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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 65	96.26 40	72.84 30	99.38 1	92.64 31	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 101	71.10 27	97.02 3	96.04 6	88.70 2	91.57 18	96.19 42	70.12 47	98.91 18	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 165	76.68 2	97.29 1	95.35 17	82.87 35	91.58 17	97.22 7	79.93 5	99.10 9	83.12 119	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 82	94.37 57	72.48 223	92.07 10	96.85 21	83.82 2	99.15 2	91.53 41	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 97	92.83 80	64.03 216	93.06 126	94.33 59	82.19 43	93.65 3	96.15 44	85.89 1	97.19 92	91.02 45	97.75 1	96.43 31
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
MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 37	97.00 5	93.73 78	87.30 4	92.15 7	96.15 44	66.38 71	98.94 17	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 33	84.83 15	89.07 38	96.80 24	70.86 43	99.06 15	92.64 31	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 71	73.88 9	97.01 4	94.40 55	88.32 3	85.71 66	94.91 85	74.11 21	98.91 18	87.26 73	95.94 8	97.03 12
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
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 59	96.89 6	94.44 51	71.65 253	92.11 8	97.21 8	76.79 9	99.11 6	92.34 33	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 153	93.00 76	58.16 354	96.72 9	94.41 53	86.50 9	90.25 29	97.83 1	75.46 14	98.67 25	92.78 30	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 100	96.04 24	63.70 230	95.04 42	95.19 22	86.74 8	91.53 19	95.15 78	73.86 22	97.58 64	93.38 25	92.00 70	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 52	96.53 13	93.78 71	86.89 7	89.68 35	95.78 51	65.94 76	99.10 9	92.99 28	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 47	71.92 239	90.55 25	96.93 15	73.77 23	99.08 11	91.91 39	94.90 22	96.29 35
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
HPM-MVS++		89.37 14	89.95 13	87.64 34	95.10 30	68.23 91	95.24 34	94.49 49	82.43 40	88.90 39	96.35 35	71.89 40	98.63 26	88.76 59	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 20	89.81 6	93.66 54	75.15 5	90.61 251	93.43 93	84.06 22	86.20 60	90.17 210	72.42 35	96.98 109	93.09 27	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 68	96.38 15	94.64 42	84.42 19	86.74 55	96.20 41	66.56 70	98.76 24	89.03 58	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 17	87.45 43	96.26 20	67.56 109	94.17 68	94.15 64	68.77 304	90.74 23	97.27 5	76.09 12	98.49 29	90.58 49	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_9		88.24 18	89.36 16	84.85 142	88.15 223	61.94 280	95.65 25	89.70 278	85.54 11	92.07 10	97.33 4	67.51 62	97.27 87	96.23 5	92.07 69	95.35 69

fmvsm_s_conf0.5_n_9		88.14 19	89.21 17	84.92 137	89.29 176	61.41 296	92.97 131	88.36 330	86.96 6	91.49 20	97.49 3	69.48 51	97.46 71	97.00 1	89.88 105	95.89 47

SMA-MVS		88.14 19	88.29 26	87.67 33	93.21 68	68.72 77	93.85 89	94.03 67	74.18 186	91.74 14	96.67 27	65.61 81	98.42 33	89.24 55	96.08 7	95.88 48
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
PS-MVSNAJ		88.14 19	87.61 36	89.71 7	92.06 104	76.72 1	95.75 20	93.26 99	83.86 23	89.55 36	96.06 46	53.55 248	97.89 46	91.10 43	93.31 53	94.54 120

TSAR-MVS + MP.		88.11 22	88.64 22	86.54 77	91.73 119	68.04 95	90.36 258	93.55 85	82.89 33	91.29 21	92.89 140	72.27 37	96.03 163	87.99 63	94.77 26	95.54 59
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_8		87.96 23	88.93 19	85.07 132	88.43 210	61.78 283	94.73 55	91.74 172	85.87 10	91.66 16	97.50 2	64.03 102	98.33 34	96.28 4	90.08 101	95.10 85

TSAR-MVS + GP.		87.96 23	88.37 25	86.70 67	93.51 62	65.32 174	95.15 37	93.84 70	78.17 119	85.93 64	94.80 88	75.80 13	98.21 36	89.38 52	88.78 117	96.59 19

DeepC-MVS_fast	79.48 2	87.95 25	88.00 30	87.79 31	95.86 27	68.32 85	95.74 21	94.11 65	83.82 24	83.49 91	96.19 42	64.53 97	98.44 31	83.42 118	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 26	87.38 40	89.55 12	91.41 131	76.43 3	95.74 21	93.12 107	83.53 27	89.55 36	95.95 49	53.45 252	97.68 54	91.07 44	92.62 60	94.54 120

EPNet		87.84 27	88.38 24	86.23 87	93.30 65	66.05 153	95.26 33	94.84 32	87.09 5	88.06 42	94.53 94	66.79 67	97.34 80	83.89 111	91.68 76	95.29 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 28	87.77 33	87.63 38	89.24 181	71.18 24	96.57 12	92.90 116	82.70 37	87.13 50	95.27 71	64.99 87	95.80 171	89.34 53	91.80 74	95.93 45

test_fmvsm_n_1920		87.69 29	88.50 23	85.27 125	87.05 254	63.55 237	93.69 99	91.08 211	84.18 21	90.17 31	97.04 12	67.58 61	97.99 41	95.72 8	90.03 102	94.26 136

fmvsm_l_conf0.5_n_3		87.54 30	88.29 26	85.30 122	86.92 260	62.63 263	95.02 44	90.28 250	84.95 14	90.27 28	96.86 19	65.36 83	97.52 69	94.93 13	90.03 102	95.76 51

APDe-MVS		87.54 30	87.84 32	86.65 69	96.07 23	66.30 148	94.84 50	93.78 71	69.35 293	88.39 41	96.34 36	67.74 60	97.66 59	90.62 48	93.44 51	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 32	88.72 21	83.84 190	86.89 262	60.04 330	95.05 40	92.17 151	84.80 16	92.27 6	96.37 33	64.62 94	96.54 135	94.43 17	91.86 72	94.94 94

fmvsm_l_conf0.5_n		87.49 33	88.19 28	85.39 116	86.95 255	64.37 204	94.30 65	88.45 328	80.51 67	92.70 4	96.86 19	69.98 48	97.15 97	95.83 7	88.08 125	94.65 114

SD-MVS		87.49 33	87.49 38	87.50 42	93.60 56	68.82 74	93.90 86	92.63 131	76.86 143	87.90 44	95.76 52	66.17 73	97.63 61	89.06 57	91.48 80	96.05 42
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
fmvsm_l_conf0.5_n_a		87.44 35	88.15 29	85.30 122	87.10 252	64.19 211	94.41 60	88.14 338	80.24 76	92.54 5	96.97 14	69.52 50	97.17 93	95.89 6	88.51 120	94.56 117

dcpmvs_2		87.37 36	87.55 37	86.85 58	95.04 32	68.20 92	90.36 258	90.66 231	79.37 94	81.20 115	93.67 124	74.73 16	96.55 134	90.88 46	92.00 70	95.82 49

alignmvs		87.28 37	86.97 44	88.24 27	91.30 133	71.14 26	95.61 26	93.56 84	79.30 95	87.07 52	95.25 73	68.43 53	96.93 117	87.87 64	84.33 173	96.65 17

train_agg		87.21 38	87.42 39	86.60 72	94.18 41	67.28 116	94.16 69	93.51 87	71.87 244	85.52 69	95.33 65	68.19 55	97.27 87	89.09 56	94.90 22	95.25 80

MG-MVS		87.11 39	86.27 57	89.62 8	97.79 1	76.27 4	94.96 46	94.49 49	78.74 110	83.87 87	92.94 138	64.34 98	96.94 115	75.19 190	94.09 38	95.66 54

SF-MVS		87.03 40	87.09 42	86.84 59	92.70 86	67.45 114	93.64 102	93.76 74	70.78 277	86.25 58	96.44 32	66.98 65	97.79 50	88.68 60	94.56 34	95.28 76

fmvsm_s_conf0.5_n_3		86.88 41	87.99 31	83.58 204	87.26 247	60.74 310	93.21 123	87.94 345	84.22 20	91.70 15	97.27 5	65.91 78	95.02 212	93.95 22	90.42 97	94.99 91

CSCG		86.87 42	86.26 58	88.72 17	95.05 31	70.79 29	93.83 94	95.33 18	68.48 308	77.63 166	94.35 103	73.04 28	98.45 30	84.92 98	93.71 47	96.92 14

sasdasda		86.85 43	86.25 59	88.66 20	91.80 117	71.92 16	93.54 107	91.71 175	80.26 73	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 171	97.11 9

canonicalmvs		86.85 43	86.25 59	88.66 20	91.80 117	71.92 16	93.54 107	91.71 175	80.26 73	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 171	97.11 9

UBG		86.83 45	86.70 50	87.20 48	93.07 74	69.81 47	93.43 115	95.56 13	81.52 50	81.50 110	92.12 160	73.58 26	96.28 147	84.37 105	85.20 162	95.51 60

PHI-MVS		86.83 45	86.85 49	86.78 63	93.47 63	65.55 169	95.39 31	95.10 25	71.77 249	85.69 67	96.52 29	62.07 137	98.77 23	86.06 86	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 47	86.90 47	86.58 75	90.42 150	66.38 145	96.09 17	93.87 69	77.73 128	84.01 86	95.66 54	63.39 116	97.94 42	87.40 71	93.55 50	95.42 62
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 48	87.63 34	84.27 177	86.15 278	61.48 293	94.69 56	91.16 201	83.79 26	90.51 27	96.28 38	64.24 99	98.22 35	95.00 12	86.88 137	93.11 185

PVSNet_Blended		86.73 49	86.86 48	86.31 86	93.76 50	67.53 111	96.33 16	93.61 82	82.34 42	81.00 120	93.08 134	63.19 120	97.29 83	87.08 77	91.38 82	94.13 145

testing11		86.71 50	86.44 55	87.55 40	93.54 60	71.35 21	93.65 101	95.58 11	81.36 57	80.69 125	92.21 159	72.30 36	96.46 140	85.18 94	83.43 183	94.82 103

test_fmvsmconf_n		86.58 51	87.17 41	84.82 144	85.28 296	62.55 264	94.26 67	89.78 269	83.81 25	87.78 46	96.33 37	65.33 84	96.98 109	94.40 18	87.55 131	94.95 93

BP-MVS1		86.54 52	86.68 52	86.13 90	87.80 235	67.18 120	92.97 131	95.62 10	79.92 80	82.84 98	94.14 112	74.95 15	96.46 140	82.91 123	88.96 116	94.74 106

jason		86.40 53	86.17 61	87.11 51	86.16 277	70.54 32	95.71 24	92.19 148	82.00 45	84.58 79	94.34 104	61.86 139	95.53 194	87.76 65	90.89 90	95.27 77
jason: jason.
NormalMVS		86.39 54	86.66 53	85.60 110	92.12 101	65.95 158	94.88 47	90.83 219	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 90	91.15 86	93.93 156

fmvsm_s_conf0.5_n		86.39 54	86.91 46	84.82 144	87.36 246	63.54 238	94.74 52	90.02 262	82.52 38	90.14 32	96.92 17	62.93 127	97.84 49	95.28 11	82.26 193	93.07 188

fmvsm_s_conf0.5_n_5		86.38 56	86.94 45	84.71 155	84.67 308	63.29 243	94.04 78	89.99 264	82.88 34	87.85 45	96.03 47	62.89 129	96.36 144	94.15 19	89.95 104	94.48 126

SymmetryMVS		86.32 57	86.39 56	86.12 91	90.52 148	65.95 158	94.88 47	94.58 46	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 90	86.59 146	95.51 60

WTY-MVS		86.32 57	85.81 69	87.85 29	92.82 82	69.37 61	95.20 35	95.25 20	82.71 36	81.91 106	94.73 89	67.93 59	97.63 61	79.55 155	82.25 195	96.54 22

myMVS_eth3d28		86.31 59	86.15 62	86.78 63	93.56 58	70.49 33	92.94 134	95.28 19	82.47 39	78.70 157	92.07 162	72.45 34	95.41 196	82.11 130	85.78 155	94.44 128

MSLP-MVS++		86.27 60	85.91 68	87.35 45	92.01 108	68.97 71	95.04 42	92.70 122	79.04 105	81.50 110	96.50 31	58.98 180	96.78 125	83.49 117	93.93 41	96.29 35

VNet		86.20 61	85.65 73	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 115	86.00 63	93.07 135	58.22 189	97.00 105	85.22 92	84.33 173	96.52 23

MVS_111021_HR		86.19 62	85.80 70	87.37 44	93.17 70	69.79 48	93.99 81	93.76 74	79.08 102	78.88 153	93.99 118	62.25 136	98.15 38	85.93 87	91.15 86	94.15 144

SPE-MVS-test		86.14 63	87.01 43	83.52 205	92.63 88	59.36 342	95.49 28	91.92 161	80.09 77	85.46 71	95.53 60	61.82 141	95.77 174	86.77 81	93.37 52	95.41 63

ACMMP_NAP		86.05 64	85.80 70	86.80 62	91.58 123	67.53 111	91.79 195	93.49 90	74.93 176	84.61 78	95.30 67	59.42 171	97.92 43	86.13 84	94.92 20	94.94 94

testing99		86.01 65	85.47 75	87.63 38	93.62 55	71.25 23	93.47 113	95.23 21	80.42 70	80.60 127	91.95 168	71.73 41	96.50 138	80.02 152	82.22 196	95.13 83

