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.

9.14			88.26 15		92.84 68		91.52 47	94.75 1	73.93 134	88.57 24	94.67 19	75.57 24	95.79 59	86.77 23	95.76 28

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 22	96.19 45	87.07 21	96.01 17	94.79 18

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 48	94.70 3	74.47 121	88.86 22	94.61 21	75.23 25	95.84 58	86.62 26	95.92 21	94.78 20

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 88	72.45 41	90.02 82	94.37 4	71.76 167	87.28 34	94.27 36	75.18 26	96.08 49	85.16 30	95.77 26	93.80 65

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

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 42

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

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

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 65	94.67 24
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
DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 44	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 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PHI-MVS		86.43 46	86.17 52	87.24 46	90.88 99	70.96 72	92.27 32	94.07 11	72.45 158	85.22 53	91.90 90	69.47 78	96.42 38	83.28 60	95.94 20	94.35 36

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

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 41

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

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 65	94.05 15	70.80 186	87.59 33	93.51 56	77.57 14	96.63 31	83.31 57	95.77 26	94.72 23

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 31

DeepC-MVS	79.81 2	87.08 37	86.88 39	87.69 36	91.16 92	72.32 47	90.31 75	93.94 17	77.12 58	82.82 97	94.23 39	72.13 54	97.09 14	84.83 36	95.37 35	93.65 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 120	91.30 15

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 87	93.82 19	73.07 153	84.86 63	92.89 72	76.22 18	96.33 39	84.89 35	95.13 41	94.40 34

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 52	94.32 35	71.76 56	96.93 18	85.53 29	95.79 25	94.32 38

CS-MVS-test		86.29 50	86.48 44	85.71 77	91.02 95	67.21 155	92.36 28	93.78 21	78.97 30	83.51 89	91.20 109	70.65 68	95.15 88	81.96 77	94.89 46	94.77 21

3Dnovator+	77.84 4	85.48 62	84.47 76	88.51 6	91.08 93	73.49 17	93.18 11	93.78 21	80.79 10	76.66 192	93.37 60	60.40 190	96.75 25	77.20 119	93.73 69	95.29 3

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 23	80.19 14	88.10 27	94.80 16	73.76 41	97.11 13	87.51 18	95.82 24	94.90 10
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 23	93.63 24	74.77 113	92.29 7	95.97 2	74.28 36	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
DROMVSNet		86.01 53	86.38 45	84.91 97	89.31 144	66.27 170	92.32 30	93.63 24	79.37 22	84.17 78	91.88 91	69.04 85	95.43 74	83.93 53	93.77 68	93.01 100

ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 60	93.59 26	76.27 82	88.14 26	95.09 15	71.06 62	96.67 28	87.67 16	96.37 14	94.09 46

CSCG		86.41 48	86.19 51	87.07 50	92.91 65	72.48 38	90.81 61	93.56 27	73.95 132	83.16 92	91.07 114	75.94 19	95.19 86	79.94 96	94.38 61	93.55 79

MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 89	93.50 28	75.17 103	86.34 41	95.29 12	70.86 63	96.00 53	88.78 12	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 33	93.49 29	74.75 114	88.33 25	94.43 32	73.27 44	97.02 16	84.18 50	94.84 49	93.82 62

FIs		82.07 105	82.42 92	81.04 222	88.80 164	58.34 288	88.26 140	93.49 29	76.93 63	78.47 151	91.04 115	69.92 74	92.34 210	69.87 185	84.97 169	92.44 119

DELS-MVS		85.41 65	85.30 65	85.77 76	88.49 175	67.93 138	85.52 223	93.44 31	78.70 31	83.63 88	89.03 167	74.57 29	95.71 62	80.26 93	94.04 66	93.66 68
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
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 64	93.99 50	70.67 67	96.82 21	84.18 50	95.01 42	93.90 57

FC-MVSNet-test		81.52 118	82.02 102	80.03 242	88.42 179	55.97 324	87.95 150	93.42 33	77.10 59	77.38 175	90.98 120	69.96 73	91.79 228	68.46 200	84.50 174	92.33 120

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 109	89.57 95	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 27

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 34	93.33 35	76.07 85	83.81 84	93.95 51	69.77 76	96.01 52	85.15 31	94.66 53	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 41	86.95 36	85.90 75	90.76 102	67.57 146	92.83 17	93.30 36	79.67 19	84.57 70	92.27 83	71.47 59	95.02 96	84.24 48	93.46 70	95.13 4

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 58	94.44 30	70.78 64	96.61 32	84.53 41	94.89 46	93.66 68

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 29	93.24 37	75.23 100	84.91 58	94.44 30	70.78 64	96.61 32	83.75 55	94.89 46	93.66 68

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 67	94.52 23	68.81 86	96.65 29	84.53 41	94.90 45	94.00 52

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 42	93.19 40	77.87 38	90.32 17	94.00 48	74.83 28	93.78 150	87.63 17	94.27 64	93.65 73
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
ACMMP		85.89 56	85.39 62	87.38 44	93.59 51	72.63 34	92.74 20	93.18 41	76.78 68	80.73 122	93.82 53	64.33 125	96.29 40	82.67 72	90.69 102	93.23 89
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
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 82	96.70 26	84.37 44	94.83 51	94.03 49

DPM-MVS		84.93 72	84.29 77	86.84 53	90.20 112	73.04 24	87.12 173	93.04 43	69.80 205	82.85 96	91.22 108	73.06 46	96.02 51	76.72 127	94.63 54	91.46 151

PGM-MVS		86.68 42	86.27 48	87.90 21	94.22 35	73.38 19	90.22 78	93.04 43	75.53 94	83.86 82	94.42 33	67.87 92	96.64 30	82.70 71	94.57 56	93.66 68

DeepC-MVS_fast	79.65 3	86.91 38	86.62 42	87.76 29	93.52 52	72.37 44	91.26 50	93.04 43	76.62 73	84.22 76	93.36 61	71.44 60	96.76 24	80.82 86	95.33 38	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		81.60 117	81.11 113	83.09 166	88.38 180	64.41 212	87.60 160	93.02 46	78.42 34	78.56 148	88.16 192	69.78 75	93.26 174	69.58 188	76.49 269	91.60 143

canonicalmvs		85.91 55	85.87 57	86.04 72	89.84 124	69.44 107	90.45 73	93.00 47	76.70 72	88.01 29	91.23 107	73.28 43	93.91 145	81.50 80	88.80 124	94.77 21

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 67	93.00 47	80.90 9	88.06 28	94.06 46	76.43 17	96.84 20	88.48 14	95.99 19	94.34 37

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 32

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 32

XVS		87.18 34	86.91 38	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 23	83.67 86	94.17 40	67.45 95	96.60 34	83.06 62	94.50 57	94.07 47

X-MVStestdata		80.37 148	77.83 186	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 23	83.67 86	12.47 376	67.45 95	96.60 34	83.06 62	94.50 57	94.07 47

APD-MVS_3200maxsize		85.97 54	85.88 56	86.22 67	92.69 72	69.53 102	91.93 38	92.99 49	73.54 144	85.94 42	94.51 26	65.80 114	95.61 63	83.04 64	92.51 80	93.53 81

test_prior3		86.73 39	86.86 40	86.33 63	92.61 74	69.59 100	88.85 114	92.97 54	75.41 96	84.91 58	93.54 54	74.28 36	95.48 70	83.31 57	95.86 22	93.91 55

test_prior				86.33 63	92.61 74	69.59 100		92.97 54					95.48 70			93.91 55

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 248	92.39 6				88.94 11	96.63 4	94.85 16

test_part1		82.78 96	82.08 100	84.89 98	90.66 103	66.97 160	90.96 59	92.93 57	77.19 55	80.53 124	90.04 138	63.44 132	95.39 78	76.04 131	76.90 262	92.31 122

baseline		84.93 72	84.98 69	84.80 102	87.30 216	65.39 192	87.30 169	92.88 58	77.62 41	84.04 81	92.26 84	71.81 55	93.96 138	81.31 81	90.30 107	95.03 6

MSLP-MVS++		85.43 64	85.76 58	84.45 112	91.93 83	70.24 86	90.71 63	92.86 59	77.46 49	84.22 76	92.81 76	67.16 99	92.94 191	80.36 91	94.35 62	90.16 193

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 20	96.81 22	87.45 19	96.44 9	93.05 97

casdiffmvs		85.11 70	85.14 67	85.01 91	87.20 218	65.77 183	87.75 157	92.83 61	77.84 39	84.36 75	92.38 82	72.15 53	93.93 144	81.27 82	90.48 105	95.33 2

APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 41	92.83 61	73.01 155	88.58 23	94.52 23	73.36 42	96.49 37	84.26 46	95.01 42	92.70 108
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 54	92.83 61	81.50 6	85.79 46	93.47 59	73.02 47	97.00 17	84.90 33	94.94 44	94.10 45

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 90	95.95 56	84.20 49	94.39 60	93.23 89

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 86
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
EIA-MVS		83.31 88	82.80 90	84.82 100	89.59 127	65.59 185	88.21 141	92.68 66	74.66 116	78.96 139	86.42 243	69.06 83	95.26 84	75.54 137	90.09 111	93.62 76

ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 184	87.75 31	94.07 45	74.01 40	96.70 26	84.66 39	94.84 49

nrg030		83.88 76	83.53 79	84.96 93	86.77 227	69.28 108	90.46 72	92.67 67	74.79 112	82.95 93	91.33 106	72.70 48	93.09 185	80.79 88	79.28 243	92.50 115

WR-MVS_H		78.51 192	78.49 169	78.56 266	88.02 190	56.38 319	88.43 128	92.67 67	77.14 57	73.89 246	87.55 206	66.25 107	89.24 277	58.92 277	73.55 309	90.06 203

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 52	96.28 41	85.65 28	95.30 40	93.62 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		84.90 74	84.67 74	85.59 79	89.39 136	68.66 125	88.74 121	92.64 71	79.97 17	84.10 79	85.71 255	69.32 80	95.38 79	80.82 86	91.37 95	92.72 107

CANet		86.45 45	86.10 54	87.51 40	90.09 114	70.94 74	89.70 93	92.59 72	81.78 4	81.32 113	91.43 104	70.34 69	97.23 12	84.26 46	93.36 71	94.37 35

SR-MVS		86.73 39	86.67 41	86.91 52	94.11 40	72.11 51	92.37 27	92.56 73	74.50 119	86.84 37	94.65 20	67.31 97	95.77 60	84.80 37	92.85 75	92.84 106

alignmvs		85.48 62	85.32 64	85.96 74	89.51 131	69.47 104	89.74 91	92.47 74	76.17 83	87.73 32	91.46 103	70.32 70	93.78 150	81.51 79	88.95 121	94.63 26

