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	80.16 140	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	92.08 220

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	91.76 231

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	91.76 231

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

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

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 47	87.16 37	97.60 71	92.73 177

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	92.19 215

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

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 67	86.15 24	93.37 10	95.10 14	90.28 10	92.11 64	95.03 54	89.75 21	94.93 70	79.95 128	98.27 28	95.04 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 15	91.87 20	92.03 12	95.53 27	85.91 28	93.35 11	94.16 33	82.52 73	92.39 62	94.14 93	89.15 26	95.62 39	87.35 32	98.24 32	94.56 89
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 16	91.47 27	92.37 6	96.04 13	88.48 8	92.72 18	92.60 107	83.09 67	91.54 74	94.25 87	87.67 45	95.51 47	87.21 36	98.11 40	93.12 162

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	92.55 190

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

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 34	91.81 133	84.07 55	92.00 67	94.40 80	86.63 55	95.28 58	88.59 11	98.31 26	92.30 207

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

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

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

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

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 76	81.99 76	91.47 75	93.96 105	88.35 32	95.56 42	87.74 21	97.74 62	92.85 174

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

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 87	78.04 96	92.84 16	94.14 37	83.33 64	93.90 29	95.73 34	88.77 28	96.41 3	87.60 26	97.98 48	92.98 170
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 27	90.95 44	91.93 15	95.67 23	85.85 31	90.00 63	93.90 49	80.32 96	91.74 73	94.41 79	88.17 35	95.98 13	86.37 47	97.99 46	93.96 118

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 62	80.97 70	91.49 41	93.48 67	82.82 71	92.60 58	93.97 102	88.19 34	96.29 6	87.61 25	98.20 36	94.39 101
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 45	91.83 20	96.18 11	86.88 17	92.20 31	93.03 90	82.59 72	88.52 143	94.37 82	86.74 54	95.41 53	86.32 48	98.21 34	93.19 158
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 30	91.01 41	90.82 37	95.45 28	82.73 59	91.75 39	93.74 55	80.98 89	91.38 77	93.80 112	87.20 50	95.80 28	87.10 39	97.69 65	93.93 119

MP-MVS-pluss		90.81 31	91.08 38	89.99 50	95.97 14	79.88 77	88.13 105	94.51 19	75.79 155	92.94 48	94.96 55	88.36 31	95.01 68	90.70 3	98.40 22	95.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 32	91.50 26	88.44 81	93.00 86	76.26 122	89.65 76	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 136	78.35 149	98.76 4	95.61 55

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

ACMM	79.39 9	90.65 33	90.99 42	89.63 58	95.03 34	83.53 51	89.62 77	93.35 70	79.20 112	93.83 32	93.60 122	90.81 8	92.96 152	85.02 72	98.45 19	92.41 197
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 52	91.38 28	89.03 199	84.23 49	93.58 6	94.68 18	90.65 8	90.33 100	93.95 107	84.50 78	95.37 54	80.87 118	95.50 153	94.53 92

ACMP	79.16 10	90.54 36	90.60 50	90.35 45	94.36 48	80.98 69	89.16 87	94.05 42	79.03 115	92.87 50	93.74 117	90.60 12	95.21 61	82.87 96	98.76 4	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 37	91.08 38	88.88 71	93.38 76	78.65 90	89.15 88	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 79	97.81 58	91.70 235
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	92.29 209

SMA-MVS		90.31 39	90.48 51	89.83 55	95.31 30	79.52 83	90.98 48	93.24 78	75.37 164	92.84 52	95.28 48	85.58 69	96.09 8	87.92 18	97.76 60	93.88 122
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SF-MVS		90.27 40	90.80 47	88.68 78	92.86 91	77.09 111	91.19 45	95.74 6	81.38 84	92.28 63	93.80 112	86.89 53	94.64 81	85.52 64	97.51 78	94.30 105

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

PMVS	80.48 6	90.08 42	90.66 49	88.34 84	96.71 3	92.97 2	90.31 60	89.57 207	88.51 21	90.11 102	95.12 53	90.98 7	88.92 269	77.55 163	97.07 88	83.13 391
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	81.12 128	95.88 18	87.41 30	95.94 133	92.48 193

DVP-MVS		90.06 44	91.32 33	86.29 117	94.16 55	72.56 158	90.54 53	91.01 159	83.61 61	93.75 35	94.65 65	89.76 19	95.78 32	86.42 45	97.97 49	90.55 272
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	58.83 345	89.04 89	92.74 101	91.40 6	96.12 5	96.06 29	87.23 49	95.57 41	79.42 138	98.74 6	99.00 2

PEN-MVS		90.03 46	91.88 19	84.48 166	96.57 5	58.88 342	88.95 90	93.19 79	91.62 5	96.01 7	96.16 27	87.02 51	95.60 40	78.69 145	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	72.94 239	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	57.84 353	88.93 91	92.84 98	91.92 4	96.16 4	96.23 24	86.95 52	95.99 12	79.05 142	98.57 15	98.80 6

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

3Dnovator+	83.92 2	89.97 50	89.66 58	90.92 35	91.27 144	81.66 66	91.25 43	94.13 38	88.89 15	88.83 134	94.26 86	77.55 168	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	81.42 125	95.90 17	80.94 117	98.80 3	98.84 5

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

anonymousdsp		89.73 54	88.88 74	92.27 8	89.82 180	86.67 18	90.51 55	90.20 189	69.87 248	95.06 15	96.14 28	84.28 81	93.07 149	87.68 23	96.34 111	97.09 20

test_djsdf		89.62 55	89.01 68	91.45 26	92.36 103	82.98 57	91.98 35	90.08 192	71.54 227	94.28 25	96.54 19	81.57 123	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	86.75 189	93.26 128	93.64 2	90.93 210	84.60 78	90.75 301	93.97 117

APD-MVS		89.54 57	89.63 59	89.26 65	92.57 96	81.34 68	90.19 62	93.08 86	80.87 91	91.13 82	93.19 130	86.22 63	95.97 14	82.23 106	97.18 86	90.45 274
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	90.29 186	69.27 253	94.39 21	96.38 21	86.02 66	93.52 132	83.96 83	95.92 135	95.34 60

CPTT-MVS		89.39 59	88.98 70	90.63 40	95.09 33	86.95 16	92.09 33	92.30 116	79.74 103	87.50 173	92.38 161	81.42 125	93.28 141	83.07 92	97.24 84	91.67 236

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	88.31 283	83.33 88	98.30 27	93.20 157
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 261	74.12 208	96.10 124	94.45 95

APD_test2		89.30 61	89.12 65	89.84 53	88.67 210	85.64 35	90.61 51	93.17 80	86.02 38	93.12 45	95.30 46	84.94 73	89.44 261	74.12 208	96.10 124	94.45 95

CP-MVSNet		89.27 63	90.91 45	84.37 168	96.34 8	58.61 348	88.66 98	92.06 122	90.78 7	95.67 8	95.17 51	81.80 121	95.54 44	79.00 143	98.69 10	98.95 4

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

DeepC-MVS	82.31 4	89.15 65	89.08 67	89.37 63	93.64 68	79.07 86	88.54 101	94.20 31	73.53 189	89.71 114	94.82 60	85.09 72	95.77 34	84.17 82	98.03 43	93.26 155
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	88.38 227	88.84 17	94.29 23	97.57 7	90.48 14	91.26 198	72.57 238	97.65 66	97.34 15

MSP-MVS		89.08 67	88.16 84	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 123	89.16 129	92.25 170	72.03 253	96.36 4	88.21 13	90.93 294	92.98 170
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SD-MVS		88.96 68	89.88 54	86.22 121	91.63 129	77.07 112	89.82 70	93.77 54	78.90 116	92.88 49	92.29 168	86.11 64	90.22 235	86.24 52	97.24 84	91.36 244
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS++		88.93 69	88.45 80	90.38 44	94.92 36	85.85 31	89.70 72	91.27 151	78.20 126	86.69 193	92.28 169	80.36 138	95.06 67	86.17 53	96.49 105	90.22 278

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	74.07 217	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	74.07 217	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	89.44 210	88.63 20	94.38 22	95.77 32	86.38 62	93.59 128	79.84 129	95.21 162	91.82 229

DP-MVS		88.60 73	89.01 68	87.36 98	91.30 142	77.50 104	87.55 114	92.97 94	87.95 26	89.62 118	92.87 145	84.56 77	93.89 113	77.65 161	96.62 100	90.70 264

APD_test1		88.40 74	87.91 86	89.88 52	89.50 186	86.65 20	89.98 66	91.91 128	84.26 53	90.87 92	93.92 109	82.18 112	89.29 265	73.75 216	94.81 181	93.70 133

Anonymous20231211		88.40 74	89.62 60	84.73 158	90.46 165	65.27 254	88.86 92	93.02 91	87.15 30	93.05 47	97.10 11	82.28 110	92.02 178	76.70 173	97.99 46	96.88 26

PS-MVSNAJss		88.31 76	87.90 87	89.56 60	93.31 78	77.96 99	87.94 110	91.97 125	70.73 238	94.19 26	96.67 17	76.94 180	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	91.22 205	84.24 82	93.37 139	77.97 159	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	84.32 254	89.33 266	83.87 83	94.53 87	82.45 102	94.89 177	94.90 76

TSAR-MVS + MP.		88.14 78	87.82 88	89.09 69	95.72 22	76.74 115	92.49 26	91.19 154	67.85 278	86.63 194	94.84 59	79.58 146	95.96 15	87.62 24	94.50 190	94.56 89
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 80	89.79 56	82.98 214	93.26 80	63.94 268	91.10 46	89.64 204	85.07 45	90.91 88	91.09 210	89.16 25	91.87 183	82.03 107	95.87 139	93.13 160

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	85.12 230	89.67 261	84.47 79	95.46 50	82.56 101	96.26 116	93.77 131

RPSCF		88.00 82	86.93 105	91.22 31	90.08 173	89.30 5	89.68 74	91.11 155	79.26 111	89.68 115	94.81 63	82.44 101	87.74 294	76.54 178	88.74 335	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	70.81 251	96.14 121	94.16 110

TranMVSNet+NR-MVSNet		87.86 84	88.76 78	85.18 146	94.02 60	64.13 265	84.38 185	91.29 148	84.88 48	92.06 66	93.84 111	86.45 59	93.73 118	73.22 229	98.66 11	97.69 9

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

CNVR-MVS		87.81 86	87.68 89	88.21 86	92.87 89	77.30 110	85.25 163	91.23 152	77.31 139	87.07 183	91.47 197	82.94 95	94.71 77	84.67 77	96.27 115	92.62 185

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	86.55 195	92.95 142	74.84 205	95.22 59	80.78 120	95.83 141	94.46 93

sc_t1		87.70 88	88.94 71	83.99 181	93.47 71	67.15 232	85.05 168	88.21 234	86.81 32	91.87 70	97.65 5	85.51 71	87.91 289	74.22 203	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	86.76 265	84.54 50	83.58 272	93.78 114	73.36 234	96.48 2	87.98 17	96.21 117	94.41 100

MVSMamba_PlusPlus		87.53 90	88.86 75	83.54 200	92.03 116	62.26 297	91.49 41	92.62 105	88.07 25	88.07 155	96.17 26	72.24 248	95.79 31	84.85 74	94.16 203	92.58 188

NCCC		87.36 91	86.87 106	88.83 72	92.32 106	78.84 89	86.58 136	91.09 157	78.77 119	84.85 241	90.89 220	80.85 131	95.29 56	81.14 115	95.32 158	92.34 205

DeepPCF-MVS	81.24 5	87.28 92	86.21 116	90.49 42	91.48 139	84.90 42	83.41 216	92.38 112	70.25 244	89.35 126	90.68 230	82.85 96	94.57 84	79.55 135	95.95 132	92.00 224

SixPastTwentyTwo		87.20 93	87.45 93	86.45 114	92.52 98	69.19 211	87.84 112	88.05 235	81.66 81	94.64 18	96.53 20	65.94 290	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	89.74 199	74.40 178	89.92 110	93.41 125	80.45 136	90.63 224	86.66 44	94.37 196	94.73 86

SPE-MVS-test		87.00 95	86.43 112	88.71 76	89.46 187	77.46 105	89.42 85	95.73 7	77.87 132	81.64 312	87.25 311	82.43 102	94.53 87	77.65 161	96.46 107	94.14 112

