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		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 23	93.63 23	74.77 112	92.29 7	95.97 2	74.28 35	97.24 11	88.58 13	96.91 1	94.87 13
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
DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 35	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 7

PC_three_1452									68.21 235	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 7

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 52	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 11

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 243	92.39 6				88.94 11	96.63 4	94.85 16

test_241102_TWO								94.06 12	77.24 52	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 40

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 23

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 55	96.48 8	94.88 11

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 31

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 31

HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 60	80.26 13	87.78 30	94.27 36	75.89 19	96.81 22	87.45 19	96.44 9	93.05 96

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 50	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 85
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				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 41

ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 57	93.59 25	76.27 82	88.14 26	95.09 15	71.06 61	96.67 28	87.67 16	96.37 14	94.09 45

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 43	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 53
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 84	93.50 28	75.17 103	86.34 40	95.29 12	70.86 62	96.00 53	88.78 12	96.04 16	94.58 26
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 67	89.69 168	74.31 122	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 18

SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 23	94.74 2	75.71 90	89.16 19	95.10 13	75.65 21	96.19 45	87.07 21	96.01 17	94.79 18

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 64	93.00 47	80.90 9	88.06 28	94.06 46	76.43 16	96.84 20	88.48 14	95.99 19	94.34 36

PHI-MVS		86.43 46	86.17 52	87.24 46	90.88 97	70.96 72	92.27 31	94.07 11	72.45 153	85.22 52	91.90 89	69.47 77	96.42 38	83.28 60	95.94 20	94.35 35

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 47	94.70 3	74.47 119	88.86 22	94.61 21	75.23 24	95.84 58	86.62 26	95.92 21	94.78 20

test_prior3		86.73 39	86.86 40	86.33 63	92.61 74	69.59 101	88.85 109	92.97 54	75.41 96	84.91 57	93.54 54	74.28 35	95.48 70	83.31 57	95.86 22	93.91 54

test_prior2							88.85 109		75.41 96	84.91 57	93.54 54	74.28 35		83.31 57	95.86 22

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 22	80.19 14	88.10 27	94.80 16	73.76 40	97.11 13	87.51 18	95.82 24	94.90 10
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 66	85.24 51	94.32 35	71.76 55	96.93 18	85.53 29	95.79 25	94.32 37

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 87	72.45 41	90.02 78	94.37 4	71.76 164	87.28 34	94.27 36	75.18 25	96.08 49	85.16 30	95.77 26	93.80 64

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 62	94.05 15	70.80 182	87.59 33	93.51 56	77.57 14	96.63 31	83.31 57	95.77 26	94.72 22

9.14			88.26 15		92.84 68		91.52 46	94.75 1	73.93 132	88.57 24	94.67 19	75.57 23	95.79 59	86.77 23	95.76 28

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 110	89.57 90	93.39 34	77.53 47	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 26

train_agg		86.43 46	86.20 50	87.13 49	93.26 56	72.96 26	88.75 113	91.89 103	68.69 229	85.00 55	93.10 65	74.43 31	95.41 76	84.97 32	95.71 30	93.02 98

test9_res														84.90 33	95.70 31	92.87 104

APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 30

agg_prior2														82.91 65	95.45 33	92.70 107

CDPH-MVS		85.76 58	85.29 65	87.17 48	93.49 53	71.08 68	88.58 121	92.42 78	68.32 234	84.61 67	93.48 57	72.32 50	96.15 48	79.00 95	95.43 34	94.28 39

DeepC-MVS	79.81 2	87.08 37	86.88 39	87.69 36	91.16 91	72.32 47	90.31 72	93.94 17	77.12 58	82.82 96	94.23 39	72.13 53	97.09 14	84.83 36	95.37 35	93.65 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 74	92.02 94	79.45 21	85.88 42	94.80 16	68.07 87	96.21 43	86.69 24	95.34 36	93.23 88

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 185	92.02 94	79.45 21	85.88 42	94.80 16	68.07 87	96.21 43	86.69 24	95.34 36	93.23 88

agg_prior1		86.22 51	86.09 54	86.62 59	92.85 66	71.94 54	88.59 120	91.78 109	68.96 224	84.41 72	93.18 64	74.94 26	94.93 98	84.75 38	95.33 38	93.01 99

DeepC-MVS_fast	79.65 3	86.91 38	86.62 42	87.76 29	93.52 52	72.37 44	91.26 49	93.04 43	76.62 73	84.22 76	93.36 61	71.44 59	96.76 24	80.82 85	95.33 38	94.16 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 22	92.65 70	77.57 43	83.84 83	94.40 34	72.24 51	96.28 41	85.65 28	95.30 40	93.62 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 82	93.82 19	73.07 148	84.86 62	92.89 71	76.22 17	96.33 39	84.89 35	95.13 41	94.40 33

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 25	93.43 32	76.89 64	84.68 63	93.99 50	70.67 66	96.82 21	84.18 51	95.01 42	93.90 56

APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 40	92.83 61	73.01 150	88.58 23	94.52 23	73.36 41	96.49 37	84.26 47	95.01 42	92.70 107
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 52	92.83 61	81.50 6	85.79 45	93.47 59	73.02 46	97.00 17	84.90 33	94.94 44	94.10 44

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 68	84.66 66	94.52 23	68.81 85	96.65 29	84.53 41	94.90 45	94.00 51

HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 68	84.91 57	94.44 30	70.78 63	96.61 32	84.53 41	94.89 46	93.66 67

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 28	93.24 37	75.23 100	84.91 57	94.44 30	70.78 63	96.61 32	83.75 56	94.89 46	93.66 67

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 180	87.75 31	94.07 45	74.01 39	96.70 26	84.66 39	94.84 48

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 32	93.49 29	74.75 113	88.33 25	94.43 32	73.27 43	97.02 16	84.18 51	94.84 48	93.82 61

region2R		87.42 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 71	84.45 71	94.52 23	69.09 81	96.70 26	84.37 44	94.83 50	94.03 48

原ACMM1				84.35 115	93.01 64	68.79 116		92.44 75	63.96 284	81.09 117	91.57 98	66.06 109	95.45 72	67.19 207	94.82 51	88.81 241

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 33	93.33 35	76.07 85	83.81 84	93.95 51	69.77 75	96.01 52	85.15 31	94.66 52	94.32 37
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DPM-MVS		84.93 71	84.29 76	86.84 53	90.20 111	73.04 24	87.12 167	93.04 43	69.80 202	82.85 95	91.22 107	73.06 45	96.02 51	76.72 122	94.63 53	91.46 147

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 48	92.35 80	74.62 116	88.90 21	93.85 52	75.75 20	96.00 53	87.80 15	94.63 53	95.04 5
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		86.68 41	86.27 48	87.90 21	94.22 35	73.38 19	90.22 75	93.04 43	75.53 94	83.86 82	94.42 33	67.87 91	96.64 30	82.70 71	94.57 55	93.66 67

XVS		87.18 34	86.91 38	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 24	83.67 86	94.17 40	67.45 94	96.60 34	83.06 62	94.50 56	94.07 46

X-MVStestdata		80.37 144	77.83 180	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 24	83.67 86	12.47 372	67.45 94	96.60 34	83.06 62	94.50 56	94.07 46

test12				86.80 55	92.63 73	70.70 81		91.79 108		82.71 98		71.67 56	96.16 47		94.50 56	93.54 79

CP-MVS		87.11 35	86.92 37	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 73	83.68 85	94.46 27	67.93 89	95.95 56	84.20 50	94.39 59	93.23 88

CSCG		86.41 48	86.19 51	87.07 50	92.91 65	72.48 38	90.81 58	93.56 26	73.95 130	83.16 91	91.07 112	75.94 18	95.19 86	79.94 93	94.38 60	93.55 78

MSLP-MVS++		85.43 63	85.76 57	84.45 110	91.93 83	70.24 87	90.71 60	92.86 59	77.46 49	84.22 76	92.81 75	67.16 98	92.94 186	80.36 89	94.35 61	90.16 188

mPP-MVS		86.67 42	86.32 47	87.72 32	94.41 24	73.55 13	92.74 20	92.22 87	76.87 65	82.81 97	94.25 38	66.44 103	96.24 42	82.88 66	94.28 62	93.38 82

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 41	93.19 40	77.87 38	90.32 17	94.00 48	74.83 27	93.78 145	87.63 17	94.27 63	93.65 72
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
MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 64	94.67 23
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
DELS-MVS		85.41 64	85.30 64	85.77 75	88.49 173	67.93 139	85.52 217	93.44 31	78.70 31	83.63 88	89.03 162	74.57 28	95.71 62	80.26 91	94.04 65	93.66 67
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
EPNet		83.72 78	82.92 87	86.14 69	84.22 256	69.48 104	91.05 55	85.27 258	81.30 7	76.83 183	91.65 94	66.09 108	95.56 65	76.00 127	93.85 66	93.38 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DROMVSNet		86.01 53	86.38 45	84.91 97	89.31 142	66.27 169	92.32 29	93.63 23	79.37 23	84.17 78	91.88 90	69.04 84	95.43 74	83.93 54	93.77 67	93.01 99

3Dnovator+	77.84 4	85.48 61	84.47 75	88.51 6	91.08 92	73.49 17	93.18 11	93.78 21	80.79 10	76.66 188	93.37 60	60.40 189	96.75 25	77.20 115	93.73 68	95.29 2

CS-MVS		86.61 44	86.96 36	85.56 79	90.78 101	66.54 165	92.84 17	93.30 36	79.67 20	84.55 70	92.25 83	71.46 58	95.00 97	84.25 49	93.48 69	95.15 4

CS-MVS-test		86.26 50	86.48 44	85.60 77	90.84 98	66.60 163	91.16 53	93.56 26	79.82 18	84.57 69	89.89 136	70.67 66	95.04 94	84.30 46	93.48 69	95.16 3

CANet		86.45 45	86.10 53	87.51 40	90.09 113	70.94 74	89.70 88	92.59 72	81.78 4	81.32 112	91.43 103	70.34 68	97.23 12	84.26 47	93.36 71	94.37 34

新几何1				83.42 145	93.13 58	70.71 80		85.48 256	57.43 335	81.80 107	91.98 87	63.28 134	92.27 207	64.60 228	92.99 72	87.27 274

1121		80.84 127	79.77 134	84.05 127	93.11 60	70.78 79	84.66 230	85.42 257	57.37 336	81.76 110	92.02 86	63.41 132	94.12 129	67.28 204	92.93 73	87.26 275

HPM-MVS_fast		85.35 65	84.95 70	86.57 61	93.69 48	70.58 85	92.15 35	91.62 113	73.89 133	82.67 99	94.09 44	62.60 146	95.54 68	80.93 83	92.93 73	93.57 77

