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	80.16 143	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	92.08 225

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	91.76 236

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	91.76 236

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	93.63 140

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	92.73 182

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	92.19 220

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	94.18 110

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 90
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	92.60 108	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 165

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	92.55 195

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	94.20 107

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 135	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 212

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 100	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	93.99 117

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	93.83 126

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	92.85 179

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	94.12 114

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	92.98 175
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	93.96 119

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	94.39 102
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	93.19 161
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	93.93 120

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	93.27 156

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	92.41 202
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 93

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	91.70 240
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	92.29 214

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	93.88 123
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	94.30 106

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	89.57 210	88.51 21	90.11 102	95.12 53	90.98 7	88.92 271	77.55 164	97.07 88	83.13 397
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	81.12 131	95.88 18	87.41 30	95.94 133	92.48 198

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 161	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 277
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	58.83 350	89.04 89	92.74 102	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	58.88 347	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	72.94 244	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	57.84 358	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	93.64 139

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	77.55 172	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	62.39 294	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 128	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	92.78 101	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 75	97.55 74	94.10 115
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	90.63 171	70.00 248	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	90.20 192	69.87 249	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	90.08 195	71.54 228	94.28 25	96.54 19	81.57 126	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	86.75 191	93.26 128	93.64 2	90.93 210	84.60 78	90.75 306	93.97 118

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	90.45 279
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	90.29 189	69.27 256	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	92.30 117	79.74 103	87.50 175	92.38 162	81.42 128	93.28 141	83.07 92	97.24 84	91.67 241

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	88.31 287	83.33 88	98.30 27	93.20 160
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	89.44 263	74.12 212	96.10 124	94.45 96

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	89.44 263	74.12 212	96.10 124	94.45 96

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 353	88.66 98	92.06 124	90.78 7	95.67 8	95.17 51	81.80 123	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	93.60 143

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	93.26 158
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	64.33 265	89.67 75	88.38 230	88.84 17	94.29 23	97.57 7	90.48 14	91.26 198	72.57 242	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	92.25 171	72.03 258	96.36 4	88.21 13	90.93 298	92.98 175
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	92.29 169	86.11 64	90.22 237	86.24 52	97.24 84	91.36 249
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	91.27 153	78.20 126	86.69 195	92.28 170	80.36 141	95.06 67	86.17 53	96.49 105	90.22 283

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	74.07 222	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	74.07 222	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	89.44 213	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 129	95.21 162	91.82 234

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	92.87 146	84.56 77	93.89 113	77.65 162	96.62 100	90.70 269

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 130	84.26 53	90.87 92	93.92 109	82.18 112	89.29 267	73.75 220	94.81 181	93.70 134

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	91.97 127	70.73 239	94.19 26	96.67 17	76.94 184	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	91.22 208	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	84.32 258	89.33 272	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	91.19 156	67.85 282	86.63 196	94.84 59	79.58 149	95.96 15	87.62 24	94.50 190	94.56 90
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	63.94 269	91.10 46	89.64 207	85.07 45	90.91 88	91.09 213	89.16 25	91.87 183	82.03 107	95.87 139	93.13 163

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	85.12 234	89.67 266	84.47 79	95.46 50	82.56 101	96.26 116	93.77 132

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 157	79.26 111	89.68 115	94.81 63	82.44 101	87.74 298	76.54 179	88.74 341	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	70.81 255	96.14 121	94.16 111

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 266	84.38 185	91.29 150	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 233	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	91.23 154	77.31 139	87.07 185	91.47 198	82.94 95	94.71 77	84.67 77	96.27 115	92.62 190

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	86.55 197	92.95 143	74.84 209	95.22 59	80.78 120	95.83 141	94.46 94

sc_t1		87.70 88	88.94 71	83.99 181	93.47 71	67.15 232	85.05 168	88.21 237	86.81 32	91.87 70	97.65 5	85.51 71	87.91 293	74.22 207	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	86.76 268	84.54 50	83.58 277	93.78 114	73.36 239	96.48 2	87.98 17	96.21 117	94.41 101

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 200	92.03 116	62.26 298	91.49 41	92.62 106	88.07 25	88.07 155	96.17 26	72.24 253	95.79 31	84.85 74	94.16 203	92.58 193

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 159	78.77 119	84.85 245	90.89 223	80.85 134	95.29 56	81.14 115	95.32 158	92.34 210

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 216	92.38 113	70.25 245	89.35 126	90.68 233	82.85 96	94.57 84	79.55 135	95.95 132	92.00 229

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 211	87.84 112	88.05 238	81.66 81	94.64 18	96.53 20	65.94 295	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	89.74 202	74.40 178	89.92 110	93.41 125	80.45 139	90.63 225	86.66 44	94.37 196	94.73 87

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	81.64 318	87.25 317	82.43 102	94.53 87	77.65 162	96.46 107	94.14 113

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	91.81 184	77.72 168	94.18 100	75.00 201	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	92.09 123	78.87 117	84.27 263	94.05 98	78.35 160	93.65 121	80.54 124	91.58 285	92.08 225
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	82.55 243	91.56 140	83.08 68	90.92 86	91.82 183	78.25 161	93.99 107	74.16 210	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 226	92.21 118	81.73 80	90.92 86	91.97 176	77.20 178	93.99 107	74.16 210	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	85.94 295	78.30 92	86.93 125	92.20 119	65.94 302	89.16 129	93.16 132	83.10 93	89.89 252	87.81 20	94.43 194	93.35 151

tt0320-xc		86.67 102	88.41 81	81.44 255	93.45 72	60.44 325	83.96 195	88.50 226	87.26 29	90.90 90	97.90 3	85.61 68	86.40 324	70.14 267	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	88.64 223	86.30 37	89.60 121	92.59 155	69.22 276	94.91 71	73.89 217	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 256	93.57 69	60.73 322	84.37 186	88.61 225	87.00 31	90.75 93	97.98 2	85.54 70	86.45 322	69.75 272	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 152	88.16 225	63.28 276	86.64 135	92.20 119	75.42 163	92.81 54	94.50 72	74.05 225	94.06 106	83.88 84	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 241	92.22 109	62.28 297	84.66 177	89.15 217	83.54 63	89.85 111	97.32 8	88.08 39	86.80 315	70.43 264	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	85.12 234	90.34 246	66.19 292	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	84.76 247	87.70 306	78.87 154	94.18 100	80.67 122	96.29 112	92.73 182

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 131	80.35 95	89.54 124	88.01 293	79.09 152	92.13 174	75.51 194	95.06 169	90.41 280

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 126	70.56 240	84.96 240	90.69 232	80.01 145	95.14 64	78.37 149	95.78 145	91.82 234
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	62.35 296	86.42 139	91.33 149	76.78 143	92.73 56	94.48 74	73.41 236	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	90.71 168	85.85 40	89.94 109	95.24 50	82.13 113	90.40 232	69.19 279	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	92.61 107	75.59 159	88.32 150	92.87 146	82.22 111	88.63 279	88.80 9	92.82 248	89.83 293

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 310	78.25 93	85.82 151	91.82 133	65.33 316	88.55 141	92.35 168	82.62 100	89.80 254	86.87 40	94.32 198	93.18 162

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 200	92.01 125	65.91 304	86.19 208	91.75 188	83.77 86	94.98 69	77.43 167	96.71 98	93.73 133

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 261	82.21 257	90.46 177	80.99 88	88.42 146	91.97 176	77.56 171	93.85 114	72.46 243	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	90.32 184	65.79 306	84.49 252	90.97 217	81.93 119	93.63 123	81.21 114	96.54 103	90.88 263

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 199	83.70 206	89.83 201	80.42 93	87.76 169	93.24 129	73.76 230	91.54 189	85.03 71	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 227	92.25 107	56.44 369	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 211	93.41 138	75.11 200	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 319	78.21 94	85.40 161	91.39 147	65.32 317	87.72 171	91.81 184	82.33 105	89.78 255	86.68 42	94.20 201	92.99 173

Effi-MVS+-dtu		85.82 121	83.38 186	93.14 4	87.13 254	91.15 3	87.70 113	88.42 229	74.57 172	83.56 278	85.65 341	78.49 159	94.21 96	72.04 245	92.88 244	94.05 116

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	69.17 257	85.80 218	89.56 267	80.76 135	92.13 174	73.21 238	95.51 152	93.25 159
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	87.54 309	86.45 59	91.06 205	75.76 192	93.76 214	92.54 196

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	87.54 309	86.45 59	91.06 205	75.76 192	93.76 214	92.54 196

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 161	87.10 257	69.98 197	84.28 187	92.68 103	74.77 169	87.90 162	92.36 167	73.94 226	90.41 231	85.95 60	92.74 250	93.66 135

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 241	81.51 83	87.05 186	91.83 182	66.18 294	95.29 56	70.75 258	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	91.39 147	72.33 219	87.59 173	90.25 251	84.85 75	92.37 168	78.00 158	91.94 275	93.66 135

MVS_0304		85.37 128	84.58 158	87.75 93	85.28 309	73.36 141	86.54 138	85.71 285	77.56 137	81.78 316	92.47 160	70.29 270	96.02 11	85.59 63	95.96 130	93.87 124

FIs		85.35 129	86.27 114	82.60 226	91.86 122	57.31 362	85.10 167	93.05 87	75.83 154	91.02 85	93.97 102	73.57 232	92.91 156	73.97 216	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 297	76.13 125	85.15 166	92.32 116	61.40 352	91.33 78	90.85 226	83.76 87	86.16 330	84.31 80	93.28 232	92.15 223

casdiffmvs		85.21 131	85.85 125	83.31 205	86.17 288	62.77 283	83.03 228	93.93 47	74.69 171	88.21 152	92.68 154	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	86.30 283	62.37 295	84.55 180	93.96 45	74.48 175	87.12 180	92.03 175	82.30 107	91.94 179	78.39 148	94.21 200	94.74 86

SSM_0404		85.16 133	85.09 142	85.36 142	90.14 172	69.52 204	86.17 144	91.58 138	74.41 176	86.55 197	91.49 195	78.54 155	93.97 109	73.71 221	93.21 236	92.59 192

K. test v3		85.14 134	84.73 151	86.37 115	91.13 151	69.63 203	85.45 159	76.68 370	84.06 56	92.44 61	96.99 13	62.03 322	94.65 80	80.58 123	93.24 233	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 210	84.65 321	74.71 132	85.87 149	90.35 183	77.94 129	83.82 270	96.96 15	77.75 166	80.03 389	78.44 147	96.21 117	94.79 85

EI-MVSNet-Vis-set		85.12 136	84.53 161	86.88 106	84.01 334	72.76 151	83.91 199	85.18 295	80.44 92	88.75 136	85.49 345	80.08 144	91.92 180	82.02 108	90.85 303	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	90.61 173	67.64 286	84.88 243	92.05 174	82.30 107	88.36 285	83.84 86	91.10 291	92.62 190