UniMVSNet (Re)		86.87 96	86.98 104	86.55 112	93.11 84	68.48 221	83.80 203	92.87 96	80.37 94	89.61 120	91.81 183	77.72 165	94.18 100	75.00 199	98.53 16	96.99 24

Vis-MVSNet		86.86 97	86.58 109	87.72 94	92.09 113	77.43 107	87.35 118	92.09 121	78.87 117	84.27 259	94.05 98	78.35 157	93.65 121	80.54 124	91.58 281	92.08 220
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 98	87.06 100	86.17 124	92.86 91	67.02 236	82.55 242	91.56 138	83.08 68	90.92 86	91.82 182	78.25 158	93.99 107	74.16 206	98.35 24	97.49 13

DU-MVS		86.80 99	86.99 103	86.21 122	93.24 81	67.02 236	83.16 225	92.21 117	81.73 80	90.92 86	91.97 175	77.20 174	93.99 107	74.16 206	98.35 24	97.61 10

casdiffmvs_mvg		86.72 100	87.51 92	84.36 170	87.09 259	65.22 255	84.16 189	94.23 28	77.89 130	91.28 81	93.66 120	84.35 80	92.71 158	80.07 125	94.87 180	95.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		86.68 101	86.52 110	87.18 99	85.94 293	78.30 92	86.93 125	92.20 118	65.94 296	89.16 129	93.16 132	83.10 93	89.89 250	87.81 20	94.43 194	93.35 149

tt0320-xc		86.67 102	88.41 81	81.44 253	93.45 72	60.44 322	83.96 195	88.50 223	87.26 29	90.90 90	97.90 3	85.61 68	86.40 319	70.14 262	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	88.64 220	86.30 37	89.60 121	92.59 154	69.22 271	94.91 71	73.89 213	97.89 55	96.72 29

tt0320		86.63 104	88.36 82	81.41 254	93.57 69	60.73 319	84.37 186	88.61 222	87.00 31	90.75 93	97.98 2	85.54 70	86.45 317	69.75 267	97.70 64	97.06 22

v10		86.54 105	87.10 99	84.84 152	88.16 225	63.28 275	86.64 135	92.20 118	75.42 163	92.81 54	94.50 72	74.05 220	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	89.15 214	83.54 63	89.85 111	97.32 8	88.08 39	86.80 310	70.43 259	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	85.12 230	90.34 242	66.19 287	94.20 97	76.57 176	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	84.76 243	87.70 300	78.87 151	94.18 100	80.67 122	96.29 112	92.73 177

CSCG		86.26 109	86.47 111	85.60 136	90.87 157	74.26 136	87.98 109	91.85 129	80.35 95	89.54 124	88.01 287	79.09 149	92.13 174	75.51 192	95.06 169	90.41 275

DeepC-MVS_fast	80.27 8	86.23 110	85.65 132	87.96 92	91.30 142	76.92 113	87.19 120	91.99 124	70.56 239	84.96 236	90.69 229	80.01 142	95.14 64	78.37 148	95.78 145	91.82 229
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 111	86.83 107	84.36 170	87.82 233	62.35 295	86.42 139	91.33 147	76.78 143	92.73 56	94.48 74	73.41 231	93.72 119	83.10 91	95.41 154	97.01 23

Anonymous20240529		86.20 112	87.13 98	83.42 202	90.19 170	64.55 262	84.55 180	90.71 166	85.85 40	89.94 109	95.24 50	82.13 113	90.40 231	69.19 274	96.40 110	95.31 62

fmvsm_s_conf0.5_n_3		86.19 113	87.27 96	82.95 216	86.91 266	70.38 192	85.31 162	92.61 106	75.59 159	88.32 150	92.87 145	82.22 111	88.63 277	88.80 9	92.82 245	89.83 288

test_fmvsmconf0.1_n		86.18 114	85.88 124	87.08 101	85.26 307	78.25 93	85.82 151	91.82 131	65.33 310	88.55 141	92.35 167	82.62 100	89.80 252	86.87 40	94.32 198	93.18 159

CDPH-MVS		86.17 115	85.54 133	88.05 91	92.25 107	75.45 129	83.85 200	92.01 123	65.91 298	86.19 206	91.75 187	83.77 86	94.98 69	77.43 166	96.71 98	93.73 132

NR-MVSNet		86.00 116	86.22 115	85.34 143	93.24 81	64.56 261	82.21 256	90.46 174	80.99 88	88.42 146	91.97 175	77.56 167	93.85 114	72.46 239	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	90.32 181	65.79 300	84.49 248	90.97 214	81.93 117	93.63 123	81.21 114	96.54 103	90.88 258

KinetiMVS		85.95 118	86.10 119	85.50 140	87.56 243	69.78 199	83.70 206	89.83 198	80.42 93	87.76 167	93.24 129	73.76 225	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	56.44 364	85.75 152	93.09 85	77.33 138	91.94 69	94.65 65	74.78 207	93.41 138	75.11 198	98.58 14	97.88 7

test_fmvsmconf_n		85.88 120	85.51 134	86.99 104	84.77 316	78.21 94	85.40 161	91.39 145	65.32 311	87.72 169	91.81 183	82.33 105	89.78 253	86.68 42	94.20 201	92.99 168

Effi-MVS+-dtu		85.82 121	83.38 185	93.14 4	87.13 254	91.15 3	87.70 113	88.42 226	74.57 172	83.56 273	85.65 335	78.49 156	94.21 96	72.04 241	92.88 243	94.05 115

TAPA-MVS	77.73 12	85.71 122	84.83 149	88.37 83	88.78 209	79.72 79	87.15 122	93.50 66	69.17 254	85.80 215	89.56 262	80.76 132	92.13 174	73.21 234	95.51 152	93.25 156
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	87.54 303	86.45 59	91.06 205	75.76 190	93.76 214	92.54 191

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	87.54 303	86.45 59	91.06 205	75.76 190	93.76 214	92.54 191

fmvsm_s_conf0.5_n_8		85.48 125	85.75 129	84.68 161	87.10 257	69.98 197	84.28 187	92.68 102	74.77 169	87.90 162	92.36 166	73.94 221	90.41 230	85.95 60	92.74 247	93.66 134

EPP-MVSNet		85.47 126	85.04 144	86.77 109	91.52 138	69.37 206	91.63 40	87.98 238	81.51 83	87.05 184	91.83 181	66.18 289	95.29 56	70.75 254	96.89 91	95.64 53

GeoE		85.45 127	85.81 126	84.37 168	90.08 173	67.07 235	85.86 150	91.39 145	72.33 219	87.59 171	90.25 247	84.85 75	92.37 168	78.00 157	91.94 271	93.66 134

MVS_0304		85.37 128	84.58 157	87.75 93	85.28 306	73.36 141	86.54 138	85.71 280	77.56 137	81.78 310	92.47 159	70.29 265	96.02 11	85.59 63	95.96 130	93.87 123

FIs		85.35 129	86.27 114	82.60 225	91.86 122	57.31 357	85.10 167	93.05 87	75.83 154	91.02 85	93.97 102	73.57 227	92.91 156	73.97 212	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 130	85.36 138	84.81 154	85.80 295	76.13 125	85.15 166	92.32 115	61.40 346	91.33 78	90.85 223	83.76 87	86.16 325	84.31 80	93.28 232	92.15 218

casdiffmvs		85.21 131	85.85 125	83.31 205	86.17 286	62.77 282	83.03 227	93.93 47	74.69 171	88.21 152	92.68 153	82.29 109	91.89 182	77.87 160	93.75 217	95.27 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		85.20 132	85.93 122	83.02 212	86.30 281	62.37 294	84.55 180	93.96 45	74.48 175	87.12 178	92.03 174	82.30 107	91.94 179	78.39 147	94.21 200	94.74 85

mamba_0404		85.16 133	85.09 142	85.36 142	90.14 172	69.52 204	86.17 144	91.58 136	74.41 176	86.55 195	91.49 194	78.54 152	93.97 109	73.71 217	93.21 235	92.59 187

K. test v3		85.14 134	84.73 150	86.37 115	91.13 151	69.63 203	85.45 159	76.68 364	84.06 56	92.44 61	96.99 13	62.03 316	94.65 80	80.58 123	93.24 233	94.83 83

mmtdpeth		85.13 135	85.78 128	83.17 210	84.65 318	74.71 132	85.87 149	90.35 180	77.94 129	83.82 266	96.96 15	77.75 163	80.03 383	78.44 146	96.21 117	94.79 84

EI-MVSNet-Vis-set		85.12 136	84.53 160	86.88 106	84.01 331	72.76 151	83.91 199	85.18 289	80.44 92	88.75 136	85.49 339	80.08 141	91.92 180	82.02 108	90.85 299	95.97 44

fmvsm_l_conf0.5_n_3		85.11 137	84.96 146	85.56 137	87.49 246	75.69 128	84.71 175	90.61 171	67.64 281	84.88 239	92.05 173	82.30 107	88.36 281	83.84 86	91.10 287	92.62 185

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	87.83 298	86.46 58	90.87 215	76.17 184	93.89 211	92.47 195

EI-MVSNet-UG-set		85.04 138	84.44 163	86.85 107	83.87 335	72.52 160	83.82 201	85.15 290	80.27 97	88.75 136	85.45 341	79.95 143	91.90 181	81.92 111	90.80 300	96.13 39

X-MVStestdata		85.04 138	82.70 201	92.08 9	95.64 24	86.25 22	92.64 20	93.33 71	85.07 45	89.99 106	16.05 460	86.57 56	95.80 28	87.35 32	97.62 69	94.20 106

MSLP-MVS++		85.00 141	86.03 120	81.90 240	91.84 125	71.56 179	86.75 133	93.02 91	75.95 152	87.12 178	89.39 264	77.98 160	89.40 264	77.46 164	94.78 182	84.75 363

F-COLMAP		84.97 142	83.42 184	89.63 58	92.39 102	83.40 52	88.83 93	91.92 127	73.19 201	80.18 334	89.15 270	77.04 178	93.28 141	65.82 307	92.28 260	92.21 214

mamba_test_0407		84.89 143	84.85 148	85.01 150	89.13 195	68.97 214	85.60 156	91.58 136	74.41 176	85.68 216	91.49 194	78.54 152	93.69 120	73.71 217	93.47 225	92.38 202

balanced_conf03		84.80 144	85.40 136	83.00 213	88.95 202	61.44 305	90.42 59	92.37 114	71.48 229	88.72 138	93.13 133	70.16 267	95.15 63	79.26 140	94.11 204	92.41 197

3Dnovator	80.37 7	84.80 144	84.71 153	85.06 149	86.36 279	74.71 132	88.77 95	90.00 194	75.65 157	84.96 236	93.17 131	74.06 219	91.19 200	78.28 151	91.09 288	89.29 298

SymmetryMVS		84.79 146	83.54 179	88.55 79	92.44 101	80.42 72	88.63 99	82.37 324	74.56 173	85.12 230	90.34 242	66.19 287	94.20 97	76.57 176	95.68 149	91.03 252

IterMVS-LS		84.73 147	84.98 145	83.96 183	87.35 248	63.66 269	83.25 221	89.88 197	76.06 147	89.62 118	92.37 164	73.40 233	92.52 163	78.16 154	94.77 184	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 148	84.34 167	85.49 141	90.18 171	75.86 127	79.23 304	87.13 255	73.35 194	85.56 223	89.34 265	83.60 89	90.50 227	76.64 175	94.05 208	90.09 284

HQP-MVS		84.61 149	84.06 172	86.27 118	91.19 147	70.66 187	84.77 170	92.68 102	73.30 197	80.55 326	90.17 252	72.10 249	94.61 82	77.30 168	94.47 192	93.56 145

v1192		84.57 150	84.69 155	84.21 176	87.75 235	62.88 279	83.02 228	91.43 142	69.08 256	89.98 108	90.89 220	72.70 243	93.62 126	82.41 103	94.97 174	96.13 39

fmvsm_s_conf0.5_n_5		84.56 151	84.71 153	84.11 179	87.92 230	72.09 168	84.80 169	88.64 220	64.43 320	88.77 135	91.78 185	78.07 159	87.95 288	85.85 61	92.18 264	92.30 207

