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 2	98.67 1	85.39 36	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 15	85.07 70	99.27 1	99.54 1

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 45	94.47 42	85.95 26	86.84 133	93.91 47	80.07 103	86.75 204	93.26 135	93.64 2	90.93 226	84.60 82	90.75 332	93.97 130

ACMH+	77.89 11	90.73 33	91.50 26	88.44 82	93.00 88	76.26 121	89.65 80	95.55 8	87.72 26	93.89 30	94.94 55	91.62 3	93.44 142	78.35 155	98.76 3	95.61 55

lecture		92.43 8	93.50 2	89.21 65	94.43 43	79.31 83	92.69 19	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 81	90.26 3	98.44 19	93.63 154

LPG-MVS_test		91.47 21	91.68 21	90.82 36	94.75 40	81.69 62	90.00 67	94.27 24	82.35 77	93.67 38	94.82 61	91.18 5	95.52 46	85.36 66	98.73 6	95.23 66

LGP-MVS_train				90.82 36	94.75 40	81.69 62		94.27 24	82.35 77	93.67 38	94.82 61	91.18 5	95.52 46	85.36 66	98.73 6	95.23 66

PMVS	80.48 6	90.08 44	90.66 50	88.34 87	96.71 3	92.97 1	90.31 64	89.57 229	88.51 20	90.11 105	95.12 52	90.98 7	88.92 290	77.55 173	97.07 91	83.13 427
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMM	79.39 9	90.65 34	90.99 43	89.63 57	95.03 33	83.53 50	89.62 81	93.35 81	79.20 115	93.83 32	93.60 124	90.81 8	92.96 158	85.02 73	98.45 18	92.41 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 62	91.14 37	83.96 191	92.50 102	70.36 200	89.55 82	93.84 54	81.89 82	94.70 16	95.44 43	90.69 9	88.31 314	83.33 93	98.30 26	93.20 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 32	92.99 12	94.23 27	85.21 45	92.51 61	95.13 51	90.65 10	95.34 57	88.06 15	98.15 38	95.95 45

RE-MVS-def			92.61 8		94.13 59	88.95 5	92.87 13	94.16 32	88.75 17	93.79 33	94.43 77	90.64 11		87.16 37	97.60 73	92.73 199

ACMP	79.16 10	90.54 37	90.60 52	90.35 44	94.36 50	80.98 68	89.16 92	94.05 41	79.03 118	92.87 52	93.74 119	90.60 12	95.21 63	82.87 101	98.76 3	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 45	93.51 8	94.85 15	82.88 73	91.77 75	93.94 110	90.55 13	95.73 36	88.50 11	98.23 32	95.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 181	97.00 2	64.33 274	89.67 79	88.38 250	88.84 16	94.29 22	97.57 7	90.48 14	91.26 209	72.57 259	97.65 68	97.34 15

SED-MVS		90.46 39	91.64 22	86.93 108	94.18 54	72.65 156	90.47 60	93.69 63	83.77 60	94.11 26	94.27 84	90.28 15	95.84 25	86.03 56	97.92 51	92.29 233

test_241102_ONE					94.18 54	72.65 156		93.69 63	83.62 63	94.11 26	93.78 116	90.28 15	95.50 50

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 37	87.85 9	92.51 25	93.87 51	88.20 23	93.24 43	94.02 102	90.15 17	95.67 39	86.82 42	97.34 84	92.19 239

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 87	85.17 38	92.47 27	95.05 14	87.65 27	93.21 46	94.39 82	90.09 18	95.08 69	86.67 44	97.60 73	94.18 119

DVP-MVS		90.06 46	91.32 34	86.29 120	94.16 57	72.56 162	90.54 57	91.01 178	83.61 64	93.75 35	94.65 66	89.76 19	95.78 33	86.42 46	97.97 48	90.55 296
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
test0726					94.16 57	72.56 162	90.63 54	93.90 48	83.61 64	93.75 35	94.49 74	89.76 19

COLMAP_ROB	83.01 3	91.97 13	91.95 15	92.04 10	93.68 69	86.15 23	93.37 10	95.10 13	90.28 9	92.11 67	95.03 53	89.75 21	94.93 73	79.95 133	98.27 27	95.04 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 45	87.71 10	92.40 28	94.38 19	89.82 12	95.51 11	95.49 41	89.64 22	95.82 27	89.13 6	98.26 29	91.76 255

our_new_method		92.86 5	93.22 5	91.76 22	94.39 45	87.71 10	92.40 28	94.38 19	89.82 12	95.51 11	95.49 41	89.64 22	95.82 27	89.13 6	98.26 29	91.76 255

test_241102_TWO								93.71 59	83.77 60	93.49 40	94.27 84	89.27 24	95.84 25	86.03 56	97.82 56	92.04 246

tt0805		88.09 82	89.79 58	82.98 222	93.26 82	63.94 278	91.10 50	89.64 226	85.07 46	90.91 91	91.09 230	89.16 25	91.87 189	82.03 112	95.87 143	93.13 179

ACMMP		91.91 14	91.87 20	92.03 11	95.53 26	85.91 27	93.35 11	94.16 32	82.52 76	92.39 64	94.14 94	89.15 26	95.62 40	87.35 32	98.24 31	94.56 95
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
SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 59	88.95 5	92.87 13	94.16 32	88.75 17	93.79 33	94.43 77	88.83 27	95.51 48	87.16 37	97.60 73	92.73 199

APDe-MVS		91.22 25	91.92 16	89.14 67	92.97 89	78.04 95	92.84 16	94.14 36	83.33 67	93.90 28	95.73 33	88.77 28	96.41 2	87.60 26	97.98 47	92.98 192
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
TestfortrainingZip a		91.12 29	92.04 14	88.36 86	94.38 47	76.05 128	92.12 33	93.73 58	85.28 43	93.85 31	94.84 58	88.66 29	95.18 65	87.89 18	97.59 76	93.84 137

test_one_0601					93.85 66	73.27 148		94.11 38	86.57 33	93.47 42	94.64 69	88.42 30

ACMMP_NAP		90.65 34	91.07 41	89.42 61	95.93 15	79.54 81	89.95 71	93.68 67	77.65 137	91.97 71	94.89 56	88.38 31	95.45 53	89.27 5	97.87 55	93.27 172

MP-MVS-pluss		90.81 31	91.08 39	89.99 49	95.97 13	79.88 76	88.13 110	94.51 18	75.79 160	92.94 50	94.96 54	88.36 32	95.01 71	90.70 2	98.40 21	95.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		91.30 23	91.39 29	91.02 32	95.43 28	84.66 46	92.58 23	93.29 87	81.99 79	91.47 78	93.96 107	88.35 33	95.56 43	87.74 21	97.74 61	92.85 196

CP-MVS		91.67 16	91.58 24	91.96 13	95.29 30	87.62 12	93.38 9	93.36 78	83.16 69	91.06 87	94.00 103	88.26 34	95.71 38	87.28 35	98.39 22	92.55 213

SteuartSystems-ACMMP		91.16 27	91.36 30	90.55 40	93.91 64	80.97 69	91.49 45	93.48 76	82.82 74	92.60 60	93.97 104	88.19 35	96.29 5	87.61 25	98.20 35	94.39 110
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		91.20 26	90.95 45	91.93 14	95.67 22	85.85 30	90.00 67	93.90 48	80.32 99	91.74 76	94.41 80	88.17 36	95.98 12	86.37 48	97.99 45	93.96 131

TDRefinement		93.52 2	93.39 4	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 53	96.29 21	88.16 37	94.17 106	86.07 55	98.48 17	97.22 18

DPE-MVS		90.53 38	91.08 39	88.88 70	93.38 78	78.65 89	89.15 93	94.05 41	84.68 51	93.90 28	94.11 96	88.13 38	96.30 4	84.51 83	97.81 57	91.70 259
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 39	79.86 77	86.76 137	92.78 115	78.78 121	92.51 61	93.64 123	88.13 38	93.84 121	84.83 79	97.55 77	94.10 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs6		86.52 108	88.06 87	81.90 259	92.22 112	62.28 306	84.66 186	89.15 237	83.54 66	89.85 114	97.32 8	88.08 40	86.80 345	70.43 282	97.30 86	96.62 31

ME-MVS		90.09 43	90.66 50	88.38 84	92.82 96	76.12 125	89.40 90	93.70 60	83.72 62	92.39 64	93.18 138	88.02 41	95.47 51	84.99 74	97.69 64	93.54 164

mvs_tets		89.78 55	89.27 66	91.30 28	93.51 72	84.79 43	89.89 73	90.63 190	70.00 268	94.55 18	96.67 16	87.94 42	93.59 133	84.27 85	95.97 133	95.52 56

ZNCC-MVS		91.26 24	91.34 33	91.01 33	95.73 20	83.05 55	92.18 32	94.22 29	80.14 102	91.29 83	93.97 104	87.93 43	95.87 19	88.65 9	97.96 50	94.12 124

reproduce_model		92.89 4	93.18 7	92.01 12	94.20 53	88.23 8	92.87 13	94.32 21	90.25 10	95.65 8	95.74 32	87.75 44	95.72 37	89.60 4	98.27 27	92.08 244

region2R		91.44 22	91.30 36	91.87 18	95.75 18	85.90 28	92.63 22	93.30 86	81.91 81	90.88 94	94.21 89	87.75 44	95.87 19	87.60 26	97.71 62	93.83 140

wuyk23d		75.13 346	79.30 289	62.63 462	75.56 459	75.18 134	80.89 298	73.10 427	75.06 172	94.76 15	95.32 44	87.73 46	52.85 494	34.16 491	97.11 90	59.85 490

mPP-MVS		91.69 15	91.47 28	92.37 5	96.04 12	88.48 7	92.72 18	92.60 123	83.09 70	91.54 77	94.25 88	87.67 47	95.51 48	87.21 36	98.11 39	93.12 182

ACMMPR		91.49 19	91.35 32	91.92 15	95.74 19	85.88 29	92.58 23	93.25 88	81.99 79	91.40 79	94.17 93	87.51 48	95.87 19	87.74 21	97.76 59	93.99 128

test_0728_THIRD									85.33 41	93.75 35	94.65 66	87.44 49	95.78 33	87.41 30	98.21 33	92.98 192

9.14			89.29 65		91.84 128		88.80 99	95.32 12	75.14 171	91.07 86	92.89 154	87.27 50	93.78 122	83.69 92	97.55 77

PS-CasMVS		90.06 46	91.92 16	84.47 173	96.56 6	58.83 375	89.04 94	92.74 116	91.40 5	96.12 4	96.06 28	87.23 51	95.57 42	79.42 143	98.74 5	99.00 2

GST-MVS		90.96 30	91.01 42	90.82 36	95.45 27	82.73 58	91.75 43	93.74 57	80.98 92	91.38 80	93.80 114	87.20 52	95.80 29	87.10 39	97.69 64	93.93 132

MED-MVS		90.77 32	91.49 27	88.60 78	94.38 47	76.12 125	92.12 33	93.85 52	85.28 43	93.24 43	94.84 58	87.06 53	95.85 23	84.99 74	97.69 64	93.84 137

PEN-MVS		90.03 48	91.88 19	84.48 172	96.57 5	58.88 372	88.95 95	93.19 91	91.62 4	96.01 6	96.16 26	87.02 54	95.60 41	78.69 151	98.72 8	98.97 3

DTE-MVSNet		89.98 50	91.91 18	84.21 183	96.51 7	57.84 386	88.93 96	92.84 112	91.92 3	96.16 3	96.23 23	86.95 55	95.99 11	79.05 147	98.57 14	98.80 6

SF-MVS		90.27 41	90.80 48	88.68 77	92.86 93	77.09 110	91.19 49	95.74 5	81.38 87	92.28 66	93.80 114	86.89 56	94.64 84	85.52 65	97.51 81	94.30 115

MP-MVS		91.14 28	90.91 46	91.83 19	96.18 10	86.88 16	92.20 31	93.03 103	82.59 75	88.52 147	94.37 83	86.74 57	95.41 55	86.32 49	98.21 33	93.19 177
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		91.52 18	91.60 23	91.29 29	96.59 4	86.29 20	92.02 38	91.81 150	84.07 57	92.00 70	94.40 81	86.63 58	95.28 60	88.59 10	98.31 25	92.30 231

XVS		91.54 17	91.36 30	92.08 8	95.64 23	86.25 21	92.64 20	93.33 82	85.07 46	89.99 109	94.03 101	86.57 59	95.80 29	87.35 32	97.62 71	94.20 116

X-MVStestdata		85.04 146	82.70 222	92.08 8	95.64 23	86.25 21	92.64 20	93.33 82	85.07 46	89.99 109	16.05 499	86.57 59	95.80 29	87.35 32	97.62 71	94.20 116

MGCFI-Net		85.04 146	85.95 128	82.31 250	87.52 255	63.59 281	86.23 148	93.96 44	73.46 201	88.07 161	87.83 325	86.46 61	90.87 231	76.17 196	93.89 218	92.47 218

sasdasda		85.50 126	86.14 123	83.58 204	87.97 238	67.13 240	87.55 119	94.32 21	73.44 203	88.47 148	87.54 330	86.45 62	91.06 221	75.76 202	93.76 222	92.54 214

canonicalmvs		85.50 126	86.14 123	83.58 204	87.97 238	67.13 240	87.55 119	94.32 21	73.44 203	88.47 148	87.54 330	86.45 62	91.06 221	75.76 202	93.76 222	92.54 214

TranMVSNet+NR-MVSNet		87.86 86	88.76 80	85.18 149	94.02 62	64.13 275	84.38 194	91.29 166	84.88 49	92.06 69	93.84 113	86.45 62	93.73 123	73.22 250	98.66 10	97.69 9

test_0402		88.65 74	89.58 63	85.88 133	92.55 100	72.22 170	84.01 202	89.44 232	88.63 19	94.38 21	95.77 31	86.38 65	93.59 133	79.84 134	95.21 167	91.82 253

APD-MVS		89.54 59	89.63 61	89.26 64	92.57 99	81.34 67	90.19 66	93.08 99	80.87 94	91.13 85	93.19 137	86.22 66	95.97 13	82.23 111	97.18 89	90.45 298
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 70	89.88 56	86.22 124	91.63 132	77.07 111	89.82 74	93.77 56	78.90 119	92.88 51	92.29 181	86.11 67	90.22 254	86.24 53	97.24 87	91.36 268
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
ZD-MVS					92.22 112	80.48 70		91.85 146	71.22 251	90.38 101	92.98 148	86.06 68	96.11 6	81.99 114	96.75 101

jajsoiax		89.41 60	88.81 79	91.19 31	93.38 78	84.72 44	89.70 76	90.29 208	69.27 276	94.39 20	96.38 20	86.02 69	93.52 138	83.96 87	95.92 139	95.34 60

nrg030		87.85 87	88.49 81	85.91 131	90.07 179	69.73 208	87.86 116	94.20 30	74.04 188	92.70 59	94.66 65	85.88 70	91.50 197	79.72 136	97.32 85	96.50 34

