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.

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

region2R		97.07 26	96.84 34	97.77 33	99.46 2	93.79 54	98.52 15	98.24 47	93.19 100	97.14 55	98.34 54	91.59 53	99.87 7	95.46 92	99.59 19	99.64 15

DVP-MVS		97.91 3	97.81 4	98.22 13	99.45 3	95.36 13	98.21 42	97.85 116	94.92 32	98.73 18	98.87 15	95.08 8	99.84 23	97.52 25	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
test_0728_SECOND				98.51 4	99.45 3	95.93 5	98.21 42	98.28 36					99.86 9	97.52 25	99.67 6	99.75 6

test0726					99.45 3	95.36 13	98.31 27	98.29 34	94.92 32	98.99 7	98.92 10	95.08 8

ACMMPR		97.07 26	96.84 34	97.79 30	99.44 6	93.88 52	98.52 15	98.31 31	93.21 97	97.15 54	98.33 57	91.35 58	99.86 9	95.63 86	99.59 19	99.62 17

SED-MVS		98.05 2	97.99 1	98.24 10	99.42 7	95.30 17	98.25 35	98.27 39	95.13 23	99.19 4	98.89 13	95.54 5	99.85 18	97.52 25	99.66 10	99.56 28

IU-MVS					99.42 7	95.39 11		97.94 104	90.40 201	98.94 8				97.41 32	99.66 10	99.74 8

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

HFP-MVS		97.14 23	96.92 30	97.83 26	99.42 7	94.12 46	98.52 15	98.32 30	93.21 97	97.18 52	98.29 63	92.08 43	99.83 26	95.63 86	99.59 19	99.54 33

MSP-MVS		97.59 10	97.54 10	97.73 37	99.40 11	93.77 56	98.53 14	98.29 34	95.55 13	98.56 22	97.81 99	93.90 15	99.65 58	96.62 45	99.21 71	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
mPP-MVS		96.86 36	96.60 47	97.64 44	99.40 11	93.44 61	98.50 18	98.09 73	93.27 96	95.95 104	98.33 57	91.04 66	99.88 4	95.20 95	99.57 25	99.60 20

MP-MVS		96.77 44	96.45 58	97.72 38	99.39 13	93.80 53	98.41 23	98.06 82	93.37 92	95.54 119	98.34 54	90.59 75	99.88 4	94.83 105	99.54 28	99.49 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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 77	98.29 63	91.70 49	99.80 30	95.66 81	99.40 52	99.62 17

X-MVStestdata		91.71 218	89.67 281	97.81 28	99.38 14	94.03 50	98.59 12	98.20 52	94.85 34	96.59 77	32.69 413	91.70 49	99.80 30	95.66 81	99.40 52	99.62 17

ZNCC-MVS		96.96 30	96.67 45	97.85 25	99.37 16	94.12 46	98.49 19	98.18 57	92.64 125	96.39 87	98.18 70	91.61 51	99.88 4	95.59 91	99.55 26	99.57 25

MTAPA		97.08 25	96.78 40	97.97 23	99.37 16	94.42 36	97.24 154	98.08 74	95.07 27	96.11 96	98.59 29	90.88 71	99.90 2	96.18 65	99.50 35	99.58 24

GST-MVS		96.85 38	96.52 51	97.82 27	99.36 18	94.14 45	98.29 29	98.13 65	92.72 122	96.70 69	98.06 77	91.35 58	99.86 9	94.83 105	99.28 63	99.47 46

HPM-MVS		96.69 50	96.45 58	97.40 50	99.36 18	93.11 71	98.87 6	98.06 82	91.17 169	96.40 86	97.99 84	90.99 67	99.58 77	95.61 88	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
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 55	98.44 43	91.17 64	99.85 18	94.35 117	99.46 41	99.57 25

CP-MVS		97.02 28	96.81 38	97.64 44	99.33 21	93.54 59	98.80 8	98.28 36	92.99 109	96.45 85	98.30 62	91.90 46	99.85 18	95.61 88	99.68 4	99.54 33

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

HPM-MVS_fast		96.51 56	96.27 63	97.22 61	99.32 22	92.74 79	98.74 9	98.06 82	90.57 195	96.77 66	98.35 51	90.21 78	99.53 91	94.80 108	99.63 16	99.38 58

MCST-MVS		97.18 21	96.84 34	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 54	99.55 31

test_part2					99.28 25	95.74 8				98.10 30

CPTT-MVS		95.57 84	95.19 88	96.70 73	99.27 26	91.48 125	98.33 26	98.11 70	87.79 282	95.17 125	98.03 80	87.09 128	99.61 69	93.51 132	99.42 48	99.02 87

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 35	99.46 41	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
CSCG		96.05 69	95.91 69	96.46 93	99.24 28	90.47 165	98.30 28	98.57 18	89.01 239	93.97 152	97.57 119	92.62 34	99.76 38	94.66 111	99.27 64	99.15 75

ACMMP		96.27 65	95.93 68	97.28 57	99.24 28	92.62 82	98.25 35	98.81 5	92.99 109	94.56 137	98.39 47	88.96 91	99.85 18	94.57 116	97.63 136	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
MP-MVS-pluss		96.70 48	96.27 63	97.98 22	99.23 30	94.71 29	96.96 180	98.06 82	90.67 186	95.55 117	98.78 24	91.07 65	99.86 9	96.58 47	99.55 26	99.38 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DP-MVS Recon		95.68 80	95.12 91	97.37 51	99.19 31	94.19 42	97.03 171	98.08 74	88.35 265	95.09 127	97.65 111	89.97 82	99.48 101	92.08 161	98.59 105	98.44 147

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 44	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
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
SR-MVS		97.01 29	96.86 32	97.47 48	99.09 34	93.27 68	97.98 62	98.07 79	93.75 76	97.45 43	98.48 40	91.43 56	99.59 74	96.22 56	99.27 64	99.54 33

ACMMP_NAP		97.20 20	96.86 32	98.23 11	99.09 34	95.16 22	97.60 115	98.19 55	92.82 120	97.93 35	98.74 25	91.60 52	99.86 9	96.26 53	99.52 30	99.67 13

HPM-MVS++		97.34 17	96.97 27	98.47 5	99.08 36	96.16 4	97.55 123	97.97 101	95.59 11	96.61 75	97.89 90	92.57 35	99.84 23	95.95 72	99.51 33	99.40 54

114514_t		93.95 133	93.06 146	96.63 77	99.07 37	91.61 118	97.46 134	97.96 102	77.99 387	93.00 173	97.57 119	86.14 142	99.33 115	89.22 222	99.15 78	98.94 98

SMA-MVS		97.35 16	97.03 24	98.30 8	99.06 38	95.42 10	97.94 72	98.18 57	90.57 195	98.85 15	98.94 9	93.33 23	99.83 26	96.72 43	99.68 4	99.63 16
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
patch_mono-2		96.83 41	97.44 13	95.01 176	99.05 39	85.39 303	96.98 178	98.77 7	94.70 45	97.99 33	98.66 26	93.61 19	99.91 1	97.67 21	99.50 35	99.72 11

ZD-MVS					99.05 39	94.59 32		98.08 74	89.22 232	97.03 60	98.10 73	92.52 36	99.65 58	94.58 115	99.31 62

APD-MVS		96.95 31	96.60 47	98.01 20	99.03 41	94.93 27	97.72 99	98.10 72	91.50 153	98.01 32	98.32 59	92.33 39	99.58 77	94.85 103	99.51 33	99.53 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS-dyc-post		96.88 35	96.80 39	97.11 67	99.02 42	92.34 91	97.98 62	98.03 91	93.52 87	97.43 46	98.51 35	91.40 57	99.56 85	96.05 67	99.26 66	99.43 51

RE-MVS-def			96.72 43		99.02 42	92.34 91	97.98 62	98.03 91	93.52 87	97.43 46	98.51 35	90.71 73		96.05 67	99.26 66	99.43 51

SF-MVS		97.39 15	97.13 16	98.17 15	99.02 42	95.28 19	98.23 39	98.27 39	92.37 129	98.27 27	98.65 28	93.33 23	99.72 45	96.49 50	99.52 30	99.51 37

APD-MVS_3200maxsize		96.81 42	96.71 44	97.12 66	99.01 45	92.31 93	97.98 62	98.06 82	93.11 106	97.44 44	98.55 32	90.93 69	99.55 87	96.06 66	99.25 68	99.51 37

dcpmvs_2		96.37 62	97.05 22	94.31 218	98.96 46	84.11 323	97.56 119	97.51 158	93.92 71	97.43 46	98.52 34	92.75 30	99.32 117	97.32 33	99.50 35	99.51 37

9.14			96.75 42		98.93 47		97.73 96	98.23 50	91.28 164	97.88 36	98.44 43	93.00 26	99.65 58	95.76 79	99.47 40

CDPH-MVS		95.97 73	95.38 83	97.77 33	98.93 47	94.44 35	96.35 234	97.88 109	86.98 301	96.65 73	97.89 90	91.99 45	99.47 102	92.26 152	99.46 41	99.39 56

save fliter					98.91 49	94.28 38	97.02 173	98.02 94	95.35 16

CNVR-MVS		97.68 6	97.44 13	98.37 7	98.90 50	95.86 6	97.27 152	98.08 74	95.81 9	97.87 37	98.31 60	94.26 13	99.68 54	97.02 36	99.49 38	99.57 25

PAPM_NR		95.01 97	94.59 101	96.26 110	98.89 51	90.68 160	97.24 154	97.73 129	91.80 145	92.93 178	96.62 177	89.13 89	99.14 140	89.21 223	97.78 133	98.97 94

OPU-MVS				98.55 3	98.82 52	96.86 3	98.25 35				98.26 66	96.04 2	99.24 125	95.36 93	99.59 19	99.56 28

NCCC		97.30 18	97.03 24	98.11 17	98.77 53	95.06 25	97.34 145	98.04 89	95.96 6	97.09 58	97.88 92	93.18 25	99.71 46	95.84 77	99.17 75	99.56 28

DP-MVS		92.76 182	91.51 204	96.52 84	98.77 53	90.99 146	97.38 142	96.08 276	82.38 363	89.29 272	97.87 93	83.77 171	99.69 52	81.37 337	96.69 165	98.89 108

MSLP-MVS++		96.94 32	97.06 19	96.59 80	98.72 55	91.86 109	97.67 103	98.49 19	94.66 48	97.24 51	98.41 46	92.31 41	98.94 165	96.61 46	99.46 41	98.96 95

TEST9					98.70 56	94.19 42	96.41 226	98.02 94	88.17 269	96.03 99	97.56 121	92.74 31	99.59 74

train_agg		96.30 64	95.83 72	97.72 38	98.70 56	94.19 42	96.41 226	98.02 94	88.58 256	96.03 99	97.56 121	92.73 32	99.59 74	95.04 98	99.37 58	99.39 56

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 9	97.45 29	99.58 23	99.59 21

MSC_two_6792asdad				98.86 1	98.67 58	96.94 1		97.93 105					99.86 9	97.68 19	99.67 6	99.77 2

No_MVS				98.86 1	98.67 58	96.94 1		97.93 105					99.86 9	97.68 19	99.67 6	99.77 2

test_8					98.67 58	94.06 49	96.37 233	98.01 97	88.58 256	95.98 103	97.55 123	92.73 32	99.58 77

agg_prior					98.67 58	93.79 54		98.00 98		95.68 113			99.57 84

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

DeepC-MVS_fast	93.89 2	96.93 33	96.64 46	97.78 31	98.64 64	94.30 37	97.41 135	98.04 89	94.81 39	96.59 77	98.37 49	91.24 61	99.64 66	95.16 96	99.52 30	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
新几何1				97.32 53	98.60 65	93.59 58		97.75 126	81.58 370	95.75 110	97.85 96	90.04 80	99.67 56	86.50 275	99.13 80	98.69 123

原ACMM1				96.38 100	98.59 66	91.09 145		97.89 107	87.41 293	95.22 124	97.68 107	90.25 77	99.54 89	87.95 243	99.12 82	98.49 139

AdaColmap		94.34 117	93.68 124	96.31 104	98.59 66	91.68 116	96.59 217	97.81 122	89.87 210	92.15 192	97.06 148	83.62 175	99.54 89	89.34 217	98.07 126	97.70 194

