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 71	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 51	97.23 2	95.32 2	99.01 2	97.26 9	80.16 154	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 54	96.29 22	88.16 36	94.17 106	86.07 56	98.48 18	97.22 18

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 69	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 82	93.16 150	91.10 2	97.53 81	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 54	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 43	95.72 39	89.60 5	98.27 28	92.08 241

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 29	89.13 7	98.26 30	91.76 252

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 29	89.13 7	98.26 30	91.76 252

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 62	95.13 52	90.65 10	95.34 59	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 82	90.26 4	98.44 20	93.63 151

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 60	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 50	87.16 38	97.60 75	92.73 197

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	94.02 101	90.15 17	95.67 41	86.82 43	97.34 85	92.19 236

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 72	91.77 76	93.94 109	90.55 13	95.73 38	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 88	85.17 39	92.47 27	95.05 15	87.65 28	93.21 47	94.39 81	90.09 18	95.08 70	86.67 45	97.60 75	94.18 117

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 70	86.15 24	93.37 10	95.10 14	90.28 10	92.11 68	95.03 54	89.75 21	94.93 74	79.95 134	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 75	92.39 65	94.14 93	89.15 26	95.62 42	87.35 33	98.24 32	94.56 93
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	92.60 121	83.09 69	91.54 78	94.25 87	87.67 46	95.51 50	87.21 37	98.11 40	93.12 180

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 77	83.16 68	91.06 88	94.00 102	88.26 33	95.71 40	87.28 36	98.39 23	92.55 211

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 81	85.07 45	89.99 110	94.03 100	86.57 59	95.80 31	87.35 33	97.62 73	94.20 114

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 38	91.81 148	84.07 55	92.00 71	94.40 80	86.63 58	95.28 62	88.59 11	98.31 26	92.30 228

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 112	88.22 23	88.53 147	97.64 6	83.45 97	94.55 90	86.02 60	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 87	81.99 78	91.40 80	94.17 92	87.51 47	95.87 20	87.74 22	97.76 60	93.99 126

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 67	94.27 25	82.35 76	93.67 38	94.82 60	91.18 5	95.52 48	85.36 67	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 85	81.91 80	90.88 95	94.21 88	87.75 43	95.87 20	87.60 27	97.71 63	93.83 136

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 86	81.99 78	91.47 79	93.96 106	88.35 32	95.56 45	87.74 22	97.74 62	92.85 194

ZNCC-MVS		91.26 25	91.34 32	91.01 34	95.73 21	83.05 56	92.18 32	94.22 30	80.14 101	91.29 84	93.97 103	87.93 42	95.87 20	88.65 10	97.96 51	94.12 122

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 90	78.04 96	92.84 16	94.14 37	83.33 66	93.90 29	95.73 34	88.77 28	96.41 3	87.60 27	97.98 48	92.98 190
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 45	91.93 15	95.67 23	85.85 31	90.00 67	93.90 49	80.32 98	91.74 77	94.41 79	88.17 35	95.98 13	86.37 49	97.99 46	93.96 129

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 65	80.97 70	91.49 45	93.48 75	82.82 73	92.60 61	93.97 103	88.19 34	96.29 6	87.61 26	98.20 36	94.39 108
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 46	91.83 20	96.18 11	86.88 17	92.20 31	93.03 102	82.59 74	88.52 148	94.37 82	86.74 57	95.41 57	86.32 50	98.21 34	93.19 175
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 42	90.82 37	95.45 28	82.73 59	91.75 43	93.74 59	80.98 91	91.38 81	93.80 113	87.20 51	95.80 31	87.10 40	97.69 65	93.93 130

MP-MVS-pluss		90.81 31	91.08 39	89.99 50	95.97 14	79.88 77	88.13 110	94.51 19	75.79 159	92.94 51	94.96 55	88.36 31	95.01 72	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 83	93.00 89	76.26 122	89.65 80	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 141	78.35 156	98.76 4	95.61 55

ACMMP_NAP		90.65 33	91.07 41	89.42 62	95.93 16	79.54 82	89.95 71	93.68 66	77.65 136	91.97 72	94.89 57	88.38 30	95.45 55	89.27 6	97.87 56	93.27 170

ACMM	79.39 9	90.65 33	90.99 43	89.63 58	95.03 34	83.53 51	89.62 81	93.35 80	79.20 114	93.83 32	93.60 123	90.81 8	92.96 157	85.02 74	98.45 19	92.41 218
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 55	91.38 28	89.03 204	84.23 49	93.58 6	94.68 18	90.65 8	90.33 104	93.95 108	84.50 84	95.37 58	80.87 124	95.50 158	94.53 97

ACMP	79.16 10	90.54 36	90.60 53	90.35 45	94.36 51	80.98 69	89.16 92	94.05 42	79.03 117	92.87 53	93.74 118	90.60 12	95.21 65	82.87 102	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 39	88.88 71	93.38 79	78.65 90	89.15 93	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 84	97.81 58	91.70 256
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		90.48 38	91.14 36	88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	83.72 60	93.24 43	93.18 134	87.06 52	95.85 24	84.99 75	97.69 65	93.54 161

SED-MVS		90.46 39	91.64 22	86.93 109	94.18 55	72.65 156	90.47 60	93.69 63	83.77 58	94.11 27	94.27 83	90.28 15	95.84 27	86.03 57	97.92 52	92.29 230

SMA-MVS		90.31 40	90.48 54	89.83 55	95.31 30	79.52 83	90.98 52	93.24 88	75.37 168	92.84 55	95.28 48	85.58 74	96.09 8	87.92 18	97.76 60	93.88 133
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 41	90.80 48	88.68 78	92.86 94	77.09 111	91.19 49	95.74 6	81.38 86	92.28 67	93.80 113	86.89 56	94.64 85	85.52 66	97.51 82	94.30 113

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 190	89.99 69	93.05 99	86.53 35	94.29 23	96.27 23	82.69 106	94.08 109	86.25 53	97.63 71	97.82 8

ME-MVS		90.09 43	90.66 51	88.38 85	92.82 97	76.12 126	89.40 90	93.70 61	83.72 60	92.39 65	93.18 134	88.02 40	95.47 53	84.99 75	97.69 65	93.54 161

PMVS	80.48 6	90.08 44	90.66 51	88.34 87	96.71 3	92.97 2	90.31 64	89.57 225	88.51 21	90.11 106	95.12 53	90.98 7	88.92 285	77.55 172	97.07 92	83.13 416
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 45	91.09 38	87.00 107	91.55 139	72.64 158	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 141	95.88 18	87.41 31	95.94 137	92.48 214

DVP-MVS		90.06 46	91.32 33	86.29 121	94.16 58	72.56 162	90.54 57	91.01 176	83.61 63	93.75 35	94.65 65	89.76 19	95.78 35	86.42 47	97.97 49	90.55 293
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 46	91.92 16	84.47 173	96.56 6	58.83 367	89.04 94	92.74 114	91.40 6	96.12 5	96.06 29	87.23 50	95.57 44	79.42 144	98.74 6	99.00 2

PEN-MVS		90.03 48	91.88 19	84.48 172	96.57 5	58.88 364	88.95 95	93.19 90	91.62 5	96.01 7	96.16 27	87.02 54	95.60 43	78.69 152	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 49	89.74 60	90.83 36	93.16 86	80.37 74	91.91 41	93.11 95	81.10 89	95.32 14	97.24 10	72.94 259	94.85 76	85.07 71	97.78 59	97.26 16

DTE-MVSNet		89.98 50	91.91 18	84.21 182	96.51 7	57.84 376	88.93 96	92.84 110	91.92 4	96.16 4	96.23 24	86.95 55	95.99 12	79.05 148	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 50	89.84 58	90.41 43	94.91 37	84.50 48	89.49 86	93.98 44	79.68 106	92.09 69	93.89 111	83.80 92	93.10 153	82.67 106	98.04 41	93.64 150

TestfortrainingZip a		89.97 52	90.77 49	87.58 100	94.38 48	73.21 150	92.12 33	93.85 53	77.53 140	93.24 43	93.18 134	87.06 52	95.85 24	87.89 19	97.69 65	93.68 145

3Dnovator+	83.92 2	89.97 52	89.66 61	90.92 35	91.27 148	81.66 66	91.25 47	94.13 38	88.89 15	88.83 139	94.26 86	77.55 183	95.86 23	84.88 78	95.87 143	95.24 65

WR-MVS_H		89.91 54	91.31 34	85.71 138	96.32 9	62.39 301	89.54 84	93.31 84	90.21 12	95.57 11	95.66 37	81.42 138	95.90 17	80.94 123	98.80 3	98.84 5

OPM-MVS		89.80 55	89.97 56	89.27 64	94.76 40	79.86 78	86.76 137	92.78 113	78.78 120	92.51 62	93.64 122	88.13 37	93.84 120	84.83 80	97.55 78	94.10 123
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 56	89.27 67	91.30 29	93.51 73	84.79 44	89.89 73	90.63 186	70.00 263	94.55 19	96.67 17	87.94 41	93.59 132	84.27 86	95.97 133	95.52 56

anonymousdsp		89.73 57	88.88 77	92.27 8	89.82 184	86.67 18	90.51 59	90.20 207	69.87 264	95.06 15	96.14 28	84.28 87	93.07 154	87.68 24	96.34 115	97.09 20

test_djsdf		89.62 58	89.01 71	91.45 26	92.36 107	82.98 57	91.98 39	90.08 210	71.54 239	94.28 25	96.54 19	81.57 136	94.27 96	86.26 51	96.49 109	97.09 20

XVG-OURS-SEG-HR		89.59 59	89.37 65	90.28 46	94.47 43	85.95 27	86.84 133	93.91 48	80.07 102	86.75 201	93.26 131	93.64 2	90.93 222	84.60 83	90.75 322	93.97 128

APD-MVS		89.54 60	89.63 62	89.26 65	92.57 100	81.34 68	90.19 66	93.08 98	80.87 93	91.13 86	93.19 133	86.22 66	95.97 14	82.23 112	97.18 90	90.45 295
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 61	88.81 80	91.19 32	93.38 79	84.72 45	89.70 76	90.29 204	69.27 271	94.39 21	96.38 21	86.02 69	93.52 137	83.96 88	95.92 139	95.34 60

CPTT-MVS		89.39 62	88.98 73	90.63 40	95.09 33	86.95 16	92.09 37	92.30 130	79.74 105	87.50 184	92.38 171	81.42 138	93.28 146	83.07 98	97.24 88	91.67 257

ACMH	76.49 14	89.34 63	91.14 36	83.96 190	92.50 103	70.36 199	89.55 82	93.84 56	81.89 81	94.70 17	95.44 44	90.69 9	88.31 305	83.33 94	98.30 27	93.20 174
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 64	89.12 68	89.84 53	88.67 215	85.64 35	90.61 55	93.17 91	86.02 38	93.12 48	95.30 46	84.94 79	89.44 277	74.12 225	96.10 128	94.45 102

APD_test2		89.30 64	89.12 68	89.84 53	88.67 215	85.64 35	90.61 55	93.17 91	86.02 38	93.12 48	95.30 46	84.94 79	89.44 277	74.12 225	96.10 128	94.45 102

CP-MVSNet		89.27 66	90.91 46	84.37 174	96.34 8	58.61 370	88.66 103	92.06 137	90.78 7	95.67 8	95.17 51	81.80 133	95.54 47	79.00 149	98.69 10	98.95 4

XVG-OURS		89.18 67	88.83 79	90.23 47	94.28 52	86.11 26	85.91 152	93.60 69	80.16 100	89.13 135	93.44 125	83.82 91	90.98 219	83.86 90	95.30 166	93.60 154

DeepC-MVS	82.31 4	89.15 68	89.08 70	89.37 63	93.64 71	79.07 86	88.54 106	94.20 31	73.53 196	89.71 118	94.82 60	85.09 78	95.77 37	84.17 87	98.03 43	93.26 172
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 69	90.72 50	84.31 180	97.00 2	64.33 272	89.67 79	88.38 246	88.84 17	94.29 23	97.57 7	90.48 14	91.26 207	72.57 256	97.65 70	97.34 15

MSP-MVS		89.08 70	88.16 87	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 125	89.16 133	92.25 180	72.03 273	96.36 4	88.21 13	90.93 313	92.98 190
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 71	89.88 57	86.22 125	91.63 133	77.07 112	89.82 74	93.77 58	78.90 118	92.88 52	92.29 178	86.11 67	90.22 250	86.24 54	97.24 88	91.36 265
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 72	88.45 83	90.38 44	94.92 36	85.85 31	89.70 76	91.27 168	78.20 128	86.69 205	92.28 179	80.36 152	95.06 71	86.17 55	96.49 109	90.22 299

Elysia		88.71 73	88.89 75	88.19 90	91.26 149	72.96 152	88.10 111	93.59 70	84.31 51	90.42 100	94.10 96	74.07 236	94.82 77	88.19 14	95.92 139	96.80 27

StellarMVS		88.71 73	88.89 75	88.19 90	91.26 149	72.96 152	88.10 111	93.59 70	84.31 51	90.42 100	94.10 96	74.07 236	94.82 77	88.19 14	95.92 139	96.80 27

test_0402		88.65 75	89.58 64	85.88 134	92.55 101	72.22 170	84.01 201	89.44 228	88.63 20	94.38 22	95.77 32	86.38 65	93.59 132	79.84 135	95.21 167	91.82 250

DP-MVS		88.60 76	89.01 71	87.36 102	91.30 146	77.50 104	87.55 119	92.97 106	87.95 26	89.62 122	92.87 153	84.56 83	93.89 117	77.65 170	96.62 104	90.70 285

APD_test1		88.40 77	87.91 89	89.88 52	89.50 190	86.65 20	89.98 70	91.91 143	84.26 53	90.87 96	93.92 110	82.18 121	89.29 281	73.75 233	94.81 186	93.70 144

Anonymous20231211		88.40 77	89.62 63	84.73 164	90.46 169	65.27 261	88.86 97	93.02 103	87.15 30	93.05 50	97.10 11	82.28 119	92.02 183	76.70 183	97.99 46	96.88 26

