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 69	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 47	97.23 2	95.32 2	99.01 2	97.26 9	80.16 141	98.99 1	95.15 1	99.14 2	96.47 35

TDRefinement		93.52 3	93.39 5	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 51	96.29 22	88.16 36	94.17 102	86.07 54	98.48 18	97.22 18

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 66	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 76	93.16 145	91.10 2	97.53 77	96.58 33
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
reproduce_model		92.89 5	93.18 8	92.01 13	94.20 51	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 42	95.72 36	89.60 5	98.27 28	92.08 222

reproduce-ours		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 26	89.13 7	98.26 30	91.76 233

our_new_method		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 26	89.13 7	98.26 30	91.76 233

HPM-MVS_fast		92.50 8	92.54 10	92.37 6	95.93 16	85.81 33	92.99 12	94.23 28	85.21 44	92.51 59	95.13 52	90.65 10	95.34 55	88.06 16	98.15 39	95.95 46

lecture		92.43 9	93.50 3	89.21 66	94.43 44	79.31 84	92.69 19	95.72 8	88.48 22	94.43 20	95.73 34	91.34 4	94.68 78	90.26 4	98.44 20	93.63 139

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 47	87.16 37	97.60 71	92.73 179

SR-MVS		92.23 11	92.34 12	91.91 17	94.89 38	87.85 10	92.51 25	93.87 52	88.20 24	93.24 43	94.02 100	90.15 17	95.67 38	86.82 41	97.34 81	92.19 217

HPM-MVS		92.13 12	92.20 14	91.91 17	95.58 26	84.67 46	93.51 8	94.85 16	82.88 70	91.77 72	93.94 108	90.55 13	95.73 35	88.50 12	98.23 33	95.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 13	92.24 13	91.48 25	93.02 85	85.17 39	92.47 27	95.05 15	87.65 28	93.21 44	94.39 81	90.09 18	95.08 66	86.67 43	97.60 71	94.18 109

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 67	86.15 24	93.37 10	95.10 14	90.28 10	92.11 64	95.03 54	89.75 21	94.93 70	79.95 128	98.27 28	95.04 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 15	91.87 20	92.03 12	95.53 27	85.91 28	93.35 11	94.16 33	82.52 73	92.39 62	94.14 93	89.15 26	95.62 39	87.35 32	98.24 32	94.56 89
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
mPP-MVS		91.69 16	91.47 27	92.37 6	96.04 13	88.48 8	92.72 18	92.60 107	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 163

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 69	83.16 66	91.06 84	94.00 101	88.26 33	95.71 37	87.28 35	98.39 23	92.55 192

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	94.03 99	86.57 56	95.80 28	87.35 32	97.62 69	94.20 106

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 133	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 209

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 99	88.22 23	88.53 142	97.64 6	83.45 90	94.55 86	86.02 58	98.60 13	96.67 30

ACMMPR		91.49 20	91.35 31	91.92 16	95.74 20	85.88 30	92.58 23	93.25 77	81.99 76	91.40 76	94.17 92	87.51 46	95.87 20	87.74 21	97.76 60	93.99 116

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 63	94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 65	98.73 7	95.23 66

region2R		91.44 23	91.30 35	91.87 19	95.75 19	85.90 29	92.63 22	93.30 75	81.91 78	90.88 91	94.21 88	87.75 42	95.87 20	87.60 26	97.71 63	93.83 125

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 76	81.99 76	91.47 75	93.96 105	88.35 32	95.56 42	87.74 21	97.74 62	92.85 176

ZNCC-MVS		91.26 25	91.34 32	91.01 34	95.73 21	83.05 56	92.18 32	94.22 30	80.14 99	91.29 80	93.97 102	87.93 41	95.87 20	88.65 10	97.96 51	94.12 113

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 87	78.04 96	92.84 16	94.14 37	83.33 64	93.90 29	95.73 34	88.77 28	96.41 3	87.60 26	97.98 48	92.98 172
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 27	90.95 44	91.93 15	95.67 23	85.85 31	90.00 63	93.90 49	80.32 96	91.74 73	94.41 79	88.17 35	95.98 13	86.37 47	97.99 46	93.96 118

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 62	80.97 70	91.49 41	93.48 67	82.82 71	92.60 58	93.97 102	88.19 34	96.29 6	87.61 25	98.20 36	94.39 101
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 45	91.83 20	96.18 11	86.88 17	92.20 31	93.03 90	82.59 72	88.52 143	94.37 82	86.74 54	95.41 53	86.32 48	98.21 34	93.19 159
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 30	91.01 41	90.82 37	95.45 28	82.73 59	91.75 39	93.74 55	80.98 89	91.38 77	93.80 112	87.20 50	95.80 28	87.10 39	97.69 65	93.93 119

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 105	94.51 19	75.79 155	92.94 48	94.96 55	88.36 31	95.01 68	90.70 3	98.40 22	95.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 32	91.50 26	88.44 81	93.00 86	76.26 122	89.65 76	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 136	78.35 150	98.76 4	95.61 55

ACMMP_NAP		90.65 33	91.07 40	89.42 62	95.93 16	79.54 82	89.95 67	93.68 59	77.65 134	91.97 68	94.89 57	88.38 30	95.45 51	89.27 6	97.87 56	93.27 154

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 77	93.35 70	79.20 112	93.83 32	93.60 122	90.81 8	92.96 152	85.02 72	98.45 19	92.41 199
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 52	91.38 28	89.03 199	84.23 49	93.58 6	94.68 18	90.65 8	90.33 100	93.95 107	84.50 78	95.37 54	80.87 118	95.50 153	94.53 92

ACMP	79.16 10	90.54 36	90.60 50	90.35 45	94.36 48	80.98 69	89.16 87	94.05 42	79.03 115	92.87 50	93.74 117	90.60 12	95.21 61	82.87 96	98.76 4	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 37	91.08 38	88.88 71	93.38 76	78.65 90	89.15 88	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 79	97.81 58	91.70 237
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		90.46 38	91.64 22	86.93 105	94.18 52	72.65 152	90.47 56	93.69 57	83.77 58	94.11 27	94.27 83	90.28 15	95.84 24	86.03 55	97.92 52	92.29 211

SMA-MVS		90.31 39	90.48 51	89.83 55	95.31 30	79.52 83	90.98 48	93.24 78	75.37 164	92.84 52	95.28 48	85.58 69	96.09 8	87.92 18	97.76 60	93.88 122
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
SF-MVS		90.27 40	90.80 47	88.68 78	92.86 91	77.09 111	91.19 45	95.74 6	81.38 84	92.28 63	93.80 112	86.89 53	94.64 81	85.52 64	97.51 78	94.30 105

v7n		90.13 41	90.96 43	87.65 96	91.95 118	71.06 184	89.99 65	93.05 87	86.53 35	94.29 23	96.27 23	82.69 97	94.08 105	86.25 51	97.63 67	97.82 8

PMVS	80.48 6	90.08 42	90.66 49	88.34 84	96.71 3	92.97 2	90.31 60	89.57 208	88.51 21	90.11 102	95.12 53	90.98 7	88.92 269	77.55 164	97.07 88	83.13 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 43	91.09 37	87.00 103	91.55 135	72.64 154	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 129	95.88 18	87.41 30	95.94 133	92.48 195

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 159	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 274
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
PS-CasMVS		90.06 44	91.92 16	84.47 167	96.56 6	58.83 347	89.04 89	92.74 101	91.40 6	96.12 5	96.06 29	87.23 49	95.57 41	79.42 138	98.74 6	99.00 2

PEN-MVS		90.03 46	91.88 19	84.48 166	96.57 5	58.88 344	88.95 90	93.19 79	91.62 5	96.01 7	96.16 27	87.02 51	95.60 40	78.69 146	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 47	89.74 57	90.83 36	93.16 83	80.37 74	91.91 37	93.11 83	81.10 87	95.32 14	97.24 10	72.94 241	94.85 72	85.07 69	97.78 59	97.26 16

DTE-MVSNet		89.98 48	91.91 18	84.21 176	96.51 7	57.84 355	88.93 91	92.84 98	91.92 4	96.16 4	96.23 24	86.95 52	95.99 12	79.05 142	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 48	89.84 55	90.41 43	94.91 37	84.50 48	89.49 82	93.98 44	79.68 104	92.09 65	93.89 110	83.80 85	93.10 148	82.67 100	98.04 41	93.64 138

3Dnovator+	83.92 2	89.97 50	89.66 58	90.92 35	91.27 144	81.66 66	91.25 43	94.13 38	88.89 15	88.83 134	94.26 86	77.55 169	95.86 23	84.88 73	95.87 139	95.24 65

WR-MVS_H		89.91 51	91.31 34	85.71 134	96.32 9	62.39 293	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 126	95.90 17	80.94 117	98.80 3	98.84 5

OPM-MVS		89.80 52	89.97 53	89.27 64	94.76 40	79.86 78	86.76 132	92.78 100	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 75	97.55 74	94.10 114
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 53	89.27 64	91.30 29	93.51 70	84.79 44	89.89 69	90.63 169	70.00 247	94.55 19	96.67 17	87.94 40	93.59 128	84.27 81	95.97 129	95.52 56

anonymousdsp		89.73 54	88.88 74	92.27 8	89.82 180	86.67 18	90.51 55	90.20 190	69.87 248	95.06 15	96.14 28	84.28 81	93.07 149	87.68 23	96.34 111	97.09 20

test_djsdf		89.62 55	89.01 68	91.45 26	92.36 103	82.98 57	91.98 35	90.08 193	71.54 227	94.28 25	96.54 19	81.57 124	94.27 92	86.26 49	96.49 105	97.09 20

XVG-OURS-SEG-HR		89.59 56	89.37 62	90.28 46	94.47 43	85.95 27	86.84 128	93.91 48	80.07 100	86.75 190	93.26 128	93.64 2	90.93 210	84.60 78	90.75 303	93.97 117

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 86	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 106	97.18 86	90.45 276
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 58	88.81 77	91.19 32	93.38 76	84.72 45	89.70 72	90.29 187	69.27 254	94.39 21	96.38 21	86.02 66	93.52 132	83.96 83	95.92 135	95.34 60

CPTT-MVS		89.39 59	88.98 70	90.63 40	95.09 33	86.95 16	92.09 33	92.30 116	79.74 103	87.50 174	92.38 161	81.42 126	93.28 141	83.07 92	97.24 84	91.67 238

ACMH	76.49 14	89.34 60	91.14 36	83.96 183	92.50 99	70.36 193	89.55 78	93.84 53	81.89 79	94.70 17	95.44 44	90.69 9	88.31 284	83.33 88	98.30 27	93.20 158
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 261	74.12 210	96.10 124	94.45 95

APD_test2		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 261	74.12 210	96.10 124	94.45 95

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 350	88.66 98	92.06 122	90.78 7	95.67 8	95.17 51	81.80 122	95.54 44	79.00 143	98.69 10	98.95 4

XVG-OURS		89.18 64	88.83 76	90.23 47	94.28 49	86.11 26	85.91 147	93.60 62	80.16 98	89.13 131	93.44 124	83.82 84	90.98 207	83.86 85	95.30 161	93.60 142

DeepC-MVS	82.31 4	89.15 65	89.08 67	89.37 63	93.64 68	79.07 86	88.54 101	94.20 31	73.53 189	89.71 114	94.82 60	85.09 72	95.77 34	84.17 82	98.03 43	93.26 156
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 66	90.72 48	84.31 174	97.00 2	64.33 264	89.67 75	88.38 228	88.84 17	94.29 23	97.57 7	90.48 14	91.26 198	72.57 240	97.65 66	97.34 15

MSP-MVS		89.08 67	88.16 84	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 123	89.16 129	92.25 170	72.03 255	96.36 4	88.21 13	90.93 295	92.98 172
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
SD-MVS		88.96 68	89.88 54	86.22 121	91.63 129	77.07 112	89.82 70	93.77 54	78.90 116	92.88 49	92.29 168	86.11 64	90.22 235	86.24 52	97.24 84	91.36 246
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
HPM-MVS++		88.93 69	88.45 80	90.38 44	94.92 36	85.85 31	89.70 72	91.27 151	78.20 126	86.69 194	92.28 169	80.36 139	95.06 67	86.17 53	96.49 105	90.22 280

Elysia		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 63	84.31 51	90.42 96	94.10 96	74.07 219	94.82 73	88.19 14	95.92 135	96.80 27

StellarMVS		88.71 70	88.89 72	88.19 87	91.26 145	72.96 148	88.10 106	93.59 63	84.31 51	90.42 96	94.10 96	74.07 219	94.82 73	88.19 14	95.92 135	96.80 27

test_0402		88.65 72	89.58 61	85.88 130	92.55 97	72.22 166	84.01 193	89.44 211	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 129	95.21 162	91.82 231

