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	78.35 128	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	77.55 142	97.07 82	83.13 332
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	70.36 177	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 79	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	64.33 233	89.67 69	88.38 196	88.84 13	94.29 18	97.57 3	90.48 13	91.26 183	72.57 202	97.65 60	97.34 15

SED-MVS		90.46 33	91.64 17	86.93 97	94.18 46	72.65 143	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	72.65 143		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	72.56 149	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	72.56 149	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	79.95 111	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	82.98 189	93.26 73	63.94 237	91.10 41	89.64 178	85.07 36	90.91 85	91.09 182	89.16 22	91.87 169	82.03 90	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	73.27 137		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	81.90 209	92.22 102	62.28 260	84.66 154	89.15 186	83.54 52	89.85 103	97.32 4	88.08 36	86.80 276	70.43 219	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		75.13 269	79.30 222	62.63 363	75.56 364	75.18 124	80.89 241	73.10 344	75.06 150	94.76 12	95.32 35	87.73 40	52.85 393	34.16 393	97.11 80	59.85 390

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	58.83 303	89.04 83	92.74 90	91.40 5	96.12 4	96.06 22	87.23 45	95.57 38	79.42 120	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	58.88 300	88.95 84	93.19 69	91.62 4	96.01 6	96.16 20	87.02 47	95.60 36	78.69 125	98.72 8	98.97 3

DTE-MVSNet		89.98 43	91.91 13	84.21 157	96.51 7	57.84 310	88.93 85	92.84 87	91.92 3	96.16 3	96.23 18	86.95 48	95.99 10	79.05 122	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	16.05 397	86.57 52	95.80 25	87.35 24	97.62 62	94.20 92

canonicalmvs		85.50 110	86.14 105	83.58 174	87.97 207	67.13 204	87.55 106	94.32 18	73.44 168	88.47 131	87.54 257	86.45 54	91.06 190	75.76 163	93.76 197	92.54 168

TranMVSNet+NR-MVSNet		87.86 79	88.76 69	85.18 133	94.02 54	64.13 234	84.38 161	91.29 131	84.88 39	92.06 63	93.84 102	86.45 54	93.73 106	73.22 193	98.66 10	97.69 9

test_0402		88.65 65	89.58 56	85.88 121	92.55 90	72.22 157	84.01 168	89.44 183	88.63 16	94.38 17	95.77 26	86.38 56	93.59 115	79.84 112	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 89	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 92	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	69.73 181	87.86 103	94.20 25	74.04 158	92.70 53	94.66 56	85.88 61	91.50 175	79.72 115	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	74.12 178	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	74.12 178	96.10 118	94.45 82

GeoE		85.45 112	85.81 112	84.37 149	90.08 162	67.07 205	85.86 134	91.39 128	72.33 192	87.59 147	90.25 211	84.85 66	92.37 154	78.00 136	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	77.65 140	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	80.87 101	95.50 143	94.53 79

EC-MVSNet		88.01 75	88.32 72	87.09 93	89.28 177	72.03 159	90.31 54	96.31 3	80.88 80	85.12 196	89.67 223	84.47 70	95.46 47	82.56 84	96.26 111	93.77 118

casdiffmvs_mvg		86.72 91	87.51 82	84.36 151	87.09 230	65.22 224	84.16 163	94.23 22	77.89 116	91.28 77	93.66 109	84.35 71	92.71 144	80.07 108	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	77.97 138	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	89.33 229	83.87 74	94.53 79	82.45 85	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 83	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	77.43 145	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	86.16 289	84.31 64	93.28 208	92.15 187

Effi-MVS+		83.90 149	84.01 147	83.57 175	87.22 224	65.61 222	86.55 127	92.40 96	78.64 109	81.34 272	84.18 309	83.65 79	92.93 140	74.22 175	87.87 298	92.17 186

MVS_111021_HR		84.63 126	84.34 143	85.49 130	90.18 161	75.86 120	79.23 265	87.13 216	73.35 169	85.56 191	89.34 228	83.60 80	90.50 208	76.64 153	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	83.57 175	90.05 165	72.26 156	86.29 130	90.00 171	78.19 114	81.65 266	87.16 265	83.40 82	94.24 87	61.69 296	94.76 175	84.21 314

LCM-MVSNet-Re		83.48 157	85.06 124	78.75 256	85.94 258	55.75 326	80.05 249	94.27 19	76.47 129	96.09 5	94.54 63	83.31 83	89.75 233	59.95 306	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	78.85 254	91.00 144	55.20 331	82.29 220	87.26 212	79.65 93	88.38 134	95.52 33	83.00 85	86.88 274	67.97 246	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	79.55 117	95.95 125	92.00 192

v7n		90.13 36	90.96 38	87.65 89	91.95 110	71.06 171	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	70.81 211	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	70.81 211	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	76.54 155	88.74 287	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	81.64 267	87.25 263	82.43 93	94.53 79	77.65 140	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	73.58 188	94.66 176	94.56 76

SDMVSNet		81.90 187	83.17 159	78.10 269	88.81 189	62.45 256	76.08 311	86.05 235	73.67 163	83.41 235	93.04 120	82.35 95	80.65 330	70.06 222	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+		81.04 197	80.57 202	82.46 204	87.50 219	63.22 244	78.37 277	89.63 179	68.01 237	81.87 260	82.08 332	82.31 97	92.65 147	67.10 248	88.30 294	91.51 207

baseline		85.20 116	85.93 107	83.02 188	86.30 247	62.37 258	84.55 156	93.96 39	74.48 155	87.12 153	92.03 154	82.30 98	91.94 165	78.39 126	94.21 188	94.74 73

casdiffmvs		85.21 115	85.85 111	83.31 181	86.17 253	62.77 250	83.03 197	93.93 40	74.69 153	88.21 137	92.68 137	82.29 99	91.89 168	77.87 139	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	65.27 223	88.86 86	93.02 81	87.15 23	93.05 43	97.10 6	82.28 100	92.02 164	76.70 152	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	73.75 185	94.81 171	93.70 120

Anonymous20240529		86.20 101	87.13 87	83.42 178	90.19 160	64.55 231	84.55 156	90.71 146	85.85 31	89.94 102	95.24 40	82.13 102	90.40 210	69.19 231	96.40 105	95.31 55

CLD-MVS		83.18 162	82.64 169	84.79 139	89.05 182	67.82 202	77.93 281	92.52 94	68.33 233	85.07 197	81.54 338	82.06 103	92.96 138	69.35 227	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 97	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		79.54 118	96.97 84

EGC-MVSNET		74.79 276	69.99 316	89.19 63	94.89 37	87.00 11	91.89 34	86.28 229	1.09 398	2.23 400	95.98 23	81.87 109	89.48 234	79.76 114	95.96 124	91.10 214

CP-MVSNet		89.27 58	90.91 40	84.37 149	96.34 8	58.61 306	88.66 92	92.06 106	90.78 6	95.67 7	95.17 43	81.80 110	95.54 41	79.00 123	98.69 9	98.95 4

MVS_111021_LR		84.28 136	83.76 151	85.83 123	89.23 179	83.07 51	80.99 240	83.56 269	72.71 184	86.07 181	89.07 234	81.75 111	86.19 288	77.11 149	93.36 204	88.24 268

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		20.81 364	27.75 367	0.00 384	0.00 406	0.00 409	0.00 395	85.44 243	0.00 402	0.00 403	82.82 324	81.46 113	0.00 403	0.00 402	0.00 401	0.00 399

WR-MVS_H		89.91 46	91.31 29	85.71 125	96.32 9	62.39 257	89.54 74	93.31 64	90.21 10	95.57 9	95.66 29	81.42 114	95.90 15	80.94 100	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	79.91 241	90.04 166	59.66 290	82.43 216	87.44 209	75.52 144	87.85 143	95.26 39	81.25 116	85.65 299	68.74 238	96.04 120	94.42 85

DVP-MVS++		90.07 38	91.09 32	87.00 95	91.55 127	72.64 145	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	74.48 173	95.35 146	92.29 179

sd_testset		79.95 221	81.39 192	75.64 301	88.81 189	58.07 308	76.16 310	82.81 276	73.67 163	83.41 235	93.04 120	80.96 119	77.65 341	58.62 312	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 98	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	89.56 224	80.76 121	92.13 160	73.21 198	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	81.41 191	85.90 120	85.60 261	76.53 111	83.07 196	89.62 180	73.02 179	79.11 300	83.51 314	80.74 122	90.24 213	68.76 237	89.29 278	90.94 217

PC_three_1452									58.96 315	90.06 96	91.33 174	80.66 123	93.03 137	75.78 162	95.94 126	92.48 169

VPA-MVSNet		83.47 158	84.73 129	79.69 245	90.29 158	57.52 313	81.30 236	88.69 192	76.29 130	87.58 148	94.44 67	80.60 124	87.20 268	66.60 254	96.82 89	94.34 89

