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 33	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 14	85.07 55	99.27 1	99.54 1

XVG-OURS-SEG-HR		89.59 51	89.37 57	90.28 42	94.47 42	85.95 23	86.84 118	93.91 41	80.07 89	86.75 164	93.26 115	93.64 2	90.93 193	84.60 62	90.75 263	93.97 105

ACMH+	77.89 11	90.73 27	91.50 21	88.44 76	93.00 79	76.26 116	89.65 70	95.55 7	87.72 21	93.89 26	94.94 48	91.62 3	93.44 123	78.35 126	98.76 3	95.61 48

LPG-MVS_test		91.47 17	91.68 16	90.82 33	94.75 40	81.69 59	90.00 57	94.27 19	82.35 63	93.67 33	94.82 52	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

LGP-MVS_train				90.82 33	94.75 40	81.69 59		94.27 19	82.35 63	93.67 33	94.82 52	91.18 4	95.52 42	85.36 52	98.73 6	95.23 59

PMVS	80.48 6	90.08 37	90.66 44	88.34 79	96.71 3	92.97 1	90.31 54	89.57 181	88.51 17	90.11 95	95.12 45	90.98 6	88.92 247	77.55 140	97.07 82	83.13 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMM	79.39 9	90.65 28	90.99 37	89.63 55	95.03 33	83.53 47	89.62 71	93.35 60	79.20 100	93.83 27	93.60 111	90.81 7	92.96 138	85.02 57	98.45 18	92.41 172
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	76.49 14	89.34 55	91.14 31	83.96 162	92.50 92	70.36 175	89.55 72	93.84 46	81.89 68	94.70 13	95.44 34	90.69 8	88.31 257	83.33 71	98.30 24	93.20 141
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		92.50 4	92.54 5	92.37 5	95.93 15	85.81 29	92.99 12	94.23 22	85.21 35	92.51 55	95.13 44	90.65 9	95.34 52	88.06 8	98.15 34	95.95 41

RE-MVS-def			92.61 4		94.13 51	88.95 5	92.87 13	94.16 27	88.75 14	93.79 28	94.43 68	90.64 10		87.16 29	97.60 64	92.73 158

ACMP	79.16 10	90.54 31	90.60 45	90.35 41	94.36 43	80.98 65	89.16 81	94.05 36	79.03 103	92.87 46	93.74 107	90.60 11	95.21 58	82.87 78	98.76 3	94.87 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		92.13 7	92.20 9	91.91 15	95.58 25	84.67 42	93.51 8	94.85 14	82.88 59	91.77 68	93.94 99	90.55 12	95.73 31	88.50 6	98.23 27	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 61	90.72 43	84.31 155	97.00 2	64.33 231	89.67 69	88.38 196	88.84 13	94.29 18	97.57 3	90.48 13	91.26 183	72.57 200	97.65 60	97.34 15

SED-MVS		90.46 33	91.64 17	86.93 97	94.18 46	72.65 141	90.47 51	93.69 50	83.77 47	94.11 22	94.27 75	90.28 14	95.84 23	86.03 46	97.92 46	92.29 179

test_241102_ONE					94.18 46	72.65 141		93.69 50	83.62 49	94.11 22	93.78 105	90.28 14	95.50 46

SR-MVS		92.23 6	92.34 7	91.91 15	94.89 37	87.85 8	92.51 23	93.87 45	88.20 19	93.24 39	94.02 91	90.15 16	95.67 34	86.82 33	97.34 74	92.19 185

APD-MVS_3200maxsize		92.05 8	92.24 8	91.48 21	93.02 78	85.17 35	92.47 25	95.05 13	87.65 22	93.21 40	94.39 73	90.09 17	95.08 61	86.67 35	97.60 64	94.18 95

DVP-MVS		90.06 39	91.32 28	86.29 109	94.16 49	72.56 147	90.54 48	91.01 139	83.61 50	93.75 30	94.65 57	89.76 18	95.78 28	86.42 36	97.97 43	90.55 231
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 49	72.56 147	90.63 45	93.90 42	83.61 50	93.75 30	94.49 65	89.76 18

COLMAP_ROB	83.01 3	91.97 9	91.95 10	92.04 10	93.68 62	86.15 20	93.37 10	95.10 12	90.28 9	92.11 61	95.03 46	89.75 20	94.93 65	79.95 109	98.27 25	95.04 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_241102_TWO								93.71 49	83.77 47	93.49 36	94.27 75	89.27 21	95.84 23	86.03 46	97.82 51	92.04 190

tt0805		88.09 74	89.79 51	82.98 187	93.26 73	63.94 235	91.10 41	89.64 178	85.07 36	90.91 85	91.09 182	89.16 22	91.87 169	82.03 89	95.87 130	93.13 144

ACMMP		91.91 10	91.87 15	92.03 11	95.53 26	85.91 24	93.35 11	94.16 27	82.52 62	92.39 58	94.14 85	89.15 23	95.62 35	87.35 24	98.24 26	94.56 76
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 5	92.41 6	92.39 4	94.13 51	88.95 5	92.87 13	94.16 27	88.75 14	93.79 28	94.43 68	88.83 24	95.51 44	87.16 29	97.60 64	92.73 158

APDe-MVS		91.22 21	91.92 11	89.14 64	92.97 80	78.04 89	92.84 15	94.14 31	83.33 53	93.90 24	95.73 27	88.77 25	96.41 2	87.60 18	97.98 42	92.98 152
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_one_0601					93.85 58	73.27 136		94.11 33	86.57 25	93.47 38	94.64 60	88.42 26

ACMMP_NAP		90.65 28	91.07 35	89.42 59	95.93 15	79.54 76	89.95 61	93.68 52	77.65 119	91.97 65	94.89 49	88.38 27	95.45 48	89.27 3	97.87 50	93.27 138

MP-MVS-pluss		90.81 26	91.08 33	89.99 46	95.97 13	79.88 71	88.13 99	94.51 17	75.79 140	92.94 44	94.96 47	88.36 28	95.01 63	90.70 2	98.40 19	95.09 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		91.30 19	91.39 23	91.02 29	95.43 28	84.66 43	92.58 21	93.29 66	81.99 65	91.47 71	93.96 96	88.35 29	95.56 39	87.74 13	97.74 57	92.85 155

CP-MVS		91.67 12	91.58 19	91.96 12	95.29 30	87.62 9	93.38 9	93.36 59	83.16 55	91.06 81	94.00 92	88.26 30	95.71 32	87.28 27	98.39 20	92.55 167

SteuartSystems-ACMMP		91.16 23	91.36 24	90.55 37	93.91 56	80.97 66	91.49 37	93.48 57	82.82 60	92.60 54	93.97 93	88.19 31	96.29 5	87.61 17	98.20 31	94.39 87
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		91.20 22	90.95 39	91.93 13	95.67 22	85.85 27	90.00 57	93.90 42	80.32 85	91.74 69	94.41 71	88.17 32	95.98 11	86.37 38	97.99 40	93.96 106

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 47	96.29 16	88.16 33	94.17 92	86.07 45	98.48 17	97.22 19

DPE-MVS		90.53 32	91.08 33	88.88 67	93.38 69	78.65 83	89.15 82	94.05 36	84.68 40	93.90 24	94.11 88	88.13 34	96.30 4	84.51 63	97.81 52	91.70 201
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		89.80 47	89.97 48	89.27 61	94.76 39	79.86 72	86.76 122	92.78 89	78.78 106	92.51 55	93.64 110	88.13 34	93.84 104	84.83 60	97.55 67	94.10 101
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs6		86.52 95	88.06 74	81.90 207	92.22 102	62.28 258	84.66 154	89.15 186	83.54 52	89.85 103	97.32 4	88.08 36	86.80 275	70.43 217	97.30 76	96.62 28

mvs_tets		89.78 48	89.27 59	91.30 25	93.51 65	84.79 40	89.89 63	90.63 149	70.00 218	94.55 15	96.67 11	87.94 37	93.59 115	84.27 65	95.97 123	95.52 49

ZNCC-MVS		91.26 20	91.34 27	91.01 30	95.73 20	83.05 52	92.18 28	94.22 24	80.14 88	91.29 76	93.97 93	87.93 38	95.87 19	88.65 4	97.96 45	94.12 99

region2R		91.44 18	91.30 30	91.87 17	95.75 18	85.90 25	92.63 20	93.30 65	81.91 67	90.88 87	94.21 80	87.75 39	95.87 19	87.60 18	97.71 58	93.83 112

wuyk23d		75.13 267	79.30 220	62.63 361	75.56 362	75.18 124	80.89 239	73.10 342	75.06 150	94.76 12	95.32 35	87.73 40	52.85 391	34.16 391	97.11 80	59.85 388

mPP-MVS		91.69 11	91.47 22	92.37 5	96.04 12	88.48 7	92.72 17	92.60 93	83.09 56	91.54 70	94.25 79	87.67 41	95.51 44	87.21 28	98.11 35	93.12 146

ACMMPR		91.49 15	91.35 26	91.92 14	95.74 19	85.88 26	92.58 21	93.25 67	81.99 65	91.40 72	94.17 84	87.51 42	95.87 19	87.74 13	97.76 55	93.99 103

test_0728_THIRD									85.33 33	93.75 30	94.65 57	87.44 43	95.78 28	87.41 22	98.21 29	92.98 152

9.14			89.29 58		91.84 117		88.80 88	95.32 11	75.14 149	91.07 80	92.89 129	87.27 44	93.78 105	83.69 70	97.55 67

PS-CasMVS		90.06 39	91.92 11	84.47 148	96.56 6	58.83 301	89.04 83	92.74 90	91.40 5	96.12 4	96.06 22	87.23 45	95.57 38	79.42 118	98.74 5	99.00 2

GST-MVS		90.96 25	91.01 36	90.82 33	95.45 27	82.73 55	91.75 35	93.74 48	80.98 79	91.38 73	93.80 103	87.20 46	95.80 25	87.10 31	97.69 59	93.93 107

PEN-MVS		90.03 41	91.88 14	84.48 147	96.57 5	58.88 298	88.95 84	93.19 69	91.62 4	96.01 6	96.16 20	87.02 47	95.60 36	78.69 123	98.72 8	98.97 3

DTE-MVSNet		89.98 43	91.91 13	84.21 157	96.51 7	57.84 308	88.93 85	92.84 87	91.92 3	96.16 3	96.23 18	86.95 48	95.99 10	79.05 120	98.57 14	98.80 6

SF-MVS		90.27 35	90.80 42	88.68 74	92.86 84	77.09 104	91.19 40	95.74 5	81.38 73	92.28 59	93.80 103	86.89 49	94.64 73	85.52 51	97.51 71	94.30 91

MP-MVS		91.14 24	90.91 40	91.83 18	96.18 10	86.88 13	92.20 27	93.03 80	82.59 61	88.52 130	94.37 74	86.74 50	95.41 50	86.32 39	98.21 29	93.19 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		91.52 14	91.60 18	91.29 26	96.59 4	86.29 17	92.02 30	91.81 118	84.07 44	92.00 64	94.40 72	86.63 51	95.28 55	88.59 5	98.31 23	92.30 178

XVS		91.54 13	91.36 24	92.08 8	95.64 23	86.25 18	92.64 18	93.33 61	85.07 36	89.99 99	94.03 90	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

X-MVStestdata		85.04 119	82.70 167	92.08 8	95.64 23	86.25 18	92.64 18	93.33 61	85.07 36	89.99 99	16.05 395	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

canonicalmvs		85.50 110	86.14 105	83.58 173	87.97 207	67.13 202	87.55 106	94.32 18	73.44 168	88.47 131	87.54 255	86.45 54	91.06 190	75.76 161	93.76 197	92.54 168

TranMVSNet+NR-MVSNet		87.86 79	88.76 69	85.18 133	94.02 54	64.13 232	84.38 161	91.29 131	84.88 39	92.06 63	93.84 102	86.45 54	93.73 106	73.22 191	98.66 10	97.69 9

test_0402		88.65 65	89.58 56	85.88 121	92.55 90	72.22 155	84.01 168	89.44 183	88.63 16	94.38 17	95.77 26	86.38 56	93.59 115	79.84 110	95.21 152	91.82 197