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 94	87.95 26	89.62 118	92.87 145	84.56 77	93.89 113	77.65 162	96.62 100	90.70 266

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 128	84.26 53	90.87 92	93.92 109	82.18 112	89.29 265	73.75 218	94.81 181	93.70 133

Anonymous20231211		88.40 74	89.62 60	84.73 158	90.46 165	65.27 254	88.86 92	93.02 91	87.15 30	93.05 47	97.10 11	82.28 110	92.02 178	76.70 174	97.99 46	96.88 26

PS-MVSNAJss		88.31 76	87.90 87	89.56 60	93.31 78	77.96 99	87.94 110	91.97 125	70.73 238	94.19 26	96.67 17	76.94 181	94.57 84	83.07 92	96.28 113	96.15 38

OMC-MVS		88.19 77	87.52 91	90.19 48	91.94 120	81.68 65	87.49 117	93.17 80	76.02 149	88.64 139	91.22 206	84.24 82	93.37 139	77.97 160	97.03 89	95.52 56

CS-MVS		88.14 78	87.67 90	89.54 61	89.56 184	79.18 85	90.47 56	94.77 17	79.37 110	84.32 255	89.33 268	83.87 83	94.53 87	82.45 102	94.89 177	94.90 76

TSAR-MVS + MP.		88.14 78	87.82 88	89.09 69	95.72 22	76.74 115	92.49 26	91.19 154	67.85 279	86.63 195	94.84 59	79.58 147	95.96 15	87.62 24	94.50 190	94.56 89
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 80	89.79 56	82.98 214	93.26 80	63.94 268	91.10 46	89.64 205	85.07 45	90.91 88	91.09 211	89.16 25	91.87 183	82.03 107	95.87 139	93.13 161

EC-MVSNet		88.01 81	88.32 83	87.09 100	89.28 191	72.03 169	90.31 60	96.31 4	80.88 90	85.12 231	89.67 262	84.47 79	95.46 50	82.56 101	96.26 116	93.77 131

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 155	79.26 111	89.68 115	94.81 63	82.44 101	87.74 295	76.54 179	88.74 337	96.61 32

AllTest		87.97 83	87.40 95	89.68 56	91.59 130	83.40 52	89.50 81	95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 191	70.81 253	96.14 121	94.16 110

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 265	84.38 185	91.29 148	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 231	98.66 11	97.69 9

nrg030		87.85 85	88.49 79	85.91 128	90.07 175	69.73 201	87.86 111	94.20 31	74.04 181	92.70 57	94.66 64	85.88 67	91.50 190	79.72 131	97.32 82	96.50 34

CNVR-MVS		87.81 86	87.68 89	88.21 86	92.87 89	77.30 110	85.25 163	91.23 152	77.31 139	87.07 184	91.47 197	82.94 95	94.71 77	84.67 77	96.27 115	92.62 187

HQP_MVS		87.75 87	87.43 94	88.70 77	93.45 72	76.42 119	89.45 83	93.61 60	79.44 108	86.55 196	92.95 142	74.84 206	95.22 59	80.78 120	95.83 141	94.46 93

sc_t1		87.70 88	88.94 71	83.99 181	93.47 71	67.15 232	85.05 168	88.21 235	86.81 32	91.87 70	97.65 5	85.51 71	87.91 290	74.22 205	97.63 67	96.92 25

MM		87.64 89	87.15 97	89.09 69	89.51 185	76.39 121	88.68 97	86.76 266	84.54 50	83.58 273	93.78 114	73.36 236	96.48 2	87.98 17	96.21 117	94.41 100

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 200	92.03 116	62.26 297	91.49 41	92.62 105	88.07 25	88.07 155	96.17 26	72.24 250	95.79 31	84.85 74	94.16 203	92.58 190

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 157	78.77 119	84.85 242	90.89 221	80.85 132	95.29 56	81.14 115	95.32 158	92.34 207

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 216	92.38 112	70.25 244	89.35 126	90.68 231	82.85 96	94.57 84	79.55 135	95.95 132	92.00 226

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 211	87.84 112	88.05 236	81.66 81	94.64 18	96.53 20	65.94 292	94.75 76	83.02 94	96.83 94	95.41 58

fmvsm_s_conf0.5_n_9		87.04 94	87.02 102	87.08 101	89.67 182	75.87 126	84.60 178	89.74 200	74.40 178	89.92 110	93.41 125	80.45 137	90.63 224	86.66 44	94.37 196	94.73 86

SPE-MVS-test		87.00 95	86.43 112	88.71 76	89.46 187	77.46 105	89.42 85	95.73 7	77.87 132	81.64 314	87.25 313	82.43 102	94.53 87	77.65 162	96.46 107	94.14 112

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 221	83.80 203	92.87 96	80.37 94	89.61 120	91.81 183	77.72 166	94.18 100	75.00 200	98.53 16	96.99 24

Vis-MVSNet		86.86 97	86.58 109	87.72 94	92.09 113	77.43 107	87.35 118	92.09 121	78.87 117	84.27 260	94.05 98	78.35 158	93.65 121	80.54 124	91.58 282	92.08 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 98	87.06 100	86.17 124	92.86 91	67.02 236	82.55 242	91.56 138	83.08 68	90.92 86	91.82 182	78.25 159	93.99 107	74.16 208	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 225	92.21 117	81.73 80	90.92 86	91.97 175	77.20 175	93.99 107	74.16 208	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 170	87.09 259	65.22 255	84.16 189	94.23 28	77.89 130	91.28 81	93.66 120	84.35 80	92.71 158	80.07 125	94.87 180	95.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		86.68 101	86.52 110	87.18 99	85.94 293	78.30 92	86.93 125	92.20 118	65.94 298	89.16 129	93.16 132	83.10 93	89.89 250	87.81 20	94.43 194	93.35 149

tt0320-xc		86.67 102	88.41 81	81.44 253	93.45 72	60.44 323	83.96 195	88.50 224	87.26 29	90.90 90	97.90 3	85.61 68	86.40 321	70.14 264	98.01 45	97.47 14

IS-MVSNet		86.66 103	86.82 108	86.17 124	92.05 115	66.87 239	91.21 44	88.64 221	86.30 37	89.60 121	92.59 154	69.22 273	94.91 71	73.89 215	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 254	93.57 69	60.73 320	84.37 186	88.61 223	87.00 31	90.75 93	97.98 2	85.54 70	86.45 319	69.75 269	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 152	88.16 225	63.28 275	86.64 135	92.20 118	75.42 163	92.81 54	94.50 72	74.05 222	94.06 106	83.88 84	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 240	92.22 109	62.28 296	84.66 177	89.15 215	83.54 63	89.85 111	97.32 8	88.08 39	86.80 312	70.43 261	97.30 83	96.62 31

NormalMVS		86.47 107	85.32 139	89.94 51	94.43 44	80.42 72	88.63 99	93.59 63	74.56 173	85.12 231	90.34 243	66.19 289	94.20 97	76.57 177	98.44 20	95.19 68

PHI-MVS		86.38 108	85.81 126	88.08 89	88.44 219	77.34 108	89.35 86	93.05 87	73.15 202	84.76 244	87.70 302	78.87 152	94.18 100	80.67 122	96.29 112	92.73 179

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 129	80.35 95	89.54 124	88.01 289	79.09 150	92.13 174	75.51 193	95.06 169	90.41 277

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 124	70.56 239	84.96 237	90.69 230	80.01 143	95.14 64	78.37 149	95.78 145	91.82 231
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 111	86.83 107	84.36 170	87.82 233	62.35 295	86.42 139	91.33 147	76.78 143	92.73 56	94.48 74	73.41 233	93.72 119	83.10 91	95.41 154	97.01 23

Anonymous20240529		86.20 112	87.13 98	83.42 202	90.19 170	64.55 262	84.55 180	90.71 166	85.85 40	89.94 109	95.24 50	82.13 113	90.40 231	69.19 276	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 216	86.91 266	70.38 192	85.31 162	92.61 106	75.59 159	88.32 150	92.87 145	82.22 111	88.63 277	88.80 9	92.82 246	89.83 290

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 307	78.25 93	85.82 151	91.82 131	65.33 312	88.55 141	92.35 167	82.62 100	89.80 252	86.87 40	94.32 198	93.18 160

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 200	92.01 123	65.91 300	86.19 207	91.75 187	83.77 86	94.98 69	77.43 167	96.71 98	93.73 132

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 261	82.21 256	90.46 175	80.99 88	88.42 146	91.97 175	77.56 168	93.85 114	72.46 241	98.65 12	97.61 10

train_agg		85.98 117	85.28 140	88.07 90	92.34 104	79.70 80	83.94 196	90.32 182	65.79 302	84.49 249	90.97 215	81.93 118	93.63 123	81.21 114	96.54 103	90.88 260

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 199	83.70 206	89.83 199	80.42 93	87.76 168	93.24 129	73.76 227	91.54 189	85.03 71	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 226	92.25 107	56.44 366	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 208	93.41 138	75.11 199	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 316	78.21 94	85.40 161	91.39 145	65.32 313	87.72 170	91.81 183	82.33 105	89.78 253	86.68 42	94.20 201	92.99 170

Effi-MVS+-dtu		85.82 121	83.38 185	93.14 4	87.13 254	91.15 3	87.70 113	88.42 227	74.57 172	83.56 274	85.65 337	78.49 157	94.21 96	72.04 243	92.88 243	94.05 115

TAPA-MVS	77.73 12	85.71 122	84.83 149	88.37 83	88.78 209	79.72 79	87.15 122	93.50 66	69.17 255	85.80 216	89.56 263	80.76 133	92.13 174	73.21 236	95.51 152	93.25 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 123	86.14 117	83.58 196	87.97 227	67.13 233	87.55 114	94.32 22	73.44 192	88.47 144	87.54 305	86.45 59	91.06 205	75.76 191	93.76 214	92.54 193

canonicalmvs		85.50 123	86.14 117	83.58 196	87.97 227	67.13 233	87.55 114	94.32 22	73.44 192	88.47 144	87.54 305	86.45 59	91.06 205	75.76 191	93.76 214	92.54 193

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 161	87.10 257	69.98 197	84.28 187	92.68 102	74.77 169	87.90 162	92.36 166	73.94 223	90.41 230	85.95 60	92.74 248	93.66 134

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 239	81.51 83	87.05 185	91.83 181	66.18 291	95.29 56	70.75 256	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 168	90.08 173	67.07 235	85.86 150	91.39 145	72.33 219	87.59 172	90.25 248	84.85 75	92.37 168	78.00 158	91.94 272	93.66 134

MVS_0304		85.37 128	84.58 157	87.75 93	85.28 306	73.36 141	86.54 138	85.71 282	77.56 137	81.78 312	92.47 159	70.29 267	96.02 11	85.59 63	95.96 130	93.87 123

FIs		85.35 129	86.27 114	82.60 225	91.86 122	57.31 359	85.10 167	93.05 87	75.83 154	91.02 85	93.97 102	73.57 229	92.91 156	73.97 214	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 295	76.13 125	85.15 166	92.32 115	61.40 348	91.33 78	90.85 224	83.76 87	86.16 327	84.31 80	93.28 232	92.15 220

casdiffmvs		85.21 131	85.85 125	83.31 205	86.17 286	62.77 282	83.03 227	93.93 47	74.69 171	88.21 152	92.68 153	82.29 109	91.89 182	77.87 161	93.75 217	95.27 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
baseline		85.20 132	85.93 122	83.02 212	86.30 281	62.37 294	84.55 180	93.96 45	74.48 175	87.12 179	92.03 174	82.30 107	91.94 179	78.39 148	94.21 200	94.74 85

SSM_0404		85.16 133	85.09 142	85.36 142	90.14 172	69.52 204	86.17 144	91.58 136	74.41 176	86.55 196	91.49 194	78.54 153	93.97 109	73.71 219	93.21 235	92.59 189

K. test v3		85.14 134	84.73 150	86.37 115	91.13 151	69.63 203	85.45 159	76.68 366	84.06 56	92.44 61	96.99 13	62.03 318	94.65 80	80.58 123	93.24 233	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 210	84.65 318	74.71 132	85.87 149	90.35 181	77.94 129	83.82 267	96.96 15	77.75 164	80.03 385	78.44 147	96.21 117	94.79 84

EI-MVSNet-Vis-set		85.12 136	84.53 160	86.88 106	84.01 331	72.76 151	83.91 199	85.18 291	80.44 92	88.75 136	85.49 341	80.08 142	91.92 180	82.02 108	90.85 300	95.97 44

fmvsm_l_conf0.5_n_3		85.11 137	84.96 146	85.56 137	87.49 246	75.69 128	84.71 175	90.61 171	67.64 283	84.88 240	92.05 173	82.30 107	88.36 282	83.84 86	91.10 288	92.62 187

