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					99.82 1	98.66 24	99.69 1	98.95 46	97.46 34	99.39 30

MTAPA		98.58 23	98.29 43	99.46 14	99.76 2	98.64 25	98.90 102	98.74 108	97.27 49	98.02 115	99.39 31	94.81 80	99.96 4	97.91 71	99.79 27	99.77 26

MSP-MVS		98.74 13	98.55 17	99.29 29	99.75 3	98.23 47	99.26 27	98.88 62	97.52 29	99.41 28	98.78 132	96.00 36	99.79 98	97.79 79	99.59 79	99.85 9
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
MP-MVS		98.33 55	98.01 63	99.28 32	99.75 3	98.18 51	99.22 36	98.79 98	96.13 104	97.92 126	99.23 62	94.54 83	99.94 9	96.74 139	99.78 31	99.73 41
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		98.51 33	98.26 44	99.25 35	99.75 3	98.04 59	99.28 24	98.81 86	96.24 99	98.35 98	99.23 62	95.46 52	99.94 9	97.42 106	99.81 14	99.77 26

HPM-MVS_fast		98.38 47	98.13 55	99.12 50	99.75 3	97.86 65	99.44 9	98.82 81	94.46 192	98.94 55	99.20 67	95.16 70	99.74 111	97.58 95	99.85 6	99.77 26

region2R		98.61 18	98.38 28	99.29 29	99.74 7	98.16 53	99.23 32	98.93 50	96.15 103	98.94 55	99.17 74	95.91 40	99.94 9	97.55 99	99.79 27	99.78 20

ACMMPR		98.59 21	98.36 30	99.29 29	99.74 7	98.15 54	99.23 32	98.95 46	96.10 106	98.93 59	99.19 72	95.70 46	99.94 9	97.62 92	99.79 27	99.78 20

HPM-MVS		98.36 50	98.10 59	99.13 48	99.74 7	97.82 69	99.53 6	98.80 93	94.63 182	98.61 83	98.97 105	95.13 72	99.77 106	97.65 90	99.83 13	99.79 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		98.23 58	97.95 65	99.09 52	99.74 7	97.62 73	99.03 73	99.41 6	95.98 108	97.60 149	99.36 41	94.45 88	99.93 25	97.14 113	98.85 139	99.70 53
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
ZNCC-MVS		98.49 35	98.20 52	99.35 22	99.73 11	98.39 34	99.19 43	98.86 75	95.77 119	98.31 101	99.10 86	95.46 52	99.93 25	97.57 98	99.81 14	99.74 36

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 12	99.25 2	98.97 86	98.58 151	97.62 24	99.45 25	99.46 24	97.42 9	99.94 9	98.47 41	99.81 14	99.69 56
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				99.71 1	99.72 12	99.35 1	98.97 86	98.88 62					99.94 9	98.47 41	99.81 14	99.84 11

test0726					99.72 12	99.25 2	99.06 65	98.88 62	97.62 24	99.56 20	99.50 15	97.42 9

GST-MVS		98.43 43	98.12 56	99.34 23	99.72 12	98.38 35	99.09 62	98.82 81	95.71 123	98.73 74	99.06 96	95.27 63	99.93 25	97.07 116	99.63 72	99.72 45

MP-MVS-pluss		98.31 56	97.92 66	99.49 12	99.72 12	98.88 18	98.43 205	98.78 100	94.10 201	97.69 140	99.42 28	95.25 65	99.92 31	98.09 61	99.80 21	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		98.63 17	98.40 26	99.32 28	99.72 12	98.29 45	99.23 32	98.96 45	96.10 106	98.94 55	99.17 74	96.06 33	99.92 31	97.62 92	99.78 31	99.75 34

PGM-MVS		98.49 35	98.23 49	99.27 34	99.72 12	98.08 58	98.99 83	99.49 5	95.43 135	99.03 48	99.32 48	95.56 49	99.94 9	96.80 136	99.77 33	99.78 20

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 19	99.24 5	99.02 76	98.87 69	97.65 22	99.73 10	99.48 18	97.53 7	99.94 9	98.43 45	99.81 14	99.70 53

IU-MVS					99.71 19	99.23 7		98.64 137	95.28 145	99.63 18				98.35 50	99.81 14	99.83 12

test_241102_ONE					99.71 19	99.24 5		98.87 69	97.62 24	99.73 10	99.39 31	97.53 7	99.74 111

XVS		98.70 14	98.49 21	99.34 23	99.70 22	98.35 42	99.29 22	98.88 62	97.40 36	98.46 88	99.20 67	95.90 42	99.89 47	97.85 75	99.74 48	99.78 20

X-MVStestdata		94.06 294	92.30 317	99.34 23	99.70 22	98.35 42	99.29 22	98.88 62	97.40 36	98.46 88	43.50 413	95.90 42	99.89 47	97.85 75	99.74 48	99.78 20

TSAR-MVS + MP.		98.78 11	98.62 13	99.24 36	99.69 24	98.28 46	99.14 52	98.66 132	96.84 71	99.56 20	99.31 50	96.34 25	99.70 119	98.32 51	99.73 51	99.73 41
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		97.85 73	97.74 71	98.20 120	99.67 25	95.16 193	99.22 36	99.32 11	93.04 264	97.02 167	98.92 116	95.36 58	99.91 39	97.43 105	99.64 71	99.52 86

test_one_0601					99.66 26	99.25 2		98.86 75	97.55 28	99.20 39	99.47 20	97.57 6

CP-MVS		98.57 27	98.36 30	99.19 40	99.66 26	97.86 65	99.34 16	98.87 69	95.96 109	98.60 84	99.13 82	96.05 34	99.94 9	97.77 80	99.86 2	99.77 26

CPTT-MVS		97.72 78	97.32 94	98.92 64	99.64 28	97.10 98	99.12 56	98.81 86	92.34 290	98.09 107	99.08 94	93.01 107	99.92 31	96.06 158	99.77 33	99.75 34

test_part2					99.63 29	99.18 10				99.27 36

ACMMP_NAP		98.61 18	98.30 42	99.55 9	99.62 30	98.95 17	98.82 127	98.81 86	95.80 117	99.16 45	99.47 20	95.37 57	99.92 31	97.89 73	99.75 44	99.79 18

MCST-MVS		98.65 15	98.37 29	99.48 13	99.60 31	98.87 19	98.41 208	98.68 124	97.04 63	98.52 87	98.80 130	96.78 16	99.83 69	97.93 69	99.61 75	99.74 36

DPE-MVS		98.92 7	98.67 12	99.65 2	99.58 32	99.20 9	98.42 207	98.91 56	97.58 27	99.54 22	99.46 24	97.10 12	99.94 9	97.64 91	99.84 11	99.83 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
dcpmvs_2		98.08 62	98.59 14	96.56 244	99.57 33	90.34 336	99.15 50	98.38 197	96.82 73	99.29 35	99.49 17	95.78 44	99.57 144	98.94 22	99.86 2	99.77 26

APDe-MVS		99.02 6	98.84 8	99.55 9	99.57 33	98.96 16	99.39 10	98.93 50	97.38 39	99.41 28	99.54 8	96.66 18	99.84 67	98.86 24	99.85 6	99.87 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SF-MVS		98.59 21	98.32 41	99.41 17	99.54 35	98.71 22	99.04 70	98.81 86	95.12 153	99.32 34	99.39 31	96.22 27	99.84 67	97.72 83	99.73 51	99.67 65

patch_mono-2		98.36 50	98.87 6	96.82 220	99.53 36	90.68 327	98.64 171	99.29 14	97.88 15	99.19 41	99.52 11	96.80 15	99.97 1	99.11 18	99.86 2	99.82 15

SR-MVS		98.57 27	98.35 32	99.24 36	99.53 36	98.18 51	99.09 62	98.82 81	96.58 86	99.10 47	99.32 48	95.39 55	99.82 76	97.70 87	99.63 72	99.72 45

DP-MVS Recon		97.86 71	97.46 86	99.06 54	99.53 36	98.35 42	98.33 212	98.89 59	92.62 279	98.05 110	98.94 113	95.34 59	99.65 129	96.04 159	99.42 108	99.19 146

SMA-MVS		98.58 23	98.25 45	99.56 8	99.51 39	99.04 15	98.95 92	98.80 93	93.67 235	99.37 31	99.52 11	96.52 22	99.89 47	98.06 62	99.81 14	99.76 33
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
APD-MVS		98.35 52	98.00 64	99.42 16	99.51 39	98.72 21	98.80 136	98.82 81	94.52 189	99.23 38	99.25 61	95.54 51	99.80 88	96.52 143	99.77 33	99.74 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		98.58 23	98.25 45	99.55 9	99.50 41	99.08 11	98.72 155	98.66 132	97.51 30	98.15 102	98.83 127	95.70 46	99.92 31	97.53 101	99.67 61	99.66 68

APD-MVS_3200maxsize		98.53 32	98.33 40	99.15 46	99.50 41	97.92 64	99.15 50	98.81 86	96.24 99	99.20 39	99.37 37	95.30 61	99.80 88	97.73 82	99.67 61	99.72 45

114514_t		96.93 127	96.27 142	98.92 64	99.50 41	97.63 72	98.85 119	98.90 57	84.80 387	97.77 131	99.11 84	92.84 109	99.66 128	94.85 198	99.77 33	99.47 100

PAPM_NR		97.46 96	97.11 103	98.50 91	99.50 41	96.41 131	98.63 174	98.60 142	95.18 150	97.06 165	98.06 205	94.26 93	99.57 144	93.80 237	98.87 138	99.52 86

SR-MVS-dyc-post		98.54 31	98.35 32	99.13 48	99.49 45	97.86 65	99.11 58	98.80 93	96.49 89	99.17 42	99.35 43	95.34 59	99.82 76	97.72 83	99.65 67	99.71 49

RE-MVS-def			98.34 36		99.49 45	97.86 65	99.11 58	98.80 93	96.49 89	99.17 42	99.35 43	95.29 62		97.72 83	99.65 67	99.71 49

9.14			98.06 60		99.47 47		98.71 156	98.82 81	94.36 195	99.16 45	99.29 52	96.05 34	99.81 81	97.00 117	99.71 56

CDPH-MVS		97.94 68	97.49 83	99.28 32	99.47 47	98.44 31	97.91 268	98.67 129	92.57 282	98.77 70	98.85 124	95.93 39	99.72 113	95.56 177	99.69 58	99.68 61

ZD-MVS					99.46 49	98.70 23		98.79 98	93.21 255	98.67 76	98.97 105	95.70 46	99.83 69	96.07 155	99.58 82

save fliter					99.46 49	98.38 35	98.21 229	98.71 116	97.95 13

EI-MVSNet-Vis-set		98.47 38	98.39 27	98.69 75	99.46 49	96.49 126	98.30 219	98.69 121	97.21 52	98.84 65	99.36 41	95.41 54	99.78 101	98.62 29	99.65 67	99.80 17

EI-MVSNet-UG-set		98.41 45	98.34 36	98.61 81	99.45 52	96.32 136	98.28 222	98.68 124	97.17 55	98.74 72	99.37 37	95.25 65	99.79 98	98.57 30	99.54 92	99.73 41

F-COLMAP		97.09 122	96.80 117	97.97 139	99.45 52	94.95 206	98.55 188	98.62 141	93.02 265	96.17 205	98.58 155	94.01 97	99.81 81	93.95 231	98.90 134	99.14 155

fmvsm_l_conf0.5_n_a		99.09 1	99.08 1	99.11 51	99.43 54	97.48 79	98.88 111	99.30 13	98.47 9	99.85 4	99.43 27	96.71 17	99.96 4	99.86 1	99.80 21	99.89 4

test_fmvsm_n_1920		98.87 10	99.01 3	98.45 97	99.42 55	96.43 129	98.96 91	99.36 9	98.63 4	99.86 2	99.51 13	95.91 40	99.97 1	99.72 4	99.75 44	98.94 181

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 47	99.41 56	97.54 77	98.89 106	99.31 12	98.49 8	99.86 2	99.42 28	96.45 24	99.96 4	99.86 1	99.74 48	99.90 3

新几何1				99.16 45	99.34 57	98.01 61		98.69 121	90.06 348	98.13 104	98.95 112	94.60 82	99.89 47	91.97 290	99.47 102	99.59 79

DP-MVS		96.59 140	95.93 155	98.57 83	99.34 57	96.19 142	98.70 160	98.39 193	89.45 359	94.52 241	99.35 43	91.85 135	99.85 63	92.89 265	98.88 136	99.68 61

