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 58	99.27 1	99.54 1

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 46	94.47 43	85.95 27	86.84 123	93.91 47	80.07 93	86.75 172	93.26 121	93.64 2	90.93 199	84.60 65	90.75 270	93.97 104

ACMH+	77.89 11	90.73 31	91.50 25	88.44 78	93.00 81	76.26 119	89.65 75	95.55 8	87.72 26	93.89 30	94.94 52	91.62 3	93.44 127	78.35 132	98.76 4	95.61 48

LPG-MVS_test		91.47 21	91.68 20	90.82 37	94.75 41	81.69 63	90.00 62	94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 55	98.73 7	95.23 59

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 55	98.73 7	95.23 59

PMVS	80.48 6	90.08 41	90.66 48	88.34 81	96.71 3	92.97 2	90.31 59	89.57 191	88.51 21	90.11 96	95.12 49	90.98 6	88.92 253	77.55 146	97.07 83	83.13 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMM	79.39 9	90.65 32	90.99 41	89.63 57	95.03 34	83.53 51	89.62 76	93.35 66	79.20 105	93.83 31	93.60 116	90.81 7	92.96 143	85.02 60	98.45 19	92.41 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 59	91.14 35	83.96 164	92.50 94	70.36 179	89.55 77	93.84 52	81.89 73	94.70 17	95.44 40	90.69 8	88.31 263	83.33 74	98.30 25	93.20 142
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		92.50 8	92.54 9	92.37 6	95.93 16	85.81 33	92.99 12	94.23 27	85.21 40	92.51 58	95.13 48	90.65 9	95.34 55	88.06 12	98.15 37	95.95 40

RE-MVS-def			92.61 8		94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	90.64 10		87.16 32	97.60 66	92.73 159

ACMP	79.16 10	90.54 35	90.60 49	90.35 45	94.36 46	80.98 69	89.16 86	94.05 41	79.03 108	92.87 49	93.74 111	90.60 11	95.21 61	82.87 82	98.76 4	94.87 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		92.13 11	92.20 13	91.91 17	95.58 26	84.67 46	93.51 8	94.85 15	82.88 64	91.77 70	93.94 102	90.55 12	95.73 35	88.50 10	98.23 31	95.33 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
UniMVSNet_ETH3D		89.12 65	90.72 47	84.31 157	97.00 2	64.33 238	89.67 74	88.38 205	88.84 17	94.29 22	97.57 4	90.48 13	91.26 188	72.57 210	97.65 62	97.34 14

SED-MVS		90.46 37	91.64 21	86.93 99	94.18 50	72.65 145	90.47 55	93.69 56	83.77 52	94.11 26	94.27 79	90.28 14	95.84 24	86.03 49	97.92 49	92.29 184

test_241102_ONE					94.18 50	72.65 145		93.69 56	83.62 54	94.11 26	93.78 108	90.28 14	95.50 49

SR-MVS		92.23 10	92.34 11	91.91 17	94.89 38	87.85 10	92.51 24	93.87 51	88.20 23	93.24 42	94.02 94	90.15 16	95.67 38	86.82 36	97.34 76	92.19 190

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 25	93.02 80	85.17 39	92.47 26	95.05 14	87.65 27	93.21 43	94.39 77	90.09 17	95.08 66	86.67 38	97.60 66	94.18 96

DVP-MVS		90.06 43	91.32 32	86.29 111	94.16 53	72.56 151	90.54 52	91.01 146	83.61 55	93.75 34	94.65 61	89.76 18	95.78 32	86.42 39	97.97 46	90.55 241
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 53	72.56 151	90.63 49	93.90 48	83.61 55	93.75 34	94.49 69	89.76 18

COLMAP_ROB	83.01 3	91.97 13	91.95 14	92.04 11	93.68 65	86.15 24	93.37 10	95.10 13	90.28 10	92.11 63	95.03 50	89.75 20	94.93 70	79.95 114	98.27 26	95.04 64
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 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 205

our_new_method		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 205

test_241102_TWO								93.71 55	83.77 52	93.49 39	94.27 79	89.27 23	95.84 24	86.03 49	97.82 54	92.04 196

tt0805		88.09 77	89.79 55	82.98 194	93.26 75	63.94 242	91.10 45	89.64 188	85.07 41	90.91 86	91.09 190	89.16 24	91.87 174	82.03 93	95.87 132	93.13 145

ACMMP		91.91 14	91.87 19	92.03 12	95.53 27	85.91 28	93.35 11	94.16 32	82.52 67	92.39 61	94.14 89	89.15 25	95.62 39	87.35 27	98.24 30	94.56 77
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 5	94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	88.83 26	95.51 47	87.16 32	97.60 66	92.73 159

APDe-MVS		91.22 25	91.92 15	89.14 66	92.97 82	78.04 93	92.84 16	94.14 36	83.33 58	93.90 28	95.73 31	88.77 27	96.41 3	87.60 21	97.98 45	92.98 153
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_one_0601					93.85 62	73.27 140		94.11 38	86.57 30	93.47 41	94.64 64	88.42 28

ACMMP_NAP		90.65 32	91.07 39	89.42 61	95.93 16	79.54 80	89.95 66	93.68 58	77.65 126	91.97 67	94.89 53	88.38 29	95.45 51	89.27 5	97.87 53	93.27 139

MP-MVS-pluss		90.81 30	91.08 37	89.99 50	95.97 14	79.88 75	88.13 102	94.51 18	75.79 147	92.94 47	94.96 51	88.36 30	95.01 68	90.70 3	98.40 20	95.09 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		91.30 23	91.39 27	91.02 33	95.43 29	84.66 47	92.58 22	93.29 72	81.99 70	91.47 73	93.96 99	88.35 31	95.56 42	87.74 16	97.74 59	92.85 156

CP-MVS		91.67 16	91.58 23	91.96 14	95.29 31	87.62 13	93.38 9	93.36 65	83.16 60	91.06 82	94.00 95	88.26 32	95.71 37	87.28 30	98.39 21	92.55 169

SteuartSystems-ACMMP		91.16 27	91.36 28	90.55 41	93.91 60	80.97 70	91.49 40	93.48 63	82.82 65	92.60 57	93.97 96	88.19 33	96.29 6	87.61 20	98.20 34	94.39 88
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		91.20 26	90.95 43	91.93 15	95.67 23	85.85 31	90.00 62	93.90 48	80.32 89	91.74 71	94.41 75	88.17 34	95.98 13	86.37 41	97.99 43	93.96 105

TDRefinement		93.52 3	93.39 4	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 50	96.29 19	88.16 35	94.17 96	86.07 48	98.48 18	97.22 17

DPE-MVS		90.53 36	91.08 37	88.88 69	93.38 71	78.65 87	89.15 87	94.05 41	84.68 45	93.90 28	94.11 91	88.13 36	96.30 5	84.51 66	97.81 55	91.70 209
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 76	86.76 127	92.78 95	78.78 111	92.51 58	93.64 115	88.13 36	93.84 108	84.83 63	97.55 69	94.10 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs6		86.52 99	88.06 79	81.90 215	92.22 103	62.28 266	84.66 164	89.15 196	83.54 57	89.85 104	97.32 5	88.08 38	86.80 282	70.43 227	97.30 78	96.62 26

mvs_tets		89.78 52	89.27 63	91.30 29	93.51 67	84.79 44	89.89 68	90.63 156	70.00 228	94.55 19	96.67 14	87.94 39	93.59 119	84.27 68	95.97 124	95.52 49

ZNCC-MVS		91.26 24	91.34 31	91.01 34	95.73 21	83.05 56	92.18 31	94.22 29	80.14 92	91.29 78	93.97 96	87.93 40	95.87 20	88.65 8	97.96 48	94.12 100

reproduce_model		92.89 5	93.18 7	92.01 13	94.20 49	88.23 9	92.87 13	94.32 21	90.25 11	95.65 9	95.74 30	87.75 41	95.72 36	89.60 4	98.27 26	92.08 194

region2R		91.44 22	91.30 34	91.87 19	95.75 19	85.90 29	92.63 21	93.30 71	81.91 72	90.88 88	94.21 84	87.75 41	95.87 20	87.60 21	97.71 60	93.83 112

wuyk23d		75.13 279	79.30 232	62.63 384	75.56 384	75.18 126	80.89 250	73.10 355	75.06 158	94.76 16	95.32 41	87.73 43	52.85 415	34.16 414	97.11 82	59.85 411

mPP-MVS		91.69 15	91.47 26	92.37 6	96.04 13	88.48 8	92.72 18	92.60 100	83.09 61	91.54 72	94.25 83	87.67 44	95.51 47	87.21 31	98.11 38	93.12 147

ACMMPR		91.49 19	91.35 30	91.92 16	95.74 20	85.88 30	92.58 22	93.25 73	81.99 70	91.40 74	94.17 88	87.51 45	95.87 20	87.74 16	97.76 57	93.99 103

test_0728_THIRD									85.33 38	93.75 34	94.65 61	87.44 46	95.78 32	87.41 25	98.21 32	92.98 153

9.14			89.29 62		91.84 119		88.80 93	95.32 12	75.14 157	91.07 81	92.89 136	87.27 47	93.78 109	83.69 73	97.55 69

PS-CasMVS		90.06 43	91.92 15	84.47 150	96.56 6	58.83 310	89.04 88	92.74 96	91.40 6	96.12 5	96.06 26	87.23 48	95.57 41	79.42 122	98.74 6	99.00 2

GST-MVS		90.96 29	91.01 40	90.82 37	95.45 28	82.73 59	91.75 38	93.74 54	80.98 83	91.38 75	93.80 106	87.20 49	95.80 28	87.10 34	97.69 61	93.93 106

PEN-MVS		90.03 45	91.88 18	84.48 149	96.57 5	58.88 307	88.95 89	93.19 75	91.62 5	96.01 7	96.16 24	87.02 50	95.60 40	78.69 128	98.72 9	98.97 3

DTE-MVSNet		89.98 47	91.91 17	84.21 159	96.51 7	57.84 318	88.93 90	92.84 93	91.92 4	96.16 4	96.23 21	86.95 51	95.99 12	79.05 125	98.57 15	98.80 6

SF-MVS		90.27 39	90.80 46	88.68 76	92.86 86	77.09 108	91.19 44	95.74 6	81.38 78	92.28 62	93.80 106	86.89 52	94.64 78	85.52 54	97.51 73	94.30 92

MP-MVS		91.14 28	90.91 44	91.83 20	96.18 11	86.88 17	92.20 30	93.03 86	82.59 66	88.52 134	94.37 78	86.74 53	95.41 53	86.32 42	98.21 32	93.19 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		91.52 18	91.60 22	91.29 30	96.59 4	86.29 21	92.02 33	91.81 125	84.07 49	92.00 66	94.40 76	86.63 54	95.28 58	88.59 9	98.31 24	92.30 183

XVS		91.54 17	91.36 28	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	94.03 93	86.57 55	95.80 28	87.35 27	97.62 64	94.20 93

X-MVStestdata		85.04 125	82.70 176	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	16.05 420	86.57 55	95.80 28	87.35 27	97.62 64	94.20 93

MGCFI-Net		85.04 125	85.95 112	82.31 211	87.52 225	63.59 245	86.23 138	93.96 44	73.46 175	88.07 145	87.83 262	86.46 57	90.87 204	76.17 164	93.89 200	92.47 174

sasdasda		85.50 113	86.14 109	83.58 176	87.97 211	67.13 209	87.55 109	94.32 21	73.44 177	88.47 135	87.54 267	86.45 58	91.06 195	75.76 169	93.76 202	92.54 170

canonicalmvs		85.50 113	86.14 109	83.58 176	87.97 211	67.13 209	87.55 109	94.32 21	73.44 177	88.47 135	87.54 267	86.45 58	91.06 195	75.76 169	93.76 202	92.54 170

TranMVSNet+NR-MVSNet		87.86 81	88.76 74	85.18 135	94.02 58	64.13 239	84.38 171	91.29 138	84.88 44	92.06 65	93.84 105	86.45 58	93.73 110	73.22 201	98.66 11	97.69 9

test_0402		88.65 69	89.58 60	85.88 123	92.55 92	72.22 159	84.01 177	89.44 193	88.63 20	94.38 21	95.77 29	86.38 61	93.59 119	79.84 115	95.21 154	91.82 203

