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.

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 23	96.63 4	94.88 16

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 18	96.68 2	94.95 12

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 55	82.45 3	96.87 20	83.77 76	96.48 8	94.88 16

PC_three_1452									68.21 293	92.02 12	94.00 57	82.09 5	95.98 57	84.58 65	96.68 2	94.95 12

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 43	96.34 15	93.95 69
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 18	96.57 7	94.67 30

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 23	96.58 6	94.26 54

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 21	96.41 12	93.33 107
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
test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 50	78.98 12	96.58 35	85.66 52	95.72 24	94.58 36

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 48	78.35 13	96.77 24	89.59 16	94.22 62	94.67 30
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
APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 103	91.06 16	96.03 1	76.84 14	97.03 17	89.09 20	95.65 27	94.47 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
dcpmvs_2		85.63 65	86.15 55	84.06 149	91.71 80	64.94 224	86.47 219	91.87 108	73.63 160	86.60 61	93.02 87	76.57 15	91.87 246	83.36 78	92.15 84	95.35 3

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 38	94.06 53	76.43 16	96.84 21	88.48 34	95.99 18	94.34 50

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 180	84.86 79	92.89 89	76.22 17	96.33 41	84.89 60	95.13 36	94.40 46

CSCG		86.41 48	86.19 53	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 151	83.16 114	91.07 142	75.94 18	95.19 85	79.94 118	94.38 58	93.55 98

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 43	94.27 42	75.89 19	96.81 23	87.45 42	96.44 9	93.05 125

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 135	88.90 27	93.85 65	75.75 20	96.00 55	87.80 38	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 101	89.16 24	95.10 18	75.65 21	96.19 47	87.07 44	96.01 17	94.79 23

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 153	88.57 30	94.67 25	75.57 22	95.79 59	86.77 46	95.76 23

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 57	74.83 23	93.78 152	87.63 40	94.27 61	93.65 90
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
DELS-MVS		85.41 71	85.30 75	85.77 75	88.49 178	67.93 148	85.52 251	93.44 28	78.70 34	83.63 109	89.03 202	74.57 24	95.71 62	80.26 115	94.04 63	93.66 86
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
fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 149	88.59 139	89.05 217	80.19 12	90.70 17	95.40 15	74.56 25	93.92 145	91.54 2	92.07 86	95.31 5

patch_mono-2		83.65 99	84.54 84	80.99 258	90.06 116	65.83 198	84.21 284	88.74 233	71.60 205	85.01 73	92.44 99	74.51 26	83.50 385	82.15 95	92.15 84	93.64 92

train_agg		86.43 46	86.20 51	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 285	85.00 74	93.10 82	74.43 27	95.41 76	84.97 57	95.71 25	93.02 127

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 285	84.87 78	93.10 82	74.43 27	95.16 86

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 290	85.00 74	93.10 82	74.36 29	95.41 76

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 131	92.29 7	95.97 2	74.28 30	97.24 13	88.58 31	96.91 1	94.87 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test_prior2							88.85 125		75.41 109	84.91 76	93.54 70	74.28 30		83.31 79	95.86 20

TSAR-MVS + GP.		85.71 64	85.33 73	86.84 52	91.34 84	72.50 36	89.07 117	87.28 268	76.41 85	85.80 65	90.22 169	74.15 32	95.37 81	81.82 97	91.88 88	92.65 143

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 222	87.75 45	94.07 52	74.01 33	96.70 27	84.66 64	94.84 44

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 37	94.80 23	73.76 34	97.11 15	87.51 41	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 182	88.58 29	94.52 27	73.36 35	96.49 38	84.26 69	95.01 37	92.70 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
casdiffmvs_mvg		85.99 54	86.09 57	85.70 77	87.65 220	67.22 173	88.69 135	93.04 42	79.64 21	85.33 70	92.54 98	73.30 36	94.50 119	83.49 77	91.14 102	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sasdasda		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 134	73.28 37	93.91 146	81.50 99	88.80 144	94.77 25

canonicalmvs		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 134	73.28 37	93.91 146	81.50 99	88.80 144	94.77 25

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 191	87.08 244	65.21 214	89.09 116	90.21 166	79.67 19	89.98 19	95.02 20	73.17 39	91.71 252	91.30 3	91.60 93	92.34 156

segment_acmp												73.08 40

DPM-MVS		84.93 81	84.29 88	86.84 52	90.20 109	73.04 23	87.12 192	93.04 42	69.80 256	82.85 119	91.22 136	73.06 41	96.02 53	76.72 158	94.63 50	91.46 193

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 66	93.47 74	73.02 42	97.00 18	84.90 58	94.94 40	94.10 60

fmvsm_l_conf0.5_n_9		85.84 61	86.63 44	83.46 174	87.12 243	66.01 192	88.56 141	89.43 193	75.59 105	89.32 23	94.32 39	72.89 43	91.21 277	90.11 10	92.33 83	93.16 117

fmvsm_l_conf0.5_n_3		86.02 52	86.32 48	85.14 92	87.20 235	68.54 126	89.57 93	90.44 155	75.31 113	87.49 49	94.39 37	72.86 44	92.72 209	89.04 25	90.56 112	94.16 56

test_fmvsmconf_n		85.92 57	86.04 58	85.57 82	85.03 299	69.51 96	89.62 92	90.58 150	73.42 168	87.75 45	94.02 55	72.85 45	93.24 179	90.37 7	90.75 109	93.96 67

MGCFI-Net		85.06 80	85.51 69	83.70 167	89.42 135	63.01 275	89.43 97	92.62 74	76.43 84	87.53 48	91.34 132	72.82 46	93.42 172	81.28 102	88.74 147	94.66 33

nrg030		83.88 91	83.53 99	84.96 101	86.77 253	69.28 105	90.46 70	92.67 68	74.79 130	82.95 116	91.33 133	72.70 47	93.09 193	80.79 109	79.28 296	92.50 149

CDPH-MVS		85.76 63	85.29 76	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 292	84.61 85	93.48 72	72.32 48	96.15 49	79.00 125	95.43 30	94.28 53

lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 49	96.27 44	86.87 45	94.65 48	93.70 85

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 103	94.40 36	72.24 50	96.28 43	85.65 53	95.30 35	93.62 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		85.11 78	85.14 77	85.01 99	87.20 235	65.77 202	87.75 172	92.83 61	77.84 43	84.36 93	92.38 100	72.15 51	93.93 144	81.27 103	90.48 113	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 120	94.23 45	72.13 52	97.09 16	84.83 61	95.37 31	93.65 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_l_conf0.5_n		84.47 85	84.54 84	84.27 133	85.42 286	68.81 112	88.49 143	87.26 270	68.08 294	88.03 39	93.49 71	72.04 53	91.77 248	88.90 27	89.14 140	92.24 163

MVSMamba_PlusPlus		85.99 54	85.96 59	86.05 69	91.09 88	67.64 156	89.63 91	92.65 71	72.89 185	84.64 84	91.71 117	71.85 54	96.03 51	84.77 63	94.45 56	94.49 42

baseline		84.93 81	84.98 78	84.80 111	87.30 233	65.39 211	87.30 188	92.88 58	77.62 47	84.04 99	92.26 102	71.81 55	93.96 138	81.31 101	90.30 116	95.03 11

ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 71	94.32 39	71.76 56	96.93 19	85.53 55	95.79 22	94.32 51

test_fmvsmconf0.1_n		85.61 66	85.65 66	85.50 83	82.99 351	69.39 103	89.65 89	90.29 164	73.31 172	87.77 44	94.15 49	71.72 57	93.23 180	90.31 8	90.67 111	93.89 73

MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 135	86.57 1	87.39 52	94.97 21	71.70 58	97.68 1	92.19 1	95.63 28	95.57 1

test12				86.80 54	92.63 69	70.70 77		91.79 113		82.71 122		71.67 59	96.16 48		94.50 53	93.54 99

UniMVSNet_NR-MVSNet		81.88 136	81.54 135	82.92 201	88.46 180	63.46 265	87.13 191	92.37 82	80.19 12	78.38 195	89.14 198	71.66 60	93.05 196	70.05 232	76.46 331	92.25 161

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 159	92.83 18	93.30 33	79.67 19	84.57 87	92.27 101	71.47 61	95.02 96	84.24 71	93.46 69	95.13 9

DeepC-MVS_fast	79.65 3	86.91 38	86.62 45	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 94	93.36 78	71.44 62	96.76 25	80.82 107	95.33 33	94.16 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsm_n_1920		85.29 75	85.34 72	85.13 95	86.12 269	69.93 88	88.65 137	90.78 146	69.97 252	88.27 33	93.98 60	71.39 63	91.54 262	88.49 33	90.45 114	93.91 70

MVS_111021_HR		85.14 77	84.75 82	86.32 61	91.65 81	72.70 30	85.98 233	90.33 161	76.11 94	82.08 129	91.61 124	71.36 64	94.17 133	81.02 104	92.58 78	92.08 172

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 157	90.51 65	92.90 57	77.26 59	87.44 51	91.63 122	71.27 65	96.06 50	85.62 54	95.01 37	94.78 24

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 122	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 123

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 122	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 123

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 36	95.09 19	71.06 68	96.67 29	87.67 39	96.37 14	94.09 61

fmvsm_l_conf0.5_n_a		84.13 88	84.16 89	84.06 149	85.38 287	68.40 129	88.34 150	86.85 280	67.48 301	87.48 50	93.40 76	70.89 69	91.61 253	88.38 35	89.22 137	92.16 170

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 119	86.34 62	95.29 17	70.86 70	96.00 55	88.78 29	96.04 16	94.58 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 76	94.44 34	70.78 71	96.61 32	84.53 66	94.89 42	93.66 86

EI-MVSNet-Vis-set		84.19 87	83.81 93	85.31 88	88.18 190	67.85 150	87.66 174	89.73 183	80.05 15	82.95 116	89.59 187	70.74 72	94.82 104	80.66 112	84.72 214	93.28 109

GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 80	93.99 59	70.67 73	96.82 22	84.18 73	95.01 37	93.90 72

SPE-MVS-test		86.29 50	86.48 46	85.71 76	91.02 91	67.21 174	92.36 30	93.78 19	78.97 33	83.51 110	91.20 137	70.65 74	95.15 87	81.96 96	94.89 42	94.77 25

CANet		86.45 45	86.10 56	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 141	91.43 130	70.34 75	97.23 14	84.26 69	93.36 70	94.37 48

alignmvs		85.48 68	85.32 74	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 47	91.46 129	70.32 76	93.78 152	81.51 98	88.95 141	94.63 34

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 132	88.80 28	95.61 11	70.29 77	96.44 39	86.20 51	93.08 71	93.16 117

EI-MVSNet-UG-set		83.81 92	83.38 102	85.09 97	87.87 207	67.53 161	87.44 183	89.66 184	79.74 18	82.23 126	89.41 196	70.24 78	94.74 109	79.95 117	83.92 229	92.99 130

MVS_Test		83.15 114	83.06 107	83.41 178	86.86 248	63.21 271	86.11 231	92.00 100	74.31 142	82.87 118	89.44 195	70.03 79	93.21 182	77.39 145	88.50 152	93.81 78

FC-MVSNet-test		81.52 148	82.02 129	80.03 280	88.42 183	55.97 375	87.95 164	93.42 30	77.10 67	77.38 218	90.98 149	69.96 80	91.79 247	68.46 251	84.50 217	92.33 157

FIs		82.07 132	82.42 117	81.04 257	88.80 167	58.34 336	88.26 153	93.49 27	76.93 71	78.47 194	91.04 143	69.92 81	92.34 228	69.87 236	84.97 210	92.44 154

UniMVSNet (Re)		81.60 144	81.11 140	83.09 191	88.38 184	64.41 239	87.60 175	93.02 46	78.42 37	78.56 190	88.16 231	69.78 82	93.26 178	69.58 239	76.49 330	91.60 184

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 104	93.95 62	69.77 83	96.01 54	85.15 56	94.66 47	94.32 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_5		85.22 76	85.55 68	84.25 136	86.26 263	67.40 165	89.18 108	89.31 202	72.50 187	88.31 32	93.86 64	69.66 84	91.96 240	89.81 12	91.05 103	93.38 103

Effi-MVS+		83.62 102	83.08 106	85.24 90	88.38 184	67.45 162	88.89 122	89.15 213	75.50 107	82.27 125	88.28 227	69.61 85	94.45 122	77.81 139	87.84 160	93.84 76

PHI-MVS		86.43 46	86.17 54	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 188	85.22 72	91.90 111	69.47 86	96.42 40	83.28 80	95.94 19	94.35 49

UA-Net		85.08 79	84.96 79	85.45 84	92.07 75	68.07 141	89.78 85	90.86 145	82.48 2	84.60 86	93.20 81	69.35 87	95.22 84	71.39 217	90.88 108	93.07 122

ETV-MVS		84.90 83	84.67 83	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 97	85.71 298	69.32 88	95.38 78	80.82 107	91.37 99	92.72 138