SD-MVS		98.64 16	98.68 11	98.53 89	99.33 59	98.36 41	98.90 102	98.85 78	97.28 45	99.72 12	99.39 31	96.63 20	97.60 357	98.17 57	99.85 6	99.64 71
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
HyFIR lowres test		96.90 129	96.49 135	98.14 123	99.33 59	95.56 171	97.38 314	99.65 2	92.34 290	97.61 148	98.20 196	89.29 191	99.10 214	96.97 119	97.60 192	99.77 26

OMC-MVS		97.55 94	97.34 93	98.20 120	99.33 59	95.92 159	98.28 222	98.59 146	95.52 131	97.97 120	99.10 86	93.28 105	99.49 163	95.09 192	98.88 136	99.19 146

原ACMM1				98.65 79	99.32 62	96.62 116		98.67 129	93.27 254	97.81 130	98.97 105	95.18 69	99.83 69	93.84 235	99.46 105	99.50 91

CNVR-MVS		98.78 11	98.56 16	99.45 15	99.32 62	98.87 19	98.47 199	98.81 86	97.72 17	98.76 71	99.16 77	97.05 13	99.78 101	98.06 62	99.66 64	99.69 56

TEST9					99.31 64	98.50 29	97.92 266	98.73 111	92.63 278	97.74 135	98.68 145	96.20 29	99.80 88

train_agg		97.97 65	97.52 82	99.33 26	99.31 64	98.50 29	97.92 266	98.73 111	92.98 266	97.74 135	98.68 145	96.20 29	99.80 88	96.59 140	99.57 83	99.68 61

test_prior				99.19 40	99.31 64	98.22 48		98.84 79					99.70 119			99.65 69

PatchMatch-RL		96.59 140	96.03 151	98.27 111	99.31 64	96.51 125	97.91 268	99.06 34	93.72 227	96.92 172	98.06 205	88.50 216	99.65 129	91.77 294	99.00 131	98.66 207

fmvsm_s_conf0.5_n		98.42 44	98.51 18	98.13 126	99.30 68	95.25 189	98.85 119	99.39 7	97.94 14	99.74 9	99.62 3	92.59 113	99.91 39	99.65 6	99.52 95	99.25 135

SDMVSNet		96.85 131	96.42 136	98.14 123	99.30 68	96.38 132	99.21 39	99.23 20	95.92 110	95.96 212	98.76 139	85.88 268	99.44 173	97.93 69	95.59 247	98.60 211

sd_testset		96.17 158	95.76 160	97.42 180	99.30 68	94.34 236	98.82 127	99.08 32	95.92 110	95.96 212	98.76 139	82.83 318	99.32 184	95.56 177	95.59 247	98.60 211

agg_prior					99.30 68	98.38 35		98.72 113		97.57 151			99.81 81

CHOSEN 1792x2688		97.12 120	96.80 117	98.08 132	99.30 68	94.56 227	98.05 253	99.71 1	93.57 240	97.09 161	98.91 117	88.17 221	99.89 47	96.87 131	99.56 89	99.81 16

test_8					99.29 73	98.44 31	97.89 274	98.72 113	92.98 266	97.70 139	98.66 148	96.20 29	99.80 88

旧先验1					99.29 73	97.48 79		98.70 120			99.09 92	95.56 49			99.47 102	99.61 75

PLC	95.07 4	97.20 115	96.78 120	98.44 99	99.29 73	96.31 138	98.14 241	98.76 104	92.41 288	96.39 199	98.31 185	94.92 79	99.78 101	94.06 229	98.77 143	99.23 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
COLMAP_ROB	93.27 12	95.33 206	94.87 207	96.71 225	99.29 73	93.24 280	98.58 180	98.11 249	89.92 350	93.57 288	99.10 86	86.37 260	99.79 98	90.78 313	98.10 174	97.09 264
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
NCCC		98.61 18	98.35 32	99.38 18	99.28 77	98.61 26	98.45 200	98.76 104	97.82 16	98.45 91	98.93 114	96.65 19	99.83 69	97.38 108	99.41 109	99.71 49

PVSNet_Blended_VisFu		97.70 80	97.46 86	98.44 99	99.27 78	95.91 160	98.63 174	99.16 27	94.48 191	97.67 141	98.88 121	92.80 110	99.91 39	97.11 114	99.12 124	99.50 91

MVS_111021_LR		98.34 53	98.23 49	98.67 77	99.27 78	96.90 105	97.95 263	99.58 3	97.14 58	98.44 93	99.01 102	95.03 76	99.62 138	97.91 71	99.75 44	99.50 91

MSLP-MVS++		98.56 29	98.57 15	98.55 85	99.26 80	96.80 109	98.71 156	99.05 36	97.28 45	98.84 65	99.28 53	96.47 23	99.40 175	98.52 39	99.70 57	99.47 100

AllTest		95.24 211	94.65 216	96.99 206	99.25 81	93.21 281	98.59 178	98.18 233	91.36 318	93.52 290	98.77 134	84.67 293	99.72 113	89.70 331	97.87 181	98.02 238

TestCases				96.99 206	99.25 81	93.21 281		98.18 233	91.36 318	93.52 290	98.77 134	84.67 293	99.72 113	89.70 331	97.87 181	98.02 238

PVSNet_BlendedMVS		96.73 135	96.60 130	97.12 199	99.25 81	95.35 184	98.26 225	99.26 15	94.28 196	97.94 123	97.46 259	92.74 111	99.81 81	96.88 128	93.32 283	96.20 348

PVSNet_Blended		97.38 105	97.12 102	98.14 123	99.25 81	95.35 184	97.28 325	99.26 15	93.13 260	97.94 123	98.21 195	92.74 111	99.81 81	96.88 128	99.40 112	99.27 130

DeepC-MVS	95.98 3	97.88 70	97.58 76	98.77 71	99.25 81	96.93 103	98.83 125	98.75 106	96.96 67	96.89 174	99.50 15	90.46 168	99.87 58	97.84 77	99.76 39	99.52 86
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	96.70 1	98.55 30	98.34 36	99.18 42	99.25 81	98.04 59	98.50 196	98.78 100	97.72 17	98.92 61	99.28 53	95.27 63	99.82 76	97.55 99	99.77 33	99.69 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS				99.37 20	99.24 87	99.05 14	99.02 76				99.16 77	97.81 3	99.37 179	97.24 111	99.73 51	99.70 53

test222					99.23 88	97.17 96	97.40 312	98.66 132	88.68 367	98.05 110	98.96 110	94.14 95			99.53 94	99.61 75

TSAR-MVS + GP.		98.38 47	98.24 47	98.81 70	99.22 89	97.25 92	98.11 246	98.29 217	97.19 54	98.99 53	99.02 98	96.22 27	99.67 126	98.52 39	98.56 153	99.51 89

SteuartSystems-ACMMP		98.90 9	98.75 10	99.36 21	99.22 89	98.43 33	99.10 61	98.87 69	97.38 39	99.35 32	99.40 30	97.78 5	99.87 58	97.77 80	99.85 6	99.78 20
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		98.47 38	98.34 36	98.88 68	99.22 89	97.32 85	97.91 268	99.58 3	97.20 53	98.33 99	99.00 103	95.99 37	99.64 131	98.05 64	99.76 39	99.69 56

CS-MVS-test		98.49 35	98.50 20	98.46 96	99.20 92	97.05 99	99.64 4	98.50 172	97.45 35	98.88 62	99.14 81	95.25 65	99.15 203	98.83 25	99.56 89	99.20 142

testdata				98.26 114	99.20 92	95.36 182		98.68 124	91.89 304	98.60 84	99.10 86	94.44 89	99.82 76	94.27 221	99.44 106	99.58 83

DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 94	99.23 7	99.69 1	98.88 62	97.32 42	99.53 23	99.47 20	97.81 3	99.94 9	98.47 41	99.72 54	99.74 36

MSC_two_6792asdad				99.62 6	99.17 94	99.08 11		98.63 139					99.94 9	98.53 33	99.80 21	99.86 7

No_MVS				99.62 6	99.17 94	99.08 11		98.63 139					99.94 9	98.53 33	99.80 21	99.86 7

PVSNet	91.96 18	96.35 151	96.15 146	96.96 210	99.17 94	92.05 300	96.08 373	98.68 124	93.69 231	97.75 134	97.80 233	88.86 206	99.69 124	94.26 222	99.01 129	99.15 153

test12				99.18 42	99.16 98	98.19 50		98.53 162		98.07 108		95.13 72	99.72 113		99.56 89	99.63 73

AdaColmap		97.15 118	96.70 125	98.48 94	99.16 98	96.69 115	98.01 257	98.89 59	94.44 193	96.83 175	98.68 145	90.69 165	99.76 107	94.36 216	99.29 119	98.98 176

PHI-MVS		98.34 53	98.06 60	99.18 42	99.15 100	98.12 57	99.04 70	99.09 31	93.32 250	98.83 67	99.10 86	96.54 21	99.83 69	97.70 87	99.76 39	99.59 79

TAPA-MVS	93.98 7	95.35 204	94.56 220	97.74 157	99.13 101	94.83 212	98.33 212	98.64 137	86.62 375	96.29 201	98.61 150	94.00 98	99.29 187	80.00 390	99.41 109	99.09 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MM		98.51 33	98.24 47	99.33 26	99.12 102	98.14 56	98.93 97	97.02 343	98.96 1	99.17 42	99.47 20	91.97 134	99.94 9	99.85 3	99.69 58	99.91 2

MG-MVS		97.81 74	97.60 75	98.44 99	99.12 102	95.97 152	97.75 289	98.78 100	96.89 70	98.46 88	99.22 64	93.90 99	99.68 125	94.81 201	99.52 95	99.67 65

test_vis1_n_1920		96.71 136	96.84 116	96.31 268	99.11 104	89.74 344	99.05 67	98.58 151	98.08 12	99.87 1	99.37 37	78.48 349	99.93 25	99.29 14	99.69 58	99.27 130

Anonymous20231211		94.10 290	93.26 299	96.61 237	99.11 104	94.28 238	99.01 78	98.88 62	86.43 377	92.81 314	97.57 253	81.66 323	98.68 268	94.83 199	89.02 341	96.88 282

fmvsm_s_conf0.5_n_a		98.38 47	98.42 25	98.27 111	99.09 106	95.41 179	98.86 117	99.37 8	97.69 21	99.78 6	99.61 4	92.38 116	99.91 39	99.58 10	99.43 107	99.49 96

CS-MVS		98.44 41	98.49 21	98.31 109	99.08 107	96.73 113	99.67 3	98.47 178	97.17 55	98.94 55	99.10 86	95.73 45	99.13 206	98.71 27	99.49 99	99.09 161

CNLPA		97.45 99	97.03 107	98.73 73	99.05 108	97.44 83	98.07 251	98.53 162	95.32 143	96.80 179	98.53 160	93.32 103	99.72 113	94.31 220	99.31 118	99.02 172

DPM-MVS		97.55 94	96.99 109	99.23 38	99.04 109	98.55 27	97.17 335	98.35 202	94.85 173	97.93 125	98.58 155	95.07 74	99.71 118	92.60 269	99.34 116	99.43 109

h-mvs33		96.17 158	95.62 171	97.81 149	99.03 110	94.45 229	98.64 171	98.75 106	97.48 32	98.67 76	98.72 142	89.76 178	99.86 62	97.95 67	81.59 385	99.11 159

test2506		94.44 266	93.91 263	96.04 278	99.02 111	88.99 359	99.06 65	79.47 418	96.96 67	98.36 96	99.26 56	77.21 361	99.52 160	96.78 137	99.04 126	99.59 79

ECVR-MVS		95.95 166	95.71 165	96.65 230	99.02 111	90.86 322	99.03 73	91.80 405	96.96 67	98.10 106	99.26 56	81.31 325	99.51 161	96.90 125	99.04 126	99.59 79

Anonymous20240529		95.10 219	94.22 238	97.75 156	99.01 113	94.26 240	98.87 114	98.83 80	85.79 383	96.64 183	98.97 105	78.73 346	99.85 63	96.27 150	94.89 252	99.12 157

Anonymous202405211		95.28 209	94.49 223	97.67 165	99.00 114	93.75 255	98.70 160	97.04 340	90.66 336	96.49 194	98.80 130	78.13 353	99.83 69	96.21 154	95.36 251	99.44 107