PS-MVSNAJss		88.31 79	87.90 90	89.56 60	93.31 81	77.96 99	87.94 115	91.97 140	70.73 252	94.19 26	96.67 17	76.94 197	94.57 88	83.07 98	96.28 117	96.15 38

OMC-MVS		88.19 80	87.52 94	90.19 48	91.94 124	81.68 65	87.49 122	93.17 91	76.02 153	88.64 144	91.22 220	84.24 88	93.37 144	77.97 168	97.03 93	95.52 56

CS-MVS		88.14 81	87.67 93	89.54 61	89.56 188	79.18 85	90.47 60	94.77 17	79.37 112	84.32 273	89.33 288	83.87 90	94.53 91	82.45 108	94.89 182	94.90 76

TSAR-MVS + MP.		88.14 81	87.82 91	89.09 69	95.72 22	76.74 115	92.49 26	91.19 171	67.85 298	86.63 206	94.84 59	79.58 160	95.96 15	87.62 25	94.50 195	94.56 93
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 83	89.79 59	82.98 221	93.26 83	63.94 276	91.10 50	89.64 222	85.07 45	90.91 92	91.09 226	89.16 25	91.87 188	82.03 113	95.87 143	93.13 177

EC-MVSNet		88.01 84	88.32 86	87.09 104	89.28 195	72.03 173	90.31 64	96.31 4	80.88 92	85.12 247	89.67 281	84.47 85	95.46 54	82.56 107	96.26 120	93.77 142

RPSCF		88.00 85	86.93 108	91.22 31	90.08 177	89.30 5	89.68 78	91.11 172	79.26 113	89.68 119	94.81 63	82.44 110	87.74 316	76.54 188	88.74 359	96.61 32

AllTest		87.97 86	87.40 98	89.68 56	91.59 134	83.40 52	89.50 85	95.44 11	79.47 108	88.00 164	93.03 144	82.66 107	91.47 197	70.81 270	96.14 125	94.16 119

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 150	94.02 63	64.13 273	84.38 193	91.29 164	84.88 48	92.06 70	93.84 112	86.45 62	93.73 122	73.22 247	98.66 11	97.69 9

nrg030		87.85 88	88.49 82	85.91 132	90.07 179	69.73 207	87.86 116	94.20 31	74.04 186	92.70 60	94.66 64	85.88 70	91.50 195	79.72 137	97.32 86	96.50 34

CNVR-MVS		87.81 89	87.68 92	88.21 89	92.87 92	77.30 110	85.25 170	91.23 169	77.31 143	87.07 194	91.47 210	82.94 102	94.71 81	84.67 82	96.27 119	92.62 205

HQP_MVS		87.75 90	87.43 97	88.70 77	93.45 75	76.42 119	89.45 87	93.61 67	79.44 110	86.55 207	92.95 150	74.84 223	95.22 63	80.78 126	95.83 145	94.46 100

sc_t1		87.70 91	88.94 74	83.99 188	93.47 74	67.15 238	85.05 175	88.21 254	86.81 32	91.87 74	97.65 5	85.51 76	87.91 311	74.22 219	97.63 71	96.92 25

MM		87.64 92	87.15 100	89.09 69	89.51 189	76.39 121	88.68 102	86.76 285	84.54 50	83.58 292	93.78 115	73.36 254	96.48 2	87.98 17	96.21 121	94.41 107

MVSMamba_PlusPlus		87.53 93	88.86 78	83.54 207	92.03 120	62.26 305	91.49 45	92.62 118	88.07 25	88.07 161	96.17 26	72.24 268	95.79 34	84.85 79	94.16 208	92.58 209

NCCC		87.36 94	86.87 109	88.83 72	92.32 110	78.84 89	86.58 141	91.09 174	78.77 121	84.85 259	90.89 237	80.85 144	95.29 60	81.14 121	95.32 163	92.34 226

DeepPCF-MVS	81.24 5	87.28 95	86.21 121	90.49 42	91.48 143	84.90 42	83.41 226	92.38 126	70.25 260	89.35 130	90.68 247	82.85 105	94.57 88	79.55 141	95.95 136	92.00 245

SixPastTwentyTwo		87.20 96	87.45 96	86.45 118	92.52 102	69.19 217	87.84 117	88.05 255	81.66 83	94.64 18	96.53 20	65.94 310	94.75 80	83.02 100	96.83 98	95.41 58

fmvsm_s_conf0.5_n_9		87.04 97	87.02 105	87.08 105	89.67 186	75.87 129	84.60 186	89.74 217	74.40 183	89.92 114	93.41 126	80.45 150	90.63 237	86.66 46	94.37 201	94.73 90

SPE-MVS-test		87.00 98	86.43 115	88.71 76	89.46 191	77.46 105	89.42 89	95.73 7	77.87 134	81.64 334	87.25 333	82.43 111	94.53 91	77.65 170	96.46 111	94.14 121

UniMVSNet (Re)		86.87 99	86.98 107	86.55 116	93.11 87	68.48 227	83.80 211	92.87 108	80.37 96	89.61 124	91.81 196	77.72 179	94.18 104	75.00 212	98.53 16	96.99 24

Vis-MVSNet		86.86 100	86.58 112	87.72 97	92.09 117	77.43 107	87.35 123	92.09 136	78.87 119	84.27 278	94.05 99	78.35 171	93.65 125	80.54 130	91.58 297	92.08 241
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 101	87.06 103	86.17 128	92.86 94	67.02 242	82.55 254	91.56 154	83.08 70	90.92 90	91.82 195	78.25 172	93.99 111	74.16 223	98.35 24	97.49 13

DU-MVS		86.80 102	86.99 106	86.21 126	93.24 84	67.02 242	83.16 237	92.21 131	81.73 82	90.92 90	91.97 186	77.20 191	93.99 111	74.16 223	98.35 24	97.61 10

casdiffmvs_mvg		86.72 103	87.51 95	84.36 176	87.09 270	65.22 262	84.16 197	94.23 28	77.89 132	91.28 85	93.66 121	84.35 86	92.71 163	80.07 131	94.87 185	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		86.68 104	86.52 113	87.18 103	85.94 310	78.30 92	86.93 130	92.20 132	65.94 318	89.16 133	93.16 139	83.10 100	89.89 265	87.81 21	94.43 199	93.35 165

tt0320-xc		86.67 105	88.41 84	81.44 269	93.45 75	60.44 339	83.96 203	88.50 242	87.26 29	90.90 94	97.90 3	85.61 73	86.40 343	70.14 282	98.01 45	97.47 14

IS-MVSNet		86.66 106	86.82 111	86.17 128	92.05 119	66.87 246	91.21 48	88.64 239	86.30 37	89.60 125	92.59 163	69.22 291	94.91 75	73.89 230	97.89 55	96.72 29

tt0320		86.63 107	88.36 85	81.41 270	93.57 72	60.73 336	84.37 194	88.61 241	87.00 31	90.75 97	97.98 2	85.54 75	86.45 341	69.75 287	97.70 64	97.06 22

v10		86.54 108	87.10 102	84.84 158	88.16 233	63.28 283	86.64 140	92.20 132	75.42 167	92.81 57	94.50 72	74.05 239	94.06 110	83.88 89	96.28 117	97.17 19

pmmvs6		86.52 109	88.06 88	81.90 255	92.22 113	62.28 304	84.66 185	89.15 233	83.54 65	89.85 115	97.32 8	88.08 39	86.80 334	70.43 279	97.30 87	96.62 31

NormalMVS		86.47 110	85.32 145	89.94 51	94.43 44	80.42 72	88.63 104	93.59 70	74.56 178	85.12 247	90.34 260	66.19 307	94.20 101	76.57 186	98.44 20	95.19 68

PHI-MVS		86.38 111	85.81 131	88.08 92	88.44 224	77.34 108	89.35 91	93.05 99	73.15 209	84.76 262	87.70 322	78.87 165	94.18 104	80.67 128	96.29 116	92.73 197

CSCG		86.26 112	86.47 114	85.60 140	90.87 161	74.26 139	87.98 114	91.85 144	80.35 97	89.54 128	88.01 309	79.09 163	92.13 179	75.51 205	95.06 174	90.41 296

DeepC-MVS_fast	80.27 8	86.23 113	85.65 137	87.96 95	91.30 146	76.92 113	87.19 125	91.99 139	70.56 253	84.96 254	90.69 246	80.01 156	95.14 68	78.37 155	95.78 149	91.82 250
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 114	86.83 110	84.36 176	87.82 241	62.35 303	86.42 144	91.33 163	76.78 147	92.73 59	94.48 74	73.41 251	93.72 123	83.10 97	95.41 159	97.01 23

Anonymous20240529		86.20 115	87.13 101	83.42 209	90.19 174	64.55 269	84.55 188	90.71 183	85.85 40	89.94 113	95.24 50	82.13 122	90.40 245	69.19 294	96.40 114	95.31 62

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 223	86.91 278	70.38 198	85.31 169	92.61 120	75.59 163	88.32 155	92.87 153	82.22 120	88.63 296	88.80 9	92.82 257	89.83 309

test_fmvsmconf0.1_n		86.18 117	85.88 129	87.08 105	85.26 326	78.25 93	85.82 156	91.82 146	65.33 333	88.55 146	92.35 177	82.62 109	89.80 267	86.87 42	94.32 203	93.18 176

CDPH-MVS		86.17 118	85.54 138	88.05 94	92.25 111	75.45 132	83.85 208	92.01 138	65.91 320	86.19 218	91.75 200	83.77 93	94.98 73	77.43 175	96.71 102	93.73 143

NR-MVSNet		86.00 119	86.22 120	85.34 147	93.24 84	64.56 268	82.21 269	90.46 192	80.99 90	88.42 151	91.97 186	77.56 182	93.85 118	72.46 257	98.65 12	97.61 10

train_agg		85.98 120	85.28 146	88.07 93	92.34 108	79.70 80	83.94 204	90.32 199	65.79 322	84.49 267	90.97 231	81.93 128	93.63 127	81.21 120	96.54 107	90.88 279

KinetiMVS		85.95 121	86.10 124	85.50 144	87.56 251	69.78 205	83.70 214	89.83 216	80.42 95	87.76 175	93.24 132	73.76 245	91.54 194	85.03 73	93.62 229	95.19 68

FC-MVSNet-test		85.93 122	87.05 104	82.58 237	92.25 111	56.44 387	85.75 157	93.09 97	77.33 142	91.94 73	94.65 65	74.78 225	93.41 143	75.11 211	98.58 14	97.88 7

test_fmvsmconf_n		85.88 123	85.51 139	86.99 108	84.77 335	78.21 94	85.40 167	91.39 161	65.32 334	87.72 177	91.81 196	82.33 114	89.78 268	86.68 44	94.20 206	92.99 188

Effi-MVS+-dtu		85.82 124	83.38 200	93.14 4	87.13 265	91.15 3	87.70 118	88.42 245	74.57 177	83.56 293	85.65 357	78.49 170	94.21 100	72.04 259	92.88 253	94.05 125

TAPA-MVS	77.73 12	85.71 125	84.83 156	88.37 86	88.78 214	79.72 79	87.15 127	93.50 74	69.17 272	85.80 230	89.56 282	80.76 146	92.13 179	73.21 252	95.51 157	93.25 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 126	86.14 122	83.58 203	87.97 235	67.13 239	87.55 119	94.32 22	73.44 199	88.47 149	87.54 325	86.45 62	91.06 217	75.76 201	93.76 220	92.54 212

canonicalmvs		85.50 126	86.14 122	83.58 203	87.97 235	67.13 239	87.55 119	94.32 22	73.44 199	88.47 149	87.54 325	86.45 62	91.06 217	75.76 201	93.76 220	92.54 212

fmvsm_s_conf0.5_n_8		85.48 128	85.75 134	84.68 167	87.10 268	69.98 203	84.28 195	92.68 115	74.77 174	87.90 168	92.36 176	73.94 240	90.41 244	85.95 62	92.74 259	93.66 146

EPP-MVSNet		85.47 129	85.04 151	86.77 113	91.52 142	69.37 212	91.63 44	87.98 258	81.51 85	87.05 195	91.83 194	66.18 309	95.29 60	70.75 273	96.89 95	95.64 53

GeoE		85.45 130	85.81 131	84.37 174	90.08 177	67.07 241	85.86 155	91.39 161	72.33 228	87.59 181	90.25 266	84.85 81	92.37 173	78.00 166	91.94 286	93.66 146

E6		85.44 131	86.37 116	82.66 233	88.23 228	61.86 310	83.59 218	93.69 63	73.64 193	87.61 180	93.30 129	85.85 71	91.26 207	78.02 164	93.40 234	94.86 82

E5		85.44 131	86.37 116	82.66 233	88.22 229	61.86 310	83.59 218	93.70 61	73.64 193	87.62 179	93.30 129	85.85 71	91.26 207	78.02 164	93.40 234	94.86 82

MGCNet		85.37 133	84.58 168	87.75 96	85.28 325	73.36 144	86.54 143	85.71 302	77.56 139	81.78 332	92.47 169	70.29 285	96.02 11	85.59 65	95.96 134	93.87 134

FIs		85.35 134	86.27 119	82.60 236	91.86 126	57.31 380	85.10 174	93.05 99	75.83 158	91.02 89	93.97 103	73.57 247	92.91 161	73.97 229	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 135	85.36 144	84.81 160	85.80 313	76.13 125	85.15 173	92.32 129	61.40 371	91.33 82	90.85 240	83.76 94	86.16 349	84.31 85	93.28 240	92.15 239

casdiffmvs		85.21 136	85.85 130	83.31 212	86.17 302	62.77 290	83.03 239	93.93 47	74.69 176	88.21 158	92.68 162	82.29 118	91.89 187	77.87 169	93.75 223	95.27 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_10		85.20 137	85.25 147	85.02 155	86.01 308	71.31 185	84.96 176	91.76 150	69.10 274	88.90 136	92.56 166	73.84 243	90.63 237	86.88 41	93.26 241	93.13 177

baseline		85.20 137	85.93 127	83.02 219	86.30 297	62.37 302	84.55 188	93.96 45	74.48 180	87.12 189	92.03 185	82.30 116	91.94 184	78.39 154	94.21 205	94.74 89

