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.

SMA-MVS		95.20 8	95.07 12	95.59 6	98.14 35	88.48 8	96.26 46	97.28 32	85.90 166	97.67 3	98.10 10	88.41 20	99.56 12	94.66 36	99.19 1	98.71 20
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
DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 25	87.28 18	95.56 103	97.51 5	89.13 74	97.14 10	97.91 25	91.64 7	99.62 2	94.61 37	99.17 2	98.86 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 52	97.13 14	97.44 15	90.31 32	97.71 1	98.07 14	92.31 4	99.58 10	95.66 21	99.13 3	98.84 14

IU-MVS					98.77 5	86.00 50		96.84 70	81.26 284	97.26 8				95.50 27	99.13 3	99.03 8

test_0728_THIRD									90.75 21	97.04 14	98.05 18	92.09 6	99.55 16	95.64 23	99.13 3	99.13 2

test_241102_TWO								97.44 15	90.31 32	97.62 5	98.07 14	91.46 10	99.58 10	95.66 21	99.12 6	98.98 10

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 55	97.09 16	96.73 84	90.27 36	97.04 14	98.05 18	91.47 8	99.55 16	95.62 25	99.08 7	98.45 36
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
test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 16	97.49 6					99.61 4	95.62 25	99.08 7	98.99 9

MSC_two_6792asdad				96.52 1	97.78 54	90.86 1		96.85 68					99.61 4	96.03 18	99.06 9	99.07 5

No_MVS				96.52 1	97.78 54	90.86 1		96.85 68					99.61 4	96.03 18	99.06 9	99.07 5

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 28	86.29 46	97.46 6	97.40 20	89.03 79	96.20 23	98.10 10	89.39 16	99.34 37	95.88 20	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 54	86.00 50	98.29 1	97.49 6	90.75 21	97.62 5	98.06 16	92.59 2	99.61 4	95.64 23	99.02 12	98.86 11

PC_three_1452									82.47 248	97.09 12	97.07 58	92.72 1	98.04 173	92.70 66	99.02 12	98.86 11

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 56	92.59 2	98.94 83	92.25 77	98.99 14	98.84 14

ACMMP_NAP		94.74 20	94.56 24	95.28 10	98.02 41	87.70 11	95.68 92	97.34 24	88.28 103	95.30 37	97.67 32	85.90 50	99.54 20	93.91 44	98.95 15	98.60 23

HPM-MVS++		95.14 10	94.91 18	95.83 4	98.25 29	89.65 4	95.92 78	96.96 57	91.75 10	94.02 57	96.83 68	88.12 24	99.55 16	93.41 52	98.94 16	98.28 55

MP-MVS-pluss		94.21 36	94.00 47	94.85 25	98.17 33	86.65 31	94.82 146	97.17 41	86.26 158	92.83 83	97.87 27	85.57 54	99.56 12	94.37 40	98.92 17	98.34 43
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 25	96.99 75	86.33 42	97.33 7	97.30 30	91.38 13	95.39 35	97.46 36	88.98 19	99.40 30	94.12 41	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
balanced_conf03		93.98 47	94.22 37	93.26 81	96.13 101	83.29 127	96.27 45	96.52 101	89.82 48	95.56 34	95.51 126	84.50 72	98.79 98	94.83 34	98.86 19	97.72 98

SD-MVS		94.96 13	95.33 8	93.88 63	97.25 72	86.69 28	96.19 49	97.11 48	90.42 29	96.95 16	97.27 44	89.53 14	96.91 265	94.38 39	98.85 20	98.03 78
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
CNVR-MVS		95.40 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 113	96.96 57	92.09 7	95.32 36	97.08 56	89.49 15	99.33 40	95.10 32	98.85 20	98.66 21

CP-MVS		94.34 31	94.21 39	94.74 37	98.39 23	86.64 32	97.60 4	97.24 33	88.53 96	92.73 89	97.23 47	85.20 59	99.32 41	92.15 81	98.83 22	98.25 62

ZNCC-MVS		94.47 25	94.28 34	95.03 16	98.52 15	86.96 20	96.85 28	97.32 28	88.24 104	93.15 73	97.04 59	86.17 47	99.62 2	92.40 71	98.81 23	98.52 26

MP-MVS		94.25 33	94.07 44	94.77 35	98.47 18	86.31 44	96.71 31	96.98 54	89.04 77	91.98 106	97.19 51	85.43 56	99.56 12	92.06 87	98.79 24	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		93.89 49	93.65 61	94.62 41	96.84 78	86.43 39	96.69 32	97.49 6	85.15 185	93.56 67	96.28 91	85.60 53	99.31 42	92.45 68	98.79 24	98.12 72

SF-MVS		94.97 12	94.90 20	95.20 12	97.84 50	87.76 10	96.65 34	97.48 10	87.76 124	95.71 31	97.70 31	88.28 23	99.35 36	93.89 45	98.78 26	98.48 30

ACMMPR		94.43 28	94.28 34	94.91 21	98.63 9	86.69 28	96.94 20	97.32 28	88.63 91	93.53 68	97.26 46	85.04 62	99.54 20	92.35 74	98.78 26	98.50 27

HFP-MVS		94.52 23	94.40 28	94.86 24	98.61 10	86.81 25	96.94 20	97.34 24	88.63 91	93.65 63	97.21 48	86.10 48	99.49 26	92.35 74	98.77 28	98.30 50

MM		95.10 11	94.91 18	95.68 5	96.09 106	88.34 9	96.68 33	94.37 248	95.08 1	94.68 44	97.72 30	82.94 90	99.64 1	97.85 2	98.76 29	99.06 7

MTAPA		94.42 30	94.22 37	95.00 18	98.42 21	86.95 21	94.36 181	96.97 55	91.07 14	93.14 74	97.56 33	84.30 74	99.56 12	93.43 50	98.75 30	98.47 33

region2R		94.43 28	94.27 36	94.92 20	98.65 8	86.67 30	96.92 24	97.23 35	88.60 94	93.58 65	97.27 44	85.22 58	99.54 20	92.21 78	98.74 31	98.56 25

test9_res														91.91 92	98.71 32	98.07 74

DeepPCF-MVS	89.96 1	94.20 38	94.77 22	92.49 125	96.52 91	80.00 229	94.00 206	97.08 49	90.05 40	95.65 33	97.29 43	89.66 13	98.97 78	93.95 43	98.71 32	98.50 27

9.14			94.47 25		97.79 52		96.08 61	97.44 15	86.13 164	95.10 41	97.40 39	88.34 22	99.22 47	93.25 54	98.70 34

train_agg		93.44 62	93.08 71	94.52 44	97.53 61	86.49 37	94.07 198	96.78 77	81.86 267	92.77 86	96.20 94	87.63 29	99.12 54	92.14 82	98.69 35	97.94 82

DeepC-MVS_fast	89.43 2	94.04 43	93.79 53	94.80 33	97.48 64	86.78 26	95.65 97	96.89 65	89.40 64	92.81 84	96.97 61	85.37 57	99.24 46	90.87 111	98.69 35	98.38 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		94.18 41	93.80 52	95.34 9	94.91 166	87.62 14	95.97 73	93.01 288	92.58 4	94.22 49	97.20 50	80.56 120	99.59 8	97.04 12	98.68 37	98.81 17

TSAR-MVS + MP.		94.85 14	94.94 16	94.58 42	98.25 29	86.33 42	96.11 59	96.62 93	88.14 109	96.10 24	96.96 62	89.09 18	98.94 83	94.48 38	98.68 37	98.48 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
agg_prior2														90.54 114	98.68 37	98.27 57

test_prior2							94.12 191		87.67 127	92.63 92	96.39 89	86.62 40		91.50 100	98.67 40

MVSMamba_PlusPlus		93.44 62	93.54 63	93.14 87	96.58 87	83.05 139	96.06 65	96.50 103	84.42 205	94.09 53	95.56 125	85.01 66	98.69 107	94.96 33	98.66 41	97.67 101

MSLP-MVS++		93.72 54	94.08 43	92.65 116	97.31 68	83.43 122	95.79 87	97.33 26	90.03 41	93.58 65	96.96 62	84.87 68	97.76 188	92.19 80	98.66 41	96.76 148

CDPH-MVS		92.83 80	92.30 86	94.44 45	97.79 52	86.11 49	94.06 200	96.66 90	80.09 298	92.77 86	96.63 80	86.62 40	99.04 60	87.40 149	98.66 41	98.17 67

HPM-MVS		94.02 44	93.88 49	94.43 47	98.39 23	85.78 63	97.25 10	97.07 50	86.90 143	92.62 93	96.80 72	84.85 69	99.17 50	92.43 69	98.65 44	98.33 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 46	93.78 54	94.63 40	98.50 16	85.90 60	96.87 26	96.91 63	88.70 89	91.83 115	97.17 53	83.96 78	99.55 16	91.44 101	98.64 45	98.43 38

SPE-MVS-test		94.02 44	94.29 33	93.24 82	96.69 81	83.24 128	97.49 5	96.92 62	92.14 6	92.90 79	95.77 117	85.02 63	98.33 145	93.03 58	98.62 46	98.13 69

MCST-MVS		94.45 26	94.20 40	95.19 13	98.46 19	87.50 16	95.00 134	97.12 46	87.13 135	92.51 96	96.30 90	89.24 17	99.34 37	93.46 49	98.62 46	98.73 18

APD-MVS		94.24 34	94.07 44	94.75 36	98.06 39	86.90 23	95.88 80	96.94 60	85.68 172	95.05 42	97.18 52	87.31 35	99.07 56	91.90 94	98.61 48	98.28 55
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		93.96 48	93.72 57	94.68 38	98.43 20	86.22 47	95.30 111	97.78 1	87.45 131	93.26 70	97.33 42	84.62 71	99.51 24	90.75 113	98.57 49	98.32 49

XVS		94.45 26	94.32 30	94.85 25	98.54 13	86.60 34	96.93 22	97.19 36	90.66 26	92.85 81	97.16 54	85.02 63	99.49 26	91.99 88	98.56 50	98.47 33

X-MVStestdata		88.31 186	86.13 233	94.85 25	98.54 13	86.60 34	96.93 22	97.19 36	90.66 26	92.85 81	23.41 424	85.02 63	99.49 26	91.99 88	98.56 50	98.47 33

DELS-MVS		93.43 66	93.25 68	93.97 60	95.42 139	85.04 76	93.06 250	97.13 45	90.74 23	91.84 113	95.09 145	86.32 45	99.21 48	91.22 103	98.45 52	97.65 102
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
ZD-MVS					98.15 34	86.62 33		97.07 50	83.63 220	94.19 51	96.91 64	87.57 31	99.26 45	91.99 88	98.44 53

GST-MVS		94.21 36	93.97 48	94.90 23	98.41 22	86.82 24	96.54 36	97.19 36	88.24 104	93.26 70	96.83 68	85.48 55	99.59 8	91.43 102	98.40 54	98.30 50

HPM-MVS_fast		93.40 67	93.22 69	93.94 62	98.36 25	84.83 80	97.15 13	96.80 76	85.77 169	92.47 97	97.13 55	82.38 97	99.07 56	90.51 116	98.40 54	97.92 85

NCCC		94.81 17	94.69 23	95.17 14	97.83 51	87.46 17	95.66 95	96.93 61	92.34 5	93.94 58	96.58 83	87.74 27	99.44 29	92.83 61	98.40 54	98.62 22

DeepC-MVS	88.79 3	93.31 68	92.99 74	94.26 55	96.07 108	85.83 61	94.89 140	96.99 53	89.02 80	89.56 146	97.37 41	82.51 96	99.38 31	92.20 79	98.30 57	97.57 107
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		93.23 72	93.05 72	93.76 70	98.04 40	84.07 104	96.22 48	97.37 21	84.15 208	90.05 142	95.66 121	87.77 26	99.15 53	89.91 121	98.27 58	98.07 74

原ACMM1				92.01 144	97.34 67	81.05 196		96.81 75	78.89 314	90.45 133	95.92 108	82.65 94	98.84 94	80.68 254	98.26 59	96.14 172

reproduce-ours		94.82 15	94.97 14	94.38 50	97.91 47	85.46 68	95.86 81	97.15 43	89.82 48	95.23 39	98.10 10	87.09 37	99.37 33	95.30 29	98.25 60	98.30 50

our_new_method		94.82 15	94.97 14	94.38 50	97.91 47	85.46 68	95.86 81	97.15 43	89.82 48	95.23 39	98.10 10	87.09 37	99.37 33	95.30 29	98.25 60	98.30 50

CS-MVS		94.12 42	94.44 27	93.17 85	96.55 88	83.08 138	97.63 3	96.95 59	91.71 12	93.50 69	96.21 93	85.61 52	98.24 150	93.64 47	98.17 62	98.19 65