PLC	91.00 6	94.11 127	93.43 137	96.13 118	98.58 68	91.15 144	96.69 204	97.39 182	87.29 296	91.37 213	96.71 163	88.39 101	99.52 95	87.33 262	97.13 156	97.73 192
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		97.41 14	97.53 11	97.06 68	98.57 69	94.46 34	97.92 74	98.14 64	94.82 38	99.01 6	98.55 32	94.18 14	97.41 330	96.94 37	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
test12				97.65 42	98.46 70	94.26 39		97.66 137		95.52 120		90.89 70	99.46 103		99.25 68	99.22 70

MVS_111021_HR		96.68 52	96.58 49	96.99 70	98.46 70	92.31 93	96.20 247	98.90 3	94.30 63	95.86 106	97.74 104	92.33 39	99.38 113	96.04 69	99.42 48	99.28 65

OMC-MVS		95.09 96	94.70 99	96.25 113	98.46 70	91.28 132	96.43 224	97.57 150	92.04 140	94.77 133	97.96 87	87.01 129	99.09 147	91.31 178	96.77 161	98.36 154

MG-MVS		95.61 82	95.38 83	96.31 104	98.42 73	90.53 163	96.04 253	97.48 161	93.47 89	95.67 114	98.10 73	89.17 88	99.25 124	91.27 179	98.77 97	99.13 77

test_fmvsm_n_1920		97.55 11	97.89 3	96.53 83	98.41 74	91.73 111	98.01 59	99.02 1	96.37 4	99.30 1	98.92 10	92.39 38	99.79 33	99.16 4	99.46 41	98.08 174

PHI-MVS		96.77 44	96.46 57	97.71 40	98.40 75	94.07 48	98.21 42	98.45 22	89.86 211	97.11 57	98.01 83	92.52 36	99.69 52	96.03 70	99.53 29	99.36 60

F-COLMAP		93.58 146	92.98 148	95.37 162	98.40 75	88.98 217	97.18 162	97.29 193	87.75 285	90.49 232	97.10 146	85.21 151	99.50 99	86.70 272	96.72 164	97.63 196

SteuartSystems-ACMMP		97.62 9	97.53 11	97.87 24	98.39 77	94.25 40	98.43 22	98.27 39	95.34 17	98.11 29	98.56 30	94.53 12	99.71 46	96.57 48	99.62 17	99.65 14
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旧先验1					98.38 78	93.38 63		97.75 126			98.09 75	92.30 42			99.01 89	99.16 73

CNLPA		94.28 118	93.53 130	96.52 84	98.38 78	92.55 85	96.59 217	96.88 231	90.13 206	91.91 199	97.24 138	85.21 151	99.09 147	87.64 255	97.83 131	97.92 181

TAPA-MVS	90.10 7	92.30 197	91.22 215	95.56 150	98.33 80	89.60 189	96.79 193	97.65 139	81.83 367	91.52 209	97.23 139	87.94 108	98.91 169	71.31 389	98.37 114	98.17 166
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + GP.		96.69 50	96.49 52	97.27 58	98.31 81	93.39 62	96.79 193	96.72 240	94.17 64	97.44 44	97.66 110	92.76 29	99.33 115	96.86 40	97.76 135	99.08 84

CS-MVS-test		96.89 34	97.04 23	96.45 94	98.29 82	91.66 117	99.03 4	97.85 116	95.84 7	96.90 62	97.97 86	91.24 61	98.75 186	96.92 38	99.33 60	98.94 98

CHOSEN 1792x2688		94.15 123	93.51 133	96.06 121	98.27 83	89.38 201	95.18 301	98.48 21	85.60 324	93.76 156	97.11 145	83.15 183	99.61 69	91.33 177	98.72 99	99.19 71

PVSNet_BlendedMVS		94.06 129	93.92 119	94.47 207	98.27 83	89.46 198	96.73 198	98.36 24	90.17 203	94.36 141	95.24 247	88.02 106	99.58 77	93.44 134	90.72 272	94.36 341

PVSNet_Blended		94.87 105	94.56 103	95.81 135	98.27 83	89.46 198	95.47 285	98.36 24	88.84 247	94.36 141	96.09 206	88.02 106	99.58 77	93.44 134	98.18 122	98.40 150

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 2	99.21 71	99.40 54

Anonymous20231211		90.63 273	89.42 288	94.27 221	98.24 87	89.19 213	98.05 56	97.89 107	79.95 379	88.25 299	94.96 255	72.56 325	98.13 242	89.70 207	85.14 329	95.49 275

EI-MVSNet-Vis-set		96.51 56	96.47 54	96.63 77	98.24 87	91.20 138	96.89 184	97.73 129	94.74 44	96.49 81	98.49 37	90.88 71	99.58 77	96.44 51	98.32 116	99.13 77

test222					98.24 87	92.21 97	95.33 290	97.60 145	79.22 383	95.25 122	97.84 98	88.80 94			99.15 78	98.72 120

HyFIR lowres test		93.66 144	92.92 150	95.87 131	98.24 87	89.88 183	94.58 315	98.49 19	85.06 334	93.78 155	95.78 221	82.86 191	98.67 196	91.77 167	95.71 183	99.07 86

MVS_111021_LR		96.24 66	96.19 65	96.39 99	98.23 91	91.35 131	96.24 245	98.79 6	93.99 69	95.80 108	97.65 111	89.92 83	99.24 125	95.87 73	99.20 73	98.58 130

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 2	99.13 80	99.50 40

EI-MVSNet-UG-set		96.34 63	96.30 62	96.47 91	98.20 93	90.93 150	96.86 186	97.72 131	94.67 47	96.16 95	98.46 41	90.43 76	99.58 77	96.23 55	97.96 129	98.90 105

PVSNet_Blended_VisFu		95.27 90	94.91 94	96.38 100	98.20 93	90.86 152	97.27 152	98.25 45	90.21 202	94.18 146	97.27 136	87.48 121	99.73 42	93.53 131	97.77 134	98.55 131

Anonymous202405211		92.07 207	90.83 229	95.76 136	98.19 95	88.75 221	97.58 116	95.00 325	86.00 319	93.64 157	97.45 125	66.24 370	99.53 91	90.68 190	92.71 238	99.01 90

PatchMatch-RL		92.90 175	92.02 184	95.56 150	98.19 95	90.80 154	95.27 295	97.18 197	87.96 274	91.86 202	95.68 227	80.44 236	98.99 162	84.01 310	97.54 138	96.89 228

testdata				95.46 160	98.18 97	88.90 219		97.66 137	82.73 361	97.03 60	98.07 76	90.06 79	98.85 174	89.67 208	98.98 90	98.64 126

CS-MVS		96.86 36	97.06 19	96.26 110	98.16 98	91.16 143	99.09 3	97.87 111	95.30 18	97.06 59	98.03 80	91.72 47	98.71 193	97.10 34	99.17 75	98.90 105

Anonymous20240529		91.98 210	90.73 235	95.73 141	98.14 99	89.40 200	97.99 61	97.72 131	79.63 381	93.54 160	97.41 129	69.94 344	99.56 85	91.04 184	91.11 265	98.22 160

LFMVS		93.60 145	92.63 163	96.52 84	98.13 100	91.27 133	97.94 72	93.39 367	90.57 195	96.29 89	98.31 60	69.00 348	99.16 137	94.18 119	95.87 178	99.12 80

SDMVSNet		94.17 121	93.61 126	95.86 133	98.09 101	91.37 130	97.35 144	98.20 52	93.18 102	91.79 203	97.28 134	79.13 259	98.93 166	94.61 114	92.84 235	97.28 216

sd_testset		93.10 164	92.45 173	95.05 173	98.09 101	89.21 210	96.89 184	97.64 141	93.18 102	91.79 203	97.28 134	75.35 306	98.65 198	88.99 228	92.84 235	97.28 216

DeepPCF-MVS	93.97 1	96.61 53	97.09 18	95.15 168	98.09 101	86.63 279	96.00 256	98.15 62	95.43 14	97.95 34	98.56 30	93.40 21	99.36 114	96.77 41	99.48 39	99.45 47

DPM-MVS		95.69 79	94.92 93	98.01 20	98.08 104	95.71 9	95.27 295	97.62 144	90.43 199	95.55 117	97.07 147	91.72 47	99.50 99	89.62 210	98.94 92	98.82 115

MVSMamba_PlusPlus		96.51 56	96.48 53	96.59 80	98.07 105	91.97 106	98.14 49	97.79 123	90.43 199	97.34 49	97.52 124	91.29 60	99.19 130	98.12 15	99.64 14	98.60 128

fmvsm_s_conf0.5_n		96.85 38	97.13 16	96.04 123	98.07 105	90.28 170	97.97 68	98.76 8	94.93 30	98.84 16	99.06 4	88.80 94	99.65 58	99.06 6	98.63 102	98.18 163

VNet		95.89 76	95.45 78	97.21 62	98.07 105	92.94 75	97.50 126	98.15 62	93.87 73	97.52 41	97.61 117	85.29 150	99.53 91	95.81 78	95.27 191	99.16 73

MM		97.29 19	96.98 26	98.23 11	98.01 108	95.03 26	98.07 54	95.76 288	97.78 1	97.52 41	98.80 22	88.09 104	99.86 9	99.44 1	99.37 58	99.80 1

mamv4		94.66 111	96.10 66	90.37 345	98.01 108	73.41 393	96.82 191	97.78 124	89.95 209	94.52 138	97.43 128	92.91 27	99.09 147	98.28 14	99.16 77	98.60 128

MAR-MVS		94.22 119	93.46 135	96.51 87	98.00 110	92.19 100	97.67 103	97.47 164	88.13 272	93.00 173	95.84 214	84.86 156	99.51 96	87.99 242	98.17 123	97.83 188
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
DeepC-MVS	93.07 3	96.06 68	95.66 73	97.29 55	97.96 111	93.17 70	97.30 150	98.06 82	93.92 71	93.38 165	98.66 26	86.83 130	99.73 42	95.60 90	99.22 70	98.96 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	87.81 15	90.40 279	89.28 291	93.79 247	97.95 112	87.13 267	96.92 182	95.89 283	82.83 360	86.88 331	97.18 141	73.77 319	99.29 122	78.44 357	93.62 228	94.95 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		90.23 284	88.98 296	93.98 233	97.94 113	86.64 276	96.51 221	95.54 302	85.38 327	85.49 341	96.77 161	70.28 339	99.15 138	80.02 347	92.87 233	96.15 248

TestCases				93.98 233	97.94 113	86.64 276		95.54 302	85.38 327	85.49 341	96.77 161	70.28 339	99.15 138	80.02 347	92.87 233	96.15 248

thres100view900		92.43 189	91.58 199	94.98 179	97.92 115	89.37 202	97.71 101	94.66 338	92.20 133	93.31 167	94.90 259	78.06 282	99.08 150	81.40 334	94.08 217	96.48 238

thres600view7		92.49 188	91.60 198	95.18 167	97.91 116	89.47 196	97.65 106	94.66 338	92.18 137	93.33 166	94.91 258	78.06 282	99.10 144	81.61 331	94.06 221	96.98 223

API-MVS		94.84 106	94.49 108	95.90 130	97.90 117	92.00 105	97.80 90	97.48 161	89.19 233	94.81 131	96.71 163	88.84 93	99.17 135	88.91 230	98.76 98	96.53 235

VDD-MVS		93.82 139	93.08 145	96.02 125	97.88 118	89.96 181	97.72 99	95.85 284	92.43 127	95.86 106	98.44 43	68.42 355	99.39 111	96.31 52	94.85 198	98.71 122

tfpn200view9		92.38 192	91.52 202	94.95 183	97.85 119	89.29 206	97.41 135	94.88 332	92.19 135	93.27 169	94.46 284	78.17 278	99.08 150	81.40 334	94.08 217	96.48 238

thres400		92.42 190	91.52 202	95.12 171	97.85 119	89.29 206	97.41 135	94.88 332	92.19 135	93.27 169	94.46 284	78.17 278	99.08 150	81.40 334	94.08 217	96.98 223

h-mvs33		94.15 123	93.52 132	96.04 123	97.81 121	90.22 172	97.62 114	97.58 149	95.19 20	96.74 67	97.45 125	83.67 173	99.61 69	95.85 75	79.73 370	98.29 157