SSM_0404		85.16 139	85.09 149	85.36 146	90.14 176	69.52 210	86.17 149	91.58 152	74.41 181	86.55 207	91.49 207	78.54 166	93.97 113	73.71 234	93.21 245	92.59 208

K. test v3		85.14 140	84.73 158	86.37 119	91.13 155	69.63 209	85.45 165	76.68 389	84.06 56	92.44 64	96.99 13	62.03 337	94.65 84	80.58 129	93.24 242	94.83 85

mmtdpeth		85.13 141	85.78 133	83.17 217	84.65 337	74.71 135	85.87 154	90.35 198	77.94 131	83.82 285	96.96 15	77.75 177	80.03 408	78.44 153	96.21 121	94.79 88

EI-MVSNet-Vis-set		85.12 142	84.53 171	86.88 110	84.01 351	72.76 155	83.91 207	85.18 312	80.44 94	88.75 141	85.49 361	80.08 155	91.92 185	82.02 114	90.85 318	95.97 44

fmvsm_l_conf0.5_n_3		85.11 143	84.96 153	85.56 141	87.49 254	75.69 131	84.71 183	90.61 188	67.64 302	84.88 257	92.05 184	82.30 116	88.36 303	83.84 91	91.10 306	92.62 205

MGCFI-Net		85.04 144	85.95 126	82.31 247	87.52 252	63.59 279	86.23 148	93.96 45	73.46 197	88.07 161	87.83 320	86.46 61	90.87 227	76.17 195	93.89 216	92.47 216

EI-MVSNet-UG-set		85.04 144	84.44 174	86.85 111	83.87 355	72.52 164	83.82 209	85.15 313	80.27 99	88.75 141	85.45 363	79.95 157	91.90 186	81.92 117	90.80 321	96.13 39

X-MVStestdata		85.04 144	82.70 219	92.08 9	95.64 24	86.25 22	92.64 20	93.33 81	85.07 45	89.99 110	16.05 485	86.57 59	95.80 31	87.35 33	97.62 73	94.20 114

MSLP-MVS++		85.00 147	86.03 125	81.90 255	91.84 129	71.56 183	86.75 138	93.02 103	75.95 156	87.12 189	89.39 286	77.98 174	89.40 280	77.46 173	94.78 187	84.75 388

F-COLMAP		84.97 148	83.42 198	89.63 58	92.39 106	83.40 52	88.83 98	91.92 142	73.19 208	80.18 356	89.15 292	77.04 195	93.28 146	65.82 328	92.28 274	92.21 235

SSM_0407		84.89 149	84.85 155	85.01 156	89.13 199	68.97 220	85.60 161	91.58 152	74.41 181	85.68 231	91.49 207	78.54 166	93.69 124	73.71 234	93.47 231	92.38 223

balanced_conf03		84.80 150	85.40 142	83.00 220	88.95 207	61.44 316	90.42 63	92.37 128	71.48 241	88.72 143	93.13 140	70.16 287	95.15 67	79.26 146	94.11 209	92.41 218

3Dnovator	80.37 7	84.80 150	84.71 161	85.06 153	86.36 295	74.71 135	88.77 100	90.00 212	75.65 161	84.96 254	93.17 138	74.06 238	91.19 212	78.28 158	91.09 307	89.29 320

SymmetryMVS		84.79 152	83.54 192	88.55 79	92.44 105	80.42 72	88.63 104	82.37 349	74.56 178	85.12 247	90.34 260	66.19 307	94.20 101	76.57 186	95.68 153	91.03 273

E4		84.75 153	85.46 140	82.61 235	88.17 231	61.55 315	81.39 283	93.55 73	73.13 211	86.83 198	92.83 155	84.17 89	91.48 196	76.92 182	92.19 278	94.80 87

IterMVS-LS		84.73 154	84.98 152	83.96 190	87.35 258	63.66 277	83.25 231	89.88 215	76.06 151	89.62 122	92.37 174	73.40 253	92.52 168	78.16 161	94.77 189	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 155	84.34 179	85.49 145	90.18 175	75.86 130	79.23 326	87.13 275	73.35 201	85.56 238	89.34 287	83.60 96	90.50 241	76.64 185	94.05 213	90.09 305

HQP-MVS		84.61 156	84.06 184	86.27 122	91.19 151	70.66 193	84.77 178	92.68 115	73.30 204	80.55 348	90.17 271	72.10 269	94.61 86	77.30 177	94.47 197	93.56 158

v1192		84.57 157	84.69 163	84.21 182	87.75 243	62.88 287	83.02 240	91.43 158	69.08 275	89.98 112	90.89 237	72.70 263	93.62 130	82.41 109	94.97 179	96.13 39

fmvsm_s_conf0.5_n_11		84.56 158	84.69 163	84.15 185	86.53 284	71.29 186	85.53 162	92.62 118	70.54 254	82.75 309	91.20 222	77.33 186	88.55 299	83.80 92	91.93 287	92.61 207

fmvsm_s_conf0.5_n_5		84.56 158	84.71 161	84.11 186	87.92 238	72.09 172	84.80 177	88.64 239	64.43 344	88.77 140	91.78 198	78.07 173	87.95 310	85.85 63	92.18 279	92.30 228

FMVSNet1		84.55 160	85.45 141	81.85 257	90.27 173	61.05 326	86.83 134	88.27 251	78.57 124	89.66 121	95.64 38	75.43 215	90.68 234	69.09 295	95.33 162	93.82 137

v1144		84.54 161	84.72 160	84.00 187	87.67 247	62.55 294	82.97 242	90.93 179	70.32 258	89.80 116	90.99 230	73.50 248	93.48 139	81.69 119	94.65 193	95.97 44

Gipuma		84.44 162	86.33 118	78.78 318	84.20 347	73.57 143	89.55 82	90.44 193	84.24 54	84.38 270	94.89 57	76.35 210	80.40 405	76.14 196	96.80 100	82.36 426
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 163	84.27 180	84.74 163	87.25 261	70.84 192	83.55 221	88.45 244	68.64 284	86.29 217	91.31 216	74.97 221	88.42 301	87.87 20	90.07 337	94.95 75

MCST-MVS		84.36 164	83.93 188	85.63 139	91.59 134	71.58 181	83.52 222	92.13 134	61.82 364	83.96 283	89.75 279	79.93 158	93.46 140	78.33 157	94.34 202	91.87 249

VDDNet		84.35 165	85.39 143	81.25 272	95.13 32	59.32 355	85.42 166	81.11 360	86.41 36	87.41 185	96.21 25	73.61 246	90.61 239	66.33 320	96.85 96	93.81 140

ETV-MVS		84.31 166	83.91 189	85.52 142	88.58 220	70.40 197	84.50 192	93.37 76	78.76 122	84.07 281	78.72 437	80.39 151	95.13 69	73.82 232	92.98 251	91.04 272

v1240		84.30 167	84.51 172	83.65 200	87.65 248	61.26 322	82.85 246	91.54 155	67.94 295	90.68 99	90.65 251	71.71 277	93.64 126	82.84 103	94.78 187	96.07 41

MVS_111021_LR		84.28 168	83.76 190	85.83 136	89.23 197	83.07 55	80.99 293	83.56 337	72.71 220	86.07 221	89.07 294	81.75 135	86.19 348	77.11 179	93.36 236	88.24 340

h-mvs33		84.25 169	82.76 218	88.72 75	91.82 131	82.60 60	84.00 202	84.98 319	71.27 242	86.70 203	90.55 256	63.04 334	93.92 116	78.26 159	94.20 206	89.63 312

v144192		84.24 170	84.41 175	83.71 199	87.59 250	61.57 314	82.95 243	91.03 175	67.82 299	89.80 116	90.49 257	73.28 255	93.51 138	81.88 118	94.89 182	96.04 43

dcpmvs_2		84.23 171	85.14 148	81.50 267	88.61 219	61.98 309	82.90 245	93.11 95	68.66 283	92.77 58	92.39 170	78.50 169	87.63 319	76.99 181	92.30 271	94.90 76

v1921920		84.23 171	84.37 177	83.79 195	87.64 249	61.71 313	82.91 244	91.20 170	67.94 295	90.06 107	90.34 260	72.04 272	93.59 132	82.32 110	94.91 180	96.07 41

VDD-MVS		84.23 171	84.58 168	83.20 215	91.17 154	65.16 264	83.25 231	84.97 320	79.79 104	87.18 188	94.27 83	74.77 226	90.89 225	69.24 291	96.54 107	93.55 160

v2v482		84.09 174	84.24 181	83.62 201	87.13 265	61.40 317	82.71 249	89.71 220	72.19 231	89.55 126	91.41 211	70.70 283	93.20 148	81.02 122	93.76 220	96.25 37

EG-PatchMatch MVS		84.08 175	84.11 183	83.98 189	92.22 113	72.61 161	82.20 271	87.02 281	72.63 221	88.86 137	91.02 229	78.52 168	91.11 215	73.41 242	91.09 307	88.21 341

E2		84.06 176	84.61 165	82.40 245	87.49 254	61.31 319	81.03 291	93.36 77	71.83 236	86.02 223	91.87 188	82.91 103	91.37 204	75.66 203	91.33 301	94.53 97

E3		84.06 176	84.61 165	82.40 245	87.49 254	61.30 320	81.03 291	93.36 77	71.83 236	86.01 224	91.87 188	82.91 103	91.36 205	75.66 203	91.33 301	94.53 97

fmvsm_s_conf0.5_n_6		84.05 178	84.14 182	83.81 193	87.75 243	71.17 188	83.42 225	91.10 173	67.90 297	84.53 265	90.70 245	73.01 258	88.73 293	85.09 70	93.72 225	91.53 262

DP-MVS Recon		84.05 178	83.22 203	86.52 117	91.73 132	75.27 133	83.23 234	92.40 124	72.04 233	82.04 323	88.33 305	77.91 176	93.95 115	66.17 321	95.12 172	90.34 298

viewmacassd2359aftdt		84.04 180	84.78 157	81.81 260	86.43 289	60.32 341	81.95 273	92.82 111	71.56 238	86.06 222	92.98 146	81.79 134	90.28 246	76.18 194	93.24 242	94.82 86

TransMVSNet (Re)		84.02 181	85.74 135	78.85 316	91.00 158	55.20 400	82.29 265	87.26 270	79.65 107	88.38 153	95.52 41	83.00 101	86.88 332	67.97 309	96.60 105	94.45 102

Baseline_NR-MVSNet		84.00 182	85.90 128	78.29 329	91.47 144	53.44 412	82.29 265	87.00 284	79.06 116	89.55 126	95.72 36	77.20 191	86.14 350	72.30 258	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 183	84.46 173	82.53 240	86.11 305	70.65 195	82.45 259	89.17 232	67.72 301	86.74 202	91.49 207	79.20 161	85.86 359	84.71 81	92.60 264	91.07 271

TSAR-MVS + GP.		83.95 184	82.69 220	87.72 97	89.27 196	81.45 67	83.72 213	81.58 358	74.73 175	85.66 234	86.06 352	72.56 265	92.69 165	75.44 207	95.21 167	89.01 333

LuminaMVS		83.94 185	83.51 193	85.23 148	89.78 185	71.74 176	84.76 181	87.27 269	72.60 222	89.31 131	90.60 255	64.04 323	90.95 220	79.08 147	94.11 209	92.99 188

alignmvs		83.94 185	83.98 186	83.80 194	87.80 242	67.88 234	84.54 190	91.42 160	73.27 207	88.41 152	87.96 310	72.33 266	90.83 228	76.02 198	94.11 209	92.69 201

Effi-MVS+		83.90 187	84.01 185	83.57 205	87.22 263	65.61 260	86.55 142	92.40 124	78.64 123	81.34 339	84.18 383	83.65 95	92.93 159	74.22 219	87.87 373	92.17 238

fmvsm_s_conf0.1_n_2		83.82 188	83.49 195	84.84 158	85.99 309	70.19 201	80.93 294	87.58 265	67.26 308	87.94 167	92.37 174	71.40 279	88.01 307	86.03 57	91.87 288	96.31 36

mvs5depth		83.82 188	84.54 170	81.68 263	82.23 380	68.65 225	86.89 131	89.90 214	80.02 103	87.74 176	97.86 4	64.19 322	82.02 393	76.37 190	95.63 156	94.35 109

CANet		83.79 190	82.85 217	86.63 114	86.17 302	72.21 171	83.76 212	91.43 158	77.24 144	74.39 414	87.45 329	75.36 216	95.42 56	77.03 180	92.83 256	92.25 234

pm-mvs1		83.69 191	84.95 154	79.91 301	90.04 181	59.66 352	82.43 260	87.44 266	75.52 165	87.85 171	95.26 49	81.25 140	85.65 363	68.74 301	96.04 130	94.42 106

AdaColmap		83.66 192	83.69 191	83.57 205	90.05 180	72.26 169	86.29 146	90.00 212	78.19 129	81.65 333	87.16 335	83.40 98	94.24 99	61.69 364	94.76 190	84.21 398

viewdifsd2359ckpt09		83.64 193	83.18 206	85.03 154	87.26 260	66.99 244	85.32 168	93.83 57	65.57 328	84.99 253	89.40 285	77.30 187	93.57 135	71.16 269	93.80 219	94.54 96

MIMVSNet1		83.63 194	84.59 167	80.74 283	94.06 62	62.77 290	82.72 248	84.53 328	77.57 138	90.34 103	95.92 31	76.88 203	85.83 360	61.88 362	97.42 83	93.62 152

fmvsm_s_conf0.5_n_2		83.62 195	83.29 202	84.62 168	85.43 323	70.18 202	80.61 301	87.24 271	67.14 309	87.79 173	91.87 188	71.79 276	87.98 309	86.00 61	91.77 291	95.71 50

test_fmvsm_n_1920		83.60 196	82.89 214	85.74 137	85.22 327	77.74 102	84.12 199	90.48 190	59.87 391	86.45 216	91.12 225	75.65 213	85.89 357	82.28 111	90.87 316	93.58 156