旧先验1					91.96 76	65.79 201		86.37 290			93.08 86	69.31 89			92.74 76	88.74 302

fmvsm_s_conf0.5_n_4		85.39 72	85.75 65	84.30 129	86.70 255	65.83 198	88.77 129	89.78 178	75.46 108	88.35 31	93.73 68	69.19 90	93.06 195	91.30 3	88.44 153	94.02 65

fmvsm_s_conf0.5_n_a		83.63 101	83.41 101	84.28 131	86.14 268	68.12 139	89.43 97	82.87 345	70.27 245	87.27 54	93.80 67	69.09 91	91.58 255	88.21 36	83.65 237	93.14 120

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 88	94.52 27	69.09 91	96.70 27	84.37 68	94.83 45	94.03 64

EIA-MVS		83.31 112	82.80 113	84.82 109	89.59 126	65.59 206	88.21 154	92.68 67	74.66 134	78.96 180	86.42 285	69.06 93	95.26 83	75.54 171	90.09 120	93.62 93

EPP-MVSNet		83.40 108	83.02 108	84.57 117	90.13 110	64.47 237	92.32 31	90.73 147	74.45 139	79.35 176	91.10 140	69.05 94	95.12 88	72.78 200	87.22 170	94.13 58

EC-MVSNet		86.01 53	86.38 47	84.91 106	89.31 143	66.27 188	92.32 31	93.63 22	79.37 23	84.17 96	91.88 112	69.04 95	95.43 73	83.93 75	93.77 65	93.01 128

fmvsm_s_conf0.5_n		83.80 93	83.71 95	84.07 146	86.69 256	67.31 168	89.46 96	83.07 340	71.09 217	86.96 58	93.70 69	69.02 96	91.47 267	88.79 28	84.62 216	93.44 102

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 83	94.52 27	68.81 97	96.65 30	84.53 66	94.90 41	94.00 66

test_fmvsmvis_n_1920		84.02 90	83.87 92	84.49 121	84.12 317	69.37 104	88.15 158	87.96 251	70.01 250	83.95 101	93.23 80	68.80 98	91.51 265	88.61 30	89.96 123	92.57 144

viewmanbaseed2359cas		83.66 98	83.55 98	84.00 157	86.81 251	64.53 232	86.65 213	91.75 116	74.89 126	83.15 115	91.68 118	68.74 99	92.83 207	79.02 123	89.24 136	94.63 34

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 114	87.76 216	65.62 205	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 100	94.20 130	90.83 5	91.39 98	94.38 47

fmvsm_s_conf0.5_n_6		85.55 67	86.20 51	83.60 169	87.32 232	65.13 217	88.86 123	91.63 119	75.41 109	88.23 35	93.45 75	68.56 101	92.47 220	89.52 17	92.78 75	93.20 115

mvs_anonymous		79.42 204	79.11 193	80.34 273	84.45 312	57.97 342	82.59 318	87.62 261	67.40 302	76.17 253	88.56 220	68.47 102	89.59 310	70.65 225	86.05 192	93.47 101

fmvsm_s_conf0.1_n		83.56 103	83.38 102	84.10 140	84.86 301	67.28 169	89.40 101	83.01 341	70.67 229	87.08 55	93.96 61	68.38 103	91.45 268	88.56 32	84.50 217	93.56 97

fmvsm_s_conf0.1_n_a		83.32 111	82.99 109	84.28 131	83.79 325	68.07 141	89.34 104	82.85 346	69.80 256	87.36 53	94.06 53	68.34 104	91.56 258	87.95 37	83.46 243	93.21 113

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 189	82.14 3	86.65 60	94.28 41	68.28 105	97.46 6	90.81 6	95.31 34	95.15 8

viewmacassd2359aftdt		83.76 95	83.66 97	84.07 146	86.59 259	64.56 231	86.88 203	91.82 111	75.72 100	83.34 111	92.15 107	68.24 106	92.88 203	79.05 122	89.15 139	94.77 25

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 216	92.02 98	79.45 22	85.88 64	94.80 23	68.07 107	96.21 46	86.69 47	95.34 32	93.23 110

mamv4		76.81 270	78.23 214	72.54 388	86.12 269	65.75 203	78.76 373	82.07 354	64.12 344	72.97 314	91.02 146	67.97 108	68.08 453	83.04 83	78.02 310	83.80 398

CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 106	94.46 31	67.93 109	95.95 58	84.20 72	94.39 57	93.23 110

PAPM_NR		83.02 118	82.41 118	84.82 109	92.47 72	66.37 186	87.93 166	91.80 112	73.82 155	77.32 220	90.66 154	67.90 110	94.90 100	70.37 227	89.48 133	93.19 116

PGM-MVS		86.68 42	86.27 50	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 106	83.86 102	94.42 35	67.87 111	96.64 31	82.70 92	94.57 52	93.66 86

PAPR		81.66 143	80.89 145	83.99 158	90.27 107	64.00 245	86.76 210	91.77 115	68.84 283	77.13 230	89.50 188	67.63 112	94.88 102	67.55 257	88.52 151	93.09 121

Fast-Effi-MVS+		80.81 162	79.92 167	83.47 173	88.85 159	64.51 234	85.53 249	89.39 195	70.79 226	78.49 192	85.06 318	67.54 113	93.58 160	67.03 265	86.58 182	92.32 158

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	94.17 47	67.45 114	96.60 33	83.06 81	94.50 53	94.07 62

X-MVStestdata		80.37 183	77.83 223	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	12.47 465	67.45 114	96.60 33	83.06 81	94.50 53	94.07 62

SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 136	86.84 59	94.65 26	67.31 116	95.77 60	84.80 62	92.85 74	92.84 137

NR-MVSNet		80.23 187	79.38 184	82.78 212	87.80 211	63.34 268	86.31 225	91.09 139	79.01 31	72.17 326	89.07 200	67.20 117	92.81 208	66.08 271	75.65 344	92.20 164

MSLP-MVS++		85.43 70	85.76 64	84.45 122	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 94	92.81 93	67.16 118	92.94 200	80.36 113	94.35 59	90.16 241

viewdifsd2359ckpt13		82.91 120	82.29 122	84.77 112	86.96 247	66.90 181	87.47 179	91.62 120	72.19 193	81.68 137	90.71 153	66.92 119	93.28 175	75.90 165	87.15 172	94.12 59

MG-MVS		83.41 107	83.45 100	83.28 181	92.74 67	62.28 291	88.17 156	89.50 191	75.22 114	81.49 139	92.74 97	66.75 120	95.11 90	72.85 199	91.58 95	92.45 153

fmvsm_s_conf0.5_n_7		83.34 110	84.03 91	81.28 249	85.73 277	65.13 217	85.40 252	89.90 176	74.96 124	82.13 128	93.89 63	66.65 121	87.92 339	86.56 48	91.05 103	90.80 212

test_fmvsmconf0.01_n		84.73 84	84.52 86	85.34 87	80.25 393	69.03 106	89.47 95	89.65 185	73.24 176	86.98 57	94.27 42	66.62 122	93.23 180	90.26 9	89.95 124	93.78 82

EI-MVSNet		80.52 178	79.98 166	82.12 227	84.28 313	63.19 273	86.41 221	88.95 224	74.18 147	78.69 185	87.54 250	66.62 122	92.43 222	72.57 203	80.57 280	90.74 217

IterMVS-LS		80.06 190	79.38 184	82.11 229	85.89 273	63.20 272	86.79 207	89.34 196	74.19 146	75.45 266	86.72 270	66.62 122	92.39 224	72.58 202	76.86 324	90.75 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		78.59 228	77.76 228	81.08 256	82.66 359	61.56 300	83.65 297	89.15 213	68.87 282	75.55 262	83.79 345	66.49 125	92.03 237	73.25 195	76.39 333	89.64 268

mPP-MVS		86.67 43	86.32 48	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 121	94.25 44	66.44 126	96.24 45	82.88 86	94.28 60	93.38 103

c3_l		78.75 222	77.91 219	81.26 250	82.89 354	61.56 300	84.09 289	89.13 215	69.97 252	75.56 261	84.29 333	66.36 127	92.09 236	73.47 192	75.48 348	90.12 244

GeoE		81.71 140	81.01 143	83.80 166	89.51 130	64.45 238	88.97 119	88.73 234	71.27 213	78.63 188	89.76 180	66.32 128	93.20 185	69.89 235	86.02 193	93.74 83

diffmvs_AUTHOR		82.38 127	82.27 123	82.73 216	83.26 339	63.80 251	83.89 291	89.76 180	73.35 171	82.37 124	90.84 150	66.25 129	90.79 289	82.77 87	87.93 159	93.59 95

WR-MVS_H		78.51 230	78.49 204	78.56 310	88.02 200	56.38 369	88.43 144	92.67 68	77.14 64	73.89 302	87.55 249	66.25 129	89.24 317	58.92 334	73.55 374	90.06 251

viewmambaseed2359dif		80.41 179	79.84 171	82.12 227	82.95 353	62.50 285	83.39 304	88.06 248	67.11 303	80.98 148	90.31 164	66.20 131	91.01 285	74.62 179	84.90 211	92.86 135

PCF-MVS	73.52 7	80.38 181	78.84 199	85.01 99	87.71 217	68.99 109	83.65 297	91.46 129	63.00 358	77.77 212	90.28 165	66.10 132	95.09 94	61.40 312	88.22 156	90.94 209
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		83.72 97	82.92 111	86.14 68	84.22 315	69.48 97	91.05 59	85.27 304	81.30 6	76.83 232	91.65 120	66.09 133	95.56 64	76.00 164	93.85 64	93.38 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				84.35 126	93.01 62	68.79 113		92.44 78	63.96 350	81.09 146	91.57 125	66.06 134	95.45 71	67.19 262	94.82 46	88.81 297

PVSNet_BlendedMVS		80.60 174	80.02 165	82.36 224	88.85 159	65.40 209	86.16 230	92.00 100	69.34 266	78.11 202	86.09 293	66.02 135	94.27 126	71.52 214	82.06 261	87.39 331

PVSNet_Blended		80.98 157	80.34 156	82.90 202	88.85 159	65.40 209	84.43 279	92.00 100	67.62 298	78.11 202	85.05 319	66.02 135	94.27 126	71.52 214	89.50 132	89.01 287

diffmvs		82.10 130	81.88 132	82.76 214	83.00 349	63.78 253	83.68 296	89.76 180	72.94 183	82.02 130	89.85 174	65.96 137	90.79 289	82.38 94	87.30 169	93.71 84
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 56	85.88 60	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 164	85.94 63	94.51 30	65.80 138	95.61 63	83.04 83	92.51 79	93.53 100

miper_enhance_ethall		77.87 248	76.86 249	80.92 261	81.65 373	61.38 302	82.68 317	88.98 221	65.52 327	75.47 263	82.30 374	65.76 139	92.00 239	72.95 198	76.39 333	89.39 275

PVSNet_Blended_VisFu		82.62 123	81.83 133	84.96 101	90.80 97	69.76 93	88.74 133	91.70 117	69.39 264	78.96 180	88.46 222	65.47 140	94.87 103	74.42 182	88.57 149	90.24 239

API-MVS		81.99 134	81.23 138	84.26 135	90.94 93	70.18 87	91.10 58	89.32 201	71.51 207	78.66 187	88.28 227	65.26 141	95.10 93	64.74 282	91.23 101	87.51 329

TranMVSNet+NR-MVSNet		80.84 160	80.31 157	82.42 222	87.85 208	62.33 289	87.74 173	91.33 130	80.55 9	77.99 206	89.86 173	65.23 142	92.62 210	67.05 264	75.24 358	92.30 159

IS-MVSNet		83.15 114	82.81 112	84.18 138	89.94 119	63.30 269	91.59 46	88.46 241	79.04 30	79.49 171	92.16 105	65.10 143	94.28 125	67.71 255	91.86 91	94.95 12

DU-MVS		81.12 156	80.52 152	82.90 202	87.80 211	63.46 265	87.02 196	91.87 108	79.01 31	78.38 195	89.07 200	65.02 144	93.05 196	70.05 232	76.46 331	92.20 164

Baseline_NR-MVSNet		78.15 239	78.33 210	77.61 331	85.79 275	56.21 373	86.78 208	85.76 300	73.60 162	77.93 207	87.57 247	65.02 144	88.99 322	67.14 263	75.33 355	87.63 325

SR-MVS-dyc-post		85.77 62	85.61 67	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 165	85.69 67	94.45 32	65.00 146	95.56 64	82.75 88	91.87 89	92.50 149

VNet		82.21 129	82.41 118	81.62 238	90.82 96	60.93 307	84.47 275	89.78 178	76.36 90	84.07 98	91.88 112	64.71 147	90.26 297	70.68 224	88.89 142	93.66 86

NormalMVS		86.29 50	85.88 60	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 90	92.18 103	64.64 148	95.53 67	80.70 110	94.65 48	94.56 39

SymmetryMVS		85.38 73	84.81 81	87.07 46	91.47 83	72.47 38	91.65 43	88.06 248	79.31 24	84.39 90	92.18 103	64.64 148	95.53 67	80.70 110	90.91 107	93.21 113

