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 93	96.80 97	98.51 102	99.99 1	95.60 156	99.09 232	98.84 52	93.32 155	96.74 153	99.72 78	86.04 208	100.00 1	98.01 106	99.43 107	99.94 71

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 12	98.69 62	98.20 4	99.93 1	99.98 2	96.82 23	100.00 1	99.75 25	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 22	98.64 70	98.47 2	99.13 78	99.92 13	96.38 30	100.00 1	99.74 27	100.00 1	100.00 1

mPP-MVS		98.39 44	98.20 43	98.97 71	99.97 3	96.92 109	99.95 46	98.38 145	95.04 88	98.61 103	99.80 51	93.39 95	100.00 1	98.64 80	100.00 1	99.98 48

CPTT-MVS		97.64 78	97.32 81	98.58 94	99.97 3	95.77 147	99.96 29	98.35 151	89.90 253	98.36 113	99.79 54	91.18 148	99.99 36	98.37 90	99.99 21	99.99 23

DP-MVS Recon		98.41 42	98.02 54	99.56 24	99.97 3	98.70 45	99.92 71	98.44 111	92.06 203	98.40 112	99.84 41	95.68 40	100.00 1	98.19 96	99.71 83	99.97 56

PAPR		98.52 32	98.16 46	99.58 23	99.97 3	98.77 39	99.95 46	98.43 119	95.35 82	98.03 123	99.75 67	94.03 82	99.98 43	98.11 101	99.83 72	99.99 23

HFP-MVS		98.56 29	98.37 33	99.14 56	99.96 8	97.43 91	99.95 46	98.61 75	94.77 96	99.31 69	99.85 30	94.22 76	100.00 1	98.70 75	99.98 32	99.98 48

region2R		98.54 30	98.37 33	99.05 63	99.96 8	97.18 98	99.96 29	98.55 88	94.87 94	99.45 58	99.85 30	94.07 81	100.00 1	98.67 77	100.00 1	99.98 48

ACMMPR		98.50 33	98.32 37	99.05 63	99.96 8	97.18 98	99.95 46	98.60 76	94.77 96	99.31 69	99.84 41	93.73 90	100.00 1	98.70 75	99.98 32	99.98 48

NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 22	98.62 74	98.02 9	99.90 2	99.95 3	97.33 17	100.00 1	99.54 35	100.00 1	100.00 1

CP-MVS		98.45 37	98.32 37	98.87 76	99.96 8	96.62 117	99.97 22	98.39 141	94.43 108	98.90 87	99.87 24	94.30 74	100.00 1	99.04 55	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 11		98.41 134	96.63 49	99.75 27	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 46	98.43 119					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 23	98.55 23	99.15 54	99.94 13	97.50 87	99.94 62	98.42 130	96.22 62	99.41 62	99.78 58	94.34 72	99.96 57	98.92 61	99.95 49	99.99 23

X-MVStestdata		93.83 193	92.06 225	99.15 54	99.94 13	97.50 87	99.94 62	98.42 130	96.22 62	99.41 62	41.37 388	94.34 72	99.96 57	98.92 61	99.95 49	99.99 23

test_prior				99.43 34	99.94 13	98.49 57		98.65 68					99.80 112			99.99 23

MSLP-MVS++		99.13 8	99.01 11	99.49 31	99.94 13	98.46 58	99.98 12	98.86 49	97.10 33	99.80 15	99.94 4	95.92 36	100.00 1	99.51 36	100.00 1	100.00 1

APDe-MVS		99.06 11	98.91 14	99.51 28	99.94 13	98.76 42	99.91 75	98.39 141	97.20 32	99.46 57	99.85 30	95.53 44	99.79 114	99.86 18	100.00 1	99.99 23

MP-MVS		98.23 54	97.97 56	99.03 65	99.94 13	97.17 101	99.95 46	98.39 141	94.70 100	98.26 119	99.81 50	91.84 139	100.00 1	98.85 67	99.97 42	99.93 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 25	98.36 35	99.49 31	99.94 13	98.73 43	99.87 92	98.33 154	93.97 134	99.76 26	99.87 24	94.99 57	99.75 123	98.55 84	100.00 1	99.98 48

PAPM_NR		98.12 57	97.93 61	98.70 84	99.94 13	96.13 138	99.82 120	98.43 119	94.56 104	97.52 134	99.70 82	94.40 67	99.98 43	97.00 140	99.98 32	99.99 23

MG-MVS		98.91 16	98.65 18	99.68 14	99.94 13	99.07 23	99.64 165	99.44 19	97.33 25	99.00 83	99.72 78	94.03 82	99.98 43	98.73 74	100.00 1	100.00 1

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 24	99.30 11	99.96 29	98.43 119	97.27 28	99.80 15	99.94 4	96.71 24	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 24	99.31 9		98.41 134	97.71 14	99.84 10				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 11		98.43 119	97.26 30	99.80 15	99.88 21	96.71 24	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 24	99.29 14	99.95 46	98.32 156	97.28 26	99.83 11	99.91 14	97.22 19	100.00 1	99.99 5	100.00 1	99.89 81
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 14	99.96 29	98.42 130	97.28 26	99.86 5	99.94 4	97.22 19

MSP-MVS		99.09 9	99.12 5	98.98 70	99.93 24	97.24 95	99.95 46	98.42 130	97.50 21	99.52 54	99.88 21	97.43 16	99.71 129	99.50 37	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 39		98.43 119		99.63 39			99.85 100

FOURS1					99.92 31	97.66 80	99.95 46	98.36 149	95.58 76	99.52 54

ZD-MVS					99.92 31	98.57 53		98.52 94	92.34 195	99.31 69	99.83 43	95.06 52	99.80 112	99.70 31	99.97 42

GST-MVS		98.27 49	97.97 56	99.17 50	99.92 31	97.57 82	99.93 68	98.39 141	94.04 132	98.80 90	99.74 73	92.98 108	100.00 1	98.16 98	99.76 80	99.93 73

TEST9					99.92 31	98.92 27	99.96 29	98.43 119	93.90 139	99.71 31	99.86 26	95.88 37	99.85 100

train_agg		98.88 17	98.65 18	99.59 22	99.92 31	98.92 27	99.96 29	98.43 119	94.35 113	99.71 31	99.86 26	95.94 34	99.85 100	99.69 32	99.98 32	99.99 23

test_8					99.92 31	98.88 30	99.96 29	98.43 119	94.35 113	99.69 33	99.85 30	95.94 34	99.85 100

PGM-MVS		98.34 45	98.13 48	98.99 69	99.92 31	97.00 105	99.75 140	99.50 17	93.90 139	99.37 66	99.76 62	93.24 103	100.00 1	97.75 123	99.96 46	99.98 48

ACMMP		97.74 75	97.44 76	98.66 87	99.92 31	96.13 138	99.18 226	99.45 18	94.84 95	96.41 163	99.71 80	91.40 142	99.99 36	97.99 108	98.03 148	99.87 84
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 9	99.95 46	98.43 119	96.48 52	99.80 15	99.93 11	97.44 14	100.00 1	99.92 12	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 134					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 134					100.00 1	99.96 9	100.00 1	100.00 1

HPM-MVS++		99.07 10	98.88 15	99.63 16	99.90 42	99.02 24	99.95 46	98.56 82	97.56 20	99.44 59	99.85 30	95.38 46	100.00 1	99.31 45	99.99 21	99.87 84

APD-MVS		98.62 26	98.35 36	99.41 37	99.90 42	98.51 56	99.87 92	98.36 149	94.08 126	99.74 28	99.73 75	94.08 80	99.74 125	99.42 41	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 20	98.54 24	99.62 19	99.90 42	98.85 33	99.24 221	98.47 104	98.14 6	99.08 79	99.91 14	93.09 106	100.00 1	99.04 55	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 29				99.80 51	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 10	99.89 45	99.24 18	99.87 92	98.44 111	97.48 22	99.64 38	99.94 4	96.68 26	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 17				99.49 56

CSCG		97.10 96	97.04 90	97.27 165	99.89 45	91.92 249	99.90 80	99.07 32	88.67 276	95.26 185	99.82 46	93.17 105	99.98 43	98.15 99	99.47 101	99.90 80

ZNCC-MVS		98.31 46	98.03 53	99.17 50	99.88 49	97.59 81	99.94 62	98.44 111	94.31 116	98.50 107	99.82 46	93.06 107	99.99 36	98.30 94	99.99 21	99.93 73

SR-MVS		98.46 36	98.30 40	98.93 74	99.88 49	97.04 103	99.84 112	98.35 151	94.92 92	99.32 68	99.80 51	93.35 96	99.78 116	99.30 46	99.95 49	99.96 62

9.14			98.38 31		99.87 51		99.91 75	98.33 154	93.22 158	99.78 24	99.89 19	94.57 64	99.85 100	99.84 19	99.97 42

SMA-MVS		98.76 21	98.48 26	99.62 19	99.87 51	98.87 31	99.86 105	98.38 145	93.19 159	99.77 25	99.94 4	95.54 42	100.00 1	99.74 27	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
PHI-MVS		98.41 42	98.21 42	99.03 65	99.86 53	97.10 102	99.98 12	98.80 56	90.78 240	99.62 41	99.78 58	95.30 47	100.00 1	99.80 22	99.93 60	99.99 23

MTAPA		98.29 48	97.96 59	99.30 41	99.85 54	97.93 72	99.39 202	98.28 163	95.76 71	97.18 142	99.88 21	92.74 116	100.00 1	98.67 77	99.88 68	99.99 23

LS3D		95.84 143	95.11 153	98.02 126	99.85 54	95.10 175	98.74 274	98.50 102	87.22 297	93.66 202	99.86 26	87.45 194	99.95 64	90.94 243	99.81 78	99.02 189

HPM-MVS		97.96 60	97.72 67	98.68 85	99.84 56	96.39 126	99.90 80	98.17 175	92.61 181	98.62 102	99.57 100	91.87 138	99.67 136	98.87 66	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 49	98.11 50	98.75 82	99.83 57	96.59 119	99.40 198	98.51 97	95.29 84	98.51 106	99.76 62	93.60 94	99.71 129	98.53 85	99.52 97	99.95 69

save fliter					99.82 58	98.79 37	99.96 29	98.40 138	97.66 16

PLC	95.54 3	97.93 62	97.89 64	98.05 125	99.82 58	94.77 184	99.92 71	98.46 106	93.93 137	97.20 141	99.27 123	95.44 45	99.97 53	97.41 128	99.51 99	99.41 157
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 52	98.08 52	98.78 79	99.81 60	96.60 118	99.82 120	98.30 161	93.95 136	99.37 66	99.77 60	92.84 112	99.76 122	98.95 58	99.92 63	99.97 56

EI-MVSNet-UG-set		98.14 56	97.99 55	98.60 91	99.80 61	96.27 129	99.36 207	98.50 102	95.21 86	98.30 116	99.75 67	93.29 100	99.73 128	98.37 90	99.30 112	99.81 90

SR-MVS-dyc-post		98.31 46	98.17 45	98.71 83	99.79 62	96.37 127	99.76 137	98.31 158	94.43 108	99.40 64	99.75 67	93.28 101	99.78 116	98.90 64	99.92 63	99.97 56

RE-MVS-def			98.13 48		99.79 62	96.37 127	99.76 137	98.31 158	94.43 108	99.40 64	99.75 67	92.95 109		98.90 64	99.92 63	99.97 56