APD-MVS		89.54 56	89.63 58	89.26 64	92.57 91	81.34 68	90.19 61	93.08 82	80.87 85	91.13 80	93.19 122	86.22 62	95.97 14	82.23 92	97.18 81	90.45 243
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 67	89.88 53	86.22 115	91.63 123	77.07 109	89.82 69	93.77 53	78.90 109	92.88 48	92.29 157	86.11 63	90.22 221	86.24 46	97.24 79	91.36 217
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					92.22 103	80.48 71		91.85 121	71.22 214	90.38 92	92.98 131	86.06 64	96.11 7	81.99 95	96.75 92

jajsoiax		89.41 57	88.81 73	91.19 32	93.38 71	84.72 45	89.70 71	90.29 172	69.27 232	94.39 20	96.38 18	86.02 65	93.52 123	83.96 70	95.92 130	95.34 53

nrg030		87.85 82	88.49 75	85.91 121	90.07 166	69.73 183	87.86 106	94.20 30	74.04 166	92.70 56	94.66 60	85.88 66	91.50 180	79.72 117	97.32 77	96.50 29

SMA-MVS		90.31 38	90.48 50	89.83 54	95.31 30	79.52 81	90.98 47	93.24 74	75.37 155	92.84 51	95.28 44	85.58 67	96.09 8	87.92 14	97.76 57	93.88 109
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
DeepC-MVS	82.31 4	89.15 64	89.08 66	89.37 62	93.64 66	79.07 83	88.54 98	94.20 30	73.53 174	89.71 107	94.82 56	85.09 68	95.77 34	84.17 69	98.03 41	93.26 140
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 60	89.12 64	89.84 52	88.67 195	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 245	74.12 185	96.10 119	94.45 83

APD_test2		89.30 60	89.12 64	89.84 52	88.67 195	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 245	74.12 185	96.10 119	94.45 83

GeoE		85.45 116	85.81 117	84.37 151	90.08 164	67.07 211	85.86 144	91.39 135	72.33 201	87.59 155	90.25 220	84.85 71	92.37 159	78.00 140	91.94 244	93.66 121

LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 22	93.73 64	85.72 34	96.79 1	95.51 9	88.86 16	95.63 10	96.99 10	84.81 72	93.16 136	91.10 2	97.53 72	96.58 28
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 70	89.01 67	87.36 93	91.30 136	77.50 101	87.55 109	92.97 89	87.95 25	89.62 111	92.87 137	84.56 73	93.89 105	77.65 144	96.62 95	90.70 235

LS3D		90.60 34	90.34 51	91.38 28	89.03 185	84.23 49	93.58 6	94.68 17	90.65 8	90.33 94	93.95 101	84.50 74	95.37 54	80.87 104	95.50 145	94.53 80

EC-MVSNet		88.01 78	88.32 77	87.09 95	89.28 180	72.03 161	90.31 59	96.31 4	80.88 84	85.12 207	89.67 233	84.47 75	95.46 50	82.56 87	96.26 111	93.77 118

casdiffmvs_mvg		86.72 95	87.51 86	84.36 153	87.09 237	65.22 229	84.16 173	94.23 27	77.89 122	91.28 79	93.66 114	84.35 76	92.71 149	80.07 111	94.87 172	95.16 61
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 53	88.88 70	92.27 8	89.82 171	86.67 18	90.51 54	90.20 175	69.87 229	95.06 15	96.14 25	84.28 77	93.07 140	87.68 18	96.34 106	97.09 19

OMC-MVS		88.19 74	87.52 85	90.19 48	91.94 114	81.68 65	87.49 112	93.17 76	76.02 141	88.64 130	91.22 185	84.24 78	93.37 130	77.97 142	97.03 84	95.52 49

CS-MVS		88.14 75	87.67 84	89.54 60	89.56 173	79.18 82	90.47 55	94.77 16	79.37 103	84.32 226	89.33 237	83.87 79	94.53 84	82.45 88	94.89 169	94.90 65

XVG-OURS		89.18 63	88.83 72	90.23 47	94.28 47	86.11 26	85.91 141	93.60 61	80.16 91	89.13 123	93.44 118	83.82 80	90.98 197	83.86 72	95.30 153	93.60 127

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 43	94.91 37	84.50 48	89.49 81	93.98 43	79.68 97	92.09 64	93.89 104	83.80 81	93.10 139	82.67 86	98.04 39	93.64 124

CDPH-MVS		86.17 106	85.54 123	88.05 86	92.25 101	75.45 124	83.85 183	92.01 115	65.91 266	86.19 186	91.75 173	83.77 82	94.98 69	77.43 149	96.71 93	93.73 119

test_fmvsmvis_n_1920		85.22 119	85.36 128	84.81 140	85.80 266	76.13 122	85.15 157	92.32 107	61.40 308	91.33 76	90.85 202	83.76 83	86.16 295	84.31 67	93.28 214	92.15 192

Effi-MVS+		83.90 156	84.01 155	83.57 178	87.22 231	65.61 227	86.55 132	92.40 103	78.64 114	81.34 284	84.18 322	83.65 84	92.93 145	74.22 182	87.87 312	92.17 191

MVS_111021_HR		84.63 133	84.34 151	85.49 132	90.18 163	75.86 123	79.23 274	87.13 224	73.35 179	85.56 200	89.34 236	83.60 85	90.50 215	76.64 157	94.05 197	90.09 253

UA-Net		91.49 19	91.53 24	91.39 27	94.98 35	82.95 58	93.52 7	92.79 94	88.22 22	88.53 133	97.64 3	83.45 86	94.55 83	86.02 51	98.60 13	96.67 25

AdaColmap		83.66 160	83.69 160	83.57 178	90.05 167	72.26 158	86.29 136	90.00 180	78.19 119	81.65 278	87.16 276	83.40 87	94.24 91	61.69 307	94.76 177	84.21 334

LCM-MVSNet-Re		83.48 165	85.06 131	78.75 264	85.94 264	55.75 335	80.05 258	94.27 24	76.47 136	96.09 6	94.54 67	83.31 88	89.75 240	59.95 318	94.89 169	90.75 232

test_fmvsmconf0.01_n		86.68 96	86.52 102	87.18 94	85.94 264	78.30 89	86.93 120	92.20 110	65.94 264	89.16 121	93.16 124	83.10 89	89.89 234	87.81 15	94.43 185	93.35 134

TransMVSNet (Re)		84.02 152	85.74 120	78.85 262	91.00 146	55.20 341	82.29 229	87.26 220	79.65 98	88.38 139	95.52 37	83.00 90	86.88 280	67.97 255	96.60 96	94.45 83

CNVR-MVS		87.81 83	87.68 83	88.21 83	92.87 84	77.30 107	85.25 154	91.23 140	77.31 131	87.07 166	91.47 179	82.94 91	94.71 75	84.67 64	96.27 110	92.62 166

DeepPCF-MVS	81.24 5	87.28 88	86.21 108	90.49 42	91.48 133	84.90 42	83.41 195	92.38 105	70.25 225	89.35 119	90.68 208	82.85 92	94.57 81	79.55 119	95.95 127	92.00 198

v7n		90.13 40	90.96 42	87.65 91	91.95 112	71.06 173	89.99 64	93.05 83	86.53 31	94.29 22	96.27 20	82.69 93	94.08 99	86.25 45	97.63 63	97.82 8

AllTest		87.97 80	87.40 89	89.68 55	91.59 124	83.40 52	89.50 80	95.44 10	79.47 99	88.00 148	93.03 129	82.66 94	91.47 181	70.81 219	96.14 116	94.16 97

TestCases				89.68 55	91.59 124	83.40 52		95.44 10	79.47 99	88.00 148	93.03 129	82.66 94	91.47 181	70.81 219	96.14 116	94.16 97

test_fmvsmconf0.1_n		86.18 105	85.88 115	87.08 96	85.26 273	78.25 90	85.82 145	91.82 123	65.33 278	88.55 132	92.35 156	82.62 96	89.80 236	86.87 35	94.32 188	93.18 144

RPSCF		88.00 79	86.93 97	91.22 31	90.08 164	89.30 5	89.68 73	91.11 143	79.26 104	89.68 108	94.81 59	82.44 97	87.74 267	76.54 158	88.74 298	96.61 27

SPE-MVS-test		87.00 90	86.43 104	88.71 74	89.46 176	77.46 102	89.42 84	95.73 7	77.87 124	81.64 279	87.25 274	82.43 98	94.53 84	77.65 144	96.46 102	94.14 99

ITE_SJBPF				90.11 49	90.72 152	84.97 41		90.30 170	81.56 76	90.02 99	91.20 187	82.40 99	90.81 206	73.58 196	94.66 179	94.56 77

SDMVSNet		81.90 197	83.17 168	78.10 277	88.81 192	62.45 262	76.08 323	86.05 243	73.67 171	83.41 246	93.04 127	82.35 100	80.65 343	70.06 231	95.03 162	91.21 219

test_fmvsmconf_n		85.88 110	85.51 124	86.99 98	84.77 281	78.21 91	85.40 153	91.39 135	65.32 279	87.72 153	91.81 170	82.33 101	89.78 237	86.68 37	94.20 191	92.99 152

Fast-Effi-MVS+		81.04 207	80.57 212	82.46 209	87.50 226	63.22 250	78.37 286	89.63 189	68.01 247	81.87 271	82.08 345	82.31 102	92.65 152	67.10 257	88.30 307	91.51 215

baseline		85.20 121	85.93 113	83.02 192	86.30 254	62.37 264	84.55 166	93.96 44	74.48 163	87.12 161	92.03 162	82.30 103	91.94 170	78.39 130	94.21 190	94.74 74

casdiffmvs		85.21 120	85.85 116	83.31 185	86.17 259	62.77 256	83.03 206	93.93 46	74.69 161	88.21 142	92.68 144	82.29 104	91.89 173	77.87 143	93.75 205	95.27 57
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 71	89.62 59	84.73 143	90.46 157	65.27 228	88.86 91	93.02 87	87.15 28	93.05 46	97.10 8	82.28 105	92.02 169	76.70 156	97.99 43	96.88 23

APD_test1		88.40 71	87.91 80	89.88 51	89.50 175	86.65 20	89.98 65	91.91 120	84.26 47	90.87 89	93.92 103	82.18 106	89.29 249	73.75 193	94.81 173	93.70 120

Anonymous20240529		86.20 104	87.13 91	83.42 182	90.19 162	64.55 236	84.55 166	90.71 153	85.85 36	89.94 103	95.24 46	82.13 107	90.40 217	69.19 240	96.40 105	95.31 55

CLD-MVS		83.18 170	82.64 178	84.79 141	89.05 184	67.82 206	77.93 290	92.52 101	68.33 243	85.07 208	81.54 351	82.06 108	92.96 143	69.35 236	97.91 51	93.57 129
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 98	79.70 78	83.94 179	90.32 167	65.41 277	84.49 220	90.97 194	82.03 109	93.63 114

segment_acmp												81.94 110

train_agg		85.98 108	85.28 129	88.07 85	92.34 98	79.70 78	83.94 179	90.32 167	65.79 268	84.49 220	90.97 194	81.93 111	93.63 114	81.21 100	96.54 98	90.88 229

test_8					92.09 107	78.87 85	83.82 184	90.31 169	65.79 268	84.36 224	90.96 196	81.93 111	93.44 127

test_prior2							83.37 196		75.43 153	84.58 218	91.57 176	81.92 113		79.54 120	96.97 85

EGC-MVSNET		74.79 286	69.99 328	89.19 65	94.89 38	87.00 15	91.89 37	86.28 237	1.09 421	2.23 423	95.98 27	81.87 114	89.48 241	79.76 116	95.96 125	91.10 222

CP-MVSNet		89.27 62	90.91 44	84.37 151	96.34 8	58.61 313	88.66 97	92.06 114	90.78 7	95.67 8	95.17 47	81.80 115	95.54 44	79.00 126	98.69 10	98.95 4

MVS_111021_LR		84.28 143	83.76 159	85.83 125	89.23 182	83.07 55	80.99 249	83.56 279	72.71 194	86.07 189	89.07 242	81.75 116	86.19 294	77.11 153	93.36 210	88.24 280

test_djsdf		89.62 54	89.01 67	91.45 26	92.36 97	82.98 57	91.98 34	90.08 178	71.54 208	94.28 24	96.54 16	81.57 117	94.27 88	86.26 43	96.49 100	97.09 19

cdsmvs_eth3d_5k		20.81 388	27.75 391	0.00 407	0.00 430	0.00 432	0.00 418	85.44 252	0.00 425	0.00 426	82.82 337	81.46 118	0.00 426	0.00 425	0.00 424	0.00 422