Test By Simon												64.33 150

ACMMP		85.89 60	85.39 71	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 155	93.82 66	64.33 150	96.29 42	82.67 93	90.69 110	93.23 110
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
DP-MVS Recon		83.11 117	82.09 127	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 234	79.17 178	91.03 145	64.12 152	96.03 51	68.39 252	90.14 119	91.50 189

CLD-MVS		82.31 128	81.65 134	84.29 130	88.47 179	67.73 154	85.81 241	92.35 83	75.78 99	78.33 197	86.58 280	64.01 153	94.35 123	76.05 163	87.48 166	90.79 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RE-MVS-def			85.48 70		93.06 60	70.63 78	91.88 39	92.27 85	73.53 165	85.69 67	94.45 32	63.87 154		82.75 88	91.87 89	92.50 149

MVS		78.19 238	76.99 247	81.78 235	85.66 278	66.99 176	84.66 269	90.47 154	55.08 422	72.02 328	85.27 311	63.83 155	94.11 135	66.10 270	89.80 127	84.24 391

WR-MVS		79.49 200	79.22 191	80.27 275	88.79 168	58.35 335	85.06 260	88.61 239	78.56 35	77.65 213	88.34 225	63.81 156	90.66 294	64.98 280	77.22 319	91.80 178

VPA-MVSNet		80.60 174	80.55 151	80.76 264	88.07 198	60.80 310	86.86 204	91.58 123	75.67 104	80.24 162	89.45 194	63.34 157	90.25 298	70.51 226	79.22 297	91.23 197

新几何1				83.42 176	93.13 56	70.71 76		85.48 303	57.43 412	81.80 134	91.98 109	63.28 158	92.27 230	64.60 283	92.99 72	87.27 336

HY-MVS	69.67 12	77.95 245	77.15 243	80.36 272	87.57 225	60.21 320	83.37 306	87.78 258	66.11 318	75.37 270	87.06 265	63.27 159	90.48 296	61.38 313	82.43 257	90.40 232

IMVS_0403		80.80 165	80.12 164	82.87 204	87.13 238	63.59 258	85.19 254	89.33 197	70.51 235	78.49 192	89.03 202	63.26 160	93.27 177	72.56 205	85.56 203	91.74 179

XXY-MVS		75.41 295	75.56 273	74.96 360	83.59 332	57.82 346	80.59 345	83.87 325	66.54 315	74.93 288	88.31 226	63.24 161	80.09 406	62.16 304	76.85 325	86.97 346

ab-mvs		79.51 199	78.97 196	81.14 254	88.46 180	60.91 308	83.84 292	89.24 209	70.36 240	79.03 179	88.87 210	63.23 162	90.21 299	65.12 278	82.57 256	92.28 160

xiu_mvs_v2_base		81.69 141	81.05 141	83.60 169	89.15 151	68.03 143	84.46 277	90.02 171	70.67 229	81.30 144	86.53 283	63.17 163	94.19 132	75.60 170	88.54 150	88.57 307

pcd_1.5k_mvsjas		5.26 439	7.02 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	0.00 471	63.15 164	0.00 472	0.00 471	0.00 470	0.00 468

PS-MVSNAJss		82.07 132	81.31 136	84.34 127	86.51 261	67.27 170	89.27 105	91.51 125	71.75 200	79.37 175	90.22 169	63.15 164	94.27 126	77.69 141	82.36 258	91.49 190

PS-MVSNAJ		81.69 141	81.02 142	83.70 167	89.51 130	68.21 138	84.28 283	90.09 170	70.79 226	81.26 145	85.62 303	63.15 164	94.29 124	75.62 169	88.87 143	88.59 306

WTY-MVS		75.65 290	75.68 270	75.57 351	86.40 262	56.82 360	77.92 387	82.40 350	65.10 331	76.18 251	87.72 242	63.13 167	80.90 403	60.31 321	81.96 262	89.00 289

TransMVSNet (Re)		75.39 297	74.56 290	77.86 325	85.50 285	57.10 357	86.78 208	86.09 296	72.17 195	71.53 333	87.34 253	63.01 168	89.31 315	56.84 357	61.83 424	87.17 338

viewdifsd2359ckpt11		80.37 183	79.73 174	82.30 225	83.70 329	62.39 286	84.20 285	86.67 282	73.22 177	80.90 150	90.62 155	63.00 169	91.56 258	76.81 155	78.44 303	92.95 132

viewmsd2359difaftdt		80.37 183	79.73 174	82.30 225	83.70 329	62.39 286	84.20 285	86.67 282	73.22 177	80.90 150	90.62 155	63.00 169	91.56 258	76.81 155	78.44 303	92.95 132

v8		79.97 193	79.02 195	82.80 208	84.09 318	64.50 236	87.96 163	90.29 164	74.13 149	75.24 278	86.81 267	62.88 171	93.89 149	74.39 183	75.40 353	90.00 253

HPM-MVS_fast		85.35 74	84.95 80	86.57 59	93.69 42	70.58 80	92.15 36	91.62 120	73.89 154	82.67 123	94.09 51	62.60 172	95.54 66	80.93 105	92.93 73	93.57 96

PAPM		77.68 254	76.40 263	81.51 241	87.29 234	61.85 296	83.78 293	89.59 188	64.74 336	71.23 336	88.70 213	62.59 173	93.66 159	52.66 380	87.03 175	89.01 287

1112_ss		77.40 260	76.43 261	80.32 274	89.11 156	60.41 317	83.65 297	87.72 260	62.13 371	73.05 313	86.72 270	62.58 174	89.97 303	62.11 306	80.80 276	90.59 224

LCM-MVSNet-Re		77.05 265	76.94 248	77.36 335	87.20 235	51.60 415	80.06 354	80.46 374	75.20 116	67.69 372	86.72 270	62.48 175	88.98 323	63.44 290	89.25 135	91.51 188

v148		78.72 224	77.80 225	81.47 242	82.73 357	61.96 295	86.30 226	88.08 246	73.26 174	76.18 251	85.47 307	62.46 176	92.36 226	71.92 213	73.82 372	90.09 247

baseline1		76.98 267	76.75 255	77.66 329	88.13 194	55.66 380	85.12 258	81.89 355	73.04 181	76.79 233	88.90 208	62.43 177	87.78 342	63.30 292	71.18 392	89.55 271

MAR-MVS		81.84 137	80.70 147	85.27 89	91.32 85	71.53 58	89.82 82	90.92 141	69.77 258	78.50 191	86.21 289	62.36 178	94.52 118	65.36 276	92.05 87	89.77 265
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_111021_LR		82.61 124	82.11 125	84.11 139	88.82 162	71.58 57	85.15 257	86.16 294	74.69 132	80.47 160	91.04 143	62.29 179	90.55 295	80.33 114	90.08 121	90.20 240

TAMVS		78.89 221	77.51 237	83.03 196	87.80 211	67.79 153	84.72 267	85.05 309	67.63 297	76.75 235	87.70 243	62.25 180	90.82 288	58.53 339	87.13 173	90.49 228

CP-MVSNet		78.22 235	78.34 209	77.84 326	87.83 210	54.54 392	87.94 165	91.17 135	77.65 46	73.48 308	88.49 221	62.24 181	88.43 333	62.19 303	74.07 367	90.55 225

OMC-MVS		82.69 122	81.97 131	84.85 108	88.75 170	67.42 163	87.98 162	90.87 144	74.92 125	79.72 168	91.65 120	62.19 182	93.96 138	75.26 175	86.42 185	93.16 117

cl____		77.72 251	76.76 253	80.58 268	82.49 363	60.48 315	83.09 312	87.87 254	69.22 271	74.38 298	85.22 314	62.10 183	91.53 263	71.09 219	75.41 352	89.73 267

DIV-MVS_self_test		77.72 251	76.76 253	80.58 268	82.48 364	60.48 315	83.09 312	87.86 255	69.22 271	74.38 298	85.24 312	62.10 183	91.53 263	71.09 219	75.40 353	89.74 266

testdata				79.97 281	90.90 94	64.21 242		84.71 311	59.27 394	85.40 69	92.91 88	62.02 185	89.08 321	68.95 245	91.37 99	86.63 354

icg_test_0407_2		78.92 220	78.93 197	78.90 303	87.13 238	63.59 258	76.58 395	89.33 197	70.51 235	77.82 208	89.03 202	61.84 186	81.38 400	72.56 205	85.56 203	91.74 179

IMVS_0407		80.61 172	79.90 169	82.75 215	87.13 238	63.59 258	85.33 253	89.33 197	70.51 235	77.82 208	89.03 202	61.84 186	92.91 201	72.56 205	85.56 203	91.74 179

fmvsm_s_conf0.5_n_2		84.04 89	84.11 90	83.81 165	86.17 267	65.00 222	86.96 198	87.28 268	74.35 140	88.25 34	94.23 45	61.82 188	92.60 212	89.85 11	88.09 158	93.84 76

eth_miper_zixun_eth		77.92 246	76.69 256	81.61 240	83.00 349	61.98 294	83.15 310	89.20 211	69.52 263	74.86 289	84.35 332	61.76 189	92.56 215	71.50 216	72.89 380	90.28 238

MVSFormer		82.85 121	82.05 128	85.24 90	87.35 226	70.21 82	90.50 67	90.38 157	68.55 287	81.32 141	89.47 190	61.68 190	93.46 169	78.98 126	90.26 117	92.05 173

lupinMVS		81.39 151	80.27 159	84.76 113	87.35 226	70.21 82	85.55 247	86.41 288	62.85 361	81.32 141	88.61 217	61.68 190	92.24 232	78.41 133	90.26 117	91.83 176

cdsmvs_eth3d_5k		19.96 433	26.61 435	0.00 453	0.00 476	0.00 478	0.00 464	89.26 206	0.00 471	0.00 472	88.61 217	61.62 192	0.00 472	0.00 471	0.00 470	0.00 468

h-mvs33		83.15 114	82.19 124	86.02 72	90.56 101	70.85 75	88.15 158	89.16 212	76.02 96	84.67 81	91.39 131	61.54 193	95.50 69	82.71 90	75.48 348	91.72 183

hse-mvs2		81.72 139	80.94 144	84.07 146	88.72 171	67.68 155	85.87 237	87.26 270	76.02 96	84.67 81	88.22 230	61.54 193	93.48 167	82.71 90	73.44 376	91.06 202

CDS-MVSNet		79.07 215	77.70 230	83.17 188	87.60 221	68.23 137	84.40 281	86.20 293	67.49 300	76.36 246	86.54 282	61.54 193	90.79 289	61.86 308	87.33 168	90.49 228
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v10		79.74 195	78.67 200	82.97 200	84.06 319	64.95 223	87.88 169	90.62 149	73.11 179	75.11 282	86.56 281	61.46 196	94.05 137	73.68 188	75.55 346	89.90 259

v1144		80.03 191	79.03 194	83.01 197	83.78 326	64.51 234	87.11 193	90.57 152	71.96 199	78.08 204	86.20 290	61.41 197	93.94 141	74.93 177	77.23 318	90.60 223

cl22		78.07 241	77.01 245	81.23 251	82.37 366	61.83 297	83.55 301	87.98 250	68.96 281	75.06 284	83.87 341	61.40 198	91.88 245	73.53 190	76.39 333	89.98 256

BH-w/o		78.21 236	77.33 241	80.84 262	88.81 163	65.13 217	84.87 264	87.85 256	69.75 259	74.52 295	84.74 325	61.34 199	93.11 192	58.24 343	85.84 199	84.27 390

Test_1112_low_res		76.40 280	75.44 275	79.27 296	89.28 145	58.09 338	81.69 327	87.07 274	59.53 392	72.48 321	86.67 275	61.30 200	89.33 314	60.81 318	80.15 285	90.41 231

Vis-MVSNet (Re-imp)		78.36 233	78.45 205	78.07 322	88.64 174	51.78 414	86.70 211	79.63 386	74.14 148	75.11 282	90.83 151	61.29 201	89.75 307	58.10 344	91.60 93	92.69 141

PEN-MVS		77.73 250	77.69 231	77.84 326	87.07 246	53.91 397	87.91 167	91.18 134	77.56 51	73.14 312	88.82 211	61.23 202	89.17 319	59.95 323	72.37 382	90.43 230

pm-mvs1		77.25 263	76.68 257	78.93 302	84.22 315	58.62 333	86.41 221	88.36 242	71.37 209	73.31 309	88.01 237	61.22 203	89.15 320	64.24 286	73.01 379	89.03 286