WR-MVS		83.56 197	84.40 176	81.06 277	93.43 78	54.88 401	78.67 335	85.02 317	81.24 87	90.74 98	91.56 205	72.85 260	91.08 216	68.00 308	98.04 41	97.23 17

CNLPA		83.55 198	83.10 209	84.90 157	89.34 194	83.87 50	84.54 190	88.77 236	79.09 115	83.54 294	88.66 302	74.87 222	81.73 395	66.84 315	92.29 273	89.11 326

viewcassd2359sk11		83.53 199	83.96 187	82.25 248	86.97 277	61.13 324	80.80 298	93.22 89	70.97 249	85.36 242	91.08 227	81.84 132	91.29 206	74.79 214	90.58 333	94.33 111

LCM-MVSNet-Re		83.48 200	85.06 150	78.75 319	85.94 310	55.75 393	80.05 307	94.27 25	76.47 148	96.09 6	94.54 71	83.31 99	89.75 271	59.95 375	94.89 182	90.75 282

hse-mvs2		83.47 201	81.81 235	88.47 82	91.03 157	82.27 61	82.61 250	83.69 335	71.27 242	86.70 203	86.05 353	63.04 334	92.41 171	78.26 159	93.62 229	90.71 284

V42		83.47 201	83.37 201	83.75 197	83.16 374	63.33 282	81.31 285	90.23 206	69.51 268	90.91 92	90.81 242	74.16 235	92.29 177	80.06 132	90.22 335	95.62 54

VPA-MVSNet		83.47 201	84.73 158	79.69 306	90.29 172	57.52 379	81.30 287	88.69 238	76.29 149	87.58 183	94.44 75	80.60 149	87.20 326	66.60 318	96.82 99	94.34 110

mamba_0408		83.44 204	82.88 215	85.11 151	89.13 199	68.97 220	72.73 412	91.28 165	72.90 214	85.68 231	90.61 253	76.78 204	93.97 113	73.37 244	93.47 231	92.38 223

viewdifsd2359ckpt07		83.41 205	84.35 178	80.56 290	85.84 312	58.93 363	79.47 318	91.28 165	73.01 213	87.59 181	92.07 183	85.24 77	88.68 294	73.59 239	91.11 305	94.09 124

PAPM_NR		83.23 206	83.19 205	83.33 211	90.90 160	65.98 256	88.19 109	90.78 182	78.13 130	80.87 344	87.92 314	73.49 250	92.42 170	70.07 283	88.40 362	91.60 259

CLD-MVS		83.18 207	82.64 221	84.79 161	89.05 203	67.82 235	77.93 345	92.52 122	68.33 287	85.07 250	81.54 412	82.06 125	92.96 157	69.35 290	97.91 54	93.57 157
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 208	85.68 136	75.65 368	81.24 392	45.26 456	79.94 309	92.91 107	83.83 57	91.33 82	96.88 16	80.25 153	85.92 353	68.89 298	95.89 142	95.76 48

FA-MVS(test-final)		83.13 209	83.02 210	83.43 208	86.16 304	66.08 255	88.00 113	88.36 247	75.55 164	85.02 251	92.75 160	65.12 316	92.50 169	74.94 213	91.30 303	91.72 254

114514_t		83.10 210	82.54 224	84.77 162	92.90 91	69.10 219	86.65 139	90.62 187	54.66 423	81.46 336	90.81 242	76.98 196	94.38 94	72.62 255	96.18 123	90.82 281

E3new		83.08 211	83.39 199	82.14 250	86.49 286	61.00 329	80.64 299	93.12 94	70.30 259	84.78 261	90.34 260	80.85 144	91.24 210	74.20 222	89.83 342	94.17 118

RRT-MVS		82.97 212	83.44 196	81.57 265	85.06 330	58.04 374	87.20 124	90.37 196	77.88 133	88.59 145	93.70 120	63.17 331	93.05 155	76.49 189	88.47 361	93.62 152

viewmanbaseed2359cas		82.95 213	83.43 197	81.52 266	85.18 328	60.03 346	81.36 284	92.38 126	69.55 267	84.84 260	91.38 212	79.85 159	90.09 259	74.22 219	92.09 281	94.43 105

BP-MVS1		82.81 214	81.67 237	86.23 123	87.88 240	68.53 226	86.06 151	84.36 329	75.65 161	85.14 246	90.19 268	45.84 425	94.42 93	85.18 69	94.72 191	95.75 49

FE-MVSNET2		82.80 215	83.51 193	80.67 288	89.08 202	58.46 371	82.40 262	89.26 230	71.25 245	88.24 157	94.07 98	75.75 212	89.56 272	65.91 326	95.67 155	93.98 127

UGNet		82.78 216	81.64 238	86.21 126	86.20 301	76.24 123	86.86 132	85.68 303	77.07 145	73.76 418	92.82 156	69.64 288	91.82 190	69.04 297	93.69 226	90.56 292
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		82.75 217	81.93 233	85.19 149	82.08 381	80.15 76	85.53 162	88.76 237	68.01 292	85.58 237	87.75 321	71.80 275	86.85 333	74.02 228	93.87 217	88.58 336

EI-MVSNet		82.61 218	82.42 226	83.20 215	83.25 371	63.66 277	83.50 223	85.07 314	76.06 151	86.55 207	85.10 369	73.41 251	90.25 247	78.15 163	90.67 328	95.68 52

QAPM		82.59 219	82.59 223	82.58 237	86.44 288	66.69 247	89.94 72	90.36 197	67.97 294	84.94 256	92.58 165	72.71 262	92.18 178	70.63 276	87.73 376	88.85 334

fmvsm_s_conf0.1_n_a		82.58 220	81.93 233	84.50 171	87.68 246	73.35 145	86.14 150	77.70 378	61.64 369	85.02 251	91.62 202	77.75 177	86.24 345	82.79 104	87.07 384	93.91 132

Fast-Effi-MVS+-dtu		82.54 221	81.41 247	85.90 133	85.60 318	76.53 118	83.07 238	89.62 224	73.02 212	79.11 366	83.51 388	80.74 147	90.24 249	68.76 300	89.29 349	90.94 276

MVS_Test		82.47 222	83.22 203	80.22 297	82.62 379	57.75 378	82.54 255	91.96 141	71.16 247	82.89 305	92.52 168	77.41 184	90.50 241	80.04 133	87.84 375	92.40 220

viewdifsd2359ckpt11		82.46 223	82.98 212	80.88 280	83.53 358	61.00 329	79.46 319	85.97 298	69.48 269	87.89 169	91.31 216	82.10 123	88.61 297	74.28 217	92.86 254	93.02 184

viewmsd2359difaftdt		82.46 223	82.99 211	80.88 280	83.52 359	61.00 329	79.46 319	85.97 298	69.48 269	87.89 169	91.31 216	82.10 123	88.61 297	74.28 217	92.86 254	93.02 184

v148		82.31 225	82.48 225	81.81 260	85.59 319	59.66 352	81.47 281	86.02 296	72.85 216	88.05 163	90.65 251	70.73 282	90.91 224	75.15 210	91.79 289	94.87 78

API-MVS		82.28 226	82.61 222	81.30 271	86.29 298	69.79 204	88.71 101	87.67 264	78.42 126	82.15 319	84.15 384	77.98 174	91.59 193	65.39 331	92.75 258	82.51 425

MVSFormer		82.23 227	81.57 243	84.19 184	85.54 320	69.26 214	91.98 39	90.08 210	71.54 239	76.23 394	85.07 372	58.69 359	94.27 96	86.26 51	88.77 357	89.03 331

viewdifsd2359ckpt13		82.22 228	81.98 232	82.95 223	85.48 322	64.44 270	83.17 236	92.11 135	65.97 317	83.72 288	89.73 280	77.60 181	90.80 230	70.61 277	89.42 347	93.59 155

fmvsm_s_conf0.5_n_a		82.21 229	81.51 246	84.32 179	86.56 283	73.35 145	85.46 164	77.30 382	61.81 365	84.51 266	90.88 239	77.36 185	86.21 347	82.72 105	86.97 389	93.38 164

EIA-MVS		82.19 230	81.23 254	85.10 152	87.95 237	69.17 218	83.22 235	93.33 81	70.42 255	78.58 371	79.77 428	77.29 188	94.20 101	71.51 265	88.96 355	91.93 248

GDP-MVS		82.17 231	80.85 262	86.15 130	88.65 217	68.95 223	85.65 160	93.02 103	68.42 285	83.73 287	89.54 283	45.07 436	94.31 95	79.66 139	93.87 217	95.19 68

fmvsm_s_conf0.1_n		82.17 231	81.59 241	83.94 192	86.87 281	71.57 182	85.19 172	77.42 381	62.27 363	84.47 269	91.33 214	76.43 207	85.91 355	83.14 95	87.14 382	94.33 111

PCF-MVS	74.62 15	82.15 233	80.92 260	85.84 135	89.43 192	72.30 168	80.53 302	91.82 146	57.36 407	87.81 172	89.92 276	77.67 180	93.63 127	58.69 383	95.08 173	91.58 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 234	80.31 269	87.45 101	90.86 162	80.29 75	85.88 153	90.65 185	68.17 290	76.32 393	86.33 347	73.12 257	92.61 167	61.40 367	90.02 339	89.44 315
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 235	81.54 245	83.60 202	83.94 352	73.90 141	83.35 228	86.10 292	58.97 393	83.80 286	90.36 259	74.23 233	86.94 331	82.90 101	90.22 335	89.94 307

fmvsm_s_conf0.5_n_7		82.04 236	82.05 230	82.01 253	86.98 276	71.07 189	78.70 333	89.45 227	68.07 291	78.14 374	91.61 203	74.19 234	85.92 353	79.61 140	91.73 292	89.05 330

GBi-Net		82.02 237	82.07 228	81.85 257	86.38 292	61.05 326	86.83 134	88.27 251	72.43 223	86.00 225	95.64 38	63.78 327	90.68 234	65.95 323	93.34 237	93.82 137

test1		82.02 237	82.07 228	81.85 257	86.38 292	61.05 326	86.83 134	88.27 251	72.43 223	86.00 225	95.64 38	63.78 327	90.68 234	65.95 323	93.34 237	93.82 137

OpenMVS	76.72 13	81.98 239	82.00 231	81.93 254	84.42 342	68.22 229	88.50 107	89.48 226	66.92 312	81.80 330	91.86 191	72.59 264	90.16 253	71.19 268	91.25 304	87.40 358

KD-MVS_self_test		81.93 240	83.14 208	78.30 328	84.75 336	52.75 416	80.37 304	89.42 229	70.24 261	90.26 105	93.39 127	74.55 232	86.77 335	68.61 303	96.64 103	95.38 59

fmvsm_s_conf0.5_n		81.91 241	81.30 251	83.75 197	86.02 307	71.56 183	84.73 182	77.11 385	62.44 360	84.00 282	90.68 247	76.42 208	85.89 357	83.14 95	87.11 383	93.81 140

SDMVSNet		81.90 242	83.17 207	78.10 332	88.81 212	62.45 300	76.08 379	86.05 295	73.67 191	83.41 295	93.04 142	82.35 113	80.65 402	70.06 284	95.03 175	91.21 267

tfpnnormal		81.79 243	82.95 213	78.31 327	88.93 208	55.40 396	80.83 297	82.85 344	76.81 146	85.90 229	94.14 93	74.58 230	86.51 339	66.82 316	95.68 153	93.01 187

AstraMVS		81.67 244	81.40 248	82.48 242	87.06 273	66.47 250	81.41 282	81.68 355	68.78 280	88.00 164	90.95 235	65.70 312	87.86 315	76.66 184	92.38 268	93.12 180

c3_l		81.64 245	81.59 241	81.79 262	80.86 399	59.15 360	78.61 336	90.18 208	68.36 286	87.20 187	87.11 337	69.39 289	91.62 192	78.16 161	94.43 199	94.60 92

guyue		81.57 246	81.37 250	82.15 249	86.39 290	66.13 254	81.54 280	83.21 339	69.79 265	87.77 174	89.95 274	65.36 315	87.64 318	75.88 199	92.49 266	92.67 202

PVSNet_Blended_VisFu		81.55 247	80.49 267	84.70 166	91.58 137	73.24 149	84.21 196	91.67 151	62.86 353	80.94 342	87.16 335	67.27 301	92.87 162	69.82 286	88.94 356	87.99 347

fmvsm_l_conf0.5_n_a		81.46 248	80.87 261	83.25 213	83.73 357	73.21 150	83.00 241	85.59 305	58.22 399	82.96 304	90.09 273	72.30 267	86.65 337	81.97 116	89.95 340	89.88 308

SSM_04072		81.44 249	82.88 215	77.10 348	89.13 199	68.97 220	72.73 412	91.28 165	72.90 214	85.68 231	90.61 253	76.78 204	69.94 445	73.37 244	93.47 231	92.38 223

DELS-MVS		81.44 249	81.25 252	82.03 252	84.27 346	62.87 288	76.47 373	92.49 123	70.97 249	81.64 334	83.83 385	75.03 219	92.70 164	74.29 216	92.22 277	90.51 294
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		81.31 251	81.61 240	80.41 293	86.38 292	58.75 368	83.93 206	86.58 287	72.43 223	87.65 178	92.98 146	63.78 327	90.22 250	66.86 313	93.92 215	92.27 232

TinyColmap		81.25 252	82.34 227	77.99 335	85.33 324	60.68 337	82.32 264	88.33 248	71.26 244	86.97 196	92.22 182	77.10 194	86.98 330	62.37 356	95.17 169	86.31 371

diffmvs_AUTHOR		81.24 253	81.55 244	80.30 295	80.61 404	60.22 342	77.98 344	90.48 190	67.77 300	83.34 297	89.50 284	74.69 228	87.42 322	78.78 151	90.81 320	93.27 170

AUN-MVS		81.18 254	78.78 292	88.39 84	90.93 159	82.14 62	82.51 256	83.67 336	64.69 343	80.29 352	85.91 356	51.07 400	92.38 172	76.29 193	93.63 228	90.65 289

