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 70	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	80.16 145	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 55	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 77	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	92.08 228

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	91.76 239

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	91.76 239

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	93.63 142

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	92.73 185

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 42	97.34 81	92.19 223

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 44	97.60 71	94.18 111

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	79.95 129	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	92.60 109	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 168

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	92.55 198

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	94.20 108

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 136	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 215

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 101	88.22 23	88.53 143	97.64 6	83.45 91	94.55 86	86.02 59	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	93.99 119

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 66	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	93.83 128

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	92.85 182

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	94.12 115

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	92.98 178
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 48	97.99 46	93.96 121

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 91	82.59 72	88.52 144	94.37 82	86.74 54	95.41 53	86.32 49	98.21 34	93.19 163
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	93.93 122

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	78.35 151	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	93.27 158

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 73	98.45 19	92.41 205
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 79	95.37 54	80.87 119	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 97	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 80	97.81 58	91.70 243
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 56	97.92 52	92.29 217

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	93.88 125
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 65	97.51 78	94.30 107

v7n		90.13 41	90.96 43	87.65 96	91.95 118	71.06 185	89.99 65	93.05 88	86.53 35	94.29 23	96.27 23	82.69 98	94.08 105	86.25 52	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	89.57 213	88.51 21	90.11 102	95.12 53	90.98 7	88.92 273	77.55 165	97.07 88	83.13 400
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	81.12 133	95.88 18	87.41 30	95.94 133	92.48 201

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 164	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 46	97.97 49	90.55 280
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	84.47 168	96.56 6	58.83 353	89.04 89	92.74 103	91.40 6	96.12 5	96.06 29	87.23 49	95.57 41	79.42 139	98.74 6	99.00 2

PEN-MVS		90.03 46	91.88 19	84.48 167	96.57 5	58.88 350	88.95 90	93.19 80	91.62 5	96.01 7	96.16 27	87.02 51	95.60 40	78.69 147	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 84	81.10 87	95.32 14	97.24 10	72.94 247	94.85 72	85.07 70	97.78 59	97.26 16

DTE-MVSNet		89.98 48	91.91 18	84.21 177	96.51 7	57.84 361	88.93 91	92.84 99	91.92 4	96.16 4	96.23 24	86.95 52	95.99 12	79.05 143	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 86	93.10 148	82.67 101	98.04 41	93.64 141

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	88.83 135	94.26 86	77.55 174	95.86 23	84.88 74	95.87 139	95.24 65

WR-MVS_H		89.91 51	91.31 34	85.71 134	96.32 9	62.39 295	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 130	95.90 17	80.94 118	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	92.78 102	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 76	97.55 74	94.10 116
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	90.63 174	70.00 250	94.55 19	96.67 17	87.94 40	93.59 128	84.27 82	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	90.20 195	69.87 251	95.06 15	96.14 28	84.28 82	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	90.08 198	71.54 229	94.28 25	96.54 19	81.57 128	94.27 92	86.26 50	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	86.75 193	93.26 128	93.64 2	90.93 211	84.60 79	90.75 308	93.97 120

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 87	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 107	97.18 86	90.45 282
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	90.29 192	69.27 258	94.39 21	96.38 21	86.02 66	93.52 132	83.96 84	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	92.30 118	79.74 103	87.50 177	92.38 163	81.42 130	93.28 141	83.07 93	97.24 84	91.67 244

ACMH	76.49 14	89.34 60	91.14 36	83.96 184	92.50 99	70.36 194	89.55 78	93.84 53	81.89 79	94.70 17	95.44 44	90.69 9	88.31 290	83.33 89	98.30 27	93.20 162
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 81	86.02 38	93.12 45	95.30 46	84.94 74	89.44 265	74.12 214	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 81	86.02 38	93.12 45	95.30 46	84.94 74	89.44 265	74.12 214	96.10 124	94.45 96

CP-MVSNet		89.27 63	90.91 45	84.37 169	96.34 8	58.61 356	88.66 98	92.06 125	90.78 7	95.67 8	95.17 51	81.80 125	95.54 44	79.00 144	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 85	90.98 208	83.86 86	95.30 161	93.60 145

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 73	95.77 34	84.17 83	98.03 43	93.26 160
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	84.31 175	97.00 2	64.33 266	89.67 75	88.38 233	88.84 17	94.29 23	97.57 7	90.48 14	91.26 199	72.57 245	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	92.25 172	72.03 261	96.36 4	88.21 13	90.93 300	92.98 178
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	92.29 170	86.11 64	90.22 239	86.24 53	97.24 84	91.36 252
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	91.27 156	78.20 126	86.69 197	92.28 171	80.36 143	95.06 67	86.17 54	96.49 105	90.22 286

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	74.07 224	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	74.07 224	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	84.01 194	89.44 216	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 130	95.21 162	91.82 237

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 95	87.95 26	89.62 118	92.87 146	84.56 78	93.89 113	77.65 163	96.62 100	90.70 272

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 131	84.26 53	90.87 92	93.92 109	82.18 113	89.29 269	73.75 222	94.81 181	93.70 136

Anonymous20231211		88.40 74	89.62 60	84.73 159	90.46 165	65.27 255	88.86 92	93.02 92	87.15 30	93.05 47	97.10 11	82.28 111	92.02 178	76.70 175	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	91.97 128	70.73 241	94.19 26	96.67 17	76.94 186	94.57 84	83.07 93	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 81	76.02 149	88.64 140	91.22 210	84.24 83	93.37 139	77.97 161	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	84.32 261	89.33 275	83.87 84	94.53 87	82.45 103	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	91.19 159	67.85 285	86.63 198	94.84 59	79.58 151	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	82.98 215	93.26 80	63.94 270	91.10 46	89.64 210	85.07 45	90.91 88	91.09 215	89.16 25	91.87 183	82.03 108	95.87 139	93.13 165

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	85.12 237	89.67 269	84.47 80	95.46 50	82.56 102	96.26 116	93.77 134

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 160	79.26 111	89.68 115	94.81 63	82.44 102	87.74 301	76.54 180	88.74 344	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	88.00 159	93.03 137	82.66 99	91.47 191	70.81 258	96.14 121	94.16 112

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 267	84.38 186	91.29 152	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 236	98.66 11	97.69 9

nrg030		87.85 85	88.49 79	85.91 128	90.07 175	69.73 202	87.86 111	94.20 31	74.04 181	92.70 57	94.66 64	85.88 67	91.50 190	79.72 132	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	91.23 157	77.31 139	87.07 187	91.47 200	82.94 96	94.71 77	84.67 78	96.27 115	92.62 193

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	86.55 199	92.95 143	74.84 211	95.22 59	80.78 121	95.83 141	94.46 94

sc_t1		87.70 88	88.94 71	83.99 182	93.47 71	67.15 233	85.05 168	88.21 240	86.81 32	91.87 70	97.65 5	85.51 71	87.91 296	74.22 209	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	86.76 271	84.54 50	83.58 280	93.78 114	73.36 242	96.48 2	87.98 17	96.21 117	94.41 101

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 201	92.03 116	62.26 299	91.49 41	92.62 107	88.07 25	88.07 156	96.17 26	72.24 256	95.79 31	84.85 75	94.16 203	92.58 196

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 162	78.77 119	84.85 248	90.89 226	80.85 136	95.29 56	81.14 116	95.32 158	92.34 213

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 217	92.38 114	70.25 247	89.35 126	90.68 236	82.85 97	94.57 84	79.55 136	95.95 132	92.00 232

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 212	87.84 112	88.05 241	81.66 81	94.64 18	96.53 20	65.94 298	94.75 76	83.02 95	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	84.60 179	89.74 205	74.40 178	89.92 110	93.41 125	80.45 141	90.63 226	86.66 45	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	81.64 321	87.25 320	82.43 103	94.53 87	77.65 163	96.46 107	94.14 114

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 222	83.80 204	92.87 97	80.37 94	89.61 120	91.81 186	77.72 170	94.18 100	75.00 202	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	92.09 124	78.87 117	84.27 266	94.05 98	78.35 162	93.65 121	80.54 125	91.58 286	92.08 228
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	67.02 237	82.55 244	91.56 142	83.08 68	90.92 86	91.82 185	78.25 163	93.99 107	74.16 212	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 237	83.16 227	92.21 119	81.73 80	90.92 86	91.97 178	77.20 180	93.99 107	74.16 212	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 171	87.09 259	65.22 256	84.16 190	94.23 28	77.89 130	91.28 81	93.66 120	84.35 81	92.71 158	80.07 126	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	85.94 297	78.30 92	86.93 125	92.20 120	65.94 305	89.16 129	93.16 132	83.10 94	89.89 254	87.81 20	94.43 194	93.35 153

tt0320-xc		86.67 102	88.41 81	81.44 257	93.45 72	60.44 327	83.96 196	88.50 229	87.26 29	90.90 90	97.90 3	85.61 68	86.40 327	70.14 270	98.01 45	97.47 14

IS-MVSNet		86.66 103	86.82 108	86.17 124	92.05 115	66.87 240	91.21 44	88.64 226	86.30 37	89.60 121	92.59 155	69.22 279	94.91 71	73.89 219	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 258	93.57 69	60.73 324	84.37 187	88.61 228	87.00 31	90.75 93	97.98 2	85.54 70	86.45 325	69.75 275	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 153	88.16 225	63.28 277	86.64 135	92.20 120	75.42 163	92.81 54	94.50 72	74.05 227	94.06 106	83.88 85	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 243	92.22 109	62.28 298	84.66 178	89.15 220	83.54 63	89.85 111	97.32 8	88.08 39	86.80 318	70.43 267	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	85.12 237	90.34 249	66.19 295	94.20 97	76.57 178	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 88	73.15 202	84.76 250	87.70 309	78.87 156	94.18 100	80.67 123	96.29 112	92.73 185

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 132	80.35 95	89.54 124	88.01 296	79.09 154	92.13 174	75.51 195	95.06 169	90.41 283

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 127	70.56 242	84.96 243	90.69 235	80.01 147	95.14 64	78.37 150	95.78 145	91.82 237
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	84.36 171	87.82 233	62.35 297	86.42 139	91.33 151	76.78 143	92.73 56	94.48 74	73.41 239	93.72 119	83.10 92	95.41 154	97.01 23

Anonymous20240529		86.20 112	87.13 98	83.42 203	90.19 170	64.55 263	84.55 181	90.71 171	85.85 40	89.94 109	95.24 50	82.13 114	90.40 234	69.19 282	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 217	86.91 267	70.38 193	85.31 162	92.61 108	75.59 159	88.32 151	92.87 146	82.22 112	88.63 282	88.80 9	92.82 249	89.83 296

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 313	78.25 93	85.82 151	91.82 134	65.33 319	88.55 142	92.35 169	82.62 101	89.80 256	86.87 41	94.32 198	93.18 164

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 201	92.01 126	65.91 307	86.19 210	91.75 190	83.77 87	94.98 69	77.43 168	96.71 98	93.73 135

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 262	82.21 258	90.46 180	80.99 88	88.42 147	91.97 178	77.56 173	93.85 114	72.46 246	98.65 12	97.61 10

train_agg		85.98 117	85.28 140	88.07 90	92.34 104	79.70 80	83.94 197	90.32 187	65.79 309	84.49 255	90.97 220	81.93 120	93.63 123	81.21 115	96.54 103	90.88 266

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 200	83.70 207	89.83 204	80.42 93	87.76 170	93.24 129	73.76 233	91.54 189	85.03 72	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 228	92.25 107	56.44 372	85.75 152	93.09 86	77.33 138	91.94 69	94.65 65	74.78 213	93.41 138	75.11 201	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 322	78.21 94	85.40 161	91.39 149	65.32 320	87.72 172	91.81 186	82.33 106	89.78 257	86.68 43	94.20 201	92.99 176