APD-MVS		89.54 52	89.63 54	89.26 62	92.57 89	81.34 64	90.19 56	93.08 76	80.87 81	91.13 79	93.19 116	86.22 57	95.97 12	82.23 88	97.18 79	90.45 233
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.96 63	89.88 49	86.22 112	91.63 121	77.07 105	89.82 64	93.77 47	78.90 104	92.88 45	92.29 149	86.11 58	90.22 214	86.24 43	97.24 77	91.36 209
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					92.22 102	80.48 67		91.85 114	71.22 204	90.38 91	92.98 124	86.06 59	96.11 6	81.99 91	96.75 91

jajsoiax		89.41 53	88.81 68	91.19 28	93.38 69	84.72 41	89.70 66	90.29 163	69.27 222	94.39 16	96.38 15	86.02 60	93.52 119	83.96 67	95.92 128	95.34 53

nrg030		87.85 80	88.49 70	85.91 119	90.07 164	69.73 179	87.86 103	94.20 25	74.04 158	92.70 53	94.66 56	85.88 61	91.50 175	79.72 113	97.32 75	96.50 31

SMA-MVS		90.31 34	90.48 46	89.83 50	95.31 29	79.52 77	90.98 43	93.24 68	75.37 147	92.84 48	95.28 38	85.58 62	96.09 7	87.92 10	97.76 55	93.88 110
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 60	89.08 62	89.37 60	93.64 63	79.07 79	88.54 94	94.20 25	73.53 166	89.71 106	94.82 52	85.09 63	95.77 30	84.17 66	98.03 38	93.26 139
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 56	89.12 60	89.84 48	88.67 192	85.64 31	90.61 46	93.17 70	86.02 29	93.12 41	95.30 36	84.94 64	89.44 238	74.12 176	96.10 118	94.45 82

APD_test2		89.30 56	89.12 60	89.84 48	88.67 192	85.64 31	90.61 46	93.17 70	86.02 29	93.12 41	95.30 36	84.94 64	89.44 238	74.12 176	96.10 118	94.45 82

GeoE		85.45 112	85.81 112	84.37 149	90.08 162	67.07 203	85.86 134	91.39 128	72.33 192	87.59 147	90.25 210	84.85 66	92.37 154	78.00 134	91.94 238	93.66 121

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 20	93.73 60	85.72 30	96.79 1	95.51 8	88.86 12	95.63 8	96.99 8	84.81 67	93.16 132	91.10 1	97.53 70	96.58 30
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 66	89.01 63	87.36 91	91.30 134	77.50 97	87.55 106	92.97 83	87.95 20	89.62 110	92.87 130	84.56 68	93.89 101	77.65 138	96.62 94	90.70 225

LS3D		90.60 30	90.34 47	91.38 24	89.03 183	84.23 45	93.58 6	94.68 16	90.65 7	90.33 93	93.95 98	84.50 69	95.37 51	80.87 99	95.50 143	94.53 79

EC-MVSNet		88.01 75	88.32 72	87.09 93	89.28 177	72.03 157	90.31 54	96.31 3	80.88 80	85.12 196	89.67 221	84.47 70	95.46 47	82.56 83	96.26 111	93.77 118

casdiffmvs_mvg		86.72 91	87.51 82	84.36 151	87.09 230	65.22 222	84.16 163	94.23 22	77.89 116	91.28 77	93.66 109	84.35 71	92.71 144	80.07 106	94.87 170	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 49	88.88 66	92.27 7	89.82 169	86.67 14	90.51 50	90.20 166	69.87 219	95.06 11	96.14 21	84.28 72	93.07 136	87.68 15	96.34 106	97.09 21

OMC-MVS		88.19 71	87.52 81	90.19 44	91.94 112	81.68 61	87.49 108	93.17 70	76.02 134	88.64 127	91.22 177	84.24 73	93.37 126	77.97 136	97.03 83	95.52 49

CS-MVS		88.14 72	87.67 80	89.54 58	89.56 171	79.18 78	90.47 51	94.77 15	79.37 98	84.32 215	89.33 227	83.87 74	94.53 79	82.45 84	94.89 167	94.90 65

XVG-OURS		89.18 59	88.83 67	90.23 43	94.28 44	86.11 22	85.91 132	93.60 55	80.16 87	89.13 121	93.44 113	83.82 75	90.98 191	83.86 69	95.30 151	93.60 126

XVG-ACMP-BASELINE		89.98 43	89.84 50	90.41 39	94.91 36	84.50 44	89.49 76	93.98 38	79.68 92	92.09 62	93.89 101	83.80 76	93.10 135	82.67 82	98.04 36	93.64 124

CDPH-MVS		86.17 103	85.54 117	88.05 84	92.25 100	75.45 122	83.85 174	92.01 107	65.91 255	86.19 178	91.75 165	83.77 77	94.98 64	77.43 143	96.71 92	93.73 119

test_fmvsmvis_n_1920		85.22 114	85.36 121	84.81 138	85.80 260	76.13 119	85.15 147	92.32 99	61.40 294	91.33 74	90.85 193	83.76 78	86.16 287	84.31 64	93.28 208	92.15 187

Effi-MVS+		83.90 149	84.01 147	83.57 174	87.22 224	65.61 220	86.55 127	92.40 96	78.64 109	81.34 270	84.18 307	83.65 79	92.93 140	74.22 173	87.87 296	92.17 186

MVS_111021_HR		84.63 126	84.34 143	85.49 130	90.18 161	75.86 120	79.23 263	87.13 216	73.35 169	85.56 191	89.34 226	83.60 80	90.50 208	76.64 151	94.05 193	90.09 242

UA-Net		91.49 15	91.53 20	91.39 23	94.98 34	82.95 54	93.52 7	92.79 88	88.22 18	88.53 129	97.64 2	83.45 81	94.55 78	86.02 48	98.60 12	96.67 27

AdaColmap		83.66 152	83.69 152	83.57 174	90.05 165	72.26 154	86.29 130	90.00 171	78.19 114	81.65 264	87.16 263	83.40 82	94.24 87	61.69 294	94.76 175	84.21 312

LCM-MVSNet-Re		83.48 157	85.06 124	78.75 254	85.94 258	55.75 324	80.05 247	94.27 19	76.47 129	96.09 5	94.54 63	83.31 83	89.75 233	59.95 304	94.89 167	90.75 222

test_fmvsmconf0.01_n		86.68 92	86.52 98	87.18 92	85.94 258	78.30 85	86.93 116	92.20 102	65.94 253	89.16 119	93.16 118	83.10 84	89.89 227	87.81 11	94.43 182	93.35 134

TransMVSNet (Re)		84.02 145	85.74 114	78.85 252	91.00 144	55.20 329	82.29 218	87.26 212	79.65 93	88.38 134	95.52 33	83.00 85	86.88 273	67.97 244	96.60 95	94.45 82

CNVR-MVS		87.81 81	87.68 79	88.21 81	92.87 82	77.30 103	85.25 144	91.23 133	77.31 124	87.07 158	91.47 171	82.94 86	94.71 70	84.67 61	96.27 110	92.62 165

DeepPCF-MVS	81.24 5	87.28 84	86.21 104	90.49 38	91.48 131	84.90 38	83.41 186	92.38 98	70.25 215	89.35 118	90.68 199	82.85 87	94.57 76	79.55 115	95.95 125	92.00 192

v7n		90.13 36	90.96 38	87.65 89	91.95 110	71.06 169	89.99 59	93.05 77	86.53 26	94.29 18	96.27 17	82.69 88	94.08 95	86.25 42	97.63 61	97.82 8

AllTest		87.97 77	87.40 85	89.68 53	91.59 122	83.40 48	89.50 75	95.44 9	79.47 94	88.00 141	93.03 122	82.66 89	91.47 176	70.81 209	96.14 115	94.16 96

TestCases				89.68 53	91.59 122	83.40 48		95.44 9	79.47 94	88.00 141	93.03 122	82.66 89	91.47 176	70.81 209	96.14 115	94.16 96

test_fmvsmconf0.1_n		86.18 102	85.88 110	87.08 94	85.26 266	78.25 86	85.82 135	91.82 116	65.33 266	88.55 128	92.35 148	82.62 91	89.80 229	86.87 32	94.32 185	93.18 143

RPSCF		88.00 76	86.93 93	91.22 27	90.08 162	89.30 4	89.68 68	91.11 136	79.26 99	89.68 107	94.81 55	82.44 92	87.74 261	76.54 153	88.74 285	96.61 29

CS-MVS-test		87.00 86	86.43 100	88.71 72	89.46 173	77.46 98	89.42 79	95.73 6	77.87 117	81.64 265	87.25 261	82.43 93	94.53 79	77.65 138	96.46 102	94.14 98

ITE_SJBPF				90.11 45	90.72 150	84.97 37		90.30 161	81.56 71	90.02 98	91.20 179	82.40 94	90.81 199	73.58 186	94.66 176	94.56 76

SDMVSNet		81.90 186	83.17 159	78.10 267	88.81 189	62.45 254	76.08 309	86.05 234	73.67 163	83.41 234	93.04 120	82.35 95	80.65 328	70.06 220	95.03 160	91.21 211

test_fmvsmconf_n		85.88 107	85.51 118	86.99 96	84.77 273	78.21 87	85.40 143	91.39 128	65.32 267	87.72 145	91.81 162	82.33 96	89.78 230	86.68 34	94.20 189	92.99 151

Fast-Effi-MVS+		81.04 195	80.57 200	82.46 202	87.50 219	63.22 242	78.37 275	89.63 179	68.01 237	81.87 258	82.08 330	82.31 97	92.65 147	67.10 246	88.30 292	91.51 207

baseline		85.20 116	85.93 107	83.02 186	86.30 247	62.37 256	84.55 156	93.96 39	74.48 155	87.12 153	92.03 154	82.30 98	91.94 165	78.39 124	94.21 188	94.74 73

casdiffmvs		85.21 115	85.85 111	83.31 180	86.17 253	62.77 248	83.03 196	93.93 40	74.69 153	88.21 137	92.68 137	82.29 99	91.89 168	77.87 137	93.75 199	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 67	89.62 55	84.73 142	90.46 155	65.27 221	88.86 86	93.02 81	87.15 23	93.05 43	97.10 6	82.28 100	92.02 164	76.70 150	97.99 40	96.88 25

APD_test1		88.40 67	87.91 75	89.88 47	89.50 172	86.65 16	89.98 60	91.91 112	84.26 42	90.87 88	93.92 100	82.18 101	89.29 242	73.75 183	94.81 171	93.70 120

Anonymous20240529		86.20 101	87.13 87	83.42 177	90.19 160	64.55 229	84.55 156	90.71 146	85.85 31	89.94 102	95.24 40	82.13 102	90.40 210	69.19 229	96.40 105	95.31 55

CLD-MVS		83.18 162	82.64 169	84.79 139	89.05 182	67.82 200	77.93 279	92.52 94	68.33 233	85.07 197	81.54 336	82.06 103	92.96 138	69.35 225	97.91 48	93.57 128
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 96	79.70 74	83.94 170	90.32 158	65.41 265	84.49 209	90.97 186	82.03 104	93.63 110

segment_acmp												81.94 105

train_agg		85.98 105	85.28 122	88.07 83	92.34 96	79.70 74	83.94 170	90.32 158	65.79 256	84.49 209	90.97 186	81.93 106	93.63 110	81.21 95	96.54 97	90.88 219

test_8					92.09 106	78.87 81	83.82 175	90.31 160	65.79 256	84.36 213	90.96 188	81.93 106	93.44 123

test_prior2							83.37 187		75.43 145	84.58 207	91.57 168	81.92 108		79.54 116	96.97 84

