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 10	95.59 5	98.14 38	88.48 8	96.26 47	97.28 31	85.90 152	97.67 3	98.10 2	88.41 20	99.56 10	94.66 13	99.19 1	98.71 18
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 9	98.36 27	87.28 17	95.56 88	97.51 4	89.13 63	97.14 8	97.91 11	91.64 7	99.62 1	94.61 14	99.17 2	98.86 10
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 36	98.77 5	85.99 57	97.13 14	97.44 14	90.31 31	97.71 1	98.07 4	92.31 4	99.58 8	95.66 4	99.13 3	98.84 13

IU-MVS					98.77 5	86.00 55		96.84 70	81.26 257	97.26 7				95.50 10	99.13 3	99.03 7

test_0728_THIRD									90.75 22	97.04 10	98.05 8	92.09 6	99.55 15	95.64 6	99.13 3	99.13 2

test_241102_TWO								97.44 14	90.31 31	97.62 5	98.07 4	91.46 10	99.58 8	95.66 4	99.12 6	98.98 9

DVP-MVS		95.67 3	96.02 3	94.64 43	98.78 3	85.93 60	97.09 16	96.73 84	90.27 33	97.04 10	98.05 8	91.47 8	99.55 15	95.62 8	99.08 7	98.45 38
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				95.01 17	98.79 2	86.43 43	97.09 16	97.49 5					99.61 3	95.62 8	99.08 7	98.99 8

MSC_two_6792asdad				96.52 1	97.78 57	90.86 1		96.85 68					99.61 3	96.03 1	99.06 9	99.07 5

No_MVS				96.52 1	97.78 57	90.86 1		96.85 68					99.61 3	96.03 1	99.06 9	99.07 5

APDe-MVS		95.46 5	95.64 5	94.91 24	98.26 30	86.29 51	97.46 6	97.40 20	89.03 67	96.20 16	98.10 2	89.39 16	99.34 36	95.88 3	99.03 11	99.10 4

DVP-MVS++		95.98 1	96.36 1	94.82 34	97.78 57	86.00 55	98.29 1	97.49 5	90.75 22	97.62 5	98.06 6	92.59 2	99.61 3	95.64 6	99.02 12	98.86 10

PC_three_1452									82.47 225	97.09 9	97.07 45	92.72 1	98.04 159	92.70 46	99.02 12	98.86 10

OPU-MVS				96.21 3	98.00 46	90.85 3	97.13 14				97.08 43	92.59 2	98.94 87	92.25 54	98.99 14	98.84 13

ACMMP_NAP		94.74 15	94.56 17	95.28 8	98.02 45	87.70 12	95.68 81	97.34 22	88.28 89	95.30 24	97.67 15	85.90 54	99.54 19	93.91 21	98.95 15	98.60 23

HPM-MVS++		95.14 10	94.91 12	95.83 4	98.25 31	89.65 4	95.92 70	96.96 56	91.75 8	94.02 41	96.83 56	88.12 25	99.55 15	93.41 31	98.94 16	98.28 54

MP-MVS-pluss		94.21 34	94.00 42	94.85 29	98.17 36	86.65 35	94.82 134	97.17 42	86.26 144	92.83 72	97.87 12	85.57 58	99.56 10	94.37 17	98.92 17	98.34 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 29	96.99 83	86.33 47	97.33 7	97.30 29	91.38 13	95.39 22	97.46 19	88.98 19	99.40 31	94.12 18	98.89 18	98.82 15
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		94.96 12	95.33 8	93.88 65	97.25 80	86.69 32	96.19 51	97.11 46	90.42 30	96.95 12	97.27 29	89.53 14	96.91 250	94.38 16	98.85 19	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 7	98.11 39	88.51 7	95.29 100	96.96 56	92.09 4	95.32 23	97.08 43	89.49 15	99.33 39	95.10 11	98.85 19	98.66 20

CP-MVS		94.34 27	94.21 31	94.74 40	98.39 25	86.64 36	97.60 4	97.24 33	88.53 81	92.73 77	97.23 32	85.20 63	99.32 40	92.15 58	98.83 21	98.25 59

ZNCC-MVS		94.47 19	94.28 26	95.03 16	98.52 16	86.96 19	96.85 28	97.32 27	88.24 90	93.15 62	97.04 46	86.17 49	99.62 1	92.40 50	98.81 22	98.52 26

MP-MVS		94.25 30	94.07 39	94.77 38	98.47 19	86.31 49	96.71 31	96.98 52	89.04 66	91.98 93	97.19 37	85.43 60	99.56 10	92.06 63	98.79 23	98.44 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		93.89 46	93.65 53	94.62 45	96.84 86	86.43 43	96.69 32	97.49 5	85.15 172	93.56 55	96.28 84	85.60 57	99.31 41	92.45 47	98.79 23	98.12 70

xxxxxxxxxxxxxcwj		94.65 16	94.70 15	94.48 50	97.85 50	85.63 71	95.21 106	95.47 177	89.44 52	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 30

SF-MVS		94.97 11	94.90 13	95.20 10	97.84 52	87.76 10	96.65 34	97.48 9	87.76 109	95.71 19	97.70 13	88.28 23	99.35 34	93.89 22	98.78 25	98.48 30

ACMMPR		94.43 23	94.28 26	94.91 24	98.63 9	86.69 32	96.94 20	97.32 27	88.63 77	93.53 56	97.26 31	85.04 65	99.54 19	92.35 52	98.78 25	98.50 28

HFP-MVS		94.52 18	94.40 21	94.86 27	98.61 10	86.81 26	96.94 20	97.34 22	88.63 77	93.65 49	97.21 34	86.10 50	99.49 26	92.35 52	98.77 28	98.30 50

#test#		94.32 29	94.14 36	94.86 27	98.61 10	86.81 26	96.43 38	97.34 22	87.51 115	93.65 49	97.21 34	86.10 50	99.49 26	91.68 76	98.77 28	98.30 50

ETH3D-3000-0.1		94.61 17	94.44 19	95.12 13	97.70 60	87.71 11	95.98 67	97.44 14	86.67 136	95.25 25	97.31 27	87.73 30	99.24 47	93.11 38	98.76 30	98.40 41

zzz-MVS		94.47 19	94.30 24	95.00 18	98.42 22	86.95 20	95.06 120	96.97 53	91.07 15	93.14 63	97.56 16	84.30 74	99.56 10	93.43 29	98.75 31	98.47 34

MTAPA		94.42 25	94.22 29	95.00 18	98.42 22	86.95 20	94.36 169	96.97 53	91.07 15	93.14 63	97.56 16	84.30 74	99.56 10	93.43 29	98.75 31	98.47 34

region2R		94.43 23	94.27 28	94.92 22	98.65 8	86.67 34	96.92 24	97.23 35	88.60 79	93.58 53	97.27 29	85.22 62	99.54 19	92.21 55	98.74 33	98.56 25

test9_res														91.91 69	98.71 34	98.07 74

DeepPCF-MVS	89.96 1	94.20 36	94.77 14	92.49 116	96.52 99	80.00 220	94.00 193	97.08 47	90.05 37	95.65 21	97.29 28	89.66 13	98.97 83	93.95 20	98.71 34	98.50 28

9.14			94.47 18		97.79 54		96.08 59	97.44 14	86.13 150	95.10 26	97.40 23	88.34 22	99.22 49	93.25 35	98.70 36

train_agg		93.44 58	93.08 63	94.52 48	97.53 65	86.49 41	94.07 186	96.78 77	81.86 243	92.77 74	96.20 89	87.63 32	99.12 58	92.14 59	98.69 37	97.94 83

DeepC-MVS_fast	89.43 2	94.04 38	93.79 47	94.80 36	97.48 69	86.78 28	95.65 85	96.89 64	89.40 55	92.81 73	96.97 49	85.37 61	99.24 47	90.87 92	98.69 37	98.38 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		94.85 13	94.94 11	94.58 46	98.25 31	86.33 47	96.11 58	96.62 96	88.14 96	96.10 17	96.96 50	89.09 18	98.94 87	94.48 15	98.68 39	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 97	98.68 39	98.27 56

ETH3 D test6400		93.64 53	93.22 60	94.92 22	97.79 54	86.84 24	95.31 95	97.26 32	82.67 223	93.81 45	96.29 83	87.29 37	99.27 45	89.87 103	98.67 41	98.65 21

test_prior3		93.60 54	93.53 55	93.82 67	97.29 76	84.49 89	94.12 179	96.88 65	87.67 112	92.63 79	96.39 80	86.62 44	98.87 91	91.50 79	98.67 41	98.11 72

test_prior2							94.12 179		87.67 112	92.63 79	96.39 80	86.62 44		91.50 79	98.67 41

MSLP-MVS++		93.72 50	94.08 38	92.65 108	97.31 74	83.43 120	95.79 75	97.33 25	90.03 38	93.58 53	96.96 50	84.87 69	97.76 175	92.19 57	98.66 44	96.76 132

CDPH-MVS		92.83 70	92.30 76	94.44 51	97.79 54	86.11 54	94.06 188	96.66 93	80.09 269	92.77 74	96.63 70	86.62 44	99.04 66	87.40 131	98.66 44	98.17 64

HPM-MVS		94.02 39	93.88 44	94.43 53	98.39 25	85.78 68	97.25 10	97.07 48	86.90 131	92.62 81	96.80 60	84.85 70	99.17 53	92.43 48	98.65 46	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 41	93.78 48	94.63 44	98.50 17	85.90 65	96.87 26	96.91 62	88.70 75	91.83 99	97.17 39	83.96 80	99.55 15	91.44 81	98.64 47	98.43 40

CS-MVS-test		94.02 39	94.29 25	93.24 79	96.69 90	83.24 124	97.49 5	96.92 61	92.14 3	92.90 68	95.77 107	85.02 66	98.33 132	93.03 39	98.62 48	98.13 68

MCST-MVS		94.45 21	94.20 32	95.19 11	98.46 20	87.50 15	95.00 122	97.12 44	87.13 123	92.51 84	96.30 82	89.24 17	99.34 36	93.46 28	98.62 48	98.73 16

APD-MVS		94.24 31	94.07 39	94.75 39	98.06 43	86.90 23	95.88 71	96.94 59	85.68 158	95.05 27	97.18 38	87.31 36	99.07 61	91.90 72	98.61 50	98.28 54
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		93.96 43	93.72 50	94.68 41	98.43 21	86.22 52	95.30 98	97.78 1	87.45 118	93.26 58	97.33 26	84.62 72	99.51 24	90.75 94	98.57 51	98.32 49

XVS		94.45 21	94.32 22	94.85 29	98.54 14	86.60 38	96.93 22	97.19 38	90.66 27	92.85 70	97.16 40	85.02 66	99.49 26	91.99 64	98.56 52	98.47 34

X-MVStestdata		88.31 175	86.13 218	94.85 29	98.54 14	86.60 38	96.93 22	97.19 38	90.66 27	92.85 70	23.41 376	85.02 66	99.49 26	91.99 64	98.56 52	98.47 34

agg_prior1		93.29 63	92.97 67	94.26 58	97.38 71	85.92 62	93.92 197	96.72 86	81.96 237	92.16 89	96.23 86	87.85 27	98.97 83	91.95 68	98.55 54	97.90 87

