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 18		92.84 68		91.52 55	94.75 1	73.93 161	88.57 34	94.67 29	75.57 24	95.79 62	86.77 49	95.76 26

SF-MVS		88.46 14	88.74 14	87.64 37	92.78 69	71.95 51	92.40 28	94.74 2	75.71 105	89.16 28	95.10 18	75.65 23	96.19 50	87.07 47	96.01 17	94.79 23

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 119	92.25 9	95.03 20		97.39 11	88.15 38	95.96 19	94.75 29

TestfortrainingZip a		89.27 6	89.82 6	87.60 38	94.57 17	70.90 76	93.28 12	94.36 3	75.24 119	92.25 9	95.03 20	81.59 7	97.39 11	86.12 55	95.96 19	94.52 48

ME-MVS		88.98 11	89.39 8	87.75 29	94.54 19	71.43 60	91.61 48	94.25 5	76.30 92	90.62 20	95.03 20	78.06 15	97.07 19	88.15 38	95.96 19	94.75 29

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 30	71.25 63	95.06 1	94.23 6	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

test_0728_SECOND				87.71 34	95.34 1	71.43 60	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 63

lecture		88.09 16	88.59 15	86.58 61	93.26 55	69.77 95	93.70 6	94.16 8	77.13 65	89.76 25	95.52 14	72.26 51	96.27 47	86.87 48	94.65 51	93.70 94

test_one_0601					95.07 7	71.46 59		94.14 9	78.27 41	92.05 13	95.74 6	80.83 12

test0726					95.27 5	71.25 63	93.60 7	94.11 10	77.33 57	92.81 3	95.79 3	80.98 10

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 33	73.46 17	92.90 20	94.11 10	80.27 10	91.35 16	94.16 52	78.35 14	96.77 27	89.59 17	94.22 65	94.67 32
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 16	94.80 11	72.69 31	91.59 50	94.10 12	75.90 100	92.29 7	95.66 10	81.67 6	97.38 13	87.44 46	96.34 15	93.95 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PHI-MVS		86.43 48	86.17 57	87.24 45	90.88 98	70.96 72	92.27 36	94.07 13	72.45 196	85.22 76	91.90 115	69.47 91	96.42 43	83.28 84	95.94 22	94.35 56

SED-MVS		90.08 2	90.85 2	87.77 27	95.30 2	70.98 70	93.57 8	94.06 14	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 24	96.63 4	94.88 16

test_241102_TWO								94.06 14	77.24 60	92.78 4	95.72 8	81.26 9	97.44 7	89.07 24	96.58 6	94.26 62

test_241102_ONE					95.30 2	70.98 70		94.06 14	77.17 63	93.10 1	95.39 16	82.99 1	97.27 14

APDe-MVS		89.15 8	89.63 7	87.73 30	94.49 21	71.69 54	93.83 4	93.96 17	75.70 107	91.06 18	96.03 1	76.84 16	97.03 20	89.09 21	95.65 30	94.47 50
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS	79.81 2	87.08 39	86.88 44	87.69 35	91.16 90	72.32 45	90.31 78	93.94 18	77.12 66	82.82 127	94.23 49	72.13 54	97.09 18	84.83 65	95.37 34	93.65 99
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 41	95.06 1	93.84 19	74.49 145	91.30 17

MCST-MVS		87.37 33	87.25 34	87.73 30	94.53 20	72.46 40	89.82 86	93.82 20	73.07 188	84.86 83	92.89 93	76.22 19	96.33 44	84.89 64	95.13 39	94.40 53

ZNCC-MVS		87.94 21	87.85 23	88.20 12	94.39 27	73.33 19	93.03 18	93.81 21	76.81 74	85.24 75	94.32 43	71.76 58	96.93 22	85.53 59	95.79 25	94.32 59

SPE-MVS-test		86.29 53	86.48 49	85.71 79	91.02 94	67.21 179	92.36 33	93.78 22	78.97 33	83.51 114	91.20 145	70.65 76	95.15 90	81.96 100	94.89 45	94.77 25

3Dnovator+	77.84 4	85.48 71	84.47 90	88.51 7	91.08 92	73.49 16	93.18 15	93.78 22	80.79 8	76.66 246	93.37 81	60.40 228	96.75 29	77.20 154	93.73 69	95.29 6

SteuartSystems-ACMMP		88.72 13	88.86 13	88.32 9	92.14 77	72.96 25	93.73 5	93.67 24	80.19 12	88.10 41	94.80 26	73.76 36	97.11 17	87.51 44	95.82 24	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 34	73.73 9	92.40 28	93.63 25	74.77 139	92.29 7	95.97 2	74.28 32	97.24 15	88.58 32	96.91 1	94.87 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
EC-MVSNet		86.01 56	86.38 50	84.91 111	89.31 146	66.27 193	92.32 34	93.63 25	79.37 23	84.17 100	91.88 116	69.04 101	95.43 76	83.93 79	93.77 68	93.01 137

ACMMP_NAP		88.05 19	88.08 20	87.94 19	93.70 44	73.05 22	90.86 64	93.59 27	76.27 93	88.14 40	95.09 19	71.06 70	96.67 32	87.67 42	96.37 14	94.09 70

CSCG		86.41 50	86.19 56	87.07 49	92.91 66	72.48 37	90.81 65	93.56 28	73.95 159	83.16 120	91.07 150	75.94 20	95.19 88	79.94 122	94.38 61	93.55 107

MP-MVS-pluss		87.67 24	87.72 24	87.54 39	93.64 47	72.04 50	89.80 88	93.50 29	75.17 127	86.34 66	95.29 17	70.86 72	96.00 58	88.78 30	96.04 16	94.58 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FIs		82.07 140	82.42 125	81.04 266	88.80 170	58.34 345	88.26 159	93.49 30	76.93 71	78.47 203	91.04 151	69.92 86	92.34 236	69.87 245	84.97 219	92.44 163

DELS-MVS		85.41 74	85.30 78	85.77 78	88.49 181	67.93 151	85.52 260	93.44 31	78.70 34	83.63 113	89.03 211	74.57 26	95.71 65	80.26 119	94.04 66	93.66 95
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 30	87.26 33	87.89 24	94.12 39	72.97 24	92.39 30	93.43 32	76.89 72	84.68 84	93.99 63	70.67 75	96.82 25	84.18 77	95.01 40	93.90 81

FC-MVSNet-test		81.52 156	82.02 137	80.03 289	88.42 186	55.97 384	87.95 170	93.42 33	77.10 67	77.38 227	90.98 157	69.96 85	91.79 256	68.46 260	84.50 226	92.33 166

DeepPCF-MVS	80.84 1	88.10 15	88.56 16	86.73 58	92.24 76	69.03 109	89.57 97	93.39 34	77.53 53	89.79 24	94.12 54	78.98 13	96.58 38	85.66 56	95.72 27	94.58 41

HPM-MVS		87.11 37	86.98 40	87.50 42	93.88 42	72.16 47	92.19 37	93.33 35	76.07 97	83.81 108	93.95 66	69.77 88	96.01 57	85.15 60	94.66 50	94.32 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 43	86.95 41	85.90 77	90.76 102	67.57 163	92.83 21	93.30 36	79.67 19	84.57 91	92.27 105	71.47 63	95.02 99	84.24 75	93.46 72	95.13 9

HFP-MVS		87.58 25	87.47 30	87.94 19	94.58 16	73.54 15	93.04 16	93.24 37	76.78 76	84.91 80	94.44 38	70.78 73	96.61 35	84.53 70	94.89 45	93.66 95

ACMMPR		87.44 28	87.23 35	88.08 15	94.64 13	73.59 12	93.04 16	93.20 38	76.78 76	84.66 87	94.52 31	68.81 103	96.65 33	84.53 70	94.90 44	94.00 75

reproduce_model		87.28 34	87.39 32	86.95 53	93.10 61	71.24 67	91.60 49	93.19 39	74.69 140	88.80 32	95.61 11	70.29 79	96.44 42	86.20 54	93.08 74	93.16 126

reproduce-ours		87.47 26	87.61 26	87.07 49	93.27 53	71.60 55	91.56 53	93.19 39	74.98 130	88.96 29	95.54 12	71.20 68	96.54 39	86.28 52	93.49 70	93.06 132

our_new_method		87.47 26	87.61 26	87.07 49	93.27 53	71.60 55	91.56 53	93.19 39	74.98 130	88.96 29	95.54 12	71.20 68	96.54 39	86.28 52	93.49 70	93.06 132

SD-MVS		88.06 17	88.50 17	86.71 59	92.60 74	72.71 29	91.81 45	93.19 39	77.87 42	90.32 22	94.00 61	74.83 25	93.78 157	87.63 43	94.27 64	93.65 99
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 63	85.39 74	87.38 43	93.59 48	72.63 33	92.74 24	93.18 43	76.78 76	80.73 164	93.82 70	64.33 158	96.29 45	82.67 97	90.69 114	93.23 119
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 30	87.20 36	88.09 14	94.63 14	73.55 13	93.03 18	93.12 44	76.73 79	84.45 92	94.52 31	69.09 97	96.70 30	84.37 72	94.83 48	94.03 73

DPM-MVS		84.93 84	84.29 91	86.84 55	90.20 112	73.04 23	87.12 198	93.04 45	69.80 265	82.85 126	91.22 144	73.06 43	96.02 56	76.72 166	94.63 53	91.46 202

PGM-MVS		86.68 44	86.27 53	87.90 22	94.22 36	73.38 18	90.22 80	93.04 45	75.53 110	83.86 106	94.42 39	67.87 118	96.64 34	82.70 96	94.57 55	93.66 95

casdiffmvs_mvg		85.99 57	86.09 60	85.70 80	87.65 226	67.22 178	88.69 140	93.04 45	79.64 21	85.33 74	92.54 102	73.30 38	94.50 123	83.49 81	91.14 106	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS_fast	79.65 3	86.91 40	86.62 48	87.76 28	93.52 49	72.37 43	91.26 58	93.04 45	76.62 82	84.22 98	93.36 82	71.44 64	96.76 28	80.82 111	95.33 36	94.16 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		81.60 152	81.11 148	83.09 199	88.38 187	64.41 247	87.60 181	93.02 49	78.42 37	78.56 199	88.16 240	69.78 87	93.26 186	69.58 248	76.49 339	91.60 193

sasdasda		85.91 61	85.87 65	86.04 73	89.84 124	69.44 104	90.45 75	93.00 50	76.70 80	88.01 44	91.23 142	73.28 39	93.91 151	81.50 103	88.80 148	94.77 25

canonicalmvs		85.91 61	85.87 65	86.04 73	89.84 124	69.44 104	90.45 75	93.00 50	76.70 80	88.01 44	91.23 142	73.28 39	93.91 151	81.50 103	88.80 148	94.77 25

CNVR-MVS		88.93 12	89.13 12	88.33 8	94.77 12	73.82 8	90.51 69	93.00 50	80.90 7	88.06 42	94.06 57	76.43 18	96.84 24	88.48 35	95.99 18	94.34 57

MSC_two_6792asdad				89.16 1	94.34 30	75.53 2		92.99 53					97.53 2	89.67 15	96.44 9	94.41 51

No_MVS				89.16 1	94.34 30	75.53 2		92.99 53					97.53 2	89.67 15	96.44 9	94.41 51

XVS		87.18 36	86.91 43	88.00 17	94.42 23	73.33 19	92.78 22	92.99 53	79.14 26	83.67 111	94.17 51	67.45 121	96.60 36	83.06 85	94.50 56	94.07 71

X-MVStestdata		80.37 191	77.83 231	88.00 17	94.42 23	73.33 19	92.78 22	92.99 53	79.14 26	83.67 111	12.47 474	67.45 121	96.60 36	83.06 85	94.50 56	94.07 71

APD-MVS_3200maxsize		85.97 59	85.88 63	86.22 66	92.69 71	69.53 98	91.93 41	92.99 53	73.54 172	85.94 67	94.51 34	65.80 146	95.61 66	83.04 87	92.51 82	93.53 109

test_prior				86.33 63	92.61 73	69.59 97		92.97 58					95.48 73			93.91 79

IU-MVS					95.30 2	71.25 63		92.95 59	66.81 314	92.39 6				88.94 27	96.63 4	94.85 21

balanced_conf03		86.78 41	86.99 39	86.15 69	91.24 89	67.61 161	90.51 69	92.90 60	77.26 59	87.44 55	91.63 128	71.27 67	96.06 53	85.62 58	95.01 40	94.78 24

baseline		84.93 84	84.98 81	84.80 116	87.30 241	65.39 216	87.30 194	92.88 61	77.62 47	84.04 103	92.26 106	71.81 57	93.96 143	81.31 105	90.30 120	95.03 11

MSLP-MVS++		85.43 73	85.76 67	84.45 127	91.93 80	70.24 84	90.71 66	92.86 62	77.46 55	84.22 98	92.81 97	67.16 125	92.94 208	80.36 117	94.35 62	90.16 250

HPM-MVS++		89.02 10	89.15 11	88.63 5	95.01 9	76.03 1	92.38 31	92.85 63	80.26 11	87.78 47	94.27 46	75.89 21	96.81 26	87.45 45	96.44 9	93.05 134

casdiffmvs		85.11 81	85.14 80	85.01 104	87.20 243	65.77 207	87.75 178	92.83 64	77.84 43	84.36 97	92.38 104	72.15 53	93.93 149	81.27 107	90.48 117	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS		87.44 28	87.52 29	87.19 46	94.24 35	72.39 41	91.86 44	92.83 64	73.01 190	88.58 33	94.52 31	73.36 37	96.49 41	84.26 73	95.01 40	92.70 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 17	88.01 21	88.24 11	94.41 25	73.62 11	91.22 61	92.83 64	81.50 5	85.79 70	93.47 78	73.02 44	97.00 21	84.90 62	94.94 43	94.10 69

CP-MVS		87.11 37	86.92 42	87.68 36	94.20 37	73.86 7	93.98 3	92.82 67	76.62 82	83.68 110	94.46 35	67.93 116	95.95 61	84.20 76	94.39 60	93.23 119

DVP-MVS		89.60 3	90.35 3	87.33 44	95.27 5	71.25 63	93.49 10	92.73 68	77.33 57	92.12 11	95.78 4	80.98 10	97.40 9	89.08 22	96.41 12	93.33 116
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
GDP-MVS		83.52 110	82.64 122	86.16 68	88.14 196	68.45 131	89.13 119	92.69 69	72.82 194	83.71 109	91.86 118	55.69 265	95.35 85	80.03 120	89.74 132	94.69 31