DELS-MVS		98.40 46	98.20 52	98.99 57	99.00 114	97.66 70	97.75 289	98.89 59	97.71 19	98.33 99	98.97 105	94.97 77	99.88 56	98.42 47	99.76 39	99.42 111
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
DeepPCF-MVS	96.37 2	97.93 69	98.48 23	96.30 269	99.00 114	89.54 349	97.43 311	98.87 69	98.16 11	99.26 37	99.38 36	96.12 32	99.64 131	98.30 52	99.77 33	99.72 45

test1111		95.94 168	95.78 159	96.41 261	98.99 117	90.12 338	99.04 70	92.45 404	96.99 66	98.03 113	99.27 55	81.40 324	99.48 168	96.87 131	99.04 126	99.63 73

thres100view900		95.38 200	94.70 214	97.41 181	98.98 118	94.92 207	98.87 114	96.90 350	95.38 138	96.61 186	96.88 316	84.29 299	99.56 147	88.11 349	96.29 229	97.76 243

thres600view7		95.49 191	94.77 209	97.67 165	98.98 118	95.02 199	98.85 119	96.90 350	95.38 138	96.63 184	96.90 315	84.29 299	99.59 141	88.65 346	96.33 225	98.40 223

mamv4		97.13 119	98.11 57	94.17 346	98.97 120	83.70 388	98.66 168	98.71 116	94.63 182	97.83 129	98.90 118	96.25 26	99.55 154	99.27 15	99.76 39	99.27 130

MVSMamba_PlusPlus		98.31 56	98.19 54	98.67 77	98.96 121	97.36 84	99.24 30	98.57 153	94.81 174	98.99 53	98.90 118	95.22 68	99.59 141	99.15 17	99.84 11	99.07 169

test_cas_vis1_n_1920		97.38 105	97.36 92	97.45 177	98.95 122	93.25 279	99.00 80	98.53 162	97.70 20	99.77 7	99.35 43	84.71 292	99.85 63	98.57 30	99.66 64	99.26 133

tfpn200view9		95.32 207	94.62 217	97.43 179	98.94 123	94.98 203	98.68 163	96.93 348	95.33 141	96.55 190	96.53 334	84.23 303	99.56 147	88.11 349	96.29 229	97.76 243

thres400		95.38 200	94.62 217	97.65 169	98.94 123	94.98 203	98.68 163	96.93 348	95.33 141	96.55 190	96.53 334	84.23 303	99.56 147	88.11 349	96.29 229	98.40 223

MSDG		95.93 169	95.30 186	97.83 146	98.90 125	95.36 182	96.83 360	98.37 199	91.32 322	94.43 248	98.73 141	90.27 172	99.60 140	90.05 324	98.82 141	98.52 217

RPSCF		94.87 235	95.40 175	93.26 357	98.89 126	82.06 395	98.33 212	98.06 264	90.30 345	96.56 188	99.26 56	87.09 245	99.49 163	93.82 236	96.32 226	98.24 230

test_fmvsmconf_n		98.92 7	98.87 6	99.04 55	98.88 127	97.25 92	98.82 127	99.34 10	98.75 2	99.80 5	99.61 4	95.16 70	99.95 7	99.70 5	99.80 21	99.93 1

VNet		97.79 75	97.40 90	98.96 62	98.88 127	97.55 75	98.63 174	98.93 50	96.74 78	99.02 49	98.84 125	90.33 171	99.83 69	98.53 33	96.66 214	99.50 91

LFMVS		95.86 173	94.98 201	98.47 95	98.87 129	96.32 136	98.84 123	96.02 372	93.40 247	98.62 82	99.20 67	74.99 376	99.63 134	97.72 83	97.20 199	99.46 104

UA-Net		97.96 66	97.62 74	98.98 59	98.86 130	97.47 81	98.89 106	99.08 32	96.67 83	98.72 75	99.54 8	93.15 106	99.81 81	94.87 197	98.83 140	99.65 69

WTY-MVS		97.37 107	96.92 113	98.72 74	98.86 130	96.89 107	98.31 217	98.71 116	95.26 146	97.67 141	98.56 159	92.21 124	99.78 101	95.89 163	96.85 209	99.48 98

IS-MVSNet		97.22 112	96.88 114	98.25 115	98.85 132	96.36 134	99.19 43	97.97 269	95.39 137	97.23 157	98.99 104	91.11 157	98.93 239	94.60 208	98.59 151	99.47 100

VDD-MVS		95.82 176	95.23 188	97.61 171	98.84 133	93.98 247	98.68 163	97.40 316	95.02 161	97.95 121	99.34 47	74.37 381	99.78 101	98.64 28	96.80 210	99.08 165

test_fmvs1		96.42 147	96.67 128	95.66 296	98.82 134	88.53 367	98.80 136	98.20 228	96.39 95	99.64 17	99.20 67	80.35 337	99.67 126	99.04 19	99.57 83	98.78 194

CHOSEN 280x420		97.18 116	97.18 101	97.20 190	98.81 135	93.27 276	95.78 380	99.15 28	95.25 147	96.79 180	98.11 202	92.29 119	99.07 217	98.56 32	99.85 6	99.25 135

thres200		95.25 210	94.57 219	97.28 187	98.81 135	94.92 207	98.20 231	97.11 333	95.24 149	96.54 192	96.22 345	84.58 296	99.53 157	87.93 354	96.50 221	97.39 257

XVG-OURS-SEG-HR		96.51 144	96.34 139	97.02 205	98.77 137	93.76 253	97.79 287	98.50 172	95.45 134	96.94 169	99.09 92	87.87 232	99.55 154	96.76 138	95.83 246	97.74 245

XVG-OURS		96.55 143	96.41 137	96.99 206	98.75 138	93.76 253	97.50 308	98.52 165	95.67 125	96.83 175	99.30 51	88.95 205	99.53 157	95.88 164	96.26 234	97.69 248

test_yl		97.22 112	96.78 120	98.54 87	98.73 139	96.60 119	98.45 200	98.31 209	94.70 176	98.02 115	98.42 170	90.80 162	99.70 119	96.81 134	96.79 211	99.34 117

DCV-MVSNet		97.22 112	96.78 120	98.54 87	98.73 139	96.60 119	98.45 200	98.31 209	94.70 176	98.02 115	98.42 170	90.80 162	99.70 119	96.81 134	96.79 211	99.34 117

CANet		98.05 64	97.76 70	98.90 67	98.73 139	97.27 87	98.35 210	98.78 100	97.37 41	97.72 138	98.96 110	91.53 146	99.92 31	98.79 26	99.65 67	99.51 89

Vis-MVSNet (Re-imp)		96.87 130	96.55 132	97.83 146	98.73 139	95.46 177	99.20 41	98.30 215	94.96 165	96.60 187	98.87 122	90.05 174	98.59 276	93.67 241	98.60 150	99.46 104

PAPR		96.84 132	96.24 144	98.65 79	98.72 143	96.92 104	97.36 318	98.57 153	93.33 249	96.67 182	97.57 253	94.30 91	99.56 147	91.05 310	98.59 151	99.47 100

sasdasda		97.67 82	97.23 97	98.98 59	98.70 144	98.38 35	99.34 16	98.39 193	96.76 76	97.67 141	97.40 266	92.26 120	99.49 163	98.28 53	96.28 232	99.08 165

canonicalmvs		97.67 82	97.23 97	98.98 59	98.70 144	98.38 35	99.34 16	98.39 193	96.76 76	97.67 141	97.40 266	92.26 120	99.49 163	98.28 53	96.28 232	99.08 165

API-MVS		97.41 103	97.25 96	97.91 142	98.70 144	96.80 109	98.82 127	98.69 121	94.53 187	98.11 105	98.28 187	94.50 87	99.57 144	94.12 226	99.49 99	97.37 259

MAR-MVS		96.91 128	96.40 138	98.45 97	98.69 147	96.90 105	98.66 168	98.68 124	92.40 289	97.07 164	97.96 215	91.54 145	99.75 109	93.68 239	98.92 133	98.69 202
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
PS-MVSNAJ		97.73 77	97.77 69	97.62 170	98.68 148	95.58 170	97.34 320	98.51 167	97.29 44	98.66 80	97.88 223	94.51 84	99.90 45	97.87 74	99.17 123	97.39 257

test_fmvs1_n		95.90 171	95.99 153	95.63 297	98.67 149	88.32 371	99.26 27	98.22 225	96.40 94	99.67 14	99.26 56	73.91 382	99.70 119	99.02 20	99.50 97	98.87 185

MGCFI-Net		97.62 87	97.19 100	98.92 64	98.66 150	98.20 49	99.32 21	98.38 197	96.69 82	97.58 150	97.42 265	92.10 128	99.50 162	98.28 53	96.25 235	99.08 165

alignmvs		97.56 93	97.07 106	99.01 56	98.66 150	98.37 40	98.83 125	98.06 264	96.74 78	98.00 119	97.65 245	90.80 162	99.48 168	98.37 49	96.56 218	99.19 146

Vis-MVSNet		97.42 102	97.11 103	98.34 107	98.66 150	96.23 139	99.22 36	99.00 39	96.63 85	98.04 112	99.21 65	88.05 227	99.35 180	96.01 161	99.21 120	99.45 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		98.45 40	98.35 32	98.74 72	98.65 153	97.55 75	99.19 43	98.60 142	96.72 81	99.35 32	98.77 134	95.06 75	99.55 154	98.95 21	99.87 1	99.12 157

EPP-MVSNet		97.46 96	97.28 95	97.99 138	98.64 154	95.38 181	99.33 20	98.31 209	93.61 239	97.19 158	99.07 95	94.05 96	99.23 193	96.89 126	98.43 162	99.37 114

ab-mvs		96.42 147	95.71 165	98.55 85	98.63 155	96.75 112	97.88 275	98.74 108	93.84 217	96.54 192	98.18 198	85.34 278	99.75 109	95.93 162	96.35 224	99.15 153

PCF-MVS	93.45 11	94.68 243	93.43 294	98.42 103	98.62 156	96.77 111	95.48 384	98.20 228	84.63 388	93.34 299	98.32 184	88.55 214	99.81 81	84.80 376	98.96 132	98.68 203
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v2_base		97.66 84	97.70 72	97.56 174	98.61 157	95.46 177	97.44 309	98.46 179	97.15 57	98.65 81	98.15 199	94.33 90	99.80 88	97.84 77	98.66 148	97.41 255

sss		97.39 104	96.98 111	98.61 81	98.60 158	96.61 118	98.22 228	98.93 50	93.97 210	98.01 118	98.48 165	91.98 132	99.85 63	96.45 145	98.15 172	99.39 112

Test_1112_low_res		96.34 152	95.66 170	98.36 106	98.56 159	95.94 155	97.71 292	98.07 259	92.10 299	94.79 236	97.29 274	91.75 137	99.56 147	94.17 224	96.50 221	99.58 83

1112_ss		96.63 138	96.00 152	98.50 91	98.56 159	96.37 133	98.18 239	98.10 252	92.92 269	94.84 232	98.43 168	92.14 126	99.58 143	94.35 217	96.51 220	99.56 85

BH-untuned		95.95 166	95.72 162	96.65 230	98.55 161	92.26 295	98.23 227	97.79 280	93.73 225	94.62 238	98.01 210	88.97 204	99.00 228	93.04 258	98.51 156	98.68 203

fmvsm_s_conf0.1_n		98.18 61	98.21 51	98.11 130	98.54 162	95.24 190	98.87 114	99.24 17	97.50 31	99.70 13	99.67 1	91.33 150	99.89 47	99.47 12	99.54 92	99.21 141

LS3D		97.16 117	96.66 129	98.68 76	98.53 163	97.19 95	98.93 97	98.90 57	92.83 273	95.99 210	99.37 37	92.12 127	99.87 58	93.67 241	99.57 83	98.97 177

hse-mvs2		95.71 180	95.30 186	96.93 212	98.50 164	93.53 264	98.36 209	98.10 252	97.48 32	98.67 76	97.99 212	89.76 178	99.02 225	97.95 67	80.91 390	98.22 232

AUN-MVS		94.53 257	93.73 279	96.92 215	98.50 164	93.52 265	98.34 211	98.10 252	93.83 219	95.94 214	97.98 214	85.59 273	99.03 222	94.35 217	80.94 389	98.22 232

baseline1		95.84 174	95.12 194	98.01 137	98.49 166	95.98 147	98.73 151	97.03 341	95.37 140	96.22 202	98.19 197	89.96 176	99.16 200	94.60 208	87.48 355	98.90 184

HY-MVS	93.96 8	96.82 133	96.23 145	98.57 83	98.46 167	97.00 100	98.14 241	98.21 226	93.95 211	96.72 181	97.99 212	91.58 141	99.76 107	94.51 212	96.54 219	98.95 180

