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		97.23 101	96.80 108	98.51 106	99.99 1	95.60 164	99.09 251	98.84 58	93.32 166	96.74 169	99.72 83	86.04 221	100.00 1	98.01 117	99.43 111	99.94 74

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 15	98.69 68	98.20 7	99.93 1	99.98 2	96.82 22	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 28	98.64 76	98.47 2	99.13 86	99.92 13	96.38 30	100.00 1	99.74 30	100.00 1	100.00 1

mPP-MVS		98.39 47	98.20 46	98.97 74	99.97 3	96.92 112	99.95 53	98.38 159	95.04 99	98.61 113	99.80 51	93.39 101	100.00 1	98.64 91	100.00 1	99.98 48

CPTT-MVS		97.64 84	97.32 87	98.58 98	99.97 3	95.77 153	99.96 35	98.35 165	89.90 272	98.36 123	99.79 57	91.18 157	99.99 36	98.37 101	99.99 21	99.99 23

DP-MVS Recon		98.41 45	98.02 57	99.56 25	99.97 3	98.70 46	99.92 79	98.44 123	92.06 217	98.40 122	99.84 41	95.68 40	100.00 1	98.19 107	99.71 84	99.97 58

PAPR		98.52 35	98.16 49	99.58 24	99.97 3	98.77 40	99.95 53	98.43 131	95.35 93	98.03 135	99.75 71	94.03 87	99.98 43	98.11 112	99.83 73	99.99 23

HFP-MVS		98.56 32	98.37 36	99.14 59	99.96 8	97.43 94	99.95 53	98.61 82	94.77 107	99.31 77	99.85 30	94.22 80	100.00 1	98.70 86	99.98 32	99.98 48

region2R		98.54 33	98.37 36	99.05 66	99.96 8	97.18 101	99.96 35	98.55 98	94.87 105	99.45 65	99.85 30	94.07 86	100.00 1	98.67 88	100.00 1	99.98 48

ACMMPR		98.50 36	98.32 40	99.05 66	99.96 8	97.18 101	99.95 53	98.60 84	94.77 107	99.31 77	99.84 41	93.73 96	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 28	98.62 81	98.02 13	99.90 3	99.95 3	97.33 16	100.00 1	99.54 39	100.00 1	100.00 1

CP-MVS		98.45 40	98.32 40	98.87 79	99.96 8	96.62 121	99.97 28	98.39 155	94.43 119	98.90 95	99.87 24	94.30 78	100.00 1	99.04 63	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 12		98.41 148	96.63 56	99.75 30	99.93 11	97.49 9

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 53	98.43 131					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 26	98.55 25	99.15 57	99.94 13	97.50 90	99.94 69	98.42 143	96.22 73	99.41 69	99.78 61	94.34 76	99.96 61	98.92 70	99.95 49	99.99 23

X-MVStestdata		93.83 211	92.06 244	99.15 57	99.94 13	97.50 90	99.94 69	98.42 143	96.22 73	99.41 69	41.37 407	94.34 76	99.96 61	98.92 70	99.95 49	99.99 23

test_prior				99.43 35	99.94 13	98.49 58		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 59	99.98 15	98.86 53	97.10 40	99.80 18	99.94 4	95.92 36	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 43	99.91 84	98.39 155	97.20 38	99.46 64	99.85 30	95.53 44	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 68	99.94 13	97.17 104	99.95 53	98.39 155	94.70 111	98.26 129	99.81 50	91.84 148	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 44	99.87 106	98.33 170	93.97 144	99.76 29	99.87 24	94.99 58	99.75 132	98.55 95	100.00 1	99.98 48

PAPM_NR		98.12 60	97.93 64	98.70 87	99.94 13	96.13 143	99.82 136	98.43 131	94.56 115	97.52 147	99.70 87	94.40 71	99.98 43	97.00 151	99.98 32	99.99 23

MG-MVS		98.91 18	98.65 20	99.68 15	99.94 13	99.07 24	99.64 185	99.44 20	97.33 31	99.00 91	99.72 83	94.03 87	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 35	98.43 131	97.27 34	99.80 18	99.94 4	96.71 23	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 24	99.31 10		98.41 148	97.71 19	99.84 12				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 131	97.26 36	99.80 18	99.88 21	96.71 23	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 12	99.93 24	99.29 15	99.95 53	98.32 172	97.28 32	99.83 13	99.91 14	97.22 18	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 35	98.42 143	97.28 32	99.86 7	99.94 4	97.22 18

MSP-MVS		99.09 9	99.12 5	98.98 73	99.93 24	97.24 98	99.95 53	98.42 143	97.50 26	99.52 60	99.88 21	97.43 15	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 40		98.43 131		99.63 44			99.85 108

FOURS1					99.92 31	97.66 83	99.95 53	98.36 163	95.58 87	99.52 60

ZD-MVS					99.92 31	98.57 54		98.52 104	92.34 209	99.31 77	99.83 43	95.06 53	99.80 121	99.70 34	99.97 42

GST-MVS		98.27 52	97.97 59	99.17 53	99.92 31	97.57 85	99.93 76	98.39 155	94.04 142	98.80 100	99.74 78	92.98 116	100.00 1	98.16 109	99.76 81	99.93 76

TEST9					99.92 31	98.92 28	99.96 35	98.43 131	93.90 149	99.71 35	99.86 26	95.88 37	99.85 108

train_agg		98.88 19	98.65 20	99.59 23	99.92 31	98.92 28	99.96 35	98.43 131	94.35 124	99.71 35	99.86 26	95.94 34	99.85 108	99.69 35	99.98 32	99.99 23

test_8					99.92 31	98.88 31	99.96 35	98.43 131	94.35 124	99.69 37	99.85 30	95.94 34	99.85 108

PGM-MVS		98.34 48	98.13 51	98.99 72	99.92 31	97.00 108	99.75 156	99.50 18	93.90 149	99.37 74	99.76 65	93.24 110	100.00 1	97.75 135	99.96 46	99.98 48

ACMMP		97.74 79	97.44 81	98.66 90	99.92 31	96.13 143	99.18 246	99.45 19	94.84 106	96.41 179	99.71 85	91.40 151	99.99 36	97.99 119	98.03 158	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 53	98.43 131	96.48 61	99.80 18	99.93 11	97.44 13	100.00 1	99.92 12	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 148					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 148					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 53	98.56 92	97.56 25	99.44 66	99.85 30	95.38 47	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 57	99.87 106	98.36 163	94.08 137	99.74 32	99.73 80	94.08 85	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 34	99.24 241	98.47 115	98.14 10	99.08 87	99.91 14	93.09 113	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 35				99.80 51	97.44 13	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	98.44 123	97.48 27	99.64 43	99.94 4	96.68 25	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 63

CSCG		97.10 106	97.04 98	97.27 181	99.89 45	91.92 265	99.90 91	99.07 34	88.67 295	95.26 201	99.82 46	93.17 112	99.98 43	98.15 110	99.47 105	99.90 83

ZNCC-MVS		98.31 49	98.03 56	99.17 53	99.88 49	97.59 84	99.94 69	98.44 123	94.31 127	98.50 117	99.82 46	93.06 114	99.99 36	98.30 105	99.99 21	99.93 76

SR-MVS		98.46 39	98.30 43	98.93 77	99.88 49	97.04 107	99.84 126	98.35 165	94.92 103	99.32 76	99.80 51	93.35 103	99.78 125	99.30 52	99.95 49	99.96 64

9.14			98.38 34		99.87 51		99.91 84	98.33 170	93.22 169	99.78 27	99.89 19	94.57 68	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 32	99.86 118	98.38 159	93.19 170	99.77 28	99.94 4	95.54 42	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 68	99.86 53	97.10 106	99.98 15	98.80 62	90.78 258	99.62 47	99.78 61	95.30 48	100.00 1	99.80 25	99.93 60	99.99 23

MTAPA		98.29 51	97.96 62	99.30 42	99.85 54	97.93 73	99.39 222	98.28 179	95.76 82	97.18 157	99.88 21	92.74 124	100.00 1	98.67 88	99.88 69	99.99 23

LS3D		95.84 158	95.11 168	98.02 135	99.85 54	95.10 185	98.74 293	98.50 112	87.22 316	93.66 219	99.86 26	87.45 205	99.95 69	90.94 258	99.81 79	99.02 201

HPM-MVS		97.96 63	97.72 71	98.68 88	99.84 56	96.39 130	99.90 91	98.17 191	92.61 194	98.62 112	99.57 109	91.87 147	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 85	99.83 57	96.59 123	99.40 218	98.51 107	95.29 95	98.51 116	99.76 65	93.60 100	99.71 138	98.53 96	99.52 100	99.95 71

save fliter					99.82 58	98.79 38	99.96 35	98.40 152	97.66 21

PLC	95.54 3	97.93 65	97.89 67	98.05 134	99.82 58	94.77 195	99.92 79	98.46 117	93.93 147	97.20 156	99.27 134	95.44 46	99.97 53	97.41 140	99.51 103	99.41 164
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 82	99.81 60	96.60 122	99.82 136	98.30 177	93.95 146	99.37 74	99.77 63	92.84 120	99.76 131	98.95 67	99.92 63	99.97 58

EI-MVSNet-UG-set		98.14 59	97.99 58	98.60 95	99.80 61	96.27 133	99.36 227	98.50 112	95.21 97	98.30 126	99.75 71	93.29 107	99.73 137	98.37 101	99.30 117	99.81 94

SR-MVS-dyc-post		98.31 49	98.17 48	98.71 86	99.79 62	96.37 131	99.76 153	98.31 174	94.43 119	99.40 71	99.75 71	93.28 108	99.78 125	98.90 75	99.92 63	99.97 58

RE-MVS-def			98.13 51		99.79 62	96.37 131	99.76 153	98.31 174	94.43 119	99.40 71	99.75 71	92.95 117		98.90 75	99.92 63	99.97 58

HPM-MVS_fast		97.80 74	97.50 79	98.68 88	99.79 62	96.42 126	99.88 103	98.16 195	91.75 227	98.94 93	99.54 112	91.82 149	99.65 147	97.62 138	99.99 21	99.99 23

SF-MVS		98.67 27	98.40 32	99.50 30	99.77 65	98.67 47	99.90 91	98.21 186	93.53 159	99.81 15	99.89 19	94.70 66	99.86 107	99.84 22	99.93 60	99.96 64

旧先验1					99.76 66	97.52 87		98.64 76			99.85 30	95.63 41			99.94 54	99.99 23

OMC-MVS		97.28 98	97.23 90	97.41 172	99.76 66	93.36 234	99.65 181	97.95 214	96.03 77	97.41 151	99.70 87	89.61 181	99.51 152	96.73 160	98.25 150	99.38 166

新几何1				99.42 37	99.75 68	98.27 61		98.63 80	92.69 189	99.55 55	99.82 46	94.40 71	100.00 1	91.21 250	99.94 54	99.99 23

MP-MVS-pluss		98.07 62	97.64 74	99.38 41	99.74 69	98.41 60	99.74 159	98.18 190	93.35 164	96.45 176	99.85 30	92.64 126	99.97 53	98.91 74	99.89 67	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 47	99.77 148	98.38 159	96.73 53	99.88 6	99.74 78	94.89 60	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 55		98.40 152		99.65 41		94.76 63	99.75 132		99.98 32	99.99 23

原ACMM1				98.96 75	99.73 72	96.99 109		98.51 107	94.06 140	99.62 47	99.85 30	94.97 59	99.96 61	95.11 180	99.95 49	99.92 81

TSAR-MVS + GP.		98.60 30	98.51 28	98.86 80	99.73 72	96.63 120	99.97 28	97.92 219	98.07 11	98.76 104	99.55 110	95.00 57	99.94 77	99.91 15	97.68 163	99.99 23

