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.

MSC_two_6792asdad				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

No_MVS				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 48	90.85 3	97.13 19				97.08 70	92.59 2	98.94 91	92.25 92	98.99 14	98.84 19

HPM-MVS++		95.14 13	94.91 26	95.83 4	98.25 35	89.65 4	95.92 87	96.96 68	91.75 13	94.02 71	96.83 82	88.12 27	99.55 20	93.41 66	98.94 16	98.28 61

MM		95.10 14	94.91 26	95.68 5	96.09 115	88.34 9	96.68 38	94.37 295	95.08 1	94.68 57	97.72 39	82.94 100	99.64 4	97.85 5	98.76 33	99.06 7

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 8	96.26 54	97.28 40	85.90 203	97.67 4	98.10 14	88.41 23	99.56 16	94.66 49	99.19 1	98.71 25
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
3Dnovator+	87.14 4	92.42 103	91.37 124	95.55 7	95.63 143	88.73 6	97.07 23	96.77 91	90.84 26	84.02 329	96.62 95	75.95 212	99.34 42	87.77 180	97.68 97	98.59 29

TestfortrainingZip a		95.70 4	95.76 5	95.51 8	98.88 1	87.98 10	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44	92.42 4	99.67 1	93.66 60	98.85 20	98.89 15

CNVR-MVS		95.40 9	95.37 11	95.50 9	98.11 42	88.51 7	95.29 130	96.96 68	92.09 10	95.32 49	97.08 70	89.49 17	99.33 45	95.10 44	98.85 20	98.66 26

MGCNet		94.18 50	93.80 64	95.34 10	94.91 182	87.62 15	95.97 82	93.01 342	92.58 6	94.22 62	97.20 64	80.56 139	99.59 11	97.04 20	98.68 41	98.81 22

ACMMP_NAP		94.74 25	94.56 33	95.28 11	98.02 47	87.70 12	95.68 106	97.34 30	88.28 120	95.30 50	97.67 41	85.90 54	99.54 24	93.91 57	98.95 15	98.60 28

DPE-MVS		95.57 6	95.67 6	95.25 12	98.36 31	87.28 19	95.56 118	97.51 11	89.13 87	97.14 18	97.91 32	91.64 9	99.62 5	94.61 50	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 17	94.90 28	95.20 13	97.84 56	87.76 11	96.65 39	97.48 16	87.76 148	95.71 44	97.70 40	88.28 26	99.35 41	93.89 58	98.78 30	98.48 35

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 22	87.50 17	95.00 153	97.12 55	87.13 169	92.51 112	96.30 104	89.24 19	99.34 42	93.46 63	98.62 50	98.73 23

NCCC		94.81 22	94.69 32	95.17 15	97.83 57	87.46 18	95.66 109	96.93 72	92.34 7	93.94 72	96.58 97	87.74 30	99.44 33	92.83 75	98.40 58	98.62 27

DPM-MVS		92.58 99	91.74 109	95.08 16	96.19 106	89.31 5	92.66 310	96.56 112	83.44 277	91.68 137	95.04 184	86.60 46	98.99 81	85.60 214	97.92 85	96.93 183

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 121	93.15 87	97.04 73	86.17 51	99.62 5	92.40 86	98.81 27	98.52 31

test_0728_SECOND				95.01 18	98.79 5	86.43 40	97.09 21	97.49 12					99.61 7	95.62 34	99.08 7	98.99 9

MTAPA		94.42 39	94.22 48	95.00 19	98.42 24	86.95 22	94.36 206	96.97 65	91.07 22	93.14 88	97.56 43	84.30 81	99.56 16	93.43 64	98.75 34	98.47 38

MSP-MVS		95.42 8	95.56 8	94.98 20	98.49 20	86.52 37	96.91 30	97.47 17	91.73 14	96.10 36	96.69 87	89.90 14	99.30 48	94.70 48	98.04 80	99.13 2
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
region2R		94.43 36	94.27 47	94.92 21	98.65 11	86.67 31	96.92 29	97.23 43	88.60 111	93.58 79	97.27 58	85.22 64	99.54 24	92.21 94	98.74 35	98.56 30

APDe-MVS		95.46 7	95.64 7	94.91 22	98.26 34	86.29 47	97.46 7	97.40 26	89.03 92	96.20 35	98.10 14	89.39 18	99.34 42	95.88 29	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		94.43 36	94.28 45	94.91 22	98.63 12	86.69 29	96.94 25	97.32 34	88.63 108	93.53 82	97.26 60	85.04 68	99.54 24	92.35 89	98.78 30	98.50 32

GST-MVS		94.21 45	93.97 60	94.90 24	98.41 25	86.82 25	96.54 41	97.19 44	88.24 121	93.26 84	96.83 82	85.48 60	99.59 11	91.43 120	98.40 58	98.30 55

HFP-MVS		94.52 31	94.40 38	94.86 25	98.61 13	86.81 26	96.94 25	97.34 30	88.63 108	93.65 77	97.21 62	86.10 52	99.49 30	92.35 89	98.77 32	98.30 55

sasdasda		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 208	82.33 109	98.62 131	92.40 86	92.86 228	98.27 63

MP-MVS-pluss		94.21 45	94.00 59	94.85 26	98.17 39	86.65 32	94.82 166	97.17 49	86.26 195	92.83 97	97.87 34	85.57 59	99.56 16	94.37 53	98.92 17	98.34 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 208	82.33 109	98.62 131	92.40 86	92.86 228	98.27 63

XVS		94.45 34	94.32 41	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	97.16 68	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

X-MVStestdata		88.31 232	86.13 281	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 486	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 26	96.99 81	86.33 43	97.33 8	97.30 37	91.38 19	95.39 48	97.46 50	88.98 22	99.40 34	94.12 54	98.89 18	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
MED-MVS test				94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44		99.67 1	95.27 40	98.85 20	98.95 11

MED-MVS		95.74 3	96.04 3	94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	89.01 94	97.21 14	97.54 44	92.42 4	99.67 1	95.27 40	98.85 20	98.95 11

DVP-MVS++		95.98 1	96.36 1	94.82 34	97.78 60	86.00 53	98.29 1	97.49 12	90.75 29	97.62 8	98.06 22	92.59 2	99.61 7	95.64 32	99.02 12	98.86 16

ME-MVS		95.17 12	95.29 14	94.81 35	98.39 28	85.89 64	95.91 88	97.55 9	89.01 94	95.86 42	97.54 44	89.24 19	99.59 11	95.27 40	98.85 20	98.95 11

alignmvs		93.08 90	92.50 98	94.81 35	95.62 144	87.61 16	95.99 79	96.07 166	89.77 64	94.12 66	94.87 192	80.56 139	98.66 123	92.42 85	93.10 224	98.15 75

SED-MVS		95.91 2	96.28 2	94.80 37	98.77 8	85.99 55	97.13 19	97.44 21	90.31 41	97.71 2	98.07 20	92.31 6	99.58 14	95.66 30	99.13 3	98.84 19

DeepC-MVS_fast	89.43 2	94.04 53	93.79 65	94.80 37	97.48 70	86.78 27	95.65 111	96.89 77	89.40 75	92.81 98	96.97 75	85.37 62	99.24 51	90.87 129	98.69 39	98.38 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		94.25 42	94.07 56	94.77 39	98.47 21	86.31 45	96.71 36	96.98 64	89.04 90	91.98 123	97.19 65	85.43 61	99.56 16	92.06 103	98.79 28	98.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 43	94.07 56	94.75 40	98.06 45	86.90 24	95.88 90	96.94 71	85.68 210	95.05 55	97.18 66	87.31 38	99.07 64	91.90 111	98.61 52	98.28 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 40	94.21 50	94.74 41	98.39 28	86.64 33	97.60 5	97.24 41	88.53 113	92.73 103	97.23 61	85.20 65	99.32 46	92.15 97	98.83 26	98.25 68

PGM-MVS		93.96 58	93.72 70	94.68 42	98.43 23	86.22 48	95.30 128	97.78 4	87.45 159	93.26 84	97.33 56	84.62 78	99.51 28	90.75 131	98.57 53	98.32 54

DVP-MVS		95.67 5	96.02 4	94.64 43	98.78 6	85.93 58	97.09 21	96.73 97	90.27 45	97.04 22	98.05 25	91.47 10	99.55 20	95.62 34	99.08 7	98.45 41
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
mPP-MVS		93.99 56	93.78 66	94.63 44	98.50 19	85.90 63	96.87 31	96.91 75	88.70 106	91.83 132	97.17 67	83.96 85	99.55 20	91.44 119	98.64 49	98.43 43

PHI-MVS		93.89 60	93.65 74	94.62 45	96.84 84	86.43 40	96.69 37	97.49 12	85.15 234	93.56 81	96.28 105	85.60 58	99.31 47	92.45 83	98.79 28	98.12 80

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 46	98.25 35	86.33 43	96.11 67	96.62 107	88.14 126	96.10 36	96.96 76	89.09 21	98.94 91	94.48 51	98.68 41	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		93.54 69	93.20 83	94.55 47	95.65 141	85.73 71	94.94 156	96.69 103	91.89 12	90.69 162	95.88 135	81.99 121	99.54 24	93.14 70	97.95 84	98.39 45

train_agg		93.44 75	93.08 85	94.52 48	97.53 67	86.49 38	94.07 226	96.78 89	81.86 321	92.77 100	96.20 108	87.63 32	99.12 62	92.14 98	98.69 39	97.94 96

CDPH-MVS		92.83 94	92.30 101	94.44 49	97.79 58	86.11 52	94.06 228	96.66 104	80.09 352	92.77 100	96.63 94	86.62 44	99.04 68	87.40 187	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 120	90.82 139	94.44 49	94.59 208	86.37 42	97.18 17	97.02 62	89.20 84	84.31 324	96.66 90	73.74 252	99.17 56	86.74 197	97.96 83	97.79 117

SR-MVS		94.23 44	94.17 54	94.43 51	98.21 38	85.78 69	96.40 43	96.90 76	88.20 124	94.33 61	97.40 53	84.75 77	99.03 69	93.35 67	97.99 82	98.48 35

HPM-MVS		94.02 54	93.88 61	94.43 51	98.39 28	85.78 69	97.25 15	97.07 60	86.90 179	92.62 109	96.80 86	84.85 75	99.17 56	92.43 84	98.65 48	98.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 67	93.41 78	94.41 53	96.59 91	86.78 27	94.40 198	93.93 313	89.77 64	94.21 63	95.59 155	87.35 37	98.61 133	92.72 78	96.15 135	97.83 114

reproduce-ours		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

our_new_method		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

NormalMVS		93.46 72	93.16 84	94.37 56	98.40 26	86.20 49	96.30 47	96.27 135	91.65 17	92.68 105	96.13 117	77.97 182	98.84 105	90.75 131	98.26 63	98.07 82

test12				94.34 57	97.13 79	86.15 51		96.29 131		91.04 158		85.08 67	99.01 74		98.13 75	97.86 109

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 291	91.65 17	92.68 105	96.13 117	77.97 182	98.84 105	90.75 131	94.72 167	97.92 103

ACMMP		93.24 84	92.88 90	94.30 59	98.09 44	85.33 78	96.86 32	97.45 20	88.33 117	90.15 177	97.03 74	81.44 129	99.51 28	90.85 130	95.74 142	98.04 89
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
reproduce_model		94.76 24	94.92 25	94.29 60	97.92 49	85.18 80	95.95 85	97.19 44	89.67 67	95.27 51	98.16 6	86.53 47	99.36 40	95.42 37	98.15 73	98.33 50

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 61	96.07 117	85.83 67	94.89 159	96.99 63	89.02 93	89.56 186	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 132
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		93.03 91	92.63 95	94.23 62	95.62 144	85.92 60	96.08 69	96.33 129	89.86 55	93.89 74	94.66 205	82.11 116	98.50 139	92.33 91	92.82 231	98.27 63

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 63	95.64 142	85.08 81	96.09 68	97.36 28	90.98 24	97.09 20	98.12 10	84.98 73	98.94 91	97.07 17	97.80 92	98.43 43

EPNet		91.79 111	91.02 133	94.10 64	90.10 407	85.25 79	96.03 76	92.05 369	92.83 5	87.39 235	95.78 146	79.39 164	99.01 74	88.13 174	97.48 100	98.05 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 14	95.46 10	94.01 65	98.40 26	84.36 106	97.70 3	97.78 4	91.19 20	96.22 34	98.08 19	86.64 43	99.37 37	94.91 46	98.26 63	98.29 60

test_fmvsmconf_n		94.60 28	94.81 30	93.98 66	94.62 204	84.96 84	96.15 62	97.35 29	89.37 76	96.03 39	98.11 11	86.36 48	99.01 74	97.45 10	97.83 90	97.96 95

DELS-MVS		93.43 79	93.25 81	93.97 67	95.42 152	85.04 82	93.06 292	97.13 54	90.74 31	91.84 130	95.09 183	86.32 49	99.21 54	91.22 121	98.45 56	97.65 126
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
DP-MVS Recon		91.95 108	91.28 127	93.96 68	98.33 33	85.92 60	94.66 178	96.66 104	82.69 298	90.03 179	95.82 142	82.30 111	99.03 69	84.57 232	96.48 128	96.91 185

HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 207	92.47 113	97.13 69	82.38 107	99.07 64	90.51 136	98.40 58	97.92 103

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 322	84.80 87	96.18 59	96.82 85	89.29 81	95.68 45	98.11 11	85.10 66	98.99 81	97.38 11	97.75 96	97.86 109

SD-MVS		94.96 18	95.33 12	93.88 70	97.25 78	86.69 29	96.19 57	97.11 58	90.42 37	96.95 24	97.27 58	89.53 16	96.91 310	94.38 52	98.85 20	98.03 90
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
MVS_111021_HR		93.45 74	93.31 79	93.84 72	96.99 81	84.84 85	93.24 283	97.24 41	88.76 103	91.60 138	95.85 139	86.07 53	98.66 123	91.91 109	98.16 71	98.03 90

