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	83.12 118	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	72.48 222	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 96	92.83 80	64.03 215	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	71.65 252	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	84.72 152	93.00 76	58.16 353	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 99	96.04 24	63.70 229	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	71.92 238	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 90	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	90.61 250	93.43 93	84.06 22	86.20 60	90.17 209	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 67	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	67.56 108	94.17 68	94.15 64	68.77 303	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	84.85 141	88.15 222	61.94 279	95.65 25	89.70 277	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	84.92 136	89.29 176	61.41 295	92.97 131	88.36 329	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 76	93.85 89	94.03 67	74.18 185	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	53.55 247	97.89 46	91.10 43	93.31 53	94.54 119

TSAR-MVS + MP.		88.11 22	88.64 22	86.54 76	91.73 119	68.04 94	90.36 257	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	85.07 131	88.43 209	61.78 282	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	65.32 173	95.15 37	93.84 70	78.17 118	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 84	95.74 21	94.11 65	83.82 24	83.49 91	96.19 42	64.53 97	98.44 31	83.42 117	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	53.45 251	97.68 54	91.07 44	92.62 60	94.54 119

EPNet		87.84 27	88.38 24	86.23 86	93.30 65	66.05 152	95.26 33	94.84 32	87.09 5	88.06 42	94.53 94	66.79 67	97.34 80	83.89 110	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	95.80 170	89.34 53	91.80 74	95.93 45

test_fmvsm_n_1920		87.69 29	88.50 23	85.27 124	87.05 253	63.55 236	93.69 99	91.08 210	84.18 21	90.17 31	97.04 12	67.58 61	97.99 41	95.72 8	90.03 102	94.26 135

fmvsm_l_conf0.5_n_3		87.54 30	88.29 26	85.30 121	86.92 259	62.63 262	95.02 44	90.28 249	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	66.30 147	94.84 50	93.78 71	69.35 292	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	83.84 189	86.89 261	60.04 329	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	85.39 115	86.95 254	64.37 203	94.30 65	88.45 327	80.51 66	92.70 4	96.86 19	69.98 48	97.15 97	95.83 7	88.08 125	94.65 113

SD-MVS		87.49 33	87.49 38	87.50 42	93.60 56	68.82 73	93.90 86	92.63 131	76.86 142	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	85.30 121	87.10 251	64.19 210	94.41 60	88.14 337	80.24 75	92.54 5	96.97 14	69.52 50	97.17 93	95.89 6	88.51 120	94.56 116

dcpmvs_2		87.37 36	87.55 37	86.85 58	95.04 32	68.20 91	90.36 257	90.66 230	79.37 93	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 94	87.07 52	95.25 73	68.43 53	96.93 117	87.87 64	84.33 172	96.65 17

train_agg		87.21 38	87.42 39	86.60 71	94.18 41	67.28 115	94.16 69	93.51 87	71.87 243	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	78.74 109	83.87 87	92.94 138	64.34 98	96.94 115	75.19 189	94.09 38	95.66 54

SF-MVS		87.03 40	87.09 42	86.84 59	92.70 86	67.45 113	93.64 102	93.76 74	70.78 276	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	83.58 203	87.26 246	60.74 309	93.21 123	87.94 344	84.22 20	91.70 15	97.27 5	65.91 78	95.02 211	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	68.48 307	77.63 165	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 72	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 170	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 72	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 170	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	85.20 161	95.51 60

PHI-MVS		86.83 45	86.85 49	86.78 63	93.47 63	65.55 168	95.39 31	95.10 25	71.77 248	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 74	90.42 150	66.38 144	96.09 17	93.87 69	77.73 127	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	84.27 176	86.15 277	61.48 292	94.69 56	91.16 200	83.79 26	90.51 27	96.28 38	64.24 99	98.22 35	95.00 12	86.88 137	93.11 184

PVSNet_Blended		86.73 49	86.86 48	86.31 85	93.76 50	67.53 110	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	94.13 144

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	80.69 124	92.21 159	72.30 36	96.46 140	85.18 94	83.43 182	94.82 103

test_fmvsmconf_n		86.58 51	87.17 41	84.82 143	85.28 295	62.55 263	94.26 67	89.78 268	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 89	87.80 234	67.18 119	92.97 131	95.62 10	79.92 79	82.84 98	94.14 112	74.95 15	96.46 140	82.91 122	88.96 116	94.74 105

jason		86.40 53	86.17 61	87.11 51	86.16 276	70.54 32	95.71 24	92.19 148	82.00 45	84.58 79	94.34 104	61.86 139	95.53 193	87.76 65	90.89 90	95.27 77
jason: jason.
NormalMVS		86.39 54	86.66 53	85.60 109	92.12 101	65.95 157	94.88 47	90.83 218	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 90	91.15 86	93.93 155

fmvsm_s_conf0.5_n		86.39 54	86.91 46	84.82 143	87.36 245	63.54 237	94.74 52	90.02 261	82.52 38	90.14 32	96.92 17	62.93 127	97.84 49	95.28 11	82.26 192	93.07 187

fmvsm_s_conf0.5_n_5		86.38 56	86.94 45	84.71 154	84.67 307	63.29 242	94.04 78	89.99 263	82.88 34	87.85 45	96.03 47	62.89 129	96.36 144	94.15 19	89.95 104	94.48 125

SymmetryMVS		86.32 57	86.39 56	86.12 90	90.52 148	65.95 157	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	79.55 154	82.25 194	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	78.70 156	92.07 162	72.45 34	95.41 195	82.11 129	85.78 155	94.44 127

MSLP-MVS++		86.27 60	85.91 68	87.35 45	92.01 108	68.97 70	95.04 42	92.70 122	79.04 104	81.50 110	96.50 31	58.98 179	96.78 125	83.49 116	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	78.43 114	86.00 63	93.07 135	58.22 188	97.00 105	85.22 92	84.33 172	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	79.08 101	78.88 152	93.99 118	62.25 136	98.15 38	85.93 87	91.15 86	94.15 143

SPE-MVS-test		86.14 63	87.01 43	83.52 204	92.63 88	59.36 341	95.49 28	91.92 161	80.09 76	85.46 71	95.53 60	61.82 141	95.77 173	86.77 81	93.37 52	95.41 63

ACMMP_NAP		86.05 64	85.80 70	86.80 62	91.58 123	67.53 110	91.79 194	93.49 90	74.93 175	84.61 78	95.30 67	59.42 170	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 69	80.60 126	91.95 167	71.73 41	96.50 138	80.02 151	82.22 195	95.13 83

ETV-MVS		86.01 65	86.11 63	85.70 106	90.21 155	67.02 126	93.43 115	91.92 161	81.21 59	84.13 85	94.07 117	60.93 149	95.63 182	89.28 54	89.81 106	94.46 126

testing91		85.93 67	85.31 79	87.78 32	93.59 57	71.47 19	93.50 110	95.08 28	80.26 72	80.53 127	91.93 168	70.43 45	96.51 137	80.32 149	82.13 197	95.37 66

APD-MVS		85.93 67	85.99 66	85.76 103	95.98 26	65.21 176	93.59 105	92.58 133	66.54 325	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	88.20 221	72.42 15	92.41 164	92.77 120	82.11 44	80.34 130	93.07 135	68.27 54	95.02 211	78.39 170	93.59 49	94.09 146

CS-MVS		85.80 70	86.65 54	83.27 216	92.00 109	58.92 345	95.31 32	91.86 166	79.97 77	84.82 77	95.40 63	62.26 135	95.51 194	86.11 85	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 71	86.09 64	84.72 152	85.73 288	63.58 234	93.79 95	89.32 287	81.42 55	90.21 30	96.91 18	62.41 134	97.67 56	94.48 16	80.56 219	92.90 193

test_fmvsmconf0.1_n		85.71 72	86.08 65	84.62 162	80.83 356	62.33 268	93.84 92	88.81 315	83.50 28	87.00 53	96.01 48	63.36 117	96.93 117	94.04 21	87.29 134	94.61 115

CDPH-MVS		85.71 72	85.46 76	86.46 78	94.75 34	67.19 117	93.89 87	92.83 118	70.90 272	83.09 96	95.28 69	63.62 111	97.36 78	80.63 145	94.18 37	94.84 99

casdiffmvs_mvg		85.66 74	85.18 81	87.09 52	88.22 220	69.35 62	93.74 98	91.89 164	81.47 51	80.10 132	91.45 178	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	84.68 156	82.95 339	63.48 239	94.03 80	89.46 281	81.69 48	89.86 33	96.74 25	61.85 140	97.75 52	94.74 15	82.01 199	92.81 197

MGCFI-Net		85.59 76	85.73 72	85.17 128	91.41 131	62.44 264	92.87 139	91.31 192	79.65 86	86.99 54	95.14 79	62.90 128	96.12 155	87.13 76	84.13 177	96.96 13

GDP-MVS		85.54 77	85.32 78	86.18 87	87.64 237	67.95 98	92.91 137	92.36 138	77.81 124	83.69 88	94.31 106	72.84 30	96.41 142	80.39 148	85.95 153	94.19 139

DeepC-MVS	77.85 3	85.52 78	85.24 80	86.37 82	88.80 191	66.64 138	92.15 173	93.68 80	81.07 61	76.91 176	93.64 125	62.59 131	98.44 31	85.50 88	92.84 59	94.03 150
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	88.25 218	69.07 66	93.04 128	91.76 171	81.27 58	80.84 122	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 91	93.09 73	65.65 164	93.89 87	93.41 95	73.75 196	79.94 134	94.68 91	60.61 152	98.03 40	82.63 125	93.72 46	94.52 121

fmvsm_s_conf0.5_n_7		85.24 81	86.69 51	80.91 284	84.52 312	60.10 327	93.35 118	90.35 242	83.41 29	86.54 57	96.27 39	60.50 153	90.02 368	94.84 14	90.38 98	92.61 201

MP-MVS-pluss		85.24 81	85.13 82	85.56 110	91.42 128	65.59 166	91.54 204	92.51 135	74.56 178	80.62 125	95.64 55	59.15 174	97.00 105	86.94 79	93.80 43	94.07 148
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 83	84.69 91	86.63 70	92.91 78	69.91 43	92.61 153	95.80 9	80.31 71	80.38 129	92.27 155	68.73 52	95.19 208	75.94 183	83.27 184	94.81 104

PAPR		85.15 84	84.47 92	87.18 49	96.02 25	68.29 85	91.85 192	93.00 113	76.59 153	79.03 148	95.00 80	61.59 142	97.61 63	78.16 171	89.00 115	95.63 55

fmvsm_s_conf0.5_n_2		85.06 85	85.60 74	83.44 210	86.92 259	60.53 316	94.41 60	87.31 352	83.30 30	88.72 40	96.72 26	54.28 239	97.75 52	94.07 20	84.68 169	92.04 224

MP-MVS		85.02 86	84.97 85	85.17 128	92.60 89	64.27 208	93.24 120	92.27 141	73.13 207	79.63 140	94.43 97	61.90 138	97.17 93	85.00 96	92.56 61	94.06 149
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	88.33 215	68.73 75	90.24 262	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	83.45 109	89.57 11	89.94 160	75.14 6	92.07 179	92.32 139	81.87 46	75.68 185	88.27 243	60.18 157	98.60 27	80.46 147	90.27 100	94.96 92

MVSMamba_PlusPlus		84.97 89	83.65 102	88.93 14	90.17 156	74.04 8	87.84 320	92.69 125	62.18 363	81.47 112	87.64 257	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	88.50 200	69.77 50	92.69 150	91.13 206	81.11 60	81.54 109	91.98 165	60.35 154	95.73 175	84.47 103	86.56 147	94.84 99

EIA-MVS		84.84 91	84.88 86	84.69 155	91.30 133	62.36 267	93.85 89	92.04 154	79.45 89	79.33 145	94.28 108	62.42 133	96.35 145	80.05 150	91.25 85	95.38 65

lecture		84.77 92	84.81 89	84.65 158	92.12 101	62.27 271	94.74 52	92.64 130	68.35 308	85.53 68	95.30 67	59.77 164	97.91 44	83.73 112	91.15 86	93.77 164

fmvsm_s_conf0.1_n_a		84.76 93	84.84 88	84.53 164	80.23 369	63.50 238	92.79 141	88.73 318	80.46 67	89.84 34	96.65 28	60.96 148	97.57 66	93.80 23	80.14 221	92.53 206

HFP-MVS		84.73 94	84.40 94	85.72 105	93.75 52	65.01 182	93.50 110	93.19 103	72.19 232	79.22 146	94.93 83	59.04 177	97.67 56	81.55 134	92.21 64	94.49 124