IMVS_0407		81.08 255	81.23 254	80.62 289	85.76 314	62.46 296	82.46 257	87.91 259	65.23 335	82.12 320	87.92 314	77.27 189	90.18 252	71.67 261	90.74 323	89.20 321

tttt0517		81.07 256	79.58 282	85.52 142	88.99 206	66.45 251	87.03 129	75.51 397	73.76 190	88.32 155	90.20 267	37.96 457	94.16 108	79.36 145	95.13 170	95.93 47

Fast-Effi-MVS+		81.04 257	80.57 264	82.46 243	87.50 253	63.22 284	78.37 339	89.63 223	68.01 292	81.87 326	82.08 406	82.31 115	92.65 166	67.10 312	88.30 368	91.51 263

BH-untuned		80.96 258	80.99 258	80.84 282	88.55 221	68.23 228	80.33 305	88.46 243	72.79 219	86.55 207	86.76 341	74.72 227	91.77 191	61.79 363	88.99 354	82.52 424

IMVS_0403		80.93 259	81.00 257	80.72 285	85.76 314	62.46 296	81.82 274	87.91 259	65.23 335	82.07 322	87.92 314	75.91 211	90.50 241	71.67 261	90.74 323	89.20 321

eth_miper_zixun_eth		80.84 260	80.22 273	82.71 231	81.41 390	60.98 332	77.81 347	90.14 209	67.31 307	86.95 197	87.24 334	64.26 320	92.31 175	75.23 209	91.61 295	94.85 84

xiu_mvs_v1_base_debu		80.84 260	80.14 275	82.93 226	88.31 225	71.73 177	79.53 314	87.17 272	65.43 329	79.59 358	82.73 400	76.94 197	90.14 256	73.22 247	88.33 364	86.90 365

xiu_mvs_v1_base		80.84 260	80.14 275	82.93 226	88.31 225	71.73 177	79.53 314	87.17 272	65.43 329	79.59 358	82.73 400	76.94 197	90.14 256	73.22 247	88.33 364	86.90 365

xiu_mvs_v1_base_debi		80.84 260	80.14 275	82.93 226	88.31 225	71.73 177	79.53 314	87.17 272	65.43 329	79.59 358	82.73 400	76.94 197	90.14 256	73.22 247	88.33 364	86.90 365

IterMVS-SCA-FT		80.64 264	79.41 283	84.34 178	83.93 353	69.66 208	76.28 375	81.09 361	72.43 223	86.47 214	90.19 268	60.46 344	93.15 151	77.45 174	86.39 395	90.22 299

BH-RMVSNet		80.53 265	80.22 273	81.49 268	87.19 264	66.21 253	77.79 348	86.23 290	74.21 185	83.69 289	88.50 303	73.25 256	90.75 231	63.18 352	87.90 372	87.52 356

VortexMVS		80.51 266	80.63 263	80.15 299	83.36 367	61.82 312	80.63 300	88.00 257	67.11 310	87.23 186	89.10 293	63.98 324	88.00 308	73.63 238	92.63 262	90.64 290

Anonymous202405211		80.51 266	81.19 256	78.49 324	88.48 222	57.26 381	76.63 368	82.49 347	81.21 88	84.30 276	92.24 181	67.99 297	86.24 345	62.22 357	95.13 170	91.98 247

DIV-MVS_self_test		80.43 268	80.23 271	81.02 278	79.99 410	59.25 357	77.07 361	87.02 281	67.38 304	86.19 218	89.22 289	63.09 332	90.16 253	76.32 191	95.80 147	93.66 146

cl____		80.42 269	80.23 271	81.02 278	79.99 410	59.25 357	77.07 361	87.02 281	67.37 305	86.18 220	89.21 290	63.08 333	90.16 253	76.31 192	95.80 147	93.65 149

diffmvs		80.40 270	80.48 268	80.17 298	79.02 423	60.04 344	77.54 352	90.28 205	66.65 315	82.40 313	87.33 332	73.50 248	87.35 324	77.98 167	89.62 345	93.13 177
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		80.37 271	78.41 300	86.23 123	76.75 437	73.28 147	87.18 126	77.45 380	76.24 150	68.14 448	88.93 296	65.41 314	93.85 118	69.47 289	96.12 127	91.55 261
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 272	80.04 278	81.24 274	79.82 413	58.95 362	77.66 349	89.66 221	65.75 325	85.99 228	85.11 368	68.29 296	91.42 201	76.03 197	92.03 282	93.33 166

MG-MVS		80.32 273	80.94 259	78.47 325	88.18 230	52.62 419	82.29 265	85.01 318	72.01 234	79.24 365	92.54 167	69.36 290	93.36 145	70.65 275	89.19 352	89.45 314

mvsmamba		80.30 274	78.87 289	84.58 170	88.12 234	67.55 236	92.35 30	84.88 322	63.15 351	85.33 243	90.91 236	50.71 402	95.20 66	66.36 319	87.98 371	90.99 274

VPNet		80.25 275	81.68 236	75.94 364	92.46 104	47.98 443	76.70 366	81.67 356	73.45 198	84.87 258	92.82 156	74.66 229	86.51 339	61.66 365	96.85 96	93.33 166

MAR-MVS		80.24 276	78.74 294	84.73 164	86.87 281	78.18 95	85.75 157	87.81 263	65.67 327	77.84 378	78.50 438	73.79 244	90.53 240	61.59 366	90.87 316	85.49 381
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		80.20 277	79.00 288	83.78 196	88.17 231	86.66 19	81.31 285	66.81 453	69.64 266	88.33 154	90.19 268	64.58 317	83.63 384	71.99 260	90.03 338	81.06 444

Anonymous20240521		80.18 278	81.25 252	76.95 350	83.15 375	60.84 334	82.46 257	85.99 297	68.76 281	86.78 199	93.73 119	59.13 356	77.44 419	73.71 234	97.55 78	92.56 210

LFMVS		80.15 279	80.56 265	78.89 315	89.19 198	55.93 389	85.22 171	73.78 409	82.96 71	84.28 277	92.72 161	57.38 369	90.07 261	63.80 346	95.75 150	90.68 286

DPM-MVS		80.10 280	79.18 287	82.88 229	90.71 165	69.74 206	78.87 331	90.84 180	60.29 387	75.64 403	85.92 355	67.28 300	93.11 152	71.24 267	91.79 289	85.77 377

MSDG		80.06 281	79.99 280	80.25 296	83.91 354	68.04 233	77.51 353	89.19 231	77.65 136	81.94 324	83.45 390	76.37 209	86.31 344	63.31 351	86.59 392	86.41 369

FE-MVS		79.98 282	78.86 290	83.36 210	86.47 287	66.45 251	89.73 75	84.74 326	72.80 218	84.22 280	91.38 212	44.95 437	93.60 131	63.93 344	91.50 298	90.04 306

sd_testset		79.95 283	81.39 249	75.64 369	88.81 212	58.07 373	76.16 378	82.81 345	73.67 191	83.41 295	93.04 142	80.96 143	77.65 418	58.62 384	95.03 175	91.21 267

ab-mvs		79.67 284	80.56 265	76.99 349	88.48 222	56.93 383	84.70 184	86.06 294	68.95 278	80.78 345	93.08 141	75.30 217	84.62 371	56.78 393	90.90 314	89.43 316

VNet		79.31 285	80.27 270	76.44 358	87.92 238	53.95 408	75.58 385	84.35 330	74.39 184	82.23 317	90.72 244	72.84 261	84.39 376	60.38 373	93.98 214	90.97 275

thisisatest0530		79.07 286	77.33 310	84.26 181	87.13 265	64.58 267	83.66 216	75.95 392	68.86 279	85.22 245	87.36 331	38.10 454	93.57 135	75.47 206	94.28 204	94.62 91

cl22		78.97 287	78.21 302	81.24 274	77.74 427	59.01 361	77.46 356	87.13 275	65.79 322	84.32 273	85.10 369	58.96 358	90.88 226	75.36 208	92.03 282	93.84 135

patch_mono-2		78.89 288	79.39 284	77.41 345	84.78 334	68.11 231	75.60 383	83.11 341	60.96 379	79.36 362	89.89 277	75.18 218	72.97 434	73.32 246	92.30 271	91.15 269

RPMNet		78.88 289	78.28 301	80.68 287	79.58 414	62.64 292	82.58 252	94.16 33	74.80 173	75.72 401	92.59 163	48.69 409	95.56 45	73.48 241	82.91 431	83.85 403

PAPR		78.84 290	78.10 303	81.07 276	85.17 329	60.22 342	82.21 269	90.57 189	62.51 355	75.32 407	84.61 377	74.99 220	92.30 176	59.48 378	88.04 370	90.68 286

viewmambaseed2359dif		78.80 291	78.47 299	79.78 302	80.26 409	59.28 356	77.31 358	87.13 275	60.42 385	82.37 314	88.67 301	74.58 230	87.87 314	67.78 311	87.73 376	92.19 236

PVSNet_BlendedMVS		78.80 291	77.84 304	81.65 264	84.43 340	63.41 280	79.49 317	90.44 193	61.70 368	75.43 404	87.07 338	69.11 292	91.44 199	60.68 371	92.24 275	90.11 304

FMVSNet3		78.80 291	78.55 296	79.57 308	82.89 378	56.89 385	81.76 275	85.77 301	69.04 276	86.00 225	90.44 258	51.75 398	90.09 259	65.95 323	93.34 237	91.72 254

test_yl		78.71 294	78.51 297	79.32 311	84.32 344	58.84 365	78.38 337	85.33 309	75.99 154	82.49 311	86.57 343	58.01 363	90.02 263	62.74 353	92.73 260	89.10 327

DCV-MVSNet		78.71 294	78.51 297	79.32 311	84.32 344	58.84 365	78.38 337	85.33 309	75.99 154	82.49 311	86.57 343	58.01 363	90.02 263	62.74 353	92.73 260	89.10 327

test1111		78.53 296	78.85 291	77.56 341	92.22 113	47.49 445	82.61 250	69.24 441	72.43 223	85.28 244	94.20 89	51.91 396	90.07 261	65.36 332	96.45 112	95.11 72

FE-MVSNET		78.46 297	79.36 285	75.75 366	86.53 284	54.53 403	78.03 341	85.35 308	69.01 277	85.41 241	90.68 247	64.27 319	85.73 361	62.59 355	92.35 270	87.00 364

icg_test_0407_2		78.46 297	79.68 281	74.78 376	85.76 314	62.46 296	68.51 441	87.91 259	65.23 335	82.12 320	87.92 314	77.27 189	72.67 435	71.67 261	90.74 323	89.20 321

ECVR-MVS		78.44 299	78.63 295	77.88 337	91.85 127	48.95 439	83.68 215	69.91 437	72.30 229	84.26 279	94.20 89	51.89 397	89.82 266	63.58 347	96.02 131	94.87 78

pmmvs-eth3d		78.42 300	77.04 313	82.57 239	87.44 257	74.41 138	80.86 296	79.67 369	55.68 416	84.69 263	90.31 265	60.91 342	85.42 364	62.20 358	91.59 296	87.88 351

mvs_anonymous		78.13 301	78.76 293	76.23 363	79.24 420	50.31 435	78.69 334	84.82 324	61.60 370	83.09 303	92.82 156	73.89 242	87.01 327	68.33 307	86.41 394	91.37 264

TAMVS		78.08 302	76.36 321	83.23 214	90.62 166	72.87 154	79.08 327	80.01 368	61.72 367	81.35 338	86.92 340	63.96 326	88.78 291	50.61 432	93.01 250	88.04 346

miper_enhance_ethall		77.83 303	76.93 314	80.51 291	76.15 444	58.01 375	75.47 387	88.82 235	58.05 401	83.59 291	80.69 416	64.41 318	91.20 211	73.16 253	92.03 282	92.33 227

Vis-MVSNet (Re-imp)		77.82 304	77.79 305	77.92 336	88.82 211	51.29 429	83.28 229	71.97 425	74.04 186	82.23 317	89.78 278	57.38 369	89.41 279	57.22 392	95.41 159	93.05 183

CANet_DTU		77.81 305	77.05 312	80.09 300	81.37 391	59.90 348	83.26 230	88.29 250	69.16 273	67.83 451	83.72 386	60.93 341	89.47 274	69.22 293	89.70 344	90.88 279

OpenMVS_ROB	70.19 17	77.77 306	77.46 307	78.71 320	84.39 343	61.15 323	81.18 289	82.52 346	62.45 359	83.34 297	87.37 330	66.20 306	88.66 295	64.69 339	85.02 411	86.32 370

SSC-MVS		77.55 307	81.64 238	65.29 442	90.46 169	20.33 489	73.56 405	68.28 443	85.44 41	88.18 160	94.64 68	70.93 281	81.33 397	71.25 266	92.03 282	94.20 114

MDA-MVSNet-bldmvs		77.47 308	76.90 315	79.16 313	79.03 422	64.59 266	66.58 453	75.67 395	73.15 209	88.86 137	88.99 295	66.94 302	81.23 398	64.71 338	88.22 369	91.64 258

jason		77.42 309	75.75 327	82.43 244	87.10 268	69.27 213	77.99 343	81.94 353	51.47 443	77.84 378	85.07 372	60.32 346	89.00 283	70.74 274	89.27 351	89.03 331
jason: jason.
CDS-MVSNet		77.32 310	75.40 331	83.06 218	89.00 205	72.48 165	77.90 346	82.17 351	60.81 380	78.94 368	83.49 389	59.30 354	88.76 292	54.64 412	92.37 269	87.93 350
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 311	77.75 306	75.73 367	85.76 314	62.46 296	70.84 427	87.91 259	65.23 335	72.21 426	87.92 314	67.48 299	75.53 427	71.67 261	90.74 323	89.20 321

xiu_mvs_v2_base		77.19 312	76.75 317	78.52 323	87.01 274	61.30 320	75.55 386	87.12 279	61.24 376	74.45 413	78.79 436	77.20 191	90.93 222	64.62 341	84.80 418	83.32 412