CANet		98.27 52	97.82 69	99.63 17	99.72 74	99.10 23	99.98 15	98.51 107	97.00 43	98.52 115	99.71 85	87.80 200	99.95 69	99.75 28	99.38 113	99.83 91

F-COLMAP		96.93 115	96.95 101	96.87 191	99.71 75	91.74 270	99.85 121	97.95 214	93.11 173	95.72 194	99.16 145	92.35 136	99.94 77	95.32 178	99.35 115	98.92 204

SD-MVS		98.92 17	98.70 19	99.56 25	99.70 76	98.73 44	99.94 69	98.34 169	96.38 67	99.81 15	99.76 65	94.59 67	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	95.59 224	99.67 77	86.91 343	99.95 53	98.89 49	97.60 22	99.90 3	99.76 65	96.54 28	99.98 43	99.94 11	99.82 77	99.88 85

ACMMP_NAP		98.49 37	98.14 50	99.54 27	99.66 78	98.62 53	99.85 121	98.37 162	94.68 112	99.53 58	99.83 43	92.87 119	100.00 1	98.66 90	99.84 72	99.99 23

DeepPCF-MVS	95.94 2	97.71 82	98.98 12	93.92 287	99.63 79	81.76 370	99.96 35	98.56 92	99.47 1	99.19 84	99.99 1	94.16 84	100.00 1	99.92 12	99.93 60	100.00 1

EPNet		98.49 37	98.40 32	98.77 84	99.62 80	96.80 117	99.90 91	99.51 17	97.60 22	99.20 82	99.36 128	93.71 97	99.91 89	97.99 119	98.71 138	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 5	99.76 6	98.39 3	99.39 73	99.80 51	90.49 171	99.96 61	99.89 16	99.43 111	99.98 48

PVSNet_BlendedMVS		96.05 152	95.82 147	96.72 196	99.59 81	96.99 109	99.95 53	99.10 31	94.06 140	98.27 127	95.80 291	89.00 192	99.95 69	99.12 58	87.53 284	93.24 342

PVSNet_Blended		97.94 64	97.64 74	98.83 81	99.59 81	96.99 109	100.00 1	99.10 31	95.38 92	98.27 127	99.08 148	89.00 192	99.95 69	99.12 58	99.25 119	99.57 139

PatchMatch-RL		96.04 153	95.40 157	97.95 138	99.59 81	95.22 181	99.52 203	99.07 34	93.96 145	96.49 175	98.35 216	82.28 250	99.82 120	90.15 274	99.22 122	98.81 211

dcpmvs_2		97.42 93	98.09 54	95.42 229	99.58 85	87.24 339	99.23 242	96.95 312	94.28 129	98.93 94	99.73 80	94.39 74	99.16 174	99.89 16	99.82 77	99.86 89

test222					99.55 86	97.41 96	99.34 228	98.55 98	91.86 222	99.27 81	99.83 43	93.84 94			99.95 49	99.99 23

CNLPA		97.76 78	97.38 83	98.92 78	99.53 87	96.84 114	99.87 106	98.14 199	93.78 152	96.55 174	99.69 89	92.28 138	99.98 43	97.13 147	99.44 109	99.93 76

API-MVS		97.86 68	97.66 73	98.47 108	99.52 88	95.41 172	99.47 212	98.87 52	91.68 228	98.84 97	99.85 30	92.34 137	99.99 36	98.44 98	99.96 46	100.00 1

PVSNet	91.05 13	97.13 105	96.69 113	98.45 110	99.52 88	95.81 151	99.95 53	99.65 12	94.73 109	99.04 89	99.21 141	84.48 236	99.95 69	94.92 186	98.74 137	99.58 138

114514_t		97.41 94	96.83 106	99.14 59	99.51 90	97.83 75	99.89 99	98.27 181	88.48 299	99.06 88	99.66 98	90.30 173	99.64 148	96.32 164	99.97 42	99.96 64

cl22		93.77 215	93.25 218	95.33 233	99.49 91	94.43 199	99.61 189	98.09 201	90.38 263	89.16 287	95.61 298	90.56 169	97.34 277	91.93 242	84.45 305	94.21 288

testdata				98.42 113	99.47 92	95.33 175		98.56 92	93.78 152	99.79 26	99.85 30	93.64 99	99.94 77	94.97 184	99.94 54	100.00 1

MAR-MVS		97.43 89	97.19 92	98.15 127	99.47 92	94.79 194	99.05 262	98.76 63	92.65 192	98.66 110	99.82 46	88.52 197	99.98 43	98.12 111	99.63 89	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		94.54 192	93.42 211	97.91 143	99.46 94	94.04 212	98.93 274	97.48 259	81.15 366	90.04 260	99.55 110	87.02 211	99.95 69	88.97 284	98.11 154	99.73 105

MVS_111021_LR		98.42 44	98.38 34	98.53 105	99.39 95	95.79 152	99.87 106	99.86 2	96.70 54	98.78 101	99.79 57	92.03 144	99.90 91	99.17 57	99.86 71	99.88 85

CHOSEN 280x420		99.01 13	99.03 10	98.95 76	99.38 96	98.87 32	98.46 310	99.42 22	97.03 42	99.02 90	99.09 147	99.35 1	98.21 241	99.73 32	99.78 80	99.77 101

MVS_111021_HR		98.72 25	98.62 22	99.01 71	99.36 97	97.18 101	99.93 76	99.90 1	96.81 51	98.67 109	99.77 63	93.92 89	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 35	98.44 123	97.96 14	99.55 55	99.94 4	97.18 20	100.00 1	93.81 214	99.94 54	99.98 48

TAPA-MVS	92.12 8	94.42 197	93.60 204	96.90 190	99.33 98	91.78 269	99.78 145	98.00 208	89.89 273	94.52 207	99.47 116	91.97 145	99.18 172	69.90 380	99.52 100	99.73 105
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS-test		97.88 67	97.94 63	97.70 156	99.28 100	95.20 182	99.98 15	97.15 291	95.53 89	99.62 47	99.79 57	92.08 143	98.38 224	98.75 84	99.28 118	99.52 149

test_fmvsm_n_1920		98.44 41	98.61 23	97.92 141	99.27 101	95.18 183	100.00 1	98.90 47	98.05 12	99.80 18	99.73 80	92.64 126	99.99 36	99.58 38	99.51 103	98.59 221

fmvsm_l_conf0.5_n_a		99.00 14	98.91 14	99.28 43	99.21 102	97.91 74	99.98 15	98.85 56	98.25 4	99.92 2	99.75 71	94.72 64	99.97 53	99.87 19	99.64 88	99.95 71

test_yl		97.83 70	97.37 84	99.21 47	99.18 103	97.98 70	99.64 185	99.27 27	91.43 237	97.88 141	98.99 157	95.84 38	99.84 116	98.82 79	95.32 216	99.79 97

DCV-MVSNet		97.83 70	97.37 84	99.21 47	99.18 103	97.98 70	99.64 185	99.27 27	91.43 237	97.88 141	98.99 157	95.84 38	99.84 116	98.82 79	95.32 216	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	98.75 106	99.75 71	92.76 123	99.94 77	99.88 18	99.44 109	99.94 74

fmvsm_l_conf0.5_n		98.94 15	98.84 17	99.25 44	99.17 106	97.81 77	99.98 15	98.86 53	98.25 4	99.90 3	99.76 65	94.21 82	99.97 53	99.87 19	99.52 100	99.98 48

DeepC-MVS	94.51 4	96.92 116	96.40 123	98.45 110	99.16 107	95.90 149	99.66 179	98.06 204	96.37 70	94.37 210	99.49 115	83.29 246	99.90 91	97.63 137	99.61 94	99.55 141
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 51	100.00 1	98.58 87	97.70 20	98.21 131	99.24 139	92.58 129	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 76	97.91 65	97.43 171	99.10 109	94.42 200	99.99 5	97.10 296	95.07 98	99.68 38	99.75 71	92.95 117	98.34 228	98.38 100	99.14 124	99.54 145

Anonymous202405211		93.10 233	91.99 245	96.40 206	99.10 109	89.65 314	98.88 279	97.93 216	83.71 353	94.00 216	98.75 186	68.79 347	99.88 102	95.08 182	91.71 244	99.68 111

fmvsm_s_conf0.5_n		97.80 74	97.85 68	97.67 157	99.06 111	94.41 201	99.98 15	98.97 40	97.34 29	99.63 44	99.69 89	87.27 207	99.97 53	99.62 37	99.06 128	98.62 220

HyFIR lowres test		96.66 130	96.43 122	97.36 177	99.05 112	93.91 217	99.70 173	99.80 3	90.54 261	96.26 182	98.08 223	92.15 141	98.23 240	96.84 159	95.46 211	99.93 76

LFMVS		94.75 186	93.56 207	98.30 119	99.03 113	95.70 158	98.74 293	97.98 211	87.81 309	98.47 118	99.39 125	67.43 356	99.53 150	98.01 117	95.20 219	99.67 115

AllTest		92.48 247	91.64 250	95.00 243	99.01 114	88.43 328	98.94 273	96.82 327	86.50 325	88.71 293	98.47 212	74.73 323	99.88 102	85.39 322	96.18 193	96.71 248

TestCases				95.00 243	99.01 114	88.43 328		96.82 327	86.50 325	88.71 293	98.47 212	74.73 323	99.88 102	85.39 322	96.18 193	96.71 248

COLMAP_ROB	90.47 14	92.18 254	91.49 256	94.25 275	99.00 116	88.04 334	98.42 315	96.70 334	82.30 362	88.43 300	99.01 154	76.97 299	99.85 108	86.11 318	96.50 188	94.86 259
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvs1		95.35 172	95.68 152	94.36 272	98.99 117	84.98 352	99.96 35	96.65 336	97.60 22	99.73 33	98.96 163	71.58 336	99.93 85	98.31 104	99.37 114	98.17 228

HY-MVS	92.50 7	97.79 76	97.17 94	99.63 17	98.98 118	99.32 9	97.49 339	99.52 15	95.69 84	98.32 125	97.41 242	93.32 105	99.77 128	98.08 115	95.75 207	99.81 94

VNet		97.21 102	96.57 118	99.13 63	98.97 119	97.82 76	99.03 265	99.21 29	94.31 127	99.18 85	98.88 174	86.26 220	99.89 96	98.93 69	94.32 228	99.69 110

thres200		96.96 113	96.21 127	99.22 46	98.97 119	98.84 35	99.85 121	99.71 7	93.17 171	96.26 182	98.88 174	89.87 178	99.51 152	94.26 204	94.91 221	99.31 176

tfpn200view9		96.79 120	95.99 132	99.19 49	98.94 121	98.82 36	99.78 145	99.71 7	92.86 179	96.02 187	98.87 177	89.33 185	99.50 154	93.84 211	94.57 224	99.27 182

thres400		96.78 122	95.99 132	99.16 55	98.94 121	98.82 36	99.78 145	99.71 7	92.86 179	96.02 187	98.87 177	89.33 185	99.50 154	93.84 211	94.57 224	99.16 189

Anonymous20231211		89.86 303	88.44 310	94.13 278	98.93 123	90.68 292	98.54 307	98.26 182	76.28 378	86.73 321	95.54 302	70.60 342	97.56 270	90.82 261	80.27 340	94.15 296

canonicalmvs		97.09 108	96.32 124	99.39 40	98.93 123	98.95 27	99.72 167	97.35 270	94.45 117	97.88 141	99.42 120	86.71 214	99.52 151	98.48 97	93.97 234	99.72 107