BH-untuned		79.47 201	78.60 202	82.05 230	89.19 150	65.91 196	86.07 232	88.52 240	72.18 194	75.42 267	87.69 244	61.15 204	93.54 164	60.38 320	86.83 179	86.70 352

v2v482		80.23 187	79.29 188	83.05 195	83.62 331	64.14 243	87.04 194	89.97 173	73.61 161	78.18 201	87.22 258	61.10 205	93.82 150	76.11 161	76.78 327	91.18 198

jason		81.39 151	80.29 158	84.70 115	86.63 258	69.90 90	85.95 234	86.77 281	63.24 354	81.07 147	89.47 190	61.08 206	92.15 234	78.33 134	90.07 122	92.05 173
jason: jason.
Vis-MVSNet		83.46 106	82.80 113	85.43 85	90.25 108	68.74 117	90.30 75	90.13 169	76.33 91	80.87 152	92.89 89	61.00 207	94.20 130	72.45 209	90.97 105	93.35 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	73.13 9	79.15 212	77.94 218	82.79 211	89.59 126	62.99 279	88.16 157	91.51 125	65.77 323	77.14 229	91.09 141	60.91 208	93.21 182	50.26 396	87.05 174	92.17 169
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PS-CasMVS		78.01 244	78.09 215	77.77 328	87.71 217	54.39 394	88.02 161	91.22 132	77.50 54	73.26 310	88.64 216	60.73 209	88.41 334	61.88 307	73.88 371	90.53 226

OPM-MVS		83.50 105	82.95 110	85.14 92	88.79 168	70.95 71	89.13 114	91.52 124	77.55 52	80.96 149	91.75 116	60.71 210	94.50 119	79.67 121	86.51 184	89.97 257
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-OURS-SEG-HR		80.81 162	79.76 173	83.96 160	85.60 281	68.78 114	83.54 303	90.50 153	70.66 232	76.71 236	91.66 119	60.69 211	91.26 274	76.94 150	81.58 266	91.83 176

fmvsm_s_conf0.1_n_2		83.80 93	83.79 94	83.83 163	85.62 280	64.94 224	87.03 195	86.62 286	74.32 141	87.97 42	94.33 38	60.67 212	92.60 212	89.72 13	87.79 161	93.96 67

v144192		79.47 201	78.37 208	82.78 212	83.35 336	63.96 246	86.96 198	90.36 160	69.99 251	77.50 215	85.67 301	60.66 213	93.77 154	74.27 184	76.58 328	90.62 221

V42		79.38 207	78.24 212	82.83 205	81.10 385	65.50 208	85.55 247	89.82 177	71.57 206	78.21 199	86.12 292	60.66 213	93.18 188	75.64 168	75.46 350	89.81 264

SDMVSNet		80.38 181	80.18 160	80.99 258	89.03 157	64.94 224	80.45 348	89.40 194	75.19 117	76.61 240	89.98 171	60.61 215	87.69 343	76.83 154	83.55 239	90.33 235

CPTT-MVS		83.73 96	83.33 104	84.92 105	93.28 49	70.86 74	92.09 37	90.38 157	68.75 284	79.57 170	92.83 91	60.60 216	93.04 198	80.92 106	91.56 96	90.86 211

DTE-MVSNet		76.99 266	76.80 251	77.54 334	86.24 264	53.06 406	87.52 177	90.66 148	77.08 68	72.50 320	88.67 215	60.48 217	89.52 311	57.33 351	70.74 394	90.05 252

HQP_MVS		83.64 100	83.14 105	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 182	91.00 147	60.42 218	95.38 78	78.71 129	86.32 186	91.33 194

plane_prior6					89.84 121	68.70 121						60.42 218

3Dnovator+	77.84 4	85.48 68	84.47 87	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 237	93.37 77	60.40 220	96.75 26	77.20 146	93.73 66	95.29 6

HQP2-MVS												60.17 221

HQP-MVS		82.61 124	82.02 129	84.37 124	89.33 140	66.98 177	89.17 109	92.19 92	76.41 85	77.23 223	90.23 168	60.17 221	95.11 90	77.47 143	85.99 194	91.03 204

SSM_0407		81.58 145	80.48 153	84.87 107	88.81 163	67.96 145	87.37 184	89.25 207	71.06 219	79.48 172	90.39 162	59.57 223	94.48 121	72.45 209	85.93 196	92.18 166

SSM_0404		81.91 135	80.84 146	85.13 95	89.24 147	68.26 133	87.84 171	89.25 207	71.06 219	80.62 156	90.39 162	59.57 223	94.65 114	72.45 209	87.19 171	92.47 152

SD_0403		74.65 303	74.77 287	74.29 369	86.20 266	47.42 433	83.71 295	85.12 306	69.30 267	68.50 367	87.95 239	59.40 225	86.05 359	49.38 400	83.35 244	89.40 274

VPNet		78.69 225	78.66 201	78.76 305	88.31 186	55.72 379	84.45 278	86.63 285	76.79 75	78.26 198	90.55 159	59.30 226	89.70 309	66.63 266	77.05 321	90.88 210

v1192		79.59 198	78.43 207	83.07 194	83.55 333	64.52 233	86.93 201	90.58 150	70.83 225	77.78 211	85.90 294	59.15 227	93.94 141	73.96 187	77.19 320	90.76 215

test222					91.50 82	68.26 133	84.16 287	83.20 338	54.63 423	79.74 167	91.63 122	58.97 228			91.42 97	86.77 350

mamba_0408		79.37 208	77.52 235	84.93 104	88.81 163	67.96 145	65.03 448	88.66 235	70.96 223	79.48 172	89.80 177	58.69 229	94.65 114	70.35 228	85.93 196	92.18 166

SSM_04072		77.67 255	77.52 235	78.12 320	88.81 163	67.96 145	65.03 448	88.66 235	70.96 223	79.48 172	89.80 177	58.69 229	74.23 441	70.35 228	85.93 196	92.18 166

CHOSEN 1792x2688		77.63 256	75.69 269	83.44 175	89.98 118	68.58 125	78.70 374	87.50 264	56.38 417	75.80 258	86.84 266	58.67 231	91.40 270	61.58 311	85.75 201	90.34 234

3Dnovator	76.31 5	83.38 109	82.31 121	86.59 57	87.94 204	72.94 28	90.64 63	92.14 97	77.21 62	75.47 263	92.83 91	58.56 232	94.72 110	73.24 196	92.71 77	92.13 171

v1921920		79.22 210	78.03 216	82.80 208	83.30 338	63.94 248	86.80 206	90.33 161	69.91 254	77.48 216	85.53 305	58.44 233	93.75 156	73.60 189	76.85 325	90.71 219

FA-MVS(test-final)		80.96 158	79.91 168	84.10 140	88.30 187	65.01 221	84.55 274	90.01 172	73.25 175	79.61 169	87.57 247	58.35 234	94.72 110	71.29 218	86.25 188	92.56 145

114514_t		80.68 170	79.51 181	84.20 137	94.09 38	67.27 170	89.64 90	91.11 138	58.75 401	74.08 300	90.72 152	58.10 235	95.04 95	69.70 237	89.42 134	90.30 237

v7n		78.97 218	77.58 234	83.14 189	83.45 335	65.51 207	88.32 151	91.21 133	73.69 159	72.41 322	86.32 288	57.93 236	93.81 151	69.18 242	75.65 344	90.11 245

CL-MVSNet_self_test		72.37 333	71.46 328	75.09 359	79.49 406	53.53 399	80.76 341	85.01 310	69.12 275	70.51 340	82.05 378	57.92 237	84.13 379	52.27 382	66.00 413	87.60 326

baseline2		75.70 289	73.83 302	81.30 248	83.26 339	61.79 298	82.57 319	80.65 369	66.81 305	66.88 383	83.42 355	57.86 238	92.19 233	63.47 289	79.57 290	89.91 258

QAPM		80.88 159	79.50 182	85.03 98	88.01 202	68.97 110	91.59 46	92.00 100	66.63 314	75.15 281	92.16 105	57.70 239	95.45 71	63.52 288	88.76 146	90.66 220

HyFIR lowres test		77.53 257	75.40 277	83.94 161	89.59 126	66.62 182	80.36 349	88.64 238	56.29 418	76.45 243	85.17 315	57.64 240	93.28 175	61.34 314	83.10 249	91.91 175

CNLPA		78.08 240	76.79 252	81.97 233	90.40 105	71.07 67	87.59 176	84.55 314	66.03 321	72.38 323	89.64 184	57.56 241	86.04 360	59.61 327	83.35 244	88.79 298

test_yl		81.17 153	80.47 154	83.24 184	89.13 152	63.62 254	86.21 228	89.95 174	72.43 191	81.78 135	89.61 185	57.50 242	93.58 160	70.75 222	86.90 176	92.52 147

DCV-MVSNet		81.17 153	80.47 154	83.24 184	89.13 152	63.62 254	86.21 228	89.95 174	72.43 191	81.78 135	89.61 185	57.50 242	93.58 160	70.75 222	86.90 176	92.52 147

sss		73.60 316	73.64 304	73.51 377	82.80 355	55.01 388	76.12 397	81.69 358	62.47 367	74.68 292	85.85 297	57.32 244	78.11 414	60.86 317	80.93 272	87.39 331

KinetiMVS		83.31 112	82.61 116	85.39 86	87.08 244	67.56 160	88.06 160	91.65 118	77.80 44	82.21 127	91.79 115	57.27 245	94.07 136	77.77 140	89.89 126	94.56 39

Effi-MVS+-dtu		80.03 191	78.57 203	84.42 123	85.13 296	68.74 117	88.77 129	88.10 245	74.99 121	74.97 287	83.49 354	57.27 245	93.36 173	73.53 190	80.88 274	91.18 198

AdaColmap		80.58 177	79.42 183	84.06 149	93.09 59	68.91 111	89.36 103	88.97 223	69.27 268	75.70 259	89.69 181	57.20 247	95.77 60	63.06 293	88.41 154	87.50 330

v1240		78.99 217	77.78 226	82.64 217	83.21 341	63.54 262	86.62 215	90.30 163	69.74 261	77.33 219	85.68 300	57.04 248	93.76 155	73.13 197	76.92 322	90.62 221

miper_lstm_enhance		74.11 309	73.11 311	77.13 339	80.11 395	59.62 325	72.23 419	86.92 279	66.76 307	70.40 342	82.92 364	56.93 249	82.92 389	69.06 244	72.63 381	88.87 294

BP-MVS1		84.32 86	83.71 95	86.17 64	87.84 209	67.85 150	89.38 102	89.64 186	77.73 45	83.98 100	92.12 108	56.89 250	95.43 73	84.03 74	91.75 92	95.24 7

guyue		81.13 155	80.64 149	82.60 219	86.52 260	63.92 249	86.69 212	87.73 259	73.97 150	80.83 154	89.69 181	56.70 251	91.33 273	78.26 138	85.40 207	92.54 146

BH-RMVSNet		79.61 196	78.44 206	83.14 189	89.38 139	65.93 195	84.95 263	87.15 273	73.56 163	78.19 200	89.79 179	56.67 252	93.36 173	59.53 328	86.74 180	90.13 243

RRT-MVS		82.60 126	82.10 126	84.10 140	87.98 203	62.94 280	87.45 182	91.27 131	77.42 56	79.85 166	90.28 165	56.62 253	94.70 112	79.87 119	88.15 157	94.67 30

test_djsdf		80.30 186	79.32 187	83.27 182	83.98 321	65.37 212	90.50 67	90.38 157	68.55 287	76.19 250	88.70 213	56.44 254	93.46 169	78.98 126	80.14 286	90.97 207

EPNet_dtu		75.46 293	74.86 285	77.23 338	82.57 361	54.60 391	86.89 202	83.09 339	71.64 201	66.25 394	85.86 296	55.99 255	88.04 338	54.92 368	86.55 183	89.05 285
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VortexMVS		78.57 229	77.89 221	80.59 267	85.89 273	62.76 282	85.61 242	89.62 187	72.06 197	74.99 286	85.38 309	55.94 256	90.77 292	74.99 176	76.58 328	88.23 313

GDP-MVS		83.52 104	82.64 115	86.16 65	88.14 193	68.45 128	89.13 114	92.69 66	72.82 186	83.71 105	91.86 114	55.69 257	95.35 82	80.03 116	89.74 128	94.69 29

CostFormer		75.24 298	73.90 300	79.27 296	82.65 360	58.27 337	80.80 338	82.73 348	61.57 375	75.33 275	83.13 360	55.52 258	91.07 284	64.98 280	78.34 308	88.45 309

tpmrst		72.39 331	72.13 322	73.18 382	80.54 390	49.91 426	79.91 358	79.08 392	63.11 356	71.69 331	79.95 399	55.32 259	82.77 391	65.66 275	73.89 370	86.87 347

1314		76.53 274	75.30 281	80.21 277	83.93 322	62.32 290	84.66 269	88.81 227	60.23 385	70.16 347	84.07 340	55.30 260	90.73 293	67.37 259	83.21 247	87.59 328

tfpnnormal		74.39 304	73.16 310	78.08 321	86.10 271	58.05 339	84.65 271	87.53 263	70.32 243	71.22 337	85.63 302	54.97 261	89.86 304	43.03 430	75.02 360	86.32 356

sd_testset		77.70 253	77.40 238	78.60 308	89.03 157	60.02 321	79.00 369	85.83 299	75.19 117	76.61 240	89.98 171	54.81 262	85.46 368	62.63 299	83.55 239	90.33 235