DELS-MVS		96.61 53	96.38 60	97.30 54	97.79 122	93.19 69	95.96 258	98.18 57	95.23 19	95.87 105	97.65 111	91.45 54	99.70 51	95.87 73	99.44 47	99.00 93
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
PVSNet	86.66 18	92.24 201	91.74 195	93.73 249	97.77 123	83.69 330	92.88 367	96.72 240	87.91 276	93.00 173	94.86 261	78.51 273	99.05 157	86.53 273	97.45 143	98.47 142

test_yl		94.78 108	94.23 114	96.43 95	97.74 124	91.22 134	96.85 187	97.10 204	91.23 166	95.71 111	96.93 152	84.30 163	99.31 119	93.10 140	95.12 194	98.75 117

DCV-MVSNet		94.78 108	94.23 114	96.43 95	97.74 124	91.22 134	96.85 187	97.10 204	91.23 166	95.71 111	96.93 152	84.30 163	99.31 119	93.10 140	95.12 194	98.75 117

WTY-MVS		94.71 110	94.02 117	96.79 72	97.71 126	92.05 103	96.59 217	97.35 188	90.61 192	94.64 135	96.93 152	86.41 136	99.39 111	91.20 181	94.71 206	98.94 98

UA-Net		95.95 74	95.53 75	97.20 63	97.67 127	92.98 74	97.65 106	98.13 65	94.81 39	96.61 75	98.35 51	88.87 92	99.51 96	90.36 194	97.35 146	99.11 81

IS-MVSNet		94.90 103	94.52 107	96.05 122	97.67 127	90.56 162	98.44 21	96.22 270	93.21 97	93.99 150	97.74 104	85.55 148	98.45 215	89.98 199	97.86 130	99.14 76

test2506		91.60 224	90.78 230	94.04 230	97.66 129	83.81 326	98.27 32	75.53 414	93.43 90	95.23 123	98.21 67	67.21 361	99.07 154	93.01 147	98.49 108	99.25 68

ECVR-MVS		93.19 160	92.73 160	94.57 204	97.66 129	85.41 301	98.21 42	88.23 399	93.43 90	94.70 134	98.21 67	72.57 324	99.07 154	93.05 144	98.49 108	99.25 68

fmvsm_s_conf0.5_n_a		96.75 46	96.93 29	96.20 115	97.64 131	90.72 158	98.00 60	98.73 9	94.55 50	98.91 13	99.08 3	88.22 103	99.63 67	98.91 9	98.37 114	98.25 158

PAPR		94.18 120	93.42 139	96.48 90	97.64 131	91.42 129	95.55 280	97.71 135	88.99 240	92.34 188	95.82 216	89.19 87	99.11 143	86.14 281	97.38 144	98.90 105

balanced_conf03		96.84 40	96.89 31	96.68 74	97.63 133	92.22 96	98.17 48	97.82 121	94.44 56	98.23 28	97.36 131	90.97 68	99.22 127	97.74 18	99.66 10	98.61 127

CANet		96.39 61	96.02 67	97.50 47	97.62 134	93.38 63	97.02 173	97.96 102	95.42 15	94.86 130	97.81 99	87.38 124	99.82 28	96.88 39	99.20 73	99.29 63

thres200		92.23 202	91.39 205	94.75 196	97.61 135	89.03 216	96.60 216	95.09 322	92.08 139	93.28 168	94.00 310	78.39 276	99.04 160	81.26 340	94.18 213	96.19 245

Vis-MVSNet (Re-imp)		94.15 123	93.88 120	94.95 183	97.61 135	87.92 247	98.10 51	95.80 287	92.22 131	93.02 172	97.45 125	84.53 160	97.91 285	88.24 238	97.97 128	99.02 87

MGCFI-Net		95.94 75	95.40 82	97.56 46	97.59 137	94.62 31	98.21 42	97.57 150	94.41 58	96.17 94	96.16 199	87.54 117	99.17 135	96.19 63	94.73 205	98.91 102

sasdasda		96.02 70	95.45 78	97.75 35	97.59 137	95.15 23	98.28 30	97.60 145	94.52 52	96.27 90	96.12 201	87.65 113	99.18 133	96.20 61	94.82 200	98.91 102

canonicalmvs		96.02 70	95.45 78	97.75 35	97.59 137	95.15 23	98.28 30	97.60 145	94.52 52	96.27 90	96.12 201	87.65 113	99.18 133	96.20 61	94.82 200	98.91 102

LS3D		93.57 147	92.61 165	96.47 91	97.59 137	91.61 118	97.67 103	97.72 131	85.17 332	90.29 236	98.34 54	84.60 158	99.73 42	83.85 315	98.27 118	98.06 175

test1111		93.19 160	92.82 154	94.30 219	97.58 141	84.56 318	98.21 42	89.02 397	93.53 86	94.58 136	98.21 67	72.69 323	99.05 157	93.06 143	98.48 110	99.28 65

alignmvs		95.87 77	95.23 87	97.78 31	97.56 142	95.19 21	97.86 79	97.17 199	94.39 60	96.47 83	96.40 187	85.89 143	99.20 129	96.21 60	95.11 196	98.95 97

EPP-MVSNet		95.22 93	95.04 92	95.76 136	97.49 143	89.56 191	98.67 10	97.00 218	90.69 184	94.24 144	97.62 116	89.79 84	98.81 178	93.39 137	96.49 169	98.92 101

test_fmvsmconf_n		97.49 12	97.56 9	97.29 55	97.44 144	92.37 90	97.91 75	98.88 4	95.83 8	98.92 12	99.05 5	91.45 54	99.80 30	99.12 5	99.46 41	99.69 12

test_vis1_n_1920		94.17 121	94.58 102	92.91 282	97.42 145	82.02 347	97.83 85	97.85 116	94.68 46	98.10 30	98.49 37	70.15 342	99.32 117	97.91 17	98.82 95	97.40 210

PS-MVSNAJ		95.37 87	95.33 85	95.49 156	97.35 146	90.66 161	95.31 292	97.48 161	93.85 74	96.51 80	95.70 226	88.65 97	99.65 58	94.80 108	98.27 118	96.17 246

ab-mvs		93.57 147	92.55 167	96.64 75	97.28 147	91.96 108	95.40 287	97.45 171	89.81 215	93.22 171	96.28 192	79.62 253	99.46 103	90.74 188	93.11 232	98.50 137

xiu_mvs_v2_base		95.32 89	95.29 86	95.40 161	97.22 148	90.50 164	95.44 286	97.44 175	93.70 79	96.46 84	96.18 196	88.59 100	99.53 91	94.79 110	97.81 132	96.17 246

BH-untuned		92.94 173	92.62 164	93.92 242	97.22 148	86.16 292	96.40 230	96.25 269	90.06 207	89.79 255	96.17 198	83.19 181	98.35 225	87.19 265	97.27 151	97.24 218

baseline1		92.82 180	91.90 188	95.55 152	97.20 150	90.77 156	97.19 161	94.58 341	92.20 133	92.36 185	96.34 190	84.16 167	98.21 235	89.20 224	83.90 350	97.68 195

Vis-MVSNet		95.23 92	94.81 95	96.51 87	97.18 151	91.58 121	98.26 34	98.12 67	94.38 61	94.90 129	98.15 72	82.28 205	98.92 167	91.45 176	98.58 106	99.01 90
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		96.02 70	95.89 70	96.40 97	97.16 152	92.44 88	97.47 132	97.77 125	94.55 50	96.48 82	94.51 279	91.23 63	98.92 167	95.65 84	98.19 121	97.82 189

BH-RMVSNet		92.72 184	91.97 186	94.97 181	97.16 152	87.99 245	96.15 249	95.60 298	90.62 191	91.87 201	97.15 144	78.41 275	98.57 207	83.16 317	97.60 137	98.36 154

MSDG		91.42 236	90.24 255	94.96 182	97.15 154	88.91 218	93.69 350	96.32 264	85.72 323	86.93 329	96.47 183	80.24 240	98.98 163	80.57 343	95.05 197	96.98 223

tttt0517		92.96 171	92.33 176	94.87 186	97.11 155	87.16 266	97.97 68	92.09 381	90.63 190	93.88 154	97.01 151	76.50 294	99.06 156	90.29 196	95.45 188	98.38 152

HY-MVS	89.66 9	93.87 137	92.95 149	96.63 77	97.10 156	92.49 87	95.64 278	96.64 248	89.05 238	93.00 173	95.79 220	85.77 146	99.45 105	89.16 226	94.35 208	97.96 179

thisisatest0530		93.03 168	92.21 179	95.49 156	97.07 157	89.11 215	97.49 131	92.19 380	90.16 204	94.09 148	96.41 186	76.43 297	99.05 157	90.38 193	95.68 184	98.31 156

XVG-OURS		93.72 143	93.35 140	94.80 192	97.07 157	88.61 224	94.79 310	97.46 166	91.97 143	93.99 150	97.86 95	81.74 216	98.88 171	92.64 151	92.67 240	96.92 227

sss		94.51 113	93.80 121	96.64 75	97.07 157	91.97 106	96.32 237	98.06 82	88.94 243	94.50 139	96.78 160	84.60 158	99.27 123	91.90 162	96.02 174	98.68 124

EIA-MVS		95.53 85	95.47 77	95.71 143	97.06 160	89.63 187	97.82 87	97.87 111	93.57 81	93.92 153	95.04 253	90.61 74	98.95 164	94.62 113	98.68 100	98.54 132

XVG-OURS-SEG-HR		93.86 138	93.55 128	94.81 189	97.06 160	88.53 229	95.28 293	97.45 171	91.68 149	94.08 149	97.68 107	82.41 203	98.90 170	93.84 128	92.47 241	96.98 223

1112_ss		93.37 153	92.42 174	96.21 114	97.05 162	90.99 146	96.31 238	96.72 240	86.87 304	89.83 254	96.69 167	86.51 134	99.14 140	88.12 239	93.67 226	98.50 137

Test_1112_low_res		92.84 179	91.84 190	95.85 134	97.04 163	89.97 180	95.53 282	96.64 248	85.38 327	89.65 260	95.18 248	85.86 144	99.10 144	87.70 250	93.58 231	98.49 139

mvsmamba		94.57 112	94.14 116	95.87 131	97.03 164	89.93 182	97.84 83	95.85 284	91.34 160	94.79 132	96.80 159	80.67 231	98.81 178	94.85 103	98.12 125	98.85 111

hse-mvs2		93.45 151	92.99 147	94.81 189	97.02 165	88.59 225	96.69 204	96.47 258	95.19 20	96.74 67	96.16 199	83.67 173	98.48 214	95.85 75	79.13 374	97.35 213

EC-MVSNet		96.42 59	96.47 54	96.26 110	97.01 166	91.52 123	98.89 5	97.75 126	94.42 57	96.64 74	97.68 107	89.32 86	98.60 203	97.45 29	99.11 83	98.67 125

AUN-MVS		91.76 217	90.75 233	94.81 189	97.00 167	88.57 226	96.65 208	96.49 257	89.63 218	92.15 192	96.12 201	78.66 271	98.50 211	90.83 185	79.18 373	97.36 211

BH-w/o		92.14 206	91.75 193	93.31 268	96.99 168	85.73 296	95.67 273	95.69 293	88.73 254	89.26 274	94.82 264	82.97 189	98.07 255	85.26 296	96.32 172	96.13 250

GeoE		93.89 136	93.28 142	95.72 142	96.96 169	89.75 186	98.24 38	96.92 227	89.47 224	92.12 194	97.21 140	84.42 161	98.39 222	87.71 249	96.50 168	99.01 90

3Dnovator+	91.43 4	95.40 86	94.48 109	98.16 16	96.90 170	95.34 16	98.48 20	97.87 111	94.65 49	88.53 291	98.02 82	83.69 172	99.71 46	93.18 139	98.96 91	99.44 49

MVS_0304		96.74 47	96.31 61	98.02 19	96.87 171	94.65 30	97.58 116	94.39 347	96.47 3	97.16 53	98.39 47	87.53 118	99.87 7	98.97 8	99.41 50	99.55 31

casdiffmvs_mvg		95.81 78	95.57 74	96.51 87	96.87 171	91.49 124	97.50 126	97.56 154	93.99 69	95.13 126	97.92 89	87.89 109	98.78 181	95.97 71	97.33 147	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
UGNet		94.04 131	93.28 142	96.31 104	96.85 173	91.19 139	97.88 78	97.68 136	94.40 59	93.00 173	96.18 196	73.39 322	99.61 69	91.72 168	98.46 111	98.13 168
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
VDDNet		93.05 167	92.07 181	96.02 125	96.84 174	90.39 169	98.08 53	95.85 284	86.22 316	95.79 109	98.46 41	67.59 358	99.19 130	94.92 102	94.85 198	98.47 142

