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.

AdaColmap		97.23 126	96.80 135	98.51 128	99.99 1	95.60 194	99.09 296	98.84 62	93.32 197	96.74 206	99.72 89	86.04 254	100.00 1	98.01 147	99.43 124	99.94 82

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 20	98.69 77	98.20 9	99.93 2	99.98 2	96.82 24	100.00 1	99.75 37	100.00 1	99.99 23

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 38	98.64 86	98.47 3	99.13 102	99.92 13	96.38 34	100.00 1	99.74 39	100.00 1	100.00 1

mPP-MVS		98.39 53	98.20 51	98.97 88	99.97 3	96.92 133	99.95 67	98.38 179	95.04 118	98.61 133	99.80 54	93.39 114	100.00 1	98.64 110	100.00 1	99.98 52

CPTT-MVS		97.64 106	97.32 110	98.58 117	99.97 3	95.77 183	99.96 48	98.35 185	89.90 328	98.36 148	99.79 58	91.18 173	99.99 36	98.37 126	99.99 21	99.99 23

DP-MVS Recon		98.41 50	98.02 65	99.56 25	99.97 3	98.70 48	99.92 94	98.44 143	92.06 261	98.40 147	99.84 44	95.68 44	100.00 1	98.19 136	99.71 88	99.97 62

PAPR		98.52 40	98.16 55	99.58 24	99.97 3	98.77 42	99.95 67	98.43 151	95.35 112	98.03 162	99.75 75	94.03 99	99.98 47	98.11 141	99.83 77	99.99 23

HFP-MVS		98.56 36	98.37 40	99.14 68	99.96 8	97.43 109	99.95 67	98.61 94	94.77 128	99.31 91	99.85 33	94.22 92	100.00 1	98.70 105	99.98 32	99.98 52

region2R		98.54 38	98.37 40	99.05 78	99.96 8	97.18 119	99.96 48	98.55 114	94.87 125	99.45 78	99.85 33	94.07 98	100.00 1	98.67 107	100.00 1	99.98 52

ACMMPR		98.50 41	98.32 44	99.05 78	99.96 8	97.18 119	99.95 67	98.60 96	94.77 128	99.31 91	99.84 44	93.73 108	100.00 1	98.70 105	99.98 32	99.98 52

NCCC		99.37 2	99.25 2	99.71 15	99.96 8	99.15 22	99.97 38	98.62 93	98.02 21	99.90 6	99.95 3	97.33 17	100.00 1	99.54 54	100.00 1	100.00 1

CP-MVS		98.45 45	98.32 44	98.87 93	99.96 8	96.62 146	99.97 38	98.39 175	94.43 145	98.90 114	99.87 27	94.30 89	100.00 1	99.04 80	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 12		98.41 168	96.63 72	99.75 39	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 67	98.43 151					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 29	98.55 28	99.15 66	99.94 13	97.50 105	99.94 84	98.42 163	96.22 89	99.41 83	99.78 62	94.34 86	99.96 71	98.92 90	99.95 50	99.99 23

X-MVStestdata		93.83 265	92.06 300	99.15 66	99.94 13	97.50 105	99.94 84	98.42 163	96.22 89	99.41 83	41.37 473	94.34 86	99.96 71	98.92 90	99.95 50	99.99 23

test_prior				99.43 36	99.94 13	98.49 61		98.65 83					99.80 137			99.99 23

MSLP-MVS++		99.13 8	99.01 11	99.49 32	99.94 13	98.46 62	99.98 20	98.86 56	97.10 52	99.80 25	99.94 4	95.92 40	100.00 1	99.51 55	100.00 1	100.00 1

APDe-MVS		99.06 11	98.91 14	99.51 29	99.94 13	98.76 45	99.91 102	98.39 175	97.20 50	99.46 77	99.85 33	95.53 48	99.79 139	99.86 23	100.00 1	99.99 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS		98.23 68	97.97 69	99.03 80	99.94 13	97.17 122	99.95 67	98.39 175	94.70 132	98.26 154	99.81 53	91.84 164	100.00 1	98.85 96	99.97 42	99.93 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 32	98.36 42	99.49 32	99.94 13	98.73 46	99.87 124	98.33 190	93.97 170	99.76 38	99.87 27	94.99 64	99.75 148	98.55 114	100.00 1	99.98 52

PAPM_NR		98.12 72	97.93 75	98.70 104	99.94 13	96.13 172	99.82 153	98.43 151	94.56 136	97.52 179	99.70 95	94.40 81	99.98 47	97.00 184	99.98 32	99.99 23

MG-MVS		98.91 19	98.65 24	99.68 16	99.94 13	99.07 24	99.64 213	99.44 19	97.33 43	99.00 110	99.72 89	94.03 99	99.98 47	98.73 104	100.00 1	100.00 1

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 24	99.30 12	99.96 48	98.43 151	97.27 46	99.80 25	99.94 4	96.71 27	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 24	99.31 10		98.41 168	97.71 30	99.84 20				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 151	97.26 48	99.80 25	99.88 24	96.71 27	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 12	99.93 24	99.29 15	99.95 67	98.32 192	97.28 44	99.83 21	99.91 14	97.22 19	100.00 1	99.99 5	100.00 1	99.89 92
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					99.93 24	99.29 15	99.96 48	98.42 163	97.28 44	99.86 14	99.94 4	97.22 19

MSP-MVS		99.09 9	99.12 5	98.98 87	99.93 24	97.24 116	99.95 67	98.42 163	97.50 37	99.52 73	99.88 24	97.43 16	99.71 154	99.50 57	99.98 32	100.00 1
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
agg_prior					99.93 24	98.77 42		98.43 151		99.63 56			99.85 124

FOURS1					99.92 31	97.66 99	99.95 67	98.36 183	95.58 106	99.52 73

ZD-MVS					99.92 31	98.57 56		98.52 123	92.34 249	99.31 91	99.83 46	95.06 59	99.80 137	99.70 45	99.97 42

GST-MVS		98.27 60	97.97 69	99.17 61	99.92 31	97.57 101	99.93 91	98.39 175	94.04 168	98.80 119	99.74 82	92.98 130	100.00 1	98.16 138	99.76 85	99.93 83

TEST9					99.92 31	98.92 29	99.96 48	98.43 151	93.90 176	99.71 46	99.86 29	95.88 41	99.85 124

train_agg		98.88 20	98.65 24	99.59 23	99.92 31	98.92 29	99.96 48	98.43 151	94.35 150	99.71 46	99.86 29	95.94 38	99.85 124	99.69 46	99.98 32	99.99 23

test_8					99.92 31	98.88 32	99.96 48	98.43 151	94.35 150	99.69 48	99.85 33	95.94 38	99.85 124

PGM-MVS		98.34 55	98.13 57	98.99 85	99.92 31	97.00 129	99.75 177	99.50 17	93.90 176	99.37 88	99.76 67	93.24 123	100.00 1	97.75 166	99.96 46	99.98 52

ACMMP		97.74 99	97.44 103	98.66 108	99.92 31	96.13 172	99.18 289	99.45 18	94.84 126	96.41 220	99.71 92	91.40 167	99.99 36	97.99 149	98.03 184	99.87 95
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
DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 39	99.31 10	99.95 67	98.43 151	96.48 77	99.80 25	99.93 11	97.44 14	100.00 1	99.92 13	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 168					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 168					100.00 1	99.96 9	100.00 1	100.00 1

HPM-MVS++		99.07 10	98.88 16	99.63 17	99.90 42	99.02 25	99.95 67	98.56 108	97.56 36	99.44 79	99.85 33	95.38 52	100.00 1	99.31 67	99.99 21	99.87 95

APD-MVS		98.62 33	98.35 43	99.41 39	99.90 42	98.51 59	99.87 124	98.36 183	94.08 163	99.74 42	99.73 86	94.08 97	99.74 150	99.42 63	99.99 21	99.99 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 23	98.54 29	99.62 20	99.90 42	98.85 35	99.24 284	98.47 135	98.14 15	99.08 105	99.91 14	93.09 127	100.00 1	99.04 80	99.99 21	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS				99.93 2	99.89 45	99.80 2	99.96 48				99.80 54	97.44 14	100.00 1	100.00 1	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 11	99.89 45	99.24 19	99.87 124	98.44 143	97.48 38	99.64 55	99.94 4	96.68 29	99.99 36	99.99 5	100.00 1	99.99 23
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 45	99.25 18				99.49 76

CSCG		97.10 132	97.04 122	97.27 222	99.89 45	91.92 311	99.90 108	99.07 37	88.67 352	95.26 252	99.82 49	93.17 126	99.98 47	98.15 139	99.47 119	99.90 91

ZNCC-MVS		98.31 57	98.03 64	99.17 61	99.88 49	97.59 100	99.94 84	98.44 143	94.31 153	98.50 140	99.82 49	93.06 128	99.99 36	98.30 130	99.99 21	99.93 83

SR-MVS		98.46 44	98.30 47	98.93 91	99.88 49	97.04 128	99.84 143	98.35 185	94.92 122	99.32 90	99.80 54	93.35 116	99.78 141	99.30 68	99.95 50	99.96 70

9.14			98.38 38		99.87 51		99.91 102	98.33 190	93.22 200	99.78 36	99.89 22	94.57 77	99.85 124	99.84 25	99.97 42

SMA-MVS		98.76 26	98.48 32	99.62 20	99.87 51	98.87 33	99.86 135	98.38 179	93.19 201	99.77 37	99.94 4	95.54 46	100.00 1	99.74 39	99.99 21	100.00 1
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
NormalMVS		97.90 81	97.85 81	98.04 160	99.86 53	95.39 204	99.61 220	97.78 259	96.52 75	98.61 133	99.31 151	92.73 138	99.67 162	96.77 194	99.48 116	99.06 235

lecture		98.67 30	98.46 33	99.28 48	99.86 53	97.88 87	99.97 38	99.25 30	96.07 93	99.79 34	99.70 95	92.53 146	99.98 47	99.51 55	99.48 116	99.97 62

PHI-MVS		98.41 50	98.21 50	99.03 80	99.86 53	97.10 126	99.98 20	98.80 68	90.78 308	99.62 59	99.78 62	95.30 53	100.00 1	99.80 28	99.93 61	99.99 23

MTAPA		98.29 59	97.96 72	99.30 47	99.85 56	97.93 85	99.39 262	98.28 199	95.76 100	97.18 193	99.88 24	92.74 137	100.00 1	98.67 107	99.88 73	99.99 23

LS3D		95.84 197	95.11 210	98.02 161	99.85 56	95.10 222	98.74 346	98.50 132	87.22 374	93.66 273	99.86 29	87.45 231	99.95 80	90.94 311	99.81 83	99.02 239

HPM-MVS		97.96 76	97.72 86	98.68 105	99.84 58	96.39 158	99.90 108	98.17 214	92.61 234	98.62 132	99.57 125	91.87 163	99.67 162	98.87 95	99.99 21	99.99 23
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 101	99.83 59	96.59 150	99.40 258	98.51 126	95.29 114	98.51 139	99.76 67	93.60 112	99.71 154	98.53 117	99.52 109	99.95 78

save fliter					99.82 60	98.79 40	99.96 48	98.40 172	97.66 32

PLC	95.54 3	97.93 79	97.89 79	98.05 159	99.82 60	94.77 232	99.92 94	98.46 137	93.93 173	97.20 191	99.27 156	95.44 51	99.97 59	97.41 171	99.51 112	99.41 186
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 65	98.08 61	98.78 98	99.81 62	96.60 148	99.82 153	98.30 197	93.95 172	99.37 88	99.77 65	92.84 134	99.76 147	98.95 86	99.92 64	99.97 62

EI-MVSNet-UG-set		98.14 71	97.99 67	98.60 113	99.80 63	96.27 161	99.36 268	98.50 132	95.21 116	98.30 151	99.75 75	93.29 120	99.73 153	98.37 126	99.30 133	99.81 104

SR-MVS-dyc-post		98.31 57	98.17 54	98.71 103	99.79 64	96.37 159	99.76 173	98.31 194	94.43 145	99.40 85	99.75 75	93.28 121	99.78 141	98.90 93	99.92 64	99.97 62

RE-MVS-def			98.13 57		99.79 64	96.37 159	99.76 173	98.31 194	94.43 145	99.40 85	99.75 75	92.95 131		98.90 93	99.92 64	99.97 62

HPM-MVS_fast		97.80 93	97.50 99	98.68 105	99.79 64	96.42 154	99.88 121	98.16 219	91.75 272	98.94 112	99.54 128	91.82 165	99.65 166	97.62 169	99.99 21	99.99 23

SF-MVS		98.67 30	98.40 36	99.50 30	99.77 67	98.67 49	99.90 108	98.21 209	93.53 188	99.81 23	99.89 22	94.70 73	99.86 123	99.84 25	99.93 61	99.96 70

MGCNet		99.06 11	98.84 17	99.72 13	99.76 68	99.21 21	99.99 5	99.34 25	98.70 2	99.44 79	99.75 75	93.24 123	99.99 36	99.94 11	99.41 126	99.95 78

旧先验1					99.76 68	97.52 103		98.64 86			99.85 33	95.63 45			99.94 55	99.99 23

OMC-MVS		97.28 122	97.23 114	97.41 212	99.76 68	93.36 278	99.65 209	97.95 240	96.03 94	97.41 184	99.70 95	89.61 199	99.51 172	96.73 196	98.25 174	99.38 188

新几何1				99.42 38	99.75 71	98.27 66		98.63 92	92.69 229	99.55 68	99.82 49	94.40 81	100.00 1	91.21 303	99.94 55	99.99 23

MP-MVS-pluss		98.07 75	97.64 92	99.38 44	99.74 72	98.41 64	99.74 180	98.18 213	93.35 195	96.45 217	99.85 33	92.64 141	99.97 59	98.91 92	99.89 70	99.77 111
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 17	98.77 19	99.41 39	99.74 72	98.67 49	99.77 167	98.38 179	96.73 68	99.88 11	99.74 82	94.89 66	99.59 168	99.80 28	99.98 32	99.97 62
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				99.43 36	99.74 72	98.56 57		98.40 172		99.65 52		94.76 69	99.75 148		99.98 32	99.99 23

