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	80.16 142	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	92.08 223

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	91.76 234

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	91.76 234

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	92.73 180

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	92.19 218

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	93.12 164

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	92.55 193

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	91.81 134	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 210

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	92.85 177

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	92.98 173
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	93.19 160
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	93.27 155

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	92.41 200
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	91.70 238
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	92.29 212

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	89.57 209	88.51 21	90.11 102	95.12 53	90.98 7	88.92 270	77.55 164	97.07 88	83.13 394
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	81.12 130	95.88 18	87.41 30	95.94 133	92.48 196

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 160	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 275
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	58.83 348	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	58.88 345	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	72.94 242	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	57.84 356	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	77.55 170	95.86 23	84.88 73	95.87 139	95.24 65

WR-MVS_H		89.91 51	91.31 34	85.71 134	96.32 9	62.39 293	89.54 80	93.31 74	90.21 12	95.57 11	95.66 37	81.42 127	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	90.63 170	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	90.20 191	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	90.08 194	71.54 228	94.28 25	96.54 19	81.57 125	94.27 92	86.26 49	96.49 105	97.09 20

XVG-OURS-SEG-HR		89.59 56	89.37 62	90.28 46	94.47 43	85.95 27	86.84 128	93.91 48	80.07 100	86.75 190	93.26 128	93.64 2	90.93 210	84.60 78	90.75 304	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	90.45 277
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	90.29 188	69.27 255	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	87.50 174	92.38 162	81.42 127	93.28 141	83.07 92	97.24 84	91.67 239

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	88.31 285	83.33 88	98.30 27	93.20 159
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	89.44 262	74.12 211	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	89.44 262	74.12 211	96.10 124	94.45 96

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 351	88.66 98	92.06 123	90.78 7	95.67 8	95.17 51	81.80 122	95.54 44	79.00 143	98.69 10	98.95 4

XVG-OURS		89.18 64	88.83 76	90.23 47	94.28 49	86.11 26	85.91 147	93.60 62	80.16 98	89.13 131	93.44 124	83.82 84	90.98 207	83.86 85	95.30 161	93.60 143

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	93.26 157
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 66	90.72 48	84.31 174	97.00 2	64.33 264	89.67 75	88.38 229	88.84 17	94.29 23	97.57 7	90.48 14	91.26 198	72.57 241	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	72.03 256	96.36 4	88.21 13	90.93 296	92.98 173
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	90.22 236	86.24 52	97.24 84	91.36 247
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	91.27 152	78.20 126	86.69 194	92.28 170	80.36 140	95.06 67	86.17 53	96.49 105	90.22 281

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	74.07 220	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	74.07 220	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	89.44 212	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 129	95.21 162	91.82 232

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	90.70 267

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 129	84.26 53	90.87 92	93.92 109	82.18 112	89.29 266	73.75 219	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	91.97 126	70.73 239	94.19 26	96.67 17	76.94 182	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	91.22 207	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	84.32 256	89.33 269	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	91.19 155	67.85 280	86.63 195	94.84 59	79.58 148	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	63.94 268	91.10 46	89.64 206	85.07 45	90.91 88	91.09 212	89.16 25	91.87 183	82.03 107	95.87 139	93.13 162

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	85.12 232	89.67 263	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	91.11 156	79.26 111	89.68 115	94.81 63	82.44 101	87.74 296	76.54 179	88.74 338	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	70.81 254	96.14 121	94.16 111

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 265	84.38 185	91.29 149	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 232	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	91.23 153	77.31 139	87.07 184	91.47 198	82.94 95	94.71 77	84.67 77	96.27 115	92.62 188

HQP_MVS		87.75 87	87.43 94	88.70 77	93.45 72	76.42 119	89.45 83	93.61 60	79.44 108	86.55 196	92.95 143	74.84 207	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	88.21 236	86.81 32	91.87 70	97.65 5	85.51 71	87.91 291	74.22 206	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	86.76 267	84.54 50	83.58 274	93.78 114	73.36 237	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	62.26 297	91.49 41	92.62 106	88.07 25	88.07 155	96.17 26	72.24 251	95.79 31	84.85 74	94.16 203	92.58 191

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 158	78.77 119	84.85 243	90.89 222	80.85 133	95.29 56	81.14 115	95.32 158	92.34 208

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	90.68 232	82.85 96	94.57 84	79.55 135	95.95 132	92.00 227

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 211	87.84 112	88.05 237	81.66 81	94.64 18	96.53 20	65.94 293	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	89.74 201	74.40 178	89.92 110	93.41 125	80.45 138	90.63 224	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	81.64 315	87.25 314	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	77.72 167	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	92.09 122	78.87 117	84.27 261	94.05 98	78.35 159	93.65 121	80.54 124	91.58 283	92.08 223
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 98	87.06 100	86.17 124	92.86 91	67.02 236	82.55 242	91.56 139	83.08 68	90.92 86	91.82 183	78.25 160	93.99 107	74.16 209	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 225	92.21 118	81.73 80	90.92 86	91.97 176	77.20 176	93.99 107	74.16 209	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	85.94 294	78.30 92	86.93 125	92.20 119	65.94 299	89.16 129	93.16 132	83.10 93	89.89 251	87.81 20	94.43 194	93.35 150

tt0320-xc		86.67 102	88.41 81	81.44 254	93.45 72	60.44 323	83.96 195	88.50 225	87.26 29	90.90 90	97.90 3	85.61 68	86.40 322	70.14 265	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	88.64 222	86.30 37	89.60 121	92.59 155	69.22 274	94.91 71	73.89 216	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 255	93.57 69	60.73 320	84.37 186	88.61 224	87.00 31	90.75 93	97.98 2	85.54 70	86.45 320	69.75 270	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 152	88.16 225	63.28 275	86.64 135	92.20 119	75.42 163	92.81 54	94.50 72	74.05 223	94.06 106	83.88 84	96.28 113	97.17 19

pmmvs6		86.52 106	88.06 85	81.90 240	92.22 109	62.28 296	84.66 177	89.15 216	83.54 63	89.85 111	97.32 8	88.08 39	86.80 313	70.43 262	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	85.12 232	90.34 244	66.19 290	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	84.76 245	87.70 303	78.87 153	94.18 100	80.67 122	96.29 112	92.73 180

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 130	80.35 95	89.54 124	88.01 290	79.09 151	92.13 174	75.51 194	95.06 169	90.41 278

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 125	70.56 240	84.96 238	90.69 231	80.01 144	95.14 64	78.37 149	95.78 145	91.82 232
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 111	86.83 107	84.36 170	87.82 233	62.35 295	86.42 139	91.33 148	76.78 143	92.73 56	94.48 74	73.41 234	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	90.71 167	85.85 40	89.94 109	95.24 50	82.13 113	90.40 231	69.19 277	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	88.63 278	88.80 9	92.82 247	89.83 291

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 308	78.25 93	85.82 151	91.82 132	65.33 313	88.55 141	92.35 168	82.62 100	89.80 253	86.87 40	94.32 198	93.18 161

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 200	92.01 124	65.91 301	86.19 207	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	82.21 256	90.46 176	80.99 88	88.42 146	91.97 176	77.56 169	93.85 114	72.46 242	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	90.32 183	65.79 303	84.49 250	90.97 216	81.93 118	93.63 123	81.21 114	96.54 103	90.88 261

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 199	83.70 206	89.83 200	80.42 93	87.76 168	93.24 129	73.76 228	91.54 189	85.03 71	93.62 223	95.19 68

FC-MVSNet-test		85.93 119	87.05 101	82.58 226	92.25 107	56.44 367	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 209	93.41 138	75.11 200	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 317	78.21 94	85.40 161	91.39 146	65.32 314	87.72 170	91.81 184	82.33 105	89.78 254	86.68 42	94.20 201	92.99 171

Effi-MVS+-dtu		85.82 121	83.38 186	93.14 4	87.13 254	91.15 3	87.70 113	88.42 228	74.57 172	83.56 275	85.65 338	78.49 158	94.21 96	72.04 244	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	69.17 256	85.80 217	89.56 264	80.76 134	92.13 174	73.21 237	95.51 152	93.25 158
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	87.54 306	86.45 59	91.06 205	75.76 192	93.76 214	92.54 194

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	87.54 306	86.45 59	91.06 205	75.76 192	93.76 214	92.54 194

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	73.94 224	90.41 230	85.95 60	92.74 249	93.66 135

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 240	81.51 83	87.05 185	91.83 182	66.18 292	95.29 56	70.75 257	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	91.39 146	72.33 219	87.59 172	90.25 249	84.85 75	92.37 168	78.00 158	91.94 273	93.66 135

MVS_0304		85.37 128	84.58 158	87.75 93	85.28 307	73.36 141	86.54 138	85.71 283	77.56 137	81.78 313	92.47 160	70.29 268	96.02 11	85.59 63	95.96 130	93.87 124

FIs		85.35 129	86.27 114	82.60 225	91.86 122	57.31 360	85.10 167	93.05 87	75.83 154	91.02 85	93.97 102	73.57 230	92.91 156	73.97 215	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 296	76.13 125	85.15 166	92.32 116	61.40 349	91.33 78	90.85 225	83.76 87	86.16 328	84.31 80	93.28 232	92.15 221

casdiffmvs		85.21 131	85.85 125	83.31 205	86.17 287	62.77 282	83.03 227	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	86.30 282	62.37 294	84.55 180	93.96 45	74.48 175	87.12 179	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	91.58 137	74.41 176	86.55 196	91.49 195	78.54 154	93.97 109	73.71 220	93.21 236	92.59 190

K. test v3		85.14 134	84.73 151	86.37 115	91.13 151	69.63 203	85.45 159	76.68 367	84.06 56	92.44 61	96.99 13	62.03 319	94.65 80	80.58 123	93.24 233	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 210	84.65 319	74.71 132	85.87 149	90.35 182	77.94 129	83.82 268	96.96 15	77.75 165	80.03 386	78.44 147	96.21 117	94.79 85

EI-MVSNet-Vis-set		85.12 136	84.53 161	86.88 106	84.01 332	72.76 151	83.91 199	85.18 292	80.44 92	88.75 136	85.49 342	80.08 143	91.92 180	82.02 108	90.85 301	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	90.61 172	67.64 284	84.88 241	92.05 174	82.30 107	88.36 283	83.84 86	91.10 289	92.62 188

MGCFI-Net		85.04 138	85.95 121	82.31 234	87.52 244	63.59 271	86.23 143	93.96 45	73.46 190	88.07 155	87.83 301	86.46 58	90.87 215	76.17 186	93.89 211	92.47 198

