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.

ETH3D-3000-0.1		78.58 16	78.91 14	77.61 48	83.06 79	57.86 92	84.14 45	88.31 1	60.37 101	79.14 22	90.35 29	57.76 28	87.00 65	77.16 22	89.90 18	87.97 20

9.14			78.75 16		83.10 78		84.15 43	88.26 2	59.90 113	78.57 27	90.36 28	57.51 31	86.86 68	77.39 18	89.52 25

SF-MVS		78.82 13	79.22 12	77.60 49	82.88 84	57.83 93	84.99 32	88.13 3	61.86 78	79.16 20	90.75 17	57.96 25	87.09 62	77.08 23	90.18 15	87.87 23

ETH3 D test6400		79.14 11	79.32 10	78.61 32	86.34 31	58.11 89	84.65 34	87.66 4	58.56 141	78.87 24	89.54 53	63.67 13	89.57 16	74.60 36	89.98 17	88.14 15

ETH3D cwj APD-0.16		78.02 25	78.13 25	77.71 47	82.10 89	58.65 82	82.72 70	87.55 5	58.33 146	78.05 30	90.06 40	58.35 24	87.65 51	76.15 28	89.86 19	86.82 62

DVP-MVS++		81.67 1	82.40 1	79.47 9	87.24 14	59.15 68	88.18 1	87.15 6	65.04 20	84.26 5	91.86 6	67.01 1	90.84 3	79.48 4	91.38 2	88.42 7

test_0728_SECOND				79.19 15	87.82 3	59.11 71	87.85 5	87.15 6					90.84 3	78.66 13	90.61 11	87.62 36

test_part1		74.74 61	74.42 62	75.70 81	81.69 96	51.26 186	83.98 48	87.05 8	65.31 16	73.10 78	86.20 104	53.94 62	88.06 39	65.32 106	73.17 197	87.77 30

MCST-MVS		77.48 33	77.45 30	77.54 50	86.67 22	58.36 86	83.22 60	86.93 9	56.91 165	74.91 48	88.19 69	59.15 21	87.68 50	73.67 45	87.45 47	86.57 69

DeepC-MVS	69.38 2	78.56 18	78.14 24	79.83 6	83.60 72	61.62 26	84.17 42	86.85 10	63.23 49	73.84 67	90.25 35	57.68 29	89.96 13	74.62 35	89.03 26	87.89 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_one_0601					87.58 9	59.30 65		86.84 11	65.01 23	83.80 11	91.86 6	64.03 11

test0726					87.75 7	59.07 72	87.86 4	86.83 12	64.26 32	84.19 7	91.92 5	64.82 8

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 34	62.73 12	86.09 17	86.83 12	65.51 14	83.81 10	90.51 23	63.71 12	89.23 20	81.51 1	88.44 32	88.09 17
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
SED-MVS		81.56 2	82.30 2	79.32 12	87.77 4	58.90 77	87.82 7	86.78 14	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 15	90.87 5	88.23 12

test_241102_ONE					87.77 4	58.90 77		86.78 14	64.20 34	85.97 1	91.34 12	66.87 3	90.78 7

test_241102_TWO								86.73 16	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 15	90.70 7	87.65 34

CSCG		76.92 38	76.75 37	77.41 52	83.96 71	59.60 59	82.95 63	86.50 17	60.78 91	75.27 41	84.83 127	60.76 15	86.56 79	67.86 84	87.87 46	86.06 87

DPE-MVS		80.56 5	80.98 5	79.29 14	87.27 13	60.56 48	85.71 26	86.42 18	63.28 47	83.27 13	91.83 10	64.96 7	90.47 10	76.41 27	89.67 22	86.84 61
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		80.16 7	80.59 6	78.86 28	86.64 23	60.02 53	88.12 3	86.42 18	62.94 54	82.40 14	92.12 2	59.64 18	89.76 14	78.70 11	88.32 36	86.79 64

3Dnovator+	66.72 4	75.84 50	74.57 60	79.66 8	82.40 87	59.92 56	85.83 22	86.32 20	66.92 8	67.80 157	89.24 58	42.03 194	89.38 18	64.07 115	86.50 61	89.69 1

CS-MVS-test		74.96 56	74.82 57	75.40 87	79.45 141	52.03 181	82.95 63	86.18 21	63.24 48	70.07 110	84.50 137	55.21 47	88.77 27	67.89 83	83.85 78	85.40 117

CS-MVS		74.01 72	74.24 65	73.32 143	76.47 214	48.51 228	79.19 124	86.17 22	60.56 95	71.62 98	83.71 153	55.16 48	87.94 43	69.21 74	86.11 63	83.51 180

DROMVSNet		75.84 50	75.87 47	75.74 78	78.86 153	52.65 166	83.73 53	86.08 23	63.47 45	72.77 84	87.25 84	53.13 74	87.93 44	71.97 56	85.57 67	86.66 67

ZNCC-MVS		78.82 13	78.67 17	79.30 13	86.43 30	62.05 21	86.62 11	86.01 24	63.32 46	75.08 43	90.47 27	53.96 61	88.68 28	76.48 26	89.63 24	87.16 53

SteuartSystems-ACMMP		79.48 10	79.31 11	79.98 2	83.01 82	62.18 19	87.60 9	85.83 25	66.69 10	78.03 31	90.98 14	54.26 57	90.06 12	78.42 17	89.02 27	87.69 32
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		75.87 49	75.36 50	77.41 52	80.62 116	55.91 127	84.28 39	85.78 26	56.08 186	73.41 72	86.58 97	50.94 99	88.54 29	70.79 63	89.71 21	87.79 29

SMA-MVS		80.28 6	80.39 7	79.95 3	86.60 25	61.95 22	86.33 13	85.75 27	62.49 65	82.20 15	92.28 1	56.53 34	89.70 15	79.85 3	91.48 1	88.19 14
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
DPM-MVS		75.47 53	75.00 52	76.88 59	81.38 103	59.16 67	79.94 110	85.71 28	56.59 173	72.46 89	86.76 87	56.89 32	87.86 47	66.36 96	88.91 30	83.64 177

MSC_two_6792asdad				79.95 3	87.24 14	61.04 36		85.62 29					90.96 1	79.31 7	90.65 8	87.85 25

No_MVS				79.95 3	87.24 14	61.04 36		85.62 29					90.96 1	79.31 7	90.65 8	87.85 25

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 223	84.64 3				79.07 9	90.87 5	88.37 9

MP-MVS-pluss		78.35 22	78.46 18	78.03 43	84.96 60	59.52 61	82.93 65	85.39 32	62.15 70	76.41 36	91.51 11	52.47 79	86.78 71	80.66 2	89.64 23	87.80 28
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testtj		78.47 19	78.43 19	78.61 32	86.82 17	60.67 46	86.07 18	85.38 33	62.12 71	78.65 26	90.29 33	55.76 42	89.31 19	73.55 47	87.22 49	85.84 93

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 18	84.92 64	60.32 51	83.03 62	85.33 34	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 38	88.67 31	88.12 16

GST-MVS		78.14 24	77.85 27	78.99 25	86.05 43	61.82 25	85.84 21	85.21 35	63.56 44	74.29 59	90.03 43	52.56 76	88.53 30	74.79 34	88.34 34	86.63 68

ACMMP_NAP		78.77 15	78.78 15	78.74 30	85.44 52	61.04 36	83.84 52	85.16 36	62.88 56	78.10 28	91.26 13	52.51 77	88.39 31	79.34 6	90.52 13	86.78 65

HPM-MVS		77.28 34	76.85 36	78.54 34	85.00 59	60.81 43	82.91 66	85.08 37	62.57 63	73.09 79	89.97 46	50.90 100	87.48 53	75.30 30	86.85 56	87.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
casdiffmvs		74.80 59	74.89 56	74.53 108	75.59 229	50.37 201	78.17 139	85.06 38	62.80 61	74.40 57	87.86 76	57.88 27	83.61 147	69.46 72	82.79 91	89.59 2

DVP-MVS		80.84 4	81.64 3	78.42 36	87.75 7	59.07 72	87.85 5	85.03 39	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 13	90.61 11	85.45 112
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
CNVR-MVS		79.84 9	79.97 9	79.45 10	87.90 2	62.17 20	84.37 36	85.03 39	66.96 5	77.58 32	90.06 40	59.47 20	89.13 22	78.67 12	89.73 20	87.03 56

ETV-MVS		74.46 66	73.84 70	76.33 70	79.27 144	55.24 139	79.22 123	85.00 41	64.97 24	72.65 86	79.46 243	53.65 70	87.87 46	67.45 89	82.91 87	85.89 92

test_prior3		76.89 40	76.96 35	76.69 62	84.20 69	57.27 101	81.75 85	84.88 42	60.37 101	75.01 44	89.06 59	56.22 38	86.43 84	72.19 53	88.96 28	86.38 71

test_prior				76.69 62	84.20 69	57.27 101		84.88 42					86.43 84			86.38 71

DeepC-MVS_fast	68.24 3	77.25 35	76.63 39	79.12 19	86.15 38	60.86 41	84.71 33	84.85 44	61.98 77	73.06 80	88.88 64	53.72 66	89.06 23	68.27 77	88.04 42	87.42 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CLD-MVS		73.33 78	72.68 82	75.29 92	78.82 155	53.33 158	78.23 138	84.79 45	61.30 85	70.41 105	81.04 206	52.41 80	87.12 60	64.61 114	82.49 95	85.41 116
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline		74.61 64	74.70 59	74.34 112	75.70 225	49.99 208	77.54 150	84.63 46	62.73 62	73.98 61	87.79 78	57.67 30	83.82 143	69.49 70	82.74 92	89.20 3

ACMMP		76.02 47	75.33 51	78.07 41	85.20 56	61.91 23	85.49 30	84.44 47	63.04 52	69.80 120	89.74 52	45.43 163	87.16 59	72.01 55	82.87 89	85.14 123
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
NCCC		78.58 16	78.31 20	79.39 11	87.51 12	62.61 16	85.20 31	84.42 48	66.73 9	74.67 54	89.38 56	55.30 46	89.18 21	74.19 39	87.34 48	86.38 71

APD-MVS		78.02 25	78.04 26	77.98 44	86.44 29	60.81 43	85.52 28	84.36 49	60.61 93	79.05 23	90.30 32	55.54 45	88.32 34	73.48 48	87.03 52	84.83 133
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 27	77.65 28	79.10 20	86.71 20	62.81 10	86.29 14	84.32 50	62.82 58	73.96 62	90.50 24	53.20 72	88.35 32	74.02 41	87.05 50	86.13 84

#test#		77.83 28	77.41 31	79.10 20	86.71 20	62.81 10	85.69 27	84.32 50	61.61 81	73.96 62	90.50 24	53.20 72	88.35 32	73.68 44	87.05 50	86.13 84

ACMMPR		77.71 29	77.23 33	79.16 16	86.75 19	62.93 9	86.29 14	84.24 52	62.82 58	73.55 71	90.56 22	49.80 106	88.24 35	74.02 41	87.03 52	86.32 79

DELS-MVS		74.76 60	74.46 61	75.65 83	77.84 182	52.25 175	75.59 190	84.17 53	63.76 41	73.15 75	82.79 166	59.58 19	86.80 69	67.24 90	86.04 64	87.89 21
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
region2R		77.67 31	77.18 34	79.15 17	86.76 18	62.95 8	86.29 14	84.16 54	62.81 60	73.30 73	90.58 21	49.90 104	88.21 36	73.78 43	87.03 52	86.29 82

