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 57	90.86 1		96.85 77					99.61 4	96.03 26	99.06 9	99.07 5

No_MVS				96.52 1	97.78 57	90.86 1		96.85 77					99.61 4	96.03 26	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 45	90.85 3	97.13 16				97.08 66	92.59 2	98.94 88	92.25 88	98.99 14	98.84 16

HPM-MVS++		95.14 11	94.91 23	95.83 4	98.25 32	89.65 4	95.92 83	96.96 65	91.75 12	94.02 67	96.83 78	88.12 25	99.55 17	93.41 62	98.94 16	98.28 58

MM		95.10 12	94.91 23	95.68 5	96.09 112	88.34 9	96.68 35	94.37 282	95.08 1	94.68 53	97.72 38	82.94 97	99.64 1	97.85 5	98.76 30	99.06 7

SMA-MVS		95.20 8	95.07 17	95.59 6	98.14 38	88.48 8	96.26 50	97.28 37	85.90 192	97.67 4	98.10 13	88.41 21	99.56 13	94.66 46	99.19 1	98.71 22
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	92.42 100	91.37 114	95.55 7	95.63 139	88.73 6	97.07 20	96.77 88	90.84 25	84.02 318	96.62 91	75.95 202	99.34 39	87.77 169	97.68 93	98.59 26

CNVR-MVS		95.40 7	95.37 9	95.50 8	98.11 39	88.51 7	95.29 126	96.96 65	92.09 9	95.32 45	97.08 66	89.49 15	99.33 42	95.10 41	98.85 20	98.66 23

MGCNet		94.18 47	93.80 61	95.34 9	94.91 178	87.62 14	95.97 78	93.01 326	92.58 6	94.22 58	97.20 60	80.56 132	99.59 8	97.04 19	98.68 38	98.81 19

ACMMP_NAP		94.74 23	94.56 30	95.28 10	98.02 44	87.70 11	95.68 102	97.34 27	88.28 118	95.30 46	97.67 40	85.90 52	99.54 21	93.91 54	98.95 15	98.60 25

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 28	87.28 18	95.56 114	97.51 8	89.13 86	97.14 15	97.91 31	91.64 7	99.62 2	94.61 47	99.17 2	98.86 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 15	94.90 25	95.20 12	97.84 53	87.76 10	96.65 36	97.48 13	87.76 140	95.71 40	97.70 39	88.28 24	99.35 38	93.89 55	98.78 27	98.48 32

MCST-MVS		94.45 31	94.20 48	95.19 13	98.46 19	87.50 16	95.00 149	97.12 52	87.13 158	92.51 108	96.30 100	89.24 17	99.34 39	93.46 59	98.62 47	98.73 20

NCCC		94.81 20	94.69 29	95.17 14	97.83 54	87.46 17	95.66 105	96.93 69	92.34 7	93.94 68	96.58 93	87.74 28	99.44 30	92.83 71	98.40 55	98.62 24

DPM-MVS		92.58 96	91.74 106	95.08 15	96.19 103	89.31 5	92.66 298	96.56 108	83.44 266	91.68 133	95.04 173	86.60 44	98.99 78	85.60 203	97.92 81	96.93 172

ZNCC-MVS		94.47 30	94.28 42	95.03 16	98.52 15	86.96 20	96.85 30	97.32 31	88.24 119	93.15 83	97.04 69	86.17 49	99.62 2	92.40 82	98.81 24	98.52 28

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 18	97.49 9					99.61 4	95.62 33	99.08 7	98.99 9

MTAPA		94.42 36	94.22 45	95.00 18	98.42 21	86.95 21	94.36 199	96.97 62	91.07 21	93.14 84	97.56 42	84.30 78	99.56 13	93.43 60	98.75 31	98.47 35

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 27	97.47 14	91.73 13	96.10 32	96.69 83	89.90 12	99.30 45	94.70 45	98.04 76	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 33	94.27 44	94.92 20	98.65 8	86.67 30	96.92 26	97.23 40	88.60 109	93.58 75	97.27 54	85.22 61	99.54 21	92.21 90	98.74 32	98.56 27

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 31	86.29 46	97.46 7	97.40 23	89.03 91	96.20 31	98.10 13	89.39 16	99.34 39	95.88 28	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 33	94.28 42	94.91 21	98.63 9	86.69 28	96.94 22	97.32 31	88.63 106	93.53 78	97.26 56	85.04 65	99.54 21	92.35 85	98.78 27	98.50 29

GST-MVS		94.21 42	93.97 57	94.90 23	98.41 22	86.82 24	96.54 38	97.19 41	88.24 119	93.26 80	96.83 78	85.48 58	99.59 8	91.43 116	98.40 55	98.30 52

HFP-MVS		94.52 28	94.40 35	94.86 24	98.61 10	86.81 25	96.94 22	97.34 27	88.63 106	93.65 73	97.21 58	86.10 50	99.49 27	92.35 85	98.77 29	98.30 52

sasdasda		93.27 79	92.75 89	94.85 25	95.70 134	87.66 12	96.33 41	96.41 118	90.00 50	94.09 63	94.60 197	82.33 106	98.62 127	92.40 82	92.86 217	98.27 60

MP-MVS-pluss		94.21 42	94.00 56	94.85 25	98.17 36	86.65 31	94.82 162	97.17 46	86.26 184	92.83 93	97.87 33	85.57 57	99.56 13	94.37 50	98.92 17	98.34 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 79	92.75 89	94.85 25	95.70 134	87.66 12	96.33 41	96.41 118	90.00 50	94.09 63	94.60 197	82.33 106	98.62 127	92.40 82	92.86 217	98.27 60

XVS		94.45 31	94.32 38	94.85 25	98.54 13	86.60 34	96.93 24	97.19 41	90.66 33	92.85 91	97.16 64	85.02 66	99.49 27	91.99 101	98.56 51	98.47 35

X-MVStestdata		88.31 222	86.13 271	94.85 25	98.54 13	86.60 34	96.93 24	97.19 41	90.66 33	92.85 91	23.41 470	85.02 66	99.49 27	91.99 101	98.56 51	98.47 35

SteuartSystems-ACMMP		95.20 8	95.32 11	94.85 25	96.99 78	86.33 42	97.33 8	97.30 34	91.38 18	95.39 44	97.46 46	88.98 20	99.40 31	94.12 51	98.89 18	98.82 18
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 57	86.00 52	98.29 1	97.49 9	90.75 28	97.62 8	98.06 21	92.59 2	99.61 4	95.64 31	99.02 12	98.86 13

MED-MVS		95.17 10	95.29 12	94.81 32	98.39 25	85.89 63	95.91 84	97.55 6	89.01 93	95.86 38	97.54 43	89.24 17	99.59 8	95.27 39	98.85 20	98.95 11

alignmvs		93.08 87	92.50 95	94.81 32	95.62 140	87.61 15	95.99 75	96.07 155	89.77 63	94.12 62	94.87 181	80.56 132	98.66 119	92.42 81	93.10 213	98.15 72

SED-MVS		95.91 2	96.28 2	94.80 34	98.77 5	85.99 54	97.13 16	97.44 18	90.31 40	97.71 2	98.07 19	92.31 4	99.58 11	95.66 29	99.13 3	98.84 16

DeepC-MVS_fast	89.43 2	94.04 50	93.79 62	94.80 34	97.48 67	86.78 26	95.65 107	96.89 74	89.40 74	92.81 94	96.97 71	85.37 60	99.24 48	90.87 125	98.69 36	98.38 44
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 39	94.07 53	94.77 36	98.47 18	86.31 44	96.71 33	96.98 61	89.04 89	91.98 119	97.19 61	85.43 59	99.56 13	92.06 99	98.79 25	98.44 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 40	94.07 53	94.75 37	98.06 42	86.90 23	95.88 86	96.94 68	85.68 199	95.05 51	97.18 62	87.31 36	99.07 61	91.90 107	98.61 49	98.28 58
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 37	94.21 47	94.74 38	98.39 25	86.64 32	97.60 5	97.24 38	88.53 111	92.73 99	97.23 57	85.20 62	99.32 43	92.15 93	98.83 23	98.25 65

PGM-MVS		93.96 55	93.72 67	94.68 39	98.43 20	86.22 47	95.30 124	97.78 1	87.45 149	93.26 80	97.33 52	84.62 75	99.51 25	90.75 127	98.57 50	98.32 51

DVP-MVS		95.67 3	96.02 3	94.64 40	98.78 3	85.93 57	97.09 18	96.73 94	90.27 44	97.04 19	98.05 24	91.47 8	99.55 17	95.62 33	99.08 7	98.45 38
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 53	93.78 63	94.63 41	98.50 16	85.90 62	96.87 28	96.91 72	88.70 104	91.83 128	97.17 63	83.96 82	99.55 17	91.44 115	98.64 46	98.43 40

PHI-MVS		93.89 57	93.65 71	94.62 42	96.84 81	86.43 39	96.69 34	97.49 9	85.15 223	93.56 77	96.28 101	85.60 56	99.31 44	92.45 79	98.79 25	98.12 77

TSAR-MVS + MP.		94.85 17	94.94 21	94.58 43	98.25 32	86.33 42	96.11 63	96.62 103	88.14 124	96.10 32	96.96 72	89.09 19	98.94 88	94.48 48	98.68 38	98.48 32
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 66	93.20 80	94.55 44	95.65 137	85.73 68	94.94 152	96.69 99	91.89 11	90.69 151	95.88 125	81.99 118	99.54 21	93.14 66	97.95 80	98.39 42

train_agg		93.44 72	93.08 82	94.52 45	97.53 64	86.49 37	94.07 219	96.78 86	81.86 308	92.77 96	96.20 104	87.63 30	99.12 59	92.14 94	98.69 36	97.94 90

CDPH-MVS		92.83 91	92.30 98	94.44 46	97.79 55	86.11 51	94.06 221	96.66 100	80.09 339	92.77 96	96.63 90	86.62 42	99.04 65	87.40 176	98.66 42	98.17 70

3Dnovator	86.66 5	91.73 113	90.82 129	94.44 46	94.59 203	86.37 41	97.18 14	97.02 59	89.20 83	84.31 313	96.66 86	73.74 242	99.17 53	86.74 186	97.96 79	97.79 107

SR-MVS		94.23 41	94.17 51	94.43 48	98.21 35	85.78 66	96.40 40	96.90 73	88.20 122	94.33 57	97.40 49	84.75 74	99.03 66	93.35 63	97.99 78	98.48 32

HPM-MVS		94.02 51	93.88 58	94.43 48	98.39 25	85.78 66	97.25 12	97.07 57	86.90 168	92.62 105	96.80 82	84.85 72	99.17 53	92.43 80	98.65 45	98.33 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 64	93.41 75	94.41 50	96.59 88	86.78 26	94.40 191	93.93 300	89.77 63	94.21 59	95.59 144	87.35 35	98.61 129	92.72 74	96.15 131	97.83 104

reproduce-ours		94.82 18	94.97 19	94.38 51	97.91 50	85.46 71	95.86 87	97.15 48	89.82 56	95.23 48	98.10 13	87.09 38	99.37 34	95.30 37	98.25 64	98.30 52

our_new_method		94.82 18	94.97 19	94.38 51	97.91 50	85.46 71	95.86 87	97.15 48	89.82 56	95.23 48	98.10 13	87.09 38	99.37 34	95.30 37	98.25 64	98.30 52

NormalMVS		93.46 69	93.16 81	94.37 53	98.40 23	86.20 48	96.30 43	96.27 131	91.65 16	92.68 101	96.13 110	77.97 172	98.84 101	90.75 127	98.26 60	98.07 79

test12				94.34 54	97.13 76	86.15 50		96.29 127		91.04 147		85.08 64	99.01 71		98.13 71	97.86 99

SymmetryMVS		92.81 93	92.31 97	94.32 55	96.15 104	86.20 48	96.30 43	94.43 278	91.65 16	92.68 101	96.13 110	77.97 172	98.84 101	90.75 127	94.72 163	97.92 94

ACMMP		93.24 81	92.88 87	94.30 56	98.09 41	85.33 75	96.86 29	97.45 17	88.33 115	90.15 166	97.03 70	81.44 125	99.51 25	90.85 126	95.74 138	98.04 85
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 22	94.92 22	94.29 57	97.92 46	85.18 77	95.95 81	97.19 41	89.67 66	95.27 47	98.16 5	86.53 45	99.36 37	95.42 36	98.15 69	98.33 47

DeepC-MVS	88.79 3	93.31 78	92.99 85	94.26 58	96.07 114	85.83 64	94.89 155	96.99 60	89.02 92	89.56 175	97.37 51	82.51 103	99.38 32	92.20 91	98.30 58	97.57 122
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 88	92.63 92	94.23 59	95.62 140	85.92 59	96.08 65	96.33 125	89.86 54	93.89 70	94.66 194	82.11 113	98.50 135	92.33 87	92.82 220	98.27 60

fmvsm_l_conf0.5_n_3		94.80 21	95.01 18	94.15 60	95.64 138	85.08 78	96.09 64	97.36 25	90.98 23	97.09 17	98.12 9	84.98 70	98.94 88	97.07 16	97.80 88	98.43 40

EPNet		91.79 108	91.02 123	94.10 61	90.10 395	85.25 76	96.03 72	92.05 353	92.83 5	87.39 224	95.78 136	79.39 154	99.01 71	88.13 163	97.48 96	98.05 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 12	95.46 8	94.01 62	98.40 23	84.36 103	97.70 3	97.78 1	91.19 19	96.22 30	98.08 18	86.64 41	99.37 34	94.91 43	98.26 60	98.29 57

test_fmvsmconf_n		94.60 26	94.81 27	93.98 63	94.62 199	84.96 81	96.15 58	97.35 26	89.37 75	96.03 35	98.11 10	86.36 46	99.01 71	97.45 10	97.83 86	97.96 89

DELS-MVS		93.43 76	93.25 78	93.97 64	95.42 148	85.04 79	93.06 281	97.13 51	90.74 30	91.84 126	95.09 172	86.32 47	99.21 51	91.22 117	98.45 53	97.65 116
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		91.95 105	91.28 117	93.96 65	98.33 30	85.92 59	94.66 174	96.66 100	82.69 286	90.03 168	95.82 132	82.30 108	99.03 66	84.57 221	96.48 124	96.91 174