SR-MVS-dyc-post		93.82 62	93.82 63	93.82 73	97.92 49	84.57 93	96.28 51	96.76 92	87.46 157	93.75 75	97.43 51	84.24 82	99.01 74	92.73 76	97.80 92	97.88 107

test_prior				93.82 73	97.29 76	84.49 97		96.88 78					98.87 99			98.11 81

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 75	97.92 49	84.19 110	96.30 47	96.87 79	86.96 175	93.92 73	97.47 49	83.88 86	98.96 88	92.71 79	97.87 88	98.26 67

fmvsm_l_conf0.5_n		94.29 41	94.46 36	93.79 76	95.28 157	85.43 76	95.68 106	96.43 120	86.56 187	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 118	97.16 164

CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 258	90.05 178	95.66 152	87.77 29	99.15 60	89.91 144	98.27 62	98.07 82

GDP-MVS		92.04 106	91.46 121	93.75 78	94.55 214	84.69 90	95.60 117	96.56 112	87.83 145	93.07 91	95.89 134	73.44 256	98.65 125	90.22 139	96.03 137	97.91 105

BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 217	84.39 105	95.90 89	94.30 298	90.39 38	92.67 107	95.94 130	74.46 235	98.65 125	93.14 70	97.35 104	98.13 77

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 80	89.25 420	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 166	98.98 85	97.22 13	97.24 106	97.74 120

UA-Net		92.83 94	92.54 97	93.68 81	96.10 114	84.71 89	95.66 109	96.39 124	91.92 11	93.22 86	96.49 100	83.16 95	98.87 99	84.47 234	95.47 149	97.45 138

fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 82	95.29 156	84.98 83	95.61 114	96.28 134	86.31 193	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 169

QAPM		89.51 189	88.15 216	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 379	83.41 346	96.19 111	73.18 261	99.30 48	77.11 349	96.54 125	96.89 186

test_fmvsm_n_1920		94.71 26	95.11 19	93.50 84	95.79 132	84.62 91	96.15 62	97.64 6	89.85 56	97.19 17	97.89 33	86.28 50	98.71 121	97.11 16	98.08 79	97.17 157

fmvsm_s_conf0.5_n_9		94.99 16	95.50 9	93.44 85	96.51 99	82.25 183	95.76 101	96.92 73	93.37 3	97.63 7	98.43 1	84.82 76	99.16 59	98.15 1	97.92 85	98.90 14

KinetiMVS		91.82 110	91.30 125	93.39 86	94.72 196	83.36 137	95.45 121	96.37 126	90.33 40	92.17 118	96.03 124	72.32 273	98.75 115	87.94 177	96.34 130	98.07 82

casdiffmvs_mvg		92.96 93	92.83 91	93.35 87	94.59 208	83.40 135	95.00 153	96.34 128	90.30 43	92.05 121	96.05 121	83.43 89	98.15 175	92.07 100	95.67 143	98.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		93.96 58	94.18 53	93.30 88	94.79 189	83.81 121	95.77 99	96.74 96	88.02 134	96.23 33	97.84 36	83.36 93	98.83 108	97.49 8	97.34 105	97.25 150

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 187	95.77 194	90.87 25	92.52 111	96.67 89	84.50 79	99.00 79	91.99 105	94.44 180	97.36 141

Vis-MVSNet		91.75 118	91.23 128	93.29 89	95.32 155	83.78 122	96.14 64	95.98 173	89.89 53	90.45 166	96.58 97	75.09 224	98.31 166	84.75 226	96.90 114	97.78 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 57	94.22 48	93.26 91	96.13 109	83.29 139	96.27 53	96.52 115	89.82 57	95.56 47	95.51 158	84.50 79	98.79 112	94.83 47	98.86 19	97.72 122

SPE-MVS-test		94.02 54	94.29 44	93.24 92	96.69 87	83.24 140	97.49 6	96.92 73	92.14 9	92.90 93	95.77 147	85.02 69	98.33 163	93.03 72	98.62 50	98.13 77

VNet		92.24 105	91.91 106	93.24 92	96.59 91	83.43 133	94.84 165	96.44 119	89.19 85	94.08 70	95.90 133	77.85 188	98.17 173	88.90 164	93.38 213	98.13 77

fmvsm_s_conf0.5_n_10		94.43 36	94.84 29	93.20 94	95.73 135	83.19 143	95.99 79	97.31 36	91.08 21	97.67 4	98.11 11	81.87 123	99.22 52	97.86 4	97.91 87	97.20 155

VDD-MVS		90.74 147	89.92 161	93.20 94	96.27 104	83.02 155	95.73 103	93.86 317	88.42 116	92.53 110	96.84 81	62.09 382	98.64 128	90.95 127	92.62 238	97.93 102

Elysia		90.12 166	89.10 184	93.18 96	93.16 292	84.05 114	95.22 137	96.27 135	85.16 232	90.59 163	94.68 201	64.64 364	98.37 156	86.38 203	95.77 140	97.12 166

StellarMVS		90.12 166	89.10 184	93.18 96	93.16 292	84.05 114	95.22 137	96.27 135	85.16 232	90.59 163	94.68 201	64.64 364	98.37 156	86.38 203	95.77 140	97.12 166

CS-MVS		94.12 51	94.44 37	93.17 98	96.55 94	83.08 152	97.63 4	96.95 70	91.71 15	93.50 83	96.21 107	85.61 57	98.24 168	93.64 61	98.17 70	98.19 71

nrg030		91.08 141	90.39 145	93.17 98	93.07 299	86.91 23	96.41 42	96.26 139	88.30 119	88.37 211	94.85 195	82.19 115	97.64 236	91.09 122	82.95 363	94.96 271

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 255	94.09 67	95.56 157	85.01 72	98.69 122	94.96 45	98.66 45	97.67 125

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 198	95.74 197	90.71 33	92.05 121	96.60 96	84.00 84	98.99 81	91.55 117	93.63 203	97.17 157

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 278	84.26 108	95.83 95	96.14 157	89.00 96	92.43 114	97.50 48	83.37 92	98.72 119	96.61 24	97.44 101	96.32 211

新几何1				93.10 102	97.30 75	84.35 107		95.56 214	71.09 449	91.26 147	96.24 106	82.87 102	98.86 101	79.19 328	98.10 76	96.07 227

OMC-MVS		91.23 133	90.62 144	93.08 104	96.27 104	84.07 112	93.52 265	95.93 179	86.95 176	89.51 187	96.13 117	78.50 176	98.35 160	85.84 212	92.90 227	96.83 193

OpenMVS	83.78 11	88.74 219	87.29 238	93.08 104	92.70 317	85.39 77	96.57 40	96.43 120	78.74 374	80.85 378	96.07 120	69.64 309	99.01 74	78.01 340	96.65 123	94.83 279

MAR-MVS		90.30 162	89.37 177	93.07 106	96.61 90	84.48 98	95.68 106	95.67 205	82.36 303	87.85 222	92.85 275	76.63 201	98.80 110	80.01 312	96.68 122	95.91 233
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
lupinMVS		90.92 142	90.21 149	93.03 107	93.86 263	83.88 119	92.81 304	93.86 317	79.84 355	91.76 134	94.29 222	77.92 185	98.04 196	90.48 137	97.11 107	97.17 157

Effi-MVS+		91.59 127	91.11 130	93.01 108	94.35 232	83.39 136	94.60 180	95.10 249	87.10 170	90.57 165	93.10 270	81.43 130	98.07 190	89.29 156	94.48 178	97.59 131

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 163	87.27 163	95.98 40	98.05 25	83.07 99	98.45 149	96.68 23	95.51 146	96.88 187

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 289	96.09 164	88.20 124	91.12 152	95.72 150	81.33 131	97.76 225	91.74 113	97.37 103	96.75 195

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 320	83.62 128	96.02 77	95.72 201	86.78 181	96.04 38	98.19 4	82.30 111	98.43 153	96.38 25	95.42 152	96.86 188

ETV-MVS		92.74 97	92.66 94	92.97 111	95.20 163	84.04 116	95.07 149	96.51 116	90.73 32	92.96 92	91.19 336	84.06 83	98.34 161	91.72 114	96.54 125	96.54 206

LFMVS		90.08 169	89.13 183	92.95 113	96.71 86	82.32 182	96.08 69	89.91 426	86.79 180	92.15 120	96.81 84	62.60 380	98.34 161	87.18 191	93.90 194	98.19 71

UGNet		89.95 176	88.95 192	92.95 113	94.51 216	83.31 138	95.70 105	95.23 242	89.37 76	87.58 229	93.94 238	64.00 370	98.78 113	83.92 241	96.31 131	96.74 196
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
jason		90.80 145	90.10 153	92.90 115	93.04 302	83.53 131	93.08 289	94.15 306	80.22 349	91.41 144	94.91 189	76.87 195	97.93 215	90.28 138	96.90 114	97.24 151
jason: jason.
DP-MVS		87.25 273	85.36 311	92.90 115	97.65 64	83.24 140	94.81 167	92.00 371	74.99 416	81.92 367	95.00 185	72.66 266	99.05 66	66.92 430	92.33 243	96.40 208

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 117	95.96 127	81.32 210	95.76 101	97.57 8	93.48 2	97.53 10	98.32 3	81.78 126	99.13 61	97.91 2	97.81 91	98.16 74

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 118	95.62 144	83.17 144	96.14 64	96.12 161	88.13 127	95.82 43	98.04 28	83.43 89	98.48 141	96.97 21	96.23 132	96.92 184

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 270	83.13 146	96.02 77	95.74 197	87.68 151	95.89 41	98.17 5	82.78 103	98.46 145	96.71 22	96.17 134	96.98 178

CANet_DTU		90.26 164	89.41 176	92.81 120	93.46 285	83.01 156	93.48 266	94.47 290	89.43 74	87.76 227	94.23 227	70.54 297	99.03 69	84.97 221	96.39 129	96.38 209

MVSFormer		91.68 125	91.30 125	92.80 121	93.86 263	83.88 119	95.96 83	95.90 183	84.66 251	91.76 134	94.91 189	77.92 185	97.30 277	89.64 152	97.11 107	97.24 151

PVSNet_Blended_VisFu		91.38 130	90.91 136	92.80 121	96.39 101	83.17 144	94.87 161	96.66 104	83.29 282	89.27 193	94.46 217	80.29 142	99.17 56	87.57 184	95.37 153	96.05 230

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 123	95.95 128	81.83 194	95.53 119	97.12 55	91.68 16	97.89 1	98.06 22	85.71 56	98.65 125	97.32 12	98.26 63	97.83 114

LuminaMVS		90.55 158	89.81 163	92.77 123	92.78 315	84.21 109	94.09 224	94.17 305	85.82 204	91.54 139	94.14 229	69.93 303	97.92 216	91.62 116	94.21 188	96.18 219

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 125	94.98 175	81.96 192	95.79 97	97.29 39	89.31 79	97.52 11	97.61 42	83.25 94	98.88 98	97.05 19	98.22 69	97.43 140

VDDNet		89.56 188	88.49 207	92.76 125	95.07 169	82.09 186	96.30 47	93.19 337	81.05 343	91.88 128	96.86 80	61.16 398	98.33 163	88.43 171	92.49 242	97.84 113

viewdifsd2359ckpt09		91.18 136	90.65 143	92.75 127	94.61 207	82.36 181	94.32 207	95.74 197	84.72 248	89.66 185	95.15 181	79.69 159	98.04 196	87.70 181	94.27 187	97.85 112

h-mvs33		90.80 145	90.15 152	92.75 127	96.01 120	82.66 169	95.43 122	95.53 218	89.80 60	93.08 89	95.64 153	75.77 213	99.00 79	92.07 100	78.05 420	96.60 201

casdiffmvs		92.51 100	92.43 99	92.74 129	94.41 227	81.98 190	94.54 184	96.23 143	89.57 70	91.96 125	96.17 112	82.58 105	98.01 202	90.95 127	95.45 151	98.23 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl		90.69 150	90.02 159	92.71 130	95.72 136	82.41 179	94.11 220	95.12 247	85.63 211	91.49 141	94.70 199	74.75 228	98.42 154	86.13 207	92.53 240	97.31 142

DCV-MVSNet		90.69 150	90.02 159	92.71 130	95.72 136	82.41 179	94.11 220	95.12 247	85.63 211	91.49 141	94.70 199	74.75 228	98.42 154	86.13 207	92.53 240	97.31 142

PCF-MVS	84.11 10	87.74 247	86.08 285	92.70 132	94.02 252	84.43 102	89.27 404	95.87 188	73.62 431	84.43 316	94.33 219	78.48 178	98.86 101	70.27 404	94.45 179	94.81 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_11		94.60 28	95.23 16	92.69 133	96.05 119	82.00 188	96.31 46	96.71 100	92.27 8	96.68 30	98.39 2	85.32 63	98.92 94	97.20 14	98.16 71	97.17 157

SSM_0404		90.73 148	90.08 154	92.69 133	95.00 174	83.13 146	94.32 207	95.00 257	85.41 222	89.84 180	95.35 166	76.13 204	97.98 207	85.46 217	94.18 189	96.95 180

baseline		92.39 104	92.29 102	92.69 133	94.46 222	81.77 197	94.14 217	96.27 135	89.22 83	91.88 128	96.00 125	82.35 108	97.99 204	91.05 123	95.27 157	98.30 55

MSLP-MVS++		93.72 66	94.08 55	92.65 136	97.31 74	83.43 133	95.79 97	97.33 32	90.03 50	93.58 79	96.96 76	84.87 74	97.76 225	92.19 96	98.66 45	96.76 194

EC-MVSNet		93.44 75	93.71 71	92.63 137	95.21 162	82.43 176	97.27 14	96.71 100	90.57 36	92.88 94	95.80 143	83.16 95	98.16 174	93.68 59	98.14 74	97.31 142

ab-mvs		89.41 196	88.35 209	92.60 138	95.15 167	82.65 173	92.20 330	95.60 212	83.97 262	88.55 207	93.70 252	74.16 243	98.21 172	82.46 265	89.37 287	96.94 182