Effi-MVS+-dtu		85.82 121	83.38 189	93.14 4	87.13 254	91.15 3	87.70 113	88.42 232	74.57 172	83.56 281	85.65 344	78.49 161	94.21 96	72.04 248	92.88 245	94.05 118

TAPA-MVS	77.73 12	85.71 122	84.83 150	88.37 83	88.78 209	79.72 79	87.15 122	93.50 66	69.17 259	85.80 220	89.56 270	80.76 137	92.13 174	73.21 241	95.51 152	93.25 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 123	86.14 117	83.58 197	87.97 227	67.13 234	87.55 114	94.32 22	73.44 192	88.47 145	87.54 312	86.45 59	91.06 206	75.76 193	93.76 214	92.54 199

canonicalmvs		85.50 123	86.14 117	83.58 197	87.97 227	67.13 234	87.55 114	94.32 22	73.44 192	88.47 145	87.54 312	86.45 59	91.06 206	75.76 193	93.76 214	92.54 199

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 162	87.10 257	69.98 198	84.28 188	92.68 104	74.77 169	87.90 163	92.36 168	73.94 228	90.41 233	85.95 61	92.74 251	93.66 137

EPP-MVSNet		85.47 126	85.04 145	86.77 109	91.52 138	69.37 207	91.63 40	87.98 244	81.51 83	87.05 188	91.83 184	66.18 297	95.29 56	70.75 261	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 169	90.08 173	67.07 236	85.86 150	91.39 149	72.33 220	87.59 174	90.25 254	84.85 76	92.37 168	78.00 159	91.94 276	93.66 137

MVS_0304		85.37 128	84.58 159	87.75 93	85.28 312	73.36 141	86.54 138	85.71 288	77.56 137	81.78 319	92.47 161	70.29 273	96.02 11	85.59 64	95.96 130	93.87 126

FIs		85.35 129	86.27 114	82.60 227	91.86 122	57.31 365	85.10 167	93.05 88	75.83 154	91.02 85	93.97 102	73.57 235	92.91 156	73.97 218	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 155	85.80 300	76.13 125	85.15 166	92.32 117	61.40 355	91.33 78	90.85 229	83.76 88	86.16 333	84.31 81	93.28 232	92.15 226

casdiffmvs		85.21 131	85.85 125	83.31 206	86.17 289	62.77 284	83.03 229	93.93 47	74.69 171	88.21 153	92.68 154	82.29 110	91.89 182	77.87 162	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
fmvsm_s_conf0.5_n_10		85.20 132	85.25 141	85.02 150	86.01 295	71.31 181	84.96 169	91.76 138	69.10 261	88.90 132	92.56 158	73.84 231	90.63 226	86.88 40	93.26 233	93.13 165

baseline		85.20 132	85.93 122	83.02 213	86.30 284	62.37 296	84.55 181	93.96 45	74.48 175	87.12 182	92.03 177	82.30 108	91.94 179	78.39 149	94.21 200	94.74 86

SSM_0404		85.16 134	85.09 143	85.36 142	90.14 172	69.52 205	86.17 144	91.58 140	74.41 176	86.55 199	91.49 197	78.54 157	93.97 109	73.71 223	93.21 237	92.59 195

K. test v3		85.14 135	84.73 152	86.37 115	91.13 151	69.63 204	85.45 159	76.68 373	84.06 56	92.44 61	96.99 13	62.03 325	94.65 80	80.58 124	93.24 234	94.83 83

mmtdpeth		85.13 136	85.78 128	83.17 211	84.65 324	74.71 132	85.87 149	90.35 186	77.94 129	83.82 273	96.96 15	77.75 168	80.03 392	78.44 148	96.21 117	94.79 85

EI-MVSNet-Vis-set		85.12 137	84.53 162	86.88 106	84.01 337	72.76 151	83.91 200	85.18 298	80.44 92	88.75 137	85.49 348	80.08 146	91.92 180	82.02 109	90.85 305	95.97 44

fmvsm_l_conf0.5_n_3		85.11 138	84.96 147	85.56 137	87.49 246	75.69 128	84.71 176	90.61 176	67.64 289	84.88 246	92.05 176	82.30 108	88.36 288	83.84 87	91.10 293	92.62 193

MGCFI-Net		85.04 139	85.95 121	82.31 236	87.52 244	63.59 273	86.23 143	93.96 45	73.46 190	88.07 156	87.83 307	86.46 58	90.87 216	76.17 187	93.89 211	92.47 203

EI-MVSNet-UG-set		85.04 139	84.44 165	86.85 107	83.87 341	72.52 160	83.82 202	85.15 299	80.27 97	88.75 137	85.45 350	79.95 148	91.90 181	81.92 112	90.80 307	96.13 39

X-MVStestdata		85.04 139	82.70 207	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 469	86.57 56	95.80 28	87.35 32	97.62 69	94.20 108

MSLP-MVS++		85.00 142	86.03 120	81.90 243	91.84 125	71.56 179	86.75 133	93.02 92	75.95 152	87.12 182	89.39 273	77.98 165	89.40 268	77.46 166	94.78 182	84.75 372

F-COLMAP		84.97 143	83.42 188	89.63 58	92.39 102	83.40 52	88.83 93	91.92 130	73.19 201	80.18 343	89.15 279	77.04 184	93.28 141	65.82 315	92.28 265	92.21 222

SSM_0407		84.89 144	84.85 149	85.01 151	89.13 195	68.97 215	85.60 156	91.58 140	74.41 176	85.68 221	91.49 197	78.54 157	93.69 120	73.71 223	93.47 225	92.38 210

balanced_conf03		84.80 145	85.40 136	83.00 214	88.95 202	61.44 307	90.42 59	92.37 116	71.48 231	88.72 139	93.13 133	70.16 275	95.15 63	79.26 141	94.11 204	92.41 205

3Dnovator	80.37 7	84.80 145	84.71 155	85.06 149	86.36 282	74.71 132	88.77 95	90.00 200	75.65 157	84.96 243	93.17 131	74.06 226	91.19 201	78.28 153	91.09 294	89.29 306

SymmetryMVS		84.79 147	83.54 183	88.55 79	92.44 101	80.42 72	88.63 99	82.37 333	74.56 173	85.12 237	90.34 249	66.19 295	94.20 97	76.57 178	95.68 149	91.03 260

IterMVS-LS		84.73 148	84.98 146	83.96 184	87.35 248	63.66 271	83.25 222	89.88 203	76.06 147	89.62 118	92.37 166	73.40 241	92.52 163	78.16 156	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		84.63 149	84.34 170	85.49 141	90.18 171	75.86 127	79.23 311	87.13 261	73.35 194	85.56 228	89.34 274	83.60 90	90.50 230	76.64 177	94.05 208	90.09 292

HQP-MVS		84.61 150	84.06 175	86.27 118	91.19 147	70.66 188	84.77 171	92.68 104	73.30 197	80.55 335	90.17 259	72.10 257	94.61 82	77.30 170	94.47 192	93.56 149

v1192		84.57 151	84.69 157	84.21 177	87.75 235	62.88 281	83.02 230	91.43 146	69.08 262	89.98 108	90.89 226	72.70 251	93.62 126	82.41 104	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 152	84.71 155	84.11 180	87.92 230	72.09 168	84.80 170	88.64 226	64.43 329	88.77 136	91.78 188	78.07 164	87.95 295	85.85 62	92.18 269	92.30 215

FMVSNet1		84.55 153	85.45 135	81.85 245	90.27 169	61.05 315	86.83 129	88.27 237	78.57 122	89.66 117	95.64 38	75.43 203	90.68 223	69.09 283	95.33 157	93.82 129

v1144		84.54 154	84.72 154	84.00 181	87.67 239	62.55 288	82.97 232	90.93 167	70.32 246	89.80 112	90.99 219	73.50 236	93.48 134	81.69 114	94.65 188	95.97 44

Gipuma		84.44 155	86.33 113	78.78 303	84.20 334	73.57 140	89.55 78	90.44 181	84.24 54	84.38 258	94.89 57	76.35 199	80.40 389	76.14 188	96.80 96	82.36 410
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 156	84.27 171	84.74 158	87.25 250	70.84 187	83.55 212	88.45 231	68.64 271	86.29 209	91.31 206	74.97 209	88.42 286	87.87 19	90.07 323	94.95 75

MCST-MVS		84.36 157	83.93 179	85.63 135	91.59 130	71.58 177	83.52 213	92.13 122	61.82 348	83.96 271	89.75 267	79.93 149	93.46 135	78.33 152	94.34 197	91.87 236

VDDNet		84.35 158	85.39 137	81.25 260	95.13 32	59.32 341	85.42 160	81.11 344	86.41 36	87.41 178	96.21 25	73.61 234	90.61 228	66.33 308	96.85 92	93.81 132

ETV-MVS		84.31 159	83.91 180	85.52 138	88.58 215	70.40 192	84.50 185	93.37 68	78.76 120	84.07 269	78.72 423	80.39 142	95.13 65	73.82 221	92.98 243	91.04 259

v1240		84.30 160	84.51 163	83.65 194	87.65 240	61.26 311	82.85 236	91.54 143	67.94 282	90.68 95	90.65 240	71.71 265	93.64 122	82.84 98	94.78 182	96.07 41

MVS_111021_LR		84.28 161	83.76 181	85.83 132	89.23 193	83.07 55	80.99 279	83.56 321	72.71 212	86.07 213	89.07 281	81.75 127	86.19 332	77.11 172	93.36 228	88.24 325

h-mvs33		84.25 162	82.76 206	88.72 75	91.82 127	82.60 60	84.00 195	84.98 305	71.27 232	86.70 195	90.55 245	63.04 322	93.92 112	78.26 154	94.20 201	89.63 298

v144192		84.24 163	84.41 166	83.71 193	87.59 242	61.57 306	82.95 233	91.03 163	67.82 286	89.80 112	90.49 246	73.28 243	93.51 133	81.88 113	94.89 177	96.04 43

dcpmvs_2		84.23 164	85.14 142	81.50 255	88.61 214	61.98 303	82.90 235	93.11 84	68.66 270	92.77 55	92.39 162	78.50 160	87.63 304	76.99 174	92.30 262	94.90 76

v1921920		84.23 164	84.37 168	83.79 189	87.64 241	61.71 305	82.91 234	91.20 158	67.94 282	90.06 103	90.34 249	72.04 260	93.59 128	82.32 105	94.91 175	96.07 41

VDD-MVS		84.23 164	84.58 159	83.20 209	91.17 150	65.16 258	83.25 222	84.97 306	79.79 102	87.18 181	94.27 83	74.77 214	90.89 214	69.24 279	96.54 103	93.55 151

v2v482		84.09 167	84.24 172	83.62 195	87.13 254	61.40 308	82.71 239	89.71 208	72.19 223	89.55 122	91.41 201	70.70 271	93.20 143	81.02 117	93.76 214	96.25 37

EG-PatchMatch MVS		84.08 168	84.11 174	83.98 183	92.22 109	72.61 157	82.20 260	87.02 267	72.63 213	88.86 133	91.02 218	78.52 159	91.11 204	73.41 231	91.09 294	88.21 326

fmvsm_s_conf0.5_n_6		84.05 169	84.14 173	83.81 187	87.75 235	71.17 183	83.42 216	91.10 161	67.90 284	84.53 253	90.70 234	73.01 246	88.73 279	85.09 69	93.72 219	91.53 249

DP-MVS Recon		84.05 169	83.22 192	86.52 113	91.73 128	75.27 130	83.23 224	92.40 112	72.04 225	82.04 310	88.33 292	77.91 167	93.95 111	66.17 309	95.12 167	90.34 285

viewmacassd2359aftdt		84.04 171	84.78 151	81.81 248	86.43 276	60.32 329	81.95 262	92.82 100	71.56 228	86.06 214	92.98 139	81.79 126	90.28 235	76.18 186	93.24 234	94.82 84