RPSCF		90.75 267	90.86 225	90.42 344	96.84 174	76.29 387	95.61 279	96.34 263	83.89 348	91.38 212	97.87 93	76.45 295	98.78 181	87.16 267	92.23 244	96.20 244

FE-MVS		92.05 208	91.05 219	95.08 172	96.83 176	87.93 246	93.91 343	95.70 291	86.30 313	94.15 147	94.97 254	76.59 293	99.21 128	84.10 308	96.86 158	98.09 173

MVS_Test		94.89 104	94.62 100	95.68 144	96.83 176	89.55 192	96.70 202	97.17 199	91.17 169	95.60 116	96.11 205	87.87 110	98.76 185	93.01 147	97.17 155	98.72 120

LCM-MVSNet-Re		92.50 186	92.52 170	92.44 295	96.82 178	81.89 348	96.92 182	93.71 364	92.41 128	84.30 351	94.60 274	85.08 153	97.03 343	91.51 173	97.36 145	98.40 150

ETVMVS		90.52 276	89.14 295	94.67 198	96.81 179	87.85 251	95.91 261	93.97 358	89.71 217	92.34 188	92.48 349	65.41 375	97.96 274	81.37 337	94.27 211	98.21 161

test_cas_vis1_n_1920		94.48 115	94.55 106	94.28 220	96.78 180	86.45 284	97.63 112	97.64 141	93.32 95	97.68 39	98.36 50	73.75 320	99.08 150	96.73 42	99.05 86	97.31 215

baseline		95.58 83	95.42 81	96.08 119	96.78 180	90.41 168	97.16 164	97.45 171	93.69 80	95.65 115	97.85 96	87.29 125	98.68 195	95.66 81	97.25 152	99.13 77

FA-MVS(test-final)		93.52 149	92.92 150	95.31 163	96.77 182	88.54 228	94.82 309	96.21 272	89.61 219	94.20 145	95.25 246	83.24 180	99.14 140	90.01 198	96.16 173	98.25 158

Fast-Effi-MVS+		93.46 150	92.75 158	95.59 149	96.77 182	90.03 174	96.81 192	97.13 201	88.19 268	91.30 217	94.27 296	86.21 139	98.63 200	87.66 254	96.46 171	98.12 169

QAPM		93.45 151	92.27 177	96.98 71	96.77 182	92.62 82	98.39 24	98.12 67	84.50 342	88.27 298	97.77 102	82.39 204	99.81 29	85.40 294	98.81 96	98.51 136

casdiffmvs		95.64 81	95.49 76	96.08 119	96.76 185	90.45 166	97.29 151	97.44 175	94.00 68	95.46 121	97.98 85	87.52 120	98.73 189	95.64 85	97.33 147	99.08 84
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CHOSEN 280x420		93.12 163	92.72 161	94.34 215	96.71 186	87.27 260	90.29 387	97.72 131	86.61 308	91.34 214	95.29 242	84.29 165	98.41 217	93.25 138	98.94 92	97.35 213

fmvsm_s_conf0.1_n		96.58 55	96.77 41	96.01 127	96.67 187	90.25 171	97.91 75	98.38 23	94.48 54	98.84 16	99.14 1	88.06 105	99.62 68	98.82 11	98.60 104	98.15 167

test_fmvsmvis_n_1920		96.70 48	96.84 34	96.31 104	96.62 188	91.73 111	97.98 62	98.30 32	96.19 5	96.10 97	98.95 8	89.42 85	99.76 38	98.90 10	99.08 84	97.43 208

Effi-MVS+		94.93 102	94.45 110	96.36 102	96.61 189	91.47 126	96.41 226	97.41 180	91.02 175	94.50 139	95.92 210	87.53 118	98.78 181	93.89 126	96.81 160	98.84 114

thisisatest0515		92.29 198	91.30 210	95.25 165	96.60 190	88.90 219	94.36 325	92.32 379	87.92 275	93.43 164	94.57 275	77.28 289	99.00 161	89.42 215	95.86 179	97.86 185

PCF-MVS	89.48 11	91.56 228	89.95 269	96.36 102	96.60 190	92.52 86	92.51 372	97.26 194	79.41 382	88.90 280	96.56 179	84.04 169	99.55 87	77.01 366	97.30 150	97.01 222
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		95.01 97	94.76 96	95.75 138	96.58 192	91.71 113	96.25 242	97.35 188	92.99 109	96.70 69	96.63 174	82.67 195	99.44 106	96.22 56	97.46 139	96.11 251

xiu_mvs_v1_base		95.01 97	94.76 96	95.75 138	96.58 192	91.71 113	96.25 242	97.35 188	92.99 109	96.70 69	96.63 174	82.67 195	99.44 106	96.22 56	97.46 139	96.11 251

xiu_mvs_v1_base_debi		95.01 97	94.76 96	95.75 138	96.58 192	91.71 113	96.25 242	97.35 188	92.99 109	96.70 69	96.63 174	82.67 195	99.44 106	96.22 56	97.46 139	96.11 251

MVSTER		93.20 159	92.81 155	94.37 212	96.56 195	89.59 190	97.06 170	97.12 202	91.24 165	91.30 217	95.96 208	82.02 210	98.05 258	93.48 133	90.55 274	95.47 278

3Dnovator	91.36 5	95.19 95	94.44 111	97.44 49	96.56 195	93.36 65	98.65 11	98.36 24	94.12 65	89.25 275	98.06 77	82.20 207	99.77 37	93.41 136	99.32 61	99.18 72

test_fmvs1		93.21 158	93.53 130	92.25 303	96.55 197	81.20 354	97.40 139	96.96 220	90.68 185	96.80 64	98.04 79	69.25 347	98.40 218	97.58 24	98.50 107	97.16 220

testing91		91.90 213	91.02 220	94.53 206	96.54 198	86.55 282	95.86 263	95.64 297	91.77 146	91.89 200	93.47 332	69.94 344	98.86 172	90.23 197	93.86 224	98.18 163

testing222		90.31 280	88.96 297	94.35 213	96.54 198	87.29 258	95.50 283	93.84 362	90.97 176	91.75 205	92.96 340	62.18 385	98.00 265	82.86 320	94.08 217	97.76 191

testing11		91.68 221	90.75 233	94.47 207	96.53 200	86.56 281	95.76 270	94.51 344	91.10 173	91.24 222	93.59 327	68.59 352	98.86 172	91.10 182	94.29 210	98.00 178

FMVSNet3		91.78 216	90.69 238	95.03 175	96.53 200	92.27 95	97.02 173	96.93 223	89.79 216	89.35 269	94.65 272	77.01 290	97.47 324	86.12 282	88.82 289	95.35 288

UBG		91.55 229	90.76 231	93.94 239	96.52 202	85.06 310	95.22 298	94.54 342	90.47 198	91.98 198	92.71 343	72.02 327	98.74 188	88.10 240	95.26 192	98.01 177

GBi-Net		91.35 241	90.27 253	94.59 199	96.51 203	91.18 140	97.50 126	96.93 223	88.82 249	89.35 269	94.51 279	73.87 316	97.29 336	86.12 282	88.82 289	95.31 291

test1		91.35 241	90.27 253	94.59 199	96.51 203	91.18 140	97.50 126	96.93 223	88.82 249	89.35 269	94.51 279	73.87 316	97.29 336	86.12 282	88.82 289	95.31 291

FMVSNet2		91.31 244	90.08 262	94.99 177	96.51 203	92.21 97	97.41 135	96.95 221	88.82 249	88.62 288	94.75 267	73.87 316	97.42 329	85.20 297	88.55 294	95.35 288

WBMVS		90.69 272	89.99 268	92.81 287	96.48 206	85.00 311	95.21 300	96.30 265	89.46 225	89.04 279	94.05 308	72.45 326	97.82 292	89.46 213	87.41 306	95.61 273

testing99		91.62 223	90.72 236	94.32 216	96.48 206	86.11 293	95.81 266	94.76 336	91.55 151	91.75 205	93.44 333	68.55 353	98.82 176	90.43 191	93.69 225	98.04 176

ACMH+	87.92 14	90.20 286	89.18 293	93.25 270	96.48 206	86.45 284	96.99 177	96.68 245	88.83 248	84.79 348	96.22 195	70.16 341	98.53 209	84.42 306	88.04 297	94.77 329

CANet_DTU		94.37 116	93.65 125	96.55 82	96.46 209	92.13 101	96.21 246	96.67 247	94.38 61	93.53 161	97.03 150	79.34 256	99.71 46	90.76 187	98.45 112	97.82 189

mvs_anonymous		93.82 139	93.74 122	94.06 228	96.44 210	85.41 301	95.81 266	97.05 212	89.85 213	90.09 247	96.36 189	87.44 122	97.75 300	93.97 122	96.69 165	99.02 87

diffmvs		95.25 91	95.13 90	95.63 146	96.43 211	89.34 203	95.99 257	97.35 188	92.83 119	96.31 88	97.37 130	86.44 135	98.67 196	96.26 53	97.19 154	98.87 110
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ET-MVSNet_ETH3D		91.49 233	90.11 261	95.63 146	96.40 212	91.57 122	95.34 289	93.48 366	90.60 194	75.58 389	95.49 237	80.08 243	96.79 352	94.25 118	89.76 282	98.52 134

RRT-MVS		94.51 113	94.35 113	94.98 179	96.40 212	86.55 282	97.56 119	97.41 180	93.19 100	94.93 128	97.04 149	79.12 260	99.30 121	96.19 63	97.32 149	99.09 83

TR-MVS		91.48 234	90.59 241	94.16 224	96.40 212	87.33 257	95.67 273	95.34 311	87.68 287	91.46 211	95.52 236	76.77 292	98.35 225	82.85 322	93.61 229	96.79 231

ACMP	89.59 10	92.62 185	92.14 180	94.05 229	96.40 212	88.20 239	97.36 143	97.25 196	91.52 152	88.30 296	96.64 170	78.46 274	98.72 192	91.86 165	91.48 258	95.23 298
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		95.37 87	95.16 89	95.99 128	96.34 216	91.21 136	98.22 40	97.57 150	91.42 157	96.22 92	97.32 132	86.20 140	97.92 282	94.07 120	99.05 86	98.85 111

lupinMVS		94.99 101	94.56 103	96.29 108	96.34 216	91.21 136	95.83 265	96.27 267	88.93 244	96.22 92	96.88 157	86.20 140	98.85 174	95.27 94	99.05 86	98.82 115

ACMM	89.79 8	92.96 171	92.50 171	94.35 213	96.30 218	88.71 222	97.58 116	97.36 187	91.40 159	90.53 231	96.65 169	79.77 249	98.75 186	91.24 180	91.64 254	95.59 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		92.29 198	91.94 187	93.34 267	96.25 219	86.97 270	96.57 220	97.05 212	90.67 186	89.50 266	94.80 265	86.59 131	97.64 308	89.91 201	86.11 317	95.40 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP_MVS		93.78 141	93.43 137	94.82 187	96.21 220	89.99 177	97.74 94	97.51 158	94.85 34	91.34 214	96.64 170	81.32 221	98.60 203	93.02 145	92.23 244	95.86 256

plane_prior7					96.21 220	89.98 179

ACMH	87.59 16	90.53 275	89.42 288	93.87 243	96.21 220	87.92 247	97.24 154	96.94 222	88.45 262	83.91 359	96.27 193	71.92 328	98.62 202	84.43 305	89.43 285	95.05 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDS-MVSNet		94.14 126	93.54 129	95.93 129	96.18 223	91.46 127	96.33 236	97.04 214	88.97 242	93.56 158	96.51 181	87.55 116	97.89 286	89.80 204	95.95 176	98.44 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LTVRE_ROB	88.41 13	90.99 258	89.92 271	94.19 222	96.18 223	89.55 192	96.31 238	97.09 206	87.88 277	85.67 339	95.91 211	78.79 270	98.57 207	81.50 332	89.98 279	94.44 339
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
LPG-MVS_test		92.94 173	92.56 166	94.10 226	96.16 225	88.26 236	97.65 106	97.46 166	91.29 161	90.12 244	97.16 142	79.05 262	98.73 189	92.25 154	91.89 252	95.31 291