GBi-Net		78.40 231	77.40 238	81.40 245	87.60 221	63.01 275	88.39 146	89.28 203	71.63 202	75.34 271	87.28 254	54.80 263	91.11 278	62.72 295	79.57 290	90.09 247

test1		78.40 231	77.40 238	81.40 245	87.60 221	63.01 275	88.39 146	89.28 203	71.63 202	75.34 271	87.28 254	54.80 263	91.11 278	62.72 295	79.57 290	90.09 247

FMVSNet2		78.20 237	77.21 242	81.20 252	87.60 221	62.89 281	87.47 179	89.02 219	71.63 202	75.29 277	87.28 254	54.80 263	91.10 281	62.38 300	79.38 294	89.61 269

Fast-Effi-MVS+-dtu		78.02 243	76.49 259	82.62 218	83.16 345	66.96 179	86.94 200	87.45 266	72.45 188	71.49 334	84.17 338	54.79 266	91.58 255	67.61 256	80.31 283	89.30 278

MVSTER		79.01 216	77.88 222	82.38 223	83.07 346	64.80 228	84.08 290	88.95 224	69.01 280	78.69 185	87.17 261	54.70 267	92.43 222	74.69 178	80.57 280	89.89 260

OpenMVS	72.83 10	79.77 194	78.33 210	84.09 144	85.17 292	69.91 89	90.57 64	90.97 140	66.70 308	72.17 326	91.91 110	54.70 267	93.96 138	61.81 309	90.95 106	88.41 311

XVG-OURS		80.41 179	79.23 190	83.97 159	85.64 279	69.02 108	83.03 316	90.39 156	71.09 217	77.63 214	91.49 128	54.62 269	91.35 271	75.71 167	83.47 242	91.54 187

LPG-MVS_test		82.08 131	81.27 137	84.50 119	89.23 148	68.76 115	90.22 76	91.94 104	75.37 111	76.64 238	91.51 126	54.29 270	94.91 98	78.44 131	83.78 230	89.83 262

LGP-MVS_train				84.50 119	89.23 148	68.76 115		91.94 104	75.37 111	76.64 238	91.51 126	54.29 270	94.91 98	78.44 131	83.78 230	89.83 262

TR-MVS		77.44 258	76.18 265	81.20 252	88.24 188	63.24 270	84.61 272	86.40 289	67.55 299	77.81 210	86.48 284	54.10 272	93.15 189	57.75 347	82.72 254	87.20 337

FMVSNet3		77.88 247	76.85 250	80.97 260	86.84 250	62.36 288	86.52 218	88.77 229	71.13 215	75.34 271	86.66 276	54.07 273	91.10 281	62.72 295	79.57 290	89.45 273

AstraMVS		80.81 162	80.14 163	82.80 208	86.05 272	63.96 246	86.46 220	85.90 298	73.71 158	80.85 153	90.56 158	54.06 274	91.57 257	79.72 120	83.97 228	92.86 135

DP-MVS		76.78 271	74.57 289	83.42 176	93.29 48	69.46 100	88.55 142	83.70 326	63.98 349	70.20 344	88.89 209	54.01 275	94.80 107	46.66 415	81.88 264	86.01 364

ACMP	74.13 6	81.51 150	80.57 150	84.36 125	89.42 135	68.69 122	89.97 80	91.50 128	74.46 138	75.04 285	90.41 161	53.82 276	94.54 116	77.56 142	82.91 250	89.86 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	70.83 11	78.05 242	76.37 264	83.08 193	91.88 79	67.80 152	88.19 155	89.46 192	64.33 342	69.87 353	88.38 224	53.66 277	93.58 160	58.86 335	82.73 253	87.86 321
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dmvs_testset		62.63 400	64.11 391	58.19 430	78.55 412	24.76 468	75.28 404	65.94 445	67.91 296	60.34 424	76.01 427	53.56 278	73.94 443	31.79 448	67.65 406	75.88 437

CANet_DTU		80.61 172	79.87 170	82.83 205	85.60 281	63.17 274	87.36 185	88.65 237	76.37 89	75.88 256	88.44 223	53.51 279	93.07 194	73.30 194	89.74 128	92.25 161

WB-MVSnew		71.96 339	71.65 326	72.89 384	84.67 309	51.88 412	82.29 321	77.57 401	62.31 368	73.67 306	83.00 362	53.49 280	81.10 402	45.75 422	82.13 260	85.70 370

ACMM	73.20 8	80.78 169	79.84 171	83.58 171	89.31 143	68.37 130	89.99 79	91.60 122	70.28 244	77.25 221	89.66 183	53.37 281	93.53 165	74.24 185	82.85 251	88.85 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		76.12 283	74.46 293	81.13 255	85.37 288	69.79 91	84.42 280	87.95 252	65.03 333	67.46 375	85.33 310	53.28 282	91.73 251	58.01 345	83.27 246	81.85 418
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		79.21 211	77.60 233	84.05 152	88.71 172	67.61 157	85.84 239	87.26 270	69.08 276	77.23 223	88.14 235	53.20 283	93.47 168	75.50 172	73.45 375	91.06 202

SSC-MVS3.2		73.35 322	73.39 306	73.23 378	85.30 290	49.01 429	74.58 412	81.57 359	75.21 115	73.68 305	85.58 304	52.53 284	82.05 395	54.33 372	77.69 315	88.63 305

anonymousdsp		78.60 227	77.15 243	82.98 199	80.51 391	67.08 175	87.24 190	89.53 190	65.66 325	75.16 280	87.19 260	52.52 285	92.25 231	77.17 147	79.34 295	89.61 269

CR-MVSNet		73.37 319	71.27 332	79.67 289	81.32 383	65.19 215	75.92 399	80.30 378	59.92 388	72.73 317	81.19 382	52.50 286	86.69 351	59.84 324	77.71 313	87.11 342

Patchmtry		70.74 348	69.16 351	75.49 354	80.72 387	54.07 396	74.94 410	80.30 378	58.34 402	70.01 348	81.19 382	52.50 286	86.54 353	53.37 377	71.09 393	85.87 369

pmmvs4		74.03 312	71.91 323	80.39 271	81.96 369	68.32 131	81.45 331	82.14 352	59.32 393	69.87 353	85.13 316	52.40 288	88.13 337	60.21 322	74.74 363	84.73 387

RPMNet		73.51 317	70.49 340	82.58 220	81.32 383	65.19 215	75.92 399	92.27 85	57.60 410	72.73 317	76.45 425	52.30 289	95.43 73	48.14 410	77.71 313	87.11 342

LFMVS		81.82 138	81.23 138	83.57 172	91.89 78	63.43 267	89.84 81	81.85 357	77.04 69	83.21 112	93.10 82	52.26 290	93.43 171	71.98 212	89.95 124	93.85 74

VDD-MVS		83.01 119	82.36 120	84.96 101	91.02 91	66.40 185	88.91 121	88.11 244	77.57 49	84.39 90	93.29 79	52.19 291	93.91 146	77.05 149	88.70 148	94.57 38

tfpn200view9		76.42 279	75.37 279	79.55 293	89.13 152	57.65 349	85.17 255	83.60 327	73.41 169	76.45 243	86.39 286	52.12 292	91.95 241	48.33 406	83.75 233	89.07 280

thres400		76.50 275	75.37 279	79.86 283	89.13 152	57.65 349	85.17 255	83.60 327	73.41 169	76.45 243	86.39 286	52.12 292	91.95 241	48.33 406	83.75 233	90.00 253

Syy-MVS		68.05 375	67.85 364	68.67 412	84.68 306	40.97 455	78.62 375	73.08 426	66.65 312	66.74 386	79.46 404	52.11 294	82.30 393	32.89 447	76.38 336	82.75 410

thres200		75.55 291	74.47 292	78.82 304	87.78 214	57.85 345	83.07 314	83.51 330	72.44 190	75.84 257	84.42 328	52.08 295	91.75 249	47.41 413	83.64 238	86.86 348

PMMVS		69.34 364	68.67 353	71.35 397	75.67 424	62.03 293	75.17 405	73.46 424	50.00 435	68.68 363	79.05 407	52.07 296	78.13 413	61.16 315	82.77 252	73.90 439

tpm cat1		70.57 350	68.31 356	77.35 336	82.41 365	57.95 343	78.08 383	80.22 380	52.04 429	68.54 366	77.66 420	52.00 297	87.84 341	51.77 383	72.07 387	86.25 357

IterMVS-SCA-FT		75.43 294	73.87 301	80.11 279	82.69 358	64.85 227	81.57 329	83.47 331	69.16 274	70.49 341	84.15 339	51.95 298	88.15 336	69.23 241	72.14 386	87.34 333

SCA		74.22 307	72.33 320	79.91 282	84.05 320	62.17 292	79.96 357	79.29 390	66.30 317	72.38 323	80.13 397	51.95 298	88.60 331	59.25 330	77.67 316	88.96 291

thres100view900		76.50 275	75.55 274	79.33 295	89.52 129	56.99 358	85.83 240	83.23 335	73.94 152	76.32 247	87.12 262	51.89 300	91.95 241	48.33 406	83.75 233	89.07 280

thres600view7		76.50 275	75.44 275	79.68 288	89.40 137	57.16 355	85.53 249	83.23 335	73.79 156	76.26 248	87.09 263	51.89 300	91.89 244	48.05 411	83.72 236	90.00 253

tpm2		73.26 323	71.46 328	78.63 306	83.34 337	56.71 363	80.65 344	80.40 377	56.63 416	73.55 307	82.02 379	51.80 302	91.24 275	56.35 362	78.42 306	87.95 318

MonoMVSNet		76.49 278	75.80 267	78.58 309	81.55 376	58.45 334	86.36 224	86.22 292	74.87 129	74.73 291	83.73 347	51.79 303	88.73 328	70.78 221	72.15 385	88.55 308

LS3D		76.95 268	74.82 286	83.37 179	90.45 103	67.36 167	89.15 113	86.94 277	61.87 374	69.52 356	90.61 157	51.71 304	94.53 117	46.38 418	86.71 181	88.21 315

IterMVS		74.29 305	72.94 313	78.35 316	81.53 377	63.49 264	81.58 328	82.49 349	68.06 295	69.99 350	83.69 349	51.66 305	85.54 366	65.85 273	71.64 389	86.01 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 333	71.71 325	74.35 368	82.19 367	52.00 409	79.22 365	77.29 406	64.56 338	72.95 315	83.68 350	51.35 306	83.26 388	58.33 342	75.80 342	87.81 322

sam_mvs1												51.32 307				88.96 291

mvsmamba		80.60 174	79.38 184	84.27 133	89.74 124	67.24 172	87.47 179	86.95 276	70.02 249	75.38 269	88.93 207	51.24 308	92.56 215	75.47 173	89.22 137	93.00 129

PatchmatchNet		73.12 325	71.33 331	78.49 314	83.18 343	60.85 309	79.63 359	78.57 395	64.13 343	71.73 330	79.81 402	51.20 309	85.97 361	57.40 350	76.36 338	88.66 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											74.00 434	51.12 310	88.60 331

xiu_mvs_v1_base_debu		80.80 165	79.72 176	84.03 154	87.35 226	70.19 84	85.56 244	88.77 229	69.06 277	81.83 131	88.16 231	50.91 311	92.85 204	78.29 135	87.56 163	89.06 282

xiu_mvs_v1_base		80.80 165	79.72 176	84.03 154	87.35 226	70.19 84	85.56 244	88.77 229	69.06 277	81.83 131	88.16 231	50.91 311	92.85 204	78.29 135	87.56 163	89.06 282

xiu_mvs_v1_base_debi		80.80 165	79.72 176	84.03 154	87.35 226	70.19 84	85.56 244	88.77 229	69.06 277	81.83 131	88.16 231	50.91 311	92.85 204	78.29 135	87.56 163	89.06 282

Patchmatch-test		64.82 395	63.24 396	69.57 405	79.42 407	49.82 427	63.49 452	69.05 437	51.98 431	59.95 427	80.13 397	50.91 311	70.98 446	40.66 436	73.57 373	87.90 320

Patchmatch-RL test		70.24 355	67.78 368	77.61 331	77.43 416	59.57 327	71.16 423	70.33 431	62.94 360	68.65 364	72.77 437	50.62 315	85.49 367	69.58 239	66.58 410	87.77 323

Anonymous20231211		78.97 218	77.69 231	82.81 207	90.54 102	64.29 241	90.11 78	91.51 125	65.01 334	76.16 254	88.13 236	50.56 316	93.03 199	69.68 238	77.56 317	91.11 200

VDDNet		81.52 148	80.67 148	84.05 152	90.44 104	64.13 244	89.73 87	85.91 297	71.11 216	83.18 113	93.48 72	50.54 317	93.49 166	73.40 193	88.25 155	94.54 41

pmmvs6		74.69 302	73.39 306	78.61 307	81.38 380	57.48 352	86.64 214	87.95 252	64.99 335	70.18 345	86.61 277	50.43 318	89.52 311	62.12 305	70.18 397	88.83 296

