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 7	95.07 9	95.59 3	98.14 36	88.48 6	96.26 39	97.28 28	85.90 134	97.67 3	98.10 2	88.41 17	99.56 7	94.66 10	99.19 1	98.71 12
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 3	95.67 3	95.25 7	98.36 25	87.28 15	95.56 75	97.51 4	89.13 55	97.14 7	97.91 9	91.64 5	99.62 1	94.61 11	99.17 2	98.86 7

SED-MVS		95.91 1	96.28 1	94.80 33	98.77 4	85.99 55	97.13 9	97.44 12	90.31 26	97.71 1	98.07 4	92.31 2	99.58 5	95.66 2	99.13 3	98.84 8

IU-MVS					98.77 4	86.00 54		96.84 63	81.26 239	97.26 6				95.50 7	99.13 3	99.03 4

test_0728_THIRD									90.75 19	97.04 8	98.05 6	92.09 4	99.55 12	95.64 4	99.13 3	99.13 1

test_241102_TWO								97.44 12	90.31 26	97.62 5	98.07 4	91.46 8	99.58 5	95.66 2	99.12 6	98.98 6

DVP-MVS		95.67 2	96.02 2	94.64 40	98.78 2	85.93 58	97.09 11	96.73 77	90.27 28	97.04 8	98.05 6	91.47 6	99.55 12	95.62 5	99.08 7	98.45 32
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 15	98.79 1	86.43 41	97.09 11	97.49 5					99.61 3	95.62 5	99.08 7	98.99 5

APDe-MVS		95.46 4	95.64 4	94.91 22	98.26 28	86.29 49	97.46 2	97.40 17	89.03 58	96.20 12	98.10 2	89.39 13	99.34 33	95.88 1	99.03 9	99.10 3

OPU-MVS				96.21 1	98.00 44	90.85 1	97.13 9				97.08 40	92.59 1	98.94 83	92.25 45	98.99 10	98.84 8

ACMMP_NAP		94.74 14	94.56 16	95.28 6	98.02 43	87.70 10	95.68 68	97.34 19	88.28 79	95.30 20	97.67 13	85.90 49	99.54 16	93.91 17	98.95 11	98.60 17

HPM-MVS++		95.14 9	94.91 11	95.83 2	98.25 29	89.65 2	95.92 59	96.96 52	91.75 7	94.02 35	96.83 51	88.12 21	99.55 12	93.41 24	98.94 12	98.28 45

MP-MVS-pluss		94.21 33	94.00 37	94.85 27	98.17 34	86.65 33	94.82 118	97.17 38	86.26 128	92.83 61	97.87 10	85.57 52	99.56 7	94.37 14	98.92 13	98.34 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP		95.20 7	95.32 8	94.85 27	96.99 76	86.33 45	97.33 3	97.30 26	91.38 11	95.39 18	97.46 17	88.98 16	99.40 28	94.12 15	98.89 14	98.82 10
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		94.96 11	95.33 7	93.88 62	97.25 73	86.69 30	96.19 42	97.11 42	90.42 25	96.95 10	97.27 27	89.53 11	96.91 234	94.38 13	98.85 15	98.03 67
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 6	95.37 6	95.50 5	98.11 37	88.51 5	95.29 86	96.96 52	92.09 3	95.32 19	97.08 40	89.49 12	99.33 36	95.10 8	98.85 15	98.66 14

CP-MVS		94.34 26	94.21 27	94.74 37	98.39 23	86.64 34	97.60 1	97.24 30	88.53 72	92.73 66	97.23 30	85.20 57	99.32 37	92.15 49	98.83 17	98.25 50

ZNCC-MVS		94.47 18	94.28 22	95.03 14	98.52 14	86.96 17	96.85 23	97.32 24	88.24 80	93.15 55	97.04 42	86.17 44	99.62 1	92.40 41	98.81 18	98.52 20

MP-MVS		94.25 29	94.07 34	94.77 35	98.47 17	86.31 47	96.71 26	96.98 48	89.04 57	91.98 82	97.19 34	85.43 54	99.56 7	92.06 52	98.79 19	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		93.89 43	93.65 47	94.62 42	96.84 79	86.43 41	96.69 27	97.49 5	85.15 156	93.56 49	96.28 76	85.60 51	99.31 38	92.45 38	98.79 19	98.12 59

xxxxxxxxxxxxxcwj		94.65 15	94.70 14	94.48 47	97.85 48	85.63 68	95.21 92	95.47 168	89.44 44	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

SF-MVS		94.97 10	94.90 12	95.20 8	97.84 50	87.76 8	96.65 28	97.48 7	87.76 94	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

ACMMPR		94.43 22	94.28 22	94.91 22	98.63 8	86.69 30	96.94 15	97.32 24	88.63 68	93.53 50	97.26 29	85.04 59	99.54 16	92.35 43	98.78 21	98.50 22

HFP-MVS		94.52 17	94.40 19	94.86 25	98.61 9	86.81 24	96.94 15	97.34 19	88.63 68	93.65 43	97.21 32	86.10 45	99.49 23	92.35 43	98.77 24	98.30 41

#test#		94.32 28	94.14 31	94.86 25	98.61 9	86.81 24	96.43 31	97.34 19	87.51 100	93.65 43	97.21 32	86.10 45	99.49 23	91.68 65	98.77 24	98.30 41

ETH3D-3000-0.1		94.61 16	94.44 18	95.12 11	97.70 55	87.71 9	95.98 56	97.44 12	86.67 120	95.25 21	97.31 25	87.73 25	99.24 44	93.11 31	98.76 26	98.40 35

zzz-MVS		94.47 18	94.30 21	95.00 16	98.42 20	86.95 18	95.06 104	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

MTAPA		94.42 24	94.22 25	95.00 16	98.42 20	86.95 18	94.36 154	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

region2R		94.43 22	94.27 24	94.92 20	98.65 7	86.67 32	96.92 19	97.23 32	88.60 70	93.58 47	97.27 27	85.22 56	99.54 16	92.21 46	98.74 29	98.56 19

test9_res														91.91 58	98.71 30	98.07 63

DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 110	96.52 90	80.00 207	94.00 178	97.08 43	90.05 32	95.65 17	97.29 26	89.66 10	98.97 79	93.95 16	98.71 30	98.50 22

9.14			94.47 17		97.79 52		96.08 50	97.44 12	86.13 132	95.10 22	97.40 21	88.34 18	99.22 46	93.25 28	98.70 32

train_agg		93.44 53	93.08 57	94.52 45	97.53 58	86.49 39	94.07 171	96.78 70	81.86 225	92.77 63	96.20 80	87.63 27	99.12 55	92.14 50	98.69 33	97.94 72

DeepC-MVS_fast	89.43 2	94.04 36	93.79 42	94.80 33	97.48 62	86.78 26	95.65 72	96.89 58	89.40 47	92.81 62	96.97 45	85.37 55	99.24 44	90.87 81	98.69 33	98.38 37
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 12	94.94 10	94.58 43	98.25 29	86.33 45	96.11 49	96.62 88	88.14 85	96.10 13	96.96 46	89.09 15	98.94 83	94.48 12	98.68 35	98.48 24
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 84	98.68 35	98.27 47

ETH3 D test6400		93.64 49	93.22 54	94.92 20	97.79 52	86.84 22	95.31 81	97.26 29	82.67 206	93.81 39	96.29 75	87.29 32	99.27 42	89.87 89	98.67 37	98.65 15

test_prior3		93.60 50	93.53 49	93.82 64	97.29 69	84.49 85	94.12 164	96.88 59	87.67 97	92.63 68	96.39 72	86.62 39	98.87 87	91.50 68	98.67 37	98.11 61

test_prior2							94.12 164		87.67 97	92.63 68	96.39 72	86.62 39		91.50 68	98.67 37

MSLP-MVS++		93.72 46	94.08 33	92.65 103	97.31 67	83.43 116	95.79 63	97.33 22	90.03 33	93.58 47	96.96 46	84.87 62	97.76 163	92.19 48	98.66 40	96.76 120

CDPH-MVS		92.83 64	92.30 71	94.44 48	97.79 52	86.11 52	94.06 173	96.66 85	80.09 252	92.77 63	96.63 62	86.62 39	99.04 63	87.40 117	98.66 40	98.17 54

HPM-MVS		94.02 37	93.88 39	94.43 50	98.39 23	85.78 65	97.25 5	97.07 44	86.90 115	92.62 70	96.80 55	84.85 63	99.17 50	92.43 39	98.65 42	98.33 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 38	93.78 43	94.63 41	98.50 15	85.90 63	96.87 21	96.91 56	88.70 66	91.83 88	97.17 36	83.96 74	99.55 12	91.44 70	98.64 43	98.43 34

MCST-MVS		94.45 20	94.20 28	95.19 9	98.46 18	87.50 13	95.00 106	97.12 40	87.13 107	92.51 73	96.30 74	89.24 14	99.34 33	93.46 21	98.62 44	98.73 11

APD-MVS		94.24 30	94.07 34	94.75 36	98.06 41	86.90 21	95.88 60	96.94 54	85.68 140	95.05 23	97.18 35	87.31 31	99.07 58	91.90 61	98.61 45	98.28 45
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		93.96 40	93.72 45	94.68 38	98.43 19	86.22 50	95.30 84	97.78 1	87.45 103	93.26 51	97.33 24	84.62 65	99.51 21	90.75 83	98.57 46	98.32 40

XVS		94.45 20	94.32 20	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 59	97.16 37	85.02 60	99.49 23	91.99 53	98.56 47	98.47 28

X-MVStestdata		88.31 160	86.13 202	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 59	23.41 357	85.02 60	99.49 23	91.99 53	98.56 47	98.47 28

agg_prior1		93.29 57	92.97 61	94.26 55	97.38 64	85.92 60	93.92 181	96.72 79	81.96 219	92.16 78	96.23 78	87.85 22	98.97 79	91.95 57	98.55 49	97.90 76

DELS-MVS		93.43 54	93.25 53	93.97 59	95.42 127	85.04 74	93.06 218	97.13 39	90.74 20	91.84 86	95.09 115	86.32 43	99.21 47	91.22 73	98.45 50	97.65 85
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 35	86.62 35		97.07 44	83.63 182	94.19 30	96.91 48	87.57 29	99.26 43	91.99 53	98.44 51

ETH3D cwj APD-0.16		93.91 41	93.53 49	95.06 13	96.76 81	87.78 7	94.92 111	97.21 34	84.33 170	93.89 38	97.09 39	87.20 33	99.29 41	91.90 61	98.44 51	98.12 59

GST-MVS		94.21 33	93.97 38	94.90 24	98.41 22	86.82 23	96.54 30	97.19 35	88.24 80	93.26 51	96.83 51	85.48 53	99.59 4	91.43 71	98.40 53	98.30 41

