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.

MSC_two_6792asdad				98.86 1	98.67 58	96.94 1		97.93 105					99.86 8	97.68 18	99.67 6	99.77 2

No_MVS				98.86 1	98.67 58	96.94 1		97.93 105					99.86 8	97.68 18	99.67 6	99.77 2

OPU-MVS				98.55 3	98.82 52	96.86 3	98.25 36				98.26 66	96.04 2	99.24 124	95.36 95	99.59 19	99.56 29

HPM-MVS++		97.34 17	96.97 27	98.47 5	99.08 36	96.16 4	97.55 121	97.97 101	95.59 11	96.61 74	97.89 90	92.57 35	99.84 23	95.95 73	99.51 34	99.40 54

test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 43	98.28 36					99.86 8	97.52 24	99.67 6	99.75 6

CNVR-MVS		97.68 6	97.44 13	98.37 7	98.90 50	95.86 6	97.27 151	98.08 74	95.81 9	97.87 36	98.31 60	94.26 13	99.68 54	97.02 35	99.49 39	99.57 26

DPE-MVS		97.86 4	97.65 8	98.47 5	99.17 32	95.78 7	97.21 160	98.35 27	95.16 22	98.71 20	98.80 22	95.05 10	99.89 3	96.70 43	99.73 1	99.73 10
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.28 25	95.74 8				98.10 29

DPM-MVS		95.69 80	94.92 94	98.01 19	98.08 104	95.71 9	95.27 293	97.62 145	90.43 197	95.55 117	97.07 148	91.72 47	99.50 99	89.62 210	98.94 94	98.82 113

SMA-MVS		97.35 16	97.03 24	98.30 8	99.06 38	95.42 10	97.94 73	98.18 57	90.57 194	98.85 15	98.94 9	93.33 23	99.83 26	96.72 42	99.68 4	99.63 17
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
DVP-MVS++		98.06 1	97.99 1	98.28 9	98.67 58	95.39 11	99.29 1	98.28 36	94.78 41	98.93 9	98.87 15	96.04 2	99.86 8	97.45 28	99.58 23	99.59 22

IU-MVS					99.42 7	95.39 11		97.94 104	90.40 199	98.94 8				97.41 31	99.66 11	99.74 8

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 43	97.85 116	94.92 32	98.73 18	98.87 15	95.08 8	99.84 23	97.52 24	99.67 6	99.48 44
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					99.45 3	95.36 13	98.31 28	98.29 34	94.92 32	98.99 7	98.92 10	95.08 8

MCST-MVS		97.18 21	96.84 33	98.20 14	99.30 24	95.35 15	97.12 167	98.07 79	93.54 85	96.08 98	97.69 106	93.86 16	99.71 46	96.50 49	99.39 55	99.55 32

3Dnovator+	91.43 4	95.40 87	94.48 110	98.16 16	96.90 172	95.34 16	98.48 21	97.87 111	94.65 49	88.53 289	98.02 82	83.69 174	99.71 46	93.18 140	98.96 93	99.44 49

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 36	98.27 39	95.13 23	99.19 4	98.89 13	95.54 5	99.85 18	97.52 24	99.66 11	99.56 29

test_241102_ONE					99.42 7	95.30 17		98.27 39	95.09 26	99.19 4	98.81 21	95.54 5	99.65 58

SF-MVS		97.39 15	97.13 16	98.17 15	99.02 42	95.28 19	98.23 40	98.27 39	92.37 129	98.27 27	98.65 29	93.33 23	99.72 45	96.49 50	99.52 31	99.51 37

test_one_0601					99.32 22	95.20 20		98.25 45	95.13 23	98.48 24	98.87 15	95.16 7

alignmvs		95.87 78	95.23 88	97.78 31	97.56 143	95.19 21	97.86 80	97.17 200	94.39 59	96.47 82	96.40 188	85.89 145	99.20 127	96.21 62	95.11 195	98.95 96

ACMMP_NAP		97.20 20	96.86 31	98.23 11	99.09 34	95.16 22	97.60 115	98.19 55	92.82 118	97.93 34	98.74 26	91.60 52	99.86 8	96.26 55	99.52 31	99.67 13

sasdasda		96.02 71	95.45 79	97.75 35	97.59 138	95.15 23	98.28 31	97.60 146	94.52 52	96.27 90	96.12 202	87.65 116	99.18 131	96.20 63	94.82 199	98.91 101

canonicalmvs		96.02 71	95.45 79	97.75 35	97.59 138	95.15 23	98.28 31	97.60 146	94.52 52	96.27 90	96.12 202	87.65 116	99.18 131	96.20 63	94.82 199	98.91 101

NCCC		97.30 18	97.03 24	98.11 17	98.77 53	95.06 25	97.34 143	98.04 89	95.96 6	97.09 56	97.88 92	93.18 25	99.71 46	95.84 78	99.17 76	99.56 29

MM		97.29 19	96.98 26	98.23 11	98.01 108	95.03 26	98.07 54	95.76 287	97.78 1	97.52 40	98.80 22	88.09 107	99.86 8	99.44 1	99.37 59	99.80 1

APD-MVS		96.95 32	96.60 47	98.01 19	99.03 41	94.93 27	97.72 99	98.10 72	91.50 153	98.01 31	98.32 59	92.33 39	99.58 77	94.85 105	99.51 34	99.53 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APDe-MVS		97.82 5	97.73 7	98.08 18	99.15 33	94.82 28	98.81 7	98.30 32	94.76 43	98.30 26	98.90 12	93.77 17	99.68 54	97.93 16	99.69 3	99.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS-pluss		96.70 47	96.27 61	97.98 21	99.23 30	94.71 29	96.96 179	98.06 82	90.67 185	95.55 117	98.78 25	91.07 65	99.86 8	96.58 46	99.55 27	99.38 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MGCFI-Net		95.94 76	95.40 83	97.56 46	97.59 138	94.62 30	98.21 43	97.57 151	94.41 57	96.17 94	96.16 200	87.54 120	99.17 133	96.19 65	94.73 204	98.91 101

ZD-MVS					99.05 39	94.59 31		98.08 74	89.22 229	97.03 58	98.10 73	92.52 36	99.65 58	94.58 116	99.31 63

nrg030		94.05 130	93.31 142	96.27 109	95.22 274	94.59 31	98.34 26	97.46 168	92.93 115	91.21 223	96.64 171	87.23 129	98.22 233	94.99 103	85.80 317	95.98 253

SD-MVS		97.41 14	97.53 11	97.06 68	98.57 69	94.46 33	97.92 75	98.14 64	94.82 38	99.01 6	98.55 33	94.18 14	97.41 328	96.94 36	99.64 14	99.32 62
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CDPH-MVS		95.97 74	95.38 84	97.77 33	98.93 47	94.44 34	96.35 233	97.88 109	86.98 298	96.65 72	97.89 90	91.99 45	99.47 102	92.26 152	99.46 42	99.39 56

MTAPA		97.08 25	96.78 39	97.97 22	99.37 16	94.42 35	97.24 154	98.08 74	95.07 27	96.11 96	98.59 30	90.88 70	99.90 2	96.18 66	99.50 36	99.58 25

MVS_0304		97.04 28	96.73 42	97.96 23	97.60 137	94.36 36	98.01 59	94.09 351	97.33 2	96.29 88	98.79 24	89.73 84	99.86 8	99.36 2	99.42 49	99.67 13

DeepC-MVS_fast	93.89 2	96.93 34	96.64 46	97.78 31	98.64 64	94.30 37	97.41 133	98.04 89	94.81 39	96.59 76	98.37 49	91.24 61	99.64 66	95.16 98	99.52 31	99.42 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter					98.91 49	94.28 38	97.02 172	98.02 94	95.35 16

test12				97.65 42	98.46 70	94.26 39		97.66 137		95.52 120		90.89 69	99.46 103		99.25 69	99.22 70

SteuartSystems-ACMMP		97.62 9	97.53 11	97.87 24	98.39 77	94.25 40	98.43 23	98.27 39	95.34 17	98.11 28	98.56 31	94.53 12	99.71 46	96.57 47	99.62 17	99.65 15
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		97.42 13	97.33 15	97.69 41	99.25 27	94.24 41	98.07 54	97.85 116	93.72 77	98.57 21	98.35 51	93.69 18	99.40 110	97.06 34	99.46 42	99.44 49
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TEST9					98.70 56	94.19 42	96.41 225	98.02 94	88.17 266	96.03 99	97.56 121	92.74 31	99.59 74

train_agg		96.30 62	95.83 73	97.72 38	98.70 56	94.19 42	96.41 225	98.02 94	88.58 253	96.03 99	97.56 121	92.73 32	99.59 74	95.04 100	99.37 59	99.39 56

DP-MVS Recon		95.68 81	95.12 92	97.37 51	99.19 31	94.19 42	97.03 170	98.08 74	88.35 262	95.09 127	97.65 111	89.97 81	99.48 101	92.08 161	98.59 107	98.44 145

GST-MVS		96.85 39	96.52 51	97.82 27	99.36 18	94.14 45	98.29 30	98.13 65	92.72 121	96.70 68	98.06 77	91.35 58	99.86 8	94.83 106	99.28 64	99.47 46

ZNCC-MVS		96.96 31	96.67 45	97.85 25	99.37 16	94.12 46	98.49 20	98.18 57	92.64 124	96.39 86	98.18 70	91.61 51	99.88 4	95.59 92	99.55 27	99.57 26

HFP-MVS		97.14 23	96.92 30	97.83 26	99.42 7	94.12 46	98.52 16	98.32 30	93.21 97	97.18 51	98.29 63	92.08 43	99.83 26	95.63 87	99.59 19	99.54 33

PHI-MVS		96.77 44	96.46 56	97.71 40	98.40 75	94.07 48	98.21 43	98.45 22	89.86 209	97.11 55	98.01 83	92.52 36	99.69 52	96.03 71	99.53 30	99.36 60

test_8					98.67 58	94.06 49	96.37 232	98.01 97	88.58 253	95.98 103	97.55 123	92.73 32	99.58 77

XVS		97.18 21	96.96 28	97.81 28	99.38 14	94.03 50	98.59 12	98.20 52	94.85 34	96.59 76	98.29 63	91.70 49	99.80 30	95.66 82	99.40 53	99.62 18

X-MVStestdata		91.71 218	89.67 279	97.81 28	99.38 14	94.03 50	98.59 12	98.20 52	94.85 34	96.59 76	32.69 408	91.70 49	99.80 30	95.66 82	99.40 53	99.62 18

ACMMPR		97.07 26	96.84 33	97.79 30	99.44 6	93.88 52	98.52 16	98.31 31	93.21 97	97.15 52	98.33 57	91.35 58	99.86 8	95.63 87	99.59 19	99.62 18

MP-MVS		96.77 44	96.45 57	97.72 38	99.39 13	93.80 53	98.41 24	98.06 82	93.37 92	95.54 119	98.34 54	90.59 74	99.88 4	94.83 106	99.54 29	99.49 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
agg_prior					98.67 58	93.79 54		98.00 98		95.68 113			99.57 84

region2R		97.07 26	96.84 33	97.77 33	99.46 2	93.79 54	98.52 16	98.24 47	93.19 100	97.14 53	98.34 54	91.59 53	99.87 7	95.46 94	99.59 19	99.64 16

MSP-MVS		97.59 10	97.54 10	97.73 37	99.40 11	93.77 56	98.53 15	98.29 34	95.55 13	98.56 22	97.81 99	93.90 15	99.65 58	96.62 44	99.21 72	99.77 2
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
test_prior4						93.66 57	96.42 224

新几何1				97.32 53	98.60 65	93.59 58		97.75 126	81.58 366	95.75 110	97.85 96	90.04 79	99.67 56	86.50 273	99.13 81	98.69 122

CP-MVS		97.02 29	96.81 37	97.64 44	99.33 21	93.54 59	98.80 8	98.28 36	92.99 108	96.45 84	98.30 62	91.90 46	99.85 18	95.61 89	99.68 4	99.54 33

PGM-MVS		96.81 42	96.53 50	97.65 42	99.35 20	93.53 60	97.65 106	98.98 2	92.22 131	97.14 53	98.44 44	91.17 64	99.85 18	94.35 118	99.46 42	99.57 26

mPP-MVS		96.86 37	96.60 47	97.64 44	99.40 11	93.44 61	98.50 19	98.09 73	93.27 96	95.95 104	98.33 57	91.04 66	99.88 4	95.20 97	99.57 25	99.60 21