TransMVSNet (Re)		84.02 172	85.74 130	78.85 302	91.00 154	55.20 384	82.29 254	87.26 256	79.65 105	88.38 149	95.52 41	83.00 95	86.88 316	67.97 297	96.60 101	94.45 96

Baseline_NR-MVSNet		84.00 173	85.90 123	78.29 314	91.47 140	53.44 396	82.29 254	87.00 270	79.06 114	89.55 122	95.72 36	77.20 180	86.14 334	72.30 247	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 174	84.46 164	82.53 231	86.11 292	70.65 190	82.45 249	89.17 219	67.72 288	86.74 194	91.49 197	79.20 152	85.86 343	84.71 77	92.60 255	91.07 258

TSAR-MVS + GP.		83.95 175	82.69 208	87.72 94	89.27 192	81.45 67	83.72 206	81.58 342	74.73 170	85.66 224	86.06 339	72.56 253	92.69 160	75.44 197	95.21 162	89.01 319

LuminaMVS		83.94 176	83.51 184	85.23 144	89.78 181	71.74 172	84.76 174	87.27 255	72.60 214	89.31 127	90.60 244	64.04 311	90.95 209	79.08 142	94.11 204	92.99 176

alignmvs		83.94 176	83.98 177	83.80 188	87.80 234	67.88 229	84.54 183	91.42 148	73.27 200	88.41 148	87.96 297	72.33 254	90.83 217	76.02 190	94.11 204	92.69 189

Effi-MVS+		83.90 178	84.01 176	83.57 199	87.22 252	65.61 254	86.55 137	92.40 112	78.64 121	81.34 326	84.18 369	83.65 89	92.93 154	74.22 209	87.87 358	92.17 225

fmvsm_s_conf0.1_n_2		83.82 179	83.49 185	84.84 153	85.99 296	70.19 196	80.93 280	87.58 251	67.26 295	87.94 162	92.37 166	71.40 267	88.01 292	86.03 56	91.87 277	96.31 36

mvs5depth		83.82 179	84.54 161	81.68 251	82.23 366	68.65 220	86.89 126	89.90 202	80.02 101	87.74 171	97.86 4	64.19 310	82.02 377	76.37 182	95.63 151	94.35 103

CANet		83.79 181	82.85 205	86.63 110	86.17 289	72.21 167	83.76 205	91.43 146	77.24 140	74.39 398	87.45 316	75.36 204	95.42 52	77.03 173	92.83 248	92.25 221

pm-mvs1		83.69 182	84.95 148	79.91 288	90.04 177	59.66 338	82.43 250	87.44 252	75.52 161	87.85 166	95.26 49	81.25 132	85.65 347	68.74 289	96.04 126	94.42 100

AdaColmap		83.66 183	83.69 182	83.57 199	90.05 176	72.26 165	86.29 141	90.00 200	78.19 127	81.65 320	87.16 322	83.40 92	94.24 95	61.69 351	94.76 185	84.21 382

MIMVSNet1		83.63 184	84.59 158	80.74 271	94.06 59	62.77 284	82.72 238	84.53 313	77.57 136	90.34 99	95.92 31	76.88 192	85.83 344	61.88 349	97.42 79	93.62 143

fmvsm_s_conf0.5_n_2		83.62 185	83.29 191	84.62 163	85.43 310	70.18 197	80.61 286	87.24 257	67.14 296	87.79 168	91.87 180	71.79 264	87.98 294	86.00 60	91.77 280	95.71 50

test_fmvsm_n_1920		83.60 186	82.89 202	85.74 133	85.22 314	77.74 102	84.12 192	90.48 178	59.87 375	86.45 208	91.12 214	75.65 201	85.89 341	82.28 106	90.87 303	93.58 147

WR-MVS		83.56 187	84.40 167	81.06 265	93.43 75	54.88 385	78.67 320	85.02 303	81.24 85	90.74 94	91.56 195	72.85 248	91.08 205	68.00 296	98.04 41	97.23 17

CNLPA		83.55 188	83.10 197	84.90 152	89.34 190	83.87 50	84.54 183	88.77 223	79.09 113	83.54 282	88.66 289	74.87 210	81.73 379	66.84 303	92.29 264	89.11 312

viewcassd2359sk11		83.53 189	83.96 178	82.25 237	86.97 266	61.13 313	80.80 284	93.22 79	70.97 238	85.36 232	91.08 216	81.84 124	91.29 198	74.79 204	90.58 319	94.33 105

LCM-MVSNet-Re		83.48 190	85.06 144	78.75 304	85.94 297	55.75 378	80.05 292	94.27 25	76.47 144	96.09 6	94.54 71	83.31 93	89.75 260	59.95 362	94.89 177	90.75 269

hse-mvs2		83.47 191	81.81 223	88.47 80	91.03 153	82.27 61	82.61 240	83.69 319	71.27 232	86.70 195	86.05 340	63.04 322	92.41 166	78.26 154	93.62 223	90.71 271

V42		83.47 191	83.37 190	83.75 191	83.16 360	63.33 276	81.31 273	90.23 194	69.51 255	90.91 88	90.81 231	74.16 223	92.29 172	80.06 127	90.22 321	95.62 54

VPA-MVSNet		83.47 191	84.73 152	79.69 293	90.29 168	57.52 364	81.30 275	88.69 225	76.29 145	87.58 176	94.44 75	80.60 140	87.20 310	66.60 306	96.82 95	94.34 104

mamba_0408		83.44 194	82.88 203	85.11 147	89.13 195	68.97 215	72.73 395	91.28 153	72.90 206	85.68 221	90.61 242	76.78 193	93.97 109	73.37 233	93.47 225	92.38 210

viewdifsd2359ckpt07		83.41 195	84.35 169	80.56 277	85.84 299	58.93 349	79.47 303	91.28 153	73.01 205	87.59 174	92.07 175	85.24 72	88.68 280	73.59 228	91.11 292	94.09 117

PAPM_NR		83.23 196	83.19 194	83.33 205	90.90 156	65.98 250	88.19 104	90.78 170	78.13 128	80.87 331	87.92 301	73.49 238	92.42 165	70.07 271	88.40 347	91.60 246

CLD-MVS		83.18 197	82.64 209	84.79 156	89.05 198	67.82 230	77.93 330	92.52 110	68.33 274	85.07 240	81.54 398	82.06 117	92.96 152	69.35 278	97.91 54	93.57 148
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 198	85.68 131	75.65 352	81.24 378	45.26 440	79.94 294	92.91 96	83.83 57	91.33 78	96.88 16	80.25 144	85.92 337	68.89 286	95.89 138	95.76 48

FA-MVS(test-final)		83.13 199	83.02 198	83.43 202	86.16 291	66.08 249	88.00 108	88.36 234	75.55 160	85.02 241	92.75 152	65.12 304	92.50 164	74.94 203	91.30 290	91.72 241

114514_t		83.10 200	82.54 212	84.77 157	92.90 88	69.10 214	86.65 134	90.62 175	54.66 407	81.46 323	90.81 231	76.98 185	94.38 90	72.62 244	96.18 119	90.82 268

RRT-MVS		82.97 201	83.44 186	81.57 253	85.06 317	58.04 359	87.20 119	90.37 184	77.88 131	88.59 141	93.70 119	63.17 319	93.05 150	76.49 181	88.47 346	93.62 143

viewmanbaseed2359cas		82.95 202	83.43 187	81.52 254	85.18 315	60.03 334	81.36 272	92.38 114	69.55 254	84.84 249	91.38 202	79.85 150	90.09 248	74.22 209	92.09 271	94.43 99

BP-MVS1		82.81 203	81.67 225	86.23 119	87.88 232	68.53 221	86.06 146	84.36 314	75.65 157	85.14 236	90.19 256	45.84 411	94.42 89	85.18 68	94.72 186	95.75 49

UGNet		82.78 204	81.64 226	86.21 122	86.20 288	76.24 123	86.86 127	85.68 289	77.07 141	73.76 402	92.82 148	69.64 276	91.82 185	69.04 285	93.69 220	90.56 279
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		82.75 205	81.93 221	85.19 145	82.08 367	80.15 76	85.53 157	88.76 224	68.01 279	85.58 227	87.75 308	71.80 263	86.85 317	74.02 217	93.87 212	88.58 322

EI-MVSNet		82.61 206	82.42 214	83.20 209	83.25 357	63.66 271	83.50 214	85.07 300	76.06 147	86.55 199	85.10 356	73.41 239	90.25 236	78.15 158	90.67 314	95.68 52

QAPM		82.59 207	82.59 211	82.58 228	86.44 275	66.69 241	89.94 68	90.36 185	67.97 281	84.94 245	92.58 157	72.71 250	92.18 173	70.63 264	87.73 361	88.85 320

fmvsm_s_conf0.1_n_a		82.58 208	81.93 221	84.50 166	87.68 238	73.35 142	86.14 145	77.70 362	61.64 353	85.02 241	91.62 192	77.75 168	86.24 329	82.79 99	87.07 369	93.91 124

Fast-Effi-MVS+-dtu		82.54 209	81.41 235	85.90 129	85.60 305	76.53 118	83.07 228	89.62 212	73.02 204	79.11 353	83.51 374	80.74 138	90.24 238	68.76 288	89.29 334	90.94 263

MVS_Test		82.47 210	83.22 192	80.22 284	82.62 365	57.75 363	82.54 245	91.96 129	71.16 236	82.89 293	92.52 160	77.41 175	90.50 230	80.04 128	87.84 360	92.40 207

viewdifsd2359ckpt11		82.46 211	82.98 200	80.88 268	83.53 344	61.00 318	79.46 304	85.97 284	69.48 256	87.89 164	91.31 206	82.10 115	88.61 283	74.28 207	92.86 246	93.02 172

viewmsd2359difaftdt		82.46 211	82.99 199	80.88 268	83.52 345	61.00 318	79.46 304	85.97 284	69.48 256	87.89 164	91.31 206	82.10 115	88.61 283	74.28 207	92.86 246	93.02 172

v148		82.31 213	82.48 213	81.81 248	85.59 306	59.66 338	81.47 270	86.02 282	72.85 208	88.05 158	90.65 240	70.73 270	90.91 213	75.15 200	91.79 278	94.87 78

API-MVS		82.28 214	82.61 210	81.30 259	86.29 285	69.79 199	88.71 96	87.67 250	78.42 124	82.15 306	84.15 370	77.98 165	91.59 188	65.39 318	92.75 250	82.51 409

MVSFormer		82.23 215	81.57 231	84.19 179	85.54 307	69.26 209	91.98 35	90.08 198	71.54 229	76.23 378	85.07 359	58.69 347	94.27 92	86.26 50	88.77 342	89.03 317

viewdifsd2359ckpt13		82.22 216	81.98 220	82.95 217	85.48 309	64.44 264	83.17 226	92.11 123	65.97 304	83.72 276	89.73 268	77.60 172	90.80 219	70.61 265	89.42 332	93.59 146

fmvsm_s_conf0.5_n_a		82.21 217	81.51 234	84.32 174	86.56 272	73.35 142	85.46 158	77.30 366	61.81 349	84.51 254	90.88 228	77.36 176	86.21 331	82.72 100	86.97 374	93.38 152

EIA-MVS		82.19 218	81.23 242	85.10 148	87.95 229	69.17 213	83.22 225	93.33 71	70.42 243	78.58 358	79.77 414	77.29 177	94.20 97	71.51 254	88.96 340	91.93 235

GDP-MVS		82.17 219	80.85 250	86.15 126	88.65 212	68.95 218	85.65 155	93.02 92	68.42 272	83.73 275	89.54 271	45.07 422	94.31 91	79.66 134	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 219	81.59 229	83.94 186	86.87 270	71.57 178	85.19 165	77.42 365	62.27 347	84.47 257	91.33 204	76.43 196	85.91 339	83.14 90	87.14 367	94.33 105

