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 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 16

PC_three_1452									68.21 311	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 106	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 84
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 97	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 125	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 53

test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 37

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 68

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 122
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 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 46

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 37
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
ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 97	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 113	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 56
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
dcpmvs_2		85.63 70	86.15 60	84.06 163	91.71 84	64.94 237	86.47 231	91.87 120	73.63 175	86.60 67	93.02 93	76.57 18	91.87 262	83.36 84	92.15 90	95.35 3

CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 63

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 195	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 59

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 166	83.16 126	91.07 157	75.94 21	95.19 89	79.94 124	94.38 62	93.55 113

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 141

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 150	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	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 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 111	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 168	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 158	87.63 45	94.27 65	93.65 105
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 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 267	93.44 32	78.70 34	83.63 115	89.03 218	74.57 27	95.71 66	80.26 121	94.04 67	93.66 101
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 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 233	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

patch_mono-2		83.65 112	84.54 89	80.99 274	90.06 120	65.83 205	84.21 303	88.74 251	71.60 221	85.01 79	92.44 105	74.51 29	83.50 405	82.15 101	92.15 90	93.64 107

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 118	68.69 303	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 143

test_8					93.13 60	72.57 35	88.68 143	91.84 122	68.69 303	84.87 84	93.10 88	74.43 30	95.16 90

TEST9					93.26 56	72.96 25	88.75 138	91.89 118	68.44 308	85.00 80	93.10 88	74.36 32	95.41 80

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 146	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	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							88.85 132		75.41 120	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 287	76.41 89	85.80 71	90.22 185	74.15 35	95.37 85	81.82 103	91.88 94	92.65 159

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 238	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 197	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 155
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 229	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 124	83.49 83	91.14 108	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 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 148	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 148	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 206	87.08 259	65.21 224	89.09 123	90.21 182	79.67 19	89.98 24	95.02 24	73.17 42	91.71 268	91.30 3	91.60 99	92.34 172

segment_acmp												73.08 43

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 203	93.04 46	69.80 272	82.85 133	91.22 151	73.06 44	96.02 57	76.72 173	94.63 54	91.46 209

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 75

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 189	87.12 258	66.01 199	88.56 148	89.43 209	75.59 115	89.32 28	94.32 44	72.89 46	91.21 295	90.11 11	92.33 87	93.16 132

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 250	68.54 130	89.57 99	90.44 171	75.31 124	87.49 54	94.39 42	72.86 47	92.72 224	89.04 27	90.56 118	94.16 71

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 315	69.51 100	89.62 98	90.58 166	73.42 183	87.75 50	94.02 61	72.85 48	93.24 193	90.37 8	90.75 115	93.96 82

MGCFI-Net		85.06 85	85.51 74	83.70 182	89.42 139	63.01 290	89.43 104	92.62 78	76.43 88	87.53 53	91.34 146	72.82 49	93.42 186	81.28 108	88.74 153	94.66 40

nrg030		83.88 103	83.53 112	84.96 107	86.77 268	69.28 109	90.46 75	92.67 72	74.79 145	82.95 129	91.33 147	72.70 50	93.09 207	80.79 115	79.28 313	92.50 165

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 171	87.78 218	66.09 196	89.96 86	90.80 161	77.37 57	86.72 65	94.20 52	72.51 51	92.78 223	89.08 22	92.33 87	93.13 136

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 310	84.61 91	93.48 78	72.32 52	96.15 53	79.00 139	95.43 34	94.28 67

lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 100

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 250	65.77 209	87.75 180	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 150	81.27 109	90.48 119	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 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 134	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 105
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 90	84.54 89	84.27 147	85.42 302	68.81 116	88.49 150	87.26 290	68.08 312	88.03 44	93.49 77	72.04 57	91.77 264	88.90 29	89.14 146	92.24 179

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 200	84.64 90	91.71 129	71.85 58	96.03 55	84.77 69	94.45 60	94.49 55

baseline		84.93 86	84.98 83	84.80 117	87.30 248	65.39 218	87.30 199	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 65

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 368	69.39 107	89.65 95	90.29 180	73.31 187	87.77 49	94.15 55	71.72 61	93.23 194	90.31 9	90.67 117	93.89 88

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 148	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

test12				86.80 58	92.63 73	70.70 81		91.79 125		82.71 136		71.67 63	96.16 52		94.50 57	93.54 114

UniMVSNet_NR-MVSNet		81.88 151	81.54 150	82.92 217	88.46 184	63.46 280	87.13 202	92.37 86	80.19 12	78.38 211	89.14 214	71.66 64	93.05 210	70.05 248	76.46 348	92.25 177

CS-MVS		86.69 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 71
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 80	85.34 77	85.13 101	86.12 285	69.93 92	88.65 144	90.78 162	69.97 268	88.27 38	93.98 66	71.39 67	91.54 278	88.49 35	90.45 120	93.91 85

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 249	90.33 177	76.11 102	82.08 144	91.61 137	71.36 68	94.17 139	81.02 110	92.58 82	92.08 188

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 134	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 137	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 139

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 137	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 139

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 99	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 76

fmvsm_l_conf0.5_n_a		84.13 96	84.16 94	84.06 163	85.38 303	68.40 133	88.34 158	86.85 300	67.48 319	87.48 55	93.40 82	70.89 73	91.61 269	88.38 37	89.22 143	92.16 186

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 133	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 101

EI-MVSNet-Vis-set		84.19 95	83.81 104	85.31 93	88.18 194	67.85 154	87.66 182	89.73 199	80.05 15	82.95 129	89.59 203	70.74 76	94.82 108	80.66 118	84.72 230	93.28 124

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 87

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 119	91.20 152	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 157	91.43 144	70.34 79	97.23 17	84.26 75	93.36 74	94.37 61

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 101	87.73 52	91.46 143	70.32 80	93.78 158	81.51 104	88.95 147	94.63 43

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 147	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 132

EI-MVSNet-UG-set		83.81 104	83.38 115	85.09 103	87.87 211	67.53 166	87.44 194	89.66 200	79.74 18	82.23 141	89.41 212	70.24 82	94.74 114	79.95 123	83.92 245	92.99 146

viewcassd2359sk11		83.89 102	83.74 106	84.34 139	87.76 221	64.91 240	86.30 240	92.22 99	75.47 118	83.04 128	91.52 139	70.15 83	93.53 174	79.26 134	87.96 171	94.57 48

E3new		83.78 107	83.60 110	84.31 141	87.76 221	64.89 241	86.24 243	92.20 102	75.15 134	82.87 131	91.23 148	70.11 84	93.52 176	79.05 135	87.79 174	94.51 54

E2		84.00 100	83.87 101	84.39 134	87.70 226	64.95 234	86.40 236	92.23 96	75.85 107	83.21 122	91.78 126	70.09 85	93.55 171	79.52 132	88.05 168	94.66 40

E3		84.00 100	83.87 101	84.39 134	87.70 226	64.95 234	86.40 236	92.23 96	75.85 107	83.21 122	91.78 126	70.09 85	93.55 171	79.52 132	88.05 168	94.66 40

MVS_Test		83.15 128	83.06 120	83.41 193	86.86 263	63.21 286	86.11 247	92.00 112	74.31 157	82.87 131	89.44 211	70.03 87	93.21 196	77.39 160	88.50 158	93.81 93

FC-MVSNet-test		81.52 163	82.02 144	80.03 298	88.42 187	55.97 395	87.95 172	93.42 34	77.10 68	77.38 234	90.98 164	69.96 88	91.79 263	68.46 267	84.50 233	92.33 173

FIs		82.07 147	82.42 132	81.04 273	88.80 171	58.34 355	88.26 161	93.49 31	76.93 72	78.47 210	91.04 158	69.92 89	92.34 243	69.87 252	84.97 226	92.44 170

E4		84.10 97	83.99 100	84.45 131	87.58 238	64.99 233	86.54 229	92.25 95	76.38 93	83.37 120	92.09 118	69.88 90	93.58 166	79.78 129	88.03 170	94.77 25

UniMVSNet (Re)		81.60 159	81.11 155	83.09 206	88.38 188	64.41 254	87.60 183	93.02 50	78.42 37	78.56 206	88.16 247	69.78 91	93.26 192	69.58 255	76.49 347	91.60 200

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 103	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 65
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 81	85.55 73	84.25 150	86.26 279	67.40 170	89.18 115	89.31 218	72.50 202	88.31 37	93.86 70	69.66 93	91.96 256	89.81 13	91.05 109	93.38 118

Effi-MVS+		83.62 115	83.08 119	85.24 95	88.38 188	67.45 167	88.89 129	89.15 229	75.50 117	82.27 140	88.28 243	69.61 94	94.45 127	77.81 153	87.84 173	93.84 91

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 203	85.22 78	91.90 121	69.47 95	96.42 44	83.28 86	95.94 23	94.35 62

viewdifsd2359ckpt07		82.83 136	82.78 128	82.99 213	86.51 276	62.58 298	85.09 276	90.83 160	75.22 127	82.28 139	91.63 134	69.43 96	92.03 252	77.71 155	86.32 201	94.34 63

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 159	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 233	90.88 114	93.07 138

ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 142	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 314	69.32 98	95.38 82	80.82 113	91.37 105	92.72 154

旧先验1					91.96 80	65.79 208		86.37 310			93.08 92	69.31 99			92.74 80	88.74 318

E6new		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 90	76.51 85	83.53 116	92.26 108	69.26 100	93.49 178	79.88 125	88.26 161	94.69 33

E6		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 90	76.51 85	83.53 116	92.26 108	69.26 100	93.49 178	79.88 125	88.26 161	94.69 33

E5		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 143	86.70 270	65.83 205	88.77 136	89.78 194	75.46 119	88.35 36	93.73 74	69.19 103	93.06 209	91.30 3	88.44 159	94.02 80

fmvsm_s_conf0.5_n_a		83.63 114	83.41 114	84.28 145	86.14 284	68.12 143	89.43 104	82.87 365	70.27 261	87.27 59	93.80 73	69.09 104	91.58 271	88.21 38	83.65 253	93.14 135

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 104	96.70 31	84.37 74	94.83 49	94.03 79

EIA-MVS		83.31 126	82.80 126	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 149	78.96 196	86.42 301	69.06 106	95.26 87	75.54 187	90.09 126	93.62 108

EPP-MVSNet		83.40 121	83.02 121	84.57 123	90.13 114	64.47 252	92.32 35	90.73 163	74.45 154	79.35 192	91.10 155	69.05 107	95.12 92	72.78 216	87.22 185	94.13 73

EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 122	69.04 108	95.43 77	83.93 81	93.77 69	93.01 144

fmvsm_s_conf0.5_n		83.80 105	83.71 107	84.07 160	86.69 271	67.31 173	89.46 103	83.07 360	71.09 233	86.96 63	93.70 75	69.02 109	91.47 283	88.79 30	84.62 232	93.44 117

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 110	96.65 34	84.53 72	94.90 45	94.00 81

test_fmvsmvis_n_1920		84.02 99	83.87 101	84.49 130	84.12 333	69.37 108	88.15 166	87.96 269	70.01 266	83.95 107	93.23 86	68.80 111	91.51 281	88.61 32	89.96 129	92.57 160

viewmanbaseed2359cas		83.66 111	83.55 111	84.00 171	86.81 266	64.53 247	86.65 224	91.75 128	74.89 141	83.15 127	91.68 130	68.74 112	92.83 221	79.02 137	89.24 142	94.63 43

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 101	79.94 17	89.74 27	94.86 26	68.63 113	94.20 136	90.83 5	91.39 104	94.38 60

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 184	87.32 247	65.13 227	88.86 130	91.63 132	75.41 120	88.23 40	93.45 81	68.56 114	92.47 235	89.52 18	92.78 79	93.20 130

mvs_anonymous		79.42 219	79.11 208	80.34 289	84.45 328	57.97 361	82.59 337	87.62 279	67.40 320	76.17 269	88.56 236	68.47 115	89.59 329	70.65 241	86.05 208	93.47 116

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 245	67.30 174	89.50 101	90.98 153	76.25 100	90.56 22	94.75 29	68.38 116	94.24 135	90.80 7	92.32 89	94.19 70

fmvsm_s_conf0.1_n		83.56 116	83.38 115	84.10 154	84.86 317	67.28 175	89.40 108	83.01 361	70.67 245	87.08 60	93.96 67	68.38 116	91.45 284	88.56 34	84.50 233	93.56 112

fmvsm_s_conf0.1_n_a		83.32 125	82.99 122	84.28 145	83.79 341	68.07 145	89.34 111	82.85 366	69.80 272	87.36 58	94.06 59	68.34 118	91.56 274	87.95 42	83.46 259	93.21 128