WR-MVS_H		89.91 50	91.31 33	85.71 127	96.32 9	62.39 263	89.54 79	93.31 70	90.21 12	95.57 11	95.66 33	81.42 119	95.90 17	80.94 103	98.80 3	98.84 5

CPTT-MVS		89.39 58	88.98 69	90.63 40	95.09 33	86.95 16	92.09 32	92.30 108	79.74 96	87.50 157	92.38 152	81.42 119	93.28 132	83.07 78	97.24 79	91.67 210

pm-mvs1		83.69 159	84.95 134	79.91 249	90.04 168	59.66 297	82.43 225	87.44 217	75.52 152	87.85 150	95.26 45	81.25 121	85.65 306	68.74 247	96.04 121	94.42 86

DVP-MVS++		90.07 42	91.09 36	87.00 97	91.55 129	72.64 147	96.19 2	94.10 39	85.33 38	93.49 39	94.64 64	81.12 122	95.88 18	87.41 25	95.94 128	92.48 172

OPU-MVS				88.27 82	91.89 115	77.83 97	90.47 55				91.22 185	81.12 122	94.68 76	74.48 180	95.35 148	92.29 184

sd_testset		79.95 231	81.39 202	75.64 310	88.81 192	58.07 315	76.16 322	82.81 286	73.67 171	83.41 246	93.04 127	80.96 124	77.65 359	58.62 324	95.03 162	91.21 219

NCCC		87.36 87	86.87 98	88.83 70	92.32 100	78.84 86	86.58 131	91.09 144	78.77 112	84.85 215	90.89 199	80.85 125	95.29 56	81.14 101	95.32 150	92.34 181

TAPA-MVS	77.73 12	85.71 112	84.83 135	88.37 80	88.78 194	79.72 77	87.15 117	93.50 62	69.17 233	85.80 195	89.56 234	80.76 126	92.13 165	73.21 206	95.51 144	93.25 141
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 181	81.41 201	85.90 122	85.60 267	76.53 115	83.07 205	89.62 190	73.02 189	79.11 311	83.51 327	80.74 127	90.24 220	68.76 246	89.29 289	90.94 226

PC_three_1452									58.96 330	90.06 97	91.33 182	80.66 128	93.03 142	75.78 168	95.94 128	92.48 172

VPA-MVSNet		83.47 166	84.73 136	79.69 253	90.29 160	57.52 321	81.30 245	88.69 201	76.29 137	87.58 156	94.44 71	80.60 129	87.20 274	66.60 263	96.82 90	94.34 90

ETV-MVS		84.31 141	83.91 158	85.52 130	88.58 199	70.40 178	84.50 170	93.37 64	78.76 113	84.07 234	78.72 375	80.39 130	95.13 65	73.82 192	92.98 222	91.04 223

HPM-MVS++		88.93 68	88.45 76	90.38 44	94.92 36	85.85 31	89.70 71	91.27 139	78.20 118	86.69 175	92.28 158	80.36 131	95.06 67	86.17 47	96.49 100	90.22 247

ANet_high		83.17 171	85.68 121	75.65 309	81.24 332	45.26 395	79.94 260	92.91 90	83.83 51	91.33 76	96.88 13	80.25 132	85.92 299	68.89 244	95.89 131	95.76 42

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 46	97.23 2	95.32 2	99.01 2	97.26 6	80.16 133	98.99 1	95.15 1	99.14 2	96.47 30

EI-MVSNet-Vis-set		85.12 124	84.53 145	86.88 100	84.01 295	72.76 144	83.91 182	85.18 257	80.44 86	88.75 127	85.49 301	80.08 134	91.92 171	82.02 94	90.85 268	95.97 38

DeepC-MVS_fast	80.27 8	86.23 102	85.65 122	87.96 87	91.30 136	76.92 110	87.19 115	91.99 116	70.56 220	84.96 211	90.69 207	80.01 135	95.14 64	78.37 131	95.78 138	91.82 203
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		85.04 125	84.44 147	86.85 101	83.87 299	72.52 153	83.82 184	85.15 258	80.27 90	88.75 127	85.45 303	79.95 136	91.90 172	81.92 97	90.80 269	96.13 33

MCST-MVS		84.36 139	83.93 157	85.63 128	91.59 124	71.58 168	83.52 192	92.13 112	61.82 301	83.96 236	89.75 232	79.93 137	93.46 126	78.33 133	94.34 187	91.87 202

TSAR-MVS + MP.		88.14 75	87.82 82	89.09 67	95.72 22	76.74 112	92.49 25	91.19 142	67.85 252	86.63 176	94.84 55	79.58 138	95.96 15	87.62 19	94.50 182	94.56 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				86.57 105	90.74 151	72.63 149		90.69 154		82.76 258		79.20 139	94.80 73		95.32 150	92.27 186

CSCG		86.26 101	86.47 103	85.60 129	90.87 149	74.26 131	87.98 104	91.85 121	80.35 88	89.54 117	88.01 256	79.09 140	92.13 165	75.51 171	95.06 161	90.41 244

Test By Simon												79.09 140

PHI-MVS		86.38 100	85.81 117	88.08 84	88.44 203	77.34 105	89.35 85	93.05 83	73.15 187	84.76 216	87.70 264	78.87 142	94.18 94	80.67 108	96.29 107	92.73 159

EG-PatchMatch MVS		84.08 150	84.11 153	83.98 163	92.22 103	72.61 150	82.20 235	87.02 229	72.63 195	88.86 124	91.02 192	78.52 143	91.11 193	73.41 198	91.09 257	88.21 281

dcpmvs_2		84.23 146	85.14 130	81.50 225	88.61 198	61.98 271	82.90 212	93.11 79	68.66 241	92.77 54	92.39 151	78.50 144	87.63 269	76.99 155	92.30 232	94.90 65

Effi-MVS+-dtu		85.82 111	83.38 163	93.14 4	87.13 233	91.15 3	87.70 108	88.42 204	74.57 162	83.56 244	85.65 298	78.49 145	94.21 92	72.04 213	92.88 224	94.05 102

Vis-MVSNet		86.86 92	86.58 101	87.72 89	92.09 107	77.43 104	87.35 113	92.09 113	78.87 110	84.27 231	94.05 92	78.35 146	93.65 112	80.54 110	91.58 251	92.08 194
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 93	87.06 93	86.17 118	92.86 86	67.02 212	82.55 221	91.56 128	83.08 62	90.92 84	91.82 169	78.25 147	93.99 101	74.16 183	98.35 22	97.49 13

MSLP-MVS++		85.00 128	86.03 111	81.90 215	91.84 119	71.56 170	86.75 128	93.02 87	75.95 144	87.12 161	89.39 235	77.98 148	89.40 248	77.46 147	94.78 174	84.75 324

API-MVS		82.28 184	82.61 179	81.30 227	86.29 255	69.79 181	88.71 95	87.67 216	78.42 117	82.15 267	84.15 323	77.98 148	91.59 179	65.39 275	92.75 226	82.51 361

DP-MVS Recon		84.05 151	83.22 165	86.52 107	91.73 122	75.27 125	83.23 202	92.40 103	72.04 205	82.04 268	88.33 252	77.91 150	93.95 103	66.17 266	95.12 159	90.34 246

mmtdpeth		85.13 123	85.78 119	83.17 190	84.65 283	74.71 127	85.87 143	90.35 166	77.94 121	83.82 238	96.96 12	77.75 151	80.03 349	78.44 129	96.21 112	94.79 73

fmvsm_s_conf0.1_n_a		82.58 180	81.93 188	84.50 148	87.68 220	73.35 137	86.14 139	77.70 317	61.64 306	85.02 209	91.62 175	77.75 151	86.24 291	82.79 84	87.07 321	93.91 108

UniMVSNet (Re)		86.87 91	86.98 96	86.55 106	93.11 79	68.48 198	83.80 186	92.87 91	80.37 87	89.61 113	91.81 170	77.72 153	94.18 94	75.00 178	98.53 16	96.99 22

PCF-MVS	74.62 15	82.15 189	80.92 210	85.84 124	89.43 177	72.30 157	80.53 253	91.82 123	57.36 343	87.81 151	89.92 229	77.67 154	93.63 114	58.69 323	95.08 160	91.58 213
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		86.00 107	86.22 107	85.34 133	93.24 76	64.56 235	82.21 233	90.46 160	80.99 82	88.42 137	91.97 163	77.56 155	93.85 106	72.46 211	98.65 12	97.61 10

3Dnovator+	83.92 2	89.97 49	89.66 57	90.92 35	91.27 138	81.66 66	91.25 42	94.13 37	88.89 15	88.83 126	94.26 82	77.55 156	95.86 23	84.88 61	95.87 132	95.24 58

MVS_Test		82.47 182	83.22 165	80.22 246	82.62 319	57.75 320	82.54 222	91.96 118	71.16 215	82.89 255	92.52 149	77.41 157	90.50 215	80.04 113	87.84 313	92.40 178

fmvsm_s_conf0.5_n_a		82.21 186	81.51 200	84.32 156	86.56 245	73.35 137	85.46 150	77.30 321	61.81 302	84.51 219	90.88 201	77.36 158	86.21 293	82.72 85	86.97 326	93.38 133

EIA-MVS		82.19 187	81.23 206	85.10 136	87.95 213	69.17 193	83.22 203	93.33 67	70.42 221	78.58 315	79.77 367	77.29 159	94.20 93	71.51 215	88.96 294	91.93 201

xiu_mvs_v2_base		77.19 256	76.75 260	78.52 268	87.01 239	61.30 277	75.55 330	87.12 227	61.24 313	74.45 351	78.79 374	77.20 160	90.93 199	64.62 285	84.80 355	83.32 348

DU-MVS		86.80 94	86.99 95	86.21 116	93.24 76	67.02 212	83.16 204	92.21 109	81.73 74	90.92 84	91.97 163	77.20 160	93.99 101	74.16 183	98.35 22	97.61 10

Baseline_NR-MVSNet		84.00 153	85.90 114	78.29 274	91.47 134	53.44 352	82.29 229	87.00 232	79.06 107	89.55 115	95.72 32	77.20 160	86.14 296	72.30 212	98.51 17	95.28 56

TinyColmap		81.25 204	82.34 184	77.99 280	85.33 271	60.68 288	82.32 228	88.33 207	71.26 213	86.97 168	92.22 161	77.10 163	86.98 278	62.37 299	95.17 156	86.31 307

F-COLMAP		84.97 129	83.42 162	89.63 57	92.39 96	83.40 52	88.83 92	91.92 119	73.19 186	80.18 301	89.15 241	77.04 164	93.28 132	65.82 272	92.28 235	92.21 189

114514_t		83.10 173	82.54 181	84.77 142	92.90 83	69.10 194	86.65 129	90.62 157	54.66 359	81.46 281	90.81 204	76.98 165	94.38 87	72.62 209	96.18 114	90.82 231

xiu_mvs_v1_base_debu		80.84 209	80.14 223	82.93 197	88.31 204	71.73 164	79.53 265	87.17 221	65.43 274	79.59 303	82.73 339	76.94 166	90.14 226	73.22 201	88.33 303	86.90 301

xiu_mvs_v1_base		80.84 209	80.14 223	82.93 197	88.31 204	71.73 164	79.53 265	87.17 221	65.43 274	79.59 303	82.73 339	76.94 166	90.14 226	73.22 201	88.33 303	86.90 301

xiu_mvs_v1_base_debi		80.84 209	80.14 223	82.93 197	88.31 204	71.73 164	79.53 265	87.17 221	65.43 274	79.59 303	82.73 339	76.94 166	90.14 226	73.22 201	88.33 303	86.90 301

pcd_1.5k_mvsjas		6.41 391	8.55 394	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	76.94 166	0.00 426	0.00 425	0.00 424	0.00 422

PS-MVSNAJss		88.31 73	87.90 81	89.56 59	93.31 73	77.96 96	87.94 105	91.97 117	70.73 219	94.19 25	96.67 14	76.94 166	94.57 81	83.07 78	96.28 108	96.15 32

PS-MVSNAJ		77.04 258	76.53 262	78.56 267	87.09 237	61.40 275	75.26 332	87.13 224	61.25 312	74.38 353	77.22 387	76.94 166	90.94 198	64.63 284	84.83 354	83.35 347

MIMVSNet1		83.63 161	84.59 141	80.74 237	94.06 57	62.77 256	82.72 215	84.53 271	77.57 128	90.34 93	95.92 28	76.88 172	85.83 304	61.88 305	97.42 74	93.62 125