MVS_111021_HR		93.45 61	93.31 66	93.84 65	96.99 75	84.84 79	93.24 243	97.24 33	88.76 86	91.60 120	95.85 112	86.07 49	98.66 108	91.91 92	98.16 63	98.03 78

reproduce_model		94.76 19	94.92 17	94.29 54	97.92 43	85.18 74	95.95 76	97.19 36	89.67 58	95.27 38	98.16 3	86.53 43	99.36 35	95.42 28	98.15 64	98.33 45

EC-MVSNet		93.44 62	93.71 58	92.63 117	95.21 149	82.43 159	97.27 9	96.71 87	90.57 28	92.88 80	95.80 115	83.16 86	98.16 156	93.68 46	98.14 65	97.31 114

test12				94.34 52	97.13 73	86.15 48		96.29 116		91.04 128		85.08 61	99.01 66		98.13 66	97.86 89

新几何1				93.10 89	97.30 69	84.35 100		95.56 182	71.09 390	91.26 126	96.24 92	82.87 92	98.86 90	79.19 275	98.10 67	96.07 178

patch_mono-2		93.74 53	94.32 30	92.01 144	97.54 60	78.37 266	93.40 231	97.19 36	88.02 112	94.99 43	97.21 48	88.35 21	98.44 135	94.07 42	98.09 68	99.23 1

dcpmvs_2		93.49 58	94.19 41	91.38 178	97.69 57	76.78 299	94.25 184	96.29 116	88.33 100	94.46 46	96.88 65	88.07 25	98.64 112	93.62 48	98.09 68	98.73 18

test_fmvsm_n_1920		94.71 21	95.11 11	93.50 77	95.79 120	84.62 85	96.15 54	97.64 2	89.85 47	97.19 9	97.89 26	86.28 46	98.71 106	97.11 9	98.08 70	97.17 122

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 25	97.47 11	91.73 11	96.10 24	96.69 73	89.90 12	99.30 43	94.70 35	98.04 71	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
SR-MVS		94.23 35	94.17 42	94.43 47	98.21 32	85.78 63	96.40 38	96.90 64	88.20 107	94.33 48	97.40 39	84.75 70	99.03 61	93.35 53	97.99 72	98.48 30

3Dnovator	86.66 5	91.73 98	90.82 110	94.44 45	94.59 183	86.37 41	97.18 12	97.02 52	89.20 71	84.31 275	96.66 76	73.74 212	99.17 50	86.74 159	97.96 73	97.79 94

CANet		93.54 57	93.20 70	94.55 43	95.65 128	85.73 65	94.94 137	96.69 89	91.89 9	90.69 131	95.88 111	81.99 111	99.54 20	93.14 56	97.95 74	98.39 40

DPM-MVS		92.58 84	91.74 94	95.08 15	96.19 99	89.31 5	92.66 262	96.56 98	83.44 226	91.68 119	95.04 146	86.60 42	98.99 73	85.60 174	97.92 75	96.93 140

APD-MVS_3200maxsize		93.78 51	93.77 55	93.80 68	97.92 43	84.19 102	96.30 41	96.87 67	86.96 139	93.92 59	97.47 35	83.88 79	98.96 80	92.71 65	97.87 76	98.26 61

CPTT-MVS		91.99 92	91.80 92	92.55 122	98.24 31	81.98 169	96.76 30	96.49 104	81.89 266	90.24 136	96.44 88	78.59 146	98.61 117	89.68 122	97.85 77	97.06 129

test_fmvsmconf_n		94.60 22	94.81 21	93.98 59	94.62 181	84.96 78	96.15 54	97.35 23	89.37 65	96.03 27	98.11 8	86.36 44	99.01 66	97.45 5	97.83 78	97.96 81

fmvsm_l_conf0.5_n_3		94.80 18	95.01 13	94.15 57	95.64 129	85.08 75	96.09 60	97.36 22	90.98 16	97.09 12	98.12 7	84.98 67	98.94 83	97.07 10	97.80 79	98.43 38

SR-MVS-dyc-post		93.82 50	93.82 51	93.82 66	97.92 43	84.57 87	96.28 43	96.76 80	87.46 129	93.75 61	97.43 37	84.24 75	99.01 66	92.73 62	97.80 79	97.88 87

RE-MVS-def			93.68 59		97.92 43	84.57 87	96.28 43	96.76 80	87.46 129	93.75 61	97.43 37	82.94 90		92.73 62	97.80 79	97.88 87

test222					96.55 88	81.70 174	92.22 278	95.01 216	68.36 397	90.20 138	96.14 99	80.26 124			97.80 79	96.05 181

test_fmvsmconf0.1_n		94.20 38	94.31 32	93.88 63	92.46 270	84.80 81	96.18 51	96.82 73	89.29 68	95.68 32	98.11 8	85.10 60	98.99 73	97.38 6	97.75 83	97.86 89

3Dnovator+	87.14 4	92.42 88	91.37 98	95.55 7	95.63 130	88.73 6	97.07 18	96.77 79	90.84 18	84.02 280	96.62 81	75.95 175	99.34 37	87.77 144	97.68 84	98.59 24

旧先验1					96.79 79	81.81 172		95.67 174			96.81 70	86.69 39			97.66 85	96.97 138

fmvsm_s_conf0.5_n_3		94.49 24	95.13 10	92.56 121	95.49 137	81.10 195	95.93 77	97.16 42	92.96 2	97.39 7	98.13 4	83.63 81	98.80 96	97.89 1	97.61 86	97.78 95

EPNet		91.79 95	91.02 106	94.10 58	90.10 351	85.25 73	96.03 68	92.05 314	92.83 3	87.39 188	95.78 116	79.39 136	99.01 66	88.13 140	97.48 87	98.05 76
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmvis_n_1920		93.44 62	93.55 62	93.10 89	93.67 235	84.26 101	95.83 85	96.14 131	89.00 81	92.43 98	97.50 34	83.37 85	98.72 104	96.61 16	97.44 88	96.32 163

testdata				90.49 214	96.40 93	77.89 278		95.37 200	72.51 382	93.63 64	96.69 73	82.08 108	97.65 196	83.08 204	97.39 89	95.94 183

MVS_111021_LR		92.47 87	92.29 87	92.98 97	95.99 114	84.43 96	93.08 248	96.09 138	88.20 107	91.12 127	95.72 120	81.33 116	97.76 188	91.74 96	97.37 90	96.75 149

BP-MVS1		92.48 86	92.07 89	93.72 72	94.50 191	84.39 99	95.90 79	94.30 251	90.39 30	92.67 91	95.94 107	74.46 196	98.65 110	93.14 56	97.35 91	98.13 69

test_fmvsmconf0.01_n		93.19 73	93.02 73	93.71 73	89.25 364	84.42 98	96.06 65	96.29 116	89.06 75	94.68 44	98.13 4	79.22 138	98.98 77	97.22 7	97.24 92	97.74 97

MVSFormer		91.68 100	91.30 99	92.80 107	93.86 225	83.88 109	95.96 74	95.90 155	84.66 201	91.76 116	94.91 149	77.92 154	97.30 232	89.64 123	97.11 93	97.24 118

lupinMVS		90.92 111	90.21 117	93.03 94	93.86 225	83.88 109	92.81 259	93.86 269	79.84 301	91.76 116	94.29 176	77.92 154	98.04 173	90.48 117	97.11 93	97.17 122

EIA-MVS		91.95 93	91.94 90	91.98 148	95.16 152	80.01 228	95.36 106	96.73 84	88.44 97	89.34 151	92.16 250	83.82 80	98.45 133	89.35 125	97.06 95	97.48 110

MG-MVS		91.77 96	91.70 95	92.00 147	97.08 74	80.03 227	93.60 224	95.18 208	87.85 120	90.89 129	96.47 87	82.06 109	98.36 140	85.07 178	97.04 96	97.62 103

fmvsm_l_conf0.5_n_a		94.20 38	94.40 28	93.60 75	95.29 143	84.98 77	95.61 99	96.28 119	86.31 156	96.75 19	97.86 28	87.40 33	98.74 103	97.07 10	97.02 97	97.07 128

test2506		87.21 230	86.28 228	90.02 237	95.62 131	73.64 338	96.25 47	71.38 422	87.89 118	90.45 133	96.65 77	55.29 376	98.09 168	86.03 169	96.94 98	98.33 45

ECVR-MVS		89.09 164	88.53 160	90.77 205	95.62 131	75.89 312	96.16 52	84.22 400	87.89 118	90.20 138	96.65 77	63.19 326	98.10 160	85.90 170	96.94 98	98.33 45

test1111		89.10 162	88.64 157	90.48 215	95.53 136	74.97 322	96.08 61	84.89 398	88.13 110	90.16 140	96.65 77	63.29 324	98.10 160	86.14 165	96.90 100	98.39 40

jason		90.80 114	90.10 121	92.90 102	93.04 256	83.53 120	93.08 248	94.15 258	80.22 295	91.41 123	94.91 149	76.87 162	97.93 182	90.28 118	96.90 100	97.24 118
jason: jason.
Vis-MVSNet		91.75 97	91.23 101	93.29 79	95.32 142	83.78 111	96.14 56	95.98 147	89.89 44	90.45 133	96.58 83	75.09 187	98.31 148	84.75 184	96.90 100	97.78 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
114514_t		89.51 149	88.50 162	92.54 123	98.11 36	81.99 168	95.16 126	96.36 112	70.19 394	85.81 221	95.25 136	76.70 166	98.63 114	82.07 227	96.86 103	97.00 136

fmvsm_l_conf0.5_n		94.29 32	94.46 26	93.79 69	95.28 144	85.43 70	95.68 92	96.43 106	86.56 150	96.84 17	97.81 29	87.56 32	98.77 100	97.14 8	96.82 104	97.16 126

Vis-MVSNet (Re-imp)		89.59 147	89.44 136	90.03 235	95.74 122	75.85 313	95.61 99	90.80 351	87.66 128	87.83 177	95.40 131	76.79 164	96.46 293	78.37 280	96.73 105	97.80 93

API-MVS		90.66 120	90.07 122	92.45 127	96.36 95	84.57 87	96.06 65	95.22 207	82.39 249	89.13 154	94.27 179	80.32 122	98.46 129	80.16 262	96.71 106	94.33 252

MAR-MVS		90.30 127	89.37 139	93.07 93	96.61 84	84.48 92	95.68 92	95.67 174	82.36 251	87.85 176	92.85 227	76.63 168	98.80 96	80.01 263	96.68 107	95.91 184
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
OpenMVS	83.78 11	88.74 175	87.29 192	93.08 91	92.70 265	85.39 71	96.57 35	96.43 106	78.74 319	80.85 327	96.07 101	69.64 263	99.01 66	78.01 287	96.65 108	94.83 228

fmvsm_s_conf0.5_n_2		93.47 59	93.83 50	92.39 131	95.36 140	81.19 191	95.20 123	96.56 98	90.37 31	97.13 11	98.03 22	77.47 158	98.96 80	97.79 3	96.58 109	97.03 132

ETV-MVS		92.74 82	92.66 80	92.97 98	95.20 150	84.04 106	95.07 130	96.51 102	90.73 24	92.96 78	91.19 286	84.06 76	98.34 143	91.72 97	96.54 110	96.54 159

QAPM		89.51 149	88.15 173	93.59 76	94.92 164	84.58 86	96.82 29	96.70 88	78.43 324	83.41 295	96.19 97	73.18 220	99.30 43	77.11 296	96.54 110	96.89 143

IS-MVSNet		91.43 102	91.09 105	92.46 126	95.87 119	81.38 185	96.95 19	93.69 275	89.72 57	89.50 149	95.98 105	78.57 147	97.77 187	83.02 206	96.50 112	98.22 64

DP-MVS Recon		91.95 93	91.28 100	93.96 61	98.33 27	85.92 57	94.66 157	96.66 90	82.69 246	90.03 143	95.82 114	82.30 101	99.03 61	84.57 186	96.48 113	96.91 142

CANet_DTU		90.26 129	89.41 138	92.81 106	93.46 242	83.01 142	93.48 227	94.47 244	89.43 63	87.76 180	94.23 181	70.54 252	99.03 61	84.97 179	96.39 114	96.38 162

UGNet		89.95 137	88.95 149	92.95 100	94.51 190	83.31 126	95.70 91	95.23 205	89.37 65	87.58 182	93.94 191	64.00 319	98.78 99	83.92 195	96.31 115	96.74 150
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
fmvsm_s_conf0.5_n		93.76 52	94.06 46	92.86 104	95.62 131	83.17 131	96.14 56	96.12 135	88.13 110	95.82 30	98.04 21	83.43 82	98.48 125	96.97 13	96.23 116	96.92 141

fmvsm_s_conf0.1_n_2		93.16 75	93.42 64	92.37 132	94.62 181	81.13 193	95.23 118	95.89 157	90.30 34	96.74 20	98.02 23	76.14 170	98.95 82	97.64 4	96.21 117	97.03 132

fmvsm_s_conf0.1_n		93.46 60	93.66 60	92.85 105	93.75 231	83.13 133	96.02 69	95.74 168	87.68 126	95.89 29	98.17 2	82.78 93	98.46 129	96.71 14	96.17 118	96.98 137