原ACMM1				98.96 89	99.73 75	96.99 130		98.51 126	94.06 166	99.62 59	99.85 33	94.97 65	99.96 71	95.11 224	99.95 50	99.92 88

TSAR-MVS + GP.		98.60 34	98.51 31	98.86 94	99.73 75	96.63 145	99.97 38	97.92 245	98.07 18	98.76 125	99.55 126	95.00 63	99.94 88	99.91 16	97.68 191	99.99 23

CANet		98.27 60	97.82 83	99.63 17	99.72 77	99.10 23	99.98 20	98.51 126	97.00 58	98.52 137	99.71 92	87.80 223	99.95 80	99.75 37	99.38 128	99.83 100

reproduce_model		98.75 27	98.66 23	99.03 80	99.71 78	97.10 126	99.73 187	98.23 207	97.02 57	99.18 100	99.90 18	94.54 78	99.99 36	99.77 33	99.90 69	99.99 23

F-COLMAP		96.93 144	96.95 125	96.87 235	99.71 78	91.74 316	99.85 138	97.95 240	93.11 207	95.72 241	99.16 174	92.35 152	99.94 88	95.32 220	99.35 131	98.92 247

reproduce-ours		98.78 24	98.67 21	99.09 75	99.70 80	97.30 113	99.74 180	98.25 203	97.10 52	99.10 103	99.90 18	94.59 74	99.99 36	99.77 33	99.91 67	99.99 23

our_new_method		98.78 24	98.67 21	99.09 75	99.70 80	97.30 113	99.74 180	98.25 203	97.10 52	99.10 103	99.90 18	94.59 74	99.99 36	99.77 33	99.91 67	99.99 23

SD-MVS		98.92 18	98.70 20	99.56 25	99.70 80	98.73 46	99.94 84	98.34 189	96.38 83	99.81 23	99.76 67	94.59 74	99.98 47	99.84 25	99.96 46	99.97 62
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
patch_mono-2		98.24 66	99.12 5	95.59 276	99.67 83	86.91 399	99.95 67	98.89 52	97.60 33	99.90 6	99.76 67	96.54 32	99.98 47	99.94 11	99.82 81	99.88 93

ACMMP_NAP		98.49 42	98.14 56	99.54 27	99.66 84	98.62 55	99.85 138	98.37 182	94.68 133	99.53 71	99.83 46	92.87 133	100.00 1	98.66 109	99.84 76	99.99 23

DeepPCF-MVS	95.94 2	97.71 103	98.98 12	93.92 342	99.63 85	81.76 434	99.96 48	98.56 108	99.47 1	99.19 99	99.99 1	94.16 96	100.00 1	99.92 13	99.93 61	100.00 1

EPNet		98.49 42	98.40 36	98.77 100	99.62 86	96.80 139	99.90 108	99.51 16	97.60 33	99.20 97	99.36 146	93.71 109	99.91 105	97.99 149	98.71 159	99.61 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 21	98.53 30	99.76 10	99.59 87	99.33 8	99.99 5	99.76 6	98.39 4	99.39 87	99.80 54	90.49 188	99.96 71	99.89 18	99.43 124	99.98 52

PVSNet_BlendedMVS		96.05 188	95.82 182	96.72 241	99.59 87	96.99 130	99.95 67	99.10 34	94.06 166	98.27 152	95.80 347	89.00 211	99.95 80	99.12 74	87.53 339	93.24 401

PVSNet_Blended		97.94 78	97.64 92	98.83 95	99.59 87	96.99 130	100.00 1	99.10 34	95.38 111	98.27 152	99.08 178	89.00 211	99.95 80	99.12 74	99.25 135	99.57 153

PatchMatch-RL		96.04 189	95.40 197	97.95 163	99.59 87	95.22 217	99.52 240	99.07 37	93.96 171	96.49 216	98.35 262	82.28 300	99.82 136	90.15 327	99.22 138	98.81 254

dcpmvs_2		97.42 117	98.09 60	95.42 283	99.58 91	87.24 395	99.23 285	96.95 374	94.28 156	98.93 113	99.73 86	94.39 84	99.16 200	99.89 18	99.82 81	99.86 97

test222					99.55 92	97.41 111	99.34 270	98.55 114	91.86 267	99.27 95	99.83 46	93.84 106			99.95 50	99.99 23

CNLPA		97.76 97	97.38 106	98.92 92	99.53 93	96.84 135	99.87 124	98.14 223	93.78 180	96.55 214	99.69 99	92.28 154	99.98 47	97.13 179	99.44 123	99.93 83

API-MVS		97.86 84	97.66 90	98.47 130	99.52 94	95.41 202	99.47 250	98.87 55	91.68 273	98.84 116	99.85 33	92.34 153	99.99 36	98.44 122	99.96 46	100.00 1

PVSNet	91.05 13	97.13 131	96.69 141	98.45 132	99.52 94	95.81 181	99.95 67	99.65 12	94.73 130	99.04 108	99.21 167	84.48 283	99.95 80	94.92 230	98.74 158	99.58 151

114514_t		97.41 118	96.83 132	99.14 68	99.51 96	97.83 89	99.89 118	98.27 201	88.48 356	99.06 107	99.66 110	90.30 191	99.64 167	96.32 205	99.97 42	99.96 70

cl22		93.77 270	93.25 274	95.33 287	99.49 97	94.43 240	99.61 220	98.09 226	90.38 316	89.16 343	95.61 355	90.56 186	97.34 329	91.93 294	84.45 360	94.21 346

testdata				98.42 136	99.47 98	95.33 208		98.56 108	93.78 180	99.79 34	99.85 33	93.64 111	99.94 88	94.97 228	99.94 55	100.00 1

MAR-MVS		97.43 113	97.19 116	98.15 152	99.47 98	94.79 231	99.05 307	98.76 69	92.65 232	98.66 130	99.82 49	88.52 217	99.98 47	98.12 140	99.63 94	99.67 125
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
DP-MVS		94.54 242	93.42 264	97.91 169	99.46 100	94.04 255	98.93 325	97.48 296	81.15 428	90.04 314	99.55 126	87.02 239	99.95 80	88.97 339	98.11 180	99.73 115

MVS_111021_LR		98.42 49	98.38 38	98.53 125	99.39 101	95.79 182	99.87 124	99.86 2	96.70 69	98.78 120	99.79 58	92.03 160	99.90 107	99.17 73	99.86 75	99.88 93

CHOSEN 280x420		99.01 14	99.03 10	98.95 90	99.38 102	98.87 33	98.46 365	99.42 21	97.03 56	99.02 109	99.09 177	99.35 2	98.21 291	99.73 41	99.78 84	99.77 111

MVS_111021_HR		98.72 28	98.62 26	99.01 84	99.36 103	97.18 119	99.93 91	99.90 1	96.81 66	98.67 129	99.77 65	93.92 101	99.89 112	99.27 69	99.94 55	99.96 70

DPM-MVS		98.83 21	98.46 33	99.97 1	99.33 104	99.92 1	99.96 48	98.44 143	97.96 22	99.55 68	99.94 4	97.18 21	100.00 1	93.81 261	99.94 55	99.98 52

TAPA-MVS	92.12 8	94.42 250	93.60 256	96.90 234	99.33 104	91.78 315	99.78 162	98.00 234	89.89 329	94.52 258	99.47 132	91.97 161	99.18 197	69.90 445	99.52 109	99.73 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 214	95.07 212	96.32 256	99.32 106	96.60 148	99.76 173	98.85 59	96.65 71	87.83 365	96.05 344	99.52 1	98.11 296	96.58 200	81.07 389	94.25 341

fmvsm_s_conf0.5_n_9		98.15 70	98.02 65	98.55 119	99.28 107	95.84 180	99.99 5	98.57 102	98.17 12	99.93 2	99.74 82	87.04 238	99.97 59	99.86 23	99.59 103	99.83 100

SPE-MVS-test		97.88 82	97.94 74	97.70 186	99.28 107	95.20 218	99.98 20	97.15 341	95.53 108	99.62 59	99.79 58	92.08 159	98.38 274	98.75 103	99.28 134	99.52 165

test_fmvsm_n_1920		98.44 46	98.61 27	97.92 167	99.27 109	95.18 219	100.00 1	98.90 50	98.05 19	99.80 25	99.73 86	92.64 141	99.99 36	99.58 53	99.51 112	98.59 264

fmvsm_s_conf0.5_n_10		98.24 66	97.90 77	99.26 50	99.24 110	97.88 87	99.99 5	98.76 69	98.20 9	99.92 4	99.74 82	85.97 256	99.94 88	99.72 42	99.53 108	99.96 70

fmvsm_l_conf0.5_n_a		99.00 15	98.91 14	99.28 48	99.21 111	97.91 86	99.98 20	98.85 59	98.25 5	99.92 4	99.75 75	94.72 71	99.97 59	99.87 21	99.64 92	99.95 78

fmvsm_s_conf0.5_n_8		98.38 54	98.05 63	99.35 45	99.20 112	98.12 72	99.98 20	98.81 64	98.22 7	99.80 25	99.71 92	87.37 233	99.97 59	99.91 16	99.48 116	99.97 62

test_yl		97.83 88	97.37 107	99.21 55	99.18 113	97.98 81	99.64 213	99.27 27	91.43 282	97.88 169	98.99 188	95.84 42	99.84 132	98.82 97	95.32 266	99.79 107

DCV-MVSNet		97.83 88	97.37 107	99.21 55	99.18 113	97.98 81	99.64 213	99.27 27	91.43 282	97.88 169	98.99 188	95.84 42	99.84 132	98.82 97	95.32 266	99.79 107

fmvsm_l_conf0.5_n		98.94 16	98.84 17	99.25 51	99.17 115	97.81 91	99.98 20	98.86 56	98.25 5	99.90 6	99.76 67	94.21 94	99.97 59	99.87 21	99.52 109	99.98 52

DeepC-MVS	94.51 4	96.92 145	96.40 154	98.45 132	99.16 116	95.90 178	99.66 208	98.06 229	96.37 86	94.37 264	99.49 131	83.29 293	99.90 107	97.63 168	99.61 99	99.55 155
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.54 38	98.22 49	99.50 30	99.15 117	98.65 53	100.00 1	98.58 100	97.70 31	98.21 157	99.24 163	92.58 144	99.94 88	98.63 112	99.94 55	99.92 88
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_l_conf0.5_n_3		98.41 50	98.08 61	99.39 41	99.12 118	98.29 65	99.98 20	98.64 86	98.14 15	99.86 14	99.76 67	87.99 222	99.97 59	99.72 42	99.54 106	99.91 90

fmvsm_l_conf0.5_n_9		98.55 37	98.23 48	99.49 32	99.10 119	98.50 60	99.99 5	98.70 75	98.14 15	99.94 1	99.68 106	89.02 210	99.98 47	99.89 18	99.61 99	99.99 23

CS-MVS		97.79 95	97.91 76	97.43 210	99.10 119	94.42 241	99.99 5	97.10 353	95.07 117	99.68 49	99.75 75	92.95 131	98.34 278	98.38 124	99.14 140	99.54 159

Anonymous202405211		93.10 288	91.99 301	96.40 252	99.10 119	89.65 365	98.88 331	97.93 242	83.71 412	94.00 270	98.75 224	68.79 406	99.88 118	95.08 225	91.71 299	99.68 123

fmvsm_s_conf0.5_n		97.80 93	97.85 81	97.67 187	99.06 122	94.41 242	99.98 20	98.97 43	97.34 41	99.63 56	99.69 99	87.27 234	99.97 59	99.62 51	99.06 145	98.62 263

HyFIR lowres test		96.66 160	96.43 152	97.36 217	99.05 123	93.91 260	99.70 199	99.80 3	90.54 312	96.26 223	98.08 275	92.15 157	98.23 290	96.84 193	95.46 261	99.93 83

LFMVS		94.75 236	93.56 259	98.30 142	99.03 124	95.70 188	98.74 346	97.98 237	87.81 367	98.47 141	99.39 143	67.43 415	99.53 169	98.01 147	95.20 269	99.67 125

fmvsm_s_conf0.5_n_4		97.75 98	97.86 80	97.42 211	99.01 125	94.69 234	99.97 38	98.76 69	97.91 24	99.87 12	99.76 67	86.70 245	99.93 98	99.67 48	99.12 143	97.64 292

fmvsm_s_conf0.5_n_2		97.59 108	97.28 111	98.53 125	99.01 125	98.15 67	99.98 20	98.59 98	98.17 12	99.75 39	99.63 116	81.83 306	99.94 88	99.78 31	98.79 156	97.51 300

AllTest		92.48 303	91.64 306	95.00 296	99.01 125	88.43 383	98.94 323	96.82 388	86.50 383	88.71 348	98.47 257	74.73 381	99.88 118	85.39 379	96.18 236	96.71 306

TestCases				95.00 296	99.01 125	88.43 383		96.82 388	86.50 383	88.71 348	98.47 257	74.73 381	99.88 118	85.39 379	96.18 236	96.71 306

COLMAP_ROB	90.47 14	92.18 310	91.49 312	94.25 330	99.00 129	88.04 389	98.42 371	96.70 395	82.30 423	88.43 357	99.01 185	76.97 356	99.85 124	86.11 375	96.50 228	94.86 317
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_3		97.95 77	97.66 90	98.81 96	98.99 130	98.07 75	99.98 20	98.81 64	98.18 11	99.89 9	99.70 95	84.15 286	99.97 59	99.76 36	99.50 114	98.39 271

test_fmvs1		95.35 215	95.68 189	94.36 326	98.99 130	84.98 410	99.96 48	96.65 397	97.60 33	99.73 44	98.96 194	71.58 396	99.93 98	98.31 129	99.37 129	98.17 276

HY-MVS	92.50 7	97.79 95	97.17 118	99.63 17	98.98 132	99.32 9	97.49 399	99.52 14	95.69 103	98.32 150	97.41 295	93.32 118	99.77 144	98.08 144	95.75 251	99.81 104