ETV-MVS		84.31 134	83.91 150	85.52 128	88.58 196	70.40 176	84.50 160	93.37 58	78.76 108	84.07 224	78.72 360	80.39 125	95.13 60	73.82 184	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	75.65 300	81.24 314	45.26 379	79.94 251	92.91 84	83.83 46	91.33 74	96.88 10	80.25 127	85.92 292	68.89 235	95.89 129	95.76 43

EI-MVSNet-Vis-set		85.12 118	84.53 136	86.88 98	84.01 285	72.76 142	83.91 173	85.18 248	80.44 82	88.75 125	85.49 288	80.08 128	91.92 166	82.02 91	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	78.37 127	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	83.87 289	72.52 151	83.82 175	85.15 249	80.27 86	88.75 125	85.45 290	79.95 130	91.90 167	81.92 94	90.80 262	96.13 34

MCST-MVS		84.36 132	83.93 149	85.63 126	91.59 122	71.58 166	83.52 183	92.13 104	61.82 287	83.96 226	89.75 222	79.93 131	93.46 122	78.33 129	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	72.63 147		90.69 147		82.76 246		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	88.01 247	79.09 134	92.13 160	75.51 164	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	87.70 254	78.87 136	94.18 90	80.67 105	96.29 107	92.73 158

EG-PatchMatch MVS		84.08 143	84.11 145	83.98 161	92.22 102	72.61 148	82.20 226	87.02 221	72.63 185	88.86 122	91.02 184	78.52 137	91.11 188	73.41 190	91.09 251	88.21 269

dcpmvs_2		84.23 139	85.14 123	81.50 217	88.61 195	61.98 264	82.90 203	93.11 73	68.66 231	92.77 51	92.39 143	78.50 138	87.63 263	76.99 151	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	83.56 233	85.65 286	78.49 139	94.21 88	72.04 205	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	80.54 107	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	67.02 206	82.55 212	91.56 121	83.08 57	90.92 83	91.82 161	78.25 141	93.99 97	74.16 176	98.35 21	97.49 13

MSLP-MVS++		85.00 121	86.03 106	81.90 209	91.84 117	71.56 168	86.75 123	93.02 81	75.95 137	87.12 153	89.39 227	77.98 142	89.40 241	77.46 143	94.78 172	84.75 307

API-MVS		82.28 174	82.61 170	81.30 219	86.29 248	69.79 179	88.71 90	87.67 208	78.42 112	82.15 255	84.15 310	77.98 142	91.59 174	65.39 265	92.75 220	82.51 340

DP-MVS Recon		84.05 144	83.22 156	86.52 105	91.73 120	75.27 123	83.23 193	92.40 96	72.04 196	82.04 257	88.33 243	77.91 144	93.95 99	66.17 256	95.12 157	90.34 236

fmvsm_s_conf0.1_n_a		82.58 170	81.93 179	84.50 146	87.68 214	73.35 134	86.14 131	77.70 307	61.64 292	85.02 198	91.62 167	77.75 145	86.24 285	82.79 81	87.07 307	93.91 109

UniMVSNet (Re)		86.87 87	86.98 92	86.55 104	93.11 77	68.48 194	83.80 177	92.87 85	80.37 83	89.61 112	91.81 162	77.72 146	94.18 90	75.00 171	98.53 15	96.99 24

PCF-MVS	74.62 15	82.15 179	80.92 200	85.84 122	89.43 174	72.30 155	80.53 244	91.82 116	57.36 328	87.81 144	89.92 219	77.67 147	93.63 110	58.69 311	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	64.56 230	82.21 224	90.46 153	80.99 78	88.42 132	91.97 155	77.56 148	93.85 102	72.46 203	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	80.22 238	82.62 302	57.75 312	82.54 213	91.96 110	71.16 205	82.89 244	92.52 142	77.41 150	90.50 208	80.04 110	87.84 299	92.40 173

fmvsm_s_conf0.5_n_a		82.21 176	81.51 190	84.32 154	86.56 238	73.35 134	85.46 140	77.30 311	61.81 288	84.51 208	90.88 192	77.36 151	86.21 287	82.72 82	86.97 311	93.38 133

EIA-MVS		82.19 177	81.23 196	85.10 134	87.95 208	69.17 191	83.22 194	93.33 61	70.42 211	78.58 303	79.77 354	77.29 152	94.20 89	71.51 207	88.96 283	91.93 195

xiu_mvs_v2_base		77.19 247	76.75 250	78.52 260	87.01 232	61.30 269	75.55 318	87.12 219	61.24 298	74.45 337	78.79 359	77.20 153	90.93 193	64.62 275	84.80 337	83.32 328

DU-MVS		86.80 90	86.99 91	86.21 113	93.24 74	67.02 206	83.16 195	92.21 101	81.73 69	90.92 83	91.97 155	77.20 153	93.99 97	74.16 176	98.35 21	97.61 10

Baseline_NR-MVSNet		84.00 146	85.90 109	78.29 266	91.47 132	53.44 340	82.29 220	87.00 224	79.06 102	89.55 114	95.72 28	77.20 153	86.14 290	72.30 204	98.51 16	95.28 56

TinyColmap		81.25 194	82.34 175	77.99 272	85.33 265	60.68 281	82.32 219	88.33 199	71.26 203	86.97 160	92.22 153	77.10 156	86.98 272	62.37 288	95.17 154	86.31 291

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	80.18 290	89.15 233	77.04 157	93.28 128	65.82 262	92.28 229	92.21 184

114514_t		83.10 165	82.54 172	84.77 141	92.90 81	69.10 192	86.65 124	90.62 150	54.66 338	81.46 269	90.81 195	76.98 158	94.38 83	72.62 201	96.18 113	90.82 221

xiu_mvs_v1_base_debu		80.84 199	80.14 213	82.93 192	88.31 201	71.73 162	79.53 256	87.17 213	65.43 262	79.59 292	82.73 326	76.94 159	90.14 219	73.22 193	88.33 290	86.90 286

xiu_mvs_v1_base		80.84 199	80.14 213	82.93 192	88.31 201	71.73 162	79.53 256	87.17 213	65.43 262	79.59 292	82.73 326	76.94 159	90.14 219	73.22 193	88.33 290	86.90 286

xiu_mvs_v1_base_debi		80.84 199	80.14 213	82.93 192	88.31 201	71.73 162	79.53 256	87.17 213	65.43 262	79.59 292	82.73 326	76.94 159	90.14 219	73.22 193	88.33 290	86.90 286

pcd_1.5k_mvsjas		6.41 367	8.55 370	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	76.94 159	0.00 403	0.00 402	0.00 401	0.00 399

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		77.04 249	76.53 252	78.56 259	87.09 230	61.40 267	75.26 320	87.13 216	61.25 297	74.38 339	77.22 370	76.94 159	90.94 192	64.63 274	84.83 336	83.35 327

MIMVSNet1		83.63 153	84.59 134	80.74 229	94.06 53	62.77 250	82.72 206	84.53 261	77.57 121	90.34 92	95.92 24	76.88 165	85.83 297	61.88 294	97.42 72	93.62 125

原ACMM1				84.60 145	92.81 87	74.01 130		91.50 123	62.59 279	82.73 247	90.67 201	76.53 166	94.25 86	69.24 228	95.69 140	85.55 298

fmvsm_s_conf0.1_n		82.17 178	81.59 186	83.94 164	86.87 236	71.57 167	85.19 146	77.42 310	62.27 286	84.47 211	91.33 174	76.43 167	85.91 293	83.14 72	87.14 305	94.33 90

fmvsm_s_conf0.5_n		81.91 186	81.30 193	83.75 168	86.02 257	71.56 168	84.73 152	77.11 314	62.44 283	84.00 225	90.68 199	76.42 168	85.89 295	83.14 72	87.11 306	93.81 116

MSDG		80.06 219	79.99 218	80.25 237	83.91 288	68.04 200	77.51 289	89.19 185	77.65 119	81.94 258	83.45 316	76.37 169	86.31 284	63.31 284	86.59 314	86.41 289

Gipuma		84.44 131	86.33 101	78.78 255	84.20 284	73.57 133	89.55 72	90.44 154	84.24 43	84.38 212	94.89 49	76.35 170	80.40 331	76.14 159	96.80 90	82.36 341
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	85.89 295	82.28 88	90.87 259	93.58 127

XXY-MVS		74.44 280	76.19 255	69.21 338	84.61 275	52.43 348	71.70 344	77.18 313	60.73 304	80.60 280	90.96 188	75.44 172	69.35 363	56.13 326	88.33 290	85.86 296

FMVSNet1		84.55 129	85.45 119	81.85 211	90.27 159	61.05 273	86.83 119	88.27 201	78.57 110	89.66 109	95.64 30	75.43 173	90.68 203	69.09 232	95.33 147	93.82 113