DELS-MVS		93.43 60	93.25 59	93.97 62	95.42 139	85.04 78	93.06 235	97.13 43	90.74 24	91.84 97	95.09 127	86.32 48	99.21 50	91.22 83	98.45 55	97.65 97
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 37	86.62 37		97.07 48	83.63 199	94.19 36	96.91 52	87.57 34	99.26 46	91.99 64	98.44 56

ETH3D cwj APD-0.16		93.91 44	93.53 55	95.06 15	96.76 88	87.78 9	94.92 127	97.21 37	84.33 186	93.89 44	97.09 42	87.20 38	99.29 44	91.90 72	98.44 56	98.12 70

GST-MVS		94.21 34	93.97 43	94.90 26	98.41 24	86.82 25	96.54 36	97.19 38	88.24 90	93.26 58	96.83 56	85.48 59	99.59 7	91.43 82	98.40 58	98.30 50

HPM-MVS_fast		93.40 61	93.22 60	93.94 64	98.36 27	84.83 80	97.15 13	96.80 76	85.77 155	92.47 85	97.13 41	82.38 93	99.07 61	90.51 98	98.40 58	97.92 86

NCCC		94.81 14	94.69 16	95.17 12	97.83 53	87.46 16	95.66 83	96.93 60	92.34 2	93.94 42	96.58 73	87.74 29	99.44 30	92.83 41	98.40 58	98.62 22

testtj		94.39 26	94.18 34	95.00 18	98.24 33	86.77 30	96.16 52	97.23 35	87.28 121	94.85 29	97.04 46	86.99 42	99.52 23	91.54 78	98.33 61	98.71 18

DeepC-MVS	88.79 3	93.31 62	92.99 66	94.26 58	96.07 114	85.83 66	94.89 129	96.99 51	89.02 69	89.56 128	97.37 25	82.51 92	99.38 32	92.20 56	98.30 62	97.57 102
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 66	93.05 64	93.76 72	98.04 44	84.07 102	96.22 50	97.37 21	84.15 188	90.05 125	95.66 111	87.77 28	99.15 56	89.91 102	98.27 63	98.07 74

原ACMM1				92.01 133	97.34 73	81.05 188		96.81 75	78.89 283	90.45 117	95.92 100	82.65 90	98.84 99	80.68 229	98.26 64	96.14 150

CS-MVS		94.12 37	94.44 19	93.17 83	96.55 96	83.08 131	97.63 3	96.95 58	91.71 10	93.50 57	96.21 87	85.61 56	98.24 137	93.64 25	98.17 65	98.19 62

MVS_111021_HR		93.45 57	93.31 58	93.84 66	96.99 83	84.84 79	93.24 228	97.24 33	88.76 74	91.60 104	95.85 103	86.07 52	98.66 105	91.91 69	98.16 66	98.03 78

DROMVSNet		93.44 58	93.71 51	92.63 109	95.21 148	82.43 152	97.27 9	96.71 88	90.57 29	92.88 69	95.80 105	83.16 84	98.16 143	93.68 24	98.14 67	97.31 109

test12				94.34 56	97.13 81	86.15 53		96.29 113		91.04 113		85.08 64	99.01 73		98.13 68	97.86 90

新几何1				93.10 86	97.30 75	84.35 98		95.56 169	71.09 351	91.26 110	96.24 85	82.87 89	98.86 94	79.19 250	98.10 69	96.07 157

patch_mono-2		93.74 49	94.32 22	92.01 133	97.54 64	78.37 257	93.40 216	97.19 38	88.02 98	94.99 28	97.21 34	88.35 21	98.44 123	94.07 19	98.09 70	99.23 1

dcpmvs_2		93.49 56	94.19 33	91.38 165	97.69 61	76.78 290	94.25 172	96.29 113	88.33 85	94.46 30	96.88 53	88.07 26	98.64 107	93.62 26	98.09 70	98.73 16

1121		90.42 112	89.49 119	93.20 81	97.27 78	84.46 92	92.63 246	95.51 175	71.01 352	91.20 111	96.21 87	82.92 88	99.05 63	80.56 231	98.07 72	96.10 155

MSP-MVS		95.42 6	95.56 6	94.98 21	98.49 18	86.52 40	96.91 25	97.47 10	91.73 9	96.10 17	96.69 63	89.90 12	99.30 42	94.70 12	98.04 73	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 32	94.17 35	94.43 53	98.21 35	85.78 68	96.40 41	96.90 63	88.20 94	94.33 33	97.40 23	84.75 71	99.03 67	93.35 32	97.99 74	98.48 30

test1172		93.97 42	94.07 39	93.66 74	98.11 39	83.45 119	96.26 47	96.84 70	88.33 85	94.19 36	97.43 20	84.24 76	99.01 73	93.26 34	97.98 75	98.52 26

3Dnovator	86.66 5	91.73 86	90.82 97	94.44 51	94.59 178	86.37 45	97.18 12	97.02 50	89.20 60	84.31 248	96.66 66	73.74 199	99.17 53	86.74 141	97.96 76	97.79 94

CANet		93.54 55	93.20 62	94.55 47	95.65 130	85.73 70	94.94 125	96.69 91	91.89 6	90.69 115	95.88 102	81.99 104	99.54 19	93.14 37	97.95 77	98.39 42

DPM-MVS		92.58 74	91.74 82	95.08 14	96.19 107	89.31 5	92.66 245	96.56 101	83.44 205	91.68 103	95.04 128	86.60 47	98.99 80	85.60 155	97.92 78	96.93 128

APD-MVS_3200maxsize		93.78 48	93.77 49	93.80 71	97.92 47	84.19 100	96.30 43	96.87 67	86.96 127	93.92 43	97.47 18	83.88 81	98.96 86	92.71 45	97.87 79	98.26 58

CPTT-MVS		91.99 80	91.80 81	92.55 113	98.24 33	81.98 162	96.76 30	96.49 103	81.89 242	90.24 120	96.44 79	78.59 138	98.61 111	89.68 104	97.85 80	97.06 121

SR-MVS-dyc-post		93.82 47	93.82 45	93.82 67	97.92 47	84.57 85	96.28 45	96.76 80	87.46 116	93.75 46	97.43 20	84.24 76	99.01 73	92.73 42	97.80 81	97.88 88

RE-MVS-def			93.68 52		97.92 47	84.57 85	96.28 45	96.76 80	87.46 116	93.75 46	97.43 20	82.94 87		92.73 42	97.80 81	97.88 88

test222					96.55 96	81.70 168	92.22 260	95.01 204	68.36 357	90.20 121	96.14 94	80.26 117			97.80 81	96.05 159

3Dnovator+	87.14 4	92.42 77	91.37 85	95.55 6	95.63 131	88.73 6	97.07 18	96.77 79	90.84 19	84.02 252	96.62 71	75.95 164	99.34 36	87.77 125	97.68 84	98.59 24

旧先验1					96.79 87	81.81 166		95.67 161			96.81 58	86.69 43			97.66 85	96.97 126

Regformer-1		94.22 33	94.13 37	94.51 49	95.54 135	86.36 46	94.57 150	96.44 104	91.69 11	94.32 34	96.56 75	87.05 41	99.03 67	93.35 32	97.65 86	98.15 66

Regformer-2		94.33 28	94.22 29	94.68 41	95.54 135	86.75 31	94.57 150	96.70 89	91.84 7	94.41 31	96.56 75	87.19 39	99.13 57	93.50 27	97.65 86	98.16 65

EPNet		91.79 83	91.02 93	94.10 61	90.10 325	85.25 77	96.03 64	92.05 292	92.83 1	87.39 165	95.78 106	79.39 129	99.01 73	88.13 122	97.48 88	98.05 76
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testdata				90.49 202	96.40 101	77.89 269		95.37 189	72.51 344	93.63 51	96.69 63	82.08 101	97.65 183	83.08 183	97.39 89	95.94 161

MVS_111021_LR		92.47 76	92.29 77	92.98 92	95.99 118	84.43 96	93.08 233	96.09 129	88.20 94	91.12 112	95.72 110	81.33 109	97.76 175	91.74 74	97.37 90	96.75 133

abl_6		93.18 67	93.05 64	93.57 76	97.52 67	84.27 99	95.53 89	96.67 92	87.85 106	93.20 61	97.22 33	80.35 114	99.18 52	91.91 69	97.21 91	97.26 112

MVSFormer		91.68 88	91.30 86	92.80 99	93.86 208	83.88 107	95.96 68	95.90 144	84.66 182	91.76 100	94.91 132	77.92 146	97.30 221	89.64 105	97.11 92	97.24 113

lupinMVS		90.92 99	90.21 103	93.03 90	93.86 208	83.88 107	92.81 242	93.86 252	79.84 272	91.76 100	94.29 160	77.92 146	98.04 159	90.48 99	97.11 92	97.17 117

EIA-MVS		91.95 81	91.94 79	91.98 137	95.16 150	80.01 219	95.36 92	96.73 84	88.44 82	89.34 132	92.16 234	83.82 82	98.45 122	89.35 107	97.06 94	97.48 105

MG-MVS		91.77 84	91.70 83	92.00 136	97.08 82	80.03 218	93.60 210	95.18 197	87.85 106	90.89 114	96.47 78	82.06 102	98.36 127	85.07 159	97.04 95	97.62 98

test2506		87.21 218	86.28 214	90.02 228	95.62 132	73.64 319	96.25 49	71.38 377	87.89 104	90.45 117	96.65 67	55.29 346	98.09 154	86.03 150	96.94 96	98.33 46

ECVR-MVS		89.09 151	88.53 148	90.77 193	95.62 132	75.89 301	96.16 52	84.22 364	87.89 104	90.20 121	96.65 67	63.19 309	98.10 146	85.90 151	96.94 96	98.33 46

test1111		89.10 149	88.64 142	90.48 205	95.53 137	74.97 307	96.08 59	84.89 362	88.13 97	90.16 123	96.65 67	63.29 307	98.10 146	86.14 146	96.90 98	98.39 42

jason		90.80 100	90.10 107	92.90 96	93.04 234	83.53 117	93.08 233	94.15 242	80.22 266	91.41 107	94.91 132	76.87 152	97.93 169	90.28 100	96.90 98	97.24 113
jason: jason.
Vis-MVSNet		91.75 85	91.23 88	93.29 77	95.32 143	83.78 110	96.14 55	95.98 137	89.89 40	90.45 117	96.58 73	75.09 176	98.31 135	84.75 165	96.90 98	97.78 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
114514_t		89.51 134	88.50 150	92.54 114	98.11 39	81.99 161	95.16 112	96.36 111	70.19 354	85.81 196	95.25 121	76.70 156	98.63 109	82.07 202	96.86 101	97.00 125

Vis-MVSNet (Re-imp)		89.59 132	89.44 121	90.03 226	95.74 126	75.85 302	95.61 86	90.80 327	87.66 114	87.83 154	95.40 118	76.79 154	96.46 277	78.37 254	96.73 102	97.80 93

API-MVS		90.66 106	90.07 108	92.45 118	96.36 103	84.57 85	96.06 63	95.22 196	82.39 226	89.13 134	94.27 163	80.32 115	98.46 119	80.16 238	96.71 103	94.33 227

MAR-MVS		90.30 113	89.37 124	93.07 89	96.61 93	84.48 91	95.68 81	95.67 161	82.36 228	87.85 153	92.85 212	76.63 158	98.80 101	80.01 239	96.68 104	95.91 162
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
Regformer-3		93.68 51	93.64 54	93.81 70	95.36 140	84.61 83	94.68 142	95.83 150	91.27 14	93.60 52	96.71 61	85.75 55	98.86 94	92.87 40	96.65 105	97.96 82