FMVSNet1		84.55 152	85.45 135	81.85 242	90.27 169	61.05 312	86.83 129	88.27 231	78.57 122	89.66 117	95.64 38	75.43 197	90.68 221	69.09 275	95.33 157	93.82 126

v1144		84.54 153	84.72 152	84.00 180	87.67 239	62.55 286	82.97 230	90.93 162	70.32 243	89.80 112	90.99 213	73.50 228	93.48 134	81.69 113	94.65 188	95.97 44

Gipuma		84.44 154	86.33 113	78.78 295	84.20 328	73.57 140	89.55 78	90.44 175	84.24 54	84.38 251	94.89 57	76.35 193	80.40 380	76.14 185	96.80 96	82.36 401
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 155	84.27 168	84.74 157	87.25 250	70.84 186	83.55 211	88.45 225	68.64 264	86.29 205	91.31 203	74.97 203	88.42 279	87.87 19	90.07 315	94.95 75

MCST-MVS		84.36 156	83.93 175	85.63 135	91.59 130	71.58 177	83.52 212	92.13 120	61.82 339	83.96 264	89.75 260	79.93 144	93.46 135	78.33 150	94.34 197	91.87 228

VDDNet		84.35 157	85.39 137	81.25 256	95.13 32	59.32 334	85.42 160	81.11 335	86.41 36	87.41 174	96.21 25	73.61 226	90.61 225	66.33 300	96.85 92	93.81 129

ETV-MVS		84.31 158	83.91 176	85.52 138	88.58 215	70.40 191	84.50 184	93.37 68	78.76 120	84.07 262	78.72 414	80.39 137	95.13 65	73.82 215	92.98 241	91.04 251

v1240		84.30 159	84.51 161	83.65 193	87.65 240	61.26 309	82.85 234	91.54 139	67.94 275	90.68 95	90.65 233	71.71 257	93.64 122	82.84 97	94.78 182	96.07 41

MVS_111021_LR		84.28 160	83.76 177	85.83 132	89.23 193	83.07 55	80.99 276	83.56 312	72.71 211	86.07 209	89.07 272	81.75 122	86.19 324	77.11 170	93.36 228	88.24 317

h-mvs33		84.25 161	82.76 200	88.72 75	91.82 127	82.60 60	84.00 194	84.98 296	71.27 230	86.70 191	90.55 238	63.04 313	93.92 112	78.26 152	94.20 201	89.63 290

v144192		84.24 162	84.41 164	83.71 192	87.59 242	61.57 304	82.95 231	91.03 158	67.82 279	89.80 112	90.49 239	73.28 235	93.51 133	81.88 112	94.89 177	96.04 43

dcpmvs_2		84.23 163	85.14 141	81.50 251	88.61 214	61.98 301	82.90 233	93.11 83	68.66 263	92.77 55	92.39 160	78.50 155	87.63 297	76.99 172	92.30 257	94.90 76

v1921920		84.23 163	84.37 166	83.79 188	87.64 241	61.71 303	82.91 232	91.20 153	67.94 275	90.06 103	90.34 242	72.04 252	93.59 128	82.32 104	94.91 175	96.07 41

VDD-MVS		84.23 163	84.58 157	83.20 208	91.17 150	65.16 257	83.25 221	84.97 297	79.79 102	87.18 177	94.27 83	74.77 208	90.89 213	69.24 271	96.54 103	93.55 147

v2v482		84.09 166	84.24 169	83.62 194	87.13 254	61.40 306	82.71 237	89.71 202	72.19 222	89.55 122	91.41 198	70.70 263	93.20 143	81.02 116	93.76 214	96.25 37

EG-PatchMatch MVS		84.08 167	84.11 171	83.98 182	92.22 109	72.61 157	82.20 258	87.02 261	72.63 212	88.86 132	91.02 212	78.52 154	91.11 203	73.41 224	91.09 288	88.21 318

fmvsm_s_conf0.5_n_6		84.05 168	84.14 170	83.81 186	87.75 235	71.17 182	83.42 215	91.10 156	67.90 277	84.53 246	90.70 228	73.01 238	88.73 275	85.09 68	93.72 219	91.53 241

DP-MVS Recon		84.05 168	83.22 188	86.52 113	91.73 128	75.27 130	83.23 223	92.40 110	72.04 224	82.04 301	88.33 283	77.91 162	93.95 111	66.17 301	95.12 167	90.34 277

TransMVSNet (Re)		84.02 170	85.74 130	78.85 294	91.00 154	55.20 376	82.29 252	87.26 250	79.65 105	88.38 148	95.52 41	83.00 94	86.88 308	67.97 289	96.60 101	94.45 95

Baseline_NR-MVSNet		84.00 171	85.90 123	78.29 306	91.47 140	53.44 387	82.29 252	87.00 264	79.06 114	89.55 122	95.72 36	77.20 174	86.14 326	72.30 240	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 172	84.46 162	82.53 229	86.11 289	70.65 189	82.45 247	89.17 213	67.72 280	86.74 190	91.49 194	79.20 147	85.86 335	84.71 76	92.60 251	91.07 250

TSAR-MVS + GP.		83.95 173	82.69 202	87.72 94	89.27 192	81.45 67	83.72 205	81.58 333	74.73 170	85.66 219	86.06 330	72.56 245	92.69 160	75.44 194	95.21 162	89.01 311

LuminaMVS		83.94 174	83.51 180	85.23 144	89.78 181	71.74 172	84.76 173	87.27 249	72.60 213	89.31 127	90.60 237	64.04 302	90.95 208	79.08 141	94.11 204	92.99 168

alignmvs		83.94 174	83.98 174	83.80 187	87.80 234	67.88 228	84.54 182	91.42 144	73.27 200	88.41 147	87.96 288	72.33 246	90.83 216	76.02 187	94.11 204	92.69 181

Effi-MVS+		83.90 176	84.01 173	83.57 198	87.22 252	65.61 253	86.55 137	92.40 110	78.64 121	81.34 317	84.18 360	83.65 88	92.93 154	74.22 203	87.87 349	92.17 217

fmvsm_s_conf0.1_n_2		83.82 177	83.49 181	84.84 152	85.99 292	70.19 195	80.93 277	87.58 245	67.26 287	87.94 161	92.37 164	71.40 259	88.01 285	86.03 55	91.87 272	96.31 36

mvs5depth		83.82 177	84.54 159	81.68 247	82.23 358	68.65 219	86.89 126	89.90 196	80.02 101	87.74 168	97.86 4	64.19 301	82.02 368	76.37 180	95.63 151	94.35 102

CANet		83.79 179	82.85 199	86.63 110	86.17 286	72.21 167	83.76 204	91.43 142	77.24 140	74.39 389	87.45 307	75.36 198	95.42 52	77.03 171	92.83 244	92.25 213

pm-mvs1		83.69 180	84.95 147	79.91 280	90.04 177	59.66 331	82.43 248	87.44 246	75.52 161	87.85 163	95.26 49	81.25 127	85.65 338	68.74 281	96.04 126	94.42 99

AdaColmap		83.66 181	83.69 178	83.57 198	90.05 176	72.26 165	86.29 141	90.00 194	78.19 127	81.65 311	87.16 313	83.40 91	94.24 95	61.69 342	94.76 185	84.21 373

MIMVSNet1		83.63 182	84.59 156	80.74 265	94.06 59	62.77 282	82.72 236	84.53 304	77.57 136	90.34 99	95.92 31	76.88 186	85.83 336	61.88 340	97.42 79	93.62 140

fmvsm_s_conf0.5_n_2		83.62 183	83.29 187	84.62 162	85.43 304	70.18 196	80.61 282	87.24 251	67.14 288	87.79 165	91.87 177	71.79 256	87.98 287	86.00 59	91.77 275	95.71 50

test_fmvsm_n_1920		83.60 184	82.89 196	85.74 133	85.22 308	77.74 102	84.12 191	90.48 173	59.87 366	86.45 204	91.12 209	75.65 195	85.89 333	82.28 105	90.87 297	93.58 143

WR-MVS		83.56 185	84.40 165	81.06 261	93.43 75	54.88 377	78.67 313	85.02 294	81.24 85	90.74 94	91.56 192	72.85 240	91.08 204	68.00 288	98.04 41	97.23 17

CNLPA		83.55 186	83.10 193	84.90 151	89.34 190	83.87 50	84.54 182	88.77 217	79.09 113	83.54 274	88.66 280	74.87 204	81.73 370	66.84 295	92.29 259	89.11 304

LCM-MVSNet-Re		83.48 187	85.06 143	78.75 296	85.94 293	55.75 370	80.05 288	94.27 25	76.47 144	96.09 6	94.54 71	83.31 92	89.75 256	59.95 353	94.89 177	90.75 261

hse-mvs2		83.47 188	81.81 216	88.47 80	91.03 153	82.27 61	82.61 238	83.69 310	71.27 230	86.70 191	86.05 331	63.04 313	92.41 166	78.26 152	93.62 223	90.71 263

V42		83.47 188	83.37 186	83.75 190	83.16 352	63.33 274	81.31 270	90.23 188	69.51 252	90.91 88	90.81 225	74.16 216	92.29 172	80.06 126	90.22 313	95.62 54

VPA-MVSNet		83.47 188	84.73 150	79.69 285	90.29 168	57.52 356	81.30 272	88.69 219	76.29 145	87.58 172	94.44 75	80.60 135	87.20 302	66.60 298	96.82 95	94.34 103

mamba_0408		83.44 191	82.88 197	85.11 147	89.13 195	68.97 214	72.73 386	91.28 149	72.90 205	85.68 216	90.61 235	76.78 187	93.97 109	73.37 226	93.47 225	92.38 202

PAPM_NR		83.23 192	83.19 190	83.33 204	90.90 156	65.98 249	88.19 104	90.78 165	78.13 128	80.87 322	87.92 292	73.49 230	92.42 165	70.07 263	88.40 338	91.60 238

CLD-MVS		83.18 193	82.64 203	84.79 155	89.05 198	67.82 229	77.93 321	92.52 108	68.33 267	85.07 233	81.54 389	82.06 114	92.96 152	69.35 270	97.91 54	93.57 144
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 194	85.68 131	75.65 343	81.24 370	45.26 431	79.94 290	92.91 95	83.83 57	91.33 78	96.88 16	80.25 139	85.92 329	68.89 278	95.89 138	95.76 48

FA-MVS(test-final)		83.13 195	83.02 194	83.43 201	86.16 288	66.08 248	88.00 108	88.36 228	75.55 160	85.02 234	92.75 151	65.12 296	92.50 164	74.94 200	91.30 285	91.72 233

114514_t		83.10 196	82.54 206	84.77 156	92.90 88	69.10 213	86.65 134	90.62 170	54.66 398	81.46 314	90.81 225	76.98 179	94.38 90	72.62 237	96.18 119	90.82 260

RRT-MVS		82.97 197	83.44 182	81.57 249	85.06 311	58.04 351	87.20 119	90.37 178	77.88 131	88.59 140	93.70 119	63.17 310	93.05 150	76.49 179	88.47 337	93.62 140

viewmanbaseed2359cas		82.95 198	83.43 183	81.52 250	85.18 309	60.03 327	81.36 269	92.38 112	69.55 251	84.84 242	91.38 199	79.85 145	90.09 244	74.22 203	92.09 266	94.43 98

BP-MVS1		82.81 199	81.67 218	86.23 119	87.88 232	68.53 220	86.06 146	84.36 305	75.65 157	85.14 229	90.19 249	45.84 402	94.42 89	85.18 67	94.72 186	95.75 49

UGNet		82.78 200	81.64 219	86.21 122	86.20 285	76.24 123	86.86 127	85.68 281	77.07 141	73.76 393	92.82 147	69.64 268	91.82 185	69.04 277	93.69 220	90.56 271
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		82.75 201	81.93 214	85.19 145	82.08 359	80.15 76	85.53 157	88.76 218	68.01 272	85.58 222	87.75 299	71.80 255	86.85 309	74.02 211	93.87 212	88.58 314

EI-MVSNet		82.61 202	82.42 208	83.20 208	83.25 349	63.66 269	83.50 213	85.07 291	76.06 147	86.55 195	85.10 347	73.41 231	90.25 232	78.15 156	90.67 307	95.68 52