MVS		84.66 95	82.86 130	90.06 2	90.93 140	74.56 7	87.91 318	95.54 14	68.55 305	72.35 242	94.71 90	59.78 163	98.90 20	81.29 140	94.69 32	96.74 16

GST-MVS		84.63 96	84.29 95	85.66 107	92.82 82	65.27 174	93.04 128	93.13 106	73.20 205	78.89 149	94.18 111	59.41 171	97.85 48	81.45 136	92.48 63	93.86 161

EC-MVSNet		84.53 97	85.04 84	83.01 222	89.34 172	61.37 296	94.42 59	91.09 208	77.91 122	83.24 92	94.20 110	58.37 186	95.40 196	85.35 89	91.41 81	92.27 218

fmvsm_s_conf0.1_n_2		84.40 98	84.78 90	83.27 216	85.25 296	60.41 319	94.13 72	85.69 376	83.05 32	87.99 43	96.37 33	52.75 256	97.68 54	93.75 24	84.05 178	91.71 232

ACMMPR		84.37 99	84.06 97	85.28 123	93.56 58	64.37 203	93.50 110	93.15 105	72.19 232	78.85 154	94.86 86	56.69 208	97.45 72	81.55 134	92.20 65	94.02 151

region2R		84.36 100	84.03 98	85.36 119	93.54 60	64.31 206	93.43 115	92.95 114	72.16 235	78.86 153	94.84 87	56.97 203	97.53 68	81.38 138	92.11 67	94.24 137

LFMVS		84.34 101	82.73 132	89.18 13	94.76 33	73.25 11	94.99 45	91.89 164	71.90 240	82.16 105	93.49 129	47.98 309	97.05 100	82.55 126	84.82 165	97.25 8

test_yl		84.28 102	83.16 121	87.64 34	94.52 37	69.24 63	95.78 18	95.09 26	69.19 295	81.09 117	92.88 141	57.00 201	97.44 73	81.11 142	81.76 202	96.23 38

DCV-MVSNet		84.28 102	83.16 121	87.64 34	94.52 37	69.24 63	95.78 18	95.09 26	69.19 295	81.09 117	92.88 141	57.00 201	97.44 73	81.11 142	81.76 202	96.23 38

diffmvs		84.28 102	83.83 99	85.61 108	87.40 243	68.02 95	90.88 235	89.24 290	80.54 65	81.64 108	92.52 146	59.83 162	94.52 240	87.32 72	85.11 162	94.29 134
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	84.28 102	83.36 115	87.02 55	92.22 96	67.74 103	84.65 347	94.50 48	79.15 98	82.23 104	87.93 252	66.88 66	96.94 115	80.53 146	82.20 196	96.39 33

ETVMVS		84.22 106	83.71 100	85.76 103	92.58 90	68.25 89	92.45 162	95.53 15	79.54 88	79.46 142	91.64 176	70.29 46	94.18 254	69.16 252	82.76 190	94.84 99

MAR-MVS		84.18 107	83.43 110	86.44 79	96.25 21	65.93 159	94.28 66	94.27 61	74.41 180	79.16 147	95.61 56	53.99 242	98.88 22	69.62 246	93.26 54	94.50 123
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		84.16 108	83.20 119	87.05 54	91.56 124	69.82 46	89.99 271	92.05 153	77.77 126	82.84 98	86.57 274	63.93 105	96.09 157	74.91 194	89.18 112	95.25 80

CANet_DTU		84.09 109	83.52 103	85.81 100	90.30 153	66.82 133	91.87 190	89.01 306	85.27 12	86.09 62	93.74 122	47.71 315	96.98 109	77.90 173	89.78 108	93.65 167

viewdifsd2359ckpt13		84.08 110	83.21 118	86.70 67	88.49 204	69.55 55	92.25 167	91.14 204	79.71 84	79.73 137	91.72 174	58.83 180	95.89 167	82.06 130	84.99 163	94.66 112

viewmacassd2359aftdt		84.03 111	83.18 120	86.59 73	86.76 262	69.44 56	92.44 163	90.85 217	80.38 70	80.78 123	91.33 183	58.54 183	95.62 184	82.15 128	85.41 159	94.72 107

ET-MVSNet_ETH3D		84.01 112	83.15 123	86.58 74	90.78 145	70.89 28	94.74 52	94.62 43	81.44 54	58.19 381	93.64 125	73.64 25	92.35 323	82.66 124	78.66 241	96.50 27

diffmvs_AUTHOR		83.97 113	83.49 106	85.39 115	86.09 278	67.83 100	90.76 240	89.05 304	79.94 78	81.43 113	92.23 158	59.53 167	94.42 243	87.18 75	85.22 160	93.92 157

PVSNet_Blended_VisFu		83.97 113	83.50 105	85.39 115	90.02 158	66.59 141	93.77 96	91.73 173	77.43 135	77.08 175	89.81 219	63.77 108	96.97 112	79.67 153	88.21 123	92.60 202

MTAPA		83.91 115	83.38 114	85.50 111	91.89 115	65.16 178	81.75 378	92.23 142	75.32 170	80.53 127	95.21 76	56.06 217	97.16 96	84.86 99	92.55 62	94.18 140

XVS		83.87 116	83.47 108	85.05 132	93.22 66	63.78 222	92.92 135	92.66 127	73.99 188	78.18 159	94.31 106	55.25 223	97.41 75	79.16 160	91.58 78	93.95 153

Effi-MVS+		83.82 117	82.76 131	86.99 56	89.56 168	69.40 57	91.35 215	86.12 370	72.59 219	83.22 95	92.81 144	59.60 166	96.01 165	81.76 133	87.80 128	95.56 58

test_fmvsmvis_n_1920		83.80 118	83.48 107	84.77 147	82.51 342	63.72 227	91.37 213	83.99 394	81.42 55	77.68 164	95.74 53	58.37 186	97.58 64	93.38 25	86.87 138	93.00 190

EI-MVSNet-Vis-set		83.77 119	83.67 101	84.06 180	92.79 85	63.56 235	91.76 197	94.81 34	79.65 86	77.87 162	94.09 115	63.35 118	97.90 45	79.35 158	79.36 231	90.74 253

MVSFormer		83.75 120	82.88 129	86.37 82	89.24 181	71.18 24	89.07 296	90.69 227	65.80 330	87.13 50	94.34 104	64.99 87	92.67 309	72.83 211	91.80 74	95.27 77

CP-MVS		83.71 121	83.40 113	84.65 158	93.14 71	63.84 220	94.59 57	92.28 140	71.03 270	77.41 168	94.92 84	55.21 226	96.19 152	81.32 139	90.70 92	93.91 158

test_fmvsmconf0.01_n		83.70 122	83.52 103	84.25 177	75.26 415	61.72 286	92.17 172	87.24 354	82.36 41	84.91 76	95.41 62	55.60 221	96.83 124	92.85 29	85.87 154	94.21 138

baseline2		83.68 123	83.42 112	84.48 167	87.37 244	66.00 154	90.06 266	95.93 8	79.71 84	69.08 280	90.39 197	77.92 6	96.28 147	78.91 165	81.38 206	91.16 246

reproduce-ours		83.51 124	83.33 116	84.06 180	92.18 99	60.49 317	90.74 242	92.04 154	64.35 340	83.24 92	95.59 58	59.05 175	97.27 87	83.61 113	89.17 113	94.41 132

our_new_method		83.51 124	83.33 116	84.06 180	92.18 99	60.49 317	90.74 242	92.04 154	64.35 340	83.24 92	95.59 58	59.05 175	97.27 87	83.61 113	89.17 113	94.41 132

thisisatest0515		83.41 126	82.49 136	86.16 88	89.46 171	68.26 87	93.54 107	94.70 39	74.31 183	75.75 183	90.92 187	72.62 32	96.52 136	69.64 244	81.50 205	93.71 165

PVSNet_BlendedMVS		83.38 127	83.43 110	83.22 218	93.76 50	67.53 110	94.06 74	93.61 82	79.13 99	81.00 120	85.14 292	63.19 120	97.29 83	87.08 77	73.91 279	84.83 353

test2506		83.29 128	82.92 128	84.37 171	88.39 212	63.18 248	92.01 182	91.35 191	77.66 129	78.49 158	91.42 179	64.58 96	95.09 210	73.19 207	89.23 110	94.85 96

PGM-MVS		83.25 129	82.70 133	84.92 136	92.81 84	64.07 214	90.44 252	92.20 146	71.28 264	77.23 172	94.43 97	55.17 227	97.31 82	79.33 159	91.38 82	93.37 174

HPM-MVS		83.25 129	82.95 127	84.17 178	92.25 95	62.88 257	90.91 232	91.86 166	70.30 281	77.12 173	93.96 119	56.75 206	96.28 147	82.04 131	91.34 84	93.34 175
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 131	82.96 125	83.73 195	92.02 105	59.74 333	90.37 256	92.08 152	63.70 347	82.86 97	95.48 61	58.62 182	97.17 93	83.06 119	88.42 121	94.26 135

EI-MVSNet-UG-set		83.14 132	82.96 125	83.67 200	92.28 94	63.19 247	91.38 212	94.68 40	79.22 96	76.60 178	93.75 121	62.64 130	97.76 51	78.07 172	78.01 244	90.05 262

testing3-2		83.11 133	83.15 123	82.98 223	91.92 112	64.01 216	94.39 63	95.37 16	78.32 115	75.53 190	90.06 215	73.18 27	93.18 288	74.34 199	75.27 268	91.77 231

VDD-MVS		83.06 134	81.81 146	86.81 61	90.86 143	67.70 104	95.40 30	91.50 186	75.46 165	81.78 107	92.34 154	40.09 359	97.13 98	86.85 80	82.04 198	95.60 56

h-mvs33		83.01 135	82.56 135	84.35 172	89.34 172	62.02 275	92.72 144	93.76 74	81.45 52	82.73 101	92.25 157	60.11 158	97.13 98	87.69 66	62.96 362	93.91 158

PAPM_NR		82.97 136	81.84 145	86.37 82	94.10 44	66.76 136	87.66 324	92.84 117	69.96 285	74.07 215	93.57 127	63.10 125	97.50 70	70.66 239	90.58 94	94.85 96

mPP-MVS		82.96 137	82.44 137	84.52 165	92.83 80	62.92 255	92.76 142	91.85 168	71.52 260	75.61 188	94.24 109	53.48 250	96.99 108	78.97 163	90.73 91	93.64 168

SR-MVS		82.81 138	82.58 134	83.50 207	93.35 64	61.16 299	92.23 170	91.28 197	64.48 339	81.27 114	95.28 69	53.71 246	95.86 168	82.87 123	88.77 118	93.49 172

DP-MVS Recon		82.73 139	81.65 147	85.98 93	97.31 4	67.06 122	95.15 37	91.99 158	69.08 300	76.50 180	93.89 120	54.48 235	98.20 37	70.76 237	85.66 157	92.69 198

CLD-MVS		82.73 139	82.35 139	83.86 188	87.90 229	67.65 106	95.45 29	92.18 149	85.06 13	72.58 233	92.27 155	52.46 259	95.78 171	84.18 106	79.06 236	88.16 290
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 141	82.38 138	83.73 195	89.25 178	59.58 336	92.24 169	94.89 31	77.96 120	79.86 135	92.38 152	56.70 207	97.05 100	77.26 176	80.86 214	94.55 117

3Dnovator	73.91 6	82.69 142	80.82 160	88.31 26	89.57 167	71.26 22	92.60 154	94.39 56	78.84 106	67.89 301	92.48 150	48.42 304	98.52 28	68.80 257	94.40 36	95.15 82

RRT-MVS		82.61 143	81.16 151	86.96 57	91.10 137	68.75 74	87.70 323	92.20 146	76.97 140	72.68 229	87.10 268	51.30 273	96.41 142	83.56 115	87.84 127	95.74 52

viewmambaseed2359dif		82.60 144	81.91 144	84.67 157	85.83 285	66.09 151	90.50 251	89.01 306	75.46 165	79.64 139	92.01 164	59.51 168	94.38 245	82.99 121	82.26 192	93.54 170

MVSTER		82.47 145	82.05 140	83.74 193	92.68 87	69.01 68	91.90 189	93.21 100	79.83 80	72.14 243	85.71 287	74.72 17	94.72 225	75.72 185	72.49 289	87.50 297

TESTMET0.1,1		82.41 146	81.98 143	83.72 197	88.08 223	63.74 224	92.70 146	93.77 73	79.30 94	77.61 166	87.57 259	58.19 189	94.08 259	73.91 201	86.68 145	93.33 177

CostFormer		82.33 147	81.15 152	85.86 98	89.01 186	68.46 81	82.39 375	93.01 111	75.59 163	80.25 131	81.57 337	72.03 39	94.96 215	79.06 162	77.48 252	94.16 142

