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		97.23 104	96.80 111	98.51 110	99.99 1	95.60 168	99.09 254	98.84 58	93.32 169	96.74 173	99.72 81	86.04 229	100.00 1	98.01 121	99.43 114	99.94 74

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 14	98.69 68	98.20 7	99.93 1	99.98 2	96.82 21	100.00 1	99.75 28	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 27	98.64 76	98.47 2	99.13 88	99.92 13	96.38 29	100.00 1	99.74 30	100.00 1	100.00 1

mPP-MVS		98.39 47	98.20 46	98.97 76	99.97 3	96.92 115	99.95 52	98.38 161	95.04 98	98.61 115	99.80 51	93.39 100	100.00 1	98.64 91	100.00 1	99.98 48

CPTT-MVS		97.64 86	97.32 90	98.58 102	99.97 3	95.77 157	99.96 34	98.35 167	89.90 274	98.36 125	99.79 55	91.18 156	99.99 36	98.37 105	99.99 21	99.99 23

DP-MVS Recon		98.41 45	98.02 57	99.56 25	99.97 3	98.70 48	99.92 80	98.44 125	92.06 219	98.40 124	99.84 41	95.68 39	100.00 1	98.19 111	99.71 86	99.97 58

PAPR		98.52 35	98.16 49	99.58 24	99.97 3	98.77 42	99.95 52	98.43 133	95.35 92	98.03 137	99.75 69	94.03 86	99.98 43	98.11 116	99.83 75	99.99 23

HFP-MVS		98.56 32	98.37 36	99.14 61	99.96 8	97.43 97	99.95 52	98.61 82	94.77 106	99.31 78	99.85 30	94.22 79	100.00 1	98.70 86	99.98 32	99.98 48

region2R		98.54 33	98.37 36	99.05 68	99.96 8	97.18 104	99.96 34	98.55 98	94.87 104	99.45 66	99.85 30	94.07 85	100.00 1	98.67 88	100.00 1	99.98 48

ACMMPR		98.50 36	98.32 40	99.05 68	99.96 8	97.18 104	99.95 52	98.60 84	94.77 106	99.31 78	99.84 41	93.73 95	100.00 1	98.70 86	99.98 32	99.98 48

NCCC		99.37 2	99.25 2	99.71 14	99.96 8	99.15 21	99.97 27	98.62 81	98.02 13	99.90 3	99.95 3	97.33 15	100.00 1	99.54 39	100.00 1	100.00 1

CP-MVS		98.45 40	98.32 40	98.87 83	99.96 8	96.62 124	99.97 27	98.39 157	94.43 119	98.90 97	99.87 24	94.30 77	100.00 1	99.04 63	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 12		98.41 150	96.63 56	99.75 29	99.93 11	97.49 8

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 52	98.43 133					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 26	98.55 25	99.15 59	99.94 13	97.50 93	99.94 68	98.42 145	96.22 72	99.41 70	99.78 59	94.34 75	99.96 61	98.92 72	99.95 49	99.99 23

X-MVStestdata		93.83 215	92.06 248	99.15 59	99.94 13	97.50 93	99.94 68	98.42 145	96.22 72	99.41 70	41.37 412	94.34 75	99.96 61	98.92 72	99.95 49	99.99 23

test_prior				99.43 35	99.94 13	98.49 60		98.65 74					99.80 121			99.99 23

MSLP-MVS++		99.13 8	99.01 11	99.49 32	99.94 13	98.46 61	99.98 14	98.86 53	97.10 40	99.80 17	99.94 4	95.92 35	100.00 1	99.51 40	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 86	98.39 157	97.20 38	99.46 65	99.85 30	95.53 43	99.79 123	99.86 21	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 57	97.97 59	99.03 70	99.94 13	97.17 107	99.95 52	98.39 157	94.70 110	98.26 131	99.81 50	91.84 147	100.00 1	98.85 78	99.97 42	99.93 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 28	98.36 38	99.49 32	99.94 13	98.73 46	99.87 106	98.33 172	93.97 146	99.76 28	99.87 24	94.99 57	99.75 132	98.55 97	100.00 1	99.98 48

PAPM_NR		98.12 60	97.93 64	98.70 91	99.94 13	96.13 147	99.82 136	98.43 133	94.56 114	97.52 151	99.70 85	94.40 70	99.98 43	97.00 155	99.98 32	99.99 23

MG-MVS		98.91 18	98.65 20	99.68 15	99.94 13	99.07 24	99.64 186	99.44 20	97.33 31	99.00 93	99.72 81	94.03 86	99.98 43	98.73 85	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 34	98.43 133	97.27 34	99.80 17	99.94 4	96.71 22	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 24	99.31 10		98.41 150	97.71 19	99.84 12				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 133	97.26 36	99.80 17	99.88 21	96.71 22	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 12	99.93 24	99.29 15	99.95 52	98.32 174	97.28 32	99.83 13	99.91 14	97.22 17	100.00 1	99.99 5	100.00 1	99.89 84
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 34	98.42 145	97.28 32	99.86 7	99.94 4	97.22 17

MSP-MVS		99.09 9	99.12 5	98.98 75	99.93 24	97.24 101	99.95 52	98.42 145	97.50 26	99.52 61	99.88 21	97.43 14	99.71 138	99.50 41	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		98.43 133		99.63 45			99.85 108

FOURS1					99.92 31	97.66 87	99.95 52	98.36 165	95.58 86	99.52 61

ZD-MVS					99.92 31	98.57 56		98.52 105	92.34 211	99.31 78	99.83 43	95.06 52	99.80 121	99.70 34	99.97 42

GST-MVS		98.27 52	97.97 59	99.17 55	99.92 31	97.57 89	99.93 75	98.39 157	94.04 142	98.80 102	99.74 76	92.98 115	100.00 1	98.16 113	99.76 83	99.93 76

TEST9					99.92 31	98.92 29	99.96 34	98.43 133	93.90 151	99.71 34	99.86 26	95.88 36	99.85 108

train_agg		98.88 19	98.65 20	99.59 23	99.92 31	98.92 29	99.96 34	98.43 133	94.35 124	99.71 34	99.86 26	95.94 33	99.85 108	99.69 35	99.98 32	99.99 23

test_8					99.92 31	98.88 32	99.96 34	98.43 133	94.35 124	99.69 37	99.85 30	95.94 33	99.85 108

PGM-MVS		98.34 48	98.13 51	98.99 74	99.92 31	97.00 111	99.75 156	99.50 18	93.90 151	99.37 75	99.76 63	93.24 109	100.00 1	97.75 139	99.96 46	99.98 48

ACMMP		97.74 81	97.44 83	98.66 94	99.92 31	96.13 147	99.18 249	99.45 19	94.84 105	96.41 183	99.71 83	91.40 150	99.99 36	97.99 123	98.03 161	99.87 87
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 52	98.43 133	96.48 59	99.80 17	99.93 11	97.44 12	100.00 1	99.92 12	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 150					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 150					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 52	98.56 92	97.56 25	99.44 67	99.85 30	95.38 46	100.00 1	99.31 51	99.99 21	99.87 87

APD-MVS		98.62 29	98.35 39	99.41 38	99.90 42	98.51 59	99.87 106	98.36 165	94.08 137	99.74 31	99.73 78	94.08 84	99.74 134	99.42 47	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 26	99.62 20	99.90 42	98.85 35	99.24 244	98.47 117	98.14 10	99.08 89	99.91 14	93.09 112	100.00 1	99.04 63	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 34				99.80 51	97.44 12	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	99.87 106	98.44 125	97.48 27	99.64 44	99.94 4	96.68 24	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 64

CSCG		97.10 109	97.04 101	97.27 185	99.89 45	91.92 268	99.90 92	99.07 34	88.67 297	95.26 205	99.82 46	93.17 111	99.98 43	98.15 114	99.47 108	99.90 83

ZNCC-MVS		98.31 49	98.03 56	99.17 55	99.88 49	97.59 88	99.94 68	98.44 125	94.31 127	98.50 119	99.82 46	93.06 113	99.99 36	98.30 109	99.99 21	99.93 76

SR-MVS		98.46 39	98.30 43	98.93 80	99.88 49	97.04 110	99.84 126	98.35 167	94.92 102	99.32 77	99.80 51	93.35 102	99.78 125	99.30 52	99.95 49	99.96 64

9.14			98.38 34		99.87 51		99.91 86	98.33 172	93.22 172	99.78 26	99.89 19	94.57 67	99.85 108	99.84 22	99.97 42

SMA-MVS		98.76 24	98.48 29	99.62 20	99.87 51	98.87 33	99.86 118	98.38 161	93.19 173	99.77 27	99.94 4	95.54 41	100.00 1	99.74 30	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 45	98.21 45	99.03 70	99.86 53	97.10 109	99.98 14	98.80 62	90.78 260	99.62 48	99.78 59	95.30 47	100.00 1	99.80 25	99.93 60	99.99 23

MTAPA		98.29 51	97.96 62	99.30 44	99.85 54	97.93 77	99.39 225	98.28 181	95.76 81	97.18 161	99.88 21	92.74 123	100.00 1	98.67 88	99.88 69	99.99 23

LS3D		95.84 161	95.11 171	98.02 138	99.85 54	95.10 189	98.74 296	98.50 114	87.22 318	93.66 223	99.86 26	87.45 211	99.95 69	90.94 261	99.81 81	99.02 204

HPM-MVS		97.96 64	97.72 72	98.68 92	99.84 56	96.39 134	99.90 92	98.17 193	92.61 197	98.62 114	99.57 107	91.87 146	99.67 145	98.87 77	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 52	98.11 53	98.75 89	99.83 57	96.59 126	99.40 221	98.51 108	95.29 94	98.51 118	99.76 63	93.60 99	99.71 138	98.53 98	99.52 103	99.95 71

save fliter					99.82 58	98.79 40	99.96 34	98.40 154	97.66 21

PLC	95.54 3	97.93 66	97.89 67	98.05 137	99.82 58	94.77 198	99.92 80	98.46 119	93.93 149	97.20 160	99.27 134	95.44 45	99.97 53	97.41 144	99.51 106	99.41 167
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 55	98.08 55	98.78 86	99.81 60	96.60 125	99.82 136	98.30 179	93.95 148	99.37 75	99.77 61	92.84 119	99.76 131	98.95 69	99.92 63	99.97 58

EI-MVSNet-UG-set		98.14 59	97.99 58	98.60 99	99.80 61	96.27 137	99.36 230	98.50 114	95.21 96	98.30 128	99.75 69	93.29 106	99.73 137	98.37 105	99.30 120	99.81 94

SR-MVS-dyc-post		98.31 49	98.17 48	98.71 90	99.79 62	96.37 135	99.76 153	98.31 176	94.43 119	99.40 72	99.75 69	93.28 107	99.78 125	98.90 75	99.92 63	99.97 58

RE-MVS-def			98.13 51		99.79 62	96.37 135	99.76 153	98.31 176	94.43 119	99.40 72	99.75 69	92.95 116		98.90 75	99.92 63	99.97 58

HPM-MVS_fast		97.80 76	97.50 81	98.68 92	99.79 62	96.42 130	99.88 103	98.16 197	91.75 229	98.94 95	99.54 110	91.82 148	99.65 147	97.62 142	99.99 21	99.99 23

SF-MVS		98.67 27	98.40 32	99.50 30	99.77 65	98.67 49	99.90 92	98.21 188	93.53 162	99.81 15	99.89 19	94.70 65	99.86 107	99.84 22	99.93 60	99.96 64

旧先验1					99.76 66	97.52 91		98.64 76			99.85 30	95.63 40			99.94 54	99.99 23

OMC-MVS		97.28 101	97.23 93	97.41 176	99.76 66	93.36 237	99.65 182	97.95 216	96.03 76	97.41 155	99.70 85	89.61 187	99.51 153	96.73 164	98.25 153	99.38 169

新几何1				99.42 37	99.75 68	98.27 63		98.63 80	92.69 192	99.55 56	99.82 46	94.40 70	100.00 1	91.21 253	99.94 54	99.99 23