SR-MVS		86.73 39	86.67 41	86.91 52	94.11 40	72.11 51	92.37 27	92.56 73	74.50 117	86.84 37	94.65 20	67.31 96	95.77 60	84.80 37	92.85 75	92.84 105

旧先验1					91.96 82	65.79 180		86.37 247			93.08 69	69.31 80			92.74 76	88.74 244

3Dnovator	76.31 5	83.38 86	82.31 95	86.59 60	87.94 190	72.94 29	90.64 61	92.14 91	77.21 54	75.47 213	92.83 73	58.56 196	94.72 110	73.24 152	92.71 77	92.13 129

test1172		86.20 52	86.22 49	86.12 70	93.95 42	69.89 96	91.79 42	92.28 82	75.07 104	86.40 39	94.58 22	65.00 121	95.56 65	84.34 45	92.60 78	92.90 103

MVS_111021_HR		85.14 68	84.75 72	86.32 65	91.65 86	72.70 31	85.98 200	90.33 150	76.11 84	82.08 102	91.61 97	71.36 60	94.17 128	81.02 82	92.58 79	92.08 130

APD-MVS_3200maxsize		85.97 54	85.88 55	86.22 67	92.69 72	69.53 103	91.93 37	92.99 49	73.54 140	85.94 41	94.51 26	65.80 113	95.61 63	83.04 64	92.51 80	93.53 80

test2506		77.30 216	76.49 213	79.74 243	90.08 114	52.02 342	87.86 150	63.10 369	74.88 109	80.16 128	92.79 76	38.29 342	92.35 204	68.74 192	92.50 81	94.86 14

ECVR-MVS		79.61 156	79.26 148	80.67 225	90.08 114	54.69 326	87.89 148	77.44 336	74.88 109	80.27 125	92.79 76	48.96 286	92.45 198	68.55 193	92.50 81	94.86 14

test1111		79.43 163	79.18 151	80.15 235	89.99 119	53.31 339	87.33 162	77.05 339	75.04 105	80.23 127	92.77 78	48.97 285	92.33 206	68.87 190	92.40 83	94.81 17

patch_mono-2		83.65 79	84.54 74	80.99 218	90.06 118	65.83 178	84.21 245	88.74 204	71.60 170	85.01 53	92.44 80	74.51 29	83.50 324	82.15 76	92.15 84	93.64 74

MAR-MVS		81.84 108	80.70 118	85.27 84	91.32 90	71.53 60	89.82 82	90.92 134	69.77 203	78.50 148	86.21 241	62.36 152	94.52 114	65.36 221	92.05 85	89.77 212
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
TSAR-MVS + GP.		85.71 59	85.33 62	86.84 53	91.34 89	72.50 37	89.07 102	87.28 233	76.41 75	85.80 44	90.22 129	74.15 38	95.37 82	81.82 77	91.88 86	92.65 111

SR-MVS-dyc-post		85.77 57	85.61 58	86.23 66	93.06 62	70.63 82	91.88 38	92.27 83	73.53 141	85.69 46	94.45 28	65.00 121	95.56 65	82.75 67	91.87 87	92.50 114

RE-MVS-def			85.48 59		93.06 62	70.63 82	91.88 38	92.27 83	73.53 141	85.69 46	94.45 28	63.87 128		82.75 67	91.87 87	92.50 114

IS-MVSNet		83.15 88	82.81 88	84.18 121	89.94 121	63.30 229	91.59 43	88.46 210	79.04 28	79.49 133	92.16 84	65.10 118	94.28 119	67.71 199	91.86 89	94.95 7

Vis-MVSNet (Re-imp)		78.36 190	78.45 163	78.07 270	88.64 169	51.78 345	86.70 182	79.63 325	74.14 128	75.11 228	90.83 119	61.29 172	89.75 265	58.10 282	91.60 90	92.69 109

MG-MVS		83.41 84	83.45 79	83.28 150	92.74 71	62.28 245	88.17 138	89.50 172	75.22 101	81.49 111	92.74 79	66.75 99	95.11 89	72.85 155	91.58 91	92.45 117

CPTT-MVS		83.73 77	83.33 81	84.92 96	93.28 55	70.86 77	92.09 36	90.38 146	68.75 228	79.57 132	92.83 73	60.60 185	93.04 184	80.92 84	91.56 92	90.86 164

test222					91.50 88	68.26 133	84.16 246	83.20 290	54.63 347	79.74 130	91.63 96	58.97 194			91.42 93	86.77 287

ETV-MVS		84.90 73	84.67 73	85.59 78	89.39 135	68.66 126	88.74 115	92.64 71	79.97 17	84.10 79	85.71 249	69.32 79	95.38 79	80.82 85	91.37 94	92.72 106

testdata				79.97 238	90.90 96	64.21 209		84.71 263	59.27 322	85.40 48	92.91 70	62.02 159	89.08 277	68.95 189	91.37 94	86.63 291

abl_6		85.23 66	84.95 70	86.07 71	92.23 79	70.48 86	90.80 59	92.08 92	73.51 143	85.26 50	94.16 41	62.75 145	95.92 57	82.46 74	91.30 96	91.81 137

API-MVS		81.99 106	81.23 110	84.26 119	90.94 95	70.18 93	91.10 54	89.32 176	71.51 172	78.66 145	88.28 181	65.26 116	95.10 92	64.74 227	91.23 97	87.51 268

Vis-MVSNet		83.46 83	82.80 89	85.43 81	90.25 110	68.74 120	90.30 73	90.13 156	76.33 81	80.87 120	92.89 71	61.00 178	94.20 125	72.45 159	90.97 98	93.35 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 154	78.33 169	84.09 124	85.17 241	69.91 94	90.57 63	90.97 133	66.70 246	72.17 259	91.91 88	54.70 224	93.96 133	61.81 250	90.95 99	88.41 252

UA-Net		85.08 70	84.96 69	85.45 80	92.07 81	68.07 137	89.78 85	90.86 137	82.48 2	84.60 68	93.20 63	69.35 78	95.22 85	71.39 165	90.88 100	93.07 95

ACMMP		85.89 56	85.39 61	87.38 44	93.59 51	72.63 34	92.74 20	93.18 41	76.78 68	80.73 121	93.82 53	64.33 124	96.29 40	82.67 72	90.69 101	93.23 88
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
Regformer-1		86.41 48	86.33 46	86.64 58	89.33 137	70.93 75	88.43 123	91.39 122	82.14 3	86.65 38	90.09 131	74.39 33	95.01 96	83.97 53	90.63 102	93.97 52

Regformer-2		86.63 43	86.53 43	86.95 51	89.33 137	71.24 67	88.43 123	92.05 93	82.50 1	86.88 36	90.09 131	74.45 30	95.61 63	84.38 43	90.63 102	94.01 50

casdiffmvs		85.11 69	85.14 66	85.01 91	87.20 213	65.77 181	87.75 151	92.83 61	77.84 39	84.36 75	92.38 81	72.15 52	93.93 139	81.27 81	90.48 104	95.33 1

UGNet		80.83 129	79.59 139	84.54 107	88.04 187	68.09 136	89.42 91	88.16 212	76.95 62	76.22 198	89.46 151	49.30 281	93.94 136	68.48 194	90.31 105	91.60 140
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
baseline		84.93 71	84.98 68	84.80 102	87.30 211	65.39 188	87.30 163	92.88 58	77.62 41	84.04 81	92.26 82	71.81 54	93.96 133	81.31 80	90.30 106	95.03 6

MVSFormer		82.85 94	82.05 100	85.24 85	87.35 206	70.21 88	90.50 65	90.38 146	68.55 231	81.32 112	89.47 149	61.68 161	93.46 163	78.98 96	90.26 107	92.05 131

lupinMVS		81.39 119	80.27 128	84.76 103	87.35 206	70.21 88	85.55 213	86.41 245	62.85 293	81.32 112	88.61 171	61.68 161	92.24 210	78.41 104	90.26 107	91.83 135

DP-MVS Recon		83.11 91	82.09 98	86.15 68	94.44 21	70.92 76	88.79 111	92.20 88	70.53 189	79.17 136	91.03 115	64.12 126	96.03 50	68.39 196	90.14 109	91.50 144

EIA-MVS		83.31 87	82.80 89	84.82 100	89.59 126	65.59 183	88.21 136	92.68 66	74.66 115	78.96 138	86.42 237	69.06 82	95.26 84	75.54 132	90.09 110	93.62 75

MVS_111021_LR		82.61 98	82.11 97	84.11 122	88.82 160	71.58 59	85.15 220	86.16 250	74.69 114	80.47 124	91.04 113	62.29 153	90.55 255	80.33 90	90.08 111	90.20 187

jason		81.39 119	80.29 127	84.70 104	86.63 223	69.90 95	85.95 201	86.77 241	63.24 286	81.07 118	89.47 149	61.08 177	92.15 213	78.33 105	90.07 112	92.05 131
jason: jason.
LFMVS		81.82 109	81.23 110	83.57 142	91.89 84	63.43 227	89.84 81	81.85 304	77.04 61	83.21 89	93.10 65	52.26 243	93.43 165	71.98 160	89.95 113	93.85 58

MVS		78.19 195	76.99 201	81.78 196	85.66 233	66.99 155	84.66 230	90.47 144	55.08 346	72.02 261	85.27 259	63.83 129	94.11 131	66.10 215	89.80 114	84.24 320

CANet_DTU		80.61 137	79.87 132	82.83 172	85.60 235	63.17 234	87.36 160	88.65 206	76.37 79	75.88 207	88.44 177	53.51 234	93.07 181	73.30 150	89.74 115	92.25 124

PVSNet_Blended		80.98 124	80.34 125	82.90 170	88.85 157	65.40 186	84.43 240	92.00 97	67.62 238	78.11 158	85.05 266	66.02 110	94.27 120	71.52 162	89.50 116	89.01 231

PAPM_NR		83.02 92	82.41 92	84.82 100	92.47 77	66.37 167	87.93 146	91.80 107	73.82 134	77.32 173	90.66 121	67.90 90	94.90 102	70.37 173	89.48 117	93.19 92

114514_t		80.68 136	79.51 140	84.20 120	94.09 41	67.27 152	89.64 89	91.11 131	58.75 327	74.08 241	90.72 120	58.10 198	95.04 94	69.70 181	89.42 118	90.30 184

LCM-MVSNet-Re		77.05 219	76.94 202	77.36 280	87.20 213	51.60 346	80.06 294	80.46 317	75.20 102	67.69 300	86.72 221	62.48 149	88.98 279	63.44 233	89.25 119	91.51 143

alignmvs		85.48 61	85.32 63	85.96 74	89.51 130	69.47 105	89.74 86	92.47 74	76.17 83	87.73 32	91.46 102	70.32 69	93.78 145	81.51 78	88.95 120	94.63 25

