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.

test_fmvsmvis_n_1920		99.65 6	99.61 6	99.77 55	99.38 212	99.37 100	99.58 109	99.62 41	99.41 9	99.87 25	99.92 14	98.81 44	100.00 1	99.97 1	99.93 22	99.94 11

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 94	99.58 109	99.69 18	99.43 7	99.98 6	99.91 20	98.62 70	100.00 1	99.97 1	99.95 16	99.90 17

test_vis1_n_1920		98.63 160	98.40 167	99.31 143	99.86 20	97.94 248	99.67 64	99.62 41	99.43 7	99.99 2	99.91 20	87.29 365	100.00 1	99.92 12	99.92 24	99.98 2

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 28	99.86 20	99.61 67	99.56 122	99.63 39	99.48 3	99.98 6	99.83 68	98.75 55	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 28	99.84 32	99.63 64	99.56 122	99.63 39	99.47 4	99.98 6	99.82 76	98.75 55	99.99 4	99.97 1	99.97 7	99.94 11

test_fmvsmconf_n		99.70 3	99.64 4	99.87 11	99.80 52	99.66 53	99.48 178	99.64 36	99.45 5	99.92 15	99.92 14	98.62 70	99.99 4	99.96 7	99.99 1	99.96 7

patch_mono-2		99.26 69	99.62 5	98.16 294	99.81 46	94.59 357	99.52 148	99.64 36	99.33 13	99.73 62	99.90 26	99.00 22	99.99 4	99.69 19	99.98 4	99.89 20

h-mvs33		97.70 268	97.28 289	98.97 192	99.70 101	97.27 271	99.36 230	99.45 193	98.94 54	99.66 83	99.64 192	94.93 204	99.99 4	99.48 41	84.36 388	99.65 129

xiu_mvs_v1_base_debu		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 293	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 225

xiu_mvs_v1_base		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 293	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 225

xiu_mvs_v1_base_debi		99.29 63	99.27 58	99.34 136	99.63 131	98.97 153	99.12 293	99.51 115	98.86 60	99.84 29	99.47 255	98.18 96	99.99 4	99.50 36	99.31 157	99.08 225

EPNet		98.86 128	98.71 132	99.30 148	97.20 381	98.18 230	99.62 88	98.91 333	99.28 16	98.63 295	99.81 90	95.96 167	99.99 4	99.24 68	99.72 118	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		99.40 50	99.28 55	99.74 61	99.67 111	99.31 107	99.52 148	98.87 340	99.55 1	99.74 60	99.80 103	96.47 151	99.98 13	99.97 1	99.97 7	99.94 11

test_cas_vis1_n_1920		99.16 83	99.01 95	99.61 84	99.81 46	98.86 175	99.65 75	99.64 36	99.39 10	99.97 13	99.94 6	93.20 273	99.98 13	99.55 29	99.91 31	99.99 1

test_vis1_n		97.92 231	97.44 266	99.34 136	99.53 163	98.08 236	99.74 44	99.49 143	99.15 20	100.00 1	99.94 6	79.51 389	99.98 13	99.88 14	99.76 110	99.97 4

xiu_mvs_v2_base		99.26 69	99.25 62	99.29 151	99.53 163	98.91 169	99.02 316	99.45 193	98.80 69	99.71 68	99.26 309	98.94 29	99.98 13	99.34 55	99.23 161	98.98 239

PS-MVSNAJ		99.32 59	99.32 40	99.30 148	99.57 152	98.94 165	98.97 330	99.46 182	98.92 57	99.71 68	99.24 311	99.01 18	99.98 13	99.35 51	99.66 128	98.97 240

QAPM		98.67 156	98.30 174	99.80 46	99.20 257	99.67 51	99.77 34	99.72 11	94.74 355	98.73 276	99.90 26	95.78 177	99.98 13	96.96 289	99.88 51	99.76 87

3Dnovator	97.25 9	99.24 74	99.05 83	99.81 44	99.12 277	99.66 53	99.84 13	99.74 10	99.09 32	98.92 251	99.90 26	95.94 170	99.98 13	98.95 93	99.92 24	99.79 74

OpenMVS	96.50 16	98.47 166	98.12 186	99.52 111	99.04 296	99.53 82	99.82 17	99.72 11	94.56 358	98.08 327	99.88 36	94.73 221	99.98 13	97.47 257	99.76 110	99.06 231

test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 197	99.65 57	99.50 163	99.61 48	99.45 5	99.87 25	99.92 14	97.31 121	99.97 21	99.95 8	99.99 1	99.97 4

test_fmvs1_n		98.41 172	98.14 183	99.21 163	99.82 42	97.71 260	99.74 44	99.49 143	99.32 14	99.99 2	99.95 3	85.32 372	99.97 21	99.82 16	99.84 77	99.96 7

CANet_DTU		98.97 118	98.87 115	99.25 158	99.33 225	98.42 222	99.08 302	99.30 275	99.16 19	99.43 140	99.75 138	95.27 195	99.97 21	98.56 158	99.95 16	99.36 201

MTAPA		99.52 17	99.39 27	99.89 4	99.90 4	99.86 13	99.66 69	99.47 173	98.79 70	99.68 74	99.81 90	98.43 83	99.97 21	98.88 102	99.90 39	99.83 49

PGM-MVS		99.45 33	99.31 47	99.86 21	99.87 15	99.78 36	99.58 109	99.65 33	97.84 172	99.71 68	99.80 103	99.12 13	99.97 21	98.33 179	99.87 54	99.83 49

mPP-MVS		99.44 37	99.30 49	99.86 21	99.88 11	99.79 30	99.69 55	99.48 155	98.12 138	99.50 126	99.75 138	98.78 48	99.97 21	98.57 155	99.89 48	99.83 49

CP-MVS		99.45 33	99.32 40	99.85 28	99.83 39	99.75 39	99.69 55	99.52 101	98.07 148	99.53 121	99.63 198	98.93 33	99.97 21	98.74 127	99.91 31	99.83 49

SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 101	99.51 115	98.62 83	99.79 42	99.83 68	99.28 4	99.97 21	98.48 165	99.90 39	99.84 40
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3Dnovator+	97.12 13	99.18 79	98.97 101	99.82 41	99.17 269	99.68 48	99.81 20	99.51 115	99.20 18	98.72 277	99.89 30	95.68 182	99.97 21	98.86 110	99.86 62	99.81 61

fmvsm_s_conf0.5_n_a		99.56 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 148	99.65 33	99.10 27	99.98 6	99.92 14	97.35 120	99.96 30	99.94 10	99.92 24	99.95 9

fmvsm_s_conf0.5_n		99.51 18	99.40 25	99.85 28	99.84 32	99.65 57	99.51 156	99.67 23	99.13 22	99.98 6	99.92 14	96.60 146	99.96 30	99.95 8	99.96 12	99.95 9

mvsany_test1		99.50 20	99.46 20	99.62 83	99.61 141	99.09 136	98.94 336	99.48 155	99.10 27	99.96 14	99.91 20	98.85 39	99.96 30	99.72 18	99.58 137	99.82 54

test_fmvs1		98.88 124	98.79 126	99.16 168	99.69 106	97.61 263	99.55 134	99.49 143	99.32 14	99.98 6	99.91 20	91.41 320	99.96 30	99.82 16	99.92 24	99.90 17

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 170	99.88 8	99.87 9	99.51 115	98.99 45	99.88 20	99.81 90	99.27 5	99.96 30	98.85 112	99.80 97	99.81 61

MSC_two_6792asdad				99.87 11	99.51 170	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

No_MVS				99.87 11	99.51 170	99.76 37		99.33 257					99.96 30	98.87 105	99.84 77	99.89 20

ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 274	99.56 114	99.54 231	98.58 72	99.96 30	96.93 292	99.75 112

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 95	99.48 155	99.08 33	99.91 16	99.81 90	99.20 7	99.96 30	98.91 99	99.85 69	99.79 74

test_241102_TWO								99.48 155	99.08 33	99.88 20	99.81 90	98.94 29	99.96 30	98.91 99	99.84 77	99.88 26

ZNCC-MVS		99.47 29	99.33 38	99.87 11	99.87 15	99.81 25	99.64 78	99.67 23	98.08 147	99.55 118	99.64 192	98.91 34	99.96 30	98.72 130	99.90 39	99.82 54

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 116	99.37 239	99.10 27	99.81 37	99.80 103	98.94 29	99.96 30	98.93 96	99.86 62	99.81 61
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_THIRD									98.99 45	99.81 37	99.80 103	99.09 14	99.96 30	98.85 112	99.90 39	99.88 26

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 116	99.51 115					99.96 30	98.93 96	99.86 62	99.88 26

SR-MVS		99.43 40	99.29 53	99.86 21	99.75 73	99.83 16	99.59 101	99.62 41	98.21 124	99.73 62	99.79 115	98.68 64	99.96 30	98.44 170	99.77 107	99.79 74

DPE-MVS		99.46 31	99.32 40	99.91 2	99.78 56	99.88 8	99.36 230	99.51 115	98.73 76	99.88 20	99.84 64	98.72 61	99.96 30	98.16 192	99.87 54	99.88 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MVS_0304		99.42 42	99.32 40	99.72 65	99.70 101	99.27 113	99.52 148	97.57 385	99.51 2	99.82 35	99.78 121	98.09 100	99.96 30	99.97 1	99.97 7	99.94 11

UA-Net		99.42 42	99.29 53	99.80 46	99.62 137	99.55 77	99.50 163	99.70 15	98.79 70	99.77 51	99.96 1	97.45 115	99.96 30	98.92 98	99.90 39	99.89 20

HFP-MVS		99.49 22	99.37 30	99.86 21	99.87 15	99.80 27	99.66 69	99.67 23	98.15 133	99.68 74	99.69 168	99.06 16	99.96 30	98.69 135	99.87 54	99.84 40

region2R		99.48 26	99.35 34	99.87 11	99.88 11	99.80 27	99.65 75	99.66 28	98.13 137	99.66 83	99.68 174	98.96 24	99.96 30	98.62 143	99.87 54	99.84 40

HPM-MVS++		99.39 52	99.23 65	99.87 11	99.75 73	99.84 15	99.43 198	99.51 115	98.68 81	99.27 184	99.53 235	98.64 69	99.96 30	98.44 170	99.80 97	99.79 74

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 41	99.56 69	99.02 38	99.88 20	99.85 54	99.18 10	99.96 30	99.22 69	99.92 24	99.90 17
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 22	99.36 32	99.86 21	99.87 15	99.79 30	99.66 69	99.67 23	98.15 133	99.67 78	99.69 168	98.95 27	99.96 30	98.69 135	99.87 54	99.84 40

MP-MVS		99.33 58	99.15 71	99.87 11	99.88 11	99.82 22	99.66 69	99.46 182	98.09 143	99.48 130	99.74 143	98.29 91	99.96 30	97.93 209	99.87 54	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 99	98.90 110	99.74 61	99.80 52	99.46 92	99.59 101	99.49 143	97.03 261	99.63 96	99.69 168	97.27 124	99.96 30	97.82 220	99.84 77	99.81 61

PVSNet_Blended_VisFu		99.36 55	99.28 55	99.61 84	99.86 20	99.07 141	99.47 184	99.93 2	97.66 195	99.71 68	99.86 49	97.73 110	99.96 30	99.47 43	99.82 90	99.79 74

UGNet		98.87 125	98.69 134	99.40 130	99.22 254	98.72 189	99.44 194	99.68 20	99.24 17	99.18 208	99.42 265	92.74 283	99.96 30	99.34 55	99.94 21	99.53 166
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
CSCG		99.32 59	99.32 40	99.32 142	99.85 26	98.29 225	99.71 51	99.66 28	98.11 140	99.41 147	99.80 103	98.37 88	99.96 30	98.99 89	99.96 12	99.72 103