VNet		97.21 127	96.57 146	99.13 72	98.97 133	97.82 90	99.03 310	99.21 32	94.31 153	99.18 100	98.88 206	86.26 252	99.89 112	98.93 88	94.32 279	99.69 122

thres200		96.96 141	96.21 161	99.22 54	98.97 133	98.84 36	99.85 138	99.71 7	93.17 202	96.26 223	98.88 206	89.87 196	99.51 172	94.26 249	94.91 271	99.31 205

tfpn200view9		96.79 149	95.99 168	99.19 57	98.94 135	98.82 37	99.78 162	99.71 7	92.86 217	96.02 231	98.87 213	89.33 203	99.50 174	93.84 258	94.57 275	99.27 214

thres400		96.78 151	95.99 168	99.16 64	98.94 135	98.82 37	99.78 162	99.71 7	92.86 217	96.02 231	98.87 213	89.33 203	99.50 174	93.84 258	94.57 275	99.16 223

sasdasda		97.09 134	96.32 155	99.39 41	98.93 137	98.95 27	99.72 191	97.35 309	94.45 141	97.88 169	99.42 136	86.71 243	99.52 170	98.48 119	93.97 285	99.72 117

Anonymous20231211		89.86 360	88.44 368	94.13 333	98.93 137	90.68 343	98.54 362	98.26 202	76.28 440	86.73 379	95.54 359	70.60 402	97.56 322	90.82 314	80.27 398	94.15 354

canonicalmvs		97.09 134	96.32 155	99.39 41	98.93 137	98.95 27	99.72 191	97.35 309	94.45 141	97.88 169	99.42 136	86.71 243	99.52 170	98.48 119	93.97 285	99.72 117

SDMVSNet		94.80 231	93.96 246	97.33 220	98.92 140	95.42 201	99.59 225	98.99 40	92.41 245	92.55 288	97.85 286	75.81 371	98.93 215	97.90 155	91.62 300	97.64 292

sd_testset		93.55 277	92.83 281	95.74 274	98.92 140	90.89 339	98.24 378	98.85 59	92.41 245	92.55 288	97.85 286	71.07 401	98.68 243	93.93 255	91.62 300	97.64 292

EPNet_dtu		95.71 203	95.39 198	96.66 243	98.92 140	93.41 274	99.57 230	98.90 50	96.19 91	97.52 179	98.56 247	92.65 140	97.36 327	77.89 426	98.33 169	99.20 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 73	97.60 94	99.60 22	98.92 140	99.28 17	99.89 118	99.52 14	95.58 106	98.24 156	99.39 143	93.33 117	99.74 150	97.98 151	95.58 260	99.78 110

CHOSEN 1792x2688		96.81 148	96.53 147	97.64 190	98.91 144	93.07 280	99.65 209	99.80 3	95.64 104	95.39 248	98.86 215	84.35 285	99.90 107	96.98 186	99.16 139	99.95 78

thres100view900		96.74 155	95.92 178	99.18 58	98.90 145	98.77 42	99.74 180	99.71 7	92.59 236	95.84 235	98.86 215	89.25 205	99.50 174	93.84 258	94.57 275	99.27 214

thres600view7		96.69 158	95.87 181	99.14 68	98.90 145	98.78 41	99.74 180	99.71 7	92.59 236	95.84 235	98.86 215	89.25 205	99.50 174	93.44 271	94.50 278	99.16 223

MSDG		94.37 252	93.36 271	97.40 213	98.88 147	93.95 259	99.37 266	97.38 305	85.75 394	90.80 307	99.17 171	84.11 288	99.88 118	86.35 371	98.43 167	98.36 273

MGCFI-Net		97.00 139	96.22 160	99.34 46	98.86 148	98.80 39	99.67 207	97.30 318	94.31 153	97.77 175	99.41 140	86.36 250	99.50 174	98.38 124	93.90 287	99.72 117

h-mvs33		94.92 228	94.36 232	96.59 245	98.85 149	91.29 331	98.93 325	98.94 44	95.90 96	98.77 122	98.42 260	90.89 181	99.77 144	97.80 159	70.76 437	98.72 260

Anonymous20240529		92.10 311	90.65 323	96.47 247	98.82 150	90.61 345	98.72 348	98.67 82	75.54 444	93.90 272	98.58 245	66.23 419	99.90 107	94.70 239	90.67 303	98.90 250

PVSNet_Blended_VisFu		97.27 123	96.81 134	98.66 108	98.81 151	96.67 144	99.92 94	98.64 86	94.51 138	96.38 221	98.49 253	89.05 209	99.88 118	97.10 181	98.34 168	99.43 183

PS-MVSNAJ		98.44 46	98.20 51	99.16 64	98.80 152	98.92 29	99.54 238	98.17 214	97.34 41	99.85 17	99.85 33	91.20 170	99.89 112	99.41 64	99.67 90	98.69 261

CANet_DTU		96.76 152	96.15 163	98.60 113	98.78 153	97.53 102	99.84 143	97.63 274	97.25 49	99.20 97	99.64 113	81.36 312	99.98 47	92.77 282	98.89 150	98.28 275

mvsany_test1		97.82 91	97.90 77	97.55 199	98.77 154	93.04 283	99.80 159	97.93 242	96.95 60	99.61 66	99.68 106	90.92 178	99.83 134	99.18 72	98.29 173	99.80 106

alignmvs		97.81 92	97.33 109	99.25 51	98.77 154	98.66 51	99.99 5	98.44 143	94.40 149	98.41 145	99.47 132	93.65 110	99.42 184	98.57 113	94.26 281	99.67 125

SymmetryMVS		97.64 106	97.46 100	98.17 148	98.74 156	95.39 204	99.61 220	99.26 29	96.52 75	98.61 133	99.31 151	92.73 138	99.67 162	96.77 194	95.63 258	99.45 179

SteuartSystems-ACMMP		99.02 13	98.97 13	99.18 58	98.72 157	97.71 94	99.98 20	98.44 143	96.85 61	99.80 25	99.91 14	97.57 8	99.85 124	99.44 62	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 68	97.97 69	99.02 83	98.69 158	98.66 51	99.52 240	98.08 228	97.05 55	99.86 14	99.86 29	90.65 183	99.71 154	99.39 66	98.63 160	98.69 261

miper_enhance_ethall		94.36 254	93.98 245	95.49 277	98.68 159	95.24 215	99.73 187	97.29 321	93.28 199	89.86 319	95.97 345	94.37 85	97.05 350	92.20 286	84.45 360	94.19 347

fmvsm_s_conf0.5_n_5		98.08 74	97.71 88	99.17 61	98.67 160	97.69 98	99.99 5	98.57 102	97.40 39	99.89 9	99.69 99	85.99 255	99.96 71	99.80 28	99.40 127	99.85 98

ETVMVS		97.03 138	96.64 142	98.20 147	98.67 160	97.12 123	99.89 118	98.57 102	91.10 294	98.17 158	98.59 242	93.86 105	98.19 292	95.64 217	95.24 268	99.28 212

test2506		97.53 110	97.19 116	98.58 117	98.66 162	96.90 134	98.81 340	99.77 5	94.93 120	97.95 164	98.96 194	92.51 147	99.20 195	94.93 229	98.15 177	99.64 131

ECVR-MVS		95.66 206	95.05 213	97.51 204	98.66 162	93.71 264	98.85 337	98.45 138	94.93 120	96.86 202	98.96 194	75.22 377	99.20 195	95.34 219	98.15 177	99.64 131

mamv4		95.24 218	96.90 127	90.25 404	98.65 164	72.11 452	98.28 376	97.64 273	89.99 327	95.93 233	98.25 270	94.74 70	99.11 201	99.01 85	99.64 92	99.53 163

balanced_conf03		98.27 60	97.99 67	99.11 73	98.64 165	98.43 63	99.47 250	97.79 257	94.56 136	99.74 42	98.35 262	94.33 88	99.25 189	99.12 74	99.96 46	99.64 131

fmvsm_s_conf0.5_n_a		97.73 101	97.72 86	97.77 181	98.63 166	94.26 249	99.96 48	98.92 49	97.18 51	99.75 39	99.69 99	87.00 240	99.97 59	99.46 60	98.89 150	99.08 233

MVSMamba_PlusPlus		97.83 88	97.45 102	98.99 85	98.60 167	98.15 67	99.58 227	97.74 264	90.34 319	99.26 96	98.32 265	94.29 90	99.23 190	99.03 83	99.89 70	99.58 151

testing222		97.08 137	96.75 137	98.06 158	98.56 168	96.82 136	99.85 138	98.61 94	92.53 240	98.84 116	98.84 219	93.36 115	98.30 282	95.84 213	94.30 280	99.05 237

test1111		95.57 209	94.98 216	97.37 215	98.56 168	93.37 277	98.86 335	98.45 138	94.95 119	96.63 208	98.95 199	75.21 378	99.11 201	95.02 226	98.14 179	99.64 131

MVSTER		95.53 210	95.22 205	96.45 250	98.56 168	97.72 93	99.91 102	97.67 269	92.38 248	91.39 298	97.14 302	97.24 18	97.30 334	94.80 235	87.85 332	94.34 336

testing3-2		97.72 102	97.43 105	98.60 113	98.55 171	97.11 125	100.00 1	99.23 31	93.78 180	97.90 166	98.73 226	95.50 49	99.69 158	98.53 117	94.63 273	98.99 241

VDD-MVS		93.77 270	92.94 279	96.27 257	98.55 171	90.22 354	98.77 345	97.79 257	90.85 300	96.82 204	99.42 136	61.18 439	99.77 144	98.95 86	94.13 282	98.82 253

tpmvs		94.28 256	93.57 258	96.40 252	98.55 171	91.50 329	95.70 436	98.55 114	87.47 369	92.15 291	94.26 410	91.42 166	98.95 214	88.15 350	95.85 247	98.76 256

UGNet		95.33 216	94.57 228	97.62 194	98.55 171	94.85 227	98.67 354	99.32 26	95.75 101	96.80 205	96.27 334	72.18 393	99.96 71	94.58 242	99.05 146	98.04 281
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
PCF-MVS	94.20 5	95.18 220	94.10 239	98.43 134	98.55 171	95.99 176	97.91 392	97.31 317	90.35 318	89.48 332	99.22 164	85.19 269	99.89 112	90.40 324	98.47 166	99.41 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 191	96.49 148	94.34 327	98.51 176	89.99 359	99.39 262	98.57 102	93.14 204	97.33 187	98.31 267	93.44 113	94.68 427	93.69 268	95.98 241	98.34 274

UWE-MVS		96.79 149	96.72 139	97.00 229	98.51 176	93.70 265	99.71 194	98.60 96	92.96 212	97.09 194	98.34 264	96.67 31	98.85 221	92.11 292	96.50 228	98.44 269

myMVS_eth3d28		97.86 84	97.59 96	98.68 105	98.50 178	97.26 115	99.92 94	98.55 114	93.79 179	98.26 154	98.75 224	95.20 54	99.48 180	98.93 88	96.40 231	99.29 210

test_vis1_n_1920		95.44 212	95.31 201	95.82 271	98.50 178	88.74 377	99.98 20	97.30 318	97.84 27	99.85 17	99.19 169	66.82 417	99.97 59	98.82 97	99.46 121	98.76 256

BH-w/o		95.71 203	95.38 199	96.68 242	98.49 180	92.28 302	99.84 143	97.50 294	92.12 258	92.06 294	98.79 222	84.69 279	98.67 245	95.29 221	99.66 91	99.09 231

baseline1		95.78 199	94.86 219	98.54 123	98.47 181	98.07 75	99.06 303	97.99 235	92.68 230	94.13 269	98.62 239	93.28 121	98.69 242	93.79 263	85.76 347	98.84 252

fmvsm_s_conf0.5_n_7		97.70 104	97.74 85	97.59 197	98.44 182	95.16 221	99.97 38	98.65 83	97.95 23	99.62 59	99.78 62	86.09 253	99.94 88	99.69 46	99.50 114	97.66 291

EPMVS		96.53 167	96.01 167	98.09 156	98.43 183	96.12 174	96.36 423	99.43 20	93.53 188	97.64 177	95.04 387	94.41 80	98.38 274	91.13 305	98.11 180	99.75 113

kuosan		93.17 285	92.60 287	94.86 303	98.40 184	89.54 367	98.44 367	98.53 121	84.46 407	88.49 353	97.92 283	90.57 185	97.05 350	83.10 396	93.49 290	97.99 282

WBMVS		94.52 245	94.03 243	95.98 263	98.38 185	96.68 143	99.92 94	97.63 274	90.75 309	89.64 327	95.25 380	96.77 25	96.90 362	94.35 247	83.57 367	94.35 334

UBG		97.84 87	97.69 89	98.29 143	98.38 185	96.59 150	99.90 108	98.53 121	93.91 175	98.52 137	98.42 260	96.77 25	99.17 198	98.54 115	96.20 235	99.11 230

sss		97.57 109	97.03 123	99.18 58	98.37 187	98.04 78	99.73 187	99.38 22	93.46 191	98.76 125	99.06 180	91.21 169	99.89 112	96.33 204	97.01 219	99.62 138

testing11		97.48 112	97.27 112	98.10 155	98.36 188	96.02 175	99.92 94	98.45 138	93.45 193	98.15 159	98.70 229	95.48 50	99.22 191	97.85 157	95.05 270	99.07 234

BH-untuned		95.18 220	94.83 220	96.22 258	98.36 188	91.22 332	99.80 159	97.32 316	90.91 298	91.08 301	98.67 231	83.51 290	98.54 256	94.23 250	99.61 99	98.92 247

testing91		97.16 129	96.90 127	97.97 162	98.35 190	95.67 191	99.91 102	98.42 163	92.91 215	97.33 187	98.72 227	94.81 68	99.21 192	96.98 186	94.63 273	99.03 238

testing99		97.17 128	96.91 126	97.95 163	98.35 190	95.70 188	99.91 102	98.43 151	92.94 213	97.36 185	98.72 227	94.83 67	99.21 192	97.00 184	94.64 272	98.95 243

ET-MVSNet_ETH3D		94.37 252	93.28 273	97.64 190	98.30 192	97.99 80	99.99 5	97.61 280	94.35 150	71.57 450	99.45 135	96.23 35	95.34 417	96.91 191	85.14 354	99.59 145