EGC-MVSNET		74.79 274	69.99 314	89.19 63	94.89 37	87.00 11	91.89 34	86.28 229	1.09 396	2.23 398	95.98 23	81.87 109	89.48 234	79.76 112	95.96 124	91.10 214

CP-MVSNet		89.27 58	90.91 40	84.37 149	96.34 8	58.61 304	88.66 92	92.06 106	90.78 6	95.67 7	95.17 43	81.80 110	95.54 41	79.00 121	98.69 9	98.95 4

MVS_111021_LR		84.28 136	83.76 151	85.83 123	89.23 179	83.07 51	80.99 238	83.56 267	72.71 184	86.07 181	89.07 232	81.75 111	86.19 286	77.11 147	93.36 204	88.24 266

test_djsdf		89.62 50	89.01 63	91.45 22	92.36 95	82.98 53	91.98 31	90.08 169	71.54 199	94.28 20	96.54 13	81.57 112	94.27 84	86.26 40	96.49 100	97.09 21

cdsmvs_eth3d_5k		20.81 362	27.75 365	0.00 382	0.00 404	0.00 407	0.00 393	85.44 241	0.00 400	0.00 401	82.82 322	81.46 113	0.00 401	0.00 400	0.00 399	0.00 397

WR-MVS_H		89.91 46	91.31 29	85.71 125	96.32 9	62.39 255	89.54 74	93.31 64	90.21 10	95.57 9	95.66 29	81.42 114	95.90 15	80.94 98	98.80 2	98.84 5

CPTT-MVS		89.39 54	88.98 65	90.63 36	95.09 32	86.95 12	92.09 29	92.30 100	79.74 91	87.50 149	92.38 144	81.42 114	93.28 128	83.07 75	97.24 77	91.67 202

pm-mvs1		83.69 151	84.95 127	79.91 239	90.04 166	59.66 288	82.43 214	87.44 209	75.52 144	87.85 143	95.26 39	81.25 116	85.65 297	68.74 236	96.04 120	94.42 85

DVP-MVS++		90.07 38	91.09 32	87.00 95	91.55 127	72.64 143	96.19 2	94.10 34	85.33 33	93.49 36	94.64 60	81.12 117	95.88 17	87.41 22	95.94 126	92.48 169

OPU-MVS				88.27 80	91.89 113	77.83 93	90.47 51				91.22 177	81.12 117	94.68 71	74.48 171	95.35 146	92.29 179

sd_testset		79.95 219	81.39 191	75.64 299	88.81 189	58.07 306	76.16 308	82.81 274	73.67 163	83.41 234	93.04 120	80.96 119	77.65 339	58.62 310	95.03 160	91.21 211

NCCC		87.36 83	86.87 94	88.83 68	92.32 98	78.84 82	86.58 126	91.09 137	78.77 107	84.85 204	90.89 190	80.85 120	95.29 53	81.14 96	95.32 148	92.34 176

TAPA-MVS	77.73 12	85.71 109	84.83 128	88.37 78	88.78 191	79.72 73	87.15 112	93.50 56	69.17 223	85.80 187	89.56 222	80.76 121	92.13 160	73.21 196	95.51 142	93.25 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+-dtu		82.54 171	81.41 190	85.90 120	85.60 261	76.53 111	83.07 195	89.62 180	73.02 179	79.11 298	83.51 312	80.74 122	90.24 213	68.76 235	89.29 276	90.94 217

PC_three_1452									58.96 314	90.06 96	91.33 174	80.66 123	93.03 137	75.78 160	95.94 126	92.48 169

VPA-MVSNet		83.47 158	84.73 129	79.69 243	90.29 158	57.52 311	81.30 234	88.69 192	76.29 130	87.58 148	94.44 67	80.60 124	87.20 268	66.60 252	96.82 89	94.34 89

ETV-MVS		84.31 134	83.91 150	85.52 128	88.58 196	70.40 174	84.50 160	93.37 58	78.76 108	84.07 224	78.72 358	80.39 125	95.13 60	73.82 182	92.98 216	91.04 215

HPM-MVS++		88.93 64	88.45 71	90.38 40	94.92 35	85.85 27	89.70 66	91.27 132	78.20 113	86.69 167	92.28 150	80.36 126	95.06 62	86.17 44	96.49 100	90.22 237

ANet_high		83.17 163	85.68 115	75.65 298	81.24 312	45.26 377	79.94 249	92.91 84	83.83 46	91.33 74	96.88 10	80.25 127	85.92 290	68.89 233	95.89 129	95.76 43

EI-MVSNet-Vis-set		85.12 118	84.53 136	86.88 98	84.01 285	72.76 140	83.91 173	85.18 246	80.44 82	88.75 125	85.49 286	80.08 128	91.92 166	82.02 90	90.85 261	95.97 39

DeepC-MVS_fast	80.27 8	86.23 99	85.65 116	87.96 85	91.30 134	76.92 106	87.19 110	91.99 108	70.56 209	84.96 200	90.69 198	80.01 129	95.14 59	78.37 125	95.78 137	91.82 197
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		85.04 119	84.44 138	86.85 99	83.87 288	72.52 149	83.82 175	85.15 247	80.27 86	88.75 125	85.45 288	79.95 130	91.90 167	81.92 92	90.80 262	96.13 34

MCST-MVS		84.36 132	83.93 149	85.63 126	91.59 122	71.58 164	83.52 183	92.13 104	61.82 287	83.96 226	89.75 220	79.93 131	93.46 122	78.33 127	94.34 184	91.87 196

TSAR-MVS + MP.		88.14 72	87.82 78	89.09 65	95.72 21	76.74 108	92.49 24	91.19 135	67.85 242	86.63 168	94.84 51	79.58 132	95.96 13	87.62 16	94.50 179	94.56 76
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				86.57 103	90.74 149	72.63 145		90.69 147		82.76 244		79.20 133	94.80 68		95.32 148	92.27 181

CSCG		86.26 98	86.47 99	85.60 127	90.87 147	74.26 129	87.98 101	91.85 114	80.35 84	89.54 116	88.01 245	79.09 134	92.13 160	75.51 162	95.06 159	90.41 234

Test By Simon												79.09 134

PHI-MVS		86.38 96	85.81 112	88.08 82	88.44 200	77.34 101	89.35 80	93.05 77	73.15 177	84.76 205	87.70 252	78.87 136	94.18 90	80.67 103	96.29 107	92.73 158

EG-PatchMatch MVS		84.08 143	84.11 145	83.98 161	92.22 102	72.61 146	82.20 224	87.02 221	72.63 185	88.86 122	91.02 184	78.52 137	91.11 188	73.41 188	91.09 251	88.21 267

dcpmvs_2		84.23 139	85.14 123	81.50 215	88.61 195	61.98 262	82.90 201	93.11 73	68.66 231	92.77 51	92.39 143	78.50 138	87.63 263	76.99 149	92.30 226	94.90 65

Effi-MVS+-dtu		85.82 108	83.38 154	93.14 3	87.13 226	91.15 2	87.70 105	88.42 195	74.57 154	83.56 232	85.65 284	78.49 139	94.21 88	72.04 203	92.88 218	94.05 102

Vis-MVSNet		86.86 88	86.58 97	87.72 86	92.09 106	77.43 100	87.35 109	92.09 105	78.87 105	84.27 220	94.05 89	78.35 140	93.65 108	80.54 105	91.58 245	92.08 189
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 89	87.06 89	86.17 115	92.86 84	67.02 204	82.55 210	91.56 121	83.08 57	90.92 83	91.82 161	78.25 141	93.99 97	74.16 174	98.35 21	97.49 13

MSLP-MVS++		85.00 121	86.03 106	81.90 207	91.84 117	71.56 166	86.75 123	93.02 81	75.95 137	87.12 153	89.39 225	77.98 142	89.40 241	77.46 141	94.78 172	84.75 305

API-MVS		82.28 174	82.61 170	81.30 217	86.29 248	69.79 177	88.71 90	87.67 208	78.42 112	82.15 253	84.15 308	77.98 142	91.59 174	65.39 263	92.75 220	82.51 338

DP-MVS Recon		84.05 144	83.22 156	86.52 105	91.73 120	75.27 123	83.23 192	92.40 96	72.04 196	82.04 255	88.33 241	77.91 144	93.95 99	66.17 254	95.12 157	90.34 236

fmvsm_s_conf0.1_n_a		82.58 170	81.93 179	84.50 146	87.68 214	73.35 133	86.14 131	77.70 305	61.64 292	85.02 198	91.62 167	77.75 145	86.24 283	82.79 80	87.07 305	93.91 109

UniMVSNet (Re)		86.87 87	86.98 92	86.55 104	93.11 77	68.48 192	83.80 177	92.87 85	80.37 83	89.61 112	91.81 162	77.72 146	94.18 90	75.00 169	98.53 15	96.99 24

PCF-MVS	74.62 15	82.15 179	80.92 199	85.84 122	89.43 174	72.30 153	80.53 242	91.82 116	57.36 326	87.81 144	89.92 217	77.67 147	93.63 110	58.69 309	95.08 158	91.58 205
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		86.00 104	86.22 103	85.34 131	93.24 74	64.56 228	82.21 222	90.46 153	80.99 78	88.42 132	91.97 155	77.56 148	93.85 102	72.46 201	98.65 11	97.61 10

3Dnovator+	83.92 2	89.97 45	89.66 53	90.92 31	91.27 136	81.66 62	91.25 38	94.13 32	88.89 11	88.83 124	94.26 78	77.55 149	95.86 22	84.88 59	95.87 130	95.24 58

MVS_Test		82.47 172	83.22 156	80.22 236	82.62 300	57.75 310	82.54 211	91.96 110	71.16 205	82.89 242	92.52 142	77.41 150	90.50 208	80.04 108	87.84 297	92.40 173

fmvsm_s_conf0.5_n_a		82.21 176	81.51 189	84.32 154	86.56 238	73.35 133	85.46 140	77.30 309	61.81 288	84.51 208	90.88 192	77.36 151	86.21 285	82.72 81	86.97 309	93.38 133

EIA-MVS		82.19 177	81.23 195	85.10 134	87.95 208	69.17 189	83.22 193	93.33 61	70.42 211	78.58 301	79.77 352	77.29 152	94.20 89	71.51 205	88.96 281	91.93 195

xiu_mvs_v2_base		77.19 245	76.75 248	78.52 258	87.01 232	61.30 267	75.55 316	87.12 219	61.24 298	74.45 335	78.79 357	77.20 153	90.93 193	64.62 273	84.80 335	83.32 326

DU-MVS		86.80 90	86.99 91	86.21 113	93.24 74	67.02 204	83.16 194	92.21 101	81.73 69	90.92 83	91.97 155	77.20 153	93.99 97	74.16 174	98.35 21	97.61 10

Baseline_NR-MVSNet		84.00 146	85.90 109	78.29 264	91.47 132	53.44 338	82.29 218	87.00 224	79.06 102	89.55 114	95.72 28	77.20 153	86.14 288	72.30 202	98.51 16	95.28 56

TinyColmap		81.25 192	82.34 175	77.99 270	85.33 265	60.68 279	82.32 217	88.33 199	71.26 203	86.97 160	92.22 153	77.10 156	86.98 272	62.37 286	95.17 154	86.31 289

F-COLMAP		84.97 122	83.42 153	89.63 55	92.39 94	83.40 48	88.83 87	91.92 111	73.19 176	80.18 288	89.15 231	77.04 157	93.28 128	65.82 260	92.28 229	92.21 184