API-MVS		82.28 148	80.53 169	87.54 41	96.13 22	70.59 31	93.63 103	91.04 214	65.72 332	75.45 191	92.83 143	56.11 216	98.89 21	64.10 305	89.75 109	93.15 182

IB-MVS	77.80 4	82.18 149	80.46 171	87.35 45	89.14 183	70.28 36	95.59 27	95.17 24	78.85 105	70.19 268	85.82 285	70.66 44	97.67 56	72.19 223	66.52 332	94.09 146
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		82.16 150	81.12 153	85.26 125	86.42 269	68.72 76	92.59 156	90.44 239	73.12 208	84.20 82	94.36 99	38.04 372	95.73 175	84.12 107	86.81 139	91.33 239

xiu_mvs_v1_base		82.16 150	81.12 153	85.26 125	86.42 269	68.72 76	92.59 156	90.44 239	73.12 208	84.20 82	94.36 99	38.04 372	95.73 175	84.12 107	86.81 139	91.33 239

xiu_mvs_v1_base_debi		82.16 150	81.12 153	85.26 125	86.42 269	68.72 76	92.59 156	90.44 239	73.12 208	84.20 82	94.36 99	38.04 372	95.73 175	84.12 107	86.81 139	91.33 239

3Dnovator+	73.60 7	82.10 153	80.60 167	86.60 71	90.89 142	66.80 135	95.20 35	93.44 92	74.05 187	67.42 308	92.49 149	49.46 294	97.65 60	70.80 236	91.68 76	95.33 70

MVS_111021_LR		82.02 154	81.52 148	83.51 206	88.42 210	62.88 257	89.77 274	88.93 311	76.78 145	75.55 189	93.10 132	50.31 283	95.38 198	83.82 111	87.02 136	92.26 219

PMMVS		81.98 155	82.04 141	81.78 258	89.76 164	56.17 373	91.13 228	90.69 227	77.96 120	80.09 133	93.57 127	46.33 328	94.99 214	81.41 137	87.46 132	94.17 141

baseline1		81.84 156	81.03 157	84.28 175	91.60 122	66.62 139	91.08 229	91.66 180	81.87 46	74.86 201	91.67 175	69.98 48	94.92 218	71.76 226	64.75 349	91.29 244

EPP-MVSNet		81.79 157	81.52 148	82.61 233	88.77 192	60.21 325	93.02 130	93.66 81	68.52 306	72.90 227	90.39 197	72.19 38	94.96 215	74.93 193	79.29 234	92.67 199

WBMVS		81.67 158	80.98 159	83.72 197	93.07 74	69.40 57	94.33 64	93.05 109	76.84 143	72.05 245	84.14 303	74.49 19	93.88 273	72.76 214	68.09 318	87.88 292

test_vis1_n_1920		81.66 159	82.01 142	80.64 287	82.24 344	55.09 382	94.76 51	86.87 358	81.67 49	84.40 81	94.63 92	38.17 369	94.67 231	91.98 38	83.34 183	92.16 222

APD-MVS_3200maxsize		81.64 160	81.32 150	82.59 235	92.36 92	58.74 347	91.39 210	91.01 215	63.35 351	79.72 138	94.62 93	51.82 262	96.14 154	79.71 152	87.93 126	92.89 194

mvsmamba		81.55 161	80.72 162	84.03 184	91.42 128	66.93 131	83.08 366	89.13 298	78.55 113	67.50 306	87.02 269	51.79 264	90.07 367	87.48 69	90.49 96	95.10 85

ACMMP		81.49 162	80.67 164	83.93 186	91.71 120	62.90 256	92.13 174	92.22 145	71.79 247	71.68 251	93.49 129	50.32 282	96.96 113	78.47 169	84.22 176	91.93 229
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		81.43 163	80.11 173	85.38 118	86.60 265	65.47 172	92.90 138	93.54 86	75.33 169	77.31 170	90.39 197	46.81 320	96.75 126	71.65 229	86.46 150	93.93 155

CDS-MVSNet		81.43 163	80.74 161	83.52 204	86.26 273	64.45 197	92.09 177	90.65 231	75.83 161	73.95 217	89.81 219	63.97 104	92.91 299	71.27 230	82.82 187	93.20 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 165	79.99 177	85.46 112	90.39 152	68.40 82	86.88 335	90.61 232	74.41 180	70.31 267	84.67 297	63.79 107	92.32 325	73.13 208	85.70 156	95.67 53

ECVR-MVS		81.29 166	80.38 172	84.01 185	88.39 212	61.96 277	92.56 159	86.79 360	77.66 129	76.63 177	91.42 179	46.34 327	95.24 207	74.36 198	89.23 110	94.85 96

guyue		81.23 167	80.57 168	83.21 220	86.64 263	61.85 280	92.52 160	92.78 119	78.69 110	74.92 200	89.42 223	50.07 286	95.35 199	80.79 144	79.31 233	92.42 208

IMVS_0403		81.19 168	79.88 179	85.13 130	88.54 195	64.75 187	88.84 301	90.80 221	76.73 148	75.21 194	90.18 203	54.22 240	96.21 151	73.47 203	80.95 209	94.43 128

thisisatest0530		81.15 169	80.07 174	84.39 170	88.26 217	65.63 165	91.40 208	94.62 43	71.27 265	70.93 258	89.18 228	72.47 33	96.04 162	65.62 294	76.89 259	91.49 235

Fast-Effi-MVS+		81.14 170	80.01 176	84.51 166	90.24 154	65.86 160	94.12 73	89.15 296	73.81 195	75.37 193	88.26 244	57.26 196	94.53 239	66.97 279	84.92 164	93.15 182

HQP-MVS		81.14 170	80.64 165	82.64 232	87.54 239	63.66 232	94.06 74	91.70 178	79.80 81	74.18 208	90.30 200	51.63 267	95.61 185	77.63 174	78.90 237	88.63 281

hse-mvs2		81.12 172	81.11 156	81.16 272	86.52 268	57.48 362	89.40 287	91.16 200	81.45 52	82.73 101	90.49 195	60.11 158	94.58 232	87.69 66	60.41 389	91.41 238

SR-MVS-dyc-post		81.06 173	80.70 163	82.15 249	92.02 105	58.56 350	90.90 233	90.45 235	62.76 358	78.89 149	94.46 95	51.26 274	95.61 185	78.77 167	86.77 142	92.28 215

HyFIR lowres test		81.03 174	79.56 186	85.43 113	87.81 233	68.11 93	90.18 263	90.01 262	70.65 278	72.95 226	86.06 281	63.61 112	94.50 241	75.01 192	79.75 225	93.67 166

nrg030		80.93 175	79.86 180	84.13 179	83.69 328	68.83 72	93.23 121	91.20 198	75.55 164	75.06 196	88.22 247	63.04 126	94.74 224	81.88 132	66.88 329	88.82 279

Vis-MVSNet		80.92 176	79.98 178	83.74 193	88.48 206	61.80 281	93.44 114	88.26 336	73.96 191	77.73 163	91.76 171	49.94 288	94.76 222	65.84 291	90.37 99	94.65 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 177	80.02 175	83.33 211	87.87 230	60.76 307	92.62 152	86.86 359	77.86 123	75.73 184	91.39 181	46.35 326	94.70 230	72.79 213	88.68 119	94.52 121

UWE-MVS		80.81 178	81.01 158	80.20 297	89.33 174	57.05 367	91.91 188	94.71 38	75.67 162	75.01 197	89.37 224	63.13 124	91.44 350	67.19 276	82.80 189	92.12 223

IMVS_0407		80.80 179	79.39 192	85.00 135	88.54 195	64.75 187	88.40 309	90.80 221	76.73 148	73.95 217	90.18 203	51.55 269	95.81 169	73.47 203	80.95 209	94.43 128

1314		80.70 180	78.95 200	85.94 95	87.77 236	67.56 108	87.91 318	92.55 134	72.17 234	67.44 307	93.09 133	50.27 284	97.04 103	71.68 228	87.64 130	93.23 179

AstraMVS		80.66 181	79.79 182	83.28 215	85.07 302	61.64 288	92.19 171	90.58 233	79.40 91	74.77 203	90.18 203	45.93 332	95.61 185	83.04 120	76.96 258	92.60 202

tpmrst		80.57 182	79.14 198	84.84 142	90.10 157	68.28 86	81.70 379	89.72 275	77.63 131	75.96 182	79.54 369	64.94 89	92.71 306	75.43 187	77.28 255	93.55 169

1112_ss		80.56 183	79.83 181	82.77 227	88.65 193	60.78 305	92.29 166	88.36 329	72.58 220	72.46 239	94.95 81	65.09 86	93.42 285	66.38 285	77.71 246	94.10 145

VDDNet		80.50 184	78.26 208	87.21 47	86.19 274	69.79 48	94.48 58	91.31 192	60.42 379	79.34 144	90.91 188	38.48 367	96.56 133	82.16 127	81.05 208	95.27 77

BH-w/o		80.49 185	79.30 194	84.05 183	90.83 144	64.36 205	93.60 104	89.42 284	74.35 182	69.09 279	90.15 211	55.23 225	95.61 185	64.61 302	86.43 151	92.17 221

test_cas_vis1_n_1920		80.45 186	80.61 166	79.97 306	78.25 396	57.01 369	94.04 78	88.33 331	79.06 103	82.81 100	93.70 123	38.65 364	91.63 341	90.82 47	79.81 223	91.27 245

icg_test_0407_2		80.38 187	79.22 196	83.88 187	88.54 195	64.75 187	86.79 336	90.80 221	76.73 148	73.95 217	90.18 203	51.55 269	92.45 318	73.47 203	80.95 209	94.43 128

TAMVS		80.37 188	79.45 189	83.13 221	85.14 299	63.37 240	91.23 222	90.76 226	74.81 177	72.65 231	88.49 237	60.63 151	92.95 294	69.41 248	81.95 200	93.08 186

HQP_MVS		80.34 189	79.75 183	82.12 251	86.94 255	62.42 265	93.13 124	91.31 192	78.81 107	72.53 234	89.14 230	50.66 279	95.55 191	76.74 177	78.53 242	88.39 287

SDMVSNet		80.26 190	78.88 201	84.40 169	89.25 178	67.63 107	85.35 343	93.02 110	76.77 146	70.84 259	87.12 266	47.95 312	96.09 157	85.04 95	74.55 270	89.48 272

HPM-MVS_fast		80.25 191	79.55 188	82.33 241	91.55 125	59.95 330	91.32 217	89.16 295	65.23 336	74.71 205	93.07 135	47.81 314	95.74 174	74.87 196	88.23 122	91.31 243

ab-mvs		80.18 192	78.31 207	85.80 101	88.44 208	65.49 171	83.00 369	92.67 126	71.82 246	77.36 169	85.01 293	54.50 232	96.59 130	76.35 182	75.63 266	95.32 72

IS-MVSNet		80.14 193	79.41 190	82.33 241	87.91 228	60.08 328	91.97 186	88.27 334	72.90 215	71.44 255	91.73 173	61.44 143	93.66 280	62.47 319	86.53 148	93.24 178

test-LLR		80.10 194	79.56 186	81.72 260	86.93 257	61.17 297	92.70 146	91.54 183	71.51 261	75.62 186	86.94 270	53.83 243	92.38 320	72.21 221	84.76 167	91.60 233

PVSNet	73.49 8	80.05 195	78.63 203	84.31 173	90.92 141	64.97 183	92.47 161	91.05 213	79.18 97	72.43 240	90.51 194	37.05 384	94.06 261	68.06 263	86.00 152	93.90 160

UA-Net		80.02 196	79.65 184	81.11 275	89.33 174	57.72 357	86.33 340	89.00 310	77.44 134	81.01 119	89.15 229	59.33 172	95.90 166	61.01 326	84.28 174	89.73 268

test-mter		79.96 197	79.38 193	81.72 260	86.93 257	61.17 297	92.70 146	91.54 183	73.85 193	75.62 186	86.94 270	49.84 290	92.38 320	72.21 221	84.76 167	91.60 233

QAPM		79.95 198	77.39 229	87.64 34	89.63 166	71.41 20	93.30 119	93.70 79	65.34 335	67.39 310	91.75 172	47.83 313	98.96 16	57.71 342	89.81 106	92.54 205

UGNet		79.87 199	78.68 202	83.45 209	89.96 159	61.51 290	92.13 174	90.79 225	76.83 144	78.85 154	86.33 278	38.16 370	96.17 153	67.93 266	87.17 135	92.67 199
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		79.80 200	77.95 215	85.34 120	88.28 216	68.26 87	81.56 381	91.42 189	70.11 283	77.59 167	80.50 355	67.40 63	94.26 252	67.34 273	77.35 253	93.51 171

thres200		79.66 201	78.33 206	83.66 201	92.54 91	65.82 162	93.06 126	96.31 3	74.90 176	73.30 223	88.66 235	59.67 165	95.61 185	47.84 385	78.67 240	89.56 271

