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	99.38 219	99.37 104	99.58 111	99.62 41	99.41 9	99.87 27	99.92 14	98.81 44	100.00 1	99.97 1	99.93 27	99.94 11

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 52	99.84 32	99.44 98	99.58 111	99.69 18	99.43 7	99.98 6	99.91 21	98.62 70	100.00 1	99.97 1	99.95 18	99.90 16

test_vis1_n_1920		98.63 164	98.40 170	99.31 150	99.86 20	97.94 250	99.67 67	99.62 41	99.43 7	99.99 2	99.91 21	87.29 368	100.00 1	99.92 11	99.92 29	99.98 2

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 28	99.86 20	99.61 67	99.56 124	99.63 39	99.48 3	99.98 6	99.83 70	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	99.56 124	99.63 39	99.47 4	99.98 6	99.82 79	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	99.48 179	99.64 36	99.45 5	99.92 17	99.92 14	98.62 70	99.99 4	99.96 6	99.99 1	99.96 7

patch_mono-2		99.26 73	99.62 5	98.16 299	99.81 46	94.59 362	99.52 149	99.64 36	99.33 13	99.73 66	99.90 28	99.00 22	99.99 4	99.69 20	99.98 4	99.89 19

h-mvs33		97.70 271	97.28 292	98.97 197	99.70 101	97.27 274	99.36 233	99.45 199	98.94 54	99.66 87	99.64 193	94.93 209	99.99 4	99.48 45	84.36 395	99.65 129

xiu_mvs_v1_base_debu		99.29 67	99.27 62	99.34 143	99.63 133	98.97 158	99.12 298	99.51 117	98.86 60	99.84 34	99.47 256	98.18 97	99.99 4	99.50 40	99.31 163	99.08 237

xiu_mvs_v1_base		99.29 67	99.27 62	99.34 143	99.63 133	98.97 158	99.12 298	99.51 117	98.86 60	99.84 34	99.47 256	98.18 97	99.99 4	99.50 40	99.31 163	99.08 237

xiu_mvs_v1_base_debi		99.29 67	99.27 62	99.34 143	99.63 133	98.97 158	99.12 298	99.51 117	98.86 60	99.84 34	99.47 256	98.18 97	99.99 4	99.50 40	99.31 163	99.08 237

EPNet		98.86 135	98.71 139	99.30 155	97.20 388	98.18 232	99.62 90	98.91 337	99.28 16	98.63 298	99.81 93	95.96 173	99.99 4	99.24 71	99.72 123	99.73 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		99.40 52	99.28 59	99.74 61	99.67 112	99.31 113	99.52 149	98.87 344	99.55 1	99.74 64	99.80 106	96.47 157	99.98 13	99.97 1	99.97 7	99.94 11

test_cas_vis1_n_1920		99.16 87	99.01 99	99.61 88	99.81 46	98.86 178	99.65 78	99.64 36	99.39 10	99.97 13	99.94 6	93.20 276	99.98 13	99.55 33	99.91 36	99.99 1

test_vis1_n		97.92 234	97.44 269	99.34 143	99.53 166	98.08 238	99.74 45	99.49 147	99.15 20	100.00 1	99.94 6	79.51 396	99.98 13	99.88 13	99.76 115	99.97 4

xiu_mvs_v2_base		99.26 73	99.25 66	99.29 158	99.53 166	98.91 172	99.02 321	99.45 199	98.80 69	99.71 72	99.26 311	98.94 29	99.98 13	99.34 59	99.23 167	98.98 251

PS-MVSNAJ		99.32 63	99.32 44	99.30 155	99.57 154	98.94 168	98.97 335	99.46 188	98.92 57	99.71 72	99.24 313	99.01 18	99.98 13	99.35 55	99.66 133	98.97 252

QAPM		98.67 160	98.30 177	99.80 46	99.20 263	99.67 51	99.77 33	99.72 11	94.74 360	98.73 279	99.90 28	95.78 183	99.98 13	96.96 294	99.88 56	99.76 87

3Dnovator	97.25 9	99.24 78	99.05 87	99.81 44	99.12 285	99.66 53	99.84 11	99.74 10	99.09 32	98.92 254	99.90 28	95.94 176	99.98 13	98.95 96	99.92 29	99.79 74

OpenMVS	96.50 16	98.47 169	98.12 189	99.52 115	99.04 303	99.53 84	99.82 15	99.72 11	94.56 363	98.08 332	99.88 37	94.73 225	99.98 13	97.47 262	99.76 115	99.06 243

test_fmvsmconf0.1_n		99.55 14	99.45 21	99.86 21	99.44 201	99.65 57	99.50 163	99.61 48	99.45 5	99.87 27	99.92 14	97.31 125	99.97 21	99.95 7	99.99 1	99.97 4

test_fmvs1_n		98.41 175	98.14 186	99.21 170	99.82 42	97.71 263	99.74 45	99.49 147	99.32 14	99.99 2	99.95 3	85.32 377	99.97 21	99.82 16	99.84 82	99.96 7

CANet_DTU		98.97 126	98.87 121	99.25 165	99.33 231	98.42 224	99.08 307	99.30 278	99.16 19	99.43 146	99.75 139	95.27 200	99.97 21	98.56 161	99.95 18	99.36 212

MVS_0304		99.15 89	98.96 109	99.73 64	98.92 319	99.37 104	99.37 228	96.92 393	99.51 2	99.66 87	99.78 124	96.69 148	99.97 21	99.84 15	99.97 7	99.84 39

MTAPA		99.52 17	99.39 30	99.89 4	99.90 4	99.86 13	99.66 72	99.47 179	98.79 70	99.68 78	99.81 93	98.43 83	99.97 21	98.88 105	99.90 44	99.83 49

PGM-MVS		99.45 36	99.31 51	99.86 21	99.87 15	99.78 36	99.58 111	99.65 33	97.84 176	99.71 72	99.80 106	99.12 13	99.97 21	98.33 183	99.87 59	99.83 49

mPP-MVS		99.44 40	99.30 53	99.86 21	99.88 11	99.79 30	99.69 58	99.48 159	98.12 142	99.50 132	99.75 139	98.78 48	99.97 21	98.57 158	99.89 53	99.83 49

CP-MVS		99.45 36	99.32 44	99.85 28	99.83 39	99.75 39	99.69 58	99.52 103	98.07 152	99.53 127	99.63 199	98.93 33	99.97 21	98.74 130	99.91 36	99.83 49

SteuartSystems-ACMMP		99.54 15	99.42 22	99.87 11	99.82 42	99.81 25	99.59 103	99.51 117	98.62 84	99.79 46	99.83 70	99.28 4	99.97 21	98.48 168	99.90 44	99.84 39
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	97.12 13	99.18 83	98.97 105	99.82 41	99.17 277	99.68 48	99.81 19	99.51 117	99.20 18	98.72 280	99.89 32	95.68 187	99.97 21	98.86 113	99.86 67	99.81 61

fmvsm_s_conf0.5_n_a		99.56 13	99.47 17	99.85 28	99.83 39	99.64 63	99.52 149	99.65 33	99.10 27	99.98 6	99.92 14	97.35 124	99.96 31	99.94 9	99.92 29	99.95 9

fmvsm_s_conf0.5_n		99.51 18	99.40 27	99.85 28	99.84 32	99.65 57	99.51 156	99.67 23	99.13 22	99.98 6	99.92 14	96.60 151	99.96 31	99.95 7	99.96 12	99.95 9

mvsany_test1		99.50 20	99.46 20	99.62 86	99.61 143	99.09 141	98.94 341	99.48 159	99.10 27	99.96 14	99.91 21	98.85 39	99.96 31	99.72 18	99.58 143	99.82 54

test_fmvs1		98.88 131	98.79 133	99.16 175	99.69 105	97.61 266	99.55 136	99.49 147	99.32 14	99.98 6	99.91 21	91.41 323	99.96 31	99.82 16	99.92 29	99.90 16