MP-MVS-pluss		98.07 62	97.64 75	99.38 42	99.74 69	98.41 62	99.74 159	98.18 192	93.35 167	96.45 180	99.85 30	92.64 125	99.97 53	98.91 74	99.89 66	99.77 101
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 16	98.77 18	99.41 38	99.74 69	98.67 49	99.77 148	98.38 161	96.73 53	99.88 6	99.74 76	94.89 59	99.59 149	99.80 25	99.98 32	99.97 58
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 35	99.74 69	98.56 57		98.40 154		99.65 41		94.76 62	99.75 132		99.98 32	99.99 23

原ACMM1				98.96 77	99.73 72	96.99 112		98.51 108	94.06 140	99.62 48	99.85 30	94.97 58	99.96 61	95.11 184	99.95 49	99.92 81

TSAR-MVS + GP.		98.60 30	98.51 28	98.86 84	99.73 72	96.63 123	99.97 27	97.92 221	98.07 11	98.76 106	99.55 108	95.00 56	99.94 77	99.91 15	97.68 166	99.99 23

CANet		98.27 52	97.82 69	99.63 17	99.72 74	99.10 23	99.98 14	98.51 108	97.00 43	98.52 117	99.71 83	87.80 206	99.95 69	99.75 28	99.38 116	99.83 91

F-COLMAP		96.93 120	96.95 104	96.87 195	99.71 75	91.74 273	99.85 121	97.95 216	93.11 176	95.72 198	99.16 145	92.35 135	99.94 77	95.32 182	99.35 118	98.92 207

SD-MVS		98.92 17	98.70 19	99.56 25	99.70 76	98.73 46	99.94 68	98.34 171	96.38 65	99.81 15	99.76 63	94.59 66	99.98 43	99.84 22	99.96 46	99.97 58
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 56	99.12 5	95.59 227	99.67 77	86.91 347	99.95 52	98.89 49	97.60 22	99.90 3	99.76 63	96.54 27	99.98 43	99.94 11	99.82 79	99.88 85

ACMMP_NAP		98.49 37	98.14 50	99.54 27	99.66 78	98.62 55	99.85 121	98.37 164	94.68 111	99.53 59	99.83 43	92.87 118	100.00 1	98.66 90	99.84 74	99.99 23

DeepPCF-MVS	95.94 2	97.71 84	98.98 12	93.92 292	99.63 79	81.76 375	99.96 34	98.56 92	99.47 1	99.19 86	99.99 1	94.16 83	100.00 1	99.92 12	99.93 60	100.00 1

EPNet		98.49 37	98.40 32	98.77 88	99.62 80	96.80 120	99.90 92	99.51 17	97.60 22	99.20 84	99.36 128	93.71 96	99.91 89	97.99 123	98.71 141	99.61 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 21	98.53 27	99.76 10	99.59 81	99.33 8	99.99 4	99.76 6	98.39 3	99.39 74	99.80 51	90.49 175	99.96 61	99.89 16	99.43 114	99.98 48

PVSNet_BlendedMVS		96.05 155	95.82 151	96.72 200	99.59 81	96.99 112	99.95 52	99.10 31	94.06 140	98.27 129	95.80 296	89.00 198	99.95 69	99.12 58	87.53 290	93.24 347

PVSNet_Blended		97.94 65	97.64 75	98.83 85	99.59 81	96.99 112	100.00 1	99.10 31	95.38 91	98.27 129	99.08 148	89.00 198	99.95 69	99.12 58	99.25 122	99.57 141

PatchMatch-RL		96.04 156	95.40 160	97.95 140	99.59 81	95.22 184	99.52 205	99.07 34	93.96 147	96.49 179	98.35 220	82.28 257	99.82 120	90.15 277	99.22 125	98.81 214

dcpmvs_2		97.42 96	98.09 54	95.42 232	99.58 85	87.24 343	99.23 245	96.95 319	94.28 130	98.93 96	99.73 78	94.39 73	99.16 175	99.89 16	99.82 79	99.86 89

test222					99.55 86	97.41 99	99.34 231	98.55 98	91.86 224	99.27 82	99.83 43	93.84 93			99.95 49	99.99 23

CNLPA		97.76 80	97.38 85	98.92 81	99.53 87	96.84 117	99.87 106	98.14 201	93.78 154	96.55 178	99.69 87	92.28 137	99.98 43	97.13 150	99.44 112	99.93 76

API-MVS		97.86 70	97.66 74	98.47 112	99.52 88	95.41 175	99.47 214	98.87 52	91.68 230	98.84 99	99.85 30	92.34 136	99.99 36	98.44 101	99.96 46	100.00 1

PVSNet	91.05 13	97.13 108	96.69 116	98.45 114	99.52 88	95.81 155	99.95 52	99.65 12	94.73 108	99.04 91	99.21 141	84.48 244	99.95 69	94.92 189	98.74 140	99.58 139

114514_t		97.41 97	96.83 109	99.14 61	99.51 90	97.83 79	99.89 100	98.27 183	88.48 301	99.06 90	99.66 96	90.30 179	99.64 148	96.32 168	99.97 42	99.96 64

cl22		93.77 219	93.25 223	95.33 236	99.49 91	94.43 202	99.61 190	98.09 203	90.38 265	89.16 291	95.61 303	90.56 171	97.34 281	91.93 245	84.45 310	94.21 293

testdata				98.42 117	99.47 92	95.33 178		98.56 92	93.78 154	99.79 25	99.85 30	93.64 98	99.94 77	94.97 187	99.94 54	100.00 1

MAR-MVS		97.43 92	97.19 95	98.15 131	99.47 92	94.79 197	99.05 265	98.76 63	92.65 195	98.66 112	99.82 46	88.52 203	99.98 43	98.12 115	99.63 91	99.67 115
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		94.54 197	93.42 216	97.91 146	99.46 94	94.04 215	98.93 277	97.48 263	81.15 370	90.04 264	99.55 108	87.02 217	99.95 69	88.97 287	98.11 157	99.73 105

MVS_111021_LR		98.42 44	98.38 34	98.53 109	99.39 95	95.79 156	99.87 106	99.86 2	96.70 54	98.78 103	99.79 55	92.03 143	99.90 91	99.17 57	99.86 73	99.88 85

CHOSEN 280x420		99.01 13	99.03 10	98.95 78	99.38 96	98.87 33	98.46 313	99.42 22	97.03 42	99.02 92	99.09 147	99.35 1	98.21 245	99.73 32	99.78 82	99.77 101

MVS_111021_HR		98.72 25	98.62 22	99.01 73	99.36 97	97.18 104	99.93 75	99.90 1	96.81 51	98.67 111	99.77 61	93.92 88	99.89 96	99.27 53	99.94 54	99.96 64

DPM-MVS		98.83 21	98.46 30	99.97 1	99.33 98	99.92 1	99.96 34	98.44 125	97.96 14	99.55 56	99.94 4	97.18 19	100.00 1	93.81 217	99.94 54	99.98 48

TAPA-MVS	92.12 8	94.42 202	93.60 209	96.90 194	99.33 98	91.78 272	99.78 145	98.00 210	89.89 275	94.52 211	99.47 114	91.97 144	99.18 173	69.90 385	99.52 103	99.73 105
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS-test		97.88 68	97.94 63	97.70 159	99.28 100	95.20 185	99.98 14	97.15 298	95.53 88	99.62 48	99.79 55	92.08 142	98.38 228	98.75 84	99.28 121	99.52 152

test_fmvsm_n_1920		98.44 41	98.61 23	97.92 144	99.27 101	95.18 186	100.00 1	98.90 47	98.05 12	99.80 17	99.73 78	92.64 125	99.99 36	99.58 38	99.51 106	98.59 224

fmvsm_l_conf0.5_n_a		99.00 14	98.91 14	99.28 45	99.21 102	97.91 78	99.98 14	98.85 56	98.25 4	99.92 2	99.75 69	94.72 63	99.97 53	99.87 19	99.64 90	99.95 71

test_yl		97.83 72	97.37 86	99.21 49	99.18 103	97.98 74	99.64 186	99.27 27	91.43 239	97.88 143	98.99 157	95.84 37	99.84 116	98.82 79	95.32 219	99.79 97

DCV-MVSNet		97.83 72	97.37 86	99.21 49	99.18 103	97.98 74	99.64 186	99.27 27	91.43 239	97.88 143	98.99 157	95.84 37	99.84 116	98.82 79	95.32 219	99.79 97

MVS_0304		98.87 20	98.61 23	99.67 16	99.18 103	99.13 22	99.87 106	99.65 12	98.17 8	98.75 108	99.75 69	92.76 122	99.94 77	99.88 18	99.44 112	99.94 74

fmvsm_l_conf0.5_n		98.94 15	98.84 17	99.25 46	99.17 106	97.81 81	99.98 14	98.86 53	98.25 4	99.90 3	99.76 63	94.21 81	99.97 53	99.87 19	99.52 103	99.98 48

DeepC-MVS	94.51 4	96.92 121	96.40 126	98.45 114	99.16 107	95.90 153	99.66 181	98.06 206	96.37 68	94.37 214	99.49 113	83.29 253	99.90 91	97.63 141	99.61 96	99.55 143
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 33	98.22 44	99.50 30	99.15 108	98.65 53	100.00 1	98.58 87	97.70 20	98.21 133	99.24 139	92.58 128	99.94 77	98.63 93	99.94 54	99.92 81
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 78	97.91 65	97.43 175	99.10 109	94.42 203	99.99 4	97.10 303	95.07 97	99.68 38	99.75 69	92.95 116	98.34 232	98.38 103	99.14 127	99.54 147

Anonymous202405211		93.10 237	91.99 249	96.40 209	99.10 109	89.65 317	98.88 282	97.93 218	83.71 356	94.00 220	98.75 186	68.79 353	99.88 102	95.08 185	91.71 251	99.68 113

fmvsm_s_conf0.5_n		97.80 76	97.85 68	97.67 160	99.06 111	94.41 204	99.98 14	98.97 40	97.34 29	99.63 45	99.69 87	87.27 213	99.97 53	99.62 37	99.06 131	98.62 223

HyFIR lowres test		96.66 135	96.43 125	97.36 181	99.05 112	93.91 220	99.70 174	99.80 3	90.54 263	96.26 186	98.08 227	92.15 140	98.23 244	96.84 163	95.46 214	99.93 76

LFMVS		94.75 190	93.56 212	98.30 123	99.03 113	95.70 162	98.74 296	97.98 213	87.81 311	98.47 120	99.39 125	67.43 361	99.53 150	98.01 121	95.20 222	99.67 115

AllTest		92.48 251	91.64 254	95.00 246	99.01 114	88.43 332	98.94 276	96.82 333	86.50 327	88.71 296	98.47 212	74.73 330	99.88 102	85.39 325	96.18 196	96.71 254

TestCases				95.00 246	99.01 114	88.43 332		96.82 333	86.50 327	88.71 296	98.47 212	74.73 330	99.88 102	85.39 325	96.18 196	96.71 254

COLMAP_ROB	90.47 14	92.18 258	91.49 260	94.25 280	99.00 116	88.04 338	98.42 319	96.70 340	82.30 366	88.43 304	99.01 154	76.97 306	99.85 108	86.11 321	96.50 191	94.86 265
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvs1		95.35 175	95.68 156	94.36 276	98.99 117	84.98 357	99.96 34	96.65 342	97.60 22	99.73 32	98.96 163	71.58 343	99.93 85	98.31 108	99.37 117	98.17 231

HY-MVS	92.50 7	97.79 78	97.17 97	99.63 17	98.98 118	99.32 9	97.49 343	99.52 15	95.69 83	98.32 127	97.41 247	93.32 104	99.77 128	98.08 119	95.75 210	99.81 94