EIA-MVS		83.31 119	82.80 119	84.82 114	89.59 129	65.59 211	88.21 160	92.68 70	74.66 142	78.96 189	86.42 294	69.06 99	95.26 86	75.54 180	90.09 124	93.62 102

ZD-MVS					94.38 28	72.22 46		92.67 71	70.98 231	87.75 49	94.07 56	74.01 35	96.70 30	84.66 68	94.84 47

nrg030		83.88 97	83.53 105	84.96 106	86.77 261	69.28 108	90.46 74	92.67 71	74.79 138	82.95 123	91.33 141	72.70 49	93.09 201	80.79 113	79.28 305	92.50 158

WR-MVS_H		78.51 238	78.49 212	78.56 319	88.02 203	56.38 378	88.43 149	92.67 71	77.14 64	73.89 311	87.55 258	66.25 137	89.24 326	58.92 343	73.55 383	90.06 260

MVSMamba_PlusPlus		85.99 57	85.96 62	86.05 72	91.09 91	67.64 160	89.63 95	92.65 74	72.89 193	84.64 88	91.71 123	71.85 56	96.03 54	84.77 67	94.45 59	94.49 49

MP-MVS		87.71 22	87.64 25	87.93 21	94.36 29	73.88 6	92.71 26	92.65 74	77.57 49	83.84 107	94.40 40	72.24 52	96.28 46	85.65 57	95.30 38	93.62 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		84.90 86	84.67 86	85.59 85	89.39 141	68.66 126	88.74 138	92.64 76	79.97 16	84.10 101	85.71 307	69.32 94	95.38 81	80.82 111	91.37 103	92.72 147

MGCFI-Net		85.06 83	85.51 72	83.70 175	89.42 138	63.01 283	89.43 102	92.62 77	76.43 84	87.53 52	91.34 140	72.82 48	93.42 180	81.28 106	88.74 151	94.66 35

CANet		86.45 47	86.10 59	87.51 41	90.09 114	70.94 74	89.70 92	92.59 78	81.78 4	81.32 150	91.43 138	70.34 77	97.23 16	84.26 73	93.36 73	94.37 55

SR-MVS		86.73 42	86.67 46	86.91 54	94.11 40	72.11 49	92.37 32	92.56 79	74.50 144	86.84 63	94.65 30	67.31 123	95.77 63	84.80 66	92.85 77	92.84 146

alignmvs		85.48 71	85.32 77	85.96 76	89.51 133	69.47 101	89.74 90	92.47 80	76.17 95	87.73 51	91.46 137	70.32 78	93.78 157	81.51 102	88.95 145	94.63 38

原ACMM1				84.35 133	93.01 65	68.79 116		92.44 81	63.96 359	81.09 155	91.57 132	66.06 142	95.45 74	67.19 271	94.82 49	88.81 306

HQP_MVS		83.64 106	83.14 111	85.14 97	90.08 115	68.71 122	91.25 59	92.44 81	79.12 28	78.92 191	91.00 155	60.42 226	95.38 81	78.71 136	86.32 194	91.33 203

plane_prior5								92.44 81					95.38 81	78.71 136	86.32 194	91.33 203

CDPH-MVS		85.76 66	85.29 79	87.17 47	93.49 50	71.08 68	88.58 145	92.42 84	68.32 301	84.61 89	93.48 76	72.32 50	96.15 52	79.00 132	95.43 33	94.28 61

UniMVSNet_NR-MVSNet		81.88 144	81.54 143	82.92 210	88.46 183	63.46 273	87.13 197	92.37 85	80.19 12	78.38 204	89.14 207	71.66 62	93.05 204	70.05 241	76.46 340	92.25 170

TSAR-MVS + MP.		88.02 20	88.11 19	87.72 32	93.68 46	72.13 48	91.41 57	92.35 86	74.62 143	88.90 31	93.85 69	75.75 22	96.00 58	87.80 41	94.63 53	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CLD-MVS		82.31 136	81.65 142	84.29 138	88.47 182	67.73 157	85.81 250	92.35 86	75.78 103	78.33 206	86.58 289	64.01 161	94.35 127	76.05 172	87.48 174	90.79 222
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SR-MVS-dyc-post		85.77 65	85.61 70	86.23 65	93.06 63	70.63 81	91.88 42	92.27 88	73.53 173	85.69 71	94.45 36	65.00 154	95.56 67	82.75 92	91.87 93	92.50 158

RE-MVS-def			85.48 73		93.06 63	70.63 81	91.88 42	92.27 88	73.53 173	85.69 71	94.45 36	63.87 162		82.75 92	91.87 93	92.50 158

RPMNet		73.51 325	70.49 348	82.58 229	81.32 392	65.19 220	75.92 409	92.27 88	57.60 419	72.73 326	76.45 434	52.30 297	95.43 76	48.14 419	77.71 322	87.11 351

E2		84.00 94	83.87 95	84.39 129	87.70 223	64.95 228	86.40 230	92.23 91	75.85 101	83.21 116	91.78 120	70.09 82	93.55 169	79.52 126	88.05 163	94.66 35

E3		84.00 94	83.87 95	84.39 129	87.70 223	64.95 228	86.40 230	92.23 91	75.85 101	83.21 116	91.78 120	70.09 82	93.55 169	79.52 126	88.05 163	94.66 35

test11								92.23 91

viewcassd2359sk11		83.89 96	83.74 100	84.34 134	87.76 219	64.91 234	86.30 234	92.22 94	75.47 112	83.04 122	91.52 133	70.15 81	93.53 172	79.26 128	87.96 165	94.57 43

mPP-MVS		86.67 45	86.32 51	87.72 32	94.41 25	73.55 13	92.74 24	92.22 94	76.87 73	82.81 128	94.25 48	66.44 134	96.24 48	82.88 90	94.28 63	93.38 112

fmvsm_s_conf0.5_n_8		86.56 46	87.17 37	84.73 119	87.76 219	65.62 210	89.20 112	92.21 96	79.94 17	89.74 26	94.86 25	68.63 106	94.20 135	90.83 5	91.39 102	94.38 54

DP-MVS Recon		83.11 124	82.09 135	86.15 69	94.44 22	70.92 75	88.79 133	92.20 97	70.53 243	79.17 187	91.03 153	64.12 160	96.03 54	68.39 261	90.14 123	91.50 198

NormalMVS		86.29 53	85.88 63	87.52 40	93.26 55	72.47 38	91.65 46	92.19 98	79.31 24	84.39 94	92.18 107	64.64 156	95.53 70	80.70 114	94.65 51	94.56 45

Elysia		81.53 154	80.16 169	85.62 83	85.51 292	68.25 138	88.84 131	92.19 98	71.31 219	80.50 167	89.83 184	46.89 359	94.82 107	76.85 159	89.57 134	93.80 89

StellarMVS		81.53 154	80.16 169	85.62 83	85.51 292	68.25 138	88.84 131	92.19 98	71.31 219	80.50 167	89.83 184	46.89 359	94.82 107	76.85 159	89.57 134	93.80 89

HQP3-MVS								92.19 98							85.99 203

HQP-MVS		82.61 132	82.02 137	84.37 131	89.33 143	66.98 182	89.17 114	92.19 98	76.41 85	77.23 232	90.23 177	60.17 229	95.11 93	77.47 151	85.99 203	91.03 213

3Dnovator	76.31 5	83.38 115	82.31 129	86.59 60	87.94 207	72.94 28	90.64 67	92.14 103	77.21 62	75.47 272	92.83 95	58.56 240	94.72 114	73.24 205	92.71 80	92.13 180

MTGPA								92.02 104

MTAPA		87.23 35	87.00 38	87.90 22	94.18 38	74.25 5	86.58 222	92.02 104	79.45 22	85.88 68	94.80 26	68.07 114	96.21 49	86.69 50	95.34 35	93.23 119

MVS_Test		83.15 121	83.06 113	83.41 186	86.86 256	63.21 279	86.11 240	92.00 106	74.31 150	82.87 125	89.44 204	70.03 84	93.21 190	77.39 153	88.50 156	93.81 87

PVSNet_BlendedMVS		80.60 182	80.02 173	82.36 233	88.85 162	65.40 214	86.16 239	92.00 106	69.34 275	78.11 211	86.09 302	66.02 143	94.27 130	71.52 223	82.06 270	87.39 340

PVSNet_Blended		80.98 165	80.34 164	82.90 211	88.85 162	65.40 214	84.43 289	92.00 106	67.62 307	78.11 211	85.05 328	66.02 143	94.27 130	71.52 223	89.50 136	89.01 296

QAPM		80.88 167	79.50 190	85.03 103	88.01 205	68.97 113	91.59 50	92.00 106	66.63 323	75.15 290	92.16 109	57.70 247	95.45 74	63.52 297	88.76 150	90.66 229

LPG-MVS_test		82.08 139	81.27 145	84.50 124	89.23 151	68.76 118	90.22 80	91.94 110	75.37 116	76.64 247	91.51 134	54.29 278	94.91 101	78.44 138	83.78 239	89.83 271

LGP-MVS_train				84.50 124	89.23 151	68.76 118		91.94 110	75.37 116	76.64 247	91.51 134	54.29 278	94.91 101	78.44 138	83.78 239	89.83 271

TEST9					93.26 55	72.96 25	88.75 136	91.89 112	68.44 299	85.00 78	93.10 86	74.36 31	95.41 79

train_agg		86.43 48	86.20 54	87.13 48	93.26 55	72.96 25	88.75 136	91.89 112	68.69 294	85.00 78	93.10 86	74.43 29	95.41 79	84.97 61	95.71 28	93.02 136

dcpmvs_2		85.63 68	86.15 58	84.06 157	91.71 83	64.94 231	86.47 225	91.87 114	73.63 168	86.60 65	93.02 91	76.57 17	91.87 255	83.36 82	92.15 88	95.35 3

DU-MVS		81.12 164	80.52 160	82.90 211	87.80 214	63.46 273	87.02 202	91.87 114	79.01 31	78.38 204	89.07 209	65.02 152	93.05 204	70.05 241	76.46 340	92.20 173

test_8					93.13 59	72.57 35	88.68 141	91.84 116	68.69 294	84.87 82	93.10 86	74.43 29	95.16 89

viewmacassd2359aftdt		83.76 101	83.66 103	84.07 154	86.59 267	64.56 239	86.88 209	91.82 117	75.72 104	83.34 115	92.15 111	68.24 113	92.88 211	79.05 129	89.15 143	94.77 25

PAPM_NR		83.02 125	82.41 126	84.82 114	92.47 75	66.37 191	87.93 172	91.80 118	73.82 163	77.32 229	90.66 163	67.90 117	94.90 103	70.37 236	89.48 137	93.19 125

test12				86.80 57	92.63 72	70.70 80		91.79 119		82.71 129		71.67 61	96.16 51		94.50 56	93.54 108

agg_prior					92.85 67	71.94 52		91.78 120		84.41 93			94.93 100

PAPR		81.66 151	80.89 153	83.99 166	90.27 110	64.00 253	86.76 216	91.77 121	68.84 292	77.13 239	89.50 197	67.63 119	94.88 105	67.55 266	88.52 155	93.09 130

viewmanbaseed2359cas		83.66 104	83.55 104	84.00 165	86.81 259	64.53 240	86.65 219	91.75 122	74.89 134	83.15 121	91.68 124	68.74 105	92.83 215	79.02 130	89.24 140	94.63 38

PVSNet_Blended_VisFu		82.62 131	81.83 141	84.96 106	90.80 100	69.76 96	88.74 138	91.70 123	69.39 273	78.96 189	88.46 231	65.47 148	94.87 106	74.42 191	88.57 153	90.24 248

viewdifsd2359ckpt09		83.34 116	82.55 124	85.70 80	87.64 227	67.72 158	88.43 149	91.68 124	71.91 208	81.65 146	90.68 162	67.10 126	94.75 112	76.17 169	87.70 170	94.62 40

KinetiMVS		83.31 119	82.61 123	85.39 90	87.08 252	67.56 164	88.06 166	91.65 125	77.80 44	82.21 135	91.79 119	57.27 253	94.07 141	77.77 147	89.89 130	94.56 45

fmvsm_s_conf0.5_n_6		85.55 70	86.20 54	83.60 177	87.32 240	65.13 222	88.86 128	91.63 126	75.41 114	88.23 39	93.45 79	68.56 107	92.47 228	89.52 18	92.78 78	93.20 124

viewdifsd2359ckpt13		82.91 127	82.29 130	84.77 117	86.96 255	66.90 186	87.47 185	91.62 127	72.19 201	81.68 145	90.71 161	66.92 127	93.28 183	75.90 174	87.15 180	94.12 68

HPM-MVS_fast		85.35 77	84.95 83	86.57 62	93.69 45	70.58 83	92.15 39	91.62 127	73.89 162	82.67 130	94.09 55	62.60 180	95.54 69	80.93 109	92.93 76	93.57 105

ACMM	73.20 8	80.78 177	79.84 179	83.58 179	89.31 146	68.37 133	89.99 83	91.60 129	70.28 253	77.25 230	89.66 192	53.37 289	93.53 172	74.24 194	82.85 260	88.85 304
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		80.60 182	80.55 159	80.76 273	88.07 201	60.80 319	86.86 210	91.58 130	75.67 108	80.24 171	89.45 203	63.34 165	90.25 307	70.51 235	79.22 306	91.23 206

OPM-MVS		83.50 111	82.95 116	85.14 97	88.79 171	70.95 73	89.13 119	91.52 131	77.55 52	80.96 158	91.75 122	60.71 218	94.50 123	79.67 125	86.51 192	89.97 266
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous20231211		78.97 226	77.69 239	82.81 216	90.54 105	64.29 249	90.11 82	91.51 132	65.01 343	76.16 263	88.13 245	50.56 324	93.03 207	69.68 247	77.56 326	91.11 209

PS-MVSNAJss		82.07 140	81.31 144	84.34 134	86.51 269	67.27 175	89.27 110	91.51 132	71.75 209	79.37 184	90.22 178	63.15 172	94.27 130	77.69 149	82.36 267	91.49 199