MGCFI-Net		85.04 138	85.95 121	82.31 235	87.52 244	63.59 272	86.23 143	93.96 45	73.46 190	88.07 155	87.83 304	86.46 58	90.87 215	76.17 186	93.89 211	92.47 200

EI-MVSNet-UG-set		85.04 138	84.44 164	86.85 107	83.87 338	72.52 160	83.82 201	85.15 296	80.27 97	88.75 136	85.45 347	79.95 146	91.90 181	81.92 111	90.80 305	96.13 39

X-MVStestdata		85.04 138	82.70 204	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 466	86.57 56	95.80 28	87.35 32	97.62 69	94.20 107

MSLP-MVS++		85.00 141	86.03 120	81.90 241	91.84 125	71.56 179	86.75 133	93.02 91	75.95 152	87.12 180	89.39 270	77.98 163	89.40 266	77.46 165	94.78 182	84.75 369

F-COLMAP		84.97 142	83.42 185	89.63 58	92.39 102	83.40 52	88.83 93	91.92 129	73.19 201	80.18 340	89.15 276	77.04 182	93.28 141	65.82 312	92.28 264	92.21 219

SSM_0407		84.89 143	84.85 148	85.01 150	89.13 195	68.97 214	85.60 156	91.58 138	74.41 176	85.68 219	91.49 195	78.54 155	93.69 120	73.71 221	93.47 225	92.38 207

balanced_conf03		84.80 144	85.40 136	83.00 213	88.95 202	61.44 306	90.42 59	92.37 115	71.48 230	88.72 138	93.13 133	70.16 272	95.15 63	79.26 140	94.11 204	92.41 202

3Dnovator	80.37 7	84.80 144	84.71 154	85.06 149	86.36 281	74.71 132	88.77 95	90.00 197	75.65 157	84.96 240	93.17 131	74.06 224	91.19 200	78.28 152	91.09 292	89.29 303

SymmetryMVS		84.79 146	83.54 180	88.55 79	92.44 101	80.42 72	88.63 99	82.37 330	74.56 173	85.12 234	90.34 246	66.19 292	94.20 97	76.57 177	95.68 149	91.03 257

IterMVS-LS		84.73 147	84.98 145	83.96 183	87.35 248	63.66 270	83.25 221	89.88 200	76.06 147	89.62 118	92.37 165	73.40 238	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	84.34 168	85.49 141	90.18 171	75.86 127	79.23 308	87.13 258	73.35 194	85.56 226	89.34 271	83.60 89	90.50 228	76.64 176	94.05 208	90.09 289

HQP-MVS		84.61 149	84.06 173	86.27 118	91.19 147	70.66 187	84.77 170	92.68 103	73.30 197	80.55 332	90.17 256	72.10 254	94.61 82	77.30 169	94.47 192	93.56 147

v1192		84.57 150	84.69 156	84.21 176	87.75 235	62.88 280	83.02 229	91.43 144	69.08 259	89.98 108	90.89 223	72.70 248	93.62 126	82.41 103	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 151	84.71 154	84.11 179	87.92 230	72.09 168	84.80 169	88.64 223	64.43 326	88.77 135	91.78 186	78.07 162	87.95 292	85.85 61	92.18 268	92.30 212

FMVSNet1		84.55 152	85.45 135	81.85 243	90.27 169	61.05 313	86.83 129	88.27 234	78.57 122	89.66 117	95.64 38	75.43 201	90.68 222	69.09 280	95.33 157	93.82 127

v1144		84.54 153	84.72 153	84.00 180	87.67 239	62.55 287	82.97 231	90.93 164	70.32 244	89.80 112	90.99 216	73.50 233	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 300	84.20 331	73.57 140	89.55 78	90.44 178	84.24 54	84.38 255	94.89 57	76.35 197	80.40 386	76.14 187	96.80 96	82.36 407
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	84.27 169	84.74 157	87.25 250	70.84 186	83.55 211	88.45 228	68.64 268	86.29 207	91.31 204	74.97 207	88.42 283	87.87 19	90.07 320	94.95 75

MCST-MVS		84.36 156	83.93 176	85.63 135	91.59 130	71.58 177	83.52 212	92.13 121	61.82 345	83.96 268	89.75 264	79.93 147	93.46 135	78.33 151	94.34 197	91.87 233

VDDNet		84.35 157	85.39 137	81.25 258	95.13 32	59.32 339	85.42 160	81.11 341	86.41 36	87.41 176	96.21 25	73.61 231	90.61 226	66.33 305	96.85 92	93.81 130

ETV-MVS		84.31 158	83.91 177	85.52 138	88.58 215	70.40 191	84.50 184	93.37 68	78.76 120	84.07 266	78.72 420	80.39 140	95.13 65	73.82 219	92.98 242	91.04 256

v1240		84.30 159	84.51 162	83.65 193	87.65 240	61.26 310	82.85 235	91.54 141	67.94 279	90.68 95	90.65 237	71.71 262	93.64 122	82.84 97	94.78 182	96.07 41

MVS_111021_LR		84.28 160	83.76 178	85.83 132	89.23 193	83.07 55	80.99 278	83.56 318	72.71 211	86.07 211	89.07 278	81.75 125	86.19 329	77.11 171	93.36 228	88.24 322

h-mvs33		84.25 161	82.76 203	88.72 75	91.82 127	82.60 60	84.00 194	84.98 302	71.27 231	86.70 193	90.55 242	63.04 319	93.92 112	78.26 153	94.20 201	89.63 295

v144192		84.24 162	84.41 165	83.71 192	87.59 242	61.57 305	82.95 232	91.03 160	67.82 283	89.80 112	90.49 243	73.28 240	93.51 133	81.88 112	94.89 177	96.04 43

dcpmvs_2		84.23 163	85.14 141	81.50 253	88.61 214	61.98 302	82.90 234	93.11 83	68.66 267	92.77 55	92.39 161	78.50 158	87.63 301	76.99 173	92.30 261	94.90 76

v1921920		84.23 163	84.37 167	83.79 188	87.64 241	61.71 304	82.91 233	91.20 155	67.94 279	90.06 103	90.34 246	72.04 257	93.59 128	82.32 104	94.91 175	96.07 41

VDD-MVS		84.23 163	84.58 158	83.20 208	91.17 150	65.16 257	83.25 221	84.97 303	79.79 102	87.18 179	94.27 83	74.77 212	90.89 213	69.24 276	96.54 103	93.55 149

v2v482		84.09 166	84.24 170	83.62 194	87.13 254	61.40 307	82.71 238	89.71 205	72.19 222	89.55 122	91.41 199	70.70 268	93.20 143	81.02 116	93.76 214	96.25 37

EG-PatchMatch MVS		84.08 167	84.11 172	83.98 182	92.22 109	72.61 157	82.20 259	87.02 264	72.63 212	88.86 132	91.02 215	78.52 157	91.11 203	73.41 228	91.09 292	88.21 323

fmvsm_s_conf0.5_n_6		84.05 168	84.14 171	83.81 186	87.75 235	71.17 182	83.42 215	91.10 158	67.90 281	84.53 250	90.70 231	73.01 243	88.73 277	85.09 68	93.72 219	91.53 246

DP-MVS Recon		84.05 168	83.22 189	86.52 113	91.73 128	75.27 130	83.23 223	92.40 111	72.04 224	82.04 307	88.33 289	77.91 165	93.95 111	66.17 306	95.12 167	90.34 282

viewmacassd2359aftdt		84.04 170	84.78 150	81.81 246	86.43 275	60.32 327	81.95 261	92.82 99	71.56 227	86.06 212	92.98 139	81.79 124	90.28 233	76.18 185	93.24 233	94.82 84

TransMVSNet (Re)		84.02 171	85.74 130	78.85 299	91.00 154	55.20 381	82.29 253	87.26 253	79.65 105	88.38 148	95.52 41	83.00 94	86.88 313	67.97 294	96.60 101	94.45 96

Baseline_NR-MVSNet		84.00 172	85.90 123	78.29 311	91.47 140	53.44 393	82.29 253	87.00 267	79.06 114	89.55 122	95.72 36	77.20 178	86.14 331	72.30 244	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 173	84.46 163	82.53 230	86.11 291	70.65 189	82.45 248	89.17 216	67.72 285	86.74 192	91.49 195	79.20 150	85.86 340	84.71 76	92.60 254	91.07 255

TSAR-MVS + GP.		83.95 174	82.69 205	87.72 94	89.27 192	81.45 67	83.72 205	81.58 339	74.73 170	85.66 222	86.06 336	72.56 250	92.69 160	75.44 196	95.21 162	89.01 316

LuminaMVS		83.94 175	83.51 181	85.23 144	89.78 181	71.74 172	84.76 173	87.27 252	72.60 213	89.31 127	90.60 241	64.04 308	90.95 208	79.08 141	94.11 204	92.99 173

alignmvs		83.94 175	83.98 175	83.80 187	87.80 234	67.88 228	84.54 182	91.42 146	73.27 200	88.41 147	87.96 294	72.33 251	90.83 216	76.02 189	94.11 204	92.69 186

Effi-MVS+		83.90 177	84.01 174	83.57 198	87.22 252	65.61 253	86.55 137	92.40 111	78.64 121	81.34 323	84.18 366	83.65 88	92.93 154	74.22 207	87.87 355	92.17 222

fmvsm_s_conf0.1_n_2		83.82 178	83.49 182	84.84 152	85.99 294	70.19 195	80.93 279	87.58 248	67.26 292	87.94 161	92.37 165	71.40 264	88.01 289	86.03 55	91.87 276	96.31 36

mvs5depth		83.82 178	84.54 160	81.68 249	82.23 363	68.65 219	86.89 126	89.90 199	80.02 101	87.74 170	97.86 4	64.19 307	82.02 374	76.37 181	95.63 151	94.35 103

CANet		83.79 180	82.85 202	86.63 110	86.17 288	72.21 167	83.76 204	91.43 144	77.24 140	74.39 395	87.45 313	75.36 202	95.42 52	77.03 172	92.83 247	92.25 218

pm-mvs1		83.69 181	84.95 147	79.91 285	90.04 177	59.66 336	82.43 249	87.44 249	75.52 161	87.85 165	95.26 49	81.25 130	85.65 344	68.74 286	96.04 126	94.42 100

AdaColmap		83.66 182	83.69 179	83.57 198	90.05 176	72.26 165	86.29 141	90.00 197	78.19 127	81.65 317	87.16 319	83.40 91	94.24 95	61.69 348	94.76 185	84.21 379

MIMVSNet1		83.63 183	84.59 157	80.74 269	94.06 59	62.77 283	82.72 237	84.53 310	77.57 136	90.34 99	95.92 31	76.88 190	85.83 341	61.88 346	97.42 79	93.62 141

fmvsm_s_conf0.5_n_2		83.62 184	83.29 188	84.62 162	85.43 307	70.18 196	80.61 284	87.24 254	67.14 293	87.79 167	91.87 178	71.79 261	87.98 291	86.00 59	91.77 279	95.71 50

test_fmvsm_n_1920		83.60 185	82.89 199	85.74 133	85.22 311	77.74 102	84.12 191	90.48 175	59.87 372	86.45 206	91.12 212	75.65 199	85.89 338	82.28 105	90.87 301	93.58 145