QAPM		82.59 203	82.59 205	82.58 226	86.44 273	66.69 240	89.94 68	90.36 179	67.97 274	84.94 238	92.58 156	72.71 242	92.18 173	70.63 257	87.73 352	88.85 312

fmvsm_s_conf0.1_n_a		82.58 204	81.93 214	84.50 165	87.68 238	73.35 142	86.14 145	77.70 353	61.64 344	85.02 234	91.62 189	77.75 163	86.24 321	82.79 98	87.07 360	93.91 121

Fast-Effi-MVS+-dtu		82.54 205	81.41 227	85.90 129	85.60 300	76.53 118	83.07 226	89.62 206	73.02 204	79.11 344	83.51 365	80.74 133	90.24 234	68.76 280	89.29 325	90.94 255

MVS_Test		82.47 206	83.22 188	80.22 276	82.62 357	57.75 355	82.54 243	91.96 126	71.16 234	82.89 284	92.52 158	77.41 169	90.50 227	80.04 127	87.84 351	92.40 199

v148		82.31 207	82.48 207	81.81 245	85.59 301	59.66 331	81.47 267	86.02 276	72.85 207	88.05 157	90.65 233	70.73 262	90.91 212	75.15 197	91.79 273	94.87 78

API-MVS		82.28 208	82.61 204	81.30 255	86.29 282	69.79 198	88.71 96	87.67 244	78.42 124	82.15 297	84.15 361	77.98 160	91.59 188	65.39 310	92.75 246	82.51 400

MVSFormer		82.23 209	81.57 224	84.19 178	85.54 302	69.26 208	91.98 35	90.08 192	71.54 227	76.23 369	85.07 350	58.69 338	94.27 92	86.26 49	88.77 333	89.03 309

fmvsm_s_conf0.5_n_a		82.21 210	81.51 226	84.32 173	86.56 271	73.35 142	85.46 158	77.30 357	61.81 340	84.51 247	90.88 222	77.36 170	86.21 323	82.72 99	86.97 365	93.38 148

EIA-MVS		82.19 211	81.23 234	85.10 148	87.95 229	69.17 212	83.22 224	93.33 71	70.42 240	78.58 349	79.77 405	77.29 171	94.20 97	71.51 247	88.96 331	91.93 227

GDP-MVS		82.17 212	80.85 242	86.15 126	88.65 212	68.95 217	85.65 155	93.02 91	68.42 265	83.73 268	89.54 263	45.07 413	94.31 91	79.66 133	93.87 212	95.19 68

fmvsm_s_conf0.1_n		82.17 212	81.59 222	83.94 185	86.87 269	71.57 178	85.19 165	77.42 356	62.27 338	84.47 250	91.33 201	76.43 190	85.91 331	83.14 89	87.14 358	94.33 104

PCF-MVS	74.62 15	82.15 214	80.92 240	85.84 131	89.43 188	72.30 164	80.53 283	91.82 131	57.36 382	87.81 164	89.92 257	77.67 166	93.63 123	58.69 358	95.08 168	91.58 239
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 215	80.31 249	87.45 97	90.86 158	80.29 75	85.88 148	90.65 168	68.17 270	76.32 368	86.33 325	73.12 237	92.61 162	61.40 345	90.02 317	89.44 293
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 216	81.54 225	83.60 195	83.94 332	73.90 138	83.35 218	86.10 272	58.97 368	83.80 267	90.36 241	74.23 214	86.94 307	82.90 95	90.22 313	89.94 286

fmvsm_s_conf0.5_n_7		82.04 217	82.05 212	82.01 238	86.98 265	71.07 183	78.70 311	89.45 209	68.07 271	78.14 351	91.61 190	74.19 215	85.92 329	79.61 134	91.73 276	89.05 308

GBi-Net		82.02 218	82.07 210	81.85 242	86.38 276	61.05 312	86.83 129	88.27 231	72.43 214	86.00 210	95.64 38	63.78 306	90.68 221	65.95 303	93.34 229	93.82 126

test1		82.02 218	82.07 210	81.85 242	86.38 276	61.05 312	86.83 129	88.27 231	72.43 214	86.00 210	95.64 38	63.78 306	90.68 221	65.95 303	93.34 229	93.82 126

OpenMVS	76.72 13	81.98 220	82.00 213	81.93 239	84.42 323	68.22 223	88.50 102	89.48 208	66.92 291	81.80 308	91.86 178	72.59 244	90.16 238	71.19 250	91.25 286	87.40 334

KD-MVS_self_test		81.93 221	83.14 192	78.30 305	84.75 317	52.75 391	80.37 285	89.42 211	70.24 245	90.26 101	93.39 126	74.55 213	86.77 311	68.61 283	96.64 99	95.38 59

fmvsm_s_conf0.5_n		81.91 222	81.30 231	83.75 190	86.02 291	71.56 179	84.73 174	77.11 360	62.44 335	84.00 263	90.68 230	76.42 191	85.89 333	83.14 89	87.11 359	93.81 129

SDMVSNet		81.90 223	83.17 191	78.10 309	88.81 207	62.45 292	76.08 355	86.05 275	73.67 186	83.41 275	93.04 135	82.35 104	80.65 377	70.06 264	95.03 170	91.21 246

tfpnnormal		81.79 224	82.95 195	78.31 304	88.93 203	55.40 372	80.83 280	82.85 319	76.81 142	85.90 214	94.14 93	74.58 211	86.51 315	66.82 296	95.68 149	93.01 167

AstraMVS		81.67 225	81.40 228	82.48 231	87.06 262	66.47 243	81.41 268	81.68 330	68.78 260	88.00 158	90.95 218	65.70 292	87.86 293	76.66 174	92.38 255	93.12 162

c3_l		81.64 226	81.59 222	81.79 246	80.86 376	59.15 339	78.61 314	90.18 190	68.36 266	87.20 176	87.11 315	69.39 269	91.62 187	78.16 154	94.43 194	94.60 88

guyue		81.57 227	81.37 230	82.15 235	86.39 274	66.13 247	81.54 266	83.21 314	69.79 249	87.77 166	89.95 255	65.36 295	87.64 296	75.88 188	92.49 253	92.67 182

PVSNet_Blended_VisFu		81.55 228	80.49 247	84.70 160	91.58 133	73.24 146	84.21 188	91.67 135	62.86 329	80.94 320	87.16 313	67.27 281	92.87 157	69.82 266	88.94 332	87.99 324

fmvsm_l_conf0.5_n_a		81.46 229	80.87 241	83.25 206	83.73 337	73.21 147	83.00 229	85.59 283	58.22 374	82.96 283	90.09 254	72.30 247	86.65 313	81.97 110	89.95 318	89.88 287

mamba_test_0407_2		81.44 230	82.88 197	77.10 324	89.13 195	68.97 214	72.73 386	91.28 149	72.90 205	85.68 216	90.61 235	76.78 187	69.94 420	73.37 226	93.47 225	92.38 202

DELS-MVS		81.44 230	81.25 232	82.03 237	84.27 327	62.87 280	76.47 349	92.49 109	70.97 236	81.64 312	83.83 362	75.03 201	92.70 159	74.29 202	92.22 263	90.51 273
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		81.31 232	81.61 221	80.41 273	86.38 276	58.75 346	83.93 198	86.58 267	72.43 214	87.65 170	92.98 139	63.78 306	90.22 235	66.86 293	93.92 210	92.27 211

TinyColmap		81.25 233	82.34 209	77.99 312	85.33 305	60.68 320	82.32 251	88.33 229	71.26 232	86.97 185	92.22 172	77.10 177	86.98 306	62.37 334	95.17 164	86.31 346

AUN-MVS		81.18 234	78.78 271	88.39 82	90.93 155	82.14 62	82.51 244	83.67 311	64.69 319	80.29 330	85.91 334	51.07 377	92.38 167	76.29 183	93.63 222	90.65 268

icg_test_0407		81.08 235	81.23 234	80.62 270	85.76 296	62.46 288	82.46 245	87.91 239	65.23 312	82.12 298	87.92 292	77.27 172	90.18 237	71.67 243	90.74 302	89.20 299

tttt0517		81.07 236	79.58 262	85.52 138	88.99 201	66.45 244	87.03 124	75.51 372	73.76 185	88.32 150	90.20 248	37.96 434	94.16 104	79.36 139	95.13 165	95.93 47

Fast-Effi-MVS+		81.04 237	80.57 244	82.46 232	87.50 245	63.22 276	78.37 317	89.63 205	68.01 272	81.87 304	82.08 383	82.31 106	92.65 161	67.10 292	88.30 344	91.51 242

BH-untuned		80.96 238	80.99 238	80.84 264	88.55 216	68.23 222	80.33 286	88.46 224	72.79 210	86.55 195	86.76 319	74.72 209	91.77 186	61.79 341	88.99 330	82.52 399

icg_test_0403		80.93 239	81.00 237	80.72 267	85.76 296	62.46 288	81.82 260	87.91 239	65.23 312	82.07 300	87.92 292	75.91 194	90.50 227	71.67 243	90.74 302	89.20 299

eth_miper_zixun_eth		80.84 240	80.22 253	82.71 223	81.41 368	60.98 315	77.81 323	90.14 191	67.31 286	86.95 186	87.24 312	64.26 299	92.31 170	75.23 196	91.61 279	94.85 82

xiu_mvs_v1_base_debu		80.84 240	80.14 255	82.93 218	88.31 220	71.73 173	79.53 295	87.17 252	65.43 306	79.59 336	82.73 377	76.94 180	90.14 241	73.22 229	88.33 340	86.90 340

xiu_mvs_v1_base		80.84 240	80.14 255	82.93 218	88.31 220	71.73 173	79.53 295	87.17 252	65.43 306	79.59 336	82.73 377	76.94 180	90.14 241	73.22 229	88.33 340	86.90 340

xiu_mvs_v1_base_debi		80.84 240	80.14 255	82.93 218	88.31 220	71.73 173	79.53 295	87.17 252	65.43 306	79.59 336	82.73 377	76.94 180	90.14 241	73.22 229	88.33 340	86.90 340

IterMVS-SCA-FT		80.64 244	79.41 263	84.34 172	83.93 333	69.66 202	76.28 351	81.09 336	72.43 214	86.47 202	90.19 249	60.46 323	93.15 146	77.45 165	86.39 371	90.22 278

BH-RMVSNet		80.53 245	80.22 253	81.49 252	87.19 253	66.21 246	77.79 324	86.23 270	74.21 180	83.69 269	88.50 281	73.25 236	90.75 218	63.18 331	87.90 348	87.52 332

VortexMVS		80.51 246	80.63 243	80.15 278	83.36 345	61.82 302	80.63 281	88.00 237	67.11 289	87.23 175	89.10 271	63.98 303	88.00 286	73.63 221	92.63 250	90.64 269

Anonymous202405211		80.51 246	81.19 236	78.49 301	88.48 217	57.26 358	76.63 344	82.49 322	81.21 86	84.30 257	92.24 171	67.99 277	86.24 321	62.22 335	95.13 165	91.98 226

DIV-MVS_self_test		80.43 248	80.23 251	81.02 262	79.99 385	59.25 336	77.07 337	87.02 261	67.38 283	86.19 206	89.22 267	63.09 311	90.16 238	76.32 181	95.80 143	93.66 134

cl____		80.42 249	80.23 251	81.02 262	79.99 385	59.25 336	77.07 337	87.02 261	67.37 284	86.18 208	89.21 268	63.08 312	90.16 238	76.31 182	95.80 143	93.65 137

diffmvs		80.40 250	80.48 248	80.17 277	79.02 398	60.04 325	77.54 328	90.28 187	66.65 294	82.40 291	87.33 310	73.50 228	87.35 300	77.98 158	89.62 322	93.13 160
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		80.37 251	78.41 279	86.23 119	76.75 412	73.28 144	87.18 121	77.45 355	76.24 146	68.14 423	88.93 274	65.41 294	93.85 114	69.47 269	96.12 123	91.55 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 252	80.04 258	81.24 258	79.82 388	58.95 341	77.66 325	89.66 203	65.75 303	85.99 213	85.11 346	68.29 276	91.42 195	76.03 186	92.03 267	93.33 150