MGCFI-Net		85.04 138	85.95 121	82.31 234	87.52 244	63.59 271	86.23 143	93.96 45	73.46 190	88.07 155	87.83 300	86.46 58	90.87 215	76.17 185	93.89 211	92.47 197

EI-MVSNet-UG-set		85.04 138	84.44 163	86.85 107	83.87 335	72.52 160	83.82 201	85.15 292	80.27 97	88.75 136	85.45 343	79.95 144	91.90 181	81.92 111	90.80 302	96.13 39

X-MVStestdata		85.04 138	82.70 202	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 462	86.57 56	95.80 28	87.35 32	97.62 69	94.20 106

MSLP-MVS++		85.00 141	86.03 120	81.90 240	91.84 125	71.56 179	86.75 133	93.02 91	75.95 152	87.12 179	89.39 266	77.98 161	89.40 264	77.46 165	94.78 182	84.75 365

F-COLMAP		84.97 142	83.42 184	89.63 58	92.39 102	83.40 52	88.83 93	91.92 127	73.19 201	80.18 336	89.15 272	77.04 179	93.28 141	65.82 309	92.28 261	92.21 216

SSM_0407		84.89 143	84.85 148	85.01 150	89.13 195	68.97 214	85.60 156	91.58 136	74.41 176	85.68 217	91.49 194	78.54 153	93.69 120	73.71 219	93.47 225	92.38 204

balanced_conf03		84.80 144	85.40 136	83.00 213	88.95 202	61.44 305	90.42 59	92.37 114	71.48 229	88.72 138	93.13 133	70.16 269	95.15 63	79.26 140	94.11 204	92.41 199

3Dnovator	80.37 7	84.80 144	84.71 153	85.06 149	86.36 279	74.71 132	88.77 95	90.00 195	75.65 157	84.96 237	93.17 131	74.06 221	91.19 200	78.28 152	91.09 289	89.29 300

SymmetryMVS		84.79 146	83.54 179	88.55 79	92.44 101	80.42 72	88.63 99	82.37 326	74.56 173	85.12 231	90.34 243	66.19 289	94.20 97	76.57 177	95.68 149	91.03 254

IterMVS-LS		84.73 147	84.98 145	83.96 183	87.35 248	63.66 269	83.25 221	89.88 198	76.06 147	89.62 118	92.37 164	73.40 235	92.52 163	78.16 155	94.77 184	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 148	84.34 167	85.49 141	90.18 171	75.86 127	79.23 305	87.13 256	73.35 194	85.56 224	89.34 267	83.60 89	90.50 227	76.64 176	94.05 208	90.09 286

HQP-MVS		84.61 149	84.06 172	86.27 118	91.19 147	70.66 187	84.77 170	92.68 102	73.30 197	80.55 328	90.17 253	72.10 251	94.61 82	77.30 169	94.47 192	93.56 145

v1192		84.57 150	84.69 155	84.21 176	87.75 235	62.88 279	83.02 228	91.43 142	69.08 257	89.98 108	90.89 221	72.70 245	93.62 126	82.41 103	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 151	84.71 153	84.11 179	87.92 230	72.09 168	84.80 169	88.64 221	64.43 322	88.77 135	91.78 185	78.07 160	87.95 289	85.85 61	92.18 265	92.30 209

FMVSNet1		84.55 152	85.45 135	81.85 242	90.27 169	61.05 312	86.83 129	88.27 232	78.57 122	89.66 117	95.64 38	75.43 198	90.68 221	69.09 277	95.33 157	93.82 126

v1144		84.54 153	84.72 152	84.00 180	87.67 239	62.55 286	82.97 230	90.93 162	70.32 243	89.80 112	90.99 214	73.50 230	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 297	84.20 328	73.57 140	89.55 78	90.44 176	84.24 54	84.38 252	94.89 57	76.35 194	80.40 382	76.14 186	96.80 96	82.36 403
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 155	84.27 168	84.74 157	87.25 250	70.84 186	83.55 211	88.45 226	68.64 265	86.29 206	91.31 203	74.97 204	88.42 280	87.87 19	90.07 317	94.95 75

MCST-MVS		84.36 156	83.93 175	85.63 135	91.59 130	71.58 177	83.52 212	92.13 120	61.82 341	83.96 265	89.75 261	79.93 145	93.46 135	78.33 151	94.34 197	91.87 230

VDDNet		84.35 157	85.39 137	81.25 256	95.13 32	59.32 336	85.42 160	81.11 337	86.41 36	87.41 175	96.21 25	73.61 228	90.61 225	66.33 302	96.85 92	93.81 129

ETV-MVS		84.31 158	83.91 176	85.52 138	88.58 215	70.40 191	84.50 184	93.37 68	78.76 120	84.07 263	78.72 416	80.39 138	95.13 65	73.82 217	92.98 241	91.04 253

v1240		84.30 159	84.51 161	83.65 193	87.65 240	61.26 309	82.85 234	91.54 139	67.94 276	90.68 95	90.65 234	71.71 259	93.64 122	82.84 97	94.78 182	96.07 41

MVS_111021_LR		84.28 160	83.76 177	85.83 132	89.23 193	83.07 55	80.99 276	83.56 314	72.71 211	86.07 210	89.07 274	81.75 123	86.19 326	77.11 171	93.36 228	88.24 319

h-mvs33		84.25 161	82.76 201	88.72 75	91.82 127	82.60 60	84.00 194	84.98 298	71.27 230	86.70 192	90.55 239	63.04 315	93.92 112	78.26 153	94.20 201	89.63 292

v144192		84.24 162	84.41 164	83.71 192	87.59 242	61.57 304	82.95 231	91.03 158	67.82 280	89.80 112	90.49 240	73.28 237	93.51 133	81.88 112	94.89 177	96.04 43

dcpmvs_2		84.23 163	85.14 141	81.50 251	88.61 214	61.98 301	82.90 233	93.11 83	68.66 264	92.77 55	92.39 160	78.50 156	87.63 298	76.99 173	92.30 258	94.90 76

v1921920		84.23 163	84.37 166	83.79 188	87.64 241	61.71 303	82.91 232	91.20 153	67.94 276	90.06 103	90.34 243	72.04 254	93.59 128	82.32 104	94.91 175	96.07 41

VDD-MVS		84.23 163	84.58 157	83.20 208	91.17 150	65.16 257	83.25 221	84.97 299	79.79 102	87.18 178	94.27 83	74.77 209	90.89 213	69.24 273	96.54 103	93.55 147

v2v482		84.09 166	84.24 169	83.62 194	87.13 254	61.40 306	82.71 237	89.71 203	72.19 222	89.55 122	91.41 198	70.70 265	93.20 143	81.02 116	93.76 214	96.25 37

EG-PatchMatch MVS		84.08 167	84.11 171	83.98 182	92.22 109	72.61 157	82.20 258	87.02 262	72.63 212	88.86 132	91.02 213	78.52 155	91.11 203	73.41 226	91.09 289	88.21 320

fmvsm_s_conf0.5_n_6		84.05 168	84.14 170	83.81 186	87.75 235	71.17 182	83.42 215	91.10 156	67.90 278	84.53 247	90.70 229	73.01 240	88.73 275	85.09 68	93.72 219	91.53 243

DP-MVS Recon		84.05 168	83.22 188	86.52 113	91.73 128	75.27 130	83.23 223	92.40 110	72.04 224	82.04 303	88.33 285	77.91 163	93.95 111	66.17 303	95.12 167	90.34 279

TransMVSNet (Re)		84.02 170	85.74 130	78.85 296	91.00 154	55.20 378	82.29 252	87.26 251	79.65 105	88.38 148	95.52 41	83.00 94	86.88 310	67.97 291	96.60 101	94.45 95

Baseline_NR-MVSNet		84.00 171	85.90 123	78.29 308	91.47 140	53.44 389	82.29 252	87.00 265	79.06 114	89.55 122	95.72 36	77.20 175	86.14 328	72.30 242	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 172	84.46 162	82.53 229	86.11 289	70.65 189	82.45 247	89.17 214	67.72 282	86.74 191	91.49 194	79.20 148	85.86 337	84.71 76	92.60 252	91.07 252

TSAR-MVS + GP.		83.95 173	82.69 203	87.72 94	89.27 192	81.45 67	83.72 205	81.58 335	74.73 170	85.66 220	86.06 332	72.56 247	92.69 160	75.44 195	95.21 162	89.01 313

LuminaMVS		83.94 174	83.51 180	85.23 144	89.78 181	71.74 172	84.76 173	87.27 250	72.60 213	89.31 127	90.60 238	64.04 304	90.95 208	79.08 141	94.11 204	92.99 170

alignmvs		83.94 174	83.98 174	83.80 187	87.80 234	67.88 228	84.54 182	91.42 144	73.27 200	88.41 147	87.96 290	72.33 248	90.83 216	76.02 188	94.11 204	92.69 183

Effi-MVS+		83.90 176	84.01 173	83.57 198	87.22 252	65.61 253	86.55 137	92.40 110	78.64 121	81.34 319	84.18 362	83.65 88	92.93 154	74.22 205	87.87 351	92.17 219

fmvsm_s_conf0.1_n_2		83.82 177	83.49 181	84.84 152	85.99 292	70.19 195	80.93 277	87.58 246	67.26 289	87.94 161	92.37 164	71.40 261	88.01 286	86.03 55	91.87 273	96.31 36

mvs5depth		83.82 177	84.54 159	81.68 247	82.23 359	68.65 219	86.89 126	89.90 197	80.02 101	87.74 169	97.86 4	64.19 303	82.02 370	76.37 181	95.63 151	94.35 102

CANet		83.79 179	82.85 200	86.63 110	86.17 286	72.21 167	83.76 204	91.43 142	77.24 140	74.39 391	87.45 309	75.36 199	95.42 52	77.03 172	92.83 245	92.25 215

pm-mvs1		83.69 180	84.95 147	79.91 282	90.04 177	59.66 333	82.43 248	87.44 247	75.52 161	87.85 164	95.26 49	81.25 128	85.65 340	68.74 283	96.04 126	94.42 99

AdaColmap		83.66 181	83.69 178	83.57 198	90.05 176	72.26 165	86.29 141	90.00 195	78.19 127	81.65 313	87.16 315	83.40 91	94.24 95	61.69 344	94.76 185	84.21 375

MIMVSNet1		83.63 182	84.59 156	80.74 266	94.06 59	62.77 282	82.72 236	84.53 306	77.57 136	90.34 99	95.92 31	76.88 187	85.83 338	61.88 342	97.42 79	93.62 140

fmvsm_s_conf0.5_n_2		83.62 183	83.29 187	84.62 162	85.43 304	70.18 196	80.61 282	87.24 252	67.14 290	87.79 166	91.87 177	71.79 258	87.98 288	86.00 59	91.77 276	95.71 50

test_fmvsm_n_1920		83.60 184	82.89 197	85.74 133	85.22 308	77.74 102	84.12 191	90.48 173	59.87 368	86.45 205	91.12 210	75.65 196	85.89 335	82.28 105	90.87 298	93.58 143

WR-MVS		83.56 185	84.40 165	81.06 261	93.43 75	54.88 379	78.67 314	85.02 296	81.24 85	90.74 94	91.56 192	72.85 242	91.08 204	68.00 290	98.04 41	97.23 17

CNLPA		83.55 186	83.10 193	84.90 151	89.34 190	83.87 50	84.54 182	88.77 218	79.09 113	83.54 275	88.66 282	74.87 205	81.73 372	66.84 297	92.29 260	89.11 306

LCM-MVSNet-Re		83.48 187	85.06 143	78.75 298	85.94 293	55.75 372	80.05 288	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 256	59.95 355	94.89 177	90.75 263

hse-mvs2		83.47 188	81.81 217	88.47 80	91.03 153	82.27 61	82.61 238	83.69 312	71.27 230	86.70 192	86.05 333	63.04 315	92.41 166	78.26 153	93.62 223	90.71 265

V42		83.47 188	83.37 186	83.75 190	83.16 353	63.33 274	81.31 270	90.23 189	69.51 252	90.91 88	90.81 226	74.16 218	92.29 172	80.06 126	90.22 315	95.62 54

VPA-MVSNet		83.47 188	84.73 150	79.69 287	90.29 168	57.52 358	81.30 272	88.69 220	76.29 145	87.58 173	94.44 75	80.60 136	87.20 304	66.60 300	96.82 95	94.34 103

mamba_0408		83.44 191	82.88 198	85.11 147	89.13 195	68.97 214	72.73 388	91.28 149	72.90 205	85.68 217	90.61 236	76.78 188	93.97 109	73.37 228	93.47 225	92.38 204

PAPM_NR		83.23 192	83.19 190	83.33 204	90.90 156	65.98 249	88.19 104	90.78 165	78.13 128	80.87 324	87.92 294	73.49 232	92.42 165	70.07 265	88.40 340	91.60 240

