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.

MED-MVS test				98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51		99.86 9	97.40 49	99.58 23	99.65 20

MED-MVS		97.91 4	97.88 4	98.00 23	99.56 1	94.50 35	98.69 11	98.70 16	94.23 87	98.73 30	98.53 51	95.46 7	99.86 9	97.40 49	99.58 23	99.65 20

TestfortrainingZip a		97.92 3	97.70 10	98.58 3	99.56 1	96.08 5	98.69 11	98.70 16	93.45 118	98.73 30	98.53 51	95.46 7	99.86 9	96.63 69	99.58 23	99.80 1

FOURS1					99.55 4	93.34 71	99.29 1	98.35 42	94.98 46	98.49 39

region2R		97.07 41	96.84 51	97.77 38	99.46 5	93.79 59	98.52 20	98.24 63	93.19 130	97.14 76	98.34 75	91.59 59	99.87 7	95.46 119	99.59 19	99.64 25

DVP-MVS		97.91 4	97.81 5	98.22 14	99.45 6	95.36 14	98.21 48	97.85 137	94.92 50	98.73 30	98.87 31	95.08 10	99.84 26	97.52 41	99.67 6	99.48 56
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
test_0728_SECOND				98.51 5	99.45 6	95.93 6	98.21 48	98.28 52					99.86 9	97.52 41	99.67 6	99.75 7

test0726					99.45 6	95.36 14	98.31 32	98.29 50	94.92 50	98.99 18	98.92 23	95.08 10

ACMMPR		97.07 41	96.84 51	97.79 34	99.44 9	93.88 57	98.52 20	98.31 47	93.21 127	97.15 75	98.33 78	91.35 64	99.86 9	95.63 113	99.59 19	99.62 27

SED-MVS		98.05 2	97.99 1	98.24 11	99.42 10	95.30 18	98.25 40	98.27 55	95.13 40	99.19 13	98.89 28	95.54 5	99.85 21	97.52 41	99.66 10	99.56 40

IU-MVS					99.42 10	95.39 12		97.94 124	90.40 264	98.94 19				97.41 48	99.66 10	99.74 9

test_241102_ONE					99.42 10	95.30 18		98.27 55	95.09 43	99.19 13	98.81 37	95.54 5	99.65 79

HFP-MVS		97.14 37	96.92 47	97.83 30	99.42 10	94.12 50	98.52 20	98.32 46	93.21 127	97.18 73	98.29 84	92.08 48	99.83 31	95.63 113	99.59 19	99.54 45

MSP-MVS		97.59 13	97.54 17	97.73 42	99.40 14	93.77 61	98.53 19	98.29 50	95.55 27	98.56 38	97.81 136	93.90 17	99.65 79	96.62 70	99.21 83	99.77 3
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
mPP-MVS		96.86 52	96.60 66	97.64 49	99.40 14	93.44 66	98.50 23	98.09 92	93.27 126	95.95 132	98.33 78	91.04 72	99.88 4	95.20 122	99.57 29	99.60 31

MP-MVS		96.77 60	96.45 77	97.72 43	99.39 16	93.80 58	98.41 28	98.06 101	93.37 122	95.54 150	98.34 75	90.59 82	99.88 4	94.83 140	99.54 32	99.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
XVS		97.18 34	96.96 45	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	98.29 84	91.70 55	99.80 40	95.66 108	99.40 61	99.62 27

X-MVStestdata		91.71 276	89.67 344	97.81 32	99.38 17	94.03 54	98.59 17	98.20 69	94.85 53	96.59 99	32.69 493	91.70 55	99.80 40	95.66 108	99.40 61	99.62 27

NormalMVS		96.36 82	96.11 86	97.12 76	99.37 19	92.90 87	97.99 69	97.63 165	95.92 16	96.57 102	97.93 111	85.34 185	99.50 120	94.99 129	99.21 83	98.97 111

lecture		97.58 15	97.63 12	97.43 58	99.37 19	92.93 86	98.86 7	98.85 5	95.27 34	98.65 36	98.90 25	91.97 51	99.80 40	97.63 37	99.21 83	99.57 36

ZNCC-MVS		96.96 46	96.67 64	97.85 29	99.37 19	94.12 50	98.49 24	98.18 76	92.64 162	96.39 113	98.18 91	91.61 57	99.88 4	95.59 118	99.55 30	99.57 36

MTAPA		97.08 39	96.78 59	97.97 27	99.37 19	94.42 40	97.24 191	98.08 93	95.07 44	96.11 124	98.59 46	90.88 78	99.90 2	96.18 92	99.50 40	99.58 35

GST-MVS		96.85 54	96.52 70	97.82 31	99.36 23	94.14 49	98.29 34	98.13 84	92.72 158	96.70 91	98.06 98	91.35 64	99.86 9	94.83 140	99.28 74	99.47 58

HPM-MVS		96.69 67	96.45 77	97.40 59	99.36 23	93.11 80	98.87 6	98.06 101	91.17 226	96.40 112	97.99 107	90.99 73	99.58 98	95.61 115	99.61 18	99.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		96.81 58	96.53 69	97.65 47	99.35 25	93.53 65	97.65 130	98.98 2	92.22 178	97.14 76	98.44 64	91.17 70	99.85 21	94.35 163	99.46 46	99.57 36

CP-MVS		97.02 43	96.81 56	97.64 49	99.33 26	93.54 64	98.80 9	98.28 52	92.99 140	96.45 111	98.30 83	91.90 52	99.85 21	95.61 115	99.68 4	99.54 45

test_one_0601					99.32 27	95.20 21		98.25 61	95.13 40	98.48 40	98.87 31	95.16 9

HPM-MVS_fast		96.51 74	96.27 83	97.22 70	99.32 27	92.74 93	98.74 10	98.06 101	90.57 257	96.77 88	98.35 72	90.21 85	99.53 112	94.80 144	99.63 16	99.38 70

MCST-MVS		97.18 34	96.84 51	98.20 15	99.30 29	95.35 16	97.12 205	98.07 98	93.54 112	96.08 126	97.69 149	93.86 18	99.71 67	96.50 74	99.39 63	99.55 43

test_part2					99.28 30	95.74 9				98.10 48

CPTT-MVS		95.57 108	95.19 112	96.70 92	99.27 31	91.48 146	98.33 31	98.11 89	87.79 350	95.17 162	98.03 101	87.09 147	99.61 90	93.51 181	99.42 56	99.02 103

TSAR-MVS + MP.		97.42 22	97.33 29	97.69 46	99.25 32	94.24 45	98.07 61	97.85 137	93.72 103	98.57 37	98.35 72	93.69 20	99.40 133	97.06 56	99.46 46	99.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		96.05 90	95.91 89	96.46 117	99.24 33	90.47 193	98.30 33	98.57 29	89.01 304	93.97 203	97.57 164	92.62 39	99.76 54	94.66 151	99.27 75	99.15 87

ACMMP		96.27 86	95.93 88	97.28 66	99.24 33	92.62 98	98.25 40	98.81 6	92.99 140	94.56 183	98.39 68	88.96 101	99.85 21	94.57 157	97.63 163	99.36 72
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
MP-MVS-pluss		96.70 65	96.27 83	97.98 26	99.23 35	94.71 30	96.96 220	98.06 101	90.67 247	95.55 148	98.78 40	91.07 71	99.86 9	96.58 72	99.55 30	99.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ME-MVS		97.54 17	97.39 27	98.00 23	99.21 36	94.50 35	97.75 111	98.34 44	94.23 87	98.15 46	98.53 51	93.32 27	99.84 26	97.40 49	99.58 23	99.65 20

DP-MVS Recon		95.68 103	95.12 116	97.37 60	99.19 37	94.19 46	97.03 209	98.08 93	88.35 331	95.09 164	97.65 154	89.97 89	99.48 124	92.08 214	98.59 126	98.44 190

DPE-MVS		97.86 6	97.65 11	98.47 6	99.17 38	95.78 8	97.21 198	98.35 42	95.16 38	98.71 35	98.80 38	95.05 12	99.89 3	96.70 68	99.73 1	99.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.82 7	97.73 9	98.08 19	99.15 39	94.82 29	98.81 8	98.30 48	94.76 64	98.30 43	98.90 25	93.77 19	99.68 75	97.93 28	99.69 3	99.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SR-MVS		97.01 44	96.86 49	97.47 56	99.09 40	93.27 75	97.98 72	98.07 98	93.75 102	97.45 64	98.48 61	91.43 62	99.59 95	96.22 83	99.27 75	99.54 45

ACMMP_NAP		97.20 33	96.86 49	98.23 12	99.09 40	95.16 23	97.60 140	98.19 74	92.82 155	97.93 54	98.74 42	91.60 58	99.86 9	96.26 80	99.52 35	99.67 15

HPM-MVS++		97.34 26	96.97 43	98.47 6	99.08 42	96.16 4	97.55 150	97.97 121	95.59 25	96.61 97	97.89 118	92.57 40	99.84 26	95.95 99	99.51 38	99.40 66

114514_t		93.95 182	93.06 199	96.63 98	99.07 43	91.61 138	97.46 165	97.96 122	77.99 463	93.00 231	97.57 164	86.14 165	99.33 139	89.22 283	99.15 94	98.94 121

SMA-MVS		97.35 25	97.03 40	98.30 9	99.06 44	95.42 11	97.94 82	98.18 76	90.57 257	98.85 27	98.94 21	93.33 25	99.83 31	96.72 66	99.68 4	99.63 26
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
patch_mono-2		96.83 57	97.44 24	95.01 226	99.05 45	85.39 375	96.98 218	98.77 8	94.70 66	97.99 51	98.66 43	93.61 21	99.91 1	97.67 36	99.50 40	99.72 13

ZD-MVS					99.05 45	94.59 33		98.08 93	89.22 297	97.03 81	98.10 94	92.52 41	99.65 79	94.58 156	99.31 72

APD-MVS		96.95 47	96.60 66	98.01 21	99.03 47	94.93 28	97.72 119	98.10 91	91.50 207	98.01 50	98.32 80	92.33 44	99.58 98	94.85 137	99.51 38	99.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS-dyc-post		96.88 51	96.80 57	97.11 78	99.02 48	92.34 108	97.98 72	98.03 110	93.52 115	97.43 67	98.51 56	91.40 63	99.56 106	96.05 94	99.26 78	99.43 63

RE-MVS-def			96.72 62		99.02 48	92.34 108	97.98 72	98.03 110	93.52 115	97.43 67	98.51 56	90.71 80		96.05 94	99.26 78	99.43 63

SF-MVS		97.39 24	97.13 31	98.17 16	99.02 48	95.28 20	98.23 44	98.27 55	92.37 172	98.27 44	98.65 45	93.33 25	99.72 65	96.49 75	99.52 35	99.51 49

APD-MVS_3200maxsize		96.81 58	96.71 63	97.12 76	99.01 51	92.31 110	97.98 72	98.06 101	93.11 136	97.44 65	98.55 49	90.93 76	99.55 108	96.06 93	99.25 80	99.51 49

reproduce_model		97.51 20	97.51 20	97.50 54	98.99 52	93.01 82	97.79 107	98.21 67	95.73 24	97.99 51	99.03 15	92.63 38	99.82 33	97.80 30	99.42 56	99.67 15

reproduce-ours		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 121	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 32	99.43 53	99.67 15

our_new_method		97.53 18	97.51 20	97.60 51	98.97 53	93.31 73	97.71 121	98.20 69	95.80 21	97.88 55	98.98 18	92.91 30	99.81 35	97.68 32	99.43 53	99.67 15

dcpmvs_2		96.37 81	97.05 38	94.31 274	98.96 55	84.11 396	97.56 145	97.51 192	93.92 97	97.43 67	98.52 55	92.75 34	99.32 141	97.32 54	99.50 40	99.51 49

9.14			96.75 61		98.93 56		97.73 116	98.23 66	91.28 218	97.88 55	98.44 64	93.00 29	99.65 79	95.76 106	99.47 45

CDPH-MVS		95.97 94	95.38 106	97.77 38	98.93 56	94.44 39	96.35 289	97.88 130	86.98 369	96.65 95	97.89 118	91.99 50	99.47 125	92.26 203	99.46 46	99.39 68

save fliter					98.91 58	94.28 42	97.02 211	98.02 113	95.35 31

CNVR-MVS		97.68 8	97.44 24	98.37 8	98.90 59	95.86 7	97.27 189	98.08 93	95.81 20	97.87 58	98.31 81	94.26 15	99.68 75	97.02 57	99.49 43	99.57 36

PAPM_NR		95.01 136	94.59 141	96.26 135	98.89 60	90.68 188	97.24 191	97.73 151	91.80 194	92.93 236	96.62 234	89.13 99	99.14 167	89.21 284	97.78 160	98.97 111

OPU-MVS				98.55 4	98.82 61	96.86 3	98.25 40				98.26 87	96.04 2	99.24 150	95.36 120	99.59 19	99.56 40

NCCC		97.30 29	97.03 40	98.11 18	98.77 62	95.06 26	97.34 178	98.04 108	95.96 15	97.09 79	97.88 123	93.18 28	99.71 67	95.84 104	99.17 91	99.56 40

DP-MVS		92.76 238	91.51 262	96.52 107	98.77 62	90.99 170	97.38 175	96.08 343	82.38 438	89.29 332	97.87 124	83.77 215	99.69 73	81.37 412	96.69 204	98.89 136

MSLP-MVS++		96.94 48	97.06 35	96.59 102	98.72 64	91.86 128	97.67 126	98.49 32	94.66 69	97.24 72	98.41 67	92.31 46	98.94 195	96.61 71	99.46 46	98.96 114

TEST9					98.70 65	94.19 46	96.41 280	98.02 113	88.17 335	96.03 127	97.56 166	92.74 35	99.59 95

train_agg		96.30 85	95.83 92	97.72 43	98.70 65	94.19 46	96.41 280	98.02 113	88.58 322	96.03 127	97.56 166	92.73 36	99.59 95	95.04 126	99.37 67	99.39 68

DVP-MVS++		98.06 1	97.99 1	98.28 10	98.67 67	95.39 12	99.29 1	98.28 52	94.78 61	98.93 20	98.87 31	96.04 2	99.86 9	97.45 45	99.58 23	99.59 32

MSC_two_6792asdad				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 32	99.67 6	99.77 3

No_MVS				98.86 1	98.67 67	96.94 1		97.93 125					99.86 9	97.68 32	99.67 6	99.77 3

test_8					98.67 67	94.06 53	96.37 288	98.01 116	88.58 322	95.98 131	97.55 168	92.73 36	99.58 98

agg_prior					98.67 67	93.79 59		98.00 117		95.68 144			99.57 105

test_prior				97.23 69	98.67 67	92.99 83		98.00 117					99.41 132			99.29 75

DeepC-MVS_fast	93.89 2	96.93 49	96.64 65	97.78 36	98.64 73	94.30 41	97.41 168	98.04 108	94.81 59	96.59 99	98.37 70	91.24 67	99.64 87	95.16 124	99.52 35	99.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何1				97.32 62	98.60 74	93.59 63		97.75 148	81.58 445	95.75 139	97.85 128	90.04 87	99.67 77	86.50 346	99.13 97	98.69 164