ETV-MVS		97.96 66	97.81 68	98.40 104	98.42 168	97.27 87	98.73 151	98.55 158	96.84 71	98.38 95	97.44 262	95.39 55	99.35 180	97.62 92	98.89 135	98.58 215

casdiffmvs_mvg		97.72 78	97.48 85	98.44 99	98.42 168	96.59 121	98.92 99	98.44 183	96.20 101	97.76 132	99.20 67	91.66 140	99.23 193	98.27 56	98.41 163	99.49 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tttt0517		96.07 161	95.51 173	97.78 151	98.41 170	94.84 210	99.28 24	94.33 393	94.26 198	97.64 146	98.64 149	84.05 307	99.47 170	95.34 183	97.60 192	99.03 171

EIA-MVS		97.75 76	97.58 76	98.27 111	98.38 171	96.44 128	99.01 78	98.60 142	95.88 113	97.26 156	97.53 256	94.97 77	99.33 183	97.38 108	99.20 121	99.05 170

thisisatest0530		96.01 163	95.36 180	97.97 139	98.38 171	95.52 175	98.88 111	94.19 395	94.04 203	97.64 146	98.31 185	83.82 314	99.46 171	95.29 187	97.70 189	98.93 182

FE-MVS		95.62 186	94.90 205	97.78 151	98.37 173	94.92 207	97.17 335	97.38 318	90.95 333	97.73 137	97.70 239	85.32 280	99.63 134	91.18 302	98.33 167	98.79 191

GeoE		96.58 142	96.07 148	98.10 131	98.35 174	95.89 162	99.34 16	98.12 246	93.12 261	96.09 206	98.87 122	89.71 180	98.97 229	92.95 261	98.08 175	99.43 109

xiu_mvs_v1_base_debu		97.60 88	97.56 78	97.72 158	98.35 174	95.98 147	97.86 278	98.51 167	97.13 59	99.01 50	98.40 172	91.56 142	99.80 88	98.53 33	98.68 144	97.37 259

xiu_mvs_v1_base		97.60 88	97.56 78	97.72 158	98.35 174	95.98 147	97.86 278	98.51 167	97.13 59	99.01 50	98.40 172	91.56 142	99.80 88	98.53 33	98.68 144	97.37 259

xiu_mvs_v1_base_debi		97.60 88	97.56 78	97.72 158	98.35 174	95.98 147	97.86 278	98.51 167	97.13 59	99.01 50	98.40 172	91.56 142	99.80 88	98.53 33	98.68 144	97.37 259

baseline		97.64 85	97.44 88	98.25 115	98.35 174	96.20 140	99.00 80	98.32 207	96.33 98	98.03 113	99.17 74	91.35 149	99.16 200	98.10 60	98.29 170	99.39 112

mvsmamba		97.25 111	96.99 109	98.02 136	98.34 179	95.54 174	99.18 47	97.47 307	95.04 159	98.15 102	98.57 158	89.46 186	99.31 185	97.68 89	99.01 129	99.22 139

BH-w/o		95.38 200	95.08 196	96.26 271	98.34 179	91.79 303	97.70 293	97.43 314	92.87 271	94.24 259	97.22 280	88.66 209	98.84 252	91.55 298	97.70 189	98.16 235

EC-MVSNet		98.21 60	98.11 57	98.49 93	98.34 179	97.26 91	99.61 5	98.43 187	96.78 74	98.87 63	98.84 125	93.72 100	99.01 227	98.91 23	99.50 97	99.19 146

test_fmvsmvis_n_1920		98.44 41	98.51 18	98.23 117	98.33 182	96.15 143	98.97 86	99.15 28	98.55 7	98.45 91	99.55 6	94.26 93	99.97 1	99.65 6	99.66 64	98.57 216

MVS_Test		97.28 109	97.00 108	98.13 126	98.33 182	95.97 152	98.74 147	98.07 259	94.27 197	98.44 93	98.07 204	92.48 114	99.26 189	96.43 146	98.19 171	99.16 152

casdiffmvs		97.63 86	97.41 89	98.28 110	98.33 182	96.14 144	98.82 127	98.32 207	96.38 96	97.95 121	99.21 65	91.23 154	99.23 193	98.12 59	98.37 164	99.48 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		97.58 91	97.40 90	98.13 126	98.32 185	95.81 165	98.06 252	98.37 199	96.20 101	98.74 72	98.89 120	91.31 152	99.25 190	98.16 58	98.52 155	99.34 117
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-RMVSNet		95.92 170	95.32 184	97.69 162	98.32 185	94.64 219	98.19 234	97.45 312	94.56 185	96.03 208	98.61 150	85.02 283	99.12 208	90.68 315	99.06 125	99.30 126

Fast-Effi-MVS+		96.28 155	95.70 167	98.03 135	98.29 187	95.97 152	98.58 180	98.25 223	91.74 307	95.29 225	97.23 279	91.03 160	99.15 203	92.90 263	97.96 178	98.97 177

mvsany_test1		97.69 81	97.70 72	97.66 168	98.24 188	94.18 243	97.53 305	97.53 301	95.52 131	99.66 15	99.51 13	94.30 91	99.56 147	98.38 48	98.62 149	99.23 137

UGNet		96.78 134	96.30 141	98.19 122	98.24 188	95.89 162	98.88 111	98.93 50	97.39 38	96.81 178	97.84 227	82.60 319	99.90 45	96.53 142	99.49 99	98.79 191
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
MVSTER		96.06 162	95.72 162	97.08 202	98.23 190	95.93 158	98.73 151	98.27 218	94.86 171	95.07 227	98.09 203	88.21 220	98.54 279	96.59 140	93.46 278	96.79 291

ET-MVSNet_ETH3D		94.13 286	92.98 303	97.58 172	98.22 191	96.20 140	97.31 323	95.37 382	94.53 187	79.56 399	97.63 249	86.51 254	97.53 361	96.91 122	90.74 315	99.02 172

FA-MVS(test-final)		96.41 150	95.94 154	97.82 148	98.21 192	95.20 192	97.80 285	97.58 291	93.21 255	97.36 154	97.70 239	89.47 185	99.56 147	94.12 226	97.99 176	98.71 201

GBi-Net		94.49 261	93.80 272	96.56 244	98.21 192	95.00 200	98.82 127	98.18 233	92.46 283	94.09 266	97.07 292	81.16 327	97.95 339	92.08 283	92.14 296	96.72 299

test1		94.49 261	93.80 272	96.56 244	98.21 192	95.00 200	98.82 127	98.18 233	92.46 283	94.09 266	97.07 292	81.16 327	97.95 339	92.08 283	92.14 296	96.72 299

FMVSNet2		94.47 264	93.61 285	97.04 204	98.21 192	96.43 129	98.79 141	98.27 218	92.46 283	93.50 293	97.09 289	81.16 327	98.00 336	91.09 305	91.93 299	96.70 303

Effi-MVS+		97.12 120	96.69 126	98.39 105	98.19 196	96.72 114	97.37 316	98.43 187	93.71 228	97.65 145	98.02 208	92.20 125	99.25 190	96.87 131	97.79 184	99.19 146

mvs_anonymous		96.70 137	96.53 134	97.18 193	98.19 196	93.78 252	98.31 217	98.19 230	94.01 207	94.47 243	98.27 190	92.08 130	98.46 287	97.39 107	97.91 179	99.31 123

ETVMVS		94.50 260	93.44 293	97.68 164	98.18 198	95.35 184	98.19 234	97.11 333	93.73 225	96.40 198	95.39 368	74.53 378	98.84 252	91.10 304	96.31 227	98.84 188

LCM-MVSNet-Re		95.22 212	95.32 184	94.91 320	98.18 198	87.85 377	98.75 144	95.66 379	95.11 154	88.96 364	96.85 319	90.26 173	97.65 355	95.65 175	98.44 160	99.22 139

FMVSNet3		94.97 230	94.26 236	97.11 200	98.18 198	96.62 116	98.56 187	98.26 222	93.67 235	94.09 266	97.10 285	84.25 301	98.01 334	92.08 283	92.14 296	96.70 303

CANet_DTU		96.96 126	96.55 132	98.21 118	98.17 201	96.07 146	97.98 261	98.21 226	97.24 50	97.13 160	98.93 114	86.88 250	99.91 39	95.00 195	99.37 115	98.66 207

thisisatest0515		95.61 189	94.89 206	97.76 155	98.15 202	95.15 195	96.77 361	94.41 391	92.95 268	97.18 159	97.43 263	84.78 289	99.45 172	94.63 205	97.73 188	98.68 203

IterMVS-LS		95.46 193	95.21 189	96.22 272	98.12 203	93.72 258	98.32 216	98.13 245	93.71 228	94.26 257	97.31 273	92.24 122	98.10 327	94.63 205	90.12 322	96.84 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl22		94.68 243	94.19 240	96.13 275	98.11 204	93.60 260	96.94 347	98.31 209	92.43 287	93.32 300	96.87 318	86.51 254	98.28 317	94.10 228	91.16 310	96.51 331

VDDNet		95.36 203	94.53 221	97.86 144	98.10 205	95.13 196	98.85 119	97.75 282	90.46 340	98.36 96	99.39 31	73.27 384	99.64 131	97.98 66	96.58 217	98.81 190

testing3		93.19 312	92.48 314	95.30 310	98.07 206	92.27 294	98.64 171	97.17 331	93.94 213	93.98 272	97.04 300	67.97 392	96.01 387	88.40 347	97.14 200	97.63 250

MVSFormer		97.57 92	97.49 83	97.84 145	98.07 206	95.76 166	99.47 7	98.40 191	94.98 163	98.79 68	98.83 127	92.34 117	98.41 299	96.91 122	99.59 79	99.34 117

lupinMVS		97.44 100	97.22 99	98.12 129	98.07 206	95.76 166	97.68 294	97.76 281	94.50 190	98.79 68	98.61 150	92.34 117	99.30 186	97.58 95	99.59 79	99.31 123

MVS_0304		98.23 58	97.91 67	99.21 39	98.06 209	97.96 63	98.58 180	95.51 380	98.58 5	98.87 63	99.26 56	92.99 108	99.95 7	99.62 9	99.67 61	99.73 41

TAMVS		97.02 124	96.79 119	97.70 161	98.06 209	95.31 187	98.52 190	98.31 209	93.95 211	97.05 166	98.61 150	93.49 102	98.52 281	95.33 184	97.81 183	99.29 128

UBG		95.32 207	94.72 213	97.13 197	98.05 211	93.26 277	97.87 276	97.20 329	94.96 165	96.18 204	95.66 365	80.97 330	99.35 180	94.47 214	97.08 201	98.78 194

CDS-MVSNet		96.99 125	96.69 126	97.90 143	98.05 211	95.98 147	98.20 231	98.33 206	93.67 235	96.95 168	98.49 164	93.54 101	98.42 292	95.24 190	97.74 187	99.31 123
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WBMVS		94.56 253	94.04 250	96.10 277	98.03 213	93.08 287	97.82 284	98.18 233	94.02 205	93.77 283	96.82 321	81.28 326	98.34 306	95.47 182	91.00 313	96.88 282

testing222		94.12 288	93.03 302	97.37 186	98.02 214	94.66 217	97.94 265	96.65 364	94.63 182	95.78 215	95.76 357	71.49 386	98.92 240	91.17 303	95.88 244	98.52 217

ADS-MVSNet2		94.58 252	94.40 232	95.11 315	98.00 215	88.74 363	96.04 374	97.30 321	90.15 346	96.47 195	96.64 331	87.89 230	97.56 360	90.08 322	97.06 202	99.02 172

ADS-MVSNet		95.00 224	94.45 228	96.63 234	98.00 215	91.91 302	96.04 374	97.74 283	90.15 346	96.47 195	96.64 331	87.89 230	98.96 233	90.08 322	97.06 202	99.02 172

IterMVS		94.09 291	93.85 269	94.80 327	97.99 217	90.35 335	97.18 333	98.12 246	93.68 233	92.46 328	97.34 269	84.05 307	97.41 364	92.51 276	91.33 306	96.62 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet_0	88.72 19	91.28 332	90.03 339	95.00 318	97.99 217	87.29 380	94.84 390	98.50 172	92.06 300	89.86 357	95.19 371	79.81 340	99.39 178	92.27 280	69.79 406	98.33 228

tt0805		94.54 255	93.85 269	96.63 234	97.98 219	93.06 288	98.77 143	97.84 278	93.67 235	93.80 281	98.04 207	76.88 367	98.96 233	94.79 202	92.86 289	97.86 242