EI-MVSNet-UG-set		85.04 138	84.44 164	86.85 107	83.87 336	72.52 160	83.82 201	85.15 293	80.27 97	88.75 136	85.45 344	79.95 145	91.90 181	81.92 111	90.80 303	96.13 39

X-MVStestdata		85.04 138	82.70 203	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 463	86.57 56	95.80 28	87.35 32	97.62 69	94.20 107

MSLP-MVS++		85.00 141	86.03 120	81.90 240	91.84 125	71.56 179	86.75 133	93.02 91	75.95 152	87.12 179	89.39 267	77.98 162	89.40 265	77.46 165	94.78 182	84.75 366

F-COLMAP		84.97 142	83.42 185	89.63 58	92.39 102	83.40 52	88.83 93	91.92 128	73.19 201	80.18 337	89.15 273	77.04 180	93.28 141	65.82 310	92.28 262	92.21 217

SSM_0407		84.89 143	84.85 148	85.01 150	89.13 195	68.97 214	85.60 156	91.58 137	74.41 176	85.68 218	91.49 195	78.54 154	93.69 120	73.71 220	93.47 225	92.38 205

balanced_conf03		84.80 144	85.40 136	83.00 213	88.95 202	61.44 305	90.42 59	92.37 115	71.48 230	88.72 138	93.13 133	70.16 270	95.15 63	79.26 140	94.11 204	92.41 200

3Dnovator	80.37 7	84.80 144	84.71 154	85.06 149	86.36 280	74.71 132	88.77 95	90.00 196	75.65 157	84.96 238	93.17 131	74.06 222	91.19 200	78.28 152	91.09 290	89.29 301

SymmetryMVS		84.79 146	83.54 180	88.55 79	92.44 101	80.42 72	88.63 99	82.37 327	74.56 173	85.12 232	90.34 244	66.19 290	94.20 97	76.57 177	95.68 149	91.03 255

IterMVS-LS		84.73 147	84.98 145	83.96 183	87.35 248	63.66 269	83.25 221	89.88 199	76.06 147	89.62 118	92.37 165	73.40 236	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	79.23 306	87.13 257	73.35 194	85.56 225	89.34 268	83.60 89	90.50 227	76.64 176	94.05 208	90.09 287

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	80.55 329	90.17 254	72.10 252	94.61 82	77.30 169	94.47 192	93.56 146

v1192		84.57 150	84.69 156	84.21 176	87.75 235	62.88 279	83.02 228	91.43 143	69.08 258	89.98 108	90.89 222	72.70 246	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	88.64 222	64.43 323	88.77 135	91.78 186	78.07 161	87.95 290	85.85 61	92.18 266	92.30 210

FMVSNet1		84.55 152	85.45 135	81.85 242	90.27 169	61.05 312	86.83 129	88.27 233	78.57 122	89.66 117	95.64 38	75.43 199	90.68 221	69.09 278	95.33 157	93.82 127

v1144		84.54 153	84.72 153	84.00 180	87.67 239	62.55 286	82.97 230	90.93 163	70.32 244	89.80 112	90.99 215	73.50 231	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 298	84.20 329	73.57 140	89.55 78	90.44 177	84.24 54	84.38 253	94.89 57	76.35 195	80.40 383	76.14 187	96.80 96	82.36 404
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	88.45 227	68.64 266	86.29 206	91.31 204	74.97 205	88.42 281	87.87 19	90.07 318	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	61.82 342	83.96 266	89.75 262	79.93 146	93.46 135	78.33 151	94.34 197	91.87 231

VDDNet		84.35 157	85.39 137	81.25 257	95.13 32	59.32 337	85.42 160	81.11 338	86.41 36	87.41 175	96.21 25	73.61 229	90.61 225	66.33 303	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	84.07 264	78.72 417	80.39 139	95.13 65	73.82 218	92.98 242	91.04 254

v1240		84.30 159	84.51 162	83.65 193	87.65 240	61.26 309	82.85 234	91.54 140	67.94 277	90.68 95	90.65 235	71.71 260	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	80.99 277	83.56 315	72.71 211	86.07 210	89.07 275	81.75 124	86.19 327	77.11 171	93.36 228	88.24 320

h-mvs33		84.25 161	82.76 202	88.72 75	91.82 127	82.60 60	84.00 194	84.98 299	71.27 231	86.70 192	90.55 240	63.04 316	93.92 112	78.26 153	94.20 201	89.63 293

v144192		84.24 162	84.41 165	83.71 192	87.59 242	61.57 304	82.95 231	91.03 159	67.82 281	89.80 112	90.49 241	73.28 238	93.51 133	81.88 112	94.89 177	96.04 43

dcpmvs_2		84.23 163	85.14 141	81.50 252	88.61 214	61.98 301	82.90 233	93.11 83	68.66 265	92.77 55	92.39 161	78.50 157	87.63 299	76.99 173	92.30 259	94.90 76

v1921920		84.23 163	84.37 167	83.79 188	87.64 241	61.71 303	82.91 232	91.20 154	67.94 277	90.06 103	90.34 244	72.04 255	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	84.97 300	79.79 102	87.18 178	94.27 83	74.77 210	90.89 213	69.24 274	96.54 103	93.55 148

v2v482		84.09 166	84.24 170	83.62 194	87.13 254	61.40 306	82.71 237	89.71 204	72.19 222	89.55 122	91.41 199	70.70 266	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	82.20 258	87.02 263	72.63 212	88.86 132	91.02 214	78.52 156	91.11 203	73.41 227	91.09 290	88.21 321

fmvsm_s_conf0.5_n_6		84.05 168	84.14 171	83.81 186	87.75 235	71.17 182	83.42 215	91.10 157	67.90 279	84.53 248	90.70 230	73.01 241	88.73 276	85.09 68	93.72 219	91.53 244

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	82.04 304	88.33 286	77.91 164	93.95 111	66.17 304	95.12 167	90.34 280

viewmacassd2359aftdt		84.04 170	84.78 150	81.81 245	86.43 274	60.32 325	81.95 260	92.82 99	71.56 227	86.06 211	92.98 139	81.79 123	90.28 232	76.18 185	93.24 233	94.82 84

TransMVSNet (Re)		84.02 171	85.74 130	78.85 297	91.00 154	55.20 379	82.29 252	87.26 252	79.65 105	88.38 148	95.52 41	83.00 94	86.88 311	67.97 292	96.60 101	94.45 96

Baseline_NR-MVSNet		84.00 172	85.90 123	78.29 309	91.47 140	53.44 390	82.29 252	87.00 266	79.06 114	89.55 122	95.72 36	77.20 176	86.14 329	72.30 243	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 173	84.46 163	82.53 229	86.11 290	70.65 189	82.45 247	89.17 215	67.72 283	86.74 191	91.49 195	79.20 149	85.86 338	84.71 76	92.60 253	91.07 253

TSAR-MVS + GP.		83.95 174	82.69 204	87.72 94	89.27 192	81.45 67	83.72 205	81.58 336	74.73 170	85.66 221	86.06 333	72.56 248	92.69 160	75.44 196	95.21 162	89.01 314

LuminaMVS		83.94 175	83.51 181	85.23 144	89.78 181	71.74 172	84.76 173	87.27 251	72.60 213	89.31 127	90.60 239	64.04 305	90.95 208	79.08 141	94.11 204	92.99 171

alignmvs		83.94 175	83.98 175	83.80 187	87.80 234	67.88 228	84.54 182	91.42 145	73.27 200	88.41 147	87.96 291	72.33 249	90.83 216	76.02 189	94.11 204	92.69 184

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	81.34 320	84.18 363	83.65 88	92.93 154	74.22 206	87.87 352	92.17 220

fmvsm_s_conf0.1_n_2		83.82 178	83.49 182	84.84 152	85.99 293	70.19 195	80.93 278	87.58 247	67.26 290	87.94 161	92.37 165	71.40 262	88.01 287	86.03 55	91.87 274	96.31 36

mvs5depth		83.82 178	84.54 160	81.68 248	82.23 360	68.65 219	86.89 126	89.90 198	80.02 101	87.74 169	97.86 4	64.19 304	82.02 371	76.37 181	95.63 151	94.35 103

CANet		83.79 180	82.85 201	86.63 110	86.17 287	72.21 167	83.76 204	91.43 143	77.24 140	74.39 392	87.45 310	75.36 200	95.42 52	77.03 172	92.83 246	92.25 216

pm-mvs1		83.69 181	84.95 147	79.91 283	90.04 177	59.66 334	82.43 248	87.44 248	75.52 161	87.85 164	95.26 49	81.25 129	85.65 341	68.74 284	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	90.00 196	78.19 127	81.65 314	87.16 316	83.40 91	94.24 95	61.69 345	94.76 185	84.21 376

MIMVSNet1		83.63 183	84.59 157	80.74 267	94.06 59	62.77 282	82.72 236	84.53 307	77.57 136	90.34 99	95.92 31	76.88 188	85.83 339	61.88 343	97.42 79	93.62 141

fmvsm_s_conf0.5_n_2		83.62 184	83.29 188	84.62 162	85.43 305	70.18 196	80.61 283	87.24 253	67.14 291	87.79 166	91.87 178	71.79 259	87.98 289	86.00 59	91.77 277	95.71 50

test_fmvsm_n_1920		83.60 185	82.89 198	85.74 133	85.22 309	77.74 102	84.12 191	90.48 174	59.87 369	86.45 205	91.12 211	75.65 197	85.89 336	82.28 105	90.87 299	93.58 144

WR-MVS		83.56 186	84.40 166	81.06 262	93.43 75	54.88 380	78.67 315	85.02 297	81.24 85	90.74 94	91.56 193	72.85 243	91.08 204	68.00 291	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	88.77 219	79.09 113	83.54 276	88.66 283	74.87 206	81.73 373	66.84 298	92.29 261	89.11 307

LCM-MVSNet-Re		83.48 188	85.06 143	78.75 299	85.94 294	55.75 373	80.05 289	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 257	59.95 356	94.89 177	90.75 264

hse-mvs2		83.47 189	81.81 218	88.47 80	91.03 153	82.27 61	82.61 238	83.69 313	71.27 231	86.70 192	86.05 334	63.04 316	92.41 166	78.26 153	93.62 223	90.71 266

V42		83.47 189	83.37 187	83.75 190	83.16 354	63.33 274	81.31 271	90.23 190	69.51 253	90.91 88	90.81 227	74.16 219	92.29 172	80.06 126	90.22 316	95.62 54