DVP-MVS++		99.59 8	99.50 13	99.88 5	99.51 174	99.88 8	99.87 7	99.51 117	98.99 45	99.88 22	99.81 93	99.27 5	99.96 31	98.85 115	99.80 102	99.81 61

MSC_two_6792asdad				99.87 11	99.51 174	99.76 37		99.33 260					99.96 31	98.87 108	99.84 82	99.89 19

No_MVS				99.87 11	99.51 174	99.76 37		99.33 260					99.96 31	98.87 108	99.84 82	99.89 19

ZD-MVS					99.71 96	99.79 30		99.61 48	96.84 279	99.56 120	99.54 232	98.58 72	99.96 31	96.93 297	99.75 117

SED-MVS		99.61 7	99.52 11	99.88 5	99.84 32	99.90 2	99.60 97	99.48 159	99.08 33	99.91 18	99.81 93	99.20 7	99.96 31	98.91 102	99.85 74	99.79 74

test_241102_TWO								99.48 159	99.08 33	99.88 22	99.81 93	98.94 29	99.96 31	98.91 102	99.84 82	99.88 25

ZNCC-MVS		99.47 30	99.33 42	99.87 11	99.87 15	99.81 25	99.64 81	99.67 23	98.08 151	99.55 124	99.64 193	98.91 34	99.96 31	98.72 133	99.90 44	99.82 54

DVP-MVS		99.57 12	99.47 17	99.88 5	99.85 26	99.89 4	99.57 118	99.37 242	99.10 27	99.81 41	99.80 106	98.94 29	99.96 31	98.93 99	99.86 67	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 41	99.80 106	99.09 14	99.96 31	98.85 115	99.90 44	99.88 25

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 118	99.51 117					99.96 31	98.93 99	99.86 67	99.88 25

SR-MVS		99.43 43	99.29 57	99.86 21	99.75 73	99.83 16	99.59 103	99.62 41	98.21 129	99.73 66	99.79 118	98.68 64	99.96 31	98.44 174	99.77 112	99.79 74

DPE-MVS		99.46 32	99.32 44	99.91 2	99.78 56	99.88 8	99.36 233	99.51 117	98.73 76	99.88 22	99.84 65	98.72 61	99.96 31	98.16 196	99.87 59	99.88 25
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 45	99.29 57	99.80 46	99.62 139	99.55 79	99.50 163	99.70 15	98.79 70	99.77 55	99.96 1	97.45 119	99.96 31	98.92 101	99.90 44	99.89 19

HFP-MVS		99.49 22	99.37 34	99.86 21	99.87 15	99.80 27	99.66 72	99.67 23	98.15 136	99.68 78	99.69 169	99.06 16	99.96 31	98.69 138	99.87 59	99.84 39

region2R		99.48 26	99.35 38	99.87 11	99.88 11	99.80 27	99.65 78	99.66 28	98.13 140	99.66 87	99.68 175	98.96 24	99.96 31	98.62 146	99.87 59	99.84 39

HPM-MVS++		99.39 54	99.23 69	99.87 11	99.75 73	99.84 15	99.43 199	99.51 117	98.68 81	99.27 188	99.53 236	98.64 69	99.96 31	98.44 174	99.80 102	99.79 74

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 62	99.89 4	99.75 40	99.56 71	99.02 38	99.88 22	99.85 54	99.18 10	99.96 31	99.22 72	99.92 29	99.90 16
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 22	99.36 36	99.86 21	99.87 15	99.79 30	99.66 72	99.67 23	98.15 136	99.67 82	99.69 169	98.95 27	99.96 31	98.69 138	99.87 59	99.84 39

MP-MVS		99.33 61	99.15 75	99.87 11	99.88 11	99.82 22	99.66 72	99.46 188	98.09 147	99.48 136	99.74 144	98.29 92	99.96 31	97.93 214	99.87 59	99.82 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 104	98.90 116	99.74 61	99.80 52	99.46 96	99.59 103	99.49 147	97.03 266	99.63 102	99.69 169	97.27 128	99.96 31	97.82 225	99.84 82	99.81 61

PVSNet_Blended_VisFu		99.36 58	99.28 59	99.61 88	99.86 20	99.07 146	99.47 185	99.93 2	97.66 199	99.71 72	99.86 49	97.73 114	99.96 31	99.47 47	99.82 95	99.79 74

UGNet		98.87 132	98.69 141	99.40 136	99.22 260	98.72 192	99.44 195	99.68 20	99.24 17	99.18 212	99.42 266	92.74 286	99.96 31	99.34 59	99.94 25	99.53 169
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 63	99.32 44	99.32 149	99.85 26	98.29 227	99.71 53	99.66 28	98.11 144	99.41 153	99.80 106	98.37 89	99.96 31	98.99 92	99.96 12	99.72 103

ACMMP		99.45 36	99.32 44	99.82 41	99.89 8	99.67 51	99.62 90	99.69 18	98.12 142	99.63 102	99.84 65	98.73 60	99.96 31	98.55 164	99.83 91	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 80	99.03 91	99.79 49	98.42 368	99.48 93	99.55 136	99.51 117	99.39 10	99.78 51	99.93 9	94.80 217	99.95 59	99.93 10	99.95 18	99.94 11

SR-MVS-dyc-post		99.45 36	99.31 51	99.85 28	99.76 65	99.82 22	99.63 85	99.52 103	98.38 105	99.76 60	99.82 79	98.53 76	99.95 59	98.61 149	99.81 98	99.77 82

GST-MVS		99.40 52	99.24 67	99.85 28	99.86 20	99.79 30	99.60 97	99.67 23	97.97 163	99.63 102	99.68 175	98.52 77	99.95 59	98.38 177	99.86 67	99.81 61

CANet		99.25 77	99.14 76	99.59 91	99.41 209	99.16 131	99.35 238	99.57 66	98.82 65	99.51 131	99.61 208	96.46 158	99.95 59	99.59 28	99.98 4	99.65 129

MP-MVS-pluss		99.37 57	99.20 71	99.88 5	99.90 4	99.87 12	99.30 249	99.52 103	97.18 248	99.60 112	99.79 118	98.79 47	99.95 59	98.83 121	99.91 36	99.83 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 45	99.27 62	99.88 5	99.89 8	99.80 27	99.67 67	99.50 137	98.70 78	99.77 55	99.49 248	98.21 95	99.95 59	98.46 172	99.77 112	99.88 25
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	96.67 309

APD-MVS_3200maxsize		99.48 26	99.35 38	99.85 28	99.76 65	99.83 16	99.63 85	99.54 87	98.36 109	99.79 46	99.82 79	98.86 38	99.95 59	98.62 146	99.81 98	99.78 80