HPM-MVS_fast		97.80 71	97.50 74	98.68 85	99.79 62	96.42 122	99.88 89	98.16 179	91.75 213	98.94 85	99.54 103	91.82 140	99.65 138	97.62 126	99.99 21	99.99 23

SF-MVS		98.67 24	98.40 29	99.50 29	99.77 65	98.67 46	99.90 80	98.21 170	93.53 149	99.81 13	99.89 19	94.70 62	99.86 99	99.84 19	99.93 60	99.96 62

旧先验1					99.76 66	97.52 84		98.64 70			99.85 30	95.63 41			99.94 54	99.99 23

OMC-MVS		97.28 90	97.23 83	97.41 156	99.76 66	93.36 218	99.65 161	97.95 196	96.03 66	97.41 138	99.70 82	89.61 171	99.51 143	96.73 147	98.25 140	99.38 159

新几何1				99.42 36	99.75 68	98.27 60		98.63 73	92.69 176	99.55 49	99.82 46	94.40 67	100.00 1	91.21 235	99.94 54	99.99 23

MP-MVS-pluss		98.07 59	97.64 69	99.38 40	99.74 69	98.41 59	99.74 143	98.18 174	93.35 153	96.45 160	99.85 30	92.64 118	99.97 53	98.91 63	99.89 66	99.77 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 14	98.77 16	99.41 37	99.74 69	98.67 46	99.77 132	98.38 145	96.73 46	99.88 4	99.74 73	94.89 59	99.59 140	99.80 22	99.98 32	99.97 56
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 34	99.74 69	98.56 54		98.40 138		99.65 37		94.76 60	99.75 123		99.98 32	99.99 23

原ACMM1				98.96 72	99.73 72	96.99 106		98.51 97	94.06 129	99.62 41	99.85 30	94.97 58	99.96 57	95.11 165	99.95 49	99.92 78

TSAR-MVS + GP.		98.60 27	98.51 25	98.86 77	99.73 72	96.63 116	99.97 22	97.92 201	98.07 7	98.76 94	99.55 101	95.00 56	99.94 72	99.91 15	97.68 153	99.99 23

CANet		98.27 49	97.82 65	99.63 16	99.72 74	99.10 22	99.98 12	98.51 97	97.00 36	98.52 105	99.71 80	87.80 190	99.95 64	99.75 25	99.38 108	99.83 88

F-COLMAP		96.93 102	96.95 93	96.87 174	99.71 75	91.74 254	99.85 108	97.95 196	93.11 162	95.72 178	99.16 134	92.35 127	99.94 72	95.32 163	99.35 110	98.92 191

SD-MVS		98.92 15	98.70 17	99.56 24	99.70 76	98.73 43	99.94 62	98.34 153	96.38 57	99.81 13	99.76 62	94.59 63	99.98 43	99.84 19	99.96 46	99.97 56
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 53	99.12 5	95.59 208	99.67 77	86.91 324	99.95 46	98.89 45	97.60 17	99.90 2	99.76 62	96.54 28	99.98 43	99.94 11	99.82 76	99.88 82

ACMMP_NAP		98.49 34	98.14 47	99.54 26	99.66 78	98.62 52	99.85 108	98.37 148	94.68 101	99.53 52	99.83 43	92.87 111	100.00 1	98.66 79	99.84 71	99.99 23

DeepPCF-MVS	95.94 2	97.71 76	98.98 12	93.92 272	99.63 79	81.76 351	99.96 29	98.56 82	99.47 1	99.19 76	99.99 1	94.16 79	100.00 1	99.92 12	99.93 60	100.00 1

EPNet		98.49 34	98.40 29	98.77 81	99.62 80	96.80 113	99.90 80	99.51 16	97.60 17	99.20 74	99.36 118	93.71 91	99.91 82	97.99 108	98.71 128	99.61 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		96.05 136	95.82 134	96.72 179	99.59 81	96.99 106	99.95 46	99.10 29	94.06 129	98.27 117	95.80 272	89.00 182	99.95 64	99.12 50	87.53 265	93.24 324

PVSNet_Blended		97.94 61	97.64 69	98.83 78	99.59 81	96.99 106	100.00 1	99.10 29	95.38 81	98.27 117	99.08 137	89.00 182	99.95 64	99.12 50	99.25 114	99.57 133

PatchMatch-RL		96.04 137	95.40 142	97.95 127	99.59 81	95.22 171	99.52 183	99.07 32	93.96 135	96.49 159	98.35 197	82.28 235	99.82 111	90.15 259	99.22 116	98.81 198

dcpmvs_2		97.42 86	98.09 51	95.42 213	99.58 84	87.24 320	99.23 222	96.95 294	94.28 118	98.93 86	99.73 75	94.39 70	99.16 161	99.89 16	99.82 76	99.86 86

test222					99.55 85	97.41 93	99.34 208	98.55 88	91.86 208	99.27 73	99.83 43	93.84 88			99.95 49	99.99 23

CNLPA		97.76 74	97.38 77	98.92 75	99.53 86	96.84 111	99.87 92	98.14 182	93.78 142	96.55 158	99.69 84	92.28 129	99.98 43	97.13 135	99.44 105	99.93 73

API-MVS		97.86 65	97.66 68	98.47 104	99.52 87	95.41 162	99.47 192	98.87 48	91.68 214	98.84 88	99.85 30	92.34 128	99.99 36	98.44 87	99.96 46	100.00 1

PVSNet	91.05 13	97.13 95	96.69 100	98.45 106	99.52 87	95.81 145	99.95 46	99.65 11	94.73 98	99.04 81	99.21 130	84.48 221	99.95 64	94.92 171	98.74 127	99.58 132

114514_t		97.41 87	96.83 95	99.14 56	99.51 89	97.83 73	99.89 87	98.27 165	88.48 280	99.06 80	99.66 91	90.30 163	99.64 139	96.32 151	99.97 42	99.96 62

cl22		93.77 197	93.25 201	95.33 217	99.49 90	94.43 188	99.61 169	98.09 184	90.38 244	89.16 268	95.61 279	90.56 160	97.34 260	91.93 227	84.45 286	94.21 269

testdata				98.42 109	99.47 91	95.33 165		98.56 82	93.78 142	99.79 23	99.85 30	93.64 93	99.94 72	94.97 169	99.94 54	100.00 1

MAR-MVS		97.43 82	97.19 84	98.15 121	99.47 91	94.79 183	99.05 243	98.76 57	92.65 179	98.66 100	99.82 46	88.52 187	99.98 43	98.12 100	99.63 87	99.67 109
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		94.54 177	93.42 194	97.91 131	99.46 93	94.04 197	98.93 255	97.48 241	81.15 347	90.04 241	99.55 101	87.02 199	99.95 64	88.97 269	98.11 144	99.73 101

MVS_111021_LR		98.42 41	98.38 31	98.53 101	99.39 94	95.79 146	99.87 92	99.86 2	96.70 47	98.78 91	99.79 54	92.03 135	99.90 84	99.17 49	99.86 70	99.88 82

CHOSEN 280x420		99.01 13	99.03 10	98.95 73	99.38 95	98.87 31	98.46 291	99.42 21	97.03 35	99.02 82	99.09 136	99.35 1	98.21 225	99.73 29	99.78 79	99.77 97

MVS_111021_HR		98.72 22	98.62 20	99.01 68	99.36 96	97.18 98	99.93 68	99.90 1	96.81 44	98.67 99	99.77 60	93.92 84	99.89 88	99.27 47	99.94 54	99.96 62

DPM-MVS		98.83 19	98.46 27	99.97 1	99.33 97	99.92 1	99.96 29	98.44 111	97.96 10	99.55 49	99.94 4	97.18 21	100.00 1	93.81 200	99.94 54	99.98 48

TAPA-MVS	92.12 8	94.42 181	93.60 187	96.90 173	99.33 97	91.78 253	99.78 129	98.00 190	89.89 254	94.52 191	99.47 107	91.97 136	99.18 159	69.90 361	99.52 97	99.73 101
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS-test		97.88 64	97.94 60	97.70 143	99.28 99	95.20 172	99.98 12	97.15 272	95.53 78	99.62 41	99.79 54	92.08 134	98.38 209	98.75 73	99.28 113	99.52 142

test_fmvsm_n_1920		98.44 38	98.61 21	97.92 129	99.27 100	95.18 173	100.00 1	98.90 43	98.05 8	99.80 15	99.73 75	92.64 118	99.99 36	99.58 34	99.51 99	98.59 206

test_yl		97.83 67	97.37 78	99.21 44	99.18 101	97.98 69	99.64 165	99.27 26	91.43 222	97.88 128	98.99 146	95.84 38	99.84 107	98.82 68	95.32 201	99.79 93

DCV-MVSNet		97.83 67	97.37 78	99.21 44	99.18 101	97.98 69	99.64 165	99.27 26	91.43 222	97.88 128	98.99 146	95.84 38	99.84 107	98.82 68	95.32 201	99.79 93

MVS_0304		98.87 18	98.61 21	99.67 15	99.18 101	99.13 21	99.87 92	99.65 11	98.17 5	98.75 96	99.75 67	92.76 115	99.94 72	99.88 17	99.44 105	99.94 71

DeepC-MVS	94.51 4	96.92 103	96.40 109	98.45 106	99.16 104	95.90 143	99.66 159	98.06 187	96.37 60	94.37 194	99.49 106	83.29 231	99.90 84	97.63 125	99.61 92	99.55 135
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 30	98.22 41	99.50 29	99.15 105	98.65 50	100.00 1	98.58 78	97.70 15	98.21 121	99.24 128	92.58 121	99.94 72	98.63 82	99.94 54	99.92 78
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
CS-MVS		97.79 72	97.91 62	97.43 155	99.10 106	94.42 189	99.99 4	97.10 277	95.07 87	99.68 34	99.75 67	92.95 109	98.34 213	98.38 89	99.14 118	99.54 138

Anonymous202405211		93.10 215	91.99 227	96.40 189	99.10 106	89.65 295	98.88 260	97.93 198	83.71 334	94.00 199	98.75 174	68.79 329	99.88 94	95.08 167	91.71 224	99.68 107

HyFIR lowres test		96.66 116	96.43 108	97.36 161	99.05 108	93.91 202	99.70 153	99.80 3	90.54 242	96.26 166	98.08 202	92.15 132	98.23 224	96.84 146	95.46 198	99.93 73

LFMVS		94.75 171	93.56 190	98.30 114	99.03 109	95.70 152	98.74 274	97.98 193	87.81 290	98.47 108	99.39 115	67.43 337	99.53 141	98.01 106	95.20 203	99.67 109

AllTest		92.48 229	91.64 232	95.00 227	99.01 110	88.43 309	98.94 254	96.82 309	86.50 306	88.71 274	98.47 193	74.73 306	99.88 94	85.39 305	96.18 182	96.71 231

TestCases				95.00 227	99.01 110	88.43 309		96.82 309	86.50 306	88.71 274	98.47 193	74.73 306	99.88 94	85.39 305	96.18 182	96.71 231

COLMAP_ROB	90.47 14	92.18 236	91.49 238	94.25 260	99.00 112	88.04 315	98.42 296	96.70 316	82.30 343	88.43 281	99.01 143	76.97 282	99.85 100	86.11 302	96.50 178	94.86 239
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvs1		95.35 157	95.68 138	94.36 257	98.99 113	84.98 333	99.96 29	96.65 318	97.60 17	99.73 29	98.96 152	71.58 319	99.93 80	98.31 93	99.37 109	98.17 211