IMVS_0404		77.16 264	76.42 262	79.37 294	87.13 238	63.59 258	77.12 393	89.33 197	70.51 235	66.22 395	89.03 202	50.36 319	82.78 390	72.56 205	85.56 203	91.74 179

test_post											5.46 466	50.36 319	84.24 378

ET-MVSNet_ETH3D		78.63 226	76.63 258	84.64 116	86.73 254	69.47 98	85.01 261	84.61 313	69.54 262	66.51 392	86.59 278	50.16 321	91.75 249	76.26 160	84.24 225	92.69 141

LuminaMVS		80.68 170	79.62 179	83.83 163	85.07 298	68.01 144	86.99 197	88.83 226	70.36 240	81.38 140	87.99 238	50.11 322	92.51 219	79.02 123	86.89 178	90.97 207

sam_mvs												50.01 323

Anonymous20240529		80.19 189	78.89 198	84.10 140	90.60 100	64.75 229	88.95 120	90.90 142	65.97 322	80.59 157	91.17 139	49.97 324	93.73 158	69.16 243	82.70 255	93.81 78

thisisatest0530		79.40 205	77.76 228	84.31 128	87.69 219	65.10 220	87.36 185	84.26 320	70.04 248	77.42 217	88.26 229	49.94 325	94.79 108	70.20 230	84.70 215	93.03 126

PatchT		68.46 373	67.85 364	70.29 403	80.70 388	43.93 447	72.47 418	74.88 418	60.15 386	70.55 339	76.57 424	49.94 325	81.59 397	50.58 390	74.83 362	85.34 375

tttt0517		79.40 205	77.91 219	83.90 162	88.10 196	63.84 250	88.37 149	84.05 322	71.45 208	76.78 234	89.12 199	49.93 327	94.89 101	70.18 231	83.18 248	92.96 131

tpmvs		71.09 344	69.29 349	76.49 343	82.04 368	56.04 374	78.92 371	81.37 363	64.05 347	67.18 380	78.28 415	49.74 328	89.77 306	49.67 399	72.37 382	83.67 399

thisisatest0515		77.33 261	75.38 278	83.18 187	85.27 291	63.80 251	82.11 323	83.27 334	65.06 332	75.91 255	83.84 343	49.54 329	94.27 126	67.24 261	86.19 189	91.48 191

UniMVSNet_ETH3D		79.10 214	78.24 212	81.70 237	86.85 249	60.24 319	87.28 189	88.79 228	74.25 145	76.84 231	90.53 160	49.48 330	91.56 258	67.98 253	82.15 259	93.29 108

dmvs_re		71.14 343	70.58 338	72.80 385	81.96 369	59.68 324	75.60 403	79.34 389	68.55 287	69.27 360	80.72 390	49.42 331	76.54 422	52.56 381	77.79 312	82.19 415

CVMVSNet		72.99 328	72.58 317	74.25 370	84.28 313	50.85 422	86.41 221	83.45 332	44.56 442	73.23 311	87.54 250	49.38 332	85.70 363	65.90 272	78.44 303	86.19 359

MDTV_nov1_ep13_2view						37.79 458	75.16 406		55.10 421	66.53 389		49.34 333		53.98 373		87.94 319

UGNet		80.83 161	79.59 180	84.54 118	88.04 199	68.09 140	89.42 99	88.16 243	76.95 70	76.22 249	89.46 192	49.30 334	93.94 141	68.48 250	90.31 115	91.60 184
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
pmmvs5		71.55 340	70.20 345	75.61 350	77.83 414	56.39 368	81.74 326	80.89 365	57.76 408	67.46 375	84.49 326	49.26 335	85.32 370	57.08 353	75.29 356	85.11 381

mvsany_test1		62.30 401	61.26 405	65.41 422	69.52 446	54.86 389	66.86 440	49.78 462	46.65 439	68.50 367	83.21 358	49.15 336	66.28 454	56.93 356	60.77 427	75.11 438

LTVRE_ROB	69.57 13	76.25 282	74.54 291	81.41 244	88.60 175	64.38 240	79.24 364	89.12 216	70.76 228	69.79 355	87.86 240	49.09 337	93.20 185	56.21 363	80.16 284	86.65 353
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
FMVSNet1		77.44 258	76.12 266	81.40 245	86.81 251	63.01 275	88.39 146	89.28 203	70.49 239	74.39 297	87.28 254	49.06 338	91.11 278	60.91 316	78.52 301	90.09 247

test1111		79.43 203	79.18 192	80.15 278	89.99 117	53.31 403	87.33 187	77.05 408	75.04 120	80.23 163	92.77 96	48.97 339	92.33 229	68.87 246	92.40 82	94.81 22

ECVR-MVS		79.61 196	79.26 189	80.67 266	90.08 112	54.69 390	87.89 168	77.44 404	74.88 127	80.27 161	92.79 94	48.96 340	92.45 221	68.55 249	92.50 80	94.86 19

MDTV_nov1_ep13			69.97 346		83.18 343	53.48 400	77.10 394	80.18 382	60.45 382	69.33 359	80.44 391	48.89 341	86.90 350	51.60 385	78.51 302

test_post1							78.90 372				5.43 467	48.81 342	85.44 369	59.25 330

test-LLR		72.94 329	72.43 318	74.48 366	81.35 381	58.04 340	78.38 378	77.46 402	66.66 309	69.95 351	79.00 409	48.06 343	79.24 408	66.13 268	84.83 212	86.15 360

test0.0.03 1		68.00 376	67.69 369	68.90 409	77.55 415	47.43 432	75.70 402	72.95 428	66.66 309	66.56 388	82.29 375	48.06 343	75.87 431	44.97 426	74.51 365	83.41 401

our_test_3		69.14 365	67.00 378	75.57 351	79.80 401	58.80 331	77.96 385	77.81 399	59.55 391	62.90 416	78.25 416	47.43 345	83.97 380	51.71 384	67.58 407	83.93 396

MS-PatchMatch		73.83 313	72.67 315	77.30 337	83.87 324	66.02 191	81.82 324	84.66 312	61.37 378	68.61 365	82.82 367	47.29 346	88.21 335	59.27 329	84.32 224	77.68 433

cascas		76.72 272	74.64 288	82.99 198	85.78 276	65.88 197	82.33 320	89.21 210	60.85 380	72.74 316	81.02 385	47.28 347	93.75 156	67.48 258	85.02 209	89.34 277

WB-MVS		54.94 410	54.72 411	55.60 436	73.50 435	20.90 470	74.27 414	61.19 453	59.16 395	50.61 445	74.15 433	47.19 348	75.78 432	17.31 461	35.07 455	70.12 443

test20.03		67.45 378	66.95 379	68.94 408	75.48 426	44.84 445	77.50 389	77.67 400	66.66 309	63.01 414	83.80 344	47.02 349	78.40 412	42.53 433	68.86 404	83.58 400

test_0402		72.79 330	70.44 341	79.84 284	88.13 194	65.99 194	85.93 235	84.29 318	65.57 326	67.40 378	85.49 306	46.92 350	92.61 211	35.88 444	74.38 366	80.94 423

Elysia		81.53 146	80.16 161	85.62 79	85.51 283	68.25 135	88.84 126	92.19 92	71.31 210	80.50 158	89.83 175	46.89 351	94.82 104	76.85 151	89.57 130	93.80 80

StellarMVS		81.53 146	80.16 161	85.62 79	85.51 283	68.25 135	88.84 126	92.19 92	71.31 210	80.50 158	89.83 175	46.89 351	94.82 104	76.85 151	89.57 130	93.80 80

F-COLMAP		76.38 281	74.33 295	82.50 221	89.28 145	66.95 180	88.41 145	89.03 218	64.05 347	66.83 384	88.61 217	46.78 353	92.89 202	57.48 348	78.55 300	87.67 324

ppachtmachnet_test		70.04 358	67.34 376	78.14 319	79.80 401	61.13 303	79.19 366	80.59 370	59.16 395	65.27 400	79.29 406	46.75 354	87.29 347	49.33 401	66.72 408	86.00 366

WBMVS		73.43 318	72.81 314	75.28 357	87.91 205	50.99 421	78.59 377	81.31 364	65.51 329	74.47 296	84.83 322	46.39 355	86.68 352	58.41 340	77.86 311	88.17 316

tt0805		78.73 223	77.83 223	81.43 243	85.17 292	60.30 318	89.41 100	90.90 142	71.21 214	77.17 228	88.73 212	46.38 356	93.21 182	72.57 203	78.96 298	90.79 213

D2MVS		74.82 301	73.21 309	79.64 290	79.81 400	62.56 284	80.34 350	87.35 267	64.37 341	68.86 362	82.66 369	46.37 357	90.10 300	67.91 254	81.24 269	86.25 357

Anonymous20231206		68.60 369	67.80 367	71.02 400	80.23 394	50.75 423	78.30 382	80.47 373	56.79 415	66.11 396	82.63 370	46.35 358	78.95 410	43.62 428	75.70 343	83.36 402

SSC-MVS		53.88 413	53.59 413	54.75 438	72.87 441	19.59 471	73.84 416	60.53 455	57.58 411	49.18 449	73.45 436	46.34 359	75.47 435	16.20 464	32.28 457	69.20 444

CHOSEN 280x420		66.51 386	64.71 388	71.90 391	81.45 378	63.52 263	57.98 455	68.95 438	53.57 425	62.59 417	76.70 423	46.22 360	75.29 437	55.25 365	79.68 289	76.88 435

testing91		76.54 273	75.66 272	79.18 299	88.43 182	55.89 376	81.08 335	83.00 342	73.76 157	75.34 271	84.29 333	46.20 361	90.07 301	64.33 284	84.50 217	91.58 186

GA-MVS		76.87 269	75.17 283	81.97 233	82.75 356	62.58 283	81.44 332	86.35 291	72.16 196	74.74 290	82.89 365	46.20 361	92.02 238	68.85 247	81.09 271	91.30 196

MDA-MVSNet_test_wron		65.03 393	62.92 397	71.37 395	75.93 420	56.73 361	69.09 435	74.73 420	57.28 413	54.03 442	77.89 417	45.88 363	74.39 440	49.89 398	61.55 425	82.99 408

YYNet1		65.03 393	62.91 398	71.38 394	75.85 423	56.60 365	69.12 434	74.66 422	57.28 413	54.12 441	77.87 418	45.85 364	74.48 439	49.95 397	61.52 426	83.05 406

EPMVS		69.02 366	68.16 358	71.59 393	79.61 404	49.80 428	77.40 390	66.93 442	62.82 363	70.01 348	79.05 407	45.79 365	77.86 416	56.58 360	75.26 357	87.13 341

IB-MVS	68.01 15	75.85 288	73.36 308	83.31 180	84.76 304	66.03 190	83.38 305	85.06 308	70.21 247	69.40 357	81.05 384	45.76 366	94.66 113	65.10 279	75.49 347	89.25 279
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
jajsoiax		79.29 209	77.96 217	83.27 182	84.68 306	66.57 184	89.25 106	90.16 168	69.20 273	75.46 265	89.49 189	45.75 367	93.13 191	76.84 153	80.80 276	90.11 245

UBG		73.08 326	72.27 321	75.51 353	88.02 200	51.29 419	78.35 381	77.38 405	65.52 327	73.87 303	82.36 372	45.55 368	86.48 355	55.02 367	84.39 223	88.75 300

PatchMatch-RL		72.38 332	70.90 336	76.80 342	88.60 175	67.38 166	79.53 360	76.17 414	62.75 364	69.36 358	82.00 380	45.51 369	84.89 374	53.62 375	80.58 279	78.12 432

FE-MVS		77.78 249	75.68 270	84.08 145	88.09 197	66.00 193	83.13 311	87.79 257	68.42 291	78.01 205	85.23 313	45.50 370	95.12 88	59.11 332	85.83 200	91.11 200

RPSCF		73.23 324	71.46 328	78.54 311	82.50 362	59.85 322	82.18 322	82.84 347	58.96 397	71.15 338	89.41 196	45.48 371	84.77 375	58.82 336	71.83 388	91.02 206

test_vis1_n_1920		75.52 292	75.78 268	74.75 365	79.84 399	57.44 353	83.26 308	85.52 302	62.83 362	79.34 177	86.17 291	45.10 372	79.71 407	78.75 128	81.21 270	87.10 344

myMVS_eth3d28		73.62 315	73.53 305	73.90 374	88.20 189	47.41 434	78.06 384	79.37 388	74.29 144	73.98 301	84.29 333	44.67 373	83.54 384	51.47 386	87.39 167	90.74 217

MSDG		73.36 321	70.99 335	80.49 270	84.51 311	65.80 200	80.71 343	86.13 295	65.70 324	65.46 398	83.74 346	44.60 374	90.91 287	51.13 389	76.89 323	84.74 386