TSAR-MVS + GP.		96.69 49	96.49 52	97.27 58	98.31 81	93.39 62	96.79 192	96.72 241	94.17 64	97.44 43	97.66 110	92.76 29	99.33 115	96.86 39	97.76 136	99.08 83

CANet		96.39 59	96.02 67	97.50 47	97.62 134	93.38 63	97.02 172	97.96 102	95.42 15	94.86 129	97.81 99	87.38 126	99.82 28	96.88 38	99.20 74	99.29 63

旧先验1					98.38 78	93.38 63		97.75 126			98.09 75	92.30 42			99.01 90	99.16 73

3Dnovator	91.36 5	95.19 96	94.44 112	97.44 49	96.56 196	93.36 65	98.65 11	98.36 24	94.12 65	89.25 274	98.06 77	82.20 210	99.77 37	93.41 137	99.32 62	99.18 72

FOURS1					99.55 1	93.34 66	99.29 1	98.35 27	94.98 29	98.49 23

UniMVSNet (Re)		93.31 156	92.55 168	95.61 149	95.39 257	93.34 66	97.39 138	98.71 11	93.14 104	90.10 245	94.83 263	87.71 114	98.03 262	91.67 172	83.99 344	95.46 277

SR-MVS		97.01 30	96.86 31	97.47 48	99.09 34	93.27 68	97.98 63	98.07 79	93.75 76	97.45 42	98.48 41	91.43 56	99.59 74	96.22 58	99.27 65	99.54 33

DELS-MVS		96.61 52	96.38 59	97.30 54	97.79 122	93.19 69	95.96 257	98.18 57	95.23 19	95.87 105	97.65 111	91.45 54	99.70 51	95.87 74	99.44 48	99.00 92
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
DeepC-MVS	93.07 3	96.06 68	95.66 74	97.29 55	97.96 111	93.17 70	97.30 149	98.06 82	93.92 71	93.38 164	98.66 27	86.83 132	99.73 42	95.60 91	99.22 71	98.96 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS		96.69 49	96.45 57	97.40 50	99.36 18	93.11 71	98.87 6	98.06 82	91.17 168	96.40 85	97.99 84	90.99 67	99.58 77	95.61 89	99.61 18	99.49 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
NR-MVSNet		92.34 195	91.27 212	95.53 154	94.95 288	93.05 72	97.39 138	98.07 79	92.65 123	84.46 346	95.71 225	85.00 156	97.77 297	89.71 206	83.52 351	95.78 262

test_prior				97.23 60	98.67 58	92.99 73		98.00 98					99.41 109			99.29 63

UA-Net		95.95 75	95.53 76	97.20 63	97.67 127	92.98 74	97.65 106	98.13 65	94.81 39	96.61 74	98.35 51	88.87 93	99.51 96	90.36 194	97.35 147	99.11 81

VNet		95.89 77	95.45 79	97.21 62	98.07 105	92.94 75	97.50 124	98.15 62	93.87 73	97.52 40	97.61 117	85.29 152	99.53 91	95.81 79	95.27 191	99.16 73

UniMVSNet_NR-MVSNet		93.37 154	92.67 163	95.47 160	95.34 263	92.83 76	97.17 163	98.58 17	92.98 113	90.13 241	95.80 218	88.37 105	97.85 288	91.71 169	83.93 345	95.73 269

DU-MVS		92.90 176	92.04 183	95.49 157	94.95 288	92.83 76	97.16 164	98.24 47	93.02 107	90.13 241	95.71 225	83.47 178	97.85 288	91.71 169	83.93 345	95.78 262

fmvsm_l_conf0.5_n		97.65 7	97.75 6	97.34 52	98.21 92	92.75 78	97.83 85	98.73 9	95.04 28	99.30 1	98.84 20	93.34 22	99.78 35	99.32 3	99.13 81	99.50 40

HPM-MVS_fast		96.51 55	96.27 61	97.22 61	99.32 22	92.74 79	98.74 9	98.06 82	90.57 194	96.77 65	98.35 51	90.21 77	99.53 91	94.80 109	99.63 16	99.38 58

OpenMVS	89.19 12	92.86 178	91.68 196	96.40 96	95.34 263	92.73 80	98.27 33	98.12 67	84.86 333	85.78 335	97.75 103	78.89 269	99.74 41	87.50 257	98.65 103	96.73 230

EPNet		95.20 95	94.56 104	97.14 65	92.80 357	92.68 81	97.85 83	94.87 334	96.64 3	92.46 180	97.80 101	86.23 139	99.65 58	93.72 131	98.62 105	99.10 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		93.45 152	92.27 178	96.98 71	96.77 183	92.62 82	98.39 25	98.12 67	84.50 338	88.27 296	97.77 102	82.39 207	99.81 29	85.40 292	98.81 98	98.51 134

ACMMP		96.27 63	95.93 68	97.28 57	99.24 28	92.62 82	98.25 36	98.81 5	92.99 108	94.56 136	98.39 48	88.96 92	99.85 18	94.57 117	97.63 137	99.36 60
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
fmvsm_l_conf0.5_n_a		97.63 8	97.76 5	97.26 59	98.25 86	92.59 84	97.81 89	98.68 13	94.93 30	99.24 3	98.87 15	93.52 20	99.79 33	99.32 3	99.21 72	99.40 54

CNLPA		94.28 118	93.53 130	96.52 82	98.38 78	92.55 85	96.59 216	96.88 232	90.13 204	91.91 197	97.24 138	85.21 153	99.09 146	87.64 253	97.83 132	97.92 179

PCF-MVS	89.48 11	91.56 228	89.95 267	96.36 101	96.60 191	92.52 86	92.51 368	97.26 195	79.41 378	88.90 278	96.56 180	84.04 171	99.55 87	77.01 362	97.30 150	97.01 219
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	89.66 9	93.87 137	92.95 150	96.63 76	97.10 158	92.49 87	95.64 276	96.64 249	89.05 235	93.00 172	95.79 221	85.77 148	99.45 105	89.16 225	94.35 207	97.96 177

ETV-MVS		96.02 71	95.89 71	96.40 96	97.16 154	92.44 88	97.47 130	97.77 124	94.55 50	96.48 81	94.51 278	91.23 63	98.92 168	95.65 85	98.19 123	97.82 187

VPA-MVSNet		93.24 158	92.48 173	95.51 155	95.70 242	92.39 89	97.86 80	98.66 16	92.30 130	92.09 195	95.37 241	80.49 236	98.40 217	93.95 124	85.86 316	95.75 267

test_fmvsmconf_n		97.49 12	97.56 9	97.29 55	97.44 146	92.37 90	97.91 76	98.88 4	95.83 8	98.92 12	99.05 5	91.45 54	99.80 30	99.12 6	99.46 42	99.69 12

SR-MVS-dyc-post		96.88 36	96.80 38	97.11 67	99.02 42	92.34 91	97.98 63	98.03 91	93.52 87	97.43 45	98.51 36	91.40 57	99.56 85	96.05 68	99.26 67	99.43 51

RE-MVS-def			96.72 43		99.02 42	92.34 91	97.98 63	98.03 91	93.52 87	97.43 45	98.51 36	90.71 72		96.05 68	99.26 67	99.43 51

APD-MVS_3200maxsize		96.81 42	96.71 44	97.12 66	99.01 45	92.31 93	97.98 63	98.06 82	93.11 105	97.44 43	98.55 33	90.93 68	99.55 87	96.06 67	99.25 69	99.51 37

MVS_111021_HR		96.68 51	96.58 49	96.99 70	98.46 70	92.31 93	96.20 246	98.90 3	94.30 62	95.86 106	97.74 104	92.33 39	99.38 113	96.04 70	99.42 49	99.28 65

FMVSNet3		91.78 216	90.69 237	95.03 176	96.53 201	92.27 95	97.02 172	96.93 224	89.79 214	89.35 268	94.65 272	77.01 290	97.47 322	86.12 280	88.82 289	95.35 286

test_fmvsmconf0.1_n		97.09 24	97.06 19	97.19 64	95.67 244	92.21 96	97.95 72	98.27 39	95.78 10	98.40 25	99.00 6	89.99 80	99.78 35	99.06 7	99.41 52	99.59 22

test222					98.24 87	92.21 96	95.33 288	97.60 146	79.22 379	95.25 122	97.84 98	88.80 95			99.15 79	98.72 119

FMVSNet2		91.31 243	90.08 261	94.99 178	96.51 203	92.21 96	97.41 133	96.95 222	88.82 246	88.62 286	94.75 267	73.87 316	97.42 327	85.20 295	88.55 294	95.35 286

MAR-MVS		94.22 119	93.46 135	96.51 85	98.00 110	92.19 99	97.67 103	97.47 165	88.13 269	93.00 172	95.84 215	84.86 158	99.51 96	87.99 240	98.17 125	97.83 186
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
CANet_DTU		94.37 115	93.65 125	96.55 80	96.46 208	92.13 100	96.21 245	96.67 248	94.38 60	93.53 160	97.03 150	79.34 257	99.71 46	90.76 187	98.45 114	97.82 187

TranMVSNet+NR-MVSNet		92.50 187	91.63 197	95.14 170	94.76 300	92.07 101	97.53 122	98.11 70	92.90 116	89.56 262	96.12 202	83.16 184	97.60 311	89.30 217	83.20 354	95.75 267

WTY-MVS		94.71 111	94.02 116	96.79 72	97.71 126	92.05 102	96.59 216	97.35 189	90.61 191	94.64 134	96.93 153	86.41 138	99.39 111	91.20 181	94.71 205	98.94 97

FIs		94.09 128	93.70 123	95.27 165	95.70 242	92.03 103	98.10 51	98.68 13	93.36 94	90.39 233	96.70 166	87.63 118	97.94 279	92.25 154	90.50 276	95.84 257

API-MVS		94.84 107	94.49 109	95.90 131	97.90 117	92.00 104	97.80 90	97.48 162	89.19 230	94.81 130	96.71 164	88.84 94	99.17 133	88.91 229	98.76 100	96.53 233

sss		94.51 113	93.80 121	96.64 74	97.07 159	91.97 105	96.32 236	98.06 82	88.94 240	94.50 138	96.78 161	84.60 160	99.27 122	91.90 162	96.02 174	98.68 123

ab-mvs		93.57 148	92.55 168	96.64 74	97.28 149	91.96 106	95.40 285	97.45 173	89.81 213	93.22 170	96.28 193	79.62 254	99.46 103	90.74 188	93.11 231	98.50 135

MSLP-MVS++		96.94 33	97.06 19	96.59 79	98.72 55	91.86 107	97.67 103	98.49 19	94.66 48	97.24 50	98.41 47	92.31 41	98.94 166	96.61 45	99.46 42	98.96 94

test_fmvsmconf0.01_n		96.15 66	95.85 72	97.03 69	92.66 360	91.83 108	97.97 69	97.84 120	95.57 12	97.53 39	99.00 6	84.20 168	99.76 38	98.82 11	99.08 85	99.48 44

MVSMamba_pp		96.06 68	95.92 69	96.50 88	97.00 168	91.81 109	97.33 146	97.77 124	92.49 126	96.78 64	97.19 141	88.50 103	99.07 153	96.54 48	99.67 6	98.60 126

test_fmvsmvis_n_1920		96.70 47	96.84 33	96.31 103	96.62 189	91.73 110	97.98 63	98.30 32	96.19 5	96.10 97	98.95 8	89.42 85	99.76 38	98.90 10	99.08 85	97.43 205

test_fmvsm_n_1920		97.55 11	97.89 3	96.53 81	98.41 74	91.73 110	98.01 59	99.02 1	96.37 4	99.30 1	98.92 10	92.39 38	99.79 33	99.16 5	99.46 42	98.08 173

xiu_mvs_v1_base_debu		95.01 98	94.76 97	95.75 138	96.58 193	91.71 112	96.25 241	97.35 189	92.99 108	96.70 68	96.63 175	82.67 198	99.44 106	96.22 58	97.46 140	96.11 249

xiu_mvs_v1_base		95.01 98	94.76 97	95.75 138	96.58 193	91.71 112	96.25 241	97.35 189	92.99 108	96.70 68	96.63 175	82.67 198	99.44 106	96.22 58	97.46 140	96.11 249

xiu_mvs_v1_base_debi		95.01 98	94.76 97	95.75 138	96.58 193	91.71 112	96.25 241	97.35 189	92.99 108	96.70 68	96.63 175	82.67 198	99.44 106	96.22 58	97.46 140	96.11 249