原ACMM1				96.38 125	98.59 75	91.09 168		97.89 128	87.41 361	95.22 161	97.68 150	90.25 84	99.54 110	87.95 306	99.12 99	98.49 182

AdaColmap		94.34 162	93.68 172	96.31 129	98.59 75	91.68 136	96.59 269	97.81 144	89.87 273	92.15 250	97.06 200	83.62 219	99.54 110	89.34 278	98.07 149	97.70 254

PLC	91.00 6	94.11 173	93.43 186	96.13 143	98.58 77	91.15 167	96.69 256	97.39 220	87.29 364	91.37 272	96.71 220	88.39 113	99.52 116	87.33 333	97.13 186	97.73 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		97.41 23	97.53 18	97.06 82	98.57 78	94.46 38	97.92 85	98.14 83	94.82 57	99.01 17	98.55 49	94.18 16	97.41 400	96.94 58	99.64 14	99.32 74
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
test12				97.65 47	98.46 79	94.26 43		97.66 159		95.52 151		90.89 77	99.46 126		99.25 80	99.22 82

MVS_111021_HR		96.68 69	96.58 68	96.99 84	98.46 79	92.31 110	96.20 305	98.90 3	94.30 86	95.86 135	97.74 144	92.33 44	99.38 136	96.04 96	99.42 56	99.28 77

OMC-MVS		95.09 129	94.70 137	96.25 138	98.46 79	91.28 154	96.43 276	97.57 177	92.04 189	94.77 178	97.96 110	87.01 148	99.09 175	91.31 231	96.77 198	98.36 197

fmvsm_s_conf0.5_n_9		97.33 27	97.57 15	96.62 101	98.43 82	90.32 204	97.80 105	98.53 30	97.24 4	99.62 2	99.14 2	88.65 108	99.80 40	99.54 1	99.15 94	99.74 9

fmvsm_s_conf0.5_n_11		97.30 29	97.59 14	96.43 119	98.42 83	91.37 151	98.04 64	98.00 117	97.30 3	99.45 4	99.21 1	89.28 96	99.80 40	99.27 10	99.35 69	98.12 220

MG-MVS		95.61 106	95.38 106	96.31 129	98.42 83	90.53 191	96.04 314	97.48 197	93.47 117	95.67 145	98.10 94	89.17 98	99.25 149	91.27 232	98.77 117	99.13 89

test_fmvsm_n_1920		97.55 16	97.89 3	96.53 105	98.41 85	91.73 130	98.01 67	99.02 1	96.37 13	99.30 7	98.92 23	92.39 43	99.79 46	99.16 14	99.46 46	98.08 228

PHI-MVS		96.77 60	96.46 76	97.71 45	98.40 86	94.07 52	98.21 48	98.45 37	89.86 274	97.11 78	98.01 104	92.52 41	99.69 73	96.03 97	99.53 33	99.36 72

F-COLMAP		93.58 197	92.98 203	95.37 209	98.40 86	88.98 267	97.18 200	97.29 235	87.75 353	90.49 291	97.10 198	85.21 189	99.50 120	86.70 343	96.72 203	97.63 256

SteuartSystems-ACMMP		97.62 12	97.53 18	97.87 28	98.39 88	94.25 44	98.43 27	98.27 55	95.34 32	98.11 47	98.56 47	94.53 14	99.71 67	96.57 73	99.62 17	99.65 20
Skip Steuart: Steuart Systems R&D Blog.
旧先验1					98.38 89	93.38 68		97.75 148			98.09 96	92.30 47			99.01 107	99.16 85

CNLPA		94.28 163	93.53 178	96.52 107	98.38 89	92.55 102	96.59 269	96.88 289	90.13 270	91.91 258	97.24 187	85.21 189	99.09 175	87.64 324	97.83 158	97.92 238

TAPA-MVS	90.10 7	92.30 254	91.22 273	95.56 192	98.33 91	89.60 232	96.79 242	97.65 161	81.83 442	91.52 268	97.23 188	87.94 122	98.91 200	71.31 466	98.37 136	98.17 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		96.69 67	96.49 71	97.27 67	98.31 92	93.39 67	96.79 242	96.72 298	94.17 89	97.44 65	97.66 153	92.76 33	99.33 139	96.86 62	97.76 162	99.08 98

SPE-MVS-test		96.89 50	97.04 39	96.45 118	98.29 93	91.66 137	99.03 4	97.85 137	95.84 18	96.90 83	97.97 109	91.24 67	98.75 231	96.92 59	99.33 70	98.94 121

fmvsm_l_conf0.5_n_9		97.59 13	97.79 6	96.97 86	98.28 94	91.49 144	97.61 139	98.71 13	97.10 5	99.70 1	98.93 22	90.95 75	99.77 52	99.35 6	99.53 33	99.65 20

fmvsm_s_conf0.5_n_8		97.32 28	97.48 23	96.85 88	98.28 94	91.07 169	97.76 109	98.62 26	97.53 2	99.20 12	99.12 5	88.24 116	99.81 35	99.41 3	99.17 91	99.67 15

CHOSEN 1792x2688		94.15 169	93.51 181	96.06 148	98.27 96	89.38 246	95.18 371	98.48 34	85.60 392	93.76 207	97.11 196	83.15 229	99.61 90	91.33 230	98.72 119	99.19 83

PVSNet_BlendedMVS		94.06 175	93.92 165	94.47 263	98.27 96	89.46 243	96.73 250	98.36 39	90.17 267	94.36 188	95.24 307	88.02 120	99.58 98	93.44 183	90.72 331	94.36 415

PVSNet_Blended		94.87 145	94.56 143	95.81 170	98.27 96	89.46 243	95.47 350	98.36 39	88.84 313	94.36 188	96.09 264	88.02 120	99.58 98	93.44 183	98.18 145	98.40 193

fmvsm_l_conf0.5_n_a		97.63 11	97.76 7	97.26 68	98.25 99	92.59 100	97.81 104	98.68 19	94.93 48	99.24 10	98.87 31	93.52 22	99.79 46	99.32 7	99.21 83	99.40 66

fmvsm_s_conf0.5_n_10		97.29 31	97.40 26	96.97 86	98.24 100	91.96 126	97.89 89	98.72 12	96.77 7	99.46 3	99.06 12	87.78 126	99.84 26	99.40 4	99.27 75	99.12 92

Anonymous20231211		90.63 335	89.42 351	94.27 277	98.24 100	89.19 258	98.05 63	97.89 128	79.95 454	88.25 363	94.96 316	72.56 390	98.13 300	89.70 268	85.14 394	95.49 338

EI-MVSNet-Vis-set		96.51 74	96.47 73	96.63 98	98.24 100	91.20 160	96.89 228	97.73 151	94.74 65	96.49 106	98.49 58	90.88 78	99.58 98	96.44 76	98.32 138	99.13 89

test222					98.24 100	92.21 114	95.33 357	97.60 170	79.22 458	95.25 159	97.84 130	88.80 105			99.15 94	98.72 161

HyFIR lowres test		93.66 195	92.92 205	95.87 163	98.24 100	89.88 220	94.58 388	98.49 32	85.06 402	93.78 206	95.78 279	82.86 239	98.67 248	91.77 220	95.71 234	99.07 100

MVS_111021_LR		96.24 87	96.19 85	96.39 124	98.23 105	91.35 153	96.24 302	98.79 7	93.99 95	95.80 137	97.65 154	89.92 90	99.24 150	95.87 100	99.20 88	98.58 171

fmvsm_l_conf0.5_n		97.65 9	97.75 8	97.34 61	98.21 106	92.75 92	97.83 99	98.73 10	95.04 45	99.30 7	98.84 36	93.34 24	99.78 49	99.32 7	99.13 97	99.50 52

EI-MVSNet-UG-set		96.34 83	96.30 82	96.47 115	98.20 107	90.93 176	96.86 231	97.72 153	94.67 68	96.16 123	98.46 62	90.43 83	99.58 98	96.23 82	97.96 155	98.90 130

PVSNet_Blended_VisFu		95.27 117	94.91 125	96.38 125	98.20 107	90.86 179	97.27 189	98.25 61	90.21 266	94.18 196	97.27 185	87.48 139	99.73 61	93.53 180	97.77 161	98.55 174

Anonymous202405211		92.07 265	90.83 289	95.76 178	98.19 109	88.75 271	97.58 141	95.00 395	86.00 387	93.64 211	97.45 171	66.24 441	99.53 112	90.68 247	92.71 297	99.01 106

PatchMatch-RL		92.90 230	92.02 241	95.56 192	98.19 109	90.80 181	95.27 362	97.18 246	87.96 341	91.86 261	95.68 285	80.44 293	98.99 191	84.01 383	97.54 165	96.89 289

testdata				95.46 207	98.18 111	88.90 269		97.66 159	82.73 434	97.03 81	98.07 97	90.06 86	98.85 205	89.67 269	98.98 108	98.64 167

CS-MVS		96.86 52	97.06 35	96.26 135	98.16 112	91.16 166	99.09 3	97.87 132	95.30 33	97.06 80	98.03 101	91.72 53	98.71 241	97.10 55	99.17 91	98.90 130

fmvsm_l_conf0.5_n_3		97.64 10	97.60 13	97.79 34	98.14 113	93.94 56	97.93 84	98.65 24	96.70 8	99.38 5	99.07 11	89.92 90	99.81 35	99.16 14	99.43 53	99.61 30

Anonymous20240529		91.98 268	90.73 295	95.73 183	98.14 113	89.40 245	97.99 69	97.72 153	79.63 456	93.54 215	97.41 175	69.94 411	99.56 106	91.04 237	91.11 324	98.22 210

LFMVS		93.60 196	92.63 219	96.52 107	98.13 115	91.27 155	97.94 82	93.39 444	90.57 257	96.29 117	98.31 81	69.00 419	99.16 162	94.18 165	95.87 229	99.12 92

SDMVSNet		94.17 167	93.61 174	95.86 166	98.09 116	91.37 151	97.35 177	98.20 69	93.18 132	91.79 262	97.28 183	79.13 316	98.93 196	94.61 154	92.84 294	97.28 276

sd_testset		93.10 219	92.45 229	95.05 222	98.09 116	89.21 255	96.89 228	97.64 163	93.18 132	91.79 262	97.28 183	75.35 364	98.65 251	88.99 289	92.84 294	97.28 276

DeepPCF-MVS	93.97 1	96.61 71	97.09 33	95.15 217	98.09 116	86.63 341	96.00 317	98.15 81	95.43 28	97.95 53	98.56 47	93.40 23	99.36 137	96.77 63	99.48 44	99.45 59

DPM-MVS		95.69 102	94.92 124	98.01 21	98.08 119	95.71 10	95.27 362	97.62 169	90.43 262	95.55 148	97.07 199	91.72 53	99.50 120	89.62 271	98.94 110	98.82 146

MVSMamba_PlusPlus		96.51 74	96.48 72	96.59 102	98.07 120	91.97 124	98.14 55	97.79 145	90.43 262	97.34 70	97.52 169	91.29 66	99.19 155	98.12 27	99.64 14	98.60 169

fmvsm_s_conf0.5_n		96.85 54	97.13 31	96.04 150	98.07 120	90.28 205	97.97 78	98.76 9	94.93 48	98.84 28	99.06 12	88.80 105	99.65 79	99.06 18	98.63 123	98.18 213

VNet		95.89 98	95.45 100	97.21 71	98.07 120	92.94 85	97.50 154	98.15 81	93.87 99	97.52 62	97.61 160	85.29 187	99.53 112	95.81 105	95.27 247	99.16 85

MM		97.29 31	96.98 42	98.23 12	98.01 123	95.03 27	98.07 61	95.76 355	97.78 1	97.52 62	98.80 38	88.09 118	99.86 9	99.44 2	99.37 67	99.80 1

MAR-MVS		94.22 165	93.46 183	96.51 111	98.00 124	92.19 117	97.67 126	97.47 201	88.13 339	93.00 231	95.84 272	84.86 198	99.51 117	87.99 305	98.17 146	97.83 248
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
fmvsm_s_conf0.5_n_3		97.15 36	97.36 28	96.52 107	97.98 125	91.19 161	97.84 96	98.65 24	97.08 6	99.25 9	99.10 6	87.88 124	99.79 46	99.32 7	99.18 90	98.59 170

fmvsm_s_conf0.5_n_2		96.62 70	96.82 55	96.02 152	97.98 125	90.43 196	97.50 154	98.59 27	96.59 10	99.31 6	99.08 8	84.47 203	99.75 58	99.37 5	98.45 133	97.88 241

DeepC-MVS	93.07 3	96.06 89	95.66 93	97.29 64	97.96 127	93.17 79	97.30 183	98.06 101	93.92 97	93.38 222	98.66 43	86.83 149	99.73 61	95.60 117	99.22 82	98.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	87.81 15	90.40 341	89.28 354	93.79 308	97.95 128	87.13 328	96.92 224	95.89 350	82.83 430	86.88 397	97.18 190	73.77 379	99.29 146	78.44 433	93.62 287	94.95 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		90.23 346	88.98 360	93.98 293	97.94 129	86.64 338	96.51 273	95.54 370	85.38 395	85.49 415	96.77 218	70.28 406	99.15 164	80.02 423	92.87 292	96.15 311

TestCases				93.98 293	97.94 129	86.64 338		95.54 370	85.38 395	85.49 415	96.77 218	70.28 406	99.15 164	80.02 423	92.87 292	96.15 311

thres100view900		92.43 246	91.58 257	94.98 230	97.92 131	89.37 247	97.71 121	94.66 411	92.20 181	93.31 224	94.90 320	78.06 339	99.08 177	81.40 409	94.08 275	96.48 300

thres600view7		92.49 244	91.60 256	95.18 216	97.91 132	89.47 241	97.65 130	94.66 411	92.18 185	93.33 223	94.91 319	78.06 339	99.10 172	81.61 405	94.06 279	96.98 284

API-MVS		94.84 147	94.49 149	95.90 161	97.90 133	92.00 123	97.80 105	97.48 197	89.19 298	94.81 176	96.71 220	88.84 104	99.17 160	88.91 291	98.76 118	96.53 297

VDD-MVS		93.82 189	93.08 198	96.02 152	97.88 134	89.96 218	97.72 119	95.85 351	92.43 170	95.86 135	98.44 64	68.42 426	99.39 134	96.31 79	94.85 254	98.71 163