LS3D		87.89 242	86.32 274	92.59 139	96.07 117	82.92 159	95.23 135	94.92 265	75.66 408	82.89 353	95.98 127	72.48 270	99.21 54	68.43 418	95.23 158	95.64 247

Anonymous20240529		88.09 238	86.59 263	92.58 140	96.53 96	81.92 193	95.99 79	95.84 190	74.11 426	89.06 197	95.21 176	61.44 390	98.81 109	83.67 248	87.47 318	97.01 176

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 141	95.49 150	81.10 220	95.93 86	97.16 50	92.96 4	97.39 12	98.13 7	83.63 88	98.80 110	97.89 3	97.61 99	97.78 118

CPTT-MVS		91.99 107	91.80 107	92.55 142	98.24 37	81.98 190	96.76 35	96.49 118	81.89 320	90.24 170	96.44 102	78.59 174	98.61 133	89.68 150	97.85 89	97.06 170

viewdifsd2359ckpt13		91.20 135	90.75 141	92.54 143	94.30 237	82.13 185	94.03 230	95.89 185	85.60 213	90.20 172	95.36 165	79.69 159	97.90 219	87.85 179	93.86 195	97.61 128

114514_t		89.51 189	88.50 205	92.54 143	98.11 42	81.99 189	95.16 145	96.36 127	70.19 453	85.81 269	95.25 172	76.70 199	98.63 130	82.07 275	96.86 117	97.00 177

PAPM_NR		91.22 134	90.78 140	92.52 145	97.60 65	81.46 206	94.37 204	96.24 142	86.39 192	87.41 232	94.80 197	82.06 119	98.48 141	82.80 260	95.37 153	97.61 128

mamba_0408		89.06 209	87.92 223	92.50 146	94.76 190	82.66 169	79.84 473	94.64 283	85.18 227	88.96 199	95.00 185	76.00 209	97.98 207	83.74 245	93.15 221	96.85 189

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 147	96.52 97	80.00 269	94.00 235	97.08 59	90.05 49	95.65 46	97.29 57	89.66 15	98.97 86	93.95 56	98.71 36	98.50 32

SSM_0407		90.47 160	89.80 164	92.46 148	94.76 190	82.66 169	93.98 237	95.00 257	85.41 222	88.96 199	95.35 166	76.13 204	97.88 220	85.46 217	93.15 221	96.85 189

IS-MVSNet		91.43 129	91.09 132	92.46 148	95.87 131	81.38 209	96.95 24	93.69 327	89.72 66	89.50 189	95.98 127	78.57 175	97.77 224	83.02 254	96.50 127	98.22 70

API-MVS		90.66 153	90.07 155	92.45 150	96.36 102	84.57 93	96.06 73	95.22 244	82.39 301	89.13 194	94.27 225	80.32 141	98.46 145	80.16 311	96.71 121	94.33 303

xiu_mvs_v1_base_debu		90.64 154	90.05 156	92.40 151	93.97 258	84.46 99	93.32 274	95.46 222	85.17 229	92.25 115	94.03 230	70.59 293	98.57 136	90.97 124	94.67 169	94.18 306

xiu_mvs_v1_base		90.64 154	90.05 156	92.40 151	93.97 258	84.46 99	93.32 274	95.46 222	85.17 229	92.25 115	94.03 230	70.59 293	98.57 136	90.97 124	94.67 169	94.18 306

xiu_mvs_v1_base_debi		90.64 154	90.05 156	92.40 151	93.97 258	84.46 99	93.32 274	95.46 222	85.17 229	92.25 115	94.03 230	70.59 293	98.57 136	90.97 124	94.67 169	94.18 306

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 154	95.36 153	81.19 216	95.20 142	96.56 112	90.37 39	97.13 19	98.03 29	77.47 191	98.96 88	97.79 6	96.58 124	97.03 173

viewmacassd2359aftdt		91.67 126	91.43 123	92.37 155	93.95 261	81.00 224	93.90 245	95.97 176	87.75 149	91.45 143	96.04 123	79.92 148	97.97 209	89.26 157	94.67 169	98.14 76

viewmanbaseed2359cas		91.78 114	91.58 114	92.37 155	94.32 234	81.07 221	93.76 251	95.96 177	87.26 164	91.50 140	95.88 135	80.92 137	97.97 209	89.70 149	94.92 163	98.07 82

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 155	94.62 204	81.13 218	95.23 135	95.89 185	90.30 43	96.74 29	98.02 30	76.14 203	98.95 90	97.64 7	96.21 133	97.03 173

AdaColmap		89.89 179	89.07 186	92.37 155	97.41 71	83.03 154	94.42 194	95.92 180	82.81 295	86.34 258	94.65 206	73.89 248	99.02 72	80.69 302	95.51 146	95.05 266

CNLPA		89.07 208	87.98 220	92.34 159	96.87 83	84.78 88	94.08 225	93.24 334	81.41 334	84.46 314	95.13 182	75.57 220	96.62 325	77.21 347	93.84 197	95.61 250

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 160	95.13 168	80.95 227	95.64 112	96.97 65	89.60 69	96.85 25	97.77 38	83.08 98	98.92 94	97.49 8	96.78 119	97.13 165

ET-MVSNet_ETH3D		87.51 261	85.91 293	92.32 161	93.70 277	83.93 117	92.33 324	90.94 403	84.16 257	72.09 451	92.52 288	69.90 304	95.85 374	89.20 158	88.36 305	97.17 157

E4		91.74 119	91.55 117	92.31 162	94.27 239	80.80 237	93.81 248	96.17 154	87.97 136	91.11 153	96.05 121	80.75 138	98.08 188	89.78 145	94.02 191	98.06 87

E2		91.79 111	91.61 112	92.31 162	94.49 218	80.86 233	93.74 253	96.19 148	87.63 154	91.16 148	95.94 130	81.31 132	98.06 191	89.76 146	94.29 185	97.99 92

Anonymous202405211		87.68 248	86.13 281	92.31 162	96.66 88	80.74 239	94.87 161	91.49 388	80.47 348	89.46 190	95.44 161	54.72 437	98.23 169	82.19 271	89.89 277	97.97 94

E3		91.78 114	91.61 112	92.30 165	94.48 219	80.86 233	93.73 254	96.19 148	87.63 154	91.16 148	95.95 129	81.30 133	98.06 191	89.76 146	94.29 185	97.99 92

CHOSEN 1792x2688		88.84 215	87.69 228	92.30 165	96.14 108	81.42 208	90.01 391	95.86 189	74.52 421	87.41 232	93.94 238	75.46 221	98.36 158	80.36 307	95.53 145	97.12 166

viewcassd2359sk11		91.79 111	91.62 111	92.29 167	94.62 204	80.88 231	93.70 258	96.18 153	87.38 161	91.13 151	95.85 139	81.62 128	98.06 191	89.71 148	94.40 181	97.94 96

HY-MVS	83.01 12	89.03 211	87.94 222	92.29 167	94.86 185	82.77 161	92.08 335	94.49 289	81.52 333	86.93 239	92.79 281	78.32 180	98.23 169	79.93 313	90.55 264	95.88 236

CDS-MVSNet		89.45 192	88.51 204	92.29 167	93.62 280	83.61 130	93.01 293	94.68 281	81.95 314	87.82 225	93.24 264	78.69 172	96.99 304	80.34 308	93.23 218	96.28 214
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 172	89.27 182	92.29 167	95.78 133	80.95 227	92.68 309	96.22 144	81.91 316	86.66 249	93.75 250	82.23 113	98.44 151	79.40 327	94.79 166	97.48 136

E3new		91.76 117	91.58 114	92.28 171	94.69 201	80.90 230	93.68 261	96.17 154	87.15 167	91.09 157	95.70 151	81.75 127	98.05 195	89.67 151	94.35 182	97.90 106

mvsmamba		90.33 161	89.69 167	92.25 172	95.17 164	81.64 199	95.27 133	93.36 332	84.88 241	89.51 187	94.27 225	69.29 318	97.42 262	89.34 155	96.12 136	97.68 124

E6new		91.71 121	91.55 117	92.20 173	94.32 234	80.62 243	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

E6		91.71 121	91.55 117	92.20 173	94.32 234	80.62 243	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

E5		91.71 121	91.55 117	92.20 173	94.33 233	80.62 243	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

PLC	84.53 7	89.06 209	88.03 218	92.15 176	97.27 77	82.69 168	94.29 209	95.44 227	79.71 357	84.01 330	94.18 228	76.68 200	98.75 115	77.28 346	93.41 212	95.02 267
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 124	91.56 116	92.13 177	95.88 129	80.50 249	97.33 8	95.25 241	86.15 198	89.76 184	95.60 154	83.42 91	98.32 165	87.37 189	93.25 217	97.56 133

patch_mono-2		93.74 65	94.32 41	92.01 178	97.54 66	78.37 312	93.40 270	97.19 44	88.02 134	94.99 56	97.21 62	88.35 24	98.44 151	94.07 55	98.09 77	99.23 1

原ACMM1				92.01 178	97.34 73	81.05 222		96.81 87	78.89 368	90.45 166	95.92 132	82.65 104	98.84 105	80.68 303	98.26 63	96.14 221

UniMVSNet (Re)		89.80 182	89.07 186	92.01 178	93.60 281	84.52 96	94.78 169	97.47 17	89.26 82	86.44 255	92.32 294	82.10 117	97.39 273	84.81 225	80.84 397	94.12 310

MG-MVS		91.77 116	91.70 110	92.00 181	97.08 80	80.03 267	93.60 263	95.18 245	87.85 144	90.89 160	96.47 101	82.06 119	98.36 158	85.07 220	97.04 110	97.62 127

EIA-MVS		91.95 108	91.94 105	91.98 182	95.16 165	80.01 268	95.36 123	96.73 97	88.44 114	89.34 191	92.16 299	83.82 87	98.45 149	89.35 154	97.06 109	97.48 136

PVSNet_Blended		90.73 148	90.32 147	91.98 182	96.12 110	81.25 212	92.55 314	96.83 83	82.04 312	89.10 195	92.56 287	81.04 135	98.85 103	86.72 199	95.91 138	95.84 238

guyue		91.12 139	90.84 138	91.96 184	94.59 208	80.57 247	94.87 161	93.71 326	88.96 97	91.14 150	95.22 173	73.22 260	97.76 225	92.01 104	93.81 198	97.54 135

PS-MVSNAJ		91.18 136	90.92 135	91.96 184	95.26 160	82.60 175	92.09 334	95.70 203	86.27 194	91.84 130	92.46 289	79.70 156	98.99 81	89.08 159	95.86 139	94.29 304

TAMVS		89.21 202	88.29 213	91.96 184	93.71 275	82.62 174	93.30 278	94.19 303	82.22 306	87.78 226	93.94 238	78.83 169	96.95 307	77.70 342	92.98 226	96.32 211

SDMVSNet		90.19 165	89.61 170	91.93 187	96.00 121	83.09 151	92.89 301	95.98 173	88.73 104	86.85 245	95.20 177	72.09 276	97.08 296	88.90 164	89.85 279	95.63 248

FA-MVS(test-final)		89.66 184	88.91 194	91.93 187	94.57 212	80.27 253	91.36 352	94.74 278	84.87 242	89.82 181	92.61 286	74.72 231	98.47 144	83.97 240	93.53 207	97.04 172

MVS_Test		91.31 132	91.11 130	91.93 187	94.37 228	80.14 258	93.46 268	95.80 192	86.46 190	91.35 146	93.77 248	82.21 114	98.09 186	87.57 184	94.95 162	97.55 134

NR-MVSNet		88.58 225	87.47 234	91.93 187	93.04 302	84.16 111	94.77 170	96.25 141	89.05 89	80.04 392	93.29 262	79.02 168	97.05 301	81.71 286	80.05 407	94.59 287

HyFIR lowres test		88.09 238	86.81 251	91.93 187	96.00 121	80.63 241	90.01 391	95.79 193	73.42 433	87.68 228	92.10 305	73.86 249	97.96 211	80.75 301	91.70 247	97.19 156

GeoE		90.05 170	89.43 175	91.90 192	95.16 165	80.37 252	95.80 96	94.65 282	83.90 263	87.55 231	94.75 198	78.18 181	97.62 238	81.28 291	93.63 203	97.71 123

thisisatest0530		88.67 220	87.61 230	91.86 193	94.87 184	80.07 263	94.63 179	89.90 427	84.00 261	88.46 209	93.78 247	66.88 342	98.46 145	83.30 250	92.65 233	97.06 170

xiu_mvs_v2_base		91.13 138	90.89 137	91.86 193	94.97 176	82.42 177	92.24 327	95.64 210	86.11 202	91.74 136	93.14 268	79.67 161	98.89 97	89.06 160	95.46 150	94.28 305

DU-MVS		89.34 201	88.50 205	91.85 195	93.04 302	83.72 123	94.47 190	96.59 109	89.50 71	86.46 252	93.29 262	77.25 193	97.23 286	84.92 222	81.02 393	94.59 287

AstraMVS		90.69 150	90.30 148	91.84 196	93.81 266	79.85 274	94.76 171	92.39 357	88.96 97	91.01 159	95.87 138	70.69 291	97.94 214	92.49 82	92.70 232	97.73 121

OPM-MVS		90.12 166	89.56 171	91.82 197	93.14 294	83.90 118	94.16 216	95.74 197	88.96 97	87.86 221	95.43 163	72.48 270	97.91 217	88.10 176	90.18 271	93.65 341
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 157	90.19 150	91.82 197	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 241	94.86 193	74.23 239	98.12 176	88.15 172	89.99 273	94.63 284

UniMVSNet_NR-MVSNet		89.92 178	89.29 180	91.81 199	93.39 287	83.72 123	94.43 193	97.12 55	89.80 60	86.46 252	93.32 259	83.16 95	97.23 286	84.92 222	81.02 393	94.49 297

diffmvs		91.37 131	91.23 128	91.77 200	93.09 297	80.27 253	92.36 320	95.52 219	87.03 172	91.40 145	94.93 188	80.08 145	97.44 260	92.13 99	94.56 175	97.61 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.51 128	91.44 122	91.73 201	93.09 297	80.27 253	92.51 315	95.58 213	87.22 165	91.80 133	95.57 156	79.96 147	97.48 252	92.23 93	94.97 161	97.45 138