VNet		97.21 105	96.57 121	99.13 65	98.97 119	97.82 80	99.03 268	99.21 29	94.31 127	99.18 87	98.88 174	86.26 228	99.89 96	98.93 71	94.32 231	99.69 112

thres200		96.96 118	96.21 132	99.22 48	98.97 119	98.84 36	99.85 121	99.71 7	93.17 174	96.26 186	98.88 174	89.87 184	99.51 153	94.26 207	94.91 224	99.31 179

tfpn200view9		96.79 125	95.99 137	99.19 51	98.94 121	98.82 37	99.78 145	99.71 7	92.86 182	96.02 191	98.87 177	89.33 191	99.50 155	93.84 214	94.57 227	99.27 185

thres400		96.78 127	95.99 137	99.16 57	98.94 121	98.82 37	99.78 145	99.71 7	92.86 182	96.02 191	98.87 177	89.33 191	99.50 155	93.84 214	94.57 227	99.16 192

sasdasda		97.09 111	96.32 127	99.39 40	98.93 123	98.95 27	99.72 167	97.35 275	94.45 116	97.88 143	99.42 118	86.71 220	99.52 151	98.48 99	93.97 237	99.72 107

Anonymous20231211		89.86 308	88.44 315	94.13 283	98.93 123	90.68 296	98.54 310	98.26 184	76.28 383	86.73 325	95.54 307	70.60 349	97.56 274	90.82 264	80.27 345	94.15 301

canonicalmvs		97.09 111	96.32 127	99.39 40	98.93 123	98.95 27	99.72 167	97.35 275	94.45 116	97.88 143	99.42 118	86.71 220	99.52 151	98.48 99	93.97 237	99.72 107

SDMVSNet		94.80 186	93.96 200	97.33 183	98.92 126	95.42 174	99.59 192	98.99 37	92.41 208	92.55 238	97.85 237	75.81 320	98.93 187	97.90 129	91.62 252	97.64 243

sd_testset		93.55 226	92.83 229	95.74 225	98.92 126	90.89 293	98.24 325	98.85 56	92.41 208	92.55 238	97.85 237	71.07 348	98.68 204	93.93 211	91.62 252	97.64 243

EPNet_dtu		95.71 165	95.39 161	96.66 202	98.92 126	93.41 234	99.57 197	98.90 47	96.19 74	97.52 151	98.56 204	92.65 124	97.36 279	77.89 367	98.33 148	99.20 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 61	97.60 77	99.60 22	98.92 126	99.28 17	99.89 100	99.52 15	95.58 86	98.24 132	99.39 125	93.33 103	99.74 134	97.98 125	95.58 213	99.78 100

CHOSEN 1792x2688		96.81 124	96.53 122	97.64 162	98.91 130	93.07 239	99.65 182	99.80 3	95.64 84	95.39 202	98.86 179	84.35 246	99.90 91	96.98 157	99.16 126	99.95 71

thres100view900		96.74 130	95.92 147	99.18 52	98.90 131	98.77 42	99.74 159	99.71 7	92.59 199	95.84 194	98.86 179	89.25 193	99.50 155	93.84 214	94.57 227	99.27 185

thres600view7		96.69 133	95.87 150	99.14 61	98.90 131	98.78 41	99.74 159	99.71 7	92.59 199	95.84 194	98.86 179	89.25 193	99.50 155	93.44 226	94.50 230	99.16 192

MSDG		94.37 204	93.36 220	97.40 177	98.88 133	93.95 219	99.37 228	97.38 273	85.75 338	90.80 257	99.17 144	84.11 248	99.88 102	86.35 318	98.43 146	98.36 229

MGCFI-Net		97.00 116	96.22 131	99.34 43	98.86 134	98.80 39	99.67 179	97.30 282	94.31 127	97.77 147	99.41 122	86.36 226	99.50 155	98.38 103	93.90 239	99.72 107

h-mvs33		94.92 184	94.36 189	96.59 204	98.85 135	91.29 284	98.93 277	98.94 41	95.90 77	98.77 104	98.42 218	90.89 164	99.77 128	97.80 132	70.76 379	98.72 220

Anonymous20240529		92.10 259	90.65 271	96.47 205	98.82 136	90.61 298	98.72 298	98.67 73	75.54 387	93.90 222	98.58 202	66.23 365	99.90 91	94.70 198	90.67 254	98.90 210

PVSNet_Blended_VisFu		97.27 102	96.81 110	98.66 94	98.81 137	96.67 122	99.92 80	98.64 76	94.51 115	96.38 184	98.49 208	89.05 197	99.88 102	97.10 153	98.34 147	99.43 165

PS-MVSNAJ		98.44 41	98.20 46	99.16 57	98.80 138	98.92 29	99.54 203	98.17 193	97.34 29	99.85 9	99.85 30	91.20 153	99.89 96	99.41 48	99.67 88	98.69 221

CANet_DTU		96.76 128	96.15 133	98.60 99	98.78 139	97.53 90	99.84 126	97.63 242	97.25 37	99.20 84	99.64 99	81.36 266	99.98 43	92.77 237	98.89 134	98.28 230

mvsany_test1		97.82 74	97.90 66	97.55 167	98.77 140	93.04 242	99.80 142	97.93 218	96.95 45	99.61 54	99.68 93	90.92 161	99.83 118	99.18 56	98.29 152	99.80 96

alignmvs		97.81 75	97.33 89	99.25 46	98.77 140	98.66 51	99.99 4	98.44 125	94.40 123	98.41 122	99.47 114	93.65 97	99.42 164	98.57 96	94.26 233	99.67 115

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 52	98.72 142	97.71 83	99.98 14	98.44 125	96.85 46	99.80 17	99.91 14	97.57 6	99.85 108	99.44 46	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 57	97.97 59	99.02 72	98.69 143	98.66 51	99.52 205	98.08 205	97.05 41	99.86 7	99.86 26	90.65 167	99.71 138	99.39 50	98.63 142	98.69 221

miper_enhance_ethall		94.36 206	93.98 199	95.49 228	98.68 144	95.24 182	99.73 164	97.29 285	93.28 171	89.86 269	95.97 294	94.37 74	97.05 302	92.20 241	84.45 310	94.19 294

ETVMVS		97.03 115	96.64 117	98.20 127	98.67 145	97.12 108	99.89 100	98.57 89	91.10 250	98.17 134	98.59 199	93.86 92	98.19 246	95.64 179	95.24 221	99.28 184

test2506		97.53 88	97.19 95	98.58 102	98.66 146	96.90 116	98.81 291	99.77 5	94.93 100	97.95 139	98.96 163	92.51 130	99.20 171	94.93 188	98.15 154	99.64 121

ECVR-MVS		95.66 168	95.05 173	97.51 170	98.66 146	93.71 224	98.85 288	98.45 120	94.93 100	96.86 169	98.96 163	75.22 326	99.20 171	95.34 181	98.15 154	99.64 121

fmvsm_s_conf0.5_n_a		97.73 83	97.72 72	97.77 154	98.63 148	94.26 209	99.96 34	98.92 46	97.18 39	99.75 29	99.69 87	87.00 218	99.97 53	99.46 44	98.89 134	99.08 200

MVSMamba_pp		98.05 63	97.76 71	98.92 81	98.56 149	98.06 69	99.92 80	97.75 234	96.28 70	99.71 34	98.43 217	90.37 177	99.11 176	98.99 68	99.88 69	99.58 139

testing222		97.08 114	96.75 113	98.06 136	98.56 149	96.82 118	99.85 121	98.61 82	92.53 203	98.84 99	98.84 183	93.36 101	98.30 236	95.84 176	94.30 232	99.05 202

test1111		95.57 170	94.98 176	97.37 179	98.56 149	93.37 236	98.86 286	98.45 120	94.95 99	96.63 175	98.95 168	75.21 327	99.11 176	95.02 186	98.14 156	99.64 121

MVSTER		95.53 171	95.22 167	96.45 207	98.56 149	97.72 82	99.91 86	97.67 239	92.38 210	91.39 249	97.14 254	97.24 16	97.30 285	94.80 194	87.85 285	94.34 285

mamv4		97.88 68	97.52 80	98.95 78	98.55 153	98.15 64	99.93 75	97.74 235	94.01 143	99.65 41	98.44 216	90.50 174	99.11 176	99.00 67	99.89 66	99.59 132

VDD-MVS		93.77 219	92.94 227	96.27 213	98.55 153	90.22 307	98.77 295	97.79 232	90.85 256	96.82 171	99.42 118	61.18 382	99.77 128	98.95 69	94.13 234	98.82 213

tpmvs		94.28 208	93.57 211	96.40 209	98.55 153	91.50 282	95.70 378	98.55 98	87.47 313	92.15 241	94.26 353	91.42 149	98.95 186	88.15 297	95.85 206	98.76 216

UGNet		95.33 176	94.57 185	97.62 165	98.55 153	94.85 193	98.67 304	99.32 26	95.75 82	96.80 172	96.27 285	72.18 340	99.96 61	94.58 201	99.05 132	98.04 235
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	95.18 178	94.10 196	98.43 116	98.55 153	95.99 151	97.91 338	97.31 281	90.35 267	89.48 280	99.22 140	85.19 237	99.89 96	90.40 274	98.47 145	99.41 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS		96.79 125	96.72 114	97.00 190	98.51 158	93.70 225	99.71 170	98.60 84	92.96 178	97.09 162	98.34 221	96.67 26	98.85 190	92.11 243	96.50 191	98.44 226

iter_conf05_11		97.49 90	97.34 88	97.93 143	98.51 158	95.15 188	99.58 194	97.64 241	93.76 156	99.26 83	98.38 219	90.51 173	99.10 179	98.58 95	99.88 69	99.54 147

test_vis1_n_1920		95.44 173	95.31 164	95.82 223	98.50 160	88.74 326	99.98 14	97.30 282	97.84 16	99.85 9	99.19 142	66.82 363	99.97 53	98.82 79	99.46 110	98.76 216

BH-w/o		95.71 165	95.38 162	96.68 201	98.49 161	92.28 259	99.84 126	97.50 261	92.12 216	92.06 244	98.79 184	84.69 242	98.67 205	95.29 183	99.66 89	99.09 198

baseline1		95.78 162	94.86 178	98.54 107	98.47 162	98.07 68	99.06 261	97.99 211	92.68 193	94.13 219	98.62 198	93.28 107	98.69 203	93.79 219	85.76 298	98.84 212

EPMVS		96.53 139	96.01 136	98.09 134	98.43 163	96.12 149	96.36 365	99.43 21	93.53 162	97.64 149	95.04 331	94.41 69	98.38 228	91.13 255	98.11 157	99.75 103

kuosan		93.17 234	92.60 235	94.86 253	98.40 164	89.54 319	98.44 315	98.53 104	84.46 351	88.49 300	97.92 235	90.57 170	97.05 302	83.10 340	93.49 242	97.99 236

sss		97.57 87	97.03 102	99.18 52	98.37 165	98.04 71	99.73 164	99.38 23	93.46 164	98.76 106	99.06 150	91.21 152	99.89 96	96.33 167	97.01 183	99.62 126

testing11		97.48 91	97.27 91	98.10 133	98.36 166	96.02 150	99.92 80	98.45 120	93.45 166	98.15 135	98.70 189	95.48 44	99.22 167	97.85 131	95.05 223	99.07 201

BH-untuned		95.18 178	94.83 179	96.22 214	98.36 166	91.22 285	99.80 142	97.32 280	90.91 254	91.08 253	98.67 191	83.51 250	98.54 211	94.23 208	99.61 96	98.92 207

testing91		97.16 107	96.90 106	97.97 139	98.35 168	95.67 165	99.91 86	98.42 145	92.91 181	97.33 157	98.72 187	94.81 61	99.21 168	96.98 157	94.63 226	99.03 203

testing99		97.17 106	96.91 105	97.95 140	98.35 168	95.70 162	99.91 86	98.43 133	92.94 179	97.36 156	98.72 187	94.83 60	99.21 168	97.00 155	94.64 225	98.95 206

ET-MVSNet_ETH3D		94.37 204	93.28 222	97.64 162	98.30 170	97.99 73	99.99 4	97.61 247	94.35 124	71.57 390	99.45 117	96.23 30	95.34 359	96.91 162	85.14 305	99.59 132

AUN-MVS		93.28 231	92.60 235	95.34 235	98.29 171	90.09 310	99.31 235	98.56 92	91.80 228	96.35 185	98.00 231	89.38 190	98.28 239	92.46 238	69.22 384	97.64 243