ACMMP		99.45 33	99.32 40	99.82 41	99.89 8	99.67 51	99.62 88	99.69 18	98.12 138	99.63 96	99.84 64	98.73 60	99.96 30	98.55 161	99.83 86	99.81 61
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
test_fmvsmconf0.01_n		99.22 76	99.03 87	99.79 49	98.42 361	99.48 89	99.55 134	99.51 115	99.39 10	99.78 47	99.93 9	94.80 213	99.95 59	99.93 11	99.95 16	99.94 11

SR-MVS-dyc-post		99.45 33	99.31 47	99.85 28	99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.53 76	99.95 59	98.61 146	99.81 93	99.77 82

GST-MVS		99.40 50	99.24 63	99.85 28	99.86 20	99.79 30	99.60 95	99.67 23	97.97 159	99.63 96	99.68 174	98.52 77	99.95 59	98.38 173	99.86 62	99.81 61

CANet		99.25 73	99.14 72	99.59 87	99.41 203	99.16 125	99.35 235	99.57 64	98.82 65	99.51 125	99.61 207	96.46 152	99.95 59	99.59 25	99.98 4	99.65 129

MP-MVS-pluss		99.37 54	99.20 67	99.88 5	99.90 4	99.87 12	99.30 246	99.52 101	97.18 243	99.60 106	99.79 115	98.79 47	99.95 59	98.83 118	99.91 31	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 42	99.27 58	99.88 5	99.89 8	99.80 27	99.67 64	99.50 135	98.70 78	99.77 51	99.49 247	98.21 94	99.95 59	98.46 169	99.77 107	99.88 26
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
testdata2													99.95 59	96.67 304

APD-MVS_3200maxsize		99.48 26	99.35 34	99.85 28	99.76 65	99.83 16	99.63 82	99.54 85	98.36 106	99.79 42	99.82 76	98.86 38	99.95 59	98.62 143	99.81 93	99.78 80

RPMNet		96.72 313	95.90 325	99.19 165	99.18 262	98.49 213	99.22 278	99.52 101	88.72 388	99.56 114	97.38 382	94.08 251	99.95 59	86.87 392	98.58 207	99.14 217

sss		99.17 81	99.05 83	99.53 105	99.62 137	98.97 153	99.36 230	99.62 41	97.83 173	99.67 78	99.65 186	97.37 119	99.95 59	99.19 71	99.19 164	99.68 119

fmvsm_s_conf0.1_n_a		99.26 69	99.06 82	99.85 28	99.52 167	99.62 65	99.54 139	99.62 41	98.69 79	99.99 2	99.96 1	94.47 237	99.94 69	99.88 14	99.92 24	99.98 2

fmvsm_s_conf0.1_n		99.29 63	99.10 76	99.86 21	99.70 101	99.65 57	99.53 147	99.62 41	98.74 75	99.99 2	99.95 3	94.53 235	99.94 69	99.89 13	99.96 12	99.97 4

TSAR-MVS + MP.		99.58 9	99.50 13	99.81 44	99.91 1	99.66 53	99.63 82	99.39 223	98.91 58	99.78 47	99.85 54	99.36 2	99.94 69	98.84 115	99.88 51	99.82 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	99.74 143	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

X-MVStestdata		96.55 315	95.45 333	99.87 11	99.85 26	99.83 16	99.69 55	99.68 20	98.98 48	99.37 160	64.01 405	98.81 44	99.94 69	98.79 123	99.86 62	99.84 40

旧先验2							98.96 331		96.70 281	99.47 131			99.94 69	98.19 188

新几何1				99.75 58	99.75 73	99.59 70		99.54 85	96.76 277	99.29 179	99.64 192	98.43 83	99.94 69	96.92 294	99.66 128	99.72 103

testdata				99.54 97	99.75 73	98.95 162		99.51 115	97.07 255	99.43 140	99.70 158	98.87 37	99.94 69	97.76 227	99.64 131	99.72 103

HPM-MVS		99.42 42	99.28 55	99.83 40	99.90 4	99.72 42	99.81 20	99.54 85	97.59 199	99.68 74	99.63 198	98.91 34	99.94 69	98.58 152	99.91 31	99.84 40
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 77	99.10 76	99.45 123	99.89 8	98.52 209	99.39 219	99.94 1	98.73 76	99.11 217	99.89 30	95.50 187	99.94 69	99.50 36	99.97 7	99.89 20

APD-MVS		99.27 67	99.08 80	99.84 39	99.75 73	99.79 30	99.50 163	99.50 135	97.16 245	99.77 51	99.82 76	98.78 48	99.94 69	97.56 248	99.86 62	99.80 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 26	99.42 22	99.65 73	99.72 91	99.40 99	99.05 308	99.66 28	99.14 21	99.57 113	99.80 103	98.46 81	99.94 69	99.57 27	99.84 77	99.60 146
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
WTY-MVS		99.06 107	98.88 114	99.61 84	99.62 137	99.16 125	99.37 226	99.56 69	98.04 154	99.53 121	99.62 203	96.84 138	99.94 69	98.85 112	98.49 215	99.72 103

DeepC-MVS	98.35 2	99.30 61	99.19 68	99.64 78	99.82 42	99.23 118	99.62 88	99.55 77	98.94 54	99.63 96	99.95 3	95.82 176	99.94 69	99.37 50	99.97 7	99.73 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		99.27 67	99.12 74	99.74 61	99.18 262	99.75 39	99.56 122	99.57 64	98.45 96	99.49 129	99.85 54	97.77 109	99.94 69	98.33 179	99.84 77	99.52 167

SDMVSNet		99.11 99	98.90 110	99.75 58	99.81 46	99.59 70	99.81 20	99.65 33	98.78 73	99.64 93	99.88 36	94.56 231	99.93 84	99.67 21	98.26 226	99.72 103

FE-MVS		98.48 165	98.17 179	99.40 130	99.54 162	98.96 157	99.68 61	98.81 346	95.54 339	99.62 100	99.70 158	93.82 260	99.93 84	97.35 266	99.46 144	99.32 207

SF-MVS		99.38 53	99.24 63	99.79 49	99.79 54	99.68 48	99.57 116	99.54 85	97.82 177	99.71 68	99.80 103	98.95 27	99.93 84	98.19 188	99.84 77	99.74 92

dcpmvs_2		99.23 75	99.58 7	98.16 294	99.83 39	94.68 355	99.76 37	99.52 101	99.07 35	99.98 6	99.88 36	98.56 74	99.93 84	99.67 21	99.98 4	99.87 31

Anonymous20240529		98.09 201	97.68 237	99.34 136	99.66 120	98.44 219	99.40 215	99.43 207	93.67 365	99.22 195	99.89 30	90.23 336	99.93 84	99.26 67	98.33 220	99.66 125

ACMMP_NAP		99.47 29	99.34 36	99.88 5	99.87 15	99.86 13	99.47 184	99.48 155	98.05 153	99.76 56	99.86 49	98.82 43	99.93 84	98.82 122	99.91 31	99.84 40

EI-MVSNet-UG-set		99.58 9	99.57 8	99.64 78	99.78 56	99.14 131	99.60 95	99.45 193	99.01 40	99.90 18	99.83 68	98.98 23	99.93 84	99.59 25	99.95 16	99.86 33

无先验							98.99 324	99.51 115	96.89 271				99.93 84	97.53 251		99.72 103

VDDNet		97.55 282	97.02 300	99.16 168	99.49 181	98.12 235	99.38 224	99.30 275	95.35 341	99.68 74	99.90 26	82.62 384	99.93 84	99.31 58	98.13 237	99.42 193

ab-mvs		98.86 128	98.63 142	99.54 97	99.64 128	99.19 120	99.44 194	99.54 85	97.77 180	99.30 176	99.81 90	94.20 245	99.93 84	99.17 74	98.82 196	99.49 177

F-COLMAP		99.19 77	99.04 85	99.64 78	99.78 56	99.27 113	99.42 205	99.54 85	97.29 234	99.41 147	99.59 212	98.42 85	99.93 84	98.19 188	99.69 123	99.73 97

Anonymous202405211		98.30 182	97.98 203	99.26 157	99.57 152	98.16 231	99.41 207	98.55 366	96.03 333	99.19 204	99.74 143	91.87 307	99.92 95	99.16 75	98.29 225	99.70 113

EI-MVSNet-Vis-set		99.58 9	99.56 10	99.64 78	99.78 56	99.15 130	99.61 94	99.45 193	99.01 40	99.89 19	99.82 76	99.01 18	99.92 95	99.56 28	99.95 16	99.85 36

VDD-MVS		97.73 262	97.35 278	98.88 211	99.47 190	97.12 279	99.34 238	98.85 342	98.19 127	99.67 78	99.85 54	82.98 382	99.92 95	99.49 40	98.32 224	99.60 146

VNet		99.11 99	98.90 110	99.73 64	99.52 167	99.56 75	99.41 207	99.39 223	99.01 40	99.74 60	99.78 121	95.56 185	99.92 95	99.52 34	98.18 233	99.72 103

XVG-OURS-SEG-HR		98.69 153	98.62 147	98.89 209	99.71 96	97.74 255	99.12 293	99.54 85	98.44 99	99.42 143	99.71 154	94.20 245	99.92 95	98.54 162	98.90 190	99.00 236

HPM-MVS_fast		99.51 18	99.40 25	99.85 28	99.91 1	99.79 30	99.76 37	99.56 69	97.72 186	99.76 56	99.75 138	99.13 12	99.92 95	99.07 83	99.92 24	99.85 36

HY-MVS	97.30 7	98.85 135	98.64 141	99.47 120	99.42 200	99.08 139	99.62 88	99.36 240	97.39 226	99.28 180	99.68 174	96.44 154	99.92 95	98.37 175	98.22 228	99.40 197

DP-MVS		99.16 83	98.95 105	99.78 52	99.77 62	99.53 82	99.41 207	99.50 135	97.03 261	99.04 233	99.88 36	97.39 116	99.92 95	98.66 139	99.90 39	99.87 31

IB-MVS	95.67 18	96.22 321	95.44 334	98.57 249	99.21 255	96.70 305	98.65 364	97.74 383	96.71 280	97.27 351	98.54 362	86.03 368	99.92 95	98.47 168	86.30 386	99.10 220
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
DeepC-MVS_fast	98.69 1	99.49 22	99.39 27	99.77 55	99.63 131	99.59 70	99.36 230	99.46 182	99.07 35	99.79 42	99.82 76	98.85 39	99.92 95	98.68 137	99.87 54	99.82 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
9.14			99.10 76		99.72 91		99.40 215	99.51 115	97.53 209	99.64 93	99.78 121	98.84 41	99.91 105	97.63 239	99.82 90

SMA-MVS		99.44 37	99.30 49	99.85 28	99.73 87	99.83 16	99.56 122	99.47 173	97.45 218	99.78 47	99.82 76	99.18 10	99.91 105	98.79 123	99.89 48	99.81 61
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
TEST9					99.67 111	99.65 57	99.05 308	99.41 212	96.22 318	98.95 246	99.49 247	98.77 51	99.91 105

train_agg		99.02 112	98.77 127	99.77 55	99.67 111	99.65 57	99.05 308	99.41 212	96.28 312	98.95 246	99.49 247	98.76 52	99.91 105	97.63 239	99.72 118	99.75 88

test_8					99.67 111	99.61 67	99.03 313	99.41 212	96.28 312	98.93 250	99.48 252	98.76 52	99.91 105

agg_prior					99.67 111	99.62 65		99.40 220		98.87 260			99.91 105

原ACMM1				99.65 73	99.73 87	99.33 103		99.47 173	97.46 215	99.12 215	99.66 185	98.67 66	99.91 105	97.70 236	99.69 123	99.71 112

LFMVS		97.90 234	97.35 278	99.54 97	99.52 167	99.01 148	99.39 219	98.24 373	97.10 253	99.65 89	99.79 115	84.79 375	99.91 105	99.28 63	98.38 217	99.69 115

XVG-OURS		98.73 149	98.68 135	98.88 211	99.70 101	97.73 256	98.92 338	99.55 77	98.52 91	99.45 134	99.84 64	95.27 195	99.91 105	98.08 199	98.84 194	99.00 236