CDPH-MVS		76.31 44	75.67 49	78.22 40	85.35 55	59.14 70	81.31 94	84.02 55	56.32 178	74.05 60	88.98 62	53.34 71	87.92 45	69.23 73	88.42 33	87.59 37

HQP_MVS		74.31 68	73.73 71	76.06 72	81.41 101	56.31 116	84.22 40	84.01 56	64.52 28	69.27 128	86.10 107	45.26 167	87.21 57	68.16 80	80.58 110	84.65 139

plane_prior5								84.01 56					87.21 57	68.16 80	80.58 110	84.65 139

XVS		77.17 36	76.56 40	79.00 23	86.32 32	62.62 14	85.83 22	83.92 58	64.55 26	72.17 92	90.01 45	47.95 127	88.01 41	71.55 59	86.74 58	86.37 74

X-MVStestdata		70.21 124	67.28 166	79.00 23	86.32 32	62.62 14	85.83 22	83.92 58	64.55 26	72.17 92	6.49 371	47.95 127	88.01 41	71.55 59	86.74 58	86.37 74

HQP3-MVS								83.90 60							80.35 116

HQP-MVS		73.45 77	72.80 81	75.40 87	80.66 113	54.94 140	82.31 78	83.90 60	62.10 72	67.85 152	85.54 121	45.46 161	86.93 66	67.04 92	80.35 116	84.32 147

canonicalmvs		74.67 63	74.98 54	73.71 127	78.94 152	50.56 199	80.23 105	83.87 62	60.30 107	77.15 33	86.56 98	59.65 17	82.00 183	66.01 99	82.12 96	88.58 6

SD-MVS		77.70 30	77.62 29	77.93 45	84.47 67	61.88 24	84.55 35	83.87 62	60.37 101	79.89 18	89.38 56	54.97 50	85.58 103	76.12 29	84.94 69	86.33 77
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
TSAR-MVS + MP.		78.44 20	78.28 21	78.90 26	84.96 60	61.41 29	84.03 46	83.82 64	59.34 126	79.37 19	89.76 51	59.84 16	87.62 52	76.69 25	86.74 58	87.68 33
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PGM-MVS		76.77 41	76.06 43	78.88 27	86.14 39	62.73 12	82.55 74	83.74 65	61.71 79	72.45 91	90.34 31	48.48 123	88.13 37	72.32 52	86.85 56	85.78 95

HPM-MVS++		79.88 8	80.14 8	79.10 20	88.17 1	64.80 1	86.59 12	83.70 66	65.37 15	78.78 25	90.64 19	58.63 23	87.24 55	79.00 10	90.37 14	85.26 122

OPM-MVS		74.73 62	74.25 64	76.19 71	80.81 112	59.01 75	82.60 73	83.64 67	63.74 42	72.52 88	87.49 79	47.18 141	85.88 96	69.47 71	80.78 106	83.66 175
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
FOURS1					86.12 40	60.82 42	88.18 1	83.61 68	60.87 88	81.50 16

FIs		70.82 112	71.43 93	68.98 225	78.33 168	38.14 319	76.96 164	83.59 69	61.02 87	67.33 164	86.73 89	55.07 49	81.64 189	54.61 186	79.22 132	87.14 54

MP-MVS		78.35 22	78.26 22	78.64 31	86.54 27	63.47 5	86.02 20	83.55 70	63.89 40	73.60 70	90.60 20	54.85 53	86.72 72	77.20 21	88.06 41	85.74 101
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
QAPM		70.05 126	68.81 134	73.78 121	76.54 212	53.43 156	83.23 59	83.48 71	52.89 232	65.90 191	86.29 103	41.55 205	86.49 83	51.01 214	78.40 146	81.42 217

test11								83.47 72

CP-MVS		77.12 37	76.68 38	78.43 35	86.05 43	63.18 7	87.55 10	83.45 73	62.44 67	72.68 85	90.50 24	48.18 125	87.34 54	73.59 46	85.71 65	84.76 138

原ACMM1				74.69 99	85.39 54	59.40 62		83.42 74	51.47 247	70.27 108	86.61 95	48.61 121	86.51 82	53.85 191	87.96 43	78.16 263

LPG-MVS_test		72.74 84	71.74 89	75.76 76	80.22 122	57.51 99	82.55 74	83.40 75	61.32 83	66.67 176	87.33 82	39.15 225	86.59 77	67.70 85	77.30 156	83.19 189

LGP-MVS_train				75.76 76	80.22 122	57.51 99		83.40 75	61.32 83	66.67 176	87.33 82	39.15 225	86.59 77	67.70 85	77.30 156	83.19 189

test12				77.76 46	84.52 66	58.41 85		83.36 77		72.93 82		54.61 55	88.05 40		88.12 40	86.81 63

PAPR		71.72 101	70.82 104	74.41 111	81.20 108	51.17 187	79.55 119	83.33 78	55.81 191	66.93 171	84.61 132	50.95 98	86.06 89	55.79 174	79.20 133	86.00 88

CANet		76.46 43	75.93 45	78.06 42	81.29 104	57.53 98	82.35 76	83.31 79	67.78 3	70.09 109	86.34 102	54.92 51	88.90 25	72.68 51	84.55 71	87.76 31

APD-MVS_3200maxsize		74.96 56	74.39 63	76.67 64	82.20 88	58.24 88	83.67 54	83.29 80	58.41 143	73.71 68	90.14 37	45.62 156	85.99 92	69.64 69	82.85 90	85.78 95

PAPM_NR		72.63 86	71.80 88	75.13 93	81.72 95	53.42 157	79.91 112	83.28 81	59.14 128	66.31 184	85.90 113	51.86 87	86.06 89	57.45 163	80.62 108	85.91 91

EIA-MVS		71.78 99	70.60 106	75.30 91	79.85 130	53.54 154	77.27 158	83.26 82	57.92 153	66.49 179	79.39 244	52.07 85	86.69 73	60.05 150	79.14 135	85.66 103

FC-MVSNet-test		69.80 131	70.58 108	67.46 241	77.61 192	34.73 344	76.05 184	83.19 83	60.84 89	65.88 192	86.46 99	54.52 56	80.76 214	52.52 201	78.12 147	86.91 58

3Dnovator	64.47 5	72.49 88	71.39 95	75.79 75	77.70 184	58.99 76	80.66 101	83.15 84	62.24 69	65.46 198	86.59 96	42.38 192	85.52 105	59.59 155	84.72 70	82.85 198

MVS_Test		72.45 89	72.46 84	72.42 162	74.88 237	48.50 229	76.28 178	83.14 85	59.40 124	72.46 89	84.68 129	55.66 44	81.12 201	65.98 100	79.66 124	87.63 35

DP-MVS Recon		72.15 96	70.73 105	76.40 68	86.57 26	57.99 91	81.15 96	82.96 86	57.03 162	66.78 172	85.56 119	44.50 174	88.11 38	51.77 210	80.23 119	83.10 193

Regformer-2		75.63 52	74.99 53	77.54 50	80.43 118	58.32 87	79.50 120	82.92 87	67.84 1	75.94 37	80.75 216	55.73 43	86.80 69	71.44 61	80.38 114	87.50 40

UniMVSNet (Re)		70.63 115	70.20 112	71.89 166	78.55 161	45.29 265	75.94 187	82.92 87	63.68 43	68.16 146	83.59 156	53.89 64	83.49 150	53.97 189	71.12 222	86.89 59

MAR-MVS		71.51 103	70.15 114	75.60 85	81.84 94	59.39 63	81.38 93	82.90 89	54.90 213	68.08 149	78.70 251	47.73 129	85.51 106	51.68 212	84.17 76	81.88 213
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
nrg030		72.96 82	73.01 79	72.84 151	75.41 232	50.24 202	80.02 108	82.89 90	58.36 145	74.44 56	86.73 89	58.90 22	80.83 210	65.84 101	74.46 176	87.44 43

ACMP	63.53 6	72.30 91	71.20 100	75.59 86	80.28 120	57.54 97	82.74 69	82.84 91	60.58 94	65.24 205	86.18 105	39.25 223	86.03 91	66.95 94	76.79 163	83.22 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ZD-MVS					86.64 23	60.38 50		82.70 92	57.95 152	78.10 28	90.06 40	56.12 40	88.84 26	74.05 40	87.00 55

UniMVSNet_NR-MVSNet		71.11 107	71.00 102	71.44 177	79.20 146	44.13 274	76.02 186	82.60 93	66.48 13	68.20 143	84.60 133	56.82 33	82.82 168	54.62 184	70.43 229	87.36 49

alignmvs		73.86 75	73.99 67	73.45 137	78.20 171	50.50 200	78.57 132	82.43 94	59.40 124	76.57 34	86.71 91	56.42 37	81.23 200	65.84 101	81.79 98	88.62 4

Anonymous20231211		69.28 143	68.47 140	71.73 170	80.28 120	47.18 245	79.98 109	82.37 95	54.61 215	67.24 165	84.01 145	39.43 221	82.41 178	55.45 178	72.83 201	85.62 106

mPP-MVS		76.54 42	75.93 45	78.34 39	86.47 28	63.50 4	85.74 25	82.28 96	62.90 55	71.77 95	90.26 34	46.61 150	86.55 80	71.71 57	85.66 66	84.97 130

SR-MVS		76.13 46	75.70 48	77.40 54	85.87 45	61.20 33	85.52 28	82.19 97	59.99 112	75.10 42	90.35 29	47.66 131	86.52 81	71.64 58	82.99 84	84.47 144

Regformer-1		75.47 53	74.93 55	77.09 57	80.43 118	57.70 96	79.50 120	82.13 98	67.84 1	75.73 40	80.75 216	56.50 35	86.07 88	71.07 62	80.38 114	87.50 40

PS-MVSNAJss		72.24 92	71.21 99	75.31 90	78.50 162	55.93 126	81.63 87	82.12 99	56.24 181	70.02 114	85.68 118	47.05 143	84.34 131	65.27 107	74.41 178	85.67 102

WR-MVS_H		67.02 192	66.92 175	67.33 245	77.95 180	37.75 322	77.57 148	82.11 100	62.03 76	62.65 236	82.48 175	50.57 101	79.46 230	42.91 279	64.01 296	84.79 136

ACMM	61.98 7	70.80 113	69.73 119	74.02 116	80.59 117	58.59 83	82.68 71	82.02 101	55.46 199	67.18 167	84.39 139	38.51 230	83.17 155	60.65 145	76.10 167	80.30 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSLP-MVS++		73.77 76	73.47 75	74.66 101	83.02 81	59.29 66	82.30 81	81.88 102	59.34 126	71.59 99	86.83 86	45.94 154	83.65 146	65.09 109	85.22 68	81.06 228

abl_6		74.34 67	73.50 73	76.86 60	82.43 86	60.16 52	83.48 57	81.86 103	58.81 133	73.95 64	89.86 48	41.87 197	86.62 76	67.98 82	81.23 105	83.80 168

MVS		67.37 182	66.33 188	70.51 201	75.46 231	50.94 189	73.95 222	81.85 104	41.57 335	62.54 239	78.57 256	47.98 126	85.47 109	52.97 199	82.05 97	75.14 295

