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 16	95.59 6	98.14 37	88.48 8	96.26 49	97.28 35	85.90 185	97.67 4	98.10 12	88.41 20	99.56 12	94.66 44	99.19 1	98.71 21
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 27	87.28 18	95.56 111	97.51 7	89.13 85	97.14 14	97.91 30	91.64 7	99.62 2	94.61 45	99.17 2	98.86 12
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 54	97.13 15	97.44 17	90.31 39	97.71 2	98.07 18	92.31 4	99.58 10	95.66 28	99.13 3	98.84 15

IU-MVS					98.77 5	86.00 52		96.84 77	81.26 316	97.26 12				95.50 34	99.13 3	99.03 8

test_0728_THIRD									90.75 27	97.04 18	98.05 23	92.09 6	99.55 16	95.64 30	99.13 3	99.13 2

test_241102_TWO								97.44 17	90.31 39	97.62 7	98.07 18	91.46 10	99.58 10	95.66 28	99.12 6	98.98 10

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 57	97.09 17	96.73 92	90.27 43	97.04 18	98.05 23	91.47 8	99.55 16	95.62 32	99.08 7	98.45 37
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 17	97.49 8					99.61 4	95.62 32	99.08 7	98.99 9

MSC_two_6792asdad				96.52 1	97.78 56	90.86 1		96.85 75					99.61 4	96.03 25	99.06 9	99.07 5

No_MVS				96.52 1	97.78 56	90.86 1		96.85 75					99.61 4	96.03 25	99.06 9	99.07 5

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 30	86.29 46	97.46 7	97.40 22	89.03 90	96.20 30	98.10 12	89.39 16	99.34 38	95.88 27	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 56	86.00 52	98.29 1	97.49 8	90.75 27	97.62 7	98.06 20	92.59 2	99.61 4	95.64 30	99.02 12	98.86 12

PC_three_1452									82.47 279	97.09 16	97.07 66	92.72 1	98.04 185	92.70 74	99.02 12	98.86 12

OPU-MVS				96.21 3	98.00 44	90.85 3	97.13 15				97.08 64	92.59 2	98.94 86	92.25 86	98.99 14	98.84 15

ACMMP_NAP		94.74 22	94.56 28	95.28 10	98.02 43	87.70 11	95.68 99	97.34 26	88.28 116	95.30 44	97.67 39	85.90 51	99.54 20	93.91 52	98.95 15	98.60 24

HPM-MVS++		95.14 10	94.91 22	95.83 4	98.25 31	89.65 4	95.92 81	96.96 63	91.75 12	94.02 65	96.83 76	88.12 24	99.55 16	93.41 60	98.94 16	98.28 57

MP-MVS-pluss		94.21 40	94.00 54	94.85 25	98.17 35	86.65 31	94.82 159	97.17 44	86.26 177	92.83 91	97.87 32	85.57 56	99.56 12	94.37 48	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 8	95.32 11	94.85 25	96.99 77	86.33 42	97.33 8	97.30 32	91.38 18	95.39 42	97.46 44	88.98 19	99.40 30	94.12 49	98.89 18	98.82 17
Skip Steuart: Steuart Systems R&D Blog.
balanced_conf03		93.98 52	94.22 43	93.26 87	96.13 105	83.29 135	96.27 48	96.52 109	89.82 55	95.56 41	95.51 140	84.50 75	98.79 106	94.83 42	98.86 19	97.72 107

SD-MVS		94.96 15	95.33 10	93.88 66	97.25 74	86.69 28	96.19 52	97.11 53	90.42 35	96.95 20	97.27 52	89.53 14	96.91 290	94.38 47	98.85 20	98.03 84
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 9	95.50 8	98.11 38	88.51 7	95.29 123	96.96 63	92.09 9	95.32 43	97.08 64	89.49 15	99.33 41	95.10 39	98.85 20	98.66 22

CP-MVS		94.34 35	94.21 45	94.74 37	98.39 25	86.64 32	97.60 5	97.24 36	88.53 109	92.73 97	97.23 55	85.20 61	99.32 42	92.15 91	98.83 22	98.25 64

ZNCC-MVS		94.47 29	94.28 40	95.03 16	98.52 15	86.96 20	96.85 29	97.32 30	88.24 117	93.15 81	97.04 67	86.17 48	99.62 2	92.40 80	98.81 23	98.52 27

MP-MVS		94.25 37	94.07 51	94.77 35	98.47 18	86.31 44	96.71 32	96.98 59	89.04 88	91.98 117	97.19 59	85.43 58	99.56 12	92.06 97	98.79 24	98.44 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		93.89 55	93.65 69	94.62 41	96.84 80	86.43 39	96.69 33	97.49 8	85.15 215	93.56 75	96.28 99	85.60 55	99.31 43	92.45 77	98.79 24	98.12 75

SF-MVS		94.97 14	94.90 24	95.20 12	97.84 52	87.76 10	96.65 35	97.48 12	87.76 138	95.71 38	97.70 38	88.28 23	99.35 37	93.89 53	98.78 26	98.48 31

ACMMPR		94.43 32	94.28 40	94.91 21	98.63 9	86.69 28	96.94 21	97.32 30	88.63 104	93.53 76	97.26 54	85.04 64	99.54 20	92.35 83	98.78 26	98.50 28

HFP-MVS		94.52 27	94.40 33	94.86 24	98.61 10	86.81 25	96.94 21	97.34 26	88.63 104	93.65 71	97.21 56	86.10 49	99.49 26	92.35 83	98.77 28	98.30 51

MM		95.10 11	94.91 22	95.68 5	96.09 111	88.34 9	96.68 34	94.37 273	95.08 1	94.68 51	97.72 37	82.94 96	99.64 1	97.85 4	98.76 29	99.06 7

MTAPA		94.42 34	94.22 43	95.00 18	98.42 21	86.95 21	94.36 196	96.97 60	91.07 20	93.14 82	97.56 41	84.30 77	99.56 12	93.43 58	98.75 30	98.47 34

region2R		94.43 32	94.27 42	94.92 20	98.65 8	86.67 30	96.92 25	97.23 38	88.60 107	93.58 73	97.27 52	85.22 60	99.54 20	92.21 88	98.74 31	98.56 26

test9_res														91.91 103	98.71 32	98.07 77

DeepPCF-MVS	89.96 1	94.20 42	94.77 26	92.49 139	96.52 93	80.00 249	94.00 223	97.08 54	90.05 47	95.65 40	97.29 51	89.66 13	98.97 81	93.95 51	98.71 32	98.50 28

9.14			94.47 30		97.79 54		96.08 64	97.44 17	86.13 183	95.10 48	97.40 47	88.34 22	99.22 48	93.25 62	98.70 34

train_agg		93.44 70	93.08 80	94.52 44	97.53 63	86.49 37	94.07 215	96.78 84	81.86 299	92.77 94	96.20 102	87.63 29	99.12 57	92.14 92	98.69 35	97.94 88

DeepC-MVS_fast	89.43 2	94.04 48	93.79 60	94.80 33	97.48 66	86.78 26	95.65 104	96.89 72	89.40 73	92.81 92	96.97 69	85.37 59	99.24 47	90.87 123	98.69 35	98.38 43
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 45	93.80 59	95.34 9	94.91 176	87.62 14	95.97 76	93.01 317	92.58 6	94.22 56	97.20 58	80.56 129	99.59 8	97.04 18	98.68 37	98.81 18

TSAR-MVS + MP.		94.85 16	94.94 20	94.58 42	98.25 31	86.33 42	96.11 62	96.62 101	88.14 122	96.10 31	96.96 70	89.09 18	98.94 86	94.48 46	98.68 37	98.48 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
agg_prior2														90.54 128	98.68 37	98.27 59

test_prior2							94.12 207		87.67 141	92.63 102	96.39 97	86.62 41		91.50 112	98.67 40

MVSMamba_PlusPlus		93.44 70	93.54 71	93.14 95	96.58 89	83.05 148	96.06 68	96.50 111	84.42 235	94.09 61	95.56 139	85.01 68	98.69 116	94.96 40	98.66 41	97.67 110

MSLP-MVS++		93.72 61	94.08 50	92.65 129	97.31 70	83.43 129	95.79 90	97.33 28	90.03 48	93.58 73	96.96 70	84.87 70	97.76 207	92.19 90	98.66 41	96.76 174

CDPH-MVS		92.83 89	92.30 96	94.44 45	97.79 54	86.11 51	94.06 217	96.66 98	80.09 330	92.77 94	96.63 88	86.62 41	99.04 63	87.40 167	98.66 41	98.17 69

HPM-MVS		94.02 49	93.88 56	94.43 47	98.39 25	85.78 65	97.25 11	97.07 55	86.90 161	92.62 103	96.80 80	84.85 71	99.17 51	92.43 78	98.65 44	98.33 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 51	93.78 61	94.63 40	98.50 16	85.90 62	96.87 27	96.91 70	88.70 102	91.83 126	97.17 61	83.96 81	99.55 16	91.44 113	98.64 45	98.43 39

SPE-MVS-test		94.02 49	94.29 39	93.24 88	96.69 83	83.24 136	97.49 6	96.92 68	92.14 8	92.90 87	95.77 130	85.02 65	98.33 157	93.03 66	98.62 46	98.13 72

MCST-MVS		94.45 30	94.20 46	95.19 13	98.46 19	87.50 16	95.00 146	97.12 50	87.13 153	92.51 106	96.30 98	89.24 17	99.34 38	93.46 57	98.62 46	98.73 19

APD-MVS		94.24 38	94.07 51	94.75 36	98.06 41	86.90 23	95.88 83	96.94 66	85.68 192	95.05 49	97.18 60	87.31 35	99.07 59	91.90 105	98.61 48	98.28 57
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		93.96 53	93.72 65	94.68 38	98.43 20	86.22 47	95.30 121	97.78 1	87.45 145	93.26 78	97.33 50	84.62 74	99.51 24	90.75 125	98.57 49	98.32 50

XVS		94.45 30	94.32 36	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 32	92.85 89	97.16 62	85.02 65	99.49 26	91.99 99	98.56 50	98.47 34

X-MVStestdata		88.31 213	86.13 262	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 32	92.85 89	23.41 460	85.02 65	99.49 26	91.99 99	98.56 50	98.47 34

DELS-MVS		93.43 74	93.25 76	93.97 63	95.42 146	85.04 78	93.06 274	97.13 49	90.74 29	91.84 124	95.09 163	86.32 46	99.21 49	91.22 115	98.45 52	97.65 111
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 36	86.62 33		97.07 55	83.63 251	94.19 58	96.91 72	87.57 31	99.26 46	91.99 99	98.44 53

GST-MVS		94.21 40	93.97 55	94.90 23	98.41 22	86.82 24	96.54 37	97.19 39	88.24 117	93.26 78	96.83 76	85.48 57	99.59 8	91.43 114	98.40 54	98.30 51

HPM-MVS_fast		93.40 75	93.22 77	93.94 65	98.36 27	84.83 82	97.15 14	96.80 83	85.77 189	92.47 107	97.13 63	82.38 103	99.07 59	90.51 130	98.40 54	97.92 91

NCCC		94.81 19	94.69 27	95.17 14	97.83 53	87.46 17	95.66 102	96.93 67	92.34 7	93.94 66	96.58 91	87.74 27	99.44 29	92.83 69	98.40 54	98.62 23

DeepC-MVS	88.79 3	93.31 76	92.99 83	94.26 57	96.07 113	85.83 63	94.89 152	96.99 58	89.02 91	89.56 166	97.37 49	82.51 102	99.38 31	92.20 89	98.30 57	97.57 116
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 80	93.05 81	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 238	90.05 161	95.66 134	87.77 26	99.15 55	89.91 135	98.27 58	98.07 77

fmvsm_l_conf0.5_n_9		94.65 24	95.28 12	92.77 118	95.95 123	81.83 186	95.53 112	97.12 50	91.68 15	97.89 1	98.06 20	85.71 53	98.65 119	97.32 11	98.26 59	97.83 99

NormalMVS		93.46 67	93.16 79	94.37 52	98.40 23	86.20 48	96.30 42	96.27 129	91.65 16	92.68 99	96.13 108	77.97 165	98.84 99	90.75 125	98.26 59	98.07 77

lecture		95.10 11	95.46 8	94.01 61	98.40 23	84.36 102	97.70 3	97.78 1	91.19 19	96.22 29	98.08 17	86.64 40	99.37 33	94.91 41	98.26 59	98.29 56

原ACMM1				92.01 161	97.34 69	81.05 213		96.81 82	78.89 346	90.45 151	95.92 119	82.65 100	98.84 99	80.68 283	98.26 59	96.14 201

reproduce-ours		94.82 17	94.97 18	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 55	95.23 46	98.10 12	87.09 37	99.37 33	95.30 36	98.25 63	98.30 51

our_new_method		94.82 17	94.97 18	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 55	95.23 46	98.10 12	87.09 37	99.37 33	95.30 36	98.25 63	98.30 51

fmvsm_s_conf0.5_n_6		94.11 47	94.56 28	92.76 120	94.98 169	81.96 184	95.79 90	97.29 34	89.31 77	97.52 10	97.61 40	83.25 90	98.88 92	97.05 17	98.22 65	97.43 124

CS-MVS		94.12 46	94.44 32	93.17 93	96.55 90	83.08 147	97.63 4	96.95 65	91.71 14	93.50 77	96.21 101	85.61 54	98.24 162	93.64 55	98.17 66	98.19 67

MVS_111021_HR		93.45 69	93.31 74	93.84 68	96.99 77	84.84 81	93.24 265	97.24 36	88.76 99	91.60 132	95.85 125	86.07 50	98.66 117	91.91 103	98.16 67	98.03 84

reproduce_model		94.76 21	94.92 21	94.29 56	97.92 45	85.18 76	95.95 79	97.19 39	89.67 65	95.27 45	98.16 5	86.53 44	99.36 36	95.42 35	98.15 68	98.33 46