CLD-MVS		83.18 193	82.64 204	84.79 155	89.05 198	67.82 229	77.93 323	92.52 108	68.33 268	85.07 234	81.54 391	82.06 115	92.96 152	69.35 272	97.91 54	93.57 144
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 194	85.68 131	75.65 345	81.24 371	45.26 433	79.94 290	92.91 95	83.83 57	91.33 78	96.88 16	80.25 140	85.92 331	68.89 280	95.89 138	95.76 48

FA-MVS(test-final)		83.13 195	83.02 194	83.43 201	86.16 288	66.08 248	88.00 108	88.36 229	75.55 160	85.02 235	92.75 151	65.12 298	92.50 164	74.94 201	91.30 286	91.72 235

114514_t		83.10 196	82.54 207	84.77 156	92.90 88	69.10 213	86.65 134	90.62 170	54.66 400	81.46 316	90.81 226	76.98 180	94.38 90	72.62 239	96.18 119	90.82 262

RRT-MVS		82.97 197	83.44 182	81.57 249	85.06 311	58.04 353	87.20 119	90.37 179	77.88 131	88.59 140	93.70 119	63.17 312	93.05 150	76.49 180	88.47 339	93.62 140

viewmanbaseed2359cas		82.95 198	83.43 183	81.52 250	85.18 309	60.03 329	81.36 269	92.38 112	69.55 251	84.84 243	91.38 199	79.85 146	90.09 244	74.22 205	92.09 267	94.43 98

BP-MVS1		82.81 199	81.67 219	86.23 119	87.88 232	68.53 220	86.06 146	84.36 307	75.65 157	85.14 230	90.19 250	45.84 404	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 200	81.64 220	86.21 122	86.20 285	76.24 123	86.86 127	85.68 283	77.07 141	73.76 395	92.82 147	69.64 270	91.82 185	69.04 279	93.69 220	90.56 273
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
LF4IMVS		82.75 201	81.93 215	85.19 145	82.08 360	80.15 76	85.53 157	88.76 219	68.01 273	85.58 223	87.75 301	71.80 257	86.85 311	74.02 213	93.87 212	88.58 316

EI-MVSNet		82.61 202	82.42 209	83.20 208	83.25 350	63.66 269	83.50 213	85.07 293	76.06 147	86.55 196	85.10 349	73.41 233	90.25 232	78.15 157	90.67 309	95.68 52

QAPM		82.59 203	82.59 206	82.58 226	86.44 273	66.69 240	89.94 68	90.36 180	67.97 275	84.94 239	92.58 156	72.71 244	92.18 173	70.63 259	87.73 354	88.85 314

fmvsm_s_conf0.1_n_a		82.58 204	81.93 215	84.50 165	87.68 238	73.35 142	86.14 145	77.70 355	61.64 346	85.02 235	91.62 189	77.75 164	86.24 323	82.79 98	87.07 362	93.91 121

Fast-Effi-MVS+-dtu		82.54 205	81.41 229	85.90 129	85.60 300	76.53 118	83.07 226	89.62 207	73.02 204	79.11 346	83.51 367	80.74 134	90.24 234	68.76 282	89.29 327	90.94 257

MVS_Test		82.47 206	83.22 188	80.22 278	82.62 358	57.75 357	82.54 243	91.96 126	71.16 234	82.89 286	92.52 158	77.41 170	90.50 227	80.04 127	87.84 353	92.40 201

viewmsd2359difaftdt		82.46 207	82.99 195	80.88 264	83.52 338	61.00 315	79.46 299	85.97 279	69.48 253	87.89 163	91.31 203	82.10 114	88.61 278	74.28 204	92.86 244	93.02 167

v148		82.31 208	82.48 208	81.81 245	85.59 301	59.66 333	81.47 267	86.02 277	72.85 207	88.05 157	90.65 234	70.73 264	90.91 212	75.15 198	91.79 274	94.87 78

API-MVS		82.28 209	82.61 205	81.30 255	86.29 282	69.79 198	88.71 96	87.67 245	78.42 124	82.15 299	84.15 363	77.98 161	91.59 188	65.39 312	92.75 247	82.51 402

MVSFormer		82.23 210	81.57 225	84.19 178	85.54 302	69.26 208	91.98 35	90.08 193	71.54 227	76.23 371	85.07 352	58.69 340	94.27 92	86.26 49	88.77 335	89.03 311

fmvsm_s_conf0.5_n_a		82.21 211	81.51 228	84.32 173	86.56 271	73.35 142	85.46 158	77.30 359	61.81 342	84.51 248	90.88 223	77.36 171	86.21 325	82.72 99	86.97 367	93.38 148

EIA-MVS		82.19 212	81.23 236	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 240	78.58 351	79.77 407	77.29 172	94.20 97	71.51 249	88.96 333	91.93 229

GDP-MVS		82.17 213	80.85 244	86.15 126	88.65 212	68.95 217	85.65 155	93.02 91	68.42 266	83.73 269	89.54 264	45.07 415	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 213	81.59 223	83.94 185	86.87 269	71.57 178	85.19 165	77.42 358	62.27 340	84.47 251	91.33 201	76.43 191	85.91 333	83.14 89	87.14 360	94.33 104

PCF-MVS	74.62 15	82.15 215	80.92 242	85.84 131	89.43 188	72.30 164	80.53 283	91.82 131	57.36 384	87.81 165	89.92 258	77.67 167	93.63 123	58.69 360	95.08 168	91.58 241
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 216	80.31 251	87.45 97	90.86 158	80.29 75	85.88 148	90.65 168	68.17 271	76.32 370	86.33 327	73.12 239	92.61 162	61.40 347	90.02 319	89.44 295
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 217	81.54 227	83.60 195	83.94 332	73.90 138	83.35 218	86.10 273	58.97 370	83.80 268	90.36 242	74.23 216	86.94 309	82.90 95	90.22 315	89.94 288

fmvsm_s_conf0.5_n_7		82.04 218	82.05 213	82.01 238	86.98 265	71.07 183	78.70 312	89.45 210	68.07 272	78.14 353	91.61 190	74.19 217	85.92 331	79.61 134	91.73 277	89.05 310

GBi-Net		82.02 219	82.07 211	81.85 242	86.38 276	61.05 312	86.83 129	88.27 232	72.43 214	86.00 211	95.64 38	63.78 308	90.68 221	65.95 305	93.34 229	93.82 126

test1		82.02 219	82.07 211	81.85 242	86.38 276	61.05 312	86.83 129	88.27 232	72.43 214	86.00 211	95.64 38	63.78 308	90.68 221	65.95 305	93.34 229	93.82 126

OpenMVS	76.72 13	81.98 221	82.00 214	81.93 239	84.42 323	68.22 223	88.50 102	89.48 209	66.92 293	81.80 310	91.86 178	72.59 246	90.16 238	71.19 252	91.25 287	87.40 336

KD-MVS_self_test		81.93 222	83.14 192	78.30 307	84.75 317	52.75 393	80.37 285	89.42 212	70.24 245	90.26 101	93.39 126	74.55 215	86.77 313	68.61 285	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 223	81.30 233	83.75 190	86.02 291	71.56 179	84.73 174	77.11 362	62.44 337	84.00 264	90.68 231	76.42 192	85.89 335	83.14 89	87.11 361	93.81 129

SDMVSNet		81.90 224	83.17 191	78.10 311	88.81 207	62.45 292	76.08 357	86.05 276	73.67 186	83.41 276	93.04 135	82.35 104	80.65 379	70.06 266	95.03 170	91.21 248

tfpnnormal		81.79 225	82.95 196	78.31 306	88.93 203	55.40 374	80.83 280	82.85 321	76.81 142	85.90 215	94.14 93	74.58 213	86.51 317	66.82 298	95.68 149	93.01 169

AstraMVS		81.67 226	81.40 230	82.48 231	87.06 262	66.47 243	81.41 268	81.68 332	68.78 261	88.00 158	90.95 219	65.70 294	87.86 294	76.66 175	92.38 256	93.12 163

c3_l		81.64 227	81.59 223	81.79 246	80.86 377	59.15 341	78.61 315	90.18 191	68.36 267	87.20 177	87.11 317	69.39 271	91.62 187	78.16 155	94.43 194	94.60 88

guyue		81.57 228	81.37 232	82.15 235	86.39 274	66.13 247	81.54 266	83.21 316	69.79 249	87.77 167	89.95 256	65.36 297	87.64 297	75.88 189	92.49 254	92.67 184

PVSNet_Blended_VisFu		81.55 229	80.49 249	84.70 160	91.58 133	73.24 146	84.21 188	91.67 135	62.86 331	80.94 322	87.16 315	67.27 283	92.87 157	69.82 268	88.94 334	87.99 326

fmvsm_l_conf0.5_n_a		81.46 230	80.87 243	83.25 206	83.73 337	73.21 147	83.00 229	85.59 285	58.22 376	82.96 285	90.09 255	72.30 249	86.65 315	81.97 110	89.95 320	89.88 289

SSM_04072		81.44 231	82.88 198	77.10 326	89.13 195	68.97 214	72.73 388	91.28 149	72.90 205	85.68 217	90.61 236	76.78 188	69.94 422	73.37 228	93.47 225	92.38 204

DELS-MVS		81.44 231	81.25 234	82.03 237	84.27 327	62.87 280	76.47 351	92.49 109	70.97 236	81.64 314	83.83 364	75.03 202	92.70 159	74.29 203	92.22 264	90.51 275
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
FMVSNet2		81.31 233	81.61 222	80.41 274	86.38 276	58.75 348	83.93 198	86.58 268	72.43 214	87.65 171	92.98 139	63.78 308	90.22 235	66.86 295	93.92 210	92.27 213

TinyColmap		81.25 234	82.34 210	77.99 314	85.33 305	60.68 321	82.32 251	88.33 230	71.26 232	86.97 186	92.22 172	77.10 178	86.98 308	62.37 336	95.17 164	86.31 348

diffmvs_AUTHOR		81.24 235	81.55 226	80.30 276	80.61 382	60.22 325	77.98 322	90.48 173	67.77 281	83.34 278	89.50 265	74.69 211	87.42 300	78.78 145	90.81 301	93.27 154

AUN-MVS		81.18 236	78.78 273	88.39 82	90.93 155	82.14 62	82.51 244	83.67 313	64.69 321	80.29 332	85.91 336	51.07 379	92.38 167	76.29 184	93.63 222	90.65 270

IMVS_0407		81.08 237	81.23 236	80.62 271	85.76 296	62.46 288	82.46 245	87.91 240	65.23 314	82.12 300	87.92 294	77.27 173	90.18 237	71.67 245	90.74 304	89.20 301

tttt0517		81.07 238	79.58 264	85.52 138	88.99 201	66.45 244	87.03 124	75.51 374	73.76 185	88.32 150	90.20 249	37.96 436	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 239	80.57 246	82.46 232	87.50 245	63.22 276	78.37 318	89.63 206	68.01 273	81.87 306	82.08 385	82.31 106	92.65 161	67.10 294	88.30 346	91.51 244

BH-untuned		80.96 240	80.99 240	80.84 265	88.55 216	68.23 222	80.33 286	88.46 225	72.79 210	86.55 196	86.76 321	74.72 210	91.77 186	61.79 343	88.99 332	82.52 401

IMVS_0403		80.93 241	81.00 239	80.72 268	85.76 296	62.46 288	81.82 260	87.91 240	65.23 314	82.07 302	87.92 294	75.91 195	90.50 227	71.67 245	90.74 304	89.20 301

eth_miper_zixun_eth		80.84 242	80.22 255	82.71 223	81.41 369	60.98 316	77.81 325	90.14 192	67.31 288	86.95 187	87.24 314	64.26 301	92.31 170	75.23 197	91.61 280	94.85 82

xiu_mvs_v1_base_debu		80.84 242	80.14 257	82.93 218	88.31 220	71.73 173	79.53 295	87.17 253	65.43 308	79.59 338	82.73 379	76.94 181	90.14 241	73.22 231	88.33 342	86.90 342

xiu_mvs_v1_base		80.84 242	80.14 257	82.93 218	88.31 220	71.73 173	79.53 295	87.17 253	65.43 308	79.59 338	82.73 379	76.94 181	90.14 241	73.22 231	88.33 342	86.90 342

xiu_mvs_v1_base_debi		80.84 242	80.14 257	82.93 218	88.31 220	71.73 173	79.53 295	87.17 253	65.43 308	79.59 338	82.73 379	76.94 181	90.14 241	73.22 231	88.33 342	86.90 342

IterMVS-SCA-FT		80.64 246	79.41 265	84.34 172	83.93 333	69.66 202	76.28 353	81.09 338	72.43 214	86.47 203	90.19 250	60.46 325	93.15 146	77.45 166	86.39 373	90.22 280

BH-RMVSNet		80.53 247	80.22 255	81.49 252	87.19 253	66.21 246	77.79 326	86.23 271	74.21 180	83.69 270	88.50 283	73.25 238	90.75 218	63.18 333	87.90 350	87.52 334