原ACMM1				84.60 146	92.81 89	74.01 132		91.50 130	62.59 292	82.73 259	90.67 210	76.53 173	94.25 90	69.24 237	95.69 141	85.55 315

fmvsm_s_conf0.1_n		82.17 188	81.59 196	83.94 166	86.87 243	71.57 169	85.19 156	77.42 320	62.27 300	84.47 222	91.33 182	76.43 174	85.91 300	83.14 75	87.14 319	94.33 91

fmvsm_s_conf0.5_n		81.91 196	81.30 203	83.75 170	86.02 263	71.56 170	84.73 162	77.11 324	62.44 297	84.00 235	90.68 208	76.42 175	85.89 302	83.14 75	87.11 320	93.81 116

MSDG		80.06 229	79.99 228	80.25 245	83.91 298	68.04 204	77.51 298	89.19 195	77.65 126	81.94 269	83.45 329	76.37 176	86.31 290	63.31 295	86.59 329	86.41 305

Gipuma		84.44 138	86.33 105	78.78 263	84.20 293	73.57 135	89.55 77	90.44 161	84.24 48	84.38 223	94.89 53	76.35 177	80.40 346	76.14 165	96.80 91	82.36 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsm_n_1920		83.60 162	82.89 173	85.74 126	85.22 274	77.74 99	84.12 175	90.48 159	59.87 327	86.45 185	91.12 189	75.65 178	85.89 302	82.28 91	90.87 266	93.58 128

XXY-MVS		74.44 290	76.19 265	69.21 355	84.61 284	52.43 360	71.70 359	77.18 323	60.73 319	80.60 291	90.96 196	75.44 179	69.35 384	56.13 338	88.33 303	85.86 312

FMVSNet1		84.55 136	85.45 125	81.85 217	90.27 161	61.05 281	86.83 124	88.27 209	78.57 115	89.66 110	95.64 34	75.43 180	90.68 210	69.09 241	95.33 149	93.82 113

CANet		83.79 158	82.85 174	86.63 104	86.17 259	72.21 160	83.76 187	91.43 132	77.24 132	74.39 352	87.45 270	75.36 181	95.42 52	77.03 154	92.83 225	92.25 188

ab-mvs		79.67 232	80.56 213	76.99 292	88.48 201	56.93 325	84.70 163	86.06 242	68.95 237	80.78 290	93.08 126	75.30 182	84.62 314	56.78 333	90.90 264	89.43 262

patch_mono-2		78.89 236	79.39 231	77.41 289	84.78 280	68.11 202	75.60 327	83.11 282	60.96 316	79.36 307	89.89 230	75.18 183	72.97 372	73.32 200	92.30 232	91.15 221

DELS-MVS		81.44 202	81.25 204	82.03 213	84.27 292	62.87 254	76.47 317	92.49 102	70.97 217	81.64 279	83.83 324	75.03 184	92.70 150	74.29 181	92.22 238	90.51 242
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		78.84 238	78.10 248	81.07 232	85.17 275	60.22 291	82.21 233	90.57 158	62.51 293	75.32 346	84.61 317	74.99 185	92.30 162	59.48 321	88.04 309	90.68 236

CNLPA		83.55 164	83.10 170	84.90 138	89.34 179	83.87 50	84.54 168	88.77 199	79.09 106	83.54 245	88.66 249	74.87 186	81.73 336	66.84 260	92.29 234	89.11 268

HQP_MVS		87.75 84	87.43 88	88.70 75	93.45 68	76.42 116	89.45 82	93.61 59	79.44 101	86.55 177	92.95 134	74.84 187	95.22 59	80.78 106	95.83 134	94.46 81

plane_prior6					92.61 90	76.54 113						74.84 187

FC-MVSNet-test		85.93 109	87.05 94	82.58 205	92.25 101	56.44 329	85.75 146	93.09 81	77.33 130	91.94 68	94.65 61	74.78 189	93.41 129	75.11 177	98.58 14	97.88 7

VDD-MVS		84.23 146	84.58 142	83.20 188	91.17 142	65.16 231	83.25 200	84.97 265	79.79 95	87.18 160	94.27 79	74.77 190	90.89 202	69.24 237	96.54 98	93.55 132

BH-untuned		80.96 208	80.99 208	80.84 236	88.55 200	68.23 199	80.33 256	88.46 203	72.79 193	86.55 177	86.76 282	74.72 191	91.77 177	61.79 306	88.99 293	82.52 360

VPNet		80.25 223	81.68 191	75.94 307	92.46 95	47.98 382	76.70 310	81.67 295	73.45 176	84.87 214	92.82 138	74.66 192	86.51 287	61.66 308	96.85 87	93.33 135

tfpnnormal		81.79 198	82.95 172	78.31 272	88.93 189	55.40 337	80.83 252	82.85 285	76.81 134	85.90 194	94.14 89	74.58 193	86.51 287	66.82 261	95.68 142	93.01 151

KD-MVS_self_test		81.93 195	83.14 169	78.30 273	84.75 282	52.75 356	80.37 255	89.42 194	70.24 226	90.26 95	93.39 119	74.55 194	86.77 283	68.61 249	96.64 94	95.38 52

fmvsm_l_conf0.5_n		82.06 191	81.54 199	83.60 175	83.94 296	73.90 133	83.35 197	86.10 240	58.97 329	83.80 239	90.36 216	74.23 195	86.94 279	82.90 81	90.22 278	89.94 255

V42		83.47 166	83.37 164	83.75 170	83.16 314	63.33 248	81.31 243	90.23 174	69.51 231	90.91 86	90.81 204	74.16 196	92.29 163	80.06 112	90.22 278	95.62 47

3Dnovator	80.37 7	84.80 130	84.71 139	85.06 137	86.36 252	74.71 127	88.77 94	90.00 180	75.65 149	84.96 211	93.17 123	74.06 197	91.19 190	78.28 134	91.09 257	89.29 266

v10		86.54 98	87.10 92	84.84 139	88.16 209	63.28 249	86.64 130	92.20 110	75.42 154	92.81 53	94.50 68	74.05 198	94.06 100	83.88 71	96.28 108	97.17 18

旧先验1					91.97 111	71.77 163		81.78 294			91.84 167	73.92 199			93.65 207	83.61 342

mvs_anonymous		78.13 246	78.76 239	76.23 306	79.24 356	50.31 375	78.69 281	84.82 268	61.60 307	83.09 253	92.82 138	73.89 200	87.01 275	68.33 253	86.41 331	91.37 216

MAR-MVS		80.24 224	78.74 240	84.73 143	86.87 243	78.18 92	85.75 146	87.81 215	65.67 273	77.84 320	78.50 376	73.79 201	90.53 214	61.59 309	90.87 266	85.49 317
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
VDDNet		84.35 140	85.39 127	81.25 228	95.13 32	59.32 300	85.42 152	81.11 299	86.41 32	87.41 158	96.21 22	73.61 202	90.61 213	66.33 265	96.85 87	93.81 116

FIs		85.35 118	86.27 106	82.60 204	91.86 116	57.31 322	85.10 158	93.05 83	75.83 146	91.02 83	93.97 96	73.57 203	92.91 147	73.97 189	98.02 42	97.58 12

v1144		84.54 137	84.72 138	84.00 162	87.67 221	62.55 260	82.97 209	90.93 149	70.32 224	89.80 105	90.99 193	73.50 204	93.48 125	81.69 99	94.65 180	95.97 38

diffmvs		80.40 218	80.48 216	80.17 247	79.02 359	60.04 292	77.54 297	90.28 173	66.65 262	82.40 262	87.33 273	73.50 204	87.35 272	77.98 141	89.62 286	93.13 145
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		83.23 169	83.19 167	83.33 184	90.90 148	65.98 223	88.19 101	90.78 152	78.13 120	80.87 289	87.92 260	73.49 206	92.42 156	70.07 230	88.40 301	91.60 212

v8		86.22 103	86.83 99	84.36 153	87.82 216	62.35 265	86.42 134	91.33 137	76.78 135	92.73 55	94.48 70	73.41 207	93.72 111	83.10 77	95.41 146	97.01 21

EI-MVSNet		82.61 178	82.42 183	83.20 188	83.25 311	63.66 243	83.50 193	85.07 259	76.06 139	86.55 177	85.10 309	73.41 207	90.25 218	78.15 139	90.67 272	95.68 45

IterMVS-LS		84.73 132	84.98 133	83.96 164	87.35 228	63.66 243	83.25 200	89.88 183	76.06 139	89.62 111	92.37 155	73.40 209	92.52 154	78.16 137	94.77 176	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MM		87.64 85	87.15 90	89.09 67	89.51 174	76.39 118	88.68 96	86.76 233	84.54 46	83.58 243	93.78 108	73.36 210	96.48 2	87.98 13	96.21 112	94.41 87

v144192		84.24 145	84.41 148	83.71 172	87.59 224	61.57 273	82.95 210	91.03 145	67.82 253	89.80 105	90.49 214	73.28 211	93.51 124	81.88 98	94.89 169	96.04 37

BH-RMVSNet		80.53 214	80.22 221	81.49 226	87.19 232	66.21 221	77.79 293	86.23 238	74.21 165	83.69 240	88.50 250	73.25 212	90.75 207	63.18 296	87.90 311	87.52 294

PLC	73.85 16	82.09 190	80.31 217	87.45 92	90.86 150	80.29 73	85.88 142	90.65 155	68.17 246	76.32 332	86.33 288	73.12 213	92.61 153	61.40 310	90.02 281	89.44 261
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OurMVSNet-221017-0		90.01 46	89.74 56	90.83 36	93.16 78	80.37 72	91.91 36	93.11 79	81.10 81	95.32 14	97.24 7	72.94 214	94.85 72	85.07 58	97.78 56	97.26 15

WR-MVS		83.56 163	84.40 149	81.06 233	93.43 70	54.88 342	78.67 282	85.02 262	81.24 79	90.74 90	91.56 177	72.85 215	91.08 194	68.00 254	98.04 39	97.23 16

VNet		79.31 233	80.27 218	76.44 301	87.92 214	53.95 348	75.58 329	84.35 273	74.39 164	82.23 265	90.72 206	72.84 216	84.39 318	60.38 316	93.98 198	90.97 225

QAPM		82.59 179	82.59 180	82.58 205	86.44 247	66.69 216	89.94 67	90.36 165	67.97 249	84.94 213	92.58 147	72.71 217	92.18 164	70.63 225	87.73 314	88.85 275

v1192		84.57 135	84.69 140	84.21 159	87.75 218	62.88 253	83.02 207	91.43 132	69.08 235	89.98 102	90.89 199	72.70 218	93.62 117	82.41 89	94.97 166	96.13 33

OpenMVS	76.72 13	81.98 194	82.00 187	81.93 214	84.42 288	68.22 200	88.50 99	89.48 192	66.92 259	81.80 275	91.86 165	72.59 219	90.16 223	71.19 218	91.25 256	87.40 296

TSAR-MVS + GP.		83.95 154	82.69 177	87.72 89	89.27 181	81.45 67	83.72 188	81.58 297	74.73 160	85.66 196	86.06 293	72.56 220	92.69 151	75.44 173	95.21 154	89.01 274

alignmvs		83.94 155	83.98 156	83.80 167	87.80 217	67.88 205	84.54 168	91.42 134	73.27 185	88.41 138	87.96 257	72.33 221	90.83 205	76.02 167	94.11 194	92.69 163

fmvsm_l_conf0.5_n_a		81.46 201	80.87 211	83.25 186	83.73 301	73.21 142	83.00 208	85.59 251	58.22 335	82.96 254	90.09 227	72.30 222	86.65 285	81.97 96	89.95 282	89.88 256

MVSMamba_PlusPlus		87.53 86	88.86 71	83.54 180	92.03 110	62.26 267	91.49 40	92.62 99	88.07 24	88.07 145	96.17 23	72.24 223	95.79 31	84.85 62	94.16 193	92.58 167

HQP2-MVS												72.10 224

HQP-MVS		84.61 134	84.06 154	86.27 112	91.19 139	70.66 175	84.77 159	92.68 97	73.30 182	80.55 293	90.17 225	72.10 224	94.61 79	77.30 151	94.47 183	93.56 130

testgi		72.36 306	74.61 278	65.59 375	80.56 343	42.82 403	68.29 378	73.35 352	66.87 260	81.84 272	89.93 228	72.08 226	66.92 397	46.05 393	92.54 229	87.01 300

v1921920		84.23 146	84.37 150	83.79 168	87.64 223	61.71 272	82.91 211	91.20 141	67.94 250	90.06 97	90.34 217	72.04 227	93.59 119	82.32 90	94.91 167	96.07 35