LGP-MVS_train				94.10 226	96.16 225	88.26 236		97.46 166	91.29 161	90.12 244	97.16 142	79.05 262	98.73 189	92.25 154	91.89 252	95.31 291

TAMVS		94.01 132	93.46 135	95.64 145	96.16 225	90.45 166	96.71 201	96.89 230	89.27 231	93.46 163	96.92 155	87.29 125	97.94 279	88.70 234	95.74 181	98.53 133

testing3		87.67 320	86.88 321	90.05 349	96.14 228	80.71 357	97.10 168	92.85 373	90.15 205	87.54 312	94.55 276	55.70 394	94.10 385	73.77 381	94.10 216	95.35 288

plane_prior1					96.14 228

CLD-MVS		92.98 170	92.53 169	94.32 216	96.12 230	89.20 211	95.28 293	97.47 164	92.66 123	89.90 251	95.62 230	80.58 233	98.40 218	92.73 150	92.40 242	95.38 286
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					96.10 231	90.00 175						81.32 221

cl22		91.21 248	90.56 243	93.14 275	96.09 232	86.80 272	94.41 323	96.58 254	87.80 281	88.58 290	93.99 311	80.85 230	97.62 311	89.87 203	86.93 309	94.99 307

test_fmvs1_n		92.73 183	92.88 152	92.29 301	96.08 233	81.05 355	97.98 62	97.08 207	90.72 183	96.79 65	98.18 70	63.07 380	98.45 215	97.62 23	98.42 113	97.36 211

Effi-MVS+-dtu		93.08 165	93.21 144	92.68 293	96.02 234	83.25 333	97.14 166	96.72 240	93.85 74	91.20 224	93.44 333	83.08 185	98.30 229	91.69 171	95.73 182	96.50 237

NP-MVS					95.99 235	89.81 185					95.87 212

UWE-MVS		89.91 291	89.48 287	91.21 328	95.88 236	78.23 383	94.91 308	90.26 393	89.11 235	92.35 187	94.52 278	68.76 350	97.96 274	83.95 312	95.59 186	97.42 209

ADS-MVSNet2		89.45 300	88.59 302	92.03 307	95.86 237	82.26 345	90.93 383	94.32 352	83.23 358	91.28 220	91.81 363	79.01 266	95.99 361	79.52 349	91.39 260	97.84 186

ADS-MVSNet		89.89 293	88.68 301	93.53 260	95.86 237	84.89 315	90.93 383	95.07 323	83.23 358	91.28 220	91.81 363	79.01 266	97.85 288	79.52 349	91.39 260	97.84 186

HQP-NCC					95.86 237		96.65 208		93.55 82	90.14 238

ACMP_Plane					95.86 237		96.65 208		93.55 82	90.14 238

HQP-MVS		93.19 160	92.74 159	94.54 205	95.86 237	89.33 204	96.65 208	97.39 182	93.55 82	90.14 238	95.87 212	80.95 225	98.50 211	92.13 158	92.10 249	95.78 264

EI-MVSNet		93.03 168	92.88 152	93.48 262	95.77 242	86.98 269	96.44 222	97.12 202	90.66 188	91.30 217	97.64 114	86.56 132	98.05 258	89.91 201	90.55 274	95.41 281

CVMVSNet		91.23 247	91.75 193	89.67 353	95.77 242	74.69 389	96.44 222	94.88 332	85.81 321	92.18 191	97.64 114	79.07 261	95.58 372	88.06 241	95.86 179	98.74 119

FIs		94.09 128	93.70 123	95.27 164	95.70 244	92.03 104	98.10 51	98.68 13	93.36 94	90.39 234	96.70 165	87.63 115	97.94 279	92.25 154	90.50 276	95.84 259

VPA-MVSNet		93.24 157	92.48 172	95.51 154	95.70 244	92.39 89	97.86 79	98.66 16	92.30 130	92.09 196	95.37 240	80.49 235	98.40 218	93.95 123	85.86 318	95.75 268

test_fmvsmconf0.1_n		97.09 24	97.06 19	97.19 64	95.67 246	92.21 97	97.95 71	98.27 39	95.78 10	98.40 25	99.00 6	89.99 81	99.78 35	99.06 6	99.41 50	99.59 21

tt0805		91.09 253	90.07 265	94.16 224	95.61 247	88.31 233	97.56 119	96.51 256	89.56 220	89.17 276	95.64 229	67.08 365	98.38 223	91.07 183	88.44 295	95.80 262

SCA		91.84 215	91.18 217	93.83 244	95.59 248	84.95 314	94.72 311	95.58 300	90.82 178	92.25 190	93.69 321	75.80 301	98.10 247	86.20 279	95.98 175	98.45 144

c3_l		91.38 238	90.89 223	92.88 284	95.58 249	86.30 287	94.68 312	96.84 235	88.17 269	88.83 285	94.23 299	85.65 147	97.47 324	89.36 216	84.63 337	94.89 316

VPNet		92.23 202	91.31 209	94.99 177	95.56 250	90.96 148	97.22 159	97.86 115	92.96 115	90.96 225	96.62 177	75.06 307	98.20 236	91.90 162	83.65 352	95.80 262

miper_ehance_all_eth		91.59 225	91.13 218	92.97 280	95.55 251	86.57 280	94.47 319	96.88 231	87.77 283	88.88 282	94.01 309	86.22 138	97.54 317	89.49 212	86.93 309	94.79 326

IterMVS-SCA-FT		90.31 280	89.81 275	91.82 313	95.52 252	84.20 322	94.30 329	96.15 274	90.61 192	87.39 316	94.27 296	75.80 301	96.44 356	87.34 261	86.88 313	94.82 321

jason		94.84 106	94.39 112	96.18 116	95.52 252	90.93 150	96.09 251	96.52 255	89.28 230	96.01 102	97.32 132	84.70 157	98.77 184	95.15 97	98.91 94	98.85 111
jason: jason.
fmvsm_s_conf0.1_n_a		96.40 60	96.47 54	96.16 117	95.48 254	90.69 159	97.91 75	98.33 29	94.07 66	98.93 9	99.14 1	87.44 122	99.61 69	98.63 13	98.32 116	98.18 163

FC-MVSNet-test		93.94 134	93.57 127	95.04 174	95.48 254	91.45 128	98.12 50	98.71 11	93.37 92	90.23 237	96.70 165	87.66 112	97.85 288	91.49 174	90.39 277	95.83 260

IterMVS		90.15 288	89.67 281	91.61 320	95.48 254	83.72 328	94.33 327	96.12 275	89.99 208	87.31 319	94.15 304	75.78 303	96.27 359	86.97 270	86.89 312	94.83 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		90.21 285	89.50 286	92.35 298	95.47 257	85.15 307	95.70 272	94.37 349	90.94 177	88.42 292	93.57 328	74.63 311	95.67 369	82.80 323	89.57 284	96.22 243

FMVSNet1		89.88 294	88.31 305	94.59 199	95.41 258	91.18 140	97.50 126	96.93 223	86.62 307	87.41 315	94.51 279	65.94 373	97.29 336	83.04 319	87.43 304	95.31 291

UniMVSNet (Re)		93.31 155	92.55 167	95.61 148	95.39 259	93.34 66	97.39 140	98.71 11	93.14 105	90.10 246	94.83 263	87.71 111	98.03 262	91.67 172	83.99 346	95.46 279

MVS-HIRNet		82.47 355	81.21 358	86.26 372	95.38 260	69.21 399	88.96 396	89.49 395	66.28 401	80.79 373	74.08 406	68.48 354	97.39 331	71.93 387	95.47 187	92.18 377

PatchmatchNet		91.91 212	91.35 206	93.59 257	95.38 260	84.11 323	93.15 362	95.39 305	89.54 221	92.10 195	93.68 323	82.82 193	98.13 242	84.81 300	95.32 190	98.52 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl____		90.96 261	90.32 249	92.89 283	95.37 262	86.21 290	94.46 321	96.64 248	87.82 279	88.15 302	94.18 302	82.98 188	97.54 317	87.70 250	85.59 320	94.92 314

DIV-MVS_self_test		90.97 260	90.33 248	92.88 284	95.36 263	86.19 291	94.46 321	96.63 251	87.82 279	88.18 301	94.23 299	82.99 187	97.53 319	87.72 247	85.57 321	94.93 312

miper_enhance_ethall		91.54 231	91.01 221	93.15 274	95.35 264	87.07 268	93.97 338	96.90 228	86.79 305	89.17 276	93.43 336	86.55 133	97.64 308	89.97 200	86.93 309	94.74 330

UniMVSNet_NR-MVSNet		93.37 153	92.67 162	95.47 159	95.34 265	92.83 76	97.17 163	98.58 17	92.98 114	90.13 242	95.80 217	88.37 102	97.85 288	91.71 169	83.93 347	95.73 270

ITE_SJBPF				92.43 296	95.34 265	85.37 304		95.92 279	91.47 154	87.75 309	96.39 188	71.00 335	97.96 274	82.36 328	89.86 281	93.97 350

OpenMVS	89.19 12	92.86 177	91.68 196	96.40 97	95.34 265	92.73 80	98.27 32	98.12 67	84.86 337	85.78 338	97.75 103	78.89 269	99.74 41	87.50 259	98.65 101	96.73 232

eth_miper_zixun_eth		91.02 257	90.59 241	92.34 300	95.33 268	84.35 319	94.10 335	96.90 228	88.56 258	88.84 284	94.33 291	84.08 168	97.60 313	88.77 233	84.37 343	95.06 305

miper_lstm_enhance		90.50 278	90.06 266	91.83 312	95.33 268	83.74 327	93.86 344	96.70 244	87.56 290	87.79 307	93.81 317	83.45 178	96.92 348	87.39 260	84.62 338	94.82 321

1314		92.81 181	92.03 183	95.14 169	95.33 268	89.52 195	96.04 253	97.44 175	87.72 286	86.25 335	95.33 241	83.84 170	98.79 180	89.26 220	97.05 157	97.11 221

PAPM		91.52 232	90.30 251	95.20 166	95.30 271	89.83 184	93.38 358	96.85 234	86.26 315	88.59 289	95.80 217	84.88 155	98.15 241	75.67 371	95.93 177	97.63 196

Fast-Effi-MVS+-dtu		92.29 198	91.99 185	93.21 273	95.27 272	85.52 299	97.03 171	96.63 251	92.09 138	89.11 278	95.14 250	80.33 239	98.08 251	87.54 258	94.74 204	96.03 254

Patchmatch-test		89.42 301	87.99 308	93.70 252	95.27 272	85.11 308	88.98 395	94.37 349	81.11 371	87.10 323	93.69 321	82.28 205	97.50 322	74.37 377	94.76 202	98.48 141

PVSNet_0	82.17 19	85.46 344	83.64 347	90.92 333	95.27 272	79.49 375	90.55 386	95.60 298	83.76 352	83.00 366	89.95 376	71.09 334	97.97 270	82.75 325	60.79 406	95.31 291

IB-MVS	87.33 17	89.91 291	88.28 306	94.79 193	95.26 275	87.70 254	95.12 303	93.95 359	89.35 229	87.03 324	92.49 348	70.74 337	99.19 130	89.18 225	81.37 364	97.49 205
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
nrg030		94.05 130	93.31 141	96.27 109	95.22 276	94.59 32	98.34 25	97.46 166	92.93 116	91.21 223	96.64 170	87.23 127	98.22 234	94.99 101	85.80 319	95.98 255

MDTV_nov1_ep13			90.76 231		95.22 276	80.33 364	93.03 365	95.28 312	88.14 271	92.84 179	93.83 314	81.34 220	98.08 251	82.86 320	94.34 209

MVS		91.71 218	90.44 245	95.51 154	95.20 278	91.59 120	96.04 253	97.45 171	73.44 397	87.36 317	95.60 231	85.42 149	99.10 144	85.97 286	97.46 139	95.83 260