TSAR-MVS + GP.		93.66 55	93.41 65	94.41 49	96.59 85	86.78 26	94.40 173	93.93 265	89.77 55	94.21 50	95.59 124	87.35 34	98.61 117	92.72 64	96.15 119	97.83 92

mvsmamba		90.33 126	89.69 131	92.25 141	95.17 151	81.64 175	95.27 116	93.36 280	84.88 192	89.51 147	94.27 179	69.29 272	97.42 218	89.34 126	96.12 120	97.68 100

GDP-MVS		92.04 91	91.46 97	93.75 71	94.55 188	84.69 84	95.60 102	96.56 98	87.83 121	93.07 77	95.89 110	73.44 216	98.65 110	90.22 119	96.03 121	97.91 86

PVSNet_Blended		90.73 117	90.32 116	91.98 148	96.12 102	81.25 187	92.55 266	96.83 71	82.04 259	89.10 155	92.56 238	81.04 118	98.85 92	86.72 161	95.91 122	95.84 188

PS-MVSNAJ		91.18 108	90.92 107	91.96 150	95.26 147	82.60 158	92.09 283	95.70 172	86.27 157	91.84 113	92.46 240	79.70 131	98.99 73	89.08 129	95.86 123	94.29 253

ACMMP		93.24 71	92.88 76	94.30 53	98.09 38	85.33 72	96.86 27	97.45 14	88.33 100	90.15 141	97.03 60	81.44 114	99.51 24	90.85 112	95.74 124	98.04 77
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
casdiffmvs_mvg		92.96 79	92.83 77	93.35 78	94.59 183	83.40 124	95.00 134	96.34 113	90.30 34	92.05 104	96.05 102	83.43 82	98.15 157	92.07 84	95.67 125	98.49 29
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LCM-MVSNet-Re		88.30 187	88.32 169	88.27 293	94.71 176	72.41 357	93.15 244	90.98 345	87.77 123	79.25 349	91.96 262	78.35 150	95.75 328	83.04 205	95.62 126	96.65 153

CHOSEN 1792x2688		88.84 171	87.69 182	92.30 137	96.14 100	81.42 184	90.01 333	95.86 160	74.52 363	87.41 185	93.94 191	75.46 184	98.36 140	80.36 258	95.53 127	97.12 127

fmvsm_s_conf0.5_n_a		93.57 56	93.76 56	93.00 96	95.02 156	83.67 114	96.19 49	96.10 137	87.27 133	95.98 28	98.05 18	83.07 89	98.45 133	96.68 15	95.51 128	96.88 144

AdaColmap		89.89 140	89.07 146	92.37 132	97.41 65	83.03 140	94.42 172	95.92 152	82.81 243	86.34 211	94.65 164	73.89 208	99.02 64	80.69 253	95.51 128	95.05 216

MVS		87.44 217	86.10 236	91.44 176	92.61 267	83.62 117	92.63 263	95.66 176	67.26 399	81.47 319	92.15 251	77.95 153	98.22 153	79.71 266	95.48 130	92.47 330

UA-Net		92.83 80	92.54 83	93.68 74	96.10 105	84.71 83	95.66 95	96.39 110	91.92 8	93.22 72	96.49 86	83.16 86	98.87 88	84.47 188	95.47 131	97.45 112

xiu_mvs_v2_base		91.13 109	90.89 109	91.86 158	94.97 160	82.42 160	92.24 277	95.64 179	86.11 165	91.74 118	93.14 220	79.67 134	98.89 87	89.06 130	95.46 132	94.28 254

casdiffmvs		92.51 85	92.43 85	92.74 111	94.41 198	81.98 169	94.54 163	96.23 125	89.57 60	91.96 108	96.17 98	82.58 95	98.01 175	90.95 109	95.45 133	98.23 63
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_conf0.1_n_a		93.19 73	93.26 67	92.97 98	92.49 268	83.62 117	96.02 69	95.72 171	86.78 145	96.04 26	98.19 1	82.30 101	98.43 137	96.38 17	95.42 134	96.86 145

PVSNet_Blended_VisFu		91.38 103	90.91 108	92.80 107	96.39 94	83.17 131	94.87 142	96.66 90	83.29 231	89.27 153	94.46 171	80.29 123	99.17 50	87.57 147	95.37 135	96.05 181

PAPM_NR		91.22 107	90.78 111	92.52 124	97.60 59	81.46 182	94.37 179	96.24 124	86.39 155	87.41 185	94.80 157	82.06 109	98.48 125	82.80 212	95.37 135	97.61 104

CHOSEN 280x420		85.15 285	83.99 290	88.65 283	92.47 269	78.40 265	79.68 412	92.76 295	74.90 360	81.41 321	89.59 333	69.85 261	95.51 336	79.92 265	95.29 137	92.03 342

TAPA-MVS	84.62 6	88.16 190	87.01 200	91.62 168	96.64 83	80.65 207	94.39 175	96.21 129	76.38 343	86.19 215	95.44 128	79.75 129	98.08 170	62.75 388	95.29 137	96.13 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline		92.39 89	92.29 87	92.69 115	94.46 194	81.77 173	94.14 190	96.27 120	89.22 70	91.88 111	96.00 103	82.35 98	97.99 177	91.05 105	95.27 139	98.30 50

LS3D		87.89 196	86.32 226	92.59 119	96.07 108	82.92 145	95.23 118	94.92 224	75.66 350	82.89 302	95.98 105	72.48 228	99.21 48	68.43 359	95.23 140	95.64 197

test_vis1_n_1920		89.39 157	89.84 129	88.04 300	92.97 260	72.64 352	94.71 154	96.03 145	86.18 160	91.94 110	96.56 85	61.63 334	95.74 329	93.42 51	95.11 141	95.74 193

mamv4		90.92 111	91.78 93	88.33 292	95.67 127	70.75 375	92.92 255	96.02 146	81.90 264	88.11 168	95.34 132	85.88 51	96.97 260	95.22 31	95.01 142	97.26 117

MVS_Test		91.31 105	91.11 103	91.93 152	94.37 199	80.14 220	93.46 229	95.80 163	86.46 153	91.35 125	93.77 201	82.21 104	98.09 168	87.57 147	94.95 143	97.55 109

RRT-MVS		90.85 113	90.70 112	91.30 181	94.25 205	76.83 298	94.85 144	96.13 134	89.04 77	90.23 137	94.88 151	70.15 257	98.72 104	91.86 95	94.88 144	98.34 43

test_cas_vis1_n_1920		88.83 174	88.85 155	88.78 277	91.15 318	76.72 300	93.85 214	94.93 223	83.23 234	92.81 84	96.00 103	61.17 345	94.45 351	91.67 98	94.84 145	95.17 212

PAPR		90.02 134	89.27 144	92.29 138	95.78 121	80.95 200	92.68 261	96.22 126	81.91 263	86.66 202	93.75 203	82.23 103	98.44 135	79.40 274	94.79 146	97.48 110

test_fmvs1		87.34 221	87.56 185	86.68 337	90.59 341	71.80 361	94.01 204	94.04 263	78.30 326	91.97 107	95.22 137	56.28 370	93.71 366	92.89 60	94.71 147	94.52 241

xiu_mvs_v1_base_debu		90.64 121	90.05 123	92.40 128	93.97 222	84.46 93	93.32 234	95.46 189	85.17 182	92.25 99	94.03 183	70.59 248	98.57 120	90.97 106	94.67 148	94.18 255

xiu_mvs_v1_base		90.64 121	90.05 123	92.40 128	93.97 222	84.46 93	93.32 234	95.46 189	85.17 182	92.25 99	94.03 183	70.59 248	98.57 120	90.97 106	94.67 148	94.18 255

xiu_mvs_v1_base_debi		90.64 121	90.05 123	92.40 128	93.97 222	84.46 93	93.32 234	95.46 189	85.17 182	92.25 99	94.03 183	70.59 248	98.57 120	90.97 106	94.67 148	94.18 255

gg-mvs-nofinetune		81.77 323	79.37 338	88.99 274	90.85 333	77.73 286	86.29 382	79.63 411	74.88 361	83.19 300	69.05 413	60.34 349	96.11 310	75.46 311	94.64 151	93.11 311

BH-RMVSNet		88.37 184	87.48 187	91.02 195	95.28 144	79.45 241	92.89 256	93.07 286	85.45 178	86.91 194	94.84 156	70.35 253	97.76 188	73.97 324	94.59 152	95.85 187

test_fmvs1_n		87.03 238	87.04 199	86.97 329	89.74 359	71.86 359	94.55 162	94.43 245	78.47 322	91.95 109	95.50 127	51.16 390	93.81 364	93.02 59	94.56 153	95.26 209

diffmvs		91.37 104	91.23 101	91.77 164	93.09 252	80.27 216	92.36 271	95.52 187	87.03 138	91.40 124	94.93 148	80.08 125	97.44 216	92.13 83	94.56 153	97.61 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		88.60 179	88.13 174	90.01 238	95.24 148	78.50 262	93.29 239	94.15 258	84.75 198	84.46 265	93.40 208	75.76 178	97.40 226	77.59 290	94.52 155	94.12 259

Effi-MVS+		91.59 101	91.11 103	93.01 95	94.35 203	83.39 125	94.60 159	95.10 212	87.10 136	90.57 132	93.10 222	81.43 115	98.07 171	89.29 127	94.48 156	97.59 106

PCF-MVS	84.11 10	87.74 201	86.08 237	92.70 114	94.02 216	84.43 96	89.27 346	95.87 159	73.62 372	84.43 267	94.33 173	78.48 149	98.86 90	70.27 345	94.45 157	94.81 229
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		93.01 78	92.92 75	93.29 79	95.01 157	83.51 121	94.48 165	95.77 165	90.87 17	92.52 95	96.67 75	84.50 72	99.00 71	91.99 88	94.44 158	97.36 113

MS-PatchMatch		85.05 287	84.16 285	87.73 307	91.42 306	78.51 261	91.25 304	93.53 276	77.50 333	80.15 336	91.58 277	61.99 331	95.51 336	75.69 309	94.35 159	89.16 386

FE-MVS		87.40 219	86.02 239	91.57 170	94.56 187	79.69 237	90.27 320	93.72 274	80.57 292	88.80 160	91.62 275	65.32 311	98.59 119	74.97 318	94.33 160	96.44 160

mvs_anonymous		89.37 158	89.32 141	89.51 261	93.47 241	74.22 331	91.65 294	94.83 231	82.91 241	85.45 236	93.79 199	81.23 117	96.36 300	86.47 163	94.09 161	97.94 82

test_vis1_n		86.56 254	86.49 221	86.78 336	88.51 370	72.69 349	94.68 155	93.78 273	79.55 305	90.70 130	95.31 133	48.75 395	93.28 372	93.15 55	93.99 162	94.38 251

MVP-Stereo		85.97 267	84.86 274	89.32 264	90.92 329	82.19 165	92.11 282	94.19 256	78.76 318	78.77 354	91.63 274	68.38 285	96.56 284	75.01 317	93.95 163	89.20 385
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LFMVS		90.08 132	89.13 145	92.95 100	96.71 80	82.32 164	96.08 61	89.91 367	86.79 144	92.15 103	96.81 70	62.60 328	98.34 143	87.18 153	93.90 164	98.19 65

PVSNet	78.82 18	85.55 274	84.65 278	88.23 296	94.72 175	71.93 358	87.12 377	92.75 296	78.80 317	84.95 254	90.53 309	64.43 317	96.71 272	74.74 319	93.86 165	96.06 180

CNLPA		89.07 165	87.98 176	92.34 134	96.87 77	84.78 82	94.08 197	93.24 281	81.41 280	84.46 265	95.13 144	75.57 183	96.62 276	77.21 294	93.84 166	95.61 200

EPNet_dtu		86.49 259	85.94 244	88.14 298	90.24 349	72.82 347	94.11 193	92.20 310	86.66 149	79.42 348	92.36 244	73.52 213	95.81 325	71.26 337	93.66 167	95.80 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GeoE		90.05 133	89.43 137	91.90 157	95.16 152	80.37 215	95.80 86	94.65 241	83.90 213	87.55 184	94.75 158	78.18 152	97.62 200	81.28 242	93.63 168	97.71 99

EI-MVSNet-UG-set		92.74 82	92.62 82	93.12 88	94.86 169	83.20 130	94.40 173	95.74 168	90.71 25	92.05 104	96.60 82	84.00 77	98.99 73	91.55 99	93.63 168	97.17 122

