High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 69	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 47	97.23 2	95.32 2	99.01 2	97.26 9	80.16 143	98.99 1	95.15 1	99.14 2	96.47 35

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

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 66	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 76	93.16 145	91.10 2	97.53 77	96.58 33
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
reproduce_model		92.89 5	93.18 8	92.01 13	94.20 51	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 42	95.72 36	89.60 5	98.27 28	92.08 224

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	91.76 235

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	91.76 235

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	92.73 181

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	92.19 219

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	92.55 194

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	92.30 211

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	92.85 178

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	92.98 174
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	92.41 201
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	91.70 239
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	92.29 213

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	83.13 395
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 43	91.09 37	87.00 103	91.55 135	72.64 154	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 131	95.88 18	87.41 30	95.94 133	92.48 197

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	90.55 276
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	58.83 349	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	58.88 346	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	72.94 243	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	57.84 357	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	77.55 171	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	81.42 128	95.90 17	80.94 117	98.80 3	98.84 5

OPM-MVS		89.80 52	89.97 53	89.27 64	94.76 40	79.86 78	86.76 132	92.78 101	78.78 118	92.51 59	93.64 121	88.13 37	93.84 116	84.83 75	97.55 74	94.10 115
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 53	89.27 64	91.30 29	93.51 70	84.79 44	89.89 69	90.63 170	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	81.57 126	94.27 92	86.26 49	96.49 105	97.09 20

XVG-OURS-SEG-HR		89.59 56	89.37 62	90.28 46	94.47 43	85.95 27	86.84 128	93.91 48	80.07 100	86.75 191	93.26 128	93.64 2	90.93 210	84.60 78	90.75 305	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	90.45 278
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	69.27 256	94.39 21	96.38 21	86.02 66	93.52 132	83.96 83	95.92 135	95.34 60

CPTT-MVS		89.39 59	88.98 70	90.63 40	95.09 33	86.95 16	92.09 33	92.30 117	79.74 103	87.50 175	92.38 162	81.42 128	93.28 141	83.07 92	97.24 84	91.67 240

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	88.31 286	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	74.12 212	96.10 124	94.45 96

APD_test2		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 262	74.12 212	96.10 124	94.45 96

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 352	88.66 98	92.06 123	90.78 7	95.67 8	95.17 51	81.80 123	95.54 44	79.00 143	98.69 10	98.95 4

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

DeepC-MVS	82.31 4	89.15 65	89.08 67	89.37 63	93.64 68	79.07 86	88.54 101	94.20 31	73.53 189	89.71 114	94.82 60	85.09 72	95.77 34	84.17 82	98.03 43	93.26 157
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	72.57 242	97.65 66	97.34 15

MSP-MVS		89.08 67	88.16 84	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 123	89.16 129	92.25 171	72.03 257	96.36 4	88.21 13	90.93 297	92.98 174
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	91.36 248
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	86.69 195	92.28 170	80.36 141	95.06 67	86.17 53	96.49 105	90.22 282

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	74.07 221	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	74.07 221	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	91.82 233

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	90.70 268

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	73.75 220	94.81 181	93.70 134

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

PS-MVSNAJss		88.31 76	87.90 87	89.56 60	93.31 78	77.96 99	87.94 110	91.97 126	70.73 239	94.19 26	96.67 17	76.94 183	94.57 84	83.07 92	96.28 113	96.15 38

OMC-MVS		88.19 77	87.52 91	90.19 48	91.94 120	81.68 65	87.49 117	93.17 80	76.02 149	88.64 139	91.22 208	84.24 82	93.37 139	77.97 160	97.03 89	95.52 56

CS-MVS		88.14 78	87.67 90	89.54 61	89.56 184	79.18 85	90.47 56	94.77 17	79.37 110	84.32 257	89.33 270	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	67.85 281	86.63 196	94.84 59	79.58 149	95.96 15	87.62 24	94.50 190	94.56 90
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 80	89.79 56	82.98 214	93.26 80	63.94 268	91.10 46	89.64 206	85.07 45	90.91 88	91.09 213	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	85.12 233	89.67 264	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	87.74 297	76.54 179	88.74 339	96.61 32

AllTest		87.97 83	87.40 95	89.68 56	91.59 130	83.40 52	89.50 81	95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 191	70.81 255	96.14 121	94.16 111

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 265	84.38 185	91.29 149	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 233	98.66 11	97.69 9

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

CNVR-MVS		87.81 86	87.68 89	88.21 86	92.87 89	77.30 110	85.25 163	91.23 153	77.31 139	87.07 185	91.47 198	82.94 95	94.71 77	84.67 77	96.27 115	92.62 189

HQP_MVS		87.75 87	87.43 94	88.70 77	93.45 72	76.42 119	89.45 83	93.61 60	79.44 108	86.55 197	92.95 143	74.84 208	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	87.91 292	74.22 207	97.63 67	96.92 25

MM		87.64 89	87.15 97	89.09 69	89.51 185	76.39 121	88.68 97	86.76 267	84.54 50	83.58 275	93.78 114	73.36 238	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	72.24 252	95.79 31	84.85 74	94.16 203	92.58 192

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	84.85 244	90.89 223	80.85 134	95.29 56	81.14 115	95.32 158	92.34 209

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 216	92.38 113	70.25 245	89.35 126	90.68 233	82.85 96	94.57 84	79.55 135	95.95 132	92.00 228

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	65.94 294	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	80.45 139	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	81.64 316	87.25 315	82.43 102	94.53 87	77.65 162	96.46 107	94.14 113

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 221	83.80 203	92.87 96	80.37 94	89.61 120	91.81 184	77.72 168	94.18 100	75.00 201	98.53 16	96.99 24

Vis-MVSNet		86.86 97	86.58 109	87.72 94	92.09 113	77.43 107	87.35 118	92.09 122	78.87 117	84.27 262	94.05 98	78.35 160	93.65 121	80.54 124	91.58 284	92.08 224
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	78.25 161	93.99 107	74.16 210	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 225	92.21 118	81.73 80	90.92 86	91.97 176	77.20 177	93.99 107	74.16 210	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 170	87.09 259	65.22 255	84.16 189	94.23 28	77.89 130	91.28 81	93.66 120	84.35 80	92.71 158	80.07 125	94.87 180	95.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		86.68 101	86.52 110	87.18 99	85.94 294	78.30 92	86.93 125	92.20 119	65.94 300	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	60.44 324	83.96 195	88.50 225	87.26 29	90.90 90	97.90 3	85.61 68	86.40 323	70.14 266	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	69.22 275	94.91 71	73.89 217	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 255	93.57 69	60.73 321	84.37 186	88.61 224	87.00 31	90.75 93	97.98 2	85.54 70	86.45 321	69.75 271	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	74.05 224	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	86.80 314	70.43 263	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	85.12 233	90.34 245	66.19 291	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	84.76 246	87.70 304	78.87 154	94.18 100	80.67 122	96.29 112	92.73 181

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	88.01 291	79.09 152	92.13 174	75.51 194	95.06 169	90.41 279

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	84.96 239	90.69 232	80.01 145	95.14 64	78.37 149	95.78 145	91.82 233
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	73.41 235	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	69.19 278	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	92.82 248	89.83 292

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	65.33 314	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	65.91 302	86.19 208	91.75 188	83.77 86	94.98 69	77.43 167	96.71 98	93.73 133

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 261	82.21 256	90.46 176	80.99 88	88.42 146	91.97 176	77.56 170	93.85 114	72.46 243	98.65 12	97.61 10

train_agg		85.98 117	85.28 140	88.07 90	92.34 104	79.70 80	83.94 196	90.32 183	65.79 304	84.49 251	90.97 217	81.93 119	93.63 123	81.21 114	96.54 103	90.88 262

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	87.76 169	93.24 129	73.76 229	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	56.44 368	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 210	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	65.32 315	87.72 171	91.81 184	82.33 105	89.78 254	86.68 42	94.20 201	92.99 172

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	83.56 276	85.65 339	78.49 159	94.21 96	72.04 245	92.88 244	94.05 116

TAPA-MVS	77.73 12	85.71 122	84.83 149	88.37 83	88.78 209	79.72 79	87.15 122	93.50 66	69.17 257	85.80 218	89.56 265	80.76 135	92.13 174	73.21 238	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	87.54 307	86.45 59	91.06 205	75.76 192	93.76 214	92.54 195

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	87.54 307	86.45 59	91.06 205	75.76 192	93.76 214	92.54 195

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	73.94 225	90.41 230	85.95 60	92.74 250	93.66 135

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 240	81.51 83	87.05 186	91.83 182	66.18 293	95.29 56	70.75 258	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 168	90.08 173	67.07 235	85.86 150	91.39 146	72.33 219	87.59 173	90.25 250	84.85 75	92.37 168	78.00 158	91.94 274	93.66 135

MVS_0304		85.37 128	84.58 158	87.75 93	85.28 307	73.36 141	86.54 138	85.71 284	77.56 137	81.78 314	92.47 160	70.29 269	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	57.31 361	85.10 167	93.05 87	75.83 154	91.02 85	93.97 102	73.57 231	92.91 156	73.97 216	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 296	76.13 125	85.15 166	92.32 116	61.40 350	91.33 78	90.85 226	83.76 87	86.16 329	84.31 80	93.28 232	92.15 222

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	87.12 180	92.03 175	82.30 107	91.94 179	78.39 148	94.21 200	94.74 86

SSM_0404		85.16 133	85.09 142	85.36 142	90.14 172	69.52 204	86.17 144	91.58 137	74.41 176	86.55 197	91.49 195	78.54 155	93.97 109	73.71 221	93.21 236	92.59 191

K. test v3		85.14 134	84.73 151	86.37 115	91.13 151	69.63 203	85.45 159	76.68 368	84.06 56	92.44 61	96.99 13	62.03 320	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	83.82 269	96.96 15	77.75 166	80.03 387	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	85.18 293	80.44 92	88.75 136	85.49 343	80.08 144	91.92 180	82.02 108	90.85 302	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	67.64 285	84.88 242	92.05 174	82.30 107	88.36 284	83.84 86	91.10 290	92.62 189