FMVSNet3		92.69 247	91.58 256	95.99 218	98.29 171	97.42 98	99.26 243	97.62 244	89.80 276	89.68 273	95.32 321	81.62 264	96.27 339	87.01 314	85.65 299	94.29 287

PMMVS		96.76 128	96.76 112	96.76 198	98.28 173	92.10 263	99.91 86	97.98 213	94.12 135	99.53 59	99.39 125	86.93 219	98.73 198	96.95 160	97.73 164	99.45 162

hse-mvs2		94.38 203	94.08 197	95.31 237	98.27 174	90.02 311	99.29 240	98.56 92	95.90 77	98.77 104	98.00 231	90.89 164	98.26 243	97.80 132	69.20 385	97.64 243

PVSNet_0	88.03 19	91.80 266	90.27 280	96.38 211	98.27 174	90.46 302	99.94 68	99.61 14	93.99 145	86.26 335	97.39 249	71.13 347	99.89 96	98.77 82	67.05 390	98.79 215

UA-Net		96.54 138	95.96 143	98.27 124	98.23 176	95.71 161	98.00 336	98.45 120	93.72 158	98.41 122	99.27 134	88.71 202	99.66 146	91.19 254	97.69 165	99.44 164

test_cas_vis1_n_1920		96.59 137	96.23 130	97.65 161	98.22 177	94.23 210	99.99 4	97.25 289	97.77 17	99.58 55	99.08 148	77.10 303	99.97 53	97.64 140	99.45 111	98.74 218

FE-MVS		95.70 167	95.01 175	97.79 151	98.21 178	94.57 199	95.03 379	98.69 68	88.90 292	97.50 153	96.19 287	92.60 127	99.49 160	89.99 279	97.94 163	99.31 179

GG-mvs-BLEND				98.54 107	98.21 178	98.01 72	93.87 384	98.52 105		97.92 140	97.92 235	99.02 2	97.94 262	98.17 112	99.58 99	99.67 115

mvs_anonymous		95.65 169	95.03 174	97.53 168	98.19 180	95.74 159	99.33 232	97.49 262	90.87 255	90.47 260	97.10 256	88.23 204	97.16 293	95.92 174	97.66 167	99.68 113

MVS_Test		96.46 141	95.74 152	98.61 98	98.18 181	97.23 102	99.31 235	97.15 298	91.07 251	98.84 99	97.05 260	88.17 205	98.97 184	94.39 203	97.50 169	99.61 129

bld_raw_dy_0_64		95.27 177	94.55 186	97.45 172	98.17 182	96.49 127	96.67 360	97.42 268	79.97 375	91.83 245	98.04 229	90.64 168	99.01 183	98.60 94	99.57 100	96.85 252

BH-RMVSNet		95.18 178	94.31 192	97.80 149	98.17 182	95.23 183	99.76 153	97.53 257	92.52 204	94.27 217	99.25 138	76.84 308	98.80 192	90.89 263	99.54 102	99.35 174

dongtai		91.55 272	91.13 265	92.82 322	98.16 184	86.35 348	99.47 214	98.51 108	83.24 359	85.07 344	97.56 243	90.33 178	94.94 365	76.09 375	91.73 250	97.18 250

RPSCF		91.80 266	92.79 231	88.83 355	98.15 185	69.87 393	98.11 332	96.60 344	83.93 354	94.33 215	99.27 134	79.60 286	99.46 163	91.99 244	93.16 247	97.18 250

ETV-MVS		97.92 67	97.80 70	98.25 125	98.14 186	96.48 128	99.98 14	97.63 242	95.61 85	99.29 81	99.46 116	92.55 129	98.82 191	99.02 66	98.54 143	99.46 160

IS-MVSNet		96.29 151	95.90 148	97.45 172	98.13 187	94.80 196	99.08 256	97.61 247	92.02 221	95.54 201	98.96 163	90.64 168	98.08 251	93.73 222	97.41 173	99.47 159

test_fmvsmconf_n		98.43 43	98.32 40	98.78 86	98.12 188	96.41 131	99.99 4	98.83 59	98.22 6	99.67 39	99.64 99	91.11 157	99.94 77	99.67 36	99.62 92	99.98 48

ab-mvs		94.69 191	93.42 216	98.51 110	98.07 189	96.26 138	96.49 363	98.68 70	90.31 268	94.54 210	97.00 262	76.30 315	99.71 138	95.98 173	93.38 245	99.56 142

XVG-OURS-SEG-HR		94.79 187	94.70 184	95.08 243	98.05 190	89.19 321	99.08 256	97.54 255	93.66 159	94.87 208	99.58 106	78.78 294	99.79 123	97.31 146	93.40 244	96.25 258

EIA-MVS		97.53 88	97.46 82	97.76 156	98.04 191	94.84 194	99.98 14	97.61 247	94.41 122	97.90 141	99.59 104	92.40 134	98.87 188	98.04 120	99.13 128	99.59 132

XVG-OURS		94.82 185	94.74 182	95.06 244	98.00 192	89.19 321	99.08 256	97.55 253	94.10 136	94.71 209	99.62 102	80.51 278	99.74 134	96.04 172	93.06 249	96.25 258

dp		95.05 181	94.43 188	96.91 193	97.99 193	92.73 249	96.29 368	97.98 213	89.70 277	95.93 193	94.67 344	93.83 94	98.45 217	86.91 317	96.53 190	99.54 147

tpmrst		96.27 153	95.98 139	97.13 187	97.96 194	93.15 238	96.34 366	98.17 193	92.07 217	98.71 110	95.12 329	93.91 89	98.73 198	94.91 191	96.62 188	99.50 156

TR-MVS		94.54 197	93.56 212	97.49 171	97.96 194	94.34 207	98.71 299	97.51 260	90.30 269	94.51 212	98.69 190	75.56 321	98.77 195	92.82 236	95.99 200	99.35 174

Vis-MVSNet (Re-imp)		96.32 148	95.98 139	97.35 182	97.93 196	94.82 195	99.47 214	98.15 200	91.83 225	95.09 206	99.11 146	91.37 151	97.47 277	93.47 225	97.43 170	99.74 104

MDTV_nov1_ep13			95.69 154		97.90 197	94.15 212	95.98 374	98.44 125	93.12 175	97.98 138	95.74 298	95.10 50	98.58 208	90.02 278	96.92 185

Fast-Effi-MVS+		95.02 182	94.19 194	97.52 169	97.88 198	94.55 200	99.97 27	97.08 306	88.85 294	94.47 213	97.96 234	84.59 243	98.41 220	89.84 281	97.10 178	99.59 132

ADS-MVSNet2		93.80 218	93.88 203	93.55 305	97.87 199	85.94 351	94.24 380	96.84 330	90.07 271	96.43 181	94.48 349	90.29 180	95.37 358	87.44 304	97.23 175	99.36 172

ADS-MVSNet		94.79 187	94.02 198	97.11 189	97.87 199	93.79 221	94.24 380	98.16 197	90.07 271	96.43 181	94.48 349	90.29 180	98.19 246	87.44 304	97.23 175	99.36 172

Effi-MVS+		96.30 150	95.69 154	98.16 128	97.85 201	96.26 138	97.41 345	97.21 291	90.37 266	98.65 113	98.58 202	86.61 223	98.70 202	97.11 152	97.37 174	99.52 152

PatchmatchNet		95.94 158	95.45 159	97.39 178	97.83 202	94.41 204	96.05 372	98.40 154	92.86 182	97.09 162	95.28 326	94.21 81	98.07 253	89.26 285	98.11 157	99.70 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 194	93.61 207	97.74 158	97.82 203	96.26 138	99.96 34	97.78 233	85.76 336	94.00 220	97.54 244	76.95 307	99.21 168	97.23 148	95.43 216	97.76 242

1112_ss		96.01 157	95.20 168	98.42 117	97.80 204	96.41 131	99.65 182	96.66 341	92.71 190	92.88 234	99.40 123	92.16 139	99.30 165	91.92 246	93.66 240	99.55 143

Test_1112_low_res		95.72 163	94.83 179	98.42 117	97.79 205	96.41 131	99.65 182	96.65 342	92.70 191	92.86 235	96.13 290	92.15 140	99.30 165	91.88 247	93.64 241	99.55 143

Effi-MVS+-dtu		94.53 199	95.30 165	92.22 328	97.77 206	82.54 368	99.59 192	97.06 308	94.92 102	95.29 204	95.37 319	85.81 230	97.89 263	94.80 194	97.07 179	96.23 260

tpm cat1		93.51 227	92.52 241	96.47 205	97.77 206	91.47 283	96.13 370	98.06 206	80.98 371	92.91 233	93.78 357	89.66 185	98.87 188	87.03 313	96.39 194	99.09 198

FA-MVS(test-final)		95.86 159	95.09 172	98.15 131	97.74 208	95.62 167	96.31 367	98.17 193	91.42 241	96.26 186	96.13 290	90.56 171	99.47 162	92.18 242	97.07 179	99.35 174

xiu_mvs_v1_base_debu		97.43 92	97.06 98	98.55 104	97.74 208	98.14 65	99.31 235	97.86 227	96.43 62	99.62 48	99.69 87	85.56 232	99.68 142	99.05 60	98.31 149	97.83 238

xiu_mvs_v1_base		97.43 92	97.06 98	98.55 104	97.74 208	98.14 65	99.31 235	97.86 227	96.43 62	99.62 48	99.69 87	85.56 232	99.68 142	99.05 60	98.31 149	97.83 238

xiu_mvs_v1_base_debi		97.43 92	97.06 98	98.55 104	97.74 208	98.14 65	99.31 235	97.86 227	96.43 62	99.62 48	99.69 87	85.56 232	99.68 142	99.05 60	98.31 149	97.83 238

EPP-MVSNet		96.69 133	96.60 119	96.96 192	97.74 208	93.05 241	99.37 228	98.56 92	88.75 295	95.83 196	99.01 154	96.01 31	98.56 209	96.92 161	97.20 177	99.25 187

gg-mvs-nofinetune		93.51 227	91.86 253	98.47 112	97.72 213	97.96 76	92.62 388	98.51 108	74.70 390	97.33 157	69.59 403	98.91 3	97.79 266	97.77 137	99.56 101	99.67 115

IB-MVS	92.85 6	94.99 183	93.94 201	98.16 128	97.72 213	95.69 164	99.99 4	98.81 60	94.28 130	92.70 236	96.90 264	95.08 51	99.17 174	96.07 171	73.88 374	99.60 131
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 97	97.02 103	98.59 101	97.71 215	97.52 91	99.97 27	98.54 101	91.83 225	97.45 154	99.04 151	97.50 7	99.10 179	94.75 196	96.37 195	99.16 192

Syy-MVS		90.00 306	90.63 272	88.11 362	97.68 216	74.66 390	99.71 170	98.35 167	90.79 258	92.10 242	98.67 191	79.10 292	93.09 382	63.35 396	95.95 203	96.59 256

myMVS_eth3d		94.46 201	94.76 181	93.55 305	97.68 216	90.97 288	99.71 170	98.35 167	90.79 258	92.10 242	98.67 191	92.46 133	93.09 382	87.13 310	95.95 203	96.59 256

test_fmvs1_n		94.25 209	94.36 189	93.92 292	97.68 216	83.70 363	99.90 92	96.57 345	97.40 28	99.67 39	98.88 174	61.82 379	99.92 88	98.23 110	99.13 128	98.14 234

diffmvs		97.00 116	96.64 117	98.09 134	97.64 219	96.17 146	99.81 138	97.19 292	94.67 112	98.95 94	99.28 131	86.43 224	98.76 196	98.37 105	97.42 172	99.33 177
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		95.72 163	95.15 170	97.45 172	97.62 220	94.28 208	99.28 241	98.24 185	94.27 132	96.84 170	98.94 170	79.39 287	98.76 196	93.25 227	98.49 144	99.30 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 109	96.72 114	98.22 126	97.60 221	96.70 121	99.92 80	98.54 101	91.11 249	97.07 164	98.97 161	97.47 10	99.03 181	93.73 222	96.09 198	98.92 207

miper_ehance_all_eth		93.16 235	92.60 235	94.82 254	97.57 222	93.56 229	99.50 209	97.07 307	88.75 295	88.85 295	95.52 309	90.97 160	96.74 320	90.77 265	84.45 310	94.17 295

testing3		93.92 213	94.23 193	92.99 319	97.54 223	90.23 306	99.99 4	99.16 30	90.57 262	91.33 252	98.63 197	92.99 114	92.52 386	82.46 344	95.39 217	96.22 261