SDMVSNet		94.80 182	93.96 195	97.33 179	98.92 125	95.42 171	99.59 191	98.99 37	92.41 206	92.55 234	97.85 232	75.81 313	98.93 183	97.90 125	91.62 245	97.64 239

sd_testset		93.55 222	92.83 226	95.74 222	98.92 125	90.89 289	98.24 321	98.85 56	92.41 206	92.55 234	97.85 232	71.07 341	98.68 200	93.93 208	91.62 245	97.64 239

EPNet_dtu		95.71 162	95.39 158	96.66 198	98.92 125	93.41 231	99.57 195	98.90 47	96.19 75	97.52 147	98.56 204	92.65 125	97.36 275	77.89 363	98.33 145	99.20 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 61	97.60 76	99.60 22	98.92 125	99.28 17	99.89 99	99.52 15	95.58 87	98.24 130	99.39 125	93.33 104	99.74 134	97.98 121	95.58 210	99.78 100

CHOSEN 1792x2688		96.81 119	96.53 119	97.64 159	98.91 129	93.07 236	99.65 181	99.80 3	95.64 85	95.39 198	98.86 179	84.35 239	99.90 91	96.98 153	99.16 123	99.95 71

thres100view900		96.74 125	95.92 143	99.18 50	98.90 130	98.77 40	99.74 159	99.71 7	92.59 196	95.84 190	98.86 179	89.25 187	99.50 154	93.84 211	94.57 224	99.27 182

thres600view7		96.69 128	95.87 146	99.14 59	98.90 130	98.78 39	99.74 159	99.71 7	92.59 196	95.84 190	98.86 179	89.25 187	99.50 154	93.44 223	94.50 227	99.16 189

MSDG		94.37 199	93.36 215	97.40 173	98.88 132	93.95 216	99.37 225	97.38 268	85.75 336	90.80 253	99.17 144	84.11 241	99.88 102	86.35 315	98.43 143	98.36 226

h-mvs33		94.92 180	94.36 184	96.59 200	98.85 133	91.29 281	98.93 274	98.94 41	95.90 78	98.77 102	98.42 215	90.89 165	99.77 128	97.80 128	70.76 374	98.72 217

Anonymous20240529		92.10 255	90.65 266	96.47 201	98.82 134	90.61 294	98.72 295	98.67 73	75.54 382	93.90 218	98.58 202	66.23 360	99.90 91	94.70 195	90.67 247	98.90 207

PVSNet_Blended_VisFu		97.27 99	96.81 107	98.66 90	98.81 135	96.67 119	99.92 79	98.64 76	94.51 116	96.38 180	98.49 208	89.05 191	99.88 102	97.10 149	98.34 144	99.43 162

PS-MVSNAJ		98.44 41	98.20 46	99.16 55	98.80 136	98.92 28	99.54 201	98.17 191	97.34 29	99.85 9	99.85 30	91.20 154	99.89 96	99.41 48	99.67 86	98.69 218

CANet_DTU		96.76 123	96.15 128	98.60 95	98.78 137	97.53 86	99.84 126	97.63 238	97.25 37	99.20 82	99.64 101	81.36 259	99.98 43	92.77 234	98.89 131	98.28 227

mvsany_test1		97.82 72	97.90 66	97.55 164	98.77 138	93.04 239	99.80 142	97.93 216	96.95 45	99.61 53	99.68 95	90.92 162	99.83 118	99.18 56	98.29 149	99.80 96

alignmvs		97.81 73	97.33 86	99.25 44	98.77 138	98.66 49	99.99 5	98.44 123	94.40 123	98.41 120	99.47 116	93.65 98	99.42 162	98.57 94	94.26 230	99.67 115

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 50	98.72 140	97.71 79	99.98 15	98.44 123	96.85 46	99.80 18	99.91 14	97.57 7	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 70	98.69 141	98.66 49	99.52 203	98.08 203	97.05 41	99.86 7	99.86 26	90.65 167	99.71 138	99.39 50	98.63 139	98.69 218

miper_enhance_ethall		94.36 201	93.98 194	95.49 225	98.68 142	95.24 179	99.73 164	97.29 278	93.28 168	89.86 265	95.97 289	94.37 75	97.05 298	92.20 238	84.45 305	94.19 289

ETVMVS		97.03 111	96.64 114	98.20 123	98.67 143	97.12 105	99.89 99	98.57 89	91.10 248	98.17 132	98.59 199	93.86 93	98.19 242	95.64 175	95.24 218	99.28 181

test2506		97.53 86	97.19 92	98.58 98	98.66 144	96.90 113	98.81 288	99.77 5	94.93 101	97.95 137	98.96 163	92.51 131	99.20 169	94.93 185	98.15 151	99.64 121

ECVR-MVS		95.66 165	95.05 170	97.51 167	98.66 144	93.71 221	98.85 285	98.45 118	94.93 101	96.86 165	98.96 163	75.22 319	99.20 169	95.34 177	98.15 151	99.64 121

fmvsm_s_conf0.5_n_a		97.73 81	97.72 71	97.77 151	98.63 146	94.26 206	99.96 35	98.92 46	97.18 39	99.75 30	99.69 89	87.00 212	99.97 53	99.46 44	98.89 131	99.08 197

iter_conf05_11		96.12 149	95.46 155	98.10 129	98.62 147	95.52 167	100.00 1	96.30 348	96.54 60	99.81 15	99.80 51	69.19 346	99.10 176	98.92 70	99.91 66	99.68 111

bld_raw_dy_0_64		94.22 205	92.97 222	97.98 136	98.62 147	95.09 186	99.89 99	93.09 389	96.55 59	92.59 232	99.80 51	68.57 350	99.19 171	98.92 70	88.69 264	99.68 111

testing222		97.08 110	96.75 110	98.06 133	98.56 149	96.82 115	99.85 121	98.61 82	92.53 200	98.84 97	98.84 183	93.36 102	98.30 232	95.84 172	94.30 229	99.05 199

test1111		95.57 167	94.98 173	97.37 175	98.56 149	93.37 233	98.86 283	98.45 118	94.95 100	96.63 171	98.95 168	75.21 320	99.11 175	95.02 183	98.14 153	99.64 121

MVSTER		95.53 168	95.22 164	96.45 203	98.56 149	97.72 78	99.91 84	97.67 236	92.38 208	91.39 245	97.14 249	97.24 17	97.30 281	94.80 191	87.85 279	94.34 280

VDD-MVS		93.77 215	92.94 223	96.27 210	98.55 152	90.22 303	98.77 292	97.79 230	90.85 254	96.82 167	99.42 120	61.18 377	99.77 128	98.95 67	94.13 231	98.82 210

tpmvs		94.28 203	93.57 206	96.40 206	98.55 152	91.50 279	95.70 373	98.55 98	87.47 311	92.15 238	94.26 348	91.42 150	98.95 182	88.15 294	95.85 203	98.76 213

UGNet		95.33 173	94.57 181	97.62 162	98.55 152	94.85 190	98.67 301	99.32 26	95.75 83	96.80 168	96.27 280	72.18 333	99.96 61	94.58 198	99.05 129	98.04 232
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	95.18 174	94.10 191	98.43 112	98.55 152	95.99 147	97.91 334	97.31 275	90.35 265	89.48 276	99.22 140	85.19 229	99.89 96	90.40 271	98.47 142	99.41 164
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS		96.79 120	96.72 111	97.00 186	98.51 156	93.70 222	99.71 169	98.60 84	92.96 175	97.09 158	98.34 217	96.67 27	98.85 186	92.11 240	96.50 188	98.44 223

test_vis1_n_1920		95.44 170	95.31 161	95.82 220	98.50 157	88.74 322	99.98 15	97.30 276	97.84 16	99.85 9	99.19 142	66.82 358	99.97 53	98.82 79	99.46 107	98.76 213

BH-w/o		95.71 162	95.38 159	96.68 197	98.49 158	92.28 256	99.84 126	97.50 257	92.12 214	92.06 241	98.79 184	84.69 234	98.67 201	95.29 179	99.66 87	99.09 195

baseline1		95.78 159	94.86 175	98.54 103	98.47 159	98.07 65	99.06 258	97.99 209	92.68 190	94.13 215	98.62 198	93.28 108	98.69 199	93.79 216	85.76 293	98.84 209

iter_conf05		96.07 151	95.95 140	96.44 205	98.43 160	97.52 87	99.91 84	96.85 323	94.16 133	92.49 236	97.98 229	98.20 4	97.34 277	97.26 144	88.29 272	94.45 270

EPMVS		96.53 134	96.01 131	98.09 131	98.43 160	96.12 145	96.36 360	99.43 21	93.53 159	97.64 145	95.04 326	94.41 70	98.38 224	91.13 252	98.11 154	99.75 103

sss		97.57 85	97.03 99	99.18 50	98.37 162	98.04 67	99.73 164	99.38 23	93.46 161	98.76 104	99.06 150	91.21 153	99.89 96	96.33 163	97.01 180	99.62 126

testing11		97.48 88	97.27 88	98.10 129	98.36 163	96.02 146	99.92 79	98.45 118	93.45 163	98.15 133	98.70 189	95.48 45	99.22 165	97.85 127	95.05 220	99.07 198

BH-untuned		95.18 174	94.83 176	96.22 211	98.36 163	91.22 282	99.80 142	97.32 274	90.91 252	91.08 249	98.67 191	83.51 243	98.54 207	94.23 205	99.61 94	98.92 204

testing91		97.16 104	96.90 103	97.97 137	98.35 165	95.67 161	99.91 84	98.42 143	92.91 178	97.33 153	98.72 187	94.81 62	99.21 166	96.98 153	94.63 223	99.03 200

testing99		97.17 103	96.91 102	97.95 138	98.35 165	95.70 158	99.91 84	98.43 131	92.94 176	97.36 152	98.72 187	94.83 61	99.21 166	97.00 151	94.64 222	98.95 203

ET-MVSNet_ETH3D		94.37 199	93.28 217	97.64 159	98.30 167	97.99 69	99.99 5	97.61 243	94.35 124	71.57 385	99.45 119	96.23 31	95.34 355	96.91 158	85.14 300	99.59 132

AUN-MVS		93.28 227	92.60 232	95.34 232	98.29 168	90.09 306	99.31 232	98.56 92	91.80 226	96.35 181	98.00 226	89.38 184	98.28 235	92.46 235	69.22 379	97.64 239

FMVSNet3		92.69 243	91.58 252	95.99 215	98.29 168	97.42 95	99.26 240	97.62 240	89.80 274	89.68 269	95.32 316	81.62 257	96.27 335	87.01 311	85.65 294	94.29 282

PMMVS		96.76 123	96.76 109	96.76 194	98.28 170	92.10 260	99.91 84	97.98 211	94.12 135	99.53 58	99.39 125	86.93 213	98.73 194	96.95 156	97.73 161	99.45 159

hse-mvs2		94.38 198	94.08 192	95.31 234	98.27 171	90.02 308	99.29 237	98.56 92	95.90 78	98.77 102	98.00 226	90.89 165	98.26 239	97.80 128	69.20 380	97.64 239

PVSNet_0	88.03 19	91.80 262	90.27 275	96.38 208	98.27 171	90.46 298	99.94 69	99.61 14	93.99 143	86.26 331	97.39 244	71.13 340	99.89 96	98.77 82	67.05 385	98.79 212

UA-Net		96.54 133	95.96 138	98.27 120	98.23 173	95.71 157	98.00 332	98.45 118	93.72 155	98.41 120	99.27 134	88.71 196	99.66 146	91.19 251	97.69 162	99.44 161

test_cas_vis1_n_1920		96.59 132	96.23 126	97.65 158	98.22 174	94.23 207	99.99 5	97.25 282	97.77 17	99.58 54	99.08 148	77.10 296	99.97 53	97.64 136	99.45 108	98.74 215