MSP-MVS		89.08 66	88.16 78	91.83 20	95.76 18	86.14 25	92.75 17	93.90 48	78.43 116	89.16 121	92.25 159	72.03 228	96.36 4	88.21 11	90.93 263	92.98 153
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
LF4IMVS		82.75 177	81.93 188	85.19 134	82.08 321	80.15 74	85.53 149	88.76 200	68.01 247	85.58 199	87.75 263	71.80 229	86.85 281	74.02 188	93.87 201	88.58 277

v1240		84.30 142	84.51 146	83.65 173	87.65 222	61.26 278	82.85 213	91.54 129	67.94 250	90.68 91	90.65 211	71.71 230	93.64 113	82.84 83	94.78 174	96.07 35

ambc				82.98 194	90.55 156	64.86 232	88.20 100	89.15 196		89.40 118	93.96 99	71.67 231	91.38 187	78.83 127	96.55 97	92.71 162

新几何1				82.95 196	93.96 59	78.56 88		80.24 305	55.45 353	83.93 237	91.08 191	71.19 232	88.33 262	65.84 271	93.07 219	81.95 366

SSC-MVS		77.55 252	81.64 193	65.29 378	90.46 157	20.33 424	73.56 347	68.28 381	85.44 37	88.18 144	94.64 64	70.93 233	81.33 338	71.25 216	92.03 240	94.20 93

v148		82.31 183	82.48 182	81.81 220	85.59 268	59.66 297	81.47 242	86.02 244	72.85 190	88.05 147	90.65 211	70.73 234	90.91 201	75.15 176	91.79 245	94.87 67

v2v482		84.09 149	84.24 152	83.62 174	87.13 233	61.40 275	82.71 216	89.71 186	72.19 204	89.55 115	91.41 180	70.70 235	93.20 134	81.02 102	93.76 202	96.25 31

MVS_0304		85.37 117	84.58 142	87.75 88	85.28 272	73.36 136	86.54 133	85.71 248	77.56 129	81.78 277	92.47 150	70.29 236	96.02 11	85.59 53	95.96 125	93.87 110

WB-MVS		76.06 271	80.01 227	64.19 381	89.96 170	20.58 423	72.18 356	68.19 382	83.21 59	86.46 184	93.49 117	70.19 237	78.97 354	65.96 267	90.46 277	93.02 150

balanced_conf03		84.80 130	85.40 126	83.00 193	88.95 188	61.44 274	90.42 58	92.37 106	71.48 210	88.72 129	93.13 125	70.16 238	95.15 63	79.26 124	94.11 194	92.41 176

UGNet		82.78 176	81.64 193	86.21 116	86.20 258	76.24 120	86.86 122	85.68 249	77.07 133	73.76 356	92.82 138	69.64 239	91.82 176	69.04 243	93.69 206	90.56 240
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		81.64 199	81.59 196	81.79 221	80.86 338	59.15 304	78.61 283	90.18 176	68.36 242	87.20 159	87.11 278	69.39 240	91.62 178	78.16 137	94.43 185	94.60 76

MG-MVS		80.32 221	80.94 209	78.47 270	88.18 207	52.62 359	82.29 229	85.01 263	72.01 206	79.24 310	92.54 148	69.36 241	93.36 131	70.65 224	89.19 292	89.45 260

IS-MVSNet		86.66 97	86.82 100	86.17 118	92.05 109	66.87 215	91.21 43	88.64 202	86.30 33	89.60 114	92.59 145	69.22 242	94.91 71	73.89 190	97.89 52	96.72 24

PVSNet_BlendedMVS		78.80 239	77.84 249	81.65 223	84.43 286	63.41 246	79.49 268	90.44 161	61.70 305	75.43 343	87.07 279	69.11 243	91.44 183	60.68 314	92.24 236	90.11 252

PVSNet_Blended		76.49 267	75.40 272	79.76 251	84.43 286	63.41 246	75.14 333	90.44 161	57.36 343	75.43 343	78.30 377	69.11 243	91.44 183	60.68 314	87.70 315	84.42 329

BH-w/o		76.57 265	76.07 267	78.10 277	86.88 242	65.92 224	77.63 295	86.33 236	65.69 272	80.89 288	79.95 364	68.97 245	90.74 208	53.01 361	85.25 343	77.62 390

MVS		73.21 300	72.59 302	75.06 314	80.97 335	60.81 287	81.64 240	85.92 246	46.03 396	71.68 366	77.54 382	68.47 246	89.77 238	55.70 341	85.39 340	74.60 396

miper_ehance_all_eth		80.34 220	80.04 226	81.24 230	79.82 349	58.95 306	77.66 294	89.66 187	65.75 271	85.99 193	85.11 308	68.29 247	91.42 185	76.03 166	92.03 240	93.33 135

Anonymous202405211		80.51 215	81.19 207	78.49 269	88.48 201	57.26 323	76.63 312	82.49 288	81.21 80	84.30 229	92.24 160	67.99 248	86.24 291	62.22 300	95.13 157	91.98 200

testdata				79.54 256	92.87 84	72.34 156		80.14 306	59.91 326	85.47 202	91.75 173	67.96 249	85.24 308	68.57 251	92.18 239	81.06 379

DPM-MVS		80.10 228	79.18 233	82.88 200	90.71 153	69.74 182	78.87 279	90.84 150	60.29 323	75.64 342	85.92 296	67.28 250	93.11 138	71.24 217	91.79 245	85.77 313

PVSNet_Blended_VisFu		81.55 200	80.49 215	84.70 145	91.58 127	73.24 141	84.21 172	91.67 127	62.86 291	80.94 287	87.16 276	67.27 251	92.87 148	69.82 233	88.94 295	87.99 287

MDA-MVSNet-bldmvs		77.47 253	76.90 259	79.16 260	79.03 358	64.59 233	66.58 388	75.67 334	73.15 187	88.86 124	88.99 243	66.94 252	81.23 339	64.71 282	88.22 308	91.64 211

CL-MVSNet_self_test		76.81 261	77.38 253	75.12 313	86.90 241	51.34 367	73.20 351	80.63 304	68.30 244	81.80 275	88.40 251	66.92 253	80.90 340	55.35 345	94.90 168	93.12 147

test222					93.31 73	76.54 113	79.38 269	77.79 316	52.59 369	82.36 263	90.84 203	66.83 254			91.69 247	81.25 374

TR-MVS		76.77 262	75.79 268	79.72 252	86.10 262	65.79 225	77.14 303	83.02 283	65.20 280	81.40 282	82.10 343	66.30 255	90.73 209	55.57 342	85.27 342	82.65 355

OpenMVS_ROB	70.19 17	77.77 251	77.46 251	78.71 265	84.39 289	61.15 279	81.18 247	82.52 287	62.45 296	83.34 248	87.37 271	66.20 256	88.66 259	64.69 283	85.02 348	86.32 306

EPP-MVSNet		85.47 115	85.04 132	86.77 103	91.52 132	69.37 187	91.63 39	87.98 214	81.51 77	87.05 167	91.83 168	66.18 257	95.29 56	70.75 222	96.89 86	95.64 46

SixPastTwentyTwo		87.20 89	87.45 87	86.45 108	92.52 93	69.19 192	87.84 107	88.05 212	81.66 75	94.64 18	96.53 17	65.94 258	94.75 74	83.02 80	96.83 89	95.41 51

PatchMatch-RL		74.48 288	73.22 294	78.27 275	87.70 219	85.26 38	75.92 325	70.09 373	64.34 284	76.09 336	81.25 353	65.87 259	78.07 358	53.86 353	83.82 361	71.48 399

WB-MVSnew		68.72 343	69.01 336	67.85 364	83.22 313	43.98 399	74.93 335	65.98 391	55.09 354	73.83 355	79.11 370	65.63 260	71.89 376	38.21 410	85.04 347	87.69 293

EPNet		80.37 219	78.41 245	86.23 113	76.75 373	73.28 139	87.18 116	77.45 319	76.24 138	68.14 384	88.93 244	65.41 261	93.85 106	69.47 235	96.12 118	91.55 214
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FA-MVS(test-final)		83.13 172	83.02 171	83.43 181	86.16 261	66.08 222	88.00 103	88.36 206	75.55 151	85.02 209	92.75 142	65.12 262	92.50 155	74.94 179	91.30 255	91.72 207

PM-MVS		80.20 225	79.00 234	83.78 169	88.17 208	86.66 19	81.31 243	66.81 390	69.64 230	88.33 140	90.19 222	64.58 263	83.63 326	71.99 214	90.03 280	81.06 379

miper_enhance_ethall		77.83 248	76.93 258	80.51 241	76.15 380	58.01 317	75.47 331	88.82 198	58.05 337	83.59 242	80.69 355	64.41 264	91.20 189	73.16 207	92.03 240	92.33 182

eth_miper_zixun_eth		80.84 209	80.22 221	82.71 202	81.41 330	60.98 284	77.81 292	90.14 177	67.31 257	86.95 169	87.24 275	64.26 265	92.31 161	75.23 175	91.61 249	94.85 71

test20.03		73.75 295	74.59 280	71.22 342	81.11 334	51.12 371	70.15 372	72.10 362	70.42 221	80.28 299	91.50 178	64.21 266	74.72 371	46.96 390	94.58 181	87.82 292

mvs5depth		83.82 157	84.54 144	81.68 222	82.23 320	68.65 196	86.89 121	89.90 182	80.02 94	87.74 152	97.86 2	64.19 267	82.02 334	76.37 160	95.63 143	94.35 89

cascas		76.29 270	74.81 277	80.72 239	84.47 285	62.94 252	73.89 345	87.34 218	55.94 350	75.16 348	76.53 392	63.97 268	91.16 191	65.00 279	90.97 262	88.06 285

TAMVS		78.08 247	76.36 263	83.23 187	90.62 154	72.87 143	79.08 275	80.01 307	61.72 304	81.35 283	86.92 281	63.96 269	88.78 257	50.61 371	93.01 221	88.04 286

GBi-Net		82.02 192	82.07 185	81.85 217	86.38 249	61.05 281	86.83 124	88.27 209	72.43 196	86.00 190	95.64 34	63.78 270	90.68 210	65.95 268	93.34 211	93.82 113

test1		82.02 192	82.07 185	81.85 217	86.38 249	61.05 281	86.83 124	88.27 209	72.43 196	86.00 190	95.64 34	63.78 270	90.68 210	65.95 268	93.34 211	93.82 113

FMVSNet2		81.31 203	81.61 195	80.41 243	86.38 249	58.75 311	83.93 181	86.58 235	72.43 196	87.65 154	92.98 131	63.78 270	90.22 221	66.86 258	93.92 199	92.27 186

USDC		76.63 264	76.73 261	76.34 303	83.46 304	57.20 324	80.02 259	88.04 213	52.14 374	83.65 241	91.25 184	63.24 273	86.65 285	54.66 350	94.11 194	85.17 319

RRT-MVS		82.97 174	83.44 161	81.57 224	85.06 276	58.04 316	87.20 114	90.37 164	77.88 123	88.59 131	93.70 113	63.17 274	93.05 141	76.49 159	88.47 300	93.62 125

DIV-MVS_self_test		80.43 216	80.23 219	81.02 234	79.99 346	59.25 301	77.07 305	87.02 229	67.38 254	86.19 186	89.22 238	63.09 275	90.16 223	76.32 161	95.80 136	93.66 121

cl____		80.42 217	80.23 219	81.02 234	79.99 346	59.25 301	77.07 305	87.02 229	67.37 255	86.18 188	89.21 239	63.08 276	90.16 223	76.31 162	95.80 136	93.65 123

h-mvs33		84.25 144	82.76 175	88.72 73	91.82 121	82.60 60	84.00 178	84.98 264	71.27 211	86.70 173	90.55 213	63.04 277	93.92 104	78.26 135	94.20 191	89.63 258

hse-mvs2		83.47 166	81.81 190	88.47 77	91.03 145	82.27 61	82.61 217	83.69 277	71.27 211	86.70 173	86.05 294	63.04 277	92.41 157	78.26 135	93.62 209	90.71 234

new-patchmatchnet		70.10 327	73.37 292	60.29 391	81.23 333	16.95 426	59.54 402	74.62 339	62.93 290	80.97 285	87.93 259	62.83 279	71.90 375	55.24 346	95.01 165	92.00 198

K. test v3		85.14 122	84.73 136	86.37 109	91.13 143	69.63 185	85.45 151	76.68 328	84.06 50	92.44 60	96.99 10	62.03 280	94.65 77	80.58 109	93.24 215	94.83 72

lessismore_v0				85.95 120	91.10 144	70.99 174		70.91 371		91.79 69	94.42 74	61.76 281	92.93 145	79.52 121	93.03 220	93.93 106