VPA-MVSNet		83.47 189	84.73 151	79.69 288	90.29 168	57.52 359	81.30 273	88.69 221	76.29 145	87.58 173	94.44 75	80.60 137	87.20 305	66.60 301	96.82 95	94.34 104

mamba_0408		83.44 192	82.88 199	85.11 147	89.13 195	68.97 214	72.73 389	91.28 150	72.90 205	85.68 218	90.61 237	76.78 189	93.97 109	73.37 229	93.47 225	92.38 205

PAPM_NR		83.23 193	83.19 191	83.33 204	90.90 156	65.98 249	88.19 104	90.78 166	78.13 128	80.87 325	87.92 295	73.49 233	92.42 165	70.07 266	88.40 341	91.60 241

CLD-MVS		83.18 194	82.64 205	84.79 155	89.05 198	67.82 229	77.93 324	92.52 109	68.33 269	85.07 235	81.54 392	82.06 115	92.96 152	69.35 273	97.91 54	93.57 145
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	75.65 346	81.24 372	45.26 434	79.94 291	92.91 95	83.83 57	91.33 78	96.88 16	80.25 141	85.92 332	68.89 281	95.89 138	95.76 48

FA-MVS(test-final)		83.13 196	83.02 195	83.43 201	86.16 289	66.08 248	88.00 108	88.36 230	75.55 160	85.02 236	92.75 152	65.12 299	92.50 164	74.94 202	91.30 287	91.72 236

114514_t		83.10 197	82.54 208	84.77 156	92.90 88	69.10 213	86.65 134	90.62 171	54.66 401	81.46 317	90.81 227	76.98 181	94.38 90	72.62 240	96.18 119	90.82 263

RRT-MVS		82.97 198	83.44 183	81.57 250	85.06 312	58.04 354	87.20 119	90.37 180	77.88 131	88.59 140	93.70 119	63.17 313	93.05 150	76.49 180	88.47 340	93.62 141

viewmanbaseed2359cas		82.95 199	83.43 184	81.52 251	85.18 310	60.03 330	81.36 270	92.38 113	69.55 252	84.84 244	91.38 200	79.85 147	90.09 245	74.22 206	92.09 268	94.43 99

BP-MVS1		82.81 200	81.67 220	86.23 119	87.88 232	68.53 220	86.06 146	84.36 308	75.65 157	85.14 231	90.19 251	45.84 405	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 201	81.64 221	86.21 122	86.20 286	76.24 123	86.86 127	85.68 284	77.07 141	73.76 396	92.82 148	69.64 271	91.82 185	69.04 280	93.69 220	90.56 274
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	81.93 216	85.19 145	82.08 361	80.15 76	85.53 157	88.76 220	68.01 274	85.58 224	87.75 302	71.80 258	86.85 312	74.02 214	93.87 212	88.58 317

EI-MVSNet		82.61 203	82.42 210	83.20 208	83.25 351	63.66 269	83.50 213	85.07 294	76.06 147	86.55 196	85.10 350	73.41 234	90.25 233	78.15 157	90.67 310	95.68 52

QAPM		82.59 204	82.59 207	82.58 226	86.44 273	66.69 240	89.94 68	90.36 181	67.97 276	84.94 240	92.58 157	72.71 245	92.18 173	70.63 260	87.73 355	88.85 315

fmvsm_s_conf0.1_n_a		82.58 205	81.93 216	84.50 165	87.68 238	73.35 142	86.14 145	77.70 356	61.64 347	85.02 236	91.62 190	77.75 165	86.24 324	82.79 98	87.07 363	93.91 122

Fast-Effi-MVS+-dtu		82.54 206	81.41 230	85.90 129	85.60 301	76.53 118	83.07 226	89.62 208	73.02 204	79.11 347	83.51 368	80.74 135	90.24 235	68.76 283	89.29 328	90.94 258

MVS_Test		82.47 207	83.22 189	80.22 279	82.62 359	57.75 358	82.54 243	91.96 127	71.16 235	82.89 287	92.52 159	77.41 171	90.50 227	80.04 127	87.84 354	92.40 202

viewmsd2359difaftdt		82.46 208	82.99 196	80.88 265	83.52 339	61.00 315	79.46 300	85.97 280	69.48 254	87.89 163	91.31 204	82.10 114	88.61 279	74.28 205	92.86 245	93.02 168

v148		82.31 209	82.48 209	81.81 245	85.59 302	59.66 334	81.47 268	86.02 278	72.85 207	88.05 157	90.65 235	70.73 265	90.91 212	75.15 199	91.79 275	94.87 78

API-MVS		82.28 210	82.61 206	81.30 256	86.29 283	69.79 198	88.71 96	87.67 246	78.42 124	82.15 300	84.15 364	77.98 162	91.59 188	65.39 313	92.75 248	82.51 403

MVSFormer		82.23 211	81.57 226	84.19 178	85.54 303	69.26 208	91.98 35	90.08 194	71.54 228	76.23 372	85.07 353	58.69 341	94.27 92	86.26 49	88.77 336	89.03 312

fmvsm_s_conf0.5_n_a		82.21 212	81.51 229	84.32 173	86.56 271	73.35 142	85.46 158	77.30 360	61.81 343	84.51 249	90.88 224	77.36 172	86.21 326	82.72 99	86.97 368	93.38 149

EIA-MVS		82.19 213	81.23 237	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 241	78.58 352	79.77 408	77.29 173	94.20 97	71.51 250	88.96 334	91.93 230

GDP-MVS		82.17 214	80.85 245	86.15 126	88.65 212	68.95 217	85.65 155	93.02 91	68.42 267	83.73 270	89.54 265	45.07 416	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 214	81.59 224	83.94 185	86.87 269	71.57 178	85.19 165	77.42 359	62.27 341	84.47 252	91.33 202	76.43 192	85.91 334	83.14 89	87.14 361	94.33 105

PCF-MVS	74.62 15	82.15 216	80.92 243	85.84 131	89.43 188	72.30 164	80.53 284	91.82 132	57.36 385	87.81 165	89.92 259	77.67 168	93.63 123	58.69 361	95.08 168	91.58 242
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 217	80.31 252	87.45 97	90.86 158	80.29 75	85.88 148	90.65 169	68.17 272	76.32 371	86.33 328	73.12 240	92.61 162	61.40 348	90.02 320	89.44 296
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 218	81.54 228	83.60 195	83.94 333	73.90 138	83.35 218	86.10 274	58.97 371	83.80 269	90.36 243	74.23 217	86.94 310	82.90 95	90.22 316	89.94 289

fmvsm_s_conf0.5_n_7		82.04 219	82.05 214	82.01 238	86.98 265	71.07 183	78.70 313	89.45 211	68.07 273	78.14 354	91.61 191	74.19 218	85.92 332	79.61 134	91.73 278	89.05 311

GBi-Net		82.02 220	82.07 212	81.85 242	86.38 277	61.05 312	86.83 129	88.27 233	72.43 214	86.00 212	95.64 38	63.78 309	90.68 221	65.95 306	93.34 229	93.82 127

test1		82.02 220	82.07 212	81.85 242	86.38 277	61.05 312	86.83 129	88.27 233	72.43 214	86.00 212	95.64 38	63.78 309	90.68 221	65.95 306	93.34 229	93.82 127

OpenMVS	76.72 13	81.98 222	82.00 215	81.93 239	84.42 324	68.22 223	88.50 102	89.48 210	66.92 294	81.80 311	91.86 179	72.59 247	90.16 239	71.19 253	91.25 288	87.40 337

KD-MVS_self_test		81.93 223	83.14 193	78.30 308	84.75 318	52.75 394	80.37 286	89.42 213	70.24 246	90.26 101	93.39 126	74.55 216	86.77 314	68.61 286	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 224	81.30 234	83.75 190	86.02 292	71.56 179	84.73 174	77.11 363	62.44 338	84.00 265	90.68 232	76.42 193	85.89 336	83.14 89	87.11 362	93.81 130

SDMVSNet		81.90 225	83.17 192	78.10 312	88.81 207	62.45 292	76.08 358	86.05 277	73.67 186	83.41 277	93.04 135	82.35 104	80.65 380	70.06 267	95.03 170	91.21 249

tfpnnormal		81.79 226	82.95 197	78.31 307	88.93 203	55.40 375	80.83 281	82.85 322	76.81 142	85.90 216	94.14 93	74.58 214	86.51 318	66.82 299	95.68 149	93.01 170

AstraMVS		81.67 227	81.40 231	82.48 231	87.06 262	66.47 243	81.41 269	81.68 333	68.78 262	88.00 158	90.95 220	65.70 295	87.86 295	76.66 175	92.38 257	93.12 164

c3_l		81.64 228	81.59 224	81.79 247	80.86 378	59.15 342	78.61 316	90.18 192	68.36 268	87.20 177	87.11 318	69.39 272	91.62 187	78.16 155	94.43 194	94.60 89

guyue		81.57 229	81.37 233	82.15 235	86.39 275	66.13 247	81.54 267	83.21 317	69.79 250	87.77 167	89.95 257	65.36 298	87.64 298	75.88 190	92.49 255	92.67 185

PVSNet_Blended_VisFu		81.55 230	80.49 250	84.70 160	91.58 133	73.24 146	84.21 188	91.67 136	62.86 332	80.94 323	87.16 316	67.27 284	92.87 157	69.82 269	88.94 335	87.99 327

fmvsm_l_conf0.5_n_a		81.46 231	80.87 244	83.25 206	83.73 338	73.21 147	83.00 229	85.59 286	58.22 377	82.96 286	90.09 256	72.30 250	86.65 316	81.97 110	89.95 321	89.88 290

SSM_04072		81.44 232	82.88 199	77.10 327	89.13 195	68.97 214	72.73 389	91.28 150	72.90 205	85.68 218	90.61 237	76.78 189	69.94 423	73.37 229	93.47 225	92.38 205

DELS-MVS		81.44 232	81.25 235	82.03 237	84.27 328	62.87 280	76.47 352	92.49 110	70.97 237	81.64 315	83.83 365	75.03 203	92.70 159	74.29 204	92.22 265	90.51 276
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		81.31 234	81.61 223	80.41 275	86.38 277	58.75 349	83.93 198	86.58 269	72.43 214	87.65 171	92.98 139	63.78 309	90.22 236	66.86 296	93.92 210	92.27 214