WR-MVS		83.56 186	84.40 166	81.06 263	93.43 75	54.88 382	78.67 317	85.02 300	81.24 85	90.74 94	91.56 193	72.85 245	91.08 204	68.00 293	98.04 41	97.23 17

CNLPA		83.55 187	83.10 194	84.90 151	89.34 190	83.87 50	84.54 182	88.77 220	79.09 113	83.54 279	88.66 286	74.87 208	81.73 376	66.84 300	92.29 263	89.11 309

LCM-MVSNet-Re		83.48 188	85.06 143	78.75 301	85.94 295	55.75 375	80.05 290	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 258	59.95 359	94.89 177	90.75 266

hse-mvs2		83.47 189	81.81 220	88.47 80	91.03 153	82.27 61	82.61 239	83.69 316	71.27 231	86.70 193	86.05 337	63.04 319	92.41 166	78.26 153	93.62 223	90.71 268

V42		83.47 189	83.37 187	83.75 190	83.16 357	63.33 275	81.31 272	90.23 191	69.51 253	90.91 88	90.81 228	74.16 221	92.29 172	80.06 126	90.22 318	95.62 54

VPA-MVSNet		83.47 189	84.73 151	79.69 290	90.29 168	57.52 361	81.30 274	88.69 222	76.29 145	87.58 174	94.44 75	80.60 138	87.20 307	66.60 303	96.82 95	94.34 104

mamba_0408		83.44 192	82.88 200	85.11 147	89.13 195	68.97 214	72.73 392	91.28 151	72.90 205	85.68 219	90.61 239	76.78 191	93.97 109	73.37 230	93.47 225	92.38 207

PAPM_NR		83.23 193	83.19 191	83.33 204	90.90 156	65.98 249	88.19 104	90.78 167	78.13 128	80.87 328	87.92 298	73.49 235	92.42 165	70.07 268	88.40 344	91.60 243

CLD-MVS		83.18 194	82.64 206	84.79 155	89.05 198	67.82 229	77.93 327	92.52 109	68.33 271	85.07 237	81.54 395	82.06 116	92.96 152	69.35 275	97.91 54	93.57 146
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 195	85.68 131	75.65 349	81.24 375	45.26 437	79.94 292	92.91 95	83.83 57	91.33 78	96.88 16	80.25 142	85.92 334	68.89 283	95.89 138	95.76 48

FA-MVS(test-final)		83.13 196	83.02 195	83.43 201	86.16 290	66.08 248	88.00 108	88.36 231	75.55 160	85.02 238	92.75 152	65.12 301	92.50 164	74.94 202	91.30 289	91.72 238

114514_t		83.10 197	82.54 209	84.77 156	92.90 88	69.10 213	86.65 134	90.62 172	54.66 404	81.46 320	90.81 228	76.98 183	94.38 90	72.62 241	96.18 119	90.82 265

RRT-MVS		82.97 198	83.44 183	81.57 251	85.06 314	58.04 356	87.20 119	90.37 181	77.88 131	88.59 140	93.70 119	63.17 316	93.05 150	76.49 180	88.47 343	93.62 141

viewmanbaseed2359cas		82.95 199	83.43 184	81.52 252	85.18 312	60.03 332	81.36 271	92.38 113	69.55 252	84.84 246	91.38 200	79.85 148	90.09 246	74.22 207	92.09 270	94.43 99

BP-MVS1		82.81 200	81.67 222	86.23 119	87.88 232	68.53 220	86.06 146	84.36 311	75.65 157	85.14 233	90.19 253	45.84 408	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 201	81.64 223	86.21 122	86.20 287	76.24 123	86.86 127	85.68 286	77.07 141	73.76 399	92.82 148	69.64 273	91.82 185	69.04 282	93.69 220	90.56 276
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		82.75 202	81.93 218	85.19 145	82.08 364	80.15 76	85.53 157	88.76 221	68.01 276	85.58 225	87.75 305	71.80 260	86.85 314	74.02 215	93.87 212	88.58 319

EI-MVSNet		82.61 203	82.42 211	83.20 208	83.25 354	63.66 270	83.50 213	85.07 297	76.06 147	86.55 197	85.10 353	73.41 236	90.25 234	78.15 157	90.67 312	95.68 52

QAPM		82.59 204	82.59 208	82.58 227	86.44 274	66.69 240	89.94 68	90.36 182	67.97 278	84.94 242	92.58 157	72.71 247	92.18 173	70.63 261	87.73 358	88.85 317

fmvsm_s_conf0.1_n_a		82.58 205	81.93 218	84.50 165	87.68 238	73.35 142	86.14 145	77.70 359	61.64 350	85.02 238	91.62 190	77.75 166	86.24 326	82.79 98	87.07 366	93.91 122

Fast-Effi-MVS+-dtu		82.54 206	81.41 232	85.90 129	85.60 302	76.53 118	83.07 227	89.62 209	73.02 204	79.11 350	83.51 371	80.74 136	90.24 236	68.76 285	89.29 331	90.94 260

MVS_Test		82.47 207	83.22 189	80.22 281	82.62 362	57.75 360	82.54 244	91.96 128	71.16 235	82.89 290	92.52 159	77.41 173	90.50 228	80.04 127	87.84 357	92.40 204

viewdifsd2359ckpt11		82.46 208	82.98 197	80.88 266	83.53 341	61.00 316	79.46 301	85.97 281	69.48 254	87.89 163	91.31 204	82.10 114	88.61 280	74.28 205	92.86 245	93.02 169

viewmsd2359difaftdt		82.46 208	82.99 196	80.88 266	83.52 342	61.00 316	79.46 301	85.97 281	69.48 254	87.89 163	91.31 204	82.10 114	88.61 280	74.28 205	92.86 245	93.02 169

v148		82.31 210	82.48 210	81.81 246	85.59 303	59.66 336	81.47 269	86.02 279	72.85 207	88.05 157	90.65 237	70.73 267	90.91 212	75.15 199	91.79 277	94.87 78

API-MVS		82.28 211	82.61 207	81.30 257	86.29 284	69.79 198	88.71 96	87.67 247	78.42 124	82.15 303	84.15 367	77.98 163	91.59 188	65.39 315	92.75 249	82.51 406

MVSFormer		82.23 212	81.57 228	84.19 178	85.54 304	69.26 208	91.98 35	90.08 195	71.54 228	76.23 375	85.07 356	58.69 344	94.27 92	86.26 49	88.77 339	89.03 314

viewdifsd2359ckpt13		82.22 213	81.98 217	82.95 216	85.48 306	64.44 263	83.17 225	92.11 122	65.97 301	83.72 273	89.73 265	77.60 170	90.80 218	70.61 262	89.42 329	93.59 144

fmvsm_s_conf0.5_n_a		82.21 214	81.51 231	84.32 173	86.56 271	73.35 142	85.46 158	77.30 363	61.81 346	84.51 251	90.88 225	77.36 174	86.21 328	82.72 99	86.97 371	93.38 150

EIA-MVS		82.19 215	81.23 239	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 241	78.58 355	79.77 411	77.29 175	94.20 97	71.51 251	88.96 337	91.93 232

GDP-MVS		82.17 216	80.85 247	86.15 126	88.65 212	68.95 217	85.65 155	93.02 91	68.42 269	83.73 272	89.54 268	45.07 419	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 216	81.59 226	83.94 185	86.87 269	71.57 178	85.19 165	77.42 362	62.27 344	84.47 254	91.33 202	76.43 194	85.91 336	83.14 89	87.14 364	94.33 105

PCF-MVS	74.62 15	82.15 218	80.92 245	85.84 131	89.43 188	72.30 164	80.53 285	91.82 133	57.36 388	87.81 166	89.92 261	77.67 169	93.63 123	58.69 364	95.08 168	91.58 244
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 219	80.31 254	87.45 97	90.86 158	80.29 75	85.88 148	90.65 170	68.17 274	76.32 374	86.33 331	73.12 242	92.61 162	61.40 351	90.02 322	89.44 298
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 220	81.54 230	83.60 195	83.94 335	73.90 138	83.35 218	86.10 275	58.97 374	83.80 271	90.36 245	74.23 219	86.94 312	82.90 95	90.22 318	89.94 291

fmvsm_s_conf0.5_n_7		82.04 221	82.05 215	82.01 239	86.98 265	71.07 183	78.70 315	89.45 212	68.07 275	78.14 357	91.61 191	74.19 220	85.92 334	79.61 134	91.73 280	89.05 313

GBi-Net		82.02 222	82.07 213	81.85 243	86.38 278	61.05 313	86.83 129	88.27 234	72.43 214	86.00 213	95.64 38	63.78 312	90.68 222	65.95 308	93.34 229	93.82 127

test1		82.02 222	82.07 213	81.85 243	86.38 278	61.05 313	86.83 129	88.27 234	72.43 214	86.00 213	95.64 38	63.78 312	90.68 222	65.95 308	93.34 229	93.82 127

OpenMVS	76.72 13	81.98 224	82.00 216	81.93 240	84.42 326	68.22 223	88.50 102	89.48 211	66.92 296	81.80 314	91.86 179	72.59 249	90.16 240	71.19 254	91.25 290	87.40 339

KD-MVS_self_test		81.93 225	83.14 193	78.30 310	84.75 320	52.75 397	80.37 287	89.42 214	70.24 246	90.26 101	93.39 126	74.55 218	86.77 316	68.61 288	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 226	81.30 236	83.75 190	86.02 293	71.56 179	84.73 174	77.11 366	62.44 341	84.00 267	90.68 233	76.42 195	85.89 338	83.14 89	87.11 365	93.81 130

SDMVSNet		81.90 227	83.17 192	78.10 314	88.81 207	62.45 293	76.08 361	86.05 278	73.67 186	83.41 280	93.04 135	82.35 104	80.65 383	70.06 269	95.03 170	91.21 251

tfpnnormal		81.79 228	82.95 198	78.31 309	88.93 203	55.40 377	80.83 282	82.85 325	76.81 142	85.90 217	94.14 93	74.58 216	86.51 320	66.82 301	95.68 149	93.01 172

AstraMVS		81.67 229	81.40 233	82.48 232	87.06 262	66.47 243	81.41 270	81.68 336	68.78 264	88.00 158	90.95 221	65.70 297	87.86 297	76.66 175	92.38 258	93.12 165

c3_l		81.64 230	81.59 226	81.79 248	80.86 381	59.15 344	78.61 318	90.18 193	68.36 270	87.20 178	87.11 321	69.39 274	91.62 187	78.16 155	94.43 194	94.60 89

guyue		81.57 231	81.37 235	82.15 236	86.39 276	66.13 247	81.54 268	83.21 320	69.79 250	87.77 168	89.95 259	65.36 300	87.64 300	75.88 190	92.49 256	92.67 187