VNet		82.21 101	82.41 92	81.62 199	90.82 99	60.93 259	84.47 236	89.78 164	76.36 80	84.07 80	91.88 90	64.71 123	90.26 257	70.68 170	88.89 121	93.66 67

PS-MVSNAJ		81.69 112	81.02 114	83.70 139	89.51 130	68.21 135	84.28 244	90.09 157	70.79 183	81.26 116	85.62 253	63.15 139	94.29 118	75.62 130	88.87 122	88.59 247

canonicalmvs		85.91 55	85.87 56	86.04 72	89.84 123	69.44 108	90.45 70	93.00 47	76.70 72	88.01 29	91.23 106	73.28 42	93.91 140	81.50 79	88.80 123	94.77 21

QAPM		80.88 125	79.50 141	85.03 90	88.01 189	68.97 114	91.59 43	92.00 97	66.63 250	75.15 227	92.16 84	57.70 202	95.45 72	63.52 231	88.76 124	90.66 170

VDD-MVS		83.01 93	82.36 94	84.96 93	91.02 94	66.40 166	88.91 106	88.11 213	77.57 43	84.39 74	93.29 62	52.19 244	93.91 140	77.05 117	88.70 125	94.57 28

PVSNet_Blended_VisFu		82.62 97	81.83 105	84.96 93	90.80 100	69.76 98	88.74 115	91.70 112	69.39 209	78.96 138	88.46 176	65.47 115	94.87 105	74.42 137	88.57 126	90.24 186

xiu_mvs_v2_base		81.69 112	81.05 113	83.60 140	89.15 149	68.03 138	84.46 238	90.02 158	70.67 186	81.30 115	86.53 235	63.17 138	94.19 126	75.60 131	88.54 127	88.57 248

PAPR		81.66 114	80.89 117	83.99 133	90.27 109	64.00 212	86.76 181	91.77 111	68.84 227	77.13 181	89.50 147	67.63 92	94.88 104	67.55 201	88.52 128	93.09 94

MVS_Test		83.15 88	83.06 84	83.41 147	86.86 217	63.21 231	86.11 198	92.00 97	74.31 122	82.87 94	89.44 154	70.03 71	93.21 170	77.39 114	88.50 129	93.81 62

AdaColmap		80.58 140	79.42 142	84.06 126	93.09 61	68.91 115	89.36 92	88.97 194	69.27 212	75.70 210	89.69 141	57.20 210	95.77 60	63.06 237	88.41 130	87.50 269

VDDNet		81.52 116	80.67 119	84.05 127	90.44 107	64.13 211	89.73 87	85.91 253	71.11 177	83.18 90	93.48 57	50.54 267	93.49 160	73.40 149	88.25 131	94.54 29

PCF-MVS	73.52 7	80.38 143	78.84 157	85.01 91	87.71 198	68.99 113	83.65 254	91.46 121	63.00 290	77.77 165	90.28 126	66.10 107	95.09 93	61.40 253	88.22 132	90.94 162
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		83.62 81	83.08 83	85.24 85	88.38 178	67.45 147	88.89 107	89.15 185	75.50 95	82.27 100	88.28 181	69.61 76	94.45 116	77.81 109	87.84 133	93.84 60

gg-mvs-nofinetune		69.95 290	67.96 293	75.94 292	83.07 280	54.51 329	77.23 320	70.29 355	63.11 288	70.32 275	62.33 358	43.62 317	88.69 284	53.88 304	87.76 134	84.62 318

Regformer-3		85.23 66	85.07 67	85.70 76	88.95 155	69.01 112	88.29 133	89.91 162	80.95 8	85.01 53	90.01 134	72.45 49	94.19 126	82.50 73	87.57 135	93.90 56

Regformer-4		85.68 60	85.45 60	86.35 62	88.95 155	69.67 100	88.29 133	91.29 124	81.73 5	85.36 49	90.01 134	72.62 48	95.35 83	83.28 60	87.57 135	94.03 48

xiu_mvs_v1_base_debu		80.80 132	79.72 136	84.03 130	87.35 206	70.19 90	85.56 210	88.77 199	69.06 220	81.83 104	88.16 185	50.91 261	92.85 188	78.29 106	87.56 137	89.06 226

xiu_mvs_v1_base		80.80 132	79.72 136	84.03 130	87.35 206	70.19 90	85.56 210	88.77 199	69.06 220	81.83 104	88.16 185	50.91 261	92.85 188	78.29 106	87.56 137	89.06 226

xiu_mvs_v1_base_debi		80.80 132	79.72 136	84.03 130	87.35 206	70.19 90	85.56 210	88.77 199	69.06 220	81.83 104	88.16 185	50.91 261	92.85 188	78.29 106	87.56 137	89.06 226

CLD-MVS		82.31 100	81.65 106	84.29 118	88.47 174	67.73 143	85.81 208	92.35 80	75.78 89	78.33 153	86.58 232	64.01 127	94.35 117	76.05 125	87.48 140	90.79 165
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet		79.07 174	77.70 186	83.17 157	87.60 201	68.23 134	84.40 242	86.20 249	67.49 240	76.36 195	86.54 234	61.54 164	90.79 251	61.86 249	87.33 141	90.49 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
diffmvs		82.10 102	81.88 104	82.76 180	83.00 283	63.78 217	83.68 253	89.76 165	72.94 151	82.02 103	89.85 138	65.96 112	90.79 251	82.38 75	87.30 142	93.71 66

EPP-MVSNet		83.40 85	83.02 85	84.57 106	90.13 112	64.47 204	92.32 29	90.73 138	74.45 121	79.35 135	91.10 110	69.05 83	95.12 88	72.78 156	87.22 143	94.13 43

TAMVS		78.89 179	77.51 191	83.03 164	87.80 194	67.79 142	84.72 229	85.05 261	67.63 237	76.75 186	87.70 194	62.25 154	90.82 250	58.53 278	87.13 144	90.49 177

TAPA-MVS	73.13 9	79.15 171	77.94 176	82.79 177	89.59 126	62.99 238	88.16 139	91.51 117	65.77 259	77.14 180	91.09 111	60.91 179	93.21 170	50.26 322	87.05 145	92.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 209	76.40 216	81.51 202	87.29 212	61.85 250	83.78 252	89.59 170	64.74 271	71.23 267	88.70 167	62.59 147	93.66 153	52.66 309	87.03 146	89.01 231

test_yl		81.17 121	80.47 123	83.24 153	89.13 150	63.62 218	86.21 195	89.95 160	72.43 156	81.78 108	89.61 144	57.50 205	93.58 154	70.75 168	86.90 147	92.52 112

DCV-MVSNet		81.17 121	80.47 123	83.24 153	89.13 150	63.62 218	86.21 195	89.95 160	72.43 156	81.78 108	89.61 144	57.50 205	93.58 154	70.75 168	86.90 147	92.52 112

BH-untuned		79.47 161	78.60 160	82.05 191	89.19 148	65.91 176	86.07 199	88.52 209	72.18 159	75.42 217	87.69 195	61.15 175	93.54 158	60.38 260	86.83 149	86.70 289

BH-RMVSNet		79.61 156	78.44 164	83.14 158	89.38 136	65.93 175	84.95 225	87.15 236	73.56 139	78.19 156	89.79 139	56.67 213	93.36 166	59.53 267	86.74 150	90.13 190

LS3D		76.95 222	74.82 234	83.37 148	90.45 106	67.36 151	89.15 100	86.94 239	61.87 303	69.52 288	90.61 122	51.71 255	94.53 113	46.38 342	86.71 151	88.21 254

Fast-Effi-MVS+		80.81 130	79.92 131	83.47 143	88.85 157	64.51 201	85.53 215	89.39 174	70.79 183	78.49 149	85.06 265	67.54 93	93.58 154	67.03 210	86.58 152	92.32 120

EPNet_dtu		75.46 242	74.86 233	77.23 284	82.57 294	54.60 327	86.89 174	83.09 292	71.64 166	66.25 318	85.86 247	55.99 215	88.04 292	54.92 300	86.55 153	89.05 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 82	82.95 86	85.14 87	88.79 163	70.95 73	89.13 101	91.52 116	77.55 46	80.96 119	91.75 92	60.71 181	94.50 115	79.67 94	86.51 154	89.97 204
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
OMC-MVS		82.69 96	81.97 103	84.85 99	88.75 165	67.42 148	87.98 142	90.87 136	74.92 108	79.72 131	91.65 94	62.19 156	93.96 133	75.26 134	86.42 155	93.16 93

HQP_MVS		83.64 80	83.14 82	85.14 87	90.08 114	68.71 122	91.25 50	92.44 75	79.12 26	78.92 140	91.00 116	60.42 187	95.38 79	78.71 98	86.32 156	91.33 149

plane_prior5								92.44 75					95.38 79	78.71 98	86.32 156	91.33 149

thisisatest0515		77.33 215	75.38 228	83.18 156	85.27 240	63.80 216	82.11 274	83.27 287	65.06 267	75.91 205	83.84 279	49.54 277	94.27 120	67.24 206	86.19 158	91.48 146

plane_prior						68.71 122	90.38 71		77.62 41						86.16 159

mvs_anonymous		79.42 164	79.11 152	80.34 231	84.45 253	57.97 290	82.59 269	87.62 226	67.40 241	76.17 202	88.56 174	68.47 86	89.59 268	70.65 171	86.05 160	93.47 81

GeoE		81.71 111	81.01 115	83.80 138	89.51 130	64.45 205	88.97 104	88.73 205	71.27 175	78.63 146	89.76 140	66.32 105	93.20 172	69.89 179	86.02 161	93.74 65

HQP3-MVS								92.19 89							85.99 162

HQP-MVS		82.61 98	82.02 101	84.37 113	89.33 137	66.98 156	89.17 96	92.19 89	76.41 75	77.23 176	90.23 128	60.17 190	95.11 89	77.47 112	85.99 162	91.03 158

BH-w/o		78.21 193	77.33 195	80.84 221	88.81 161	65.13 193	84.87 226	87.85 223	69.75 204	74.52 237	84.74 269	61.34 170	93.11 179	58.24 281	85.84 164	84.27 319

CHOSEN 1792x2688		77.63 210	75.69 220	83.44 144	89.98 120	68.58 128	78.70 309	87.50 229	56.38 341	75.80 209	86.84 217	58.67 195	91.40 235	61.58 252	85.75 165	90.34 183