VortexMVS		80.51 248	80.63 245	80.15 280	83.36 346	61.82 302	80.63 281	88.00 238	67.11 291	87.23 176	89.10 273	63.98 305	88.00 287	73.63 223	92.63 251	90.64 271

Anonymous202405211		80.51 248	81.19 238	78.49 303	88.48 217	57.26 360	76.63 346	82.49 324	81.21 86	84.30 258	92.24 171	67.99 279	86.24 323	62.22 337	95.13 165	91.98 228

DIV-MVS_self_test		80.43 250	80.23 253	81.02 262	79.99 387	59.25 338	77.07 339	87.02 262	67.38 285	86.19 207	89.22 269	63.09 313	90.16 238	76.32 182	95.80 143	93.66 134

cl____		80.42 251	80.23 253	81.02 262	79.99 387	59.25 338	77.07 339	87.02 262	67.37 286	86.18 209	89.21 270	63.08 314	90.16 238	76.31 183	95.80 143	93.65 137

diffmvs		80.40 252	80.48 250	80.17 279	79.02 400	60.04 327	77.54 330	90.28 188	66.65 296	82.40 293	87.33 312	73.50 230	87.35 302	77.98 159	89.62 324	93.13 161
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet		80.37 253	78.41 281	86.23 119	76.75 414	73.28 144	87.18 121	77.45 357	76.24 146	68.14 425	88.93 276	65.41 296	93.85 114	69.47 271	96.12 123	91.55 242
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 254	80.04 260	81.24 258	79.82 390	58.95 343	77.66 327	89.66 204	65.75 305	85.99 214	85.11 348	68.29 278	91.42 195	76.03 187	92.03 268	93.33 150

MG-MVS		80.32 255	80.94 241	78.47 304	88.18 223	52.62 396	82.29 252	85.01 297	72.01 225	79.24 345	92.54 157	69.36 272	93.36 140	70.65 258	89.19 330	89.45 294

mvsmamba		80.30 256	78.87 270	84.58 164	88.12 226	67.55 230	92.35 30	84.88 300	63.15 329	85.33 227	90.91 220	50.71 381	95.20 62	66.36 301	87.98 349	90.99 255

VPNet		80.25 257	81.68 218	75.94 342	92.46 100	47.98 420	76.70 344	81.67 333	73.45 191	84.87 241	92.82 147	74.66 212	86.51 317	61.66 345	96.85 92	93.33 150

MAR-MVS		80.24 258	78.74 275	84.73 158	86.87 269	78.18 95	85.75 152	87.81 244	65.67 307	77.84 357	78.50 417	73.79 226	90.53 226	61.59 346	90.87 298	85.49 358
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
PM-MVS		80.20 259	79.00 269	83.78 189	88.17 224	86.66 19	81.31 270	66.81 430	69.64 250	88.33 149	90.19 250	64.58 299	83.63 361	71.99 244	90.03 318	81.06 421

Anonymous20240521		80.18 260	81.25 234	76.95 328	83.15 354	60.84 318	82.46 245	85.99 278	68.76 262	86.78 188	93.73 118	59.13 337	77.44 396	73.71 219	97.55 74	92.56 191

LFMVS		80.15 261	80.56 247	78.89 295	89.19 194	55.93 368	85.22 164	73.78 386	82.96 69	84.28 259	92.72 152	57.38 349	90.07 246	63.80 327	95.75 146	90.68 267

DPM-MVS		80.10 262	79.18 268	82.88 221	90.71 161	69.74 200	78.87 310	90.84 163	60.29 364	75.64 380	85.92 335	67.28 282	93.11 147	71.24 251	91.79 274	85.77 354

MSDG		80.06 263	79.99 262	80.25 277	83.91 334	68.04 227	77.51 331	89.19 213	77.65 134	81.94 304	83.45 369	76.37 193	86.31 322	63.31 332	86.59 370	86.41 346

FE-MVS		79.98 264	78.86 271	83.36 203	86.47 272	66.45 244	89.73 71	84.74 304	72.80 209	84.22 262	91.38 199	44.95 416	93.60 127	63.93 325	91.50 283	90.04 287

sd_testset		79.95 265	81.39 231	75.64 346	88.81 207	58.07 352	76.16 356	82.81 322	73.67 186	83.41 276	93.04 135	80.96 131	77.65 395	58.62 361	95.03 170	91.21 248

ab-mvs		79.67 266	80.56 247	76.99 327	88.48 217	56.93 362	84.70 176	86.06 275	68.95 259	80.78 325	93.08 134	75.30 200	84.62 348	56.78 370	90.90 296	89.43 296

VNet		79.31 267	80.27 252	76.44 336	87.92 230	53.95 385	75.58 363	84.35 308	74.39 179	82.23 297	90.72 228	72.84 243	84.39 353	60.38 353	93.98 209	90.97 256

thisisatest0530		79.07 268	77.33 291	84.26 175	87.13 254	64.58 260	83.66 208	75.95 369	68.86 260	85.22 229	87.36 311	38.10 433	93.57 131	75.47 194	94.28 199	94.62 87

cl22		78.97 269	78.21 283	81.24 258	77.74 404	59.01 342	77.46 334	87.13 256	65.79 302	84.32 255	85.10 349	58.96 339	90.88 214	75.36 196	92.03 268	93.84 124

patch_mono-2		78.89 270	79.39 266	77.41 323	84.78 315	68.11 225	75.60 361	83.11 318	60.96 356	79.36 342	89.89 259	75.18 201	72.97 411	73.32 230	92.30 258	91.15 250

RPMNet		78.88 271	78.28 282	80.68 270	79.58 391	62.64 284	82.58 240	94.16 33	74.80 168	75.72 378	92.59 154	48.69 388	95.56 42	73.48 225	82.91 409	83.85 380

PAPR		78.84 272	78.10 284	81.07 260	85.17 310	60.22 325	82.21 256	90.57 172	62.51 333	75.32 384	84.61 357	74.99 203	92.30 171	59.48 358	88.04 348	90.68 267

viewmambaseed2359dif		78.80 273	78.47 280	79.78 283	80.26 386	59.28 337	77.31 336	87.13 256	60.42 362	82.37 294	88.67 281	74.58 213	87.87 293	67.78 293	87.73 354	92.19 217

PVSNet_BlendedMVS		78.80 273	77.84 285	81.65 248	84.43 321	63.41 272	79.49 298	90.44 176	61.70 345	75.43 381	87.07 318	69.11 274	91.44 193	60.68 351	92.24 262	90.11 285

FMVSNet3		78.80 273	78.55 277	79.57 289	82.89 357	56.89 364	81.76 261	85.77 281	69.04 258	86.00 211	90.44 241	51.75 377	90.09 244	65.95 305	93.34 229	91.72 235

test_yl		78.71 276	78.51 278	79.32 292	84.32 325	58.84 345	78.38 316	85.33 288	75.99 150	82.49 291	86.57 323	58.01 343	90.02 248	62.74 334	92.73 249	89.10 307

DCV-MVSNet		78.71 276	78.51 278	79.32 292	84.32 325	58.84 345	78.38 316	85.33 288	75.99 150	82.49 291	86.57 323	58.01 343	90.02 248	62.74 334	92.73 249	89.10 307

test1111		78.53 278	78.85 272	77.56 320	92.22 109	47.49 422	82.61 238	69.24 418	72.43 214	85.28 228	94.20 89	51.91 375	90.07 246	65.36 313	96.45 108	95.11 72

icg_test_0407_2		78.46 279	79.68 263	74.78 353	85.76 296	62.46 288	68.51 417	87.91 240	65.23 314	82.12 300	87.92 294	77.27 173	72.67 412	71.67 245	90.74 304	89.20 301

ECVR-MVS		78.44 280	78.63 276	77.88 316	91.85 123	48.95 416	83.68 207	69.91 414	72.30 220	84.26 261	94.20 89	51.89 376	89.82 251	63.58 328	96.02 127	94.87 78

pmmvs-eth3d		78.42 281	77.04 294	82.57 228	87.44 247	74.41 135	80.86 279	79.67 346	55.68 393	84.69 245	90.31 247	60.91 323	85.42 341	62.20 338	91.59 281	87.88 330

mvs_anonymous		78.13 282	78.76 274	76.23 341	79.24 397	50.31 412	78.69 313	84.82 302	61.60 347	83.09 284	92.82 147	73.89 225	87.01 305	68.33 289	86.41 372	91.37 245

TAMVS		78.08 283	76.36 301	83.23 207	90.62 162	72.87 150	79.08 306	80.01 345	61.72 344	81.35 318	86.92 320	63.96 307	88.78 273	50.61 409	93.01 240	88.04 325

miper_enhance_ethall		77.83 284	76.93 295	80.51 272	76.15 421	58.01 354	75.47 365	88.82 217	58.05 378	83.59 272	80.69 395	64.41 300	91.20 199	73.16 237	92.03 268	92.33 208

Vis-MVSNet (Re-imp)		77.82 285	77.79 286	77.92 315	88.82 206	51.29 406	83.28 219	71.97 402	74.04 181	82.23 297	89.78 260	57.38 349	89.41 263	57.22 369	95.41 154	93.05 166

CANet_DTU		77.81 286	77.05 293	80.09 281	81.37 370	59.90 331	83.26 220	88.29 231	69.16 256	67.83 428	83.72 365	60.93 322	89.47 258	69.22 275	89.70 323	90.88 260

OpenMVS_ROB	70.19 17	77.77 287	77.46 288	78.71 299	84.39 324	61.15 310	81.18 274	82.52 323	62.45 336	83.34 278	87.37 310	66.20 288	88.66 276	64.69 320	85.02 389	86.32 347

SSC-MVS		77.55 288	81.64 220	65.29 419	90.46 165	20.33 466	73.56 381	68.28 420	85.44 41	88.18 154	94.64 68	70.93 263	81.33 374	71.25 250	92.03 268	94.20 106

MDA-MVSNet-bldmvs		77.47 289	76.90 296	79.16 294	79.03 399	64.59 259	66.58 429	75.67 372	73.15 202	88.86 132	88.99 275	66.94 284	81.23 375	64.71 319	88.22 347	91.64 239

jason		77.42 290	75.75 307	82.43 233	87.10 257	69.27 207	77.99 321	81.94 330	51.47 420	77.84 357	85.07 352	60.32 327	89.00 267	70.74 257	89.27 329	89.03 311
jason: jason.
CDS-MVSNet		77.32 291	75.40 311	83.06 211	89.00 200	72.48 161	77.90 324	82.17 328	60.81 357	78.94 348	83.49 368	59.30 335	88.76 274	54.64 389	92.37 257	87.93 329
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 292	77.75 287	75.73 344	85.76 296	62.46 288	70.84 403	87.91 240	65.23 314	72.21 403	87.92 294	67.48 281	75.53 404	71.67 245	90.74 304	89.20 301

xiu_mvs_v2_base		77.19 293	76.75 298	78.52 302	87.01 263	61.30 308	75.55 364	87.12 260	61.24 353	74.45 390	78.79 415	77.20 175	90.93 210	64.62 322	84.80 396	83.32 389

MVSTER		77.09 294	75.70 308	81.25 256	75.27 429	61.08 311	77.49 333	85.07 293	60.78 358	86.55 196	88.68 279	43.14 425	90.25 232	73.69 222	90.67 309	92.42 198

PS-MVSNAJ		77.04 295	76.53 300	78.56 301	87.09 259	61.40 306	75.26 366	87.13 256	61.25 352	74.38 392	77.22 429	76.94 181	90.94 209	64.63 321	84.83 395	83.35 388

IterMVS		76.91 296	76.34 302	78.64 300	80.91 375	64.03 266	76.30 352	79.03 349	64.88 320	83.11 282	89.16 271	59.90 331	84.46 351	68.61 285	85.15 387	87.42 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 297	75.67 309	80.34 275	80.48 384	62.16 300	73.50 382	84.80 303	57.61 382	82.24 296	87.54 305	51.31 378	87.65 296	70.40 262	93.19 236	91.23 247

CL-MVSNet_self_test		76.81 298	77.38 290	75.12 349	86.90 267	51.34 404	73.20 385	80.63 342	68.30 269	81.80 310	88.40 284	66.92 285	80.90 376	55.35 383	94.90 176	93.12 163

TR-MVS		76.77 299	75.79 306	79.72 286	86.10 290	65.79 251	77.14 337	83.02 319	65.20 318	81.40 317	82.10 383	66.30 287	90.73 220	55.57 380	85.27 383	82.65 396

MonoMVSNet		76.66 300	77.26 292	74.86 351	79.86 389	54.34 382	86.26 142	86.08 274	71.08 235	85.59 222	88.68 279	53.95 367	85.93 330	63.86 326	80.02 425	84.32 371