TAPA-MVS	73.13 9	79.15 220	77.94 226	82.79 220	89.59 129	62.99 287	88.16 163	91.51 132	65.77 332	77.14 238	91.09 149	60.91 216	93.21 190	50.26 405	87.05 182	92.17 178
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	74.13 6	81.51 158	80.57 158	84.36 132	89.42 138	68.69 125	89.97 84	91.50 135	74.46 146	75.04 294	90.41 170	53.82 284	94.54 120	77.56 150	82.91 259	89.86 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 189	78.84 207	85.01 104	87.71 221	68.99 112	83.65 307	91.46 136	63.00 367	77.77 221	90.28 174	66.10 140	95.09 97	61.40 321	88.22 160	90.94 218
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		80.84 168	80.31 165	82.42 231	87.85 211	62.33 298	87.74 179	91.33 137	80.55 9	77.99 215	89.86 182	65.23 150	92.62 218	67.05 273	75.24 367	92.30 168

RRT-MVS		82.60 134	82.10 134	84.10 148	87.98 206	62.94 288	87.45 188	91.27 138	77.42 56	79.85 175	90.28 174	56.62 261	94.70 116	79.87 123	88.15 161	94.67 32

PS-CasMVS		78.01 252	78.09 223	77.77 337	87.71 221	54.39 403	88.02 167	91.22 139	77.50 54	73.26 319	88.64 225	60.73 217	88.41 343	61.88 316	73.88 380	90.53 235

v7n		78.97 226	77.58 242	83.14 197	83.45 344	65.51 212	88.32 157	91.21 140	73.69 167	72.41 331	86.32 297	57.93 244	93.81 156	69.18 251	75.65 353	90.11 254

PEN-MVS		77.73 258	77.69 239	77.84 335	87.07 254	53.91 406	87.91 173	91.18 141	77.56 51	73.14 321	88.82 220	61.23 210	89.17 328	59.95 332	72.37 391	90.43 239

MM		89.16 7	89.23 9	88.97 4	90.79 101	73.65 10	92.66 27	91.17 142	86.57 1	87.39 56	94.97 24	71.70 60	97.68 1	92.19 1	95.63 31	95.57 1

save fliter					93.80 43	72.35 44	90.47 73	91.17 142	74.31 150

CP-MVSNet		78.22 243	78.34 217	77.84 335	87.83 213	54.54 401	87.94 171	91.17 142	77.65 46	73.48 317	88.49 230	62.24 189	88.43 342	62.19 312	74.07 376	90.55 234

114514_t		80.68 178	79.51 189	84.20 145	94.09 41	67.27 175	89.64 94	91.11 145	58.75 410	74.08 309	90.72 160	58.10 243	95.04 98	69.70 246	89.42 138	90.30 246

NR-MVSNet		80.23 195	79.38 192	82.78 221	87.80 214	63.34 276	86.31 233	91.09 146	79.01 31	72.17 335	89.07 209	67.20 124	92.81 216	66.08 280	75.65 353	92.20 173

fmvsm_s_conf0.5_n_10		86.38 51	86.76 45	85.24 94	87.33 238	67.30 173	89.50 99	90.98 147	76.25 94	90.56 21	94.75 28	68.38 109	94.24 134	90.80 7	92.32 87	94.19 64

OpenMVS	72.83 10	79.77 202	78.33 218	84.09 152	85.17 301	69.91 92	90.57 68	90.97 148	66.70 317	72.17 335	91.91 114	54.70 275	93.96 143	61.81 318	90.95 110	88.41 320

MAR-MVS		81.84 145	80.70 155	85.27 93	91.32 88	71.53 58	89.82 86	90.92 149	69.77 267	78.50 200	86.21 298	62.36 186	94.52 122	65.36 285	92.05 91	89.77 274
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
tt0805		78.73 231	77.83 231	81.43 252	85.17 301	60.30 327	89.41 105	90.90 150	71.21 223	77.17 237	88.73 221	46.38 364	93.21 190	72.57 212	78.96 307	90.79 222

Anonymous20240529		80.19 197	78.89 206	84.10 148	90.60 103	64.75 237	88.95 125	90.90 150	65.97 331	80.59 166	91.17 147	49.97 332	93.73 163	69.16 252	82.70 264	93.81 87

OMC-MVS		82.69 130	81.97 139	84.85 113	88.75 173	67.42 167	87.98 168	90.87 152	74.92 133	79.72 177	91.65 126	62.19 190	93.96 143	75.26 184	86.42 193	93.16 126

UA-Net		85.08 82	84.96 82	85.45 88	92.07 78	68.07 144	89.78 89	90.86 153	82.48 2	84.60 90	93.20 85	69.35 93	95.22 87	71.39 226	90.88 112	93.07 131

viewdifsd2359ckpt07		82.83 129	82.78 121	82.99 206	86.51 269	62.58 291	85.09 269	90.83 154	75.22 121	82.28 132	91.63 128	69.43 92	92.03 245	77.71 148	86.32 194	94.34 57

test_fmvsm_n_1920		85.29 78	85.34 75	85.13 100	86.12 278	69.93 91	88.65 142	90.78 155	69.97 261	88.27 37	93.98 64	71.39 65	91.54 271	88.49 34	90.45 118	93.91 79

EPP-MVSNet		83.40 114	83.02 114	84.57 122	90.13 113	64.47 245	92.32 34	90.73 156	74.45 147	79.35 185	91.10 148	69.05 100	95.12 91	72.78 209	87.22 178	94.13 67

DTE-MVSNet		76.99 274	76.80 259	77.54 343	86.24 273	53.06 415	87.52 183	90.66 157	77.08 68	72.50 329	88.67 224	60.48 225	89.52 320	57.33 360	70.74 403	90.05 261

v10		79.74 203	78.67 208	82.97 209	84.06 328	64.95 228	87.88 175	90.62 158	73.11 187	75.11 291	86.56 290	61.46 204	94.05 142	73.68 197	75.55 355	89.90 268

test_fmvsmconf_n		85.92 60	86.04 61	85.57 86	85.03 308	69.51 99	89.62 96	90.58 159	73.42 176	87.75 49	94.02 59	72.85 47	93.24 187	90.37 8	90.75 113	93.96 76

v1192		79.59 206	78.43 215	83.07 202	83.55 342	64.52 241	86.93 207	90.58 159	70.83 234	77.78 220	85.90 303	59.15 235	93.94 146	73.96 196	77.19 329	90.76 224

v1144		80.03 199	79.03 202	83.01 205	83.78 335	64.51 242	87.11 199	90.57 161	71.96 207	78.08 213	86.20 299	61.41 205	93.94 146	74.93 186	77.23 327	90.60 232

XVG-OURS-SEG-HR		80.81 170	79.76 181	83.96 168	85.60 290	68.78 117	83.54 313	90.50 162	70.66 241	76.71 245	91.66 125	60.69 219	91.26 283	76.94 158	81.58 275	91.83 185

MVS		78.19 246	76.99 255	81.78 244	85.66 287	66.99 181	84.66 279	90.47 163	55.08 431	72.02 337	85.27 320	63.83 163	94.11 140	66.10 279	89.80 131	84.24 400

fmvsm_l_conf0.5_n_3		86.02 55	86.32 51	85.14 97	87.20 243	68.54 129	89.57 97	90.44 164	75.31 118	87.49 53	94.39 41	72.86 46	92.72 217	89.04 26	90.56 116	94.16 65

XVG-OURS		80.41 187	79.23 198	83.97 167	85.64 288	69.02 111	83.03 326	90.39 165	71.09 226	77.63 223	91.49 136	54.62 277	91.35 280	75.71 176	83.47 251	91.54 196

MVSFormer		82.85 128	82.05 136	85.24 94	87.35 233	70.21 85	90.50 71	90.38 166	68.55 296	81.32 150	89.47 199	61.68 198	93.46 177	78.98 133	90.26 121	92.05 182

test_djsdf		80.30 194	79.32 195	83.27 190	83.98 330	65.37 217	90.50 71	90.38 166	68.55 296	76.19 259	88.70 222	56.44 262	93.46 177	78.98 133	80.14 295	90.97 216

CPTT-MVS		83.73 102	83.33 110	84.92 110	93.28 52	70.86 77	92.09 40	90.38 166	68.75 293	79.57 179	92.83 95	60.60 224	93.04 206	80.92 110	91.56 100	90.86 220

v144192		79.47 209	78.37 216	82.78 221	83.35 345	63.96 254	86.96 204	90.36 169	69.99 260	77.50 224	85.67 310	60.66 221	93.77 159	74.27 193	76.58 337	90.62 230

v1921920		79.22 218	78.03 224	82.80 217	83.30 347	63.94 256	86.80 212	90.33 170	69.91 263	77.48 225	85.53 314	58.44 241	93.75 161	73.60 198	76.85 334	90.71 228

MVS_111021_HR		85.14 80	84.75 85	86.32 64	91.65 84	72.70 30	85.98 242	90.33 170	76.11 96	82.08 137	91.61 131	71.36 66	94.17 138	81.02 108	92.58 81	92.08 181

v1240		78.99 225	77.78 234	82.64 226	83.21 350	63.54 270	86.62 221	90.30 172	69.74 270	77.33 228	85.68 309	57.04 256	93.76 160	73.13 206	76.92 331	90.62 230

test_fmvsmconf0.1_n		85.61 69	85.65 69	85.50 87	82.99 360	69.39 106	89.65 93	90.29 173	73.31 180	87.77 48	94.15 53	71.72 59	93.23 188	90.31 9	90.67 115	93.89 82

v8		79.97 201	79.02 203	82.80 217	84.09 327	64.50 244	87.96 169	90.29 173	74.13 157	75.24 287	86.81 276	62.88 179	93.89 154	74.39 192	75.40 362	90.00 262

fmvsm_s_conf0.5_n_3		86.36 52	87.46 31	83.09 199	87.08 252	65.21 219	89.09 121	90.21 175	79.67 19	89.98 23	95.02 23	73.17 41	91.71 261	91.30 3	91.60 97	92.34 165

mvs_tets		79.13 221	77.77 235	83.22 194	84.70 314	66.37 191	89.17 114	90.19 176	69.38 274	75.40 277	89.46 201	44.17 387	93.15 197	76.78 165	80.70 287	90.14 251

jajsoiax		79.29 217	77.96 225	83.27 190	84.68 315	66.57 189	89.25 111	90.16 177	69.20 282	75.46 274	89.49 198	45.75 375	93.13 199	76.84 161	80.80 285	90.11 254

Vis-MVSNet		83.46 112	82.80 119	85.43 89	90.25 111	68.74 120	90.30 79	90.13 178	76.33 91	80.87 161	92.89 93	61.00 215	94.20 135	72.45 218	90.97 109	93.35 115
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJ		81.69 149	81.02 150	83.70 175	89.51 133	68.21 141	84.28 293	90.09 179	70.79 235	81.26 154	85.62 312	63.15 172	94.29 128	75.62 178	88.87 147	88.59 315

xiu_mvs_v2_base		81.69 149	81.05 149	83.60 177	89.15 154	68.03 146	84.46 287	90.02 180	70.67 238	81.30 153	86.53 292	63.17 171	94.19 137	75.60 179	88.54 154	88.57 316

FA-MVS(test-final)		80.96 166	79.91 176	84.10 148	88.30 190	65.01 226	84.55 284	90.01 181	73.25 183	79.61 178	87.57 256	58.35 242	94.72 114	71.29 227	86.25 197	92.56 154

v2v482		80.23 195	79.29 196	83.05 203	83.62 340	64.14 251	87.04 200	89.97 182	73.61 169	78.18 210	87.22 267	61.10 213	93.82 155	76.11 170	76.78 336	91.18 207

test_yl		81.17 161	80.47 162	83.24 192	89.13 155	63.62 262	86.21 237	89.95 183	72.43 199	81.78 143	89.61 194	57.50 250	93.58 165	70.75 231	86.90 184	92.52 156

DCV-MVSNet		81.17 161	80.47 162	83.24 192	89.13 155	63.62 262	86.21 237	89.95 183	72.43 199	81.78 143	89.61 194	57.50 250	93.58 165	70.75 231	86.90 184	92.52 156

fmvsm_s_conf0.5_n_7		83.34 116	84.03 94	81.28 258	85.73 286	65.13 222	85.40 261	89.90 185	74.96 132	82.13 136	93.89 67	66.65 129	87.92 348	86.56 51	91.05 107	90.80 221

V42		79.38 215	78.24 220	82.83 214	81.10 394	65.50 213	85.55 256	89.82 186	71.57 215	78.21 208	86.12 301	60.66 221	93.18 196	75.64 177	75.46 359	89.81 273

fmvsm_s_conf0.5_n_4		85.39 75	85.75 68	84.30 137	86.70 263	65.83 203	88.77 134	89.78 187	75.46 113	88.35 35	93.73 72	69.19 96	93.06 203	91.30 3	88.44 157	94.02 74

VNet		82.21 137	82.41 126	81.62 247	90.82 99	60.93 316	84.47 285	89.78 187	76.36 90	84.07 102	91.88 116	64.71 155	90.26 306	70.68 233	88.89 146	93.66 95

diffmvs_AUTHOR		82.38 135	82.27 131	82.73 225	83.26 348	63.80 259	83.89 301	89.76 189	73.35 179	82.37 131	90.84 158	66.25 137	90.79 298	82.77 91	87.93 166	93.59 104

diffmvs		82.10 138	81.88 140	82.76 223	83.00 358	63.78 261	83.68 306	89.76 189	72.94 191	82.02 138	89.85 183	65.96 145	90.79 298	82.38 98	87.30 177	93.71 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XVG-ACMP-BASELINE		76.11 292	74.27 304	81.62 247	83.20 351	64.67 238	83.60 310	89.75 191	69.75 268	71.85 338	87.09 272	32.78 438	92.11 243	69.99 243	80.43 291	88.09 326

EI-MVSNet-Vis-set		84.19 90	83.81 98	85.31 92	88.18 193	67.85 153	87.66 180	89.73 192	80.05 15	82.95 123	89.59 196	70.74 74	94.82 107	80.66 116	84.72 223	93.28 118

EI-MVSNet-UG-set		83.81 98	83.38 108	85.09 102	87.87 210	67.53 165	87.44 189	89.66 193	79.74 18	82.23 134	89.41 205	70.24 80	94.74 113	79.95 121	83.92 238	92.99 139

test_fmvsmconf0.01_n		84.73 87	84.52 89	85.34 91	80.25 402	69.03 109	89.47 100	89.65 194	73.24 184	86.98 61	94.27 46	66.62 130	93.23 188	90.26 10	89.95 128	93.78 91