MG-MVS		80.32 253	80.94 239	78.47 302	88.18 223	52.62 394	82.29 252	85.01 295	72.01 225	79.24 343	92.54 157	69.36 270	93.36 140	70.65 256	89.19 328	89.45 292

mvsmamba		80.30 254	78.87 268	84.58 164	88.12 226	67.55 230	92.35 30	84.88 298	63.15 327	85.33 226	90.91 219	50.71 379	95.20 62	66.36 299	87.98 347	90.99 253

VPNet		80.25 255	81.68 217	75.94 340	92.46 100	47.98 418	76.70 342	81.67 331	73.45 191	84.87 240	92.82 147	74.66 210	86.51 315	61.66 343	96.85 92	93.33 150

MAR-MVS		80.24 256	78.74 273	84.73 158	86.87 269	78.18 95	85.75 152	87.81 243	65.67 305	77.84 355	78.50 415	73.79 224	90.53 226	61.59 344	90.87 297	85.49 356
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		80.20 257	79.00 267	83.78 189	88.17 224	86.66 19	81.31 270	66.81 428	69.64 250	88.33 149	90.19 249	64.58 297	83.63 359	71.99 242	90.03 316	81.06 419

Anonymous20240521		80.18 258	81.25 232	76.95 326	83.15 353	60.84 317	82.46 245	85.99 277	68.76 261	86.78 187	93.73 118	59.13 335	77.44 394	73.71 217	97.55 74	92.56 189

LFMVS		80.15 259	80.56 245	78.89 293	89.19 194	55.93 366	85.22 164	73.78 384	82.96 69	84.28 258	92.72 152	57.38 347	90.07 246	63.80 325	95.75 146	90.68 265

DPM-MVS		80.10 260	79.18 266	82.88 221	90.71 161	69.74 200	78.87 309	90.84 163	60.29 362	75.64 378	85.92 333	67.28 280	93.11 147	71.24 249	91.79 273	85.77 352

MSDG		80.06 261	79.99 260	80.25 275	83.91 334	68.04 227	77.51 329	89.19 212	77.65 134	81.94 302	83.45 367	76.37 192	86.31 320	63.31 330	86.59 368	86.41 344

FE-MVS		79.98 262	78.86 269	83.36 203	86.47 272	66.45 244	89.73 71	84.74 302	72.80 209	84.22 261	91.38 199	44.95 414	93.60 127	63.93 323	91.50 282	90.04 285

sd_testset		79.95 263	81.39 229	75.64 344	88.81 207	58.07 350	76.16 354	82.81 320	73.67 186	83.41 275	93.04 135	80.96 130	77.65 393	58.62 359	95.03 170	91.21 246

ab-mvs		79.67 264	80.56 245	76.99 325	88.48 217	56.93 360	84.70 176	86.06 274	68.95 258	80.78 323	93.08 134	75.30 199	84.62 346	56.78 368	90.90 295	89.43 294

VNet		79.31 265	80.27 250	76.44 334	87.92 230	53.95 383	75.58 361	84.35 306	74.39 179	82.23 295	90.72 227	72.84 241	84.39 351	60.38 351	93.98 209	90.97 254

thisisatest0530		79.07 266	77.33 289	84.26 175	87.13 254	64.58 260	83.66 208	75.95 367	68.86 259	85.22 228	87.36 309	38.10 431	93.57 131	75.47 193	94.28 199	94.62 87

cl22		78.97 267	78.21 281	81.24 258	77.74 402	59.01 340	77.46 332	87.13 255	65.79 300	84.32 254	85.10 347	58.96 337	90.88 214	75.36 195	92.03 267	93.84 124

patch_mono-2		78.89 268	79.39 264	77.41 321	84.78 315	68.11 225	75.60 359	83.11 316	60.96 354	79.36 340	89.89 258	75.18 200	72.97 409	73.32 228	92.30 257	91.15 248

RPMNet		78.88 269	78.28 280	80.68 269	79.58 389	62.64 284	82.58 240	94.16 33	74.80 168	75.72 376	92.59 154	48.69 386	95.56 42	73.48 223	82.91 407	83.85 378

PAPR		78.84 270	78.10 282	81.07 260	85.17 310	60.22 324	82.21 256	90.57 172	62.51 331	75.32 382	84.61 355	74.99 202	92.30 171	59.48 356	88.04 346	90.68 265

viewmambaseed2359dif		78.80 271	78.47 278	79.78 281	80.26 384	59.28 335	77.31 334	87.13 255	60.42 360	82.37 292	88.67 279	74.58 211	87.87 292	67.78 291	87.73 352	92.19 215

PVSNet_BlendedMVS		78.80 271	77.84 283	81.65 248	84.43 321	63.41 272	79.49 298	90.44 175	61.70 343	75.43 379	87.07 316	69.11 272	91.44 193	60.68 349	92.24 261	90.11 283

FMVSNet3		78.80 271	78.55 275	79.57 287	82.89 356	56.89 362	81.76 261	85.77 279	69.04 257	86.00 210	90.44 240	51.75 375	90.09 244	65.95 303	93.34 229	91.72 233

test_yl		78.71 274	78.51 276	79.32 290	84.32 325	58.84 343	78.38 315	85.33 286	75.99 150	82.49 289	86.57 321	58.01 341	90.02 248	62.74 332	92.73 248	89.10 305

DCV-MVSNet		78.71 274	78.51 276	79.32 290	84.32 325	58.84 343	78.38 315	85.33 286	75.99 150	82.49 289	86.57 321	58.01 341	90.02 248	62.74 332	92.73 248	89.10 305

test1111		78.53 276	78.85 270	77.56 318	92.22 109	47.49 420	82.61 238	69.24 416	72.43 214	85.28 227	94.20 89	51.91 373	90.07 246	65.36 311	96.45 108	95.11 72

icg_test_0407_2		78.46 277	79.68 261	74.78 351	85.76 296	62.46 288	68.51 415	87.91 239	65.23 312	82.12 298	87.92 292	77.27 172	72.67 410	71.67 243	90.74 302	89.20 299

ECVR-MVS		78.44 278	78.63 274	77.88 314	91.85 123	48.95 414	83.68 207	69.91 412	72.30 220	84.26 260	94.20 89	51.89 374	89.82 251	63.58 326	96.02 127	94.87 78

pmmvs-eth3d		78.42 279	77.04 292	82.57 228	87.44 247	74.41 135	80.86 279	79.67 344	55.68 391	84.69 244	90.31 246	60.91 321	85.42 339	62.20 336	91.59 280	87.88 328

mvs_anonymous		78.13 280	78.76 272	76.23 339	79.24 395	50.31 410	78.69 312	84.82 300	61.60 345	83.09 282	92.82 147	73.89 223	87.01 303	68.33 287	86.41 370	91.37 243

TAMVS		78.08 281	76.36 299	83.23 207	90.62 162	72.87 150	79.08 305	80.01 343	61.72 342	81.35 316	86.92 318	63.96 305	88.78 273	50.61 407	93.01 240	88.04 323

miper_enhance_ethall		77.83 282	76.93 293	80.51 271	76.15 419	58.01 352	75.47 363	88.82 216	58.05 376	83.59 271	80.69 393	64.41 298	91.20 199	73.16 235	92.03 267	92.33 206

Vis-MVSNet (Re-imp)		77.82 283	77.79 284	77.92 313	88.82 206	51.29 404	83.28 219	71.97 400	74.04 181	82.23 295	89.78 259	57.38 347	89.41 263	57.22 367	95.41 154	93.05 165

CANet_DTU		77.81 284	77.05 291	80.09 279	81.37 369	59.90 329	83.26 220	88.29 230	69.16 255	67.83 426	83.72 363	60.93 320	89.47 258	69.22 273	89.70 321	90.88 258

OpenMVS_ROB	70.19 17	77.77 285	77.46 286	78.71 297	84.39 324	61.15 310	81.18 274	82.52 321	62.45 334	83.34 277	87.37 308	66.20 286	88.66 276	64.69 318	85.02 387	86.32 345

SSC-MVS		77.55 286	81.64 219	65.29 417	90.46 165	20.33 464	73.56 379	68.28 418	85.44 41	88.18 154	94.64 68	70.93 261	81.33 372	71.25 248	92.03 267	94.20 106

MDA-MVSNet-bldmvs		77.47 287	76.90 294	79.16 292	79.03 397	64.59 259	66.58 427	75.67 370	73.15 202	88.86 132	88.99 273	66.94 282	81.23 373	64.71 317	88.22 345	91.64 237

jason		77.42 288	75.75 305	82.43 233	87.10 257	69.27 207	77.99 320	81.94 328	51.47 418	77.84 355	85.07 350	60.32 325	89.00 267	70.74 255	89.27 327	89.03 309
jason: jason.
CDS-MVSNet		77.32 289	75.40 309	83.06 211	89.00 200	72.48 161	77.90 322	82.17 326	60.81 355	78.94 346	83.49 366	59.30 333	88.76 274	54.64 387	92.37 256	87.93 327
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ICG_test_0404		77.24 290	77.75 285	75.73 342	85.76 296	62.46 288	70.84 401	87.91 239	65.23 312	72.21 401	87.92 292	67.48 279	75.53 402	71.67 243	90.74 302	89.20 299

xiu_mvs_v2_base		77.19 291	76.75 296	78.52 300	87.01 263	61.30 308	75.55 362	87.12 259	61.24 351	74.45 388	78.79 413	77.20 174	90.93 210	64.62 320	84.80 394	83.32 387

MVSTER		77.09 292	75.70 306	81.25 256	75.27 427	61.08 311	77.49 331	85.07 291	60.78 356	86.55 195	88.68 277	43.14 423	90.25 232	73.69 220	90.67 307	92.42 196

PS-MVSNAJ		77.04 293	76.53 298	78.56 299	87.09 259	61.40 306	75.26 364	87.13 255	61.25 350	74.38 390	77.22 427	76.94 180	90.94 209	64.63 319	84.83 393	83.35 386

IterMVS		76.91 294	76.34 300	78.64 298	80.91 374	64.03 266	76.30 350	79.03 347	64.88 318	83.11 280	89.16 269	59.90 329	84.46 349	68.61 283	85.15 385	87.42 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 295	75.67 307	80.34 274	80.48 382	62.16 300	73.50 380	84.80 301	57.61 380	82.24 294	87.54 303	51.31 376	87.65 295	70.40 260	93.19 236	91.23 245

CL-MVSNet_self_test		76.81 296	77.38 288	75.12 347	86.90 267	51.34 402	73.20 383	80.63 340	68.30 268	81.80 308	88.40 282	66.92 283	80.90 374	55.35 381	94.90 176	93.12 162

TR-MVS		76.77 297	75.79 304	79.72 284	86.10 290	65.79 251	77.14 335	83.02 317	65.20 316	81.40 315	82.10 381	66.30 285	90.73 220	55.57 378	85.27 381	82.65 394

MonoMVSNet		76.66 298	77.26 290	74.86 349	79.86 387	54.34 380	86.26 142	86.08 273	71.08 235	85.59 221	88.68 277	53.95 365	85.93 328	63.86 324	80.02 423	84.32 369

USDC		76.63 299	76.73 297	76.34 336	83.46 340	57.20 359	80.02 289	88.04 236	52.14 414	83.65 270	91.25 204	63.24 309	86.65 313	54.66 386	94.11 204	85.17 358

BH-w/o		76.57 300	76.07 303	78.10 309	86.88 268	65.92 250	77.63 326	86.33 268	65.69 304	80.89 321	79.95 402	68.97 274	90.74 219	53.01 397	85.25 382	77.62 430

Patchmtry		76.56 301	77.46 286	73.83 357	79.37 394	46.60 424	82.41 249	76.90 361	73.81 184	85.56 223	92.38 161	48.07 389	83.98 356	63.36 329	95.31 160	90.92 256

PVSNet_Blended		76.49 302	75.40 309	79.76 283	84.43 321	63.41 272	75.14 365	90.44 175	57.36 382	75.43 379	78.30 416	69.11 272	91.44 193	60.68 349	87.70 354	84.42 368

miper_lstm_enhance		76.45 303	76.10 302	77.51 319	76.72 413	60.97 316	64.69 431	85.04 293	63.98 323	83.20 279	88.22 284	56.67 351	78.79 390	73.22 229	93.12 237	92.78 176

lupinMVS		76.37 304	74.46 318	82.09 236	85.54 302	69.26 208	76.79 340	80.77 339	50.68 425	76.23 369	82.82 375	58.69 338	88.94 268	69.85 265	88.77 333	88.07 320

cascas		76.29 305	74.81 314	80.72 267	84.47 320	62.94 278	73.89 377	87.34 247	55.94 389	75.16 384	76.53 432	63.97 304	91.16 201	65.00 314	90.97 293	88.06 322