RPMNet		96.72 318	95.90 330	99.19 172	99.18 269	98.49 216	99.22 282	99.52 103	88.72 395	99.56 120	97.38 389	94.08 255	99.95 59	86.87 397	98.58 213	99.14 229

sss		99.17 85	99.05 87	99.53 109	99.62 139	98.97 158	99.36 233	99.62 41	97.83 177	99.67 82	99.65 187	97.37 123	99.95 59	99.19 74	99.19 170	99.68 119

MVSMamba_PlusPlus		99.46 32	99.41 26	99.64 80	99.68 109	99.50 89	99.75 40	99.50 137	98.27 118	99.87 27	99.92 14	98.09 101	99.94 69	99.65 24	99.95 18	99.47 189

fmvsm_s_conf0.1_n_a		99.26 73	99.06 86	99.85 28	99.52 171	99.62 65	99.54 140	99.62 41	98.69 79	99.99 2	99.96 1	94.47 241	99.94 69	99.88 13	99.92 29	99.98 2

fmvsm_s_conf0.1_n		99.29 67	99.10 80	99.86 21	99.70 101	99.65 57	99.53 148	99.62 41	98.74 75	99.99 2	99.95 3	94.53 239	99.94 69	99.89 12	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 85	99.39 226	98.91 58	99.78 51	99.85 54	99.36 2	99.94 69	98.84 118	99.88 56	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
iter_conf05		99.48 26	99.40 27	99.71 67	99.68 109	99.61 67	99.49 174	99.58 62	98.27 118	99.95 15	99.92 14	98.09 101	99.94 69	99.65 24	99.96 12	99.58 154

mamv4		99.33 61	99.42 22	99.07 183	99.67 112	97.73 258	99.42 206	99.60 54	98.15 136	99.94 16	99.91 21	98.42 85	99.94 69	99.72 18	99.96 12	99.54 163

XVS		99.53 16	99.42 22	99.87 11	99.85 26	99.83 16	99.69 58	99.68 20	98.98 48	99.37 164	99.74 144	98.81 44	99.94 69	98.79 126	99.86 67	99.84 39

X-MVStestdata		96.55 320	95.45 338	99.87 11	99.85 26	99.83 16	99.69 58	99.68 20	98.98 48	99.37 164	64.01 412	98.81 44	99.94 69	98.79 126	99.86 67	99.84 39

旧先验2							98.96 336		96.70 286	99.47 137			99.94 69	98.19 192

新几何1				99.75 58	99.75 73	99.59 71		99.54 87	96.76 282	99.29 182	99.64 193	98.43 83	99.94 69	96.92 299	99.66 133	99.72 103

testdata				99.54 101	99.75 73	98.95 165		99.51 117	97.07 260	99.43 146	99.70 159	98.87 37	99.94 69	97.76 232	99.64 136	99.72 103

HPM-MVS		99.42 45	99.28 59	99.83 40	99.90 4	99.72 42	99.81 19	99.54 87	97.59 204	99.68 78	99.63 199	98.91 34	99.94 69	98.58 155	99.91 36	99.84 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 81	99.10 80	99.45 129	99.89 8	98.52 212	99.39 221	99.94 1	98.73 76	99.11 221	99.89 32	95.50 192	99.94 69	99.50 40	99.97 7	99.89 19

APD-MVS		99.27 71	99.08 84	99.84 39	99.75 73	99.79 30	99.50 163	99.50 137	97.16 250	99.77 55	99.82 79	98.78 48	99.94 69	97.56 253	99.86 67	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 75	99.72 91	99.40 103	99.05 313	99.66 28	99.14 21	99.57 119	99.80 106	98.46 81	99.94 69	99.57 31	99.84 82	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		99.06 112	98.88 120	99.61 88	99.62 139	99.16 131	99.37 228	99.56 71	98.04 158	99.53 127	99.62 204	96.84 142	99.94 69	98.85 115	98.49 221	99.72 103

DeepC-MVS	98.35 2	99.30 65	99.19 72	99.64 80	99.82 42	99.23 124	99.62 90	99.55 79	98.94 54	99.63 102	99.95 3	95.82 182	99.94 69	99.37 54	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 71	99.12 78	99.74 61	99.18 269	99.75 39	99.56 124	99.57 66	98.45 98	99.49 135	99.85 54	97.77 113	99.94 69	98.33 183	99.84 82	99.52 170

SDMVSNet		99.11 104	98.90 116	99.75 58	99.81 46	99.59 71	99.81 19	99.65 33	98.78 73	99.64 99	99.88 37	94.56 235	99.93 87	99.67 22	98.26 232	99.72 103

FE-MVS		98.48 168	98.17 182	99.40 136	99.54 165	98.96 162	99.68 64	98.81 351	95.54 344	99.62 106	99.70 159	93.82 264	99.93 87	97.35 271	99.46 150	99.32 218

SF-MVS		99.38 56	99.24 67	99.79 49	99.79 54	99.68 48	99.57 118	99.54 87	97.82 181	99.71 72	99.80 106	98.95 27	99.93 87	98.19 192	99.84 82	99.74 92

dcpmvs_2		99.23 79	99.58 7	98.16 299	99.83 39	94.68 360	99.76 36	99.52 103	99.07 35	99.98 6	99.88 37	98.56 74	99.93 87	99.67 22	99.98 4	99.87 30

Anonymous20240529		98.09 204	97.68 240	99.34 143	99.66 122	98.44 221	99.40 217	99.43 213	93.67 370	99.22 199	99.89 32	90.23 339	99.93 87	99.26 70	98.33 226	99.66 125

ACMMP_NAP		99.47 30	99.34 40	99.88 5	99.87 15	99.86 13	99.47 185	99.48 159	98.05 157	99.76 60	99.86 49	98.82 43	99.93 87	98.82 125	99.91 36	99.84 39

EI-MVSNet-UG-set		99.58 9	99.57 8	99.64 80	99.78 56	99.14 136	99.60 97	99.45 199	99.01 40	99.90 20	99.83 70	98.98 23	99.93 87	99.59 28	99.95 18	99.86 32

无先验							98.99 329	99.51 117	96.89 276				99.93 87	97.53 256		99.72 103

VDDNet		97.55 285	97.02 304	99.16 175	99.49 185	98.12 237	99.38 226	99.30 278	95.35 346	99.68 78	99.90 28	82.62 389	99.93 87	99.31 62	98.13 243	99.42 202

ab-mvs		98.86 135	98.63 148	99.54 101	99.64 130	99.19 126	99.44 195	99.54 87	97.77 185	99.30 179	99.81 93	94.20 249	99.93 87	99.17 78	98.82 202	99.49 182

F-COLMAP		99.19 81	99.04 89	99.64 80	99.78 56	99.27 119	99.42 206	99.54 87	97.29 239	99.41 153	99.59 213	98.42 85	99.93 87	98.19 192	99.69 128	99.73 97

Anonymous202405211		98.30 185	97.98 206	99.26 164	99.57 154	98.16 233	99.41 209	98.55 371	96.03 338	99.19 208	99.74 144	91.87 310	99.92 98	99.16 79	98.29 231	99.70 113

EI-MVSNet-Vis-set		99.58 9	99.56 10	99.64 80	99.78 56	99.15 135	99.61 96	99.45 199	99.01 40	99.89 21	99.82 79	99.01 18	99.92 98	99.56 32	99.95 18	99.85 35