LCM-MVSNet-Re		92.31 255	92.60 235	91.43 335	97.53 224	79.27 385	99.02 269	91.83 399	92.07 217	80.31 365	94.38 352	83.50 251	95.48 356	97.22 149	97.58 168	99.54 147

GBi-Net		90.88 283	89.82 289	94.08 284	97.53 224	91.97 264	98.43 316	96.95 319	87.05 319	89.68 273	94.72 340	71.34 344	96.11 344	87.01 314	85.65 299	94.17 295

test1		90.88 283	89.82 289	94.08 284	97.53 224	91.97 264	98.43 316	96.95 319	87.05 319	89.68 273	94.72 340	71.34 344	96.11 344	87.01 314	85.65 299	94.17 295

FMVSNet2		91.02 280	89.56 294	95.41 233	97.53 224	95.74 159	98.98 271	97.41 271	87.05 319	88.43 304	95.00 334	71.34 344	96.24 341	85.12 327	85.21 304	94.25 290

tttt0517		96.85 122	96.49 123	97.92 144	97.48 228	95.89 154	99.85 121	98.54 101	90.72 261	96.63 175	98.93 172	97.47 10	99.02 182	93.03 234	95.76 209	98.85 211

casdiffmvs_mvg		96.43 142	95.94 145	97.89 148	97.44 229	95.47 171	99.86 118	97.29 285	93.35 167	96.03 190	99.19 142	85.39 235	98.72 200	97.89 130	97.04 181	99.49 158
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 99	97.24 92	97.80 149	97.41 230	95.64 166	99.99 4	97.06 308	94.59 113	99.63 45	99.32 130	89.20 196	98.14 248	98.76 83	99.23 124	99.62 126

c3_l		92.53 250	91.87 252	94.52 266	97.40 231	92.99 243	99.40 221	96.93 324	87.86 309	88.69 298	95.44 313	89.95 183	96.44 332	90.45 271	80.69 341	94.14 304

fmvsm_s_conf0.1_n		97.30 100	97.21 94	97.60 166	97.38 232	94.40 206	99.90 92	98.64 76	96.47 61	99.51 63	99.65 98	84.99 240	99.93 85	99.22 55	99.09 130	98.46 225

CDS-MVSNet		96.34 147	96.07 134	97.13 187	97.37 233	94.96 191	99.53 204	97.91 222	91.55 233	95.37 203	98.32 222	95.05 53	97.13 296	93.80 218	95.75 210	99.30 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 130	96.26 129	98.16 128	97.36 234	96.48 128	99.96 34	98.29 180	91.93 222	95.77 197	98.07 228	95.54 41	98.29 237	90.55 269	98.89 134	99.70 110

miper_lstm_enhance		91.81 263	91.39 262	93.06 318	97.34 235	89.18 323	99.38 226	96.79 335	86.70 326	87.47 317	95.22 327	90.00 182	95.86 353	88.26 295	81.37 331	94.15 301

baseline		96.43 142	95.98 139	97.76 156	97.34 235	95.17 187	99.51 207	97.17 295	93.92 150	96.90 168	99.28 131	85.37 236	98.64 206	97.50 143	96.86 187	99.46 160

cl____		92.31 255	91.58 256	94.52 266	97.33 237	92.77 245	99.57 197	96.78 336	86.97 323	87.56 315	95.51 310	89.43 189	96.62 325	88.60 290	82.44 323	94.16 300

DIV-MVS_self_test		92.32 254	91.60 255	94.47 270	97.31 238	92.74 247	99.58 194	96.75 337	86.99 322	87.64 313	95.54 307	89.55 188	96.50 329	88.58 291	82.44 323	94.17 295

casdiffmvs		96.42 144	95.97 142	97.77 154	97.30 239	94.98 190	99.84 126	97.09 305	93.75 157	96.58 177	99.26 137	85.07 238	98.78 194	97.77 137	97.04 181	99.54 147
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		94.36 206	93.48 214	96.99 191	97.29 240	93.54 230	99.96 34	96.72 339	88.35 304	93.43 224	98.94 170	82.05 258	98.05 254	88.12 299	96.48 193	99.37 171

eth_miper_zixun_eth		92.41 253	91.93 250	93.84 296	97.28 241	90.68 296	98.83 289	96.97 318	88.57 300	89.19 290	95.73 300	89.24 195	96.69 323	89.97 280	81.55 329	94.15 301

MVSFormer		96.94 119	96.60 119	97.95 140	97.28 241	97.70 85	99.55 201	97.27 287	91.17 246	99.43 68	99.54 110	90.92 161	96.89 313	94.67 199	99.62 92	99.25 187

lupinMVS		97.85 71	97.60 77	98.62 97	97.28 241	97.70 85	99.99 4	97.55 253	95.50 90	99.43 68	99.67 94	90.92 161	98.71 201	98.40 102	99.62 92	99.45 162

SCA		94.69 191	93.81 205	97.33 183	97.10 244	94.44 201	98.86 286	98.32 174	93.30 170	96.17 189	95.59 305	76.48 313	97.95 260	91.06 257	97.43 170	99.59 132

TAMVS		95.85 160	95.58 157	96.65 203	97.07 245	93.50 231	99.17 250	97.82 231	91.39 243	95.02 207	98.01 230	92.20 138	97.30 285	93.75 221	95.83 207	99.14 195

Fast-Effi-MVS+-dtu		93.72 222	93.86 204	93.29 310	97.06 246	86.16 349	99.80 142	96.83 331	92.66 194	92.58 237	97.83 239	81.39 265	97.67 271	89.75 282	96.87 186	96.05 263

CostFormer		96.10 154	95.88 149	96.78 197	97.03 247	92.55 255	97.08 353	97.83 230	90.04 273	98.72 109	94.89 338	95.01 55	98.29 237	96.54 166	95.77 208	99.50 156

test_fmvsmvis_n_1920		97.67 85	97.59 79	97.91 146	97.02 248	95.34 177	99.95 52	98.45 120	97.87 15	97.02 165	99.59 104	89.64 186	99.98 43	99.41 48	99.34 119	98.42 227

test-LLR		96.47 140	96.04 135	97.78 152	97.02 248	95.44 172	99.96 34	98.21 188	94.07 138	95.55 199	96.38 281	93.90 90	98.27 241	90.42 272	98.83 138	99.64 121

test-mter		96.39 145	95.93 146	97.78 152	97.02 248	95.44 172	99.96 34	98.21 188	91.81 227	95.55 199	96.38 281	95.17 48	98.27 241	90.42 272	98.83 138	99.64 121

gm-plane-assit					96.97 251	93.76 223			91.47 237		98.96 163		98.79 193	94.92 189

WB-MVSnew		92.90 241	92.77 232	93.26 312	96.95 252	93.63 227	99.71 170	98.16 197	91.49 234	94.28 216	98.14 225	81.33 267	96.48 330	79.47 359	95.46 214	89.68 383

QAPM		95.40 174	94.17 195	99.10 66	96.92 253	97.71 83	99.40 221	98.68 70	89.31 280	88.94 294	98.89 173	82.48 256	99.96 61	93.12 233	99.83 75	99.62 126

KD-MVS_2432*1600		88.00 325	86.10 329	93.70 301	96.91 254	94.04 215	97.17 350	97.12 301	84.93 346	81.96 356	92.41 368	92.48 131	94.51 370	79.23 360	52.68 402	92.56 357

miper_refine_blended		88.00 325	86.10 329	93.70 301	96.91 254	94.04 215	97.17 350	97.12 301	84.93 346	81.96 356	92.41 368	92.48 131	94.51 370	79.23 360	52.68 402	92.56 357

tpm2		95.47 172	95.18 169	96.35 212	96.91 254	91.70 277	96.96 356	97.93 218	88.04 308	98.44 121	95.40 315	93.32 104	97.97 257	94.00 210	95.61 212	99.38 169

FMVSNet5		88.32 322	87.47 324	90.88 338	96.90 257	88.39 334	97.28 347	95.68 366	82.60 365	84.67 345	92.40 370	79.83 284	91.16 391	76.39 374	81.51 330	93.09 349

3Dnovator+	91.53 11	96.31 149	95.24 166	99.52 28	96.88 258	98.64 54	99.72 167	98.24 185	95.27 95	88.42 306	98.98 159	82.76 255	99.94 77	97.10 153	99.83 75	99.96 64

Patchmatch-test		92.65 249	91.50 259	96.10 217	96.85 259	90.49 301	91.50 393	97.19 292	82.76 364	90.23 261	95.59 305	95.02 54	98.00 256	77.41 369	96.98 184	99.82 92

MVS		96.60 136	95.56 158	99.72 13	96.85 259	99.22 20	98.31 322	98.94 41	91.57 232	90.90 256	99.61 103	86.66 222	99.96 61	97.36 145	99.88 69	99.99 23

3Dnovator	91.47 12	96.28 152	95.34 163	99.08 67	96.82 261	97.47 96	99.45 218	98.81 60	95.52 89	89.39 281	99.00 156	81.97 259	99.95 69	97.27 147	99.83 75	99.84 90

EI-MVSNet		93.73 221	93.40 219	94.74 255	96.80 262	92.69 250	99.06 261	97.67 239	88.96 289	91.39 249	99.02 152	88.75 201	97.30 285	91.07 256	87.85 285	94.22 291

CVMVSNet		94.68 193	94.94 177	93.89 295	96.80 262	86.92 346	99.06 261	98.98 38	94.45 116	94.23 218	99.02 152	85.60 231	95.31 360	90.91 262	95.39 217	99.43 165

IterMVS-LS		92.69 247	92.11 246	94.43 274	96.80 262	92.74 247	99.45 218	96.89 327	88.98 287	89.65 276	95.38 318	88.77 200	96.34 336	90.98 260	82.04 326	94.22 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.91 282	90.17 284	93.12 315	96.78 265	90.42 304	98.89 280	97.05 310	89.03 284	86.49 330	95.42 314	76.59 311	95.02 362	87.22 309	84.09 313	93.93 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 123	95.96 143	99.48 34	96.74 266	98.52 58	98.31 322	98.86 53	95.82 79	89.91 267	98.98 159	87.49 210	99.96 61	97.80 132	99.73 85	99.96 64

IterMVS-SCA-FT		90.85 285	90.16 285	92.93 320	96.72 267	89.96 312	98.89 280	96.99 314	88.95 290	86.63 327	95.67 301	76.48 313	95.00 363	87.04 312	84.04 316	93.84 328

MVS-HIRNet		86.22 332	83.19 345	95.31 237	96.71 268	90.29 305	92.12 390	97.33 279	62.85 397	86.82 324	70.37 402	69.37 352	97.49 276	75.12 377	97.99 162	98.15 232

VDDNet		93.12 236	91.91 251	96.76 198	96.67 269	92.65 253	98.69 302	98.21 188	82.81 363	97.75 148	99.28 131	61.57 380	99.48 161	98.09 118	94.09 235	98.15 232

dmvs_re		93.20 233	93.15 224	93.34 308	96.54 270	83.81 362	98.71 299	98.51 108	91.39 243	92.37 240	98.56 204	78.66 296	97.83 265	93.89 212	89.74 255	98.38 228

MIMVSNet		90.30 298	88.67 312	95.17 242	96.45 271	91.64 279	92.39 389	97.15 298	85.99 333	90.50 259	93.19 364	66.95 362	94.86 367	82.01 348	93.43 243	99.01 205

CR-MVSNet		93.45 230	92.62 234	95.94 219	96.29 272	92.66 251	92.01 391	96.23 355	92.62 196	96.94 166	93.31 362	91.04 158	96.03 349	79.23 360	95.96 201	99.13 196