MVSTER		77.09 313	75.70 328	81.25 272	75.27 452	61.08 325	77.49 355	85.07 314	60.78 381	86.55 207	88.68 299	43.14 446	90.25 247	73.69 237	90.67 328	92.42 217

usedtu_blend_shiyan5		77.07 314	76.43 320	78.99 314	80.36 408	59.77 350	83.25 231	88.32 249	74.91 172	77.62 383	75.71 459	56.22 378	88.89 286	58.91 381	92.61 263	88.32 339

PS-MVSNAJ		77.04 315	76.53 319	78.56 322	87.09 270	61.40 317	75.26 388	87.13 275	61.25 375	74.38 415	77.22 450	76.94 197	90.94 221	64.63 340	84.83 417	83.35 411

IterMVS		76.91 316	76.34 322	78.64 321	80.91 397	64.03 274	76.30 374	79.03 372	64.88 342	83.11 301	89.16 291	59.90 350	84.46 374	68.61 303	85.15 409	87.42 357
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 317	75.67 329	80.34 294	80.48 406	62.16 308	73.50 406	84.80 325	57.61 405	82.24 316	87.54 325	51.31 399	87.65 317	70.40 280	93.19 246	91.23 266

CL-MVSNet_self_test		76.81 318	77.38 309	75.12 372	86.90 279	51.34 427	73.20 409	80.63 365	68.30 288	81.80 330	88.40 304	66.92 303	80.90 399	55.35 406	94.90 181	93.12 180

TR-MVS		76.77 319	75.79 326	79.72 305	86.10 306	65.79 258	77.14 359	83.02 342	65.20 339	81.40 337	82.10 404	66.30 305	90.73 233	55.57 403	85.27 405	82.65 419

MonoMVSNet		76.66 320	77.26 311	74.86 374	79.86 412	54.34 405	86.26 147	86.08 293	71.08 248	85.59 236	88.68 299	53.95 388	85.93 352	63.86 345	80.02 447	84.32 394

USDC		76.63 321	76.73 318	76.34 360	83.46 362	57.20 382	80.02 308	88.04 256	52.14 439	83.65 290	91.25 219	63.24 330	86.65 337	54.66 411	94.11 209	85.17 383

BH-w/o		76.57 322	76.07 325	78.10 332	86.88 280	65.92 257	77.63 350	86.33 288	65.69 326	80.89 343	79.95 425	68.97 294	90.74 232	53.01 422	85.25 406	77.62 455

Patchmtry		76.56 323	77.46 307	73.83 382	79.37 419	46.60 449	82.41 261	76.90 386	73.81 189	85.56 238	92.38 171	48.07 412	83.98 381	63.36 350	95.31 165	90.92 277

PVSNet_Blended		76.49 324	75.40 331	79.76 304	84.43 340	63.41 280	75.14 389	90.44 193	57.36 407	75.43 404	78.30 439	69.11 292	91.44 199	60.68 371	87.70 378	84.42 393

miper_lstm_enhance		76.45 325	76.10 324	77.51 343	76.72 438	60.97 333	64.69 457	85.04 316	63.98 347	83.20 300	88.22 306	56.67 373	78.79 415	73.22 247	93.12 247	92.78 196

lupinMVS		76.37 326	74.46 341	82.09 251	85.54 320	69.26 214	76.79 364	80.77 364	50.68 450	76.23 394	82.82 398	58.69 359	88.94 284	69.85 285	88.77 357	88.07 343

cascas		76.29 327	74.81 337	80.72 285	84.47 339	62.94 286	73.89 402	87.34 267	55.94 414	75.16 409	76.53 455	63.97 325	91.16 213	65.00 335	90.97 312	88.06 345

SD_0403		76.08 328	76.77 316	73.98 380	87.08 272	49.45 438	83.62 217	84.68 327	63.31 348	75.13 410	87.47 328	71.85 274	84.56 372	49.97 434	87.86 374	87.94 349

WB-MVS		76.06 329	80.01 279	64.19 445	89.96 183	20.58 488	72.18 416	68.19 444	83.21 67	86.46 215	93.49 124	70.19 286	78.97 413	65.96 322	90.46 334	93.02 184

thres600view7		75.97 330	75.35 333	77.85 339	87.01 274	51.84 425	80.45 303	73.26 414	75.20 169	83.10 302	86.31 349	45.54 427	89.05 282	55.03 409	92.24 275	92.66 203

GA-MVS		75.83 331	74.61 338	79.48 310	81.87 383	59.25 357	73.42 407	82.88 343	68.68 282	79.75 357	81.80 409	50.62 403	89.46 275	66.85 314	85.64 402	89.72 310

MVP-Stereo		75.81 332	73.51 350	82.71 231	89.35 193	73.62 142	80.06 306	85.20 311	60.30 386	73.96 416	87.94 311	57.89 367	89.45 276	52.02 426	74.87 465	85.06 385
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 333	75.20 334	77.27 346	75.01 455	69.47 211	78.93 328	84.88 322	46.67 457	87.08 193	87.84 319	50.44 405	71.62 440	77.42 176	88.53 360	90.72 283

FE-MVSNET3		75.70 334	75.08 335	77.56 341	84.10 350	55.50 395	73.58 404	84.89 321	62.48 356	78.16 373	84.24 381	58.14 362	87.47 321	59.34 379	90.82 319	89.72 310

thres100view900		75.45 335	75.05 336	76.66 356	87.27 259	51.88 424	81.07 290	73.26 414	75.68 160	83.25 299	86.37 346	45.54 427	88.80 288	51.98 427	90.99 309	89.31 318

ET-MVSNet_ETH3D		75.28 336	72.77 359	82.81 230	83.03 377	68.11 231	77.09 360	76.51 390	60.67 383	77.60 384	80.52 420	38.04 455	91.15 214	70.78 272	90.68 327	89.17 325

thres400		75.14 337	74.23 343	77.86 338	86.24 299	52.12 421	79.24 324	73.87 407	73.34 202	81.82 328	84.60 378	46.02 420	88.80 288	51.98 427	90.99 309	92.66 203

wuyk23d		75.13 338	79.30 286	62.63 448	75.56 448	75.18 134	80.89 295	73.10 416	75.06 171	94.76 16	95.32 45	87.73 45	52.85 480	34.16 478	97.11 91	59.85 476

EU-MVSNet		75.12 339	74.43 342	77.18 347	83.11 376	59.48 354	85.71 159	82.43 348	39.76 477	85.64 235	88.76 297	44.71 439	87.88 313	73.86 231	85.88 401	84.16 399

HyFIR lowres test		75.12 339	72.66 361	82.50 241	91.44 145	65.19 263	72.47 414	87.31 268	46.79 456	80.29 352	84.30 380	52.70 393	92.10 182	51.88 431	86.73 390	90.22 299

CMPMVS	59.41 20	75.12 339	73.57 348	79.77 303	75.84 447	67.22 237	81.21 288	82.18 350	50.78 448	76.50 390	87.66 323	55.20 384	82.99 387	62.17 360	90.64 332	89.09 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 342	72.98 357	80.73 284	84.95 331	71.71 180	76.23 376	77.59 379	52.83 433	77.73 382	86.38 345	56.35 376	84.97 368	57.72 391	87.05 385	85.51 380

tfpn200view9		74.86 343	74.23 343	76.74 355	86.24 299	52.12 421	79.24 324	73.87 407	73.34 202	81.82 328	84.60 378	46.02 420	88.80 288	51.98 427	90.99 309	89.31 318

1112_ss		74.82 344	73.74 346	78.04 334	89.57 187	60.04 344	76.49 372	87.09 280	54.31 424	73.66 419	79.80 426	60.25 347	86.76 336	58.37 385	84.15 422	87.32 359

EGC-MVSNET		74.79 345	69.99 389	89.19 67	94.89 38	87.00 15	91.89 42	86.28 289	1.09 486	2.23 488	95.98 30	81.87 131	89.48 273	79.76 136	95.96 134	91.10 270

ppachtmachnet_test		74.73 346	74.00 345	76.90 352	80.71 402	56.89 385	71.53 422	78.42 374	58.24 398	79.32 364	82.92 397	57.91 366	84.26 378	65.60 330	91.36 300	89.56 313

Patchmatch-RL test		74.48 347	73.68 347	76.89 353	84.83 333	66.54 248	72.29 415	69.16 442	57.70 403	86.76 200	86.33 347	45.79 426	82.59 388	69.63 288	90.65 331	81.54 435

PatchMatch-RL		74.48 347	73.22 354	78.27 330	87.70 245	85.26 38	75.92 381	70.09 435	64.34 345	76.09 397	81.25 414	65.87 311	78.07 417	53.86 414	83.82 424	71.48 464

XXY-MVS		74.44 349	76.19 323	69.21 417	84.61 338	52.43 420	71.70 419	77.18 384	60.73 382	80.60 346	90.96 233	75.44 214	69.35 448	56.13 398	88.33 364	85.86 376

test2506		74.12 350	73.39 351	76.28 361	91.85 127	44.20 459	84.06 200	48.20 484	72.30 229	81.90 325	94.20 89	27.22 483	89.77 269	64.81 337	96.02 131	94.87 78

reproduce_monomvs		74.09 351	73.23 353	76.65 357	76.52 439	54.54 402	77.50 354	81.40 359	65.85 321	82.86 307	86.67 342	27.38 481	84.53 373	70.24 281	90.66 330	90.89 278

CR-MVSNet		74.00 352	73.04 356	76.85 354	79.58 414	62.64 292	82.58 252	76.90 386	50.50 451	75.72 401	92.38 171	48.07 412	84.07 380	68.72 302	82.91 431	83.85 403

SSC-MVS3.2		73.90 353	75.67 329	68.61 425	84.11 349	41.28 467	64.17 459	72.83 417	72.09 232	79.08 367	87.94 311	70.31 284	73.89 433	55.99 399	94.49 196	90.67 288

Test_1112_low_res		73.90 353	73.08 355	76.35 359	90.35 171	55.95 388	73.40 408	86.17 291	50.70 449	73.14 420	85.94 354	58.31 361	85.90 356	56.51 395	83.22 428	87.20 361

test20.03		73.75 355	74.59 340	71.22 403	81.11 394	51.12 431	70.15 433	72.10 424	70.42 255	80.28 354	91.50 206	64.21 321	74.72 431	46.96 452	94.58 194	87.82 354

test_fmvs2		73.57 356	72.80 358	75.90 365	72.74 469	68.84 224	77.07 361	84.32 331	45.14 463	82.89 305	84.22 382	48.37 410	70.36 444	73.40 243	87.03 386	88.52 337

SCA		73.32 357	72.57 363	75.58 370	81.62 387	55.86 391	78.89 330	71.37 430	61.73 366	74.93 411	83.42 391	60.46 344	87.01 327	58.11 389	82.63 436	83.88 400

baseline1		73.26 358	73.54 349	72.43 396	84.92 332	47.79 444	79.89 310	74.00 405	65.93 319	78.81 369	86.28 350	56.36 375	81.63 396	56.63 394	79.04 454	87.87 352

1314		73.22 359	72.56 364	75.20 371	80.41 407	57.84 376	81.64 278	85.36 307	51.68 442	73.10 421	76.65 454	61.45 339	85.19 366	63.54 348	79.21 452	82.59 420

MVS		73.21 360	72.59 362	75.06 373	80.97 396	60.81 335	81.64 278	85.92 300	46.03 461	71.68 429	77.54 445	68.47 295	89.77 269	55.70 402	85.39 403	74.60 461

HY-MVS	64.64 18	73.03 361	72.47 365	74.71 377	83.36 367	54.19 406	82.14 272	81.96 352	56.76 413	69.57 443	86.21 351	60.03 348	84.83 370	49.58 439	82.65 434	85.11 384

thisisatest0515		73.00 362	70.52 381	80.46 292	81.45 389	59.90 348	73.16 410	74.31 404	57.86 402	76.08 398	77.78 442	37.60 458	92.12 181	65.00 335	91.45 299	89.35 317

EPNet_dtu		72.87 363	71.33 375	77.49 344	77.72 428	60.55 338	82.35 263	75.79 393	66.49 316	58.39 479	81.06 415	53.68 389	85.98 351	53.55 417	92.97 252	85.95 374
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 364	71.41 374	76.28 361	83.25 371	60.34 340	83.50 223	79.02 373	37.77 481	76.33 392	85.10 369	49.60 408	87.41 323	70.54 278	77.54 460	81.08 442

CHOSEN 1792x2688		72.45 365	70.56 380	78.13 331	90.02 182	63.08 285	68.72 440	83.16 340	42.99 471	75.92 399	85.46 362	57.22 371	85.18 367	49.87 437	81.67 438	86.14 372

testgi		72.36 366	74.61 338	65.59 439	80.56 405	42.82 464	68.29 442	73.35 413	66.87 313	81.84 327	89.93 275	72.08 271	66.92 462	46.05 456	92.54 265	87.01 363

thres200		72.34 367	71.55 373	74.70 378	83.48 361	51.60 426	75.02 390	73.71 410	70.14 262	78.56 372	80.57 419	46.20 418	88.20 306	46.99 451	89.29 349	84.32 394

FPMVS		72.29 368	72.00 367	73.14 387	88.63 218	85.00 40	74.65 394	67.39 447	71.94 235	77.80 380	87.66 323	50.48 404	75.83 425	49.95 435	79.51 448	58.58 478

FMVSNet5		72.10 369	71.69 369	73.32 385	81.57 388	53.02 415	76.77 365	78.37 375	63.31 348	76.37 391	91.85 192	36.68 459	78.98 412	47.87 448	92.45 267	87.95 348

our_test_3		71.85 370	71.59 370	72.62 393	80.71 402	53.78 409	69.72 436	71.71 429	58.80 395	78.03 375	80.51 421	56.61 374	78.84 414	62.20 358	86.04 400	85.23 382