SD_0403		76.08 306	76.77 295	73.98 355	87.08 261	49.45 413	83.62 209	84.68 303	63.31 324	75.13 385	87.47 306	71.85 254	84.56 347	49.97 409	87.86 350	87.94 326

WB-MVS		76.06 307	80.01 259	64.19 420	89.96 179	20.58 463	72.18 390	68.19 419	83.21 65	86.46 203	93.49 123	70.19 266	78.97 388	65.96 302	90.46 312	93.02 166

thres600view7		75.97 308	75.35 311	77.85 316	87.01 263	51.84 400	80.45 284	73.26 389	75.20 165	83.10 281	86.31 327	45.54 404	89.05 266	55.03 384	92.24 261	92.66 183

GA-MVS		75.83 309	74.61 315	79.48 289	81.87 361	59.25 336	73.42 381	82.88 318	68.68 262	79.75 335	81.80 386	50.62 380	89.46 259	66.85 294	85.64 378	89.72 289

MVP-Stereo		75.81 310	73.51 327	82.71 223	89.35 189	73.62 139	80.06 287	85.20 288	60.30 361	73.96 391	87.94 289	57.89 345	89.45 260	52.02 401	74.87 441	85.06 360
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 311	75.20 312	77.27 322	75.01 430	69.47 205	78.93 306	84.88 298	46.67 432	87.08 182	87.84 297	50.44 382	71.62 415	77.42 167	88.53 336	90.72 262

thres100view900		75.45 312	75.05 313	76.66 332	87.27 249	51.88 399	81.07 275	73.26 389	75.68 156	83.25 278	86.37 324	45.54 404	88.80 270	51.98 402	90.99 290	89.31 296

ET-MVSNet_ETH3D		75.28 313	72.77 336	82.81 222	83.03 355	68.11 225	77.09 336	76.51 365	60.67 358	77.60 360	80.52 397	38.04 432	91.15 202	70.78 253	90.68 306	89.17 303

thres400		75.14 314	74.23 320	77.86 315	86.24 283	52.12 396	79.24 302	73.87 382	73.34 195	81.82 306	84.60 356	46.02 397	88.80 270	51.98 402	90.99 290	92.66 183

wuyk23d		75.13 315	79.30 265	62.63 423	75.56 423	75.18 131	80.89 278	73.10 391	75.06 167	94.76 16	95.32 45	87.73 44	52.85 455	34.16 453	97.11 87	59.85 451

EU-MVSNet		75.12 316	74.43 319	77.18 323	83.11 354	59.48 333	85.71 154	82.43 323	39.76 452	85.64 220	88.76 275	44.71 416	87.88 291	73.86 214	85.88 377	84.16 374

HyFIR lowres test		75.12 316	72.66 338	82.50 230	91.44 141	65.19 256	72.47 388	87.31 248	46.79 431	80.29 330	84.30 358	52.70 370	92.10 177	51.88 406	86.73 366	90.22 278

CMPMVS	59.41 20	75.12 316	73.57 325	79.77 282	75.84 422	67.22 231	81.21 273	82.18 325	50.78 423	76.50 365	87.66 301	55.20 361	82.99 362	62.17 338	90.64 311	89.09 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 319	72.98 334	80.73 266	84.95 312	71.71 176	76.23 352	77.59 354	52.83 408	77.73 359	86.38 323	56.35 354	84.97 343	57.72 366	87.05 361	85.51 355

tfpn200view9		74.86 320	74.23 320	76.74 331	86.24 283	52.12 396	79.24 302	73.87 382	73.34 195	81.82 306	84.60 356	46.02 397	88.80 270	51.98 402	90.99 290	89.31 296

1112_ss		74.82 321	73.74 323	78.04 311	89.57 183	60.04 325	76.49 348	87.09 260	54.31 399	73.66 394	79.80 403	60.25 326	86.76 312	58.37 360	84.15 398	87.32 335

EGC-MVSNET		74.79 322	69.99 366	89.19 67	94.89 38	87.00 15	91.89 38	86.28 269	1.09 461	2.23 463	95.98 30	81.87 120	89.48 257	79.76 130	95.96 130	91.10 249

ppachtmachnet_test		74.73 323	74.00 322	76.90 328	80.71 379	56.89 362	71.53 396	78.42 349	58.24 373	79.32 342	82.92 374	57.91 344	84.26 353	65.60 309	91.36 284	89.56 291

Patchmatch-RL test		74.48 324	73.68 324	76.89 329	84.83 314	66.54 241	72.29 389	69.16 417	57.70 378	86.76 188	86.33 325	45.79 403	82.59 363	69.63 268	90.65 310	81.54 410

PatchMatch-RL		74.48 324	73.22 331	78.27 307	87.70 237	85.26 38	75.92 357	70.09 410	64.34 321	76.09 372	81.25 391	65.87 291	78.07 392	53.86 389	83.82 400	71.48 439

XXY-MVS		74.44 326	76.19 301	69.21 392	84.61 319	52.43 395	71.70 393	77.18 359	60.73 357	80.60 324	90.96 216	75.44 196	69.35 423	56.13 373	88.33 340	85.86 351

test2506		74.12 327	73.39 328	76.28 337	91.85 123	44.20 434	84.06 192	48.20 459	72.30 220	81.90 303	94.20 89	27.22 459	89.77 254	64.81 316	96.02 127	94.87 78

reproduce_monomvs		74.09 328	73.23 330	76.65 333	76.52 414	54.54 378	77.50 330	81.40 334	65.85 299	82.86 286	86.67 320	27.38 457	84.53 348	70.24 261	90.66 309	90.89 257

CR-MVSNet		74.00 329	73.04 333	76.85 330	79.58 389	62.64 284	82.58 240	76.90 361	50.50 426	75.72 376	92.38 161	48.07 389	84.07 355	68.72 282	82.91 407	83.85 378

SSC-MVS3.2		73.90 330	75.67 307	68.61 400	84.11 330	41.28 442	64.17 433	72.83 392	72.09 223	79.08 345	87.94 289	70.31 264	73.89 408	55.99 374	94.49 191	90.67 267

Test_1112_low_res		73.90 330	73.08 332	76.35 335	90.35 167	55.95 365	73.40 382	86.17 271	50.70 424	73.14 395	85.94 332	58.31 340	85.90 332	56.51 370	83.22 404	87.20 337

test20.03		73.75 332	74.59 317	71.22 378	81.11 372	51.12 406	70.15 407	72.10 399	70.42 240	80.28 332	91.50 193	64.21 300	74.72 406	46.96 427	94.58 189	87.82 330

test_fmvs2		73.57 333	72.80 335	75.90 341	72.74 444	68.84 218	77.07 337	84.32 307	45.14 438	82.89 284	84.22 359	48.37 387	70.36 419	73.40 225	87.03 362	88.52 315

SCA		73.32 334	72.57 340	75.58 345	81.62 365	55.86 368	78.89 308	71.37 405	61.73 341	74.93 386	83.42 368	60.46 323	87.01 303	58.11 364	82.63 412	83.88 375

baseline1		73.26 335	73.54 326	72.43 371	84.92 313	47.79 419	79.89 291	74.00 380	65.93 297	78.81 347	86.28 328	56.36 353	81.63 371	56.63 369	79.04 430	87.87 329

1314		73.22 336	72.56 341	75.20 346	80.41 383	57.84 353	81.64 264	85.36 285	51.68 417	73.10 396	76.65 431	61.45 318	85.19 341	63.54 327	79.21 428	82.59 395

MVS		73.21 337	72.59 339	75.06 348	80.97 373	60.81 318	81.64 264	85.92 278	46.03 436	71.68 404	77.54 422	68.47 275	89.77 254	55.70 377	85.39 379	74.60 436

HY-MVS	64.64 18	73.03 338	72.47 342	74.71 352	83.36 345	54.19 381	82.14 259	81.96 327	56.76 388	69.57 418	86.21 329	60.03 327	84.83 345	49.58 414	82.65 410	85.11 359

thisisatest0515		73.00 339	70.52 358	80.46 272	81.45 367	59.90 329	73.16 384	74.31 379	57.86 377	76.08 373	77.78 419	37.60 435	92.12 176	65.00 314	91.45 283	89.35 295

EPNet_dtu		72.87 340	71.33 352	77.49 320	77.72 403	60.55 321	82.35 250	75.79 368	66.49 295	58.39 454	81.06 392	53.68 366	85.98 327	53.55 392	92.97 242	85.95 349
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 341	71.41 351	76.28 337	83.25 349	60.34 323	83.50 213	79.02 348	37.77 456	76.33 367	85.10 347	49.60 385	87.41 299	70.54 258	77.54 436	81.08 417

CHOSEN 1792x2688		72.45 342	70.56 357	78.13 308	90.02 178	63.08 277	68.72 414	83.16 315	42.99 446	75.92 374	85.46 340	57.22 349	85.18 342	49.87 412	81.67 414	86.14 347

testgi		72.36 343	74.61 315	65.59 414	80.56 381	42.82 439	68.29 416	73.35 388	66.87 292	81.84 305	89.93 256	72.08 251	66.92 437	46.05 431	92.54 252	87.01 339

thres200		72.34 344	71.55 350	74.70 353	83.48 339	51.60 401	75.02 366	73.71 385	70.14 246	78.56 350	80.57 396	46.20 395	88.20 284	46.99 426	89.29 325	84.32 369

FPMVS		72.29 345	72.00 344	73.14 362	88.63 213	85.00 40	74.65 370	67.39 422	71.94 226	77.80 357	87.66 301	50.48 381	75.83 400	49.95 410	79.51 424	58.58 453

FMVSNet5		72.10 346	71.69 346	73.32 360	81.57 366	53.02 390	76.77 341	78.37 350	63.31 324	76.37 366	91.85 179	36.68 436	78.98 387	47.87 423	92.45 254	87.95 325

our_test_3		71.85 347	71.59 347	72.62 368	80.71 379	53.78 384	69.72 410	71.71 404	58.80 370	78.03 352	80.51 398	56.61 352	78.84 389	62.20 336	86.04 376	85.23 357

PAPM		71.77 348	70.06 364	76.92 327	86.39 274	53.97 382	76.62 345	86.62 266	53.44 403	63.97 443	84.73 354	57.79 346	92.34 169	39.65 443	81.33 418	84.45 367

ttmdpeth		71.72 349	70.67 355	74.86 349	73.08 441	55.88 367	77.41 333	69.27 415	55.86 390	78.66 348	93.77 116	38.01 433	75.39 403	60.12 352	89.87 319	93.31 152

IB-MVS	62.13 19	71.64 350	68.97 376	79.66 286	80.80 378	62.26 297	73.94 376	76.90 361	63.27 326	68.63 422	76.79 429	33.83 440	91.84 184	59.28 357	87.26 356	84.88 361
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		71.63 351	72.30 343	69.62 389	76.47 416	52.70 393	70.03 408	80.97 337	59.18 367	79.36 340	88.21 285	60.50 322	69.12 424	58.33 362	77.62 435	87.04 338

testing3		71.53 352	70.79 354	73.77 358	88.89 205	41.86 441	76.60 347	59.12 448	72.83 208	80.97 318	82.08 383	19.80 465	87.33 301	65.12 313	91.68 278	92.13 219

test_vis3_rt		71.42 353	70.67 355	73.64 359	69.66 451	70.46 190	66.97 426	89.73 200	42.68 448	88.20 153	83.04 370	43.77 418	60.07 449	65.35 312	86.66 367	90.39 276

Anonymous20231206		71.38 354	71.88 345	69.88 386	86.31 280	54.37 379	70.39 405	74.62 375	52.57 410	76.73 364	88.76 275	59.94 328	72.06 412	44.35 435	93.23 234	83.23 389

test_vis1_n_1920		71.30 355	71.58 349	70.47 381	77.58 405	59.99 328	74.25 371	84.22 308	51.06 420	74.85 387	79.10 409	55.10 362	68.83 426	68.86 279	79.20 429	82.58 396