Regformer-4		93.91 44	93.81 46	94.19 60	95.36 140	85.47 74	94.68 142	96.41 107	91.60 12	93.75 46	96.71 61	85.95 53	99.10 60	93.21 36	96.65 105	98.01 80

OpenMVS	83.78 11	88.74 165	87.29 180	93.08 87	92.70 244	85.39 75	96.57 35	96.43 106	78.74 288	80.85 296	96.07 96	69.64 250	99.01 73	78.01 260	96.65 105	94.83 200

ETV-MVS		92.74 72	92.66 71	92.97 93	95.20 149	84.04 104	95.07 117	96.51 102	90.73 25	92.96 67	91.19 267	84.06 78	98.34 130	91.72 75	96.54 108	96.54 141

QAPM		89.51 134	88.15 161	93.59 75	94.92 162	84.58 84	96.82 29	96.70 89	78.43 292	83.41 267	96.19 92	73.18 207	99.30 42	77.11 269	96.54 108	96.89 130

IS-MVSNet		91.43 90	91.09 92	92.46 117	95.87 124	81.38 179	96.95 19	93.69 257	89.72 48	89.50 130	95.98 98	78.57 139	97.77 174	83.02 185	96.50 110	98.22 61

DP-MVS Recon		91.95 81	91.28 87	93.96 63	98.33 29	85.92 62	94.66 145	96.66 93	82.69 222	90.03 126	95.82 104	82.30 96	99.03 67	84.57 167	96.48 111	96.91 129

CANet_DTU		90.26 115	89.41 123	92.81 98	93.46 222	83.01 134	93.48 213	94.47 229	89.43 54	87.76 157	94.23 164	70.54 240	99.03 67	84.97 160	96.39 112	96.38 143

UGNet		89.95 123	88.95 135	92.95 94	94.51 181	83.31 123	95.70 80	95.23 194	89.37 56	87.58 159	93.94 176	64.00 302	98.78 102	83.92 174	96.31 113	96.74 134
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
TSAR-MVS + GP.		93.66 52	93.41 57	94.41 55	96.59 94	86.78 28	94.40 162	93.93 248	89.77 46	94.21 35	95.59 114	87.35 35	98.61 111	92.72 44	96.15 114	97.83 92

PVSNet_Blended		90.73 103	90.32 102	91.98 137	96.12 109	81.25 181	92.55 250	96.83 72	82.04 235	89.10 135	92.56 222	81.04 111	98.85 97	86.72 143	95.91 115	95.84 166

PS-MVSNAJ		91.18 96	90.92 94	91.96 139	95.26 146	82.60 151	92.09 265	95.70 159	86.27 143	91.84 97	92.46 224	79.70 124	98.99 80	89.08 111	95.86 116	94.29 230

ACMMP		93.24 65	92.88 69	94.30 57	98.09 42	85.33 76	96.86 27	97.45 13	88.33 85	90.15 124	97.03 48	81.44 107	99.51 24	90.85 93	95.74 117	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
LCM-MVSNet-Re		88.30 176	88.32 157	88.27 276	94.71 173	72.41 334	93.15 229	90.98 322	87.77 108	79.25 318	91.96 246	78.35 142	95.75 308	83.04 184	95.62 118	96.65 136

CHOSEN 1792x2688		88.84 162	87.69 171	92.30 126	96.14 108	81.42 178	90.01 302	95.86 148	74.52 328	87.41 162	93.94 176	75.46 173	98.36 127	80.36 234	95.53 119	97.12 120

AdaColmap		89.89 126	89.07 132	92.37 122	97.41 70	83.03 132	94.42 161	95.92 141	82.81 220	86.34 187	94.65 148	73.89 195	99.02 71	80.69 228	95.51 120	95.05 188

MVS		87.44 207	86.10 221	91.44 163	92.61 247	83.62 115	92.63 246	95.66 163	67.26 358	81.47 288	92.15 235	77.95 145	98.22 140	79.71 242	95.48 121	92.47 305

UA-Net		92.83 70	92.54 73	93.68 73	96.10 112	84.71 82	95.66 83	96.39 109	91.92 5	93.22 60	96.49 77	83.16 84	98.87 91	84.47 168	95.47 122	97.45 107

xiu_mvs_v2_base		91.13 97	90.89 96	91.86 145	94.97 158	82.42 153	92.24 259	95.64 166	86.11 151	91.74 102	93.14 205	79.67 127	98.89 90	89.06 112	95.46 123	94.28 231

casdiffmvs		92.51 75	92.43 75	92.74 103	94.41 187	81.98 162	94.54 152	96.23 120	89.57 50	91.96 94	96.17 93	82.58 91	98.01 162	90.95 90	95.45 124	98.23 60

PVSNet_Blended_VisFu		91.38 91	90.91 95	92.80 99	96.39 102	83.17 127	94.87 131	96.66 93	83.29 209	89.27 133	94.46 154	80.29 116	99.17 53	87.57 129	95.37 125	96.05 159

PAPM_NR		91.22 95	90.78 98	92.52 115	97.60 63	81.46 176	94.37 168	96.24 119	86.39 142	87.41 162	94.80 141	82.06 102	98.48 117	82.80 191	95.37 125	97.61 99

CHOSEN 280x420		85.15 263	83.99 265	88.65 268	92.47 248	78.40 256	79.68 364	92.76 272	74.90 325	81.41 290	89.59 304	69.85 248	95.51 315	79.92 241	95.29 127	92.03 315

TAPA-MVS	84.62 6	88.16 179	87.01 187	91.62 155	96.64 92	80.65 199	94.39 164	96.21 124	76.38 308	86.19 191	95.44 115	79.75 122	98.08 156	62.75 352	95.29 127	96.13 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline		92.39 78	92.29 77	92.69 107	94.46 184	81.77 167	94.14 178	96.27 115	89.22 59	91.88 95	96.00 97	82.35 94	97.99 164	91.05 85	95.27 129	98.30 50

LS3D		87.89 185	86.32 212	92.59 111	96.07 114	82.92 138	95.23 104	94.92 211	75.66 315	82.89 274	95.98 98	72.48 216	99.21 50	68.43 326	95.23 130	95.64 174

MVS_Test		91.31 93	91.11 90	91.93 141	94.37 188	80.14 211	93.46 215	95.80 152	86.46 139	91.35 109	93.77 186	82.21 98	98.09 154	87.57 129	94.95 131	97.55 104

PAPR		90.02 119	89.27 129	92.29 127	95.78 125	80.95 192	92.68 244	96.22 121	81.91 240	86.66 180	93.75 188	82.23 97	98.44 123	79.40 249	94.79 132	97.48 105

xiu_mvs_v1_base_debu		90.64 107	90.05 109	92.40 119	93.97 205	84.46 92	93.32 218	95.46 178	85.17 169	92.25 86	94.03 168	70.59 236	98.57 113	90.97 87	94.67 133	94.18 232

xiu_mvs_v1_base		90.64 107	90.05 109	92.40 119	93.97 205	84.46 92	93.32 218	95.46 178	85.17 169	92.25 86	94.03 168	70.59 236	98.57 113	90.97 87	94.67 133	94.18 232

xiu_mvs_v1_base_debi		90.64 107	90.05 109	92.40 119	93.97 205	84.46 92	93.32 218	95.46 178	85.17 169	92.25 86	94.03 168	70.59 236	98.57 113	90.97 87	94.67 133	94.18 232

gg-mvs-nofinetune		81.77 294	79.37 307	88.99 261	90.85 307	77.73 276	86.29 341	79.63 373	74.88 326	83.19 272	69.05 367	60.34 326	96.11 292	75.46 283	94.64 136	93.11 286

BH-RMVSNet		88.37 173	87.48 175	91.02 184	95.28 144	79.45 232	92.89 240	93.07 266	85.45 165	86.91 175	94.84 139	70.35 241	97.76 175	73.97 295	94.59 137	95.85 165

diffmvs		91.37 92	91.23 88	91.77 151	93.09 231	80.27 207	92.36 255	95.52 174	87.03 126	91.40 108	94.93 131	80.08 118	97.44 204	92.13 60	94.56 138	97.61 99

BH-untuned		88.60 169	88.13 162	90.01 229	95.24 147	78.50 253	93.29 223	94.15 242	84.75 180	84.46 238	93.40 193	75.76 167	97.40 213	77.59 263	94.52 139	94.12 236

Effi-MVS+		91.59 89	91.11 90	93.01 91	94.35 191	83.39 122	94.60 147	95.10 201	87.10 124	90.57 116	93.10 207	81.43 108	98.07 157	89.29 109	94.48 140	97.59 101

PCF-MVS	84.11 10	87.74 190	86.08 222	92.70 106	94.02 199	84.43 96	89.27 312	95.87 147	73.62 335	84.43 240	94.33 157	78.48 141	98.86 94	70.27 312	94.45 141	94.81 201
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		93.01 69	92.92 68	93.29 77	95.01 155	83.51 118	94.48 154	95.77 154	90.87 18	92.52 83	96.67 65	84.50 73	99.00 78	91.99 64	94.44 142	97.36 108

MS-PatchMatch		85.05 265	84.16 262	87.73 288	91.42 281	78.51 252	91.25 281	93.53 258	77.50 299	80.15 306	91.58 258	61.99 314	95.51 315	75.69 281	94.35 143	89.16 351

mvs_anonymous		89.37 144	89.32 126	89.51 249	93.47 221	74.22 314	91.65 275	94.83 218	82.91 218	85.45 213	93.79 184	81.23 110	96.36 283	86.47 145	94.09 144	97.94 83

MVP-Stereo		85.97 247	84.86 253	89.32 251	90.92 303	82.19 158	92.11 264	94.19 240	78.76 287	78.77 320	91.63 256	68.38 271	96.56 269	75.01 289	93.95 145	89.20 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LFMVS		90.08 117	89.13 131	92.95 94	96.71 89	82.32 157	96.08 59	89.91 341	86.79 132	92.15 91	96.81 58	62.60 311	98.34 130	87.18 135	93.90 146	98.19 62

PVSNet	78.82 18	85.55 254	84.65 257	88.23 279	94.72 172	71.93 335	87.12 337	92.75 273	78.80 286	84.95 229	90.53 286	64.43 301	96.71 257	74.74 290	93.86 147	96.06 158

CNLPA		89.07 153	87.98 166	92.34 123	96.87 85	84.78 81	94.08 185	93.24 262	81.41 253	84.46 238	95.13 126	75.57 172	96.62 261	77.21 267	93.84 148	95.61 175

EPNet_dtu		86.49 241	85.94 228	88.14 281	90.24 323	72.82 326	94.11 181	92.20 286	86.66 137	79.42 317	92.36 228	73.52 200	95.81 306	71.26 306	93.66 149	95.80 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GeoE		90.05 118	89.43 122	91.90 144	95.16 150	80.37 206	95.80 74	94.65 226	83.90 193	87.55 161	94.75 143	78.18 144	97.62 189	81.28 217	93.63 150	97.71 96

EI-MVSNet-UG-set		92.74 72	92.62 72	93.12 85	94.86 166	83.20 126	94.40 162	95.74 157	90.71 26	92.05 92	96.60 72	84.00 79	98.99 80	91.55 77	93.63 150	97.17 117

Fast-Effi-MVS+		89.41 141	88.64 142	91.71 153	94.74 170	80.81 196	93.54 211	95.10 201	83.11 212	86.82 178	90.67 284	79.74 123	97.75 178	80.51 233	93.55 152	96.57 139