HPM-MVS_fast		93.40 77	93.22 79	93.94 66	98.36 28	84.83 83	97.15 15	96.80 85	85.77 196	92.47 109	97.13 65	82.38 104	99.07 61	90.51 132	98.40 55	97.92 94

test_fmvsmconf0.1_n		94.20 44	94.31 40	93.88 67	92.46 311	84.80 84	96.18 55	96.82 82	89.29 80	95.68 41	98.11 10	85.10 63	98.99 78	97.38 11	97.75 92	97.86 99

SD-MVS		94.96 16	95.33 10	93.88 67	97.25 75	86.69 28	96.19 53	97.11 55	90.42 36	96.95 21	97.27 54	89.53 14	96.91 299	94.38 49	98.85 20	98.03 86
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 71	93.31 76	93.84 69	96.99 78	84.84 82	93.24 272	97.24 38	88.76 101	91.60 134	95.85 129	86.07 51	98.66 119	91.91 105	98.16 68	98.03 86

SR-MVS-dyc-post		93.82 59	93.82 60	93.82 70	97.92 46	84.57 90	96.28 47	96.76 89	87.46 147	93.75 71	97.43 47	84.24 79	99.01 71	92.73 72	97.80 88	97.88 97

test_prior				93.82 70	97.29 73	84.49 94		96.88 75					98.87 95			98.11 78

APD-MVS_3200maxsize		93.78 60	93.77 64	93.80 72	97.92 46	84.19 107	96.30 43	96.87 76	86.96 164	93.92 69	97.47 45	83.88 83	98.96 85	92.71 75	97.87 84	98.26 64

fmvsm_l_conf0.5_n		94.29 38	94.46 33	93.79 73	95.28 153	85.43 73	95.68 102	96.43 116	86.56 176	96.84 23	97.81 36	87.56 33	98.77 110	97.14 14	96.82 114	97.16 153

CSCG		93.23 82	93.05 83	93.76 74	98.04 43	84.07 109	96.22 52	97.37 24	84.15 247	90.05 167	95.66 141	87.77 27	99.15 57	89.91 137	98.27 59	98.07 79

GDP-MVS		92.04 103	91.46 111	93.75 75	94.55 209	84.69 87	95.60 113	96.56 108	87.83 137	93.07 87	95.89 124	73.44 246	98.65 121	90.22 135	96.03 133	97.91 96

BP-MVS1		92.48 98	92.07 101	93.72 76	94.50 212	84.39 102	95.90 85	94.30 285	90.39 37	92.67 103	95.94 121	74.46 225	98.65 121	93.14 66	97.35 100	98.13 74

test_fmvsmconf0.01_n		93.19 83	93.02 84	93.71 77	89.25 408	84.42 101	96.06 69	96.29 127	89.06 87	94.68 53	98.13 6	79.22 156	98.98 82	97.22 13	97.24 102	97.74 110

UA-Net		92.83 91	92.54 94	93.68 78	96.10 111	84.71 86	95.66 105	96.39 120	91.92 10	93.22 82	96.49 96	83.16 92	98.87 95	84.47 223	95.47 145	97.45 128

fmvsm_l_conf0.5_n_a		94.20 44	94.40 35	93.60 79	95.29 152	84.98 80	95.61 110	96.28 130	86.31 182	96.75 25	97.86 34	87.40 34	98.74 114	97.07 16	97.02 107	97.07 158

QAPM		89.51 179	88.15 206	93.59 80	94.92 176	84.58 89	96.82 31	96.70 98	78.43 366	83.41 334	96.19 107	73.18 251	99.30 45	77.11 334	96.54 121	96.89 175

test_fmvsm_n_1920		94.71 24	95.11 16	93.50 81	95.79 128	84.62 88	96.15 58	97.64 3	89.85 55	97.19 14	97.89 32	86.28 48	98.71 117	97.11 15	98.08 75	97.17 147

fmvsm_s_conf0.5_n_9		94.99 14	95.50 7	93.44 82	96.51 96	82.25 180	95.76 97	96.92 70	93.37 3	97.63 7	98.43 1	84.82 73	99.16 56	98.15 1	97.92 81	98.90 12

KinetiMVS		91.82 107	91.30 115	93.39 83	94.72 192	83.36 134	95.45 117	96.37 122	90.33 39	92.17 114	96.03 116	72.32 263	98.75 111	87.94 166	96.34 126	98.07 79

casdiffmvs_mvg		92.96 90	92.83 88	93.35 84	94.59 203	83.40 132	95.00 149	96.34 124	90.30 42	92.05 117	96.05 114	83.43 86	98.15 171	92.07 96	95.67 139	98.49 31
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 55	94.18 50	93.30 85	94.79 185	83.81 118	95.77 95	96.74 93	88.02 127	96.23 29	97.84 35	83.36 90	98.83 104	97.49 8	97.34 101	97.25 140

EI-MVSNet-Vis-set		93.01 89	92.92 86	93.29 86	95.01 167	83.51 129	94.48 183	95.77 183	90.87 24	92.52 107	96.67 85	84.50 76	99.00 76	91.99 101	94.44 176	97.36 131

Vis-MVSNet		91.75 112	91.23 118	93.29 86	95.32 151	83.78 119	96.14 60	95.98 162	89.89 52	90.45 155	96.58 93	75.09 214	98.31 162	84.75 215	96.90 110	97.78 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 54	94.22 45	93.26 88	96.13 106	83.29 136	96.27 49	96.52 111	89.82 56	95.56 43	95.51 147	84.50 76	98.79 108	94.83 44	98.86 19	97.72 112

SPE-MVS-test		94.02 51	94.29 41	93.24 89	96.69 84	83.24 137	97.49 6	96.92 70	92.14 8	92.90 89	95.77 137	85.02 66	98.33 159	93.03 68	98.62 47	98.13 74

VNet		92.24 102	91.91 103	93.24 89	96.59 88	83.43 130	94.84 161	96.44 115	89.19 84	94.08 66	95.90 123	77.85 178	98.17 169	88.90 153	93.38 202	98.13 74

fmvsm_s_conf0.5_n_10		94.43 33	94.84 26	93.20 91	95.73 131	83.19 140	95.99 75	97.31 33	91.08 20	97.67 4	98.11 10	81.87 120	99.22 49	97.86 4	97.91 83	97.20 145

VDD-MVS		90.74 137	89.92 151	93.20 91	96.27 101	83.02 152	95.73 99	93.86 304	88.42 114	92.53 106	96.84 77	62.09 369	98.64 124	90.95 123	92.62 227	97.93 93

Elysia		90.12 156	89.10 174	93.18 93	93.16 281	84.05 111	95.22 133	96.27 131	85.16 221	90.59 152	94.68 190	64.64 352	98.37 152	86.38 192	95.77 136	97.12 155

StellarMVS		90.12 156	89.10 174	93.18 93	93.16 281	84.05 111	95.22 133	96.27 131	85.16 221	90.59 152	94.68 190	64.64 352	98.37 152	86.38 192	95.77 136	97.12 155

CS-MVS		94.12 48	94.44 34	93.17 95	96.55 91	83.08 149	97.63 4	96.95 67	91.71 14	93.50 79	96.21 103	85.61 55	98.24 164	93.64 57	98.17 67	98.19 68

nrg030		91.08 131	90.39 135	93.17 95	93.07 288	86.91 22	96.41 39	96.26 135	88.30 117	88.37 200	94.85 184	82.19 112	97.64 225	91.09 118	82.95 351	94.96 260

MVSMamba_PlusPlus		93.44 72	93.54 73	93.14 97	96.58 90	83.05 150	96.06 69	96.50 113	84.42 244	94.09 63	95.56 146	85.01 69	98.69 118	94.96 42	98.66 42	97.67 115

EI-MVSNet-UG-set		92.74 94	92.62 93	93.12 98	94.86 181	83.20 139	94.40 191	95.74 186	90.71 32	92.05 117	96.60 92	84.00 81	98.99 78	91.55 113	93.63 192	97.17 147

test_fmvsmvis_n_1920		93.44 72	93.55 72	93.10 99	93.67 267	84.26 105	95.83 91	96.14 146	89.00 94	92.43 110	97.50 44	83.37 89	98.72 115	96.61 23	97.44 97	96.32 200

新几何1				93.10 99	97.30 72	84.35 104		95.56 203	71.09 433	91.26 143	96.24 102	82.87 99	98.86 97	79.19 313	98.10 72	96.07 216

OMC-MVS		91.23 123	90.62 134	93.08 101	96.27 101	84.07 109	93.52 254	95.93 168	86.95 165	89.51 176	96.13 110	78.50 166	98.35 156	85.84 201	92.90 216	96.83 182

OpenMVS	83.78 11	88.74 209	87.29 228	93.08 101	92.70 306	85.39 74	96.57 37	96.43 116	78.74 361	80.85 366	96.07 113	69.64 298	99.01 71	78.01 325	96.65 119	94.83 268

MAR-MVS		90.30 152	89.37 167	93.07 103	96.61 87	84.48 95	95.68 102	95.67 194	82.36 291	87.85 211	92.85 264	76.63 191	98.80 106	80.01 301	96.68 118	95.91 222
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		90.92 132	90.21 139	93.03 104	93.86 252	83.88 116	92.81 293	93.86 304	79.84 342	91.76 130	94.29 211	77.92 175	98.04 188	90.48 133	97.11 103	97.17 147

Effi-MVS+		91.59 117	91.11 120	93.01 105	94.35 225	83.39 133	94.60 176	95.10 238	87.10 159	90.57 154	93.10 259	81.43 126	98.07 185	89.29 145	94.48 174	97.59 121

fmvsm_s_conf0.5_n_a		93.57 65	93.76 65	93.00 106	95.02 166	83.67 122	96.19 53	96.10 152	87.27 153	95.98 36	98.05 24	83.07 96	98.45 145	96.68 22	95.51 142	96.88 176

MVS_111021_LR		92.47 99	92.29 99	92.98 107	95.99 120	84.43 99	93.08 278	96.09 153	88.20 122	91.12 146	95.72 140	81.33 127	97.76 214	91.74 109	97.37 99	96.75 184

fmvsm_s_conf0.1_n_a		93.19 83	93.26 77	92.97 108	92.49 309	83.62 125	96.02 73	95.72 190	86.78 170	96.04 34	98.19 3	82.30 108	98.43 149	96.38 24	95.42 148	96.86 177

ETV-MVS		92.74 94	92.66 91	92.97 108	95.20 159	84.04 113	95.07 145	96.51 112	90.73 31	92.96 88	91.19 325	84.06 80	98.34 157	91.72 110	96.54 121	96.54 195

LFMVS		90.08 159	89.13 173	92.95 110	96.71 83	82.32 179	96.08 65	89.91 410	86.79 169	92.15 116	96.81 80	62.60 367	98.34 157	87.18 180	93.90 186	98.19 68

UGNet		89.95 166	88.95 182	92.95 110	94.51 211	83.31 135	95.70 101	95.23 231	89.37 75	87.58 218	93.94 227	64.00 357	98.78 109	83.92 230	96.31 127	96.74 185
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		90.80 135	90.10 143	92.90 112	93.04 291	83.53 128	93.08 278	94.15 293	80.22 336	91.41 140	94.91 178	76.87 185	97.93 204	90.28 134	96.90 110	97.24 141
jason: jason.
DP-MVS		87.25 263	85.36 300	92.90 112	97.65 61	83.24 137	94.81 163	92.00 355	74.99 401	81.92 355	95.00 174	72.66 256	99.05 63	66.92 414	92.33 232	96.40 197

fmvsm_s_conf0.5_n_8		94.56 27	95.12 15	92.87 114	95.96 123	81.32 205	95.76 97	97.57 5	93.48 2	97.53 10	98.32 2	81.78 123	99.13 58	97.91 2	97.81 87	98.16 71

fmvsm_s_conf0.5_n		93.76 61	94.06 55	92.86 115	95.62 140	83.17 141	96.14 60	96.12 150	88.13 125	95.82 39	98.04 27	83.43 86	98.48 137	96.97 20	96.23 128	96.92 173

fmvsm_s_conf0.1_n		93.46 69	93.66 70	92.85 116	93.75 259	83.13 143	96.02 73	95.74 186	87.68 143	95.89 37	98.17 4	82.78 100	98.46 141	96.71 21	96.17 130	96.98 167

CANet_DTU		90.26 154	89.41 166	92.81 117	93.46 274	83.01 153	93.48 255	94.47 277	89.43 73	87.76 216	94.23 216	70.54 286	99.03 66	84.97 210	96.39 125	96.38 198

MVSFormer		91.68 115	91.30 115	92.80 118	93.86 252	83.88 116	95.96 79	95.90 172	84.66 240	91.76 130	94.91 178	77.92 175	97.30 266	89.64 141	97.11 103	97.24 141

PVSNet_Blended_VisFu		91.38 120	90.91 126	92.80 118	96.39 98	83.17 141	94.87 157	96.66 100	83.29 271	89.27 182	94.46 206	80.29 135	99.17 53	87.57 173	95.37 149	96.05 219

fmvsm_l_conf0.5_n_9		94.65 25	95.28 13	92.77 120	95.95 124	81.83 190	95.53 115	97.12 52	91.68 15	97.89 1	98.06 21	85.71 54	98.65 121	97.32 12	98.26 60	97.83 104

LuminaMVS		90.55 148	89.81 153	92.77 120	92.78 304	84.21 106	94.09 217	94.17 292	85.82 193	91.54 135	94.14 218	69.93 292	97.92 205	91.62 112	94.21 181	96.18 208

fmvsm_s_conf0.5_n_6		94.11 49	94.56 30	92.76 122	94.98 171	81.96 188	95.79 93	97.29 36	89.31 78	97.52 11	97.61 41	83.25 91	98.88 94	97.05 18	98.22 66	97.43 130