HPM-MVS_fast		93.40 55	93.22 54	93.94 61	98.36 25	84.83 76	97.15 8	96.80 69	85.77 137	92.47 74	97.13 38	82.38 86	99.07 58	90.51 85	98.40 53	97.92 75

NCCC		94.81 13	94.69 15	95.17 10	97.83 51	87.46 14	95.66 70	96.93 55	92.34 2	93.94 36	96.58 65	87.74 24	99.44 27	92.83 33	98.40 53	98.62 16

testtj		94.39 25	94.18 29	95.00 16	98.24 31	86.77 28	96.16 43	97.23 32	87.28 105	94.85 24	97.04 42	86.99 37	99.52 20	91.54 67	98.33 56	98.71 12

DeepC-MVS	88.79 3	93.31 56	92.99 60	94.26 55	96.07 105	85.83 64	94.89 113	96.99 47	89.02 59	89.56 115	97.37 23	82.51 85	99.38 29	92.20 47	98.30 57	97.57 90
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 60	93.05 58	93.76 69	98.04 42	84.07 98	96.22 41	97.37 18	84.15 172	90.05 112	95.66 100	87.77 23	99.15 53	89.91 88	98.27 58	98.07 63

原ACMM1				92.01 128	97.34 66	81.05 177		96.81 68	78.89 266	90.45 107	95.92 91	82.65 83	98.84 95	80.68 211	98.26 59	96.14 137

MVS_111021_HR		93.45 52	93.31 52	93.84 63	96.99 76	84.84 75	93.24 211	97.24 30	88.76 64	91.60 93	95.85 94	86.07 47	98.66 101	91.91 58	98.16 60	98.03 67

test12				94.34 53	97.13 74	86.15 51		96.29 105		91.04 103		85.08 58	99.01 70		98.13 61	97.86 79

新几何1				93.10 81	97.30 68	84.35 94		95.56 161	71.09 333	91.26 100	96.24 77	82.87 82	98.86 90	79.19 232	98.10 62	96.07 144

1121		90.42 106	89.49 112	93.20 77	97.27 71	84.46 88	92.63 228	95.51 166	71.01 334	91.20 101	96.21 79	82.92 81	99.05 60	80.56 213	98.07 63	96.10 142

MSP-MVS		95.42 5	95.56 5	94.98 19	98.49 16	86.52 38	96.91 20	97.47 8	91.73 8	96.10 13	96.69 58	89.90 9	99.30 39	94.70 9	98.04 64	99.13 1
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 31	94.17 30	94.43 50	98.21 33	85.78 65	96.40 33	96.90 57	88.20 83	94.33 27	97.40 21	84.75 64	99.03 64	93.35 25	97.99 65	98.48 24

test1172		93.97 39	94.07 34	93.66 71	98.11 37	83.45 115	96.26 39	96.84 63	88.33 76	94.19 30	97.43 18	84.24 69	99.01 70	93.26 27	97.98 66	98.52 20

3Dnovator	86.66 5	91.73 81	90.82 92	94.44 48	94.59 162	86.37 43	97.18 7	97.02 46	89.20 52	84.31 229	96.66 61	73.74 189	99.17 50	86.74 127	97.96 67	97.79 83

CANet		93.54 51	93.20 56	94.55 44	95.65 120	85.73 67	94.94 109	96.69 83	91.89 5	90.69 105	95.88 93	81.99 97	99.54 16	93.14 30	97.95 68	98.39 36

DPM-MVS		92.58 69	91.74 77	95.08 12	96.19 98	89.31 3	92.66 227	96.56 93	83.44 188	91.68 92	95.04 116	86.60 42	98.99 76	85.60 138	97.92 69	96.93 116

APD-MVS_3200maxsize		93.78 45	93.77 44	93.80 68	97.92 45	84.19 96	96.30 35	96.87 61	86.96 111	93.92 37	97.47 16	83.88 75	98.96 82	92.71 37	97.87 70	98.26 49

CPTT-MVS		91.99 75	91.80 76	92.55 107	98.24 31	81.98 154	96.76 25	96.49 95	81.89 224	90.24 109	96.44 71	78.59 131	98.61 106	89.68 90	97.85 71	97.06 109

SR-MVS-dyc-post		93.82 44	93.82 40	93.82 64	97.92 45	84.57 81	96.28 37	96.76 73	87.46 101	93.75 40	97.43 18	84.24 69	99.01 70	92.73 34	97.80 72	97.88 77

RE-MVS-def			93.68 46		97.92 45	84.57 81	96.28 37	96.76 73	87.46 101	93.75 40	97.43 18	82.94 80		92.73 34	97.80 72	97.88 77

test222					96.55 88	81.70 159	92.22 242	95.01 195	68.36 339	90.20 110	96.14 85	80.26 110			97.80 72	96.05 146

3Dnovator+	87.14 4	92.42 72	91.37 80	95.55 4	95.63 121	88.73 4	97.07 13	96.77 72	90.84 16	84.02 234	96.62 63	75.95 156	99.34 33	87.77 112	97.68 75	98.59 18

旧先验1					96.79 80	81.81 157		95.67 153			96.81 53	86.69 38			97.66 76	96.97 114

Regformer-1		94.22 32	94.13 32	94.51 46	95.54 124	86.36 44	94.57 134	96.44 96	91.69 9	94.32 28	96.56 67	87.05 36	99.03 64	93.35 25	97.65 77	98.15 56

Regformer-2		94.33 27	94.22 25	94.68 38	95.54 124	86.75 29	94.57 134	96.70 81	91.84 6	94.41 25	96.56 67	87.19 34	99.13 54	93.50 20	97.65 77	98.16 55

EPNet		91.79 78	91.02 88	94.10 58	90.10 306	85.25 73	96.03 53	92.05 276	92.83 1	87.39 151	95.78 96	79.39 122	99.01 70	88.13 109	97.48 79	98.05 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CS-MVS		92.60 68	92.56 67	92.73 98	95.55 123	82.35 148	96.14 45	96.85 62	88.71 65	91.44 96	91.51 242	84.13 71	98.48 112	91.27 72	97.47 80	97.34 97

testdata				90.49 189	96.40 92	77.89 253		95.37 180	72.51 326	93.63 45	96.69 58	82.08 94	97.65 171	83.08 166	97.39 81	95.94 148

MVS_111021_LR		92.47 71	92.29 72	92.98 87	95.99 108	84.43 92	93.08 216	96.09 120	88.20 83	91.12 102	95.72 99	81.33 102	97.76 163	91.74 63	97.37 82	96.75 121

abl_6		93.18 61	93.05 58	93.57 73	97.52 60	84.27 95	95.53 76	96.67 84	87.85 91	93.20 54	97.22 31	80.35 107	99.18 49	91.91 58	97.21 83	97.26 100

MVSFormer		91.68 83	91.30 81	92.80 94	93.86 191	83.88 103	95.96 57	95.90 136	84.66 166	91.76 89	94.91 119	77.92 138	97.30 202	89.64 91	97.11 84	97.24 101

lupinMVS		90.92 94	90.21 98	93.03 85	93.86 191	83.88 103	92.81 224	93.86 240	79.84 255	91.76 89	94.29 142	77.92 138	98.04 148	90.48 86	97.11 84	97.17 105

EIA-MVS		91.95 76	91.94 74	91.98 131	95.16 137	80.01 206	95.36 78	96.73 77	88.44 73	89.34 119	92.16 215	83.82 76	98.45 118	89.35 94	97.06 86	97.48 93

MG-MVS		91.77 79	91.70 78	92.00 130	97.08 75	80.03 205	93.60 194	95.18 188	87.85 91	90.89 104	96.47 70	82.06 95	98.36 122	85.07 142	97.04 87	97.62 86

jason		90.80 95	90.10 101	92.90 91	93.04 217	83.53 113	93.08 216	94.15 231	80.22 249	91.41 97	94.91 119	76.87 144	97.93 157	90.28 87	96.90 88	97.24 101
jason: jason.
Vis-MVSNet		91.75 80	91.23 83	93.29 74	95.32 131	83.78 106	96.14 45	95.98 128	89.89 35	90.45 107	96.58 65	75.09 166	98.31 129	84.75 148	96.90 88	97.78 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
114514_t		89.51 125	88.50 135	92.54 108	98.11 37	81.99 153	95.16 97	96.36 103	70.19 336	85.81 178	95.25 109	76.70 148	98.63 104	82.07 185	96.86 90	97.00 113

Vis-MVSNet (Re-imp)		89.59 123	89.44 114	90.03 209	95.74 116	75.85 284	95.61 73	90.80 311	87.66 99	87.83 141	95.40 106	76.79 146	96.46 260	78.37 237	96.73 91	97.80 82

API-MVS		90.66 100	90.07 102	92.45 112	96.36 94	84.57 81	96.06 52	95.22 187	82.39 208	89.13 121	94.27 145	80.32 108	98.46 115	80.16 220	96.71 92	94.33 211

MAR-MVS		90.30 107	89.37 116	93.07 84	96.61 85	84.48 87	95.68 68	95.67 153	82.36 210	87.85 140	92.85 193	76.63 150	98.80 97	80.01 221	96.68 93	95.91 149
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 47	93.64 48	93.81 67	95.36 128	84.61 79	94.68 126	95.83 142	91.27 12	93.60 46	96.71 56	85.75 50	98.86 90	92.87 32	96.65 94	97.96 71

Regformer-4		93.91 41	93.81 41	94.19 57	95.36 128	85.47 70	94.68 126	96.41 99	91.60 10	93.75 40	96.71 56	85.95 48	99.10 57	93.21 29	96.65 94	98.01 69

OpenMVS	83.78 11	88.74 150	87.29 165	93.08 82	92.70 226	85.39 71	96.57 29	96.43 98	78.74 271	80.85 279	96.07 87	69.64 239	99.01 70	78.01 243	96.65 94	94.83 187

ETV-MVS		92.74 66	92.66 65	92.97 88	95.20 136	84.04 100	95.07 101	96.51 94	90.73 21	92.96 58	91.19 249	84.06 72	98.34 125	91.72 64	96.54 97	96.54 128

QAPM		89.51 125	88.15 146	93.59 72	94.92 147	84.58 80	96.82 24	96.70 81	78.43 275	83.41 250	96.19 83	73.18 197	99.30 39	77.11 252	96.54 97	96.89 118

IS-MVSNet		91.43 85	91.09 87	92.46 111	95.87 114	81.38 170	96.95 14	93.69 245	89.72 40	89.50 117	95.98 89	78.57 132	97.77 162	83.02 168	96.50 99	98.22 52

DP-MVS Recon		91.95 76	91.28 82	93.96 60	98.33 27	85.92 60	94.66 129	96.66 85	82.69 205	90.03 113	95.82 95	82.30 89	99.03 64	84.57 150	96.48 100	96.91 117