EC-MVSNet		93.44 70	93.71 66	92.63 130	95.21 156	82.43 171	97.27 10	96.71 95	90.57 34	92.88 88	95.80 128	83.16 91	98.16 168	93.68 54	98.14 69	97.31 126

test12				94.34 53	97.13 75	86.15 50		96.29 125		91.04 143		85.08 63	99.01 69		98.13 70	97.86 96

新几何1				93.10 97	97.30 71	84.35 103		95.56 196	71.09 423	91.26 140	96.24 100	82.87 98	98.86 95	79.19 304	98.10 71	96.07 207

patch_mono-2		93.74 60	94.32 36	92.01 161	97.54 62	78.37 291	93.40 252	97.19 39	88.02 125	94.99 50	97.21 56	88.35 21	98.44 145	94.07 50	98.09 72	99.23 1

dcpmvs_2		93.49 65	94.19 47	91.38 198	97.69 59	76.78 330	94.25 200	96.29 125	88.33 113	94.46 53	96.88 73	88.07 25	98.64 122	93.62 56	98.09 72	98.73 19

test_fmvsm_n_1920		94.71 23	95.11 15	93.50 80	95.79 127	84.62 87	96.15 57	97.64 3	89.85 54	97.19 13	97.89 31	86.28 47	98.71 115	97.11 14	98.08 74	97.17 140

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 26	97.47 13	91.73 13	96.10 31	96.69 81	89.90 12	99.30 44	94.70 43	98.04 75	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
fmvsm_s_conf0.5_n_7		93.15 84	93.76 63	91.31 201	94.42 215	79.48 261	94.52 178	97.14 48	89.33 76	94.17 59	98.09 16	81.83 119	97.49 231	96.33 24	98.02 76	96.95 160

SR-MVS		94.23 39	94.17 49	94.43 47	98.21 34	85.78 65	96.40 39	96.90 71	88.20 120	94.33 55	97.40 47	84.75 73	99.03 64	93.35 61	97.99 77	98.48 31

3Dnovator	86.66 5	91.73 110	90.82 124	94.44 45	94.59 199	86.37 41	97.18 13	97.02 57	89.20 82	84.31 304	96.66 84	73.74 233	99.17 51	86.74 177	97.96 78	97.79 102

CANet		93.54 64	93.20 78	94.55 43	95.65 135	85.73 67	94.94 149	96.69 97	91.89 11	90.69 147	95.88 122	81.99 117	99.54 20	93.14 64	97.95 79	98.39 41

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 177	95.76 94	96.92 68	93.37 3	97.63 6	98.43 1	84.82 72	99.16 54	98.15 1	97.92 80	98.90 11

DPM-MVS		92.58 94	91.74 104	95.08 15	96.19 102	89.31 5	92.66 288	96.56 106	83.44 257	91.68 131	95.04 164	86.60 43	98.99 76	85.60 194	97.92 80	96.93 163

APD-MVS_3200maxsize		93.78 58	93.77 62	93.80 71	97.92 45	84.19 106	96.30 42	96.87 74	86.96 157	93.92 67	97.47 43	83.88 82	98.96 83	92.71 73	97.87 82	98.26 63

CPTT-MVS		91.99 102	91.80 102	92.55 135	98.24 33	81.98 182	96.76 31	96.49 112	81.89 298	90.24 154	96.44 96	78.59 157	98.61 127	89.68 137	97.85 83	97.06 150

test_fmvsmconf_n		94.60 25	94.81 25	93.98 62	94.62 197	84.96 80	96.15 57	97.35 25	89.37 74	96.03 34	98.11 10	86.36 45	99.01 69	97.45 9	97.83 84	97.96 87

fmvsm_s_conf0.5_n_8		94.56 26	95.12 14	92.87 112	95.96 122	81.32 201	95.76 94	97.57 5	93.48 2	97.53 9	98.32 2	81.78 121	99.13 56	97.91 2	97.81 85	98.16 70

fmvsm_l_conf0.5_n_3		94.80 20	95.01 17	94.15 59	95.64 136	85.08 77	96.09 63	97.36 24	90.98 22	97.09 16	98.12 9	84.98 69	98.94 86	97.07 15	97.80 86	98.43 39

SR-MVS-dyc-post		93.82 57	93.82 58	93.82 69	97.92 45	84.57 89	96.28 46	96.76 87	87.46 143	93.75 69	97.43 45	84.24 78	99.01 69	92.73 70	97.80 86	97.88 94

RE-MVS-def			93.68 67		97.92 45	84.57 89	96.28 46	96.76 87	87.46 143	93.75 69	97.43 45	82.94 96		92.73 70	97.80 86	97.88 94

test222					96.55 90	81.70 189	92.22 306	95.01 235	68.36 431	90.20 156	96.14 107	80.26 133			97.80 86	96.05 210

test_fmvsmconf0.1_n		94.20 42	94.31 38	93.88 66	92.46 302	84.80 83	96.18 54	96.82 80	89.29 79	95.68 39	98.11 10	85.10 62	98.99 76	97.38 10	97.75 90	97.86 96

3Dnovator+	87.14 4	92.42 98	91.37 110	95.55 7	95.63 137	88.73 6	97.07 19	96.77 86	90.84 24	84.02 309	96.62 89	75.95 195	99.34 38	87.77 161	97.68 91	98.59 25

旧先验1					96.79 81	81.81 187		95.67 187			96.81 78	86.69 39			97.66 92	96.97 159

fmvsm_s_conf0.5_n_3		94.49 28	95.13 13	92.56 134	95.49 144	81.10 211	95.93 80	97.16 45	92.96 4	97.39 11	98.13 6	83.63 84	98.80 104	97.89 3	97.61 93	97.78 103

EPNet		91.79 106	91.02 119	94.10 60	90.10 386	85.25 75	96.03 71	92.05 344	92.83 5	87.39 215	95.78 129	79.39 147	99.01 69	88.13 156	97.48 94	98.05 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmvis_n_1920		93.44 70	93.55 70	93.10 97	93.67 260	84.26 104	95.83 88	96.14 143	89.00 92	92.43 108	97.50 42	83.37 88	98.72 113	96.61 22	97.44 95	96.32 191

testdata				90.49 239	96.40 96	77.89 305		95.37 215	72.51 415	93.63 72	96.69 81	82.08 114	97.65 216	83.08 232	97.39 96	95.94 212

MVS_111021_LR		92.47 97	92.29 97	92.98 105	95.99 119	84.43 98	93.08 271	96.09 150	88.20 120	91.12 142	95.72 133	81.33 124	97.76 207	91.74 107	97.37 97	96.75 175

BP-MVS1		92.48 96	92.07 99	93.72 75	94.50 208	84.39 101	95.90 82	94.30 276	90.39 36	92.67 101	95.94 118	74.46 216	98.65 119	93.14 64	97.35 98	98.13 72

fmvsm_s_conf0.5_n_5		93.96 53	94.18 48	93.30 84	94.79 183	83.81 117	95.77 92	96.74 91	88.02 125	96.23 28	97.84 34	83.36 89	98.83 102	97.49 7	97.34 99	97.25 134

test_fmvsmconf0.01_n		93.19 81	93.02 82	93.71 76	89.25 399	84.42 100	96.06 68	96.29 125	89.06 86	94.68 51	98.13 6	79.22 149	98.98 80	97.22 12	97.24 100	97.74 105

MVSFormer		91.68 112	91.30 111	92.80 116	93.86 245	83.88 115	95.96 77	95.90 168	84.66 231	91.76 128	94.91 169	77.92 168	97.30 257	89.64 138	97.11 101	97.24 135

lupinMVS		90.92 125	90.21 132	93.03 102	93.86 245	83.88 115	92.81 285	93.86 295	79.84 333	91.76 128	94.29 202	77.92 168	98.04 185	90.48 131	97.11 101	97.17 140

EIA-MVS		91.95 103	91.94 100	91.98 165	95.16 159	80.01 248	95.36 116	96.73 92	88.44 110	89.34 171	92.16 279	83.82 83	98.45 143	89.35 140	97.06 103	97.48 120

MG-MVS		91.77 108	91.70 105	92.00 164	97.08 76	80.03 247	93.60 245	95.18 227	87.85 134	90.89 145	96.47 95	82.06 115	98.36 152	85.07 200	97.04 104	97.62 112

fmvsm_l_conf0.5_n_a		94.20 42	94.40 33	93.60 78	95.29 150	84.98 79	95.61 107	96.28 128	86.31 175	96.75 24	97.86 33	87.40 33	98.74 112	97.07 15	97.02 105	97.07 149

test2506		87.21 258	86.28 257	90.02 263	95.62 138	73.64 369	96.25 50	71.38 458	87.89 132	90.45 151	96.65 85	55.29 409	98.09 180	86.03 189	96.94 106	98.33 46

ECVR-MVS		89.09 188	88.53 184	90.77 229	95.62 138	75.89 343	96.16 55	84.22 435	87.89 132	90.20 156	96.65 85	63.19 355	98.10 172	85.90 190	96.94 106	98.33 46

test1111		89.10 186	88.64 181	90.48 240	95.53 143	74.97 353	96.08 64	84.89 433	88.13 123	90.16 159	96.65 85	63.29 353	98.10 172	86.14 185	96.90 108	98.39 41

jason		90.80 128	90.10 136	92.90 110	93.04 284	83.53 127	93.08 271	94.15 284	80.22 327	91.41 137	94.91 169	76.87 178	97.93 198	90.28 132	96.90 108	97.24 135
jason: jason.
Vis-MVSNet		91.75 109	91.23 114	93.29 85	95.32 149	83.78 118	96.14 59	95.98 159	89.89 51	90.45 151	96.58 91	75.09 207	98.31 160	84.75 206	96.90 108	97.78 103
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
114514_t		89.51 172	88.50 186	92.54 136	98.11 38	81.99 181	95.16 138	96.36 121	70.19 427	85.81 249	95.25 153	76.70 182	98.63 124	82.07 255	96.86 111	97.00 157

fmvsm_l_conf0.5_n		94.29 36	94.46 31	93.79 72	95.28 151	85.43 72	95.68 99	96.43 114	86.56 169	96.84 22	97.81 35	87.56 32	98.77 108	97.14 13	96.82 112	97.16 144

fmvsm_s_conf0.5_n_4		93.86 56	94.37 35	92.33 151	95.13 162	80.95 217	95.64 105	96.97 60	89.60 67	96.85 21	97.77 36	83.08 94	98.92 89	97.49 7	96.78 113	97.13 145

Vis-MVSNet (Re-imp)		89.59 170	89.44 157	90.03 261	95.74 129	75.85 344	95.61 107	90.80 382	87.66 142	87.83 204	95.40 146	76.79 180	96.46 320	78.37 309	96.73 114	97.80 101

API-MVS		90.66 136	90.07 138	92.45 142	96.36 98	84.57 89	96.06 68	95.22 226	82.39 280	89.13 174	94.27 205	80.32 131	98.46 139	80.16 291	96.71 115	94.33 283

MAR-MVS		90.30 145	89.37 160	93.07 101	96.61 86	84.48 94	95.68 99	95.67 187	82.36 282	87.85 202	92.85 255	76.63 184	98.80 104	80.01 292	96.68 116	95.91 213
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	88.74 200	87.29 219	93.08 99	92.70 297	85.39 73	96.57 36	96.43 114	78.74 352	80.85 357	96.07 111	69.64 289	99.01 69	78.01 316	96.65 117	94.83 259

fmvsm_s_conf0.5_n_2		93.47 66	93.83 57	92.39 146	95.36 147	81.19 207	95.20 135	96.56 106	90.37 37	97.13 15	98.03 27	77.47 174	98.96 83	97.79 5	96.58 118	97.03 153

ETV-MVS		92.74 92	92.66 89	92.97 106	95.20 157	84.04 112	95.07 142	96.51 110	90.73 30	92.96 86	91.19 316	84.06 79	98.34 155	91.72 108	96.54 119	96.54 186

QAPM		89.51 172	88.15 197	93.59 79	94.92 174	84.58 88	96.82 30	96.70 96	78.43 357	83.41 325	96.19 105	73.18 242	99.30 44	77.11 325	96.54 119	96.89 166

IS-MVSNet		91.43 115	91.09 118	92.46 140	95.87 126	81.38 200	96.95 20	93.69 303	89.72 64	89.50 169	95.98 116	78.57 158	97.77 206	83.02 234	96.50 121	98.22 66

DP-MVS Recon		91.95 103	91.28 113	93.96 64	98.33 29	85.92 59	94.66 171	96.66 98	82.69 277	90.03 162	95.82 127	82.30 107	99.03 64	84.57 212	96.48 122	96.91 165

CANet_DTU		90.26 147	89.41 159	92.81 115	93.46 267	83.01 151	93.48 248	94.47 268	89.43 72	87.76 207	94.23 207	70.54 277	99.03 64	84.97 201	96.39 123	96.38 189

KinetiMVS		91.82 105	91.30 111	93.39 82	94.72 190	83.36 133	95.45 114	96.37 120	90.33 38	92.17 112	96.03 113	72.32 254	98.75 109	87.94 159	96.34 124	98.07 77

UGNet		89.95 159	88.95 173	92.95 108	94.51 207	83.31 134	95.70 98	95.23 224	89.37 74	87.58 209	93.94 218	64.00 348	98.78 107	83.92 221	96.31 125	96.74 176
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 59	94.06 53	92.86 113	95.62 138	83.17 139	96.14 59	96.12 147	88.13 123	95.82 37	98.04 26	83.43 85	98.48 135	96.97 19	96.23 126	96.92 164

fmvsm_s_conf0.1_n_2		93.16 83	93.42 72	92.37 147	94.62 197	81.13 209	95.23 128	95.89 170	90.30 41	96.74 25	98.02 28	76.14 186	98.95 85	97.64 6	96.21 127	97.03 153

fmvsm_s_conf0.1_n		93.46 67	93.66 68	92.85 114	93.75 252	83.13 141	96.02 72	95.74 181	87.68 140	95.89 36	98.17 4	82.78 99	98.46 139	96.71 20	96.17 128	96.98 158