RPMNet		89.76 310	87.28 325	97.19 186	96.29 272	92.66 251	92.01 391	98.31 176	70.19 396	96.94 166	85.87 395	87.25 214	99.78 125	62.69 397	95.96 201	99.13 196

tt0805		91.28 275	90.18 283	94.60 261	96.26 274	87.55 340	98.39 320	98.72 65	89.00 286	89.22 287	98.47 212	62.98 376	98.96 185	90.57 268	88.00 284	97.28 249

Patchmtry		89.70 311	88.49 314	93.33 309	96.24 275	89.94 315	91.37 394	96.23 355	78.22 380	87.69 312	93.31 362	91.04 158	96.03 349	80.18 358	82.10 325	94.02 311

test_vis1_rt		86.87 330	86.05 332	89.34 351	96.12 276	78.07 386	99.87 106	83.54 410	92.03 220	78.21 375	89.51 381	45.80 396	99.91 89	96.25 169	93.11 248	90.03 380

JIA-IIPM		91.76 269	90.70 270	94.94 248	96.11 277	87.51 341	93.16 387	98.13 202	75.79 386	97.58 150	77.68 400	92.84 119	97.97 257	88.47 294	96.54 189	99.33 177

OpenMVS	90.15 15	94.77 189	93.59 210	98.33 121	96.07 278	97.48 95	99.56 199	98.57 89	90.46 264	86.51 329	98.95 168	78.57 297	99.94 77	93.86 213	99.74 84	97.57 247

PAPM		98.60 30	98.42 31	99.14 61	96.05 279	98.96 26	99.90 92	99.35 25	96.68 55	98.35 126	99.66 96	96.45 28	98.51 212	99.45 45	99.89 66	99.96 64

CLD-MVS		94.06 212	93.90 202	94.55 265	96.02 280	90.69 295	99.98 14	97.72 236	96.62 58	91.05 255	98.85 182	77.21 302	98.47 213	98.11 116	89.51 261	94.48 271
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 295	88.75 311	95.25 239	95.99 281	90.16 308	91.22 395	97.54 255	76.80 382	97.26 159	86.01 394	91.88 145	96.07 348	66.16 393	95.91 205	99.51 154

ACMH+	89.98 16	90.35 296	89.54 295	92.78 324	95.99 281	86.12 350	98.81 291	97.18 294	89.38 279	83.14 352	97.76 241	68.42 357	98.43 218	89.11 286	86.05 297	93.78 331

DeepMVS_CX				82.92 372	95.98 283	58.66 403		96.01 360	92.72 189	78.34 374	95.51 310	58.29 385	98.08 251	82.57 343	85.29 302	92.03 365

ACMP	92.05 9	92.74 245	92.42 243	93.73 297	95.91 284	88.72 327	99.81 138	97.53 257	94.13 134	87.00 323	98.23 223	74.07 334	98.47 213	96.22 170	88.86 268	93.99 316
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 225	93.03 226	95.35 234	95.86 285	86.94 345	99.87 106	96.36 352	96.85 46	99.54 58	98.79 184	52.41 392	99.83 118	98.64 91	98.97 133	99.29 183

HQP-NCC					95.78 286		99.87 106		96.82 48	93.37 225

ACMP_Plane					95.78 286		99.87 106		96.82 48	93.37 225

HQP-MVS		94.61 195	94.50 187	94.92 249	95.78 286	91.85 269	99.87 106	97.89 223	96.82 48	93.37 225	98.65 194	80.65 276	98.39 224	97.92 127	89.60 256	94.53 266

NP-MVS					95.77 289	91.79 271					98.65 194

test_fmvsmconf0.1_n		97.74 81	97.44 83	98.64 96	95.76 290	96.20 143	99.94 68	98.05 208	98.17 8	98.89 98	99.42 118	87.65 208	99.90 91	99.50 41	99.60 98	99.82 92

plane_prior6					95.76 290	91.72 276						80.47 280

ACMM	91.95 10	92.88 242	92.52 241	93.98 291	95.75 292	89.08 324	99.77 148	97.52 259	93.00 177	89.95 266	97.99 233	76.17 317	98.46 216	93.63 224	88.87 267	94.39 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 215	92.84 228	96.80 196	95.73 293	93.57 228	99.88 103	97.24 290	92.57 201	92.92 231	96.66 273	78.73 295	97.67 271	87.75 302	94.06 236	99.17 191

plane_prior1					95.73 293

jason		97.24 103	96.86 108	98.38 120	95.73 293	97.32 100	99.97 27	97.40 272	95.34 93	98.60 116	99.54 110	87.70 207	98.56 209	97.94 126	99.47 108	99.25 187
jason: jason.
HQP_MVS		94.49 200	94.36 189	94.87 250	95.71 296	91.74 273	99.84 126	97.87 225	96.38 65	93.01 229	98.59 199	80.47 280	98.37 230	97.79 135	89.55 259	94.52 268

plane_prior7					95.71 296	91.59 281

ITE_SJBPF				92.38 326	95.69 298	85.14 355		95.71 365	92.81 185	89.33 284	98.11 226	70.23 350	98.42 219	85.91 323	88.16 281	93.59 339

fmvsm_s_conf0.1_n_a		97.09 111	96.90 106	97.63 164	95.65 299	94.21 211	99.83 133	98.50 114	96.27 71	99.65 41	99.64 99	84.72 241	99.93 85	99.04 63	98.84 137	98.74 218

ACMH	89.72 17	90.64 289	89.63 292	93.66 303	95.64 300	88.64 330	98.55 308	97.45 264	89.03 284	81.62 359	97.61 242	69.75 351	98.41 220	89.37 283	87.62 289	93.92 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 132	96.49 123	97.37 179	95.63 301	95.96 152	99.74 159	98.88 51	92.94 179	91.61 247	98.97 161	97.72 5	98.62 207	94.83 193	98.08 160	97.53 248

FMVSNet1		88.50 321	86.64 327	94.08 284	95.62 302	91.97 264	98.43 316	96.95 319	83.00 361	86.08 337	94.72 340	59.09 384	96.11 344	81.82 350	84.07 314	94.17 295

LPG-MVS_test		92.96 239	92.71 233	93.71 299	95.43 303	88.67 328	99.75 156	97.62 244	92.81 185	90.05 262	98.49 208	75.24 324	98.40 222	95.84 176	89.12 263	94.07 308

LGP-MVS_train				93.71 299	95.43 303	88.67 328		97.62 244	92.81 185	90.05 262	98.49 208	75.24 324	98.40 222	95.84 176	89.12 263	94.07 308

tpm		93.70 223	93.41 218	94.58 263	95.36 305	87.41 342	97.01 354	96.90 326	90.85 256	96.72 174	94.14 354	90.40 176	96.84 316	90.75 266	88.54 276	99.51 154

D2MVS		92.76 244	92.59 239	93.27 311	95.13 306	89.54 319	99.69 175	99.38 23	92.26 213	87.59 314	94.61 346	85.05 239	97.79 266	91.59 250	88.01 283	92.47 360

VPA-MVSNet		92.70 246	91.55 258	96.16 215	95.09 307	96.20 143	98.88 282	99.00 36	91.02 253	91.82 246	95.29 325	76.05 319	97.96 259	95.62 180	81.19 332	94.30 286

LTVRE_ROB	88.28 18	90.29 299	89.05 306	94.02 287	95.08 308	90.15 309	97.19 349	97.43 266	84.91 348	83.99 348	97.06 259	74.00 335	98.28 239	84.08 332	87.71 287	93.62 338
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		87.87 327	86.51 328	91.94 331	95.05 309	85.57 353	97.65 342	94.08 387	84.40 352	81.82 358	96.85 268	62.14 378	98.33 233	80.25 357	86.37 296	91.91 367

test0.0.03 1		93.86 214	93.61 207	94.64 259	95.02 310	92.18 262	99.93 75	98.58 87	94.07 138	87.96 310	98.50 207	93.90 90	94.96 364	81.33 351	93.17 246	96.78 253

UniMVSNet (Re)		93.07 238	92.13 245	95.88 220	94.84 311	96.24 142	99.88 103	98.98 38	92.49 206	89.25 285	95.40 315	87.09 216	97.14 295	93.13 232	78.16 355	94.26 288

USDC		90.00 306	88.96 307	93.10 317	94.81 312	88.16 336	98.71 299	95.54 370	93.66 159	83.75 350	97.20 253	65.58 367	98.31 235	83.96 335	87.49 291	92.85 354

VPNet		91.81 263	90.46 274	95.85 222	94.74 313	95.54 170	98.98 271	98.59 86	92.14 215	90.77 258	97.44 246	68.73 355	97.54 275	94.89 192	77.89 357	94.46 272

FIs		94.10 210	93.43 215	96.11 216	94.70 314	96.82 118	99.58 194	98.93 45	92.54 202	89.34 283	97.31 250	87.62 209	97.10 299	94.22 209	86.58 294	94.40 278

UniMVSNet_ETH3D		90.06 305	88.58 313	94.49 269	94.67 315	88.09 337	97.81 341	97.57 252	83.91 355	88.44 302	97.41 247	57.44 386	97.62 273	91.41 251	88.59 275	97.77 241

UniMVSNet_NR-MVSNet		92.95 240	92.11 246	95.49 228	94.61 316	95.28 180	99.83 133	99.08 33	91.49 234	89.21 288	96.86 267	87.14 215	96.73 321	93.20 228	77.52 360	94.46 272

test_fmvs2		89.47 314	89.70 291	88.77 358	94.54 317	75.74 387	99.83 133	94.70 383	94.71 109	91.08 253	96.82 272	54.46 389	97.78 268	92.87 235	88.27 279	92.80 355

WR-MVS		92.31 255	91.25 263	95.48 231	94.45 318	95.29 179	99.60 191	98.68 70	90.10 270	88.07 309	96.89 265	80.68 275	96.80 319	93.14 231	79.67 348	94.36 281

nrg030		93.51 227	92.53 240	96.45 207	94.36 319	97.20 103	99.81 138	97.16 297	91.60 231	89.86 269	97.46 245	86.37 225	97.68 270	95.88 175	80.31 344	94.46 272

tfpnnormal		89.29 317	87.61 323	94.34 277	94.35 320	94.13 213	98.95 275	98.94 41	83.94 353	84.47 346	95.51 310	74.84 329	97.39 278	77.05 372	80.41 342	91.48 370

iter_conf05		94.61 195	94.72 183	94.27 278	94.31 321	91.01 287	99.93 75	96.30 354	94.01 143	92.92 231	98.46 215	90.66 166	97.32 283	97.12 151	88.75 270	94.49 270

FC-MVSNet-test		93.81 217	93.15 224	95.80 224	94.30 322	96.20 143	99.42 220	98.89 49	92.33 212	89.03 293	97.27 252	87.39 212	96.83 317	93.20 228	86.48 295	94.36 281

MS-PatchMatch		90.65 288	90.30 279	91.71 334	94.22 323	85.50 354	98.24 325	97.70 237	88.67 297	86.42 332	96.37 283	67.82 359	98.03 255	83.62 337	99.62 92	91.60 368

WR-MVS_H		91.30 273	90.35 277	94.15 281	94.17 324	92.62 254	99.17 250	98.94 41	88.87 293	86.48 331	94.46 351	84.36 245	96.61 326	88.19 296	78.51 353	93.21 348

DU-MVS		92.46 252	91.45 261	95.49 228	94.05 325	95.28 180	99.81 138	98.74 64	92.25 214	89.21 288	96.64 275	81.66 262	96.73 321	93.20 228	77.52 360	94.46 272

NR-MVSNet		91.56 271	90.22 281	95.60 226	94.05 325	95.76 158	98.25 324	98.70 67	91.16 248	80.78 364	96.64 275	83.23 254	96.57 327	91.41 251	77.73 359	94.46 272

CP-MVSNet		91.23 277	90.22 281	94.26 279	93.96 327	92.39 258	99.09 254	98.57 89	88.95 290	86.42 332	96.57 278	79.19 290	96.37 334	90.29 275	78.95 350	94.02 311