CANet_DTU		90.26 109	89.41 115	92.81 93	93.46 205	83.01 128	93.48 197	94.47 219	89.43 46	87.76 144	94.23 146	70.54 229	99.03 64	84.97 143	96.39 101	96.38 130

UGNet		89.95 115	88.95 126	92.95 89	94.51 165	83.31 119	95.70 67	95.23 185	89.37 48	87.58 146	93.94 156	64.00 289	98.78 98	83.92 157	96.31 102	96.74 122
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 48	93.41 51	94.41 52	96.59 86	86.78 26	94.40 146	93.93 237	89.77 38	94.21 29	95.59 102	87.35 30	98.61 106	92.72 36	96.15 103	97.83 81

PVSNet_Blended		90.73 97	90.32 97	91.98 131	96.12 100	81.25 172	92.55 232	96.83 65	82.04 217	89.10 122	92.56 203	81.04 104	98.85 93	86.72 129	95.91 104	95.84 153

PS-MVSNAJ		91.18 91	90.92 89	91.96 133	95.26 134	82.60 143	92.09 247	95.70 151	86.27 127	91.84 86	92.46 205	79.70 117	98.99 76	89.08 97	95.86 105	94.29 212

ACMMP		93.24 59	92.88 63	94.30 54	98.09 40	85.33 72	96.86 22	97.45 11	88.33 76	90.15 111	97.03 44	81.44 100	99.51 21	90.85 82	95.74 106	98.04 66
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		88.30 161	88.32 142	88.27 260	94.71 157	72.41 315	93.15 212	90.98 305	87.77 93	79.25 301	91.96 227	78.35 135	95.75 291	83.04 167	95.62 107	96.65 124

CHOSEN 1792x2688		88.84 147	87.69 155	92.30 121	96.14 99	81.42 169	90.01 285	95.86 140	74.52 310	87.41 148	93.94 156	75.46 163	98.36 122	80.36 216	95.53 108	97.12 108

AdaColmap		89.89 118	89.07 123	92.37 117	97.41 63	83.03 126	94.42 145	95.92 133	82.81 203	86.34 171	94.65 131	73.89 185	99.02 68	80.69 210	95.51 109	95.05 175

MVS		87.44 191	86.10 205	91.44 154	92.61 228	83.62 111	92.63 228	95.66 155	67.26 340	81.47 271	92.15 216	77.95 137	98.22 133	79.71 224	95.48 110	92.47 288

UA-Net		92.83 64	92.54 68	93.68 70	96.10 103	84.71 78	95.66 70	96.39 101	91.92 4	93.22 53	96.49 69	83.16 78	98.87 87	84.47 151	95.47 111	97.45 95

xiu_mvs_v2_base		91.13 92	90.89 91	91.86 138	94.97 143	82.42 144	92.24 241	95.64 158	86.11 133	91.74 91	93.14 185	79.67 120	98.89 86	89.06 98	95.46 112	94.28 213

casdiffmvs		92.51 70	92.43 70	92.74 97	94.41 171	81.98 154	94.54 136	96.23 111	89.57 42	91.96 83	96.17 84	82.58 84	98.01 150	90.95 79	95.45 113	98.23 51

PVSNet_Blended_VisFu		91.38 86	90.91 90	92.80 94	96.39 93	83.17 122	94.87 115	96.66 85	83.29 192	89.27 120	94.46 137	80.29 109	99.17 50	87.57 115	95.37 114	96.05 146

PAPM_NR		91.22 90	90.78 93	92.52 109	97.60 57	81.46 167	94.37 153	96.24 110	86.39 126	87.41 148	94.80 126	82.06 95	98.48 112	82.80 174	95.37 114	97.61 87

CHOSEN 280x420		85.15 248	83.99 250	88.65 251	92.47 229	78.40 241	79.68 347	92.76 259	74.90 307	81.41 273	89.59 287	69.85 237	95.51 298	79.92 223	95.29 116	92.03 298

TAPA-MVS	84.62 6	88.16 164	87.01 172	91.62 148	96.64 84	80.65 188	94.39 148	96.21 115	76.38 291	86.19 174	95.44 103	79.75 115	98.08 145	62.75 334	95.29 116	96.13 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline		92.39 73	92.29 72	92.69 102	94.46 168	81.77 158	94.14 163	96.27 106	89.22 51	91.88 84	96.00 88	82.35 87	97.99 152	91.05 75	95.27 118	98.30 41

LS3D		87.89 170	86.32 197	92.59 105	96.07 105	82.92 131	95.23 90	94.92 203	75.66 298	82.89 257	95.98 89	72.48 205	99.21 47	68.43 308	95.23 119	95.64 160

MVS_Test		91.31 88	91.11 85	91.93 135	94.37 172	80.14 198	93.46 199	95.80 144	86.46 123	91.35 99	93.77 167	82.21 91	98.09 144	87.57 115	94.95 120	97.55 92

PAPR		90.02 112	89.27 120	92.29 122	95.78 115	80.95 181	92.68 226	96.22 112	81.91 222	86.66 164	93.75 169	82.23 90	98.44 119	79.40 231	94.79 121	97.48 93

xiu_mvs_v1_base_debu		90.64 101	90.05 103	92.40 113	93.97 188	84.46 88	93.32 201	95.46 169	85.17 153	92.25 75	94.03 148	70.59 225	98.57 108	90.97 76	94.67 122	94.18 214

xiu_mvs_v1_base		90.64 101	90.05 103	92.40 113	93.97 188	84.46 88	93.32 201	95.46 169	85.17 153	92.25 75	94.03 148	70.59 225	98.57 108	90.97 76	94.67 122	94.18 214

xiu_mvs_v1_base_debi		90.64 101	90.05 103	92.40 113	93.97 188	84.46 88	93.32 201	95.46 169	85.17 153	92.25 75	94.03 148	70.59 225	98.57 108	90.97 76	94.67 122	94.18 214

gg-mvs-nofinetune		81.77 280	79.37 293	88.99 243	90.85 288	77.73 260	86.29 324	79.63 353	74.88 308	83.19 255	69.05 348	60.34 311	96.11 275	75.46 266	94.64 125	93.11 269

BH-RMVSNet		88.37 158	87.48 160	91.02 171	95.28 132	79.45 217	92.89 223	93.07 253	85.45 147	86.91 159	94.84 125	70.35 230	97.76 163	73.97 278	94.59 126	95.85 152

diffmvs		91.37 87	91.23 83	91.77 144	93.09 214	80.27 195	92.36 237	95.52 165	87.03 110	91.40 98	94.93 118	80.08 111	97.44 187	92.13 51	94.56 127	97.61 87

BH-untuned		88.60 154	88.13 147	90.01 211	95.24 135	78.50 238	93.29 206	94.15 231	84.75 164	84.46 219	93.40 173	75.76 157	97.40 196	77.59 246	94.52 128	94.12 218

Effi-MVS+		91.59 84	91.11 85	93.01 86	94.35 175	83.39 118	94.60 131	95.10 192	87.10 108	90.57 106	93.10 187	81.43 101	98.07 146	89.29 95	94.48 129	97.59 89

PCF-MVS	84.11 10	87.74 175	86.08 206	92.70 101	94.02 182	84.43 92	89.27 295	95.87 139	73.62 317	84.43 221	94.33 139	78.48 134	98.86 90	70.27 294	94.45 130	94.81 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-Vis-set		93.01 63	92.92 62	93.29 74	95.01 140	83.51 114	94.48 138	95.77 146	90.87 15	92.52 72	96.67 60	84.50 66	99.00 75	91.99 53	94.44 131	97.36 96

MS-PatchMatch		85.05 250	84.16 247	87.73 272	91.42 262	78.51 237	91.25 264	93.53 246	77.50 282	80.15 289	91.58 239	61.99 298	95.51 298	75.69 264	94.35 132	89.16 333

mvs_anonymous		89.37 134	89.32 117	89.51 231	93.47 204	74.22 295	91.65 258	94.83 209	82.91 201	85.45 194	93.79 165	81.23 103	96.36 266	86.47 131	94.09 133	97.94 72

MVP-Stereo		85.97 232	84.86 238	89.32 233	90.92 284	82.19 150	92.11 246	94.19 229	78.76 270	78.77 303	91.63 237	68.38 258	96.56 252	75.01 272	93.95 134	89.20 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
LFMVS		90.08 111	89.13 122	92.95 89	96.71 82	82.32 149	96.08 50	89.91 326	86.79 116	92.15 80	96.81 53	62.60 294	98.34 125	87.18 121	93.90 135	98.19 53

PVSNet	78.82 18	85.55 239	84.65 242	88.23 263	94.72 156	71.93 316	87.12 320	92.75 260	78.80 269	84.95 210	90.53 269	64.43 288	96.71 241	74.74 273	93.86 136	96.06 145

CNLPA		89.07 139	87.98 150	92.34 118	96.87 78	84.78 77	94.08 170	93.24 250	81.41 235	84.46 219	95.13 114	75.57 162	96.62 244	77.21 250	93.84 137	95.61 161

EPNet_dtu		86.49 226	85.94 212	88.14 265	90.24 304	72.82 307	94.11 166	92.20 272	86.66 121	79.42 300	92.36 209	73.52 190	95.81 289	71.26 289	93.66 138	95.80 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EI-MVSNet-UG-set		92.74 66	92.62 66	93.12 80	94.86 151	83.20 121	94.40 146	95.74 149	90.71 22	92.05 81	96.60 64	84.00 73	98.99 76	91.55 66	93.63 139	97.17 105

Fast-Effi-MVS+		89.41 131	88.64 132	91.71 146	94.74 154	80.81 185	93.54 195	95.10 192	83.11 195	86.82 162	90.67 267	79.74 116	97.75 166	80.51 215	93.55 140	96.57 126

1314		87.51 188	86.57 188	90.34 198	92.42 231	79.74 213	92.63 228	95.35 182	78.35 276	80.14 290	91.62 238	74.05 182	97.15 215	81.05 201	93.53 141	94.12 218

BH-w/o		87.57 186	87.05 171	89.12 238	94.90 149	77.90 252	92.41 234	93.51 247	82.89 202	83.70 242	91.34 243	75.75 158	97.07 223	75.49 265	93.49 142	92.39 291

PMMVS		85.71 238	84.96 235	87.95 269	88.90 319	77.09 270	88.68 305	90.06 322	72.32 327	86.47 165	90.76 265	72.15 208	94.40 313	81.78 193	93.49 142	92.36 292

PatchMatch-RL		86.77 217	85.54 222	90.47 191	95.88 112	82.71 139	90.54 274	92.31 269	79.82 256	84.32 227	91.57 241	68.77 253	96.39 263	73.16 283	93.48 144	92.32 294