TSAR-MVS + GP.		93.66 62	93.41 73	94.41 49	96.59 87	86.78 26	94.40 188	93.93 291	89.77 62	94.21 57	95.59 137	87.35 34	98.61 127	92.72 72	96.15 129	97.83 99

mvsmamba		90.33 144	89.69 150	92.25 158	95.17 158	81.64 190	95.27 126	93.36 308	84.88 222	89.51 167	94.27 205	69.29 298	97.42 242	89.34 141	96.12 130	97.68 109

GDP-MVS		92.04 101	91.46 108	93.75 74	94.55 205	84.69 86	95.60 110	96.56 106	87.83 135	93.07 85	95.89 121	73.44 237	98.65 119	90.22 133	96.03 131	97.91 93

PVSNet_Blended		90.73 131	90.32 130	91.98 165	96.12 106	81.25 203	92.55 292	96.83 78	82.04 290	89.10 175	92.56 267	81.04 126	98.85 97	86.72 179	95.91 132	95.84 218

PS-MVSNAJ		91.18 121	90.92 120	91.96 167	95.26 154	82.60 170	92.09 311	95.70 185	86.27 176	91.84 124	92.46 269	79.70 141	98.99 76	89.08 144	95.86 133	94.29 284

Elysia		90.12 149	89.10 167	93.18 91	93.16 274	84.05 110	95.22 130	96.27 129	85.16 213	90.59 148	94.68 181	64.64 343	98.37 150	86.38 183	95.77 134	97.12 146

StellarMVS		90.12 149	89.10 167	93.18 91	93.16 274	84.05 110	95.22 130	96.27 129	85.16 213	90.59 148	94.68 181	64.64 343	98.37 150	86.38 183	95.77 134	97.12 146

ACMMP		93.24 79	92.88 85	94.30 55	98.09 40	85.33 74	96.86 28	97.45 16	88.33 113	90.15 160	97.03 68	81.44 122	99.51 24	90.85 124	95.74 136	98.04 83
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 88	92.83 86	93.35 83	94.59 199	83.40 131	95.00 146	96.34 122	90.30 41	92.05 115	96.05 112	83.43 85	98.15 169	92.07 94	95.67 137	98.49 30
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		88.30 214	88.32 193	88.27 324	94.71 192	72.41 388	93.15 266	90.98 375	87.77 137	79.25 381	91.96 292	78.35 162	95.75 356	83.04 233	95.62 138	96.65 180

CHOSEN 1792x2688		88.84 196	87.69 209	92.30 154	96.14 104	81.42 199	90.01 364	95.86 173	74.52 396	87.41 212	93.94 218	75.46 204	98.36 152	80.36 287	95.53 139	97.12 146

fmvsm_s_conf0.5_n_a		93.57 63	93.76 63	93.00 104	95.02 164	83.67 121	96.19 52	96.10 149	87.27 148	95.98 35	98.05 23	83.07 95	98.45 143	96.68 21	95.51 140	96.88 167

AdaColmap		89.89 162	89.07 169	92.37 147	97.41 67	83.03 149	94.42 187	95.92 165	82.81 274	86.34 238	94.65 186	73.89 229	99.02 67	80.69 282	95.51 140	95.05 246

MVS		87.44 245	86.10 265	91.44 196	92.61 299	83.62 124	92.63 289	95.66 189	67.26 433	81.47 349	92.15 280	77.95 167	98.22 165	79.71 295	95.48 142	92.47 364

UA-Net		92.83 89	92.54 92	93.68 77	96.10 110	84.71 85	95.66 102	96.39 118	91.92 10	93.22 80	96.49 94	83.16 91	98.87 93	84.47 214	95.47 143	97.45 122

xiu_mvs_v2_base		91.13 122	90.89 122	91.86 176	94.97 170	82.42 172	92.24 304	95.64 192	86.11 184	91.74 130	93.14 248	79.67 144	98.89 91	89.06 145	95.46 144	94.28 285

casdiffmvs		92.51 95	92.43 94	92.74 123	94.41 216	81.98 182	94.54 177	96.23 137	89.57 68	91.96 119	96.17 106	82.58 101	98.01 187	90.95 121	95.45 145	98.23 65
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 81	93.26 75	92.97 106	92.49 300	83.62 124	96.02 72	95.72 184	86.78 163	96.04 33	98.19 3	82.30 107	98.43 147	96.38 23	95.42 146	96.86 168

PVSNet_Blended_VisFu		91.38 116	90.91 121	92.80 116	96.39 97	83.17 139	94.87 154	96.66 98	83.29 262	89.27 173	94.46 197	80.29 132	99.17 51	87.57 164	95.37 147	96.05 210

PAPM_NR		91.22 120	90.78 125	92.52 137	97.60 61	81.46 197	94.37 194	96.24 136	86.39 174	87.41 212	94.80 177	82.06 115	98.48 135	82.80 240	95.37 147	97.61 113

CHOSEN 280x420		85.15 315	83.99 322	88.65 312	92.47 301	78.40 290	79.68 448	92.76 324	74.90 393	81.41 351	89.59 367	69.85 287	95.51 365	79.92 294	95.29 149	92.03 376

TAPA-MVS	84.62 6	88.16 217	87.01 227	91.62 188	96.64 85	80.65 225	94.39 190	96.21 141	76.38 376	86.19 242	95.44 143	79.75 139	98.08 182	62.75 423	95.29 149	96.13 202
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline		92.39 99	92.29 97	92.69 127	94.46 211	81.77 188	94.14 206	96.27 129	89.22 81	91.88 122	96.00 114	82.35 104	97.99 189	91.05 117	95.27 151	98.30 51

LS3D		87.89 223	86.32 255	92.59 132	96.07 113	82.92 154	95.23 128	94.92 246	75.66 383	82.89 332	95.98 116	72.48 251	99.21 49	68.43 393	95.23 152	95.64 227

test_vis1_n_1920		89.39 180	89.84 145	88.04 331	92.97 288	72.64 383	94.71 168	96.03 157	86.18 179	91.94 121	96.56 93	61.63 364	95.74 357	93.42 59	95.11 153	95.74 223

mamv4		90.92 125	91.78 103	88.33 322	95.67 134	70.75 406	92.92 281	96.02 158	81.90 295	88.11 194	95.34 149	85.88 52	96.97 285	95.22 38	95.01 154	97.26 133

diffmvs_AUTHOR		91.51 114	91.44 109	91.73 184	93.09 279	80.27 235	92.51 293	95.58 195	87.22 150	91.80 127	95.57 138	79.96 136	97.48 232	92.23 87	94.97 155	97.45 122

MVS_Test		91.31 118	91.11 116	91.93 170	94.37 217	80.14 240	93.46 250	95.80 176	86.46 172	91.35 139	93.77 228	82.21 110	98.09 180	87.57 164	94.95 156	97.55 118

viewmanbaseed2359cas		91.78 107	91.58 106	92.37 147	94.32 222	81.07 212	93.76 237	95.96 162	87.26 149	91.50 134	95.88 122	80.92 128	97.97 193	89.70 136	94.92 157	98.07 77

RRT-MVS		90.85 127	90.70 126	91.30 202	94.25 224	76.83 329	94.85 157	96.13 146	89.04 88	90.23 155	94.88 171	70.15 282	98.72 113	91.86 106	94.88 158	98.34 44

test_cas_vis1_n_1920		88.83 199	88.85 179	88.78 306	91.15 350	76.72 331	93.85 233	94.93 245	83.23 265	92.81 92	96.00 114	61.17 375	94.45 383	91.67 109	94.84 159	95.17 242

PAPR		90.02 155	89.27 165	92.29 155	95.78 128	80.95 217	92.68 287	96.22 138	81.91 294	86.66 229	93.75 230	82.23 109	98.44 145	79.40 303	94.79 160	97.48 120

SymmetryMVS		92.81 91	92.31 95	94.32 54	96.15 103	86.20 48	96.30 42	94.43 269	91.65 16	92.68 99	96.13 108	77.97 165	98.84 99	90.75 125	94.72 161	97.92 91

test_fmvs1		87.34 249	87.56 212	86.68 370	90.59 375	71.80 392	94.01 221	94.04 289	78.30 359	91.97 118	95.22 154	56.28 403	93.71 399	92.89 68	94.71 162	94.52 272

xiu_mvs_v1_base_debu		90.64 137	90.05 139	92.40 143	93.97 241	84.46 95	93.32 256	95.46 204	85.17 210	92.25 109	94.03 210	70.59 273	98.57 130	90.97 118	94.67 163	94.18 286

xiu_mvs_v1_base		90.64 137	90.05 139	92.40 143	93.97 241	84.46 95	93.32 256	95.46 204	85.17 210	92.25 109	94.03 210	70.59 273	98.57 130	90.97 118	94.67 163	94.18 286

xiu_mvs_v1_base_debi		90.64 137	90.05 139	92.40 143	93.97 241	84.46 95	93.32 256	95.46 204	85.17 210	92.25 109	94.03 210	70.59 273	98.57 130	90.97 118	94.67 163	94.18 286

gg-mvs-nofinetune		81.77 355	79.37 370	88.99 303	90.85 366	77.73 317	86.29 416	79.63 446	74.88 394	83.19 330	69.05 449	60.34 379	96.11 337	75.46 341	94.64 166	93.11 344

BH-RMVSNet		88.37 211	87.48 214	91.02 216	95.28 151	79.45 263	92.89 282	93.07 315	85.45 202	86.91 221	94.84 176	70.35 278	97.76 207	73.97 356	94.59 167	95.85 217

test_fmvs1_n		87.03 266	87.04 226	86.97 361	89.74 394	71.86 390	94.55 176	94.43 269	78.47 355	91.95 120	95.50 141	51.16 423	93.81 397	93.02 67	94.56 168	95.26 239

diffmvs		91.37 117	91.23 114	91.77 183	93.09 279	80.27 235	92.36 298	95.52 201	87.03 156	91.40 138	94.93 168	80.08 134	97.44 240	92.13 93	94.56 168	97.61 113
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		88.60 204	88.13 198	90.01 264	95.24 155	78.50 287	93.29 261	94.15 284	84.75 228	84.46 294	93.40 236	75.76 198	97.40 250	77.59 319	94.52 170	94.12 290

Effi-MVS+		91.59 113	91.11 116	93.01 103	94.35 221	83.39 132	94.60 173	95.10 231	87.10 154	90.57 150	93.10 250	81.43 123	98.07 183	89.29 142	94.48 171	97.59 115

PCF-MVS	84.11 10	87.74 228	86.08 266	92.70 126	94.02 235	84.43 98	89.27 377	95.87 172	73.62 405	84.43 296	94.33 199	78.48 161	98.86 95	70.27 379	94.45 172	94.81 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		93.01 87	92.92 84	93.29 85	95.01 165	83.51 128	94.48 180	95.77 178	90.87 23	92.52 105	96.67 83	84.50 75	99.00 74	91.99 99	94.44 173	97.36 125

MS-PatchMatch		85.05 317	84.16 317	87.73 338	91.42 338	78.51 286	91.25 333	93.53 304	77.50 366	80.15 367	91.58 307	61.99 361	95.51 365	75.69 339	94.35 174	89.16 422

FE-MVS		87.40 247	86.02 268	91.57 190	94.56 204	79.69 258	90.27 351	93.72 301	80.57 324	88.80 183	91.62 305	65.32 338	98.59 129	74.97 348	94.33 175	96.44 187

LuminaMVS		90.55 141	89.81 146	92.77 118	92.78 295	84.21 105	94.09 213	94.17 283	85.82 186	91.54 133	94.14 209	69.93 283	97.92 199	91.62 110	94.21 176	96.18 199

SSM_0404		90.73 131	90.08 137	92.69 127	95.00 168	83.13 141	94.32 197	95.00 239	85.41 203	89.84 163	95.35 147	76.13 187	97.98 191	85.46 197	94.18 177	96.95 160

mvs_anonymous		89.37 181	89.32 162	89.51 290	93.47 266	74.22 362	91.65 323	94.83 253	82.91 272	85.45 264	93.79 226	81.23 125	96.36 327	86.47 181	94.09 178	97.94 88

test_vis1_n		86.56 283	86.49 250	86.78 368	88.51 405	72.69 380	94.68 169	93.78 300	79.55 337	90.70 146	95.31 150	48.75 429	93.28 405	93.15 63	93.99 179	94.38 282

MVP-Stereo		85.97 296	84.86 304	89.32 293	90.92 362	82.19 178	92.11 310	94.19 281	78.76 351	78.77 387	91.63 304	68.38 311	96.56 311	75.01 347	93.95 180	89.20 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LFMVS		90.08 152	89.13 166	92.95 108	96.71 82	82.32 176	96.08 64	89.91 400	86.79 162	92.15 114	96.81 78	62.60 358	98.34 155	87.18 171	93.90 181	98.19 67

PVSNet	78.82 18	85.55 304	84.65 308	88.23 327	94.72 190	71.93 389	87.12 411	92.75 325	78.80 350	84.95 282	90.53 341	64.43 346	96.71 298	74.74 350	93.86 182	96.06 209

CNLPA		89.07 189	87.98 201	92.34 150	96.87 79	84.78 84	94.08 214	93.24 309	81.41 312	84.46 294	95.13 162	75.57 203	96.62 303	77.21 323	93.84 183	95.61 230

guyue		91.12 123	90.84 123	91.96 167	94.59 199	80.57 229	94.87 154	93.71 302	88.96 93	91.14 141	95.22 154	73.22 241	97.76 207	92.01 98	93.81 184	97.54 119