CPTT-MVS		79.59 202	79.16 197	80.89 285	91.54 126	59.80 332	92.10 176	88.54 326	60.42 379	72.96 225	93.28 131	48.27 305	92.80 303	78.89 166	86.50 149	90.06 261

Test_1112_low_res		79.56 203	78.60 204	82.43 237	88.24 219	60.39 321	92.09 177	87.99 341	72.10 236	71.84 247	87.42 261	64.62 94	93.04 290	65.80 292	77.30 254	93.85 162

tttt0517		79.50 204	78.53 205	82.41 240	87.22 248	61.43 294	89.75 275	94.76 35	69.29 293	67.91 299	88.06 251	72.92 29	95.63 182	62.91 315	73.90 280	90.16 260

reproduce_monomvs		79.49 205	79.11 199	80.64 287	92.91 78	61.47 293	91.17 227	93.28 98	83.09 31	64.04 340	82.38 323	66.19 72	94.57 234	81.19 141	57.71 397	85.88 336

FIs		79.47 206	79.41 190	79.67 314	85.95 281	59.40 338	91.68 201	93.94 68	78.06 119	68.96 285	88.28 242	66.61 69	91.77 337	66.20 288	74.99 269	87.82 293

SSM_0404		79.46 207	77.65 219	84.91 138	88.37 214	67.04 124	89.59 276	87.03 355	67.99 311	75.45 191	89.32 225	47.98 309	95.34 201	71.23 231	81.90 201	92.34 211

BH-RMVSNet		79.46 207	77.65 219	84.89 139	91.68 121	65.66 163	93.55 106	88.09 339	72.93 212	73.37 222	91.12 186	46.20 330	96.12 155	56.28 348	85.61 158	92.91 192

viewdifsd2359ckpt11		79.42 209	77.95 215	83.81 190	83.87 325	63.85 218	89.54 281	87.38 348	77.39 137	74.94 198	89.95 216	51.11 275	94.72 225	79.52 155	67.90 321	92.88 195

viewmsd2359difaftdt		79.42 209	77.96 214	83.81 190	83.88 324	63.85 218	89.54 281	87.38 348	77.39 137	74.94 198	89.95 216	51.11 275	94.72 225	79.52 155	67.90 321	92.88 195

PCF-MVS	73.15 9	79.29 211	77.63 221	84.29 174	86.06 279	65.96 156	87.03 331	91.10 207	69.86 287	69.79 275	90.64 190	57.54 195	96.59 130	64.37 304	82.29 191	90.32 258
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 212	79.57 185	78.24 335	88.46 207	52.29 393	90.41 254	89.12 299	74.24 184	69.13 278	91.91 169	65.77 79	90.09 366	59.00 338	88.09 124	92.33 212

114514_t		79.17 213	77.67 218	83.68 199	95.32 29	65.53 169	92.85 140	91.60 182	63.49 349	67.92 298	90.63 192	46.65 323	95.72 180	67.01 278	83.54 181	89.79 266

FA-MVS(test-final)		79.12 214	77.23 231	84.81 146	90.54 147	63.98 217	81.35 384	91.71 175	71.09 269	74.85 202	82.94 316	52.85 254	97.05 100	67.97 264	81.73 204	93.41 173

SSM_0407		79.09 215	77.21 232	84.75 150	88.50 200	66.98 127	89.21 292	87.03 355	67.99 311	74.12 212	89.32 225	47.98 309	95.29 206	71.23 231	79.52 226	91.98 226

VPA-MVSNet		79.03 216	78.00 212	82.11 254	85.95 281	64.48 196	93.22 122	94.66 41	75.05 174	74.04 216	84.95 294	52.17 261	93.52 282	74.90 195	67.04 328	88.32 289

OPM-MVS		79.00 217	78.09 210	81.73 259	83.52 331	63.83 221	91.64 203	90.30 247	76.36 157	71.97 246	89.93 218	46.30 329	95.17 209	75.10 190	77.70 247	86.19 324
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 218	78.22 209	81.25 269	85.33 292	62.73 260	89.53 284	93.21 100	72.39 227	72.14 243	90.13 212	60.99 146	94.72 225	67.73 268	72.49 289	86.29 321

AdaColmap		78.94 219	77.00 236	84.76 149	96.34 17	65.86 160	92.66 151	87.97 343	62.18 363	70.56 261	92.37 153	43.53 344	97.35 79	64.50 303	82.86 186	91.05 248

GeoE		78.90 220	77.43 225	83.29 214	88.95 187	62.02 275	92.31 165	86.23 366	70.24 282	71.34 256	89.27 227	54.43 236	94.04 264	63.31 311	80.81 216	93.81 163

miper_enhance_ethall		78.86 221	77.97 213	81.54 264	88.00 227	65.17 177	91.41 206	89.15 296	75.19 172	68.79 288	83.98 306	67.17 64	92.82 301	72.73 215	65.30 339	86.62 318

VPNet		78.82 222	77.53 224	82.70 230	84.52 312	66.44 143	93.93 84	92.23 142	80.46 67	72.60 232	88.38 241	49.18 298	93.13 289	72.47 219	63.97 359	88.55 284

EPNet_dtu		78.80 223	79.26 195	77.43 343	88.06 224	49.71 410	91.96 187	91.95 160	77.67 128	76.56 179	91.28 184	58.51 184	90.20 364	56.37 347	80.95 209	92.39 209
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 224	77.43 225	82.88 225	92.21 97	64.49 194	92.05 180	96.28 4	73.48 202	71.75 249	88.26 244	60.07 160	95.32 202	45.16 398	77.58 249	88.83 277

TR-MVS		78.77 225	77.37 230	82.95 224	90.49 149	60.88 303	93.67 100	90.07 257	70.08 284	74.51 206	91.37 182	45.69 333	95.70 181	60.12 332	80.32 220	92.29 214

thres400		78.68 226	77.43 225	82.43 237	92.21 97	64.49 194	92.05 180	96.28 4	73.48 202	71.75 249	88.26 244	60.07 160	95.32 202	45.16 398	77.58 249	87.48 298

BH-untuned		78.68 226	77.08 233	83.48 208	89.84 161	63.74 224	92.70 146	88.59 324	71.57 258	66.83 317	88.65 236	51.75 265	95.39 197	59.03 337	84.77 166	91.32 242

OMC-MVS		78.67 228	77.91 217	80.95 282	85.76 287	57.40 364	88.49 307	88.67 321	73.85 193	72.43 240	92.10 161	49.29 297	94.55 238	72.73 215	77.89 245	90.91 252

tpm		78.58 229	77.03 234	83.22 218	85.94 283	64.56 192	83.21 365	91.14 204	78.31 116	73.67 220	79.68 367	64.01 103	92.09 331	66.07 289	71.26 299	93.03 188

OpenMVS	70.45 11	78.54 230	75.92 253	86.41 81	85.93 284	71.68 18	92.74 143	92.51 135	66.49 326	64.56 334	91.96 166	43.88 343	98.10 39	54.61 353	90.65 93	89.44 274

EPMVS		78.49 231	75.98 252	86.02 92	91.21 135	69.68 53	80.23 393	91.20 198	75.25 171	72.48 238	78.11 378	54.65 231	93.69 279	57.66 343	83.04 185	94.69 108

AUN-MVS		78.37 232	77.43 225	81.17 271	86.60 265	57.45 363	89.46 286	91.16 200	74.11 186	74.40 207	90.49 195	55.52 222	94.57 234	74.73 197	60.43 388	91.48 236

thres100view900		78.37 232	77.01 235	82.46 236	91.89 115	63.21 246	91.19 226	96.33 1	72.28 230	70.45 264	87.89 253	60.31 155	95.32 202	45.16 398	77.58 249	88.83 277

GA-MVS		78.33 234	76.23 248	84.65 158	83.65 329	66.30 147	91.44 205	90.14 255	76.01 159	70.32 266	84.02 305	42.50 348	94.72 225	70.98 234	77.00 257	92.94 191

cascas		78.18 235	75.77 255	85.41 114	87.14 250	69.11 65	92.96 133	91.15 203	66.71 324	70.47 262	86.07 280	37.49 378	96.48 139	70.15 242	79.80 224	90.65 254

UniMVSNet_NR-MVSNet		78.15 236	77.55 223	79.98 304	84.46 315	60.26 323	92.25 167	93.20 102	77.50 133	68.88 286	86.61 273	66.10 74	92.13 329	66.38 285	62.55 366	87.54 296

LuminaMVS		78.14 237	76.66 240	82.60 234	80.82 357	64.64 191	89.33 288	90.45 235	68.25 309	74.73 204	85.51 289	41.15 354	94.14 255	78.96 164	80.69 218	89.04 275

IMVS_0404		78.11 238	76.29 247	83.59 202	88.54 195	64.75 187	84.63 348	90.80 221	76.73 148	61.16 362	90.18 203	40.17 358	91.58 343	73.47 203	80.95 209	94.43 128

thres600view7		78.00 239	76.66 240	82.03 256	91.93 111	63.69 230	91.30 218	96.33 1	72.43 225	70.46 263	87.89 253	60.31 155	94.92 218	42.64 410	76.64 260	87.48 298

FC-MVSNet-test		77.99 240	78.08 211	77.70 338	84.89 305	55.51 379	90.27 260	93.75 77	76.87 141	66.80 318	87.59 258	65.71 80	90.23 363	62.89 316	73.94 278	87.37 301

Anonymous202405211		77.96 241	75.33 261	85.87 97	93.73 53	64.52 193	94.85 49	85.36 379	62.52 361	76.11 181	90.18 203	29.43 416	97.29 83	68.51 259	77.24 256	95.81 50

cl22		77.94 242	76.78 238	81.42 266	87.57 238	64.93 185	90.67 245	88.86 314	72.45 224	67.63 305	82.68 320	64.07 101	92.91 299	71.79 224	65.30 339	86.44 319

XXY-MVS		77.94 242	76.44 243	82.43 237	82.60 341	64.44 198	92.01 182	91.83 169	73.59 201	70.00 271	85.82 285	54.43 236	94.76 222	69.63 245	68.02 320	88.10 291

MS-PatchMatch		77.90 244	76.50 242	82.12 251	85.99 280	69.95 42	91.75 199	92.70 122	73.97 190	62.58 357	84.44 301	41.11 355	95.78 171	63.76 308	92.17 66	80.62 401

FMVSNet3		77.73 245	76.04 251	82.80 226	91.20 136	68.99 69	91.87 190	91.99 158	73.35 204	67.04 313	83.19 315	56.62 209	92.14 328	59.80 334	69.34 306	87.28 304

VortexMVS		77.62 246	76.44 243	81.13 273	88.58 194	63.73 226	91.24 221	91.30 196	77.81 124	65.76 323	81.97 329	49.69 292	93.72 277	76.40 181	65.26 342	85.94 334

miper_ehance_all_eth		77.60 247	76.44 243	81.09 279	85.70 289	64.41 201	90.65 246	88.64 323	72.31 228	67.37 311	82.52 321	64.77 93	92.64 312	70.67 238	65.30 339	86.24 323

UniMVSNet (Re)		77.58 248	76.78 238	79.98 304	84.11 321	60.80 304	91.76 197	93.17 104	76.56 154	69.93 274	84.78 296	63.32 119	92.36 322	64.89 301	62.51 368	86.78 312

PatchmatchNet		77.46 249	74.63 268	85.96 94	89.55 169	70.35 35	79.97 398	89.55 279	72.23 231	70.94 257	76.91 390	57.03 199	92.79 304	54.27 355	81.17 207	94.74 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 250	75.65 257	82.73 228	80.38 365	67.13 121	91.85 192	90.23 252	75.09 173	69.37 276	83.39 312	53.79 245	94.44 242	71.77 225	65.00 346	86.63 317

CHOSEN 280x420		77.35 251	76.95 237	78.55 330	87.07 252	62.68 261	69.71 430	82.95 401	68.80 302	71.48 254	87.27 265	66.03 75	84.00 414	76.47 180	82.81 188	88.95 276

PS-MVSNAJss		77.26 252	76.31 246	80.13 299	80.64 361	59.16 343	90.63 249	91.06 212	72.80 216	68.58 292	84.57 299	53.55 247	93.96 269	72.97 209	71.96 293	87.27 305

gg-mvs-nofinetune		77.18 253	74.31 275	85.80 101	91.42 128	68.36 83	71.78 424	94.72 37	49.61 423	77.12 173	45.92 452	77.41 8	93.98 268	67.62 269	93.16 55	95.05 88