SymmetryMVS		95.94 96	95.54 95	97.15 74	97.85 135	92.90 87	97.99 69	96.91 285	95.92 16	96.57 102	97.93 111	85.34 185	99.50 120	94.99 129	96.39 221	99.05 102

tfpn200view9		92.38 249	91.52 260	94.95 234	97.85 135	89.29 251	97.41 168	94.88 403	92.19 183	93.27 226	94.46 346	78.17 335	99.08 177	81.40 409	94.08 275	96.48 300

thres400		92.42 247	91.52 260	95.12 220	97.85 135	89.29 251	97.41 168	94.88 403	92.19 183	93.27 226	94.46 346	78.17 335	99.08 177	81.40 409	94.08 275	96.98 284

h-mvs33		94.15 169	93.52 180	96.04 150	97.81 138	90.22 207	97.62 138	97.58 174	95.19 36	96.74 89	97.45 171	83.67 217	99.61 90	95.85 102	79.73 436	98.29 206

DELS-MVS		96.61 71	96.38 80	97.30 63	97.79 139	93.19 78	95.96 319	98.18 76	95.23 35	95.87 134	97.65 154	91.45 60	99.70 72	95.87 100	99.44 52	99.00 109
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
PVSNet	86.66 18	92.24 258	91.74 253	93.73 310	97.77 140	83.69 403	92.88 445	96.72 298	87.91 343	93.00 231	94.86 322	78.51 330	99.05 186	86.53 344	97.45 171	98.47 185

fmvsm_s_conf0.5_n_4		96.75 62	97.07 34	95.79 174	97.76 141	89.57 234	97.66 129	98.66 22	95.36 30	99.03 16	98.90 25	88.39 113	99.73 61	99.17 13	98.66 121	98.08 228

test_yl		94.78 151	94.23 158	96.43 119	97.74 142	91.22 156	96.85 232	97.10 256	91.23 223	95.71 141	96.93 208	84.30 206	99.31 143	93.10 190	95.12 250	98.75 157

DCV-MVSNet		94.78 151	94.23 158	96.43 119	97.74 142	91.22 156	96.85 232	97.10 256	91.23 223	95.71 141	96.93 208	84.30 206	99.31 143	93.10 190	95.12 250	98.75 157

testing3-2		92.10 264	92.05 238	92.27 373	97.71 144	79.56 451	97.42 167	94.41 421	93.53 113	93.22 228	95.49 295	69.16 418	99.11 170	93.25 187	94.22 269	98.13 218

WTY-MVS		94.71 155	94.02 163	96.79 90	97.71 144	92.05 120	96.59 269	97.35 228	90.61 253	94.64 181	96.93 208	86.41 159	99.39 134	91.20 234	94.71 262	98.94 121

UA-Net		95.95 95	95.53 96	97.20 72	97.67 146	92.98 84	97.65 130	98.13 84	94.81 59	96.61 97	98.35 72	88.87 103	99.51 117	90.36 255	97.35 174	99.11 94

IS-MVSNet		94.90 142	94.52 147	96.05 149	97.67 146	90.56 190	98.44 26	96.22 336	93.21 127	93.99 201	97.74 144	85.55 182	98.45 271	89.98 260	97.86 157	99.14 88

test2506		91.60 284	90.78 290	94.04 289	97.66 148	83.81 399	98.27 37	75.53 494	93.43 120	95.23 160	98.21 88	67.21 432	99.07 181	93.01 197	98.49 129	99.25 80

ECVR-MVS		93.19 215	92.73 215	94.57 258	97.66 148	85.41 373	98.21 48	88.23 478	93.43 120	94.70 179	98.21 88	72.57 389	99.07 181	93.05 194	98.49 129	99.25 80

fmvsm_s_conf0.5_n_a		96.75 62	96.93 46	96.20 140	97.64 150	90.72 186	98.00 68	98.73 10	94.55 73	98.91 24	99.08 8	88.22 117	99.63 88	98.91 21	98.37 136	98.25 208

PAPR		94.18 166	93.42 188	96.48 114	97.64 150	91.42 150	95.55 345	97.71 157	88.99 306	92.34 246	95.82 274	89.19 97	99.11 170	86.14 352	97.38 172	98.90 130

balanced_conf03		96.84 56	96.89 48	96.68 93	97.63 152	92.22 113	98.17 54	97.82 143	94.44 79	98.23 45	97.36 178	90.97 74	99.22 152	97.74 31	99.66 10	98.61 168

CANet		96.39 80	96.02 87	97.50 54	97.62 153	93.38 68	97.02 211	97.96 122	95.42 29	94.86 173	97.81 136	87.38 142	99.82 33	96.88 60	99.20 88	99.29 75

thres200		92.23 259	91.39 263	94.75 247	97.61 154	89.03 262	96.60 268	95.09 392	92.08 187	93.28 225	94.00 374	78.39 333	99.04 189	81.26 415	94.18 271	96.19 307

Vis-MVSNet (Re-imp)		94.15 169	93.88 166	94.95 234	97.61 154	87.92 305	98.10 57	95.80 354	92.22 178	93.02 230	97.45 171	84.53 202	97.91 345	88.24 301	97.97 154	99.02 103

MGCFI-Net		95.94 96	95.40 104	97.56 53	97.59 156	94.62 32	98.21 48	97.57 177	94.41 81	96.17 122	96.16 257	87.54 134	99.17 160	96.19 90	94.73 261	98.91 127

sasdasda		96.02 91	95.45 100	97.75 40	97.59 156	95.15 24	98.28 35	97.60 170	94.52 75	96.27 118	96.12 259	87.65 129	99.18 158	96.20 88	94.82 256	98.91 127

canonicalmvs		96.02 91	95.45 100	97.75 40	97.59 156	95.15 24	98.28 35	97.60 170	94.52 75	96.27 118	96.12 259	87.65 129	99.18 158	96.20 88	94.82 256	98.91 127

LS3D		93.57 199	92.61 221	96.47 115	97.59 156	91.61 138	97.67 126	97.72 153	85.17 400	90.29 295	98.34 75	84.60 200	99.73 61	83.85 388	98.27 141	98.06 230

fmvsm_s_conf0.5_n_5		97.00 45	96.97 43	97.09 79	97.58 160	92.56 101	97.68 125	98.47 35	94.02 93	98.90 25	98.89 28	88.94 102	99.78 49	99.18 12	99.03 106	98.93 125

test1111		93.19 215	92.82 209	94.30 275	97.58 160	84.56 390	98.21 48	89.02 476	93.53 113	94.58 182	98.21 88	72.69 388	99.05 186	93.06 193	98.48 131	99.28 77

alignmvs		95.87 100	95.23 111	97.78 36	97.56 162	95.19 22	97.86 92	97.17 248	94.39 83	96.47 108	96.40 244	85.89 168	99.20 154	96.21 87	95.11 252	98.95 118

EPP-MVSNet		95.22 123	95.04 119	95.76 178	97.49 163	89.56 235	98.67 15	97.00 275	90.69 245	94.24 192	97.62 159	89.79 92	98.81 211	93.39 186	96.49 214	98.92 126

test_fmvsmconf_n		97.49 21	97.56 16	97.29 64	97.44 164	92.37 107	97.91 86	98.88 4	95.83 19	98.92 23	99.05 14	91.45 60	99.80 40	99.12 16	99.46 46	99.69 14

test_vis1_n_1920		94.17 167	94.58 142	92.91 352	97.42 165	82.02 423	97.83 99	97.85 137	94.68 67	98.10 48	98.49 58	70.15 409	99.32 141	97.91 29	98.82 113	97.40 270

PS-MVSNAJ		95.37 112	95.33 108	95.49 203	97.35 166	90.66 189	95.31 359	97.48 197	93.85 100	96.51 105	95.70 284	88.65 108	99.65 79	94.80 144	98.27 141	96.17 308

fmvsm_s_conf0.1_n_2		96.33 84	96.44 79	96.00 156	97.30 167	90.37 202	97.53 151	97.92 127	96.52 11	99.14 15	99.08 8	83.21 226	99.74 59	99.22 11	98.06 150	97.88 241

fmvsm_s_conf0.5_n_7		96.45 77	96.80 57	95.37 209	97.29 168	88.38 287	97.23 195	98.47 35	95.14 39	98.43 41	99.09 7	87.58 132	99.72 65	98.80 25	99.21 83	98.02 232

fmvsm_s_conf0.5_n_6		97.08 39	97.17 30	96.81 89	97.28 169	91.73 130	97.75 111	98.50 31	94.86 52	99.22 11	98.78 40	89.75 93	99.76 54	99.10 17	99.29 73	98.94 121

ab-mvs		93.57 199	92.55 223	96.64 94	97.28 169	91.96 126	95.40 353	97.45 208	89.81 278	93.22 228	96.28 250	79.62 310	99.46 126	90.74 245	93.11 291	98.50 180

xiu_mvs_v2_base		95.32 115	95.29 109	95.40 208	97.22 171	90.50 192	95.44 352	97.44 212	93.70 105	96.46 109	96.18 254	88.59 112	99.53 112	94.79 147	97.81 159	96.17 308

BH-untuned		92.94 228	92.62 220	93.92 303	97.22 171	86.16 356	96.40 284	96.25 335	90.06 271	89.79 314	96.17 256	83.19 227	98.35 282	87.19 336	97.27 180	97.24 278

baseline1		92.82 236	91.90 246	95.55 194	97.20 173	90.77 183	97.19 199	94.58 414	92.20 181	92.36 243	96.34 247	84.16 210	98.21 293	89.20 285	83.90 416	97.68 255

Vis-MVSNet		95.23 122	94.81 131	96.51 111	97.18 174	91.58 141	98.26 39	98.12 86	94.38 84	94.90 172	98.15 93	82.28 254	98.92 198	91.45 229	98.58 127	99.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		96.02 91	95.89 90	96.40 122	97.16 175	92.44 105	97.47 163	97.77 147	94.55 73	96.48 107	94.51 341	91.23 69	98.92 198	95.65 111	98.19 144	97.82 249

balanced_ft_v1		95.56 109	95.40 104	96.07 147	97.16 175	90.36 203	98.23 44	97.31 233	92.89 152	96.36 114	97.11 196	83.28 224	99.26 148	97.40 49	98.80 115	98.58 171

BH-RMVSNet		92.72 240	91.97 243	94.97 232	97.16 175	87.99 303	96.15 309	95.60 365	90.62 252	91.87 260	97.15 193	78.41 332	98.57 262	83.16 390	97.60 164	98.36 197

MSDG		91.42 297	90.24 317	94.96 233	97.15 178	88.91 268	93.69 428	96.32 324	85.72 391	86.93 395	96.47 240	80.24 297	98.98 192	80.57 419	95.05 253	96.98 284

tttt0517		92.96 226	92.33 232	94.87 237	97.11 179	87.16 327	97.97 78	92.09 460	90.63 251	93.88 205	97.01 207	76.50 352	99.06 183	90.29 257	95.45 244	98.38 195

HY-MVS	89.66 9	93.87 187	92.95 204	96.63 98	97.10 180	92.49 104	95.64 342	96.64 306	89.05 303	93.00 231	95.79 278	85.77 173	99.45 128	89.16 287	94.35 264	97.96 235

thisisatest0530		93.03 223	92.21 235	95.49 203	97.07 181	89.11 260	97.49 162	92.19 459	90.16 268	94.09 199	96.41 243	76.43 355	99.05 186	90.38 254	95.68 235	98.31 205

XVG-OURS		93.72 193	93.35 189	94.80 243	97.07 181	88.61 276	94.79 383	97.46 203	91.97 192	93.99 201	97.86 126	81.74 267	98.88 202	92.64 201	92.67 299	96.92 288

sss		94.51 158	93.80 167	96.64 94	97.07 181	91.97 124	96.32 294	98.06 101	88.94 309	94.50 185	96.78 217	84.60 200	99.27 147	91.90 215	96.02 224	98.68 165

EIA-MVS		95.53 110	95.47 99	95.71 185	97.06 184	89.63 230	97.82 101	97.87 132	93.57 108	93.92 204	95.04 313	90.61 81	98.95 193	94.62 153	98.68 120	98.54 175

XVG-OURS-SEG-HR		93.86 188	93.55 176	94.81 240	97.06 184	88.53 282	95.28 360	97.45 208	91.68 199	94.08 200	97.68 150	82.41 252	98.90 201	93.84 174	92.47 300	96.98 284

SSM_0404		94.73 154	94.31 157	95.98 158	97.05 186	90.90 178	97.01 214	97.29 235	91.24 220	94.17 197	97.60 161	85.03 192	98.76 225	92.14 208	97.30 178	98.29 206

1112_ss		93.37 208	92.42 230	96.21 139	97.05 186	90.99 170	96.31 295	96.72 298	86.87 372	89.83 313	96.69 224	86.51 155	99.14 167	88.12 302	93.67 285	98.50 180

Test_1112_low_res		92.84 235	91.84 248	95.85 167	97.04 188	89.97 217	95.53 347	96.64 306	85.38 395	89.65 320	95.18 308	85.86 169	99.10 172	87.70 316	93.58 290	98.49 182

E3new		95.28 116	95.11 117	95.80 171	97.03 189	89.76 224	96.78 246	97.54 189	92.06 188	95.40 154	97.75 141	87.49 138	98.76 225	94.85 137	97.10 187	98.88 138

mvsmamba		94.57 156	94.14 160	95.87 163	97.03 189	89.93 219	97.84 96	95.85 351	91.34 214	94.79 177	96.80 216	80.67 287	98.81 211	94.85 137	98.12 148	98.85 142

hse-mvs2		93.45 206	92.99 200	94.81 240	97.02 191	88.59 277	96.69 256	96.47 316	95.19 36	96.74 89	96.16 257	83.67 217	98.48 270	95.85 102	79.13 440	97.35 273

EC-MVSNet		96.42 78	96.47 73	96.26 135	97.01 192	91.52 143	98.89 5	97.75 148	94.42 80	96.64 96	97.68 150	89.32 95	98.60 257	97.45 45	99.11 100	98.67 166

AUN-MVS		91.76 275	90.75 293	94.81 240	97.00 193	88.57 278	96.65 260	96.49 315	89.63 283	92.15 250	96.12 259	78.66 328	98.50 267	90.83 240	79.18 439	97.36 271

KinetiMVS		95.26 118	94.75 136	96.79 90	96.99 194	92.05 120	97.82 101	97.78 146	94.77 63	96.46 109	97.70 147	80.62 289	99.34 138	92.37 202	98.28 140	98.97 111

BH-w/o		92.14 263	91.75 251	93.31 337	96.99 194	85.73 366	95.67 337	95.69 360	88.73 320	89.26 334	94.82 325	82.97 236	98.07 314	85.26 368	96.32 222	96.13 313

guyue		95.17 128	94.96 123	95.82 169	96.97 196	89.65 229	97.56 145	95.58 367	94.82 57	95.72 140	97.42 174	82.90 238	98.84 207	96.71 67	96.93 192	98.96 114