1314		87.51 204	86.57 204	90.34 214	92.42 250	79.74 226	92.63 246	95.35 191	78.35 293	80.14 307	91.62 257	74.05 192	97.15 234	81.05 219	93.53 153	94.12 236

BH-w/o		87.57 202	87.05 186	89.12 256	94.90 164	77.90 268	92.41 252	93.51 259	82.89 219	83.70 259	91.34 261	75.75 168	97.07 240	75.49 282	93.49 154	92.39 308

PMMVS		85.71 253	84.96 250	87.95 285	88.90 339	77.09 286	88.68 322	90.06 337	72.32 345	86.47 181	90.76 283	72.15 219	94.40 330	81.78 210	93.49 154	92.36 309

PatchMatch-RL		86.77 232	85.54 237	90.47 207	95.88 122	82.71 146	90.54 291	92.31 283	79.82 273	84.32 246	91.57 260	68.77 266	96.39 280	73.16 300	93.48 156	92.32 311

PLC	84.53 7	89.06 155	88.03 164	92.15 130	97.27 78	82.69 147	94.29 170	95.44 183	79.71 274	84.01 253	94.18 165	76.68 157	98.75 103	77.28 266	93.41 157	95.02 189
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		92.24 79	91.91 80	93.24 79	96.59 94	83.43 120	94.84 133	96.44 104	89.19 61	94.08 40	95.90 101	77.85 149	98.17 142	88.90 113	93.38 158	98.13 68

test-LLR		85.87 249	85.41 240	87.25 300	90.95 299	71.67 337	89.55 306	89.88 343	83.41 206	84.54 235	87.95 327	67.25 275	95.11 324	81.82 208	93.37 159	94.97 190

test-mter		84.54 272	83.64 270	87.25 300	90.95 299	71.67 337	89.55 306	89.88 343	79.17 279	84.54 235	87.95 327	55.56 343	95.11 324	81.82 208	93.37 159	94.97 190

EPP-MVSNet		91.70 87	91.56 84	92.13 131	95.88 122	80.50 204	97.33 7	95.25 193	86.15 148	89.76 127	95.60 113	83.42 83	98.32 134	87.37 133	93.25 161	97.56 103

CDS-MVSNet		89.45 137	88.51 149	92.29 127	93.62 217	83.61 116	93.01 236	94.68 225	81.95 238	87.82 155	93.24 201	78.69 136	96.99 245	80.34 235	93.23 162	96.28 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 233	85.39 241	90.53 198	93.05 233	79.33 239	89.79 305	94.77 223	78.82 285	81.95 285	93.24 201	76.81 153	97.30 221	66.94 335	93.16 163	94.95 196

alignmvs		93.08 68	92.50 74	94.81 35	95.62 132	87.61 14	95.99 65	96.07 131	89.77 46	94.12 38	94.87 134	80.56 113	98.66 105	92.42 49	93.10 164	98.15 66

mvs-test1		89.45 137	89.14 130	90.38 211	93.33 224	77.63 278	94.95 124	94.36 233	87.70 110	87.10 170	92.81 216	73.45 202	98.03 161	85.57 156	93.04 165	95.48 177

thisisatest0515		87.33 210	85.99 224	91.37 166	93.49 220	79.55 229	90.63 290	89.56 348	80.17 267	87.56 160	90.86 278	67.07 279	98.28 136	81.50 215	93.02 166	96.29 145

TAMVS		89.21 146	88.29 158	91.96 139	93.71 214	82.62 150	93.30 222	94.19 240	82.22 230	87.78 156	93.94 176	78.83 133	96.95 247	77.70 262	92.98 167	96.32 144

OMC-MVS		91.23 94	90.62 99	93.08 87	96.27 105	84.07 102	93.52 212	95.93 140	86.95 128	89.51 129	96.13 95	78.50 140	98.35 129	85.84 152	92.90 168	96.83 131

canonicalmvs		93.27 64	92.75 70	94.85 29	95.70 129	87.66 13	96.33 42	96.41 107	90.00 39	94.09 39	94.60 150	82.33 95	98.62 110	92.40 50	92.86 169	98.27 56

TESTMET0.1,1		83.74 280	82.85 279	86.42 315	89.96 329	71.21 341	89.55 306	87.88 353	77.41 300	83.37 268	87.31 335	56.71 340	93.65 342	80.62 230	92.85 170	94.40 226

thisisatest0530		88.67 166	87.61 173	91.86 145	94.87 165	80.07 214	94.63 146	89.90 342	84.00 191	88.46 143	93.78 185	66.88 282	98.46 119	83.30 181	92.65 171	97.06 121

VDD-MVS		90.74 102	89.92 114	93.20 81	96.27 105	83.02 133	95.73 78	93.86 252	88.42 84	92.53 82	96.84 55	62.09 313	98.64 107	90.95 90	92.62 172	97.93 85

test_yl		90.69 104	90.02 112	92.71 104	95.72 127	82.41 155	94.11 181	95.12 199	85.63 160	91.49 105	94.70 144	74.75 180	98.42 125	86.13 148	92.53 173	97.31 109

DCV-MVSNet		90.69 104	90.02 112	92.71 104	95.72 127	82.41 155	94.11 181	95.12 199	85.63 160	91.49 105	94.70 144	74.75 180	98.42 125	86.13 148	92.53 173	97.31 109

VDDNet		89.56 133	88.49 152	92.76 101	95.07 154	82.09 159	96.30 43	93.19 264	81.05 262	91.88 95	96.86 54	61.16 323	98.33 132	88.43 119	92.49 175	97.84 91

DP-MVS		87.25 214	85.36 243	92.90 96	97.65 62	83.24 124	94.81 135	92.00 294	74.99 323	81.92 286	95.00 129	72.66 213	99.05 63	66.92 337	92.33 176	96.40 142

GG-mvs-BLEND				87.94 286	89.73 333	77.91 267	87.80 330	78.23 375		80.58 300	83.86 351	59.88 330	95.33 321	71.20 307	92.22 177	90.60 340

tttt0517		88.61 168	87.78 170	91.11 178	94.96 159	77.81 272	95.35 93	89.69 345	85.09 174	88.05 150	94.59 151	66.93 280	98.48 117	83.27 182	92.13 178	97.03 123

HyFIR lowres test		88.09 181	86.81 191	91.93 141	96.00 117	80.63 200	90.01 302	95.79 153	73.42 336	87.68 158	92.10 240	73.86 196	97.96 166	80.75 227	91.70 179	97.19 116

sss		88.93 160	88.26 160	90.94 190	94.05 198	80.78 197	91.71 272	95.38 187	81.55 251	88.63 140	93.91 180	75.04 177	95.47 319	82.47 195	91.61 180	96.57 139

cascas		86.43 242	84.98 249	90.80 192	92.10 259	80.92 193	90.24 296	95.91 143	73.10 339	83.57 264	88.39 320	65.15 297	97.46 201	84.90 163	91.43 181	94.03 243

Effi-MVS+-dtu		88.65 167	88.35 154	89.54 246	93.33 224	76.39 296	94.47 157	94.36 233	87.70 110	85.43 216	89.56 306	73.45 202	97.26 227	85.57 156	91.28 182	94.97 190

thres100view900		87.63 196	86.71 196	90.38 211	96.12 109	78.55 250	95.03 121	91.58 305	87.15 122	88.06 149	92.29 231	68.91 264	98.10 146	70.13 316	91.10 183	94.48 221

tfpn200view9		87.58 201	86.64 199	90.41 208	95.99 118	78.64 248	94.58 148	91.98 296	86.94 129	88.09 146	91.77 250	69.18 261	98.10 146	70.13 316	91.10 183	94.48 221

thres600view7		87.65 193	86.67 198	90.59 195	96.08 113	78.72 246	94.88 130	91.58 305	87.06 125	88.08 148	92.30 230	68.91 264	98.10 146	70.05 319	91.10 183	94.96 193

thres400		87.62 198	86.64 199	90.57 196	95.99 118	78.64 248	94.58 148	91.98 296	86.94 129	88.09 146	91.77 250	69.18 261	98.10 146	70.13 316	91.10 183	94.96 193

F-COLMAP		87.95 184	86.80 192	91.40 164	96.35 104	80.88 194	94.73 140	95.45 181	79.65 275	82.04 284	94.61 149	71.13 227	98.50 116	76.24 277	91.05 187	94.80 202

thres200		87.21 218	86.24 216	90.12 222	95.36 140	78.53 251	93.26 225	92.10 290	86.42 141	88.00 151	91.11 273	69.24 260	98.00 163	69.58 320	91.04 188	93.83 253

WTY-MVS		89.60 131	88.92 136	91.67 154	95.47 138	81.15 186	92.38 254	94.78 222	83.11 212	89.06 137	94.32 158	78.67 137	96.61 264	81.57 214	90.89 189	97.24 113

HY-MVS	83.01 12	89.03 156	87.94 168	92.29 127	94.86 166	82.77 140	92.08 266	94.49 228	81.52 252	86.93 173	92.79 218	78.32 143	98.23 138	79.93 240	90.55 190	95.88 164

CLD-MVS		89.47 136	88.90 137	91.18 173	94.22 193	82.07 160	92.13 263	96.09 129	87.90 102	85.37 222	92.45 225	74.38 185	97.56 192	87.15 136	90.43 191	93.93 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CVMVSNet		84.69 271	84.79 255	84.37 332	91.84 266	64.92 366	93.70 207	91.47 310	66.19 360	86.16 192	95.28 119	67.18 277	93.33 345	80.89 225	90.42 192	94.88 198

SCA		86.32 243	85.18 245	89.73 241	92.15 255	76.60 292	91.12 283	91.69 303	83.53 203	85.50 209	88.81 313	66.79 283	96.48 274	76.65 272	90.35 193	96.12 152

Fast-Effi-MVS+-dtu		87.44 207	86.72 195	89.63 244	92.04 260	77.68 277	94.03 190	93.94 247	85.81 153	82.42 278	91.32 264	70.33 242	97.06 241	80.33 236	90.23 194	94.14 235

OPM-MVS		90.12 116	89.56 117	91.82 148	93.14 229	83.90 106	94.16 177	95.74 157	88.96 70	87.86 152	95.43 117	72.48 216	97.91 170	88.10 123	90.18 195	93.65 265
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 110	90.19 104	91.82 148	94.70 174	82.73 144	95.85 72	96.22 121	90.81 20	86.91 175	94.86 135	74.23 187	98.12 144	88.15 120	89.99 196	94.63 206

plane_prior5								96.22 121					98.12 144	88.15 120	89.99 196	94.63 206

XVG-OURS		89.40 143	88.70 140	91.52 158	94.06 197	81.46 176	91.27 280	96.07 131	86.14 149	88.89 139	95.77 107	68.73 267	97.26 227	87.39 132	89.96 198	95.83 167

baseline2		86.50 239	85.39 241	89.84 234	91.12 293	76.70 291	91.88 267	88.58 351	82.35 229	79.95 311	90.95 277	73.42 204	97.63 187	80.27 237	89.95 199	95.19 185