WB-MVSnew		77.14 254	76.18 250	80.01 303	86.18 275	63.24 244	91.26 219	94.11 65	71.72 250	73.52 221	87.29 264	45.14 338	93.00 292	56.98 345	79.42 229	83.80 362

MVP-Stereo		77.12 255	76.23 248	79.79 311	81.72 349	66.34 146	89.29 289	90.88 216	70.56 279	62.01 360	82.88 317	49.34 295	94.13 256	65.55 296	93.80 43	78.88 416
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 256	75.37 259	82.20 247	89.25 178	62.11 274	82.06 376	89.09 301	76.77 146	70.84 259	87.12 266	41.43 353	95.01 213	67.23 275	74.55 270	89.48 272

MonoMVSNet		76.99 257	75.08 264	82.73 228	83.32 333	63.24 244	86.47 339	86.37 362	79.08 101	66.31 321	79.30 371	49.80 291	91.72 338	79.37 157	65.70 337	93.23 179

dmvs_re		76.93 258	75.36 260	81.61 262	87.78 235	60.71 311	80.00 397	87.99 341	79.42 90	69.02 282	89.47 222	46.77 321	94.32 246	63.38 310	74.45 273	89.81 265

X-MVStestdata		76.86 259	74.13 281	85.05 132	93.22 66	63.78 222	92.92 135	92.66 127	73.99 188	78.18 159	10.19 467	55.25 223	97.41 75	79.16 160	91.58 78	93.95 153

DU-MVS		76.86 259	75.84 254	79.91 307	82.96 337	60.26 323	91.26 219	91.54 183	76.46 156	68.88 286	86.35 276	56.16 214	92.13 329	66.38 285	62.55 366	87.35 302

Anonymous20240529		76.84 261	74.15 280	84.88 140	91.02 138	64.95 184	93.84 92	91.09 208	53.57 411	73.00 224	87.42 261	35.91 388	97.32 81	69.14 253	72.41 291	92.36 210

UWE-MVS-28		76.83 262	77.60 222	74.51 372	84.58 311	50.34 406	88.22 312	94.60 45	74.46 179	66.66 319	88.98 234	62.53 132	85.50 406	57.55 344	80.80 217	87.69 295

c3_l		76.83 262	75.47 258	80.93 283	85.02 303	64.18 211	90.39 255	88.11 338	71.66 251	66.65 320	81.64 335	63.58 115	92.56 313	69.31 250	62.86 363	86.04 329

WR-MVS		76.76 264	75.74 256	79.82 310	84.60 309	62.27 271	92.60 154	92.51 135	76.06 158	67.87 302	85.34 290	56.76 205	90.24 362	62.20 320	63.69 361	86.94 310

v1144		76.73 265	74.88 265	82.27 243	80.23 369	66.60 140	91.68 201	90.21 254	73.69 198	69.06 281	81.89 330	52.73 257	94.40 244	69.21 251	65.23 343	85.80 337

IterMVS-LS		76.49 266	75.18 263	80.43 291	84.49 314	62.74 259	90.64 247	88.80 316	72.40 226	65.16 329	81.72 333	60.98 147	92.27 326	67.74 267	64.65 351	86.29 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 267	74.55 271	82.19 248	79.14 383	67.82 101	90.26 261	89.42 284	73.75 196	68.63 291	81.89 330	51.31 272	94.09 258	71.69 227	64.84 347	84.66 354

Elysia		76.45 268	74.17 278	83.30 212	80.43 363	64.12 212	89.58 277	90.83 218	61.78 371	72.53 234	85.92 283	34.30 395	94.81 220	68.10 261	84.01 179	90.97 249

StellarMVS		76.45 268	74.17 278	83.30 212	80.43 363	64.12 212	89.58 277	90.83 218	61.78 371	72.53 234	85.92 283	34.30 395	94.81 220	68.10 261	84.01 179	90.97 249

mamba_0408		76.22 270	73.37 292	84.77 147	88.50 200	66.98 127	58.80 451	86.18 368	69.12 298	74.12 212	89.01 232	47.50 316	95.35 199	67.57 270	79.52 226	91.98 226

v148		76.19 271	74.47 273	81.36 267	80.05 371	64.44 198	91.75 199	90.23 252	73.68 199	67.13 312	80.84 350	55.92 219	93.86 276	68.95 255	61.73 377	85.76 340

Effi-MVS+-dtu		76.14 272	75.28 262	78.72 329	83.22 334	55.17 381	89.87 272	87.78 345	75.42 167	67.98 297	81.43 339	45.08 339	92.52 315	75.08 191	71.63 294	88.48 285

cl____		76.07 273	74.67 266	80.28 294	85.15 298	61.76 284	90.12 264	88.73 318	71.16 266	65.43 326	81.57 337	61.15 144	92.95 294	66.54 282	62.17 370	86.13 327

DIV-MVS_self_test		76.07 273	74.67 266	80.28 294	85.14 299	61.75 285	90.12 264	88.73 318	71.16 266	65.42 327	81.60 336	61.15 144	92.94 298	66.54 282	62.16 372	86.14 325

FMVSNet2		76.07 273	74.01 283	82.26 245	88.85 188	67.66 105	91.33 216	91.61 181	70.84 273	65.98 322	82.25 325	48.03 306	92.00 333	58.46 339	68.73 314	87.10 307

v144192		76.05 276	74.03 282	82.12 251	79.50 377	66.55 142	91.39 210	89.71 276	72.30 229	68.17 295	81.33 342	51.75 265	94.03 266	67.94 265	64.19 354	85.77 338

NR-MVSNet		76.05 276	74.59 269	80.44 290	82.96 337	62.18 273	90.83 237	91.73 173	77.12 139	60.96 364	86.35 276	59.28 173	91.80 336	60.74 327	61.34 381	87.35 302

v1192		75.98 278	73.92 284	82.15 249	79.73 373	66.24 149	91.22 223	89.75 270	72.67 218	68.49 293	81.42 340	49.86 289	94.27 250	67.08 277	65.02 345	85.95 332

FE-MVS		75.97 279	73.02 298	84.82 143	89.78 162	65.56 167	77.44 409	91.07 211	64.55 338	72.66 230	79.85 365	46.05 331	96.69 128	54.97 352	80.82 215	92.21 220

eth_miper_zixun_eth		75.96 280	74.40 274	80.66 286	84.66 308	63.02 250	89.28 290	88.27 334	71.88 242	65.73 324	81.65 334	59.45 169	92.81 302	68.13 260	60.53 386	86.14 325

TranMVSNet+NR-MVSNet		75.86 281	74.52 272	79.89 308	82.44 343	60.64 314	91.37 213	91.37 190	76.63 152	67.65 304	86.21 279	52.37 260	91.55 344	61.84 322	60.81 384	87.48 298

SCA		75.82 282	72.76 301	85.01 134	86.63 264	70.08 38	81.06 386	89.19 293	71.60 257	70.01 270	77.09 388	45.53 334	90.25 359	60.43 329	73.27 282	94.68 109

LPG-MVS_test		75.82 282	74.58 270	79.56 318	84.31 318	59.37 339	90.44 252	89.73 273	69.49 290	64.86 330	88.42 239	38.65 364	94.30 248	72.56 217	72.76 286	85.01 351

GBi-Net		75.65 284	73.83 285	81.10 276	88.85 188	65.11 179	90.01 268	90.32 243	70.84 273	67.04 313	80.25 360	48.03 306	91.54 345	59.80 334	69.34 306	86.64 314

test1		75.65 284	73.83 285	81.10 276	88.85 188	65.11 179	90.01 268	90.32 243	70.84 273	67.04 313	80.25 360	48.03 306	91.54 345	59.80 334	69.34 306	86.64 314

v1921920		75.63 286	73.49 290	82.06 255	79.38 378	66.35 145	91.07 231	89.48 280	71.98 237	67.99 296	81.22 345	49.16 300	93.90 272	66.56 281	64.56 352	85.92 335

ACMP	71.68 10	75.58 287	74.23 277	79.62 316	84.97 304	59.64 334	90.80 238	89.07 303	70.39 280	62.95 353	87.30 263	38.28 368	93.87 274	72.89 210	71.45 297	85.36 347
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 288	73.26 296	81.61 262	80.67 360	66.82 133	89.54 281	89.27 289	71.65 252	63.30 348	80.30 359	54.99 229	94.06 261	67.33 274	62.33 369	83.94 360

tpm cat1		75.30 289	72.21 310	84.58 163	88.52 199	67.77 102	78.16 407	88.02 340	61.88 369	68.45 294	76.37 394	60.65 150	94.03 266	53.77 358	74.11 276	91.93 229

PLC	68.80 14	75.23 290	73.68 288	79.86 309	92.93 77	58.68 348	90.64 247	88.30 332	60.90 376	64.43 338	90.53 193	42.38 349	94.57 234	56.52 346	76.54 261	86.33 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 291	72.98 299	81.88 257	79.20 380	66.00 154	90.75 241	89.11 300	71.63 256	67.41 309	81.22 345	47.36 318	93.87 274	65.46 297	64.72 350	85.77 338

Fast-Effi-MVS+-dtu		75.04 292	73.37 292	80.07 300	80.86 355	59.52 337	91.20 225	85.38 378	71.90 240	65.20 328	84.84 295	41.46 352	92.97 293	66.50 284	72.96 285	87.73 294

dp		75.01 293	72.09 311	83.76 192	89.28 177	66.22 150	79.96 399	89.75 270	71.16 266	67.80 303	77.19 387	51.81 263	92.54 314	50.39 368	71.44 298	92.51 207

TAPA-MVS	70.22 12	74.94 294	73.53 289	79.17 324	90.40 151	52.07 394	89.19 294	89.61 278	62.69 360	70.07 269	92.67 145	48.89 303	94.32 246	38.26 424	79.97 222	91.12 247
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 295	73.32 295	79.74 313	86.53 267	60.31 322	89.03 299	92.70 122	78.61 112	68.98 284	83.34 313	41.93 351	92.23 327	52.77 362	65.97 335	86.69 313

SSM_04072		74.86 296	73.37 292	79.35 321	88.50 200	66.98 127	58.80 451	86.18 368	69.12 298	74.12 212	89.01 232	47.50 316	79.09 436	67.57 270	79.52 226	91.98 226

v10		74.77 297	72.54 307	81.46 265	80.33 367	66.71 137	89.15 295	89.08 302	70.94 271	63.08 351	79.86 364	52.52 258	94.04 264	65.70 293	62.17 370	83.64 363

XVG-OURS-SEG-HR		74.70 298	73.08 297	79.57 317	78.25 396	57.33 365	80.49 389	87.32 350	63.22 353	68.76 289	90.12 214	44.89 340	91.59 342	70.55 240	74.09 277	89.79 266

ACMM	69.62 13	74.34 299	72.73 303	79.17 324	84.25 320	57.87 355	90.36 257	89.93 264	63.17 355	65.64 325	86.04 282	37.79 376	94.10 257	65.89 290	71.52 296	85.55 343
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 300	72.30 309	80.32 292	91.49 127	61.66 287	90.85 236	80.72 407	56.67 403	63.85 343	90.64 190	46.75 322	90.84 353	53.79 357	75.99 265	88.47 286

XVG-OURS		74.25 301	72.46 308	79.63 315	78.45 394	57.59 361	80.33 391	87.39 347	63.86 345	68.76 289	89.62 221	40.50 357	91.72 338	69.00 254	74.25 275	89.58 269

test_fmvs1		74.07 302	73.69 287	75.22 363	78.91 387	47.34 423	89.06 298	74.69 424	63.68 348	79.41 143	91.59 177	24.36 427	87.77 389	85.22 92	76.26 263	90.55 257

CVMVSNet		74.04 303	74.27 276	73.33 382	85.33 292	43.94 437	89.53 284	88.39 328	54.33 410	70.37 265	90.13 212	49.17 299	84.05 412	61.83 323	79.36 231	91.99 225

Baseline_NR-MVSNet		73.99 304	72.83 300	77.48 342	80.78 358	59.29 342	91.79 194	84.55 387	68.85 301	68.99 283	80.70 351	56.16 214	92.04 332	62.67 317	60.98 383	81.11 395

pmmvs4		73.92 305	71.81 315	80.25 296	79.17 381	65.24 175	87.43 327	87.26 353	67.64 317	63.46 346	83.91 307	48.96 302	91.53 348	62.94 314	65.49 338	83.96 359

D2MVS		73.80 306	72.02 312	79.15 326	79.15 382	62.97 251	88.58 306	90.07 257	72.94 211	59.22 374	78.30 375	42.31 350	92.70 308	65.59 295	72.00 292	81.79 390