1314		73.22 299	72.56 304	75.20 312	80.41 345	57.84 318	81.64 240	85.36 253	51.68 377	73.10 359	76.65 391	61.45 282	85.19 309	63.54 292	79.21 388	82.59 356

Syy-MVS		69.40 337	70.03 327	67.49 367	81.72 325	38.94 410	71.00 364	61.99 400	61.38 309	70.81 371	72.36 403	61.37 283	79.30 351	64.50 287	85.18 344	84.22 332

CANet_DTU		77.81 250	77.05 256	80.09 248	81.37 331	59.90 295	83.26 199	88.29 208	69.16 234	67.83 387	83.72 325	60.93 284	89.47 242	69.22 239	89.70 285	90.88 229

pmmvs-eth3d		78.42 245	77.04 257	82.57 207	87.44 227	74.41 130	80.86 251	79.67 308	55.68 352	84.69 217	90.31 219	60.91 285	85.42 307	62.20 301	91.59 250	87.88 290

UnsupCasMVSNet_eth		71.63 314	72.30 306	69.62 352	76.47 377	52.70 358	70.03 373	80.97 301	59.18 328	79.36 307	88.21 254	60.50 286	69.12 385	58.33 327	77.62 395	87.04 299

IterMVS-SCA-FT		80.64 213	79.41 230	84.34 155	83.93 297	69.66 184	76.28 319	81.09 300	72.43 196	86.47 183	90.19 222	60.46 287	93.15 137	77.45 148	86.39 332	90.22 247

SCA		73.32 297	72.57 303	75.58 311	81.62 327	55.86 333	78.89 278	71.37 368	61.73 303	74.93 349	83.42 330	60.46 287	87.01 275	58.11 329	82.63 373	83.88 336

jason		77.42 254	75.75 269	82.43 210	87.10 236	69.27 188	77.99 289	81.94 293	51.47 378	77.84 320	85.07 312	60.32 289	89.00 251	70.74 223	89.27 291	89.03 272
jason: jason.
1112_ss		74.82 285	73.74 286	78.04 279	89.57 172	60.04 292	76.49 316	87.09 228	54.31 360	73.66 357	79.80 365	60.25 290	86.76 284	58.37 325	84.15 359	87.32 297

HY-MVS	64.64 18	73.03 301	72.47 305	74.71 317	83.36 308	54.19 346	82.14 236	81.96 292	56.76 349	69.57 379	86.21 292	60.03 291	84.83 313	49.58 377	82.65 371	85.11 320

Anonymous20231206		71.38 317	71.88 308	69.88 349	86.31 253	54.37 344	70.39 370	74.62 339	52.57 370	76.73 328	88.76 245	59.94 292	72.06 374	44.35 397	93.23 216	83.23 350

IterMVS		76.91 259	76.34 264	78.64 266	80.91 336	64.03 240	76.30 318	79.03 311	64.88 282	83.11 251	89.16 240	59.90 293	84.46 316	68.61 249	85.15 346	87.42 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 328	70.44 321	68.90 357	73.76 395	53.42 353	58.99 405	67.20 386	58.42 333	87.10 163	85.39 305	59.82 294	67.32 394	59.79 319	83.50 364	85.96 309

MDA-MVSNet_test_wron		70.05 329	70.44 321	68.88 358	73.84 394	53.47 351	58.93 406	67.28 385	58.43 332	87.09 164	85.40 304	59.80 295	67.25 395	59.66 320	83.54 363	85.92 311

PMMVS		61.65 371	60.38 378	65.47 377	65.40 420	69.26 189	63.97 394	61.73 404	36.80 417	60.11 409	68.43 408	59.42 296	66.35 399	48.97 380	78.57 391	60.81 410

CDS-MVSNet		77.32 255	75.40 272	83.06 191	89.00 186	72.48 154	77.90 291	82.17 291	60.81 317	78.94 312	83.49 328	59.30 297	88.76 258	54.64 351	92.37 231	87.93 289
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 339	69.68 330	67.82 365	79.42 353	51.15 370	67.82 382	75.79 332	54.15 361	77.47 326	85.36 307	59.26 298	70.64 380	48.46 383	79.35 386	81.66 368

Anonymous20240521		80.18 226	81.25 204	76.95 293	83.15 315	60.84 286	82.46 224	85.99 245	68.76 239	86.78 170	93.73 112	59.13 299	77.44 360	73.71 194	97.55 69	92.56 168

WTY-MVS		67.91 346	68.35 343	66.58 372	80.82 339	48.12 381	65.96 389	72.60 356	53.67 363	71.20 368	81.68 350	58.97 300	69.06 386	48.57 382	81.67 375	82.55 358

cl22		78.97 235	78.21 247	81.24 230	77.74 363	59.01 305	77.46 301	87.13 224	65.79 268	84.32 226	85.10 309	58.96 301	90.88 203	75.36 174	92.03 240	93.84 111

MVSFormer		82.23 185	81.57 198	84.19 161	85.54 269	69.26 189	91.98 34	90.08 178	71.54 208	76.23 333	85.07 312	58.69 302	94.27 88	86.26 43	88.77 296	89.03 272

lupinMVS		76.37 269	74.46 281	82.09 212	85.54 269	69.26 189	76.79 308	80.77 303	50.68 385	76.23 333	82.82 337	58.69 302	88.94 252	69.85 232	88.77 296	88.07 283

Test_1112_low_res		73.90 294	73.08 295	76.35 302	90.35 159	55.95 330	73.40 350	86.17 239	50.70 384	73.14 358	85.94 295	58.31 304	85.90 301	56.51 335	83.22 365	87.20 298

test_yl		78.71 241	78.51 243	79.32 258	84.32 290	58.84 308	78.38 284	85.33 254	75.99 142	82.49 260	86.57 284	58.01 305	90.02 232	62.74 297	92.73 227	89.10 269

DCV-MVSNet		78.71 241	78.51 243	79.32 258	84.32 290	58.84 308	78.38 284	85.33 254	75.99 142	82.49 260	86.57 284	58.01 305	90.02 232	62.74 297	92.73 227	89.10 269

sss		66.92 350	67.26 348	65.90 374	77.23 368	51.10 372	64.79 391	71.72 366	52.12 375	70.13 376	80.18 362	57.96 307	65.36 403	50.21 372	81.01 381	81.25 374

ppachtmachnet_test		74.73 287	74.00 285	76.90 295	80.71 341	56.89 327	71.53 362	78.42 313	58.24 334	79.32 309	82.92 336	57.91 308	84.26 320	65.60 274	91.36 254	89.56 259

MVP-Stereo		75.81 274	73.51 290	82.71 202	89.35 178	73.62 134	80.06 257	85.20 256	60.30 322	73.96 354	87.94 258	57.89 309	89.45 244	52.02 365	74.87 401	85.06 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 311	70.06 326	76.92 294	86.39 248	53.97 347	76.62 313	86.62 234	53.44 364	63.97 404	84.73 316	57.79 310	92.34 160	39.65 405	81.33 379	84.45 328

LFMVS		80.15 227	80.56 213	78.89 261	89.19 183	55.93 331	85.22 155	73.78 348	82.96 63	84.28 230	92.72 143	57.38 311	90.07 230	63.80 290	95.75 139	90.68 236

Vis-MVSNet (Re-imp)		77.82 249	77.79 250	77.92 281	88.82 191	51.29 369	83.28 198	71.97 363	74.04 166	82.23 265	89.78 231	57.38 311	89.41 247	57.22 332	95.41 146	93.05 149

CHOSEN 1792x2688		72.45 305	70.56 319	78.13 276	90.02 169	63.08 251	68.72 377	83.16 281	42.99 406	75.92 338	85.46 302	57.22 313	85.18 310	49.87 375	81.67 375	86.14 308

mvsany_test1		58.48 380	56.47 385	64.50 380	65.90 419	68.21 201	56.95 409	42.11 422	38.30 414	65.69 395	77.19 388	56.96 314	59.35 412	46.16 391	58.96 415	65.93 406

miper_lstm_enhance		76.45 268	76.10 266	77.51 287	76.72 374	60.97 285	64.69 392	85.04 261	63.98 286	83.20 250	88.22 253	56.67 315	78.79 356	73.22 201	93.12 218	92.78 158

our_test_3		71.85 310	71.59 310	72.62 332	80.71 341	53.78 349	69.72 374	71.71 367	58.80 331	78.03 317	80.51 360	56.61 316	78.84 355	62.20 301	86.04 337	85.23 318

baseline1		73.26 298	73.54 289	72.43 335	84.92 278	47.79 383	79.89 261	74.00 344	65.93 265	78.81 313	86.28 291	56.36 317	81.63 337	56.63 334	79.04 390	87.87 291

pmmvs4		74.92 283	72.98 297	80.73 238	84.95 277	71.71 167	76.23 320	77.59 318	52.83 368	77.73 324	86.38 286	56.35 318	84.97 311	57.72 331	87.05 322	85.51 316

MVE	40.22 23	51.82 384	50.47 387	55.87 395	62.66 422	51.91 363	31.61 416	39.28 423	40.65 409	50.76 418	74.98 398	56.24 319	44.67 419	33.94 415	64.11 413	71.04 401
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		60.59 378	62.54 373	54.72 397	77.26 367	27.74 420	74.05 342	61.00 407	60.48 321	65.62 396	67.03 410	55.93 320	68.23 392	32.07 417	69.46 411	68.17 404

N_pmnet		70.20 325	68.80 340	74.38 319	80.91 336	84.81 43	59.12 404	76.45 330	55.06 355	75.31 347	82.36 342	55.74 321	54.82 414	47.02 388	87.24 318	83.52 343

MS-PatchMatch		70.93 321	70.22 324	73.06 327	81.85 324	62.50 261	73.82 346	77.90 315	52.44 371	75.92 338	81.27 352	55.67 322	81.75 335	55.37 344	77.70 394	74.94 395

DSMNet-mixed		60.98 376	61.61 376	59.09 394	72.88 402	45.05 396	74.70 337	46.61 420	26.20 418	65.34 397	90.32 218	55.46 323	63.12 407	41.72 401	81.30 380	69.09 403

pmmvs5		70.73 322	70.07 325	72.72 330	77.03 371	52.73 357	74.14 340	75.65 335	50.36 387	72.17 364	85.37 306	55.42 324	80.67 342	52.86 362	87.59 316	84.77 323

CMPMVS	59.41 20	75.12 280	73.57 288	79.77 250	75.84 383	67.22 208	81.21 246	82.18 290	50.78 383	76.50 329	87.66 265	55.20 325	82.99 329	62.17 303	90.64 276	89.09 271
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_vis1_n_1920		71.30 318	71.58 312	70.47 345	77.58 366	59.99 294	74.25 339	84.22 275	51.06 380	74.85 350	79.10 371	55.10 326	68.83 387	68.86 245	79.20 389	82.58 357

MIMVSNet		71.09 319	71.59 310	69.57 353	87.23 230	50.07 376	78.91 277	71.83 364	60.20 325	71.26 367	91.76 172	55.08 327	76.09 364	41.06 402	87.02 324	82.54 359

PVSNet_0	51.08 22	56.10 381	54.97 386	59.48 393	75.12 389	53.28 354	55.16 410	61.89 402	44.30 400	59.16 410	62.48 413	54.22 328	65.91 401	35.40 412	47.01 416	59.25 412

MonoMVSNet		76.66 263	77.26 255	74.86 315	79.86 348	54.34 345	86.26 137	86.08 241	71.08 216	85.59 198	88.68 247	53.95 329	85.93 298	63.86 289	80.02 383	84.32 330

EPNet_dtu		72.87 303	71.33 315	77.49 288	77.72 364	60.55 289	82.35 227	75.79 332	66.49 263	58.39 414	81.06 354	53.68 330	85.98 297	53.55 356	92.97 223	85.95 310
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 383	59.27 382	44.74 399	64.30 421	12.32 427	40.60 414	49.79 418	53.19 366	65.06 401	84.81 314	53.60 331	49.76 417	32.68 416	89.41 288	72.15 398

test_vis1_rt		65.64 360	64.09 364	70.31 346	66.09 417	70.20 180	61.16 399	81.60 296	38.65 413	72.87 360	69.66 406	52.84 332	60.04 410	56.16 337	77.77 393	80.68 381

mvsany_test3		65.48 361	62.97 370	73.03 328	69.99 410	76.17 121	64.83 390	43.71 421	43.68 403	80.25 300	87.05 280	52.83 333	63.09 408	51.92 369	72.44 403	79.84 386