RRT_test8_iter05		68.17 170	66.86 176	72.07 165	75.81 223	46.33 251	76.41 175	81.81 105	56.43 176	66.52 178	81.30 202	31.90 298	84.25 132	63.77 122	67.83 269	85.64 105

114514_t		70.83 111	69.56 121	74.64 103	86.21 34	54.63 144	82.34 77	81.81 105	48.22 278	63.01 231	85.83 115	40.92 213	87.10 61	57.91 161	79.79 121	82.18 207

PCF-MVS	61.88 8	70.95 110	69.49 123	75.35 89	77.63 187	55.71 129	76.04 185	81.81 105	50.30 259	69.66 121	85.40 124	52.51 77	84.89 120	51.82 209	80.24 118	85.45 112
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPP-MVSNet		72.16 95	71.31 98	74.71 98	78.68 159	49.70 211	82.10 82	81.65 108	60.40 98	65.94 189	85.84 114	51.74 89	86.37 86	55.93 171	79.55 127	88.07 19

test1172		75.36 55	74.81 58	77.02 58	85.47 51	60.79 45	83.94 51	81.63 109	59.52 123	74.66 55	90.18 36	44.74 170	85.84 97	70.63 65	82.52 93	84.42 145

PVSNet_BlendedMVS		68.56 160	67.72 150	71.07 191	77.03 202	50.57 197	74.50 213	81.52 110	53.66 226	64.22 223	79.72 237	49.13 114	82.87 164	55.82 172	73.92 182	79.77 250

PVSNet_Blended		68.59 156	67.72 150	71.19 186	77.03 202	50.57 197	72.51 245	81.52 110	51.91 240	64.22 223	77.77 268	49.13 114	82.87 164	55.82 172	79.58 125	80.14 241

DU-MVS		70.01 127	69.53 122	71.44 177	78.05 177	44.13 274	75.01 202	81.51 112	64.37 31	68.20 143	84.52 134	49.12 116	82.82 168	54.62 184	70.43 229	87.37 47

Regformer-4		74.25 70	73.48 74	76.57 67	79.75 131	56.54 115	78.54 134	81.49 113	66.93 7	73.90 65	80.30 224	53.84 65	85.98 93	69.76 68	76.84 161	87.17 52

v1144		70.42 120	69.31 126	73.76 123	73.22 257	50.64 196	77.83 143	81.43 114	58.58 139	69.40 126	81.16 203	47.53 134	85.29 114	64.01 117	70.64 225	85.34 118

v10		70.21 124	69.02 131	73.81 120	73.51 256	50.92 191	78.74 128	81.39 115	60.05 111	66.39 182	81.83 191	47.58 133	85.41 112	62.80 128	68.86 260	85.09 126

SR-MVS-dyc-post		74.57 65	73.90 68	76.58 66	83.49 74	59.87 57	84.29 37	81.36 116	58.07 149	73.14 76	90.07 38	44.74 170	85.84 97	68.20 78	81.76 100	84.03 155

RE-MVS-def			73.71 72		83.49 74	59.87 57	84.29 37	81.36 116	58.07 149	73.14 76	90.07 38	43.06 186		68.20 78	81.76 100	84.03 155

v1192		69.97 129	68.68 136	73.85 119	73.19 258	50.94 189	77.68 146	81.36 116	57.51 157	68.95 134	80.85 213	45.28 166	85.33 113	62.97 127	70.37 231	85.27 121

RPMNet		61.53 256	58.42 266	70.86 193	69.96 307	52.07 178	65.31 307	81.36 116	43.20 325	59.36 268	70.15 331	35.37 259	85.47 109	36.42 318	64.65 293	75.06 296

OpenMVS	61.03 9	68.85 149	67.56 153	72.70 155	74.26 251	53.99 148	81.21 95	81.34 120	52.70 233	62.75 234	85.55 120	38.86 228	84.14 134	48.41 234	83.01 83	79.97 244

v7n		69.01 148	67.36 163	73.98 117	72.51 272	52.65 166	78.54 134	81.30 121	60.26 108	62.67 235	81.62 194	43.61 181	84.49 128	57.01 165	68.70 262	84.79 136

MG-MVS		73.96 73	73.89 69	74.16 115	85.65 47	49.69 213	81.59 90	81.29 122	61.45 82	71.05 101	88.11 70	51.77 88	87.73 49	61.05 143	83.09 82	85.05 127

TEST9					85.58 49	61.59 27	81.62 88	81.26 123	55.65 196	74.93 46	88.81 65	53.70 67	84.68 124

train_agg		76.27 45	76.15 42	76.64 65	85.58 49	61.59 27	81.62 88	81.26 123	55.86 188	74.93 46	88.81 65	53.70 67	84.68 124	75.24 32	88.33 35	83.65 176

PAPM		67.92 174	66.69 178	71.63 174	78.09 175	49.02 221	77.09 161	81.24 125	51.04 253	60.91 255	83.98 146	47.71 130	84.99 115	40.81 292	79.32 131	80.90 230

test_8					85.40 53	60.96 39	81.54 91	81.18 126	55.86 188	74.81 49	88.80 67	53.70 67	84.45 129

TranMVSNet+NR-MVSNet		70.36 121	70.10 116	71.17 188	78.64 160	42.97 285	76.53 172	81.16 127	66.95 6	68.53 139	85.42 123	51.61 90	83.07 156	52.32 202	69.70 246	87.46 42

HPM-MVS_fast		74.30 69	73.46 76	76.80 61	84.45 68	59.04 74	83.65 55	81.05 128	60.15 109	70.43 104	89.84 49	41.09 212	85.59 102	67.61 87	82.90 88	85.77 98

agg_prior1		75.94 48	76.01 44	75.72 79	85.04 57	59.96 54	81.44 92	81.04 129	56.14 184	74.68 52	88.90 63	53.91 63	84.04 136	75.01 33	87.92 45	83.16 192

agg_prior					85.04 57	59.96 54		81.04 129		74.68 52			84.04 136

Anonymous20240529		69.91 130	69.02 131	72.56 157	80.19 125	47.65 239	77.56 149	80.99 131	55.45 200	69.88 118	86.76 87	39.24 224	82.18 181	54.04 188	77.10 158	87.85 25

zzz-MVS		77.61 32	77.36 32	78.35 37	86.08 41	63.57 2	83.37 58	80.97 132	65.13 18	75.77 38	90.88 15	48.63 119	86.66 74	77.23 19	88.17 38	84.81 134

MTGPA								80.97 132

MTAPA		76.90 39	76.42 41	78.35 37	86.08 41	63.57 2	74.92 205	80.97 132	65.13 18	75.77 38	90.88 15	48.63 119	86.66 74	77.23 19	88.17 38	84.81 134

NR-MVSNet		69.54 139	68.85 133	71.59 175	78.05 177	43.81 278	74.20 217	80.86 135	65.18 17	62.76 233	84.52 134	52.35 82	83.59 148	50.96 215	70.78 224	87.37 47

v8		70.33 122	69.28 127	73.49 135	73.15 259	50.22 203	78.62 131	80.78 136	60.79 90	66.45 181	82.11 186	49.35 109	84.98 117	63.58 123	68.71 261	85.28 120

v144192		69.71 132	68.51 138	73.33 142	73.10 260	50.13 205	77.54 150	80.64 137	56.65 167	68.57 138	80.55 218	46.87 148	84.96 119	62.98 126	69.66 247	84.89 132

v1921920		69.47 141	68.17 146	73.36 141	73.06 261	50.10 206	77.39 153	80.56 138	56.58 174	68.59 136	80.37 220	44.72 172	84.98 117	62.47 132	69.82 242	85.00 128

v1240		69.24 145	67.91 148	73.25 146	73.02 263	49.82 209	77.21 159	80.54 139	56.43 176	68.34 142	80.51 219	43.33 184	84.99 115	62.03 136	69.77 245	84.95 131

v2v482		70.50 118	69.45 125	73.66 129	72.62 269	50.03 207	77.58 147	80.51 140	59.90 113	69.52 122	82.14 185	47.53 134	84.88 122	65.07 110	70.17 235	86.09 86

PEN-MVS		66.60 201	66.45 181	67.04 246	77.11 200	36.56 332	77.03 163	80.42 141	62.95 53	62.51 241	84.03 144	46.69 149	79.07 241	44.22 264	63.08 305	85.51 109

API-MVS		72.17 94	71.41 94	74.45 110	81.95 93	57.22 103	84.03 46	80.38 142	59.89 116	68.40 140	82.33 178	49.64 107	87.83 48	51.87 208	84.16 77	78.30 261

PVSNet_Blended_VisFu		71.45 105	70.39 110	74.65 102	82.01 90	58.82 79	79.93 111	80.35 143	55.09 206	65.82 194	82.16 184	49.17 113	82.64 173	60.34 148	78.62 144	82.50 203

test_yl		69.69 133	69.13 128	71.36 181	78.37 166	45.74 259	74.71 209	80.20 144	57.91 154	70.01 115	83.83 149	42.44 190	82.87 164	54.97 180	79.72 122	85.48 110

DCV-MVSNet		69.69 133	69.13 128	71.36 181	78.37 166	45.74 259	74.71 209	80.20 144	57.91 154	70.01 115	83.83 149	42.44 190	82.87 164	54.97 180	79.72 122	85.48 110

TAPA-MVS	59.36 10	66.60 201	65.20 207	70.81 194	76.63 209	48.75 225	76.52 173	80.04 146	50.64 257	65.24 205	84.93 126	39.15 225	78.54 248	36.77 311	76.88 160	85.14 123
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
xxxxxxxxxxxxxcwj		78.37 21	78.25 23	78.76 29	86.17 36	61.30 31	83.98 48	79.95 147	59.00 129	79.16 20	90.75 17	57.96 25	87.09 62	77.08 23	90.18 15	87.87 23

Regformer-3		73.89 74	73.28 78	75.71 80	79.75 131	55.48 136	78.54 134	79.93 148	66.58 11	73.62 69	80.30 224	54.87 52	84.54 127	69.09 75	76.84 161	87.10 55

OMC-MVS		71.40 106	70.60 106	73.78 121	76.60 210	53.15 160	79.74 116	79.78 149	58.37 144	68.75 135	86.45 100	45.43 163	80.60 215	62.58 129	77.73 150	87.58 38

ACMH	55.70 15	65.20 220	63.57 222	70.07 207	78.07 176	52.01 182	79.48 122	79.69 150	55.75 193	56.59 291	80.98 208	27.12 328	80.94 206	42.90 280	71.58 218	77.25 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		69.02 147	69.47 124	67.69 239	77.42 195	41.00 302	74.04 219	79.68 151	60.06 110	69.26 130	84.81 128	51.06 97	77.58 261	54.44 187	74.43 177	84.48 143

save fliter					86.17 36	61.30 31	83.98 48	79.66 152	59.00 129

Effi-MVS+		73.31 79	72.54 83	75.62 84	77.87 181	53.64 151	79.62 118	79.61 153	61.63 80	72.02 94	82.61 171	56.44 36	85.97 94	63.99 118	79.07 136	87.25 51

PS-CasMVS		66.42 205	66.32 189	66.70 250	77.60 193	36.30 337	76.94 165	79.61 153	62.36 68	62.43 243	83.66 154	45.69 155	78.37 249	45.35 261	63.26 303	85.42 115