CANet		83.79 150	82.85 165	86.63 102	86.17 253	72.21 158	83.76 178	91.43 125	77.24 125	74.39 338	87.45 259	75.36 174	95.42 49	77.03 150	92.83 219	92.25 183

ab-mvs		79.67 222	80.56 203	76.99 284	88.48 198	56.93 317	84.70 153	86.06 234	68.95 227	80.78 279	93.08 119	75.30 175	84.62 307	56.78 321	90.90 258	89.43 251

patch_mono-2		78.89 226	79.39 221	77.41 281	84.78 272	68.11 198	75.60 315	83.11 272	60.96 301	79.36 296	89.89 220	75.18 176	72.97 353	73.32 192	92.30 226	91.15 213

DELS-MVS		81.44 192	81.25 194	82.03 207	84.27 283	62.87 248	76.47 305	92.49 95	70.97 206	81.64 267	83.83 311	75.03 177	92.70 145	74.29 174	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		78.84 228	78.10 238	81.07 224	85.17 268	60.22 284	82.21 224	90.57 151	62.51 280	75.32 332	84.61 304	74.99 178	92.30 157	59.48 309	88.04 296	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	83.54 234	88.66 240	74.87 179	81.73 323	66.84 251	92.29 228	89.11 257

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	80.78 103	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	82.58 200	92.25 100	56.44 321	85.75 136	93.09 75	77.33 123	91.94 66	94.65 57	74.78 182	93.41 125	75.11 170	98.58 13	97.88 7

VDD-MVS		84.23 139	84.58 135	83.20 185	91.17 140	65.16 226	83.25 191	84.97 256	79.79 90	87.18 152	94.27 75	74.77 183	90.89 196	69.24 228	96.54 97	93.55 131

BH-untuned		80.96 198	80.99 198	80.84 228	88.55 197	68.23 195	80.33 247	88.46 194	72.79 183	86.55 169	86.76 271	74.72 184	91.77 172	61.79 295	88.99 282	82.52 339

VPNet		80.25 213	81.68 182	75.94 298	92.46 93	47.98 370	76.70 299	81.67 286	73.45 167	84.87 203	92.82 131	74.66 185	86.51 281	61.66 297	96.85 86	93.33 135

tfpnnormal		81.79 188	82.95 163	78.31 264	88.93 186	55.40 327	80.83 243	82.85 275	76.81 127	85.90 186	94.14 85	74.58 186	86.51 281	66.82 252	95.68 141	93.01 150

KD-MVS_self_test		81.93 185	83.14 160	78.30 265	84.75 274	52.75 344	80.37 246	89.42 184	70.24 216	90.26 94	93.39 114	74.55 187	86.77 277	68.61 240	96.64 93	95.38 52

fmvsm_l_conf0.5_n		82.06 181	81.54 189	83.60 173	83.94 286	73.90 131	83.35 188	86.10 233	58.97 314	83.80 228	90.36 207	74.23 188	86.94 273	82.90 78	90.22 270	89.94 244

V42		83.47 158	83.37 155	83.75 168	83.16 296	63.33 242	81.31 234	90.23 165	69.51 221	90.91 85	90.81 195	74.16 189	92.29 158	80.06 109	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	74.06 190	91.19 185	78.28 130	91.09 251	89.29 255

v10		86.54 94	87.10 88	84.84 137	88.16 206	63.28 243	86.64 125	92.20 102	75.42 146	92.81 50	94.50 64	74.05 191	94.06 96	83.88 68	96.28 108	97.17 20

旧先验1					91.97 109	71.77 161		81.78 285			91.84 159	73.92 192			93.65 201	83.61 322

mvs_anonymous		78.13 237	78.76 229	76.23 297	79.24 337	50.31 363	78.69 272	84.82 258	61.60 293	83.09 242	92.82 131	73.89 193	87.01 269	68.33 244	86.41 316	91.37 208

MAR-MVS		80.24 214	78.74 230	84.73 142	86.87 236	78.18 88	85.75 136	87.81 207	65.67 261	77.84 308	78.50 361	73.79 194	90.53 207	61.59 298	90.87 259	85.49 300
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	81.25 220	95.13 31	59.32 293	85.42 142	81.11 289	86.41 27	87.41 150	96.21 19	73.61 195	90.61 206	66.33 255	96.85 86	93.81 116

FIs		85.35 113	86.27 102	82.60 199	91.86 114	57.31 314	85.10 148	93.05 77	75.83 139	91.02 82	93.97 93	73.57 196	92.91 142	73.97 181	98.02 39	97.58 12

v1144		84.54 130	84.72 131	84.00 160	87.67 215	62.55 254	82.97 200	90.93 142	70.32 214	89.80 104	90.99 185	73.50 197	93.48 121	81.69 96	94.65 177	95.97 39

diffmvs		80.40 208	80.48 206	80.17 239	79.02 340	60.04 285	77.54 288	90.28 164	66.65 251	82.40 250	87.33 262	73.50 197	87.35 266	77.98 137	89.62 276	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	83.33 180	90.90 146	65.98 218	88.19 98	90.78 145	78.13 115	80.87 277	87.92 251	73.49 199	92.42 151	70.07 221	88.40 289	91.60 204

v8		86.22 100	86.83 95	84.36 151	87.82 210	62.35 259	86.42 128	91.33 130	76.78 128	92.73 52	94.48 66	73.41 200	93.72 107	83.10 74	95.41 144	97.01 23

EI-MVSNet		82.61 168	82.42 174	83.20 185	83.25 294	63.66 238	83.50 184	85.07 250	76.06 132	86.55 169	85.10 296	73.41 200	90.25 211	78.15 135	90.67 265	95.68 45

IterMVS-LS		84.73 125	84.98 126	83.96 162	87.35 221	63.66 238	83.25 191	89.88 173	76.06 132	89.62 110	92.37 147	73.40 202	92.52 149	78.16 133	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	83.58 232	93.78 105	73.36 203	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	61.57 266	82.95 201	91.03 138	67.82 243	89.80 104	90.49 205	73.28 204	93.51 120	81.88 95	94.89 167	96.04 38

BH-RMVSNet		80.53 204	80.22 211	81.49 218	87.19 225	66.21 216	77.79 284	86.23 231	74.21 157	83.69 229	88.50 241	73.25 205	90.75 200	63.18 285	87.90 297	87.52 279

PLC	73.85 16	82.09 180	80.31 207	87.45 90	90.86 148	80.29 69	85.88 133	90.65 148	68.17 235	76.32 319	86.33 276	73.12 206	92.61 148	61.40 299	90.02 273	89.44 250
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	72.94 207	94.85 67	85.07 55	97.78 53	97.26 16

WR-MVS		83.56 155	84.40 141	81.06 225	93.43 68	54.88 332	78.67 273	85.02 253	81.24 75	90.74 89	91.56 169	72.85 208	91.08 189	68.00 245	98.04 36	97.23 18

VNet		79.31 223	80.27 208	76.44 292	87.92 209	53.95 336	75.58 317	84.35 262	74.39 156	82.23 253	90.72 197	72.84 209	84.39 309	60.38 305	93.98 194	90.97 216

QAPM		82.59 169	82.59 171	82.58 200	86.44 240	66.69 210	89.94 62	90.36 157	67.97 239	84.94 202	92.58 140	72.71 210	92.18 159	70.63 217	87.73 300	88.85 264

v1192		84.57 128	84.69 133	84.21 157	87.75 212	62.88 247	83.02 198	91.43 125	69.08 225	89.98 101	90.89 190	72.70 211	93.62 113	82.41 86	94.97 164	96.13 34

OpenMVS	76.72 13	81.98 184	82.00 178	81.93 208	84.42 279	68.22 196	88.50 95	89.48 182	66.92 248	81.80 264	91.86 157	72.59 212	90.16 216	71.19 210	91.25 250	87.40 281

TSAR-MVS + GP.		83.95 147	82.69 168	87.72 86	89.27 178	81.45 63	83.72 179	81.58 288	74.73 152	85.66 188	86.06 281	72.56 213	92.69 146	75.44 166	95.21 152	89.01 263

alignmvs		83.94 148	83.98 148	83.80 165	87.80 211	67.88 201	84.54 158	91.42 127	73.27 175	88.41 133	87.96 248	72.33 214	90.83 198	76.02 161	94.11 191	92.69 162

fmvsm_l_conf0.5_n_a		81.46 191	80.87 201	83.25 182	83.73 290	73.21 139	83.00 199	85.59 242	58.22 320	82.96 243	90.09 217	72.30 215	86.65 279	81.97 93	89.95 274	89.88 245