BP-MVS1		84.32 89	83.71 101	86.17 67	87.84 212	67.85 153	89.38 107	89.64 195	77.73 45	83.98 104	92.12 112	56.89 258	95.43 76	84.03 78	91.75 96	95.24 7

VortexMVS		78.57 237	77.89 229	80.59 276	85.89 282	62.76 290	85.61 251	89.62 196	72.06 205	74.99 295	85.38 318	55.94 264	90.77 301	74.99 185	76.58 337	88.23 322

PAPM		77.68 262	76.40 271	81.51 250	87.29 242	61.85 305	83.78 303	89.59 197	64.74 345	71.23 345	88.70 222	62.59 181	93.66 164	52.66 389	87.03 183	89.01 296

MGCNet		87.69 23	87.55 28	88.12 13	89.45 137	71.76 53	91.47 56	89.54 198	82.14 3	86.65 64	94.28 45	68.28 112	97.46 6	90.81 6	95.31 37	95.15 8

anonymousdsp		78.60 235	77.15 251	82.98 208	80.51 400	67.08 180	87.24 196	89.53 199	65.66 334	75.16 289	87.19 269	52.52 293	92.25 239	77.17 155	79.34 304	89.61 278

MG-MVS		83.41 113	83.45 106	83.28 189	92.74 70	62.28 300	88.17 162	89.50 200	75.22 121	81.49 148	92.74 101	66.75 128	95.11 93	72.85 208	91.58 99	92.45 162

PLC	70.83 11	78.05 250	76.37 272	83.08 201	91.88 82	67.80 155	88.19 161	89.46 201	64.33 351	69.87 362	88.38 233	53.66 285	93.58 165	58.86 344	82.73 262	87.86 330
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n_9		85.84 64	86.63 47	83.46 182	87.12 251	66.01 197	88.56 146	89.43 202	75.59 109	89.32 27	94.32 43	72.89 45	91.21 286	90.11 11	92.33 86	93.16 126

SDMVSNet		80.38 189	80.18 168	80.99 267	89.03 160	64.94 231	80.45 358	89.40 203	75.19 125	76.61 249	89.98 180	60.61 223	87.69 352	76.83 162	83.55 248	90.33 244

Fast-Effi-MVS+		80.81 170	79.92 175	83.47 181	88.85 162	64.51 242	85.53 258	89.39 204	70.79 235	78.49 201	85.06 327	67.54 120	93.58 165	67.03 274	86.58 190	92.32 167

IterMVS-LS		80.06 198	79.38 192	82.11 238	85.89 282	63.20 280	86.79 213	89.34 205	74.19 154	75.45 275	86.72 279	66.62 130	92.39 232	72.58 211	76.86 333	90.75 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
icg_test_0407_2		78.92 228	78.93 205	78.90 312	87.13 246	63.59 266	76.58 405	89.33 206	70.51 244	77.82 217	89.03 211	61.84 194	81.38 409	72.56 214	85.56 212	91.74 188

IMVS_0407		80.61 180	79.90 177	82.75 224	87.13 246	63.59 266	85.33 262	89.33 206	70.51 244	77.82 217	89.03 211	61.84 194	92.91 209	72.56 214	85.56 212	91.74 188

IMVS_0404		77.16 272	76.42 270	79.37 303	87.13 246	63.59 266	77.12 403	89.33 206	70.51 244	66.22 404	89.03 211	50.36 327	82.78 399	72.56 214	85.56 212	91.74 188

IMVS_0403		80.80 173	80.12 172	82.87 213	87.13 246	63.59 266	85.19 263	89.33 206	70.51 244	78.49 201	89.03 211	63.26 168	93.27 185	72.56 214	85.56 212	91.74 188

API-MVS		81.99 142	81.23 146	84.26 143	90.94 96	70.18 90	91.10 62	89.32 210	71.51 216	78.66 196	88.28 236	65.26 149	95.10 96	64.74 291	91.23 105	87.51 338

fmvsm_s_conf0.5_n_5		85.22 79	85.55 71	84.25 144	86.26 272	67.40 169	89.18 113	89.31 211	72.50 195	88.31 36	93.86 68	69.66 89	91.96 249	89.81 13	91.05 107	93.38 112

GBi-Net		78.40 239	77.40 246	81.40 254	87.60 228	63.01 283	88.39 152	89.28 212	71.63 211	75.34 280	87.28 263	54.80 271	91.11 287	62.72 304	79.57 299	90.09 256

test1		78.40 239	77.40 246	81.40 254	87.60 228	63.01 283	88.39 152	89.28 212	71.63 211	75.34 280	87.28 263	54.80 271	91.11 287	62.72 304	79.57 299	90.09 256

FMVSNet1		77.44 266	76.12 274	81.40 254	86.81 259	63.01 283	88.39 152	89.28 212	70.49 248	74.39 306	87.28 263	49.06 346	91.11 287	60.91 325	78.52 310	90.09 256

cdsmvs_eth3d_5k		19.96 441	26.61 443	0.00 462	0.00 485	0.00 487	0.00 474	89.26 215	0.00 480	0.00 481	88.61 226	61.62 200	0.00 481	0.00 480	0.00 479	0.00 477

SSM_0407		81.58 153	80.48 161	84.87 112	88.81 166	67.96 148	87.37 190	89.25 216	71.06 228	79.48 181	90.39 171	59.57 231	94.48 125	72.45 218	85.93 205	92.18 175

SSM_0404		81.91 143	80.84 154	85.13 100	89.24 150	68.26 136	87.84 177	89.25 216	71.06 228	80.62 165	90.39 171	59.57 231	94.65 118	72.45 218	87.19 179	92.47 161

ab-mvs		79.51 207	78.97 204	81.14 263	88.46 183	60.91 317	83.84 302	89.24 218	70.36 249	79.03 188	88.87 219	63.23 170	90.21 308	65.12 287	82.57 265	92.28 169

cascas		76.72 280	74.64 296	82.99 206	85.78 285	65.88 202	82.33 330	89.21 219	60.85 389	72.74 325	81.02 394	47.28 355	93.75 161	67.48 267	85.02 218	89.34 286

eth_miper_zixun_eth		77.92 254	76.69 264	81.61 249	83.00 358	61.98 303	83.15 320	89.20 220	69.52 272	74.86 298	84.35 341	61.76 197	92.56 223	71.50 225	72.89 389	90.28 247

h-mvs33		83.15 121	82.19 132	86.02 75	90.56 104	70.85 78	88.15 164	89.16 221	76.02 98	84.67 85	91.39 139	61.54 201	95.50 72	82.71 94	75.48 357	91.72 192

miper_ehance_all_eth		78.59 236	77.76 236	81.08 265	82.66 368	61.56 309	83.65 307	89.15 222	68.87 291	75.55 271	83.79 354	66.49 133	92.03 245	73.25 204	76.39 342	89.64 277

Effi-MVS+		83.62 108	83.08 112	85.24 94	88.38 187	67.45 166	88.89 127	89.15 222	75.50 111	82.27 133	88.28 236	69.61 90	94.45 126	77.81 146	87.84 167	93.84 85

c3_l		78.75 230	77.91 227	81.26 259	82.89 363	61.56 309	84.09 299	89.13 224	69.97 261	75.56 270	84.29 342	66.36 135	92.09 244	73.47 201	75.48 357	90.12 253

LTVRE_ROB	69.57 13	76.25 290	74.54 299	81.41 253	88.60 178	64.38 248	79.24 374	89.12 225	70.76 237	69.79 364	87.86 249	49.09 345	93.20 193	56.21 372	80.16 293	86.65 362
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
fmvsm_s_conf0.5_n_9		87.39 32	87.95 22	85.70 80	89.48 136	67.88 152	88.59 144	89.05 226	80.19 12	90.70 19	95.40 15	74.56 27	93.92 150	91.54 2	92.07 90	95.31 5

F-COLMAP		76.38 289	74.33 303	82.50 230	89.28 148	66.95 185	88.41 151	89.03 227	64.05 356	66.83 393	88.61 226	46.78 361	92.89 210	57.48 357	78.55 309	87.67 333

FMVSNet2		78.20 245	77.21 250	81.20 261	87.60 228	62.89 289	87.47 185	89.02 228	71.63 211	75.29 286	87.28 263	54.80 271	91.10 290	62.38 309	79.38 303	89.61 278

ACMH	67.68 16	75.89 295	73.93 307	81.77 245	88.71 175	66.61 188	88.62 143	89.01 229	69.81 264	66.78 394	86.70 283	41.95 403	91.51 274	55.64 373	78.14 318	87.17 347
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_enhance_ethall		77.87 256	76.86 257	80.92 270	81.65 382	61.38 311	82.68 327	88.98 230	65.52 336	75.47 272	82.30 383	65.76 147	92.00 248	72.95 207	76.39 342	89.39 284

无先验							87.48 184	88.98 230	60.00 396				94.12 139	67.28 269		88.97 299

AdaColmap		80.58 185	79.42 191	84.06 157	93.09 62	68.91 114	89.36 108	88.97 232	69.27 277	75.70 268	89.69 190	57.20 255	95.77 63	63.06 302	88.41 158	87.50 339

EI-MVSNet		80.52 186	79.98 174	82.12 236	84.28 322	63.19 281	86.41 227	88.95 233	74.18 155	78.69 194	87.54 259	66.62 130	92.43 230	72.57 212	80.57 289	90.74 226

MVSTER		79.01 224	77.88 230	82.38 232	83.07 355	64.80 236	84.08 300	88.95 233	69.01 289	78.69 194	87.17 270	54.70 275	92.43 230	74.69 187	80.57 289	89.89 269

LuminaMVS		80.68 178	79.62 187	83.83 171	85.07 307	68.01 147	86.99 203	88.83 235	70.36 249	81.38 149	87.99 247	50.11 330	92.51 227	79.02 130	86.89 186	90.97 216

1314		76.53 282	75.30 289	80.21 286	83.93 331	62.32 299	84.66 279	88.81 236	60.23 394	70.16 356	84.07 349	55.30 268	90.73 302	67.37 268	83.21 256	87.59 337

UniMVSNet_ETH3D		79.10 222	78.24 220	81.70 246	86.85 257	60.24 328	87.28 195	88.79 237	74.25 153	76.84 240	90.53 169	49.48 338	91.56 267	67.98 262	82.15 268	93.29 117

xiu_mvs_v1_base_debu		80.80 173	79.72 184	84.03 162	87.35 233	70.19 87	85.56 253	88.77 238	69.06 286	81.83 139	88.16 240	50.91 319	92.85 212	78.29 142	87.56 171	89.06 291

xiu_mvs_v1_base		80.80 173	79.72 184	84.03 162	87.35 233	70.19 87	85.56 253	88.77 238	69.06 286	81.83 139	88.16 240	50.91 319	92.85 212	78.29 142	87.56 171	89.06 291

xiu_mvs_v1_base_debi		80.80 173	79.72 184	84.03 162	87.35 233	70.19 87	85.56 253	88.77 238	69.06 286	81.83 139	88.16 240	50.91 319	92.85 212	78.29 142	87.56 171	89.06 291

FMVSNet3		77.88 255	76.85 258	80.97 269	86.84 258	62.36 297	86.52 224	88.77 238	71.13 224	75.34 280	86.66 285	54.07 281	91.10 290	62.72 304	79.57 299	89.45 282

patch_mono-2		83.65 105	84.54 87	80.99 267	90.06 119	65.83 203	84.21 294	88.74 242	71.60 214	85.01 77	92.44 103	74.51 28	83.50 394	82.15 99	92.15 88	93.64 101

GeoE		81.71 148	81.01 151	83.80 174	89.51 133	64.45 246	88.97 124	88.73 243	71.27 222	78.63 197	89.76 189	66.32 136	93.20 193	69.89 244	86.02 202	93.74 92

mamba_0408		79.37 216	77.52 243	84.93 109	88.81 166	67.96 148	65.03 458	88.66 244	70.96 232	79.48 181	89.80 186	58.69 237	94.65 118	70.35 237	85.93 205	92.18 175

SSM_04072		77.67 263	77.52 243	78.12 329	88.81 166	67.96 148	65.03 458	88.66 244	70.96 232	79.48 181	89.80 186	58.69 237	74.23 450	70.35 237	85.93 205	92.18 175

CANet_DTU		80.61 180	79.87 178	82.83 214	85.60 290	63.17 282	87.36 191	88.65 246	76.37 89	75.88 265	88.44 232	53.51 287	93.07 202	73.30 203	89.74 132	92.25 170

HyFIR lowres test		77.53 265	75.40 285	83.94 169	89.59 129	66.62 187	80.36 359	88.64 247	56.29 427	76.45 252	85.17 324	57.64 248	93.28 183	61.34 323	83.10 258	91.91 184

WR-MVS		79.49 208	79.22 199	80.27 284	88.79 171	58.35 344	85.06 270	88.61 248	78.56 35	77.65 222	88.34 234	63.81 164	90.66 303	64.98 289	77.22 328	91.80 187

BH-untuned		79.47 209	78.60 210	82.05 239	89.19 153	65.91 201	86.07 241	88.52 249	72.18 202	75.42 276	87.69 253	61.15 212	93.54 171	60.38 329	86.83 187	86.70 361

IS-MVSNet		83.15 121	82.81 118	84.18 146	89.94 122	63.30 277	91.59 50	88.46 250	79.04 30	79.49 180	92.16 109	65.10 151	94.28 129	67.71 264	91.86 95	94.95 12

pm-mvs1		77.25 271	76.68 265	78.93 311	84.22 324	58.62 342	86.41 227	88.36 251	71.37 218	73.31 318	88.01 246	61.22 211	89.15 329	64.24 295	73.01 388	89.03 295

UGNet		80.83 169	79.59 188	84.54 123	88.04 202	68.09 143	89.42 104	88.16 252	76.95 70	76.22 258	89.46 201	49.30 342	93.94 146	68.48 259	90.31 119	91.60 193
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		83.01 126	82.36 128	84.96 106	91.02 94	66.40 190	88.91 126	88.11 253	77.57 49	84.39 94	93.29 83	52.19 299	93.91 151	77.05 157	88.70 152	94.57 43