PCF-MVS	74.62 15	82.15 221	80.92 248	85.84 131	89.43 188	72.30 164	80.53 287	91.82 134	57.36 391	87.81 167	89.92 264	77.67 171	93.63 123	58.69 367	95.08 168	91.58 247
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 222	80.31 257	87.45 97	90.86 158	80.29 75	85.88 148	90.65 173	68.17 277	76.32 377	86.33 334	73.12 245	92.61 162	61.40 354	90.02 325	89.44 301
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 223	81.54 233	83.60 196	83.94 338	73.90 138	83.35 219	86.10 278	58.97 377	83.80 274	90.36 248	74.23 221	86.94 315	82.90 96	90.22 321	89.94 294

fmvsm_s_conf0.5_n_7		82.04 224	82.05 218	82.01 241	86.98 265	71.07 184	78.70 318	89.45 215	68.07 278	78.14 360	91.61 193	74.19 222	85.92 337	79.61 135	91.73 281	89.05 316

GBi-Net		82.02 225	82.07 216	81.85 245	86.38 279	61.05 315	86.83 129	88.27 237	72.43 215	86.00 215	95.64 38	63.78 315	90.68 223	65.95 311	93.34 229	93.82 129

test1		82.02 225	82.07 216	81.85 245	86.38 279	61.05 315	86.83 129	88.27 237	72.43 215	86.00 215	95.64 38	63.78 315	90.68 223	65.95 311	93.34 229	93.82 129

OpenMVS	76.72 13	81.98 227	82.00 219	81.93 242	84.42 329	68.22 224	88.50 102	89.48 214	66.92 299	81.80 317	91.86 181	72.59 252	90.16 242	71.19 257	91.25 291	87.40 342

KD-MVS_self_test		81.93 228	83.14 196	78.30 313	84.75 323	52.75 400	80.37 289	89.42 217	70.24 248	90.26 101	93.39 126	74.55 220	86.77 319	68.61 291	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 229	81.30 239	83.75 191	86.02 294	71.56 179	84.73 175	77.11 369	62.44 344	84.00 270	90.68 236	76.42 197	85.89 341	83.14 90	87.11 368	93.81 132

SDMVSNet		81.90 230	83.17 195	78.10 317	88.81 207	62.45 294	76.08 364	86.05 281	73.67 186	83.41 283	93.04 135	82.35 105	80.65 386	70.06 272	95.03 170	91.21 254

tfpnnormal		81.79 231	82.95 201	78.31 312	88.93 203	55.40 380	80.83 283	82.85 328	76.81 142	85.90 219	94.14 93	74.58 218	86.51 323	66.82 304	95.68 149	93.01 175

AstraMVS		81.67 232	81.40 236	82.48 233	87.06 262	66.47 244	81.41 271	81.68 339	68.78 267	88.00 159	90.95 224	65.70 300	87.86 300	76.66 176	92.38 259	93.12 168

c3_l		81.64 233	81.59 229	81.79 250	80.86 384	59.15 346	78.61 321	90.18 196	68.36 273	87.20 180	87.11 324	69.39 277	91.62 187	78.16 156	94.43 194	94.60 89

guyue		81.57 234	81.37 238	82.15 238	86.39 277	66.13 248	81.54 269	83.21 323	69.79 252	87.77 169	89.95 262	65.36 303	87.64 303	75.88 191	92.49 257	92.67 190

PVSNet_Blended_VisFu		81.55 235	80.49 255	84.70 161	91.58 133	73.24 146	84.21 189	91.67 139	62.86 338	80.94 329	87.16 322	67.27 289	92.87 157	69.82 274	88.94 341	87.99 332

fmvsm_l_conf0.5_n_a		81.46 236	80.87 249	83.25 207	83.73 343	73.21 147	83.00 231	85.59 291	58.22 383	82.96 292	90.09 261	72.30 255	86.65 321	81.97 111	89.95 326	89.88 295

SSM_04072		81.44 237	82.88 203	77.10 332	89.13 195	68.97 215	72.73 395	91.28 153	72.90 206	85.68 221	90.61 242	76.78 193	69.94 429	73.37 233	93.47 225	92.38 210

DELS-MVS		81.44 237	81.25 240	82.03 240	84.27 333	62.87 282	76.47 358	92.49 111	70.97 238	81.64 321	83.83 371	75.03 207	92.70 159	74.29 206	92.22 268	90.51 281
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		81.31 239	81.61 228	80.41 280	86.38 279	58.75 354	83.93 199	86.58 273	72.43 215	87.65 173	92.98 139	63.78 315	90.22 239	66.86 301	93.92 210	92.27 219

TinyColmap		81.25 240	82.34 215	77.99 320	85.33 311	60.68 325	82.32 253	88.33 235	71.26 234	86.97 189	92.22 174	77.10 183	86.98 314	62.37 343	95.17 164	86.31 355

diffmvs_AUTHOR		81.24 241	81.55 232	80.30 282	80.61 389	60.22 330	77.98 329	90.48 178	67.77 287	83.34 285	89.50 272	74.69 216	87.42 306	78.78 146	90.81 306	93.27 158

AUN-MVS		81.18 242	78.78 280	88.39 82	90.93 155	82.14 62	82.51 246	83.67 320	64.69 328	80.29 339	85.91 343	51.07 386	92.38 167	76.29 185	93.63 222	90.65 276

IMVS_0407		81.08 243	81.23 242	80.62 276	85.76 301	62.46 290	82.46 247	87.91 245	65.23 321	82.12 307	87.92 301	77.27 178	90.18 241	71.67 250	90.74 309	89.20 307

tttt0517		81.07 244	79.58 270	85.52 138	88.99 201	66.45 245	87.03 124	75.51 381	73.76 185	88.32 151	90.20 255	37.96 443	94.16 104	79.36 140	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 245	80.57 252	82.46 234	87.50 245	63.22 278	78.37 324	89.63 211	68.01 279	81.87 313	82.08 392	82.31 107	92.65 161	67.10 300	88.30 353	91.51 250

BH-untuned		80.96 246	80.99 246	80.84 270	88.55 216	68.23 223	80.33 290	88.46 230	72.79 211	86.55 199	86.76 328	74.72 215	91.77 186	61.79 350	88.99 339	82.52 408

IMVS_0403		80.93 247	81.00 245	80.72 273	85.76 301	62.46 290	81.82 263	87.91 245	65.23 321	82.07 309	87.92 301	75.91 200	90.50 230	71.67 250	90.74 309	89.20 307

eth_miper_zixun_eth		80.84 248	80.22 261	82.71 225	81.41 376	60.98 320	77.81 332	90.14 197	67.31 294	86.95 190	87.24 321	64.26 308	92.31 170	75.23 199	91.61 284	94.85 82

xiu_mvs_v1_base_debu		80.84 248	80.14 263	82.93 220	88.31 220	71.73 173	79.53 299	87.17 258	65.43 315	79.59 345	82.73 386	76.94 186	90.14 245	73.22 236	88.33 349	86.90 349

xiu_mvs_v1_base		80.84 248	80.14 263	82.93 220	88.31 220	71.73 173	79.53 299	87.17 258	65.43 315	79.59 345	82.73 386	76.94 186	90.14 245	73.22 236	88.33 349	86.90 349

xiu_mvs_v1_base_debi		80.84 248	80.14 263	82.93 220	88.31 220	71.73 173	79.53 299	87.17 258	65.43 315	79.59 345	82.73 386	76.94 186	90.14 245	73.22 236	88.33 349	86.90 349

IterMVS-SCA-FT		80.64 252	79.41 271	84.34 173	83.93 339	69.66 203	76.28 360	81.09 345	72.43 215	86.47 206	90.19 256	60.46 332	93.15 146	77.45 167	86.39 380	90.22 286

BH-RMVSNet		80.53 253	80.22 261	81.49 256	87.19 253	66.21 247	77.79 333	86.23 276	74.21 180	83.69 277	88.50 290	73.25 244	90.75 220	63.18 339	87.90 357	87.52 340

VortexMVS		80.51 254	80.63 251	80.15 286	83.36 353	61.82 304	80.63 285	88.00 243	67.11 297	87.23 179	89.10 280	63.98 312	88.00 293	73.63 227	92.63 254	90.64 277

Anonymous202405211		80.51 254	81.19 244	78.49 309	88.48 217	57.26 366	76.63 353	82.49 331	81.21 86	84.30 264	92.24 173	67.99 285	86.24 329	62.22 344	95.13 165	91.98 234

DIV-MVS_self_test		80.43 256	80.23 259	81.02 266	79.99 394	59.25 343	77.07 346	87.02 267	67.38 291	86.19 210	89.22 276	63.09 320	90.16 242	76.32 183	95.80 143	93.66 137

cl____		80.42 257	80.23 259	81.02 266	79.99 394	59.25 343	77.07 346	87.02 267	67.37 292	86.18 212	89.21 277	63.08 321	90.16 242	76.31 184	95.80 143	93.65 140

diffmvs		80.40 258	80.48 256	80.17 285	79.02 407	60.04 332	77.54 337	90.28 193	66.65 302	82.40 300	87.33 319	73.50 236	87.35 308	77.98 160	89.62 330	93.13 165
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		80.37 259	78.41 288	86.23 119	76.75 421	73.28 144	87.18 121	77.45 364	76.24 146	68.14 432	88.93 283	65.41 302	93.85 114	69.47 277	96.12 123	91.55 248
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 260	80.04 266	81.24 262	79.82 397	58.95 348	77.66 334	89.66 209	65.75 312	85.99 218	85.11 355	68.29 284	91.42 195	76.03 189	92.03 272	93.33 154

MG-MVS		80.32 261	80.94 247	78.47 310	88.18 223	52.62 403	82.29 254	85.01 304	72.01 226	79.24 352	92.54 159	69.36 278	93.36 140	70.65 263	89.19 337	89.45 300

mvsmamba		80.30 262	78.87 277	84.58 165	88.12 226	67.55 231	92.35 30	84.88 307	63.15 336	85.33 233	90.91 225	50.71 388	95.20 62	66.36 307	87.98 356	90.99 261

VPNet		80.25 263	81.68 224	75.94 348	92.46 100	47.98 427	76.70 351	81.67 340	73.45 191	84.87 247	92.82 148	74.66 217	86.51 323	61.66 352	96.85 92	93.33 154

MAR-MVS		80.24 264	78.74 282	84.73 159	86.87 270	78.18 95	85.75 152	87.81 249	65.67 314	77.84 364	78.50 424	73.79 232	90.53 229	61.59 353	90.87 303	85.49 365
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		80.20 265	79.00 276	83.78 190	88.17 224	86.66 19	81.31 273	66.81 437	69.64 253	88.33 150	90.19 256	64.58 305	83.63 368	71.99 249	90.03 324	81.06 428

Anonymous20240521		80.18 266	81.25 240	76.95 334	83.15 361	60.84 322	82.46 247	85.99 283	68.76 268	86.78 191	93.73 118	59.13 344	77.44 403	73.71 223	97.55 74	92.56 197

LFMVS		80.15 267	80.56 253	78.89 301	89.19 194	55.93 374	85.22 164	73.78 393	82.96 69	84.28 265	92.72 153	57.38 356	90.07 250	63.80 333	95.75 146	90.68 273

DPM-MVS		80.10 268	79.18 275	82.88 223	90.71 161	69.74 201	78.87 316	90.84 168	60.29 371	75.64 387	85.92 342	67.28 288	93.11 147	71.24 256	91.79 278	85.77 361

MSDG		80.06 269	79.99 268	80.25 283	83.91 340	68.04 228	77.51 338	89.19 218	77.65 134	81.94 311	83.45 376	76.37 198	86.31 328	63.31 338	86.59 377	86.41 353