AUN-MVS		93.28 282	92.60 287	95.34 286	98.29 193	90.09 357	99.31 274	98.56 108	91.80 271	96.35 222	98.00 278	89.38 202	98.28 285	92.46 283	69.22 442	97.64 292

FMVSNet3		92.69 298	91.58 308	95.99 262	98.29 193	97.42 110	99.26 283	97.62 277	89.80 330	89.68 323	95.32 374	81.62 310	96.27 393	87.01 367	85.65 348	94.29 338

PMMVS		96.76 152	96.76 136	96.76 239	98.28 195	92.10 306	99.91 102	97.98 237	94.12 161	99.53 71	99.39 143	86.93 241	98.73 235	96.95 189	97.73 188	99.45 179

hse-mvs2		94.38 251	94.08 242	95.31 288	98.27 196	90.02 358	99.29 279	98.56 108	95.90 96	98.77 122	98.00 278	90.89 181	98.26 289	97.80 159	69.20 443	97.64 292

PVSNet_0	88.03 19	91.80 318	90.27 332	96.38 254	98.27 196	90.46 349	99.94 84	99.61 13	93.99 169	86.26 389	97.39 297	71.13 400	99.89 112	98.77 101	67.05 449	98.79 255

UA-Net		96.54 166	95.96 174	98.27 144	98.23 198	95.71 187	98.00 390	98.45 138	93.72 184	98.41 145	99.27 156	88.71 216	99.66 165	91.19 304	97.69 189	99.44 182

test_cas_vis1_n_1920		96.59 163	96.23 158	97.65 189	98.22 199	94.23 250	99.99 5	97.25 326	97.77 28	99.58 67	99.08 178	77.10 351	99.97 59	97.64 167	99.45 122	98.74 258

FE-MVS		95.70 205	95.01 215	97.79 177	98.21 200	94.57 236	95.03 437	98.69 77	88.90 346	97.50 181	96.19 336	92.60 143	99.49 179	89.99 329	97.94 186	99.31 205

GG-mvs-BLEND				98.54 123	98.21 200	98.01 79	93.87 442	98.52 123		97.92 165	97.92 283	99.02 3	97.94 309	98.17 137	99.58 104	99.67 125

mvs_anonymous		95.65 207	95.03 214	97.53 201	98.19 202	95.74 185	99.33 271	97.49 295	90.87 299	90.47 310	97.10 304	88.23 219	97.16 341	95.92 211	97.66 192	99.68 123

MVS_Test		96.46 169	95.74 185	98.61 112	98.18 203	97.23 117	99.31 274	97.15 341	91.07 295	98.84 116	97.05 308	88.17 220	98.97 211	94.39 244	97.50 194	99.61 142

BH-RMVSNet		95.18 220	94.31 235	97.80 175	98.17 204	95.23 216	99.76 173	97.53 290	92.52 241	94.27 267	99.25 162	76.84 358	98.80 225	90.89 313	99.54 106	99.35 196

dongtai		91.55 324	91.13 317	92.82 372	98.16 205	86.35 400	99.47 250	98.51 126	83.24 415	85.07 399	97.56 291	90.33 190	94.94 423	76.09 434	91.73 298	97.18 303

RPSCF		91.80 318	92.79 283	88.83 415	98.15 206	69.87 454	98.11 386	96.60 399	83.93 410	94.33 265	99.27 156	79.60 334	99.46 183	91.99 293	93.16 295	97.18 303

ETV-MVS		97.92 80	97.80 84	98.25 145	98.14 207	96.48 152	99.98 20	97.63 274	95.61 105	99.29 94	99.46 134	92.55 145	98.82 223	99.02 84	98.54 164	99.46 176

IS-MVSNet		96.29 180	95.90 179	97.45 207	98.13 208	94.80 230	99.08 298	97.61 280	92.02 263	95.54 246	98.96 194	90.64 184	98.08 298	93.73 266	97.41 198	99.47 174

test_fmvsmconf_n		98.43 48	98.32 44	98.78 98	98.12 209	96.41 155	99.99 5	98.83 63	98.22 7	99.67 50	99.64 113	91.11 174	99.94 88	99.67 48	99.62 95	99.98 52

fmvsm_s_conf0.1_n_2		97.25 124	96.85 131	98.43 134	98.08 210	98.08 74	99.92 94	97.76 263	98.05 19	99.65 52	99.58 122	80.88 319	99.93 98	99.59 52	98.17 175	97.29 301

ab-mvs		94.69 237	93.42 264	98.51 128	98.07 211	96.26 162	96.49 421	98.68 79	90.31 320	94.54 257	97.00 310	76.30 366	99.71 154	95.98 210	93.38 293	99.56 154

XVG-OURS-SEG-HR		94.79 232	94.70 227	95.08 293	98.05 212	89.19 369	99.08 298	97.54 288	93.66 185	94.87 255	99.58 122	78.78 342	99.79 139	97.31 174	93.40 292	96.25 310

EIA-MVS		97.53 110	97.46 100	97.76 183	98.04 213	94.84 228	99.98 20	97.61 280	94.41 148	97.90 166	99.59 119	92.40 151	98.87 219	98.04 146	99.13 141	99.59 145

XVG-OURS		94.82 229	94.74 226	95.06 294	98.00 214	89.19 369	99.08 298	97.55 286	94.10 162	94.71 256	99.62 117	80.51 325	99.74 150	96.04 209	93.06 297	96.25 310

mvsmamba		96.94 142	96.73 138	97.55 199	97.99 215	94.37 246	99.62 216	97.70 266	93.13 205	98.42 144	97.92 283	88.02 221	98.75 233	98.78 100	99.01 147	99.52 165

dp		95.05 223	94.43 230	96.91 232	97.99 215	92.73 291	96.29 426	97.98 237	89.70 331	95.93 233	94.67 400	93.83 107	98.45 262	86.91 370	96.53 227	99.54 159

tpmrst		96.27 182	95.98 170	97.13 224	97.96 217	93.15 279	96.34 424	98.17 214	92.07 259	98.71 128	95.12 384	93.91 102	98.73 235	94.91 232	96.62 225	99.50 171

TR-MVS		94.54 242	93.56 259	97.49 206	97.96 217	94.34 247	98.71 349	97.51 293	90.30 321	94.51 259	98.69 230	75.56 372	98.77 229	92.82 281	95.99 240	99.35 196

Vis-MVSNet (Re-imp)		96.32 177	95.98 170	97.35 219	97.93 219	94.82 229	99.47 250	98.15 222	91.83 268	95.09 253	99.11 176	91.37 168	97.47 325	93.47 270	97.43 195	99.74 114

MDTV_nov1_ep13			95.69 187		97.90 220	94.15 252	95.98 432	98.44 143	93.12 206	97.98 163	95.74 349	95.10 57	98.58 252	90.02 328	96.92 221

Fast-Effi-MVS+		95.02 225	94.19 237	97.52 203	97.88 221	94.55 237	99.97 38	97.08 357	88.85 348	94.47 260	97.96 282	84.59 280	98.41 266	89.84 331	97.10 212	99.59 145

ADS-MVSNet2		93.80 269	93.88 249	93.55 355	97.87 222	85.94 404	94.24 438	96.84 385	90.07 324	96.43 218	94.48 405	90.29 192	95.37 416	87.44 357	97.23 205	99.36 192

ADS-MVSNet		94.79 232	94.02 244	97.11 226	97.87 222	93.79 261	94.24 438	98.16 219	90.07 324	96.43 218	94.48 405	90.29 192	98.19 292	87.44 357	97.23 205	99.36 192

Effi-MVS+		96.30 179	95.69 187	98.16 149	97.85 224	96.26 162	97.41 401	97.21 333	90.37 317	98.65 131	98.58 245	86.61 247	98.70 241	97.11 180	97.37 199	99.52 165

PatchmatchNet		95.94 192	95.45 194	97.39 214	97.83 225	94.41 242	96.05 430	98.40 172	92.86 217	97.09 194	95.28 379	94.21 94	98.07 300	89.26 337	98.11 180	99.70 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 240	93.61 254	97.74 185	97.82 226	96.26 162	99.96 48	97.78 259	85.76 392	94.00 270	97.54 292	76.95 357	99.21 192	97.23 177	95.43 263	97.76 290

1112_ss		96.01 190	95.20 206	98.42 136	97.80 227	96.41 155	99.65 209	96.66 396	92.71 227	92.88 284	99.40 141	92.16 156	99.30 187	91.92 295	93.66 288	99.55 155

Test_1112_low_res		95.72 201	94.83 220	98.42 136	97.79 228	96.41 155	99.65 209	96.65 397	92.70 228	92.86 285	96.13 340	92.15 157	99.30 187	91.88 296	93.64 289	99.55 155

Effi-MVS+-dtu		94.53 244	95.30 202	92.22 380	97.77 229	82.54 427	99.59 225	97.06 361	94.92 122	95.29 250	95.37 372	85.81 257	97.89 310	94.80 235	97.07 213	96.23 312

tpm cat1		93.51 278	92.52 293	96.47 247	97.77 229	91.47 330	96.13 428	98.06 229	80.98 429	92.91 283	93.78 414	89.66 197	98.87 219	87.03 366	96.39 232	99.09 231

FA-MVS(test-final)		95.86 195	95.09 211	98.15 152	97.74 231	95.62 193	96.31 425	98.17 214	91.42 284	96.26 223	96.13 340	90.56 186	99.47 182	92.18 287	97.07 213	99.35 196

xiu_mvs_v1_base_debu		97.43 113	97.06 119	98.55 119	97.74 231	98.14 69	99.31 274	97.86 251	96.43 80	99.62 59	99.69 99	85.56 264	99.68 159	99.05 77	98.31 170	97.83 286

xiu_mvs_v1_base		97.43 113	97.06 119	98.55 119	97.74 231	98.14 69	99.31 274	97.86 251	96.43 80	99.62 59	99.69 99	85.56 264	99.68 159	99.05 77	98.31 170	97.83 286

xiu_mvs_v1_base_debi		97.43 113	97.06 119	98.55 119	97.74 231	98.14 69	99.31 274	97.86 251	96.43 80	99.62 59	99.69 99	85.56 264	99.68 159	99.05 77	98.31 170	97.83 286

EPP-MVSNet		96.69 158	96.60 144	96.96 231	97.74 231	93.05 282	99.37 266	98.56 108	88.75 350	95.83 237	99.01 185	96.01 36	98.56 254	96.92 190	97.20 207	99.25 216

gg-mvs-nofinetune		93.51 278	91.86 305	98.47 130	97.72 236	97.96 84	92.62 448	98.51 126	74.70 447	97.33 187	69.59 464	98.91 4	97.79 313	97.77 164	99.56 105	99.67 125

IB-MVS	92.85 6	94.99 226	93.94 247	98.16 149	97.72 236	95.69 190	99.99 5	98.81 64	94.28 156	92.70 286	96.90 312	95.08 58	99.17 198	96.07 208	73.88 430	99.60 144
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
thisisatest0515		97.41 118	97.02 124	98.59 116	97.71 238	97.52 103	99.97 38	98.54 118	91.83 268	97.45 182	99.04 182	97.50 9	99.10 203	94.75 237	96.37 233	99.16 223

VortexMVS		94.11 258	93.50 261	95.94 265	97.70 239	96.61 147	99.35 269	97.18 336	93.52 190	89.57 330	95.74 349	87.55 228	96.97 358	95.76 216	85.13 355	94.23 343

viewdifsd2359ckpt09		96.21 184	95.77 183	97.53 201	97.69 240	94.50 238	99.78 162	97.23 331	92.88 216	96.58 211	99.26 160	84.85 274	98.66 248	96.61 198	97.02 218	99.43 183

Syy-MVS		90.00 358	90.63 324	88.11 422	97.68 241	74.66 450	99.71 194	98.35 185	90.79 306	92.10 292	98.67 231	79.10 340	93.09 442	63.35 457	95.95 244	96.59 308

myMVS_eth3d		94.46 249	94.76 225	93.55 355	97.68 241	90.97 334	99.71 194	98.35 185	90.79 306	92.10 292	98.67 231	92.46 150	93.09 442	87.13 363	95.95 244	96.59 308

test_fmvs1_n		94.25 257	94.36 232	93.92 342	97.68 241	83.70 417	99.90 108	96.57 400	97.40 39	99.67 50	98.88 206	61.82 436	99.92 104	98.23 135	99.13 141	98.14 279

fmvsm_s_conf0.5_n_6		98.27 60	97.96 72	99.23 53	97.66 244	98.11 73	99.98 20	98.64 86	97.85 26	99.87 12	99.72 89	88.86 213	99.93 98	99.64 50	99.36 130	99.63 137

RRT-MVS		96.24 183	95.68 189	97.94 166	97.65 245	94.92 226	99.27 282	97.10 353	92.79 223	97.43 183	97.99 280	81.85 305	99.37 186	98.46 121	98.57 161	99.53 163

diffmvs		97.00 139	96.64 142	98.09 156	97.64 246	96.17 171	99.81 155	97.19 334	94.67 134	98.95 111	99.28 153	86.43 248	98.76 231	98.37 126	97.42 197	99.33 199
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		96.59 163	96.23 158	97.66 188	97.63 247	94.70 233	99.77 167	97.33 313	93.41 194	97.34 186	99.17 171	86.72 242	98.83 222	97.40 172	97.32 202	99.46 176

viewdifsd2359ckpt13		96.19 185	95.77 183	97.45 207	97.62 248	94.40 244	99.70 199	97.23 331	92.76 225	96.63 208	99.05 181	84.96 273	98.64 249	96.65 197	97.35 200	99.31 205

Vis-MVSNet		95.72 201	95.15 209	97.45 207	97.62 248	94.28 248	99.28 280	98.24 205	94.27 158	96.84 203	98.94 201	79.39 335	98.76 231	93.25 272	98.49 165	99.30 208
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 132	96.72 139	98.22 146	97.60 250	96.70 140	99.92 94	98.54 118	91.11 293	97.07 196	98.97 192	97.47 12	99.03 206	93.73 266	96.09 238	98.92 247