EPNet_dtu		86.49 288	85.94 273	88.14 329	90.24 384	72.82 378	94.11 209	92.20 340	86.66 168	79.42 380	92.36 273	73.52 234	95.81 353	71.26 371	93.66 185	95.80 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GeoE		90.05 153	89.43 158	91.90 175	95.16 159	80.37 234	95.80 89	94.65 263	83.90 243	87.55 211	94.75 178	78.18 164	97.62 220	81.28 271	93.63 186	97.71 108

EI-MVSNet-UG-set		92.74 92	92.62 91	93.12 96	94.86 179	83.20 138	94.40 188	95.74 181	90.71 31	92.05 115	96.60 90	84.00 80	98.99 76	91.55 111	93.63 186	97.17 140

Fast-Effi-MVS+		89.41 177	88.64 181	91.71 186	94.74 187	80.81 222	93.54 246	95.10 231	83.11 266	86.82 227	90.67 339	79.74 140	97.75 211	80.51 286	93.55 188	96.57 184

FA-MVS(test-final)		89.66 167	88.91 175	91.93 170	94.57 203	80.27 235	91.36 328	94.74 259	84.87 223	89.82 164	92.61 266	74.72 214	98.47 138	83.97 220	93.53 189	97.04 152

1314		87.51 242	86.57 245	90.34 249	92.42 304	79.74 257	92.63 289	95.35 217	78.35 358	80.14 368	91.62 305	74.05 225	97.15 270	81.05 273	93.53 189	94.12 290

BH-w/o		87.57 240	87.05 225	89.12 298	94.90 177	77.90 304	92.41 295	93.51 305	82.89 273	83.70 317	91.34 310	75.75 199	97.07 278	75.49 340	93.49 191	92.39 368

PMMVS		85.71 303	84.96 301	87.95 333	88.90 403	77.09 325	88.68 387	90.06 396	72.32 417	86.47 231	90.76 335	72.15 255	94.40 385	81.78 263	93.49 191	92.36 369

PatchMatch-RL		86.77 276	85.54 285	90.47 243	95.88 124	82.71 162	90.54 348	92.31 336	79.82 334	84.32 302	91.57 309	68.77 306	96.39 324	73.16 362	93.48 193	92.32 371

PLC	84.53 7	89.06 190	88.03 199	92.15 159	97.27 73	82.69 163	94.29 198	95.44 209	79.71 335	84.01 310	94.18 208	76.68 183	98.75 109	77.28 322	93.41 194	95.02 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		92.24 100	91.91 101	93.24 88	96.59 87	83.43 129	94.84 158	96.44 113	89.19 83	94.08 64	95.90 120	77.85 171	98.17 167	88.90 147	93.38 195	98.13 72

test-LLR		85.87 298	85.41 288	87.25 353	90.95 358	71.67 395	89.55 371	89.88 402	83.41 258	84.54 290	87.95 394	67.25 316	95.11 376	81.82 261	93.37 196	94.97 248

test-mter		84.54 329	83.64 327	87.25 353	90.95 358	71.67 395	89.55 371	89.88 402	79.17 341	84.54 290	87.95 394	55.56 405	95.11 376	81.82 261	93.37 196	94.97 248

myMVS_eth3d28		85.80 301	85.26 295	87.42 348	94.73 188	69.92 413	90.60 347	90.95 377	87.21 151	86.06 245	90.04 356	59.47 385	96.02 340	74.89 349	93.35 198	96.33 190

EPP-MVSNet		91.70 111	91.56 107	92.13 160	95.88 124	80.50 231	97.33 8	95.25 223	86.15 180	89.76 165	95.60 136	83.42 87	98.32 159	87.37 169	93.25 199	97.56 117

CDS-MVSNet		89.45 175	88.51 185	92.29 155	93.62 262	83.61 126	93.01 275	94.68 262	81.95 292	87.82 205	93.24 244	78.69 155	96.99 284	80.34 288	93.23 200	96.28 194
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewmambaseed2359dif		90.04 154	89.78 148	90.83 225	92.85 292	77.92 302	92.23 305	95.01 235	81.90 295	90.20 156	95.45 142	79.64 146	97.34 255	87.52 166	93.17 201	97.23 138

PAPM		86.68 279	85.39 289	90.53 234	93.05 283	79.33 270	89.79 367	94.77 258	78.82 349	81.95 345	93.24 244	76.81 179	97.30 257	66.94 403	93.16 202	94.95 255

mamba_0408		89.06 190	87.92 204	92.50 138	94.76 184	82.66 164	79.84 446	94.64 264	85.18 208	88.96 179	95.00 165	76.00 192	97.98 191	83.74 225	93.15 203	96.85 169

SSM_04072		88.57 207	87.92 204	90.51 237	94.76 184	82.66 164	79.84 446	94.64 264	85.18 208	88.96 179	95.00 165	76.00 192	92.03 417	83.74 225	93.15 203	96.85 169

SSM_0407		90.47 143	89.80 147	92.46 140	94.76 184	82.66 164	93.98 225	95.00 239	85.41 203	88.96 179	95.35 147	76.13 187	97.88 202	85.46 197	93.15 203	96.85 169

alignmvs		93.08 85	92.50 93	94.81 32	95.62 138	87.61 15	95.99 74	96.07 152	89.77 62	94.12 60	94.87 172	80.56 129	98.66 117	92.42 79	93.10 206	98.15 71

thisisatest0515		87.33 250	85.99 269	91.37 199	93.49 265	79.55 259	90.63 346	89.56 409	80.17 328	87.56 210	90.86 329	67.07 319	98.28 161	81.50 268	93.02 207	96.29 193

TAMVS		89.21 183	88.29 194	91.96 167	93.71 257	82.62 169	93.30 260	94.19 281	82.22 285	87.78 206	93.94 218	78.83 152	96.95 287	77.70 318	92.98 208	96.32 191

OMC-MVS		91.23 119	90.62 127	93.08 99	96.27 100	84.07 108	93.52 247	95.93 164	86.95 158	89.51 167	96.13 108	78.50 159	98.35 154	85.84 192	92.90 209	96.83 173

sasdasda		93.27 77	92.75 87	94.85 25	95.70 132	87.66 12	96.33 40	96.41 116	90.00 49	94.09 61	94.60 188	82.33 105	98.62 125	92.40 80	92.86 210	98.27 59

canonicalmvs		93.27 77	92.75 87	94.85 25	95.70 132	87.66 12	96.33 40	96.41 116	90.00 49	94.09 61	94.60 188	82.33 105	98.62 125	92.40 80	92.86 210	98.27 59

TESTMET0.1,1		83.74 341	82.85 341	86.42 374	89.96 390	71.21 400	89.55 371	87.88 417	77.41 367	83.37 326	87.31 402	56.71 401	93.65 401	80.62 284	92.85 212	94.40 281

MGCFI-Net		93.03 86	92.63 90	94.23 58	95.62 138	85.92 59	96.08 64	96.33 123	89.86 53	93.89 68	94.66 185	82.11 112	98.50 133	92.33 85	92.82 213	98.27 59

AstraMVS		90.69 133	90.30 131	91.84 179	93.81 248	79.85 254	94.76 164	92.39 332	88.96 93	91.01 144	95.87 124	70.69 271	97.94 197	92.49 76	92.70 214	97.73 106

icg_test_0407_2		89.15 184	88.97 171	89.68 283	93.72 253	77.75 313	88.26 394	95.34 218	85.53 198	88.34 192	94.49 193	77.69 172	93.99 393	84.75 206	92.65 215	97.28 129

IMVS_0407		89.85 164	89.51 155	90.88 224	93.72 253	77.75 313	93.07 273	95.34 218	85.53 198	88.34 192	94.49 193	77.69 172	97.60 221	84.75 206	92.65 215	97.28 129

IMVS_0404		87.60 238	86.84 231	89.89 268	93.72 253	77.75 313	88.56 389	95.34 218	85.53 198	79.98 372	94.49 193	66.54 330	94.64 382	84.75 206	92.65 215	97.28 129

IMVS_0403		89.97 157	89.64 151	90.96 222	93.72 253	77.75 313	93.00 276	95.34 218	85.53 198	88.77 184	94.49 193	78.49 160	97.84 203	84.75 206	92.65 215	97.28 129

thisisatest0530		88.67 201	87.61 211	91.86 176	94.87 178	80.07 243	94.63 172	89.90 401	84.00 241	88.46 189	93.78 227	66.88 322	98.46 139	83.30 230	92.65 215	97.06 150

UWE-MVS		83.69 342	83.09 335	85.48 384	93.06 282	65.27 433	90.92 340	86.14 425	79.90 332	86.26 240	90.72 338	57.17 400	95.81 353	71.03 377	92.62 220	95.35 237

VDD-MVS		90.74 130	89.92 144	93.20 90	96.27 100	83.02 150	95.73 96	93.86 295	88.42 112	92.53 104	96.84 75	62.09 360	98.64 122	90.95 121	92.62 220	97.93 90

test_yl		90.69 133	90.02 142	92.71 124	95.72 130	82.41 174	94.11 209	95.12 229	85.63 193	91.49 135	94.70 179	74.75 211	98.42 148	86.13 187	92.53 222	97.31 126

DCV-MVSNet		90.69 133	90.02 142	92.71 124	95.72 130	82.41 174	94.11 209	95.12 229	85.63 193	91.49 135	94.70 179	74.75 211	98.42 148	86.13 187	92.53 222	97.31 126

VDDNet		89.56 171	88.49 188	92.76 120	95.07 163	82.09 179	96.30 42	93.19 312	81.05 321	91.88 122	96.86 74	61.16 376	98.33 157	88.43 153	92.49 224	97.84 98

DP-MVS		87.25 254	85.36 291	92.90 110	97.65 60	83.24 136	94.81 160	92.00 346	74.99 391	81.92 346	95.00 165	72.66 247	99.05 61	66.92 405	92.33 225	96.40 188

GG-mvs-BLEND				87.94 334	89.73 395	77.91 303	87.80 400	78.23 451		80.58 362	83.86 426	59.88 383	95.33 372	71.20 372	92.22 226	90.60 407

tttt0517		88.61 203	87.78 208	91.11 211	94.96 171	77.81 308	95.35 117	89.69 404	85.09 217	88.05 199	94.59 190	66.93 320	98.48 135	83.27 231	92.13 227	97.03 153

UBG		85.51 305	84.57 312	88.35 319	94.21 227	71.78 393	90.07 362	89.66 406	82.28 284	85.91 248	89.01 376	61.30 369	97.06 279	76.58 331	92.06 228	96.22 196

HyFIR lowres test		88.09 219	86.81 232	91.93 170	96.00 116	80.63 226	90.01 364	95.79 177	73.42 407	87.68 208	92.10 285	73.86 230	97.96 194	80.75 281	91.70 229	97.19 139

sss		88.93 195	88.26 196	90.94 223	94.05 234	80.78 223	91.71 320	95.38 213	81.55 310	88.63 186	93.91 222	75.04 208	95.47 369	82.47 244	91.61 230	96.57 184

testing222		84.84 323	83.32 330	89.43 292	94.15 231	75.94 342	91.09 337	89.41 411	84.90 221	85.78 250	89.44 370	52.70 420	96.28 331	70.80 378	91.57 231	96.07 207

cascas		86.43 290	84.98 300	90.80 228	92.10 313	80.92 219	90.24 355	95.91 167	73.10 410	83.57 322	88.39 387	65.15 340	97.46 236	84.90 204	91.43 232	94.03 297

ETVMVS		84.43 330	82.92 339	88.97 304	94.37 217	74.67 356	91.23 334	88.35 415	83.37 260	86.06 245	89.04 375	55.38 407	95.67 360	67.12 401	91.34 233	96.58 183

Effi-MVS+-dtu		88.65 202	88.35 190	89.54 287	93.33 270	76.39 337	94.47 183	94.36 274	87.70 139	85.43 267	89.56 369	73.45 236	97.26 263	85.57 195	91.28 234	94.97 248

thres100view900		87.63 234	86.71 236	90.38 247	96.12 106	78.55 284	95.03 145	91.58 359	87.15 152	88.06 198	92.29 276	68.91 304	98.10 172	70.13 383	91.10 235	94.48 278

tfpn200view9		87.58 239	86.64 240	90.41 244	95.99 119	78.64 281	94.58 174	91.98 348	86.94 159	88.09 195	91.77 297	69.18 300	98.10 172	70.13 383	91.10 235	94.48 278

thres600view7		87.65 231	86.67 239	90.59 231	96.08 112	78.72 278	94.88 153	91.58 359	87.06 155	88.08 197	92.30 275	68.91 304	98.10 172	70.05 386	91.10 235	94.96 251

thres400		87.62 236	86.64 240	90.57 232	95.99 119	78.64 281	94.58 174	91.98 348	86.94 159	88.09 195	91.77 297	69.18 300	98.10 172	70.13 383	91.10 235	94.96 251

testing11		86.44 289	85.35 292	89.69 280	94.29 223	75.40 351	91.30 330	90.53 386	84.76 227	85.06 279	90.13 353	58.95 393	97.45 237	82.08 254	91.09 239	96.21 198

F-COLMAP		87.95 222	86.80 233	91.40 197	96.35 99	80.88 220	94.73 166	95.45 207	79.65 336	82.04 344	94.61 187	71.13 263	98.50 133	76.24 335	91.05 240	94.80 261

thres200		87.21 258	86.24 259	90.12 256	95.36 147	78.53 285	93.26 263	92.10 342	86.42 173	88.00 200	91.11 322	69.24 299	98.00 188	69.58 387	91.04 241	93.83 308