VDDNet		89.56 178	88.49 197	92.76 122	95.07 165	82.09 183	96.30 43	93.19 321	81.05 330	91.88 124	96.86 76	61.16 385	98.33 159	88.43 160	92.49 231	97.84 103

viewdifsd2359ckpt09		91.18 126	90.65 133	92.75 124	94.61 202	82.36 178	94.32 200	95.74 186	84.72 237	89.66 174	95.15 170	79.69 149	98.04 188	87.70 170	94.27 180	97.85 102

h-mvs33		90.80 135	90.15 142	92.75 124	96.01 116	82.66 166	95.43 118	95.53 207	89.80 59	93.08 85	95.64 142	75.77 203	99.00 76	92.07 96	78.05 408	96.60 190

casdiffmvs		92.51 97	92.43 96	92.74 126	94.41 220	81.98 186	94.54 180	96.23 139	89.57 69	91.96 121	96.17 108	82.58 102	98.01 191	90.95 123	95.45 147	98.23 66
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		90.69 140	90.02 149	92.71 127	95.72 132	82.41 176	94.11 213	95.12 236	85.63 200	91.49 137	94.70 188	74.75 218	98.42 150	86.13 196	92.53 229	97.31 132

DCV-MVSNet		90.69 140	90.02 149	92.71 127	95.72 132	82.41 176	94.11 213	95.12 236	85.63 200	91.49 137	94.70 188	74.75 218	98.42 150	86.13 196	92.53 229	97.31 132

PCF-MVS	84.11 10	87.74 237	86.08 275	92.70 129	94.02 241	84.43 99	89.27 388	95.87 177	73.62 415	84.43 305	94.33 208	78.48 168	98.86 97	70.27 388	94.45 175	94.81 269
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		90.73 138	90.08 144	92.69 130	95.00 170	83.13 143	94.32 200	95.00 246	85.41 211	89.84 169	95.35 155	76.13 194	97.98 196	85.46 206	94.18 182	96.95 169

baseline		92.39 101	92.29 99	92.69 130	94.46 215	81.77 192	94.14 210	96.27 131	89.22 82	91.88 124	96.00 117	82.35 105	97.99 193	91.05 119	95.27 153	98.30 52

MSLP-MVS++		93.72 63	94.08 52	92.65 132	97.31 71	83.43 130	95.79 93	97.33 29	90.03 49	93.58 75	96.96 72	84.87 71	97.76 214	92.19 92	98.66 42	96.76 183

EC-MVSNet		93.44 72	93.71 68	92.63 133	95.21 158	82.43 173	97.27 11	96.71 97	90.57 35	92.88 90	95.80 133	83.16 92	98.16 170	93.68 56	98.14 70	97.31 132

ab-mvs		89.41 186	88.35 199	92.60 134	95.15 163	82.65 170	92.20 317	95.60 201	83.97 251	88.55 196	93.70 241	74.16 233	98.21 168	82.46 254	89.37 276	96.94 171

LS3D		87.89 232	86.32 264	92.59 135	96.07 114	82.92 156	95.23 131	94.92 253	75.66 393	82.89 341	95.98 119	72.48 260	99.21 51	68.43 402	95.23 154	95.64 236

Anonymous20240529		88.09 228	86.59 253	92.58 136	96.53 93	81.92 189	95.99 75	95.84 179	74.11 410	89.06 186	95.21 165	61.44 377	98.81 105	83.67 237	87.47 307	97.01 165

fmvsm_s_conf0.5_n_3		94.49 29	95.13 14	92.56 137	95.49 146	81.10 215	95.93 82	97.16 47	92.96 4	97.39 12	98.13 6	83.63 85	98.80 106	97.89 3	97.61 95	97.78 108

CPTT-MVS		91.99 104	91.80 104	92.55 138	98.24 34	81.98 186	96.76 32	96.49 114	81.89 307	90.24 159	96.44 98	78.59 164	98.61 129	89.68 140	97.85 85	97.06 159

viewdifsd2359ckpt13		91.20 125	90.75 131	92.54 139	94.30 227	82.13 182	94.03 223	95.89 174	85.60 202	90.20 161	95.36 154	79.69 149	97.90 208	87.85 168	93.86 187	97.61 118

114514_t		89.51 179	88.50 195	92.54 139	98.11 39	81.99 185	95.16 141	96.36 123	70.19 437	85.81 258	95.25 161	76.70 189	98.63 126	82.07 264	96.86 113	97.00 166

PAPM_NR		91.22 124	90.78 130	92.52 141	97.60 62	81.46 201	94.37 197	96.24 138	86.39 181	87.41 221	94.80 186	82.06 116	98.48 137	82.80 249	95.37 149	97.61 118

mamba_0408		89.06 199	87.92 213	92.50 142	94.76 186	82.66 166	79.84 457	94.64 271	85.18 216	88.96 188	95.00 174	76.00 199	97.98 196	83.74 234	93.15 210	96.85 178

DeepPCF-MVS	89.96 1	94.20 44	94.77 28	92.49 143	96.52 94	80.00 255	94.00 228	97.08 56	90.05 48	95.65 42	97.29 53	89.66 13	98.97 83	93.95 53	98.71 33	98.50 29

SSM_0407		90.47 150	89.80 154	92.46 144	94.76 186	82.66 166	93.98 230	95.00 246	85.41 211	88.96 188	95.35 155	76.13 194	97.88 209	85.46 206	93.15 210	96.85 178

IS-MVSNet		91.43 119	91.09 122	92.46 144	95.87 127	81.38 204	96.95 21	93.69 312	89.72 65	89.50 178	95.98 119	78.57 165	97.77 213	83.02 243	96.50 123	98.22 67

API-MVS		90.66 143	90.07 145	92.45 146	96.36 99	84.57 90	96.06 69	95.22 233	82.39 289	89.13 183	94.27 214	80.32 134	98.46 141	80.16 300	96.71 117	94.33 292

xiu_mvs_v1_base_debu		90.64 144	90.05 146	92.40 147	93.97 247	84.46 96	93.32 263	95.46 211	85.17 218	92.25 111	94.03 219	70.59 282	98.57 132	90.97 120	94.67 165	94.18 295

xiu_mvs_v1_base		90.64 144	90.05 146	92.40 147	93.97 247	84.46 96	93.32 263	95.46 211	85.17 218	92.25 111	94.03 219	70.59 282	98.57 132	90.97 120	94.67 165	94.18 295

xiu_mvs_v1_base_debi		90.64 144	90.05 146	92.40 147	93.97 247	84.46 96	93.32 263	95.46 211	85.17 218	92.25 111	94.03 219	70.59 282	98.57 132	90.97 120	94.67 165	94.18 295

fmvsm_s_conf0.5_n_2		93.47 68	93.83 59	92.39 150	95.36 149	81.19 211	95.20 138	96.56 108	90.37 38	97.13 16	98.03 28	77.47 181	98.96 85	97.79 6	96.58 120	97.03 162

viewmacassd2359aftdt		91.67 116	91.43 113	92.37 151	93.95 250	81.00 219	93.90 238	95.97 165	87.75 141	91.45 139	96.04 115	79.92 141	97.97 198	89.26 146	94.67 165	98.14 73

viewmanbaseed2359cas		91.78 110	91.58 109	92.37 151	94.32 226	81.07 216	93.76 243	95.96 166	87.26 154	91.50 136	95.88 125	80.92 131	97.97 198	89.70 139	94.92 159	98.07 79

fmvsm_s_conf0.1_n_2		93.16 85	93.42 74	92.37 151	94.62 199	81.13 213	95.23 131	95.89 174	90.30 42	96.74 26	98.02 29	76.14 193	98.95 87	97.64 7	96.21 129	97.03 162

AdaColmap		89.89 169	89.07 176	92.37 151	97.41 68	83.03 151	94.42 190	95.92 169	82.81 283	86.34 247	94.65 195	73.89 238	99.02 69	80.69 291	95.51 142	95.05 255

CNLPA		89.07 198	87.98 210	92.34 155	96.87 80	84.78 85	94.08 218	93.24 318	81.41 321	84.46 303	95.13 171	75.57 210	96.62 312	77.21 332	93.84 189	95.61 239

fmvsm_s_conf0.5_n_4		93.86 58	94.37 37	92.33 156	95.13 164	80.95 222	95.64 108	96.97 62	89.60 68	96.85 22	97.77 37	83.08 95	98.92 91	97.49 8	96.78 115	97.13 154

ET-MVSNet_ETH3D		87.51 251	85.91 283	92.32 157	93.70 266	83.93 114	92.33 311	90.94 387	84.16 246	72.09 435	92.52 277	69.90 293	95.85 359	89.20 147	88.36 294	97.17 147

Anonymous202405211		87.68 238	86.13 271	92.31 158	96.66 85	80.74 230	94.87 157	91.49 372	80.47 335	89.46 179	95.44 150	54.72 422	98.23 165	82.19 260	89.89 266	97.97 88

CHOSEN 1792x2688		88.84 205	87.69 218	92.30 159	96.14 105	81.42 203	90.01 375	95.86 178	74.52 406	87.41 221	93.94 227	75.46 211	98.36 154	80.36 296	95.53 141	97.12 155

viewcassd2359sk11		91.79 108	91.62 108	92.29 160	94.62 199	80.88 225	93.70 248	96.18 144	87.38 151	91.13 145	95.85 129	81.62 124	98.06 186	89.71 138	94.40 177	97.94 90

HY-MVS	83.01 12	89.03 201	87.94 212	92.29 160	94.86 181	82.77 158	92.08 322	94.49 276	81.52 320	86.93 228	92.79 270	78.32 170	98.23 165	79.93 302	90.55 253	95.88 225

CDS-MVSNet		89.45 182	88.51 194	92.29 160	93.62 269	83.61 127	93.01 282	94.68 269	81.95 301	87.82 214	93.24 253	78.69 162	96.99 293	80.34 297	93.23 207	96.28 203
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 162	89.27 172	92.29 160	95.78 129	80.95 222	92.68 297	96.22 140	81.91 303	86.66 238	93.75 239	82.23 110	98.44 147	79.40 312	94.79 162	97.48 126

mvsmamba		90.33 151	89.69 157	92.25 164	95.17 160	81.64 194	95.27 129	93.36 317	84.88 230	89.51 176	94.27 214	69.29 307	97.42 251	89.34 144	96.12 132	97.68 114

PLC	84.53 7	89.06 199	88.03 208	92.15 165	97.27 74	82.69 165	94.29 202	95.44 216	79.71 344	84.01 319	94.18 217	76.68 190	98.75 111	77.28 331	93.41 201	95.02 256
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 114	91.56 110	92.13 166	95.88 125	80.50 237	97.33 8	95.25 230	86.15 187	89.76 173	95.60 143	83.42 88	98.32 161	87.37 178	93.25 206	97.56 123

patch_mono-2		93.74 62	94.32 38	92.01 167	97.54 63	78.37 297	93.40 259	97.19 41	88.02 127	94.99 52	97.21 58	88.35 22	98.44 147	94.07 52	98.09 73	99.23 1

原ACMM1				92.01 167	97.34 70	81.05 217		96.81 84	78.89 355	90.45 155	95.92 122	82.65 101	98.84 101	80.68 292	98.26 60	96.14 210

UniMVSNet (Re)		89.80 172	89.07 176	92.01 167	93.60 270	84.52 93	94.78 165	97.47 14	89.26 81	86.44 244	92.32 283	82.10 114	97.39 262	84.81 214	80.84 385	94.12 299

MG-MVS		91.77 111	91.70 107	92.00 170	97.08 77	80.03 253	93.60 252	95.18 234	87.85 136	90.89 149	96.47 97	82.06 116	98.36 154	85.07 209	97.04 106	97.62 117

EIA-MVS		91.95 105	91.94 102	91.98 171	95.16 161	80.01 254	95.36 119	96.73 94	88.44 112	89.34 180	92.16 288	83.82 84	98.45 145	89.35 143	97.06 105	97.48 126

PVSNet_Blended		90.73 138	90.32 137	91.98 171	96.12 107	81.25 207	92.55 302	96.83 80	82.04 299	89.10 184	92.56 276	81.04 129	98.85 99	86.72 188	95.91 134	95.84 227

guyue		91.12 129	90.84 128	91.96 173	94.59 203	80.57 235	94.87 157	93.71 311	88.96 95	91.14 144	95.22 162	73.22 250	97.76 214	92.01 100	93.81 190	97.54 125

PS-MVSNAJ		91.18 126	90.92 125	91.96 173	95.26 156	82.60 172	92.09 321	95.70 192	86.27 183	91.84 126	92.46 278	79.70 146	98.99 78	89.08 148	95.86 135	94.29 293

TAMVS		89.21 192	88.29 203	91.96 173	93.71 264	82.62 171	93.30 267	94.19 290	82.22 294	87.78 215	93.94 227	78.83 159	96.95 296	77.70 327	92.98 215	96.32 200

SDMVSNet		90.19 155	89.61 160	91.93 176	96.00 117	83.09 148	92.89 290	95.98 162	88.73 102	86.85 234	95.20 166	72.09 265	97.08 285	88.90 153	89.85 268	95.63 237

FA-MVS(test-final)		89.66 174	88.91 184	91.93 176	94.57 207	80.27 241	91.36 338	94.74 266	84.87 231	89.82 170	92.61 275	74.72 221	98.47 140	83.97 229	93.53 196	97.04 161

MVS_Test		91.31 122	91.11 120	91.93 176	94.37 221	80.14 246	93.46 257	95.80 181	86.46 179	91.35 142	93.77 237	82.21 111	98.09 182	87.57 173	94.95 158	97.55 124

NR-MVSNet		88.58 215	87.47 224	91.93 176	93.04 291	84.16 108	94.77 166	96.25 137	89.05 88	80.04 380	93.29 251	79.02 158	97.05 290	81.71 275	80.05 395	94.59 276

HyFIR lowres test		88.09 228	86.81 241	91.93 176	96.00 117	80.63 232	90.01 375	95.79 182	73.42 417	87.68 217	92.10 294	73.86 239	97.96 200	80.75 290	91.70 236	97.19 146

GeoE		90.05 160	89.43 165	91.90 181	95.16 161	80.37 240	95.80 92	94.65 270	83.90 252	87.55 220	94.75 187	78.18 171	97.62 227	81.28 280	93.63 192	97.71 113

thisisatest0530		88.67 210	87.61 220	91.86 182	94.87 180	80.07 249	94.63 175	89.90 411	84.00 250	88.46 198	93.78 236	66.88 331	98.46 141	83.30 239	92.65 222	97.06 159

xiu_mvs_v2_base		91.13 128	90.89 127	91.86 182	94.97 172	82.42 174	92.24 314	95.64 199	86.11 191	91.74 132	93.14 257	79.67 151	98.89 93	89.06 149	95.46 146	94.28 294