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	87.83 302	86.46 58	90.87 215	76.17 186	93.89 211	92.47 199

EI-MVSNet-UG-set		85.04 138	84.44 164	86.85 107	83.87 336	72.52 160	83.82 201	85.15 294	80.27 97	88.75 136	85.45 345	79.95 146	91.90 181	81.92 111	90.80 304	96.13 39

X-MVStestdata		85.04 138	82.70 204	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 464	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	87.12 180	89.39 268	77.98 163	89.40 265	77.46 165	94.78 182	84.75 367

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	80.18 338	89.15 274	77.04 181	93.28 141	65.82 311	92.28 263	92.21 218

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	85.68 219	91.49 195	78.54 155	93.69 120	73.71 221	93.47 225	92.38 206

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	70.16 271	95.15 63	79.26 140	94.11 204	92.41 201

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	84.96 239	93.17 131	74.06 223	91.19 200	78.28 152	91.09 291	89.29 302

SymmetryMVS		84.79 146	83.54 180	88.55 79	92.44 101	80.42 72	88.63 99	82.37 328	74.56 173	85.12 233	90.34 245	66.19 291	94.20 97	76.57 177	95.68 149	91.03 256

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	73.40 237	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	79.23 307	87.13 257	73.35 194	85.56 226	89.34 269	83.60 89	90.50 227	76.64 176	94.05 208	90.09 288

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	80.55 330	90.17 255	72.10 253	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	69.08 259	89.98 108	90.89 223	72.70 247	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	64.43 324	88.77 135	91.78 186	78.07 162	87.95 291	85.85 61	92.18 267	92.30 211

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	75.43 200	90.68 221	69.09 279	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	90.99 216	73.50 232	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 299	84.20 329	73.57 140	89.55 78	90.44 177	84.24 54	84.38 254	94.89 57	76.35 196	80.40 384	76.14 187	96.80 96	82.36 405
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	68.64 267	86.29 207	91.31 204	74.97 206	88.42 282	87.87 19	90.07 319	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	61.82 343	83.96 267	89.75 263	79.93 147	93.46 135	78.33 151	94.34 197	91.87 232

VDDNet		84.35 157	85.39 137	81.25 257	95.13 32	59.32 338	85.42 160	81.11 339	86.41 36	87.41 176	96.21 25	73.61 230	90.61 225	66.33 304	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	84.07 265	78.72 418	80.39 140	95.13 65	73.82 219	92.98 242	91.04 255

v1240		84.30 159	84.51 162	83.65 193	87.65 240	61.26 309	82.85 234	91.54 140	67.94 278	90.68 95	90.65 236	71.71 261	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	83.56 316	72.71 211	86.07 211	89.07 276	81.75 125	86.19 328	77.11 171	93.36 228	88.24 321

h-mvs33		84.25 161	82.76 203	88.72 75	91.82 127	82.60 60	84.00 194	84.98 300	71.27 231	86.70 193	90.55 241	63.04 317	93.92 112	78.26 153	94.20 201	89.63 294

v144192		84.24 162	84.41 165	83.71 192	87.59 242	61.57 304	82.95 231	91.03 159	67.82 282	89.80 112	90.49 242	73.28 239	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	68.66 266	92.77 55	92.39 161	78.50 158	87.63 300	76.99 173	92.30 260	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	67.94 278	90.06 103	90.34 245	72.04 256	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	84.97 301	79.79 102	87.18 179	94.27 83	74.77 211	90.89 213	69.24 275	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	70.70 267	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	91.02 215	78.52 157	91.11 203	73.41 228	91.09 291	88.21 322

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	67.90 280	84.53 249	90.70 231	73.01 242	88.73 276	85.09 68	93.72 219	91.53 245

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	82.04 305	88.33 287	77.91 165	93.95 111	66.17 305	95.12 167	90.34 281

viewmacassd2359aftdt		84.04 170	84.78 150	81.81 245	86.43 274	60.32 326	81.95 260	92.82 99	71.56 227	86.06 212	92.98 139	81.79 124	90.28 232	76.18 185	93.24 233	94.82 84

TransMVSNet (Re)		84.02 171	85.74 130	78.85 298	91.00 154	55.20 380	82.29 252	87.26 252	79.65 105	88.38 148	95.52 41	83.00 94	86.88 312	67.97 293	96.60 101	94.45 96

Baseline_NR-MVSNet		84.00 172	85.90 123	78.29 310	91.47 140	53.44 391	82.29 252	87.00 266	79.06 114	89.55 122	95.72 36	77.20 177	86.14 330	72.30 244	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	67.72 284	86.74 192	91.49 195	79.20 150	85.86 339	84.71 76	92.60 254	91.07 254

TSAR-MVS + GP.		83.95 174	82.69 205	87.72 94	89.27 192	81.45 67	83.72 205	81.58 337	74.73 170	85.66 222	86.06 334	72.56 249	92.69 160	75.44 196	95.21 162	89.01 315

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	90.60 240	64.04 306	90.95 208	79.08 141	94.11 204	92.99 172

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	87.96 292	72.33 250	90.83 216	76.02 189	94.11 204	92.69 185

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	81.34 321	84.18 364	83.65 88	92.93 154	74.22 207	87.87 353	92.17 221

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	67.26 291	87.94 161	92.37 165	71.40 263	88.01 288	86.03 55	91.87 275	96.31 36

mvs5depth		83.82 178	84.54 160	81.68 248	82.23 361	68.65 219	86.89 126	89.90 198	80.02 101	87.74 170	97.86 4	64.19 305	82.02 372	76.37 181	95.63 151	94.35 103

CANet		83.79 180	82.85 202	86.63 110	86.17 287	72.21 167	83.76 204	91.43 143	77.24 140	74.39 393	87.45 311	75.36 201	95.42 52	77.03 172	92.83 247	92.25 217

pm-mvs1		83.69 181	84.95 147	79.91 284	90.04 177	59.66 335	82.43 248	87.44 248	75.52 161	87.85 165	95.26 49	81.25 130	85.65 342	68.74 285	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	81.65 315	87.16 317	83.40 91	94.24 95	61.69 346	94.76 185	84.21 377

MIMVSNet1		83.63 183	84.59 157	80.74 268	94.06 59	62.77 282	82.72 236	84.53 308	77.57 136	90.34 99	95.92 31	76.88 189	85.83 340	61.88 344	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	67.14 292	87.79 167	91.87 178	71.79 260	87.98 290	86.00 59	91.77 278	95.71 50

test_fmvsm_n_1920		83.60 185	82.89 199	85.74 133	85.22 309	77.74 102	84.12 191	90.48 174	59.87 370	86.45 206	91.12 212	75.65 198	85.89 337	82.28 105	90.87 300	93.58 144

WR-MVS		83.56 186	84.40 166	81.06 262	93.43 75	54.88 381	78.67 316	85.02 298	81.24 85	90.74 94	91.56 193	72.85 244	91.08 204	68.00 292	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	83.54 277	88.66 284	74.87 207	81.73 374	66.84 299	92.29 262	89.11 308

LCM-MVSNet-Re		83.48 188	85.06 143	78.75 300	85.94 294	55.75 374	80.05 289	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 257	59.95 357	94.89 177	90.75 265

hse-mvs2		83.47 189	81.81 219	88.47 80	91.03 153	82.27 61	82.61 238	83.69 314	71.27 231	86.70 193	86.05 335	63.04 317	92.41 166	78.26 153	93.62 223	90.71 267

V42		83.47 189	83.37 187	83.75 190	83.16 355	63.33 274	81.31 271	90.23 190	69.51 253	90.91 88	90.81 228	74.16 220	92.29 172	80.06 126	90.22 317	95.62 54

VPA-MVSNet		83.47 189	84.73 151	79.69 289	90.29 168	57.52 360	81.30 273	88.69 221	76.29 145	87.58 174	94.44 75	80.60 138	87.20 306	66.60 302	96.82 95	94.34 104

mamba_0408		83.44 192	82.88 200	85.11 147	89.13 195	68.97 214	72.73 390	91.28 150	72.90 205	85.68 219	90.61 238	76.78 190	93.97 109	73.37 230	93.47 225	92.38 206

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	80.87 326	87.92 296	73.49 234	92.42 165	70.07 267	88.40 342	91.60 242

CLD-MVS		83.18 194	82.64 206	84.79 155	89.05 198	67.82 229	77.93 325	92.52 109	68.33 270	85.07 236	81.54 393	82.06 116	92.96 152	69.35 274	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	75.65 347	81.24 373	45.26 435	79.94 291	92.91 95	83.83 57	91.33 78	96.88 16	80.25 142	85.92 333	68.89 282	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	85.02 237	92.75 152	65.12 300	92.50 164	74.94 202	91.30 288	91.72 237

114514_t		83.10 197	82.54 209	84.77 156	92.90 88	69.10 213	86.65 134	90.62 171	54.66 402	81.46 318	90.81 228	76.98 182	94.38 90	72.62 241	96.18 119	90.82 264

RRT-MVS		82.97 198	83.44 183	81.57 250	85.06 312	58.04 355	87.20 119	90.37 180	77.88 131	88.59 140	93.70 119	63.17 314	93.05 150	76.49 180	88.47 341	93.62 141

viewmanbaseed2359cas		82.95 199	83.43 184	81.52 251	85.18 310	60.03 331	81.36 270	92.38 113	69.55 252	84.84 245	91.38 200	79.85 148	90.09 245	74.22 207	92.09 269	94.43 99

BP-MVS1		82.81 200	81.67 221	86.23 119	87.88 232	68.53 220	86.06 146	84.36 309	75.65 157	85.14 232	90.19 252	45.84 406	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 201	81.64 222	86.21 122	86.20 286	76.24 123	86.86 127	85.68 285	77.07 141	73.76 397	92.82 148	69.64 272	91.82 185	69.04 281	93.69 220	90.56 275
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	81.93 217	85.19 145	82.08 362	80.15 76	85.53 157	88.76 220	68.01 275	85.58 225	87.75 303	71.80 259	86.85 313	74.02 215	93.87 212	88.58 318