WTY-MVS		89.60 169	88.92 174	91.67 187	95.47 145	81.15 208	92.38 297	94.78 257	83.11 266	89.06 177	94.32 200	78.67 156	96.61 306	81.57 267	90.89 242	97.24 135

testing91		87.11 263	86.18 260	89.92 267	94.43 214	75.38 352	91.53 325	92.27 338	86.48 170	86.50 230	90.24 347	61.19 374	97.53 227	82.10 253	90.88 243	96.84 172

testing3-2		86.72 277	86.71 236	86.74 369	96.11 109	65.92 428	93.39 253	89.65 407	89.46 70	87.84 203	92.79 261	59.17 390	97.60 221	81.31 270	90.72 244	96.70 178

testing99		86.72 277	85.73 284	89.69 280	94.23 225	74.91 355	91.35 329	90.97 376	86.14 181	86.36 236	90.22 348	59.41 387	97.48 232	82.24 250	90.66 245	96.69 179

HY-MVS	83.01 12	89.03 192	87.94 203	92.29 155	94.86 179	82.77 156	92.08 312	94.49 267	81.52 311	86.93 219	92.79 261	78.32 163	98.23 163	79.93 293	90.55 246	95.88 216

WB-MVSnew		83.77 340	83.28 331	85.26 389	91.48 334	71.03 402	91.89 314	87.98 416	78.91 344	84.78 284	90.22 348	69.11 302	94.02 392	64.70 415	90.44 247	90.71 403

CLD-MVS		89.47 174	88.90 176	91.18 207	94.22 226	82.07 180	92.13 309	96.09 150	87.90 130	85.37 273	92.45 270	74.38 218	97.56 225	87.15 172	90.43 248	93.93 299
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CVMVSNet		84.69 327	84.79 306	84.37 396	91.84 322	64.92 434	93.70 242	91.47 364	66.19 436	86.16 243	95.28 151	67.18 318	93.33 404	80.89 279	90.42 249	94.88 257

SCA		86.32 292	85.18 296	89.73 278	92.15 309	76.60 333	91.12 336	91.69 355	83.53 255	85.50 261	88.81 380	66.79 323	96.48 317	76.65 328	90.35 250	96.12 203

UWE-MVS-28		78.98 389	78.38 383	80.80 414	88.18 414	60.66 444	90.65 345	78.51 448	78.84 348	77.93 392	90.93 328	59.08 391	89.02 438	50.96 443	90.33 251	92.72 357

Fast-Effi-MVS+-dtu		87.44 245	86.72 235	89.63 284	92.04 314	77.68 318	94.03 219	93.94 290	85.81 187	82.42 337	91.32 313	70.33 279	97.06 279	80.33 289	90.23 252	94.14 289

OPM-MVS		90.12 149	89.56 154	91.82 180	93.14 276	83.90 114	94.16 205	95.74 181	88.96 93	87.86 201	95.43 145	72.48 251	97.91 200	88.10 158	90.18 253	93.65 321
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SD_0403		84.71 326	84.65 308	84.92 392	92.95 289	65.95 427	92.07 313	93.23 310	83.82 247	79.03 382	93.73 231	73.90 228	92.91 411	63.02 422	90.05 254	95.89 215

HQP_MVS		90.60 140	90.19 133	91.82 180	94.70 193	82.73 160	95.85 86	96.22 138	90.81 25	86.91 221	94.86 173	74.23 220	98.12 170	88.15 154	89.99 255	94.63 264

plane_prior5								96.22 138					98.12 170	88.15 154	89.99 255	94.63 264

XVG-OURS		89.40 179	88.70 180	91.52 191	94.06 233	81.46 197	91.27 332	96.07 152	86.14 181	88.89 182	95.77 130	68.73 307	97.26 263	87.39 168	89.96 257	95.83 219

baseline2		86.50 286	85.39 289	89.84 271	91.12 351	76.70 332	91.88 315	88.58 413	82.35 283	79.95 373	90.95 327	73.42 238	97.63 219	80.27 290	89.95 258	95.19 241

Anonymous202405211		87.68 229	86.13 262	92.31 153	96.66 84	80.74 224	94.87 154	91.49 363	80.47 326	89.46 170	95.44 143	54.72 412	98.23 163	82.19 251	89.89 259	97.97 86

plane_prior						82.73 160	95.21 133		89.66 66						89.88 260

SDMVSNet		90.19 148	89.61 153	91.93 170	96.00 116	83.09 146	92.89 282	95.98 159	88.73 100	86.85 225	95.20 158	72.09 256	97.08 276	88.90 147	89.85 261	95.63 228

sd_testset		88.59 205	87.85 207	90.83 225	96.00 116	80.42 233	92.35 299	94.71 260	88.73 100	86.85 225	95.20 158	67.31 314	96.43 322	79.64 297	89.85 261	95.63 228

TR-MVS		86.78 273	85.76 281	89.82 272	94.37 217	78.41 289	92.47 294	92.83 321	81.11 320	86.36 236	92.40 271	68.73 307	97.48 232	73.75 360	89.85 261	93.57 323

HQP3-MVS								96.04 155							89.77 264

HQP-MVS		89.80 165	89.28 164	91.34 200	94.17 228	81.56 191	94.39 190	96.04 155	88.81 96	85.43 267	93.97 217	73.83 231	97.96 194	87.11 174	89.77 264	94.50 275

XVG-OURS-SEG-HR		89.95 159	89.45 156	91.47 195	94.00 239	81.21 206	91.87 316	96.06 154	85.78 188	88.55 187	95.73 132	74.67 215	97.27 261	88.71 150	89.64 266	95.91 213

GA-MVS		86.61 280	85.27 294	90.66 230	91.33 343	78.71 280	90.40 350	93.81 298	85.34 206	85.12 277	89.57 368	61.25 371	97.11 275	80.99 277	89.59 267	96.15 200

1112_ss		88.42 208	87.33 218	91.72 185	94.92 174	80.98 215	92.97 279	94.54 266	78.16 363	83.82 313	93.88 223	78.78 154	97.91 200	79.45 299	89.41 268	96.26 195

ab-mvs		89.41 177	88.35 190	92.60 131	95.15 161	82.65 168	92.20 307	95.60 194	83.97 242	88.55 187	93.70 232	74.16 224	98.21 166	82.46 245	89.37 269	96.94 162

CR-MVSNet		85.35 310	83.76 325	90.12 256	90.58 376	79.34 267	85.24 424	91.96 350	78.27 360	85.55 256	87.87 397	71.03 265	95.61 361	73.96 357	89.36 270	95.40 234

RPMNet		83.95 337	81.53 348	91.21 205	90.58 376	79.34 267	85.24 424	96.76 87	71.44 421	85.55 256	82.97 433	70.87 268	98.91 90	61.01 427	89.36 270	95.40 234

DSMNet-mixed		76.94 397	76.29 396	78.89 418	83.10 439	56.11 454	87.78 402	79.77 445	60.65 444	75.64 410	88.71 383	61.56 367	88.34 440	60.07 430	89.29 272	92.21 374

LPG-MVS_test		89.45 175	88.90 176	91.12 208	94.47 209	81.49 195	95.30 121	96.14 143	86.73 165	85.45 264	95.16 160	69.89 285	98.10 172	87.70 162	89.23 273	93.77 314

LGP-MVS_train				91.12 208	94.47 209	81.49 195		96.14 143	86.73 165	85.45 264	95.16 160	69.89 285	98.10 172	87.70 162	89.23 273	93.77 314

Test_1112_low_res		87.65 231	86.51 248	91.08 212	94.94 173	79.28 271	91.77 318	94.30 276	76.04 381	83.51 323	92.37 272	77.86 170	97.73 212	78.69 308	89.13 275	96.22 196

PatchmatchNet		85.85 299	84.70 307	89.29 294	91.76 326	75.54 348	88.49 390	91.30 367	81.63 307	85.05 280	88.70 384	71.71 257	96.24 332	74.61 352	89.05 276	96.08 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.56 328		91.69 330	69.93 412	87.75 404	91.54 361	78.60 354	84.86 283	88.90 379	69.54 291	96.03 339	70.25 380	88.93 277

MIMVSNet		82.59 349	80.53 354	88.76 307	91.51 333	78.32 292	86.57 415	90.13 394	79.32 338	80.70 360	88.69 385	52.98 419	93.07 409	66.03 409	88.86 278	94.90 256

ACMM	84.12 9	89.14 185	88.48 189	91.12 208	94.65 196	81.22 205	95.31 119	96.12 147	85.31 207	85.92 247	94.34 198	70.19 281	98.06 184	85.65 193	88.86 278	94.08 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	84.23 8	89.01 194	88.35 190	90.99 219	94.73 188	81.27 202	95.07 142	95.89 170	86.48 170	83.67 318	94.30 201	69.33 294	97.99 189	87.10 176	88.55 280	93.72 319
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		89.03 192	88.64 181	90.21 251	90.74 371	79.28 271	95.96 77	95.90 168	84.66 231	85.33 275	92.94 254	74.02 226	97.30 257	89.64 138	88.53 281	94.05 296

jajsoiax		88.24 215	87.50 213	90.48 240	90.89 364	80.14 240	95.31 119	95.65 191	84.97 220	84.24 305	94.02 213	65.31 339	97.42 242	88.56 151	88.52 282	93.89 300

PatchT		82.68 348	81.27 350	86.89 365	90.09 387	70.94 405	84.06 431	90.15 393	74.91 392	85.63 255	83.57 428	69.37 293	94.87 381	65.19 411	88.50 283	94.84 258

MSDG		84.86 322	83.09 335	90.14 255	93.80 249	80.05 245	89.18 380	93.09 314	78.89 346	78.19 388	91.91 294	65.86 337	97.27 261	68.47 392	88.45 284	93.11 344

MVS-HIRNet		73.70 403	72.20 406	78.18 421	91.81 325	56.42 453	82.94 437	82.58 439	55.24 447	68.88 434	66.48 450	55.32 408	95.13 375	58.12 435	88.42 285	83.01 438

mvs_tets		88.06 221	87.28 220	90.38 247	90.94 360	79.88 252	95.22 130	95.66 189	85.10 216	84.21 306	93.94 218	63.53 351	97.40 250	88.50 152	88.40 286	93.87 304

ET-MVSNet_ETH3D		87.51 242	85.91 274	92.32 152	93.70 259	83.93 113	92.33 301	90.94 378	84.16 237	72.09 426	92.52 268	69.90 284	95.85 350	89.20 143	88.36 287	97.17 140

FIs		90.51 142	90.35 129	90.99 219	93.99 240	80.98 215	95.73 96	97.54 6	89.15 84	86.72 228	94.68 181	81.83 119	97.24 265	85.18 199	88.31 288	94.76 262

PS-MVSNAJss		89.97 157	89.62 152	91.02 216	91.90 320	80.85 221	95.26 127	95.98 159	86.26 177	86.21 241	94.29 202	79.70 141	97.65 216	88.87 149	88.10 289	94.57 269

CMPMVS	59.16 21	80.52 372	79.20 374	84.48 395	83.98 435	67.63 424	89.95 366	93.84 297	64.79 439	66.81 437	91.14 321	57.93 396	95.17 374	76.25 334	88.10 289	90.65 404
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FC-MVSNet-test		90.27 146	90.18 134	90.53 234	93.71 257	79.85 254	95.77 92	97.59 4	89.31 77	86.27 239	94.67 184	81.93 118	97.01 283	84.26 216	88.09 291	94.71 263

ACMMP++															88.01 292

D2MVS		85.90 297	85.09 298	88.35 319	90.79 367	77.42 321	91.83 317	95.70 185	80.77 323	80.08 370	90.02 357	66.74 325	96.37 325	81.88 260	87.97 293	91.26 394

UniMVSNet_ETH3D		87.53 241	86.37 252	91.00 218	92.44 303	78.96 276	94.74 165	95.61 193	84.07 240	85.36 274	94.52 192	59.78 384	97.34 255	82.93 235	87.88 294	96.71 177

PVSNet_BlendedMVS		89.98 156	89.70 149	90.82 227	96.12 106	81.25 203	93.92 229	96.83 78	83.49 256	89.10 175	92.26 277	81.04 126	98.85 97	86.72 179	87.86 295	92.35 370

Syy-MVS		80.07 378	79.78 364	80.94 413	91.92 318	59.93 445	89.75 369	87.40 422	81.72 303	78.82 384	87.20 404	66.29 332	91.29 425	47.06 446	87.84 296	91.60 384

myMVS_eth3d		79.67 383	78.79 380	82.32 410	91.92 318	64.08 436	89.75 369	87.40 422	81.72 303	78.82 384	87.20 404	45.33 439	91.29 425	59.09 433	87.84 296	91.60 384

anonymousdsp		87.84 224	87.09 223	90.12 256	89.13 400	80.54 230	94.67 170	95.55 197	82.05 288	83.82 313	92.12 282	71.47 261	97.15 270	87.15 172	87.80 298	92.67 358

testing3		80.46 373	79.59 369	83.06 404	93.44 268	64.64 435	93.33 255	85.47 430	84.34 236	79.93 374	90.84 331	44.35 441	92.39 414	57.06 438	87.56 299	92.16 375

Anonymous20240529		88.09 219	86.59 244	92.58 133	96.53 92	81.92 185	95.99 74	95.84 174	74.11 400	89.06 177	95.21 157	61.44 368	98.81 103	83.67 228	87.47 300	97.01 156

ACMMP++_ref															87.47 300

XVG-ACMP-BASELINE		86.00 295	84.84 305	89.45 291	91.20 345	78.00 300	91.70 321	95.55 197	85.05 218	82.97 331	92.25 278	54.49 413	97.48 232	82.93 235	87.45 302	92.89 352

EI-MVSNet		89.10 186	88.86 178	89.80 275	91.84 322	78.30 293	93.70 242	95.01 235	85.73 190	87.15 216	95.28 151	79.87 138	97.21 268	83.81 223	87.36 303	93.88 303

MVSTER		88.84 196	88.29 194	90.51 237	92.95 289	80.44 232	93.73 239	95.01 235	84.66 231	87.15 216	93.12 249	72.79 246	97.21 268	87.86 160	87.36 303	93.87 304

EG-PatchMatch MVS		82.37 351	80.34 357	88.46 316	90.27 383	79.35 266	92.80 286	94.33 275	77.14 371	73.26 423	90.18 351	47.47 432	96.72 296	70.25 380	87.32 305	89.30 418