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 205	82.14 3	86.65 66	94.28 46	68.28 119	97.46 6	90.81 6	95.31 38	95.15 8

viewmacassd2359aftdt		83.76 108	83.66 109	84.07 160	86.59 274	64.56 246	86.88 214	91.82 123	75.72 110	83.34 121	92.15 116	68.24 120	92.88 217	79.05 135	89.15 145	94.77 25

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 227	92.02 110	79.45 22	85.88 70	94.80 27	68.07 121	96.21 50	86.69 52	95.34 36	93.23 125

mamv4		76.81 285	78.23 229	72.54 408	86.12 285	65.75 210	78.76 394	82.07 374	64.12 364	72.97 331	91.02 161	67.97 122	68.08 473	83.04 89	78.02 327	83.80 418

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 123	95.95 62	84.20 78	94.39 61	93.23 125

PAPM_NR		83.02 132	82.41 133	84.82 115	92.47 76	66.37 192	87.93 174	91.80 124	73.82 170	77.32 236	90.66 170	67.90 124	94.90 104	70.37 243	89.48 139	93.19 131

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 116	83.86 108	94.42 40	67.87 125	96.64 35	82.70 98	94.57 56	93.66 101

PAPR		81.66 158	80.89 160	83.99 173	90.27 111	64.00 260	86.76 221	91.77 127	68.84 301	77.13 246	89.50 204	67.63 126	94.88 106	67.55 273	88.52 157	93.09 137

Fast-Effi-MVS+		80.81 177	79.92 182	83.47 188	88.85 163	64.51 249	85.53 265	89.39 211	70.79 242	78.49 208	85.06 334	67.54 127	93.58 166	67.03 281	86.58 197	92.32 174

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 128	96.60 37	83.06 87	94.50 57	94.07 77

X-MVStestdata		80.37 198	77.83 238	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 485	67.45 128	96.60 37	83.06 87	94.50 57	94.07 77

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 151	86.84 64	94.65 31	67.31 130	95.77 64	84.80 68	92.85 78	92.84 153

NR-MVSNet		80.23 202	79.38 199	82.78 228	87.80 215	63.34 283	86.31 239	91.09 152	79.01 31	72.17 343	89.07 216	67.20 131	92.81 222	66.08 287	75.65 361	92.20 180

MSLP-MVS++		85.43 75	85.76 69	84.45 131	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 132	92.94 214	80.36 119	94.35 63	90.16 257

viewdifsd2359ckpt09		83.34 123	82.55 131	85.70 81	87.64 230	67.72 159	88.43 151	91.68 130	71.91 215	81.65 153	90.68 169	67.10 133	94.75 113	76.17 176	87.70 177	94.62 45

viewdifsd2359ckpt13		82.91 134	82.29 137	84.77 118	86.96 262	66.90 187	87.47 187	91.62 133	72.19 208	81.68 152	90.71 168	66.92 134	93.28 189	75.90 181	87.15 187	94.12 74

MG-MVS		83.41 120	83.45 113	83.28 196	92.74 71	62.28 307	88.17 164	89.50 207	75.22 127	81.49 155	92.74 103	66.75 135	95.11 94	72.85 215	91.58 101	92.45 169

fmvsm_s_conf0.5_n_7		83.34 123	84.03 99	81.28 265	85.73 293	65.13 227	85.40 268	89.90 192	74.96 139	82.13 143	93.89 69	66.65 136	87.92 359	86.56 53	91.05 109	90.80 228

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 410	69.03 110	89.47 102	89.65 201	73.24 191	86.98 62	94.27 47	66.62 137	93.23 194	90.26 10	89.95 130	93.78 97

EI-MVSNet		80.52 193	79.98 181	82.12 243	84.28 329	63.19 288	86.41 233	88.95 240	74.18 162	78.69 201	87.54 266	66.62 137	92.43 237	72.57 219	80.57 296	90.74 233

IterMVS-LS		80.06 205	79.38 199	82.11 245	85.89 289	63.20 287	86.79 218	89.34 212	74.19 161	75.45 282	86.72 286	66.62 137	92.39 239	72.58 218	76.86 341	90.75 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		78.59 243	77.76 243	81.08 272	82.66 376	61.56 318	83.65 316	89.15 229	68.87 300	75.55 278	83.79 362	66.49 140	92.03 252	73.25 211	76.39 350	89.64 284

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 99	76.87 74	82.81 135	94.25 49	66.44 141	96.24 49	82.88 92	94.28 64	93.38 118

c3_l		78.75 237	77.91 234	81.26 266	82.89 371	61.56 318	84.09 308	89.13 231	69.97 268	75.56 277	84.29 349	66.36 142	92.09 251	73.47 208	75.48 365	90.12 260

GeoE		81.71 155	81.01 158	83.80 181	89.51 134	64.45 253	88.97 126	88.73 252	71.27 229	78.63 204	89.76 196	66.32 143	93.20 199	69.89 251	86.02 209	93.74 98

diffmvs_AUTHOR		82.38 142	82.27 138	82.73 232	83.26 355	63.80 266	83.89 310	89.76 196	73.35 186	82.37 138	90.84 165	66.25 144	90.79 307	82.77 93	87.93 172	93.59 110

WR-MVS_H		78.51 245	78.49 219	78.56 329	88.02 204	56.38 389	88.43 151	92.67 72	77.14 65	73.89 318	87.55 265	66.25 144	89.24 336	58.92 354	73.55 391	90.06 267

viewmambaseed2359dif		80.41 194	79.84 186	82.12 243	82.95 370	62.50 301	83.39 323	88.06 266	67.11 321	80.98 164	90.31 180	66.20 146	91.01 303	74.62 195	84.90 227	92.86 151

PCF-MVS	73.52 7	80.38 196	78.84 214	85.01 105	87.71 224	68.99 113	83.65 316	91.46 142	63.00 378	77.77 228	90.28 181	66.10 147	95.09 98	61.40 331	88.22 165	90.94 225
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		83.72 110	82.92 124	86.14 72	84.22 331	69.48 101	91.05 64	85.27 324	81.30 6	76.83 248	91.65 132	66.09 148	95.56 68	76.00 180	93.85 68	93.38 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				84.35 138	93.01 66	68.79 117		92.44 82	63.96 370	81.09 162	91.57 138	66.06 149	95.45 75	67.19 278	94.82 50	88.81 313

PVSNet_BlendedMVS		80.60 189	80.02 180	82.36 240	88.85 163	65.40 216	86.16 246	92.00 112	69.34 283	78.11 218	86.09 309	66.02 150	94.27 131	71.52 230	82.06 277	87.39 348

PVSNet_Blended		80.98 172	80.34 171	82.90 218	88.85 163	65.40 216	84.43 297	92.00 112	67.62 316	78.11 218	85.05 335	66.02 150	94.27 131	71.52 230	89.50 138	89.01 303

diffmvs		82.10 145	81.88 147	82.76 230	83.00 365	63.78 268	83.68 315	89.76 196	72.94 198	82.02 145	89.85 190	65.96 152	90.79 307	82.38 100	87.30 184	93.71 99
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 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 179	85.94 69	94.51 35	65.80 153	95.61 67	83.04 89	92.51 83	93.53 115

miper_enhance_ethall		77.87 263	76.86 264	80.92 277	81.65 390	61.38 320	82.68 336	88.98 237	65.52 345	75.47 279	82.30 391	65.76 154	92.00 255	72.95 214	76.39 350	89.39 291

PVSNet_Blended_VisFu		82.62 138	81.83 148	84.96 107	90.80 101	69.76 97	88.74 140	91.70 129	69.39 281	78.96 196	88.46 238	65.47 155	94.87 107	74.42 198	88.57 155	90.24 255

API-MVS		81.99 149	81.23 153	84.26 149	90.94 97	70.18 91	91.10 63	89.32 217	71.51 223	78.66 203	88.28 243	65.26 156	95.10 97	64.74 298	91.23 107	87.51 346

TranMVSNet+NR-MVSNet		80.84 175	80.31 172	82.42 238	87.85 212	62.33 305	87.74 181	91.33 143	80.55 9	77.99 222	89.86 189	65.23 157	92.62 225	67.05 280	75.24 375	92.30 175

IS-MVSNet		83.15 128	82.81 125	84.18 152	89.94 123	63.30 284	91.59 51	88.46 259	79.04 30	79.49 187	92.16 114	65.10 158	94.28 130	67.71 271	91.86 97	94.95 12

DU-MVS		81.12 171	80.52 167	82.90 218	87.80 215	63.46 280	87.02 207	91.87 120	79.01 31	78.38 211	89.07 216	65.02 159	93.05 210	70.05 248	76.46 348	92.20 180

Baseline_NR-MVSNet		78.15 254	78.33 225	77.61 351	85.79 291	56.21 393	86.78 219	85.76 320	73.60 177	77.93 223	87.57 263	65.02 159	88.99 341	67.14 279	75.33 372	87.63 342

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 92	73.53 180	85.69 73	94.45 37	65.00 161	95.56 68	82.75 94	91.87 95	92.50 165

VNet		82.21 144	82.41 133	81.62 254	90.82 100	60.93 325	84.47 292	89.78 194	76.36 95	84.07 104	91.88 122	64.71 162	90.26 316	70.68 240	88.89 148	93.66 101

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 104	79.31 24	84.39 96	92.18 112	64.64 163	95.53 71	80.70 116	94.65 52	94.56 50

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 266	79.31 24	84.39 96	92.18 112	64.64 163	95.53 71	80.70 116	90.91 113	93.21 128

Test By Simon												64.33 165

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 171	93.82 72	64.33 165	96.29 46	82.67 99	90.69 116	93.23 125
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		83.11 131	82.09 142	86.15 70	94.44 23	70.92 76	88.79 135	92.20 102	70.53 250	79.17 194	91.03 160	64.12 167	96.03 55	68.39 268	90.14 125	91.50 205

CLD-MVS		82.31 143	81.65 149	84.29 144	88.47 183	67.73 158	85.81 257	92.35 87	75.78 109	78.33 213	86.58 296	64.01 168	94.35 128	76.05 179	87.48 181	90.79 229
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 75		93.06 64	70.63 82	91.88 43	92.27 92	73.53 180	85.69 73	94.45 37	63.87 169		82.75 94	91.87 95	92.50 165

MVS		78.19 253	76.99 262	81.78 251	85.66 294	66.99 182	84.66 286	90.47 170	55.08 442	72.02 345	85.27 327	63.83 170	94.11 141	66.10 286	89.80 133	84.24 411

WR-MVS		79.49 215	79.22 206	80.27 291	88.79 172	58.35 354	85.06 277	88.61 257	78.56 35	77.65 229	88.34 241	63.81 171	90.66 312	64.98 296	77.22 336	91.80 194

VPA-MVSNet		80.60 189	80.55 166	80.76 280	88.07 202	60.80 328	86.86 215	91.58 136	75.67 114	80.24 178	89.45 210	63.34 172	90.25 317	70.51 242	79.22 314	91.23 213

新几何1				83.42 191	93.13 60	70.71 80		85.48 323	57.43 432	81.80 149	91.98 119	63.28 173	92.27 245	64.60 299	92.99 76	87.27 354

HY-MVS	69.67 12	77.95 260	77.15 258	80.36 288	87.57 239	60.21 339	83.37 325	87.78 276	66.11 336	75.37 286	87.06 281	63.27 174	90.48 314	61.38 332	82.43 273	90.40 248

IMVS_0403		80.80 180	80.12 179	82.87 220	87.13 253	63.59 273	85.19 270	89.33 213	70.51 251	78.49 208	89.03 218	63.26 175	93.27 191	72.56 221	85.56 219	91.74 195

XXY-MVS		75.41 311	75.56 288	74.96 380	83.59 348	57.82 365	80.59 366	83.87 345	66.54 333	74.93 304	88.31 242	63.24 176	80.09 426	62.16 323	76.85 342	86.97 365

ab-mvs		79.51 214	78.97 211	81.14 270	88.46 184	60.91 326	83.84 311	89.24 225	70.36 256	79.03 195	88.87 226	63.23 177	90.21 318	65.12 294	82.57 272	92.28 176

xiu_mvs_v2_base		81.69 156	81.05 156	83.60 184	89.15 155	68.03 147	84.46 294	90.02 187	70.67 245	81.30 160	86.53 299	63.17 178	94.19 138	75.60 186	88.54 156	88.57 323

pcd_1.5k_mvsjas		5.26 458	7.02 461	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	63.15 179	0.00 492	0.00 491	0.00 490	0.00 488