E5new		85.44 131	86.37 115	82.66 234	88.22 231	61.86 312	83.59 219	93.70 60	73.64 195	87.62 179	93.30 131	85.85 71	91.26 209	78.02 163	93.40 236	94.86 82

E6new		85.44 131	86.37 115	82.66 234	88.23 229	61.86 312	83.59 219	93.69 63	73.64 195	87.61 181	93.30 131	85.85 71	91.26 209	78.02 163	93.40 236	94.86 82

E6		85.44 131	86.37 115	82.66 234	88.23 229	61.86 312	83.59 219	93.69 63	73.64 195	87.61 181	93.30 131	85.85 71	91.26 209	78.02 163	93.40 236	94.86 82

E5		85.44 131	86.37 115	82.66 234	88.22 231	61.86 312	83.59 219	93.70 60	73.64 195	87.62 179	93.30 131	85.85 71	91.26 209	78.02 163	93.40 236	94.86 82

tt0320-xc		86.67 104	88.41 83	81.44 273	93.45 74	60.44 343	83.96 204	88.50 246	87.26 28	90.90 93	97.90 3	85.61 75	86.40 356	70.14 285	98.01 44	97.47 14

SMA-MVS		90.31 40	90.48 53	89.83 54	95.31 29	79.52 82	90.98 51	93.24 89	75.37 169	92.84 54	95.28 47	85.58 76	96.09 7	87.92 17	97.76 59	93.88 135
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
tt0320		86.63 106	88.36 84	81.41 274	93.57 71	60.73 340	84.37 195	88.61 245	87.00 30	90.75 96	97.98 2	85.54 77	86.45 353	69.75 290	97.70 63	97.06 22

sc_t1		87.70 90	88.94 73	83.99 189	93.47 73	67.15 239	85.05 176	88.21 258	86.81 31	91.87 73	97.65 5	85.51 78	87.91 320	74.22 221	97.63 69	96.92 25

viewdifsd2359ckpt07		83.41 208	84.35 180	80.56 295	85.84 317	58.93 371	79.47 322	91.28 167	73.01 217	87.59 183	92.07 186	85.24 79	88.68 300	73.59 242	91.11 314	94.09 126

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 62	93.64 70	79.07 85	88.54 106	94.20 30	73.53 200	89.71 117	94.82 61	85.09 80	95.77 35	84.17 86	98.03 42	93.26 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testf1		89.30 63	89.12 67	89.84 52	88.67 216	85.64 34	90.61 55	93.17 92	86.02 37	93.12 47	95.30 45	84.94 81	89.44 282	74.12 227	96.10 128	94.45 104

APD_test2		89.30 63	89.12 67	89.84 52	88.67 216	85.64 34	90.61 55	93.17 92	86.02 37	93.12 47	95.30 45	84.94 81	89.44 282	74.12 227	96.10 128	94.45 104

GeoE		85.45 130	85.81 133	84.37 174	90.08 177	67.07 242	85.86 155	91.39 163	72.33 232	87.59 183	90.25 270	84.85 83	92.37 174	78.00 167	91.94 295	93.66 149

LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 68	85.72 33	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 84	93.16 151	91.10 1	97.53 80	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
DP-MVS		88.60 75	89.01 70	87.36 101	91.30 146	77.50 103	87.55 119	92.97 108	87.95 25	89.62 121	92.87 155	84.56 85	93.89 118	77.65 171	96.62 104	90.70 288

LS3D		90.60 36	90.34 54	91.38 27	89.03 204	84.23 48	93.58 6	94.68 17	90.65 7	90.33 103	93.95 109	84.50 86	95.37 56	80.87 123	95.50 158	94.53 99

EC-MVSNet		88.01 83	88.32 85	87.09 103	89.28 195	72.03 173	90.31 64	96.31 3	80.88 93	85.12 250	89.67 285	84.47 87	95.46 52	82.56 106	96.26 120	93.77 146

casdiffmvs_mvg		86.72 102	87.51 94	84.36 176	87.09 274	65.22 264	84.16 198	94.23 27	77.89 133	91.28 84	93.66 122	84.35 88	92.71 164	80.07 130	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
anonymousdsp		89.73 56	88.88 76	92.27 7	89.82 184	86.67 17	90.51 59	90.20 211	69.87 269	95.06 14	96.14 27	84.28 89	93.07 155	87.68 23	96.34 115	97.09 20

OMC-MVS		88.19 79	87.52 93	90.19 47	91.94 123	81.68 64	87.49 122	93.17 92	76.02 153	88.64 143	91.22 224	84.24 90	93.37 145	77.97 169	97.03 92	95.52 56

E4		84.75 155	85.46 142	82.61 238	88.17 234	61.55 319	81.39 286	93.55 74	73.13 215	86.83 201	92.83 157	84.17 91	91.48 198	76.92 183	92.19 287	94.80 89

CS-MVS		88.14 80	87.67 92	89.54 60	89.56 188	79.18 84	90.47 60	94.77 16	79.37 113	84.32 276	89.33 292	83.87 92	94.53 91	82.45 107	94.89 182	94.90 76

XVG-OURS		89.18 66	88.83 78	90.23 46	94.28 51	86.11 25	85.91 152	93.60 70	80.16 101	89.13 134	93.44 126	83.82 93	90.98 223	83.86 89	95.30 166	93.60 157

XVG-ACMP-BASELINE		89.98 50	89.84 57	90.41 42	94.91 36	84.50 47	89.49 86	93.98 43	79.68 107	92.09 68	93.89 112	83.80 94	93.10 154	82.67 105	98.04 40	93.64 153

CDPH-MVS		86.17 117	85.54 140	88.05 94	92.25 110	75.45 132	83.85 209	92.01 140	65.91 326	86.19 221	91.75 204	83.77 95	94.98 72	77.43 176	96.71 102	93.73 147

test_fmvsmvis_n_1920		85.22 137	85.36 146	84.81 159	85.80 318	76.13 124	85.15 174	92.32 131	61.40 382	91.33 81	90.85 244	83.76 96	86.16 362	84.31 84	93.28 244	92.15 242

Effi-MVS+		83.90 189	84.01 187	83.57 206	87.22 267	65.61 261	86.55 142	92.40 126	78.64 124	81.34 343	84.18 389	83.65 97	92.93 160	74.22 221	87.87 384	92.17 241

MVS_111021_HR		84.63 157	84.34 181	85.49 144	90.18 175	75.86 130	79.23 330	87.13 279	73.35 205	85.56 241	89.34 291	83.60 98	90.50 245	76.64 186	94.05 214	90.09 308

UA-Net		91.49 19	91.53 25	91.39 26	94.98 34	82.95 57	93.52 7	92.79 114	88.22 22	88.53 146	97.64 6	83.45 99	94.55 89	86.02 59	98.60 12	96.67 30

AdaColmap		83.66 194	83.69 194	83.57 206	90.05 180	72.26 169	86.29 146	90.00 216	78.19 130	81.65 337	87.16 340	83.40 100	94.24 99	61.69 370	94.76 191	84.21 409

LCM-MVSNet-Re		83.48 203	85.06 152	78.75 327	85.94 315	55.75 403	80.05 311	94.27 24	76.47 148	96.09 5	94.54 72	83.31 101	89.75 276	59.95 383	94.89 182	90.75 285

casdiffseed414692147		85.64 125	86.08 126	84.32 179	87.49 257	65.55 262	85.81 157	93.00 107	75.85 158	87.50 186	93.40 128	83.10 102	91.71 193	73.70 239	94.84 186	95.69 50

test_fmvsmconf0.01_n		86.68 103	86.52 112	87.18 102	85.94 315	78.30 91	86.93 130	92.20 134	65.94 324	89.16 132	93.16 141	83.10 102	89.89 270	87.81 20	94.43 200	93.35 167

TransMVSNet (Re)		84.02 183	85.74 137	78.85 324	91.00 158	55.20 412	82.29 268	87.26 274	79.65 108	88.38 152	95.52 40	83.00 104	86.88 342	67.97 312	96.60 105	94.45 104

CNVR-MVS		87.81 88	87.68 91	88.21 89	92.87 91	77.30 109	85.25 171	91.23 171	77.31 143	87.07 197	91.47 214	82.94 105	94.71 80	84.67 81	96.27 119	92.62 207

E2		84.06 178	84.61 167	82.40 248	87.49 257	61.31 323	81.03 294	93.36 78	71.83 240	86.02 226	91.87 192	82.91 106	91.37 206	75.66 204	91.33 310	94.53 99

E3		84.06 178	84.61 167	82.40 248	87.49 257	61.30 324	81.03 294	93.36 78	71.83 240	86.01 227	91.87 192	82.91 106	91.36 207	75.66 204	91.33 310	94.53 99

DeepPCF-MVS	81.24 5	87.28 94	86.21 122	90.49 41	91.48 142	84.90 41	83.41 229	92.38 128	70.25 265	89.35 129	90.68 251	82.85 108	94.57 87	79.55 140	95.95 136	92.00 248

v7n		90.13 42	90.96 44	87.65 99	91.95 121	71.06 190	89.99 69	93.05 100	86.53 34	94.29 22	96.27 22	82.69 109	94.08 109	86.25 52	97.63 69	97.82 8

AllTest		87.97 85	87.40 97	89.68 55	91.59 133	83.40 51	89.50 85	95.44 10	79.47 109	88.00 164	93.03 146	82.66 110	91.47 199	70.81 273	96.14 125	94.16 121

TestCases				89.68 55	91.59 133	83.40 51		95.44 10	79.47 109	88.00 164	93.03 146	82.66 110	91.47 199	70.81 273	96.14 125	94.16 121

test_fmvsmconf0.1_n		86.18 116	85.88 131	87.08 104	85.26 331	78.25 92	85.82 156	91.82 148	65.33 339	88.55 145	92.35 180	82.62 112	89.80 272	86.87 41	94.32 204	93.18 178

RPSCF		88.00 84	86.93 107	91.22 30	90.08 177	89.30 4	89.68 78	91.11 174	79.26 114	89.68 118	94.81 64	82.44 113	87.74 325	76.54 189	88.74 370	96.61 32

SPE-MVS-test		87.00 97	86.43 114	88.71 75	89.46 191	77.46 104	89.42 89	95.73 6	77.87 135	81.64 338	87.25 338	82.43 114	94.53 91	77.65 171	96.46 111	94.14 123

ITE_SJBPF				90.11 48	90.72 164	84.97 40		90.30 206	81.56 85	90.02 108	91.20 226	82.40 115	90.81 233	73.58 243	94.66 193	94.56 95

SDMVSNet		81.90 245	83.17 210	78.10 342	88.81 213	62.45 302	76.08 383	86.05 299	73.67 193	83.41 298	93.04 144	82.35 116	80.65 416	70.06 287	95.03 175	91.21 270

test_fmvsmconf_n		85.88 122	85.51 141	86.99 107	84.77 340	78.21 93	85.40 168	91.39 163	65.32 340	87.72 177	91.81 200	82.33 117	89.78 273	86.68 43	94.20 207	92.99 190

Fast-Effi-MVS+		81.04 260	80.57 267	82.46 246	87.50 256	63.22 286	78.37 343	89.63 227	68.01 297	81.87 330	82.08 412	82.31 118	92.65 167	67.10 315	88.30 379	91.51 266

fmvsm_l_conf0.5_n_3		85.11 145	84.96 155	85.56 140	87.49 257	75.69 131	84.71 184	90.61 192	67.64 307	84.88 260	92.05 187	82.30 119	88.36 312	83.84 90	91.10 315	92.62 207

baseline		85.20 139	85.93 129	83.02 220	86.30 302	62.37 304	84.55 189	93.96 44	74.48 181	87.12 192	92.03 189	82.30 119	91.94 185	78.39 153	94.21 206	94.74 91

casdiffmvs		85.21 138	85.85 132	83.31 213	86.17 307	62.77 292	83.03 242	93.93 46	74.69 177	88.21 157	92.68 164	82.29 121	91.89 188	77.87 170	93.75 225	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
Anonymous20231211		88.40 76	89.62 62	84.73 163	90.46 169	65.27 263	88.86 97	93.02 104	87.15 29	93.05 49	97.10 10	82.28 122	92.02 184	76.70 184	97.99 45	96.88 26

fmvsm_s_conf0.5_n_3		86.19 115	87.27 98	82.95 224	86.91 282	70.38 199	85.31 170	92.61 122	75.59 164	88.32 154	92.87 155	82.22 123	88.63 303	88.80 8	92.82 261	89.83 312

APD_test1		88.40 76	87.91 88	89.88 51	89.50 190	86.65 19	89.98 70	91.91 145	84.26 55	90.87 95	93.92 111	82.18 124	89.29 286	73.75 235	94.81 187	93.70 148

Anonymous20240529		86.20 114	87.13 100	83.42 210	90.19 174	64.55 271	84.55 189	90.71 187	85.85 39	89.94 112	95.24 49	82.13 125	90.40 249	69.19 297	96.40 114	95.31 62

viewdifsd2359ckpt11		82.46 226	82.98 215	80.88 285	83.53 364	61.00 333	79.46 323	85.97 302	69.48 274	87.89 169	91.31 220	82.10 126	88.61 304	74.28 219	92.86 258	93.02 186

viewmsd2359difaftdt		82.46 226	82.99 214	80.88 285	83.52 365	61.00 333	79.46 323	85.97 302	69.48 274	87.89 169	91.31 220	82.10 126	88.61 304	74.28 219	92.86 258	93.02 186

CLD-MVS		83.18 210	82.64 224	84.79 160	89.05 203	67.82 236	77.93 349	92.52 124	68.33 292	85.07 253	81.54 418	82.06 128	92.96 158	69.35 293	97.91 53	93.57 160
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TEST9					92.34 107	79.70 79	83.94 205	90.32 203	65.41 338	84.49 270	90.97 235	82.03 129	93.63 128

segment_acmp												81.94 130

train_agg		85.98 119	85.28 148	88.07 93	92.34 107	79.70 79	83.94 205	90.32 203	65.79 328	84.49 270	90.97 235	81.93 131	93.63 128	81.21 119	96.54 107	90.88 282

test_8					92.09 116	78.87 87	83.82 210	90.31 205	65.79 328	84.36 274	90.96 237	81.93 131	93.44 142

test_prior2							83.37 230		75.43 167	84.58 267	91.57 208	81.92 133		79.54 141	96.97 93

EGC-MVSNET		74.79 355	69.99 399	89.19 66	94.89 37	87.00 14	91.89 42	86.28 293	1.09 500	2.23 502	95.98 29	81.87 134	89.48 278	79.76 135	95.96 134	91.10 273

viewcassd2359sk11		83.53 201	83.96 189	82.25 251	86.97 281	61.13 328	80.80 301	93.22 90	70.97 254	85.36 245	91.08 231	81.84 135	91.29 208	74.79 216	90.58 344	94.33 113