EPMVS		83.90 339	82.70 343	87.51 343	90.23 385	72.67 381	88.62 388	81.96 441	81.37 313	85.01 281	88.34 388	66.31 331	94.45 383	75.30 343	87.12 306	95.43 233

tpm2		84.08 334	82.94 338	87.48 346	91.39 339	71.27 398	89.23 379	90.37 388	71.95 419	84.64 287	89.33 371	67.30 315	96.55 313	75.17 344	87.09 307	94.63 264

CostFormer		85.77 302	84.94 302	88.26 325	91.16 349	72.58 386	89.47 375	91.04 374	76.26 379	86.45 234	89.97 359	70.74 270	96.86 293	82.35 247	87.07 308	95.34 238

Patchmatch-test		81.37 364	79.30 371	87.58 342	90.92 362	74.16 364	80.99 441	87.68 420	70.52 425	76.63 402	88.81 380	71.21 262	92.76 412	60.01 431	86.93 309	95.83 219

mvsany_test1		85.42 308	85.30 293	85.77 382	87.95 417	75.41 350	87.61 408	80.97 443	76.82 373	88.68 185	95.83 126	77.44 175	90.82 429	85.90 190	86.51 310	91.08 401

test_fmvs2		83.98 335	84.03 320	83.83 401	87.16 420	67.53 425	93.93 228	92.89 319	77.62 365	86.89 224	93.53 234	47.18 433	92.02 419	90.54 128	86.51 310	91.93 378

LTVRE_ROB	82.13 13	86.26 293	84.90 303	90.34 249	94.44 213	81.50 193	92.31 303	94.89 247	83.03 268	79.63 378	92.67 263	69.69 288	97.79 205	71.20 372	86.26 312	91.72 381
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	83.96 336	82.04 345	89.74 276	95.28 151	79.75 256	94.25 200	92.28 337	75.17 389	78.02 391	93.77 228	58.60 394	97.84 203	65.06 414	85.92 313	91.63 383
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		85.07 316	84.27 314	87.48 346	92.91 291	70.62 408	91.69 322	92.46 330	76.20 380	82.67 335	95.22 154	63.94 349	97.29 260	77.51 321	85.80 314	94.53 271

USDC		82.76 346	81.26 351	87.26 352	91.17 347	74.55 358	89.27 377	93.39 307	78.26 361	75.30 412	92.08 286	54.43 414	96.63 302	71.64 369	85.79 315	90.61 405

dmvs_re		84.20 333	83.22 334	87.14 359	91.83 324	77.81 308	90.04 363	90.19 392	84.70 230	81.49 348	89.17 373	64.37 347	91.13 427	71.58 370	85.65 316	92.46 365

GBi-Net		87.26 252	85.98 270	91.08 212	94.01 236	83.10 143	95.14 139	94.94 241	83.57 252	84.37 297	91.64 301	66.59 327	96.34 328	78.23 313	85.36 317	93.79 309

test1		87.26 252	85.98 270	91.08 212	94.01 236	83.10 143	95.14 139	94.94 241	83.57 252	84.37 297	91.64 301	66.59 327	96.34 328	78.23 313	85.36 317	93.79 309

FMVSNet3		87.40 247	86.11 264	91.30 202	93.79 251	83.64 123	94.20 204	94.81 255	83.89 244	84.37 297	91.87 296	68.45 310	96.56 311	78.23 313	85.36 317	93.70 320

FMVSNet2		87.19 260	85.82 277	91.30 202	94.01 236	83.67 121	94.79 161	94.94 241	83.57 252	83.88 312	92.05 289	66.59 327	96.51 315	77.56 320	85.01 320	93.73 318

ACMH	80.38 17	85.36 309	83.68 326	90.39 245	94.45 212	80.63 226	94.73 166	94.85 251	82.09 287	77.24 396	92.65 264	60.01 382	97.58 223	72.25 367	84.87 321	92.96 349
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				88.24 326	91.88 321	77.05 326		92.92 318	85.54 196	80.13 369	93.30 241	57.29 399	96.20 333	72.46 366	84.71 322	91.49 388

JIA-IIPM		81.04 367	78.98 379	87.25 353	88.64 404	73.48 371	81.75 440	89.61 408	73.19 409	82.05 343	73.71 445	66.07 336	95.87 349	71.18 374	84.60 323	92.41 367

tt0805		86.92 268	85.74 283	90.48 240	92.22 307	79.98 250	95.63 106	94.88 249	83.83 246	84.74 286	92.80 260	57.61 398	97.67 213	85.48 196	84.42 324	93.79 309

OpenMVS_ROB	74.94 19	79.51 385	77.03 392	86.93 362	87.00 421	76.23 340	92.33 301	90.74 383	68.93 429	74.52 417	88.23 391	49.58 426	96.62 303	57.64 436	84.29 325	87.94 432

AllTest		83.42 343	81.39 349	89.52 288	95.01 165	77.79 310	93.12 267	90.89 380	77.41 367	76.12 405	93.34 237	54.08 415	97.51 229	68.31 394	84.27 326	93.26 334

TestCases				89.52 288	95.01 165	77.79 310		90.89 380	77.41 367	76.12 405	93.34 237	54.08 415	97.51 229	68.31 394	84.27 326	93.26 334

VortexMVS		88.42 208	88.01 200	89.63 284	93.89 244	78.82 277	93.82 234	95.47 203	86.67 167	84.53 292	91.99 291	72.62 249	96.65 301	89.02 146	84.09 328	93.41 331

tpm		84.73 324	84.02 321	86.87 366	90.33 382	68.90 416	89.06 382	89.94 399	80.85 322	85.75 251	89.86 362	68.54 309	95.97 343	77.76 317	84.05 329	95.75 222

WBMVS		84.97 320	84.18 316	87.34 349	94.14 232	71.62 397	90.20 358	92.35 333	81.61 308	84.06 307	90.76 335	61.82 363	96.52 314	78.93 306	83.81 330	93.89 300

FMVSNet1		85.85 299	84.11 319	91.08 212	92.81 293	83.10 143	95.14 139	94.94 241	81.64 306	82.68 334	91.64 301	59.01 392	96.34 328	75.37 342	83.78 331	93.79 309

ADS-MVSNet2		81.66 358	79.71 367	87.50 344	91.35 341	74.19 363	83.33 434	88.48 414	72.90 412	82.24 340	85.77 419	64.98 341	93.20 407	64.57 416	83.74 332	95.12 243

ADS-MVSNet		81.56 360	79.78 364	86.90 364	91.35 341	71.82 391	83.33 434	89.16 412	72.90 412	82.24 340	85.77 419	64.98 341	93.76 398	64.57 416	83.74 332	95.12 243

XXY-MVS		87.65 231	86.85 230	90.03 261	92.14 310	80.60 228	93.76 237	95.23 224	82.94 271	84.60 288	94.02 213	74.27 219	95.49 368	81.04 274	83.68 334	94.01 298

test_0402		81.30 366	79.17 375	87.67 340	93.19 273	78.17 296	92.98 278	91.71 353	75.25 388	76.02 408	90.31 346	59.23 388	96.37 325	50.22 444	83.63 335	88.47 429

tpmvs		83.35 345	82.07 344	87.20 357	91.07 353	71.00 404	88.31 393	91.70 354	78.91 344	80.49 364	87.18 406	69.30 297	97.08 276	68.12 397	83.56 336	93.51 327

pmmvs5		84.21 332	82.84 342	88.34 321	88.95 402	76.94 327	92.41 295	91.91 352	75.63 384	80.28 365	91.18 318	64.59 345	95.57 362	77.09 326	83.47 337	92.53 362

pmmvs4		85.43 307	83.86 324	90.16 253	90.02 389	82.97 153	90.27 351	92.67 327	75.93 382	80.73 359	91.74 299	71.05 264	95.73 358	78.85 307	83.46 338	91.78 380

test0.0.03 1		82.41 350	81.69 346	84.59 394	88.23 411	72.89 377	90.24 355	87.83 418	83.41 258	79.86 375	89.78 364	67.25 316	88.99 439	65.18 412	83.42 339	91.90 379

tpmrst		85.35 310	84.99 299	86.43 373	90.88 365	67.88 421	88.71 386	91.43 365	80.13 329	86.08 244	88.80 382	73.05 243	96.02 340	82.48 243	83.40 340	95.40 234

SSC-MVS3.2		84.60 328	84.19 315	85.85 381	92.74 296	68.07 418	88.15 396	93.81 298	87.42 146	83.76 315	91.07 324	62.91 356	95.73 358	74.56 353	83.24 341	93.75 316

nrg030		91.08 124	90.39 128	93.17 93	93.07 281	86.91 22	96.41 38	96.26 133	88.30 115	88.37 191	94.85 175	82.19 111	97.64 218	91.09 116	82.95 342	94.96 251

cl22		86.78 273	85.98 270	89.18 297	92.34 305	77.62 319	90.84 342	94.13 286	81.33 314	83.97 311	90.15 352	73.96 227	96.60 308	84.19 217	82.94 343	93.33 332

miper_ehance_all_eth		87.22 257	86.62 243	89.02 302	92.13 311	77.40 322	90.91 341	94.81 255	81.28 315	84.32 302	90.08 355	79.26 148	96.62 303	83.81 223	82.94 343	93.04 347

miper_enhance_ethall		86.90 269	86.18 260	89.06 300	91.66 331	77.58 320	90.22 357	94.82 254	79.16 342	84.48 293	89.10 374	79.19 150	96.66 300	84.06 218	82.94 343	92.94 350

ACMH+	81.04 14	85.05 317	83.46 329	89.82 272	94.66 195	79.37 265	94.44 185	94.12 287	82.19 286	78.04 390	92.82 258	58.23 395	97.54 226	73.77 359	82.90 346	92.54 361

VPA-MVSNet		89.62 168	88.96 172	91.60 189	93.86 245	82.89 155	95.46 113	97.33 28	87.91 129	88.43 190	93.31 240	74.17 223	97.40 250	87.32 170	82.86 347	94.52 272

IterMVS-LS		88.36 212	87.91 206	89.70 279	93.80 249	78.29 294	93.73 239	95.08 233	85.73 190	84.75 285	91.90 295	79.88 137	96.92 289	83.83 222	82.51 348	93.89 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MonoMVSNet		86.89 270	86.55 246	87.92 335	89.46 398	73.75 366	94.12 207	93.10 313	87.82 136	85.10 278	90.76 335	69.59 290	94.94 380	86.47 181	82.50 349	95.07 245

testgi		80.94 371	80.20 360	83.18 402	87.96 416	66.29 426	91.28 331	90.70 385	83.70 249	78.12 389	92.84 256	51.37 422	90.82 429	63.34 419	82.46 350	92.43 366

test_vis1_rt		77.96 394	76.46 394	82.48 408	85.89 427	71.74 394	90.25 353	78.89 447	71.03 424	71.30 430	81.35 437	42.49 443	91.05 428	84.55 213	82.37 351	84.65 435

WR-MVS		88.38 210	87.67 210	90.52 236	93.30 271	80.18 238	93.26 263	95.96 162	88.57 108	85.47 263	92.81 259	76.12 189	96.91 290	81.24 272	82.29 352	94.47 280

tpm cat1		81.96 352	80.27 358	87.01 360	91.09 352	71.02 403	87.38 409	91.53 362	66.25 435	80.17 366	86.35 415	68.22 312	96.15 336	69.16 388	82.29 352	93.86 306

v1192		87.25 254	86.33 254	90.00 265	90.76 370	79.04 275	93.80 235	95.48 202	82.57 278	85.48 262	91.18 318	73.38 240	97.42 242	82.30 248	82.06 354	93.53 324

v1144		87.61 237	86.79 234	90.06 260	91.01 355	79.34 267	93.95 226	95.42 212	83.36 261	85.66 254	91.31 314	74.98 209	97.42 242	83.37 229	82.06 354	93.42 330

v1240		86.78 273	85.85 276	89.56 286	90.45 381	77.79 310	93.61 244	95.37 215	81.65 305	85.43 267	91.15 320	71.50 260	97.43 241	81.47 269	82.05 356	93.47 328

Anonymous20231206		81.03 368	79.77 366	84.82 393	87.85 418	70.26 410	91.42 327	92.08 343	73.67 404	77.75 393	89.25 372	62.43 359	93.08 408	61.50 426	82.00 357	91.12 398

V42		87.68 229	86.86 229	90.15 254	90.58 376	80.14 240	94.24 202	95.28 222	83.66 250	85.67 253	91.33 311	74.73 213	97.41 248	84.43 215	81.83 358	92.89 352

v1921920		86.97 267	86.06 267	89.69 280	90.53 379	78.11 298	93.80 235	95.43 210	81.90 295	85.33 275	91.05 325	72.66 247	97.41 248	82.05 256	81.80 359	93.53 324

v2v482		87.84 224	87.06 224	90.17 252	90.99 356	79.23 274	94.00 223	95.13 228	84.87 223	85.53 258	92.07 288	74.45 217	97.45 237	84.71 211	81.75 360	93.85 307

Anonymous20231211		86.59 282	85.13 297	90.98 221	96.52 93	81.50 193	96.14 59	96.16 142	73.78 403	83.65 319	92.15 280	63.26 354	97.37 254	82.82 239	81.74 361	94.06 295

v144192		87.19 260	86.35 253	89.74 276	90.64 374	78.24 295	93.92 229	95.43 210	81.93 293	85.51 260	91.05 325	74.21 222	97.45 237	82.86 237	81.56 362	93.53 324

cl____		86.52 285	85.78 278	88.75 308	92.03 315	76.46 335	90.74 343	94.30 276	81.83 301	83.34 327	90.78 334	75.74 201	96.57 309	81.74 264	81.54 363	93.22 338