ETV-MVS		86.01 65	86.11 63	85.70 107	90.21 155	67.02 127	93.43 115	91.92 161	81.21 59	84.13 85	94.07 117	60.93 150	95.63 183	89.28 54	89.81 106	94.46 127

testing91		85.93 67	85.31 79	87.78 32	93.59 57	71.47 19	93.50 110	95.08 28	80.26 73	80.53 128	91.93 169	70.43 45	96.51 137	80.32 150	82.13 198	95.37 66

APD-MVS		85.93 67	85.99 66	85.76 104	95.98 26	65.21 177	93.59 105	92.58 133	66.54 326	86.17 61	95.88 50	63.83 106	97.00 105	86.39 83	92.94 57	95.06 87
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 69	85.46 76	87.18 49	88.20 222	72.42 15	92.41 165	92.77 120	82.11 44	80.34 131	93.07 135	68.27 54	95.02 212	78.39 171	93.59 49	94.09 147

CS-MVS		85.80 70	86.65 54	83.27 217	92.00 109	58.92 346	95.31 32	91.86 166	79.97 78	84.82 77	95.40 63	62.26 135	95.51 195	86.11 85	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 71	86.09 64	84.72 153	85.73 289	63.58 235	93.79 95	89.32 288	81.42 55	90.21 30	96.91 18	62.41 134	97.67 56	94.48 16	80.56 220	92.90 194

test_fmvsmconf0.1_n		85.71 72	86.08 65	84.62 163	80.83 357	62.33 269	93.84 92	88.81 316	83.50 28	87.00 53	96.01 48	63.36 117	96.93 117	94.04 21	87.29 134	94.61 116

CDPH-MVS		85.71 72	85.46 76	86.46 79	94.75 34	67.19 118	93.89 87	92.83 118	70.90 273	83.09 96	95.28 69	63.62 111	97.36 78	80.63 146	94.18 37	94.84 99

casdiffmvs_mvg		85.66 74	85.18 81	87.09 52	88.22 221	69.35 62	93.74 98	91.89 164	81.47 51	80.10 133	91.45 179	64.80 92	96.35 145	87.23 74	87.69 129	95.58 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 75	85.93 67	84.68 157	82.95 340	63.48 240	94.03 80	89.46 282	81.69 48	89.86 33	96.74 25	61.85 140	97.75 52	94.74 15	82.01 200	92.81 198

MGCFI-Net		85.59 76	85.73 72	85.17 129	91.41 131	62.44 265	92.87 139	91.31 192	79.65 87	86.99 54	95.14 79	62.90 128	96.12 155	87.13 76	84.13 178	96.96 13

GDP-MVS		85.54 77	85.32 78	86.18 88	87.64 238	67.95 99	92.91 137	92.36 138	77.81 125	83.69 88	94.31 106	72.84 30	96.41 142	80.39 149	85.95 153	94.19 140

DeepC-MVS	77.85 3	85.52 78	85.24 80	86.37 83	88.80 191	66.64 139	92.15 174	93.68 80	81.07 61	76.91 177	93.64 125	62.59 131	98.44 31	85.50 88	92.84 59	94.03 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 79	84.87 87	86.84 59	88.25 219	69.07 67	93.04 128	91.76 171	81.27 58	80.84 123	92.07 162	64.23 100	96.06 161	84.98 97	87.43 133	95.39 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 80	85.08 83	86.06 92	93.09 73	65.65 165	93.89 87	93.41 95	73.75 197	79.94 135	94.68 91	60.61 153	98.03 40	82.63 126	93.72 46	94.52 122

fmvsm_s_conf0.5_n_7		85.24 81	86.69 51	80.91 285	84.52 313	60.10 328	93.35 118	90.35 243	83.41 29	86.54 57	96.27 39	60.50 154	90.02 369	94.84 14	90.38 98	92.61 202

MP-MVS-pluss		85.24 81	85.13 82	85.56 111	91.42 128	65.59 167	91.54 205	92.51 135	74.56 179	80.62 126	95.64 55	59.15 175	97.00 105	86.94 79	93.80 43	94.07 149
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 83	84.69 91	86.63 71	92.91 78	69.91 43	92.61 153	95.80 9	80.31 72	80.38 130	92.27 155	68.73 52	95.19 209	75.94 184	83.27 185	94.81 105

PAPR		85.15 84	84.47 92	87.18 49	96.02 25	68.29 86	91.85 193	93.00 113	76.59 154	79.03 149	95.00 80	61.59 142	97.61 63	78.16 172	89.00 115	95.63 55

fmvsm_s_conf0.5_n_2		85.06 85	85.60 74	83.44 211	86.92 260	60.53 317	94.41 60	87.31 353	83.30 30	88.72 40	96.72 26	54.28 240	97.75 52	94.07 20	84.68 170	92.04 225

MP-MVS		85.02 86	84.97 85	85.17 129	92.60 89	64.27 209	93.24 120	92.27 141	73.13 208	79.63 141	94.43 97	61.90 138	97.17 93	85.00 96	92.56 61	94.06 150
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 87	84.44 93	86.71 66	88.33 216	68.73 76	90.24 263	91.82 170	81.05 62	81.18 116	92.50 147	63.69 109	96.08 160	84.45 104	86.71 144	95.32 72

CHOSEN 1792x2688		84.98 88	83.45 110	89.57 11	89.94 160	75.14 6	92.07 180	92.32 139	81.87 46	75.68 186	88.27 244	60.18 158	98.60 27	80.46 148	90.27 100	94.96 92

MVSMamba_PlusPlus		84.97 89	83.65 103	88.93 14	90.17 156	74.04 8	87.84 321	92.69 125	62.18 364	81.47 112	87.64 258	71.47 42	96.28 147	84.69 100	94.74 31	96.47 28

viewmanbaseed2359cas		84.89 90	84.26 96	86.78 63	88.50 201	69.77 50	92.69 150	91.13 207	81.11 60	81.54 109	91.98 165	60.35 155	95.73 176	84.47 103	86.56 147	94.84 99

EIA-MVS		84.84 91	84.88 86	84.69 156	91.30 133	62.36 268	93.85 89	92.04 154	79.45 90	79.33 146	94.28 108	62.42 133	96.35 145	80.05 151	91.25 85	95.38 65

lecture		84.77 92	84.81 89	84.65 159	92.12 101	62.27 272	94.74 52	92.64 130	68.35 309	85.53 68	95.30 67	59.77 165	97.91 44	83.73 113	91.15 86	93.77 165

fmvsm_s_conf0.1_n_a		84.76 93	84.84 88	84.53 165	80.23 370	63.50 239	92.79 141	88.73 319	80.46 68	89.84 34	96.65 28	60.96 149	97.57 66	93.80 23	80.14 222	92.53 207

viewcassd2359sk11		84.74 94	84.11 97	86.64 70	88.57 195	69.20 65	92.61 153	91.23 198	80.58 65	80.85 122	91.96 166	61.39 144	95.89 167	84.28 106	85.49 159	94.82 103

HFP-MVS		84.73 95	84.40 94	85.72 106	93.75 52	65.01 183	93.50 110	93.19 103	72.19 233	79.22 147	94.93 83	59.04 178	97.67 56	81.55 135	92.21 64	94.49 125

MVS		84.66 96	82.86 131	90.06 2	90.93 140	74.56 7	87.91 319	95.54 14	68.55 306	72.35 243	94.71 90	59.78 164	98.90 20	81.29 141	94.69 32	96.74 16

GST-MVS		84.63 97	84.29 95	85.66 108	92.82 82	65.27 175	93.04 128	93.13 106	73.20 206	78.89 150	94.18 111	59.41 172	97.85 48	81.45 137	92.48 63	93.86 162

EC-MVSNet		84.53 98	85.04 84	83.01 223	89.34 172	61.37 297	94.42 59	91.09 209	77.91 123	83.24 92	94.20 110	58.37 187	95.40 197	85.35 89	91.41 81	92.27 219

fmvsm_s_conf0.1_n_2		84.40 99	84.78 90	83.27 217	85.25 297	60.41 320	94.13 72	85.69 377	83.05 32	87.99 43	96.37 33	52.75 257	97.68 54	93.75 24	84.05 179	91.71 233

ACMMPR		84.37 100	84.06 98	85.28 124	93.56 58	64.37 204	93.50 110	93.15 105	72.19 233	78.85 155	94.86 86	56.69 209	97.45 72	81.55 135	92.20 65	94.02 152

region2R		84.36 101	84.03 99	85.36 120	93.54 60	64.31 207	93.43 115	92.95 114	72.16 236	78.86 154	94.84 87	56.97 204	97.53 68	81.38 139	92.11 67	94.24 138

LFMVS		84.34 102	82.73 133	89.18 13	94.76 33	73.25 11	94.99 45	91.89 164	71.90 241	82.16 105	93.49 129	47.98 310	97.05 100	82.55 127	84.82 166	97.25 8

test_yl		84.28 103	83.16 122	87.64 34	94.52 37	69.24 63	95.78 18	95.09 26	69.19 296	81.09 117	92.88 141	57.00 202	97.44 73	81.11 143	81.76 203	96.23 38

DCV-MVSNet		84.28 103	83.16 122	87.64 34	94.52 37	69.24 63	95.78 18	95.09 26	69.19 296	81.09 117	92.88 141	57.00 202	97.44 73	81.11 143	81.76 203	96.23 38

diffmvs		84.28 103	83.83 100	85.61 109	87.40 244	68.02 96	90.88 236	89.24 291	80.54 66	81.64 108	92.52 146	59.83 163	94.52 241	87.32 72	85.11 163	94.29 135
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 103	83.36 116	87.02 55	92.22 96	67.74 104	84.65 348	94.50 48	79.15 99	82.23 104	87.93 253	66.88 66	96.94 115	80.53 147	82.20 197	96.39 33

ETVMVS		84.22 107	83.71 101	85.76 104	92.58 90	68.25 90	92.45 163	95.53 15	79.54 89	79.46 143	91.64 177	70.29 46	94.18 255	69.16 253	82.76 191	94.84 99

MAR-MVS		84.18 108	83.43 111	86.44 80	96.25 21	65.93 160	94.28 66	94.27 61	74.41 181	79.16 148	95.61 56	53.99 243	98.88 22	69.62 247	93.26 54	94.50 124
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
MVS_Test		84.16 109	83.20 120	87.05 54	91.56 124	69.82 46	89.99 272	92.05 153	77.77 127	82.84 98	86.57 275	63.93 105	96.09 157	74.91 195	89.18 112	95.25 80

CANet_DTU		84.09 110	83.52 104	85.81 101	90.30 153	66.82 134	91.87 191	89.01 307	85.27 12	86.09 62	93.74 122	47.71 316	96.98 109	77.90 174	89.78 108	93.65 168

viewdifsd2359ckpt13		84.08 111	83.21 119	86.70 67	88.49 205	69.55 55	92.25 168	91.14 205	79.71 85	79.73 138	91.72 175	58.83 181	95.89 167	82.06 131	84.99 164	94.66 113

viewmacassd2359aftdt		84.03 112	83.18 121	86.59 74	86.76 263	69.44 56	92.44 164	90.85 218	80.38 71	80.78 124	91.33 184	58.54 184	95.62 185	82.15 129	85.41 160	94.72 108

ET-MVSNet_ETH3D		84.01 113	83.15 124	86.58 75	90.78 145	70.89 28	94.74 52	94.62 43	81.44 54	58.19 382	93.64 125	73.64 25	92.35 324	82.66 125	78.66 242	96.50 27

diffmvs_AUTHOR		83.97 114	83.49 107	85.39 116	86.09 279	67.83 101	90.76 241	89.05 305	79.94 79	81.43 113	92.23 158	59.53 168	94.42 244	87.18 75	85.22 161	93.92 158

PVSNet_Blended_VisFu		83.97 114	83.50 106	85.39 116	90.02 158	66.59 142	93.77 96	91.73 173	77.43 136	77.08 176	89.81 220	63.77 108	96.97 112	79.67 154	88.21 123	92.60 203

MTAPA		83.91 116	83.38 115	85.50 112	91.89 115	65.16 179	81.75 379	92.23 142	75.32 171	80.53 128	95.21 76	56.06 218	97.16 96	84.86 99	92.55 62	94.18 141

XVS		83.87 117	83.47 109	85.05 133	93.22 66	63.78 223	92.92 135	92.66 127	73.99 189	78.18 160	94.31 106	55.25 224	97.41 75	79.16 161	91.58 78	93.95 154

Effi-MVS+		83.82 118	82.76 132	86.99 56	89.56 168	69.40 57	91.35 216	86.12 371	72.59 220	83.22 95	92.81 144	59.60 167	96.01 165	81.76 134	87.80 128	95.56 58

test_fmvsmvis_n_1920		83.80 119	83.48 108	84.77 148	82.51 343	63.72 228	91.37 214	83.99 395	81.42 55	77.68 165	95.74 53	58.37 187	97.58 64	93.38 25	86.87 138	93.00 191

EI-MVSNet-Vis-set		83.77 120	83.67 102	84.06 181	92.79 85	63.56 236	91.76 198	94.81 34	79.65 87	77.87 163	94.09 115	63.35 118	97.90 45	79.35 159	79.36 232	90.74 254

MVSFormer		83.75 121	82.88 130	86.37 83	89.24 181	71.18 24	89.07 297	90.69 228	65.80 331	87.13 50	94.34 104	64.99 87	92.67 310	72.83 212	91.80 74	95.27 77