GeoE		93.89 186	93.28 191	95.72 184	96.96 197	89.75 225	98.24 43	96.92 284	89.47 289	92.12 252	97.21 189	84.42 204	98.39 279	87.71 315	96.50 213	99.01 106

viewcassd2359sk11		95.26 118	95.09 118	95.80 171	96.95 198	89.72 226	96.80 241	97.56 185	92.21 180	95.37 155	97.80 138	87.17 146	98.77 219	94.82 142	97.10 187	98.90 130

viewdifsd2359ckpt09		94.81 150	94.37 154	96.12 144	96.91 199	90.75 185	96.94 221	97.31 233	90.51 260	94.31 190	97.38 176	85.70 174	98.71 241	93.54 179	96.75 200	98.90 130

myMVS_eth3d28		91.52 292	90.97 281	93.17 343	96.91 199	83.24 407	95.61 343	94.96 399	92.24 177	91.98 256	93.28 403	69.31 416	98.40 274	88.71 296	95.68 235	97.88 241

3Dnovator+	91.43 4	95.40 111	94.48 150	98.16 17	96.90 201	95.34 17	98.48 25	97.87 132	94.65 70	88.53 354	98.02 103	83.69 216	99.71 67	93.18 189	98.96 109	99.44 61

viewdifsd2359ckpt13		94.87 145	94.52 147	95.90 161	96.88 202	90.19 208	96.92 224	97.36 226	91.26 219	94.65 180	97.46 170	85.79 172	98.64 252	93.64 178	96.76 199	98.88 138

viewmanbaseed2359cas		95.24 121	95.02 120	95.91 160	96.87 203	89.98 215	96.82 237	97.49 195	92.26 176	95.47 152	97.82 134	86.47 156	98.69 243	94.80 144	97.20 183	99.06 101

MGCNet		96.74 64	96.31 81	98.02 20	96.87 203	94.65 31	97.58 141	94.39 422	96.47 12	97.16 74	98.39 68	87.53 135	99.87 7	98.97 20	99.41 59	99.55 43

casdiffmvs_mvg		95.81 101	95.57 94	96.51 111	96.87 203	91.49 144	97.50 154	97.56 185	93.99 95	95.13 163	97.92 114	87.89 123	98.78 215	95.97 98	97.33 175	99.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UGNet		94.04 177	93.28 191	96.31 129	96.85 206	91.19 161	97.88 91	97.68 158	94.40 82	93.00 231	96.18 254	73.39 384	99.61 90	91.72 221	98.46 132	98.13 218
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
VDDNet		93.05 222	92.07 237	96.02 152	96.84 207	90.39 198	98.08 59	95.85 351	86.22 384	95.79 138	98.46 62	67.59 429	99.19 155	94.92 132	94.85 254	98.47 185

RPSCF		90.75 329	90.86 285	90.42 421	96.84 207	76.29 467	95.61 343	96.34 323	83.89 416	91.38 271	97.87 124	76.45 353	98.78 215	87.16 338	92.23 303	96.20 306

FE-MVS		92.05 266	91.05 278	95.08 221	96.83 209	87.93 304	93.91 419	95.70 358	86.30 381	94.15 198	94.97 315	76.59 351	99.21 153	84.10 381	96.86 195	98.09 227

MVS_Test		94.89 143	94.62 140	95.68 186	96.83 209	89.55 237	96.70 254	97.17 248	91.17 226	95.60 147	96.11 263	87.87 125	98.76 225	93.01 197	97.17 185	98.72 161

reproduce_monomvs		91.30 306	91.10 277	91.92 382	96.82 211	82.48 417	97.01 214	97.49 195	94.64 71	88.35 357	95.27 304	70.53 404	98.10 305	95.20 122	84.60 404	95.19 367

LCM-MVSNet-Re		92.50 242	92.52 226	92.44 365	96.82 211	81.89 424	96.92 224	93.71 441	92.41 171	84.30 426	94.60 336	85.08 191	97.03 414	91.51 226	97.36 173	98.40 193

ETVMVS		90.52 338	89.14 359	94.67 250	96.81 213	87.85 309	95.91 323	93.97 435	89.71 280	92.34 246	92.48 416	65.41 447	97.96 333	81.37 412	94.27 268	98.21 211

E2		95.20 124	95.00 121	95.79 174	96.79 214	89.66 227	96.82 237	97.58 174	92.35 173	95.28 157	97.83 132	86.68 151	98.76 225	94.79 147	96.92 193	98.95 118

mamba_0408		93.70 194	92.99 200	95.83 168	96.79 214	90.38 199	88.69 476	97.07 262	90.96 236	93.68 208	97.31 181	84.97 195	98.76 225	90.95 238	96.51 210	98.35 199

SSM_04072		93.51 202	92.99 200	95.05 222	96.79 214	90.38 199	88.69 476	97.07 262	90.96 236	93.68 208	97.31 181	84.97 195	96.42 431	90.95 238	96.51 210	98.35 199

SSM_0407		94.54 157	94.12 162	95.80 171	96.79 214	90.38 199	96.79 242	97.29 235	91.24 220	93.68 208	97.60 161	85.03 192	98.67 248	92.14 208	96.51 210	98.35 199

GDP-MVS		95.62 105	95.13 114	97.09 79	96.79 214	93.26 76	97.89 89	97.83 142	93.58 107	96.80 85	97.82 134	83.06 233	99.16 162	94.40 160	97.95 156	98.87 140

test_cas_vis1_n_1920		94.48 160	94.55 146	94.28 276	96.78 219	86.45 347	97.63 136	97.64 163	93.32 125	97.68 60	98.36 71	73.75 380	99.08 177	96.73 65	99.05 103	97.31 275

baseline		95.58 107	95.42 103	96.08 145	96.78 219	90.41 197	97.16 202	97.45 208	93.69 106	95.65 146	97.85 128	87.29 143	98.68 245	95.66 108	97.25 181	99.13 89

E3		95.20 124	95.00 121	95.79 174	96.77 221	89.66 227	96.82 237	97.58 174	92.35 173	95.28 157	97.83 132	86.69 150	98.76 225	94.79 147	96.92 193	98.95 118

FA-MVS(test-final)		93.52 201	92.92 205	95.31 212	96.77 221	88.54 280	94.82 382	96.21 338	89.61 284	94.20 194	95.25 306	83.24 225	99.14 167	90.01 259	96.16 223	98.25 208

Fast-Effi-MVS+		93.46 203	92.75 213	95.59 191	96.77 221	90.03 210	96.81 240	97.13 250	88.19 334	91.30 276	94.27 359	86.21 162	98.63 254	87.66 323	96.46 216	98.12 220

QAPM		93.45 206	92.27 233	96.98 85	96.77 221	92.62 98	98.39 29	98.12 86	84.50 410	88.27 362	97.77 140	82.39 253	99.81 35	85.40 365	98.81 114	98.51 179

viewdifsd2359ckpt07		94.76 153	94.68 138	95.01 226	96.76 225	87.41 317	96.38 286	97.43 215	92.65 160	94.52 184	97.75 141	85.55 182	98.81 211	94.36 162	96.69 204	98.82 146

casdiffmvs		95.64 104	95.49 97	96.08 145	96.76 225	90.45 194	97.29 184	97.44 212	94.00 94	95.46 153	97.98 108	87.52 137	98.73 235	95.64 112	97.33 175	99.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CHOSEN 280x420		93.12 218	92.72 216	94.34 271	96.71 227	87.27 321	90.29 466	97.72 153	86.61 376	91.34 273	95.29 301	84.29 208	98.41 273	93.25 187	98.94 110	97.35 273

BP-MVS1		95.89 98	95.49 97	97.08 81	96.67 228	93.20 77	98.08 59	96.32 324	94.56 72	96.32 115	97.84 130	84.07 212	99.15 164	96.75 64	98.78 116	98.90 130

fmvsm_s_conf0.1_n		96.58 73	96.77 60	96.01 155	96.67 228	90.25 206	97.91 86	98.38 38	94.48 77	98.84 28	99.14 2	88.06 119	99.62 89	98.82 23	98.60 125	98.15 217

E5new		95.04 132	94.88 126	95.52 196	96.62 230	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.89 118	85.65 177	98.77 219	94.92 132	96.44 217	98.78 149

E5		95.04 132	94.88 126	95.52 196	96.62 230	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.89 118	85.65 177	98.77 219	94.92 132	96.44 217	98.78 149

test_fmvsmvis_n_1920		96.70 65	96.84 51	96.31 129	96.62 230	91.73 130	97.98 72	98.30 48	96.19 14	96.10 125	98.95 20	89.42 94	99.76 54	98.90 22	99.08 101	97.43 268

Effi-MVS+		94.93 141	94.45 151	96.36 127	96.61 233	91.47 147	96.41 280	97.41 218	91.02 234	94.50 185	95.92 268	87.53 135	98.78 215	93.89 172	96.81 197	98.84 145

E6new		95.04 132	94.88 126	95.52 196	96.60 234	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.90 116	85.66 175	98.77 219	94.92 132	96.44 217	98.78 149

E6		95.04 132	94.88 126	95.52 196	96.60 234	89.02 263	97.29 184	97.57 177	92.54 163	95.04 165	97.90 116	85.66 175	98.77 219	94.92 132	96.44 217	98.78 149

thisisatest0515		92.29 255	91.30 268	95.25 214	96.60 234	88.90 269	94.36 401	92.32 458	87.92 342	93.43 221	94.57 337	77.28 346	99.00 190	89.42 276	95.86 230	97.86 245

PCF-MVS	89.48 11	91.56 288	89.95 332	96.36 127	96.60 234	92.52 103	92.51 450	97.26 239	79.41 457	88.90 342	96.56 236	84.04 213	99.55 108	77.01 442	97.30 178	97.01 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
E4		95.09 129	94.86 130	95.77 177	96.58 238	89.56 235	96.85 232	97.56 185	92.50 167	95.03 169	97.86 126	86.03 166	98.78 215	94.71 150	96.65 207	98.96 114

VortexMVS		92.88 232	92.64 218	93.58 324	96.58 238	87.53 316	96.93 223	97.28 238	92.78 157	89.75 315	94.99 314	82.73 243	97.76 360	94.60 155	88.16 360	95.46 342

xiu_mvs_v1_base_debu		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 314

xiu_mvs_v1_base		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 314

xiu_mvs_v1_base_debi		95.01 136	94.76 133	95.75 180	96.58 238	91.71 133	96.25 299	97.35 228	92.99 140	96.70 91	96.63 231	82.67 244	99.44 129	96.22 83	97.46 167	96.11 314

MVSTER		93.20 214	92.81 210	94.37 268	96.56 243	89.59 233	97.06 208	97.12 251	91.24 220	91.30 276	95.96 266	82.02 260	98.05 317	93.48 182	90.55 333	95.47 341

3Dnovator	91.36 5	95.19 127	94.44 152	97.44 57	96.56 243	93.36 70	98.65 16	98.36 39	94.12 90	89.25 335	98.06 98	82.20 256	99.77 52	93.41 185	99.32 71	99.18 84

test_fmvs1		93.21 213	93.53 178	92.25 375	96.55 245	81.20 430	97.40 172	96.96 277	90.68 246	96.80 85	98.04 100	69.25 417	98.40 274	97.58 40	98.50 128	97.16 281

testing91		91.90 271	91.02 279	94.53 261	96.54 246	86.55 344	95.86 325	95.64 364	91.77 196	91.89 259	93.47 398	69.94 411	98.86 203	90.23 258	93.86 282	98.18 213

testing222		90.31 342	88.96 361	94.35 269	96.54 246	87.29 319	95.50 348	93.84 439	90.97 235	91.75 264	92.96 407	62.18 460	98.00 324	82.86 393	94.08 275	97.76 251

viewmacassd2359aftdt		95.07 131	94.80 132	95.87 163	96.53 248	89.84 221	96.90 227	97.48 197	92.44 169	95.36 156	97.89 118	85.23 188	98.68 245	94.40 160	97.00 191	99.09 96

testing11		91.68 279	90.75 293	94.47 263	96.53 248	86.56 343	95.76 333	94.51 418	91.10 232	91.24 281	93.59 393	68.59 423	98.86 203	91.10 235	94.29 267	98.00 234

FMVSNet3		91.78 274	90.69 298	95.03 225	96.53 248	92.27 112	97.02 211	96.93 280	89.79 279	89.35 329	94.65 334	77.01 347	97.47 394	86.12 353	88.82 352	95.35 353

UBG		91.55 289	90.76 291	93.94 299	96.52 251	85.06 382	95.22 366	94.54 416	90.47 261	91.98 256	92.71 410	72.02 392	98.74 233	88.10 303	95.26 248	98.01 233

GBi-Net		91.35 302	90.27 315	94.59 253	96.51 252	91.18 163	97.50 154	96.93 280	88.82 315	89.35 329	94.51 341	73.87 376	97.29 406	86.12 353	88.82 352	95.31 356

test1		91.35 302	90.27 315	94.59 253	96.51 252	91.18 163	97.50 154	96.93 280	88.82 315	89.35 329	94.51 341	73.87 376	97.29 406	86.12 353	88.82 352	95.31 356

FMVSNet2		91.31 305	90.08 324	94.99 228	96.51 252	92.21 114	97.41 168	96.95 278	88.82 315	88.62 351	94.75 328	73.87 376	97.42 399	85.20 369	88.55 357	95.35 353

WBMVS		90.69 334	89.99 331	92.81 357	96.48 255	85.00 383	95.21 368	96.30 326	89.46 290	89.04 341	94.05 372	72.45 391	97.82 352	89.46 274	87.41 370	95.61 336

testing99		91.62 283	90.72 296	94.32 272	96.48 255	86.11 361	95.81 329	94.76 408	91.55 201	91.75 264	93.44 399	68.55 424	98.82 209	90.43 252	93.69 284	98.04 231

ACMH+	87.92 14	90.20 348	89.18 357	93.25 339	96.48 255	86.45 347	96.99 217	96.68 303	88.83 314	84.79 423	96.22 253	70.16 408	98.53 265	84.42 378	88.04 361	94.77 402

CANet_DTU		94.37 161	93.65 173	96.55 104	96.46 258	92.13 118	96.21 303	96.67 305	94.38 84	93.53 216	97.03 206	79.34 313	99.71 67	90.76 244	98.45 133	97.82 249

mvs_anonymous		93.82 189	93.74 170	94.06 287	96.44 259	85.41 373	95.81 329	97.05 268	89.85 276	90.09 306	96.36 246	87.44 140	97.75 362	93.97 168	96.69 204	99.02 103

diffmvs		95.25 120	95.13 114	95.63 188	96.43 260	89.34 248	95.99 318	97.35 228	92.83 154	96.31 116	97.37 177	86.44 158	98.67 248	96.26 80	97.19 184	98.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ET-MVSNet_ETH3D		91.49 294	90.11 323	95.63 188	96.40 261	91.57 142	95.34 356	93.48 443	90.60 255	75.58 469	95.49 295	80.08 300	96.79 425	94.25 164	89.76 341	98.52 177

RRT-MVS		94.51 158	94.35 155	94.98 230	96.40 261	86.55 344	97.56 145	97.41 218	93.19 130	94.93 171	97.04 201	79.12 317	99.30 145	96.19 90	97.32 177	99.09 96