CP-MVSNet		89.27 65	90.91 46	84.37 174	96.34 8	58.61 378	88.66 103	92.06 139	90.78 6	95.67 7	95.17 50	81.80 136	95.54 45	79.00 148	98.69 9	98.95 4

viewmacassd2359aftdt		84.04 182	84.78 159	81.81 264	86.43 294	60.32 345	81.95 276	92.82 113	71.56 243	86.06 225	92.98 148	81.79 137	90.28 250	76.18 195	93.24 246	94.82 88

MVS_111021_LR		84.28 170	83.76 193	85.83 135	89.23 197	83.07 54	80.99 296	83.56 344	72.71 224	86.07 224	89.07 299	81.75 138	86.19 361	77.11 180	93.36 240	88.24 351

test_djsdf		89.62 57	89.01 70	91.45 25	92.36 106	82.98 56	91.98 39	90.08 214	71.54 244	94.28 24	96.54 18	81.57 139	94.27 96	86.26 50	96.49 109	97.09 20

cdsmvs_eth3d_5k		20.81 465	27.75 468	0.00 486	0.00 509	0.00 511	0.00 497	85.44 310	0.00 504	0.00 505	82.82 404	81.46 140	0.00 505	0.00 503	0.00 503	0.00 501

WR-MVS_H		89.91 53	91.31 35	85.71 137	96.32 9	62.39 303	89.54 84	93.31 85	90.21 11	95.57 10	95.66 36	81.42 141	95.90 16	80.94 122	98.80 2	98.84 5

CPTT-MVS		89.39 61	88.98 72	90.63 39	95.09 32	86.95 15	92.09 37	92.30 132	79.74 106	87.50 186	92.38 174	81.42 141	93.28 147	83.07 97	97.24 87	91.67 260

pm-mvs1		83.69 193	84.95 156	79.91 307	90.04 181	59.66 357	82.43 263	87.44 270	75.52 166	87.85 171	95.26 48	81.25 143	85.65 376	68.74 304	96.04 130	94.42 108

DVP-MVS++		90.07 45	91.09 38	87.00 106	91.55 138	72.64 158	96.19 2	94.10 39	85.33 41	93.49 40	94.64 69	81.12 144	95.88 17	87.41 30	95.94 137	92.48 216

OPU-MVS				88.27 88	91.89 124	77.83 99	90.47 60				91.22 224	81.12 144	94.68 81	74.48 217	95.35 161	92.29 233

sd_testset		79.95 286	81.39 252	75.64 380	88.81 213	58.07 383	76.16 382	82.81 354	73.67 193	83.41 298	93.04 144	80.96 146	77.65 432	58.62 393	95.03 175	91.21 270

E3new		83.08 214	83.39 202	82.14 254	86.49 290	61.00 333	80.64 303	93.12 95	70.30 264	84.78 264	90.34 264	80.85 147	91.24 214	74.20 224	89.83 353	94.17 120

NCCC		87.36 93	86.87 108	88.83 71	92.32 109	78.84 88	86.58 141	91.09 176	78.77 122	84.85 262	90.89 241	80.85 147	95.29 58	81.14 120	95.32 163	92.34 229

TAPA-MVS	77.73 12	85.71 124	84.83 158	88.37 85	88.78 215	79.72 78	87.15 127	93.50 75	69.17 277	85.80 233	89.56 286	80.76 149	92.13 180	73.21 255	95.51 157	93.25 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 224	81.41 250	85.90 132	85.60 323	76.53 117	83.07 241	89.62 228	73.02 216	79.11 374	83.51 394	80.74 150	90.24 253	68.76 303	89.29 360	90.94 279

PC_three_1452									58.96 405	90.06 106	91.33 218	80.66 151	93.03 157	75.78 201	95.94 137	92.48 216

VPA-MVSNet		83.47 204	84.73 160	79.69 312	90.29 172	57.52 389	81.30 290	88.69 242	76.29 149	87.58 185	94.44 76	80.60 152	87.20 336	66.60 321	96.82 98	94.34 112

fmvsm_s_conf0.5_n_9		87.04 96	87.02 104	87.08 104	89.67 186	75.87 129	84.60 187	89.74 221	74.40 184	89.92 113	93.41 127	80.45 153	90.63 241	86.66 45	94.37 202	94.73 92

ETV-MVS		84.31 168	83.91 192	85.52 141	88.58 221	70.40 198	84.50 193	93.37 77	78.76 123	84.07 284	78.72 443	80.39 154	95.13 68	73.82 234	92.98 255	91.04 275

HPM-MVS++		88.93 71	88.45 82	90.38 43	94.92 35	85.85 30	89.70 76	91.27 170	78.20 129	86.69 208	92.28 182	80.36 155	95.06 70	86.17 54	96.49 109	90.22 302

ANet_high		83.17 211	85.68 138	75.65 379	81.24 400	45.26 470	79.94 313	92.91 109	83.83 59	91.33 81	96.88 15	80.25 156	85.92 366	68.89 301	95.89 142	95.76 47

EI-MVSNet-Vis-set		85.12 144	84.53 173	86.88 109	84.01 357	72.76 155	83.91 208	85.18 316	80.44 95	88.75 140	85.49 366	80.08 157	91.92 186	82.02 113	90.85 327	95.97 43

DeepC-MVS_fast	80.27 8	86.23 112	85.65 139	87.96 95	91.30 146	76.92 112	87.19 125	91.99 141	70.56 258	84.96 257	90.69 250	80.01 158	95.14 67	78.37 154	95.78 149	91.82 253
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-UG-set		85.04 146	84.44 176	86.85 110	83.87 361	72.52 164	83.82 210	85.15 317	80.27 100	88.75 140	85.45 368	79.95 159	91.90 187	81.92 116	90.80 331	96.13 38

MCST-MVS		84.36 166	83.93 190	85.63 138	91.59 133	71.58 181	83.52 225	92.13 136	61.82 375	83.96 286	89.75 283	79.93 160	93.46 141	78.33 156	94.34 203	91.87 252

viewmanbaseed2359cas		82.95 216	83.43 200	81.52 270	85.18 333	60.03 350	81.36 287	92.38 128	69.55 272	84.84 263	91.38 216	79.85 161	90.09 264	74.22 221	92.09 290	94.43 107

TSAR-MVS + MP.		88.14 80	87.82 90	89.09 68	95.72 21	76.74 114	92.49 26	91.19 173	67.85 303	86.63 209	94.84 58	79.58 162	95.96 14	87.62 24	94.50 196	94.56 95
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_l_conf0.5_n_9		83.98 185	84.46 175	82.53 243	86.11 310	70.65 195	82.45 262	89.17 236	67.72 306	86.74 205	91.49 211	79.20 163	85.86 372	84.71 80	92.60 271	91.07 274

test12				86.57 114	90.74 163	72.63 160		90.69 188		82.76 312		79.20 163	94.80 78		95.32 163	92.27 235

CSCG		86.26 111	86.47 113	85.60 139	90.87 161	74.26 139	87.98 114	91.85 146	80.35 98	89.54 127	88.01 314	79.09 165	92.13 180	75.51 206	95.06 174	90.41 299

Test By Simon												79.09 165

PHI-MVS		86.38 110	85.81 133	88.08 92	88.44 225	77.34 107	89.35 91	93.05 100	73.15 213	84.76 265	87.70 327	78.87 167	94.18 104	80.67 127	96.29 116	92.73 199

SSM_0407		84.89 151	84.85 157	85.01 155	89.13 199	68.97 221	85.60 162	91.58 154	74.41 182	85.68 234	91.49 211	78.54 168	93.69 125	73.71 236	93.47 233	92.38 226

SSM_0404		85.16 141	85.09 151	85.36 145	90.14 176	69.52 211	86.17 149	91.58 154	74.41 182	86.55 210	91.49 211	78.54 168	93.97 114	73.71 236	93.21 249	92.59 210

EG-PatchMatch MVS		84.08 177	84.11 185	83.98 190	92.22 112	72.61 161	82.20 274	87.02 285	72.63 225	88.86 136	91.02 233	78.52 170	91.11 219	73.41 245	91.09 316	88.21 352

dcpmvs_2		84.23 173	85.14 150	81.50 271	88.61 220	61.98 311	82.90 248	93.11 96	68.66 288	92.77 57	92.39 173	78.50 171	87.63 328	76.99 182	92.30 280	94.90 76

Effi-MVS+-dtu		85.82 123	83.38 203	93.14 3	87.13 269	91.15 2	87.70 118	88.42 249	74.57 178	83.56 296	85.65 362	78.49 172	94.21 100	72.04 262	92.88 257	94.05 127

Vis-MVSNet		86.86 99	86.58 111	87.72 97	92.09 116	77.43 106	87.35 123	92.09 138	78.87 120	84.27 281	94.05 100	78.35 173	93.65 126	80.54 129	91.58 306	92.08 244
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 100	87.06 102	86.17 127	92.86 93	67.02 243	82.55 257	91.56 156	83.08 71	90.92 89	91.82 199	78.25 174	93.99 112	74.16 225	98.35 23	97.49 13

fmvsm_s_conf0.5_n_5		84.56 160	84.71 163	84.11 187	87.92 241	72.09 172	84.80 178	88.64 243	64.43 352	88.77 139	91.78 202	78.07 175	87.95 319	85.85 62	92.18 288	92.30 231

MSLP-MVS++		85.00 149	86.03 127	81.90 259	91.84 128	71.56 183	86.75 138	93.02 104	75.95 156	87.12 192	89.39 290	77.98 176	89.40 285	77.46 174	94.78 188	84.75 399

API-MVS		82.28 229	82.61 225	81.30 275	86.29 303	69.79 205	88.71 101	87.67 268	78.42 127	82.15 323	84.15 390	77.98 176	91.59 195	65.39 334	92.75 262	82.51 436

DP-MVS Recon		84.05 180	83.22 206	86.52 116	91.73 131	75.27 133	83.23 237	92.40 126	72.04 237	82.04 327	88.33 310	77.91 178	93.95 116	66.17 324	95.12 172	90.34 301

mmtdpeth		85.13 143	85.78 135	83.17 218	84.65 342	74.71 135	85.87 154	90.35 202	77.94 132	83.82 288	96.96 14	77.75 179	80.03 422	78.44 152	96.21 121	94.79 90

fmvsm_s_conf0.1_n_a		82.58 223	81.93 236	84.50 170	87.68 249	73.35 145	86.14 150	77.70 389	61.64 380	85.02 254	91.62 206	77.75 179	86.24 358	82.79 103	87.07 395	93.91 134

UniMVSNet (Re)		86.87 98	86.98 106	86.55 115	93.11 86	68.48 228	83.80 212	92.87 110	80.37 97	89.61 123	91.81 200	77.72 181	94.18 104	75.00 214	98.53 15	96.99 24

PCF-MVS	74.62 15	82.15 236	80.92 263	85.84 134	89.43 192	72.30 168	80.53 306	91.82 148	57.36 418	87.81 172	89.92 280	77.67 182	93.63 128	58.69 392	95.08 173	91.58 263
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
viewdifsd2359ckpt13		82.22 231	81.98 235	82.95 224	85.48 327	64.44 272	83.17 239	92.11 137	65.97 323	83.72 291	89.73 284	77.60 183	90.80 234	70.61 280	89.42 358	93.59 158

NR-MVSNet		86.00 118	86.22 121	85.34 146	93.24 83	64.56 270	82.21 272	90.46 196	80.99 91	88.42 150	91.97 190	77.56 184	93.85 119	72.46 260	98.65 11	97.61 10

3Dnovator+	83.92 2	89.97 52	89.66 60	90.92 34	91.27 148	81.66 65	91.25 47	94.13 37	88.89 14	88.83 138	94.26 87	77.55 185	95.86 22	84.88 77	95.87 143	95.24 65

MVS_Test		82.47 225	83.22 206	80.22 302	82.62 385	57.75 388	82.54 258	91.96 143	71.16 252	82.89 308	92.52 171	77.41 186	90.50 245	80.04 132	87.84 386	92.40 223

fmvsm_s_conf0.5_n_a		82.21 232	81.51 249	84.32 179	86.56 287	73.35 145	85.46 165	77.30 393	61.81 376	84.51 269	90.88 243	77.36 187	86.21 360	82.72 104	86.97 400	93.38 166

fmvsm_s_conf0.5_n_11		84.56 160	84.69 165	84.15 186	86.53 288	71.29 186	85.53 163	92.62 120	70.54 259	82.75 313	91.20 226	77.33 188	88.55 308	83.80 91	91.93 296	92.61 209

viewdifsd2359ckpt09		83.64 195	83.18 209	85.03 153	87.26 264	66.99 245	85.32 169	93.83 55	65.57 334	84.99 256	89.40 289	77.30 189	93.57 136	71.16 272	93.80 221	94.54 98

EIA-MVS		82.19 233	81.23 257	85.10 151	87.95 240	69.17 219	83.22 238	93.33 82	70.42 260	78.58 379	79.77 434	77.29 190	94.20 101	71.51 268	88.96 366	91.93 251

icg_test_0407_2		78.46 301	79.68 284	74.78 387	85.76 319	62.46 298	68.51 451	87.91 263	65.23 341	82.12 324	87.92 319	77.27 191	72.67 449	71.67 264	90.74 333	89.20 327

IMVS_0407		81.08 258	81.23 257	80.62 294	85.76 319	62.46 298	82.46 260	87.91 263	65.23 341	82.12 324	87.92 319	77.27 191	90.18 256	71.67 264	90.74 333	89.20 327

xiu_mvs_v2_base		77.19 316	76.75 321	78.52 331	87.01 278	61.30 324	75.55 391	87.12 283	61.24 387	74.45 424	78.79 442	77.20 193	90.93 226	64.62 344	84.80 429	83.32 423

DU-MVS		86.80 101	86.99 105	86.21 125	93.24 83	67.02 243	83.16 240	92.21 133	81.73 83	90.92 89	91.97 190	77.20 193	93.99 112	74.16 225	98.35 23	97.61 10

Baseline_NR-MVSNet		84.00 184	85.90 130	78.29 339	91.47 143	53.44 426	82.29 268	87.00 288	79.06 117	89.55 125	95.72 35	77.20 193	86.14 363	72.30 261	98.51 16	95.28 63

TinyColmap		81.25 255	82.34 230	77.99 345	85.33 329	60.68 341	82.32 267	88.33 252	71.26 249	86.97 199	92.22 185	77.10 196	86.98 340	62.37 360	95.17 169	86.31 382

F-COLMAP		84.97 150	83.42 201	89.63 57	92.39 105	83.40 51	88.83 98	91.92 144	73.19 212	80.18 360	89.15 297	77.04 197	93.28 147	65.82 331	92.28 283	92.21 238