EI-MVSNet		82.61 203	82.42 211	83.20 208	83.25 352	63.66 269	83.50 213	85.07 295	76.06 147	86.55 197	85.10 351	73.41 235	90.25 233	78.15 157	90.67 311	95.68 52

QAPM		82.59 204	82.59 208	82.58 226	86.44 273	66.69 240	89.94 68	90.36 181	67.97 277	84.94 241	92.58 157	72.71 246	92.18 173	70.63 261	87.73 356	88.85 316

fmvsm_s_conf0.1_n_a		82.58 205	81.93 217	84.50 165	87.68 238	73.35 142	86.14 145	77.70 357	61.64 348	85.02 237	91.62 190	77.75 166	86.24 325	82.79 98	87.07 364	93.91 122

Fast-Effi-MVS+-dtu		82.54 206	81.41 231	85.90 129	85.60 301	76.53 118	83.07 226	89.62 208	73.02 204	79.11 348	83.51 369	80.74 136	90.24 235	68.76 284	89.29 329	90.94 259

MVS_Test		82.47 207	83.22 189	80.22 280	82.62 360	57.75 359	82.54 243	91.96 127	71.16 235	82.89 288	92.52 159	77.41 172	90.50 227	80.04 127	87.84 355	92.40 203

viewdifsd2359ckpt11		82.46 208	82.98 197	80.88 265	83.53 339	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

viewmsd2359difaftdt		82.46 208	82.99 196	80.88 265	83.52 340	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		82.31 210	82.48 210	81.81 245	85.59 302	59.66 335	81.47 268	86.02 278	72.85 207	88.05 157	90.65 236	70.73 266	90.91 212	75.15 199	91.79 276	94.87 78

API-MVS		82.28 211	82.61 207	81.30 256	86.29 283	69.79 198	88.71 96	87.67 246	78.42 124	82.15 301	84.15 365	77.98 163	91.59 188	65.39 314	92.75 249	82.51 404

MVSFormer		82.23 212	81.57 227	84.19 178	85.54 303	69.26 208	91.98 35	90.08 194	71.54 228	76.23 373	85.07 354	58.69 342	94.27 92	86.26 49	88.77 337	89.03 313

fmvsm_s_conf0.5_n_a		82.21 213	81.51 230	84.32 173	86.56 271	73.35 142	85.46 158	77.30 361	61.81 344	84.51 250	90.88 225	77.36 173	86.21 327	82.72 99	86.97 369	93.38 149

EIA-MVS		82.19 214	81.23 238	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 241	78.58 353	79.77 409	77.29 174	94.20 97	71.51 251	88.96 335	91.93 231

GDP-MVS		82.17 215	80.85 246	86.15 126	88.65 212	68.95 217	85.65 155	93.02 91	68.42 268	83.73 271	89.54 266	45.07 417	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 215	81.59 225	83.94 185	86.87 269	71.57 178	85.19 165	77.42 360	62.27 342	84.47 253	91.33 202	76.43 193	85.91 335	83.14 89	87.14 362	94.33 105

PCF-MVS	74.62 15	82.15 217	80.92 244	85.84 131	89.43 188	72.30 164	80.53 284	91.82 132	57.36 386	87.81 166	89.92 260	77.67 169	93.63 123	58.69 362	95.08 168	91.58 243
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 218	80.31 253	87.45 97	90.86 158	80.29 75	85.88 148	90.65 169	68.17 273	76.32 372	86.33 329	73.12 241	92.61 162	61.40 349	90.02 321	89.44 297
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 219	81.54 229	83.60 195	83.94 333	73.90 138	83.35 218	86.10 274	58.97 372	83.80 270	90.36 244	74.23 218	86.94 311	82.90 95	90.22 317	89.94 290

fmvsm_s_conf0.5_n_7		82.04 220	82.05 215	82.01 238	86.98 265	71.07 183	78.70 314	89.45 211	68.07 274	78.14 355	91.61 191	74.19 219	85.92 333	79.61 134	91.73 279	89.05 312

GBi-Net		82.02 221	82.07 213	81.85 242	86.38 277	61.05 312	86.83 129	88.27 233	72.43 214	86.00 213	95.64 38	63.78 310	90.68 221	65.95 307	93.34 229	93.82 127

test1		82.02 221	82.07 213	81.85 242	86.38 277	61.05 312	86.83 129	88.27 233	72.43 214	86.00 213	95.64 38	63.78 310	90.68 221	65.95 307	93.34 229	93.82 127

OpenMVS	76.72 13	81.98 223	82.00 216	81.93 239	84.42 324	68.22 223	88.50 102	89.48 210	66.92 295	81.80 312	91.86 179	72.59 248	90.16 239	71.19 254	91.25 289	87.40 338

KD-MVS_self_test		81.93 224	83.14 193	78.30 309	84.75 318	52.75 395	80.37 286	89.42 213	70.24 246	90.26 101	93.39 126	74.55 217	86.77 315	68.61 287	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 225	81.30 235	83.75 190	86.02 292	71.56 179	84.73 174	77.11 364	62.44 339	84.00 266	90.68 233	76.42 194	85.89 337	83.14 89	87.11 363	93.81 130

SDMVSNet		81.90 226	83.17 192	78.10 313	88.81 207	62.45 292	76.08 359	86.05 277	73.67 186	83.41 278	93.04 135	82.35 104	80.65 381	70.06 268	95.03 170	91.21 250

tfpnnormal		81.79 227	82.95 198	78.31 308	88.93 203	55.40 376	80.83 281	82.85 323	76.81 142	85.90 217	94.14 93	74.58 215	86.51 319	66.82 300	95.68 149	93.01 171

AstraMVS		81.67 228	81.40 232	82.48 231	87.06 262	66.47 243	81.41 269	81.68 334	68.78 263	88.00 158	90.95 221	65.70 296	87.86 296	76.66 175	92.38 258	93.12 164

c3_l		81.64 229	81.59 225	81.79 247	80.86 379	59.15 343	78.61 317	90.18 192	68.36 269	87.20 178	87.11 319	69.39 273	91.62 187	78.16 155	94.43 194	94.60 89

guyue		81.57 230	81.37 234	82.15 235	86.39 275	66.13 247	81.54 267	83.21 318	69.79 250	87.77 168	89.95 258	65.36 299	87.64 299	75.88 190	92.49 256	92.67 186

PVSNet_Blended_VisFu		81.55 231	80.49 251	84.70 160	91.58 133	73.24 146	84.21 188	91.67 136	62.86 333	80.94 324	87.16 317	67.27 285	92.87 157	69.82 270	88.94 336	87.99 328

fmvsm_l_conf0.5_n_a		81.46 232	80.87 245	83.25 206	83.73 338	73.21 147	83.00 229	85.59 287	58.22 378	82.96 287	90.09 257	72.30 251	86.65 317	81.97 110	89.95 322	89.88 291

SSM_04072		81.44 233	82.88 200	77.10 328	89.13 195	68.97 214	72.73 390	91.28 150	72.90 205	85.68 219	90.61 238	76.78 190	69.94 424	73.37 230	93.47 225	92.38 206

DELS-MVS		81.44 233	81.25 236	82.03 237	84.27 328	62.87 280	76.47 353	92.49 110	70.97 237	81.64 316	83.83 366	75.03 204	92.70 159	74.29 204	92.22 266	90.51 277
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		81.31 235	81.61 224	80.41 276	86.38 277	58.75 350	83.93 198	86.58 269	72.43 214	87.65 172	92.98 139	63.78 310	90.22 236	66.86 297	93.92 210	92.27 215

TinyColmap		81.25 236	82.34 212	77.99 316	85.33 306	60.68 322	82.32 251	88.33 231	71.26 233	86.97 187	92.22 173	77.10 180	86.98 310	62.37 338	95.17 164	86.31 350

diffmvs_AUTHOR		81.24 237	81.55 228	80.30 278	80.61 384	60.22 327	77.98 324	90.48 174	67.77 283	83.34 280	89.50 267	74.69 213	87.42 302	78.78 145	90.81 303	93.27 155

AUN-MVS		81.18 238	78.78 275	88.39 82	90.93 155	82.14 62	82.51 244	83.67 315	64.69 323	80.29 334	85.91 338	51.07 381	92.38 167	76.29 184	93.63 222	90.65 272

IMVS_0407		81.08 239	81.23 238	80.62 273	85.76 297	62.46 288	82.46 245	87.91 241	65.23 316	82.12 302	87.92 296	77.27 175	90.18 238	71.67 247	90.74 306	89.20 303

tttt0517		81.07 240	79.58 266	85.52 138	88.99 201	66.45 244	87.03 124	75.51 376	73.76 185	88.32 150	90.20 251	37.96 438	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 241	80.57 248	82.46 232	87.50 245	63.22 276	78.37 320	89.63 207	68.01 275	81.87 308	82.08 387	82.31 106	92.65 161	67.10 296	88.30 348	91.51 246

BH-untuned		80.96 242	80.99 242	80.84 267	88.55 216	68.23 222	80.33 287	88.46 226	72.79 210	86.55 197	86.76 323	74.72 212	91.77 186	61.79 345	88.99 334	82.52 403

IMVS_0403		80.93 243	81.00 241	80.72 270	85.76 297	62.46 288	81.82 261	87.91 241	65.23 316	82.07 304	87.92 296	75.91 197	90.50 227	71.67 247	90.74 306	89.20 303

eth_miper_zixun_eth		80.84 244	80.22 257	82.71 223	81.41 371	60.98 317	77.81 327	90.14 193	67.31 290	86.95 188	87.24 316	64.26 303	92.31 170	75.23 198	91.61 282	94.85 82

xiu_mvs_v1_base_debu		80.84 244	80.14 259	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 310	79.59 340	82.73 381	76.94 183	90.14 242	73.22 233	88.33 344	86.90 344

xiu_mvs_v1_base		80.84 244	80.14 259	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 310	79.59 340	82.73 381	76.94 183	90.14 242	73.22 233	88.33 344	86.90 344

xiu_mvs_v1_base_debi		80.84 244	80.14 259	82.93 218	88.31 220	71.73 173	79.53 296	87.17 254	65.43 310	79.59 340	82.73 381	76.94 183	90.14 242	73.22 233	88.33 344	86.90 344