114514_t		83.10 165	82.54 172	84.77 141	92.90 81	69.10 190	86.65 124	90.62 150	54.66 336	81.46 267	90.81 195	76.98 158	94.38 83	72.62 199	96.18 113	90.82 221

xiu_mvs_v1_base_debu		80.84 197	80.14 211	82.93 190	88.31 201	71.73 160	79.53 254	87.17 213	65.43 262	79.59 290	82.73 324	76.94 159	90.14 219	73.22 191	88.33 288	86.90 284

xiu_mvs_v1_base		80.84 197	80.14 211	82.93 190	88.31 201	71.73 160	79.53 254	87.17 213	65.43 262	79.59 290	82.73 324	76.94 159	90.14 219	73.22 191	88.33 288	86.90 284

xiu_mvs_v1_base_debi		80.84 197	80.14 211	82.93 190	88.31 201	71.73 160	79.53 254	87.17 213	65.43 262	79.59 290	82.73 324	76.94 159	90.14 219	73.22 191	88.33 288	86.90 284

pcd_1.5k_mvsjas		6.41 365	8.55 368	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	76.94 159	0.00 401	0.00 400	0.00 399	0.00 397

PS-MVSNAJss		88.31 69	87.90 76	89.56 57	93.31 71	77.96 92	87.94 102	91.97 109	70.73 208	94.19 21	96.67 11	76.94 159	94.57 76	83.07 75	96.28 108	96.15 33

PS-MVSNAJ		77.04 247	76.53 250	78.56 257	87.09 230	61.40 265	75.26 318	87.13 216	61.25 297	74.38 337	77.22 368	76.94 159	90.94 192	64.63 272	84.83 334	83.35 325

MIMVSNet1		83.63 153	84.59 134	80.74 227	94.06 53	62.77 248	82.72 204	84.53 259	77.57 121	90.34 92	95.92 24	76.88 165	85.83 295	61.88 292	97.42 72	93.62 125

原ACMM1				84.60 145	92.81 87	74.01 130		91.50 123	62.59 279	82.73 245	90.67 201	76.53 166	94.25 86	69.24 226	95.69 140	85.55 296

fmvsm_s_conf0.1_n		82.17 178	81.59 186	83.94 164	86.87 236	71.57 165	85.19 146	77.42 308	62.27 286	84.47 211	91.33 174	76.43 167	85.91 291	83.14 72	87.14 303	94.33 90

fmvsm_s_conf0.5_n		81.91 185	81.30 192	83.75 168	86.02 257	71.56 166	84.73 152	77.11 312	62.44 283	84.00 225	90.68 199	76.42 168	85.89 293	83.14 72	87.11 304	93.81 116

MSDG		80.06 217	79.99 216	80.25 235	83.91 287	68.04 198	77.51 287	89.19 185	77.65 119	81.94 256	83.45 314	76.37 169	86.31 282	63.31 282	86.59 312	86.41 287

Gipuma		84.44 131	86.33 101	78.78 253	84.20 284	73.57 132	89.55 72	90.44 154	84.24 43	84.38 212	94.89 49	76.35 170	80.40 329	76.14 157	96.80 90	82.36 339
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsm_n_1920		83.60 154	82.89 164	85.74 124	85.22 267	77.74 95	84.12 165	90.48 152	59.87 312	86.45 177	91.12 181	75.65 171	85.89 293	82.28 87	90.87 259	93.58 127

XXY-MVS		74.44 278	76.19 253	69.21 336	84.61 275	52.43 346	71.70 342	77.18 311	60.73 304	80.60 278	90.96 188	75.44 172	69.35 361	56.13 324	88.33 288	85.86 294

FMVSNet1		84.55 129	85.45 119	81.85 209	90.27 159	61.05 271	86.83 119	88.27 201	78.57 110	89.66 109	95.64 30	75.43 173	90.68 203	69.09 230	95.33 147	93.82 113

CANet		83.79 150	82.85 165	86.63 102	86.17 253	72.21 156	83.76 178	91.43 125	77.24 125	74.39 336	87.45 257	75.36 174	95.42 49	77.03 148	92.83 219	92.25 183

ab-mvs		79.67 220	80.56 201	76.99 282	88.48 198	56.93 315	84.70 153	86.06 233	68.95 227	80.78 277	93.08 119	75.30 175	84.62 305	56.78 319	90.90 258	89.43 249

patch_mono-2		78.89 224	79.39 219	77.41 279	84.78 272	68.11 196	75.60 313	83.11 270	60.96 301	79.36 294	89.89 218	75.18 176	72.97 351	73.32 190	92.30 226	91.15 213

DELS-MVS		81.44 190	81.25 193	82.03 205	84.27 283	62.87 246	76.47 303	92.49 95	70.97 206	81.64 265	83.83 309	75.03 177	92.70 145	74.29 172	92.22 232	90.51 232
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		78.84 226	78.10 236	81.07 222	85.17 268	60.22 282	82.21 222	90.57 151	62.51 280	75.32 330	84.61 302	74.99 178	92.30 157	59.48 307	88.04 294	90.68 226

CNLPA		83.55 156	83.10 161	84.90 136	89.34 176	83.87 46	84.54 158	88.77 190	79.09 101	83.54 233	88.66 238	74.87 179	81.73 321	66.84 249	92.29 228	89.11 255

HQP_MVS		87.75 82	87.43 84	88.70 73	93.45 66	76.42 113	89.45 77	93.61 53	79.44 96	86.55 169	92.95 127	74.84 180	95.22 56	80.78 101	95.83 132	94.46 80

plane_prior6					92.61 88	76.54 109						74.84 180

FC-MVSNet-test		85.93 106	87.05 90	82.58 198	92.25 100	56.44 319	85.75 136	93.09 75	77.33 123	91.94 66	94.65 57	74.78 182	93.41 125	75.11 168	98.58 13	97.88 7

VDD-MVS		84.23 139	84.58 135	83.20 183	91.17 140	65.16 224	83.25 190	84.97 254	79.79 90	87.18 152	94.27 75	74.77 183	90.89 196	69.24 226	96.54 97	93.55 131

BH-untuned		80.96 196	80.99 197	80.84 226	88.55 197	68.23 193	80.33 245	88.46 194	72.79 183	86.55 169	86.76 269	74.72 184	91.77 172	61.79 293	88.99 280	82.52 337

VPNet		80.25 211	81.68 182	75.94 296	92.46 93	47.98 368	76.70 297	81.67 284	73.45 167	84.87 203	92.82 131	74.66 185	86.51 279	61.66 295	96.85 86	93.33 135

tfpnnormal		81.79 187	82.95 163	78.31 262	88.93 186	55.40 325	80.83 241	82.85 273	76.81 127	85.90 186	94.14 85	74.58 186	86.51 279	66.82 250	95.68 141	93.01 150

KD-MVS_self_test		81.93 184	83.14 160	78.30 263	84.75 274	52.75 342	80.37 244	89.42 184	70.24 216	90.26 94	93.39 114	74.55 187	86.77 276	68.61 238	96.64 93	95.38 52

V42		83.47 158	83.37 155	83.75 168	83.16 294	63.33 240	81.31 232	90.23 165	69.51 221	90.91 85	90.81 195	74.16 188	92.29 158	80.06 107	90.22 270	95.62 47

3Dnovator	80.37 7	84.80 124	84.71 132	85.06 135	86.36 245	74.71 126	88.77 89	90.00 171	75.65 142	84.96 200	93.17 117	74.06 189	91.19 185	78.28 128	91.09 251	89.29 253

v10		86.54 94	87.10 88	84.84 137	88.16 206	63.28 241	86.64 125	92.20 102	75.42 146	92.81 50	94.50 64	74.05 190	94.06 96	83.88 68	96.28 108	97.17 20

旧先验1					91.97 109	71.77 159		81.78 283			91.84 159	73.92 191			93.65 201	83.61 320

mvs_anonymous		78.13 235	78.76 227	76.23 295	79.24 335	50.31 361	78.69 270	84.82 256	61.60 293	83.09 241	92.82 131	73.89 192	87.01 269	68.33 242	86.41 314	91.37 208

MAR-MVS		80.24 212	78.74 228	84.73 142	86.87 236	78.18 88	85.75 136	87.81 207	65.67 261	77.84 306	78.50 359	73.79 193	90.53 207	61.59 296	90.87 259	85.49 298
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		84.35 133	85.39 120	81.25 218	95.13 31	59.32 291	85.42 142	81.11 287	86.41 27	87.41 150	96.21 19	73.61 194	90.61 206	66.33 253	96.85 86	93.81 116

FIs		85.35 113	86.27 102	82.60 197	91.86 114	57.31 312	85.10 148	93.05 77	75.83 139	91.02 82	93.97 93	73.57 195	92.91 142	73.97 179	98.02 39	97.58 12

v1144		84.54 130	84.72 131	84.00 160	87.67 215	62.55 252	82.97 198	90.93 142	70.32 214	89.80 104	90.99 185	73.50 196	93.48 121	81.69 94	94.65 177	95.97 39

diffmvs		80.40 206	80.48 204	80.17 237	79.02 338	60.04 283	77.54 286	90.28 164	66.65 251	82.40 248	87.33 260	73.50 196	87.35 266	77.98 135	89.62 274	93.13 144
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		83.23 161	83.19 158	83.33 179	90.90 146	65.98 216	88.19 98	90.78 145	78.13 115	80.87 275	87.92 249	73.49 198	92.42 151	70.07 219	88.40 287	91.60 204

v8		86.22 100	86.83 95	84.36 151	87.82 210	62.35 257	86.42 128	91.33 130	76.78 128	92.73 52	94.48 66	73.41 199	93.72 107	83.10 74	95.41 144	97.01 23

EI-MVSNet		82.61 168	82.42 174	83.20 183	83.25 292	63.66 236	83.50 184	85.07 248	76.06 132	86.55 169	85.10 294	73.41 199	90.25 211	78.15 133	90.67 265	95.68 45

IterMVS-LS		84.73 125	84.98 126	83.96 162	87.35 221	63.66 236	83.25 190	89.88 173	76.06 132	89.62 110	92.37 147	73.40 201	92.52 149	78.16 131	94.77 174	95.69 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MM				89.09 65		76.39 115	88.68 91	86.76 225	84.54 41	83.58 231	93.78 105	73.36 202	96.48 1	87.98 9	96.21 112	94.41 86

v144192		84.24 138	84.41 140	83.71 170	87.59 218	61.57 264	82.95 199	91.03 138	67.82 243	89.80 104	90.49 205	73.28 203	93.51 120	81.88 93	94.89 167	96.04 38

BH-RMVSNet		80.53 202	80.22 209	81.49 216	87.19 225	66.21 214	77.79 282	86.23 231	74.21 157	83.69 228	88.50 239	73.25 204	90.75 200	63.18 283	87.90 295	87.52 277

PLC	73.85 16	82.09 180	80.31 205	87.45 90	90.86 148	80.29 69	85.88 133	90.65 148	68.17 235	76.32 317	86.33 274	73.12 205	92.61 148	61.40 297	90.02 272	89.44 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OurMVSNet-221017-0		90.01 42	89.74 52	90.83 32	93.16 76	80.37 68	91.91 33	93.11 73	81.10 77	95.32 10	97.24 5	72.94 206	94.85 67	85.07 55	97.78 53	97.26 16

WR-MVS		83.56 155	84.40 141	81.06 223	93.43 68	54.88 330	78.67 271	85.02 251	81.24 75	90.74 89	91.56 169	72.85 207	91.08 189	68.00 243	98.04 36	97.23 18

VNet		79.31 221	80.27 206	76.44 290	87.92 209	53.95 334	75.58 315	84.35 260	74.39 156	82.23 251	90.72 197	72.84 208	84.39 307	60.38 303	93.98 194	90.97 216

QAPM		82.59 169	82.59 171	82.58 198	86.44 240	66.69 208	89.94 62	90.36 157	67.97 239	84.94 202	92.58 140	72.71 209	92.18 159	70.63 215	87.73 298	88.85 262

v1192		84.57 128	84.69 133	84.21 157	87.75 212	62.88 245	83.02 197	91.43 125	69.08 225	89.98 101	90.89 190	72.70 210	93.62 113	82.41 85	94.97 164	96.13 34

OpenMVS	76.72 13	81.98 183	82.00 178	81.93 206	84.42 279	68.22 194	88.50 95	89.48 182	66.92 248	81.80 262	91.86 157	72.59 211	90.16 216	71.19 208	91.25 250	87.40 279