114514_t		83.10 213	82.54 227	84.77 161	92.90 90	69.10 220	86.65 139	90.62 191	54.66 435	81.46 340	90.81 246	76.98 198	94.38 94	72.62 258	96.18 123	90.82 284

xiu_mvs_v1_base_debu		80.84 263	80.14 278	82.93 227	88.31 226	71.73 177	79.53 318	87.17 276	65.43 335	79.59 362	82.73 406	76.94 199	90.14 261	73.22 250	88.33 375	86.90 376

xiu_mvs_v1_base		80.84 263	80.14 278	82.93 227	88.31 226	71.73 177	79.53 318	87.17 276	65.43 335	79.59 362	82.73 406	76.94 199	90.14 261	73.22 250	88.33 375	86.90 376

xiu_mvs_v1_base_debi		80.84 263	80.14 278	82.93 227	88.31 226	71.73 177	79.53 318	87.17 276	65.43 335	79.59 362	82.73 406	76.94 199	90.14 261	73.22 250	88.33 375	86.90 376

pcd_1.5k_mvsjas		6.41 468	8.55 471	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	76.94 199	0.00 505	0.00 503	0.00 503	0.00 501

PS-MVSNAJss		88.31 78	87.90 89	89.56 59	93.31 80	77.96 98	87.94 115	91.97 142	70.73 257	94.19 25	96.67 16	76.94 199	94.57 87	83.07 97	96.28 117	96.15 37

PS-MVSNAJ		77.04 319	76.53 323	78.56 330	87.09 274	61.40 321	75.26 393	87.13 279	61.25 386	74.38 426	77.22 458	76.94 199	90.94 225	64.63 343	84.83 428	83.35 422

MIMVSNet1		83.63 196	84.59 169	80.74 288	94.06 61	62.77 292	82.72 251	84.53 333	77.57 139	90.34 102	95.92 30	76.88 205	85.83 373	61.88 368	97.42 82	93.62 155

mamba_0408		83.44 207	82.88 218	85.11 150	89.13 199	68.97 221	72.73 422	91.28 167	72.90 218	85.68 234	90.61 257	76.78 206	93.97 114	73.37 247	93.47 233	92.38 226

SSM_04072		81.44 252	82.88 218	77.10 359	89.13 199	68.97 221	72.73 422	91.28 167	72.90 218	85.68 234	90.61 257	76.78 206	69.94 459	73.37 247	93.47 233	92.38 226

原ACMM1				84.60 168	92.81 97	74.01 140		91.50 158	62.59 364	82.73 314	90.67 254	76.53 208	94.25 98	69.24 294	95.69 152	85.55 390

fmvsm_s_conf0.1_n		82.17 234	81.59 244	83.94 193	86.87 285	71.57 182	85.19 173	77.42 392	62.27 374	84.47 272	91.33 218	76.43 209	85.91 368	83.14 94	87.14 393	94.33 113

fmvsm_s_conf0.5_n		81.91 244	81.30 254	83.75 198	86.02 312	71.56 183	84.73 183	77.11 396	62.44 371	84.00 285	90.68 251	76.42 210	85.89 370	83.14 94	87.11 394	93.81 144

MSDG		80.06 284	79.99 283	80.25 301	83.91 360	68.04 234	77.51 357	89.19 235	77.65 137	81.94 328	83.45 396	76.37 211	86.31 357	63.31 355	86.59 403	86.41 380

Gipuma		84.44 164	86.33 119	78.78 326	84.20 352	73.57 143	89.55 82	90.44 197	84.24 56	84.38 273	94.89 56	76.35 212	80.40 419	76.14 197	96.80 100	82.36 437
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
IMVS_0403		80.93 262	81.00 260	80.72 290	85.76 319	62.46 298	81.82 277	87.91 263	65.23 341	82.07 326	87.92 319	75.91 213	90.50 245	71.67 264	90.74 333	89.20 327

FE-MVSNET2		82.80 218	83.51 196	80.67 293	89.08 202	58.46 379	82.40 265	89.26 234	71.25 250	88.24 156	94.07 99	75.75 214	89.56 277	65.91 329	95.67 155	93.98 129

test_fmvsm_n_1920		83.60 198	82.89 217	85.74 136	85.22 332	77.74 101	84.12 200	90.48 194	59.87 402	86.45 219	91.12 229	75.65 215	85.89 370	82.28 110	90.87 325	93.58 159

XXY-MVS		74.44 359	76.19 327	69.21 431	84.61 343	52.43 434	71.70 429	77.18 395	60.73 393	80.60 350	90.96 237	75.44 216	69.35 462	56.13 407	88.33 375	85.86 387

FMVSNet1		84.55 162	85.45 143	81.85 261	90.27 173	61.05 330	86.83 134	88.27 255	78.57 125	89.66 120	95.64 37	75.43 217	90.68 238	69.09 298	95.33 162	93.82 141

CANet		83.79 192	82.85 220	86.63 113	86.17 307	72.21 171	83.76 213	91.43 160	77.24 144	74.39 425	87.45 334	75.36 218	95.42 54	77.03 181	92.83 260	92.25 237

ab-mvs		79.67 287	80.56 268	76.99 360	88.48 223	56.93 393	84.70 185	86.06 298	68.95 283	80.78 349	93.08 143	75.30 219	84.62 384	56.78 402	90.90 323	89.43 321

patch_mono-2		78.89 292	79.39 287	77.41 356	84.78 339	68.11 232	75.60 388	83.11 350	60.96 390	79.36 368	89.89 281	75.18 220	72.97 448	73.32 249	92.30 280	91.15 272

DELS-MVS		81.44 252	81.25 255	82.03 256	84.27 351	62.87 290	76.47 377	92.49 125	70.97 254	81.64 338	83.83 391	75.03 221	92.70 165	74.29 218	92.22 286	90.51 297
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
PAPR		78.84 294	78.10 307	81.07 281	85.17 334	60.22 346	82.21 272	90.57 193	62.51 365	75.32 418	84.61 382	74.99 222	92.30 177	59.48 386	88.04 381	90.68 289

fmvsm_s_conf0.5_n_4		84.38 165	84.27 182	84.74 162	87.25 265	70.84 192	83.55 224	88.45 248	68.64 289	86.29 220	91.31 220	74.97 223	88.42 310	87.87 19	90.07 348	94.95 75

CNLPA		83.55 200	83.10 212	84.90 156	89.34 194	83.87 49	84.54 191	88.77 240	79.09 116	83.54 297	88.66 307	74.87 224	81.73 408	66.84 318	92.29 282	89.11 332

HQP_MVS		87.75 89	87.43 96	88.70 76	93.45 74	76.42 118	89.45 87	93.61 68	79.44 111	86.55 210	92.95 152	74.84 225	95.22 61	80.78 125	95.83 145	94.46 102

plane_prior6					92.61 98	76.54 115						74.84 225

FC-MVSNet-test		85.93 121	87.05 103	82.58 240	92.25 110	56.44 397	85.75 158	93.09 98	77.33 142	91.94 72	94.65 66	74.78 227	93.41 144	75.11 213	98.58 13	97.88 7

VDD-MVS		84.23 173	84.58 170	83.20 216	91.17 154	65.16 266	83.25 234	84.97 324	79.79 105	87.18 191	94.27 84	74.77 228	90.89 229	69.24 294	96.54 107	93.55 163

BH-untuned		80.96 261	80.99 261	80.84 287	88.55 222	68.23 229	80.33 309	88.46 247	72.79 223	86.55 210	86.76 346	74.72 229	91.77 192	61.79 369	88.99 365	82.52 435

diffmvs_AUTHOR		81.24 256	81.55 247	80.30 300	80.61 412	60.22 346	77.98 348	90.48 194	67.77 305	83.34 300	89.50 288	74.69 230	87.42 332	78.78 150	90.81 330	93.27 172

VPNet		80.25 278	81.68 239	75.94 375	92.46 103	47.98 457	76.70 370	81.67 366	73.45 202	84.87 261	92.82 158	74.66 231	86.51 351	61.66 371	96.85 95	93.33 168

viewmambaseed2359dif		78.80 295	78.47 302	79.78 308	80.26 420	59.28 362	77.31 362	87.13 279	60.42 396	82.37 318	88.67 306	74.58 232	87.87 323	67.78 314	87.73 387	92.19 239

tfpnnormal		81.79 246	82.95 216	78.31 337	88.93 208	55.40 408	80.83 300	82.85 353	76.81 146	85.90 232	94.14 94	74.58 232	86.51 351	66.82 319	95.68 153	93.01 189

KD-MVS_self_test		81.93 243	83.14 211	78.30 338	84.75 341	52.75 430	80.37 308	89.42 233	70.24 266	90.26 104	93.39 129	74.55 234	86.77 346	68.61 306	96.64 103	95.38 59

TestfortrainingZip				84.49 171	88.84 211	70.49 196	92.12 33	91.01 178	84.70 50	82.82 311	89.25 293	74.30 235	94.06 110		90.73 337	88.92 340

fmvsm_l_conf0.5_n		82.06 238	81.54 248	83.60 203	83.94 358	73.90 141	83.35 231	86.10 296	58.97 404	83.80 289	90.36 263	74.23 236	86.94 341	82.90 100	90.22 346	89.94 310

fmvsm_s_conf0.5_n_7		82.04 239	82.05 233	82.01 257	86.98 280	71.07 189	78.70 337	89.45 231	68.07 296	78.14 383	91.61 207	74.19 237	85.92 366	79.61 139	91.73 301	89.05 336

V42		83.47 204	83.37 204	83.75 198	83.16 380	63.33 284	81.31 288	90.23 210	69.51 273	90.91 91	90.81 246	74.16 238	92.29 178	80.06 131	90.22 346	95.62 54

Elysia		88.71 72	88.89 74	88.19 90	91.26 149	72.96 152	88.10 111	93.59 71	84.31 53	90.42 99	94.10 97	74.07 239	94.82 76	88.19 13	95.92 139	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 90	91.26 149	72.96 152	88.10 111	93.59 71	84.31 53	90.42 99	94.10 97	74.07 239	94.82 76	88.19 13	95.92 139	96.80 27

3Dnovator	80.37 7	84.80 152	84.71 163	85.06 152	86.36 300	74.71 135	88.77 100	90.00 216	75.65 162	84.96 257	93.17 140	74.06 241	91.19 216	78.28 157	91.09 316	89.29 326

v10		86.54 107	87.10 101	84.84 157	88.16 236	63.28 285	86.64 140	92.20 134	75.42 168	92.81 56	94.50 73	74.05 242	94.06 110	83.88 88	96.28 117	97.17 19

fmvsm_s_conf0.5_n_8		85.48 128	85.75 136	84.68 166	87.10 272	69.98 204	84.28 196	92.68 117	74.77 175	87.90 168	92.36 179	73.94 243	90.41 248	85.95 61	92.74 263	93.66 149

旧先验1					91.97 120	71.77 175		81.78 364			91.84 197	73.92 244			93.65 229	83.61 417

mvs_anonymous		78.13 305	78.76 296	76.23 374	79.24 431	50.31 449	78.69 338	84.82 329	61.60 381	83.09 306	92.82 158	73.89 245	87.01 337	68.33 310	86.41 405	91.37 267

fmvsm_s_conf0.5_n_10		85.20 139	85.25 149	85.02 154	86.01 313	71.31 185	84.96 177	91.76 152	69.10 279	88.90 135	92.56 168	73.84 246	90.63 241	86.88 40	93.26 245	93.13 179

MAR-MVS		80.24 279	78.74 297	84.73 163	86.87 285	78.18 94	85.75 158	87.81 267	65.67 333	77.84 387	78.50 444	73.79 247	90.53 244	61.59 372	90.87 325	85.49 392
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
KinetiMVS		85.95 120	86.10 125	85.50 143	87.56 254	69.78 206	83.70 215	89.83 220	80.42 96	87.76 175	93.24 136	73.76 248	91.54 196	85.03 72	93.62 231	95.19 68

VDDNet		84.35 167	85.39 145	81.25 276	95.13 31	59.32 361	85.42 167	81.11 370	86.41 35	87.41 188	96.21 24	73.61 249	90.61 243	66.33 323	96.85 95	93.81 144

FIs		85.35 136	86.27 120	82.60 239	91.86 125	57.31 390	85.10 175	93.05 100	75.83 159	91.02 88	93.97 104	73.57 250	92.91 162	73.97 231	98.02 43	97.58 12

v1144		84.54 163	84.72 162	84.00 188	87.67 250	62.55 296	82.97 245	90.93 182	70.32 263	89.80 115	90.99 234	73.50 251	93.48 140	81.69 118	94.65 194	95.97 43

diffmvs		80.40 273	80.48 271	80.17 303	79.02 434	60.04 348	77.54 356	90.28 209	66.65 320	82.40 317	87.33 337	73.50 251	87.35 334	77.98 168	89.62 356	93.13 179
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPM_NR		83.23 209	83.19 208	83.33 212	90.90 160	65.98 257	88.19 109	90.78 186	78.13 131	80.87 348	87.92 319	73.49 253	92.42 171	70.07 286	88.40 373	91.60 262

v8		86.22 113	86.83 109	84.36 176	87.82 244	62.35 305	86.42 144	91.33 165	76.78 147	92.73 58	94.48 75	73.41 254	93.72 124	83.10 96	95.41 159	97.01 23

EI-MVSNet		82.61 221	82.42 229	83.20 216	83.25 377	63.66 279	83.50 226	85.07 318	76.06 151	86.55 210	85.10 374	73.41 254	90.25 251	78.15 162	90.67 339	95.68 52

IterMVS-LS		84.73 156	84.98 154	83.96 191	87.35 262	63.66 279	83.25 234	89.88 219	76.06 151	89.62 121	92.37 177	73.40 256	92.52 169	78.16 160	94.77 190	95.69 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MM		87.64 91	87.15 99	89.09 68	89.51 189	76.39 120	88.68 102	86.76 289	84.54 52	83.58 295	93.78 116	73.36 257	96.48 1	87.98 16	96.21 121	94.41 109

v144192		84.24 172	84.41 177	83.71 200	87.59 253	61.57 318	82.95 246	91.03 177	67.82 304	89.80 115	90.49 261	73.28 258	93.51 139	81.88 117	94.89 182	96.04 42

BH-RMVSNet		80.53 268	80.22 276	81.49 272	87.19 268	66.21 254	77.79 352	86.23 294	74.21 186	83.69 292	88.50 308	73.25 259	90.75 235	63.18 356	87.90 383	87.52 367

PLC	73.85 16	82.09 237	80.31 272	87.45 100	90.86 162	80.29 74	85.88 153	90.65 189	68.17 295	76.32 404	86.33 352	73.12 260	92.61 168	61.40 375	90.02 350	89.44 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_s_conf0.5_n_6		84.05 180	84.14 184	83.81 194	87.75 246	71.17 188	83.42 228	91.10 175	67.90 302	84.53 268	90.70 249	73.01 261	88.73 298	85.09 69	93.72 227	91.53 265

OurMVSNet-221017-0		90.01 49	89.74 59	90.83 35	93.16 85	80.37 73	91.91 41	93.11 96	81.10 90	95.32 13	97.24 9	72.94 262	94.85 75	85.07 70	97.78 58	97.26 16

WR-MVS		83.56 199	84.40 178	81.06 282	93.43 77	54.88 414	78.67 339	85.02 321	81.24 88	90.74 97	91.56 209	72.85 263	91.08 220	68.00 311	98.04 40	97.23 17