HQP2-MVS												72.10 216

HQP-MVS		84.61 127	84.06 146	86.27 110	91.19 137	70.66 173	84.77 149	92.68 91	73.30 172	80.55 282	90.17 215	72.10 216	94.61 74	77.30 147	94.47 180	93.56 129

testgi		72.36 295	74.61 268	65.59 354	80.56 325	42.82 386	68.29 359	73.35 341	66.87 249	81.84 261	89.93 218	72.08 218	66.92 376	46.05 377	92.54 223	87.01 285

v1921920		84.23 139	84.37 142	83.79 166	87.64 217	61.71 265	82.91 202	91.20 134	67.94 240	90.06 96	90.34 208	72.04 219	93.59 115	82.32 87	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	72.03 220	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	82.08 303	80.15 70	85.53 139	88.76 191	68.01 237	85.58 190	87.75 253	71.80 221	86.85 275	74.02 180	93.87 196	88.58 266

v1240		84.30 135	84.51 137	83.65 171	87.65 216	61.26 270	82.85 204	91.54 122	67.94 240	90.68 90	90.65 202	71.71 222	93.64 109	82.84 80	94.78 172	96.07 36

ambc				82.98 189	90.55 154	64.86 227	88.20 97	89.15 186		89.40 117	93.96 96	71.67 223	91.38 182	78.83 124	96.55 96	92.71 161

MVS_0304		86.35 97	85.92 108	87.66 88	89.21 180	73.16 140	88.40 96	83.63 268	81.27 74	80.87 277	94.12 87	71.49 224	95.71 32	87.79 12	96.50 99	94.11 100

新几何1				82.95 191	93.96 55	78.56 84		80.24 295	55.45 335	83.93 227	91.08 183	71.19 225	88.33 256	65.84 261	93.07 213	81.95 345

SSC-MVS		77.55 243	81.64 183	65.29 357	90.46 155	20.33 402	73.56 334	68.28 366	85.44 32	88.18 139	94.64 60	70.93 226	81.33 325	71.25 208	92.03 234	94.20 92

v148		82.31 173	82.48 173	81.81 214	85.59 262	59.66 290	81.47 233	86.02 236	72.85 180	88.05 140	90.65 202	70.73 227	90.91 195	75.15 169	91.79 239	94.87 67

v2v482		84.09 142	84.24 144	83.62 172	87.13 226	61.40 267	82.71 207	89.71 176	72.19 195	89.55 114	91.41 172	70.70 228	93.20 130	81.02 99	93.76 197	96.25 32

WB-MVS		76.06 261	80.01 217	64.19 360	89.96 168	20.58 401	72.18 342	68.19 367	83.21 54	86.46 176	93.49 112	70.19 229	78.97 337	65.96 257	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	85.68 240	77.07 126	73.76 341	92.82 131	69.64 230	91.82 171	69.04 234	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		81.64 189	81.59 186	81.79 215	80.86 320	59.15 297	78.61 274	90.18 167	68.36 232	87.20 151	87.11 267	69.39 231	91.62 173	78.16 133	94.43 182	94.60 75

MG-MVS		80.32 212	80.94 199	78.47 262	88.18 204	52.62 347	82.29 220	85.01 254	72.01 197	79.24 299	92.54 141	69.36 232	93.36 127	70.65 216	89.19 281	89.45 249

IS-MVSNet		86.66 93	86.82 96	86.17 115	92.05 108	66.87 209	91.21 39	88.64 193	86.30 28	89.60 113	92.59 138	69.22 233	94.91 66	73.89 182	97.89 49	96.72 26

PVSNet_BlendedMVS		78.80 230	77.84 239	81.65 216	84.43 277	63.41 240	79.49 259	90.44 154	61.70 291	75.43 329	87.07 268	69.11 234	91.44 178	60.68 303	92.24 230	90.11 241

PVSNet_Blended		76.49 257	75.40 262	79.76 243	84.43 277	63.41 240	75.14 321	90.44 154	57.36 328	75.43 329	78.30 362	69.11 234	91.44 178	60.68 303	87.70 301	84.42 310

BH-w/o		76.57 255	76.07 257	78.10 269	86.88 235	65.92 219	77.63 286	86.33 228	65.69 260	80.89 276	79.95 351	68.97 236	90.74 201	53.01 347	85.25 326	77.62 369

MVS		73.21 289	72.59 291	75.06 305	80.97 317	60.81 279	81.64 231	85.92 238	46.03 374	71.68 351	77.54 365	68.47 237	89.77 231	55.70 329	85.39 323	74.60 375

miper_ehance_all_eth		80.34 211	80.04 216	81.24 222	79.82 330	58.95 299	77.66 285	89.66 177	65.75 259	85.99 185	85.11 295	68.29 238	91.42 180	76.03 160	92.03 234	93.33 135

Anonymous202405211		80.51 205	81.19 197	78.49 261	88.48 198	57.26 315	76.63 301	82.49 278	81.21 76	84.30 218	92.24 152	67.99 239	86.24 285	62.22 289	95.13 155	91.98 194

testdata				79.54 248	92.87 82	72.34 154		80.14 296	59.91 311	85.47 193	91.75 165	67.96 240	85.24 301	68.57 242	92.18 233	81.06 358

DPM-MVS		80.10 218	79.18 223	82.88 195	90.71 151	69.74 180	78.87 270	90.84 143	60.29 308	75.64 328	85.92 284	67.28 241	93.11 134	71.24 209	91.79 239	85.77 297

PVSNet_Blended_VisFu		81.55 190	80.49 205	84.70 144	91.58 125	73.24 138	84.21 162	91.67 120	62.86 278	80.94 275	87.16 265	67.27 242	92.87 143	69.82 224	88.94 284	87.99 273

MDA-MVSNet-bldmvs		77.47 244	76.90 249	79.16 252	79.03 339	64.59 228	66.58 367	75.67 324	73.15 177	88.86 122	88.99 235	66.94 243	81.23 326	64.71 272	88.22 295	91.64 203

CL-MVSNet_self_test		76.81 252	77.38 243	75.12 304	86.90 234	51.34 355	73.20 338	80.63 294	68.30 234	81.80 264	88.40 242	66.92 244	80.90 327	55.35 333	94.90 166	93.12 146

test222					93.31 71	76.54 109	79.38 260	77.79 306	52.59 347	82.36 251	90.84 194	66.83 245			91.69 241	81.25 353

TR-MVS		76.77 253	75.79 258	79.72 244	86.10 256	65.79 220	77.14 292	83.02 273	65.20 268	81.40 270	82.10 330	66.30 246	90.73 202	55.57 330	85.27 325	82.65 334

OpenMVS_ROB	70.19 17	77.77 242	77.46 241	78.71 257	84.39 280	61.15 271	81.18 238	82.52 277	62.45 282	83.34 237	87.37 260	66.20 247	88.66 253	64.69 273	85.02 330	86.32 290

EPP-MVSNet		85.47 111	85.04 125	86.77 101	91.52 130	69.37 185	91.63 36	87.98 206	81.51 72	87.05 159	91.83 160	66.18 248	95.29 53	70.75 214	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	66.01 249	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	69.19 190	87.84 104	88.05 204	81.66 70	94.64 14	96.53 14	65.94 250	94.75 69	83.02 77	96.83 88	95.41 51

PatchMatch-RL		74.48 278	73.22 283	78.27 267	87.70 213	85.26 34	75.92 313	70.09 360	64.34 272	76.09 322	81.25 340	65.87 251	78.07 340	53.86 341	83.82 342	71.48 378

EPNet		80.37 209	78.41 235	86.23 111	76.75 354	73.28 136	87.18 111	77.45 309	76.24 131	68.14 365	88.93 236	65.41 252	93.85 102	69.47 226	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	65.40 253	95.90 15	85.01 58	98.23 27	97.49 13

FA-MVS(test-final)		83.13 164	83.02 162	83.43 177	86.16 255	66.08 217	88.00 100	88.36 197	75.55 143	85.02 198	92.75 135	65.12 254	92.50 150	74.94 172	91.30 249	91.72 199

PM-MVS		80.20 215	79.00 224	83.78 167	88.17 205	86.66 15	81.31 234	66.81 374	69.64 220	88.33 135	90.19 213	64.58 255	83.63 315	71.99 206	90.03 272	81.06 358

miper_enhance_ethall		77.83 239	76.93 248	80.51 233	76.15 360	58.01 309	75.47 319	88.82 189	58.05 322	83.59 231	80.69 342	64.41 256	91.20 184	73.16 199	92.03 234	92.33 177

eth_miper_zixun_eth		80.84 199	80.22 211	82.71 197	81.41 312	60.98 276	77.81 283	90.14 168	67.31 246	86.95 161	87.24 264	64.26 257	92.31 156	75.23 168	91.61 243	94.85 71

test20.03		73.75 284	74.59 270	71.22 326	81.11 316	51.12 359	70.15 354	72.10 350	70.42 211	80.28 288	91.50 170	64.21 258	74.72 352	46.96 374	94.58 178	87.82 278

cascas		76.29 260	74.81 267	80.72 231	84.47 276	62.94 246	73.89 332	87.34 210	55.94 333	75.16 334	76.53 374	63.97 259	91.16 186	65.00 269	90.97 256	88.06 271

TAMVS		78.08 238	76.36 253	83.23 183	90.62 152	72.87 141	79.08 266	80.01 297	61.72 290	81.35 271	86.92 270	63.96 260	88.78 251	50.61 357	93.01 215	88.04 272