CP-MVS		83.71 122	83.40 114	84.65 159	93.14 71	63.84 221	94.59 57	92.28 140	71.03 271	77.41 169	94.92 84	55.21 227	96.19 152	81.32 140	90.70 92	93.91 159

test_fmvsmconf0.01_n		83.70 123	83.52 104	84.25 178	75.26 416	61.72 287	92.17 173	87.24 355	82.36 41	84.91 76	95.41 62	55.60 222	96.83 124	92.85 29	85.87 154	94.21 139

baseline2		83.68 124	83.42 113	84.48 168	87.37 245	66.00 155	90.06 267	95.93 8	79.71 85	69.08 281	90.39 198	77.92 6	96.28 147	78.91 166	81.38 207	91.16 247

reproduce-ours		83.51 125	83.33 117	84.06 181	92.18 99	60.49 318	90.74 243	92.04 154	64.35 341	83.24 92	95.59 58	59.05 176	97.27 87	83.61 114	89.17 113	94.41 133

our_new_method		83.51 125	83.33 117	84.06 181	92.18 99	60.49 318	90.74 243	92.04 154	64.35 341	83.24 92	95.59 58	59.05 176	97.27 87	83.61 114	89.17 113	94.41 133

thisisatest0515		83.41 127	82.49 137	86.16 89	89.46 171	68.26 88	93.54 107	94.70 39	74.31 184	75.75 184	90.92 188	72.62 32	96.52 136	69.64 245	81.50 206	93.71 166

PVSNet_BlendedMVS		83.38 128	83.43 111	83.22 219	93.76 50	67.53 111	94.06 74	93.61 82	79.13 100	81.00 120	85.14 293	63.19 120	97.29 83	87.08 77	73.91 280	84.83 354

test2506		83.29 129	82.92 129	84.37 172	88.39 213	63.18 249	92.01 183	91.35 191	77.66 130	78.49 159	91.42 180	64.58 96	95.09 211	73.19 208	89.23 110	94.85 96

PGM-MVS		83.25 130	82.70 134	84.92 137	92.81 84	64.07 215	90.44 253	92.20 146	71.28 265	77.23 173	94.43 97	55.17 228	97.31 82	79.33 160	91.38 82	93.37 175

HPM-MVS		83.25 130	82.95 128	84.17 179	92.25 95	62.88 258	90.91 233	91.86 166	70.30 282	77.12 174	93.96 119	56.75 207	96.28 147	82.04 132	91.34 84	93.34 176
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 132	82.96 126	83.73 196	92.02 105	59.74 334	90.37 257	92.08 152	63.70 348	82.86 97	95.48 61	58.62 183	97.17 93	83.06 120	88.42 121	94.26 136

EI-MVSNet-UG-set		83.14 133	82.96 126	83.67 201	92.28 94	63.19 248	91.38 213	94.68 40	79.22 97	76.60 179	93.75 121	62.64 130	97.76 51	78.07 173	78.01 245	90.05 263

testing3-2		83.11 134	83.15 124	82.98 224	91.92 112	64.01 217	94.39 63	95.37 16	78.32 116	75.53 191	90.06 216	73.18 27	93.18 289	74.34 200	75.27 269	91.77 232

VDD-MVS		83.06 135	81.81 147	86.81 61	90.86 143	67.70 105	95.40 30	91.50 186	75.46 166	81.78 107	92.34 154	40.09 360	97.13 98	86.85 80	82.04 199	95.60 56

h-mvs33		83.01 136	82.56 136	84.35 173	89.34 172	62.02 276	92.72 144	93.76 74	81.45 52	82.73 101	92.25 157	60.11 159	97.13 98	87.69 66	62.96 363	93.91 159

PAPM_NR		82.97 137	81.84 146	86.37 83	94.10 44	66.76 137	87.66 325	92.84 117	69.96 286	74.07 216	93.57 127	63.10 125	97.50 70	70.66 240	90.58 94	94.85 96

mPP-MVS		82.96 138	82.44 138	84.52 166	92.83 80	62.92 256	92.76 142	91.85 168	71.52 261	75.61 189	94.24 109	53.48 251	96.99 108	78.97 164	90.73 91	93.64 169

SR-MVS		82.81 139	82.58 135	83.50 208	93.35 64	61.16 300	92.23 171	91.28 197	64.48 340	81.27 114	95.28 69	53.71 247	95.86 169	82.87 124	88.77 118	93.49 173

DP-MVS Recon		82.73 140	81.65 148	85.98 94	97.31 4	67.06 123	95.15 37	91.99 158	69.08 301	76.50 181	93.89 120	54.48 236	98.20 37	70.76 238	85.66 157	92.69 199

CLD-MVS		82.73 140	82.35 140	83.86 189	87.90 230	67.65 107	95.45 29	92.18 149	85.06 13	72.58 234	92.27 155	52.46 260	95.78 172	84.18 107	79.06 237	88.16 291
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 142	82.38 139	83.73 196	89.25 178	59.58 337	92.24 170	94.89 31	77.96 121	79.86 136	92.38 152	56.70 208	97.05 100	77.26 177	80.86 215	94.55 118

3Dnovator	73.91 6	82.69 143	80.82 161	88.31 26	89.57 167	71.26 22	92.60 155	94.39 56	78.84 107	67.89 302	92.48 150	48.42 305	98.52 28	68.80 258	94.40 36	95.15 82

RRT-MVS		82.61 144	81.16 152	86.96 57	91.10 137	68.75 75	87.70 324	92.20 146	76.97 141	72.68 230	87.10 269	51.30 274	96.41 142	83.56 116	87.84 127	95.74 52

viewmambaseed2359dif		82.60 145	81.91 145	84.67 158	85.83 286	66.09 152	90.50 252	89.01 307	75.46 166	79.64 140	92.01 164	59.51 169	94.38 246	82.99 122	82.26 193	93.54 171

MVSTER		82.47 146	82.05 141	83.74 194	92.68 87	69.01 69	91.90 190	93.21 100	79.83 81	72.14 244	85.71 288	74.72 17	94.72 226	75.72 186	72.49 290	87.50 298

TESTMET0.1,1		82.41 147	81.98 144	83.72 198	88.08 224	63.74 225	92.70 146	93.77 73	79.30 95	77.61 167	87.57 260	58.19 190	94.08 260	73.91 202	86.68 145	93.33 178

CostFormer		82.33 148	81.15 153	85.86 99	89.01 186	68.46 82	82.39 376	93.01 111	75.59 164	80.25 132	81.57 338	72.03 39	94.96 216	79.06 163	77.48 253	94.16 143

API-MVS		82.28 149	80.53 170	87.54 41	96.13 22	70.59 31	93.63 103	91.04 215	65.72 333	75.45 192	92.83 143	56.11 217	98.89 21	64.10 306	89.75 109	93.15 183

IB-MVS	77.80 4	82.18 150	80.46 172	87.35 45	89.14 183	70.28 36	95.59 27	95.17 24	78.85 106	70.19 269	85.82 286	70.66 44	97.67 56	72.19 224	66.52 333	94.09 147
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
xiu_mvs_v1_base_debu		82.16 151	81.12 154	85.26 126	86.42 270	68.72 77	92.59 157	90.44 240	73.12 209	84.20 82	94.36 99	38.04 373	95.73 176	84.12 108	86.81 139	91.33 240

xiu_mvs_v1_base		82.16 151	81.12 154	85.26 126	86.42 270	68.72 77	92.59 157	90.44 240	73.12 209	84.20 82	94.36 99	38.04 373	95.73 176	84.12 108	86.81 139	91.33 240

xiu_mvs_v1_base_debi		82.16 151	81.12 154	85.26 126	86.42 270	68.72 77	92.59 157	90.44 240	73.12 209	84.20 82	94.36 99	38.04 373	95.73 176	84.12 108	86.81 139	91.33 240

3Dnovator+	73.60 7	82.10 154	80.60 168	86.60 72	90.89 142	66.80 136	95.20 35	93.44 92	74.05 188	67.42 309	92.49 149	49.46 295	97.65 60	70.80 237	91.68 76	95.33 70

MVS_111021_LR		82.02 155	81.52 149	83.51 207	88.42 211	62.88 258	89.77 275	88.93 312	76.78 146	75.55 190	93.10 132	50.31 284	95.38 199	83.82 112	87.02 136	92.26 220

PMMVS		81.98 156	82.04 142	81.78 259	89.76 164	56.17 374	91.13 229	90.69 228	77.96 121	80.09 134	93.57 127	46.33 329	94.99 215	81.41 138	87.46 132	94.17 142

baseline1		81.84 157	81.03 158	84.28 176	91.60 122	66.62 140	91.08 230	91.66 180	81.87 46	74.86 202	91.67 176	69.98 48	94.92 219	71.76 227	64.75 350	91.29 245

EPP-MVSNet		81.79 158	81.52 149	82.61 234	88.77 192	60.21 326	93.02 130	93.66 81	68.52 307	72.90 228	90.39 198	72.19 38	94.96 216	74.93 194	79.29 235	92.67 200

WBMVS		81.67 159	80.98 160	83.72 198	93.07 74	69.40 57	94.33 64	93.05 109	76.84 144	72.05 246	84.14 304	74.49 19	93.88 274	72.76 215	68.09 319	87.88 293

test_vis1_n_1920		81.66 160	82.01 143	80.64 288	82.24 345	55.09 383	94.76 51	86.87 359	81.67 49	84.40 81	94.63 92	38.17 370	94.67 232	91.98 38	83.34 184	92.16 223

APD-MVS_3200maxsize		81.64 161	81.32 151	82.59 236	92.36 92	58.74 348	91.39 211	91.01 216	63.35 352	79.72 139	94.62 93	51.82 263	96.14 154	79.71 153	87.93 126	92.89 195

mvsmamba		81.55 162	80.72 163	84.03 185	91.42 128	66.93 132	83.08 367	89.13 299	78.55 114	67.50 307	87.02 270	51.79 265	90.07 368	87.48 69	90.49 96	95.10 85

ACMMP		81.49 163	80.67 165	83.93 187	91.71 120	62.90 257	92.13 175	92.22 145	71.79 248	71.68 252	93.49 129	50.32 283	96.96 113	78.47 170	84.22 177	91.93 230
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
KinetiMVS		81.43 164	80.11 174	85.38 119	86.60 266	65.47 173	92.90 138	93.54 86	75.33 170	77.31 171	90.39 198	46.81 321	96.75 126	71.65 230	86.46 150	93.93 156

CDS-MVSNet		81.43 164	80.74 162	83.52 205	86.26 274	64.45 198	92.09 178	90.65 232	75.83 162	73.95 218	89.81 220	63.97 104	92.91 300	71.27 231	82.82 188	93.20 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 166	79.99 178	85.46 113	90.39 152	68.40 83	86.88 336	90.61 233	74.41 181	70.31 268	84.67 298	63.79 107	92.32 326	73.13 209	85.70 156	95.67 53

ECVR-MVS		81.29 167	80.38 173	84.01 186	88.39 213	61.96 278	92.56 160	86.79 361	77.66 130	76.63 178	91.42 180	46.34 328	95.24 208	74.36 199	89.23 110	94.85 96

guyue		81.23 168	80.57 169	83.21 221	86.64 264	61.85 281	92.52 161	92.78 119	78.69 111	74.92 201	89.42 224	50.07 287	95.35 200	80.79 145	79.31 234	92.42 209

IMVS_0403		81.19 169	79.88 180	85.13 131	88.54 196	64.75 188	88.84 302	90.80 222	76.73 149	75.21 195	90.18 204	54.22 241	96.21 151	73.47 204	80.95 210	94.43 129

thisisatest0530		81.15 170	80.07 175	84.39 171	88.26 218	65.63 166	91.40 209	94.62 43	71.27 266	70.93 259	89.18 229	72.47 33	96.04 162	65.62 295	76.89 260	91.49 236

Fast-Effi-MVS+		81.14 171	80.01 177	84.51 167	90.24 154	65.86 161	94.12 73	89.15 297	73.81 196	75.37 194	88.26 245	57.26 197	94.53 240	66.97 280	84.92 165	93.15 183

HQP-MVS		81.14 171	80.64 166	82.64 233	87.54 240	63.66 233	94.06 74	91.70 178	79.80 82	74.18 209	90.30 201	51.63 268	95.61 186	77.63 175	78.90 238	88.63 282

hse-mvs2		81.12 173	81.11 157	81.16 273	86.52 269	57.48 363	89.40 288	91.16 201	81.45 52	82.73 101	90.49 196	60.11 159	94.58 233	87.69 66	60.41 390	91.41 239

SR-MVS-dyc-post		81.06 174	80.70 164	82.15 250	92.02 105	58.56 351	90.90 234	90.45 236	62.76 359	78.89 150	94.46 95	51.26 275	95.61 186	78.77 168	86.77 142	92.28 216

HyFIR lowres test		81.03 175	79.56 187	85.43 114	87.81 234	68.11 94	90.18 264	90.01 263	70.65 279	72.95 227	86.06 282	63.61 112	94.50 242	75.01 193	79.75 226	93.67 167

nrg030		80.93 176	79.86 181	84.13 180	83.69 329	68.83 73	93.23 121	91.20 199	75.55 165	75.06 197	88.22 248	63.04 126	94.74 225	81.88 133	66.88 330	88.82 280

Vis-MVSNet		80.92 177	79.98 179	83.74 194	88.48 207	61.80 282	93.44 114	88.26 337	73.96 192	77.73 164	91.76 172	49.94 289	94.76 223	65.84 292	90.37 99	94.65 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 178	80.02 176	83.33 212	87.87 231	60.76 308	92.62 152	86.86 360	77.86 124	75.73 185	91.39 182	46.35 327	94.70 231	72.79 214	88.68 119	94.52 122

UWE-MVS		80.81 179	81.01 159	80.20 298	89.33 174	57.05 368	91.91 189	94.71 38	75.67 163	75.01 198	89.37 225	63.13 124	91.44 351	67.19 277	82.80 190	92.12 224

IMVS_0407		80.80 180	79.39 193	85.00 136	88.54 196	64.75 188	88.40 310	90.80 222	76.73 149	73.95 218	90.18 204	51.55 270	95.81 170	73.47 204	80.95 210	94.43 129