Anonymous202405211		78.25 191	77.01 199	81.99 193	91.03 93	60.67 264	84.77 228	83.90 276	70.65 188	80.00 129	91.20 108	41.08 332	91.43 234	65.21 222	85.26 166	93.85 58

cascas		76.72 225	74.64 235	82.99 166	85.78 232	65.88 177	82.33 272	89.21 182	60.85 309	72.74 251	81.02 310	47.28 294	93.75 149	67.48 202	85.02 167	89.34 221

FIs		82.07 104	82.42 91	81.04 217	88.80 162	58.34 284	88.26 135	93.49 29	76.93 63	78.47 150	91.04 113	69.92 73	92.34 205	69.87 180	84.97 168	92.44 118

test-LLR		72.94 268	72.43 257	74.48 306	81.35 313	58.04 288	78.38 310	77.46 334	66.66 247	69.95 283	79.00 328	48.06 289	79.24 340	66.13 213	84.83 169	86.15 297

test-mter		71.41 277	70.39 278	74.48 306	81.35 313	58.04 288	78.38 310	77.46 334	60.32 312	69.95 283	79.00 328	36.08 349	79.24 340	66.13 213	84.83 169	86.15 297

EI-MVSNet-Vis-set		84.19 74	83.81 77	85.31 82	88.18 182	67.85 140	87.66 153	89.73 167	80.05 16	82.95 92	89.59 146	70.74 65	94.82 106	80.66 88	84.72 171	93.28 87

thisisatest0530		79.40 165	77.76 184	84.31 117	87.69 200	65.10 194	87.36 160	84.26 272	70.04 196	77.42 170	88.26 183	49.94 273	94.79 108	70.20 174	84.70 172	93.03 97

GG-mvs-BLEND				75.38 299	81.59 308	55.80 321	79.32 301	69.63 357		67.19 305	73.67 351	43.24 318	88.90 283	50.41 317	84.50 173	81.45 341

FC-MVSNet-test		81.52 116	82.02 101	80.03 237	88.42 177	55.97 320	87.95 144	93.42 33	77.10 59	77.38 171	90.98 118	69.96 72	91.79 224	68.46 195	84.50 173	92.33 119

PVSNet	64.34 18	72.08 275	70.87 274	75.69 294	86.21 227	56.44 313	74.37 335	80.73 312	62.06 302	70.17 278	82.23 301	42.86 321	83.31 326	54.77 301	84.45 175	87.32 273

MS-PatchMatch		73.83 256	72.67 255	77.30 282	83.87 263	66.02 172	81.82 275	84.66 264	61.37 307	68.61 295	82.82 293	47.29 293	88.21 289	59.27 268	84.32 176	77.68 353

ET-MVSNet_ETH3D		78.63 183	76.63 212	84.64 105	86.73 222	69.47 105	85.01 223	84.61 265	69.54 207	66.51 316	86.59 230	50.16 270	91.75 225	76.26 123	84.24 177	92.69 109

TESTMET0.1,1		69.89 291	69.00 284	72.55 318	79.27 340	56.85 305	78.38 310	74.71 348	57.64 333	68.09 297	77.19 341	37.75 344	76.70 351	63.92 230	84.09 178	84.10 323

EI-MVSNet-UG-set		83.81 76	83.38 80	85.09 89	87.87 191	67.53 146	87.44 159	89.66 169	79.74 19	82.23 101	89.41 155	70.24 70	94.74 109	79.95 92	83.92 179	92.99 101

LPG-MVS_test		82.08 103	81.27 109	84.50 108	89.23 146	68.76 118	90.22 75	91.94 101	75.37 98	76.64 189	91.51 99	54.29 227	94.91 100	78.44 102	83.78 180	89.83 209

LGP-MVS_train				84.50 108	89.23 146	68.76 118		91.94 101	75.37 98	76.64 189	91.51 99	54.29 227	94.91 100	78.44 102	83.78 180	89.83 209

thres100view900		76.50 227	75.55 223	79.33 251	89.52 129	56.99 304	85.83 207	83.23 288	73.94 131	76.32 196	87.12 213	51.89 252	91.95 219	48.33 330	83.75 182	89.07 224

tfpn200view9		76.42 230	75.37 229	79.55 250	89.13 150	57.65 296	85.17 218	83.60 279	73.41 144	76.45 191	86.39 238	52.12 245	91.95 219	48.33 330	83.75 182	89.07 224

thres400		76.50 227	75.37 229	79.86 240	89.13 150	57.65 296	85.17 218	83.60 279	73.41 144	76.45 191	86.39 238	52.12 245	91.95 219	48.33 330	83.75 182	90.00 200

thres600view7		76.50 227	75.44 225	79.68 245	89.40 134	57.16 301	85.53 215	83.23 288	73.79 135	76.26 197	87.09 214	51.89 252	91.89 222	48.05 335	83.72 185	90.00 200

thres200		75.55 241	74.47 239	78.82 258	87.78 197	57.85 293	83.07 266	83.51 282	72.44 155	75.84 208	84.42 271	52.08 247	91.75 225	47.41 337	83.64 186	86.86 285

XVG-OURS		80.41 142	79.23 149	83.97 134	85.64 234	69.02 111	83.03 267	90.39 145	71.09 178	77.63 167	91.49 101	54.62 226	91.35 236	75.71 128	83.47 187	91.54 142

CNLPA		78.08 197	76.79 206	81.97 194	90.40 108	71.07 69	87.59 155	84.55 266	66.03 257	72.38 257	89.64 143	57.56 204	86.04 306	59.61 266	83.35 188	88.79 242

MVP-Stereo		76.12 234	74.46 240	81.13 215	85.37 239	69.79 97	84.42 241	87.95 219	65.03 268	67.46 302	85.33 258	53.28 236	91.73 227	58.01 283	83.27 189	81.85 339
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 226	75.30 231	80.21 234	83.93 262	62.32 244	84.66 230	88.81 197	60.23 313	70.16 279	84.07 276	55.30 218	90.73 253	67.37 203	83.21 190	87.59 267

tttt0517		79.40 165	77.91 177	83.90 137	88.10 185	63.84 215	88.37 130	84.05 274	71.45 173	76.78 185	89.12 159	49.93 275	94.89 103	70.18 175	83.18 191	92.96 102

mvs-test1		80.88 125	79.40 143	85.29 83	85.13 244	69.75 99	89.28 93	88.10 214	74.99 106	76.44 194	86.72 221	57.27 208	94.26 124	73.53 145	83.18 191	91.87 134

HyFIR lowres test		77.53 211	75.40 227	83.94 136	89.59 126	66.62 161	80.36 291	88.64 207	56.29 342	76.45 191	85.17 262	57.64 203	93.28 168	61.34 255	83.10 193	91.91 133

ACMP	74.13 6	81.51 118	80.57 120	84.36 114	89.42 133	68.69 125	89.97 80	91.50 120	74.46 120	75.04 231	90.41 125	53.82 232	94.54 112	77.56 111	82.91 194	89.86 208
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 135	79.84 133	83.58 141	89.31 142	68.37 130	89.99 79	91.60 114	70.28 193	77.25 174	89.66 142	53.37 235	93.53 159	74.24 140	82.85 195	88.85 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 293	68.67 285	71.35 325	75.67 351	62.03 247	75.17 329	73.46 350	50.00 355	68.68 293	79.05 326	52.07 248	78.13 345	61.16 256	82.77 196	73.90 356

PLC	70.83 11	78.05 199	76.37 217	83.08 161	91.88 85	67.80 141	88.19 137	89.46 173	64.33 277	69.87 285	88.38 178	53.66 233	93.58 154	58.86 274	82.73 197	87.86 260
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 212	76.18 218	81.20 212	88.24 181	63.24 230	84.61 234	86.40 246	67.55 239	77.81 163	86.48 236	54.10 229	93.15 176	57.75 285	82.72 198	87.20 276

Anonymous20240529		80.19 148	78.89 156	84.10 123	90.60 103	64.75 198	88.95 105	90.90 135	65.97 258	80.59 122	91.17 109	49.97 272	93.73 151	69.16 187	82.70 199	93.81 62

ab-mvs		79.51 159	78.97 155	81.14 214	88.46 175	60.91 260	83.84 251	89.24 181	70.36 191	79.03 137	88.87 165	63.23 137	90.21 259	65.12 223	82.57 200	92.28 123

HY-MVS	69.67 12	77.95 202	77.15 197	80.36 230	87.57 205	60.21 271	83.37 261	87.78 224	66.11 254	75.37 219	87.06 216	63.27 135	90.48 256	61.38 254	82.43 201	90.40 181

PS-MVSNAJss		82.07 104	81.31 108	84.34 116	86.51 224	67.27 152	89.27 94	91.51 117	71.75 165	79.37 134	90.22 129	63.15 139	94.27 120	77.69 110	82.36 202	91.49 145

UniMVSNet_ETH3D		79.10 173	78.24 171	81.70 198	86.85 218	60.24 270	87.28 164	88.79 198	74.25 125	76.84 182	90.53 124	49.48 278	91.56 230	67.98 197	82.15 203	93.29 86

PVSNet_BlendedMVS		80.60 138	80.02 129	82.36 187	88.85 157	65.40 186	86.16 197	92.00 97	69.34 211	78.11 158	86.09 244	66.02 110	94.27 120	71.52 162	82.06 204	87.39 270

WTY-MVS		75.65 240	75.68 221	75.57 296	86.40 225	56.82 306	77.92 317	82.40 298	65.10 266	76.18 200	87.72 193	63.13 142	80.90 336	60.31 261	81.96 205	89.00 233

ACMMP++_ref															81.95 206

DP-MVS		76.78 224	74.57 236	83.42 145	93.29 54	69.46 107	88.55 122	83.70 278	63.98 283	70.20 276	88.89 164	54.01 231	94.80 107	46.66 339	81.88 207	86.01 301

CMPMVS	51.72 21	70.19 288	68.16 290	76.28 290	73.15 363	57.55 298	79.47 300	83.92 275	48.02 356	56.48 355	84.81 267	43.13 319	86.42 304	62.67 241	81.81 208	84.89 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS-SEG-HR		80.81 130	79.76 135	83.96 135	85.60 235	68.78 117	83.54 259	90.50 143	70.66 187	76.71 187	91.66 93	60.69 182	91.26 238	76.94 119	81.58 209	91.83 135

MIMVSNet		70.69 282	69.30 281	74.88 303	84.52 251	56.35 316	75.87 327	79.42 326	64.59 272	67.76 298	82.41 297	41.10 331	81.54 333	46.64 341	81.34 210	86.75 288

ACMMP++															81.25 211

D2MVS		74.82 247	73.21 251	79.64 247	79.81 331	62.56 241	80.34 292	87.35 232	64.37 276	68.86 292	82.66 295	46.37 299	90.10 261	67.91 198	81.24 212	86.25 294