PLC	97.94 4	99.02 112	98.85 119	99.53 105	99.66 120	99.01 148	99.24 273	99.52 101	96.85 273	99.27 184	99.48 252	98.25 93	99.91 105	97.76 227	99.62 134	99.65 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 275	97.06 299	99.47 120	99.61 141	99.09 136	98.04 389	99.25 287	91.24 380	98.51 305	99.70 158	94.55 233	99.91 105	92.76 369	99.85 69	99.42 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS		97.58 281	97.29 288	98.48 260	99.09 285	96.25 323	99.01 321	96.61 394	97.86 167	99.19 204	99.01 336	88.72 348	99.90 116	97.38 264	98.69 202	99.28 210

test_vis1_rt		95.81 330	95.65 330	96.32 356	99.67 111	91.35 383	99.49 174	96.74 392	98.25 117	95.24 371	98.10 375	74.96 390	99.90 116	99.53 32	98.85 193	97.70 375

FA-MVS(test-final)		98.75 146	98.53 160	99.41 129	99.55 160	99.05 144	99.80 25	99.01 318	96.59 294	99.58 110	99.59 212	95.39 190	99.90 116	97.78 223	99.49 143	99.28 210

MCST-MVS		99.43 40	99.30 49	99.82 41	99.79 54	99.74 41	99.29 251	99.40 220	98.79 70	99.52 123	99.62 203	98.91 34	99.90 116	98.64 141	99.75 112	99.82 54

CDPH-MVS		99.13 89	98.91 109	99.80 46	99.75 73	99.71 44	99.15 287	99.41 212	96.60 292	99.60 106	99.55 226	98.83 42	99.90 116	97.48 255	99.83 86	99.78 80

NCCC		99.34 57	99.19 68	99.79 49	99.61 141	99.65 57	99.30 246	99.48 155	98.86 60	99.21 198	99.63 198	98.72 61	99.90 116	98.25 184	99.63 133	99.80 70

114514_t		98.93 120	98.67 136	99.72 65	99.85 26	99.53 82	99.62 88	99.59 57	92.65 375	99.71 68	99.78 121	98.06 102	99.90 116	98.84 115	99.91 31	99.74 92

1112_ss		98.98 116	98.77 127	99.59 87	99.68 110	99.02 146	99.25 271	99.48 155	97.23 240	99.13 213	99.58 216	96.93 137	99.90 116	98.87 105	98.78 199	99.84 40

PHI-MVS		99.30 61	99.17 70	99.70 67	99.56 156	99.52 85	99.58 109	99.80 8	97.12 249	99.62 100	99.73 149	98.58 72	99.90 116	98.61 146	99.91 31	99.68 119

AdaColmap		99.01 115	98.80 123	99.66 69	99.56 156	99.54 79	99.18 282	99.70 15	98.18 131	99.35 167	99.63 198	96.32 157	99.90 116	97.48 255	99.77 107	99.55 159

COLMAP_ROB	97.56 6	98.86 128	98.75 129	99.17 167	99.88 11	98.53 205	99.34 238	99.59 57	97.55 205	98.70 284	99.89 30	95.83 175	99.90 116	98.10 194	99.90 39	99.08 225
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 178	98.03 198	99.31 143	99.63 131	98.56 202	99.54 139	96.75 391	97.53 209	99.73 62	99.65 186	91.25 324	99.89 127	98.62 143	99.56 138	99.48 178

tttt0517		98.42 170	98.14 183	99.28 155	99.66 120	98.38 223	99.74 44	96.85 389	97.68 192	99.79 42	99.74 143	91.39 321	99.89 127	98.83 118	99.56 138	99.57 156

test12				99.75 58	99.64 128	99.61 67		99.29 279		99.21 198		98.38 87	99.89 127		99.74 115	99.74 92

Test_1112_low_res		98.89 123	98.66 139	99.57 92	99.69 106	98.95 162	99.03 313	99.47 173	96.98 263	99.15 211	99.23 312	96.77 141	99.89 127	98.83 118	98.78 199	99.86 33

CNLPA		99.14 87	98.99 97	99.59 87	99.58 150	99.41 98	99.16 284	99.44 201	98.45 96	99.19 204	99.49 247	98.08 101	99.89 127	97.73 231	99.75 112	99.48 178

sd_testset		98.75 146	98.57 156	99.29 151	99.81 46	98.26 227	99.56 122	99.62 41	98.78 73	99.64 93	99.88 36	92.02 304	99.88 132	99.54 30	98.26 226	99.72 103

APD_test1		95.87 328	96.49 312	94.00 362	99.53 163	84.01 390	99.54 139	99.32 267	95.91 335	97.99 332	99.85 54	85.49 371	99.88 132	91.96 372	98.84 194	98.12 357

diffmvs		99.14 87	99.02 91	99.51 113	99.61 141	98.96 157	99.28 255	99.49 143	98.46 95	99.72 67	99.71 154	96.50 150	99.88 132	99.31 58	99.11 171	99.67 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 128	98.80 123	99.03 182	99.76 65	98.79 184	99.28 255	99.91 3	97.42 223	99.67 78	99.37 280	97.53 113	99.88 132	98.98 90	97.29 281	98.42 340

PVSNet_Blended		99.08 105	98.97 101	99.42 128	99.76 65	98.79 184	98.78 352	99.91 3	96.74 278	99.67 78	99.49 247	97.53 113	99.88 132	98.98 90	99.85 69	99.60 146

MVS		97.28 298	96.55 310	99.48 117	98.78 331	98.95 162	99.27 260	99.39 223	83.53 392	98.08 327	99.54 231	96.97 135	99.87 137	94.23 351	99.16 165	99.63 140

MG-MVS		99.13 89	99.02 91	99.45 123	99.57 152	98.63 196	99.07 303	99.34 250	98.99 45	99.61 103	99.82 76	97.98 104	99.87 137	97.00 285	99.80 97	99.85 36

MSDG		98.98 116	98.80 123	99.53 105	99.76 65	99.19 120	98.75 355	99.55 77	97.25 237	99.47 131	99.77 129	97.82 107	99.87 137	96.93 292	99.90 39	99.54 161

ETV-MVS		99.26 69	99.21 66	99.40 130	99.46 191	99.30 109	99.56 122	99.52 101	98.52 91	99.44 139	99.27 307	98.41 86	99.86 140	99.10 79	99.59 136	99.04 232

thisisatest0515		98.14 196	97.79 221	99.19 165	99.50 179	98.50 212	98.61 366	96.82 390	96.95 267	99.54 119	99.43 263	91.66 316	99.86 140	98.08 199	99.51 142	99.22 214

thres600view7		97.86 239	97.51 254	98.92 200	99.72 91	97.95 246	99.59 101	98.74 353	97.94 161	99.27 184	98.62 359	91.75 310	99.86 140	93.73 356	98.19 232	98.96 242

lupinMVS		99.13 89	99.01 95	99.46 122	99.51 170	98.94 165	99.05 308	99.16 301	97.86 167	99.80 40	99.56 223	97.39 116	99.86 140	98.94 94	99.85 69	99.58 154

PVSNet	96.02 17	98.85 135	98.84 120	98.89 209	99.73 87	97.28 270	98.32 382	99.60 54	97.86 167	99.50 126	99.57 220	96.75 142	99.86 140	98.56 158	99.70 122	99.54 161

MAR-MVS		98.86 128	98.63 142	99.54 97	99.37 215	99.66 53	99.45 188	99.54 85	96.61 290	99.01 236	99.40 272	97.09 129	99.86 140	97.68 238	99.53 141	99.10 220
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
test2506		96.81 312	96.65 308	97.29 337	99.74 80	92.21 380	99.60 95	85.06 409	99.13 22	99.77 51	99.93 9	87.82 363	99.85 146	99.38 48	99.38 149	99.80 70

AllTest		98.87 125	98.72 130	99.31 143	99.86 20	98.48 215	99.56 122	99.61 48	97.85 170	99.36 164	99.85 54	95.95 168	99.85 146	96.66 305	99.83 86	99.59 150

TestCases				99.31 143	99.86 20	98.48 215		99.61 48	97.85 170	99.36 164	99.85 54	95.95 168	99.85 146	96.66 305	99.83 86	99.59 150

jason		99.13 89	99.03 87	99.45 123	99.46 191	98.87 172	99.12 293	99.26 285	98.03 156	99.79 42	99.65 186	97.02 132	99.85 146	99.02 87	99.90 39	99.65 129
jason: jason.
CNVR-MVS		99.42 42	99.30 49	99.78 52	99.62 137	99.71 44	99.26 269	99.52 101	98.82 65	99.39 155	99.71 154	98.96 24	99.85 146	98.59 151	99.80 97	99.77 82

PAPM_NR		99.04 109	98.84 120	99.66 69	99.74 80	99.44 94	99.39 219	99.38 231	97.70 190	99.28 180	99.28 304	98.34 89	99.85 146	96.96 289	99.45 145	99.69 115

testing99		97.36 295	96.94 302	98.63 242	99.18 262	96.70 305	99.30 246	98.93 326	97.71 187	98.23 320	98.26 369	84.92 374	99.84 152	98.04 204	97.85 245	99.35 202

testing222		97.16 303	96.50 311	99.16 168	99.16 271	98.47 217	99.27 260	98.66 362	97.71 187	98.23 320	98.15 371	82.28 386	99.84 152	97.36 265	97.66 250	99.18 216

test1111		98.04 211	98.11 187	97.83 317	99.74 80	93.82 365	99.58 109	95.40 398	99.12 25	99.65 89	99.93 9	90.73 329	99.84 152	99.43 46	99.38 149	99.82 54

ECVR-MVS		98.04 211	98.05 196	98.00 306	99.74 80	94.37 360	99.59 101	94.98 399	99.13 22	99.66 83	99.93 9	90.67 330	99.84 152	99.40 47	99.38 149	99.80 70

test_yl		98.86 128	98.63 142	99.54 97	99.49 181	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 191	99.84 152	98.60 149	98.33 220	99.59 150

DCV-MVSNet		98.86 128	98.63 142	99.54 97	99.49 181	99.18 122	99.50 163	99.07 313	98.22 122	99.61 103	99.51 241	95.37 191	99.84 152	98.60 149	98.33 220	99.59 150

Fast-Effi-MVS+		98.70 151	98.43 164	99.51 113	99.51 170	99.28 111	99.52 148	99.47 173	96.11 328	99.01 236	99.34 290	96.20 161	99.84 152	97.88 212	98.82 196	99.39 198

TSAR-MVS + GP.		99.36 55	99.36 32	99.36 135	99.67 111	98.61 199	99.07 303	99.33 257	99.00 43	99.82 35	99.81 90	99.06 16	99.84 152	99.09 80	99.42 147	99.65 129

tpmrst		98.33 179	98.48 162	97.90 312	99.16 271	94.78 353	99.31 244	99.11 306	97.27 235	99.45 134	99.59 212	95.33 193	99.84 152	98.48 165	98.61 204	99.09 224

Vis-MVSNet		99.12 95	98.97 101	99.56 94	99.78 56	99.10 135	99.68 61	99.66 28	98.49 93	99.86 27	99.87 44	94.77 218	99.84 152	99.19 71	99.41 148	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 160	98.34 170	99.51 113	99.40 208	99.03 145	98.80 350	99.36 240	96.33 309	99.00 240	99.12 326	98.46 81	99.84 152	95.23 338	99.37 156	99.66 125

PatchMatch-RL		98.84 138	98.62 147	99.52 111	99.71 96	99.28 111	99.06 306	99.77 9	97.74 185	99.50 126	99.53 235	95.41 189	99.84 152	97.17 279	99.64 131	99.44 191