AdaColmap		94.34 116	93.68 124	96.31 103	98.59 66	91.68 115	96.59 216	97.81 121	89.87 208	92.15 191	97.06 149	83.62 177	99.54 89	89.34 216	98.07 127	97.70 192

CS-MVS-test		96.89 35	97.04 23	96.45 93	98.29 82	91.66 116	99.03 4	97.85 116	95.84 7	96.90 60	97.97 86	91.24 61	98.75 186	96.92 37	99.33 61	98.94 97

114514_t		93.95 133	93.06 147	96.63 76	99.07 37	91.61 117	97.46 132	97.96 102	77.99 383	93.00 172	97.57 119	86.14 144	99.33 115	89.22 221	99.15 79	98.94 97

LS3D		93.57 148	92.61 166	96.47 90	97.59 138	91.61 117	97.67 103	97.72 131	85.17 328	90.29 235	98.34 54	84.60 160	99.73 42	83.85 313	98.27 120	98.06 174

MVS		91.71 218	90.44 244	95.51 155	95.20 276	91.59 119	96.04 252	97.45 173	73.44 392	87.36 315	95.60 232	85.42 151	99.10 142	85.97 284	97.46 140	95.83 258

Vis-MVSNet		95.23 93	94.81 96	96.51 85	97.18 153	91.58 120	98.26 35	98.12 67	94.38 60	94.90 128	98.15 72	82.28 208	98.92 168	91.45 176	98.58 108	99.01 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ET-MVSNet_ETH3D		91.49 232	90.11 260	95.63 146	96.40 211	91.57 121	95.34 287	93.48 363	90.60 193	75.58 385	95.49 238	80.08 244	96.79 350	94.25 119	89.76 282	98.52 132

EC-MVSNet		96.42 57	96.47 53	96.26 110	97.01 167	91.52 122	98.89 5	97.75 126	94.42 56	96.64 73	97.68 107	89.32 86	98.60 202	97.45 28	99.11 84	98.67 124

casdiffmvs_mvg		95.81 79	95.57 75	96.51 85	96.87 173	91.49 123	97.50 124	97.56 155	93.99 69	95.13 126	97.92 89	87.89 112	98.78 181	95.97 72	97.33 148	99.26 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
iter_conf05		96.12 67	96.06 66	96.29 107	98.07 105	91.48 124	97.25 153	97.65 139	90.43 197	94.65 133	97.52 124	91.29 60	99.19 128	98.12 15	99.56 26	98.22 158

CPTT-MVS		95.57 85	95.19 89	96.70 73	99.27 26	91.48 124	98.33 27	98.11 70	87.79 279	95.17 125	98.03 80	87.09 130	99.61 69	93.51 133	99.42 49	99.02 86

Effi-MVS+		94.93 103	94.45 111	96.36 101	96.61 190	91.47 126	96.41 225	97.41 182	91.02 174	94.50 138	95.92 211	87.53 121	98.78 181	93.89 127	96.81 160	98.84 112

CDS-MVSNet		94.14 126	93.54 129	95.93 130	96.18 221	91.46 127	96.33 235	97.04 215	88.97 239	93.56 157	96.51 182	87.55 119	97.89 286	89.80 204	95.95 176	98.44 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FC-MVSNet-test		93.94 134	93.57 127	95.04 175	95.48 252	91.45 128	98.12 50	98.71 11	93.37 92	90.23 236	96.70 166	87.66 115	97.85 288	91.49 174	90.39 277	95.83 258

PAPR		94.18 120	93.42 140	96.48 89	97.64 131	91.42 129	95.55 278	97.71 135	88.99 237	92.34 187	95.82 217	89.19 87	99.11 141	86.14 279	97.38 145	98.90 104

SDMVSNet		94.17 121	93.61 126	95.86 133	98.09 101	91.37 130	97.35 142	98.20 52	93.18 101	91.79 201	97.28 134	79.13 260	98.93 167	94.61 115	92.84 234	97.28 213

MVS_111021_LR		96.24 64	96.19 63	96.39 98	98.23 91	91.35 131	96.24 244	98.79 6	93.99 69	95.80 108	97.65 111	89.92 82	99.24 124	95.87 74	99.20 74	98.58 128

OMC-MVS		95.09 97	94.70 100	96.25 113	98.46 70	91.28 132	96.43 223	97.57 151	92.04 140	94.77 131	97.96 87	87.01 131	99.09 146	91.31 178	96.77 161	98.36 152

LFMVS		93.60 146	92.63 164	96.52 82	98.13 100	91.27 133	97.94 73	93.39 364	90.57 194	96.29 88	98.31 60	69.00 346	99.16 135	94.18 120	95.87 178	99.12 80

test_yl		94.78 109	94.23 114	96.43 94	97.74 124	91.22 134	96.85 186	97.10 205	91.23 165	95.71 111	96.93 153	84.30 165	99.31 119	93.10 141	95.12 193	98.75 116

DCV-MVSNet		94.78 109	94.23 114	96.43 94	97.74 124	91.22 134	96.85 186	97.10 205	91.23 165	95.71 111	96.93 153	84.30 165	99.31 119	93.10 141	95.12 193	98.75 116

MVSFormer		95.37 88	95.16 90	95.99 129	96.34 214	91.21 136	98.22 41	97.57 151	91.42 157	96.22 92	97.32 131	86.20 142	97.92 282	94.07 121	99.05 87	98.85 110

lupinMVS		94.99 102	94.56 104	96.29 107	96.34 214	91.21 136	95.83 264	96.27 267	88.93 241	96.22 92	96.88 158	86.20 142	98.85 175	95.27 96	99.05 87	98.82 113

EI-MVSNet-Vis-set		96.51 55	96.47 53	96.63 76	98.24 87	91.20 138	96.89 183	97.73 129	94.74 44	96.49 80	98.49 38	90.88 70	99.58 77	96.44 51	98.32 118	99.13 77

UGNet		94.04 131	93.28 143	96.31 103	96.85 174	91.19 139	97.88 79	97.68 136	94.40 58	93.00 172	96.18 197	73.39 322	99.61 69	91.72 168	98.46 113	98.13 167
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
GBi-Net		91.35 240	90.27 252	94.59 200	96.51 203	91.18 140	97.50 124	96.93 224	88.82 246	89.35 268	94.51 278	73.87 316	97.29 334	86.12 280	88.82 289	95.31 289

test1		91.35 240	90.27 252	94.59 200	96.51 203	91.18 140	97.50 124	96.93 224	88.82 246	89.35 268	94.51 278	73.87 316	97.29 334	86.12 280	88.82 289	95.31 289

FMVSNet1		89.88 292	88.31 303	94.59 200	95.41 256	91.18 140	97.50 124	96.93 224	86.62 304	87.41 313	94.51 278	65.94 369	97.29 334	83.04 317	87.43 303	95.31 289

CS-MVS		96.86 37	97.06 19	96.26 110	98.16 98	91.16 143	99.09 3	97.87 111	95.30 18	97.06 57	98.03 80	91.72 47	98.71 192	97.10 33	99.17 76	98.90 104

PLC	91.00 6	94.11 127	93.43 138	96.13 119	98.58 68	91.15 144	96.69 203	97.39 183	87.29 293	91.37 211	96.71 164	88.39 104	99.52 95	87.33 260	97.13 156	97.73 190
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
原ACMM1				96.38 99	98.59 66	91.09 145		97.89 107	87.41 290	95.22 124	97.68 107	90.25 76	99.54 89	87.95 241	99.12 83	98.49 137

1112_ss		93.37 154	92.42 175	96.21 114	97.05 164	90.99 146	96.31 237	96.72 241	86.87 301	89.83 253	96.69 168	86.51 136	99.14 138	88.12 238	93.67 225	98.50 135

DP-MVS		92.76 183	91.51 204	96.52 82	98.77 53	90.99 146	97.38 140	96.08 276	82.38 358	89.29 271	97.87 93	83.77 173	99.69 52	81.37 335	96.69 165	98.89 107

VPNet		92.23 203	91.31 209	94.99 178	95.56 248	90.96 148	97.22 159	97.86 115	92.96 114	90.96 225	96.62 178	75.06 307	98.20 235	91.90 162	83.65 350	95.80 260

bld_raw_dy_0_64		94.33 117	93.90 119	95.62 148	97.64 131	90.95 149	95.17 298	97.47 165	82.34 359	91.28 219	96.84 160	89.10 90	99.04 160	96.27 54	99.00 91	96.85 226

XXY-MVS		92.16 205	91.23 214	94.95 183	94.75 301	90.94 150	97.47 130	97.43 180	89.14 231	88.90 278	96.43 186	79.71 251	98.24 231	89.56 211	87.68 300	95.67 271

EI-MVSNet-UG-set		96.34 61	96.30 60	96.47 90	98.20 93	90.93 151	96.86 185	97.72 131	94.67 47	96.16 95	98.46 42	90.43 75	99.58 77	96.23 57	97.96 130	98.90 104

jason		94.84 107	94.39 113	96.18 117	95.52 250	90.93 151	96.09 250	96.52 256	89.28 227	96.01 102	97.32 131	84.70 159	98.77 184	95.15 99	98.91 96	98.85 110
jason: jason.
PVSNet_Blended_VisFu		95.27 91	94.91 95	96.38 99	98.20 93	90.86 153	97.27 151	98.25 45	90.21 200	94.18 145	97.27 136	87.48 123	99.73 42	93.53 132	97.77 135	98.55 129

mvsmamba		93.83 139	93.46 135	94.93 186	94.88 295	90.85 154	98.55 14	95.49 302	94.24 63	91.29 218	96.97 152	83.04 189	98.14 241	95.56 93	91.17 263	95.78 262

WR-MVS		92.34 195	91.53 201	94.77 195	95.13 281	90.83 155	96.40 229	97.98 100	91.88 144	89.29 271	95.54 236	82.50 203	97.80 293	89.79 205	85.27 325	95.69 270

PatchMatch-RL		92.90 176	92.02 185	95.56 151	98.19 95	90.80 156	95.27 293	97.18 198	87.96 271	91.86 200	95.68 228	80.44 237	98.99 163	84.01 308	97.54 139	96.89 225

pmmvs4		90.93 261	89.85 271	94.17 224	93.34 348	90.79 157	94.60 310	96.02 277	84.62 336	87.45 311	95.15 249	81.88 217	97.45 324	87.70 248	87.87 299	94.27 344

OPM-MVS		93.28 157	92.76 157	94.82 188	94.63 307	90.77 158	96.65 207	97.18 198	93.72 77	91.68 205	97.26 137	79.33 258	98.63 199	92.13 158	92.28 242	95.07 302
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline1		92.82 181	91.90 189	95.55 153	97.20 152	90.77 158	97.19 161	94.58 340	92.20 133	92.36 184	96.34 191	84.16 169	98.21 234	89.20 223	83.90 348	97.68 193

iter_conf05_11		96.17 65	96.16 64	96.21 114	97.48 145	90.74 160	98.14 49	97.80 122	92.80 119	97.34 48	97.29 133	88.54 102	99.10 142	96.40 52	99.64 14	98.80 115

fmvsm_s_conf0.5_n_a		96.75 46	96.93 29	96.20 116	97.64 131	90.72 161	98.00 61	98.73 9	94.55 50	98.91 13	99.08 3	88.22 106	99.63 67	98.91 9	98.37 116	98.25 156

fmvsm_s_conf0.1_n_a		96.40 58	96.47 53	96.16 118	95.48 252	90.69 162	97.91 76	98.33 29	94.07 66	98.93 9	99.14 1	87.44 124	99.61 69	98.63 13	98.32 118	98.18 162

PAPM_NR		95.01 98	94.59 102	96.26 110	98.89 51	90.68 163	97.24 154	97.73 129	91.80 145	92.93 177	96.62 178	89.13 89	99.14 138	89.21 222	97.78 134	98.97 93

PS-MVSNAJ		95.37 88	95.33 86	95.49 157	97.35 148	90.66 164	95.31 290	97.48 162	93.85 74	96.51 79	95.70 227	88.65 98	99.65 58	94.80 109	98.27 120	96.17 244

IS-MVSNet		94.90 104	94.52 108	96.05 123	97.67 127	90.56 165	98.44 22	96.22 270	93.21 97	93.99 149	97.74 104	85.55 150	98.45 214	89.98 199	97.86 131	99.14 76