GDP-MVS		97.88 82	97.59 96	98.75 101	97.59 251	97.81 91	99.95 67	97.37 308	94.44 144	99.08 105	99.58 122	97.13 23	99.08 204	94.99 227	98.17 175	99.37 190

miper_ehance_all_eth		93.16 286	92.60 287	94.82 304	97.57 252	93.56 269	99.50 244	97.07 360	88.75 350	88.85 347	95.52 361	90.97 177	96.74 372	90.77 315	84.45 360	94.17 348

guyue		97.15 130	96.82 133	98.15 152	97.56 253	96.25 166	99.71 194	97.84 254	95.75 101	98.13 160	98.65 234	87.58 227	98.82 223	98.29 131	97.91 187	99.36 192

viewmanbaseed2359cas		96.45 170	96.07 164	97.59 197	97.55 254	94.59 235	99.70 199	97.33 313	93.62 187	97.00 198	99.32 148	85.57 263	98.71 238	97.26 176	97.33 201	99.47 174

testing3		93.92 263	94.23 236	92.99 369	97.54 255	90.23 353	99.99 5	99.16 33	90.57 311	91.33 300	98.63 238	92.99 129	92.52 446	82.46 400	95.39 264	96.22 313

SSM_0404		95.75 200	95.16 208	97.50 205	97.53 256	95.39 204	99.11 294	97.25 326	90.81 302	95.27 251	98.83 220	84.74 276	98.67 245	95.24 222	97.69 189	98.45 268

LCM-MVSNet-Re		92.31 307	92.60 287	91.43 389	97.53 256	79.27 444	99.02 312	91.83 459	92.07 259	80.31 423	94.38 408	83.50 291	95.48 413	97.22 178	97.58 193	99.54 159

GBi-Net		90.88 335	89.82 341	94.08 334	97.53 256	91.97 307	98.43 368	96.95 374	87.05 375	89.68 323	94.72 396	71.34 397	96.11 399	87.01 367	85.65 348	94.17 348

test1		90.88 335	89.82 341	94.08 334	97.53 256	91.97 307	98.43 368	96.95 374	87.05 375	89.68 323	94.72 396	71.34 397	96.11 399	87.01 367	85.65 348	94.17 348

FMVSNet2		91.02 332	89.56 346	95.41 284	97.53 256	95.74 185	98.98 315	97.41 303	87.05 375	88.43 357	95.00 390	71.34 397	96.24 395	85.12 382	85.21 353	94.25 341

tttt0517		96.85 146	96.49 148	97.92 167	97.48 261	95.89 179	99.85 138	98.54 118	90.72 310	96.63 208	98.93 204	97.47 12	99.02 207	93.03 279	95.76 250	98.85 251

BP-MVS1		98.33 56	98.18 53	98.81 96	97.44 262	97.98 81	99.96 48	98.17 214	94.88 124	98.77 122	99.59 119	97.59 7	99.08 204	98.24 134	98.93 149	99.36 192

casdiffmvs_mvg		96.43 171	95.94 176	97.89 171	97.44 262	95.47 197	99.86 135	97.29 321	93.35 195	96.03 230	99.19 169	85.39 267	98.72 237	97.89 156	97.04 215	99.49 173
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		97.38 120	97.24 113	97.80 175	97.41 264	95.64 192	99.99 5	97.06 361	94.59 135	99.63 56	99.32 148	89.20 208	98.14 294	98.76 102	99.23 137	99.62 138

viewdifsd2359ckpt07		95.83 198	95.42 196	97.07 227	97.40 265	93.04 283	99.60 223	97.24 329	92.39 247	96.09 229	99.14 175	83.07 296	98.93 215	97.02 183	96.87 222	99.23 219

c3_l		92.53 302	91.87 304	94.52 316	97.40 265	92.99 285	99.40 258	96.93 379	87.86 365	88.69 350	95.44 366	89.95 195	96.44 385	90.45 321	80.69 394	94.14 357

viewmambaseed2359dif		95.92 194	95.55 193	97.04 228	97.38 267	93.41 274	99.78 162	96.97 372	91.14 292	96.58 211	99.27 156	84.85 274	98.75 233	96.87 192	97.12 211	98.97 242

fmvsm_s_conf0.1_n		97.30 121	97.21 115	97.60 196	97.38 267	94.40 244	99.90 108	98.64 86	96.47 79	99.51 75	99.65 112	84.99 272	99.93 98	99.22 71	99.09 144	98.46 267

CDS-MVSNet		96.34 176	96.07 164	97.13 224	97.37 269	94.96 224	99.53 239	97.91 246	91.55 276	95.37 249	98.32 265	95.05 60	97.13 344	93.80 262	95.75 251	99.30 208
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 155	96.26 157	98.16 149	97.36 270	96.48 152	99.96 48	98.29 198	91.93 264	95.77 238	98.07 276	95.54 46	98.29 283	90.55 319	98.89 150	99.70 120

miper_lstm_enhance		91.81 315	91.39 314	93.06 368	97.34 271	89.18 371	99.38 264	96.79 390	86.70 382	87.47 371	95.22 381	90.00 194	95.86 408	88.26 348	81.37 383	94.15 354

baseline		96.43 171	95.98 170	97.76 183	97.34 271	95.17 220	99.51 242	97.17 338	93.92 174	96.90 201	99.28 153	85.37 268	98.64 249	97.50 170	96.86 224	99.46 176

cl____		92.31 307	91.58 308	94.52 316	97.33 273	92.77 287	99.57 230	96.78 391	86.97 379	87.56 369	95.51 362	89.43 201	96.62 377	88.60 342	82.44 375	94.16 353

SD_0403		92.63 301	93.38 268	90.40 403	97.32 274	77.91 446	97.75 397	98.03 233	91.89 265	90.83 306	98.29 269	82.00 302	93.79 436	88.51 346	95.75 251	99.52 165

DIV-MVS_self_test		92.32 306	91.60 307	94.47 320	97.31 275	92.74 289	99.58 227	96.75 392	86.99 378	87.64 367	95.54 359	89.55 200	96.50 382	88.58 343	82.44 375	94.17 348

casdiffmvs		96.42 173	95.97 173	97.77 181	97.30 276	94.98 223	99.84 143	97.09 356	93.75 183	96.58 211	99.26 160	85.07 270	98.78 228	97.77 164	97.04 215	99.54 159
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		94.36 254	93.48 262	96.99 230	97.29 277	93.54 270	99.96 48	96.72 394	88.35 359	93.43 274	98.94 201	82.05 301	98.05 301	88.12 352	96.48 230	99.37 190

eth_miper_zixun_eth		92.41 305	91.93 302	93.84 346	97.28 278	90.68 343	98.83 338	96.97 372	88.57 355	89.19 342	95.73 352	89.24 207	96.69 375	89.97 330	81.55 381	94.15 354

MVSFormer		96.94 142	96.60 144	97.95 163	97.28 278	97.70 96	99.55 236	97.27 323	91.17 289	99.43 81	99.54 128	90.92 178	96.89 363	94.67 240	99.62 95	99.25 216

lupinMVS		97.85 86	97.60 94	98.62 111	97.28 278	97.70 96	99.99 5	97.55 286	95.50 110	99.43 81	99.67 108	90.92 178	98.71 238	98.40 123	99.62 95	99.45 179

diffmvs_AUTHOR		96.75 154	96.41 153	97.79 177	97.20 281	95.46 198	99.69 202	97.15 341	94.46 140	98.78 120	99.21 167	85.64 261	98.77 229	98.27 132	97.31 203	99.13 227

mamba_0408		94.98 227	94.09 240	97.64 190	97.14 282	95.31 209	93.48 445	97.08 357	90.48 313	94.40 261	98.62 239	84.49 281	98.67 245	93.99 253	97.18 208	98.93 244

SSM_04072		94.77 234	94.09 240	96.82 236	97.14 282	95.31 209	93.48 445	97.08 357	90.48 313	94.40 261	98.62 239	84.49 281	96.21 396	93.99 253	97.18 208	98.93 244

SSM_0407		95.62 208	94.95 217	97.61 195	97.14 282	95.31 209	99.00 313	97.25 326	90.81 302	94.40 261	98.83 220	84.74 276	98.58 252	95.24 222	97.18 208	98.93 244

SCA		94.69 237	93.81 251	97.33 220	97.10 285	94.44 239	98.86 335	98.32 192	93.30 198	96.17 228	95.59 357	76.48 364	97.95 307	91.06 307	97.43 195	99.59 145

viewmacassd2359aftdt		95.93 193	95.45 194	97.36 217	97.09 286	94.12 254	99.57 230	97.26 325	93.05 210	96.50 215	99.17 171	82.76 297	98.68 243	96.61 198	97.04 215	99.28 212

KinetiMVS		96.10 186	95.29 203	98.53 125	97.08 287	97.12 123	99.56 233	98.12 225	94.78 127	98.44 142	98.94 201	80.30 329	99.39 185	91.56 300	98.79 156	99.06 235

TAMVS		95.85 196	95.58 191	96.65 244	97.07 288	93.50 271	99.17 290	97.82 256	91.39 286	95.02 254	98.01 277	92.20 155	97.30 334	93.75 265	95.83 248	99.14 226

Fast-Effi-MVS+-dtu		93.72 273	93.86 250	93.29 360	97.06 289	86.16 401	99.80 159	96.83 386	92.66 231	92.58 287	97.83 288	81.39 311	97.67 318	89.75 332	96.87 222	96.05 315

CostFormer		96.10 186	95.88 180	96.78 238	97.03 290	92.55 297	97.08 410	97.83 255	90.04 326	98.72 127	94.89 394	95.01 62	98.29 283	96.54 201	95.77 249	99.50 171

test_fmvsmvis_n_1920		97.67 105	97.59 96	97.91 169	97.02 291	95.34 207	99.95 67	98.45 138	97.87 25	97.02 197	99.59 119	89.64 198	99.98 47	99.41 64	99.34 132	98.42 270

test-LLR		96.47 168	96.04 166	97.78 179	97.02 291	95.44 199	99.96 48	98.21 209	94.07 164	95.55 244	96.38 329	93.90 103	98.27 287	90.42 322	98.83 154	99.64 131

test-mter		96.39 174	95.93 177	97.78 179	97.02 291	95.44 199	99.96 48	98.21 209	91.81 270	95.55 244	96.38 329	95.17 55	98.27 287	90.42 322	98.83 154	99.64 131

icg_test_0407_2		95.04 224	94.78 224	95.84 270	96.97 294	91.64 322	98.63 357	97.12 346	92.33 250	95.60 242	98.88 206	85.65 259	96.56 380	92.12 288	95.70 254	99.32 201

IMVS_0407		95.21 219	94.80 223	96.46 249	96.97 294	91.64 322	98.81 340	97.12 346	92.33 250	95.60 242	98.88 206	85.65 259	98.42 264	92.12 288	95.70 254	99.32 201

IMVS_0404		93.83 265	93.17 275	95.80 272	96.97 294	91.64 322	97.78 396	97.12 346	92.33 250	90.87 305	98.88 206	76.78 359	96.43 386	92.12 288	95.70 254	99.32 201

IMVS_0403		95.25 217	94.81 222	96.58 246	96.97 294	91.64 322	98.97 320	97.12 346	92.33 250	95.43 247	98.88 206	85.78 258	98.79 226	92.12 288	95.70 254	99.32 201

gm-plane-assit					96.97 294	93.76 263			91.47 280		98.96 194		98.79 226	94.92 230

WB-MVSnew		92.90 292	92.77 284	93.26 362	96.95 299	93.63 267	99.71 194	98.16 219	91.49 277	94.28 266	98.14 273	81.33 313	96.48 383	79.47 417	95.46 261	89.68 443

QAPM		95.40 213	94.17 238	99.10 74	96.92 300	97.71 94	99.40 258	98.68 79	89.31 334	88.94 346	98.89 205	82.48 299	99.96 71	93.12 278	99.83 77	99.62 138

KD-MVS_2432*1600		88.00 380	86.10 384	93.70 351	96.91 301	94.04 255	97.17 407	97.12 346	84.93 402	81.96 413	92.41 427	92.48 148	94.51 429	79.23 418	52.68 463	92.56 413

miper_refine_blended		88.00 380	86.10 384	93.70 351	96.91 301	94.04 255	97.17 407	97.12 346	84.93 402	81.96 413	92.41 427	92.48 148	94.51 429	79.23 418	52.68 463	92.56 413

tpm2		95.47 211	95.18 207	96.35 255	96.91 301	91.70 320	96.96 413	97.93 242	88.04 363	98.44 142	95.40 368	93.32 118	97.97 304	94.00 252	95.61 259	99.38 188

FMVSNet5		88.32 376	87.47 378	90.88 392	96.90 304	88.39 385	97.28 404	95.68 421	82.60 422	84.67 401	92.40 429	79.83 332	91.16 451	76.39 433	81.51 382	93.09 404

3Dnovator+	91.53 11	96.31 178	95.24 204	99.52 28	96.88 305	98.64 54	99.72 191	98.24 205	95.27 115	88.42 359	98.98 190	82.76 297	99.94 88	97.10 181	99.83 77	99.96 70

Patchmatch-test		92.65 300	91.50 311	96.10 261	96.85 306	90.49 348	91.50 453	97.19 334	82.76 421	90.23 311	95.59 357	95.02 61	98.00 303	77.41 428	96.98 220	99.82 102

MVS		96.60 162	95.56 192	99.72 13	96.85 306	99.22 20	98.31 374	98.94 44	91.57 275	90.90 304	99.61 118	86.66 246	99.96 71	97.36 173	99.88 73	99.99 23

3Dnovator	91.47 12	96.28 181	95.34 200	99.08 77	96.82 308	97.47 108	99.45 255	98.81 64	95.52 109	89.39 333	99.00 187	81.97 303	99.95 80	97.27 175	99.83 77	99.84 99

EI-MVSNet		93.73 272	93.40 267	94.74 305	96.80 309	92.69 292	99.06 303	97.67 269	88.96 343	91.39 298	99.02 183	88.75 215	97.30 334	91.07 306	87.85 332	94.22 344