EPP-MVSNet		99.13 89	98.99 97	99.53 105	99.65 126	99.06 142	99.81 20	99.33 257	97.43 221	99.60 106	99.88 36	97.14 126	99.84 152	99.13 76	98.94 185	99.69 115

thres100view900		97.76 255	97.45 261	98.69 239	99.72 91	97.86 252	99.59 101	98.74 353	97.93 162	99.26 188	98.62 359	91.75 310	99.83 165	93.22 361	98.18 233	98.37 346

tfpn200view9		97.72 264	97.38 274	98.72 237	99.69 106	97.96 244	99.50 163	98.73 358	97.83 173	99.17 209	98.45 364	91.67 314	99.83 165	93.22 361	98.18 233	98.37 346

test_prior				99.68 68	99.67 111	99.48 89		99.56 69					99.83 165			99.74 92

1314		98.68 155	98.54 159	99.11 174	98.89 315	98.65 194	99.27 260	99.49 143	96.89 271	97.99 332	99.56 223	97.72 111	99.83 165	97.74 230	99.27 160	98.84 248

thres400		97.77 254	97.38 274	98.92 200	99.69 106	97.96 244	99.50 163	98.73 358	97.83 173	99.17 209	98.45 364	91.67 314	99.83 165	93.22 361	98.18 233	98.96 242

casdiffmvs		99.13 89	98.98 100	99.56 94	99.65 126	99.16 125	99.56 122	99.50 135	98.33 110	99.41 147	99.86 49	95.92 171	99.83 165	99.45 45	99.16 165	99.70 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS-test		99.49 22	99.48 15	99.54 97	99.78 56	99.30 109	99.89 2	99.58 61	98.56 87	99.73 62	99.69 168	98.55 75	99.82 171	99.69 19	99.85 69	99.48 178

MVS_Test		99.10 103	98.97 101	99.48 117	99.49 181	99.14 131	99.67 64	99.34 250	97.31 232	99.58 110	99.76 135	97.65 112	99.82 171	98.87 105	99.07 177	99.46 186

dp		97.75 259	97.80 220	97.59 329	99.10 282	93.71 368	99.32 241	98.88 338	96.48 301	99.08 224	99.55 226	92.67 289	99.82 171	96.52 308	98.58 207	99.24 213

RPSCF		98.22 186	98.62 147	96.99 343	99.82 42	91.58 382	99.72 49	99.44 201	96.61 290	99.66 83	99.89 30	95.92 171	99.82 171	97.46 258	99.10 174	99.57 156

PMMVS		98.80 142	98.62 147	99.34 136	99.27 242	98.70 190	98.76 354	99.31 271	97.34 229	99.21 198	99.07 328	97.20 125	99.82 171	98.56 158	98.87 191	99.52 167

EIA-MVS		99.18 79	99.09 79	99.45 123	99.49 181	99.18 122	99.67 64	99.53 96	97.66 195	99.40 152	99.44 261	98.10 99	99.81 176	98.94 94	99.62 134	99.35 202

Effi-MVS+		98.81 139	98.59 154	99.48 117	99.46 191	99.12 134	98.08 388	99.50 135	97.50 213	99.38 158	99.41 269	96.37 156	99.81 176	99.11 78	98.54 212	99.51 173

thres200		97.61 279	97.28 289	98.62 243	99.64 128	98.03 238	99.26 269	98.74 353	97.68 192	99.09 223	98.32 368	91.66 316	99.81 176	92.88 366	98.22 228	98.03 362

tpmvs		97.98 222	98.02 200	97.84 316	99.04 296	94.73 354	99.31 244	99.20 296	96.10 332	98.76 274	99.42 265	94.94 203	99.81 176	96.97 288	98.45 216	98.97 240

casdiffmvs_mvg		99.15 85	99.02 91	99.55 96	99.66 120	99.09 136	99.64 78	99.56 69	98.26 116	99.45 134	99.87 44	96.03 165	99.81 176	99.54 30	99.15 168	99.73 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepPCF-MVS	98.18 3	98.81 139	99.37 30	97.12 341	99.60 146	91.75 381	98.61 366	99.44 201	99.35 12	99.83 34	99.85 54	98.70 63	99.81 176	99.02 87	99.91 31	99.81 61

DPM-MVS		98.95 119	98.71 132	99.66 69	99.63 131	99.55 77	98.64 365	99.10 307	97.93 162	99.42 143	99.55 226	98.67 66	99.80 182	95.80 323	99.68 126	99.61 144

DP-MVS Recon		99.12 95	98.95 105	99.65 73	99.74 80	99.70 46	99.27 260	99.57 64	96.40 308	99.42 143	99.68 174	98.75 55	99.80 182	97.98 206	99.72 118	99.44 191

MVS_111021_LR		99.41 47	99.33 38	99.65 73	99.77 62	99.51 86	98.94 336	99.85 6	98.82 65	99.65 89	99.74 143	98.51 78	99.80 182	98.83 118	99.89 48	99.64 136

CS-MVS		99.50 20	99.48 15	99.54 97	99.76 65	99.42 96	99.90 1	99.55 77	98.56 87	99.78 47	99.70 158	98.65 68	99.79 185	99.65 23	99.78 104	99.41 195

Fast-Effi-MVS+-dtu		98.77 145	98.83 122	98.60 244	99.41 203	96.99 293	99.52 148	99.49 143	98.11 140	99.24 190	99.34 290	96.96 136	99.79 185	97.95 208	99.45 145	99.02 235

baseline1		98.31 180	97.95 207	99.38 134	99.50 179	98.74 187	99.59 101	98.93 326	98.41 100	99.14 212	99.60 210	94.59 229	99.79 185	98.48 165	93.29 359	99.61 144

baseline		99.15 85	99.02 91	99.53 105	99.66 120	99.14 131	99.72 49	99.48 155	98.35 107	99.42 143	99.84 64	96.07 163	99.79 185	99.51 35	99.14 169	99.67 122

PVSNet_0	94.43 19	96.09 326	95.47 332	97.94 309	99.31 232	94.34 362	97.81 390	99.70 15	97.12 249	97.46 345	98.75 356	89.71 340	99.79 185	97.69 237	81.69 392	99.68 119

API-MVS		99.04 109	99.03 87	99.06 178	99.40 208	99.31 107	99.55 134	99.56 69	98.54 89	99.33 171	99.39 276	98.76 52	99.78 190	96.98 287	99.78 104	98.07 359

OMC-MVS		99.08 105	99.04 85	99.20 164	99.67 111	98.22 229	99.28 255	99.52 101	98.07 148	99.66 83	99.81 90	97.79 108	99.78 190	97.79 222	99.81 93	99.60 146

GeoE		98.85 135	98.62 147	99.53 105	99.61 141	99.08 139	99.80 25	99.51 115	97.10 253	99.31 174	99.78 121	95.23 199	99.77 192	98.21 186	99.03 180	99.75 88

alignmvs		98.81 139	98.56 158	99.58 90	99.43 198	99.42 96	99.51 156	98.96 324	98.61 84	99.35 167	98.92 348	94.78 215	99.77 192	99.35 51	98.11 238	99.54 161

tpm cat1		97.39 294	97.36 276	97.50 332	99.17 269	93.73 367	99.43 198	99.31 271	91.27 379	98.71 278	99.08 327	94.31 243	99.77 192	96.41 312	98.50 214	99.00 236

CostFormer		97.72 264	97.73 233	97.71 324	99.15 275	94.02 364	99.54 139	99.02 317	94.67 356	99.04 233	99.35 286	92.35 301	99.77 192	98.50 164	97.94 241	99.34 205

test_241102_ONE					99.84 32	99.90 2		99.48 155	99.07 35	99.91 16	99.74 143	99.20 7	99.76 196

MDTV_nov1_ep13			98.32 172		99.11 279	94.44 359	99.27 260	98.74 353	97.51 212	99.40 152	99.62 203	94.78 215	99.76 196	97.59 242	98.81 198

canonicalmvs		99.02 112	98.86 118	99.51 113	99.42 200	99.32 104	99.80 25	99.48 155	98.63 82	99.31 174	98.81 353	97.09 129	99.75 198	99.27 66	97.90 242	99.47 184

Effi-MVS+-dtu		98.78 143	98.89 113	98.47 265	99.33 225	96.91 299	99.57 116	99.30 275	98.47 94	99.41 147	98.99 338	96.78 140	99.74 199	98.73 129	99.38 149	98.74 262

patchmatchnet-post											98.70 357	94.79 214	99.74 199

SCA		98.19 190	98.16 180	98.27 289	99.30 233	95.55 336	99.07 303	98.97 322	97.57 202	99.43 140	99.57 220	92.72 284	99.74 199	97.58 243	99.20 163	99.52 167

BH-untuned		98.42 170	98.36 168	98.59 245	99.49 181	96.70 305	99.27 260	99.13 305	97.24 239	98.80 269	99.38 277	95.75 178	99.74 199	97.07 283	99.16 165	99.33 206

BH-RMVSNet		98.41 172	98.08 192	99.40 130	99.41 203	98.83 180	99.30 246	98.77 349	97.70 190	98.94 248	99.65 186	92.91 279	99.74 199	96.52 308	99.55 140	99.64 136

MVS_111021_HR		99.41 47	99.32 40	99.66 69	99.72 91	99.47 91	98.95 334	99.85 6	98.82 65	99.54 119	99.73 149	98.51 78	99.74 199	98.91 99	99.88 51	99.77 82

test_post											65.99 403	94.65 228	99.73 205

XVG-ACMP-BASELINE		97.83 245	97.71 235	98.20 291	99.11 279	96.33 320	99.41 207	99.52 101	98.06 152	99.05 232	99.50 244	89.64 342	99.73 205	97.73 231	97.38 279	98.53 328

HyFIR lowres test		99.11 99	98.92 107	99.65 73	99.90 4	99.37 100	99.02 316	99.91 3	97.67 194	99.59 109	99.75 138	95.90 173	99.73 205	99.53 32	99.02 182	99.86 33

DeepMVS_CX				93.34 365	99.29 237	82.27 393		99.22 292	85.15 390	96.33 364	99.05 331	90.97 327	99.73 205	93.57 358	97.77 247	98.01 363

Patchmatch-test		97.93 228	97.65 240	98.77 234	99.18 262	97.07 284	99.03 313	99.14 304	96.16 323	98.74 275	99.57 220	94.56 231	99.72 209	93.36 360	99.11 171	99.52 167

LPG-MVS_test		98.22 186	98.13 185	98.49 258	99.33 225	97.05 286	99.58 109	99.55 77	97.46 215	99.24 190	99.83 68	92.58 291	99.72 209	98.09 195	97.51 263	98.68 281

LGP-MVS_train				98.49 258	99.33 225	97.05 286		99.55 77	97.46 215	99.24 190	99.83 68	92.58 291	99.72 209	98.09 195	97.51 263	98.68 281

BH-w/o		98.00 220	97.89 216	98.32 282	99.35 219	96.20 325	99.01 321	98.90 335	96.42 306	98.38 312	99.00 337	95.26 197	99.72 209	96.06 316	98.61 204	99.03 233

ACMP	97.20 11	98.06 205	97.94 209	98.45 267	99.37 215	97.01 291	99.44 194	99.49 143	97.54 208	98.45 309	99.79 115	91.95 306	99.72 209	97.91 210	97.49 268	98.62 311
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 215	97.90 212	98.40 275	99.23 250	96.80 303	99.70 52	99.60 54	97.12 249	98.18 324	99.70 158	91.73 312	99.72 209	98.39 172	97.45 271	98.68 281
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
test_post1							99.23 274				65.14 404	94.18 248	99.71 215	97.58 243

ADS-MVSNet		98.20 189	98.08 192	98.56 252	99.33 225	96.48 315	99.23 274	99.15 302	96.24 316	99.10 220	99.67 180	94.11 249	99.71 215	96.81 297	99.05 178	99.48 178

JIA-IIPM		97.50 287	97.02 300	98.93 198	98.73 338	97.80 254	99.30 246	98.97 322	91.73 378	98.91 252	94.86 392	95.10 201	99.71 215	97.58 243	97.98 240	99.28 210

EPMVS		97.82 248	97.65 240	98.35 279	98.88 316	95.98 328	99.49 174	94.71 401	97.57 202	99.26 188	99.48 252	92.46 298	99.71 215	97.87 214	99.08 176	99.35 202