PLC	84.53 7	89.06 140	88.03 148	92.15 125	97.27 71	82.69 140	94.29 156	95.44 174	79.71 257	84.01 235	94.18 147	76.68 149	98.75 99	77.28 249	93.41 145	95.02 176
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VNet		92.24 74	91.91 75	93.24 76	96.59 86	83.43 116	94.84 117	96.44 96	89.19 53	94.08 34	95.90 92	77.85 141	98.17 135	88.90 99	93.38 146	98.13 58

test-LLR		85.87 234	85.41 225	87.25 284	90.95 280	71.67 318	89.55 289	89.88 328	83.41 189	84.54 216	87.95 310	67.25 261	95.11 307	81.82 191	93.37 147	94.97 177

test-mter		84.54 258	83.64 256	87.25 284	90.95 280	71.67 318	89.55 289	89.88 328	79.17 262	84.54 216	87.95 310	55.56 328	95.11 307	81.82 191	93.37 147	94.97 177

EPP-MVSNet		91.70 82	91.56 79	92.13 126	95.88 112	80.50 193	97.33 3	95.25 184	86.15 130	89.76 114	95.60 101	83.42 77	98.32 128	87.37 119	93.25 149	97.56 91

CDS-MVSNet		89.45 128	88.51 134	92.29 122	93.62 200	83.61 112	93.01 219	94.68 216	81.95 220	87.82 142	93.24 181	78.69 129	96.99 229	80.34 217	93.23 150	96.28 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		86.68 218	85.39 226	90.53 185	93.05 216	79.33 224	89.79 288	94.77 214	78.82 268	81.95 268	93.24 181	76.81 145	97.30 202	66.94 317	93.16 151	94.95 183

alignmvs		93.08 62	92.50 69	94.81 32	95.62 122	87.61 12	95.99 54	96.07 122	89.77 38	94.12 32	94.87 121	80.56 106	98.66 101	92.42 40	93.10 152	98.15 56

mvs-test1		89.45 128	89.14 121	90.38 195	93.33 207	77.63 262	94.95 108	94.36 222	87.70 95	87.10 155	92.81 197	73.45 192	98.03 149	85.57 139	93.04 153	95.48 163

thisisatest0515		87.33 194	85.99 208	91.37 156	93.49 203	79.55 214	90.63 273	89.56 333	80.17 250	87.56 147	90.86 260	67.07 265	98.28 130	81.50 198	93.02 154	96.29 132

TAMVS		89.21 136	88.29 143	91.96 133	93.71 197	82.62 142	93.30 205	94.19 229	82.22 212	87.78 143	93.94 156	78.83 126	96.95 231	77.70 245	92.98 155	96.32 131

OMC-MVS		91.23 89	90.62 94	93.08 82	96.27 96	84.07 98	93.52 196	95.93 132	86.95 112	89.51 116	96.13 86	78.50 133	98.35 124	85.84 135	92.90 156	96.83 119

canonicalmvs		93.27 58	92.75 64	94.85 27	95.70 119	87.66 11	96.33 34	96.41 99	90.00 34	94.09 33	94.60 133	82.33 88	98.62 105	92.40 41	92.86 157	98.27 47

TESTMET0.1,1		83.74 266	82.85 265	86.42 299	89.96 310	71.21 322	89.55 289	87.88 337	77.41 283	83.37 251	87.31 318	56.71 325	93.65 324	80.62 212	92.85 158	94.40 210

thisisatest0530		88.67 151	87.61 158	91.86 138	94.87 150	80.07 201	94.63 130	89.90 327	84.00 175	88.46 130	93.78 166	66.88 268	98.46 115	83.30 164	92.65 159	97.06 109

VDD-MVS		90.74 96	89.92 108	93.20 77	96.27 96	83.02 127	95.73 65	93.86 240	88.42 75	92.53 71	96.84 50	62.09 297	98.64 103	90.95 79	92.62 160	97.93 74

test_yl		90.69 98	90.02 106	92.71 99	95.72 117	82.41 146	94.11 166	95.12 190	85.63 141	91.49 94	94.70 127	74.75 170	98.42 120	86.13 133	92.53 161	97.31 98

DCV-MVSNet		90.69 98	90.02 106	92.71 99	95.72 117	82.41 146	94.11 166	95.12 190	85.63 141	91.49 94	94.70 127	74.75 170	98.42 120	86.13 133	92.53 161	97.31 98

VDDNet		89.56 124	88.49 137	92.76 96	95.07 139	82.09 151	96.30 35	93.19 251	81.05 244	91.88 84	96.86 49	61.16 307	98.33 127	88.43 105	92.49 163	97.84 80

DP-MVS		87.25 198	85.36 228	92.90 91	97.65 56	83.24 120	94.81 119	92.00 278	74.99 305	81.92 269	95.00 117	72.66 202	99.05 60	66.92 319	92.33 164	96.40 129

GG-mvs-BLEND				87.94 270	89.73 314	77.91 251	87.80 313	78.23 355		80.58 283	83.86 333	59.88 315	95.33 304	71.20 290	92.22 165	90.60 323

tttt0517		88.61 153	87.78 154	91.11 166	94.96 144	77.81 256	95.35 79	89.69 330	85.09 158	88.05 137	94.59 134	66.93 266	98.48 112	83.27 165	92.13 166	97.03 111

HyFIR lowres test		88.09 166	86.81 176	91.93 135	96.00 107	80.63 189	90.01 285	95.79 145	73.42 318	87.68 145	92.10 221	73.86 186	97.96 154	80.75 209	91.70 167	97.19 104

sss		88.93 145	88.26 145	90.94 177	94.05 181	80.78 186	91.71 255	95.38 178	81.55 233	88.63 127	93.91 160	75.04 167	95.47 302	82.47 178	91.61 168	96.57 126

cascas		86.43 227	84.98 234	90.80 179	92.10 239	80.92 182	90.24 279	95.91 135	73.10 321	83.57 247	88.39 303	65.15 284	97.46 184	84.90 146	91.43 169	94.03 225

Effi-MVS+-dtu		88.65 152	88.35 139	89.54 228	93.33 207	76.39 279	94.47 141	94.36 222	87.70 95	85.43 197	89.56 289	73.45 192	97.26 208	85.57 139	91.28 170	94.97 177

thres100view900		87.63 181	86.71 180	90.38 195	96.12 100	78.55 235	95.03 105	91.58 289	87.15 106	88.06 136	92.29 212	68.91 251	98.10 138	70.13 298	91.10 171	94.48 207

tfpn200view9		87.58 185	86.64 183	90.41 192	95.99 108	78.64 233	94.58 132	91.98 280	86.94 113	88.09 133	91.77 231	69.18 248	98.10 138	70.13 298	91.10 171	94.48 207

thres600view7		87.65 178	86.67 182	90.59 182	96.08 104	78.72 231	94.88 114	91.58 289	87.06 109	88.08 135	92.30 211	68.91 251	98.10 138	70.05 301	91.10 171	94.96 180

thres400		87.62 183	86.64 183	90.57 183	95.99 108	78.64 233	94.58 132	91.98 280	86.94 113	88.09 133	91.77 231	69.18 248	98.10 138	70.13 298	91.10 171	94.96 180

F-COLMAP		87.95 169	86.80 177	91.40 155	96.35 95	80.88 183	94.73 124	95.45 172	79.65 258	82.04 267	94.61 132	71.13 216	98.50 111	76.24 260	91.05 175	94.80 189

thres200		87.21 202	86.24 200	90.12 205	95.36 128	78.53 236	93.26 208	92.10 274	86.42 125	88.00 138	91.11 255	69.24 247	98.00 151	69.58 302	91.04 176	93.83 236

WTY-MVS		89.60 122	88.92 127	91.67 147	95.47 126	81.15 176	92.38 236	94.78 213	83.11 195	89.06 124	94.32 140	78.67 130	96.61 247	81.57 197	90.89 177	97.24 101

HY-MVS	83.01 12	89.03 141	87.94 152	92.29 122	94.86 151	82.77 133	92.08 248	94.49 218	81.52 234	86.93 157	92.79 199	78.32 136	98.23 131	79.93 222	90.55 178	95.88 151

CLD-MVS		89.47 127	88.90 128	91.18 162	94.22 176	82.07 152	92.13 245	96.09 120	87.90 89	85.37 203	92.45 206	74.38 175	97.56 177	87.15 122	90.43 179	93.93 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CVMVSNet		84.69 257	84.79 240	84.37 316	91.84 247	64.92 346	93.70 191	91.47 294	66.19 342	86.16 175	95.28 107	67.18 263	93.33 327	80.89 207	90.42 180	94.88 185

SCA		86.32 228	85.18 230	89.73 223	92.15 235	76.60 275	91.12 266	91.69 287	83.53 186	85.50 190	88.81 296	66.79 269	96.48 257	76.65 255	90.35 181	96.12 139

Fast-Effi-MVS+-dtu		87.44 191	86.72 179	89.63 226	92.04 240	77.68 261	94.03 175	93.94 236	85.81 135	82.42 261	91.32 246	70.33 231	97.06 224	80.33 218	90.23 182	94.14 217

OPM-MVS		90.12 110	89.56 111	91.82 141	93.14 212	83.90 102	94.16 162	95.74 149	88.96 60	87.86 139	95.43 105	72.48 205	97.91 158	88.10 110	90.18 183	93.65 247

HQP_MVS		90.60 104	90.19 99	91.82 141	94.70 158	82.73 137	95.85 61	96.22 112	90.81 17	86.91 159	94.86 122	74.23 177	98.12 136	88.15 107	89.99 184	94.63 193

plane_prior5								96.22 112					98.12 136	88.15 107	89.99 184	94.63 193

XVG-OURS		89.40 133	88.70 131	91.52 150	94.06 180	81.46 167	91.27 263	96.07 122	86.14 131	88.89 126	95.77 97	68.73 254	97.26 208	87.39 118	89.96 186	95.83 154

baseline2		86.50 224	85.39 226	89.84 216	91.12 274	76.70 274	91.88 249	88.58 335	82.35 211	79.95 294	90.95 259	73.42 194	97.63 174	80.27 219	89.95 187	95.19 172

Anonymous202405211		87.68 176	86.13 202	92.31 120	96.66 83	80.74 187	94.87 115	91.49 293	80.47 248	89.46 118	95.44 103	54.72 332	98.23 131	82.19 183	89.89 188	97.97 70

plane_prior						82.73 137	95.21 92		89.66 41						89.88 189

TR-MVS		86.78 214	85.76 219	89.82 217	94.37 172	78.41 240	92.47 233	92.83 257	81.11 243	86.36 170	92.40 207	68.73 254	97.48 182	73.75 281	89.85 190	93.57 249