FE-MVS		79.98 270	78.86 278	83.36 204	86.47 274	66.45 245	89.73 71	84.74 311	72.80 210	84.22 268	91.38 202	44.95 423	93.60 127	63.93 331	91.50 287	90.04 293

sd_testset		79.95 271	81.39 237	75.64 353	88.81 207	58.07 358	76.16 363	82.81 329	73.67 186	83.41 283	93.04 135	80.96 135	77.65 402	58.62 368	95.03 170	91.21 254

ab-mvs		79.67 272	80.56 253	76.99 333	88.48 217	56.93 368	84.70 177	86.06 280	68.95 265	80.78 332	93.08 134	75.30 205	84.62 355	56.78 377	90.90 301	89.43 302

VNet		79.31 273	80.27 258	76.44 342	87.92 230	53.95 392	75.58 370	84.35 315	74.39 179	82.23 304	90.72 233	72.84 249	84.39 360	60.38 360	93.98 209	90.97 262

thisisatest0530		79.07 274	77.33 298	84.26 176	87.13 254	64.58 261	83.66 209	75.95 376	68.86 266	85.22 235	87.36 318	38.10 440	93.57 131	75.47 196	94.28 199	94.62 88

cl22		78.97 275	78.21 290	81.24 262	77.74 411	59.01 347	77.46 341	87.13 261	65.79 309	84.32 261	85.10 356	58.96 346	90.88 215	75.36 198	92.03 272	93.84 127

patch_mono-2		78.89 276	79.39 272	77.41 329	84.78 321	68.11 226	75.60 368	83.11 325	60.96 363	79.36 349	89.89 265	75.18 206	72.97 418	73.32 235	92.30 262	91.15 256

RPMNet		78.88 277	78.28 289	80.68 275	79.58 398	62.64 286	82.58 242	94.16 33	74.80 168	75.72 385	92.59 155	48.69 395	95.56 42	73.48 230	82.91 416	83.85 387

PAPR		78.84 278	78.10 291	81.07 264	85.17 316	60.22 330	82.21 258	90.57 177	62.51 340	75.32 391	84.61 364	74.99 208	92.30 171	59.48 365	88.04 355	90.68 273

viewmambaseed2359dif		78.80 279	78.47 287	79.78 289	80.26 393	59.28 342	77.31 343	87.13 261	60.42 369	82.37 301	88.67 288	74.58 218	87.87 299	67.78 299	87.73 361	92.19 223

PVSNet_BlendedMVS		78.80 279	77.84 292	81.65 252	84.43 327	63.41 274	79.49 302	90.44 181	61.70 352	75.43 388	87.07 325	69.11 280	91.44 193	60.68 358	92.24 266	90.11 291

FMVSNet3		78.80 279	78.55 284	79.57 295	82.89 364	56.89 370	81.76 264	85.77 287	69.04 263	86.00 215	90.44 247	51.75 384	90.09 248	65.95 311	93.34 229	91.72 241

test_yl		78.71 282	78.51 285	79.32 298	84.32 331	58.84 351	78.38 322	85.33 295	75.99 150	82.49 298	86.57 330	58.01 350	90.02 252	62.74 340	92.73 252	89.10 313

DCV-MVSNet		78.71 282	78.51 285	79.32 298	84.32 331	58.84 351	78.38 322	85.33 295	75.99 150	82.49 298	86.57 330	58.01 350	90.02 252	62.74 340	92.73 252	89.10 313

test1111		78.53 284	78.85 279	77.56 326	92.22 109	47.49 429	82.61 240	69.24 425	72.43 215	85.28 234	94.20 89	51.91 382	90.07 250	65.36 319	96.45 108	95.11 72

FE-MVSNET		78.46 285	79.36 273	75.75 350	86.53 273	54.53 387	78.03 326	85.35 294	69.01 264	85.41 231	90.68 236	64.27 307	85.73 345	62.59 342	92.35 261	87.00 348

icg_test_0407_2		78.46 285	79.68 269	74.78 360	85.76 301	62.46 290	68.51 424	87.91 245	65.23 321	82.12 307	87.92 301	77.27 178	72.67 419	71.67 250	90.74 309	89.20 307

ECVR-MVS		78.44 287	78.63 283	77.88 322	91.85 123	48.95 423	83.68 208	69.91 421	72.30 221	84.26 267	94.20 89	51.89 383	89.82 255	63.58 334	96.02 127	94.87 78

pmmvs-eth3d		78.42 288	77.04 301	82.57 230	87.44 247	74.41 135	80.86 282	79.67 353	55.68 400	84.69 251	90.31 253	60.91 330	85.42 348	62.20 345	91.59 285	87.88 336

mvs_anonymous		78.13 289	78.76 281	76.23 347	79.24 404	50.31 419	78.69 319	84.82 309	61.60 354	83.09 291	92.82 148	73.89 230	87.01 311	68.33 295	86.41 379	91.37 251

TAMVS		78.08 290	76.36 308	83.23 208	90.62 162	72.87 150	79.08 312	80.01 352	61.72 351	81.35 325	86.92 327	63.96 314	88.78 277	50.61 416	93.01 242	88.04 331

miper_enhance_ethall		77.83 291	76.93 302	80.51 278	76.15 428	58.01 360	75.47 372	88.82 222	58.05 385	83.59 279	80.69 402	64.41 306	91.20 200	73.16 242	92.03 272	92.33 214

Vis-MVSNet (Re-imp)		77.82 292	77.79 293	77.92 321	88.82 206	51.29 413	83.28 220	71.97 409	74.04 181	82.23 304	89.78 266	57.38 356	89.41 267	57.22 376	95.41 154	93.05 171

CANet_DTU		77.81 293	77.05 300	80.09 287	81.37 377	59.90 336	83.26 221	88.29 236	69.16 260	67.83 435	83.72 372	60.93 329	89.47 262	69.22 281	89.70 329	90.88 266

OpenMVS_ROB	70.19 17	77.77 294	77.46 295	78.71 305	84.39 330	61.15 312	81.18 277	82.52 330	62.45 343	83.34 285	87.37 317	66.20 294	88.66 281	64.69 326	85.02 396	86.32 354

SSC-MVS		77.55 295	81.64 226	65.29 426	90.46 165	20.33 473	73.56 388	68.28 427	85.44 41	88.18 155	94.64 68	70.93 269	81.33 381	71.25 255	92.03 272	94.20 108

MDA-MVSNet-bldmvs		77.47 296	76.90 303	79.16 300	79.03 406	64.59 260	66.58 436	75.67 379	73.15 202	88.86 133	88.99 282	66.94 290	81.23 382	64.71 325	88.22 354	91.64 245

jason		77.42 297	75.75 314	82.43 235	87.10 257	69.27 208	77.99 328	81.94 337	51.47 427	77.84 364	85.07 359	60.32 334	89.00 271	70.74 262	89.27 336	89.03 317
jason: jason.
CDS-MVSNet		77.32 298	75.40 318	83.06 212	89.00 200	72.48 161	77.90 331	82.17 335	60.81 364	78.94 355	83.49 375	59.30 342	88.76 278	54.64 396	92.37 260	87.93 335
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 299	77.75 294	75.73 351	85.76 301	62.46 290	70.84 410	87.91 245	65.23 321	72.21 410	87.92 301	67.48 287	75.53 411	71.67 250	90.74 309	89.20 307

xiu_mvs_v2_base		77.19 300	76.75 305	78.52 308	87.01 263	61.30 310	75.55 371	87.12 265	61.24 360	74.45 397	78.79 422	77.20 180	90.93 211	64.62 328	84.80 403	83.32 396

MVSTER		77.09 301	75.70 315	81.25 260	75.27 436	61.08 314	77.49 340	85.07 300	60.78 365	86.55 199	88.68 286	43.14 432	90.25 236	73.69 226	90.67 314	92.42 204

PS-MVSNAJ		77.04 302	76.53 307	78.56 307	87.09 259	61.40 308	75.26 373	87.13 261	61.25 359	74.38 399	77.22 436	76.94 186	90.94 210	64.63 327	84.83 402	83.35 395

IterMVS		76.91 303	76.34 309	78.64 306	80.91 382	64.03 268	76.30 359	79.03 356	64.88 327	83.11 289	89.16 278	59.90 338	84.46 358	68.61 291	85.15 394	87.42 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 304	75.67 316	80.34 281	80.48 391	62.16 302	73.50 389	84.80 310	57.61 389	82.24 303	87.54 312	51.31 385	87.65 302	70.40 268	93.19 238	91.23 253

CL-MVSNet_self_test		76.81 305	77.38 297	75.12 356	86.90 268	51.34 411	73.20 392	80.63 349	68.30 275	81.80 317	88.40 291	66.92 291	80.90 383	55.35 390	94.90 176	93.12 168

TR-MVS		76.77 306	75.79 313	79.72 292	86.10 293	65.79 252	77.14 344	83.02 326	65.20 325	81.40 324	82.10 390	66.30 293	90.73 222	55.57 387	85.27 390	82.65 403

MonoMVSNet		76.66 307	77.26 299	74.86 358	79.86 396	54.34 389	86.26 142	86.08 279	71.08 237	85.59 226	88.68 286	53.95 374	85.93 336	63.86 332	80.02 432	84.32 378

USDC		76.63 308	76.73 306	76.34 344	83.46 348	57.20 367	80.02 293	88.04 242	52.14 423	83.65 278	91.25 209	63.24 318	86.65 321	54.66 395	94.11 204	85.17 367

BH-w/o		76.57 309	76.07 312	78.10 317	86.88 269	65.92 251	77.63 335	86.33 274	65.69 313	80.89 330	79.95 411	68.97 282	90.74 221	53.01 406	85.25 391	77.62 439

Patchmtry		76.56 310	77.46 295	73.83 366	79.37 403	46.60 433	82.41 251	76.90 370	73.81 184	85.56 228	92.38 163	48.07 398	83.98 365	63.36 337	95.31 160	90.92 264

PVSNet_Blended		76.49 311	75.40 318	79.76 291	84.43 327	63.41 274	75.14 374	90.44 181	57.36 391	75.43 388	78.30 425	69.11 280	91.44 193	60.68 358	87.70 363	84.42 377

miper_lstm_enhance		76.45 312	76.10 311	77.51 327	76.72 422	60.97 321	64.69 440	85.04 302	63.98 332	83.20 288	88.22 293	56.67 360	78.79 399	73.22 236	93.12 239	92.78 184

lupinMVS		76.37 313	74.46 327	82.09 239	85.54 307	69.26 209	76.79 349	80.77 348	50.68 434	76.23 378	82.82 384	58.69 347	88.94 272	69.85 273	88.77 342	88.07 328

cascas		76.29 314	74.81 323	80.72 273	84.47 326	62.94 280	73.89 386	87.34 253	55.94 398	75.16 393	76.53 441	63.97 313	91.16 202	65.00 322	90.97 299	88.06 330

SD_0403		76.08 315	76.77 304	73.98 364	87.08 261	49.45 422	83.62 210	84.68 312	63.31 333	75.13 394	87.47 315	71.85 262	84.56 356	49.97 418	87.86 359	87.94 334

WB-MVS		76.06 316	80.01 267	64.19 429	89.96 179	20.58 472	72.18 399	68.19 428	83.21 65	86.46 207	93.49 123	70.19 274	78.97 397	65.96 310	90.46 320	93.02 172

thres600view7		75.97 317	75.35 320	77.85 324	87.01 263	51.84 409	80.45 288	73.26 398	75.20 165	83.10 290	86.31 336	45.54 413	89.05 270	55.03 393	92.24 266	92.66 191

GA-MVS		75.83 318	74.61 324	79.48 297	81.87 369	59.25 343	73.42 390	82.88 327	68.68 269	79.75 344	81.80 395	50.62 389	89.46 263	66.85 302	85.64 387	89.72 297