1112_ss		88.42 227	87.33 237	91.72 202	94.92 180	80.98 225	92.97 297	94.54 286	78.16 385	83.82 333	93.88 243	78.78 171	97.91 217	79.45 323	89.41 286	96.26 215

Fast-Effi-MVS+		89.41 196	88.64 200	91.71 203	94.74 193	80.81 236	93.54 264	95.10 249	83.11 286	86.82 247	90.67 359	79.74 155	97.75 229	80.51 306	93.55 205	96.57 204

WTY-MVS		89.60 186	88.92 193	91.67 204	95.47 151	81.15 217	92.38 319	94.78 276	83.11 286	89.06 197	94.32 220	78.67 173	96.61 328	81.57 287	90.89 260	97.24 151

TAPA-MVS	84.62 6	88.16 236	87.01 246	91.62 205	96.64 89	80.65 240	94.39 200	96.21 147	76.38 401	86.19 262	95.44 161	79.75 154	98.08 188	62.75 448	95.29 155	96.13 222
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 185	88.96 191	91.60 206	93.86 263	82.89 160	95.46 120	97.33 32	87.91 139	88.43 210	93.31 260	74.17 242	97.40 270	87.32 190	82.86 368	94.52 292

FE-MVS		87.40 266	86.02 287	91.57 207	94.56 213	79.69 279	90.27 378	93.72 325	80.57 346	88.80 203	91.62 325	65.32 358	98.59 135	74.97 372	94.33 184	96.44 207

XVG-OURS		89.40 198	88.70 199	91.52 208	94.06 250	81.46 206	91.27 357	96.07 166	86.14 199	88.89 202	95.77 147	68.73 327	97.26 283	87.39 188	89.96 275	95.83 239

hse-mvs2		89.88 180	89.34 178	91.51 209	94.83 187	81.12 219	93.94 239	93.91 316	89.80 60	93.08 89	93.60 253	75.77 213	97.66 233	92.07 100	77.07 427	95.74 243

TranMVSNet+NR-MVSNet		88.84 215	87.95 221	91.49 210	92.68 318	83.01 156	94.92 158	96.31 130	89.88 54	85.53 278	93.85 245	76.63 201	96.96 306	81.91 279	79.87 410	94.50 295

AUN-MVS		87.78 246	86.54 266	91.48 211	94.82 188	81.05 222	93.91 243	93.93 313	83.00 290	86.93 239	93.53 254	69.50 312	97.67 231	86.14 205	77.12 426	95.73 245

XVG-OURS-SEG-HR		89.95 176	89.45 173	91.47 212	94.00 256	81.21 215	91.87 339	96.06 168	85.78 206	88.55 207	95.73 149	74.67 232	97.27 281	88.71 168	89.64 284	95.91 233

MVS		87.44 264	86.10 284	91.44 213	92.61 319	83.62 128	92.63 311	95.66 207	67.26 459	81.47 370	92.15 300	77.95 184	98.22 171	79.71 315	95.48 148	92.47 387

viewdifsd2359ckpt07		91.11 140	91.02 133	91.41 214	94.21 243	78.37 312	92.91 300	95.71 202	87.50 156	90.32 169	95.88 135	80.27 143	97.99 204	88.78 167	93.55 205	97.86 109

F-COLMAP		87.95 241	86.80 252	91.40 215	96.35 103	80.88 231	94.73 173	95.45 225	79.65 358	82.04 365	94.61 207	71.13 283	98.50 139	76.24 359	91.05 258	94.80 281

dcpmvs_2		93.49 70	94.19 52	91.38 216	97.69 63	76.78 355	94.25 211	96.29 131	88.33 117	94.46 59	96.88 79	88.07 28	98.64 128	93.62 62	98.09 77	98.73 23

thisisatest0515		87.33 269	85.99 288	91.37 217	93.49 283	79.55 280	90.63 372	89.56 435	80.17 350	87.56 230	90.86 349	67.07 339	98.28 167	81.50 288	93.02 225	96.29 213

HQP-MVS		89.80 182	89.28 181	91.34 218	94.17 245	81.56 200	94.39 200	96.04 169	88.81 100	85.43 287	93.97 237	73.83 250	97.96 211	87.11 194	89.77 282	94.50 295

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 219	94.42 226	79.48 282	94.52 185	97.14 53	89.33 78	94.17 65	98.09 18	81.83 124	97.49 251	96.33 26	98.02 81	96.95 180

RRT-MVS		90.85 144	90.70 142	91.30 220	94.25 240	76.83 354	94.85 164	96.13 160	89.04 90	90.23 171	94.88 191	70.15 302	98.72 119	91.86 112	94.88 164	98.34 48

FMVSNet3		87.40 266	86.11 283	91.30 220	93.79 269	83.64 127	94.20 215	94.81 274	83.89 264	84.37 317	91.87 316	68.45 330	96.56 335	78.23 337	85.36 336	93.70 340

FMVSNet2		87.19 279	85.82 296	91.30 220	94.01 253	83.67 125	94.79 168	94.94 260	83.57 272	83.88 332	92.05 309	66.59 347	96.51 339	77.56 344	85.01 339	93.73 338

RPMNet		83.95 357	81.53 368	91.21 223	90.58 396	79.34 288	85.24 451	96.76 92	71.44 447	85.55 276	82.97 457	70.87 288	98.91 96	61.01 452	89.36 288	95.40 254

IB-MVS	80.51 15	85.24 334	83.26 352	91.19 224	92.13 331	79.86 273	91.75 342	91.29 393	83.28 283	80.66 382	88.49 407	61.28 392	98.46 145	80.99 297	79.46 414	95.25 260
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
CLD-MVS		89.47 191	88.90 195	91.18 225	94.22 242	82.07 187	92.13 332	96.09 164	87.90 140	85.37 293	92.45 290	74.38 237	97.56 243	87.15 192	90.43 266	93.93 319
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 192	88.90 195	91.12 226	94.47 220	81.49 204	95.30 128	96.14 157	86.73 183	85.45 284	95.16 179	69.89 305	98.10 178	87.70 181	89.23 291	93.77 334

LGP-MVS_train				91.12 226	94.47 220	81.49 204		96.14 157	86.73 183	85.45 284	95.16 179	69.89 305	98.10 178	87.70 181	89.23 291	93.77 334

ACMM	84.12 9	89.14 204	88.48 208	91.12 226	94.65 203	81.22 214	95.31 126	96.12 161	85.31 226	85.92 267	94.34 218	70.19 301	98.06 191	85.65 213	88.86 296	94.08 314
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 222	87.78 227	91.11 229	94.96 177	77.81 330	95.35 124	89.69 430	85.09 236	88.05 219	94.59 210	66.93 340	98.48 141	83.27 251	92.13 245	97.03 173

GBi-Net		87.26 271	85.98 289	91.08 230	94.01 253	83.10 148	95.14 146	94.94 260	83.57 272	84.37 317	91.64 321	66.59 347	96.34 352	78.23 337	85.36 336	93.79 329

test1		87.26 271	85.98 289	91.08 230	94.01 253	83.10 148	95.14 146	94.94 260	83.57 272	84.37 317	91.64 321	66.59 347	96.34 352	78.23 337	85.36 336	93.79 329

FMVSNet1		85.85 319	84.11 339	91.08 230	92.81 313	83.10 148	95.14 146	94.94 260	81.64 328	82.68 355	91.64 321	59.01 414	96.34 352	75.37 366	83.78 352	93.79 329

Test_1112_low_res		87.65 250	86.51 267	91.08 230	94.94 179	79.28 292	91.77 341	94.30 298	76.04 406	83.51 343	92.37 292	77.86 187	97.73 230	78.69 332	89.13 293	96.22 216

PS-MVSNAJss		89.97 174	89.62 169	91.02 234	91.90 340	80.85 235	95.26 134	95.98 173	86.26 195	86.21 261	94.29 222	79.70 156	97.65 234	88.87 166	88.10 307	94.57 289

BH-RMVSNet		88.37 230	87.48 233	91.02 234	95.28 157	79.45 284	92.89 301	93.07 340	85.45 221	86.91 241	94.84 196	70.35 298	97.76 225	73.97 381	94.59 174	95.85 237

UniMVSNet_ETH3D		87.53 260	86.37 271	91.00 236	92.44 323	78.96 297	94.74 172	95.61 211	84.07 260	85.36 294	94.52 212	59.78 406	97.34 275	82.93 255	87.88 312	96.71 197

FIs		90.51 159	90.35 146	90.99 237	93.99 257	80.98 225	95.73 103	97.54 10	89.15 86	86.72 248	94.68 201	81.83 124	97.24 285	85.18 219	88.31 306	94.76 282

ACMP	84.23 8	89.01 213	88.35 209	90.99 237	94.73 194	81.27 211	95.07 149	95.89 185	86.48 188	83.67 338	94.30 221	69.33 314	97.99 204	87.10 196	88.55 298	93.72 339
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 302	85.13 317	90.98 239	96.52 97	81.50 202	96.14 64	96.16 156	73.78 429	83.65 339	92.15 300	63.26 376	97.37 274	82.82 259	81.74 382	94.06 315

IMVS_0403		89.97 174	89.64 168	90.96 240	93.72 271	77.75 335	93.00 294	95.34 236	85.53 217	88.77 204	94.49 213	78.49 177	97.84 221	84.75 226	92.65 233	97.28 145

sss		88.93 214	88.26 215	90.94 241	94.05 251	80.78 238	91.71 343	95.38 231	81.55 332	88.63 206	93.91 242	75.04 225	95.47 393	82.47 264	91.61 248	96.57 204

IMVS_0407		89.85 181	89.51 172	90.88 242	93.72 271	77.75 335	93.07 291	95.34 236	85.53 217	88.34 212	94.49 213	77.69 189	97.60 239	84.75 226	92.65 233	97.28 145

viewmambaseed2359dif		90.04 171	89.78 165	90.83 243	92.85 312	77.92 324	92.23 328	95.01 253	81.90 317	90.20 172	95.45 160	79.64 163	97.34 275	87.52 186	93.17 219	97.23 154

sd_testset		88.59 224	87.85 226	90.83 243	96.00 121	80.42 251	92.35 322	94.71 279	88.73 104	86.85 245	95.20 177	67.31 334	96.43 346	79.64 318	89.85 279	95.63 248

PVSNet_BlendedMVS		89.98 173	89.70 166	90.82 245	96.12 110	81.25 212	93.92 241	96.83 83	83.49 276	89.10 195	92.26 297	81.04 135	98.85 103	86.72 199	87.86 313	92.35 394

cascas		86.43 310	84.98 320	90.80 246	92.10 333	80.92 229	90.24 382	95.91 182	73.10 436	83.57 342	88.39 408	65.15 360	97.46 256	84.90 224	91.43 250	94.03 317

ECVR-MVS		89.09 207	88.53 203	90.77 247	95.62 144	75.89 368	96.16 60	84.22 461	87.89 142	90.20 172	96.65 91	63.19 377	98.10 178	85.90 210	96.94 112	98.33 50

GA-MVS		86.61 300	85.27 314	90.66 248	91.33 363	78.71 301	90.40 377	93.81 322	85.34 225	85.12 297	89.57 389	61.25 393	97.11 295	80.99 297	89.59 285	96.15 220

thres600view7		87.65 250	86.67 258	90.59 249	96.08 116	78.72 299	94.88 160	91.58 384	87.06 171	88.08 217	92.30 295	68.91 324	98.10 178	70.05 411	91.10 253	94.96 271

thres400		87.62 255	86.64 259	90.57 250	95.99 124	78.64 302	94.58 181	91.98 373	86.94 177	88.09 215	91.77 317	69.18 320	98.10 178	70.13 408	91.10 253	94.96 271

baseline1		88.10 237	87.28 239	90.57 250	94.96 177	80.07 263	94.27 210	91.29 393	86.74 182	87.41 232	94.00 235	76.77 198	96.20 357	80.77 300	79.31 416	95.44 252

viewdifsd2359ckpt11		89.43 194	89.05 188	90.56 252	92.89 310	77.00 350	92.81 304	94.52 287	87.03 172	89.77 182	95.79 144	74.67 232	97.51 247	88.97 162	84.98 340	97.17 157

viewmsd2359difaftdt		89.43 194	89.05 188	90.56 252	92.89 310	77.00 350	92.81 304	94.52 287	87.03 172	89.77 182	95.79 144	74.67 232	97.51 247	88.97 162	84.98 340	97.17 157

FE-MVSNET3		86.84 290	85.61 304	90.53 254	90.50 400	81.80 196	90.97 365	94.96 259	83.05 288	83.50 344	90.32 366	72.15 274	96.65 321	79.49 321	85.55 335	93.15 364

FC-MVSNet-test		90.27 163	90.18 151	90.53 254	93.71 275	79.85 274	95.77 99	97.59 7	89.31 79	86.27 259	94.67 204	81.93 122	97.01 303	84.26 236	88.09 309	94.71 283

PAPM		86.68 299	85.39 309	90.53 254	93.05 301	79.33 291	89.79 394	94.77 277	78.82 371	81.95 366	93.24 264	76.81 196	97.30 277	66.94 428	93.16 220	94.95 275

WR-MVS		88.38 229	87.67 229	90.52 257	93.30 289	80.18 256	93.26 281	95.96 177	88.57 112	85.47 283	92.81 279	76.12 206	96.91 310	81.24 292	82.29 373	94.47 300

SSM_04072		88.57 226	87.92 223	90.51 258	94.76 190	82.66 169	79.84 473	94.64 283	85.18 227	88.96 199	95.00 185	76.00 209	92.03 442	83.74 245	93.15 221	96.85 189