PAPM		71.77 371	70.06 387	76.92 351	86.39 290	53.97 407	76.62 369	86.62 286	53.44 428	63.97 468	84.73 376	57.79 368	92.34 174	39.65 468	81.33 442	84.45 392

ttmdpeth		71.72 372	70.67 378	74.86 374	73.08 466	55.88 390	77.41 357	69.27 440	55.86 415	78.66 370	93.77 117	38.01 456	75.39 428	60.12 374	89.87 341	93.31 168

IB-MVS	62.13 19	71.64 373	68.97 399	79.66 307	80.80 401	62.26 305	73.94 401	76.90 386	63.27 350	68.63 447	76.79 452	33.83 463	91.84 189	59.28 380	87.26 380	84.88 386
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		71.63 374	72.30 366	69.62 414	76.47 441	52.70 418	70.03 434	80.97 362	59.18 392	79.36 362	88.21 307	60.50 343	69.12 449	58.33 387	77.62 459	87.04 362

testing3		71.53 375	70.79 377	73.77 383	88.89 210	41.86 466	76.60 371	59.12 473	72.83 217	80.97 340	82.08 406	19.80 489	87.33 325	65.12 334	91.68 294	92.13 240

test_vis3_rt		71.42 376	70.67 378	73.64 384	69.66 476	70.46 196	66.97 452	89.73 218	42.68 473	88.20 159	83.04 393	43.77 441	60.07 474	65.35 333	86.66 391	90.39 297

Anonymous20231206		71.38 377	71.88 368	69.88 411	86.31 296	54.37 404	70.39 431	74.62 400	52.57 435	76.73 389	88.76 297	59.94 349	72.06 437	44.35 460	93.23 244	83.23 414

test_vis1_n_1920		71.30 378	71.58 372	70.47 406	77.58 430	59.99 347	74.25 396	84.22 332	51.06 445	74.85 412	79.10 432	55.10 385	68.83 451	68.86 299	79.20 453	82.58 421

MIMVSNet		71.09 379	71.59 370	69.57 415	87.23 262	50.07 436	78.91 329	71.83 426	60.20 389	71.26 430	91.76 199	55.08 386	76.09 423	41.06 465	87.02 387	82.54 423

test_fmvs1_n		70.94 380	70.41 384	72.53 395	73.92 457	66.93 245	75.99 380	84.21 333	43.31 470	79.40 361	79.39 430	43.47 442	68.55 453	69.05 296	84.91 414	82.10 429

MS-PatchMatch		70.93 381	70.22 385	73.06 388	81.85 384	62.50 295	73.82 403	77.90 376	52.44 436	75.92 399	81.27 413	55.67 381	81.75 394	55.37 405	77.70 458	74.94 460

blend_shiyan4		70.82 382	68.15 406	78.83 317	81.06 395	59.77 350	74.58 395	83.79 334	64.94 341	77.34 387	75.47 461	29.39 475	88.89 286	58.91 381	67.86 477	87.84 353

pmmvs5		70.73 383	70.07 386	72.72 391	77.03 435	52.73 417	74.14 397	75.65 396	50.36 452	72.17 427	85.37 366	55.42 383	80.67 401	52.86 423	87.59 379	84.77 387

testing3-2		70.72 384	70.97 376	69.95 410	88.93 208	34.80 480	69.85 435	66.59 454	78.42 126	77.58 385	85.55 358	31.83 469	82.08 392	46.28 453	93.73 224	92.98 190

PatchT		70.52 385	72.76 360	63.79 447	79.38 418	33.53 481	77.63 350	65.37 458	73.61 195	71.77 428	92.79 159	44.38 440	75.65 426	64.53 342	85.37 404	82.18 428

test_vis1_n		70.29 386	69.99 389	71.20 404	75.97 446	66.50 249	76.69 367	80.81 363	44.22 466	75.43 404	77.23 449	50.00 406	68.59 452	66.71 317	82.85 433	78.52 454

N_pmnet		70.20 387	68.80 401	74.38 379	80.91 397	84.81 43	59.12 470	76.45 391	55.06 419	75.31 408	82.36 403	55.74 380	54.82 479	47.02 450	87.24 381	83.52 407

tpmvs		70.16 388	69.56 392	71.96 399	74.71 456	48.13 441	79.63 312	75.45 398	65.02 340	70.26 438	81.88 408	45.34 432	85.68 362	58.34 386	75.39 464	82.08 430

new-patchmatchnet		70.10 389	73.37 352	60.29 456	81.23 393	16.95 491	59.54 468	74.62 400	62.93 352	80.97 340	87.93 313	62.83 336	71.90 438	55.24 407	95.01 178	92.00 245

YYNet1		70.06 390	70.44 382	68.90 419	73.76 459	53.42 413	58.99 471	67.20 449	58.42 397	87.10 191	85.39 365	59.82 351	67.32 459	59.79 376	83.50 427	85.96 373

MVStest1		70.05 391	69.26 393	72.41 397	58.62 488	55.59 394	76.61 370	65.58 456	53.44 428	89.28 132	93.32 128	22.91 487	71.44 442	74.08 227	89.52 346	90.21 303

MDA-MVSNet_test_wron		70.05 391	70.44 382	68.88 420	73.84 458	53.47 411	58.93 472	67.28 448	58.43 396	87.09 192	85.40 364	59.80 352	67.25 460	59.66 377	83.54 426	85.92 375

CostFormer		69.98 393	68.68 402	73.87 381	77.14 433	50.72 433	79.26 323	74.51 402	51.94 441	70.97 433	84.75 375	45.16 435	87.49 320	55.16 408	79.23 451	83.40 410

testing91		69.94 394	68.99 398	72.80 390	83.81 356	45.89 452	71.57 421	73.64 412	68.24 289	70.77 436	77.82 441	34.37 462	84.44 375	53.64 416	87.00 388	88.07 343

baseline2		69.77 395	66.89 412	78.41 326	79.51 416	58.09 372	76.23 376	69.57 438	57.50 406	64.82 466	77.45 447	46.02 420	88.44 300	53.08 419	77.83 456	88.70 335

PatchmatchNet		69.71 396	68.83 400	72.33 398	77.66 429	53.60 410	79.29 322	69.99 436	57.66 404	72.53 424	82.93 396	46.45 417	80.08 407	60.91 370	72.09 468	83.31 413
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 397	69.05 396	71.14 405	69.15 477	65.77 259	73.98 400	83.32 338	42.83 472	77.77 381	78.27 440	43.39 445	68.50 454	68.39 306	84.38 421	79.15 452

JIA-IIPM		69.41 398	66.64 416	77.70 340	73.19 463	71.24 187	75.67 382	65.56 457	70.42 255	65.18 462	92.97 149	33.64 465	83.06 385	53.52 418	69.61 474	78.79 453

Syy-MVS		69.40 399	70.03 388	67.49 430	81.72 385	38.94 472	71.00 424	61.99 464	61.38 372	70.81 434	72.36 468	61.37 340	79.30 410	64.50 343	85.18 407	84.22 396

testing99		69.27 400	68.15 406	72.63 392	83.29 369	45.45 454	71.15 423	71.08 431	67.34 306	70.43 437	77.77 443	32.24 468	84.35 377	53.72 415	86.33 396	88.10 342

UnsupCasMVSNet_bld		69.21 401	69.68 391	67.82 428	79.42 417	51.15 430	67.82 446	75.79 393	54.15 425	77.47 386	85.36 367	59.26 355	70.64 443	48.46 445	79.35 450	81.66 433

test_cas_vis1_n_1920		69.20 402	69.12 394	69.43 416	73.68 460	62.82 289	70.38 432	77.21 383	46.18 460	80.46 351	78.95 434	52.03 395	65.53 467	65.77 329	77.45 461	79.95 450

gg-mvs-nofinetune		68.96 403	69.11 395	68.52 426	76.12 445	45.32 455	83.59 218	55.88 478	86.68 33	64.62 467	97.01 12	30.36 473	83.97 382	44.78 459	82.94 430	76.26 457

WBMVS		68.76 404	68.43 403	69.75 413	83.29 369	40.30 470	67.36 448	72.21 423	57.09 410	77.05 388	85.53 360	33.68 464	80.51 403	48.79 443	90.90 314	88.45 338

WB-MVSnew		68.72 405	69.01 397	67.85 427	83.22 373	43.98 460	74.93 391	65.98 455	55.09 418	73.83 417	79.11 431	65.63 313	71.89 439	38.21 473	85.04 410	87.69 355

tpm2		68.45 406	66.83 413	73.30 386	78.93 424	48.50 440	79.76 311	71.76 427	47.50 455	69.92 440	83.60 387	42.07 448	88.40 302	48.44 446	79.51 448	83.01 417

tpm		67.95 407	68.08 408	67.55 429	78.74 425	43.53 462	75.60 383	67.10 452	54.92 420	72.23 425	88.10 308	42.87 447	75.97 424	52.21 425	80.95 446	83.15 415

WTY-MVS		67.91 408	68.35 404	66.58 435	80.82 400	48.12 442	65.96 454	72.60 418	53.67 427	71.20 431	81.68 411	58.97 357	69.06 450	48.57 444	81.67 438	82.55 422

testing11		67.38 409	65.93 417	71.73 401	83.37 366	46.60 449	70.95 426	69.40 439	62.47 358	66.14 455	76.66 453	31.22 470	84.10 379	49.10 441	84.10 423	84.49 390

test-LLR		67.21 410	66.74 414	68.63 423	76.45 442	55.21 398	67.89 443	67.14 450	62.43 361	65.08 463	72.39 466	43.41 443	69.37 446	61.00 368	84.89 415	81.31 437

testing222		66.93 411	65.30 424	71.81 400	83.38 365	45.83 453	72.06 417	67.50 446	64.12 346	69.68 442	76.37 456	27.34 482	83.00 386	38.88 469	88.38 363	86.62 368

sss		66.92 412	67.26 410	65.90 437	77.23 432	51.10 432	64.79 456	71.72 428	52.12 440	70.13 439	80.18 423	57.96 365	65.36 468	50.21 433	81.01 444	81.25 439

KD-MVS_2432*1600		66.87 413	65.81 420	70.04 408	67.50 478	47.49 445	62.56 462	79.16 370	61.21 377	77.98 376	80.61 417	25.29 485	82.48 389	53.02 420	84.92 412	80.16 448

miper_refine_blended		66.87 413	65.81 420	70.04 408	67.50 478	47.49 445	62.56 462	79.16 370	61.21 377	77.98 376	80.61 417	25.29 485	82.48 389	53.02 420	84.92 412	80.16 448

dmvs_re		66.81 415	66.98 411	66.28 436	76.87 436	58.68 369	71.66 420	72.24 421	60.29 387	69.52 444	73.53 465	52.38 394	64.40 470	44.90 458	81.44 441	75.76 458

tpm cat1		66.76 416	65.21 425	71.42 402	77.09 434	50.62 434	78.01 342	73.68 411	44.89 464	68.64 446	79.00 433	45.51 429	82.42 391	49.91 436	70.15 471	81.23 441

UWE-MVS		66.43 417	65.56 423	69.05 418	84.15 348	40.98 468	73.06 411	64.71 460	54.84 421	76.18 396	79.62 429	29.21 476	80.50 404	38.54 472	89.75 343	85.66 378

PVSNet	58.17 21	66.41 418	65.63 422	68.75 421	81.96 382	49.88 437	62.19 464	72.51 420	51.03 446	68.04 449	75.34 462	50.84 401	74.77 429	45.82 457	82.96 429	81.60 434

tpmrst		66.28 419	66.69 415	65.05 443	72.82 468	39.33 471	78.20 340	70.69 434	53.16 431	67.88 450	80.36 422	48.18 411	74.75 430	58.13 388	70.79 470	81.08 442

Patchmatch-test		65.91 420	67.38 409	61.48 453	75.51 449	43.21 463	68.84 439	63.79 462	62.48 356	72.80 423	83.42 391	44.89 438	59.52 476	48.27 447	86.45 393	81.70 432

ADS-MVSNet2		65.87 421	63.64 430	72.55 394	73.16 464	56.92 384	67.10 450	74.81 399	49.74 453	66.04 457	82.97 394	46.71 415	77.26 420	42.29 462	69.96 472	83.46 408

myMVS_eth3d28		65.83 422	65.85 418	65.78 438	83.42 364	35.71 478	67.29 449	68.01 445	67.58 303	69.80 441	77.72 444	32.29 467	74.30 432	37.49 474	89.06 353	87.32 359

test_vis1_rt		65.64 423	64.09 427	70.31 407	66.09 482	70.20 200	61.16 465	81.60 357	38.65 478	72.87 422	69.66 471	52.84 391	60.04 475	56.16 397	77.77 457	80.68 446

mvsany_test3		65.48 424	62.97 433	73.03 389	69.99 475	76.17 124	64.83 455	43.71 486	43.68 468	80.25 355	87.05 339	52.83 392	63.09 473	51.92 430	72.44 467	79.84 451

test-mter		65.00 425	63.79 429	68.63 423	76.45 442	55.21 398	67.89 443	67.14 450	50.98 447	65.08 463	72.39 466	28.27 479	69.37 446	61.00 368	84.89 415	81.31 437

ETVMVS		64.67 426	63.34 432	68.64 422	83.44 363	41.89 465	69.56 438	61.70 469	61.33 374	68.74 445	75.76 458	28.76 477	79.35 409	34.65 477	86.16 399	84.67 389

myMVS_eth3d		64.66 427	63.89 428	66.97 433	81.72 385	37.39 475	71.00 424	61.99 464	61.38 372	70.81 434	72.36 468	20.96 488	79.30 410	49.59 438	85.18 407	84.22 396

test0.0.03 1		64.66 427	64.36 426	65.57 440	75.03 454	46.89 448	64.69 457	61.58 470	62.43 361	71.18 432	77.54 445	43.41 443	68.47 455	40.75 467	82.65 434	81.35 436