MVP-Stereo		75.81 319	73.51 336	82.71 225	89.35 189	73.62 139	80.06 291	85.20 297	60.30 370	73.96 400	87.94 298	57.89 354	89.45 264	52.02 410	74.87 450	85.06 369
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 320	75.20 321	77.27 330	75.01 439	69.47 206	78.93 313	84.88 307	46.67 441	87.08 186	87.84 306	50.44 391	71.62 424	77.42 169	88.53 345	90.72 270

thres100view900		75.45 321	75.05 322	76.66 340	87.27 249	51.88 408	81.07 278	73.26 398	75.68 156	83.25 287	86.37 333	45.54 413	88.80 274	51.98 411	90.99 296	89.31 304

ET-MVSNet_ETH3D		75.28 322	72.77 345	82.81 224	83.03 363	68.11 226	77.09 345	76.51 374	60.67 367	77.60 369	80.52 406	38.04 441	91.15 203	70.78 260	90.68 313	89.17 311

thres400		75.14 323	74.23 329	77.86 323	86.24 286	52.12 405	79.24 309	73.87 391	73.34 195	81.82 315	84.60 365	46.02 406	88.80 274	51.98 411	90.99 296	92.66 191

wuyk23d		75.13 324	79.30 274	62.63 432	75.56 432	75.18 131	80.89 281	73.10 400	75.06 167	94.76 16	95.32 45	87.73 44	52.85 464	34.16 462	97.11 87	59.85 460

EU-MVSNet		75.12 325	74.43 328	77.18 331	83.11 362	59.48 340	85.71 154	82.43 332	39.76 461	85.64 225	88.76 284	44.71 425	87.88 298	73.86 220	85.88 386	84.16 383

HyFIR lowres test		75.12 325	72.66 347	82.50 232	91.44 141	65.19 257	72.47 397	87.31 254	46.79 440	80.29 339	84.30 367	52.70 379	92.10 177	51.88 415	86.73 375	90.22 286

CMPMVS	59.41 20	75.12 325	73.57 334	79.77 290	75.84 431	67.22 232	81.21 276	82.18 334	50.78 432	76.50 374	87.66 310	55.20 370	82.99 371	62.17 347	90.64 318	89.09 315
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 328	72.98 343	80.73 272	84.95 318	71.71 176	76.23 361	77.59 363	52.83 417	77.73 368	86.38 332	56.35 363	84.97 352	57.72 375	87.05 370	85.51 364

tfpn200view9		74.86 329	74.23 329	76.74 339	86.24 286	52.12 405	79.24 309	73.87 391	73.34 195	81.82 315	84.60 365	46.02 406	88.80 274	51.98 411	90.99 296	89.31 304

1112_ss		74.82 330	73.74 332	78.04 319	89.57 183	60.04 332	76.49 357	87.09 266	54.31 408	73.66 403	79.80 412	60.25 335	86.76 320	58.37 369	84.15 407	87.32 343

EGC-MVSNET		74.79 331	69.99 375	89.19 67	94.89 38	87.00 15	91.89 38	86.28 275	1.09 470	2.23 472	95.98 30	81.87 123	89.48 261	79.76 131	95.96 130	91.10 257

ppachtmachnet_test		74.73 332	74.00 331	76.90 336	80.71 387	56.89 370	71.53 405	78.42 358	58.24 382	79.32 351	82.92 383	57.91 353	84.26 362	65.60 317	91.36 289	89.56 299

Patchmatch-RL test		74.48 333	73.68 333	76.89 337	84.83 320	66.54 242	72.29 398	69.16 426	57.70 387	86.76 192	86.33 334	45.79 412	82.59 372	69.63 276	90.65 317	81.54 419

PatchMatch-RL		74.48 333	73.22 340	78.27 315	87.70 237	85.26 38	75.92 366	70.09 419	64.34 330	76.09 381	81.25 400	65.87 299	78.07 401	53.86 398	83.82 409	71.48 448

XXY-MVS		74.44 335	76.19 310	69.21 401	84.61 325	52.43 404	71.70 402	77.18 368	60.73 366	80.60 333	90.96 222	75.44 202	69.35 432	56.13 382	88.33 349	85.86 360

test2506		74.12 336	73.39 337	76.28 345	91.85 123	44.20 443	84.06 193	48.20 468	72.30 221	81.90 312	94.20 89	27.22 468	89.77 258	64.81 324	96.02 127	94.87 78

reproduce_monomvs		74.09 337	73.23 339	76.65 341	76.52 423	54.54 386	77.50 339	81.40 343	65.85 308	82.86 295	86.67 329	27.38 466	84.53 357	70.24 269	90.66 316	90.89 265

CR-MVSNet		74.00 338	73.04 342	76.85 338	79.58 398	62.64 286	82.58 242	76.90 370	50.50 435	75.72 385	92.38 163	48.07 398	84.07 364	68.72 290	82.91 416	83.85 387

SSC-MVS3.2		73.90 339	75.67 316	68.61 409	84.11 336	41.28 451	64.17 442	72.83 401	72.09 224	79.08 354	87.94 298	70.31 272	73.89 417	55.99 383	94.49 191	90.67 275

Test_1112_low_res		73.90 339	73.08 341	76.35 343	90.35 167	55.95 373	73.40 391	86.17 277	50.70 433	73.14 404	85.94 341	58.31 349	85.90 340	56.51 379	83.22 413	87.20 345

test20.03		73.75 341	74.59 326	71.22 387	81.11 380	51.12 415	70.15 416	72.10 408	70.42 243	80.28 341	91.50 196	64.21 309	74.72 415	46.96 436	94.58 189	87.82 338

test_fmvs2		73.57 342	72.80 344	75.90 349	72.74 453	68.84 219	77.07 346	84.32 316	45.14 447	82.89 293	84.22 368	48.37 396	70.36 428	73.40 232	87.03 371	88.52 323

SCA		73.32 343	72.57 349	75.58 354	81.62 373	55.86 376	78.89 315	71.37 414	61.73 350	74.93 395	83.42 377	60.46 332	87.01 311	58.11 373	82.63 421	83.88 384

baseline1		73.26 344	73.54 335	72.43 380	84.92 319	47.79 428	79.89 295	74.00 389	65.93 306	78.81 356	86.28 337	56.36 362	81.63 380	56.63 378	79.04 439	87.87 337

1314		73.22 345	72.56 350	75.20 355	80.41 392	57.84 361	81.64 267	85.36 293	51.68 426	73.10 405	76.65 440	61.45 327	85.19 350	63.54 335	79.21 437	82.59 404

MVS		73.21 346	72.59 348	75.06 357	80.97 381	60.81 323	81.64 267	85.92 286	46.03 445	71.68 413	77.54 431	68.47 283	89.77 258	55.70 386	85.39 388	74.60 445

HY-MVS	64.64 18	73.03 347	72.47 351	74.71 361	83.36 353	54.19 390	82.14 261	81.96 336	56.76 397	69.57 427	86.21 338	60.03 336	84.83 354	49.58 423	82.65 419	85.11 368

thisisatest0515		73.00 348	70.52 367	80.46 279	81.45 375	59.90 336	73.16 393	74.31 388	57.86 386	76.08 382	77.78 428	37.60 444	92.12 176	65.00 322	91.45 288	89.35 303

EPNet_dtu		72.87 349	71.33 361	77.49 328	77.72 412	60.55 326	82.35 252	75.79 377	66.49 303	58.39 463	81.06 401	53.68 375	85.98 335	53.55 401	92.97 244	85.95 358
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 350	71.41 360	76.28 345	83.25 357	60.34 328	83.50 214	79.02 357	37.77 465	76.33 376	85.10 356	49.60 394	87.41 307	70.54 266	77.54 445	81.08 426

CHOSEN 1792x2688		72.45 351	70.56 366	78.13 316	90.02 178	63.08 279	68.72 423	83.16 324	42.99 455	75.92 383	85.46 349	57.22 358	85.18 351	49.87 421	81.67 423	86.14 356

testgi		72.36 352	74.61 324	65.59 423	80.56 390	42.82 448	68.29 425	73.35 397	66.87 300	81.84 314	89.93 263	72.08 259	66.92 446	46.05 440	92.54 256	87.01 347

thres200		72.34 353	71.55 359	74.70 362	83.48 347	51.60 410	75.02 375	73.71 394	70.14 249	78.56 359	80.57 405	46.20 404	88.20 291	46.99 435	89.29 334	84.32 378

FPMVS		72.29 354	72.00 353	73.14 371	88.63 213	85.00 40	74.65 379	67.39 431	71.94 227	77.80 366	87.66 310	50.48 390	75.83 409	49.95 419	79.51 433	58.58 462

FMVSNet5		72.10 355	71.69 355	73.32 369	81.57 374	53.02 399	76.77 350	78.37 359	63.31 333	76.37 375	91.85 182	36.68 445	78.98 396	47.87 432	92.45 258	87.95 333

our_test_3		71.85 356	71.59 356	72.62 377	80.71 387	53.78 393	69.72 419	71.71 413	58.80 379	78.03 361	80.51 407	56.61 361	78.84 398	62.20 345	86.04 385	85.23 366

PAPM		71.77 357	70.06 373	76.92 335	86.39 277	53.97 391	76.62 354	86.62 272	53.44 412	63.97 452	84.73 363	57.79 355	92.34 169	39.65 452	81.33 427	84.45 376

ttmdpeth		71.72 358	70.67 364	74.86 358	73.08 450	55.88 375	77.41 342	69.27 424	55.86 399	78.66 357	93.77 116	38.01 442	75.39 412	60.12 361	89.87 327	93.31 156

IB-MVS	62.13 19	71.64 359	68.97 385	79.66 294	80.80 386	62.26 299	73.94 385	76.90 370	63.27 335	68.63 431	76.79 438	33.83 449	91.84 184	59.28 366	87.26 365	84.88 370
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		71.63 360	72.30 352	69.62 398	76.47 425	52.70 402	70.03 417	80.97 346	59.18 376	79.36 349	88.21 294	60.50 331	69.12 433	58.33 371	77.62 444	87.04 346

testing3		71.53 361	70.79 363	73.77 367	88.89 205	41.86 450	76.60 356	59.12 457	72.83 209	80.97 327	82.08 392	19.80 474	87.33 309	65.12 321	91.68 283	92.13 227

test_vis3_rt		71.42 362	70.67 364	73.64 368	69.66 460	70.46 191	66.97 435	89.73 206	42.68 457	88.20 154	83.04 379	43.77 427	60.07 458	65.35 320	86.66 376	90.39 284

Anonymous20231206		71.38 363	71.88 354	69.88 395	86.31 283	54.37 388	70.39 414	74.62 384	52.57 419	76.73 373	88.76 284	59.94 337	72.06 421	44.35 444	93.23 236	83.23 398

test_vis1_n_1920		71.30 364	71.58 358	70.47 390	77.58 414	59.99 335	74.25 380	84.22 317	51.06 429	74.85 396	79.10 418	55.10 371	68.83 435	68.86 287	79.20 438	82.58 405

MIMVSNet		71.09 365	71.59 356	69.57 399	87.23 251	50.07 420	78.91 314	71.83 410	60.20 373	71.26 414	91.76 189	55.08 372	76.09 407	41.06 449	87.02 372	82.54 407

test_fmvs1_n		70.94 366	70.41 370	72.53 379	73.92 441	66.93 239	75.99 365	84.21 318	43.31 454	79.40 348	79.39 416	43.47 428	68.55 437	69.05 284	84.91 399	82.10 413

MS-PatchMatch		70.93 367	70.22 371	73.06 372	81.85 370	62.50 289	73.82 387	77.90 360	52.44 420	75.92 383	81.27 399	55.67 367	81.75 378	55.37 389	77.70 443	74.94 444

pmmvs5		70.73 368	70.07 372	72.72 375	77.03 419	52.73 401	74.14 381	75.65 380	50.36 436	72.17 411	85.37 353	55.42 369	80.67 385	52.86 407	87.59 364	84.77 371

testing3-2		70.72 369	70.97 362	69.95 394	88.93 203	34.80 464	69.85 418	66.59 438	78.42 124	77.58 370	85.55 345	31.83 455	82.08 376	46.28 437	93.73 218	92.98 178