CVMVSNet		94.68 239	94.94 218	93.89 345	96.80 309	86.92 398	99.06 303	98.98 41	94.45 141	94.23 268	99.02 183	85.60 262	95.31 418	90.91 312	95.39 264	99.43 183

IterMVS-LS		92.69 298	92.11 298	94.43 324	96.80 309	92.74 289	99.45 255	96.89 382	88.98 341	89.65 326	95.38 371	88.77 214	96.34 390	90.98 310	82.04 378	94.22 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 165	96.46 151	96.91 232	96.79 312	92.50 298	99.90 108	97.38 305	96.02 95	97.79 174	99.32 148	86.36 250	98.99 208	98.26 133	96.33 234	99.23 219

IterMVS		90.91 334	90.17 336	93.12 365	96.78 313	90.42 351	98.89 329	97.05 364	89.03 338	86.49 384	95.42 367	76.59 362	95.02 420	87.22 362	84.09 363	93.93 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 147	95.96 174	99.48 35	96.74 314	98.52 58	98.31 374	98.86 56	95.82 98	89.91 317	98.98 190	87.49 230	99.96 71	97.80 159	99.73 87	99.96 70

IterMVS-SCA-FT		90.85 337	90.16 337	92.93 370	96.72 315	89.96 360	98.89 329	96.99 368	88.95 344	86.63 381	95.67 353	76.48 364	95.00 421	87.04 365	84.04 366	93.84 382

MVS-HIRNet		86.22 387	83.19 400	95.31 288	96.71 316	90.29 352	92.12 450	97.33 313	62.85 458	86.82 378	70.37 463	69.37 405	97.49 324	75.12 436	97.99 185	98.15 277

viewdifsd2359ckpt11		94.09 260	93.63 253	95.46 281	96.68 317	88.92 374	99.62 216	97.12 346	93.07 208	95.73 239	99.22 164	77.05 352	98.88 218	96.52 202	87.69 337	98.58 265

viewmsd2359difaftdt		94.09 260	93.64 252	95.46 281	96.68 317	88.92 374	99.62 216	97.13 345	93.07 208	95.73 239	99.22 164	77.05 352	98.89 217	96.52 202	87.70 336	98.58 265

VDDNet		93.12 287	91.91 303	96.76 239	96.67 319	92.65 295	98.69 352	98.21 209	82.81 420	97.75 176	99.28 153	61.57 437	99.48 180	98.09 143	94.09 283	98.15 277

dmvs_re		93.20 284	93.15 276	93.34 358	96.54 320	83.81 416	98.71 349	98.51 126	91.39 286	92.37 290	98.56 247	78.66 344	97.83 312	93.89 256	89.74 304	98.38 272

Elysia		94.50 246	93.38 268	97.85 173	96.49 321	96.70 140	98.98 315	97.78 259	90.81 302	96.19 226	98.55 249	73.63 388	98.98 209	89.41 333	98.56 162	97.88 284

StellarMVS		94.50 246	93.38 268	97.85 173	96.49 321	96.70 140	98.98 315	97.78 259	90.81 302	96.19 226	98.55 249	73.63 388	98.98 209	89.41 333	98.56 162	97.88 284

MIMVSNet		90.30 350	88.67 364	95.17 292	96.45 323	91.64 322	92.39 449	97.15 341	85.99 389	90.50 309	93.19 422	66.95 416	94.86 425	82.01 404	93.43 291	99.01 240

CR-MVSNet		93.45 281	92.62 286	95.94 265	96.29 324	92.66 293	92.01 451	96.23 408	92.62 233	96.94 199	93.31 420	91.04 175	96.03 404	79.23 418	95.96 242	99.13 227

RPMNet		89.76 362	87.28 379	97.19 223	96.29 324	92.66 293	92.01 451	98.31 194	70.19 454	96.94 199	85.87 456	87.25 235	99.78 141	62.69 458	95.96 242	99.13 227

tt0805		91.28 327	90.18 335	94.60 311	96.26 326	87.55 391	98.39 372	98.72 73	89.00 340	89.22 339	98.47 257	62.98 432	98.96 213	90.57 318	88.00 331	97.28 302

Patchmtry		89.70 363	88.49 367	93.33 359	96.24 327	89.94 363	91.37 454	96.23 408	78.22 437	87.69 366	93.31 420	91.04 175	96.03 404	80.18 416	82.10 377	94.02 365

test_vis1_rt		86.87 385	86.05 387	89.34 411	96.12 328	78.07 445	99.87 124	83.54 471	92.03 262	78.21 434	89.51 441	45.80 457	99.91 105	96.25 206	93.11 296	90.03 440

JIA-IIPM		91.76 321	90.70 322	94.94 298	96.11 329	87.51 392	93.16 447	98.13 224	75.79 443	97.58 178	77.68 461	92.84 134	97.97 304	88.47 347	96.54 226	99.33 199

OpenMVS	90.15 15	94.77 234	93.59 257	98.33 140	96.07 330	97.48 107	99.56 233	98.57 102	90.46 315	86.51 383	98.95 199	78.57 345	99.94 88	93.86 257	99.74 86	97.57 297

PAPM		98.60 34	98.42 35	99.14 68	96.05 331	98.96 26	99.90 108	99.35 24	96.68 70	98.35 149	99.66 110	96.45 33	98.51 257	99.45 61	99.89 70	99.96 70

CLD-MVS		94.06 262	93.90 248	94.55 315	96.02 332	90.69 342	99.98 20	97.72 265	96.62 74	91.05 303	98.85 218	77.21 350	98.47 258	98.11 141	89.51 310	94.48 322
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 347	88.75 363	95.25 290	95.99 333	90.16 355	91.22 455	97.54 288	76.80 439	97.26 190	86.01 455	91.88 162	96.07 403	66.16 453	95.91 246	99.51 169

ACMH+	89.98 16	90.35 348	89.54 347	92.78 374	95.99 333	86.12 402	98.81 340	97.18 336	89.38 333	83.14 409	97.76 289	68.42 410	98.43 263	89.11 338	86.05 346	93.78 385

DeepMVS_CX				82.92 433	95.98 335	58.66 464		96.01 413	92.72 226	78.34 433	95.51 362	58.29 443	98.08 298	82.57 399	85.29 351	92.03 421

ACMP	92.05 9	92.74 296	92.42 295	93.73 347	95.91 336	88.72 378	99.81 155	97.53 290	94.13 160	87.00 377	98.23 271	74.07 385	98.47 258	96.22 207	88.86 317	93.99 370
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 276	93.03 278	95.35 285	95.86 337	86.94 397	99.87 124	96.36 406	96.85 61	99.54 70	98.79 222	52.41 451	99.83 134	98.64 110	98.97 148	99.29 210

HQP-NCC					95.78 338		99.87 124		96.82 63	93.37 275

ACMP_Plane					95.78 338		99.87 124		96.82 63	93.37 275

HQP-MVS		94.61 241	94.50 229	94.92 299	95.78 338	91.85 312	99.87 124	97.89 247	96.82 63	93.37 275	98.65 234	80.65 323	98.39 270	97.92 153	89.60 305	94.53 318

NP-MVS					95.77 341	91.79 314					98.65 234

test_fmvsmconf0.1_n		97.74 99	97.44 103	98.64 110	95.76 342	96.20 168	99.94 84	98.05 231	98.17 12	98.89 115	99.42 136	87.65 225	99.90 107	99.50 57	99.60 102	99.82 102

plane_prior6					95.76 342	91.72 319						80.47 327

ACMM	91.95 10	92.88 293	92.52 293	93.98 341	95.75 344	89.08 373	99.77 167	97.52 292	93.00 211	89.95 316	97.99 280	76.17 368	98.46 261	93.63 269	88.87 316	94.39 330
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 265	92.84 280	96.80 237	95.73 345	93.57 268	99.88 121	97.24 329	92.57 238	92.92 282	96.66 321	78.73 343	97.67 318	87.75 355	94.06 284	99.17 222

plane_prior1					95.73 345

jason		97.24 125	96.86 130	98.38 139	95.73 345	97.32 112	99.97 38	97.40 304	95.34 113	98.60 136	99.54 128	87.70 224	98.56 254	97.94 152	99.47 119	99.25 216
jason: jason.
mmtdpeth		88.52 374	87.75 376	90.85 394	95.71 348	83.47 422	98.94 323	94.85 436	88.78 349	97.19 192	89.58 440	63.29 430	98.97 211	98.54 115	62.86 457	90.10 439

HQP_MVS		94.49 248	94.36 232	94.87 300	95.71 348	91.74 316	99.84 143	97.87 249	96.38 83	93.01 280	98.59 242	80.47 327	98.37 276	97.79 162	89.55 308	94.52 320

plane_prior7					95.71 348	91.59 328

ITE_SJBPF				92.38 377	95.69 351	85.14 408		95.71 420	92.81 220	89.33 336	98.11 274	70.23 403	98.42 264	85.91 377	88.16 329	93.59 393

fmvsm_s_conf0.1_n_a		97.09 134	96.90 127	97.63 193	95.65 352	94.21 251	99.83 150	98.50 132	96.27 88	99.65 52	99.64 113	84.72 278	99.93 98	99.04 80	98.84 153	98.74 258

ACMH	89.72 17	90.64 341	89.63 344	93.66 353	95.64 353	88.64 381	98.55 360	97.45 297	89.03 338	81.62 416	97.61 290	69.75 404	98.41 266	89.37 335	87.62 338	93.92 376
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 157	96.49 148	97.37 215	95.63 354	95.96 177	99.74 180	98.88 54	92.94 213	91.61 296	98.97 192	97.72 6	98.62 251	94.83 234	98.08 183	97.53 299

FMVSNet1		88.50 375	86.64 382	94.08 334	95.62 355	91.97 307	98.43 368	96.95 374	83.00 418	86.08 391	94.72 396	59.09 442	96.11 399	81.82 406	84.07 364	94.17 348

LuminaMVS		96.63 161	96.21 161	97.87 172	95.58 356	96.82 136	99.12 292	97.67 269	94.47 139	97.88 169	98.31 267	87.50 229	98.71 238	98.07 145	97.29 204	98.10 280

LPG-MVS_test		92.96 290	92.71 285	93.71 349	95.43 357	88.67 379	99.75 177	97.62 277	92.81 220	90.05 312	98.49 253	75.24 375	98.40 268	95.84 213	89.12 312	94.07 362

LGP-MVS_train				93.71 349	95.43 357	88.67 379		97.62 277	92.81 220	90.05 312	98.49 253	75.24 375	98.40 268	95.84 213	89.12 312	94.07 362

tpm		93.70 274	93.41 266	94.58 313	95.36 359	87.41 393	97.01 411	96.90 381	90.85 300	96.72 207	94.14 411	90.40 189	96.84 367	90.75 316	88.54 324	99.51 169

D2MVS		92.76 295	92.59 291	93.27 361	95.13 360	89.54 367	99.69 202	99.38 22	92.26 255	87.59 368	94.61 402	85.05 271	97.79 313	91.59 299	88.01 330	92.47 416

VPA-MVSNet		92.70 297	91.55 310	96.16 259	95.09 361	96.20 168	98.88 331	99.00 39	91.02 297	91.82 295	95.29 378	76.05 370	97.96 306	95.62 218	81.19 384	94.30 337

LTVRE_ROB	88.28 18	90.29 351	89.05 358	94.02 337	95.08 362	90.15 356	97.19 406	97.43 299	84.91 404	83.99 405	97.06 307	74.00 386	98.28 285	84.08 388	87.71 334	93.62 392
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
TinyColmap		87.87 382	86.51 383	91.94 383	95.05 363	85.57 406	97.65 398	94.08 446	84.40 408	81.82 415	96.85 316	62.14 435	98.33 279	80.25 415	86.37 345	91.91 423

test0.0.03 1		93.86 264	93.61 254	94.64 309	95.02 364	92.18 305	99.93 91	98.58 100	94.07 164	87.96 363	98.50 252	93.90 103	94.96 422	81.33 407	93.17 294	96.78 305

UniMVSNet (Re)		93.07 289	92.13 297	95.88 267	94.84 365	96.24 167	99.88 121	98.98 41	92.49 243	89.25 337	95.40 368	87.09 237	97.14 343	93.13 277	78.16 408	94.26 339

USDC		90.00 358	88.96 359	93.10 367	94.81 366	88.16 387	98.71 349	95.54 425	93.66 185	83.75 407	97.20 301	65.58 421	98.31 281	83.96 391	87.49 340	92.85 410

VPNet		91.81 315	90.46 326	95.85 269	94.74 367	95.54 196	98.98 315	98.59 98	92.14 257	90.77 308	97.44 294	68.73 408	97.54 323	94.89 233	77.89 410	94.46 323

FIs		94.10 259	93.43 263	96.11 260	94.70 368	96.82 136	99.58 227	98.93 48	92.54 239	89.34 335	97.31 298	87.62 226	97.10 347	94.22 251	86.58 343	94.40 329

UniMVSNet_ETH3D		90.06 357	88.58 366	94.49 319	94.67 369	88.09 388	97.81 395	97.57 285	83.91 411	88.44 355	97.41 295	57.44 444	97.62 320	91.41 301	88.59 323	97.77 289

UniMVSNet_NR-MVSNet		92.95 291	92.11 298	95.49 277	94.61 370	95.28 213	99.83 150	99.08 36	91.49 277	89.21 340	96.86 315	87.14 236	96.73 373	93.20 273	77.52 413	94.46 323

test_fmvs2		89.47 367	89.70 343	88.77 418	94.54 371	75.74 447	99.83 150	94.70 442	94.71 131	91.08 301	96.82 320	54.46 447	97.78 315	92.87 280	88.27 327	92.80 411

MonoMVSNet		94.82 229	94.43 230	95.98 263	94.54 371	90.73 341	99.03 310	97.06 361	93.16 203	93.15 279	95.47 365	88.29 218	97.57 321	97.85 157	91.33 302	99.62 138