VDD-MVS		97.73 265	97.35 281	98.88 216	99.47 193	97.12 282	99.34 241	98.85 346	98.19 131	99.67 82	99.85 54	82.98 387	99.92 98	99.49 44	98.32 230	99.60 146

VNet		99.11 104	98.90 116	99.73 64	99.52 171	99.56 77	99.41 209	99.39 226	99.01 40	99.74 64	99.78 124	95.56 190	99.92 98	99.52 38	98.18 239	99.72 103

XVG-OURS-SEG-HR		98.69 158	98.62 153	98.89 214	99.71 96	97.74 257	99.12 298	99.54 87	98.44 101	99.42 149	99.71 155	94.20 249	99.92 98	98.54 165	98.90 196	99.00 248

mvsmamba		99.06 112	98.96 109	99.36 141	99.47 193	98.64 199	99.70 54	99.05 318	97.61 203	99.65 94	99.83 70	96.54 154	99.92 98	99.19 74	99.62 139	99.51 177

HPM-MVS_fast		99.51 18	99.40 27	99.85 28	99.91 1	99.79 30	99.76 36	99.56 71	97.72 190	99.76 60	99.75 139	99.13 12	99.92 98	99.07 86	99.92 29	99.85 35

HY-MVS	97.30 7	98.85 142	98.64 147	99.47 126	99.42 204	99.08 144	99.62 90	99.36 243	97.39 231	99.28 183	99.68 175	96.44 160	99.92 98	98.37 179	98.22 234	99.40 206

DP-MVS		99.16 87	98.95 111	99.78 52	99.77 62	99.53 84	99.41 209	99.50 137	97.03 266	99.04 237	99.88 37	97.39 120	99.92 98	98.66 142	99.90 44	99.87 30

IB-MVS	95.67 18	96.22 326	95.44 339	98.57 254	99.21 261	96.70 310	98.65 369	97.74 388	96.71 285	97.27 356	98.54 366	86.03 371	99.92 98	98.47 171	86.30 393	99.10 232
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 30	99.77 55	99.63 133	99.59 71	99.36 233	99.46 188	99.07 35	99.79 46	99.82 79	98.85 39	99.92 98	98.68 140	99.87 59	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
balanced_conf03		99.46 32	99.39 30	99.67 70	99.55 162	99.58 76	99.74 45	99.51 117	98.42 102	99.87 27	99.84 65	98.05 105	99.91 109	99.58 30	99.94 25	99.52 170

9.14			99.10 80		99.72 91		99.40 217	99.51 117	97.53 214	99.64 99	99.78 124	98.84 41	99.91 109	97.63 244	99.82 95

SMA-MVS		99.44 40	99.30 53	99.85 28	99.73 87	99.83 16	99.56 124	99.47 179	97.45 223	99.78 51	99.82 79	99.18 10	99.91 109	98.79 126	99.89 53	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					99.67 112	99.65 57	99.05 313	99.41 217	96.22 323	98.95 250	99.49 248	98.77 51	99.91 109

train_agg		99.02 118	98.77 134	99.77 55	99.67 112	99.65 57	99.05 313	99.41 217	96.28 317	98.95 250	99.49 248	98.76 52	99.91 109	97.63 244	99.72 123	99.75 88

test_8					99.67 112	99.61 67	99.03 318	99.41 217	96.28 317	98.93 253	99.48 253	98.76 52	99.91 109

agg_prior					99.67 112	99.62 65		99.40 223		98.87 263			99.91 109

原ACMM1				99.65 75	99.73 87	99.33 108		99.47 179	97.46 220	99.12 219	99.66 186	98.67 66	99.91 109	97.70 241	99.69 128	99.71 112

LFMVS		97.90 237	97.35 281	99.54 101	99.52 171	99.01 153	99.39 221	98.24 378	97.10 258	99.65 94	99.79 118	84.79 380	99.91 109	99.28 66	98.38 223	99.69 115

XVG-OURS		98.73 156	98.68 142	98.88 216	99.70 101	97.73 258	98.92 343	99.55 79	98.52 93	99.45 140	99.84 65	95.27 200	99.91 109	98.08 203	98.84 200	99.00 248

PLC	97.94 4	99.02 118	98.85 125	99.53 109	99.66 122	99.01 153	99.24 277	99.52 103	96.85 278	99.27 188	99.48 253	98.25 94	99.91 109	97.76 232	99.62 139	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	97.66 278	97.06 303	99.47 126	99.61 143	99.09 141	98.04 395	99.25 289	91.24 386	98.51 308	99.70 159	94.55 237	99.91 109	92.76 374	99.85 74	99.42 202
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS		97.58 284	97.29 291	98.48 265	99.09 293	96.25 328	99.01 326	96.61 399	97.86 171	99.19 208	99.01 338	88.72 351	99.90 121	97.38 269	98.69 208	99.28 221

test_vis1_rt		95.81 335	95.65 335	96.32 361	99.67 112	91.35 388	99.49 174	96.74 397	98.25 122	95.24 377	98.10 382	74.96 397	99.90 121	99.53 36	98.85 199	97.70 382

FA-MVS(test-final)		98.75 153	98.53 164	99.41 135	99.55 162	99.05 149	99.80 24	99.01 322	96.59 299	99.58 116	99.59 213	95.39 195	99.90 121	97.78 228	99.49 149	99.28 221

bld_raw_conf03		99.39 54	99.32 44	99.62 86	99.53 166	99.50 89	99.75 40	99.50 137	98.13 140	99.87 27	99.85 54	97.89 108	99.90 121	99.39 52	99.95 18	99.47 189

MCST-MVS		99.43 43	99.30 53	99.82 41	99.79 54	99.74 41	99.29 254	99.40 223	98.79 70	99.52 129	99.62 204	98.91 34	99.90 121	98.64 144	99.75 117	99.82 54

CDPH-MVS		99.13 94	98.91 115	99.80 46	99.75 73	99.71 44	99.15 292	99.41 217	96.60 297	99.60 112	99.55 227	98.83 42	99.90 121	97.48 260	99.83 91	99.78 80

NCCC		99.34 60	99.19 72	99.79 49	99.61 143	99.65 57	99.30 249	99.48 159	98.86 60	99.21 202	99.63 199	98.72 61	99.90 121	98.25 188	99.63 138	99.80 70

114514_t		98.93 128	98.67 143	99.72 66	99.85 26	99.53 84	99.62 90	99.59 58	92.65 381	99.71 72	99.78 124	98.06 104	99.90 121	98.84 118	99.91 36	99.74 92

1112_ss		98.98 124	98.77 134	99.59 91	99.68 109	99.02 151	99.25 275	99.48 159	97.23 245	99.13 217	99.58 217	96.93 141	99.90 121	98.87 108	98.78 205	99.84 39

PHI-MVS		99.30 65	99.17 74	99.70 68	99.56 158	99.52 87	99.58 111	99.80 8	97.12 254	99.62 106	99.73 150	98.58 72	99.90 121	98.61 149	99.91 36	99.68 119

AdaColmap		99.01 122	98.80 130	99.66 71	99.56 158	99.54 81	99.18 287	99.70 15	98.18 134	99.35 170	99.63 199	96.32 163	99.90 121	97.48 260	99.77 112	99.55 161