DU-MVS		89.34 191	88.50 195	91.85 184	93.04 291	83.72 120	94.47 186	96.59 105	89.50 70	86.46 241	93.29 251	77.25 183	97.23 275	84.92 211	81.02 381	94.59 276

AstraMVS		90.69 140	90.30 138	91.84 185	93.81 255	79.85 260	94.76 167	92.39 341	88.96 95	91.01 148	95.87 128	70.69 280	97.94 203	92.49 78	92.70 221	97.73 111

OPM-MVS		90.12 156	89.56 161	91.82 186	93.14 283	83.90 115	94.16 209	95.74 186	88.96 95	87.86 210	95.43 152	72.48 260	97.91 206	88.10 165	90.18 260	93.65 330
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 147	90.19 140	91.82 186	94.70 195	82.73 162	95.85 89	96.22 140	90.81 26	86.91 230	94.86 182	74.23 229	98.12 172	88.15 161	89.99 262	94.63 273

UniMVSNet_NR-MVSNet		89.92 168	89.29 170	91.81 188	93.39 276	83.72 120	94.43 189	97.12 52	89.80 59	86.46 241	93.32 248	83.16 92	97.23 275	84.92 211	81.02 381	94.49 286

diffmvs		91.37 121	91.23 118	91.77 189	93.09 286	80.27 241	92.36 308	95.52 208	87.03 161	91.40 141	94.93 177	80.08 138	97.44 249	92.13 95	94.56 171	97.61 118
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		91.51 118	91.44 112	91.73 190	93.09 286	80.27 241	92.51 303	95.58 202	87.22 155	91.80 129	95.57 145	79.96 140	97.48 241	92.23 89	94.97 157	97.45 128

1112_ss		88.42 217	87.33 227	91.72 191	94.92 176	80.98 220	92.97 286	94.54 273	78.16 372	83.82 322	93.88 232	78.78 161	97.91 206	79.45 308	89.41 275	96.26 204

Fast-Effi-MVS+		89.41 186	88.64 190	91.71 192	94.74 189	80.81 228	93.54 253	95.10 238	83.11 275	86.82 236	90.67 348	79.74 145	97.75 218	80.51 295	93.55 194	96.57 193

WTY-MVS		89.60 176	88.92 183	91.67 193	95.47 147	81.15 212	92.38 307	94.78 264	83.11 275	89.06 186	94.32 209	78.67 163	96.61 315	81.57 276	90.89 249	97.24 141

TAPA-MVS	84.62 6	88.16 226	87.01 236	91.62 194	96.64 86	80.65 231	94.39 193	96.21 143	76.38 386	86.19 251	95.44 150	79.75 144	98.08 184	62.75 432	95.29 151	96.13 211
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 175	88.96 181	91.60 195	93.86 252	82.89 157	95.46 116	97.33 29	87.91 131	88.43 199	93.31 249	74.17 232	97.40 259	87.32 179	82.86 356	94.52 281

FE-MVS		87.40 256	86.02 277	91.57 196	94.56 208	79.69 264	90.27 362	93.72 310	80.57 333	88.80 192	91.62 314	65.32 347	98.59 131	74.97 357	94.33 179	96.44 196

XVG-OURS		89.40 188	88.70 189	91.52 197	94.06 239	81.46 201	91.27 342	96.07 155	86.14 188	88.89 191	95.77 137	68.73 316	97.26 272	87.39 177	89.96 264	95.83 228

hse-mvs2		89.88 170	89.34 168	91.51 198	94.83 183	81.12 214	93.94 232	93.91 303	89.80 59	93.08 85	93.60 242	75.77 203	97.66 222	92.07 96	77.07 415	95.74 232

TranMVSNet+NR-MVSNet		88.84 205	87.95 211	91.49 199	92.68 307	83.01 153	94.92 154	96.31 126	89.88 53	85.53 267	93.85 234	76.63 191	96.96 295	81.91 268	79.87 398	94.50 284

AUN-MVS		87.78 236	86.54 256	91.48 200	94.82 184	81.05 217	93.91 236	93.93 300	83.00 278	86.93 228	93.53 243	69.50 301	97.67 220	86.14 194	77.12 414	95.73 234

XVG-OURS-SEG-HR		89.95 166	89.45 163	91.47 201	94.00 245	81.21 210	91.87 326	96.06 157	85.78 195	88.55 196	95.73 139	74.67 222	97.27 270	88.71 157	89.64 273	95.91 222

MVS		87.44 254	86.10 274	91.44 202	92.61 308	83.62 125	92.63 299	95.66 196	67.26 443	81.47 358	92.15 289	77.95 174	98.22 167	79.71 304	95.48 144	92.47 373

viewdifsd2359ckpt07		91.11 130	91.02 123	91.41 203	94.21 232	78.37 297	92.91 289	95.71 191	87.50 146	90.32 158	95.88 125	80.27 136	97.99 193	88.78 156	93.55 194	97.86 99

F-COLMAP		87.95 231	86.80 242	91.40 204	96.35 100	80.88 225	94.73 169	95.45 214	79.65 345	82.04 353	94.61 196	71.13 272	98.50 135	76.24 344	91.05 247	94.80 270

dcpmvs_2		93.49 67	94.19 49	91.38 205	97.69 60	76.78 339	94.25 204	96.29 127	88.33 115	94.46 55	96.88 75	88.07 26	98.64 124	93.62 58	98.09 73	98.73 20

thisisatest0515		87.33 259	85.99 278	91.37 206	93.49 272	79.55 265	90.63 356	89.56 419	80.17 337	87.56 219	90.86 338	67.07 328	98.28 163	81.50 277	93.02 214	96.29 202

HQP-MVS		89.80 172	89.28 171	91.34 207	94.17 234	81.56 195	94.39 193	96.04 158	88.81 98	85.43 276	93.97 226	73.83 240	97.96 200	87.11 183	89.77 271	94.50 284

fmvsm_s_conf0.5_n_7		93.15 86	93.76 65	91.31 208	94.42 219	79.48 267	94.52 181	97.14 50	89.33 77	94.17 61	98.09 17	81.83 121	97.49 240	96.33 25	98.02 77	96.95 169

RRT-MVS		90.85 134	90.70 132	91.30 209	94.25 229	76.83 338	94.85 160	96.13 149	89.04 89	90.23 160	94.88 180	70.15 291	98.72 115	91.86 108	94.88 160	98.34 45

FMVSNet3		87.40 256	86.11 273	91.30 209	93.79 258	83.64 124	94.20 208	94.81 262	83.89 253	84.37 306	91.87 305	68.45 319	96.56 320	78.23 322	85.36 324	93.70 329

FMVSNet2		87.19 269	85.82 286	91.30 209	94.01 242	83.67 122	94.79 164	94.94 248	83.57 261	83.88 321	92.05 298	66.59 336	96.51 324	77.56 329	85.01 327	93.73 327

RPMNet		83.95 346	81.53 357	91.21 212	90.58 385	79.34 273	85.24 435	96.76 89	71.44 431	85.55 265	82.97 443	70.87 277	98.91 92	61.01 436	89.36 277	95.40 243

IB-MVS	80.51 15	85.24 323	83.26 341	91.19 213	92.13 320	79.86 259	91.75 329	91.29 377	83.28 272	80.66 370	88.49 395	61.28 379	98.46 141	80.99 286	79.46 402	95.25 249
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		89.47 181	88.90 185	91.18 214	94.22 231	82.07 184	92.13 319	96.09 153	87.90 132	85.37 282	92.45 279	74.38 227	97.56 232	87.15 181	90.43 255	93.93 308
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		89.45 182	88.90 185	91.12 215	94.47 213	81.49 199	95.30 124	96.14 146	86.73 172	85.45 273	95.16 168	69.89 294	98.10 174	87.70 170	89.23 280	93.77 323

LGP-MVS_train				91.12 215	94.47 213	81.49 199		96.14 146	86.73 172	85.45 273	95.16 168	69.89 294	98.10 174	87.70 170	89.23 280	93.77 323

ACMM	84.12 9	89.14 194	88.48 198	91.12 215	94.65 198	81.22 209	95.31 122	96.12 150	85.31 215	85.92 256	94.34 207	70.19 290	98.06 186	85.65 202	88.86 285	94.08 303
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 212	87.78 217	91.11 218	94.96 173	77.81 315	95.35 120	89.69 414	85.09 225	88.05 208	94.59 199	66.93 329	98.48 137	83.27 240	92.13 234	97.03 162

GBi-Net		87.26 261	85.98 279	91.08 219	94.01 242	83.10 145	95.14 142	94.94 248	83.57 261	84.37 306	91.64 310	66.59 336	96.34 337	78.23 322	85.36 324	93.79 318

test1		87.26 261	85.98 279	91.08 219	94.01 242	83.10 145	95.14 142	94.94 248	83.57 261	84.37 306	91.64 310	66.59 336	96.34 337	78.23 322	85.36 324	93.79 318

FMVSNet1		85.85 308	84.11 328	91.08 219	92.81 302	83.10 145	95.14 142	94.94 248	81.64 315	82.68 343	91.64 310	59.01 401	96.34 337	75.37 351	83.78 340	93.79 318

Test_1112_low_res		87.65 240	86.51 257	91.08 219	94.94 175	79.28 277	91.77 328	94.30 285	76.04 391	83.51 332	92.37 281	77.86 177	97.73 219	78.69 317	89.13 282	96.22 205

PS-MVSNAJss		89.97 164	89.62 159	91.02 223	91.90 329	80.85 227	95.26 130	95.98 162	86.26 184	86.21 250	94.29 211	79.70 146	97.65 223	88.87 155	88.10 296	94.57 278

BH-RMVSNet		88.37 220	87.48 223	91.02 223	95.28 153	79.45 269	92.89 290	93.07 324	85.45 210	86.91 230	94.84 185	70.35 287	97.76 214	73.97 365	94.59 170	95.85 226

UniMVSNet_ETH3D		87.53 250	86.37 261	91.00 225	92.44 312	78.96 282	94.74 168	95.61 200	84.07 249	85.36 283	94.52 201	59.78 393	97.34 264	82.93 244	87.88 301	96.71 186

FIs		90.51 149	90.35 136	90.99 226	93.99 246	80.98 220	95.73 99	97.54 7	89.15 85	86.72 237	94.68 190	81.83 121	97.24 274	85.18 208	88.31 295	94.76 271

ACMP	84.23 8	89.01 203	88.35 199	90.99 226	94.73 190	81.27 206	95.07 145	95.89 174	86.48 177	83.67 327	94.30 210	69.33 303	97.99 193	87.10 185	88.55 287	93.72 328
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 291	85.13 306	90.98 228	96.52 94	81.50 197	96.14 60	96.16 145	73.78 413	83.65 328	92.15 289	63.26 363	97.37 263	82.82 248	81.74 370	94.06 304

IMVS_0403		89.97 164	89.64 158	90.96 229	93.72 260	77.75 320	93.00 283	95.34 225	85.53 206	88.77 193	94.49 202	78.49 167	97.84 210	84.75 215	92.65 222	97.28 135

sss		88.93 204	88.26 205	90.94 230	94.05 240	80.78 229	91.71 330	95.38 220	81.55 319	88.63 195	93.91 231	75.04 215	95.47 378	82.47 253	91.61 237	96.57 193

IMVS_0407		89.85 171	89.51 162	90.88 231	93.72 260	77.75 320	93.07 280	95.34 225	85.53 206	88.34 201	94.49 202	77.69 179	97.60 228	84.75 215	92.65 222	97.28 135

viewmambaseed2359dif		90.04 161	89.78 155	90.83 232	92.85 301	77.92 309	92.23 315	95.01 242	81.90 304	90.20 161	95.45 149	79.64 153	97.34 264	87.52 175	93.17 208	97.23 144

sd_testset		88.59 214	87.85 216	90.83 232	96.00 117	80.42 239	92.35 309	94.71 267	88.73 102	86.85 234	95.20 166	67.31 323	96.43 331	79.64 306	89.85 268	95.63 237

PVSNet_BlendedMVS		89.98 163	89.70 156	90.82 234	96.12 107	81.25 207	93.92 234	96.83 80	83.49 265	89.10 184	92.26 286	81.04 129	98.85 99	86.72 188	87.86 302	92.35 379

cascas		86.43 299	84.98 309	90.80 235	92.10 322	80.92 224	90.24 366	95.91 171	73.10 420	83.57 331	88.39 396	65.15 349	97.46 245	84.90 213	91.43 239	94.03 306

ECVR-MVS		89.09 197	88.53 193	90.77 236	95.62 140	75.89 352	96.16 56	84.22 445	87.89 134	90.20 161	96.65 87	63.19 364	98.10 174	85.90 199	96.94 108	98.33 47

GA-MVS		86.61 289	85.27 303	90.66 237	91.33 352	78.71 286	90.40 361	93.81 307	85.34 214	85.12 286	89.57 377	61.25 380	97.11 284	80.99 286	89.59 274	96.15 209

thres600view7		87.65 240	86.67 248	90.59 238	96.08 113	78.72 284	94.88 156	91.58 368	87.06 160	88.08 206	92.30 284	68.91 313	98.10 174	70.05 395	91.10 242	94.96 260

thres400		87.62 245	86.64 249	90.57 239	95.99 120	78.64 287	94.58 177	91.98 357	86.94 166	88.09 204	91.77 306	69.18 309	98.10 174	70.13 392	91.10 242	94.96 260

baseline1		88.10 227	87.28 229	90.57 239	94.96 173	80.07 249	94.27 203	91.29 377	86.74 171	87.41 221	94.00 224	76.77 188	96.20 342	80.77 289	79.31 404	95.44 241

viewdifsd2359ckpt11		89.43 184	89.05 178	90.56 241	92.89 299	77.00 334	92.81 293	94.52 274	87.03 161	89.77 171	95.79 134	74.67 222	97.51 236	88.97 151	84.98 328	97.17 147

viewmsd2359difaftdt		89.43 184	89.05 178	90.56 241	92.89 299	77.00 334	92.81 293	94.52 274	87.03 161	89.77 171	95.79 134	74.67 222	97.51 236	88.97 151	84.98 328	97.17 147