UBG		64.34 429	63.35 431	67.30 431	83.50 360	40.53 469	67.46 447	65.02 459	54.77 422	67.54 453	74.47 464	32.99 466	78.50 416	40.82 466	83.58 425	82.88 418

test_f		64.31 430	65.85 418	59.67 457	66.54 481	62.24 307	57.76 474	70.96 432	40.13 475	84.36 271	82.09 405	46.93 414	51.67 481	61.99 361	81.89 437	65.12 472

pmmvs3		62.47 431	60.02 444	69.80 412	71.58 472	64.00 275	70.52 430	58.44 476	39.77 476	66.05 456	75.84 457	27.10 484	72.28 436	46.15 455	84.77 419	73.11 462

EPMVS		62.47 431	62.63 435	62.01 449	70.63 474	38.74 473	74.76 392	52.86 480	53.91 426	67.71 452	80.01 424	39.40 452	66.60 463	55.54 404	68.81 476	80.68 446

ADS-MVSNet		61.90 433	62.19 437	61.03 454	73.16 464	36.42 477	67.10 450	61.75 467	49.74 453	66.04 457	82.97 394	46.71 415	63.21 471	42.29 462	69.96 472	83.46 408

PMMVS		61.65 434	60.38 441	65.47 441	65.40 485	69.26 214	63.97 460	61.73 468	36.80 482	60.11 474	68.43 473	59.42 353	66.35 464	48.97 442	78.57 455	60.81 475

E-PMN		61.59 435	61.62 438	61.49 452	66.81 480	55.40 396	53.77 477	60.34 472	66.80 314	58.90 477	65.50 476	40.48 451	66.12 465	55.72 401	86.25 397	62.95 474

TESTMET0.1,1		61.29 436	60.32 442	64.19 445	72.06 470	51.30 428	67.89 443	62.09 463	45.27 462	60.65 473	69.01 472	27.93 480	64.74 469	56.31 396	81.65 440	76.53 456

MVS-HIRNet		61.16 437	62.92 434	55.87 460	79.09 421	35.34 479	71.83 418	57.98 477	46.56 458	59.05 476	91.14 224	49.95 407	76.43 422	38.74 470	71.92 469	55.84 479

EMVS		61.10 438	60.81 440	61.99 450	65.96 483	55.86 391	53.10 478	58.97 475	67.06 311	56.89 481	63.33 477	40.98 449	67.03 461	54.79 410	86.18 398	63.08 473

DSMNet-mixed		60.98 439	61.61 439	59.09 459	72.88 467	45.05 457	74.70 393	46.61 485	26.20 483	65.34 461	90.32 264	55.46 382	63.12 472	41.72 464	81.30 443	69.09 468

dp		60.70 440	60.29 443	61.92 451	72.04 471	38.67 474	70.83 428	64.08 461	51.28 444	60.75 472	77.28 448	36.59 460	71.58 441	47.41 449	62.34 479	75.52 459

dmvs_testset		60.59 441	62.54 436	54.72 462	77.26 431	27.74 485	74.05 399	61.00 471	60.48 384	65.62 460	67.03 475	55.93 379	68.23 457	32.07 481	69.46 475	68.17 469

CHOSEN 280x420		59.08 442	56.52 448	66.76 434	76.51 440	64.39 271	49.62 479	59.00 474	43.86 467	55.66 482	68.41 474	35.55 461	68.21 458	43.25 461	76.78 463	67.69 470

mvsany_test1		58.48 443	56.47 449	64.50 444	65.90 484	68.21 230	56.95 475	42.11 487	38.30 479	65.69 459	77.19 451	56.96 372	59.35 477	46.16 454	58.96 480	65.93 471

UWE-MVS-28		58.44 444	57.71 446	60.65 455	73.58 461	31.23 482	69.68 437	48.80 483	53.12 432	61.79 470	78.83 435	30.98 471	68.40 456	21.58 484	80.99 445	82.33 427

PVSNet_0	51.08 22	56.10 445	54.97 450	59.48 458	75.12 453	53.28 414	55.16 476	61.89 466	44.30 465	59.16 475	62.48 478	54.22 387	65.91 466	35.40 476	47.01 481	59.25 477

new_pmnet		55.69 446	57.66 447	49.76 463	75.47 450	30.59 483	59.56 467	51.45 481	43.62 469	62.49 469	75.48 460	40.96 450	49.15 483	37.39 475	72.52 466	69.55 467

PMMVS2		55.64 447	59.27 445	44.74 464	64.30 486	12.32 492	40.60 480	49.79 482	53.19 430	65.06 465	84.81 374	53.60 390	49.76 482	32.68 480	89.41 348	72.15 463

MVE	40.22 23	51.82 448	50.47 451	55.87 460	62.66 487	51.91 423	31.61 482	39.28 488	40.65 474	50.76 483	74.98 463	56.24 377	44.67 484	33.94 479	64.11 478	71.04 466
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 449	42.65 452	39.67 465	70.86 473	21.11 487	61.01 466	21.42 492	57.36 407	57.97 480	50.06 481	16.40 490	58.73 478	21.03 485	27.69 485	39.17 481

kuosan		30.83 450	32.17 453	26.83 467	53.36 489	19.02 490	57.90 473	20.44 493	38.29 480	38.01 484	37.82 483	15.18 491	33.45 486	7.74 487	20.76 486	28.03 482

test_method		30.46 451	29.60 454	33.06 466	17.99 491	3.84 494	13.62 483	73.92 406	2.79 485	18.29 487	53.41 480	28.53 478	43.25 485	22.56 482	35.27 483	52.11 480

cdsmvs_eth3d_5k		20.81 452	27.75 455	0.00 472	0.00 495	0.00 497	0.00 484	85.44 306	0.00 490	0.00 491	82.82 398	81.46 137	0.00 491	0.00 490	0.00 489	0.00 487

tmp_tt		20.25 453	24.50 456	7.49 469	4.47 492	8.70 493	34.17 481	25.16 490	1.00 487	32.43 486	18.49 484	39.37 453	9.21 488	21.64 483	43.75 482	4.57 484

ab-mvs-re		6.65 454	8.87 457	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	79.80 426	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

pcd_1.5k_mvsjas		6.41 455	8.55 458	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	76.94 197	0.00 491	0.00 490	0.00 489	0.00 487

test123		6.27 456	8.08 459	0.84 470	1.11 494	0.57 495	62.90 461	0.82 494	0.54 488	1.07 490	2.75 489	1.26 492	0.30 489	1.04 488	1.26 488	1.66 485

testmvs		5.91 457	7.65 460	0.72 471	1.20 493	0.37 496	59.14 469	0.67 495	0.49 489	1.11 489	2.76 488	0.94 493	0.24 490	1.02 489	1.47 487	1.55 486

mmdepth		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

monomultidepth		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

test_blank		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uanet_test		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

DCPMVS		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

sosnet-low-res		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

sosnet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uncertanet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

Regformer		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uanet		0.00 458	0.00 461	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	0.00 490	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 140	93.24 43	93.18 134		95.85 24	84.99 75	97.69 65	93.54 161

TestfortrainingZip							92.12 33

WAC-MVS						37.39 475								52.61 424

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	91.55 139	77.99 97		91.01 176					96.05 9	87.45 29	98.17 37	92.40 220

PC_three_1452									58.96 394	90.06 107	91.33 214	80.66 148	93.03 156	75.78 200	95.94 137	92.48 214

No_MVS				88.81 73	91.55 139	77.99 97		91.01 176					96.05 9	87.45 29	98.17 37	92.40 220

test_one_0601					93.85 67	73.27 148		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 495

eth-test					0.00 495

ZD-MVS					92.22 113	80.48 71		91.85 144	71.22 246	90.38 102	92.98 146	86.06 68	96.11 7	81.99 115	96.75 101

RE-MVS-def			92.61 9		94.13 60	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 38	97.60 75	92.73 197

IU-MVS					94.18 55	72.64 158		90.82 181	56.98 411	89.67 120				85.78 64	97.92 52	93.28 169

OPU-MVS				88.27 88	91.89 125	77.83 100	90.47 60				91.22 220	81.12 141	94.68 82	74.48 215	95.35 161	92.29 230

test_241102_TWO								93.71 60	83.77 58	93.49 40	94.27 83	89.27 24	95.84 27	86.03 57	97.82 57	92.04 243

test_241102_ONE					94.18 55	72.65 156		93.69 63	83.62 62	94.11 27	93.78 115	90.28 15	95.50 52

9.14			89.29 66		91.84 129		88.80 99	95.32 13	75.14 170	91.07 87	92.89 152	87.27 49	93.78 121	83.69 93	97.55 78

save fliter					93.75 68	77.44 106	86.31 145	89.72 219	70.80 251

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 48	95.78 35	87.41 31	98.21 34	92.98 190

test_0728_SECOND				86.79 112	94.25 53	72.45 166	90.54 57	94.10 40					95.88 18	86.42 47	97.97 49	92.02 244

test0726					94.16 58	72.56 162	90.63 54	93.90 49	83.61 63	93.75 35	94.49 73	89.76 19

GSMVS																83.88 400

test_part2					93.86 66	77.77 101				92.84 55

sam_mvs1												46.11 419				83.88 400

sam_mvs												45.92 424

ambc				82.98 221	90.55 168	64.86 265	88.20 108	89.15 233		89.40 129	93.96 106	71.67 278	91.38 203	78.83 150	96.55 106	92.71 200

MTGPA								91.81 148

test_post1							78.85 332				3.13 486	45.19 434	80.13 406	58.11 389

test_post											3.10 487	45.43 430	77.22 421

patchmatchnet-post											81.71 410	45.93 423	87.01 327

GG-mvs-BLEND				67.16 432	73.36 462	46.54 451	84.15 198	55.04 479		58.64 478	61.95 479	29.93 474	83.87 383	38.71 471	76.92 462	71.07 465

MTMP							90.66 53	33.14 489

gm-plane-assit					75.42 451	44.97 458			52.17 437		72.36 468		87.90 312	54.10 413

test9_res														80.83 125	96.45 112	90.57 291

TEST9					92.34 108	79.70 80	83.94 204	90.32 199	65.41 332	84.49 267	90.97 231	82.03 126	93.63 127

test_8					92.09 117	78.87 88	83.82 209	90.31 201	65.79 322	84.36 271	90.96 233	81.93 128	93.44 141

agg_prior2														79.68 138	96.16 124	90.22 299

agg_prior					91.58 137	77.69 103		90.30 202		84.32 273			93.18 149

TestCases				89.68 56	91.59 134	83.40 52		95.44 11	79.47 108	88.00 164	93.03 144	82.66 107	91.47 197	70.81 270	96.14 125	94.16 119

test_prior4						78.97 87	84.59 187

test_prior2							83.37 227		75.43 166	84.58 264	91.57 204	81.92 130		79.54 142	96.97 94

test_prior				86.32 120	90.59 167	71.99 174		92.85 109					94.17 106			92.80 195

旧先验2							81.73 276		56.88 412	86.54 213			84.90 369	72.81 254

新几何2							81.72 277

新几何1				82.95 223	93.96 64	78.56 91		80.24 366	55.45 417	83.93 284	91.08 227	71.19 280	88.33 304	65.84 327	93.07 248	81.95 431

旧先验1					91.97 121	71.77 175		81.78 354			91.84 193	73.92 241			93.65 227	83.61 406

无先验							82.81 247	85.62 304	58.09 400				91.41 202	67.95 310		84.48 391

原ACMM2							82.26 268

原ACMM1				84.60 169	92.81 98	74.01 140		91.50 156	62.59 354	82.73 310	90.67 250	76.53 206	94.25 98	69.24 291	95.69 152	85.55 379

test222					93.31 81	76.54 116	79.38 321	77.79 377	52.59 434	82.36 315	90.84 241	66.83 304			91.69 293	81.25 439

testdata2													86.43 342	63.52 349

segment_acmp												81.94 127

testdata				79.54 309	92.87 92	72.34 167		80.14 367	59.91 390	85.47 240	91.75 200	67.96 298	85.24 365	68.57 305	92.18 279	81.06 444

testdata1							79.62 313		73.95 188

test12				86.57 115	90.74 163	72.63 160		90.69 184		82.76 308		79.20 161	94.80 79		95.32 163	92.27 232

plane_prior7					93.45 75	77.31 109

plane_prior6					92.61 99	76.54 116						74.84 223

plane_prior5								93.61 67					95.22 63	80.78 126	95.83 145	94.46 100

plane_prior4											92.95 150

plane_prior3						76.85 114			77.79 135	86.55 207

plane_prior2							89.45 87		79.44 110

plane_prior1					92.83 96

plane_prior						76.42 119	87.15 127		75.94 157						95.03 175

n2								0.00 496

nn								0.00 496

door-mid								74.45 403

lessismore_v0				85.95 131	91.10 156	70.99 191		70.91 433		91.79 75	94.42 78	61.76 338	92.93 159	79.52 143	93.03 249	93.93 130

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 76	93.67 38	94.82 60	91.18 5	95.52 48	85.36 67	98.73 7	95.23 66

test11								91.46 157

door								72.57 419

HQP5-MVS						70.66 193

HQP-NCC					91.19 151		84.77 178		73.30 204	80.55 348

ACMP_Plane					91.19 151		84.77 178		73.30 204	80.55 348

BP-MVS														77.30 177

HQP4-MVS										80.56 347			94.61 86			93.56 158

HQP3-MVS								92.68 115							94.47 197

HQP2-MVS												72.10 269

NP-MVS					91.95 122	74.55 137					90.17 271

MDTV_nov1_ep13_2view						27.60 486	70.76 429		46.47 459	61.27 471		45.20 433		49.18 440		83.75 405

MDTV_nov1_ep13			68.29 405		78.03 426	43.87 461	74.12 398	72.22 422	52.17 437	67.02 454	85.54 359	45.36 431	80.85 400	55.73 400	84.42 420

ACMMP++_ref															95.74 151

ACMMP++															97.35 84

Test By Simon												79.09 163

ITE_SJBPF				90.11 49	90.72 164	84.97 41		90.30 202	81.56 84	90.02 109	91.20 222	82.40 112	90.81 229	73.58 240	94.66 192	94.56 93

DeepMVS_CX				24.13 468	32.95 490	29.49 484		21.63 491	12.07 484	37.95 485	45.07 482	30.84 472	19.21 487	17.94 486	33.06 484	23.69 483

High-resolution multi-view benchmark