HY-MVS	92.50 7	97.79 72	97.17 86	99.63 16	98.98 114	99.32 8	97.49 320	99.52 14	95.69 73	98.32 115	97.41 223	93.32 98	99.77 119	98.08 104	95.75 194	99.81 90

VNet		97.21 94	96.57 104	99.13 60	98.97 115	97.82 74	99.03 246	99.21 28	94.31 116	99.18 77	98.88 163	86.26 207	99.89 88	98.93 60	94.32 209	99.69 106

thres200		96.96 100	96.21 113	99.22 43	98.97 115	98.84 34	99.85 108	99.71 6	93.17 160	96.26 166	98.88 163	89.87 168	99.51 143	94.26 189	94.91 204	99.31 169

tfpn200view9		96.79 107	95.99 118	99.19 46	98.94 117	98.82 35	99.78 129	99.71 6	92.86 165	96.02 171	98.87 166	89.33 175	99.50 145	93.84 197	94.57 205	99.27 174

thres400		96.78 108	95.99 118	99.16 52	98.94 117	98.82 35	99.78 129	99.71 6	92.86 165	96.02 171	98.87 166	89.33 175	99.50 145	93.84 197	94.57 205	99.16 181

Anonymous20231211		89.86 284	88.44 291	94.13 263	98.93 119	90.68 274	98.54 288	98.26 166	76.28 359	86.73 302	95.54 283	70.60 325	97.56 253	90.82 246	80.27 321	94.15 277

canonicalmvs		97.09 98	96.32 110	99.39 39	98.93 119	98.95 26	99.72 151	97.35 252	94.45 106	97.88 128	99.42 111	86.71 201	99.52 142	98.48 86	93.97 214	99.72 103

SDMVSNet		94.80 167	93.96 178	97.33 163	98.92 121	95.42 161	99.59 171	98.99 35	92.41 192	92.55 217	97.85 212	75.81 296	98.93 169	97.90 114	91.62 225	97.64 222

sd_testset		93.55 204	92.83 208	95.74 206	98.92 121	90.89 271	98.24 302	98.85 51	92.41 192	92.55 217	97.85 212	71.07 324	98.68 185	93.93 194	91.62 225	97.64 222

EPNet_dtu		95.71 147	95.39 143	96.66 181	98.92 121	93.41 215	99.57 175	98.90 43	96.19 64	97.52 134	98.56 185	92.65 117	97.36 258	77.89 344	98.33 135	99.20 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 58	97.60 71	99.60 21	98.92 121	99.28 16	99.89 87	99.52 14	95.58 76	98.24 120	99.39 115	93.33 97	99.74 125	97.98 110	95.58 197	99.78 96

CHOSEN 1792x2688		96.81 106	96.53 105	97.64 145	98.91 125	93.07 220	99.65 161	99.80 3	95.64 74	95.39 182	98.86 168	84.35 224	99.90 84	96.98 141	99.16 117	99.95 69

thres100view900		96.74 111	95.92 130	99.18 47	98.90 126	98.77 39	99.74 143	99.71 6	92.59 183	95.84 174	98.86 168	89.25 177	99.50 145	93.84 197	94.57 205	99.27 174

thres600view7		96.69 114	95.87 133	99.14 56	98.90 126	98.78 38	99.74 143	99.71 6	92.59 183	95.84 174	98.86 168	89.25 177	99.50 145	93.44 209	94.50 208	99.16 181

MSDG		94.37 183	93.36 198	97.40 157	98.88 128	93.95 201	99.37 205	97.38 250	85.75 317	90.80 233	99.17 133	84.11 226	99.88 94	86.35 299	98.43 133	98.36 209

h-mvs33		94.92 165	94.36 168	96.59 183	98.85 129	91.29 265	98.93 255	98.94 38	95.90 67	98.77 92	98.42 196	90.89 156	99.77 119	97.80 116	70.76 355	98.72 203

Anonymous20240529		92.10 237	90.65 248	96.47 184	98.82 130	90.61 276	98.72 276	98.67 67	75.54 363	93.90 201	98.58 183	66.23 341	99.90 84	94.70 180	90.67 227	98.90 194

PVSNet_Blended_VisFu		97.27 91	96.81 96	98.66 87	98.81 131	96.67 115	99.92 71	98.64 70	94.51 105	96.38 164	98.49 189	89.05 181	99.88 94	97.10 137	98.34 134	99.43 155

PS-MVSNAJ		98.44 38	98.20 43	99.16 52	98.80 132	98.92 27	99.54 181	98.17 175	97.34 24	99.85 7	99.85 30	91.20 145	99.89 88	99.41 42	99.67 85	98.69 204

CANet_DTU		96.76 109	96.15 114	98.60 91	98.78 133	97.53 83	99.84 112	97.63 220	97.25 31	99.20 74	99.64 93	81.36 244	99.98 43	92.77 220	98.89 123	98.28 210

mvsany_test1		97.82 69	97.90 63	97.55 148	98.77 134	93.04 223	99.80 126	97.93 198	96.95 38	99.61 47	99.68 88	90.92 153	99.83 109	99.18 48	98.29 139	99.80 92

alignmvs		97.81 70	97.33 80	99.25 42	98.77 134	98.66 48	99.99 4	98.44 111	94.40 112	98.41 110	99.47 107	93.65 92	99.42 153	98.57 83	94.26 210	99.67 109

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 47	98.72 136	97.71 76	99.98 12	98.44 111	96.85 39	99.80 15	99.91 14	97.57 8	99.85 100	99.44 40	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 54	97.97 56	99.02 67	98.69 137	98.66 48	99.52 183	98.08 186	97.05 34	99.86 5	99.86 26	90.65 158	99.71 129	99.39 44	98.63 129	98.69 204

miper_enhance_ethall		94.36 185	93.98 177	95.49 209	98.68 138	95.24 169	99.73 148	97.29 259	93.28 157	89.86 246	95.97 270	94.37 71	97.05 282	92.20 224	84.45 286	94.19 270

test2506		97.53 80	97.19 84	98.58 94	98.66 139	96.90 110	98.81 269	99.77 5	94.93 90	97.95 125	98.96 152	92.51 123	99.20 157	94.93 170	98.15 141	99.64 115

ECVR-MVS		95.66 150	95.05 155	97.51 151	98.66 139	93.71 206	98.85 266	98.45 107	94.93 90	96.86 149	98.96 152	75.22 302	99.20 157	95.34 162	98.15 141	99.64 115

test1111		95.57 152	94.98 158	97.37 159	98.56 141	93.37 217	98.86 264	98.45 107	94.95 89	96.63 155	98.95 157	75.21 303	99.11 162	95.02 168	98.14 143	99.64 115

MVSTER		95.53 153	95.22 149	96.45 186	98.56 141	97.72 75	99.91 75	97.67 218	92.38 194	91.39 226	97.14 230	97.24 18	97.30 265	94.80 176	87.85 259	94.34 261

VDD-MVS		93.77 197	92.94 205	96.27 194	98.55 143	90.22 284	98.77 273	97.79 212	90.85 238	96.82 151	99.42 111	61.18 358	99.77 119	98.95 58	94.13 211	98.82 197

tpmvs		94.28 187	93.57 189	96.40 189	98.55 143	91.50 263	95.70 354	98.55 88	87.47 292	92.15 221	94.26 329	91.42 141	98.95 168	88.15 279	95.85 190	98.76 200

UGNet		95.33 158	94.57 165	97.62 147	98.55 143	94.85 179	98.67 282	99.32 25	95.75 72	96.80 152	96.27 261	72.18 316	99.96 57	94.58 183	99.05 121	98.04 215
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	95.18 159	94.10 174	98.43 108	98.55 143	95.99 141	97.91 315	97.31 257	90.35 246	89.48 257	99.22 129	85.19 216	99.89 88	90.40 256	98.47 132	99.41 157
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_vis1_n_1920		95.44 155	95.31 146	95.82 204	98.50 147	88.74 303	99.98 12	97.30 258	97.84 12	99.85 7	99.19 131	66.82 339	99.97 53	98.82 68	99.46 103	98.76 200

BH-w/o		95.71 147	95.38 144	96.68 180	98.49 148	92.28 240	99.84 112	97.50 239	92.12 200	92.06 222	98.79 172	84.69 219	98.67 186	95.29 164	99.66 86	99.09 187

baseline1		95.78 144	94.86 160	98.54 99	98.47 149	98.07 64	99.06 239	97.99 191	92.68 177	94.13 198	98.62 180	93.28 101	98.69 184	93.79 202	85.76 274	98.84 196

iter_conf05		96.07 135	95.95 126	96.44 188	98.43 150	97.52 84	99.91 75	96.85 305	94.16 122	92.49 219	97.98 208	98.20 4	97.34 260	97.26 132	88.29 252	94.45 251

EPMVS		96.53 120	96.01 117	98.09 123	98.43 150	96.12 140	96.36 341	99.43 20	93.53 149	97.64 132	95.04 307	94.41 66	98.38 209	91.13 237	98.11 144	99.75 99

iter_conf_final		96.01 138	95.93 128	96.28 193	98.38 152	97.03 104	99.87 92	97.03 285	94.05 131	92.61 215	97.98 208	98.01 5	97.34 260	97.02 139	88.39 251	94.47 245

sss		97.57 79	97.03 91	99.18 47	98.37 153	98.04 66	99.73 148	99.38 22	93.46 151	98.76 94	99.06 139	91.21 144	99.89 88	96.33 150	97.01 170	99.62 120

BH-untuned		95.18 159	94.83 161	96.22 195	98.36 154	91.22 266	99.80 126	97.32 256	90.91 236	91.08 229	98.67 176	83.51 228	98.54 192	94.23 190	99.61 92	98.92 191

ET-MVSNet_ETH3D		94.37 183	93.28 200	97.64 145	98.30 155	97.99 68	99.99 4	97.61 225	94.35 113	71.57 366	99.45 110	96.23 31	95.34 339	96.91 145	85.14 281	99.59 126

AUN-MVS		93.28 209	92.60 213	95.34 216	98.29 156	90.09 287	99.31 212	98.56 82	91.80 212	96.35 165	98.00 205	89.38 174	98.28 219	92.46 221	69.22 360	97.64 222

FMVSNet3		92.69 225	91.58 234	95.99 199	98.29 156	97.42 92	99.26 220	97.62 222	89.80 255	89.68 250	95.32 297	81.62 242	96.27 319	87.01 295	85.65 275	94.29 263

PMMVS		96.76 109	96.76 98	96.76 177	98.28 158	92.10 244	99.91 75	97.98 193	94.12 124	99.53 52	99.39 115	86.93 200	98.73 179	96.95 143	97.73 151	99.45 152

hse-mvs2		94.38 182	94.08 175	95.31 218	98.27 159	90.02 289	99.29 217	98.56 82	95.90 67	98.77 92	98.00 205	90.89 156	98.26 223	97.80 116	69.20 361	97.64 222

PVSNet_0	88.03 19	91.80 244	90.27 256	96.38 191	98.27 159	90.46 280	99.94 62	99.61 13	93.99 133	86.26 312	97.39 225	71.13 323	99.89 88	98.77 71	67.05 366	98.79 199

UA-Net		96.54 119	95.96 124	98.27 115	98.23 161	95.71 151	98.00 313	98.45 107	93.72 145	98.41 110	99.27 123	88.71 186	99.66 137	91.19 236	97.69 152	99.44 154

test_cas_vis1_n_1920		96.59 118	96.23 112	97.65 144	98.22 162	94.23 193	99.99 4	97.25 263	97.77 13	99.58 48	99.08 137	77.10 279	99.97 53	97.64 124	99.45 104	98.74 202