TR-MVS		91.48 295	90.59 303	94.16 283	96.40 261	87.33 318	95.67 337	95.34 381	87.68 355	91.46 270	95.52 294	76.77 350	98.35 282	82.85 395	93.61 288	96.79 292

ACMP	89.59 10	92.62 241	92.14 236	94.05 288	96.40 261	88.20 296	97.36 176	97.25 241	91.52 206	88.30 360	96.64 227	78.46 331	98.72 240	91.86 218	91.48 317	95.23 363
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
diffmvs_AUTHOR		95.33 114	95.27 110	95.50 202	96.37 265	89.08 261	96.08 312	97.38 223	93.09 138	96.53 104	97.74 144	86.45 157	98.68 245	96.32 78	97.48 166	98.75 157

AstraMVS		94.82 149	94.64 139	95.34 211	96.36 266	88.09 301	97.58 141	94.56 415	94.98 46	95.70 143	97.92 114	81.93 264	98.93 196	96.87 61	95.88 228	98.99 110

MVSFormer		95.37 112	95.16 113	95.99 157	96.34 267	91.21 158	98.22 46	97.57 177	91.42 211	96.22 120	97.32 179	86.20 163	97.92 342	94.07 166	99.05 103	98.85 142

lupinMVS		94.99 140	94.56 143	96.29 133	96.34 267	91.21 158	95.83 327	96.27 331	88.93 310	96.22 120	96.88 213	86.20 163	98.85 205	95.27 121	99.05 103	98.82 146

ACMM	89.79 8	92.96 226	92.50 227	94.35 269	96.30 269	88.71 272	97.58 141	97.36 226	91.40 213	90.53 290	96.65 226	79.77 306	98.75 231	91.24 233	91.64 313	95.59 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		92.29 255	91.94 244	93.34 336	96.25 270	86.97 331	96.57 272	97.05 268	90.67 247	89.50 326	94.80 326	86.59 152	97.64 372	89.91 262	86.11 382	95.40 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewmambaseed2359dif		94.28 163	94.14 160	94.71 248	96.21 271	86.97 331	95.93 321	97.11 255	89.00 305	95.00 170	97.70 147	86.02 167	98.59 261	93.71 177	96.59 209	98.57 173

HQP_MVS		93.78 191	93.43 186	94.82 238	96.21 271	89.99 213	97.74 114	97.51 192	94.85 53	91.34 273	96.64 227	81.32 273	98.60 257	93.02 195	92.23 303	95.86 319

plane_prior7					96.21 271	89.98 215

ACMH	87.59 16	90.53 337	89.42 351	93.87 304	96.21 271	87.92 305	97.24 191	96.94 279	88.45 328	83.91 434	96.27 251	71.92 393	98.62 256	84.43 377	89.43 344	95.05 374
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
icg_test_0407_2		93.58 197	93.46 183	93.94 299	96.19 275	86.16 356	93.73 425	97.24 242	91.54 202	93.50 217	97.04 201	85.64 180	96.91 420	90.68 247	95.59 238	98.76 153

IMVS_0407		93.94 183	93.75 169	94.49 262	96.19 275	86.16 356	96.35 289	97.24 242	91.54 202	93.50 217	97.04 201	85.64 180	98.54 264	90.68 247	95.59 238	98.76 153

IMVS_0404		92.44 245	91.92 245	94.00 291	96.19 275	86.16 356	93.84 422	97.24 242	91.54 202	88.17 366	97.04 201	76.96 349	97.09 411	90.68 247	95.59 238	98.76 153

IMVS_0403		93.98 181	93.79 168	94.55 259	96.19 275	86.16 356	96.35 289	97.24 242	91.54 202	93.59 212	97.04 201	85.86 169	98.73 235	90.68 247	95.59 238	98.76 153

CDS-MVSNet		94.14 172	93.54 177	95.93 159	96.18 279	91.46 148	96.33 293	97.04 270	88.97 308	93.56 213	96.51 238	87.55 133	97.89 346	89.80 265	95.95 226	98.44 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LTVRE_ROB	88.41 13	90.99 320	89.92 334	94.19 279	96.18 279	89.55 237	96.31 295	97.09 258	87.88 344	85.67 413	95.91 269	78.79 327	98.57 262	81.50 406	89.98 338	94.44 413
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
LPG-MVS_test		92.94 228	92.56 222	94.10 285	96.16 281	88.26 291	97.65 130	97.46 203	91.29 215	90.12 303	97.16 191	79.05 319	98.73 235	92.25 205	91.89 311	95.31 356

LGP-MVS_train				94.10 285	96.16 281	88.26 291		97.46 203	91.29 215	90.12 303	97.16 191	79.05 319	98.73 235	92.25 205	91.89 311	95.31 356

TAMVS		94.01 178	93.46 183	95.64 187	96.16 281	90.45 194	96.71 253	96.89 288	89.27 296	93.46 220	96.92 211	87.29 143	97.94 339	88.70 297	95.74 232	98.53 176

testing3		87.67 385	86.88 386	90.05 425	96.14 284	80.71 433	97.10 206	92.85 451	90.15 269	87.54 377	94.55 338	55.70 470	94.10 463	73.77 457	94.10 274	95.35 353

plane_prior1					96.14 284

viewmsd2359difaftdt		93.46 203	93.23 193	94.17 280	96.12 286	85.42 371	96.43 276	97.08 259	92.91 148	94.21 193	98.00 105	80.82 285	98.74 233	94.41 159	89.05 349	98.34 203

CLD-MVS		92.98 225	92.53 225	94.32 272	96.12 286	89.20 256	95.28 360	97.47 201	92.66 159	89.90 310	95.62 288	80.58 290	98.40 274	92.73 200	92.40 301	95.38 351
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewdifsd2359ckpt11		93.46 203	93.22 194	94.17 280	96.11 288	85.42 371	96.43 276	97.07 262	92.91 148	94.20 194	98.00 105	80.82 285	98.73 235	94.42 158	89.04 351	98.34 203

plane_prior6					96.10 289	90.00 211						81.32 273

cl22		91.21 310	90.56 305	93.14 345	96.09 290	86.80 334	94.41 399	96.58 312	87.80 349	88.58 353	93.99 375	80.85 284	97.62 375	89.87 264	86.93 373	94.99 375

Elysia		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 154	97.63 165	91.15 228	94.82 174	97.12 194	74.98 367	99.06 183	90.78 242	98.02 151	98.12 220

StellarMVS		94.00 179	93.12 196	96.64 94	96.08 291	92.72 95	97.50 154	97.63 165	91.15 228	94.82 174	97.12 194	74.98 367	99.06 183	90.78 242	98.02 151	98.12 220

test_fmvs1_n		92.73 239	92.88 207	92.29 372	96.08 291	81.05 431	97.98 72	97.08 259	90.72 244	96.79 87	98.18 91	63.07 455	98.45 271	97.62 39	98.42 135	97.36 271

Effi-MVS+-dtu		93.08 220	93.21 195	92.68 363	96.02 294	83.25 406	97.14 204	96.72 298	93.85 100	91.20 283	93.44 399	83.08 231	98.30 286	91.69 224	95.73 233	96.50 299

NP-MVS					95.99 295	89.81 223					95.87 270

UWE-MVS		89.91 354	89.48 350	91.21 404	95.88 296	78.23 462	94.91 379	90.26 472	89.11 300	92.35 245	94.52 340	68.76 421	97.96 333	83.95 385	95.59 238	97.42 269

ADS-MVSNet2		89.45 365	88.59 367	92.03 380	95.86 297	82.26 421	90.93 462	94.32 427	83.23 428	91.28 279	91.81 431	79.01 323	95.99 436	79.52 425	91.39 319	97.84 246

ADS-MVSNet		89.89 356	88.68 366	93.53 327	95.86 297	84.89 387	90.93 462	95.07 393	83.23 428	91.28 279	91.81 431	79.01 323	97.85 348	79.52 425	91.39 319	97.84 246

HQP-NCC					95.86 297		96.65 260		93.55 109	90.14 297

ACMP_Plane					95.86 297		96.65 260		93.55 109	90.14 297

HQP-MVS		93.19 215	92.74 214	94.54 260	95.86 297	89.33 249	96.65 260	97.39 220	93.55 109	90.14 297	95.87 270	80.95 279	98.50 267	92.13 211	92.10 308	95.78 327

mmtdpeth		89.70 363	88.96 361	91.90 384	95.84 302	84.42 391	97.46 165	95.53 373	90.27 265	94.46 187	90.50 440	69.74 415	98.95 193	97.39 53	69.48 474	92.34 450

EI-MVSNet		93.03 223	92.88 207	93.48 331	95.77 303	86.98 330	96.44 274	97.12 251	90.66 249	91.30 276	97.64 157	86.56 153	98.05 317	89.91 262	90.55 333	95.41 346

CVMVSNet		91.23 309	91.75 251	89.67 430	95.77 303	74.69 469	96.44 274	94.88 403	85.81 389	92.18 249	97.64 157	79.07 318	95.58 447	88.06 304	95.86 230	98.74 160

FIs		94.09 174	93.70 171	95.27 213	95.70 305	92.03 122	98.10 57	98.68 19	93.36 124	90.39 293	96.70 222	87.63 131	97.94 339	92.25 205	90.50 335	95.84 322

VPA-MVSNet		93.24 212	92.48 228	95.51 200	95.70 305	92.39 106	97.86 92	98.66 22	92.30 175	92.09 254	95.37 299	80.49 292	98.40 274	93.95 169	85.86 383	95.75 331

test_fmvsmconf0.1_n		97.09 38	97.06 35	97.19 73	95.67 307	92.21 114	97.95 81	98.27 55	95.78 23	98.40 42	99.00 16	89.99 88	99.78 49	99.06 18	99.41 59	99.59 32

SD_0403		90.01 352	90.02 330	89.96 427	95.65 308	76.76 464	95.76 333	96.46 317	90.58 256	86.59 399	96.29 249	82.12 258	94.78 456	73.00 461	93.76 283	98.35 199

tt0805		91.09 315	90.07 327	94.16 283	95.61 309	88.31 288	97.56 145	96.51 314	89.56 285	89.17 338	95.64 287	67.08 436	98.38 280	91.07 236	88.44 358	95.80 325

SCA		91.84 273	91.18 275	93.83 305	95.59 310	84.95 386	94.72 384	95.58 367	90.82 239	92.25 248	93.69 385	75.80 359	98.10 305	86.20 350	95.98 225	98.45 187

c3_l		91.38 299	90.89 283	92.88 354	95.58 311	86.30 350	94.68 385	96.84 293	88.17 335	88.83 348	94.23 362	85.65 177	97.47 394	89.36 277	84.63 402	94.89 384

VPNet		92.23 259	91.31 267	94.99 228	95.56 312	90.96 172	97.22 197	97.86 136	92.96 146	90.96 284	96.62 234	75.06 365	98.20 294	91.90 215	83.65 418	95.80 325

miper_ehance_all_eth		91.59 285	91.13 276	92.97 350	95.55 313	86.57 342	94.47 395	96.88 289	87.77 351	88.88 344	94.01 373	86.22 161	97.54 387	89.49 273	86.93 373	94.79 399

IterMVS-SCA-FT		90.31 342	89.81 338	91.82 388	95.52 314	84.20 395	94.30 405	96.15 341	90.61 253	87.39 381	94.27 359	75.80 359	96.44 430	87.34 332	86.88 377	94.82 394

jason		94.84 147	94.39 153	96.18 141	95.52 314	90.93 176	96.09 311	96.52 313	89.28 295	96.01 130	97.32 179	84.70 199	98.77 219	95.15 125	98.91 112	98.85 142
jason: jason.
LuminaMVS		94.89 143	94.35 155	96.53 105	95.48 316	92.80 91	96.88 230	96.18 340	92.85 153	95.92 133	96.87 215	81.44 271	98.83 208	96.43 77	97.10 187	97.94 237

fmvsm_s_conf0.1_n_a		96.40 79	96.47 73	96.16 142	95.48 316	90.69 187	97.91 86	98.33 45	94.07 91	98.93 20	99.14 2	87.44 140	99.61 90	98.63 26	98.32 138	98.18 213

FC-MVSNet-test		93.94 183	93.57 175	95.04 224	95.48 316	91.45 149	98.12 56	98.71 13	93.37 122	90.23 296	96.70 222	87.66 128	97.85 348	91.49 227	90.39 336	95.83 323

IterMVS		90.15 350	89.67 344	91.61 395	95.48 316	83.72 401	94.33 403	96.12 342	89.99 272	87.31 384	94.15 367	75.78 361	96.27 434	86.97 341	86.89 376	94.83 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		90.21 347	89.50 349	92.35 368	95.47 320	85.15 379	95.70 336	94.37 424	90.94 238	88.42 355	93.57 394	74.63 371	95.67 444	82.80 396	89.57 343	96.22 305

FMVSNet1		89.88 357	88.31 370	94.59 253	95.41 321	91.18 163	97.50 154	96.93 280	86.62 375	87.41 380	94.51 341	65.94 444	97.29 406	83.04 392	87.43 368	95.31 356

UniMVSNet (Re)		93.31 210	92.55 223	95.61 190	95.39 322	93.34 71	97.39 173	98.71 13	93.14 135	90.10 305	94.83 324	87.71 127	98.03 321	91.67 225	83.99 412	95.46 342

MVS-HIRNet		82.47 433	81.21 436	86.26 452	95.38 323	69.21 479	88.96 475	89.49 474	66.28 481	80.79 450	74.08 486	68.48 425	97.39 401	71.93 464	95.47 243	92.18 455

PatchmatchNet		91.91 270	91.35 264	93.59 323	95.38 323	84.11 396	93.15 440	95.39 375	89.54 286	92.10 253	93.68 387	82.82 241	98.13 300	84.81 372	95.32 246	98.52 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl____		90.96 323	90.32 311	92.89 353	95.37 325	86.21 353	94.46 397	96.64 306	87.82 347	88.15 367	94.18 365	82.98 235	97.54 387	87.70 316	85.59 385	94.92 382

DIV-MVS_self_test		90.97 322	90.33 310	92.88 354	95.36 326	86.19 355	94.46 397	96.63 309	87.82 347	88.18 365	94.23 362	82.99 234	97.53 389	87.72 313	85.57 386	94.93 380

miper_enhance_ethall		91.54 291	91.01 280	93.15 344	95.35 327	87.07 329	93.97 414	96.90 286	86.79 373	89.17 338	93.43 402	86.55 154	97.64 372	89.97 261	86.93 373	94.74 404

UniMVSNet_NR-MVSNet		93.37 208	92.67 217	95.47 206	95.34 328	92.83 89	97.17 201	98.58 28	92.98 145	90.13 301	95.80 275	88.37 115	97.85 348	91.71 222	83.93 413	95.73 333

ITE_SJBPF				92.43 366	95.34 328	85.37 376		95.92 346	91.47 208	87.75 374	96.39 245	71.00 400	97.96 333	82.36 402	89.86 340	93.97 426