PVSNet_0	57.27 20	61.67 403	59.27 406	68.85 410	79.61 404	57.44 353	68.01 436	73.44 425	55.93 419	58.54 431	70.41 442	44.58 375	77.55 417	47.01 414	35.91 454	71.55 442

testing99		76.09 285	75.12 284	79.00 300	88.16 191	55.50 382	80.79 339	81.40 362	73.30 173	75.17 279	84.27 336	44.48 376	90.02 302	64.28 285	84.22 226	91.48 191

testing3-2		75.12 300	75.19 282	74.91 361	90.40 105	45.09 444	80.29 351	78.42 396	78.37 40	76.54 242	87.75 241	44.36 377	87.28 348	57.04 354	83.49 241	92.37 155

test_cas_vis1_n_1920		73.76 314	73.74 303	73.81 375	75.90 421	59.77 323	80.51 346	82.40 350	58.30 403	81.62 138	85.69 299	44.35 378	76.41 425	76.29 159	78.61 299	85.23 377

mvs_tets		79.13 213	77.77 227	83.22 186	84.70 305	66.37 186	89.17 109	90.19 167	69.38 265	75.40 268	89.46 192	44.17 379	93.15 189	76.78 157	80.70 278	90.14 242

MDA-MVSNet-bldmvs		66.68 384	63.66 394	75.75 348	79.28 408	60.56 314	73.92 415	78.35 397	64.43 339	50.13 447	79.87 401	44.02 380	83.67 382	46.10 420	56.86 433	83.03 407

mmtdpeth		74.16 308	73.01 312	77.60 333	83.72 328	61.13 303	85.10 259	85.10 307	72.06 197	77.21 227	80.33 394	43.84 381	85.75 362	77.14 148	52.61 443	85.91 367

gg-mvs-nofinetune		69.95 359	67.96 362	75.94 346	83.07 346	54.51 393	77.23 392	70.29 432	63.11 356	70.32 343	62.33 446	43.62 382	88.69 329	53.88 374	87.76 162	84.62 388

testing11		75.14 299	74.01 297	78.53 312	88.16 191	56.38 369	80.74 342	80.42 376	70.67 229	72.69 319	83.72 348	43.61 383	89.86 304	62.29 302	83.76 232	89.36 276

GG-mvs-BLEND				75.38 356	81.59 375	55.80 378	79.32 363	69.63 434		67.19 379	73.67 435	43.24 384	88.90 327	50.41 391	84.50 217	81.45 420

CMPMVS	51.72 21	70.19 356	68.16 358	76.28 344	73.15 440	57.55 351	79.47 361	83.92 323	48.02 438	56.48 438	84.81 323	43.13 385	86.42 356	62.67 298	81.81 265	84.89 384
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		66.80 383	65.43 384	70.90 402	79.74 403	48.82 430	75.12 408	74.77 419	59.61 390	64.08 409	77.23 421	42.89 386	80.72 404	48.86 404	66.58 410	83.16 404

PVSNet	64.34 18	72.08 338	70.87 337	75.69 349	86.21 265	56.44 367	74.37 413	80.73 368	62.06 372	70.17 346	82.23 376	42.86 387	83.31 387	54.77 369	84.45 221	87.32 334

pmmvs-eth3d		70.50 352	67.83 366	78.52 313	77.37 417	66.18 189	81.82 324	81.51 360	58.90 398	63.90 411	80.42 392	42.69 388	86.28 357	58.56 338	65.30 415	83.11 405

UnsupCasMVSNet_eth		67.33 379	65.99 383	71.37 395	73.48 436	51.47 417	75.16 406	85.19 305	65.20 330	60.78 422	80.93 389	42.35 389	77.20 418	57.12 352	53.69 441	85.44 374

KD-MVS_self_test		68.81 367	67.59 372	72.46 389	74.29 430	45.45 439	77.93 386	87.00 275	63.12 355	63.99 410	78.99 411	42.32 390	84.77 375	56.55 361	64.09 418	87.16 340

ADS-MVSNet2		66.20 391	63.33 395	74.82 363	79.92 397	58.75 332	67.55 438	75.19 416	53.37 426	65.25 401	75.86 428	42.32 390	80.53 405	41.57 434	68.91 402	85.18 378

ADS-MVSNet		64.36 396	62.88 399	68.78 411	79.92 397	47.17 435	67.55 438	71.18 430	53.37 426	65.25 401	75.86 428	42.32 390	73.99 442	41.57 434	68.91 402	85.18 378

SixPastTwentyTwo		73.37 319	71.26 333	79.70 287	85.08 297	57.89 344	85.57 243	83.56 329	71.03 221	65.66 397	85.88 295	42.10 393	92.57 214	59.11 332	63.34 419	88.65 304

JIA-IIPM		66.32 388	62.82 400	76.82 341	77.09 418	61.72 299	65.34 446	75.38 415	58.04 407	64.51 405	62.32 447	42.05 394	86.51 354	51.45 387	69.22 401	82.21 414

ACMH	67.68 16	75.89 287	73.93 299	81.77 236	88.71 172	66.61 183	88.62 138	89.01 220	69.81 255	66.78 385	86.70 274	41.95 395	91.51 265	55.64 364	78.14 309	87.17 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UWE-MVS-28		65.32 392	64.93 386	66.49 420	78.70 411	38.55 457	77.86 388	64.39 449	62.00 373	64.13 408	83.60 351	41.44 396	76.00 429	31.39 449	80.89 273	84.92 383

FE-MVSNET		67.25 381	65.33 385	73.02 383	75.86 422	52.54 407	80.26 353	80.56 371	63.80 352	60.39 423	79.70 403	41.41 397	84.66 377	43.34 429	62.62 422	81.86 417

ACMH+	68.96 14	76.01 286	74.01 297	82.03 231	88.60 175	65.31 213	88.86 123	87.55 262	70.25 246	67.75 371	87.47 252	41.27 398	93.19 187	58.37 341	75.94 341	87.60 326

MIMVSNet		70.69 349	69.30 348	74.88 362	84.52 310	56.35 371	75.87 401	79.42 387	64.59 337	67.76 370	82.41 371	41.10 399	81.54 398	46.64 417	81.34 267	86.75 351

Anonymous202405211		78.25 234	77.01 245	81.99 232	91.03 90	60.67 312	84.77 266	83.90 324	70.65 233	80.00 165	91.20 137	41.08 400	91.43 269	65.21 277	85.26 208	93.85 74

N_pmnet		52.79 416	53.26 414	51.40 440	78.99 410	7.68 474	69.52 430	3.89 473	51.63 432	57.01 436	74.98 432	40.83 401	65.96 455	37.78 441	64.67 416	80.56 427

ETVMVS		72.25 335	71.05 334	75.84 347	87.77 215	51.91 411	79.39 362	74.98 417	69.26 269	73.71 304	82.95 363	40.82 402	86.14 358	46.17 419	84.43 222	89.47 272

EU-MVSNet		68.53 372	67.61 371	71.31 398	78.51 413	47.01 436	84.47 275	84.27 319	42.27 445	66.44 393	84.79 324	40.44 403	83.76 381	58.76 337	68.54 405	83.17 403

DSMNet-mixed		57.77 408	56.90 410	60.38 428	67.70 449	35.61 459	69.18 432	53.97 460	32.30 458	57.49 435	79.88 400	40.39 404	68.57 452	38.78 440	72.37 382	76.97 434

UWE-MVS		72.13 337	71.49 327	74.03 372	86.66 257	47.70 431	81.40 333	76.89 410	63.60 353	75.59 260	84.22 337	39.94 405	85.62 365	48.98 403	86.13 191	88.77 299

OurMVSNet-221017-0		74.26 306	72.42 319	79.80 285	83.76 327	59.59 326	85.92 236	86.64 284	66.39 316	66.96 382	87.58 246	39.46 406	91.60 254	65.76 274	69.27 400	88.22 314

K. test v3		71.19 342	68.51 354	79.21 298	83.04 348	57.78 348	84.35 282	76.91 409	72.90 184	62.99 415	82.86 366	39.27 407	91.09 283	61.65 310	52.66 442	88.75 300

tt0320		70.49 353	68.03 361	77.89 324	84.78 303	59.12 330	83.55 301	80.44 375	58.13 405	67.43 377	80.41 393	39.26 408	87.54 345	55.12 366	63.18 421	86.99 345

lessismore_v0				78.97 301	81.01 386	57.15 356		65.99 444		61.16 421	82.82 367	39.12 409	91.34 272	59.67 326	46.92 449	88.43 310

testing222		74.04 310	72.66 316	78.19 318	87.89 206	55.36 383	81.06 336	79.20 391	71.30 212	74.65 293	83.57 353	39.11 410	88.67 330	51.43 388	85.75 201	90.53 226

reproduce_monomvs		75.40 296	74.38 294	78.46 315	83.92 323	57.80 347	83.78 293	86.94 277	73.47 167	72.25 325	84.47 327	38.74 411	89.27 316	75.32 174	70.53 395	88.31 312

UnsupCasMVSNet_bld		63.70 398	61.53 404	70.21 404	73.69 434	51.39 418	72.82 417	81.89 355	55.63 420	57.81 434	71.80 439	38.67 412	78.61 411	49.26 402	52.21 444	80.63 425

new-patchmatchnet		61.73 402	61.73 403	61.70 426	72.74 442	24.50 469	69.16 433	78.03 398	61.40 376	56.72 437	75.53 431	38.42 413	76.48 424	45.95 421	57.67 432	84.13 393

MVS-HIRNet		59.14 406	57.67 408	63.57 424	81.65 373	43.50 448	71.73 420	65.06 447	39.59 449	51.43 444	57.73 452	38.34 414	82.58 392	39.53 437	73.95 369	64.62 448

test2506		77.30 262	76.49 259	79.74 286	90.08 112	52.02 408	87.86 170	63.10 451	74.88 127	80.16 164	92.79 94	38.29 415	92.35 227	68.74 248	92.50 80	94.86 19

COLMAP_ROB	66.92 17	73.01 327	70.41 342	80.81 263	87.13 238	65.63 204	88.30 152	84.19 321	62.96 359	63.80 412	87.69 244	38.04 416	92.56 215	46.66 415	74.91 361	84.24 391
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 360	69.00 352	72.55 387	79.27 409	56.85 359	78.38 378	74.71 421	57.64 409	68.09 369	77.19 422	37.75 417	76.70 421	63.92 287	84.09 227	84.10 394

OpenMVS_ROB	64.09 19	70.56 351	68.19 357	77.65 330	80.26 392	59.41 329	85.01 261	82.96 344	58.76 400	65.43 399	82.33 373	37.63 418	91.23 276	45.34 425	76.03 340	82.32 413

FMVSNet5		69.50 362	67.96 362	74.15 371	82.97 352	55.35 384	80.01 356	82.12 353	62.56 366	63.02 413	81.53 381	36.92 419	81.92 396	48.42 405	74.06 368	85.17 380

tt0320-xc		70.11 357	67.45 374	78.07 322	85.33 289	59.51 328	83.28 307	78.96 393	58.77 399	67.10 381	80.28 395	36.73 420	87.42 346	56.83 358	59.77 431	87.29 335

sc_t1		72.19 336	69.51 347	80.23 276	84.81 302	61.09 305	84.68 268	80.22 380	60.70 381	71.27 335	83.58 352	36.59 421	89.24 317	60.41 319	63.31 420	90.37 233

MIMVSNet1		68.58 370	66.78 380	73.98 373	80.07 396	51.82 413	80.77 340	84.37 315	64.40 340	59.75 428	82.16 377	36.47 422	83.63 383	42.73 431	70.33 396	86.48 355

ITE_SJBPF				78.22 317	81.77 372	60.57 313		83.30 333	69.25 270	67.54 373	87.20 259	36.33 423	87.28 348	54.34 371	74.62 364	86.80 349

test-mter		71.41 341	70.39 343	74.48 366	81.35 381	58.04 340	78.38 378	77.46 402	60.32 384	69.95 351	79.00 409	36.08 424	79.24 408	66.13 268	84.83 212	86.15 360

testgi		66.67 385	66.53 381	67.08 419	75.62 425	41.69 454	75.93 398	76.50 411	66.11 318	65.20 403	86.59 278	35.72 425	74.71 438	43.71 427	73.38 377	84.84 385

EG-PatchMatch MVS		74.04 310	71.82 324	80.71 265	84.92 300	67.42 163	85.86 238	88.08 246	66.04 320	64.22 407	83.85 342	35.10 426	92.56 215	57.44 349	80.83 275	82.16 416

KD-MVS_2432*1600		66.22 389	63.89 392	73.21 379	75.47 427	53.42 401	70.76 426	84.35 316	64.10 345	66.52 390	78.52 413	34.55 427	84.98 372	50.40 392	50.33 446	81.23 421

miper_refine_blended		66.22 389	63.89 392	73.21 379	75.47 427	53.42 401	70.76 426	84.35 316	64.10 345	66.52 390	78.52 413	34.55 427	84.98 372	50.40 392	50.33 446	81.23 421

mvs5depth		69.45 363	67.45 374	75.46 355	73.93 431	55.83 377	79.19 366	83.23 335	66.89 304	71.63 332	83.32 356	33.69 429	85.09 371	59.81 325	55.34 439	85.46 373