MVSTER		88.84 215	88.29 213	90.51 258	92.95 307	80.44 250	93.73 254	95.01 253	84.66 251	87.15 236	93.12 269	72.79 265	97.21 288	87.86 178	87.36 321	93.87 324

testdata				90.49 260	96.40 100	77.89 327		95.37 233	72.51 441	93.63 78	96.69 87	82.08 118	97.65 234	83.08 252	97.39 102	95.94 232

test1111		89.10 205	88.64 200	90.48 261	95.53 149	74.97 378	96.08 69	84.89 459	88.13 127	90.16 176	96.65 91	63.29 375	98.10 178	86.14 205	96.90 114	98.39 45

tt0805		86.92 287	85.74 302	90.48 261	92.22 327	79.98 270	95.63 113	94.88 268	83.83 266	84.74 306	92.80 280	57.61 421	97.67 231	85.48 216	84.42 345	93.79 329

jajsoiax		88.24 234	87.50 232	90.48 261	90.89 384	80.14 258	95.31 126	95.65 209	84.97 239	84.24 325	94.02 233	65.31 359	97.42 262	88.56 169	88.52 300	93.89 320

PatchMatch-RL		86.77 296	85.54 305	90.47 264	95.88 129	82.71 167	90.54 375	92.31 361	79.82 356	84.32 322	91.57 329	68.77 326	96.39 348	73.16 387	93.48 211	92.32 395

tfpn200view9		87.58 258	86.64 259	90.41 265	95.99 124	78.64 302	94.58 181	91.98 373	86.94 177	88.09 215	91.77 317	69.18 320	98.10 178	70.13 408	91.10 253	94.48 298

VPNet		88.20 235	87.47 234	90.39 266	93.56 282	79.46 283	94.04 229	95.54 217	88.67 107	86.96 238	94.58 211	69.33 314	97.15 290	84.05 239	80.53 402	94.56 290

ACMH	80.38 17	85.36 329	83.68 346	90.39 266	94.45 223	80.63 241	94.73 173	94.85 270	82.09 308	77.24 420	92.65 284	60.01 404	97.58 241	72.25 392	84.87 342	92.96 370
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 253	86.71 255	90.38 268	96.12 110	78.55 305	95.03 152	91.58 384	87.15 167	88.06 218	92.29 296	68.91 324	98.10 178	70.13 408	91.10 253	94.48 298

mvs_tets		88.06 240	87.28 239	90.38 268	90.94 380	79.88 272	95.22 137	95.66 207	85.10 235	84.21 326	93.94 238	63.53 373	97.40 270	88.50 170	88.40 304	93.87 324

1314		87.51 261	86.57 264	90.34 270	92.42 324	79.74 278	92.63 311	95.35 235	78.35 380	80.14 389	91.62 325	74.05 244	97.15 290	81.05 293	93.53 207	94.12 310

LTVRE_ROB	82.13 13	86.26 313	84.90 323	90.34 270	94.44 224	81.50 202	92.31 326	94.89 266	83.03 289	79.63 399	92.67 283	69.69 308	97.79 223	71.20 397	86.26 330	91.72 405
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
test_djsdf		89.03 211	88.64 200	90.21 272	90.74 391	79.28 292	95.96 83	95.90 183	84.66 251	85.33 295	92.94 274	74.02 245	97.30 277	89.64 152	88.53 299	94.05 316

v2v482		87.84 243	87.06 243	90.17 273	90.99 376	79.23 295	94.00 235	95.13 246	84.87 242	85.53 278	92.07 308	74.45 236	97.45 257	84.71 231	81.75 381	93.85 327

pmmvs4		85.43 327	83.86 344	90.16 274	90.02 410	82.97 158	90.27 378	92.67 352	75.93 407	80.73 380	91.74 319	71.05 284	95.73 382	78.85 331	83.46 359	91.78 404

V42		87.68 248	86.86 248	90.15 275	90.58 396	80.14 258	94.24 213	95.28 240	83.66 270	85.67 273	91.33 331	74.73 230	97.41 268	84.43 235	81.83 379	92.89 373

MSDG		84.86 342	83.09 355	90.14 276	93.80 267	80.05 265	89.18 407	93.09 339	78.89 368	78.19 412	91.91 314	65.86 357	97.27 281	68.47 417	88.45 302	93.11 365

sc_t1		81.53 385	78.67 406	90.12 277	90.78 388	78.64 302	93.91 243	90.20 416	68.42 456	80.82 379	89.88 382	46.48 460	96.76 315	76.03 362	71.47 442	94.96 271

anonymousdsp		87.84 243	87.09 242	90.12 277	89.13 421	80.54 248	94.67 177	95.55 215	82.05 310	83.82 333	92.12 302	71.47 281	97.15 290	87.15 192	87.80 316	92.67 379

thres200		87.21 277	86.24 278	90.12 277	95.36 153	78.53 306	93.26 281	92.10 367	86.42 191	88.00 220	91.11 342	69.24 319	98.00 203	69.58 412	91.04 259	93.83 328

CR-MVSNet		85.35 330	83.76 345	90.12 277	90.58 396	79.34 288	85.24 451	91.96 375	78.27 382	85.55 276	87.87 418	71.03 285	95.61 385	73.96 382	89.36 288	95.40 254

v1144		87.61 256	86.79 253	90.06 281	91.01 375	79.34 288	93.95 238	95.42 230	83.36 281	85.66 274	91.31 334	74.98 226	97.42 262	83.37 249	82.06 375	93.42 350

XXY-MVS		87.65 250	86.85 249	90.03 282	92.14 330	80.60 246	93.76 251	95.23 242	82.94 292	84.60 308	94.02 233	74.27 238	95.49 392	81.04 294	83.68 355	94.01 318

Vis-MVSNet (Re-imp)		89.59 187	89.44 174	90.03 282	95.74 134	75.85 369	95.61 114	90.80 407	87.66 153	87.83 224	95.40 164	76.79 197	96.46 344	78.37 333	96.73 120	97.80 116

test2506		87.21 277	86.28 276	90.02 284	95.62 144	73.64 394	96.25 55	71.38 484	87.89 142	90.45 166	96.65 91	55.29 433	98.09 186	86.03 209	96.94 112	98.33 50

BH-untuned		88.60 223	88.13 217	90.01 285	95.24 161	78.50 308	93.29 279	94.15 306	84.75 247	84.46 314	93.40 256	75.76 215	97.40 270	77.59 343	94.52 177	94.12 310

v1192		87.25 273	86.33 273	90.00 286	90.76 390	79.04 296	93.80 249	95.48 220	82.57 299	85.48 282	91.18 338	73.38 259	97.42 262	82.30 268	82.06 375	93.53 344

v7n		86.81 291	85.76 300	89.95 287	90.72 392	79.25 294	95.07 149	95.92 180	84.45 254	82.29 359	90.86 349	72.60 269	97.53 245	79.42 326	80.52 403	93.08 367

testing91		87.11 282	86.18 279	89.92 288	94.43 225	75.38 377	91.53 348	92.27 363	86.48 188	86.50 250	90.24 368	61.19 396	97.53 245	82.10 273	90.88 261	96.84 192

IMVS_0404		87.60 257	86.84 250	89.89 289	93.72 271	77.75 335	88.56 416	95.34 236	85.53 217	79.98 393	94.49 213	66.54 350	94.64 406	84.75 226	92.65 233	97.28 145

v8		87.50 263	86.71 255	89.89 289	91.37 360	79.40 285	94.50 186	95.38 231	84.81 245	83.60 341	91.33 331	76.05 207	97.42 262	82.84 258	80.51 404	92.84 375

v10		87.25 273	86.38 270	89.85 291	91.19 366	79.50 281	94.48 187	95.45 225	83.79 268	83.62 340	91.19 336	75.13 223	97.42 262	81.94 278	80.60 399	92.63 381

baseline2		86.50 306	85.39 309	89.84 292	91.12 371	76.70 357	91.88 338	88.58 439	82.35 304	79.95 394	90.95 347	73.42 257	97.63 237	80.27 310	89.95 276	95.19 261

pm-mvs1		86.61 300	85.54 305	89.82 293	91.44 355	80.18 256	95.28 132	94.85 270	83.84 265	81.66 368	92.62 285	72.45 272	96.48 341	79.67 317	78.06 419	92.82 376

TR-MVS		86.78 293	85.76 300	89.82 293	94.37 228	78.41 310	92.47 316	92.83 346	81.11 342	86.36 256	92.40 291	68.73 327	97.48 252	73.75 385	89.85 279	93.57 343

ACMH+	81.04 14	85.05 337	83.46 349	89.82 293	94.66 202	79.37 286	94.44 192	94.12 309	82.19 307	78.04 414	92.82 278	58.23 417	97.54 244	73.77 384	82.90 367	92.54 384

EI-MVSNet		89.10 205	88.86 197	89.80 296	91.84 342	78.30 315	93.70 258	95.01 253	85.73 208	87.15 236	95.28 170	79.87 153	97.21 288	83.81 243	87.36 321	93.88 323

usedtu_blend_shiyan5		82.39 372	79.93 386	89.75 297	85.12 456	80.08 261	92.36 320	93.26 333	74.29 424	79.00 406	82.72 459	64.29 369	96.60 330	79.60 319	68.75 454	92.55 383

v144192		87.19 279	86.35 272	89.74 298	90.64 394	78.24 317	93.92 241	95.43 228	81.93 315	85.51 280	91.05 345	74.21 241	97.45 257	82.86 257	81.56 383	93.53 344

COLMAP_ROB	80.39 16	83.96 356	82.04 365	89.74 298	95.28 157	79.75 277	94.25 211	92.28 362	75.17 414	78.02 415	93.77 248	58.60 416	97.84 221	65.06 439	85.92 331	91.63 407
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 312	85.18 316	89.73 300	92.15 329	76.60 358	91.12 361	91.69 380	83.53 275	85.50 281	88.81 401	66.79 343	96.48 341	76.65 352	90.35 268	96.12 223

blend_shiyan4		81.94 375	79.35 394	89.70 301	85.52 453	80.08 261	91.29 355	93.82 320	77.12 395	79.31 402	82.94 458	54.81 435	96.60 330	79.60 319	69.78 447	92.41 390

IterMVS-LS		88.36 231	87.91 225	89.70 301	93.80 267	78.29 316	93.73 254	95.08 251	85.73 208	84.75 305	91.90 315	79.88 152	96.92 309	83.83 242	82.51 369	93.89 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 309	85.35 312	89.69 303	94.29 238	75.40 376	91.30 354	90.53 411	84.76 246	85.06 299	90.13 374	58.95 415	97.45 257	82.08 274	91.09 257	96.21 218

testing99		86.72 297	85.73 303	89.69 303	94.23 241	74.91 380	91.35 353	90.97 401	86.14 199	86.36 256	90.22 369	59.41 409	97.48 252	82.24 270	90.66 263	96.69 199

v1921920		86.97 286	86.06 286	89.69 303	90.53 399	78.11 320	93.80 249	95.43 228	81.90 317	85.33 295	91.05 345	72.66 266	97.41 268	82.05 276	81.80 380	93.53 344

icg_test_0407_2		89.15 203	88.97 190	89.68 306	93.72 271	77.75 335	88.26 421	95.34 236	85.53 217	88.34 212	94.49 213	77.69 189	93.99 418	84.75 226	92.65 233	97.28 145

blended_shiyan6		82.78 366	80.48 376	89.67 307	85.53 452	79.76 276	91.37 351	93.82 320	77.14 393	79.30 403	83.73 451	64.96 363	96.63 323	79.68 316	68.75 454	92.63 381

VortexMVS		88.42 227	88.01 219	89.63 308	93.89 262	78.82 298	93.82 247	95.47 221	86.67 185	84.53 312	91.99 311	72.62 268	96.65 321	89.02 161	84.09 349	93.41 351

Fast-Effi-MVS+-dtu		87.44 264	86.72 254	89.63 308	92.04 334	77.68 340	94.03 230	93.94 312	85.81 205	82.42 358	91.32 333	70.33 299	97.06 299	80.33 309	90.23 270	94.14 309

v1240		86.78 293	85.85 295	89.56 310	90.45 402	77.79 332	93.61 262	95.37 233	81.65 327	85.43 287	91.15 340	71.50 280	97.43 261	81.47 289	82.05 377	93.47 348

Effi-MVS+-dtu		88.65 221	88.35 209	89.54 311	93.33 288	76.39 362	94.47 190	94.36 296	87.70 150	85.43 287	89.56 390	73.45 255	97.26 283	85.57 215	91.28 252	94.97 268

AllTest		83.42 363	81.39 369	89.52 312	95.01 171	77.79 332	93.12 285	90.89 405	77.41 389	76.12 429	93.34 257	54.08 440	97.51 247	68.31 419	84.27 347	93.26 354

TestCases				89.52 312	95.01 171	77.79 332		90.89 405	77.41 389	76.12 429	93.34 257	54.08 440	97.51 247	68.31 419	84.27 347	93.26 354

mvs_anonymous		89.37 200	89.32 179	89.51 314	93.47 284	74.22 387	91.65 346	94.83 272	82.91 293	85.45 284	93.79 246	81.23 134	96.36 351	86.47 201	94.09 190	97.94 96

XVG-ACMP-BASELINE		86.00 315	84.84 325	89.45 315	91.20 365	78.00 322	91.70 344	95.55 215	85.05 237	82.97 352	92.25 298	54.49 438	97.48 252	82.93 255	87.45 320	92.89 373

testing222		84.84 343	83.32 350	89.43 316	94.15 248	75.94 367	91.09 362	89.41 437	84.90 240	85.78 270	89.44 391	52.70 445	96.28 355	70.80 403	91.57 249	96.07 227

MVP-Stereo		85.97 316	84.86 324	89.32 317	90.92 382	82.19 184	92.11 333	94.19 303	78.76 373	78.77 411	91.63 324	68.38 331	96.56 335	75.01 371	93.95 193	89.20 446
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 319	84.70 327	89.29 318	91.76 346	75.54 373	88.49 417	91.30 392	81.63 329	85.05 300	88.70 405	71.71 277	96.24 356	74.61 377	89.05 294	96.08 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 284	86.32 274	89.21 319	90.94 380	77.26 346	93.71 257	94.43 291	84.84 244	84.36 320	90.80 353	76.04 208	97.05 301	82.12 272	79.60 413	93.31 353

tfpnnormal		84.72 345	83.23 353	89.20 320	92.79 314	80.05 265	94.48 187	95.81 191	82.38 302	81.08 376	91.21 335	69.01 323	96.95 307	61.69 450	80.59 400	90.58 432

cl22		86.78 293	85.98 289	89.18 321	92.34 325	77.62 341	90.84 368	94.13 308	81.33 336	83.97 331	90.15 373	73.96 246	96.60 330	84.19 237	82.94 364	93.33 352