FE-MVS		95.70 149	95.01 157	97.79 136	98.21 163	94.57 185	95.03 355	98.69 62	88.90 271	97.50 136	96.19 263	92.60 120	99.49 149	89.99 261	97.94 150	99.31 169

GG-mvs-BLEND				98.54 99	98.21 163	98.01 67	93.87 360	98.52 94		97.92 126	97.92 211	99.02 2	97.94 241	98.17 97	99.58 94	99.67 109

mvs_anonymous		95.65 151	95.03 156	97.53 149	98.19 165	95.74 149	99.33 209	97.49 240	90.87 237	90.47 236	97.10 232	88.23 188	97.16 273	95.92 157	97.66 154	99.68 107

MVS_Test		96.46 122	95.74 135	98.61 90	98.18 166	97.23 96	99.31 212	97.15 272	91.07 233	98.84 88	97.05 236	88.17 189	98.97 166	94.39 185	97.50 156	99.61 123

BH-RMVSNet		95.18 159	94.31 171	97.80 134	98.17 167	95.23 170	99.76 137	97.53 235	92.52 188	94.27 196	99.25 127	76.84 284	98.80 173	90.89 245	99.54 96	99.35 164

RPSCF		91.80 244	92.79 210	88.83 332	98.15 168	69.87 368	98.11 309	96.60 320	83.93 332	94.33 195	99.27 123	79.60 262	99.46 152	91.99 226	93.16 221	97.18 229

ETV-MVS		97.92 63	97.80 66	98.25 116	98.14 169	96.48 120	99.98 12	97.63 220	95.61 75	99.29 72	99.46 109	92.55 122	98.82 172	99.02 57	98.54 130	99.46 150

IS-MVSNet		96.29 131	95.90 131	97.45 153	98.13 170	94.80 182	99.08 234	97.61 225	92.02 205	95.54 181	98.96 152	90.64 159	98.08 230	93.73 205	97.41 160	99.47 149

test_fmvsmconf_n		98.43 40	98.32 37	98.78 79	98.12 171	96.41 123	99.99 4	98.83 53	98.22 3	99.67 35	99.64 93	91.11 149	99.94 72	99.67 33	99.62 88	99.98 48

ab-mvs		94.69 172	93.42 194	98.51 102	98.07 172	96.26 130	96.49 339	98.68 64	90.31 247	94.54 190	97.00 238	76.30 291	99.71 129	95.98 156	93.38 219	99.56 134

XVG-OURS-SEG-HR		94.79 168	94.70 164	95.08 224	98.05 173	89.19 298	99.08 234	97.54 233	93.66 146	94.87 188	99.58 99	78.78 269	99.79 114	97.31 130	93.40 218	96.25 233

EIA-MVS		97.53 80	97.46 75	97.76 140	98.04 174	94.84 180	99.98 12	97.61 225	94.41 111	97.90 127	99.59 97	92.40 126	98.87 170	98.04 105	99.13 119	99.59 126

XVG-OURS		94.82 166	94.74 163	95.06 225	98.00 175	89.19 298	99.08 234	97.55 231	94.10 125	94.71 189	99.62 95	80.51 255	99.74 125	96.04 155	93.06 223	96.25 233

dp		95.05 162	94.43 167	96.91 172	97.99 176	92.73 230	96.29 344	97.98 193	89.70 256	95.93 173	94.67 320	93.83 89	98.45 198	86.91 298	96.53 177	99.54 138

tpmrst		96.27 133	95.98 120	97.13 167	97.96 177	93.15 219	96.34 342	98.17 175	92.07 201	98.71 98	95.12 305	93.91 85	98.73 179	94.91 173	96.62 175	99.50 146

TR-MVS		94.54 177	93.56 190	97.49 152	97.96 177	94.34 191	98.71 277	97.51 238	90.30 248	94.51 192	98.69 175	75.56 297	98.77 176	92.82 219	95.99 186	99.35 164

Vis-MVSNet (Re-imp)		96.32 128	95.98 120	97.35 162	97.93 179	94.82 181	99.47 192	98.15 181	91.83 209	95.09 186	99.11 135	91.37 143	97.47 256	93.47 208	97.43 157	99.74 100

MDTV_nov1_ep13			95.69 136		97.90 180	94.15 194	95.98 350	98.44 111	93.12 161	97.98 124	95.74 274	95.10 50	98.58 189	90.02 260	96.92 172

Fast-Effi-MVS+		95.02 163	94.19 172	97.52 150	97.88 181	94.55 186	99.97 22	97.08 280	88.85 273	94.47 193	97.96 210	84.59 220	98.41 201	89.84 263	97.10 165	99.59 126

ADS-MVSNet2		93.80 196	93.88 181	93.55 286	97.87 182	85.94 327	94.24 356	96.84 306	90.07 250	96.43 161	94.48 325	90.29 164	95.37 338	87.44 286	97.23 162	99.36 162

ADS-MVSNet		94.79 168	94.02 176	97.11 169	97.87 182	93.79 203	94.24 356	98.16 179	90.07 250	96.43 161	94.48 325	90.29 164	98.19 226	87.44 286	97.23 162	99.36 162

Effi-MVS+		96.30 130	95.69 136	98.16 118	97.85 184	96.26 130	97.41 322	97.21 265	90.37 245	98.65 101	98.58 183	86.61 203	98.70 183	97.11 136	97.37 161	99.52 142

PatchmatchNet		95.94 140	95.45 141	97.39 158	97.83 185	94.41 190	96.05 348	98.40 138	92.86 165	97.09 143	95.28 302	94.21 78	98.07 232	89.26 267	98.11 144	99.70 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 175	93.61 185	97.74 142	97.82 186	96.26 130	99.96 29	97.78 213	85.76 315	94.00 199	97.54 219	76.95 283	99.21 156	97.23 133	95.43 199	97.76 221

1112_ss		96.01 138	95.20 150	98.42 109	97.80 187	96.41 123	99.65 161	96.66 317	92.71 174	92.88 212	99.40 113	92.16 131	99.30 154	91.92 228	93.66 215	99.55 135

Test_1112_low_res		95.72 145	94.83 161	98.42 109	97.79 188	96.41 123	99.65 161	96.65 318	92.70 175	92.86 213	96.13 266	92.15 132	99.30 154	91.88 229	93.64 216	99.55 135

Effi-MVS+-dtu		94.53 179	95.30 147	92.22 305	97.77 189	82.54 344	99.59 171	97.06 282	94.92 92	95.29 184	95.37 295	85.81 209	97.89 242	94.80 176	97.07 166	96.23 235

tpm cat1		93.51 205	92.52 218	96.47 184	97.77 189	91.47 264	96.13 346	98.06 187	80.98 348	92.91 211	93.78 333	89.66 169	98.87 170	87.03 294	96.39 180	99.09 187

FA-MVS(test-final)		95.86 141	95.09 154	98.15 121	97.74 191	95.62 155	96.31 343	98.17 175	91.42 224	96.26 166	96.13 266	90.56 160	99.47 151	92.18 225	97.07 166	99.35 164

xiu_mvs_v1_base_debu		97.43 82	97.06 87	98.55 96	97.74 191	98.14 61	99.31 212	97.86 207	96.43 54	99.62 41	99.69 84	85.56 211	99.68 133	99.05 52	98.31 136	97.83 217

xiu_mvs_v1_base		97.43 82	97.06 87	98.55 96	97.74 191	98.14 61	99.31 212	97.86 207	96.43 54	99.62 41	99.69 84	85.56 211	99.68 133	99.05 52	98.31 136	97.83 217

xiu_mvs_v1_base_debi		97.43 82	97.06 87	98.55 96	97.74 191	98.14 61	99.31 212	97.86 207	96.43 54	99.62 41	99.69 84	85.56 211	99.68 133	99.05 52	98.31 136	97.83 217

EPP-MVSNet		96.69 114	96.60 102	96.96 171	97.74 191	93.05 222	99.37 205	98.56 82	88.75 274	95.83 176	99.01 143	96.01 32	98.56 190	96.92 144	97.20 164	99.25 176

gg-mvs-nofinetune		93.51 205	91.86 231	98.47 104	97.72 196	97.96 71	92.62 364	98.51 97	74.70 366	97.33 139	69.59 379	98.91 3	97.79 245	97.77 121	99.56 95	99.67 109

IB-MVS	92.85 6	94.99 164	93.94 179	98.16 118	97.72 196	95.69 153	99.99 4	98.81 54	94.28 118	92.70 214	96.90 240	95.08 51	99.17 160	96.07 154	73.88 350	99.60 125
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 87	97.02 92	98.59 93	97.71 198	97.52 84	99.97 22	98.54 91	91.83 209	97.45 137	99.04 140	97.50 9	99.10 163	94.75 178	96.37 181	99.16 181

test_fmvs1_n		94.25 188	94.36 168	93.92 272	97.68 199	83.70 339	99.90 80	96.57 321	97.40 23	99.67 35	98.88 163	61.82 355	99.92 81	98.23 95	99.13 119	98.14 214

diffmvs		97.00 99	96.64 101	98.09 123	97.64 200	96.17 137	99.81 122	97.19 266	94.67 102	98.95 84	99.28 120	86.43 204	98.76 177	98.37 90	97.42 159	99.33 167
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		95.72 145	95.15 152	97.45 153	97.62 201	94.28 192	99.28 218	98.24 167	94.27 120	96.84 150	98.94 159	79.39 263	98.76 177	93.25 210	98.49 131	99.30 171
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 96	96.72 99	98.22 117	97.60 202	96.70 114	99.92 71	98.54 91	91.11 232	97.07 144	98.97 150	97.47 12	99.03 164	93.73 205	96.09 184	98.92 191

miper_ehance_all_eth		93.16 212	92.60 213	94.82 235	97.57 203	93.56 209	99.50 187	97.07 281	88.75 274	88.85 273	95.52 285	90.97 152	96.74 301	90.77 247	84.45 286	94.17 271

LCM-MVSNet-Re		92.31 233	92.60 213	91.43 312	97.53 204	79.27 361	99.02 247	91.83 375	92.07 201	80.31 341	94.38 328	83.50 229	95.48 336	97.22 134	97.58 155	99.54 138

GBi-Net		90.88 260	89.82 265	94.08 264	97.53 204	91.97 245	98.43 293	96.95 294	87.05 298	89.68 250	94.72 316	71.34 320	96.11 324	87.01 295	85.65 275	94.17 271

test1		90.88 260	89.82 265	94.08 264	97.53 204	91.97 245	98.43 293	96.95 294	87.05 298	89.68 250	94.72 316	71.34 320	96.11 324	87.01 295	85.65 275	94.17 271

FMVSNet2		91.02 257	89.56 270	95.41 214	97.53 204	95.74 149	98.98 249	97.41 248	87.05 298	88.43 281	95.00 310	71.34 320	96.24 321	85.12 307	85.21 280	94.25 266

tttt0517		96.85 104	96.49 106	97.92 129	97.48 208	95.89 144	99.85 108	98.54 91	90.72 241	96.63 155	98.93 161	97.47 12	99.02 165	93.03 217	95.76 193	98.85 195

casdiffmvs_mvg		96.43 123	95.94 127	97.89 133	97.44 209	95.47 158	99.86 105	97.29 259	93.35 153	96.03 170	99.19 131	85.39 214	98.72 181	97.89 115	97.04 168	99.49 148
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 89	97.24 82	97.80 134	97.41 210	95.64 154	99.99 4	97.06 282	94.59 103	99.63 39	99.32 119	89.20 180	98.14 227	98.76 72	99.23 115	99.62 120

c3_l		92.53 228	91.87 230	94.52 247	97.40 211	92.99 224	99.40 198	96.93 299	87.86 288	88.69 276	95.44 289	89.95 167	96.44 312	90.45 253	80.69 317	94.14 280