TDRefinement		95.42 334	94.57 341	97.97 308	89.83 402	96.11 327	99.48 178	98.75 350	96.74 278	96.68 361	99.88 36	88.65 352	99.71 215	98.37 175	82.74 391	98.09 358

ACMM	97.58 5	98.37 177	98.34 170	98.48 260	99.41 203	97.10 280	99.56 122	99.45 193	98.53 90	99.04 233	99.85 54	93.00 275	99.71 215	98.74 127	97.45 271	98.64 300
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 225	97.77 226	98.57 249	99.59 148	96.61 311	99.45 188	99.08 310	98.21 124	98.88 257	99.80 103	88.66 351	99.70 221	98.58 152	97.72 248	99.39 198

CHOSEN 280x420		99.12 95	99.13 73	99.08 175	99.66 120	97.89 249	98.43 376	99.71 13	98.88 59	99.62 100	99.76 135	96.63 145	99.70 221	99.46 44	99.99 1	99.66 125

EC-MVSNet		99.44 37	99.39 27	99.58 90	99.56 156	99.49 87	99.88 4	99.58 61	98.38 102	99.73 62	99.69 168	98.20 95	99.70 221	99.64 24	99.82 90	99.54 161

PatchmatchNet		98.31 180	98.36 168	98.19 292	99.16 271	95.32 344	99.27 260	98.92 329	97.37 227	99.37 160	99.58 216	94.90 207	99.70 221	97.43 261	99.21 162	99.54 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 200	97.99 202	98.44 270	99.41 203	96.96 297	99.60 95	99.56 69	98.09 143	98.15 325	99.91 20	90.87 328	99.70 221	98.88 102	97.45 271	98.67 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 287	96.90 303	99.29 151	99.23 250	98.78 186	99.32 241	98.90 335	97.52 211	98.56 302	98.09 376	84.72 376	99.69 226	97.86 215	97.88 243	99.39 198

HQP_MVS		98.27 185	98.22 178	98.44 270	99.29 237	96.97 295	99.39 219	99.47 173	98.97 51	99.11 217	99.61 207	92.71 286	99.69 226	97.78 223	97.63 251	98.67 288

plane_prior5								99.47 173					99.69 226	97.78 223	97.63 251	98.67 288

D2MVS		98.41 172	98.50 161	98.15 297	99.26 244	96.62 310	99.40 215	99.61 48	97.71 187	98.98 242	99.36 283	96.04 164	99.67 229	98.70 132	97.41 276	98.15 356

IS-MVSNet		99.05 108	98.87 115	99.57 92	99.73 87	99.32 104	99.75 41	99.20 296	98.02 157	99.56 114	99.86 49	96.54 149	99.67 229	98.09 195	99.13 170	99.73 97

CLD-MVS		98.16 194	98.10 188	98.33 280	99.29 237	96.82 302	98.75 355	99.44 201	97.83 173	99.13 213	99.55 226	92.92 277	99.67 229	98.32 181	97.69 249	98.48 332
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 300	97.30 286	97.09 342	99.43 198	93.31 373	99.73 47	98.87 340	98.83 64	99.28 180	99.80 103	84.45 377	99.66 232	97.88 212	97.45 271	98.30 348

AUN-MVS		96.88 310	96.31 316	98.59 245	99.48 189	97.04 289	99.27 260	99.22 292	97.44 220	98.51 305	99.41 269	91.97 305	99.66 232	97.71 234	83.83 389	99.07 230

UniMVSNet_ETH3D		97.32 297	96.81 305	98.87 215	99.40 208	97.46 266	99.51 156	99.53 96	95.86 336	98.54 304	99.77 129	82.44 385	99.66 232	98.68 137	97.52 261	99.50 176

OPM-MVS		98.19 190	98.10 188	98.45 267	98.88 316	97.07 284	99.28 255	99.38 231	98.57 86	99.22 195	99.81 90	92.12 302	99.66 232	98.08 199	97.54 260	98.61 320
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 231	97.78 224	98.32 282	99.46 191	96.68 308	99.56 122	99.54 85	98.41 100	97.79 341	99.87 44	90.18 337	99.66 232	98.05 203	97.18 287	98.62 311

hse-mvs2		97.50 287	97.14 295	98.59 245	99.49 181	97.05 286	99.28 255	99.22 292	98.94 54	99.66 83	99.42 265	94.93 204	99.65 237	99.48 41	83.80 390	99.08 225

VPA-MVSNet		98.29 183	97.95 207	99.30 148	99.16 271	99.54 79	99.50 163	99.58 61	98.27 115	99.35 167	99.37 280	92.53 293	99.65 237	99.35 51	94.46 342	98.72 265

TR-MVS		97.76 255	97.41 272	98.82 226	99.06 292	97.87 250	98.87 344	98.56 365	96.63 289	98.68 286	99.22 313	92.49 294	99.65 237	95.40 334	97.79 246	98.95 244

gm-plane-assit					98.54 357	92.96 375			94.65 357		99.15 321		99.64 240	97.56 248

HQP4-MVS										98.66 287			99.64 240			98.64 300

HQP-MVS		98.02 215	97.90 212	98.37 278	99.19 259	96.83 300	98.98 327	99.39 223	98.24 118	98.66 287	99.40 272	92.47 295	99.64 240	97.19 276	97.58 256	98.64 300

PAPM		97.59 280	97.09 298	99.07 177	99.06 292	98.26 227	98.30 383	99.10 307	94.88 351	98.08 327	99.34 290	96.27 159	99.64 240	89.87 380	98.92 188	99.31 208

TAPA-MVS	97.07 15	97.74 261	97.34 281	98.94 196	99.70 101	97.53 264	99.25 271	99.51 115	91.90 377	99.30 176	99.63 198	98.78 48	99.64 240	88.09 387	99.87 54	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 176	98.09 191	99.24 160	99.26 244	99.32 104	99.56 122	99.55 77	97.45 218	98.71 278	99.83 68	93.23 270	99.63 245	98.88 102	96.32 302	98.76 257

ITE_SJBPF				98.08 299	99.29 237	96.37 318		98.92 329	98.34 108	98.83 265	99.75 138	91.09 325	99.62 246	95.82 321	97.40 277	98.25 352

LF4IMVS		97.52 284	97.46 260	97.70 325	98.98 306	95.55 336	99.29 251	98.82 345	98.07 148	98.66 287	99.64 192	89.97 338	99.61 247	97.01 284	96.68 292	97.94 369

tpm		97.67 274	97.55 248	98.03 301	99.02 298	95.01 350	99.43 198	98.54 367	96.44 304	99.12 215	99.34 290	91.83 309	99.60 248	97.75 229	96.46 298	99.48 178

tpm2		97.44 293	97.34 281	97.74 323	99.15 275	94.36 361	99.45 188	98.94 325	93.45 370	98.90 254	99.44 261	91.35 322	99.59 249	97.31 267	98.07 239	99.29 209

baseline2		97.87 237	97.55 248	98.82 226	99.18 262	98.02 239	99.41 207	96.58 395	96.97 264	96.51 362	99.17 318	93.43 267	99.57 250	97.71 234	99.03 180	98.86 246

MS-PatchMatch		97.24 302	97.32 284	96.99 343	98.45 360	93.51 372	98.82 348	99.32 267	97.41 224	98.13 326	99.30 300	88.99 346	99.56 251	95.68 327	99.80 97	97.90 372

TinyColmap		97.12 305	96.89 304	97.83 317	99.07 289	95.52 339	98.57 369	98.74 353	97.58 201	97.81 340	99.79 115	88.16 358	99.56 251	95.10 339	97.21 285	98.39 344

USDC		97.34 296	97.20 293	97.75 322	99.07 289	95.20 346	98.51 373	99.04 316	97.99 158	98.31 316	99.86 49	89.02 345	99.55 253	95.67 328	97.36 280	98.49 331

MSLP-MVS++		99.46 31	99.47 17	99.44 127	99.60 146	99.16 125	99.41 207	99.71 13	98.98 48	99.45 134	99.78 121	99.19 9	99.54 254	99.28 63	99.84 77	99.63 140

TAMVS		99.12 95	99.08 80	99.24 160	99.46 191	98.55 203	99.51 156	99.46 182	98.09 143	99.45 134	99.82 76	98.34 89	99.51 255	98.70 132	98.93 186	99.67 122

EPNet_dtu		98.03 213	97.96 205	98.23 290	98.27 363	95.54 338	99.23 274	98.75 350	99.02 38	97.82 339	99.71 154	96.11 162	99.48 256	93.04 364	99.65 130	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		95.97 327	95.69 329	96.81 350	97.78 370	92.79 376	99.16 284	98.93 326	96.16 323	94.08 379	99.22 313	82.72 383	99.47 257	95.67 328	97.50 265	98.17 355

MVP-Stereo		97.81 250	97.75 231	97.99 307	97.53 374	96.60 312	98.96 331	98.85 342	97.22 241	97.23 352	99.36 283	95.28 194	99.46 258	95.51 330	99.78 104	97.92 371
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 162	98.67 136	98.30 284	99.35 219	95.59 335	99.50 163	99.55 77	98.60 85	99.39 155	99.83 68	94.48 236	99.45 259	98.75 126	98.56 210	99.85 36

test-LLR		98.06 205	97.90 212	98.55 254	98.79 328	97.10 280	98.67 361	97.75 381	97.34 229	98.61 298	98.85 350	94.45 238	99.45 259	97.25 270	99.38 149	99.10 220

TESTMET0.1,1		97.55 282	97.27 292	98.40 275	98.93 311	96.53 313	98.67 361	97.61 384	96.96 265	98.64 294	99.28 304	88.63 353	99.45 259	97.30 268	99.38 149	99.21 215

test-mter		97.49 291	97.13 297	98.55 254	98.79 328	97.10 280	98.67 361	97.75 381	96.65 285	98.61 298	98.85 350	88.23 357	99.45 259	97.25 270	99.38 149	99.10 220

mvs_anonymous		99.03 111	98.99 97	99.16 168	99.38 212	98.52 209	99.51 156	99.38 231	97.79 178	99.38 158	99.81 90	97.30 122	99.45 259	99.35 51	98.99 183	99.51 173

tfpnnormal		97.84 243	97.47 258	98.98 190	99.20 257	99.22 119	99.64 78	99.61 48	96.32 310	98.27 319	99.70 158	93.35 269	99.44 264	95.69 326	95.40 325	98.27 350

v7n		97.87 237	97.52 252	98.92 200	98.76 336	98.58 201	99.84 13	99.46 182	96.20 319	98.91 252	99.70 158	94.89 208	99.44 264	96.03 317	93.89 353	98.75 259

jajsoiax		98.43 169	98.28 175	98.88 211	98.60 353	98.43 220	99.82 17	99.53 96	98.19 127	98.63 295	99.80 103	93.22 272	99.44 264	99.22 69	97.50 265	98.77 255

mvs_tets		98.40 175	98.23 177	98.91 204	98.67 346	98.51 211	99.66 69	99.53 96	98.19 127	98.65 293	99.81 90	92.75 281	99.44 264	99.31 58	97.48 269	98.77 255

Vis-MVSNet (Re-imp)		98.87 125	98.72 130	99.31 143	99.71 96	98.88 171	99.80 25	99.44 201	97.91 164	99.36 164	99.78 121	95.49 188	99.43 268	97.91 210	99.11 171	99.62 142

OPU-MVS				99.64 78	99.56 156	99.72 42	99.60 95				99.70 158	99.27 5	99.42 269	98.24 185	99.80 97	99.79 74

Anonymous20231211		97.88 235	97.54 251	98.90 206	99.71 96	98.53 205	99.48 178	99.57 64	94.16 361	98.81 267	99.68 174	93.23 270	99.42 269	98.84 115	94.42 344	98.76 257