Effi-MVS+-dtu		80.03 199	78.57 211	84.42 128	85.13 305	68.74 120	88.77 134	88.10 254	74.99 129	74.97 296	83.49 363	57.27 253	93.36 181	73.53 199	80.88 283	91.18 207

v148		78.72 232	77.80 233	81.47 251	82.73 366	61.96 304	86.30 234	88.08 255	73.26 182	76.18 260	85.47 316	62.46 184	92.36 234	71.92 222	73.82 381	90.09 256

EG-PatchMatch MVS		74.04 318	71.82 332	80.71 274	84.92 309	67.42 167	85.86 247	88.08 255	66.04 329	64.22 416	83.85 351	35.10 434	92.56 223	57.44 358	80.83 284	82.16 425

viewmambaseed2359dif		80.41 187	79.84 179	82.12 236	82.95 362	62.50 294	83.39 314	88.06 257	67.11 312	80.98 157	90.31 173	66.20 139	91.01 294	74.62 188	84.90 220	92.86 144

SymmetryMVS		85.38 76	84.81 84	87.07 49	91.47 86	72.47 38	91.65 46	88.06 257	79.31 24	84.39 94	92.18 107	64.64 156	95.53 70	80.70 114	90.91 111	93.21 122

cl22		78.07 249	77.01 253	81.23 260	82.37 375	61.83 306	83.55 311	87.98 259	68.96 290	75.06 293	83.87 350	61.40 206	91.88 254	73.53 199	76.39 342	89.98 265

test_fmvsmvis_n_1920		84.02 93	83.87 95	84.49 126	84.12 326	69.37 107	88.15 164	87.96 260	70.01 259	83.95 105	93.23 84	68.80 104	91.51 274	88.61 31	89.96 127	92.57 153

pmmvs6		74.69 310	73.39 314	78.61 316	81.38 389	57.48 361	86.64 220	87.95 261	64.99 344	70.18 354	86.61 286	50.43 326	89.52 320	62.12 314	70.18 406	88.83 305

MVP-Stereo		76.12 291	74.46 301	81.13 264	85.37 297	69.79 94	84.42 290	87.95 261	65.03 342	67.46 384	85.33 319	53.28 290	91.73 260	58.01 354	83.27 255	81.85 427
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl____		77.72 259	76.76 261	80.58 277	82.49 372	60.48 324	83.09 322	87.87 263	69.22 280	74.38 307	85.22 323	62.10 191	91.53 272	71.09 228	75.41 361	89.73 276

DIV-MVS_self_test		77.72 259	76.76 261	80.58 277	82.48 373	60.48 324	83.09 322	87.86 264	69.22 280	74.38 307	85.24 321	62.10 191	91.53 272	71.09 228	75.40 362	89.74 275

BH-w/o		78.21 244	77.33 249	80.84 271	88.81 166	65.13 222	84.87 274	87.85 265	69.75 268	74.52 304	84.74 334	61.34 207	93.11 200	58.24 352	85.84 208	84.27 399

FE-MVS		77.78 257	75.68 278	84.08 153	88.09 200	66.00 198	83.13 321	87.79 266	68.42 300	78.01 214	85.23 322	45.50 378	95.12 91	59.11 341	85.83 209	91.11 209

HY-MVS	69.67 12	77.95 253	77.15 251	80.36 281	87.57 232	60.21 329	83.37 316	87.78 267	66.11 327	75.37 279	87.06 274	63.27 167	90.48 305	61.38 322	82.43 266	90.40 241

guyue		81.13 163	80.64 157	82.60 228	86.52 268	63.92 257	86.69 218	87.73 268	73.97 158	80.83 163	89.69 190	56.70 259	91.33 282	78.26 145	85.40 216	92.54 155

1112_ss		77.40 268	76.43 269	80.32 283	89.11 159	60.41 326	83.65 307	87.72 269	62.13 380	73.05 322	86.72 279	62.58 182	89.97 312	62.11 315	80.80 285	90.59 233

mvs_anonymous		79.42 212	79.11 201	80.34 282	84.45 321	57.97 351	82.59 328	87.62 270	67.40 311	76.17 262	88.56 229	68.47 108	89.59 319	70.65 234	86.05 201	93.47 110

ACMH+	68.96 14	76.01 294	74.01 305	82.03 240	88.60 178	65.31 218	88.86 128	87.55 271	70.25 255	67.75 380	87.47 261	41.27 406	93.19 195	58.37 350	75.94 350	87.60 335

tfpnnormal		74.39 312	73.16 318	78.08 330	86.10 280	58.05 348	84.65 281	87.53 272	70.32 252	71.22 346	85.63 311	54.97 269	89.86 313	43.03 439	75.02 369	86.32 365

CHOSEN 1792x2688		77.63 264	75.69 277	83.44 183	89.98 121	68.58 128	78.70 384	87.50 273	56.38 426	75.80 267	86.84 275	58.67 239	91.40 279	61.58 320	85.75 210	90.34 243

ambc				75.24 367	73.16 448	50.51 433	63.05 463	87.47 274		64.28 415	77.81 428	17.80 463	89.73 317	57.88 355	60.64 437	85.49 381

Fast-Effi-MVS+-dtu		78.02 251	76.49 267	82.62 227	83.16 354	66.96 184	86.94 206	87.45 275	72.45 196	71.49 343	84.17 347	54.79 274	91.58 264	67.61 265	80.31 292	89.30 287

D2MVS		74.82 309	73.21 317	79.64 299	79.81 409	62.56 293	80.34 360	87.35 276	64.37 350	68.86 371	82.66 378	46.37 365	90.10 309	67.91 263	81.24 278	86.25 366

fmvsm_s_conf0.5_n_2		84.04 92	84.11 93	83.81 173	86.17 276	65.00 227	86.96 204	87.28 277	74.35 148	88.25 38	94.23 49	61.82 196	92.60 220	89.85 12	88.09 162	93.84 85

TSAR-MVS + GP.		85.71 67	85.33 76	86.84 55	91.34 87	72.50 36	89.07 122	87.28 277	76.41 85	85.80 69	90.22 178	74.15 34	95.37 84	81.82 101	91.88 92	92.65 152

fmvsm_l_conf0.5_n		84.47 88	84.54 87	84.27 141	85.42 295	68.81 115	88.49 148	87.26 279	68.08 303	88.03 43	93.49 75	72.04 55	91.77 257	88.90 28	89.14 144	92.24 172

hse-mvs2		81.72 147	80.94 152	84.07 154	88.72 174	67.68 159	85.87 246	87.26 279	76.02 98	84.67 85	88.22 239	61.54 201	93.48 175	82.71 94	73.44 385	91.06 211

AUN-MVS		79.21 219	77.60 241	84.05 160	88.71 175	67.61 161	85.84 248	87.26 279	69.08 285	77.23 232	88.14 244	53.20 291	93.47 176	75.50 181	73.45 384	91.06 211

BH-RMVSNet		79.61 204	78.44 214	83.14 197	89.38 142	65.93 200	84.95 273	87.15 282	73.56 171	78.19 209	89.79 188	56.67 260	93.36 181	59.53 337	86.74 188	90.13 252

Test_1112_low_res		76.40 288	75.44 283	79.27 305	89.28 148	58.09 347	81.69 337	87.07 283	59.53 401	72.48 330	86.67 284	61.30 208	89.33 323	60.81 327	80.15 294	90.41 240

KD-MVS_self_test		68.81 375	67.59 380	72.46 398	74.29 439	45.45 448	77.93 396	87.00 284	63.12 364	63.99 419	78.99 420	42.32 398	84.77 384	56.55 370	64.09 427	87.16 349

mvsmamba		80.60 182	79.38 192	84.27 141	89.74 127	67.24 177	87.47 185	86.95 285	70.02 258	75.38 278	88.93 216	51.24 316	92.56 223	75.47 182	89.22 141	93.00 138

reproduce_monomvs		75.40 304	74.38 302	78.46 324	83.92 332	57.80 356	83.78 303	86.94 286	73.47 175	72.25 334	84.47 336	38.74 419	89.27 325	75.32 183	70.53 404	88.31 321

LS3D		76.95 276	74.82 294	83.37 187	90.45 106	67.36 171	89.15 118	86.94 286	61.87 383	69.52 365	90.61 166	51.71 312	94.53 121	46.38 427	86.71 189	88.21 324

miper_lstm_enhance		74.11 317	73.11 319	77.13 348	80.11 404	59.62 334	72.23 429	86.92 288	66.76 316	70.40 351	82.92 373	56.93 257	82.92 398	69.06 253	72.63 390	88.87 303

fmvsm_l_conf0.5_n_a		84.13 91	84.16 92	84.06 157	85.38 296	68.40 132	88.34 156	86.85 289	67.48 310	87.48 54	93.40 80	70.89 71	91.61 262	88.38 36	89.22 141	92.16 179

jason		81.39 159	80.29 166	84.70 120	86.63 266	69.90 93	85.95 243	86.77 290	63.24 363	81.07 156	89.47 199	61.08 214	92.15 242	78.33 141	90.07 126	92.05 182
jason: jason.
viewdifsd2359ckpt11		80.37 191	79.73 182	82.30 234	83.70 338	62.39 295	84.20 295	86.67 291	73.22 185	80.90 159	90.62 164	63.00 177	91.56 267	76.81 163	78.44 312	92.95 141

viewmsd2359difaftdt		80.37 191	79.73 182	82.30 234	83.70 338	62.39 295	84.20 295	86.67 291	73.22 185	80.90 159	90.62 164	63.00 177	91.56 267	76.81 163	78.44 312	92.95 141

OurMVSNet-221017-0		74.26 314	72.42 327	79.80 294	83.76 336	59.59 335	85.92 245	86.64 293	66.39 325	66.96 391	87.58 255	39.46 414	91.60 263	65.76 283	69.27 409	88.22 323

VPNet		78.69 233	78.66 209	78.76 314	88.31 189	55.72 388	84.45 288	86.63 294	76.79 75	78.26 207	90.55 168	59.30 234	89.70 318	66.63 275	77.05 330	90.88 219

fmvsm_s_conf0.1_n_2		83.80 99	83.79 99	83.83 171	85.62 289	64.94 231	87.03 201	86.62 295	74.32 149	87.97 46	94.33 42	60.67 220	92.60 220	89.72 14	87.79 168	93.96 76

USDC		70.33 362	68.37 363	76.21 354	80.60 398	56.23 381	79.19 376	86.49 296	60.89 388	61.29 429	85.47 316	31.78 441	89.47 322	53.37 386	76.21 348	82.94 418

lupinMVS		81.39 159	80.27 167	84.76 118	87.35 233	70.21 85	85.55 256	86.41 297	62.85 370	81.32 150	88.61 226	61.68 198	92.24 240	78.41 140	90.26 121	91.83 185

TR-MVS		77.44 266	76.18 273	81.20 261	88.24 191	63.24 278	84.61 282	86.40 298	67.55 308	77.81 219	86.48 293	54.10 280	93.15 197	57.75 356	82.72 263	87.20 346

旧先验1					91.96 79	65.79 206		86.37 299			93.08 90	69.31 95			92.74 79	88.74 311

GA-MVS		76.87 277	75.17 291	81.97 242	82.75 365	62.58 291	81.44 342	86.35 300	72.16 204	74.74 299	82.89 374	46.20 369	92.02 247	68.85 256	81.09 280	91.30 205

MonoMVSNet		76.49 286	75.80 275	78.58 318	81.55 385	58.45 343	86.36 232	86.22 301	74.87 137	74.73 300	83.73 356	51.79 311	88.73 337	70.78 230	72.15 394	88.55 317

CDS-MVSNet		79.07 223	77.70 238	83.17 196	87.60 228	68.23 140	84.40 291	86.20 302	67.49 309	76.36 255	86.54 291	61.54 201	90.79 298	61.86 317	87.33 176	90.49 237
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		82.61 132	82.11 133	84.11 147	88.82 165	71.58 57	85.15 266	86.16 303	74.69 140	80.47 169	91.04 151	62.29 187	90.55 304	80.33 118	90.08 125	90.20 249

MSDG		73.36 329	70.99 343	80.49 279	84.51 320	65.80 205	80.71 353	86.13 304	65.70 333	65.46 407	83.74 355	44.60 382	90.91 296	51.13 398	76.89 332	84.74 395

TransMVSNet (Re)		75.39 305	74.56 298	77.86 334	85.50 294	57.10 366	86.78 214	86.09 305	72.17 203	71.53 342	87.34 262	63.01 176	89.31 324	56.84 366	61.83 433	87.17 347

VDDNet		81.52 156	80.67 156	84.05 160	90.44 107	64.13 252	89.73 91	85.91 306	71.11 225	83.18 119	93.48 76	50.54 325	93.49 174	73.40 202	88.25 159	94.54 47

AstraMVS		80.81 170	80.14 171	82.80 217	86.05 281	63.96 254	86.46 226	85.90 307	73.71 166	80.85 162	90.56 167	54.06 282	91.57 266	79.72 124	83.97 237	92.86 144

sd_testset		77.70 261	77.40 246	78.60 317	89.03 160	60.02 330	79.00 379	85.83 308	75.19 125	76.61 249	89.98 180	54.81 270	85.46 377	62.63 308	83.55 248	90.33 244

Baseline_NR-MVSNet		78.15 247	78.33 218	77.61 340	85.79 284	56.21 382	86.78 214	85.76 309	73.60 170	77.93 216	87.57 256	65.02 152	88.99 331	67.14 272	75.33 364	87.63 334

Anonymous20240521		68.80 376	67.22 385	73.55 385	74.33 438	54.11 404	83.18 319	85.61 310	58.15 413	61.68 428	80.94 396	30.71 444	81.27 410	57.00 364	73.34 387	85.28 385

test_vis1_n_1920		75.52 300	75.78 276	74.75 374	79.84 408	57.44 362	83.26 318	85.52 311	62.83 371	79.34 186	86.17 300	45.10 380	79.71 416	78.75 135	81.21 279	87.10 353

新几何1				83.42 184	93.13 59	70.71 79		85.48 312	57.43 421	81.80 142	91.98 113	63.28 166	92.27 238	64.60 292	92.99 75	87.27 345

EPNet		83.72 103	82.92 117	86.14 71	84.22 324	69.48 100	91.05 63	85.27 313	81.30 6	76.83 241	91.65 126	66.09 141	95.56 67	76.00 173	93.85 67	93.38 112
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		67.33 387	65.99 391	71.37 404	73.48 445	51.47 426	75.16 416	85.19 314	65.20 339	60.78 431	80.93 398	42.35 397	77.20 427	57.12 361	53.69 450	85.44 383