Syy-MVS		87.13 325	87.02 320	87.47 366	95.16 279	73.21 394	95.00 305	93.93 360	88.55 259	86.96 326	91.99 359	75.90 299	94.00 386	61.59 400	94.11 214	95.20 299

myMVS_eth3d		87.18 324	86.38 324	89.58 354	95.16 279	79.53 373	95.00 305	93.93 360	88.55 259	86.96 326	91.99 359	56.23 393	94.00 386	75.47 373	94.11 214	95.20 299

tfpnnormal		89.70 299	88.40 304	93.60 256	95.15 281	90.10 173	97.56 119	98.16 61	87.28 297	86.16 336	94.63 273	77.57 287	98.05 258	74.48 375	84.59 339	92.65 367

tpmrst		91.44 235	91.32 208	91.79 315	95.15 281	79.20 378	93.42 357	95.37 307	88.55 259	93.49 162	93.67 324	82.49 201	98.27 231	90.41 192	89.34 286	97.90 182

WR-MVS		92.34 194	91.53 201	94.77 194	95.13 283	90.83 153	96.40 230	97.98 100	91.88 144	89.29 272	95.54 235	82.50 200	97.80 294	89.79 205	85.27 327	95.69 271

tpm cat1		88.36 313	87.21 316	91.81 314	95.13 283	80.55 361	92.58 371	95.70 291	74.97 393	87.45 313	91.96 361	78.01 284	98.17 240	80.39 345	88.74 292	96.72 233

WR-MVS_H		92.00 209	91.35 206	93.95 237	95.09 285	89.47 196	98.04 57	98.68 13	91.46 155	88.34 294	94.68 270	85.86 144	97.56 315	85.77 289	84.24 344	94.82 321

CP-MVSNet		91.89 214	91.24 213	93.82 245	95.05 286	88.57 226	97.82 87	98.19 55	91.70 148	88.21 300	95.76 222	81.96 211	97.52 321	87.86 244	84.65 336	95.37 287

test_0402		86.46 331	84.79 340	91.45 323	95.02 287	85.55 298	96.29 240	94.89 331	80.90 372	82.21 368	93.97 312	68.21 356	97.29 336	62.98 398	88.68 293	91.51 383

cascas		91.20 249	90.08 262	94.58 203	94.97 288	89.16 214	93.65 352	97.59 148	79.90 380	89.40 267	92.92 341	75.36 305	98.36 224	92.14 157	94.75 203	96.23 242

PS-CasMVS		91.55 229	90.84 228	93.69 253	94.96 289	88.28 235	97.84 83	98.24 47	91.46 155	88.04 304	95.80 217	79.67 251	97.48 323	87.02 269	84.54 341	95.31 291

DU-MVS		92.90 175	92.04 182	95.49 156	94.95 290	92.83 76	97.16 164	98.24 47	93.02 108	90.13 242	95.71 224	83.47 176	97.85 288	91.71 169	83.93 347	95.78 264

NR-MVSNet		92.34 194	91.27 212	95.53 153	94.95 290	93.05 72	97.39 140	98.07 79	92.65 124	84.46 349	95.71 224	85.00 154	97.77 298	89.71 206	83.52 353	95.78 264

mvsany_test1		93.93 135	93.98 118	93.78 248	94.94 292	86.80 272	94.62 313	92.55 378	88.77 253	96.85 63	98.49 37	88.98 90	98.08 251	95.03 99	95.62 185	96.46 240

tpmvs		89.83 297	89.15 294	91.89 310	94.92 293	80.30 365	93.11 363	95.46 304	86.28 314	88.08 303	92.65 344	80.44 236	98.52 210	81.47 333	89.92 280	96.84 229

PMMVS		92.86 177	92.34 175	94.42 211	94.92 293	86.73 275	94.53 317	96.38 262	84.78 339	94.27 143	95.12 252	83.13 184	98.40 218	91.47 175	96.49 169	98.12 169

tpm2		89.96 290	89.21 292	92.23 304	94.91 295	81.25 352	93.78 346	94.42 346	80.62 377	91.56 208	93.44 333	76.44 296	97.94 279	85.60 291	92.08 251	97.49 205

TinyColmap		86.82 328	85.35 334	91.21 328	94.91 295	82.99 336	93.94 340	94.02 357	83.58 354	81.56 370	94.68 270	62.34 384	98.13 242	75.78 369	87.35 308	92.52 371

UniMVSNet_ETH3D		91.34 243	90.22 258	94.68 197	94.86 297	87.86 250	97.23 158	97.46 166	87.99 273	89.90 251	96.92 155	66.35 368	98.23 233	90.30 195	90.99 268	97.96 179

CostFormer		91.18 252	90.70 237	92.62 294	94.84 298	81.76 349	94.09 336	94.43 345	84.15 345	92.72 180	93.77 318	79.43 255	98.20 236	90.70 189	92.18 247	97.90 182

MIMVSNet		88.50 312	86.76 322	93.72 251	94.84 298	87.77 253	91.39 378	94.05 355	86.41 311	87.99 305	92.59 347	63.27 379	95.82 366	77.44 360	92.84 235	97.57 203

FMVSNet5		87.29 323	85.79 329	91.78 316	94.80 300	87.28 259	95.49 284	95.28 312	84.09 346	83.85 360	91.82 362	62.95 381	94.17 384	78.48 356	85.34 326	93.91 351

TranMVSNet+NR-MVSNet		92.50 186	91.63 197	95.14 169	94.76 301	92.07 102	97.53 124	98.11 70	92.90 118	89.56 263	96.12 201	83.16 182	97.60 313	89.30 218	83.20 356	95.75 268

test_vis1_n		92.37 193	92.26 178	92.72 290	94.75 302	82.64 338	98.02 58	96.80 237	91.18 168	97.77 38	97.93 88	58.02 389	98.29 230	97.63 22	98.21 120	97.23 219

XXY-MVS		92.16 204	91.23 214	94.95 183	94.75 302	90.94 149	97.47 132	97.43 178	89.14 234	88.90 280	96.43 185	79.71 250	98.24 232	89.56 211	87.68 301	95.67 272

EPMVS		90.70 270	89.81 275	93.37 266	94.73 304	84.21 321	93.67 351	88.02 400	89.50 223	92.38 184	93.49 330	77.82 286	97.78 296	86.03 285	92.68 239	98.11 172

D2MVS		91.30 245	90.95 222	92.35 298	94.71 305	85.52 299	96.18 248	98.21 51	88.89 245	86.60 332	93.82 316	79.92 247	97.95 278	89.29 219	90.95 269	93.56 354

USDC		88.94 305	87.83 310	92.27 302	94.66 306	84.96 313	93.86 344	95.90 281	87.34 295	83.40 361	95.56 233	67.43 359	98.19 238	82.64 327	89.67 283	93.66 353

GA-MVS		91.38 238	90.31 250	94.59 199	94.65 307	87.62 255	94.34 326	96.19 273	90.73 182	90.35 235	93.83 314	71.84 329	97.96 274	87.22 264	93.61 229	98.21 161

OPM-MVS		93.28 156	92.76 156	94.82 187	94.63 308	90.77 156	96.65 208	97.18 197	93.72 77	91.68 207	97.26 137	79.33 257	98.63 200	92.13 158	92.28 243	95.07 304
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test-LLR		91.42 236	91.19 216	92.12 305	94.59 309	80.66 358	94.29 330	92.98 371	91.11 171	90.76 229	92.37 351	79.02 264	98.07 255	88.81 231	96.74 162	97.63 196

test-mter		90.19 287	89.54 285	92.12 305	94.59 309	80.66 358	94.29 330	92.98 371	87.68 287	90.76 229	92.37 351	67.67 357	98.07 255	88.81 231	96.74 162	97.63 196

dp		88.90 307	88.26 307	90.81 337	94.58 311	76.62 385	92.85 368	94.93 329	85.12 333	90.07 249	93.07 338	75.81 300	98.12 245	80.53 344	87.42 305	97.71 193

WB-MVSnew		89.88 294	89.56 284	90.82 336	94.57 312	83.06 335	95.65 277	92.85 373	87.86 278	90.83 228	94.10 305	79.66 252	96.88 349	76.34 367	94.19 212	92.54 370

PEN-MVS		91.20 249	90.44 245	93.48 262	94.49 313	87.91 249	97.76 92	98.18 57	91.29 161	87.78 308	95.74 223	80.35 238	97.33 334	85.46 293	82.96 357	95.19 302

gg-mvs-nofinetune		87.82 318	85.61 330	94.44 209	94.46 314	89.27 209	91.21 382	84.61 408	80.88 373	89.89 253	74.98 404	71.50 331	97.53 319	85.75 290	97.21 153	96.51 236

CR-MVSNet		90.82 265	89.77 277	93.95 237	94.45 315	87.19 264	90.23 388	95.68 295	86.89 303	92.40 182	92.36 354	80.91 227	97.05 342	81.09 341	93.95 222	97.60 201

RPMNet		88.98 304	87.05 318	94.77 194	94.45 315	87.19 264	90.23 388	98.03 91	77.87 389	92.40 182	87.55 393	80.17 242	99.51 96	68.84 394	93.95 222	97.60 201

TESTMET0.1,1		90.06 289	89.42 288	91.97 308	94.41 317	80.62 360	94.29 330	91.97 383	87.28 297	90.44 233	92.47 350	68.79 349	97.67 305	88.50 237	96.60 167	97.61 200

TransMVSNet (Re)		88.94 305	87.56 311	93.08 277	94.35 318	88.45 232	97.73 96	95.23 316	87.47 291	84.26 352	95.29 242	79.86 248	97.33 334	79.44 353	74.44 387	93.45 357

MS-PatchMatch		90.27 282	89.77 277	91.78 316	94.33 319	84.72 317	95.55 280	96.73 239	86.17 317	86.36 334	95.28 244	71.28 333	97.80 294	84.09 309	98.14 124	92.81 364

baseline2		91.63 222	90.86 225	93.94 239	94.33 319	86.32 286	95.92 260	91.64 385	89.37 228	86.94 328	94.69 269	81.62 218	98.69 194	88.64 235	94.57 207	96.81 230

XVG-ACMP-BASELINE		90.93 262	90.21 259	93.09 276	94.31 321	85.89 294	95.33 290	97.26 194	91.06 174	89.38 268	95.44 239	68.61 351	98.60 203	89.46 213	91.05 266	94.79 326

pm-mvs1		90.72 269	89.65 283	93.96 236	94.29 322	89.63 187	97.79 91	96.82 236	89.07 236	86.12 337	95.48 238	78.61 272	97.78 296	86.97 270	81.67 362	94.46 337

v8		91.29 246	90.53 244	93.57 259	94.15 323	88.12 243	97.34 145	97.06 211	88.99 240	88.32 295	94.26 298	83.08 185	98.01 264	87.62 256	83.92 349	94.57 335

v10		91.04 256	90.23 256	93.49 261	94.12 324	88.16 242	97.32 148	97.08 207	88.26 267	88.29 297	94.22 301	82.17 208	97.97 270	86.45 276	84.12 345	94.33 342

Patchmtry		88.64 311	87.25 314	92.78 289	94.09 325	86.64 276	89.82 392	95.68 295	80.81 375	87.63 311	92.36 354	80.91 227	97.03 343	78.86 355	85.12 330	94.67 332

PatchT		88.87 308	87.42 312	93.22 272	94.08 326	85.10 309	89.51 393	94.64 340	81.92 366	92.36 185	88.15 389	80.05 244	97.01 345	72.43 385	93.65 227	97.54 204

V42		91.58 227	90.87 224	93.73 249	94.05 327	88.50 230	97.32 148	96.97 219	88.80 252	89.71 256	94.33 291	82.54 199	98.05 258	89.01 227	85.07 331	94.64 334

DTE-MVSNet		90.56 274	89.75 279	93.01 278	93.95 328	87.25 261	97.64 110	97.65 139	90.74 181	87.12 320	95.68 227	79.97 246	97.00 346	83.33 316	81.66 363	94.78 328

tpm		90.25 283	89.74 280	91.76 318	93.92 329	79.73 372	93.98 337	93.54 365	88.28 266	91.99 197	93.25 337	77.51 288	97.44 327	87.30 263	87.94 298	98.12 169

PS-MVSNAJss		93.74 142	93.51 133	94.44 209	93.91 330	89.28 208	97.75 93	97.56 154	92.50 126	89.94 250	96.54 180	88.65 97	98.18 239	93.83 129	90.90 270	95.86 256

v1144		91.37 240	90.60 240	93.68 254	93.89 331	88.23 238	96.84 189	97.03 216	88.37 264	89.69 258	94.39 286	82.04 209	97.98 267	87.80 246	85.37 324	94.84 318

v2v482		91.59 225	90.85 227	93.80 246	93.87 332	88.17 241	96.94 181	96.88 231	89.54 221	89.53 264	94.90 259	81.70 217	98.02 263	89.25 221	85.04 333	95.20 299

v148		90.99 258	90.38 247	92.81 287	93.83 333	85.80 295	96.78 195	96.68 245	89.45 226	88.75 287	93.93 313	82.96 190	97.82 292	87.83 245	83.25 354	94.80 324