IterMVS-SCA-FT		94.11 289	93.87 267	94.85 324	97.98 219	90.56 331	97.18 333	98.11 249	93.75 222	92.58 322	97.48 258	83.97 309	97.41 364	92.48 278	91.30 307	96.58 316

testing11		95.00 224	94.28 235	97.16 195	97.96 221	93.36 274	98.09 249	97.06 339	94.94 169	95.33 224	96.15 347	76.89 366	99.40 175	95.77 170	96.30 228	98.72 198

testing91		94.98 228	94.25 237	97.20 190	97.94 222	93.41 269	98.00 259	97.58 291	94.99 162	95.45 220	96.04 351	77.20 362	99.42 174	94.97 196	96.02 242	98.78 194

testing99		94.83 236	94.08 248	97.07 203	97.94 222	93.13 283	98.10 248	97.17 331	94.86 171	95.34 221	96.00 354	76.31 369	99.40 175	95.08 193	95.90 243	98.68 203

EI-MVSNet		95.96 165	95.83 158	96.36 264	97.93 224	93.70 259	98.12 244	98.27 218	93.70 230	95.07 227	99.02 98	92.23 123	98.54 279	94.68 203	93.46 278	96.84 288

CVMVSNet		95.43 196	96.04 150	93.57 351	97.93 224	83.62 389	98.12 244	98.59 146	95.68 124	96.56 188	99.02 98	87.51 238	97.51 362	93.56 245	97.44 195	99.60 77

RRT-MVS		97.03 123	96.78 120	97.77 154	97.90 226	94.34 236	99.12 56	98.35 202	95.87 114	98.06 109	98.70 143	86.45 258	99.63 134	98.04 65	98.54 154	99.35 115

PMMVS		96.60 139	96.33 140	97.41 181	97.90 226	93.93 248	97.35 319	98.41 189	92.84 272	97.76 132	97.45 261	91.10 158	99.20 197	96.26 151	97.91 179	99.11 159

Effi-MVS+-dtu		96.29 153	96.56 131	95.51 301	97.89 228	90.22 337	98.80 136	98.10 252	96.57 88	96.45 197	96.66 328	90.81 161	98.91 242	95.72 171	97.99 176	97.40 256

QAPM		96.29 153	95.40 175	98.96 62	97.85 229	97.60 74	99.23 32	98.93 50	89.76 353	93.11 308	99.02 98	89.11 197	99.93 25	91.99 288	99.62 74	99.34 117

UWE-MVS		94.30 273	93.89 266	95.53 300	97.83 230	88.95 360	97.52 307	93.25 399	94.44 193	96.63 184	97.07 292	78.70 347	99.28 188	91.99 288	97.56 194	98.36 226

3Dnovator+	94.38 6	97.43 101	96.78 120	99.38 18	97.83 230	98.52 28	99.37 12	98.71 116	97.09 62	92.99 311	99.13 82	89.36 189	99.89 47	96.97 119	99.57 83	99.71 49

ACMH+	92.99 14	94.30 273	93.77 275	95.88 288	97.81 232	92.04 301	98.71 156	98.37 199	93.99 209	90.60 351	98.47 166	80.86 333	99.05 218	92.75 267	92.40 295	96.55 322

3Dnovator	94.51 5	97.46 96	96.93 112	99.07 53	97.78 233	97.64 71	99.35 15	99.06 34	97.02 64	93.75 284	99.16 77	89.25 192	99.92 31	97.22 112	99.75 44	99.64 71

test_vis1_n		95.47 192	95.13 192	96.49 252	97.77 234	90.41 334	99.27 26	98.11 249	96.58 86	99.66 15	99.18 73	67.00 395	99.62 138	99.21 16	99.40 112	99.44 107

miper_lstm_enhance		94.33 271	94.07 249	95.11 315	97.75 235	90.97 318	97.22 328	98.03 266	91.67 311	92.76 316	96.97 308	90.03 175	97.78 351	92.51 276	89.64 328	96.56 320

c3_l		94.79 238	94.43 230	95.89 287	97.75 235	93.12 285	97.16 337	98.03 266	92.23 295	93.46 295	97.05 299	91.39 147	98.01 334	93.58 244	89.21 337	96.53 325

TR-MVS		94.94 233	94.20 239	97.17 194	97.75 235	94.14 244	97.59 302	97.02 343	92.28 294	95.75 216	97.64 247	83.88 311	98.96 233	89.77 328	96.15 239	98.40 223

Fast-Effi-MVS+-dtu		95.87 172	95.85 157	95.91 285	97.74 238	91.74 306	98.69 162	98.15 242	95.56 129	94.92 230	97.68 244	88.98 203	98.79 259	93.19 253	97.78 185	97.20 263

test_fmvsmconf0.1_n		98.58 23	98.44 24	98.99 57	97.73 239	97.15 97	98.84 123	98.97 42	98.75 2	99.43 27	99.54 8	93.29 104	99.93 25	99.64 8	99.79 27	99.89 4

MIMVSNet		93.26 309	92.21 318	96.41 261	97.73 239	93.13 283	95.65 381	97.03 341	91.27 326	94.04 269	96.06 350	75.33 374	97.19 367	86.56 360	96.23 237	98.92 183

miper_ehance_all_eth		95.01 223	94.69 215	95.97 282	97.70 241	93.31 275	97.02 343	98.07 259	92.23 295	93.51 292	96.96 310	91.85 135	98.15 323	93.68 239	91.16 310	96.44 339

dmvs_re		94.48 263	94.18 242	95.37 307	97.68 242	90.11 339	98.54 189	97.08 335	94.56 185	94.42 249	97.24 278	84.25 301	97.76 352	91.02 311	92.83 290	98.24 230

SCA		95.46 193	95.13 192	96.46 258	97.67 243	91.29 314	97.33 321	97.60 290	94.68 179	96.92 172	97.10 285	83.97 309	98.89 246	92.59 271	98.32 169	99.20 142

ACMP	93.49 10	95.34 205	94.98 201	96.43 260	97.67 243	93.48 266	98.73 151	98.44 183	94.94 169	92.53 324	98.53 160	84.50 298	99.14 205	95.48 181	94.00 266	96.66 309
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.1_n_a		98.08 62	98.04 62	98.21 118	97.66 245	95.39 180	98.89 106	99.17 26	97.24 50	99.76 8	99.67 1	91.13 155	99.88 56	99.39 13	99.41 109	99.35 115

eth_miper_zixun_eth		94.68 243	94.41 231	95.47 303	97.64 246	91.71 307	96.73 364	98.07 259	92.71 276	93.64 285	97.21 281	90.54 167	98.17 322	93.38 247	89.76 326	96.54 323

ACMH	92.88 16	94.55 254	93.95 260	96.34 266	97.63 247	93.26 277	98.81 135	98.49 177	93.43 246	89.74 358	98.53 160	81.91 321	99.08 216	93.69 238	93.30 284	96.70 303
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMM	93.85 9	95.69 183	95.38 179	96.61 237	97.61 248	93.84 251	98.91 101	98.44 183	95.25 147	94.28 256	98.47 166	86.04 267	99.12 208	95.50 180	93.95 268	96.87 285
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmatch-test		94.42 267	93.68 283	96.63 234	97.60 249	91.76 304	94.83 391	97.49 306	89.45 359	94.14 264	97.10 285	88.99 200	98.83 255	85.37 370	98.13 173	99.29 128

cl____		94.51 259	94.01 255	96.02 279	97.58 250	93.40 271	97.05 341	97.96 271	91.73 309	92.76 316	97.08 291	89.06 199	98.13 325	92.61 268	90.29 320	96.52 328

tpm cat1		93.36 304	92.80 306	95.07 317	97.58 250	87.97 375	96.76 362	97.86 277	82.17 395	93.53 289	96.04 351	86.13 263	99.13 206	89.24 339	95.87 245	98.10 236

MVS-HIRNet		89.46 351	88.40 350	92.64 362	97.58 250	82.15 394	94.16 400	93.05 403	75.73 402	90.90 347	82.52 405	79.42 343	98.33 308	83.53 381	98.68 144	97.43 254

PatchmatchNet		95.71 180	95.52 172	96.29 270	97.58 250	90.72 326	96.84 359	97.52 302	94.06 202	97.08 162	96.96 310	89.24 193	98.90 245	92.03 287	98.37 164	99.26 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DIV-MVS_self_test		94.52 258	94.03 252	95.99 280	97.57 254	93.38 272	97.05 341	97.94 272	91.74 307	92.81 314	97.10 285	89.12 196	98.07 331	92.60 269	90.30 319	96.53 325

tpmrst		95.63 185	95.69 168	95.44 305	97.54 255	88.54 366	96.97 345	97.56 294	93.50 242	97.52 152	96.93 314	89.49 183	99.16 200	95.25 189	96.42 223	98.64 209

FMVSNet1		93.19 312	92.07 319	96.56 244	97.54 255	95.00 200	98.82 127	98.18 233	90.38 343	92.27 331	97.07 292	73.68 383	97.95 339	89.36 338	91.30 307	96.72 299

miper_enhance_ethall		95.10 219	94.75 211	96.12 276	97.53 257	93.73 257	96.61 367	98.08 257	92.20 298	93.89 275	96.65 330	92.44 115	98.30 313	94.21 223	91.16 310	96.34 342

CLD-MVS		95.62 186	95.34 181	96.46 258	97.52 258	93.75 255	97.27 326	98.46 179	95.53 130	94.42 249	98.00 211	86.21 262	98.97 229	96.25 153	94.37 253	96.66 309
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MDTV_nov1_ep13			95.40 175		97.48 259	88.34 370	96.85 358	97.29 322	93.74 224	97.48 153	97.26 275	89.18 194	99.05 218	91.92 291	97.43 196

IB-MVS	91.98 17	93.27 308	91.97 321	97.19 192	97.47 260	93.41 269	97.09 340	95.99 373	93.32 250	92.47 327	95.73 360	78.06 354	99.53 157	94.59 210	82.98 380	98.62 210
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
tpmvs		94.60 249	94.36 233	95.33 309	97.46 261	88.60 365	96.88 356	97.68 284	91.29 324	93.80 281	96.42 338	88.58 210	99.24 192	91.06 308	96.04 241	98.17 234

LPG-MVS_test		95.62 186	95.34 181	96.47 255	97.46 261	93.54 262	98.99 83	98.54 160	94.67 180	94.36 252	98.77 134	85.39 275	99.11 210	95.71 172	94.15 261	96.76 294

LGP-MVS_train				96.47 255	97.46 261	93.54 262		98.54 160	94.67 180	94.36 252	98.77 134	85.39 275	99.11 210	95.71 172	94.15 261	96.76 294

test_vis1_rt		91.29 331	90.65 331	93.19 359	97.45 264	86.25 383	98.57 186	90.90 409	93.30 252	86.94 377	93.59 387	62.07 401	99.11 210	97.48 104	95.58 249	94.22 382

jason		97.32 108	97.08 105	98.06 134	97.45 264	95.59 169	97.87 276	97.91 275	94.79 175	98.55 86	98.83 127	91.12 156	99.23 193	97.58 95	99.60 77	99.34 117
jason: jason.
HQP_MVS		96.14 160	95.90 156	96.85 218	97.42 266	94.60 225	98.80 136	98.56 156	97.28 45	95.34 221	98.28 187	87.09 245	99.03 222	96.07 155	94.27 255	96.92 273

plane_prior7					97.42 266	94.63 220

ITE_SJBPF				95.44 305	97.42 266	91.32 313		97.50 304	95.09 157	93.59 286	98.35 178	81.70 322	98.88 248	89.71 330	93.39 282	96.12 350

LTVRE_ROB	92.95 15	94.60 249	93.90 264	96.68 229	97.41 269	94.42 231	98.52 190	98.59 146	91.69 310	91.21 344	98.35 178	84.87 286	99.04 221	91.06 308	93.44 281	96.60 314
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
Syy-MVS		92.55 321	92.61 311	92.38 364	97.39 270	83.41 390	97.91 268	97.46 308	93.16 258	93.42 296	95.37 369	84.75 290	96.12 385	77.00 398	96.99 204	97.60 251

myMVS_eth3d		92.73 318	92.01 320	94.89 322	97.39 270	90.94 319	97.91 268	97.46 308	93.16 258	93.42 296	95.37 369	68.09 391	96.12 385	88.34 348	96.99 204	97.60 251

plane_prior1					97.37 272

plane_prior6					97.35 273	94.61 223						87.09 245

dp		94.15 285	93.90 264	94.90 321	97.31 274	86.82 382	96.97 345	97.19 330	91.22 328	96.02 209	96.61 333	85.51 274	99.02 225	90.00 326	94.30 254	98.85 186