PS-MVSNAJss		82.07 147	81.31 151	84.34 139	86.51 276	67.27 176	89.27 112	91.51 138	71.75 216	79.37 191	90.22 185	63.15 179	94.27 131	77.69 156	82.36 274	91.49 206

PS-MVSNAJ		81.69 156	81.02 157	83.70 182	89.51 134	68.21 142	84.28 302	90.09 186	70.79 242	81.26 161	85.62 319	63.15 179	94.29 129	75.62 185	88.87 149	88.59 322

WTY-MVS		75.65 306	75.68 285	75.57 371	86.40 278	56.82 380	77.92 408	82.40 370	65.10 350	76.18 267	87.72 258	63.13 182	80.90 423	60.31 340	81.96 278	89.00 305

TransMVSNet (Re)		75.39 313	74.56 306	77.86 344	85.50 301	57.10 377	86.78 219	86.09 316	72.17 210	71.53 350	87.34 269	63.01 183	89.31 334	56.84 377	61.83 444	87.17 357

viewdifsd2359ckpt11		80.37 198	79.73 189	82.30 241	83.70 345	62.39 302	84.20 304	86.67 302	73.22 192	80.90 166	90.62 171	63.00 184	91.56 274	76.81 170	78.44 320	92.95 148

viewmsd2359difaftdt		80.37 198	79.73 189	82.30 241	83.70 345	62.39 302	84.20 304	86.67 302	73.22 192	80.90 166	90.62 171	63.00 184	91.56 274	76.81 170	78.44 320	92.95 148

v8		79.97 208	79.02 210	82.80 224	84.09 334	64.50 251	87.96 171	90.29 180	74.13 164	75.24 294	86.81 283	62.88 186	93.89 155	74.39 199	75.40 370	90.00 269

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 133	73.89 169	82.67 137	94.09 57	62.60 187	95.54 70	80.93 111	92.93 77	93.57 111

PAPM		77.68 269	76.40 278	81.51 257	87.29 249	61.85 314	83.78 312	89.59 204	64.74 355	71.23 353	88.70 229	62.59 188	93.66 165	52.66 400	87.03 190	89.01 303

1112_ss		77.40 275	76.43 276	80.32 290	89.11 160	60.41 336	83.65 316	87.72 278	62.13 391	73.05 329	86.72 286	62.58 189	89.97 322	62.11 325	80.80 292	90.59 240

LCM-MVSNet-Re		77.05 280	76.94 263	77.36 355	87.20 250	51.60 435	80.06 375	80.46 394	75.20 130	67.69 391	86.72 286	62.48 190	88.98 342	63.44 306	89.25 141	91.51 204

v148		78.72 239	77.80 240	81.47 258	82.73 374	61.96 313	86.30 240	88.08 264	73.26 189	76.18 267	85.47 323	62.46 191	92.36 241	71.92 229	73.82 389	90.09 263

baseline1		76.98 282	76.75 270	77.66 349	88.13 198	55.66 400	85.12 274	81.89 375	73.04 196	76.79 249	88.90 224	62.43 192	87.78 362	63.30 308	71.18 409	89.55 287

MAR-MVS		81.84 152	80.70 162	85.27 94	91.32 89	71.53 58	89.82 88	90.92 155	69.77 274	78.50 207	86.21 305	62.36 193	94.52 123	65.36 292	92.05 93	89.77 281
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		82.61 139	82.11 140	84.11 153	88.82 166	71.58 57	85.15 273	86.16 314	74.69 147	80.47 176	91.04 158	62.29 194	90.55 313	80.33 120	90.08 127	90.20 256

TAMVS		78.89 236	77.51 252	83.03 211	87.80 215	67.79 157	84.72 284	85.05 329	67.63 315	76.75 251	87.70 259	62.25 195	90.82 306	58.53 359	87.13 188	90.49 244

CP-MVSNet		78.22 250	78.34 224	77.84 345	87.83 214	54.54 412	87.94 173	91.17 148	77.65 46	73.48 324	88.49 237	62.24 196	88.43 353	62.19 322	74.07 384	90.55 241

OMC-MVS		82.69 137	81.97 146	84.85 114	88.75 174	67.42 168	87.98 170	90.87 158	74.92 140	79.72 184	91.65 132	62.19 197	93.96 144	75.26 191	86.42 200	93.16 132

cl____		77.72 266	76.76 268	80.58 284	82.49 380	60.48 334	83.09 331	87.87 272	69.22 288	74.38 314	85.22 330	62.10 198	91.53 279	71.09 235	75.41 369	89.73 283

DIV-MVS_self_test		77.72 266	76.76 268	80.58 284	82.48 381	60.48 334	83.09 331	87.86 273	69.22 288	74.38 314	85.24 328	62.10 198	91.53 279	71.09 235	75.40 370	89.74 282

testdata				79.97 299	90.90 98	64.21 257		84.71 331	59.27 414	85.40 75	92.91 94	62.02 200	89.08 340	68.95 261	91.37 105	86.63 374

icg_test_0407_2		78.92 235	78.93 212	78.90 322	87.13 253	63.59 273	76.58 416	89.33 213	70.51 251	77.82 224	89.03 218	61.84 201	81.38 420	72.56 221	85.56 219	91.74 195

IMVS_0407		80.61 187	79.90 184	82.75 231	87.13 253	63.59 273	85.33 269	89.33 213	70.51 251	77.82 224	89.03 218	61.84 201	92.91 215	72.56 221	85.56 219	91.74 195

fmvsm_s_conf0.5_n_2		84.04 98	84.11 98	83.81 180	86.17 283	65.00 232	86.96 209	87.28 287	74.35 155	88.25 39	94.23 50	61.82 203	92.60 227	89.85 12	88.09 167	93.84 91

eth_miper_zixun_eth		77.92 261	76.69 271	81.61 256	83.00 365	61.98 312	83.15 329	89.20 227	69.52 280	74.86 305	84.35 348	61.76 204	92.56 230	71.50 232	72.89 397	90.28 254

MVSFormer		82.85 135	82.05 143	85.24 95	87.35 240	70.21 86	90.50 72	90.38 173	68.55 305	81.32 157	89.47 206	61.68 205	93.46 183	78.98 140	90.26 123	92.05 189

lupinMVS		81.39 166	80.27 174	84.76 119	87.35 240	70.21 86	85.55 263	86.41 308	62.85 381	81.32 157	88.61 233	61.68 205	92.24 247	78.41 147	90.26 123	91.83 192

cdsmvs_eth3d_5k		19.96 452	26.61 454	0.00 473	0.00 496	0.00 498	0.00 485	89.26 222	0.00 491	0.00 492	88.61 233	61.62 207	0.00 492	0.00 491	0.00 490	0.00 488

h-mvs33		83.15 128	82.19 139	86.02 76	90.56 105	70.85 79	88.15 166	89.16 228	76.02 104	84.67 87	91.39 145	61.54 208	95.50 73	82.71 96	75.48 365	91.72 199

hse-mvs2		81.72 154	80.94 159	84.07 160	88.72 175	67.68 160	85.87 253	87.26 290	76.02 104	84.67 87	88.22 246	61.54 208	93.48 181	82.71 96	73.44 393	91.06 218

CDS-MVSNet		79.07 230	77.70 245	83.17 203	87.60 231	68.23 141	84.40 300	86.20 313	67.49 318	76.36 262	86.54 298	61.54 208	90.79 307	61.86 327	87.33 183	90.49 244
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v10		79.74 210	78.67 215	82.97 216	84.06 335	64.95 234	87.88 177	90.62 165	73.11 194	75.11 298	86.56 297	61.46 211	94.05 143	73.68 204	75.55 363	89.90 275

v1144		80.03 206	79.03 209	83.01 212	83.78 342	64.51 249	87.11 204	90.57 168	71.96 214	78.08 220	86.20 306	61.41 212	93.94 147	74.93 193	77.23 335	90.60 239

cl22		78.07 256	77.01 260	81.23 267	82.37 383	61.83 315	83.55 320	87.98 268	68.96 299	75.06 300	83.87 358	61.40 213	91.88 261	73.53 206	76.39 350	89.98 272

BH-w/o		78.21 251	77.33 256	80.84 278	88.81 167	65.13 227	84.87 281	87.85 274	69.75 275	74.52 311	84.74 341	61.34 214	93.11 206	58.24 363	85.84 215	84.27 410

Test_1112_low_res		76.40 296	75.44 290	79.27 315	89.28 149	58.09 357	81.69 348	87.07 294	59.53 412	72.48 338	86.67 291	61.30 215	89.33 333	60.81 337	80.15 301	90.41 247

Vis-MVSNet (Re-imp)		78.36 248	78.45 220	78.07 341	88.64 178	51.78 434	86.70 222	79.63 406	74.14 163	75.11 298	90.83 166	61.29 216	89.75 326	58.10 364	91.60 99	92.69 157

PEN-MVS		77.73 265	77.69 246	77.84 345	87.07 261	53.91 417	87.91 175	91.18 147	77.56 51	73.14 328	88.82 227	61.23 217	89.17 338	59.95 342	72.37 399	90.43 246

pm-mvs1		77.25 278	76.68 272	78.93 321	84.22 331	58.62 352	86.41 233	88.36 260	71.37 225	73.31 325	88.01 253	61.22 218	89.15 339	64.24 302	73.01 396	89.03 302

BH-untuned		79.47 216	78.60 217	82.05 246	89.19 154	65.91 203	86.07 248	88.52 258	72.18 209	75.42 283	87.69 260	61.15 219	93.54 173	60.38 339	86.83 194	86.70 371

v2v482		80.23 202	79.29 203	83.05 210	83.62 347	64.14 258	87.04 205	89.97 189	73.61 176	78.18 217	87.22 274	61.10 220	93.82 156	76.11 177	76.78 344	91.18 214

jason		81.39 166	80.29 173	84.70 121	86.63 273	69.90 94	85.95 250	86.77 301	63.24 374	81.07 163	89.47 206	61.08 221	92.15 249	78.33 148	90.07 128	92.05 189
jason: jason.
Vis-MVSNet		83.46 119	82.80 126	85.43 90	90.25 112	68.74 121	90.30 80	90.13 185	76.33 96	80.87 168	92.89 95	61.00 222	94.20 136	72.45 225	90.97 111	93.35 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	73.13 9	79.15 227	77.94 233	82.79 227	89.59 130	62.99 294	88.16 165	91.51 138	65.77 341	77.14 245	91.09 156	60.91 223	93.21 196	50.26 416	87.05 189	92.17 185
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PS-CasMVS		78.01 259	78.09 230	77.77 347	87.71 224	54.39 414	88.02 169	91.22 145	77.50 54	73.26 326	88.64 232	60.73 224	88.41 354	61.88 326	73.88 388	90.53 242

OPM-MVS		83.50 118	82.95 123	85.14 98	88.79 172	70.95 74	89.13 121	91.52 137	77.55 52	80.96 165	91.75 128	60.71 225	94.50 124	79.67 131	86.51 199	89.97 273
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-OURS-SEG-HR		80.81 177	79.76 188	83.96 175	85.60 297	68.78 118	83.54 322	90.50 169	70.66 248	76.71 252	91.66 131	60.69 226	91.26 290	76.94 165	81.58 282	91.83 192

fmvsm_s_conf0.1_n_2		83.80 105	83.79 105	83.83 178	85.62 296	64.94 237	87.03 206	86.62 306	74.32 156	87.97 47	94.33 43	60.67 227	92.60 227	89.72 14	87.79 174	93.96 82

v144192		79.47 216	78.37 223	82.78 228	83.35 352	63.96 261	86.96 209	90.36 176	69.99 267	77.50 231	85.67 317	60.66 228	93.77 160	74.27 200	76.58 345	90.62 237

V42		79.38 222	78.24 227	82.83 221	81.10 402	65.50 215	85.55 263	89.82 193	71.57 222	78.21 215	86.12 308	60.66 228	93.18 202	75.64 184	75.46 367	89.81 280

SDMVSNet		80.38 196	80.18 175	80.99 274	89.03 161	64.94 237	80.45 369	89.40 210	75.19 131	76.61 256	89.98 187	60.61 230	87.69 363	76.83 169	83.55 255	90.33 251

CPTT-MVS		83.73 109	83.33 117	84.92 111	93.28 53	70.86 78	92.09 41	90.38 173	68.75 302	79.57 186	92.83 97	60.60 231	93.04 212	80.92 112	91.56 102	90.86 227

DTE-MVSNet		76.99 281	76.80 266	77.54 354	86.24 280	53.06 426	87.52 185	90.66 164	77.08 69	72.50 337	88.67 231	60.48 232	89.52 330	57.33 371	70.74 411	90.05 268