HyFIR lowres test		75.12 280	72.66 301	82.50 208	91.44 135	65.19 230	72.47 354	87.31 219	46.79 391	80.29 297	84.30 320	52.70 334	92.10 168	51.88 370	86.73 327	90.22 247

dmvs_re		66.81 353	66.98 349	66.28 373	76.87 372	58.68 312	71.66 360	72.24 359	60.29 323	69.52 380	73.53 400	52.38 335	64.40 405	44.90 395	81.44 378	75.76 393

test_cas_vis1_n_1920		69.20 340	69.12 333	69.43 354	73.68 396	62.82 255	70.38 371	77.21 322	46.18 395	80.46 296	78.95 373	52.03 336	65.53 402	65.77 273	77.45 397	79.95 385

test1111		78.53 243	78.85 237	77.56 286	92.22 103	47.49 384	82.61 217	69.24 379	72.43 196	85.28 204	94.20 85	51.91 337	90.07 230	65.36 276	96.45 103	95.11 62

ECVR-MVS		78.44 244	78.63 241	77.88 282	91.85 117	48.95 378	83.68 189	69.91 375	72.30 202	84.26 232	94.20 85	51.89 338	89.82 235	63.58 291	96.02 122	94.87 67

FMVSNet3		78.80 239	78.55 242	79.57 255	82.89 318	56.89 327	81.76 237	85.77 247	69.04 236	86.00 190	90.44 215	51.75 339	90.09 229	65.95 268	93.34 211	91.72 207

D2MVS		76.84 260	75.67 271	80.34 244	80.48 344	62.16 270	73.50 348	84.80 269	57.61 341	82.24 264	87.54 267	51.31 340	87.65 268	70.40 228	93.19 217	91.23 218

AUN-MVS		81.18 205	78.78 238	88.39 79	90.93 147	82.14 62	82.51 223	83.67 278	64.69 283	80.29 297	85.91 297	51.07 341	92.38 158	76.29 163	93.63 208	90.65 238

PVSNet	58.17 21	66.41 356	65.63 359	68.75 359	81.96 322	49.88 377	62.19 398	72.51 358	51.03 381	68.04 385	75.34 397	50.84 342	74.77 369	45.82 394	82.96 366	81.60 369

mvsmamba		80.30 222	78.87 235	84.58 147	88.12 210	67.55 207	92.35 29	84.88 266	63.15 289	85.33 203	90.91 198	50.71 343	95.20 62	66.36 264	87.98 310	90.99 224

GA-MVS		75.83 273	74.61 278	79.48 257	81.87 323	59.25 301	73.42 349	82.88 284	68.68 240	79.75 302	81.80 348	50.62 344	89.46 243	66.85 259	85.64 339	89.72 257

FPMVS		72.29 308	72.00 307	73.14 326	88.63 197	85.00 40	74.65 338	67.39 384	71.94 207	77.80 322	87.66 265	50.48 345	75.83 366	49.95 373	79.51 384	58.58 413

test_fmvs3		75.72 275	75.20 275	77.27 290	75.01 391	69.47 186	78.93 276	84.88 266	46.67 392	87.08 165	87.84 261	50.44 346	71.62 377	77.42 150	88.53 299	90.72 233

test_vis1_n		70.29 324	69.99 328	71.20 343	75.97 382	66.50 218	76.69 311	80.81 302	44.22 401	75.43 343	77.23 386	50.00 347	68.59 388	66.71 262	82.85 370	78.52 389

MVS-HIRNet		61.16 374	62.92 371	55.87 395	79.09 357	35.34 416	71.83 358	57.98 413	46.56 393	59.05 411	91.14 188	49.95 348	76.43 363	38.74 407	71.92 405	55.84 414

CVMVSNet		72.62 304	71.41 314	76.28 304	83.25 311	60.34 290	83.50 193	79.02 312	37.77 416	76.33 331	85.10 309	49.60 349	87.41 271	70.54 226	77.54 396	81.08 377

RPMNet		78.88 237	78.28 246	80.68 240	79.58 350	62.64 258	82.58 219	94.16 32	74.80 159	75.72 340	92.59 145	48.69 350	95.56 42	73.48 197	82.91 368	83.85 339

test_fmvs2		73.57 296	72.80 298	75.90 308	72.74 404	68.84 195	77.07 305	84.32 274	45.14 398	82.89 255	84.22 321	48.37 351	70.36 381	73.40 199	87.03 323	88.52 278

tpmrst		66.28 357	66.69 353	65.05 379	72.82 403	39.33 409	78.20 287	70.69 372	53.16 367	67.88 386	80.36 361	48.18 352	74.75 370	58.13 328	70.79 406	81.08 377

CR-MVSNet		74.00 293	73.04 296	76.85 297	79.58 350	62.64 258	82.58 219	76.90 325	50.50 386	75.72 340	92.38 152	48.07 353	84.07 322	68.72 248	82.91 368	83.85 339

Patchmtry		76.56 266	77.46 251	73.83 321	79.37 355	46.60 388	82.41 226	76.90 325	73.81 169	85.56 200	92.38 152	48.07 353	83.98 323	63.36 294	95.31 152	90.92 227

test_f		64.31 367	65.85 356	59.67 392	66.54 416	62.24 269	57.76 408	70.96 370	40.13 410	84.36 224	82.09 344	46.93 355	51.67 416	61.99 304	81.89 374	65.12 407

ADS-MVSNet2		65.87 359	63.64 367	72.55 333	73.16 399	56.92 326	67.10 385	74.81 338	49.74 388	66.04 393	82.97 333	46.71 356	77.26 361	42.29 399	69.96 408	83.46 344

ADS-MVSNet		61.90 370	62.19 374	61.03 390	73.16 399	36.42 415	67.10 385	61.75 403	49.74 388	66.04 393	82.97 333	46.71 356	63.21 406	42.29 399	69.96 408	83.46 344

PatchmatchNet		69.71 334	68.83 339	72.33 337	77.66 365	53.60 350	79.29 270	69.99 374	57.66 340	72.53 362	82.93 335	46.45 358	80.08 348	60.91 313	72.09 404	83.31 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 307	71.55 313	74.70 318	83.48 303	51.60 366	75.02 334	73.71 349	70.14 227	78.56 316	80.57 358	46.20 359	88.20 264	46.99 389	89.29 289	84.32 330

sam_mvs1												46.11 360				83.88 336

tfpn200view9		74.86 284	74.23 283	76.74 298	86.24 256	52.12 361	79.24 272	73.87 346	73.34 180	81.82 273	84.60 318	46.02 361	88.80 254	51.98 366	90.99 259	89.31 264

thres400		75.14 278	74.23 283	77.86 283	86.24 256	52.12 361	79.24 272	73.87 346	73.34 180	81.82 273	84.60 318	46.02 361	88.80 254	51.98 366	90.99 259	92.66 164

baseline2		69.77 333	66.89 350	78.41 271	79.51 352	58.09 314	76.23 320	69.57 376	57.50 342	64.82 402	77.45 384	46.02 361	88.44 260	53.08 358	77.83 392	88.70 276

patchmatchnet-post											81.71 349	45.93 364	87.01 275

sam_mvs												45.92 365

BP-MVS1		82.81 175	81.67 192	86.23 113	87.88 215	68.53 197	86.06 140	84.36 272	75.65 149	85.14 206	90.19 222	45.84 366	94.42 86	85.18 57	94.72 178	95.75 43

Patchmatch-RL test		74.48 288	73.68 287	76.89 296	84.83 279	66.54 217	72.29 355	69.16 380	57.70 339	86.76 171	86.33 288	45.79 367	82.59 330	69.63 234	90.65 275	81.54 370

thres100view900		75.45 276	75.05 276	76.66 299	87.27 229	51.88 364	81.07 248	73.26 353	75.68 148	83.25 249	86.37 287	45.54 368	88.80 254	51.98 366	90.99 259	89.31 264

thres600view7		75.97 272	75.35 274	77.85 284	87.01 239	51.84 365	80.45 254	73.26 353	75.20 156	83.10 252	86.31 290	45.54 368	89.05 250	55.03 348	92.24 236	92.66 164

tpm cat1		66.76 354	65.21 362	71.42 341	77.09 370	50.62 374	78.01 288	73.68 350	44.89 399	68.64 382	79.00 372	45.51 370	82.42 333	49.91 374	70.15 407	81.23 376

test_post											3.10 422	45.43 371	77.22 362

MDTV_nov1_ep13			68.29 344		78.03 362	43.87 400	74.12 341	72.22 360	52.17 372	67.02 390	85.54 299	45.36 372	80.85 341	55.73 339	84.42 357

tpmvs		70.16 326	69.56 331	71.96 338	74.71 392	48.13 380	79.63 263	75.45 337	65.02 281	70.26 375	81.88 347	45.34 373	85.68 305	58.34 326	75.39 400	82.08 365

MDTV_nov1_ep13_2view						27.60 421	70.76 368		46.47 394	61.27 406		45.20 374		49.18 378		83.75 341

test_post1							78.85 280				3.13 421	45.19 375	80.13 347	58.11 329

CostFormer		69.98 331	68.68 341	73.87 320	77.14 369	50.72 373	79.26 271	74.51 341	51.94 376	70.97 370	84.75 315	45.16 376	87.49 270	55.16 347	79.23 387	83.40 346

FE-MVS		79.98 230	78.86 236	83.36 183	86.47 246	66.45 219	89.73 70	84.74 270	72.80 192	84.22 233	91.38 181	44.95 377	93.60 118	63.93 288	91.50 252	90.04 254

Patchmatch-test		65.91 358	67.38 347	61.48 389	75.51 385	43.21 402	68.84 376	63.79 398	62.48 294	72.80 361	83.42 330	44.89 378	59.52 411	48.27 385	86.45 330	81.70 367

EU-MVSNet		75.12 280	74.43 282	77.18 291	83.11 316	59.48 299	85.71 148	82.43 289	39.76 412	85.64 197	88.76 245	44.71 379	87.88 266	73.86 191	85.88 338	84.16 335

PatchT		70.52 323	72.76 300	63.79 383	79.38 354	33.53 417	77.63 295	65.37 394	73.61 173	71.77 365	92.79 141	44.38 380	75.65 367	64.53 286	85.37 341	82.18 363

test_vis3_rt		71.42 316	70.67 317	73.64 323	69.66 411	70.46 177	66.97 387	89.73 184	42.68 408	88.20 143	83.04 332	43.77 381	60.07 409	65.35 277	86.66 328	90.39 245

test_fmvs1_n		70.94 320	70.41 323	72.53 334	73.92 393	66.93 214	75.99 324	84.21 276	43.31 405	79.40 306	79.39 369	43.47 382	68.55 389	69.05 242	84.91 351	82.10 364

test-LLR		67.21 348	66.74 352	68.63 361	76.45 378	55.21 339	67.89 379	67.14 387	62.43 298	65.08 399	72.39 401	43.41 383	69.37 382	61.00 311	84.89 352	81.31 372

test0.0.03 1		64.66 364	64.36 363	65.57 376	75.03 390	46.89 387	64.69 392	61.58 406	62.43 298	71.18 369	77.54 382	43.41 383	68.47 391	40.75 404	82.65 371	81.35 371

test_fmvs1		69.57 335	69.05 335	71.14 344	69.15 412	65.77 226	73.98 343	83.32 280	42.83 407	77.77 323	78.27 378	43.39 385	68.50 390	68.39 252	84.38 358	79.15 387

MVSTER		77.09 257	75.70 270	81.25 228	75.27 388	61.08 280	77.49 300	85.07 259	60.78 318	86.55 177	88.68 247	43.14 386	90.25 218	73.69 195	90.67 272	92.42 175

tpm		67.95 345	68.08 346	67.55 366	78.74 361	43.53 401	75.60 327	67.10 389	54.92 356	72.23 363	88.10 255	42.87 387	75.97 365	52.21 364	80.95 382	83.15 351

tpm2		68.45 344	66.83 351	73.30 325	78.93 360	48.50 379	79.76 262	71.76 365	47.50 390	69.92 377	83.60 326	42.07 388	88.40 261	48.44 384	79.51 384	83.01 353

EMVS		61.10 375	60.81 377	61.99 386	65.96 418	55.86 333	53.10 412	58.97 411	67.06 258	56.89 416	63.33 412	40.98 389	67.03 396	54.79 349	86.18 335	63.08 408

new_pmnet		55.69 382	57.66 383	49.76 398	75.47 386	30.59 418	59.56 401	51.45 417	43.62 404	62.49 405	75.48 396	40.96 390	49.15 418	37.39 411	72.52 402	69.55 402