VNet		79.31 288	80.27 273	76.44 369	87.92 241	53.95 422	75.58 390	84.35 335	74.39 185	82.23 321	90.72 248	72.84 264	84.39 389	60.38 381	93.98 215	90.97 278

QAPM		82.59 222	82.59 226	82.58 240	86.44 293	66.69 248	89.94 72	90.36 201	67.97 299	84.94 259	92.58 167	72.71 265	92.18 179	70.63 279	87.73 387	88.85 341

v1192		84.57 159	84.69 165	84.21 183	87.75 246	62.88 289	83.02 243	91.43 160	69.08 280	89.98 111	90.89 241	72.70 266	93.62 131	82.41 108	94.97 179	96.13 38

OpenMVS	76.72 13	81.98 242	82.00 234	81.93 258	84.42 347	68.22 230	88.50 107	89.48 230	66.92 317	81.80 334	91.86 195	72.59 267	90.16 258	71.19 271	91.25 313	87.40 369

TSAR-MVS + GP.		83.95 186	82.69 223	87.72 97	89.27 196	81.45 66	83.72 214	81.58 368	74.73 176	85.66 237	86.06 357	72.56 268	92.69 166	75.44 208	95.21 167	89.01 339

alignmvs		83.94 187	83.98 188	83.80 195	87.80 245	67.88 235	84.54 191	91.42 162	73.27 211	88.41 151	87.96 315	72.33 269	90.83 232	76.02 199	94.11 210	92.69 203

fmvsm_l_conf0.5_n_a		81.46 251	80.87 264	83.25 214	83.73 363	73.21 150	83.00 244	85.59 309	58.22 410	82.96 307	90.09 277	72.30 270	86.65 348	81.97 115	89.95 351	89.88 311

MVSMamba_PlusPlus		87.53 92	88.86 77	83.54 208	92.03 119	62.26 307	91.49 45	92.62 120	88.07 24	88.07 161	96.17 25	72.24 271	95.79 32	84.85 78	94.16 209	92.58 211

HQP2-MVS												72.10 272

HQP-MVS		84.61 158	84.06 186	86.27 121	91.19 151	70.66 193	84.77 179	92.68 117	73.30 208	80.55 352	90.17 275	72.10 272	94.61 85	77.30 178	94.47 198	93.56 161

testgi		72.36 376	74.61 346	65.59 453	80.56 413	42.82 478	68.29 452	73.35 424	66.87 318	81.84 331	89.93 279	72.08 274	66.92 476	46.05 468	92.54 273	87.01 374

v1921920		84.23 173	84.37 179	83.79 196	87.64 252	61.71 317	82.91 247	91.20 172	67.94 300	90.06 106	90.34 264	72.04 275	93.59 133	82.32 109	94.91 180	96.07 40

MSP-MVS		89.08 69	88.16 86	91.83 19	95.76 17	86.14 24	92.75 17	93.90 48	78.43 126	89.16 132	92.25 183	72.03 276	96.36 3	88.21 12	90.93 322	92.98 192
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_0403		76.08 333	76.77 320	73.98 392	87.08 276	49.45 452	83.62 218	84.68 332	63.31 358	75.13 421	87.47 333	71.85 277	84.56 385	49.97 446	87.86 385	87.94 360

LF4IMVS		82.75 220	81.93 236	85.19 148	82.08 387	80.15 75	85.53 163	88.76 241	68.01 297	85.58 240	87.75 326	71.80 278	86.85 344	74.02 230	93.87 219	88.58 345

fmvsm_s_conf0.5_n_2		83.62 197	83.29 205	84.62 167	85.43 328	70.18 203	80.61 305	87.24 275	67.14 314	87.79 173	91.87 192	71.79 279	87.98 318	86.00 60	91.77 300	95.71 49

v1240		84.30 169	84.51 174	83.65 201	87.65 251	61.26 326	82.85 249	91.54 157	67.94 300	90.68 98	90.65 255	71.71 280	93.64 127	82.84 102	94.78 188	96.07 40

ambc				82.98 222	90.55 168	64.86 267	88.20 108	89.15 237		89.40 128	93.96 107	71.67 281	91.38 205	78.83 149	96.55 106	92.71 202

fmvsm_s_conf0.1_n_2		83.82 190	83.49 198	84.84 157	85.99 314	70.19 202	80.93 297	87.58 269	67.26 313	87.94 167	92.37 177	71.40 282	88.01 316	86.03 56	91.87 297	96.31 35

新几何1				82.95 224	93.96 63	78.56 90		80.24 376	55.45 429	83.93 287	91.08 231	71.19 283	88.33 313	65.84 330	93.07 252	81.95 442

SSC-MVS		77.55 311	81.64 241	65.29 456	90.46 169	20.33 503	73.56 413	68.28 457	85.44 40	88.18 159	94.64 69	70.93 284	81.33 410	71.25 269	92.03 291	94.20 116

v148		82.31 228	82.48 228	81.81 264	85.59 324	59.66 357	81.47 284	86.02 300	72.85 220	88.05 163	90.65 255	70.73 285	90.91 228	75.15 212	91.79 298	94.87 78

v2v482		84.09 176	84.24 183	83.62 202	87.13 269	61.40 321	82.71 252	89.71 224	72.19 235	89.55 125	91.41 215	70.70 286	93.20 149	81.02 121	93.76 222	96.25 36

SSC-MVS3.2		73.90 363	75.67 333	68.61 439	84.11 354	41.28 481	64.17 472	72.83 430	72.09 236	79.08 375	87.94 316	70.31 287	73.89 447	55.99 408	94.49 197	90.67 291

MGCNet		85.37 135	84.58 170	87.75 96	85.28 330	73.36 144	86.54 143	85.71 306	77.56 140	81.78 336	92.47 172	70.29 288	96.02 10	85.59 64	95.96 134	93.87 136

WB-MVS		76.06 334	80.01 282	64.19 459	89.96 183	20.58 502	72.18 426	68.19 458	83.21 68	86.46 218	93.49 125	70.19 289	78.97 427	65.96 325	90.46 345	93.02 186

BridgeMVS		84.80 152	85.40 144	83.00 221	88.95 207	61.44 320	90.42 63	92.37 130	71.48 246	88.72 142	93.13 142	70.16 290	95.15 66	79.26 145	94.11 210	92.41 221

UGNet		82.78 219	81.64 241	86.21 125	86.20 306	76.24 122	86.86 132	85.68 307	77.07 145	73.76 429	92.82 158	69.64 291	91.82 191	69.04 300	93.69 228	90.56 295
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
c3_l		81.64 248	81.59 244	81.79 266	80.86 407	59.15 367	78.61 340	90.18 212	68.36 291	87.20 190	87.11 342	69.39 292	91.62 194	78.16 160	94.43 200	94.60 94

MG-MVS		80.32 276	80.94 262	78.47 333	88.18 233	52.62 433	82.29 268	85.01 322	72.01 238	79.24 371	92.54 170	69.36 293	93.36 146	70.65 278	89.19 363	89.45 319

IS-MVSNet		86.66 105	86.82 110	86.17 127	92.05 118	66.87 247	91.21 48	88.64 243	86.30 36	89.60 124	92.59 165	69.22 294	94.91 74	73.89 232	97.89 54	96.72 29

PVSNet_BlendedMVS		78.80 295	77.84 308	81.65 268	84.43 345	63.41 282	79.49 321	90.44 197	61.70 379	75.43 415	87.07 343	69.11 295	91.44 201	60.68 379	92.24 284	90.11 307

PVSNet_Blended		76.49 328	75.40 335	79.76 310	84.43 345	63.41 282	75.14 394	90.44 197	57.36 418	75.43 415	78.30 447	69.11 295	91.44 201	60.68 379	87.70 389	84.42 404

BH-w/o		76.57 326	76.07 329	78.10 342	86.88 284	65.92 258	77.63 354	86.33 292	65.69 332	80.89 347	79.95 431	68.97 297	90.74 236	53.01 431	85.25 417	77.62 469

MVS		73.21 370	72.59 372	75.06 384	80.97 404	60.81 339	81.64 281	85.92 304	46.03 475	71.68 440	77.54 453	68.47 298	89.77 274	55.70 411	85.39 414	74.60 475

miper_ehance_all_eth		80.34 275	80.04 281	81.24 279	79.82 424	58.95 370	77.66 353	89.66 225	65.75 331	85.99 231	85.11 373	68.29 299	91.42 203	76.03 198	92.03 291	93.33 168

Anonymous202405211		80.51 269	81.19 259	78.49 332	88.48 223	57.26 391	76.63 372	82.49 356	81.21 89	84.30 279	92.24 184	67.99 300	86.24 358	62.22 361	95.13 170	91.98 250

testdata				79.54 315	92.87 91	72.34 167		80.14 377	59.91 401	85.47 243	91.75 204	67.96 301	85.24 378	68.57 308	92.18 288	81.06 455

IMVS_0404		77.24 315	77.75 310	75.73 378	85.76 319	62.46 298	70.84 437	87.91 263	65.23 341	72.21 437	87.92 319	67.48 302	75.53 441	71.67 264	90.74 333	89.20 327

DPM-MVS		80.10 283	79.18 290	82.88 230	90.71 165	69.74 207	78.87 335	90.84 184	60.29 398	75.64 414	85.92 360	67.28 303	93.11 153	71.24 270	91.79 298	85.77 388

PVSNet_Blended_VisFu		81.55 250	80.49 270	84.70 165	91.58 136	73.24 149	84.21 197	91.67 153	62.86 363	80.94 346	87.16 340	67.27 304	92.87 163	69.82 289	88.94 367	87.99 358

usedtu_dtu_shiyan2		78.92 291	78.15 306	81.25 276	91.33 145	73.10 151	80.75 302	79.00 384	74.19 187	79.17 373	92.04 188	67.17 305	81.33 410	42.86 474	96.81 99	89.31 323

balanced_ft_v1		83.49 202	83.93 190	82.19 252	86.46 292	59.61 359	90.81 52	90.92 183	71.78 242	88.08 160	92.56 168	66.97 306	94.54 90	75.34 210	92.42 276	92.42 219

MDA-MVSNet-bldmvs		77.47 312	76.90 319	79.16 319	79.03 433	64.59 268	66.58 464	75.67 406	73.15 213	88.86 136	88.99 300	66.94 307	81.23 412	64.71 341	88.22 380	91.64 261

CL-MVSNet_self_test		76.81 322	77.38 313	75.12 383	86.90 283	51.34 441	73.20 417	80.63 375	68.30 293	81.80 334	88.40 309	66.92 308	80.90 413	55.35 415	94.90 181	93.12 182

test222					93.31 80	76.54 115	79.38 325	77.79 388	52.59 448	82.36 319	90.84 245	66.83 309			91.69 302	81.25 450

TR-MVS		76.77 323	75.79 330	79.72 311	86.10 311	65.79 259	77.14 363	83.02 351	65.20 345	81.40 341	82.10 410	66.30 310	90.73 237	55.57 412	85.27 416	82.65 430

OpenMVS_ROB	70.19 17	77.77 310	77.46 311	78.71 328	84.39 348	61.15 327	81.18 292	82.52 355	62.45 370	83.34 300	87.37 335	66.20 311	88.66 302	64.69 342	85.02 422	86.32 381

NormalMVS		86.47 109	85.32 147	89.94 50	94.43 43	80.42 71	88.63 104	93.59 71	74.56 179	85.12 250	90.34 264	66.19 312	94.20 101	76.57 187	98.44 19	95.19 68

SymmetryMVS		84.79 154	83.54 195	88.55 79	92.44 104	80.42 71	88.63 104	82.37 359	74.56 179	85.12 250	90.34 264	66.19 312	94.20 101	76.57 187	95.68 153	91.03 276

EPP-MVSNet		85.47 129	85.04 153	86.77 112	91.52 141	69.37 213	91.63 44	87.98 262	81.51 86	87.05 198	91.83 198	66.18 314	95.29 58	70.75 276	96.89 94	95.64 53

SixPastTwentyTwo		87.20 95	87.45 95	86.45 117	92.52 101	69.19 218	87.84 117	88.05 259	81.66 84	94.64 17	96.53 19	65.94 315	94.75 79	83.02 99	96.83 97	95.41 58

PatchMatch-RL		74.48 357	73.22 364	78.27 340	87.70 248	85.26 37	75.92 385	70.09 449	64.34 353	76.09 408	81.25 420	65.87 316	78.07 431	53.86 423	83.82 435	71.48 478

AstraMVS		81.67 247	81.40 251	82.48 245	87.06 277	66.47 251	81.41 285	81.68 365	68.78 285	88.00 164	90.95 239	65.70 317	87.86 324	76.66 185	92.38 277	93.12 182

WB-MVSnew		68.72 415	69.01 407	67.85 441	83.22 379	43.98 474	74.93 398	65.98 469	55.09 430	73.83 428	79.11 437	65.63 318	71.89 453	38.21 486	85.04 421	87.69 366

EPNet		80.37 274	78.41 303	86.23 122	76.75 448	73.28 147	87.18 126	77.45 391	76.24 150	68.14 459	88.93 301	65.41 319	93.85 119	69.47 292	96.12 127	91.55 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
guyue		81.57 249	81.37 253	82.15 253	86.39 295	66.13 255	81.54 283	83.21 348	69.79 270	87.77 174	89.95 278	65.36 320	87.64 327	75.88 200	92.49 274	92.67 204

FA-MVS(test-final)		83.13 212	83.02 213	83.43 209	86.16 309	66.08 256	88.00 113	88.36 251	75.55 165	85.02 254	92.75 162	65.12 321	92.50 170	74.94 215	91.30 312	91.72 257

PM-MVS		80.20 280	79.00 291	83.78 197	88.17 234	86.66 18	81.31 288	66.81 467	69.64 271	88.33 153	90.19 272	64.58 322	83.63 397	71.99 263	90.03 349	81.06 455

miper_enhance_ethall		77.83 307	76.93 318	80.51 296	76.15 455	58.01 385	75.47 392	88.82 239	58.05 412	83.59 294	80.69 422	64.41 323	91.20 215	73.16 256	92.03 291	92.33 230

FE-MVSNET		78.46 301	79.36 288	75.75 377	86.53 288	54.53 416	78.03 345	85.35 312	69.01 282	85.41 244	90.68 251	64.27 324	85.73 374	62.59 359	92.35 279	87.00 375

eth_miper_zixun_eth		80.84 263	80.22 276	82.71 232	81.41 398	60.98 336	77.81 351	90.14 213	67.31 312	86.95 200	87.24 339	64.26 325	92.31 176	75.23 211	91.61 304	94.85 86

test20.03		73.75 365	74.59 348	71.22 417	81.11 402	51.12 445	70.15 443	72.10 438	70.42 260	80.28 358	91.50 210	64.21 326	74.72 445	46.96 464	94.58 195	87.82 365

mvs5depth		83.82 190	84.54 172	81.68 267	82.23 386	68.65 226	86.89 131	89.90 218	80.02 104	87.74 176	97.86 4	64.19 327	82.02 406	76.37 191	95.63 156	94.35 111