CP-MVSNet		66.49 204	66.41 185	66.72 248	77.67 186	36.33 335	76.83 169	79.52 155	62.45 66	62.54 239	83.47 160	46.32 151	78.37 249	45.47 259	63.43 302	85.45 112

V42		68.65 155	67.35 164	72.56 157	68.93 318	50.18 204	72.90 239	79.47 156	56.92 164	69.45 125	80.26 226	46.29 152	82.99 157	64.07 115	67.82 270	84.53 141

Fast-Effi-MVS+		70.28 123	69.12 130	73.73 126	78.50 162	51.50 185	75.01 202	79.46 157	56.16 183	68.59 136	79.55 241	53.97 60	84.05 135	53.34 196	77.53 152	85.65 104

DTE-MVSNet		65.58 212	65.34 204	66.31 253	76.06 220	34.79 341	76.43 174	79.38 158	62.55 64	61.66 250	83.83 149	45.60 157	79.15 239	41.64 291	60.88 319	85.00 128

EI-MVSNet-Vis-set		72.42 90	71.59 90	74.91 95	78.47 164	54.02 147	77.05 162	79.33 159	65.03 22	71.68 97	79.35 246	52.75 75	84.89 120	66.46 95	74.23 179	85.83 94

EI-MVSNet-UG-set		71.92 97	71.06 101	74.52 109	77.98 179	53.56 153	76.62 170	79.16 160	64.40 30	71.18 100	78.95 250	52.19 83	84.66 126	65.47 105	73.57 187	85.32 119

XVG-OURS-SEG-HR		68.81 150	67.47 159	72.82 153	74.40 248	56.87 113	70.59 272	79.04 161	54.77 214	66.99 169	86.01 110	39.57 220	78.21 252	62.54 130	73.33 192	83.37 182

PS-MVSNAJ		70.51 117	69.70 120	72.93 149	81.52 98	55.79 128	74.92 205	79.00 162	55.04 211	69.88 118	78.66 252	47.05 143	82.19 180	61.61 139	79.58 125	80.83 231

xiu_mvs_v2_base		70.52 116	69.75 118	72.84 151	81.21 107	55.63 132	75.11 199	78.92 163	54.92 212	69.96 117	79.68 238	47.00 147	82.09 182	61.60 140	79.37 128	80.81 232

EG-PatchMatch MVS		64.71 224	62.87 229	70.22 203	77.68 185	53.48 155	77.99 141	78.82 164	53.37 227	56.03 294	77.41 271	24.75 343	84.04 136	46.37 247	73.42 191	73.14 315

XVG-OURS		68.76 154	67.37 162	72.90 150	74.32 250	57.22 103	70.09 279	78.81 165	55.24 202	67.79 158	85.81 117	36.54 253	78.28 251	62.04 135	75.74 170	83.19 189

c3_l		68.33 164	67.56 153	70.62 198	70.87 292	46.21 254	74.47 214	78.80 166	56.22 182	66.19 185	78.53 257	51.88 86	81.40 194	62.08 133	69.04 256	84.25 149

ambc				65.13 272	63.72 346	37.07 327	47.66 356	78.78 167		54.37 313	71.42 320	11.24 365	80.94 206	45.64 254	53.85 342	77.38 272

AdaColmap		69.99 128	68.66 137	73.97 118	84.94 62	57.83 93	82.63 72	78.71 168	56.28 180	64.34 218	84.14 141	41.57 202	87.06 64	46.45 246	78.88 137	77.02 278

IS-MVSNet		71.57 102	71.00 102	73.27 144	78.86 153	45.63 263	80.22 106	78.69 169	64.14 38	66.46 180	87.36 81	49.30 110	85.60 101	50.26 219	83.71 80	88.59 5

miper_ehance_all_eth		68.03 171	67.24 170	70.40 202	70.54 296	46.21 254	73.98 220	78.68 170	55.07 209	66.05 187	77.80 266	52.16 84	81.31 197	61.53 142	69.32 250	83.67 173

cdsmvs_eth3d_5k		17.50 339	23.34 338	0.00 359	0.00 382	0.00 382	0.00 370	78.63 171	0.00 377	0.00 378	82.18 181	49.25 112	0.00 376	0.00 376	0.00 374	0.00 374

TSAR-MVS + GP.		74.90 58	74.15 66	77.17 56	82.00 91	58.77 80	81.80 84	78.57 172	58.58 139	74.32 58	84.51 136	55.94 41	87.22 56	67.11 91	84.48 73	85.52 108

mvs_tets		68.18 168	66.36 187	73.63 132	75.61 228	55.35 138	80.77 99	78.56 173	52.48 236	64.27 221	84.10 143	27.45 326	81.84 186	63.45 125	70.56 228	83.69 172

MVP-Stereo		65.41 216	63.80 218	70.22 203	77.62 191	55.53 134	76.30 177	78.53 174	50.59 258	56.47 292	78.65 253	39.84 217	82.68 171	44.10 268	72.12 213	72.44 324
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		68.25 166	66.45 181	73.66 129	75.62 227	55.49 135	80.82 98	78.51 175	52.33 237	64.33 219	84.11 142	28.28 320	81.81 187	63.48 124	70.62 226	83.67 173

MVSFormer		71.50 104	70.38 111	74.88 96	78.76 156	57.15 108	82.79 67	78.48 176	51.26 251	69.49 123	83.22 161	43.99 179	83.24 153	66.06 97	79.37 128	84.23 150

test_djsdf		69.45 142	67.74 149	74.58 106	74.57 244	54.92 142	82.79 67	78.48 176	51.26 251	65.41 199	83.49 159	38.37 232	83.24 153	66.06 97	69.25 253	85.56 107

diffmvs		70.69 114	70.43 109	71.46 176	69.45 313	48.95 223	72.93 238	78.46 178	57.27 159	71.69 96	83.97 147	51.48 91	77.92 256	70.70 64	77.95 149	87.53 39

EI-MVSNet		69.27 144	68.44 142	71.73 170	74.47 245	49.39 218	75.20 197	78.45 179	59.60 119	69.16 132	76.51 282	51.29 92	82.50 175	59.86 154	71.45 220	83.30 184

XVG-ACMP-BASELINE		64.36 228	62.23 237	70.74 196	72.35 274	52.45 173	70.80 271	78.45 179	53.84 224	59.87 263	81.10 205	16.24 357	79.32 233	55.64 177	71.76 215	80.47 235

MVSTER		67.16 189	65.58 202	71.88 167	70.37 300	49.70 211	70.25 278	78.45 179	51.52 245	69.16 132	80.37 220	38.45 231	82.50 175	60.19 149	71.46 219	83.44 181

miper_enhance_ethall		67.11 190	66.09 194	70.17 206	69.21 315	45.98 257	72.85 240	78.41 182	51.38 248	65.65 195	75.98 291	51.17 95	81.25 198	60.82 144	69.32 250	83.29 186

MVS_111021_HR		74.02 71	73.46 76	75.69 82	83.01 82	60.63 47	77.29 157	78.40 183	61.18 86	70.58 103	85.97 111	54.18 59	84.00 140	67.52 88	82.98 86	82.45 204

1314		64.61 226	63.21 226	68.80 227	71.87 282	47.46 242	73.95 222	78.39 184	42.88 328	59.97 261	76.60 281	38.11 236	79.39 232	54.84 182	72.32 210	79.55 251

Vis-MVSNet		72.18 93	71.37 96	74.61 104	81.29 104	55.41 137	80.90 97	78.28 185	60.73 92	69.23 131	88.09 71	44.36 176	82.65 172	57.68 162	81.75 102	85.77 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		71.01 109	70.15 114	73.60 133	79.57 137	52.17 176	78.93 126	78.12 186	58.02 151	67.76 160	83.87 148	52.36 81	82.72 170	56.90 166	75.79 169	85.92 90

ACMH+	57.40 11	66.12 207	64.06 213	72.30 164	77.79 183	52.83 164	80.39 104	78.03 187	57.30 158	57.47 286	82.55 173	27.68 324	84.17 133	45.54 256	69.78 243	79.90 245

eth_miper_zixun_eth		67.63 178	66.28 191	71.67 172	71.60 284	48.33 231	73.68 231	77.88 188	55.80 192	65.91 190	78.62 255	47.35 140	82.88 163	59.45 156	66.25 281	83.81 164

CPTT-MVS		72.78 83	72.08 87	74.87 97	84.88 65	61.41 29	84.15 43	77.86 189	55.27 201	67.51 162	88.08 72	41.93 196	81.85 185	69.04 76	80.01 120	81.35 221

GBi-Net		67.21 184	66.55 179	69.19 221	77.63 187	43.33 281	77.31 154	77.83 190	56.62 170	65.04 209	82.70 167	41.85 198	80.33 221	47.18 240	72.76 203	83.92 159

test1		67.21 184	66.55 179	69.19 221	77.63 187	43.33 281	77.31 154	77.83 190	56.62 170	65.04 209	82.70 167	41.85 198	80.33 221	47.18 240	72.76 203	83.92 159

FMVSNet1		66.70 199	65.87 196	69.19 221	77.49 194	43.33 281	77.31 154	77.83 190	56.45 175	64.60 217	82.70 167	38.08 237	80.33 221	46.08 249	72.31 211	83.92 159

UA-Net		73.13 80	72.93 80	73.76 123	83.58 73	51.66 184	78.75 127	77.66 193	67.75 4	72.61 87	89.42 54	49.82 105	83.29 152	53.61 194	83.14 81	86.32 79

VDD-MVS		72.50 87	72.09 86	73.75 125	81.58 97	49.69 213	77.76 145	77.63 194	63.21 50	73.21 74	89.02 61	42.14 193	83.32 151	61.72 138	82.50 94	88.25 11

IterMVS-LS		69.22 146	68.48 139	71.43 179	74.44 247	49.40 217	76.23 179	77.55 195	59.60 119	65.85 193	81.59 197	51.28 93	81.58 192	59.87 153	69.90 241	83.30 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		66.93 194	66.31 190	68.79 228	77.63 187	42.98 284	76.11 181	77.47 196	56.62 170	65.22 207	82.17 183	41.85 198	80.18 224	47.05 243	72.72 206	83.20 188

PLC	56.13 14	65.09 221	63.21 226	70.72 197	81.04 110	54.87 143	78.57 132	77.47 196	48.51 274	55.71 295	81.89 189	33.71 275	79.71 226	41.66 289	70.37 231	77.58 270
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		68.27 165	67.29 165	71.21 185	79.74 133	53.22 159	76.06 183	77.46 198	57.19 160	66.10 186	81.61 195	45.37 165	83.50 149	45.42 260	76.68 165	76.91 282

VNet		69.68 135	70.19 113	68.16 235	79.73 134	41.63 298	70.53 273	77.38 199	60.37 101	70.69 102	86.63 94	51.08 96	77.09 267	53.61 194	81.69 104	85.75 100

cl22		67.47 181	66.45 181	70.54 200	69.85 309	46.49 249	73.85 228	77.35 200	55.07 209	65.51 197	77.92 262	47.64 132	81.10 202	61.58 141	69.32 250	84.01 157

anonymousdsp		67.00 193	64.82 210	73.57 134	70.09 304	56.13 121	76.35 176	77.35 200	48.43 276	64.99 212	80.84 214	33.01 283	80.34 220	64.66 112	67.64 272	84.23 150

cascas		65.98 208	63.42 224	73.64 131	77.26 198	52.58 169	72.26 249	77.21 202	48.56 273	61.21 254	74.60 303	32.57 294	85.82 99	50.38 218	76.75 164	82.52 202

