58.96 336	90.06 97	91.33 186	80.66 130	93.03 143	75.78 173	95.94 128	92.48 176

No_MVS				88.81 71	91.55 129	77.99 94		91.01 148					96.05 9	87.45 24	98.17 35	92.40 182

test_one_0601					93.85 62	73.27 141		94.11 38	86.57 30	93.47 41	94.64 64	88.42 28

eth-test2					0.00 437

eth-test					0.00 437

ZD-MVS					92.22 103	80.48 71		91.85 123	71.22 215	90.38 92	92.98 131	86.06 64	96.11 7	81.99 99	96.75 92

IU-MVS					94.18 50	72.64 148		90.82 153	56.98 353	89.67 109				85.78 55	97.92 49	93.28 141

OPU-MVS				88.27 82	91.89 115	77.83 97	90.47 55				91.22 189	81.12 124	94.68 76	74.48 185	95.35 148	92.29 188

test_241102_TWO								93.71 55	83.77 52	93.49 39	94.27 79	89.27 23	95.84 24	86.03 50	97.82 54	92.04 200

test_241102_ONE					94.18 50	72.65 146		93.69 56	83.62 54	94.11 26	93.78 108	90.28 14	95.50 49

save fliter					93.75 63	77.44 103	86.31 135	89.72 188	70.80 219

test_0728_THIRD									85.33 38	93.75 34	94.65 61	87.44 46	95.78 32	87.41 26	98.21 32	92.98 156

test_0728_SECOND				86.79 102	94.25 48	72.45 156	90.54 52	94.10 39					95.88 18	86.42 40	97.97 46	92.02 201

test0726					94.16 53	72.56 152	90.63 49	93.90 48	83.61 55	93.75 34	94.49 69	89.76 18

GSMVS																83.88 342

test_part2					93.86 61	77.77 98				92.84 51

sam_mvs1												46.11 364				83.88 342

sam_mvs												45.92 369

ambc				82.98 198	90.55 156	64.86 237	88.20 100	89.15 199		89.40 118	93.96 99	71.67 234	91.38 188	78.83 132	96.55 97	92.71 165

MTGPA								91.81 127

test_post1							78.85 285				3.13 428	45.19 379	80.13 352	58.11 334

test_post											3.10 429	45.43 375	77.22 367

patchmatchnet-post											81.71 354	45.93 368	87.01 280

GG-mvs-BLEND				67.16 374	73.36 404	46.54 395	84.15 177	55.04 421		58.64 420	61.95 421	29.93 418	83.87 330	38.71 413	76.92 405	71.07 407

MTMP							90.66 48	33.14 431

gm-plane-assit					75.42 393	44.97 402			52.17 379		72.36 410		87.90 270	54.10 357

test9_res														80.83 109	96.45 103	90.57 243

TEST9					92.34 98	79.70 78	83.94 182	90.32 170	65.41 283	84.49 224	90.97 198	82.03 111	93.63 115

test_8					92.09 107	78.87 85	83.82 187	90.31 172	65.79 274	84.36 228	90.96 200	81.93 113	93.44 128

agg_prior2														79.68 122	96.16 115	90.22 251

agg_prior					91.58 127	77.69 100		90.30 173		84.32 230			93.18 136

TestCases				89.68 55	91.59 124	83.40 52		95.44 10	79.47 99	88.00 149	93.03 129	82.66 94	91.47 182	70.81 224	96.14 116	94.16 100

test_prior4						78.97 84	84.59 168

test_prior2							83.37 199		75.43 154	84.58 222	91.57 180	81.92 115		79.54 125	96.97 85

test_prior				86.32 110	90.59 155	71.99 163		92.85 93					94.17 97			92.80 160

旧先验2							81.73 241		56.88 354	86.54 185			84.90 317	72.81 213

新几何2							81.72 242

新几何1				82.95 200	93.96 59	78.56 88		80.24 310	55.45 359	83.93 241	91.08 195	71.19 236	88.33 265	65.84 276	93.07 220	81.95 373

旧先验1					91.97 111	71.77 164		81.78 299			91.84 171	73.92 201			93.65 208	83.61 348

无先验							82.81 217	85.62 255	58.09 342				91.41 187	67.95 261		84.48 333

原ACMM2							82.26 235

原ACMM1				84.60 150	92.81 89	74.01 133		91.50 132	62.59 298	82.73 264	90.67 214	76.53 175	94.25 91	69.24 242	95.69 141	85.55 321

test222					93.31 73	76.54 113	79.38 274	77.79 321	52.59 376	82.36 268	90.84 207	66.83 258			91.69 251	81.25 381

testdata2													86.43 294	63.52 298

segment_acmp												81.94 112

testdata				79.54 261	92.87 84	72.34 157		80.14 311	59.91 332	85.47 205	91.75 177	67.96 253	85.24 313	68.57 256	92.18 241	81.06 386

testdata1							79.62 269		73.95 169

test12				86.57 105	90.74 151	72.63 150		90.69 156		82.76 263		79.20 141	94.80 73		95.32 150	92.27 190

plane_prior7					93.45 68	77.31 106

plane_prior6					92.61 90	76.54 113						74.84 189

plane_prior5								93.61 59					95.22 59	80.78 110	95.83 134	94.46 84

plane_prior4											92.95 134

plane_prior3						76.85 111			77.79 125	86.55 180

plane_prior2							89.45 82		79.44 101

plane_prior1					92.83 88

plane_prior						76.42 116	87.15 117		75.94 145						95.03 162

n2								0.00 438

nn								0.00 438

door-mid								74.45 347

lessismore_v0				85.95 121	91.10 144	70.99 175		70.91 376		91.79 69	94.42 74	61.76 285	92.93 146	79.52 126	93.03 221	93.93 109

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 58	98.73 7	95.23 61

test11								91.46 133

door								72.57 362

HQP5-MVS						70.66 176

HQP-NCC					91.19 139		84.77 161		73.30 183	80.55 298

ACMP_Plane					91.19 139		84.77 161		73.30 183	80.55 298

BP-MVS														77.30 156

HQP4-MVS										80.56 297			94.61 79			93.56 133

HQP3-MVS								92.68 98							94.47 183

HQP2-MVS												72.10 226

NP-MVS					91.95 112	74.55 130					90.17 229

MDTV_nov1_ep13_2view						27.60 428	70.76 373		46.47 401	61.27 413		45.20 378		49.18 383		83.75 347

ACMMP++_ref															95.74 140

ACMMP++															97.35 75

Test By Simon												79.09 142

ITE_SJBPF				90.11 49	90.72 152	84.97 41		90.30 173	81.56 76	90.02 99	91.20 191	82.40 99	90.81 207	73.58 201	94.66 179	94.56 80

DeepMVS_CX				24.13 410	32.95 432	29.49 426		21.63 433	12.07 426	37.95 427	45.07 424	30.84 416	19.21 429	17.94 428	33.06 426	23.69 425

High-resolution multi-view benchmark