HQP3-MVS								96.04 126							89.77 191

HQP-MVS		89.80 119	89.28 119	91.34 157	94.17 177	81.56 161	94.39 148	96.04 126	88.81 61	85.43 197	93.97 155	73.83 187	97.96 154	87.11 124	89.77 191	94.50 204

XVG-OURS-SEG-HR		89.95 115	89.45 113	91.47 153	94.00 186	81.21 175	91.87 250	96.06 124	85.78 136	88.55 128	95.73 98	74.67 173	97.27 206	88.71 102	89.64 193	95.91 149

GA-MVS		86.61 219	85.27 229	90.66 180	91.33 267	78.71 232	90.40 276	93.81 243	85.34 150	85.12 207	89.57 288	61.25 304	97.11 219	80.99 205	89.59 194	96.15 136

1112_ss		88.42 156	87.33 164	91.72 145	94.92 147	80.98 179	92.97 221	94.54 217	78.16 280	83.82 239	93.88 161	78.78 128	97.91 158	79.45 227	89.41 195	96.26 134

ab-mvs		89.41 131	88.35 139	92.60 104	95.15 138	82.65 141	92.20 243	95.60 160	83.97 176	88.55 128	93.70 171	74.16 181	98.21 134	82.46 179	89.37 196	96.94 115

CR-MVSNet		85.35 243	83.76 253	90.12 205	90.58 297	79.34 221	85.24 330	91.96 282	78.27 277	85.55 185	87.87 313	71.03 218	95.61 293	73.96 279	89.36 197	95.40 167

RPMNet		83.95 263	81.53 273	91.21 160	90.58 297	79.34 221	85.24 330	96.76 73	71.44 331	85.55 185	82.97 338	70.87 221	98.91 85	61.01 338	89.36 197	95.40 167

DSMNet-mixed		76.94 311	76.29 310	78.89 327	83.10 347	56.11 354	87.78 314	79.77 352	60.65 346	75.64 322	88.71 299	61.56 301	88.34 347	60.07 341	89.29 199	92.21 297

LPG-MVS_test		89.45 128	88.90 128	91.12 163	94.47 166	81.49 165	95.30 84	96.14 117	86.73 118	85.45 194	95.16 112	69.89 235	98.10 138	87.70 113	89.23 200	93.77 241

LGP-MVS_train				91.12 163	94.47 166	81.49 165		96.14 117	86.73 118	85.45 194	95.16 112	69.89 235	98.10 138	87.70 113	89.23 200	93.77 241

Test_1112_low_res		87.65 178	86.51 190	91.08 167	94.94 146	79.28 225	91.77 252	94.30 225	76.04 296	83.51 248	92.37 208	77.86 140	97.73 167	78.69 236	89.13 202	96.22 135

PatchmatchNet		85.85 235	84.70 241	89.29 234	91.76 250	75.54 287	88.49 307	91.30 297	81.63 231	85.05 208	88.70 300	71.71 209	96.24 270	74.61 275	89.05 203	96.08 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			83.56 257		91.69 254	69.93 332	87.75 315	91.54 291	78.60 273	84.86 211	88.90 295	69.54 240	96.03 277	70.25 295	88.93 204

MIMVSNet		82.59 274	80.53 279	88.76 246	91.51 257	78.32 242	86.57 323	90.13 320	79.32 259	80.70 281	88.69 301	52.98 339	93.07 331	66.03 322	88.86 205	94.90 184

ACMM	84.12 9	89.14 137	88.48 138	91.12 163	94.65 161	81.22 174	95.31 81	96.12 119	85.31 151	85.92 177	94.34 138	70.19 233	98.06 147	85.65 137	88.86 205	94.08 222
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	84.23 8	89.01 143	88.35 139	90.99 174	94.73 155	81.27 171	95.07 101	95.89 138	86.48 122	83.67 243	94.30 141	69.33 243	97.99 152	87.10 126	88.55 207	93.72 245
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		89.03 141	88.64 132	90.21 200	90.74 292	79.28 225	95.96 57	95.90 136	84.66 166	85.33 205	92.94 191	74.02 183	97.30 202	89.64 91	88.53 208	94.05 224

jajsoiax		88.24 162	87.50 159	90.48 190	90.89 286	80.14 198	95.31 81	95.65 157	84.97 160	84.24 231	94.02 151	65.31 283	97.42 189	88.56 103	88.52 209	93.89 229

PatchT		82.68 273	81.27 275	86.89 294	90.09 307	70.94 326	84.06 336	90.15 319	74.91 306	85.63 184	83.57 335	69.37 242	94.87 311	65.19 324	88.50 210	94.84 186

MSDG		84.86 254	83.09 261	90.14 204	93.80 194	80.05 203	89.18 298	93.09 252	78.89 266	78.19 304	91.91 228	65.86 282	97.27 206	68.47 307	88.45 211	93.11 269

MVS-HIRNet		73.70 314	72.20 317	78.18 329	91.81 249	56.42 353	82.94 342	82.58 348	55.24 348	68.88 341	66.48 349	55.32 330	95.13 306	58.12 343	88.42 212	83.01 344

mvs_tets		88.06 168	87.28 166	90.38 195	90.94 282	79.88 209	95.22 91	95.66 155	85.10 157	84.21 232	93.94 156	63.53 291	97.40 196	88.50 104	88.40 213	93.87 232

ET-MVSNet_ETH3D		87.51 188	85.91 213	92.32 119	93.70 199	83.93 101	92.33 238	90.94 307	84.16 171	72.09 336	92.52 204	69.90 234	95.85 286	89.20 96	88.36 214	97.17 105

FIs		90.51 105	90.35 96	90.99 174	93.99 187	80.98 179	95.73 65	97.54 3	89.15 54	86.72 163	94.68 129	81.83 99	97.24 210	85.18 141	88.31 215	94.76 190

MVS_0304		83.46 267	81.92 270	88.10 266	90.63 296	77.49 265	93.26 208	93.75 244	80.04 253	80.44 286	87.24 320	47.94 346	95.55 295	75.79 263	88.16 216	91.26 311

PS-MVSNAJss		89.97 114	89.62 110	91.02 171	91.90 245	80.85 184	95.26 89	95.98 128	86.26 128	86.21 173	94.29 142	79.70 117	97.65 171	88.87 100	88.10 217	94.57 199

CMPMVS	59.16 21	80.52 295	79.20 296	84.48 315	83.98 344	67.63 340	89.95 287	93.84 242	64.79 343	66.81 344	91.14 254	57.93 323	95.17 305	76.25 259	88.10 217	90.65 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FC-MVSNet-test		90.27 108	90.18 100	90.53 185	93.71 197	79.85 211	95.77 64	97.59 2	89.31 49	86.27 172	94.67 130	81.93 98	97.01 228	84.26 153	88.09 219	94.71 191

ACMMP++															88.01 220

D2MVS		85.90 233	85.09 232	88.35 258	90.79 289	77.42 266	91.83 251	95.70 151	80.77 246	80.08 292	90.02 279	66.74 271	96.37 264	81.88 190	87.97 221	91.26 311

UniMVSNet_ETH3D		87.53 187	86.37 193	91.00 173	92.44 230	78.96 230	94.74 123	95.61 159	84.07 174	85.36 204	94.52 136	59.78 316	97.34 201	82.93 169	87.88 222	96.71 123

PVSNet_BlendedMVS		89.98 113	89.70 109	90.82 178	96.12 100	81.25 172	93.92 181	96.83 65	83.49 187	89.10 122	92.26 213	81.04 104	98.85 93	86.72 129	87.86 223	92.35 293

anonymousdsp		87.84 171	87.09 169	90.12 205	89.13 316	80.54 192	94.67 128	95.55 162	82.05 215	83.82 239	92.12 218	71.47 214	97.15 215	87.15 122	87.80 224	92.67 282

Anonymous20240529		88.09 166	86.59 187	92.58 106	96.53 89	81.92 156	95.99 54	95.84 141	74.11 313	89.06 124	95.21 111	61.44 302	98.81 96	83.67 162	87.47 225	97.01 112

ACMMP++_ref															87.47 225

XVG-ACMP-BASELINE		86.00 231	84.84 239	89.45 232	91.20 269	78.00 249	91.70 256	95.55 162	85.05 159	82.97 256	92.25 214	54.49 333	97.48 182	82.93 169	87.45 227	92.89 277

EI-MVSNet		89.10 138	88.86 130	89.80 220	91.84 247	78.30 243	93.70 191	95.01 195	85.73 138	87.15 152	95.28 107	79.87 114	97.21 213	83.81 159	87.36 228	93.88 231

MVSTER		88.84 147	88.29 143	90.51 188	92.95 222	80.44 194	93.73 188	95.01 195	84.66 166	87.15 152	93.12 186	72.79 201	97.21 213	87.86 111	87.36 228	93.87 232

EG-PatchMatch MVS		82.37 276	80.34 282	88.46 255	90.27 303	79.35 220	92.80 225	94.33 224	77.14 287	73.26 333	90.18 275	47.47 348	96.72 239	70.25 295	87.32 230	89.30 330

EPMVS		83.90 265	82.70 267	87.51 276	90.23 305	72.67 309	88.62 306	81.96 350	81.37 236	85.01 209	88.34 304	66.31 276	94.45 312	75.30 268	87.12 231	95.43 166

tpm2		84.08 261	82.94 263	87.48 279	91.39 263	71.27 320	89.23 297	90.37 316	71.95 329	84.64 213	89.33 290	67.30 260	96.55 254	75.17 269	87.09 232	94.63 193

CostFormer		85.77 237	84.94 236	88.26 261	91.16 273	72.58 313	89.47 293	91.04 304	76.26 294	86.45 168	89.97 281	70.74 223	96.86 237	82.35 180	87.07 233	95.34 170

Patchmatch-test		81.37 288	79.30 294	87.58 275	90.92 284	74.16 297	80.99 345	87.68 340	70.52 335	76.63 316	88.81 296	71.21 215	92.76 333	60.01 342	86.93 234	95.83 154

RRT_MVS		88.86 146	87.68 156	92.39 116	92.02 242	86.09 53	94.38 152	94.94 198	85.45 147	87.14 154	93.84 164	65.88 281	97.11 219	88.73 101	86.77 235	93.98 227

LTVRE_ROB	82.13 13	86.26 229	84.90 237	90.34 198	94.44 170	81.50 163	92.31 240	94.89 204	83.03 197	79.63 298	92.67 200	69.69 238	97.79 161	71.20 290	86.26 236	91.72 302
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	83.96 262	82.04 269	89.74 221	95.28 132	79.75 212	94.25 158	92.28 270	75.17 303	78.02 307	93.77 167	58.60 321	97.84 160	65.06 327	85.92 237	91.63 304
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DWT-MVSNet_test		84.95 252	83.68 254	88.77 245	91.43 261	73.75 299	91.74 254	90.98 305	80.66 247	83.84 238	87.36 317	62.44 295	97.11 219	78.84 235	85.81 238	95.46 164