原ACMM1				84.35 116	93.01 64	68.79 115		92.44 75	63.96 288	81.09 118	91.57 99	66.06 110	95.45 72	67.19 212	94.82 52	88.81 247

HQP_MVS		83.64 81	83.14 83	85.14 87	90.08 115	68.71 121	91.25 52	92.44 75	79.12 25	78.92 141	91.00 118	60.42 188	95.38 79	78.71 103	86.32 157	91.33 153

plane_prior5								92.44 75					95.38 79	78.71 103	86.32 157	91.33 153

CDPH-MVS		85.76 58	85.29 66	87.17 48	93.49 53	71.08 68	88.58 126	92.42 78	68.32 239	84.61 68	93.48 57	72.32 51	96.15 48	79.00 100	95.43 34	94.28 40

UniMVSNet_NR-MVSNet		81.88 108	81.54 108	82.92 175	88.46 177	63.46 231	87.13 172	92.37 79	80.19 14	78.38 152	89.14 163	71.66 58	93.05 187	70.05 181	76.46 270	92.25 125

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 49	92.35 80	74.62 118	88.90 21	93.85 52	75.75 21	96.00 53	87.80 15	94.63 54	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
CLD-MVS		82.31 101	81.65 107	84.29 120	88.47 176	67.73 142	85.81 214	92.35 80	75.78 89	78.33 154	86.58 238	64.01 128	94.35 121	76.05 130	87.48 141	90.79 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test1172		86.20 52	86.22 49	86.12 70	93.95 42	69.89 95	91.79 43	92.28 82	75.07 105	86.40 40	94.58 22	65.00 122	95.56 65	84.34 45	92.60 78	92.90 104

SR-MVS-dyc-post		85.77 57	85.61 59	86.23 66	93.06 62	70.63 82	91.88 39	92.27 83	73.53 145	85.69 47	94.45 28	65.00 122	95.56 65	82.75 67	91.87 88	92.50 115

RE-MVS-def			85.48 60		93.06 62	70.63 82	91.88 39	92.27 83	73.53 145	85.69 47	94.45 28	63.87 129		82.75 67	91.87 88	92.50 115

RPMNet		73.51 263	70.49 279	82.58 189	81.32 320	65.19 195	75.92 329	92.27 83	57.60 338	72.73 257	76.45 348	52.30 244	95.43 74	48.14 338	77.71 253	87.11 285

test11								92.23 86

mPP-MVS		86.67 43	86.32 47	87.72 32	94.41 24	73.55 13	92.74 20	92.22 87	76.87 65	82.81 98	94.25 38	66.44 104	96.24 42	82.88 66	94.28 63	93.38 83

DP-MVS Recon		83.11 92	82.09 99	86.15 68	94.44 21	70.92 76	88.79 117	92.20 88	70.53 193	79.17 137	91.03 117	64.12 127	96.03 50	68.39 201	90.14 110	91.50 148

HQP3-MVS								92.19 89							85.99 163

HQP-MVS		82.61 99	82.02 102	84.37 114	89.33 139	66.98 158	89.17 101	92.19 89	76.41 75	77.23 180	90.23 132	60.17 191	95.11 90	77.47 116	85.99 163	91.03 164

3Dnovator	76.31 5	83.38 87	82.31 96	86.59 60	87.94 192	72.94 29	90.64 64	92.14 91	77.21 54	75.47 217	92.83 74	58.56 197	94.72 112	73.24 157	92.71 77	92.13 130

abl_6		85.23 67	84.95 71	86.07 71	92.23 79	70.48 85	90.80 62	92.08 92	73.51 147	85.26 51	94.16 41	62.75 146	95.92 57	82.46 74	91.30 97	91.81 140

Regformer-2		86.63 44	86.53 43	86.95 51	89.33 139	71.24 67	88.43 128	92.05 93	82.50 1	86.88 36	90.09 136	74.45 31	95.61 63	84.38 43	90.63 103	94.01 51

zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 77	92.02 94	79.45 20	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

MTGPA								92.02 94

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 191	92.02 94	79.45 20	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

MVS_Test		83.15 89	83.06 85	83.41 152	86.86 223	63.21 237	86.11 204	92.00 97	74.31 124	82.87 95	89.44 159	70.03 72	93.21 175	77.39 118	88.50 130	93.81 63

PVSNet_BlendedMVS		80.60 142	80.02 131	82.36 193	88.85 159	65.40 190	86.16 203	92.00 97	69.34 215	78.11 161	86.09 250	66.02 111	94.27 124	71.52 167	82.06 210	87.39 274

PVSNet_Blended		80.98 127	80.34 127	82.90 176	88.85 159	65.40 190	84.43 246	92.00 97	67.62 243	78.11 161	85.05 272	66.02 111	94.27 124	71.52 167	89.50 117	89.01 237

QAPM		80.88 128	79.50 144	85.03 90	88.01 191	68.97 113	91.59 44	92.00 97	66.63 255	75.15 231	92.16 85	57.70 203	95.45 72	63.52 236	88.76 125	90.66 176

LPG-MVS_test		82.08 104	81.27 110	84.50 109	89.23 148	68.76 117	90.22 78	91.94 101	75.37 98	76.64 193	91.51 100	54.29 229	94.91 100	78.44 105	83.78 183	89.83 214

LGP-MVS_train				84.50 109	89.23 148	68.76 117		91.94 101	75.37 98	76.64 193	91.51 100	54.29 229	94.91 100	78.44 105	83.78 183	89.83 214

TEST9					93.26 56	72.96 26	88.75 119	91.89 103	68.44 238	85.00 56	93.10 65	74.36 35	95.41 76

train_agg		86.43 46	86.20 50	87.13 49	93.26 56	72.96 26	88.75 119	91.89 103	68.69 234	85.00 56	93.10 65	74.43 32	95.41 76	84.97 32	95.71 30	93.02 99

dcpmvs_2		85.63 61	86.15 53	84.06 128	91.71 86	64.94 200	86.47 194	91.87 105	73.63 140	86.60 39	93.02 70	76.57 16	91.87 227	83.36 56	92.15 84	95.35 1

DU-MVS		81.12 125	80.52 124	82.90 176	87.80 197	63.46 231	87.02 176	91.87 105	79.01 28	78.38 152	89.07 165	65.02 120	93.05 187	70.05 181	76.46 270	92.20 127

test_8					93.13 58	72.57 36	88.68 123	91.84 107	68.69 234	84.87 62	93.10 65	74.43 32	95.16 87

PAPM_NR		83.02 93	82.41 93	84.82 100	92.47 77	66.37 168	87.93 152	91.80 108	73.82 136	77.32 177	90.66 123	67.90 91	94.90 104	70.37 178	89.48 118	93.19 93

test12				86.80 55	92.63 73	70.70 81		91.79 109		82.71 99		71.67 57	96.16 47		94.50 57	93.54 80

agg_prior1		86.22 51	86.09 55	86.62 59	92.85 66	71.94 54	88.59 125	91.78 110	68.96 229	84.41 72	93.18 64	74.94 27	94.93 98	84.75 38	95.33 38	93.01 100

agg_prior					92.85 66	71.94 54		91.78 110		84.41 72			94.93 98

PAPR		81.66 116	80.89 118	83.99 136	90.27 110	64.00 218	86.76 187	91.77 112	68.84 232	77.13 185	89.50 152	67.63 93	94.88 106	67.55 206	88.52 129	93.09 95

PVSNet_Blended_VisFu		82.62 98	81.83 106	84.96 93	90.80 101	69.76 97	88.74 121	91.70 113	69.39 213	78.96 139	88.46 183	65.47 116	94.87 107	74.42 142	88.57 127	90.24 191

HPM-MVS_fast		85.35 66	84.95 71	86.57 61	93.69 48	70.58 84	92.15 36	91.62 114	73.89 135	82.67 100	94.09 44	62.60 147	95.54 68	80.93 84	92.93 73	93.57 78

ACMM	73.20 8	80.78 138	79.84 136	83.58 146	89.31 144	68.37 129	89.99 83	91.60 115	70.28 197	77.25 178	89.66 147	53.37 237	93.53 164	74.24 145	82.85 200	88.85 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		80.60 142	80.55 123	80.76 228	88.07 188	60.80 268	86.86 181	91.58 116	75.67 93	80.24 127	89.45 158	63.34 134	90.25 262	70.51 177	79.22 244	91.23 157

OPM-MVS		83.50 83	82.95 87	85.14 87	88.79 165	70.95 73	89.13 106	91.52 117	77.55 46	80.96 120	91.75 93	60.71 182	94.50 119	79.67 97	86.51 155	89.97 209
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous20231211		78.97 183	77.69 193	82.81 180	90.54 106	64.29 214	90.11 81	91.51 118	65.01 273	76.16 208	88.13 197	50.56 268	93.03 190	69.68 187	77.56 256	91.11 160

PS-MVSNAJss		82.07 105	81.31 109	84.34 118	86.51 231	67.27 153	89.27 99	91.51 118	71.75 168	79.37 135	90.22 134	63.15 140	94.27 124	77.69 114	82.36 207	91.49 149

TAPA-MVS	73.13 9	79.15 177	77.94 182	82.79 183	89.59 127	62.99 244	88.16 144	91.51 118	65.77 264	77.14 184	91.09 113	60.91 180	93.21 175	50.26 326	87.05 146	92.17 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	74.13 6	81.51 120	80.57 122	84.36 115	89.42 134	68.69 124	89.97 84	91.50 121	74.46 122	75.04 235	90.41 129	53.82 234	94.54 116	77.56 115	82.91 199	89.86 213
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 147	78.84 163	85.01 91	87.71 201	68.99 112	83.65 260	91.46 122	63.00 294	77.77 168	90.28 130	66.10 108	95.09 94	61.40 257	88.22 133	90.94 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Regformer-1		86.41 48	86.33 46	86.64 58	89.33 139	70.93 75	88.43 128	91.39 123	82.14 3	86.65 38	90.09 136	74.39 34	95.01 97	83.97 52	90.63 103	93.97 53

TranMVSNet+NR-MVSNet		80.84 130	80.31 128	82.42 191	87.85 194	62.33 249	87.74 158	91.33 124	80.55 11	77.99 164	89.86 142	65.23 118	92.62 197	67.05 214	75.24 294	92.30 123

Regformer-4		85.68 60	85.45 61	86.35 62	88.95 157	69.67 99	88.29 138	91.29 125	81.73 5	85.36 50	90.01 140	72.62 49	95.35 83	83.28 60	87.57 136	94.03 49