PatchT		70.52 370	72.76 346	63.79 431	79.38 402	33.53 465	77.63 335	65.37 442	73.61 188	71.77 412	92.79 151	44.38 426	75.65 410	64.53 329	85.37 389	82.18 412

test_vis1_n		70.29 371	69.99 375	71.20 388	75.97 430	66.50 243	76.69 352	80.81 347	44.22 450	75.43 388	77.23 435	50.00 392	68.59 436	66.71 305	82.85 418	78.52 438

N_pmnet		70.20 372	68.80 387	74.38 363	80.91 382	84.81 43	59.12 453	76.45 375	55.06 403	75.31 392	82.36 389	55.74 366	54.82 463	47.02 434	87.24 366	83.52 391

tpmvs		70.16 373	69.56 378	71.96 383	74.71 440	48.13 425	79.63 297	75.45 382	65.02 326	70.26 422	81.88 394	45.34 418	85.68 346	58.34 370	75.39 449	82.08 414

new-patchmatchnet		70.10 374	73.37 338	60.29 440	81.23 379	16.95 475	59.54 451	74.62 384	62.93 337	80.97 327	87.93 300	62.83 324	71.90 422	55.24 391	95.01 173	92.00 232

YYNet1		70.06 375	70.44 368	68.90 403	73.76 443	53.42 397	58.99 454	67.20 433	58.42 381	87.10 184	85.39 352	59.82 339	67.32 443	59.79 363	83.50 412	85.96 357

MVStest1		70.05 376	69.26 379	72.41 381	58.62 472	55.59 379	76.61 355	65.58 440	53.44 412	89.28 128	93.32 127	22.91 472	71.44 426	74.08 216	89.52 331	90.21 290

MDA-MVSNet_test_wron		70.05 376	70.44 368	68.88 404	73.84 442	53.47 395	58.93 455	67.28 432	58.43 380	87.09 185	85.40 351	59.80 340	67.25 444	59.66 364	83.54 411	85.92 359

CostFormer		69.98 378	68.68 388	73.87 365	77.14 417	50.72 417	79.26 308	74.51 386	51.94 425	70.97 417	84.75 362	45.16 421	87.49 305	55.16 392	79.23 436	83.40 394

testing91		69.94 379	68.99 384	72.80 374	83.81 342	45.89 436	71.57 404	73.64 396	68.24 276	70.77 420	77.82 427	34.37 448	84.44 359	53.64 400	87.00 373	88.07 328

baseline2		69.77 380	66.89 397	78.41 311	79.51 400	58.09 357	76.23 361	69.57 422	57.50 390	64.82 450	77.45 433	46.02 406	88.44 285	53.08 403	77.83 441	88.70 321

PatchmatchNet		69.71 381	68.83 386	72.33 382	77.66 413	53.60 394	79.29 307	69.99 420	57.66 388	72.53 408	82.93 382	46.45 403	80.08 391	60.91 357	72.09 453	83.31 397
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 382	69.05 382	71.14 389	69.15 461	65.77 253	73.98 384	83.32 322	42.83 456	77.77 367	78.27 426	43.39 431	68.50 438	68.39 294	84.38 406	79.15 436

JIA-IIPM		69.41 383	66.64 401	77.70 325	73.19 447	71.24 182	75.67 367	65.56 441	70.42 243	65.18 446	92.97 142	33.64 451	83.06 369	53.52 402	69.61 459	78.79 437

Syy-MVS		69.40 384	70.03 374	67.49 414	81.72 371	38.94 456	71.00 407	61.99 448	61.38 356	70.81 418	72.36 452	61.37 328	79.30 394	64.50 330	85.18 392	84.22 380

testing99		69.27 385	68.15 392	72.63 376	83.29 355	45.45 438	71.15 406	71.08 415	67.34 293	70.43 421	77.77 429	32.24 454	84.35 361	53.72 399	86.33 381	88.10 327

UnsupCasMVSNet_bld		69.21 386	69.68 377	67.82 412	79.42 401	51.15 414	67.82 429	75.79 377	54.15 409	77.47 371	85.36 354	59.26 343	70.64 427	48.46 429	79.35 435	81.66 417

test_cas_vis1_n_1920		69.20 387	69.12 380	69.43 400	73.68 444	62.82 283	70.38 415	77.21 367	46.18 444	80.46 338	78.95 420	52.03 381	65.53 451	65.77 316	77.45 446	79.95 434

gg-mvs-nofinetune		68.96 388	69.11 381	68.52 410	76.12 429	45.32 439	83.59 211	55.88 462	86.68 33	64.62 451	97.01 12	30.36 459	83.97 366	44.78 443	82.94 415	76.26 441

WBMVS		68.76 389	68.43 389	69.75 397	83.29 355	40.30 454	67.36 431	72.21 407	57.09 394	77.05 372	85.53 347	33.68 450	80.51 387	48.79 427	90.90 301	88.45 324

WB-MVSnew		68.72 390	69.01 383	67.85 411	83.22 359	43.98 444	74.93 376	65.98 439	55.09 402	73.83 401	79.11 417	65.63 301	71.89 423	38.21 457	85.04 395	87.69 339

tpm2		68.45 391	66.83 398	73.30 370	78.93 408	48.50 424	79.76 296	71.76 411	47.50 439	69.92 424	83.60 373	42.07 434	88.40 287	48.44 430	79.51 433	83.01 401

tpm		67.95 392	68.08 393	67.55 413	78.74 409	43.53 446	75.60 368	67.10 436	54.92 404	72.23 409	88.10 295	42.87 433	75.97 408	52.21 409	80.95 431	83.15 399

WTY-MVS		67.91 393	68.35 390	66.58 419	80.82 385	48.12 426	65.96 437	72.60 402	53.67 411	71.20 415	81.68 397	58.97 345	69.06 434	48.57 428	81.67 423	82.55 406

testing11		67.38 394	65.93 402	71.73 385	83.37 352	46.60 433	70.95 409	69.40 423	62.47 342	66.14 439	76.66 439	31.22 456	84.10 363	49.10 425	84.10 408	84.49 374

test-LLR		67.21 395	66.74 399	68.63 407	76.45 426	55.21 382	67.89 426	67.14 434	62.43 345	65.08 447	72.39 450	43.41 429	69.37 430	61.00 355	84.89 400	81.31 421

testing222		66.93 396	65.30 409	71.81 384	83.38 351	45.83 437	72.06 400	67.50 430	64.12 331	69.68 426	76.37 442	27.34 467	83.00 370	38.88 453	88.38 348	86.62 352

sss		66.92 397	67.26 395	65.90 421	77.23 416	51.10 416	64.79 439	71.72 412	52.12 424	70.13 423	80.18 409	57.96 352	65.36 452	50.21 417	81.01 429	81.25 423

KD-MVS_2432*1600		66.87 398	65.81 405	70.04 392	67.50 462	47.49 429	62.56 445	79.16 354	61.21 361	77.98 362	80.61 403	25.29 470	82.48 373	53.02 404	84.92 397	80.16 432

miper_refine_blended		66.87 398	65.81 405	70.04 392	67.50 462	47.49 429	62.56 445	79.16 354	61.21 361	77.98 362	80.61 403	25.29 470	82.48 373	53.02 404	84.92 397	80.16 432

dmvs_re		66.81 400	66.98 396	66.28 420	76.87 420	58.68 355	71.66 403	72.24 405	60.29 371	69.52 428	73.53 449	52.38 380	64.40 454	44.90 442	81.44 426	75.76 442

tpm cat1		66.76 401	65.21 410	71.42 386	77.09 418	50.62 418	78.01 327	73.68 395	44.89 448	68.64 430	79.00 419	45.51 415	82.42 375	49.91 420	70.15 456	81.23 425

UWE-MVS		66.43 402	65.56 408	69.05 402	84.15 335	40.98 452	73.06 394	64.71 444	54.84 405	76.18 380	79.62 415	29.21 461	80.50 388	38.54 456	89.75 328	85.66 362

PVSNet	58.17 21	66.41 403	65.63 407	68.75 405	81.96 368	49.88 421	62.19 447	72.51 404	51.03 430	68.04 433	75.34 446	50.84 387	74.77 413	45.82 441	82.96 414	81.60 418

tpmrst		66.28 404	66.69 400	65.05 427	72.82 452	39.33 455	78.20 325	70.69 418	53.16 415	67.88 434	80.36 408	48.18 397	74.75 414	58.13 372	70.79 455	81.08 426

Patchmatch-test		65.91 405	67.38 394	61.48 437	75.51 433	43.21 447	68.84 422	63.79 446	62.48 341	72.80 407	83.42 377	44.89 424	59.52 460	48.27 431	86.45 378	81.70 416

ADS-MVSNet2		65.87 406	63.64 415	72.55 378	73.16 448	56.92 369	67.10 433	74.81 383	49.74 437	66.04 441	82.97 380	46.71 401	77.26 404	42.29 446	69.96 457	83.46 392

myMVS_eth3d28		65.83 407	65.85 403	65.78 422	83.42 350	35.71 462	67.29 432	68.01 429	67.58 290	69.80 425	77.72 430	32.29 453	74.30 416	37.49 458	89.06 338	87.32 343

test_vis1_rt		65.64 408	64.09 412	70.31 391	66.09 466	70.20 195	61.16 448	81.60 341	38.65 462	72.87 406	69.66 455	52.84 377	60.04 459	56.16 381	77.77 442	80.68 430

mvsany_test3		65.48 409	62.97 418	73.03 373	69.99 459	76.17 124	64.83 438	43.71 470	43.68 452	80.25 342	87.05 326	52.83 378	63.09 457	51.92 414	72.44 452	79.84 435

test-mter		65.00 410	63.79 414	68.63 407	76.45 426	55.21 382	67.89 426	67.14 434	50.98 431	65.08 447	72.39 450	28.27 464	69.37 430	61.00 355	84.89 400	81.31 421

ETVMVS		64.67 411	63.34 417	68.64 406	83.44 349	41.89 449	69.56 421	61.70 453	61.33 358	68.74 429	75.76 444	28.76 462	79.35 393	34.65 461	86.16 384	84.67 373

myMVS_eth3d		64.66 412	63.89 413	66.97 417	81.72 371	37.39 459	71.00 407	61.99 448	61.38 356	70.81 418	72.36 452	20.96 473	79.30 394	49.59 422	85.18 392	84.22 380

test0.0.03 1		64.66 412	64.36 411	65.57 424	75.03 438	46.89 432	64.69 440	61.58 454	62.43 345	71.18 416	77.54 431	43.41 429	68.47 439	40.75 451	82.65 419	81.35 420

UBG		64.34 414	63.35 416	67.30 415	83.50 346	40.53 453	67.46 430	65.02 443	54.77 406	67.54 437	74.47 448	32.99 452	78.50 400	40.82 450	83.58 410	82.88 402

test_f		64.31 415	65.85 403	59.67 441	66.54 465	62.24 301	57.76 457	70.96 416	40.13 459	84.36 259	82.09 391	46.93 400	51.67 465	61.99 348	81.89 422	65.12 456

pmmvs3		62.47 416	60.02 429	69.80 396	71.58 456	64.00 269	70.52 413	58.44 460	39.77 460	66.05 440	75.84 443	27.10 469	72.28 420	46.15 439	84.77 404	73.11 446

EPMVS		62.47 416	62.63 420	62.01 433	70.63 458	38.74 457	74.76 377	52.86 464	53.91 410	67.71 436	80.01 410	39.40 438	66.60 447	55.54 388	68.81 461	80.68 430