FE-MVS		95.70 164	95.01 172	97.79 148	98.21 175	94.57 196	95.03 374	98.69 68	88.90 290	97.50 149	96.19 282	92.60 128	99.49 158	89.99 276	97.94 160	99.31 176

GG-mvs-BLEND				98.54 103	98.21 175	98.01 68	93.87 379	98.52 104		97.92 138	97.92 231	99.02 2	97.94 258	98.17 108	99.58 97	99.67 115

mvs_anonymous		95.65 166	95.03 171	97.53 165	98.19 177	95.74 155	99.33 229	97.49 258	90.87 253	90.47 256	97.10 251	88.23 198	97.16 289	95.92 170	97.66 164	99.68 111

MVS_Test		96.46 136	95.74 148	98.61 94	98.18 178	97.23 99	99.31 232	97.15 291	91.07 249	98.84 97	97.05 255	88.17 199	98.97 180	94.39 200	97.50 166	99.61 129

BH-RMVSNet		95.18 174	94.31 187	97.80 146	98.17 179	95.23 180	99.76 153	97.53 253	92.52 202	94.27 213	99.25 138	76.84 301	98.80 188	90.89 260	99.54 99	99.35 171

RPSCF		91.80 262	92.79 228	88.83 350	98.15 180	69.87 388	98.11 328	96.60 338	83.93 351	94.33 211	99.27 134	79.60 279	99.46 161	91.99 241	93.16 241	97.18 246

ETV-MVS		97.92 66	97.80 70	98.25 121	98.14 181	96.48 124	99.98 15	97.63 238	95.61 86	99.29 80	99.46 118	92.55 130	98.82 187	99.02 66	98.54 140	99.46 157

IS-MVSNet		96.29 146	95.90 144	97.45 169	98.13 182	94.80 193	99.08 253	97.61 243	92.02 219	95.54 197	98.96 163	90.64 168	98.08 247	93.73 219	97.41 170	99.47 156

test_fmvsmconf_n		98.43 43	98.32 40	98.78 82	98.12 183	96.41 127	99.99 5	98.83 59	98.22 6	99.67 39	99.64 101	91.11 158	99.94 77	99.67 36	99.62 90	99.98 48

ab-mvs		94.69 187	93.42 211	98.51 106	98.07 184	96.26 134	96.49 358	98.68 70	90.31 266	94.54 206	97.00 257	76.30 308	99.71 138	95.98 169	93.38 239	99.56 140

XVG-OURS-SEG-HR		94.79 183	94.70 180	95.08 240	98.05 185	89.19 317	99.08 253	97.54 251	93.66 156	94.87 204	99.58 108	78.78 287	99.79 123	97.31 142	93.40 238	96.25 252

EIA-MVS		97.53 86	97.46 80	97.76 153	98.04 186	94.84 191	99.98 15	97.61 243	94.41 122	97.90 139	99.59 106	92.40 135	98.87 184	98.04 116	99.13 125	99.59 132

XVG-OURS		94.82 181	94.74 179	95.06 241	98.00 187	89.19 317	99.08 253	97.55 249	94.10 136	94.71 205	99.62 104	80.51 271	99.74 134	96.04 168	93.06 243	96.25 252

dp		95.05 177	94.43 183	96.91 189	97.99 188	92.73 246	96.29 363	97.98 211	89.70 275	95.93 189	94.67 339	93.83 95	98.45 213	86.91 314	96.53 187	99.54 145

tpmrst		96.27 148	95.98 134	97.13 183	97.96 189	93.15 235	96.34 361	98.17 191	92.07 215	98.71 108	95.12 324	93.91 90	98.73 194	94.91 188	96.62 185	99.50 153

TR-MVS		94.54 192	93.56 207	97.49 168	97.96 189	94.34 204	98.71 296	97.51 256	90.30 267	94.51 208	98.69 190	75.56 314	98.77 191	92.82 233	95.99 197	99.35 171

Vis-MVSNet (Re-imp)		96.32 143	95.98 134	97.35 178	97.93 191	94.82 192	99.47 212	98.15 198	91.83 223	95.09 202	99.11 146	91.37 152	97.47 273	93.47 222	97.43 167	99.74 104

MDTV_nov1_ep13			95.69 150		97.90 192	94.15 209	95.98 369	98.44 123	93.12 172	97.98 136	95.74 293	95.10 51	98.58 204	90.02 275	96.92 182

Fast-Effi-MVS+		95.02 178	94.19 189	97.52 166	97.88 193	94.55 197	99.97 28	97.08 299	88.85 292	94.47 209	97.96 230	84.59 235	98.41 216	89.84 278	97.10 175	99.59 132

ADS-MVSNet2		93.80 214	93.88 198	93.55 301	97.87 194	85.94 346	94.24 375	96.84 324	90.07 269	96.43 177	94.48 344	90.29 174	95.37 354	87.44 301	97.23 172	99.36 169

ADS-MVSNet		94.79 183	94.02 193	97.11 185	97.87 194	93.79 218	94.24 375	98.16 195	90.07 269	96.43 177	94.48 344	90.29 174	98.19 242	87.44 301	97.23 172	99.36 169

Effi-MVS+		96.30 145	95.69 150	98.16 124	97.85 196	96.26 134	97.41 341	97.21 284	90.37 264	98.65 111	98.58 202	86.61 216	98.70 198	97.11 148	97.37 171	99.52 149

PatchmatchNet		95.94 155	95.45 156	97.39 174	97.83 197	94.41 201	96.05 367	98.40 152	92.86 179	97.09 158	95.28 321	94.21 82	98.07 249	89.26 282	98.11 154	99.70 108
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 190	93.61 202	97.74 155	97.82 198	96.26 134	99.96 35	97.78 231	85.76 334	94.00 216	97.54 239	76.95 300	99.21 166	97.23 145	95.43 213	97.76 238

1112_ss		96.01 154	95.20 165	98.42 113	97.80 199	96.41 127	99.65 181	96.66 335	92.71 187	92.88 229	99.40 123	92.16 140	99.30 163	91.92 243	93.66 235	99.55 141

Test_1112_low_res		95.72 160	94.83 176	98.42 113	97.79 200	96.41 127	99.65 181	96.65 336	92.70 188	92.86 230	96.13 285	92.15 141	99.30 163	91.88 244	93.64 236	99.55 141

Effi-MVS+-dtu		94.53 194	95.30 162	92.22 323	97.77 201	82.54 363	99.59 191	97.06 301	94.92 103	95.29 200	95.37 314	85.81 222	97.89 259	94.80 191	97.07 176	96.23 254

tpm cat1		93.51 223	92.52 237	96.47 201	97.77 201	91.47 280	96.13 365	98.06 204	80.98 367	92.91 228	93.78 352	89.66 179	98.87 184	87.03 310	96.39 191	99.09 195

FA-MVS(test-final)		95.86 156	95.09 169	98.15 127	97.74 203	95.62 163	96.31 362	98.17 191	91.42 239	96.26 182	96.13 285	90.56 169	99.47 160	92.18 239	97.07 176	99.35 171

xiu_mvs_v1_base_debu		97.43 89	97.06 95	98.55 100	97.74 203	98.14 62	99.31 232	97.86 225	96.43 64	99.62 47	99.69 89	85.56 224	99.68 142	99.05 60	98.31 146	97.83 234

xiu_mvs_v1_base		97.43 89	97.06 95	98.55 100	97.74 203	98.14 62	99.31 232	97.86 225	96.43 64	99.62 47	99.69 89	85.56 224	99.68 142	99.05 60	98.31 146	97.83 234

xiu_mvs_v1_base_debi		97.43 89	97.06 95	98.55 100	97.74 203	98.14 62	99.31 232	97.86 225	96.43 64	99.62 47	99.69 89	85.56 224	99.68 142	99.05 60	98.31 146	97.83 234

EPP-MVSNet		96.69 128	96.60 116	96.96 188	97.74 203	93.05 238	99.37 225	98.56 92	88.75 293	95.83 192	99.01 154	96.01 32	98.56 205	96.92 157	97.20 174	99.25 184

gg-mvs-nofinetune		93.51 223	91.86 249	98.47 108	97.72 208	97.96 72	92.62 383	98.51 107	74.70 385	97.33 153	69.59 398	98.91 3	97.79 262	97.77 133	99.56 98	99.67 115

IB-MVS	92.85 6	94.99 179	93.94 196	98.16 124	97.72 208	95.69 160	99.99 5	98.81 60	94.28 129	92.70 231	96.90 259	95.08 52	99.17 173	96.07 167	73.88 369	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 94	97.02 100	98.59 97	97.71 210	97.52 87	99.97 28	98.54 101	91.83 223	97.45 150	99.04 151	97.50 8	99.10 176	94.75 193	96.37 192	99.16 189

Syy-MVS		90.00 301	90.63 267	88.11 357	97.68 211	74.66 385	99.71 169	98.35 165	90.79 256	92.10 239	98.67 191	79.10 285	93.09 377	63.35 391	95.95 200	96.59 250

myMVS_eth3d		94.46 196	94.76 178	93.55 301	97.68 211	90.97 284	99.71 169	98.35 165	90.79 256	92.10 239	98.67 191	92.46 134	93.09 377	87.13 307	95.95 200	96.59 250

test_fmvs1_n		94.25 204	94.36 184	93.92 287	97.68 211	83.70 358	99.90 91	96.57 339	97.40 28	99.67 39	98.88 174	61.82 374	99.92 88	98.23 106	99.13 125	98.14 231

diffmvs		97.00 112	96.64 114	98.09 131	97.64 214	96.17 142	99.81 138	97.19 285	94.67 113	98.95 92	99.28 131	86.43 217	98.76 192	98.37 101	97.42 169	99.33 174
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		95.72 160	95.15 167	97.45 169	97.62 215	94.28 205	99.28 238	98.24 183	94.27 131	96.84 166	98.94 170	79.39 280	98.76 192	93.25 224	98.49 141	99.30 178
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 106	96.72 111	98.22 122	97.60 216	96.70 118	99.92 79	98.54 101	91.11 247	97.07 160	98.97 161	97.47 11	99.03 178	93.73 219	96.09 195	98.92 204

miper_ehance_all_eth		93.16 230	92.60 232	94.82 250	97.57 217	93.56 226	99.50 207	97.07 300	88.75 293	88.85 292	95.52 304	90.97 161	96.74 316	90.77 262	84.45 305	94.17 290

testing3		93.92 209	94.23 188	92.99 315	97.54 218	90.23 302	99.99 5	99.16 30	90.57 260	91.33 248	98.63 197	92.99 115	92.52 381	82.46 340	95.39 214	96.22 255

LCM-MVSNet-Re		92.31 251	92.60 232	91.43 330	97.53 219	79.27 380	99.02 266	91.83 394	92.07 215	80.31 360	94.38 347	83.50 244	95.48 352	97.22 146	97.58 165	99.54 145

GBi-Net		90.88 278	89.82 284	94.08 279	97.53 219	91.97 261	98.43 312	96.95 312	87.05 317	89.68 269	94.72 335	71.34 337	96.11 340	87.01 311	85.65 294	94.17 290

test1		90.88 278	89.82 284	94.08 279	97.53 219	91.97 261	98.43 312	96.95 312	87.05 317	89.68 269	94.72 335	71.34 337	96.11 340	87.01 311	85.65 294	94.17 290