LuminaMVS		83.94 187	83.51 196	85.23 147	89.78 185	71.74 176	84.76 182	87.27 273	72.60 226	89.31 130	90.60 259	64.04 328	90.95 224	79.08 146	94.11 210	92.99 190

VortexMVS		80.51 269	80.63 266	80.15 304	83.36 373	61.82 316	80.63 304	88.00 261	67.11 315	87.23 189	89.10 298	63.98 329	88.00 317	73.63 241	92.63 266	90.64 293

cascas		76.29 331	74.81 345	80.72 290	84.47 344	62.94 288	73.89 409	87.34 271	55.94 425	75.16 420	76.53 463	63.97 330	91.16 217	65.00 338	90.97 321	88.06 356

TAMVS		78.08 306	76.36 325	83.23 215	90.62 166	72.87 154	79.08 331	80.01 378	61.72 378	81.35 342	86.92 345	63.96 331	88.78 296	50.61 444	93.01 254	88.04 357

GBi-Net		82.02 240	82.07 231	81.85 261	86.38 297	61.05 330	86.83 134	88.27 255	72.43 227	86.00 228	95.64 37	63.78 332	90.68 238	65.95 326	93.34 241	93.82 141

test1		82.02 240	82.07 231	81.85 261	86.38 297	61.05 330	86.83 134	88.27 255	72.43 227	86.00 228	95.64 37	63.78 332	90.68 238	65.95 326	93.34 241	93.82 141

FMVSNet2		81.31 254	81.61 243	80.41 298	86.38 297	58.75 376	83.93 207	86.58 291	72.43 227	87.65 178	92.98 148	63.78 332	90.22 254	66.86 316	93.92 217	92.27 235

USDC		76.63 325	76.73 322	76.34 371	83.46 368	57.20 392	80.02 312	88.04 260	52.14 453	83.65 293	91.25 223	63.24 335	86.65 348	54.66 420	94.11 210	85.17 394

RRT-MVS		82.97 215	83.44 199	81.57 269	85.06 335	58.04 384	87.20 124	90.37 200	77.88 134	88.59 144	93.70 121	63.17 336	93.05 156	76.49 190	88.47 372	93.62 155

DIV-MVS_self_test		80.43 271	80.23 274	81.02 283	79.99 421	59.25 363	77.07 365	87.02 285	67.38 309	86.19 221	89.22 294	63.09 337	90.16 258	76.32 192	95.80 147	93.66 149

cl____		80.42 272	80.23 274	81.02 283	79.99 421	59.25 363	77.07 365	87.02 285	67.37 310	86.18 223	89.21 295	63.08 338	90.16 258	76.31 193	95.80 147	93.65 152

h-mvs33		84.25 171	82.76 221	88.72 74	91.82 130	82.60 59	84.00 203	84.98 323	71.27 247	86.70 206	90.55 260	63.04 339	93.92 117	78.26 158	94.20 207	89.63 316

hse-mvs2		83.47 204	81.81 238	88.47 81	91.03 157	82.27 60	82.61 253	83.69 342	71.27 247	86.70 206	86.05 358	63.04 339	92.41 172	78.26 158	93.62 231	90.71 287

new-patchmatchnet		70.10 399	73.37 362	60.29 470	81.23 401	16.95 505	59.54 481	74.62 411	62.93 362	80.97 344	87.93 318	62.83 341	71.90 452	55.24 416	95.01 178	92.00 248

K. test v3		85.14 142	84.73 160	86.37 118	91.13 155	69.63 210	85.45 166	76.68 400	84.06 58	92.44 63	96.99 12	62.03 342	94.65 83	80.58 128	93.24 246	94.83 87

lessismore_v0				85.95 130	91.10 156	70.99 191		70.91 447		91.79 74	94.42 79	61.76 343	92.93 160	79.52 142	93.03 253	93.93 132

1314		73.22 369	72.56 374	75.20 382	80.41 415	57.84 386	81.64 281	85.36 311	51.68 456	73.10 432	76.65 462	61.45 344	85.19 379	63.54 352	79.21 463	82.59 431

Syy-MVS		69.40 409	70.03 398	67.49 444	81.72 393	38.94 486	71.00 434	61.99 478	61.38 383	70.81 445	72.36 479	61.37 345	79.30 424	64.50 347	85.18 418	84.22 407

CANet_DTU		77.81 309	77.05 316	80.09 305	81.37 399	59.90 353	83.26 233	88.29 254	69.16 278	67.83 462	83.72 392	60.93 346	89.47 279	69.22 296	89.70 355	90.88 282

pmmvs-eth3d		78.42 304	77.04 317	82.57 242	87.44 261	74.41 138	80.86 299	79.67 379	55.68 427	84.69 266	90.31 269	60.91 347	85.42 377	62.20 362	91.59 305	87.88 362

UnsupCasMVSNet_eth		71.63 384	72.30 376	69.62 428	76.47 452	52.70 432	70.03 444	80.97 372	59.18 403	79.36 368	88.21 312	60.50 348	69.12 463	58.33 396	77.62 470	87.04 373

IterMVS-SCA-FT		80.64 267	79.41 286	84.34 178	83.93 359	69.66 209	76.28 379	81.09 371	72.43 227	86.47 217	90.19 272	60.46 349	93.15 152	77.45 175	86.39 406	90.22 302

SCA		73.32 367	72.57 373	75.58 381	81.62 395	55.86 401	78.89 334	71.37 444	61.73 377	74.93 422	83.42 397	60.46 349	87.01 337	58.11 398	82.63 447	83.88 411

jason		77.42 313	75.75 331	82.43 247	87.10 272	69.27 214	77.99 347	81.94 363	51.47 457	77.84 387	85.07 377	60.32 351	89.00 288	70.74 277	89.27 362	89.03 337
jason: jason.
1112_ss		74.82 354	73.74 356	78.04 344	89.57 187	60.04 348	76.49 376	87.09 284	54.31 436	73.66 430	79.80 432	60.25 352	86.76 347	58.37 394	84.15 433	87.32 370

HY-MVS	64.64 18	73.03 371	72.47 375	74.71 388	83.36 373	54.19 420	82.14 275	81.96 362	56.76 424	69.57 454	86.21 356	60.03 353	84.83 383	49.58 451	82.65 445	85.11 395

Anonymous20231206		71.38 387	71.88 378	69.88 425	86.31 301	54.37 417	70.39 441	74.62 411	52.57 449	76.73 400	88.76 302	59.94 354	72.06 451	44.35 472	93.23 248	83.23 425

IterMVS		76.91 320	76.34 326	78.64 329	80.91 405	64.03 276	76.30 378	79.03 382	64.88 348	83.11 304	89.16 296	59.90 355	84.46 387	68.61 306	85.15 420	87.42 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 400	70.44 392	68.90 433	73.76 471	53.42 427	58.99 484	67.20 463	58.42 408	87.10 194	85.39 370	59.82 356	67.32 473	59.79 384	83.50 438	85.96 384

MDA-MVSNet_test_wron		70.05 401	70.44 392	68.88 434	73.84 470	53.47 425	58.93 485	67.28 462	58.43 407	87.09 195	85.40 369	59.80 357	67.25 474	59.66 385	83.54 437	85.92 386

PMMVS		61.65 447	60.38 452	65.47 455	65.40 499	69.26 215	63.97 473	61.73 482	36.80 496	60.11 486	68.43 484	59.42 358	66.35 478	48.97 454	78.57 466	60.81 489

CDS-MVSNet		77.32 314	75.40 335	83.06 219	89.00 205	72.48 165	77.90 350	82.17 361	60.81 391	78.94 376	83.49 395	59.30 359	88.76 297	54.64 421	92.37 278	87.93 361
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 411	69.68 401	67.82 442	79.42 428	51.15 444	67.82 456	75.79 404	54.15 438	77.47 397	85.36 372	59.26 360	70.64 457	48.46 457	79.35 461	81.66 444

Anonymous20240521		80.18 281	81.25 255	76.95 361	83.15 381	60.84 338	82.46 260	85.99 301	68.76 286	86.78 202	93.73 120	59.13 361	77.44 433	73.71 236	97.55 77	92.56 212

WTY-MVS		67.91 418	68.35 414	66.58 449	80.82 408	48.12 456	65.96 465	72.60 432	53.67 441	71.20 442	81.68 417	58.97 362	69.06 464	48.57 456	81.67 449	82.55 433

cl22		78.97 290	78.21 305	81.24 279	77.74 438	59.01 369	77.46 360	87.13 279	65.79 328	84.32 276	85.10 374	58.96 363	90.88 230	75.36 209	92.03 291	93.84 137

MVSFormer		82.23 230	81.57 246	84.19 185	85.54 325	69.26 215	91.98 39	90.08 214	71.54 244	76.23 405	85.07 377	58.69 364	94.27 96	86.26 50	88.77 368	89.03 337

lupinMVS		76.37 330	74.46 349	82.09 255	85.54 325	69.26 215	76.79 368	80.77 374	50.68 464	76.23 405	82.82 404	58.69 364	88.94 289	69.85 288	88.77 368	88.07 354

Test_1112_low_res		73.90 363	73.08 365	76.35 370	90.35 171	55.95 398	73.40 416	86.17 295	50.70 463	73.14 431	85.94 359	58.31 366	85.90 369	56.51 404	83.22 439	87.20 372

blended_shiyan8		76.05 335	75.11 339	78.86 323	81.76 391	59.18 366	75.09 395	83.81 339	64.70 349	79.37 366	78.35 446	58.30 367	88.68 300	62.03 365	92.56 272	88.73 343

blended_shiyan6		76.05 335	75.11 339	78.87 322	81.74 392	59.15 367	75.08 396	83.79 340	64.69 350	79.37 366	78.37 445	58.30 367	88.69 299	61.99 366	92.61 267	88.77 342

usedtu_dtu_shiyan1		75.70 341	75.08 341	77.56 351	84.10 355	55.50 406	73.58 411	84.89 325	62.48 366	78.16 381	84.24 386	58.14 369	87.47 330	59.35 387	90.82 328	89.72 313

FE-MVSNET3		75.70 341	75.08 341	77.56 351	84.10 355	55.50 406	73.58 411	84.89 325	62.48 366	78.16 381	84.24 386	58.14 369	87.47 330	59.34 388	90.82 328	89.72 313

test_yl		78.71 298	78.51 300	79.32 317	84.32 349	58.84 373	78.38 341	85.33 313	75.99 154	82.49 315	86.57 348	58.01 371	90.02 268	62.74 357	92.73 264	89.10 333

DCV-MVSNet		78.71 298	78.51 300	79.32 317	84.32 349	58.84 373	78.38 341	85.33 313	75.99 154	82.49 315	86.57 348	58.01 371	90.02 268	62.74 357	92.73 264	89.10 333

sss		66.92 422	67.26 420	65.90 451	77.23 443	51.10 446	64.79 468	71.72 442	52.12 454	70.13 450	80.18 429	57.96 373	65.36 482	50.21 445	81.01 455	81.25 450

ppachtmachnet_test		74.73 356	74.00 353	76.90 363	80.71 410	56.89 395	71.53 432	78.42 385	58.24 409	79.32 370	82.92 403	57.91 374	84.26 391	65.60 333	91.36 309	89.56 318

MVP-Stereo		75.81 339	73.51 360	82.71 232	89.35 193	73.62 142	80.06 310	85.20 315	60.30 397	73.96 427	87.94 316	57.89 375	89.45 281	52.02 437	74.87 476	85.06 396
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 381	70.06 397	76.92 362	86.39 295	53.97 421	76.62 373	86.62 290	53.44 442	63.97 479	84.73 381	57.79 376	92.34 175	39.65 481	81.33 453	84.45 403

LFMVS		80.15 282	80.56 268	78.89 321	89.19 198	55.93 399	85.22 172	73.78 420	82.96 72	84.28 280	92.72 163	57.38 377	90.07 266	63.80 350	95.75 150	90.68 289

Vis-MVSNet (Re-imp)		77.82 308	77.79 309	77.92 346	88.82 212	51.29 443	83.28 232	71.97 439	74.04 188	82.23 321	89.78 282	57.38 377	89.41 284	57.22 401	95.41 159	93.05 185

CHOSEN 1792x2688		72.45 375	70.56 390	78.13 341	90.02 182	63.08 287	68.72 450	83.16 349	42.99 485	75.92 410	85.46 367	57.22 379	85.18 380	49.87 449	81.67 449	86.14 383

mvsany_test1		58.48 456	56.47 462	64.50 458	65.90 498	68.21 231	56.95 488	42.11 501	38.30 493	65.69 470	77.19 459	56.96 380	59.35 491	46.16 466	58.96 494	65.93 485

miper_lstm_enhance		76.45 329	76.10 328	77.51 354	76.72 449	60.97 337	64.69 469	85.04 320	63.98 357	83.20 303	88.22 311	56.67 381	78.79 429	73.22 250	93.12 251	92.78 198

our_test_3		71.85 380	71.59 380	72.62 407	80.71 410	53.78 423	69.72 446	71.71 443	58.80 406	78.03 384	80.51 427	56.61 382	78.84 428	62.20 362	86.04 411	85.23 393

baseline1		73.26 368	73.54 359	72.43 410	84.92 337	47.79 458	79.89 314	74.00 416	65.93 325	78.81 377	86.28 355	56.36 383	81.63 409	56.63 403	79.04 465	87.87 363

pmmvs4		74.92 352	72.98 367	80.73 289	84.95 336	71.71 180	76.23 380	77.59 390	52.83 447	77.73 391	86.38 350	56.35 384	84.97 381	57.72 400	87.05 396	85.51 391

MVE	40.22 23	51.82 461	50.47 464	55.87 474	62.66 501	51.91 437	31.61 495	39.28 502	40.65 488	50.76 497	74.98 474	56.24 385	44.67 498	33.94 492	64.11 492	71.04 480
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wanda-best-256-512		74.97 350	73.85 354	78.35 335	80.36 416	58.13 380	73.10 419	83.53 345	64.04 355	77.62 392	75.71 467	56.22 386	88.60 306	61.42 373	92.61 267	88.32 348

FE-blended-shiyan7		74.97 350	73.85 354	78.35 335	80.36 416	58.13 380	73.10 419	83.53 345	64.03 356	77.62 392	75.71 467	56.22 386	88.60 306	61.42 373	92.61 267	88.32 348

usedtu_blend_shiyan5		77.07 318	76.43 324	78.99 320	80.36 416	59.77 355	83.25 234	88.32 253	74.91 173	77.62 392	75.71 467	56.22 386	88.89 291	58.91 390	92.61 267	88.32 348

gbinet_0.2-2-1-0.02		76.14 332	74.88 344	79.92 306	80.33 419	60.02 351	75.80 386	82.44 357	66.36 322	79.24 371	75.07 473	56.11 389	90.17 257	64.60 345	93.95 216	89.58 317

dmvs_testset		60.59 454	62.54 446	54.72 476	77.26 442	27.74 499	74.05 406	61.00 485	60.48 395	65.62 471	67.03 486	55.93 390	68.23 471	32.07 494	69.46 489	68.17 483