PS-CasMVS		78.01 206	78.09 179	77.77 278	87.71 201	54.39 334	88.02 147	91.22 126	77.50 48	73.26 251	88.64 177	60.73 181	88.41 292	61.88 252	73.88 306	90.53 182

v7n		78.97 183	77.58 196	83.14 164	83.45 276	65.51 186	88.32 136	91.21 127	73.69 139	72.41 261	86.32 246	57.93 200	93.81 149	69.18 191	75.65 281	90.11 197

PEN-MVS		77.73 211	77.69 193	77.84 276	87.07 222	53.91 337	87.91 153	91.18 128	77.56 45	73.14 253	88.82 173	61.23 174	89.17 278	59.95 267	72.37 317	90.43 185

save fliter					93.80 44	72.35 45	90.47 70	91.17 129	74.31 124

CP-MVSNet		78.22 197	78.34 174	77.84 276	87.83 196	54.54 332	87.94 151	91.17 129	77.65 40	73.48 249	88.49 182	62.24 156	88.43 291	62.19 248	74.07 302	90.55 181

114514_t		80.68 139	79.51 143	84.20 122	94.09 41	67.27 153	89.64 94	91.11 131	58.75 331	74.08 245	90.72 122	58.10 199	95.04 95	69.70 186	89.42 119	90.30 189

NR-MVSNet		80.23 151	79.38 147	82.78 184	87.80 197	63.34 234	86.31 198	91.09 132	79.01 28	72.17 264	89.07 165	67.20 98	92.81 196	66.08 221	75.65 281	92.20 127

OpenMVS	72.83 10	79.77 160	78.33 175	84.09 126	85.17 246	69.91 93	90.57 66	90.97 133	66.70 251	72.17 264	91.91 89	54.70 225	93.96 138	61.81 254	90.95 100	88.41 257

MAR-MVS		81.84 109	80.70 120	85.27 84	91.32 91	71.53 60	89.82 87	90.92 134	69.77 206	78.50 149	86.21 247	62.36 153	94.52 118	65.36 226	92.05 86	89.77 217
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
Anonymous20240529		80.19 153	78.89 162	84.10 125	90.60 104	64.75 203	88.95 110	90.90 135	65.97 263	80.59 123	91.17 111	49.97 275	93.73 156	69.16 192	82.70 204	93.81 63

OMC-MVS		82.69 97	81.97 104	84.85 99	88.75 167	67.42 149	87.98 148	90.87 136	74.92 109	79.72 132	91.65 95	62.19 157	93.96 138	75.26 139	86.42 156	93.16 94

UA-Net		85.08 71	84.96 70	85.45 80	92.07 81	68.07 136	89.78 90	90.86 137	82.48 2	84.60 69	93.20 63	69.35 79	95.22 85	71.39 170	90.88 101	93.07 96

EPP-MVSNet		83.40 86	83.02 86	84.57 106	90.13 113	64.47 210	92.32 30	90.73 138	74.45 123	79.35 136	91.10 112	69.05 84	95.12 89	72.78 161	87.22 144	94.13 44

DTE-MVSNet		76.99 225	76.80 210	77.54 283	86.24 233	53.06 345	87.52 162	90.66 139	77.08 60	72.50 259	88.67 176	60.48 187	89.52 272	57.33 293	70.74 328	90.05 204

v10		79.74 161	78.67 165	82.97 174	84.06 265	64.95 199	87.88 155	90.62 140	73.11 152	75.11 232	86.56 239	61.46 168	94.05 137	73.68 148	75.55 283	89.90 211

v1192		79.59 164	78.43 172	83.07 168	83.55 274	64.52 206	86.93 179	90.58 141	70.83 185	77.78 167	85.90 251	59.15 194	93.94 141	73.96 147	77.19 259	90.76 172

v1144		80.03 156	79.03 159	83.01 171	83.78 270	64.51 207	87.11 174	90.57 142	71.96 166	78.08 163	86.20 248	61.41 169	93.94 141	74.93 140	77.23 257	90.60 179

XVG-OURS-SEG-HR		80.81 133	79.76 138	83.96 138	85.60 241	68.78 116	83.54 265	90.50 143	70.66 191	76.71 191	91.66 94	60.69 183	91.26 242	76.94 122	81.58 216	91.83 138

MVS		78.19 200	76.99 206	81.78 201	85.66 239	66.99 157	84.66 236	90.47 144	55.08 350	72.02 266	85.27 265	63.83 130	94.11 136	66.10 220	89.80 115	84.24 324

XVG-OURS		80.41 146	79.23 153	83.97 137	85.64 240	69.02 110	83.03 273	90.39 145	71.09 182	77.63 171	91.49 102	54.62 227	91.35 240	75.71 133	83.47 191	91.54 145

MVSFormer		82.85 95	82.05 101	85.24 85	87.35 211	70.21 87	90.50 68	90.38 146	68.55 236	81.32 113	89.47 154	61.68 162	93.46 168	78.98 101	90.26 108	92.05 132

test_djsdf		80.30 150	79.32 150	83.27 156	83.98 267	65.37 193	90.50 68	90.38 146	68.55 236	76.19 204	88.70 174	56.44 215	93.46 168	78.98 101	80.14 234	90.97 167

CPTT-MVS		83.73 78	83.33 82	84.92 96	93.28 55	70.86 77	92.09 37	90.38 146	68.75 233	79.57 133	92.83 74	60.60 186	93.04 189	80.92 85	91.56 93	90.86 170

v144192		79.47 167	78.37 173	82.78 184	83.35 277	63.96 219	86.96 177	90.36 149	69.99 201	77.50 172	85.67 257	60.66 184	93.77 152	74.27 144	76.58 268	90.62 177

v1921920		79.22 175	78.03 180	82.80 181	83.30 279	63.94 220	86.80 183	90.33 150	69.91 203	77.48 173	85.53 260	58.44 198	93.75 154	73.60 149	76.85 265	90.71 175

MVS_111021_HR		85.14 69	84.75 73	86.32 65	91.65 87	72.70 31	85.98 206	90.33 150	76.11 84	82.08 103	91.61 98	71.36 61	94.17 133	81.02 83	92.58 79	92.08 131

v1240		78.99 182	77.78 188	82.64 187	83.21 281	63.54 228	86.62 190	90.30 152	69.74 209	77.33 176	85.68 256	57.04 212	93.76 153	73.13 158	76.92 261	90.62 177

v8		79.97 159	79.02 160	82.80 181	84.09 264	64.50 209	87.96 149	90.29 153	74.13 131	75.24 229	86.81 224	62.88 145	93.89 147	74.39 143	75.40 289	90.00 205

mvs_tets		79.13 178	77.77 189	83.22 160	84.70 255	66.37 168	89.17 101	90.19 154	69.38 214	75.40 222	89.46 156	44.17 317	93.15 181	76.78 125	80.70 226	90.14 194

jajsoiax		79.29 174	77.96 181	83.27 156	84.68 256	66.57 166	89.25 100	90.16 155	69.20 220	75.46 219	89.49 153	45.75 312	93.13 183	76.84 123	80.80 224	90.11 197

Vis-MVSNet		83.46 84	82.80 90	85.43 81	90.25 111	68.74 119	90.30 76	90.13 156	76.33 81	80.87 121	92.89 72	61.00 179	94.20 130	72.45 164	90.97 99	93.35 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJ		81.69 113	81.02 115	83.70 144	89.51 131	68.21 134	84.28 250	90.09 157	70.79 187	81.26 117	85.62 259	63.15 140	94.29 122	75.62 135	88.87 123	88.59 253

xiu_mvs_v2_base		81.69 113	81.05 114	83.60 145	89.15 151	68.03 137	84.46 244	90.02 158	70.67 190	81.30 116	86.53 241	63.17 139	94.19 131	75.60 136	88.54 128	88.57 254

v2v482		80.23 151	79.29 151	83.05 169	83.62 272	64.14 216	87.04 175	89.97 159	73.61 141	78.18 160	87.22 215	61.10 177	93.82 148	76.11 129	76.78 267	91.18 158

test_yl		81.17 123	80.47 125	83.24 158	89.13 152	63.62 224	86.21 201	89.95 160	72.43 161	81.78 109	89.61 149	57.50 206	93.58 159	70.75 173	86.90 148	92.52 113

DCV-MVSNet		81.17 123	80.47 125	83.24 158	89.13 152	63.62 224	86.21 201	89.95 160	72.43 161	81.78 109	89.61 149	57.50 206	93.58 159	70.75 173	86.90 148	92.52 113

Regformer-3		85.23 67	85.07 68	85.70 78	88.95 157	69.01 111	88.29 138	89.91 162	80.95 8	85.01 54	90.01 140	72.45 50	94.19 131	82.50 73	87.57 136	93.90 57

V42		79.38 173	78.24 177	82.83 178	81.10 322	65.50 187	85.55 219	89.82 163	71.57 175	78.21 158	86.12 249	60.66 184	93.18 180	75.64 134	75.46 287	89.81 216

VNet		82.21 102	82.41 93	81.62 204	90.82 100	60.93 265	84.47 242	89.78 164	76.36 80	84.07 80	91.88 91	64.71 124	90.26 261	70.68 175	88.89 122	93.66 68

diffmvs		82.10 103	81.88 105	82.76 186	83.00 289	63.78 223	83.68 259	89.76 165	72.94 156	82.02 104	89.85 143	65.96 113	90.79 255	82.38 75	87.30 143	93.71 67

XVG-ACMP-BASELINE		76.11 240	74.27 247	81.62 204	83.20 282	64.67 204	83.60 263	89.75 166	69.75 207	71.85 267	87.09 220	32.78 359	92.11 217	69.99 183	80.43 230	88.09 260

EI-MVSNet-Vis-set		84.19 75	83.81 78	85.31 82	88.18 184	67.85 139	87.66 159	89.73 167	80.05 16	82.95 93	89.59 151	70.74 66	94.82 108	80.66 90	84.72 172	93.28 88

xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 70	89.69 168	74.31 124	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 18

EI-MVSNet-UG-set		83.81 77	83.38 81	85.09 89	87.87 193	67.53 147	87.44 165	89.66 169	79.74 18	82.23 102	89.41 160	70.24 71	94.74 111	79.95 95	83.92 182	92.99 102

PAPM		77.68 214	76.40 221	81.51 207	87.29 217	61.85 256	83.78 258	89.59 170	64.74 275	71.23 272	88.70 174	62.59 148	93.66 157	52.66 313	87.03 147	89.01 237

anonymousdsp		78.60 190	77.15 202	82.98 173	80.51 328	67.08 156	87.24 171	89.53 171	65.66 266	75.16 230	87.19 217	52.52 240	92.25 213	77.17 120	79.34 242	89.61 221