FMVSNet2		91.02 275	89.56 289	95.41 230	97.53 219	95.74 155	98.98 268	97.41 266	87.05 317	88.43 300	95.00 329	71.34 337	96.24 337	85.12 324	85.21 299	94.25 285

tttt0517		96.85 117	96.49 120	97.92 141	97.48 223	95.89 150	99.85 121	98.54 101	90.72 259	96.63 171	98.93 172	97.47 11	99.02 179	93.03 231	95.76 206	98.85 208

casdiffmvs_mvg		96.43 137	95.94 141	97.89 145	97.44 224	95.47 168	99.86 118	97.29 278	93.35 164	96.03 186	99.19 142	85.39 227	98.72 196	97.89 126	97.04 178	99.49 155
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 96	97.24 89	97.80 146	97.41 225	95.64 162	99.99 5	97.06 301	94.59 114	99.63 44	99.32 130	89.20 190	98.14 244	98.76 83	99.23 121	99.62 126

c3_l		92.53 246	91.87 248	94.52 262	97.40 226	92.99 240	99.40 218	96.93 317	87.86 307	88.69 295	95.44 308	89.95 177	96.44 328	90.45 268	80.69 336	94.14 299

fmvsm_s_conf0.1_n		97.30 97	97.21 91	97.60 163	97.38 227	94.40 203	99.90 91	98.64 76	96.47 63	99.51 62	99.65 100	84.99 232	99.93 85	99.22 55	99.09 127	98.46 222

CDS-MVSNet		96.34 142	96.07 129	97.13 183	97.37 228	94.96 188	99.53 202	97.91 220	91.55 231	95.37 199	98.32 218	95.05 54	97.13 292	93.80 215	95.75 207	99.30 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 125	96.26 125	98.16 124	97.36 229	96.48 124	99.96 35	98.29 178	91.93 220	95.77 193	98.07 224	95.54 42	98.29 233	90.55 266	98.89 131	99.70 108

miper_lstm_enhance		91.81 259	91.39 258	93.06 314	97.34 230	89.18 319	99.38 223	96.79 329	86.70 324	87.47 313	95.22 322	90.00 176	95.86 349	88.26 292	81.37 326	94.15 296

baseline		96.43 137	95.98 134	97.76 153	97.34 230	95.17 184	99.51 205	97.17 288	93.92 148	96.90 164	99.28 131	85.37 228	98.64 202	97.50 139	96.86 184	99.46 157

cl____		92.31 251	91.58 252	94.52 262	97.33 232	92.77 242	99.57 195	96.78 330	86.97 321	87.56 311	95.51 305	89.43 183	96.62 321	88.60 287	82.44 318	94.16 295

DIV-MVS_self_test		92.32 250	91.60 251	94.47 266	97.31 233	92.74 244	99.58 193	96.75 331	86.99 320	87.64 309	95.54 302	89.55 182	96.50 325	88.58 288	82.44 318	94.17 290

casdiffmvs		96.42 139	95.97 137	97.77 151	97.30 234	94.98 187	99.84 126	97.09 298	93.75 154	96.58 173	99.26 137	85.07 230	98.78 190	97.77 133	97.04 178	99.54 145
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		94.36 201	93.48 209	96.99 187	97.29 235	93.54 227	99.96 35	96.72 333	88.35 302	93.43 220	98.94 170	82.05 251	98.05 250	88.12 296	96.48 190	99.37 168

eth_miper_zixun_eth		92.41 249	91.93 246	93.84 291	97.28 236	90.68 292	98.83 286	96.97 311	88.57 298	89.19 286	95.73 295	89.24 189	96.69 319	89.97 277	81.55 324	94.15 296

MVSFormer		96.94 114	96.60 116	97.95 138	97.28 236	97.70 81	99.55 199	97.27 280	91.17 244	99.43 67	99.54 112	90.92 162	96.89 309	94.67 196	99.62 90	99.25 184

lupinMVS		97.85 69	97.60 76	98.62 93	97.28 236	97.70 81	99.99 5	97.55 249	95.50 91	99.43 67	99.67 96	90.92 162	98.71 197	98.40 99	99.62 90	99.45 159

SCA		94.69 187	93.81 200	97.33 179	97.10 239	94.44 198	98.86 283	98.32 172	93.30 167	96.17 185	95.59 300	76.48 306	97.95 256	91.06 254	97.43 167	99.59 132

TAMVS		95.85 157	95.58 153	96.65 199	97.07 240	93.50 228	99.17 247	97.82 229	91.39 241	95.02 203	98.01 225	92.20 139	97.30 281	93.75 218	95.83 204	99.14 192

Fast-Effi-MVS+-dtu		93.72 218	93.86 199	93.29 306	97.06 241	86.16 344	99.80 142	96.83 325	92.66 191	92.58 233	97.83 234	81.39 258	97.67 267	89.75 279	96.87 183	96.05 257

CostFormer		96.10 150	95.88 145	96.78 193	97.03 242	92.55 252	97.08 349	97.83 228	90.04 271	98.72 107	94.89 333	95.01 56	98.29 233	96.54 162	95.77 205	99.50 153

test_fmvsmvis_n_1920		97.67 83	97.59 78	97.91 143	97.02 243	95.34 174	99.95 53	98.45 118	97.87 15	97.02 161	99.59 106	89.64 180	99.98 43	99.41 48	99.34 116	98.42 224

test-LLR		96.47 135	96.04 130	97.78 149	97.02 243	95.44 169	99.96 35	98.21 186	94.07 138	95.55 195	96.38 276	93.90 91	98.27 237	90.42 269	98.83 135	99.64 121

test-mter		96.39 140	95.93 142	97.78 149	97.02 243	95.44 169	99.96 35	98.21 186	91.81 225	95.55 195	96.38 276	95.17 49	98.27 237	90.42 269	98.83 135	99.64 121

gm-plane-assit					96.97 246	93.76 220			91.47 235		98.96 163		98.79 189	94.92 186

WB-MVSnew		92.90 237	92.77 229	93.26 308	96.95 247	93.63 224	99.71 169	98.16 195	91.49 232	94.28 212	98.14 221	81.33 260	96.48 326	79.47 355	95.46 211	89.68 378

QAPM		95.40 171	94.17 190	99.10 64	96.92 248	97.71 79	99.40 218	98.68 70	89.31 278	88.94 290	98.89 173	82.48 249	99.96 61	93.12 230	99.83 73	99.62 126

KD-MVS_2432*1600		88.00 320	86.10 324	93.70 297	96.91 249	94.04 212	97.17 346	97.12 294	84.93 344	81.96 351	92.41 363	92.48 132	94.51 365	79.23 356	52.68 397	92.56 352

miper_refine_blended		88.00 320	86.10 324	93.70 297	96.91 249	94.04 212	97.17 346	97.12 294	84.93 344	81.96 351	92.41 363	92.48 132	94.51 365	79.23 356	52.68 397	92.56 352

tpm2		95.47 169	95.18 166	96.35 209	96.91 249	91.70 274	96.96 352	97.93 216	88.04 306	98.44 119	95.40 310	93.32 105	97.97 253	94.00 207	95.61 209	99.38 166

FMVSNet5		88.32 317	87.47 319	90.88 333	96.90 252	88.39 330	97.28 343	95.68 360	82.60 361	84.67 340	92.40 365	79.83 277	91.16 386	76.39 370	81.51 325	93.09 344

3Dnovator+	91.53 11	96.31 144	95.24 163	99.52 28	96.88 253	98.64 52	99.72 167	98.24 183	95.27 96	88.42 302	98.98 159	82.76 248	99.94 77	97.10 149	99.83 73	99.96 64

Patchmatch-test		92.65 245	91.50 255	96.10 214	96.85 254	90.49 297	91.50 388	97.19 285	82.76 360	90.23 257	95.59 300	95.02 55	98.00 252	77.41 365	96.98 181	99.82 92

MVS		96.60 131	95.56 154	99.72 13	96.85 254	99.22 20	98.31 318	98.94 41	91.57 230	90.90 252	99.61 105	86.66 215	99.96 61	97.36 141	99.88 69	99.99 23

3Dnovator	91.47 12	96.28 147	95.34 160	99.08 65	96.82 256	97.47 93	99.45 215	98.81 60	95.52 90	89.39 277	99.00 156	81.97 252	99.95 69	97.27 143	99.83 73	99.84 90

EI-MVSNet		93.73 217	93.40 214	94.74 251	96.80 257	92.69 247	99.06 258	97.67 236	88.96 287	91.39 245	99.02 152	88.75 195	97.30 281	91.07 253	87.85 279	94.22 286

CVMVSNet		94.68 189	94.94 174	93.89 290	96.80 257	86.92 342	99.06 258	98.98 38	94.45 117	94.23 214	99.02 152	85.60 223	95.31 356	90.91 259	95.39 214	99.43 162

IterMVS-LS		92.69 243	92.11 242	94.43 270	96.80 257	92.74 244	99.45 215	96.89 320	88.98 285	89.65 272	95.38 313	88.77 194	96.34 332	90.98 257	82.04 321	94.22 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.91 277	90.17 279	93.12 311	96.78 260	90.42 300	98.89 277	97.05 303	89.03 282	86.49 326	95.42 309	76.59 304	95.02 358	87.22 306	84.09 308	93.93 316
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 118	95.96 138	99.48 34	96.74 261	98.52 56	98.31 318	98.86 53	95.82 80	89.91 263	98.98 159	87.49 204	99.96 61	97.80 128	99.73 83	99.96 64

IterMVS-SCA-FT		90.85 280	90.16 280	92.93 316	96.72 262	89.96 309	98.89 277	96.99 307	88.95 288	86.63 323	95.67 296	76.48 306	95.00 359	87.04 309	84.04 311	93.84 323

MVS-HIRNet		86.22 327	83.19 340	95.31 234	96.71 263	90.29 301	92.12 385	97.33 273	62.85 392	86.82 320	70.37 397	69.37 345	97.49 272	75.12 372	97.99 159	98.15 229

VDDNet		93.12 232	91.91 247	96.76 194	96.67 264	92.65 250	98.69 299	98.21 186	82.81 359	97.75 144	99.28 131	61.57 375	99.48 159	98.09 114	94.09 232	98.15 229

dmvs_re		93.20 229	93.15 219	93.34 304	96.54 265	83.81 357	98.71 296	98.51 107	91.39 241	92.37 237	98.56 204	78.66 289	97.83 261	93.89 209	89.74 248	98.38 225

MIMVSNet		90.30 293	88.67 307	95.17 239	96.45 266	91.64 276	92.39 384	97.15 291	85.99 331	90.50 255	93.19 359	66.95 357	94.86 362	82.01 344	93.43 237	99.01 202

CR-MVSNet		93.45 226	92.62 231	95.94 216	96.29 267	92.66 248	92.01 386	96.23 349	92.62 193	96.94 162	93.31 357	91.04 159	96.03 345	79.23 356	95.96 198	99.13 193

RPMNet		89.76 305	87.28 320	97.19 182	96.29 267	92.66 248	92.01 386	98.31 174	70.19 391	96.94 162	85.87 390	87.25 208	99.78 125	62.69 392	95.96 198	99.13 193

tt0805		91.28 270	90.18 278	94.60 257	96.26 269	87.55 336	98.39 316	98.72 65	89.00 284	89.22 283	98.47 212	62.98 371	98.96 181	90.57 265	88.00 278	97.28 245

Patchmtry		89.70 306	88.49 309	93.33 305	96.24 270	89.94 312	91.37 389	96.23 349	78.22 375	87.69 308	93.31 357	91.04 159	96.03 345	80.18 354	82.10 320	94.02 306

test_vis1_rt		86.87 325	86.05 327	89.34 346	96.12 271	78.07 381	99.87 106	83.54 405	92.03 218	78.21 370	89.51 376	45.80 391	99.91 89	96.25 165	93.11 242	90.03 375

JIA-IIPM		91.76 265	90.70 265	94.94 245	96.11 272	87.51 337	93.16 382	98.13 200	75.79 381	97.58 146	77.68 395	92.84 120	97.97 253	88.47 291	96.54 186	99.33 174