VPNet		97.84 243	97.44 266	99.01 184	99.21 255	98.94 165	99.48 178	99.57 64	98.38 102	99.28 180	99.73 149	88.89 347	99.39 271	99.19 71	93.27 360	98.71 267

iter_conf_final		98.71 150	98.61 153	98.99 188	99.49 181	98.96 157	99.63 82	99.41 212	98.19 127	99.39 155	99.77 129	94.82 210	99.38 272	99.30 61	97.52 261	98.64 300

nrg030		98.64 159	98.42 165	99.28 155	99.05 295	99.69 47	99.81 20	99.46 182	98.04 154	99.01 236	99.82 76	96.69 144	99.38 272	99.34 55	94.59 341	98.78 252

iter_conf05		98.55 163	98.44 163	98.87 215	99.34 223	98.60 200	99.55 134	99.42 209	98.21 124	99.37 160	99.77 129	93.55 266	99.38 272	99.30 61	97.48 269	98.63 308

GA-MVS		97.85 240	97.47 258	99.00 186	99.38 212	97.99 241	98.57 369	99.15 302	97.04 260	98.90 254	99.30 300	89.83 339	99.38 272	96.70 302	98.33 220	99.62 142

UniMVSNet (Re)		98.29 183	98.00 201	99.13 173	99.00 300	99.36 102	99.49 174	99.51 115	97.95 160	98.97 244	99.13 323	96.30 158	99.38 272	98.36 177	93.34 358	98.66 296

FIs		98.78 143	98.63 142	99.23 162	99.18 262	99.54 79	99.83 16	99.59 57	98.28 113	98.79 271	99.81 90	96.75 142	99.37 277	99.08 82	96.38 300	98.78 252

PS-MVSNAJss		98.92 121	98.92 107	98.90 206	98.78 331	98.53 205	99.78 32	99.54 85	98.07 148	99.00 240	99.76 135	99.01 18	99.37 277	99.13 76	97.23 284	98.81 249

CDS-MVSNet		99.09 104	99.03 87	99.25 158	99.42 200	98.73 188	99.45 188	99.46 182	98.11 140	99.46 133	99.77 129	98.01 103	99.37 277	98.70 132	98.92 188	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 331	95.16 336	97.51 331	99.30 233	93.69 369	98.88 342	95.78 396	85.09 391	98.78 272	92.65 394	91.29 323	99.37 277	94.85 343	99.85 69	99.46 186

v1192		97.81 250	97.44 266	98.91 204	98.88 316	98.68 191	99.51 156	99.34 250	96.18 321	99.20 201	99.34 290	94.03 252	99.36 281	95.32 336	95.18 329	98.69 276

EI-MVSNet		98.67 156	98.67 136	98.68 240	99.35 219	97.97 242	99.50 163	99.38 231	96.93 270	99.20 201	99.83 68	97.87 105	99.36 281	98.38 173	97.56 258	98.71 267

MVSTER		98.49 164	98.32 172	99.00 186	99.35 219	99.02 146	99.54 139	99.38 231	97.41 224	99.20 201	99.73 149	93.86 259	99.36 281	98.87 105	97.56 258	98.62 311

gg-mvs-nofinetune		96.17 324	95.32 335	98.73 236	98.79 328	98.14 233	99.38 224	94.09 402	91.07 382	98.07 330	91.04 398	89.62 343	99.35 284	96.75 299	99.09 175	98.68 281

pm-mvs1		97.68 271	97.28 289	98.88 211	99.06 292	98.62 197	99.50 163	99.45 193	96.32 310	97.87 337	99.79 115	92.47 295	99.35 284	97.54 250	93.54 357	98.67 288

OurMVSNet-221017-0		97.88 235	97.77 226	98.19 292	98.71 342	96.53 313	99.88 4	99.00 319	97.79 178	98.78 272	99.94 6	91.68 313	99.35 284	97.21 272	96.99 291	98.69 276

EGC-MVSNET		82.80 363	77.86 369	97.62 327	97.91 367	96.12 326	99.33 240	99.28 281	8.40 406	25.05 407	99.27 307	84.11 378	99.33 287	89.20 382	98.22 228	97.42 380

pmmvs6		96.53 316	96.09 321	97.82 319	98.69 344	95.47 340	99.37 226	99.47 173	93.46 369	97.41 346	99.78 121	87.06 366	99.33 287	96.92 294	92.70 367	98.65 298

mvsmamba		98.92 121	98.87 115	99.08 175	99.07 289	99.16 125	99.88 4	99.51 115	98.15 133	99.40 152	99.89 30	97.12 127	99.33 287	99.38 48	97.40 277	98.73 264

V42		98.06 205	97.79 221	98.86 219	98.98 306	98.84 177	99.69 55	99.34 250	96.53 296	99.30 176	99.37 280	94.67 226	99.32 290	97.57 247	94.66 339	98.42 340

lessismore_v0				97.79 321	98.69 344	95.44 342		94.75 400		95.71 370	99.87 44	88.69 350	99.32 290	95.89 320	94.93 336	98.62 311

OpenMVS_ROB	92.34 20	94.38 345	93.70 351	96.41 355	97.38 376	93.17 374	99.06 306	98.75 350	86.58 389	94.84 377	98.26 369	81.53 387	99.32 290	89.01 383	97.87 244	96.76 383

bld_raw_dy_0_64		98.69 153	98.58 155	98.99 188	98.88 316	98.96 157	99.80 25	99.41 212	97.91 164	99.32 172	99.87 44	95.70 181	99.31 293	99.09 80	97.27 282	98.71 267

v8		97.95 227	97.63 244	98.93 198	98.95 310	98.81 183	99.80 25	99.41 212	96.03 333	99.10 220	99.42 265	94.92 206	99.30 294	96.94 291	94.08 350	98.66 296

v1921920		97.80 252	97.45 261	98.84 223	98.80 327	98.53 205	99.52 148	99.34 250	96.15 325	99.24 190	99.47 255	93.98 254	99.29 295	95.40 334	95.13 331	98.69 276

anonymousdsp		98.44 168	98.28 175	98.94 196	98.50 358	98.96 157	99.77 34	99.50 135	97.07 255	98.87 260	99.77 129	94.76 219	99.28 296	98.66 139	97.60 254	98.57 326

MVSFormer		99.17 81	99.12 74	99.29 151	99.51 170	98.94 165	99.88 4	99.46 182	97.55 205	99.80 40	99.65 186	97.39 116	99.28 296	99.03 85	99.85 69	99.65 129

test_djsdf		98.67 156	98.57 156	98.98 190	98.70 343	98.91 169	99.88 4	99.46 182	97.55 205	99.22 195	99.88 36	95.73 179	99.28 296	99.03 85	97.62 253	98.75 259

cascas		97.69 269	97.43 270	98.48 260	98.60 353	97.30 269	98.18 387	99.39 223	92.96 373	98.41 310	98.78 355	93.77 262	99.27 299	98.16 192	98.61 204	98.86 246

v144192		97.92 231	97.60 246	98.87 215	98.83 326	98.65 194	99.55 134	99.34 250	96.20 319	99.32 172	99.40 272	94.36 240	99.26 300	96.37 313	95.03 333	98.70 272

dmvs_re		98.08 203	98.16 180	97.85 314	99.55 160	94.67 356	99.70 52	98.92 329	98.15 133	99.06 230	99.35 286	93.67 265	99.25 301	97.77 226	97.25 283	99.64 136

RRT_MVS		98.70 151	98.66 139	98.83 225	98.90 313	98.45 218	99.89 2	99.28 281	97.76 181	98.94 248	99.92 14	96.98 134	99.25 301	99.28 63	97.00 290	98.80 250

v2v482		98.06 205	97.77 226	98.92 200	98.90 313	98.82 181	99.57 116	99.36 240	96.65 285	99.19 204	99.35 286	94.20 245	99.25 301	97.72 233	94.97 334	98.69 276

v1240		97.69 269	97.32 284	98.79 232	98.85 324	98.43 220	99.48 178	99.36 240	96.11 328	99.27 184	99.36 283	93.76 263	99.24 304	94.46 347	95.23 328	98.70 272

v1144		97.98 222	97.69 236	98.85 222	98.87 320	98.66 193	99.54 139	99.35 246	96.27 314	99.23 194	99.35 286	94.67 226	99.23 305	96.73 300	95.16 330	98.68 281

v10		97.85 240	97.52 252	98.86 219	98.99 303	98.67 192	99.75 41	99.41 212	95.70 337	98.98 242	99.41 269	94.75 220	99.23 305	96.01 319	94.63 340	98.67 288

WR-MVS_H		98.13 197	97.87 217	98.90 206	99.02 298	98.84 177	99.70 52	99.59 57	97.27 235	98.40 311	99.19 317	95.53 186	99.23 305	98.34 178	93.78 355	98.61 320

miper_enhance_ethall		98.16 194	98.08 192	98.41 273	98.96 309	97.72 257	98.45 375	99.32 267	96.95 267	98.97 244	99.17 318	97.06 131	99.22 308	97.86 215	95.99 309	98.29 349

GG-mvs-BLEND				98.45 267	98.55 356	98.16 231	99.43 198	93.68 403		97.23 352	98.46 363	89.30 344	99.22 308	95.43 333	98.22 228	97.98 367

FC-MVSNet-test		98.75 146	98.62 147	99.15 172	99.08 288	99.45 93	99.86 12	99.60 54	98.23 121	98.70 284	99.82 76	96.80 139	99.22 308	99.07 83	96.38 300	98.79 251

UniMVSNet_NR-MVSNet		98.22 186	97.97 204	98.96 193	98.92 312	98.98 150	99.48 178	99.53 96	97.76 181	98.71 278	99.46 259	96.43 155	99.22 308	98.57 155	92.87 365	98.69 276

DU-MVS		98.08 203	97.79 221	98.96 193	98.87 320	98.98 150	99.41 207	99.45 193	97.87 166	98.71 278	99.50 244	94.82 210	99.22 308	98.57 155	92.87 365	98.68 281

cl____		98.01 218	97.84 219	98.55 254	99.25 248	97.97 242	98.71 359	99.34 250	96.47 303	98.59 301	99.54 231	95.65 183	99.21 313	97.21 272	95.77 315	98.46 337

WR-MVS		98.06 205	97.73 233	99.06 178	98.86 323	99.25 116	99.19 281	99.35 246	97.30 233	98.66 287	99.43 263	93.94 255	99.21 313	98.58 152	94.28 346	98.71 267

test_0402		96.64 314	96.24 317	97.85 314	98.85 324	96.43 317	99.44 194	99.26 285	93.52 367	96.98 359	99.52 238	88.52 354	99.20 315	92.58 371	97.50 265	97.93 370

SixPastTwentyTwo		97.50 287	97.33 283	98.03 301	98.65 347	96.23 324	99.77 34	98.68 361	97.14 246	97.90 335	99.93 9	90.45 331	99.18 316	97.00 285	96.43 299	98.67 288

cl22		97.85 240	97.64 243	98.48 260	99.09 285	97.87 250	98.60 368	99.33 257	97.11 252	98.87 260	99.22 313	92.38 300	99.17 317	98.21 186	95.99 309	98.42 340

WB-MVSnew		97.65 276	97.65 240	97.63 326	98.78 331	97.62 262	99.13 290	98.33 370	97.36 228	99.07 225	98.94 344	95.64 184	99.15 318	92.95 365	98.68 203	96.12 390

IterMVS-SCA-FT		97.82 248	97.75 231	98.06 300	99.57 152	96.36 319	99.02 316	99.49 143	97.18 243	98.71 278	99.72 153	92.72 284	99.14 319	97.44 260	95.86 314	98.67 288

pmmvs5		97.52 284	97.30 286	98.16 294	98.57 355	96.73 304	99.27 260	98.90 335	96.14 326	98.37 313	99.53 235	91.54 319	99.14 319	97.51 252	95.87 313	98.63 308

v148		97.79 253	97.55 248	98.50 257	98.74 337	97.72 257	99.54 139	99.33 257	96.26 315	98.90 254	99.51 241	94.68 225	99.14 319	97.83 219	93.15 362	98.63 308

miper_ehance_all_eth		98.18 192	98.10 188	98.41 273	99.23 250	97.72 257	98.72 358	99.31 271	96.60 292	98.88 257	99.29 302	97.29 123	99.13 322	97.60 241	95.99 309	98.38 345