N_pmnet		70.20 397	68.80 411	74.38 390	80.91 405	84.81 42	59.12 483	76.45 402	55.06 431	75.31 419	82.36 409	55.74 391	54.82 493	47.02 462	87.24 392	83.52 418

MS-PatchMatch		70.93 391	70.22 395	73.06 402	81.85 390	62.50 297	73.82 410	77.90 387	52.44 450	75.92 410	81.27 419	55.67 392	81.75 407	55.37 414	77.70 469	74.94 474

DSMNet-mixed		60.98 452	61.61 449	59.09 473	72.88 479	45.05 471	74.70 400	46.61 499	26.20 497	65.34 472	90.32 268	55.46 393	63.12 486	41.72 477	81.30 454	69.09 482

pmmvs5		70.73 393	70.07 396	72.72 405	77.03 446	52.73 431	74.14 404	75.65 407	50.36 466	72.17 438	85.37 371	55.42 394	80.67 415	52.86 432	87.59 390	84.77 398

CMPMVS	59.41 20	75.12 347	73.57 358	79.77 309	75.84 458	67.22 238	81.21 291	82.18 360	50.78 462	76.50 401	87.66 328	55.20 395	82.99 400	62.17 364	90.64 343	89.09 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_vis1_n_1920		71.30 388	71.58 382	70.47 420	77.58 441	59.99 352	74.25 403	84.22 337	51.06 459	74.85 423	79.10 438	55.10 396	68.83 465	68.86 302	79.20 464	82.58 432

MIMVSNet		71.09 389	71.59 380	69.57 429	87.23 266	50.07 450	78.91 333	71.83 440	60.20 400	71.26 441	91.76 203	55.08 397	76.09 437	41.06 478	87.02 398	82.54 434

PVSNet_0	51.08 22	56.10 458	54.97 463	59.48 472	75.12 464	53.28 428	55.16 489	61.89 480	44.30 479	59.16 487	62.48 489	54.22 398	65.91 480	35.40 489	47.01 495	59.25 491

MonoMVSNet		76.66 324	77.26 315	74.86 385	79.86 423	54.34 418	86.26 147	86.08 297	71.08 253	85.59 239	88.68 304	53.95 399	85.93 365	63.86 349	80.02 458	84.32 405

EPNet_dtu		72.87 373	71.33 385	77.49 355	77.72 439	60.55 342	82.35 266	75.79 404	66.49 321	58.39 491	81.06 421	53.68 400	85.98 364	53.55 426	92.97 256	85.95 385
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 460	59.27 456	44.74 478	64.30 500	12.32 506	40.60 493	49.79 496	53.19 444	65.06 476	84.81 379	53.60 401	49.76 496	32.68 493	89.41 359	72.15 477

test_vis1_rt		65.64 433	64.09 437	70.31 421	66.09 496	70.20 201	61.16 478	81.60 367	38.65 492	72.87 433	69.66 482	52.84 402	60.04 489	56.16 406	77.77 468	80.68 457

mvsany_test3		65.48 434	62.97 443	73.03 403	69.99 489	76.17 123	64.83 467	43.71 500	43.68 482	80.25 359	87.05 344	52.83 403	63.09 487	51.92 441	72.44 481	79.84 464

HyFIR lowres test		75.12 347	72.66 371	82.50 244	91.44 144	65.19 265	72.47 424	87.31 272	46.79 470	80.29 356	84.30 385	52.70 404	92.10 183	51.88 442	86.73 401	90.22 302

dmvs_re		66.81 425	66.98 421	66.28 450	76.87 447	58.68 377	71.66 430	72.24 435	60.29 398	69.52 455	73.53 476	52.38 405	64.40 484	44.90 470	81.44 452	75.76 472

test_cas_vis1_n_1920		69.20 412	69.12 404	69.43 430	73.68 472	62.82 291	70.38 442	77.21 394	46.18 474	80.46 355	78.95 440	52.03 406	65.53 481	65.77 332	77.45 472	79.95 462

test1111		78.53 300	78.85 294	77.56 351	92.22 112	47.49 459	82.61 253	69.24 455	72.43 227	85.28 247	94.20 90	51.91 407	90.07 266	65.36 335	96.45 112	95.11 72

ECVR-MVS		78.44 303	78.63 298	77.88 347	91.85 126	48.95 453	83.68 216	69.91 451	72.30 233	84.26 282	94.20 90	51.89 408	89.82 271	63.58 351	96.02 131	94.87 78

FMVSNet3		78.80 295	78.55 299	79.57 314	82.89 384	56.89 395	81.76 278	85.77 305	69.04 281	86.00 228	90.44 262	51.75 409	90.09 264	65.95 326	93.34 241	91.72 257

D2MVS		76.84 321	75.67 333	80.34 299	80.48 414	62.16 310	73.50 414	84.80 330	57.61 416	82.24 320	87.54 330	51.31 410	87.65 326	70.40 283	93.19 250	91.23 269

AUN-MVS		81.18 257	78.78 295	88.39 83	90.93 159	82.14 61	82.51 259	83.67 343	64.69 350	80.29 356	85.91 361	51.07 411	92.38 173	76.29 194	93.63 230	90.65 292

PVSNet	58.17 21	66.41 428	65.63 432	68.75 435	81.96 388	49.88 451	62.19 477	72.51 434	51.03 460	68.04 460	75.34 472	50.84 412	74.77 443	45.82 469	82.96 440	81.60 445

mvsmamba		80.30 277	78.87 292	84.58 169	88.12 237	67.55 237	92.35 30	84.88 327	63.15 361	85.33 246	90.91 240	50.71 413	95.20 64	66.36 322	87.98 382	90.99 277

GA-MVS		75.83 338	74.61 346	79.48 316	81.87 389	59.25 363	73.42 415	82.88 352	68.68 287	79.75 361	81.80 415	50.62 414	89.46 280	66.85 317	85.64 413	89.72 313

FPMVS		72.29 378	72.00 377	73.14 401	88.63 219	85.00 39	74.65 401	67.39 461	71.94 239	77.80 389	87.66 328	50.48 415	75.83 439	49.95 447	79.51 459	58.58 492

test_fmvs3		75.72 340	75.20 338	77.27 357	75.01 466	69.47 212	78.93 332	84.88 327	46.67 471	87.08 196	87.84 324	50.44 416	71.62 454	77.42 177	88.53 371	90.72 286

test_vis1_n		70.29 396	69.99 399	71.20 418	75.97 457	66.50 250	76.69 371	80.81 373	44.22 480	75.43 415	77.23 457	50.00 417	68.59 466	66.71 320	82.85 444	78.52 468

MVS-HIRNet		61.16 450	62.92 444	55.87 474	79.09 432	35.34 493	71.83 428	57.98 491	46.56 472	59.05 488	91.14 228	49.95 418	76.43 436	38.74 483	71.92 483	55.84 493

CVMVSNet		72.62 374	71.41 384	76.28 372	83.25 377	60.34 344	83.50 226	79.02 383	37.77 495	76.33 403	85.10 374	49.60 419	87.41 333	70.54 281	77.54 471	81.08 453

RPMNet		78.88 293	78.28 304	80.68 292	79.58 425	62.64 294	82.58 255	94.16 32	74.80 174	75.72 412	92.59 165	48.69 420	95.56 43	73.48 244	82.91 442	83.85 414

test_fmvs2		73.57 366	72.80 368	75.90 376	72.74 481	68.84 225	77.07 365	84.32 336	45.14 477	82.89 308	84.22 388	48.37 421	70.36 458	73.40 246	87.03 397	88.52 346

tpmrst		66.28 429	66.69 425	65.05 457	72.82 480	39.33 485	78.20 344	70.69 448	53.16 445	67.88 461	80.36 428	48.18 422	74.75 444	58.13 397	70.79 484	81.08 453

CR-MVSNet		74.00 362	73.04 366	76.85 365	79.58 425	62.64 294	82.58 255	76.90 397	50.50 465	75.72 412	92.38 174	48.07 423	84.07 393	68.72 305	82.91 442	83.85 414

Patchmtry		76.56 327	77.46 311	73.83 394	79.37 430	46.60 463	82.41 264	76.90 397	73.81 191	85.56 241	92.38 174	48.07 423	83.98 394	63.36 354	95.31 165	90.92 280

test_f		64.31 440	65.85 428	59.67 471	66.54 495	62.24 309	57.76 487	70.96 446	40.13 489	84.36 274	82.09 411	46.93 425	51.67 495	61.99 366	81.89 448	65.12 486

ADS-MVSNet2		65.87 431	63.64 440	72.55 408	73.16 476	56.92 394	67.10 461	74.81 410	49.74 467	66.04 468	82.97 400	46.71 426	77.26 434	42.29 475	69.96 486	83.46 419

ADS-MVSNet		61.90 446	62.19 447	61.03 468	73.16 476	36.42 491	67.10 461	61.75 481	49.74 467	66.04 468	82.97 400	46.71 426	63.21 485	42.29 475	69.96 486	83.46 419

PatchmatchNet		69.71 406	68.83 410	72.33 412	77.66 440	53.60 424	79.29 326	69.99 450	57.66 415	72.53 435	82.93 402	46.45 428	80.08 421	60.91 378	72.09 482	83.31 424
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 377	71.55 383	74.70 389	83.48 367	51.60 440	75.02 397	73.71 421	70.14 267	78.56 380	80.57 425	46.20 429	88.20 315	46.99 463	89.29 360	84.32 405

sam_mvs1												46.11 430				83.88 411

tfpn200view9		74.86 353	74.23 351	76.74 366	86.24 304	52.12 435	79.24 328	73.87 418	73.34 206	81.82 332	84.60 383	46.02 431	88.80 293	51.98 438	90.99 318	89.31 323

thres400		75.14 345	74.23 351	77.86 348	86.24 304	52.12 435	79.24 328	73.87 418	73.34 206	81.82 332	84.60 383	46.02 431	88.80 293	51.98 438	90.99 318	92.66 205

baseline2		69.77 405	66.89 422	78.41 334	79.51 427	58.09 382	76.23 380	69.57 452	57.50 417	64.82 477	77.45 455	46.02 431	88.44 309	53.08 428	77.83 467	88.70 344

patchmatchnet-post											81.71 416	45.93 434	87.01 337

sam_mvs												45.92 435

BP-MVS1		82.81 217	81.67 240	86.23 122	87.88 243	68.53 227	86.06 151	84.36 334	75.65 162	85.14 249	90.19 272	45.84 436	94.42 93	85.18 68	94.72 192	95.75 48

Patchmatch-RL test		74.48 357	73.68 357	76.89 364	84.83 338	66.54 249	72.29 425	69.16 456	57.70 414	86.76 203	86.33 352	45.79 437	82.59 401	69.63 291	90.65 342	81.54 446

thres100view900		75.45 343	75.05 343	76.66 367	87.27 263	51.88 438	81.07 293	73.26 425	75.68 161	83.25 302	86.37 351	45.54 438	88.80 293	51.98 438	90.99 318	89.31 323

thres600view7		75.97 337	75.35 337	77.85 349	87.01 278	51.84 439	80.45 307	73.26 425	75.20 170	83.10 305	86.31 354	45.54 438	89.05 287	55.03 418	92.24 284	92.66 205

tpm cat1		66.76 426	65.21 435	71.42 416	77.09 445	50.62 448	78.01 346	73.68 422	44.89 478	68.64 457	79.00 439	45.51 440	82.42 404	49.91 448	70.15 485	81.23 452

test_post											3.10 501	45.43 441	77.22 435

MDTV_nov1_ep13			68.29 415		78.03 437	43.87 475	74.12 405	72.22 436	52.17 451	67.02 465	85.54 364	45.36 442	80.85 414	55.73 409	84.42 431

tpmvs		70.16 398	69.56 402	71.96 413	74.71 467	48.13 455	79.63 316	75.45 409	65.02 346	70.26 449	81.88 414	45.34 443	85.68 375	58.34 395	75.39 475	82.08 441

MDTV_nov1_ep13_2view						27.60 500	70.76 439		46.47 473	61.27 482		45.20 444		49.18 452		83.75 416

test_post1							78.85 336				3.13 500	45.19 445	80.13 420	58.11 398

CostFormer		69.98 403	68.68 412	73.87 393	77.14 444	50.72 447	79.26 327	74.51 413	51.94 455	70.97 444	84.75 380	45.16 446	87.49 329	55.16 417	79.23 462	83.40 421

GDP-MVS		82.17 234	80.85 265	86.15 129	88.65 218	68.95 224	85.65 161	93.02 104	68.42 290	83.73 290	89.54 287	45.07 447	94.31 95	79.66 138	93.87 219	95.19 68

FE-MVS		79.98 285	78.86 293	83.36 211	86.47 291	66.45 252	89.73 75	84.74 331	72.80 222	84.22 283	91.38 216	44.95 448	93.60 132	63.93 348	91.50 307	90.04 309

Patchmatch-test		65.91 430	67.38 419	61.48 467	75.51 460	43.21 477	68.84 449	63.79 476	62.48 366	72.80 434	83.42 397	44.89 449	59.52 490	48.27 459	86.45 404	81.70 443

EU-MVSNet		75.12 347	74.43 350	77.18 358	83.11 382	59.48 360	85.71 160	82.43 358	39.76 491	85.64 238	88.76 302	44.71 450	87.88 322	73.86 233	85.88 412	84.16 410

PatchT		70.52 395	72.76 370	63.79 461	79.38 429	33.53 495	77.63 354	65.37 472	73.61 199	71.77 439	92.79 161	44.38 451	75.65 440	64.53 346	85.37 415	82.18 439

test_vis3_rt		71.42 386	70.67 388	73.64 397	69.66 490	70.46 197	66.97 463	89.73 222	42.68 487	88.20 158	83.04 399	43.77 452	60.07 488	65.35 336	86.66 402	90.39 300

test_fmvs1_n		70.94 390	70.41 394	72.53 409	73.92 469	66.93 246	75.99 384	84.21 338	43.31 484	79.40 365	79.39 436	43.47 453	68.55 467	69.05 299	84.91 425	82.10 440

test-LLR		67.21 420	66.74 424	68.63 437	76.45 453	55.21 410	67.89 453	67.14 464	62.43 372	65.08 474	72.39 477	43.41 454	69.37 460	61.00 376	84.89 426	81.31 448

test0.0.03 1		64.66 437	64.36 436	65.57 454	75.03 465	46.89 462	64.69 469	61.58 484	62.43 372	71.18 443	77.54 453	43.41 454	68.47 469	40.75 480	82.65 445	81.35 447

test_fmvs1		69.57 407	69.05 406	71.14 419	69.15 491	65.77 260	73.98 407	83.32 347	42.83 486	77.77 390	78.27 448	43.39 456	68.50 468	68.39 309	84.38 432	79.15 466