OpenMVS	90.15 15	94.77 185	93.59 205	98.33 117	96.07 273	97.48 92	99.56 197	98.57 89	90.46 262	86.51 325	98.95 168	78.57 290	99.94 77	93.86 210	99.74 82	97.57 243

PAPM		98.60 30	98.42 31	99.14 59	96.05 274	98.96 26	99.90 91	99.35 25	96.68 55	98.35 124	99.66 98	96.45 29	98.51 208	99.45 45	99.89 67	99.96 64

CLD-MVS		94.06 208	93.90 197	94.55 261	96.02 275	90.69 291	99.98 15	97.72 232	96.62 58	91.05 251	98.85 182	77.21 295	98.47 209	98.11 112	89.51 254	94.48 264
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 290	88.75 306	95.25 236	95.99 276	90.16 304	91.22 390	97.54 251	76.80 377	97.26 155	86.01 389	91.88 146	96.07 344	66.16 388	95.91 202	99.51 151

ACMH+	89.98 16	90.35 291	89.54 290	92.78 319	95.99 276	86.12 345	98.81 288	97.18 287	89.38 277	83.14 347	97.76 236	68.42 352	98.43 214	89.11 283	86.05 292	93.78 326

DeepMVS_CX				82.92 367	95.98 278	58.66 398		96.01 354	92.72 186	78.34 369	95.51 305	58.29 380	98.08 247	82.57 339	85.29 297	92.03 360

ACMP	92.05 9	92.74 241	92.42 239	93.73 293	95.91 279	88.72 323	99.81 138	97.53 253	94.13 134	87.00 319	98.23 219	74.07 327	98.47 209	96.22 166	88.86 261	93.99 311
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 221	93.03 221	95.35 231	95.86 280	86.94 341	99.87 106	96.36 346	96.85 46	99.54 57	98.79 184	52.41 387	99.83 118	98.64 91	98.97 130	99.29 180

HQP-NCC					95.78 281		99.87 106		96.82 48	93.37 221

ACMP_Plane					95.78 281		99.87 106		96.82 48	93.37 221

HQP-MVS		94.61 191	94.50 182	94.92 246	95.78 281	91.85 266	99.87 106	97.89 221	96.82 48	93.37 221	98.65 194	80.65 269	98.39 220	97.92 123	89.60 249	94.53 260

NP-MVS					95.77 284	91.79 268					98.65 194

test_fmvsmconf0.1_n		97.74 79	97.44 81	98.64 92	95.76 285	96.20 139	99.94 69	98.05 206	98.17 8	98.89 96	99.42 120	87.65 202	99.90 91	99.50 41	99.60 96	99.82 92

plane_prior6					95.76 285	91.72 273						80.47 273

ACMM	91.95 10	92.88 238	92.52 237	93.98 286	95.75 287	89.08 320	99.77 148	97.52 255	93.00 174	89.95 262	97.99 228	76.17 310	98.46 212	93.63 221	88.87 260	94.39 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 211	92.84 225	96.80 192	95.73 288	93.57 225	99.88 103	97.24 283	92.57 198	92.92 227	96.66 268	78.73 288	97.67 267	87.75 299	94.06 233	99.17 188

plane_prior1					95.73 288

jason		97.24 100	96.86 105	98.38 116	95.73 288	97.32 97	99.97 28	97.40 267	95.34 94	98.60 114	99.54 112	87.70 201	98.56 205	97.94 122	99.47 105	99.25 184
jason: jason.
HQP_MVS		94.49 195	94.36 184	94.87 247	95.71 291	91.74 270	99.84 126	97.87 223	96.38 67	93.01 225	98.59 199	80.47 273	98.37 226	97.79 131	89.55 252	94.52 262

plane_prior7					95.71 291	91.59 278

ITE_SJBPF				92.38 321	95.69 293	85.14 350		95.71 359	92.81 182	89.33 280	98.11 222	70.23 343	98.42 215	85.91 320	88.16 275	93.59 334

fmvsm_s_conf0.1_n_a		97.09 108	96.90 103	97.63 161	95.65 294	94.21 208	99.83 133	98.50 112	96.27 72	99.65 41	99.64 101	84.72 233	99.93 85	99.04 63	98.84 134	98.74 215

ACMH	89.72 17	90.64 284	89.63 287	93.66 299	95.64 295	88.64 326	98.55 305	97.45 260	89.03 282	81.62 354	97.61 238	69.75 344	98.41 216	89.37 280	87.62 283	93.92 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 127	96.49 120	97.37 175	95.63 296	95.96 148	99.74 159	98.88 51	92.94 176	91.61 243	98.97 161	97.72 6	98.62 203	94.83 190	98.08 157	97.53 244

FMVSNet1		88.50 316	86.64 322	94.08 279	95.62 297	91.97 261	98.43 312	96.95 312	83.00 357	86.08 333	94.72 335	59.09 379	96.11 340	81.82 346	84.07 309	94.17 290

LPG-MVS_test		92.96 235	92.71 230	93.71 295	95.43 298	88.67 324	99.75 156	97.62 240	92.81 182	90.05 258	98.49 208	75.24 317	98.40 218	95.84 172	89.12 256	94.07 303

LGP-MVS_train				93.71 295	95.43 298	88.67 324		97.62 240	92.81 182	90.05 258	98.49 208	75.24 317	98.40 218	95.84 172	89.12 256	94.07 303

tpm		93.70 219	93.41 213	94.58 259	95.36 300	87.41 338	97.01 350	96.90 319	90.85 254	96.72 170	94.14 349	90.40 172	96.84 312	90.75 263	88.54 269	99.51 151

D2MVS		92.76 240	92.59 235	93.27 307	95.13 301	89.54 316	99.69 174	99.38 23	92.26 211	87.59 310	94.61 341	85.05 231	97.79 262	91.59 247	88.01 277	92.47 355

VPA-MVSNet		92.70 242	91.55 254	96.16 212	95.09 302	96.20 139	98.88 279	99.00 36	91.02 251	91.82 242	95.29 320	76.05 312	97.96 255	95.62 176	81.19 327	94.30 281

LTVRE_ROB	88.28 18	90.29 294	89.05 301	94.02 282	95.08 303	90.15 305	97.19 345	97.43 262	84.91 346	83.99 343	97.06 254	74.00 328	98.28 235	84.08 329	87.71 281	93.62 333
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		87.87 322	86.51 323	91.94 326	95.05 304	85.57 348	97.65 338	94.08 381	84.40 349	81.82 353	96.85 263	62.14 373	98.33 229	80.25 353	86.37 291	91.91 362

test0.0.03 1		93.86 210	93.61 202	94.64 255	95.02 305	92.18 259	99.93 76	98.58 87	94.07 138	87.96 306	98.50 207	93.90 91	94.96 360	81.33 347	93.17 240	96.78 247

UniMVSNet (Re)		93.07 234	92.13 241	95.88 217	94.84 306	96.24 138	99.88 103	98.98 38	92.49 204	89.25 281	95.40 310	87.09 210	97.14 291	93.13 229	78.16 350	94.26 283

USDC		90.00 301	88.96 302	93.10 313	94.81 307	88.16 332	98.71 296	95.54 364	93.66 156	83.75 345	97.20 248	65.58 362	98.31 231	83.96 332	87.49 285	92.85 349

VPNet		91.81 259	90.46 269	95.85 219	94.74 308	95.54 166	98.98 268	98.59 86	92.14 213	90.77 254	97.44 241	68.73 349	97.54 271	94.89 189	77.89 352	94.46 265

FIs		94.10 206	93.43 210	96.11 213	94.70 309	96.82 115	99.58 193	98.93 45	92.54 199	89.34 279	97.31 245	87.62 203	97.10 295	94.22 206	86.58 289	94.40 272

UniMVSNet_ETH3D		90.06 300	88.58 308	94.49 265	94.67 310	88.09 333	97.81 337	97.57 248	83.91 352	88.44 298	97.41 242	57.44 381	97.62 269	91.41 248	88.59 268	97.77 237

UniMVSNet_NR-MVSNet		92.95 236	92.11 242	95.49 225	94.61 311	95.28 177	99.83 133	99.08 33	91.49 232	89.21 284	96.86 262	87.14 209	96.73 317	93.20 225	77.52 355	94.46 265

test_fmvs2		89.47 309	89.70 286	88.77 353	94.54 312	75.74 382	99.83 133	94.70 377	94.71 110	91.08 249	96.82 267	54.46 384	97.78 264	92.87 232	88.27 273	92.80 350

WR-MVS		92.31 251	91.25 259	95.48 228	94.45 313	95.29 176	99.60 190	98.68 70	90.10 268	88.07 305	96.89 260	80.68 268	96.80 315	93.14 228	79.67 343	94.36 276

nrg030		93.51 223	92.53 236	96.45 203	94.36 314	97.20 100	99.81 138	97.16 290	91.60 229	89.86 265	97.46 240	86.37 218	97.68 266	95.88 171	80.31 339	94.46 265

tfpnnormal		89.29 312	87.61 318	94.34 273	94.35 315	94.13 210	98.95 272	98.94 41	83.94 350	84.47 341	95.51 305	74.84 322	97.39 274	77.05 368	80.41 337	91.48 365

FC-MVSNet-test		93.81 213	93.15 219	95.80 221	94.30 316	96.20 139	99.42 217	98.89 49	92.33 210	89.03 289	97.27 247	87.39 206	96.83 313	93.20 225	86.48 290	94.36 276

MS-PatchMatch		90.65 283	90.30 274	91.71 329	94.22 317	85.50 349	98.24 321	97.70 233	88.67 295	86.42 328	96.37 278	67.82 354	98.03 251	83.62 334	99.62 90	91.60 363

WR-MVS_H		91.30 268	90.35 272	94.15 276	94.17 318	92.62 251	99.17 247	98.94 41	88.87 291	86.48 327	94.46 346	84.36 237	96.61 322	88.19 293	78.51 348	93.21 343

DU-MVS		92.46 248	91.45 257	95.49 225	94.05 319	95.28 177	99.81 138	98.74 64	92.25 212	89.21 284	96.64 270	81.66 255	96.73 317	93.20 225	77.52 355	94.46 265

NR-MVSNet		91.56 267	90.22 276	95.60 223	94.05 319	95.76 154	98.25 320	98.70 67	91.16 246	80.78 359	96.64 270	83.23 247	96.57 323	91.41 248	77.73 354	94.46 265

CP-MVSNet		91.23 272	90.22 276	94.26 274	93.96 321	92.39 255	99.09 251	98.57 89	88.95 288	86.42 328	96.57 273	79.19 283	96.37 330	90.29 272	78.95 345	94.02 306

XXY-MVS		91.82 258	90.46 269	95.88 217	93.91 322	95.40 173	98.87 282	97.69 234	88.63 297	87.87 307	97.08 252	74.38 326	97.89 259	91.66 246	84.07 309	94.35 279

PS-CasMVS		90.63 285	89.51 292	93.99 285	93.83 323	91.70 274	98.98 268	98.52 104	88.48 299	86.15 332	96.53 275	75.46 315	96.31 334	88.83 285	78.86 347	93.95 314

test_0402		85.58 329	83.94 334	90.50 337	93.81 324	85.04 351	98.55 305	95.20 371	76.01 379	79.72 364	95.13 323	64.15 368	96.26 336	66.04 389	86.88 288	90.21 374

XVG-ACMP-BASELINE		91.22 273	90.75 264	92.63 320	93.73 325	85.61 347	98.52 309	97.44 261	92.77 185	89.90 264	96.85 263	66.64 359	98.39 220	92.29 237	88.61 266	93.89 319