TinyColmap		81.25 235	82.34 211	77.99 315	85.33 306	60.68 321	82.32 251	88.33 231	71.26 233	86.97 186	92.22 173	77.10 179	86.98 309	62.37 337	95.17 164	86.31 349

diffmvs_AUTHOR		81.24 236	81.55 227	80.30 277	80.61 383	60.22 326	77.98 323	90.48 174	67.77 282	83.34 279	89.50 266	74.69 212	87.42 301	78.78 145	90.81 302	93.27 155

AUN-MVS		81.18 237	78.78 274	88.39 82	90.93 155	82.14 62	82.51 244	83.67 314	64.69 322	80.29 333	85.91 337	51.07 380	92.38 167	76.29 184	93.63 222	90.65 271

IMVS_0407		81.08 238	81.23 237	80.62 272	85.76 297	62.46 288	82.46 245	87.91 241	65.23 315	82.12 301	87.92 295	77.27 174	90.18 238	71.67 246	90.74 305	89.20 302

tttt0517		81.07 239	79.58 265	85.52 138	88.99 201	66.45 244	87.03 124	75.51 375	73.76 185	88.32 150	90.20 250	37.96 437	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 240	80.57 247	82.46 232	87.50 245	63.22 276	78.37 319	89.63 207	68.01 274	81.87 307	82.08 386	82.31 106	92.65 161	67.10 295	88.30 347	91.51 245

BH-untuned		80.96 241	80.99 241	80.84 266	88.55 216	68.23 222	80.33 287	88.46 226	72.79 210	86.55 196	86.76 322	74.72 211	91.77 186	61.79 344	88.99 333	82.52 402

IMVS_0403		80.93 242	81.00 240	80.72 269	85.76 297	62.46 288	81.82 261	87.91 241	65.23 315	82.07 303	87.92 295	75.91 196	90.50 227	71.67 246	90.74 305	89.20 302

eth_miper_zixun_eth		80.84 243	80.22 256	82.71 223	81.41 370	60.98 316	77.81 326	90.14 193	67.31 289	86.95 187	87.24 315	64.26 302	92.31 170	75.23 198	91.61 281	94.85 82

xiu_mvs_v1_base_debu		80.84 243	80.14 258	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 309	79.59 339	82.73 380	76.94 182	90.14 242	73.22 232	88.33 343	86.90 343

xiu_mvs_v1_base		80.84 243	80.14 258	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 309	79.59 339	82.73 380	76.94 182	90.14 242	73.22 232	88.33 343	86.90 343

xiu_mvs_v1_base_debi		80.84 243	80.14 258	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 309	79.59 339	82.73 380	76.94 182	90.14 242	73.22 232	88.33 343	86.90 343

IterMVS-SCA-FT		80.64 247	79.41 266	84.34 172	83.93 334	69.66 202	76.28 354	81.09 339	72.43 214	86.47 203	90.19 251	60.46 326	93.15 146	77.45 166	86.39 374	90.22 281

BH-RMVSNet		80.53 248	80.22 256	81.49 253	87.19 253	66.21 246	77.79 327	86.23 272	74.21 180	83.69 271	88.50 284	73.25 239	90.75 218	63.18 334	87.90 351	87.52 335

VortexMVS		80.51 249	80.63 246	80.15 281	83.36 347	61.82 302	80.63 282	88.00 239	67.11 292	87.23 176	89.10 274	63.98 306	88.00 288	73.63 224	92.63 252	90.64 272

Anonymous202405211		80.51 249	81.19 239	78.49 304	88.48 217	57.26 361	76.63 347	82.49 325	81.21 86	84.30 259	92.24 172	67.99 280	86.24 324	62.22 338	95.13 165	91.98 229

DIV-MVS_self_test		80.43 251	80.23 254	81.02 263	79.99 388	59.25 339	77.07 340	87.02 263	67.38 286	86.19 207	89.22 270	63.09 314	90.16 239	76.32 182	95.80 143	93.66 135

cl____		80.42 252	80.23 254	81.02 263	79.99 388	59.25 339	77.07 340	87.02 263	67.37 287	86.18 209	89.21 271	63.08 315	90.16 239	76.31 183	95.80 143	93.65 138

diffmvs		80.40 253	80.48 251	80.17 280	79.02 401	60.04 328	77.54 331	90.28 189	66.65 297	82.40 294	87.33 313	73.50 231	87.35 303	77.98 159	89.62 325	93.13 162
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		80.37 254	78.41 282	86.23 119	76.75 415	73.28 144	87.18 121	77.45 358	76.24 146	68.14 426	88.93 277	65.41 297	93.85 114	69.47 272	96.12 123	91.55 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 255	80.04 261	81.24 259	79.82 391	58.95 344	77.66 328	89.66 205	65.75 306	85.99 215	85.11 349	68.29 279	91.42 195	76.03 188	92.03 269	93.33 151

MG-MVS		80.32 256	80.94 242	78.47 305	88.18 223	52.62 397	82.29 252	85.01 298	72.01 225	79.24 346	92.54 158	69.36 273	93.36 140	70.65 259	89.19 331	89.45 295

mvsmamba		80.30 257	78.87 271	84.58 164	88.12 226	67.55 230	92.35 30	84.88 301	63.15 330	85.33 228	90.91 221	50.71 382	95.20 62	66.36 302	87.98 350	90.99 256

VPNet		80.25 258	81.68 219	75.94 343	92.46 100	47.98 421	76.70 345	81.67 334	73.45 191	84.87 242	92.82 148	74.66 213	86.51 318	61.66 346	96.85 92	93.33 151

MAR-MVS		80.24 259	78.74 276	84.73 158	86.87 269	78.18 95	85.75 152	87.81 245	65.67 308	77.84 358	78.50 418	73.79 227	90.53 226	61.59 347	90.87 299	85.49 359
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		80.20 260	79.00 270	83.78 189	88.17 224	86.66 19	81.31 271	66.81 431	69.64 251	88.33 149	90.19 251	64.58 300	83.63 362	71.99 245	90.03 319	81.06 422

Anonymous20240521		80.18 261	81.25 235	76.95 329	83.15 355	60.84 318	82.46 245	85.99 279	68.76 263	86.78 188	93.73 118	59.13 338	77.44 397	73.71 220	97.55 74	92.56 192

LFMVS		80.15 262	80.56 248	78.89 296	89.19 194	55.93 369	85.22 164	73.78 387	82.96 69	84.28 260	92.72 153	57.38 350	90.07 247	63.80 328	95.75 146	90.68 268

DPM-MVS		80.10 263	79.18 269	82.88 221	90.71 161	69.74 200	78.87 311	90.84 164	60.29 365	75.64 381	85.92 336	67.28 283	93.11 147	71.24 252	91.79 275	85.77 355

MSDG		80.06 264	79.99 263	80.25 278	83.91 335	68.04 227	77.51 332	89.19 214	77.65 134	81.94 305	83.45 370	76.37 194	86.31 323	63.31 333	86.59 371	86.41 347

FE-MVS		79.98 265	78.86 272	83.36 203	86.47 272	66.45 244	89.73 71	84.74 305	72.80 209	84.22 263	91.38 200	44.95 417	93.60 127	63.93 326	91.50 284	90.04 288

sd_testset		79.95 266	81.39 232	75.64 347	88.81 207	58.07 353	76.16 357	82.81 323	73.67 186	83.41 277	93.04 135	80.96 132	77.65 396	58.62 362	95.03 170	91.21 249

ab-mvs		79.67 267	80.56 248	76.99 328	88.48 217	56.93 363	84.70 176	86.06 276	68.95 260	80.78 326	93.08 134	75.30 201	84.62 349	56.78 371	90.90 297	89.43 297

VNet		79.31 268	80.27 253	76.44 337	87.92 230	53.95 386	75.58 364	84.35 309	74.39 179	82.23 298	90.72 229	72.84 244	84.39 354	60.38 354	93.98 209	90.97 257

thisisatest0530		79.07 269	77.33 292	84.26 175	87.13 254	64.58 260	83.66 208	75.95 370	68.86 261	85.22 230	87.36 312	38.10 434	93.57 131	75.47 195	94.28 199	94.62 88

cl22		78.97 270	78.21 284	81.24 259	77.74 405	59.01 343	77.46 335	87.13 257	65.79 303	84.32 256	85.10 350	58.96 340	90.88 214	75.36 197	92.03 269	93.84 125

patch_mono-2		78.89 271	79.39 267	77.41 324	84.78 316	68.11 225	75.60 362	83.11 319	60.96 357	79.36 343	89.89 260	75.18 202	72.97 412	73.32 231	92.30 259	91.15 251

RPMNet		78.88 272	78.28 283	80.68 271	79.58 392	62.64 284	82.58 240	94.16 33	74.80 168	75.72 379	92.59 155	48.69 389	95.56 42	73.48 226	82.91 410	83.85 381

PAPR		78.84 273	78.10 285	81.07 261	85.17 311	60.22 326	82.21 256	90.57 173	62.51 334	75.32 385	84.61 358	74.99 204	92.30 171	59.48 359	88.04 349	90.68 268

viewmambaseed2359dif		78.80 274	78.47 281	79.78 284	80.26 387	59.28 338	77.31 337	87.13 257	60.42 363	82.37 295	88.67 282	74.58 214	87.87 294	67.78 294	87.73 355	92.19 218

PVSNet_BlendedMVS		78.80 274	77.84 286	81.65 249	84.43 322	63.41 272	79.49 299	90.44 177	61.70 346	75.43 382	87.07 319	69.11 275	91.44 193	60.68 352	92.24 263	90.11 286

FMVSNet3		78.80 274	78.55 278	79.57 290	82.89 358	56.89 365	81.76 262	85.77 282	69.04 259	86.00 212	90.44 242	51.75 378	90.09 245	65.95 306	93.34 229	91.72 236

test_yl		78.71 277	78.51 279	79.32 293	84.32 326	58.84 346	78.38 317	85.33 289	75.99 150	82.49 292	86.57 324	58.01 344	90.02 249	62.74 335	92.73 250	89.10 308

DCV-MVSNet		78.71 277	78.51 279	79.32 293	84.32 326	58.84 346	78.38 317	85.33 289	75.99 150	82.49 292	86.57 324	58.01 344	90.02 249	62.74 335	92.73 250	89.10 308

test1111		78.53 279	78.85 273	77.56 321	92.22 109	47.49 423	82.61 238	69.24 419	72.43 214	85.28 229	94.20 89	51.91 376	90.07 247	65.36 314	96.45 108	95.11 72

icg_test_0407_2		78.46 280	79.68 264	74.78 354	85.76 297	62.46 288	68.51 418	87.91 241	65.23 315	82.12 301	87.92 295	77.27 174	72.67 413	71.67 246	90.74 305	89.20 302