FMVSNet3		66.32 206	65.61 201	68.46 231	76.48 213	42.34 288	74.98 204	77.15 203	55.83 190	65.04 209	81.16 203	39.91 216	80.14 225	47.18 240	72.76 203	82.90 197

v148		68.24 167	67.19 171	71.40 180	70.43 298	47.77 238	75.76 189	77.03 204	58.91 131	67.36 163	80.10 229	48.60 122	81.89 184	60.01 151	66.52 280	84.53 141

Fast-Effi-MVS+-dtu		67.37 182	65.33 205	73.48 136	72.94 264	57.78 95	77.47 152	76.88 205	57.60 156	61.97 246	76.85 276	39.31 222	80.49 219	54.72 183	70.28 234	82.17 209

CANet_DTU		68.18 168	67.71 152	69.59 216	74.83 238	46.24 253	78.66 130	76.85 206	59.60 119	63.45 227	82.09 187	35.25 260	77.41 263	59.88 152	78.76 141	85.14 123

cl____		67.18 187	66.26 192	69.94 209	70.20 301	45.74 259	73.30 233	76.83 207	55.10 204	65.27 201	79.57 240	47.39 138	80.53 216	59.41 158	69.22 254	83.53 179

DIV-MVS_self_test		67.18 187	66.26 192	69.94 209	70.20 301	45.74 259	73.29 234	76.83 207	55.10 204	65.27 201	79.58 239	47.38 139	80.53 216	59.43 157	69.22 254	83.54 178

h-mvs33		72.71 85	71.49 92	76.40 68	81.99 92	59.58 60	76.92 166	76.74 209	60.40 98	74.81 49	85.95 112	45.54 159	85.76 100	70.41 66	70.61 227	83.86 163

BH-w/o		66.85 195	65.83 197	69.90 212	79.29 142	52.46 172	74.66 211	76.65 210	54.51 219	64.85 213	78.12 258	45.59 158	82.95 160	43.26 275	75.54 172	74.27 308

LTVRE_ROB	55.42 16	63.15 240	61.23 249	68.92 226	76.57 211	47.80 236	59.92 329	76.39 211	54.35 221	58.67 276	82.46 176	29.44 313	81.49 193	42.12 285	71.14 221	77.46 271
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
BH-RMVSNet		68.81 150	67.42 160	72.97 148	80.11 127	52.53 170	74.26 216	76.29 212	58.48 142	68.38 141	84.20 140	42.59 188	83.83 142	46.53 245	75.91 168	82.56 200

F-COLMAP		63.05 241	60.87 254	69.58 218	76.99 204	53.63 152	78.12 140	76.16 213	47.97 282	52.41 325	81.61 195	27.87 322	78.11 253	40.07 295	66.66 278	77.00 279

ab-mvs		66.65 200	66.42 184	67.37 243	76.17 217	41.73 295	70.41 276	76.14 214	53.99 222	65.98 188	83.51 158	49.48 108	76.24 276	48.60 232	73.46 190	84.14 153

WR-MVS		68.47 162	68.47 140	68.44 232	80.20 124	39.84 305	73.75 230	76.07 215	64.68 25	68.11 148	83.63 155	50.39 103	79.14 240	49.78 220	69.66 247	86.34 76

Effi-MVS+-dtu		69.64 137	67.53 156	75.95 73	76.10 218	62.29 18	80.20 107	76.06 216	59.83 117	65.26 204	77.09 272	41.56 203	84.02 139	60.60 146	71.09 223	81.53 216

mvs-test1		70.44 119	68.19 145	77.18 55	76.10 218	63.22 6	80.59 102	76.06 216	59.83 117	66.32 183	79.87 232	41.56 203	85.53 104	60.60 146	72.77 202	82.80 199

RRT_MVS		68.77 153	66.71 177	74.95 94	75.93 222	58.55 84	80.50 103	75.84 218	56.09 185	68.17 145	83.74 152	28.50 318	82.98 158	65.67 103	65.91 283	83.33 183

MSDG		61.81 254	59.23 260	69.55 219	72.64 268	52.63 168	70.45 275	75.81 219	51.38 248	53.70 317	76.11 287	29.52 311	81.08 204	37.70 306	65.79 286	74.93 300

miper_lstm_enhance		62.03 250	60.88 253	65.49 268	66.71 331	46.25 252	56.29 341	75.70 220	50.68 255	61.27 253	75.48 296	40.21 215	68.03 310	56.31 169	65.25 289	82.18 207

pm-mvs1		65.24 219	64.97 209	66.04 260	72.38 273	39.40 310	72.62 243	75.63 221	55.53 198	62.35 245	83.18 163	47.45 136	76.47 273	49.06 229	66.54 279	82.24 206

UniMVSNet_ETH3D		67.60 179	67.07 174	69.18 224	77.39 196	42.29 289	74.18 218	75.59 222	60.37 101	66.77 173	86.06 109	37.64 239	78.93 247	52.16 204	73.49 189	86.32 79

HyFIR lowres test		65.67 211	63.01 228	73.67 128	79.97 129	55.65 131	69.07 287	75.52 223	42.68 329	63.53 226	77.95 260	40.43 214	81.64 189	46.01 250	71.91 214	83.73 171

pmmvs6		63.69 232	62.82 231	66.27 255	70.63 295	39.27 311	73.13 236	75.47 224	52.69 234	59.75 266	82.30 179	39.71 219	77.03 268	47.40 238	64.35 295	82.53 201

UGNet		68.81 150	67.39 161	73.06 147	78.33 168	54.47 145	79.77 114	75.40 225	60.45 97	63.22 228	84.40 138	32.71 290	80.91 209	51.71 211	80.56 112	83.81 164
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
VDDNet		71.81 98	71.33 97	73.26 145	82.80 85	47.60 241	78.74 128	75.27 226	59.59 122	72.94 81	89.40 55	41.51 206	83.91 141	58.75 159	82.99 84	88.26 10

hse-mvs2		71.04 108	69.86 117	74.60 105	79.58 136	57.12 110	73.96 221	75.25 227	60.40 98	74.81 49	81.95 188	45.54 159	82.90 161	70.41 66	66.83 277	83.77 169

AUN-MVS		68.45 163	66.41 185	74.57 107	79.53 138	57.08 111	73.93 225	75.23 228	54.44 220	66.69 175	81.85 190	37.10 248	82.89 162	62.07 134	66.84 276	83.75 170

mvs_anonymous		68.03 171	67.51 157	69.59 216	72.08 277	44.57 272	71.99 252	75.23 228	51.67 241	67.06 168	82.57 172	54.68 54	77.94 255	56.56 167	75.71 171	86.26 83

TR-MVS		66.59 203	65.07 208	71.17 188	79.18 147	49.63 215	73.48 232	75.20 230	52.95 230	67.90 150	80.33 223	39.81 218	83.68 145	43.20 276	73.56 188	80.20 239

IB-MVS	56.42 12	65.40 217	62.73 232	73.40 140	74.89 236	52.78 165	73.09 237	75.13 231	55.69 194	58.48 280	73.73 309	32.86 285	86.32 87	50.63 216	70.11 236	81.10 227
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
xiu_mvs_v1_base_debu		68.58 157	67.28 166	72.48 159	78.19 172	57.19 105	75.28 194	75.09 232	51.61 242	70.04 111	81.41 199	32.79 286	79.02 242	63.81 119	77.31 153	81.22 223

xiu_mvs_v1_base		68.58 157	67.28 166	72.48 159	78.19 172	57.19 105	75.28 194	75.09 232	51.61 242	70.04 111	81.41 199	32.79 286	79.02 242	63.81 119	77.31 153	81.22 223

xiu_mvs_v1_base_debi		68.58 157	67.28 166	72.48 159	78.19 172	57.19 105	75.28 194	75.09 232	51.61 242	70.04 111	81.41 199	32.79 286	79.02 242	63.81 119	77.31 153	81.22 223

TransMVSNet (Re)		64.72 223	64.33 212	65.87 264	75.22 234	38.56 316	74.66 211	75.08 235	58.90 132	61.79 249	82.63 170	51.18 94	78.07 254	43.63 272	55.87 335	80.99 229

ET-MVSNet_ETH3D		67.96 173	65.72 199	74.68 100	76.67 208	55.62 133	75.11 199	74.74 236	52.91 231	60.03 260	80.12 228	33.68 276	82.64 173	61.86 137	76.34 166	85.78 95

LS3D		64.71 224	62.50 234	71.34 183	79.72 135	55.71 129	79.82 113	74.72 237	48.50 275	56.62 290	84.62 131	33.59 278	82.34 179	29.65 349	75.23 174	75.97 286

Baseline_NR-MVSNet		67.05 191	67.56 153	65.50 267	75.65 226	37.70 323	75.42 192	74.65 238	59.90 113	68.14 147	83.15 164	49.12 116	77.20 265	52.23 203	69.78 243	81.60 215

HY-MVS	56.14 13	64.55 227	63.89 215	66.55 251	74.73 241	41.02 300	69.96 280	74.43 239	49.29 267	61.66 250	80.92 210	47.43 137	76.68 271	44.91 263	71.69 216	81.94 211

GA-MVS		65.53 214	63.70 219	71.02 192	70.87 292	48.10 233	70.48 274	74.40 240	56.69 166	64.70 215	76.77 277	33.66 277	81.10 202	55.42 179	70.32 233	83.87 162

KD-MVS_self_test		55.22 295	53.89 301	59.21 303	57.80 364	27.47 364	57.75 336	74.32 241	47.38 288	50.90 331	70.00 332	28.45 319	70.30 301	40.44 294	57.92 329	79.87 246

无先验							79.66 117	74.30 242	48.40 277				80.78 212	53.62 192		79.03 257

thisisatest0530		67.92 174	65.78 198	74.33 113	76.29 215	51.03 188	76.89 167	74.25 243	53.67 225	65.59 196	81.76 192	35.15 261	85.50 107	55.94 170	72.47 207	86.47 70

CHOSEN 1792x2688		65.08 222	62.84 230	71.82 168	81.49 100	56.26 119	66.32 298	74.20 244	40.53 340	63.16 230	78.65 253	41.30 208	77.80 258	45.80 252	74.09 180	81.40 218

MS-PatchMatch		62.42 245	61.46 245	65.31 271	75.21 235	52.10 177	72.05 251	74.05 245	46.41 297	57.42 287	74.36 304	34.35 270	77.57 262	45.62 255	73.67 184	66.26 348

tttt0517		67.83 176	65.66 200	74.33 113	76.69 207	50.82 193	77.86 142	73.99 246	54.54 218	64.64 216	82.53 174	35.06 262	85.50 107	55.71 175	69.91 240	86.67 66

USDC		56.35 287	54.24 298	62.69 287	64.74 341	40.31 303	65.05 309	73.83 247	43.93 320	47.58 341	77.71 269	15.36 359	75.05 280	38.19 305	61.81 314	72.70 319

tfpnnormal		62.47 244	61.63 243	64.99 273	74.81 239	39.01 312	71.22 262	73.72 248	55.22 203	60.21 258	80.09 230	41.26 211	76.98 269	30.02 347	68.09 266	78.97 258

jason		69.65 136	68.39 143	73.43 139	78.27 170	56.88 112	77.12 160	73.71 249	46.53 296	69.34 127	83.22 161	43.37 183	79.18 235	64.77 111	79.20 133	84.23 150
jason: jason.
D2MVS		62.30 247	60.29 256	68.34 234	66.46 333	48.42 230	65.70 301	73.42 250	47.71 284	58.16 282	75.02 299	30.51 303	77.71 259	53.96 190	71.68 217	78.90 259