NP-MVS					97.28 275	94.51 228					97.73 236

CostFormer		94.95 231	94.73 212	95.60 299	97.28 275	89.06 356	97.53 305	96.89 352	89.66 355	96.82 177	96.72 326	86.05 265	98.95 238	95.53 179	96.13 240	98.79 191

VPA-MVSNet		95.75 178	95.11 195	97.69 162	97.24 277	97.27 87	98.94 95	99.23 20	95.13 152	95.51 219	97.32 272	85.73 270	98.91 242	97.33 110	89.55 331	96.89 281

tpm2		94.19 281	93.76 277	95.46 304	97.23 278	89.04 357	97.31 323	96.85 356	87.08 374	96.21 203	96.79 323	83.75 315	98.74 262	92.43 279	96.23 237	98.59 213

EPMVS		94.99 226	94.48 224	96.52 250	97.22 279	91.75 305	97.23 327	91.66 406	94.11 200	97.28 155	96.81 322	85.70 271	98.84 252	93.04 258	97.28 198	98.97 177

FMVSNet5		91.81 326	90.92 329	94.49 337	97.21 280	92.09 298	98.00 259	97.55 299	89.31 362	90.86 348	95.61 366	74.48 379	95.32 393	85.57 367	89.70 327	96.07 352

HQP-NCC					97.20 281		98.05 253		96.43 91	94.45 244

ACMP_Plane					97.20 281		98.05 253		96.43 91	94.45 244

HQP-MVS		95.72 179	95.40 175	96.69 228	97.20 281	94.25 241	98.05 253	98.46 179	96.43 91	94.45 244	97.73 236	86.75 251	98.96 233	95.30 185	94.18 259	96.86 287

UniMVSNet_ETH3D		94.24 278	93.33 296	96.97 209	97.19 284	93.38 272	98.74 147	98.57 153	91.21 329	93.81 280	98.58 155	72.85 385	98.77 261	95.05 194	93.93 269	98.77 197

OpenMVS	93.04 13	95.83 175	95.00 199	98.32 108	97.18 285	97.32 85	99.21 39	98.97 42	89.96 349	91.14 345	99.05 97	86.64 253	99.92 31	93.38 247	99.47 102	97.73 246

VPNet		94.99 226	94.19 240	97.40 183	97.16 286	96.57 122	98.71 156	98.97 42	95.67 125	94.84 232	98.24 194	80.36 336	98.67 269	96.46 144	87.32 359	96.96 270

GA-MVS		94.81 237	94.03 252	97.14 196	97.15 287	93.86 250	96.76 362	97.58 291	94.00 208	94.76 237	97.04 300	80.91 331	98.48 283	91.79 293	96.25 235	99.09 161

FIs		96.51 144	96.12 147	97.67 165	97.13 288	97.54 77	99.36 13	99.22 23	95.89 112	94.03 270	98.35 178	91.98 132	98.44 290	96.40 147	92.76 291	97.01 267

1314		96.25 157	95.73 161	97.79 150	97.13 288	95.55 173	98.19 234	98.59 146	93.47 244	92.03 336	97.82 231	91.33 150	99.49 163	94.62 207	98.44 160	98.32 229

D2MVS		95.18 215	95.08 196	95.48 302	97.10 290	92.07 299	98.30 219	99.13 30	94.02 205	92.90 312	96.73 325	89.48 184	98.73 263	94.48 213	93.60 277	95.65 361

DeepMVS_CX				86.78 377	97.09 291	72.30 407		95.17 386	75.92 401	84.34 390	95.19 371	70.58 387	95.35 391	79.98 391	89.04 340	92.68 395

PAPM		94.95 231	94.00 256	97.78 151	97.04 292	95.65 168	96.03 376	98.25 223	91.23 327	94.19 262	97.80 233	91.27 153	98.86 251	82.61 384	97.61 191	98.84 188

CR-MVSNet		94.76 240	94.15 244	96.59 240	97.00 293	93.43 267	94.96 387	97.56 294	92.46 283	96.93 170	96.24 341	88.15 222	97.88 347	87.38 356	96.65 215	98.46 221

RPMNet		92.81 317	91.34 327	97.24 188	97.00 293	93.43 267	94.96 387	98.80 93	82.27 394	96.93 170	92.12 398	86.98 248	99.82 76	76.32 399	96.65 215	98.46 221

UniMVSNet (Re)		95.78 177	95.19 190	97.58 172	96.99 295	97.47 81	98.79 141	99.18 25	95.60 127	93.92 274	97.04 300	91.68 138	98.48 283	95.80 168	87.66 354	96.79 291

test_fmvs2		93.43 303	93.58 286	92.95 361	96.97 296	83.91 387	99.19 43	97.24 327	95.74 120	95.20 226	98.27 190	69.65 388	98.72 264	96.26 151	93.73 272	96.24 346

FC-MVSNet-test		96.42 147	96.05 149	97.53 175	96.95 297	97.27 87	99.36 13	99.23 20	95.83 116	93.93 273	98.37 176	92.00 131	98.32 309	96.02 160	92.72 292	97.00 268

tfpnnormal		93.66 299	92.70 309	96.55 248	96.94 298	95.94 155	98.97 86	99.19 24	91.04 331	91.38 343	97.34 269	84.94 285	98.61 273	85.45 369	89.02 341	95.11 370

TESTMET0.1,1		94.18 284	93.69 282	95.63 297	96.92 299	89.12 355	96.91 350	94.78 388	93.17 257	94.88 231	96.45 337	78.52 348	98.92 240	93.09 255	98.50 157	98.85 186

TinyColmap		92.31 324	91.53 325	94.65 332	96.92 299	89.75 343	96.92 348	96.68 361	90.45 341	89.62 359	97.85 226	76.06 372	98.81 257	86.74 359	92.51 294	95.41 363

cascas		94.63 248	93.86 268	96.93 212	96.91 301	94.27 239	96.00 377	98.51 167	85.55 384	94.54 240	96.23 343	84.20 305	98.87 249	95.80 168	96.98 207	97.66 249

nrg030		96.28 155	95.72 162	97.96 141	96.90 302	98.15 54	99.39 10	98.31 209	95.47 133	94.42 249	98.35 178	92.09 129	98.69 265	97.50 103	89.05 339	97.04 266

MVS		94.67 246	93.54 289	98.08 132	96.88 303	96.56 123	98.19 234	98.50 172	78.05 399	92.69 319	98.02 208	91.07 159	99.63 134	90.09 321	98.36 166	98.04 237

WR-MVS_H		95.05 222	94.46 226	96.81 221	96.86 304	95.82 164	99.24 30	99.24 17	93.87 216	92.53 324	96.84 320	90.37 169	98.24 319	93.24 251	87.93 351	96.38 341

UniMVSNet_NR-MVSNet		95.71 180	95.15 191	97.40 183	96.84 305	96.97 101	98.74 147	99.24 17	95.16 151	93.88 276	97.72 238	91.68 138	98.31 311	95.81 166	87.25 360	96.92 273

USDC		93.33 307	92.71 308	95.21 311	96.83 306	90.83 324	96.91 350	97.50 304	93.84 217	90.72 349	98.14 200	77.69 356	98.82 256	89.51 335	93.21 286	95.97 354

WB-MVSnew		94.19 281	94.04 250	94.66 331	96.82 307	92.14 296	97.86 278	95.96 375	93.50 242	95.64 217	96.77 324	88.06 226	97.99 337	84.87 373	96.86 208	93.85 390

test-LLR		95.10 219	94.87 207	95.80 290	96.77 308	89.70 345	96.91 350	95.21 383	95.11 154	94.83 234	95.72 362	87.71 234	98.97 229	93.06 256	98.50 157	98.72 198

test-mter		94.08 292	93.51 290	95.80 290	96.77 308	89.70 345	96.91 350	95.21 383	92.89 270	94.83 234	95.72 362	77.69 356	98.97 229	93.06 256	98.50 157	98.72 198

Patchmtry		93.22 310	92.35 316	95.84 289	96.77 308	93.09 286	94.66 394	97.56 294	87.37 373	92.90 312	96.24 341	88.15 222	97.90 343	87.37 357	90.10 323	96.53 325

gg-mvs-nofinetune		92.21 325	90.58 333	97.13 197	96.75 311	95.09 197	95.85 378	89.40 411	85.43 385	94.50 242	81.98 406	80.80 334	98.40 305	92.16 281	98.33 167	97.88 240

XXY-MVS		95.20 214	94.45 228	97.46 176	96.75 311	96.56 123	98.86 117	98.65 136	93.30 252	93.27 301	98.27 190	84.85 287	98.87 249	94.82 200	91.26 309	96.96 270

CP-MVSNet		94.94 233	94.30 234	96.83 219	96.72 313	95.56 171	99.11 58	98.95 46	93.89 214	92.42 329	97.90 220	87.19 244	98.12 326	94.32 219	88.21 348	96.82 290

PatchT		93.06 315	91.97 321	96.35 265	96.69 314	92.67 291	94.48 397	97.08 335	86.62 375	97.08 162	92.23 397	87.94 229	97.90 343	78.89 394	96.69 213	98.49 219

PS-CasMVS		94.67 246	93.99 258	96.71 225	96.68 315	95.26 188	99.13 55	99.03 37	93.68 233	92.33 330	97.95 216	85.35 277	98.10 327	93.59 243	88.16 350	96.79 291

WR-MVS		95.15 216	94.46 226	97.22 189	96.67 316	96.45 127	98.21 229	98.81 86	94.15 199	93.16 304	97.69 241	87.51 238	98.30 313	95.29 187	88.62 345	96.90 280

baseline2		95.11 218	94.52 222	96.87 217	96.65 317	93.56 261	98.27 224	94.10 397	93.45 245	92.02 337	97.43 263	87.45 242	99.19 198	93.88 234	97.41 197	97.87 241

test_0402		91.32 330	90.27 336	94.48 338	96.60 318	91.12 316	98.50 196	97.22 328	86.10 380	88.30 370	96.98 307	77.65 358	97.99 337	78.13 396	92.94 288	94.34 379

TransMVSNet (Re)		92.67 319	91.51 326	96.15 273	96.58 319	94.65 218	98.90 102	96.73 358	90.86 334	89.46 362	97.86 224	85.62 272	98.09 329	86.45 361	81.12 387	95.71 359

XVG-ACMP-BASELINE		94.54 255	94.14 245	95.75 293	96.55 320	91.65 308	98.11 246	98.44 183	94.96 165	94.22 260	97.90 220	79.18 345	99.11 210	94.05 230	93.85 270	96.48 336

DU-MVS		95.42 197	94.76 210	97.40 183	96.53 321	96.97 101	98.66 168	98.99 41	95.43 135	93.88 276	97.69 241	88.57 211	98.31 311	95.81 166	87.25 360	96.92 273

NR-MVSNet		94.98 228	94.16 243	97.44 178	96.53 321	97.22 94	98.74 147	98.95 46	94.96 165	89.25 363	97.69 241	89.32 190	98.18 321	94.59 210	87.40 357	96.92 273

tpm		94.13 286	93.80 272	95.12 314	96.50 323	87.91 376	97.44 309	95.89 378	92.62 279	96.37 200	96.30 340	84.13 306	98.30 313	93.24 251	91.66 304	99.14 155

pm-mvs1		93.94 297	93.06 301	96.59 240	96.49 324	95.16 193	98.95 92	98.03 266	92.32 292	91.08 346	97.84 227	84.54 297	98.41 299	92.16 281	86.13 372	96.19 349

JIA-IIPM		93.35 305	92.49 313	95.92 284	96.48 325	90.65 328	95.01 386	96.96 346	85.93 381	96.08 207	87.33 403	87.70 236	98.78 260	91.35 300	95.58 249	98.34 227

TranMVSNet+NR-MVSNet		95.14 217	94.48 224	97.11 200	96.45 326	96.36 134	99.03 73	99.03 37	95.04 159	93.58 287	97.93 217	88.27 219	98.03 333	94.13 225	86.90 365	96.95 272

testgi		93.06 315	92.45 315	94.88 323	96.43 327	89.90 340	98.75 144	97.54 300	95.60 127	91.63 342	97.91 219	74.46 380	97.02 369	86.10 363	93.67 273	97.72 247

v10		94.29 275	93.55 288	96.51 251	96.39 328	94.80 214	98.99 83	98.19 230	91.35 320	93.02 310	96.99 306	88.09 224	98.41 299	90.50 317	88.41 347	96.33 344

v8		94.47 264	93.77 275	96.57 243	96.36 329	94.83 212	99.05 67	98.19 230	91.92 303	93.16 304	96.97 308	88.82 208	98.48 283	91.69 296	87.79 352	96.39 340