1314		80.70 181	78.95 201	85.94 96	87.77 237	67.56 109	87.91 319	92.55 134	72.17 235	67.44 308	93.09 133	50.27 285	97.04 103	71.68 229	87.64 130	93.23 180

AstraMVS		80.66 182	79.79 183	83.28 216	85.07 303	61.64 289	92.19 172	90.58 234	79.40 92	74.77 204	90.18 204	45.93 333	95.61 186	83.04 121	76.96 259	92.60 203

tpmrst		80.57 183	79.14 199	84.84 143	90.10 157	68.28 87	81.70 380	89.72 276	77.63 132	75.96 183	79.54 370	64.94 89	92.71 307	75.43 188	77.28 256	93.55 170

1112_ss		80.56 184	79.83 182	82.77 228	88.65 193	60.78 306	92.29 167	88.36 330	72.58 221	72.46 240	94.95 81	65.09 86	93.42 286	66.38 286	77.71 247	94.10 146

VDDNet		80.50 185	78.26 209	87.21 47	86.19 275	69.79 48	94.48 58	91.31 192	60.42 380	79.34 145	90.91 189	38.48 368	96.56 133	82.16 128	81.05 209	95.27 77

BH-w/o		80.49 186	79.30 195	84.05 184	90.83 144	64.36 206	93.60 104	89.42 285	74.35 183	69.09 280	90.15 212	55.23 226	95.61 186	64.61 303	86.43 151	92.17 222

test_cas_vis1_n_1920		80.45 187	80.61 167	79.97 307	78.25 397	57.01 370	94.04 78	88.33 332	79.06 104	82.81 100	93.70 123	38.65 365	91.63 342	90.82 47	79.81 224	91.27 246

icg_test_0407_2		80.38 188	79.22 197	83.88 188	88.54 196	64.75 188	86.79 337	90.80 222	76.73 149	73.95 218	90.18 204	51.55 270	92.45 319	73.47 204	80.95 210	94.43 129

TAMVS		80.37 189	79.45 190	83.13 222	85.14 300	63.37 241	91.23 223	90.76 227	74.81 178	72.65 232	88.49 238	60.63 152	92.95 295	69.41 249	81.95 201	93.08 187

HQP_MVS		80.34 190	79.75 184	82.12 252	86.94 256	62.42 266	93.13 124	91.31 192	78.81 108	72.53 235	89.14 231	50.66 280	95.55 192	76.74 178	78.53 243	88.39 288

SDMVSNet		80.26 191	78.88 202	84.40 170	89.25 178	67.63 108	85.35 344	93.02 110	76.77 147	70.84 260	87.12 267	47.95 313	96.09 157	85.04 95	74.55 271	89.48 273

HPM-MVS_fast		80.25 192	79.55 189	82.33 242	91.55 125	59.95 331	91.32 218	89.16 296	65.23 337	74.71 206	93.07 135	47.81 315	95.74 175	74.87 197	88.23 122	91.31 244

ab-mvs		80.18 193	78.31 208	85.80 102	88.44 209	65.49 172	83.00 370	92.67 126	71.82 247	77.36 170	85.01 294	54.50 233	96.59 130	76.35 183	75.63 267	95.32 72

IS-MVSNet		80.14 194	79.41 191	82.33 242	87.91 229	60.08 329	91.97 187	88.27 335	72.90 216	71.44 256	91.73 174	61.44 143	93.66 281	62.47 320	86.53 148	93.24 179

test-LLR		80.10 195	79.56 187	81.72 261	86.93 258	61.17 298	92.70 146	91.54 183	71.51 262	75.62 187	86.94 271	53.83 244	92.38 321	72.21 222	84.76 168	91.60 234

PVSNet	73.49 8	80.05 196	78.63 204	84.31 174	90.92 141	64.97 184	92.47 162	91.05 214	79.18 98	72.43 241	90.51 195	37.05 385	94.06 262	68.06 264	86.00 152	93.90 161

UA-Net		80.02 197	79.65 185	81.11 276	89.33 174	57.72 358	86.33 341	89.00 311	77.44 135	81.01 119	89.15 230	59.33 173	95.90 166	61.01 327	84.28 175	89.73 269

test-mter		79.96 198	79.38 194	81.72 261	86.93 258	61.17 298	92.70 146	91.54 183	73.85 194	75.62 187	86.94 271	49.84 291	92.38 321	72.21 222	84.76 168	91.60 234

QAPM		79.95 199	77.39 230	87.64 34	89.63 166	71.41 20	93.30 119	93.70 79	65.34 336	67.39 311	91.75 173	47.83 314	98.96 16	57.71 343	89.81 106	92.54 206

UGNet		79.87 200	78.68 203	83.45 210	89.96 159	61.51 291	92.13 175	90.79 226	76.83 145	78.85 155	86.33 279	38.16 371	96.17 153	67.93 267	87.17 135	92.67 200
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
tpm2		79.80 201	77.95 216	85.34 121	88.28 217	68.26 88	81.56 382	91.42 189	70.11 284	77.59 168	80.50 356	67.40 63	94.26 253	67.34 274	77.35 254	93.51 172

thres200		79.66 202	78.33 207	83.66 202	92.54 91	65.82 163	93.06 126	96.31 3	74.90 177	73.30 224	88.66 236	59.67 166	95.61 186	47.84 386	78.67 241	89.56 272

CPTT-MVS		79.59 203	79.16 198	80.89 286	91.54 126	59.80 333	92.10 177	88.54 327	60.42 380	72.96 226	93.28 131	48.27 306	92.80 304	78.89 167	86.50 149	90.06 262

Test_1112_low_res		79.56 204	78.60 205	82.43 238	88.24 220	60.39 322	92.09 178	87.99 342	72.10 237	71.84 248	87.42 262	64.62 94	93.04 291	65.80 293	77.30 255	93.85 163

tttt0517		79.50 205	78.53 206	82.41 241	87.22 249	61.43 295	89.75 276	94.76 35	69.29 294	67.91 300	88.06 252	72.92 29	95.63 183	62.91 316	73.90 281	90.16 261

reproduce_monomvs		79.49 206	79.11 200	80.64 288	92.91 78	61.47 294	91.17 228	93.28 98	83.09 31	64.04 341	82.38 324	66.19 72	94.57 235	81.19 142	57.71 398	85.88 337

FIs		79.47 207	79.41 191	79.67 315	85.95 282	59.40 339	91.68 202	93.94 68	78.06 120	68.96 286	88.28 243	66.61 69	91.77 338	66.20 289	74.99 270	87.82 294

SSM_0404		79.46 208	77.65 220	84.91 139	88.37 215	67.04 125	89.59 277	87.03 356	67.99 312	75.45 192	89.32 226	47.98 310	95.34 202	71.23 232	81.90 202	92.34 212

BH-RMVSNet		79.46 208	77.65 220	84.89 140	91.68 121	65.66 164	93.55 106	88.09 340	72.93 213	73.37 223	91.12 187	46.20 331	96.12 155	56.28 349	85.61 158	92.91 193

viewdifsd2359ckpt11		79.42 210	77.95 216	83.81 191	83.87 326	63.85 219	89.54 282	87.38 349	77.39 138	74.94 199	89.95 217	51.11 276	94.72 226	79.52 156	67.90 322	92.88 196

viewmsd2359difaftdt		79.42 210	77.96 215	83.81 191	83.88 325	63.85 219	89.54 282	87.38 349	77.39 138	74.94 199	89.95 217	51.11 276	94.72 226	79.52 156	67.90 322	92.88 196

PCF-MVS	73.15 9	79.29 212	77.63 222	84.29 175	86.06 280	65.96 157	87.03 332	91.10 208	69.86 288	69.79 276	90.64 191	57.54 196	96.59 130	64.37 305	82.29 192	90.32 259
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 213	79.57 186	78.24 336	88.46 208	52.29 394	90.41 255	89.12 300	74.24 185	69.13 279	91.91 170	65.77 79	90.09 367	59.00 339	88.09 124	92.33 213

114514_t		79.17 214	77.67 219	83.68 200	95.32 29	65.53 170	92.85 140	91.60 182	63.49 350	67.92 299	90.63 193	46.65 324	95.72 181	67.01 279	83.54 182	89.79 267

FA-MVS(test-final)		79.12 215	77.23 232	84.81 147	90.54 147	63.98 218	81.35 385	91.71 175	71.09 270	74.85 203	82.94 317	52.85 255	97.05 100	67.97 265	81.73 205	93.41 174

SSM_0407		79.09 216	77.21 233	84.75 151	88.50 201	66.98 128	89.21 293	87.03 356	67.99 312	74.12 213	89.32 226	47.98 310	95.29 207	71.23 232	79.52 227	91.98 227

VPA-MVSNet		79.03 217	78.00 213	82.11 255	85.95 282	64.48 197	93.22 122	94.66 41	75.05 175	74.04 217	84.95 295	52.17 262	93.52 283	74.90 196	67.04 329	88.32 290

OPM-MVS		79.00 218	78.09 211	81.73 260	83.52 332	63.83 222	91.64 204	90.30 248	76.36 158	71.97 247	89.93 219	46.30 330	95.17 210	75.10 191	77.70 248	86.19 325
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 219	78.22 210	81.25 270	85.33 293	62.73 261	89.53 285	93.21 100	72.39 228	72.14 244	90.13 213	60.99 147	94.72 226	67.73 269	72.49 290	86.29 322

AdaColmap		78.94 220	77.00 237	84.76 150	96.34 17	65.86 161	92.66 151	87.97 344	62.18 364	70.56 262	92.37 153	43.53 345	97.35 79	64.50 304	82.86 187	91.05 249

GeoE		78.90 221	77.43 226	83.29 215	88.95 187	62.02 276	92.31 166	86.23 367	70.24 283	71.34 257	89.27 228	54.43 237	94.04 265	63.31 312	80.81 217	93.81 164

miper_enhance_ethall		78.86 222	77.97 214	81.54 265	88.00 228	65.17 178	91.41 207	89.15 297	75.19 173	68.79 289	83.98 307	67.17 64	92.82 302	72.73 216	65.30 340	86.62 319

VPNet		78.82 223	77.53 225	82.70 231	84.52 313	66.44 144	93.93 84	92.23 142	80.46 68	72.60 233	88.38 242	49.18 299	93.13 290	72.47 220	63.97 360	88.55 285

EPNet_dtu		78.80 224	79.26 196	77.43 344	88.06 225	49.71 411	91.96 188	91.95 160	77.67 129	76.56 180	91.28 185	58.51 185	90.20 365	56.37 348	80.95 210	92.39 210
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 225	77.43 226	82.88 226	92.21 97	64.49 195	92.05 181	96.28 4	73.48 203	71.75 250	88.26 245	60.07 161	95.32 203	45.16 399	77.58 250	88.83 278

TR-MVS		78.77 226	77.37 231	82.95 225	90.49 149	60.88 304	93.67 100	90.07 258	70.08 285	74.51 207	91.37 183	45.69 334	95.70 182	60.12 333	80.32 221	92.29 215

thres400		78.68 227	77.43 226	82.43 238	92.21 97	64.49 195	92.05 181	96.28 4	73.48 203	71.75 250	88.26 245	60.07 161	95.32 203	45.16 399	77.58 250	87.48 299

BH-untuned		78.68 227	77.08 234	83.48 209	89.84 161	63.74 225	92.70 146	88.59 325	71.57 259	66.83 318	88.65 237	51.75 266	95.39 198	59.03 338	84.77 167	91.32 243

OMC-MVS		78.67 229	77.91 218	80.95 283	85.76 288	57.40 365	88.49 308	88.67 322	73.85 194	72.43 241	92.10 161	49.29 298	94.55 239	72.73 216	77.89 246	90.91 253

tpm		78.58 230	77.03 235	83.22 219	85.94 284	64.56 193	83.21 366	91.14 205	78.31 117	73.67 221	79.68 368	64.01 103	92.09 332	66.07 290	71.26 300	93.03 189

OpenMVS	70.45 11	78.54 231	75.92 254	86.41 82	85.93 285	71.68 18	92.74 143	92.51 135	66.49 327	64.56 335	91.96 166	43.88 344	98.10 39	54.61 354	90.65 93	89.44 275

EPMVS		78.49 232	75.98 253	86.02 93	91.21 135	69.68 53	80.23 394	91.20 199	75.25 172	72.48 239	78.11 379	54.65 232	93.69 280	57.66 344	83.04 186	94.69 109

AUN-MVS		78.37 233	77.43 226	81.17 272	86.60 266	57.45 364	89.46 287	91.16 201	74.11 187	74.40 208	90.49 196	55.52 223	94.57 235	74.73 198	60.43 389	91.48 237

thres100view900		78.37 233	77.01 236	82.46 237	91.89 115	63.21 247	91.19 227	96.33 1	72.28 231	70.45 265	87.89 254	60.31 156	95.32 203	45.16 399	77.58 250	88.83 278

GA-MVS		78.33 235	76.23 249	84.65 159	83.65 330	66.30 148	91.44 206	90.14 256	76.01 160	70.32 267	84.02 306	42.50 349	94.72 226	70.98 235	77.00 258	92.94 192

cascas		78.18 236	75.77 256	85.41 115	87.14 251	69.11 66	92.96 133	91.15 204	66.71 325	70.47 263	86.07 281	37.49 379	96.48 139	70.15 243	79.80 225	90.65 255

UniMVSNet_NR-MVSNet		78.15 237	77.55 224	79.98 305	84.46 316	60.26 324	92.25 168	93.20 102	77.50 134	68.88 287	86.61 274	66.10 74	92.13 330	66.38 286	62.55 367	87.54 297

LuminaMVS		78.14 238	76.66 241	82.60 235	80.82 358	64.64 192	89.33 289	90.45 236	68.25 310	74.73 205	85.51 290	41.15 355	94.14 256	78.96 165	80.69 219	89.04 276