FC-MVSNet-test		90.27 153	90.18 141	90.53 243	93.71 264	79.85 260	95.77 95	97.59 4	89.31 78	86.27 248	94.67 193	81.93 119	97.01 292	84.26 225	88.09 298	94.71 272

PAPM		86.68 288	85.39 298	90.53 243	93.05 290	79.33 276	89.79 378	94.77 265	78.82 358	81.95 354	93.24 253	76.81 186	97.30 266	66.94 412	93.16 209	94.95 264

WR-MVS		88.38 219	87.67 219	90.52 245	93.30 278	80.18 244	93.26 270	95.96 166	88.57 110	85.47 272	92.81 268	76.12 196	96.91 299	81.24 281	82.29 361	94.47 289

SSM_04072		88.57 216	87.92 213	90.51 246	94.76 186	82.66 166	79.84 457	94.64 271	85.18 216	88.96 188	95.00 174	76.00 199	92.03 426	83.74 234	93.15 210	96.85 178

MVSTER		88.84 205	88.29 203	90.51 246	92.95 296	80.44 238	93.73 245	95.01 242	84.66 240	87.15 225	93.12 258	72.79 255	97.21 277	87.86 167	87.36 310	93.87 313

testdata				90.49 248	96.40 97	77.89 312		95.37 222	72.51 425	93.63 74	96.69 83	82.08 115	97.65 223	83.08 241	97.39 98	95.94 221

test1111		89.10 195	88.64 190	90.48 249	95.53 145	74.97 362	96.08 65	84.89 443	88.13 125	90.16 165	96.65 87	63.29 362	98.10 174	86.14 194	96.90 110	98.39 42

tt0805		86.92 277	85.74 292	90.48 249	92.22 316	79.98 256	95.63 109	94.88 256	83.83 255	84.74 295	92.80 269	57.61 407	97.67 220	85.48 205	84.42 333	93.79 318

jajsoiax		88.24 224	87.50 222	90.48 249	90.89 373	80.14 246	95.31 122	95.65 198	84.97 228	84.24 314	94.02 222	65.31 348	97.42 251	88.56 158	88.52 289	93.89 309

PatchMatch-RL		86.77 285	85.54 294	90.47 252	95.88 125	82.71 164	90.54 359	92.31 345	79.82 343	84.32 311	91.57 318	68.77 315	96.39 333	73.16 371	93.48 200	92.32 380

tfpn200view9		87.58 248	86.64 249	90.41 253	95.99 120	78.64 287	94.58 177	91.98 357	86.94 166	88.09 204	91.77 306	69.18 309	98.10 174	70.13 392	91.10 242	94.48 287

VPNet		88.20 225	87.47 224	90.39 254	93.56 271	79.46 268	94.04 222	95.54 206	88.67 105	86.96 227	94.58 200	69.33 303	97.15 279	84.05 228	80.53 390	94.56 279

ACMH	80.38 17	85.36 318	83.68 335	90.39 254	94.45 216	80.63 232	94.73 169	94.85 258	82.09 296	77.24 405	92.65 273	60.01 391	97.58 230	72.25 376	84.87 330	92.96 358
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 243	86.71 245	90.38 256	96.12 107	78.55 290	95.03 148	91.58 368	87.15 157	88.06 207	92.29 285	68.91 313	98.10 174	70.13 392	91.10 242	94.48 287

mvs_tets		88.06 230	87.28 229	90.38 256	90.94 369	79.88 258	95.22 133	95.66 196	85.10 224	84.21 315	93.94 227	63.53 360	97.40 259	88.50 159	88.40 293	93.87 313

1314		87.51 251	86.57 254	90.34 258	92.42 313	79.74 263	92.63 299	95.35 224	78.35 367	80.14 377	91.62 314	74.05 234	97.15 279	81.05 282	93.53 196	94.12 299

LTVRE_ROB	82.13 13	86.26 302	84.90 312	90.34 258	94.44 217	81.50 197	92.31 313	94.89 254	83.03 277	79.63 387	92.67 272	69.69 297	97.79 212	71.20 381	86.26 319	91.72 390
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		89.03 201	88.64 190	90.21 260	90.74 380	79.28 277	95.96 79	95.90 172	84.66 240	85.33 284	92.94 263	74.02 235	97.30 266	89.64 141	88.53 288	94.05 305

v2v482		87.84 233	87.06 233	90.17 261	90.99 365	79.23 280	94.00 228	95.13 235	84.87 231	85.53 267	92.07 297	74.45 226	97.45 246	84.71 220	81.75 369	93.85 316

pmmvs4		85.43 316	83.86 333	90.16 262	90.02 398	82.97 155	90.27 362	92.67 336	75.93 392	80.73 368	91.74 308	71.05 273	95.73 367	78.85 316	83.46 347	91.78 389

V42		87.68 238	86.86 238	90.15 263	90.58 385	80.14 246	94.24 206	95.28 229	83.66 259	85.67 262	91.33 320	74.73 220	97.41 257	84.43 224	81.83 367	92.89 361

MSDG		84.86 331	83.09 344	90.14 264	93.80 256	80.05 251	89.18 391	93.09 323	78.89 355	78.19 397	91.91 303	65.86 346	97.27 270	68.47 401	88.45 291	93.11 353

sc_t1		81.53 370	78.67 391	90.12 265	90.78 377	78.64 287	93.91 236	90.20 400	68.42 440	80.82 367	89.88 370	46.48 445	96.76 304	76.03 347	71.47 429	94.96 260

anonymousdsp		87.84 233	87.09 232	90.12 265	89.13 409	80.54 236	94.67 173	95.55 204	82.05 297	83.82 322	92.12 291	71.47 270	97.15 279	87.15 181	87.80 305	92.67 367

thres200		87.21 267	86.24 268	90.12 265	95.36 149	78.53 291	93.26 270	92.10 351	86.42 180	88.00 209	91.11 331	69.24 308	98.00 192	69.58 396	91.04 248	93.83 317

CR-MVSNet		85.35 319	83.76 334	90.12 265	90.58 385	79.34 273	85.24 435	91.96 359	78.27 369	85.55 265	87.87 406	71.03 274	95.61 370	73.96 366	89.36 277	95.40 243

v1144		87.61 246	86.79 243	90.06 269	91.01 364	79.34 273	93.95 231	95.42 219	83.36 270	85.66 263	91.31 323	74.98 216	97.42 251	83.37 238	82.06 363	93.42 339

XXY-MVS		87.65 240	86.85 239	90.03 270	92.14 319	80.60 234	93.76 243	95.23 231	82.94 280	84.60 297	94.02 222	74.27 228	95.49 377	81.04 283	83.68 343	94.01 307

Vis-MVSNet (Re-imp)		89.59 177	89.44 164	90.03 270	95.74 130	75.85 353	95.61 110	90.80 391	87.66 145	87.83 213	95.40 153	76.79 187	96.46 329	78.37 318	96.73 116	97.80 106

test2506		87.21 267	86.28 266	90.02 272	95.62 140	73.64 378	96.25 51	71.38 468	87.89 134	90.45 155	96.65 87	55.29 419	98.09 182	86.03 198	96.94 108	98.33 47

BH-untuned		88.60 213	88.13 207	90.01 273	95.24 157	78.50 293	93.29 268	94.15 293	84.75 236	84.46 303	93.40 245	75.76 205	97.40 259	77.59 328	94.52 173	94.12 299

v1192		87.25 263	86.33 263	90.00 274	90.76 379	79.04 281	93.80 241	95.48 209	82.57 287	85.48 271	91.18 327	73.38 249	97.42 251	82.30 257	82.06 363	93.53 333

v7n		86.81 280	85.76 290	89.95 275	90.72 381	79.25 279	95.07 145	95.92 169	84.45 243	82.29 347	90.86 338	72.60 259	97.53 234	79.42 311	80.52 391	93.08 355

testing91		87.11 272	86.18 269	89.92 276	94.43 218	75.38 361	91.53 335	92.27 347	86.48 177	86.50 239	90.24 356	61.19 383	97.53 234	82.10 262	90.88 250	96.84 181

IMVS_0404		87.60 247	86.84 240	89.89 277	93.72 260	77.75 320	88.56 400	95.34 225	85.53 206	79.98 381	94.49 202	66.54 339	94.64 391	84.75 215	92.65 222	97.28 135

v8		87.50 253	86.71 245	89.89 277	91.37 349	79.40 270	94.50 182	95.38 220	84.81 234	83.60 330	91.33 320	76.05 197	97.42 251	82.84 247	80.51 392	92.84 363

v10		87.25 263	86.38 260	89.85 279	91.19 355	79.50 266	94.48 183	95.45 214	83.79 257	83.62 329	91.19 325	75.13 213	97.42 251	81.94 267	80.60 387	92.63 369

baseline2		86.50 295	85.39 298	89.84 280	91.12 360	76.70 341	91.88 325	88.58 423	82.35 292	79.95 382	90.95 336	73.42 247	97.63 226	80.27 299	89.95 265	95.19 250

pm-mvs1		86.61 289	85.54 294	89.82 281	91.44 344	80.18 244	95.28 128	94.85 258	83.84 254	81.66 356	92.62 274	72.45 262	96.48 326	79.67 305	78.06 407	92.82 364

TR-MVS		86.78 282	85.76 290	89.82 281	94.37 221	78.41 295	92.47 304	92.83 330	81.11 329	86.36 245	92.40 280	68.73 316	97.48 241	73.75 369	89.85 268	93.57 332

ACMH+	81.04 14	85.05 326	83.46 338	89.82 281	94.66 197	79.37 271	94.44 188	94.12 296	82.19 295	78.04 399	92.82 267	58.23 404	97.54 233	73.77 368	82.90 355	92.54 370

EI-MVSNet		89.10 195	88.86 187	89.80 284	91.84 331	78.30 300	93.70 248	95.01 242	85.73 197	87.15 225	95.28 159	79.87 143	97.21 277	83.81 232	87.36 310	93.88 312

v144192		87.19 269	86.35 262	89.74 285	90.64 383	78.24 302	93.92 234	95.43 217	81.93 302	85.51 269	91.05 334	74.21 231	97.45 246	82.86 246	81.56 371	93.53 333

COLMAP_ROB	80.39 16	83.96 345	82.04 354	89.74 285	95.28 153	79.75 262	94.25 204	92.28 346	75.17 399	78.02 400	93.77 237	58.60 403	97.84 210	65.06 423	85.92 320	91.63 392
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 301	85.18 305	89.73 287	92.15 318	76.60 342	91.12 346	91.69 364	83.53 264	85.50 270	88.81 389	66.79 332	96.48 326	76.65 337	90.35 257	96.12 212

IterMVS-LS		88.36 221	87.91 215	89.70 288	93.80 256	78.29 301	93.73 245	95.08 240	85.73 197	84.75 294	91.90 304	79.88 142	96.92 298	83.83 231	82.51 357	93.89 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 298	85.35 301	89.69 289	94.29 228	75.40 360	91.30 340	90.53 395	84.76 235	85.06 288	90.13 362	58.95 402	97.45 246	82.08 263	91.09 246	96.21 207

testing99		86.72 286	85.73 293	89.69 289	94.23 230	74.91 364	91.35 339	90.97 385	86.14 188	86.36 245	90.22 357	59.41 396	97.48 241	82.24 259	90.66 252	96.69 188

v1921920		86.97 276	86.06 276	89.69 289	90.53 388	78.11 305	93.80 241	95.43 217	81.90 304	85.33 284	91.05 334	72.66 256	97.41 257	82.05 265	81.80 368	93.53 333

icg_test_0407_2		89.15 193	88.97 180	89.68 292	93.72 260	77.75 320	88.26 405	95.34 225	85.53 206	88.34 201	94.49 202	77.69 179	93.99 402	84.75 215	92.65 222	97.28 135

VortexMVS		88.42 217	88.01 209	89.63 293	93.89 251	78.82 283	93.82 240	95.47 210	86.67 174	84.53 301	91.99 300	72.62 258	96.65 310	89.02 150	84.09 337	93.41 340

Fast-Effi-MVS+-dtu		87.44 254	86.72 244	89.63 293	92.04 323	77.68 325	94.03 223	93.94 299	85.81 194	82.42 346	91.32 322	70.33 288	97.06 288	80.33 298	90.23 259	94.14 298

v1240		86.78 282	85.85 285	89.56 295	90.45 390	77.79 317	93.61 251	95.37 222	81.65 314	85.43 276	91.15 329	71.50 269	97.43 250	81.47 278	82.05 365	93.47 337

Effi-MVS+-dtu		88.65 211	88.35 199	89.54 296	93.33 277	76.39 346	94.47 186	94.36 283	87.70 142	85.43 276	89.56 378	73.45 245	97.26 272	85.57 204	91.28 241	94.97 257

AllTest		83.42 352	81.39 358	89.52 297	95.01 167	77.79 317	93.12 274	90.89 389	77.41 376	76.12 414	93.34 246	54.08 425	97.51 236	68.31 403	84.27 335	93.26 343

TestCases				89.52 297	95.01 167	77.79 317		90.89 389	77.41 376	76.12 414	93.34 246	54.08 425	97.51 236	68.31 403	84.27 335	93.26 343

mvs_anonymous		89.37 190	89.32 169	89.51 299	93.47 273	74.22 371	91.65 333	94.83 260	82.91 281	85.45 273	93.79 235	81.23 128	96.36 336	86.47 190	94.09 183	97.94 90

XVG-ACMP-BASELINE		86.00 304	84.84 314	89.45 300	91.20 354	78.00 307	91.70 331	95.55 204	85.05 226	82.97 340	92.25 287	54.49 423	97.48 241	82.93 244	87.45 309	92.89 361

testing222		84.84 332	83.32 339	89.43 301	94.15 237	75.94 351	91.09 347	89.41 421	84.90 229	85.78 259	89.44 379	52.70 430	96.28 340	70.80 387	91.57 238	96.07 216