COLMAP_ROB	97.56 6	98.86 135	98.75 136	99.17 174	99.88 11	98.53 208	99.34 241	99.59 58	97.55 210	98.70 287	99.89 32	95.83 181	99.90 121	98.10 198	99.90 44	99.08 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 181	98.03 201	99.31 150	99.63 133	98.56 205	99.54 140	96.75 396	97.53 214	99.73 66	99.65 187	91.25 327	99.89 133	98.62 146	99.56 144	99.48 183

tttt0517		98.42 173	98.14 186	99.28 162	99.66 122	98.38 225	99.74 45	96.85 394	97.68 196	99.79 46	99.74 144	91.39 324	99.89 133	98.83 121	99.56 144	99.57 158

test12				99.75 58	99.64 130	99.61 67		99.29 282		99.21 202		98.38 88	99.89 133		99.74 120	99.74 92

Test_1112_low_res		98.89 130	98.66 146	99.57 96	99.69 105	98.95 165	99.03 318	99.47 179	96.98 268	99.15 215	99.23 314	96.77 145	99.89 133	98.83 121	98.78 205	99.86 32

CNLPA		99.14 92	98.99 101	99.59 91	99.58 152	99.41 102	99.16 289	99.44 207	98.45 98	99.19 208	99.49 248	98.08 103	99.89 133	97.73 236	99.75 117	99.48 183

sd_testset		98.75 153	98.57 160	99.29 158	99.81 46	98.26 229	99.56 124	99.62 41	98.78 73	99.64 99	99.88 37	92.02 307	99.88 138	99.54 34	98.26 232	99.72 103

APD_test1		95.87 333	96.49 317	94.00 368	99.53 166	84.01 397	99.54 140	99.32 270	95.91 340	97.99 337	99.85 54	85.49 375	99.88 138	91.96 377	98.84 200	98.12 364

diffmvs		99.14 92	99.02 95	99.51 117	99.61 143	98.96 162	99.28 259	99.49 147	98.46 97	99.72 71	99.71 155	96.50 156	99.88 138	99.31 62	99.11 177	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		98.86 135	98.80 130	99.03 189	99.76 65	98.79 187	99.28 259	99.91 3	97.42 228	99.67 82	99.37 282	97.53 117	99.88 138	98.98 93	97.29 289	98.42 347

PVSNet_Blended		99.08 110	98.97 105	99.42 134	99.76 65	98.79 187	98.78 357	99.91 3	96.74 283	99.67 82	99.49 248	97.53 117	99.88 138	98.98 93	99.85 74	99.60 146

MVS		97.28 303	96.55 315	99.48 123	98.78 337	98.95 165	99.27 264	99.39 226	83.53 399	98.08 332	99.54 232	96.97 139	99.87 143	94.23 356	99.16 171	99.63 140

MG-MVS		99.13 94	99.02 95	99.45 129	99.57 154	98.63 200	99.07 308	99.34 253	98.99 45	99.61 109	99.82 79	97.98 107	99.87 143	97.00 290	99.80 102	99.85 35

MSDG		98.98 124	98.80 130	99.53 109	99.76 65	99.19 126	98.75 360	99.55 79	97.25 242	99.47 137	99.77 132	97.82 111	99.87 143	96.93 297	99.90 44	99.54 163

ETV-MVS		99.26 73	99.21 70	99.40 136	99.46 195	99.30 115	99.56 124	99.52 103	98.52 93	99.44 145	99.27 309	98.41 87	99.86 146	99.10 83	99.59 142	99.04 244

thisisatest0515		98.14 199	97.79 224	99.19 172	99.50 183	98.50 215	98.61 371	96.82 395	96.95 272	99.54 125	99.43 264	91.66 319	99.86 146	98.08 203	99.51 148	99.22 226

thres600view7		97.86 242	97.51 257	98.92 205	99.72 91	97.95 248	99.59 103	98.74 358	97.94 165	99.27 188	98.62 363	91.75 313	99.86 146	93.73 361	98.19 238	98.96 254

lupinMVS		99.13 94	99.01 99	99.46 128	99.51 174	98.94 168	99.05 313	99.16 303	97.86 171	99.80 44	99.56 224	97.39 120	99.86 146	98.94 97	99.85 74	99.58 154

PVSNet	96.02 17	98.85 142	98.84 127	98.89 214	99.73 87	97.28 273	98.32 387	99.60 54	97.86 171	99.50 132	99.57 221	96.75 146	99.86 146	98.56 161	99.70 127	99.54 163

MAR-MVS		98.86 135	98.63 148	99.54 101	99.37 222	99.66 53	99.45 189	99.54 87	96.61 295	99.01 240	99.40 273	97.09 132	99.86 146	97.68 243	99.53 147	99.10 232
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
testing91		97.44 297	97.02 304	98.71 242	99.18 269	96.89 304	99.19 285	99.04 319	97.78 184	98.31 319	98.29 375	85.41 376	99.85 152	98.01 209	97.95 248	99.39 207

test2506		96.81 317	96.65 313	97.29 342	99.74 80	92.21 385	99.60 97	85.06 416	99.13 22	99.77 55	99.93 9	87.82 366	99.85 152	99.38 53	99.38 155	99.80 70

AllTest		98.87 132	98.72 137	99.31 150	99.86 20	98.48 218	99.56 124	99.61 48	97.85 174	99.36 167	99.85 54	95.95 174	99.85 152	96.66 310	99.83 91	99.59 150

TestCases				99.31 150	99.86 20	98.48 218		99.61 48	97.85 174	99.36 167	99.85 54	95.95 174	99.85 152	96.66 310	99.83 91	99.59 150

jason		99.13 94	99.03 91	99.45 129	99.46 195	98.87 175	99.12 298	99.26 287	98.03 160	99.79 46	99.65 187	97.02 137	99.85 152	99.02 90	99.90 44	99.65 129
jason: jason.
CNVR-MVS		99.42 45	99.30 53	99.78 52	99.62 139	99.71 44	99.26 273	99.52 103	98.82 65	99.39 160	99.71 155	98.96 24	99.85 152	98.59 154	99.80 102	99.77 82

PAPM_NR		99.04 115	98.84 127	99.66 71	99.74 80	99.44 98	99.39 221	99.38 234	97.70 194	99.28 183	99.28 306	98.34 90	99.85 152	96.96 294	99.45 151	99.69 115

testing99		97.36 300	96.94 307	98.63 247	99.18 269	96.70 310	99.30 249	98.93 330	97.71 191	98.23 324	98.26 376	84.92 379	99.84 159	98.04 208	97.85 255	99.35 213

testing222		97.16 308	96.50 316	99.16 175	99.16 279	98.47 220	99.27 264	98.66 367	97.71 191	98.23 324	98.15 378	82.28 392	99.84 159	97.36 270	97.66 261	99.18 228

test1111		98.04 214	98.11 190	97.83 322	99.74 80	93.82 370	99.58 111	95.40 403	99.12 25	99.65 94	99.93 9	90.73 332	99.84 159	99.43 50	99.38 155	99.82 54