MG-MVS		95.61 83	95.38 84	96.31 103	98.42 73	90.53 166	96.04 252	97.48 162	93.47 89	95.67 114	98.10 73	89.17 88	99.25 123	91.27 179	98.77 99	99.13 77

xiu_mvs_v2_base		95.32 90	95.29 87	95.40 162	97.22 150	90.50 167	95.44 284	97.44 177	93.70 79	96.46 83	96.18 197	88.59 101	99.53 91	94.79 111	97.81 133	96.17 244

CSCG		96.05 70	95.91 70	96.46 92	99.24 28	90.47 168	98.30 29	98.57 18	89.01 236	93.97 151	97.57 119	92.62 34	99.76 38	94.66 112	99.27 65	99.15 75

casdiffmvs		95.64 82	95.49 77	96.08 120	96.76 186	90.45 169	97.29 150	97.44 177	94.00 68	95.46 121	97.98 85	87.52 122	98.73 188	95.64 86	97.33 148	99.08 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TAMVS		94.01 132	93.46 135	95.64 145	96.16 223	90.45 169	96.71 200	96.89 231	89.27 228	93.46 162	96.92 156	87.29 127	97.94 279	88.70 233	95.74 181	98.53 131

baseline		95.58 84	95.42 82	96.08 120	96.78 181	90.41 171	97.16 164	97.45 173	93.69 80	95.65 115	97.85 96	87.29 127	98.68 194	95.66 82	97.25 152	99.13 77

VDDNet		93.05 168	92.07 182	96.02 126	96.84 175	90.39 172	98.08 53	95.85 284	86.22 312	95.79 109	98.46 42	67.59 356	99.19 128	94.92 104	94.85 197	98.47 140

fmvsm_s_conf0.5_n		96.85 39	97.13 16	96.04 124	98.07 105	90.28 173	97.97 69	98.76 8	94.93 30	98.84 16	99.06 4	88.80 95	99.65 58	99.06 7	98.63 104	98.18 162

fmvsm_s_conf0.1_n		96.58 54	96.77 40	96.01 128	96.67 188	90.25 174	97.91 76	98.38 23	94.48 54	98.84 16	99.14 1	88.06 108	99.62 68	98.82 11	98.60 106	98.15 166

h-mvs33		94.15 123	93.52 132	96.04 124	97.81 121	90.22 175	97.62 114	97.58 150	95.19 20	96.74 66	97.45 125	83.67 175	99.61 69	95.85 76	79.73 368	98.29 155

tfpnnormal		89.70 297	88.40 302	93.60 256	95.15 279	90.10 176	97.56 118	98.16 61	87.28 294	86.16 333	94.63 273	77.57 287	98.05 258	74.48 371	84.59 337	92.65 365

Fast-Effi-MVS+		93.46 151	92.75 159	95.59 150	96.77 183	90.03 177	96.81 191	97.13 202	88.19 265	91.30 215	94.27 294	86.21 141	98.63 199	87.66 252	96.46 171	98.12 168

plane_prior6					96.10 229	90.00 178						81.32 224

plane_prior3						90.00 178			94.46 55	91.34 212

HQP_MVS		93.78 142	93.43 138	94.82 188	96.21 218	89.99 180	97.74 94	97.51 159	94.85 34	91.34 212	96.64 171	81.32 224	98.60 202	93.02 146	92.23 243	95.86 254

plane_prior						89.99 180	97.24 154		94.06 67						92.16 247

plane_prior7					96.21 218	89.98 182

Test_1112_low_res		92.84 180	91.84 191	95.85 134	97.04 165	89.97 183	95.53 280	96.64 249	85.38 323	89.65 259	95.18 248	85.86 146	99.10 142	87.70 248	93.58 230	98.49 137

VDD-MVS		93.82 140	93.08 146	96.02 126	97.88 118	89.96 184	97.72 99	95.85 284	92.43 127	95.86 106	98.44 44	68.42 353	99.39 111	96.31 53	94.85 197	98.71 121

HyFIR lowres test		93.66 145	92.92 151	95.87 132	98.24 87	89.88 185	94.58 311	98.49 19	85.06 330	93.78 154	95.78 222	82.86 194	98.67 195	91.77 167	95.71 183	99.07 85

PAPM		91.52 231	90.30 250	95.20 167	95.30 269	89.83 186	93.38 354	96.85 235	86.26 311	88.59 287	95.80 218	84.88 157	98.15 240	75.67 367	95.93 177	97.63 194

NP-MVS					95.99 233	89.81 187					95.87 213

GeoE		93.89 136	93.28 143	95.72 142	96.96 171	89.75 188	98.24 39	96.92 228	89.47 222	92.12 193	97.21 140	84.42 163	98.39 221	87.71 247	96.50 168	99.01 89

EIA-MVS		95.53 86	95.47 78	95.71 143	97.06 162	89.63 189	97.82 87	97.87 111	93.57 81	93.92 152	95.04 253	90.61 73	98.95 165	94.62 114	98.68 102	98.54 130

pm-mvs1		90.72 268	89.65 281	93.96 237	94.29 321	89.63 189	97.79 91	96.82 237	89.07 233	86.12 334	95.48 239	78.61 272	97.78 295	86.97 268	81.67 360	94.46 335

TAPA-MVS	90.10 7	92.30 198	91.22 215	95.56 151	98.33 80	89.60 191	96.79 192	97.65 139	81.83 363	91.52 207	97.23 139	87.94 111	98.91 170	71.31 384	98.37 116	98.17 165
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVSTER		93.20 160	92.81 156	94.37 213	96.56 196	89.59 192	97.06 169	97.12 203	91.24 164	91.30 215	95.96 209	82.02 213	98.05 258	93.48 134	90.55 274	95.47 276

EPP-MVSNet		95.22 94	95.04 93	95.76 136	97.49 144	89.56 193	98.67 10	97.00 219	90.69 183	94.24 143	97.62 116	89.79 83	98.81 179	93.39 138	96.49 169	98.92 100

anonymousdsp		92.16 205	91.55 200	93.97 236	92.58 362	89.55 194	97.51 123	97.42 181	89.42 224	88.40 291	94.84 262	80.66 233	97.88 287	91.87 164	91.28 261	94.48 334

MVS_Test		94.89 105	94.62 101	95.68 144	96.83 177	89.55 194	96.70 201	97.17 200	91.17 168	95.60 116	96.11 206	87.87 113	98.76 185	93.01 148	97.17 155	98.72 119

LTVRE_ROB	88.41 13	90.99 257	89.92 269	94.19 223	96.18 221	89.55 194	96.31 237	97.09 207	87.88 274	85.67 336	95.91 212	78.79 270	98.57 206	81.50 330	89.98 279	94.44 337
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
1314		92.81 182	92.03 184	95.14 170	95.33 266	89.52 197	96.04 252	97.44 177	87.72 283	86.25 332	95.33 242	83.84 172	98.79 180	89.26 219	97.05 157	97.11 218

thres600view7		92.49 189	91.60 198	95.18 168	97.91 116	89.47 198	97.65 106	94.66 337	92.18 137	93.33 165	94.91 258	78.06 282	99.10 142	81.61 329	94.06 220	96.98 220

WR-MVS_H		92.00 210	91.35 206	93.95 238	95.09 283	89.47 198	98.04 57	98.68 13	91.46 155	88.34 292	94.68 270	85.86 146	97.56 313	85.77 287	84.24 342	94.82 319

PVSNet_BlendedMVS		94.06 129	93.92 118	94.47 208	98.27 83	89.46 200	96.73 197	98.36 24	90.17 201	94.36 140	95.24 247	88.02 109	99.58 77	93.44 135	90.72 272	94.36 339

PVSNet_Blended		94.87 106	94.56 104	95.81 135	98.27 83	89.46 200	95.47 283	98.36 24	88.84 244	94.36 140	96.09 207	88.02 109	99.58 77	93.44 135	98.18 124	98.40 148

Anonymous20240529		91.98 211	90.73 234	95.73 141	98.14 99	89.40 202	97.99 62	97.72 131	79.63 377	93.54 159	97.41 129	69.94 342	99.56 85	91.04 184	91.11 265	98.22 158

CHOSEN 1792x2688		94.15 123	93.51 133	96.06 122	98.27 83	89.38 203	95.18 297	98.48 21	85.60 320	93.76 155	97.11 146	83.15 185	99.61 69	91.33 177	98.72 101	99.19 71

thres100view900		92.43 190	91.58 199	94.98 180	97.92 115	89.37 204	97.71 101	94.66 337	92.20 133	93.31 166	94.90 259	78.06 282	99.08 149	81.40 332	94.08 216	96.48 236

diffmvs		95.25 92	95.13 91	95.63 146	96.43 210	89.34 205	95.99 256	97.35 189	92.83 117	96.31 87	97.37 130	86.44 137	98.67 195	96.26 55	97.19 154	98.87 109
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HQP5-MVS						89.33 206

HQP-MVS		93.19 161	92.74 160	94.54 206	95.86 235	89.33 206	96.65 207	97.39 183	93.55 82	90.14 237	95.87 213	80.95 227	98.50 210	92.13 158	92.10 248	95.78 262

tfpn200view9		92.38 193	91.52 202	94.95 183	97.85 119	89.29 208	97.41 133	94.88 331	92.19 135	93.27 168	94.46 283	78.17 278	99.08 149	81.40 332	94.08 216	96.48 236

thres400		92.42 191	91.52 202	95.12 172	97.85 119	89.29 208	97.41 133	94.88 331	92.19 135	93.27 168	94.46 283	78.17 278	99.08 149	81.40 332	94.08 216	96.98 220

PS-MVSNAJss		93.74 143	93.51 133	94.44 210	93.91 329	89.28 210	97.75 93	97.56 155	92.50 125	89.94 249	96.54 181	88.65 98	98.18 238	93.83 130	90.90 270	95.86 254

gg-mvs-nofinetune		87.82 316	85.61 328	94.44 210	94.46 313	89.27 211	91.21 377	84.61 403	80.88 369	89.89 252	74.98 399	71.50 329	97.53 317	85.75 288	97.21 153	96.51 234

sd_testset		93.10 165	92.45 174	95.05 174	98.09 101	89.21 212	96.89 183	97.64 142	93.18 101	91.79 201	97.28 134	75.35 306	98.65 197	88.99 227	92.84 234	97.28 213

GG-mvs-BLEND				93.62 255	93.69 336	89.20 213	92.39 370	83.33 405		87.98 304	89.84 373	71.00 333	96.87 348	82.08 328	95.40 189	94.80 322

CLD-MVS		92.98 171	92.53 170	94.32 217	96.12 228	89.20 213	95.28 291	97.47 165	92.66 122	89.90 250	95.62 231	80.58 234	98.40 217	92.73 150	92.40 241	95.38 284
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		90.63 271	89.42 286	94.27 222	98.24 87	89.19 215	98.05 56	97.89 107	79.95 375	88.25 297	94.96 255	72.56 325	98.13 242	89.70 207	85.14 327	95.49 273

cascas		91.20 248	90.08 261	94.58 204	94.97 286	89.16 216	93.65 348	97.59 149	79.90 376	89.40 266	92.92 337	75.36 305	98.36 223	92.14 157	94.75 202	96.23 240

thisisatest0530		93.03 169	92.21 180	95.49 157	97.07 159	89.11 217	97.49 129	92.19 375	90.16 202	94.09 147	96.41 187	76.43 297	99.05 157	90.38 193	95.68 184	98.31 154

thres200		92.23 203	91.39 205	94.75 197	97.61 135	89.03 218	96.60 215	95.09 321	92.08 139	93.28 167	94.00 307	78.39 276	99.04 160	81.26 337	94.18 212	96.19 243

F-COLMAP		93.58 147	92.98 149	95.37 163	98.40 75	88.98 219	97.18 162	97.29 194	87.75 282	90.49 231	97.10 147	85.21 153	99.50 99	86.70 270	96.72 164	97.63 194

MSDG		91.42 235	90.24 254	94.96 182	97.15 156	88.91 220	93.69 346	96.32 265	85.72 319	86.93 326	96.47 184	80.24 241	98.98 164	80.57 339	95.05 196	96.98 220

thisisatest0515		92.29 199	91.30 210	95.25 166	96.60 191	88.90 221	94.36 321	92.32 374	87.92 272	93.43 163	94.57 275	77.28 289	99.00 162	89.42 214	95.86 179	97.86 183

testdata				95.46 161	98.18 97	88.90 221		97.66 137	82.73 356	97.03 58	98.07 76	90.06 78	98.85 175	89.67 208	98.98 92	98.64 125