TSAR-MVS + GP.		83.95 147	82.69 168	87.72 86	89.27 178	81.45 63	83.72 179	81.58 286	74.73 152	85.66 188	86.06 279	72.56 212	92.69 146	75.44 164	95.21 152	89.01 261

alignmvs		83.94 148	83.98 148	83.80 165	87.80 211	67.88 199	84.54 158	91.42 127	73.27 175	88.41 133	87.96 246	72.33 213	90.83 198	76.02 159	94.11 191	92.69 162

HQP2-MVS												72.10 214

HQP-MVS		84.61 127	84.06 146	86.27 110	91.19 137	70.66 171	84.77 149	92.68 91	73.30 172	80.55 280	90.17 214	72.10 214	94.61 74	77.30 145	94.47 180	93.56 129

testgi		72.36 293	74.61 266	65.59 352	80.56 323	42.82 384	68.29 357	73.35 339	66.87 249	81.84 259	89.93 216	72.08 216	66.92 374	46.05 375	92.54 223	87.01 283

v1921920		84.23 139	84.37 142	83.79 166	87.64 217	61.71 263	82.91 200	91.20 134	67.94 240	90.06 96	90.34 207	72.04 217	93.59 115	82.32 86	94.91 165	96.07 36

MSP-MVS		89.08 62	88.16 73	91.83 18	95.76 17	86.14 21	92.75 16	93.90 42	78.43 111	89.16 119	92.25 151	72.03 218	96.36 3	88.21 7	90.93 257	92.98 152
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		82.75 167	81.93 179	85.19 132	82.08 301	80.15 70	85.53 139	88.76 191	68.01 237	85.58 190	87.75 251	71.80 219	86.85 274	74.02 178	93.87 196	88.58 264

v1240		84.30 135	84.51 137	83.65 171	87.65 216	61.26 268	82.85 202	91.54 122	67.94 240	90.68 90	90.65 202	71.71 220	93.64 109	82.84 79	94.78 172	96.07 36

ambc				82.98 187	90.55 154	64.86 225	88.20 97	89.15 186		89.40 117	93.96 96	71.67 221	91.38 182	78.83 122	96.55 96	92.71 161

MVS_0304		86.35 97	85.92 108	87.66 88	89.21 180	73.16 138	88.40 96	83.63 266	81.27 74	80.87 275	94.12 87	71.49 222	95.71 32	87.79 12	96.50 99	94.11 100

新几何1				82.95 189	93.96 55	78.56 84		80.24 293	55.45 333	83.93 227	91.08 183	71.19 223	88.33 256	65.84 259	93.07 213	81.95 343

SSC-MVS		77.55 241	81.64 183	65.29 355	90.46 155	20.33 400	73.56 332	68.28 364	85.44 32	88.18 139	94.64 60	70.93 224	81.33 323	71.25 206	92.03 234	94.20 92

v148		82.31 173	82.48 173	81.81 212	85.59 262	59.66 288	81.47 231	86.02 235	72.85 180	88.05 140	90.65 202	70.73 225	90.91 195	75.15 167	91.79 239	94.87 67

v2v482		84.09 142	84.24 144	83.62 172	87.13 226	61.40 265	82.71 205	89.71 176	72.19 195	89.55 114	91.41 172	70.70 226	93.20 130	81.02 97	93.76 197	96.25 32

WB-MVS		76.06 259	80.01 215	64.19 358	89.96 168	20.58 399	72.18 340	68.19 365	83.21 54	86.46 176	93.49 112	70.19 227	78.97 335	65.96 255	90.46 269	93.02 149

UGNet		82.78 166	81.64 183	86.21 113	86.20 252	76.24 117	86.86 117	85.68 239	77.07 126	73.76 339	92.82 131	69.64 228	91.82 171	69.04 232	93.69 200	90.56 230
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		81.64 188	81.59 186	81.79 213	80.86 318	59.15 295	78.61 272	90.18 167	68.36 232	87.20 151	87.11 265	69.39 229	91.62 173	78.16 131	94.43 182	94.60 75

MG-MVS		80.32 210	80.94 198	78.47 260	88.18 204	52.62 345	82.29 218	85.01 252	72.01 197	79.24 297	92.54 141	69.36 230	93.36 127	70.65 214	89.19 279	89.45 247

IS-MVSNet		86.66 93	86.82 96	86.17 115	92.05 108	66.87 207	91.21 39	88.64 193	86.30 28	89.60 113	92.59 138	69.22 231	94.91 66	73.89 180	97.89 49	96.72 26

PVSNet_BlendedMVS		78.80 228	77.84 237	81.65 214	84.43 277	63.41 238	79.49 257	90.44 154	61.70 291	75.43 327	87.07 266	69.11 232	91.44 178	60.68 301	92.24 230	90.11 241

PVSNet_Blended		76.49 255	75.40 260	79.76 241	84.43 277	63.41 238	75.14 319	90.44 154	57.36 326	75.43 327	78.30 360	69.11 232	91.44 178	60.68 301	87.70 299	84.42 308

BH-w/o		76.57 253	76.07 255	78.10 267	86.88 235	65.92 217	77.63 284	86.33 228	65.69 260	80.89 274	79.95 349	68.97 234	90.74 201	53.01 345	85.25 324	77.62 367

MVS		73.21 287	72.59 289	75.06 303	80.97 315	60.81 277	81.64 229	85.92 237	46.03 372	71.68 349	77.54 363	68.47 235	89.77 231	55.70 327	85.39 321	74.60 373

miper_ehance_all_eth		80.34 209	80.04 214	81.24 220	79.82 328	58.95 297	77.66 283	89.66 177	65.75 259	85.99 185	85.11 293	68.29 236	91.42 180	76.03 158	92.03 234	93.33 135

Anonymous202405211		80.51 203	81.19 196	78.49 259	88.48 198	57.26 313	76.63 299	82.49 276	81.21 76	84.30 218	92.24 152	67.99 237	86.24 283	62.22 287	95.13 155	91.98 194

testdata				79.54 246	92.87 82	72.34 152		80.14 294	59.91 311	85.47 193	91.75 165	67.96 238	85.24 299	68.57 240	92.18 233	81.06 356

DPM-MVS		80.10 216	79.18 221	82.88 193	90.71 151	69.74 178	78.87 268	90.84 143	60.29 308	75.64 326	85.92 282	67.28 239	93.11 134	71.24 207	91.79 239	85.77 295

PVSNet_Blended_VisFu		81.55 189	80.49 203	84.70 144	91.58 125	73.24 137	84.21 162	91.67 120	62.86 278	80.94 273	87.16 263	67.27 240	92.87 143	69.82 222	88.94 282	87.99 271

MDA-MVSNet-bldmvs		77.47 242	76.90 247	79.16 250	79.03 337	64.59 226	66.58 365	75.67 322	73.15 177	88.86 122	88.99 233	66.94 241	81.23 324	64.71 270	88.22 293	91.64 203

CL-MVSNet_self_test		76.81 250	77.38 241	75.12 302	86.90 234	51.34 353	73.20 336	80.63 292	68.30 234	81.80 262	88.40 240	66.92 242	80.90 325	55.35 331	94.90 166	93.12 146

test222					93.31 71	76.54 109	79.38 258	77.79 304	52.59 345	82.36 249	90.84 194	66.83 243			91.69 241	81.25 351

TR-MVS		76.77 251	75.79 256	79.72 242	86.10 256	65.79 218	77.14 290	83.02 271	65.20 268	81.40 268	82.10 328	66.30 244	90.73 202	55.57 328	85.27 323	82.65 332

OpenMVS_ROB	70.19 17	77.77 240	77.46 239	78.71 255	84.39 280	61.15 269	81.18 236	82.52 275	62.45 282	83.34 236	87.37 258	66.20 245	88.66 253	64.69 271	85.02 328	86.32 288

EPP-MVSNet		85.47 111	85.04 125	86.77 101	91.52 130	69.37 183	91.63 36	87.98 206	81.51 72	87.05 159	91.83 160	66.18 246	95.29 53	70.75 212	96.89 85	95.64 46

mvsmamba		87.87 78	87.23 86	89.78 51	92.31 99	76.51 112	91.09 42	91.87 113	72.61 186	92.16 60	95.23 41	66.01 247	95.59 37	86.02 48	97.78 53	97.24 17

SixPastTwentyTwo		87.20 85	87.45 83	86.45 106	92.52 91	69.19 188	87.84 104	88.05 204	81.66 70	94.64 14	96.53 14	65.94 248	94.75 69	83.02 77	96.83 88	95.41 51

PatchMatch-RL		74.48 276	73.22 281	78.27 265	87.70 213	85.26 34	75.92 311	70.09 358	64.34 272	76.09 320	81.25 338	65.87 249	78.07 338	53.86 339	83.82 340	71.48 376

EPNet		80.37 207	78.41 233	86.23 111	76.75 352	73.28 135	87.18 111	77.45 307	76.24 131	68.14 363	88.93 234	65.41 250	93.85 102	69.47 224	96.12 117	91.55 206
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RRT_MVS		88.30 70	87.83 77	89.70 52	93.62 64	75.70 121	92.36 26	89.06 188	77.34 122	93.63 35	95.83 25	65.40 251	95.90 15	85.01 58	98.23 27	97.49 13

FA-MVS(test-final)		83.13 164	83.02 162	83.43 176	86.16 255	66.08 215	88.00 100	88.36 197	75.55 143	85.02 198	92.75 135	65.12 252	92.50 150	74.94 170	91.30 249	91.72 199

PM-MVS		80.20 213	79.00 222	83.78 167	88.17 205	86.66 15	81.31 232	66.81 372	69.64 220	88.33 135	90.19 212	64.58 253	83.63 313	71.99 204	90.03 271	81.06 356

miper_enhance_ethall		77.83 237	76.93 246	80.51 231	76.15 358	58.01 307	75.47 317	88.82 189	58.05 320	83.59 230	80.69 340	64.41 254	91.20 184	73.16 197	92.03 234	92.33 177

eth_miper_zixun_eth		80.84 197	80.22 209	82.71 195	81.41 310	60.98 274	77.81 281	90.14 168	67.31 246	86.95 161	87.24 262	64.26 255	92.31 156	75.23 166	91.61 243	94.85 71

test20.03		73.75 282	74.59 268	71.22 324	81.11 314	51.12 357	70.15 352	72.10 348	70.42 211	80.28 286	91.50 170	64.21 256	74.72 350	46.96 372	94.58 178	87.82 276

cascas		76.29 258	74.81 265	80.72 229	84.47 276	62.94 244	73.89 330	87.34 210	55.94 331	75.16 332	76.53 372	63.97 257	91.16 186	65.00 267	90.97 256	88.06 269

TAMVS		78.08 236	76.36 251	83.23 181	90.62 152	72.87 139	79.08 264	80.01 295	61.72 290	81.35 269	86.92 268	63.96 258	88.78 251	50.61 355	93.01 215	88.04 270

GBi-Net		82.02 181	82.07 176	81.85 209	86.38 242	61.05 271	86.83 119	88.27 201	72.43 187	86.00 182	95.64 30	63.78 259	90.68 203	65.95 256	93.34 205	93.82 113

test1		82.02 181	82.07 176	81.85 209	86.38 242	61.05 271	86.83 119	88.27 201	72.43 187	86.00 182	95.64 30	63.78 259	90.68 203	65.95 256	93.34 205	93.82 113

FMVSNet2		81.31 191	81.61 185	80.41 233	86.38 242	58.75 302	83.93 172	86.58 227	72.43 187	87.65 146	92.98 124	63.78 259	90.22 214	66.86 247	93.92 195	92.27 181

USDC		76.63 252	76.73 249	76.34 292	83.46 290	57.20 314	80.02 248	88.04 205	52.14 350	83.65 229	91.25 176	63.24 262	86.65 278	54.66 336	94.11 191	85.17 300