GG-mvs-BLEND				96.59 240	96.34 330	94.98 203	96.51 370	88.58 412		93.10 309	94.34 383	80.34 338	98.05 332	89.53 334	96.99 204	96.74 296

V42		94.78 239	94.14 245	96.70 227	96.33 331	95.22 191	98.97 86	98.09 256	92.32 292	94.31 255	97.06 296	88.39 217	98.55 278	92.90 263	88.87 343	96.34 342

PEN-MVS		94.42 267	93.73 279	96.49 252	96.28 332	94.84 210	99.17 48	99.00 39	93.51 241	92.23 332	97.83 230	86.10 264	97.90 343	92.55 274	86.92 364	96.74 296

v1144		94.59 251	93.92 261	96.60 239	96.21 333	94.78 216	98.59 178	98.14 244	91.86 306	94.21 261	97.02 303	87.97 228	98.41 299	91.72 295	89.57 329	96.61 313

Baseline_NR-MVSNet		94.35 270	93.81 271	95.96 283	96.20 334	94.05 246	98.61 177	96.67 362	91.44 316	93.85 278	97.60 250	88.57 211	98.14 324	94.39 215	86.93 363	95.68 360

MS-PatchMatch		93.84 298	93.63 284	94.46 340	96.18 335	89.45 350	97.76 288	98.27 218	92.23 295	92.13 334	97.49 257	79.50 342	98.69 265	89.75 329	99.38 114	95.25 366

v2v482		94.69 241	94.03 252	96.65 230	96.17 336	94.79 215	98.67 166	98.08 257	92.72 275	94.00 271	97.16 283	87.69 237	98.45 288	92.91 262	88.87 343	96.72 299

EPNet_dtu		95.21 213	94.95 203	95.99 280	96.17 336	90.45 332	98.16 240	97.27 325	96.77 75	93.14 307	98.33 183	90.34 170	98.42 292	85.57 367	98.81 142	99.09 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		95.69 183	95.33 183	96.76 223	96.16 338	94.63 220	98.43 205	98.39 193	96.64 84	95.02 229	98.78 132	85.15 282	99.05 218	95.21 191	94.20 258	96.60 314
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v1192		94.32 272	93.58 286	96.53 249	96.10 339	94.45 229	98.50 196	98.17 239	91.54 313	94.19 262	97.06 296	86.95 249	98.43 291	90.14 320	89.57 329	96.70 303

v148		94.29 275	93.76 277	95.91 285	96.10 339	92.93 289	98.58 180	97.97 269	92.59 281	93.47 294	96.95 312	88.53 215	98.32 309	92.56 273	87.06 362	96.49 334

v144192		94.39 269	93.70 281	96.48 254	96.06 341	94.35 235	98.58 180	98.16 241	91.45 315	94.33 254	97.02 303	87.50 240	98.45 288	91.08 307	89.11 338	96.63 311

DTE-MVSNet		93.98 296	93.26 299	96.14 274	96.06 341	94.39 233	99.20 41	98.86 75	93.06 263	91.78 338	97.81 232	85.87 269	97.58 359	90.53 316	86.17 369	96.46 338

v1240		94.06 294	93.29 298	96.34 266	96.03 343	93.90 249	98.44 203	98.17 239	91.18 330	94.13 265	97.01 305	86.05 265	98.42 292	89.13 341	89.50 333	96.70 303

APD_test1		88.22 355	88.01 354	88.86 374	95.98 344	74.66 406	97.21 329	96.44 368	83.96 390	86.66 380	97.90 220	60.95 402	97.84 349	82.73 382	90.23 321	94.09 385

v1921920		94.20 280	93.47 292	96.40 263	95.98 344	94.08 245	98.52 190	98.15 242	91.33 321	94.25 258	97.20 282	86.41 259	98.42 292	90.04 325	89.39 335	96.69 308

EU-MVSNet		93.66 299	94.14 245	92.25 367	95.96 346	83.38 391	98.52 190	98.12 246	94.69 178	92.61 321	98.13 201	87.36 243	96.39 383	91.82 292	90.00 324	96.98 269

v7n		94.19 281	93.43 294	96.47 255	95.90 347	94.38 234	99.26 27	98.34 205	91.99 301	92.76 316	97.13 284	88.31 218	98.52 281	89.48 336	87.70 353	96.52 328

gm-plane-assit					95.88 348	87.47 378			89.74 354		96.94 313		99.19 198	93.32 250

LF4IMVS		93.14 314	92.79 307	94.20 344	95.88 348	88.67 364	97.66 296	97.07 337	93.81 220	91.71 339	97.65 245	77.96 355	98.81 257	91.47 299	91.92 300	95.12 369

PS-MVSNAJss		96.43 146	96.26 143	96.92 215	95.84 350	95.08 198	99.16 49	98.50 172	95.87 114	93.84 279	98.34 182	94.51 84	98.61 273	96.88 128	93.45 280	97.06 265

pmmvs4		94.69 241	93.99 258	96.81 221	95.74 351	95.94 155	97.40 312	97.67 285	90.42 342	93.37 298	97.59 251	89.08 198	98.20 320	92.97 260	91.67 303	96.30 345

test_djsdf		96.00 164	95.69 168	96.93 212	95.72 352	95.49 176	99.47 7	98.40 191	94.98 163	94.58 239	97.86 224	89.16 195	98.41 299	96.91 122	94.12 263	96.88 282

SixPastTwentyTwo		93.34 306	92.86 305	94.75 328	95.67 353	89.41 352	98.75 144	96.67 362	93.89 214	90.15 356	98.25 193	80.87 332	98.27 318	90.90 312	90.64 316	96.57 318

K. test v3		92.55 321	91.91 324	94.48 338	95.64 354	89.24 353	99.07 64	94.88 387	94.04 203	86.78 378	97.59 251	77.64 359	97.64 356	92.08 283	89.43 334	96.57 318

OurMVSNet-221017-0		94.21 279	94.00 256	94.85 324	95.60 355	89.22 354	98.89 106	97.43 314	95.29 144	92.18 333	98.52 163	82.86 317	98.59 276	93.46 246	91.76 301	96.74 296

mvs_tets		95.41 199	95.00 199	96.65 230	95.58 356	94.42 231	99.00 80	98.55 158	95.73 122	93.21 303	98.38 175	83.45 316	98.63 271	97.09 115	94.00 266	96.91 278

MonoMVSNet		95.51 190	95.45 174	95.68 294	95.54 357	90.87 321	98.92 99	97.37 319	95.79 118	95.53 218	97.38 268	89.58 182	97.68 354	96.40 147	92.59 293	98.49 219

Gipuma		78.40 372	76.75 375	83.38 385	95.54 357	80.43 397	79.42 410	97.40 316	64.67 407	73.46 404	80.82 408	45.65 407	93.14 402	66.32 406	87.43 356	76.56 410
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		94.08 292	93.51 290	95.80 290	95.53 359	92.89 290	97.38 314	95.97 374	95.11 154	92.51 326	96.66 328	87.71 234	96.94 371	87.03 358	93.67 273	97.57 253

pmmvs5		93.65 301	92.97 304	95.68 294	95.49 360	92.37 293	98.20 231	97.28 324	89.66 355	92.58 322	97.26 275	82.14 320	98.09 329	93.18 254	90.95 314	96.58 316

test_fmvsmconf0.01_n		97.86 71	97.54 81	98.83 69	95.48 361	96.83 108	98.95 92	98.60 142	98.58 5	98.93 59	99.55 6	88.57 211	99.91 39	99.54 11	99.61 75	99.77 26

N_pmnet		87.12 360	87.77 358	85.17 380	95.46 362	61.92 416	97.37 316	70.66 421	85.83 382	88.73 369	96.04 351	85.33 279	97.76 352	80.02 389	90.48 317	95.84 356

our_test_3		93.65 301	93.30 297	94.69 329	95.45 363	89.68 347	96.91 350	97.65 286	91.97 302	91.66 341	96.88 316	89.67 181	97.93 342	88.02 352	91.49 305	96.48 336

ppachtmachnet_test		93.22 310	92.63 310	94.97 319	95.45 363	90.84 323	96.88 356	97.88 276	90.60 337	92.08 335	97.26 275	88.08 225	97.86 348	85.12 372	90.33 318	96.22 347

jajsoiax		95.45 195	95.03 198	96.73 224	95.42 365	94.63 220	99.14 52	98.52 165	95.74 120	93.22 302	98.36 177	83.87 312	98.65 270	96.95 121	94.04 264	96.91 278

dmvs_testset		87.64 357	88.93 349	83.79 383	95.25 366	63.36 415	97.20 330	91.17 407	93.07 262	85.64 386	95.98 355	85.30 281	91.52 405	69.42 404	87.33 358	96.49 334

MDA-MVSNet-bldmvs		89.97 345	88.35 351	94.83 326	95.21 367	91.34 312	97.64 298	97.51 303	88.36 369	71.17 407	96.13 348	79.22 344	96.63 380	83.65 380	86.27 368	96.52 328

dongtai		82.47 365	81.88 368	84.22 382	95.19 368	76.03 399	94.59 396	74.14 420	82.63 392	87.19 376	96.09 349	64.10 398	87.85 410	58.91 408	84.11 377	88.78 402

anonymousdsp		95.42 197	94.91 204	96.94 211	95.10 369	95.90 161	99.14 52	98.41 189	93.75 222	93.16 304	97.46 259	87.50 240	98.41 299	95.63 176	94.03 265	96.50 333

EPNet		97.28 109	96.87 115	98.51 90	94.98 370	96.14 144	98.90 102	97.02 343	98.28 10	95.99 210	99.11 84	91.36 148	99.89 47	96.98 118	99.19 122	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVP-Stereo		94.28 277	93.92 261	95.35 308	94.95 371	92.60 292	97.97 262	97.65 286	91.61 312	90.68 350	97.09 289	86.32 261	98.42 292	89.70 331	99.34 116	95.02 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lessismore_v0				94.45 341	94.93 372	88.44 369		91.03 408		86.77 379	97.64 247	76.23 370	98.42 292	90.31 319	85.64 373	96.51 331

MDA-MVSNet_test_wron		90.71 339	89.38 344	94.68 330	94.83 373	90.78 325	97.19 332	97.46 308	87.60 371	72.41 406	95.72 362	86.51 254	96.71 378	85.92 365	86.80 366	96.56 320

EGC-MVSNET		75.22 375	69.54 378	92.28 366	94.81 374	89.58 348	97.64 298	96.50 366	1.82 418	5.57 419	95.74 358	68.21 390	96.26 384	73.80 401	91.71 302	90.99 396

YYNet1		90.70 340	89.39 343	94.62 333	94.79 375	90.65 328	97.20 330	97.46 308	87.54 372	72.54 405	95.74 358	86.51 254	96.66 379	86.00 364	86.76 367	96.54 323

EG-PatchMatch MVS		91.13 335	90.12 338	94.17 346	94.73 376	89.00 358	98.13 243	97.81 279	89.22 363	85.32 388	96.46 336	67.71 393	98.42 292	87.89 355	93.82 271	95.08 371

pmmvs6		91.77 327	90.63 332	95.17 313	94.69 377	91.24 315	98.67 166	97.92 274	86.14 379	89.62 359	97.56 255	75.79 373	98.34 306	90.75 314	84.56 374	95.94 355

MVStest1		89.53 350	87.99 355	94.14 348	94.39 378	90.42 333	98.25 226	96.84 357	82.81 391	81.18 396	97.33 271	77.09 365	96.94 371	85.27 371	78.79 395	95.06 372

new_pmnet		90.06 344	89.00 348	93.22 358	94.18 379	88.32 371	96.42 372	96.89 352	86.19 378	85.67 385	93.62 386	77.18 363	97.10 368	81.61 386	89.29 336	94.23 381

DSMNet-mixed		92.52 323	92.58 312	92.33 365	94.15 380	82.65 393	98.30 219	94.26 394	89.08 364	92.65 320	95.73 360	85.01 284	95.76 389	86.24 362	97.76 186	98.59 213

m2depth		92.61 320	91.96 323	94.55 334	94.10 381	90.60 330	98.52 190	97.29 322	92.67 277	90.18 354	97.92 218	79.75 341	97.79 350	91.09 305	86.15 371	95.26 365

UnsupCasMVSNet_eth		90.99 337	89.92 340	94.19 345	94.08 382	89.83 341	97.13 339	98.67 129	93.69 231	85.83 384	96.19 346	75.15 375	96.74 375	89.14 340	79.41 394	96.00 353