Anonymous202405211		87.68 191	86.13 218	92.31 125	96.66 91	80.74 198	94.87 131	91.49 309	80.47 265	89.46 131	95.44 115	54.72 348	98.23 138	82.19 200	89.89 200	97.97 81

plane_prior						82.73 144	95.21 106		89.66 49						89.88 201

TR-MVS		86.78 229	85.76 235	89.82 235	94.37 188	78.41 255	92.47 251	92.83 270	81.11 261	86.36 186	92.40 226	68.73 267	97.48 199	73.75 298	89.85 202	93.57 267

HQP3-MVS								96.04 135							89.77 203

HQP-MVS		89.80 128	89.28 128	91.34 167	94.17 194	81.56 170	94.39 164	96.04 135	88.81 71	85.43 216	93.97 175	73.83 197	97.96 166	87.11 138	89.77 203	94.50 218

XVG-OURS-SEG-HR		89.95 123	89.45 120	91.47 162	94.00 203	81.21 184	91.87 268	96.06 133	85.78 154	88.55 141	95.73 109	74.67 183	97.27 225	88.71 115	89.64 205	95.91 162

GA-MVS		86.61 234	85.27 244	90.66 194	91.33 286	78.71 247	90.40 293	93.81 255	85.34 167	85.12 226	89.57 305	61.25 320	97.11 238	80.99 223	89.59 206	96.15 149

1112_ss		88.42 171	87.33 179	91.72 152	94.92 162	80.98 190	92.97 238	94.54 227	78.16 297	83.82 256	93.88 181	78.78 135	97.91 170	79.45 245	89.41 207	96.26 147

ab-mvs		89.41 141	88.35 154	92.60 110	95.15 152	82.65 149	92.20 261	95.60 168	83.97 192	88.55 141	93.70 190	74.16 191	98.21 141	82.46 196	89.37 208	96.94 127

CR-MVSNet		85.35 258	83.76 268	90.12 222	90.58 316	79.34 236	85.24 347	91.96 298	78.27 294	85.55 203	87.87 330	71.03 229	95.61 310	73.96 296	89.36 209	95.40 180

RPMNet		83.95 277	81.53 287	91.21 171	90.58 316	79.34 236	85.24 347	96.76 80	71.44 349	85.55 203	82.97 356	70.87 232	98.91 89	61.01 356	89.36 209	95.40 180

DSMNet-mixed		76.94 326	76.29 325	78.89 344	83.10 367	56.11 374	87.78 331	79.77 372	60.65 364	75.64 339	88.71 316	61.56 317	88.34 366	60.07 359	89.29 211	92.21 314

LPG-MVS_test		89.45 137	88.90 137	91.12 175	94.47 182	81.49 174	95.30 98	96.14 126	86.73 134	85.45 213	95.16 124	69.89 246	98.10 146	87.70 127	89.23 212	93.77 258

LGP-MVS_train				91.12 175	94.47 182	81.49 174		96.14 126	86.73 134	85.45 213	95.16 124	69.89 246	98.10 146	87.70 127	89.23 212	93.77 258

Test_1112_low_res		87.65 193	86.51 206	91.08 180	94.94 161	79.28 240	91.77 270	94.30 236	76.04 313	83.51 265	92.37 227	77.86 148	97.73 179	78.69 253	89.13 214	96.22 148

PatchmatchNet		85.85 250	84.70 256	89.29 252	91.76 269	75.54 305	88.49 324	91.30 313	81.63 249	85.05 227	88.70 317	71.71 220	96.24 287	74.61 292	89.05 215	96.08 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
iter_conf_final		89.42 140	88.69 141	91.60 156	95.12 153	82.93 137	95.75 77	92.14 289	87.32 120	87.12 169	94.07 166	67.09 278	97.55 193	90.61 96	89.01 216	94.32 228

iter_conf05		88.85 161	88.08 163	91.17 174	94.27 192	81.64 169	95.18 109	92.15 288	86.23 147	87.28 166	94.07 166	63.89 305	97.55 193	90.63 95	89.00 217	94.32 228

MDTV_nov1_ep13			83.56 271		91.69 274	69.93 351	87.75 332	91.54 307	78.60 290	84.86 230	88.90 312	69.54 252	96.03 294	70.25 313	88.93 218

MIMVSNet		82.59 288	80.53 293	88.76 263	91.51 277	78.32 258	86.57 340	90.13 335	79.32 276	80.70 298	88.69 318	52.98 355	93.07 349	66.03 340	88.86 219	94.90 197

ACMM	84.12 9	89.14 148	88.48 153	91.12 175	94.65 177	81.22 183	95.31 95	96.12 128	85.31 168	85.92 195	94.34 156	70.19 244	98.06 158	85.65 154	88.86 219	94.08 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvsmamba		89.96 122	89.50 118	91.33 168	92.90 240	81.82 165	96.68 33	92.37 280	89.03 67	87.00 171	94.85 137	73.05 208	97.65 183	91.03 86	88.63 221	94.51 215

ACMP	84.23 8	89.01 158	88.35 154	90.99 187	94.73 171	81.27 180	95.07 117	95.89 146	86.48 138	83.67 260	94.30 159	69.33 256	97.99 164	87.10 140	88.55 222	93.72 262
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		89.03 156	88.64 142	90.21 216	90.74 311	79.28 240	95.96 68	95.90 144	84.66 182	85.33 224	92.94 211	74.02 193	97.30 221	89.64 105	88.53 223	94.05 242

jajsoiax		88.24 177	87.50 174	90.48 205	90.89 305	80.14 211	95.31 95	95.65 165	84.97 176	84.24 249	94.02 171	65.31 296	97.42 206	88.56 117	88.52 224	93.89 246

PatchT		82.68 287	81.27 289	86.89 310	90.09 326	70.94 345	84.06 353	90.15 334	74.91 324	85.63 202	83.57 353	69.37 254	94.87 328	65.19 342	88.50 225	94.84 199

bld_raw_conf005		89.19 147	88.56 147	91.09 179	92.62 246	81.17 185	96.45 37	91.24 316	89.08 64	86.16 192	94.82 140	68.16 273	97.63 187	90.03 101	88.46 226	94.47 223

MSDG		84.86 268	83.09 275	90.14 221	93.80 211	80.05 216	89.18 315	93.09 265	78.89 283	78.19 321	91.91 247	65.86 295	97.27 225	68.47 325	88.45 227	93.11 286

MVS-HIRNet		73.70 329	72.20 332	78.18 347	91.81 268	56.42 373	82.94 359	82.58 367	55.24 366	68.88 359	66.48 368	55.32 345	95.13 323	58.12 361	88.42 228	83.01 362

mvs_tets		88.06 183	87.28 181	90.38 211	90.94 301	79.88 222	95.22 105	95.66 163	85.10 173	84.21 250	93.94 176	63.53 306	97.40 213	88.50 118	88.40 229	93.87 249

ET-MVSNet_ETH3D		87.51 204	85.91 229	92.32 124	93.70 216	83.93 105	92.33 256	90.94 323	84.16 187	72.09 354	92.52 223	69.90 245	95.85 303	89.20 110	88.36 230	97.17 117

test_low_dy_conf_001		89.07 153	88.60 146	90.49 202	92.39 251	79.71 227	96.07 62	94.84 217	86.25 146	86.34 187	94.97 130	69.61 251	97.31 219	88.59 116	88.35 231	94.44 225

FIs		90.51 111	90.35 101	90.99 187	93.99 204	80.98 190	95.73 78	97.54 3	89.15 62	86.72 179	94.68 146	81.83 106	97.24 229	85.18 158	88.31 232	94.76 203

MVS_0304		83.46 281	81.92 284	88.10 282	90.63 315	77.49 281	93.26 225	93.75 256	80.04 270	80.44 303	87.24 337	47.94 363	95.55 312	75.79 280	88.16 233	91.26 328

PS-MVSNAJss		89.97 121	89.62 116	91.02 184	91.90 264	80.85 195	95.26 103	95.98 137	86.26 144	86.21 190	94.29 160	79.70 124	97.65 183	88.87 114	88.10 234	94.57 211

CMPMVS	59.16 21	80.52 309	79.20 311	84.48 331	83.98 364	67.63 360	89.95 304	93.84 254	64.79 361	66.81 362	91.14 272	57.93 338	95.17 322	76.25 276	88.10 234	90.65 337
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FC-MVSNet-test		90.27 114	90.18 105	90.53 198	93.71 214	79.85 224	95.77 76	97.59 2	89.31 57	86.27 189	94.67 147	81.93 105	97.01 244	84.26 170	88.09 236	94.71 204

ACMMP++															88.01 237

D2MVS		85.90 248	85.09 247	88.35 274	90.79 308	77.42 282	91.83 269	95.70 159	80.77 264	80.08 309	90.02 296	66.74 285	96.37 281	81.88 207	87.97 238	91.26 328

UniMVSNet_ETH3D		87.53 203	86.37 209	91.00 186	92.44 249	78.96 245	94.74 139	95.61 167	84.07 190	85.36 223	94.52 153	59.78 331	97.34 218	82.93 186	87.88 239	96.71 135

PVSNet_BlendedMVS		89.98 120	89.70 115	90.82 191	96.12 109	81.25 181	93.92 197	96.83 72	83.49 204	89.10 135	92.26 232	81.04 111	98.85 97	86.72 143	87.86 240	92.35 310

bld_raw_dy_0_64		87.60 200	86.73 194	90.21 216	91.72 270	80.26 208	95.09 116	88.61 350	85.68 158	85.55 203	94.38 155	63.93 304	96.66 258	87.73 126	87.84 241	93.72 262

anonymousdsp		87.84 186	87.09 184	90.12 222	89.13 336	80.54 203	94.67 144	95.55 170	82.05 233	83.82 256	92.12 237	71.47 225	97.15 234	87.15 136	87.80 242	92.67 299

Anonymous20240529		88.09 181	86.59 203	92.58 112	96.53 98	81.92 164	95.99 65	95.84 149	74.11 331	89.06 137	95.21 123	61.44 318	98.81 100	83.67 179	87.47 243	97.01 124

ACMMP++_ref															87.47 243

XVG-ACMP-BASELINE		86.00 246	84.84 254	89.45 250	91.20 288	78.00 265	91.70 273	95.55 170	85.05 175	82.97 273	92.25 233	54.49 349	97.48 199	82.93 186	87.45 245	92.89 294

EI-MVSNet		89.10 149	88.86 139	89.80 238	91.84 266	78.30 259	93.70 207	95.01 204	85.73 156	87.15 167	95.28 119	79.87 121	97.21 232	83.81 176	87.36 246	93.88 248

MVSTER		88.84 162	88.29 158	90.51 201	92.95 238	80.44 205	93.73 204	95.01 204	84.66 182	87.15 167	93.12 206	72.79 212	97.21 232	87.86 124	87.36 246	93.87 249

EG-PatchMatch MVS		82.37 290	80.34 296	88.46 271	90.27 322	79.35 235	92.80 243	94.33 235	77.14 304	73.26 351	90.18 292	47.47 365	96.72 255	70.25 313	87.32 248	89.30 348

EPMVS		83.90 279	82.70 281	87.51 292	90.23 324	72.67 328	88.62 323	81.96 369	81.37 254	85.01 228	88.34 321	66.31 290	94.45 329	75.30 285	87.12 249	95.43 179

tpm2		84.08 275	82.94 277	87.48 295	91.39 282	71.27 339	89.23 314	90.37 331	71.95 347	84.64 232	89.33 307	67.30 274	96.55 271	75.17 286	87.09 250	94.63 206

CostFormer		85.77 252	84.94 251	88.26 277	91.16 292	72.58 332	89.47 310	91.04 321	76.26 311	86.45 184	89.97 298	70.74 234	96.86 253	82.35 197	87.07 251	95.34 183