Baseline_NR-MVSNet		91.20 249	90.62 239	92.95 281	93.83 333	88.03 244	97.01 176	95.12 321	88.42 263	89.70 257	95.13 251	83.47 176	97.44 327	89.66 209	83.24 355	93.37 358

EPNet_dtu		91.71 218	91.28 211	92.99 279	93.76 335	83.71 329	96.69 204	95.28 312	93.15 104	87.02 325	95.95 209	83.37 179	97.38 332	79.46 352	96.84 159	97.88 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1192		91.07 254	90.23 256	93.58 258	93.70 336	87.82 252	96.73 198	97.07 209	87.77 283	89.58 261	94.32 293	80.90 229	97.97 270	86.52 274	85.48 322	94.95 308

GG-mvs-BLEND				93.62 255	93.69 337	89.20 211	92.39 374	83.33 410		87.98 306	89.84 378	71.00 335	96.87 350	82.08 330	95.40 189	94.80 324

test_fmvs2		89.77 298	89.93 270	89.31 359	93.68 338	76.37 386	97.64 110	95.90 281	89.84 214	91.49 210	96.26 194	58.77 388	97.10 340	94.65 112	91.13 264	94.46 337

v144192		91.06 255	90.28 252	93.39 265	93.66 339	87.23 263	96.83 190	97.07 209	87.43 292	89.69 258	94.28 295	81.48 219	98.00 265	87.18 266	84.92 335	94.93 312

v1921920		90.85 264	90.03 267	93.29 269	93.55 340	86.96 271	96.74 197	97.04 214	87.36 294	89.52 265	94.34 290	80.23 241	97.97 270	86.27 277	85.21 328	94.94 310

v7n		90.76 266	89.86 272	93.45 264	93.54 341	87.60 256	97.70 102	97.37 185	88.85 246	87.65 310	94.08 307	81.08 224	98.10 247	84.68 302	83.79 351	94.66 333

JIA-IIPM		88.26 315	87.04 319	91.91 309	93.52 342	81.42 351	89.38 394	94.38 348	80.84 374	90.93 226	80.74 401	79.22 258	97.92 282	82.76 324	91.62 255	96.38 241

v1240		90.70 270	89.85 273	93.23 271	93.51 343	86.80 272	96.61 214	97.02 217	87.16 299	89.58 261	94.31 294	79.55 254	97.98 267	85.52 292	85.44 323	94.90 315

test_djsdf		93.07 166	92.76 156	94.00 232	93.49 344	88.70 223	98.22 40	97.57 150	91.42 157	90.08 248	95.55 234	82.85 192	97.92 282	94.07 120	91.58 256	95.40 284

SixPastTwentyTwo		89.15 303	88.54 303	90.98 332	93.49 344	80.28 366	96.70 202	94.70 337	90.78 179	84.15 354	95.57 232	71.78 330	97.71 303	84.63 303	85.07 331	94.94 310

test_vis1_rt		86.16 336	85.06 337	89.46 355	93.47 346	80.46 362	96.41 226	86.61 405	85.22 330	79.15 382	88.64 384	52.41 397	97.06 341	93.08 142	90.57 273	90.87 388

mvs_tets		92.31 196	91.76 192	93.94 239	93.41 347	88.29 234	97.63 112	97.53 156	92.04 140	88.76 286	96.45 184	74.62 312	98.09 250	93.91 125	91.48 258	95.45 280

OurMVSNet-221017-0		90.51 277	90.19 260	91.44 324	93.41 347	81.25 352	96.98 178	96.28 266	91.68 149	86.55 333	96.30 191	74.20 315	97.98 267	88.96 229	87.40 307	95.09 303

pmmvs4		90.93 262	89.85 273	94.17 223	93.34 349	90.79 155	94.60 314	96.02 277	84.62 340	87.45 313	95.15 249	81.88 214	97.45 326	87.70 250	87.87 299	94.27 346

jajsoiax		92.42 190	91.89 189	94.03 231	93.33 350	88.50 230	97.73 96	97.53 156	92.00 142	88.85 283	96.50 182	75.62 304	98.11 246	93.88 127	91.56 257	95.48 276

gm-plane-assit					93.22 351	78.89 381			84.82 338		93.52 329		98.64 199	87.72 247

MVP-Stereo		90.74 268	90.08 262	92.71 291	93.19 352	88.20 239	95.86 263	96.27 267	86.07 318	84.86 347	94.76 266	77.84 285	97.75 300	83.88 314	98.01 127	92.17 378
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		88.72 310	88.90 298	88.20 363	93.15 353	74.21 390	96.63 213	94.22 354	85.18 331	87.32 318	95.97 207	76.16 298	94.98 378	85.27 295	86.17 315	95.41 281

MDA-MVSNet-bldmvs		85.00 345	82.95 350	91.17 331	93.13 354	83.33 332	94.56 316	95.00 325	84.57 341	65.13 403	92.65 344	70.45 338	95.85 364	73.57 382	77.49 377	94.33 342

K. test v3		87.64 321	86.75 323	90.32 346	93.02 355	79.48 376	96.61 214	92.08 382	90.66 188	80.25 378	94.09 306	67.21 361	96.65 354	85.96 287	80.83 366	94.83 319

MonoMVSNet		91.92 211	91.77 191	92.37 297	92.94 356	83.11 334	97.09 169	95.55 301	92.91 117	90.85 227	94.55 276	81.27 223	96.52 355	93.01 147	87.76 300	97.47 207

pmmvs5		89.86 296	88.87 299	92.82 286	92.86 357	86.23 289	96.26 241	95.39 305	84.24 344	87.12 320	94.51 279	74.27 314	97.36 333	87.61 257	87.57 302	94.86 317

testgi		87.97 316	87.21 316	90.24 347	92.86 357	80.76 356	96.67 207	94.97 327	91.74 147	85.52 340	95.83 215	62.66 383	94.47 382	76.25 368	88.36 296	95.48 276

EPNet		95.20 94	94.56 103	97.14 65	92.80 359	92.68 81	97.85 82	94.87 335	96.64 2	92.46 181	97.80 101	86.23 137	99.65 58	93.72 130	98.62 103	99.10 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		78.73 362	78.71 363	78.79 380	92.80 359	46.50 419	94.14 334	43.71 421	78.61 385	80.83 372	91.66 365	74.94 309	96.36 357	67.24 395	84.45 342	93.50 355

EG-PatchMatch MVS		87.02 327	85.44 331	91.76 318	92.67 361	85.00 311	96.08 252	96.45 259	83.41 357	79.52 380	93.49 330	57.10 391	97.72 302	79.34 354	90.87 271	92.56 369

test_fmvsmconf0.01_n		96.15 67	95.85 71	97.03 69	92.66 362	91.83 110	97.97 68	97.84 120	95.57 12	97.53 40	99.00 6	84.20 166	99.76 38	98.82 11	99.08 84	99.48 44

Gipuma		67.86 373	65.41 375	75.18 388	92.66 362	73.45 392	66.50 409	94.52 343	53.33 408	57.80 409	66.07 409	30.81 409	89.20 401	48.15 407	78.88 376	62.90 409
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		92.16 204	91.55 200	93.97 235	92.58 364	89.55 192	97.51 125	97.42 179	89.42 227	88.40 293	94.84 262	80.66 232	97.88 287	91.87 164	91.28 262	94.48 336

EGC-MVSNET		68.77 372	63.01 378	86.07 373	92.49 365	82.24 346	93.96 339	90.96 390	0.71 418	2.62 419	90.89 369	53.66 395	93.46 390	57.25 403	84.55 340	82.51 399

test0.0.03 1		89.37 302	88.70 300	91.41 325	92.47 366	85.63 297	95.22 298	92.70 376	91.11 171	86.91 330	93.65 325	79.02 264	93.19 394	78.00 359	89.18 287	95.41 281

our_test_3		88.78 309	87.98 309	91.20 330	92.45 367	82.53 340	93.61 354	95.69 293	85.77 322	84.88 346	93.71 319	79.99 245	96.78 353	79.47 351	86.24 314	94.28 345

ppachtmachnet_test		88.35 314	87.29 313	91.53 321	92.45 367	83.57 331	93.75 347	95.97 278	84.28 343	85.32 344	94.18 302	79.00 268	96.93 347	75.71 370	84.99 334	94.10 347

YYNet1		85.87 341	84.23 345	90.78 340	92.38 369	82.46 343	93.17 360	95.14 320	82.12 365	67.69 397	92.36 354	78.16 280	95.50 374	77.31 362	79.73 370	94.39 340

MDA-MVSNet_test_wron		85.87 341	84.23 345	90.80 339	92.38 369	82.57 339	93.17 360	95.15 319	82.15 364	67.65 399	92.33 357	78.20 277	95.51 373	77.33 361	79.74 369	94.31 344

LF4IMVS		87.94 317	87.25 314	89.98 350	92.38 369	80.05 370	94.38 324	95.25 315	87.59 289	84.34 350	94.74 268	64.31 377	97.66 307	84.83 299	87.45 303	92.23 375

lessismore_v0				90.45 343	91.96 372	79.09 380		87.19 403		80.32 377	94.39 286	66.31 369	97.55 316	84.00 311	76.84 379	94.70 331

dmvs_testset		81.38 358	82.60 353	77.73 381	91.74 373	51.49 416	93.03 365	84.21 409	89.07 236	78.28 385	91.25 368	76.97 291	88.53 404	56.57 404	82.24 361	93.16 359

pmmvs6		87.81 319	86.19 326	92.69 292	91.32 374	86.30 287	97.34 145	96.41 261	80.59 378	84.05 358	94.37 288	67.37 360	97.67 305	84.75 301	79.51 372	94.09 349

Anonymous20231206		87.09 326	86.14 327	89.93 351	91.22 375	80.35 363	96.11 250	95.35 308	83.57 355	84.16 353	93.02 339	73.54 321	95.61 370	72.16 386	86.14 316	93.84 352

KD-MVS_2432*1600		84.81 347	82.64 351	91.31 326	91.07 376	85.34 305	91.22 380	95.75 289	85.56 325	83.09 364	90.21 374	67.21 361	95.89 362	77.18 364	62.48 404	92.69 365

miper_refine_blended		84.81 347	82.64 351	91.31 326	91.07 376	85.34 305	91.22 380	95.75 289	85.56 325	83.09 364	90.21 374	67.21 361	95.89 362	77.18 364	62.48 404	92.69 365

DeepMVS_CX				74.68 389	90.84 378	64.34 407		81.61 412	65.34 402	67.47 400	88.01 391	48.60 401	80.13 411	62.33 399	73.68 389	79.58 401

Anonymous20240521		86.42 332	85.44 331	89.34 358	90.33 379	79.79 371	96.73 198	95.92 279	83.71 353	83.25 363	91.36 367	63.92 378	96.01 360	78.39 358	85.36 325	92.22 376

test20.03		86.14 337	85.40 333	88.35 361	90.12 380	80.06 369	95.90 262	95.20 317	88.59 255	81.29 371	93.62 326	71.43 332	92.65 395	71.26 390	81.17 365	92.34 373

OpenMVS_ROB	81.14 20	84.42 349	82.28 355	90.83 335	90.06 381	84.05 325	95.73 271	94.04 356	73.89 396	80.17 379	91.53 366	59.15 387	97.64 308	66.92 396	89.05 288	90.80 389

UnsupCasMVSNet_eth		85.99 338	84.45 343	90.62 341	89.97 382	82.40 344	93.62 353	97.37 185	89.86 211	78.59 384	92.37 351	65.25 376	95.35 376	82.27 329	70.75 393	94.10 347

DSMNet-mixed		86.34 333	86.12 328	87.00 370	89.88 383	70.43 396	94.93 307	90.08 394	77.97 388	85.42 343	92.78 342	74.44 313	93.96 388	74.43 376	95.14 193	96.62 234

new_pmnet		82.89 354	81.12 359	88.18 364	89.63 384	80.18 368	91.77 377	92.57 377	76.79 391	75.56 390	88.23 388	61.22 386	94.48 381	71.43 388	82.92 358	89.87 392