PVSNet_Blended_VisFu		81.55 232	80.49 252	84.70 160	91.58 133	73.24 146	84.21 188	91.67 137	62.86 335	80.94 326	87.16 319	67.27 286	92.87 157	69.82 271	88.94 338	87.99 329

fmvsm_l_conf0.5_n_a		81.46 233	80.87 246	83.25 206	83.73 340	73.21 147	83.00 230	85.59 288	58.22 380	82.96 289	90.09 258	72.30 252	86.65 318	81.97 110	89.95 323	89.88 292

SSM_04072		81.44 234	82.88 200	77.10 329	89.13 195	68.97 214	72.73 392	91.28 151	72.90 205	85.68 219	90.61 239	76.78 191	69.94 426	73.37 230	93.47 225	92.38 207

DELS-MVS		81.44 234	81.25 237	82.03 238	84.27 330	62.87 281	76.47 355	92.49 110	70.97 237	81.64 318	83.83 368	75.03 205	92.70 159	74.29 204	92.22 267	90.51 278
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		81.31 236	81.61 225	80.41 277	86.38 278	58.75 351	83.93 198	86.58 270	72.43 214	87.65 172	92.98 139	63.78 312	90.22 237	66.86 298	93.92 210	92.27 216

TinyColmap		81.25 237	82.34 212	77.99 317	85.33 308	60.68 323	82.32 252	88.33 232	71.26 233	86.97 187	92.22 173	77.10 181	86.98 311	62.37 340	95.17 164	86.31 352

diffmvs_AUTHOR		81.24 238	81.55 229	80.30 279	80.61 386	60.22 328	77.98 326	90.48 175	67.77 284	83.34 282	89.50 269	74.69 214	87.42 303	78.78 145	90.81 304	93.27 156

AUN-MVS		81.18 239	78.78 277	88.39 82	90.93 155	82.14 62	82.51 245	83.67 317	64.69 325	80.29 336	85.91 340	51.07 383	92.38 167	76.29 184	93.63 222	90.65 273

IMVS_0407		81.08 240	81.23 239	80.62 274	85.76 298	62.46 289	82.46 246	87.91 242	65.23 318	82.12 304	87.92 298	77.27 176	90.18 239	71.67 247	90.74 307	89.20 304

tttt0517		81.07 241	79.58 267	85.52 138	88.99 201	66.45 244	87.03 124	75.51 378	73.76 185	88.32 150	90.20 252	37.96 440	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 242	80.57 249	82.46 233	87.50 245	63.22 277	78.37 321	89.63 208	68.01 276	81.87 310	82.08 389	82.31 106	92.65 161	67.10 297	88.30 350	91.51 247

BH-untuned		80.96 243	80.99 243	80.84 268	88.55 216	68.23 222	80.33 288	88.46 227	72.79 210	86.55 197	86.76 325	74.72 213	91.77 186	61.79 347	88.99 336	82.52 405

IMVS_0403		80.93 244	81.00 242	80.72 271	85.76 298	62.46 289	81.82 262	87.91 242	65.23 318	82.07 306	87.92 298	75.91 198	90.50 228	71.67 247	90.74 307	89.20 304

eth_miper_zixun_eth		80.84 245	80.22 258	82.71 224	81.41 373	60.98 318	77.81 329	90.14 194	67.31 291	86.95 188	87.24 318	64.26 305	92.31 170	75.23 198	91.61 283	94.85 82

xiu_mvs_v1_base_debu		80.84 245	80.14 260	82.93 219	88.31 220	71.73 173	79.53 297	87.17 255	65.43 312	79.59 342	82.73 383	76.94 184	90.14 243	73.22 233	88.33 346	86.90 346

xiu_mvs_v1_base		80.84 245	80.14 260	82.93 219	88.31 220	71.73 173	79.53 297	87.17 255	65.43 312	79.59 342	82.73 383	76.94 184	90.14 243	73.22 233	88.33 346	86.90 346

xiu_mvs_v1_base_debi		80.84 245	80.14 260	82.93 219	88.31 220	71.73 173	79.53 297	87.17 255	65.43 312	79.59 342	82.73 383	76.94 184	90.14 243	73.22 233	88.33 346	86.90 346

IterMVS-SCA-FT		80.64 249	79.41 268	84.34 172	83.93 336	69.66 202	76.28 357	81.09 342	72.43 214	86.47 204	90.19 253	60.46 329	93.15 146	77.45 166	86.39 377	90.22 283

BH-RMVSNet		80.53 250	80.22 258	81.49 254	87.19 253	66.21 246	77.79 330	86.23 273	74.21 180	83.69 274	88.50 287	73.25 241	90.75 219	63.18 336	87.90 354	87.52 337

VortexMVS		80.51 251	80.63 248	80.15 283	83.36 350	61.82 303	80.63 283	88.00 240	67.11 294	87.23 177	89.10 277	63.98 309	88.00 290	73.63 225	92.63 253	90.64 274

Anonymous202405211		80.51 251	81.19 241	78.49 306	88.48 217	57.26 363	76.63 350	82.49 328	81.21 86	84.30 261	92.24 172	67.99 282	86.24 326	62.22 341	95.13 165	91.98 231

DIV-MVS_self_test		80.43 253	80.23 256	81.02 264	79.99 391	59.25 341	77.07 343	87.02 264	67.38 288	86.19 208	89.22 273	63.09 317	90.16 240	76.32 182	95.80 143	93.66 135

cl____		80.42 254	80.23 256	81.02 264	79.99 391	59.25 341	77.07 343	87.02 264	67.37 289	86.18 210	89.21 274	63.08 318	90.16 240	76.31 183	95.80 143	93.65 138

diffmvs		80.40 255	80.48 253	80.17 282	79.02 404	60.04 330	77.54 334	90.28 190	66.65 299	82.40 297	87.33 316	73.50 233	87.35 305	77.98 159	89.62 327	93.13 163
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		80.37 256	78.41 285	86.23 119	76.75 418	73.28 144	87.18 121	77.45 361	76.24 146	68.14 429	88.93 280	65.41 299	93.85 114	69.47 274	96.12 123	91.55 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 257	80.04 263	81.24 260	79.82 394	58.95 346	77.66 331	89.66 206	65.75 309	85.99 216	85.11 352	68.29 281	91.42 195	76.03 188	92.03 271	93.33 152

MG-MVS		80.32 258	80.94 244	78.47 307	88.18 223	52.62 400	82.29 253	85.01 301	72.01 225	79.24 349	92.54 158	69.36 275	93.36 140	70.65 260	89.19 334	89.45 297

mvsmamba		80.30 259	78.87 274	84.58 164	88.12 226	67.55 230	92.35 30	84.88 304	63.15 333	85.33 230	90.91 222	50.71 385	95.20 62	66.36 304	87.98 353	90.99 258

VPNet		80.25 260	81.68 221	75.94 345	92.46 100	47.98 424	76.70 348	81.67 337	73.45 191	84.87 244	92.82 148	74.66 215	86.51 320	61.66 349	96.85 92	93.33 152

MAR-MVS		80.24 261	78.74 279	84.73 158	86.87 269	78.18 95	85.75 152	87.81 246	65.67 311	77.84 361	78.50 421	73.79 229	90.53 227	61.59 350	90.87 301	85.49 362
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		80.20 262	79.00 273	83.78 189	88.17 224	86.66 19	81.31 272	66.81 434	69.64 251	88.33 149	90.19 253	64.58 302	83.63 365	71.99 246	90.03 321	81.06 425

Anonymous20240521		80.18 263	81.25 237	76.95 331	83.15 358	60.84 320	82.46 246	85.99 280	68.76 265	86.78 189	93.73 118	59.13 341	77.44 400	73.71 221	97.55 74	92.56 194

LFMVS		80.15 264	80.56 250	78.89 298	89.19 194	55.93 371	85.22 164	73.78 390	82.96 69	84.28 262	92.72 153	57.38 353	90.07 248	63.80 330	95.75 146	90.68 270

DPM-MVS		80.10 265	79.18 272	82.88 222	90.71 161	69.74 200	78.87 313	90.84 165	60.29 368	75.64 384	85.92 339	67.28 285	93.11 147	71.24 253	91.79 277	85.77 358

MSDG		80.06 266	79.99 265	80.25 280	83.91 337	68.04 227	77.51 335	89.19 215	77.65 134	81.94 308	83.45 373	76.37 196	86.31 325	63.31 335	86.59 374	86.41 350

FE-MVS		79.98 267	78.86 275	83.36 203	86.47 273	66.45 244	89.73 71	84.74 308	72.80 209	84.22 265	91.38 200	44.95 420	93.60 127	63.93 328	91.50 286	90.04 290

sd_testset		79.95 268	81.39 234	75.64 350	88.81 207	58.07 355	76.16 360	82.81 326	73.67 186	83.41 280	93.04 135	80.96 133	77.65 399	58.62 365	95.03 170	91.21 251

ab-mvs		79.67 269	80.56 250	76.99 330	88.48 217	56.93 365	84.70 176	86.06 277	68.95 262	80.78 329	93.08 134	75.30 203	84.62 352	56.78 374	90.90 299	89.43 299

VNet		79.31 270	80.27 255	76.44 339	87.92 230	53.95 389	75.58 367	84.35 312	74.39 179	82.23 301	90.72 230	72.84 246	84.39 357	60.38 357	93.98 209	90.97 259

thisisatest0530		79.07 271	77.33 295	84.26 175	87.13 254	64.58 260	83.66 208	75.95 373	68.86 263	85.22 232	87.36 315	38.10 437	93.57 131	75.47 195	94.28 199	94.62 88

cl22		78.97 272	78.21 287	81.24 260	77.74 408	59.01 345	77.46 338	87.13 258	65.79 306	84.32 258	85.10 353	58.96 343	90.88 214	75.36 197	92.03 271	93.84 125

patch_mono-2		78.89 273	79.39 269	77.41 326	84.78 318	68.11 225	75.60 365	83.11 322	60.96 360	79.36 346	89.89 262	75.18 204	72.97 415	73.32 232	92.30 261	91.15 253

RPMNet		78.88 274	78.28 286	80.68 273	79.58 395	62.64 285	82.58 241	94.16 33	74.80 168	75.72 382	92.59 155	48.69 392	95.56 42	73.48 227	82.91 413	83.85 384

PAPR		78.84 275	78.10 288	81.07 262	85.17 313	60.22 328	82.21 257	90.57 174	62.51 337	75.32 388	84.61 361	74.99 206	92.30 171	59.48 362	88.04 352	90.68 270

viewmambaseed2359dif		78.80 276	78.47 284	79.78 286	80.26 390	59.28 340	77.31 340	87.13 258	60.42 366	82.37 298	88.67 285	74.58 216	87.87 296	67.78 296	87.73 358	92.19 220

PVSNet_BlendedMVS		78.80 276	77.84 289	81.65 250	84.43 324	63.41 273	79.49 300	90.44 178	61.70 349	75.43 385	87.07 322	69.11 277	91.44 193	60.68 355	92.24 265	90.11 288