GA-MVS		76.87 223	75.17 232	81.97 194	82.75 289	62.58 240	81.44 282	86.35 248	72.16 161	74.74 235	82.89 291	46.20 302	92.02 217	68.85 191	81.09 213	91.30 151

sss		73.60 258	73.64 248	73.51 313	82.80 288	55.01 325	76.12 323	81.69 305	62.47 298	74.68 236	85.85 248	57.32 207	78.11 346	60.86 258	80.93 214	87.39 270

Effi-MVS+-dtu		80.03 150	78.57 161	84.42 111	85.13 244	68.74 120	88.77 112	88.10 214	74.99 106	74.97 232	83.49 285	57.27 208	93.36 166	73.53 145	80.88 215	91.18 153

EG-PatchMatch MVS		74.04 254	71.82 263	80.71 224	84.92 247	67.42 148	85.86 205	88.08 216	66.04 256	64.22 330	83.85 278	35.10 351	92.56 195	57.44 287	80.83 216	82.16 338

jajsoiax		79.29 168	77.96 175	83.27 151	84.68 250	66.57 164	89.25 95	90.16 155	69.20 216	75.46 215	89.49 148	45.75 307	93.13 178	76.84 120	80.80 217	90.11 192

1112_ss		77.40 214	76.43 215	80.32 232	89.11 154	60.41 269	83.65 254	87.72 225	62.13 301	73.05 249	86.72 221	62.58 148	89.97 262	62.11 247	80.80 217	90.59 174

mvs_tets		79.13 172	77.77 183	83.22 155	84.70 249	66.37 167	89.17 96	90.19 154	69.38 210	75.40 218	89.46 151	44.17 315	93.15 176	76.78 121	80.70 219	90.14 189

PatchMatch-RL		72.38 272	70.90 272	76.80 288	88.60 170	67.38 150	79.53 299	76.17 342	62.75 295	69.36 290	82.00 305	45.51 308	84.89 315	53.62 305	80.58 220	78.12 352

EI-MVSNet		80.52 141	79.98 130	82.12 188	84.28 254	63.19 233	86.41 189	88.95 195	74.18 127	78.69 143	87.54 200	66.62 100	92.43 199	72.57 158	80.57 221	90.74 168

MVSTER		79.01 175	77.88 179	82.38 186	83.07 280	64.80 197	84.08 250	88.95 195	69.01 223	78.69 143	87.17 212	54.70 224	92.43 199	74.69 136	80.57 221	89.89 207

XVG-ACMP-BASELINE		76.11 235	74.27 242	81.62 199	83.20 276	64.67 199	83.60 257	89.75 166	69.75 204	71.85 262	87.09 214	32.78 355	92.11 214	69.99 178	80.43 223	88.09 255

Fast-Effi-MVS+-dtu		78.02 200	76.49 213	82.62 182	83.16 279	66.96 159	86.94 172	87.45 231	72.45 153	71.49 266	84.17 274	54.79 223	91.58 229	67.61 200	80.31 224	89.30 222

LTVRE_ROB	69.57 13	76.25 233	74.54 238	81.41 204	88.60 170	64.38 207	79.24 302	89.12 188	70.76 185	69.79 287	87.86 192	49.09 283	93.20 172	56.21 297	80.16 225	86.65 290
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Test_1112_low_res		76.40 231	75.44 225	79.27 252	89.28 144	58.09 286	81.69 278	87.07 237	59.53 320	72.48 255	86.67 227	61.30 171	89.33 272	60.81 259	80.15 226	90.41 180

test_djsdf		80.30 145	79.32 146	83.27 151	83.98 261	65.37 189	90.50 65	90.38 146	68.55 231	76.19 199	88.70 167	56.44 214	93.46 163	78.98 96	80.14 227	90.97 161

CHOSEN 280x420		66.51 310	64.71 311	71.90 320	81.45 310	63.52 223	57.98 362	68.95 361	53.57 348	62.59 339	76.70 342	46.22 301	75.29 358	55.25 299	79.68 228	76.88 355

MVS_0304		72.48 270	70.89 273	77.24 283	82.20 300	59.68 273	84.11 248	83.49 283	67.10 242	66.87 309	80.59 315	35.00 352	87.40 297	59.07 272	79.58 229	84.63 317

baseline2		75.70 239	73.83 247	81.30 209	83.26 274	61.79 252	82.57 270	80.65 313	66.81 243	66.88 308	83.42 286	57.86 201	92.19 211	63.47 232	79.57 230	89.91 205

GBi-Net		78.40 187	77.40 192	81.40 205	87.60 201	63.01 235	88.39 127	89.28 177	71.63 167	75.34 220	87.28 205	54.80 220	91.11 241	62.72 238	79.57 230	90.09 194

test1		78.40 187	77.40 192	81.40 205	87.60 201	63.01 235	88.39 127	89.28 177	71.63 167	75.34 220	87.28 205	54.80 220	91.11 241	62.72 238	79.57 230	90.09 194

FMVSNet3		77.88 204	76.85 204	80.97 219	86.84 219	62.36 242	86.52 188	88.77 199	71.13 176	75.34 220	86.66 228	54.07 230	91.10 244	62.72 238	79.57 230	89.45 219

FMVSNet2		78.20 194	77.21 196	81.20 212	87.60 201	62.89 239	87.47 158	89.02 190	71.63 167	75.29 224	87.28 205	54.80 220	91.10 244	62.38 242	79.38 234	89.61 216

anonymousdsp		78.60 184	77.15 197	82.98 167	80.51 324	67.08 154	87.24 165	89.53 171	65.66 261	75.16 226	87.19 211	52.52 238	92.25 209	77.17 116	79.34 235	89.61 216

RRT_MVS		79.88 153	78.38 166	84.38 112	85.42 238	70.60 84	88.71 117	88.75 203	72.30 158	78.83 142	89.14 157	44.44 313	92.18 212	78.50 101	79.33 236	90.35 182

nrg030		83.88 75	83.53 78	84.96 93	86.77 221	69.28 109	90.46 69	92.67 67	74.79 111	82.95 92	91.33 105	72.70 47	93.09 180	80.79 87	79.28 237	92.50 114

VPA-MVSNet		80.60 138	80.55 121	80.76 223	88.07 186	60.80 262	86.86 175	91.58 115	75.67 93	80.24 126	89.45 153	63.34 133	90.25 258	70.51 172	79.22 238	91.23 152

F-COLMAP		76.38 232	74.33 241	82.50 184	89.28 144	66.95 160	88.41 126	89.03 189	64.05 281	66.83 310	88.61 171	46.78 297	92.89 187	57.48 286	78.55 239	87.67 263

FMVSNet1		77.44 212	76.12 219	81.40 205	86.81 220	63.01 235	88.39 127	89.28 177	70.49 190	74.39 238	87.28 205	49.06 284	91.11 241	60.91 257	78.52 240	90.09 194

MDTV_nov1_ep13			69.97 280		83.18 277	53.48 336	77.10 321	80.18 322	60.45 310	69.33 291	80.44 316	48.89 287	86.90 300	51.60 313	78.51 241

CVMVSNet		72.99 267	72.58 256	74.25 309	84.28 254	50.85 351	86.41 189	83.45 285	44.56 357	73.23 247	87.54 200	49.38 279	85.70 308	65.90 217	78.44 242	86.19 296

tpm2		73.26 263	71.46 265	78.63 260	83.34 272	56.71 309	80.65 288	80.40 318	56.63 340	73.55 243	82.02 304	51.80 254	91.24 239	56.35 296	78.42 243	87.95 256

CostFormer		75.24 246	73.90 245	79.27 252	82.65 293	58.27 285	80.80 284	82.73 296	61.57 304	75.33 223	83.13 289	55.52 216	91.07 247	64.98 225	78.34 244	88.45 250

ACMH	67.68 16	75.89 237	73.93 244	81.77 197	88.71 167	66.61 162	88.62 119	89.01 191	69.81 201	66.78 311	86.70 226	41.95 329	91.51 233	55.64 298	78.14 245	87.17 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
bset_n11_16_dypcd		77.12 218	75.47 224	82.06 190	81.12 317	65.99 173	81.37 283	83.20 290	69.94 199	76.09 204	83.38 287	47.75 291	92.26 208	78.51 100	77.91 246	87.95 256

CR-MVSNet		73.37 260	71.27 269	79.67 246	81.32 315	65.19 191	75.92 325	80.30 319	59.92 316	72.73 252	81.19 307	52.50 239	86.69 301	59.84 264	77.71 247	87.11 281

RPMNet		73.51 259	70.49 275	82.58 183	81.32 315	65.19 191	75.92 325	92.27 83	57.60 334	72.73 252	76.45 344	52.30 242	95.43 74	48.14 334	77.71 247	87.11 281

SCA		74.22 252	72.33 259	79.91 239	84.05 260	62.17 246	79.96 296	79.29 327	66.30 253	72.38 257	80.13 319	51.95 250	88.60 285	59.25 269	77.67 249	88.96 235

Anonymous20231211		78.97 177	77.69 187	82.81 174	90.54 105	64.29 208	90.11 77	91.51 117	65.01 269	76.16 203	88.13 190	50.56 266	93.03 185	69.68 182	77.56 250	91.11 155

DWT-MVSNet_test		73.70 257	71.86 262	79.21 254	82.91 286	58.94 278	82.34 271	82.17 299	65.21 264	71.05 270	78.31 333	44.21 314	90.17 260	63.29 236	77.28 251	88.53 249

v1144		80.03 150	79.03 153	83.01 165	83.78 264	64.51 201	87.11 168	90.57 142	71.96 163	78.08 160	86.20 242	61.41 168	93.94 136	74.93 135	77.23 252	90.60 173

WR-MVS		79.49 160	79.22 150	80.27 233	88.79 163	58.35 283	85.06 222	88.61 208	78.56 32	77.65 166	88.34 179	63.81 130	90.66 254	64.98 225	77.22 253	91.80 138

v1192		79.59 158	78.43 165	83.07 162	83.55 268	64.52 200	86.93 173	90.58 141	70.83 181	77.78 164	85.90 245	59.15 193	93.94 136	73.96 142	77.19 254	90.76 166

VPNet		78.69 182	78.66 159	78.76 259	88.31 180	55.72 322	84.45 239	86.63 243	76.79 67	78.26 154	90.55 123	59.30 192	89.70 267	66.63 211	77.05 255	90.88 163

v1240		78.99 176	77.78 182	82.64 181	83.21 275	63.54 222	86.62 184	90.30 152	69.74 206	77.33 172	85.68 250	57.04 211	93.76 148	73.13 153	76.92 256	90.62 171

test_part1		82.78 95	82.08 99	84.89 98	90.66 102	66.97 158	90.96 56	92.93 57	77.19 55	80.53 123	90.04 133	63.44 131	95.39 78	76.04 126	76.90 257	92.31 121