WR-MVS		92.31 307	91.25 315	95.48 280	94.45 373	95.29 212	99.60 223	98.68 79	90.10 323	88.07 362	96.89 313	80.68 322	96.80 371	93.14 276	79.67 401	94.36 331

nrg030		93.51 278	92.53 292	96.45 250	94.36 374	97.20 118	99.81 155	97.16 340	91.60 274	89.86 319	97.46 293	86.37 249	97.68 317	95.88 212	80.31 397	94.46 323

tfpnnormal		89.29 370	87.61 377	94.34 327	94.35 375	94.13 253	98.95 322	98.94 44	83.94 409	84.47 402	95.51 362	74.84 380	97.39 326	77.05 431	80.41 395	91.48 426

FC-MVSNet-test		93.81 268	93.15 276	95.80 272	94.30 376	96.20 168	99.42 257	98.89 52	92.33 250	89.03 345	97.27 300	87.39 232	96.83 369	93.20 273	86.48 344	94.36 331

SSC-MVS3.2		89.59 365	88.66 365	92.38 377	94.29 377	86.12 402	99.49 246	97.66 272	90.28 322	88.63 352	95.18 382	64.46 426	96.88 365	85.30 381	82.66 372	94.14 357

MS-PatchMatch		90.65 340	90.30 331	91.71 388	94.22 378	85.50 407	98.24 378	97.70 266	88.67 352	86.42 386	96.37 331	67.82 413	98.03 302	83.62 393	99.62 95	91.60 424

WR-MVS_H		91.30 325	90.35 329	94.15 331	94.17 379	92.62 296	99.17 290	98.94 44	88.87 347	86.48 385	94.46 407	84.36 284	96.61 378	88.19 349	78.51 406	93.21 402

DU-MVS		92.46 304	91.45 313	95.49 277	94.05 380	95.28 213	99.81 155	98.74 72	92.25 256	89.21 340	96.64 323	81.66 308	96.73 373	93.20 273	77.52 413	94.46 323

NR-MVSNet		91.56 323	90.22 333	95.60 275	94.05 380	95.76 184	98.25 377	98.70 75	91.16 291	80.78 422	96.64 323	83.23 294	96.57 379	91.41 301	77.73 412	94.46 323

CP-MVSNet		91.23 329	90.22 333	94.26 329	93.96 382	92.39 301	99.09 296	98.57 102	88.95 344	86.42 386	96.57 326	79.19 338	96.37 388	90.29 325	78.95 403	94.02 365

XXY-MVS		91.82 314	90.46 326	95.88 267	93.91 383	95.40 203	98.87 334	97.69 268	88.63 354	87.87 364	97.08 305	74.38 384	97.89 310	91.66 298	84.07 364	94.35 334

PS-CasMVS		90.63 342	89.51 349	93.99 340	93.83 384	91.70 320	98.98 315	98.52 123	88.48 356	86.15 390	96.53 328	75.46 373	96.31 392	88.83 340	78.86 405	93.95 373

test_0402		85.58 389	83.94 394	90.50 400	93.81 385	85.04 409	98.55 360	95.20 433	76.01 441	79.72 428	95.13 383	64.15 428	96.26 394	66.04 454	86.88 342	90.21 437

XVG-ACMP-BASELINE		91.22 330	90.75 321	92.63 376	93.73 386	85.61 405	98.52 364	97.44 298	92.77 224	89.90 318	96.85 316	66.64 418	98.39 270	92.29 285	88.61 321	93.89 378

TranMVSNet+NR-MVSNet		91.68 322	90.61 325	94.87 300	93.69 387	93.98 258	99.69 202	98.65 83	91.03 296	88.44 355	96.83 319	80.05 331	96.18 397	90.26 326	76.89 421	94.45 328

TransMVSNet (Re)		87.25 383	85.28 390	93.16 364	93.56 388	91.03 333	98.54 362	94.05 448	83.69 413	81.09 420	96.16 337	75.32 374	96.40 387	76.69 432	68.41 445	92.06 420

v10		90.25 352	88.82 361	94.57 314	93.53 389	93.43 273	99.08 298	96.87 384	85.00 401	87.34 375	94.51 403	80.93 318	97.02 357	82.85 398	79.23 402	93.26 400

testgi		89.01 372	88.04 373	91.90 384	93.49 390	84.89 411	99.73 187	95.66 422	93.89 178	85.14 397	98.17 272	59.68 441	94.66 428	77.73 427	88.88 315	96.16 314

v8		90.54 344	89.17 354	94.66 308	93.43 391	93.40 276	99.20 287	96.94 378	85.76 392	87.56 369	94.51 403	81.96 304	97.19 340	84.94 384	78.25 407	93.38 398

V42		91.28 327	90.12 338	94.74 305	93.42 392	93.46 272	99.68 205	97.02 365	87.36 371	89.85 321	95.05 386	81.31 314	97.34 329	87.34 360	80.07 399	93.40 396

pm-mvs1		89.36 369	87.81 375	94.01 338	93.40 393	91.93 310	98.62 358	96.48 404	86.25 387	83.86 406	96.14 339	73.68 387	97.04 353	86.16 374	75.73 426	93.04 406

v1144		91.09 331	89.83 340	94.87 300	93.25 394	93.69 266	99.62 216	96.98 370	86.83 381	89.64 327	94.99 391	80.94 317	97.05 350	85.08 383	81.16 385	93.87 380

v1192		90.62 343	89.25 353	94.72 307	93.13 395	93.07 280	99.50 244	97.02 365	86.33 386	89.56 331	95.01 388	79.22 337	97.09 349	82.34 402	81.16 385	94.01 367

v2v482		91.30 325	90.07 339	95.01 295	93.13 395	93.79 261	99.77 167	97.02 365	88.05 362	89.25 337	95.37 372	80.73 321	97.15 342	87.28 361	80.04 400	94.09 361

OPM-MVS		93.21 283	92.80 282	94.44 322	93.12 397	90.85 340	99.77 167	97.61 280	96.19 91	91.56 297	98.65 234	75.16 379	98.47 258	93.78 264	89.39 311	93.99 370
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 338	89.52 348	94.59 312	93.11 398	92.77 287	99.56 233	96.99 368	86.38 385	89.82 322	94.95 393	80.50 326	97.10 347	83.98 390	80.41 395	93.90 377

PEN-MVS		90.19 354	89.06 357	93.57 354	93.06 399	90.90 338	99.06 303	98.47 135	88.11 361	85.91 392	96.30 333	76.67 360	95.94 407	87.07 364	76.91 420	93.89 378

v1240		90.20 353	88.79 362	94.44 322	93.05 400	92.27 303	99.38 264	96.92 380	85.89 390	89.36 334	94.87 395	77.89 349	97.03 355	80.66 411	81.08 388	94.01 367

v148		90.70 339	89.63 344	93.92 342	92.97 401	90.97 334	99.75 177	96.89 382	87.51 368	88.27 360	95.01 388	81.67 307	97.04 353	87.40 359	77.17 418	93.75 386

v1921920		90.46 345	89.12 355	94.50 318	92.96 402	92.46 299	99.49 246	96.98 370	86.10 388	89.61 329	95.30 375	78.55 346	97.03 355	82.17 403	80.89 393	94.01 367

MVStest1		85.03 395	82.76 404	91.83 385	92.95 403	89.16 372	98.57 359	94.82 437	71.68 452	68.54 455	95.11 385	83.17 295	95.66 411	74.69 437	65.32 452	90.65 433

tt0320-xc		82.94 409	80.35 416	90.72 398	92.90 404	83.54 420	96.85 416	94.73 440	63.12 457	79.85 427	93.77 415	49.43 455	95.46 414	80.98 410	71.54 435	93.16 403

Baseline_NR-MVSNet		90.33 349	89.51 349	92.81 373	92.84 405	89.95 361	99.77 167	93.94 449	84.69 406	89.04 344	95.66 354	81.66 308	96.52 381	90.99 309	76.98 419	91.97 422

test_method		80.79 415	79.70 418	84.08 430	92.83 406	67.06 456	99.51 242	95.42 427	54.34 462	81.07 421	93.53 417	44.48 458	92.22 448	78.90 422	77.23 417	92.94 408

pmmvs4		92.10 311	91.07 319	95.18 291	92.82 407	94.96 224	99.48 249	96.83 386	87.45 370	88.66 351	96.56 327	83.78 289	96.83 369	89.29 336	84.77 358	93.75 386

LF4IMVS		89.25 371	88.85 360	90.45 402	92.81 408	81.19 437	98.12 385	94.79 438	91.44 281	86.29 388	97.11 303	65.30 424	98.11 296	88.53 345	85.25 352	92.07 419

tt0320		83.56 408	81.15 411	90.77 396	92.77 409	83.58 419	96.83 417	95.52 426	63.26 456	81.36 418	92.54 425	53.26 449	95.77 409	80.45 412	74.38 429	92.96 407

DTE-MVSNet		89.40 368	88.24 371	92.88 371	92.66 410	89.95 361	99.10 295	98.22 208	87.29 372	85.12 398	96.22 335	76.27 367	95.30 419	83.56 394	75.74 425	93.41 395

EU-MVSNet		90.14 356	90.34 330	89.54 410	92.55 411	81.06 438	98.69 352	98.04 232	91.41 285	86.59 382	96.84 318	80.83 320	93.31 441	86.20 373	81.91 379	94.26 339

APD_test1		81.15 413	80.92 413	81.86 434	92.45 412	59.76 463	96.04 431	93.61 452	73.29 450	77.06 437	96.64 323	44.28 459	96.16 398	72.35 441	82.52 373	89.67 444

sc_t1		85.01 396	82.46 406	92.67 375	92.44 413	83.09 423	97.39 402	95.72 419	65.06 455	85.64 395	96.16 337	49.50 454	97.34 329	84.86 385	75.39 427	97.57 297

our_test_3		90.39 346	89.48 351	93.12 365	92.40 414	89.57 366	99.33 271	96.35 407	87.84 366	85.30 396	94.99 391	84.14 287	96.09 402	80.38 413	84.56 359	93.71 391

ppachtmachnet_test		89.58 366	88.35 369	93.25 363	92.40 414	90.44 350	99.33 271	96.73 393	85.49 397	85.90 393	95.77 348	81.09 316	96.00 406	76.00 435	82.49 374	93.30 399

v7n		89.65 364	88.29 370	93.72 348	92.22 416	90.56 347	99.07 302	97.10 353	85.42 399	86.73 379	94.72 396	80.06 330	97.13 344	81.14 408	78.12 409	93.49 394

dmvs_testset		83.79 405	86.07 386	76.94 438	92.14 417	48.60 473	96.75 418	90.27 463	89.48 332	78.65 431	98.55 249	79.25 336	86.65 461	66.85 451	82.69 371	95.57 316

PS-MVSNAJss		93.64 275	93.31 272	94.61 310	92.11 418	92.19 304	99.12 292	97.38 305	92.51 242	88.45 354	96.99 311	91.20 170	97.29 337	94.36 245	87.71 334	94.36 331

pmmvs5		90.17 355	89.09 356	93.40 357	92.10 419	89.77 364	99.74 180	95.58 424	85.88 391	87.24 376	95.74 349	73.41 390	96.48 383	88.54 344	83.56 368	93.95 373

N_pmnet		80.06 418	80.78 414	77.89 437	91.94 420	45.28 475	98.80 343	56.82 477	78.10 438	80.08 425	93.33 418	77.03 354	95.76 410	68.14 449	82.81 370	92.64 412

test_djsdf		92.83 294	92.29 296	94.47 320	91.90 421	92.46 299	99.55 236	97.27 323	91.17 289	89.96 315	96.07 343	81.10 315	96.89 363	94.67 240	88.91 314	94.05 364

SixPastTwentyTwo		88.73 373	88.01 374	90.88 392	91.85 422	82.24 429	98.22 382	95.18 434	88.97 342	82.26 412	96.89 313	71.75 395	96.67 376	84.00 389	82.98 369	93.72 390

K. test v3		88.05 379	87.24 380	90.47 401	91.82 423	82.23 430	98.96 321	97.42 301	89.05 337	76.93 439	95.60 356	68.49 409	95.42 415	85.87 378	81.01 391	93.75 386

OurMVSNet-221017-0		89.81 361	89.48 351	90.83 395	91.64 424	81.21 436	98.17 384	95.38 429	91.48 279	85.65 394	97.31 298	72.66 391	97.29 337	88.15 350	84.83 357	93.97 372

mvs_tets		91.81 315	91.08 318	94.00 339	91.63 425	90.58 346	98.67 354	97.43 299	92.43 244	87.37 374	97.05 308	71.76 394	97.32 332	94.75 237	88.68 320	94.11 360

Gipuma		66.95 431	65.00 431	72.79 443	91.52 426	67.96 455	66.16 466	95.15 435	47.89 464	58.54 461	67.99 466	29.74 463	87.54 460	50.20 465	77.83 411	62.87 466
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 174	95.74 185	98.32 141	91.47 427	95.56 195	99.84 143	97.30 318	97.74 29	97.89 168	99.35 147	79.62 333	99.85 124	99.25 70	99.24 136	99.55 155

jajsoiax		91.92 313	91.18 316	94.15 331	91.35 428	90.95 337	99.00 313	97.42 301	92.61 234	87.38 373	97.08 305	72.46 392	97.36 327	94.53 243	88.77 318	94.13 359

MDA-MVSNet-bldmvs		84.09 403	81.52 410	91.81 386	91.32 429	88.00 390	98.67 354	95.92 415	80.22 432	55.60 464	93.32 419	68.29 411	93.60 439	73.76 438	76.61 422	93.82 384

MVP-Stereo		90.93 333	90.45 328	92.37 379	91.25 430	88.76 376	98.05 389	96.17 410	87.27 373	84.04 403	95.30 375	78.46 347	97.27 339	83.78 392	99.70 89	91.09 427
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 391	83.32 399	92.10 381	90.96 431	88.58 382	99.20 287	96.52 402	79.70 434	57.12 463	92.69 424	79.11 339	93.86 435	77.10 430	77.46 415	93.86 381

YYNet1		85.50 392	83.33 398	92.00 382	90.89 432	88.38 386	99.22 286	96.55 401	79.60 435	57.26 462	92.72 423	79.09 341	93.78 437	77.25 429	77.37 416	93.84 382

anonymousdsp		91.79 320	90.92 320	94.41 325	90.76 433	92.93 286	98.93 325	97.17 338	89.08 336	87.46 372	95.30 375	78.43 348	96.92 361	92.38 284	88.73 319	93.39 397

lessismore_v0				90.53 399	90.58 434	80.90 439		95.80 416		77.01 438	95.84 346	66.15 420	96.95 359	83.03 397	75.05 428	93.74 389