RPSCF		85.07 249	84.27 246	87.48 279	92.91 223	70.62 328	91.69 257	92.46 265	76.20 295	82.67 260	95.22 110	63.94 290	97.29 205	77.51 248	85.80 239	94.53 201

USDC		82.76 271	81.26 276	87.26 283	91.17 271	74.55 291	89.27 295	93.39 249	78.26 278	75.30 323	92.08 222	54.43 334	96.63 243	71.64 288	85.79 240	90.61 321

GBi-Net		87.26 196	85.98 209	91.08 167	94.01 183	83.10 123	95.14 98	94.94 198	83.57 183	84.37 222	91.64 234	66.59 273	96.34 267	78.23 240	85.36 241	93.79 237

test1		87.26 196	85.98 209	91.08 167	94.01 183	83.10 123	95.14 98	94.94 198	83.57 183	84.37 222	91.64 234	66.59 273	96.34 267	78.23 240	85.36 241	93.79 237

FMVSNet3		87.40 193	86.11 204	91.30 158	93.79 196	83.64 110	94.20 161	94.81 211	83.89 177	84.37 222	91.87 230	68.45 257	96.56 252	78.23 240	85.36 241	93.70 246

FMVSNet2		87.19 203	85.82 215	91.30 158	94.01 183	83.67 109	94.79 120	94.94 198	83.57 183	83.88 237	92.05 225	66.59 273	96.51 255	77.56 247	85.01 244	93.73 244

ACMH	80.38 17	85.36 242	83.68 254	90.39 193	94.45 169	80.63 189	94.73 124	94.85 207	82.09 214	77.24 311	92.65 201	60.01 314	97.58 175	72.25 287	84.87 245	92.96 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				88.24 262	91.88 246	77.05 271		92.92 255	85.54 144	80.13 291	93.30 178	57.29 324	96.20 271	72.46 286	84.71 246	91.49 306

JIA-IIPM		81.04 291	78.98 300	87.25 284	88.64 320	73.48 301	81.75 344	89.61 332	73.19 320	82.05 266	73.71 345	66.07 280	95.87 285	71.18 292	84.60 247	92.41 290

bset_n11_16_dypcd		86.83 211	85.55 221	90.65 181	88.22 327	81.70 159	88.88 302	90.42 314	85.26 152	85.49 191	90.69 266	67.11 264	97.02 227	89.51 93	84.39 248	93.23 263

OpenMVS_ROB	74.94 19	79.51 302	77.03 308	86.93 291	87.00 333	76.23 282	92.33 238	90.74 312	68.93 338	74.52 327	88.23 307	49.58 343	96.62 244	57.64 344	84.29 249	87.94 341

AllTest		83.42 268	81.39 274	89.52 229	95.01 140	77.79 257	93.12 213	90.89 309	77.41 283	76.12 319	93.34 174	54.08 335	97.51 180	68.31 309	84.27 250	93.26 259

TestCases				89.52 229	95.01 140	77.79 257		90.89 309	77.41 283	76.12 319	93.34 174	54.08 335	97.51 180	68.31 309	84.27 250	93.26 259

tpm		84.73 255	84.02 249	86.87 295	90.33 302	68.90 335	89.06 299	89.94 325	80.85 245	85.75 179	89.86 283	68.54 256	95.97 280	77.76 244	84.05 252	95.75 157

FMVSNet1		85.85 235	84.11 248	91.08 167	92.81 224	83.10 123	95.14 98	94.94 198	81.64 230	82.68 259	91.64 234	59.01 320	96.34 267	75.37 267	83.78 253	93.79 237

ADS-MVSNet2		81.66 283	79.71 291	87.50 277	91.35 265	74.19 296	83.33 339	88.48 336	72.90 323	82.24 264	85.77 329	64.98 285	93.20 329	64.57 328	83.74 254	95.12 173

ADS-MVSNet		81.56 285	79.78 289	86.90 293	91.35 265	71.82 317	83.33 339	89.16 334	72.90 323	82.24 264	85.77 329	64.98 285	93.76 322	64.57 328	83.74 254	95.12 173

XXY-MVS		87.65 178	86.85 175	90.03 209	92.14 236	80.60 191	93.76 187	95.23 185	82.94 200	84.60 214	94.02 151	74.27 176	95.49 301	81.04 202	83.68 256	94.01 226

test_0402		81.30 290	79.17 297	87.67 273	93.19 211	78.17 246	92.98 220	91.71 285	75.25 302	76.02 321	90.31 273	59.23 318	96.37 264	50.22 348	83.63 257	88.47 339

tpmvs		83.35 270	82.07 268	87.20 288	91.07 276	71.00 325	88.31 310	91.70 286	78.91 265	80.49 285	87.18 321	69.30 246	97.08 222	68.12 312	83.56 258	93.51 253

pmmvs5		84.21 260	82.84 266	88.34 259	88.95 318	76.94 272	92.41 234	91.91 284	75.63 299	80.28 287	91.18 251	64.59 287	95.57 294	77.09 253	83.47 259	92.53 286

pmmvs4		85.43 241	83.86 252	90.16 202	90.02 309	82.97 130	90.27 277	92.67 262	75.93 297	80.73 280	91.74 233	71.05 217	95.73 292	78.85 234	83.46 260	91.78 301

test0.0.03 1		82.41 275	81.69 271	84.59 314	88.23 326	72.89 306	90.24 279	87.83 338	83.41 189	79.86 295	89.78 285	67.25 261	88.99 346	65.18 325	83.42 261	91.90 300

tpmrst		85.35 243	84.99 233	86.43 298	90.88 287	67.88 338	88.71 304	91.43 295	80.13 251	86.08 176	88.80 298	73.05 198	96.02 278	82.48 177	83.40 262	95.40 167

nrg030		91.08 93	90.39 95	93.17 79	93.07 215	86.91 20	96.41 32	96.26 107	88.30 78	88.37 132	94.85 124	82.19 92	97.64 173	91.09 74	82.95 263	94.96 180

cl-mvsnet2		86.78 214	85.98 209	89.18 237	92.34 232	77.62 263	90.84 270	94.13 233	81.33 237	83.97 236	90.15 276	73.96 184	96.60 249	84.19 154	82.94 264	93.33 257

miper_ehance_all_eth		87.22 201	86.62 186	89.02 242	92.13 237	77.40 267	90.91 269	94.81 211	81.28 238	84.32 227	90.08 278	79.26 123	96.62 244	83.81 159	82.94 264	93.04 272

miper_enhance_ethall		86.90 209	86.18 201	89.06 240	91.66 255	77.58 264	90.22 281	94.82 210	79.16 263	84.48 218	89.10 292	79.19 124	96.66 242	84.06 155	82.94 264	92.94 275

ACMH+	81.04 14	85.05 250	83.46 258	89.82 217	94.66 160	79.37 219	94.44 143	94.12 234	82.19 213	78.04 306	92.82 196	58.23 322	97.54 178	73.77 280	82.90 267	92.54 285

VPA-MVSNet		89.62 121	88.96 125	91.60 149	93.86 191	82.89 132	95.46 77	97.33 22	87.91 88	88.43 131	93.31 177	74.17 180	97.40 196	87.32 120	82.86 268	94.52 202

IterMVS-LS		88.36 159	87.91 153	89.70 224	93.80 194	78.29 244	93.73 188	95.08 194	85.73 138	84.75 212	91.90 229	79.88 113	96.92 233	83.83 158	82.51 269	93.89 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testgi		80.94 294	80.20 285	83.18 320	87.96 331	66.29 341	91.28 262	90.70 313	83.70 180	78.12 305	92.84 194	51.37 341	90.82 343	63.34 331	82.46 270	92.43 289

WR-MVS		88.38 157	87.67 157	90.52 187	93.30 209	80.18 196	93.26 208	95.96 130	88.57 71	85.47 193	92.81 197	76.12 152	96.91 234	81.24 200	82.29 271	94.47 209

RRT_test8_iter05		86.90 209	86.36 194	88.52 254	93.00 220	73.27 303	94.32 155	95.96 130	85.50 146	84.26 230	92.86 192	60.76 309	97.70 168	88.32 106	82.29 271	94.60 196

tpm cat1		81.96 277	80.27 283	87.01 290	91.09 275	71.02 324	87.38 319	91.53 292	66.25 341	80.17 288	86.35 325	68.22 259	96.15 274	69.16 303	82.29 271	93.86 234

v1192		87.25 198	86.33 196	90.00 212	90.76 291	79.04 229	93.80 185	95.48 167	82.57 207	85.48 192	91.18 251	73.38 196	97.42 189	82.30 181	82.06 274	93.53 250

v1144		87.61 184	86.79 178	90.06 208	91.01 277	79.34 221	93.95 180	95.42 177	83.36 191	85.66 183	91.31 247	74.98 168	97.42 189	83.37 163	82.06 274	93.42 256

v1240		86.78 214	85.85 214	89.56 227	90.45 301	77.79 257	93.61 193	95.37 180	81.65 229	85.43 197	91.15 253	71.50 213	97.43 188	81.47 199	82.05 276	93.47 254

Anonymous20231206		81.03 292	79.77 290	84.82 313	87.85 332	70.26 330	91.42 261	92.08 275	73.67 316	77.75 308	89.25 291	62.43 296	93.08 330	61.50 337	82.00 277	91.12 316

V42		87.68 176	86.86 174	90.15 203	90.58 297	80.14 198	94.24 159	95.28 183	83.66 181	85.67 182	91.33 244	74.73 172	97.41 194	84.43 152	81.83 278	92.89 277

v1921920		86.97 208	86.06 207	89.69 225	90.53 300	78.11 248	93.80 185	95.43 175	81.90 223	85.33 205	91.05 257	72.66 202	97.41 194	82.05 186	81.80 279	93.53 250

v2v482		87.84 171	87.06 170	90.17 201	90.99 278	79.23 228	94.00 178	95.13 189	84.87 161	85.53 187	92.07 224	74.45 174	97.45 185	84.71 149	81.75 280	93.85 235

Anonymous20231211		86.59 221	85.13 231	90.98 176	96.52 90	81.50 163	96.14 45	96.16 116	73.78 315	83.65 244	92.15 216	63.26 292	97.37 200	82.82 173	81.74 281	94.06 223

v144192		87.19 203	86.35 195	89.74 221	90.64 295	78.24 245	93.92 181	95.43 175	81.93 221	85.51 189	91.05 257	74.21 179	97.45 185	82.86 171	81.56 282	93.53 250

cl-mvsnet_		86.52 223	85.78 216	88.75 247	92.03 241	76.46 277	90.74 271	94.30 225	81.83 227	83.34 252	90.78 264	75.74 160	96.57 250	81.74 194	81.54 283	93.22 264

cl-mvsnet1		86.53 222	85.78 216	88.75 247	92.02 242	76.45 278	90.74 271	94.30 225	81.83 227	83.34 252	90.82 262	75.75 158	96.57 250	81.73 195	81.52 284	93.24 262