HQP_MVS		83.64 113	83.14 118	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 198	91.00 162	60.42 233	95.38 82	78.71 143	86.32 201	91.33 210

plane_prior6					89.84 125	68.70 125						60.42 233

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 253	93.37 83	60.40 235	96.75 30	77.20 161	93.73 70	95.29 6

HQP2-MVS												60.17 236

HQP-MVS		82.61 139	82.02 144	84.37 136	89.33 144	66.98 183	89.17 116	92.19 104	76.41 89	77.23 239	90.23 184	60.17 236	95.11 94	77.47 158	85.99 210	91.03 220

SSM_0407		81.58 160	80.48 168	84.87 113	88.81 167	67.96 149	87.37 195	89.25 223	71.06 235	79.48 188	90.39 178	59.57 238	94.48 126	72.45 225	85.93 212	92.18 182

SSM_0404		81.91 150	80.84 161	85.13 101	89.24 151	68.26 137	87.84 179	89.25 223	71.06 235	80.62 172	90.39 178	59.57 238	94.65 119	72.45 225	87.19 186	92.47 168

SD_0403		74.65 319	74.77 303	74.29 389	86.20 282	47.42 453	83.71 314	85.12 326	69.30 284	68.50 384	87.95 255	59.40 240	86.05 379	49.38 420	83.35 260	89.40 290

VPNet		78.69 240	78.66 216	78.76 324	88.31 190	55.72 399	84.45 295	86.63 305	76.79 76	78.26 214	90.55 175	59.30 241	89.70 328	66.63 282	77.05 338	90.88 226

v1192		79.59 213	78.43 222	83.07 209	83.55 349	64.52 248	86.93 212	90.58 166	70.83 241	77.78 227	85.90 310	59.15 242	93.94 147	73.96 203	77.19 337	90.76 231

test222					91.50 86	68.26 137	84.16 306	83.20 358	54.63 443	79.74 183	91.63 134	58.97 243			91.42 103	86.77 369

mamba_0408		79.37 223	77.52 250	84.93 110	88.81 167	67.96 149	65.03 469	88.66 253	70.96 239	79.48 188	89.80 193	58.69 244	94.65 119	70.35 244	85.93 212	92.18 182

SSM_04072		77.67 270	77.52 250	78.12 339	88.81 167	67.96 149	65.03 469	88.66 253	70.96 239	79.48 188	89.80 193	58.69 244	74.23 461	70.35 244	85.93 212	92.18 182

CHOSEN 1792x2688		77.63 271	75.69 284	83.44 190	89.98 122	68.58 129	78.70 395	87.50 282	56.38 437	75.80 274	86.84 282	58.67 246	91.40 286	61.58 330	85.75 217	90.34 250

3Dnovator	76.31 5	83.38 122	82.31 136	86.59 61	87.94 208	72.94 28	90.64 68	92.14 109	77.21 63	75.47 279	92.83 97	58.56 247	94.72 115	73.24 212	92.71 81	92.13 187

v1921920		79.22 225	78.03 231	82.80 224	83.30 354	63.94 263	86.80 217	90.33 177	69.91 270	77.48 232	85.53 321	58.44 248	93.75 162	73.60 205	76.85 342	90.71 235

FA-MVS(test-final)		80.96 173	79.91 183	84.10 154	88.30 191	65.01 231	84.55 291	90.01 188	73.25 190	79.61 185	87.57 263	58.35 249	94.72 115	71.29 234	86.25 204	92.56 161

114514_t		80.68 185	79.51 196	84.20 151	94.09 42	67.27 176	89.64 96	91.11 151	58.75 421	74.08 316	90.72 167	58.10 250	95.04 99	69.70 253	89.42 140	90.30 253

v7n		78.97 233	77.58 249	83.14 204	83.45 351	65.51 214	88.32 159	91.21 146	73.69 174	72.41 339	86.32 304	57.93 251	93.81 157	69.18 258	75.65 361	90.11 261

CL-MVSNet_self_test		72.37 351	71.46 344	75.09 379	79.49 423	53.53 419	80.76 362	85.01 330	69.12 292	70.51 357	82.05 395	57.92 252	84.13 399	52.27 402	66.00 431	87.60 343

baseline2		75.70 305	73.83 318	81.30 264	83.26 355	61.79 316	82.57 338	80.65 389	66.81 323	66.88 402	83.42 372	57.86 253	92.19 248	63.47 305	79.57 306	89.91 274

QAPM		80.88 174	79.50 197	85.03 104	88.01 206	68.97 114	91.59 51	92.00 112	66.63 332	75.15 297	92.16 114	57.70 254	95.45 75	63.52 304	88.76 152	90.66 236

HyFIR lowres test		77.53 272	75.40 292	83.94 176	89.59 130	66.62 188	80.36 370	88.64 256	56.29 438	76.45 259	85.17 331	57.64 255	93.28 189	61.34 333	83.10 265	91.91 191

CNLPA		78.08 255	76.79 267	81.97 249	90.40 109	71.07 70	87.59 184	84.55 334	66.03 339	72.38 340	89.64 200	57.56 256	86.04 380	59.61 346	83.35 260	88.79 314

test_yl		81.17 168	80.47 169	83.24 199	89.13 156	63.62 269	86.21 244	89.95 190	72.43 206	81.78 150	89.61 201	57.50 257	93.58 166	70.75 238	86.90 191	92.52 163

DCV-MVSNet		81.17 168	80.47 169	83.24 199	89.13 156	63.62 269	86.21 244	89.95 190	72.43 206	81.78 150	89.61 201	57.50 257	93.58 166	70.75 238	86.90 191	92.52 163

sss		73.60 332	73.64 320	73.51 397	82.80 372	55.01 408	76.12 418	81.69 378	62.47 387	74.68 308	85.85 313	57.32 259	78.11 434	60.86 336	80.93 288	87.39 348

KinetiMVS		83.31 126	82.61 130	85.39 91	87.08 259	67.56 165	88.06 168	91.65 131	77.80 44	82.21 142	91.79 125	57.27 260	94.07 142	77.77 154	89.89 132	94.56 50

Effi-MVS+-dtu		80.03 206	78.57 218	84.42 133	85.13 312	68.74 121	88.77 136	88.10 263	74.99 136	74.97 303	83.49 371	57.27 260	93.36 187	73.53 206	80.88 290	91.18 214

AdaColmap		80.58 192	79.42 198	84.06 163	93.09 63	68.91 115	89.36 110	88.97 239	69.27 285	75.70 275	89.69 197	57.20 262	95.77 64	63.06 311	88.41 160	87.50 347

v1240		78.99 232	77.78 241	82.64 233	83.21 357	63.54 277	86.62 226	90.30 179	69.74 277	77.33 235	85.68 316	57.04 263	93.76 161	73.13 213	76.92 339	90.62 237

miper_lstm_enhance		74.11 325	73.11 327	77.13 359	80.11 412	59.62 344	72.23 440	86.92 299	66.76 325	70.40 359	82.92 381	56.93 264	82.92 409	69.06 260	72.63 398	88.87 310

BP-MVS1		84.32 91	83.71 107	86.17 68	87.84 213	67.85 154	89.38 109	89.64 202	77.73 45	83.98 106	92.12 117	56.89 265	95.43 77	84.03 80	91.75 98	95.24 7

guyue		81.13 170	80.64 164	82.60 235	86.52 275	63.92 264	86.69 223	87.73 277	73.97 165	80.83 170	89.69 197	56.70 266	91.33 289	78.26 152	85.40 223	92.54 162

BH-RMVSNet		79.61 211	78.44 221	83.14 204	89.38 143	65.93 202	84.95 280	87.15 293	73.56 178	78.19 216	89.79 195	56.67 267	93.36 187	59.53 347	86.74 195	90.13 259

RRT-MVS		82.60 141	82.10 141	84.10 154	87.98 207	62.94 295	87.45 190	91.27 144	77.42 56	79.85 182	90.28 181	56.62 268	94.70 117	79.87 128	88.15 166	94.67 37

test_djsdf		80.30 201	79.32 202	83.27 197	83.98 337	65.37 219	90.50 72	90.38 173	68.55 305	76.19 266	88.70 229	56.44 269	93.46 183	78.98 140	80.14 302	90.97 223

FE-MVSNET3		76.43 294	75.32 296	79.76 304	83.00 365	60.72 329	81.74 346	88.76 250	68.99 298	72.98 330	84.19 354	56.41 270	90.27 315	62.39 318	79.40 310	88.31 328

EPNet_dtu		75.46 309	74.86 301	77.23 358	82.57 378	54.60 411	86.89 213	83.09 359	71.64 217	66.25 413	85.86 312	55.99 271	88.04 358	54.92 388	86.55 198	89.05 301
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VortexMVS		78.57 244	77.89 236	80.59 283	85.89 289	62.76 297	85.61 258	89.62 203	72.06 212	74.99 302	85.38 325	55.94 272	90.77 310	74.99 192	76.58 345	88.23 330

GDP-MVS		83.52 117	82.64 129	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 201	83.71 111	91.86 124	55.69 273	95.35 86	80.03 122	89.74 134	94.69 33

CostFormer		75.24 314	73.90 316	79.27 315	82.65 377	58.27 356	80.80 359	82.73 368	61.57 395	75.33 291	83.13 377	55.52 274	91.07 302	64.98 296	78.34 325	88.45 325

tpmrst		72.39 349	72.13 338	73.18 402	80.54 407	49.91 446	79.91 379	79.08 412	63.11 376	71.69 348	79.95 417	55.32 275	82.77 411	65.66 291	73.89 387	86.87 366

1314		76.53 289	75.30 297	80.21 294	83.93 338	62.32 306	84.66 286	88.81 244	60.23 405	70.16 364	84.07 357	55.30 276	90.73 311	67.37 275	83.21 263	87.59 345

tfpnnormal		74.39 320	73.16 326	78.08 340	86.10 287	58.05 358	84.65 288	87.53 281	70.32 259	71.22 354	85.63 318	54.97 277	89.86 323	43.03 450	75.02 377	86.32 376

sd_testset		77.70 268	77.40 253	78.60 327	89.03 161	60.02 340	79.00 390	85.83 319	75.19 131	76.61 256	89.98 187	54.81 278	85.46 388	62.63 317	83.55 255	90.33 251

GBi-Net		78.40 246	77.40 253	81.40 261	87.60 231	63.01 290	88.39 154	89.28 219	71.63 218	75.34 287	87.28 270	54.80 279	91.11 296	62.72 313	79.57 306	90.09 263

test1		78.40 246	77.40 253	81.40 261	87.60 231	63.01 290	88.39 154	89.28 219	71.63 218	75.34 287	87.28 270	54.80 279	91.11 296	62.72 313	79.57 306	90.09 263

FMVSNet2		78.20 252	77.21 257	81.20 268	87.60 231	62.89 296	87.47 187	89.02 235	71.63 218	75.29 293	87.28 270	54.80 279	91.10 299	62.38 319	79.38 311	89.61 285

Fast-Effi-MVS+-dtu		78.02 258	76.49 274	82.62 234	83.16 361	66.96 185	86.94 211	87.45 284	72.45 203	71.49 351	84.17 355	54.79 282	91.58 271	67.61 272	80.31 299	89.30 294

MVSTER		79.01 231	77.88 237	82.38 239	83.07 362	64.80 243	84.08 309	88.95 240	69.01 297	78.69 201	87.17 277	54.70 283	92.43 237	74.69 194	80.57 296	89.89 276

OpenMVS	72.83 10	79.77 209	78.33 225	84.09 158	85.17 308	69.91 93	90.57 69	90.97 154	66.70 326	72.17 343	91.91 120	54.70 283	93.96 144	61.81 328	90.95 112	88.41 327

XVG-OURS		80.41 194	79.23 205	83.97 174	85.64 295	69.02 112	83.03 335	90.39 172	71.09 233	77.63 230	91.49 142	54.62 285	91.35 287	75.71 183	83.47 258	91.54 203

LPG-MVS_test		82.08 146	81.27 152	84.50 128	89.23 152	68.76 119	90.22 81	91.94 116	75.37 122	76.64 254	91.51 140	54.29 286	94.91 102	78.44 145	83.78 246	89.83 278

LGP-MVS_train				84.50 128	89.23 152	68.76 119		91.94 116	75.37 122	76.64 254	91.51 140	54.29 286	94.91 102	78.44 145	83.78 246	89.83 278

TR-MVS		77.44 273	76.18 280	81.20 268	88.24 192	63.24 285	84.61 289	86.40 309	67.55 317	77.81 226	86.48 300	54.10 288	93.15 203	57.75 367	82.72 270	87.20 356