SD_0403		73.79 307	73.48 291	74.69 369	85.33 292	45.56 433	83.80 355	85.57 377	76.55 155	62.96 352	88.45 238	50.62 281	87.59 393	48.80 378	79.28 235	90.92 251

CR-MVSNet		73.79 307	70.82 323	82.70 230	83.15 335	67.96 96	70.25 427	84.00 392	73.67 200	69.97 272	72.41 411	57.82 192	89.48 372	52.99 361	73.13 283	90.64 255

test_djsdf		73.76 309	72.56 306	77.39 344	77.00 408	53.93 387	89.07 296	90.69 227	65.80 330	63.92 341	82.03 328	43.14 347	92.67 309	72.83 211	68.53 315	85.57 342

pmmvs5		73.35 310	71.52 317	78.86 328	78.64 391	60.61 315	91.08 229	86.90 357	67.69 314	63.32 347	83.64 308	44.33 342	90.53 356	62.04 321	66.02 334	85.46 345

Anonymous20231211		73.08 311	70.39 327	81.13 273	90.62 146	63.33 241	91.40 208	90.06 259	51.84 416	64.46 337	80.67 353	36.49 386	94.07 260	63.83 307	64.17 355	85.98 331

tt0805		73.07 312	70.73 324	80.07 300	78.37 395	57.05 367	87.78 321	92.18 149	61.23 375	67.04 313	86.49 275	31.35 408	94.58 232	65.06 300	67.12 327	88.57 283

miper_lstm_enhance		73.05 313	71.73 316	77.03 349	83.80 326	58.32 352	81.76 377	88.88 312	69.80 288	61.01 363	78.23 377	57.19 197	87.51 394	65.34 298	59.53 391	85.27 350

jajsoiax		73.05 313	71.51 318	77.67 339	77.46 405	54.83 383	88.81 302	90.04 260	69.13 297	62.85 355	83.51 310	31.16 409	92.75 305	70.83 235	69.80 302	85.43 346

LCM-MVSNet-Re		72.93 315	71.84 314	76.18 358	88.49 204	48.02 418	80.07 396	70.17 439	73.96 191	52.25 408	80.09 363	49.98 287	88.24 383	67.35 272	84.23 175	92.28 215

pm-mvs1		72.89 316	71.09 320	78.26 334	79.10 384	57.62 359	90.80 238	89.30 288	67.66 315	62.91 354	81.78 332	49.11 301	92.95 294	60.29 331	58.89 394	84.22 358

tpmvs		72.88 317	69.76 333	82.22 246	90.98 139	67.05 123	78.22 406	88.30 332	63.10 356	64.35 339	74.98 401	55.09 228	94.27 250	43.25 404	69.57 305	85.34 348

test0.0.03 1		72.76 318	72.71 304	72.88 386	80.25 368	47.99 419	91.22 223	89.45 282	71.51 261	62.51 358	87.66 256	53.83 243	85.06 408	50.16 370	67.84 325	85.58 341

UniMVSNet_ETH3D		72.74 319	70.53 326	79.36 320	78.62 392	56.64 371	85.01 345	89.20 292	63.77 346	64.84 332	84.44 301	34.05 397	91.86 335	63.94 306	70.89 301	89.57 270

mvs_tets		72.71 320	71.11 319	77.52 340	77.41 406	54.52 385	88.45 308	89.76 269	68.76 304	62.70 356	83.26 314	29.49 415	92.71 306	70.51 241	69.62 304	85.34 348

FMVSNet1		72.71 320	69.91 331	81.10 276	83.60 330	65.11 179	90.01 268	90.32 243	63.92 344	63.56 345	80.25 360	36.35 387	91.54 345	54.46 354	66.75 330	86.64 314

test_fmvs1_n		72.69 322	71.92 313	74.99 367	71.15 428	47.08 425	87.34 329	75.67 419	63.48 350	78.08 161	91.17 185	20.16 441	87.87 386	84.65 101	75.57 267	90.01 263

IterMVS		72.65 323	70.83 321	78.09 336	82.17 345	62.96 252	87.64 325	86.28 364	71.56 259	60.44 367	78.85 373	45.42 336	86.66 398	63.30 312	61.83 374	84.65 355
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 324	72.74 302	72.10 394	87.87 230	49.45 412	88.07 314	89.01 306	72.91 213	63.11 349	88.10 248	63.63 110	85.54 403	32.73 440	69.23 309	81.32 393

PatchMatch-RL		72.06 325	69.98 328	78.28 333	89.51 170	55.70 378	83.49 358	83.39 399	61.24 374	63.72 344	82.76 318	34.77 392	93.03 291	53.37 360	77.59 248	86.12 328

PVSNet_0	68.08 15	71.81 326	68.32 342	82.27 243	84.68 306	62.31 270	88.68 304	90.31 246	75.84 160	57.93 386	80.65 354	37.85 375	94.19 253	69.94 243	29.05 455	90.31 259

MIMVSNet		71.64 327	68.44 340	81.23 270	81.97 348	64.44 198	73.05 421	88.80 316	69.67 289	64.59 333	74.79 403	32.79 400	87.82 387	53.99 356	76.35 262	91.42 237

test_vis1_n		71.63 328	70.73 324	74.31 376	69.63 434	47.29 424	86.91 333	72.11 432	63.21 354	75.18 195	90.17 209	20.40 439	85.76 402	84.59 102	74.42 274	89.87 264

IterMVS-SCA-FT		71.55 329	69.97 329	76.32 356	81.48 351	60.67 313	87.64 325	85.99 371	66.17 328	59.50 372	78.88 372	45.53 334	83.65 416	62.58 318	61.93 373	84.63 357

v7n		71.31 330	68.65 337	79.28 322	76.40 410	60.77 306	86.71 337	89.45 282	64.17 343	58.77 379	78.24 376	44.59 341	93.54 281	57.76 341	61.75 376	83.52 366

anonymousdsp		71.14 331	69.37 335	76.45 355	72.95 423	54.71 384	84.19 352	88.88 312	61.92 368	62.15 359	79.77 366	38.14 371	91.44 350	68.90 256	67.45 326	83.21 372

F-COLMAP		70.66 332	68.44 340	77.32 345	86.37 272	55.91 376	88.00 316	86.32 363	56.94 401	57.28 390	88.07 250	33.58 398	92.49 316	51.02 365	68.37 316	83.55 364

WR-MVS_H		70.59 333	69.94 330	72.53 388	81.03 354	51.43 398	87.35 328	92.03 157	67.38 318	60.23 369	80.70 351	55.84 220	83.45 419	46.33 393	58.58 396	82.72 379

CP-MVSNet		70.50 334	69.91 331	72.26 391	80.71 359	51.00 402	87.23 330	90.30 247	67.84 313	59.64 371	82.69 319	50.23 285	82.30 427	51.28 364	59.28 392	83.46 368

RPMNet		70.42 335	65.68 356	84.63 161	83.15 335	67.96 96	70.25 427	90.45 235	46.83 432	69.97 272	65.10 435	56.48 213	95.30 205	35.79 429	73.13 283	90.64 255

testing3		70.38 336	70.83 321	69.03 407	85.82 286	43.93 438	90.72 244	90.56 234	68.06 310	60.24 368	86.82 272	64.83 91	84.12 410	26.33 448	64.10 356	79.04 414

tfpnnormal		70.10 337	67.36 346	78.32 332	83.45 332	60.97 302	88.85 300	92.77 120	64.85 337	60.83 365	78.53 374	43.52 345	93.48 283	31.73 443	61.70 378	80.52 402

TransMVSNet (Re)		70.07 338	67.66 344	77.31 346	80.62 362	59.13 344	91.78 196	84.94 383	65.97 329	60.08 370	80.44 356	50.78 278	91.87 334	48.84 377	45.46 428	80.94 397

CL-MVSNet_self_test		69.92 339	68.09 343	75.41 361	73.25 422	55.90 377	90.05 267	89.90 265	69.96 285	61.96 361	76.54 391	51.05 277	87.64 390	49.51 374	50.59 417	82.70 381

DP-MVS		69.90 340	66.48 348	80.14 298	95.36 28	62.93 253	89.56 279	76.11 417	50.27 422	57.69 388	85.23 291	39.68 360	95.73 175	33.35 434	71.05 300	81.78 391

PS-CasMVS		69.86 341	69.13 336	72.07 395	80.35 366	50.57 405	87.02 332	89.75 270	67.27 319	59.19 375	82.28 324	46.58 324	82.24 428	50.69 367	59.02 393	83.39 370

Syy-MVS		69.65 342	69.52 334	70.03 403	87.87 230	43.21 439	88.07 314	89.01 306	72.91 213	63.11 349	88.10 248	45.28 337	85.54 403	22.07 453	69.23 309	81.32 393

MSDG		69.54 343	65.73 355	80.96 281	85.11 301	63.71 228	84.19 352	83.28 400	56.95 400	54.50 397	84.03 304	31.50 406	96.03 163	42.87 408	69.13 311	83.14 374

PEN-MVS		69.46 344	68.56 338	72.17 393	79.27 379	49.71 410	86.90 334	89.24 290	67.24 322	59.08 376	82.51 322	47.23 319	83.54 418	48.42 380	57.12 398	83.25 371

LS3D		69.17 345	66.40 350	77.50 341	91.92 112	56.12 374	85.12 344	80.37 409	46.96 430	56.50 392	87.51 260	37.25 379	93.71 278	32.52 442	79.40 230	82.68 382

PatchT		69.11 346	65.37 360	80.32 292	82.07 347	63.68 231	67.96 437	87.62 346	50.86 420	69.37 276	65.18 434	57.09 198	88.53 379	41.59 413	66.60 331	88.74 280

KD-MVS_2432*1600		69.03 347	66.37 351	77.01 350	85.56 290	61.06 300	81.44 382	90.25 250	67.27 319	58.00 384	76.53 392	54.49 233	87.63 391	48.04 382	35.77 446	82.34 385

miper_refine_blended		69.03 347	66.37 351	77.01 350	85.56 290	61.06 300	81.44 382	90.25 250	67.27 319	58.00 384	76.53 392	54.49 233	87.63 391	48.04 382	35.77 446	82.34 385

mvsany_test1		68.77 349	68.56 338	69.39 405	73.57 421	45.88 432	80.93 387	60.88 453	59.65 385	71.56 252	90.26 202	43.22 346	75.05 440	74.26 200	62.70 365	87.25 306

ACMH	63.93 17	68.62 350	64.81 362	80.03 302	85.22 297	63.25 243	87.72 322	84.66 385	60.83 377	51.57 412	79.43 370	27.29 422	94.96 215	41.76 411	64.84 347	81.88 389
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 351	65.41 359	77.96 337	78.69 390	62.93 253	89.86 273	89.17 294	60.55 378	50.27 417	77.73 382	22.60 435	94.06 261	47.18 389	72.65 288	76.88 428

ADS-MVSNet		68.54 352	64.38 369	81.03 280	88.06 224	66.90 132	68.01 435	84.02 391	57.57 394	64.48 335	69.87 422	38.68 362	89.21 374	40.87 415	67.89 323	86.97 308

DTE-MVSNet		68.46 353	67.33 347	71.87 397	77.94 400	49.00 416	86.16 341	88.58 325	66.36 327	58.19 381	82.21 326	46.36 325	83.87 415	44.97 401	55.17 405	82.73 378

mmtdpeth		68.33 354	66.37 351	74.21 377	82.81 340	51.73 395	84.34 350	80.42 408	67.01 323	71.56 252	68.58 426	30.52 413	92.35 323	75.89 184	36.21 444	78.56 421

our_test_3		68.29 355	64.69 364	79.11 327	78.92 385	64.85 186	88.40 309	85.06 381	60.32 381	52.68 406	76.12 396	40.81 356	89.80 371	44.25 403	55.65 403	82.67 383

Patchmatch-RL test		68.17 356	64.49 367	79.19 323	71.22 427	53.93 387	70.07 429	71.54 436	69.22 294	56.79 391	62.89 439	56.58 210	88.61 376	69.53 247	52.61 412	95.03 90

XVG-ACMP-BASELINE		68.04 357	65.53 358	75.56 360	74.06 420	52.37 392	78.43 403	85.88 372	62.03 366	58.91 378	81.21 347	20.38 440	91.15 352	60.69 328	68.18 317	83.16 373

FMVSNet5		68.04 357	65.66 357	75.18 365	84.43 316	57.89 354	83.54 357	86.26 365	61.83 370	53.64 403	73.30 406	37.15 382	85.08 407	48.99 376	61.77 375	82.56 384

ppachtmachnet_test		67.72 359	63.70 371	79.77 312	78.92 385	66.04 153	88.68 304	82.90 402	60.11 383	55.45 394	75.96 397	39.19 361	90.55 355	39.53 419	52.55 413	82.71 380