GBi-Net		82.02 182	82.07 176	81.85 211	86.38 242	61.05 273	86.83 119	88.27 201	72.43 187	86.00 182	95.64 30	63.78 261	90.68 203	65.95 258	93.34 205	93.82 113

test1		82.02 182	82.07 176	81.85 211	86.38 242	61.05 273	86.83 119	88.27 201	72.43 187	86.00 182	95.64 30	63.78 261	90.68 203	65.95 258	93.34 205	93.82 113

FMVSNet2		81.31 193	81.61 185	80.41 235	86.38 242	58.75 304	83.93 172	86.58 227	72.43 187	87.65 146	92.98 124	63.78 261	90.22 214	66.86 249	93.92 195	92.27 181

USDC		76.63 254	76.73 251	76.34 294	83.46 292	57.20 316	80.02 250	88.04 205	52.14 352	83.65 230	91.25 176	63.24 264	86.65 279	54.66 338	94.11 191	85.17 302

DIV-MVS_self_test		80.43 206	80.23 209	81.02 226	79.99 328	59.25 294	77.07 294	87.02 221	67.38 244	86.19 178	89.22 230	63.09 265	90.16 216	76.32 156	95.80 135	93.66 121

cl____		80.42 207	80.23 209	81.02 226	79.99 328	59.25 294	77.07 294	87.02 221	67.37 245	86.18 180	89.21 231	63.08 266	90.16 216	76.31 157	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	84.98 255	71.27 201	86.70 165	90.55 204	63.04 267	93.92 100	78.26 131	94.20 189	89.63 247

hse-mvs2		83.47 158	81.81 181	88.47 75	91.03 143	82.27 57	82.61 208	83.69 266	71.27 201	86.70 165	86.05 282	63.04 267	92.41 152	78.26 131	93.62 203	90.71 224

new-patchmatchnet		70.10 315	73.37 282	60.29 370	81.23 315	16.95 403	59.54 380	74.62 329	62.93 277	80.97 273	87.93 250	62.83 269	71.90 356	55.24 334	95.01 163	92.00 192

K. test v3		85.14 117	84.73 129	86.37 107	91.13 141	69.63 183	85.45 141	76.68 318	84.06 45	92.44 57	96.99 8	62.03 270	94.65 72	80.58 106	93.24 209	94.83 72

lessismore_v0				85.95 118	91.10 142	70.99 172		70.91 358		91.79 67	94.42 70	61.76 271	92.93 140	79.52 119	93.03 214	93.93 107

1314		73.22 288	72.56 293	75.20 303	80.41 327	57.84 310	81.64 231	85.36 244	51.68 355	73.10 344	76.65 373	61.45 272	85.19 302	63.54 281	79.21 367	82.59 335

Syy-MVS		69.40 323	70.03 315	67.49 347	81.72 307	38.94 389	71.00 347	61.99 380	61.38 295	70.81 356	72.36 382	61.37 273	79.30 334	64.50 277	85.18 327	84.22 312

CANet_DTU		77.81 241	77.05 246	80.09 240	81.37 313	59.90 288	83.26 190	88.29 200	69.16 224	67.83 368	83.72 312	60.93 274	89.47 235	69.22 230	89.70 275	90.88 219

pmmvs-eth3d		78.42 236	77.04 247	82.57 202	87.44 220	74.41 128	80.86 242	79.67 298	55.68 334	84.69 206	90.31 210	60.91 275	85.42 300	62.20 290	91.59 244	87.88 276

UnsupCasMVSNet_eth		71.63 302	72.30 295	69.62 335	76.47 357	52.70 346	70.03 355	80.97 291	59.18 313	79.36 296	88.21 245	60.50 276	69.12 364	58.33 315	77.62 374	87.04 284

IterMVS-SCA-FT		80.64 203	79.41 220	84.34 153	83.93 287	69.66 182	76.28 307	81.09 290	72.43 187	86.47 175	90.19 213	60.46 277	93.15 133	77.45 144	86.39 317	90.22 237

SCA		73.32 286	72.57 292	75.58 302	81.62 309	55.86 324	78.89 269	71.37 356	61.73 289	74.93 335	83.42 317	60.46 277	87.01 269	58.11 317	82.63 353	83.88 316

jason		77.42 245	75.75 259	82.43 205	87.10 229	69.27 186	77.99 280	81.94 283	51.47 356	77.84 308	85.07 299	60.32 279	89.00 245	70.74 215	89.27 280	89.03 261
jason: jason.
1112_ss		74.82 275	73.74 276	78.04 271	89.57 170	60.04 285	76.49 304	87.09 220	54.31 339	73.66 342	79.80 352	60.25 280	86.76 278	58.37 313	84.15 341	87.32 282

HY-MVS	64.64 18	73.03 290	72.47 294	74.71 306	83.36 293	54.19 334	82.14 227	81.96 282	56.76 332	69.57 361	86.21 280	60.03 281	84.83 306	49.58 363	82.65 351	85.11 303

Anonymous20231206		71.38 305	71.88 297	69.88 333	86.31 246	54.37 333	70.39 352	74.62 329	52.57 348	76.73 315	88.76 237	59.94 282	72.06 355	44.35 381	93.23 210	83.23 330

IterMVS		76.91 250	76.34 254	78.64 258	80.91 318	64.03 235	76.30 306	79.03 301	64.88 270	83.11 240	89.16 232	59.90 283	84.46 308	68.61 240	85.15 329	87.42 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
YYNet1		70.06 316	70.44 309	68.90 339	73.76 375	53.42 341	58.99 383	67.20 370	58.42 318	87.10 155	85.39 292	59.82 284	67.32 373	59.79 307	83.50 344	85.96 293

MDA-MVSNet_test_wron		70.05 317	70.44 309	68.88 340	73.84 374	53.47 339	58.93 384	67.28 369	58.43 317	87.09 156	85.40 291	59.80 285	67.25 374	59.66 308	83.54 343	85.92 295

PMMVS		61.65 349	60.38 356	65.47 356	65.40 398	69.26 187	63.97 373	61.73 384	36.80 394	60.11 388	68.43 387	59.42 286	66.35 378	48.97 365	78.57 370	60.81 389

CDS-MVSNet		77.32 246	75.40 262	83.06 187	89.00 184	72.48 152	77.90 282	82.17 281	60.81 302	78.94 301	83.49 315	59.30 287	88.76 252	54.64 339	92.37 225	87.93 275
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UnsupCasMVSNet_bld		69.21 324	69.68 318	67.82 345	79.42 334	51.15 358	67.82 363	75.79 322	54.15 340	77.47 314	85.36 294	59.26 288	70.64 359	48.46 367	79.35 365	81.66 347

Anonymous20240521		80.18 216	81.25 194	76.95 285	83.15 297	60.84 278	82.46 215	85.99 237	68.76 229	86.78 162	93.73 108	59.13 289	77.44 342	73.71 186	97.55 67	92.56 166

WTY-MVS		67.91 329	68.35 327	66.58 351	80.82 321	48.12 369	65.96 368	72.60 345	53.67 342	71.20 353	81.68 337	58.97 290	69.06 365	48.57 366	81.67 355	82.55 337

cl22		78.97 225	78.21 237	81.24 222	77.74 344	59.01 298	77.46 291	87.13 216	65.79 256	84.32 215	85.10 296	58.96 291	90.88 197	75.36 167	92.03 234	93.84 111

MVSFormer		82.23 175	81.57 188	84.19 159	85.54 263	69.26 187	91.98 31	90.08 169	71.54 199	76.23 320	85.07 299	58.69 292	94.27 84	86.26 40	88.77 285	89.03 261

lupinMVS		76.37 259	74.46 271	82.09 206	85.54 263	69.26 187	76.79 297	80.77 293	50.68 363	76.23 320	82.82 324	58.69 292	88.94 246	69.85 223	88.77 285	88.07 270

Test_1112_low_res		73.90 283	73.08 284	76.35 293	90.35 157	55.95 322	73.40 337	86.17 232	50.70 362	73.14 343	85.94 283	58.31 294	85.90 294	56.51 323	83.22 345	87.20 283

test_yl		78.71 232	78.51 233	79.32 250	84.32 281	58.84 301	78.38 275	85.33 245	75.99 135	82.49 248	86.57 272	58.01 295	90.02 225	62.74 286	92.73 221	89.10 258

DCV-MVSNet		78.71 232	78.51 233	79.32 250	84.32 281	58.84 301	78.38 275	85.33 245	75.99 135	82.49 248	86.57 272	58.01 295	90.02 225	62.74 286	92.73 221	89.10 258

sss		66.92 331	67.26 331	65.90 353	77.23 349	51.10 360	64.79 370	71.72 354	52.12 353	70.13 359	80.18 349	57.96 297	65.36 382	50.21 358	81.01 361	81.25 353

ppachtmachnet_test		74.73 277	74.00 275	76.90 287	80.71 323	56.89 319	71.53 346	78.42 303	58.24 319	79.32 298	82.92 323	57.91 298	84.26 310	65.60 264	91.36 248	89.56 248