DIV-MVS_self_test		80.43 204	80.23 207	81.02 224	79.99 326	59.25 292	77.07 292	87.02 221	67.38 244	86.19 178	89.22 228	63.09 263	90.16 216	76.32 154	95.80 135	93.66 121

cl____		80.42 205	80.23 207	81.02 224	79.99 326	59.25 292	77.07 292	87.02 221	67.37 245	86.18 180	89.21 229	63.08 264	90.16 216	76.31 155	95.80 135	93.65 123

h-mvs33		84.25 137	82.76 166	88.72 71	91.82 119	82.60 56	84.00 169	84.98 253	71.27 201	86.70 165	90.55 204	63.04 265	93.92 100	78.26 129	94.20 189	89.63 245

hse-mvs2		83.47 158	81.81 181	88.47 75	91.03 143	82.27 57	82.61 206	83.69 264	71.27 201	86.70 165	86.05 280	63.04 265	92.41 152	78.26 129	93.62 203	90.71 224

new-patchmatchnet		70.10 313	73.37 280	60.29 368	81.23 313	16.95 401	59.54 378	74.62 327	62.93 277	80.97 271	87.93 248	62.83 267	71.90 354	55.24 332	95.01 163	92.00 192

K. test v3		85.14 117	84.73 129	86.37 107	91.13 141	69.63 181	85.45 141	76.68 316	84.06 45	92.44 57	96.99 8	62.03 268	94.65 72	80.58 104	93.24 209	94.83 72

lessismore_v0				85.95 118	91.10 142	70.99 170		70.91 356		91.79 67	94.42 70	61.76 269	92.93 140	79.52 117	93.03 214	93.93 107

1314		73.22 286	72.56 291	75.20 301	80.41 325	57.84 308	81.64 229	85.36 242	51.68 353	73.10 342	76.65 371	61.45 270	85.19 300	63.54 279	79.21 365	82.59 333

Syy-MVS		69.40 321	70.03 313	67.49 345	81.72 305	38.94 387	71.00 345	61.99 378	61.38 295	70.81 354	72.36 380	61.37 271	79.30 332	64.50 275	85.18 325	84.22 310

CANet_DTU		77.81 239	77.05 244	80.09 238	81.37 311	59.90 286	83.26 189	88.29 200	69.16 224	67.83 366	83.72 310	60.93 272	89.47 235	69.22 228	89.70 273	90.88 219

pmmvs-eth3d		78.42 234	77.04 245	82.57 200	87.44 220	74.41 128	80.86 240	79.67 296	55.68 332	84.69 206	90.31 209	60.91 273	85.42 298	62.20 288	91.59 244	87.88 274

UnsupCasMVSNet_eth		71.63 300	72.30 293	69.62 333	76.47 355	52.70 344	70.03 353	80.97 289	59.18 313	79.36 294	88.21 243	60.50 274	69.12 362	58.33 313	77.62 372	87.04 282

IterMVS-SCA-FT		80.64 201	79.41 218	84.34 153	83.93 286	69.66 180	76.28 305	81.09 288	72.43 187	86.47 175	90.19 212	60.46 275	93.15 133	77.45 142	86.39 315	90.22 237

SCA		73.32 284	72.57 290	75.58 300	81.62 307	55.86 322	78.89 267	71.37 354	61.73 289	74.93 333	83.42 315	60.46 275	87.01 269	58.11 315	82.63 351	83.88 314

jason		77.42 243	75.75 257	82.43 203	87.10 229	69.27 184	77.99 278	81.94 281	51.47 354	77.84 306	85.07 297	60.32 277	89.00 245	70.74 213	89.27 278	89.03 259
jason: jason.
1112_ss		74.82 273	73.74 274	78.04 269	89.57 170	60.04 283	76.49 302	87.09 220	54.31 337	73.66 340	79.80 350	60.25 278	86.76 277	58.37 311	84.15 339	87.32 280

HY-MVS	64.64 18	73.03 288	72.47 292	74.71 304	83.36 291	54.19 332	82.14 225	81.96 280	56.76 330	69.57 359	86.21 278	60.03 279	84.83 304	49.58 361	82.65 349	85.11 301

Anonymous20231206		71.38 303	71.88 295	69.88 331	86.31 246	54.37 331	70.39 350	74.62 327	52.57 346	76.73 313	88.76 235	59.94 280	72.06 353	44.35 379	93.23 210	83.23 328

IterMVS		76.91 248	76.34 252	78.64 256	80.91 316	64.03 233	76.30 304	79.03 299	64.88 270	83.11 239	89.16 230	59.90 281	84.46 306	68.61 238	85.15 327	87.42 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 314	70.44 307	68.90 337	73.76 373	53.42 339	58.99 381	67.20 368	58.42 317	87.10 155	85.39 290	59.82 282	67.32 371	59.79 305	83.50 342	85.96 291

MDA-MVSNet_test_wron		70.05 315	70.44 307	68.88 338	73.84 372	53.47 337	58.93 382	67.28 367	58.43 316	87.09 156	85.40 289	59.80 283	67.25 372	59.66 306	83.54 341	85.92 293

PMMVS		61.65 347	60.38 354	65.47 354	65.40 396	69.26 185	63.97 371	61.73 382	36.80 392	60.11 386	68.43 385	59.42 284	66.35 376	48.97 363	78.57 368	60.81 387

CDS-MVSNet		77.32 244	75.40 260	83.06 185	89.00 184	72.48 150	77.90 280	82.17 279	60.81 302	78.94 299	83.49 313	59.30 285	88.76 252	54.64 337	92.37 225	87.93 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 322	69.68 316	67.82 343	79.42 332	51.15 356	67.82 361	75.79 320	54.15 338	77.47 312	85.36 292	59.26 286	70.64 357	48.46 365	79.35 363	81.66 345

Anonymous20240521		80.18 214	81.25 193	76.95 283	83.15 295	60.84 276	82.46 213	85.99 236	68.76 229	86.78 162	93.73 108	59.13 287	77.44 340	73.71 184	97.55 67	92.56 166

WTY-MVS		67.91 327	68.35 325	66.58 349	80.82 319	48.12 367	65.96 366	72.60 343	53.67 340	71.20 351	81.68 335	58.97 288	69.06 363	48.57 364	81.67 353	82.55 335

cl22		78.97 223	78.21 235	81.24 220	77.74 342	59.01 296	77.46 289	87.13 216	65.79 256	84.32 215	85.10 294	58.96 289	90.88 197	75.36 165	92.03 234	93.84 111

MVSFormer		82.23 175	81.57 188	84.19 159	85.54 263	69.26 185	91.98 31	90.08 169	71.54 199	76.23 318	85.07 297	58.69 290	94.27 84	86.26 40	88.77 283	89.03 259

lupinMVS		76.37 257	74.46 269	82.09 204	85.54 263	69.26 185	76.79 295	80.77 291	50.68 361	76.23 318	82.82 322	58.69 290	88.94 246	69.85 221	88.77 283	88.07 268

Test_1112_low_res		73.90 281	73.08 282	76.35 291	90.35 157	55.95 320	73.40 335	86.17 232	50.70 360	73.14 341	85.94 281	58.31 292	85.90 292	56.51 321	83.22 343	87.20 281

test_yl		78.71 230	78.51 231	79.32 248	84.32 281	58.84 299	78.38 273	85.33 243	75.99 135	82.49 246	86.57 270	58.01 293	90.02 225	62.74 284	92.73 221	89.10 256

DCV-MVSNet		78.71 230	78.51 231	79.32 248	84.32 281	58.84 299	78.38 273	85.33 243	75.99 135	82.49 246	86.57 270	58.01 293	90.02 225	62.74 284	92.73 221	89.10 256

sss		66.92 329	67.26 329	65.90 351	77.23 347	51.10 358	64.79 368	71.72 352	52.12 351	70.13 357	80.18 347	57.96 295	65.36 380	50.21 356	81.01 359	81.25 351

ppachtmachnet_test		74.73 275	74.00 273	76.90 285	80.71 321	56.89 317	71.53 344	78.42 301	58.24 318	79.32 296	82.92 321	57.91 296	84.26 308	65.60 262	91.36 248	89.56 246

MVP-Stereo		75.81 262	73.51 278	82.71 195	89.35 175	73.62 131	80.06 246	85.20 245	60.30 307	73.96 338	87.94 247	57.89 297	89.45 237	52.02 349	74.87 378	85.06 302
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 298	70.06 312	76.92 284	86.39 241	53.97 333	76.62 300	86.62 226	53.44 341	63.97 381	84.73 301	57.79 298	92.34 155	39.65 386	81.33 357	84.45 307

LFMVS		80.15 215	80.56 201	78.89 251	89.19 181	55.93 321	85.22 145	73.78 336	82.96 58	84.28 219	92.72 136	57.38 299	90.07 223	63.80 277	95.75 138	90.68 226

Vis-MVSNet (Re-imp)		77.82 238	77.79 238	77.92 271	88.82 188	51.29 355	83.28 188	71.97 349	74.04 158	82.23 251	89.78 219	57.38 299	89.41 240	57.22 318	95.41 144	93.05 148

CHOSEN 1792x2688		72.45 292	70.56 305	78.13 266	90.02 167	63.08 243	68.72 356	83.16 269	42.99 382	75.92 322	85.46 287	57.22 301	85.18 301	49.87 359	81.67 353	86.14 290

mvsany_test1		58.48 356	56.47 361	64.50 357	65.90 395	68.21 195	56.95 384	42.11 399	38.30 390	65.69 372	77.19 369	56.96 302	59.35 389	46.16 373	58.96 392	65.93 383

miper_lstm_enhance		76.45 256	76.10 254	77.51 277	76.72 353	60.97 275	64.69 369	85.04 250	63.98 273	83.20 238	88.22 242	56.67 303	78.79 337	73.22 191	93.12 212	92.78 157

bld_raw_dy_0_64		84.85 123	84.44 138	86.07 117	93.73 60	74.93 125	88.57 93	81.90 282	70.44 210	91.28 77	95.18 42	56.62 304	89.28 243	85.15 54	97.09 81	93.99 103

our_test_3		71.85 297	71.59 297	72.62 317	80.71 321	53.78 335	69.72 354	71.71 353	58.80 315	78.03 303	80.51 345	56.61 305	78.84 336	62.20 288	86.04 318	85.23 299

baseline1		73.26 285	73.54 277	72.43 320	84.92 270	47.79 369	79.89 250	74.00 332	65.93 254	78.81 300	86.28 277	56.36 306	81.63 322	56.63 320	79.04 367	87.87 275

pmmvs4		74.92 271	72.98 284	80.73 228	84.95 269	71.71 163	76.23 306	77.59 306	52.83 344	77.73 310	86.38 272	56.35 307	84.97 302	57.72 317	87.05 306	85.51 297

MVE	40.22 23	51.82 360	50.47 363	55.87 372	62.66 398	51.91 349	31.61 391	39.28 400	40.65 385	50.76 394	74.98 376	56.24 308	44.67 395	33.94 392	64.11 390	71.04 378
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		60.59 354	62.54 349	54.72 374	77.26 346	27.74 397	74.05 327	61.00 384	60.48 306	65.62 373	67.03 387	55.93 309	68.23 369	32.07 394	69.46 388	68.17 381

N_pmnet		70.20 311	68.80 323	74.38 306	80.91 316	84.81 39	59.12 380	76.45 318	55.06 334	75.31 331	82.36 327	55.74 310	54.82 390	47.02 370	87.24 302	83.52 321

MS-PatchMatch		70.93 307	70.22 310	73.06 314	81.85 304	62.50 253	73.82 331	77.90 303	52.44 347	75.92 322	81.27 337	55.67 311	81.75 320	55.37 330	77.70 371	74.94 372

DSMNet-mixed		60.98 352	61.61 352	59.09 371	72.88 379	45.05 378	74.70 322	46.61 397	26.20 393	65.34 374	90.32 208	55.46 312	63.12 384	41.72 383	81.30 358	69.09 380

pmmvs5		70.73 308	70.07 311	72.72 316	77.03 350	52.73 343	74.14 325	75.65 323	50.36 363	72.17 347	85.37 291	55.42 313	80.67 327	52.86 346	87.59 300	84.77 304