OpenMVS	89.19 12	92.86 233	91.68 254	96.40 122	95.34 328	92.73 94	98.27 37	98.12 86	84.86 405	85.78 412	97.75 141	78.89 326	99.74 59	87.50 330	98.65 122	96.73 293

eth_miper_zixun_eth		91.02 319	90.59 303	92.34 370	95.33 331	84.35 392	94.10 411	96.90 286	88.56 324	88.84 347	94.33 354	84.08 211	97.60 377	88.77 295	84.37 409	95.06 373

miper_lstm_enhance		90.50 340	90.06 328	91.83 387	95.33 331	83.74 400	93.86 420	96.70 302	87.56 358	87.79 372	93.81 381	83.45 222	96.92 419	87.39 331	84.62 403	94.82 394

1314		92.81 237	92.03 240	95.14 218	95.33 331	89.52 240	96.04 314	97.44 212	87.72 354	86.25 403	95.33 300	83.84 214	98.79 214	89.26 281	97.05 190	97.11 282

PAPM		91.52 292	90.30 313	95.20 215	95.30 334	89.83 222	93.38 436	96.85 292	86.26 383	88.59 352	95.80 275	84.88 197	98.15 299	75.67 447	95.93 227	97.63 256

Fast-Effi-MVS+-dtu		92.29 255	91.99 242	93.21 342	95.27 335	85.52 369	97.03 209	96.63 309	92.09 186	89.11 340	95.14 310	80.33 296	98.08 310	87.54 327	94.74 260	96.03 317

Patchmatch-test		89.42 366	87.99 373	93.70 313	95.27 335	85.11 380	88.98 474	94.37 424	81.11 446	87.10 389	93.69 385	82.28 254	97.50 392	74.37 453	94.76 258	98.48 184

PVSNet_0	82.17 19	85.46 419	83.64 421	90.92 410	95.27 335	79.49 454	90.55 465	95.60 365	83.76 420	83.00 441	89.95 446	71.09 399	97.97 329	82.75 398	60.79 486	95.31 356

IB-MVS	87.33 17	89.91 354	88.28 371	94.79 244	95.26 338	87.70 312	95.12 374	93.95 436	89.35 294	87.03 390	92.49 415	70.74 403	99.19 155	89.18 286	81.37 430	97.49 265
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
nrg030		94.05 176	93.31 190	96.27 134	95.22 339	94.59 33	98.34 30	97.46 203	92.93 147	91.21 282	96.64 227	87.23 145	98.22 292	94.99 129	85.80 384	95.98 318

MDTV_nov1_ep13			90.76 291		95.22 339	80.33 440	93.03 443	95.28 382	88.14 338	92.84 237	93.83 378	81.34 272	98.08 310	82.86 393	94.34 265

MVS		91.71 276	90.44 307	95.51 200	95.20 341	91.59 140	96.04 314	97.45 208	73.44 473	87.36 382	95.60 289	85.42 184	99.10 172	85.97 357	97.46 167	95.83 323

SSC-MVS3.2		89.74 362	89.26 355	91.19 407	95.16 342	80.29 442	94.53 390	97.03 272	91.79 195	88.86 345	94.10 368	69.94 411	97.82 352	85.29 366	86.66 378	95.45 344

Syy-MVS		87.13 394	87.02 385	87.47 446	95.16 342	73.21 474	95.00 376	93.93 437	88.55 325	86.96 392	91.99 427	75.90 357	94.00 464	61.59 480	94.11 272	95.20 364

myMVS_eth3d		87.18 393	86.38 390	89.58 431	95.16 342	79.53 452	95.00 376	93.93 437	88.55 325	86.96 392	91.99 427	56.23 469	94.00 464	75.47 449	94.11 272	95.20 364

tfpnnormal		89.70 363	88.40 369	93.60 322	95.15 345	90.10 209	97.56 145	98.16 80	87.28 365	86.16 405	94.63 335	77.57 344	98.05 317	74.48 451	84.59 405	92.65 444

tpmrst		91.44 296	91.32 266	91.79 390	95.15 345	79.20 457	93.42 435	95.37 377	88.55 325	93.49 219	93.67 388	82.49 250	98.27 289	90.41 253	89.34 345	97.90 239

WR-MVS		92.34 251	91.53 259	94.77 245	95.13 347	90.83 180	96.40 284	97.98 120	91.88 193	89.29 332	95.54 293	82.50 249	97.80 355	89.79 266	85.27 392	95.69 334

tpm cat1		88.36 378	87.21 381	91.81 389	95.13 347	80.55 437	92.58 449	95.70 358	74.97 469	87.45 378	91.96 429	78.01 341	98.17 298	80.39 421	88.74 355	96.72 294

WR-MVS_H		92.00 267	91.35 264	93.95 297	95.09 349	89.47 241	98.04 64	98.68 19	91.46 209	88.34 358	94.68 331	85.86 169	97.56 380	85.77 360	84.24 410	94.82 394

CP-MVSNet		91.89 272	91.24 271	93.82 306	95.05 350	88.57 278	97.82 101	98.19 74	91.70 198	88.21 364	95.76 280	81.96 261	97.52 391	87.86 307	84.65 401	95.37 352

test_0402		86.46 404	84.79 411	91.45 398	95.02 351	85.55 368	96.29 297	94.89 402	80.90 447	82.21 444	93.97 376	68.21 427	97.29 406	62.98 478	88.68 356	91.51 462

cascas		91.20 311	90.08 324	94.58 257	94.97 352	89.16 259	93.65 430	97.59 173	79.90 455	89.40 327	92.92 408	75.36 363	98.36 281	92.14 208	94.75 259	96.23 304

PS-CasMVS		91.55 289	90.84 288	93.69 314	94.96 353	88.28 290	97.84 96	98.24 63	91.46 209	88.04 369	95.80 275	79.67 308	97.48 393	87.02 340	84.54 407	95.31 356

DU-MVS		92.90 230	92.04 239	95.49 203	94.95 354	92.83 89	97.16 202	98.24 63	93.02 139	90.13 301	95.71 282	83.47 220	97.85 348	91.71 222	83.93 413	95.78 327

NR-MVSNet		92.34 251	91.27 270	95.53 195	94.95 354	93.05 81	97.39 173	98.07 98	92.65 160	84.46 424	95.71 282	85.00 194	97.77 359	89.71 267	83.52 419	95.78 327

mvsany_test1		93.93 185	93.98 164	93.78 309	94.94 356	86.80 334	94.62 386	92.55 456	88.77 319	96.85 84	98.49 58	88.98 100	98.08 310	95.03 127	95.62 237	96.46 302

tpmvs		89.83 360	89.15 358	91.89 385	94.92 357	80.30 441	93.11 441	95.46 374	86.28 382	88.08 368	92.65 411	80.44 293	98.52 266	81.47 408	89.92 339	96.84 290

PMMVS		92.86 233	92.34 231	94.42 267	94.92 357	86.73 337	94.53 390	96.38 322	84.78 407	94.27 191	95.12 312	83.13 230	98.40 274	91.47 228	96.49 214	98.12 220

tpm2		89.96 353	89.21 356	92.23 376	94.91 359	81.25 428	93.78 423	94.42 420	80.62 452	91.56 267	93.44 399	76.44 354	97.94 339	85.60 362	92.08 310	97.49 265

TinyColmap		86.82 399	85.35 402	91.21 404	94.91 359	82.99 411	93.94 416	94.02 434	83.58 422	81.56 447	94.68 331	62.34 459	98.13 300	75.78 445	87.35 372	92.52 448

UniMVSNet_ETH3D		91.34 304	90.22 320	94.68 249	94.86 361	87.86 308	97.23 195	97.46 203	87.99 340	89.90 310	96.92 211	66.35 439	98.23 291	90.30 256	90.99 327	97.96 235

CostFormer		91.18 314	90.70 297	92.62 364	94.84 362	81.76 425	94.09 412	94.43 419	84.15 413	92.72 238	93.77 382	79.43 312	98.20 294	90.70 246	92.18 306	97.90 239

MIMVSNet		88.50 377	86.76 387	93.72 312	94.84 362	87.77 311	91.39 456	94.05 432	86.41 379	87.99 370	92.59 414	63.27 454	95.82 441	77.44 436	92.84 294	97.57 263

FMVSNet5		87.29 390	85.79 395	91.78 391	94.80 364	87.28 320	95.49 349	95.28 382	84.09 414	83.85 435	91.82 430	62.95 456	94.17 462	78.48 432	85.34 391	93.91 427

TranMVSNet+NR-MVSNet		92.50 242	91.63 255	95.14 218	94.76 365	92.07 119	97.53 151	98.11 89	92.90 151	89.56 323	96.12 259	83.16 228	97.60 377	89.30 279	83.20 422	95.75 331

test_vis1_n		92.37 250	92.26 234	92.72 360	94.75 366	82.64 413	98.02 66	96.80 295	91.18 225	97.77 59	97.93 111	58.02 465	98.29 287	97.63 37	98.21 143	97.23 279

XXY-MVS		92.16 261	91.23 272	94.95 234	94.75 366	90.94 175	97.47 163	97.43 215	89.14 299	88.90 342	96.43 242	79.71 307	98.24 290	89.56 272	87.68 365	95.67 335

EPMVS		90.70 332	89.81 338	93.37 335	94.73 368	84.21 394	93.67 429	88.02 479	89.50 288	92.38 242	93.49 396	77.82 343	97.78 357	86.03 356	92.68 298	98.11 226

D2MVS		91.30 306	90.95 282	92.35 368	94.71 369	85.52 369	96.18 307	98.21 67	88.89 311	86.60 398	93.82 380	79.92 304	97.95 337	89.29 280	90.95 328	93.56 431

USDC		88.94 370	87.83 375	92.27 373	94.66 370	84.96 385	93.86 420	95.90 348	87.34 363	83.40 436	95.56 291	67.43 430	98.19 296	82.64 400	89.67 342	93.66 430

GA-MVS		91.38 299	90.31 312	94.59 253	94.65 371	87.62 314	94.34 402	96.19 339	90.73 243	90.35 294	93.83 378	71.84 394	97.96 333	87.22 335	93.61 288	98.21 211

OPM-MVS		93.28 211	92.76 211	94.82 238	94.63 372	90.77 183	96.65 260	97.18 246	93.72 103	91.68 266	97.26 186	79.33 314	98.63 254	92.13 211	92.28 302	95.07 372
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test-LLR		91.42 297	91.19 274	92.12 378	94.59 373	80.66 434	94.29 406	92.98 449	91.11 230	90.76 288	92.37 418	79.02 321	98.07 314	88.81 293	96.74 201	97.63 256

test-mter		90.19 349	89.54 348	92.12 378	94.59 373	80.66 434	94.29 406	92.98 449	87.68 355	90.76 288	92.37 418	67.67 428	98.07 314	88.81 293	96.74 201	97.63 256

dp		88.90 372	88.26 372	90.81 414	94.58 375	76.62 465	92.85 446	94.93 400	85.12 401	90.07 308	93.07 405	75.81 358	98.12 303	80.53 420	87.42 369	97.71 253

WB-MVSnew		89.88 357	89.56 347	90.82 413	94.57 376	83.06 410	95.65 341	92.85 451	87.86 346	90.83 287	94.10 368	79.66 309	96.88 421	76.34 443	94.19 270	92.54 447

PEN-MVS		91.20 311	90.44 307	93.48 331	94.49 377	87.91 307	97.76 109	98.18 76	91.29 215	87.78 373	95.74 281	80.35 295	97.33 404	85.46 364	82.96 423	95.19 367

gg-mvs-nofinetune		87.82 383	85.61 396	94.44 265	94.46 378	89.27 254	91.21 460	84.61 488	80.88 448	89.89 312	74.98 484	71.50 396	97.53 389	85.75 361	97.21 182	96.51 298

CR-MVSNet		90.82 327	89.77 340	93.95 297	94.45 379	87.19 325	90.23 467	95.68 362	86.89 371	92.40 240	92.36 421	80.91 281	97.05 413	81.09 416	93.95 280	97.60 261

RPMNet		88.98 369	87.05 383	94.77 245	94.45 379	87.19 325	90.23 467	98.03 110	77.87 465	92.40 240	87.55 468	80.17 299	99.51 117	68.84 473	93.95 280	97.60 261

TESTMET0.1,1		90.06 351	89.42 351	91.97 381	94.41 381	80.62 436	94.29 406	91.97 462	87.28 365	90.44 292	92.47 417	68.79 420	97.67 367	88.50 300	96.60 208	97.61 260

TransMVSNet (Re)		88.94 370	87.56 376	93.08 347	94.35 382	88.45 286	97.73 116	95.23 386	87.47 359	84.26 427	95.29 301	79.86 305	97.33 404	79.44 429	74.44 458	93.45 434

MS-PatchMatch		90.27 344	89.77 340	91.78 391	94.33 383	84.72 389	95.55 345	96.73 297	86.17 385	86.36 402	95.28 303	71.28 398	97.80 355	84.09 382	98.14 147	92.81 441

baseline2		91.63 282	90.86 285	93.94 299	94.33 383	86.32 349	95.92 322	91.64 464	89.37 293	86.94 394	94.69 330	81.62 269	98.69 243	88.64 298	94.57 263	96.81 291

XVG-ACMP-BASELINE		90.93 324	90.21 321	93.09 346	94.31 385	85.89 362	95.33 357	97.26 239	91.06 233	89.38 328	95.44 298	68.61 422	98.60 257	89.46 274	91.05 325	94.79 399

pm-mvs1		90.72 331	89.65 346	93.96 296	94.29 386	89.63 230	97.79 107	96.82 294	89.07 301	86.12 407	95.48 297	78.61 329	97.78 357	86.97 341	81.67 428	94.46 411

v8		91.29 308	90.53 306	93.57 326	94.15 387	88.12 300	97.34 178	97.06 267	88.99 306	88.32 359	94.26 361	83.08 231	98.01 323	87.62 325	83.92 415	94.57 409

v10		91.04 318	90.23 318	93.49 330	94.12 388	88.16 299	97.32 181	97.08 259	88.26 333	88.29 361	94.22 364	82.17 257	97.97 329	86.45 347	84.12 411	94.33 416

Patchmtry		88.64 376	87.25 379	92.78 359	94.09 389	86.64 338	89.82 471	95.68 362	80.81 450	87.63 376	92.36 421	80.91 281	97.03 414	78.86 431	85.12 395	94.67 406

PatchT		88.87 373	87.42 377	93.22 341	94.08 390	85.10 381	89.51 472	94.64 413	81.92 441	92.36 243	88.15 461	80.05 301	97.01 416	72.43 462	93.65 286	97.54 264

V42		91.58 287	90.87 284	93.73 310	94.05 391	88.50 283	97.32 181	96.97 276	88.80 318	89.71 316	94.33 354	82.54 248	98.05 317	89.01 288	85.07 396	94.64 408