MVP-Stereo		75.81 264	73.51 280	82.71 197	89.35 175	73.62 132	80.06 248	85.20 247	60.30 307	73.96 340	87.94 249	57.89 299	89.45 237	52.02 351	74.87 380	85.06 304
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PAPM		71.77 300	70.06 314	76.92 286	86.39 241	53.97 335	76.62 302	86.62 226	53.44 343	63.97 383	84.73 303	57.79 300	92.34 155	39.65 388	81.33 359	84.45 309

LFMVS		80.15 217	80.56 203	78.89 253	89.19 181	55.93 323	85.22 145	73.78 338	82.96 58	84.28 219	92.72 136	57.38 301	90.07 223	63.80 279	95.75 138	90.68 226

Vis-MVSNet (Re-imp)		77.82 240	77.79 240	77.92 273	88.82 188	51.29 357	83.28 189	71.97 351	74.04 158	82.23 253	89.78 221	57.38 301	89.41 240	57.22 320	95.41 144	93.05 148

CHOSEN 1792x2688		72.45 294	70.56 307	78.13 268	90.02 167	63.08 245	68.72 358	83.16 271	42.99 384	75.92 324	85.46 289	57.22 303	85.18 303	49.87 361	81.67 355	86.14 292

mvsany_test1		58.48 358	56.47 363	64.50 359	65.90 397	68.21 197	56.95 386	42.11 401	38.30 392	65.69 374	77.19 371	56.96 304	59.35 391	46.16 375	58.96 394	65.93 385

miper_lstm_enhance		76.45 258	76.10 256	77.51 279	76.72 355	60.97 277	64.69 371	85.04 252	63.98 273	83.20 239	88.22 244	56.67 305	78.79 339	73.22 193	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	81.90 284	70.44 210	91.28 77	95.18 42	56.62 306	89.28 243	85.15 54	97.09 81	93.99 103

our_test_3		71.85 299	71.59 299	72.62 319	80.71 323	53.78 337	69.72 356	71.71 355	58.80 316	78.03 305	80.51 347	56.61 307	78.84 338	62.20 290	86.04 320	85.23 301

baseline1		73.26 287	73.54 279	72.43 322	84.92 270	47.79 371	79.89 252	74.00 334	65.93 254	78.81 302	86.28 279	56.36 308	81.63 324	56.63 322	79.04 369	87.87 277

pmmvs4		74.92 273	72.98 286	80.73 230	84.95 269	71.71 165	76.23 308	77.59 308	52.83 346	77.73 312	86.38 274	56.35 309	84.97 304	57.72 319	87.05 308	85.51 299

MVE	40.22 23	51.82 362	50.47 365	55.87 374	62.66 400	51.91 351	31.61 393	39.28 402	40.65 387	50.76 396	74.98 378	56.24 310	44.67 397	33.94 394	64.11 392	71.04 380
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		60.59 356	62.54 351	54.72 376	77.26 348	27.74 399	74.05 329	61.00 386	60.48 306	65.62 375	67.03 389	55.93 311	68.23 371	32.07 396	69.46 390	68.17 383

N_pmnet		70.20 313	68.80 325	74.38 308	80.91 318	84.81 39	59.12 382	76.45 320	55.06 336	75.31 333	82.36 329	55.74 312	54.82 392	47.02 372	87.24 304	83.52 323

MS-PatchMatch		70.93 309	70.22 312	73.06 316	81.85 306	62.50 255	73.82 333	77.90 305	52.44 349	75.92 324	81.27 339	55.67 313	81.75 322	55.37 332	77.70 373	74.94 374

DSMNet-mixed		60.98 354	61.61 354	59.09 373	72.88 381	45.05 380	74.70 324	46.61 399	26.20 395	65.34 376	90.32 209	55.46 314	63.12 386	41.72 385	81.30 360	69.09 382

pmmvs5		70.73 310	70.07 313	72.72 318	77.03 352	52.73 345	74.14 327	75.65 325	50.36 365	72.17 349	85.37 293	55.42 315	80.67 329	52.86 348	87.59 302	84.77 306

CMPMVS	59.41 20	75.12 270	73.57 278	79.77 242	75.84 363	67.22 203	81.21 237	82.18 280	50.78 361	76.50 316	87.66 255	55.20 316	82.99 317	62.17 292	90.64 268	89.09 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_vis1_n_1920		71.30 306	71.58 301	70.47 329	77.58 347	59.99 287	74.25 326	84.22 264	51.06 358	74.85 336	79.10 356	55.10 317	68.83 366	68.86 236	79.20 368	82.58 336

MIMVSNet		71.09 307	71.59 299	69.57 336	87.23 223	50.07 364	78.91 268	71.83 352	60.20 310	71.26 352	91.76 164	55.08 318	76.09 346	41.06 386	87.02 310	82.54 338

PVSNet_0	51.08 22	56.10 359	54.97 364	59.48 372	75.12 369	53.28 342	55.16 387	61.89 382	44.30 378	59.16 389	62.48 392	54.22 319	65.91 380	35.40 392	47.01 395	59.25 391

EPNet_dtu		72.87 292	71.33 304	77.49 280	77.72 345	60.55 282	82.35 218	75.79 322	66.49 252	58.39 393	81.06 341	53.68 320	85.98 291	53.55 342	92.97 217	85.95 294
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS2		55.64 361	59.27 360	44.74 378	64.30 399	12.32 404	40.60 391	49.79 397	53.19 344	65.06 380	84.81 301	53.60 321	49.76 395	32.68 395	89.41 277	72.15 377

test_vis1_rt		65.64 340	64.09 344	70.31 330	66.09 395	70.20 178	61.16 378	81.60 287	38.65 391	72.87 345	69.66 385	52.84 322	60.04 389	56.16 325	77.77 372	80.68 360

mvsany_test3		65.48 341	62.97 348	73.03 317	69.99 388	76.17 118	64.83 369	43.71 400	43.68 381	80.25 289	87.05 269	52.83 323	63.09 387	51.92 355	72.44 382	79.84 365

HyFIR lowres test		75.12 270	72.66 290	82.50 203	91.44 133	65.19 225	72.47 340	87.31 211	46.79 369	80.29 286	84.30 307	52.70 324	92.10 163	51.88 356	86.73 312	90.22 237

dmvs_re		66.81 334	66.98 332	66.28 352	76.87 353	58.68 305	71.66 345	72.24 348	60.29 308	69.52 362	73.53 379	52.38 325	64.40 384	44.90 379	81.44 358	75.76 372

test_cas_vis1_n_1920		69.20 325	69.12 320	69.43 337	73.68 376	62.82 249	70.38 353	77.21 312	46.18 373	80.46 285	78.95 358	52.03 326	65.53 381	65.77 263	77.45 376	79.95 364

test1111		78.53 234	78.85 227	77.56 278	92.22 102	47.49 372	82.61 208	69.24 364	72.43 187	85.28 194	94.20 81	51.91 327	90.07 223	65.36 266	96.45 103	95.11 62

ECVR-MVS		78.44 235	78.63 231	77.88 274	91.85 115	48.95 366	83.68 180	69.91 362	72.30 193	84.26 221	94.20 81	51.89 328	89.82 228	63.58 280	96.02 121	94.87 67

FMVSNet3		78.80 230	78.55 232	79.57 247	82.89 301	56.89 319	81.76 228	85.77 239	69.04 226	86.00 182	90.44 206	51.75 329	90.09 222	65.95 258	93.34 205	91.72 199

D2MVS		76.84 251	75.67 261	80.34 236	80.48 326	62.16 263	73.50 335	84.80 259	57.61 326	82.24 252	87.54 257	51.31 330	87.65 262	70.40 220	93.19 211	91.23 210

AUN-MVS		81.18 195	78.78 228	88.39 77	90.93 145	82.14 58	82.51 214	83.67 267	64.69 271	80.29 286	85.91 285	51.07 331	92.38 153	76.29 158	93.63 202	90.65 228

PVSNet	58.17 21	66.41 336	65.63 341	68.75 341	81.96 304	49.88 365	62.19 377	72.51 347	51.03 359	68.04 366	75.34 377	50.84 332	74.77 350	45.82 378	82.96 346	81.60 348

GA-MVS		75.83 263	74.61 268	79.48 249	81.87 305	59.25 294	73.42 336	82.88 274	68.68 230	79.75 291	81.80 335	50.62 333	89.46 236	66.85 250	85.64 322	89.72 246

FPMVS		72.29 297	72.00 296	73.14 315	88.63 194	85.00 36	74.65 325	67.39 368	71.94 198	77.80 310	87.66 255	50.48 334	75.83 348	49.95 359	79.51 363	58.58 392

test_fmvs3		75.72 265	75.20 265	77.27 282	75.01 371	69.47 184	78.93 267	84.88 257	46.67 370	87.08 157	87.84 252	50.44 335	71.62 357	77.42 146	88.53 288	90.72 223

test_vis1_n		70.29 312	69.99 316	71.20 327	75.97 362	66.50 212	76.69 300	80.81 292	44.22 379	75.43 329	77.23 369	50.00 336	68.59 367	66.71 253	82.85 350	78.52 368