IterMVS-SCA-FT		80.64 248	79.41 267	84.34 172	83.93 334	69.66 202	76.28 355	81.09 340	72.43 214	86.47 204	90.19 252	60.46 327	93.15 146	77.45 166	86.39 375	90.22 282

BH-RMVSNet		80.53 249	80.22 257	81.49 253	87.19 253	66.21 246	77.79 328	86.23 272	74.21 180	83.69 272	88.50 285	73.25 240	90.75 218	63.18 335	87.90 352	87.52 336

VortexMVS		80.51 250	80.63 247	80.15 282	83.36 348	61.82 302	80.63 282	88.00 239	67.11 293	87.23 177	89.10 275	63.98 307	88.00 289	73.63 225	92.63 253	90.64 273

Anonymous202405211		80.51 250	81.19 240	78.49 305	88.48 217	57.26 362	76.63 348	82.49 326	81.21 86	84.30 260	92.24 172	67.99 281	86.24 325	62.22 339	95.13 165	91.98 230

DIV-MVS_self_test		80.43 252	80.23 255	81.02 263	79.99 389	59.25 340	77.07 341	87.02 263	67.38 287	86.19 208	89.22 271	63.09 315	90.16 239	76.32 182	95.80 143	93.66 135

cl____		80.42 253	80.23 255	81.02 263	79.99 389	59.25 340	77.07 341	87.02 263	67.37 288	86.18 210	89.21 272	63.08 316	90.16 239	76.31 183	95.80 143	93.65 138

diffmvs		80.40 254	80.48 252	80.17 281	79.02 402	60.04 329	77.54 332	90.28 189	66.65 298	82.40 295	87.33 314	73.50 232	87.35 304	77.98 159	89.62 326	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		80.37 255	78.41 283	86.23 119	76.75 416	73.28 144	87.18 121	77.45 359	76.24 146	68.14 427	88.93 278	65.41 298	93.85 114	69.47 273	96.12 123	91.55 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 256	80.04 262	81.24 259	79.82 392	58.95 345	77.66 329	89.66 205	65.75 307	85.99 216	85.11 350	68.29 280	91.42 195	76.03 188	92.03 270	93.33 151

MG-MVS		80.32 257	80.94 243	78.47 306	88.18 223	52.62 398	82.29 252	85.01 299	72.01 225	79.24 347	92.54 158	69.36 274	93.36 140	70.65 260	89.19 332	89.45 296

mvsmamba		80.30 258	78.87 272	84.58 164	88.12 226	67.55 230	92.35 30	84.88 302	63.15 331	85.33 229	90.91 222	50.71 383	95.20 62	66.36 303	87.98 351	90.99 257

VPNet		80.25 259	81.68 220	75.94 344	92.46 100	47.98 422	76.70 346	81.67 335	73.45 191	84.87 243	92.82 148	74.66 214	86.51 319	61.66 347	96.85 92	93.33 151

MAR-MVS		80.24 260	78.74 277	84.73 158	86.87 269	78.18 95	85.75 152	87.81 245	65.67 309	77.84 359	78.50 419	73.79 228	90.53 226	61.59 348	90.87 300	85.49 360
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		80.20 261	79.00 271	83.78 189	88.17 224	86.66 19	81.31 271	66.81 432	69.64 251	88.33 149	90.19 252	64.58 301	83.63 363	71.99 246	90.03 320	81.06 423

Anonymous20240521		80.18 262	81.25 236	76.95 330	83.15 356	60.84 319	82.46 245	85.99 279	68.76 264	86.78 189	93.73 118	59.13 339	77.44 398	73.71 221	97.55 74	92.56 193

LFMVS		80.15 263	80.56 249	78.89 297	89.19 194	55.93 370	85.22 164	73.78 388	82.96 69	84.28 261	92.72 153	57.38 351	90.07 247	63.80 329	95.75 146	90.68 269

DPM-MVS		80.10 264	79.18 270	82.88 221	90.71 161	69.74 200	78.87 312	90.84 164	60.29 366	75.64 382	85.92 337	67.28 284	93.11 147	71.24 253	91.79 276	85.77 356

MSDG		80.06 265	79.99 264	80.25 279	83.91 335	68.04 227	77.51 333	89.19 214	77.65 134	81.94 306	83.45 371	76.37 195	86.31 324	63.31 334	86.59 372	86.41 348

FE-MVS		79.98 266	78.86 273	83.36 203	86.47 272	66.45 244	89.73 71	84.74 306	72.80 209	84.22 264	91.38 200	44.95 418	93.60 127	63.93 327	91.50 285	90.04 289

sd_testset		79.95 267	81.39 233	75.64 348	88.81 207	58.07 354	76.16 358	82.81 324	73.67 186	83.41 278	93.04 135	80.96 133	77.65 397	58.62 363	95.03 170	91.21 250

ab-mvs		79.67 268	80.56 249	76.99 329	88.48 217	56.93 364	84.70 176	86.06 276	68.95 261	80.78 327	93.08 134	75.30 202	84.62 350	56.78 372	90.90 298	89.43 298

VNet		79.31 269	80.27 254	76.44 338	87.92 230	53.95 387	75.58 365	84.35 310	74.39 179	82.23 299	90.72 230	72.84 245	84.39 355	60.38 355	93.98 209	90.97 258

thisisatest0530		79.07 270	77.33 293	84.26 175	87.13 254	64.58 260	83.66 208	75.95 371	68.86 262	85.22 231	87.36 313	38.10 435	93.57 131	75.47 195	94.28 199	94.62 88

cl22		78.97 271	78.21 285	81.24 259	77.74 406	59.01 344	77.46 336	87.13 257	65.79 304	84.32 257	85.10 351	58.96 341	90.88 214	75.36 197	92.03 270	93.84 125

patch_mono-2		78.89 272	79.39 268	77.41 325	84.78 316	68.11 225	75.60 363	83.11 320	60.96 358	79.36 344	89.89 261	75.18 203	72.97 413	73.32 232	92.30 260	91.15 252

RPMNet		78.88 273	78.28 284	80.68 272	79.58 393	62.64 284	82.58 240	94.16 33	74.80 168	75.72 380	92.59 155	48.69 390	95.56 42	73.48 227	82.91 411	83.85 382

PAPR		78.84 274	78.10 286	81.07 261	85.17 311	60.22 327	82.21 256	90.57 173	62.51 335	75.32 386	84.61 359	74.99 205	92.30 171	59.48 360	88.04 350	90.68 269

viewmambaseed2359dif		78.80 275	78.47 282	79.78 285	80.26 388	59.28 339	77.31 338	87.13 257	60.42 364	82.37 296	88.67 283	74.58 215	87.87 295	67.78 295	87.73 356	92.19 219

PVSNet_BlendedMVS		78.80 275	77.84 287	81.65 249	84.43 322	63.41 272	79.49 299	90.44 177	61.70 347	75.43 383	87.07 320	69.11 276	91.44 193	60.68 353	92.24 264	90.11 287

FMVSNet3		78.80 275	78.55 279	79.57 291	82.89 359	56.89 366	81.76 262	85.77 283	69.04 260	86.00 213	90.44 243	51.75 379	90.09 245	65.95 307	93.34 229	91.72 237

test_yl		78.71 278	78.51 280	79.32 294	84.32 326	58.84 347	78.38 318	85.33 290	75.99 150	82.49 293	86.57 325	58.01 345	90.02 249	62.74 336	92.73 251	89.10 309

DCV-MVSNet		78.71 278	78.51 280	79.32 294	84.32 326	58.84 347	78.38 318	85.33 290	75.99 150	82.49 293	86.57 325	58.01 345	90.02 249	62.74 336	92.73 251	89.10 309

test1111		78.53 280	78.85 274	77.56 322	92.22 109	47.49 424	82.61 238	69.24 420	72.43 214	85.28 230	94.20 89	51.91 377	90.07 247	65.36 315	96.45 108	95.11 72

icg_test_0407_2		78.46 281	79.68 265	74.78 355	85.76 297	62.46 288	68.51 419	87.91 241	65.23 316	82.12 302	87.92 296	77.27 175	72.67 414	71.67 247	90.74 306	89.20 303

ECVR-MVS		78.44 282	78.63 278	77.88 318	91.85 123	48.95 418	83.68 207	69.91 416	72.30 220	84.26 263	94.20 89	51.89 378	89.82 252	63.58 330	96.02 127	94.87 78

pmmvs-eth3d		78.42 283	77.04 296	82.57 228	87.44 247	74.41 135	80.86 280	79.67 348	55.68 395	84.69 247	90.31 249	60.91 325	85.42 343	62.20 340	91.59 283	87.88 332

mvs_anonymous		78.13 284	78.76 276	76.23 343	79.24 399	50.31 414	78.69 315	84.82 304	61.60 349	83.09 286	92.82 148	73.89 227	87.01 307	68.33 291	86.41 374	91.37 247

TAMVS		78.08 285	76.36 303	83.23 207	90.62 162	72.87 150	79.08 308	80.01 347	61.72 346	81.35 320	86.92 322	63.96 309	88.78 274	50.61 411	93.01 241	88.04 327

miper_enhance_ethall		77.83 286	76.93 297	80.51 274	76.15 423	58.01 356	75.47 367	88.82 218	58.05 380	83.59 274	80.69 397	64.41 302	91.20 199	73.16 239	92.03 270	92.33 210

Vis-MVSNet (Re-imp)		77.82 287	77.79 288	77.92 317	88.82 206	51.29 408	83.28 219	71.97 404	74.04 181	82.23 299	89.78 262	57.38 351	89.41 264	57.22 371	95.41 154	93.05 167

CANet_DTU		77.81 288	77.05 295	80.09 283	81.37 372	59.90 333	83.26 220	88.29 232	69.16 258	67.83 430	83.72 367	60.93 324	89.47 259	69.22 277	89.70 325	90.88 262

OpenMVS_ROB	70.19 17	77.77 289	77.46 290	78.71 301	84.39 325	61.15 310	81.18 275	82.52 325	62.45 338	83.34 280	87.37 312	66.20 290	88.66 277	64.69 322	85.02 391	86.32 349