CDS-MVSNet		96.34 127	96.07 115	97.13 167	97.37 212	94.96 177	99.53 182	97.91 202	91.55 217	95.37 183	98.32 198	95.05 53	97.13 276	93.80 201	95.75 194	99.30 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 111	96.26 111	98.16 118	97.36 213	96.48 120	99.96 29	98.29 162	91.93 206	95.77 177	98.07 203	95.54 42	98.29 217	90.55 251	98.89 123	99.70 104

miper_lstm_enhance		91.81 241	91.39 240	93.06 297	97.34 214	89.18 300	99.38 203	96.79 311	86.70 305	87.47 294	95.22 303	90.00 166	95.86 333	88.26 277	81.37 307	94.15 277

baseline		96.43 123	95.98 120	97.76 140	97.34 214	95.17 174	99.51 185	97.17 269	93.92 138	96.90 148	99.28 120	85.37 215	98.64 187	97.50 127	96.86 174	99.46 150

cl____		92.31 233	91.58 234	94.52 247	97.33 216	92.77 226	99.57 175	96.78 312	86.97 302	87.56 292	95.51 286	89.43 173	96.62 306	88.60 272	82.44 299	94.16 276

DIV-MVS_self_test		92.32 232	91.60 233	94.47 251	97.31 217	92.74 228	99.58 173	96.75 313	86.99 301	87.64 290	95.54 283	89.55 172	96.50 310	88.58 273	82.44 299	94.17 271

casdiffmvs		96.42 125	95.97 123	97.77 139	97.30 218	94.98 176	99.84 112	97.09 279	93.75 144	96.58 157	99.26 126	85.07 217	98.78 175	97.77 121	97.04 168	99.54 138
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		94.36 185	93.48 192	96.99 170	97.29 219	93.54 210	99.96 29	96.72 315	88.35 283	93.43 203	98.94 159	82.05 236	98.05 233	88.12 281	96.48 179	99.37 161

eth_miper_zixun_eth		92.41 231	91.93 228	93.84 276	97.28 220	90.68 274	98.83 267	96.97 293	88.57 279	89.19 267	95.73 276	89.24 179	96.69 304	89.97 262	81.55 305	94.15 277

MVSFormer		96.94 101	96.60 102	97.95 127	97.28 220	97.70 78	99.55 179	97.27 261	91.17 229	99.43 60	99.54 103	90.92 153	96.89 294	94.67 181	99.62 88	99.25 176

lupinMVS		97.85 66	97.60 71	98.62 89	97.28 220	97.70 78	99.99 4	97.55 231	95.50 80	99.43 60	99.67 89	90.92 153	98.71 182	98.40 88	99.62 88	99.45 152

SCA		94.69 172	93.81 183	97.33 163	97.10 223	94.44 187	98.86 264	98.32 156	93.30 156	96.17 169	95.59 281	76.48 289	97.95 239	91.06 239	97.43 157	99.59 126

TAMVS		95.85 142	95.58 139	96.65 182	97.07 224	93.50 211	99.17 227	97.82 211	91.39 226	95.02 187	98.01 204	92.20 130	97.30 265	93.75 204	95.83 191	99.14 184

Fast-Effi-MVS+-dtu		93.72 200	93.86 182	93.29 290	97.06 225	86.16 325	99.80 126	96.83 307	92.66 178	92.58 216	97.83 214	81.39 243	97.67 250	89.75 264	96.87 173	96.05 237

CostFormer		96.10 134	95.88 132	96.78 176	97.03 226	92.55 236	97.08 330	97.83 210	90.04 252	98.72 97	94.89 314	95.01 55	98.29 217	96.54 149	95.77 192	99.50 146

test_fmvsmvis_n_1920		97.67 77	97.59 73	97.91 131	97.02 227	95.34 164	99.95 46	98.45 107	97.87 11	97.02 145	99.59 97	89.64 170	99.98 43	99.41 42	99.34 111	98.42 207

test-LLR		96.47 121	96.04 116	97.78 137	97.02 227	95.44 159	99.96 29	98.21 170	94.07 127	95.55 179	96.38 257	93.90 86	98.27 221	90.42 254	98.83 125	99.64 115

test-mter		96.39 126	95.93 128	97.78 137	97.02 227	95.44 159	99.96 29	98.21 170	91.81 211	95.55 179	96.38 257	95.17 48	98.27 221	90.42 254	98.83 125	99.64 115

gm-plane-assit					96.97 230	93.76 205			91.47 220		98.96 152		98.79 174	94.92 171

QAPM		95.40 156	94.17 173	99.10 61	96.92 231	97.71 76	99.40 198	98.68 64	89.31 259	88.94 271	98.89 162	82.48 234	99.96 57	93.12 216	99.83 72	99.62 120

KD-MVS_2432*1600		88.00 301	86.10 305	93.70 282	96.91 232	94.04 197	97.17 327	97.12 275	84.93 325	81.96 332	92.41 344	92.48 124	94.51 349	79.23 337	52.68 378	92.56 334

miper_refine_blended		88.00 301	86.10 305	93.70 282	96.91 232	94.04 197	97.17 327	97.12 275	84.93 325	81.96 332	92.41 344	92.48 124	94.51 349	79.23 337	52.68 378	92.56 334

tpm2		95.47 154	95.18 151	96.35 192	96.91 232	91.70 258	96.96 333	97.93 198	88.04 287	98.44 109	95.40 291	93.32 98	97.97 236	94.00 192	95.61 196	99.38 159

FMVSNet5		88.32 298	87.47 300	90.88 315	96.90 235	88.39 311	97.28 324	95.68 341	82.60 342	84.67 321	92.40 346	79.83 261	91.16 367	76.39 351	81.51 306	93.09 326

3Dnovator+	91.53 11	96.31 129	95.24 148	99.52 27	96.88 236	98.64 51	99.72 151	98.24 167	95.27 85	88.42 283	98.98 148	82.76 233	99.94 72	97.10 137	99.83 72	99.96 62

Patchmatch-test		92.65 227	91.50 237	96.10 198	96.85 237	90.49 279	91.50 369	97.19 266	82.76 341	90.23 238	95.59 281	95.02 54	98.00 235	77.41 346	96.98 171	99.82 89

MVS		96.60 117	95.56 140	99.72 12	96.85 237	99.22 19	98.31 299	98.94 38	91.57 216	90.90 232	99.61 96	86.66 202	99.96 57	97.36 129	99.88 68	99.99 23

3Dnovator	91.47 12	96.28 132	95.34 145	99.08 62	96.82 239	97.47 90	99.45 195	98.81 54	95.52 79	89.39 258	99.00 145	81.97 237	99.95 64	97.27 131	99.83 72	99.84 87

EI-MVSNet		93.73 199	93.40 197	94.74 236	96.80 240	92.69 231	99.06 239	97.67 218	88.96 268	91.39 226	99.02 141	88.75 185	97.30 265	91.07 238	87.85 259	94.22 267

CVMVSNet		94.68 174	94.94 159	93.89 275	96.80 240	86.92 323	99.06 239	98.98 36	94.45 106	94.23 197	99.02 141	85.60 210	95.31 340	90.91 244	95.39 200	99.43 155

IterMVS-LS		92.69 225	92.11 223	94.43 255	96.80 240	92.74 228	99.45 195	96.89 302	88.98 266	89.65 253	95.38 294	88.77 184	96.34 316	90.98 242	82.04 302	94.22 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.91 259	90.17 260	93.12 294	96.78 243	90.42 282	98.89 258	97.05 284	89.03 263	86.49 307	95.42 290	76.59 287	95.02 342	87.22 291	84.09 289	93.93 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 105	95.96 124	99.48 33	96.74 244	98.52 55	98.31 299	98.86 49	95.82 69	89.91 244	98.98 148	87.49 193	99.96 57	97.80 116	99.73 82	99.96 62

IterMVS-SCA-FT		90.85 262	90.16 261	92.93 298	96.72 245	89.96 290	98.89 258	96.99 289	88.95 269	86.63 304	95.67 277	76.48 289	95.00 343	87.04 293	84.04 292	93.84 305

MVS-HIRNet		86.22 308	83.19 321	95.31 218	96.71 246	90.29 283	92.12 366	97.33 255	62.85 373	86.82 301	70.37 378	69.37 328	97.49 255	75.12 353	97.99 149	98.15 212

VDDNet		93.12 214	91.91 229	96.76 177	96.67 247	92.65 234	98.69 280	98.21 170	82.81 340	97.75 131	99.28 120	61.57 356	99.48 150	98.09 103	94.09 212	98.15 212

dmvs_re		93.20 211	93.15 202	93.34 288	96.54 248	83.81 338	98.71 277	98.51 97	91.39 226	92.37 220	98.56 185	78.66 271	97.83 244	93.89 195	89.74 228	98.38 208

MIMVSNet		90.30 275	88.67 288	95.17 223	96.45 249	91.64 260	92.39 365	97.15 272	85.99 312	90.50 235	93.19 340	66.95 338	94.86 346	82.01 326	93.43 217	99.01 190

CR-MVSNet		93.45 208	92.62 212	95.94 200	96.29 250	92.66 232	92.01 367	96.23 330	92.62 180	96.94 146	93.31 338	91.04 150	96.03 329	79.23 337	95.96 187	99.13 185

RPMNet		89.76 286	87.28 301	97.19 166	96.29 250	92.66 232	92.01 367	98.31 158	70.19 372	96.94 146	85.87 371	87.25 196	99.78 116	62.69 372	95.96 187	99.13 185

tt0805		91.28 252	90.18 259	94.60 242	96.26 252	87.55 317	98.39 297	98.72 59	89.00 265	89.22 264	98.47 193	62.98 352	98.96 167	90.57 250	88.00 258	97.28 228

Patchmtry		89.70 287	88.49 290	93.33 289	96.24 253	89.94 293	91.37 370	96.23 330	78.22 356	87.69 289	93.31 338	91.04 150	96.03 329	80.18 336	82.10 301	94.02 288

test_vis1_rt		86.87 306	86.05 308	89.34 328	96.12 254	78.07 362	99.87 92	83.54 386	92.03 204	78.21 351	89.51 357	45.80 372	99.91 82	96.25 152	93.11 222	90.03 357

JIA-IIPM		91.76 247	90.70 247	94.94 229	96.11 255	87.51 318	93.16 363	98.13 183	75.79 362	97.58 133	77.68 376	92.84 112	97.97 236	88.47 276	96.54 176	99.33 167

OpenMVS	90.15 15	94.77 170	93.59 188	98.33 113	96.07 256	97.48 89	99.56 177	98.57 80	90.46 243	86.51 306	98.95 157	78.57 272	99.94 72	93.86 196	99.74 81	97.57 226

PAPM		98.60 27	98.42 28	99.14 56	96.05 257	98.96 25	99.90 80	99.35 24	96.68 48	98.35 114	99.66 91	96.45 29	98.51 193	99.45 39	99.89 66	99.96 62