ECVR-MVS		78.44 281	78.63 277	77.88 317	91.85 123	48.95 417	83.68 207	69.91 415	72.30 220	84.26 262	94.20 89	51.89 377	89.82 252	63.58 329	96.02 127	94.87 78

pmmvs-eth3d		78.42 282	77.04 295	82.57 228	87.44 247	74.41 135	80.86 280	79.67 347	55.68 394	84.69 246	90.31 248	60.91 324	85.42 342	62.20 339	91.59 282	87.88 331

mvs_anonymous		78.13 283	78.76 275	76.23 342	79.24 398	50.31 413	78.69 314	84.82 303	61.60 348	83.09 285	92.82 148	73.89 226	87.01 306	68.33 290	86.41 373	91.37 246

TAMVS		78.08 284	76.36 302	83.23 207	90.62 162	72.87 150	79.08 307	80.01 346	61.72 345	81.35 319	86.92 321	63.96 308	88.78 274	50.61 410	93.01 241	88.04 326

miper_enhance_ethall		77.83 285	76.93 296	80.51 273	76.15 422	58.01 355	75.47 366	88.82 218	58.05 379	83.59 273	80.69 396	64.41 301	91.20 199	73.16 238	92.03 269	92.33 209

Vis-MVSNet (Re-imp)		77.82 286	77.79 287	77.92 316	88.82 206	51.29 407	83.28 219	71.97 403	74.04 181	82.23 298	89.78 261	57.38 350	89.41 264	57.22 370	95.41 154	93.05 167

CANet_DTU		77.81 287	77.05 294	80.09 282	81.37 371	59.90 332	83.26 220	88.29 232	69.16 257	67.83 429	83.72 366	60.93 323	89.47 259	69.22 276	89.70 324	90.88 261

OpenMVS_ROB	70.19 17	77.77 288	77.46 289	78.71 300	84.39 325	61.15 310	81.18 275	82.52 324	62.45 337	83.34 279	87.37 311	66.20 289	88.66 277	64.69 321	85.02 390	86.32 348

SSC-MVS		77.55 289	81.64 221	65.29 420	90.46 165	20.33 467	73.56 382	68.28 421	85.44 41	88.18 154	94.64 68	70.93 264	81.33 375	71.25 251	92.03 269	94.20 107

MDA-MVSNet-bldmvs		77.47 290	76.90 297	79.16 295	79.03 400	64.59 259	66.58 430	75.67 373	73.15 202	88.86 132	88.99 276	66.94 285	81.23 376	64.71 320	88.22 348	91.64 240

jason		77.42 291	75.75 308	82.43 233	87.10 257	69.27 207	77.99 322	81.94 331	51.47 421	77.84 358	85.07 353	60.32 328	89.00 268	70.74 258	89.27 330	89.03 312
jason: jason.
CDS-MVSNet		77.32 292	75.40 312	83.06 211	89.00 200	72.48 161	77.90 325	82.17 329	60.81 358	78.94 349	83.49 369	59.30 336	88.76 275	54.64 390	92.37 258	87.93 330
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 293	77.75 288	75.73 345	85.76 297	62.46 288	70.84 404	87.91 241	65.23 315	72.21 404	87.92 295	67.48 282	75.53 405	71.67 246	90.74 305	89.20 302

xiu_mvs_v2_base		77.19 294	76.75 299	78.52 303	87.01 263	61.30 308	75.55 365	87.12 261	61.24 354	74.45 391	78.79 416	77.20 176	90.93 210	64.62 323	84.80 397	83.32 390

MVSTER		77.09 295	75.70 309	81.25 257	75.27 430	61.08 311	77.49 334	85.07 294	60.78 359	86.55 196	88.68 280	43.14 426	90.25 233	73.69 223	90.67 310	92.42 199

PS-MVSNAJ		77.04 296	76.53 301	78.56 302	87.09 259	61.40 306	75.26 367	87.13 257	61.25 353	74.38 393	77.22 430	76.94 182	90.94 209	64.63 322	84.83 396	83.35 389

IterMVS		76.91 297	76.34 303	78.64 301	80.91 376	64.03 266	76.30 353	79.03 350	64.88 321	83.11 283	89.16 272	59.90 332	84.46 352	68.61 286	85.15 388	87.42 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 298	75.67 310	80.34 276	80.48 385	62.16 300	73.50 383	84.80 304	57.61 383	82.24 297	87.54 306	51.31 379	87.65 297	70.40 263	93.19 237	91.23 248

CL-MVSNet_self_test		76.81 299	77.38 291	75.12 350	86.90 267	51.34 405	73.20 386	80.63 343	68.30 270	81.80 311	88.40 285	66.92 286	80.90 377	55.35 384	94.90 176	93.12 164

TR-MVS		76.77 300	75.79 307	79.72 287	86.10 291	65.79 251	77.14 338	83.02 320	65.20 319	81.40 318	82.10 384	66.30 288	90.73 220	55.57 381	85.27 384	82.65 397

MonoMVSNet		76.66 301	77.26 293	74.86 352	79.86 390	54.34 383	86.26 142	86.08 275	71.08 236	85.59 223	88.68 280	53.95 368	85.93 331	63.86 327	80.02 426	84.32 372

USDC		76.63 302	76.73 300	76.34 339	83.46 342	57.20 362	80.02 290	88.04 238	52.14 417	83.65 272	91.25 206	63.24 312	86.65 316	54.66 389	94.11 204	85.17 361

BH-w/o		76.57 303	76.07 306	78.10 312	86.88 268	65.92 250	77.63 329	86.33 270	65.69 307	80.89 324	79.95 405	68.97 277	90.74 219	53.01 400	85.25 385	77.62 433

Patchmtry		76.56 304	77.46 289	73.83 360	79.37 397	46.60 427	82.41 249	76.90 364	73.81 184	85.56 225	92.38 162	48.07 392	83.98 359	63.36 332	95.31 160	90.92 259

PVSNet_Blended		76.49 305	75.40 312	79.76 286	84.43 322	63.41 272	75.14 368	90.44 177	57.36 385	75.43 382	78.30 419	69.11 275	91.44 193	60.68 352	87.70 357	84.42 371

miper_lstm_enhance		76.45 306	76.10 305	77.51 322	76.72 416	60.97 317	64.69 434	85.04 296	63.98 326	83.20 282	88.22 287	56.67 354	78.79 393	73.22 232	93.12 238	92.78 179

lupinMVS		76.37 307	74.46 321	82.09 236	85.54 303	69.26 208	76.79 343	80.77 342	50.68 428	76.23 372	82.82 378	58.69 341	88.94 269	69.85 268	88.77 336	88.07 323

cascas		76.29 308	74.81 317	80.72 269	84.47 321	62.94 278	73.89 380	87.34 249	55.94 392	75.16 387	76.53 435	63.97 307	91.16 201	65.00 317	90.97 295	88.06 325

SD_0403		76.08 309	76.77 298	73.98 358	87.08 261	49.45 416	83.62 209	84.68 306	63.31 327	75.13 388	87.47 309	71.85 257	84.56 350	49.97 412	87.86 353	87.94 329

WB-MVS		76.06 310	80.01 262	64.19 423	89.96 179	20.58 466	72.18 393	68.19 422	83.21 65	86.46 204	93.49 123	70.19 269	78.97 391	65.96 305	90.46 315	93.02 168

thres600view7		75.97 311	75.35 314	77.85 319	87.01 263	51.84 403	80.45 285	73.26 392	75.20 165	83.10 284	86.31 330	45.54 407	89.05 267	55.03 387	92.24 263	92.66 186

GA-MVS		75.83 312	74.61 318	79.48 292	81.87 363	59.25 339	73.42 384	82.88 321	68.68 264	79.75 338	81.80 389	50.62 383	89.46 260	66.85 297	85.64 381	89.72 292

MVP-Stereo		75.81 313	73.51 330	82.71 223	89.35 189	73.62 139	80.06 288	85.20 291	60.30 364	73.96 394	87.94 292	57.89 348	89.45 261	52.02 404	74.87 444	85.06 363
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 314	75.20 315	77.27 325	75.01 433	69.47 205	78.93 308	84.88 301	46.67 435	87.08 183	87.84 300	50.44 385	71.62 418	77.42 168	88.53 339	90.72 265

thres100view900		75.45 315	75.05 316	76.66 335	87.27 249	51.88 402	81.07 276	73.26 392	75.68 156	83.25 281	86.37 327	45.54 407	88.80 271	51.98 405	90.99 292	89.31 299

ET-MVSNet_ETH3D		75.28 316	72.77 339	82.81 222	83.03 357	68.11 225	77.09 339	76.51 368	60.67 361	77.60 363	80.52 400	38.04 435	91.15 202	70.78 256	90.68 309	89.17 306

thres400		75.14 317	74.23 323	77.86 318	86.24 284	52.12 399	79.24 304	73.87 385	73.34 195	81.82 309	84.60 359	46.02 400	88.80 271	51.98 405	90.99 292	92.66 186

wuyk23d		75.13 318	79.30 268	62.63 426	75.56 426	75.18 131	80.89 279	73.10 394	75.06 167	94.76 16	95.32 45	87.73 44	52.85 458	34.16 456	97.11 87	59.85 454

EU-MVSNet		75.12 319	74.43 322	77.18 326	83.11 356	59.48 336	85.71 154	82.43 326	39.76 455	85.64 222	88.76 278	44.71 419	87.88 293	73.86 217	85.88 380	84.16 377

HyFIR lowres test		75.12 319	72.66 341	82.50 230	91.44 141	65.19 256	72.47 391	87.31 250	46.79 434	80.29 333	84.30 361	52.70 373	92.10 177	51.88 409	86.73 369	90.22 281

CMPMVS	59.41 20	75.12 319	73.57 328	79.77 285	75.84 425	67.22 231	81.21 274	82.18 328	50.78 426	76.50 368	87.66 304	55.20 364	82.99 365	62.17 341	90.64 314	89.09 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 322	72.98 337	80.73 268	84.95 313	71.71 176	76.23 355	77.59 357	52.83 411	77.73 362	86.38 326	56.35 357	84.97 346	57.72 369	87.05 364	85.51 358

tfpn200view9		74.86 323	74.23 323	76.74 334	86.24 284	52.12 399	79.24 304	73.87 385	73.34 195	81.82 309	84.60 359	46.02 400	88.80 271	51.98 405	90.99 292	89.31 299