SSC-MVS		77.55 290	81.64 222	65.29 421	90.46 165	20.33 468	73.56 383	68.28 422	85.44 41	88.18 154	94.64 68	70.93 265	81.33 376	71.25 252	92.03 270	94.20 107

MDA-MVSNet-bldmvs		77.47 291	76.90 298	79.16 296	79.03 401	64.59 259	66.58 431	75.67 374	73.15 202	88.86 132	88.99 277	66.94 286	81.23 377	64.71 321	88.22 349	91.64 241

jason		77.42 292	75.75 309	82.43 233	87.10 257	69.27 207	77.99 323	81.94 332	51.47 422	77.84 359	85.07 354	60.32 329	89.00 268	70.74 259	89.27 331	89.03 313
jason: jason.
CDS-MVSNet		77.32 293	75.40 313	83.06 211	89.00 200	72.48 161	77.90 326	82.17 330	60.81 359	78.94 350	83.49 370	59.30 337	88.76 275	54.64 391	92.37 259	87.93 331
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 294	77.75 289	75.73 346	85.76 297	62.46 288	70.84 405	87.91 241	65.23 316	72.21 405	87.92 296	67.48 283	75.53 406	71.67 247	90.74 306	89.20 303

xiu_mvs_v2_base		77.19 295	76.75 300	78.52 304	87.01 263	61.30 308	75.55 366	87.12 261	61.24 355	74.45 392	78.79 417	77.20 177	90.93 210	64.62 324	84.80 398	83.32 391

MVSTER		77.09 296	75.70 310	81.25 257	75.27 431	61.08 311	77.49 335	85.07 295	60.78 360	86.55 197	88.68 281	43.14 427	90.25 233	73.69 224	90.67 311	92.42 200

PS-MVSNAJ		77.04 297	76.53 302	78.56 303	87.09 259	61.40 306	75.26 368	87.13 257	61.25 354	74.38 394	77.22 431	76.94 183	90.94 209	64.63 323	84.83 397	83.35 390

IterMVS		76.91 298	76.34 304	78.64 302	80.91 377	64.03 266	76.30 354	79.03 351	64.88 322	83.11 284	89.16 273	59.90 333	84.46 353	68.61 287	85.15 389	87.42 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 299	75.67 311	80.34 277	80.48 386	62.16 300	73.50 384	84.80 305	57.61 384	82.24 298	87.54 307	51.31 380	87.65 298	70.40 264	93.19 237	91.23 249

CL-MVSNet_self_test		76.81 300	77.38 292	75.12 351	86.90 267	51.34 406	73.20 387	80.63 344	68.30 271	81.80 312	88.40 286	66.92 287	80.90 378	55.35 385	94.90 176	93.12 164

TR-MVS		76.77 301	75.79 308	79.72 288	86.10 291	65.79 251	77.14 339	83.02 321	65.20 320	81.40 319	82.10 385	66.30 289	90.73 220	55.57 382	85.27 385	82.65 398

MonoMVSNet		76.66 302	77.26 294	74.86 353	79.86 391	54.34 384	86.26 142	86.08 275	71.08 236	85.59 224	88.68 281	53.95 369	85.93 332	63.86 328	80.02 427	84.32 373

USDC		76.63 303	76.73 301	76.34 340	83.46 343	57.20 363	80.02 290	88.04 238	52.14 418	83.65 273	91.25 207	63.24 313	86.65 317	54.66 390	94.11 204	85.17 362

BH-w/o		76.57 304	76.07 307	78.10 313	86.88 268	65.92 250	77.63 330	86.33 270	65.69 308	80.89 325	79.95 406	68.97 278	90.74 219	53.01 401	85.25 386	77.62 434

Patchmtry		76.56 305	77.46 290	73.83 361	79.37 398	46.60 428	82.41 249	76.90 365	73.81 184	85.56 226	92.38 162	48.07 393	83.98 360	63.36 333	95.31 160	90.92 260

PVSNet_Blended		76.49 306	75.40 313	79.76 287	84.43 322	63.41 272	75.14 369	90.44 177	57.36 386	75.43 383	78.30 420	69.11 276	91.44 193	60.68 353	87.70 358	84.42 372

miper_lstm_enhance		76.45 307	76.10 306	77.51 323	76.72 417	60.97 318	64.69 435	85.04 297	63.98 327	83.20 283	88.22 288	56.67 355	78.79 394	73.22 233	93.12 238	92.78 180

lupinMVS		76.37 308	74.46 322	82.09 236	85.54 303	69.26 208	76.79 344	80.77 343	50.68 429	76.23 373	82.82 379	58.69 342	88.94 269	69.85 269	88.77 337	88.07 324

cascas		76.29 309	74.81 318	80.72 270	84.47 321	62.94 278	73.89 381	87.34 249	55.94 393	75.16 388	76.53 436	63.97 308	91.16 201	65.00 318	90.97 296	88.06 326

SD_0403		76.08 310	76.77 299	73.98 359	87.08 261	49.45 417	83.62 209	84.68 307	63.31 328	75.13 389	87.47 310	71.85 258	84.56 351	49.97 413	87.86 354	87.94 330

WB-MVS		76.06 311	80.01 263	64.19 424	89.96 179	20.58 467	72.18 394	68.19 423	83.21 65	86.46 205	93.49 123	70.19 270	78.97 392	65.96 306	90.46 316	93.02 168

thres600view7		75.97 312	75.35 315	77.85 320	87.01 263	51.84 404	80.45 285	73.26 393	75.20 165	83.10 285	86.31 331	45.54 408	89.05 267	55.03 388	92.24 264	92.66 187

GA-MVS		75.83 313	74.61 319	79.48 293	81.87 364	59.25 340	73.42 385	82.88 322	68.68 265	79.75 339	81.80 390	50.62 384	89.46 260	66.85 298	85.64 382	89.72 293

MVP-Stereo		75.81 314	73.51 331	82.71 223	89.35 189	73.62 139	80.06 288	85.20 292	60.30 365	73.96 395	87.94 293	57.89 349	89.45 261	52.02 405	74.87 445	85.06 364
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 315	75.20 316	77.27 326	75.01 434	69.47 205	78.93 309	84.88 302	46.67 436	87.08 184	87.84 301	50.44 386	71.62 419	77.42 168	88.53 340	90.72 266

thres100view900		75.45 316	75.05 317	76.66 336	87.27 249	51.88 403	81.07 276	73.26 393	75.68 156	83.25 282	86.37 328	45.54 408	88.80 271	51.98 406	90.99 293	89.31 300

ET-MVSNet_ETH3D		75.28 317	72.77 340	82.81 222	83.03 358	68.11 225	77.09 340	76.51 369	60.67 362	77.60 364	80.52 401	38.04 436	91.15 202	70.78 257	90.68 310	89.17 307

thres400		75.14 318	74.23 324	77.86 319	86.24 284	52.12 400	79.24 305	73.87 386	73.34 195	81.82 310	84.60 360	46.02 401	88.80 271	51.98 406	90.99 293	92.66 187

wuyk23d		75.13 319	79.30 269	62.63 427	75.56 427	75.18 131	80.89 279	73.10 395	75.06 167	94.76 16	95.32 45	87.73 44	52.85 459	34.16 457	97.11 87	59.85 455

EU-MVSNet		75.12 320	74.43 323	77.18 327	83.11 357	59.48 337	85.71 154	82.43 327	39.76 456	85.64 223	88.76 279	44.71 420	87.88 294	73.86 218	85.88 381	84.16 378

HyFIR lowres test		75.12 320	72.66 342	82.50 230	91.44 141	65.19 256	72.47 392	87.31 250	46.79 435	80.29 334	84.30 362	52.70 374	92.10 177	51.88 410	86.73 370	90.22 282

CMPMVS	59.41 20	75.12 320	73.57 329	79.77 286	75.84 426	67.22 231	81.21 274	82.18 329	50.78 427	76.50 369	87.66 305	55.20 365	82.99 366	62.17 342	90.64 315	89.09 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 323	72.98 338	80.73 269	84.95 313	71.71 176	76.23 356	77.59 358	52.83 412	77.73 363	86.38 327	56.35 358	84.97 347	57.72 370	87.05 365	85.51 359

tfpn200view9		74.86 324	74.23 324	76.74 335	86.24 284	52.12 400	79.24 305	73.87 386	73.34 195	81.82 310	84.60 360	46.02 401	88.80 271	51.98 406	90.99 293	89.31 300

1112_ss		74.82 325	73.74 327	78.04 315	89.57 183	60.04 329	76.49 352	87.09 262	54.31 403	73.66 398	79.80 407	60.25 330	86.76 316	58.37 364	84.15 402	87.32 339

EGC-MVSNET		74.79 326	69.99 370	89.19 67	94.89 38	87.00 15	91.89 38	86.28 271	1.09 465	2.23 467	95.98 30	81.87 122	89.48 258	79.76 130	95.96 130	91.10 253

ppachtmachnet_test		74.73 327	74.00 326	76.90 332	80.71 382	56.89 366	71.53 400	78.42 353	58.24 377	79.32 346	82.92 378	57.91 348	84.26 357	65.60 313	91.36 287	89.56 295

Patchmatch-RL test		74.48 328	73.68 328	76.89 333	84.83 315	66.54 241	72.29 393	69.16 421	57.70 382	86.76 190	86.33 329	45.79 407	82.59 367	69.63 272	90.65 314	81.54 414

PatchMatch-RL		74.48 328	73.22 335	78.27 311	87.70 237	85.26 38	75.92 361	70.09 414	64.34 325	76.09 376	81.25 395	65.87 295	78.07 396	53.86 393	83.82 404	71.48 443