IMVS_0404		78.11 239	76.29 248	83.59 203	88.54 196	64.75 188	84.63 349	90.80 222	76.73 149	61.16 363	90.18 204	40.17 359	91.58 344	73.47 204	80.95 210	94.43 129

thres600view7		78.00 240	76.66 241	82.03 257	91.93 111	63.69 231	91.30 219	96.33 1	72.43 226	70.46 264	87.89 254	60.31 156	94.92 219	42.64 411	76.64 261	87.48 299

FC-MVSNet-test		77.99 241	78.08 212	77.70 339	84.89 306	55.51 380	90.27 261	93.75 77	76.87 142	66.80 319	87.59 259	65.71 80	90.23 364	62.89 317	73.94 279	87.37 302

Anonymous202405211		77.96 242	75.33 262	85.87 98	93.73 53	64.52 194	94.85 49	85.36 380	62.52 362	76.11 182	90.18 204	29.43 417	97.29 83	68.51 260	77.24 257	95.81 50

cl22		77.94 243	76.78 239	81.42 267	87.57 239	64.93 186	90.67 246	88.86 315	72.45 225	67.63 306	82.68 321	64.07 101	92.91 300	71.79 225	65.30 340	86.44 320

XXY-MVS		77.94 243	76.44 244	82.43 238	82.60 342	64.44 199	92.01 183	91.83 169	73.59 202	70.00 272	85.82 286	54.43 237	94.76 223	69.63 246	68.02 321	88.10 292

MS-PatchMatch		77.90 245	76.50 243	82.12 252	85.99 281	69.95 42	91.75 200	92.70 122	73.97 191	62.58 358	84.44 302	41.11 356	95.78 172	63.76 309	92.17 66	80.62 402

FMVSNet3		77.73 246	76.04 252	82.80 227	91.20 136	68.99 70	91.87 191	91.99 158	73.35 205	67.04 314	83.19 316	56.62 210	92.14 329	59.80 335	69.34 307	87.28 305

VortexMVS		77.62 247	76.44 244	81.13 274	88.58 194	63.73 227	91.24 222	91.30 196	77.81 125	65.76 324	81.97 330	49.69 293	93.72 278	76.40 182	65.26 343	85.94 335

miper_ehance_all_eth		77.60 248	76.44 244	81.09 280	85.70 290	64.41 202	90.65 247	88.64 324	72.31 229	67.37 312	82.52 322	64.77 93	92.64 313	70.67 239	65.30 340	86.24 324

UniMVSNet (Re)		77.58 249	76.78 239	79.98 305	84.11 322	60.80 305	91.76 198	93.17 104	76.56 155	69.93 275	84.78 297	63.32 119	92.36 323	64.89 302	62.51 369	86.78 313

PatchmatchNet		77.46 250	74.63 269	85.96 95	89.55 169	70.35 35	79.97 399	89.55 280	72.23 232	70.94 258	76.91 391	57.03 200	92.79 305	54.27 356	81.17 208	94.74 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 251	75.65 258	82.73 229	80.38 366	67.13 122	91.85 193	90.23 253	75.09 174	69.37 277	83.39 313	53.79 246	94.44 243	71.77 226	65.00 347	86.63 318

CHOSEN 280x420		77.35 252	76.95 238	78.55 331	87.07 253	62.68 262	69.71 431	82.95 402	68.80 303	71.48 255	87.27 266	66.03 75	84.00 415	76.47 181	82.81 189	88.95 277

PS-MVSNAJss		77.26 253	76.31 247	80.13 300	80.64 362	59.16 344	90.63 250	91.06 213	72.80 217	68.58 293	84.57 300	53.55 248	93.96 270	72.97 210	71.96 294	87.27 306

gg-mvs-nofinetune		77.18 254	74.31 276	85.80 102	91.42 128	68.36 84	71.78 425	94.72 37	49.61 424	77.12 174	45.92 453	77.41 8	93.98 269	67.62 270	93.16 55	95.05 88

WB-MVSnew		77.14 255	76.18 251	80.01 304	86.18 276	63.24 245	91.26 220	94.11 65	71.72 251	73.52 222	87.29 265	45.14 339	93.00 293	56.98 346	79.42 230	83.80 363

MVP-Stereo		77.12 256	76.23 249	79.79 312	81.72 350	66.34 147	89.29 290	90.88 217	70.56 280	62.01 361	82.88 318	49.34 296	94.13 257	65.55 297	93.80 43	78.88 417
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 257	75.37 260	82.20 248	89.25 178	62.11 275	82.06 377	89.09 302	76.77 147	70.84 260	87.12 267	41.43 354	95.01 214	67.23 276	74.55 271	89.48 273

MonoMVSNet		76.99 258	75.08 265	82.73 229	83.32 334	63.24 245	86.47 340	86.37 363	79.08 102	66.31 322	79.30 372	49.80 292	91.72 339	79.37 158	65.70 338	93.23 180

dmvs_re		76.93 259	75.36 261	81.61 263	87.78 236	60.71 312	80.00 398	87.99 342	79.42 91	69.02 283	89.47 223	46.77 322	94.32 247	63.38 311	74.45 274	89.81 266

X-MVStestdata		76.86 260	74.13 282	85.05 133	93.22 66	63.78 223	92.92 135	92.66 127	73.99 189	78.18 160	10.19 468	55.25 224	97.41 75	79.16 161	91.58 78	93.95 154

DU-MVS		76.86 260	75.84 255	79.91 308	82.96 338	60.26 324	91.26 220	91.54 183	76.46 157	68.88 287	86.35 277	56.16 215	92.13 330	66.38 286	62.55 367	87.35 303

Anonymous20240529		76.84 262	74.15 281	84.88 141	91.02 138	64.95 185	93.84 92	91.09 209	53.57 412	73.00 225	87.42 262	35.91 389	97.32 81	69.14 254	72.41 292	92.36 211

UWE-MVS-28		76.83 263	77.60 223	74.51 373	84.58 312	50.34 407	88.22 313	94.60 45	74.46 180	66.66 320	88.98 235	62.53 132	85.50 407	57.55 345	80.80 218	87.69 296

c3_l		76.83 263	75.47 259	80.93 284	85.02 304	64.18 212	90.39 256	88.11 339	71.66 252	66.65 321	81.64 336	63.58 115	92.56 314	69.31 251	62.86 364	86.04 330

WR-MVS		76.76 265	75.74 257	79.82 311	84.60 310	62.27 272	92.60 155	92.51 135	76.06 159	67.87 303	85.34 291	56.76 206	90.24 363	62.20 321	63.69 362	86.94 311

v1144		76.73 266	74.88 266	82.27 244	80.23 370	66.60 141	91.68 202	90.21 255	73.69 199	69.06 282	81.89 331	52.73 258	94.40 245	69.21 252	65.23 344	85.80 338

IterMVS-LS		76.49 267	75.18 264	80.43 292	84.49 315	62.74 260	90.64 248	88.80 317	72.40 227	65.16 330	81.72 334	60.98 148	92.27 327	67.74 268	64.65 352	86.29 322
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 268	74.55 272	82.19 249	79.14 384	67.82 102	90.26 262	89.42 285	73.75 197	68.63 292	81.89 331	51.31 273	94.09 259	71.69 228	64.84 348	84.66 355

Elysia		76.45 269	74.17 279	83.30 213	80.43 364	64.12 213	89.58 278	90.83 219	61.78 372	72.53 235	85.92 284	34.30 396	94.81 221	68.10 262	84.01 180	90.97 250

StellarMVS		76.45 269	74.17 279	83.30 213	80.43 364	64.12 213	89.58 278	90.83 219	61.78 372	72.53 235	85.92 284	34.30 396	94.81 221	68.10 262	84.01 180	90.97 250

mamba_0408		76.22 271	73.37 293	84.77 148	88.50 201	66.98 128	58.80 452	86.18 369	69.12 299	74.12 213	89.01 233	47.50 317	95.35 200	67.57 271	79.52 227	91.98 227

v148		76.19 272	74.47 274	81.36 268	80.05 372	64.44 199	91.75 200	90.23 253	73.68 200	67.13 313	80.84 351	55.92 220	93.86 277	68.95 256	61.73 378	85.76 341

Effi-MVS+-dtu		76.14 273	75.28 263	78.72 330	83.22 335	55.17 382	89.87 273	87.78 346	75.42 168	67.98 298	81.43 340	45.08 340	92.52 316	75.08 192	71.63 295	88.48 286

cl____		76.07 274	74.67 267	80.28 295	85.15 299	61.76 285	90.12 265	88.73 319	71.16 267	65.43 327	81.57 338	61.15 145	92.95 295	66.54 283	62.17 371	86.13 328

DIV-MVS_self_test		76.07 274	74.67 267	80.28 295	85.14 300	61.75 286	90.12 265	88.73 319	71.16 267	65.42 328	81.60 337	61.15 145	92.94 299	66.54 283	62.16 373	86.14 326

FMVSNet2		76.07 274	74.01 284	82.26 246	88.85 188	67.66 106	91.33 217	91.61 181	70.84 274	65.98 323	82.25 326	48.03 307	92.00 334	58.46 340	68.73 315	87.10 308

v144192		76.05 277	74.03 283	82.12 252	79.50 378	66.55 143	91.39 211	89.71 277	72.30 230	68.17 296	81.33 343	51.75 266	94.03 267	67.94 266	64.19 355	85.77 339

NR-MVSNet		76.05 277	74.59 270	80.44 291	82.96 338	62.18 274	90.83 238	91.73 173	77.12 140	60.96 365	86.35 277	59.28 174	91.80 337	60.74 328	61.34 382	87.35 303

v1192		75.98 279	73.92 285	82.15 250	79.73 374	66.24 150	91.22 224	89.75 271	72.67 219	68.49 294	81.42 341	49.86 290	94.27 251	67.08 278	65.02 346	85.95 333

FE-MVS		75.97 280	73.02 299	84.82 144	89.78 162	65.56 168	77.44 410	91.07 212	64.55 339	72.66 231	79.85 366	46.05 332	96.69 128	54.97 353	80.82 216	92.21 221

eth_miper_zixun_eth		75.96 281	74.40 275	80.66 287	84.66 309	63.02 251	89.28 291	88.27 335	71.88 243	65.73 325	81.65 335	59.45 170	92.81 303	68.13 261	60.53 387	86.14 326

TranMVSNet+NR-MVSNet		75.86 282	74.52 273	79.89 309	82.44 344	60.64 315	91.37 214	91.37 190	76.63 153	67.65 305	86.21 280	52.37 261	91.55 345	61.84 323	60.81 385	87.48 299

SCA		75.82 283	72.76 302	85.01 135	86.63 265	70.08 38	81.06 387	89.19 294	71.60 258	70.01 271	77.09 389	45.53 335	90.25 360	60.43 330	73.27 283	94.68 110

LPG-MVS_test		75.82 283	74.58 271	79.56 319	84.31 319	59.37 340	90.44 253	89.73 274	69.49 291	64.86 331	88.42 240	38.65 365	94.30 249	72.56 218	72.76 287	85.01 352

GBi-Net		75.65 285	73.83 286	81.10 277	88.85 188	65.11 180	90.01 269	90.32 244	70.84 274	67.04 314	80.25 361	48.03 307	91.54 346	59.80 335	69.34 307	86.64 315

test1		75.65 285	73.83 286	81.10 277	88.85 188	65.11 180	90.01 269	90.32 244	70.84 274	67.04 314	80.25 361	48.03 307	91.54 346	59.80 335	69.34 307	86.64 315

v1921920		75.63 287	73.49 291	82.06 256	79.38 379	66.35 146	91.07 232	89.48 281	71.98 238	67.99 297	81.22 346	49.16 301	93.90 273	66.56 282	64.56 353	85.92 336

ACMP	71.68 10	75.58 288	74.23 278	79.62 317	84.97 305	59.64 335	90.80 239	89.07 304	70.39 281	62.95 354	87.30 264	38.28 369	93.87 275	72.89 211	71.45 298	85.36 348
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 289	73.26 297	81.61 263	80.67 361	66.82 134	89.54 282	89.27 290	71.65 253	63.30 349	80.30 360	54.99 230	94.06 262	67.33 275	62.33 370	83.94 361

tpm cat1		75.30 290	72.21 311	84.58 164	88.52 200	67.77 103	78.16 408	88.02 341	61.88 370	68.45 295	76.37 395	60.65 151	94.03 267	53.77 359	74.11 277	91.93 230

PLC	68.80 14	75.23 291	73.68 289	79.86 310	92.93 77	58.68 349	90.64 248	88.30 333	60.90 377	64.43 339	90.53 194	42.38 350	94.57 235	56.52 347	76.54 262	86.33 321
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 292	72.98 300	81.88 258	79.20 381	66.00 155	90.75 242	89.11 301	71.63 257	67.41 310	81.22 346	47.36 319	93.87 275	65.46 298	64.72 351	85.77 339

Fast-Effi-MVS+-dtu		75.04 293	73.37 293	80.07 301	80.86 356	59.52 338	91.20 226	85.38 379	71.90 241	65.20 329	84.84 296	41.46 353	92.97 294	66.50 285	72.96 286	87.73 295

dp		75.01 294	72.09 312	83.76 193	89.28 177	66.22 151	79.96 400	89.75 271	71.16 267	67.80 304	77.19 388	51.81 264	92.54 315	50.39 369	71.44 299	92.51 208

TAPA-MVS	70.22 12	74.94 295	73.53 290	79.17 325	90.40 151	52.07 395	89.19 295	89.61 279	62.69 361	70.07 270	92.67 145	48.89 304	94.32 247	38.26 425	79.97 223	91.12 248
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 296	73.32 296	79.74 314	86.53 268	60.31 323	89.03 300	92.70 122	78.61 113	68.98 285	83.34 314	41.93 352	92.23 328	52.77 363	65.97 336	86.69 314