ECVR-MVS		98.04 214	98.05 199	98.00 311	99.74 80	94.37 365	99.59 103	94.98 404	99.13 22	99.66 87	99.93 9	90.67 333	99.84 159	99.40 51	99.38 155	99.80 70

test_yl		98.86 135	98.63 148	99.54 101	99.49 185	99.18 128	99.50 163	99.07 315	98.22 127	99.61 109	99.51 242	95.37 196	99.84 159	98.60 152	98.33 226	99.59 150

DCV-MVSNet		98.86 135	98.63 148	99.54 101	99.49 185	99.18 128	99.50 163	99.07 315	98.22 127	99.61 109	99.51 242	95.37 196	99.84 159	98.60 152	98.33 226	99.59 150

Fast-Effi-MVS+		98.70 157	98.43 167	99.51 117	99.51 174	99.28 117	99.52 149	99.47 179	96.11 333	99.01 240	99.34 292	96.20 167	99.84 159	97.88 217	98.82 202	99.39 207

TSAR-MVS + GP.		99.36 58	99.36 36	99.36 141	99.67 112	98.61 203	99.07 308	99.33 260	99.00 43	99.82 40	99.81 93	99.06 16	99.84 159	99.09 84	99.42 153	99.65 129

tpmrst		98.33 182	98.48 166	97.90 317	99.16 279	94.78 358	99.31 247	99.11 308	97.27 240	99.45 140	99.59 213	95.33 198	99.84 159	98.48 168	98.61 210	99.09 236

Vis-MVSNet		99.12 100	98.97 105	99.56 98	99.78 56	99.10 140	99.68 64	99.66 28	98.49 95	99.86 32	99.87 45	94.77 222	99.84 159	99.19 74	99.41 154	99.74 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 164	98.34 173	99.51 117	99.40 214	99.03 150	98.80 355	99.36 243	96.33 314	99.00 244	99.12 328	98.46 81	99.84 159	95.23 343	99.37 162	99.66 125

PatchMatch-RL		98.84 145	98.62 153	99.52 115	99.71 96	99.28 117	99.06 311	99.77 9	97.74 189	99.50 132	99.53 236	95.41 194	99.84 159	97.17 284	99.64 136	99.44 200

EPP-MVSNet		99.13 94	98.99 101	99.53 109	99.65 128	99.06 147	99.81 19	99.33 260	97.43 226	99.60 112	99.88 37	97.14 130	99.84 159	99.13 80	98.94 191	99.69 115

testing11		97.50 290	97.10 301	98.71 242	99.20 263	96.91 302	99.29 254	98.82 349	97.89 169	98.21 327	98.40 370	85.63 374	99.83 172	98.45 173	98.04 246	99.37 211

thres100view900		97.76 258	97.45 264	98.69 244	99.72 91	97.86 254	99.59 103	98.74 358	97.93 166	99.26 192	98.62 363	91.75 313	99.83 172	93.22 366	98.18 239	98.37 353

tfpn200view9		97.72 267	97.38 277	98.72 240	99.69 105	97.96 246	99.50 163	98.73 363	97.83 177	99.17 213	98.45 368	91.67 317	99.83 172	93.22 366	98.18 239	98.37 353

test_prior				99.68 69	99.67 112	99.48 93		99.56 71					99.83 172			99.74 92

1314		98.68 159	98.54 163	99.11 181	98.89 322	98.65 197	99.27 264	99.49 147	96.89 276	97.99 337	99.56 224	97.72 115	99.83 172	97.74 235	99.27 166	98.84 260

thres400		97.77 257	97.38 277	98.92 205	99.69 105	97.96 246	99.50 163	98.73 363	97.83 177	99.17 213	98.45 368	91.67 317	99.83 172	93.22 366	98.18 239	98.96 254

casdiffmvs		99.13 94	98.98 104	99.56 98	99.65 128	99.16 131	99.56 124	99.50 137	98.33 113	99.41 153	99.86 49	95.92 177	99.83 172	99.45 49	99.16 171	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	99.54 101	99.78 56	99.30 115	99.89 2	99.58 62	98.56 89	99.73 66	99.69 169	98.55 75	99.82 179	99.69 20	99.85 74	99.48 183

MVS_Test		99.10 108	98.97 105	99.48 123	99.49 185	99.14 136	99.67 67	99.34 253	97.31 237	99.58 116	99.76 136	97.65 116	99.82 179	98.87 108	99.07 183	99.46 195

dp		97.75 262	97.80 223	97.59 334	99.10 290	93.71 373	99.32 244	98.88 342	96.48 306	99.08 228	99.55 227	92.67 292	99.82 179	96.52 313	98.58 213	99.24 225

RPSCF		98.22 189	98.62 153	96.99 348	99.82 42	91.58 387	99.72 51	99.44 207	96.61 295	99.66 87	99.89 32	95.92 177	99.82 179	97.46 263	99.10 180	99.57 158

PMMVS		98.80 149	98.62 153	99.34 143	99.27 248	98.70 193	98.76 359	99.31 274	97.34 234	99.21 202	99.07 330	97.20 129	99.82 179	98.56 161	98.87 197	99.52 170

EIA-MVS		99.18 83	99.09 83	99.45 129	99.49 185	99.18 128	99.67 67	99.53 98	97.66 199	99.40 158	99.44 262	98.10 100	99.81 184	98.94 97	99.62 139	99.35 213

Effi-MVS+		98.81 146	98.59 159	99.48 123	99.46 195	99.12 139	98.08 394	99.50 137	97.50 218	99.38 162	99.41 270	96.37 162	99.81 184	99.11 82	98.54 218	99.51 177

thres200		97.61 282	97.28 292	98.62 248	99.64 130	98.03 240	99.26 273	98.74 358	97.68 196	99.09 227	98.32 374	91.66 319	99.81 184	92.88 371	98.22 234	98.03 369

tpmvs		97.98 225	98.02 203	97.84 321	99.04 303	94.73 359	99.31 247	99.20 298	96.10 337	98.76 277	99.42 266	94.94 208	99.81 184	96.97 293	98.45 222	98.97 252

casdiffmvs_mvg		99.15 89	99.02 95	99.55 100	99.66 122	99.09 141	99.64 81	99.56 71	98.26 121	99.45 140	99.87 45	96.03 171	99.81 184	99.54 34	99.15 174	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	98.81 146	99.37 34	97.12 346	99.60 148	91.75 386	98.61 371	99.44 207	99.35 12	99.83 39	99.85 54	98.70 63	99.81 184	99.02 90	99.91 36	99.81 61

DPM-MVS		98.95 127	98.71 139	99.66 71	99.63 133	99.55 79	98.64 370	99.10 309	97.93 166	99.42 149	99.55 227	98.67 66	99.80 190	95.80 328	99.68 131	99.61 144

DP-MVS Recon		99.12 100	98.95 111	99.65 75	99.74 80	99.70 46	99.27 264	99.57 66	96.40 313	99.42 149	99.68 175	98.75 55	99.80 190	97.98 211	99.72 123	99.44 200

MVS_111021_LR		99.41 49	99.33 42	99.65 75	99.77 62	99.51 88	98.94 341	99.85 6	98.82 65	99.65 94	99.74 144	98.51 78	99.80 190	98.83 121	99.89 53	99.64 136

CS-MVS		99.50 20	99.48 15	99.54 101	99.76 65	99.42 100	99.90 1	99.55 79	98.56 89	99.78 51	99.70 159	98.65 68	99.79 193	99.65 24	99.78 109	99.41 204