MVS-HIRNet		61.16 352	62.92 349	55.87 374	79.09 338	35.34 395	71.83 343	57.98 392	46.56 371	59.05 390	91.14 180	49.95 337	76.43 345	38.74 389	71.92 384	55.84 393

CVMVSNet		72.62 293	71.41 303	76.28 295	83.25 294	60.34 283	83.50 184	79.02 302	37.77 393	76.33 318	85.10 296	49.60 338	87.41 265	70.54 218	77.54 375	81.08 356

RPMNet		78.88 227	78.28 236	80.68 232	79.58 331	62.64 252	82.58 210	94.16 27	74.80 151	75.72 326	92.59 138	48.69 339	95.56 39	73.48 189	82.91 348	83.85 319

test_fmvs2		73.57 285	72.80 287	75.90 299	72.74 383	68.84 193	77.07 294	84.32 263	45.14 376	82.89 244	84.22 308	48.37 340	70.36 360	73.40 191	87.03 309	88.52 267

tpmrst		66.28 337	66.69 336	65.05 358	72.82 382	39.33 388	78.20 278	70.69 359	53.16 345	67.88 367	80.36 348	48.18 341	74.75 351	58.13 316	70.79 385	81.08 356

CR-MVSNet		74.00 282	73.04 285	76.85 289	79.58 331	62.64 252	82.58 210	76.90 315	50.50 364	75.72 326	92.38 144	48.07 342	84.07 311	68.72 239	82.91 348	83.85 319

Patchmtry		76.56 256	77.46 241	73.83 310	79.37 336	46.60 376	82.41 217	76.90 315	73.81 161	85.56 191	92.38 144	48.07 342	83.98 312	63.36 283	95.31 150	90.92 218

test_f		64.31 345	65.85 338	59.67 371	66.54 394	62.24 262	57.76 385	70.96 357	40.13 388	84.36 213	82.09 331	46.93 344	51.67 394	61.99 293	81.89 354	65.12 386

ADS-MVSNet2		65.87 339	63.64 347	72.55 320	73.16 379	56.92 318	67.10 364	74.81 328	49.74 366	66.04 372	82.97 320	46.71 345	77.26 343	42.29 383	69.96 387	83.46 324

ADS-MVSNet		61.90 348	62.19 352	61.03 369	73.16 379	36.42 394	67.10 364	61.75 383	49.74 366	66.04 372	82.97 320	46.71 345	63.21 385	42.29 383	69.96 387	83.46 324

PatchmatchNet		69.71 320	68.83 324	72.33 323	77.66 346	53.60 338	79.29 261	69.99 361	57.66 325	72.53 347	82.93 322	46.45 347	80.08 333	60.91 302	72.09 383	83.31 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres200		72.34 296	71.55 302	74.70 307	83.48 291	51.60 354	75.02 322	73.71 339	70.14 217	78.56 304	80.57 345	46.20 348	88.20 258	46.99 373	89.29 278	84.32 311

sam_mvs1												46.11 349				83.88 316

tfpn200view9		74.86 274	74.23 273	76.74 290	86.24 250	52.12 349	79.24 263	73.87 336	73.34 170	81.82 262	84.60 305	46.02 350	88.80 248	51.98 352	90.99 253	89.31 253

thres400		75.14 268	74.23 273	77.86 275	86.24 250	52.12 349	79.24 263	73.87 336	73.34 170	81.82 262	84.60 305	46.02 350	88.80 248	51.98 352	90.99 253	92.66 163

baseline2		69.77 319	66.89 333	78.41 263	79.51 333	58.09 307	76.23 308	69.57 363	57.50 327	64.82 381	77.45 367	46.02 350	88.44 254	53.08 344	77.83 371	88.70 265

patchmatchnet-post											81.71 336	45.93 353	87.01 269

sam_mvs												45.92 354

Patchmatch-RL test		74.48 278	73.68 277	76.89 288	84.83 271	66.54 211	72.29 341	69.16 365	57.70 324	86.76 163	86.33 276	45.79 355	82.59 318	69.63 225	90.65 267	81.54 349

thres100view900		75.45 266	75.05 266	76.66 291	87.27 222	51.88 352	81.07 239	73.26 342	75.68 141	83.25 238	86.37 275	45.54 356	88.80 248	51.98 352	90.99 253	89.31 253

thres600view7		75.97 262	75.35 264	77.85 276	87.01 232	51.84 353	80.45 245	73.26 342	75.20 148	83.10 241	86.31 278	45.54 356	89.05 244	55.03 336	92.24 230	92.66 163

tpm cat1		66.76 335	65.21 342	71.42 325	77.09 351	50.62 362	78.01 279	73.68 340	44.89 377	68.64 363	79.00 357	45.51 358	82.42 321	49.91 360	70.15 386	81.23 355

test_post											3.10 399	45.43 359	77.22 344

MDTV_nov1_ep13			68.29 328		78.03 343	43.87 383	74.12 328	72.22 349	52.17 350	67.02 370	85.54 287	45.36 360	80.85 328	55.73 327	84.42 339

tpmvs		70.16 314	69.56 319	71.96 324	74.71 372	48.13 368	79.63 254	75.45 327	65.02 269	70.26 358	81.88 334	45.34 361	85.68 298	58.34 314	75.39 379	82.08 344

MDTV_nov1_ep13_2view						27.60 400	70.76 350		46.47 372	61.27 385		45.20 362		49.18 364		83.75 321

test_post1							78.85 271				3.13 398	45.19 363	80.13 332	58.11 317

CostFormer		69.98 318	68.68 326	73.87 309	77.14 350	50.72 361	79.26 262	74.51 331	51.94 354	70.97 355	84.75 302	45.16 364	87.49 264	55.16 335	79.23 366	83.40 326

FE-MVS		79.98 220	78.86 226	83.36 179	86.47 239	66.45 213	89.73 65	84.74 260	72.80 182	84.22 223	91.38 173	44.95 365	93.60 114	63.93 278	91.50 246	90.04 243

Patchmatch-test		65.91 338	67.38 330	61.48 368	75.51 365	43.21 385	68.84 357	63.79 378	62.48 281	72.80 346	83.42 317	44.89 366	59.52 390	48.27 369	86.45 315	81.70 346

EU-MVSNet		75.12 270	74.43 272	77.18 283	83.11 298	59.48 292	85.71 138	82.43 279	39.76 390	85.64 189	88.76 237	44.71 367	87.88 260	73.86 183	85.88 321	84.16 315

PatchT		70.52 311	72.76 289	63.79 362	79.38 335	33.53 396	77.63 286	65.37 376	73.61 165	71.77 350	92.79 134	44.38 368	75.65 349	64.53 276	85.37 324	82.18 342

test_vis3_rt		71.42 304	70.67 306	73.64 312	69.66 389	70.46 175	66.97 366	89.73 174	42.68 386	88.20 138	83.04 319	43.77 369	60.07 388	65.35 267	86.66 313	90.39 235

test_fmvs1_n		70.94 308	70.41 311	72.53 321	73.92 373	66.93 208	75.99 312	84.21 265	43.31 383	79.40 295	79.39 355	43.47 370	68.55 368	69.05 233	84.91 333	82.10 343

test-LLR		67.21 330	66.74 335	68.63 342	76.45 358	55.21 329	67.89 360	67.14 371	62.43 284	65.08 378	72.39 380	43.41 371	69.37 361	61.00 300	84.89 334	81.31 351

test0.0.03 1		64.66 343	64.36 343	65.57 355	75.03 370	46.89 375	64.69 371	61.58 385	62.43 284	71.18 354	77.54 365	43.41 371	68.47 370	40.75 387	82.65 351	81.35 350

test_fmvs1		69.57 321	69.05 322	71.14 328	69.15 390	65.77 221	73.98 330	83.32 270	42.83 385	77.77 311	78.27 363	43.39 373	68.50 369	68.39 243	84.38 340	79.15 366

MVSTER		77.09 248	75.70 260	81.25 220	75.27 368	61.08 272	77.49 290	85.07 250	60.78 303	86.55 169	88.68 239	43.14 374	90.25 211	73.69 187	90.67 265	92.42 171

tpm		67.95 328	68.08 329	67.55 346	78.74 342	43.53 384	75.60 315	67.10 373	54.92 337	72.23 348	88.10 246	42.87 375	75.97 347	52.21 350	80.95 362	83.15 331

tpm2		68.45 327	66.83 334	73.30 314	78.93 341	48.50 367	79.76 253	71.76 353	47.50 368	69.92 360	83.60 313	42.07 376	88.40 255	48.44 368	79.51 363	83.01 333