MG-MVS		83.41 85	83.45 80	83.28 155	92.74 71	62.28 251	88.17 143	89.50 172	75.22 101	81.49 112	92.74 80	66.75 100	95.11 90	72.85 160	91.58 92	92.45 118

PLC	70.83 11	78.05 204	76.37 222	83.08 167	91.88 85	67.80 140	88.19 142	89.46 173	64.33 281	69.87 289	88.38 185	53.66 235	93.58 159	58.86 278	82.73 202	87.86 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+		80.81 133	79.92 134	83.47 148	88.85 159	64.51 207	85.53 221	89.39 174	70.79 187	78.49 150	85.06 271	67.54 94	93.58 159	67.03 215	86.58 153	92.32 121

IterMVS-LS		80.06 155	79.38 147	82.11 195	85.89 236	63.20 238	86.79 184	89.34 175	74.19 128	75.45 220	86.72 227	66.62 101	92.39 206	72.58 162	76.86 264	90.75 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
API-MVS		81.99 107	81.23 111	84.26 121	90.94 97	70.18 92	91.10 55	89.32 176	71.51 176	78.66 146	88.28 188	65.26 117	95.10 93	64.74 232	91.23 98	87.51 272

GBi-Net		78.40 193	77.40 198	81.40 210	87.60 205	63.01 241	88.39 132	89.28 177	71.63 170	75.34 224	87.28 211	54.80 221	91.11 245	62.72 242	79.57 237	90.09 199

test1		78.40 193	77.40 198	81.40 210	87.60 205	63.01 241	88.39 132	89.28 177	71.63 170	75.34 224	87.28 211	54.80 221	91.11 245	62.72 242	79.57 237	90.09 199

FMVSNet1		77.44 217	76.12 224	81.40 210	86.81 226	63.01 241	88.39 132	89.28 177	70.49 194	74.39 242	87.28 211	49.06 288	91.11 245	60.91 261	78.52 247	90.09 199

cdsmvs_eth3d_5k		19.96 344	26.61 346	0.00 364	0.00 387	0.00 388	0.00 375	89.26 180	0.00 382	0.00 383	88.61 178	61.62 164	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs		79.51 165	78.97 161	81.14 219	88.46 177	60.91 266	83.84 257	89.24 181	70.36 195	79.03 138	88.87 172	63.23 138	90.21 263	65.12 228	82.57 205	92.28 124

cascas		76.72 230	74.64 240	82.99 172	85.78 238	65.88 178	82.33 277	89.21 182	60.85 313	72.74 256	81.02 315	47.28 298	93.75 154	67.48 207	85.02 168	89.34 226

eth_miper_zixun_eth		77.92 208	76.69 215	81.61 206	83.00 289	61.98 254	83.15 269	89.20 183	69.52 212	74.86 238	84.35 278	61.76 161	92.56 200	71.50 169	72.89 315	90.28 190

test_low_dy_conf_001		80.11 154	79.08 158	83.17 162	86.54 230	64.59 205	90.19 80	89.19 184	69.61 210	75.86 211	90.23 132	49.52 281	93.59 158	78.26 112	82.32 208	91.34 152

h-mvs33		83.15 89	82.19 97	86.02 73	90.56 105	70.85 78	88.15 145	89.16 185	76.02 86	84.67 65	91.39 105	61.54 165	95.50 69	82.71 69	75.48 285	91.72 142

miper_ehance_all_eth		78.59 191	77.76 190	81.08 221	82.66 297	61.56 260	83.65 260	89.15 186	68.87 231	75.55 216	83.79 287	66.49 103	92.03 219	73.25 156	76.39 272	89.64 220

Effi-MVS+		83.62 82	83.08 84	85.24 85	88.38 180	67.45 148	88.89 112	89.15 186	75.50 95	82.27 101	88.28 188	69.61 77	94.45 120	77.81 113	87.84 134	93.84 61

c3_l		78.75 186	77.91 183	81.26 214	82.89 292	61.56 260	84.09 255	89.13 188	69.97 202	75.56 215	84.29 279	66.36 105	92.09 218	73.47 153	75.48 285	90.12 196

LTVRE_ROB	69.57 13	76.25 238	74.54 243	81.41 209	88.60 172	64.38 213	79.24 306	89.12 189	70.76 189	69.79 291	87.86 199	49.09 287	93.20 177	56.21 301	80.16 232	86.65 294
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
F-COLMAP		76.38 237	74.33 246	82.50 190	89.28 146	66.95 162	88.41 131	89.03 190	64.05 285	66.83 314	88.61 178	46.78 301	92.89 192	57.48 290	78.55 246	87.67 267

FMVSNet2		78.20 199	77.21 201	81.20 217	87.60 205	62.89 245	87.47 164	89.02 191	71.63 170	75.29 228	87.28 211	54.80 221	91.10 248	62.38 246	79.38 241	89.61 221

ACMH	67.68 16	75.89 242	73.93 249	81.77 202	88.71 169	66.61 165	88.62 124	89.01 192	69.81 204	66.78 315	86.70 232	41.95 333	91.51 237	55.64 302	78.14 252	87.17 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_enhance_ethall		77.87 210	76.86 208	80.92 225	81.65 311	61.38 262	82.68 274	88.98 193	65.52 268	75.47 217	82.30 304	65.76 115	92.00 221	72.95 159	76.39 272	89.39 225

无先验							87.48 163	88.98 193	60.00 319				94.12 134	67.28 209		88.97 240

AdaColmap		80.58 144	79.42 145	84.06 128	93.09 61	68.91 114	89.36 97	88.97 195	69.27 216	75.70 214	89.69 146	57.20 211	95.77 60	63.06 241	88.41 131	87.50 273

EI-MVSNet		80.52 145	79.98 133	82.12 194	84.28 260	63.19 239	86.41 195	88.95 196	74.18 129	78.69 144	87.54 207	66.62 101	92.43 204	72.57 163	80.57 228	90.74 174

MVSTER		79.01 181	77.88 185	82.38 192	83.07 286	64.80 202	84.08 256	88.95 196	69.01 228	78.69 144	87.17 218	54.70 225	92.43 204	74.69 141	80.57 228	89.89 212

RRT_MVS		80.35 149	79.22 154	83.74 143	87.63 204	65.46 189	91.08 56	88.92 198	73.82 136	76.44 198	90.03 139	49.05 289	94.25 129	76.84 123	79.20 245	91.51 146

1314		76.53 231	75.30 236	80.21 239	83.93 268	62.32 250	84.66 236	88.81 199	60.23 317	70.16 283	84.07 282	55.30 219	90.73 257	67.37 208	83.21 195	87.59 271

UniMVSNet_ETH3D		79.10 179	78.24 177	81.70 203	86.85 224	60.24 276	87.28 170	88.79 200	74.25 127	76.84 186	90.53 128	49.48 282	91.56 234	67.98 202	82.15 209	93.29 87

xiu_mvs_v1_base_debu		80.80 135	79.72 139	84.03 133	87.35 211	70.19 89	85.56 216	88.77 201	69.06 225	81.83 105	88.16 192	50.91 263	92.85 193	78.29 109	87.56 138	89.06 232

xiu_mvs_v1_base		80.80 135	79.72 139	84.03 133	87.35 211	70.19 89	85.56 216	88.77 201	69.06 225	81.83 105	88.16 192	50.91 263	92.85 193	78.29 109	87.56 138	89.06 232

xiu_mvs_v1_base_debi		80.80 135	79.72 139	84.03 133	87.35 211	70.19 89	85.56 216	88.77 201	69.06 225	81.83 105	88.16 192	50.91 263	92.85 193	78.29 109	87.56 138	89.06 232

FMVSNet3		77.88 209	76.85 209	80.97 224	86.84 225	62.36 248	86.52 193	88.77 201	71.13 180	75.34 224	86.66 234	54.07 232	91.10 248	62.72 242	79.57 237	89.45 224

patch_mono-2		83.65 80	84.54 75	80.99 223	90.06 119	65.83 179	84.21 251	88.74 205	71.60 174	85.01 54	92.44 81	74.51 30	83.50 328	82.15 76	92.15 84	93.64 75

GeoE		81.71 112	81.01 116	83.80 142	89.51 131	64.45 211	88.97 109	88.73 206	71.27 179	78.63 147	89.76 145	66.32 106	93.20 177	69.89 184	86.02 162	93.74 66

CANet_DTU		80.61 141	79.87 135	82.83 178	85.60 241	63.17 240	87.36 166	88.65 207	76.37 79	75.88 210	88.44 184	53.51 236	93.07 186	73.30 155	89.74 116	92.25 125

HyFIR lowres test		77.53 216	75.40 232	83.94 139	89.59 127	66.62 164	80.36 295	88.64 208	56.29 346	76.45 195	85.17 268	57.64 204	93.28 173	61.34 259	83.10 198	91.91 136

WR-MVS		79.49 166	79.22 154	80.27 238	88.79 165	58.35 287	85.06 228	88.61 209	78.56 32	77.65 170	88.34 186	63.81 131	90.66 258	64.98 230	77.22 258	91.80 141

BH-untuned		79.47 167	78.60 167	82.05 196	89.19 150	65.91 177	86.07 205	88.52 210	72.18 163	75.42 221	87.69 202	61.15 176	93.54 163	60.38 264	86.83 150	86.70 293

IS-MVSNet		83.15 89	82.81 89	84.18 123	89.94 122	63.30 235	91.59 44	88.46 211	79.04 27	79.49 134	92.16 85	65.10 119	94.28 123	67.71 204	91.86 90	94.95 7

pm-mvs1		77.25 223	76.68 216	78.93 261	84.22 262	58.62 286	86.41 195	88.36 212	71.37 178	73.31 250	88.01 198	61.22 175	89.15 279	64.24 234	73.01 314	89.03 236

UGNet		80.83 132	79.59 142	84.54 108	88.04 189	68.09 135	89.42 96	88.16 213	76.95 62	76.22 203	89.46 156	49.30 285	93.94 141	68.48 199	90.31 106	91.60 143
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
VDD-MVS		83.01 94	82.36 95	84.96 93	91.02 95	66.40 167	88.91 111	88.11 214	77.57 43	84.39 74	93.29 62	52.19 246	93.91 145	77.05 121	88.70 126	94.57 29

Effi-MVS+-dtu		80.03 156	78.57 168	84.42 113	85.13 249	68.74 119	88.77 118	88.10 215	74.99 107	74.97 236	83.49 291	57.27 209	93.36 171	73.53 150	80.88 222	91.18 158

mvs-test1		80.88 128	79.40 146	85.29 83	85.13 249	69.75 98	89.28 98	88.10 215	74.99 107	76.44 198	86.72 227	57.27 209	94.26 128	73.53 150	83.18 196	91.87 137

v148		78.72 187	77.80 187	81.47 208	82.73 295	61.96 255	86.30 199	88.08 217	73.26 150	76.18 205	85.47 262	62.46 151	92.36 208	71.92 166	73.82 307	90.09 199