MVP-Stereo		85.97 305	84.86 313	89.32 302	90.92 371	82.19 181	92.11 320	94.19 290	78.76 360	78.77 396	91.63 313	68.38 320	96.56 320	75.01 356	93.95 185	89.20 430
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 308	84.70 316	89.29 303	91.76 335	75.54 357	88.49 401	91.30 376	81.63 316	85.05 289	88.70 393	71.71 266	96.24 341	74.61 361	89.05 283	96.08 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 274	86.32 264	89.21 304	90.94 369	77.26 330	93.71 247	94.43 278	84.84 233	84.36 309	90.80 342	76.04 198	97.05 290	82.12 261	79.60 401	93.31 342

tfpnnormal		84.72 334	83.23 342	89.20 305	92.79 303	80.05 251	94.48 183	95.81 180	82.38 290	81.08 364	91.21 324	69.01 312	96.95 296	61.69 434	80.59 388	90.58 417

cl22		86.78 282	85.98 279	89.18 306	92.34 314	77.62 326	90.84 352	94.13 295	81.33 323	83.97 320	90.15 361	73.96 236	96.60 317	84.19 226	82.94 352	93.33 341

BH-w/o		87.57 249	87.05 234	89.12 307	94.90 179	77.90 311	92.41 305	93.51 314	82.89 282	83.70 326	91.34 319	75.75 206	97.07 287	75.49 349	93.49 198	92.39 377

WR-MVS_H		87.80 235	87.37 226	89.10 308	93.23 279	78.12 304	95.61 110	97.30 34	87.90 132	83.72 325	92.01 299	79.65 152	96.01 351	76.36 341	80.54 389	93.16 351

miper_enhance_ethall		86.90 278	86.18 269	89.06 309	91.66 340	77.58 327	90.22 368	94.82 261	79.16 351	84.48 302	89.10 383	79.19 157	96.66 309	84.06 227	82.94 352	92.94 359

c3_l		87.14 271	86.50 258	89.04 310	92.20 317	77.26 330	91.22 345	94.70 268	82.01 300	84.34 310	90.43 353	78.81 160	96.61 315	83.70 236	81.09 378	93.25 345

miper_ehance_all_eth		87.22 266	86.62 252	89.02 311	92.13 320	77.40 329	90.91 351	94.81 262	81.28 324	84.32 311	90.08 364	79.26 155	96.62 312	83.81 232	82.94 352	93.04 356

gg-mvs-nofinetune		81.77 364	79.37 379	88.99 312	90.85 375	77.73 324	86.29 427	79.63 456	74.88 404	83.19 339	69.05 459	60.34 388	96.11 346	75.46 350	94.64 169	93.11 353

ETVMVS		84.43 339	82.92 348	88.97 313	94.37 221	74.67 365	91.23 344	88.35 425	83.37 269	86.06 254	89.04 384	55.38 417	95.67 369	67.12 410	91.34 240	96.58 192

pmmvs6		83.42 352	81.60 356	88.87 314	88.01 424	77.87 313	94.96 151	94.24 289	74.67 405	78.80 395	91.09 332	60.17 390	96.49 325	77.06 336	75.40 421	92.23 382

test_cas_vis1_n_1920		88.83 208	88.85 188	88.78 315	91.15 359	76.72 340	93.85 239	94.93 252	83.23 274	92.81 94	96.00 117	61.17 384	94.45 392	91.67 111	94.84 161	95.17 251

MIMVSNet		82.59 358	80.53 363	88.76 316	91.51 342	78.32 299	86.57 426	90.13 403	79.32 347	80.70 369	88.69 394	52.98 429	93.07 418	66.03 418	88.86 285	94.90 265

cl____		86.52 294	85.78 287	88.75 317	92.03 324	76.46 344	90.74 353	94.30 285	81.83 310	83.34 336	90.78 343	75.74 208	96.57 318	81.74 273	81.54 372	93.22 347

DIV-MVS_self_test		86.53 293	85.78 287	88.75 317	92.02 325	76.45 345	90.74 353	94.30 285	81.83 310	83.34 336	90.82 341	75.75 206	96.57 318	81.73 274	81.52 373	93.24 346

CP-MVSNet		87.63 243	87.26 231	88.74 319	93.12 284	76.59 343	95.29 126	96.58 106	88.43 113	83.49 333	92.98 262	75.28 212	95.83 360	78.97 314	81.15 377	93.79 318

eth_miper_zixun_eth		86.50 295	85.77 289	88.68 320	91.94 326	75.81 354	90.47 360	94.89 254	82.05 297	84.05 317	90.46 352	75.96 201	96.77 303	82.76 250	79.36 403	93.46 338

CHOSEN 280x420		85.15 324	83.99 331	88.65 321	92.47 310	78.40 296	79.68 459	92.76 333	74.90 403	81.41 360	89.59 376	69.85 296	95.51 374	79.92 303	95.29 151	92.03 385

PS-CasMVS		87.32 260	86.88 237	88.63 322	92.99 294	76.33 348	95.33 121	96.61 104	88.22 121	83.30 338	93.07 260	73.03 253	95.79 364	78.36 319	81.00 383	93.75 325

TransMVSNet (Re)		84.43 339	83.06 346	88.54 323	91.72 336	78.44 294	95.18 139	92.82 332	82.73 285	79.67 386	92.12 291	73.49 244	95.96 353	71.10 385	68.73 440	91.21 404

tt0320-xc		79.63 393	76.66 402	88.52 324	91.03 363	78.72 284	93.00 283	89.53 420	66.37 444	76.11 416	87.11 417	46.36 447	95.32 382	72.78 373	67.67 441	91.51 396

EG-PatchMatch MVS		82.37 360	80.34 366	88.46 325	90.27 392	79.35 272	92.80 296	94.33 284	77.14 380	73.26 432	90.18 360	47.47 442	96.72 305	70.25 389	87.32 312	89.30 427

PEN-MVS		86.80 281	86.27 267	88.40 326	92.32 315	75.71 356	95.18 139	96.38 121	87.97 129	82.82 342	93.15 256	73.39 248	95.92 355	76.15 345	79.03 406	93.59 331

Baseline_NR-MVSNet		87.07 273	86.63 251	88.40 326	91.44 344	77.87 313	94.23 207	92.57 338	84.12 248	85.74 261	92.08 295	77.25 183	96.04 347	82.29 258	79.94 396	91.30 402

UBG		85.51 314	84.57 321	88.35 328	94.21 232	71.78 403	90.07 373	89.66 416	82.28 293	85.91 257	89.01 385	61.30 378	97.06 288	76.58 340	92.06 235	96.22 205

D2MVS		85.90 306	85.09 307	88.35 328	90.79 376	77.42 328	91.83 327	95.70 192	80.77 332	80.08 379	90.02 366	66.74 334	96.37 334	81.88 269	87.97 300	91.26 403

pmmvs5		84.21 341	82.84 351	88.34 330	88.95 411	76.94 336	92.41 305	91.91 361	75.63 394	80.28 374	91.18 327	64.59 354	95.57 371	77.09 335	83.47 346	92.53 371

mamv4		90.92 132	91.78 105	88.33 331	95.67 136	70.75 416	92.92 288	96.02 161	81.90 304	88.11 203	95.34 157	85.88 53	96.97 294	95.22 40	95.01 156	97.26 139

tt0320		80.13 386	77.41 395	88.29 332	90.50 389	78.02 306	93.10 277	90.71 393	66.06 447	76.75 409	86.97 418	49.56 437	95.40 379	71.65 377	71.41 430	91.46 399

LCM-MVSNet-Re		88.30 223	88.32 202	88.27 333	94.71 194	72.41 398	93.15 273	90.98 384	87.77 139	79.25 390	91.96 301	78.35 169	95.75 365	83.04 242	95.62 140	96.65 189

CostFormer		85.77 311	84.94 311	88.26 334	91.16 358	72.58 396	89.47 386	91.04 383	76.26 389	86.45 243	89.97 368	70.74 279	96.86 302	82.35 256	87.07 315	95.34 247

ITE_SJBPF				88.24 335	91.88 330	77.05 333		92.92 327	85.54 204	80.13 378	93.30 250	57.29 408	96.20 342	72.46 375	84.71 331	91.49 397

PVSNet	78.82 18	85.55 313	84.65 317	88.23 336	94.72 192	71.93 399	87.12 422	92.75 334	78.80 359	84.95 291	90.53 350	64.43 355	96.71 307	74.74 359	93.86 187	96.06 218

IterMVS-SCA-FT		85.45 315	84.53 322	88.18 337	91.71 337	76.87 337	90.19 370	92.65 337	85.40 213	81.44 359	90.54 349	66.79 332	95.00 388	81.04 283	81.05 379	92.66 368

EPNet_dtu		86.49 297	85.94 282	88.14 338	90.24 393	72.82 388	94.11 213	92.20 349	86.66 175	79.42 389	92.36 282	73.52 243	95.81 362	71.26 380	93.66 191	95.80 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 356	80.93 362	88.06 339	90.05 397	76.37 347	84.74 440	91.96 359	72.28 428	81.32 362	87.87 406	71.03 274	95.50 376	68.97 398	80.15 394	92.32 380

test_vis1_n_1920		89.39 189	89.84 152	88.04 340	92.97 295	72.64 393	94.71 171	96.03 160	86.18 186	91.94 123	96.56 95	61.63 373	95.74 366	93.42 61	95.11 155	95.74 232

DTE-MVSNet		86.11 303	85.48 296	87.98 341	91.65 341	74.92 363	94.93 153	95.75 185	87.36 152	82.26 348	93.04 261	72.85 254	95.82 361	74.04 364	77.46 412	93.20 349

PMMVS		85.71 312	84.96 310	87.95 342	88.90 412	77.09 332	88.68 398	90.06 405	72.32 427	86.47 240	90.76 344	72.15 264	94.40 394	81.78 272	93.49 198	92.36 378

GG-mvs-BLEND				87.94 343	89.73 404	77.91 310	87.80 411	78.23 461		80.58 371	83.86 436	59.88 392	95.33 381	71.20 381	92.22 233	90.60 416

MonoMVSNet		86.89 279	86.55 255	87.92 344	89.46 407	73.75 375	94.12 211	93.10 322	87.82 138	85.10 287	90.76 344	69.59 299	94.94 389	86.47 190	82.50 358	95.07 254

reproduce_monomvs		86.37 300	85.87 284	87.87 345	93.66 268	73.71 376	93.44 258	95.02 241	88.61 108	82.64 345	91.94 302	57.88 406	96.68 308	89.96 136	79.71 400	93.22 347

pmmvs-eth3d		80.97 379	78.72 390	87.74 346	84.99 442	79.97 257	90.11 372	91.65 366	75.36 396	73.51 430	86.03 425	59.45 395	93.96 405	75.17 353	72.21 426	89.29 429

MS-PatchMatch		85.05 326	84.16 326	87.73 347	91.42 347	78.51 292	91.25 343	93.53 313	77.50 375	80.15 376	91.58 316	61.99 370	95.51 374	75.69 348	94.35 178	89.16 431

mmtdpeth		85.04 328	84.15 327	87.72 348	93.11 285	75.74 355	94.37 197	92.83 330	84.98 227	89.31 181	86.41 422	61.61 375	97.14 282	92.63 77	62.11 451	90.29 418

test_0402		81.30 375	79.17 384	87.67 349	93.19 280	78.17 303	92.98 285	91.71 362	75.25 398	76.02 417	90.31 355	59.23 397	96.37 334	50.22 454	83.63 344	88.47 439

IterMVS		84.88 330	83.98 332	87.60 350	91.44 344	76.03 350	90.18 371	92.41 340	83.24 273	81.06 365	90.42 354	66.60 335	94.28 398	79.46 307	80.98 384	92.48 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 373	79.30 380	87.58 351	90.92 371	74.16 373	80.99 452	87.68 430	70.52 435	76.63 411	88.81 389	71.21 271	92.76 421	60.01 440	86.93 316	95.83 228

EPMVS		83.90 348	82.70 352	87.51 352	90.23 394	72.67 391	88.62 399	81.96 451	81.37 322	85.01 290	88.34 397	66.31 340	94.45 392	75.30 352	87.12 313	95.43 242

ADS-MVSNet2		81.66 367	79.71 376	87.50 353	91.35 350	74.19 372	83.33 445	88.48 424	72.90 422	82.24 349	85.77 428	64.98 350	93.20 416	64.57 425	83.74 341	95.12 252

OurMVSNet-221017-0		85.35 319	84.64 319	87.49 354	90.77 378	72.59 395	94.01 226	94.40 281	84.72 237	79.62 388	93.17 255	61.91 371	96.72 305	81.99 266	81.16 375	93.16 351

tpm2		84.08 343	82.94 347	87.48 355	91.39 348	71.27 408	89.23 390	90.37 397	71.95 429	84.64 296	89.33 380	67.30 324	96.55 322	75.17 353	87.09 314	94.63 273

RPSCF		85.07 325	84.27 323	87.48 355	92.91 298	70.62 418	91.69 332	92.46 339	76.20 390	82.67 344	95.22 162	63.94 358	97.29 269	77.51 330	85.80 321	94.53 280

myMVS_eth3d28		85.80 310	85.26 304	87.42 357	94.73 190	69.92 423	90.60 357	90.95 386	87.21 156	86.06 254	90.04 365	59.47 394	96.02 349	74.89 358	93.35 205	96.33 199

WBMVS		84.97 329	84.18 325	87.34 358	94.14 238	71.62 407	90.20 369	92.35 342	81.61 317	84.06 316	90.76 344	61.82 372	96.52 323	78.93 315	83.81 339	93.89 309

miper_lstm_enhance		85.27 322	84.59 320	87.31 359	91.28 353	74.63 366	87.69 416	94.09 297	81.20 328	81.36 361	89.85 372	74.97 217	94.30 397	81.03 285	79.84 399	93.01 357

FMVSNet5		81.52 371	79.60 377	87.27 360	91.17 356	77.95 308	91.49 336	92.26 348	76.87 382	76.16 413	87.91 405	51.67 431	92.34 424	67.74 407	81.16 375	91.52 395

USDC		82.76 355	81.26 360	87.26 361	91.17 356	74.55 367	89.27 388	93.39 316	78.26 370	75.30 421	92.08 295	54.43 424	96.63 311	71.64 378	85.79 322	90.61 414

test-LLR		85.87 307	85.41 297	87.25 362	90.95 367	71.67 405	89.55 382	89.88 412	83.41 267	84.54 299	87.95 403	67.25 325	95.11 385	81.82 270	93.37 203	94.97 257

test-mter		84.54 338	83.64 336	87.25 362	90.95 367	71.67 405	89.55 382	89.88 412	79.17 350	84.54 299	87.95 403	55.56 414	95.11 385	81.82 270	93.37 203	94.97 257