SD_0403		74.65 311	74.77 295	74.29 378	86.20 275	47.42 442	83.71 305	85.12 315	69.30 276	68.50 376	87.95 248	59.40 233	86.05 368	49.38 409	83.35 253	89.40 283

mmtdpeth		74.16 316	73.01 320	77.60 342	83.72 337	61.13 312	85.10 268	85.10 316	72.06 205	77.21 236	80.33 403	43.84 389	85.75 371	77.14 156	52.61 452	85.91 376

IB-MVS	68.01 15	75.85 296	73.36 316	83.31 188	84.76 313	66.03 195	83.38 315	85.06 317	70.21 256	69.40 366	81.05 393	45.76 374	94.66 117	65.10 288	75.49 356	89.25 288
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		78.89 229	77.51 245	83.03 204	87.80 214	67.79 156	84.72 277	85.05 318	67.63 306	76.75 244	87.70 252	62.25 188	90.82 297	58.53 348	87.13 181	90.49 237

CL-MVSNet_self_test		72.37 341	71.46 336	75.09 368	79.49 415	53.53 408	80.76 351	85.01 319	69.12 284	70.51 349	82.05 387	57.92 245	84.13 388	52.27 391	66.00 422	87.60 335

testdata				79.97 290	90.90 97	64.21 250		84.71 320	59.27 403	85.40 73	92.91 92	62.02 193	89.08 330	68.95 254	91.37 103	86.63 363

MS-PatchMatch		73.83 321	72.67 323	77.30 346	83.87 333	66.02 196	81.82 334	84.66 321	61.37 387	68.61 374	82.82 376	47.29 354	88.21 344	59.27 338	84.32 233	77.68 442

ET-MVSNet_ETH3D		78.63 234	76.63 266	84.64 121	86.73 262	69.47 101	85.01 271	84.61 322	69.54 271	66.51 401	86.59 287	50.16 329	91.75 258	76.26 168	84.24 234	92.69 150

CNLPA		78.08 248	76.79 260	81.97 242	90.40 108	71.07 69	87.59 182	84.55 323	66.03 330	72.38 332	89.64 193	57.56 249	86.04 369	59.61 336	83.35 253	88.79 307

MIMVSNet1		68.58 378	66.78 388	73.98 382	80.07 405	51.82 422	80.77 350	84.37 324	64.40 349	59.75 437	82.16 386	36.47 430	83.63 392	42.73 440	70.33 405	86.48 364

KD-MVS_2432*1600		66.22 397	63.89 400	73.21 388	75.47 436	53.42 410	70.76 436	84.35 325	64.10 354	66.52 399	78.52 422	34.55 435	84.98 381	50.40 401	50.33 455	81.23 430

miper_refine_blended		66.22 397	63.89 400	73.21 388	75.47 436	53.42 410	70.76 436	84.35 325	64.10 354	66.52 399	78.52 422	34.55 435	84.98 381	50.40 401	50.33 455	81.23 430

test_0402		72.79 338	70.44 349	79.84 293	88.13 197	65.99 199	85.93 244	84.29 327	65.57 335	67.40 387	85.49 315	46.92 358	92.61 219	35.88 453	74.38 375	80.94 432

EU-MVSNet		68.53 380	67.61 379	71.31 407	78.51 422	47.01 445	84.47 285	84.27 328	42.27 454	66.44 402	84.79 333	40.44 411	83.76 390	58.76 346	68.54 414	83.17 412

thisisatest0530		79.40 213	77.76 236	84.31 136	87.69 225	65.10 225	87.36 191	84.26 329	70.04 257	77.42 226	88.26 238	49.94 333	94.79 111	70.20 239	84.70 224	93.03 135

COLMAP_ROB	66.92 17	73.01 335	70.41 350	80.81 272	87.13 246	65.63 209	88.30 158	84.19 330	62.96 368	63.80 421	87.69 253	38.04 424	92.56 223	46.66 424	74.91 370	84.24 400
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tttt0517		79.40 213	77.91 227	83.90 170	88.10 199	63.84 258	88.37 155	84.05 331	71.45 217	76.78 243	89.12 208	49.93 335	94.89 104	70.18 240	83.18 257	92.96 140

CMPMVS	51.72 21	70.19 364	68.16 366	76.28 353	73.15 449	57.55 360	79.47 371	83.92 332	48.02 447	56.48 447	84.81 332	43.13 393	86.42 365	62.67 307	81.81 274	84.89 393
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous202405211		78.25 242	77.01 253	81.99 241	91.03 93	60.67 321	84.77 276	83.90 333	70.65 242	80.00 174	91.20 145	41.08 408	91.43 278	65.21 286	85.26 217	93.85 83

XXY-MVS		75.41 303	75.56 281	74.96 369	83.59 341	57.82 355	80.59 355	83.87 334	66.54 324	74.93 297	88.31 235	63.24 169	80.09 415	62.16 313	76.85 334	86.97 355

DP-MVS		76.78 279	74.57 297	83.42 184	93.29 51	69.46 103	88.55 147	83.70 335	63.98 358	70.20 353	88.89 218	54.01 283	94.80 110	46.66 424	81.88 273	86.01 373

tfpn200view9		76.42 287	75.37 287	79.55 302	89.13 155	57.65 358	85.17 264	83.60 336	73.41 177	76.45 252	86.39 295	52.12 300	91.95 250	48.33 415	83.75 242	89.07 289

thres400		76.50 283	75.37 287	79.86 292	89.13 155	57.65 358	85.17 264	83.60 336	73.41 177	76.45 252	86.39 295	52.12 300	91.95 250	48.33 415	83.75 242	90.00 262

SixPastTwentyTwo		73.37 327	71.26 341	79.70 296	85.08 306	57.89 353	85.57 252	83.56 338	71.03 230	65.66 406	85.88 304	42.10 401	92.57 222	59.11 341	63.34 428	88.65 313

thres200		75.55 299	74.47 300	78.82 313	87.78 217	57.85 354	83.07 324	83.51 339	72.44 198	75.84 266	84.42 337	52.08 303	91.75 258	47.41 422	83.64 247	86.86 357

IterMVS-SCA-FT		75.43 302	73.87 309	80.11 288	82.69 367	64.85 235	81.57 339	83.47 340	69.16 283	70.49 350	84.15 348	51.95 306	88.15 345	69.23 250	72.14 395	87.34 342

CVMVSNet		72.99 336	72.58 325	74.25 379	84.28 322	50.85 431	86.41 227	83.45 341	44.56 451	73.23 320	87.54 259	49.38 340	85.70 372	65.90 281	78.44 312	86.19 368

ITE_SJBPF				78.22 326	81.77 381	60.57 322		83.30 342	69.25 279	67.54 382	87.20 268	36.33 431	87.28 357	54.34 380	74.62 373	86.80 358

thisisatest0515		77.33 269	75.38 286	83.18 195	85.27 300	63.80 259	82.11 333	83.27 343	65.06 341	75.91 264	83.84 352	49.54 337	94.27 130	67.24 270	86.19 198	91.48 200

mvs5depth		69.45 371	67.45 382	75.46 364	73.93 440	55.83 386	79.19 376	83.23 344	66.89 313	71.63 341	83.32 365	33.69 437	85.09 380	59.81 334	55.34 448	85.46 382

thres100view900		76.50 283	75.55 282	79.33 304	89.52 132	56.99 367	85.83 249	83.23 344	73.94 160	76.32 256	87.12 271	51.89 308	91.95 250	48.33 415	83.75 242	89.07 289

thres600view7		76.50 283	75.44 283	79.68 297	89.40 140	57.16 364	85.53 258	83.23 344	73.79 164	76.26 257	87.09 272	51.89 308	91.89 253	48.05 420	83.72 245	90.00 262

test222					91.50 85	68.26 136	84.16 297	83.20 347	54.63 432	79.74 176	91.63 128	58.97 236			91.42 101	86.77 359

EPNet_dtu		75.46 301	74.86 293	77.23 347	82.57 370	54.60 400	86.89 208	83.09 348	71.64 210	66.25 403	85.86 305	55.99 263	88.04 347	54.92 377	86.55 191	89.05 294
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		83.80 99	83.71 101	84.07 154	86.69 264	67.31 172	89.46 101	83.07 349	71.09 226	86.96 62	93.70 73	69.02 102	91.47 276	88.79 29	84.62 225	93.44 111

fmvsm_s_conf0.1_n		83.56 109	83.38 108	84.10 148	84.86 310	67.28 174	89.40 106	83.01 350	70.67 238	87.08 59	93.96 65	68.38 109	91.45 277	88.56 33	84.50 226	93.56 106

testing91		76.54 281	75.66 280	79.18 308	88.43 185	55.89 385	81.08 345	83.00 351	73.76 165	75.34 280	84.29 342	46.20 369	90.07 310	64.33 293	84.50 226	91.58 195

TDRefinement		67.49 385	64.34 397	76.92 349	73.47 446	61.07 315	84.86 275	82.98 352	59.77 398	58.30 441	85.13 325	26.06 449	87.89 349	47.92 421	60.59 438	81.81 428

OpenMVS_ROB	64.09 19	70.56 359	68.19 365	77.65 339	80.26 401	59.41 338	85.01 271	82.96 353	58.76 409	65.43 408	82.33 382	37.63 426	91.23 285	45.34 434	76.03 349	82.32 422

fmvsm_s_conf0.5_n_a		83.63 107	83.41 107	84.28 139	86.14 277	68.12 142	89.43 102	82.87 354	70.27 254	87.27 58	93.80 71	69.09 97	91.58 264	88.21 37	83.65 246	93.14 129

fmvsm_s_conf0.1_n_a		83.32 118	82.99 115	84.28 139	83.79 334	68.07 144	89.34 109	82.85 355	69.80 265	87.36 57	94.06 57	68.34 111	91.56 267	87.95 40	83.46 252	93.21 122

RPSCF		73.23 332	71.46 336	78.54 320	82.50 371	59.85 331	82.18 332	82.84 356	58.96 406	71.15 347	89.41 205	45.48 379	84.77 384	58.82 345	71.83 397	91.02 215

CostFormer		75.24 306	73.90 308	79.27 305	82.65 369	58.27 346	80.80 348	82.73 357	61.57 384	75.33 284	83.13 369	55.52 266	91.07 293	64.98 289	78.34 317	88.45 318

IterMVS		74.29 313	72.94 321	78.35 325	81.53 386	63.49 272	81.58 338	82.49 358	68.06 304	69.99 359	83.69 358	51.66 313	85.54 375	65.85 282	71.64 398	86.01 373
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_cas_vis1_n_1920		73.76 322	73.74 311	73.81 384	75.90 430	59.77 332	80.51 356	82.40 359	58.30 412	81.62 147	85.69 308	44.35 386	76.41 434	76.29 167	78.61 308	85.23 386

WTY-MVS		75.65 298	75.68 278	75.57 360	86.40 271	56.82 369	77.92 397	82.40 359	65.10 340	76.18 260	87.72 251	63.13 175	80.90 412	60.31 330	81.96 271	89.00 298

pmmvs4		74.03 320	71.91 331	80.39 280	81.96 378	68.32 134	81.45 341	82.14 361	59.32 402	69.87 362	85.13 325	52.40 296	88.13 346	60.21 331	74.74 372	84.73 396

FMVSNet5		69.50 370	67.96 370	74.15 380	82.97 361	55.35 393	80.01 366	82.12 362	62.56 375	63.02 422	81.53 390	36.92 427	81.92 405	48.42 414	74.06 377	85.17 389

mamv4		76.81 278	78.23 222	72.54 397	86.12 278	65.75 208	78.76 383	82.07 363	64.12 353	72.97 323	91.02 154	67.97 115	68.08 462	83.04 87	78.02 319	83.80 407

baseline1		76.98 275	76.75 263	77.66 338	88.13 197	55.66 389	85.12 267	81.89 364	73.04 189	76.79 242	88.90 217	62.43 185	87.78 351	63.30 301	71.18 401	89.55 280

UnsupCasMVSNet_bld		63.70 406	61.53 412	70.21 413	73.69 443	51.39 427	72.82 427	81.89 364	55.63 429	57.81 443	71.80 448	38.67 420	78.61 420	49.26 411	52.21 453	80.63 434

LFMVS		81.82 146	81.23 146	83.57 180	91.89 81	63.43 275	89.84 85	81.85 366	77.04 69	83.21 116	93.10 86	52.26 298	93.43 179	71.98 221	89.95 128	93.85 83

sss		73.60 324	73.64 312	73.51 386	82.80 364	55.01 397	76.12 407	81.69 367	62.47 376	74.68 301	85.85 306	57.32 252	78.11 423	60.86 326	80.93 281	87.39 340

SSC-MVS3.2		73.35 330	73.39 314	73.23 387	85.30 299	49.01 438	74.58 422	81.57 368	75.21 123	73.68 314	85.58 313	52.53 292	82.05 404	54.33 381	77.69 324	88.63 314

pmmvs-eth3d		70.50 360	67.83 374	78.52 322	77.37 426	66.18 194	81.82 334	81.51 369	58.90 407	63.90 420	80.42 401	42.69 396	86.28 366	58.56 347	65.30 424	83.11 414

TinyColmap		67.30 388	64.81 395	74.76 373	81.92 380	56.68 373	80.29 361	81.49 370	60.33 392	56.27 448	83.22 366	24.77 453	87.66 353	45.52 432	69.47 408	79.95 437

testing99		76.09 293	75.12 292	79.00 309	88.16 194	55.50 391	80.79 349	81.40 371	73.30 181	75.17 288	84.27 345	44.48 384	90.02 311	64.28 294	84.22 235	91.48 200

tpmvs		71.09 352	69.29 357	76.49 352	82.04 377	56.04 383	78.92 381	81.37 372	64.05 356	67.18 389	78.28 424	49.74 336	89.77 315	49.67 408	72.37 391	83.67 408

WBMVS		73.43 326	72.81 322	75.28 366	87.91 208	50.99 430	78.59 387	81.31 373	65.51 338	74.47 305	84.83 331	46.39 363	86.68 361	58.41 349	77.86 320	88.17 325

pmmvs5		71.55 348	70.20 353	75.61 359	77.83 423	56.39 377	81.74 336	80.89 374	57.76 417	67.46 384	84.49 335	49.26 343	85.32 379	57.08 362	75.29 365	85.11 390