EG-PatchMatch MVS		74.04 259	71.82 267	80.71 229	84.92 253	67.42 149	85.86 211	88.08 217	66.04 261	64.22 334	83.85 284	35.10 355	92.56 200	57.44 291	80.83 223	82.16 342

cl22		78.07 203	77.01 204	81.23 215	82.37 304	61.83 257	83.55 264	87.98 219	68.96 229	75.06 234	83.87 283	61.40 170	91.88 226	73.53 150	76.39 272	89.98 208

pmmvs6		74.69 253	73.39 254	78.61 265	81.38 317	57.48 303	86.64 189	87.95 220	64.99 274	70.18 281	86.61 235	50.43 270	89.52 272	62.12 250	70.18 330	88.83 246

MVP-Stereo		76.12 239	74.46 245	81.13 220	85.37 244	69.79 96	84.42 247	87.95 220	65.03 272	67.46 306	85.33 264	53.28 238	91.73 231	58.01 287	83.27 194	81.85 343
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl____		77.72 212	76.76 212	80.58 231	82.49 301	60.48 273	83.09 270	87.87 222	69.22 218	74.38 243	85.22 267	62.10 158	91.53 235	71.09 171	75.41 288	89.73 219

DIV-MVS_self_test		77.72 212	76.76 212	80.58 231	82.48 302	60.48 273	83.09 270	87.86 223	69.22 218	74.38 243	85.24 266	62.10 158	91.53 235	71.09 171	75.40 289	89.74 218

BH-w/o		78.21 198	77.33 200	80.84 226	88.81 163	65.13 197	84.87 232	87.85 224	69.75 207	74.52 241	84.74 275	61.34 171	93.11 184	58.24 285	85.84 165	84.27 323

HY-MVS	69.67 12	77.95 207	77.15 202	80.36 235	87.57 209	60.21 277	83.37 267	87.78 225	66.11 259	75.37 223	87.06 222	63.27 136	90.48 260	61.38 258	82.43 206	90.40 187

1112_ss		77.40 219	76.43 220	80.32 237	89.11 156	60.41 275	83.65 260	87.72 226	62.13 305	73.05 254	86.72 227	62.58 149	89.97 265	62.11 251	80.80 224	90.59 180

mvs_anonymous		79.42 170	79.11 157	80.34 236	84.45 259	57.97 294	82.59 275	87.62 227	67.40 246	76.17 207	88.56 181	68.47 87	89.59 271	70.65 176	86.05 161	93.47 82

ACMH+	68.96 14	76.01 241	74.01 248	82.03 197	88.60 172	65.31 194	88.86 113	87.55 228	70.25 198	67.75 303	87.47 209	41.27 334	93.19 179	58.37 283	75.94 278	87.60 269

tfpnnormal		74.39 254	73.16 257	78.08 273	86.10 235	58.05 291	84.65 239	87.53 229	70.32 196	71.22 273	85.63 258	54.97 220	89.86 266	43.03 355	75.02 295	86.32 297

CHOSEN 1792x2688		77.63 215	75.69 226	83.44 149	89.98 121	68.58 127	78.70 313	87.50 230	56.38 345	75.80 213	86.84 223	58.67 196	91.40 239	61.58 256	85.75 166	90.34 188

ambc				75.24 304	73.16 366	50.51 357	63.05 365	87.47 231		64.28 333	77.81 342	17.80 372	89.73 269	57.88 288	60.64 354	85.49 309

Fast-Effi-MVS+-dtu		78.02 205	76.49 218	82.62 188	83.16 285	66.96 161	86.94 178	87.45 232	72.45 158	71.49 271	84.17 280	54.79 224	91.58 233	67.61 205	80.31 231	89.30 228

D2MVS		74.82 252	73.21 256	79.64 252	79.81 335	62.56 247	80.34 296	87.35 233	64.37 280	68.86 296	82.66 300	46.37 303	90.10 264	67.91 203	81.24 219	86.25 298

TSAR-MVS + GP.		85.71 59	85.33 63	86.84 53	91.34 90	72.50 37	89.07 107	87.28 234	76.41 75	85.80 45	90.22 134	74.15 39	95.37 82	81.82 78	91.88 87	92.65 112

hse-mvs2		81.72 111	80.94 117	84.07 127	88.72 168	67.68 144	85.87 210	87.26 235	76.02 86	84.67 65	88.22 191	61.54 165	93.48 166	82.71 69	73.44 311	91.06 162

AUN-MVS		79.21 176	77.60 195	84.05 130	88.71 169	67.61 145	85.84 212	87.26 235	69.08 224	77.23 180	88.14 196	53.20 239	93.47 167	75.50 138	73.45 310	91.06 162

BH-RMVSNet		79.61 162	78.44 171	83.14 164	89.38 137	65.93 176	84.95 231	87.15 237	73.56 143	78.19 159	89.79 144	56.67 214	93.36 171	59.53 271	86.74 151	90.13 195

Test_1112_low_res		76.40 236	75.44 230	79.27 257	89.28 146	58.09 290	81.69 283	87.07 238	59.53 324	72.48 260	86.67 233	61.30 172	89.33 275	60.81 263	80.15 233	90.41 186

KD-MVS_self_test		68.81 300	67.59 305	72.46 323	74.29 361	45.45 365	77.93 320	87.00 239	63.12 291	63.99 336	78.99 335	42.32 327	84.77 320	56.55 299	64.09 348	87.16 283

LS3D		76.95 227	74.82 239	83.37 153	90.45 107	67.36 152	89.15 105	86.94 240	61.87 307	69.52 292	90.61 124	51.71 257	94.53 117	46.38 346	86.71 152	88.21 259

miper_lstm_enhance		74.11 258	73.11 258	77.13 289	80.11 331	59.62 280	72.23 343	86.92 241	66.76 250	70.40 278	82.92 295	56.93 213	82.92 332	69.06 193	72.63 316	88.87 244

jason		81.39 121	80.29 129	84.70 104	86.63 229	69.90 94	85.95 207	86.77 242	63.24 290	81.07 119	89.47 154	61.08 178	92.15 216	78.33 108	90.07 113	92.05 132
jason: jason.
OurMVSNet-221017-0		74.26 256	72.42 263	79.80 247	83.76 271	59.59 281	85.92 209	86.64 243	66.39 257	66.96 311	87.58 204	39.46 340	91.60 232	65.76 224	69.27 332	88.22 258

VPNet		78.69 188	78.66 166	78.76 263	88.31 182	55.72 326	84.45 245	86.63 244	76.79 67	78.26 156	90.55 126	59.30 193	89.70 270	66.63 216	77.05 260	90.88 169

USDC		70.33 290	68.37 291	76.21 295	80.60 326	56.23 321	79.19 308	86.49 245	60.89 312	61.29 345	85.47 262	31.78 362	89.47 274	53.37 310	76.21 276	82.94 339

lupinMVS		81.39 121	80.27 130	84.76 103	87.35 211	70.21 87	85.55 219	86.41 246	62.85 297	81.32 113	88.61 178	61.68 162	92.24 214	78.41 107	90.26 108	91.83 138

TR-MVS		77.44 217	76.18 223	81.20 217	88.24 183	63.24 236	84.61 240	86.40 247	67.55 244	77.81 166	86.48 242	54.10 231	93.15 181	57.75 289	82.72 203	87.20 280

旧先验1					91.96 82	65.79 182		86.37 248			93.08 69	69.31 81			92.74 76	88.74 250

GA-MVS		76.87 228	75.17 237	81.97 199	82.75 294	62.58 246	81.44 287	86.35 249	72.16 165	74.74 239	82.89 296	46.20 306	92.02 220	68.85 196	81.09 220	91.30 156

CDS-MVSNet		79.07 180	77.70 192	83.17 162	87.60 205	68.23 133	84.40 248	86.20 250	67.49 245	76.36 200	86.54 240	61.54 165	90.79 255	61.86 253	87.33 142	90.49 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		82.61 99	82.11 98	84.11 124	88.82 162	71.58 59	85.15 226	86.16 251	74.69 115	80.47 125	91.04 115	62.29 154	90.55 259	80.33 92	90.08 112	90.20 192

MSDG		73.36 266	70.99 275	80.49 233	84.51 258	65.80 181	80.71 291	86.13 252	65.70 265	65.46 325	83.74 288	44.60 315	90.91 253	51.13 319	76.89 263	84.74 319

TransMVSNet (Re)		75.39 250	74.56 242	77.86 275	85.50 243	57.10 307	86.78 185	86.09 253	72.17 164	71.53 270	87.34 210	63.01 144	89.31 276	56.84 297	61.83 351	87.17 281

VDDNet		81.52 118	80.67 121	84.05 130	90.44 108	64.13 217	89.73 92	85.91 254	71.11 181	83.18 91	93.48 57	50.54 269	93.49 165	73.40 154	88.25 132	94.54 30

mvsmamba		81.69 113	80.74 119	84.56 107	87.45 210	66.72 163	91.26 50	85.89 255	74.66 116	78.23 157	90.56 125	54.33 228	94.91 100	80.73 89	83.54 190	92.04 134

Baseline_NR-MVSNet		78.15 201	78.33 175	77.61 281	85.79 237	56.21 322	86.78 185	85.76 256	73.60 142	77.93 165	87.57 205	65.02 120	88.99 281	67.14 213	75.33 291	87.63 268

Anonymous20240521		68.80 301	67.22 307	73.55 316	74.33 360	54.11 335	83.18 268	85.61 257	58.15 333	61.68 344	80.94 317	30.71 363	81.27 339	57.00 296	73.34 313	85.28 312

新几何1				83.42 150	93.13 58	70.71 80		85.48 258	57.43 339	81.80 108	91.98 88	63.28 135	92.27 212	64.60 233	92.99 72	87.27 278

1121		80.84 130	79.77 137	84.05 130	93.11 60	70.78 79	84.66 236	85.42 259	57.37 340	81.76 111	92.02 87	63.41 133	94.12 134	67.28 209	92.93 73	87.26 279

EPNet		83.72 79	82.92 88	86.14 69	84.22 262	69.48 103	91.05 58	85.27 260	81.30 7	76.83 187	91.65 95	66.09 109	95.56 65	76.00 132	93.85 67	93.38 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		67.33 309	65.99 312	71.37 327	73.48 364	51.47 352	75.16 334	85.19 261	65.20 269	60.78 347	80.93 319	42.35 326	77.20 354	57.12 294	53.69 362	85.44 310