Fast-Effi-MVS+		89.41 154	88.64 157	91.71 166	94.74 173	80.81 204	93.54 225	95.10 212	83.11 235	86.82 200	90.67 307	79.74 130	97.75 191	80.51 257	93.55 170	96.57 157

FA-MVS(test-final)		89.66 144	88.91 151	91.93 152	94.57 186	80.27 216	91.36 299	94.74 237	84.87 193	89.82 144	92.61 237	74.72 194	98.47 128	83.97 194	93.53 171	97.04 131

1314		87.51 214	86.57 216	90.34 224	92.42 272	79.74 236	92.63 263	95.35 202	78.35 325	80.14 337	91.62 275	74.05 205	97.15 245	81.05 244	93.53 171	94.12 259

BH-w/o		87.57 212	87.05 198	89.12 269	94.90 167	77.90 277	92.41 268	93.51 277	82.89 242	83.70 287	91.34 280	75.75 179	97.07 253	75.49 310	93.49 173	92.39 334

PMMVS		85.71 273	84.96 271	87.95 302	88.90 368	77.09 294	88.68 356	90.06 363	72.32 384	86.47 204	90.76 303	72.15 231	94.40 353	81.78 235	93.49 173	92.36 335

PatchMatch-RL		86.77 248	85.54 256	90.47 218	95.88 117	82.71 153	90.54 317	92.31 306	79.82 302	84.32 273	91.57 279	68.77 280	96.39 297	73.16 330	93.48 175	92.32 337

PLC	84.53 7	89.06 166	88.03 175	92.15 142	97.27 71	82.69 154	94.29 182	95.44 194	79.71 303	84.01 281	94.18 182	76.68 167	98.75 101	77.28 293	93.41 176	95.02 217
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		92.24 90	91.91 91	93.24 82	96.59 85	83.43 122	94.84 145	96.44 105	89.19 72	94.08 56	95.90 109	77.85 157	98.17 155	88.90 131	93.38 177	98.13 69

test-LLR		85.87 269	85.41 259	87.25 321	90.95 325	71.67 364	89.55 340	89.88 369	83.41 227	84.54 262	87.95 360	67.25 290	95.11 345	81.82 233	93.37 178	94.97 218

test-mter		84.54 297	83.64 295	87.25 321	90.95 325	71.67 364	89.55 340	89.88 369	79.17 309	84.54 262	87.95 360	55.56 372	95.11 345	81.82 233	93.37 178	94.97 218

EPP-MVSNet		91.70 99	91.56 96	92.13 143	95.88 117	80.50 212	97.33 7	95.25 204	86.15 161	89.76 145	95.60 123	83.42 84	98.32 147	87.37 151	93.25 180	97.56 108

CDS-MVSNet		89.45 152	88.51 161	92.29 138	93.62 237	83.61 119	93.01 251	94.68 240	81.95 261	87.82 178	93.24 216	78.69 144	96.99 259	80.34 259	93.23 181	96.28 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 250	85.39 260	90.53 210	93.05 255	79.33 248	89.79 336	94.77 236	78.82 316	81.95 315	93.24 216	76.81 163	97.30 232	66.94 369	93.16 182	94.95 224

alignmvs		93.08 76	92.50 84	94.81 32	95.62 131	87.61 15	95.99 71	96.07 140	89.77 55	94.12 52	94.87 152	80.56 120	98.66 108	92.42 70	93.10 183	98.15 68

thisisatest0515		87.33 222	85.99 240	91.37 179	93.49 240	79.55 238	90.63 316	89.56 375	80.17 296	87.56 183	90.86 297	67.07 293	98.28 149	81.50 240	93.02 184	96.29 165

TAMVS		89.21 160	88.29 170	91.96 150	93.71 232	82.62 157	93.30 238	94.19 256	82.22 254	87.78 179	93.94 191	78.83 141	96.95 262	77.70 289	92.98 185	96.32 163

OMC-MVS		91.23 106	90.62 113	93.08 91	96.27 97	84.07 104	93.52 226	95.93 151	86.95 140	89.51 147	96.13 100	78.50 148	98.35 142	85.84 172	92.90 186	96.83 147

sasdasda		93.27 69	92.75 78	94.85 25	95.70 125	87.66 12	96.33 39	96.41 108	90.00 42	94.09 53	94.60 166	82.33 99	98.62 115	92.40 71	92.86 187	98.27 57

canonicalmvs		93.27 69	92.75 78	94.85 25	95.70 125	87.66 12	96.33 39	96.41 108	90.00 42	94.09 53	94.60 166	82.33 99	98.62 115	92.40 71	92.86 187	98.27 57

TESTMET0.1,1		83.74 309	82.85 309	86.42 341	89.96 355	71.21 369	89.55 340	87.88 382	77.41 334	83.37 296	87.31 368	56.71 368	93.65 368	80.62 255	92.85 189	94.40 250

MGCFI-Net		93.03 77	92.63 81	94.23 56	95.62 131	85.92 57	96.08 61	96.33 114	89.86 46	93.89 60	94.66 163	82.11 106	98.50 123	92.33 76	92.82 190	98.27 57

thisisatest0530		88.67 176	87.61 184	91.86 158	94.87 168	80.07 223	94.63 158	89.90 368	84.00 211	88.46 165	93.78 200	66.88 296	98.46 129	83.30 202	92.65 191	97.06 129

UWE-MVS		83.69 310	83.09 303	85.48 350	93.06 254	65.27 398	90.92 311	86.14 390	79.90 300	86.26 213	90.72 306	57.17 367	95.81 325	71.03 343	92.62 192	95.35 207

VDD-MVS		90.74 116	89.92 128	93.20 84	96.27 97	83.02 141	95.73 89	93.86 269	88.42 99	92.53 94	96.84 67	62.09 330	98.64 112	90.95 109	92.62 192	97.93 84

test_yl		90.69 118	90.02 126	92.71 112	95.72 123	82.41 162	94.11 193	95.12 210	85.63 173	91.49 121	94.70 159	74.75 191	98.42 138	86.13 167	92.53 194	97.31 114

DCV-MVSNet		90.69 118	90.02 126	92.71 112	95.72 123	82.41 162	94.11 193	95.12 210	85.63 173	91.49 121	94.70 159	74.75 191	98.42 138	86.13 167	92.53 194	97.31 114

VDDNet		89.56 148	88.49 164	92.76 109	95.07 155	82.09 166	96.30 41	93.19 283	81.05 289	91.88 111	96.86 66	61.16 346	98.33 145	88.43 137	92.49 196	97.84 91

DP-MVS		87.25 226	85.36 262	92.90 102	97.65 58	83.24 128	94.81 147	92.00 316	74.99 358	81.92 316	95.00 147	72.66 225	99.05 58	66.92 371	92.33 197	96.40 161

GG-mvs-BLEND				87.94 303	89.73 360	77.91 276	87.80 366	78.23 415		80.58 331	83.86 390	59.88 353	95.33 342	71.20 338	92.22 198	90.60 371

tttt0517		88.61 178	87.78 181	91.11 190	94.96 161	77.81 281	95.35 107	89.69 371	85.09 187	88.05 173	94.59 168	66.93 294	98.48 125	83.27 203	92.13 199	97.03 132

UBG		85.51 275	84.57 281	88.35 289	94.21 208	71.78 362	90.07 331	89.66 373	82.28 253	85.91 220	89.01 342	61.30 339	97.06 254	76.58 302	92.06 200	96.22 168

HyFIR lowres test		88.09 192	86.81 204	91.93 152	96.00 111	80.63 208	90.01 333	95.79 164	73.42 374	87.68 181	92.10 256	73.86 209	97.96 179	80.75 252	91.70 201	97.19 121

sss		88.93 170	88.26 172	90.94 201	94.05 215	80.78 205	91.71 291	95.38 198	81.55 278	88.63 162	93.91 195	75.04 188	95.47 340	82.47 216	91.61 202	96.57 157

testing222		84.84 293	83.32 298	89.43 263	94.15 212	75.94 311	91.09 308	89.41 376	84.90 191	85.78 222	89.44 336	52.70 387	96.28 304	70.80 344	91.57 203	96.07 178

cascas		86.43 261	84.98 270	90.80 204	92.10 281	80.92 201	90.24 324	95.91 154	73.10 377	83.57 292	88.39 353	65.15 313	97.46 212	84.90 182	91.43 204	94.03 266

ETVMVS		84.43 298	82.92 307	88.97 275	94.37 199	74.67 325	91.23 305	88.35 380	83.37 229	86.06 218	89.04 341	55.38 374	95.67 331	67.12 367	91.34 205	96.58 156

Effi-MVS+-dtu		88.65 177	88.35 166	89.54 258	93.33 245	76.39 306	94.47 168	94.36 249	87.70 125	85.43 239	89.56 335	73.45 215	97.26 238	85.57 175	91.28 206	94.97 218

thres100view900		87.63 207	86.71 208	90.38 222	96.12 102	78.55 259	95.03 133	91.58 329	87.15 134	88.06 172	92.29 247	68.91 278	98.10 160	70.13 349	91.10 207	94.48 247

tfpn200view9		87.58 211	86.64 211	90.41 219	95.99 114	78.64 257	94.58 160	91.98 318	86.94 141	88.09 169	91.77 267	69.18 274	98.10 160	70.13 349	91.10 207	94.48 247

thres600view7		87.65 204	86.67 210	90.59 207	96.08 107	78.72 255	94.88 141	91.58 329	87.06 137	88.08 171	92.30 246	68.91 278	98.10 160	70.05 352	91.10 207	94.96 221

thres400		87.62 209	86.64 211	90.57 208	95.99 114	78.64 257	94.58 160	91.98 318	86.94 141	88.09 169	91.77 267	69.18 274	98.10 160	70.13 349	91.10 207	94.96 221

testing11		86.44 260	85.35 263	89.69 253	94.29 204	75.40 320	91.30 301	90.53 354	84.76 197	85.06 251	90.13 321	58.95 360	97.45 213	82.08 226	91.09 211	96.21 170

F-COLMAP		87.95 195	86.80 205	91.40 177	96.35 96	80.88 202	94.73 152	95.45 192	79.65 304	82.04 314	94.61 165	71.13 239	98.50 123	76.24 306	91.05 212	94.80 230

thres200		87.21 230	86.24 230	90.12 231	95.36 140	78.53 260	93.26 241	92.10 312	86.42 154	88.00 174	91.11 292	69.24 273	98.00 176	69.58 353	91.04 213	93.83 277

WTY-MVS		89.60 146	88.92 150	91.67 167	95.47 138	81.15 192	92.38 270	94.78 235	83.11 235	89.06 157	94.32 174	78.67 145	96.61 279	81.57 239	90.89 214	97.24 118

testing91		87.11 235	86.18 231	89.92 241	94.43 197	75.38 321	91.53 296	92.27 308	86.48 151	86.50 203	90.24 315	61.19 344	97.53 205	82.10 225	90.88 215	96.84 146

testing99		86.72 249	85.73 255	89.69 253	94.23 206	74.91 324	91.35 300	90.97 346	86.14 162	86.36 209	90.22 316	59.41 356	97.48 209	82.24 222	90.66 216	96.69 152

HY-MVS	83.01 12	89.03 167	87.94 178	92.29 138	94.86 169	82.77 147	92.08 284	94.49 243	81.52 279	86.93 192	92.79 233	78.32 151	98.23 151	79.93 264	90.55 217	95.88 186

WB-MVSnew		83.77 308	83.28 299	85.26 355	91.48 302	71.03 371	91.89 285	87.98 381	78.91 312	84.78 256	90.22 316	69.11 276	94.02 360	64.70 381	90.44 218	90.71 367

CLD-MVS		89.47 151	88.90 152	91.18 186	94.22 207	82.07 167	92.13 281	96.09 138	87.90 116	85.37 245	92.45 241	74.38 198	97.56 203	87.15 154	90.43 219	93.93 268
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CVMVSNet		84.69 296	84.79 276	84.37 361	91.84 290	64.92 399	93.70 221	91.47 334	66.19 401	86.16 216	95.28 134	67.18 292	93.33 371	80.89 250	90.42 220	94.88 226

SCA		86.32 263	85.18 266	89.73 251	92.15 277	76.60 302	91.12 307	91.69 325	83.53 224	85.50 233	88.81 346	66.79 297	96.48 290	76.65 299	90.35 221	96.12 174

Fast-Effi-MVS+-dtu		87.44 217	86.72 207	89.63 256	92.04 282	77.68 287	94.03 202	93.94 264	85.81 167	82.42 307	91.32 283	70.33 254	97.06 254	80.33 260	90.23 222	94.14 258