FMVSNet3		77.88 262	76.85 265	80.97 276	86.84 265	62.36 304	86.52 230	88.77 246	71.13 231	75.34 287	86.66 292	54.07 289	91.10 299	62.72 313	79.57 306	89.45 289

AstraMVS		80.81 177	80.14 178	82.80 224	86.05 288	63.96 261	86.46 232	85.90 318	73.71 173	80.85 169	90.56 174	54.06 290	91.57 273	79.72 130	83.97 244	92.86 151

DP-MVS		76.78 286	74.57 305	83.42 191	93.29 52	69.46 104	88.55 149	83.70 346	63.98 369	70.20 361	88.89 225	54.01 291	94.80 111	46.66 435	81.88 280	86.01 384

ACMP	74.13 6	81.51 165	80.57 165	84.36 137	89.42 139	68.69 126	89.97 85	91.50 141	74.46 153	75.04 301	90.41 177	53.82 292	94.54 121	77.56 157	82.91 266	89.86 277
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	70.83 11	78.05 257	76.37 279	83.08 208	91.88 83	67.80 156	88.19 163	89.46 208	64.33 362	69.87 370	88.38 240	53.66 293	93.58 166	58.86 355	82.73 269	87.86 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dmvs_testset		62.63 419	64.11 410	58.19 450	78.55 429	24.76 488	75.28 425	65.94 465	67.91 314	60.34 444	76.01 447	53.56 294	73.94 463	31.79 468	67.65 424	75.88 457

CANet_DTU		80.61 187	79.87 185	82.83 221	85.60 297	63.17 289	87.36 196	88.65 255	76.37 94	75.88 272	88.44 239	53.51 295	93.07 208	73.30 210	89.74 134	92.25 177

WB-MVSnew		71.96 358	71.65 342	72.89 404	84.67 325	51.88 432	82.29 340	77.57 421	62.31 388	73.67 322	83.00 379	53.49 296	81.10 422	45.75 442	82.13 276	85.70 390

ACMM	73.20 8	80.78 184	79.84 186	83.58 186	89.31 147	68.37 134	89.99 84	91.60 135	70.28 260	77.25 237	89.66 199	53.37 297	93.53 174	74.24 201	82.85 267	88.85 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		76.12 299	74.46 309	81.13 271	85.37 304	69.79 95	84.42 299	87.95 270	65.03 352	67.46 394	85.33 326	53.28 298	91.73 267	58.01 365	83.27 262	81.85 438
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		79.21 226	77.60 248	84.05 166	88.71 176	67.61 162	85.84 255	87.26 290	69.08 293	77.23 239	88.14 251	53.20 299	93.47 182	75.50 188	73.45 392	91.06 218

SSC-MVS3.2		73.35 338	73.39 322	73.23 398	85.30 306	49.01 449	74.58 433	81.57 379	75.21 129	73.68 321	85.58 320	52.53 300	82.05 415	54.33 392	77.69 332	88.63 321

anonymousdsp		78.60 242	77.15 258	82.98 215	80.51 408	67.08 181	87.24 201	89.53 206	65.66 343	75.16 296	87.19 276	52.52 301	92.25 246	77.17 162	79.34 312	89.61 285

CR-MVSNet		73.37 335	71.27 349	79.67 308	81.32 400	65.19 225	75.92 420	80.30 398	59.92 408	72.73 334	81.19 400	52.50 302	86.69 371	59.84 343	77.71 330	87.11 361

Patchmtry		70.74 367	69.16 370	75.49 374	80.72 404	54.07 416	74.94 431	80.30 398	58.34 422	70.01 365	81.19 400	52.50 302	86.54 373	53.37 397	71.09 410	85.87 389

pmmvs4		74.03 328	71.91 339	80.39 287	81.96 386	68.32 135	81.45 352	82.14 372	59.32 413	69.87 370	85.13 332	52.40 304	88.13 357	60.21 341	74.74 380	84.73 407

RPMNet		73.51 333	70.49 358	82.58 236	81.32 400	65.19 225	75.92 420	92.27 92	57.60 430	72.73 334	76.45 443	52.30 305	95.43 77	48.14 430	77.71 330	87.11 361

LFMVS		81.82 153	81.23 153	83.57 187	91.89 82	63.43 282	89.84 87	81.85 377	77.04 70	83.21 122	93.10 88	52.26 306	93.43 185	71.98 228	89.95 130	93.85 89

VDD-MVS		83.01 133	82.36 135	84.96 107	91.02 95	66.40 191	88.91 128	88.11 262	77.57 49	84.39 96	93.29 85	52.19 307	93.91 152	77.05 164	88.70 154	94.57 48

tfpn200view9		76.42 295	75.37 294	79.55 312	89.13 156	57.65 369	85.17 271	83.60 347	73.41 184	76.45 259	86.39 302	52.12 308	91.95 257	48.33 426	83.75 249	89.07 296

thres400		76.50 290	75.37 294	79.86 301	89.13 156	57.65 369	85.17 271	83.60 347	73.41 184	76.45 259	86.39 302	52.12 308	91.95 257	48.33 426	83.75 249	90.00 269

Syy-MVS		68.05 394	67.85 383	68.67 432	84.68 322	40.97 475	78.62 396	73.08 446	66.65 330	66.74 405	79.46 422	52.11 310	82.30 413	32.89 467	76.38 353	82.75 430

thres200		75.55 307	74.47 308	78.82 323	87.78 218	57.85 364	83.07 333	83.51 350	72.44 205	75.84 273	84.42 344	52.08 311	91.75 265	47.41 433	83.64 254	86.86 367

PMMVS		69.34 383	68.67 372	71.35 417	75.67 444	62.03 311	75.17 426	73.46 444	50.00 455	68.68 380	79.05 425	52.07 312	78.13 433	61.16 334	82.77 268	73.90 459

tpm cat1		70.57 369	68.31 375	77.35 356	82.41 382	57.95 362	78.08 404	80.22 400	52.04 449	68.54 383	77.66 438	52.00 313	87.84 361	51.77 403	72.07 404	86.25 377

IterMVS-SCA-FT		75.43 310	73.87 317	80.11 297	82.69 375	64.85 242	81.57 350	83.47 351	69.16 291	70.49 358	84.15 356	51.95 314	88.15 356	69.23 257	72.14 403	87.34 351

SCA		74.22 323	72.33 336	79.91 300	84.05 336	62.17 308	79.96 378	79.29 410	66.30 335	72.38 340	80.13 415	51.95 314	88.60 350	59.25 350	77.67 333	88.96 307

thres100view900		76.50 290	75.55 289	79.33 314	89.52 133	56.99 378	85.83 256	83.23 355	73.94 167	76.32 263	87.12 278	51.89 316	91.95 257	48.33 426	83.75 249	89.07 296

thres600view7		76.50 290	75.44 290	79.68 307	89.40 141	57.16 375	85.53 265	83.23 355	73.79 171	76.26 264	87.09 279	51.89 316	91.89 260	48.05 431	83.72 252	90.00 269

tpm2		73.26 340	71.46 344	78.63 325	83.34 353	56.71 383	80.65 365	80.40 397	56.63 436	73.55 323	82.02 396	51.80 318	91.24 291	56.35 382	78.42 323	87.95 335

MonoMVSNet		76.49 293	75.80 282	78.58 328	81.55 393	58.45 353	86.36 238	86.22 312	74.87 144	74.73 307	83.73 364	51.79 319	88.73 347	70.78 237	72.15 402	88.55 324

LS3D		76.95 283	74.82 302	83.37 194	90.45 107	67.36 172	89.15 120	86.94 297	61.87 394	69.52 373	90.61 173	51.71 320	94.53 122	46.38 438	86.71 196	88.21 332

IterMVS		74.29 321	72.94 329	78.35 335	81.53 394	63.49 279	81.58 349	82.49 369	68.06 313	69.99 367	83.69 366	51.66 321	85.54 386	65.85 289	71.64 406	86.01 384
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 351	71.71 341	74.35 388	82.19 384	52.00 429	79.22 386	77.29 426	64.56 357	72.95 332	83.68 367	51.35 322	83.26 408	58.33 362	75.80 359	87.81 339

usedtu_blend_shiyan5		73.29 339	70.96 353	80.25 292	77.80 434	62.16 309	84.44 296	87.38 285	64.41 359	68.09 387	76.28 446	51.32 323	91.23 292	63.21 309	65.76 432	87.35 350

sam_mvs1												51.32 323				88.96 307

mvsmamba		80.60 189	79.38 199	84.27 147	89.74 128	67.24 178	87.47 187	86.95 296	70.02 265	75.38 285	88.93 223	51.24 325	92.56 230	75.47 189	89.22 143	93.00 145

PatchmatchNet		73.12 342	71.33 347	78.49 333	83.18 359	60.85 327	79.63 380	78.57 415	64.13 363	71.73 347	79.81 420	51.20 326	85.97 381	57.40 370	76.36 355	88.66 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											74.00 454	51.12 327	88.60 350

xiu_mvs_v1_base_debu		80.80 180	79.72 191	84.03 168	87.35 240	70.19 88	85.56 260	88.77 246	69.06 294	81.83 146	88.16 247	50.91 328	92.85 218	78.29 149	87.56 178	89.06 298

xiu_mvs_v1_base		80.80 180	79.72 191	84.03 168	87.35 240	70.19 88	85.56 260	88.77 246	69.06 294	81.83 146	88.16 247	50.91 328	92.85 218	78.29 149	87.56 178	89.06 298

xiu_mvs_v1_base_debi		80.80 180	79.72 191	84.03 168	87.35 240	70.19 88	85.56 260	88.77 246	69.06 294	81.83 146	88.16 247	50.91 328	92.85 218	78.29 149	87.56 178	89.06 298

Patchmatch-test		64.82 414	63.24 415	69.57 425	79.42 424	49.82 447	63.49 473	69.05 457	51.98 451	59.95 447	80.13 415	50.91 328	70.98 466	40.66 456	73.57 390	87.90 337

Patchmatch-RL test		70.24 374	67.78 387	77.61 351	77.43 436	59.57 346	71.16 444	70.33 451	62.94 380	68.65 381	72.77 457	50.62 332	85.49 387	69.58 255	66.58 428	87.77 340

Anonymous20231211		78.97 233	77.69 246	82.81 223	90.54 106	64.29 256	90.11 83	91.51 138	65.01 353	76.16 270	88.13 252	50.56 333	93.03 213	69.68 254	77.56 334	91.11 216

VDDNet		81.52 163	80.67 163	84.05 166	90.44 108	64.13 259	89.73 93	85.91 317	71.11 232	83.18 125	93.48 78	50.54 334	93.49 178	73.40 209	88.25 164	94.54 52

pmmvs6		74.69 318	73.39 322	78.61 326	81.38 397	57.48 372	86.64 225	87.95 270	64.99 354	70.18 362	86.61 293	50.43 335	89.52 330	62.12 324	70.18 414	88.83 312

IMVS_0404		77.16 279	76.42 277	79.37 313	87.13 253	63.59 273	77.12 414	89.33 213	70.51 251	66.22 414	89.03 218	50.36 336	82.78 410	72.56 221	85.56 219	91.74 195

test_post											5.46 486	50.36 336	84.24 398

ET-MVSNet_ETH3D		78.63 241	76.63 273	84.64 122	86.73 269	69.47 102	85.01 278	84.61 333	69.54 279	66.51 411	86.59 294	50.16 338	91.75 265	76.26 175	84.24 241	92.69 157

LuminaMVS		80.68 185	79.62 194	83.83 178	85.07 314	68.01 148	86.99 208	88.83 243	70.36 256	81.38 156	87.99 254	50.11 339	92.51 234	79.02 137	86.89 193	90.97 223

sam_mvs												50.01 340

Anonymous20240529		80.19 204	78.89 213	84.10 154	90.60 104	64.75 244	88.95 127	90.90 156	65.97 340	80.59 173	91.17 154	49.97 341	93.73 164	69.16 259	82.70 271	93.81 93

thisisatest0530		79.40 220	77.76 243	84.31 141	87.69 228	65.10 230	87.36 196	84.26 340	70.04 264	77.42 233	88.26 245	49.94 342	94.79 112	70.20 246	84.70 231	93.03 142