XXY-MVS		91.82 262	90.46 274	95.88 220	93.91 328	95.40 176	98.87 285	97.69 238	88.63 299	87.87 311	97.08 257	74.38 333	97.89 263	91.66 249	84.07 314	94.35 284

PS-CasMVS		90.63 290	89.51 297	93.99 290	93.83 329	91.70 277	98.98 271	98.52 105	88.48 301	86.15 336	96.53 280	75.46 322	96.31 338	88.83 288	78.86 352	93.95 319

test_0402		85.58 334	83.94 339	90.50 342	93.81 330	85.04 356	98.55 308	95.20 377	76.01 384	79.72 369	95.13 328	64.15 373	96.26 340	66.04 394	86.88 293	90.21 379

XVG-ACMP-BASELINE		91.22 278	90.75 269	92.63 325	93.73 331	85.61 352	98.52 312	97.44 265	92.77 188	89.90 268	96.85 268	66.64 364	98.39 224	92.29 240	88.61 273	93.89 324

TranMVSNet+NR-MVSNet		91.68 270	90.61 273	94.87 250	93.69 332	93.98 218	99.69 175	98.65 74	91.03 252	88.44 302	96.83 271	80.05 283	96.18 342	90.26 276	76.89 368	94.45 277

mvsmamba		94.10 210	93.72 206	95.25 239	93.57 333	94.13 213	99.67 179	96.45 350	93.63 161	91.34 251	97.77 240	86.29 227	97.22 291	96.65 165	88.10 282	94.40 278

TransMVSNet (Re)		87.25 328	85.28 335	93.16 314	93.56 334	91.03 286	98.54 310	94.05 389	83.69 357	81.09 362	96.16 288	75.32 323	96.40 333	76.69 373	68.41 386	92.06 364

v10		90.25 300	88.82 309	94.57 264	93.53 335	93.43 233	99.08 256	96.87 329	85.00 345	87.34 321	94.51 347	80.93 272	97.02 309	82.85 342	79.23 349	93.26 346

testgi		89.01 319	88.04 320	91.90 332	93.49 336	84.89 358	99.73 164	95.66 367	93.89 153	85.14 342	98.17 224	59.68 383	94.66 369	77.73 368	88.88 266	96.16 262

v8		90.54 292	89.17 302	94.66 258	93.43 337	93.40 235	99.20 247	96.94 323	85.76 336	87.56 315	94.51 347	81.96 260	97.19 292	84.94 329	78.25 354	93.38 344

V42		91.28 275	90.12 286	94.74 255	93.42 338	93.46 232	99.68 177	97.02 311	87.36 315	89.85 271	95.05 330	81.31 268	97.34 281	87.34 307	80.07 346	93.40 342

pm-mvs1		89.36 316	87.81 322	94.01 288	93.40 339	91.93 267	98.62 307	96.48 349	86.25 331	83.86 349	96.14 289	73.68 336	97.04 305	86.16 320	75.73 372	93.04 351

v1144		91.09 279	89.83 288	94.87 250	93.25 340	93.69 226	99.62 189	96.98 316	86.83 325	89.64 277	94.99 335	80.94 271	97.05 302	85.08 328	81.16 333	93.87 326

v1192		90.62 291	89.25 301	94.72 257	93.13 341	93.07 239	99.50 209	97.02 311	86.33 330	89.56 279	95.01 332	79.22 289	97.09 301	82.34 346	81.16 333	94.01 313

v2v482		91.30 273	90.07 287	95.01 245	93.13 341	93.79 221	99.77 148	97.02 311	88.05 307	89.25 285	95.37 319	80.73 274	97.15 294	87.28 308	80.04 347	94.09 307

OPM-MVS		93.21 232	92.80 230	94.44 272	93.12 343	90.85 294	99.77 148	97.61 247	96.19 74	91.56 248	98.65 194	75.16 328	98.47 213	93.78 220	89.39 262	93.99 316
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 286	89.52 296	94.59 262	93.11 344	92.77 245	99.56 199	96.99 314	86.38 329	89.82 272	94.95 337	80.50 279	97.10 299	83.98 334	80.41 342	93.90 323

PEN-MVS		90.19 302	89.06 305	93.57 304	93.06 345	90.90 292	99.06 261	98.47 117	88.11 306	85.91 338	96.30 284	76.67 309	95.94 352	87.07 311	76.91 367	93.89 324

v1240		90.20 301	88.79 310	94.44 272	93.05 346	92.27 260	99.38 226	96.92 325	85.89 334	89.36 282	94.87 339	77.89 301	97.03 307	80.66 354	81.08 336	94.01 313

v148		90.70 287	89.63 292	93.92 292	92.97 347	90.97 288	99.75 156	96.89 327	87.51 312	88.27 307	95.01 332	81.67 261	97.04 305	87.40 306	77.17 365	93.75 332

v1921920		90.46 293	89.12 303	94.50 268	92.96 348	92.46 256	99.49 211	96.98 316	86.10 332	89.61 278	95.30 322	78.55 298	97.03 307	82.17 347	80.89 340	94.01 313

Baseline_NR-MVSNet		90.33 297	89.51 297	92.81 323	92.84 349	89.95 313	99.77 148	93.94 390	84.69 350	89.04 292	95.66 302	81.66 262	96.52 328	90.99 259	76.98 366	91.97 366

test_method		80.79 354	79.70 358	84.08 369	92.83 350	67.06 395	99.51 207	95.42 371	54.34 401	81.07 363	93.53 359	44.48 397	92.22 388	78.90 364	77.23 364	92.94 352

pmmvs4		92.10 259	91.07 267	95.18 241	92.82 351	94.96 191	99.48 213	96.83 331	87.45 314	88.66 299	96.56 279	83.78 249	96.83 317	89.29 284	84.77 308	93.75 332

LF4IMVS		89.25 318	88.85 308	90.45 344	92.81 352	81.19 378	98.12 331	94.79 380	91.44 238	86.29 334	97.11 255	65.30 370	98.11 250	88.53 293	85.25 303	92.07 363

DTE-MVSNet		89.40 315	88.24 318	92.88 321	92.66 353	89.95 313	99.10 253	98.22 187	87.29 316	85.12 343	96.22 286	76.27 316	95.30 361	83.56 338	75.74 371	93.41 341

EU-MVSNet		90.14 304	90.34 278	89.54 350	92.55 354	81.06 379	98.69 302	98.04 209	91.41 242	86.59 328	96.84 270	80.83 273	93.31 381	86.20 319	81.91 327	94.26 288

APD_test1		81.15 353	80.92 354	81.86 373	92.45 355	59.76 402	96.04 373	93.61 393	73.29 393	77.06 378	96.64 275	44.28 398	96.16 343	72.35 381	82.52 321	89.67 384

our_test_3		90.39 294	89.48 299	93.12 315	92.40 356	89.57 318	99.33 232	96.35 353	87.84 310	85.30 341	94.99 335	84.14 247	96.09 347	80.38 355	84.56 309	93.71 337

ppachtmachnet_test		89.58 313	88.35 316	93.25 313	92.40 356	90.44 303	99.33 232	96.73 338	85.49 341	85.90 339	95.77 297	81.09 270	96.00 351	76.00 376	82.49 322	93.30 345

v7n		89.65 312	88.29 317	93.72 298	92.22 358	90.56 300	99.07 260	97.10 303	85.42 343	86.73 325	94.72 340	80.06 282	97.13 296	81.14 352	78.12 356	93.49 340

dmvs_testset		83.79 347	86.07 331	76.94 377	92.14 359	48.60 412	96.75 359	90.27 402	89.48 278	78.65 372	98.55 206	79.25 288	86.65 400	66.85 391	82.69 320	95.57 264

PS-MVSNAJss		93.64 224	93.31 221	94.61 260	92.11 360	92.19 261	99.12 252	97.38 273	92.51 205	88.45 301	96.99 263	91.20 153	97.29 288	94.36 204	87.71 287	94.36 281

pmmvs5		90.17 303	89.09 304	93.40 307	92.10 361	89.77 316	99.74 159	95.58 369	85.88 335	87.24 322	95.74 298	73.41 337	96.48 330	88.54 292	83.56 317	93.95 319

N_pmnet		80.06 357	80.78 355	77.89 376	91.94 362	45.28 414	98.80 293	56.82 416	78.10 381	80.08 367	93.33 360	77.03 304	95.76 354	68.14 389	82.81 319	92.64 356

test_djsdf		92.83 243	92.29 244	94.47 270	91.90 363	92.46 256	99.55 201	97.27 287	91.17 246	89.96 265	96.07 293	81.10 269	96.89 313	94.67 199	88.91 265	94.05 310

SixPastTwentyTwo		88.73 320	88.01 321	90.88 338	91.85 364	82.24 370	98.22 328	95.18 378	88.97 288	82.26 355	96.89 265	71.75 342	96.67 324	84.00 333	82.98 318	93.72 336

K. test v3		88.05 324	87.24 326	90.47 343	91.82 365	82.23 371	98.96 274	97.42 268	89.05 283	76.93 380	95.60 304	68.49 356	95.42 357	85.87 324	81.01 338	93.75 332

OurMVSNet-221017-0		89.81 309	89.48 299	90.83 340	91.64 366	81.21 377	98.17 330	95.38 373	91.48 236	85.65 340	97.31 250	72.66 338	97.29 288	88.15 297	84.83 307	93.97 318

mvs_tets		91.81 263	91.08 266	94.00 289	91.63 367	90.58 299	98.67 304	97.43 266	92.43 207	87.37 320	97.05 260	71.76 341	97.32 283	94.75 196	88.68 272	94.11 306

Gipuma		66.95 370	65.00 370	72.79 382	91.52 368	67.96 394	66.16 405	95.15 379	47.89 403	58.54 400	67.99 405	29.74 402	87.54 399	50.20 404	77.83 358	62.87 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 145	95.74 152	98.32 122	91.47 369	95.56 169	99.84 126	97.30 282	97.74 18	97.89 142	99.35 129	79.62 285	99.85 108	99.25 54	99.24 123	99.55 143

jajsoiax		91.92 261	91.18 264	94.15 281	91.35 370	90.95 291	99.00 270	97.42 268	92.61 197	87.38 319	97.08 257	72.46 339	97.36 279	94.53 202	88.77 269	94.13 305

MDA-MVSNet-bldmvs		84.09 345	81.52 352	91.81 333	91.32 371	88.00 339	98.67 304	95.92 362	80.22 374	55.60 403	93.32 361	68.29 358	93.60 379	73.76 378	76.61 369	93.82 330

MVP-Stereo		90.93 281	90.45 276	92.37 327	91.25 372	88.76 325	98.05 335	96.17 357	87.27 317	84.04 347	95.30 322	78.46 299	97.27 290	83.78 336	99.70 87	91.09 371
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 336	83.32 344	92.10 329	90.96 373	88.58 331	99.20 247	96.52 347	79.70 377	57.12 402	92.69 366	79.11 291	93.86 376	77.10 371	77.46 362	93.86 327

YYNet1		85.50 337	83.33 343	92.00 330	90.89 374	88.38 335	99.22 246	96.55 346	79.60 378	57.26 401	92.72 365	79.09 293	93.78 377	77.25 370	77.37 363	93.84 328

anonymousdsp		91.79 268	90.92 268	94.41 275	90.76 375	92.93 244	98.93 277	97.17 295	89.08 282	87.46 318	95.30 322	78.43 300	96.92 312	92.38 239	88.73 271	93.39 343

lessismore_v0				90.53 341	90.58 376	80.90 380		95.80 363		77.01 379	95.84 295	66.15 366	96.95 310	83.03 341	75.05 373	93.74 335

EG-PatchMatch MVS		85.35 338	83.81 341	89.99 348	90.39 377	81.89 373	98.21 329	96.09 359	81.78 368	74.73 386	93.72 358	51.56 394	97.12 298	79.16 363	88.61 273	90.96 373

EGC-MVSNET		69.38 363	63.76 373	86.26 366	90.32 378	81.66 376	96.24 369	93.85 391	0.99 413	3.22 414	92.33 371	52.44 391	92.92 384	59.53 400	84.90 306	84.21 394