Anonymous202405211		92.07 208	90.83 229	95.76 136	98.19 95	88.75 223	97.58 116	95.00 324	86.00 315	93.64 156	97.45 125	66.24 367	99.53 91	90.68 190	92.71 237	99.01 89

ACMM	89.79 8	92.96 172	92.50 172	94.35 214	96.30 216	88.71 224	97.58 116	97.36 188	91.40 159	90.53 230	96.65 170	79.77 250	98.75 186	91.24 180	91.64 253	95.59 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_djsdf		93.07 167	92.76 157	94.00 233	93.49 343	88.70 225	98.22 41	97.57 151	91.42 157	90.08 247	95.55 235	82.85 195	97.92 282	94.07 121	91.58 255	95.40 282

XVG-OURS		93.72 144	93.35 141	94.80 193	97.07 159	88.61 226	94.79 306	97.46 168	91.97 143	93.99 149	97.86 95	81.74 219	98.88 172	92.64 151	92.67 239	96.92 224

hse-mvs2		93.45 152	92.99 148	94.81 190	97.02 166	88.59 227	96.69 203	96.47 259	95.19 20	96.74 66	96.16 200	83.67 175	98.48 213	95.85 76	79.13 372	97.35 210

AUN-MVS		91.76 217	90.75 232	94.81 190	97.00 168	88.57 228	96.65 207	96.49 258	89.63 216	92.15 191	96.12 202	78.66 271	98.50 210	90.83 185	79.18 371	97.36 208

CP-MVSNet		91.89 214	91.24 213	93.82 245	95.05 284	88.57 228	97.82 87	98.19 55	91.70 148	88.21 298	95.76 223	81.96 214	97.52 319	87.86 242	84.65 334	95.37 285

FA-MVS(test-final)		93.52 150	92.92 151	95.31 164	96.77 183	88.54 230	94.82 305	96.21 272	89.61 217	94.20 144	95.25 246	83.24 182	99.14 138	90.01 198	96.16 173	98.25 156

XVG-OURS-SEG-HR		93.86 138	93.55 128	94.81 190	97.06 162	88.53 231	95.28 291	97.45 173	91.68 149	94.08 148	97.68 107	82.41 206	98.90 171	93.84 129	92.47 240	96.98 220

jajsoiax		92.42 191	91.89 190	94.03 232	93.33 349	88.50 232	97.73 96	97.53 157	92.00 142	88.85 281	96.50 183	75.62 304	98.11 246	93.88 128	91.56 256	95.48 274

V42		91.58 227	90.87 224	93.73 249	94.05 326	88.50 232	97.32 147	96.97 220	88.80 249	89.71 255	94.33 289	82.54 202	98.05 258	89.01 226	85.07 329	94.64 332

TransMVSNet (Re)		88.94 303	87.56 309	93.08 277	94.35 317	88.45 234	97.73 96	95.23 315	87.47 288	84.26 349	95.29 243	79.86 249	97.33 332	79.44 349	74.44 383	93.45 355

tt0805		91.09 252	90.07 264	94.16 225	95.61 245	88.31 235	97.56 118	96.51 257	89.56 218	89.17 275	95.64 230	67.08 363	98.38 222	91.07 183	88.44 295	95.80 260

mvs_tets		92.31 197	91.76 192	93.94 240	93.41 346	88.29 236	97.63 112	97.53 157	92.04 140	88.76 284	96.45 185	74.62 312	98.09 250	93.91 126	91.48 257	95.45 278

PS-CasMVS		91.55 229	90.84 228	93.69 253	94.96 287	88.28 237	97.84 84	98.24 47	91.46 155	88.04 302	95.80 218	79.67 252	97.48 321	87.02 267	84.54 339	95.31 289

LPG-MVS_test		92.94 174	92.56 167	94.10 227	96.16 223	88.26 238	97.65 106	97.46 168	91.29 160	90.12 243	97.16 143	79.05 262	98.73 188	92.25 154	91.89 251	95.31 289

LGP-MVS_train				94.10 227	96.16 223	88.26 238		97.46 168	91.29 160	90.12 243	97.16 143	79.05 262	98.73 188	92.25 154	91.89 251	95.31 289

v1144		91.37 239	90.60 239	93.68 254	93.89 330	88.23 240	96.84 188	97.03 217	88.37 261	89.69 257	94.39 285	82.04 212	97.98 267	87.80 244	85.37 322	94.84 316

MVP-Stereo		90.74 267	90.08 261	92.71 290	93.19 351	88.20 241	95.86 262	96.27 267	86.07 314	84.86 344	94.76 266	77.84 285	97.75 298	83.88 312	98.01 128	92.17 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	89.59 10	92.62 186	92.14 181	94.05 230	96.40 211	88.20 241	97.36 141	97.25 197	91.52 152	88.30 294	96.64 171	78.46 274	98.72 191	91.86 165	91.48 257	95.23 296
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v2v482		91.59 225	90.85 227	93.80 246	93.87 331	88.17 243	96.94 180	96.88 232	89.54 219	89.53 263	94.90 259	81.70 220	98.02 263	89.25 220	85.04 331	95.20 297

v10		91.04 255	90.23 255	93.49 261	94.12 323	88.16 244	97.32 147	97.08 208	88.26 264	88.29 295	94.22 299	82.17 211	97.97 270	86.45 274	84.12 343	94.33 340

v8		91.29 245	90.53 243	93.57 259	94.15 322	88.12 245	97.34 143	97.06 212	88.99 237	88.32 293	94.26 296	83.08 187	98.01 264	87.62 254	83.92 347	94.57 333

Baseline_NR-MVSNet		91.20 248	90.62 238	92.95 281	93.83 332	88.03 246	97.01 175	95.12 320	88.42 260	89.70 256	95.13 251	83.47 178	97.44 325	89.66 209	83.24 353	93.37 356

BH-RMVSNet		92.72 185	91.97 187	94.97 181	97.16 154	87.99 247	96.15 248	95.60 297	90.62 190	91.87 199	97.15 145	78.41 275	98.57 206	83.16 315	97.60 138	98.36 152

FE-MVS		92.05 209	91.05 219	95.08 173	96.83 177	87.93 248	93.91 339	95.70 290	86.30 309	94.15 146	94.97 254	76.59 293	99.21 126	84.10 306	96.86 158	98.09 172

Vis-MVSNet (Re-imp)		94.15 123	93.88 120	94.95 183	97.61 135	87.92 249	98.10 51	95.80 286	92.22 131	93.02 171	97.45 125	84.53 162	97.91 285	88.24 237	97.97 129	99.02 86

ACMH	87.59 16	90.53 273	89.42 286	93.87 243	96.21 218	87.92 249	97.24 154	96.94 223	88.45 259	83.91 356	96.27 194	71.92 326	98.62 201	84.43 303	89.43 285	95.05 304
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		91.20 248	90.44 244	93.48 262	94.49 312	87.91 251	97.76 92	98.18 57	91.29 160	87.78 306	95.74 224	80.35 239	97.33 332	85.46 291	82.96 355	95.19 300

UniMVSNet_ETH3D		91.34 242	90.22 257	94.68 198	94.86 296	87.86 252	97.23 158	97.46 168	87.99 270	89.90 250	96.92 156	66.35 365	98.23 232	90.30 195	90.99 268	97.96 177

ETVMVS		90.52 274	89.14 293	94.67 199	96.81 180	87.85 253	95.91 260	93.97 355	89.71 215	92.34 187	92.48 344	65.41 371	97.96 274	81.37 335	94.27 210	98.21 160

v1192		91.07 253	90.23 255	93.58 258	93.70 335	87.82 254	96.73 197	97.07 210	87.77 280	89.58 260	94.32 291	80.90 231	97.97 270	86.52 272	85.48 320	94.95 306

MIMVSNet		88.50 310	86.76 320	93.72 251	94.84 297	87.77 255	91.39 373	94.05 352	86.41 308	87.99 303	92.59 342	63.27 375	95.82 363	77.44 356	92.84 234	97.57 201

IB-MVS	87.33 17	89.91 289	88.28 304	94.79 194	95.26 273	87.70 256	95.12 300	93.95 356	89.35 226	87.03 321	92.49 343	70.74 335	99.19 128	89.18 224	81.37 362	97.49 203
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
GA-MVS		91.38 237	90.31 249	94.59 200	94.65 306	87.62 257	94.34 322	96.19 273	90.73 181	90.35 234	93.83 311	71.84 327	97.96 274	87.22 262	93.61 228	98.21 160

v7n		90.76 265	89.86 270	93.45 264	93.54 340	87.60 258	97.70 102	97.37 186	88.85 243	87.65 308	94.08 305	81.08 226	98.10 247	84.68 300	83.79 349	94.66 331

TR-MVS		91.48 233	90.59 240	94.16 225	96.40 211	87.33 259	95.67 272	95.34 310	87.68 284	91.46 209	95.52 237	76.77 292	98.35 224	82.85 320	93.61 228	96.79 229

testing222		90.31 278	88.96 295	94.35 214	96.54 199	87.29 260	95.50 281	93.84 359	90.97 175	91.75 203	92.96 336	62.18 380	98.00 265	82.86 318	94.08 216	97.76 189

FMVSNet5		87.29 321	85.79 327	91.78 314	94.80 299	87.28 261	95.49 282	95.28 311	84.09 342	83.85 357	91.82 357	62.95 377	94.17 380	78.48 352	85.34 324	93.91 349

CHOSEN 280x420		93.12 164	92.72 162	94.34 216	96.71 187	87.27 262	90.29 382	97.72 131	86.61 305	91.34 212	95.29 243	84.29 167	98.41 216	93.25 139	98.94 94	97.35 210

pmmvs-eth3d		86.22 332	84.45 339	91.53 319	88.34 390	87.25 263	94.47 315	95.01 323	83.47 351	79.51 377	89.61 374	69.75 344	95.71 364	83.13 316	76.73 379	91.64 375

DTE-MVSNet		90.56 272	89.75 277	93.01 278	93.95 327	87.25 263	97.64 110	97.65 139	90.74 180	87.12 318	95.68 228	79.97 247	97.00 344	83.33 314	81.66 361	94.78 326

v144192		91.06 254	90.28 251	93.39 265	93.66 338	87.23 265	96.83 189	97.07 210	87.43 289	89.69 257	94.28 293	81.48 222	98.00 265	87.18 264	84.92 333	94.93 310

CR-MVSNet		90.82 264	89.77 275	93.95 238	94.45 314	87.19 266	90.23 383	95.68 294	86.89 300	92.40 181	92.36 349	80.91 229	97.05 340	81.09 338	93.95 221	97.60 199

RPMNet		88.98 302	87.05 316	94.77 195	94.45 314	87.19 266	90.23 383	98.03 91	77.87 385	92.40 181	87.55 388	80.17 243	99.51 96	68.84 389	93.95 221	97.60 199

tttt0517		92.96 172	92.33 177	94.87 187	97.11 157	87.16 268	97.97 69	92.09 376	90.63 189	93.88 153	97.01 151	76.50 294	99.06 156	90.29 196	95.45 188	98.38 150

COLMAP_ROB	87.81 15	90.40 277	89.28 289	93.79 247	97.95 112	87.13 269	96.92 181	95.89 283	82.83 355	86.88 328	97.18 142	73.77 319	99.29 121	78.44 353	93.62 227	94.95 306
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
miper_enhance_ethall		91.54 230	91.01 221	93.15 274	95.35 262	87.07 270	93.97 334	96.90 229	86.79 302	89.17 275	93.43 332	86.55 135	97.64 306	89.97 200	86.93 307	94.74 328

EI-MVSNet		93.03 169	92.88 153	93.48 262	95.77 240	86.98 271	96.44 221	97.12 203	90.66 187	91.30 215	97.64 114	86.56 134	98.05 258	89.91 201	90.55 274	95.41 279

IterMVS-LS		92.29 199	91.94 188	93.34 267	96.25 217	86.97 272	96.57 219	97.05 213	90.67 185	89.50 265	94.80 265	86.59 133	97.64 306	89.91 201	86.11 315	95.40 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		90.85 263	90.03 266	93.29 269	93.55 339	86.96 273	96.74 196	97.04 215	87.36 291	89.52 264	94.34 288	80.23 242	97.97 270	86.27 275	85.21 326	94.94 308

mvsany_test1		93.93 135	93.98 117	93.78 248	94.94 290	86.80 274	94.62 309	92.55 373	88.77 250	96.85 61	98.49 38	88.98 91	98.08 251	95.03 101	95.62 185	96.46 238

cl22		91.21 247	90.56 242	93.14 275	96.09 230	86.80 274	94.41 319	96.58 255	87.80 278	88.58 288	93.99 308	80.85 232	97.62 309	89.87 203	86.93 307	94.99 305

v1240		90.70 269	89.85 271	93.23 271	93.51 342	86.80 274	96.61 213	97.02 218	87.16 296	89.58 260	94.31 292	79.55 255	97.98 267	85.52 290	85.44 321	94.90 313