PatchT		68.46 392	67.85 383	70.29 423	80.70 405	43.93 467	72.47 439	74.88 438	60.15 406	70.55 356	76.57 442	49.94 342	81.59 417	50.58 410	74.83 379	85.34 395

tttt0517		79.40 220	77.91 234	83.90 177	88.10 200	63.84 265	88.37 157	84.05 342	71.45 224	76.78 250	89.12 215	49.93 344	94.89 105	70.18 247	83.18 264	92.96 147

tpmvs		71.09 363	69.29 368	76.49 363	82.04 385	56.04 394	78.92 392	81.37 383	64.05 367	67.18 399	78.28 433	49.74 345	89.77 325	49.67 419	72.37 399	83.67 419

thisisatest0515		77.33 276	75.38 293	83.18 202	85.27 307	63.80 266	82.11 343	83.27 354	65.06 351	75.91 271	83.84 360	49.54 346	94.27 131	67.24 277	86.19 205	91.48 207

UniMVSNet_ETH3D		79.10 229	78.24 227	81.70 253	86.85 264	60.24 338	87.28 200	88.79 245	74.25 160	76.84 247	90.53 176	49.48 347	91.56 274	67.98 269	82.15 275	93.29 123

dmvs_re		71.14 362	70.58 356	72.80 405	81.96 386	59.68 343	75.60 424	79.34 409	68.55 305	69.27 377	80.72 408	49.42 348	76.54 442	52.56 401	77.79 329	82.19 435

CVMVSNet		72.99 345	72.58 333	74.25 390	84.28 329	50.85 442	86.41 233	83.45 352	44.56 462	73.23 327	87.54 266	49.38 349	85.70 383	65.90 288	78.44 320	86.19 379

MDTV_nov1_ep13_2view						37.79 478	75.16 427		55.10 441	66.53 408		49.34 350		53.98 393		87.94 336

UGNet		80.83 176	79.59 195	84.54 124	88.04 203	68.09 144	89.42 106	88.16 261	76.95 71	76.22 265	89.46 208	49.30 351	93.94 147	68.48 266	90.31 121	91.60 200
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
pmmvs5		71.55 359	70.20 363	75.61 370	77.83 433	56.39 388	81.74 346	80.89 385	57.76 428	67.46 394	84.49 342	49.26 352	85.32 390	57.08 373	75.29 373	85.11 401

mvsany_test1		62.30 420	61.26 424	65.41 442	69.52 466	54.86 409	66.86 461	49.78 482	46.65 459	68.50 384	83.21 375	49.15 353	66.28 474	56.93 376	60.77 447	75.11 458

LTVRE_ROB	69.57 13	76.25 298	74.54 307	81.41 260	88.60 179	64.38 255	79.24 385	89.12 232	70.76 244	69.79 372	87.86 256	49.09 354	93.20 199	56.21 383	80.16 300	86.65 373
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		77.44 273	76.12 281	81.40 261	86.81 266	63.01 290	88.39 154	89.28 219	70.49 255	74.39 313	87.28 270	49.06 355	91.11 296	60.91 335	78.52 318	90.09 263

test1111		79.43 218	79.18 207	80.15 296	89.99 121	53.31 423	87.33 198	77.05 428	75.04 135	80.23 179	92.77 102	48.97 356	92.33 244	68.87 262	92.40 86	94.81 22

ECVR-MVS		79.61 211	79.26 204	80.67 282	90.08 116	54.69 410	87.89 176	77.44 424	74.88 142	80.27 177	92.79 100	48.96 357	92.45 236	68.55 265	92.50 84	94.86 19

MDTV_nov1_ep13			69.97 364		83.18 359	53.48 420	77.10 415	80.18 402	60.45 402	69.33 376	80.44 409	48.89 358	86.90 370	51.60 405	78.51 319

test_post1							78.90 393				5.43 487	48.81 359	85.44 389	59.25 350

test-LLR		72.94 346	72.43 334	74.48 386	81.35 398	58.04 359	78.38 399	77.46 422	66.66 327	69.95 368	79.00 427	48.06 360	79.24 428	66.13 284	84.83 228	86.15 380

test0.0.03 1		68.00 395	67.69 388	68.90 429	77.55 435	47.43 452	75.70 423	72.95 448	66.66 327	66.56 407	82.29 392	48.06 360	75.87 451	44.97 446	74.51 382	83.41 421

our_test_3		69.14 384	67.00 397	75.57 371	79.80 418	58.80 350	77.96 406	77.81 419	59.55 411	62.90 436	78.25 434	47.43 362	83.97 400	51.71 404	67.58 425	83.93 416

MS-PatchMatch		73.83 329	72.67 331	77.30 357	83.87 340	66.02 198	81.82 344	84.66 332	61.37 398	68.61 382	82.82 384	47.29 363	88.21 355	59.27 349	84.32 240	77.68 453

cascas		76.72 287	74.64 304	82.99 213	85.78 292	65.88 204	82.33 339	89.21 226	60.85 400	72.74 333	81.02 403	47.28 364	93.75 162	67.48 274	85.02 225	89.34 293

WB-MVS		54.94 429	54.72 430	55.60 456	73.50 455	20.90 490	74.27 435	61.19 473	59.16 415	50.61 465	74.15 453	47.19 365	75.78 452	17.31 481	35.07 475	70.12 463

test20.03		67.45 397	66.95 398	68.94 428	75.48 446	44.84 465	77.50 410	77.67 420	66.66 327	63.01 434	83.80 361	47.02 366	78.40 432	42.53 453	68.86 421	83.58 420

test_0402		72.79 348	70.44 359	79.84 302	88.13 198	65.99 201	85.93 251	84.29 338	65.57 344	67.40 397	85.49 322	46.92 367	92.61 226	35.88 464	74.38 383	80.94 443

Elysia		81.53 161	80.16 176	85.62 84	85.51 299	68.25 139	88.84 133	92.19 104	71.31 226	80.50 174	89.83 191	46.89 368	94.82 108	76.85 166	89.57 136	93.80 95

StellarMVS		81.53 161	80.16 176	85.62 84	85.51 299	68.25 139	88.84 133	92.19 104	71.31 226	80.50 174	89.83 191	46.89 368	94.82 108	76.85 166	89.57 136	93.80 95

F-COLMAP		76.38 297	74.33 311	82.50 237	89.28 149	66.95 186	88.41 153	89.03 234	64.05 367	66.83 403	88.61 233	46.78 370	92.89 216	57.48 368	78.55 317	87.67 341

ppachtmachnet_test		70.04 377	67.34 395	78.14 338	79.80 418	61.13 321	79.19 387	80.59 390	59.16 415	65.27 419	79.29 424	46.75 371	87.29 367	49.33 421	66.72 426	86.00 386

FE-MVSNET2		72.88 347	71.28 348	77.67 348	78.30 431	57.78 367	84.43 297	88.92 242	69.56 278	64.61 424	81.67 398	46.73 372	88.54 352	59.33 348	67.99 423	86.69 372

WBMVS		73.43 334	72.81 330	75.28 377	87.91 209	50.99 441	78.59 398	81.31 384	65.51 347	74.47 312	84.83 338	46.39 373	86.68 372	58.41 360	77.86 328	88.17 333

tt0805		78.73 238	77.83 238	81.43 259	85.17 308	60.30 337	89.41 107	90.90 156	71.21 230	77.17 244	88.73 228	46.38 374	93.21 196	72.57 219	78.96 315	90.79 229

D2MVS		74.82 317	73.21 325	79.64 309	79.81 417	62.56 300	80.34 371	87.35 286	64.37 361	68.86 379	82.66 386	46.37 375	90.10 319	67.91 270	81.24 285	86.25 377

Anonymous20231206		68.60 388	67.80 386	71.02 420	80.23 411	50.75 443	78.30 403	80.47 393	56.79 435	66.11 415	82.63 387	46.35 376	78.95 430	43.62 448	75.70 360	83.36 422

SSC-MVS		53.88 432	53.59 432	54.75 458	72.87 461	19.59 491	73.84 437	60.53 475	57.58 431	49.18 469	73.45 456	46.34 377	75.47 455	16.20 484	32.28 477	69.20 464

CHOSEN 280x420		66.51 405	64.71 407	71.90 411	81.45 395	63.52 278	57.98 476	68.95 458	53.57 445	62.59 437	76.70 441	46.22 378	75.29 457	55.25 385	79.68 305	76.88 455

testing91		76.54 288	75.66 287	79.18 318	88.43 186	55.89 396	81.08 356	83.00 362	73.76 172	75.34 287	84.29 349	46.20 379	90.07 320	64.33 300	84.50 233	91.58 202

GA-MVS		76.87 284	75.17 299	81.97 249	82.75 373	62.58 298	81.44 353	86.35 311	72.16 211	74.74 306	82.89 382	46.20 379	92.02 254	68.85 263	81.09 287	91.30 212

MDA-MVSNet_test_wron		65.03 412	62.92 416	71.37 415	75.93 440	56.73 381	69.09 456	74.73 440	57.28 433	54.03 462	77.89 435	45.88 381	74.39 460	49.89 418	61.55 445	82.99 428

YYNet1		65.03 412	62.91 417	71.38 414	75.85 443	56.60 385	69.12 455	74.66 442	57.28 433	54.12 461	77.87 436	45.85 382	74.48 459	49.95 417	61.52 446	83.05 426

EPMVS		69.02 385	68.16 377	71.59 413	79.61 421	49.80 448	77.40 411	66.93 462	62.82 383	70.01 365	79.05 425	45.79 383	77.86 436	56.58 380	75.26 374	87.13 360

IB-MVS	68.01 15	75.85 304	73.36 324	83.31 195	84.76 320	66.03 197	83.38 324	85.06 328	70.21 263	69.40 374	81.05 402	45.76 384	94.66 118	65.10 295	75.49 364	89.25 295
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		79.29 224	77.96 232	83.27 197	84.68 322	66.57 190	89.25 113	90.16 184	69.20 290	75.46 281	89.49 205	45.75 385	93.13 205	76.84 168	80.80 292	90.11 261

UBG		73.08 343	72.27 337	75.51 373	88.02 204	51.29 439	78.35 402	77.38 425	65.52 345	73.87 319	82.36 389	45.55 386	86.48 375	55.02 387	84.39 239	88.75 316

PatchMatch-RL		72.38 350	70.90 354	76.80 362	88.60 179	67.38 171	79.53 381	76.17 434	62.75 384	69.36 375	82.00 397	45.51 387	84.89 394	53.62 395	80.58 295	78.12 452

FE-MVS		77.78 264	75.68 285	84.08 159	88.09 201	66.00 200	83.13 330	87.79 275	68.42 309	78.01 221	85.23 329	45.50 388	95.12 92	59.11 352	85.83 216	91.11 216

RPSCF		73.23 341	71.46 344	78.54 330	82.50 379	59.85 341	82.18 342	82.84 367	58.96 417	71.15 355	89.41 212	45.48 389	84.77 395	58.82 356	71.83 405	91.02 222

test_vis1_n_1920		75.52 308	75.78 283	74.75 385	79.84 416	57.44 373	83.26 327	85.52 322	62.83 382	79.34 193	86.17 307	45.10 390	79.71 427	78.75 142	81.21 286	87.10 363

myMVS_eth3d28		73.62 331	73.53 321	73.90 394	88.20 193	47.41 454	78.06 405	79.37 408	74.29 159	73.98 317	84.29 349	44.67 391	83.54 404	51.47 406	87.39 182	90.74 233

MSDG		73.36 337	70.99 352	80.49 286	84.51 327	65.80 207	80.71 364	86.13 315	65.70 342	65.46 417	83.74 363	44.60 392	90.91 305	51.13 409	76.89 340	84.74 406

PVSNet_0	57.27 20	61.67 422	59.27 425	68.85 430	79.61 421	57.44 373	68.01 457	73.44 445	55.93 439	58.54 451	70.41 462	44.58 393	77.55 437	47.01 434	35.91 474	71.55 462

testing99		76.09 301	75.12 300	79.00 319	88.16 195	55.50 402	80.79 360	81.40 382	73.30 188	75.17 295	84.27 352	44.48 394	90.02 321	64.28 301	84.22 242	91.48 207

testing3-2		75.12 316	75.19 298	74.91 381	90.40 109	45.09 464	80.29 372	78.42 416	78.37 40	76.54 258	87.75 257	44.36 395	87.28 368	57.04 374	83.49 257	92.37 171

test_cas_vis1_n_1920		73.76 330	73.74 319	73.81 395	75.90 441	59.77 342	80.51 367	82.40 370	58.30 423	81.62 154	85.69 315	44.35 396	76.41 445	76.29 174	78.61 316	85.23 397

mvs_tets		79.13 228	77.77 242	83.22 201	84.70 321	66.37 192	89.17 116	90.19 183	69.38 282	75.40 284	89.46 208	44.17 397	93.15 203	76.78 172	80.70 294	90.14 258