MSDG		73.36 262	70.99 271	80.49 228	84.51 252	65.80 179	80.71 287	86.13 251	65.70 260	65.46 321	83.74 282	44.60 311	90.91 249	51.13 315	76.89 258	84.74 315

IterMVS-LS		80.06 149	79.38 144	82.11 189	85.89 230	63.20 232	86.79 178	89.34 175	74.19 126	75.45 216	86.72 221	66.62 100	92.39 201	72.58 157	76.86 259	90.75 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 169	78.03 174	82.80 175	83.30 273	63.94 214	86.80 177	90.33 150	69.91 200	77.48 169	85.53 254	58.44 197	93.75 149	73.60 144	76.85 260	90.71 169

XXY-MVS		75.41 244	75.56 222	74.96 302	83.59 267	57.82 294	80.59 289	83.87 277	66.54 251	74.93 233	88.31 180	63.24 136	80.09 339	62.16 245	76.85 260	86.97 283

v2v482		80.23 146	79.29 147	83.05 163	83.62 266	64.14 210	87.04 169	89.97 159	73.61 137	78.18 157	87.22 209	61.10 176	93.82 143	76.11 124	76.78 262	91.18 153

v144192		79.47 161	78.37 167	82.78 178	83.35 271	63.96 213	86.96 171	90.36 149	69.99 197	77.50 168	85.67 251	60.66 183	93.77 147	74.27 139	76.58 263	90.62 171

UniMVSNet (Re)		81.60 115	81.11 112	83.09 160	88.38 178	64.41 206	87.60 154	93.02 46	78.42 34	78.56 147	88.16 185	69.78 74	93.26 169	69.58 183	76.49 264	91.60 140

UniMVSNet_NR-MVSNet		81.88 107	81.54 107	82.92 169	88.46 175	63.46 225	87.13 166	92.37 79	80.19 14	78.38 151	89.14 157	71.66 57	93.05 182	70.05 176	76.46 265	92.25 124

DU-MVS		81.12 123	80.52 122	82.90 170	87.80 194	63.46 225	87.02 170	91.87 105	79.01 29	78.38 151	89.07 160	65.02 119	93.05 182	70.05 176	76.46 265	92.20 126

cl22		78.07 198	77.01 199	81.23 211	82.37 299	61.83 251	83.55 258	87.98 218	68.96 224	75.06 230	83.87 277	61.40 169	91.88 223	73.53 145	76.39 267	89.98 203

miper_ehance_all_eth		78.59 185	77.76 184	81.08 216	82.66 292	61.56 254	83.65 254	89.15 185	68.87 226	75.55 212	83.79 281	66.49 102	92.03 216	73.25 151	76.39 267	89.64 215

miper_enhance_ethall		77.87 205	76.86 203	80.92 220	81.65 306	61.38 256	82.68 268	88.98 192	65.52 263	75.47 213	82.30 299	65.76 114	92.00 218	72.95 154	76.39 267	89.39 220

PatchmatchNet		73.12 265	71.33 268	78.49 265	83.18 277	60.85 261	79.63 298	78.57 329	64.13 278	71.73 263	79.81 324	51.20 259	85.97 307	57.40 288	76.36 270	88.66 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 286	68.37 287	76.21 291	80.60 322	56.23 317	79.19 304	86.49 244	60.89 308	61.29 341	85.47 256	31.78 358	89.47 271	53.37 306	76.21 271	82.94 335

OpenMVS_ROB	64.09 19	70.56 284	68.19 289	77.65 276	80.26 325	59.41 277	85.01 223	82.96 294	58.76 326	65.43 322	82.33 298	37.63 345	91.23 240	45.34 347	76.03 272	82.32 336

ACMH+	68.96 14	76.01 236	74.01 243	82.03 192	88.60 170	65.31 190	88.86 108	87.55 227	70.25 194	67.75 299	87.47 202	41.27 330	93.19 174	58.37 279	75.94 273	87.60 265

tpm		72.37 273	71.71 264	74.35 308	82.19 301	52.00 343	79.22 303	77.29 337	64.56 273	72.95 250	83.68 284	51.35 257	83.26 327	58.33 280	75.80 274	87.81 261

Anonymous20231206		68.60 298	67.80 297	71.02 327	80.23 326	50.75 352	78.30 313	80.47 316	56.79 339	66.11 319	82.63 296	46.35 300	78.95 342	43.62 350	75.70 275	83.36 328

v7n		78.97 177	77.58 190	83.14 158	83.45 270	65.51 184	88.32 131	91.21 127	73.69 136	72.41 256	86.32 240	57.93 199	93.81 144	69.18 186	75.65 276	90.11 192

NR-MVSNet		80.23 146	79.38 144	82.78 178	87.80 194	63.34 228	86.31 192	91.09 132	79.01 29	72.17 259	89.07 160	67.20 97	92.81 191	66.08 216	75.65 276	92.20 126

v10		79.74 155	78.67 158	82.97 168	84.06 259	64.95 195	87.88 149	90.62 140	73.11 147	75.11 228	86.56 233	61.46 167	94.05 132	73.68 143	75.55 278	89.90 206

IB-MVS	68.01 15	75.85 238	73.36 250	83.31 149	84.76 248	66.03 171	83.38 260	85.06 260	70.21 195	69.40 289	81.05 309	45.76 306	94.66 111	65.10 224	75.49 279	89.25 223
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
h-mvs33		83.15 88	82.19 96	86.02 73	90.56 104	70.85 78	88.15 140	89.16 184	76.02 86	84.67 64	91.39 104	61.54 164	95.50 69	82.71 69	75.48 280	91.72 139

c3_l		78.75 180	77.91 177	81.26 210	82.89 287	61.56 254	84.09 249	89.13 187	69.97 198	75.56 211	84.29 273	66.36 104	92.09 215	73.47 148	75.48 280	90.12 191

V42		79.38 167	78.24 171	82.83 172	81.10 318	65.50 185	85.55 213	89.82 163	71.57 171	78.21 155	86.12 243	60.66 183	93.18 175	75.64 129	75.46 282	89.81 211

cl____		77.72 207	76.76 207	80.58 226	82.49 296	60.48 267	83.09 264	87.87 221	69.22 214	74.38 239	85.22 261	62.10 157	91.53 231	71.09 166	75.41 283	89.73 214

DIV-MVS_self_test		77.72 207	76.76 207	80.58 226	82.48 297	60.48 267	83.09 264	87.86 222	69.22 214	74.38 239	85.24 260	62.10 157	91.53 231	71.09 166	75.40 284	89.74 213

v8		79.97 152	79.02 154	82.80 175	84.09 258	64.50 203	87.96 143	90.29 153	74.13 129	75.24 225	86.81 218	62.88 144	93.89 142	74.39 138	75.40 284	90.00 200

Baseline_NR-MVSNet		78.15 196	78.33 169	77.61 277	85.79 231	56.21 318	86.78 179	85.76 254	73.60 138	77.93 162	87.57 198	65.02 119	88.99 278	67.14 208	75.33 286	87.63 264

RRT_test8_iter05		78.38 189	77.40 192	81.34 208	86.00 229	58.86 279	86.55 187	91.26 125	72.13 162	75.91 205	87.42 203	44.97 310	93.73 151	77.02 118	75.30 287	91.45 148

pmmvs5		71.55 276	70.20 279	75.61 295	77.83 343	56.39 314	81.74 277	80.89 309	57.76 332	67.46 302	84.49 270	49.26 282	85.32 312	57.08 291	75.29 288	85.11 312

EPMVS		69.02 295	68.16 290	71.59 321	79.61 335	49.80 356	77.40 319	66.93 363	62.82 294	70.01 280	79.05 326	45.79 305	77.86 348	56.58 294	75.26 289	87.13 280

TranMVSNet+NR-MVSNet		80.84 127	80.31 126	82.42 185	87.85 192	62.33 243	87.74 152	91.33 123	80.55 11	77.99 161	89.86 137	65.23 117	92.62 192	67.05 209	75.24 290	92.30 122

tfpnnormal		74.39 249	73.16 252	78.08 269	86.10 228	58.05 287	84.65 233	87.53 228	70.32 192	71.22 268	85.63 252	54.97 219	89.86 263	43.03 351	75.02 291	86.32 293

COLMAP_ROB	66.92 17	73.01 266	70.41 277	80.81 222	87.13 215	65.63 182	88.30 132	84.19 273	62.96 291	63.80 334	87.69 195	38.04 343	92.56 195	46.66 339	74.91 292	84.24 320
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 301	67.85 295	70.29 329	80.70 321	43.93 364	72.47 338	74.88 345	60.15 314	70.55 271	76.57 343	49.94 273	81.59 332	50.58 316	74.83 293	85.34 307

pmmvs4		74.03 255	71.91 261	80.39 229	81.96 303	68.32 131	81.45 281	82.14 300	59.32 321	69.87 285	85.13 263	52.40 241	88.13 291	60.21 262	74.74 294	84.73 316

ITE_SJBPF				78.22 267	81.77 305	60.57 265		83.30 286	69.25 213	67.54 301	87.20 210	36.33 348	87.28 299	54.34 302	74.62 295	86.80 286

test0.0.03 1		68.00 302	67.69 299	68.90 334	77.55 344	47.43 358	75.70 328	72.95 352	66.66 247	66.56 312	82.29 300	48.06 289	75.87 355	44.97 348	74.51 296	83.41 327

test_0402		72.79 269	70.44 276	79.84 241	88.13 183	65.99 173	85.93 202	84.29 270	65.57 262	67.40 304	85.49 255	46.92 296	92.61 193	35.88 360	74.38 297	80.94 344

CP-MVSNet		78.22 192	78.34 168	77.84 272	87.83 193	54.54 328	87.94 145	91.17 129	77.65 40	73.48 244	88.49 175	62.24 155	88.43 287	62.19 244	74.07 298	90.55 175

FMVSNet5		69.50 292	67.96 293	74.15 310	82.97 285	55.35 324	80.01 295	82.12 301	62.56 297	63.02 335	81.53 306	36.92 346	81.92 331	48.42 329	74.06 299	85.17 311

MVS-HIRNet		59.14 324	57.67 327	63.57 342	81.65 306	43.50 365	71.73 340	65.06 366	39.59 362	51.43 360	57.73 362	38.34 341	82.58 330	39.53 357	73.95 300	64.62 361

tpmrst		72.39 271	72.13 260	73.18 317	80.54 323	49.91 354	79.91 297	79.08 328	63.11 288	71.69 264	79.95 321	55.32 217	82.77 329	65.66 220	73.89 301	86.87 284