OPM-MVS		90.12 131	89.56 134	91.82 161	93.14 249	83.90 108	94.16 189	95.74 168	88.96 82	87.86 175	95.43 130	72.48 228	97.91 183	88.10 142	90.18 223	93.65 289
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 124	90.19 118	91.82 161	94.70 177	82.73 151	95.85 83	96.22 126	90.81 19	86.91 194	94.86 153	74.23 200	98.12 158	88.15 138	89.99 224	94.63 233

plane_prior5								96.22 126					98.12 158	88.15 138	89.99 224	94.63 233

XVG-OURS		89.40 156	88.70 156	91.52 171	94.06 214	81.46 182	91.27 303	96.07 140	86.14 162	88.89 159	95.77 117	68.73 281	97.26 238	87.39 150	89.96 226	95.83 189

baseline2		86.50 257	85.39 260	89.84 244	91.12 319	76.70 301	91.88 286	88.58 378	82.35 252	79.95 341	90.95 296	73.42 217	97.63 199	80.27 261	89.95 227	95.19 211

Anonymous202405211		87.68 202	86.13 233	92.31 136	96.66 82	80.74 206	94.87 142	91.49 333	80.47 294	89.46 150	95.44 128	54.72 379	98.23 151	82.19 223	89.89 228	97.97 80

plane_prior						82.73 151	95.21 121		89.66 59						89.88 229

SDMVSNet		90.19 130	89.61 133	91.93 152	96.00 111	83.09 137	92.89 256	95.98 147	88.73 87	86.85 198	95.20 140	72.09 232	97.08 251	88.90 131	89.85 230	95.63 198

sd_testset		88.59 180	87.85 180	90.83 202	96.00 111	80.42 214	92.35 272	94.71 238	88.73 87	86.85 198	95.20 140	67.31 288	96.43 295	79.64 268	89.85 230	95.63 198

TR-MVS		86.78 245	85.76 252	89.82 245	94.37 199	78.41 264	92.47 267	92.83 292	81.11 288	86.36 209	92.40 242	68.73 281	97.48 209	73.75 328	89.85 230	93.57 291

HQP3-MVS								96.04 143							89.77 233

HQP-MVS		89.80 142	89.28 143	91.34 180	94.17 209	81.56 176	94.39 175	96.04 143	88.81 83	85.43 239	93.97 190	73.83 210	97.96 179	87.11 156	89.77 233	94.50 244

XVG-OURS-SEG-HR		89.95 137	89.45 135	91.47 175	94.00 220	81.21 190	91.87 287	96.06 142	85.78 168	88.55 163	95.73 119	74.67 195	97.27 236	88.71 134	89.64 235	95.91 184

GA-MVS		86.61 251	85.27 265	90.66 206	91.33 311	78.71 256	90.40 319	93.81 272	85.34 180	85.12 249	89.57 334	61.25 341	97.11 250	80.99 248	89.59 236	96.15 171

1112_ss		88.42 182	87.33 191	91.72 165	94.92 164	80.98 198	92.97 253	94.54 242	78.16 330	83.82 284	93.88 196	78.78 143	97.91 183	79.45 270	89.41 237	96.26 167

ab-mvs		89.41 154	88.35 166	92.60 118	95.15 154	82.65 156	92.20 279	95.60 181	83.97 212	88.55 163	93.70 204	74.16 204	98.21 154	82.46 217	89.37 238	96.94 139

CR-MVSNet		85.35 280	83.76 293	90.12 231	90.58 342	79.34 245	85.24 390	91.96 320	78.27 327	85.55 228	87.87 363	71.03 241	95.61 332	73.96 325	89.36 239	95.40 204

RPMNet		83.95 305	81.53 316	91.21 184	90.58 342	79.34 245	85.24 390	96.76 80	71.44 388	85.55 228	82.97 397	70.87 244	98.91 86	61.01 392	89.36 239	95.40 204

DSMNet-mixed		76.94 361	76.29 360	78.89 382	83.10 403	56.11 418	87.78 368	79.77 410	60.65 408	75.64 374	88.71 349	61.56 337	88.34 404	60.07 395	89.29 241	92.21 340

LPG-MVS_test		89.45 152	88.90 152	91.12 187	94.47 192	81.49 180	95.30 111	96.14 131	86.73 147	85.45 236	95.16 142	69.89 259	98.10 160	87.70 145	89.23 242	93.77 283

LGP-MVS_train				91.12 187	94.47 192	81.49 180		96.14 131	86.73 147	85.45 236	95.16 142	69.89 259	98.10 160	87.70 145	89.23 242	93.77 283

Test_1112_low_res		87.65 204	86.51 219	91.08 191	94.94 163	79.28 249	91.77 289	94.30 251	76.04 348	83.51 293	92.37 243	77.86 156	97.73 192	78.69 279	89.13 244	96.22 168

PatchmatchNet		85.85 270	84.70 277	89.29 265	91.76 294	75.54 317	88.49 358	91.30 337	81.63 275	85.05 252	88.70 350	71.71 233	96.24 305	74.61 321	89.05 245	96.08 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.56 296		91.69 298	69.93 381	87.75 370	91.54 331	78.60 321	84.86 255	88.90 345	69.54 265	96.03 312	70.25 346	88.93 246

MIMVSNet		82.59 317	80.53 322	88.76 278	91.51 301	78.32 267	86.57 381	90.13 361	79.32 306	80.70 329	88.69 351	52.98 386	93.07 376	66.03 375	88.86 247	94.90 225

ACMM	84.12 9	89.14 161	88.48 165	91.12 187	94.65 180	81.22 189	95.31 109	96.12 135	85.31 181	85.92 219	94.34 172	70.19 256	98.06 172	85.65 173	88.86 247	94.08 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	84.23 8	89.01 169	88.35 166	90.99 198	94.73 174	81.27 186	95.07 130	95.89 157	86.48 151	83.67 288	94.30 175	69.33 268	97.99 177	87.10 158	88.55 249	93.72 287
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		89.03 167	88.64 157	90.21 226	90.74 337	79.28 249	95.96 74	95.90 155	84.66 201	85.33 247	92.94 226	74.02 206	97.30 232	89.64 123	88.53 250	94.05 265

jajsoiax		88.24 188	87.50 186	90.48 215	90.89 331	80.14 220	95.31 109	95.65 178	84.97 190	84.24 276	94.02 186	65.31 312	97.42 218	88.56 135	88.52 251	93.89 269

PatchT		82.68 316	81.27 318	86.89 333	90.09 352	70.94 374	84.06 397	90.15 360	74.91 359	85.63 227	83.57 392	69.37 267	94.87 350	65.19 377	88.50 252	94.84 227

MSDG		84.86 292	83.09 303	90.14 230	93.80 228	80.05 225	89.18 349	93.09 285	78.89 314	78.19 355	91.91 264	65.86 310	97.27 236	68.47 358	88.45 253	93.11 311

MVS-HIRNet		73.70 367	72.20 370	78.18 385	91.81 293	56.42 417	82.94 403	82.58 404	55.24 411	68.88 398	66.48 414	55.32 375	95.13 344	58.12 400	88.42 254	83.01 402

mvs_tets		88.06 194	87.28 193	90.38 222	90.94 327	79.88 232	95.22 120	95.66 176	85.10 186	84.21 277	93.94 191	63.53 322	97.40 226	88.50 136	88.40 255	93.87 273

ET-MVSNet_ETH3D		87.51 214	85.91 245	92.32 135	93.70 234	83.93 107	92.33 274	90.94 347	84.16 207	72.09 390	92.52 239	69.90 258	95.85 322	89.20 128	88.36 256	97.17 122

FIs		90.51 125	90.35 115	90.99 198	93.99 221	80.98 198	95.73 89	97.54 4	89.15 73	86.72 201	94.68 161	81.83 113	97.24 240	85.18 177	88.31 257	94.76 231

PS-MVSNAJss		89.97 136	89.62 132	91.02 195	91.90 288	80.85 203	95.26 117	95.98 147	86.26 158	86.21 214	94.29 176	79.70 131	97.65 196	88.87 133	88.10 258	94.57 238

CMPMVS	59.16 21	80.52 339	79.20 342	84.48 360	83.98 399	67.63 391	89.95 335	93.84 271	64.79 403	66.81 401	91.14 291	57.93 363	95.17 343	76.25 305	88.10 258	90.65 368
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FC-MVSNet-test		90.27 128	90.18 119	90.53 210	93.71 232	79.85 234	95.77 88	97.59 3	89.31 67	86.27 212	94.67 162	81.93 112	97.01 258	84.26 190	88.09 260	94.71 232

ACMMP++															88.01 261

D2MVS		85.90 268	85.09 268	88.35 289	90.79 334	77.42 290	91.83 288	95.70 172	80.77 291	80.08 339	90.02 324	66.74 299	96.37 298	81.88 232	87.97 262	91.26 358

UniMVSNet_ETH3D		87.53 213	86.37 223	91.00 197	92.44 271	78.96 254	94.74 151	95.61 180	84.07 210	85.36 246	94.52 170	59.78 354	97.34 231	82.93 207	87.88 263	96.71 151

PVSNet_BlendedMVS		89.98 135	89.70 130	90.82 203	96.12 102	81.25 187	93.92 211	96.83 71	83.49 225	89.10 155	92.26 248	81.04 118	98.85 92	86.72 161	87.86 264	92.35 336

Syy-MVS		80.07 344	79.78 332	80.94 378	91.92 286	59.93 409	89.75 338	87.40 387	81.72 271	78.82 351	87.20 370	66.29 305	91.29 390	47.06 410	87.84 265	91.60 350

myMVS_eth3d		79.67 349	78.79 348	82.32 375	91.92 286	64.08 401	89.75 338	87.40 387	81.72 271	78.82 351	87.20 370	45.33 403	91.29 390	59.09 398	87.84 265	91.60 350

anonymousdsp		87.84 197	87.09 196	90.12 231	89.13 365	80.54 211	94.67 156	95.55 183	82.05 257	83.82 284	92.12 253	71.47 237	97.15 245	87.15 154	87.80 267	92.67 324

testing3		80.46 340	79.59 337	83.06 369	93.44 243	64.64 400	93.33 233	85.47 395	84.34 206	79.93 342	90.84 299	44.35 405	92.39 380	57.06 403	87.56 268	92.16 341

Anonymous20240529		88.09 192	86.59 215	92.58 120	96.53 90	81.92 171	95.99 71	95.84 161	74.11 367	89.06 157	95.21 139	61.44 338	98.81 95	83.67 200	87.47 269	97.01 135

ACMMP++_ref															87.47 269

XVG-ACMP-BASELINE		86.00 266	84.84 275	89.45 262	91.20 313	78.00 274	91.70 292	95.55 183	85.05 188	82.97 301	92.25 249	54.49 380	97.48 209	82.93 207	87.45 271	92.89 319

EI-MVSNet		89.10 162	88.86 154	89.80 248	91.84 290	78.30 268	93.70 221	95.01 216	85.73 170	87.15 189	95.28 134	79.87 128	97.21 243	83.81 197	87.36 272	93.88 272

MVSTER		88.84 171	88.29 170	90.51 213	92.95 261	80.44 213	93.73 218	95.01 216	84.66 201	87.15 189	93.12 221	72.79 224	97.21 243	87.86 143	87.36 272	93.87 273

EG-PatchMatch MVS		82.37 319	80.34 325	88.46 286	90.27 348	79.35 244	92.80 260	94.33 250	77.14 338	73.26 387	90.18 319	47.47 398	96.72 270	70.25 346	87.32 274	89.30 382

EPMVS		83.90 307	82.70 311	87.51 312	90.23 350	72.67 350	88.62 357	81.96 406	81.37 281	85.01 253	88.34 354	66.31 304	94.45 351	75.30 313	87.12 275	95.43 203

tpm2		84.08 302	82.94 306	87.48 315	91.39 307	71.27 367	89.23 348	90.37 356	71.95 386	84.64 259	89.33 337	67.30 289	96.55 286	75.17 314	87.09 276	94.63 233

CostFormer		85.77 272	84.94 272	88.26 294	91.16 317	72.58 355	89.47 344	91.04 344	76.26 346	86.45 207	89.97 326	70.74 246	96.86 268	82.35 219	87.07 277	95.34 208

Patchmatch-test		81.37 331	79.30 339	87.58 311	90.92 329	74.16 333	80.99 407	87.68 385	70.52 392	76.63 367	88.81 346	71.21 238	92.76 378	60.01 396	86.93 278	95.83 189

mvsany_test1		85.42 278	85.30 264	85.77 348	87.95 381	75.41 319	87.61 374	80.97 408	76.82 340	88.68 161	95.83 113	77.44 159	90.82 394	85.90 170	86.51 279	91.08 365

test_fmvs2		83.98 303	84.03 288	83.83 366	87.16 384	67.53 392	93.93 210	92.89 290	77.62 332	86.89 197	93.53 206	47.18 399	92.02 384	90.54 114	86.51 279	91.93 344