MIMVSNet1		84.93 346	83.05 348	90.56 342	89.56 385	84.84 316	95.40 287	95.35 308	83.91 347	80.38 376	92.21 358	57.23 390	93.34 392	70.69 392	82.75 360	93.50 355

KD-MVS_self_test		85.95 339	84.95 338	88.96 360	89.55 386	79.11 379	95.13 302	96.42 260	85.91 320	84.07 357	90.48 371	70.03 343	94.82 379	80.04 346	72.94 390	92.94 362

m2depth		85.91 340	84.76 341	89.36 357	89.14 387	80.25 367	95.66 276	93.16 370	83.77 351	83.39 362	95.26 245	66.24 370	95.26 377	80.65 342	75.57 384	92.57 368

CMPMVS	62.92 21	85.62 343	84.92 339	87.74 365	89.14 387	73.12 395	94.17 333	96.80 237	73.98 394	73.65 393	94.93 257	66.36 367	97.61 312	83.95 312	91.28 262	92.48 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
APD_test1		79.31 361	77.70 364	84.14 374	89.11 389	69.07 400	92.36 375	91.50 386	69.07 399	73.87 392	92.63 346	39.93 405	94.32 383	70.54 393	80.25 368	89.02 394

CL-MVSNet_self_test		86.31 334	85.15 335	89.80 352	88.83 390	81.74 350	93.93 341	96.22 270	86.67 306	85.03 345	90.80 370	78.09 281	94.50 380	74.92 374	71.86 392	93.15 360

dongtai		69.99 369	69.33 371	71.98 390	88.78 391	61.64 410	89.86 391	59.93 420	75.67 392	74.96 391	85.45 396	50.19 399	81.66 409	43.86 408	55.27 407	72.63 405

mvs5depth		86.53 329	85.08 336	90.87 334	88.74 392	82.52 341	91.91 376	94.23 353	86.35 312	87.11 322	93.70 320	66.52 366	97.76 299	81.37 337	75.80 383	92.31 374

Patchmatch-RL test		87.38 322	86.24 325	90.81 337	88.74 392	78.40 382	88.12 400	93.17 369	87.11 300	82.17 369	89.29 381	81.95 212	95.60 371	88.64 235	77.02 378	98.41 149

pmmvs-eth3d		86.22 335	84.45 343	91.53 321	88.34 394	87.25 261	94.47 319	95.01 324	83.47 356	79.51 381	89.61 379	69.75 346	95.71 367	83.13 318	76.73 381	91.64 380

UnsupCasMVSNet_bld		82.13 357	79.46 362	90.14 348	88.00 395	82.47 342	90.89 385	96.62 253	78.94 384	75.61 388	84.40 399	56.63 392	96.31 358	77.30 363	66.77 400	91.63 381

PM-MVS		83.48 351	81.86 357	88.31 362	87.83 396	77.59 384	93.43 356	91.75 384	86.91 302	80.63 374	89.91 377	44.42 403	95.84 365	85.17 298	76.73 381	91.50 384

MVStest1		82.38 356	80.04 360	89.37 356	87.63 397	82.83 337	95.03 304	93.37 368	73.90 395	73.50 394	94.35 289	62.89 382	93.25 393	73.80 380	65.92 401	92.04 379

new-patchmatchnet		83.18 353	81.87 356	87.11 368	86.88 398	75.99 388	93.70 348	95.18 318	85.02 335	77.30 387	88.40 386	65.99 372	93.88 389	74.19 379	70.18 394	91.47 385

test_fmvs3		83.21 352	83.02 349	83.78 375	86.77 399	68.34 401	96.76 196	94.91 330	86.49 309	84.14 355	89.48 380	36.04 407	91.73 397	91.86 165	80.77 367	91.26 387

WB-MVS		76.77 363	76.63 366	77.18 382	85.32 400	56.82 414	94.53 317	89.39 396	82.66 362	71.35 395	89.18 382	75.03 308	88.88 402	35.42 411	66.79 399	85.84 396

SSC-MVS		76.05 364	75.83 367	76.72 386	84.77 401	56.22 415	94.32 328	88.96 398	81.82 368	70.52 396	88.91 383	74.79 310	88.71 403	33.69 412	64.71 402	85.23 397

kuosan		65.27 375	64.66 377	67.11 393	83.80 402	61.32 411	88.53 397	60.77 419	68.22 400	67.67 398	80.52 402	49.12 400	70.76 415	29.67 414	53.64 409	69.26 407

mvsany_test3		83.59 350	82.44 354	87.03 369	83.80 402	73.82 391	93.70 348	90.92 391	86.42 310	82.51 367	90.26 373	46.76 402	95.71 367	90.82 186	76.76 380	91.57 382

ambc				86.56 371	83.60 404	70.00 398	85.69 402	94.97 327		80.60 375	88.45 385	37.42 406	96.84 351	82.69 326	75.44 385	92.86 363

test_f		80.57 359	79.62 361	83.41 376	83.38 405	67.80 403	93.57 355	93.72 363	80.80 376	77.91 386	87.63 392	33.40 408	92.08 396	87.14 268	79.04 375	90.34 391

pmmvs3		79.97 360	77.50 365	87.39 367	82.80 406	79.38 377	92.70 370	90.75 392	70.69 398	78.66 383	87.47 394	51.34 398	93.40 391	73.39 383	69.65 395	89.38 393

TDRefinement		86.53 329	84.76 341	91.85 311	82.23 407	84.25 320	96.38 232	95.35 308	84.97 336	84.09 356	94.94 256	65.76 374	98.34 228	84.60 304	74.52 386	92.97 361

test_vis3_rt		72.73 365	70.55 368	79.27 379	80.02 408	68.13 402	93.92 342	74.30 416	76.90 390	58.99 407	73.58 407	20.29 416	95.37 375	84.16 307	72.80 391	74.31 404

testf1		69.31 370	66.76 373	76.94 384	78.61 409	61.93 408	88.27 398	86.11 406	55.62 405	59.69 405	85.31 397	20.19 417	89.32 399	57.62 401	69.44 396	79.58 401

APD_test2		69.31 370	66.76 373	76.94 384	78.61 409	61.93 408	88.27 398	86.11 406	55.62 405	59.69 405	85.31 397	20.19 417	89.32 399	57.62 401	69.44 396	79.58 401

PMMVS2		70.19 368	66.92 372	80.01 378	76.35 411	65.67 405	86.22 401	87.58 402	64.83 403	62.38 404	80.29 403	26.78 413	88.49 405	63.79 397	54.07 408	85.88 395

FPMVS		71.27 367	69.85 369	75.50 387	74.64 412	59.03 412	91.30 379	91.50 386	58.80 404	57.92 408	88.28 387	29.98 411	85.53 407	53.43 405	82.84 359	81.95 400

E-PMN		53.28 378	52.56 382	55.43 395	74.43 413	47.13 418	83.63 405	76.30 413	42.23 410	42.59 412	62.22 411	28.57 412	74.40 412	31.53 413	31.51 411	44.78 410

wuyk23d		25.11 382	24.57 386	26.74 398	73.98 414	39.89 422	57.88 411	9.80 422	12.27 415	10.39 416	6.97 418	7.03 420	36.44 417	25.43 416	17.39 415	3.89 415

test_method		66.11 374	64.89 376	69.79 391	72.62 415	35.23 423	65.19 410	92.83 375	20.35 413	65.20 402	88.08 390	43.14 404	82.70 408	73.12 384	63.46 403	91.45 386

EMVS		52.08 380	51.31 383	54.39 396	72.62 415	45.39 420	83.84 404	75.51 415	41.13 411	40.77 413	59.65 412	30.08 410	73.60 413	28.31 415	29.90 413	44.18 411

LCM-MVSNet		72.55 366	69.39 370	82.03 377	70.81 417	65.42 406	90.12 390	94.36 351	55.02 407	65.88 401	81.72 400	24.16 415	89.96 398	74.32 378	68.10 398	90.71 390

MVE	50.73 23	53.25 379	48.81 384	66.58 394	65.34 418	57.50 413	72.49 408	70.94 417	40.15 412	39.28 414	63.51 410	6.89 421	73.48 414	38.29 410	42.38 410	68.76 408
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		63.94 376	59.58 379	77.02 383	61.24 419	66.06 404	85.66 403	87.93 401	78.53 386	42.94 411	71.04 408	25.42 414	80.71 410	52.60 406	30.83 412	84.28 398

PMVS	53.92 22	58.58 377	55.40 380	68.12 392	51.00 420	48.64 417	78.86 406	87.10 404	46.77 409	35.84 415	74.28 405	8.76 419	86.34 406	42.07 409	73.91 388	69.38 406
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 381	53.82 381	46.29 397	33.73 421	45.30 421	78.32 407	67.24 418	18.02 414	50.93 410	87.05 395	52.99 396	53.11 416	70.76 391	25.29 414	40.46 412

testmvs		13.36 384	16.33 387	4.48 400	5.04 422	2.26 425	93.18 359	3.28 423	2.70 416	8.24 417	21.66 414	2.29 423	2.19 418	7.58 417	2.96 416	9.00 414

test123		13.04 385	15.66 388	5.18 399	4.51 423	3.45 424	92.50 373	1.81 424	2.50 417	7.58 418	20.15 415	3.67 422	2.18 419	7.13 418	1.07 417	9.90 413

test_blank		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

eth-test2					0.00 424

eth-test					0.00 424

uanet_test		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

DCPMVS		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

cdsmvs_eth3d_5k		23.24 383	30.99 385	0.00 401	0.00 424	0.00 426	0.00 412	97.63 143	0.00 419	0.00 420	96.88 157	84.38 162	0.00 420	0.00 419	0.00 418	0.00 416

pcd_1.5k_mvsjas		7.39 387	9.85 390	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	88.65 97	0.00 420	0.00 419	0.00 418	0.00 416

sosnet-low-res		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

sosnet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uncertanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

Regformer		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

ab-mvs-re		8.06 386	10.74 389	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	96.69 167	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

WAC-MVS						79.53 373								75.56 372

PC_three_1452									90.77 180	98.89 14	98.28 65	96.24 1	98.35 225	95.76 79	99.58 23	99.59 21

test_241102_TWO								98.27 39	95.13 23	98.93 9	98.89 13	94.99 11	99.85 18	97.52 25	99.65 13	99.74 8

test_0728_THIRD									94.78 41	98.73 18	98.87 15	95.87 4	99.84 23	97.45 29	99.72 2	99.77 2

GSMVS																98.45 144

sam_mvs1												82.76 194				98.45 144

sam_mvs												81.94 213

MTGPA								98.08 74

test_post1							92.81 369				16.58 417	80.53 234	97.68 304	86.20 279

test_post											17.58 416	81.76 215	98.08 251

patchmatchnet-post											90.45 372	82.65 198	98.10 247

MTMP							97.86 79	82.03 411

test9_res														94.81 107	99.38 55	99.45 47

agg_prior2														93.94 124	99.38 55	99.50 40

test_prior4						93.66 57	96.42 225

test_prior2							96.35 234		92.80 121	96.03 99	97.59 118	92.01 44		95.01 100	99.38 55

旧先验2							95.94 259		81.66 369	97.34 49			98.82 176	92.26 152

新几何2							95.79 268

无先验							95.79 268	97.87 111	83.87 350				99.65 58	87.68 253		98.89 108

原ACMM2							95.67 273

testdata2													99.67 56	85.96 287

segment_acmp												92.89 28

testdata1							95.26 297		93.10 107

plane_prior5								97.51 158					98.60 203	93.02 145	92.23 244	95.86 256

plane_prior4											96.64 170

plane_prior3						90.00 175			94.46 55	91.34 214

plane_prior2							97.74 94		94.85 34

plane_prior						89.99 177	97.24 154		94.06 67						92.16 248

n2								0.00 425

nn								0.00 425

door-mid								91.06 389

test11								97.88 109

door								91.13 388

HQP5-MVS						89.33 204

BP-MVS														92.13 158

HQP4-MVS										90.14 238			98.50 211			95.78 264

HQP3-MVS								97.39 182							92.10 249

HQP2-MVS												80.95 225

MDTV_nov1_ep13_2view						70.35 397	93.10 364		83.88 349	93.55 159		82.47 202		86.25 278		98.38 152

ACMMP++_ref															90.30 278

ACMMP++															91.02 267

Test By Simon												88.73 96

High-resolution multi-view benchmark