Fast-Effi-MVS+-dtu		98.77 152	98.83 129	98.60 249	99.41 209	96.99 296	99.52 149	99.49 147	98.11 144	99.24 194	99.34 292	96.96 140	99.79 193	97.95 213	99.45 151	99.02 247

baseline1		98.31 183	97.95 210	99.38 140	99.50 183	98.74 190	99.59 103	98.93 330	98.41 103	99.14 216	99.60 211	94.59 233	99.79 193	98.48 168	93.29 366	99.61 144

baseline		99.15 89	99.02 95	99.53 109	99.66 122	99.14 136	99.72 51	99.48 159	98.35 110	99.42 149	99.84 65	96.07 169	99.79 193	99.51 39	99.14 175	99.67 122

PVSNet_0	94.43 19	96.09 331	95.47 337	97.94 314	99.31 238	94.34 367	97.81 396	99.70 15	97.12 254	97.46 350	98.75 360	89.71 343	99.79 193	97.69 242	81.69 399	99.68 119

API-MVS		99.04 115	99.03 91	99.06 185	99.40 214	99.31 113	99.55 136	99.56 71	98.54 91	99.33 174	99.39 277	98.76 52	99.78 198	96.98 292	99.78 109	98.07 366

OMC-MVS		99.08 110	99.04 89	99.20 171	99.67 112	98.22 231	99.28 259	99.52 103	98.07 152	99.66 87	99.81 93	97.79 112	99.78 198	97.79 227	99.81 98	99.60 146

GeoE		98.85 142	98.62 153	99.53 109	99.61 143	99.08 144	99.80 24	99.51 117	97.10 258	99.31 176	99.78 124	95.23 204	99.77 200	98.21 190	99.03 186	99.75 88

alignmvs		98.81 146	98.56 162	99.58 94	99.43 202	99.42 100	99.51 156	98.96 328	98.61 85	99.35 170	98.92 350	94.78 219	99.77 200	99.35 55	98.11 244	99.54 163

tpm cat1		97.39 299	97.36 279	97.50 337	99.17 277	93.73 372	99.43 199	99.31 274	91.27 385	98.71 281	99.08 329	94.31 247	99.77 200	96.41 317	98.50 220	99.00 248

CostFormer		97.72 267	97.73 236	97.71 329	99.15 283	94.02 369	99.54 140	99.02 321	94.67 361	99.04 237	99.35 288	92.35 304	99.77 200	98.50 167	97.94 249	99.34 216

MGCFI-Net		99.01 122	98.85 125	99.50 122	99.42 204	99.26 120	99.82 15	99.48 159	98.60 86	99.28 183	98.81 355	97.04 136	99.76 204	99.29 65	97.87 253	99.47 189

test_241102_ONE					99.84 32	99.90 2		99.48 159	99.07 35	99.91 18	99.74 144	99.20 7	99.76 204

MDTV_nov1_ep13			98.32 175		99.11 287	94.44 364	99.27 264	98.74 358	97.51 217	99.40 158	99.62 204	94.78 219	99.76 204	97.59 247	98.81 204

sasdasda		99.02 118	98.86 123	99.51 117	99.42 204	99.32 109	99.80 24	99.48 159	98.63 82	99.31 176	98.81 355	97.09 132	99.75 207	99.27 68	97.90 250	99.47 189

canonicalmvs		99.02 118	98.86 123	99.51 117	99.42 204	99.32 109	99.80 24	99.48 159	98.63 82	99.31 176	98.81 355	97.09 132	99.75 207	99.27 68	97.90 250	99.47 189

Effi-MVS+-dtu		98.78 150	98.89 119	98.47 270	99.33 231	96.91 302	99.57 118	99.30 278	98.47 96	99.41 153	98.99 340	96.78 144	99.74 209	98.73 132	99.38 155	98.74 273

patchmatchnet-post											98.70 361	94.79 218	99.74 209

SCA		98.19 193	98.16 183	98.27 294	99.30 239	95.55 341	99.07 308	98.97 326	97.57 207	99.43 146	99.57 221	92.72 287	99.74 209	97.58 248	99.20 169	99.52 170

BH-untuned		98.42 173	98.36 171	98.59 250	99.49 185	96.70 310	99.27 264	99.13 307	97.24 244	98.80 272	99.38 279	95.75 184	99.74 209	97.07 288	99.16 171	99.33 217

BH-RMVSNet		98.41 175	98.08 195	99.40 136	99.41 209	98.83 183	99.30 249	98.77 354	97.70 194	98.94 252	99.65 187	92.91 282	99.74 209	96.52 313	99.55 146	99.64 136

MVS_111021_HR		99.41 49	99.32 44	99.66 71	99.72 91	99.47 95	98.95 339	99.85 6	98.82 65	99.54 125	99.73 150	98.51 78	99.74 209	98.91 102	99.88 56	99.77 82

test_post											65.99 410	94.65 232	99.73 215

XVG-ACMP-BASELINE		97.83 248	97.71 238	98.20 296	99.11 287	96.33 325	99.41 209	99.52 103	98.06 156	99.05 236	99.50 245	89.64 345	99.73 215	97.73 236	97.38 287	98.53 335

HyFIR lowres test		99.11 104	98.92 113	99.65 75	99.90 4	99.37 104	99.02 321	99.91 3	97.67 198	99.59 115	99.75 139	95.90 179	99.73 215	99.53 36	99.02 188	99.86 32

DeepMVS_CX				93.34 371	99.29 243	82.27 400		99.22 294	85.15 397	96.33 370	99.05 333	90.97 330	99.73 215	93.57 363	97.77 258	98.01 370

Patchmatch-test		97.93 231	97.65 243	98.77 237	99.18 269	97.07 287	99.03 318	99.14 306	96.16 328	98.74 278	99.57 221	94.56 235	99.72 219	93.36 365	99.11 177	99.52 170

LPG-MVS_test		98.22 189	98.13 188	98.49 263	99.33 231	97.05 289	99.58 111	99.55 79	97.46 220	99.24 194	99.83 70	92.58 294	99.72 219	98.09 199	97.51 273	98.68 290

LGP-MVS_train				98.49 263	99.33 231	97.05 289		99.55 79	97.46 220	99.24 194	99.83 70	92.58 294	99.72 219	98.09 199	97.51 273	98.68 290

BH-w/o		98.00 223	97.89 219	98.32 287	99.35 226	96.20 330	99.01 326	98.90 339	96.42 311	98.38 315	99.00 339	95.26 202	99.72 219	96.06 321	98.61 210	99.03 245

ACMP	97.20 11	98.06 208	97.94 212	98.45 272	99.37 222	97.01 294	99.44 195	99.49 147	97.54 213	98.45 312	99.79 118	91.95 309	99.72 219	97.91 215	97.49 278	98.62 318
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 218	97.90 215	98.40 280	99.23 256	96.80 308	99.70 54	99.60 54	97.12 254	98.18 329	99.70 159	91.73 315	99.72 219	98.39 176	97.45 280	98.68 290
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							99.23 278				65.14 411	94.18 252	99.71 225	97.58 248

ADS-MVSNet		98.20 192	98.08 195	98.56 257	99.33 231	96.48 320	99.23 278	99.15 304	96.24 321	99.10 224	99.67 181	94.11 253	99.71 225	96.81 302	99.05 184	99.48 183