ANet_high		50.57 428	46.10 432	63.99 432	48.67 477	39.13 465	70.99 435	80.85 375	61.39 386	31.18 466	57.70 462	17.02 464	73.65 453	31.22 459	15.89 474	79.18 439

LCM-MVSNet		54.25 419	49.68 429	67.97 426	53.73 474	45.28 451	66.85 451	80.78 376	35.96 463	39.45 464	62.23 457	8.70 473	78.06 424	48.24 418	51.20 454	80.57 435

PVSNet	64.34 18	72.08 346	70.87 345	75.69 358	86.21 274	56.44 376	74.37 423	80.73 377	62.06 381	70.17 355	82.23 385	42.86 395	83.31 396	54.77 378	84.45 230	87.32 343

baseline2		75.70 297	73.83 310	81.30 257	83.26 348	61.79 307	82.57 329	80.65 378	66.81 314	66.88 392	83.42 364	57.86 246	92.19 241	63.47 298	79.57 299	89.91 267

ppachtmachnet_test		70.04 366	67.34 384	78.14 328	79.80 410	61.13 312	79.19 376	80.59 379	59.16 404	65.27 409	79.29 415	46.75 362	87.29 356	49.33 410	66.72 417	86.00 375

FE-MVSNET		67.25 389	65.33 393	73.02 392	75.86 431	52.54 416	80.26 363	80.56 380	63.80 361	60.39 432	79.70 412	41.41 405	84.66 386	43.34 438	62.62 431	81.86 426

Gipuma		45.18 433	41.86 436	55.16 446	77.03 428	51.52 425	32.50 471	80.52 381	32.46 466	27.12 469	35.02 470	9.52 472	75.50 442	22.31 467	60.21 439	38.45 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231206		68.60 377	67.80 375	71.02 409	80.23 403	50.75 432	78.30 392	80.47 382	56.79 424	66.11 405	82.63 379	46.35 366	78.95 419	43.62 437	75.70 352	83.36 411

LCM-MVSNet-Re		77.05 273	76.94 256	77.36 344	87.20 243	51.60 424	80.06 364	80.46 383	75.20 124	67.69 381	86.72 279	62.48 183	88.98 332	63.44 299	89.25 139	91.51 197

tt0320		70.49 361	68.03 369	77.89 333	84.78 312	59.12 339	83.55 311	80.44 384	58.13 414	67.43 386	80.41 402	39.26 416	87.54 354	55.12 375	63.18 430	86.99 354

testing11		75.14 307	74.01 305	78.53 321	88.16 194	56.38 378	80.74 352	80.42 385	70.67 238	72.69 328	83.72 357	43.61 391	89.86 313	62.29 311	83.76 241	89.36 285

tpm2		73.26 331	71.46 336	78.63 315	83.34 346	56.71 372	80.65 354	80.40 386	56.63 425	73.55 316	82.02 388	51.80 310	91.24 284	56.35 371	78.42 315	87.95 327

CR-MVSNet		73.37 327	71.27 340	79.67 298	81.32 392	65.19 220	75.92 409	80.30 387	59.92 397	72.73 326	81.19 391	52.50 294	86.69 360	59.84 333	77.71 322	87.11 351

Patchmtry		70.74 356	69.16 359	75.49 363	80.72 396	54.07 405	74.94 420	80.30 387	58.34 411	70.01 357	81.19 391	52.50 294	86.54 362	53.37 386	71.09 402	85.87 378

sc_t1		72.19 344	69.51 355	80.23 285	84.81 311	61.09 314	84.68 278	80.22 389	60.70 390	71.27 344	83.58 361	36.59 429	89.24 326	60.41 328	63.31 429	90.37 242

tpm cat1		70.57 358	68.31 364	77.35 345	82.41 374	57.95 352	78.08 393	80.22 389	52.04 438	68.54 375	77.66 429	52.00 305	87.84 350	51.77 392	72.07 396	86.25 366

MDTV_nov1_ep13			69.97 354		83.18 352	53.48 409	77.10 404	80.18 391	60.45 391	69.33 368	80.44 400	48.89 349	86.90 359	51.60 394	78.51 311

AllTest		70.96 353	68.09 368	79.58 300	85.15 303	63.62 262	84.58 283	79.83 392	62.31 377	60.32 434	86.73 277	32.02 439	88.96 334	50.28 403	71.57 399	86.15 369

TestCases				79.58 300	85.15 303	63.62 262		79.83 392	62.31 377	60.32 434	86.73 277	32.02 439	88.96 334	50.28 403	71.57 399	86.15 369

test_fmvs1_n		70.86 355	70.24 352	72.73 395	72.51 453	55.28 394	81.27 344	79.71 394	51.49 442	78.73 193	84.87 330	27.54 448	77.02 428	76.06 171	79.97 297	85.88 377

Vis-MVSNet (Re-imp)		78.36 241	78.45 213	78.07 331	88.64 177	51.78 423	86.70 217	79.63 395	74.14 156	75.11 291	90.83 159	61.29 209	89.75 316	58.10 353	91.60 97	92.69 150

MIMVSNet		70.69 357	69.30 356	74.88 371	84.52 319	56.35 380	75.87 411	79.42 396	64.59 346	67.76 379	82.41 380	41.10 407	81.54 407	46.64 426	81.34 276	86.75 360

myMVS_eth3d28		73.62 323	73.53 313	73.90 383	88.20 192	47.41 443	78.06 394	79.37 397	74.29 152	73.98 310	84.29 342	44.67 381	83.54 393	51.47 395	87.39 175	90.74 226

dmvs_re		71.14 351	70.58 346	72.80 394	81.96 378	59.68 333	75.60 413	79.34 398	68.55 296	69.27 369	80.72 399	49.42 339	76.54 431	52.56 390	77.79 321	82.19 424

SCA		74.22 315	72.33 328	79.91 291	84.05 329	62.17 301	79.96 367	79.29 399	66.30 326	72.38 332	80.13 406	51.95 306	88.60 340	59.25 339	77.67 325	88.96 300

testing222		74.04 318	72.66 324	78.19 327	87.89 209	55.36 392	81.06 346	79.20 400	71.30 221	74.65 302	83.57 362	39.11 418	88.67 339	51.43 397	85.75 210	90.53 235

tpmrst		72.39 339	72.13 330	73.18 391	80.54 399	49.91 435	79.91 368	79.08 401	63.11 365	71.69 340	79.95 408	55.32 267	82.77 400	65.66 284	73.89 379	86.87 356

tt0320-xc		70.11 365	67.45 382	78.07 331	85.33 298	59.51 337	83.28 317	78.96 402	58.77 408	67.10 390	80.28 404	36.73 428	87.42 355	56.83 367	59.77 440	87.29 344

test_fmvs1		70.93 354	70.52 347	72.16 399	73.71 442	55.05 396	80.82 347	78.77 403	51.21 443	78.58 198	84.41 338	31.20 443	76.94 429	75.88 175	80.12 296	84.47 398

PatchmatchNet		73.12 333	71.33 339	78.49 323	83.18 352	60.85 318	79.63 369	78.57 404	64.13 352	71.73 339	79.81 411	51.20 317	85.97 370	57.40 359	76.36 347	88.66 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		75.12 308	75.19 290	74.91 370	90.40 108	45.09 453	80.29 361	78.42 405	78.37 40	76.54 251	87.75 250	44.36 385	87.28 357	57.04 363	83.49 250	92.37 164

MDA-MVSNet-bldmvs		66.68 392	63.66 402	75.75 357	79.28 417	60.56 323	73.92 425	78.35 406	64.43 348	50.13 456	79.87 410	44.02 388	83.67 391	46.10 429	56.86 442	83.03 416

new-patchmatchnet		61.73 410	61.73 411	61.70 435	72.74 451	24.50 478	69.16 443	78.03 407	61.40 385	56.72 446	75.53 440	38.42 421	76.48 433	45.95 430	57.67 441	84.13 402

our_test_3		69.14 373	67.00 386	75.57 360	79.80 410	58.80 340	77.96 395	77.81 408	59.55 400	62.90 425	78.25 425	47.43 353	83.97 389	51.71 393	67.58 416	83.93 405

test20.03		67.45 386	66.95 387	68.94 417	75.48 435	44.84 454	77.50 399	77.67 409	66.66 318	63.01 423	83.80 353	47.02 357	78.40 421	42.53 442	68.86 413	83.58 409

WB-MVSnew		71.96 347	71.65 334	72.89 393	84.67 318	51.88 421	82.29 331	77.57 410	62.31 377	73.67 315	83.00 371	53.49 288	81.10 411	45.75 431	82.13 269	85.70 379

test-LLR		72.94 337	72.43 326	74.48 375	81.35 390	58.04 349	78.38 388	77.46 411	66.66 318	69.95 360	79.00 418	48.06 351	79.24 417	66.13 277	84.83 221	86.15 369

test-mter		71.41 349	70.39 351	74.48 375	81.35 390	58.04 349	78.38 388	77.46 411	60.32 393	69.95 360	79.00 418	36.08 432	79.24 417	66.13 277	84.83 221	86.15 369

ECVR-MVS		79.61 204	79.26 197	80.67 275	90.08 115	54.69 399	87.89 174	77.44 413	74.88 135	80.27 170	92.79 98	48.96 348	92.45 229	68.55 258	92.50 83	94.86 19

UBG		73.08 334	72.27 329	75.51 362	88.02 203	51.29 428	78.35 391	77.38 414	65.52 336	73.87 312	82.36 381	45.55 376	86.48 364	55.02 376	84.39 232	88.75 309

tpm		72.37 341	71.71 333	74.35 377	82.19 376	52.00 418	79.22 375	77.29 415	64.56 347	72.95 324	83.68 359	51.35 314	83.26 397	58.33 351	75.80 351	87.81 331

LF4IMVS		64.02 405	62.19 409	69.50 415	70.90 454	53.29 413	76.13 406	77.18 416	52.65 437	58.59 439	80.98 395	23.55 456	76.52 432	53.06 388	66.66 418	78.68 440

test1111		79.43 211	79.18 200	80.15 287	89.99 120	53.31 412	87.33 193	77.05 417	75.04 128	80.23 172	92.77 100	48.97 347	92.33 237	68.87 255	92.40 85	94.81 22

K. test v3		71.19 350	68.51 362	79.21 307	83.04 357	57.78 357	84.35 292	76.91 418	72.90 192	62.99 424	82.86 375	39.27 415	91.09 292	61.65 319	52.66 451	88.75 309

UWE-MVS		72.13 345	71.49 335	74.03 381	86.66 265	47.70 440	81.40 343	76.89 419	63.60 362	75.59 269	84.22 346	39.94 413	85.62 374	48.98 412	86.13 200	88.77 308

testgi		66.67 393	66.53 389	67.08 428	75.62 434	41.69 463	75.93 408	76.50 420	66.11 327	65.20 412	86.59 287	35.72 433	74.71 447	43.71 436	73.38 386	84.84 394

test_fmvs2		68.35 382	67.48 381	70.98 410	69.50 456	51.95 419	80.05 365	76.38 421	49.33 445	74.65 302	84.38 339	23.30 457	75.40 445	74.51 190	75.17 368	85.60 380

test_vis1_n		69.85 369	69.21 358	71.77 401	72.66 452	55.27 395	81.48 340	76.21 422	52.03 439	75.30 285	83.20 368	28.97 446	76.22 436	74.60 189	78.41 316	83.81 406

PatchMatch-RL		72.38 340	70.90 344	76.80 351	88.60 178	67.38 170	79.53 370	76.17 423	62.75 373	69.36 367	82.00 389	45.51 377	84.89 383	53.62 384	80.58 288	78.12 441

JIA-IIPM		66.32 396	62.82 408	76.82 350	77.09 427	61.72 308	65.34 456	75.38 424	58.04 416	64.51 414	62.32 456	42.05 402	86.51 363	51.45 396	69.22 410	82.21 423

ADS-MVSNet2		66.20 399	63.33 403	74.82 372	79.92 406	58.75 341	67.55 448	75.19 425	53.37 435	65.25 410	75.86 437	42.32 398	80.53 414	41.57 443	68.91 411	85.18 387

ETVMVS		72.25 343	71.05 342	75.84 356	87.77 218	51.91 420	79.39 372	74.98 426	69.26 278	73.71 313	82.95 372	40.82 410	86.14 367	46.17 428	84.43 231	89.47 281

PatchT		68.46 381	67.85 372	70.29 412	80.70 397	43.93 456	72.47 428	74.88 427	60.15 395	70.55 348	76.57 433	49.94 333	81.59 406	50.58 399	74.83 371	85.34 384

dp		66.80 391	65.43 392	70.90 411	79.74 412	48.82 439	75.12 418	74.77 428	59.61 399	64.08 418	77.23 430	42.89 394	80.72 413	48.86 413	66.58 419	83.16 413

MDA-MVSNet_test_wron		65.03 401	62.92 405	71.37 404	75.93 429	56.73 370	69.09 445	74.73 429	57.28 422	54.03 451	77.89 426	45.88 371	74.39 449	49.89 407	61.55 434	82.99 417

TESTMET0.1,1		69.89 368	69.00 360	72.55 396	79.27 418	56.85 368	78.38 388	74.71 430	57.64 418	68.09 378	77.19 431	37.75 425	76.70 430	63.92 296	84.09 236	84.10 403

YYNet1		65.03 401	62.91 406	71.38 403	75.85 432	56.60 374	69.12 444	74.66 431	57.28 422	54.12 450	77.87 427	45.85 372	74.48 448	49.95 406	61.52 435	83.05 415

test_fmvs3		63.36 407	61.82 410	67.98 425	62.51 465	46.96 446	77.37 401	74.03 432	45.24 450	67.50 383	78.79 421	12.16 469	72.98 454	72.77 210	66.02 421	83.99 404

PMMVS		69.34 372	68.67 361	71.35 406	75.67 433	62.03 302	75.17 415	73.46 433	50.00 444	68.68 372	79.05 416	52.07 304	78.13 422	61.16 324	82.77 261	73.90 448

PVSNet_0	57.27 20	61.67 411	59.27 414	68.85 419	79.61 413	57.44 362	68.01 446	73.44 434	55.93 428	58.54 440	70.41 451	44.58 383	77.55 426	47.01 423	35.91 463	71.55 451