Patchmatch-test		81.37 302	79.30 308	87.58 291	90.92 303	74.16 316	80.99 362	87.68 356	70.52 353	76.63 333	88.81 313	71.21 226	92.76 351	60.01 360	86.93 252	95.83 167

RRT_MVS		89.09 151	88.62 145	90.49 202	92.85 241	79.65 228	96.41 39	94.41 231	88.22 92	85.50 209	94.77 142	69.36 255	97.31 219	89.33 108	86.73 253	94.51 215

LTVRE_ROB	82.13 13	86.26 244	84.90 252	90.34 214	94.44 186	81.50 172	92.31 258	94.89 212	83.03 214	79.63 315	92.67 219	69.69 249	97.79 173	71.20 307	86.26 254	91.72 319
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	83.96 276	82.04 283	89.74 239	95.28 144	79.75 225	94.25 172	92.28 284	75.17 321	78.02 324	93.77 186	58.60 336	97.84 172	65.06 345	85.92 255	91.63 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		85.07 264	84.27 261	87.48 295	92.91 239	70.62 347	91.69 274	92.46 278	76.20 312	82.67 277	95.22 122	63.94 303	97.29 224	77.51 265	85.80 256	94.53 213

USDC		82.76 285	81.26 290	87.26 299	91.17 290	74.55 310	89.27 312	93.39 261	78.26 295	75.30 341	92.08 241	54.43 350	96.63 260	71.64 305	85.79 257	90.61 338

GBi-Net		87.26 212	85.98 225	91.08 180	94.01 200	83.10 128	95.14 113	94.94 207	83.57 200	84.37 241	91.64 253	66.59 287	96.34 284	78.23 257	85.36 258	93.79 254

test1		87.26 212	85.98 225	91.08 180	94.01 200	83.10 128	95.14 113	94.94 207	83.57 200	84.37 241	91.64 253	66.59 287	96.34 284	78.23 257	85.36 258	93.79 254

FMVSNet3		87.40 209	86.11 220	91.30 169	93.79 213	83.64 114	94.20 176	94.81 220	83.89 194	84.37 241	91.87 249	68.45 270	96.56 269	78.23 257	85.36 258	93.70 264

FMVSNet2		87.19 220	85.82 231	91.30 169	94.01 200	83.67 113	94.79 136	94.94 207	83.57 200	83.88 255	92.05 244	66.59 287	96.51 272	77.56 264	85.01 261	93.73 261

ACMH	80.38 17	85.36 257	83.68 269	90.39 209	94.45 185	80.63 200	94.73 140	94.85 215	82.09 232	77.24 328	92.65 220	60.01 329	97.58 190	72.25 304	84.87 262	92.96 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				88.24 278	91.88 265	77.05 287		92.92 268	85.54 163	80.13 308	93.30 198	57.29 339	96.20 288	72.46 303	84.71 263	91.49 323

JIA-IIPM		81.04 305	78.98 315	87.25 300	88.64 340	73.48 321	81.75 361	89.61 347	73.19 338	82.05 283	73.71 364	66.07 294	95.87 302	71.18 309	84.60 264	92.41 307

OpenMVS_ROB	74.94 19	79.51 317	77.03 323	86.93 307	87.00 352	76.23 299	92.33 256	90.74 328	68.93 356	74.52 345	88.23 324	49.58 360	96.62 261	57.64 362	84.29 265	87.94 359

AllTest		83.42 282	81.39 288	89.52 247	95.01 155	77.79 273	93.12 230	90.89 325	77.41 300	76.12 336	93.34 194	54.08 351	97.51 197	68.31 327	84.27 266	93.26 277

TestCases				89.52 247	95.01 155	77.79 273		90.89 325	77.41 300	76.12 336	93.34 194	54.08 351	97.51 197	68.31 327	84.27 266	93.26 277

tpm		84.73 269	84.02 264	86.87 311	90.33 321	68.90 354	89.06 317	89.94 340	80.85 263	85.75 197	89.86 300	68.54 269	95.97 297	77.76 261	84.05 268	95.75 170

FMVSNet1		85.85 250	84.11 263	91.08 180	92.81 242	83.10 128	95.14 113	94.94 207	81.64 248	82.68 276	91.64 253	59.01 335	96.34 284	75.37 284	83.78 269	93.79 254

ADS-MVSNet2		81.66 297	79.71 305	87.50 293	91.35 284	74.19 315	83.33 356	88.48 352	72.90 341	82.24 281	85.77 346	64.98 298	93.20 347	64.57 346	83.74 270	95.12 186

ADS-MVSNet		81.56 299	79.78 303	86.90 309	91.35 284	71.82 336	83.33 356	89.16 349	72.90 341	82.24 281	85.77 346	64.98 298	93.76 340	64.57 346	83.74 270	95.12 186

XXY-MVS		87.65 193	86.85 190	90.03 226	92.14 256	80.60 202	93.76 203	95.23 194	82.94 217	84.60 233	94.02 171	74.27 186	95.49 318	81.04 220	83.68 272	94.01 244

test_0402		81.30 304	79.17 312	87.67 289	93.19 228	78.17 262	92.98 237	91.71 301	75.25 320	76.02 338	90.31 290	59.23 333	96.37 281	50.22 367	83.63 273	88.47 357

tpmvs		83.35 284	82.07 282	87.20 304	91.07 295	71.00 344	88.31 327	91.70 302	78.91 282	80.49 302	87.18 338	69.30 259	97.08 239	68.12 330	83.56 274	93.51 271

pmmvs5		84.21 274	82.84 280	88.34 275	88.95 338	76.94 288	92.41 252	91.91 300	75.63 316	80.28 304	91.18 269	64.59 300	95.57 311	77.09 270	83.47 275	92.53 303

pmmvs4		85.43 256	83.86 267	90.16 219	90.02 328	82.97 136	90.27 294	92.67 275	75.93 314	80.73 297	91.74 252	71.05 228	95.73 309	78.85 252	83.46 276	91.78 318

test0.0.03 1		82.41 289	81.69 285	84.59 330	88.23 346	72.89 325	90.24 296	87.83 354	83.41 206	79.86 312	89.78 302	67.25 275	88.99 365	65.18 343	83.42 277	91.90 317

tpmrst		85.35 258	84.99 248	86.43 314	90.88 306	67.88 358	88.71 321	91.43 311	80.13 268	86.08 194	88.80 315	73.05 208	96.02 295	82.48 194	83.40 278	95.40 180

nrg030		91.08 98	90.39 100	93.17 83	93.07 232	86.91 22	96.41 39	96.26 116	88.30 88	88.37 145	94.85 137	82.19 99	97.64 186	91.09 84	82.95 279	94.96 193

cl22		86.78 229	85.98 225	89.18 255	92.34 252	77.62 279	90.84 287	94.13 244	81.33 255	83.97 254	90.15 293	73.96 194	96.60 266	84.19 171	82.94 280	93.33 275

miper_ehance_all_eth		87.22 217	86.62 202	89.02 260	92.13 257	77.40 283	90.91 286	94.81 220	81.28 256	84.32 246	90.08 295	79.26 130	96.62 261	83.81 176	82.94 280	93.04 289

miper_enhance_ethall		86.90 226	86.18 217	89.06 258	91.66 275	77.58 280	90.22 298	94.82 219	79.16 280	84.48 237	89.10 309	79.19 131	96.66 258	84.06 172	82.94 280	92.94 292

ACMH+	81.04 14	85.05 265	83.46 272	89.82 235	94.66 176	79.37 234	94.44 159	94.12 245	82.19 231	78.04 323	92.82 215	58.23 337	97.54 195	73.77 297	82.90 283	92.54 302

VPA-MVSNet		89.62 130	88.96 134	91.60 156	93.86 208	82.89 139	95.46 90	97.33 25	87.91 101	88.43 144	93.31 197	74.17 190	97.40 213	87.32 134	82.86 284	94.52 214

IterMVS-LS		88.36 174	87.91 169	89.70 242	93.80 211	78.29 260	93.73 204	95.08 203	85.73 156	84.75 231	91.90 248	79.88 120	96.92 249	83.83 175	82.51 285	93.89 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testgi		80.94 308	80.20 299	83.18 337	87.96 350	66.29 361	91.28 279	90.70 329	83.70 197	78.12 322	92.84 213	51.37 357	90.82 361	63.34 349	82.46 286	92.43 306

WR-MVS		88.38 172	87.67 172	90.52 200	93.30 226	80.18 209	93.26 225	95.96 139	88.57 80	85.47 212	92.81 216	76.12 160	96.91 250	81.24 218	82.29 287	94.47 223

tpm cat1		81.96 291	80.27 297	87.01 306	91.09 294	71.02 343	87.38 336	91.53 308	66.25 359	80.17 305	86.35 342	68.22 272	96.15 291	69.16 321	82.29 287	93.86 251

v1192		87.25 214	86.33 211	90.00 230	90.76 310	79.04 244	93.80 201	95.48 176	82.57 224	85.48 211	91.18 269	73.38 206	97.42 206	82.30 198	82.06 289	93.53 268

v1144		87.61 199	86.79 193	90.06 225	91.01 296	79.34 236	93.95 195	95.42 186	83.36 208	85.66 201	91.31 265	74.98 178	97.42 206	83.37 180	82.06 289	93.42 274

v1240		86.78 229	85.85 230	89.56 245	90.45 320	77.79 273	93.61 209	95.37 189	81.65 247	85.43 216	91.15 271	71.50 224	97.43 205	81.47 216	82.05 291	93.47 272

Anonymous20231206		81.03 306	79.77 304	84.82 329	87.85 351	70.26 349	91.42 278	92.08 291	73.67 334	77.75 325	89.25 308	62.43 312	93.08 348	61.50 355	82.00 292	91.12 333

V42		87.68 191	86.86 189	90.15 220	90.58 316	80.14 211	94.24 174	95.28 192	83.66 198	85.67 200	91.33 262	74.73 182	97.41 211	84.43 169	81.83 293	92.89 294

v1921920		86.97 225	86.06 223	89.69 243	90.53 319	78.11 264	93.80 201	95.43 184	81.90 241	85.33 224	91.05 275	72.66 213	97.41 211	82.05 203	81.80 294	93.53 268

v2v482		87.84 186	87.06 185	90.17 218	90.99 297	79.23 243	94.00 193	95.13 198	84.87 177	85.53 206	92.07 243	74.45 184	97.45 202	84.71 166	81.75 295	93.85 252

Anonymous20231211		86.59 236	85.13 246	90.98 189	96.52 99	81.50 172	96.14 55	96.16 125	73.78 333	83.65 261	92.15 235	63.26 308	97.37 217	82.82 190	81.74 296	94.06 241

v144192		87.19 220	86.35 210	89.74 239	90.64 314	78.24 261	93.92 197	95.43 184	81.93 239	85.51 208	91.05 275	74.21 189	97.45 202	82.86 188	81.56 297	93.53 268

cl____		86.52 238	85.78 232	88.75 264	92.03 261	76.46 294	90.74 288	94.30 236	81.83 245	83.34 269	90.78 282	75.74 170	96.57 267	81.74 211	81.54 298	93.22 281

DIV-MVS_self_test		86.53 237	85.78 232	88.75 264	92.02 262	76.45 295	90.74 288	94.30 236	81.83 245	83.34 269	90.82 280	75.75 168	96.57 267	81.73 212	81.52 299	93.24 280