BH-w/o		87.57 259	87.05 244	89.12 322	94.90 183	77.90 326	92.41 317	93.51 329	82.89 294	83.70 337	91.34 330	75.75 216	97.07 298	75.49 364	93.49 209	92.39 392

WR-MVS_H		87.80 245	87.37 236	89.10 323	93.23 290	78.12 319	95.61 114	97.30 37	87.90 140	83.72 336	92.01 310	79.65 162	96.01 366	76.36 356	80.54 401	93.16 362

miper_enhance_ethall		86.90 288	86.18 279	89.06 324	91.66 351	77.58 342	90.22 384	94.82 273	79.16 364	84.48 313	89.10 395	79.19 167	96.66 320	84.06 238	82.94 364	92.94 371

c3_l		87.14 281	86.50 268	89.04 325	92.20 328	77.26 346	91.22 360	94.70 280	82.01 313	84.34 321	90.43 364	78.81 170	96.61 328	83.70 247	81.09 390	93.25 356

miper_ehance_all_eth		87.22 276	86.62 262	89.02 326	92.13 331	77.40 344	90.91 367	94.81 274	81.28 337	84.32 322	90.08 376	79.26 165	96.62 325	83.81 243	82.94 364	93.04 368

gg-mvs-nofinetune		81.77 379	79.37 393	88.99 327	90.85 386	77.73 339	86.29 443	79.63 472	74.88 419	83.19 351	69.05 475	60.34 401	96.11 361	75.46 365	94.64 173	93.11 365

ETVMVS		84.43 350	82.92 359	88.97 328	94.37 228	74.67 381	91.23 359	88.35 441	83.37 280	86.06 265	89.04 396	55.38 431	95.67 384	67.12 426	91.34 251	96.58 203

pmmvs6		83.42 363	81.60 367	88.87 329	88.01 436	77.87 328	94.96 155	94.24 302	74.67 420	78.80 410	91.09 343	60.17 403	96.49 340	77.06 351	75.40 433	92.23 397

test_cas_vis1_n_1920		88.83 218	88.85 198	88.78 330	91.15 370	76.72 356	93.85 246	94.93 264	83.23 285	92.81 98	96.00 125	61.17 397	94.45 407	91.67 115	94.84 165	95.17 262

MIMVSNet		82.59 370	80.53 374	88.76 331	91.51 353	78.32 314	86.57 442	90.13 419	79.32 360	80.70 381	88.69 406	52.98 444	93.07 434	66.03 434	88.86 296	94.90 276

cl____		86.52 305	85.78 297	88.75 332	92.03 335	76.46 360	90.74 369	94.30 298	81.83 323	83.34 348	90.78 354	75.74 218	96.57 333	81.74 284	81.54 384	93.22 358

DIV-MVS_self_test		86.53 304	85.78 297	88.75 332	92.02 336	76.45 361	90.74 369	94.30 298	81.83 323	83.34 348	90.82 352	75.75 216	96.57 333	81.73 285	81.52 385	93.24 357

CP-MVSNet		87.63 253	87.26 241	88.74 334	93.12 295	76.59 359	95.29 130	96.58 110	88.43 115	83.49 345	92.98 273	75.28 222	95.83 375	78.97 329	81.15 389	93.79 329

eth_miper_zixun_eth		86.50 306	85.77 299	88.68 335	91.94 337	75.81 370	90.47 376	94.89 266	82.05 310	84.05 328	90.46 363	75.96 211	96.77 314	82.76 261	79.36 415	93.46 349

CHOSEN 280x420		85.15 335	83.99 342	88.65 336	92.47 321	78.40 311	79.68 475	92.76 349	74.90 418	81.41 372	89.59 388	69.85 307	95.51 389	79.92 314	95.29 155	92.03 400

PS-CasMVS		87.32 270	86.88 247	88.63 337	92.99 305	76.33 364	95.33 125	96.61 108	88.22 123	83.30 350	93.07 271	73.03 263	95.79 379	78.36 334	81.00 395	93.75 336

TransMVSNet (Re)		84.43 350	83.06 357	88.54 338	91.72 347	78.44 309	95.18 143	92.82 348	82.73 297	79.67 398	92.12 302	73.49 254	95.96 368	71.10 401	68.73 456	91.21 419

tt0320-xc		79.63 408	76.66 417	88.52 339	91.03 374	78.72 299	93.00 294	89.53 436	66.37 460	76.11 431	87.11 429	46.36 462	95.32 397	72.78 389	67.67 457	91.51 411

EG-PatchMatch MVS		82.37 373	80.34 378	88.46 340	90.27 404	79.35 287	92.80 307	94.33 297	77.14 393	73.26 448	90.18 372	47.47 457	96.72 316	70.25 405	87.32 323	89.30 443

PEN-MVS		86.80 292	86.27 277	88.40 341	92.32 326	75.71 372	95.18 143	96.38 125	87.97 136	82.82 354	93.15 267	73.39 258	95.92 370	76.15 360	79.03 418	93.59 342

Baseline_NR-MVSNet		87.07 283	86.63 261	88.40 341	91.44 355	77.87 328	94.23 214	92.57 354	84.12 259	85.74 272	92.08 306	77.25 193	96.04 362	82.29 269	79.94 408	91.30 417

UBG		85.51 325	84.57 332	88.35 343	94.21 243	71.78 419	90.07 389	89.66 432	82.28 305	85.91 268	89.01 397	61.30 391	97.06 299	76.58 355	92.06 246	96.22 216

D2MVS		85.90 317	85.09 318	88.35 343	90.79 387	77.42 343	91.83 340	95.70 203	80.77 345	80.08 391	90.02 378	66.74 345	96.37 349	81.88 280	87.97 311	91.26 418

pmmvs5		84.21 352	82.84 362	88.34 345	88.95 423	76.94 352	92.41 317	91.91 377	75.63 409	80.28 386	91.18 338	64.59 366	95.57 386	77.09 350	83.47 358	92.53 385

mamv4		90.92 142	91.78 108	88.33 346	95.67 140	70.75 432	92.92 299	96.02 172	81.90 317	88.11 214	95.34 168	85.88 55	96.97 305	95.22 43	95.01 160	97.26 149

tt0320		80.13 401	77.41 410	88.29 347	90.50 400	78.02 321	93.10 288	90.71 409	66.06 463	76.75 424	86.97 430	49.56 452	95.40 394	71.65 393	71.41 443	91.46 414

LCM-MVSNet-Re		88.30 233	88.32 212	88.27 348	94.71 198	72.41 414	93.15 284	90.98 400	87.77 147	79.25 404	91.96 312	78.35 179	95.75 380	83.04 253	95.62 144	96.65 200

CostFormer		85.77 322	84.94 322	88.26 349	91.16 369	72.58 412	89.47 402	91.04 399	76.26 404	86.45 254	89.97 380	70.74 290	96.86 313	82.35 267	87.07 326	95.34 258

ITE_SJBPF				88.24 350	91.88 341	77.05 349		92.92 343	85.54 215	80.13 390	93.30 261	57.29 422	96.20 357	72.46 391	84.71 343	91.49 412

PVSNet	78.82 18	85.55 324	84.65 328	88.23 351	94.72 196	71.93 415	87.12 438	92.75 350	78.80 372	84.95 302	90.53 361	64.43 367	96.71 318	74.74 374	93.86 195	96.06 229

IterMVS-SCA-FT		85.45 326	84.53 333	88.18 352	91.71 348	76.87 353	90.19 386	92.65 353	85.40 224	81.44 371	90.54 360	66.79 343	95.00 403	81.04 294	81.05 391	92.66 380

EPNet_dtu		86.49 308	85.94 292	88.14 353	90.24 405	72.82 404	94.11 220	92.20 365	86.66 186	79.42 401	92.36 293	73.52 253	95.81 377	71.26 396	93.66 202	95.80 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 368	80.93 373	88.06 354	90.05 409	76.37 363	84.74 456	91.96 375	72.28 444	81.32 374	87.87 418	71.03 285	95.50 391	68.97 414	80.15 406	92.32 395

test_vis1_n_1920		89.39 199	89.84 162	88.04 355	92.97 306	72.64 409	94.71 175	96.03 171	86.18 197	91.94 127	96.56 99	61.63 386	95.74 381	93.42 65	95.11 159	95.74 243

DTE-MVSNet		86.11 314	85.48 307	87.98 356	91.65 352	74.92 379	94.93 157	95.75 196	87.36 162	82.26 360	93.04 272	72.85 264	95.82 376	74.04 380	77.46 424	93.20 360

PMMVS		85.71 323	84.96 321	87.95 357	88.90 424	77.09 348	88.68 414	90.06 421	72.32 443	86.47 251	90.76 355	72.15 274	94.40 410	81.78 283	93.49 209	92.36 393

GG-mvs-BLEND				87.94 358	89.73 416	77.91 325	87.80 427	78.23 477		80.58 383	83.86 449	59.88 405	95.33 396	71.20 397	92.22 244	90.60 431

MonoMVSNet		86.89 289	86.55 265	87.92 359	89.46 419	73.75 391	94.12 218	93.10 338	87.82 146	85.10 298	90.76 355	69.59 310	94.94 404	86.47 201	82.50 370	95.07 265

reproduce_monomvs		86.37 311	85.87 294	87.87 360	93.66 279	73.71 392	93.44 269	95.02 252	88.61 110	82.64 357	91.94 313	57.88 419	96.68 319	89.96 143	79.71 412	93.22 358

pmmvs-eth3d		80.97 394	78.72 405	87.74 361	84.99 457	79.97 271	90.11 388	91.65 382	75.36 411	73.51 446	86.03 438	59.45 408	93.96 421	75.17 368	72.21 439	89.29 445

MS-PatchMatch		85.05 337	84.16 337	87.73 362	91.42 358	78.51 307	91.25 358	93.53 328	77.50 388	80.15 388	91.58 327	61.99 383	95.51 389	75.69 363	94.35 182	89.16 447

mmtdpeth		85.04 339	84.15 338	87.72 363	93.11 296	75.74 371	94.37 204	92.83 346	84.98 238	89.31 192	86.41 435	61.61 388	97.14 293	92.63 81	62.11 467	90.29 433

test_0402		81.30 390	79.17 399	87.67 364	93.19 291	78.17 318	92.98 296	91.71 378	75.25 413	76.02 432	90.31 367	59.23 410	96.37 349	50.22 470	83.63 356	88.47 455

IterMVS		84.88 341	83.98 343	87.60 365	91.44 355	76.03 366	90.18 387	92.41 356	83.24 284	81.06 377	90.42 365	66.60 346	94.28 414	79.46 322	80.98 396	92.48 386
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 388	79.30 395	87.58 366	90.92 382	74.16 389	80.99 468	87.68 446	70.52 451	76.63 426	88.81 401	71.21 282	92.76 437	60.01 456	86.93 327	95.83 239

EPMVS		83.90 359	82.70 363	87.51 367	90.23 406	72.67 407	88.62 415	81.96 467	81.37 335	85.01 301	88.34 409	66.31 351	94.45 407	75.30 367	87.12 324	95.43 253

ADS-MVSNet2		81.66 382	79.71 390	87.50 368	91.35 361	74.19 388	83.33 461	88.48 440	72.90 438	82.24 361	85.77 441	64.98 361	93.20 432	64.57 441	83.74 353	95.12 263

OurMVSNet-221017-0		85.35 330	84.64 330	87.49 369	90.77 389	72.59 411	94.01 233	94.40 294	84.72 248	79.62 400	93.17 266	61.91 384	96.72 316	81.99 277	81.16 387	93.16 362

tpm2		84.08 354	82.94 358	87.48 370	91.39 359	71.27 424	89.23 406	90.37 413	71.95 445	84.64 307	89.33 392	67.30 335	96.55 337	75.17 368	87.09 325	94.63 284

RPSCF		85.07 336	84.27 334	87.48 370	92.91 309	70.62 434	91.69 345	92.46 355	76.20 405	82.67 356	95.22 173	63.94 371	97.29 280	77.51 345	85.80 332	94.53 291

myMVS_eth3d28		85.80 321	85.26 315	87.42 372	94.73 194	69.92 439	90.60 373	90.95 402	87.21 166	86.06 265	90.04 377	59.47 407	96.02 364	74.89 373	93.35 216	96.33 210

FE-MVSNET2		81.82 378	79.99 384	87.34 373	84.74 458	77.36 345	92.72 308	94.55 285	82.09 308	73.79 445	86.46 432	57.80 420	94.45 407	74.65 375	73.10 435	90.20 434

WBMVS		84.97 340	84.18 336	87.34 373	94.14 249	71.62 423	90.20 385	92.35 358	81.61 330	84.06 327	90.76 355	61.82 385	96.52 338	78.93 330	83.81 351	93.89 320

miper_lstm_enhance		85.27 333	84.59 331	87.31 375	91.28 364	74.63 382	87.69 432	94.09 310	81.20 341	81.36 373	89.85 384	74.97 227	94.30 413	81.03 296	79.84 411	93.01 369

FMVSNet5		81.52 386	79.60 391	87.27 376	91.17 367	77.95 323	91.49 349	92.26 364	76.87 397	76.16 428	87.91 417	51.67 446	92.34 440	67.74 423	81.16 387	91.52 410