LTVRE_ROB	82.13 13	86.26 264	84.90 273	90.34 224	94.44 196	81.50 178	92.31 276	94.89 225	83.03 237	79.63 346	92.67 234	69.69 262	97.79 186	71.20 338	86.26 281	91.72 347
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
COLMAP_ROB	80.39 16	83.96 304	82.04 313	89.74 249	95.28 144	79.75 235	94.25 184	92.28 307	75.17 356	78.02 358	93.77 201	58.60 361	97.84 185	65.06 380	85.92 282	91.63 349
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		85.07 286	84.27 283	87.48 315	92.91 262	70.62 377	91.69 293	92.46 301	76.20 347	82.67 305	95.22 137	63.94 320	97.29 235	77.51 292	85.80 283	94.53 240

USDC		82.76 314	81.26 319	87.26 320	91.17 315	74.55 327	89.27 346	93.39 279	78.26 328	75.30 376	92.08 257	54.43 381	96.63 275	71.64 335	85.79 284	90.61 369

dmvs_re		84.20 301	83.22 302	87.14 327	91.83 292	77.81 281	90.04 332	90.19 359	84.70 200	81.49 318	89.17 339	64.37 318	91.13 392	71.58 336	85.65 285	92.46 331

GBi-Net		87.26 224	85.98 241	91.08 191	94.01 217	83.10 134	95.14 127	94.94 219	83.57 221	84.37 268	91.64 271	66.59 301	96.34 301	78.23 284	85.36 286	93.79 278

test1		87.26 224	85.98 241	91.08 191	94.01 217	83.10 134	95.14 127	94.94 219	83.57 221	84.37 268	91.64 271	66.59 301	96.34 301	78.23 284	85.36 286	93.79 278

FMVSNet3		87.40 219	86.11 235	91.30 181	93.79 230	83.64 116	94.20 188	94.81 233	83.89 214	84.37 268	91.87 266	68.45 284	96.56 284	78.23 284	85.36 286	93.70 288

FMVSNet2		87.19 232	85.82 248	91.30 181	94.01 217	83.67 114	94.79 148	94.94 219	83.57 221	83.88 283	92.05 260	66.59 301	96.51 288	77.56 291	85.01 289	93.73 286

ACMH	80.38 17	85.36 279	83.68 294	90.39 220	94.45 195	80.63 208	94.73 152	94.85 229	82.09 256	77.24 362	92.65 235	60.01 352	97.58 201	72.25 334	84.87 290	92.96 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				88.24 295	91.88 289	77.05 295		92.92 289	85.54 176	80.13 338	93.30 213	57.29 366	96.20 306	72.46 333	84.71 291	91.49 353

JIA-IIPM		81.04 334	78.98 347	87.25 321	88.64 369	73.48 340	81.75 406	89.61 374	73.19 376	82.05 313	73.71 409	66.07 309	95.87 321	71.18 340	84.60 292	92.41 333

tt0805		86.92 240	85.74 254	90.48 215	92.22 275	79.98 230	95.63 98	94.88 227	83.83 216	84.74 258	92.80 232	57.61 365	97.67 193	85.48 176	84.42 293	93.79 278

OpenMVS_ROB	74.94 19	79.51 350	77.03 357	86.93 330	87.00 385	76.23 309	92.33 274	90.74 352	68.93 396	74.52 381	88.23 357	49.58 393	96.62 276	57.64 401	84.29 294	87.94 396

AllTest		83.42 311	81.39 317	89.52 259	95.01 157	77.79 283	93.12 245	90.89 349	77.41 334	76.12 370	93.34 209	54.08 382	97.51 207	68.31 360	84.27 295	93.26 301

TestCases				89.52 259	95.01 157	77.79 283		90.89 349	77.41 334	76.12 370	93.34 209	54.08 382	97.51 207	68.31 360	84.27 295	93.26 301

tpm		84.73 294	84.02 289	86.87 334	90.33 347	68.90 384	89.06 351	89.94 366	80.85 290	85.75 223	89.86 328	68.54 283	95.97 315	77.76 288	84.05 297	95.75 192

WBMVS		84.97 290	84.18 284	87.34 317	94.14 213	71.62 366	90.20 327	92.35 303	81.61 276	84.06 278	90.76 303	61.82 333	96.52 287	78.93 277	83.81 298	93.89 269

FMVSNet1		85.85 270	84.11 287	91.08 191	92.81 263	83.10 134	95.14 127	94.94 219	81.64 274	82.68 304	91.64 271	59.01 359	96.34 301	75.37 312	83.78 299	93.79 278

ADS-MVSNet2		81.66 326	79.71 335	87.50 313	91.35 309	74.19 332	83.33 400	88.48 379	72.90 379	82.24 310	85.77 383	64.98 314	93.20 374	64.57 382	83.74 300	95.12 213

ADS-MVSNet		81.56 328	79.78 332	86.90 332	91.35 309	71.82 360	83.33 400	89.16 377	72.90 379	82.24 310	85.77 383	64.98 314	93.76 365	64.57 382	83.74 300	95.12 213

XXY-MVS		87.65 204	86.85 203	90.03 235	92.14 278	80.60 210	93.76 217	95.23 205	82.94 240	84.60 260	94.02 186	74.27 199	95.49 339	81.04 245	83.68 302	94.01 267

test_0402		81.30 333	79.17 343	87.67 309	93.19 248	78.17 271	92.98 252	91.71 323	75.25 355	76.02 372	90.31 314	59.23 357	96.37 298	50.22 408	83.63 303	88.47 393

tpmvs		83.35 313	82.07 312	87.20 325	91.07 321	71.00 373	88.31 361	91.70 324	78.91 312	80.49 333	87.18 372	69.30 271	97.08 251	68.12 363	83.56 304	93.51 295

pmmvs5		84.21 300	82.84 310	88.34 291	88.95 367	76.94 296	92.41 268	91.91 322	75.63 351	80.28 334	91.18 288	64.59 316	95.57 333	77.09 297	83.47 305	92.53 328

pmmvs4		85.43 277	83.86 292	90.16 228	90.02 354	82.97 144	90.27 320	92.67 298	75.93 349	80.73 328	91.74 269	71.05 240	95.73 330	78.85 278	83.46 306	91.78 346

test0.0.03 1		82.41 318	81.69 314	84.59 359	88.23 376	72.89 346	90.24 324	87.83 383	83.41 227	79.86 343	89.78 330	67.25 290	88.99 403	65.18 378	83.42 307	91.90 345

tpmrst		85.35 280	84.99 269	86.43 340	90.88 332	67.88 388	88.71 355	91.43 335	80.13 297	86.08 217	88.80 348	73.05 221	96.02 313	82.48 215	83.40 308	95.40 204

nrg030		91.08 110	90.39 114	93.17 85	93.07 253	86.91 22	96.41 37	96.26 121	88.30 102	88.37 167	94.85 155	82.19 105	97.64 198	91.09 104	82.95 309	94.96 221

cl22		86.78 245	85.98 241	89.18 268	92.34 273	77.62 288	90.84 313	94.13 260	81.33 282	83.97 282	90.15 320	73.96 207	96.60 281	84.19 191	82.94 310	93.33 299

miper_ehance_all_eth		87.22 229	86.62 214	89.02 273	92.13 279	77.40 291	90.91 312	94.81 233	81.28 283	84.32 273	90.08 323	79.26 137	96.62 276	83.81 197	82.94 310	93.04 314

miper_enhance_ethall		86.90 241	86.18 231	89.06 271	91.66 299	77.58 289	90.22 326	94.82 232	79.16 310	84.48 264	89.10 340	79.19 139	96.66 274	84.06 192	82.94 310	92.94 317

ACMH+	81.04 14	85.05 287	83.46 297	89.82 245	94.66 179	79.37 243	94.44 170	94.12 261	82.19 255	78.04 357	92.82 230	58.23 362	97.54 204	73.77 327	82.90 313	92.54 327

VPA-MVSNet		89.62 145	88.96 148	91.60 169	93.86 225	82.89 146	95.46 104	97.33 26	87.91 115	88.43 166	93.31 212	74.17 203	97.40 226	87.32 152	82.86 314	94.52 241

IterMVS-LS		88.36 185	87.91 179	89.70 252	93.80 228	78.29 269	93.73 218	95.08 214	85.73 170	84.75 257	91.90 265	79.88 127	96.92 264	83.83 196	82.51 315	93.89 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MonoMVSNet		86.89 242	86.55 217	87.92 304	89.46 363	73.75 335	94.12 191	93.10 284	87.82 122	85.10 250	90.76 303	69.59 264	94.94 349	86.47 163	82.50 316	95.07 215

testgi		80.94 338	80.20 328	83.18 367	87.96 380	66.29 393	91.28 302	90.70 353	83.70 218	78.12 356	92.84 228	51.37 389	90.82 394	63.34 385	82.46 317	92.43 332

test_vis1_rt		77.96 358	76.46 358	82.48 373	85.89 391	71.74 363	90.25 322	78.89 412	71.03 391	71.30 394	81.35 401	42.49 407	91.05 393	84.55 187	82.37 318	84.65 399

WR-MVS		88.38 183	87.67 183	90.52 212	93.30 246	80.18 218	93.26 241	95.96 150	88.57 95	85.47 235	92.81 231	76.12 171	96.91 265	81.24 243	82.29 319	94.47 249

tpm cat1		81.96 320	80.27 326	87.01 328	91.09 320	71.02 372	87.38 375	91.53 332	66.25 400	80.17 335	86.35 379	68.22 286	96.15 309	69.16 354	82.29 319	93.86 275

v1192		87.25 226	86.33 225	90.00 239	90.76 336	79.04 253	93.80 215	95.48 188	82.57 247	85.48 234	91.18 288	73.38 219	97.42 218	82.30 220	82.06 321	93.53 292

v1144		87.61 210	86.79 206	90.06 234	91.01 322	79.34 245	93.95 208	95.42 197	83.36 230	85.66 226	91.31 284	74.98 189	97.42 218	83.37 201	82.06 321	93.42 298

v1240		86.78 245	85.85 247	89.56 257	90.45 346	77.79 283	93.61 223	95.37 200	81.65 273	85.43 239	91.15 290	71.50 236	97.43 217	81.47 241	82.05 323	93.47 296

Anonymous20231206		81.03 335	79.77 334	84.82 358	87.85 382	70.26 379	91.42 298	92.08 313	73.67 371	77.75 359	89.25 338	62.43 329	93.08 375	61.50 391	82.00 324	91.12 362

V42		87.68 202	86.86 202	90.15 229	90.58 342	80.14 220	94.24 186	95.28 203	83.66 219	85.67 225	91.33 281	74.73 193	97.41 224	84.43 189	81.83 325	92.89 319

v1921920		86.97 239	86.06 238	89.69 253	90.53 345	78.11 273	93.80 215	95.43 195	81.90 264	85.33 247	91.05 294	72.66 225	97.41 224	82.05 228	81.80 326	93.53 292

v2v482		87.84 197	87.06 197	90.17 227	90.99 323	79.23 252	94.00 206	95.13 209	84.87 193	85.53 230	92.07 259	74.45 197	97.45 213	84.71 185	81.75 327	93.85 276

Anonymous20231211		86.59 253	85.13 267	90.98 200	96.52 91	81.50 178	96.14 56	96.16 130	73.78 370	83.65 289	92.15 251	63.26 325	97.37 230	82.82 211	81.74 328	94.06 264

v144192		87.19 232	86.35 224	89.74 249	90.64 340	78.24 270	93.92 211	95.43 195	81.93 262	85.51 232	91.05 294	74.21 202	97.45 213	82.86 209	81.56 329	93.53 292

cl____		86.52 256	85.78 249	88.75 279	92.03 283	76.46 304	90.74 314	94.30 251	81.83 269	83.34 297	90.78 302	75.74 181	96.57 282	81.74 236	81.54 330	93.22 305

DIV-MVS_self_test		86.53 255	85.78 249	88.75 279	92.02 284	76.45 305	90.74 314	94.30 251	81.83 269	83.34 297	90.82 300	75.75 179	96.57 282	81.73 237	81.52 331	93.24 304

Anonymous20240521		80.44 341	79.21 341	84.11 364	85.75 393	67.89 387	92.86 258	93.23 282	75.61 352	75.59 375	87.47 367	50.03 391	94.33 355	71.14 341	81.21 332	90.12 375

OurMVSNet-221017-0		85.35 280	84.64 279	87.49 314	90.77 335	72.59 354	94.01 204	94.40 247	84.72 199	79.62 347	93.17 218	61.91 332	96.72 270	81.99 229	81.16 333	93.16 309

FMVSNet5		81.52 329	79.60 336	87.27 319	91.17 315	77.95 275	91.49 297	92.26 309	76.87 339	76.16 369	87.91 362	51.67 388	92.34 381	67.74 364	81.16 333	91.52 352