ACMH+	65.35 16	67.65 360	64.55 365	76.96 352	84.59 310	57.10 366	88.08 313	80.79 406	58.59 392	53.00 405	81.09 349	26.63 424	92.95 294	46.51 391	61.69 379	80.82 398

pmmvs6		67.57 361	64.76 363	76.00 359	72.82 425	53.37 389	88.71 303	86.78 361	53.19 412	57.58 389	78.03 379	35.33 391	92.41 319	55.56 350	54.88 407	82.21 387

Anonymous20231206		67.53 362	65.78 354	72.79 387	74.95 416	47.59 421	88.23 311	87.32 350	61.75 373	58.07 383	77.29 385	37.79 376	87.29 396	42.91 406	63.71 360	83.48 367

Patchmtry		67.53 362	63.93 370	78.34 331	82.12 346	64.38 202	68.72 432	84.00 392	48.23 429	59.24 373	72.41 411	57.82 192	89.27 373	46.10 394	56.68 402	81.36 392

USDC		67.43 364	64.51 366	76.19 357	77.94 400	55.29 380	78.38 404	85.00 382	73.17 206	48.36 425	80.37 357	21.23 437	92.48 317	52.15 363	64.02 358	80.81 399

ADS-MVSNet2		66.90 365	63.44 373	77.26 347	88.06 224	60.70 312	68.01 435	75.56 421	57.57 394	64.48 335	69.87 422	38.68 362	84.10 411	40.87 415	67.89 323	86.97 308

CMPMVS	48.56 21	66.77 366	64.41 368	73.84 379	70.65 431	50.31 407	77.79 408	85.73 375	45.54 435	44.76 436	82.14 327	35.40 390	90.14 365	63.18 313	74.54 272	81.07 396
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 367	62.92 376	76.80 354	76.51 409	57.77 356	89.22 291	83.41 398	55.48 407	53.86 401	77.84 380	26.28 425	93.95 270	34.90 431	68.76 313	78.68 419

LTVRE_ROB	59.60 19	66.27 368	63.54 372	74.45 373	84.00 323	51.55 397	67.08 439	83.53 396	58.78 390	54.94 396	80.31 358	34.54 393	93.23 287	40.64 417	68.03 319	78.58 420
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		66.06 369	62.45 379	76.88 353	81.42 353	54.45 386	57.49 453	88.67 321	49.36 424	63.86 342	46.86 451	56.06 217	90.25 359	49.53 373	68.83 312	85.95 332

Patchmatch-test		65.86 370	60.94 385	80.62 289	83.75 327	58.83 346	58.91 450	75.26 423	44.50 438	50.95 416	77.09 388	58.81 181	87.90 385	35.13 430	64.03 357	95.12 84

UnsupCasMVSNet_eth		65.79 371	63.10 374	73.88 378	70.71 430	50.29 408	81.09 385	89.88 266	72.58 220	49.25 422	74.77 404	32.57 402	87.43 395	55.96 349	41.04 436	83.90 361

test_fmvs2		65.78 372	64.84 361	68.60 409	66.54 441	41.71 441	83.27 362	69.81 440	54.38 409	67.91 299	84.54 300	15.35 446	81.22 432	75.65 186	66.16 333	82.88 375

dmvs_testset		65.55 373	66.45 349	62.86 421	79.87 372	22.35 467	76.55 411	71.74 434	77.42 136	55.85 393	87.77 255	51.39 271	80.69 433	31.51 446	65.92 336	85.55 343

pmmvs-eth3d		65.53 374	62.32 380	75.19 364	69.39 435	59.59 335	82.80 370	83.43 397	62.52 361	51.30 414	72.49 409	32.86 399	87.16 397	55.32 351	50.73 416	78.83 417

mamv4		65.18 375	67.43 345	58.44 425	77.88 402	49.36 415	69.40 431	70.99 438	48.31 428	57.78 387	85.53 288	59.01 178	51.88 463	73.67 202	64.32 353	74.07 433

SixPastTwentyTwo		64.92 376	61.78 383	74.34 375	78.74 389	49.76 409	83.42 361	79.51 412	62.86 357	50.27 417	77.35 383	30.92 411	90.49 357	45.89 395	47.06 423	82.78 376

OurMVSNet-221017-0		64.68 377	62.17 381	72.21 392	76.08 413	47.35 422	80.67 388	81.02 405	56.19 404	51.60 411	79.66 368	27.05 423	88.56 378	53.60 359	53.63 410	80.71 400

test_0402		64.54 378	61.09 384	74.92 368	84.10 322	60.75 308	87.95 317	79.71 411	52.03 414	52.41 407	77.20 386	32.21 404	91.64 340	23.14 451	61.03 382	72.36 439

testgi		64.48 379	62.87 377	69.31 406	71.24 426	40.62 444	85.49 342	79.92 410	65.36 334	54.18 399	83.49 311	23.74 430	84.55 409	41.60 412	60.79 385	82.77 377

RPSCF		64.24 380	61.98 382	71.01 400	76.10 412	45.00 434	75.83 416	75.94 418	46.94 431	58.96 377	84.59 298	31.40 407	82.00 429	47.76 387	60.33 390	86.04 329

EU-MVSNet		64.01 381	63.01 375	67.02 415	74.40 419	38.86 450	83.27 362	86.19 367	45.11 436	54.27 398	81.15 348	36.91 385	80.01 435	48.79 379	57.02 399	82.19 388

test20.03		63.83 382	62.65 378	67.38 414	70.58 432	39.94 446	86.57 338	84.17 389	63.29 352	51.86 410	77.30 384	37.09 383	82.47 425	38.87 423	54.13 409	79.73 408

sc_t1		63.81 383	59.39 391	77.10 348	77.62 403	56.03 375	84.32 351	73.56 428	46.66 433	58.22 380	73.06 407	23.28 433	90.62 354	50.93 366	46.84 424	84.64 356

MDA-MVSNet_test_wron		63.78 384	60.16 387	74.64 370	78.15 398	60.41 319	83.49 358	84.03 390	56.17 406	39.17 446	71.59 417	37.22 380	83.24 422	42.87 408	48.73 419	80.26 405

YYNet1		63.76 385	60.14 388	74.62 371	78.06 399	60.19 326	83.46 360	83.99 394	56.18 405	39.25 445	71.56 418	37.18 381	83.34 420	42.90 407	48.70 420	80.32 404

K. test v3		63.09 386	59.61 390	73.53 381	76.26 411	49.38 414	83.27 362	77.15 415	64.35 340	47.77 427	72.32 413	28.73 417	87.79 388	49.93 372	36.69 443	83.41 369

COLMAP_ROB	57.96 20	62.98 387	59.65 389	72.98 385	81.44 352	53.00 391	83.75 356	75.53 422	48.34 427	48.81 424	81.40 341	24.14 428	90.30 358	32.95 437	60.52 387	75.65 431
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 388	59.08 392	71.10 399	67.19 439	48.72 417	83.91 354	85.23 380	50.38 421	47.84 426	71.22 420	20.74 438	85.51 405	46.47 392	58.75 395	79.06 413

tt0320		61.85 389	57.45 398	75.03 366	77.49 404	57.60 360	82.74 371	73.65 427	43.65 442	53.65 402	68.18 428	25.47 426	88.66 375	45.56 397	46.68 425	78.81 418

AllTest		61.66 390	58.06 394	72.46 389	79.57 374	51.42 399	80.17 394	68.61 442	51.25 418	45.88 430	81.23 343	19.86 442	86.58 399	38.98 421	57.01 400	79.39 410

UnsupCasMVSNet_bld		61.60 391	57.71 395	73.29 383	68.73 436	51.64 396	78.61 402	89.05 304	57.20 399	46.11 429	61.96 442	28.70 418	88.60 377	50.08 371	38.90 441	79.63 409

MDA-MVSNet-bldmvs		61.54 392	57.70 396	73.05 384	79.53 376	57.00 370	83.08 366	81.23 404	57.57 394	34.91 450	72.45 410	32.79 400	86.26 401	35.81 428	41.95 434	75.89 430

tt0320-xc		61.51 393	56.89 402	75.37 362	78.50 393	58.61 349	82.61 373	71.27 437	44.31 439	53.17 404	68.03 430	23.38 431	88.46 380	47.77 386	43.00 433	79.03 415

mvs5depth		61.03 394	57.65 397	71.18 398	67.16 440	47.04 427	72.74 422	77.49 413	57.47 397	60.52 366	72.53 408	22.84 434	88.38 381	49.15 375	38.94 440	78.11 424

KD-MVS_self_test		60.87 395	58.60 393	67.68 412	66.13 442	39.93 447	75.63 418	84.70 384	57.32 398	49.57 420	68.45 427	29.55 414	82.87 423	48.09 381	47.94 421	80.25 406

kuosan		60.86 396	60.24 386	62.71 422	81.57 350	46.43 429	75.70 417	85.88 372	57.98 393	48.95 423	69.53 424	58.42 185	76.53 438	28.25 447	35.87 445	65.15 446

FE-MVSNET		60.52 397	57.18 401	70.53 401	67.53 438	50.68 404	82.62 372	76.28 416	59.33 388	46.71 428	71.10 421	30.54 412	83.61 417	33.15 436	47.37 422	77.29 427

TinyColmap		60.32 398	56.42 405	72.00 396	78.78 388	53.18 390	78.36 405	75.64 420	52.30 413	41.59 444	75.82 399	14.76 449	88.35 382	35.84 427	54.71 408	74.46 432

MVS-HIRNet		60.25 399	55.55 406	74.35 374	84.37 317	56.57 372	71.64 425	74.11 425	34.44 449	45.54 434	42.24 457	31.11 410	89.81 369	40.36 418	76.10 264	76.67 429

MIMVSNet1		60.16 400	57.33 399	68.67 408	69.71 433	44.13 436	78.92 401	84.21 388	55.05 408	44.63 437	71.85 415	23.91 429	81.54 431	32.63 441	55.03 406	80.35 403

PM-MVS		59.40 401	56.59 403	67.84 410	63.63 445	41.86 440	76.76 410	63.22 450	59.01 389	51.07 415	72.27 414	11.72 453	83.25 421	61.34 324	50.28 418	78.39 422

new-patchmatchnet		59.30 402	56.48 404	67.79 411	65.86 443	44.19 435	82.47 374	81.77 403	59.94 384	43.65 440	66.20 433	27.67 421	81.68 430	39.34 420	41.40 435	77.50 426

test_vis1_rt		59.09 403	57.31 400	64.43 418	68.44 437	46.02 431	83.05 368	48.63 462	51.96 415	49.57 420	63.86 438	16.30 444	80.20 434	71.21 233	62.79 364	67.07 445

test_fmvs3		56.82 404	54.86 408	62.69 423	53.59 456	35.47 453	75.87 415	65.64 447	43.91 440	55.10 395	71.43 419	6.91 461	74.40 443	68.64 258	52.63 411	78.20 423

DSMNet-mixed		56.78 405	54.44 409	63.79 419	63.21 446	29.44 462	64.43 442	64.10 449	42.12 446	51.32 413	71.60 416	31.76 405	75.04 441	36.23 426	65.20 344	86.87 311

pmmvs3		55.51 406	51.50 412	67.53 413	57.90 454	50.93 403	80.37 390	73.66 426	40.63 447	44.15 439	64.75 436	16.30 444	78.97 437	44.77 402	40.98 438	72.69 437

TDRefinement		55.28 407	51.58 411	66.39 416	59.53 453	46.15 430	76.23 413	72.80 429	44.60 437	42.49 442	76.28 395	15.29 447	82.39 426	33.20 435	43.75 430	70.62 441

dongtai		55.18 408	55.46 407	54.34 433	76.03 414	36.88 451	76.07 414	84.61 386	51.28 417	43.41 441	64.61 437	56.56 211	67.81 451	18.09 456	28.50 456	58.32 449

LF4IMVS		54.01 409	52.12 410	59.69 424	62.41 448	39.91 448	68.59 433	68.28 444	42.96 444	44.55 438	75.18 400	14.09 451	68.39 450	41.36 414	51.68 414	70.78 440

ttmdpeth		53.34 410	49.96 413	63.45 420	62.07 450	40.04 445	72.06 423	65.64 447	42.54 445	51.88 409	77.79 381	13.94 452	76.48 439	32.93 438	30.82 454	73.84 434

MVStest1		51.35 411	46.89 415	64.74 417	65.06 444	51.10 401	67.33 438	72.58 430	30.20 453	35.30 448	74.82 402	27.70 420	69.89 448	24.44 450	24.57 457	73.22 435