SSM_04072		74.86 297	73.37 293	79.35 322	88.50 201	66.98 128	58.80 452	86.18 369	69.12 299	74.12 213	89.01 233	47.50 317	79.09 437	67.57 271	79.52 227	91.98 227

v10		74.77 298	72.54 308	81.46 266	80.33 368	66.71 138	89.15 296	89.08 303	70.94 272	63.08 352	79.86 365	52.52 259	94.04 265	65.70 294	62.17 371	83.64 364

XVG-OURS-SEG-HR		74.70 299	73.08 298	79.57 318	78.25 397	57.33 366	80.49 390	87.32 351	63.22 354	68.76 290	90.12 215	44.89 341	91.59 343	70.55 241	74.09 278	89.79 267

ACMM	69.62 13	74.34 300	72.73 304	79.17 325	84.25 321	57.87 356	90.36 258	89.93 265	63.17 356	65.64 326	86.04 283	37.79 377	94.10 258	65.89 291	71.52 297	85.55 344
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 301	72.30 310	80.32 293	91.49 127	61.66 288	90.85 237	80.72 408	56.67 404	63.85 344	90.64 191	46.75 323	90.84 354	53.79 358	75.99 266	88.47 287

XVG-OURS		74.25 302	72.46 309	79.63 316	78.45 395	57.59 362	80.33 392	87.39 348	63.86 346	68.76 290	89.62 222	40.50 358	91.72 339	69.00 255	74.25 276	89.58 270

test_fmvs1		74.07 303	73.69 288	75.22 364	78.91 388	47.34 424	89.06 299	74.69 425	63.68 349	79.41 144	91.59 178	24.36 428	87.77 390	85.22 92	76.26 264	90.55 258

CVMVSNet		74.04 304	74.27 277	73.33 383	85.33 293	43.94 438	89.53 285	88.39 329	54.33 411	70.37 266	90.13 213	49.17 300	84.05 413	61.83 324	79.36 232	91.99 226

Baseline_NR-MVSNet		73.99 305	72.83 301	77.48 343	80.78 359	59.29 343	91.79 195	84.55 388	68.85 302	68.99 284	80.70 352	56.16 215	92.04 333	62.67 318	60.98 384	81.11 396

pmmvs4		73.92 306	71.81 316	80.25 297	79.17 382	65.24 176	87.43 328	87.26 354	67.64 318	63.46 347	83.91 308	48.96 303	91.53 349	62.94 315	65.49 339	83.96 360

D2MVS		73.80 307	72.02 313	79.15 327	79.15 383	62.97 252	88.58 307	90.07 258	72.94 212	59.22 375	78.30 376	42.31 351	92.70 309	65.59 296	72.00 293	81.79 391

SD_0403		73.79 308	73.48 292	74.69 370	85.33 293	45.56 434	83.80 356	85.57 378	76.55 156	62.96 353	88.45 239	50.62 282	87.59 394	48.80 379	79.28 236	90.92 252

CR-MVSNet		73.79 308	70.82 324	82.70 231	83.15 336	67.96 97	70.25 428	84.00 393	73.67 201	69.97 273	72.41 412	57.82 193	89.48 373	52.99 362	73.13 284	90.64 256

test_djsdf		73.76 310	72.56 307	77.39 345	77.00 409	53.93 388	89.07 297	90.69 228	65.80 331	63.92 342	82.03 329	43.14 348	92.67 310	72.83 212	68.53 316	85.57 343

pmmvs5		73.35 311	71.52 318	78.86 329	78.64 392	60.61 316	91.08 230	86.90 358	67.69 315	63.32 348	83.64 309	44.33 343	90.53 357	62.04 322	66.02 335	85.46 346

Anonymous20231211		73.08 312	70.39 328	81.13 274	90.62 146	63.33 242	91.40 209	90.06 260	51.84 417	64.46 338	80.67 354	36.49 387	94.07 261	63.83 308	64.17 356	85.98 332

tt0805		73.07 313	70.73 325	80.07 301	78.37 396	57.05 368	87.78 322	92.18 149	61.23 376	67.04 314	86.49 276	31.35 409	94.58 233	65.06 301	67.12 328	88.57 284

miper_lstm_enhance		73.05 314	71.73 317	77.03 350	83.80 327	58.32 353	81.76 378	88.88 313	69.80 289	61.01 364	78.23 378	57.19 198	87.51 395	65.34 299	59.53 392	85.27 351

jajsoiax		73.05 314	71.51 319	77.67 340	77.46 406	54.83 384	88.81 303	90.04 261	69.13 298	62.85 356	83.51 311	31.16 410	92.75 306	70.83 236	69.80 303	85.43 347

LCM-MVSNet-Re		72.93 316	71.84 315	76.18 359	88.49 205	48.02 419	80.07 397	70.17 440	73.96 192	52.25 409	80.09 364	49.98 288	88.24 384	67.35 273	84.23 176	92.28 216

pm-mvs1		72.89 317	71.09 321	78.26 335	79.10 385	57.62 360	90.80 239	89.30 289	67.66 316	62.91 355	81.78 333	49.11 302	92.95 295	60.29 332	58.89 395	84.22 359

tpmvs		72.88 318	69.76 334	82.22 247	90.98 139	67.05 124	78.22 407	88.30 333	63.10 357	64.35 340	74.98 402	55.09 229	94.27 251	43.25 405	69.57 306	85.34 349

test0.0.03 1		72.76 319	72.71 305	72.88 387	80.25 369	47.99 420	91.22 224	89.45 283	71.51 262	62.51 359	87.66 257	53.83 244	85.06 409	50.16 371	67.84 326	85.58 342

UniMVSNet_ETH3D		72.74 320	70.53 327	79.36 321	78.62 393	56.64 372	85.01 346	89.20 293	63.77 347	64.84 333	84.44 302	34.05 398	91.86 336	63.94 307	70.89 302	89.57 271

mvs_tets		72.71 321	71.11 320	77.52 341	77.41 407	54.52 386	88.45 309	89.76 270	68.76 305	62.70 357	83.26 315	29.49 416	92.71 307	70.51 242	69.62 305	85.34 349

FMVSNet1		72.71 321	69.91 332	81.10 277	83.60 331	65.11 180	90.01 269	90.32 244	63.92 345	63.56 346	80.25 361	36.35 388	91.54 346	54.46 355	66.75 331	86.64 315

test_fmvs1_n		72.69 323	71.92 314	74.99 368	71.15 429	47.08 426	87.34 330	75.67 420	63.48 351	78.08 162	91.17 186	20.16 442	87.87 387	84.65 101	75.57 268	90.01 264

IterMVS		72.65 324	70.83 322	78.09 337	82.17 346	62.96 253	87.64 326	86.28 365	71.56 260	60.44 368	78.85 374	45.42 337	86.66 399	63.30 313	61.83 375	84.65 356
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 325	72.74 303	72.10 395	87.87 231	49.45 413	88.07 315	89.01 307	72.91 214	63.11 350	88.10 249	63.63 110	85.54 404	32.73 441	69.23 310	81.32 394

PatchMatch-RL		72.06 326	69.98 329	78.28 334	89.51 170	55.70 379	83.49 359	83.39 400	61.24 375	63.72 345	82.76 319	34.77 393	93.03 292	53.37 361	77.59 249	86.12 329

PVSNet_0	68.08 15	71.81 327	68.32 343	82.27 244	84.68 307	62.31 271	88.68 305	90.31 247	75.84 161	57.93 387	80.65 355	37.85 376	94.19 254	69.94 244	29.05 456	90.31 260

MIMVSNet		71.64 328	68.44 341	81.23 271	81.97 349	64.44 199	73.05 422	88.80 317	69.67 290	64.59 334	74.79 404	32.79 401	87.82 388	53.99 357	76.35 263	91.42 238

test_vis1_n		71.63 329	70.73 325	74.31 377	69.63 435	47.29 425	86.91 334	72.11 433	63.21 355	75.18 196	90.17 210	20.40 440	85.76 403	84.59 102	74.42 275	89.87 265

IterMVS-SCA-FT		71.55 330	69.97 330	76.32 357	81.48 352	60.67 314	87.64 326	85.99 372	66.17 329	59.50 373	78.88 373	45.53 335	83.65 417	62.58 319	61.93 374	84.63 358

v7n		71.31 331	68.65 338	79.28 323	76.40 411	60.77 307	86.71 338	89.45 283	64.17 344	58.77 380	78.24 377	44.59 342	93.54 282	57.76 342	61.75 377	83.52 367

anonymousdsp		71.14 332	69.37 336	76.45 356	72.95 424	54.71 385	84.19 353	88.88 313	61.92 369	62.15 360	79.77 367	38.14 372	91.44 351	68.90 257	67.45 327	83.21 373

F-COLMAP		70.66 333	68.44 341	77.32 346	86.37 273	55.91 377	88.00 317	86.32 364	56.94 402	57.28 391	88.07 251	33.58 399	92.49 317	51.02 366	68.37 317	83.55 365

WR-MVS_H		70.59 334	69.94 331	72.53 389	81.03 355	51.43 399	87.35 329	92.03 157	67.38 319	60.23 370	80.70 352	55.84 221	83.45 420	46.33 394	58.58 397	82.72 380

CP-MVSNet		70.50 335	69.91 332	72.26 392	80.71 360	51.00 403	87.23 331	90.30 248	67.84 314	59.64 372	82.69 320	50.23 286	82.30 428	51.28 365	59.28 393	83.46 369

RPMNet		70.42 336	65.68 357	84.63 162	83.15 336	67.96 97	70.25 428	90.45 236	46.83 433	69.97 273	65.10 436	56.48 214	95.30 206	35.79 430	73.13 284	90.64 256

testing3		70.38 337	70.83 322	69.03 408	85.82 287	43.93 439	90.72 245	90.56 235	68.06 311	60.24 369	86.82 273	64.83 91	84.12 411	26.33 449	64.10 357	79.04 415

tfpnnormal		70.10 338	67.36 347	78.32 333	83.45 333	60.97 303	88.85 301	92.77 120	64.85 338	60.83 366	78.53 375	43.52 346	93.48 284	31.73 444	61.70 379	80.52 403

TransMVSNet (Re)		70.07 339	67.66 345	77.31 347	80.62 363	59.13 345	91.78 197	84.94 384	65.97 330	60.08 371	80.44 357	50.78 279	91.87 335	48.84 378	45.46 429	80.94 398

CL-MVSNet_self_test		69.92 340	68.09 344	75.41 362	73.25 423	55.90 378	90.05 268	89.90 266	69.96 286	61.96 362	76.54 392	51.05 278	87.64 391	49.51 375	50.59 418	82.70 382

DP-MVS		69.90 341	66.48 349	80.14 299	95.36 28	62.93 254	89.56 280	76.11 418	50.27 423	57.69 389	85.23 292	39.68 361	95.73 176	33.35 435	71.05 301	81.78 392

PS-CasMVS		69.86 342	69.13 337	72.07 396	80.35 367	50.57 406	87.02 333	89.75 271	67.27 320	59.19 376	82.28 325	46.58 325	82.24 429	50.69 368	59.02 394	83.39 371

Syy-MVS		69.65 343	69.52 335	70.03 404	87.87 231	43.21 440	88.07 315	89.01 307	72.91 214	63.11 350	88.10 249	45.28 338	85.54 404	22.07 454	69.23 310	81.32 394

MSDG		69.54 344	65.73 356	80.96 282	85.11 302	63.71 229	84.19 353	83.28 401	56.95 401	54.50 398	84.03 305	31.50 407	96.03 163	42.87 409	69.13 312	83.14 375

PEN-MVS		69.46 345	68.56 339	72.17 394	79.27 380	49.71 411	86.90 335	89.24 291	67.24 323	59.08 377	82.51 323	47.23 320	83.54 419	48.42 381	57.12 399	83.25 372

LS3D		69.17 346	66.40 351	77.50 342	91.92 112	56.12 375	85.12 345	80.37 410	46.96 431	56.50 393	87.51 261	37.25 380	93.71 279	32.52 443	79.40 231	82.68 383

PatchT		69.11 347	65.37 361	80.32 293	82.07 348	63.68 232	67.96 438	87.62 347	50.86 421	69.37 277	65.18 435	57.09 199	88.53 380	41.59 414	66.60 332	88.74 281

KD-MVS_2432*1600		69.03 348	66.37 352	77.01 351	85.56 291	61.06 301	81.44 383	90.25 251	67.27 320	58.00 385	76.53 393	54.49 234	87.63 392	48.04 383	35.77 447	82.34 386

miper_refine_blended		69.03 348	66.37 352	77.01 351	85.56 291	61.06 301	81.44 383	90.25 251	67.27 320	58.00 385	76.53 393	54.49 234	87.63 392	48.04 383	35.77 447	82.34 386

mvsany_test1		68.77 350	68.56 339	69.39 406	73.57 422	45.88 433	80.93 388	60.88 454	59.65 386	71.56 253	90.26 203	43.22 347	75.05 441	74.26 201	62.70 366	87.25 307

ACMH	63.93 17	68.62 351	64.81 363	80.03 303	85.22 298	63.25 244	87.72 323	84.66 386	60.83 378	51.57 413	79.43 371	27.29 423	94.96 216	41.76 412	64.84 348	81.88 390
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 352	65.41 360	77.96 338	78.69 391	62.93 254	89.86 274	89.17 295	60.55 379	50.27 418	77.73 383	22.60 436	94.06 262	47.18 390	72.65 289	76.88 429

ADS-MVSNet		68.54 353	64.38 370	81.03 281	88.06 225	66.90 133	68.01 436	84.02 392	57.57 395	64.48 336	69.87 423	38.68 363	89.21 375	40.87 416	67.89 324	86.97 309