CLD-MVS		94.06 191	93.90 180	94.55 246	96.02 258	90.69 273	99.98 12	97.72 214	96.62 51	91.05 231	98.85 171	77.21 278	98.47 194	98.11 101	89.51 234	94.48 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 272	88.75 287	95.25 220	95.99 259	90.16 285	91.22 371	97.54 233	76.80 358	97.26 140	86.01 370	91.88 137	96.07 328	66.16 369	95.91 189	99.51 144

ACMH+	89.98 16	90.35 273	89.54 271	92.78 301	95.99 259	86.12 326	98.81 269	97.18 268	89.38 258	83.14 328	97.76 216	68.42 333	98.43 199	89.11 268	86.05 273	93.78 308

DeepMVS_CX				82.92 348	95.98 261	58.66 378		96.01 335	92.72 173	78.34 350	95.51 286	58.29 361	98.08 230	82.57 322	85.29 278	92.03 342

ACMP	92.05 9	92.74 222	92.42 220	93.73 278	95.91 262	88.72 304	99.81 122	97.53 235	94.13 123	87.00 300	98.23 199	74.07 310	98.47 194	96.22 153	88.86 241	93.99 293
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 203	93.03 204	95.35 215	95.86 263	86.94 322	99.87 92	96.36 328	96.85 39	99.54 51	98.79 172	52.41 368	99.83 109	98.64 80	98.97 122	99.29 173

HQP-NCC					95.78 264		99.87 92		96.82 41	93.37 204

ACMP_Plane					95.78 264		99.87 92		96.82 41	93.37 204

HQP-MVS		94.61 176	94.50 166	94.92 230	95.78 264	91.85 250	99.87 92	97.89 203	96.82 41	93.37 204	98.65 177	80.65 253	98.39 205	97.92 112	89.60 229	94.53 240

NP-MVS					95.77 267	91.79 252					98.65 177

plane_prior6					95.76 268	91.72 257						80.47 257

ACMM	91.95 10	92.88 219	92.52 218	93.98 271	95.75 269	89.08 301	99.77 132	97.52 237	93.00 163	89.95 243	97.99 207	76.17 293	98.46 197	93.63 207	88.87 240	94.39 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 193	92.84 207	96.80 175	95.73 270	93.57 208	99.88 89	97.24 264	92.57 185	92.92 210	96.66 249	78.73 270	97.67 250	87.75 284	94.06 213	99.17 180

plane_prior1					95.73 270

jason		97.24 92	96.86 94	98.38 112	95.73 270	97.32 94	99.97 22	97.40 249	95.34 83	98.60 104	99.54 103	87.70 191	98.56 190	97.94 111	99.47 101	99.25 176
jason: jason.
HQP_MVS		94.49 180	94.36 168	94.87 231	95.71 273	91.74 254	99.84 112	97.87 205	96.38 57	93.01 208	98.59 181	80.47 257	98.37 211	97.79 119	89.55 232	94.52 242

plane_prior7					95.71 273	91.59 262

ITE_SJBPF				92.38 303	95.69 275	85.14 331		95.71 340	92.81 169	89.33 261	98.11 201	70.23 326	98.42 200	85.91 303	88.16 255	93.59 316

ACMH	89.72 17	90.64 266	89.63 268	93.66 284	95.64 276	88.64 307	98.55 286	97.45 242	89.03 263	81.62 335	97.61 218	69.75 327	98.41 201	89.37 265	87.62 264	93.92 299
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 113	96.49 106	97.37 159	95.63 277	95.96 142	99.74 143	98.88 47	92.94 164	91.61 224	98.97 150	97.72 7	98.62 188	94.83 175	98.08 147	97.53 227

FMVSNet1		88.50 297	86.64 303	94.08 264	95.62 278	91.97 245	98.43 293	96.95 294	83.00 338	86.08 314	94.72 316	59.09 360	96.11 324	81.82 328	84.07 290	94.17 271

LPG-MVS_test		92.96 217	92.71 211	93.71 280	95.43 279	88.67 305	99.75 140	97.62 222	92.81 169	90.05 239	98.49 189	75.24 300	98.40 203	95.84 159	89.12 236	94.07 285

LGP-MVS_train				93.71 280	95.43 279	88.67 305		97.62 222	92.81 169	90.05 239	98.49 189	75.24 300	98.40 203	95.84 159	89.12 236	94.07 285

tpm		93.70 201	93.41 196	94.58 244	95.36 281	87.41 319	97.01 331	96.90 301	90.85 238	96.72 154	94.14 330	90.40 162	96.84 297	90.75 248	88.54 248	99.51 144

D2MVS		92.76 221	92.59 216	93.27 291	95.13 282	89.54 297	99.69 154	99.38 22	92.26 197	87.59 291	94.61 322	85.05 218	97.79 245	91.59 232	88.01 257	92.47 337

VPA-MVSNet		92.70 224	91.55 236	96.16 196	95.09 283	96.20 135	98.88 260	99.00 34	91.02 235	91.82 223	95.29 301	76.05 295	97.96 238	95.62 161	81.19 308	94.30 262

LTVRE_ROB	88.28 18	90.29 276	89.05 282	94.02 267	95.08 284	90.15 286	97.19 326	97.43 244	84.91 327	83.99 324	97.06 235	74.00 311	98.28 219	84.08 312	87.71 262	93.62 315
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		87.87 303	86.51 304	91.94 308	95.05 285	85.57 329	97.65 319	94.08 363	84.40 330	81.82 334	96.85 244	62.14 354	98.33 214	80.25 335	86.37 272	91.91 344

test0.0.03 1		93.86 192	93.61 185	94.64 240	95.02 286	92.18 243	99.93 68	98.58 78	94.07 127	87.96 287	98.50 188	93.90 86	94.96 344	81.33 329	93.17 220	96.78 230

UniMVSNet (Re)		93.07 216	92.13 222	95.88 201	94.84 287	96.24 134	99.88 89	98.98 36	92.49 190	89.25 262	95.40 291	87.09 198	97.14 275	93.13 215	78.16 331	94.26 264

USDC		90.00 283	88.96 283	93.10 296	94.81 288	88.16 313	98.71 277	95.54 345	93.66 146	83.75 326	97.20 229	65.58 343	98.31 216	83.96 315	87.49 266	92.85 331

VPNet		91.81 241	90.46 250	95.85 203	94.74 289	95.54 157	98.98 249	98.59 77	92.14 199	90.77 234	97.44 222	68.73 331	97.54 254	94.89 174	77.89 333	94.46 246

FIs		94.10 189	93.43 193	96.11 197	94.70 290	96.82 112	99.58 173	98.93 42	92.54 186	89.34 260	97.31 226	87.62 192	97.10 279	94.22 191	86.58 270	94.40 253

UniMVSNet_ETH3D		90.06 282	88.58 289	94.49 250	94.67 291	88.09 314	97.81 318	97.57 230	83.91 333	88.44 279	97.41 223	57.44 362	97.62 252	91.41 233	88.59 247	97.77 220

UniMVSNet_NR-MVSNet		92.95 218	92.11 223	95.49 209	94.61 292	95.28 167	99.83 118	99.08 31	91.49 218	89.21 265	96.86 243	87.14 197	96.73 302	93.20 211	77.52 336	94.46 246

test_fmvs2		89.47 290	89.70 267	88.77 335	94.54 293	75.74 363	99.83 118	94.70 359	94.71 99	91.08 229	96.82 248	54.46 365	97.78 247	92.87 218	88.27 253	92.80 332

WR-MVS		92.31 233	91.25 241	95.48 212	94.45 294	95.29 166	99.60 170	98.68 64	90.10 249	88.07 286	96.89 241	80.68 252	96.80 300	93.14 214	79.67 324	94.36 257

nrg030		93.51 205	92.53 217	96.45 186	94.36 295	97.20 97	99.81 122	97.16 271	91.60 215	89.86 246	97.46 221	86.37 205	97.68 249	95.88 158	80.31 320	94.46 246

tfpnnormal		89.29 293	87.61 299	94.34 258	94.35 296	94.13 195	98.95 253	98.94 38	83.94 331	84.47 322	95.51 286	74.84 305	97.39 257	77.05 349	80.41 318	91.48 347

FC-MVSNet-test		93.81 195	93.15 202	95.80 205	94.30 297	96.20 135	99.42 197	98.89 45	92.33 196	89.03 270	97.27 228	87.39 195	96.83 298	93.20 211	86.48 271	94.36 257

MS-PatchMatch		90.65 265	90.30 255	91.71 311	94.22 298	85.50 330	98.24 302	97.70 215	88.67 276	86.42 309	96.37 259	67.82 335	98.03 234	83.62 317	99.62 88	91.60 345

WR-MVS_H		91.30 250	90.35 253	94.15 261	94.17 299	92.62 235	99.17 227	98.94 38	88.87 272	86.48 308	94.46 327	84.36 222	96.61 307	88.19 278	78.51 329	93.21 325

DU-MVS		92.46 230	91.45 239	95.49 209	94.05 300	95.28 167	99.81 122	98.74 58	92.25 198	89.21 265	96.64 251	81.66 240	96.73 302	93.20 211	77.52 336	94.46 246

NR-MVSNet		91.56 249	90.22 257	95.60 207	94.05 300	95.76 148	98.25 301	98.70 61	91.16 231	80.78 340	96.64 251	83.23 232	96.57 308	91.41 233	77.73 335	94.46 246

CP-MVSNet		91.23 254	90.22 257	94.26 259	93.96 302	92.39 239	99.09 232	98.57 80	88.95 269	86.42 309	96.57 254	79.19 266	96.37 314	90.29 257	78.95 326	94.02 288

XXY-MVS		91.82 240	90.46 250	95.88 201	93.91 303	95.40 163	98.87 263	97.69 216	88.63 278	87.87 288	97.08 233	74.38 309	97.89 242	91.66 231	84.07 290	94.35 260

PS-CasMVS		90.63 267	89.51 273	93.99 270	93.83 304	91.70 258	98.98 249	98.52 94	88.48 280	86.15 313	96.53 256	75.46 298	96.31 318	88.83 270	78.86 328	93.95 296

test_0402		85.58 310	83.94 315	90.50 319	93.81 305	85.04 332	98.55 286	95.20 353	76.01 360	79.72 345	95.13 304	64.15 349	96.26 320	66.04 370	86.88 269	90.21 356

XVG-ACMP-BASELINE		91.22 255	90.75 246	92.63 302	93.73 306	85.61 328	98.52 290	97.44 243	92.77 172	89.90 245	96.85 244	66.64 340	98.39 205	92.29 223	88.61 245	93.89 301

TranMVSNet+NR-MVSNet		91.68 248	90.61 249	94.87 231	93.69 307	93.98 200	99.69 154	98.65 68	91.03 234	88.44 279	96.83 247	80.05 260	96.18 322	90.26 258	76.89 344	94.45 251

mvsmamba		94.10 189	93.72 184	95.25 220	93.57 308	94.13 195	99.67 158	96.45 326	93.63 148	91.34 228	97.77 215	86.29 206	97.22 271	96.65 148	88.10 256	94.40 253

TransMVSNet (Re)		87.25 304	85.28 311	93.16 293	93.56 309	91.03 267	98.54 288	94.05 365	83.69 335	81.09 338	96.16 264	75.32 299	96.40 313	76.69 350	68.41 362	92.06 341

v10		90.25 277	88.82 285	94.57 245	93.53 310	93.43 214	99.08 234	96.87 304	85.00 324	87.34 298	94.51 323	80.93 249	97.02 289	82.85 321	79.23 325	93.26 323

testgi		89.01 295	88.04 296	91.90 309	93.49 311	84.89 334	99.73 148	95.66 342	93.89 141	85.14 319	98.17 200	59.68 359	94.66 348	77.73 345	88.88 239	96.16 236

v8		90.54 269	89.17 278	94.66 239	93.43 312	93.40 216	99.20 224	96.94 298	85.76 315	87.56 292	94.51 323	81.96 238	97.19 272	84.94 309	78.25 330	93.38 321