USDC		82.76 367	81.26 371	87.26 377	91.17 367	74.55 383	89.27 404	93.39 331	78.26 383	75.30 436	92.08 306	54.43 439	96.63 323	71.64 394	85.79 333	90.61 429

test-LLR		85.87 318	85.41 308	87.25 378	90.95 378	71.67 421	89.55 398	89.88 428	83.41 278	84.54 310	87.95 415	67.25 336	95.11 400	81.82 281	93.37 214	94.97 268

test-mter		84.54 349	83.64 347	87.25 378	90.95 378	71.67 421	89.55 398	89.88 428	79.17 363	84.54 310	87.95 415	55.56 428	95.11 400	81.82 281	93.37 214	94.97 268

JIA-IIPM		81.04 391	78.98 403	87.25 378	88.64 425	73.48 396	81.75 467	89.61 434	73.19 435	82.05 364	73.71 471	66.07 356	95.87 373	71.18 399	84.60 344	92.41 390

TDRefinement		79.81 405	77.34 411	87.22 381	79.24 474	75.48 374	93.12 285	92.03 370	76.45 400	75.01 437	91.58 327	49.19 453	96.44 345	70.22 407	69.18 451	89.75 439

tpmvs		83.35 365	82.07 364	87.20 382	91.07 373	71.00 430	88.31 420	91.70 379	78.91 366	80.49 385	87.18 427	69.30 317	97.08 296	68.12 422	83.56 357	93.51 347

ppachtmachnet_test		81.84 377	80.07 383	87.15 383	88.46 429	74.43 386	89.04 410	92.16 366	75.33 412	77.75 417	88.99 398	66.20 353	95.37 395	65.12 438	77.60 422	91.65 406

dmvs_re		84.20 353	83.22 354	87.14 384	91.83 344	77.81 330	90.04 390	90.19 417	84.70 250	81.49 369	89.17 394	64.37 368	91.13 453	71.58 395	85.65 334	92.46 388

tpm cat1		81.96 374	80.27 379	87.01 385	91.09 372	71.02 429	87.38 436	91.53 387	66.25 461	80.17 387	86.35 437	68.22 332	96.15 360	69.16 413	82.29 373	93.86 326

test_fmvs1_n		87.03 285	87.04 245	86.97 386	89.74 415	71.86 416	94.55 183	94.43 291	78.47 377	91.95 126	95.50 159	51.16 448	93.81 422	93.02 73	94.56 175	95.26 259

OpenMVS_ROB	74.94 19	79.51 409	77.03 416	86.93 387	87.00 442	76.23 365	92.33 324	90.74 408	68.93 455	74.52 441	88.23 412	49.58 451	96.62 325	57.64 462	84.29 346	87.94 458

SixPastTwentyTwo		83.91 358	82.90 360	86.92 388	90.99 376	70.67 433	93.48 266	91.99 372	85.54 215	77.62 419	92.11 304	60.59 400	96.87 312	76.05 361	77.75 421	93.20 360

ADS-MVSNet		81.56 384	79.78 387	86.90 389	91.35 361	71.82 417	83.33 461	89.16 438	72.90 438	82.24 361	85.77 441	64.98 361	93.76 423	64.57 441	83.74 353	95.12 263

PatchT		82.68 369	81.27 370	86.89 390	90.09 408	70.94 431	84.06 458	90.15 418	74.91 417	85.63 275	83.57 452	69.37 313	94.87 405	65.19 436	88.50 301	94.84 278

tpm		84.73 344	84.02 341	86.87 391	90.33 403	68.90 442	89.06 409	89.94 425	80.85 344	85.75 271	89.86 383	68.54 329	95.97 367	77.76 341	84.05 350	95.75 242

Patchmatch-RL test		81.67 381	79.96 385	86.81 392	85.42 454	71.23 425	82.17 466	87.50 447	78.47 377	77.19 421	82.50 461	70.81 289	93.48 427	82.66 262	72.89 438	95.71 246

test_vis1_n		86.56 303	86.49 269	86.78 393	88.51 426	72.69 406	94.68 176	93.78 324	79.55 359	90.70 161	95.31 169	48.75 454	93.28 430	93.15 69	93.99 192	94.38 302

testing3-2		86.72 297	86.71 255	86.74 394	96.11 113	65.92 454	93.39 271	89.65 433	89.46 72	87.84 223	92.79 281	59.17 412	97.60 239	81.31 290	90.72 262	96.70 198

test_fmvs1		87.34 268	87.56 231	86.68 395	90.59 395	71.80 418	94.01 233	94.04 311	78.30 381	91.97 124	95.22 173	56.28 426	93.71 424	92.89 74	94.71 168	94.52 292

MDA-MVSNet-bldmvs		78.85 414	76.31 419	86.46 396	89.76 414	73.88 390	88.79 412	90.42 412	79.16 364	59.18 471	88.33 410	60.20 402	94.04 416	62.00 449	68.96 452	91.48 413

mvs5depth		80.98 393	79.15 400	86.45 397	84.57 459	73.29 399	87.79 428	91.67 381	80.52 347	82.20 363	89.72 386	55.14 434	95.93 369	73.93 383	66.83 459	90.12 436

tpmrst		85.35 330	84.99 319	86.43 398	90.88 385	67.88 447	88.71 413	91.43 390	80.13 351	86.08 264	88.80 403	73.05 262	96.02 364	82.48 263	83.40 361	95.40 254

TESTMET0.1,1		83.74 361	82.85 361	86.42 399	89.96 411	71.21 426	89.55 398	87.88 443	77.41 389	83.37 347	87.31 423	56.71 424	93.65 426	80.62 304	92.85 230	94.40 301

our_test_3		81.93 376	80.46 377	86.33 400	88.46 429	73.48 396	88.46 418	91.11 395	76.46 399	76.69 425	88.25 411	66.89 341	94.36 411	68.75 415	79.08 417	91.14 421

lessismore_v0				86.04 401	88.46 429	68.78 443		80.59 470		73.01 449	90.11 375	55.39 430	96.43 346	75.06 370	65.06 462	92.90 372

TinyColmap		79.76 406	77.69 409	85.97 402	91.71 348	73.12 400	89.55 398	90.36 414	75.03 415	72.03 452	90.19 371	46.22 463	96.19 359	63.11 445	81.03 392	88.59 454

KD-MVS_2432*1600		78.50 415	76.02 423	85.93 403	86.22 445	74.47 384	84.80 454	92.33 359	79.29 361	76.98 422	85.92 439	53.81 442	93.97 419	67.39 424	57.42 472	89.36 441

miper_refine_blended		78.50 415	76.02 423	85.93 403	86.22 445	74.47 384	84.80 454	92.33 359	79.29 361	76.98 422	85.92 439	53.81 442	93.97 419	67.39 424	57.42 472	89.36 441

K. test v3		81.59 383	80.15 382	85.91 405	89.89 413	69.42 441	92.57 313	87.71 445	85.56 214	73.44 447	89.71 387	55.58 427	95.52 388	77.17 348	69.76 448	92.78 377

SSC-MVS3.2		84.60 348	84.19 335	85.85 406	92.74 316	68.07 444	88.15 423	93.81 322	87.42 160	83.76 335	91.07 344	62.91 378	95.73 382	74.56 378	83.24 362	93.75 336

mvsany_test1		85.42 328	85.30 313	85.77 407	87.95 438	75.41 375	87.61 435	80.97 469	76.82 398	88.68 205	95.83 141	77.44 192	90.82 455	85.90 210	86.51 328	91.08 425

MIMVSNet1		79.38 410	77.28 412	85.69 408	86.35 444	73.67 393	91.61 347	92.75 350	78.11 386	72.64 450	88.12 413	48.16 455	91.97 446	60.32 453	77.49 423	91.43 415

UWE-MVS		83.69 362	83.09 355	85.48 409	93.06 300	65.27 459	90.92 366	86.14 451	79.90 354	86.26 260	90.72 358	57.17 423	95.81 377	71.03 402	92.62 238	95.35 257

UnsupCasMVSNet_eth		80.07 402	78.27 408	85.46 410	85.24 455	72.63 410	88.45 419	94.87 269	82.99 291	71.64 455	88.07 414	56.34 425	91.75 448	73.48 386	63.36 465	92.01 401

CL-MVSNet_self_test		81.74 380	80.53 374	85.36 411	85.96 447	72.45 413	90.25 380	93.07 340	81.24 339	79.85 397	87.29 424	70.93 287	92.52 438	66.95 427	69.23 450	91.11 423

MDA-MVSNet_test_wron		79.21 412	77.19 414	85.29 412	88.22 433	72.77 405	85.87 445	90.06 421	74.34 422	62.62 468	87.56 421	66.14 354	91.99 445	66.90 431	73.01 436	91.10 424

YYNet1		79.22 411	77.20 413	85.28 413	88.20 434	72.66 408	85.87 445	90.05 423	74.33 423	62.70 466	87.61 420	66.09 355	92.03 442	66.94 428	72.97 437	91.15 420

WB-MVSnew		83.77 360	83.28 351	85.26 414	91.48 354	71.03 428	91.89 337	87.98 442	78.91 366	84.78 304	90.22 369	69.11 322	94.02 417	64.70 440	90.44 265	90.71 427

dp		81.47 387	80.23 380	85.17 415	89.92 412	65.49 457	86.74 440	90.10 420	76.30 403	81.10 375	87.12 428	62.81 379	95.92 370	68.13 421	79.88 409	94.09 313

UnsupCasMVSNet_bld		76.23 425	73.27 429	85.09 416	83.79 461	72.92 402	85.65 448	93.47 330	71.52 446	68.84 461	79.08 466	49.77 450	93.21 431	66.81 432	60.52 469	89.13 449

SD_0403		84.71 346	84.65 328	84.92 417	92.95 307	65.95 453	92.07 336	93.23 335	83.82 267	79.03 405	93.73 251	73.90 247	92.91 436	63.02 447	90.05 272	95.89 235

Anonymous20231206		81.03 392	79.77 389	84.82 418	87.85 439	70.26 436	91.42 350	92.08 368	73.67 430	77.75 417	89.25 393	62.43 381	93.08 433	61.50 451	82.00 378	91.12 422

FE-MVSNET		78.19 417	76.03 422	84.69 419	83.70 462	73.31 398	90.58 374	90.00 424	77.11 396	71.91 453	85.47 443	55.53 429	91.94 447	59.69 457	70.24 445	88.83 451

test0.0.03 1		82.41 371	81.69 366	84.59 420	88.23 432	72.89 403	90.24 382	87.83 444	83.41 278	79.86 396	89.78 385	67.25 336	88.99 465	65.18 437	83.42 360	91.90 403

CMPMVS	59.16 21	80.52 396	79.20 398	84.48 421	83.98 460	67.63 450	89.95 393	93.84 319	64.79 465	66.81 463	91.14 341	57.93 418	95.17 398	76.25 358	88.10 307	90.65 428
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 347	84.79 326	84.37 422	91.84 342	64.92 460	93.70 258	91.47 389	66.19 462	86.16 263	95.28 170	67.18 338	93.33 429	80.89 299	90.42 267	94.88 277

PVSNet_0	73.20 20	77.22 421	74.83 427	84.37 422	90.70 393	71.10 427	83.09 463	89.67 431	72.81 440	73.93 444	83.13 454	60.79 399	93.70 425	68.54 416	50.84 477	88.30 456

LF4IMVS		80.37 399	79.07 402	84.27 424	86.64 443	69.87 440	89.39 403	91.05 398	76.38 401	74.97 438	90.00 379	47.85 456	94.25 415	74.55 379	80.82 398	88.69 453

Anonymous20240521		80.44 398	79.21 397	84.11 425	85.75 450	67.89 446	92.86 303	93.23 335	75.61 410	75.59 435	87.47 422	50.03 449	94.33 412	71.14 400	81.21 386	90.12 436

PM-MVS		78.11 418	76.12 421	84.09 426	83.54 463	70.08 437	88.97 411	85.27 458	79.93 353	74.73 440	86.43 434	34.70 474	93.48 427	79.43 325	72.06 440	88.72 452

test_fmvs2		83.98 355	84.03 340	83.83 427	87.16 441	67.53 451	93.93 240	92.89 344	77.62 387	86.89 244	93.53 254	47.18 458	92.02 444	90.54 134	86.51 328	91.93 402

testgi		80.94 395	80.20 381	83.18 428	87.96 437	66.29 452	91.28 356	90.70 410	83.70 269	78.12 413	92.84 276	51.37 447	90.82 455	63.34 444	82.46 371	92.43 389

KD-MVS_self_test		80.20 400	79.24 396	83.07 429	85.64 451	65.29 458	91.01 364	93.93 313	78.71 375	76.32 427	86.40 436	59.20 411	92.93 435	72.59 390	69.35 449	91.00 426

testing3		80.46 397	79.59 392	83.06 430	93.44 286	64.64 461	93.33 273	85.47 456	84.34 256	79.93 395	90.84 351	44.35 466	92.39 439	57.06 464	87.56 317	92.16 399

ambc				83.06 430	79.99 472	63.51 465	77.47 476	92.86 345		74.34 443	84.45 448	28.74 475	95.06 402	73.06 388	68.89 453	90.61 429

test20.03		79.95 404	79.08 401	82.55 432	85.79 449	67.74 449	91.09 362	91.08 396	81.23 340	74.48 442	89.96 381	61.63 386	90.15 457	60.08 454	76.38 429	89.76 438

MVStest1		72.91 429	69.70 434	82.54 433	78.14 475	73.05 401	88.21 422	86.21 450	60.69 469	64.70 464	90.53 361	46.44 461	85.70 472	58.78 460	53.62 474	88.87 450

test_vis1_rt		77.96 419	76.46 418	82.48 434	85.89 448	71.74 420	90.25 380	78.89 473	71.03 450	71.30 456	81.35 463	42.49 468	91.05 454	84.55 233	82.37 372	84.65 461