XXY-MVS		74.44 330	76.19 305	69.21 396	84.61 320	52.43 399	71.70 397	77.18 363	60.73 361	80.60 328	90.96 219	75.44 199	69.35 427	56.13 377	88.33 344	85.86 355

test2506		74.12 331	73.39 332	76.28 341	91.85 123	44.20 438	84.06 192	48.20 463	72.30 220	81.90 307	94.20 89	27.22 463	89.77 255	64.81 320	96.02 127	94.87 78

reproduce_monomvs		74.09 332	73.23 334	76.65 337	76.52 418	54.54 382	77.50 334	81.40 338	65.85 303	82.86 290	86.67 324	27.38 461	84.53 352	70.24 265	90.66 313	90.89 261

CR-MVSNet		74.00 333	73.04 337	76.85 334	79.58 393	62.64 284	82.58 240	76.90 365	50.50 430	75.72 380	92.38 162	48.07 393	84.07 359	68.72 286	82.91 411	83.85 382

SSC-MVS3.2		73.90 334	75.67 311	68.61 404	84.11 331	41.28 446	64.17 437	72.83 396	72.09 223	79.08 349	87.94 293	70.31 268	73.89 412	55.99 378	94.49 191	90.67 271

Test_1112_low_res		73.90 334	73.08 336	76.35 339	90.35 167	55.95 369	73.40 386	86.17 273	50.70 428	73.14 399	85.94 336	58.31 344	85.90 336	56.51 374	83.22 408	87.20 341

test20.03		73.75 336	74.59 321	71.22 382	81.11 375	51.12 410	70.15 411	72.10 403	70.42 241	80.28 336	91.50 194	64.21 304	74.72 410	46.96 431	94.58 189	87.82 334

test_fmvs2		73.57 337	72.80 339	75.90 345	72.74 448	68.84 218	77.07 341	84.32 311	45.14 442	82.89 288	84.22 363	48.37 391	70.36 423	73.40 229	87.03 366	88.52 319

SCA		73.32 338	72.57 344	75.58 349	81.62 368	55.86 372	78.89 311	71.37 409	61.73 345	74.93 390	83.42 372	60.46 327	87.01 307	58.11 368	82.63 416	83.88 379

baseline1		73.26 339	73.54 330	72.43 375	84.92 314	47.79 423	79.89 292	74.00 384	65.93 301	78.81 351	86.28 332	56.36 357	81.63 375	56.63 373	79.04 434	87.87 333

1314		73.22 340	72.56 345	75.20 350	80.41 387	57.84 357	81.64 265	85.36 289	51.68 421	73.10 400	76.65 435	61.45 322	85.19 345	63.54 331	79.21 432	82.59 399

MVS		73.21 341	72.59 343	75.06 352	80.97 376	60.81 320	81.64 265	85.92 282	46.03 440	71.68 408	77.54 426	68.47 279	89.77 255	55.70 381	85.39 383	74.60 440

HY-MVS	64.64 18	73.03 342	72.47 346	74.71 356	83.36 348	54.19 385	82.14 259	81.96 331	56.76 392	69.57 422	86.21 333	60.03 331	84.83 349	49.58 418	82.65 414	85.11 363

thisisatest0515		73.00 343	70.52 362	80.46 275	81.45 370	59.90 333	73.16 388	74.31 383	57.86 381	76.08 377	77.78 423	37.60 439	92.12 176	65.00 318	91.45 286	89.35 299

EPNet_dtu		72.87 344	71.33 356	77.49 324	77.72 407	60.55 323	82.35 250	75.79 372	66.49 299	58.39 458	81.06 396	53.68 370	85.98 331	53.55 396	92.97 243	85.95 353
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 345	71.41 355	76.28 341	83.25 352	60.34 325	83.50 213	79.02 352	37.77 460	76.33 371	85.10 351	49.60 389	87.41 303	70.54 262	77.54 440	81.08 421

CHOSEN 1792x2688		72.45 346	70.56 361	78.13 312	90.02 178	63.08 277	68.72 418	83.16 319	42.99 450	75.92 378	85.46 344	57.22 353	85.18 346	49.87 416	81.67 418	86.14 351

testgi		72.36 347	74.61 319	65.59 418	80.56 385	42.82 443	68.29 420	73.35 392	66.87 296	81.84 309	89.93 259	72.08 255	66.92 441	46.05 435	92.54 255	87.01 343

thres200		72.34 348	71.55 354	74.70 357	83.48 342	51.60 405	75.02 370	73.71 389	70.14 247	78.56 354	80.57 400	46.20 399	88.20 287	46.99 430	89.29 329	84.32 373

FPMVS		72.29 349	72.00 348	73.14 366	88.63 213	85.00 40	74.65 374	67.39 426	71.94 226	77.80 361	87.66 305	50.48 385	75.83 404	49.95 414	79.51 428	58.58 457

FMVSNet5		72.10 350	71.69 350	73.32 364	81.57 369	53.02 394	76.77 345	78.37 354	63.31 328	76.37 370	91.85 180	36.68 440	78.98 391	47.87 427	92.45 257	87.95 329

our_test_3		71.85 351	71.59 351	72.62 372	80.71 382	53.78 388	69.72 414	71.71 408	58.80 374	78.03 356	80.51 402	56.61 356	78.84 393	62.20 340	86.04 380	85.23 361

PAPM		71.77 352	70.06 368	76.92 331	86.39 275	53.97 386	76.62 349	86.62 268	53.44 407	63.97 447	84.73 358	57.79 350	92.34 169	39.65 447	81.33 422	84.45 371

ttmdpeth		71.72 353	70.67 359	74.86 353	73.08 445	55.88 371	77.41 337	69.27 419	55.86 394	78.66 352	93.77 116	38.01 437	75.39 407	60.12 356	89.87 323	93.31 153

IB-MVS	62.13 19	71.64 354	68.97 380	79.66 290	80.80 381	62.26 297	73.94 380	76.90 365	63.27 330	68.63 426	76.79 433	33.83 444	91.84 184	59.28 361	87.26 360	84.88 365
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		71.63 355	72.30 347	69.62 393	76.47 420	52.70 397	70.03 412	80.97 341	59.18 371	79.36 344	88.21 289	60.50 326	69.12 428	58.33 366	77.62 439	87.04 342

testing3		71.53 356	70.79 358	73.77 362	88.89 205	41.86 445	76.60 351	59.12 452	72.83 208	80.97 322	82.08 387	19.80 469	87.33 305	65.12 317	91.68 281	92.13 223

test_vis3_rt		71.42 357	70.67 359	73.64 363	69.66 455	70.46 190	66.97 430	89.73 202	42.68 452	88.20 153	83.04 374	43.77 422	60.07 453	65.35 316	86.66 371	90.39 280

Anonymous20231206		71.38 358	71.88 349	69.88 390	86.31 281	54.37 383	70.39 409	74.62 379	52.57 414	76.73 368	88.76 279	59.94 332	72.06 416	44.35 439	93.23 235	83.23 393

test_vis1_n_1920		71.30 359	71.58 353	70.47 385	77.58 409	59.99 332	74.25 375	84.22 312	51.06 424	74.85 391	79.10 413	55.10 366	68.83 430	68.86 283	79.20 433	82.58 400

MIMVSNet		71.09 360	71.59 351	69.57 394	87.23 251	50.07 415	78.91 310	71.83 405	60.20 368	71.26 409	91.76 187	55.08 367	76.09 402	41.06 444	87.02 367	82.54 402

test_fmvs1_n		70.94 361	70.41 365	72.53 374	73.92 436	66.93 238	75.99 360	84.21 313	43.31 449	79.40 343	79.39 411	43.47 423	68.55 432	69.05 280	84.91 394	82.10 408

MS-PatchMatch		70.93 362	70.22 366	73.06 367	81.85 365	62.50 287	73.82 382	77.90 355	52.44 415	75.92 378	81.27 394	55.67 362	81.75 373	55.37 384	77.70 438	74.94 439

pmmvs5		70.73 363	70.07 367	72.72 370	77.03 414	52.73 396	74.14 376	75.65 375	50.36 431	72.17 406	85.37 348	55.42 364	80.67 380	52.86 402	87.59 359	84.77 366

testing3-2		70.72 364	70.97 357	69.95 389	88.93 203	34.80 459	69.85 413	66.59 433	78.42 124	77.58 365	85.55 340	31.83 450	82.08 371	46.28 432	93.73 218	92.98 174

PatchT		70.52 365	72.76 341	63.79 426	79.38 397	33.53 460	77.63 330	65.37 437	73.61 188	71.77 407	92.79 151	44.38 421	75.65 405	64.53 325	85.37 384	82.18 407

test_vis1_n		70.29 366	69.99 370	71.20 383	75.97 425	66.50 242	76.69 347	80.81 342	44.22 445	75.43 383	77.23 430	50.00 387	68.59 431	66.71 301	82.85 413	78.52 433

N_pmnet		70.20 367	68.80 382	74.38 358	80.91 377	84.81 43	59.12 448	76.45 370	55.06 398	75.31 387	82.36 384	55.74 361	54.82 458	47.02 429	87.24 361	83.52 386

tpmvs		70.16 368	69.56 373	71.96 378	74.71 435	48.13 420	79.63 294	75.45 377	65.02 321	70.26 417	81.88 389	45.34 413	85.68 341	58.34 365	75.39 444	82.08 409

new-patchmatchnet		70.10 369	73.37 333	60.29 435	81.23 374	16.95 470	59.54 446	74.62 379	62.93 332	80.97 322	87.93 295	62.83 319	71.90 417	55.24 386	95.01 173	92.00 228

YYNet1		70.06 370	70.44 363	68.90 398	73.76 438	53.42 392	58.99 449	67.20 428	58.42 376	87.10 182	85.39 347	59.82 334	67.32 438	59.79 358	83.50 407	85.96 352

MVStest1		70.05 371	69.26 374	72.41 376	58.62 467	55.59 375	76.61 350	65.58 435	53.44 407	89.28 128	93.32 127	22.91 467	71.44 421	74.08 214	89.52 327	90.21 286

MDA-MVSNet_test_wron		70.05 371	70.44 363	68.88 399	73.84 437	53.47 390	58.93 450	67.28 427	58.43 375	87.09 183	85.40 346	59.80 335	67.25 439	59.66 359	83.54 406	85.92 354