PMMVS		92.86 178	92.34 176	94.42 212	94.92 291	86.73 277	94.53 313	96.38 263	84.78 335	94.27 142	95.12 252	83.13 186	98.40 217	91.47 175	96.49 169	98.12 168

AllTest		90.23 282	88.98 294	93.98 234	97.94 113	86.64 278	96.51 220	95.54 300	85.38 323	85.49 338	96.77 162	70.28 337	99.15 136	80.02 343	92.87 232	96.15 246

TestCases				93.98 234	97.94 113	86.64 278		95.54 300	85.38 323	85.49 338	96.77 162	70.28 337	99.15 136	80.02 343	92.87 232	96.15 246

Patchmtry		88.64 309	87.25 312	92.78 288	94.09 324	86.64 278	89.82 387	95.68 294	80.81 371	87.63 309	92.36 349	80.91 229	97.03 341	78.86 351	85.12 328	94.67 330

DeepPCF-MVS	93.97 1	96.61 52	97.09 18	95.15 169	98.09 101	86.63 281	96.00 255	98.15 62	95.43 14	97.95 33	98.56 31	93.40 21	99.36 114	96.77 40	99.48 40	99.45 47

miper_ehance_all_eth		91.59 225	91.13 218	92.97 280	95.55 249	86.57 282	94.47 315	96.88 232	87.77 280	88.88 280	94.01 306	86.22 140	97.54 315	89.49 212	86.93 307	94.79 324

testing11		91.68 221	90.75 232	94.47 208	96.53 201	86.56 283	95.76 269	94.51 342	91.10 172	91.24 222	93.59 323	68.59 350	98.86 173	91.10 182	94.29 209	98.00 176

testing91		91.90 213	91.02 220	94.53 207	96.54 199	86.55 284	95.86 262	95.64 296	91.77 146	91.89 198	93.47 328	69.94 342	98.86 173	90.23 197	93.86 223	98.18 162

test_cas_vis1_n_1920		94.48 114	94.55 107	94.28 221	96.78 181	86.45 285	97.63 112	97.64 142	93.32 95	97.68 38	98.36 50	73.75 320	99.08 149	96.73 41	99.05 87	97.31 212

ACMH+	87.92 14	90.20 284	89.18 291	93.25 270	96.48 206	86.45 285	96.99 176	96.68 246	88.83 245	84.79 345	96.22 196	70.16 339	98.53 208	84.42 304	88.04 297	94.77 327

baseline2		91.63 222	90.86 225	93.94 240	94.33 318	86.32 287	95.92 259	91.64 380	89.37 225	86.94 325	94.69 269	81.62 221	98.69 193	88.64 234	94.57 206	96.81 228

c3_l		91.38 237	90.89 223	92.88 284	95.58 247	86.30 288	94.68 308	96.84 236	88.17 266	88.83 283	94.23 297	85.65 149	97.47 322	89.36 215	84.63 335	94.89 314

pmmvs6		87.81 317	86.19 324	92.69 291	91.32 372	86.30 288	97.34 143	96.41 262	80.59 374	84.05 355	94.37 287	67.37 358	97.67 303	84.75 299	79.51 370	94.09 347

pmmvs5		89.86 294	88.87 297	92.82 286	92.86 355	86.23 290	96.26 240	95.39 304	84.24 340	87.12 318	94.51 278	74.27 314	97.36 331	87.61 255	87.57 301	94.86 315

cl____		90.96 260	90.32 248	92.89 283	95.37 260	86.21 291	94.46 317	96.64 249	87.82 276	88.15 300	94.18 300	82.98 191	97.54 315	87.70 248	85.59 318	94.92 312

DIV-MVS_self_test		90.97 259	90.33 247	92.88 284	95.36 261	86.19 292	94.46 317	96.63 252	87.82 276	88.18 299	94.23 297	82.99 190	97.53 317	87.72 245	85.57 319	94.93 310

BH-untuned		92.94 174	92.62 165	93.92 242	97.22 150	86.16 293	96.40 229	96.25 269	90.06 205	89.79 254	96.17 199	83.19 183	98.35 224	87.19 263	97.27 151	97.24 215

testing99		91.62 223	90.72 235	94.32 217	96.48 206	86.11 294	95.81 265	94.76 335	91.55 151	91.75 203	93.44 329	68.55 351	98.82 177	90.43 191	93.69 224	98.04 175

XVG-ACMP-BASELINE		90.93 261	90.21 258	93.09 276	94.31 320	85.89 295	95.33 288	97.26 195	91.06 173	89.38 267	95.44 240	68.61 349	98.60 202	89.46 213	91.05 266	94.79 324

v148		90.99 257	90.38 246	92.81 287	93.83 332	85.80 296	96.78 194	96.68 246	89.45 223	88.75 285	93.93 310	82.96 193	97.82 292	87.83 243	83.25 352	94.80 322

BH-w/o		92.14 207	91.75 193	93.31 268	96.99 170	85.73 297	95.67 272	95.69 292	88.73 251	89.26 273	94.82 264	82.97 192	98.07 255	85.26 294	96.32 172	96.13 248

test0.0.03 1		89.37 300	88.70 298	91.41 323	92.47 364	85.63 298	95.22 296	92.70 371	91.11 170	86.91 327	93.65 321	79.02 264	93.19 389	78.00 355	89.18 287	95.41 279

test_0402		86.46 328	84.79 337	91.45 321	95.02 285	85.55 299	96.29 239	94.89 330	80.90 368	82.21 364	93.97 309	68.21 354	97.29 334	62.98 393	88.68 293	91.51 378

D2MVS		91.30 244	90.95 222	92.35 296	94.71 304	85.52 300	96.18 247	98.21 51	88.89 242	86.60 329	93.82 313	79.92 248	97.95 278	89.29 218	90.95 269	93.56 352

Fast-Effi-MVS+-dtu		92.29 199	91.99 186	93.21 273	95.27 270	85.52 300	97.03 170	96.63 252	92.09 138	89.11 277	95.14 250	80.33 240	98.08 251	87.54 256	94.74 203	96.03 252

ECVR-MVS		93.19 161	92.73 161	94.57 205	97.66 129	85.41 302	98.21 43	88.23 394	93.43 90	94.70 132	98.21 67	72.57 324	99.07 153	93.05 145	98.49 110	99.25 68

mvs_anonymous		93.82 140	93.74 122	94.06 229	96.44 209	85.41 302	95.81 265	97.05 213	89.85 211	90.09 246	96.36 190	87.44 124	97.75 298	93.97 123	96.69 165	99.02 86

patch_mono-2		96.83 41	97.44 13	95.01 177	99.05 39	85.39 304	96.98 177	98.77 7	94.70 45	97.99 32	98.66 27	93.61 19	99.91 1	97.67 20	99.50 36	99.72 11

ITE_SJBPF				92.43 295	95.34 263	85.37 305		95.92 279	91.47 154	87.75 307	96.39 189	71.00 333	97.96 274	82.36 326	89.86 281	93.97 348

KD-MVS_2432*1600		84.81 343	82.64 347	91.31 324	91.07 374	85.34 306	91.22 375	95.75 288	85.56 321	83.09 360	90.21 369	67.21 359	95.89 359	77.18 360	62.48 399	92.69 363

miper_refine_blended		84.81 343	82.64 347	91.31 324	91.07 374	85.34 306	91.22 375	95.75 288	85.56 321	83.09 360	90.21 369	67.21 359	95.89 359	77.18 360	62.48 399	92.69 363

dmvs_re		90.21 283	89.50 284	92.35 296	95.47 255	85.15 308	95.70 271	94.37 346	90.94 176	88.42 290	93.57 324	74.63 311	95.67 366	82.80 321	89.57 284	96.22 241

Patchmatch-test		89.42 299	87.99 306	93.70 252	95.27 270	85.11 309	88.98 390	94.37 346	81.11 367	87.10 320	93.69 317	82.28 208	97.50 320	74.37 373	94.76 201	98.48 139

PatchT		88.87 306	87.42 310	93.22 272	94.08 325	85.10 310	89.51 388	94.64 339	81.92 362	92.36 184	88.15 384	80.05 245	97.01 343	72.43 380	93.65 226	97.54 202

EG-PatchMatch MVS		87.02 325	85.44 329	91.76 316	92.67 359	85.00 311	96.08 251	96.45 260	83.41 352	79.52 376	93.49 326	57.10 386	97.72 300	79.34 350	90.87 271	92.56 366

USDC		88.94 303	87.83 308	92.27 300	94.66 305	84.96 312	93.86 340	95.90 281	87.34 292	83.40 358	95.56 234	67.43 357	98.19 237	82.64 325	89.67 283	93.66 351

SCA		91.84 215	91.18 217	93.83 244	95.59 246	84.95 313	94.72 307	95.58 299	90.82 177	92.25 189	93.69 317	75.80 301	98.10 247	86.20 277	95.98 175	98.45 142

ADS-MVSNet		89.89 291	88.68 299	93.53 260	95.86 235	84.89 314	90.93 378	95.07 322	83.23 353	91.28 219	91.81 358	79.01 266	97.85 288	79.52 345	91.39 259	97.84 184

MIMVSNet1		84.93 342	83.05 344	90.56 339	89.56 383	84.84 315	95.40 285	95.35 307	83.91 343	80.38 372	92.21 353	57.23 385	93.34 388	70.69 387	82.75 358	93.50 353

MS-PatchMatch		90.27 280	89.77 275	91.78 314	94.33 318	84.72 316	95.55 278	96.73 240	86.17 313	86.36 331	95.28 245	71.28 331	97.80 293	84.09 307	98.14 126	92.81 362

test1111		93.19 161	92.82 155	94.30 220	97.58 142	84.56 317	98.21 43	89.02 392	93.53 86	94.58 135	98.21 67	72.69 323	99.05 157	93.06 144	98.48 112	99.28 65

eth_miper_zixun_eth		91.02 256	90.59 240	92.34 298	95.33 266	84.35 318	94.10 331	96.90 229	88.56 255	88.84 282	94.33 289	84.08 170	97.60 311	88.77 232	84.37 341	95.06 303

TDRefinement		86.53 327	84.76 338	91.85 309	82.23 402	84.25 319	96.38 231	95.35 307	84.97 332	84.09 353	94.94 256	65.76 370	98.34 227	84.60 302	74.52 382	92.97 359

EPMVS		90.70 269	89.81 273	93.37 266	94.73 303	84.21 320	93.67 347	88.02 395	89.50 221	92.38 183	93.49 326	77.82 286	97.78 295	86.03 283	92.68 238	98.11 171

IterMVS-SCA-FT		90.31 278	89.81 273	91.82 311	95.52 250	84.20 321	94.30 325	96.15 274	90.61 191	87.39 314	94.27 294	75.80 301	96.44 353	87.34 259	86.88 311	94.82 319

dcpmvs_2		96.37 60	97.05 22	94.31 219	98.96 46	84.11 322	97.56 118	97.51 159	93.92 71	97.43 45	98.52 35	92.75 30	99.32 117	97.32 32	99.50 36	99.51 37

PatchmatchNet		91.91 212	91.35 206	93.59 257	95.38 258	84.11 322	93.15 358	95.39 304	89.54 219	92.10 194	93.68 319	82.82 196	98.13 242	84.81 298	95.32 190	98.52 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OpenMVS_ROB	81.14 20	84.42 345	82.28 351	90.83 332	90.06 379	84.05 324	95.73 270	94.04 353	73.89 391	80.17 375	91.53 361	59.15 382	97.64 306	66.92 391	89.05 288	90.80 384

test2506		91.60 224	90.78 230	94.04 231	97.66 129	83.81 325	98.27 33	75.53 409	93.43 90	95.23 123	98.21 67	67.21 359	99.07 153	93.01 148	98.49 110	99.25 68

miper_lstm_enhance		90.50 276	90.06 265	91.83 310	95.33 266	83.74 326	93.86 340	96.70 245	87.56 287	87.79 305	93.81 314	83.45 180	96.92 346	87.39 258	84.62 336	94.82 319