CP-MVSNet		87.63 207	87.26 195	88.74 281	93.12 250	76.59 303	95.29 113	96.58 96	88.43 98	83.49 294	92.98 225	75.28 185	95.83 323	78.97 276	81.15 335	93.79 278

c3_l		87.14 234	86.50 220	89.04 272	92.20 276	77.26 292	91.22 306	94.70 239	82.01 260	84.34 272	90.43 312	78.81 142	96.61 279	83.70 199	81.09 336	93.25 303

IterMVS-SCA-FT		85.45 276	84.53 282	88.18 297	91.71 296	76.87 297	90.19 328	92.65 299	85.40 179	81.44 320	90.54 308	66.79 297	95.00 348	81.04 245	81.05 337	92.66 325

TinyColmap		79.76 348	77.69 351	85.97 344	91.71 296	73.12 343	89.55 340	90.36 357	75.03 357	72.03 391	90.19 318	46.22 402	96.19 308	63.11 386	81.03 338	88.59 392

UniMVSNet_NR-MVSNet		89.92 139	89.29 142	91.81 163	93.39 244	83.72 112	94.43 171	97.12 46	89.80 51	86.46 205	93.32 211	83.16 86	97.23 241	84.92 180	81.02 339	94.49 246

DU-MVS		89.34 159	88.50 162	91.85 160	93.04 256	83.72 112	94.47 168	96.59 95	89.50 61	86.46 205	93.29 214	77.25 160	97.23 241	84.92 180	81.02 339	94.59 236

PS-CasMVS		87.32 223	86.88 201	88.63 284	92.99 259	76.33 308	95.33 108	96.61 94	88.22 106	83.30 299	93.07 223	73.03 222	95.79 327	78.36 281	81.00 341	93.75 285

IterMVS		84.88 291	83.98 291	87.60 310	91.44 303	76.03 310	90.18 329	92.41 302	83.24 233	81.06 326	90.42 313	66.60 300	94.28 357	79.46 269	80.98 342	92.48 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		89.80 142	89.07 146	92.01 144	93.60 238	84.52 90	94.78 149	97.47 11	89.26 69	86.44 208	92.32 245	82.10 107	97.39 229	84.81 183	80.84 343	94.12 259

LF4IMVS		80.37 342	79.07 346	84.27 363	86.64 386	69.87 382	89.39 345	91.05 343	76.38 343	74.97 378	90.00 325	47.85 397	94.25 358	74.55 322	80.82 344	88.69 391

v10		87.25 226	86.38 222	89.85 243	91.19 314	79.50 239	94.48 165	95.45 192	83.79 217	83.62 290	91.19 286	75.13 186	97.42 218	81.94 230	80.60 345	92.63 326

tfpnnormal		84.72 295	83.23 301	89.20 267	92.79 264	80.05 225	94.48 165	95.81 162	82.38 250	81.08 325	91.21 285	69.01 277	96.95 262	61.69 390	80.59 346	90.58 372

WR-MVS_H		87.80 199	87.37 190	89.10 270	93.23 247	78.12 272	95.61 99	97.30 30	87.90 116	83.72 286	92.01 261	79.65 135	96.01 314	76.36 303	80.54 347	93.16 309

VPNet		88.20 189	87.47 188	90.39 220	93.56 239	79.46 240	94.04 201	95.54 185	88.67 90	86.96 191	94.58 169	69.33 268	97.15 245	84.05 193	80.53 348	94.56 239

v7n		86.81 243	85.76 252	89.95 240	90.72 338	79.25 251	95.07 130	95.92 152	84.45 204	82.29 308	90.86 297	72.60 227	97.53 205	79.42 273	80.52 349	93.08 313

v8		87.50 216	86.71 208	89.89 242	91.37 308	79.40 242	94.50 164	95.38 198	84.81 196	83.60 291	91.33 281	76.05 172	97.42 218	82.84 210	80.51 350	92.84 321

EU-MVSNet		81.32 332	80.95 320	82.42 374	88.50 372	63.67 403	93.32 234	91.33 336	64.02 404	80.57 332	92.83 229	61.21 343	92.27 382	76.34 304	80.38 351	91.32 356

Patchmtry		82.71 315	80.93 321	88.06 299	90.05 353	76.37 307	84.74 395	91.96 320	72.28 385	81.32 323	87.87 363	71.03 241	95.50 338	68.97 355	80.15 352	92.32 337

NR-MVSNet		88.58 181	87.47 188	91.93 152	93.04 256	84.16 103	94.77 150	96.25 123	89.05 76	80.04 340	93.29 214	79.02 140	97.05 256	81.71 238	80.05 353	94.59 236

Baseline_NR-MVSNet		87.07 236	86.63 213	88.40 287	91.44 303	77.87 279	94.23 187	92.57 300	84.12 209	85.74 224	92.08 257	77.25 160	96.04 311	82.29 221	79.94 354	91.30 357

dp		81.47 330	80.23 327	85.17 356	89.92 356	65.49 396	86.74 379	90.10 362	76.30 345	81.10 324	87.12 373	62.81 327	95.92 318	68.13 362	79.88 355	94.09 262

TranMVSNet+NR-MVSNet		88.84 171	87.95 177	91.49 173	92.68 266	83.01 142	94.92 139	96.31 115	89.88 45	85.53 230	93.85 198	76.63 168	96.96 261	81.91 231	79.87 356	94.50 244

miper_lstm_enhance		85.27 283	84.59 280	87.31 318	91.28 312	74.63 326	87.69 371	94.09 262	81.20 287	81.36 322	89.85 329	74.97 190	94.30 356	81.03 247	79.84 357	93.01 315

reproduce_monomvs		86.37 262	85.87 246	87.87 305	93.66 236	73.71 336	93.44 230	95.02 215	88.61 93	82.64 306	91.94 263	57.88 364	96.68 273	89.96 120	79.71 358	93.22 305

v148		87.04 237	86.32 226	89.21 266	90.94 327	77.26 292	93.71 220	94.43 245	84.84 195	84.36 271	90.80 301	76.04 173	97.05 256	82.12 224	79.60 359	93.31 300

IB-MVS	80.51 15	85.24 284	83.26 300	91.19 185	92.13 279	79.86 233	91.75 290	91.29 338	83.28 232	80.66 330	88.49 352	61.28 340	98.46 129	80.99 248	79.46 360	95.25 210
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
eth_miper_zixun_eth		86.50 257	85.77 251	88.68 282	91.94 285	75.81 314	90.47 318	94.89 225	82.05 257	84.05 279	90.46 311	75.96 174	96.77 269	82.76 213	79.36 361	93.46 297

baseline1		88.10 191	87.28 193	90.57 208	94.96 161	80.07 223	94.27 183	91.29 338	86.74 146	87.41 185	94.00 188	76.77 165	96.20 306	80.77 251	79.31 362	95.44 202

our_test_3		81.93 321	80.46 324	86.33 342	88.46 373	73.48 340	88.46 359	91.11 340	76.46 341	76.69 366	88.25 356	66.89 295	94.36 354	68.75 356	79.08 363	91.14 361

PEN-MVS		86.80 244	86.27 229	88.40 287	92.32 274	75.71 316	95.18 124	96.38 111	87.97 113	82.82 303	93.15 219	73.39 218	95.92 318	76.15 307	79.03 364	93.59 290

pm-mvs1		86.61 251	85.54 256	89.82 245	91.44 303	80.18 218	95.28 115	94.85 229	83.84 215	81.66 317	92.62 236	72.45 230	96.48 290	79.67 267	78.06 365	92.82 322

h-mvs33		90.80 114	90.15 120	92.75 110	96.01 110	82.66 155	95.43 105	95.53 186	89.80 51	93.08 75	95.64 122	75.77 176	99.00 71	92.07 84	78.05 366	96.60 154

SixPastTwentyTwo		83.91 306	82.90 308	86.92 331	90.99 323	70.67 376	93.48 227	91.99 317	85.54 176	77.62 361	92.11 255	60.59 348	96.87 267	76.05 308	77.75 367	93.20 307

ppachtmachnet_test		81.84 322	80.07 330	87.15 326	88.46 373	74.43 330	89.04 352	92.16 311	75.33 354	77.75 359	88.99 343	66.20 306	95.37 341	65.12 379	77.60 368	91.65 348

MIMVSNet1		79.38 351	77.28 353	85.69 349	86.35 387	73.67 337	91.61 295	92.75 296	78.11 331	72.64 389	88.12 358	48.16 396	91.97 386	60.32 393	77.49 369	91.43 355

DTE-MVSNet		86.11 265	85.48 258	87.98 301	91.65 300	74.92 323	94.93 138	95.75 167	87.36 132	82.26 309	93.04 224	72.85 223	95.82 324	74.04 323	77.46 370	93.20 307

N_pmnet		68.89 373	68.44 375	70.23 393	89.07 366	28.79 432	88.06 363	19.50 432	69.47 395	71.86 392	84.93 386	61.24 342	91.75 387	54.70 405	77.15 371	90.15 374

AUN-MVS		87.78 200	86.54 218	91.48 174	94.82 172	81.05 196	93.91 213	93.93 265	83.00 238	86.93 192	93.53 206	69.50 266	97.67 193	86.14 165	77.12 372	95.73 195

hse-mvs2		89.88 141	89.34 140	91.51 172	94.83 171	81.12 194	93.94 209	93.91 268	89.80 51	93.08 75	93.60 205	75.77 176	97.66 195	92.07 84	77.07 373	95.74 193

dmvs_testset		74.57 366	75.81 364	70.86 392	87.72 383	40.47 427	87.05 378	77.90 417	82.75 244	71.15 395	85.47 385	67.98 287	84.12 414	45.26 411	76.98 374	88.00 395

test20.03		79.95 346	79.08 345	82.55 371	85.79 392	67.74 390	91.09 308	91.08 341	81.23 286	74.48 382	89.96 327	61.63 334	90.15 396	60.08 394	76.38 375	89.76 377

FPMVS		64.63 378	62.55 380	70.88 391	70.80 420	56.71 413	84.42 396	84.42 399	51.78 414	49.57 414	81.61 400	23.49 419	81.48 417	40.61 417	76.25 376	74.46 410

test_fmvs3		77.67 359	77.16 356	79.22 381	79.52 411	61.14 407	92.34 273	91.64 328	73.98 368	78.86 350	86.59 374	27.38 417	87.03 405	88.12 141	75.97 377	89.50 379

EGC-MVSNET		61.97 379	56.37 384	78.77 383	89.63 361	73.50 339	89.12 350	82.79 403	0.21 429	1.24 430	84.80 387	39.48 408	90.04 397	44.13 412	75.94 378	72.79 411

pmmvs6		83.42 311	81.60 315	88.87 276	88.01 379	77.87 279	94.96 136	94.24 255	74.67 362	78.80 353	91.09 293	60.17 351	96.49 289	77.06 298	75.40 379	92.23 339

new_pmnet		72.15 369	70.13 372	78.20 384	82.95 404	65.68 394	83.91 398	82.40 405	62.94 406	64.47 403	79.82 403	42.85 406	86.26 409	57.41 402	74.44 380	82.65 404

MDA-MVSNet_test_wron		79.21 353	77.19 355	85.29 353	88.22 377	72.77 348	85.87 384	90.06 363	74.34 364	62.62 406	87.56 366	66.14 307	91.99 385	66.90 372	73.01 381	91.10 364

YYNet1		79.22 352	77.20 354	85.28 354	88.20 378	72.66 351	85.87 384	90.05 365	74.33 365	62.70 404	87.61 365	66.09 308	92.03 383	66.94 369	72.97 382	91.15 360

Patchmatch-RL test		81.67 325	79.96 331	86.81 335	85.42 395	71.23 368	82.17 405	87.50 386	78.47 322	77.19 363	82.50 399	70.81 245	93.48 369	82.66 214	72.89 383	95.71 196

pmmvs-eth3d		80.97 337	78.72 349	87.74 306	84.99 397	79.97 231	90.11 330	91.65 327	75.36 353	73.51 385	86.03 380	59.45 355	93.96 363	75.17 314	72.21 384	89.29 384

PM-MVS		78.11 357	76.12 361	84.09 365	83.54 401	70.08 380	88.97 353	85.27 397	79.93 299	74.73 380	86.43 376	34.70 413	93.48 369	79.43 272	72.06 385	88.72 390

test_f		71.95 370	70.87 371	75.21 388	74.21 418	59.37 411	85.07 392	85.82 392	65.25 402	70.42 396	83.13 394	23.62 418	82.93 416	78.32 282	71.94 386	83.33 401

Gipuma		57.99 385	54.91 387	67.24 399	88.51 370	65.59 395	52.21 420	90.33 358	43.58 417	42.84 420	51.18 421	20.29 423	85.07 411	34.77 418	70.45 387	51.05 420
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD_test1		69.04 372	66.26 378	77.36 387	80.51 409	62.79 406	85.46 389	83.51 402	54.11 413	59.14 410	84.79 388	23.40 420	89.61 399	55.22 404	70.24 388	79.68 408