JIA-IIPM		81.04 376	78.98 388	87.25 362	88.64 413	73.48 380	81.75 451	89.61 418	73.19 419	82.05 352	73.71 455	66.07 345	95.87 358	71.18 383	84.60 332	92.41 376

TDRefinement		79.81 390	77.34 396	87.22 365	79.24 458	75.48 358	93.12 274	92.03 354	76.45 385	75.01 422	91.58 316	49.19 438	96.44 330	70.22 391	69.18 437	89.75 423

tpmvs		83.35 354	82.07 353	87.20 366	91.07 362	71.00 414	88.31 404	91.70 363	78.91 353	80.49 373	87.18 415	69.30 306	97.08 285	68.12 406	83.56 345	93.51 336

ppachtmachnet_test		81.84 363	80.07 371	87.15 367	88.46 417	74.43 370	89.04 394	92.16 350	75.33 397	77.75 402	88.99 386	66.20 342	95.37 380	65.12 422	77.60 410	91.65 391

dmvs_re		84.20 342	83.22 343	87.14 368	91.83 333	77.81 315	90.04 374	90.19 401	84.70 239	81.49 357	89.17 382	64.37 356	91.13 437	71.58 379	85.65 323	92.46 374

tpm cat1		81.96 361	80.27 367	87.01 369	91.09 361	71.02 413	87.38 420	91.53 371	66.25 445	80.17 375	86.35 424	68.22 321	96.15 345	69.16 397	82.29 361	93.86 315

test_fmvs1_n		87.03 275	87.04 235	86.97 370	89.74 403	71.86 400	94.55 179	94.43 278	78.47 364	91.95 122	95.50 148	51.16 433	93.81 406	93.02 69	94.56 171	95.26 248

OpenMVS_ROB	74.94 19	79.51 394	77.03 401	86.93 371	87.00 430	76.23 349	92.33 311	90.74 392	68.93 439	74.52 426	88.23 400	49.58 436	96.62 312	57.64 446	84.29 334	87.94 442

SixPastTwentyTwo		83.91 347	82.90 349	86.92 372	90.99 365	70.67 417	93.48 255	91.99 356	85.54 204	77.62 404	92.11 293	60.59 387	96.87 301	76.05 346	77.75 409	93.20 349

ADS-MVSNet		81.56 369	79.78 373	86.90 373	91.35 350	71.82 401	83.33 445	89.16 422	72.90 422	82.24 349	85.77 428	64.98 350	93.76 407	64.57 425	83.74 341	95.12 252

PatchT		82.68 357	81.27 359	86.89 374	90.09 396	70.94 415	84.06 442	90.15 402	74.91 402	85.63 264	83.57 438	69.37 302	94.87 390	65.19 420	88.50 290	94.84 267

tpm		84.73 333	84.02 330	86.87 375	90.33 391	68.90 426	89.06 393	89.94 409	80.85 331	85.75 260	89.86 371	68.54 318	95.97 352	77.76 326	84.05 338	95.75 231

Patchmatch-RL test		81.67 366	79.96 372	86.81 376	85.42 440	71.23 409	82.17 450	87.50 431	78.47 364	77.19 406	82.50 445	70.81 278	93.48 411	82.66 251	72.89 425	95.71 235

test_vis1_n		86.56 292	86.49 259	86.78 377	88.51 414	72.69 390	94.68 172	93.78 309	79.55 346	90.70 150	95.31 158	48.75 439	93.28 414	93.15 65	93.99 184	94.38 291

testing3-2		86.72 286	86.71 245	86.74 378	96.11 110	65.92 438	93.39 260	89.65 417	89.46 71	87.84 212	92.79 270	59.17 399	97.60 228	81.31 279	90.72 251	96.70 187

test_fmvs1		87.34 258	87.56 221	86.68 379	90.59 384	71.80 402	94.01 226	94.04 298	78.30 368	91.97 120	95.22 162	56.28 412	93.71 408	92.89 70	94.71 164	94.52 281

MDA-MVSNet-bldmvs		78.85 399	76.31 404	86.46 380	89.76 402	73.88 374	88.79 396	90.42 396	79.16 351	59.18 455	88.33 398	60.20 389	94.04 400	62.00 433	68.96 438	91.48 398

mvs5depth		80.98 378	79.15 385	86.45 381	84.57 443	73.29 383	87.79 412	91.67 365	80.52 334	82.20 351	89.72 374	55.14 420	95.93 354	73.93 367	66.83 443	90.12 420

tpmrst		85.35 319	84.99 308	86.43 382	90.88 374	67.88 431	88.71 397	91.43 374	80.13 338	86.08 253	88.80 391	73.05 252	96.02 349	82.48 252	83.40 349	95.40 243

TESTMET0.1,1		83.74 350	82.85 350	86.42 383	89.96 399	71.21 410	89.55 382	87.88 427	77.41 376	83.37 335	87.31 411	56.71 410	93.65 410	80.62 293	92.85 219	94.40 290

our_test_3		81.93 362	80.46 365	86.33 384	88.46 417	73.48 380	88.46 402	91.11 379	76.46 384	76.69 410	88.25 399	66.89 330	94.36 395	68.75 399	79.08 405	91.14 406

lessismore_v0				86.04 385	88.46 417	68.78 427		80.59 454		73.01 433	90.11 363	55.39 416	96.43 331	75.06 355	65.06 446	92.90 360

TinyColmap		79.76 391	77.69 394	85.97 386	91.71 337	73.12 384	89.55 382	90.36 398	75.03 400	72.03 436	90.19 359	46.22 448	96.19 344	63.11 429	81.03 380	88.59 438

KD-MVS_2432*1600		78.50 400	76.02 408	85.93 387	86.22 433	74.47 368	84.80 438	92.33 343	79.29 348	76.98 407	85.92 426	53.81 427	93.97 403	67.39 408	57.42 456	89.36 425

miper_refine_blended		78.50 400	76.02 408	85.93 387	86.22 433	74.47 368	84.80 438	92.33 343	79.29 348	76.98 407	85.92 426	53.81 427	93.97 403	67.39 408	57.42 456	89.36 425

K. test v3		81.59 368	80.15 370	85.91 389	89.89 401	69.42 425	92.57 301	87.71 429	85.56 203	73.44 431	89.71 375	55.58 413	95.52 373	77.17 333	69.76 434	92.78 365

SSC-MVS3.2		84.60 337	84.19 324	85.85 390	92.74 305	68.07 428	88.15 407	93.81 307	87.42 150	83.76 324	91.07 333	62.91 365	95.73 367	74.56 362	83.24 350	93.75 325

mvsany_test1		85.42 317	85.30 302	85.77 391	87.95 426	75.41 359	87.61 419	80.97 453	76.82 383	88.68 194	95.83 131	77.44 182	90.82 439	85.90 199	86.51 317	91.08 410

MIMVSNet1		79.38 395	77.28 397	85.69 392	86.35 432	73.67 377	91.61 334	92.75 334	78.11 373	72.64 434	88.12 401	48.16 440	91.97 430	60.32 437	77.49 411	91.43 400

UWE-MVS		83.69 351	83.09 344	85.48 393	93.06 289	65.27 443	90.92 350	86.14 435	79.90 341	86.26 249	90.72 347	57.17 409	95.81 362	71.03 386	92.62 227	95.35 246

UnsupCasMVSNet_eth		80.07 387	78.27 393	85.46 394	85.24 441	72.63 394	88.45 403	94.87 257	82.99 279	71.64 439	88.07 402	56.34 411	91.75 432	73.48 370	63.36 449	92.01 386

CL-MVSNet_self_test		81.74 365	80.53 363	85.36 395	85.96 435	72.45 397	90.25 364	93.07 324	81.24 326	79.85 385	87.29 412	70.93 276	92.52 422	66.95 411	69.23 436	91.11 408

MDA-MVSNet_test_wron		79.21 397	77.19 399	85.29 396	88.22 421	72.77 389	85.87 429	90.06 405	74.34 407	62.62 452	87.56 409	66.14 343	91.99 429	66.90 415	73.01 423	91.10 409

YYNet1		79.22 396	77.20 398	85.28 397	88.20 422	72.66 392	85.87 429	90.05 407	74.33 408	62.70 450	87.61 408	66.09 344	92.03 426	66.94 412	72.97 424	91.15 405

WB-MVSnew		83.77 349	83.28 340	85.26 398	91.48 343	71.03 412	91.89 324	87.98 426	78.91 353	84.78 293	90.22 357	69.11 311	94.02 401	64.70 424	90.44 254	90.71 412

dp		81.47 372	80.23 368	85.17 399	89.92 400	65.49 441	86.74 424	90.10 404	76.30 388	81.10 363	87.12 416	62.81 366	95.92 355	68.13 405	79.88 397	94.09 302

UnsupCasMVSNet_bld		76.23 410	73.27 414	85.09 400	83.79 445	72.92 386	85.65 432	93.47 315	71.52 430	68.84 445	79.08 450	49.77 435	93.21 415	66.81 416	60.52 453	89.13 433

SD_0403		84.71 335	84.65 317	84.92 401	92.95 296	65.95 437	92.07 323	93.23 319	83.82 256	79.03 391	93.73 240	73.90 237	92.91 420	63.02 431	90.05 261	95.89 224

Anonymous20231206		81.03 377	79.77 375	84.82 402	87.85 427	70.26 420	91.42 337	92.08 352	73.67 414	77.75 402	89.25 381	62.43 368	93.08 417	61.50 435	82.00 366	91.12 407

FE-MVSNET		78.19 402	76.03 407	84.69 403	83.70 446	73.31 382	90.58 358	90.00 408	77.11 381	71.91 437	85.47 430	55.53 415	91.94 431	59.69 441	70.24 432	88.83 435

test0.0.03 1		82.41 359	81.69 355	84.59 404	88.23 420	72.89 387	90.24 366	87.83 428	83.41 267	79.86 384	89.78 373	67.25 325	88.99 449	65.18 421	83.42 348	91.90 388

CMPMVS	59.16 21	80.52 381	79.20 383	84.48 405	83.98 444	67.63 434	89.95 377	93.84 306	64.79 449	66.81 447	91.14 330	57.93 405	95.17 383	76.25 343	88.10 296	90.65 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 336	84.79 315	84.37 406	91.84 331	64.92 444	93.70 248	91.47 373	66.19 446	86.16 252	95.28 159	67.18 327	93.33 413	80.89 288	90.42 256	94.88 266

PVSNet_0	73.20 20	77.22 406	74.83 412	84.37 406	90.70 382	71.10 411	83.09 447	89.67 415	72.81 424	73.93 429	83.13 440	60.79 386	93.70 409	68.54 400	50.84 461	88.30 440

LF4IMVS		80.37 384	79.07 387	84.27 408	86.64 431	69.87 424	89.39 387	91.05 382	76.38 386	74.97 423	90.00 367	47.85 441	94.25 399	74.55 363	80.82 386	88.69 437

Anonymous20240521		80.44 383	79.21 382	84.11 409	85.75 438	67.89 430	92.86 292	93.23 319	75.61 395	75.59 420	87.47 410	50.03 434	94.33 396	71.14 384	81.21 374	90.12 420

PM-MVS		78.11 403	76.12 406	84.09 410	83.54 447	70.08 421	88.97 395	85.27 442	79.93 340	74.73 425	86.43 421	34.70 459	93.48 411	79.43 310	72.06 427	88.72 436

test_fmvs2		83.98 344	84.03 329	83.83 411	87.16 429	67.53 435	93.93 233	92.89 328	77.62 374	86.89 233	93.53 243	47.18 443	92.02 428	90.54 130	86.51 317	91.93 387

testgi		80.94 380	80.20 369	83.18 412	87.96 425	66.29 436	91.28 341	90.70 394	83.70 258	78.12 398	92.84 265	51.37 432	90.82 439	63.34 428	82.46 359	92.43 375

KD-MVS_self_test		80.20 385	79.24 381	83.07 413	85.64 439	65.29 442	91.01 349	93.93 300	78.71 362	76.32 412	86.40 423	59.20 398	92.93 419	72.59 374	69.35 435	91.00 411

testing3		80.46 382	79.59 378	83.06 414	93.44 275	64.64 445	93.33 262	85.47 440	84.34 245	79.93 383	90.84 340	44.35 451	92.39 423	57.06 448	87.56 306	92.16 384

ambc				83.06 414	79.99 456	63.51 449	77.47 460	92.86 329		74.34 428	84.45 435	28.74 460	95.06 387	73.06 372	68.89 439	90.61 414

test20.03		79.95 389	79.08 386	82.55 416	85.79 437	67.74 433	91.09 347	91.08 380	81.23 327	74.48 427	89.96 369	61.63 373	90.15 441	60.08 438	76.38 417	89.76 422

MVStest1		72.91 414	69.70 419	82.54 417	78.14 459	73.05 385	88.21 406	86.21 434	60.69 453	64.70 448	90.53 350	46.44 446	85.70 456	58.78 444	53.62 458	88.87 434

test_vis1_rt		77.96 404	76.46 403	82.48 418	85.89 436	71.74 404	90.25 364	78.89 457	71.03 434	71.30 440	81.35 447	42.49 453	91.05 438	84.55 222	82.37 360	84.65 445

EU-MVSNet		81.32 374	80.95 361	82.42 419	88.50 416	63.67 448	93.32 263	91.33 375	64.02 450	80.57 372	92.83 266	61.21 382	92.27 425	76.34 342	80.38 393	91.32 401

myMVS_eth3d		79.67 392	78.79 389	82.32 420	91.92 327	64.08 446	89.75 380	87.40 432	81.72 312	78.82 393	87.20 413	45.33 449	91.29 435	59.09 443	87.84 303	91.60 393

ttmdpeth		76.55 408	74.64 413	82.29 421	82.25 452	67.81 432	89.76 379	85.69 438	70.35 436	75.76 418	91.69 309	46.88 444	89.77 443	66.16 417	63.23 450	89.30 427

pmmvs3		71.81 417	68.71 420	81.11 422	75.86 461	70.42 419	86.74 424	83.66 446	58.95 456	68.64 446	80.89 448	36.93 457	89.52 445	63.10 430	63.59 448	83.39 446