V42		91.28 252	90.12 262	94.74 236	93.42 313	93.46 212	99.68 156	97.02 286	87.36 294	89.85 248	95.05 306	81.31 245	97.34 260	87.34 289	80.07 322	93.40 319

pm-mvs1		89.36 292	87.81 298	94.01 268	93.40 314	91.93 248	98.62 285	96.48 325	86.25 310	83.86 325	96.14 265	73.68 312	97.04 284	86.16 301	75.73 348	93.04 328

RRT_MVS		93.14 213	92.92 206	93.78 277	93.31 315	90.04 288	99.66 159	97.69 216	92.53 187	88.91 272	97.76 216	84.36 222	96.93 292	95.10 166	86.99 268	94.37 256

v1144		91.09 256	89.83 264	94.87 231	93.25 316	93.69 207	99.62 168	96.98 291	86.83 304	89.64 254	94.99 311	80.94 248	97.05 282	85.08 308	81.16 309	93.87 303

v1192		90.62 268	89.25 277	94.72 238	93.13 317	93.07 220	99.50 187	97.02 286	86.33 309	89.56 256	95.01 308	79.22 265	97.09 281	82.34 324	81.16 309	94.01 290

v2v482		91.30 250	90.07 263	95.01 226	93.13 317	93.79 203	99.77 132	97.02 286	88.05 286	89.25 262	95.37 295	80.73 251	97.15 274	87.28 290	80.04 323	94.09 284

OPM-MVS		93.21 210	92.80 209	94.44 253	93.12 319	90.85 272	99.77 132	97.61 225	96.19 64	91.56 225	98.65 177	75.16 304	98.47 194	93.78 203	89.39 235	93.99 293
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 263	89.52 272	94.59 243	93.11 320	92.77 226	99.56 177	96.99 289	86.38 308	89.82 249	94.95 313	80.50 256	97.10 279	83.98 314	80.41 318	93.90 300

bld_raw_dy_0_64		92.74 222	92.03 226	94.87 231	93.09 321	93.46 212	99.12 229	95.41 347	92.84 168	90.44 237	97.54 219	78.08 276	97.04 284	93.94 193	87.77 261	94.11 282

PEN-MVS		90.19 279	89.06 281	93.57 285	93.06 322	90.90 270	99.06 239	98.47 104	88.11 285	85.91 315	96.30 260	76.67 285	95.94 332	87.07 292	76.91 343	93.89 301

v1240		90.20 278	88.79 286	94.44 253	93.05 323	92.27 241	99.38 203	96.92 300	85.89 313	89.36 259	94.87 315	77.89 277	97.03 287	80.66 332	81.08 312	94.01 290

v148		90.70 264	89.63 268	93.92 272	92.97 324	90.97 268	99.75 140	96.89 302	87.51 291	88.27 284	95.01 308	81.67 239	97.04 284	87.40 288	77.17 341	93.75 309

v1921920		90.46 270	89.12 279	94.50 249	92.96 325	92.46 237	99.49 189	96.98 291	86.10 311	89.61 255	95.30 298	78.55 273	97.03 287	82.17 325	80.89 316	94.01 290

Baseline_NR-MVSNet		90.33 274	89.51 273	92.81 300	92.84 326	89.95 291	99.77 132	93.94 366	84.69 329	89.04 269	95.66 278	81.66 240	96.52 309	90.99 241	76.98 342	91.97 343

test_method		80.79 330	79.70 334	84.08 345	92.83 327	67.06 370	99.51 185	95.42 346	54.34 377	81.07 339	93.53 335	44.48 373	92.22 364	78.90 341	77.23 340	92.94 329

pmmvs4		92.10 237	91.07 244	95.18 222	92.82 328	94.96 177	99.48 191	96.83 307	87.45 293	88.66 277	96.56 255	83.78 227	96.83 298	89.29 266	84.77 284	93.75 309

LF4IMVS		89.25 294	88.85 284	90.45 321	92.81 329	81.19 354	98.12 308	94.79 356	91.44 221	86.29 311	97.11 231	65.30 346	98.11 229	88.53 275	85.25 279	92.07 340

DTE-MVSNet		89.40 291	88.24 294	92.88 299	92.66 330	89.95 291	99.10 231	98.22 169	87.29 295	85.12 320	96.22 262	76.27 292	95.30 341	83.56 318	75.74 347	93.41 318

EU-MVSNet		90.14 281	90.34 254	89.54 327	92.55 331	81.06 355	98.69 280	98.04 189	91.41 225	86.59 305	96.84 246	80.83 250	93.31 360	86.20 300	81.91 303	94.26 264

APD_test1		81.15 329	80.92 330	81.86 349	92.45 332	59.76 377	96.04 349	93.61 369	73.29 369	77.06 354	96.64 251	44.28 374	96.16 323	72.35 357	82.52 297	89.67 360

our_test_3		90.39 271	89.48 275	93.12 294	92.40 333	89.57 296	99.33 209	96.35 329	87.84 289	85.30 318	94.99 311	84.14 225	96.09 327	80.38 333	84.56 285	93.71 314

ppachtmachnet_test		89.58 289	88.35 292	93.25 292	92.40 333	90.44 281	99.33 209	96.73 314	85.49 320	85.90 316	95.77 273	81.09 247	96.00 331	76.00 352	82.49 298	93.30 322

v7n		89.65 288	88.29 293	93.72 279	92.22 335	90.56 278	99.07 238	97.10 277	85.42 322	86.73 302	94.72 316	80.06 259	97.13 276	81.14 330	78.12 332	93.49 317

dmvs_testset		83.79 323	86.07 307	76.94 353	92.14 336	48.60 387	96.75 336	90.27 378	89.48 257	78.65 348	98.55 187	79.25 264	86.65 376	66.85 367	82.69 296	95.57 238

PS-MVSNAJss		93.64 202	93.31 199	94.61 241	92.11 337	92.19 242	99.12 229	97.38 250	92.51 189	88.45 278	96.99 239	91.20 145	97.29 268	94.36 186	87.71 262	94.36 257

pmmvs5		90.17 280	89.09 280	93.40 287	92.10 338	89.77 294	99.74 143	95.58 344	85.88 314	87.24 299	95.74 274	73.41 313	96.48 311	88.54 274	83.56 293	93.95 296

N_pmnet		80.06 333	80.78 331	77.89 352	91.94 339	45.28 389	98.80 271	56.82 392	78.10 357	80.08 343	93.33 336	77.03 280	95.76 334	68.14 365	82.81 295	92.64 333

test_djsdf		92.83 220	92.29 221	94.47 251	91.90 340	92.46 237	99.55 179	97.27 261	91.17 229	89.96 242	96.07 269	81.10 246	96.89 294	94.67 181	88.91 238	94.05 287

SixPastTwentyTwo		88.73 296	88.01 297	90.88 315	91.85 341	82.24 346	98.22 305	95.18 354	88.97 267	82.26 331	96.89 241	71.75 318	96.67 305	84.00 313	82.98 294	93.72 313

K. test v3		88.05 300	87.24 302	90.47 320	91.82 342	82.23 347	98.96 252	97.42 246	89.05 262	76.93 356	95.60 280	68.49 332	95.42 337	85.87 304	81.01 314	93.75 309

OurMVSNet-221017-0		89.81 285	89.48 275	90.83 317	91.64 343	81.21 353	98.17 307	95.38 349	91.48 219	85.65 317	97.31 226	72.66 314	97.29 268	88.15 279	84.83 283	93.97 295

mvs_tets		91.81 241	91.08 243	94.00 269	91.63 344	90.58 277	98.67 282	97.43 244	92.43 191	87.37 297	97.05 236	71.76 317	97.32 264	94.75 178	88.68 244	94.11 282

Gipuma		66.95 346	65.00 346	72.79 358	91.52 345	67.96 369	66.16 381	95.15 355	47.89 379	58.54 376	67.99 381	29.74 378	87.54 375	50.20 379	77.83 334	62.87 381
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
jajsoiax		91.92 239	91.18 242	94.15 261	91.35 346	90.95 269	99.00 248	97.42 246	92.61 181	87.38 296	97.08 233	72.46 315	97.36 258	94.53 184	88.77 242	94.13 281

MDA-MVSNet-bldmvs		84.09 321	81.52 328	91.81 310	91.32 347	88.00 316	98.67 282	95.92 337	80.22 351	55.60 379	93.32 337	68.29 334	93.60 358	73.76 354	76.61 345	93.82 307

MVP-Stereo		90.93 258	90.45 252	92.37 304	91.25 348	88.76 302	98.05 312	96.17 332	87.27 296	84.04 323	95.30 298	78.46 274	97.27 270	83.78 316	99.70 84	91.09 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 312	83.32 320	92.10 306	90.96 349	88.58 308	99.20 224	96.52 323	79.70 353	57.12 378	92.69 342	79.11 267	93.86 355	77.10 348	77.46 338	93.86 304

YYNet1		85.50 313	83.33 319	92.00 307	90.89 350	88.38 312	99.22 223	96.55 322	79.60 354	57.26 377	92.72 341	79.09 268	93.78 356	77.25 347	77.37 339	93.84 305

anonymousdsp		91.79 246	90.92 245	94.41 256	90.76 351	92.93 225	98.93 255	97.17 269	89.08 261	87.46 295	95.30 298	78.43 275	96.92 293	92.38 222	88.73 243	93.39 320

lessismore_v0				90.53 318	90.58 352	80.90 356		95.80 338		77.01 355	95.84 271	66.15 342	96.95 290	83.03 320	75.05 349	93.74 312

EG-PatchMatch MVS		85.35 314	83.81 317	89.99 325	90.39 353	81.89 349	98.21 306	96.09 334	81.78 345	74.73 362	93.72 334	51.56 370	97.12 278	79.16 340	88.61 245	90.96 350

EGC-MVSNET		69.38 339	63.76 349	86.26 342	90.32 354	81.66 352	96.24 345	93.85 367	0.99 389	3.22 390	92.33 347	52.44 367	92.92 361	59.53 375	84.90 282	84.21 370

CMPMVS	61.59 21	84.75 317	85.14 312	83.57 346	90.32 354	62.54 373	96.98 332	97.59 229	74.33 367	69.95 368	96.66 249	64.17 348	98.32 215	87.88 283	88.41 250	89.84 359
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 320	82.92 323	89.21 329	90.03 356	82.60 343	96.89 335	95.62 343	80.59 349	75.77 361	89.17 358	65.04 347	94.79 347	72.12 358	81.02 313	90.23 355

pmmvs6		85.69 309	83.84 316	91.26 314	90.00 357	84.41 336	97.82 317	96.15 333	75.86 361	81.29 337	95.39 293	61.21 357	96.87 296	83.52 319	73.29 351	92.50 336

DSMNet-mixed		88.28 299	88.24 294	88.42 337	89.64 358	75.38 365	98.06 311	89.86 379	85.59 319	88.20 285	92.14 348	76.15 294	91.95 365	78.46 342	96.05 185	97.92 216

UnsupCasMVSNet_eth		85.52 311	83.99 313	90.10 323	89.36 359	83.51 340	96.65 337	97.99 191	89.14 260	75.89 360	93.83 332	63.25 351	93.92 353	81.92 327	67.90 365	92.88 330