K. test v3		81.59 327	80.15 329	85.91 347	89.89 357	69.42 383	92.57 265	87.71 384	85.56 175	73.44 386	89.71 332	55.58 371	95.52 335	77.17 295	69.76 389	92.78 323

KD-MVS_self_test		80.20 343	79.24 340	83.07 368	85.64 394	65.29 397	91.01 310	93.93 265	78.71 320	76.32 368	86.40 378	59.20 358	92.93 377	72.59 332	69.35 390	91.00 366

CL-MVSNet_self_test		81.74 324	80.53 322	85.36 352	85.96 390	72.45 356	90.25 322	93.07 286	81.24 285	79.85 344	87.29 369	70.93 243	92.52 379	66.95 368	69.23 391	91.11 363

TDRefinement		79.81 347	77.34 352	87.22 324	79.24 412	75.48 318	93.12 245	92.03 315	76.45 342	75.01 377	91.58 277	49.19 394	96.44 294	70.22 348	69.18 392	89.75 378

MDA-MVSNet-bldmvs		78.85 354	76.31 359	86.46 338	89.76 358	73.88 334	88.79 354	90.42 355	79.16 310	59.18 409	88.33 355	60.20 350	94.04 359	62.00 389	68.96 393	91.48 354

ambc				83.06 369	79.99 410	63.51 404	77.47 413	92.86 291		74.34 383	84.45 389	28.74 414	95.06 347	73.06 331	68.89 394	90.61 369

TransMVSNet (Re)		84.43 298	83.06 305	88.54 285	91.72 295	78.44 263	95.18 124	92.82 294	82.73 245	79.67 345	92.12 253	73.49 214	95.96 316	71.10 342	68.73 395	91.21 359

mvsany_test3		74.95 365	73.26 369	80.02 380	74.61 416	63.16 405	85.53 388	78.42 413	74.16 366	74.89 379	86.46 375	36.02 412	89.09 402	82.39 218	66.91 396	87.82 397

mvs5depth		80.98 336	79.15 344	86.45 339	84.57 398	73.29 342	87.79 367	91.67 326	80.52 293	82.20 312	89.72 331	55.14 377	95.93 317	73.93 326	66.83 397	90.12 375

PMVS	47.18 22	52.22 387	48.46 391	63.48 400	45.72 431	46.20 423	73.41 416	78.31 414	41.03 420	30.06 423	65.68 415	6.05 430	83.43 415	30.04 420	65.86 398	60.80 415
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		65.12 377	62.60 379	72.69 390	71.44 419	60.71 408	87.17 376	65.55 423	63.80 405	53.22 413	65.65 416	14.54 427	89.44 401	76.65 299	65.38 399	67.91 414

lessismore_v0				86.04 343	88.46 373	68.78 385		80.59 409		73.01 388	90.11 322	55.39 373	96.43 295	75.06 316	65.06 400	92.90 318

new-patchmatchnet		76.41 363	75.17 365	80.13 379	82.65 405	59.61 410	87.66 372	91.08 341	78.23 329	69.85 397	83.22 393	54.76 378	91.63 389	64.14 384	64.89 401	89.16 386

pmmvs3		71.81 371	68.71 374	81.11 377	75.86 415	70.42 378	86.74 379	83.66 401	58.95 410	68.64 400	80.89 402	36.93 411	89.52 400	63.10 387	63.59 402	83.39 400

UnsupCasMVSNet_eth		80.07 344	78.27 350	85.46 351	85.24 396	72.63 353	88.45 360	94.87 228	82.99 239	71.64 393	88.07 359	56.34 369	91.75 387	73.48 329	63.36 403	92.01 343

ttmdpeth		76.55 362	74.64 367	82.29 376	82.25 406	67.81 389	89.76 337	85.69 393	70.35 393	75.76 373	91.69 270	46.88 400	89.77 398	66.16 374	63.23 404	89.30 382

mmtdpeth		85.04 289	84.15 286	87.72 308	93.11 251	75.74 315	94.37 179	92.83 292	84.98 189	89.31 152	86.41 377	61.61 336	97.14 248	92.63 67	62.11 405	90.29 373

LCM-MVSNet		66.00 376	62.16 381	77.51 386	64.51 426	58.29 412	83.87 399	90.90 348	48.17 415	54.69 412	73.31 410	16.83 426	86.75 406	65.47 376	61.67 406	87.48 398

UnsupCasMVSNet_bld		76.23 364	73.27 368	85.09 357	83.79 400	72.92 345	85.65 387	93.47 278	71.52 387	68.84 399	79.08 404	49.77 392	93.21 373	66.81 373	60.52 407	89.13 388

testf1		59.54 381	56.11 385	69.85 394	69.28 421	56.61 415	80.37 409	76.55 420	42.58 418	45.68 417	75.61 405	11.26 428	84.18 412	43.20 414	60.44 408	68.75 412

APD_test2		59.54 381	56.11 385	69.85 394	69.28 421	56.61 415	80.37 409	76.55 420	42.58 418	45.68 417	75.61 405	11.26 428	84.18 412	43.20 414	60.44 408	68.75 412

KD-MVS_2432*1600		78.50 355	76.02 362	85.93 345	86.22 388	74.47 328	84.80 393	92.33 304	79.29 307	76.98 364	85.92 381	53.81 384	93.97 361	67.39 365	57.42 410	89.36 380

miper_refine_blended		78.50 355	76.02 362	85.93 345	86.22 388	74.47 328	84.80 393	92.33 304	79.29 307	76.98 364	85.92 381	53.81 384	93.97 361	67.39 365	57.42 410	89.36 380

MVStest1		72.91 368	69.70 373	82.54 372	78.14 413	73.05 344	88.21 362	86.21 389	60.69 407	64.70 402	90.53 309	46.44 401	85.70 410	58.78 399	53.62 412	88.87 389

DeepMVS_CX				56.31 404	74.23 417	51.81 420		56.67 428	44.85 416	48.54 416	75.16 407	27.87 416	58.74 426	40.92 416	52.22 413	58.39 418

WB-MVS		67.92 374	67.49 376	69.21 396	81.09 407	41.17 426	88.03 364	78.00 416	73.50 373	62.63 405	83.11 396	63.94 320	86.52 407	25.66 422	51.45 414	79.94 407

PVSNet_0	73.20 20	77.22 360	74.83 366	84.37 361	90.70 339	71.10 370	83.09 402	89.67 372	72.81 381	73.93 384	83.13 394	60.79 347	93.70 367	68.54 357	50.84 415	88.30 394

test_method		50.52 388	48.47 390	56.66 403	52.26 430	18.98 434	41.51 422	81.40 407	10.10 424	44.59 419	75.01 408	28.51 415	68.16 421	53.54 406	49.31 416	82.83 403

SSC-MVS		67.06 375	66.56 377	68.56 398	80.54 408	40.06 428	87.77 369	77.37 419	72.38 383	61.75 407	82.66 398	63.37 323	86.45 408	24.48 423	48.69 417	79.16 409

PMMVS2		59.60 380	56.40 383	69.21 396	68.83 423	46.58 422	73.02 417	77.48 418	55.07 412	49.21 415	72.95 411	17.43 425	80.04 418	49.32 409	44.33 418	80.99 406

dongtai		58.82 384	58.24 382	60.56 401	83.13 402	45.09 425	82.32 404	48.22 431	67.61 398	61.70 408	69.15 412	38.75 409	76.05 420	32.01 419	41.31 419	60.55 416

kuosan		53.51 386	53.30 389	54.13 405	76.06 414	45.36 424	80.11 411	48.36 430	59.63 409	54.84 411	63.43 418	37.41 410	62.07 425	20.73 425	39.10 420	54.96 419

MVE	39.65 23	43.39 389	38.59 395	57.77 402	56.52 428	48.77 421	55.38 419	58.64 427	29.33 423	28.96 424	52.65 420	4.68 431	64.62 424	28.11 421	33.07 421	59.93 417
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 390	42.29 392	46.03 406	65.58 425	37.41 429	73.51 415	64.62 424	33.99 421	28.47 425	47.87 422	19.90 424	67.91 422	22.23 424	24.45 422	32.77 421

ANet_high		58.88 383	54.22 388	72.86 389	56.50 429	56.67 414	80.75 408	86.00 391	73.09 378	37.39 421	64.63 417	22.17 421	79.49 419	43.51 413	23.96 423	82.43 405

EMVS		42.07 391	41.12 393	44.92 407	63.45 427	35.56 431	73.65 414	63.48 425	33.05 422	26.88 426	45.45 423	21.27 422	67.14 423	19.80 426	23.02 424	32.06 422

tmp_tt		35.64 392	39.24 394	24.84 408	14.87 432	23.90 433	62.71 418	51.51 429	6.58 426	36.66 422	62.08 419	44.37 404	30.34 428	52.40 407	22.00 425	20.27 423

wuyk23d		21.27 394	20.48 397	23.63 409	68.59 424	36.41 430	49.57 421	6.85 433	9.37 425	7.89 427	4.46 429	4.03 432	31.37 427	17.47 427	16.07 426	3.12 424

testmvs		8.92 395	11.52 398	1.12 411	1.06 433	0.46 436	86.02 383	0.65 434	0.62 427	2.74 428	9.52 427	0.31 434	0.45 430	2.38 428	0.39 427	2.46 426

test123		8.76 396	11.22 399	1.39 410	0.85 434	0.97 435	85.76 386	0.35 435	0.54 428	2.45 429	8.14 428	0.60 433	0.48 429	2.16 429	0.17 428	2.71 425

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

cdsmvs_eth3d_5k		22.14 393	29.52 396	0.00 412	0.00 435	0.00 437	0.00 423	95.76 166	0.00 430	0.00 431	94.29 176	75.66 182	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		6.64 398	8.86 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	79.70 131	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

ab-mvs-re		7.82 397	10.43 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	93.88 196	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						64.08 401								59.14 397

FOURS1					98.86 1	85.54 67	98.29 1	97.49 6	89.79 54	96.29 22

test_one_0601					98.58 11	85.83 61		97.44 15	91.05 15	96.78 18	98.06 16	91.45 11

eth-test2					0.00 435

eth-test					0.00 435

test_241102_ONE					98.77 5	85.99 52		97.44 15	90.26 38	97.71 1	97.96 24	92.31 4	99.38 31

save fliter					97.85 49	85.63 66	95.21 121	96.82 73	89.44 62

test0726					98.78 3	85.93 55	97.19 11	97.47 11	90.27 36	97.64 4	98.13 4	91.47 8

GSMVS																96.12 174

test_part2					98.55 12	87.22 19				96.40 21

sam_mvs1												71.70 234				96.12 174

sam_mvs												70.60 247

MTGPA								96.97 55

test_post1							88.00 365				9.81 426	69.31 270	95.53 334	76.65 299

test_post											10.29 425	70.57 251	95.91 320

patchmatchnet-post											83.76 391	71.53 235	96.48 290

MTMP							96.16 52	60.64 426

gm-plane-assit					89.60 362	68.00 386			77.28 337		88.99 343		97.57 202	79.44 271

TEST9					97.53 61	86.49 37	94.07 198	96.78 77	81.61 276	92.77 86	96.20 94	87.71 28	99.12 54

test_8					97.49 63	86.30 45	94.02 203	96.76 80	81.86 267	92.70 90	96.20 94	87.63 29	99.02 64

agg_prior					97.38 66	85.92 57		96.72 86		92.16 102			98.97 78

test_prior4						85.96 54	94.11 193

test_prior				93.82 66	97.29 70	84.49 91		96.88 66					98.87 88			98.11 73

旧先验2							93.36 232		71.25 389	94.37 47			97.13 249	86.74 159

新几何2							93.11 247

无先验							93.28 240	96.26 121	73.95 369				99.05 58	80.56 256		96.59 155

原ACMM2							92.94 254

testdata2													98.75 101	78.30 283

segment_acmp												87.16 36

testdata1							92.15 280		87.94 114

plane_prior7					94.70 177	82.74 150

plane_prior6					94.52 189	82.75 148						74.23 200

plane_prior4											94.86 153

plane_prior3						82.75 148			90.26 38	86.91 194

plane_prior2							95.85 83		90.81 19

plane_prior1					94.59 183

n2								0.00 436

nn								0.00 436

door-mid								85.49 394

test11								96.57 97

door								85.33 396

HQP5-MVS						81.56 176

HQP-NCC					94.17 209		94.39 175		88.81 83	85.43 239

ACMP_Plane					94.17 209		94.39 175		88.81 83	85.43 239

BP-MVS														87.11 156

HQP4-MVS										85.43 239			97.96 179			94.51 243

HQP2-MVS												73.83 210

NP-MVS					94.37 199	82.42 160					93.98 189

MDTV_nov1_ep13_2view						55.91 419	87.62 373		73.32 375	84.59 261		70.33 254		74.65 320		95.50 201

Test By Simon												80.02 126

High-resolution multi-view benchmark