CMPMVS	61.59 21	84.75 341	85.14 336	83.57 370	90.32 378	62.54 398	96.98 355	97.59 251	74.33 391	69.95 392	96.66 273	64.17 372	98.32 234	87.88 301	88.41 278	89.84 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 344	82.92 347	89.21 352	90.03 380	82.60 367	96.89 358	95.62 368	80.59 372	75.77 385	89.17 382	65.04 371	94.79 368	72.12 382	81.02 337	90.23 378

pmmvs6		85.69 333	83.84 340	91.26 337	90.00 381	84.41 360	97.82 340	96.15 358	75.86 385	81.29 361	95.39 317	61.21 381	96.87 315	83.52 339	73.29 375	92.50 359

DSMNet-mixed		88.28 323	88.24 318	88.42 360	89.64 382	75.38 389	98.06 334	89.86 403	85.59 340	88.20 308	92.14 372	76.15 318	91.95 389	78.46 365	96.05 199	97.92 237

UnsupCasMVSNet_eth		85.52 335	83.99 337	90.10 346	89.36 383	83.51 364	96.65 361	97.99 211	89.14 281	75.89 384	93.83 356	63.25 375	93.92 374	81.92 349	67.90 389	92.88 353

Anonymous20231206		86.32 331	85.42 334	89.02 354	89.11 384	80.53 383	99.05 265	95.28 374	85.43 342	82.82 353	93.92 355	74.40 332	93.44 380	66.99 390	81.83 328	93.08 350

Anonymous20240521		85.15 339	83.81 341	89.16 353	88.32 385	82.69 366	98.80 293	95.74 364	79.72 376	81.53 360	90.99 375	65.38 369	94.16 372	72.69 380	81.11 335	90.63 376

OpenMVS_ROB	79.82 20	83.77 348	81.68 351	90.03 347	88.30 386	82.82 365	98.46 313	95.22 376	73.92 392	76.00 383	91.29 374	55.00 388	96.94 311	68.40 388	88.51 277	90.34 377

test20.03		84.72 342	83.99 337	86.91 364	88.19 387	80.62 382	98.88 282	95.94 361	88.36 303	78.87 370	94.62 345	68.75 354	89.11 395	66.52 392	75.82 370	91.00 372

KD-MVS_self_test		83.59 349	82.06 349	88.20 361	86.93 388	80.70 381	97.21 348	96.38 351	82.87 362	82.49 354	88.97 383	67.63 360	92.32 387	73.75 379	62.30 398	91.58 369

MIMVSNet1		82.58 350	80.51 356	88.78 356	86.68 389	84.20 361	96.65 361	95.41 372	78.75 379	78.59 373	92.44 367	51.88 393	89.76 394	65.26 395	78.95 350	92.38 362

CL-MVSNet_self_test		84.50 343	83.15 346	88.53 359	86.00 390	81.79 374	98.82 290	97.35 275	85.12 344	83.62 351	90.91 377	76.66 310	91.40 390	69.53 386	60.36 399	92.40 361

UnsupCasMVSNet_bld		79.97 359	77.03 364	88.78 356	85.62 391	81.98 372	93.66 385	97.35 275	75.51 388	70.79 391	83.05 397	48.70 395	94.91 366	78.31 366	60.29 400	89.46 387

Patchmatch-RL test		86.90 329	85.98 333	89.67 349	84.45 392	75.59 388	89.71 398	92.43 396	86.89 324	77.83 377	90.94 376	94.22 79	93.63 378	87.75 302	69.61 381	99.79 97

pmmvs-eth3d		84.03 346	81.97 350	90.20 345	84.15 393	87.09 344	98.10 333	94.73 382	83.05 360	74.10 388	87.77 389	65.56 368	94.01 373	81.08 353	69.24 383	89.49 386

test_fmvs3		79.99 358	80.17 357	79.45 375	84.02 394	62.83 396	99.05 265	93.49 394	88.29 305	80.06 368	86.65 392	28.09 404	88.00 396	88.63 289	73.27 376	87.54 392

PM-MVS		80.47 355	78.88 360	85.26 367	83.79 395	72.22 391	95.89 376	91.08 400	85.71 339	76.56 382	88.30 385	36.64 400	93.90 375	82.39 345	69.57 382	89.66 385

new-patchmatchnet		81.19 352	79.34 359	86.76 365	82.86 396	80.36 384	97.92 337	95.27 375	82.09 367	72.02 389	86.87 391	62.81 377	90.74 393	71.10 383	63.08 396	89.19 389

mvsany_test3		82.12 351	81.14 353	85.06 368	81.87 397	70.41 392	97.09 352	92.14 397	91.27 245	77.84 376	88.73 384	39.31 399	95.49 355	90.75 266	71.24 378	89.29 388

WB-MVS		76.28 361	77.28 363	73.29 381	81.18 398	54.68 406	97.87 339	94.19 386	81.30 369	69.43 393	90.70 378	77.02 305	82.06 404	35.71 409	68.11 388	83.13 395

test_f		78.40 360	77.59 362	80.81 374	80.82 399	62.48 399	96.96 356	93.08 395	83.44 358	74.57 387	84.57 396	27.95 405	92.63 385	84.15 331	72.79 377	87.32 393

SSC-MVS		75.42 362	76.40 365	72.49 385	80.68 400	53.62 407	97.42 344	94.06 388	80.42 373	68.75 394	90.14 380	76.54 312	81.66 405	33.25 410	66.34 392	82.19 396

pmmvs3		80.27 356	77.77 361	87.76 363	80.32 401	82.43 369	98.23 327	91.97 398	72.74 394	78.75 371	87.97 388	57.30 387	90.99 392	70.31 384	62.37 397	89.87 381

testf1		68.38 366	66.92 367	72.78 383	78.80 402	50.36 409	90.95 396	87.35 408	55.47 399	58.95 398	88.14 386	20.64 409	87.60 397	57.28 401	64.69 393	80.39 398

APD_test2		68.38 366	66.92 367	72.78 383	78.80 402	50.36 409	90.95 396	87.35 408	55.47 399	58.95 398	88.14 386	20.64 409	87.60 397	57.28 401	64.69 393	80.39 398

ambc				83.23 371	77.17 404	62.61 397	87.38 400	94.55 385		76.72 381	86.65 392	30.16 401	96.36 335	84.85 330	69.86 380	90.73 375

test_vis3_rt		68.82 364	66.69 369	75.21 380	76.24 405	60.41 401	96.44 364	68.71 415	75.13 389	50.54 406	69.52 404	16.42 414	96.32 337	80.27 356	66.92 391	68.89 402

TDRefinement		84.76 340	82.56 348	91.38 336	74.58 406	84.80 359	97.36 346	94.56 384	84.73 349	80.21 366	96.12 292	63.56 374	98.39 224	87.92 300	63.97 395	90.95 374

E-PMN		52.30 374	52.18 376	52.67 391	71.51 407	45.40 413	93.62 386	76.60 413	36.01 407	43.50 408	64.13 407	27.11 406	67.31 410	31.06 411	26.06 406	45.30 409

EMVS		51.44 376	51.22 378	52.11 392	70.71 408	44.97 415	94.04 382	75.66 414	35.34 409	42.40 409	61.56 410	28.93 403	65.87 411	27.64 412	24.73 407	45.49 408

PMMVS2		67.15 369	64.15 372	76.14 379	70.56 409	62.07 400	93.89 383	87.52 407	58.09 398	60.02 397	78.32 399	22.38 408	84.54 402	59.56 399	47.03 404	81.80 397

FPMVS		68.72 365	68.72 366	68.71 387	65.95 410	44.27 416	95.97 375	94.74 381	51.13 402	53.26 404	90.50 379	25.11 407	83.00 403	60.80 398	80.97 339	78.87 400

wuyk23d		20.37 380	20.84 383	18.99 395	65.34 411	27.73 418	50.43 406	7.67 419	9.50 412	8.01 413	6.34 413	6.13 417	26.24 412	23.40 413	10.69 411	2.99 410

LCM-MVSNet		67.77 368	64.73 371	76.87 378	62.95 412	56.25 405	89.37 399	93.74 392	44.53 404	61.99 396	80.74 398	20.42 411	86.53 401	69.37 387	59.50 401	87.84 390

MVE	53.74 22	51.54 375	47.86 379	62.60 389	59.56 413	50.93 408	79.41 403	77.69 412	35.69 408	36.27 410	61.76 409	5.79 418	69.63 408	37.97 408	36.61 405	67.24 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 372	52.24 375	67.66 388	49.27 414	56.82 404	83.94 401	82.02 411	70.47 395	33.28 411	64.54 406	17.23 413	69.16 409	45.59 406	23.85 408	77.02 401

tmp_tt		65.23 371	62.94 374	72.13 386	44.90 415	50.03 411	81.05 402	89.42 406	38.45 405	48.51 407	99.90 18	54.09 390	78.70 407	91.84 248	18.26 409	87.64 391

PMVS	49.05 23	53.75 373	51.34 377	60.97 390	40.80 416	34.68 417	74.82 404	89.62 405	37.55 406	28.67 412	72.12 401	7.09 416	81.63 406	43.17 407	68.21 387	66.59 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 378	39.14 381	33.31 393	19.94 417	24.83 419	98.36 321	9.75 418	15.53 411	51.31 405	87.14 390	19.62 412	17.74 413	47.10 405	3.47 412	57.36 406

testmvs		40.60 377	44.45 380	29.05 394	19.49 418	14.11 420	99.68 177	18.47 417	20.74 410	64.59 395	98.48 211	10.95 415	17.09 414	56.66 403	11.01 410	55.94 407

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.02 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

eth-test2					0.00 419

eth-test					0.00 419

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		23.43 379	31.24 382	0.00 396	0.00 419	0.00 421	0.00 407	98.09 203	0.00 414	0.00 415	99.67 94	83.37 252	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		7.60 382	10.13 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	91.20 153	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.28 381	11.04 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	99.40 123	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						90.97 288								86.10 322

PC_three_1452									96.96 44	99.80 17	99.79 55	97.49 8	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 133	97.27 34	99.80 17	99.94 4	97.18 19	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 59	99.83 13	99.91 14	97.87 4	100.00 1	99.92 12	100.00 1	100.00 1

GSMVS																99.59 132

sam_mvs1												94.72 63				99.59 132

sam_mvs												94.25 78

MTGPA								98.28 181

test_post1							95.78 377				59.23 411	93.20 110	97.74 269	91.06 257

test_post											63.35 408	94.43 68	98.13 249

patchmatchnet-post											91.70 373	95.12 49	97.95 260

MTMP							99.87 106	96.49 348

test9_res														99.71 33	99.99 21	100.00 1

agg_prior2														99.48 43	100.00 1	100.00 1

test_prior4						98.05 70	99.94 68

test_prior2							99.95 52		95.78 80	99.73 32	99.76 63	96.00 32		99.78 27	100.00 1

旧先验2							99.46 217		94.21 133	99.85 9			99.95 69	96.96 159

新几何2							99.40 221

无先验							99.49 211	98.71 66	93.46 164				100.00 1	94.36 204		99.99 23

原ACMM2							99.90 92

testdata2													99.99 36	90.54 270

segment_acmp												96.68 24

testdata1							99.28 241		96.35 69

plane_prior5								97.87 225					98.37 230	97.79 135	89.55 259	94.52 268

plane_prior4											98.59 199

plane_prior3						91.64 279			96.63 56	93.01 229

plane_prior2							99.84 126		96.38 65

plane_prior						91.74 273	99.86 118		96.76 52						89.59 258

n2								0.00 420

nn								0.00 420

door-mid								89.69 404

test11								98.44 125

door								90.31 401

HQP5-MVS						91.85 269

BP-MVS														97.92 127

HQP4-MVS										93.37 225			98.39 224			94.53 266

HQP3-MVS								97.89 223							89.60 256

HQP2-MVS												80.65 276

MDTV_nov1_ep13_2view						96.26 138	96.11 371		91.89 223	98.06 136		94.40 70		94.30 206		99.67 115

ACMMP++_ref															87.04 292

ACMMP++															88.23 280

Test By Simon												92.82 121

High-resolution multi-view benchmark