COLMAP_ROB	52.97 17	61.27 260	58.81 262	68.64 229	74.63 242	52.51 171	78.42 137	73.30 251	49.92 264	50.96 330	81.51 198	23.06 345	79.40 231	31.63 339	65.85 284	74.01 311
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
lupinMVS		69.57 138	68.28 144	73.44 138	78.76 156	57.15 108	76.57 171	73.29 252	46.19 299	69.49 123	82.18 181	43.99 179	79.23 234	64.66 112	79.37 128	83.93 158

DP-MVS		65.68 210	63.66 221	71.75 169	84.93 63	56.87 113	80.74 100	73.16 253	53.06 229	59.09 272	82.35 177	36.79 252	85.94 95	32.82 331	69.96 239	72.45 323

thisisatest0515		65.83 209	63.50 223	72.82 153	73.75 254	49.50 216	71.32 260	73.12 254	49.39 266	63.82 225	76.50 284	34.95 264	84.84 123	53.20 198	75.49 173	84.13 154

VPNet		67.52 180	68.11 147	65.74 265	79.18 147	36.80 330	72.17 250	72.83 255	62.04 75	67.79 158	85.83 115	48.88 118	76.60 272	51.30 213	72.97 200	83.81 164

CL-MVSNet_self_test		61.53 256	60.94 252	63.30 282	68.95 317	36.93 329	67.60 293	72.80 256	55.67 195	59.95 262	76.63 278	45.01 169	72.22 292	39.74 299	62.09 312	80.74 233

OurMVSNet-221017-0		61.37 259	58.63 265	69.61 215	72.05 278	48.06 234	73.93 225	72.51 257	47.23 292	54.74 307	80.92 210	21.49 352	81.24 199	48.57 233	56.22 334	79.53 252

EPNet		73.09 81	72.16 85	75.90 74	75.95 221	56.28 118	83.05 61	72.39 258	66.53 12	65.27 201	87.00 85	50.40 102	85.47 109	62.48 131	86.32 62	85.94 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss		64.00 230	63.36 225	65.93 262	79.28 143	42.58 287	71.35 259	72.36 259	46.41 297	60.55 257	77.89 264	46.27 153	73.28 286	46.18 248	69.97 238	81.92 212

test_0402		63.25 238	61.01 251	69.96 208	80.00 128	54.37 146	76.86 168	72.02 260	54.58 217	58.71 275	80.79 215	35.00 263	84.36 130	26.41 357	64.71 292	71.15 335

EU-MVSNet		55.61 292	54.41 295	59.19 304	65.41 339	33.42 351	72.44 246	71.91 261	28.81 357	51.27 328	73.87 308	24.76 342	69.08 306	43.04 277	58.20 328	75.06 296

KD-MVS_2432*1600		53.45 303	51.50 310	59.30 300	62.82 347	37.14 325	55.33 342	71.79 262	47.34 290	55.09 303	70.52 327	21.91 350	70.45 299	35.72 321	42.97 358	70.31 338

miper_refine_blended		53.45 303	51.50 310	59.30 300	62.82 347	37.14 325	55.33 342	71.79 262	47.34 290	55.09 303	70.52 327	21.91 350	70.45 299	35.72 321	42.97 358	70.31 338

Anonymous202405211		66.84 196	65.99 195	69.40 220	80.19 125	42.21 290	71.11 266	71.31 264	58.80 134	67.90 150	86.39 101	29.83 310	79.65 227	49.60 226	78.78 140	86.33 77

LFMVS		71.78 99	71.59 90	72.32 163	83.40 76	46.38 250	79.75 115	71.08 265	64.18 35	72.80 83	88.64 68	42.58 189	83.72 144	57.41 164	84.49 72	86.86 60

CDS-MVSNet		66.80 197	65.37 203	71.10 190	78.98 151	53.13 162	73.27 235	71.07 266	52.15 239	64.72 214	80.23 227	43.56 182	77.10 266	45.48 258	78.88 137	83.05 194
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20240521		55.30 293	54.41 295	57.96 312	60.92 359	41.73 295	71.09 267	71.06 267	41.18 336	48.65 339	73.31 311	16.93 356	59.25 341	42.54 281	64.01 296	72.90 317

OpenMVS_ROB	52.78 18	60.03 264	58.14 270	65.69 266	70.47 297	44.82 267	75.33 193	70.86 268	45.04 307	56.06 293	76.00 288	26.89 331	79.65 227	35.36 323	67.29 273	72.60 320

CNLPA		65.43 215	64.02 214	69.68 214	78.73 158	58.07 90	77.82 144	70.71 269	51.49 246	61.57 252	83.58 157	38.23 235	70.82 296	43.90 269	70.10 237	80.16 240

CostFormer		64.04 229	62.51 233	68.61 230	71.88 281	45.77 258	71.30 261	70.60 270	47.55 286	64.31 220	76.61 280	41.63 201	79.62 229	49.74 222	69.00 258	80.42 236

bset_n11_16_dypcd		65.57 213	63.69 220	71.19 186	70.84 294	51.79 183	71.37 258	70.48 271	53.33 228	65.19 208	76.41 285	31.46 300	81.76 188	65.12 108	69.04 256	80.01 243

Test_1112_low_res		62.32 246	61.77 241	64.00 278	79.08 150	39.53 309	68.17 289	70.17 272	43.25 324	59.03 273	79.90 231	44.08 177	71.24 295	43.79 271	68.42 264	81.25 222

MVS_111021_LR		69.50 140	68.78 135	71.65 173	78.38 165	59.33 64	74.82 207	70.11 273	58.08 148	67.83 156	84.68 129	41.96 195	76.34 275	65.62 104	77.54 151	79.30 255

DWT-MVSNet_test		61.90 251	59.93 258	67.83 237	71.98 280	46.09 256	71.03 269	69.71 274	50.09 261	58.51 279	70.62 325	30.21 307	77.63 260	49.28 227	67.91 267	79.78 249

ANet_high		41.38 327	37.47 332	53.11 331	39.73 373	24.45 369	56.94 338	69.69 275	47.65 285	26.04 364	52.32 358	12.44 361	62.38 331	21.80 360	10.61 371	72.49 322

SixPastTwentyTwo		61.65 255	58.80 263	70.20 205	75.80 224	47.22 244	75.59 190	69.68 276	54.61 215	54.11 314	79.26 247	27.07 329	82.96 159	43.27 274	49.79 350	80.41 237

IterMVS-SCA-FT		62.49 243	61.52 244	65.40 269	71.99 279	50.80 194	71.15 265	69.63 277	45.71 305	60.61 256	77.93 261	37.45 241	65.99 321	55.67 176	63.50 301	79.42 253

TAMVS		66.78 198	65.27 206	71.33 184	79.16 149	53.67 150	73.84 229	69.59 278	52.32 238	65.28 200	81.72 193	44.49 175	77.40 264	42.32 283	78.66 143	82.92 195

CMPMVS	42.80 21	57.81 279	55.97 284	63.32 281	60.98 357	47.38 243	64.66 311	69.50 279	32.06 354	46.83 345	77.80 266	29.50 312	71.36 294	48.68 231	73.75 183	71.21 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn200view9		63.18 239	62.18 238	66.21 256	76.85 205	39.62 307	71.96 253	69.44 280	56.63 168	62.61 237	79.83 233	37.18 244	79.17 236	31.84 335	73.25 194	79.83 247

thres400		63.31 235	62.18 238	66.72 248	76.85 205	39.62 307	71.96 253	69.44 280	56.63 168	62.61 237	79.83 233	37.18 244	79.17 236	31.84 335	73.25 194	81.36 219

thres200		62.20 248	61.16 250	65.34 270	75.38 233	39.99 304	69.60 282	69.29 282	55.64 197	61.87 248	76.99 273	37.07 249	78.96 246	31.28 343	73.28 193	77.06 277

UnsupCasMVSNet_eth		53.16 308	52.47 306	55.23 321	59.45 361	33.39 352	59.43 331	69.13 283	45.98 301	50.35 337	72.32 315	29.30 314	58.26 344	42.02 287	44.30 356	74.05 310

thres100view900		63.28 237	62.41 235	65.89 263	77.31 197	38.66 315	72.65 241	69.11 284	57.07 161	62.45 242	81.03 207	37.01 250	79.17 236	31.84 335	73.25 194	79.83 247

thres600view7		63.30 236	62.27 236	66.41 252	77.18 199	38.87 313	72.35 247	69.11 284	56.98 163	62.37 244	80.96 209	37.01 250	79.00 245	31.43 342	73.05 199	81.36 219

CVMVSNet		59.63 268	59.14 261	61.08 298	74.47 245	38.84 314	75.20 197	68.74 286	31.15 355	58.24 281	76.51 282	32.39 295	68.58 308	49.77 221	65.84 285	75.81 289

TinyColmap		54.14 298	51.72 308	61.40 296	66.84 330	41.97 291	66.52 296	68.51 287	44.81 308	42.69 355	75.77 292	11.66 363	72.94 287	31.96 333	56.77 332	69.27 344

baseline2		63.42 234	61.26 248	69.89 213	72.55 271	47.62 240	71.54 256	68.38 288	50.11 260	54.82 306	75.55 295	43.06 186	80.96 205	48.13 235	67.16 275	81.11 226

IterMVS		62.79 242	61.27 247	67.35 244	69.37 314	52.04 180	71.17 263	68.24 289	52.63 235	59.82 264	76.91 275	37.32 243	72.36 289	52.80 200	63.19 304	77.66 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
旧先验1					83.04 80	53.15 160		67.52 290			87.85 77	44.08 177			80.76 107	78.03 268

AllTest		57.08 283	54.65 292	64.39 276	71.44 286	49.03 219	69.92 281	67.30 291	45.97 302	47.16 343	79.77 235	17.47 354	67.56 312	33.65 328	59.16 325	76.57 283

TestCases				64.39 276	71.44 286	49.03 219		67.30 291	45.97 302	47.16 343	79.77 235	17.47 354	67.56 312	33.65 328	59.16 325	76.57 283

baseline1		63.81 231	63.87 217	63.62 279	76.29 215	36.36 333	71.78 255	67.29 293	56.05 187	64.23 222	82.95 165	47.11 142	74.41 283	47.30 239	61.85 313	80.10 242

tpmvs		58.47 272	56.95 278	63.03 286	70.20 301	41.21 299	67.90 292	67.23 294	49.62 265	54.73 308	70.84 323	34.14 271	76.24 276	36.64 315	61.29 317	71.64 331

Gipuma		34.77 332	31.91 336	43.33 345	62.05 352	37.87 320	20.39 366	67.03 295	23.23 363	18.41 367	25.84 367	4.24 373	62.73 329	14.71 365	51.32 346	29.38 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ECVR-MVS		67.72 177	67.51 157	68.35 233	79.46 139	36.29 338	74.79 208	66.93 296	58.72 135	67.19 166	88.05 73	36.10 254	81.38 195	52.07 205	84.25 74	87.39 45

tpm2		62.07 249	60.10 257	67.99 236	72.79 266	43.86 277	71.05 268	66.85 297	43.14 326	62.77 232	75.39 297	38.32 233	80.80 211	41.69 288	68.88 259	79.32 254