DIV-MVS_self_test		86.53 284	85.78 278	88.75 308	92.02 316	76.45 336	90.74 343	94.30 276	81.83 301	83.34 327	90.82 332	75.75 199	96.57 309	81.73 265	81.52 364	93.24 337

Anonymous20240521		80.44 374	79.21 373	84.11 399	85.75 429	67.89 420	92.86 284	93.23 310	75.61 385	75.59 411	87.47 401	50.03 424	94.33 387	71.14 375	81.21 365	90.12 411

OurMVSNet-221017-0		85.35 310	84.64 310	87.49 345	90.77 369	72.59 385	94.01 221	94.40 272	84.72 229	79.62 379	93.17 246	61.91 362	96.72 296	81.99 257	81.16 366	93.16 342

FMVSNet5		81.52 362	79.60 368	87.27 351	91.17 347	77.95 301	91.49 326	92.26 339	76.87 372	76.16 404	87.91 396	51.67 421	92.34 415	67.74 398	81.16 366	91.52 386

CP-MVSNet		87.63 234	87.26 222	88.74 310	93.12 277	76.59 334	95.29 123	96.58 104	88.43 111	83.49 324	92.98 253	75.28 205	95.83 351	78.97 305	81.15 368	93.79 309

c3_l		87.14 262	86.50 249	89.04 301	92.20 308	77.26 323	91.22 335	94.70 261	82.01 291	84.34 301	90.43 344	78.81 153	96.61 306	83.70 227	81.09 369	93.25 336

IterMVS-SCA-FT		85.45 306	84.53 313	88.18 328	91.71 328	76.87 328	90.19 359	92.65 328	85.40 205	81.44 350	90.54 340	66.79 323	95.00 379	81.04 274	81.05 370	92.66 359

TinyColmap		79.76 382	77.69 385	85.97 377	91.71 328	73.12 374	89.55 371	90.36 389	75.03 390	72.03 427	90.19 350	46.22 438	96.19 335	63.11 420	81.03 371	88.59 428

UniMVSNet_NR-MVSNet		89.92 161	89.29 163	91.81 182	93.39 269	83.72 119	94.43 186	97.12 50	89.80 58	86.46 232	93.32 239	83.16 91	97.23 266	84.92 202	81.02 372	94.49 277

DU-MVS		89.34 182	88.50 186	91.85 178	93.04 284	83.72 119	94.47 183	96.59 103	89.50 69	86.46 232	93.29 242	77.25 176	97.23 266	84.92 202	81.02 372	94.59 267

PS-CasMVS		87.32 251	86.88 228	88.63 313	92.99 287	76.33 339	95.33 118	96.61 102	88.22 119	83.30 329	93.07 251	73.03 244	95.79 355	78.36 310	81.00 374	93.75 316

IterMVS		84.88 321	83.98 323	87.60 341	91.44 335	76.03 341	90.18 360	92.41 331	83.24 264	81.06 356	90.42 345	66.60 326	94.28 389	79.46 298	80.98 375	92.48 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		89.80 165	89.07 169	92.01 161	93.60 263	84.52 92	94.78 162	97.47 13	89.26 80	86.44 235	92.32 274	82.10 113	97.39 253	84.81 205	80.84 376	94.12 290

LF4IMVS		80.37 375	79.07 378	84.27 398	86.64 422	69.87 414	89.39 376	91.05 373	76.38 376	74.97 414	90.00 358	47.85 431	94.25 390	74.55 354	80.82 377	88.69 427

v10		87.25 254	86.38 251	89.85 270	91.19 346	79.50 260	94.48 180	95.45 207	83.79 248	83.62 320	91.19 316	75.13 206	97.42 242	81.94 258	80.60 378	92.63 360

tfpnnormal		84.72 325	83.23 333	89.20 296	92.79 294	80.05 245	94.48 180	95.81 175	82.38 281	81.08 355	91.21 315	69.01 303	96.95 287	61.69 425	80.59 379	90.58 408

WR-MVS_H		87.80 226	87.37 217	89.10 299	93.23 272	78.12 297	95.61 107	97.30 32	87.90 130	83.72 316	92.01 290	79.65 145	96.01 342	76.36 332	80.54 380	93.16 342

VPNet		88.20 216	87.47 215	90.39 245	93.56 264	79.46 262	94.04 218	95.54 199	88.67 103	86.96 218	94.58 191	69.33 294	97.15 270	84.05 219	80.53 381	94.56 270

v7n		86.81 271	85.76 281	89.95 266	90.72 372	79.25 273	95.07 142	95.92 165	84.45 234	82.29 338	90.86 329	72.60 250	97.53 227	79.42 302	80.52 382	93.08 346

v8		87.50 244	86.71 236	89.89 268	91.37 340	79.40 264	94.50 179	95.38 213	84.81 226	83.60 321	91.33 311	76.05 190	97.42 242	82.84 238	80.51 383	92.84 354

EU-MVSNet		81.32 365	80.95 352	82.42 409	88.50 407	63.67 438	93.32 256	91.33 366	64.02 440	80.57 363	92.83 257	61.21 373	92.27 416	76.34 333	80.38 384	91.32 392

Patchmtry		82.71 347	80.93 353	88.06 330	90.05 388	76.37 338	84.74 429	91.96 350	72.28 418	81.32 353	87.87 397	71.03 265	95.50 367	68.97 389	80.15 385	92.32 371

NR-MVSNet		88.58 206	87.47 215	91.93 170	93.04 284	84.16 107	94.77 163	96.25 135	89.05 87	80.04 371	93.29 242	79.02 151	97.05 281	81.71 266	80.05 386	94.59 267

Baseline_NR-MVSNet		87.07 264	86.63 242	88.40 317	91.44 335	77.87 306	94.23 203	92.57 329	84.12 239	85.74 252	92.08 286	77.25 176	96.04 338	82.29 249	79.94 387	91.30 393

dp		81.47 363	80.23 359	85.17 390	89.92 391	65.49 431	86.74 413	90.10 395	76.30 378	81.10 354	87.12 407	62.81 357	95.92 346	68.13 396	79.88 388	94.09 293

TranMVSNet+NR-MVSNet		88.84 196	87.95 202	91.49 193	92.68 298	83.01 151	94.92 151	96.31 124	89.88 52	85.53 258	93.85 225	76.63 184	96.96 286	81.91 259	79.87 389	94.50 275

miper_lstm_enhance		85.27 313	84.59 311	87.31 350	91.28 344	74.63 357	87.69 405	94.09 288	81.20 319	81.36 352	89.85 363	74.97 210	94.30 388	81.03 276	79.84 390	93.01 348

reproduce_monomvs		86.37 291	85.87 275	87.87 336	93.66 261	73.71 367	93.44 251	95.02 234	88.61 106	82.64 336	91.94 293	57.88 397	96.68 299	89.96 134	79.71 391	93.22 338

v148		87.04 265	86.32 255	89.21 295	90.94 360	77.26 323	93.71 241	94.43 269	84.84 225	84.36 300	90.80 333	76.04 191	97.05 281	82.12 252	79.60 392	93.31 333

IB-MVS	80.51 15	85.24 314	83.26 332	91.19 206	92.13 311	79.86 253	91.75 319	91.29 368	83.28 263	80.66 361	88.49 386	61.28 370	98.46 139	80.99 277	79.46 393	95.25 240
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		86.50 286	85.77 280	88.68 311	91.94 317	75.81 345	90.47 349	94.89 247	82.05 288	84.05 308	90.46 343	75.96 194	96.77 294	82.76 241	79.36 394	93.46 329

baseline1		88.10 218	87.28 220	90.57 232	94.96 171	80.07 243	94.27 199	91.29 368	86.74 164	87.41 212	94.00 215	76.77 181	96.20 333	80.77 280	79.31 395	95.44 232

our_test_3		81.93 353	80.46 356	86.33 375	88.46 408	73.48 371	88.46 391	91.11 370	76.46 374	76.69 401	88.25 390	66.89 321	94.36 386	68.75 390	79.08 396	91.14 397

PEN-MVS		86.80 272	86.27 258	88.40 317	92.32 306	75.71 347	95.18 136	96.38 119	87.97 127	82.82 333	93.15 247	73.39 239	95.92 346	76.15 336	79.03 397	93.59 322

pm-mvs1		86.61 280	85.54 285	89.82 272	91.44 335	80.18 238	95.28 125	94.85 251	83.84 245	81.66 347	92.62 265	72.45 253	96.48 317	79.67 296	78.06 398	92.82 355

h-mvs33		90.80 128	90.15 135	92.75 122	96.01 115	82.66 164	95.43 115	95.53 200	89.80 58	93.08 83	95.64 135	75.77 196	99.00 74	92.07 94	78.05 399	96.60 181

SixPastTwentyTwo		83.91 338	82.90 340	86.92 363	90.99 356	70.67 407	93.48 248	91.99 347	85.54 196	77.62 395	92.11 284	60.59 378	96.87 292	76.05 337	77.75 400	93.20 340

ppachtmachnet_test		81.84 354	80.07 362	87.15 358	88.46 408	74.43 361	89.04 383	92.16 341	75.33 387	77.75 393	88.99 377	66.20 333	95.37 371	65.12 413	77.60 401	91.65 382

MIMVSNet1		79.38 386	77.28 388	85.69 383	86.35 423	73.67 368	91.61 324	92.75 325	78.11 364	72.64 425	88.12 392	48.16 430	91.97 421	60.32 428	77.49 402	91.43 391

DTE-MVSNet		86.11 294	85.48 287	87.98 332	91.65 332	74.92 354	94.93 150	95.75 180	87.36 147	82.26 339	93.04 252	72.85 245	95.82 352	74.04 355	77.46 403	93.20 340

N_pmnet		68.89 409	68.44 411	70.23 429	89.07 401	28.79 468	88.06 397	19.50 468	69.47 428	71.86 428	84.93 422	61.24 372	91.75 422	54.70 440	77.15 404	90.15 410

AUN-MVS		87.78 227	86.54 247	91.48 194	94.82 182	81.05 213	93.91 231	93.93 291	83.00 269	86.93 219	93.53 234	69.50 292	97.67 213	86.14 185	77.12 405	95.73 225

hse-mvs2		89.88 163	89.34 161	91.51 192	94.83 181	81.12 210	93.94 227	93.91 294	89.80 58	93.08 83	93.60 233	75.77 196	97.66 215	92.07 94	77.07 406	95.74 223

dmvs_testset		74.57 402	75.81 400	70.86 428	87.72 419	40.47 463	87.05 412	77.90 453	82.75 275	71.15 431	85.47 421	67.98 313	84.12 450	45.26 447	76.98 407	88.00 431

test20.03		79.95 380	79.08 377	82.55 406	85.79 428	67.74 423	91.09 337	91.08 371	81.23 318	74.48 418	89.96 360	61.63 364	90.15 431	60.08 429	76.38 408	89.76 413

FPMVS		64.63 414	62.55 416	70.88 427	70.80 456	56.71 449	84.42 430	84.42 434	51.78 450	49.57 450	81.61 436	23.49 455	81.48 453	40.61 453	76.25 409	74.46 446

test_fmvs3		77.67 395	77.16 391	79.22 417	79.52 447	61.14 442	92.34 300	91.64 358	73.98 401	78.86 383	86.59 410	27.38 453	87.03 441	88.12 157	75.97 410	89.50 415

EGC-MVSNET		61.97 415	56.37 420	78.77 419	89.63 396	73.50 370	89.12 381	82.79 438	0.21 465	1.24 466	84.80 423	39.48 444	90.04 432	44.13 448	75.94 411	72.79 447

pmmvs6		83.42 343	81.60 347	88.87 305	88.01 415	77.87 306	94.96 148	94.24 280	74.67 395	78.80 386	91.09 323	60.17 381	96.49 316	77.06 327	75.40 412	92.23 373

new_pmnet		72.15 405	70.13 408	78.20 420	82.95 440	65.68 429	83.91 432	82.40 440	62.94 442	64.47 439	79.82 439	42.85 442	86.26 445	57.41 437	74.44 413	82.65 440

MDA-MVSNet_test_wron		79.21 388	77.19 390	85.29 387	88.22 412	72.77 379	85.87 418	90.06 396	74.34 397	62.62 442	87.56 400	66.14 334	91.99 420	66.90 406	73.01 414	91.10 400

YYNet1		79.22 387	77.20 389	85.28 388	88.20 413	72.66 382	85.87 418	90.05 398	74.33 398	62.70 440	87.61 399	66.09 335	92.03 417	66.94 403	72.97 415	91.15 396

Patchmatch-RL test		81.67 357	79.96 363	86.81 367	85.42 431	71.23 399	82.17 439	87.50 421	78.47 355	77.19 397	82.50 435	70.81 269	93.48 402	82.66 242	72.89 416	95.71 226

pmmvs-eth3d		80.97 370	78.72 381	87.74 337	84.99 433	79.97 251	90.11 361	91.65 357	75.36 386	73.51 421	86.03 416	59.45 386	93.96 396	75.17 344	72.21 417	89.29 420

PM-MVS		78.11 393	76.12 397	84.09 400	83.54 437	70.08 411	88.97 384	85.27 432	79.93 331	74.73 416	86.43 412	34.70 449	93.48 402	79.43 301	72.06 418	88.72 426

test_f		71.95 406	70.87 407	75.21 424	74.21 454	59.37 447	85.07 426	85.82 427	65.25 438	70.42 432	83.13 430	23.62 454	82.93 452	78.32 311	71.94 419	83.33 437

sc_t1		81.53 361	78.67 382	90.12 256	90.78 368	78.64 281	93.91 231	90.20 391	68.42 430	80.82 358	89.88 361	46.48 435	96.76 295	76.03 338	71.47 420	94.96 251

tt0320		80.13 377	77.41 386	88.29 323	90.50 380	78.02 299	93.10 270	90.71 384	66.06 437	76.75 400	86.97 409	49.56 427	95.40 370	71.65 368	71.41 421	91.46 390