DTE-MVSNet		90.56 336	89.75 342	93.01 348	93.95 392	87.25 322	97.64 134	97.65 161	90.74 242	87.12 386	95.68 285	79.97 303	97.00 417	83.33 389	81.66 429	94.78 401

tpm		90.25 345	89.74 343	91.76 393	93.92 393	79.73 449	93.98 413	93.54 442	88.28 332	91.99 255	93.25 404	77.51 345	97.44 397	87.30 334	87.94 362	98.12 220

PS-MVSNAJss		93.74 192	93.51 181	94.44 265	93.91 394	89.28 253	97.75 111	97.56 185	92.50 167	89.94 309	96.54 237	88.65 108	98.18 297	93.83 175	90.90 329	95.86 319

v1144		91.37 301	90.60 302	93.68 316	93.89 395	88.23 293	96.84 235	97.03 272	88.37 330	89.69 318	94.39 348	82.04 259	97.98 326	87.80 310	85.37 389	94.84 388

v2v482		91.59 285	90.85 287	93.80 307	93.87 396	88.17 298	96.94 221	96.88 289	89.54 286	89.53 324	94.90 320	81.70 268	98.02 322	89.25 282	85.04 398	95.20 364

v148		90.99 320	90.38 309	92.81 357	93.83 397	85.80 363	96.78 246	96.68 303	89.45 291	88.75 350	93.93 377	82.96 237	97.82 352	87.83 308	83.25 420	94.80 397

Baseline_NR-MVSNet		91.20 311	90.62 301	92.95 351	93.83 397	88.03 302	97.01 214	95.12 391	88.42 329	89.70 317	95.13 311	83.47 220	97.44 397	89.66 270	83.24 421	93.37 435

EPNet_dtu		91.71 276	91.28 269	92.99 349	93.76 399	83.71 402	96.69 256	95.28 382	93.15 134	87.02 391	95.95 267	83.37 223	97.38 402	79.46 428	96.84 196	97.88 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1192		91.07 316	90.23 318	93.58 324	93.70 400	87.82 310	96.73 250	97.07 262	87.77 351	89.58 321	94.32 356	80.90 283	97.97 329	86.52 345	85.48 387	94.95 376

GG-mvs-BLEND				93.62 321	93.69 401	89.20 256	92.39 452	83.33 490		87.98 371	89.84 448	71.00 400	96.87 422	82.08 404	95.40 245	94.80 397

test_fmvs2		89.77 361	89.93 333	89.31 437	93.68 402	76.37 466	97.64 134	95.90 348	89.84 277	91.49 269	96.26 252	58.77 463	97.10 410	94.65 152	91.13 323	94.46 411

tt0320-xc		84.83 423	82.33 431	92.31 371	93.66 403	86.20 354	96.17 308	94.06 431	71.26 476	82.04 446	92.22 425	55.07 472	96.72 427	81.49 407	75.04 456	94.02 424

v144192		91.06 317	90.28 314	93.39 334	93.66 403	87.23 324	96.83 236	97.07 262	87.43 360	89.69 318	94.28 358	81.48 270	98.00 324	87.18 337	84.92 400	94.93 380

usedtu_dtu_shiyan1		91.65 280	90.67 299	94.60 251	93.65 405	90.95 173	94.86 380	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 367	87.54 327	89.14 347	95.17 369

FE-MVSNET3		91.65 280	90.67 299	94.60 251	93.65 405	90.95 173	94.86 380	97.12 251	89.69 281	89.21 336	93.62 390	81.17 276	97.67 367	87.54 327	89.14 347	95.17 369

v1921920		90.85 326	90.03 329	93.29 338	93.55 407	86.96 333	96.74 249	97.04 270	87.36 362	89.52 325	94.34 353	80.23 298	97.97 329	86.27 348	85.21 393	94.94 378

v7n		90.76 328	89.86 335	93.45 333	93.54 408	87.60 315	97.70 124	97.37 224	88.85 312	87.65 375	94.08 371	81.08 278	98.10 305	84.68 374	83.79 417	94.66 407

JIA-IIPM		88.26 380	87.04 384	91.91 383	93.52 409	81.42 427	89.38 473	94.38 423	80.84 449	90.93 285	80.74 481	79.22 315	97.92 342	82.76 397	91.62 314	96.38 303

v1240		90.70 332	89.85 336	93.23 340	93.51 410	86.80 334	96.61 266	97.02 274	87.16 367	89.58 321	94.31 357	79.55 311	97.98 326	85.52 363	85.44 388	94.90 383

test_djsdf		93.07 221	92.76 211	94.00 291	93.49 411	88.70 273	98.22 46	97.57 177	91.42 211	90.08 307	95.55 292	82.85 240	97.92 342	94.07 166	91.58 315	95.40 349

SixPastTwentyTwo		89.15 368	88.54 368	90.98 409	93.49 411	80.28 443	96.70 254	94.70 410	90.78 240	84.15 429	95.57 290	71.78 395	97.71 365	84.63 375	85.07 396	94.94 378

test_vis1_rt		86.16 411	85.06 407	89.46 433	93.47 413	80.46 438	96.41 280	86.61 485	85.22 398	79.15 460	88.64 456	52.41 475	97.06 412	93.08 192	90.57 332	90.87 468

sc_t1		86.48 403	84.10 420	93.63 320	93.45 414	85.76 365	96.79 242	94.71 409	73.06 474	86.45 401	94.35 351	55.13 471	97.95 337	84.38 379	78.55 443	97.18 280

tt0320		85.39 420	83.12 423	92.19 377	93.44 415	85.79 364	96.19 306	94.87 406	71.19 477	82.92 442	91.76 433	58.43 464	96.81 424	81.03 417	78.26 444	93.98 425

mvs_tets		92.31 253	91.76 250	93.94 299	93.41 416	88.29 289	97.63 136	97.53 190	92.04 189	88.76 349	96.45 241	74.62 372	98.09 309	93.91 171	91.48 317	95.45 344

OurMVSNet-221017-0		90.51 339	90.19 322	91.44 399	93.41 416	81.25 428	96.98 218	96.28 330	91.68 199	86.55 400	96.30 248	74.20 375	97.98 326	88.96 290	87.40 371	95.09 371

pmmvs4		90.93 324	89.85 336	94.17 280	93.34 418	90.79 182	94.60 387	96.02 344	84.62 408	87.45 378	95.15 309	81.88 265	97.45 396	87.70 316	87.87 363	94.27 420

jajsoiax		92.42 247	91.89 247	94.03 290	93.33 419	88.50 283	97.73 116	97.53 190	92.00 191	88.85 346	96.50 239	75.62 362	98.11 304	93.88 173	91.56 316	95.48 339

gm-plane-assit					93.22 420	78.89 460			84.82 406		93.52 395		98.64 252	87.72 313

MVP-Stereo		90.74 330	90.08 324	92.71 361	93.19 421	88.20 296	95.86 325	96.27 331	86.07 386	84.86 422	94.76 327	77.84 342	97.75 362	83.88 387	98.01 153	92.17 456
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		88.72 375	88.90 363	88.20 442	93.15 422	74.21 471	96.63 265	94.22 429	85.18 399	87.32 383	95.97 265	76.16 356	94.98 454	85.27 367	86.17 380	95.41 346

MDA-MVSNet-bldmvs		85.00 421	82.95 426	91.17 408	93.13 423	83.33 405	94.56 389	95.00 395	84.57 409	65.13 483	92.65 411	70.45 405	95.85 439	73.57 458	77.49 445	94.33 416

K. test v3		87.64 386	86.75 388	90.32 422	93.02 424	79.48 455	96.61 266	92.08 461	90.66 249	80.25 455	94.09 370	67.21 432	96.65 428	85.96 358	80.83 432	94.83 389

MonoMVSNet		91.92 269	91.77 249	92.37 367	92.94 425	83.11 409	97.09 207	95.55 369	92.91 148	90.85 286	94.55 338	81.27 275	96.52 429	93.01 197	87.76 364	97.47 267

UWE-MVS-28		86.81 400	86.41 389	88.02 444	92.87 426	74.60 470	95.38 355	86.70 484	88.17 335	87.28 385	94.67 333	70.83 402	93.30 472	67.45 474	94.31 266	96.17 308

pmmvs5		89.86 359	88.87 364	92.82 356	92.86 427	86.23 352	96.26 298	95.39 375	84.24 412	87.12 386	94.51 341	74.27 374	97.36 403	87.61 326	87.57 366	94.86 385

testgi		87.97 381	87.21 381	90.24 423	92.86 427	80.76 432	96.67 259	94.97 397	91.74 197	85.52 414	95.83 273	62.66 458	94.47 459	76.25 444	88.36 359	95.48 339

EPNet		95.20 124	94.56 143	97.14 75	92.80 429	92.68 97	97.85 95	94.87 406	96.64 9	92.46 239	97.80 138	86.23 160	99.65 79	93.72 176	98.62 124	99.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		78.73 441	78.71 441	78.79 460	92.80 429	46.50 499	94.14 410	43.71 501	78.61 461	80.83 449	91.66 434	74.94 369	96.36 432	67.24 475	84.45 408	93.50 432

EG-PatchMatch MVS		87.02 397	85.44 399	91.76 393	92.67 431	85.00 383	96.08 312	96.45 318	83.41 427	79.52 457	93.49 396	57.10 467	97.72 364	79.34 430	90.87 330	92.56 446

test_fmvsmconf0.01_n		96.15 88	95.85 91	97.03 83	92.66 432	91.83 129	97.97 78	97.84 141	95.57 26	97.53 61	99.00 16	84.20 209	99.76 54	98.82 23	99.08 101	99.48 56

Gipuma		67.86 452	65.41 454	75.18 468	92.66 432	73.45 473	66.50 490	94.52 417	53.33 488	57.80 489	66.07 489	30.81 488	89.20 481	48.15 487	78.88 442	62.90 489
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		92.16 261	91.55 258	93.97 295	92.58 434	89.55 237	97.51 153	97.42 217	89.42 292	88.40 356	94.84 323	80.66 288	97.88 347	91.87 217	91.28 321	94.48 410

EGC-MVSNET		68.77 451	63.01 457	86.07 453	92.49 435	82.24 422	93.96 415	90.96 469	0.71 498	2.62 499	90.89 438	53.66 473	93.46 469	57.25 483	84.55 406	82.51 479

test0.0.03 1		89.37 367	88.70 365	91.41 400	92.47 436	85.63 367	95.22 366	92.70 454	91.11 230	86.91 396	93.65 389	79.02 321	93.19 474	78.00 435	89.18 346	95.41 346

our_test_3		88.78 374	87.98 374	91.20 406	92.45 437	82.53 415	93.61 432	95.69 360	85.77 390	84.88 421	93.71 383	79.99 302	96.78 426	79.47 427	86.24 379	94.28 419

ppachtmachnet_test		88.35 379	87.29 378	91.53 396	92.45 437	83.57 404	93.75 424	95.97 345	84.28 411	85.32 418	94.18 365	79.00 325	96.93 418	75.71 446	84.99 399	94.10 421

YYNet1		85.87 416	84.23 418	90.78 417	92.38 439	82.46 419	93.17 438	95.14 390	82.12 440	67.69 477	92.36 421	78.16 337	95.50 450	77.31 438	79.73 436	94.39 414

MDA-MVSNet_test_wron		85.87 416	84.23 418	90.80 416	92.38 439	82.57 414	93.17 438	95.15 389	82.15 439	67.65 479	92.33 424	78.20 334	95.51 449	77.33 437	79.74 435	94.31 418

LF4IMVS		87.94 382	87.25 379	89.98 426	92.38 439	80.05 447	94.38 400	95.25 385	87.59 357	84.34 425	94.74 329	64.31 452	97.66 371	84.83 371	87.45 367	92.23 453

lessismore_v0				90.45 420	91.96 442	79.09 459		87.19 482		80.32 454	94.39 348	66.31 440	97.55 382	84.00 384	76.84 448	94.70 405

dmvs_testset		81.38 436	82.60 429	77.73 461	91.74 443	51.49 496	93.03 443	84.21 489	89.07 301	78.28 464	91.25 437	76.97 348	88.53 484	56.57 484	82.24 427	93.16 436

pmmvs6		87.81 384	86.19 392	92.69 362	91.32 444	86.30 350	97.34 178	96.41 320	80.59 453	84.05 433	94.37 350	67.37 431	97.67 367	84.75 373	79.51 438	94.09 423

Anonymous20231206		87.09 395	86.14 393	89.93 428	91.22 445	80.35 439	96.11 310	95.35 378	83.57 423	84.16 428	93.02 406	73.54 383	95.61 445	72.16 463	86.14 381	93.84 428

KD-MVS_2432*1600		84.81 424	82.64 427	91.31 402	91.07 446	85.34 377	91.22 458	95.75 356	85.56 393	83.09 439	90.21 444	67.21 432	95.89 437	77.18 440	62.48 484	92.69 442

miper_refine_blended		84.81 424	82.64 427	91.31 402	91.07 446	85.34 377	91.22 458	95.75 356	85.56 393	83.09 439	90.21 444	67.21 432	95.89 437	77.18 440	62.48 484	92.69 442

DeepMVS_CX				74.68 469	90.84 448	64.34 487		81.61 492	65.34 482	67.47 480	88.01 463	48.60 479	80.13 491	62.33 479	73.68 461	79.58 481

0.4-1-1-0.2		86.27 409	83.62 422	94.20 278	90.38 449	87.69 313	91.04 461	92.52 457	83.43 426	85.22 419	81.49 480	65.31 448	98.29 287	88.90 292	74.30 459	96.64 295

Anonymous20240521		86.42 405	85.44 399	89.34 436	90.33 450	79.79 448	96.73 250	95.92 346	83.71 421	83.25 438	91.36 436	63.92 453	96.01 435	78.39 434	85.36 390	92.22 454

test20.03		86.14 412	85.40 401	88.35 440	90.12 451	80.06 446	95.90 324	95.20 387	88.59 321	81.29 448	93.62 390	71.43 397	92.65 475	71.26 467	81.17 431	92.34 450

OpenMVS_ROB	81.14 20	84.42 426	82.28 432	90.83 412	90.06 452	84.05 398	95.73 335	94.04 433	73.89 472	80.17 456	91.53 435	59.15 462	97.64 372	66.92 476	89.05 349	90.80 469

UnsupCasMVSNet_eth		85.99 413	84.45 416	90.62 418	89.97 453	82.40 420	93.62 431	97.37 224	89.86 274	78.59 463	92.37 418	65.25 450	95.35 452	82.27 403	70.75 471	94.10 421

DSMNet-mixed		86.34 407	86.12 394	87.00 450	89.88 454	70.43 476	94.93 378	90.08 473	77.97 464	85.42 417	92.78 409	74.44 373	93.96 466	74.43 452	95.14 249	96.62 296

new_pmnet		82.89 432	81.12 437	88.18 443	89.63 455	80.18 445	91.77 455	92.57 455	76.79 467	75.56 470	88.23 460	61.22 461	94.48 458	71.43 465	82.92 424	89.87 472