IB-MVS	68.01 15	75.85 243	73.36 255	83.31 154	84.76 254	66.03 173	83.38 266	85.06 262	70.21 199	69.40 293	81.05 314	45.76 311	94.66 113	65.10 229	75.49 284	89.25 229
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
TAMVS		78.89 185	77.51 197	83.03 170	87.80 197	67.79 141	84.72 235	85.05 263	67.63 242	76.75 190	87.70 201	62.25 155	90.82 254	58.53 282	87.13 145	90.49 183

CL-MVSNet_self_test		72.37 277	71.46 269	75.09 305	79.49 341	53.53 339	80.76 290	85.01 264	69.12 223	70.51 276	82.05 308	57.92 201	84.13 323	52.27 314	66.00 343	87.60 269

bld_raw_conf005		81.08 126	79.99 132	84.35 116	87.16 220	66.17 172	91.08 56	84.98 265	75.09 104	77.71 169	90.54 127	50.04 273	94.91 100	79.96 94	83.32 193	91.98 135

iter_conf05		80.00 158	78.70 164	83.91 140	87.84 195	65.83 179	88.84 116	84.92 266	71.61 173	78.70 143	88.94 168	43.88 319	94.56 114	79.28 99	84.28 179	91.33 153

testdata				79.97 243	90.90 98	64.21 215		84.71 267	59.27 326	85.40 49	92.91 71	62.02 160	89.08 280	68.95 194	91.37 95	86.63 295

MS-PatchMatch		73.83 261	72.67 260	77.30 286	83.87 269	66.02 174	81.82 280	84.66 268	61.37 311	68.61 299	82.82 298	47.29 297	88.21 293	59.27 272	84.32 178	77.68 357

ET-MVSNet_ETH3D		78.63 189	76.63 217	84.64 105	86.73 228	69.47 104	85.01 229	84.61 269	69.54 211	66.51 320	86.59 236	50.16 272	91.75 229	76.26 128	84.24 180	92.69 110

CNLPA		78.08 202	76.79 211	81.97 199	90.40 109	71.07 69	87.59 161	84.55 270	66.03 262	72.38 262	89.64 148	57.56 205	86.04 310	59.61 270	83.35 192	88.79 248

iter_conf_final		80.63 140	79.35 149	84.46 111	89.36 138	67.70 143	89.85 85	84.49 271	73.19 151	78.30 155	88.94 168	45.98 307	94.56 114	79.59 98	84.48 176	91.11 160

MIMVSNet1		68.58 303	66.78 310	73.98 315	80.07 332	51.82 348	80.77 289	84.37 272	64.40 279	59.75 351	82.16 307	36.47 351	83.63 327	42.73 356	70.33 329	86.48 296

KD-MVS_2432*1600		66.22 317	63.89 318	73.21 318	75.47 358	53.42 341	70.76 348	84.35 273	64.10 283	66.52 318	78.52 336	34.55 357	84.98 317	50.40 322	50.33 366	81.23 346

miper_refine_blended		66.22 317	63.89 318	73.21 318	75.47 358	53.42 341	70.76 348	84.35 273	64.10 283	66.52 318	78.52 336	34.55 357	84.98 317	50.40 322	50.33 366	81.23 346

test_0402		72.79 273	70.44 280	79.84 246	88.13 185	65.99 175	85.93 208	84.29 275	65.57 267	67.40 308	85.49 261	46.92 300	92.61 198	35.88 364	74.38 301	80.94 348

EU-MVSNet		68.53 304	67.61 304	71.31 330	78.51 346	47.01 364	84.47 242	84.27 276	42.27 362	66.44 321	84.79 274	40.44 338	83.76 325	58.76 280	68.54 337	83.17 333

thisisatest0530		79.40 171	77.76 190	84.31 119	87.69 203	65.10 198	87.36 166	84.26 277	70.04 200	77.42 174	88.26 190	49.94 276	94.79 110	70.20 179	84.70 173	93.03 98

COLMAP_ROB	66.92 17	73.01 270	70.41 281	80.81 227	87.13 221	65.63 184	88.30 137	84.19 278	62.96 295	63.80 338	87.69 202	38.04 347	92.56 200	46.66 343	74.91 296	84.24 324
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tttt0517		79.40 171	77.91 183	83.90 141	88.10 187	63.84 221	88.37 135	84.05 279	71.45 177	76.78 189	89.12 164	49.93 278	94.89 105	70.18 180	83.18 196	92.96 103

CMPMVS	51.72 21	70.19 292	68.16 294	76.28 294	73.15 367	57.55 302	79.47 304	83.92 280	48.02 360	56.48 359	84.81 273	43.13 322	86.42 308	62.67 245	81.81 214	84.89 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous202405211		78.25 196	77.01 204	81.99 198	91.03 94	60.67 270	84.77 234	83.90 281	70.65 192	80.00 130	91.20 109	41.08 336	91.43 238	65.21 227	85.26 167	93.85 59

XXY-MVS		75.41 249	75.56 228	74.96 306	83.59 273	57.82 298	80.59 293	83.87 282	66.54 256	74.93 237	88.31 187	63.24 137	80.09 343	62.16 249	76.85 265	86.97 287

DP-MVS		76.78 229	74.57 241	83.42 150	93.29 54	69.46 106	88.55 127	83.70 283	63.98 287	70.20 280	88.89 171	54.01 233	94.80 109	46.66 343	81.88 213	86.01 305

tfpn200view9		76.42 235	75.37 234	79.55 255	89.13 152	57.65 300	85.17 224	83.60 284	73.41 148	76.45 195	86.39 244	52.12 247	91.95 222	48.33 334	83.75 185	89.07 230

thres400		76.50 232	75.37 234	79.86 245	89.13 152	57.65 300	85.17 224	83.60 284	73.41 148	76.45 195	86.39 244	52.12 247	91.95 222	48.33 334	83.75 185	90.00 205

SixPastTwentyTwo		73.37 264	71.26 274	79.70 249	85.08 251	57.89 296	85.57 215	83.56 286	71.03 183	65.66 324	85.88 252	42.10 331	92.57 199	59.11 275	63.34 349	88.65 252

thres200		75.55 246	74.47 244	78.82 262	87.78 200	57.85 297	83.07 272	83.51 287	72.44 160	75.84 212	84.42 277	52.08 249	91.75 229	47.41 341	83.64 189	86.86 289

MVS_0304		72.48 274	70.89 277	77.24 287	82.20 305	59.68 279	84.11 254	83.49 288	67.10 247	66.87 313	80.59 320	35.00 356	87.40 301	59.07 276	79.58 236	84.63 321

IterMVS-SCA-FT		75.43 248	73.87 251	80.11 241	82.69 296	64.85 201	81.57 285	83.47 289	69.16 222	70.49 277	84.15 281	51.95 252	88.15 294	69.23 190	72.14 320	87.34 276

CVMVSNet		72.99 271	72.58 261	74.25 313	84.28 260	50.85 355	86.41 195	83.45 290	44.56 361	73.23 252	87.54 207	49.38 283	85.70 312	65.90 222	78.44 249	86.19 300

ITE_SJBPF				78.22 271	81.77 310	60.57 271		83.30 291	69.25 217	67.54 305	87.20 216	36.33 352	87.28 303	54.34 306	74.62 299	86.80 290

thisisatest0515		77.33 220	75.38 233	83.18 161	85.27 245	63.80 222	82.11 279	83.27 292	65.06 271	75.91 209	83.84 285	49.54 280	94.27 124	67.24 211	86.19 159	91.48 150

thres100view900		76.50 232	75.55 229	79.33 256	89.52 130	56.99 308	85.83 213	83.23 293	73.94 133	76.32 201	87.12 219	51.89 254	91.95 222	48.33 334	83.75 185	89.07 230

thres600view7		76.50 232	75.44 230	79.68 250	89.40 135	57.16 305	85.53 221	83.23 293	73.79 138	76.26 202	87.09 220	51.89 254	91.89 225	48.05 339	83.72 188	90.00 205

test222					91.50 89	68.26 132	84.16 252	83.20 295	54.63 351	79.74 131	91.63 97	58.97 195			91.42 94	86.77 291

EPNet_dtu		75.46 247	74.86 238	77.23 288	82.57 299	54.60 331	86.89 180	83.09 296	71.64 169	66.25 322	85.86 253	55.99 216	88.04 296	54.92 304	86.55 154	89.05 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TDRefinement		67.49 307	64.34 316	76.92 290	73.47 365	61.07 264	84.86 233	82.98 297	59.77 321	58.30 354	85.13 269	26.06 365	87.89 297	47.92 340	60.59 355	81.81 344

OpenMVS_ROB	64.09 19	70.56 288	68.19 293	77.65 280	80.26 329	59.41 283	85.01 229	82.96 298	58.76 330	65.43 326	82.33 303	37.63 349	91.23 244	45.34 351	76.03 277	82.32 340

RPSCF		73.23 268	71.46 269	78.54 267	82.50 300	59.85 278	82.18 278	82.84 299	58.96 328	71.15 274	89.41 160	45.48 314	84.77 320	58.82 279	71.83 322	91.02 166

CostFormer		75.24 251	73.90 250	79.27 257	82.65 298	58.27 289	80.80 288	82.73 300	61.57 308	75.33 227	83.13 294	55.52 217	91.07 251	64.98 230	78.34 251	88.45 255

IterMVS		74.29 255	72.94 259	78.35 270	81.53 314	63.49 230	81.58 284	82.49 301	68.06 241	69.99 286	83.69 289	51.66 258	85.54 313	65.85 223	71.64 323	86.01 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WTY-MVS		75.65 245	75.68 227	75.57 300	86.40 232	56.82 310	77.92 321	82.40 302	65.10 270	76.18 205	87.72 200	63.13 143	80.90 340	60.31 265	81.96 211	89.00 239

pmmvs4		74.03 260	71.91 266	80.39 234	81.96 308	68.32 130	81.45 286	82.14 303	59.32 325	69.87 289	85.13 269	52.40 243	88.13 295	60.21 266	74.74 298	84.73 320

FMVSNet5		69.50 296	67.96 297	74.15 314	82.97 291	55.35 328	80.01 299	82.12 304	62.56 301	63.02 339	81.53 311	36.92 350	81.92 335	48.42 333	74.06 303	85.17 315

baseline1		76.98 226	76.75 214	77.66 279	88.13 185	55.66 327	85.12 227	81.89 305	73.04 154	76.79 188	88.90 170	62.43 152	87.78 299	63.30 240	71.18 326	89.55 223