OurMVSNet-221017-0		85.35 243	84.64 243	87.49 278	90.77 290	72.59 312	94.01 177	94.40 221	84.72 165	79.62 299	93.17 183	61.91 299	96.72 239	81.99 187	81.16 285	93.16 267

FMVSNet5		81.52 286	79.60 292	87.27 282	91.17 271	77.95 250	91.49 260	92.26 271	76.87 288	76.16 318	87.91 312	51.67 340	92.34 335	67.74 313	81.16 285	91.52 305

CP-MVSNet		87.63 181	87.26 168	88.74 249	93.12 213	76.59 276	95.29 86	96.58 91	88.43 74	83.49 249	92.98 190	75.28 164	95.83 287	78.97 233	81.15 287	93.79 237

cl_fuxian		87.14 205	86.50 191	89.04 241	92.20 234	77.26 268	91.22 265	94.70 215	82.01 218	84.34 226	90.43 271	78.81 127	96.61 247	83.70 161	81.09 288	93.25 261

IterMVS-SCA-FT		85.45 240	84.53 245	88.18 264	91.71 252	76.87 273	90.19 282	92.65 263	85.40 149	81.44 272	90.54 268	66.79 269	95.00 310	81.04 202	81.05 289	92.66 283

TinyColmap		79.76 301	77.69 303	85.97 302	91.71 252	73.12 304	89.55 289	90.36 317	75.03 304	72.03 337	90.19 274	46.22 349	96.19 273	63.11 332	81.03 290	88.59 338

UniMVSNet_NR-MVSNet		89.92 117	89.29 118	91.81 143	93.39 206	83.72 107	94.43 144	97.12 40	89.80 37	86.46 166	93.32 176	83.16 78	97.23 211	84.92 144	81.02 291	94.49 206

DU-MVS		89.34 135	88.50 135	91.85 140	93.04 217	83.72 107	94.47 141	96.59 90	89.50 43	86.46 166	93.29 179	77.25 142	97.23 211	84.92 144	81.02 291	94.59 197

PS-CasMVS		87.32 195	86.88 173	88.63 252	92.99 221	76.33 281	95.33 80	96.61 89	88.22 82	83.30 254	93.07 188	73.03 199	95.79 290	78.36 238	81.00 293	93.75 243

IterMVS		84.88 253	83.98 251	87.60 274	91.44 258	76.03 283	90.18 283	92.41 266	83.24 194	81.06 278	90.42 272	66.60 272	94.28 316	79.46 226	80.98 294	92.48 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		89.80 119	89.07 123	92.01 128	93.60 201	84.52 84	94.78 121	97.47 8	89.26 50	86.44 169	92.32 210	82.10 93	97.39 199	84.81 147	80.84 295	94.12 218

LF4IMVS		80.37 296	79.07 299	84.27 318	86.64 334	69.87 333	89.39 294	91.05 303	76.38 291	74.97 325	90.00 280	47.85 347	94.25 317	74.55 276	80.82 296	88.69 337

v10		87.25 198	86.38 192	89.85 215	91.19 270	79.50 215	94.48 138	95.45 172	83.79 179	83.62 245	91.19 249	75.13 165	97.42 189	81.94 188	80.60 297	92.63 284

tfpnnormal		84.72 256	83.23 260	89.20 236	92.79 225	80.05 203	94.48 138	95.81 143	82.38 209	81.08 277	91.21 248	69.01 250	96.95 231	61.69 336	80.59 298	90.58 324

test_part1		89.00 144	87.99 149	92.04 127	95.94 111	83.81 105	96.14 45	96.05 125	86.44 124	85.69 181	93.73 170	71.57 211	97.66 170	85.80 136	80.54 299	94.66 192

WR-MVS_H		87.80 173	87.37 163	89.10 239	93.23 210	78.12 247	95.61 73	97.30 26	87.90 89	83.72 241	92.01 226	79.65 121	96.01 279	76.36 257	80.54 299	93.16 267

VPNet		88.20 163	87.47 161	90.39 193	93.56 202	79.46 216	94.04 174	95.54 164	88.67 67	86.96 156	94.58 135	69.33 243	97.15 215	84.05 156	80.53 301	94.56 200

v7n		86.81 212	85.76 219	89.95 213	90.72 293	79.25 227	95.07 101	95.92 133	84.45 169	82.29 262	90.86 260	72.60 204	97.53 179	79.42 230	80.52 302	93.08 271

v8		87.50 190	86.71 180	89.89 214	91.37 264	79.40 218	94.50 137	95.38 178	84.81 163	83.60 246	91.33 244	76.05 153	97.42 189	82.84 172	80.51 303	92.84 279

EU-MVSNet		81.32 289	80.95 277	82.42 324	88.50 322	63.67 347	93.32 201	91.33 296	64.02 344	80.57 284	92.83 195	61.21 306	92.27 336	76.34 258	80.38 304	91.32 309

Patchmtry		82.71 272	80.93 278	88.06 267	90.05 308	76.37 280	84.74 334	91.96 282	72.28 328	81.32 275	87.87 313	71.03 218	95.50 300	68.97 304	80.15 305	92.32 294

NR-MVSNet		88.58 155	87.47 161	91.93 135	93.04 217	84.16 97	94.77 122	96.25 109	89.05 56	80.04 293	93.29 179	79.02 125	97.05 225	81.71 196	80.05 306	94.59 197

Baseline_NR-MVSNet		87.07 206	86.63 185	88.40 256	91.44 258	77.87 254	94.23 160	92.57 264	84.12 173	85.74 180	92.08 222	77.25 142	96.04 276	82.29 182	79.94 307	91.30 310

dp		81.47 287	80.23 284	85.17 311	89.92 311	65.49 344	86.74 321	90.10 321	76.30 293	81.10 276	87.12 322	62.81 293	95.92 282	68.13 311	79.88 308	94.09 221

TranMVSNet+NR-MVSNet		88.84 147	87.95 151	91.49 151	92.68 227	83.01 128	94.92 111	96.31 104	89.88 36	85.53 187	93.85 163	76.63 150	96.96 230	81.91 189	79.87 309	94.50 204

miper_lstm_enhance		85.27 246	84.59 244	87.31 281	91.28 268	74.63 290	87.69 316	94.09 235	81.20 242	81.36 274	89.85 284	74.97 169	94.30 315	81.03 204	79.84 310	93.01 273

v148		87.04 207	86.32 197	89.21 235	90.94 282	77.26 268	93.71 190	94.43 220	84.84 162	84.36 225	90.80 263	76.04 154	97.05 225	82.12 184	79.60 311	93.31 258

IB-MVS	80.51 15	85.24 247	83.26 259	91.19 161	92.13 237	79.86 210	91.75 253	91.29 298	83.28 193	80.66 282	88.49 302	61.28 303	98.46 115	80.99 205	79.46 312	95.25 171
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		86.50 224	85.77 218	88.68 250	91.94 244	75.81 285	90.47 275	94.89 204	82.05 215	84.05 233	90.46 270	75.96 155	96.77 238	82.76 175	79.36 313	93.46 255

baseline1		88.10 165	87.28 166	90.57 183	94.96 144	80.07 201	94.27 157	91.29 298	86.74 117	87.41 148	94.00 153	76.77 147	96.20 271	80.77 208	79.31 314	95.44 165

our_test_3		81.93 278	80.46 281	86.33 300	88.46 323	73.48 301	88.46 308	91.11 300	76.46 289	76.69 315	88.25 306	66.89 267	94.36 314	68.75 305	79.08 315	91.14 315

PEN-MVS		86.80 213	86.27 199	88.40 256	92.32 233	75.71 286	95.18 95	96.38 102	87.97 86	82.82 258	93.15 184	73.39 195	95.92 282	76.15 261	79.03 316	93.59 248

pm-mvs1		86.61 219	85.54 222	89.82 217	91.44 258	80.18 196	95.28 88	94.85 207	83.84 178	81.66 270	92.62 202	72.45 207	96.48 257	79.67 225	78.06 317	92.82 280

SixPastTwentyTwo		83.91 264	82.90 264	86.92 292	90.99 278	70.67 327	93.48 197	91.99 279	85.54 144	77.62 310	92.11 220	60.59 310	96.87 236	76.05 262	77.75 318	93.20 265

ppachtmachnet_test		81.84 279	80.07 287	87.15 289	88.46 323	74.43 294	89.04 300	92.16 273	75.33 301	77.75 308	88.99 293	66.20 277	95.37 303	65.12 326	77.60 319	91.65 303

MIMVSNet1		79.38 303	77.28 305	85.69 306	86.35 335	73.67 300	91.61 259	92.75 260	78.11 281	72.64 335	88.12 308	48.16 345	91.97 339	60.32 339	77.49 320	91.43 308

DTE-MVSNet		86.11 230	85.48 224	87.98 268	91.65 256	74.92 289	94.93 110	95.75 148	87.36 104	82.26 263	93.04 189	72.85 200	95.82 288	74.04 277	77.46 321	93.20 265

N_pmnet		68.89 317	68.44 320	70.23 333	89.07 317	28.79 363	88.06 311	19.50 363	69.47 337	71.86 338	84.93 331	61.24 305	91.75 340	54.70 346	77.15 322	90.15 325

AUN-MVS		87.78 174	86.54 189	91.48 152	94.82 153	81.05 177	93.91 184	93.93 237	83.00 198	86.93 157	93.53 172	69.50 241	97.67 169	86.14 132	77.12 323	95.73 158

test20.03		79.95 299	79.08 298	82.55 323	85.79 339	67.74 339	91.09 267	91.08 301	81.23 241	74.48 328	89.96 282	61.63 300	90.15 344	60.08 340	76.38 324	89.76 326

FPMVS		64.63 319	62.55 321	70.88 332	70.80 354	56.71 351	84.42 335	84.42 346	51.78 350	49.57 350	81.61 340	23.49 356	81.48 352	40.61 352	76.25 325	74.46 348

pmmvs6		83.42 268	81.60 272	88.87 244	88.01 330	77.87 254	94.96 107	94.24 228	74.67 309	78.80 302	91.09 256	60.17 313	96.49 256	77.06 254	75.40 326	92.23 296

new_pmnet		72.15 315	70.13 318	78.20 328	82.95 348	65.68 342	83.91 337	82.40 349	62.94 345	64.47 345	79.82 342	42.85 351	86.26 349	57.41 345	74.44 327	82.65 345

MDA-MVSNet_test_wron		79.21 305	77.19 307	85.29 309	88.22 327	72.77 308	85.87 326	90.06 322	74.34 311	62.62 347	87.56 316	66.14 278	91.99 338	66.90 320	73.01 328	91.10 318