MIMVSNet1		84.93 422	83.05 424	90.56 419	89.56 456	84.84 388	95.40 353	95.35 378	83.91 415	80.38 453	92.21 426	57.23 466	93.34 471	70.69 469	82.75 426	93.50 432

KD-MVS_self_test		85.95 414	84.95 408	88.96 439	89.55 457	79.11 458	95.13 373	96.42 319	85.91 388	84.07 432	90.48 441	70.03 410	94.82 455	80.04 422	72.94 462	92.94 439

ttmdpeth		85.91 415	84.76 412	89.36 435	89.14 458	80.25 444	95.66 340	93.16 448	83.77 419	83.39 437	95.26 305	66.24 441	95.26 453	80.65 418	75.57 453	92.57 445

CMPMVS	62.92 21	85.62 418	84.92 409	87.74 445	89.14 458	73.12 475	94.17 409	96.80 295	73.98 470	73.65 473	94.93 318	66.36 438	97.61 376	83.95 385	91.28 321	92.48 449
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
APD_test1		79.31 440	77.70 442	84.14 454	89.11 460	69.07 480	92.36 453	91.50 465	69.07 479	73.87 472	92.63 413	39.93 484	94.32 460	70.54 471	80.25 434	89.02 474

blend_shiyan4		86.87 398	84.61 415	93.67 317	88.87 461	88.70 273	95.17 372	96.30 326	82.80 432	86.16 405	87.11 470	65.12 451	97.55 382	87.73 311	72.21 464	94.75 403

CL-MVSNet_self_test		86.31 408	85.15 405	89.80 429	88.83 462	81.74 426	93.93 417	96.22 336	86.67 374	85.03 420	90.80 439	78.09 338	94.50 457	74.92 450	71.86 465	93.15 437

blended_shiyan6		87.55 388	85.52 398	93.64 319	88.78 463	88.50 283	95.23 365	96.30 326	82.80 432	86.09 408	87.70 466	73.69 382	97.56 380	87.70 316	71.36 467	94.86 385

dongtai		69.99 448	69.33 450	71.98 470	88.78 463	61.64 490	89.86 470	59.93 500	75.67 468	74.96 471	85.45 475	50.19 477	81.66 489	43.86 488	55.27 487	72.63 485

blended_shiyan8		87.58 387	85.55 397	93.66 318	88.76 465	88.54 280	95.21 368	96.29 329	82.81 431	86.25 403	87.73 465	73.70 381	97.58 379	87.81 309	71.42 466	94.85 387

mvs5depth		86.53 401	85.08 406	90.87 411	88.74 466	82.52 416	91.91 454	94.23 428	86.35 380	87.11 388	93.70 384	66.52 437	97.76 360	81.37 412	75.80 452	92.31 452

Patchmatch-RL test		87.38 389	86.24 391	90.81 414	88.74 466	78.40 461	88.12 481	93.17 446	87.11 368	82.17 445	89.29 452	81.95 262	95.60 446	88.64 298	77.02 447	98.41 192

wanda-best-256-512		87.29 390	85.21 403	93.53 327	88.54 468	88.21 294	94.51 393	96.27 331	82.69 435	85.92 409	86.89 472	73.04 385	97.55 382	87.68 320	71.36 467	94.83 389

FE-blended-shiyan7		87.29 390	85.21 403	93.53 327	88.54 468	88.21 294	94.51 393	96.27 331	82.69 435	85.92 409	86.89 472	73.03 386	97.55 382	87.68 320	71.36 467	94.83 389

usedtu_blend_shiyan5		87.06 396	84.84 410	93.69 314	88.54 468	88.70 273	95.83 327	95.54 370	78.74 460	85.92 409	86.89 472	73.03 386	97.55 382	87.73 311	71.36 467	94.83 389

pmmvs-eth3d		86.22 410	84.45 416	91.53 396	88.34 471	87.25 322	94.47 395	95.01 394	83.47 424	79.51 458	89.61 450	69.75 414	95.71 442	83.13 391	76.73 450	91.64 459

UnsupCasMVSNet_bld		82.13 435	79.46 440	90.14 424	88.00 472	82.47 418	90.89 464	96.62 311	78.94 459	75.61 468	84.40 478	56.63 468	96.31 433	77.30 439	66.77 480	91.63 460

PM-MVS		83.48 429	81.86 435	88.31 441	87.83 473	77.59 463	93.43 434	91.75 463	86.91 370	80.63 451	89.91 447	44.42 482	95.84 440	85.17 370	76.73 450	91.50 464

FE-MVSNET2		86.36 406	84.68 414	91.39 401	87.67 474	86.47 346	96.21 303	96.41 320	87.87 345	79.31 459	89.64 449	65.29 449	95.58 447	82.42 401	77.28 446	92.14 457

MVStest1		82.38 434	80.04 438	89.37 434	87.63 475	82.83 412	95.03 375	93.37 445	73.90 471	73.50 474	94.35 351	62.89 457	93.25 473	73.80 456	65.92 481	92.04 458

FE-MVSNET		83.85 427	81.97 433	89.51 432	87.19 476	83.19 408	95.21 368	93.17 446	83.45 425	78.90 461	89.05 454	65.46 446	93.84 468	69.71 472	75.56 454	91.51 462

new-patchmatchnet		83.18 431	81.87 434	87.11 448	86.88 477	75.99 468	93.70 426	95.18 388	85.02 403	77.30 466	88.40 458	65.99 443	93.88 467	74.19 455	70.18 472	91.47 465

test_fmvs3		83.21 430	83.02 425	83.78 455	86.77 478	68.34 481	96.76 248	94.91 401	86.49 377	84.14 430	89.48 451	36.04 486	91.73 477	91.86 218	80.77 433	91.26 467

WB-MVS		76.77 442	76.63 445	77.18 462	85.32 479	56.82 494	94.53 390	89.39 475	82.66 437	71.35 475	89.18 453	75.03 366	88.88 482	35.42 491	66.79 479	85.84 476

SSC-MVS		76.05 443	75.83 446	76.72 466	84.77 480	56.22 495	94.32 404	88.96 477	81.82 443	70.52 476	88.91 455	74.79 370	88.71 483	33.69 492	64.71 482	85.23 477

kuosan		65.27 454	64.66 456	67.11 473	83.80 481	61.32 491	88.53 478	60.77 499	68.22 480	67.67 478	80.52 482	49.12 478	70.76 495	29.67 494	53.64 489	69.26 487

mvsany_test3		83.59 428	82.44 430	87.03 449	83.80 481	73.82 472	93.70 426	90.92 470	86.42 378	82.51 443	90.26 443	46.76 480	95.71 442	90.82 241	76.76 449	91.57 461

ambc				86.56 451	83.60 483	70.00 478	85.69 483	94.97 397		80.60 452	88.45 457	37.42 485	96.84 423	82.69 399	75.44 455	92.86 440

test_f		80.57 437	79.62 439	83.41 456	83.38 484	67.80 483	93.57 433	93.72 440	80.80 451	77.91 465	87.63 467	33.40 487	92.08 476	87.14 339	79.04 441	90.34 471

pmmvs3		79.97 439	77.50 443	87.39 447	82.80 485	79.38 456	92.70 448	90.75 471	70.69 478	78.66 462	87.47 469	51.34 476	93.40 470	73.39 459	69.65 473	89.38 473

TDRefinement		86.53 401	84.76 412	91.85 386	82.23 486	84.25 393	96.38 286	95.35 378	84.97 404	84.09 431	94.94 317	65.76 445	98.34 285	84.60 376	74.52 457	92.97 438

usedtu_dtu_shiyan2		80.00 438	76.91 444	89.27 438	82.13 487	79.69 450	95.45 351	94.20 430	72.95 475	75.80 467	87.75 464	44.44 481	94.30 461	70.64 470	68.81 477	93.84 428

test_vis3_rt		72.73 444	70.55 447	79.27 459	80.02 488	68.13 482	93.92 418	74.30 496	76.90 466	58.99 487	73.58 487	20.29 495	95.37 451	84.16 380	72.80 463	74.31 484

testf1		69.31 449	66.76 452	76.94 464	78.61 489	61.93 488	88.27 479	86.11 486	55.62 485	59.69 485	85.31 476	20.19 496	89.32 479	57.62 481	69.44 475	79.58 481

APD_test2		69.31 449	66.76 452	76.94 464	78.61 489	61.93 488	88.27 479	86.11 486	55.62 485	59.69 485	85.31 476	20.19 496	89.32 479	57.62 481	69.44 475	79.58 481

PMMVS2		70.19 447	66.92 451	80.01 458	76.35 491	65.67 485	86.22 482	87.58 481	64.83 483	62.38 484	80.29 483	26.78 492	88.49 485	63.79 477	54.07 488	85.88 475

FPMVS		71.27 446	69.85 448	75.50 467	74.64 492	59.03 492	91.30 457	91.50 465	58.80 484	57.92 488	88.28 459	29.98 490	85.53 487	53.43 485	82.84 425	81.95 480

E-PMN		53.28 457	52.56 461	55.43 475	74.43 493	47.13 498	83.63 486	76.30 493	42.23 490	42.59 492	62.22 491	28.57 491	74.40 492	31.53 493	31.51 491	44.78 490

wuyk23d		25.11 461	24.57 465	26.74 478	73.98 494	39.89 502	57.88 492	9.80 502	12.27 495	10.39 496	6.97 498	7.03 499	36.44 497	25.43 496	17.39 495	3.89 495

test_method		66.11 453	64.89 455	69.79 471	72.62 495	35.23 503	65.19 491	92.83 453	20.35 493	65.20 482	88.08 462	43.14 483	82.70 488	73.12 460	63.46 483	91.45 466

EMVS		52.08 459	51.31 462	54.39 476	72.62 495	45.39 500	83.84 485	75.51 495	41.13 491	40.77 493	59.65 492	30.08 489	73.60 493	28.31 495	29.90 493	44.18 491

LCM-MVSNet		72.55 445	69.39 449	82.03 457	70.81 497	65.42 486	90.12 469	94.36 426	55.02 487	65.88 481	81.72 479	24.16 494	89.96 478	74.32 454	68.10 478	90.71 470

MVE	50.73 23	53.25 458	48.81 463	66.58 474	65.34 498	57.50 493	72.49 489	70.94 497	40.15 492	39.28 494	63.51 490	6.89 500	73.48 494	38.29 490	42.38 490	68.76 488
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		63.94 455	59.58 458	77.02 463	61.24 499	66.06 484	85.66 484	87.93 480	78.53 462	42.94 491	71.04 488	25.42 493	80.71 490	52.60 486	30.83 492	84.28 478

PMVS	53.92 22	58.58 456	55.40 459	68.12 472	51.00 500	48.64 497	78.86 487	87.10 483	46.77 489	35.84 495	74.28 485	8.76 498	86.34 486	42.07 489	73.91 460	69.38 486
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 460	53.82 460	46.29 477	33.73 501	45.30 501	78.32 488	67.24 498	18.02 494	50.93 490	87.05 471	52.99 474	53.11 496	70.76 468	25.29 494	40.46 492

testmvs		13.36 463	16.33 466	4.48 480	5.04 502	2.26 505	93.18 437	3.28 503	2.70 496	8.24 497	21.66 494	2.29 502	2.19 498	7.58 497	2.96 496	9.00 494

test123		13.04 464	15.66 467	5.18 479	4.51 503	3.45 504	92.50 451	1.81 504	2.50 497	7.58 498	20.15 495	3.67 501	2.18 499	7.13 498	1.07 497	9.90 493

mmdepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

monomultidepth		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

test_blank		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

eth-test2					0.00 504

eth-test					0.00 504

uanet_test		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

DCPMVS		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

cdsmvs_eth3d_5k		23.24 462	30.99 464	0.00 481	0.00 504	0.00 506	0.00 493	97.63 165	0.00 499	0.00 500	96.88 213	84.38 205	0.00 500	0.00 499	0.00 498	0.00 496

pcd_1.5k_mvsjas		7.39 466	9.85 469	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	88.65 108	0.00 500	0.00 499	0.00 498	0.00 496

sosnet-low-res		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

sosnet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

uncertanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

Regformer		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

ab-mvs-re		8.06 465	10.74 468	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	96.69 224	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

uanet		0.00 467	0.00 470	0.00 481	0.00 504	0.00 506	0.00 493	0.00 505	0.00 499	0.00 500	0.00 499	0.00 503	0.00 500	0.00 499	0.00 498	0.00 496

TestfortrainingZip							98.69 11

WAC-MVS						79.53 452								75.56 448

PC_three_1452									90.77 241	98.89 26	98.28 86	96.24 1	98.35 282	95.76 106	99.58 23	99.59 32

test_241102_TWO								98.27 55	95.13 40	98.93 20	98.89 28	94.99 13	99.85 21	97.52 41	99.65 13	99.74 9

test_0728_THIRD									94.78 61	98.73 30	98.87 31	95.87 4	99.84 26	97.45 45	99.72 2	99.77 3

GSMVS																98.45 187

sam_mvs1												82.76 242				98.45 187

sam_mvs												81.94 263

MTGPA								98.08 93

test_post1							92.81 447				16.58 497	80.53 291	97.68 366	86.20 350

test_post											17.58 496	81.76 266	98.08 310

patchmatchnet-post											90.45 442	82.65 247	98.10 305

MTMP							97.86 92	82.03 491

test9_res														94.81 143	99.38 64	99.45 59

agg_prior2														93.94 170	99.38 64	99.50 52

test_prior4						93.66 62	96.42 279

test_prior2							96.35 289		92.80 156	96.03 127	97.59 163	92.01 49		95.01 128	99.38 64

旧先验2							95.94 320		81.66 444	97.34 70			98.82 209	92.26 203

新几何2							95.79 331

无先验							95.79 331	97.87 132	83.87 418				99.65 79	87.68 320		98.89 136

原ACMM2							95.67 337

testdata2													99.67 77	85.96 358

segment_acmp												92.89 32

testdata1							95.26 364		93.10 137

plane_prior5								97.51 192					98.60 257	93.02 195	92.23 303	95.86 319

plane_prior4											96.64 227

plane_prior3						90.00 211			94.46 78	91.34 273

plane_prior2							97.74 114		94.85 53

plane_prior						89.99 213	97.24 191		94.06 92						92.16 307

n2								0.00 505

nn								0.00 505

door-mid								91.06 468

test11								97.88 130

door								91.13 467

HQP5-MVS						89.33 249

BP-MVS														92.13 211

HQP4-MVS										90.14 297			98.50 267			95.78 327

HQP3-MVS								97.39 220							92.10 308

HQP2-MVS												80.95 279

MDTV_nov1_ep13_2view						70.35 477	93.10 442		83.88 417	93.55 214		82.47 251		86.25 349		98.38 195

ACMMP++_ref															90.30 337

ACMMP++															91.02 326

Test By Simon												88.73 107

High-resolution multi-view benchmark