XXY-MVS		60.68 261	61.67 242	57.70 315	70.43 298	38.45 317	64.19 313	66.47 298	48.05 281	63.22 228	80.86 212	49.28 111	60.47 336	45.25 262	67.28 274	74.19 309

1121		68.53 161	67.16 172	72.63 156	85.64 48	61.14 34	73.95 222	66.46 299	44.61 311	70.28 107	86.68 92	41.42 207	80.78 212	53.62 192	81.79 98	75.97 286

新几何1				70.76 195	85.66 46	61.13 35		66.43 300	44.68 310	70.29 106	86.64 93	41.29 209	75.23 279	49.72 223	81.75 102	75.93 288

ppachtmachnet_test		58.06 277	55.38 288	66.10 259	69.51 311	48.99 222	68.01 291	66.13 301	44.50 313	54.05 315	70.74 324	32.09 297	72.34 290	36.68 314	56.71 333	76.99 281

tpm cat1		59.25 269	56.95 278	66.15 257	72.19 276	46.96 246	68.09 290	65.76 302	40.03 343	57.81 284	70.56 326	38.32 233	74.51 282	38.26 304	61.50 316	77.00 279

test1111		67.21 184	67.14 173	67.42 242	79.24 145	34.76 343	73.89 227	65.65 303	58.71 137	66.96 170	87.95 75	36.09 255	80.53 216	52.03 206	83.79 79	86.97 57

EPNet_dtu		61.90 251	61.97 240	61.68 293	72.89 265	39.78 306	75.85 188	65.62 304	55.09 206	54.56 310	79.36 245	37.59 240	67.02 315	39.80 298	76.95 159	78.25 262
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		58.51 271	57.36 274	61.96 292	70.04 305	41.83 293	69.40 285	65.46 305	50.73 254	53.30 323	74.06 307	22.65 346	70.18 303	42.16 284	68.44 263	73.86 313

pmmvs4		61.48 258	59.39 259	67.76 238	71.57 285	53.86 149	71.42 257	65.34 306	44.20 316	59.46 267	77.92 262	35.90 256	74.71 281	43.87 270	64.87 291	74.71 304

testdata				64.66 274	81.52 98	52.93 163		65.29 307	46.09 300	73.88 66	87.46 80	38.08 237	66.26 320	53.31 197	78.48 145	74.78 303

TDRefinement		53.44 305	50.72 313	61.60 294	64.31 344	46.96 246	70.89 270	65.27 308	41.78 331	44.61 351	77.98 259	11.52 364	66.36 319	28.57 352	51.59 345	71.49 332

MIMVSNet1		55.17 296	54.31 297	57.77 314	70.03 306	32.01 355	65.68 302	64.81 309	49.19 268	46.75 346	76.00 288	25.53 339	64.04 326	28.65 351	62.13 311	77.26 275

pmmvs-eth3d		58.81 270	56.31 283	66.30 254	67.61 325	52.42 174	72.30 248	64.76 310	43.55 322	54.94 305	74.19 306	28.95 315	72.60 288	43.31 273	57.21 330	73.88 312

MDTV_nov1_ep13			57.00 277		72.73 267	38.26 318	65.02 310	64.73 311	44.74 309	55.46 297	72.48 314	32.61 293	70.47 298	37.47 307	67.75 271

UnsupCasMVSNet_bld		50.07 316	48.87 317	53.66 328	60.97 358	33.67 350	57.62 337	64.56 312	39.47 345	47.38 342	64.02 351	27.47 325	59.32 340	34.69 325	43.68 357	67.98 347

ITE_SJBPF				62.09 291	66.16 335	44.55 273		64.32 313	47.36 289	55.31 300	80.34 222	19.27 353	62.68 330	36.29 319	62.39 310	79.04 256

WTY-MVS		59.75 267	60.39 255	57.85 313	72.32 275	37.83 321	61.05 327	64.18 314	45.95 304	61.91 247	79.11 249	47.01 146	60.88 335	42.50 282	69.49 249	74.83 301

MDA-MVSNet-bldmvs		53.87 301	50.81 312	63.05 285	66.25 334	48.58 227	56.93 339	63.82 315	48.09 280	41.22 356	70.48 329	30.34 305	68.00 311	34.24 326	45.92 355	72.57 321

Vis-MVSNet (Re-imp)		63.69 232	63.88 216	63.14 284	74.75 240	31.04 358	71.16 264	63.64 316	56.32 178	59.80 265	84.99 125	44.51 173	75.46 278	39.12 300	80.62 108	82.92 195

test222					83.14 77	58.68 81	72.57 244	63.45 317	41.78 331	67.56 161	86.12 106	37.13 247			78.73 142	74.98 299

PVSNet	50.76 19	58.40 273	57.39 273	61.42 295	75.53 230	44.04 276	61.43 322	63.45 317	47.04 294	56.91 288	73.61 310	27.00 330	64.76 324	39.12 300	72.40 208	75.47 293

SCA		60.49 262	58.38 267	66.80 247	74.14 253	48.06 234	63.35 315	63.23 319	49.13 269	59.33 271	72.10 316	37.45 241	74.27 284	44.17 265	62.57 308	78.05 265

CR-MVSNet		59.91 265	57.90 272	65.96 261	69.96 307	52.07 178	65.31 307	63.15 320	42.48 330	59.36 268	74.84 300	35.83 257	70.75 297	45.50 257	64.65 293	75.06 296

Patchmtry		57.16 282	56.47 281	59.23 302	69.17 316	34.58 345	62.98 316	63.15 320	44.53 312	56.83 289	74.84 300	35.83 257	68.71 307	40.03 296	60.91 318	74.39 307

pmmvs5		56.47 285	55.68 286	58.86 306	61.41 354	36.71 331	66.37 297	62.75 322	40.38 341	53.70 317	76.62 279	34.56 266	67.05 314	40.02 297	65.27 288	72.83 318

K. test v3		60.47 263	57.11 275	70.56 199	73.74 255	48.22 232	75.10 201	62.55 323	58.27 147	53.62 319	76.31 286	27.81 323	81.59 191	47.42 237	39.18 361	81.88 213

FMVSNet5		55.86 290	54.93 290	58.66 308	71.05 291	36.35 334	64.18 314	62.48 324	46.76 295	50.66 335	74.73 302	25.80 337	64.04 326	33.11 330	65.57 287	75.59 292

PatchmatchNet		59.84 266	58.24 268	64.65 275	73.05 262	46.70 248	69.42 284	62.18 325	47.55 286	58.88 274	71.96 318	34.49 268	69.16 305	42.99 278	63.60 300	78.07 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20231206		55.10 297	55.30 289	54.48 325	69.81 310	33.94 349	62.91 317	62.13 326	41.08 337	55.18 302	75.65 293	32.75 289	56.59 351	30.32 346	67.86 268	72.91 316

sss		56.17 289	56.57 280	54.96 322	66.93 329	36.32 336	57.94 335	61.69 327	41.67 333	58.64 277	75.32 298	38.72 229	56.25 352	42.04 286	66.19 282	72.31 328

our_test_3		56.49 284	54.42 294	62.68 288	69.51 311	45.48 264	66.08 299	61.49 328	44.11 319	50.73 334	69.60 335	33.05 282	68.15 309	38.38 303	56.86 331	74.40 306

tpmrst		58.24 274	58.70 264	56.84 316	66.97 328	34.32 346	69.57 283	61.14 329	47.17 293	58.58 278	71.60 319	41.28 210	60.41 337	49.20 228	62.84 306	75.78 290

MIMVSNet		57.35 280	57.07 276	58.22 309	74.21 252	37.18 324	62.46 318	60.88 330	48.88 271	55.29 301	75.99 290	31.68 299	62.04 332	31.87 334	72.35 209	75.43 294

LCM-MVSNet		40.30 328	35.88 333	53.57 329	42.24 370	29.15 362	45.21 359	60.53 331	22.23 365	28.02 363	50.98 361	3.72 375	61.78 333	31.22 344	38.76 362	69.78 341

ADS-MVSNet2		51.33 313	48.76 318	59.07 305	66.02 337	44.60 271	50.90 350	59.76 332	36.90 347	50.74 332	66.18 346	26.38 332	63.11 328	27.17 353	54.76 338	69.50 342

new-patchmatchnet		47.56 320	47.73 321	47.06 341	58.81 362	9.37 376	48.78 354	59.21 333	43.28 323	44.22 352	68.66 337	25.67 338	57.20 348	31.57 341	49.35 351	74.62 305

test20.03		53.87 301	54.02 300	53.41 330	61.47 353	28.11 363	61.30 324	59.21 333	51.34 250	52.09 326	77.43 270	33.29 281	58.55 343	29.76 348	60.27 322	73.58 314

JIA-IIPM		51.56 312	47.68 322	63.21 283	64.61 342	50.73 195	47.71 355	58.77 335	42.90 327	48.46 340	51.72 359	24.97 341	70.24 302	36.06 320	53.89 341	68.64 346

testgi		51.90 310	52.37 307	50.51 339	60.39 360	23.55 370	58.42 333	58.15 336	49.03 270	51.83 327	79.21 248	22.39 347	55.59 355	29.24 350	62.64 307	72.40 327

LCM-MVSNet-Re		61.88 253	61.35 246	63.46 280	74.58 243	31.48 357	61.42 323	58.14 337	58.71 137	53.02 324	79.55 241	43.07 185	76.80 270	45.69 253	77.96 148	82.11 210

test-LLR		58.15 276	58.13 271	58.22 309	68.57 319	44.80 268	65.46 304	57.92 338	50.08 262	55.44 298	69.82 333	32.62 291	57.44 346	49.66 224	73.62 185	72.41 325

test-mter		56.42 286	55.82 285	58.22 309	68.57 319	44.80 268	65.46 304	57.92 338	39.94 344	55.44 298	69.82 333	21.92 349	57.44 346	49.66 224	73.62 185	72.41 325

RPSCF		55.80 291	54.22 299	60.53 299	65.13 340	42.91 286	64.30 312	57.62 340	36.84 349	58.05 283	82.28 180	28.01 321	56.24 353	37.14 309	58.61 327	82.44 205

GG-mvs-BLEND				62.34 289	71.36 290	37.04 328	69.20 286	57.33 341		54.73 308	65.48 348	30.37 304	77.82 257	34.82 324	74.93 175	72.17 329

MDA-MVSNet_test_wron		50.71 315	48.95 316	56.00 320	61.17 355	41.84 292	51.90 349	56.45 342	40.96 338	44.79 350	67.84 339	30.04 309	55.07 358	36.71 313	50.69 348	71.11 336

YYNet1		50.73 314	48.96 315	56.03 319	61.10 356	41.78 294	51.94 348	56.44 343	40.94 339	44.84 349	67.80 340	30.08 308	55.08 357	36.77 311	50.71 347	71.22 333

gg-mvs-nofinetune		57.86 278	56.43 282	62.18 290	72.62 269	35.35 340	66.57 295	56.33 344	50.65 256	57.64 285	57.10 356	30.65 302	76.36 274	37.38 308	78.88 137	74.82 302