MVSTER		77.09 317	75.70 332	81.25 276	75.27 463	61.08 329	77.49 359	85.07 318	60.78 392	86.55 210	88.68 304	43.14 457	90.25 251	73.69 240	90.67 339	92.42 219

tpm		67.95 417	68.08 418	67.55 443	78.74 436	43.53 476	75.60 388	67.10 466	54.92 432	72.23 436	88.10 313	42.87 458	75.97 438	52.21 435	80.95 457	83.15 426

tpm2		68.45 416	66.83 423	73.30 400	78.93 435	48.50 454	79.76 315	71.76 441	47.50 469	69.92 451	83.60 393	42.07 459	88.40 311	48.44 458	79.51 459	83.01 428

EMVS		61.10 451	60.81 450	61.99 464	65.96 497	55.86 401	53.10 491	58.97 489	67.06 316	56.89 495	63.33 488	40.98 460	67.03 475	54.79 419	86.18 409	63.08 487

new_pmnet		55.69 459	57.66 460	49.76 477	75.47 461	30.59 497	59.56 480	51.45 495	43.62 483	62.49 480	75.48 470	40.96 461	49.15 497	37.39 488	72.52 480	69.55 481

E-PMN		61.59 448	61.62 448	61.49 466	66.81 494	55.40 408	53.77 490	60.34 486	66.80 319	58.90 489	65.50 487	40.48 462	66.12 479	55.72 410	86.25 408	62.95 488

EPMVS		62.47 443	62.63 445	62.01 463	70.63 488	38.74 487	74.76 399	52.86 494	53.91 439	67.71 463	80.01 430	39.40 463	66.60 477	55.54 413	68.81 490	80.68 457

tmp_tt		20.25 466	24.50 469	7.49 483	4.47 506	8.70 507	34.17 494	25.16 504	1.00 501	32.43 500	18.49 498	39.37 464	9.21 502	21.64 496	43.75 496	4.57 498

thisisatest0530		79.07 289	77.33 314	84.26 182	87.13 269	64.58 269	83.66 217	75.95 403	68.86 284	85.22 248	87.36 336	38.10 465	93.57 136	75.47 207	94.28 205	94.62 93

ET-MVSNet_ETH3D		75.28 344	72.77 369	82.81 231	83.03 383	68.11 232	77.09 364	76.51 401	60.67 394	77.60 395	80.52 426	38.04 466	91.15 218	70.78 275	90.68 338	89.17 331

ttmdpeth		71.72 382	70.67 388	74.86 385	73.08 478	55.88 400	77.41 361	69.27 454	55.86 426	78.66 378	93.77 118	38.01 467	75.39 442	60.12 382	89.87 352	93.31 170

tttt0517		81.07 259	79.58 285	85.52 141	88.99 206	66.45 252	87.03 129	75.51 408	73.76 192	88.32 154	90.20 271	37.96 468	94.16 108	79.36 144	95.13 170	95.93 46

thisisatest0515		73.00 372	70.52 391	80.46 297	81.45 397	59.90 353	73.16 418	74.31 415	57.86 413	76.08 409	77.78 450	37.60 469	92.12 182	65.00 338	91.45 308	89.35 322

FMVSNet5		72.10 379	71.69 379	73.32 398	81.57 396	53.02 429	76.77 369	78.37 386	63.31 358	76.37 402	91.85 196	36.68 470	78.98 426	47.87 460	92.45 275	87.95 359

dp		60.70 453	60.29 454	61.92 465	72.04 483	38.67 488	70.83 438	64.08 475	51.28 458	60.75 483	77.28 456	36.59 471	71.58 455	47.41 461	62.34 493	75.52 473

CHOSEN 280x420		59.08 455	56.52 461	66.76 448	76.51 451	64.39 273	49.62 492	59.00 488	43.86 481	55.66 496	68.41 485	35.55 472	68.21 472	43.25 473	76.78 474	67.69 484

testing91		69.94 404	68.99 408	72.80 404	83.81 362	45.89 466	71.57 431	73.64 423	68.24 294	70.77 447	77.82 449	34.37 473	84.44 388	53.64 425	87.00 399	88.07 354

IB-MVS	62.13 19	71.64 383	68.97 409	79.66 313	80.80 409	62.26 307	73.94 408	76.90 397	63.27 360	68.63 458	76.79 460	33.83 474	91.84 190	59.28 389	87.26 391	84.88 397
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
WBMVS		68.76 414	68.43 413	69.75 427	83.29 375	40.30 484	67.36 459	72.21 437	57.09 421	77.05 399	85.53 365	33.68 475	80.51 417	48.79 455	90.90 323	88.45 347

JIA-IIPM		69.41 408	66.64 426	77.70 350	73.19 475	71.24 187	75.67 387	65.56 471	70.42 260	65.18 473	92.97 151	33.64 476	83.06 398	53.52 427	69.61 488	78.79 467

UBG		64.34 439	63.35 441	67.30 445	83.50 366	40.53 483	67.46 458	65.02 473	54.77 434	67.54 464	74.47 475	32.99 477	78.50 430	40.82 479	83.58 436	82.88 429

myMVS_eth3d28		65.83 432	65.85 428	65.78 452	83.42 370	35.71 492	67.29 460	68.01 459	67.58 308	69.80 452	77.72 452	32.29 478	74.30 446	37.49 487	89.06 364	87.32 370

testing99		69.27 410	68.15 416	72.63 406	83.29 375	45.45 468	71.15 433	71.08 445	67.34 311	70.43 448	77.77 451	32.24 479	84.35 390	53.72 424	86.33 407	88.10 353

testing3-2		70.72 394	70.97 386	69.95 424	88.93 208	34.80 494	69.85 445	66.59 468	78.42 127	77.58 396	85.55 363	31.83 480	82.08 405	46.28 465	93.73 226	92.98 192

testing11		67.38 419	65.93 427	71.73 415	83.37 372	46.60 463	70.95 436	69.40 453	62.47 369	66.14 466	76.66 461	31.22 481	84.10 392	49.10 453	84.10 434	84.49 401

UWE-MVS-28		58.44 457	57.71 459	60.65 469	73.58 473	31.23 496	69.68 447	48.80 497	53.12 446	61.79 481	78.83 441	30.98 482	68.40 470	21.58 497	80.99 456	82.33 438

DeepMVS_CX				24.13 482	32.95 504	29.49 498		21.63 505	12.07 498	37.95 499	45.07 496	30.84 483	19.21 501	17.94 499	33.06 498	23.69 497

gg-mvs-nofinetune		68.96 413	69.11 405	68.52 440	76.12 456	45.32 469	83.59 219	55.88 492	86.68 32	64.62 478	97.01 11	30.36 484	83.97 395	44.78 471	82.94 441	76.26 471

GG-mvs-BLEND				67.16 446	73.36 474	46.54 465	84.15 199	55.04 493		58.64 490	61.95 490	29.93 485	83.87 396	38.71 484	76.92 473	71.07 479

0.4-1-1-0.2		62.43 445	58.81 458	73.31 399	70.85 487	54.20 419	64.36 471	72.99 428	53.70 440	57.51 493	54.59 493	29.52 486	86.44 354	51.70 443	74.02 478	79.30 465

blend_shiyan4		70.82 392	68.15 416	78.83 325	81.06 403	59.77 355	74.58 402	83.79 340	64.94 347	77.34 398	75.47 471	29.39 487	88.89 291	58.91 390	67.86 491	87.84 364

0.4-1-1-0.1		64.02 441	60.59 451	74.31 391	73.99 468	55.62 404	67.66 457	72.78 431	55.53 428	60.35 485	58.45 491	29.26 488	86.88 342	52.84 433	74.42 477	80.42 459

UWE-MVS		66.43 427	65.56 433	69.05 432	84.15 353	40.98 482	73.06 421	64.71 474	54.84 433	76.18 407	79.62 435	29.21 489	80.50 418	38.54 485	89.75 354	85.66 389

ETVMVS		64.67 436	63.34 442	68.64 436	83.44 369	41.89 479	69.56 448	61.70 483	61.33 385	68.74 456	75.76 466	28.76 490	79.35 423	34.65 490	86.16 410	84.67 400

test_method		30.46 464	29.60 467	33.06 480	17.99 505	3.84 508	13.62 496	73.92 417	2.79 499	18.29 501	53.41 494	28.53 491	43.25 499	22.56 495	35.27 497	52.11 494

test-mter		65.00 435	63.79 439	68.63 437	76.45 453	55.21 410	67.89 453	67.14 464	50.98 461	65.08 474	72.39 477	28.27 492	69.37 460	61.00 376	84.89 426	81.31 448

TESTMET0.1,1		61.29 449	60.32 453	64.19 459	72.06 482	51.30 442	67.89 453	62.09 477	45.27 476	60.65 484	69.01 483	27.93 493	64.74 483	56.31 405	81.65 451	76.53 470

reproduce_monomvs		74.09 361	73.23 363	76.65 368	76.52 450	54.54 415	77.50 358	81.40 369	65.85 327	82.86 310	86.67 347	27.38 494	84.53 386	70.24 284	90.66 341	90.89 281

testing222		66.93 421	65.30 434	71.81 414	83.38 371	45.83 467	72.06 427	67.50 460	64.12 354	69.68 453	76.37 464	27.34 495	83.00 399	38.88 482	88.38 374	86.62 379

test2506		74.12 360	73.39 361	76.28 372	91.85 126	44.20 473	84.06 201	48.20 498	72.30 233	81.90 329	94.20 90	27.22 496	89.77 274	64.81 340	96.02 131	94.87 78

pmmvs3		62.47 443	60.02 455	69.80 426	71.58 485	64.00 277	70.52 440	58.44 490	39.77 490	66.05 467	75.84 465	27.10 497	72.28 450	46.15 467	84.77 430	73.11 476

0.3-1-1-0.015		62.57 442	58.82 457	73.82 395	71.85 484	54.96 413	65.63 466	72.97 429	54.16 437	56.95 494	55.43 492	26.76 498	86.59 350	52.05 436	73.55 479	79.92 463

KD-MVS_2432*1600		66.87 423	65.81 430	70.04 422	67.50 492	47.49 459	62.56 475	79.16 380	61.21 388	77.98 385	80.61 423	25.29 499	82.48 402	53.02 429	84.92 423	80.16 460

miper_refine_blended		66.87 423	65.81 430	70.04 422	67.50 492	47.49 459	62.56 475	79.16 380	61.21 388	77.98 385	80.61 423	25.29 499	82.48 402	53.02 429	84.92 423	80.16 460

MVStest1		70.05 401	69.26 403	72.41 411	58.62 502	55.59 405	76.61 374	65.58 470	53.44 442	89.28 131	93.32 130	22.91 501	71.44 456	74.08 229	89.52 357	90.21 306

myMVS_eth3d		64.66 437	63.89 438	66.97 447	81.72 393	37.39 489	71.00 434	61.99 478	61.38 383	70.81 445	72.36 479	20.96 502	79.30 424	49.59 450	85.18 418	84.22 407

testing3		71.53 385	70.79 387	73.77 396	88.89 210	41.86 480	76.60 375	59.12 487	72.83 221	80.97 344	82.08 412	19.80 503	87.33 335	65.12 337	91.68 303	92.13 243

dongtai		41.90 462	42.65 465	39.67 479	70.86 486	21.11 501	61.01 479	21.42 506	57.36 418	57.97 492	50.06 495	16.40 504	58.73 492	21.03 498	27.69 499	39.17 495

kuosan		30.83 463	32.17 466	26.83 481	53.36 503	19.02 504	57.90 486	20.44 507	38.29 494	38.01 498	37.82 497	15.18 505	33.45 500	7.74 500	20.76 500	28.03 496

test123		6.27 469	8.08 472	0.84 484	1.11 508	0.57 509	62.90 474	0.82 508	0.54 502	1.07 504	2.75 503	1.26 506	0.30 503	1.04 501	1.26 502	1.66 499

testmvs		5.91 470	7.65 473	0.72 485	1.20 507	0.37 510	59.14 482	0.67 509	0.49 503	1.11 503	2.76 502	0.94 507	0.24 504	1.02 502	1.47 501	1.55 500

mmdepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

monomultidepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

test_blank		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet_test		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

DCPMVS		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

sosnet-low-res		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

sosnet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uncertanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

Regformer		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

ab-mvs-re		6.65 467	8.87 470	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	79.80 432	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

MED-MVS test				88.50 80	94.38 47	76.12 125	92.12 33	93.85 52	77.53 141	93.24 43	93.18 138		95.85 23	84.99 74	97.69 64	93.54 164

WAC-MVS						37.39 489								52.61 434

FOURS1					96.08 11	87.41 13	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 72	91.55 138	77.99 96		91.01 178					96.05 8	87.45 28	98.17 36	92.40 223

No_MVS				88.81 72	91.55 138	77.99 96		91.01 178					96.05 8	87.45 28	98.17 36	92.40 223

eth-test2					0.00 509

eth-test					0.00 509

IU-MVS					94.18 54	72.64 158		90.82 185	56.98 422	89.67 119				85.78 63	97.92 51	93.28 171

save fliter					93.75 67	77.44 105	86.31 145	89.72 223	70.80 256

test_0728_SECOND				86.79 111	94.25 52	72.45 166	90.54 57	94.10 39					95.88 17	86.42 46	97.97 48	92.02 247

GSMVS																83.88 411

test_part2					93.86 65	77.77 100				92.84 54

MTGPA								91.81 150

MTMP							90.66 53	33.14 503

gm-plane-assit					75.42 462	44.97 472			52.17 451		72.36 479		87.90 321	54.10 422

test9_res														80.83 124	96.45 112	90.57 294

agg_prior2														79.68 137	96.16 124	90.22 302

agg_prior					91.58 136	77.69 102		90.30 206		84.32 276			93.18 150

test_prior4						78.97 86	84.59 188

test_prior				86.32 119	90.59 167	71.99 174		92.85 111					94.17 106			92.80 197

旧先验2							81.73 279		56.88 423	86.54 216			84.90 382	72.81 257

新几何2							81.72 280

无先验							82.81 250	85.62 308	58.09 411				91.41 204	67.95 313		84.48 402

原ACMM2							82.26 271

testdata2													86.43 355	63.52 353

testdata1							79.62 317		73.95 190

plane_prior7					93.45 74	77.31 108

plane_prior5								93.61 68					95.22 61	80.78 125	95.83 145	94.46 102

plane_prior4											92.95 152

plane_prior3						76.85 113			77.79 136	86.55 210

plane_prior2							89.45 87		79.44 111

plane_prior1					92.83 95

plane_prior						76.42 118	87.15 127		75.94 157						95.03 175

n2								0.00 510

nn								0.00 510

door-mid								74.45 414

test11								91.46 159

door								72.57 433

HQP5-MVS						70.66 193

HQP-NCC					91.19 151		84.77 179		73.30 208	80.55 352

ACMP_Plane					91.19 151		84.77 179		73.30 208	80.55 352

BP-MVS														77.30 178

HQP4-MVS										80.56 351			94.61 85			93.56 161

HQP3-MVS								92.68 117							94.47 198

NP-MVS					91.95 121	74.55 137					90.17 275

ACMMP++_ref															95.74 151

ACMMP++															97.35 83

High-resolution multi-view benchmark