1112_ss		74.82 324	73.74 326	78.04 314	89.57 183	60.04 328	76.49 351	87.09 262	54.31 402	73.66 397	79.80 406	60.25 329	86.76 315	58.37 363	84.15 401	87.32 338

EGC-MVSNET		74.79 325	69.99 369	89.19 67	94.89 38	87.00 15	91.89 38	86.28 271	1.09 464	2.23 466	95.98 30	81.87 121	89.48 258	79.76 130	95.96 130	91.10 252

ppachtmachnet_test		74.73 326	74.00 325	76.90 331	80.71 381	56.89 365	71.53 399	78.42 352	58.24 376	79.32 345	82.92 377	57.91 347	84.26 356	65.60 312	91.36 286	89.56 294

Patchmatch-RL test		74.48 327	73.68 327	76.89 332	84.83 315	66.54 241	72.29 392	69.16 420	57.70 381	86.76 189	86.33 328	45.79 406	82.59 366	69.63 271	90.65 313	81.54 413

PatchMatch-RL		74.48 327	73.22 334	78.27 310	87.70 237	85.26 38	75.92 360	70.09 413	64.34 324	76.09 375	81.25 394	65.87 294	78.07 395	53.86 392	83.82 403	71.48 442

XXY-MVS		74.44 329	76.19 304	69.21 395	84.61 320	52.43 398	71.70 396	77.18 362	60.73 360	80.60 327	90.96 218	75.44 198	69.35 426	56.13 376	88.33 343	85.86 354

test2506		74.12 330	73.39 331	76.28 340	91.85 123	44.20 437	84.06 192	48.20 462	72.30 220	81.90 306	94.20 89	27.22 462	89.77 255	64.81 319	96.02 127	94.87 78

reproduce_monomvs		74.09 331	73.23 333	76.65 336	76.52 417	54.54 381	77.50 333	81.40 337	65.85 302	82.86 289	86.67 323	27.38 460	84.53 351	70.24 264	90.66 312	90.89 260

CR-MVSNet		74.00 332	73.04 336	76.85 333	79.58 392	62.64 284	82.58 240	76.90 364	50.50 429	75.72 379	92.38 162	48.07 392	84.07 358	68.72 285	82.91 410	83.85 381

SSC-MVS3.2		73.90 333	75.67 310	68.61 403	84.11 331	41.28 445	64.17 436	72.83 395	72.09 223	79.08 348	87.94 292	70.31 267	73.89 411	55.99 377	94.49 191	90.67 270

Test_1112_low_res		73.90 333	73.08 335	76.35 338	90.35 167	55.95 368	73.40 385	86.17 273	50.70 427	73.14 398	85.94 335	58.31 343	85.90 335	56.51 373	83.22 407	87.20 340

test20.03		73.75 335	74.59 320	71.22 381	81.11 374	51.12 409	70.15 410	72.10 402	70.42 241	80.28 335	91.50 194	64.21 303	74.72 409	46.96 430	94.58 189	87.82 333

test_fmvs2		73.57 336	72.80 338	75.90 344	72.74 447	68.84 218	77.07 340	84.32 310	45.14 441	82.89 287	84.22 362	48.37 390	70.36 422	73.40 228	87.03 365	88.52 318

SCA		73.32 337	72.57 343	75.58 348	81.62 367	55.86 371	78.89 310	71.37 408	61.73 344	74.93 389	83.42 371	60.46 326	87.01 306	58.11 367	82.63 415	83.88 378

baseline1		73.26 338	73.54 329	72.43 374	84.92 314	47.79 422	79.89 292	74.00 383	65.93 300	78.81 350	86.28 331	56.36 356	81.63 374	56.63 372	79.04 433	87.87 332

1314		73.22 339	72.56 344	75.20 349	80.41 386	57.84 356	81.64 265	85.36 288	51.68 420	73.10 399	76.65 434	61.45 321	85.19 344	63.54 330	79.21 431	82.59 398

MVS		73.21 340	72.59 342	75.06 351	80.97 375	60.81 319	81.64 265	85.92 281	46.03 439	71.68 407	77.54 425	68.47 278	89.77 255	55.70 380	85.39 382	74.60 439

HY-MVS	64.64 18	73.03 341	72.47 345	74.71 355	83.36 347	54.19 384	82.14 259	81.96 330	56.76 391	69.57 421	86.21 332	60.03 330	84.83 348	49.58 417	82.65 413	85.11 362

thisisatest0515		73.00 342	70.52 361	80.46 274	81.45 369	59.90 332	73.16 387	74.31 382	57.86 380	76.08 376	77.78 422	37.60 438	92.12 176	65.00 317	91.45 285	89.35 298

EPNet_dtu		72.87 343	71.33 355	77.49 323	77.72 406	60.55 322	82.35 250	75.79 371	66.49 298	58.39 457	81.06 395	53.68 369	85.98 330	53.55 395	92.97 243	85.95 352
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 344	71.41 354	76.28 340	83.25 351	60.34 324	83.50 213	79.02 351	37.77 459	76.33 370	85.10 350	49.60 388	87.41 302	70.54 261	77.54 439	81.08 420

CHOSEN 1792x2688		72.45 345	70.56 360	78.13 311	90.02 178	63.08 277	68.72 417	83.16 318	42.99 449	75.92 377	85.46 343	57.22 352	85.18 345	49.87 415	81.67 417	86.14 350

testgi		72.36 346	74.61 318	65.59 417	80.56 384	42.82 442	68.29 419	73.35 391	66.87 295	81.84 308	89.93 258	72.08 254	66.92 440	46.05 434	92.54 254	87.01 342

thres200		72.34 347	71.55 353	74.70 356	83.48 341	51.60 404	75.02 369	73.71 388	70.14 247	78.56 353	80.57 399	46.20 398	88.20 286	46.99 429	89.29 328	84.32 372

FPMVS		72.29 348	72.00 347	73.14 365	88.63 213	85.00 40	74.65 373	67.39 425	71.94 226	77.80 360	87.66 304	50.48 384	75.83 403	49.95 413	79.51 427	58.58 456

FMVSNet5		72.10 349	71.69 349	73.32 363	81.57 368	53.02 393	76.77 344	78.37 353	63.31 327	76.37 369	91.85 180	36.68 439	78.98 390	47.87 426	92.45 256	87.95 328

our_test_3		71.85 350	71.59 350	72.62 371	80.71 381	53.78 387	69.72 413	71.71 407	58.80 373	78.03 355	80.51 401	56.61 355	78.84 392	62.20 339	86.04 379	85.23 360

PAPM		71.77 351	70.06 367	76.92 330	86.39 275	53.97 385	76.62 348	86.62 268	53.44 406	63.97 446	84.73 357	57.79 349	92.34 169	39.65 446	81.33 421	84.45 370

ttmdpeth		71.72 352	70.67 358	74.86 352	73.08 444	55.88 370	77.41 336	69.27 418	55.86 393	78.66 351	93.77 116	38.01 436	75.39 406	60.12 355	89.87 322	93.31 153

IB-MVS	62.13 19	71.64 353	68.97 379	79.66 289	80.80 380	62.26 297	73.94 379	76.90 364	63.27 329	68.63 425	76.79 432	33.83 443	91.84 184	59.28 360	87.26 359	84.88 364
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		71.63 354	72.30 346	69.62 392	76.47 419	52.70 396	70.03 411	80.97 340	59.18 370	79.36 343	88.21 288	60.50 325	69.12 427	58.33 365	77.62 438	87.04 341

testing3		71.53 355	70.79 357	73.77 361	88.89 205	41.86 444	76.60 350	59.12 451	72.83 208	80.97 321	82.08 386	19.80 468	87.33 304	65.12 316	91.68 280	92.13 222

test_vis3_rt		71.42 356	70.67 358	73.64 362	69.66 454	70.46 190	66.97 429	89.73 202	42.68 451	88.20 153	83.04 373	43.77 421	60.07 452	65.35 315	86.66 370	90.39 279

Anonymous20231206		71.38 357	71.88 348	69.88 389	86.31 281	54.37 382	70.39 408	74.62 378	52.57 413	76.73 367	88.76 278	59.94 331	72.06 415	44.35 438	93.23 235	83.23 392

test_vis1_n_1920		71.30 358	71.58 352	70.47 384	77.58 408	59.99 331	74.25 374	84.22 311	51.06 423	74.85 390	79.10 412	55.10 365	68.83 429	68.86 282	79.20 432	82.58 399

MIMVSNet		71.09 359	71.59 350	69.57 393	87.23 251	50.07 414	78.91 309	71.83 404	60.20 367	71.26 408	91.76 187	55.08 366	76.09 401	41.06 443	87.02 366	82.54 401

test_fmvs1_n		70.94 360	70.41 364	72.53 373	73.92 435	66.93 238	75.99 359	84.21 312	43.31 448	79.40 342	79.39 410	43.47 422	68.55 431	69.05 279	84.91 393	82.10 407

MS-PatchMatch		70.93 361	70.22 365	73.06 366	81.85 364	62.50 287	73.82 381	77.90 354	52.44 414	75.92 377	81.27 393	55.67 361	81.75 372	55.37 383	77.70 437	74.94 438

pmmvs5		70.73 362	70.07 366	72.72 369	77.03 413	52.73 395	74.14 375	75.65 374	50.36 430	72.17 405	85.37 347	55.42 363	80.67 379	52.86 401	87.59 358	84.77 365

testing3-2		70.72 363	70.97 356	69.95 388	88.93 203	34.80 458	69.85 412	66.59 432	78.42 124	77.58 364	85.55 339	31.83 449	82.08 370	46.28 431	93.73 218	92.98 173

PatchT		70.52 364	72.76 340	63.79 425	79.38 396	33.53 459	77.63 329	65.37 436	73.61 188	71.77 406	92.79 151	44.38 420	75.65 404	64.53 324	85.37 383	82.18 406

test_vis1_n		70.29 365	69.99 369	71.20 382	75.97 424	66.50 242	76.69 346	80.81 341	44.22 444	75.43 382	77.23 429	50.00 386	68.59 430	66.71 300	82.85 412	78.52 432

N_pmnet		70.20 366	68.80 381	74.38 357	80.91 376	84.81 43	59.12 447	76.45 369	55.06 397	75.31 386	82.36 383	55.74 360	54.82 457	47.02 428	87.24 360	83.52 385

tpmvs		70.16 367	69.56 372	71.96 377	74.71 434	48.13 419	79.63 294	75.45 376	65.02 320	70.26 416	81.88 388	45.34 412	85.68 340	58.34 364	75.39 443	82.08 408

new-patchmatchnet		70.10 368	73.37 332	60.29 434	81.23 373	16.95 469	59.54 445	74.62 378	62.93 331	80.97 321	87.93 294	62.83 318	71.90 416	55.24 385	95.01 173	92.00 227