CostFormer		69.98 373	68.68 383	73.87 360	77.14 412	50.72 412	79.26 304	74.51 381	51.94 420	70.97 412	84.75 357	45.16 416	87.49 301	55.16 387	79.23 431	83.40 389

testing91		69.94 374	68.99 379	72.80 369	83.81 337	45.89 431	71.57 399	73.64 391	68.24 272	70.77 415	77.82 422	34.37 443	84.44 354	53.64 395	87.00 368	88.07 324

baseline2		69.77 375	66.89 392	78.41 307	79.51 395	58.09 353	76.23 356	69.57 417	57.50 385	64.82 445	77.45 428	46.02 401	88.44 281	53.08 398	77.83 436	88.70 317

PatchmatchNet		69.71 376	68.83 381	72.33 377	77.66 408	53.60 389	79.29 303	69.99 415	57.66 383	72.53 403	82.93 377	46.45 398	80.08 386	60.91 352	72.09 448	83.31 392
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 377	69.05 377	71.14 384	69.15 456	65.77 252	73.98 379	83.32 317	42.83 451	77.77 362	78.27 421	43.39 426	68.50 433	68.39 290	84.38 401	79.15 431

JIA-IIPM		69.41 378	66.64 396	77.70 321	73.19 442	71.24 181	75.67 362	65.56 436	70.42 241	65.18 441	92.97 142	33.64 446	83.06 364	53.52 397	69.61 454	78.79 432

Syy-MVS		69.40 379	70.03 369	67.49 409	81.72 366	38.94 451	71.00 402	61.99 443	61.38 351	70.81 413	72.36 447	61.37 323	79.30 389	64.50 326	85.18 387	84.22 375

testing99		69.27 380	68.15 387	72.63 371	83.29 350	45.45 433	71.15 401	71.08 410	67.34 289	70.43 416	77.77 424	32.24 449	84.35 356	53.72 394	86.33 376	88.10 323

UnsupCasMVSNet_bld		69.21 381	69.68 372	67.82 407	79.42 396	51.15 409	67.82 424	75.79 372	54.15 404	77.47 366	85.36 349	59.26 338	70.64 422	48.46 424	79.35 430	81.66 412

test_cas_vis1_n_1920		69.20 382	69.12 375	69.43 395	73.68 439	62.82 281	70.38 410	77.21 362	46.18 439	80.46 333	78.95 415	52.03 376	65.53 446	65.77 312	77.45 441	79.95 429

gg-mvs-nofinetune		68.96 383	69.11 376	68.52 405	76.12 424	45.32 434	83.59 210	55.88 457	86.68 33	64.62 446	97.01 12	30.36 454	83.97 361	44.78 438	82.94 410	76.26 436

WBMVS		68.76 384	68.43 384	69.75 392	83.29 350	40.30 449	67.36 426	72.21 402	57.09 389	77.05 367	85.53 342	33.68 445	80.51 382	48.79 422	90.90 298	88.45 320

WB-MVSnew		68.72 385	69.01 378	67.85 406	83.22 354	43.98 439	74.93 371	65.98 434	55.09 397	73.83 396	79.11 412	65.63 297	71.89 418	38.21 452	85.04 390	87.69 335

tpm2		68.45 386	66.83 393	73.30 365	78.93 403	48.50 419	79.76 293	71.76 406	47.50 434	69.92 419	83.60 368	42.07 429	88.40 283	48.44 425	79.51 428	83.01 396

tpm		67.95 387	68.08 388	67.55 408	78.74 404	43.53 441	75.60 363	67.10 431	54.92 399	72.23 404	88.10 290	42.87 428	75.97 403	52.21 404	80.95 426	83.15 394

WTY-MVS		67.91 388	68.35 385	66.58 414	80.82 380	48.12 421	65.96 432	72.60 397	53.67 406	71.20 410	81.68 392	58.97 340	69.06 429	48.57 423	81.67 418	82.55 401

testing11		67.38 389	65.93 397	71.73 380	83.37 347	46.60 428	70.95 404	69.40 418	62.47 337	66.14 434	76.66 434	31.22 451	84.10 358	49.10 420	84.10 403	84.49 369

test-LLR		67.21 390	66.74 394	68.63 402	76.45 421	55.21 378	67.89 421	67.14 429	62.43 340	65.08 442	72.39 445	43.41 424	69.37 425	61.00 350	84.89 395	81.31 416

testing222		66.93 391	65.30 404	71.81 379	83.38 346	45.83 432	72.06 395	67.50 425	64.12 326	69.68 421	76.37 437	27.34 462	83.00 365	38.88 448	88.38 343	86.62 347

sss		66.92 392	67.26 390	65.90 416	77.23 411	51.10 411	64.79 434	71.72 407	52.12 419	70.13 418	80.18 404	57.96 347	65.36 447	50.21 412	81.01 424	81.25 418

KD-MVS_2432*1600		66.87 393	65.81 400	70.04 387	67.50 457	47.49 424	62.56 440	79.16 349	61.21 356	77.98 357	80.61 398	25.29 465	82.48 368	53.02 399	84.92 392	80.16 427

miper_refine_blended		66.87 393	65.81 400	70.04 387	67.50 457	47.49 424	62.56 440	79.16 349	61.21 356	77.98 357	80.61 398	25.29 465	82.48 368	53.02 399	84.92 392	80.16 427

dmvs_re		66.81 395	66.98 391	66.28 415	76.87 415	58.68 351	71.66 398	72.24 400	60.29 366	69.52 423	73.53 444	52.38 375	64.40 449	44.90 437	81.44 421	75.76 437

tpm cat1		66.76 396	65.21 405	71.42 381	77.09 413	50.62 413	78.01 322	73.68 390	44.89 443	68.64 425	79.00 414	45.51 410	82.42 370	49.91 415	70.15 451	81.23 420

UWE-MVS		66.43 397	65.56 403	69.05 397	84.15 330	40.98 447	73.06 389	64.71 439	54.84 400	76.18 375	79.62 410	29.21 456	80.50 383	38.54 451	89.75 324	85.66 357

PVSNet	58.17 21	66.41 398	65.63 402	68.75 400	81.96 363	49.88 416	62.19 442	72.51 399	51.03 425	68.04 428	75.34 441	50.84 382	74.77 408	45.82 436	82.96 409	81.60 413

tpmrst		66.28 399	66.69 395	65.05 422	72.82 447	39.33 450	78.20 321	70.69 413	53.16 410	67.88 429	80.36 403	48.18 392	74.75 409	58.13 367	70.79 450	81.08 421

Patchmatch-test		65.91 400	67.38 389	61.48 432	75.51 428	43.21 442	68.84 417	63.79 441	62.48 336	72.80 402	83.42 372	44.89 419	59.52 455	48.27 426	86.45 373	81.70 411

ADS-MVSNet2		65.87 401	63.64 410	72.55 373	73.16 443	56.92 365	67.10 428	74.81 378	49.74 432	66.04 436	82.97 375	46.71 396	77.26 399	42.29 441	69.96 452	83.46 387

myMVS_eth3d28		65.83 402	65.85 398	65.78 417	83.42 345	35.71 457	67.29 427	68.01 424	67.58 286	69.80 420	77.72 425	32.29 448	74.30 411	37.49 453	89.06 333	87.32 339

test_vis1_rt		65.64 403	64.09 407	70.31 386	66.09 461	70.20 194	61.16 443	81.60 336	38.65 457	72.87 401	69.66 450	52.84 372	60.04 454	56.16 376	77.77 437	80.68 425

mvsany_test3		65.48 404	62.97 413	73.03 368	69.99 454	76.17 124	64.83 433	43.71 465	43.68 447	80.25 337	87.05 321	52.83 373	63.09 452	51.92 409	72.44 447	79.84 430

test-mter		65.00 405	63.79 409	68.63 402	76.45 421	55.21 378	67.89 421	67.14 429	50.98 426	65.08 442	72.39 445	28.27 459	69.37 425	61.00 350	84.89 395	81.31 416

ETVMVS		64.67 406	63.34 412	68.64 401	83.44 344	41.89 444	69.56 416	61.70 448	61.33 353	68.74 424	75.76 439	28.76 457	79.35 388	34.65 456	86.16 379	84.67 368

myMVS_eth3d		64.66 407	63.89 408	66.97 412	81.72 366	37.39 454	71.00 402	61.99 443	61.38 351	70.81 413	72.36 447	20.96 468	79.30 389	49.59 417	85.18 387	84.22 375

test0.0.03 1		64.66 407	64.36 406	65.57 419	75.03 433	46.89 427	64.69 435	61.58 449	62.43 340	71.18 411	77.54 426	43.41 424	68.47 434	40.75 446	82.65 414	81.35 415

UBG		64.34 409	63.35 411	67.30 410	83.50 341	40.53 448	67.46 425	65.02 438	54.77 401	67.54 432	74.47 443	32.99 447	78.50 395	40.82 445	83.58 405	82.88 397

test_f		64.31 410	65.85 398	59.67 436	66.54 460	62.24 299	57.76 452	70.96 411	40.13 454	84.36 255	82.09 386	46.93 395	51.67 460	61.99 343	81.89 417	65.12 451

pmmvs3		62.47 411	60.02 424	69.80 391	71.58 451	64.00 267	70.52 408	58.44 455	39.77 455	66.05 435	75.84 438	27.10 464	72.28 415	46.15 434	84.77 399	73.11 441

EPMVS		62.47 411	62.63 415	62.01 428	70.63 453	38.74 452	74.76 372	52.86 459	53.91 405	67.71 431	80.01 405	39.40 433	66.60 442	55.54 383	68.81 456	80.68 425

ADS-MVSNet		61.90 413	62.19 417	61.03 433	73.16 443	36.42 456	67.10 428	61.75 446	49.74 432	66.04 436	82.97 375	46.71 396	63.21 450	42.29 441	69.96 452	83.46 387

PMMVS		61.65 414	60.38 421	65.47 420	65.40 464	69.26 208	63.97 438	61.73 447	36.80 461	60.11 453	68.43 452	59.42 336	66.35 443	48.97 421	78.57 435	60.81 454