FMVSNet3		78.80 276	78.55 281	79.57 292	82.89 361	56.89 367	81.76 263	85.77 284	69.04 260	86.00 213	90.44 244	51.75 381	90.09 246	65.95 308	93.34 229	91.72 238

test_yl		78.71 279	78.51 282	79.32 295	84.32 328	58.84 348	78.38 319	85.33 292	75.99 150	82.49 295	86.57 327	58.01 347	90.02 250	62.74 337	92.73 251	89.10 310

DCV-MVSNet		78.71 279	78.51 282	79.32 295	84.32 328	58.84 348	78.38 319	85.33 292	75.99 150	82.49 295	86.57 327	58.01 347	90.02 250	62.74 337	92.73 251	89.10 310

test1111		78.53 281	78.85 276	77.56 323	92.22 109	47.49 426	82.61 239	69.24 422	72.43 214	85.28 231	94.20 89	51.91 379	90.07 248	65.36 316	96.45 108	95.11 72

FE-MVSNET		78.46 282	79.36 270	75.75 347	86.53 272	54.53 384	78.03 323	85.35 291	69.01 261	85.41 229	90.68 233	64.27 304	85.73 342	62.59 339	92.35 260	87.00 345

icg_test_0407_2		78.46 282	79.68 266	74.78 357	85.76 298	62.46 289	68.51 421	87.91 242	65.23 318	82.12 304	87.92 298	77.27 176	72.67 416	71.67 247	90.74 307	89.20 304

ECVR-MVS		78.44 284	78.63 280	77.88 319	91.85 123	48.95 420	83.68 207	69.91 418	72.30 220	84.26 264	94.20 89	51.89 380	89.82 253	63.58 331	96.02 127	94.87 78

pmmvs-eth3d		78.42 285	77.04 298	82.57 229	87.44 247	74.41 135	80.86 281	79.67 350	55.68 397	84.69 248	90.31 250	60.91 327	85.42 345	62.20 342	91.59 284	87.88 333

mvs_anonymous		78.13 286	78.76 278	76.23 344	79.24 401	50.31 416	78.69 316	84.82 306	61.60 351	83.09 288	92.82 148	73.89 228	87.01 308	68.33 292	86.41 376	91.37 248

TAMVS		78.08 287	76.36 305	83.23 207	90.62 162	72.87 150	79.08 309	80.01 349	61.72 348	81.35 322	86.92 324	63.96 311	88.78 275	50.61 413	93.01 241	88.04 328

miper_enhance_ethall		77.83 288	76.93 299	80.51 275	76.15 425	58.01 357	75.47 369	88.82 219	58.05 382	83.59 276	80.69 399	64.41 303	91.20 199	73.16 239	92.03 271	92.33 211

Vis-MVSNet (Re-imp)		77.82 289	77.79 290	77.92 318	88.82 206	51.29 410	83.28 219	71.97 406	74.04 181	82.23 301	89.78 263	57.38 353	89.41 265	57.22 373	95.41 154	93.05 168

CANet_DTU		77.81 290	77.05 297	80.09 284	81.37 374	59.90 334	83.26 220	88.29 233	69.16 258	67.83 432	83.72 369	60.93 326	89.47 260	69.22 278	89.70 326	90.88 263

OpenMVS_ROB	70.19 17	77.77 291	77.46 292	78.71 302	84.39 327	61.15 311	81.18 276	82.52 327	62.45 340	83.34 282	87.37 314	66.20 291	88.66 278	64.69 323	85.02 393	86.32 351

SSC-MVS		77.55 292	81.64 223	65.29 423	90.46 165	20.33 470	73.56 385	68.28 424	85.44 41	88.18 154	94.64 68	70.93 266	81.33 378	71.25 252	92.03 271	94.20 107

MDA-MVSNet-bldmvs		77.47 293	76.90 300	79.16 297	79.03 403	64.59 259	66.58 433	75.67 376	73.15 202	88.86 132	88.99 279	66.94 287	81.23 379	64.71 322	88.22 351	91.64 242

jason		77.42 294	75.75 311	82.43 234	87.10 257	69.27 207	77.99 325	81.94 334	51.47 424	77.84 361	85.07 356	60.32 331	89.00 269	70.74 259	89.27 333	89.03 314
jason: jason.
CDS-MVSNet		77.32 295	75.40 315	83.06 211	89.00 200	72.48 161	77.90 328	82.17 332	60.81 361	78.94 352	83.49 372	59.30 339	88.76 276	54.64 393	92.37 259	87.93 332
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 296	77.75 291	75.73 348	85.76 298	62.46 289	70.84 407	87.91 242	65.23 318	72.21 407	87.92 298	67.48 284	75.53 408	71.67 247	90.74 307	89.20 304

xiu_mvs_v2_base		77.19 297	76.75 302	78.52 305	87.01 263	61.30 309	75.55 368	87.12 262	61.24 357	74.45 394	78.79 419	77.20 178	90.93 210	64.62 325	84.80 400	83.32 393

MVSTER		77.09 298	75.70 312	81.25 258	75.27 433	61.08 312	77.49 337	85.07 297	60.78 362	86.55 197	88.68 283	43.14 429	90.25 234	73.69 224	90.67 312	92.42 201

PS-MVSNAJ		77.04 299	76.53 304	78.56 304	87.09 259	61.40 307	75.26 370	87.13 258	61.25 356	74.38 396	77.22 433	76.94 184	90.94 209	64.63 324	84.83 399	83.35 392

IterMVS		76.91 300	76.34 306	78.64 303	80.91 379	64.03 267	76.30 356	79.03 353	64.88 324	83.11 286	89.16 275	59.90 335	84.46 355	68.61 288	85.15 391	87.42 338
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 301	75.67 313	80.34 278	80.48 388	62.16 301	73.50 386	84.80 307	57.61 386	82.24 300	87.54 309	51.31 382	87.65 299	70.40 265	93.19 237	91.23 250

CL-MVSNet_self_test		76.81 302	77.38 294	75.12 353	86.90 267	51.34 408	73.20 389	80.63 346	68.30 272	81.80 314	88.40 288	66.92 288	80.90 380	55.35 387	94.90 176	93.12 165

TR-MVS		76.77 303	75.79 310	79.72 289	86.10 292	65.79 251	77.14 341	83.02 323	65.20 322	81.40 321	82.10 387	66.30 290	90.73 221	55.57 384	85.27 387	82.65 400

MonoMVSNet		76.66 304	77.26 296	74.86 355	79.86 393	54.34 386	86.26 142	86.08 276	71.08 236	85.59 224	88.68 283	53.95 371	85.93 333	63.86 329	80.02 429	84.32 375

USDC		76.63 305	76.73 303	76.34 341	83.46 345	57.20 364	80.02 291	88.04 239	52.14 420	83.65 275	91.25 207	63.24 315	86.65 318	54.66 392	94.11 204	85.17 364

BH-w/o		76.57 306	76.07 309	78.10 314	86.88 268	65.92 250	77.63 332	86.33 271	65.69 310	80.89 327	79.95 408	68.97 279	90.74 220	53.01 403	85.25 388	77.62 436

Patchmtry		76.56 307	77.46 292	73.83 363	79.37 400	46.60 430	82.41 250	76.90 367	73.81 184	85.56 226	92.38 162	48.07 395	83.98 362	63.36 334	95.31 160	90.92 261

PVSNet_Blended		76.49 308	75.40 315	79.76 288	84.43 324	63.41 273	75.14 371	90.44 178	57.36 388	75.43 385	78.30 422	69.11 277	91.44 193	60.68 355	87.70 360	84.42 374

miper_lstm_enhance		76.45 309	76.10 308	77.51 324	76.72 419	60.97 319	64.69 437	85.04 299	63.98 329	83.20 285	88.22 290	56.67 357	78.79 396	73.22 233	93.12 238	92.78 181

lupinMVS		76.37 310	74.46 324	82.09 237	85.54 304	69.26 208	76.79 346	80.77 345	50.68 431	76.23 375	82.82 381	58.69 344	88.94 270	69.85 270	88.77 339	88.07 325

cascas		76.29 311	74.81 320	80.72 271	84.47 323	62.94 279	73.89 383	87.34 250	55.94 395	75.16 390	76.53 438	63.97 310	91.16 201	65.00 319	90.97 297	88.06 327

SD_0403		76.08 312	76.77 301	73.98 361	87.08 261	49.45 419	83.62 209	84.68 309	63.31 330	75.13 391	87.47 312	71.85 259	84.56 353	49.97 415	87.86 356	87.94 331

WB-MVS		76.06 313	80.01 264	64.19 426	89.96 179	20.58 469	72.18 396	68.19 425	83.21 65	86.46 205	93.49 123	70.19 271	78.97 394	65.96 307	90.46 317	93.02 169

thres600view7		75.97 314	75.35 317	77.85 321	87.01 263	51.84 406	80.45 286	73.26 395	75.20 165	83.10 287	86.31 333	45.54 410	89.05 268	55.03 390	92.24 265	92.66 188

GA-MVS		75.83 315	74.61 321	79.48 294	81.87 366	59.25 341	73.42 387	82.88 324	68.68 266	79.75 341	81.80 392	50.62 386	89.46 261	66.85 299	85.64 384	89.72 294

MVP-Stereo		75.81 316	73.51 333	82.71 224	89.35 189	73.62 139	80.06 289	85.20 294	60.30 367	73.96 397	87.94 295	57.89 351	89.45 262	52.02 407	74.87 447	85.06 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 317	75.20 318	77.27 327	75.01 436	69.47 205	78.93 310	84.88 304	46.67 438	87.08 184	87.84 303	50.44 388	71.62 421	77.42 168	88.53 342	90.72 267

thres100view900		75.45 318	75.05 319	76.66 337	87.27 249	51.88 405	81.07 277	73.26 395	75.68 156	83.25 284	86.37 330	45.54 410	88.80 272	51.98 408	90.99 294	89.31 301

ET-MVSNet_ETH3D		75.28 319	72.77 342	82.81 223	83.03 360	68.11 225	77.09 342	76.51 371	60.67 364	77.60 366	80.52 403	38.04 438	91.15 202	70.78 257	90.68 311	89.17 308

thres400		75.14 320	74.23 326	77.86 320	86.24 285	52.12 402	79.24 306	73.87 388	73.34 195	81.82 312	84.60 362	46.02 403	88.80 272	51.98 408	90.99 294	92.66 188

wuyk23d		75.13 321	79.30 271	62.63 429	75.56 429	75.18 131	80.89 280	73.10 397	75.06 167	94.76 16	95.32 45	87.73 44	52.85 461	34.16 459	97.11 87	59.85 457

EU-MVSNet		75.12 322	74.43 325	77.18 328	83.11 359	59.48 338	85.71 154	82.43 329	39.76 458	85.64 223	88.76 281	44.71 422	87.88 295	73.86 218	85.88 383	84.16 380