YYNet1		70.06 369	70.44 362	68.90 397	73.76 437	53.42 391	58.99 448	67.20 427	58.42 375	87.10 181	85.39 346	59.82 333	67.32 437	59.79 357	83.50 406	85.96 351

MVStest1		70.05 370	69.26 373	72.41 375	58.62 466	55.59 374	76.61 349	65.58 434	53.44 406	89.28 128	93.32 127	22.91 466	71.44 420	74.08 213	89.52 326	90.21 285

MDA-MVSNet_test_wron		70.05 370	70.44 362	68.88 398	73.84 436	53.47 389	58.93 449	67.28 426	58.43 374	87.09 182	85.40 345	59.80 334	67.25 438	59.66 358	83.54 405	85.92 353

CostFormer		69.98 372	68.68 382	73.87 359	77.14 411	50.72 411	79.26 303	74.51 380	51.94 419	70.97 411	84.75 356	45.16 415	87.49 300	55.16 386	79.23 430	83.40 388

testing91		69.94 373	68.99 378	72.80 368	83.81 337	45.89 430	71.57 398	73.64 390	68.24 271	70.77 414	77.82 421	34.37 442	84.44 353	53.64 394	87.00 367	88.07 323

baseline2		69.77 374	66.89 391	78.41 306	79.51 394	58.09 352	76.23 355	69.57 416	57.50 384	64.82 444	77.45 427	46.02 400	88.44 280	53.08 397	77.83 435	88.70 316

PatchmatchNet		69.71 375	68.83 380	72.33 376	77.66 407	53.60 388	79.29 302	69.99 414	57.66 382	72.53 402	82.93 376	46.45 397	80.08 385	60.91 351	72.09 447	83.31 391
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 376	69.05 376	71.14 383	69.15 455	65.77 252	73.98 378	83.32 316	42.83 450	77.77 361	78.27 420	43.39 425	68.50 432	68.39 289	84.38 400	79.15 430

JIA-IIPM		69.41 377	66.64 395	77.70 320	73.19 441	71.24 181	75.67 361	65.56 435	70.42 241	65.18 440	92.97 142	33.64 445	83.06 363	53.52 396	69.61 453	78.79 431

Syy-MVS		69.40 378	70.03 368	67.49 408	81.72 365	38.94 450	71.00 401	61.99 442	61.38 350	70.81 412	72.36 446	61.37 322	79.30 388	64.50 325	85.18 386	84.22 374

testing99		69.27 379	68.15 386	72.63 370	83.29 349	45.45 432	71.15 400	71.08 409	67.34 288	70.43 415	77.77 423	32.24 448	84.35 355	53.72 393	86.33 375	88.10 322

UnsupCasMVSNet_bld		69.21 380	69.68 371	67.82 406	79.42 395	51.15 408	67.82 423	75.79 371	54.15 403	77.47 365	85.36 348	59.26 337	70.64 421	48.46 423	79.35 429	81.66 411

test_cas_vis1_n_1920		69.20 381	69.12 374	69.43 394	73.68 438	62.82 281	70.38 409	77.21 361	46.18 438	80.46 332	78.95 414	52.03 375	65.53 445	65.77 311	77.45 440	79.95 428

gg-mvs-nofinetune		68.96 382	69.11 375	68.52 404	76.12 423	45.32 433	83.59 210	55.88 456	86.68 33	64.62 445	97.01 12	30.36 453	83.97 360	44.78 437	82.94 409	76.26 435

WBMVS		68.76 383	68.43 383	69.75 391	83.29 349	40.30 448	67.36 425	72.21 401	57.09 388	77.05 366	85.53 341	33.68 444	80.51 381	48.79 421	90.90 297	88.45 319

WB-MVSnew		68.72 384	69.01 377	67.85 405	83.22 353	43.98 438	74.93 370	65.98 433	55.09 396	73.83 395	79.11 411	65.63 296	71.89 417	38.21 451	85.04 389	87.69 334

tpm2		68.45 385	66.83 392	73.30 364	78.93 402	48.50 418	79.76 293	71.76 405	47.50 433	69.92 418	83.60 367	42.07 428	88.40 282	48.44 424	79.51 427	83.01 395

tpm		67.95 386	68.08 387	67.55 407	78.74 403	43.53 440	75.60 362	67.10 430	54.92 398	72.23 403	88.10 289	42.87 427	75.97 402	52.21 403	80.95 425	83.15 393

WTY-MVS		67.91 387	68.35 384	66.58 413	80.82 379	48.12 420	65.96 431	72.60 396	53.67 405	71.20 409	81.68 391	58.97 339	69.06 428	48.57 422	81.67 417	82.55 400

testing11		67.38 388	65.93 396	71.73 379	83.37 346	46.60 427	70.95 403	69.40 417	62.47 336	66.14 433	76.66 433	31.22 450	84.10 357	49.10 419	84.10 402	84.49 368

test-LLR		67.21 389	66.74 393	68.63 401	76.45 420	55.21 377	67.89 420	67.14 428	62.43 339	65.08 441	72.39 444	43.41 423	69.37 424	61.00 349	84.89 394	81.31 415

testing222		66.93 390	65.30 403	71.81 378	83.38 345	45.83 431	72.06 394	67.50 424	64.12 325	69.68 420	76.37 436	27.34 461	83.00 364	38.88 447	88.38 342	86.62 346

sss		66.92 391	67.26 389	65.90 415	77.23 410	51.10 410	64.79 433	71.72 406	52.12 418	70.13 417	80.18 403	57.96 346	65.36 446	50.21 411	81.01 423	81.25 417

KD-MVS_2432*1600		66.87 392	65.81 399	70.04 386	67.50 456	47.49 423	62.56 439	79.16 348	61.21 355	77.98 356	80.61 397	25.29 464	82.48 367	53.02 398	84.92 391	80.16 426

miper_refine_blended		66.87 392	65.81 399	70.04 386	67.50 456	47.49 423	62.56 439	79.16 348	61.21 355	77.98 356	80.61 397	25.29 464	82.48 367	53.02 398	84.92 391	80.16 426

dmvs_re		66.81 394	66.98 390	66.28 414	76.87 414	58.68 350	71.66 397	72.24 399	60.29 365	69.52 422	73.53 443	52.38 374	64.40 448	44.90 436	81.44 420	75.76 436

tpm cat1		66.76 395	65.21 404	71.42 380	77.09 412	50.62 412	78.01 321	73.68 389	44.89 442	68.64 424	79.00 413	45.51 409	82.42 369	49.91 414	70.15 450	81.23 419

UWE-MVS		66.43 396	65.56 402	69.05 396	84.15 330	40.98 446	73.06 388	64.71 438	54.84 399	76.18 374	79.62 409	29.21 455	80.50 382	38.54 450	89.75 323	85.66 356

PVSNet	58.17 21	66.41 397	65.63 401	68.75 399	81.96 362	49.88 415	62.19 441	72.51 398	51.03 424	68.04 427	75.34 440	50.84 381	74.77 407	45.82 435	82.96 408	81.60 412

tpmrst		66.28 398	66.69 394	65.05 421	72.82 446	39.33 449	78.20 320	70.69 412	53.16 409	67.88 428	80.36 402	48.18 391	74.75 408	58.13 366	70.79 449	81.08 420

Patchmatch-test		65.91 399	67.38 388	61.48 431	75.51 427	43.21 441	68.84 416	63.79 440	62.48 335	72.80 401	83.42 371	44.89 418	59.52 454	48.27 425	86.45 372	81.70 410

ADS-MVSNet2		65.87 400	63.64 409	72.55 372	73.16 442	56.92 364	67.10 427	74.81 377	49.74 431	66.04 435	82.97 374	46.71 395	77.26 398	42.29 440	69.96 451	83.46 386

myMVS_eth3d28		65.83 401	65.85 397	65.78 416	83.42 344	35.71 456	67.29 426	68.01 423	67.58 285	69.80 419	77.72 424	32.29 447	74.30 410	37.49 452	89.06 332	87.32 338

test_vis1_rt		65.64 402	64.09 406	70.31 385	66.09 460	70.20 194	61.16 442	81.60 335	38.65 456	72.87 400	69.66 449	52.84 371	60.04 453	56.16 375	77.77 436	80.68 424

mvsany_test3		65.48 403	62.97 412	73.03 367	69.99 453	76.17 124	64.83 432	43.71 464	43.68 446	80.25 336	87.05 320	52.83 372	63.09 451	51.92 408	72.44 446	79.84 429

test-mter		65.00 404	63.79 408	68.63 401	76.45 420	55.21 377	67.89 420	67.14 428	50.98 425	65.08 441	72.39 444	28.27 458	69.37 424	61.00 349	84.89 394	81.31 415

ETVMVS		64.67 405	63.34 411	68.64 400	83.44 343	41.89 443	69.56 415	61.70 447	61.33 352	68.74 423	75.76 438	28.76 456	79.35 387	34.65 455	86.16 378	84.67 367

myMVS_eth3d		64.66 406	63.89 407	66.97 411	81.72 365	37.39 453	71.00 401	61.99 442	61.38 350	70.81 412	72.36 446	20.96 467	79.30 388	49.59 416	85.18 386	84.22 374

test0.0.03 1		64.66 406	64.36 405	65.57 418	75.03 432	46.89 426	64.69 434	61.58 448	62.43 339	71.18 410	77.54 425	43.41 423	68.47 433	40.75 445	82.65 413	81.35 414

UBG		64.34 408	63.35 410	67.30 409	83.50 340	40.53 447	67.46 424	65.02 437	54.77 400	67.54 431	74.47 442	32.99 446	78.50 394	40.82 444	83.58 404	82.88 396

test_f		64.31 409	65.85 397	59.67 435	66.54 459	62.24 299	57.76 451	70.96 410	40.13 453	84.36 254	82.09 385	46.93 394	51.67 459	61.99 342	81.89 416	65.12 450

pmmvs3		62.47 410	60.02 423	69.80 390	71.58 450	64.00 267	70.52 407	58.44 454	39.77 454	66.05 434	75.84 437	27.10 463	72.28 414	46.15 433	84.77 398	73.11 440

EPMVS		62.47 410	62.63 414	62.01 427	70.63 452	38.74 451	74.76 371	52.86 458	53.91 404	67.71 430	80.01 404	39.40 432	66.60 441	55.54 382	68.81 455	80.68 424