USDC		76.63 301	76.73 299	76.34 338	83.46 341	57.20 361	80.02 289	88.04 237	52.14 416	83.65 271	91.25 205	63.24 311	86.65 315	54.66 388	94.11 204	85.17 360

BH-w/o		76.57 302	76.07 305	78.10 311	86.88 268	65.92 250	77.63 328	86.33 269	65.69 306	80.89 323	79.95 404	68.97 276	90.74 219	53.01 399	85.25 384	77.62 432

Patchmtry		76.56 303	77.46 288	73.83 359	79.37 396	46.60 426	82.41 249	76.90 363	73.81 184	85.56 224	92.38 161	48.07 391	83.98 358	63.36 331	95.31 160	90.92 258

PVSNet_Blended		76.49 304	75.40 311	79.76 285	84.43 321	63.41 272	75.14 367	90.44 176	57.36 384	75.43 381	78.30 418	69.11 274	91.44 193	60.68 351	87.70 356	84.42 370

miper_lstm_enhance		76.45 305	76.10 304	77.51 321	76.72 415	60.97 317	64.69 433	85.04 295	63.98 325	83.20 281	88.22 286	56.67 353	78.79 392	73.22 231	93.12 237	92.78 178

lupinMVS		76.37 306	74.46 320	82.09 236	85.54 302	69.26 208	76.79 342	80.77 341	50.68 427	76.23 371	82.82 377	58.69 340	88.94 268	69.85 267	88.77 335	88.07 322

cascas		76.29 307	74.81 316	80.72 268	84.47 320	62.94 278	73.89 379	87.34 248	55.94 391	75.16 386	76.53 434	63.97 306	91.16 201	65.00 316	90.97 294	88.06 324

SD_0403		76.08 308	76.77 297	73.98 357	87.08 261	49.45 415	83.62 209	84.68 305	63.31 326	75.13 387	87.47 308	71.85 256	84.56 349	49.97 411	87.86 352	87.94 328

WB-MVS		76.06 309	80.01 261	64.19 422	89.96 179	20.58 465	72.18 392	68.19 421	83.21 65	86.46 204	93.49 123	70.19 268	78.97 390	65.96 304	90.46 314	93.02 167

thres600view7		75.97 310	75.35 313	77.85 318	87.01 263	51.84 402	80.45 284	73.26 391	75.20 165	83.10 283	86.31 329	45.54 406	89.05 266	55.03 386	92.24 262	92.66 185

GA-MVS		75.83 311	74.61 317	79.48 291	81.87 362	59.25 338	73.42 383	82.88 320	68.68 263	79.75 337	81.80 388	50.62 382	89.46 259	66.85 296	85.64 380	89.72 291

MVP-Stereo		75.81 312	73.51 329	82.71 223	89.35 189	73.62 139	80.06 287	85.20 290	60.30 363	73.96 393	87.94 291	57.89 347	89.45 260	52.02 403	74.87 443	85.06 362
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 313	75.20 314	77.27 324	75.01 432	69.47 205	78.93 307	84.88 300	46.67 434	87.08 183	87.84 299	50.44 384	71.62 417	77.42 168	88.53 338	90.72 264

thres100view900		75.45 314	75.05 315	76.66 334	87.27 249	51.88 401	81.07 275	73.26 391	75.68 156	83.25 280	86.37 326	45.54 406	88.80 270	51.98 404	90.99 291	89.31 298

ET-MVSNet_ETH3D		75.28 315	72.77 338	82.81 222	83.03 356	68.11 225	77.09 338	76.51 367	60.67 360	77.60 362	80.52 399	38.04 434	91.15 202	70.78 255	90.68 308	89.17 305

thres400		75.14 316	74.23 322	77.86 317	86.24 283	52.12 398	79.24 303	73.87 384	73.34 195	81.82 308	84.60 358	46.02 399	88.80 270	51.98 404	90.99 291	92.66 185

wuyk23d		75.13 317	79.30 267	62.63 425	75.56 425	75.18 131	80.89 278	73.10 393	75.06 167	94.76 16	95.32 45	87.73 44	52.85 457	34.16 455	97.11 87	59.85 453

EU-MVSNet		75.12 318	74.43 321	77.18 325	83.11 355	59.48 335	85.71 154	82.43 325	39.76 454	85.64 221	88.76 277	44.71 418	87.88 292	73.86 216	85.88 379	84.16 376

HyFIR lowres test		75.12 318	72.66 340	82.50 230	91.44 141	65.19 256	72.47 390	87.31 249	46.79 433	80.29 332	84.30 360	52.70 372	92.10 177	51.88 408	86.73 368	90.22 280

CMPMVS	59.41 20	75.12 318	73.57 327	79.77 284	75.84 424	67.22 231	81.21 273	82.18 327	50.78 425	76.50 367	87.66 303	55.20 363	82.99 364	62.17 340	90.64 313	89.09 309
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 321	72.98 336	80.73 267	84.95 312	71.71 176	76.23 354	77.59 356	52.83 410	77.73 361	86.38 325	56.35 356	84.97 345	57.72 368	87.05 363	85.51 357

tfpn200view9		74.86 322	74.23 322	76.74 333	86.24 283	52.12 398	79.24 303	73.87 384	73.34 195	81.82 308	84.60 358	46.02 399	88.80 270	51.98 404	90.99 291	89.31 298

1112_ss		74.82 323	73.74 325	78.04 313	89.57 183	60.04 327	76.49 350	87.09 261	54.31 401	73.66 396	79.80 405	60.25 328	86.76 314	58.37 362	84.15 400	87.32 337

EGC-MVSNET		74.79 324	69.99 368	89.19 67	94.89 38	87.00 15	91.89 38	86.28 270	1.09 463	2.23 465	95.98 30	81.87 121	89.48 257	79.76 130	95.96 130	91.10 251

ppachtmachnet_test		74.73 325	74.00 324	76.90 330	80.71 380	56.89 364	71.53 398	78.42 351	58.24 375	79.32 344	82.92 376	57.91 346	84.26 355	65.60 311	91.36 285	89.56 293

Patchmatch-RL test		74.48 326	73.68 326	76.89 331	84.83 314	66.54 241	72.29 391	69.16 419	57.70 380	86.76 189	86.33 327	45.79 405	82.59 365	69.63 270	90.65 312	81.54 412

PatchMatch-RL		74.48 326	73.22 333	78.27 309	87.70 237	85.26 38	75.92 359	70.09 412	64.34 323	76.09 374	81.25 393	65.87 293	78.07 394	53.86 391	83.82 402	71.48 441

XXY-MVS		74.44 328	76.19 303	69.21 394	84.61 319	52.43 397	71.70 395	77.18 361	60.73 359	80.60 326	90.96 217	75.44 197	69.35 425	56.13 375	88.33 342	85.86 353

test2506		74.12 329	73.39 330	76.28 339	91.85 123	44.20 436	84.06 192	48.20 461	72.30 220	81.90 305	94.20 89	27.22 461	89.77 254	64.81 318	96.02 127	94.87 78

reproduce_monomvs		74.09 330	73.23 332	76.65 335	76.52 416	54.54 380	77.50 332	81.40 336	65.85 301	82.86 288	86.67 322	27.38 459	84.53 350	70.24 263	90.66 311	90.89 259

CR-MVSNet		74.00 331	73.04 335	76.85 332	79.58 391	62.64 284	82.58 240	76.90 363	50.50 428	75.72 378	92.38 161	48.07 391	84.07 357	68.72 284	82.91 409	83.85 380

SSC-MVS3.2		73.90 332	75.67 309	68.61 402	84.11 330	41.28 444	64.17 435	72.83 394	72.09 223	79.08 347	87.94 291	70.31 266	73.89 410	55.99 376	94.49 191	90.67 269

Test_1112_low_res		73.90 332	73.08 334	76.35 337	90.35 167	55.95 367	73.40 384	86.17 272	50.70 426	73.14 397	85.94 334	58.31 342	85.90 334	56.51 372	83.22 406	87.20 339

test20.03		73.75 334	74.59 319	71.22 380	81.11 373	51.12 408	70.15 409	72.10 401	70.42 240	80.28 334	91.50 193	64.21 302	74.72 408	46.96 429	94.58 189	87.82 332

test_fmvs2		73.57 335	72.80 337	75.90 343	72.74 446	68.84 218	77.07 339	84.32 309	45.14 440	82.89 286	84.22 361	48.37 389	70.36 421	73.40 227	87.03 364	88.52 317

SCA		73.32 336	72.57 342	75.58 347	81.62 366	55.86 370	78.89 309	71.37 407	61.73 343	74.93 388	83.42 370	60.46 325	87.01 305	58.11 366	82.63 414	83.88 377

baseline1		73.26 337	73.54 328	72.43 373	84.92 313	47.79 421	79.89 291	74.00 382	65.93 299	78.81 349	86.28 330	56.36 355	81.63 373	56.63 371	79.04 432	87.87 331

1314		73.22 338	72.56 343	75.20 348	80.41 385	57.84 355	81.64 264	85.36 287	51.68 419	73.10 398	76.65 433	61.45 320	85.19 343	63.54 329	79.21 430	82.59 397

MVS		73.21 339	72.59 341	75.06 350	80.97 374	60.81 319	81.64 264	85.92 280	46.03 438	71.68 406	77.54 424	68.47 277	89.77 254	55.70 379	85.39 381	74.60 438

HY-MVS	64.64 18	73.03 340	72.47 344	74.71 354	83.36 346	54.19 383	82.14 259	81.96 329	56.76 390	69.57 420	86.21 331	60.03 329	84.83 347	49.58 416	82.65 412	85.11 361

thisisatest0515		73.00 341	70.52 360	80.46 273	81.45 368	59.90 331	73.16 386	74.31 381	57.86 379	76.08 375	77.78 421	37.60 437	92.12 176	65.00 316	91.45 284	89.35 297

EPNet_dtu		72.87 342	71.33 354	77.49 322	77.72 405	60.55 322	82.35 250	75.79 370	66.49 297	58.39 456	81.06 394	53.68 368	85.98 329	53.55 394	92.97 242	85.95 351
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 343	71.41 353	76.28 339	83.25 350	60.34 324	83.50 213	79.02 350	37.77 458	76.33 369	85.10 349	49.60 387	87.41 301	70.54 260	77.54 438	81.08 419

CHOSEN 1792x2688		72.45 344	70.56 359	78.13 310	90.02 178	63.08 277	68.72 416	83.16 317	42.99 448	75.92 376	85.46 342	57.22 351	85.18 344	49.87 414	81.67 416	86.14 349

testgi		72.36 345	74.61 317	65.59 416	80.56 383	42.82 441	68.29 418	73.35 390	66.87 294	81.84 307	89.93 257	72.08 253	66.92 439	46.05 433	92.54 253	87.01 341

thres200		72.34 346	71.55 352	74.70 355	83.48 340	51.60 403	75.02 368	73.71 387	70.14 246	78.56 352	80.57 398	46.20 397	88.20 285	46.99 428	89.29 327	84.32 371

FPMVS		72.29 347	72.00 346	73.14 364	88.63 213	85.00 40	74.65 372	67.39 424	71.94 226	77.80 359	87.66 303	50.48 383	75.83 402	49.95 412	79.51 426	58.58 455

FMVSNet5		72.10 348	71.69 348	73.32 362	81.57 367	53.02 392	76.77 343	78.37 352	63.31 326	76.37 368	91.85 179	36.68 438	78.98 389	47.87 425	92.45 255	87.95 327

our_test_3		71.85 349	71.59 349	72.62 370	80.71 380	53.78 386	69.72 412	71.71 406	58.80 372	78.03 354	80.51 400	56.61 354	78.84 391	62.20 338	86.04 378	85.23 359

PAPM		71.77 350	70.06 366	76.92 329	86.39 274	53.97 384	76.62 347	86.62 267	53.44 405	63.97 445	84.73 356	57.79 348	92.34 169	39.65 445	81.33 420	84.45 369

ttmdpeth		71.72 351	70.67 357	74.86 351	73.08 443	55.88 369	77.41 335	69.27 417	55.86 392	78.66 350	93.77 116	38.01 435	75.39 405	60.12 354	89.87 321	93.31 152

IB-MVS	62.13 19	71.64 352	68.97 378	79.66 288	80.80 379	62.26 297	73.94 378	76.90 363	63.27 328	68.63 424	76.79 431	33.83 442	91.84 184	59.28 359	87.26 358	84.88 363
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
UnsupCasMVSNet_eth		71.63 353	72.30 345	69.62 391	76.47 418	52.70 395	70.03 410	80.97 339	59.18 369	79.36 342	88.21 287	60.50 324	69.12 426	58.33 364	77.62 437	87.04 340

testing3		71.53 354	70.79 356	73.77 360	88.89 205	41.86 443	76.60 349	59.12 450	72.83 208	80.97 320	82.08 385	19.80 467	87.33 303	65.12 315	91.68 279	92.13 221

test_vis3_rt		71.42 355	70.67 357	73.64 361	69.66 453	70.46 190	66.97 428	89.73 201	42.68 450	88.20 153	83.04 372	43.77 420	60.07 451	65.35 314	86.66 369	90.39 278