PS-CasMVS		78.01 201	78.09 173	77.77 274	87.71 198	54.39 330	88.02 141	91.22 126	77.50 48	73.26 246	88.64 170	60.73 180	88.41 288	61.88 248	73.88 302	90.53 176

v148		78.72 181	77.80 181	81.47 203	82.73 290	61.96 249	86.30 193	88.08 216	73.26 146	76.18 200	85.47 256	62.46 150	92.36 203	71.92 161	73.82 303	90.09 194

Patchmatch-test		64.82 318	63.24 318	69.57 331	79.42 338	49.82 355	63.49 360	69.05 360	51.98 353	59.95 346	80.13 319	50.91 261	70.98 364	40.66 356	73.57 304	87.90 259

WR-MVS_H		78.51 186	78.49 162	78.56 262	88.02 188	56.38 315	88.43 123	92.67 67	77.14 57	73.89 242	87.55 199	66.25 106	89.24 274	58.92 273	73.55 305	90.06 198

AUN-MVS		79.21 170	77.60 189	84.05 127	88.71 167	67.61 145	85.84 206	87.26 234	69.08 219	77.23 176	88.14 189	53.20 237	93.47 162	75.50 133	73.45 306	91.06 156

hse-mvs2		81.72 110	80.94 116	84.07 125	88.72 166	67.68 144	85.87 204	87.26 234	76.02 86	84.67 64	88.22 184	61.54 164	93.48 161	82.71 69	73.44 307	91.06 156

testgi		66.67 309	66.53 307	67.08 340	75.62 352	41.69 367	75.93 324	76.50 341	66.11 254	65.20 326	86.59 230	35.72 350	74.71 359	43.71 349	73.38 308	84.84 314

Anonymous20240521		68.80 297	67.22 303	73.55 312	74.33 356	54.11 331	83.18 262	85.61 255	58.15 329	61.68 340	80.94 312	30.71 359	81.27 335	57.00 292	73.34 309	85.28 308

pm-mvs1		77.25 217	76.68 211	78.93 257	84.22 256	58.62 282	86.41 189	88.36 211	71.37 174	73.31 245	88.01 191	61.22 174	89.15 276	64.24 229	73.01 310	89.03 230

eth_miper_zixun_eth		77.92 203	76.69 210	81.61 201	83.00 283	61.98 248	83.15 263	89.20 183	69.52 208	74.86 234	84.35 272	61.76 160	92.56 195	71.50 164	72.89 311	90.28 185

miper_lstm_enhance		74.11 253	73.11 253	77.13 285	80.11 327	59.62 274	72.23 339	86.92 240	66.76 245	70.40 274	82.92 290	56.93 212	82.92 328	69.06 188	72.63 312	88.87 238

tpmvs		71.09 279	69.29 282	76.49 289	82.04 302	56.04 319	78.92 307	81.37 308	64.05 281	67.18 306	78.28 334	49.74 276	89.77 264	49.67 325	72.37 313	83.67 325

PEN-MVS		77.73 206	77.69 187	77.84 272	87.07 216	53.91 333	87.91 147	91.18 128	77.56 45	73.14 248	88.82 166	61.23 173	89.17 275	59.95 263	72.37 313	90.43 179

DSMNet-mixed		57.77 326	56.90 328	60.38 344	67.70 367	35.61 370	69.18 349	53.97 372	32.30 367	57.49 352	79.88 322	40.39 335	68.57 366	38.78 358	72.37 313	76.97 354

IterMVS-SCA-FT		75.43 243	73.87 246	80.11 236	82.69 291	64.85 196	81.57 280	83.47 284	69.16 217	70.49 273	84.15 275	51.95 250	88.15 290	69.23 185	72.14 316	87.34 272

tpm cat1		70.57 283	68.31 288	77.35 281	82.41 298	57.95 291	78.08 314	80.22 321	52.04 352	68.54 296	77.66 339	52.00 249	87.84 294	51.77 311	72.07 317	86.25 294

RPSCF		73.23 264	71.46 265	78.54 263	82.50 295	59.85 272	82.18 273	82.84 295	58.96 324	71.15 269	89.41 155	45.48 309	84.77 316	58.82 275	71.83 318	91.02 160

IterMVS		74.29 250	72.94 254	78.35 266	81.53 309	63.49 224	81.58 279	82.49 297	68.06 236	69.99 282	83.69 283	51.66 256	85.54 309	65.85 218	71.64 319	86.01 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 280	68.09 292	79.58 248	85.15 242	63.62 218	84.58 235	79.83 323	62.31 299	60.32 344	86.73 219	32.02 356	88.96 281	50.28 320	71.57 320	86.15 297

TestCases				79.58 248	85.15 242	63.62 218		79.83 323	62.31 299	60.32 344	86.73 219	32.02 356	88.96 281	50.28 320	71.57 320	86.15 297

baseline1		76.98 221	76.75 209	77.66 275	88.13 183	55.66 323	85.12 221	81.89 302	73.04 149	76.79 184	88.90 163	62.43 151	87.78 295	63.30 235	71.18 322	89.55 218

Patchmtry		70.74 281	69.16 283	75.49 298	80.72 320	54.07 332	74.94 334	80.30 319	58.34 328	70.01 280	81.19 307	52.50 239	86.54 302	53.37 306	71.09 323	85.87 304

DTE-MVSNet		76.99 220	76.80 205	77.54 279	86.24 226	53.06 341	87.52 156	90.66 139	77.08 60	72.50 254	88.67 169	60.48 186	89.52 269	57.33 289	70.74 324	90.05 199

MIMVSNet1		68.58 299	66.78 306	73.98 311	80.07 328	51.82 344	80.77 285	84.37 267	64.40 275	59.75 347	82.16 302	36.47 347	83.63 323	42.73 352	70.33 325	86.48 292

pmmvs6		74.69 248	73.39 249	78.61 261	81.38 312	57.48 299	86.64 183	87.95 219	64.99 270	70.18 277	86.61 229	50.43 268	89.52 269	62.12 246	70.18 326	88.83 240

TinyColmap		67.30 306	64.81 310	74.76 305	81.92 304	56.68 310	80.29 293	81.49 307	60.33 311	56.27 356	83.22 288	24.77 362	87.66 296	45.52 345	69.47 327	79.95 348

OurMVSNet-221017-0		74.26 251	72.42 258	79.80 242	83.76 265	59.59 275	85.92 203	86.64 242	66.39 252	66.96 307	87.58 197	39.46 336	91.60 228	65.76 219	69.27 328	88.22 253

JIA-IIPM		66.32 312	62.82 322	76.82 287	77.09 347	61.72 253	65.34 357	75.38 343	58.04 331	64.51 328	62.32 359	42.05 328	86.51 303	51.45 314	69.22 329	82.21 337

ADS-MVSNet2		66.20 315	63.33 317	74.82 304	79.92 329	58.75 281	67.55 354	75.19 344	53.37 349	65.25 324	75.86 345	42.32 324	80.53 338	41.57 354	68.91 330	85.18 309

ADS-MVSNet		64.36 319	62.88 321	68.78 336	79.92 329	47.17 359	67.55 354	71.18 353	53.37 349	65.25 324	75.86 345	42.32 324	73.99 362	41.57 354	68.91 330	85.18 309

test20.03		67.45 304	66.95 305	68.94 333	75.48 353	44.84 363	77.50 318	77.67 333	66.66 247	63.01 336	83.80 280	47.02 295	78.40 344	42.53 353	68.86 332	83.58 326

EU-MVSNet		68.53 300	67.61 300	71.31 326	78.51 342	47.01 360	84.47 236	84.27 271	42.27 358	66.44 317	84.79 268	40.44 334	83.76 321	58.76 276	68.54 333	83.17 329

our_test_3		69.14 294	67.00 304	75.57 296	79.80 332	58.80 280	77.96 315	77.81 332	59.55 319	62.90 338	78.25 335	47.43 292	83.97 320	51.71 312	67.58 334	83.93 324

ppachtmachnet_test		70.04 289	67.34 302	78.14 268	79.80 332	61.13 257	79.19 304	80.59 314	59.16 323	65.27 323	79.29 325	46.75 298	87.29 298	49.33 326	66.72 335	86.00 303

LF4IMVS		64.02 320	62.19 323	69.50 332	70.90 365	53.29 340	76.13 322	77.18 338	52.65 351	58.59 348	80.98 311	23.55 364	76.52 352	53.06 308	66.66 336	78.68 351

Patchmatch-RL test		70.24 287	67.78 298	77.61 277	77.43 345	59.57 276	71.16 341	70.33 354	62.94 292	68.65 294	72.77 352	50.62 265	85.49 310	69.58 183	66.58 337	87.77 262

dp		66.80 307	65.43 309	70.90 328	79.74 334	48.82 357	75.12 332	74.77 346	59.61 318	64.08 331	77.23 340	42.89 320	80.72 337	48.86 328	66.58 337	83.16 330

CL-MVSNet_self_test		72.37 273	71.46 265	75.09 301	79.49 337	53.53 335	80.76 286	85.01 262	69.12 218	70.51 272	82.05 303	57.92 200	84.13 319	52.27 310	66.00 339	87.60 265

FPMVS		53.68 328	51.64 331	59.81 345	65.08 368	51.03 350	69.48 348	69.58 358	41.46 359	40.67 363	72.32 353	16.46 370	70.00 365	24.24 367	65.42 340	58.40 364

pmmvs-eth3d		70.50 285	67.83 296	78.52 264	77.37 346	66.18 170	81.82 275	81.51 306	58.90 325	63.90 333	80.42 317	42.69 322	86.28 305	58.56 277	65.30 341	83.11 331

N_pmnet		52.79 329	53.26 330	51.40 349	78.99 341	7.68 380	69.52 347	3.89 380	51.63 354	57.01 353	74.98 349	40.83 333	65.96 367	37.78 359	64.67 342	80.56 347

PM-MVS		66.41 311	64.14 313	73.20 316	73.92 358	56.45 312	78.97 306	64.96 367	63.88 285	64.72 327	80.24 318	19.84 367	83.44 325	66.24 212	64.52 343	79.71 349

KD-MVS_self_test		68.81 296	67.59 301	72.46 319	74.29 357	45.45 361	77.93 316	87.00 238	63.12 287	63.99 332	78.99 330	42.32 324	84.77 316	56.55 295	64.09 344	87.16 279