Anonymous20231206		86.32 307	85.42 310	89.02 331	89.11 360	80.53 359	99.05 243	95.28 350	85.43 321	82.82 329	93.92 331	74.40 308	93.44 359	66.99 366	81.83 304	93.08 327

Anonymous20240521		85.15 315	83.81 317	89.16 330	88.32 361	82.69 342	98.80 271	95.74 339	79.72 352	81.53 336	90.99 351	65.38 345	94.16 351	72.69 356	81.11 311	90.63 353

OpenMVS_ROB	79.82 20	83.77 324	81.68 327	90.03 324	88.30 362	82.82 341	98.46 291	95.22 352	73.92 368	76.00 359	91.29 350	55.00 364	96.94 291	68.40 364	88.51 249	90.34 354

test20.03		84.72 318	83.99 313	86.91 340	88.19 363	80.62 358	98.88 260	95.94 336	88.36 282	78.87 346	94.62 321	68.75 330	89.11 371	66.52 368	75.82 346	91.00 349

KD-MVS_self_test		83.59 325	82.06 325	88.20 338	86.93 364	80.70 357	97.21 325	96.38 327	82.87 339	82.49 330	88.97 359	67.63 336	92.32 363	73.75 355	62.30 374	91.58 346

MIMVSNet1		82.58 326	80.51 332	88.78 333	86.68 365	84.20 337	96.65 337	95.41 347	78.75 355	78.59 349	92.44 343	51.88 369	89.76 370	65.26 371	78.95 326	92.38 339

CL-MVSNet_self_test		84.50 319	83.15 322	88.53 336	86.00 366	81.79 350	98.82 268	97.35 252	85.12 323	83.62 327	90.91 353	76.66 286	91.40 366	69.53 362	60.36 375	92.40 338

UnsupCasMVSNet_bld		79.97 335	77.03 340	88.78 333	85.62 367	81.98 348	93.66 361	97.35 252	75.51 364	70.79 367	83.05 373	48.70 371	94.91 345	78.31 343	60.29 376	89.46 363

Patchmatch-RL test		86.90 305	85.98 309	89.67 326	84.45 368	75.59 364	89.71 374	92.43 372	86.89 303	77.83 353	90.94 352	94.22 76	93.63 357	87.75 284	69.61 357	99.79 93

pmmvs-eth3d		84.03 322	81.97 326	90.20 322	84.15 369	87.09 321	98.10 310	94.73 358	83.05 337	74.10 364	87.77 365	65.56 344	94.01 352	81.08 331	69.24 359	89.49 362

test_fmvs3		79.99 334	80.17 333	79.45 351	84.02 370	62.83 371	99.05 243	93.49 370	88.29 284	80.06 344	86.65 368	28.09 380	88.00 372	88.63 271	73.27 352	87.54 368

PM-MVS		80.47 331	78.88 336	85.26 343	83.79 371	72.22 366	95.89 352	91.08 376	85.71 318	76.56 358	88.30 361	36.64 376	93.90 354	82.39 323	69.57 358	89.66 361

new-patchmatchnet		81.19 328	79.34 335	86.76 341	82.86 372	80.36 360	97.92 314	95.27 351	82.09 344	72.02 365	86.87 367	62.81 353	90.74 369	71.10 359	63.08 372	89.19 365

mvsany_test3		82.12 327	81.14 329	85.06 344	81.87 373	70.41 367	97.09 329	92.14 373	91.27 228	77.84 352	88.73 360	39.31 375	95.49 335	90.75 248	71.24 354	89.29 364

WB-MVS		76.28 337	77.28 339	73.29 357	81.18 374	54.68 381	97.87 316	94.19 362	81.30 346	69.43 369	90.70 354	77.02 281	82.06 380	35.71 384	68.11 364	83.13 371

test_f		78.40 336	77.59 338	80.81 350	80.82 375	62.48 374	96.96 333	93.08 371	83.44 336	74.57 363	84.57 372	27.95 381	92.63 362	84.15 311	72.79 353	87.32 369

SSC-MVS		75.42 338	76.40 341	72.49 361	80.68 376	53.62 382	97.42 321	94.06 364	80.42 350	68.75 370	90.14 356	76.54 288	81.66 381	33.25 385	66.34 368	82.19 372

pmmvs3		80.27 332	77.77 337	87.76 339	80.32 377	82.43 345	98.23 304	91.97 374	72.74 370	78.75 347	87.97 364	57.30 363	90.99 368	70.31 360	62.37 373	89.87 358

testf1		68.38 342	66.92 343	72.78 359	78.80 378	50.36 384	90.95 372	87.35 384	55.47 375	58.95 374	88.14 362	20.64 385	87.60 373	57.28 376	64.69 369	80.39 374

APD_test2		68.38 342	66.92 343	72.78 359	78.80 378	50.36 384	90.95 372	87.35 384	55.47 375	58.95 374	88.14 362	20.64 385	87.60 373	57.28 376	64.69 369	80.39 374

ambc				83.23 347	77.17 380	62.61 372	87.38 376	94.55 361		76.72 357	86.65 368	30.16 377	96.36 315	84.85 310	69.86 356	90.73 352

test_vis3_rt		68.82 340	66.69 345	75.21 356	76.24 381	60.41 376	96.44 340	68.71 391	75.13 365	50.54 382	69.52 380	16.42 390	96.32 317	80.27 334	66.92 367	68.89 378

TDRefinement		84.76 316	82.56 324	91.38 313	74.58 382	84.80 335	97.36 323	94.56 360	84.73 328	80.21 342	96.12 268	63.56 350	98.39 205	87.92 282	63.97 371	90.95 351

E-PMN		52.30 350	52.18 352	52.67 367	71.51 383	45.40 388	93.62 362	76.60 389	36.01 383	43.50 384	64.13 383	27.11 382	67.31 386	31.06 386	26.06 382	45.30 385

EMVS		51.44 352	51.22 354	52.11 368	70.71 384	44.97 390	94.04 358	75.66 390	35.34 385	42.40 385	61.56 386	28.93 379	65.87 387	27.64 387	24.73 383	45.49 384

PMMVS2		67.15 345	64.15 348	76.14 355	70.56 385	62.07 375	93.89 359	87.52 383	58.09 374	60.02 373	78.32 375	22.38 384	84.54 378	59.56 374	47.03 380	81.80 373

FPMVS		68.72 341	68.72 342	68.71 363	65.95 386	44.27 391	95.97 351	94.74 357	51.13 378	53.26 380	90.50 355	25.11 383	83.00 379	60.80 373	80.97 315	78.87 376

wuyk23d		20.37 356	20.84 359	18.99 371	65.34 387	27.73 393	50.43 382	7.67 395	9.50 388	8.01 389	6.34 389	6.13 393	26.24 388	23.40 388	10.69 387	2.99 386

LCM-MVSNet		67.77 344	64.73 347	76.87 354	62.95 388	56.25 380	89.37 375	93.74 368	44.53 380	61.99 372	80.74 374	20.42 387	86.53 377	69.37 363	59.50 377	87.84 366

MVE	53.74 22	51.54 351	47.86 355	62.60 365	59.56 389	50.93 383	79.41 379	77.69 388	35.69 384	36.27 386	61.76 385	5.79 394	69.63 384	37.97 383	36.61 381	67.24 379
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 348	52.24 351	67.66 364	49.27 390	56.82 379	83.94 377	82.02 387	70.47 371	33.28 387	64.54 382	17.23 389	69.16 385	45.59 381	23.85 384	77.02 377

tmp_tt		65.23 347	62.94 350	72.13 362	44.90 391	50.03 386	81.05 378	89.42 382	38.45 381	48.51 383	99.90 18	54.09 366	78.70 383	91.84 230	18.26 385	87.64 367

PMVS	49.05 23	53.75 349	51.34 353	60.97 366	40.80 392	34.68 392	74.82 380	89.62 381	37.55 382	28.67 388	72.12 377	7.09 392	81.63 382	43.17 382	68.21 363	66.59 380
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 354	39.14 357	33.31 369	19.94 393	24.83 394	98.36 298	9.75 394	15.53 387	51.31 381	87.14 366	19.62 388	17.74 389	47.10 380	3.47 388	57.36 382

testmvs		40.60 353	44.45 356	29.05 370	19.49 394	14.11 395	99.68 156	18.47 393	20.74 386	64.59 371	98.48 192	10.95 391	17.09 390	56.66 378	11.01 386	55.94 383

test_blank		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.02 390	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

eth-test2					0.00 395

eth-test					0.00 395

uanet_test		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

DCPMVS		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

cdsmvs_eth3d_5k		23.43 355	31.24 358	0.00 372	0.00 395	0.00 396	0.00 383	98.09 184	0.00 390	0.00 391	99.67 89	83.37 230	0.00 391	0.00 389	0.00 389	0.00 387

pcd_1.5k_mvsjas		7.60 358	10.13 361	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	91.20 145	0.00 391	0.00 389	0.00 389	0.00 387

sosnet-low-res		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

sosnet		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

uncertanet		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

Regformer		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

ab-mvs-re		8.28 357	11.04 360	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	99.40 113	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

uanet		0.00 359	0.00 362	0.00 372	0.00 395	0.00 396	0.00 383	0.00 396	0.00 390	0.00 391	0.00 391	0.00 395	0.00 391	0.00 389	0.00 389	0.00 387

PC_three_1452									96.96 37	99.80 15	99.79 54	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 119	97.27 28	99.80 15	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 52	99.83 11	99.91 14	97.87 6	100.00 1	99.92 12	100.00 1	100.00 1

GSMVS																99.59 126

sam_mvs1												94.72 61				99.59 126

sam_mvs												94.25 75

MTGPA								98.28 163

test_post1							95.78 353				59.23 387	93.20 104	97.74 248	91.06 239

test_post											63.35 384	94.43 65	98.13 228

patchmatchnet-post											91.70 349	95.12 49	97.95 239

MTMP							99.87 92	96.49 324

test9_res														99.71 30	99.99 21	100.00 1

agg_prior2														99.48 38	100.00 1	100.00 1

test_prior4						98.05 65	99.94 62

test_prior2							99.95 46		95.78 70	99.73 29	99.76 62	96.00 33		99.78 24	100.00 1

旧先验2							99.46 194		94.21 121	99.85 7			99.95 64	96.96 142

新几何2							99.40 198

无先验							99.49 189	98.71 60	93.46 151				100.00 1	94.36 186		99.99 23

原ACMM2							99.90 80

testdata2													99.99 36	90.54 252

segment_acmp												96.68 26

testdata1							99.28 218		96.35 61

plane_prior5								97.87 205					98.37 211	97.79 119	89.55 232	94.52 242

plane_prior4											98.59 181

plane_prior3						91.64 260			96.63 49	93.01 208

plane_prior2							99.84 112		96.38 57

plane_prior						91.74 254	99.86 105		96.76 45						89.59 231

n2								0.00 396

nn								0.00 396

door-mid								89.69 380

test11								98.44 111

door								90.31 377

HQP5-MVS						91.85 250

BP-MVS														97.92 112

HQP4-MVS										93.37 204			98.39 205			94.53 240

HQP3-MVS								97.89 203							89.60 229

HQP2-MVS												80.65 253

MDTV_nov1_ep13_2view						96.26 130	96.11 347		91.89 207	98.06 122		94.40 67		94.30 188		99.67 109

ACMMP++_ref															87.04 267

ACMMP++															88.23 254

Test By Simon												92.82 114

High-resolution multi-view benchmark