XVG-ACMP-BASELINE		76.11 284	74.27 296	81.62 238	83.20 342	64.67 230	83.60 300	89.75 182	69.75 259	71.85 329	87.09 263	32.78 430	92.11 235	69.99 234	80.43 282	88.09 317

AllTest		70.96 345	68.09 360	79.58 291	85.15 294	63.62 254	84.58 273	79.83 383	62.31 368	60.32 425	86.73 268	32.02 431	88.96 325	50.28 394	71.57 390	86.15 360

TestCases				79.58 291	85.15 294	63.62 254		79.83 383	62.31 368	60.32 425	86.73 268	32.02 431	88.96 325	50.28 394	71.57 390	86.15 360

USDC		70.33 354	68.37 355	76.21 345	80.60 389	56.23 372	79.19 366	86.49 287	60.89 379	61.29 420	85.47 307	31.78 433	89.47 313	53.37 377	76.21 339	82.94 409

myMVS_eth3d		67.02 382	66.29 382	69.21 407	84.68 306	42.58 450	78.62 375	73.08 426	66.65 312	66.74 386	79.46 404	31.53 434	82.30 393	39.43 439	76.38 336	82.75 410

test_fmvs1		70.93 346	70.52 339	72.16 390	73.71 433	55.05 387	80.82 337	78.77 394	51.21 434	78.58 189	84.41 329	31.20 435	76.94 420	75.88 166	80.12 287	84.47 389

Anonymous20240521		68.80 368	67.22 377	73.55 376	74.33 429	54.11 395	83.18 309	85.61 301	58.15 404	61.68 419	80.94 387	30.71 436	81.27 401	57.00 355	73.34 378	85.28 376

testing3		68.56 371	67.67 370	71.22 399	87.33 231	42.87 449	83.06 315	71.54 429	70.36 240	69.08 361	84.38 330	30.33 437	85.69 364	37.50 442	75.45 351	85.09 382

test_vis1_n		69.85 361	69.21 350	71.77 392	72.66 443	55.27 386	81.48 330	76.21 413	52.03 430	75.30 276	83.20 359	28.97 438	76.22 427	74.60 180	78.41 307	83.81 397

tmp_tt		18.61 434	21.40 437	10.23 450	4.82 473	10.11 473	34.70 460	30.74 471	1.48 467	23.91 463	26.07 464	28.42 439	13.41 469	27.12 453	15.35 466	7.17 464

test_fmvs1_n		70.86 347	70.24 344	72.73 386	72.51 444	55.28 385	81.27 334	79.71 385	51.49 433	78.73 184	84.87 321	27.54 440	77.02 419	76.06 162	79.97 288	85.88 368

TDRefinement		67.49 377	64.34 389	76.92 340	73.47 437	61.07 306	84.86 265	82.98 343	59.77 389	58.30 432	85.13 316	26.06 441	87.89 340	47.92 412	60.59 429	81.81 419

dongtai		45.42 424	45.38 425	45.55 442	73.36 438	26.85 466	67.72 437	34.19 468	54.15 424	49.65 448	56.41 455	25.43 442	62.94 458	19.45 459	28.09 459	46.86 458

MVStest1		56.63 409	52.76 415	68.25 415	61.67 457	53.25 405	71.67 421	68.90 439	38.59 450	50.59 446	83.05 361	25.08 443	70.66 447	36.76 443	38.56 453	80.83 424

test_vis1_rt		60.28 404	58.42 407	65.84 421	67.25 450	55.60 381	70.44 428	60.94 454	44.33 443	59.00 429	66.64 444	24.91 444	68.67 451	62.80 294	69.48 398	73.25 440

TinyColmap		67.30 380	64.81 387	74.76 364	81.92 371	56.68 364	80.29 351	81.49 361	60.33 383	56.27 439	83.22 357	24.77 445	87.66 344	45.52 423	69.47 399	79.95 428

EGC-MVSNET		52.07 418	47.05 422	67.14 418	83.51 334	60.71 311	80.50 347	67.75 440	0.07 468	0.43 469	75.85 430	24.26 446	81.54 398	28.82 451	62.25 423	59.16 451

kuosan		39.70 428	40.40 429	37.58 445	64.52 454	26.98 464	65.62 445	33.02 469	46.12 440	42.79 452	48.99 458	24.10 447	46.56 466	12.16 467	26.30 460	39.20 459

LF4IMVS		64.02 397	62.19 401	69.50 406	70.90 445	53.29 404	76.13 396	77.18 407	52.65 428	58.59 430	80.98 386	23.55 448	76.52 423	53.06 379	66.66 409	78.68 431

test_fmvs2		68.35 374	67.48 373	70.98 401	69.50 447	51.95 410	80.05 355	76.38 412	49.33 436	74.65 293	84.38 330	23.30 449	75.40 436	74.51 181	75.17 359	85.60 371

new_pmnet		50.91 419	50.29 419	52.78 439	68.58 448	34.94 461	63.71 450	56.63 459	39.73 448	44.95 450	65.47 445	21.93 450	58.48 459	34.98 445	56.62 434	64.92 447

ttmdpeth		59.91 405	57.10 409	68.34 414	67.13 451	46.65 438	74.64 411	67.41 441	48.30 437	62.52 418	85.04 320	20.40 451	75.93 430	42.55 432	45.90 452	82.44 412

pmmvs3		57.79 407	54.26 412	68.37 413	64.02 455	56.72 362	75.12 408	65.17 446	40.20 447	52.93 443	69.86 443	20.36 452	75.48 434	45.45 424	55.25 440	72.90 441

PM-MVS		66.41 387	64.14 390	73.20 381	73.92 432	56.45 366	78.97 370	64.96 448	63.88 351	64.72 404	80.24 396	19.84 453	83.44 386	66.24 267	64.52 417	79.71 429

mvsany_test3		53.99 412	51.45 417	61.61 427	55.51 461	44.74 446	63.52 451	45.41 466	43.69 444	58.11 433	76.45 425	17.99 454	63.76 457	54.77 369	47.59 448	76.34 436

ambc				75.24 358	73.16 439	50.51 424	63.05 453	87.47 265		64.28 406	77.81 419	17.80 455	89.73 308	57.88 346	60.64 428	85.49 372

ANet_high		50.57 420	46.10 424	63.99 423	48.67 468	39.13 456	70.99 425	80.85 366	61.39 377	31.18 457	57.70 453	17.02 456	73.65 444	31.22 450	15.89 465	79.18 430

FPMVS		53.68 414	51.64 416	59.81 429	65.08 453	51.03 420	69.48 431	69.58 435	41.46 446	40.67 453	72.32 438	16.46 457	70.00 450	24.24 457	65.42 414	58.40 453

test_method		31.52 430	29.28 434	38.23 444	27.03 472	6.50 475	20.94 463	62.21 452	4.05 466	22.35 464	52.50 457	13.33 458	47.58 464	27.04 454	34.04 456	60.62 450

EMVS		30.81 431	29.65 433	34.27 447	50.96 467	25.95 467	56.58 457	46.80 465	24.01 462	15.53 467	30.68 463	12.47 459	54.43 463	12.81 466	17.05 464	22.43 463

test_f		52.09 417	50.82 418	55.90 434	53.82 464	42.31 453	59.42 454	58.31 458	36.45 453	56.12 440	70.96 441	12.18 460	57.79 460	53.51 376	56.57 435	67.60 445

test_fmvs3		63.36 399	61.82 402	67.98 416	62.51 456	46.96 437	77.37 391	74.03 423	45.24 441	67.50 374	78.79 412	12.16 461	72.98 445	72.77 201	66.02 412	83.99 395

E-PMN		31.77 429	30.64 432	35.15 446	52.87 466	27.67 463	57.09 456	47.86 464	24.64 461	16.40 466	33.05 462	11.23 462	54.90 462	14.46 465	18.15 463	22.87 462

DeepMVS_CX				27.40 448	40.17 471	26.90 465		24.59 472	17.44 464	23.95 462	48.61 459	9.77 463	26.48 467	18.06 460	24.47 461	28.83 461

Gipuma		45.18 425	41.86 428	55.16 437	77.03 419	51.52 416	32.50 461	80.52 372	32.46 457	27.12 460	35.02 461	9.52 464	75.50 433	22.31 458	60.21 430	38.45 460
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 411	49.68 421	67.97 417	53.73 465	45.28 442	66.85 441	80.78 367	35.96 454	39.45 455	62.23 448	8.70 465	78.06 415	48.24 409	51.20 445	80.57 426

APD_test1		53.31 415	49.93 420	63.42 425	65.68 452	50.13 425	71.59 422	66.90 443	34.43 455	40.58 454	71.56 440	8.65 466	76.27 426	34.64 446	55.36 438	63.86 449

PMMVS2		40.82 427	38.86 431	46.69 441	53.84 463	16.45 472	48.61 458	49.92 461	37.49 451	31.67 456	60.97 449	8.14 467	56.42 461	28.42 452	30.72 458	67.19 446

test_vis3_rt		49.26 421	47.02 423	56.00 433	54.30 462	45.27 443	66.76 442	48.08 463	36.83 452	44.38 451	53.20 456	7.17 468	64.07 456	56.77 359	55.66 436	58.65 452

testf1		45.72 422	41.96 426	57.00 431	56.90 459	45.32 440	66.14 443	59.26 456	26.19 459	30.89 458	60.96 450	4.14 469	70.64 448	26.39 455	46.73 450	55.04 454

APD_test2		45.72 422	41.96 426	57.00 431	56.90 459	45.32 440	66.14 443	59.26 456	26.19 459	30.89 458	60.96 450	4.14 469	70.64 448	26.39 455	46.73 450	55.04 454

PMVS	37.38 22	44.16 426	40.28 430	55.82 435	40.82 470	42.54 452	65.12 447	63.99 450	34.43 455	24.48 461	57.12 454	3.92 471	76.17 428	17.10 462	55.52 437	48.75 456
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 432	25.89 436	43.81 443	44.55 469	35.46 460	28.87 462	39.07 467	18.20 463	18.58 465	40.18 460	2.68 472	47.37 465	17.07 463	23.78 462	48.60 457
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		16.82 435	15.94 438	19.46 449	58.74 458	31.45 462	39.22 459	3.74 474	6.84 465	6.04 468	2.70 468	1.27 473	24.29 468	10.54 468	14.40 467	2.63 465

test123		6.12 437	8.11 440	0.14 451	0.06 475	0.09 476	71.05 424	0.03 476	0.04 470	0.25 471	1.30 470	0.05 474	0.03 471	0.21 470	0.01 469	0.29 466

testmvs		6.04 438	8.02 441	0.10 452	0.08 474	0.03 477	69.74 429	0.04 475	0.05 469	0.31 470	1.68 469	0.02 475	0.04 470	0.24 469	0.02 468	0.25 467

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	0.00 470	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	0.00 470	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	0.00 470	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	0.00 470	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	0.00 470	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	0.00 470	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	0.00 470	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	0.00 470	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	0.00 470	0.00 468

ab-mvs-re		7.23 436	9.64 439	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	86.72 270	0.00 476	0.00 472	0.00 471	0.00 470	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	0.00 470	0.00 468

WAC-MVS						42.58 450								39.46 438

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 137	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 44

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 44

eth-test2					0.00 476

eth-test					0.00 476

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 305	92.39 6				88.94 26	96.63 4	94.85 21

save fliter					93.80 40	72.35 44	90.47 69	91.17 135	74.31 142

test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 21	96.41 12	94.21 55

GSMVS																88.96 291

test_part2					95.06 8	72.65 32				91.80 13

MTGPA								92.02 98

MTMP							92.18 35	32.83 470

gm-plane-assit					81.40 379	53.83 398			62.72 365		80.94 387		92.39 224	63.40 291

test9_res														84.90 58	95.70 26	92.87 134

agg_prior2														82.91 85	95.45 29	92.70 139

agg_prior					92.85 64	71.94 52		91.78 114		84.41 89			94.93 97

test_prior4						72.60 34	89.01 118

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 70

旧先验2							86.56 217		58.10 406	87.04 56			88.98 323	74.07 186

新几何2							86.29 227

无先验							87.48 178	88.98 221	60.00 387				94.12 134	67.28 260		88.97 290

原ACMM2							86.86 204

testdata2													91.01 285	62.37 301

testdata1							84.14 288		75.71 101

plane_prior7					90.08 112	68.51 127

plane_prior5								92.44 78					95.38 78	78.71 129	86.32 186	91.33 194

plane_prior4											91.00 147

plane_prior3						68.60 124			78.44 36	78.92 182

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

plane_prior						68.71 119	90.38 73		77.62 47						86.16 190

n2								0.00 477

nn								0.00 477

door-mid								69.98 433

test11								92.23 88

door								69.44 436

HQP5-MVS						66.98 177

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 223

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 223

BP-MVS														77.47 143

HQP4-MVS										77.24 222			95.11 90			91.03 204

HQP3-MVS								92.19 92							85.99 194

NP-MVS					89.62 125	68.32 131					90.24 167

ACMMP++_ref															81.95 263

ACMMP++															81.25 268

High-resolution multi-view benchmark