Syy-MVS		68.05 383	67.85 372	68.67 421	84.68 315	40.97 464	78.62 385	73.08 435	66.65 321	66.74 395	79.46 413	52.11 302	82.30 402	32.89 456	76.38 345	82.75 419

myMVS_eth3d		67.02 390	66.29 390	69.21 416	84.68 315	42.58 459	78.62 385	73.08 435	66.65 321	66.74 395	79.46 413	31.53 442	82.30 402	39.43 448	76.38 345	82.75 419

test0.0.03 1		68.00 384	67.69 377	68.90 418	77.55 424	47.43 441	75.70 412	72.95 437	66.66 318	66.56 397	82.29 384	48.06 351	75.87 440	44.97 435	74.51 374	83.41 410

testing3		68.56 379	67.67 378	71.22 408	87.33 238	42.87 458	83.06 325	71.54 438	70.36 249	69.08 370	84.38 339	30.33 445	85.69 373	37.50 451	75.45 360	85.09 391

ADS-MVSNet		64.36 404	62.88 407	68.78 420	79.92 406	47.17 444	67.55 448	71.18 439	53.37 435	65.25 410	75.86 437	42.32 398	73.99 451	41.57 443	68.91 411	85.18 387

Patchmatch-RL test		70.24 363	67.78 376	77.61 340	77.43 425	59.57 336	71.16 433	70.33 440	62.94 369	68.65 373	72.77 446	50.62 323	85.49 376	69.58 248	66.58 419	87.77 332

gg-mvs-nofinetune		69.95 367	67.96 370	75.94 355	83.07 355	54.51 402	77.23 402	70.29 441	63.11 365	70.32 352	62.33 455	43.62 390	88.69 338	53.88 383	87.76 169	84.62 397

door-mid								69.98 442

GG-mvs-BLEND				75.38 365	81.59 384	55.80 387	79.32 373	69.63 443		67.19 388	73.67 444	43.24 392	88.90 336	50.41 400	84.50 226	81.45 429

FPMVS		53.68 422	51.64 424	59.81 438	65.08 462	51.03 429	69.48 441	69.58 444	41.46 455	40.67 462	72.32 447	16.46 465	70.00 459	24.24 466	65.42 423	58.40 462

door								69.44 445

Patchmatch-test		64.82 403	63.24 404	69.57 414	79.42 416	49.82 436	63.49 462	69.05 446	51.98 440	59.95 436	80.13 406	50.91 319	70.98 455	40.66 445	73.57 382	87.90 329

CHOSEN 280x420		66.51 394	64.71 396	71.90 400	81.45 387	63.52 271	57.98 465	68.95 447	53.57 434	62.59 426	76.70 432	46.22 368	75.29 446	55.25 374	79.68 298	76.88 444

MVStest1		56.63 417	52.76 423	68.25 424	61.67 466	53.25 414	71.67 431	68.90 448	38.59 459	50.59 455	83.05 370	25.08 451	70.66 456	36.76 452	38.56 462	80.83 433

EGC-MVSNET		52.07 426	47.05 430	67.14 427	83.51 343	60.71 320	80.50 357	67.75 449	0.07 477	0.43 478	75.85 439	24.26 454	81.54 407	28.82 460	62.25 432	59.16 460

ttmdpeth		59.91 413	57.10 417	68.34 423	67.13 460	46.65 447	74.64 421	67.41 450	48.30 446	62.52 427	85.04 329	20.40 459	75.93 439	42.55 441	45.90 461	82.44 421

EPMVS		69.02 374	68.16 366	71.59 402	79.61 413	49.80 437	77.40 400	66.93 451	62.82 372	70.01 357	79.05 416	45.79 373	77.86 425	56.58 369	75.26 366	87.13 350

APD_test1		53.31 423	49.93 428	63.42 434	65.68 461	50.13 434	71.59 432	66.90 452	34.43 464	40.58 463	71.56 449	8.65 474	76.27 435	34.64 455	55.36 447	63.86 458

lessismore_v0				78.97 310	81.01 395	57.15 365		65.99 453		61.16 430	82.82 376	39.12 417	91.34 281	59.67 335	46.92 458	88.43 319

dmvs_testset		62.63 408	64.11 399	58.19 439	78.55 421	24.76 477	75.28 414	65.94 454	67.91 305	60.34 433	76.01 436	53.56 286	73.94 452	31.79 457	67.65 415	75.88 446

pmmvs3		57.79 415	54.26 420	68.37 422	64.02 464	56.72 371	75.12 418	65.17 455	40.20 456	52.93 452	69.86 452	20.36 460	75.48 443	45.45 433	55.25 449	72.90 450

MVS-HIRNet		59.14 414	57.67 416	63.57 433	81.65 382	43.50 457	71.73 430	65.06 456	39.59 458	51.43 453	57.73 461	38.34 422	82.58 401	39.53 446	73.95 378	64.62 457

PM-MVS		66.41 395	64.14 398	73.20 390	73.92 441	56.45 375	78.97 380	64.96 457	63.88 360	64.72 413	80.24 405	19.84 461	83.44 395	66.24 276	64.52 426	79.71 438

UWE-MVS-28		65.32 400	64.93 394	66.49 429	78.70 420	38.55 466	77.86 398	64.39 458	62.00 382	64.13 417	83.60 360	41.44 404	76.00 438	31.39 458	80.89 282	84.92 392

PMVS	37.38 22	44.16 434	40.28 438	55.82 444	40.82 479	42.54 461	65.12 457	63.99 459	34.43 464	24.48 470	57.12 463	3.92 479	76.17 437	17.10 471	55.52 446	48.75 465
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2506		77.30 270	76.49 267	79.74 295	90.08 115	52.02 417	87.86 176	63.10 460	74.88 135	80.16 173	92.79 98	38.29 423	92.35 235	68.74 257	92.50 83	94.86 19

test_method		31.52 438	29.28 442	38.23 453	27.03 481	6.50 484	20.94 473	62.21 461	4.05 475	22.35 473	52.50 466	13.33 466	47.58 473	27.04 463	34.04 465	60.62 459

WB-MVS		54.94 418	54.72 419	55.60 445	73.50 444	20.90 479	74.27 424	61.19 462	59.16 404	50.61 454	74.15 442	47.19 356	75.78 441	17.31 470	35.07 464	70.12 452

test_vis1_rt		60.28 412	58.42 415	65.84 430	67.25 459	55.60 390	70.44 438	60.94 463	44.33 452	59.00 438	66.64 453	24.91 452	68.67 460	62.80 303	69.48 407	73.25 449

SSC-MVS		53.88 421	53.59 421	54.75 447	72.87 450	19.59 480	73.84 426	60.53 464	57.58 420	49.18 458	73.45 445	46.34 367	75.47 444	16.20 473	32.28 466	69.20 453

testf1		45.72 430	41.96 434	57.00 440	56.90 468	45.32 449	66.14 453	59.26 465	26.19 468	30.89 467	60.96 459	4.14 477	70.64 457	26.39 464	46.73 459	55.04 463

APD_test2		45.72 430	41.96 434	57.00 440	56.90 468	45.32 449	66.14 453	59.26 465	26.19 468	30.89 467	60.96 459	4.14 477	70.64 457	26.39 464	46.73 459	55.04 463

test_f		52.09 425	50.82 426	55.90 443	53.82 473	42.31 462	59.42 464	58.31 467	36.45 462	56.12 449	70.96 450	12.18 468	57.79 469	53.51 385	56.57 444	67.60 454

new_pmnet		50.91 427	50.29 427	52.78 448	68.58 457	34.94 470	63.71 460	56.63 468	39.73 457	44.95 459	65.47 454	21.93 458	58.48 468	34.98 454	56.62 443	64.92 456

DSMNet-mixed		57.77 416	56.90 418	60.38 437	67.70 458	35.61 468	69.18 442	53.97 469	32.30 467	57.49 444	79.88 409	40.39 412	68.57 461	38.78 449	72.37 391	76.97 443

PMMVS2		40.82 435	38.86 439	46.69 450	53.84 472	16.45 481	48.61 468	49.92 470	37.49 460	31.67 465	60.97 458	8.14 475	56.42 470	28.42 461	30.72 467	67.19 455

mvsany_test1		62.30 409	61.26 413	65.41 431	69.52 455	54.86 398	66.86 450	49.78 471	46.65 448	68.50 376	83.21 367	49.15 344	66.28 463	56.93 365	60.77 436	75.11 447

test_vis3_rt		49.26 429	47.02 431	56.00 442	54.30 471	45.27 452	66.76 452	48.08 472	36.83 461	44.38 460	53.20 465	7.17 476	64.07 465	56.77 368	55.66 445	58.65 461

E-PMN		31.77 437	30.64 440	35.15 455	52.87 475	27.67 472	57.09 466	47.86 473	24.64 470	16.40 475	33.05 471	11.23 470	54.90 471	14.46 474	18.15 472	22.87 471

EMVS		30.81 439	29.65 441	34.27 456	50.96 476	25.95 476	56.58 467	46.80 474	24.01 471	15.53 476	30.68 472	12.47 467	54.43 472	12.81 475	17.05 473	22.43 472

mvsany_test3		53.99 420	51.45 425	61.61 436	55.51 470	44.74 455	63.52 461	45.41 475	43.69 453	58.11 442	76.45 434	17.99 462	63.76 466	54.77 378	47.59 457	76.34 445

MVE	26.22 23	30.37 440	25.89 444	43.81 452	44.55 478	35.46 469	28.87 472	39.07 476	18.20 472	18.58 474	40.18 469	2.68 480	47.37 474	17.07 472	23.78 471	48.60 466
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 432	45.38 433	45.55 451	73.36 447	26.85 475	67.72 447	34.19 477	54.15 433	49.65 457	56.41 464	25.43 450	62.94 467	19.45 468	28.09 468	46.86 467

kuosan		39.70 436	40.40 437	37.58 454	64.52 463	26.98 473	65.62 455	33.02 478	46.12 449	42.79 461	48.99 467	24.10 455	46.56 475	12.16 476	26.30 469	39.20 468

MTMP							92.18 38	32.83 479

tmp_tt		18.61 442	21.40 445	10.23 459	4.82 482	10.11 482	34.70 470	30.74 480	1.48 476	23.91 472	26.07 473	28.42 447	13.41 478	27.12 462	15.35 475	7.17 473

DeepMVS_CX				27.40 457	40.17 480	26.90 474		24.59 481	17.44 473	23.95 471	48.61 468	9.77 471	26.48 476	18.06 469	24.47 470	28.83 470

N_pmnet		52.79 424	53.26 422	51.40 449	78.99 419	7.68 483	69.52 440	3.89 482	51.63 441	57.01 445	74.98 441	40.83 409	65.96 464	37.78 450	64.67 425	80.56 436

wuyk23d		16.82 443	15.94 446	19.46 458	58.74 467	31.45 471	39.22 469	3.74 483	6.84 474	6.04 477	2.70 477	1.27 481	24.29 477	10.54 477	14.40 476	2.63 474

testmvs		6.04 446	8.02 449	0.10 461	0.08 483	0.03 486	69.74 439	0.04 484	0.05 478	0.31 479	1.68 478	0.02 483	0.04 479	0.24 478	0.02 477	0.25 476

test123		6.12 445	8.11 448	0.14 460	0.06 484	0.09 485	71.05 434	0.03 485	0.04 479	0.25 480	1.30 479	0.05 482	0.03 480	0.21 479	0.01 478	0.29 475

mmdepth		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

monomultidepth		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

test_blank		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

uanet_test		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

DCPMVS		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

pcd_1.5k_mvsjas		5.26 447	7.02 450	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	63.15 172	0.00 481	0.00 480	0.00 479	0.00 477

sosnet-low-res		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

sosnet		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

uncertanet		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

Regformer		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

n2								0.00 486

nn								0.00 486

ab-mvs-re		7.23 444	9.64 447	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	86.72 279	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

uanet		0.00 448	0.00 451	0.00 462	0.00 485	0.00 487	0.00 474	0.00 486	0.00 480	0.00 481	0.00 480	0.00 484	0.00 481	0.00 480	0.00 479	0.00 477

TestfortrainingZip							93.28 12

WAC-MVS						42.58 459								39.46 447

PC_three_1452									68.21 302	92.02 14	94.00 61	82.09 5	95.98 60	84.58 69	96.68 2	94.95 12

eth-test2					0.00 485

eth-test					0.00 485

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 59	82.45 3	96.87 23	83.77 80	96.48 8	94.88 16

test_0728_THIRD									78.38 38	92.12 11	95.78 4	81.46 8	97.40 9	89.42 19	96.57 7	94.67 32

GSMVS																88.96 300

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 315				88.96 300

sam_mvs												50.01 331

test_post1							78.90 382				5.43 476	48.81 350	85.44 378	59.25 339

test_post											5.46 475	50.36 327	84.24 387

patchmatchnet-post											74.00 443	51.12 318	88.60 340

gm-plane-assit					81.40 388	53.83 407			62.72 374		80.94 396		92.39 232	63.40 300

test9_res														84.90 62	95.70 29	92.87 143

agg_prior2														82.91 89	95.45 32	92.70 148

test_prior4						72.60 34	89.01 123

test_prior2							88.85 130		75.41 114	84.91 80	93.54 74	74.28 32		83.31 83	95.86 23

旧先验2							86.56 223		58.10 415	87.04 60			88.98 332	74.07 195

新几何2							86.29 236

原ACMM2							86.86 210

testdata2													91.01 294	62.37 310

segment_acmp												73.08 42

testdata1							84.14 298		75.71 105

plane_prior7					90.08 115	68.51 130

plane_prior6					89.84 124	68.70 124						60.42 226

plane_prior4											91.00 155

plane_prior3						68.60 127			78.44 36	78.92 191

plane_prior2							91.25 59		79.12 28

plane_prior1					89.90 123

plane_prior						68.71 122	90.38 77		77.62 47						86.16 199

HQP5-MVS						66.98 182

HQP-NCC					89.33 143		89.17 114		76.41 85	77.23 232

ACMP_Plane					89.33 143		89.17 114		76.41 85	77.23 232

BP-MVS														77.47 151

HQP4-MVS										77.24 231			95.11 93			91.03 213

HQP2-MVS												60.17 229

NP-MVS					89.62 128	68.32 134					90.24 176

MDTV_nov1_ep13_2view						37.79 467	75.16 416		55.10 430	66.53 398		49.34 341		53.98 382		87.94 328

ACMMP++_ref															81.95 272

ACMMP++															81.25 277

Test By Simon												64.33 158

High-resolution multi-view benchmark