MIMVSNet		71.09 356	71.59 347	69.57 390	87.23 251	50.07 411	78.91 307	71.83 401	60.20 364	71.26 405	91.76 186	55.08 363	76.09 398	41.06 440	87.02 363	82.54 398

test_fmvs1_n		70.94 357	70.41 361	72.53 370	73.92 432	66.93 238	75.99 356	84.21 309	43.31 445	79.40 339	79.39 407	43.47 419	68.55 428	69.05 276	84.91 390	82.10 404

MS-PatchMatch		70.93 358	70.22 362	73.06 363	81.85 362	62.50 287	73.82 378	77.90 351	52.44 411	75.92 374	81.27 390	55.67 358	81.75 369	55.37 380	77.70 434	74.94 435

pmmvs5		70.73 359	70.07 363	72.72 366	77.03 410	52.73 392	74.14 372	75.65 371	50.36 427	72.17 402	85.37 344	55.42 360	80.67 376	52.86 398	87.59 355	84.77 362

testing3-2		70.72 360	70.97 353	69.95 385	88.93 203	34.80 455	69.85 409	66.59 429	78.42 124	77.58 361	85.55 336	31.83 446	82.08 367	46.28 428	93.73 218	92.98 170

PatchT		70.52 361	72.76 337	63.79 422	79.38 393	33.53 456	77.63 326	65.37 433	73.61 188	71.77 403	92.79 150	44.38 417	75.65 401	64.53 321	85.37 380	82.18 403

test_vis1_n		70.29 362	69.99 366	71.20 379	75.97 421	66.50 242	76.69 343	80.81 338	44.22 441	75.43 379	77.23 426	50.00 383	68.59 427	66.71 297	82.85 409	78.52 429

N_pmnet		70.20 363	68.80 378	74.38 354	80.91 374	84.81 43	59.12 444	76.45 366	55.06 394	75.31 383	82.36 380	55.74 357	54.82 454	47.02 425	87.24 357	83.52 382

tpmvs		70.16 364	69.56 369	71.96 374	74.71 431	48.13 416	79.63 293	75.45 373	65.02 317	70.26 413	81.88 385	45.34 409	85.68 337	58.34 361	75.39 440	82.08 405

new-patchmatchnet		70.10 365	73.37 329	60.29 431	81.23 371	16.95 466	59.54 442	74.62 375	62.93 328	80.97 318	87.93 291	62.83 315	71.90 413	55.24 382	95.01 173	92.00 224

YYNet1		70.06 366	70.44 359	68.90 394	73.76 434	53.42 388	58.99 445	67.20 424	58.42 372	87.10 180	85.39 343	59.82 330	67.32 434	59.79 354	83.50 403	85.96 348

MVStest1		70.05 367	69.26 370	72.41 372	58.62 463	55.59 371	76.61 346	65.58 431	53.44 403	89.28 128	93.32 127	22.91 463	71.44 417	74.08 210	89.52 323	90.21 282

MDA-MVSNet_test_wron		70.05 367	70.44 359	68.88 395	73.84 433	53.47 386	58.93 446	67.28 423	58.43 371	87.09 181	85.40 342	59.80 331	67.25 435	59.66 355	83.54 402	85.92 350

CostFormer		69.98 369	68.68 379	73.87 356	77.14 408	50.72 408	79.26 301	74.51 377	51.94 416	70.97 408	84.75 353	45.16 412	87.49 298	55.16 383	79.23 427	83.40 385

testing91		69.94 370	68.99 375	72.80 365	83.81 336	45.89 427	71.57 395	73.64 387	68.24 269	70.77 411	77.82 418	34.37 439	84.44 350	53.64 391	87.00 364	88.07 320

baseline2		69.77 371	66.89 388	78.41 303	79.51 391	58.09 349	76.23 352	69.57 413	57.50 381	64.82 441	77.45 424	46.02 397	88.44 278	53.08 394	77.83 432	88.70 313

PatchmatchNet		69.71 372	68.83 377	72.33 373	77.66 404	53.60 385	79.29 300	69.99 411	57.66 379	72.53 399	82.93 373	46.45 394	80.08 382	60.91 348	72.09 444	83.31 388
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 373	69.05 373	71.14 380	69.15 452	65.77 252	73.98 375	83.32 313	42.83 447	77.77 358	78.27 417	43.39 422	68.50 429	68.39 286	84.38 397	79.15 427

JIA-IIPM		69.41 374	66.64 392	77.70 317	73.19 438	71.24 181	75.67 358	65.56 432	70.42 240	65.18 437	92.97 141	33.64 442	83.06 360	53.52 393	69.61 450	78.79 428

Syy-MVS		69.40 375	70.03 365	67.49 405	81.72 363	38.94 447	71.00 398	61.99 439	61.38 347	70.81 409	72.36 443	61.37 319	79.30 385	64.50 322	85.18 383	84.22 371

testing99		69.27 376	68.15 383	72.63 367	83.29 347	45.45 429	71.15 397	71.08 406	67.34 285	70.43 412	77.77 420	32.24 445	84.35 352	53.72 390	86.33 372	88.10 319

UnsupCasMVSNet_bld		69.21 377	69.68 368	67.82 403	79.42 392	51.15 405	67.82 420	75.79 368	54.15 400	77.47 362	85.36 345	59.26 334	70.64 418	48.46 420	79.35 426	81.66 408

test_cas_vis1_n_1920		69.20 378	69.12 371	69.43 391	73.68 435	62.82 281	70.38 406	77.21 358	46.18 435	80.46 329	78.95 411	52.03 372	65.53 442	65.77 308	77.45 437	79.95 425

gg-mvs-nofinetune		68.96 379	69.11 372	68.52 401	76.12 420	45.32 430	83.59 210	55.88 453	86.68 33	64.62 442	97.01 12	30.36 450	83.97 357	44.78 434	82.94 406	76.26 432

WBMVS		68.76 380	68.43 380	69.75 388	83.29 347	40.30 445	67.36 422	72.21 398	57.09 385	77.05 363	85.53 338	33.68 441	80.51 378	48.79 418	90.90 295	88.45 316

WB-MVSnew		68.72 381	69.01 374	67.85 402	83.22 351	43.98 435	74.93 367	65.98 430	55.09 393	73.83 392	79.11 408	65.63 293	71.89 414	38.21 448	85.04 386	87.69 331

tpm2		68.45 382	66.83 389	73.30 361	78.93 399	48.50 415	79.76 292	71.76 402	47.50 430	69.92 415	83.60 364	42.07 425	88.40 280	48.44 421	79.51 424	83.01 392

tpm		67.95 383	68.08 384	67.55 404	78.74 400	43.53 437	75.60 359	67.10 427	54.92 395	72.23 400	88.10 286	42.87 424	75.97 399	52.21 400	80.95 422	83.15 390

WTY-MVS		67.91 384	68.35 381	66.58 410	80.82 377	48.12 417	65.96 428	72.60 393	53.67 402	71.20 406	81.68 388	58.97 336	69.06 425	48.57 419	81.67 414	82.55 397

testing11		67.38 385	65.93 393	71.73 376	83.37 344	46.60 424	70.95 400	69.40 414	62.47 333	66.14 430	76.66 430	31.22 447	84.10 354	49.10 416	84.10 399	84.49 365

test-LLR		67.21 386	66.74 390	68.63 398	76.45 417	55.21 374	67.89 417	67.14 425	62.43 336	65.08 438	72.39 441	43.41 420	69.37 421	61.00 346	84.89 391	81.31 412

testing222		66.93 387	65.30 400	71.81 375	83.38 343	45.83 428	72.06 391	67.50 421	64.12 322	69.68 417	76.37 433	27.34 458	83.00 361	38.88 444	88.38 339	86.62 343

sss		66.92 388	67.26 386	65.90 412	77.23 407	51.10 407	64.79 430	71.72 403	52.12 415	70.13 414	80.18 400	57.96 343	65.36 443	50.21 408	81.01 420	81.25 414

KD-MVS_2432*1600		66.87 389	65.81 396	70.04 383	67.50 453	47.49 420	62.56 436	79.16 345	61.21 352	77.98 353	80.61 394	25.29 461	82.48 364	53.02 395	84.92 388	80.16 423

miper_refine_blended		66.87 389	65.81 396	70.04 383	67.50 453	47.49 420	62.56 436	79.16 345	61.21 352	77.98 353	80.61 394	25.29 461	82.48 364	53.02 395	84.92 388	80.16 423

dmvs_re		66.81 391	66.98 387	66.28 411	76.87 411	58.68 347	71.66 394	72.24 396	60.29 362	69.52 419	73.53 440	52.38 371	64.40 445	44.90 433	81.44 417	75.76 433

tpm cat1		66.76 392	65.21 401	71.42 377	77.09 409	50.62 409	78.01 319	73.68 386	44.89 439	68.64 421	79.00 410	45.51 406	82.42 366	49.91 411	70.15 447	81.23 416

UWE-MVS		66.43 393	65.56 399	69.05 393	84.15 329	40.98 443	73.06 385	64.71 435	54.84 396	76.18 371	79.62 406	29.21 452	80.50 379	38.54 447	89.75 320	85.66 353

PVSNet	58.17 21	66.41 394	65.63 398	68.75 396	81.96 360	49.88 412	62.19 438	72.51 395	51.03 421	68.04 424	75.34 437	50.84 378	74.77 404	45.82 432	82.96 405	81.60 409

tpmrst		66.28 395	66.69 391	65.05 418	72.82 443	39.33 446	78.20 318	70.69 409	53.16 406	67.88 425	80.36 399	48.18 388	74.75 405	58.13 363	70.79 446	81.08 417

Patchmatch-test		65.91 396	67.38 385	61.48 428	75.51 424	43.21 438	68.84 413	63.79 437	62.48 332	72.80 398	83.42 368	44.89 415	59.52 451	48.27 422	86.45 369	81.70 407

ADS-MVSNet2		65.87 397	63.64 406	72.55 369	73.16 439	56.92 361	67.10 424	74.81 374	49.74 428	66.04 432	82.97 371	46.71 392	77.26 395	42.29 437	69.96 448	83.46 383

myMVS_eth3d28		65.83 398	65.85 394	65.78 413	83.42 342	35.71 453	67.29 423	68.01 420	67.58 282	69.80 416	77.72 421	32.29 444	74.30 407	37.49 449	89.06 329	87.32 335

test_vis1_rt		65.64 399	64.09 403	70.31 382	66.09 457	70.20 194	61.16 439	81.60 332	38.65 453	72.87 397	69.66 446	52.84 368	60.04 450	56.16 372	77.77 433	80.68 421

mvsany_test3		65.48 400	62.97 409	73.03 364	69.99 450	76.17 124	64.83 429	43.71 461	43.68 443	80.25 333	87.05 317	52.83 369	63.09 448	51.92 405	72.44 443	79.84 426

test-mter		65.00 401	63.79 405	68.63 398	76.45 417	55.21 374	67.89 417	67.14 425	50.98 422	65.08 438	72.39 441	28.27 455	69.37 421	61.00 346	84.89 391	81.31 412

ETVMVS		64.67 402	63.34 408	68.64 397	83.44 341	41.89 440	69.56 412	61.70 444	61.33 349	68.74 420	75.76 435	28.76 453	79.35 384	34.65 452	86.16 375	84.67 364

myMVS_eth3d		64.66 403	63.89 404	66.97 408	81.72 363	37.39 450	71.00 398	61.99 439	61.38 347	70.81 409	72.36 443	20.96 464	79.30 385	49.59 413	85.18 383	84.22 371

test0.0.03 1		64.66 403	64.36 402	65.57 415	75.03 429	46.89 423	64.69 431	61.58 445	62.43 336	71.18 407	77.54 422	43.41 420	68.47 430	40.75 442	82.65 410	81.35 411

UBG		64.34 405	63.35 407	67.30 406	83.50 338	40.53 444	67.46 421	65.02 434	54.77 397	67.54 428	74.47 439	32.99 443	78.50 391	40.82 441	83.58 401	82.88 393

test_f		64.31 406	65.85 394	59.67 432	66.54 456	62.24 299	57.76 448	70.96 407	40.13 450	84.36 252	82.09 382	46.93 391	51.67 456	61.99 339	81.89 413	65.12 447

pmmvs3		62.47 407	60.02 420	69.80 387	71.58 447	64.00 267	70.52 404	58.44 451	39.77 451	66.05 431	75.84 434	27.10 460	72.28 411	46.15 430	84.77 395	73.11 437