MDA-MVSNet-bldmvs		66.68 403	63.66 413	75.75 368	79.28 425	60.56 333	73.92 436	78.35 417	64.43 358	50.13 467	79.87 419	44.02 398	83.67 402	46.10 440	56.86 453	83.03 427

mmtdpeth		74.16 324	73.01 328	77.60 353	83.72 344	61.13 321	85.10 275	85.10 327	72.06 212	77.21 243	80.33 412	43.84 399	85.75 382	77.14 163	52.61 463	85.91 387

gg-mvs-nofinetune		69.95 378	67.96 381	75.94 366	83.07 362	54.51 413	77.23 413	70.29 452	63.11 376	70.32 360	62.33 466	43.62 400	88.69 348	53.88 394	87.76 176	84.62 408

testing11		75.14 315	74.01 313	78.53 331	88.16 195	56.38 389	80.74 363	80.42 396	70.67 245	72.69 336	83.72 365	43.61 401	89.86 323	62.29 321	83.76 248	89.36 292

GG-mvs-BLEND				75.38 376	81.59 392	55.80 398	79.32 384	69.63 454		67.19 398	73.67 455	43.24 402	88.90 346	50.41 411	84.50 233	81.45 440

CMPMVS	51.72 21	70.19 375	68.16 377	76.28 364	73.15 460	57.55 371	79.47 382	83.92 343	48.02 458	56.48 458	84.81 339	43.13 403	86.42 376	62.67 316	81.81 281	84.89 404
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		66.80 402	65.43 403	70.90 422	79.74 420	48.82 450	75.12 429	74.77 439	59.61 410	64.08 429	77.23 439	42.89 404	80.72 424	48.86 424	66.58 428	83.16 424

PVSNet	64.34 18	72.08 357	70.87 355	75.69 369	86.21 281	56.44 387	74.37 434	80.73 388	62.06 392	70.17 363	82.23 393	42.86 405	83.31 407	54.77 389	84.45 237	87.32 352

pmmvs-eth3d		70.50 371	67.83 385	78.52 332	77.37 437	66.18 195	81.82 344	81.51 380	58.90 418	63.90 431	80.42 410	42.69 406	86.28 377	58.56 358	65.30 435	83.11 425

UnsupCasMVSNet_eth		67.33 398	65.99 402	71.37 415	73.48 456	51.47 437	75.16 427	85.19 325	65.20 349	60.78 442	80.93 407	42.35 407	77.20 438	57.12 372	53.69 461	85.44 394

KD-MVS_self_test		68.81 386	67.59 391	72.46 409	74.29 450	45.45 459	77.93 407	87.00 295	63.12 375	63.99 430	78.99 429	42.32 408	84.77 395	56.55 381	64.09 438	87.16 359

ADS-MVSNet2		66.20 410	63.33 414	74.82 383	79.92 414	58.75 351	67.55 459	75.19 436	53.37 446	65.25 420	75.86 448	42.32 408	80.53 425	41.57 454	68.91 419	85.18 398

ADS-MVSNet		64.36 415	62.88 418	68.78 431	79.92 414	47.17 455	67.55 459	71.18 450	53.37 446	65.25 420	75.86 448	42.32 408	73.99 462	41.57 454	68.91 419	85.18 398

SixPastTwentyTwo		73.37 335	71.26 350	79.70 306	85.08 313	57.89 363	85.57 259	83.56 349	71.03 237	65.66 416	85.88 311	42.10 411	92.57 229	59.11 352	63.34 439	88.65 320

JIA-IIPM		66.32 407	62.82 419	76.82 361	77.09 438	61.72 317	65.34 467	75.38 435	58.04 427	64.51 425	62.32 467	42.05 412	86.51 374	51.45 407	69.22 418	82.21 434

ACMH	67.68 16	75.89 303	73.93 315	81.77 252	88.71 176	66.61 189	88.62 145	89.01 236	69.81 271	66.78 404	86.70 290	41.95 413	91.51 281	55.64 384	78.14 326	87.17 357
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UWE-MVS-28		65.32 411	64.93 405	66.49 440	78.70 428	38.55 477	77.86 409	64.39 469	62.00 393	64.13 428	83.60 368	41.44 414	76.00 449	31.39 469	80.89 289	84.92 403

FE-MVSNET		67.25 400	65.33 404	73.02 403	75.86 442	52.54 427	80.26 374	80.56 391	63.80 372	60.39 443	79.70 421	41.41 415	84.66 397	43.34 449	62.62 442	81.86 437

ACMH+	68.96 14	76.01 302	74.01 313	82.03 247	88.60 179	65.31 223	88.86 130	87.55 280	70.25 262	67.75 390	87.47 268	41.27 416	93.19 201	58.37 361	75.94 358	87.60 343

MIMVSNet		70.69 368	69.30 367	74.88 382	84.52 326	56.35 391	75.87 422	79.42 407	64.59 356	67.76 389	82.41 388	41.10 417	81.54 418	46.64 437	81.34 283	86.75 370

Anonymous202405211		78.25 249	77.01 260	81.99 248	91.03 94	60.67 331	84.77 283	83.90 344	70.65 249	80.00 181	91.20 152	41.08 418	91.43 285	65.21 293	85.26 224	93.85 89

N_pmnet		52.79 435	53.26 433	51.40 460	78.99 427	7.68 494	69.52 451	3.89 493	51.63 452	57.01 456	74.98 452	40.83 419	65.96 475	37.78 461	64.67 436	80.56 447

ETVMVS		72.25 354	71.05 351	75.84 367	87.77 220	51.91 431	79.39 383	74.98 437	69.26 286	73.71 320	82.95 380	40.82 420	86.14 378	46.17 439	84.43 238	89.47 288

EU-MVSNet		68.53 391	67.61 390	71.31 418	78.51 430	47.01 456	84.47 292	84.27 339	42.27 465	66.44 412	84.79 340	40.44 421	83.76 401	58.76 357	68.54 422	83.17 423

DSMNet-mixed		57.77 427	56.90 429	60.38 448	67.70 469	35.61 479	69.18 453	53.97 480	32.30 478	57.49 455	79.88 418	40.39 422	68.57 472	38.78 460	72.37 399	76.97 454

UWE-MVS		72.13 356	71.49 343	74.03 392	86.66 272	47.70 451	81.40 354	76.89 430	63.60 373	75.59 276	84.22 353	39.94 423	85.62 385	48.98 423	86.13 207	88.77 315

blend_shiyan4		72.29 353	69.65 365	80.21 294	78.24 432	62.16 309	82.29 340	87.27 289	65.41 348	68.43 386	76.42 445	39.91 424	91.23 292	63.21 309	65.66 433	87.22 355

OurMVSNet-221017-0		74.26 322	72.42 335	79.80 303	83.76 343	59.59 345	85.92 252	86.64 304	66.39 334	66.96 401	87.58 262	39.46 425	91.60 270	65.76 290	69.27 417	88.22 331

K. test v3		71.19 361	68.51 373	79.21 317	83.04 364	57.78 367	84.35 301	76.91 429	72.90 199	62.99 435	82.86 383	39.27 426	91.09 301	61.65 329	52.66 462	88.75 316

tt0320		70.49 372	68.03 380	77.89 343	84.78 319	59.12 349	83.55 320	80.44 395	58.13 425	67.43 396	80.41 411	39.26 427	87.54 365	55.12 386	63.18 441	86.99 364

lessismore_v0				78.97 320	81.01 403	57.15 376		65.99 464		61.16 441	82.82 384	39.12 428	91.34 288	59.67 345	46.92 469	88.43 326

testing222		74.04 326	72.66 332	78.19 337	87.89 210	55.36 403	81.06 357	79.20 411	71.30 228	74.65 309	83.57 370	39.11 429	88.67 349	51.43 408	85.75 217	90.53 242

reproduce_monomvs		75.40 312	74.38 310	78.46 334	83.92 339	57.80 366	83.78 312	86.94 297	73.47 182	72.25 342	84.47 343	38.74 430	89.27 335	75.32 190	70.53 412	88.31 328

UnsupCasMVSNet_bld		63.70 417	61.53 423	70.21 424	73.69 454	51.39 438	72.82 438	81.89 375	55.63 440	57.81 454	71.80 459	38.67 431	78.61 431	49.26 422	52.21 464	80.63 445

new-patchmatchnet		61.73 421	61.73 422	61.70 446	72.74 462	24.50 489	69.16 454	78.03 418	61.40 396	56.72 457	75.53 451	38.42 432	76.48 444	45.95 441	57.67 452	84.13 413

MVS-HIRNet		59.14 425	57.67 427	63.57 444	81.65 390	43.50 468	71.73 441	65.06 467	39.59 469	51.43 464	57.73 472	38.34 433	82.58 412	39.53 457	73.95 386	64.62 468

test2506		77.30 277	76.49 274	79.74 305	90.08 116	52.02 428	87.86 178	63.10 471	74.88 142	80.16 180	92.79 100	38.29 434	92.35 242	68.74 264	92.50 84	94.86 19

COLMAP_ROB	66.92 17	73.01 344	70.41 360	80.81 279	87.13 253	65.63 211	88.30 160	84.19 341	62.96 379	63.80 432	87.69 260	38.04 435	92.56 230	46.66 435	74.91 378	84.24 411
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 379	69.00 371	72.55 407	79.27 426	56.85 379	78.38 399	74.71 441	57.64 429	68.09 387	77.19 440	37.75 436	76.70 441	63.92 303	84.09 243	84.10 414

OpenMVS_ROB	64.09 19	70.56 370	68.19 376	77.65 350	80.26 409	59.41 348	85.01 278	82.96 364	58.76 420	65.43 418	82.33 390	37.63 437	91.23 292	45.34 445	76.03 357	82.32 433

FMVSNet5		69.50 381	67.96 381	74.15 391	82.97 369	55.35 404	80.01 377	82.12 373	62.56 386	63.02 433	81.53 399	36.92 438	81.92 416	48.42 425	74.06 385	85.17 400

tt0320-xc		70.11 376	67.45 393	78.07 341	85.33 305	59.51 347	83.28 326	78.96 413	58.77 419	67.10 400	80.28 413	36.73 439	87.42 366	56.83 378	59.77 451	87.29 353

sc_t1		72.19 355	69.51 366	80.23 293	84.81 318	61.09 323	84.68 285	80.22 400	60.70 401	71.27 352	83.58 369	36.59 440	89.24 336	60.41 338	63.31 440	90.37 249

MIMVSNet1		68.58 389	66.78 399	73.98 393	80.07 413	51.82 433	80.77 361	84.37 335	64.40 360	59.75 448	82.16 394	36.47 441	83.63 403	42.73 451	70.33 413	86.48 375

ITE_SJBPF				78.22 336	81.77 389	60.57 332		83.30 353	69.25 287	67.54 392	87.20 275	36.33 442	87.28 368	54.34 391	74.62 381	86.80 368

test-mter		71.41 360	70.39 361	74.48 386	81.35 398	58.04 359	78.38 399	77.46 422	60.32 404	69.95 368	79.00 427	36.08 443	79.24 428	66.13 284	84.83 228	86.15 380

testgi		66.67 404	66.53 400	67.08 439	75.62 445	41.69 474	75.93 419	76.50 431	66.11 336	65.20 422	86.59 294	35.72 444	74.71 458	43.71 447	73.38 394	84.84 405

EG-PatchMatch MVS		74.04 326	71.82 340	80.71 281	84.92 316	67.42 168	85.86 254	88.08 264	66.04 338	64.22 427	83.85 359	35.10 445	92.56 230	57.44 369	80.83 291	82.16 436

KD-MVS_2432*1600		66.22 408	63.89 411	73.21 399	75.47 447	53.42 421	70.76 447	84.35 336	64.10 365	66.52 409	78.52 431	34.55 446	84.98 392	50.40 412	50.33 466	81.23 441

miper_refine_blended		66.22 408	63.89 411	73.21 399	75.47 447	53.42 421	70.76 447	84.35 336	64.10 365	66.52 409	78.52 431	34.55 446	84.98 392	50.40 412	50.33 466	81.23 441

mvs5depth		69.45 382	67.45 393	75.46 375	73.93 451	55.83 397	79.19 387	83.23 355	66.89 322	71.63 349	83.32 373	33.69 448	85.09 391	59.81 344	55.34 459	85.46 393

XVG-ACMP-BASELINE		76.11 300	74.27 312	81.62 254	83.20 358	64.67 245	83.60 319	89.75 198	69.75 275	71.85 346	87.09 279	32.78 449	92.11 250	69.99 250	80.43 298	88.09 334