E-PMN		61.59 415	61.62 418	61.49 431	66.81 459	55.40 376	53.77 455	60.34 451	66.80 297	58.90 456	65.50 455	40.48 432	66.12 444	55.72 380	86.25 377	62.95 453

TESTMET0.1,1		61.29 416	60.32 422	64.19 424	72.06 449	51.30 407	67.89 421	62.09 442	45.27 441	60.65 452	69.01 451	27.93 460	64.74 448	56.31 375	81.65 420	76.53 435

MVS-HIRNet		61.16 417	62.92 414	55.87 439	79.09 400	35.34 458	71.83 396	57.98 456	46.56 437	59.05 455	91.14 211	49.95 388	76.43 401	38.74 449	71.92 449	55.84 458

EMVS		61.10 418	60.81 420	61.99 429	65.96 462	55.86 372	53.10 456	58.97 454	67.06 294	56.89 460	63.33 456	40.98 430	67.03 440	54.79 389	86.18 378	63.08 452

DSMNet-mixed		60.98 419	61.61 419	59.09 438	72.88 446	45.05 436	74.70 373	46.61 464	26.20 462	65.34 440	90.32 248	55.46 363	63.12 451	41.72 443	81.30 423	69.09 447

dp		60.70 420	60.29 423	61.92 430	72.04 450	38.67 453	70.83 406	64.08 440	51.28 423	60.75 451	77.28 429	36.59 441	71.58 420	47.41 428	62.34 458	75.52 438

dmvs_testset		60.59 421	62.54 416	54.72 441	77.26 410	27.74 464	74.05 378	61.00 450	60.48 363	65.62 439	67.03 454	55.93 360	68.23 436	32.07 460	69.46 455	68.17 448

CHOSEN 280x420		59.08 422	56.52 428	66.76 413	76.51 419	64.39 263	49.62 457	59.00 453	43.86 446	55.66 461	68.41 453	35.55 442	68.21 437	43.25 440	76.78 443	67.69 449

mvsany_test1		58.48 423	56.47 429	64.50 423	65.90 463	68.21 224	56.95 453	42.11 466	38.30 458	65.69 438	77.19 432	56.96 354	59.35 456	46.16 433	58.96 459	65.93 450

UWE-MVS-28		58.44 424	57.71 426	60.65 434	73.58 440	31.23 461	69.68 415	48.80 462	53.12 411	61.79 449	78.83 416	30.98 452	68.40 435	21.58 463	80.99 425	82.33 406

PVSNet_0	51.08 22	56.10 425	54.97 430	59.48 437	75.12 432	53.28 393	55.16 454	61.89 445	44.30 444	59.16 454	62.48 457	54.22 368	65.91 445	35.40 455	47.01 460	59.25 456

new_pmnet		55.69 426	57.66 427	49.76 442	75.47 429	30.59 462	59.56 445	51.45 460	43.62 448	62.49 448	75.48 440	40.96 431	49.15 462	37.39 454	72.52 446	69.55 446

PMMVS2		55.64 427	59.27 425	44.74 443	64.30 465	12.32 471	40.60 458	49.79 461	53.19 409	65.06 444	84.81 356	53.60 371	49.76 461	32.68 459	89.41 328	72.15 442

MVE	40.22 23	51.82 428	50.47 431	55.87 439	62.66 466	51.91 402	31.61 460	39.28 467	40.65 453	50.76 462	74.98 442	56.24 359	44.67 463	33.94 458	64.11 457	71.04 445
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 429	42.65 432	39.67 444	70.86 452	21.11 466	61.01 444	21.42 471	57.36 386	57.97 459	50.06 460	16.40 470	58.73 457	21.03 464	27.69 464	39.17 460

kuosan		30.83 430	32.17 433	26.83 446	53.36 468	19.02 469	57.90 451	20.44 472	38.29 459	38.01 463	37.82 462	15.18 471	33.45 465	7.74 466	20.76 465	28.03 461

test_method		30.46 431	29.60 434	33.06 445	17.99 470	3.84 473	13.62 461	73.92 385	2.79 464	18.29 466	53.41 459	28.53 458	43.25 464	22.56 461	35.27 462	52.11 459

cdsmvs_eth3d_5k		20.81 432	27.75 435	0.00 451	0.00 474	0.00 476	0.00 462	85.44 288	0.00 469	0.00 470	82.82 379	81.46 127	0.00 470	0.00 469	0.00 468	0.00 466

tmp_tt		20.25 433	24.50 436	7.49 448	4.47 471	8.70 472	34.17 459	25.16 469	1.00 466	32.43 465	18.49 463	39.37 434	9.21 467	21.64 462	43.75 461	4.57 463

ab-mvs-re		6.65 434	8.87 437	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	79.80 407	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

pcd_1.5k_mvsjas		6.41 435	8.55 438	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	76.94 183	0.00 470	0.00 469	0.00 468	0.00 466

test123		6.27 436	8.08 439	0.84 449	1.11 473	0.57 474	62.90 439	0.82 473	0.54 467	1.07 469	2.75 468	1.26 472	0.30 468	1.04 467	1.26 467	1.66 464

testmvs		5.91 437	7.65 440	0.72 450	1.20 472	0.37 475	59.14 447	0.67 474	0.49 468	1.11 468	2.76 467	0.94 473	0.24 469	1.02 468	1.47 466	1.55 465

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 476	0.00 462	0.00 475	0.00 469	0.00 470	0.00 469	0.00 474	0.00 470	0.00 469	0.00 468	0.00 466

WAC-MVS						37.39 454								52.61 403

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	92.40 203

PC_three_1452									58.96 373	90.06 103	91.33 202	80.66 137	93.03 151	75.78 191	95.94 133	92.48 197

No_MVS				88.81 73	91.55 135	77.99 97		91.01 160					96.05 9	87.45 28	98.17 37	92.40 203

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					0.00 474

eth-test					0.00 474

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	92.73 181

IU-MVS					94.18 52	72.64 154		90.82 165	56.98 390	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				91.22 208	81.12 131	94.68 78	74.48 203	95.35 156	92.29 213

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	92.04 226

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	92.98 174

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	92.02 227

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																83.88 379

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 400				83.88 379

sam_mvs												45.92 405

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 216		89.40 125	93.96 105	71.67 262	91.38 197	78.83 144	96.55 102	92.71 184

MTGPA								91.81 134

test_post1							78.85 313				3.13 465	45.19 415	80.13 385	58.11 368

test_post											3.10 466	45.43 411	77.22 400

patchmatchnet-post											81.71 391	45.93 404	87.01 307

GG-mvs-BLEND				67.16 411	73.36 441	46.54 430	84.15 190	55.04 458		58.64 457	61.95 458	29.93 455	83.87 362	38.71 450	76.92 442	71.07 444

MTMP							90.66 49	33.14 468

gm-plane-assit					75.42 430	44.97 437			52.17 416		72.36 447		87.90 293	54.10 392

test9_res														80.83 119	96.45 108	90.57 274

TEST9					92.34 104	79.70 80	83.94 196	90.32 183	65.41 313	84.49 251	90.97 217	82.03 117	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 185	65.79 304	84.36 255	90.96 219	81.93 119	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 282

agg_prior					91.58 133	77.69 103		90.30 186		84.32 257			93.18 144

TestCases				89.68 56	91.59 130	83.40 52		95.44 11	79.47 106	88.00 158	93.03 137	82.66 98	91.47 191	70.81 255	96.14 121	94.16 111

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 248	91.57 192	81.92 121		79.54 136	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 97					94.17 102			92.80 179

旧先验2							81.73 263		56.88 391	86.54 203			84.90 348	72.81 240

新几何2							81.72 264

新几何1				82.95 216	93.96 61	78.56 91		80.24 345	55.45 396	83.93 268	91.08 214	71.19 264	88.33 285	65.84 310	93.07 239	81.95 410

旧先验1					91.97 117	71.77 171		81.78 333			91.84 181	73.92 226			93.65 221	83.61 385

无先验							82.81 235	85.62 286	58.09 379				91.41 196	67.95 294		84.48 370

原ACMM2							82.26 255

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 141	62.59 334	82.73 292	90.67 235	76.53 192	94.25 94	69.24 275	95.69 148	85.55 358

test222					93.31 78	76.54 116	79.38 302	77.79 356	52.59 413	82.36 297	90.84 227	66.83 288			91.69 280	81.25 418

testdata2													86.43 322	63.52 332

segment_acmp												81.94 118

testdata				79.54 292	92.87 89	72.34 163		80.14 346	59.91 369	85.47 228	91.75 188	67.96 282	85.24 344	68.57 289	92.18 267	81.06 423

testdata1							79.62 295		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 168		82.76 291		79.20 150	94.80 75		95.32 158	92.27 215

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 208

plane_prior5								93.61 60					95.22 59	80.78 120	95.83 141	94.46 94

plane_prior4											92.95 143

plane_prior3						76.85 114			77.79 133	86.55 197

plane_prior2							89.45 83		79.44 108

plane_prior1					92.83 93

plane_prior						76.42 119	87.15 122		75.94 153						95.03 170

n2								0.00 475

nn								0.00 475

door-mid								74.45 382

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 412		91.79 71	94.42 78	61.76 321	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								72.57 398

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 330

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 330

BP-MVS														77.30 169

HQP4-MVS										80.56 329			94.61 82			93.56 146

HQP3-MVS								92.68 103							94.47 192

HQP2-MVS												72.10 253

NP-MVS					91.95 118	74.55 134					90.17 255

MDTV_nov1_ep13_2view						27.60 465	70.76 407		46.47 438	61.27 450		45.20 414		49.18 419		83.75 384

MDTV_nov1_ep13			68.29 386		78.03 405	43.87 440	74.12 377	72.22 401	52.17 416	67.02 433	85.54 341	45.36 412	80.85 379	55.73 379	84.42 400

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 152

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 186	81.56 82	90.02 105	91.20 210	82.40 103	90.81 217	73.58 226	94.66 187	94.56 90

DeepMVS_CX				24.13 447	32.95 469	29.49 463		21.63 470	12.07 463	37.95 464	45.07 461	30.84 453	19.21 466	17.94 465	33.06 463	23.69 462

High-resolution multi-view benchmark