EPMVS		62.47 407	62.63 411	62.01 424	70.63 449	38.74 448	74.76 368	52.86 455	53.91 401	67.71 427	80.01 401	39.40 429	66.60 438	55.54 379	68.81 452	80.68 421

ADS-MVSNet		61.90 409	62.19 413	61.03 429	73.16 439	36.42 452	67.10 424	61.75 442	49.74 428	66.04 432	82.97 371	46.71 392	63.21 446	42.29 437	69.96 448	83.46 383

PMMVS		61.65 410	60.38 417	65.47 416	65.40 460	69.26 208	63.97 434	61.73 443	36.80 457	60.11 449	68.43 448	59.42 332	66.35 439	48.97 417	78.57 431	60.81 450

E-PMN		61.59 411	61.62 414	61.49 427	66.81 455	55.40 372	53.77 451	60.34 447	66.80 293	58.90 452	65.50 451	40.48 428	66.12 440	55.72 376	86.25 373	62.95 449

TESTMET0.1,1		61.29 412	60.32 418	64.19 420	72.06 445	51.30 403	67.89 417	62.09 438	45.27 437	60.65 448	69.01 447	27.93 456	64.74 444	56.31 371	81.65 416	76.53 431

MVS-HIRNet		61.16 413	62.92 410	55.87 435	79.09 396	35.34 454	71.83 392	57.98 452	46.56 433	59.05 451	91.14 208	49.95 384	76.43 397	38.74 445	71.92 445	55.84 454

EMVS		61.10 414	60.81 416	61.99 425	65.96 458	55.86 368	53.10 452	58.97 450	67.06 290	56.89 456	63.33 452	40.98 426	67.03 436	54.79 385	86.18 374	63.08 448

DSMNet-mixed		60.98 415	61.61 415	59.09 434	72.88 442	45.05 432	74.70 369	46.61 460	26.20 458	65.34 436	90.32 245	55.46 359	63.12 447	41.72 439	81.30 419	69.09 443

dp		60.70 416	60.29 419	61.92 426	72.04 446	38.67 449	70.83 402	64.08 436	51.28 419	60.75 447	77.28 425	36.59 437	71.58 416	47.41 424	62.34 454	75.52 434

dmvs_testset		60.59 417	62.54 412	54.72 437	77.26 406	27.74 460	74.05 374	61.00 446	60.48 359	65.62 435	67.03 450	55.93 356	68.23 432	32.07 456	69.46 451	68.17 444

CHOSEN 280x420		59.08 418	56.52 424	66.76 409	76.51 415	64.39 263	49.62 453	59.00 449	43.86 442	55.66 457	68.41 449	35.55 438	68.21 433	43.25 436	76.78 439	67.69 445

mvsany_test1		58.48 419	56.47 425	64.50 419	65.90 459	68.21 224	56.95 449	42.11 462	38.30 454	65.69 434	77.19 428	56.96 350	59.35 452	46.16 429	58.96 455	65.93 446

UWE-MVS-28		58.44 420	57.71 422	60.65 430	73.58 436	31.23 457	69.68 411	48.80 458	53.12 407	61.79 445	78.83 412	30.98 448	68.40 431	21.58 459	80.99 421	82.33 402

PVSNet_0	51.08 22	56.10 421	54.97 426	59.48 433	75.12 428	53.28 389	55.16 450	61.89 441	44.30 440	59.16 450	62.48 453	54.22 364	65.91 441	35.40 451	47.01 456	59.25 452

new_pmnet		55.69 422	57.66 423	49.76 438	75.47 425	30.59 458	59.56 441	51.45 456	43.62 444	62.49 444	75.48 436	40.96 427	49.15 458	37.39 450	72.52 442	69.55 442

PMMVS2		55.64 423	59.27 421	44.74 439	64.30 461	12.32 467	40.60 454	49.79 457	53.19 405	65.06 440	84.81 352	53.60 367	49.76 457	32.68 455	89.41 324	72.15 438

MVE	40.22 23	51.82 424	50.47 427	55.87 435	62.66 462	51.91 398	31.61 456	39.28 463	40.65 449	50.76 458	74.98 438	56.24 355	44.67 459	33.94 454	64.11 453	71.04 441
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 425	42.65 428	39.67 440	70.86 448	21.11 462	61.01 440	21.42 467	57.36 382	57.97 455	50.06 456	16.40 466	58.73 453	21.03 460	27.69 460	39.17 456

kuosan		30.83 426	32.17 429	26.83 442	53.36 464	19.02 465	57.90 447	20.44 468	38.29 455	38.01 459	37.82 458	15.18 467	33.45 461	7.74 462	20.76 461	28.03 457

test_method		30.46 427	29.60 430	33.06 441	17.99 466	3.84 469	13.62 457	73.92 381	2.79 460	18.29 462	53.41 455	28.53 454	43.25 460	22.56 457	35.27 458	52.11 455

cdsmvs_eth3d_5k		20.81 428	27.75 431	0.00 447	0.00 470	0.00 472	0.00 458	85.44 284	0.00 465	0.00 466	82.82 375	81.46 124	0.00 466	0.00 465	0.00 464	0.00 462

tmp_tt		20.25 429	24.50 432	7.49 444	4.47 467	8.70 468	34.17 455	25.16 465	1.00 462	32.43 461	18.49 459	39.37 430	9.21 463	21.64 458	43.75 457	4.57 459

ab-mvs-re		6.65 430	8.87 433	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	79.80 403	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

pcd_1.5k_mvsjas		6.41 431	8.55 434	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	76.94 180	0.00 466	0.00 465	0.00 464	0.00 462

test123		6.27 432	8.08 435	0.84 445	1.11 469	0.57 470	62.90 435	0.82 469	0.54 463	1.07 465	2.75 464	1.26 468	0.30 464	1.04 463	1.26 463	1.66 460

testmvs		5.91 433	7.65 436	0.72 446	1.20 468	0.37 471	59.14 443	0.67 470	0.49 464	1.11 464	2.76 463	0.94 469	0.24 465	1.02 464	1.47 462	1.55 461

mmdepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

monomultidepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

test_blank		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet_test		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

DCPMVS		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet-low-res		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uncertanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

Regformer		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

WAC-MVS						37.39 450								52.61 399

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

MSC_two_6792asdad				88.81 73	91.55 135	77.99 97		91.01 159					96.05 9	87.45 28	98.17 37	92.40 199

PC_three_1452									58.96 369	90.06 103	91.33 201	80.66 134	93.03 151	75.78 189	95.94 133	92.48 193

No_MVS				88.81 73	91.55 135	77.99 97		91.01 159					96.05 9	87.45 28	98.17 37	92.40 199

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					0.00 470

eth-test					0.00 470

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

RE-MVS-def			92.61 9		94.13 57	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 37	97.60 71	92.73 177

IU-MVS					94.18 52	72.64 154		90.82 164	56.98 386	89.67 116				85.78 62	97.92 52	93.28 153

OPU-MVS				88.27 85	91.89 121	77.83 100	90.47 56				91.22 205	81.12 128	94.68 78	74.48 201	95.35 156	92.29 209

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	92.04 222

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

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

save fliter					93.75 65	77.44 106	86.31 140	89.72 201	70.80 237

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 47	95.78 32	87.41 30	98.21 34	92.98 170

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	92.02 223

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																83.88 375

test_part2					93.86 63	77.77 101				92.84 52

sam_mvs1												46.11 396				83.88 375

sam_mvs												45.92 401

ambc				82.98 214	90.55 164	64.86 258	88.20 103	89.15 214		89.40 125	93.96 105	71.67 258	91.38 197	78.83 144	96.55 102	92.71 180

MTGPA								91.81 133

test_post1							78.85 310				3.13 461	45.19 411	80.13 381	58.11 364

test_post											3.10 462	45.43 407	77.22 396

patchmatchnet-post											81.71 387	45.93 400	87.01 303

GG-mvs-BLEND				67.16 407	73.36 437	46.54 426	84.15 190	55.04 454		58.64 453	61.95 454	29.93 451	83.87 358	38.71 446	76.92 438	71.07 440

MTMP							90.66 49	33.14 464

gm-plane-assit					75.42 426	44.97 433			52.17 412		72.36 443		87.90 290	54.10 388

test9_res														80.83 119	96.45 108	90.57 270

TEST9					92.34 104	79.70 80	83.94 196	90.32 181	65.41 309	84.49 248	90.97 214	82.03 115	93.63 123

test_8					92.09 113	78.87 88	83.82 201	90.31 183	65.79 300	84.36 252	90.96 216	81.93 117	93.44 136

agg_prior2														79.68 132	96.16 120	90.22 278

agg_prior					91.58 133	77.69 103		90.30 184		84.32 254			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	70.81 251	96.14 121	94.16 110

test_prior4						78.97 87	84.59 179

test_prior2							83.37 217		75.43 162	84.58 245	91.57 191	81.92 119		79.54 136	96.97 90

test_prior				86.32 116	90.59 163	71.99 170		92.85 97					94.17 102			92.80 175

旧先验2							81.73 262		56.88 387	86.54 201			84.90 344	72.81 236

新几何2							81.72 263

新几何1				82.95 216	93.96 61	78.56 91		80.24 341	55.45 392	83.93 265	91.08 211	71.19 260	88.33 282	65.84 306	93.07 238	81.95 406

旧先验1					91.97 117	71.77 171		81.78 329			91.84 180	73.92 222			93.65 221	83.61 381

无先验							82.81 235	85.62 282	58.09 375				91.41 196	67.95 290		84.48 366

原ACMM2							82.26 255

原ACMM1				84.60 163	92.81 94	74.01 137		91.50 140	62.59 330	82.73 288	90.67 232	76.53 189	94.25 94	69.24 271	95.69 148	85.55 354

test222					93.31 78	76.54 116	79.38 299	77.79 352	52.59 409	82.36 293	90.84 224	66.83 284			91.69 277	81.25 414

testdata2													86.43 318	63.52 328

segment_acmp												81.94 116

testdata				79.54 288	92.87 89	72.34 163		80.14 342	59.91 365	85.47 225	91.75 187	67.96 278	85.24 340	68.57 285	92.18 264	81.06 419

testdata1							79.62 294		73.95 183

test12				86.57 111	90.74 159	72.63 156		90.69 167		82.76 287		79.20 147	94.80 75		95.32 158	92.27 211

plane_prior7					93.45 72	77.31 109

plane_prior6					92.61 95	76.54 116						74.84 205

plane_prior5								93.61 60					95.22 59	80.78 120	95.83 141	94.46 93

plane_prior4											92.95 142

plane_prior3						76.85 114			77.79 133	86.55 195

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								0.00 471

nn								0.00 471

door-mid								74.45 378

lessismore_v0				85.95 127	91.10 152	70.99 185		70.91 408		91.79 71	94.42 78	61.76 317	92.93 154	79.52 137	93.03 239	93.93 119

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

test11								91.46 141

door								72.57 394

HQP5-MVS						70.66 187

HQP-NCC					91.19 147		84.77 170		73.30 197	80.55 326

ACMP_Plane					91.19 147		84.77 170		73.30 197	80.55 326

BP-MVS														77.30 168

HQP4-MVS										80.56 325			94.61 82			93.56 145

HQP3-MVS								92.68 102							94.47 192

HQP2-MVS												72.10 249

NP-MVS					91.95 118	74.55 134					90.17 252

MDTV_nov1_ep13_2view						27.60 461	70.76 403		46.47 434	61.27 446		45.20 410		49.18 415		83.75 380

MDTV_nov1_ep13			68.29 382		78.03 401	43.87 436	74.12 373	72.22 397	52.17 412	67.02 429	85.54 337	45.36 408	80.85 375	55.73 375	84.42 396

ACMMP++_ref															95.74 147

ACMMP++															97.35 80

Test By Simon												79.09 149

ITE_SJBPF				90.11 49	90.72 160	84.97 41		90.30 184	81.56 82	90.02 105	91.20 207	82.40 103	90.81 217	73.58 222	94.66 187	94.56 89

DeepMVS_CX				24.13 443	32.95 465	29.49 459		21.63 466	12.07 459	37.95 460	45.07 457	30.84 449	19.21 462	17.94 461	33.06 459	23.69 458

High-resolution multi-view benchmark