CMPMVS	59.41 20	75.12 268	73.57 276	79.77 240	75.84 361	67.22 201	81.21 235	82.18 278	50.78 359	76.50 314	87.66 253	55.20 314	82.99 315	62.17 290	90.64 268	89.09 258
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_vis1_n_1920		71.30 304	71.58 299	70.47 327	77.58 345	59.99 285	74.25 324	84.22 262	51.06 356	74.85 334	79.10 354	55.10 315	68.83 364	68.86 234	79.20 366	82.58 334

MIMVSNet		71.09 305	71.59 297	69.57 334	87.23 223	50.07 362	78.91 266	71.83 350	60.20 310	71.26 350	91.76 164	55.08 316	76.09 344	41.06 384	87.02 308	82.54 336

PVSNet_0	51.08 22	56.10 357	54.97 362	59.48 370	75.12 367	53.28 340	55.16 385	61.89 380	44.30 376	59.16 387	62.48 390	54.22 317	65.91 378	35.40 390	47.01 393	59.25 389

EPNet_dtu		72.87 290	71.33 302	77.49 278	77.72 343	60.55 280	82.35 216	75.79 320	66.49 252	58.39 391	81.06 339	53.68 318	85.98 289	53.55 340	92.97 217	85.95 292
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 359	59.27 358	44.74 376	64.30 397	12.32 402	40.60 389	49.79 395	53.19 342	65.06 378	84.81 299	53.60 319	49.76 393	32.68 393	89.41 275	72.15 375

test_vis1_rt		65.64 338	64.09 342	70.31 328	66.09 393	70.20 176	61.16 376	81.60 285	38.65 389	72.87 343	69.66 383	52.84 320	60.04 387	56.16 323	77.77 370	80.68 358

mvsany_test3		65.48 339	62.97 346	73.03 315	69.99 386	76.17 118	64.83 367	43.71 398	43.68 379	80.25 287	87.05 267	52.83 321	63.09 385	51.92 353	72.44 380	79.84 363

HyFIR lowres test		75.12 268	72.66 288	82.50 201	91.44 133	65.19 223	72.47 338	87.31 211	46.79 367	80.29 284	84.30 305	52.70 322	92.10 163	51.88 354	86.73 310	90.22 237

dmvs_re		66.81 332	66.98 330	66.28 350	76.87 351	58.68 303	71.66 343	72.24 346	60.29 308	69.52 360	73.53 377	52.38 323	64.40 382	44.90 377	81.44 356	75.76 370

test_cas_vis1_n_1920		69.20 323	69.12 318	69.43 335	73.68 374	62.82 247	70.38 351	77.21 310	46.18 371	80.46 283	78.95 356	52.03 324	65.53 379	65.77 261	77.45 374	79.95 362

test1111		78.53 232	78.85 225	77.56 276	92.22 102	47.49 370	82.61 206	69.24 362	72.43 187	85.28 194	94.20 81	51.91 325	90.07 223	65.36 264	96.45 103	95.11 62

ECVR-MVS		78.44 233	78.63 229	77.88 272	91.85 115	48.95 364	83.68 180	69.91 360	72.30 193	84.26 221	94.20 81	51.89 326	89.82 228	63.58 278	96.02 121	94.87 67

FMVSNet3		78.80 228	78.55 230	79.57 245	82.89 299	56.89 317	81.76 226	85.77 238	69.04 226	86.00 182	90.44 206	51.75 327	90.09 222	65.95 256	93.34 205	91.72 199

D2MVS		76.84 249	75.67 259	80.34 234	80.48 324	62.16 261	73.50 333	84.80 257	57.61 324	82.24 250	87.54 255	51.31 328	87.65 262	70.40 218	93.19 211	91.23 210

AUN-MVS		81.18 193	78.78 226	88.39 77	90.93 145	82.14 58	82.51 212	83.67 265	64.69 271	80.29 284	85.91 283	51.07 329	92.38 153	76.29 156	93.63 202	90.65 228

PVSNet	58.17 21	66.41 334	65.63 339	68.75 339	81.96 302	49.88 363	62.19 375	72.51 345	51.03 357	68.04 364	75.34 375	50.84 330	74.77 348	45.82 376	82.96 344	81.60 346

GA-MVS		75.83 261	74.61 266	79.48 247	81.87 303	59.25 292	73.42 334	82.88 272	68.68 230	79.75 289	81.80 333	50.62 331	89.46 236	66.85 248	85.64 320	89.72 244

FPMVS		72.29 295	72.00 294	73.14 313	88.63 194	85.00 36	74.65 323	67.39 366	71.94 198	77.80 308	87.66 253	50.48 332	75.83 346	49.95 357	79.51 361	58.58 390

test_fmvs3		75.72 263	75.20 263	77.27 280	75.01 369	69.47 182	78.93 265	84.88 255	46.67 368	87.08 157	87.84 250	50.44 333	71.62 355	77.42 144	88.53 286	90.72 223

test_vis1_n		70.29 310	69.99 314	71.20 325	75.97 360	66.50 210	76.69 298	80.81 290	44.22 377	75.43 327	77.23 367	50.00 334	68.59 365	66.71 251	82.85 348	78.52 366

MVS-HIRNet		61.16 350	62.92 347	55.87 372	79.09 336	35.34 393	71.83 341	57.98 390	46.56 369	59.05 388	91.14 180	49.95 335	76.43 343	38.74 387	71.92 382	55.84 391

CVMVSNet		72.62 291	71.41 301	76.28 293	83.25 292	60.34 281	83.50 184	79.02 300	37.77 391	76.33 316	85.10 294	49.60 336	87.41 265	70.54 216	77.54 373	81.08 354

RPMNet		78.88 225	78.28 234	80.68 230	79.58 329	62.64 250	82.58 208	94.16 27	74.80 151	75.72 324	92.59 138	48.69 337	95.56 39	73.48 187	82.91 346	83.85 317

test_fmvs2		73.57 283	72.80 285	75.90 297	72.74 381	68.84 191	77.07 292	84.32 261	45.14 374	82.89 242	84.22 306	48.37 338	70.36 358	73.40 189	87.03 307	88.52 265

tpmrst		66.28 335	66.69 334	65.05 356	72.82 380	39.33 386	78.20 276	70.69 357	53.16 343	67.88 365	80.36 346	48.18 339	74.75 349	58.13 314	70.79 383	81.08 354

CR-MVSNet		74.00 280	73.04 283	76.85 287	79.58 329	62.64 250	82.58 208	76.90 313	50.50 362	75.72 324	92.38 144	48.07 340	84.07 309	68.72 237	82.91 346	83.85 317

Patchmtry		76.56 254	77.46 239	73.83 308	79.37 334	46.60 374	82.41 215	76.90 313	73.81 161	85.56 191	92.38 144	48.07 340	83.98 310	63.36 281	95.31 150	90.92 218

test_f		64.31 343	65.85 336	59.67 369	66.54 392	62.24 260	57.76 383	70.96 355	40.13 386	84.36 213	82.09 329	46.93 342	51.67 392	61.99 291	81.89 352	65.12 384

ADS-MVSNet2		65.87 337	63.64 345	72.55 318	73.16 377	56.92 316	67.10 362	74.81 326	49.74 364	66.04 370	82.97 318	46.71 343	77.26 341	42.29 381	69.96 385	83.46 322

ADS-MVSNet		61.90 346	62.19 350	61.03 367	73.16 377	36.42 392	67.10 362	61.75 381	49.74 364	66.04 370	82.97 318	46.71 343	63.21 383	42.29 381	69.96 385	83.46 322

PatchmatchNet		69.71 318	68.83 322	72.33 321	77.66 344	53.60 336	79.29 259	69.99 359	57.66 323	72.53 345	82.93 320	46.45 345	80.08 331	60.91 300	72.09 381	83.31 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 294	71.55 300	74.70 305	83.48 289	51.60 352	75.02 320	73.71 337	70.14 217	78.56 302	80.57 343	46.20 346	88.20 258	46.99 371	89.29 276	84.32 309

sam_mvs1												46.11 347				83.88 314

tfpn200view9		74.86 272	74.23 271	76.74 288	86.24 250	52.12 347	79.24 261	73.87 334	73.34 170	81.82 260	84.60 303	46.02 348	88.80 248	51.98 350	90.99 253	89.31 251

thres400		75.14 266	74.23 271	77.86 273	86.24 250	52.12 347	79.24 261	73.87 334	73.34 170	81.82 260	84.60 303	46.02 348	88.80 248	51.98 350	90.99 253	92.66 163

baseline2		69.77 317	66.89 331	78.41 261	79.51 331	58.09 305	76.23 306	69.57 361	57.50 325	64.82 379	77.45 365	46.02 348	88.44 254	53.08 342	77.83 369	88.70 263

patchmatchnet-post											81.71 334	45.93 351	87.01 269

sam_mvs												45.92 352

Patchmatch-RL test		74.48 276	73.68 275	76.89 286	84.83 271	66.54 209	72.29 339	69.16 363	57.70 322	86.76 163	86.33 274	45.79 353	82.59 316	69.63 223	90.65 267	81.54 347

thres100view900		75.45 264	75.05 264	76.66 289	87.27 222	51.88 350	81.07 237	73.26 340	75.68 141	83.25 237	86.37 273	45.54 354	88.80 248	51.98 350	90.99 253	89.31 251

thres600view7		75.97 260	75.35 262	77.85 274	87.01 232	51.84 351	80.45 243	73.26 340	75.20 148	83.10 240	86.31 276	45.54 354	89.05 244	55.03 334	92.24 230	92.66 163

tpm cat1		66.76 333	65.21 340	71.42 323	77.09 349	50.62 360	78.01 277	73.68 338	44.89 375	68.64 361	79.00 355	45.51 356	82.42 319	49.91 358	70.15 384	81.23 353

test_post											3.10 397	45.43 357	77.22 342

MDTV_nov1_ep13			68.29 326		78.03 341	43.87 381	74.12 326	72.22 347	52.17 348	67.02 368	85.54 285	45.36 358	80.85 326	55.73 325	84.42 337

tpmvs		70.16 312	69.56 317	71.96 322	74.71 370	48.13 366	79.63 252	75.45 325	65.02 269	70.26 356	81.88 332	45.34 359	85.68 296	58.34 312	75.39 377	82.08 342

MDTV_nov1_ep13_2view						27.60 398	70.76 348		46.47 370	61.27 383		45.20 360		49.18 362		83.75 319

test_post1							78.85 269				3.13 396	45.19 361	80.13 330	58.11 315

CostFormer		69.98 316	68.68 324	73.87 307	77.14 348	50.72 359	79.26 260	74.51 329	51.94 352	70.97 353	84.75 300	45.16 362	87.49 264	55.16 333	79.23 364	83.40 324

FE-MVS		79.98 218	78.86 224	83.36 178	86.47 239	66.45 211	89.73 65	84.74 258	72.80 182	84.22 223	91.38 173	44.95 363	93.60 114	63.93 276	91.50 246	90.04 243

Patchmatch-test		65.91 336	67.38 328	61.48 366	75.51 363	43.21 383	68.84 355	63.79 376	62.48 281	72.80 344	83.42 315	44.89 364	59.52 388	48.27 367	86.45 313	81.70 344

EU-MVSNet		75.12 268	74.43 270	77.18 281	83.11 296	59.48 290	85.71 138	82.43 277	39.76 388	85.64 189	88.76 235	44.71 365	87.88 260	73.86 181	85.88 319	84.16 313