DTE-MVSNet		68.46 354	67.33 348	71.87 398	77.94 401	49.00 417	86.16 342	88.58 326	66.36 328	58.19 382	82.21 327	46.36 326	83.87 416	44.97 402	55.17 406	82.73 379

mmtdpeth		68.33 355	66.37 352	74.21 378	82.81 341	51.73 396	84.34 351	80.42 409	67.01 324	71.56 253	68.58 427	30.52 414	92.35 324	75.89 185	36.21 445	78.56 422

our_test_3		68.29 356	64.69 365	79.11 328	78.92 386	64.85 187	88.40 310	85.06 382	60.32 382	52.68 407	76.12 397	40.81 357	89.80 372	44.25 404	55.65 404	82.67 384

Patchmatch-RL test		68.17 357	64.49 368	79.19 324	71.22 428	53.93 388	70.07 430	71.54 437	69.22 295	56.79 392	62.89 440	56.58 211	88.61 377	69.53 248	52.61 413	95.03 90

XVG-ACMP-BASELINE		68.04 358	65.53 359	75.56 361	74.06 421	52.37 393	78.43 404	85.88 373	62.03 367	58.91 379	81.21 348	20.38 441	91.15 353	60.69 329	68.18 318	83.16 374

FMVSNet5		68.04 358	65.66 358	75.18 366	84.43 317	57.89 355	83.54 358	86.26 366	61.83 371	53.64 404	73.30 407	37.15 383	85.08 408	48.99 377	61.77 376	82.56 385

ppachtmachnet_test		67.72 360	63.70 372	79.77 313	78.92 386	66.04 154	88.68 305	82.90 403	60.11 384	55.45 395	75.96 398	39.19 362	90.55 356	39.53 420	52.55 414	82.71 381

ACMH+	65.35 16	67.65 361	64.55 366	76.96 353	84.59 311	57.10 367	88.08 314	80.79 407	58.59 393	53.00 406	81.09 350	26.63 425	92.95 295	46.51 392	61.69 380	80.82 399

pmmvs6		67.57 362	64.76 364	76.00 360	72.82 426	53.37 390	88.71 304	86.78 362	53.19 413	57.58 390	78.03 380	35.33 392	92.41 320	55.56 351	54.88 408	82.21 388

Anonymous20231206		67.53 363	65.78 355	72.79 388	74.95 417	47.59 422	88.23 312	87.32 351	61.75 374	58.07 384	77.29 386	37.79 377	87.29 397	42.91 407	63.71 361	83.48 368

Patchmtry		67.53 363	63.93 371	78.34 332	82.12 347	64.38 203	68.72 433	84.00 393	48.23 430	59.24 374	72.41 412	57.82 193	89.27 374	46.10 395	56.68 403	81.36 393

USDC		67.43 365	64.51 367	76.19 358	77.94 401	55.29 381	78.38 405	85.00 383	73.17 207	48.36 426	80.37 358	21.23 438	92.48 318	52.15 364	64.02 359	80.81 400

ADS-MVSNet2		66.90 366	63.44 374	77.26 348	88.06 225	60.70 313	68.01 436	75.56 422	57.57 395	64.48 336	69.87 423	38.68 363	84.10 412	40.87 416	67.89 324	86.97 309

CMPMVS	48.56 21	66.77 367	64.41 369	73.84 380	70.65 432	50.31 408	77.79 409	85.73 376	45.54 436	44.76 437	82.14 328	35.40 391	90.14 366	63.18 314	74.54 273	81.07 397
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 368	62.92 377	76.80 355	76.51 410	57.77 357	89.22 292	83.41 399	55.48 408	53.86 402	77.84 381	26.28 426	93.95 271	34.90 432	68.76 314	78.68 420

LTVRE_ROB	59.60 19	66.27 369	63.54 373	74.45 374	84.00 324	51.55 398	67.08 440	83.53 397	58.78 391	54.94 397	80.31 359	34.54 394	93.23 288	40.64 418	68.03 320	78.58 421
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
JIA-IIPM		66.06 370	62.45 380	76.88 354	81.42 354	54.45 387	57.49 454	88.67 322	49.36 425	63.86 343	46.86 452	56.06 218	90.25 360	49.53 374	68.83 313	85.95 333

Patchmatch-test		65.86 371	60.94 386	80.62 290	83.75 328	58.83 347	58.91 451	75.26 424	44.50 439	50.95 417	77.09 389	58.81 182	87.90 386	35.13 431	64.03 358	95.12 84

UnsupCasMVSNet_eth		65.79 372	63.10 375	73.88 379	70.71 431	50.29 409	81.09 386	89.88 267	72.58 221	49.25 423	74.77 405	32.57 403	87.43 396	55.96 350	41.04 437	83.90 362

test_fmvs2		65.78 373	64.84 362	68.60 410	66.54 442	41.71 442	83.27 363	69.81 441	54.38 410	67.91 300	84.54 301	15.35 447	81.22 433	75.65 187	66.16 334	82.88 376

dmvs_testset		65.55 374	66.45 350	62.86 422	79.87 373	22.35 468	76.55 412	71.74 435	77.42 137	55.85 394	87.77 256	51.39 272	80.69 434	31.51 447	65.92 337	85.55 344

pmmvs-eth3d		65.53 375	62.32 381	75.19 365	69.39 436	59.59 336	82.80 371	83.43 398	62.52 362	51.30 415	72.49 410	32.86 400	87.16 398	55.32 352	50.73 417	78.83 418

mamv4		65.18 376	67.43 346	58.44 426	77.88 403	49.36 416	69.40 432	70.99 439	48.31 429	57.78 388	85.53 289	59.01 179	51.88 464	73.67 203	64.32 354	74.07 434

SixPastTwentyTwo		64.92 377	61.78 384	74.34 376	78.74 390	49.76 410	83.42 362	79.51 413	62.86 358	50.27 418	77.35 384	30.92 412	90.49 358	45.89 396	47.06 424	82.78 377

OurMVSNet-221017-0		64.68 378	62.17 382	72.21 393	76.08 414	47.35 423	80.67 389	81.02 406	56.19 405	51.60 412	79.66 369	27.05 424	88.56 379	53.60 360	53.63 411	80.71 401

test_0402		64.54 379	61.09 385	74.92 369	84.10 323	60.75 309	87.95 318	79.71 412	52.03 415	52.41 408	77.20 387	32.21 405	91.64 341	23.14 452	61.03 383	72.36 440

testgi		64.48 380	62.87 378	69.31 407	71.24 427	40.62 445	85.49 343	79.92 411	65.36 335	54.18 400	83.49 312	23.74 431	84.55 410	41.60 413	60.79 386	82.77 378

RPSCF		64.24 381	61.98 383	71.01 401	76.10 413	45.00 435	75.83 417	75.94 419	46.94 432	58.96 378	84.59 299	31.40 408	82.00 430	47.76 388	60.33 391	86.04 330

EU-MVSNet		64.01 382	63.01 376	67.02 416	74.40 420	38.86 451	83.27 363	86.19 368	45.11 437	54.27 399	81.15 349	36.91 386	80.01 436	48.79 380	57.02 400	82.19 389

test20.03		63.83 383	62.65 379	67.38 415	70.58 433	39.94 447	86.57 339	84.17 390	63.29 353	51.86 411	77.30 385	37.09 384	82.47 426	38.87 424	54.13 410	79.73 409

sc_t1		63.81 384	59.39 392	77.10 349	77.62 404	56.03 376	84.32 352	73.56 429	46.66 434	58.22 381	73.06 408	23.28 434	90.62 355	50.93 367	46.84 425	84.64 357

MDA-MVSNet_test_wron		63.78 385	60.16 388	74.64 371	78.15 399	60.41 320	83.49 359	84.03 391	56.17 407	39.17 447	71.59 418	37.22 381	83.24 423	42.87 409	48.73 420	80.26 406

YYNet1		63.76 386	60.14 389	74.62 372	78.06 400	60.19 327	83.46 361	83.99 395	56.18 406	39.25 446	71.56 419	37.18 382	83.34 421	42.90 408	48.70 421	80.32 405

K. test v3		63.09 387	59.61 391	73.53 382	76.26 412	49.38 415	83.27 363	77.15 416	64.35 341	47.77 428	72.32 414	28.73 418	87.79 389	49.93 373	36.69 444	83.41 370

COLMAP_ROB	57.96 20	62.98 388	59.65 390	72.98 386	81.44 353	53.00 392	83.75 357	75.53 423	48.34 428	48.81 425	81.40 342	24.14 429	90.30 359	32.95 438	60.52 388	75.65 432
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 389	59.08 393	71.10 400	67.19 440	48.72 418	83.91 355	85.23 381	50.38 422	47.84 427	71.22 421	20.74 439	85.51 406	46.47 393	58.75 396	79.06 414

tt0320		61.85 390	57.45 399	75.03 367	77.49 405	57.60 361	82.74 372	73.65 428	43.65 443	53.65 403	68.18 429	25.47 427	88.66 376	45.56 398	46.68 426	78.81 419

AllTest		61.66 391	58.06 395	72.46 390	79.57 375	51.42 400	80.17 395	68.61 443	51.25 419	45.88 431	81.23 344	19.86 443	86.58 400	38.98 422	57.01 401	79.39 411

UnsupCasMVSNet_bld		61.60 392	57.71 396	73.29 384	68.73 437	51.64 397	78.61 403	89.05 305	57.20 400	46.11 430	61.96 443	28.70 419	88.60 378	50.08 372	38.90 442	79.63 410

MDA-MVSNet-bldmvs		61.54 393	57.70 397	73.05 385	79.53 377	57.00 371	83.08 367	81.23 405	57.57 395	34.91 451	72.45 411	32.79 401	86.26 402	35.81 429	41.95 435	75.89 431

tt0320-xc		61.51 394	56.89 403	75.37 363	78.50 394	58.61 350	82.61 374	71.27 438	44.31 440	53.17 405	68.03 431	23.38 432	88.46 381	47.77 387	43.00 434	79.03 416

mvs5depth		61.03 395	57.65 398	71.18 399	67.16 441	47.04 428	72.74 423	77.49 414	57.47 398	60.52 367	72.53 409	22.84 435	88.38 382	49.15 376	38.94 441	78.11 425

KD-MVS_self_test		60.87 396	58.60 394	67.68 413	66.13 443	39.93 448	75.63 419	84.70 385	57.32 399	49.57 421	68.45 428	29.55 415	82.87 424	48.09 382	47.94 422	80.25 407

kuosan		60.86 397	60.24 387	62.71 423	81.57 351	46.43 430	75.70 418	85.88 373	57.98 394	48.95 424	69.53 425	58.42 186	76.53 439	28.25 448	35.87 446	65.15 447

FE-MVSNET		60.52 398	57.18 402	70.53 402	67.53 439	50.68 405	82.62 373	76.28 417	59.33 389	46.71 429	71.10 422	30.54 413	83.61 418	33.15 437	47.37 423	77.29 428

TinyColmap		60.32 399	56.42 406	72.00 397	78.78 389	53.18 391	78.36 406	75.64 421	52.30 414	41.59 445	75.82 400	14.76 450	88.35 383	35.84 428	54.71 409	74.46 433

MVS-HIRNet		60.25 400	55.55 407	74.35 375	84.37 318	56.57 373	71.64 426	74.11 426	34.44 450	45.54 435	42.24 458	31.11 411	89.81 370	40.36 419	76.10 265	76.67 430

MIMVSNet1		60.16 401	57.33 400	68.67 409	69.71 434	44.13 437	78.92 402	84.21 389	55.05 409	44.63 438	71.85 416	23.91 430	81.54 432	32.63 442	55.03 407	80.35 404

PM-MVS		59.40 402	56.59 404	67.84 411	63.63 446	41.86 441	76.76 411	63.22 451	59.01 390	51.07 416	72.27 415	11.72 454	83.25 422	61.34 325	50.28 419	78.39 423

new-patchmatchnet		59.30 403	56.48 405	67.79 412	65.86 444	44.19 436	82.47 375	81.77 404	59.94 385	43.65 441	66.20 434	27.67 422	81.68 431	39.34 421	41.40 436	77.50 427

test_vis1_rt		59.09 404	57.31 401	64.43 419	68.44 438	46.02 432	83.05 369	48.63 463	51.96 416	49.57 421	63.86 439	16.30 445	80.20 435	71.21 234	62.79 365	67.07 446

test_fmvs3		56.82 405	54.86 409	62.69 424	53.59 457	35.47 454	75.87 416	65.64 448	43.91 441	55.10 396	71.43 420	6.91 462	74.40 444	68.64 259	52.63 412	78.20 424

DSMNet-mixed		56.78 406	54.44 410	63.79 420	63.21 447	29.44 463	64.43 443	64.10 450	42.12 447	51.32 414	71.60 417	31.76 406	75.04 442	36.23 427	65.20 345	86.87 312

pmmvs3		55.51 407	51.50 413	67.53 414	57.90 455	50.93 404	80.37 391	73.66 427	40.63 448	44.15 440	64.75 437	16.30 445	78.97 438	44.77 403	40.98 439	72.69 438

TDRefinement		55.28 408	51.58 412	66.39 417	59.53 454	46.15 431	76.23 414	72.80 430	44.60 438	42.49 443	76.28 396	15.29 448	82.39 427	33.20 436	43.75 431	70.62 442

dongtai		55.18 409	55.46 408	54.34 434	76.03 415	36.88 452	76.07 415	84.61 387	51.28 418	43.41 442	64.61 438	56.56 212	67.81 452	18.09 457	28.50 457	58.32 450

LF4IMVS		54.01 410	52.12 411	59.69 425	62.41 449	39.91 449	68.59 434	68.28 445	42.96 445	44.55 439	75.18 401	14.09 452	68.39 451	41.36 415	51.68 415	70.78 441

ttmdpeth		53.34 411	49.96 414	63.45 421	62.07 451	40.04 446	72.06 424	65.64 448	42.54 446	51.88 410	77.79 382	13.94 453	76.48 440	32.93 439	30.82 455	73.84 435