EU-MVSNet		81.32 389	80.95 372	82.42 435	88.50 428	63.67 464	93.32 274	91.33 391	64.02 466	80.57 384	92.83 277	61.21 395	92.27 441	76.34 357	80.38 405	91.32 416

myMVS_eth3d		79.67 407	78.79 404	82.32 436	91.92 338	64.08 462	89.75 396	87.40 448	81.72 325	78.82 408	87.20 425	45.33 464	91.29 451	59.09 459	87.84 314	91.60 408

ttmdpeth		76.55 423	74.64 428	82.29 437	82.25 468	67.81 448	89.76 395	85.69 454	70.35 452	75.76 433	91.69 320	46.88 459	89.77 459	66.16 433	63.23 466	89.30 443

pmmvs3		71.81 432	68.71 435	81.11 438	75.86 477	70.42 435	86.74 440	83.66 462	58.95 472	68.64 462	80.89 464	36.93 472	89.52 461	63.10 446	63.59 464	83.39 462

Syy-MVS		80.07 402	79.78 387	80.94 439	91.92 338	59.93 471	89.75 396	87.40 448	81.72 325	78.82 408	87.20 425	66.29 352	91.29 451	47.06 472	87.84 314	91.60 408

UWE-MVS-28		78.98 413	78.38 407	80.80 440	88.18 435	60.66 470	90.65 371	78.51 474	78.84 370	77.93 416	90.93 348	59.08 413	89.02 464	50.96 469	90.33 269	92.72 378

new-patchmatchnet		76.41 424	75.17 426	80.13 441	82.65 467	59.61 472	87.66 433	91.08 396	78.23 384	69.85 459	83.22 453	54.76 436	91.63 450	64.14 443	64.89 463	89.16 447

mvsany_test3		74.95 426	73.26 430	80.02 442	74.61 478	63.16 466	85.53 449	78.42 475	74.16 425	74.89 439	86.46 432	36.02 473	89.09 463	82.39 266	66.91 458	87.82 459

test_fmvs3		77.67 420	77.16 415	79.22 443	79.52 473	61.14 468	92.34 323	91.64 383	73.98 427	78.86 407	86.59 431	27.38 478	87.03 467	88.12 175	75.97 431	89.50 440

DSMNet-mixed		76.94 422	76.29 420	78.89 444	83.10 465	56.11 480	87.78 429	79.77 471	60.65 470	75.64 434	88.71 404	61.56 389	88.34 466	60.07 455	89.29 290	92.21 398

EGC-MVSNET		61.97 440	56.37 445	78.77 445	89.63 417	73.50 395	89.12 408	82.79 464	0.21 491	1.24 492	84.80 446	39.48 469	90.04 458	44.13 474	75.94 432	72.79 473

new_pmnet		72.15 430	70.13 433	78.20 446	82.95 466	65.68 455	83.91 459	82.40 466	62.94 468	64.47 465	79.82 465	42.85 467	86.26 471	57.41 463	74.44 434	82.65 466

MVS-HIRNet		73.70 428	72.20 431	78.18 447	91.81 345	56.42 479	82.94 464	82.58 465	55.24 473	68.88 460	66.48 476	55.32 432	95.13 399	58.12 461	88.42 303	83.01 464

LCM-MVSNet		66.00 437	62.16 442	77.51 448	64.51 488	58.29 474	83.87 460	90.90 404	48.17 477	54.69 474	73.31 472	16.83 487	86.75 468	65.47 435	61.67 468	87.48 460

APD_test1		69.04 433	66.26 439	77.36 449	80.51 471	62.79 467	85.46 450	83.51 463	54.11 475	59.14 472	84.79 447	23.40 481	89.61 460	55.22 465	70.24 445	79.68 470

test_f		71.95 431	70.87 432	75.21 450	74.21 480	59.37 473	85.07 453	85.82 453	65.25 464	70.42 458	83.13 454	23.62 479	82.93 478	78.32 335	71.94 441	83.33 463

ANet_high		58.88 444	54.22 449	72.86 451	56.50 491	56.67 476	80.75 469	86.00 452	73.09 437	37.39 483	64.63 479	22.17 482	79.49 481	43.51 475	23.96 485	82.43 467

test_vis3_rt		65.12 438	62.60 440	72.69 452	71.44 481	60.71 469	87.17 437	65.55 485	63.80 467	53.22 475	65.65 478	14.54 488	89.44 462	76.65 352	65.38 461	67.91 476

FPMVS		64.63 439	62.55 441	70.88 453	70.80 482	56.71 475	84.42 457	84.42 460	51.78 476	49.57 476	81.61 462	23.49 480	81.48 479	40.61 479	76.25 430	74.46 472

dmvs_testset		74.57 427	75.81 425	70.86 454	87.72 440	40.47 489	87.05 439	77.90 479	82.75 296	71.15 457	85.47 443	67.98 333	84.12 476	45.26 473	76.98 428	88.00 457

N_pmnet		68.89 434	68.44 436	70.23 455	89.07 422	28.79 494	88.06 424	19.50 494	69.47 454	71.86 454	84.93 445	61.24 394	91.75 448	54.70 466	77.15 425	90.15 435

testf1		59.54 442	56.11 446	69.85 456	69.28 483	56.61 477	80.37 470	76.55 482	42.58 480	45.68 479	75.61 467	11.26 489	84.18 474	43.20 476	60.44 470	68.75 474

APD_test2		59.54 442	56.11 446	69.85 456	69.28 483	56.61 477	80.37 470	76.55 482	42.58 480	45.68 479	75.61 467	11.26 489	84.18 474	43.20 476	60.44 470	68.75 474

WB-MVS		67.92 435	67.49 437	69.21 458	81.09 469	41.17 488	88.03 425	78.00 478	73.50 432	62.63 467	83.11 456	63.94 371	86.52 469	25.66 484	51.45 476	79.94 469

PMMVS2		59.60 441	56.40 444	69.21 458	68.83 485	46.58 484	73.02 480	77.48 480	55.07 474	49.21 477	72.95 473	17.43 486	80.04 480	49.32 471	44.33 480	80.99 468

SSC-MVS		67.06 436	66.56 438	68.56 460	80.54 470	40.06 490	87.77 430	77.37 481	72.38 442	61.75 469	82.66 460	63.37 374	86.45 470	24.48 485	48.69 479	79.16 471

Gipuma		57.99 446	54.91 448	67.24 461	88.51 426	65.59 456	52.21 483	90.33 415	43.58 479	42.84 482	51.18 483	20.29 484	85.07 473	34.77 480	70.45 444	51.05 482
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 448	48.46 452	63.48 462	45.72 493	46.20 485	73.41 479	78.31 476	41.03 482	30.06 485	65.68 477	6.05 491	83.43 477	30.04 482	65.86 460	60.80 477
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 445	58.24 443	60.56 463	83.13 464	45.09 487	82.32 465	48.22 493	67.61 458	61.70 470	69.15 474	38.75 470	76.05 482	32.01 481	41.31 481	60.55 478

MVE	39.65 23	43.39 450	38.59 456	57.77 464	56.52 490	48.77 483	55.38 482	58.64 489	29.33 485	28.96 486	52.65 482	4.68 492	64.62 486	28.11 483	33.07 483	59.93 479
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 449	48.47 451	56.66 465	52.26 492	18.98 496	41.51 485	81.40 468	10.10 486	44.59 481	75.01 470	28.51 476	68.16 483	53.54 467	49.31 478	82.83 465

DeepMVS_CX				56.31 466	74.23 479	51.81 482		56.67 490	44.85 478	48.54 478	75.16 469	27.87 477	58.74 488	40.92 478	52.22 475	58.39 480

kuosan		53.51 447	53.30 450	54.13 467	76.06 476	45.36 486	80.11 472	48.36 492	59.63 471	54.84 473	63.43 480	37.41 471	62.07 487	20.73 487	39.10 482	54.96 481

E-PMN		43.23 451	42.29 453	46.03 468	65.58 487	37.41 491	73.51 478	64.62 486	33.99 483	28.47 487	47.87 484	19.90 485	67.91 484	22.23 486	24.45 484	32.77 483

EMVS		42.07 452	41.12 454	44.92 469	63.45 489	35.56 493	73.65 477	63.48 487	33.05 484	26.88 488	45.45 485	21.27 483	67.14 485	19.80 488	23.02 486	32.06 484

tmp_tt		35.64 453	39.24 455	24.84 470	14.87 494	23.90 495	62.71 481	51.51 491	6.58 488	36.66 484	62.08 481	44.37 465	30.34 490	52.40 468	22.00 487	20.27 485

wuyk23d		21.27 455	20.48 458	23.63 471	68.59 486	36.41 492	49.57 484	6.85 495	9.37 487	7.89 489	4.46 491	4.03 493	31.37 489	17.47 489	16.07 488	3.12 486

test123		8.76 457	11.22 460	1.39 472	0.85 496	0.97 497	85.76 447	0.35 497	0.54 490	2.45 491	8.14 490	0.60 494	0.48 491	2.16 491	0.17 490	2.71 487

testmvs		8.92 456	11.52 459	1.12 473	1.06 495	0.46 498	86.02 444	0.65 496	0.62 489	2.74 490	9.52 489	0.31 495	0.45 492	2.38 490	0.39 489	2.46 488

mmdepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

monomultidepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

test_blank		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet_test		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

DCPMVS		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

cdsmvs_eth3d_5k		22.14 454	29.52 457	0.00 474	0.00 497	0.00 499	0.00 486	95.76 195	0.00 492	0.00 493	94.29 222	75.66 219	0.00 493	0.00 492	0.00 491	0.00 489

pcd_1.5k_mvsjas		6.64 459	8.86 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	79.70 156	0.00 493	0.00 492	0.00 491	0.00 489

sosnet-low-res		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

sosnet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uncertanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

Regformer		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

ab-mvs-re		7.82 458	10.43 461	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	93.88 243	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

TestfortrainingZip							97.32 10

WAC-MVS						64.08 462								59.14 458

FOURS1					98.86 4	85.54 73	98.29 1	97.49 12	89.79 63	96.29 32

PC_three_1452									82.47 300	97.09 20	97.07 72	92.72 1	98.04 196	92.70 80	99.02 12	98.86 16

test_one_0601					98.58 14	85.83 67		97.44 21	91.05 23	96.78 27	98.06 22	91.45 13

eth-test2					0.00 497

eth-test					0.00 497

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 271	94.19 64	96.91 78	87.57 34	99.26 50	91.99 105	98.44 57

RE-MVS-def			93.68 72		97.92 49	84.57 93	96.28 51	96.76 92	87.46 157	93.75 75	97.43 51	82.94 100		92.73 76	97.80 92	97.88 107

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 338	97.26 13				95.50 36	99.13 3	99.03 8

test_241102_TWO								97.44 21	90.31 41	97.62 8	98.07 20	91.46 12	99.58 14	95.66 30	99.12 6	98.98 10

test_241102_ONE					98.77 8	85.99 55		97.44 21	90.26 47	97.71 2	97.96 31	92.31 6	99.38 35

9.14			94.47 35		97.79 58		96.08 69	97.44 21	86.13 201	95.10 54	97.40 53	88.34 25	99.22 52	93.25 68	98.70 38

save fliter					97.85 55	85.63 72	95.21 140	96.82 85	89.44 73

test_0728_THIRD									90.75 29	97.04 22	98.05 25	92.09 8	99.55 20	95.64 32	99.13 3	99.13 2

test0726					98.78 6	85.93 58	97.19 16	97.47 17	90.27 45	97.64 6	98.13 7	91.47 10

GSMVS																96.12 223

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 278				96.12 223

sam_mvs												70.60 292

MTGPA								96.97 65

test_post1							88.00 426				9.81 488	69.31 316	95.53 387	76.65 352

test_post											10.29 487	70.57 296	95.91 372

patchmatchnet-post											83.76 450	71.53 279	96.48 341

MTMP							96.16 60	60.64 488

gm-plane-assit					89.60 418	68.00 445			77.28 392		88.99 398		97.57 242	79.44 324

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 226	96.78 89	81.61 330	92.77 100	96.20 108	87.71 31	99.12 62

test_8					97.49 69	86.30 46	94.02 232	96.76 92	81.86 321	92.70 104	96.20 108	87.63 32	99.02 72

agg_prior2														90.54 134	98.68 41	98.27 63

agg_prior					97.38 72	85.92 60		96.72 99		92.16 119			98.97 86

test_prior4						85.96 57	94.11 220

test_prior2							94.12 218		87.67 152	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

旧先验2							93.36 272		71.25 448	94.37 60			97.13 294	86.74 197

新几何2							93.11 287

旧先验1					96.79 85	81.81 195		95.67 205			96.81 84	86.69 42			97.66 98	96.97 179

无先验							93.28 280	96.26 139	73.95 428				99.05 66	80.56 305		96.59 202

原ACMM2							92.94 298

test222					96.55 94	81.70 198	92.22 329	95.01 253	68.36 457	90.20 172	96.14 116	80.26 144			97.80 92	96.05 230

testdata2													98.75 115	78.30 336

segment_acmp												87.16 39

testdata1							92.15 331		87.94 138

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 215	82.75 162						74.23 239

plane_prior5								96.22 144					98.12 176	88.15 172	89.99 273	94.63 284

plane_prior4											94.86 193

plane_prior3						82.75 162			90.26 47	86.91 241

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 208

plane_prior						82.73 165	95.21 140		89.66 68						89.88 278

n2								0.00 498

nn								0.00 498

door-mid								85.49 455

test11								96.57 111

door								85.33 457

HQP5-MVS						81.56 200

HQP-NCC					94.17 245		94.39 200		88.81 100	85.43 287

ACMP_Plane					94.17 245		94.39 200		88.81 100	85.43 287

BP-MVS														87.11 194

HQP4-MVS										85.43 287			97.96 211			94.51 294

HQP3-MVS								96.04 169							89.77 282

HQP2-MVS												73.83 250

NP-MVS					94.37 228	82.42 177					93.98 236

MDTV_nov1_ep13_2view						55.91 481	87.62 434		73.32 434	84.59 309		70.33 299		74.65 375		95.50 251

MDTV_nov1_ep13			83.56 348		91.69 350	69.93 438	87.75 431	91.54 386	78.60 376	84.86 303	88.90 400	69.54 311	96.03 363	70.25 405	88.93 295

ACMMP++_ref															87.47 318

ACMMP++															88.01 310

Test By Simon												80.02 146

High-resolution multi-view benchmark