E-PMN		61.59 372	61.62 375	61.49 388	66.81 415	55.40 337	53.77 411	60.34 408	66.80 261	58.90 412	65.50 411	40.48 391	66.12 400	55.72 340	86.25 334	62.95 409

EPMVS		62.47 368	62.63 372	62.01 385	70.63 409	38.74 411	74.76 336	52.86 416	53.91 362	67.71 388	80.01 363	39.40 392	66.60 398	55.54 343	68.81 412	80.68 381

tmp_tt		20.25 389	24.50 392	7.49 404	4.47 427	8.70 428	34.17 415	25.16 425	1.00 422	32.43 421	18.49 419	39.37 393	9.21 423	21.64 419	43.75 417	4.57 419

thisisatest0530		79.07 234	77.33 254	84.26 158	87.13 233	64.58 234	83.66 190	75.95 331	68.86 238	85.22 205	87.36 272	38.10 394	93.57 122	75.47 172	94.28 189	94.62 75

ET-MVSNet_ETH3D		75.28 277	72.77 299	82.81 201	83.03 317	68.11 202	77.09 304	76.51 329	60.67 320	77.60 325	80.52 359	38.04 395	91.15 192	70.78 221	90.68 271	89.17 267

ttmdpeth		71.72 312	70.67 317	74.86 315	73.08 401	55.88 332	77.41 302	69.27 378	55.86 351	78.66 314	93.77 110	38.01 396	75.39 368	60.12 317	89.87 283	93.31 137

tttt0517		81.07 206	79.58 229	85.52 130	88.99 187	66.45 219	87.03 119	75.51 336	73.76 170	88.32 141	90.20 221	37.96 397	94.16 98	79.36 123	95.13 157	95.93 41

thisisatest0515		73.00 302	70.52 320	80.46 242	81.45 329	59.90 295	73.16 352	74.31 343	57.86 338	76.08 337	77.78 380	37.60 398	92.12 167	65.00 279	91.45 253	89.35 263

FMVSNet5		72.10 309	71.69 309	73.32 324	81.57 328	53.02 355	76.77 309	78.37 314	63.31 287	76.37 330	91.85 166	36.68 399	78.98 353	47.87 386	92.45 230	87.95 288

dp		60.70 377	60.29 380	61.92 387	72.04 406	38.67 412	70.83 367	64.08 397	51.28 379	60.75 407	77.28 385	36.59 400	71.58 378	47.41 387	62.34 414	75.52 394

CHOSEN 280x420		59.08 379	56.52 384	66.76 371	76.51 376	64.39 237	49.62 413	59.00 410	43.86 402	55.66 417	68.41 409	35.55 401	68.21 393	43.25 398	76.78 399	67.69 405

testing91		69.94 332	68.99 337	72.80 329	83.81 300	45.89 391	71.57 361	73.64 351	68.24 245	70.77 373	77.82 379	34.37 402	84.44 317	53.64 355	87.00 325	88.07 283

IB-MVS	62.13 19	71.64 313	68.97 338	79.66 254	80.80 340	62.26 267	73.94 344	76.90 325	63.27 288	68.63 383	76.79 389	33.83 403	91.84 175	59.28 322	87.26 317	84.88 322
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		68.76 342	68.43 342	69.75 351	83.29 309	40.30 408	67.36 384	72.21 361	57.09 346	77.05 327	85.53 300	33.68 404	80.51 344	48.79 381	90.90 264	88.45 279

JIA-IIPM		69.41 336	66.64 354	77.70 285	73.19 398	71.24 172	75.67 326	65.56 393	70.42 221	65.18 398	92.97 133	33.64 405	83.06 327	53.52 357	69.61 410	78.79 388

UBG		64.34 366	63.35 368	67.30 368	83.50 302	40.53 407	67.46 383	65.02 395	54.77 358	67.54 389	74.47 399	32.99 406	78.50 357	40.82 403	83.58 362	82.88 354

testing99		69.27 338	68.15 345	72.63 331	83.29 309	45.45 393	71.15 363	71.08 369	67.34 256	70.43 374	77.77 381	32.24 407	84.35 319	53.72 354	86.33 333	88.10 282

testing11		67.38 347	65.93 355	71.73 340	83.37 307	46.60 388	70.95 366	69.40 377	62.47 295	66.14 391	76.66 390	31.22 408	84.10 321	49.10 379	84.10 360	84.49 326

DeepMVS_CX				24.13 403	32.95 425	29.49 419		21.63 426	12.07 419	37.95 420	45.07 417	30.84 409	19.21 422	17.94 421	33.06 419	23.69 418

gg-mvs-nofinetune		68.96 341	69.11 334	68.52 363	76.12 381	45.32 394	83.59 191	55.88 414	86.68 29	64.62 403	97.01 9	30.36 410	83.97 324	44.78 396	82.94 367	76.26 392

GG-mvs-BLEND				67.16 369	73.36 397	46.54 390	84.15 174	55.04 415		58.64 413	61.95 414	29.93 411	83.87 325	38.71 408	76.92 398	71.07 400

UWE-MVS		66.43 355	65.56 360	69.05 356	84.15 294	40.98 406	73.06 353	64.71 396	54.84 357	76.18 335	79.62 368	29.21 412	80.50 345	38.54 409	89.75 284	85.66 314

ETVMVS		64.67 363	63.34 369	68.64 360	83.44 305	41.89 404	69.56 375	61.70 405	61.33 311	68.74 381	75.76 395	28.76 413	79.35 350	34.65 413	86.16 336	84.67 325

test_method		30.46 387	29.60 390	33.06 401	17.99 426	3.84 429	13.62 417	73.92 345	2.79 420	18.29 422	53.41 415	28.53 414	43.25 420	22.56 418	35.27 418	52.11 415

test-mter		65.00 362	63.79 366	68.63 361	76.45 378	55.21 339	67.89 379	67.14 387	50.98 382	65.08 399	72.39 401	28.27 415	69.37 382	61.00 311	84.89 352	81.31 372

TESTMET0.1,1		61.29 373	60.32 379	64.19 381	72.06 405	51.30 368	67.89 379	62.09 399	45.27 397	60.65 408	69.01 407	27.93 416	64.74 404	56.31 336	81.65 377	76.53 391

reproduce_monomvs		74.09 292	73.23 293	76.65 300	76.52 375	54.54 343	77.50 299	81.40 298	65.85 267	82.86 257	86.67 283	27.38 417	84.53 315	70.24 229	90.66 274	90.89 228

testing222		66.93 349	65.30 361	71.81 339	83.38 306	45.83 392	72.06 357	67.50 383	64.12 285	69.68 378	76.37 393	27.34 418	83.00 328	38.88 406	88.38 302	86.62 304

test2506		74.12 291	73.39 291	76.28 304	91.85 117	44.20 398	84.06 176	48.20 419	72.30 202	81.90 270	94.20 85	27.22 419	89.77 238	64.81 281	96.02 122	94.87 67

pmmvs3		62.47 368	60.02 381	69.80 350	71.58 407	64.00 241	70.52 369	58.44 412	39.77 411	66.05 392	75.84 394	27.10 420	72.28 373	46.15 392	84.77 356	73.11 397

KD-MVS_2432*1600		66.87 351	65.81 357	70.04 347	67.50 413	47.49 384	62.56 396	79.16 309	61.21 314	77.98 318	80.61 356	25.29 421	82.48 331	53.02 359	84.92 349	80.16 383

miper_refine_blended		66.87 351	65.81 357	70.04 347	67.50 413	47.49 384	62.56 396	79.16 309	61.21 314	77.98 318	80.61 356	25.29 421	82.48 331	53.02 359	84.92 349	80.16 383

MVStest1		70.05 329	69.26 332	72.41 336	58.62 423	55.59 336	76.61 314	65.58 392	53.44 364	89.28 120	93.32 120	22.91 423	71.44 379	74.08 187	89.52 287	90.21 251

myMVS_eth3d		64.66 364	63.89 365	66.97 370	81.72 325	37.39 413	71.00 364	61.99 400	61.38 309	70.81 371	72.36 403	20.96 424	79.30 351	49.59 376	85.18 344	84.22 332

testing3		71.53 315	70.79 316	73.77 322	88.89 190	41.86 405	76.60 315	59.12 409	72.83 191	80.97 285	82.08 345	19.80 425	87.33 273	65.12 278	91.68 248	92.13 193

dongtai		41.90 385	42.65 388	39.67 400	70.86 408	21.11 422	61.01 400	21.42 427	57.36 343	57.97 415	50.06 416	16.40 426	58.73 413	21.03 420	27.69 420	39.17 416

kuosan		30.83 386	32.17 389	26.83 402	53.36 424	19.02 425	57.90 407	20.44 428	38.29 415	38.01 419	37.82 418	15.18 427	33.45 421	7.74 422	20.76 421	28.03 417

test123		6.27 392	8.08 395	0.84 405	1.11 429	0.57 430	62.90 395	0.82 429	0.54 423	1.07 425	2.75 424	1.26 428	0.30 424	1.04 423	1.26 423	1.66 420

testmvs		5.91 393	7.65 396	0.72 406	1.20 428	0.37 431	59.14 403	0.67 430	0.49 424	1.11 424	2.76 423	0.94 429	0.24 425	1.02 424	1.47 422	1.55 421

mmdepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

monomultidepth		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

test_blank		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uanet_test		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

DCPMVS		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

sosnet-low-res		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

sosnet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uncertanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

Regformer		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

ab-mvs-re		6.65 390	8.87 393	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	79.80 365	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

uanet		0.00 394	0.00 397	0.00 407	0.00 430	0.00 432	0.00 418	0.00 431	0.00 425	0.00 426	0.00 425	0.00 430	0.00 426	0.00 425	0.00 424	0.00 422

WAC-MVS						37.39 413								52.61 363

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

MSC_two_6792asdad				88.81 71	91.55 129	77.99 94		91.01 146					96.05 9	87.45 23	98.17 35	92.40 178

No_MVS				88.81 71	91.55 129	77.99 94		91.01 146					96.05 9	87.45 23	98.17 35	92.40 178

eth-test2					0.00 430

eth-test					0.00 430

IU-MVS					94.18 50	72.64 147		90.82 151	56.98 347	89.67 109				85.78 52	97.92 49	93.28 138

save fliter					93.75 63	77.44 103	86.31 135	89.72 185	70.80 218

test_0728_SECOND				86.79 102	94.25 48	72.45 155	90.54 52	94.10 39					95.88 18	86.42 39	97.97 46	92.02 197

GSMVS																83.88 336

test_part2					93.86 61	77.77 98				92.84 51

MTGPA								91.81 125

MTMP							90.66 48	33.14 424

gm-plane-assit					75.42 387	44.97 397			52.17 372		72.36 403		87.90 265	54.10 352

test9_res														80.83 105	96.45 103	90.57 239

agg_prior2														79.68 118	96.16 115	90.22 247

agg_prior					91.58 127	77.69 100		90.30 170		84.32 226			93.18 135

test_prior4						78.97 84	84.59 165

test_prior				86.32 110	90.59 155	71.99 162		92.85 92					94.17 96			92.80 157

旧先验2							81.73 238		56.88 348	86.54 182			84.90 312	72.81 208

新几何2							81.72 239

无先验							82.81 214	85.62 250	58.09 336				91.41 186	67.95 256		84.48 327

原ACMM2							82.26 232

testdata2													86.43 289	63.52 293

testdata1							79.62 264		73.95 168

plane_prior7					93.45 68	77.31 106

plane_prior5								93.61 59					95.22 59	80.78 106	95.83 134	94.46 81

plane_prior4											92.95 134

plane_prior3						76.85 111			77.79 125	86.55 177

plane_prior2							89.45 82		79.44 101

plane_prior1					92.83 88

plane_prior						76.42 116	87.15 117		75.94 145						95.03 162

n2								0.00 431

nn								0.00 431

door-mid								74.45 342

test11								91.46 131

door								72.57 357

HQP5-MVS						70.66 175

HQP-NCC					91.19 139		84.77 159		73.30 182	80.55 293

ACMP_Plane					91.19 139		84.77 159		73.30 182	80.55 293

BP-MVS														77.30 151

HQP4-MVS										80.56 292			94.61 79			93.56 130

HQP3-MVS								92.68 97							94.47 183

NP-MVS					91.95 112	74.55 129					90.17 225

ACMMP++_ref															95.74 140

ACMMP++															97.35 75

High-resolution multi-view benchmark