Syy-MVS		80.07 387	79.78 373	80.94 423	91.92 327	59.93 455	89.75 380	87.40 432	81.72 312	78.82 393	87.20 413	66.29 341	91.29 435	47.06 456	87.84 303	91.60 393

UWE-MVS-28		78.98 398	78.38 392	80.80 424	88.18 423	60.66 454	90.65 355	78.51 458	78.84 357	77.93 401	90.93 337	59.08 400	89.02 448	50.96 453	90.33 258	92.72 366

new-patchmatchnet		76.41 409	75.17 411	80.13 425	82.65 451	59.61 456	87.66 417	91.08 380	78.23 371	69.85 443	83.22 439	54.76 421	91.63 434	64.14 427	64.89 447	89.16 431

mvsany_test3		74.95 411	73.26 415	80.02 426	74.61 462	63.16 450	85.53 433	78.42 459	74.16 409	74.89 424	86.46 420	36.02 458	89.09 447	82.39 255	66.91 442	87.82 443

test_fmvs3		77.67 405	77.16 400	79.22 427	79.52 457	61.14 452	92.34 310	91.64 367	73.98 411	78.86 392	86.59 419	27.38 463	87.03 451	88.12 164	75.97 419	89.50 424

DSMNet-mixed		76.94 407	76.29 405	78.89 428	83.10 449	56.11 464	87.78 413	79.77 455	60.65 454	75.64 419	88.71 392	61.56 376	88.34 450	60.07 439	89.29 279	92.21 383

EGC-MVSNET		61.97 425	56.37 430	78.77 429	89.63 405	73.50 379	89.12 392	82.79 448	0.21 475	1.24 476	84.80 433	39.48 454	90.04 442	44.13 458	75.94 420	72.79 457

new_pmnet		72.15 415	70.13 418	78.20 430	82.95 450	65.68 439	83.91 443	82.40 450	62.94 452	64.47 449	79.82 449	42.85 452	86.26 455	57.41 447	74.44 422	82.65 450

MVS-HIRNet		73.70 413	72.20 416	78.18 431	91.81 334	56.42 463	82.94 448	82.58 449	55.24 457	68.88 444	66.48 460	55.32 418	95.13 384	58.12 445	88.42 292	83.01 448

LCM-MVSNet		66.00 422	62.16 427	77.51 432	64.51 472	58.29 458	83.87 444	90.90 388	48.17 461	54.69 458	73.31 456	16.83 472	86.75 452	65.47 419	61.67 452	87.48 444

APD_test1		69.04 418	66.26 424	77.36 433	80.51 455	62.79 451	85.46 434	83.51 447	54.11 459	59.14 456	84.79 434	23.40 466	89.61 444	55.22 449	70.24 432	79.68 454

test_f		71.95 416	70.87 417	75.21 434	74.21 464	59.37 457	85.07 437	85.82 437	65.25 448	70.42 442	83.13 440	23.62 464	82.93 462	78.32 320	71.94 428	83.33 447

ANet_high		58.88 429	54.22 434	72.86 435	56.50 475	56.67 460	80.75 453	86.00 436	73.09 421	37.39 467	64.63 463	22.17 467	79.49 465	43.51 459	23.96 469	82.43 451

test_vis3_rt		65.12 423	62.60 425	72.69 436	71.44 465	60.71 453	87.17 421	65.55 469	63.80 451	53.22 459	65.65 462	14.54 473	89.44 446	76.65 337	65.38 445	67.91 460

FPMVS		64.63 424	62.55 426	70.88 437	70.80 466	56.71 459	84.42 441	84.42 444	51.78 460	49.57 460	81.61 446	23.49 465	81.48 463	40.61 463	76.25 418	74.46 456

dmvs_testset		74.57 412	75.81 410	70.86 438	87.72 428	40.47 473	87.05 423	77.90 463	82.75 284	71.15 441	85.47 430	67.98 322	84.12 460	45.26 457	76.98 416	88.00 441

N_pmnet		68.89 419	68.44 421	70.23 439	89.07 410	28.79 478	88.06 408	19.50 478	69.47 438	71.86 438	84.93 432	61.24 381	91.75 432	54.70 450	77.15 413	90.15 419

testf1		59.54 427	56.11 431	69.85 440	69.28 467	56.61 461	80.37 454	76.55 466	42.58 464	45.68 463	75.61 451	11.26 474	84.18 458	43.20 460	60.44 454	68.75 458

APD_test2		59.54 427	56.11 431	69.85 440	69.28 467	56.61 461	80.37 454	76.55 466	42.58 464	45.68 463	75.61 451	11.26 474	84.18 458	43.20 460	60.44 454	68.75 458

WB-MVS		67.92 420	67.49 422	69.21 442	81.09 453	41.17 472	88.03 409	78.00 462	73.50 416	62.63 451	83.11 442	63.94 358	86.52 453	25.66 468	51.45 460	79.94 453

PMMVS2		59.60 426	56.40 429	69.21 442	68.83 469	46.58 468	73.02 464	77.48 464	55.07 458	49.21 461	72.95 457	17.43 471	80.04 464	49.32 455	44.33 464	80.99 452

SSC-MVS		67.06 421	66.56 423	68.56 444	80.54 454	40.06 474	87.77 414	77.37 465	72.38 426	61.75 453	82.66 444	63.37 361	86.45 454	24.48 469	48.69 463	79.16 455

Gipuma		57.99 431	54.91 433	67.24 445	88.51 414	65.59 440	52.21 467	90.33 399	43.58 463	42.84 466	51.18 467	20.29 469	85.07 457	34.77 464	70.45 431	51.05 466
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 433	48.46 437	63.48 446	45.72 477	46.20 469	73.41 463	78.31 460	41.03 466	30.06 469	65.68 461	6.05 476	83.43 461	30.04 466	65.86 444	60.80 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 430	58.24 428	60.56 447	83.13 448	45.09 471	82.32 449	48.22 477	67.61 442	61.70 454	69.15 458	38.75 455	76.05 466	32.01 465	41.31 465	60.55 462

MVE	39.65 23	43.39 435	38.59 441	57.77 448	56.52 474	48.77 467	55.38 466	58.64 473	29.33 469	28.96 470	52.65 466	4.68 477	64.62 470	28.11 467	33.07 467	59.93 463
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 434	48.47 436	56.66 449	52.26 476	18.98 480	41.51 469	81.40 452	10.10 470	44.59 465	75.01 454	28.51 461	68.16 467	53.54 451	49.31 462	82.83 449

DeepMVS_CX				56.31 450	74.23 463	51.81 466		56.67 474	44.85 462	48.54 462	75.16 453	27.87 462	58.74 472	40.92 462	52.22 459	58.39 464

kuosan		53.51 432	53.30 435	54.13 451	76.06 460	45.36 470	80.11 456	48.36 476	59.63 455	54.84 457	63.43 464	37.41 456	62.07 471	20.73 471	39.10 466	54.96 465

E-PMN		43.23 436	42.29 438	46.03 452	65.58 471	37.41 475	73.51 462	64.62 470	33.99 467	28.47 471	47.87 468	19.90 470	67.91 468	22.23 470	24.45 468	32.77 467

EMVS		42.07 437	41.12 439	44.92 453	63.45 473	35.56 477	73.65 461	63.48 471	33.05 468	26.88 472	45.45 469	21.27 468	67.14 469	19.80 472	23.02 470	32.06 468

tmp_tt		35.64 438	39.24 440	24.84 454	14.87 478	23.90 479	62.71 465	51.51 475	6.58 472	36.66 468	62.08 465	44.37 450	30.34 474	52.40 452	22.00 471	20.27 469

wuyk23d		21.27 440	20.48 443	23.63 455	68.59 470	36.41 476	49.57 468	6.85 479	9.37 471	7.89 473	4.46 475	4.03 478	31.37 473	17.47 473	16.07 472	3.12 470

test123		8.76 442	11.22 445	1.39 456	0.85 480	0.97 481	85.76 431	0.35 481	0.54 474	2.45 475	8.14 474	0.60 479	0.48 475	2.16 475	0.17 474	2.71 471

testmvs		8.92 441	11.52 444	1.12 457	1.06 479	0.46 482	86.02 428	0.65 480	0.62 473	2.74 474	9.52 473	0.31 480	0.45 476	2.38 474	0.39 473	2.46 472

mmdepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

monomultidepth		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

test_blank		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uanet_test		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

DCPMVS		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

cdsmvs_eth3d_5k		22.14 439	29.52 442	0.00 458	0.00 481	0.00 483	0.00 470	95.76 184	0.00 476	0.00 477	94.29 211	75.66 209	0.00 477	0.00 476	0.00 475	0.00 473

pcd_1.5k_mvsjas		6.64 444	8.86 447	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	79.70 146	0.00 477	0.00 476	0.00 475	0.00 473

sosnet-low-res		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

sosnet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uncertanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

Regformer		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

ab-mvs-re		7.82 443	10.43 446	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	93.88 232	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

uanet		0.00 445	0.00 448	0.00 458	0.00 481	0.00 483	0.00 470	0.00 482	0.00 476	0.00 477	0.00 476	0.00 481	0.00 477	0.00 476	0.00 475	0.00 473

TestfortrainingZip							97.32 10

WAC-MVS						64.08 446								59.14 442

FOURS1					98.86 1	85.54 70	98.29 1	97.49 9	89.79 62	96.29 28

PC_three_1452									82.47 288	97.09 17	97.07 68	92.72 1	98.04 188	92.70 76	99.02 12	98.86 13

test_one_0601					98.58 11	85.83 64		97.44 18	91.05 22	96.78 24	98.06 21	91.45 11

eth-test2					0.00 481

eth-test					0.00 481

ZD-MVS					98.15 37	86.62 33		97.07 57	83.63 260	94.19 60	96.91 74	87.57 32	99.26 47	91.99 101	98.44 54

RE-MVS-def			93.68 69		97.92 46	84.57 90	96.28 47	96.76 89	87.46 147	93.75 71	97.43 47	82.94 97		92.73 72	97.80 88	97.88 97

IU-MVS					98.77 5	86.00 52		96.84 79	81.26 325	97.26 13				95.50 35	99.13 3	99.03 8

test_241102_TWO								97.44 18	90.31 40	97.62 8	98.07 19	91.46 10	99.58 11	95.66 29	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 54		97.44 18	90.26 46	97.71 2	97.96 30	92.31 4	99.38 32

9.14			94.47 32		97.79 55		96.08 65	97.44 18	86.13 190	95.10 50	97.40 49	88.34 23	99.22 49	93.25 64	98.70 35

save fliter					97.85 52	85.63 69	95.21 136	96.82 82	89.44 72

test_0728_THIRD									90.75 28	97.04 19	98.05 24	92.09 6	99.55 17	95.64 31	99.13 3	99.13 2

test0726					98.78 3	85.93 57	97.19 13	97.47 14	90.27 44	97.64 6	98.13 6	91.47 8

GSMVS																96.12 212

test_part2					98.55 12	87.22 19				96.40 27

sam_mvs1												71.70 267				96.12 212

sam_mvs												70.60 281

MTGPA								96.97 62

test_post1							88.00 410				9.81 472	69.31 305	95.53 372	76.65 337

test_post											10.29 471	70.57 285	95.91 357

patchmatchnet-post											83.76 437	71.53 268	96.48 326

MTMP							96.16 56	60.64 472

gm-plane-assit					89.60 406	68.00 429			77.28 379		88.99 386		97.57 231	79.44 309

test9_res														91.91 105	98.71 33	98.07 79

TEST9					97.53 64	86.49 37	94.07 219	96.78 86	81.61 317	92.77 96	96.20 104	87.71 29	99.12 59

test_8					97.49 66	86.30 45	94.02 225	96.76 89	81.86 308	92.70 100	96.20 104	87.63 30	99.02 69

agg_prior2														90.54 130	98.68 38	98.27 60

agg_prior					97.38 69	85.92 59		96.72 96		92.16 115			98.97 83

test_prior4						85.96 56	94.11 213

test_prior2							94.12 211		87.67 144	92.63 104	96.39 99	86.62 42		91.50 114	98.67 41

旧先验2							93.36 261		71.25 432	94.37 56			97.13 283	86.74 186

新几何2							93.11 276

旧先验1					96.79 82	81.81 191		95.67 194			96.81 80	86.69 40			97.66 94	96.97 168

无先验							93.28 269	96.26 135	73.95 412				99.05 63	80.56 294		96.59 191

原ACMM2							92.94 287

test222					96.55 91	81.70 193	92.22 316	95.01 242	68.36 441	90.20 161	96.14 109	80.26 137			97.80 88	96.05 219

testdata2													98.75 111	78.30 321

segment_acmp												87.16 37

testdata1							92.15 318		87.94 130

plane_prior7					94.70 195	82.74 161

plane_prior6					94.52 210	82.75 159						74.23 229

plane_prior5								96.22 140					98.12 172	88.15 161	89.99 262	94.63 273

plane_prior4											94.86 182

plane_prior3						82.75 159			90.26 46	86.91 230

plane_prior2							95.85 89		90.81 26

plane_prior1					94.59 203

plane_prior						82.73 162	95.21 136		89.66 67						89.88 267

n2								0.00 482

nn								0.00 482

door-mid								85.49 439

test11								96.57 107

door								85.33 441

HQP5-MVS						81.56 195

HQP-NCC					94.17 234		94.39 193		88.81 98	85.43 276

ACMP_Plane					94.17 234		94.39 193		88.81 98	85.43 276

BP-MVS														87.11 183

HQP4-MVS										85.43 276			97.96 200			94.51 283

HQP3-MVS								96.04 158							89.77 271

HQP2-MVS												73.83 240

NP-MVS					94.37 221	82.42 174					93.98 225

MDTV_nov1_ep13_2view						55.91 465	87.62 418		73.32 418	84.59 298		70.33 288		74.65 360		95.50 240

MDTV_nov1_ep13			83.56 337		91.69 339	69.93 422	87.75 415	91.54 370	78.60 363	84.86 292	88.90 388	69.54 300	96.03 348	70.25 389	88.93 284

ACMMP++_ref															87.47 307

ACMMP++															88.01 299

Test By Simon												80.02 139

High-resolution multi-view benchmark