NR-MVSNet		97.97 225	97.61 245	99.02 183	98.87 320	99.26 115	99.47 184	99.42 209	97.63 197	97.08 357	99.50 244	95.07 202	99.13 322	97.86 215	93.59 356	98.68 281

IterMVS		97.83 245	97.77 226	98.02 303	99.58 150	96.27 322	99.02 316	99.48 155	97.22 241	98.71 278	99.70 158	92.75 281	99.13 322	97.46 258	96.00 308	98.67 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 346	94.90 338	91.84 369	97.24 380	80.01 399	98.52 372	99.48 155	89.01 386	91.99 387	99.67 180	85.67 370	99.13 322	95.44 332	97.03 289	96.39 387
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 210	97.96 205	98.33 280	99.26 244	97.38 268	98.56 371	99.31 271	96.65 285	98.88 257	99.52 238	96.58 147	99.12 326	97.39 263	95.53 323	98.47 334

pmmvs4		98.13 197	97.90 212	98.81 229	98.61 352	98.87 172	98.99 324	99.21 295	96.44 304	99.06 230	99.58 216	95.90 173	99.11 327	97.18 278	96.11 306	98.46 337

TransMVSNet (Re)		97.15 304	96.58 309	98.86 219	99.12 277	98.85 176	99.49 174	98.91 333	95.48 340	97.16 355	99.80 103	93.38 268	99.11 327	94.16 353	91.73 370	98.62 311

ambc				93.06 367	92.68 398	82.36 392	98.47 374	98.73 358		95.09 375	97.41 381	55.55 399	99.10 329	96.42 311	91.32 371	97.71 373

Baseline_NR-MVSNet		97.76 255	97.45 261	98.68 240	99.09 285	98.29 225	99.41 207	98.85 342	95.65 338	98.63 295	99.67 180	94.82 210	99.10 329	98.07 202	92.89 364	98.64 300

test_vis3_rt		87.04 359	85.81 362	90.73 373	93.99 397	81.96 394	99.76 37	90.23 408	92.81 374	81.35 396	91.56 396	40.06 405	99.07 331	94.27 350	88.23 383	91.15 396

CP-MVSNet		98.09 201	97.78 224	99.01 184	98.97 308	99.24 117	99.67 64	99.46 182	97.25 237	98.48 308	99.64 192	93.79 261	99.06 332	98.63 142	94.10 349	98.74 262

PS-CasMVS		97.93 228	97.59 247	98.95 195	98.99 303	99.06 142	99.68 61	99.52 101	97.13 247	98.31 316	99.68 174	92.44 299	99.05 333	98.51 163	94.08 350	98.75 259

K. test v3		97.10 306	96.79 306	98.01 304	98.72 340	96.33 320	99.87 9	97.05 388	97.59 199	96.16 366	99.80 103	88.71 349	99.04 334	96.69 303	96.55 297	98.65 298

new_pmnet		96.38 320	96.03 322	97.41 333	98.13 366	95.16 349	99.05 308	99.20 296	93.94 362	97.39 349	98.79 354	91.61 318	99.04 334	90.43 378	95.77 315	98.05 361

DIV-MVS_self_test		98.01 218	97.85 218	98.48 260	99.24 249	97.95 246	98.71 359	99.35 246	96.50 297	98.60 300	99.54 231	95.72 180	99.03 336	97.21 272	95.77 315	98.46 337

IterMVS-LS		98.46 167	98.42 165	98.58 248	99.59 148	98.00 240	99.37 226	99.43 207	96.94 269	99.07 225	99.59 212	97.87 105	99.03 336	98.32 181	95.62 320	98.71 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 276	97.68 237	97.55 330	98.62 350	94.97 351	98.84 346	99.30 275	96.83 276	98.19 323	99.34 290	97.01 133	99.02 338	95.00 342	96.01 307	98.64 300

Patchmtry		97.75 259	97.40 273	98.81 229	99.10 282	98.87 172	99.11 299	99.33 257	94.83 353	98.81 267	99.38 277	94.33 241	99.02 338	96.10 315	95.57 321	98.53 328

N_pmnet		94.95 340	95.83 327	92.31 368	98.47 359	79.33 400	99.12 293	92.81 406	93.87 363	97.68 342	99.13 323	93.87 258	99.01 340	91.38 375	96.19 304	98.59 324

CR-MVSNet		98.17 193	97.93 210	98.87 215	99.18 262	98.49 213	99.22 278	99.33 257	96.96 265	99.56 114	99.38 277	94.33 241	99.00 341	94.83 344	98.58 207	99.14 217

c3_l		98.12 199	98.04 197	98.38 277	99.30 233	97.69 261	98.81 349	99.33 257	96.67 283	98.83 265	99.34 290	97.11 128	98.99 342	97.58 243	95.34 326	98.48 332

test0.0.03 1		97.71 267	97.42 271	98.56 252	98.41 362	97.82 253	98.78 352	98.63 363	97.34 229	98.05 331	98.98 340	94.45 238	98.98 343	95.04 341	97.15 288	98.89 245

PatchT		97.03 308	96.44 313	98.79 232	98.99 303	98.34 224	99.16 284	99.07 313	92.13 376	99.52 123	97.31 385	94.54 234	98.98 343	88.54 385	98.73 201	99.03 233

GBi-Net		97.68 271	97.48 256	98.29 285	99.51 170	97.26 273	99.43 198	99.48 155	96.49 298	99.07 225	99.32 297	90.26 333	98.98 343	97.10 280	96.65 293	98.62 311

test1		97.68 271	97.48 256	98.29 285	99.51 170	97.26 273	99.43 198	99.48 155	96.49 298	99.07 225	99.32 297	90.26 333	98.98 343	97.10 280	96.65 293	98.62 311

FMVSNet3		98.03 213	97.76 230	98.84 223	99.39 211	98.98 150	99.40 215	99.38 231	96.67 283	99.07 225	99.28 304	92.93 276	98.98 343	97.10 280	96.65 293	98.56 327

FMVSNet2		97.72 264	97.36 276	98.80 231	99.51 170	98.84 177	99.45 188	99.42 209	96.49 298	98.86 264	99.29 302	90.26 333	98.98 343	96.44 310	96.56 296	98.58 325

FMVSNet1		96.84 311	96.36 315	98.29 285	99.32 231	97.26 273	99.43 198	99.48 155	95.11 345	98.55 303	99.32 297	83.95 379	98.98 343	95.81 322	96.26 303	98.62 311

ppachtmachnet_test		97.49 291	97.45 261	97.61 328	98.62 350	95.24 345	98.80 350	99.46 182	96.11 328	98.22 322	99.62 203	96.45 153	98.97 350	93.77 355	95.97 312	98.61 320

TranMVSNet+NR-MVSNet		97.93 228	97.66 239	98.76 235	98.78 331	98.62 197	99.65 75	99.49 143	97.76 181	98.49 307	99.60 210	94.23 244	98.97 350	98.00 205	92.90 363	98.70 272

test_method		91.10 355	91.36 357	90.31 374	95.85 387	73.72 407	94.89 395	99.25 287	68.39 398	95.82 369	99.02 335	80.50 388	98.95 352	93.64 357	94.89 338	98.25 352

ADS-MVSNet2		98.02 215	98.07 195	97.87 313	99.33 225	95.19 347	99.23 274	99.08 310	96.24 316	99.10 220	99.67 180	94.11 249	98.93 353	96.81 297	99.05 178	99.48 178

ET-MVSNet_ETH3D		96.49 317	95.64 331	99.05 180	99.53 163	98.82 181	98.84 346	97.51 386	97.63 197	84.77 392	99.21 316	92.09 303	98.91 354	98.98 90	92.21 369	99.41 195

miper_lstm_enhance		98.00 220	97.91 211	98.28 288	99.34 223	97.43 267	98.88 342	99.36 240	96.48 301	98.80 269	99.55 226	95.98 166	98.91 354	97.27 269	95.50 324	98.51 330

PEN-MVS		97.76 255	97.44 266	98.72 237	98.77 335	98.54 204	99.78 32	99.51 115	97.06 257	98.29 318	99.64 192	92.63 290	98.89 356	98.09 195	93.16 361	98.72 265

testing3		97.28 298	96.76 307	98.82 226	99.37 215	98.07 237	99.45 188	99.36 240	97.56 204	97.89 336	98.95 343	83.70 380	98.82 357	96.03 317	98.56 210	99.58 154

testgi		97.65 276	97.50 255	98.13 298	99.36 218	96.45 316	99.42 205	99.48 155	97.76 181	97.87 337	99.45 260	91.09 325	98.81 358	94.53 346	98.52 213	99.13 219

testf1		90.42 357	90.68 359	89.65 377	97.78 370	73.97 405	99.13 290	98.81 346	89.62 384	91.80 388	98.93 345	62.23 397	98.80 359	86.61 393	91.17 372	96.19 388

APD_test2		90.42 357	90.68 359	89.65 377	97.78 370	73.97 405	99.13 290	98.81 346	89.62 384	91.80 388	98.93 345	62.23 397	98.80 359	86.61 393	91.17 372	96.19 388

MIMVSNet		97.73 262	97.45 261	98.57 249	99.45 196	97.50 265	99.02 316	98.98 321	96.11 328	99.41 147	99.14 322	90.28 332	98.74 361	95.74 324	98.93 186	99.47 184

LCM-MVSNet-Re		97.83 245	98.15 182	96.87 349	99.30 233	92.25 379	99.59 101	98.26 371	97.43 221	96.20 365	99.13 323	96.27 159	98.73 362	98.17 191	98.99 183	99.64 136

Syy-MVS		97.09 307	97.14 295	96.95 346	99.00 300	92.73 377	99.29 251	99.39 223	97.06 257	97.41 346	98.15 371	93.92 257	98.68 363	91.71 373	98.34 218	99.45 189

myMVS_eth3d		96.89 309	96.37 314	98.43 272	99.00 300	97.16 277	99.29 251	99.39 223	97.06 257	97.41 346	98.15 371	83.46 381	98.68 363	95.27 337	98.34 218	99.45 189

DTE-MVSNet		97.51 286	97.19 294	98.46 266	98.63 349	98.13 234	99.84 13	99.48 155	96.68 282	97.97 334	99.67 180	92.92 277	98.56 365	96.88 296	92.60 368	98.70 272

PC_three_1452									98.18 131	99.84 29	99.70 158	99.31 3	98.52 366	98.30 183	99.80 97	99.81 61

mvsany_test3		93.77 348	93.45 352	94.74 361	95.78 388	88.01 387	99.64 78	98.25 372	98.28 113	94.31 378	97.97 378	68.89 393	98.51 367	97.50 253	90.37 377	97.71 373

UnsupCasMVSNet_bld		93.53 349	92.51 354	96.58 354	97.38 376	93.82 365	98.24 384	99.48 155	91.10 381	93.10 383	96.66 387	74.89 391	98.37 368	94.03 354	87.71 384	97.56 378

Anonymous20240521		96.20 323	95.89 326	97.13 340	97.72 373	94.96 352	99.79 31	99.29 279	93.01 372	97.20 354	99.03 333	89.69 341	98.36 369	91.16 376	96.13 305	98.07 359

test_f		91.90 354	91.26 358	93.84 363	95.52 392	85.92 389	99.69 55	98.53 368	95.31 342	93.87 380	96.37 389	55.33 400	98.27 370	95.70 325	90.98 375	97.32 381

MDA-MVSNet_test_wron		95.45 333	94.60 340	98.01 304	98.16 365	97.21 276	99.11 299	99.24 289	93.49 368	80.73 398	98.98 340	93.02 274	98.18 371	94.22 352	94.45 343	98.64 300

UnsupCasMVSNet_eth		96.44 318	96.12 319	97.40 334	98.65 347	95.65 333	99.36 230	99.51 115	97.13 247	96.04 368	98.99 338	88.40 355	98.17 372	96.71 301	90.27 378	98.40 343