HyFIR lowres test		75.12 322	72.66 344	82.50 231	91.44 141	65.19 256	72.47 394	87.31 251	46.79 437	80.29 336	84.30 364	52.70 376	92.10 177	51.88 412	86.73 372	90.22 283

CMPMVS	59.41 20	75.12 322	73.57 331	79.77 287	75.84 428	67.22 231	81.21 275	82.18 331	50.78 429	76.50 371	87.66 307	55.20 367	82.99 368	62.17 344	90.64 316	89.09 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 325	72.98 340	80.73 270	84.95 315	71.71 176	76.23 358	77.59 360	52.83 414	77.73 365	86.38 329	56.35 360	84.97 349	57.72 372	87.05 367	85.51 361

tfpn200view9		74.86 326	74.23 326	76.74 336	86.24 285	52.12 402	79.24 306	73.87 388	73.34 195	81.82 312	84.60 362	46.02 403	88.80 272	51.98 408	90.99 294	89.31 301

1112_ss		74.82 327	73.74 329	78.04 316	89.57 183	60.04 330	76.49 354	87.09 263	54.31 405	73.66 400	79.80 409	60.25 332	86.76 317	58.37 366	84.15 404	87.32 340

EGC-MVSNET		74.79 328	69.99 372	89.19 67	94.89 38	87.00 15	91.89 38	86.28 272	1.09 467	2.23 469	95.98 30	81.87 122	89.48 259	79.76 130	95.96 130	91.10 254

ppachtmachnet_test		74.73 329	74.00 328	76.90 333	80.71 384	56.89 367	71.53 402	78.42 355	58.24 379	79.32 348	82.92 380	57.91 350	84.26 359	65.60 314	91.36 288	89.56 296

Patchmatch-RL test		74.48 330	73.68 330	76.89 334	84.83 317	66.54 241	72.29 395	69.16 423	57.70 384	86.76 190	86.33 331	45.79 409	82.59 369	69.63 273	90.65 315	81.54 416

PatchMatch-RL		74.48 330	73.22 337	78.27 312	87.70 237	85.26 38	75.92 363	70.09 416	64.34 327	76.09 378	81.25 397	65.87 296	78.07 398	53.86 395	83.82 406	71.48 445

XXY-MVS		74.44 332	76.19 307	69.21 398	84.61 322	52.43 401	71.70 399	77.18 365	60.73 363	80.60 330	90.96 219	75.44 200	69.35 429	56.13 379	88.33 346	85.86 357

test2506		74.12 333	73.39 334	76.28 342	91.85 123	44.20 440	84.06 192	48.20 465	72.30 220	81.90 309	94.20 89	27.22 465	89.77 256	64.81 321	96.02 127	94.87 78

reproduce_monomvs		74.09 334	73.23 336	76.65 338	76.52 420	54.54 383	77.50 336	81.40 340	65.85 305	82.86 292	86.67 326	27.38 463	84.53 354	70.24 266	90.66 314	90.89 262

CR-MVSNet		74.00 335	73.04 339	76.85 335	79.58 395	62.64 285	82.58 241	76.90 367	50.50 432	75.72 382	92.38 162	48.07 395	84.07 361	68.72 287	82.91 413	83.85 384

SSC-MVS3.2		73.90 336	75.67 313	68.61 406	84.11 333	41.28 448	64.17 439	72.83 398	72.09 223	79.08 351	87.94 295	70.31 269	73.89 414	55.99 380	94.49 191	90.67 272

Test_1112_low_res		73.90 336	73.08 338	76.35 340	90.35 167	55.95 370	73.40 388	86.17 274	50.70 430	73.14 401	85.94 338	58.31 346	85.90 337	56.51 376	83.22 410	87.20 342

test20.03		73.75 338	74.59 323	71.22 384	81.11 377	51.12 412	70.15 413	72.10 405	70.42 241	80.28 338	91.50 194	64.21 306	74.72 412	46.96 433	94.58 189	87.82 335

test_fmvs2		73.57 339	72.80 341	75.90 346	72.74 450	68.84 218	77.07 343	84.32 313	45.14 444	82.89 290	84.22 365	48.37 393	70.36 425	73.40 229	87.03 368	88.52 320

SCA		73.32 340	72.57 346	75.58 351	81.62 370	55.86 373	78.89 312	71.37 411	61.73 347	74.93 392	83.42 374	60.46 329	87.01 308	58.11 370	82.63 418	83.88 381

baseline1		73.26 341	73.54 332	72.43 377	84.92 316	47.79 425	79.89 293	74.00 386	65.93 303	78.81 353	86.28 334	56.36 359	81.63 377	56.63 375	79.04 436	87.87 334

1314		73.22 342	72.56 347	75.20 352	80.41 389	57.84 358	81.64 266	85.36 290	51.68 423	73.10 402	76.65 437	61.45 324	85.19 347	63.54 332	79.21 434	82.59 401

MVS		73.21 343	72.59 345	75.06 354	80.97 378	60.81 321	81.64 266	85.92 283	46.03 442	71.68 410	77.54 428	68.47 280	89.77 256	55.70 383	85.39 385	74.60 442

HY-MVS	64.64 18	73.03 344	72.47 348	74.71 358	83.36 350	54.19 387	82.14 260	81.96 333	56.76 394	69.57 424	86.21 335	60.03 333	84.83 351	49.58 420	82.65 416	85.11 365

thisisatest0515		73.00 345	70.52 364	80.46 276	81.45 372	59.90 334	73.16 390	74.31 385	57.86 383	76.08 379	77.78 425	37.60 441	92.12 176	65.00 319	91.45 287	89.35 300

EPNet_dtu		72.87 346	71.33 358	77.49 325	77.72 409	60.55 324	82.35 251	75.79 374	66.49 300	58.39 460	81.06 398	53.68 372	85.98 332	53.55 398	92.97 243	85.95 355
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 347	71.41 357	76.28 342	83.25 354	60.34 326	83.50 213	79.02 354	37.77 462	76.33 373	85.10 353	49.60 391	87.41 304	70.54 263	77.54 442	81.08 423

CHOSEN 1792x2688		72.45 348	70.56 363	78.13 313	90.02 178	63.08 278	68.72 420	83.16 321	42.99 452	75.92 380	85.46 346	57.22 355	85.18 348	49.87 418	81.67 420	86.14 353

testgi		72.36 349	74.61 321	65.59 420	80.56 387	42.82 445	68.29 422	73.35 394	66.87 297	81.84 311	89.93 260	72.08 256	66.92 443	46.05 437	92.54 255	87.01 344

thres200		72.34 350	71.55 356	74.70 359	83.48 344	51.60 407	75.02 372	73.71 391	70.14 247	78.56 356	80.57 402	46.20 401	88.20 288	46.99 432	89.29 331	84.32 375

FPMVS		72.29 351	72.00 350	73.14 368	88.63 213	85.00 40	74.65 376	67.39 428	71.94 226	77.80 363	87.66 307	50.48 387	75.83 406	49.95 416	79.51 430	58.58 459

FMVSNet5		72.10 352	71.69 352	73.32 366	81.57 371	53.02 396	76.77 347	78.37 356	63.31 330	76.37 372	91.85 180	36.68 442	78.98 393	47.87 429	92.45 257	87.95 330

our_test_3		71.85 353	71.59 353	72.62 374	80.71 384	53.78 390	69.72 416	71.71 410	58.80 376	78.03 358	80.51 404	56.61 358	78.84 395	62.20 342	86.04 382	85.23 363

PAPM		71.77 354	70.06 370	76.92 332	86.39 276	53.97 388	76.62 351	86.62 269	53.44 409	63.97 449	84.73 360	57.79 352	92.34 169	39.65 449	81.33 424	84.45 373

ttmdpeth		71.72 355	70.67 361	74.86 355	73.08 447	55.88 372	77.41 339	69.27 421	55.86 396	78.66 354	93.77 116	38.01 439	75.39 409	60.12 358	89.87 324	93.31 154

IB-MVS	62.13 19	71.64 356	68.97 382	79.66 291	80.80 383	62.26 298	73.94 382	76.90 367	63.27 332	68.63 428	76.79 435	33.83 446	91.84 184	59.28 363	87.26 362	84.88 367
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		71.63 357	72.30 349	69.62 395	76.47 422	52.70 399	70.03 414	80.97 343	59.18 373	79.36 346	88.21 291	60.50 328	69.12 430	58.33 368	77.62 441	87.04 343

testing3		71.53 358	70.79 360	73.77 364	88.89 205	41.86 447	76.60 353	59.12 454	72.83 208	80.97 324	82.08 389	19.80 471	87.33 306	65.12 318	91.68 282	92.13 224

test_vis3_rt		71.42 359	70.67 361	73.64 365	69.66 457	70.46 190	66.97 432	89.73 203	42.68 454	88.20 153	83.04 376	43.77 424	60.07 455	65.35 317	86.66 373	90.39 281

Anonymous20231206		71.38 360	71.88 351	69.88 392	86.31 282	54.37 385	70.39 411	74.62 381	52.57 416	76.73 370	88.76 281	59.94 334	72.06 418	44.35 441	93.23 235	83.23 395

test_vis1_n_1920		71.30 361	71.58 355	70.47 387	77.58 411	59.99 333	74.25 377	84.22 314	51.06 426	74.85 393	79.10 415	55.10 368	68.83 432	68.86 284	79.20 435	82.58 402

MIMVSNet		71.09 362	71.59 353	69.57 396	87.23 251	50.07 417	78.91 311	71.83 407	60.20 370	71.26 411	91.76 187	55.08 369	76.09 404	41.06 446	87.02 369	82.54 404

test_fmvs1_n		70.94 363	70.41 367	72.53 376	73.92 438	66.93 238	75.99 362	84.21 315	43.31 451	79.40 345	79.39 413	43.47 425	68.55 434	69.05 281	84.91 396	82.10 410

MS-PatchMatch		70.93 364	70.22 368	73.06 369	81.85 367	62.50 288	73.82 384	77.90 357	52.44 417	75.92 380	81.27 396	55.67 364	81.75 375	55.37 386	77.70 440	74.94 441

pmmvs5		70.73 365	70.07 369	72.72 372	77.03 416	52.73 398	74.14 378	75.65 377	50.36 433	72.17 408	85.37 350	55.42 366	80.67 382	52.86 404	87.59 361	84.77 368

testing3-2		70.72 366	70.97 359	69.95 391	88.93 203	34.80 461	69.85 415	66.59 435	78.42 124	77.58 367	85.55 342	31.83 452	82.08 373	46.28 434	93.73 218	92.98 175

PatchT		70.52 367	72.76 343	63.79 428	79.38 399	33.53 462	77.63 332	65.37 439	73.61 188	71.77 409	92.79 151	44.38 423	75.65 407	64.53 326	85.37 386	82.18 409

test_vis1_n		70.29 368	69.99 372	71.20 385	75.97 427	66.50 242	76.69 349	80.81 344	44.22 447	75.43 385	77.23 432	50.00 389	68.59 433	66.71 302	82.85 415	78.52 435