Anonymous20240521		80.44 310	79.21 310	84.11 335	85.75 359	67.89 357	92.86 241	93.23 263	75.61 317	75.59 340	87.47 334	50.03 358	94.33 332	71.14 310	81.21 300	90.12 343

OurMVSNet-221017-0		85.35 258	84.64 258	87.49 294	90.77 309	72.59 331	94.01 192	94.40 232	84.72 181	79.62 316	93.17 203	61.91 315	96.72 255	81.99 204	81.16 301	93.16 284

FMVSNet5		81.52 300	79.60 306	87.27 298	91.17 290	77.95 266	91.49 277	92.26 285	76.87 305	76.16 335	87.91 329	51.67 356	92.34 353	67.74 331	81.16 301	91.52 322

CP-MVSNet		87.63 196	87.26 183	88.74 266	93.12 230	76.59 293	95.29 100	96.58 99	88.43 83	83.49 266	92.98 210	75.28 174	95.83 304	78.97 251	81.15 303	93.79 254

c3_l		87.14 222	86.50 207	89.04 259	92.20 254	77.26 284	91.22 282	94.70 224	82.01 236	84.34 245	90.43 288	78.81 134	96.61 264	83.70 178	81.09 304	93.25 279

IterMVS-SCA-FT		85.45 255	84.53 260	88.18 280	91.71 272	76.87 289	90.19 299	92.65 276	85.40 166	81.44 289	90.54 285	66.79 283	95.00 327	81.04 220	81.05 305	92.66 300

TinyColmap		79.76 316	77.69 318	85.97 318	91.71 272	73.12 323	89.55 306	90.36 332	75.03 322	72.03 355	90.19 291	46.22 366	96.19 290	63.11 350	81.03 306	88.59 356

UniMVSNet_NR-MVSNet		89.92 125	89.29 127	91.81 150	93.39 223	83.72 111	94.43 160	97.12 44	89.80 42	86.46 182	93.32 196	83.16 84	97.23 230	84.92 161	81.02 307	94.49 220

DU-MVS		89.34 145	88.50 150	91.85 147	93.04 234	83.72 111	94.47 157	96.59 98	89.50 51	86.46 182	93.29 199	77.25 150	97.23 230	84.92 161	81.02 307	94.59 209

PS-CasMVS		87.32 211	86.88 188	88.63 269	92.99 237	76.33 298	95.33 94	96.61 97	88.22 92	83.30 271	93.07 208	73.03 210	95.79 307	78.36 255	81.00 309	93.75 260

IterMVS		84.88 267	83.98 266	87.60 290	91.44 278	76.03 300	90.18 300	92.41 279	83.24 211	81.06 295	90.42 289	66.60 286	94.28 334	79.46 244	80.98 310	92.48 304
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		89.80 128	89.07 132	92.01 133	93.60 218	84.52 88	94.78 137	97.47 10	89.26 58	86.44 185	92.32 229	82.10 100	97.39 216	84.81 164	80.84 311	94.12 236

LF4IMVS		80.37 311	79.07 314	84.27 334	86.64 353	69.87 352	89.39 311	91.05 320	76.38 308	74.97 343	90.00 297	47.85 364	94.25 335	74.55 293	80.82 312	88.69 355

v10		87.25 214	86.38 208	89.85 233	91.19 289	79.50 230	94.48 154	95.45 181	83.79 196	83.62 262	91.19 267	75.13 175	97.42 206	81.94 205	80.60 313	92.63 301

tfpnnormal		84.72 270	83.23 274	89.20 254	92.79 243	80.05 216	94.48 154	95.81 151	82.38 227	81.08 294	91.21 266	69.01 263	96.95 247	61.69 354	80.59 314	90.58 341

test_part1		89.00 159	87.99 165	92.04 132	95.94 121	83.81 109	96.14 55	96.05 134	86.44 140	85.69 199	93.73 189	71.57 222	97.66 181	85.80 153	80.54 315	94.66 205

WR-MVS_H		87.80 188	87.37 178	89.10 257	93.23 227	78.12 263	95.61 86	97.30 29	87.90 102	83.72 258	92.01 245	79.65 128	96.01 296	76.36 274	80.54 315	93.16 284

VPNet		88.20 178	87.47 176	90.39 209	93.56 219	79.46 231	94.04 189	95.54 172	88.67 76	86.96 172	94.58 152	69.33 256	97.15 234	84.05 173	80.53 317	94.56 212

v7n		86.81 227	85.76 235	89.95 231	90.72 312	79.25 242	95.07 117	95.92 141	84.45 185	82.29 279	90.86 278	72.60 215	97.53 196	79.42 248	80.52 318	93.08 288

v8		87.50 206	86.71 196	89.89 232	91.37 283	79.40 233	94.50 153	95.38 187	84.81 179	83.60 263	91.33 262	76.05 161	97.42 206	82.84 189	80.51 319	92.84 296

EU-MVSNet		81.32 303	80.95 291	82.42 341	88.50 342	63.67 367	93.32 218	91.33 312	64.02 362	80.57 301	92.83 214	61.21 322	92.27 354	76.34 275	80.38 320	91.32 326

Patchmtry		82.71 286	80.93 292	88.06 283	90.05 327	76.37 297	84.74 351	91.96 298	72.28 346	81.32 292	87.87 330	71.03 229	95.50 317	68.97 322	80.15 321	92.32 311

NR-MVSNet		88.58 170	87.47 176	91.93 141	93.04 234	84.16 101	94.77 138	96.25 118	89.05 65	80.04 310	93.29 199	79.02 132	97.05 242	81.71 213	80.05 322	94.59 209

Baseline_NR-MVSNet		87.07 223	86.63 201	88.40 272	91.44 278	77.87 270	94.23 175	92.57 277	84.12 189	85.74 198	92.08 241	77.25 150	96.04 293	82.29 199	79.94 323	91.30 327

dp		81.47 301	80.23 298	85.17 327	89.92 330	65.49 364	86.74 338	90.10 336	76.30 310	81.10 293	87.12 339	62.81 310	95.92 299	68.13 329	79.88 324	94.09 239

TranMVSNet+NR-MVSNet		88.84 162	87.95 167	91.49 160	92.68 245	83.01 134	94.92 127	96.31 112	89.88 41	85.53 206	93.85 183	76.63 158	96.96 246	81.91 206	79.87 325	94.50 218

miper_lstm_enhance		85.27 261	84.59 259	87.31 297	91.28 287	74.63 309	87.69 333	94.09 246	81.20 260	81.36 291	89.85 301	74.97 179	94.30 333	81.03 222	79.84 326	93.01 290

v148		87.04 224	86.32 212	89.21 253	90.94 301	77.26 284	93.71 206	94.43 230	84.84 178	84.36 244	90.80 281	76.04 162	97.05 242	82.12 201	79.60 327	93.31 276

IB-MVS	80.51 15	85.24 262	83.26 273	91.19 172	92.13 257	79.86 223	91.75 271	91.29 314	83.28 210	80.66 299	88.49 319	61.28 319	98.46 119	80.99 223	79.46 328	95.25 184
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		86.50 239	85.77 234	88.68 267	91.94 263	75.81 303	90.47 292	94.89 212	82.05 233	84.05 251	90.46 287	75.96 163	96.77 254	82.76 192	79.36 329	93.46 273

baseline1		88.10 180	87.28 181	90.57 196	94.96 159	80.07 214	94.27 171	91.29 314	86.74 133	87.41 162	94.00 173	76.77 155	96.20 288	80.77 226	79.31 330	95.44 178

our_test_3		81.93 292	80.46 295	86.33 316	88.46 343	73.48 321	88.46 325	91.11 317	76.46 306	76.69 332	88.25 323	66.89 281	94.36 331	68.75 323	79.08 331	91.14 332

PEN-MVS		86.80 228	86.27 215	88.40 272	92.32 253	75.71 304	95.18 109	96.38 110	87.97 99	82.82 275	93.15 204	73.39 205	95.92 299	76.15 278	79.03 332	93.59 266

pm-mvs1		86.61 234	85.54 237	89.82 235	91.44 278	80.18 209	95.28 102	94.85 215	83.84 195	81.66 287	92.62 221	72.45 218	96.48 274	79.67 243	78.06 333	92.82 297

h-mvs33		90.80 100	90.15 106	92.75 102	96.01 116	82.66 148	95.43 91	95.53 173	89.80 42	93.08 65	95.64 112	75.77 165	99.00 78	92.07 61	78.05 334	96.60 137

SixPastTwentyTwo		83.91 278	82.90 278	86.92 308	90.99 297	70.67 346	93.48 213	91.99 295	85.54 163	77.62 327	92.11 239	60.59 325	96.87 252	76.05 279	77.75 335	93.20 282

ppachtmachnet_test		81.84 293	80.07 301	87.15 305	88.46 343	74.43 313	89.04 318	92.16 287	75.33 319	77.75 325	88.99 310	66.20 291	95.37 320	65.12 344	77.60 336	91.65 320

MIMVSNet1		79.38 318	77.28 320	85.69 322	86.35 354	73.67 318	91.61 276	92.75 273	78.11 298	72.64 353	88.12 325	48.16 362	91.97 357	60.32 357	77.49 337	91.43 325

DTE-MVSNet		86.11 245	85.48 239	87.98 284	91.65 276	74.92 308	94.93 126	95.75 156	87.36 119	82.26 280	93.04 209	72.85 211	95.82 305	74.04 294	77.46 338	93.20 282

N_pmnet		68.89 332	68.44 335	70.23 351	89.07 337	28.79 383	88.06 328	19.50 384	69.47 355	71.86 356	84.93 348	61.24 321	91.75 358	54.70 364	77.15 339	90.15 342

AUN-MVS		87.78 189	86.54 205	91.48 161	94.82 169	81.05 188	93.91 200	93.93 248	83.00 215	86.93 173	93.53 192	69.50 253	97.67 180	86.14 146	77.12 340	95.73 172

hse-mvs2		89.88 127	89.34 125	91.51 159	94.83 168	81.12 187	93.94 196	93.91 251	89.80 42	93.08 65	93.60 191	75.77 165	97.66 181	92.07 61	77.07 341	95.74 171

test20.03		79.95 314	79.08 313	82.55 340	85.79 358	67.74 359	91.09 284	91.08 318	81.23 259	74.48 346	89.96 299	61.63 316	90.15 362	60.08 358	76.38 342	89.76 344

FPMVS		64.63 334	62.55 336	70.88 350	70.80 374	56.71 371	84.42 352	84.42 363	51.78 368	49.57 368	81.61 358	23.49 375	81.48 371	40.61 372	76.25 343	74.46 367

EGC-MVSNET		61.97 335	56.37 339	78.77 345	89.63 334	73.50 320	89.12 316	82.79 366	0.21 381	1.24 382	84.80 349	39.48 369	90.04 363	44.13 369	75.94 344	72.79 368

pmmvs6		83.42 282	81.60 286	88.87 262	88.01 349	77.87 270	94.96 123	94.24 239	74.67 327	78.80 319	91.09 274	60.17 328	96.49 273	77.06 271	75.40 345	92.23 313

new_pmnet		72.15 330	70.13 333	78.20 346	82.95 368	65.68 362	83.91 354	82.40 368	62.94 363	64.47 363	79.82 360	42.85 368	86.26 368	57.41 363	74.44 346	82.65 364