IterMVS		90.15 286	89.67 279	91.61 318	95.48 252	83.72 327	94.33 323	96.12 275	89.99 206	87.31 317	94.15 302	75.78 303	96.27 356	86.97 268	86.89 310	94.83 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPNet_dtu		91.71 218	91.28 211	92.99 279	93.76 334	83.71 328	96.69 203	95.28 311	93.15 103	87.02 322	95.95 210	83.37 181	97.38 330	79.46 348	96.84 159	97.88 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	86.66 18	92.24 202	91.74 195	93.73 249	97.77 123	83.69 329	92.88 363	96.72 241	87.91 273	93.00 172	94.86 261	78.51 273	99.05 157	86.53 271	97.45 144	98.47 140

ppachtmachnet_test		88.35 312	87.29 311	91.53 319	92.45 365	83.57 330	93.75 343	95.97 278	84.28 339	85.32 341	94.18 300	79.00 268	96.93 345	75.71 366	84.99 332	94.10 345

MDA-MVSNet-bldmvs		85.00 341	82.95 346	91.17 329	93.13 353	83.33 331	94.56 312	95.00 324	84.57 337	65.13 398	92.65 339	70.45 336	95.85 361	73.57 377	77.49 375	94.33 340

Effi-MVS+-dtu		93.08 166	93.21 145	92.68 292	96.02 232	83.25 332	97.14 166	96.72 241	93.85 74	91.20 224	93.44 329	83.08 187	98.30 228	91.69 171	95.73 182	96.50 235

WB-MVSnew		89.88 292	89.56 282	90.82 333	94.57 311	83.06 333	95.65 275	92.85 368	87.86 275	90.83 227	94.10 303	79.66 253	96.88 347	76.34 363	94.19 211	92.54 367

TinyColmap		86.82 326	85.35 332	91.21 326	94.91 293	82.99 334	93.94 336	94.02 354	83.58 349	81.56 366	94.68 270	62.34 379	98.13 242	75.78 365	87.35 306	92.52 368

test_vis1_n		92.37 194	92.26 179	92.72 289	94.75 301	82.64 335	98.02 58	96.80 238	91.18 167	97.77 37	97.93 88	58.02 384	98.29 229	97.63 21	98.21 122	97.23 216

MDA-MVSNet_test_wron		85.87 337	84.23 341	90.80 336	92.38 367	82.57 336	93.17 356	95.15 318	82.15 360	67.65 394	92.33 352	78.20 277	95.51 370	77.33 357	79.74 367	94.31 342

our_test_3		88.78 307	87.98 307	91.20 328	92.45 365	82.53 337	93.61 350	95.69 292	85.77 318	84.88 343	93.71 316	79.99 246	96.78 351	79.47 347	86.24 312	94.28 343

UnsupCasMVSNet_bld		82.13 352	79.46 357	90.14 345	88.00 391	82.47 338	90.89 380	96.62 254	78.94 380	75.61 384	84.40 394	56.63 387	96.31 355	77.30 359	66.77 396	91.63 376

YYNet1		85.87 337	84.23 341	90.78 337	92.38 367	82.46 339	93.17 356	95.14 319	82.12 361	67.69 392	92.36 349	78.16 280	95.50 371	77.31 358	79.73 368	94.39 338

UnsupCasMVSNet_eth		85.99 335	84.45 339	90.62 338	89.97 380	82.40 340	93.62 349	97.37 186	89.86 209	78.59 380	92.37 346	65.25 372	95.35 373	82.27 327	70.75 389	94.10 345

ADS-MVSNet2		89.45 298	88.59 300	92.03 305	95.86 235	82.26 341	90.93 378	94.32 349	83.23 353	91.28 219	91.81 358	79.01 266	95.99 358	79.52 345	91.39 259	97.84 184

EGC-MVSNET		68.77 367	63.01 373	86.07 368	92.49 363	82.24 342	93.96 335	90.96 385	0.71 413	2.62 414	90.89 364	53.66 390	93.46 386	57.25 398	84.55 338	82.51 394

test_vis1_n_1920		94.17 121	94.58 103	92.91 282	97.42 147	82.02 343	97.83 85	97.85 116	94.68 46	98.10 29	98.49 38	70.15 340	99.32 117	97.91 17	98.82 97	97.40 207

LCM-MVSNet-Re		92.50 187	92.52 171	92.44 294	96.82 179	81.89 344	96.92 181	93.71 361	92.41 128	84.30 348	94.60 274	85.08 155	97.03 341	91.51 173	97.36 146	98.40 148

CostFormer		91.18 251	90.70 236	92.62 293	94.84 297	81.76 345	94.09 332	94.43 343	84.15 341	92.72 179	93.77 315	79.43 256	98.20 235	90.70 189	92.18 246	97.90 180

CL-MVSNet_self_test		86.31 331	85.15 333	89.80 349	88.83 387	81.74 346	93.93 337	96.22 270	86.67 303	85.03 342	90.80 365	78.09 281	94.50 376	74.92 370	71.86 388	93.15 358

JIA-IIPM		88.26 313	87.04 317	91.91 307	93.52 341	81.42 347	89.38 389	94.38 345	80.84 370	90.93 226	80.74 396	79.22 259	97.92 282	82.76 322	91.62 254	96.38 239

OurMVSNet-221017-0		90.51 275	90.19 259	91.44 322	93.41 346	81.25 348	96.98 177	96.28 266	91.68 149	86.55 330	96.30 192	74.20 315	97.98 267	88.96 228	87.40 305	95.09 301

tpm2		89.96 288	89.21 290	92.23 302	94.91 293	81.25 348	93.78 342	94.42 344	80.62 373	91.56 206	93.44 329	76.44 296	97.94 279	85.60 289	92.08 250	97.49 203

test_fmvs1		93.21 159	93.53 130	92.25 301	96.55 198	81.20 350	97.40 137	96.96 221	90.68 184	96.80 62	98.04 79	69.25 345	98.40 217	97.58 23	98.50 109	97.16 217

test_fmvs1_n		92.73 184	92.88 153	92.29 299	96.08 231	81.05 351	97.98 63	97.08 208	90.72 182	96.79 63	98.18 70	63.07 376	98.45 214	97.62 22	98.42 115	97.36 208

testgi		87.97 314	87.21 314	90.24 344	92.86 355	80.76 352	96.67 206	94.97 326	91.74 147	85.52 337	95.83 216	62.66 378	94.47 378	76.25 364	88.36 296	95.48 274

testing3		87.67 318	86.88 319	90.05 346	96.14 226	80.71 353	97.10 168	92.85 368	90.15 203	87.54 310	94.55 276	55.70 389	94.10 381	73.77 376	94.10 215	95.35 286

test-LLR		91.42 235	91.19 216	92.12 303	94.59 308	80.66 354	94.29 326	92.98 366	91.11 170	90.76 228	92.37 346	79.02 264	98.07 255	88.81 230	96.74 162	97.63 194

test-mter		90.19 285	89.54 283	92.12 303	94.59 308	80.66 354	94.29 326	92.98 366	87.68 284	90.76 228	92.37 346	67.67 355	98.07 255	88.81 230	96.74 162	97.63 194

TESTMET0.1,1		90.06 287	89.42 286	91.97 306	94.41 316	80.62 356	94.29 326	91.97 378	87.28 294	90.44 232	92.47 345	68.79 347	97.67 303	88.50 236	96.60 167	97.61 198

tpm cat1		88.36 311	87.21 314	91.81 312	95.13 281	80.55 357	92.58 367	95.70 290	74.97 389	87.45 311	91.96 356	78.01 284	98.17 239	80.39 341	88.74 292	96.72 231

test_vis1_rt		86.16 333	85.06 334	89.46 352	93.47 345	80.46 358	96.41 225	86.61 400	85.22 326	79.15 378	88.64 379	52.41 392	97.06 339	93.08 143	90.57 273	90.87 383

Anonymous20231206		87.09 324	86.14 325	89.93 348	91.22 373	80.35 359	96.11 249	95.35 307	83.57 350	84.16 350	93.02 335	73.54 321	95.61 367	72.16 381	86.14 314	93.84 350

MDTV_nov1_ep13			90.76 231		95.22 274	80.33 360	93.03 361	95.28 311	88.14 268	92.84 178	93.83 311	81.34 223	98.08 251	82.86 318	94.34 208

tpmvs		89.83 295	89.15 292	91.89 308	94.92 291	80.30 361	93.11 359	95.46 303	86.28 310	88.08 301	92.65 339	80.44 237	98.52 209	81.47 331	89.92 280	96.84 227

SixPastTwentyTwo		89.15 301	88.54 301	90.98 330	93.49 343	80.28 362	96.70 201	94.70 336	90.78 178	84.15 351	95.57 233	71.78 328	97.71 301	84.63 301	85.07 329	94.94 308

new_pmnet		82.89 350	81.12 355	88.18 359	89.63 382	80.18 363	91.77 372	92.57 372	76.79 387	75.56 386	88.23 383	61.22 381	94.48 377	71.43 383	82.92 356	89.87 387

test20.03		86.14 334	85.40 331	88.35 356	90.12 378	80.06 364	95.90 261	95.20 316	88.59 252	81.29 367	93.62 322	71.43 330	92.65 390	71.26 385	81.17 363	92.34 370

LF4IMVS		87.94 315	87.25 312	89.98 347	92.38 367	80.05 365	94.38 320	95.25 314	87.59 286	84.34 347	94.74 268	64.31 373	97.66 305	84.83 297	87.45 302	92.23 371

Anonymous20240521		86.42 329	85.44 329	89.34 353	90.33 377	79.79 366	96.73 197	95.92 279	83.71 348	83.25 359	91.36 362	63.92 374	96.01 357	78.39 354	85.36 323	92.22 372

tpm		90.25 281	89.74 278	91.76 316	93.92 328	79.73 367	93.98 333	93.54 362	88.28 263	91.99 196	93.25 333	77.51 288	97.44 325	87.30 261	87.94 298	98.12 168

WAC-MVS						79.53 368								75.56 368

myMVS_eth3d		87.18 322	86.38 322	89.58 351	95.16 277	79.53 368	95.00 301	93.93 357	88.55 256	86.96 323	91.99 354	56.23 388	94.00 382	75.47 369	94.11 213	95.20 297

PVSNet_0	82.17 19	85.46 340	83.64 343	90.92 331	95.27 270	79.49 370	90.55 381	95.60 297	83.76 347	83.00 362	89.95 371	71.09 332	97.97 270	82.75 323	60.79 401	95.31 289

K. test v3		87.64 319	86.75 321	90.32 343	93.02 354	79.48 371	96.61 213	92.08 377	90.66 187	80.25 374	94.09 304	67.21 359	96.65 352	85.96 285	80.83 364	94.83 317

pmmvs3		79.97 355	77.50 360	87.39 362	82.80 401	79.38 372	92.70 366	90.75 387	70.69 393	78.66 379	87.47 389	51.34 393	93.40 387	73.39 378	69.65 391	89.38 388

tpmrst		91.44 234	91.32 208	91.79 313	95.15 279	79.20 373	93.42 353	95.37 306	88.55 256	93.49 161	93.67 320	82.49 204	98.27 230	90.41 192	89.34 286	97.90 180

KD-MVS_self_test		85.95 336	84.95 335	88.96 355	89.55 384	79.11 374	95.13 299	96.42 261	85.91 316	84.07 354	90.48 366	70.03 341	94.82 375	80.04 342	72.94 386	92.94 360

lessismore_v0				90.45 340	91.96 370	79.09 375		87.19 398		80.32 373	94.39 285	66.31 366	97.55 314	84.00 309	76.84 377	94.70 329

gm-plane-assit					93.22 350	78.89 376			84.82 334		93.52 325		98.64 198	87.72 245

Patchmatch-RL test		87.38 320	86.24 323	90.81 334	88.74 389	78.40 377	88.12 395	93.17 365	87.11 297	82.17 365	89.29 376	81.95 215	95.60 368	88.64 234	77.02 376	98.41 147

UWE-MVS		89.91 289	89.48 285	91.21 326	95.88 234	78.23 378	94.91 304	90.26 388	89.11 232	92.35 186	94.52 277	68.76 348	97.96 274	83.95 310	95.59 186	97.42 206