YYNet1		79.22 304	77.20 306	85.28 310	88.20 329	72.66 310	85.87 326	90.05 324	74.33 312	62.70 346	87.61 315	66.09 279	92.03 337	66.94 317	72.97 329	91.15 314

Patchmatch-RL test		81.67 282	79.96 288	86.81 296	85.42 341	71.23 321	82.17 343	87.50 341	78.47 274	77.19 312	82.50 339	70.81 222	93.48 325	82.66 176	72.89 330	95.71 159

pmmvs-eth3d		80.97 293	78.72 301	87.74 271	84.99 343	79.97 208	90.11 284	91.65 288	75.36 300	73.51 331	86.03 326	59.45 317	93.96 321	75.17 269	72.21 331	89.29 331

PM-MVS		78.11 309	76.12 311	84.09 319	83.54 346	70.08 331	88.97 301	85.27 345	79.93 254	74.73 326	86.43 323	34.70 353	93.48 325	79.43 229	72.06 332	88.72 336

Gipuma		57.99 322	54.91 324	67.24 335	88.51 321	65.59 343	52.21 355	90.33 318	43.58 353	42.84 353	51.18 354	20.29 359	85.07 350	34.77 353	70.45 333	51.05 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
K. test v3		81.59 284	80.15 286	85.91 305	89.89 312	69.42 334	92.57 231	87.71 339	85.56 143	73.44 332	89.71 286	55.58 327	95.52 297	77.17 251	69.76 334	92.78 281

DIV-MVS_2432*1600		80.20 297	79.24 295	83.07 321	85.64 340	65.29 345	91.01 268	93.93 237	78.71 272	76.32 317	86.40 324	59.20 319	92.93 332	72.59 285	69.35 335	91.00 319

CL-MVSNet_2432*1600		81.74 281	80.53 279	85.36 308	85.96 338	72.45 314	90.25 278	93.07 253	81.24 240	79.85 296	87.29 319	70.93 220	92.52 334	66.95 316	69.23 336	91.11 317

TDRefinement		79.81 300	77.34 304	87.22 287	79.24 351	75.48 288	93.12 213	92.03 277	76.45 290	75.01 324	91.58 239	49.19 344	96.44 261	70.22 297	69.18 337	89.75 327

MDA-MVSNet-bldmvs		78.85 306	76.31 309	86.46 297	89.76 313	73.88 298	88.79 303	90.42 314	79.16 263	59.18 348	88.33 305	60.20 312	94.04 318	62.00 335	68.96 338	91.48 307

ambc				83.06 322	79.99 350	63.51 348	77.47 348	92.86 256		74.34 329	84.45 332	28.74 354	95.06 309	73.06 284	68.89 339	90.61 321

TransMVSNet (Re)		84.43 259	83.06 262	88.54 253	91.72 251	78.44 239	95.18 95	92.82 258	82.73 204	79.67 297	92.12 218	73.49 191	95.96 281	71.10 293	68.73 340	91.21 313

PMVS	47.18 22	52.22 323	48.46 326	63.48 336	45.72 362	46.20 359	73.41 351	78.31 354	41.03 354	30.06 356	65.68 350	6.05 363	83.43 351	30.04 354	65.86 341	60.80 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lessismore_v0				86.04 301	88.46 323	68.78 336		80.59 351		73.01 334	90.11 277	55.39 329	96.43 262	75.06 271	65.06 342	92.90 276

new-patchmatchnet		76.41 312	75.17 314	80.13 326	82.65 349	59.61 349	87.66 317	91.08 301	78.23 279	69.85 340	83.22 336	54.76 331	91.63 342	64.14 330	64.89 343	89.16 333

pmmvs3		71.81 316	68.71 319	81.11 325	75.86 352	70.42 329	86.74 321	83.66 347	58.95 347	68.64 343	80.89 341	36.93 352	89.52 345	63.10 333	63.59 344	83.39 343

UnsupCasMVSNet_eth		80.07 298	78.27 302	85.46 307	85.24 342	72.63 311	88.45 309	94.87 206	82.99 199	71.64 339	88.07 309	56.34 326	91.75 340	73.48 282	63.36 345	92.01 299

LCM-MVSNet		66.00 318	62.16 322	77.51 330	64.51 358	58.29 350	83.87 338	90.90 308	48.17 351	54.69 349	73.31 346	16.83 362	86.75 348	65.47 323	61.67 346	87.48 342

UnsupCasMVSNet_bld		76.23 313	73.27 316	85.09 312	83.79 345	72.92 305	85.65 329	93.47 248	71.52 330	68.84 342	79.08 343	49.77 342	93.21 328	66.81 321	60.52 347	89.13 335

KD-MVS_2432*1600		78.50 307	76.02 312	85.93 303	86.22 336	74.47 292	84.80 332	92.33 267	79.29 260	76.98 313	85.92 327	53.81 337	93.97 319	67.39 314	57.42 348	89.36 328

miper_refine_blended		78.50 307	76.02 312	85.93 303	86.22 336	74.47 292	84.80 332	92.33 267	79.29 260	76.98 313	85.92 327	53.81 337	93.97 319	67.39 314	57.42 348	89.36 328

DeepMVS_CX				56.31 338	74.23 353	51.81 356		56.67 361	44.85 352	48.54 352	75.16 344	27.87 355	58.74 358	40.92 351	52.22 350	58.39 351

PVSNet_0	73.20 20	77.22 310	74.83 315	84.37 316	90.70 294	71.10 323	83.09 341	89.67 331	72.81 325	73.93 330	83.13 337	60.79 308	93.70 323	68.54 306	50.84 351	88.30 340

PMMVS2		59.60 320	56.40 323	69.21 334	68.83 355	46.58 358	73.02 352	77.48 356	55.07 349	49.21 351	72.95 347	17.43 361	80.04 353	49.32 349	44.33 352	80.99 347

MVE	39.65 23	43.39 324	38.59 330	57.77 337	56.52 360	48.77 357	55.38 354	58.64 360	29.33 357	28.96 357	52.65 353	4.68 364	64.62 357	28.11 355	33.07 353	59.93 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 325	42.29 327	46.03 339	65.58 357	37.41 360	73.51 350	64.62 357	33.99 355	28.47 358	47.87 355	19.90 360	67.91 355	22.23 356	24.45 354	32.77 353

ANet_high		58.88 321	54.22 325	72.86 331	56.50 361	56.67 352	80.75 346	86.00 342	73.09 322	37.39 354	64.63 351	22.17 357	79.49 354	43.51 350	23.96 355	82.43 346

EMVS		42.07 326	41.12 328	44.92 340	63.45 359	35.56 362	73.65 349	63.48 358	33.05 356	26.88 359	45.45 356	21.27 358	67.14 356	19.80 357	23.02 356	32.06 354

tmp_tt		35.64 327	39.24 329	24.84 341	14.87 363	23.90 364	62.71 353	51.51 362	6.58 359	36.66 355	62.08 352	44.37 350	30.34 360	52.40 347	22.00 357	20.27 355

wuyk23d		21.27 329	20.48 332	23.63 342	68.59 356	36.41 361	49.57 356	6.85 364	9.37 358	7.89 360	4.46 362	4.03 365	31.37 359	17.47 358	16.07 358	3.12 356

testmvs		8.92 330	11.52 333	1.12 344	1.06 364	0.46 366	86.02 325	0.65 365	0.62 360	2.74 361	9.52 360	0.31 367	0.45 362	2.38 359	0.39 359	2.46 358

test123		8.76 331	11.22 334	1.39 343	0.85 365	0.97 365	85.76 328	0.35 366	0.54 361	2.45 362	8.14 361	0.60 366	0.48 361	2.16 360	0.17 360	2.71 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		22.14 328	29.52 331	0.00 345	0.00 366	0.00 367	0.00 357	95.76 147	0.00 362	0.00 363	94.29 142	75.66 161	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		6.64 333	8.86 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	79.70 117	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		7.82 332	10.43 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	93.88 161	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

test_241102_ONE					98.77 4	85.99 55		97.44 12	90.26 30	97.71 1	97.96 8	92.31 2	99.38 29

save fliter					97.85 48	85.63 68	95.21 92	96.82 67	89.44 44

test0726					98.78 2	85.93 58	97.19 6	97.47 8	90.27 28	97.64 4	98.13 1	91.47 6

GSMVS																96.12 139

test_part2					98.55 11	87.22 16				96.40 11

sam_mvs1												71.70 210				96.12 139

sam_mvs												70.60 224

MTGPA								96.97 49

test_post1							88.00 312				9.81 359	69.31 245	95.53 296	76.65 255

test_post											10.29 358	70.57 228	95.91 284

patchmatchnet-post											83.76 334	71.53 212	96.48 257

MTMP							96.16 43	60.64 359

gm-plane-assit					89.60 315	68.00 337			77.28 286		88.99 293		97.57 176	79.44 228

TEST9					97.53 58	86.49 39	94.07 171	96.78 70	81.61 232	92.77 63	96.20 80	87.71 26	99.12 55

test_8					97.49 61	86.30 48	94.02 176	96.76 73	81.86 225	92.70 67	96.20 80	87.63 27	99.02 68

agg_prior					97.38 64	85.92 60		96.72 79		92.16 78			98.97 79

test_prior4						85.96 57	94.11 166

test_prior				93.82 64	97.29 69	84.49 85		96.88 59					98.87 87			98.11 61

旧先验2							93.36 200		71.25 332	94.37 26			97.13 218	86.74 127

新几何2							93.11 215

无先验							93.28 207	96.26 107	73.95 314				99.05 60	80.56 213		96.59 125

原ACMM2							92.94 222

testdata2													98.75 99	78.30 239

segment_acmp												87.16 35

testdata1							92.15 244		87.94 87

plane_prior7					94.70 158	82.74 136

plane_prior6					94.52 164	82.75 134						74.23 177

plane_prior4											94.86 122

plane_prior3						82.75 134			90.26 30	86.91 159

plane_prior2							95.85 61		90.81 17

plane_prior1					94.59 162

n2								0.00 367

nn								0.00 367

door-mid								85.49 343

test11								96.57 92

door								85.33 344

HQP5-MVS						81.56 161

HQP-NCC					94.17 177		94.39 148		88.81 61	85.43 197

ACMP_Plane					94.17 177		94.39 148		88.81 61	85.43 197

BP-MVS														87.11 124

HQP4-MVS										85.43 197			97.96 154			94.51 203

HQP2-MVS												73.83 187

NP-MVS					94.37 172	82.42 144					93.98 154

MDTV_nov1_ep13_2view						55.91 355	87.62 318		73.32 319	84.59 215		70.33 231		74.65 274		95.50 162

Test By Simon												80.02 112

High-resolution multi-view benchmark