AllTest		70.96 364	68.09 379	79.58 310	85.15 310	63.62 269	84.58 290	79.83 403	62.31 388	60.32 445	86.73 284	32.02 450	88.96 344	50.28 414	71.57 407	86.15 380

TestCases				79.58 310	85.15 310	63.62 269		79.83 403	62.31 388	60.32 445	86.73 284	32.02 450	88.96 344	50.28 414	71.57 407	86.15 380

USDC		70.33 373	68.37 374	76.21 365	80.60 406	56.23 392	79.19 387	86.49 307	60.89 399	61.29 440	85.47 323	31.78 452	89.47 332	53.37 397	76.21 356	82.94 429

myMVS_eth3d		67.02 401	66.29 401	69.21 427	84.68 322	42.58 470	78.62 396	73.08 446	66.65 330	66.74 405	79.46 422	31.53 453	82.30 413	39.43 459	76.38 353	82.75 430

test_fmvs1		70.93 365	70.52 357	72.16 410	73.71 453	55.05 407	80.82 358	78.77 414	51.21 454	78.58 205	84.41 345	31.20 454	76.94 440	75.88 182	80.12 303	84.47 409

Anonymous20240521		68.80 387	67.22 396	73.55 396	74.33 449	54.11 415	83.18 328	85.61 321	58.15 424	61.68 439	80.94 405	30.71 455	81.27 421	57.00 375	73.34 395	85.28 396

testing3		68.56 390	67.67 389	71.22 419	87.33 245	42.87 469	83.06 334	71.54 449	70.36 256	69.08 378	84.38 346	30.33 456	85.69 384	37.50 462	75.45 368	85.09 402

test_vis1_n		69.85 380	69.21 369	71.77 412	72.66 463	55.27 406	81.48 351	76.21 433	52.03 450	75.30 292	83.20 376	28.97 457	76.22 447	74.60 196	78.41 324	83.81 417

tmp_tt		18.61 453	21.40 456	10.23 470	4.82 493	10.11 493	34.70 481	30.74 491	1.48 487	23.91 483	26.07 484	28.42 458	13.41 489	27.12 473	15.35 486	7.17 484

test_fmvs1_n		70.86 366	70.24 362	72.73 406	72.51 464	55.28 405	81.27 355	79.71 405	51.49 453	78.73 200	84.87 337	27.54 459	77.02 439	76.06 178	79.97 304	85.88 388

TDRefinement		67.49 396	64.34 408	76.92 360	73.47 457	61.07 324	84.86 282	82.98 363	59.77 409	58.30 452	85.13 332	26.06 460	87.89 360	47.92 432	60.59 449	81.81 439

dongtai		45.42 443	45.38 444	45.55 462	73.36 458	26.85 486	67.72 458	34.19 488	54.15 444	49.65 468	56.41 475	25.43 461	62.94 478	19.45 479	28.09 479	46.86 478

MVStest1		56.63 428	52.76 434	68.25 435	61.67 477	53.25 425	71.67 442	68.90 459	38.59 470	50.59 466	83.05 378	25.08 462	70.66 467	36.76 463	38.56 473	80.83 444

test_vis1_rt		60.28 423	58.42 426	65.84 441	67.25 470	55.60 401	70.44 449	60.94 474	44.33 463	59.00 449	66.64 464	24.91 463	68.67 471	62.80 312	69.48 415	73.25 460

TinyColmap		67.30 399	64.81 406	74.76 384	81.92 388	56.68 384	80.29 372	81.49 381	60.33 403	56.27 459	83.22 374	24.77 464	87.66 364	45.52 443	69.47 416	79.95 448

EGC-MVSNET		52.07 437	47.05 441	67.14 438	83.51 350	60.71 330	80.50 368	67.75 460	0.07 488	0.43 489	75.85 450	24.26 465	81.54 418	28.82 471	62.25 443	59.16 471

kuosan		39.70 447	40.40 448	37.58 465	64.52 474	26.98 484	65.62 466	33.02 489	46.12 460	42.79 472	48.99 478	24.10 466	46.56 486	12.16 487	26.30 480	39.20 479

LF4IMVS		64.02 416	62.19 420	69.50 426	70.90 465	53.29 424	76.13 417	77.18 427	52.65 448	58.59 450	80.98 404	23.55 467	76.52 443	53.06 399	66.66 427	78.68 451

test_fmvs2		68.35 393	67.48 392	70.98 421	69.50 467	51.95 430	80.05 376	76.38 432	49.33 456	74.65 309	84.38 346	23.30 468	75.40 456	74.51 197	75.17 376	85.60 391

new_pmnet		50.91 438	50.29 438	52.78 459	68.58 468	34.94 481	63.71 471	56.63 479	39.73 468	44.95 470	65.47 465	21.93 469	58.48 479	34.98 465	56.62 454	64.92 467

ttmdpeth		59.91 424	57.10 428	68.34 434	67.13 471	46.65 458	74.64 432	67.41 461	48.30 457	62.52 438	85.04 336	20.40 470	75.93 450	42.55 452	45.90 472	82.44 432

pmmvs3		57.79 426	54.26 431	68.37 433	64.02 475	56.72 382	75.12 429	65.17 466	40.20 467	52.93 463	69.86 463	20.36 471	75.48 454	45.45 444	55.25 460	72.90 461

PM-MVS		66.41 406	64.14 409	73.20 401	73.92 452	56.45 386	78.97 391	64.96 468	63.88 371	64.72 423	80.24 414	19.84 472	83.44 406	66.24 283	64.52 437	79.71 449

mvsany_test3		53.99 431	51.45 436	61.61 447	55.51 481	44.74 466	63.52 472	45.41 486	43.69 464	58.11 453	76.45 443	17.99 473	63.76 477	54.77 389	47.59 468	76.34 456

ambc				75.24 378	73.16 459	50.51 444	63.05 474	87.47 283		64.28 426	77.81 437	17.80 474	89.73 327	57.88 366	60.64 448	85.49 392

ANet_high		50.57 439	46.10 443	63.99 443	48.67 488	39.13 476	70.99 446	80.85 386	61.39 397	31.18 477	57.70 473	17.02 475	73.65 464	31.22 470	15.89 485	79.18 450

FPMVS		53.68 433	51.64 435	59.81 449	65.08 473	51.03 440	69.48 452	69.58 455	41.46 466	40.67 473	72.32 458	16.46 476	70.00 470	24.24 477	65.42 434	58.40 473

test_method		31.52 449	29.28 453	38.23 464	27.03 492	6.50 495	20.94 484	62.21 472	4.05 486	22.35 484	52.50 477	13.33 477	47.58 484	27.04 474	34.04 476	60.62 470

EMVS		30.81 450	29.65 452	34.27 467	50.96 487	25.95 487	56.58 478	46.80 485	24.01 482	15.53 487	30.68 483	12.47 478	54.43 483	12.81 486	17.05 484	22.43 483

test_f		52.09 436	50.82 437	55.90 454	53.82 484	42.31 473	59.42 475	58.31 478	36.45 473	56.12 460	70.96 461	12.18 479	57.79 480	53.51 396	56.57 455	67.60 465

test_fmvs3		63.36 418	61.82 421	67.98 436	62.51 476	46.96 457	77.37 412	74.03 443	45.24 461	67.50 393	78.79 430	12.16 480	72.98 465	72.77 217	66.02 430	83.99 415

E-PMN		31.77 448	30.64 451	35.15 466	52.87 486	27.67 483	57.09 477	47.86 484	24.64 481	16.40 486	33.05 482	11.23 481	54.90 482	14.46 485	18.15 483	22.87 482

DeepMVS_CX				27.40 468	40.17 491	26.90 485		24.59 492	17.44 484	23.95 482	48.61 479	9.77 482	26.48 487	18.06 480	24.47 481	28.83 481

Gipuma		45.18 444	41.86 447	55.16 457	77.03 439	51.52 436	32.50 482	80.52 392	32.46 477	27.12 480	35.02 481	9.52 483	75.50 453	22.31 478	60.21 450	38.45 480
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 430	49.68 440	67.97 437	53.73 485	45.28 462	66.85 462	80.78 387	35.96 474	39.45 475	62.23 468	8.70 484	78.06 435	48.24 429	51.20 465	80.57 446

APD_test1		53.31 434	49.93 439	63.42 445	65.68 472	50.13 445	71.59 443	66.90 463	34.43 475	40.58 474	71.56 460	8.65 485	76.27 446	34.64 466	55.36 458	63.86 469

PMMVS2		40.82 446	38.86 450	46.69 461	53.84 483	16.45 492	48.61 479	49.92 481	37.49 471	31.67 476	60.97 469	8.14 486	56.42 481	28.42 472	30.72 478	67.19 466

test_vis3_rt		49.26 440	47.02 442	56.00 453	54.30 482	45.27 463	66.76 463	48.08 483	36.83 472	44.38 471	53.20 476	7.17 487	64.07 476	56.77 379	55.66 456	58.65 472

testf1		45.72 441	41.96 445	57.00 451	56.90 479	45.32 460	66.14 464	59.26 476	26.19 479	30.89 478	60.96 470	4.14 488	70.64 468	26.39 475	46.73 470	55.04 474

APD_test2		45.72 441	41.96 445	57.00 451	56.90 479	45.32 460	66.14 464	59.26 476	26.19 479	30.89 478	60.96 470	4.14 488	70.64 468	26.39 475	46.73 470	55.04 474

PMVS	37.38 22	44.16 445	40.28 449	55.82 455	40.82 490	42.54 472	65.12 468	63.99 470	34.43 475	24.48 481	57.12 474	3.92 490	76.17 448	17.10 482	55.52 457	48.75 476
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 451	25.89 455	43.81 463	44.55 489	35.46 480	28.87 483	39.07 487	18.20 483	18.58 485	40.18 480	2.68 491	47.37 485	17.07 483	23.78 482	48.60 477
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		16.82 454	15.94 457	19.46 469	58.74 478	31.45 482	39.22 480	3.74 494	6.84 485	6.04 488	2.70 488	1.27 492	24.29 488	10.54 488	14.40 487	2.63 485

test123		6.12 456	8.11 459	0.14 471	0.06 495	0.09 496	71.05 445	0.03 496	0.04 490	0.25 491	1.30 490	0.05 493	0.03 491	0.21 490	0.01 489	0.29 486

testmvs		6.04 457	8.02 460	0.10 472	0.08 494	0.03 497	69.74 450	0.04 495	0.05 489	0.31 490	1.68 489	0.02 494	0.04 490	0.24 489	0.02 488	0.25 487

mmdepth		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

monomultidepth		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

test_blank		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

uanet_test		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

DCPMVS		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

sosnet-low-res		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

sosnet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

uncertanet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

Regformer		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

ab-mvs-re		7.23 455	9.64 458	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	86.72 286	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

uanet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 125	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip							93.28 12

WAC-MVS						42.58 470								39.46 458

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 152	91.30 18

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 57

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 57

eth-test2					0.00 496

eth-test					0.00 496

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 323	92.39 6				88.94 28	96.63 4	94.85 21

save fliter					93.80 44	72.35 44	90.47 74	91.17 148	74.31 157

test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 69

GSMVS																88.96 307

test_part2					95.06 8	72.65 32				91.80 16

MTGPA								92.02 110

MTMP							92.18 39	32.83 490

gm-plane-assit					81.40 396	53.83 418			62.72 385		80.94 405		92.39 239	63.40 307

test9_res														84.90 64	95.70 30	92.87 150

agg_prior2														82.91 91	95.45 33	92.70 155

agg_prior					92.85 68	71.94 52		91.78 126		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 85

旧先验2							86.56 228		58.10 426	87.04 61			88.98 342	74.07 202

新几何2							86.29 242

无先验							87.48 186	88.98 237	60.00 407				94.12 140	67.28 276		88.97 306

原ACMM2							86.86 215

testdata2													91.01 303	62.37 320

testdata1							84.14 307		75.71 111

plane_prior7					90.08 116	68.51 131

plane_prior5								92.44 82					95.38 82	78.71 143	86.32 201	91.33 210

plane_prior4											91.00 162

plane_prior3						68.60 128			78.44 36	78.92 198

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 206

n2								0.00 497

nn								0.00 497

door-mid								69.98 453

test11								92.23 96

door								69.44 456

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 89	77.23 239

ACMP_Plane					89.33 144		89.17 116		76.41 89	77.23 239

BP-MVS														77.47 158

HQP4-MVS										77.24 238			95.11 94			91.03 220

HQP3-MVS								92.19 104							85.99 210

NP-MVS					89.62 129	68.32 135					90.24 183

ACMMP++_ref															81.95 279

ACMMP++															81.25 284

High-resolution multi-view benchmark