Gipuma		57.99 421	54.91 423	67.24 435	88.51 405	65.59 430	52.21 456	90.33 390	43.58 453	42.84 456	51.18 457	20.29 459	85.07 447	34.77 454	70.45 422	51.05 456
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD_test1		69.04 408	66.26 414	77.36 423	80.51 445	62.79 441	85.46 423	83.51 437	54.11 449	59.14 446	84.79 424	23.40 456	89.61 434	55.22 439	70.24 423	79.68 444

K. test v3		81.59 359	80.15 361	85.91 380	89.89 392	69.42 415	92.57 291	87.71 419	85.56 195	73.44 422	89.71 366	55.58 404	95.52 364	77.17 324	69.76 424	92.78 356

KD-MVS_self_test		80.20 376	79.24 372	83.07 403	85.64 430	65.29 432	91.01 339	93.93 291	78.71 353	76.32 403	86.40 414	59.20 389	92.93 410	72.59 365	69.35 425	91.00 402

CL-MVSNet_self_test		81.74 356	80.53 354	85.36 386	85.96 426	72.45 387	90.25 353	93.07 315	81.24 317	79.85 376	87.29 403	70.93 267	92.52 413	66.95 402	69.23 426	91.11 399

TDRefinement		79.81 381	77.34 387	87.22 356	79.24 448	75.48 349	93.12 267	92.03 345	76.45 375	75.01 413	91.58 307	49.19 428	96.44 321	70.22 382	69.18 427	89.75 414

MDA-MVSNet-bldmvs		78.85 390	76.31 395	86.46 371	89.76 393	73.88 365	88.79 385	90.42 387	79.16 342	59.18 445	88.33 389	60.20 380	94.04 391	62.00 424	68.96 428	91.48 389

ambc				83.06 404	79.99 446	63.51 439	77.47 449	92.86 320		74.34 419	84.45 425	28.74 450	95.06 378	73.06 363	68.89 429	90.61 405

TransMVSNet (Re)		84.43 330	83.06 337	88.54 314	91.72 327	78.44 288	95.18 136	92.82 323	82.73 276	79.67 377	92.12 282	73.49 235	95.96 344	71.10 376	68.73 430	91.21 395

tt0320-xc		79.63 384	76.66 393	88.52 315	91.03 354	78.72 278	93.00 276	89.53 410	66.37 434	76.11 407	87.11 408	46.36 437	95.32 373	72.78 364	67.67 431	91.51 387

mvsany_test3		74.95 401	73.26 405	80.02 416	74.61 452	63.16 440	85.53 422	78.42 449	74.16 399	74.89 415	86.46 411	36.02 448	89.09 437	82.39 246	66.91 432	87.82 433

mvs5depth		80.98 369	79.15 376	86.45 372	84.57 434	73.29 373	87.79 401	91.67 356	80.52 325	82.20 342	89.72 365	55.14 410	95.93 345	73.93 358	66.83 433	90.12 411

PMVS	47.18 22	52.22 423	48.46 427	63.48 436	45.72 467	46.20 459	73.41 452	78.31 450	41.03 456	30.06 459	65.68 451	6.05 466	83.43 451	30.04 456	65.86 434	60.80 451
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt		65.12 413	62.60 415	72.69 426	71.44 455	60.71 443	87.17 410	65.55 459	63.80 441	53.22 449	65.65 452	14.54 463	89.44 436	76.65 328	65.38 435	67.91 450

lessismore_v0				86.04 376	88.46 408	68.78 417		80.59 444		73.01 424	90.11 354	55.39 406	96.43 322	75.06 346	65.06 436	92.90 351

new-patchmatchnet		76.41 399	75.17 401	80.13 415	82.65 441	59.61 446	87.66 406	91.08 371	78.23 362	69.85 433	83.22 429	54.76 411	91.63 424	64.14 418	64.89 437	89.16 422

pmmvs3		71.81 407	68.71 410	81.11 412	75.86 451	70.42 409	86.74 413	83.66 436	58.95 446	68.64 436	80.89 438	36.93 447	89.52 435	63.10 421	63.59 438	83.39 436

UnsupCasMVSNet_eth		80.07 378	78.27 384	85.46 385	85.24 432	72.63 384	88.45 392	94.87 250	82.99 270	71.64 429	88.07 393	56.34 402	91.75 422	73.48 361	63.36 439	92.01 377

ttmdpeth		76.55 398	74.64 403	82.29 411	82.25 442	67.81 422	89.76 368	85.69 428	70.35 426	75.76 409	91.69 300	46.88 434	89.77 433	66.16 408	63.23 440	89.30 418

mmtdpeth		85.04 319	84.15 318	87.72 339	93.11 278	75.74 346	94.37 194	92.83 321	84.98 219	89.31 172	86.41 413	61.61 366	97.14 273	92.63 75	62.11 441	90.29 409

LCM-MVSNet		66.00 412	62.16 417	77.51 422	64.51 462	58.29 448	83.87 433	90.90 379	48.17 451	54.69 448	73.31 446	16.83 462	86.75 442	65.47 410	61.67 442	87.48 434

UnsupCasMVSNet_bld		76.23 400	73.27 404	85.09 391	83.79 436	72.92 376	85.65 421	93.47 306	71.52 420	68.84 435	79.08 440	49.77 425	93.21 406	66.81 407	60.52 443	89.13 424

testf1		59.54 417	56.11 421	69.85 430	69.28 457	56.61 451	80.37 443	76.55 456	42.58 454	45.68 453	75.61 441	11.26 464	84.18 448	43.20 450	60.44 444	68.75 448

APD_test2		59.54 417	56.11 421	69.85 430	69.28 457	56.61 451	80.37 443	76.55 456	42.58 454	45.68 453	75.61 441	11.26 464	84.18 448	43.20 450	60.44 444	68.75 448

KD-MVS_2432*1600		78.50 391	76.02 398	85.93 378	86.22 424	74.47 359	84.80 427	92.33 334	79.29 339	76.98 398	85.92 417	53.81 417	93.97 394	67.39 399	57.42 446	89.36 416

miper_refine_blended		78.50 391	76.02 398	85.93 378	86.22 424	74.47 359	84.80 427	92.33 334	79.29 339	76.98 398	85.92 417	53.81 417	93.97 394	67.39 399	57.42 446	89.36 416

MVStest1		72.91 404	69.70 409	82.54 407	78.14 449	73.05 375	88.21 395	86.21 424	60.69 443	64.70 438	90.53 341	46.44 436	85.70 446	58.78 434	53.62 448	88.87 425

DeepMVS_CX				56.31 440	74.23 453	51.81 456		56.67 464	44.85 452	48.54 452	75.16 443	27.87 452	58.74 462	40.92 452	52.22 449	58.39 454

WB-MVS		67.92 410	67.49 412	69.21 432	81.09 443	41.17 462	88.03 398	78.00 452	73.50 406	62.63 441	83.11 432	63.94 349	86.52 443	25.66 458	51.45 450	79.94 443

PVSNet_0	73.20 20	77.22 396	74.83 402	84.37 396	90.70 373	71.10 401	83.09 436	89.67 405	72.81 414	73.93 420	83.13 430	60.79 377	93.70 400	68.54 391	50.84 451	88.30 430

test_method		50.52 424	48.47 426	56.66 439	52.26 466	18.98 470	41.51 458	81.40 442	10.10 460	44.59 455	75.01 444	28.51 451	68.16 457	53.54 441	49.31 452	82.83 439

SSC-MVS		67.06 411	66.56 413	68.56 434	80.54 444	40.06 464	87.77 403	77.37 455	72.38 416	61.75 443	82.66 434	63.37 352	86.45 444	24.48 459	48.69 453	79.16 445

PMMVS2		59.60 416	56.40 419	69.21 432	68.83 459	46.58 458	73.02 453	77.48 454	55.07 448	49.21 451	72.95 447	17.43 461	80.04 454	49.32 445	44.33 454	80.99 442

dongtai		58.82 420	58.24 418	60.56 437	83.13 438	45.09 461	82.32 438	48.22 467	67.61 432	61.70 444	69.15 448	38.75 445	76.05 456	32.01 455	41.31 455	60.55 452

kuosan		53.51 422	53.30 425	54.13 441	76.06 450	45.36 460	80.11 445	48.36 466	59.63 445	54.84 447	63.43 454	37.41 446	62.07 461	20.73 461	39.10 456	54.96 455

MVE	39.65 23	43.39 425	38.59 431	57.77 438	56.52 464	48.77 457	55.38 455	58.64 463	29.33 459	28.96 460	52.65 456	4.68 467	64.62 460	28.11 457	33.07 457	59.93 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 426	42.29 428	46.03 442	65.58 461	37.41 465	73.51 451	64.62 460	33.99 457	28.47 461	47.87 458	19.90 460	67.91 458	22.23 460	24.45 458	32.77 457

ANet_high		58.88 419	54.22 424	72.86 425	56.50 465	56.67 450	80.75 442	86.00 426	73.09 411	37.39 457	64.63 453	22.17 457	79.49 455	43.51 449	23.96 459	82.43 441

EMVS		42.07 427	41.12 429	44.92 443	63.45 463	35.56 467	73.65 450	63.48 461	33.05 458	26.88 462	45.45 459	21.27 458	67.14 459	19.80 462	23.02 460	32.06 458

tmp_tt		35.64 428	39.24 430	24.84 444	14.87 468	23.90 469	62.71 454	51.51 465	6.58 462	36.66 458	62.08 455	44.37 440	30.34 464	52.40 442	22.00 461	20.27 459

wuyk23d		21.27 430	20.48 433	23.63 445	68.59 460	36.41 466	49.57 457	6.85 469	9.37 461	7.89 463	4.46 465	4.03 468	31.37 463	17.47 463	16.07 462	3.12 460

testmvs		8.92 431	11.52 434	1.12 447	1.06 469	0.46 472	86.02 417	0.65 470	0.62 463	2.74 464	9.52 463	0.31 470	0.45 466	2.38 464	0.39 463	2.46 462

test123		8.76 432	11.22 435	1.39 446	0.85 470	0.97 471	85.76 420	0.35 471	0.54 464	2.45 465	8.14 464	0.60 469	0.48 465	2.16 465	0.17 464	2.71 461

mmdepth		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

monomultidepth		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

test_blank		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uanet_test		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

DCPMVS		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

cdsmvs_eth3d_5k		22.14 429	29.52 432	0.00 448	0.00 471	0.00 473	0.00 459	95.76 179	0.00 466	0.00 467	94.29 202	75.66 202	0.00 467	0.00 466	0.00 465	0.00 463

pcd_1.5k_mvsjas		6.64 434	8.86 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	79.70 141	0.00 467	0.00 466	0.00 465	0.00 463

sosnet-low-res		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

sosnet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uncertanet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

Regformer		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

ab-mvs-re		7.82 433	10.43 436	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	93.88 223	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uanet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

WAC-MVS						64.08 436								59.14 432

FOURS1					98.86 1	85.54 69	98.29 1	97.49 8	89.79 61	96.29 27

test_one_0601					98.58 11	85.83 63		97.44 17	91.05 21	96.78 23	98.06 20	91.45 11

eth-test2					0.00 471

eth-test					0.00 471

test_241102_ONE					98.77 5	85.99 54		97.44 17	90.26 45	97.71 2	97.96 29	92.31 4	99.38 31

save fliter					97.85 51	85.63 68	95.21 133	96.82 80	89.44 71

test0726					98.78 3	85.93 57	97.19 12	97.47 13	90.27 43	97.64 5	98.13 6	91.47 8

GSMVS																96.12 203

test_part2					98.55 12	87.22 19				96.40 26

sam_mvs1												71.70 258				96.12 203

sam_mvs												70.60 272

MTGPA								96.97 60

test_post1							88.00 399				9.81 462	69.31 296	95.53 363	76.65 328

test_post											10.29 461	70.57 276	95.91 348

patchmatchnet-post											83.76 427	71.53 259	96.48 317

MTMP							96.16 55	60.64 462

gm-plane-assit					89.60 397	68.00 419			77.28 370		88.99 377		97.57 224	79.44 300

TEST9					97.53 63	86.49 37	94.07 215	96.78 84	81.61 308	92.77 94	96.20 102	87.71 28	99.12 57

test_8					97.49 65	86.30 45	94.02 220	96.76 87	81.86 299	92.70 98	96.20 102	87.63 29	99.02 67

agg_prior					97.38 68	85.92 59		96.72 94		92.16 113			98.97 81

test_prior4						85.96 56	94.11 209

test_prior				93.82 69	97.29 72	84.49 93		96.88 73					98.87 93			98.11 76

旧先验2							93.36 254		71.25 422	94.37 54			97.13 274	86.74 177

新几何2							93.11 269

无先验							93.28 262	96.26 133	73.95 402				99.05 61	80.56 285		96.59 182

原ACMM2							92.94 280

testdata2													98.75 109	78.30 312

segment_acmp												87.16 36

testdata1							92.15 308		87.94 128

plane_prior7					94.70 193	82.74 159

plane_prior6					94.52 206	82.75 157						74.23 220

plane_prior4											94.86 173

plane_prior3						82.75 157			90.26 45	86.91 221

plane_prior2							95.85 86		90.81 25

plane_prior1					94.59 199

n2								0.00 472

nn								0.00 472

door-mid								85.49 429

test11								96.57 105

door								85.33 431

HQP5-MVS						81.56 191

HQP-NCC					94.17 228		94.39 190		88.81 96	85.43 267

ACMP_Plane					94.17 228		94.39 190		88.81 96	85.43 267

BP-MVS														87.11 174

HQP4-MVS										85.43 267			97.96 194			94.51 274

HQP2-MVS												73.83 231

NP-MVS					94.37 217	82.42 172					93.98 216

MDTV_nov1_ep13_2view						55.91 455	87.62 407		73.32 408	84.59 289		70.33 279		74.65 351		95.50 231

Test By Simon												80.02 135

High-resolution multi-view benchmark