TESTMET0.1,1		55.28 294	54.90 291	56.42 317	66.56 332	43.67 279	65.46 304	56.27 345	39.18 346	53.83 316	67.44 342	24.21 344	55.46 356	48.04 236	73.11 198	70.13 340

PMMVS		53.96 299	53.26 305	56.04 318	62.60 350	50.92 191	61.17 326	56.09 346	32.81 353	53.51 321	66.84 344	34.04 272	59.93 339	44.14 267	68.18 265	57.27 356

tpm		57.34 281	58.16 269	54.86 323	71.80 283	34.77 342	67.47 294	56.04 347	48.20 279	60.10 259	76.92 274	37.17 246	53.41 359	40.76 293	65.01 290	76.40 285

PVSNet_0	43.31 20	47.46 321	45.64 324	52.92 332	67.60 326	44.65 270	54.06 346	54.64 348	41.59 334	46.15 347	58.75 355	30.99 301	58.66 342	32.18 332	24.81 364	55.46 357

dp		51.89 311	51.60 309	52.77 333	68.44 322	32.45 354	62.36 319	54.57 349	44.16 317	49.31 338	67.91 338	28.87 317	56.61 350	33.89 327	54.89 337	69.24 345

PatchT		53.17 307	53.44 304	52.33 335	68.29 323	25.34 368	58.21 334	54.41 350	44.46 314	54.56 310	69.05 336	33.32 280	60.94 334	36.93 310	61.76 315	70.73 337

test0.0.03 1		53.32 306	53.59 303	52.50 334	62.81 349	29.45 361	59.51 330	54.11 351	50.08 262	54.40 312	74.31 305	32.62 291	55.92 354	30.50 345	63.95 298	72.15 330

PatchMatch-RL		56.25 288	54.55 293	61.32 297	77.06 201	56.07 123	65.57 303	54.10 352	44.13 318	53.49 322	71.27 322	25.20 340	66.78 316	36.52 317	63.66 299	61.12 351

FPMVS		42.18 326	41.11 328	45.39 342	58.03 363	41.01 301	49.50 352	53.81 353	30.07 356	33.71 361	64.03 349	11.69 362	52.08 361	14.01 366	55.11 336	43.09 362

test2506		65.33 218	64.61 211	67.50 240	79.46 139	34.19 347	74.43 215	51.92 354	58.72 135	66.75 174	88.05 73	25.99 336	80.92 208	51.94 207	84.25 74	87.39 45

EGC-MVSNET		42.47 325	38.48 331	54.46 326	74.33 249	48.73 226	70.33 277	51.10 355	0.03 374	0.18 375	67.78 341	13.28 360	66.49 318	18.91 362	50.36 349	48.15 359

Patchmatch-RL test		58.16 275	55.49 287	66.15 257	67.92 324	48.89 224	60.66 328	51.07 356	47.86 283	59.36 268	62.71 353	34.02 273	72.27 291	56.41 168	59.40 324	77.30 273

lessismore_v0				69.91 211	71.42 288	47.80 236		50.90 357		50.39 336	75.56 294	27.43 327	81.33 196	45.91 251	34.10 363	80.59 234

ADS-MVSNet		48.48 318	47.77 320	50.63 338	66.02 337	29.92 360	50.90 350	50.87 358	36.90 347	50.74 332	66.18 346	26.38 332	52.47 360	27.17 353	54.76 338	69.50 342

EPMVS		53.96 299	53.69 302	54.79 324	66.12 336	31.96 356	62.34 320	49.05 359	44.42 315	55.54 296	71.33 321	30.22 306	56.70 349	41.65 290	62.54 309	75.71 291

PMVS	28.69 22	36.22 331	33.29 335	45.02 344	36.82 375	35.98 339	54.68 345	48.74 360	26.31 360	21.02 365	51.61 360	2.88 377	60.10 338	9.99 370	47.58 353	38.99 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LF4IMVS		42.95 324	42.26 326	45.04 343	48.30 368	32.50 353	54.80 344	48.49 361	28.03 358	40.51 358	70.16 330	9.24 368	43.89 365	31.63 339	49.18 352	58.72 353

Patchmatch-test		49.08 317	48.28 319	51.50 337	64.40 343	30.85 359	45.68 357	48.46 362	35.60 350	46.10 348	72.10 316	34.47 269	46.37 363	27.08 355	60.65 321	77.27 274

door								47.60 363

door-mid								47.19 364

pmmvs3		44.92 323	41.95 327	53.86 327	52.58 366	43.55 280	62.11 321	46.90 365	26.05 361	40.63 357	60.19 354	11.08 366	57.91 345	31.83 338	46.15 354	60.11 352

MVS-HIRNet		45.52 322	44.48 325	48.65 340	68.49 321	34.05 348	59.41 332	44.50 366	27.03 359	37.96 360	50.47 362	26.16 335	64.10 325	26.74 356	59.52 323	47.82 360

CHOSEN 280x420		47.83 319	46.36 323	52.24 336	67.37 327	49.78 210	38.91 363	43.11 367	35.00 351	43.27 354	63.30 352	28.95 315	49.19 362	36.53 316	60.80 320	57.76 355

test_method		19.68 338	18.10 341	24.41 353	13.68 378	3.11 379	12.06 369	42.37 368	2.00 372	11.97 370	36.38 364	5.77 372	29.35 372	15.06 364	23.65 365	40.76 363

PM-MVS		52.33 309	50.19 314	58.75 307	62.10 351	45.14 266	65.75 300	40.38 369	43.60 321	53.52 320	72.65 313	9.16 369	65.87 322	50.41 217	54.18 340	65.24 350

E-PMN		23.77 335	22.73 339	26.90 351	42.02 371	20.67 371	42.66 361	35.70 370	17.43 366	10.28 372	25.05 368	6.42 371	42.39 367	10.28 369	14.71 368	17.63 367

EMVS		22.97 336	21.84 340	26.36 352	40.20 372	19.53 373	41.95 362	34.64 371	17.09 367	9.73 373	22.83 369	7.29 370	42.22 368	9.18 371	13.66 369	17.32 368

new_pmnet		34.13 333	34.29 334	33.64 348	52.63 365	18.23 374	44.43 360	33.90 372	22.81 364	30.89 362	53.18 357	10.48 367	35.72 370	20.77 361	39.51 360	46.98 361

DSMNet-mixed		39.30 330	38.72 330	41.03 346	51.22 367	19.66 372	45.53 358	31.35 373	15.83 368	39.80 359	67.42 343	22.19 348	45.13 364	22.43 359	52.69 343	58.31 354

PMMVS2		27.40 334	25.91 337	31.87 350	39.46 374	6.57 377	31.17 364	28.52 374	23.96 362	20.45 366	48.94 363	4.20 374	37.94 369	16.51 363	19.97 366	51.09 358

MVE	17.77 23	21.41 337	17.77 342	32.34 349	34.34 376	25.44 367	16.11 367	24.11 375	11.19 369	13.22 369	31.92 365	1.58 378	30.95 371	10.47 368	17.03 367	40.62 364
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							86.03 19	17.08 376

tmp_tt		9.43 341	11.14 344	4.30 356	2.38 379	4.40 378	13.62 368	16.08 377	0.39 373	15.89 368	13.06 370	15.80 358	5.54 375	12.63 367	10.46 372	2.95 370

DeepMVS_CX				12.03 355	17.97 377	10.91 375		10.60 378	7.46 370	11.07 371	28.36 366	3.28 376	11.29 374	8.01 372	9.74 373	13.89 369

wuyk23d		13.32 340	12.52 343	15.71 354	47.54 369	26.27 365	31.06 365	1.98 379	4.93 371	5.18 374	1.94 374	0.45 379	18.54 373	6.81 373	12.83 370	2.33 371

N_pmnet		39.35 329	40.28 329	36.54 347	63.76 345	1.62 380	49.37 353	0.76 380	34.62 352	43.61 353	66.38 345	26.25 334	42.57 366	26.02 358	51.77 344	65.44 349

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

pcd_1.5k_mvsjas		3.92 345	5.23 348	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	47.05 143	0.00 376	0.00 376	0.00 374	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

testmvs		4.52 344	6.03 347	0.01 358	0.01 380	0.00 382	53.86 347	0.00 381	0.01 375	0.04 376	0.27 375	0.00 381	0.00 376	0.04 374	0.00 374	0.03 373

test123		4.73 343	6.30 346	0.02 357	0.01 380	0.01 381	56.36 340	0.00 381	0.01 375	0.04 376	0.21 376	0.01 380	0.00 376	0.03 375	0.00 374	0.04 372

n2								0.00 381

nn								0.00 381

ab-mvs-re		6.49 342	8.65 345	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	77.89 264	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 382	0.00 370	0.00 381	0.00 377	0.00 378	0.00 377	0.00 381	0.00 376	0.00 376	0.00 374	0.00 374

PC_three_1452									55.09 206	84.46 4	89.84 49	66.68 5	89.41 17	74.24 37	91.38 2	88.42 7

eth-test2					0.00 382

eth-test					0.00 382

OPU-MVS				79.83 6	87.54 11	60.93 40	87.82 7				89.89 47	67.01 1	90.33 11	73.16 49	91.15 4	88.23 12

test_0728_THIRD									65.04 20	83.82 8	92.00 3	64.69 10	90.75 8	79.48 4	90.63 10	88.09 17

GSMVS																78.05 265

test_part2					87.58 9	60.47 49				83.42 12

sam_mvs1												34.74 265				78.05 265

sam_mvs												33.43 279

test_post1							68.67 288				3.64 372	32.39 295	69.49 304	44.17 265

test_post											3.55 373	33.90 274	66.52 317

patchmatchnet-post											64.03 349	34.50 267	74.27 284

gm-plane-assit					71.40 289	41.72 297			48.85 272		73.31 311		82.48 177	48.90 230

test9_res														75.28 31	88.31 37	83.81 164

agg_prior2														73.09 50	87.93 44	84.33 146

test_prior4						62.51 17	82.08 83

test_prior2							81.75 85		60.37 101	75.01 44	89.06 59	56.22 38		72.19 53	88.96 28

旧先验2							76.08 182		45.32 306	76.55 35			65.56 323	58.75 159

新几何2							76.12 180

原ACMM2							79.02 125

testdata2													72.18 293	46.95 244

segment_acmp												54.23 58

testdata1							72.65 241		60.50 96

plane_prior7					81.41 101	55.96 125

plane_prior6					81.20 108	56.24 120						45.26 167

plane_prior4											86.10 107

plane_prior3						56.09 122			63.92 39	69.27 128

plane_prior2							84.22 40		64.52 28

plane_prior1					81.27 106

plane_prior						56.31 116	83.58 56		63.19 51						80.48 113

HQP5-MVS						54.94 140

HQP-NCC					80.66 113		82.31 78		62.10 72	67.85 152

ACMP_Plane					80.66 113		82.31 78		62.10 72	67.85 152

BP-MVS														67.04 92

HQP4-MVS										67.85 152			86.93 66			84.32 147

HQP2-MVS												45.46 161

NP-MVS					80.98 111	56.05 124					85.54 121

MDTV_nov1_ep13_2view						25.89 366	61.22 325		40.10 342	51.10 329		32.97 284		38.49 302		78.61 260

ACMMP++_ref															74.07 181

ACMMP++															72.16 212

Test By Simon												48.33 124

High-resolution multi-view benchmark