SixPastTwentyTwo		73.37 260	71.26 270	79.70 244	85.08 246	57.89 292	85.57 209	83.56 281	71.03 179	65.66 320	85.88 246	42.10 327	92.57 194	59.11 271	63.34 345	88.65 246

EGC-MVSNET		52.07 330	47.05 334	67.14 339	83.51 269	60.71 263	80.50 290	67.75 362	0.07 375	0.43 376	75.85 347	24.26 363	81.54 333	28.82 363	62.25 346	59.16 363

TransMVSNet (Re)		75.39 245	74.56 237	77.86 271	85.50 237	57.10 303	86.78 179	86.09 252	72.17 160	71.53 265	87.34 204	63.01 143	89.31 273	56.84 293	61.83 347	87.17 277

MDA-MVSNet_test_wron		65.03 316	62.92 319	71.37 323	75.93 349	56.73 307	69.09 352	74.73 347	57.28 337	54.03 358	77.89 336	45.88 303	74.39 361	49.89 324	61.55 348	82.99 334

YYNet1		65.03 316	62.91 320	71.38 322	75.85 350	56.60 311	69.12 351	74.66 349	57.28 337	54.12 357	77.87 337	45.85 304	74.48 360	49.95 323	61.52 349	83.05 332

ambc				75.24 300	73.16 362	50.51 353	63.05 361	87.47 230		64.28 329	77.81 338	17.80 368	89.73 266	57.88 284	60.64 350	85.49 305

TDRefinement		67.49 303	64.34 312	76.92 286	73.47 361	61.07 258	84.86 227	82.98 293	59.77 317	58.30 350	85.13 263	26.06 361	87.89 293	47.92 336	60.59 351	81.81 340

Gipuma		45.18 333	41.86 336	55.16 347	77.03 348	51.52 347	32.50 368	80.52 315	32.46 366	27.12 367	35.02 368	9.52 375	75.50 356	22.31 368	60.21 352	38.45 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		61.73 322	61.73 324	61.70 343	72.74 364	24.50 377	69.16 350	78.03 331	61.40 305	56.72 354	75.53 348	38.42 340	76.48 353	45.95 344	57.67 353	84.13 322

MDA-MVSNet-bldmvs		66.68 308	63.66 316	75.75 293	79.28 339	60.56 266	73.92 336	78.35 330	64.43 274	50.13 361	79.87 323	44.02 316	83.67 322	46.10 343	56.86 354	83.03 333

new_pmnet		50.91 331	50.29 332	52.78 348	68.58 366	34.94 372	63.71 359	56.63 371	39.73 361	44.95 362	65.47 357	21.93 365	58.48 368	34.98 361	56.62 355	64.92 360

PMVS	37.38 22	44.16 334	40.28 337	55.82 346	40.82 377	42.54 366	65.12 358	63.99 368	34.43 365	24.48 368	57.12 364	3.92 378	76.17 354	17.10 370	55.52 356	48.75 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		57.79 325	54.26 329	68.37 337	64.02 369	56.72 308	75.12 332	65.17 365	40.20 360	52.93 359	69.86 356	20.36 366	75.48 357	45.45 346	55.25 357	72.90 357

UnsupCasMVSNet_eth		67.33 305	65.99 308	71.37 323	73.48 360	51.47 348	75.16 330	85.19 259	65.20 265	60.78 343	80.93 314	42.35 323	77.20 350	57.12 290	53.69 358	85.44 306

K. test v3		71.19 278	68.51 286	79.21 254	83.04 282	57.78 295	84.35 243	76.91 340	72.90 152	62.99 337	82.86 292	39.27 337	91.09 246	61.65 251	52.66 359	88.75 243

UnsupCasMVSNet_bld		63.70 321	61.53 325	70.21 330	73.69 359	51.39 349	72.82 337	81.89 302	55.63 344	57.81 351	71.80 354	38.67 339	78.61 343	49.26 327	52.21 360	80.63 345

LCM-MVSNet		54.25 327	49.68 333	67.97 338	53.73 372	45.28 362	66.85 356	80.78 311	35.96 364	39.45 364	62.23 360	8.70 376	78.06 347	48.24 333	51.20 361	80.57 346

KD-MVS_2432*1600		66.22 313	63.89 314	73.21 314	75.47 354	53.42 337	70.76 344	84.35 268	64.10 279	66.52 314	78.52 331	34.55 353	84.98 313	50.40 318	50.33 362	81.23 342

miper_refine_blended		66.22 313	63.89 314	73.21 314	75.47 354	53.42 337	70.76 344	84.35 268	64.10 279	66.52 314	78.52 331	34.55 353	84.98 313	50.40 318	50.33 362	81.23 342

lessismore_v0				78.97 256	81.01 319	57.15 302		65.99 364		61.16 342	82.82 293	39.12 338	91.34 237	59.67 265	46.92 364	88.43 251

PVSNet_0	57.27 20	61.67 323	59.27 326	68.85 335	79.61 335	57.44 300	68.01 353	73.44 351	55.93 343	58.54 349	70.41 355	44.58 312	77.55 349	47.01 338	35.91 365	71.55 358

test_method		31.52 337	29.28 341	38.23 352	27.03 379	6.50 381	20.94 370	62.21 370	4.05 373	22.35 371	52.50 365	13.33 371	47.58 372	27.04 366	34.04 366	60.62 362

PMMVS2		40.82 335	38.86 338	46.69 350	53.84 371	16.45 378	48.61 365	49.92 373	37.49 363	31.67 365	60.97 361	8.14 377	56.42 369	28.42 364	30.72 367	67.19 359

DeepMVS_CX				27.40 355	40.17 378	26.90 375		24.59 379	17.44 371	23.95 369	48.61 366	9.77 374	26.48 374	18.06 369	24.47 368	28.83 368

MVE	26.22 23	30.37 339	25.89 343	43.81 351	44.55 376	35.46 371	28.87 369	39.07 376	18.20 370	18.58 372	40.18 367	2.68 379	47.37 373	17.07 371	23.78 369	48.60 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 336	30.64 339	35.15 353	52.87 373	27.67 374	57.09 363	47.86 374	24.64 368	16.40 373	33.05 369	11.23 373	54.90 370	14.46 372	18.15 370	22.87 369

EMVS		30.81 338	29.65 340	34.27 354	50.96 374	25.95 376	56.58 364	46.80 375	24.01 369	15.53 374	30.68 370	12.47 372	54.43 371	12.81 373	17.05 371	22.43 370

ANet_high		50.57 332	46.10 335	63.99 341	48.67 375	39.13 368	70.99 343	80.85 310	61.39 306	31.18 366	57.70 363	17.02 369	73.65 363	31.22 362	15.89 372	79.18 350

tmp_tt		18.61 341	21.40 344	10.23 357	4.82 380	10.11 379	34.70 367	30.74 378	1.48 374	23.91 370	26.07 371	28.42 360	13.41 376	27.12 365	15.35 373	7.17 371

wuyk23d		16.82 342	15.94 345	19.46 356	58.74 370	31.45 373	39.22 366	3.74 381	6.84 372	6.04 375	2.70 375	1.27 380	24.29 375	10.54 374	14.40 374	2.63 372

testmvs		6.04 345	8.02 348	0.10 359	0.08 381	0.03 383	69.74 346	0.04 382	0.05 376	0.31 377	1.68 376	0.02 382	0.04 377	0.24 375	0.02 375	0.25 374

test123		6.12 344	8.11 347	0.14 358	0.06 382	0.09 382	71.05 342	0.03 383	0.04 377	0.25 378	1.30 377	0.05 381	0.03 378	0.21 376	0.01 376	0.29 373

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

cdsmvs_eth3d_5k		19.96 340	26.61 342	0.00 360	0.00 383	0.00 384	0.00 371	89.26 180	0.00 378	0.00 379	88.61 171	61.62 163	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		5.26 346	7.02 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	63.15 139	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		7.23 343	9.64 346	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	86.72 221	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 118	91.30 15

test_one_0601					95.07 7	71.46 61		94.14 7	78.27 37	92.05 11	95.74 6	80.83 11

eth-test2					0.00 383

eth-test					0.00 383

test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 56	93.10 1	95.39 11	82.99 1	97.27 10

save fliter					93.80 44	72.35 45	90.47 67	91.17 129	74.31 122

test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 50	92.81 3	95.79 3	80.98 9

GSMVS																88.96 235

test_part2					95.06 8	72.65 33				91.80 13

sam_mvs1												51.32 258				88.96 235

sam_mvs												50.01 271

MTGPA								92.02 94

test_post1							78.90 308				5.43 374	48.81 288	85.44 311	59.25 269

test_post											5.46 373	50.36 269	84.24 318

patchmatchnet-post											74.00 350	51.12 260	88.60 285

MTMP							92.18 34	32.83 377

gm-plane-assit					81.40 311	53.83 334			62.72 296		80.94 312		92.39 201	63.40 234

TEST9					93.26 56	72.96 26	88.75 113	91.89 103	68.44 233	85.00 55	93.10 65	74.36 34	95.41 76

test_8					93.13 58	72.57 36	88.68 118	91.84 106	68.69 229	84.87 61	93.10 65	74.43 31	95.16 87

agg_prior					92.85 66	71.94 54		91.78 109		84.41 72			94.93 98

test_prior4						72.60 35	89.01 103

test_prior				86.33 63	92.61 74	69.59 101		92.97 54					95.48 70			93.91 54

旧先验2							86.56 186		58.10 330	87.04 35			88.98 279	74.07 141

新几何2							86.29 194

无先验							87.48 157	88.98 192	60.00 315				94.12 129	67.28 204		88.97 234

原ACMM2							86.86 175

testdata2													91.01 248	62.37 243

segment_acmp												73.08 44

testdata1							84.14 247		75.71 90

plane_prior7					90.08 114	68.51 129

plane_prior6					89.84 123	68.70 124						60.42 187

plane_prior4											91.00 116

plane_prior3						68.60 127			78.44 33	78.92 140

plane_prior2							91.25 50		79.12 26

plane_prior1					89.90 122

n2								0.00 384

nn								0.00 384

door-mid								69.98 356

test11								92.23 86

door								69.44 359

HQP5-MVS						66.98 156

HQP-NCC					89.33 137		89.17 96		76.41 75	77.23 176

ACMP_Plane					89.33 137		89.17 96		76.41 75	77.23 176

BP-MVS														77.47 112

HQP4-MVS										77.24 175			95.11 89			91.03 158

HQP2-MVS												60.17 190

NP-MVS					89.62 125	68.32 131					90.24 127

MDTV_nov1_ep13_2view						37.79 369	75.16 330		55.10 345	66.53 313		49.34 280		53.98 303		87.94 258

Test By Simon												64.33 124

High-resolution multi-view benchmark