ADS-MVSNet		61.90 412	62.19 416	61.03 432	73.16 442	36.42 455	67.10 427	61.75 445	49.74 431	66.04 435	82.97 374	46.71 395	63.21 449	42.29 440	69.96 451	83.46 386

PMMVS		61.65 413	60.38 420	65.47 419	65.40 463	69.26 208	63.97 437	61.73 446	36.80 460	60.11 452	68.43 451	59.42 335	66.35 442	48.97 420	78.57 434	60.81 453

E-PMN		61.59 414	61.62 417	61.49 430	66.81 458	55.40 375	53.77 454	60.34 450	66.80 296	58.90 455	65.50 454	40.48 431	66.12 443	55.72 379	86.25 376	62.95 452

TESTMET0.1,1		61.29 415	60.32 421	64.19 423	72.06 448	51.30 406	67.89 420	62.09 441	45.27 440	60.65 451	69.01 450	27.93 459	64.74 447	56.31 374	81.65 419	76.53 434

MVS-HIRNet		61.16 416	62.92 413	55.87 438	79.09 399	35.34 457	71.83 395	57.98 455	46.56 436	59.05 454	91.14 210	49.95 387	76.43 400	38.74 448	71.92 448	55.84 457

EMVS		61.10 417	60.81 419	61.99 428	65.96 461	55.86 371	53.10 455	58.97 453	67.06 293	56.89 459	63.33 455	40.98 429	67.03 439	54.79 388	86.18 377	63.08 451

DSMNet-mixed		60.98 418	61.61 418	59.09 437	72.88 445	45.05 435	74.70 372	46.61 463	26.20 461	65.34 439	90.32 247	55.46 362	63.12 450	41.72 442	81.30 422	69.09 446

dp		60.70 419	60.29 422	61.92 429	72.04 449	38.67 452	70.83 405	64.08 439	51.28 422	60.75 450	77.28 428	36.59 440	71.58 419	47.41 427	62.34 457	75.52 437

dmvs_testset		60.59 420	62.54 415	54.72 440	77.26 409	27.74 463	74.05 377	61.00 449	60.48 362	65.62 438	67.03 453	55.93 359	68.23 435	32.07 459	69.46 454	68.17 447

CHOSEN 280x420		59.08 421	56.52 427	66.76 412	76.51 418	64.39 263	49.62 456	59.00 452	43.86 445	55.66 460	68.41 452	35.55 441	68.21 436	43.25 439	76.78 442	67.69 448

mvsany_test1		58.48 422	56.47 428	64.50 422	65.90 462	68.21 224	56.95 452	42.11 465	38.30 457	65.69 437	77.19 431	56.96 353	59.35 455	46.16 432	58.96 458	65.93 449

UWE-MVS-28		58.44 423	57.71 425	60.65 433	73.58 439	31.23 460	69.68 414	48.80 461	53.12 410	61.79 448	78.83 415	30.98 451	68.40 434	21.58 462	80.99 424	82.33 405

PVSNet_0	51.08 22	56.10 424	54.97 429	59.48 436	75.12 431	53.28 392	55.16 453	61.89 444	44.30 443	59.16 453	62.48 456	54.22 367	65.91 444	35.40 454	47.01 459	59.25 455

new_pmnet		55.69 425	57.66 426	49.76 441	75.47 428	30.59 461	59.56 444	51.45 459	43.62 447	62.49 447	75.48 439	40.96 430	49.15 461	37.39 453	72.52 445	69.55 445

PMMVS2		55.64 426	59.27 424	44.74 442	64.30 464	12.32 470	40.60 457	49.79 460	53.19 408	65.06 443	84.81 355	53.60 370	49.76 460	32.68 458	89.41 327	72.15 441

MVE	40.22 23	51.82 427	50.47 430	55.87 438	62.66 465	51.91 401	31.61 459	39.28 466	40.65 452	50.76 461	74.98 441	56.24 358	44.67 462	33.94 457	64.11 456	71.04 444
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 428	42.65 431	39.67 443	70.86 451	21.11 465	61.01 443	21.42 470	57.36 385	57.97 458	50.06 459	16.40 469	58.73 456	21.03 463	27.69 463	39.17 459

kuosan		30.83 429	32.17 432	26.83 445	53.36 467	19.02 468	57.90 450	20.44 471	38.29 458	38.01 462	37.82 461	15.18 470	33.45 464	7.74 465	20.76 464	28.03 460

test_method		30.46 430	29.60 433	33.06 444	17.99 469	3.84 472	13.62 460	73.92 384	2.79 463	18.29 465	53.41 458	28.53 457	43.25 463	22.56 460	35.27 461	52.11 458

cdsmvs_eth3d_5k		20.81 431	27.75 434	0.00 450	0.00 473	0.00 475	0.00 461	85.44 287	0.00 468	0.00 469	82.82 378	81.46 126	0.00 469	0.00 468	0.00 467	0.00 465

tmp_tt		20.25 432	24.50 435	7.49 447	4.47 470	8.70 471	34.17 458	25.16 468	1.00 465	32.43 464	18.49 462	39.37 433	9.21 466	21.64 461	43.75 460	4.57 462

ab-mvs-re		6.65 433	8.87 436	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	79.80 406	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

pcd_1.5k_mvsjas		6.41 434	8.55 437	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	76.94 182	0.00 469	0.00 468	0.00 467	0.00 465

test123		6.27 435	8.08 438	0.84 448	1.11 472	0.57 473	62.90 438	0.82 472	0.54 466	1.07 468	2.75 467	1.26 471	0.30 467	1.04 466	1.26 466	1.66 463

testmvs		5.91 436	7.65 439	0.72 449	1.20 471	0.37 474	59.14 446	0.67 473	0.49 467	1.11 467	2.76 466	0.94 472	0.24 468	1.02 467	1.47 465	1.55 464

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

WAC-MVS						37.39 453								52.61 402

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		91.01 160					96.05 9	87.45 28	98.17 37	92.40 202

PC_three_1452									58.96 372	90.06 103	91.33 202	80.66 136	93.03 151	75.78 191	95.94 133	92.48 196

No_MVS				88.81 73	91.55 135	77.99 97		91.01 160					96.05 9	87.45 28	98.17 37	92.40 202

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					0.00 473

eth-test					0.00 473

ZD-MVS					92.22 109	80.48 71		91.85 130	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	92.73 180

IU-MVS					94.18 52	72.64 154		90.82 165	56.98 389	89.67 116				85.78 62	97.92 52	93.28 154

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 207	81.12 130	94.68 78	74.48 203	95.35 156	92.29 212

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	92.04 225

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	89.72 203	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	92.98 173

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	92.02 226

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																83.88 378

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 399				83.88 378

sam_mvs												45.92 404

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 216		89.40 125	93.96 105	71.67 261	91.38 197	78.83 144	96.55 102	92.71 183

MTGPA								91.81 134

test_post1							78.85 312				3.13 464	45.19 414	80.13 384	58.11 367

test_post											3.10 465	45.43 410	77.22 399

patchmatchnet-post											81.71 390	45.93 403	87.01 306

GG-mvs-BLEND				67.16 410	73.36 440	46.54 429	84.15 190	55.04 457		58.64 456	61.95 457	29.93 454	83.87 361	38.71 449	76.92 441	71.07 443

MTMP							90.66 49	33.14 467

gm-plane-assit					75.42 429	44.97 436			52.17 415		72.36 446		87.90 292	54.10 391

test9_res														80.83 119	96.45 108	90.57 273

TEST9					92.34 104	79.70 80	83.94 196	90.32 183	65.41 312	84.49 250	90.97 216	82.03 116	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 185	65.79 303	84.36 254	90.96 218	81.93 118	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 281

agg_prior					91.58 133	77.69 103		90.30 186		84.32 256			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	70.81 254	96.14 121	94.16 111

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 247	91.57 192	81.92 120		79.54 136	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 97					94.17 102			92.80 178

旧先验2							81.73 263		56.88 390	86.54 202			84.90 347	72.81 239

新几何2							81.72 264

新几何1				82.95 216	93.96 61	78.56 91		80.24 344	55.45 395	83.93 267	91.08 213	71.19 263	88.33 284	65.84 309	93.07 239	81.95 409

旧先验1					91.97 117	71.77 171		81.78 332			91.84 181	73.92 225			93.65 221	83.61 384

无先验							82.81 235	85.62 285	58.09 378				91.41 196	67.95 293		84.48 369

原ACMM2							82.26 255

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 141	62.59 333	82.73 291	90.67 234	76.53 191	94.25 94	69.24 274	95.69 148	85.55 357

test222					93.31 78	76.54 116	79.38 301	77.79 355	52.59 412	82.36 296	90.84 226	66.83 287			91.69 279	81.25 417

testdata2													86.43 321	63.52 331

segment_acmp												81.94 117

testdata				79.54 291	92.87 89	72.34 163		80.14 345	59.91 368	85.47 227	91.75 188	67.96 281	85.24 343	68.57 288	92.18 266	81.06 422

testdata1							79.62 295		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 168		82.76 290		79.20 149	94.80 75		95.32 158	92.27 214

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 207

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	86.55 196

plane_prior2							89.45 83		79.44 108

plane_prior1					92.83 93

plane_prior						76.42 119	87.15 122		75.94 153						95.03 170

n2								0.00 474

nn								0.00 474

door-mid								74.45 381

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 411		91.79 71	94.42 78	61.76 320	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								91.46 142

door								72.57 397

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 329

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 329

BP-MVS														77.30 169

HQP4-MVS										80.56 328			94.61 82			93.56 146

HQP3-MVS								92.68 103							94.47 192

HQP2-MVS												72.10 252

NP-MVS					91.95 118	74.55 134					90.17 254

MDTV_nov1_ep13_2view						27.60 464	70.76 406		46.47 437	61.27 449		45.20 413		49.18 418		83.75 383

MDTV_nov1_ep13			68.29 385		78.03 404	43.87 439	74.12 376	72.22 400	52.17 415	67.02 432	85.54 340	45.36 411	80.85 378	55.73 378	84.42 399

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 151

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 186	81.56 82	90.02 105	91.20 209	82.40 103	90.81 217	73.58 225	94.66 187	94.56 90

DeepMVS_CX				24.13 446	32.95 468	29.49 462		21.63 469	12.07 462	37.95 463	45.07 460	30.84 452	19.21 465	17.94 464	33.06 462	23.69 461

High-resolution multi-view benchmark