N_pmnet		70.20 369	68.80 384	74.38 360	80.91 379	84.81 43	59.12 450	76.45 372	55.06 400	75.31 389	82.36 386	55.74 363	54.82 460	47.02 431	87.24 363	83.52 388

tpmvs		70.16 370	69.56 375	71.96 380	74.71 437	48.13 422	79.63 295	75.45 379	65.02 323	70.26 419	81.88 391	45.34 415	85.68 343	58.34 367	75.39 446	82.08 411

new-patchmatchnet		70.10 371	73.37 335	60.29 437	81.23 376	16.95 472	59.54 448	74.62 381	62.93 334	80.97 324	87.93 297	62.83 321	71.90 419	55.24 388	95.01 173	92.00 229

YYNet1		70.06 372	70.44 365	68.90 400	73.76 440	53.42 394	58.99 451	67.20 430	58.42 378	87.10 182	85.39 349	59.82 336	67.32 440	59.79 360	83.50 409	85.96 354

MVStest1		70.05 373	69.26 376	72.41 378	58.62 469	55.59 376	76.61 352	65.58 437	53.44 409	89.28 128	93.32 127	22.91 469	71.44 423	74.08 214	89.52 328	90.21 287

MDA-MVSNet_test_wron		70.05 373	70.44 365	68.88 401	73.84 439	53.47 392	58.93 452	67.28 429	58.43 377	87.09 183	85.40 348	59.80 337	67.25 441	59.66 361	83.54 408	85.92 356

CostFormer		69.98 375	68.68 385	73.87 362	77.14 414	50.72 414	79.26 305	74.51 383	51.94 422	70.97 414	84.75 359	45.16 418	87.49 302	55.16 389	79.23 433	83.40 391

testing91		69.94 376	68.99 381	72.80 371	83.81 339	45.89 433	71.57 401	73.64 393	68.24 273	70.77 417	77.82 424	34.37 445	84.44 356	53.64 397	87.00 370	88.07 325

baseline2		69.77 377	66.89 394	78.41 308	79.51 397	58.09 354	76.23 358	69.57 419	57.50 387	64.82 447	77.45 430	46.02 403	88.44 282	53.08 400	77.83 438	88.70 318

PatchmatchNet		69.71 378	68.83 383	72.33 379	77.66 410	53.60 391	79.29 304	69.99 417	57.66 385	72.53 405	82.93 379	46.45 400	80.08 388	60.91 354	72.09 450	83.31 394
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 379	69.05 379	71.14 386	69.15 458	65.77 252	73.98 381	83.32 319	42.83 453	77.77 364	78.27 423	43.39 428	68.50 435	68.39 291	84.38 403	79.15 433

JIA-IIPM		69.41 380	66.64 398	77.70 322	73.19 444	71.24 181	75.67 364	65.56 438	70.42 241	65.18 443	92.97 142	33.64 448	83.06 366	53.52 399	69.61 456	78.79 434

Syy-MVS		69.40 381	70.03 371	67.49 411	81.72 368	38.94 453	71.00 404	61.99 445	61.38 353	70.81 415	72.36 449	61.37 325	79.30 391	64.50 327	85.18 389	84.22 377

testing99		69.27 382	68.15 389	72.63 373	83.29 352	45.45 435	71.15 403	71.08 412	67.34 290	70.43 418	77.77 426	32.24 451	84.35 358	53.72 396	86.33 378	88.10 324

UnsupCasMVSNet_bld		69.21 383	69.68 374	67.82 409	79.42 398	51.15 411	67.82 426	75.79 374	54.15 406	77.47 368	85.36 351	59.26 340	70.64 424	48.46 426	79.35 432	81.66 414

test_cas_vis1_n_1920		69.20 384	69.12 377	69.43 397	73.68 441	62.82 282	70.38 412	77.21 364	46.18 441	80.46 335	78.95 417	52.03 378	65.53 448	65.77 313	77.45 443	79.95 431

gg-mvs-nofinetune		68.96 385	69.11 378	68.52 407	76.12 426	45.32 436	83.59 210	55.88 459	86.68 33	64.62 448	97.01 12	30.36 456	83.97 363	44.78 440	82.94 412	76.26 438

WBMVS		68.76 386	68.43 386	69.75 394	83.29 352	40.30 451	67.36 428	72.21 404	57.09 391	77.05 369	85.53 344	33.68 447	80.51 384	48.79 424	90.90 299	88.45 321

WB-MVSnew		68.72 387	69.01 380	67.85 408	83.22 356	43.98 441	74.93 373	65.98 436	55.09 399	73.83 398	79.11 414	65.63 298	71.89 420	38.21 454	85.04 392	87.69 336

tpm2		68.45 388	66.83 395	73.30 367	78.93 405	48.50 421	79.76 294	71.76 408	47.50 436	69.92 421	83.60 370	42.07 431	88.40 284	48.44 427	79.51 430	83.01 398

tpm		67.95 389	68.08 390	67.55 410	78.74 406	43.53 443	75.60 365	67.10 433	54.92 401	72.23 406	88.10 292	42.87 430	75.97 405	52.21 406	80.95 428	83.15 396

WTY-MVS		67.91 390	68.35 387	66.58 416	80.82 382	48.12 423	65.96 434	72.60 399	53.67 408	71.20 412	81.68 394	58.97 342	69.06 431	48.57 425	81.67 420	82.55 403

testing11		67.38 391	65.93 399	71.73 382	83.37 349	46.60 430	70.95 406	69.40 420	62.47 339	66.14 436	76.66 436	31.22 453	84.10 360	49.10 422	84.10 405	84.49 371

test-LLR		67.21 392	66.74 396	68.63 404	76.45 423	55.21 379	67.89 423	67.14 431	62.43 342	65.08 444	72.39 447	43.41 426	69.37 427	61.00 352	84.89 397	81.31 418

testing222		66.93 393	65.30 406	71.81 381	83.38 348	45.83 434	72.06 397	67.50 427	64.12 328	69.68 423	76.37 439	27.34 464	83.00 367	38.88 450	88.38 345	86.62 349

sss		66.92 394	67.26 392	65.90 418	77.23 413	51.10 413	64.79 436	71.72 409	52.12 421	70.13 420	80.18 406	57.96 349	65.36 449	50.21 414	81.01 426	81.25 420

KD-MVS_2432*1600		66.87 395	65.81 402	70.04 389	67.50 459	47.49 426	62.56 442	79.16 351	61.21 358	77.98 359	80.61 400	25.29 467	82.48 370	53.02 401	84.92 394	80.16 429

miper_refine_blended		66.87 395	65.81 402	70.04 389	67.50 459	47.49 426	62.56 442	79.16 351	61.21 358	77.98 359	80.61 400	25.29 467	82.48 370	53.02 401	84.92 394	80.16 429

dmvs_re		66.81 397	66.98 393	66.28 417	76.87 417	58.68 352	71.66 400	72.24 402	60.29 368	69.52 425	73.53 446	52.38 377	64.40 451	44.90 439	81.44 423	75.76 439

tpm cat1		66.76 398	65.21 407	71.42 383	77.09 415	50.62 415	78.01 324	73.68 392	44.89 445	68.64 427	79.00 416	45.51 412	82.42 372	49.91 417	70.15 453	81.23 422

UWE-MVS		66.43 399	65.56 405	69.05 399	84.15 332	40.98 449	73.06 391	64.71 441	54.84 402	76.18 377	79.62 412	29.21 458	80.50 385	38.54 453	89.75 325	85.66 359

PVSNet	58.17 21	66.41 400	65.63 404	68.75 402	81.96 365	49.88 418	62.19 444	72.51 401	51.03 427	68.04 430	75.34 443	50.84 384	74.77 410	45.82 438	82.96 411	81.60 415

tpmrst		66.28 401	66.69 397	65.05 424	72.82 449	39.33 452	78.20 322	70.69 415	53.16 412	67.88 431	80.36 405	48.18 394	74.75 411	58.13 369	70.79 452	81.08 423

Patchmatch-test		65.91 402	67.38 391	61.48 434	75.51 430	43.21 444	68.84 419	63.79 443	62.48 338	72.80 404	83.42 374	44.89 421	59.52 457	48.27 428	86.45 375	81.70 413

ADS-MVSNet2		65.87 403	63.64 412	72.55 375	73.16 445	56.92 366	67.10 430	74.81 380	49.74 434	66.04 438	82.97 377	46.71 398	77.26 401	42.29 443	69.96 454	83.46 389

myMVS_eth3d28		65.83 404	65.85 400	65.78 419	83.42 347	35.71 459	67.29 429	68.01 426	67.58 287	69.80 422	77.72 427	32.29 450	74.30 413	37.49 455	89.06 335	87.32 340

test_vis1_rt		65.64 405	64.09 409	70.31 388	66.09 463	70.20 194	61.16 445	81.60 338	38.65 459	72.87 403	69.66 452	52.84 374	60.04 456	56.16 378	77.77 439	80.68 427

mvsany_test3		65.48 406	62.97 415	73.03 370	69.99 456	76.17 124	64.83 435	43.71 467	43.68 449	80.25 339	87.05 323	52.83 375	63.09 454	51.92 411	72.44 449	79.84 432

test-mter		65.00 407	63.79 411	68.63 404	76.45 423	55.21 379	67.89 423	67.14 431	50.98 428	65.08 444	72.39 447	28.27 461	69.37 427	61.00 352	84.89 397	81.31 418

ETVMVS		64.67 408	63.34 414	68.64 403	83.44 346	41.89 446	69.56 418	61.70 450	61.33 355	68.74 426	75.76 441	28.76 459	79.35 390	34.65 458	86.16 381	84.67 370

myMVS_eth3d		64.66 409	63.89 410	66.97 414	81.72 368	37.39 456	71.00 404	61.99 445	61.38 353	70.81 415	72.36 449	20.96 470	79.30 391	49.59 419	85.18 389	84.22 377

test0.0.03 1		64.66 409	64.36 408	65.57 421	75.03 435	46.89 429	64.69 437	61.58 451	62.43 342	71.18 413	77.54 428	43.41 426	68.47 436	40.75 448	82.65 416	81.35 417

UBG		64.34 411	63.35 413	67.30 412	83.50 343	40.53 450	67.46 427	65.02 440	54.77 403	67.54 434	74.47 445	32.99 449	78.50 397	40.82 447	83.58 407	82.88 399

test_f		64.31 412	65.85 400	59.67 438	66.54 462	62.24 300	57.76 454	70.96 413	40.13 456	84.36 256	82.09 388	46.93 397	51.67 462	61.99 345	81.89 419	65.12 453

pmmvs3		62.47 413	60.02 426	69.80 393	71.58 453	64.00 268	70.52 410	58.44 457	39.77 457	66.05 437	75.84 440	27.10 466	72.28 417	46.15 436	84.77 401	73.11 443