TranMVSNet+NR-MVSNet		91.68 266	90.61 268	94.87 247	93.69 326	93.98 215	99.69 174	98.65 74	91.03 250	88.44 298	96.83 266	80.05 276	96.18 338	90.26 273	76.89 363	94.45 270

mvsmamba		94.10 206	93.72 201	95.25 236	93.57 327	94.13 210	99.67 178	96.45 344	93.63 158	91.34 247	97.77 235	86.29 219	97.22 287	96.65 161	88.10 276	94.40 272

TransMVSNet (Re)		87.25 323	85.28 330	93.16 310	93.56 328	91.03 283	98.54 307	94.05 383	83.69 354	81.09 357	96.16 283	75.32 316	96.40 329	76.69 369	68.41 381	92.06 359

v10		90.25 295	88.82 304	94.57 260	93.53 329	93.43 230	99.08 253	96.87 322	85.00 343	87.34 317	94.51 342	80.93 265	97.02 304	82.85 338	79.23 344	93.26 341

testgi		89.01 314	88.04 315	91.90 327	93.49 330	84.89 353	99.73 164	95.66 361	93.89 151	85.14 338	98.17 220	59.68 378	94.66 364	77.73 364	88.88 259	96.16 256

v8		90.54 287	89.17 297	94.66 254	93.43 331	93.40 232	99.20 244	96.94 316	85.76 334	87.56 311	94.51 342	81.96 253	97.19 288	84.94 326	78.25 349	93.38 339

V42		91.28 270	90.12 281	94.74 251	93.42 332	93.46 229	99.68 176	97.02 304	87.36 313	89.85 267	95.05 325	81.31 261	97.34 277	87.34 304	80.07 341	93.40 337

pm-mvs1		89.36 311	87.81 317	94.01 283	93.40 333	91.93 264	98.62 304	96.48 343	86.25 329	83.86 344	96.14 284	73.68 329	97.04 300	86.16 317	75.73 367	93.04 346

RRT_MVS		93.14 231	92.92 224	93.78 292	93.31 334	90.04 307	99.66 179	97.69 234	92.53 200	88.91 291	97.76 236	84.36 237	96.93 307	95.10 181	86.99 287	94.37 275

v1144		91.09 274	89.83 283	94.87 247	93.25 335	93.69 223	99.62 188	96.98 309	86.83 323	89.64 273	94.99 330	80.94 264	97.05 298	85.08 325	81.16 328	93.87 321

v1192		90.62 286	89.25 296	94.72 253	93.13 336	93.07 236	99.50 207	97.02 304	86.33 328	89.56 275	95.01 327	79.22 282	97.09 297	82.34 342	81.16 328	94.01 308

v2v482		91.30 268	90.07 282	95.01 242	93.13 336	93.79 218	99.77 148	97.02 304	88.05 305	89.25 281	95.37 314	80.73 267	97.15 290	87.28 305	80.04 342	94.09 302

OPM-MVS		93.21 228	92.80 227	94.44 268	93.12 338	90.85 290	99.77 148	97.61 243	96.19 75	91.56 244	98.65 194	75.16 321	98.47 209	93.78 217	89.39 255	93.99 311
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 281	89.52 291	94.59 258	93.11 339	92.77 242	99.56 197	96.99 307	86.38 327	89.82 268	94.95 332	80.50 272	97.10 295	83.98 331	80.41 337	93.90 318

PEN-MVS		90.19 297	89.06 300	93.57 300	93.06 340	90.90 288	99.06 258	98.47 115	88.11 304	85.91 334	96.30 279	76.67 302	95.94 348	87.07 308	76.91 362	93.89 319

v1240		90.20 296	88.79 305	94.44 268	93.05 341	92.27 257	99.38 223	96.92 318	85.89 332	89.36 278	94.87 334	77.89 294	97.03 302	80.66 350	81.08 331	94.01 308

v148		90.70 282	89.63 287	93.92 287	92.97 342	90.97 284	99.75 156	96.89 320	87.51 310	88.27 303	95.01 327	81.67 254	97.04 300	87.40 303	77.17 360	93.75 327

v1921920		90.46 288	89.12 298	94.50 264	92.96 343	92.46 253	99.49 209	96.98 309	86.10 330	89.61 274	95.30 317	78.55 291	97.03 302	82.17 343	80.89 335	94.01 308

Baseline_NR-MVSNet		90.33 292	89.51 292	92.81 318	92.84 344	89.95 310	99.77 148	93.94 384	84.69 348	89.04 288	95.66 297	81.66 255	96.52 324	90.99 256	76.98 361	91.97 361

test_method		80.79 349	79.70 353	84.08 364	92.83 345	67.06 390	99.51 205	95.42 365	54.34 396	81.07 358	93.53 354	44.48 392	92.22 383	78.90 360	77.23 359	92.94 347

pmmvs4		92.10 255	91.07 262	95.18 238	92.82 346	94.96 188	99.48 211	96.83 325	87.45 312	88.66 296	96.56 274	83.78 242	96.83 313	89.29 281	84.77 303	93.75 327

LF4IMVS		89.25 313	88.85 303	90.45 339	92.81 347	81.19 373	98.12 327	94.79 374	91.44 236	86.29 330	97.11 250	65.30 365	98.11 246	88.53 290	85.25 298	92.07 358

DTE-MVSNet		89.40 310	88.24 313	92.88 317	92.66 348	89.95 310	99.10 250	98.22 185	87.29 314	85.12 339	96.22 281	76.27 309	95.30 357	83.56 335	75.74 366	93.41 336

EU-MVSNet		90.14 299	90.34 273	89.54 345	92.55 349	81.06 374	98.69 299	98.04 207	91.41 240	86.59 324	96.84 265	80.83 266	93.31 376	86.20 316	81.91 322	94.26 283

APD_test1		81.15 348	80.92 349	81.86 368	92.45 350	59.76 397	96.04 368	93.61 387	73.29 388	77.06 373	96.64 270	44.28 393	96.16 339	72.35 376	82.52 316	89.67 379

our_test_3		90.39 289	89.48 294	93.12 311	92.40 351	89.57 315	99.33 229	96.35 347	87.84 308	85.30 337	94.99 330	84.14 240	96.09 343	80.38 351	84.56 304	93.71 332

ppachtmachnet_test		89.58 308	88.35 311	93.25 309	92.40 351	90.44 299	99.33 229	96.73 332	85.49 339	85.90 335	95.77 292	81.09 263	96.00 347	76.00 371	82.49 317	93.30 340

v7n		89.65 307	88.29 312	93.72 294	92.22 353	90.56 296	99.07 257	97.10 296	85.42 341	86.73 321	94.72 335	80.06 275	97.13 292	81.14 348	78.12 351	93.49 335

dmvs_testset		83.79 342	86.07 326	76.94 372	92.14 354	48.60 407	96.75 355	90.27 397	89.48 276	78.65 367	98.55 206	79.25 281	86.65 395	66.85 386	82.69 315	95.57 258

PS-MVSNAJss		93.64 220	93.31 216	94.61 256	92.11 355	92.19 258	99.12 249	97.38 268	92.51 203	88.45 297	96.99 258	91.20 154	97.29 284	94.36 201	87.71 281	94.36 276

pmmvs5		90.17 298	89.09 299	93.40 303	92.10 356	89.77 313	99.74 159	95.58 363	85.88 333	87.24 318	95.74 293	73.41 330	96.48 326	88.54 289	83.56 312	93.95 314

N_pmnet		80.06 352	80.78 350	77.89 371	91.94 357	45.28 409	98.80 290	56.82 411	78.10 376	80.08 362	93.33 355	77.03 297	95.76 350	68.14 384	82.81 314	92.64 351

test_djsdf		92.83 239	92.29 240	94.47 266	91.90 358	92.46 253	99.55 199	97.27 280	91.17 244	89.96 261	96.07 288	81.10 262	96.89 309	94.67 196	88.91 258	94.05 305

SixPastTwentyTwo		88.73 315	88.01 316	90.88 333	91.85 359	82.24 365	98.22 324	95.18 372	88.97 286	82.26 350	96.89 260	71.75 335	96.67 320	84.00 330	82.98 313	93.72 331

K. test v3		88.05 319	87.24 321	90.47 338	91.82 360	82.23 366	98.96 271	97.42 264	89.05 281	76.93 375	95.60 299	68.49 351	95.42 353	85.87 321	81.01 333	93.75 327

OurMVSNet-221017-0		89.81 304	89.48 294	90.83 335	91.64 361	81.21 372	98.17 326	95.38 367	91.48 234	85.65 336	97.31 245	72.66 331	97.29 284	88.15 294	84.83 302	93.97 313

mvs_tets		91.81 259	91.08 261	94.00 284	91.63 362	90.58 295	98.67 301	97.43 262	92.43 205	87.37 316	97.05 255	71.76 334	97.32 280	94.75 193	88.68 265	94.11 301

Gipuma		66.95 365	65.00 365	72.79 377	91.52 363	67.96 389	66.16 400	95.15 373	47.89 398	58.54 395	67.99 400	29.74 397	87.54 394	50.20 399	77.83 353	62.87 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 140	95.74 148	98.32 118	91.47 364	95.56 165	99.84 126	97.30 276	97.74 18	97.89 140	99.35 129	79.62 278	99.85 108	99.25 54	99.24 120	99.55 141

jajsoiax		91.92 257	91.18 260	94.15 276	91.35 365	90.95 287	99.00 267	97.42 264	92.61 194	87.38 315	97.08 252	72.46 332	97.36 275	94.53 199	88.77 262	94.13 300

MDA-MVSNet-bldmvs		84.09 340	81.52 347	91.81 328	91.32 366	88.00 335	98.67 301	95.92 356	80.22 370	55.60 398	93.32 356	68.29 353	93.60 374	73.76 373	76.61 364	93.82 325

MVP-Stereo		90.93 276	90.45 271	92.37 322	91.25 367	88.76 321	98.05 331	96.17 351	87.27 315	84.04 342	95.30 317	78.46 292	97.27 286	83.78 333	99.70 85	91.09 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 331	83.32 339	92.10 324	90.96 368	88.58 327	99.20 244	96.52 341	79.70 372	57.12 397	92.69 361	79.11 284	93.86 371	77.10 367	77.46 357	93.86 322

YYNet1		85.50 332	83.33 338	92.00 325	90.89 369	88.38 331	99.22 243	96.55 340	79.60 373	57.26 396	92.72 360	79.09 286	93.78 372	77.25 366	77.37 358	93.84 323

anonymousdsp		91.79 264	90.92 263	94.41 271	90.76 370	92.93 241	98.93 274	97.17 288	89.08 280	87.46 314	95.30 317	78.43 293	96.92 308	92.38 236	88.73 263	93.39 338

lessismore_v0				90.53 336	90.58 371	80.90 375		95.80 357		77.01 374	95.84 290	66.15 361	96.95 305	83.03 337	75.05 368	93.74 330

EG-PatchMatch MVS		85.35 333	83.81 336	89.99 343	90.39 372	81.89 368	98.21 325	96.09 353	81.78 364	74.73 381	93.72 353	51.56 389	97.12 294	79.16 359	88.61 266	90.96 368

EGC-MVSNET		69.38 358	63.76 368	86.26 361	90.32 373	81.66 371	96.24 364	93.85 385	0.99 408	3.22 409	92.33 366	52.44 386	92.92 379	59.53 395	84.90 301	84.21 389