PatchT		70.52 309	72.76 287	63.79 360	79.38 333	33.53 394	77.63 284	65.37 374	73.61 165	71.77 348	92.79 134	44.38 366	75.65 347	64.53 274	85.37 322	82.18 340

test_vis3_rt		71.42 302	70.67 304	73.64 310	69.66 387	70.46 173	66.97 364	89.73 174	42.68 384	88.20 138	83.04 317	43.77 367	60.07 386	65.35 265	86.66 311	90.39 235

test_fmvs1_n		70.94 306	70.41 309	72.53 319	73.92 371	66.93 206	75.99 310	84.21 263	43.31 381	79.40 293	79.39 353	43.47 368	68.55 366	69.05 231	84.91 331	82.10 341

test-LLR		67.21 328	66.74 333	68.63 340	76.45 356	55.21 327	67.89 358	67.14 369	62.43 284	65.08 376	72.39 378	43.41 369	69.37 359	61.00 298	84.89 332	81.31 349

test0.0.03 1		64.66 341	64.36 341	65.57 353	75.03 368	46.89 373	64.69 369	61.58 383	62.43 284	71.18 352	77.54 363	43.41 369	68.47 368	40.75 385	82.65 349	81.35 348

test_fmvs1		69.57 319	69.05 320	71.14 326	69.15 388	65.77 219	73.98 328	83.32 268	42.83 383	77.77 309	78.27 361	43.39 371	68.50 367	68.39 241	84.38 338	79.15 364

MVSTER		77.09 246	75.70 258	81.25 218	75.27 366	61.08 270	77.49 288	85.07 248	60.78 303	86.55 169	88.68 237	43.14 372	90.25 211	73.69 185	90.67 265	92.42 171

tpm		67.95 326	68.08 327	67.55 344	78.74 340	43.53 382	75.60 313	67.10 371	54.92 335	72.23 346	88.10 244	42.87 373	75.97 345	52.21 348	80.95 360	83.15 329

tpm2		68.45 325	66.83 332	73.30 312	78.93 339	48.50 365	79.76 251	71.76 351	47.50 366	69.92 358	83.60 311	42.07 374	88.40 255	48.44 366	79.51 361	83.01 331

EMVS		61.10 351	60.81 353	61.99 363	65.96 394	55.86 322	53.10 387	58.97 388	67.06 247	56.89 392	63.33 389	40.98 375	67.03 373	54.79 335	86.18 317	63.08 385

new_pmnet		55.69 358	57.66 359	49.76 375	75.47 364	30.59 395	59.56 377	51.45 394	43.62 380	62.49 382	75.48 374	40.96 376	49.15 394	37.39 389	72.52 379	69.55 379

E-PMN		61.59 348	61.62 351	61.49 365	66.81 391	55.40 325	53.77 386	60.34 385	66.80 250	58.90 389	65.50 388	40.48 377	66.12 377	55.72 326	86.25 316	62.95 386

EPMVS		62.47 344	62.63 348	62.01 362	70.63 385	38.74 388	74.76 321	52.86 393	53.91 339	67.71 367	80.01 348	39.40 378	66.60 375	55.54 329	68.81 389	80.68 358

tmp_tt		20.25 363	24.50 366	7.49 379	4.47 401	8.70 403	34.17 390	25.16 402	1.00 397	32.43 396	18.49 394	39.37 379	9.21 398	21.64 396	43.75 394	4.57 394

thisisatest0530		79.07 222	77.33 243	84.26 156	87.13 226	64.58 227	83.66 181	75.95 319	68.86 228	85.22 195	87.36 259	38.10 380	93.57 118	75.47 163	94.28 187	94.62 74

ET-MVSNet_ETH3D		75.28 265	72.77 286	82.81 194	83.03 297	68.11 196	77.09 291	76.51 317	60.67 305	77.60 311	80.52 344	38.04 381	91.15 187	70.78 211	90.68 264	89.17 254

tttt0517		81.07 194	79.58 217	85.52 128	88.99 185	66.45 211	87.03 114	75.51 324	73.76 162	88.32 136	90.20 211	37.96 382	94.16 94	79.36 119	95.13 155	95.93 42

thisisatest0515		73.00 289	70.52 306	80.46 232	81.45 309	59.90 286	73.16 337	74.31 331	57.86 321	76.08 321	77.78 362	37.60 383	92.12 162	65.00 267	91.45 247	89.35 250

FMVSNet5		72.10 296	71.69 296	73.32 311	81.57 308	53.02 341	76.77 296	78.37 302	63.31 274	76.37 315	91.85 158	36.68 384	78.98 334	47.87 368	92.45 224	87.95 272

iter_conf_final		80.36 208	78.88 223	84.79 139	86.29 248	66.36 213	86.95 115	86.25 230	68.16 236	82.09 254	89.48 223	36.59 385	94.51 81	79.83 111	94.30 186	93.50 132

dp		60.70 353	60.29 356	61.92 364	72.04 383	38.67 389	70.83 347	64.08 375	51.28 355	60.75 384	77.28 366	36.59 385	71.58 356	47.41 369	62.34 391	75.52 371

CHOSEN 280x420		59.08 355	56.52 360	66.76 348	76.51 354	64.39 230	49.62 388	59.00 387	43.86 378	55.66 393	68.41 386	35.55 387	68.21 370	43.25 380	76.78 376	67.69 382

IB-MVS	62.13 19	71.64 299	68.97 321	79.66 244	80.80 320	62.26 259	73.94 329	76.90 313	63.27 275	68.63 362	76.79 370	33.83 388	91.84 170	59.28 308	87.26 301	84.88 303
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
JIA-IIPM		69.41 320	66.64 335	77.70 275	73.19 376	71.24 168	75.67 312	65.56 373	70.42 211	65.18 375	92.97 126	33.64 389	83.06 314	53.52 341	69.61 387	78.79 365

iter_conf05		78.81 227	77.35 242	83.21 182	82.98 298	60.75 278	84.09 166	88.34 198	63.12 276	84.25 222	89.48 223	31.41 390	94.51 81	76.64 151	95.83 132	94.38 88

DeepMVS_CX				24.13 378	32.95 399	29.49 396		21.63 403	12.07 394	37.95 395	45.07 393	30.84 391	19.21 397	17.94 397	33.06 396	23.69 393

gg-mvs-nofinetune		68.96 324	69.11 319	68.52 342	76.12 359	45.32 376	83.59 182	55.88 391	86.68 24	64.62 380	97.01 7	30.36 392	83.97 311	44.78 378	82.94 345	76.26 369

GG-mvs-BLEND				67.16 346	73.36 375	46.54 375	84.15 164	55.04 392		58.64 390	61.95 391	29.93 393	83.87 312	38.71 388	76.92 375	71.07 377

test_method		30.46 361	29.60 364	33.06 377	17.99 400	3.84 404	13.62 392	73.92 333	2.79 395	18.29 397	53.41 392	28.53 394	43.25 396	22.56 395	35.27 395	52.11 392

test-mter		65.00 340	63.79 344	68.63 340	76.45 356	55.21 327	67.89 358	67.14 369	50.98 358	65.08 376	72.39 378	28.27 395	69.37 359	61.00 298	84.89 332	81.31 349

TESTMET0.1,1		61.29 349	60.32 355	64.19 358	72.06 382	51.30 354	67.89 358	62.09 377	45.27 373	60.65 385	69.01 384	27.93 396	64.74 381	56.31 322	81.65 355	76.53 368

test2506		74.12 279	73.39 279	76.28 293	91.85 115	44.20 380	84.06 167	48.20 396	72.30 193	81.90 257	94.20 81	27.22 397	89.77 231	64.81 269	96.02 121	94.87 67

pmmvs3		62.47 344	60.02 357	69.80 332	71.58 384	64.00 234	70.52 349	58.44 389	39.77 387	66.05 369	75.84 373	27.10 398	72.28 352	46.15 374	84.77 336	73.11 374

KD-MVS_2432*1600		66.87 330	65.81 337	70.04 329	67.50 389	47.49 370	62.56 373	79.16 297	61.21 299	77.98 304	80.61 341	25.29 399	82.48 317	53.02 343	84.92 329	80.16 360

miper_refine_blended		66.87 330	65.81 337	70.04 329	67.50 389	47.49 370	62.56 373	79.16 297	61.21 299	77.98 304	80.61 341	25.29 399	82.48 317	53.02 343	84.92 329	80.16 360

myMVS_eth3d		64.66 341	63.89 343	66.97 347	81.72 305	37.39 390	71.00 345	61.99 378	61.38 295	70.81 354	72.36 380	20.96 401	79.30 332	49.59 360	85.18 325	84.22 310

testing3		71.53 301	70.79 303	73.77 309	88.89 187	41.86 385	76.60 301	59.12 386	72.83 181	80.97 271	82.08 330	19.80 402	87.33 267	65.12 266	91.68 242	92.13 188

test123		6.27 366	8.08 369	0.84 380	1.11 403	0.57 405	62.90 372	0.82 404	0.54 398	1.07 400	2.75 399	1.26 403	0.30 399	1.04 398	1.26 398	1.66 395

testmvs		5.91 367	7.65 370	0.72 381	1.20 402	0.37 406	59.14 379	0.67 405	0.49 399	1.11 399	2.76 398	0.94 404	0.24 400	1.02 399	1.47 397	1.55 396

test_blank		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uanet_test		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

DCPMVS		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

sosnet-low-res		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

sosnet		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uncertanet		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

Regformer		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

ab-mvs-re		6.65 364	8.87 367	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	79.80 350	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uanet		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

WAC-MVS						37.39 390								52.61 347

FOURS1					96.08 11	87.41 10	96.19 2	95.83 4	92.95 2	96.57 2

MSC_two_6792asdad				88.81 69	91.55 127	77.99 90		91.01 139					96.05 8	87.45 20	98.17 32	92.40 173

No_MVS				88.81 69	91.55 127	77.99 90		91.01 139					96.05 8	87.45 20	98.17 32	92.40 173

eth-test2					0.00 404

eth-test					0.00 404

IU-MVS					94.18 46	72.64 143		90.82 144	56.98 328	89.67 108				85.78 50	97.92 46	93.28 137

save fliter					93.75 59	77.44 99	86.31 129	89.72 175	70.80 207

test_0728_SECOND				86.79 100	94.25 45	72.45 151	90.54 48	94.10 34					95.88 17	86.42 36	97.97 43	92.02 191

GSMVS																83.88 314

test_part2					93.86 57	77.77 94				92.84 48

MTGPA								91.81 118

MTMP							90.66 44	33.14 401

gm-plane-assit					75.42 365	44.97 379			52.17 348		72.36 380		87.90 259	54.10 338

test9_res														80.83 100	96.45 103	90.57 229

agg_prior2														79.68 114	96.16 114	90.22 237

agg_prior					91.58 125	77.69 96		90.30 161		84.32 215			93.18 131

test_prior4						78.97 80	84.59 155

test_prior				86.32 108	90.59 153	71.99 158		92.85 86					94.17 92			92.80 156

旧先验2							81.73 227		56.88 329	86.54 174			84.90 303	72.81 198

新几何2							81.72 228

无先验							82.81 203	85.62 240	58.09 319				91.41 181	67.95 245		84.48 306

原ACMM2							82.26 221

testdata2													86.43 281	63.52 280

testdata1							79.62 253		73.95 160

plane_prior7					93.45 66	77.31 102

plane_prior5								93.61 53					95.22 56	80.78 101	95.83 132	94.46 80

plane_prior4											92.95 127

plane_prior3						76.85 107			77.79 118	86.55 169

plane_prior2							89.45 77		79.44 96

plane_prior1					92.83 86

plane_prior						76.42 113	87.15 112		75.94 138						95.03 160

n2								0.00 406

nn								0.00 406

door-mid								74.45 330

test11								91.46 124

door								72.57 344

HQP5-MVS						70.66 171

HQP-NCC					91.19 137		84.77 149		73.30 172	80.55 280

ACMP_Plane					91.19 137		84.77 149		73.30 172	80.55 280

BP-MVS														77.30 145

HQP4-MVS										80.56 279			94.61 74			93.56 129

HQP3-MVS								92.68 91							94.47 180

NP-MVS					91.95 110	74.55 127					90.17 214

ACMMP++_ref															95.74 139

ACMMP++															97.35 73

High-resolution multi-view benchmark