N_pmnet		50.55 412	49.11 414	54.88 431	77.17 407	4.02 475	84.36 349	2.00 473	48.59 425	45.86 432	68.82 425	32.22 403	82.80 424	31.58 444	51.38 415	77.81 425

new_pmnet		49.31 413	46.44 416	57.93 426	62.84 447	40.74 443	68.47 434	62.96 451	36.48 448	35.09 449	57.81 446	14.97 448	72.18 445	32.86 439	46.44 426	60.88 448

mvsany_test3		48.86 414	46.35 417	56.41 427	46.00 462	31.67 458	62.26 444	47.25 463	43.71 441	45.54 434	68.15 429	10.84 454	64.44 459	57.95 340	35.44 448	73.13 436

test_f		46.58 415	43.45 419	55.96 428	45.18 463	32.05 457	61.18 445	49.49 461	33.39 450	42.05 443	62.48 441	7.00 460	65.56 455	47.08 390	43.21 432	70.27 442

WB-MVS		46.23 416	44.94 418	50.11 436	62.13 449	21.23 469	76.48 412	55.49 455	45.89 434	35.78 447	61.44 444	35.54 389	72.83 444	9.96 463	21.75 458	56.27 451

FPMVS		45.64 417	43.10 421	53.23 434	51.42 459	36.46 452	64.97 441	71.91 433	29.13 454	27.53 454	61.55 443	9.83 456	65.01 457	16.00 460	55.58 404	58.22 450

SSC-MVS		44.51 418	43.35 420	47.99 440	61.01 452	18.90 471	74.12 420	54.36 456	43.42 443	34.10 451	60.02 445	34.42 394	70.39 447	9.14 465	19.57 459	54.68 452

EGC-MVSNET		42.35 419	38.09 422	55.11 430	74.57 417	46.62 428	71.63 426	55.77 454	0.04 468	0.24 469	62.70 440	14.24 450	74.91 442	17.59 457	46.06 427	43.80 454

LCM-MVSNet		40.54 420	35.79 425	54.76 432	36.92 469	30.81 459	51.41 456	69.02 441	22.07 456	24.63 456	45.37 453	4.56 465	65.81 454	33.67 433	34.50 449	67.67 443

APD_test1		40.50 421	37.31 424	50.09 437	51.88 457	35.27 454	59.45 449	52.59 458	21.64 457	26.12 455	57.80 447	4.56 465	66.56 453	22.64 452	39.09 439	48.43 453

test_vis3_rt		40.46 422	37.79 423	48.47 439	44.49 464	33.35 456	66.56 440	32.84 470	32.39 451	29.65 452	39.13 460	3.91 468	68.65 449	50.17 369	40.99 437	43.40 455

ANet_high		40.27 423	35.20 426	55.47 429	34.74 470	34.47 455	63.84 443	71.56 435	48.42 426	18.80 459	41.08 458	9.52 457	64.45 458	20.18 454	8.66 466	67.49 444

test_method		38.59 424	35.16 427	48.89 438	54.33 455	21.35 468	45.32 459	53.71 457	7.41 465	28.74 453	51.62 449	8.70 458	52.87 462	33.73 432	32.89 450	72.47 438

PMMVS2		37.93 425	33.61 428	50.92 435	46.31 461	24.76 465	60.55 448	50.05 459	28.94 455	20.93 457	47.59 450	4.41 467	65.13 456	25.14 449	18.55 461	62.87 447

Gipuma		34.91 426	31.44 429	45.30 441	70.99 429	39.64 449	19.85 463	72.56 431	20.10 459	16.16 463	21.47 464	5.08 464	71.16 446	13.07 461	43.70 431	25.08 461
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 427	29.47 430	42.67 443	41.89 466	30.81 459	52.07 454	43.45 464	15.45 460	18.52 460	44.82 454	2.12 469	58.38 460	16.05 458	30.87 452	38.83 456

APD_test2		32.77 427	29.47 430	42.67 443	41.89 466	30.81 459	52.07 454	43.45 464	15.45 460	18.52 460	44.82 454	2.12 469	58.38 460	16.05 458	30.87 452	38.83 456

PMVS	26.43 22	31.84 429	28.16 432	42.89 442	25.87 472	27.58 463	50.92 457	49.78 460	21.37 458	14.17 464	40.81 459	2.01 471	66.62 452	9.61 464	38.88 442	34.49 460
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 430	24.00 434	26.45 447	43.74 465	18.44 472	60.86 446	39.66 466	15.11 462	9.53 466	22.10 463	6.52 462	46.94 465	8.31 466	10.14 463	13.98 463

MVE	24.84 23	24.35 431	19.77 437	38.09 445	34.56 471	26.92 464	26.57 461	38.87 468	11.73 464	11.37 465	27.44 461	1.37 472	50.42 464	11.41 462	14.60 462	36.93 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 432	23.20 436	25.46 448	41.52 468	16.90 473	60.56 447	38.79 469	14.62 463	8.99 467	20.24 466	7.35 459	45.82 466	7.25 467	9.46 464	13.64 464

tmp_tt		22.26 433	23.75 435	17.80 449	5.23 473	12.06 474	35.26 460	39.48 467	2.82 467	18.94 458	44.20 456	22.23 436	24.64 468	36.30 425	9.31 465	16.69 462

cdsmvs_eth3d_5k		19.86 434	26.47 433	0.00 453	0.00 476	0.00 478	0.00 464	93.45 91	0.00 471	0.00 472	95.27 71	49.56 293	0.00 472	0.00 471	0.00 469	0.00 468

wuyk23d		11.30 435	10.95 438	12.33 450	48.05 460	19.89 470	25.89 462	1.92 474	3.58 466	3.12 468	1.37 468	0.64 473	15.77 469	6.23 468	7.77 467	1.35 465

ab-mvs-re		7.91 436	10.55 439	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	94.95 81	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

testmvs		7.23 437	9.62 440	0.06 452	0.04 474	0.02 477	84.98 346	0.02 475	0.03 469	0.18 470	1.21 469	0.01 475	0.02 470	0.14 469	0.01 468	0.13 467

test123		6.92 438	9.21 441	0.08 451	0.03 475	0.05 476	81.65 380	0.01 476	0.02 470	0.14 471	0.85 470	0.03 474	0.02 470	0.12 470	0.00 469	0.16 466

pcd_1.5k_mvsjas		4.46 439	5.95 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	53.55 247	0.00 472	0.00 471	0.00 469	0.00 468

mmdepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

monomultidepth		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

test_blank		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

uanet_test		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

DCPMVS		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

sosnet-low-res		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

sosnet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

uncertanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

Regformer		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

uanet		0.00 440	0.00 443	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	0.00 476	0.00 472	0.00 471	0.00 469	0.00 468

WAC-MVS						49.45 412								31.56 445

FOURS1					93.95 46	61.77 283	93.96 82	91.92 161	62.14 365	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	71.42 263	90.67 24	96.85 21	74.45 20

eth-test2					0.00 476

eth-test					0.00 476

ZD-MVS					96.63 9	65.50 170		93.50 89	70.74 277	85.26 74	95.19 77	64.92 90	97.29 83	87.51 68	93.01 56

RE-MVS-def			80.48 170		92.02 105	58.56 350	90.90 233	90.45 235	62.76 358	78.89 149	94.46 95	49.30 296		78.77 167	86.77 142	92.28 215

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	71.65 252	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	71.65 252	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	72.76 217	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 99	95.05 40	92.66 127	78.19 117

test_0728_THIRD									72.48 222	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	71.92 238	91.89 13	97.11 10	73.77 23

GSMVS																94.68 109

test_part2					96.29 19	68.16 92				90.78 22

sam_mvs1												57.85 191				94.68 109

sam_mvs												54.91 230

ambc				69.61 404	61.38 451	41.35 442	49.07 458	85.86 374		50.18 419	66.40 432	10.16 455	88.14 384	45.73 396	44.20 429	79.32 412

MTGPA								92.23 142

test_post1							78.95 400				20.70 465	53.05 252	91.50 349	60.43 329

test_post											23.01 462	56.49 212	92.67 309

patchmatchnet-post											67.62 431	57.62 194	90.25 359

GG-mvs-BLEND				86.53 77	91.91 114	69.67 54	75.02 419	94.75 36		78.67 157	90.85 189	77.91 7	94.56 237	72.25 220	93.74 45	95.36 68

MTMP							93.77 96	32.52 471

gm-plane-assit					88.42 210	67.04 124			78.62 111		91.83 170		97.37 77	76.57 179

test9_res														89.41 51	94.96 19	95.29 74

TEST9					94.18 41	67.28 115	94.16 69	93.51 87	71.75 249	85.52 69	95.33 65	68.01 57	97.27 87

test_8					94.19 40	67.19 117	94.15 71	93.42 94	71.87 243	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	66.97 130		93.31 97		84.49 80			96.75 126

TestCases				72.46 389	79.57 374	51.42 399		68.61 442	51.25 418	45.88 430	81.23 343	19.86 442	86.58 399	38.98 421	57.01 400	79.39 410

test_prior4						67.18 119	93.92 85

test_prior2							95.10 39		75.40 168	85.25 75	95.61 56	67.94 58		87.47 70	94.77 26

test_prior				86.42 80	94.71 35	67.35 114		93.10 108					96.84 123			95.05 88

旧先验2							92.00 185		59.37 387	87.54 49			93.47 284	75.39 188

新几何2							91.41 206

新几何1				84.73 151	92.32 93	64.28 207		91.46 188	59.56 386	79.77 136	92.90 139	56.95 204	96.57 132	63.40 309	92.91 58	93.34 175

旧先验1					91.94 110	60.74 309		91.50 186			94.36 99	65.23 85			91.84 73	94.55 117

无先验							92.71 145	92.61 132	62.03 366				97.01 104	66.63 280		93.97 152

原ACMM2							92.01 182

原ACMM1				84.42 168	93.21 68	64.27 208		93.40 96	65.39 333	79.51 141	92.50 147	58.11 190	96.69 128	65.27 299	93.96 40	92.32 213

test222					89.77 163	61.60 289	89.55 280	89.42 284	56.83 402	77.28 171	92.43 151	52.76 255			91.14 89	93.09 185

testdata2													96.09 157	61.26 325

segment_acmp												65.94 76

testdata				81.34 268	89.02 185	57.72 357		89.84 267	58.65 391	85.32 73	94.09 115	57.03 199	93.28 286	69.34 249	90.56 95	93.03 188

testdata1							89.21 292		77.55 132

test12				87.09 52	94.60 36	68.86 71		92.91 115		82.67 103		65.44 82	97.55 67		93.69 48	94.84 99

plane_prior7					86.94 255	61.51 290

plane_prior6					87.23 247	62.32 269						50.66 279

plane_prior5								91.31 192					95.55 191	76.74 177	78.53 242	88.39 287

plane_prior4											89.14 230

plane_prior3						61.95 278			79.09 100	72.53 234

plane_prior2							93.13 124		78.81 107

plane_prior1					87.15 249

plane_prior						62.42 265	93.85 89		79.38 92						78.80 239

n2								0.00 477

nn								0.00 477

door-mid								66.01 446

lessismore_v0				73.72 380	72.93 424	47.83 420		61.72 452		45.86 432	73.76 405	28.63 419	89.81 369	47.75 388	31.37 451	83.53 365

LGP-MVS_train				79.56 318	84.31 318	59.37 339		89.73 273	69.49 290	64.86 330	88.42 239	38.65 364	94.30 248	72.56 217	72.76 286	85.01 351

test11								93.01 111

door								66.57 445

HQP5-MVS						63.66 232

HQP-NCC					87.54 239		94.06 74		79.80 81	74.18 208

ACMP_Plane					87.54 239		94.06 74		79.80 81	74.18 208

BP-MVS														77.63 174

HQP4-MVS										74.18 208			95.61 185			88.63 281

HQP3-MVS								91.70 178							78.90 237

HQP2-MVS												51.63 267

NP-MVS					87.41 242	63.04 249					90.30 200

MDTV_nov1_ep13_2view						59.90 331	80.13 395		67.65 316	72.79 228		54.33 238		59.83 333		92.58 204

MDTV_nov1_ep13			72.61 305		89.06 184	68.48 80	80.33 391	90.11 256	71.84 245	71.81 248	75.92 398	53.01 253	93.92 271	48.04 382	73.38 281

ACMMP++_ref															71.63 294

ACMMP++															69.72 303

Test By Simon												54.21 241

ITE_SJBPF				70.43 402	74.44 418	47.06 426		77.32 414	60.16 382	54.04 400	83.53 309	23.30 432	84.01 413	43.07 405	61.58 380	80.21 407

DeepMVS_CX				34.71 446	51.45 458	24.73 466		28.48 472	31.46 452	17.49 462	52.75 448	5.80 463	42.60 467	18.18 455	19.42 460	36.81 459

High-resolution multi-view benchmark