UnsupCasMVSNet_bld		63.70 325	61.53 329	70.21 334	73.69 363	51.39 353	72.82 341	81.89 305	55.63 348	57.81 355	71.80 358	38.67 343	78.61 347	49.26 331	52.21 364	80.63 349

LFMVS		81.82 110	81.23 111	83.57 147	91.89 84	63.43 233	89.84 86	81.85 307	77.04 61	83.21 90	93.10 65	52.26 245	93.43 170	71.98 165	89.95 114	93.85 59

sss		73.60 262	73.64 253	73.51 317	82.80 293	55.01 329	76.12 327	81.69 308	62.47 302	74.68 240	85.85 254	57.32 208	78.11 350	60.86 262	80.93 221	87.39 274

pmmvs-eth3d		70.50 289	67.83 300	78.52 268	77.37 350	66.18 171	81.82 280	81.51 309	58.90 329	63.90 337	80.42 322	42.69 325	86.28 309	58.56 281	65.30 345	83.11 335

TinyColmap		67.30 310	64.81 314	74.76 309	81.92 309	56.68 314	80.29 297	81.49 310	60.33 315	56.27 360	83.22 293	24.77 366	87.66 300	45.52 349	69.47 331	79.95 352

tpmvs		71.09 283	69.29 286	76.49 293	82.04 307	56.04 323	78.92 311	81.37 311	64.05 285	67.18 310	78.28 338	49.74 279	89.77 267	49.67 329	72.37 317	83.67 329

pmmvs5		71.55 280	70.20 283	75.61 299	77.83 347	56.39 318	81.74 282	80.89 312	57.76 336	67.46 306	84.49 276	49.26 286	85.32 316	57.08 295	75.29 292	85.11 316

ANet_high		50.57 336	46.10 339	63.99 345	48.67 379	39.13 372	70.99 347	80.85 313	61.39 310	31.18 370	57.70 367	17.02 373	73.65 367	31.22 366	15.89 376	79.18 354

LCM-MVSNet		54.25 331	49.68 337	67.97 342	53.73 376	45.28 366	66.85 360	80.78 314	35.96 368	39.45 368	62.23 364	8.70 380	78.06 351	48.24 337	51.20 365	80.57 350

PVSNet	64.34 18	72.08 279	70.87 278	75.69 298	86.21 234	56.44 317	74.37 339	80.73 315	62.06 306	70.17 282	82.23 306	42.86 324	83.31 330	54.77 305	84.45 177	87.32 277

baseline2		75.70 244	73.83 252	81.30 213	83.26 280	61.79 258	82.57 276	80.65 316	66.81 248	66.88 312	83.42 292	57.86 202	92.19 215	63.47 237	79.57 237	89.91 210

ppachtmachnet_test		70.04 293	67.34 306	78.14 272	79.80 336	61.13 263	79.19 308	80.59 317	59.16 327	65.27 327	79.29 330	46.75 302	87.29 302	49.33 330	66.72 339	86.00 307

Gipuma		45.18 337	41.86 340	55.16 351	77.03 352	51.52 351	32.50 372	80.52 318	32.46 370	27.12 371	35.02 372	9.52 379	75.50 360	22.31 372	60.21 356	38.45 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231206		68.60 302	67.80 301	71.02 331	80.23 330	50.75 356	78.30 317	80.47 319	56.79 343	66.11 323	82.63 301	46.35 304	78.95 346	43.62 354	75.70 280	83.36 332

LCM-MVSNet-Re		77.05 224	76.94 207	77.36 284	87.20 218	51.60 350	80.06 298	80.46 320	75.20 102	67.69 304	86.72 227	62.48 150	88.98 282	63.44 238	89.25 120	91.51 146

tpm2		73.26 267	71.46 269	78.63 264	83.34 278	56.71 313	80.65 292	80.40 321	56.63 344	73.55 248	82.02 309	51.80 256	91.24 243	56.35 300	78.42 250	87.95 261

CR-MVSNet		73.37 264	71.27 273	79.67 251	81.32 320	65.19 195	75.92 329	80.30 322	59.92 320	72.73 257	81.19 312	52.50 241	86.69 305	59.84 268	77.71 253	87.11 285

Patchmtry		70.74 285	69.16 287	75.49 302	80.72 324	54.07 336	74.94 338	80.30 322	58.34 332	70.01 284	81.19 312	52.50 241	86.54 306	53.37 310	71.09 327	85.87 308

tpm cat1		70.57 287	68.31 292	77.35 285	82.41 303	57.95 295	78.08 318	80.22 324	52.04 356	68.54 300	77.66 343	52.00 251	87.84 298	51.77 315	72.07 321	86.25 298

MDTV_nov1_ep13			69.97 284		83.18 283	53.48 340	77.10 325	80.18 325	60.45 314	69.33 295	80.44 321	48.89 292	86.90 304	51.60 317	78.51 248

AllTest		70.96 284	68.09 296	79.58 253	85.15 247	63.62 224	84.58 241	79.83 326	62.31 303	60.32 348	86.73 225	32.02 360	88.96 284	50.28 324	71.57 324	86.15 301

TestCases				79.58 253	85.15 247	63.62 224		79.83 326	62.31 303	60.32 348	86.73 225	32.02 360	88.96 284	50.28 324	71.57 324	86.15 301

Vis-MVSNet (Re-imp)		78.36 195	78.45 170	78.07 274	88.64 171	51.78 349	86.70 188	79.63 328	74.14 130	75.11 232	90.83 121	61.29 173	89.75 268	58.10 286	91.60 91	92.69 110

MIMVSNet		70.69 286	69.30 285	74.88 307	84.52 257	56.35 320	75.87 331	79.42 329	64.59 276	67.76 302	82.41 302	41.10 335	81.54 337	46.64 345	81.34 217	86.75 292

SCA		74.22 257	72.33 264	79.91 244	84.05 266	62.17 252	79.96 300	79.29 330	66.30 258	72.38 262	80.13 324	51.95 252	88.60 289	59.25 273	77.67 255	88.96 241

tpmrst		72.39 275	72.13 265	73.18 321	80.54 327	49.91 358	79.91 301	79.08 331	63.11 292	71.69 269	79.95 326	55.32 218	82.77 333	65.66 225	73.89 305	86.87 288

PatchmatchNet		73.12 269	71.33 272	78.49 269	83.18 283	60.85 267	79.63 302	78.57 332	64.13 282	71.73 268	79.81 329	51.20 261	85.97 311	57.40 292	76.36 275	88.66 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDA-MVSNet-bldmvs		66.68 312	63.66 320	75.75 297	79.28 343	60.56 272	73.92 340	78.35 333	64.43 278	50.13 365	79.87 328	44.02 318	83.67 326	46.10 347	56.86 358	83.03 337

new-patchmatchnet		61.73 326	61.73 328	61.70 347	72.74 368	24.50 381	69.16 354	78.03 334	61.40 309	56.72 358	75.53 352	38.42 344	76.48 357	45.95 348	57.67 357	84.13 326

our_test_3		69.14 298	67.00 308	75.57 300	79.80 336	58.80 284	77.96 319	77.81 335	59.55 323	62.90 342	78.25 339	47.43 296	83.97 324	51.71 316	67.58 338	83.93 328

test20.03		67.45 308	66.95 309	68.94 337	75.48 357	44.84 367	77.50 322	77.67 336	66.66 252	63.01 340	83.80 286	47.02 299	78.40 348	42.53 357	68.86 336	83.58 330

bld_raw_dy_0_64		77.29 222	75.98 225	81.22 216	85.04 252	65.47 188	88.14 146	77.56 337	69.20 220	73.77 247	89.40 162	42.24 330	88.85 287	76.78 125	81.64 215	89.33 227

test-LLR		72.94 272	72.43 262	74.48 310	81.35 318	58.04 292	78.38 314	77.46 338	66.66 252	69.95 287	79.00 333	48.06 294	79.24 344	66.13 218	84.83 170	86.15 301

test-mter		71.41 281	70.39 282	74.48 310	81.35 318	58.04 292	78.38 314	77.46 338	60.32 316	69.95 287	79.00 333	36.08 353	79.24 344	66.13 218	84.83 170	86.15 301

ECVR-MVS		79.61 162	79.26 152	80.67 230	90.08 115	54.69 330	87.89 154	77.44 340	74.88 110	80.27 126	92.79 77	48.96 291	92.45 203	68.55 198	92.50 81	94.86 14

tpm		72.37 277	71.71 268	74.35 312	82.19 306	52.00 347	79.22 307	77.29 341	64.56 277	72.95 255	83.68 290	51.35 259	83.26 331	58.33 284	75.80 279	87.81 265

LF4IMVS		64.02 324	62.19 327	69.50 336	70.90 369	53.29 344	76.13 326	77.18 342	52.65 355	58.59 352	80.98 316	23.55 368	76.52 356	53.06 312	66.66 340	78.68 355

test1111		79.43 169	79.18 156	80.15 240	89.99 120	53.31 343	87.33 168	77.05 343	75.04 106	80.23 128	92.77 79	48.97 290	92.33 211	68.87 195	92.40 83	94.81 17

K. test v3		71.19 282	68.51 290	79.21 259	83.04 288	57.78 299	84.35 249	76.91 344	72.90 157	62.99 341	82.86 297	39.27 341	91.09 250	61.65 255	52.66 363	88.75 249

testgi		66.67 313	66.53 311	67.08 344	75.62 356	41.69 371	75.93 328	76.50 345	66.11 259	65.20 330	86.59 236	35.72 354	74.71 363	43.71 353	73.38 312	84.84 318

PatchMatch-RL		72.38 276	70.90 276	76.80 292	88.60 172	67.38 151	79.53 303	76.17 346	62.75 299	69.36 294	82.00 310	45.51 313	84.89 319	53.62 309	80.58 227	78.12 356

JIA-IIPM		66.32 316	62.82 326	76.82 291	77.09 351	61.72 259	65.34 361	75.38 347	58.04 335	64.51 332	62.32 363	42.05 332	86.51 307	51.45 318	69.22 333	82.21 341

ADS-MVSNet2		66.20 319	63.33 321	74.82 308	79.92 333	58.75 285	67.55 358	75.19 348	53.37 353	65.25 328	75.86 349	42.32 327	80.53 342	41.57 358	68.91 334	85.18 313

PatchT		68.46 305	67.85 299	70.29 333	80.70 325	43.93 368	72.47 342	74.88 349	60.15 318	70.55 275	76.57 347	49.94 276	81.59 336	50.58 320	74.83 297	85.34 311

dp		66.80 311	65.43 313	70.90 332	79.74 338	48.82 361	75.12 336	74.77 350	59.61 322	64.08 335	77.23 344	42.89 323	80.72 341	48.86 332	66.58 341	83.16 334