MVStest1		51.35 412	46.89 416	64.74 418	65.06 445	51.10 402	67.33 439	72.58 431	30.20 454	35.30 449	74.82 403	27.70 421	69.89 449	24.44 451	24.57 458	73.22 436

N_pmnet		50.55 413	49.11 415	54.88 432	77.17 408	4.02 476	84.36 350	2.00 474	48.59 426	45.86 433	68.82 426	32.22 404	82.80 425	31.58 445	51.38 416	77.81 426

new_pmnet		49.31 414	46.44 417	57.93 427	62.84 448	40.74 444	68.47 435	62.96 452	36.48 449	35.09 450	57.81 447	14.97 449	72.18 446	32.86 440	46.44 427	60.88 449

mvsany_test3		48.86 415	46.35 418	56.41 428	46.00 463	31.67 459	62.26 445	47.25 464	43.71 442	45.54 435	68.15 430	10.84 455	64.44 460	57.95 341	35.44 449	73.13 437

test_f		46.58 416	43.45 420	55.96 429	45.18 464	32.05 458	61.18 446	49.49 462	33.39 451	42.05 444	62.48 442	7.00 461	65.56 456	47.08 391	43.21 433	70.27 443

WB-MVS		46.23 417	44.94 419	50.11 437	62.13 450	21.23 470	76.48 413	55.49 456	45.89 435	35.78 448	61.44 445	35.54 390	72.83 445	9.96 464	21.75 459	56.27 452

FPMVS		45.64 418	43.10 422	53.23 435	51.42 460	36.46 453	64.97 442	71.91 434	29.13 455	27.53 455	61.55 444	9.83 457	65.01 458	16.00 461	55.58 405	58.22 451

SSC-MVS		44.51 419	43.35 421	47.99 441	61.01 453	18.90 472	74.12 421	54.36 457	43.42 444	34.10 452	60.02 446	34.42 395	70.39 448	9.14 466	19.57 460	54.68 453

EGC-MVSNET		42.35 420	38.09 423	55.11 431	74.57 418	46.62 429	71.63 427	55.77 455	0.04 469	0.24 470	62.70 441	14.24 451	74.91 443	17.59 458	46.06 428	43.80 455

LCM-MVSNet		40.54 421	35.79 426	54.76 433	36.92 470	30.81 460	51.41 457	69.02 442	22.07 457	24.63 457	45.37 454	4.56 466	65.81 455	33.67 434	34.50 450	67.67 444

APD_test1		40.50 422	37.31 425	50.09 438	51.88 458	35.27 455	59.45 450	52.59 459	21.64 458	26.12 456	57.80 448	4.56 466	66.56 454	22.64 453	39.09 440	48.43 454

test_vis3_rt		40.46 423	37.79 424	48.47 440	44.49 465	33.35 457	66.56 441	32.84 471	32.39 452	29.65 453	39.13 461	3.91 469	68.65 450	50.17 370	40.99 438	43.40 456

ANet_high		40.27 424	35.20 427	55.47 430	34.74 471	34.47 456	63.84 444	71.56 436	48.42 427	18.80 460	41.08 459	9.52 458	64.45 459	20.18 455	8.66 467	67.49 445

test_method		38.59 425	35.16 428	48.89 439	54.33 456	21.35 469	45.32 460	53.71 458	7.41 466	28.74 454	51.62 450	8.70 459	52.87 463	33.73 433	32.89 451	72.47 439

PMMVS2		37.93 426	33.61 429	50.92 436	46.31 462	24.76 466	60.55 449	50.05 460	28.94 456	20.93 458	47.59 451	4.41 468	65.13 457	25.14 450	18.55 462	62.87 448

Gipuma		34.91 427	31.44 430	45.30 442	70.99 430	39.64 450	19.85 464	72.56 432	20.10 460	16.16 464	21.47 465	5.08 465	71.16 447	13.07 462	43.70 432	25.08 462
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 428	29.47 431	42.67 444	41.89 467	30.81 460	52.07 455	43.45 465	15.45 461	18.52 461	44.82 455	2.12 470	58.38 461	16.05 459	30.87 453	38.83 457

APD_test2		32.77 428	29.47 431	42.67 444	41.89 467	30.81 460	52.07 455	43.45 465	15.45 461	18.52 461	44.82 455	2.12 470	58.38 461	16.05 459	30.87 453	38.83 457

PMVS	26.43 22	31.84 430	28.16 433	42.89 443	25.87 473	27.58 464	50.92 458	49.78 461	21.37 459	14.17 465	40.81 460	2.01 472	66.62 453	9.61 465	38.88 443	34.49 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 431	24.00 435	26.45 448	43.74 466	18.44 473	60.86 447	39.66 467	15.11 463	9.53 467	22.10 464	6.52 463	46.94 466	8.31 467	10.14 464	13.98 464

MVE	24.84 23	24.35 432	19.77 438	38.09 446	34.56 472	26.92 465	26.57 462	38.87 469	11.73 465	11.37 466	27.44 462	1.37 473	50.42 465	11.41 463	14.60 463	36.93 459
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 433	23.20 437	25.46 449	41.52 469	16.90 474	60.56 448	38.79 470	14.62 464	8.99 468	20.24 467	7.35 460	45.82 467	7.25 468	9.46 465	13.64 465

tmp_tt		22.26 434	23.75 436	17.80 450	5.23 474	12.06 475	35.26 461	39.48 468	2.82 468	18.94 459	44.20 457	22.23 437	24.64 469	36.30 426	9.31 466	16.69 463

cdsmvs_eth3d_5k		19.86 435	26.47 434	0.00 454	0.00 477	0.00 479	0.00 465	93.45 91	0.00 472	0.00 473	95.27 71	49.56 294	0.00 473	0.00 472	0.00 470	0.00 469

wuyk23d		11.30 436	10.95 439	12.33 451	48.05 461	19.89 471	25.89 463	1.92 475	3.58 467	3.12 469	1.37 469	0.64 474	15.77 470	6.23 469	7.77 468	1.35 466

ab-mvs-re		7.91 437	10.55 440	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	94.95 81	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

testmvs		7.23 438	9.62 441	0.06 453	0.04 475	0.02 478	84.98 347	0.02 476	0.03 470	0.18 471	1.21 470	0.01 476	0.02 471	0.14 470	0.01 469	0.13 468

test123		6.92 439	9.21 442	0.08 452	0.03 476	0.05 477	81.65 381	0.01 477	0.02 471	0.14 472	0.85 471	0.03 475	0.02 471	0.12 471	0.00 470	0.16 467

pcd_1.5k_mvsjas		4.46 440	5.95 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	53.55 248	0.00 473	0.00 472	0.00 470	0.00 469

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 470	0.00 469

WAC-MVS						49.45 413								31.56 446

FOURS1					93.95 46	61.77 284	93.96 82	91.92 161	62.14 366	86.57 56

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 36	94.77 26	96.51 24

PC_three_1452									80.91 63	94.07 2	96.83 23	83.57 4	99.12 5	95.70 10	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 36	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 51		94.18 62	71.42 264	90.67 24	96.85 21	74.45 20

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					96.63 9	65.50 171		93.50 89	70.74 278	85.26 74	95.19 77	64.92 90	97.29 83	87.51 68	93.01 56

RE-MVS-def			80.48 171		92.02 105	58.56 351	90.90 234	90.45 236	62.76 359	78.89 150	94.46 95	49.30 297		78.77 168	86.77 142	92.28 216

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 64	95.28 1				92.34 33	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 13	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 53	71.65 253	92.07 10	97.21 8	74.58 18	99.11 6	92.34 33	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 59		94.44 51	71.65 253	92.11 8	97.05 11	76.79 9	99.11 6

9.14			87.63 34		93.86 48		94.41 60	94.18 62	72.76 218	86.21 59	96.51 30	66.64 68	97.88 47	90.08 50	94.04 39

save fliter					93.84 49	67.89 100	95.05 40	92.66 127	78.19 118

test_0728_THIRD									72.48 223	90.55 25	96.93 15	76.24 11	99.08 11	91.53 41	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 57					99.15 2	91.91 39	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 59	71.92 239	91.89 13	97.11 10	73.77 23

GSMVS																94.68 110

test_part2					96.29 19	68.16 93				90.78 22

sam_mvs1												57.85 192				94.68 110

sam_mvs												54.91 231

ambc				69.61 405	61.38 452	41.35 443	49.07 459	85.86 375		50.18 420	66.40 433	10.16 456	88.14 385	45.73 397	44.20 430	79.32 413

MTGPA								92.23 142

test_post1							78.95 401				20.70 466	53.05 253	91.50 350	60.43 330

test_post											23.01 463	56.49 213	92.67 310

patchmatchnet-post											67.62 432	57.62 195	90.25 360

GG-mvs-BLEND				86.53 78	91.91 114	69.67 54	75.02 420	94.75 36		78.67 158	90.85 190	77.91 7	94.56 238	72.25 221	93.74 45	95.36 68

MTMP							93.77 96	32.52 472

gm-plane-assit					88.42 211	67.04 125			78.62 112		91.83 171		97.37 77	76.57 180

test9_res														89.41 51	94.96 19	95.29 74

TEST9					94.18 41	67.28 116	94.16 69	93.51 87	71.75 250	85.52 69	95.33 65	68.01 57	97.27 87

test_8					94.19 40	67.19 118	94.15 71	93.42 94	71.87 244	85.38 72	95.35 64	68.19 55	96.95 114

agg_prior2														86.41 82	94.75 30	95.33 70

agg_prior					94.16 43	66.97 131		93.31 97		84.49 80			96.75 126

TestCases				72.46 390	79.57 375	51.42 400		68.61 443	51.25 419	45.88 431	81.23 344	19.86 443	86.58 400	38.98 422	57.01 401	79.39 411

test_prior4						67.18 120	93.92 85

test_prior2							95.10 39		75.40 169	85.25 75	95.61 56	67.94 58		87.47 70	94.77 26

test_prior				86.42 81	94.71 35	67.35 115		93.10 108					96.84 123			95.05 88

旧先验2							92.00 186		59.37 388	87.54 49			93.47 285	75.39 189

新几何2							91.41 207

新几何1				84.73 152	92.32 93	64.28 208		91.46 188	59.56 387	79.77 137	92.90 139	56.95 205	96.57 132	63.40 310	92.91 58	93.34 176

旧先验1					91.94 110	60.74 310		91.50 186			94.36 99	65.23 85			91.84 73	94.55 118

无先验							92.71 145	92.61 132	62.03 367				97.01 104	66.63 281		93.97 153

原ACMM2							92.01 183

原ACMM1				84.42 169	93.21 68	64.27 209		93.40 96	65.39 334	79.51 142	92.50 147	58.11 191	96.69 128	65.27 300	93.96 40	92.32 214

test222					89.77 163	61.60 290	89.55 281	89.42 285	56.83 403	77.28 172	92.43 151	52.76 256			91.14 89	93.09 186

testdata2													96.09 157	61.26 326

segment_acmp												65.94 76

testdata				81.34 269	89.02 185	57.72 358		89.84 268	58.65 392	85.32 73	94.09 115	57.03 200	93.28 287	69.34 250	90.56 95	93.03 189

testdata1							89.21 293		77.55 133

test12				87.09 52	94.60 36	68.86 72		92.91 115		82.67 103		65.44 82	97.55 67		93.69 48	94.84 99

plane_prior7					86.94 256	61.51 291

plane_prior6					87.23 248	62.32 270						50.66 280

plane_prior5								91.31 192					95.55 192	76.74 178	78.53 243	88.39 288

plane_prior4											89.14 231

plane_prior3						61.95 279			79.09 101	72.53 235

plane_prior2							93.13 124		78.81 108

plane_prior1					87.15 250

plane_prior						62.42 266	93.85 89		79.38 93						78.80 240

n2								0.00 478

nn								0.00 478

door-mid								66.01 447

lessismore_v0				73.72 381	72.93 425	47.83 421		61.72 453		45.86 433	73.76 406	28.63 420	89.81 370	47.75 389	31.37 452	83.53 366

LGP-MVS_train				79.56 319	84.31 319	59.37 340		89.73 274	69.49 291	64.86 331	88.42 240	38.65 365	94.30 249	72.56 218	72.76 287	85.01 352

test11								93.01 111

door								66.57 446

HQP5-MVS						63.66 233

HQP-NCC					87.54 240		94.06 74		79.80 82	74.18 209

ACMP_Plane					87.54 240		94.06 74		79.80 82	74.18 209

BP-MVS														77.63 175

HQP4-MVS										74.18 209			95.61 186			88.63 282

HQP3-MVS								91.70 178							78.90 238

HQP2-MVS												51.63 268

NP-MVS					87.41 243	63.04 250					90.30 201

MDTV_nov1_ep13_2view						59.90 332	80.13 396		67.65 317	72.79 229		54.33 239		59.83 334		92.58 205

MDTV_nov1_ep13			72.61 306		89.06 184	68.48 81	80.33 392	90.11 257	71.84 246	71.81 249	75.92 399	53.01 254	93.92 272	48.04 383	73.38 282

ACMMP++_ref															71.63 295

ACMMP++															69.72 304

Test By Simon												54.21 242

ITE_SJBPF				70.43 403	74.44 419	47.06 427		77.32 415	60.16 383	54.04 401	83.53 310	23.30 433	84.01 414	43.07 406	61.58 381	80.21 408

DeepMVS_CX				34.71 447	51.45 459	24.73 467		28.48 473	31.46 453	17.49 463	52.75 449	5.80 464	42.60 468	18.18 456	19.42 461	36.81 460

High-resolution multi-view benchmark