KD-MVS_2432*1600		89.61 348	87.96 356	94.54 335	94.06 383	91.59 309	95.59 382	97.63 288	89.87 351	88.95 365	94.38 381	78.28 351	96.82 373	84.83 374	68.05 407	95.21 367

miper_refine_blended		89.61 348	87.96 356	94.54 335	94.06 383	91.59 309	95.59 382	97.63 288	89.87 351	88.95 365	94.38 381	78.28 351	96.82 373	84.83 374	68.05 407	95.21 367

Anonymous20231206		91.66 328	91.10 328	93.33 355	94.02 385	87.35 379	98.58 180	97.26 326	90.48 339	90.16 355	96.31 339	83.83 313	96.53 381	79.36 392	89.90 325	96.12 350

Anonymous20240521		91.18 334	90.44 334	93.42 352	93.70 386	88.47 368	98.94 95	97.56 294	88.46 368	89.56 361	95.08 374	77.15 364	96.97 370	83.92 379	89.55 331	94.82 376

test20.03		90.89 338	90.38 335	92.43 363	93.48 387	88.14 374	98.33 212	97.56 294	93.40 247	87.96 371	96.71 327	80.69 335	94.13 398	79.15 393	86.17 369	95.01 375

CMPMVS	66.06 21	89.70 346	89.67 342	89.78 372	93.19 388	76.56 398	97.00 344	98.35 202	80.97 396	81.57 394	97.75 235	74.75 377	98.61 273	89.85 327	93.63 275	94.17 383
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	86.42 20	89.00 352	87.43 360	93.69 350	93.08 389	89.42 351	97.91 268	96.89 352	78.58 398	85.86 383	94.69 376	69.48 389	98.29 316	77.13 397	93.29 285	93.36 392

KD-MVS_self_test		90.38 341	89.38 344	93.40 354	92.85 390	88.94 361	97.95 263	97.94 272	90.35 344	90.25 353	93.96 384	79.82 339	95.94 388	84.62 378	76.69 401	95.33 364

MIMVSNet1		89.67 347	88.28 352	93.82 349	92.81 391	91.08 317	98.01 257	97.45 312	87.95 370	87.90 372	95.87 356	67.63 394	94.56 397	78.73 395	88.18 349	95.83 357

kuosan		78.45 371	77.69 372	80.72 390	92.73 392	75.32 403	94.63 395	74.51 419	75.96 400	80.87 398	93.19 391	63.23 400	79.99 414	42.56 414	81.56 386	86.85 406

mvs5depth		91.23 333	90.17 337	94.41 342	92.09 393	89.79 342	95.26 385	96.50 366	90.73 335	91.69 340	97.06 296	76.12 371	98.62 272	88.02 352	84.11 377	94.82 376

UnsupCasMVSNet_bld		87.17 358	85.12 365	93.31 356	91.94 394	88.77 362	94.92 389	98.30 215	84.30 389	82.30 392	90.04 400	63.96 399	97.25 366	85.85 366	74.47 405	93.93 389

CL-MVSNet_self_test		90.11 343	89.14 346	93.02 360	91.86 395	88.23 373	96.51 370	98.07 259	90.49 338	90.49 352	94.41 379	84.75 290	95.34 392	80.79 388	74.95 403	95.50 362

Patchmatch-RL test		91.49 329	90.85 330	93.41 353	91.37 396	84.40 385	92.81 401	95.93 377	91.87 305	87.25 374	94.87 375	88.99 200	96.53 381	92.54 275	82.00 382	99.30 126

test_fmvs3		87.17 358	87.06 361	87.50 376	91.21 397	75.66 401	99.05 67	96.61 365	92.79 274	88.85 367	92.78 393	43.72 408	93.49 399	93.95 231	84.56 374	93.34 393

pmmvs-eth3d		90.36 342	89.05 347	94.32 343	91.10 398	92.12 297	97.63 301	96.95 347	88.86 366	84.91 389	93.13 392	78.32 350	96.74 375	88.70 344	81.81 384	94.09 385

PM-MVS		87.77 356	86.55 362	91.40 370	91.03 399	83.36 392	96.92 348	95.18 385	91.28 325	86.48 382	93.42 388	53.27 405	96.74 375	89.43 337	81.97 383	94.11 384

new-patchmatchnet		88.50 354	87.45 359	91.67 369	90.31 400	85.89 384	97.16 337	97.33 320	89.47 358	83.63 391	92.77 394	76.38 368	95.06 395	82.70 383	77.29 400	94.06 387

mvsany_test3		88.80 353	88.04 353	91.09 371	89.78 401	81.57 396	97.83 283	95.49 381	93.81 220	87.53 373	93.95 385	56.14 404	97.43 363	94.68 203	83.13 379	94.26 380

WB-MVS		84.86 363	85.33 364	83.46 384	89.48 402	69.56 410	98.19 234	96.42 369	89.55 357	81.79 393	94.67 377	84.80 288	90.12 406	52.44 410	80.64 391	90.69 397

test_f		86.07 362	85.39 363	88.10 375	89.28 403	75.57 402	97.73 291	96.33 370	89.41 361	85.35 387	91.56 399	43.31 410	95.53 390	91.32 301	84.23 376	93.21 394

SSC-MVS		84.27 364	84.71 367	82.96 388	89.19 404	68.83 411	98.08 250	96.30 371	89.04 365	81.37 395	94.47 378	84.60 295	89.89 407	49.80 412	79.52 393	90.15 398

pmmvs3		86.67 361	84.86 366	92.11 368	88.16 405	87.19 381	96.63 366	94.75 389	79.88 397	87.22 375	92.75 395	66.56 396	95.20 394	81.24 387	76.56 402	93.96 388

testf1		79.02 368	77.70 370	82.99 386	88.10 406	66.90 412	94.67 392	93.11 400	71.08 404	74.02 402	93.41 389	34.15 414	93.25 400	72.25 402	78.50 397	88.82 400

APD_test2		79.02 368	77.70 370	82.99 386	88.10 406	66.90 412	94.67 392	93.11 400	71.08 404	74.02 402	93.41 389	34.15 414	93.25 400	72.25 402	78.50 397	88.82 400

ambc				89.49 373	86.66 408	75.78 400	92.66 402	96.72 359		86.55 381	92.50 396	46.01 406	97.90 343	90.32 318	82.09 381	94.80 378

test_vis3_rt		79.22 366	77.40 373	84.67 381	86.44 409	74.85 405	97.66 296	81.43 416	84.98 386	67.12 409	81.91 407	28.09 418	97.60 357	88.96 342	80.04 392	81.55 407

test_method		79.03 367	78.17 369	81.63 389	86.06 410	54.40 421	82.75 409	96.89 352	39.54 413	80.98 397	95.57 367	58.37 403	94.73 396	84.74 377	78.61 396	95.75 358

TDRefinement		91.06 336	89.68 341	95.21 311	85.35 411	91.49 311	98.51 195	97.07 337	91.47 314	88.83 368	97.84 227	77.31 360	99.09 215	92.79 266	77.98 399	95.04 373

PMMVS2		77.95 373	75.44 377	85.46 379	82.54 412	74.95 404	94.23 399	93.08 402	72.80 403	74.68 401	87.38 402	36.36 413	91.56 404	73.95 400	63.94 409	89.87 399

E-PMN		64.94 379	64.25 381	67.02 396	82.28 413	59.36 419	91.83 404	85.63 413	52.69 410	60.22 411	77.28 410	41.06 411	80.12 413	46.15 413	41.14 411	61.57 412

EMVS		64.07 380	63.26 383	66.53 397	81.73 414	58.81 420	91.85 403	84.75 414	51.93 412	59.09 412	75.13 411	43.32 409	79.09 415	42.03 415	39.47 412	61.69 411

FPMVS		77.62 374	77.14 374	79.05 392	79.25 415	60.97 417	95.79 379	95.94 376	65.96 406	67.93 408	94.40 380	37.73 412	88.88 409	68.83 405	88.46 346	87.29 403

wuyk23d		30.17 382	30.18 386	30.16 398	78.61 416	43.29 423	66.79 411	14.21 422	17.31 415	14.82 418	11.93 418	11.55 421	41.43 417	37.08 416	19.30 415	5.76 415

LCM-MVSNet		78.70 370	76.24 376	86.08 378	77.26 417	71.99 408	94.34 398	96.72 359	61.62 408	76.53 400	89.33 401	33.91 416	92.78 403	81.85 385	74.60 404	93.46 391

MVE	62.14 22	63.28 381	59.38 384	74.99 393	74.33 418	65.47 414	85.55 407	80.50 417	52.02 411	51.10 413	75.00 412	10.91 422	80.50 412	51.60 411	53.40 410	78.99 408
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 376	65.37 380	80.22 391	65.99 419	71.96 409	90.91 405	90.09 410	82.62 393	49.93 414	78.39 409	29.36 417	81.75 411	62.49 407	38.52 413	86.95 405

PMVS	61.03 23	65.95 378	63.57 382	73.09 395	57.90 420	51.22 422	85.05 408	93.93 398	54.45 409	44.32 415	83.57 404	13.22 419	89.15 408	58.68 409	81.00 388	78.91 409
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		68.90 377	66.97 379	74.68 394	50.78 421	59.95 418	87.13 406	83.47 415	38.80 414	62.21 410	96.23 343	64.70 397	76.91 416	88.91 343	30.49 414	87.19 404

testmvs		21.48 384	24.95 387	11.09 400	14.89 422	6.47 425	96.56 368	9.87 423	7.55 416	17.93 416	39.02 414	9.43 423	5.90 419	16.56 418	12.72 416	20.91 414

test123		20.95 385	23.72 388	12.64 399	13.54 423	8.19 424	96.55 369	6.13 424	7.48 417	16.74 417	37.98 415	12.97 420	6.05 418	16.69 417	5.43 417	23.68 413

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		23.98 383	31.98 385	0.00 401	0.00 424	0.00 426	0.00 412	98.59 146	0.00 419	0.00 420	98.61 150	90.60 166	0.00 420	0.00 419	0.00 418	0.00 416

pcd_1.5k_mvsjas		7.88 387	10.50 390	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	94.51 84	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		8.20 386	10.94 389	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	98.43 168	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						90.94 319								88.66 345

PC_three_1452									95.08 158	99.60 19	99.16 77	97.86 2	98.47 286	97.52 102	99.72 54	99.74 36

test_241102_TWO								98.87 69	97.65 22	99.53 23	99.48 18	97.34 11	99.94 9	98.43 45	99.80 21	99.83 12

test_0728_THIRD									97.32 42	99.45 25	99.46 24	97.88 1	99.94 9	98.47 41	99.86 2	99.85 9

GSMVS																99.20 142

sam_mvs1												89.45 187				99.20 142

sam_mvs												88.99 200

MTGPA								98.74 108

test_post1							96.68 365				30.43 417	87.85 233	98.69 265	92.59 271

test_post											31.83 416	88.83 207	98.91 242

patchmatchnet-post											95.10 373	89.42 188	98.89 246

MTMP							98.89 106	94.14 396

test9_res														96.39 149	99.57 83	99.69 56

agg_prior2														95.87 165	99.57 83	99.68 61

test_prior4						98.01 61	97.86 278

test_prior2							97.80 285		96.12 105	97.89 128	98.69 144	95.96 38		96.89 126	99.60 77

旧先验2							97.57 304		91.30 323	98.67 76			99.80 88	95.70 174

新几何2							97.64 298

无先验							97.58 303	98.72 113	91.38 317				99.87 58	93.36 249		99.60 77

原ACMM2							97.67 295

testdata2													99.89 47	91.65 297

segment_acmp												96.85 14

testdata1							97.32 322		96.34 97

plane_prior5								98.56 156					99.03 222	96.07 155	94.27 255	96.92 273

plane_prior4											98.28 187

plane_prior3						94.61 223			97.02 64	95.34 221

plane_prior2							98.80 136		97.28 45

plane_prior						94.60 225	98.44 203		96.74 78						94.22 257

n2								0.00 425

nn								0.00 425

door-mid								94.37 392

test11								98.66 132

door								94.64 390

HQP5-MVS						94.25 241

BP-MVS														95.30 185

HQP4-MVS										94.45 244			98.96 233			96.87 285

HQP3-MVS								98.46 179							94.18 259

HQP2-MVS												86.75 251

MDTV_nov1_ep13_2view						84.26 386	96.89 355		90.97 332	97.90 127		89.89 177		93.91 233		99.18 151

ACMMP++_ref															92.97 287

ACMMP++															93.61 276

Test By Simon												94.64 81

High-resolution multi-view benchmark