EG-PatchMatch MVS		85.35 393	83.81 396	89.99 408	90.39 435	81.89 432	98.21 383	96.09 412	81.78 425	74.73 445	93.72 416	51.56 453	97.12 346	79.16 421	88.61 321	90.96 430

EGC-MVSNET		69.38 424	63.76 434	86.26 427	90.32 436	81.66 435	96.24 427	93.85 450	0.99 474	3.22 475	92.33 430	52.44 450	92.92 444	59.53 461	84.90 356	84.21 455

CMPMVS	61.59 21	84.75 399	85.14 391	83.57 431	90.32 436	62.54 459	96.98 412	97.59 284	74.33 448	69.95 452	96.66 321	64.17 427	98.32 280	87.88 354	88.41 326	89.84 442
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 402	82.92 402	89.21 412	90.03 438	82.60 426	96.89 415	95.62 423	80.59 430	75.77 444	89.17 442	65.04 425	94.79 426	72.12 442	81.02 390	90.23 436

pmmvs6		85.69 388	83.84 395	91.26 391	90.00 439	84.41 414	97.82 394	96.15 411	75.86 442	81.29 419	95.39 370	61.21 438	96.87 366	83.52 395	73.29 431	92.50 415

ttmdpeth		88.23 378	87.06 381	91.75 387	89.91 440	87.35 394	98.92 328	95.73 418	87.92 364	84.02 404	96.31 332	68.23 412	96.84 367	86.33 372	76.12 423	91.06 428

DSMNet-mixed		88.28 377	88.24 371	88.42 420	89.64 441	75.38 449	98.06 388	89.86 464	85.59 396	88.20 361	92.14 431	76.15 369	91.95 449	78.46 424	96.05 239	97.92 283

UnsupCasMVSNet_eth		85.52 390	83.99 392	90.10 406	89.36 442	83.51 421	96.65 419	97.99 235	89.14 335	75.89 443	93.83 413	63.25 431	93.92 433	81.92 405	67.90 448	92.88 409

Anonymous20231206		86.32 386	85.42 389	89.02 414	89.11 443	80.53 442	99.05 307	95.28 430	85.43 398	82.82 410	93.92 412	74.40 383	93.44 440	66.99 450	81.83 380	93.08 405

Anonymous20240521		85.15 394	83.81 396	89.16 413	88.32 444	82.69 425	98.80 343	95.74 417	79.72 433	81.53 417	90.99 434	65.38 423	94.16 431	72.69 440	81.11 387	90.63 434

OpenMVS_ROB	79.82 20	83.77 406	81.68 409	90.03 407	88.30 445	82.82 424	98.46 365	95.22 432	73.92 449	76.00 442	91.29 433	55.00 446	96.94 360	68.40 448	88.51 325	90.34 435

test20.03		84.72 400	83.99 392	86.91 425	88.19 446	80.62 441	98.88 331	95.94 414	88.36 358	78.87 429	94.62 401	68.75 407	89.11 456	66.52 452	75.82 424	91.00 429

KD-MVS_self_test		83.59 407	82.06 407	88.20 421	86.93 447	80.70 440	97.21 405	96.38 405	82.87 419	82.49 411	88.97 443	67.63 414	92.32 447	73.75 439	62.30 459	91.58 425

MIMVSNet1		82.58 410	80.51 415	88.78 416	86.68 448	84.20 415	96.65 419	95.41 428	78.75 436	78.59 432	92.44 426	51.88 452	89.76 455	65.26 455	78.95 403	92.38 418

CL-MVSNet_self_test		84.50 401	83.15 401	88.53 419	86.00 449	81.79 433	98.82 339	97.35 309	85.12 400	83.62 408	90.91 436	76.66 361	91.40 450	69.53 446	60.36 460	92.40 417

UnsupCasMVSNet_bld		79.97 420	77.03 425	88.78 416	85.62 450	81.98 431	93.66 443	97.35 309	75.51 445	70.79 451	83.05 458	48.70 456	94.91 424	78.31 425	60.29 461	89.46 447

mvs5depth		84.87 397	82.90 403	90.77 396	85.59 451	84.84 412	91.10 456	93.29 454	83.14 416	85.07 399	94.33 409	62.17 434	97.32 332	78.83 423	72.59 434	90.14 438

Patchmatch-RL test		86.90 384	85.98 388	89.67 409	84.45 452	75.59 448	89.71 459	92.43 456	86.89 380	77.83 436	90.94 435	94.22 92	93.63 438	87.75 355	69.61 439	99.79 107

pmmvs-eth3d		84.03 404	81.97 408	90.20 405	84.15 453	87.09 396	98.10 387	94.73 440	83.05 417	74.10 448	87.77 449	65.56 422	94.01 432	81.08 409	69.24 441	89.49 446

test_fmvs3		79.99 419	80.17 417	79.45 436	84.02 454	62.83 457	99.05 307	93.49 453	88.29 360	80.06 426	86.65 453	28.09 465	88.00 457	88.63 341	73.27 432	87.54 453

PM-MVS		80.47 416	78.88 420	85.26 428	83.79 455	72.22 451	95.89 434	91.08 461	85.71 395	76.56 441	88.30 445	36.64 461	93.90 434	82.39 401	69.57 440	89.66 445

new-patchmatchnet		81.19 412	79.34 419	86.76 426	82.86 456	80.36 443	97.92 391	95.27 431	82.09 424	72.02 449	86.87 452	62.81 433	90.74 453	71.10 443	63.08 456	89.19 449

FE-MVSNET		81.05 414	78.81 421	87.79 423	81.98 457	83.70 417	98.23 380	91.78 460	81.27 427	74.29 447	87.44 450	60.92 440	90.67 454	64.92 456	68.43 444	89.01 450

mvsany_test3		82.12 411	81.14 412	85.06 429	81.87 458	70.41 453	97.09 409	92.14 457	91.27 288	77.84 435	88.73 444	39.31 460	95.49 412	90.75 316	71.24 436	89.29 448

WB-MVS		76.28 422	77.28 424	73.29 442	81.18 459	54.68 467	97.87 393	94.19 445	81.30 426	69.43 453	90.70 437	77.02 355	82.06 465	35.71 470	68.11 447	83.13 456

test_f		78.40 421	77.59 423	80.81 435	80.82 460	62.48 460	96.96 413	93.08 455	83.44 414	74.57 446	84.57 457	27.95 466	92.63 445	84.15 387	72.79 433	87.32 454

SSC-MVS		75.42 423	76.40 426	72.49 446	80.68 461	53.62 468	97.42 400	94.06 447	80.42 431	68.75 454	90.14 439	76.54 363	81.66 466	33.25 471	66.34 451	82.19 457

pmmvs3		80.27 417	77.77 422	87.76 424	80.32 462	82.43 428	98.23 380	91.97 458	72.74 451	78.75 430	87.97 448	57.30 445	90.99 452	70.31 444	62.37 458	89.87 441

testf1		68.38 427	66.92 428	72.78 444	78.80 463	50.36 470	90.95 457	87.35 469	55.47 460	58.95 459	88.14 446	20.64 470	87.60 458	57.28 462	64.69 453	80.39 459

APD_test2		68.38 427	66.92 428	72.78 444	78.80 463	50.36 470	90.95 457	87.35 469	55.47 460	58.95 459	88.14 446	20.64 470	87.60 458	57.28 462	64.69 453	80.39 459

ambc				83.23 432	77.17 465	62.61 458	87.38 461	94.55 444		76.72 440	86.65 453	30.16 462	96.36 389	84.85 386	69.86 438	90.73 432

test_vis3_rt		68.82 425	66.69 430	75.21 441	76.24 466	60.41 462	96.44 422	68.71 476	75.13 446	50.54 467	69.52 465	16.42 475	96.32 391	80.27 414	66.92 450	68.89 463

TDRefinement		84.76 398	82.56 405	91.38 390	74.58 467	84.80 413	97.36 403	94.56 443	84.73 405	80.21 424	96.12 342	63.56 429	98.39 270	87.92 353	63.97 455	90.95 431

E-PMN		52.30 435	52.18 437	52.67 452	71.51 468	45.40 474	93.62 444	76.60 474	36.01 468	43.50 469	64.13 468	27.11 467	67.31 471	31.06 472	26.06 467	45.30 470

EMVS		51.44 437	51.22 439	52.11 453	70.71 469	44.97 476	94.04 440	75.66 475	35.34 470	42.40 470	61.56 471	28.93 464	65.87 472	27.64 473	24.73 468	45.49 469

PMMVS2		67.15 430	64.15 433	76.14 440	70.56 470	62.07 461	93.89 441	87.52 468	58.09 459	60.02 458	78.32 460	22.38 469	84.54 463	59.56 460	47.03 465	81.80 458

FPMVS		68.72 426	68.72 427	68.71 448	65.95 471	44.27 477	95.97 433	94.74 439	51.13 463	53.26 465	90.50 438	25.11 468	83.00 464	60.80 459	80.97 392	78.87 461

wuyk23d		20.37 441	20.84 444	18.99 456	65.34 472	27.73 479	50.43 467	7.67 480	9.50 473	8.01 474	6.34 474	6.13 478	26.24 473	23.40 474	10.69 472	2.99 471

LCM-MVSNet		67.77 429	64.73 432	76.87 439	62.95 473	56.25 466	89.37 460	93.74 451	44.53 465	61.99 457	80.74 459	20.42 472	86.53 462	69.37 447	59.50 462	87.84 451

MVE	53.74 22	51.54 436	47.86 440	62.60 450	59.56 474	50.93 469	79.41 464	77.69 473	35.69 469	36.27 471	61.76 470	5.79 479	69.63 469	37.97 469	36.61 466	67.24 464
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 433	52.24 436	67.66 449	49.27 475	56.82 465	83.94 462	82.02 472	70.47 453	33.28 472	64.54 467	17.23 474	69.16 470	45.59 467	23.85 469	77.02 462

tmp_tt		65.23 432	62.94 435	72.13 447	44.90 476	50.03 472	81.05 463	89.42 467	38.45 466	48.51 468	99.90 18	54.09 448	78.70 468	91.84 297	18.26 470	87.64 452

PMVS	49.05 23	53.75 434	51.34 438	60.97 451	40.80 477	34.68 478	74.82 465	89.62 466	37.55 467	28.67 473	72.12 462	7.09 477	81.63 467	43.17 468	68.21 446	66.59 465
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 439	39.14 442	33.31 454	19.94 478	24.83 480	98.36 373	9.75 479	15.53 472	51.31 466	87.14 451	19.62 473	17.74 474	47.10 466	3.47 473	57.36 467

testmvs		40.60 438	44.45 441	29.05 455	19.49 479	14.11 481	99.68 205	18.47 478	20.74 471	64.59 456	98.48 256	10.95 476	17.09 475	56.66 464	11.01 471	55.94 468

mmdepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

monomultidepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

test_blank		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.02 475	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

eth-test2					0.00 480

eth-test					0.00 480

uanet_test		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

DCPMVS		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

cdsmvs_eth3d_5k		23.43 440	31.24 443	0.00 457	0.00 480	0.00 482	0.00 468	98.09 226	0.00 475	0.00 476	99.67 108	83.37 292	0.00 476	0.00 475	0.00 474	0.00 472

pcd_1.5k_mvsjas		7.60 443	10.13 446	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	91.20 170	0.00 476	0.00 475	0.00 474	0.00 472

sosnet-low-res		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

sosnet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uncertanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

Regformer		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

ab-mvs-re		8.28 442	11.04 445	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	99.40 141	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

uanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 482	0.00 468	0.00 481	0.00 475	0.00 476	0.00 476	0.00 480	0.00 476	0.00 475	0.00 474	0.00 472

WAC-MVS						90.97 334								86.10 376

PC_three_1452									96.96 59	99.80 25	99.79 58	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 151	97.27 46	99.80 25	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 77	99.83 21	99.91 14	97.87 5	100.00 1	99.92 13	100.00 1	100.00 1

GSMVS																99.59 145

sam_mvs1												94.72 71				99.59 145

sam_mvs												94.25 91

MTGPA								98.28 199

test_post1							95.78 435				59.23 472	93.20 125	97.74 316	91.06 307

test_post											63.35 469	94.43 79	98.13 295

patchmatchnet-post											91.70 432	95.12 56	97.95 307

MTMP							99.87 124	96.49 403

test9_res														99.71 44	99.99 21	100.00 1

agg_prior2														99.48 59	100.00 1	100.00 1

test_prior4						98.05 77	99.94 84

test_prior2							99.95 67		95.78 99	99.73 44	99.76 67	96.00 37		99.78 31	100.00 1

旧先验2							99.46 254		94.21 159	99.85 17			99.95 80	96.96 188

新几何2							99.40 258

无先验							99.49 246	98.71 74	93.46 191				100.00 1	94.36 245		99.99 23

原ACMM2							99.90 108

testdata2													99.99 36	90.54 320

segment_acmp												96.68 29

testdata1							99.28 280		96.35 87

plane_prior5								97.87 249					98.37 276	97.79 162	89.55 308	94.52 320

plane_prior4											98.59 242

plane_prior3						91.64 322			96.63 72	93.01 280

plane_prior2							99.84 143		96.38 83

plane_prior						91.74 316	99.86 135		96.76 67						89.59 307

n2								0.00 481

nn								0.00 481

door-mid								89.69 465

test11								98.44 143

door								90.31 462

HQP5-MVS						91.85 312

BP-MVS														97.92 153

HQP4-MVS										93.37 275			98.39 270			94.53 318

HQP3-MVS								97.89 247							89.60 305

HQP2-MVS												80.65 323

MDTV_nov1_ep13_2view						96.26 162	96.11 429		91.89 265	98.06 161		94.40 81		94.30 248		99.67 125

ACMMP++_ref															87.04 341

ACMMP++															88.23 328

Test By Simon												92.82 136

High-resolution multi-view benchmark