Anonymous20231206		71.38 356	71.88 347	69.88 388	86.31 280	54.37 381	70.39 407	74.62 377	52.57 412	76.73 366	88.76 277	59.94 330	72.06 414	44.35 437	93.23 234	83.23 391

test_vis1_n_1920		71.30 357	71.58 351	70.47 383	77.58 407	59.99 330	74.25 373	84.22 310	51.06 422	74.85 389	79.10 411	55.10 364	68.83 428	68.86 281	79.20 431	82.58 398

MIMVSNet		71.09 358	71.59 349	69.57 392	87.23 251	50.07 413	78.91 308	71.83 403	60.20 366	71.26 407	91.76 186	55.08 365	76.09 400	41.06 442	87.02 365	82.54 400

test_fmvs1_n		70.94 359	70.41 363	72.53 372	73.92 434	66.93 238	75.99 358	84.21 311	43.31 447	79.40 341	79.39 409	43.47 421	68.55 430	69.05 278	84.91 392	82.10 406

MS-PatchMatch		70.93 360	70.22 364	73.06 365	81.85 363	62.50 287	73.82 380	77.90 353	52.44 413	75.92 376	81.27 392	55.67 360	81.75 371	55.37 382	77.70 436	74.94 437

pmmvs5		70.73 361	70.07 365	72.72 368	77.03 412	52.73 394	74.14 374	75.65 373	50.36 429	72.17 404	85.37 346	55.42 362	80.67 378	52.86 400	87.59 357	84.77 364

testing3-2		70.72 362	70.97 355	69.95 387	88.93 203	34.80 457	69.85 411	66.59 431	78.42 124	77.58 363	85.55 338	31.83 448	82.08 369	46.28 430	93.73 218	92.98 172

PatchT		70.52 363	72.76 339	63.79 424	79.38 395	33.53 458	77.63 328	65.37 435	73.61 188	71.77 405	92.79 150	44.38 419	75.65 403	64.53 323	85.37 382	82.18 405

test_vis1_n		70.29 364	69.99 368	71.20 381	75.97 423	66.50 242	76.69 345	80.81 340	44.22 443	75.43 381	77.23 428	50.00 385	68.59 429	66.71 299	82.85 411	78.52 431

N_pmnet		70.20 365	68.80 380	74.38 356	80.91 375	84.81 43	59.12 446	76.45 368	55.06 396	75.31 385	82.36 382	55.74 359	54.82 456	47.02 427	87.24 359	83.52 384

tpmvs		70.16 366	69.56 371	71.96 376	74.71 433	48.13 418	79.63 293	75.45 375	65.02 319	70.26 415	81.88 387	45.34 411	85.68 339	58.34 363	75.39 442	82.08 407

new-patchmatchnet		70.10 367	73.37 331	60.29 433	81.23 372	16.95 468	59.54 444	74.62 377	62.93 330	80.97 320	87.93 293	62.83 317	71.90 415	55.24 384	95.01 173	92.00 226

YYNet1		70.06 368	70.44 361	68.90 396	73.76 436	53.42 390	58.99 447	67.20 426	58.42 374	87.10 181	85.39 345	59.82 332	67.32 436	59.79 356	83.50 405	85.96 350

MVStest1		70.05 369	69.26 372	72.41 374	58.62 465	55.59 373	76.61 348	65.58 433	53.44 405	89.28 128	93.32 127	22.91 465	71.44 419	74.08 212	89.52 325	90.21 284

MDA-MVSNet_test_wron		70.05 369	70.44 361	68.88 397	73.84 435	53.47 388	58.93 448	67.28 425	58.43 373	87.09 182	85.40 344	59.80 333	67.25 437	59.66 357	83.54 404	85.92 352

CostFormer		69.98 371	68.68 381	73.87 358	77.14 410	50.72 410	79.26 302	74.51 379	51.94 418	70.97 410	84.75 355	45.16 414	87.49 299	55.16 385	79.23 429	83.40 387

testing91		69.94 372	68.99 377	72.80 367	83.81 336	45.89 429	71.57 397	73.64 389	68.24 270	70.77 413	77.82 420	34.37 441	84.44 352	53.64 393	87.00 366	88.07 322

baseline2		69.77 373	66.89 390	78.41 305	79.51 393	58.09 351	76.23 354	69.57 415	57.50 383	64.82 443	77.45 426	46.02 399	88.44 279	53.08 396	77.83 434	88.70 315

PatchmatchNet		69.71 374	68.83 379	72.33 375	77.66 406	53.60 387	79.29 301	69.99 413	57.66 381	72.53 401	82.93 375	46.45 396	80.08 384	60.91 350	72.09 446	83.31 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 375	69.05 375	71.14 382	69.15 454	65.77 252	73.98 377	83.32 315	42.83 449	77.77 360	78.27 419	43.39 424	68.50 431	68.39 288	84.38 399	79.15 429

JIA-IIPM		69.41 376	66.64 394	77.70 319	73.19 440	71.24 181	75.67 360	65.56 434	70.42 240	65.18 439	92.97 141	33.64 444	83.06 362	53.52 395	69.61 452	78.79 430

Syy-MVS		69.40 377	70.03 367	67.49 407	81.72 364	38.94 449	71.00 400	61.99 441	61.38 349	70.81 411	72.36 445	61.37 321	79.30 387	64.50 324	85.18 385	84.22 373

testing99		69.27 378	68.15 385	72.63 369	83.29 348	45.45 431	71.15 399	71.08 408	67.34 287	70.43 414	77.77 422	32.24 447	84.35 354	53.72 392	86.33 374	88.10 321

UnsupCasMVSNet_bld		69.21 379	69.68 370	67.82 405	79.42 394	51.15 407	67.82 422	75.79 370	54.15 402	77.47 364	85.36 347	59.26 336	70.64 420	48.46 422	79.35 428	81.66 410

test_cas_vis1_n_1920		69.20 380	69.12 373	69.43 393	73.68 437	62.82 281	70.38 408	77.21 360	46.18 437	80.46 331	78.95 413	52.03 374	65.53 444	65.77 310	77.45 439	79.95 427

gg-mvs-nofinetune		68.96 381	69.11 374	68.52 403	76.12 422	45.32 432	83.59 210	55.88 455	86.68 33	64.62 444	97.01 12	30.36 452	83.97 359	44.78 436	82.94 408	76.26 434

WBMVS		68.76 382	68.43 382	69.75 390	83.29 348	40.30 447	67.36 424	72.21 400	57.09 387	77.05 365	85.53 340	33.68 443	80.51 380	48.79 420	90.90 296	88.45 318

WB-MVSnew		68.72 383	69.01 376	67.85 404	83.22 352	43.98 437	74.93 369	65.98 432	55.09 395	73.83 394	79.11 410	65.63 295	71.89 416	38.21 450	85.04 388	87.69 333

tpm2		68.45 384	66.83 391	73.30 363	78.93 401	48.50 417	79.76 292	71.76 404	47.50 432	69.92 417	83.60 366	42.07 427	88.40 281	48.44 423	79.51 426	83.01 394

tpm		67.95 385	68.08 386	67.55 406	78.74 402	43.53 439	75.60 361	67.10 429	54.92 397	72.23 402	88.10 288	42.87 426	75.97 401	52.21 402	80.95 424	83.15 392

WTY-MVS		67.91 386	68.35 383	66.58 412	80.82 378	48.12 419	65.96 430	72.60 395	53.67 404	71.20 408	81.68 390	58.97 338	69.06 427	48.57 421	81.67 416	82.55 399

testing11		67.38 387	65.93 395	71.73 378	83.37 345	46.60 426	70.95 402	69.40 416	62.47 335	66.14 432	76.66 432	31.22 449	84.10 356	49.10 418	84.10 401	84.49 367

test-LLR		67.21 388	66.74 392	68.63 400	76.45 419	55.21 376	67.89 419	67.14 427	62.43 338	65.08 440	72.39 443	43.41 422	69.37 423	61.00 348	84.89 393	81.31 414

testing222		66.93 389	65.30 402	71.81 377	83.38 344	45.83 430	72.06 393	67.50 423	64.12 324	69.68 419	76.37 435	27.34 460	83.00 363	38.88 446	88.38 341	86.62 345

sss		66.92 390	67.26 388	65.90 414	77.23 409	51.10 409	64.79 432	71.72 405	52.12 417	70.13 416	80.18 402	57.96 345	65.36 445	50.21 410	81.01 422	81.25 416

KD-MVS_2432*1600		66.87 391	65.81 398	70.04 385	67.50 455	47.49 422	62.56 438	79.16 347	61.21 354	77.98 355	80.61 396	25.29 463	82.48 366	53.02 397	84.92 390	80.16 425

miper_refine_blended		66.87 391	65.81 398	70.04 385	67.50 455	47.49 422	62.56 438	79.16 347	61.21 354	77.98 355	80.61 396	25.29 463	82.48 366	53.02 397	84.92 390	80.16 425

dmvs_re		66.81 393	66.98 389	66.28 413	76.87 413	58.68 349	71.66 396	72.24 398	60.29 364	69.52 421	73.53 442	52.38 373	64.40 447	44.90 435	81.44 419	75.76 435

tpm cat1		66.76 394	65.21 403	71.42 379	77.09 411	50.62 411	78.01 320	73.68 388	44.89 441	68.64 423	79.00 412	45.51 408	82.42 368	49.91 413	70.15 449	81.23 418

UWE-MVS		66.43 395	65.56 401	69.05 395	84.15 329	40.98 445	73.06 387	64.71 437	54.84 398	76.18 373	79.62 408	29.21 454	80.50 381	38.54 449	89.75 322	85.66 355

PVSNet	58.17 21	66.41 396	65.63 400	68.75 398	81.96 361	49.88 414	62.19 440	72.51 397	51.03 423	68.04 426	75.34 439	50.84 380	74.77 406	45.82 434	82.96 407	81.60 411

tpmrst		66.28 397	66.69 393	65.05 420	72.82 445	39.33 448	78.20 319	70.69 411	53.16 408	67.88 427	80.36 401	48.18 390	74.75 407	58.13 365	70.79 448	81.08 419

Patchmatch-test		65.91 398	67.38 387	61.48 430	75.51 426	43.21 440	68.84 415	63.79 439	62.48 334	72.80 400	83.42 370	44.89 417	59.52 453	48.27 424	86.45 371	81.70 409

ADS-MVSNet2		65.87 399	63.64 408	72.55 371	73.16 441	56.92 363	67.10 426	74.81 376	49.74 430	66.04 434	82.97 373	46.71 394	77.26 397	42.29 439	69.96 450	83.46 385

myMVS_eth3d28		65.83 400	65.85 396	65.78 415	83.42 343	35.71 455	67.29 425	68.01 422	67.58 284	69.80 418	77.72 423	32.29 446	74.30 409	37.49 451	89.06 331	87.32 337

test_vis1_rt		65.64 401	64.09 405	70.31 384	66.09 459	70.20 194	61.16 441	81.60 334	38.65 455	72.87 399	69.66 448	52.84 370	60.04 452	56.16 374	77.77 435	80.68 423

mvsany_test3		65.48 402	62.97 411	73.03 366	69.99 452	76.17 124	64.83 431	43.71 463	43.68 445	80.25 335	87.05 319	52.83 371	63.09 450	51.92 407	72.44 445	79.84 428

test-mter		65.00 403	63.79 407	68.63 400	76.45 419	55.21 376	67.89 419	67.14 427	50.98 424	65.08 440	72.39 443	28.27 457	69.37 423	61.00 348	84.89 393	81.31 414

ETVMVS		64.67 404	63.34 410	68.64 399	83.44 342	41.89 442	69.56 414	61.70 446	61.33 351	68.74 422	75.76 437	28.76 455	79.35 386	34.65 454	86.16 377	84.67 366

myMVS_eth3d		64.66 405	63.89 406	66.97 410	81.72 364	37.39 452	71.00 400	61.99 441	61.38 349	70.81 411	72.36 445	20.96 466	79.30 387	49.59 415	85.18 385	84.22 373

test0.0.03 1		64.66 405	64.36 404	65.57 417	75.03 431	46.89 425	64.69 433	61.58 447	62.43 338	71.18 409	77.54 424	43.41 422	68.47 432	40.75 444	82.65 412	81.35 413