MDA-MVSNet_test_wron		79.21 320	77.19 322	85.29 325	88.22 347	72.77 327	85.87 343	90.06 337	74.34 329	62.62 365	87.56 333	66.14 292	91.99 356	66.90 338	73.01 347	91.10 335

YYNet1		79.22 319	77.20 321	85.28 326	88.20 348	72.66 329	85.87 343	90.05 339	74.33 330	62.70 364	87.61 332	66.09 293	92.03 355	66.94 335	72.97 348	91.15 331

Patchmatch-RL test		81.67 296	79.96 302	86.81 312	85.42 361	71.23 340	82.17 360	87.50 357	78.47 291	77.19 329	82.50 357	70.81 233	93.48 343	82.66 193	72.89 349	95.71 173

pmmvs-eth3d		80.97 307	78.72 316	87.74 287	84.99 363	79.97 221	90.11 301	91.65 304	75.36 318	73.51 349	86.03 343	59.45 332	93.96 339	75.17 286	72.21 350	89.29 349

PM-MVS		78.11 324	76.12 326	84.09 336	83.54 366	70.08 350	88.97 319	85.27 361	79.93 271	74.73 344	86.43 340	34.70 371	93.48 343	79.43 247	72.06 351	88.72 354

Gipuma		57.99 338	54.91 340	67.24 353	88.51 341	65.59 363	52.21 372	90.33 333	43.58 371	42.84 372	51.18 373	20.29 378	85.07 369	34.77 373	70.45 352	51.05 372
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
K. test v3		81.59 298	80.15 300	85.91 321	89.89 331	69.42 353	92.57 249	87.71 355	85.56 162	73.44 350	89.71 303	55.58 342	95.52 314	77.17 268	69.76 353	92.78 298

KD-MVS_self_test		80.20 312	79.24 309	83.07 338	85.64 360	65.29 365	91.01 285	93.93 248	78.71 289	76.32 334	86.40 341	59.20 334	92.93 350	72.59 302	69.35 354	91.00 336

CL-MVSNet_self_test		81.74 295	80.53 293	85.36 324	85.96 357	72.45 333	90.25 295	93.07 266	81.24 258	79.85 313	87.29 336	70.93 231	92.52 352	66.95 334	69.23 355	91.11 334

TDRefinement		79.81 315	77.34 319	87.22 303	79.24 371	75.48 306	93.12 230	92.03 293	76.45 307	75.01 342	91.58 258	49.19 361	96.44 278	70.22 315	69.18 356	89.75 345

MDA-MVSNet-bldmvs		78.85 321	76.31 324	86.46 313	89.76 332	73.88 317	88.79 320	90.42 330	79.16 280	59.18 366	88.33 322	60.20 327	94.04 336	62.00 353	68.96 357	91.48 324

ambc				83.06 339	79.99 370	63.51 368	77.47 365	92.86 269		74.34 347	84.45 350	28.74 372	95.06 326	73.06 301	68.89 358	90.61 338

TransMVSNet (Re)		84.43 273	83.06 276	88.54 270	91.72 270	78.44 254	95.18 109	92.82 271	82.73 221	79.67 314	92.12 237	73.49 201	95.96 298	71.10 311	68.73 359	91.21 330

PMVS	47.18 22	52.22 339	48.46 343	63.48 354	45.72 383	46.20 379	73.41 368	78.31 374	41.03 372	30.06 375	65.68 369	6.05 382	83.43 370	30.04 374	65.86 360	60.80 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lessismore_v0				86.04 317	88.46 343	68.78 355		80.59 371		73.01 352	90.11 294	55.39 344	96.43 279	75.06 288	65.06 361	92.90 293

new-patchmatchnet		76.41 327	75.17 329	80.13 343	82.65 369	59.61 369	87.66 334	91.08 318	78.23 296	69.85 358	83.22 354	54.76 347	91.63 360	64.14 348	64.89 362	89.16 351

pmmvs3		71.81 331	68.71 334	81.11 342	75.86 372	70.42 348	86.74 338	83.66 365	58.95 365	68.64 361	80.89 359	36.93 370	89.52 364	63.10 351	63.59 363	83.39 361

UnsupCasMVSNet_eth		80.07 313	78.27 317	85.46 323	85.24 362	72.63 330	88.45 326	94.87 214	82.99 216	71.64 357	88.07 326	56.34 341	91.75 358	73.48 299	63.36 364	92.01 316

LCM-MVSNet		66.00 333	62.16 337	77.51 348	64.51 378	58.29 370	83.87 355	90.90 324	48.17 369	54.69 367	73.31 365	16.83 381	86.75 367	65.47 341	61.67 365	87.48 360

UnsupCasMVSNet_bld		76.23 328	73.27 331	85.09 328	83.79 365	72.92 324	85.65 346	93.47 260	71.52 348	68.84 360	79.08 361	49.77 359	93.21 346	66.81 339	60.52 366	89.13 353

KD-MVS_2432*1600		78.50 322	76.02 327	85.93 319	86.22 355	74.47 311	84.80 349	92.33 281	79.29 277	76.98 330	85.92 344	53.81 353	93.97 337	67.39 332	57.42 367	89.36 346

miper_refine_blended		78.50 322	76.02 327	85.93 319	86.22 355	74.47 311	84.80 349	92.33 281	79.29 277	76.98 330	85.92 344	53.81 353	93.97 337	67.39 332	57.42 367	89.36 346

DeepMVS_CX				56.31 357	74.23 373	51.81 376		56.67 382	44.85 370	48.54 370	75.16 362	27.87 374	58.74 378	40.92 371	52.22 369	58.39 371

PVSNet_0	73.20 20	77.22 325	74.83 330	84.37 332	90.70 313	71.10 342	83.09 358	89.67 346	72.81 343	73.93 348	83.13 355	60.79 324	93.70 341	68.54 324	50.84 370	88.30 358

test_method		50.52 340	48.47 342	56.66 356	52.26 382	18.98 385	41.51 374	81.40 370	10.10 376	44.59 371	75.01 363	28.51 373	68.16 374	53.54 365	49.31 371	82.83 363

PMMVS2		59.60 336	56.40 338	69.21 352	68.83 375	46.58 378	73.02 369	77.48 376	55.07 367	49.21 369	72.95 366	17.43 380	80.04 372	49.32 368	44.33 372	80.99 366

MVE	39.65 23	43.39 341	38.59 347	57.77 355	56.52 380	48.77 377	55.38 371	58.64 381	29.33 375	28.96 376	52.65 372	4.68 383	64.62 377	28.11 375	33.07 373	59.93 370
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 342	42.29 344	46.03 358	65.58 377	37.41 380	73.51 367	64.62 378	33.99 373	28.47 377	47.87 374	19.90 379	67.91 375	22.23 376	24.45 374	32.77 373

ANet_high		58.88 337	54.22 341	72.86 349	56.50 381	56.67 372	80.75 363	86.00 358	73.09 340	37.39 373	64.63 370	22.17 376	79.49 373	43.51 370	23.96 375	82.43 365

EMVS		42.07 343	41.12 345	44.92 359	63.45 379	35.56 382	73.65 366	63.48 379	33.05 374	26.88 378	45.45 375	21.27 377	67.14 376	19.80 377	23.02 376	32.06 374

tmp_tt		35.64 344	39.24 346	24.84 360	14.87 384	23.90 384	62.71 370	51.51 383	6.58 378	36.66 374	62.08 371	44.37 367	30.34 380	52.40 366	22.00 377	20.27 375

wuyk23d		21.27 346	20.48 349	23.63 361	68.59 376	36.41 381	49.57 373	6.85 385	9.37 377	7.89 379	4.46 381	4.03 384	31.37 379	17.47 378	16.07 378	3.12 376

testmvs		8.92 347	11.52 350	1.12 363	1.06 385	0.46 387	86.02 342	0.65 386	0.62 379	2.74 380	9.52 379	0.31 386	0.45 382	2.38 379	0.39 379	2.46 378

test123		8.76 348	11.22 351	1.39 362	0.85 386	0.97 386	85.76 345	0.35 387	0.54 380	2.45 381	8.14 380	0.60 385	0.48 381	2.16 380	0.17 380	2.71 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		22.14 345	29.52 348	0.00 364	0.00 387	0.00 388	0.00 375	95.76 155	0.00 382	0.00 383	94.29 160	75.66 171	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		6.64 350	8.86 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	79.70 124	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		7.82 349	10.43 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	93.88 181	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					98.86 1	85.54 73	98.29 1	97.49 5	89.79 45	96.29 15

test_one_0601					98.58 12	85.83 66		97.44 14	91.05 17	96.78 13	98.06 6	91.45 11

eth-test2					0.00 387

eth-test					0.00 387

test_241102_ONE					98.77 5	85.99 57		97.44 14	90.26 35	97.71 1	97.96 10	92.31 4	99.38 32

save fliter					97.85 50	85.63 71	95.21 106	96.82 74	89.44 52

test0726					98.78 3	85.93 60	97.19 11	97.47 10	90.27 33	97.64 4	98.13 1	91.47 8

GSMVS																96.12 152

test_part2					98.55 13	87.22 18				96.40 14

sam_mvs1												71.70 221				96.12 152

sam_mvs												70.60 235

MTGPA								96.97 53

test_post1							88.00 329				9.81 378	69.31 258	95.53 313	76.65 272

test_post											10.29 377	70.57 239	95.91 301

patchmatchnet-post											83.76 352	71.53 223	96.48 274

MTMP							96.16 52	60.64 380

gm-plane-assit					89.60 335	68.00 356			77.28 303		88.99 310		97.57 191	79.44 246

TEST9					97.53 65	86.49 41	94.07 186	96.78 77	81.61 250	92.77 74	96.20 89	87.71 31	99.12 58

test_8					97.49 68	86.30 50	94.02 191	96.76 80	81.86 243	92.70 78	96.20 89	87.63 32	99.02 71

agg_prior					97.38 71	85.92 62		96.72 86		92.16 89			98.97 83

test_prior4						85.96 59	94.11 181

test_prior				93.82 67	97.29 76	84.49 89		96.88 65					98.87 91			98.11 72

旧先验2							93.36 217		71.25 350	94.37 32			97.13 237	86.74 141

新几何2							93.11 232

无先验							93.28 224	96.26 116	73.95 332				99.05 63	80.56 231		96.59 138

原ACMM2							92.94 239

testdata2													98.75 103	78.30 256

segment_acmp												87.16 40

testdata1							92.15 262		87.94 100

plane_prior7					94.70 174	82.74 143

plane_prior6					94.52 180	82.75 141						74.23 187

plane_prior4											94.86 135

plane_prior3						82.75 141			90.26 35	86.91 175

plane_prior2							95.85 72		90.81 20

plane_prior1					94.59 178

n2								0.00 388

nn								0.00 388

door-mid								85.49 359

test11								96.57 100

door								85.33 360

HQP5-MVS						81.56 170

HQP-NCC					94.17 194		94.39 164		88.81 71	85.43 216

ACMP_Plane					94.17 194		94.39 164		88.81 71	85.43 216

BP-MVS														87.11 138

HQP4-MVS										85.43 216			97.96 166			94.51 215

HQP2-MVS												73.83 197

NP-MVS					94.37 188	82.42 153					93.98 174

MDTV_nov1_ep13_2view						55.91 375	87.62 335		73.32 337	84.59 234		70.33 242		74.65 291		95.50 176

Test By Simon												80.02 119

High-resolution multi-view benchmark