MDA-MVSNet_test_wron		65.03 320	62.92 323	71.37 327	75.93 353	56.73 311	69.09 356	74.73 351	57.28 341	54.03 362	77.89 340	45.88 308	74.39 365	49.89 328	61.55 352	82.99 338

TESTMET0.1,1		69.89 295	69.00 288	72.55 322	79.27 344	56.85 309	78.38 314	74.71 352	57.64 337	68.09 301	77.19 345	37.75 348	76.70 355	63.92 235	84.09 181	84.10 327

YYNet1		65.03 320	62.91 324	71.38 326	75.85 354	56.60 315	69.12 355	74.66 353	57.28 341	54.12 361	77.87 341	45.85 309	74.48 364	49.95 327	61.52 353	83.05 336

PMMVS		69.34 297	68.67 289	71.35 329	75.67 355	62.03 253	75.17 333	73.46 354	50.00 359	68.68 297	79.05 331	52.07 250	78.13 349	61.16 260	82.77 201	73.90 360

PVSNet_0	57.27 20	61.67 327	59.27 330	68.85 339	79.61 339	57.44 304	68.01 357	73.44 355	55.93 347	58.54 353	70.41 359	44.58 316	77.55 353	47.01 342	35.91 369	71.55 362

test0.0.03 1		68.00 306	67.69 303	68.90 338	77.55 348	47.43 362	75.70 332	72.95 356	66.66 252	66.56 316	82.29 305	48.06 294	75.87 359	44.97 352	74.51 300	83.41 331

ADS-MVSNet		64.36 323	62.88 325	68.78 340	79.92 333	47.17 363	67.55 358	71.18 357	53.37 353	65.25 328	75.86 349	42.32 327	73.99 366	41.57 358	68.91 334	85.18 313

Patchmatch-RL test		70.24 291	67.78 302	77.61 281	77.43 349	59.57 282	71.16 345	70.33 358	62.94 296	68.65 298	72.77 356	50.62 267	85.49 314	69.58 188	66.58 341	87.77 266

gg-mvs-nofinetune		69.95 294	67.96 297	75.94 296	83.07 286	54.51 333	77.23 324	70.29 359	63.11 292	70.32 279	62.33 362	43.62 320	88.69 288	53.88 308	87.76 135	84.62 322

door-mid								69.98 360

GG-mvs-BLEND				75.38 303	81.59 313	55.80 325	79.32 305	69.63 361		67.19 309	73.67 355	43.24 321	88.90 286	50.41 321	84.50 174	81.45 345

FPMVS		53.68 332	51.64 335	59.81 349	65.08 372	51.03 354	69.48 352	69.58 362	41.46 363	40.67 367	72.32 357	16.46 374	70.00 369	24.24 371	65.42 344	58.40 368

door								69.44 363

Patchmatch-test		64.82 322	63.24 322	69.57 335	79.42 342	49.82 359	63.49 364	69.05 364	51.98 357	59.95 350	80.13 324	50.91 263	70.98 368	40.66 360	73.57 308	87.90 263

CHOSEN 280x420		66.51 314	64.71 315	71.90 324	81.45 315	63.52 229	57.98 366	68.95 365	53.57 352	62.59 343	76.70 346	46.22 305	75.29 362	55.25 303	79.68 235	76.88 359

EGC-MVSNET		52.07 334	47.05 338	67.14 343	83.51 275	60.71 269	80.50 294	67.75 366	0.07 379	0.43 380	75.85 351	24.26 367	81.54 337	28.82 367	62.25 350	59.16 367

EPMVS		69.02 299	68.16 294	71.59 325	79.61 339	49.80 360	77.40 323	66.93 367	62.82 298	70.01 284	79.05 331	45.79 310	77.86 352	56.58 298	75.26 293	87.13 284

lessismore_v0				78.97 260	81.01 323	57.15 306		65.99 368		61.16 346	82.82 298	39.12 342	91.34 241	59.67 269	46.92 368	88.43 256

pmmvs3		57.79 329	54.26 333	68.37 341	64.02 373	56.72 312	75.12 336	65.17 369	40.20 364	52.93 363	69.86 360	20.36 370	75.48 361	45.45 350	55.25 361	72.90 361

MVS-HIRNet		59.14 328	57.67 331	63.57 346	81.65 311	43.50 369	71.73 344	65.06 370	39.59 366	51.43 364	57.73 366	38.34 345	82.58 334	39.53 361	73.95 304	64.62 365

PM-MVS		66.41 315	64.14 317	73.20 320	73.92 362	56.45 316	78.97 310	64.96 371	63.88 289	64.72 331	80.24 323	19.84 371	83.44 329	66.24 217	64.52 347	79.71 353

PMVS	37.38 22	44.16 338	40.28 341	55.82 350	40.82 381	42.54 370	65.12 362	63.99 372	34.43 369	24.48 372	57.12 368	3.92 382	76.17 358	17.10 374	55.52 360	48.75 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2506		77.30 221	76.49 218	79.74 248	90.08 115	52.02 346	87.86 156	63.10 373	74.88 110	80.16 129	92.79 77	38.29 346	92.35 209	68.74 197	92.50 81	94.86 14

test_method		31.52 341	29.28 345	38.23 356	27.03 383	6.50 385	20.94 374	62.21 374	4.05 377	22.35 375	52.50 369	13.33 375	47.58 376	27.04 370	34.04 370	60.62 366

new_pmnet		50.91 335	50.29 336	52.78 352	68.58 370	34.94 376	63.71 363	56.63 375	39.73 365	44.95 366	65.47 361	21.93 369	58.48 372	34.98 365	56.62 359	64.92 364

DSMNet-mixed		57.77 330	56.90 332	60.38 348	67.70 371	35.61 374	69.18 353	53.97 376	32.30 371	57.49 356	79.88 327	40.39 339	68.57 370	38.78 362	72.37 317	76.97 358

PMMVS2		40.82 339	38.86 342	46.69 354	53.84 375	16.45 382	48.61 369	49.92 377	37.49 367	31.67 369	60.97 365	8.14 381	56.42 373	28.42 368	30.72 371	67.19 363

E-PMN		31.77 340	30.64 343	35.15 357	52.87 377	27.67 378	57.09 367	47.86 378	24.64 372	16.40 377	33.05 373	11.23 377	54.90 374	14.46 376	18.15 374	22.87 373

EMVS		30.81 342	29.65 344	34.27 358	50.96 378	25.95 380	56.58 368	46.80 379	24.01 373	15.53 378	30.68 374	12.47 376	54.43 375	12.81 377	17.05 375	22.43 374

MVE	26.22 23	30.37 343	25.89 347	43.81 355	44.55 380	35.46 375	28.87 373	39.07 380	18.20 374	18.58 376	40.18 371	2.68 383	47.37 377	17.07 375	23.78 373	48.60 370
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							92.18 35	32.83 381

tmp_tt		18.61 345	21.40 348	10.23 361	4.82 384	10.11 383	34.70 371	30.74 382	1.48 378	23.91 374	26.07 375	28.42 364	13.41 380	27.12 369	15.35 377	7.17 375

DeepMVS_CX				27.40 359	40.17 382	26.90 379		24.59 383	17.44 375	23.95 373	48.61 370	9.77 378	26.48 378	18.06 373	24.47 372	28.83 372

N_pmnet		52.79 333	53.26 334	51.40 353	78.99 345	7.68 384	69.52 351	3.89 384	51.63 358	57.01 357	74.98 353	40.83 337	65.96 371	37.78 363	64.67 346	80.56 351

wuyk23d		16.82 346	15.94 349	19.46 360	58.74 374	31.45 377	39.22 370	3.74 385	6.84 376	6.04 379	2.70 379	1.27 384	24.29 379	10.54 378	14.40 378	2.63 376

testmvs		6.04 349	8.02 352	0.10 363	0.08 385	0.03 387	69.74 350	0.04 386	0.05 380	0.31 381	1.68 380	0.02 386	0.04 381	0.24 379	0.02 379	0.25 378

test123		6.12 348	8.11 351	0.14 362	0.06 386	0.09 386	71.05 346	0.03 387	0.04 381	0.25 382	1.30 381	0.05 385	0.03 382	0.21 380	0.01 380	0.29 377

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

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

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

pcd_1.5k_mvsjas		5.26 350	7.02 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	63.15 140	0.00 383	0.00 381	0.00 381	0.00 379

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

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

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

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

n2								0.00 388

nn								0.00 388

ab-mvs-re		7.23 347	9.64 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	86.72 227	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

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

PC_three_1452									68.21 240	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 7

eth-test2					0.00 387

eth-test					0.00 387

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 54	96.48 8	94.88 11

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 24

GSMVS																88.96 241

test_part2					95.06 8	72.65 33				91.80 13

sam_mvs1												51.32 260				88.96 241

sam_mvs												50.01 274

test_post1							78.90 312				5.43 378	48.81 293	85.44 315	59.25 273

test_post											5.46 377	50.36 271	84.24 322

patchmatchnet-post											74.00 354	51.12 262	88.60 289

gm-plane-assit					81.40 316	53.83 338			62.72 300		80.94 317		92.39 206	63.40 239

test9_res														84.90 33	95.70 31	92.87 105

agg_prior2														82.91 65	95.45 33	92.70 108

test_prior4						72.60 35	89.01 108

test_prior2							88.85 114		75.41 96	84.91 58	93.54 54	74.28 36		83.31 57	95.86 22

旧先验2							86.56 192		58.10 334	87.04 35			88.98 282	74.07 146

新几何2							86.29 200

原ACMM2							86.86 181

testdata2													91.01 252	62.37 247

segment_acmp												73.08 45

testdata1							84.14 253		75.71 90

plane_prior7					90.08 115	68.51 128

plane_prior6					89.84 124	68.70 123						60.42 188

plane_prior4											91.00 118

plane_prior3						68.60 126			78.44 33	78.92 141

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 123

plane_prior						68.71 121	90.38 74		77.62 41						86.16 160

HQP5-MVS						66.98 158

HQP-NCC					89.33 139		89.17 101		76.41 75	77.23 180

ACMP_Plane					89.33 139		89.17 101		76.41 75	77.23 180

BP-MVS														77.47 116

HQP4-MVS										77.24 179			95.11 90			91.03 164

HQP2-MVS												60.17 191

NP-MVS					89.62 126	68.32 130					90.24 131

MDTV_nov1_ep13_2view						37.79 373	75.16 334		55.10 349	66.53 317		49.34 284		53.98 307		87.94 262

ACMMP++_ref															81.95 212

ACMMP++															81.25 218

Test By Simon												64.33 125

High-resolution multi-view benchmark