UBG		64.34 407	63.35 409	67.30 408	83.50 339	40.53 446	67.46 423	65.02 436	54.77 399	67.54 430	74.47 441	32.99 445	78.50 393	40.82 443	83.58 403	82.88 395

test_f		64.31 408	65.85 396	59.67 434	66.54 458	62.24 299	57.76 450	70.96 409	40.13 452	84.36 253	82.09 384	46.93 393	51.67 458	61.99 341	81.89 415	65.12 449

pmmvs3		62.47 409	60.02 422	69.80 389	71.58 449	64.00 267	70.52 406	58.44 453	39.77 453	66.05 433	75.84 436	27.10 462	72.28 413	46.15 432	84.77 397	73.11 439

EPMVS		62.47 409	62.63 413	62.01 426	70.63 451	38.74 450	74.76 370	52.86 457	53.91 403	67.71 429	80.01 403	39.40 431	66.60 440	55.54 381	68.81 454	80.68 423

ADS-MVSNet		61.90 411	62.19 415	61.03 431	73.16 441	36.42 454	67.10 426	61.75 444	49.74 430	66.04 434	82.97 373	46.71 394	63.21 448	42.29 439	69.96 450	83.46 385

PMMVS		61.65 412	60.38 419	65.47 418	65.40 462	69.26 208	63.97 436	61.73 445	36.80 459	60.11 451	68.43 450	59.42 334	66.35 441	48.97 419	78.57 433	60.81 452

E-PMN		61.59 413	61.62 416	61.49 429	66.81 457	55.40 374	53.77 453	60.34 449	66.80 295	58.90 454	65.50 453	40.48 430	66.12 442	55.72 378	86.25 375	62.95 451

TESTMET0.1,1		61.29 414	60.32 420	64.19 422	72.06 447	51.30 405	67.89 419	62.09 440	45.27 439	60.65 450	69.01 449	27.93 458	64.74 446	56.31 373	81.65 418	76.53 433

MVS-HIRNet		61.16 415	62.92 412	55.87 437	79.09 398	35.34 456	71.83 394	57.98 454	46.56 435	59.05 453	91.14 209	49.95 386	76.43 399	38.74 447	71.92 447	55.84 456

EMVS		61.10 416	60.81 418	61.99 427	65.96 460	55.86 370	53.10 454	58.97 452	67.06 292	56.89 458	63.33 454	40.98 428	67.03 438	54.79 387	86.18 376	63.08 450

DSMNet-mixed		60.98 417	61.61 417	59.09 436	72.88 444	45.05 434	74.70 371	46.61 462	26.20 460	65.34 438	90.32 246	55.46 361	63.12 449	41.72 441	81.30 421	69.09 445

dp		60.70 418	60.29 421	61.92 428	72.04 448	38.67 451	70.83 404	64.08 438	51.28 421	60.75 449	77.28 427	36.59 439	71.58 418	47.41 426	62.34 456	75.52 436

dmvs_testset		60.59 419	62.54 414	54.72 439	77.26 408	27.74 462	74.05 376	61.00 448	60.48 361	65.62 437	67.03 452	55.93 358	68.23 434	32.07 458	69.46 453	68.17 446

CHOSEN 280x420		59.08 420	56.52 426	66.76 411	76.51 417	64.39 263	49.62 455	59.00 451	43.86 444	55.66 459	68.41 451	35.55 440	68.21 435	43.25 438	76.78 441	67.69 447

mvsany_test1		58.48 421	56.47 427	64.50 421	65.90 461	68.21 224	56.95 451	42.11 464	38.30 456	65.69 436	77.19 430	56.96 352	59.35 454	46.16 431	58.96 457	65.93 448

UWE-MVS-28		58.44 422	57.71 424	60.65 432	73.58 438	31.23 459	69.68 413	48.80 460	53.12 409	61.79 447	78.83 414	30.98 450	68.40 433	21.58 461	80.99 423	82.33 404

PVSNet_0	51.08 22	56.10 423	54.97 428	59.48 435	75.12 430	53.28 391	55.16 452	61.89 443	44.30 442	59.16 452	62.48 455	54.22 366	65.91 443	35.40 453	47.01 458	59.25 454

new_pmnet		55.69 424	57.66 425	49.76 440	75.47 427	30.59 460	59.56 443	51.45 458	43.62 446	62.49 446	75.48 438	40.96 429	49.15 460	37.39 452	72.52 444	69.55 444

PMMVS2		55.64 425	59.27 423	44.74 441	64.30 463	12.32 469	40.60 456	49.79 459	53.19 407	65.06 442	84.81 354	53.60 369	49.76 459	32.68 457	89.41 326	72.15 440

MVE	40.22 23	51.82 426	50.47 429	55.87 437	62.66 464	51.91 400	31.61 458	39.28 465	40.65 451	50.76 460	74.98 440	56.24 357	44.67 461	33.94 456	64.11 455	71.04 443
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 427	42.65 430	39.67 442	70.86 450	21.11 464	61.01 442	21.42 469	57.36 384	57.97 457	50.06 458	16.40 468	58.73 455	21.03 462	27.69 462	39.17 458

kuosan		30.83 428	32.17 431	26.83 444	53.36 466	19.02 467	57.90 449	20.44 470	38.29 457	38.01 461	37.82 460	15.18 469	33.45 463	7.74 464	20.76 463	28.03 459

test_method		30.46 429	29.60 432	33.06 443	17.99 468	3.84 471	13.62 459	73.92 383	2.79 462	18.29 464	53.41 457	28.53 456	43.25 462	22.56 459	35.27 460	52.11 457

cdsmvs_eth3d_5k		20.81 430	27.75 433	0.00 449	0.00 472	0.00 474	0.00 460	85.44 286	0.00 467	0.00 468	82.82 377	81.46 125	0.00 468	0.00 467	0.00 466	0.00 464

tmp_tt		20.25 431	24.50 434	7.49 446	4.47 469	8.70 470	34.17 457	25.16 467	1.00 464	32.43 463	18.49 461	39.37 432	9.21 465	21.64 460	43.75 459	4.57 461

ab-mvs-re		6.65 432	8.87 435	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	79.80 405	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

pcd_1.5k_mvsjas		6.41 433	8.55 436	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	76.94 181	0.00 468	0.00 467	0.00 466	0.00 464

test123		6.27 434	8.08 437	0.84 447	1.11 471	0.57 472	62.90 437	0.82 471	0.54 465	1.07 467	2.75 466	1.26 470	0.30 466	1.04 465	1.26 465	1.66 462

testmvs		5.91 435	7.65 438	0.72 448	1.20 470	0.37 473	59.14 445	0.67 472	0.49 466	1.11 466	2.76 465	0.94 471	0.24 467	1.02 466	1.47 464	1.55 463

mmdepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

monomultidepth		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

test_blank		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uanet_test		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

DCPMVS		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

sosnet-low-res		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

sosnet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uncertanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

Regformer		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

uanet		0.00 436	0.00 439	0.00 449	0.00 472	0.00 474	0.00 460	0.00 473	0.00 467	0.00 468	0.00 467	0.00 472	0.00 468	0.00 467	0.00 466	0.00 464

WAC-MVS						37.39 452								52.61 401

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

MSC_two_6792asdad				88.81 73	91.55 135	77.99 97		91.01 159					96.05 9	87.45 28	98.17 37	92.40 201

PC_three_1452									58.96 371	90.06 103	91.33 201	80.66 135	93.03 151	75.78 190	95.94 133	92.48 195

No_MVS				88.81 73	91.55 135	77.99 97		91.01 159					96.05 9	87.45 28	98.17 37	92.40 201

test_one_0601					93.85 64	73.27 145		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 472

eth-test					0.00 472

ZD-MVS					92.22 109	80.48 71		91.85 129	71.22 233	90.38 98	92.98 139	86.06 65	96.11 7	81.99 109	96.75 97

RE-MVS-def			92.61 9		94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 37	97.60 71	92.73 179

IU-MVS					94.18 52	72.64 154		90.82 164	56.98 388	89.67 116				85.78 62	97.92 52	93.28 153

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 206	81.12 129	94.68 78	74.48 202	95.35 156	92.29 211

test_241102_TWO								93.71 56	83.77 58	93.49 40	94.27 83	89.27 24	95.84 24	86.03 55	97.82 57	92.04 224

test_241102_ONE					94.18 52	72.65 152		93.69 57	83.62 60	94.11 27	93.78 114	90.28 15	95.50 49

9.14			89.29 63		91.84 125		88.80 94	95.32 13	75.14 166	91.07 83	92.89 144	87.27 48	93.78 117	83.69 87	97.55 74

save fliter					93.75 65	77.44 106	86.31 140	89.72 202	70.80 237

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 172

test_0728_SECOND				86.79 108	94.25 50	72.45 162	90.54 53	94.10 40					95.88 18	86.42 45	97.97 49	92.02 225

test0726					94.16 55	72.56 158	90.63 50	93.90 49	83.61 61	93.75 35	94.49 73	89.76 19

GSMVS																83.88 377

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 398				83.88 377

sam_mvs												45.92 403

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 215		89.40 125	93.96 105	71.67 260	91.38 197	78.83 144	96.55 102	92.71 182

MTGPA								91.81 133

test_post1							78.85 311				3.13 463	45.19 413	80.13 383	58.11 366

test_post											3.10 464	45.43 409	77.22 398

patchmatchnet-post											81.71 389	45.93 402	87.01 305

GG-mvs-BLEND				67.16 409	73.36 439	46.54 428	84.15 190	55.04 456		58.64 455	61.95 456	29.93 453	83.87 360	38.71 448	76.92 440	71.07 442

MTMP							90.66 49	33.14 466

gm-plane-assit					75.42 428	44.97 435			52.17 414		72.36 445		87.90 291	54.10 390

test9_res														80.83 119	96.45 108	90.57 272

TEST9					92.34 104	79.70 80	83.94 196	90.32 182	65.41 311	84.49 249	90.97 215	82.03 116	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 184	65.79 302	84.36 253	90.96 217	81.93 118	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 280

agg_prior					91.58 133	77.69 103		90.30 185		84.32 255			93.18 144

TestCases				89.68 56	91.59 130	83.40 52		95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 191	70.81 253	96.14 121	94.16 110

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 246	91.57 191	81.92 120		79.54 136	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 97					94.17 102			92.80 177

旧先验2							81.73 262		56.88 389	86.54 202			84.90 346	72.81 238

新几何2							81.72 263

新几何1				82.95 216	93.96 61	78.56 91		80.24 343	55.45 394	83.93 266	91.08 212	71.19 262	88.33 283	65.84 308	93.07 238	81.95 408

旧先验1					91.97 117	71.77 171		81.78 331			91.84 180	73.92 224			93.65 221	83.61 383

无先验							82.81 235	85.62 284	58.09 377				91.41 196	67.95 292		84.48 368

原ACMM2							82.26 255

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 140	62.59 332	82.73 290	90.67 233	76.53 190	94.25 94	69.24 273	95.69 148	85.55 356

test222					93.31 78	76.54 116	79.38 300	77.79 354	52.59 411	82.36 295	90.84 225	66.83 286			91.69 278	81.25 416

testdata2													86.43 320	63.52 330

segment_acmp												81.94 117

testdata				79.54 290	92.87 89	72.34 163		80.14 344	59.91 367	85.47 226	91.75 187	67.96 280	85.24 342	68.57 287	92.18 265	81.06 421

testdata1							79.62 294		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 167		82.76 289		79.20 148	94.80 75		95.32 158	92.27 213

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 206

plane_prior5								93.61 60					95.22 59	80.78 120	95.83 141	94.46 93

plane_prior4											92.95 142

plane_prior3						76.85 114			77.79 133	86.55 196

plane_prior2							89.45 83		79.44 108

plane_prior1					92.83 93

plane_prior						76.42 119	87.15 122		75.94 153						95.03 170

n2								0.00 473

nn								0.00 473

door-mid								74.45 380

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 410		91.79 71	94.42 78	61.76 319	92.93 154	79.52 137	93.03 239	93.93 119

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 74	93.67 38	94.82 60	91.18 5	95.52 45	85.36 65	98.73 7	95.23 66

test11								91.46 141

door								72.57 396

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 328

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 328

BP-MVS														77.30 169

HQP4-MVS										80.56 327			94.61 82			93.56 145

HQP3-MVS								92.68 102							94.47 192

HQP2-MVS												72.10 251

NP-MVS					91.95 118	74.55 134					90.17 253

MDTV_nov1_ep13_2view						27.60 463	70.76 405		46.47 436	61.27 448		45.20 412		49.18 417		83.75 382

MDTV_nov1_ep13			68.29 384		78.03 403	43.87 438	74.12 375	72.22 399	52.17 414	67.02 431	85.54 339	45.36 410	80.85 377	55.73 377	84.42 398

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 150

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 185	81.56 82	90.02 105	91.20 208	82.40 103	90.81 217	73.58 224	94.66 187	94.56 89

DeepMVS_CX				24.13 445	32.95 467	29.49 461		21.63 468	12.07 461	37.95 462	45.07 459	30.84 451	19.21 464	17.94 463	33.06 461	23.69 460

High-resolution multi-view benchmark