ADS-MVSNet		61.90 418	62.19 422	61.03 438	73.16 448	36.42 461	67.10 433	61.75 451	49.74 437	66.04 441	82.97 380	46.71 401	63.21 455	42.29 446	69.96 457	83.46 392

PMMVS		61.65 419	60.38 426	65.47 425	65.40 469	69.26 209	63.97 443	61.73 452	36.80 466	60.11 458	68.43 457	59.42 341	66.35 448	48.97 426	78.57 440	60.81 459

E-PMN		61.59 420	61.62 423	61.49 436	66.81 464	55.40 380	53.77 460	60.34 456	66.80 301	58.90 461	65.50 460	40.48 437	66.12 449	55.72 385	86.25 382	62.95 458

TESTMET0.1,1		61.29 421	60.32 427	64.19 429	72.06 454	51.30 412	67.89 426	62.09 447	45.27 446	60.65 457	69.01 456	27.93 465	64.74 453	56.31 380	81.65 425	76.53 440

MVS-HIRNet		61.16 422	62.92 419	55.87 444	79.09 405	35.34 463	71.83 401	57.98 461	46.56 442	59.05 460	91.14 213	49.95 393	76.43 406	38.74 454	71.92 454	55.84 463

EMVS		61.10 423	60.81 425	61.99 434	65.96 467	55.86 376	53.10 461	58.97 459	67.06 298	56.89 465	63.33 461	40.98 435	67.03 445	54.79 394	86.18 383	63.08 457

DSMNet-mixed		60.98 424	61.61 424	59.09 443	72.88 451	45.05 441	74.70 378	46.61 469	26.20 467	65.34 445	90.32 252	55.46 368	63.12 456	41.72 448	81.30 428	69.09 452

dp		60.70 425	60.29 428	61.92 435	72.04 455	38.67 458	70.83 411	64.08 445	51.28 428	60.75 456	77.28 434	36.59 446	71.58 425	47.41 433	62.34 463	75.52 443

dmvs_testset		60.59 426	62.54 421	54.72 446	77.26 415	27.74 469	74.05 383	61.00 455	60.48 368	65.62 444	67.03 459	55.93 365	68.23 441	32.07 465	69.46 460	68.17 453

CHOSEN 280x420		59.08 427	56.52 433	66.76 418	76.51 424	64.39 265	49.62 462	59.00 458	43.86 451	55.66 466	68.41 458	35.55 447	68.21 442	43.25 445	76.78 448	67.69 454

mvsany_test1		58.48 428	56.47 434	64.50 428	65.90 468	68.21 225	56.95 458	42.11 471	38.30 463	65.69 443	77.19 437	56.96 359	59.35 461	46.16 438	58.96 464	65.93 455

UWE-MVS-28		58.44 429	57.71 431	60.65 439	73.58 445	31.23 466	69.68 420	48.80 467	53.12 416	61.79 454	78.83 421	30.98 457	68.40 440	21.58 468	80.99 430	82.33 411

PVSNet_0	51.08 22	56.10 430	54.97 435	59.48 442	75.12 437	53.28 398	55.16 459	61.89 450	44.30 449	59.16 459	62.48 462	54.22 373	65.91 450	35.40 460	47.01 465	59.25 461

new_pmnet		55.69 431	57.66 432	49.76 447	75.47 434	30.59 467	59.56 450	51.45 465	43.62 453	62.49 453	75.48 445	40.96 436	49.15 467	37.39 459	72.52 451	69.55 451

PMMVS2		55.64 432	59.27 430	44.74 448	64.30 470	12.32 476	40.60 463	49.79 466	53.19 414	65.06 449	84.81 361	53.60 376	49.76 466	32.68 464	89.41 333	72.15 447

MVE	40.22 23	51.82 433	50.47 436	55.87 444	62.66 471	51.91 407	31.61 465	39.28 472	40.65 458	50.76 467	74.98 447	56.24 364	44.67 468	33.94 463	64.11 462	71.04 450
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 434	42.65 437	39.67 449	70.86 457	21.11 471	61.01 449	21.42 476	57.36 391	57.97 464	50.06 465	16.40 475	58.73 462	21.03 469	27.69 469	39.17 465

kuosan		30.83 435	32.17 438	26.83 451	53.36 473	19.02 474	57.90 456	20.44 477	38.29 464	38.01 468	37.82 467	15.18 476	33.45 470	7.74 471	20.76 470	28.03 466

test_method		30.46 436	29.60 439	33.06 450	17.99 475	3.84 478	13.62 466	73.92 390	2.79 469	18.29 471	53.41 464	28.53 463	43.25 469	22.56 466	35.27 467	52.11 464

cdsmvs_eth3d_5k		20.81 437	27.75 440	0.00 456	0.00 479	0.00 481	0.00 467	85.44 292	0.00 474	0.00 475	82.82 384	81.46 129	0.00 475	0.00 474	0.00 473	0.00 471

tmp_tt		20.25 438	24.50 441	7.49 453	4.47 476	8.70 477	34.17 464	25.16 474	1.00 471	32.43 470	18.49 468	39.37 439	9.21 472	21.64 467	43.75 466	4.57 468

ab-mvs-re		6.65 439	8.87 442	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	79.80 412	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

pcd_1.5k_mvsjas		6.41 440	8.55 443	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	76.94 186	0.00 475	0.00 474	0.00 473	0.00 471

test123		6.27 441	8.08 444	0.84 454	1.11 478	0.57 479	62.90 444	0.82 478	0.54 472	1.07 474	2.75 473	1.26 477	0.30 473	1.04 472	1.26 472	1.66 469

testmvs		5.91 442	7.65 445	0.72 455	1.20 477	0.37 480	59.14 452	0.67 479	0.49 473	1.11 473	2.76 472	0.94 478	0.24 474	1.02 473	1.47 471	1.55 470

mmdepth		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

monomultidepth		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

test_blank		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uanet_test		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

DCPMVS		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

sosnet-low-res		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

sosnet		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uncertanet		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

Regformer		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uanet		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

WAC-MVS						37.39 459								52.61 408

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		91.01 164					96.05 9	87.45 28	98.17 37	92.40 207

PC_three_1452									58.96 378	90.06 103	91.33 204	80.66 139	93.03 151	75.78 192	95.94 133	92.48 201

No_MVS				88.81 73	91.55 135	77.99 97		91.01 164					96.05 9	87.45 28	98.17 37	92.40 207

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					0.00 479

eth-test					0.00 479

ZD-MVS					92.22 109	80.48 71		91.85 132	71.22 235	90.38 98	92.98 139	86.06 65	96.11 7	81.99 110	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	92.73 185

IU-MVS					94.18 52	72.64 154		90.82 169	56.98 395	89.67 116				85.78 63	97.92 52	93.28 157

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 210	81.12 133	94.68 78	74.48 205	95.35 156	92.29 217

test_241102_TWO								93.71 56	83.77 58	93.49 40	94.27 83	89.27 24	95.84 24	86.03 56	97.82 57	92.04 230

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 88	97.55 74

save fliter					93.75 65	77.44 106	86.31 140	89.72 207	70.80 240

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 178

test_0728_SECOND				86.79 108	94.25 50	72.45 162	90.54 53	94.10 40					95.88 18	86.42 46	97.97 49	92.02 231

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																83.88 384

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 405				83.88 384

sam_mvs												45.92 410

ambc				82.98 215	90.55 164	64.86 259	88.20 103	89.15 220		89.40 125	93.96 105	71.67 266	91.38 197	78.83 145	96.55 102	92.71 188

MTGPA								91.81 136

test_post1							78.85 317				3.13 470	45.19 420	80.13 390	58.11 373

test_post											3.10 471	45.43 416	77.22 405

patchmatchnet-post											81.71 396	45.93 409	87.01 311

GG-mvs-BLEND				67.16 416	73.36 446	46.54 435	84.15 191	55.04 463		58.64 462	61.95 463	29.93 460	83.87 367	38.71 455	76.92 447	71.07 449

MTMP							90.66 49	33.14 473

gm-plane-assit					75.42 435	44.97 442			52.17 421		72.36 452		87.90 297	54.10 397

test9_res														80.83 120	96.45 108	90.57 278

TEST9					92.34 104	79.70 80	83.94 197	90.32 187	65.41 318	84.49 255	90.97 220	82.03 118	93.63 123

test_8					92.09 113	78.87 88	83.82 202	90.31 189	65.79 309	84.36 259	90.96 222	81.93 120	93.44 136

agg_prior2														79.68 133	96.16 120	90.22 286

agg_prior					91.58 133	77.69 103		90.30 190		84.32 261			93.18 144

TestCases				89.68 56	91.59 130	83.40 52		95.44 11	79.47 106	88.00 159	93.03 137	82.66 99	91.47 191	70.81 258	96.14 121	94.16 112

test_prior4						78.97 87	84.59 180

test_prior2							83.37 218		75.43 162	84.58 252	91.57 194	81.92 122		79.54 137	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 98					94.17 102			92.80 183

旧先验2							81.73 265		56.88 396	86.54 205			84.90 353	72.81 243

新几何2							81.72 266

新几何1				82.95 217	93.96 61	78.56 91		80.24 350	55.45 401	83.93 272	91.08 216	71.19 268	88.33 289	65.84 314	93.07 240	81.95 415

旧先验1					91.97 117	71.77 171		81.78 338			91.84 183	73.92 229			93.65 221	83.61 390

无先验							82.81 237	85.62 290	58.09 384				91.41 196	67.95 298		84.48 375

原ACMM2							82.26 257

原ACMM1				84.60 164	92.81 94	74.01 137		91.50 144	62.59 339	82.73 297	90.67 239	76.53 195	94.25 94	69.24 279	95.69 148	85.55 363

test222					93.31 78	76.54 116	79.38 306	77.79 361	52.59 418	82.36 302	90.84 230	66.83 292			91.69 282	81.25 423

testdata2													86.43 326	63.52 336

segment_acmp												81.94 119

testdata				79.54 296	92.87 89	72.34 163		80.14 351	59.91 374	85.47 230	91.75 190	67.96 286	85.24 349	68.57 293	92.18 269	81.06 428

testdata1							79.62 298		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 172		82.76 296		79.20 152	94.80 75		95.32 158	92.27 219

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 211

plane_prior5								93.61 60					95.22 59	80.78 121	95.83 141	94.46 94

plane_prior4											92.95 143

plane_prior3						76.85 114			77.79 133	86.55 199

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								0.00 480

nn								0.00 480

door-mid								74.45 387

lessismore_v0				85.95 127	91.10 152	70.99 186		70.91 417		91.79 71	94.42 78	61.76 326	92.93 154	79.52 138	93.03 241	93.93 122

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 66	98.73 7	95.23 66

test11								91.46 145

door								72.57 403

HQP5-MVS						70.66 188

HQP-NCC					91.19 147		84.77 171		73.30 197	80.55 335

ACMP_Plane					91.19 147		84.77 171		73.30 197	80.55 335

BP-MVS														77.30 170

HQP4-MVS										80.56 334			94.61 82			93.56 149

HQP3-MVS								92.68 104							94.47 192

HQP2-MVS												72.10 257

NP-MVS					91.95 118	74.55 134					90.17 259

MDTV_nov1_ep13_2view						27.60 470	70.76 412		46.47 443	61.27 455		45.20 419		49.18 424		83.75 389

MDTV_nov1_ep13			68.29 391		78.03 410	43.87 445	74.12 382	72.22 406	52.17 421	67.02 438	85.54 346	45.36 417	80.85 384	55.73 384	84.42 405

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 154

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 190	81.56 82	90.02 105	91.20 212	82.40 104	90.81 218	73.58 229	94.66 187	94.56 90

DeepMVS_CX				24.13 452	32.95 474	29.49 468		21.63 475	12.07 468	37.95 469	45.07 466	30.84 458	19.21 471	17.94 470	33.06 468	23.69 467

High-resolution multi-view benchmark