PM-MVS		83.48 347	81.86 353	88.31 357	87.83 392	77.59 379	93.43 352	91.75 379	86.91 299	80.63 370	89.91 372	44.42 398	95.84 362	85.17 296	76.73 379	91.50 379

dp		88.90 305	88.26 305	90.81 334	94.58 310	76.62 380	92.85 364	94.93 328	85.12 329	90.07 248	93.07 334	75.81 300	98.12 245	80.53 340	87.42 304	97.71 191

test_fmvs2		89.77 296	89.93 268	89.31 354	93.68 337	76.37 381	97.64 110	95.90 281	89.84 212	91.49 208	96.26 195	58.77 383	97.10 338	94.65 113	91.13 264	94.46 335

RPSCF		90.75 266	90.86 225	90.42 341	96.84 175	76.29 382	95.61 277	96.34 264	83.89 344	91.38 210	97.87 93	76.45 295	98.78 181	87.16 265	92.23 243	96.20 242

new-patchmatchnet		83.18 349	81.87 352	87.11 363	86.88 393	75.99 383	93.70 344	95.18 317	85.02 331	77.30 383	88.40 381	65.99 368	93.88 385	74.19 375	70.18 390	91.47 380

CVMVSNet		91.23 246	91.75 193	89.67 350	95.77 240	74.69 384	96.44 221	94.88 331	85.81 317	92.18 190	97.64 114	79.07 261	95.58 369	88.06 239	95.86 179	98.74 118

EU-MVSNet		88.72 308	88.90 296	88.20 358	93.15 352	74.21 385	96.63 212	94.22 350	85.18 327	87.32 316	95.97 208	76.16 298	94.98 374	85.27 293	86.17 313	95.41 279

mvsany_test3		83.59 346	82.44 350	87.03 364	83.80 397	73.82 386	93.70 344	90.92 386	86.42 307	82.51 363	90.26 368	46.76 397	95.71 364	90.82 186	76.76 378	91.57 377

Gipuma		67.86 368	65.41 370	75.18 383	92.66 360	73.45 387	66.50 404	94.52 341	53.33 403	57.80 404	66.07 404	30.81 404	89.20 396	48.15 402	78.88 374	62.90 404
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mamv4		94.66 112	96.10 65	90.37 342	98.01 108	73.41 388	96.82 190	97.78 123	89.95 207	94.52 137	97.43 128	92.91 27	99.09 146	98.28 14	99.16 78	98.60 126

Syy-MVS		87.13 323	87.02 318	87.47 361	95.16 277	73.21 389	95.00 301	93.93 357	88.55 256	86.96 323	91.99 354	75.90 299	94.00 382	61.59 395	94.11 213	95.20 297

CMPMVS	62.92 21	85.62 339	84.92 336	87.74 360	89.14 385	73.12 390	94.17 329	96.80 238	73.98 390	73.65 389	94.93 257	66.36 364	97.61 310	83.95 310	91.28 261	92.48 369
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DSMNet-mixed		86.34 330	86.12 326	87.00 365	89.88 381	70.43 391	94.93 303	90.08 389	77.97 384	85.42 340	92.78 338	74.44 313	93.96 384	74.43 372	95.14 192	96.62 232

MDTV_nov1_ep13_2view						70.35 392	93.10 360		83.88 345	93.55 158		82.47 205		86.25 276		98.38 150

ambc				86.56 366	83.60 399	70.00 393	85.69 397	94.97 326		80.60 371	88.45 380	37.42 401	96.84 349	82.69 324	75.44 381	92.86 361

MVS-HIRNet		82.47 351	81.21 354	86.26 367	95.38 258	69.21 394	88.96 391	89.49 390	66.28 396	80.79 369	74.08 401	68.48 352	97.39 329	71.93 382	95.47 187	92.18 373

APD_test1		79.31 356	77.70 359	84.14 369	89.11 386	69.07 395	92.36 371	91.50 381	69.07 394	73.87 388	92.63 341	39.93 400	94.32 379	70.54 388	80.25 366	89.02 389

test_fmvs3		83.21 348	83.02 345	83.78 370	86.77 394	68.34 396	96.76 195	94.91 329	86.49 306	84.14 352	89.48 375	36.04 402	91.73 392	91.86 165	80.77 365	91.26 382

test_vis3_rt		72.73 360	70.55 363	79.27 374	80.02 403	68.13 397	93.92 338	74.30 411	76.90 386	58.99 402	73.58 402	20.29 411	95.37 372	84.16 305	72.80 387	74.31 399

test_f		80.57 354	79.62 356	83.41 371	83.38 400	67.80 398	93.57 351	93.72 360	80.80 372	77.91 382	87.63 387	33.40 403	92.08 391	87.14 266	79.04 373	90.34 386

ANet_high		63.94 371	59.58 374	77.02 378	61.24 414	66.06 399	85.66 398	87.93 396	78.53 382	42.94 406	71.04 403	25.42 409	80.71 405	52.60 401	30.83 407	84.28 393

PMMVS2		70.19 363	66.92 367	80.01 373	76.35 406	65.67 400	86.22 396	87.58 397	64.83 398	62.38 399	80.29 398	26.78 408	88.49 400	63.79 392	54.07 403	85.88 390

LCM-MVSNet		72.55 361	69.39 365	82.03 372	70.81 412	65.42 401	90.12 385	94.36 348	55.02 402	65.88 396	81.72 395	24.16 410	89.96 393	74.32 374	68.10 394	90.71 385

DeepMVS_CX				74.68 384	90.84 376	64.34 402		81.61 407	65.34 397	67.47 395	88.01 386	48.60 396	80.13 406	62.33 394	73.68 385	79.58 396

testf1		69.31 365	66.76 368	76.94 379	78.61 404	61.93 403	88.27 393	86.11 401	55.62 400	59.69 400	85.31 392	20.19 412	89.32 394	57.62 396	69.44 392	79.58 396

APD_test2		69.31 365	66.76 368	76.94 379	78.61 404	61.93 403	88.27 393	86.11 401	55.62 400	59.69 400	85.31 392	20.19 412	89.32 394	57.62 396	69.44 392	79.58 396

dongtai		69.99 364	69.33 366	71.98 385	88.78 388	61.64 405	89.86 386	59.93 415	75.67 388	74.96 387	85.45 391	50.19 394	81.66 404	43.86 403	55.27 402	72.63 400

kuosan		65.27 370	64.66 372	67.11 388	83.80 397	61.32 406	88.53 392	60.77 414	68.22 395	67.67 393	80.52 397	49.12 395	70.76 410	29.67 409	53.64 404	69.26 402

FPMVS		71.27 362	69.85 364	75.50 382	74.64 407	59.03 407	91.30 374	91.50 381	58.80 399	57.92 403	88.28 382	29.98 406	85.53 402	53.43 400	82.84 357	81.95 395

MVE	50.73 23	53.25 374	48.81 379	66.58 389	65.34 413	57.50 408	72.49 403	70.94 412	40.15 407	39.28 409	63.51 405	6.89 416	73.48 409	38.29 405	42.38 405	68.76 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WB-MVS		76.77 358	76.63 361	77.18 377	85.32 395	56.82 409	94.53 313	89.39 391	82.66 357	71.35 390	89.18 377	75.03 308	88.88 397	35.42 406	66.79 395	85.84 391

SSC-MVS		76.05 359	75.83 362	76.72 381	84.77 396	56.22 410	94.32 324	88.96 393	81.82 364	70.52 391	88.91 378	74.79 310	88.71 398	33.69 407	64.71 397	85.23 392

dmvs_testset		81.38 353	82.60 349	77.73 376	91.74 371	51.49 411	93.03 361	84.21 404	89.07 233	78.28 381	91.25 363	76.97 291	88.53 399	56.57 399	82.24 359	93.16 357

PMVS	53.92 22	58.58 372	55.40 375	68.12 387	51.00 415	48.64 412	78.86 401	87.10 399	46.77 404	35.84 410	74.28 400	8.76 414	86.34 401	42.07 404	73.91 384	69.38 401
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		53.28 373	52.56 377	55.43 390	74.43 408	47.13 413	83.63 400	76.30 408	42.23 405	42.59 407	62.22 406	28.57 407	74.40 407	31.53 408	31.51 406	44.78 405

N_pmnet		78.73 357	78.71 358	78.79 375	92.80 357	46.50 414	94.14 330	43.71 416	78.61 381	80.83 368	91.66 360	74.94 309	96.36 354	67.24 390	84.45 340	93.50 353

EMVS		52.08 375	51.31 378	54.39 391	72.62 410	45.39 415	83.84 399	75.51 410	41.13 406	40.77 408	59.65 407	30.08 405	73.60 408	28.31 410	29.90 408	44.18 406

tmp_tt		51.94 376	53.82 376	46.29 392	33.73 416	45.30 416	78.32 402	67.24 413	18.02 409	50.93 405	87.05 390	52.99 391	53.11 411	70.76 386	25.29 409	40.46 407

wuyk23d		25.11 377	24.57 381	26.74 393	73.98 409	39.89 417	57.88 406	9.80 417	12.27 410	10.39 411	6.97 413	7.03 415	36.44 412	25.43 411	17.39 410	3.89 410

test_method		66.11 369	64.89 371	69.79 386	72.62 410	35.23 418	65.19 405	92.83 370	20.35 408	65.20 397	88.08 385	43.14 399	82.70 403	73.12 379	63.46 398	91.45 381

test123		13.04 380	15.66 383	5.18 394	4.51 418	3.45 419	92.50 369	1.81 419	2.50 412	7.58 413	20.15 410	3.67 417	2.18 414	7.13 413	1.07 412	9.90 408

testmvs		13.36 379	16.33 382	4.48 395	5.04 417	2.26 420	93.18 355	3.28 418	2.70 411	8.24 412	21.66 409	2.29 418	2.19 413	7.58 412	2.96 411	9.00 409

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	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

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	0.00 414	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	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		23.24 378	30.99 380	0.00 396	0.00 419	0.00 421	0.00 407	97.63 144	0.00 414	0.00 415	96.88 158	84.38 164	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		7.39 382	9.85 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	88.65 98	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	0.00 414	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	0.00 414	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	0.00 414	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	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.06 381	10.74 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	96.69 168	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	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

PC_three_1452									90.77 179	98.89 14	98.28 65	96.24 1	98.35 224	95.76 80	99.58 23	99.59 22

eth-test2					0.00 419

eth-test					0.00 419

test_241102_TWO								98.27 39	95.13 23	98.93 9	98.89 13	94.99 11	99.85 18	97.52 24	99.65 13	99.74 8

9.14			96.75 41		98.93 47		97.73 96	98.23 50	91.28 163	97.88 35	98.44 44	93.00 26	99.65 58	95.76 80	99.47 41

test_0728_THIRD									94.78 41	98.73 18	98.87 15	95.87 4	99.84 23	97.45 28	99.72 2	99.77 2

GSMVS																98.45 142

sam_mvs1												82.76 197				98.45 142

sam_mvs												81.94 216

MTGPA								98.08 74

test_post1							92.81 365				16.58 412	80.53 235	97.68 302	86.20 277

test_post											17.58 411	81.76 218	98.08 251

patchmatchnet-post											90.45 367	82.65 201	98.10 247

MTMP							97.86 80	82.03 406

test9_res														94.81 108	99.38 56	99.45 47

agg_prior2														93.94 125	99.38 56	99.50 40

test_prior2							96.35 233		92.80 119	96.03 99	97.59 118	92.01 44		95.01 102	99.38 56

旧先验2							95.94 258		81.66 365	97.34 48			98.82 177	92.26 152

新几何2							95.79 267

无先验							95.79 267	97.87 111	83.87 346				99.65 58	87.68 251		98.89 107

原ACMM2							95.67 272

testdata2													99.67 56	85.96 285

segment_acmp												92.89 28

testdata1							95.26 295		93.10 106

plane_prior5								97.51 159					98.60 202	93.02 146	92.23 243	95.86 254

plane_prior4											96.64 171

plane_prior2							97.74 94		94.85 34

plane_prior1					96.14 226

n2								0.00 420

nn								0.00 420

door-mid								91.06 384

test11								97.88 109

door								91.13 383

HQP-NCC					95.86 235		96.65 207		93.55 82	90.14 237

ACMP_Plane					95.86 235		96.65 207		93.55 82	90.14 237

BP-MVS														92.13 158

HQP4-MVS										90.14 237			98.50 210			95.78 262

HQP3-MVS								97.39 183							92.10 248

HQP2-MVS												80.95 227

ACMMP++_ref															90.30 278

ACMMP++															91.02 267

Test By Simon												88.73 97

High-resolution multi-view benchmark