KD-MVS_2432*1600		94.62 341	93.72 349	97.31 335	97.19 382	95.82 331	98.34 379	99.20 296	95.00 349	97.57 343	98.35 366	87.95 360	98.10 373	92.87 367	77.00 396	98.01 363

miper_refine_blended		94.62 341	93.72 349	97.31 335	97.19 382	95.82 331	98.34 379	99.20 296	95.00 349	97.57 343	98.35 366	87.95 360	98.10 373	92.87 367	77.00 396	98.01 363

YYNet1		95.36 335	94.51 342	97.92 310	97.89 368	97.10 280	99.10 301	99.23 290	93.26 371	80.77 397	99.04 332	92.81 280	98.02 375	94.30 348	94.18 348	98.64 300

EU-MVSNet		97.98 222	98.03 198	97.81 320	98.72 340	96.65 309	99.66 69	99.66 28	98.09 143	98.35 314	99.82 76	95.25 198	98.01 376	97.41 262	95.30 327	98.78 252

Gipuma		90.99 356	90.15 361	93.51 364	98.73 338	90.12 385	93.98 396	99.45 193	79.32 394	92.28 386	94.91 391	69.61 392	97.98 377	87.42 389	95.67 319	92.45 394
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 336	94.73 339	97.15 338	95.53 391	95.94 329	99.35 235	99.10 307	95.13 343	93.55 381	97.54 380	88.15 359	97.91 378	94.58 345	89.69 381	97.61 376

PM-MVS		92.96 351	92.23 355	95.14 360	95.61 389	89.98 386	99.37 226	98.21 374	94.80 354	95.04 376	97.69 379	65.06 394	97.90 379	94.30 348	89.98 380	97.54 379

MDA-MVSNet-bldmvs		94.96 339	93.98 346	97.92 310	98.24 364	97.27 271	99.15 287	99.33 257	93.80 364	80.09 399	99.03 333	88.31 356	97.86 380	93.49 359	94.36 345	98.62 311

Patchmatch-RL test		95.84 329	95.81 328	95.95 358	95.61 389	90.57 384	98.24 384	98.39 369	95.10 347	95.20 373	98.67 358	94.78 215	97.77 381	96.28 314	90.02 379	99.51 173

Anonymous20231206		96.22 321	96.03 322	96.79 351	97.31 379	94.14 363	99.63 82	99.08 310	96.17 322	97.04 358	99.06 330	93.94 255	97.76 382	86.96 391	95.06 332	98.47 334

SD-MVS		99.41 47	99.52 11	99.05 180	99.74 80	99.68 48	99.46 187	99.52 101	99.11 26	99.88 20	99.91 20	99.43 1	97.70 383	98.72 130	99.93 22	99.77 82
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
DSMNet-mixed		97.25 300	97.35 278	96.95 346	97.84 369	93.61 371	99.57 116	96.63 393	96.13 327	98.87 260	98.61 361	94.59 229	97.70 383	95.08 340	98.86 192	99.55 159

pmmvs3		94.09 347	93.25 353	96.60 353	94.76 396	94.49 358	98.92 338	98.18 376	89.66 383	96.48 363	98.06 377	86.28 367	97.33 385	89.68 381	87.20 385	97.97 368

KD-MVS_self_test		95.00 338	94.34 343	96.96 345	97.07 384	95.39 343	99.56 122	99.44 201	95.11 345	97.13 356	97.32 384	91.86 308	97.27 386	90.35 379	81.23 393	98.23 354

FMVSNet5		96.43 319	96.19 318	97.15 338	99.11 279	95.89 330	99.32 241	99.52 101	94.47 360	98.34 315	99.07 328	87.54 364	97.07 387	92.61 370	95.72 318	98.47 334

new-patchmatchnet		94.48 344	94.08 345	95.67 359	95.08 394	92.41 378	99.18 282	99.28 281	94.55 359	93.49 382	97.37 383	87.86 362	97.01 388	91.57 374	88.36 382	97.61 376

LCM-MVSNet		86.80 361	85.22 365	91.53 371	87.81 403	80.96 397	98.23 386	98.99 320	71.05 396	90.13 391	96.51 388	48.45 404	96.88 389	90.51 377	85.30 387	96.76 383

CL-MVSNet_self_test		94.49 343	93.97 347	96.08 357	96.16 386	93.67 370	98.33 381	99.38 231	95.13 343	97.33 350	98.15 371	92.69 288	96.57 390	88.67 384	79.87 394	97.99 366

MIMVSNet1		95.51 332	95.04 337	96.92 348	97.38 376	95.60 334	99.52 148	99.50 135	93.65 366	96.97 360	99.17 318	85.28 373	96.56 391	88.36 386	95.55 322	98.60 323

test20.03		96.12 325	95.96 324	96.63 352	97.44 375	95.45 341	99.51 156	99.38 231	96.55 295	96.16 366	99.25 310	93.76 263	96.17 392	87.35 390	94.22 347	98.27 350

tmp_tt		82.80 363	81.52 366	86.66 379	66.61 409	68.44 408	92.79 398	97.92 378	68.96 397	80.04 400	99.85 54	85.77 369	96.15 393	97.86 215	43.89 402	95.39 392

test_fmvs3		92.10 353	91.77 356	93.08 366	96.19 385	86.25 388	99.82 17	98.62 364	96.65 285	95.19 374	96.90 386	55.05 401	95.93 394	96.63 307	90.92 376	97.06 382

dmvs_testset		95.02 337	96.12 319	91.72 370	99.10 282	80.43 398	99.58 109	97.87 380	97.47 214	95.22 372	98.82 352	93.99 253	95.18 395	88.09 387	94.91 337	99.56 158

PMMVS2		86.87 360	85.37 364	91.35 372	90.21 401	83.80 391	98.89 341	97.45 387	83.13 393	91.67 390	95.03 390	48.49 403	94.70 396	85.86 395	77.62 395	95.54 391

PMVS	70.75 22	75.98 369	74.97 370	79.01 385	70.98 408	55.18 410	93.37 397	98.21 374	65.08 402	61.78 403	93.83 393	21.74 410	92.53 397	78.59 397	91.12 374	89.34 398
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 362	85.65 363	82.75 383	86.77 404	63.39 409	98.35 378	98.92 329	74.11 395	83.39 394	98.98 340	50.85 402	92.40 398	84.54 396	94.97 334	92.46 393

WB-MVS		93.10 350	94.10 344	90.12 375	95.51 393	81.88 395	99.73 47	99.27 284	95.05 348	93.09 384	98.91 349	94.70 224	91.89 399	76.62 398	94.02 352	96.58 385

SSC-MVS		92.73 352	93.73 348	89.72 376	95.02 395	81.38 396	99.76 37	99.23 290	94.87 352	92.80 385	98.93 345	94.71 223	91.37 400	74.49 400	93.80 354	96.42 386

MVE	76.82 21	76.91 368	74.31 372	84.70 380	85.38 406	76.05 404	96.88 394	93.17 404	67.39 399	71.28 401	89.01 400	21.66 411	87.69 401	71.74 401	72.29 398	90.35 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 365	79.88 367	82.81 382	90.75 400	76.38 403	97.69 391	95.76 397	66.44 400	83.52 393	92.25 395	62.54 396	87.16 402	68.53 402	61.40 399	84.89 400

EMVS		80.02 366	79.22 368	82.43 384	91.19 399	76.40 402	97.55 393	92.49 407	66.36 401	83.01 395	91.27 397	64.63 395	85.79 403	65.82 403	60.65 400	85.08 399

ANet_high		77.30 367	74.86 371	84.62 381	75.88 407	77.61 401	97.63 392	93.15 405	88.81 387	64.27 402	89.29 399	36.51 406	83.93 404	75.89 399	52.31 401	92.33 395

wuyk23d		40.18 370	41.29 375	36.84 386	86.18 405	49.12 411	79.73 399	22.81 411	27.64 403	25.46 406	28.45 406	21.98 409	48.89 405	55.80 404	23.56 405	12.51 403

test123		39.01 372	42.50 374	28.53 387	39.17 410	20.91 412	98.75 355	19.17 412	19.83 405	38.57 404	66.67 402	33.16 407	15.42 406	37.50 406	29.66 404	49.26 401

testmvs		39.17 371	43.78 373	25.37 388	36.04 411	16.84 413	98.36 377	26.56 410	20.06 404	38.51 405	67.32 401	29.64 408	15.30 407	37.59 405	39.90 403	43.98 402

test_blank		0.13 376	0.17 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	1.57 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uanet_test		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

DCPMVS		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

cdsmvs_eth3d_5k		24.64 373	32.85 376	0.00 389	0.00 412	0.00 414	0.00 400	99.51 115	0.00 407	0.00 408	99.56 223	96.58 147	0.00 408	0.00 407	0.00 406	0.00 404

pcd_1.5k_mvsjas		8.27 375	11.03 378	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	99.01 18	0.00 408	0.00 407	0.00 406	0.00 404

sosnet-low-res		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

sosnet		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uncertanet		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

Regformer		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

ab-mvs-re		8.30 374	11.06 377	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	99.58 216	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uanet		0.02 377	0.03 380	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.27 408	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

WAC-MVS						97.16 277								95.47 331

FOURS1					99.91 1	99.93 1	99.87 9	99.56 69	99.10 27	99.81 37

test_one_0601					99.81 46	99.88 8		99.49 143	98.97 51	99.65 89	99.81 90	99.09 14

eth-test2					0.00 412

eth-test					0.00 412

RE-MVS-def			99.34 36		99.76 65	99.82 22	99.63 82	99.52 101	98.38 102	99.76 56	99.82 76	98.75 55		98.61 146	99.81 93	99.77 82

IU-MVS					99.84 32	99.88 8		99.32 267	98.30 112	99.84 29				98.86 110	99.85 69	99.89 20

save fliter					99.76 65	99.59 70	99.14 289	99.40 220	99.00 43

test0726					99.85 26	99.89 4	99.62 88	99.50 135	99.10 27	99.86 27	99.82 76	98.94 29

GSMVS																99.52 167

test_part2					99.81 46	99.83 16				99.77 51

sam_mvs1												94.86 209				99.52 167

sam_mvs												94.72 222

MTGPA								99.47 173

MTMP							99.54 139	98.88 338

test9_res														97.49 254	99.72 118	99.75 88

agg_prior2														97.21 272	99.73 117	99.75 88

test_prior4						99.56 75	98.99 324

test_prior2							98.96 331		98.34 108	99.01 236	99.52 238	98.68 64		97.96 207	99.74 115

新几何2							99.01 321

旧先验1					99.74 80	99.59 70		99.54 85			99.69 168	98.47 80			99.68 126	99.73 97

原ACMM2							98.95 334

test222					99.75 73	99.49 87	98.91 340	99.49 143	96.42 306	99.34 170	99.65 186	98.28 92			99.69 123	99.72 103

segment_acmp												98.96 24

testdata1							98.85 345		98.32 111

plane_prior7					99.29 237	97.03 290

plane_prior6					99.27 242	96.98 294						92.71 286

plane_prior4											99.61 207

plane_prior3						97.00 292			98.69 79	99.11 217

plane_prior2							99.39 219		98.97 51

plane_prior1					99.26 244

plane_prior						96.97 295	99.21 280		98.45 96						97.60 254

n2								0.00 413

nn								0.00 413

door-mid								98.05 377

test11								99.35 246

door								97.92 378

HQP5-MVS						96.83 300

HQP-NCC					99.19 259		98.98 327		98.24 118	98.66 287

ACMP_Plane					99.19 259		98.98 327		98.24 118	98.66 287

BP-MVS														97.19 276

HQP3-MVS								99.39 223							97.58 256

HQP2-MVS												92.47 295

NP-MVS					99.23 250	96.92 298					99.40 272

MDTV_nov1_ep13_2view						95.18 348	99.35 235		96.84 274	99.58 110		95.19 200		97.82 220		99.46 186

ACMMP++_ref															97.19 286

ACMMP++															97.43 275

Test By Simon												98.75 55

High-resolution multi-view benchmark