EPMVS		62.47 413	62.63 417	62.01 430	70.63 455	38.74 454	74.76 374	52.86 461	53.91 407	67.71 433	80.01 407	39.40 435	66.60 444	55.54 385	68.81 458	80.68 427

ADS-MVSNet		61.90 415	62.19 419	61.03 435	73.16 445	36.42 458	67.10 430	61.75 448	49.74 434	66.04 438	82.97 377	46.71 398	63.21 452	42.29 443	69.96 454	83.46 389

PMMVS		61.65 416	60.38 423	65.47 422	65.40 466	69.26 208	63.97 440	61.73 449	36.80 463	60.11 455	68.43 454	59.42 338	66.35 445	48.97 423	78.57 437	60.81 456

E-PMN		61.59 417	61.62 420	61.49 433	66.81 461	55.40 377	53.77 457	60.34 453	66.80 298	58.90 458	65.50 457	40.48 434	66.12 446	55.72 382	86.25 379	62.95 455

TESTMET0.1,1		61.29 418	60.32 424	64.19 426	72.06 451	51.30 409	67.89 423	62.09 444	45.27 443	60.65 454	69.01 453	27.93 462	64.74 450	56.31 377	81.65 422	76.53 437

MVS-HIRNet		61.16 419	62.92 416	55.87 441	79.09 402	35.34 460	71.83 398	57.98 458	46.56 439	59.05 457	91.14 211	49.95 390	76.43 403	38.74 451	71.92 451	55.84 460

EMVS		61.10 420	60.81 422	61.99 431	65.96 464	55.86 373	53.10 458	58.97 456	67.06 295	56.89 462	63.33 458	40.98 432	67.03 442	54.79 391	86.18 380	63.08 454

DSMNet-mixed		60.98 421	61.61 421	59.09 440	72.88 448	45.05 438	74.70 375	46.61 466	26.20 464	65.34 442	90.32 249	55.46 365	63.12 453	41.72 445	81.30 425	69.09 449

dp		60.70 422	60.29 425	61.92 432	72.04 452	38.67 455	70.83 408	64.08 442	51.28 425	60.75 453	77.28 431	36.59 443	71.58 422	47.41 430	62.34 460	75.52 440

dmvs_testset		60.59 423	62.54 418	54.72 443	77.26 412	27.74 466	74.05 380	61.00 452	60.48 365	65.62 441	67.03 456	55.93 362	68.23 438	32.07 462	69.46 457	68.17 450

CHOSEN 280x420		59.08 424	56.52 430	66.76 415	76.51 421	64.39 264	49.62 459	59.00 455	43.86 448	55.66 463	68.41 455	35.55 444	68.21 439	43.25 442	76.78 445	67.69 451

mvsany_test1		58.48 425	56.47 431	64.50 425	65.90 465	68.21 224	56.95 455	42.11 468	38.30 460	65.69 440	77.19 434	56.96 356	59.35 458	46.16 435	58.96 461	65.93 452

UWE-MVS-28		58.44 426	57.71 428	60.65 436	73.58 442	31.23 463	69.68 417	48.80 464	53.12 413	61.79 451	78.83 418	30.98 454	68.40 437	21.58 465	80.99 427	82.33 408

PVSNet_0	51.08 22	56.10 427	54.97 432	59.48 439	75.12 434	53.28 395	55.16 456	61.89 447	44.30 446	59.16 456	62.48 459	54.22 370	65.91 447	35.40 457	47.01 462	59.25 458

new_pmnet		55.69 428	57.66 429	49.76 444	75.47 431	30.59 464	59.56 447	51.45 462	43.62 450	62.49 450	75.48 442	40.96 433	49.15 464	37.39 456	72.52 448	69.55 448

PMMVS2		55.64 429	59.27 427	44.74 445	64.30 467	12.32 473	40.60 460	49.79 463	53.19 411	65.06 446	84.81 358	53.60 373	49.76 463	32.68 461	89.41 330	72.15 444

MVE	40.22 23	51.82 430	50.47 433	55.87 441	62.66 468	51.91 404	31.61 462	39.28 469	40.65 455	50.76 464	74.98 444	56.24 361	44.67 465	33.94 460	64.11 459	71.04 447
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 431	42.65 434	39.67 446	70.86 454	21.11 468	61.01 446	21.42 473	57.36 388	57.97 461	50.06 462	16.40 472	58.73 459	21.03 466	27.69 466	39.17 462

kuosan		30.83 432	32.17 435	26.83 448	53.36 470	19.02 471	57.90 453	20.44 474	38.29 461	38.01 465	37.82 464	15.18 473	33.45 467	7.74 468	20.76 467	28.03 463

test_method		30.46 433	29.60 436	33.06 447	17.99 472	3.84 475	13.62 463	73.92 387	2.79 466	18.29 468	53.41 461	28.53 460	43.25 466	22.56 463	35.27 464	52.11 461

cdsmvs_eth3d_5k		20.81 434	27.75 437	0.00 453	0.00 476	0.00 478	0.00 464	85.44 289	0.00 471	0.00 472	82.82 381	81.46 127	0.00 472	0.00 471	0.00 470	0.00 468

tmp_tt		20.25 435	24.50 438	7.49 450	4.47 473	8.70 474	34.17 461	25.16 471	1.00 468	32.43 467	18.49 465	39.37 436	9.21 469	21.64 464	43.75 463	4.57 465

ab-mvs-re		6.65 436	8.87 439	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	79.80 409	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

pcd_1.5k_mvsjas		6.41 437	8.55 440	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	76.94 184	0.00 472	0.00 471	0.00 470	0.00 468

test123		6.27 438	8.08 441	0.84 451	1.11 475	0.57 476	62.90 441	0.82 475	0.54 469	1.07 471	2.75 470	1.26 474	0.30 470	1.04 469	1.26 469	1.66 466

testmvs		5.91 439	7.65 442	0.72 452	1.20 474	0.37 477	59.14 449	0.67 476	0.49 470	1.11 470	2.76 469	0.94 475	0.24 471	1.02 470	1.47 468	1.55 467

mmdepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

monomultidepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

test_blank		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet_test		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

DCPMVS		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

sosnet-low-res		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

sosnet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uncertanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

Regformer		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

WAC-MVS						37.39 456								52.61 405

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		91.01 161					96.05 9	87.45 28	98.17 37	92.40 204

PC_three_1452									58.96 375	90.06 103	91.33 202	80.66 137	93.03 151	75.78 191	95.94 133	92.48 198

No_MVS				88.81 73	91.55 135	77.99 97		91.01 161					96.05 9	87.45 28	98.17 37	92.40 204

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					0.00 476

eth-test					0.00 476

ZD-MVS					92.22 109	80.48 71		91.85 131	71.22 234	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	92.73 182

IU-MVS					94.18 52	72.64 154		90.82 166	56.98 392	89.67 116				85.78 62	97.92 52	93.28 155

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 208	81.12 131	94.68 78	74.48 203	95.35 156	92.29 214

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	92.04 227

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	92.89 145	87.27 48	93.78 117	83.69 87	97.55 74

save fliter					93.75 65	77.44 106	86.31 140	89.72 204	70.80 238

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 175

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	92.02 228

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																83.88 381

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 402				83.88 381

sam_mvs												45.92 407

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 217		89.40 125	93.96 105	71.67 263	91.38 197	78.83 144	96.55 102	92.71 185

MTGPA								91.81 135

test_post1							78.85 314				3.13 467	45.19 417	80.13 387	58.11 370

test_post											3.10 468	45.43 413	77.22 402

patchmatchnet-post											81.71 393	45.93 406	87.01 308

GG-mvs-BLEND				67.16 413	73.36 443	46.54 432	84.15 190	55.04 460		58.64 459	61.95 460	29.93 457	83.87 364	38.71 452	76.92 444	71.07 446

MTMP							90.66 49	33.14 470

gm-plane-assit					75.42 432	44.97 439			52.17 418		72.36 449		87.90 294	54.10 394

test9_res														80.83 119	96.45 108	90.57 275

TEST9					92.34 104	79.70 80	83.94 196	90.32 184	65.41 315	84.49 252	90.97 217	82.03 117	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 186	65.79 306	84.36 256	90.96 219	81.93 119	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 283

agg_prior					91.58 133	77.69 103		90.30 187		84.32 258			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	70.81 255	96.14 121	94.16 111

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 249	91.57 192	81.92 121		79.54 136	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 97					94.17 102			92.80 180

旧先验2							81.73 264		56.88 393	86.54 203			84.90 350	72.81 240

新几何2							81.72 265

新几何1				82.95 216	93.96 61	78.56 91		80.24 347	55.45 398	83.93 269	91.08 214	71.19 265	88.33 286	65.84 311	93.07 239	81.95 412

旧先验1					91.97 117	71.77 171		81.78 335			91.84 181	73.92 227			93.65 221	83.61 387

无先验							82.81 236	85.62 287	58.09 381				91.41 196	67.95 295		84.48 372

原ACMM2							82.26 256

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 142	62.59 336	82.73 294	90.67 236	76.53 193	94.25 94	69.24 276	95.69 148	85.55 360

test222					93.31 78	76.54 116	79.38 303	77.79 358	52.59 415	82.36 299	90.84 227	66.83 289			91.69 281	81.25 420

testdata2													86.43 323	63.52 333

segment_acmp												81.94 118

testdata				79.54 293	92.87 89	72.34 163		80.14 348	59.91 371	85.47 228	91.75 188	67.96 283	85.24 346	68.57 290	92.18 268	81.06 425

testdata1							79.62 296		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 169		82.76 293		79.20 150	94.80 75		95.32 158	92.27 216

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 209

plane_prior5								93.61 60					95.22 59	80.78 120	95.83 141	94.46 94

plane_prior4											92.95 143

plane_prior3						76.85 114			77.79 133	86.55 197

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								0.00 477

nn								0.00 477

door-mid								74.45 384

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 414		91.79 71	94.42 78	61.76 323	92.93 154	79.52 137	93.03 240	93.93 120

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								91.46 143

door								72.57 400

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 332

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 332

BP-MVS														77.30 169

HQP4-MVS										80.56 331			94.61 82			93.56 147

HQP3-MVS								92.68 103							94.47 192

HQP2-MVS												72.10 254

NP-MVS					91.95 118	74.55 134					90.17 256

MDTV_nov1_ep13_2view						27.60 467	70.76 409		46.47 440	61.27 452		45.20 416		49.18 421		83.75 386

MDTV_nov1_ep13			68.29 388		78.03 407	43.87 442	74.12 379	72.22 403	52.17 418	67.02 435	85.54 343	45.36 414	80.85 381	55.73 381	84.42 402

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 152

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 187	81.56 82	90.02 105	91.20 210	82.40 103	90.81 217	73.58 226	94.66 187	94.56 90

DeepMVS_CX				24.13 449	32.95 471	29.49 465		21.63 472	12.07 465	37.95 466	45.07 463	30.84 455	19.21 468	17.94 467	33.06 465	23.69 464

High-resolution multi-view benchmark