EMVS		61.10 353	60.81 355	61.99 365	65.96 396	55.86 324	53.10 389	58.97 390	67.06 247	56.89 394	63.33 391	40.98 377	67.03 375	54.79 337	86.18 319	63.08 387

new_pmnet		55.69 360	57.66 361	49.76 377	75.47 366	30.59 397	59.56 379	51.45 396	43.62 382	62.49 384	75.48 376	40.96 378	49.15 396	37.39 391	72.52 381	69.55 381

E-PMN		61.59 350	61.62 353	61.49 367	66.81 393	55.40 327	53.77 388	60.34 387	66.80 250	58.90 391	65.50 390	40.48 379	66.12 379	55.72 328	86.25 318	62.95 388

EPMVS		62.47 346	62.63 350	62.01 364	70.63 387	38.74 390	74.76 323	52.86 395	53.91 341	67.71 369	80.01 350	39.40 380	66.60 377	55.54 331	68.81 391	80.68 360

tmp_tt		20.25 365	24.50 368	7.49 381	4.47 403	8.70 405	34.17 392	25.16 404	1.00 399	32.43 398	18.49 396	39.37 381	9.21 400	21.64 398	43.75 396	4.57 396

thisisatest0530		79.07 224	77.33 245	84.26 156	87.13 226	64.58 229	83.66 181	75.95 321	68.86 228	85.22 195	87.36 261	38.10 382	93.57 118	75.47 165	94.28 187	94.62 74

ET-MVSNet_ETH3D		75.28 267	72.77 288	82.81 196	83.03 299	68.11 198	77.09 293	76.51 319	60.67 305	77.60 313	80.52 346	38.04 383	91.15 187	70.78 213	90.68 264	89.17 256

tttt0517		81.07 196	79.58 219	85.52 128	88.99 185	66.45 213	87.03 114	75.51 326	73.76 162	88.32 136	90.20 212	37.96 384	94.16 94	79.36 121	95.13 155	95.93 42

thisisatest0515		73.00 291	70.52 308	80.46 234	81.45 311	59.90 288	73.16 339	74.31 333	57.86 323	76.08 323	77.78 364	37.60 385	92.12 162	65.00 269	91.45 247	89.35 252

FMVSNet5		72.10 298	71.69 298	73.32 313	81.57 310	53.02 343	76.77 298	78.37 304	63.31 274	76.37 317	91.85 158	36.68 386	78.98 336	47.87 370	92.45 224	87.95 274

iter_conf_final		80.36 210	78.88 225	84.79 139	86.29 248	66.36 215	86.95 115	86.25 230	68.16 236	82.09 256	89.48 225	36.59 387	94.51 81	79.83 113	94.30 186	93.50 132

dp		60.70 355	60.29 358	61.92 366	72.04 385	38.67 391	70.83 349	64.08 377	51.28 357	60.75 386	77.28 368	36.59 387	71.58 358	47.41 371	62.34 393	75.52 373

CHOSEN 280x420		59.08 357	56.52 362	66.76 350	76.51 356	64.39 232	49.62 390	59.00 389	43.86 380	55.66 395	68.41 388	35.55 389	68.21 372	43.25 382	76.78 378	67.69 384

IB-MVS	62.13 19	71.64 301	68.97 323	79.66 246	80.80 322	62.26 261	73.94 331	76.90 315	63.27 275	68.63 364	76.79 372	33.83 390	91.84 170	59.28 310	87.26 303	84.88 305
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		69.41 322	66.64 337	77.70 277	73.19 378	71.24 170	75.67 314	65.56 375	70.42 211	65.18 377	92.97 126	33.64 391	83.06 316	53.52 343	69.61 389	78.79 367

iter_conf05		78.81 229	77.35 244	83.21 184	82.98 300	60.75 280	84.09 166	88.34 198	63.12 276	84.25 222	89.48 225	31.41 392	94.51 81	76.64 153	95.83 132	94.38 88

DeepMVS_CX				24.13 380	32.95 401	29.49 398		21.63 405	12.07 396	37.95 397	45.07 395	30.84 393	19.21 399	17.94 399	33.06 398	23.69 395

gg-mvs-nofinetune		68.96 326	69.11 321	68.52 344	76.12 361	45.32 378	83.59 182	55.88 393	86.68 24	64.62 382	97.01 7	30.36 394	83.97 313	44.78 380	82.94 347	76.26 371

GG-mvs-BLEND				67.16 348	73.36 377	46.54 377	84.15 164	55.04 394		58.64 392	61.95 393	29.93 395	83.87 314	38.71 390	76.92 377	71.07 379

test_method		30.46 363	29.60 366	33.06 379	17.99 402	3.84 406	13.62 394	73.92 335	2.79 397	18.29 399	53.41 394	28.53 396	43.25 398	22.56 397	35.27 397	52.11 394

test-mter		65.00 342	63.79 346	68.63 342	76.45 358	55.21 329	67.89 360	67.14 371	50.98 360	65.08 378	72.39 380	28.27 397	69.37 361	61.00 300	84.89 334	81.31 351

TESTMET0.1,1		61.29 351	60.32 357	64.19 360	72.06 384	51.30 356	67.89 360	62.09 379	45.27 375	60.65 387	69.01 386	27.93 398	64.74 383	56.31 324	81.65 357	76.53 370

test2506		74.12 281	73.39 281	76.28 295	91.85 115	44.20 382	84.06 167	48.20 398	72.30 193	81.90 259	94.20 81	27.22 399	89.77 231	64.81 271	96.02 121	94.87 67

pmmvs3		62.47 346	60.02 359	69.80 334	71.58 386	64.00 236	70.52 351	58.44 391	39.77 389	66.05 371	75.84 375	27.10 400	72.28 354	46.15 376	84.77 338	73.11 376

KD-MVS_2432*1600		66.87 332	65.81 339	70.04 331	67.50 391	47.49 372	62.56 375	79.16 299	61.21 299	77.98 306	80.61 343	25.29 401	82.48 319	53.02 345	84.92 331	80.16 362

miper_refine_blended		66.87 332	65.81 339	70.04 331	67.50 391	47.49 372	62.56 375	79.16 299	61.21 299	77.98 306	80.61 343	25.29 401	82.48 319	53.02 345	84.92 331	80.16 362

myMVS_eth3d		64.66 343	63.89 345	66.97 349	81.72 307	37.39 392	71.00 347	61.99 380	61.38 295	70.81 356	72.36 382	20.96 403	79.30 334	49.59 362	85.18 327	84.22 312

testing3		71.53 303	70.79 305	73.77 311	88.89 187	41.86 387	76.60 303	59.12 388	72.83 181	80.97 273	82.08 332	19.80 404	87.33 267	65.12 268	91.68 242	92.13 188

test123		6.27 368	8.08 371	0.84 382	1.11 405	0.57 407	62.90 374	0.82 406	0.54 400	1.07 402	2.75 401	1.26 405	0.30 401	1.04 400	1.26 400	1.66 397

testmvs		5.91 369	7.65 372	0.72 383	1.20 404	0.37 408	59.14 381	0.67 407	0.49 401	1.11 401	2.76 400	0.94 406	0.24 402	1.02 401	1.47 399	1.55 398

test_blank		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet_test		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

DCPMVS		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet-low-res		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uncertanet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

Regformer		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

ab-mvs-re		6.65 366	8.87 369	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	79.80 352	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

WAC-MVS						37.39 392								52.61 349

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					0.00 406

eth-test					0.00 406

IU-MVS					94.18 46	72.64 145		90.82 144	56.98 330	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	72.45 153	90.54 48	94.10 34					95.88 17	86.42 36	97.97 43	92.02 191

GSMVS																83.88 316

test_part2					93.86 57	77.77 94				92.84 48

MTGPA								91.81 118

MTMP							90.66 44	33.14 403

gm-plane-assit					75.42 367	44.97 381			52.17 350		72.36 382		87.90 259	54.10 340

test9_res														80.83 102	96.45 103	90.57 229

agg_prior2														79.68 116	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	71.99 160		92.85 86					94.17 92			92.80 156

旧先验2							81.73 229		56.88 331	86.54 174			84.90 305	72.81 200

新几何2							81.72 230

无先验							82.81 205	85.62 241	58.09 321				91.41 181	67.95 247		84.48 308

原ACMM2							82.26 223

testdata2													86.43 283	63.52 282

testdata1							79.62 255		73.95 160

plane_prior7					93.45 66	77.31 102

plane_prior5								93.61 53					95.22 56	80.78 103	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								0.00 408

nn								0.00 408

door-mid								74.45 332

test11								91.46 124

door								72.57 346

HQP5-MVS						70.66 173

HQP-NCC					91.19 137		84.77 149		73.30 172	80.55 282

ACMP_Plane					91.19 137		84.77 149		73.30 172	80.55 282

BP-MVS														77.30 147

HQP4-MVS										80.56 281			94.61 74			93.56 129

HQP3-MVS								92.68 91							94.47 180

NP-MVS					91.95 110	74.55 127					90.17 215

ACMMP++_ref															95.74 139

ACMMP++															97.35 73

High-resolution multi-view benchmark