CMPMVS	61.59 21	84.75 336	85.14 331	83.57 365	90.32 373	62.54 393	96.98 351	97.59 247	74.33 386	69.95 387	96.66 268	64.17 367	98.32 230	87.88 298	88.41 271	89.84 377
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 339	82.92 342	89.21 347	90.03 375	82.60 362	96.89 354	95.62 362	80.59 368	75.77 380	89.17 377	65.04 366	94.79 363	72.12 377	81.02 332	90.23 373

pmmvs6		85.69 328	83.84 335	91.26 332	90.00 376	84.41 355	97.82 336	96.15 352	75.86 380	81.29 356	95.39 312	61.21 376	96.87 311	83.52 336	73.29 370	92.50 354

DSMNet-mixed		88.28 318	88.24 313	88.42 355	89.64 377	75.38 384	98.06 330	89.86 398	85.59 338	88.20 304	92.14 367	76.15 311	91.95 384	78.46 361	96.05 196	97.92 233

UnsupCasMVSNet_eth		85.52 330	83.99 332	90.10 341	89.36 378	83.51 359	96.65 356	97.99 209	89.14 279	75.89 379	93.83 351	63.25 370	93.92 369	81.92 345	67.90 384	92.88 348

Anonymous20231206		86.32 326	85.42 329	89.02 349	89.11 379	80.53 378	99.05 262	95.28 368	85.43 340	82.82 348	93.92 350	74.40 325	93.44 375	66.99 385	81.83 323	93.08 345

Anonymous20240521		85.15 334	83.81 336	89.16 348	88.32 380	82.69 361	98.80 290	95.74 358	79.72 371	81.53 355	90.99 370	65.38 364	94.16 367	72.69 375	81.11 330	90.63 371

OpenMVS_ROB	79.82 20	83.77 343	81.68 346	90.03 342	88.30 381	82.82 360	98.46 310	95.22 370	73.92 387	76.00 378	91.29 369	55.00 383	96.94 306	68.40 383	88.51 270	90.34 372

test20.03		84.72 337	83.99 332	86.91 359	88.19 382	80.62 377	98.88 279	95.94 355	88.36 301	78.87 365	94.62 340	68.75 348	89.11 390	66.52 387	75.82 365	91.00 367

KD-MVS_self_test		83.59 344	82.06 344	88.20 356	86.93 383	80.70 376	97.21 344	96.38 345	82.87 358	82.49 349	88.97 378	67.63 355	92.32 382	73.75 374	62.30 393	91.58 364

MIMVSNet1		82.58 345	80.51 351	88.78 351	86.68 384	84.20 356	96.65 356	95.41 366	78.75 374	78.59 368	92.44 362	51.88 388	89.76 389	65.26 390	78.95 345	92.38 357

CL-MVSNet_self_test		84.50 338	83.15 341	88.53 354	86.00 385	81.79 369	98.82 287	97.35 270	85.12 342	83.62 346	90.91 372	76.66 303	91.40 385	69.53 381	60.36 394	92.40 356

UnsupCasMVSNet_bld		79.97 354	77.03 359	88.78 351	85.62 386	81.98 367	93.66 380	97.35 270	75.51 383	70.79 386	83.05 392	48.70 390	94.91 361	78.31 362	60.29 395	89.46 382

Patchmatch-RL test		86.90 324	85.98 328	89.67 344	84.45 387	75.59 383	89.71 393	92.43 391	86.89 322	77.83 372	90.94 371	94.22 80	93.63 373	87.75 299	69.61 376	99.79 97

pmmvs-eth3d		84.03 341	81.97 345	90.20 340	84.15 388	87.09 340	98.10 329	94.73 376	83.05 356	74.10 383	87.77 384	65.56 363	94.01 368	81.08 349	69.24 378	89.49 381

test_fmvs3		79.99 353	80.17 352	79.45 370	84.02 389	62.83 391	99.05 262	93.49 388	88.29 303	80.06 363	86.65 387	28.09 399	88.00 391	88.63 286	73.27 371	87.54 387

PM-MVS		80.47 350	78.88 355	85.26 362	83.79 390	72.22 386	95.89 371	91.08 395	85.71 337	76.56 377	88.30 380	36.64 395	93.90 370	82.39 341	69.57 377	89.66 380

new-patchmatchnet		81.19 347	79.34 354	86.76 360	82.86 391	80.36 379	97.92 333	95.27 369	82.09 363	72.02 384	86.87 386	62.81 372	90.74 388	71.10 378	63.08 391	89.19 384

mvsany_test3		82.12 346	81.14 348	85.06 363	81.87 392	70.41 387	97.09 348	92.14 392	91.27 243	77.84 371	88.73 379	39.31 394	95.49 351	90.75 263	71.24 373	89.29 383

WB-MVS		76.28 356	77.28 358	73.29 376	81.18 393	54.68 401	97.87 335	94.19 380	81.30 365	69.43 388	90.70 373	77.02 298	82.06 399	35.71 404	68.11 383	83.13 390

test_f		78.40 355	77.59 357	80.81 369	80.82 394	62.48 394	96.96 352	93.08 390	83.44 355	74.57 382	84.57 391	27.95 400	92.63 380	84.15 328	72.79 372	87.32 388

SSC-MVS		75.42 357	76.40 360	72.49 380	80.68 395	53.62 402	97.42 340	94.06 382	80.42 369	68.75 389	90.14 375	76.54 305	81.66 400	33.25 405	66.34 387	82.19 391

pmmvs3		80.27 351	77.77 356	87.76 358	80.32 396	82.43 364	98.23 323	91.97 393	72.74 389	78.75 366	87.97 383	57.30 382	90.99 387	70.31 379	62.37 392	89.87 376

testf1		68.38 361	66.92 362	72.78 378	78.80 397	50.36 404	90.95 391	87.35 403	55.47 394	58.95 393	88.14 381	20.64 404	87.60 392	57.28 396	64.69 388	80.39 393

APD_test2		68.38 361	66.92 362	72.78 378	78.80 397	50.36 404	90.95 391	87.35 403	55.47 394	58.95 393	88.14 381	20.64 404	87.60 392	57.28 396	64.69 388	80.39 393

ambc				83.23 366	77.17 399	62.61 392	87.38 395	94.55 379		76.72 376	86.65 387	30.16 396	96.36 331	84.85 327	69.86 375	90.73 370

test_vis3_rt		68.82 359	66.69 364	75.21 375	76.24 400	60.41 396	96.44 359	68.71 410	75.13 384	50.54 401	69.52 399	16.42 409	96.32 333	80.27 352	66.92 386	68.89 397

TDRefinement		84.76 335	82.56 343	91.38 331	74.58 401	84.80 354	97.36 342	94.56 378	84.73 347	80.21 361	96.12 287	63.56 369	98.39 220	87.92 297	63.97 390	90.95 369

E-PMN		52.30 369	52.18 371	52.67 386	71.51 402	45.40 408	93.62 381	76.60 408	36.01 402	43.50 403	64.13 402	27.11 401	67.31 405	31.06 406	26.06 401	45.30 404

EMVS		51.44 371	51.22 373	52.11 387	70.71 403	44.97 410	94.04 377	75.66 409	35.34 404	42.40 404	61.56 405	28.93 398	65.87 406	27.64 407	24.73 402	45.49 403

PMMVS2		67.15 364	64.15 367	76.14 374	70.56 404	62.07 395	93.89 378	87.52 402	58.09 393	60.02 392	78.32 394	22.38 403	84.54 397	59.56 394	47.03 399	81.80 392

FPMVS		68.72 360	68.72 361	68.71 382	65.95 405	44.27 411	95.97 370	94.74 375	51.13 397	53.26 399	90.50 374	25.11 402	83.00 398	60.80 393	80.97 334	78.87 395

wuyk23d		20.37 375	20.84 378	18.99 390	65.34 406	27.73 413	50.43 401	7.67 414	9.50 407	8.01 408	6.34 408	6.13 412	26.24 407	23.40 408	10.69 406	2.99 405

LCM-MVSNet		67.77 363	64.73 366	76.87 373	62.95 407	56.25 400	89.37 394	93.74 386	44.53 399	61.99 391	80.74 393	20.42 406	86.53 396	69.37 382	59.50 396	87.84 385

MVE	53.74 22	51.54 370	47.86 374	62.60 384	59.56 408	50.93 403	79.41 398	77.69 407	35.69 403	36.27 405	61.76 404	5.79 413	69.63 403	37.97 403	36.61 400	67.24 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 367	52.24 370	67.66 383	49.27 409	56.82 399	83.94 396	82.02 406	70.47 390	33.28 406	64.54 401	17.23 408	69.16 404	45.59 401	23.85 403	77.02 396

tmp_tt		65.23 366	62.94 369	72.13 381	44.90 410	50.03 406	81.05 397	89.42 401	38.45 400	48.51 402	99.90 18	54.09 385	78.70 402	91.84 245	18.26 404	87.64 386

PMVS	49.05 23	53.75 368	51.34 372	60.97 385	40.80 411	34.68 412	74.82 399	89.62 400	37.55 401	28.67 407	72.12 396	7.09 411	81.63 401	43.17 402	68.21 382	66.59 399
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 373	39.14 376	33.31 388	19.94 412	24.83 414	98.36 317	9.75 413	15.53 406	51.31 400	87.14 385	19.62 407	17.74 408	47.10 400	3.47 407	57.36 401

testmvs		40.60 372	44.45 375	29.05 389	19.49 413	14.11 415	99.68 176	18.47 412	20.74 405	64.59 390	98.48 211	10.95 410	17.09 409	56.66 398	11.01 405	55.94 402

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.02 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

eth-test2					0.00 414

eth-test					0.00 414

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		23.43 374	31.24 377	0.00 391	0.00 414	0.00 416	0.00 402	98.09 201	0.00 409	0.00 410	99.67 96	83.37 245	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		7.60 377	10.13 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	91.20 154	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		8.28 376	11.04 379	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	99.40 123	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 410	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						90.97 284								86.10 319

PC_three_1452									96.96 44	99.80 18	99.79 57	97.49 9	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 131	97.27 34	99.80 18	99.94 4	97.18 20	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 61	99.83 13	99.91 14	97.87 5	100.00 1	99.92 12	100.00 1	100.00 1

GSMVS																99.59 132

sam_mvs1												94.72 64				99.59 132

sam_mvs												94.25 79

MTGPA								98.28 179

test_post1							95.78 372				59.23 406	93.20 111	97.74 265	91.06 254

test_post											63.35 403	94.43 69	98.13 245

patchmatchnet-post											91.70 368	95.12 50	97.95 256

MTMP							99.87 106	96.49 342

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 66	99.94 69

test_prior2							99.95 53		95.78 81	99.73 33	99.76 65	96.00 33		99.78 27	100.00 1

旧先验2							99.46 214		94.21 132	99.85 9			99.95 69	96.96 155

新几何2							99.40 218

无先验							99.49 209	98.71 66	93.46 161				100.00 1	94.36 201		99.99 23

原ACMM2							99.90 91

testdata2													99.99 36	90.54 267

segment_acmp												96.68 25

testdata1							99.28 238		96.35 71

plane_prior5								97.87 223					98.37 226	97.79 131	89.55 252	94.52 262

plane_prior4											98.59 199

plane_prior3						91.64 276			96.63 56	93.01 225

plane_prior2							99.84 126		96.38 67

plane_prior						91.74 270	99.86 118		96.76 52						89.59 251

n2								0.00 415

nn								0.00 415

door-mid								89.69 399

test11								98.44 123

door								90.31 396

HQP5-MVS						91.85 266

BP-MVS														97.92 123

HQP4-MVS										93.37 221			98.39 220			94.53 260

HQP3-MVS								97.89 221							89.60 249

HQP2-MVS												80.65 269

MDTV_nov1_ep13_2view						96.26 134	96.11 366		91.89 221	98.06 134		94.40 71		94.30 203		99.67 115

ACMMP++_ref															87.04 286

ACMMP++															88.23 274

Test By Simon												92.82 122

High-resolution multi-view benchmark