JIA-IIPM		97.50 290	97.02 304	98.93 203	98.73 345	97.80 256	99.30 249	98.97 326	91.73 384	98.91 255	94.86 399	95.10 206	99.71 225	97.58 248	97.98 247	99.28 221

EPMVS		97.82 251	97.65 243	98.35 284	98.88 323	95.98 333	99.49 174	94.71 406	97.57 207	99.26 192	99.48 253	92.46 301	99.71 225	97.87 219	99.08 182	99.35 213

TDRefinement		95.42 339	94.57 346	97.97 313	89.83 409	96.11 332	99.48 179	98.75 355	96.74 283	96.68 367	99.88 37	88.65 355	99.71 225	98.37 179	82.74 398	98.09 365

ACMM	97.58 5	98.37 180	98.34 173	98.48 265	99.41 209	97.10 283	99.56 124	99.45 199	98.53 92	99.04 237	99.85 54	93.00 278	99.71 225	98.74 130	97.45 280	98.64 309
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 228	97.77 229	98.57 254	99.59 150	96.61 316	99.45 189	99.08 312	98.21 129	98.88 260	99.80 106	88.66 354	99.70 231	98.58 155	97.72 259	99.39 207

CHOSEN 280x420		99.12 100	99.13 77	99.08 182	99.66 122	97.89 251	98.43 381	99.71 13	98.88 59	99.62 106	99.76 136	96.63 150	99.70 231	99.46 48	99.99 1	99.66 125

EC-MVSNet		99.44 40	99.39 30	99.58 94	99.56 158	99.49 91	99.88 3	99.58 62	98.38 105	99.73 66	99.69 169	98.20 96	99.70 231	99.64 27	99.82 95	99.54 163

PatchmatchNet		98.31 183	98.36 171	98.19 297	99.16 279	95.32 349	99.27 264	98.92 333	97.37 232	99.37 164	99.58 217	94.90 212	99.70 231	97.43 266	99.21 168	99.54 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 203	97.99 205	98.44 275	99.41 209	96.96 300	99.60 97	99.56 71	98.09 147	98.15 330	99.91 21	90.87 331	99.70 231	98.88 105	97.45 280	98.67 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 290	96.90 308	99.29 158	99.23 256	98.78 189	99.32 244	98.90 339	97.52 216	98.56 305	98.09 383	84.72 381	99.69 236	97.86 220	97.88 252	99.39 207

HQP_MVS		98.27 188	98.22 181	98.44 275	99.29 243	96.97 298	99.39 221	99.47 179	98.97 51	99.11 221	99.61 208	92.71 289	99.69 236	97.78 228	97.63 262	98.67 297

plane_prior5								99.47 179					99.69 236	97.78 228	97.63 262	98.67 297

D2MVS		98.41 175	98.50 165	98.15 302	99.26 250	96.62 315	99.40 217	99.61 48	97.71 191	98.98 246	99.36 285	96.04 170	99.67 239	98.70 135	97.41 285	98.15 363

IS-MVSNet		99.05 114	98.87 121	99.57 96	99.73 87	99.32 109	99.75 40	99.20 298	98.02 161	99.56 120	99.86 49	96.54 154	99.67 239	98.09 199	99.13 176	99.73 97

CLD-MVS		98.16 197	98.10 191	98.33 285	99.29 243	96.82 307	98.75 360	99.44 207	97.83 177	99.13 217	99.55 227	92.92 280	99.67 239	98.32 185	97.69 260	98.48 339
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 305	97.30 289	97.09 347	99.43 202	93.31 378	99.73 49	98.87 344	98.83 64	99.28 183	99.80 106	84.45 382	99.66 242	97.88 217	97.45 280	98.30 355

AUN-MVS		96.88 315	96.31 321	98.59 250	99.48 192	97.04 292	99.27 264	99.22 294	97.44 225	98.51 308	99.41 270	91.97 308	99.66 242	97.71 239	83.83 396	99.07 242

UniMVSNet_ETH3D		97.32 302	96.81 310	98.87 220	99.40 214	97.46 269	99.51 156	99.53 98	95.86 341	98.54 307	99.77 132	82.44 390	99.66 242	98.68 140	97.52 272	99.50 181

OPM-MVS		98.19 193	98.10 191	98.45 272	98.88 323	97.07 287	99.28 259	99.38 234	98.57 88	99.22 199	99.81 93	92.12 305	99.66 242	98.08 203	97.54 271	98.61 327
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 234	97.78 227	98.32 287	99.46 195	96.68 313	99.56 124	99.54 87	98.41 103	97.79 346	99.87 45	90.18 340	99.66 242	98.05 207	97.18 294	98.62 318

hse-mvs2		97.50 290	97.14 298	98.59 250	99.49 185	97.05 289	99.28 259	99.22 294	98.94 54	99.66 87	99.42 266	94.93 209	99.65 247	99.48 45	83.80 397	99.08 237

VPA-MVSNet		98.29 186	97.95 210	99.30 155	99.16 279	99.54 81	99.50 163	99.58 62	98.27 118	99.35 170	99.37 282	92.53 296	99.65 247	99.35 55	94.46 349	98.72 275

TR-MVS		97.76 258	97.41 275	98.82 229	99.06 299	97.87 252	98.87 349	98.56 370	96.63 294	98.68 289	99.22 315	92.49 297	99.65 247	95.40 339	97.79 257	98.95 256

gm-plane-assit					98.54 364	92.96 380			94.65 362		99.15 323		99.64 250	97.56 253

HQP4-MVS										98.66 290			99.64 250			98.64 309

HQP-MVS		98.02 218	97.90 215	98.37 283	99.19 266	96.83 305	98.98 332	99.39 226	98.24 123	98.66 290	99.40 273	92.47 298	99.64 250	97.19 281	97.58 267	98.64 309

PAPM		97.59 283	97.09 302	99.07 183	99.06 299	98.26 229	98.30 388	99.10 309	94.88 356	98.08 332	99.34 292	96.27 165	99.64 250	89.87 385	98.92 194	99.31 219

TAPA-MVS	97.07 15	97.74 264	97.34 284	98.94 201	99.70 101	97.53 267	99.25 275	99.51 117	91.90 383	99.30 179	99.63 199	98.78 48	99.64 250	88.09 392	99.87 59	99.65 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 179	98.09 194	99.24 167	99.26 250	99.32 109	99.56 124	99.55 79	97.45 223	98.71 281	99.83 70	93.23 273	99.63 255	98.88 105	96.32 309	98.76 268

ITE_SJBPF				98.08 304	99.29 243	96.37 323		98.92 333	98.34 111	98.83 268	99.75 139	91.09 328	99.62 256	95.82 326	97.40 286	98.25 359

LF4IMVS		97.52 287	97.46 263	97.70 330	98.98 313	95.55 341	99.29 254	98.82 349	98.07 152	98.66 290	99.64 193	89.97 341	99.61 257	97.01 289	96.68 299	97.94 376

tpm		97.67 277	97.55 251	98.03 306	99.02 305	95.01 355	99.43 199	98.54 372	96.44 309	99.12 219	99.34 292	91.83 312	99.60 258	97.75 234	96.46 305	99.48 183

tpm2		97.44 297	97.34 284	97.74 328	99.15 283	94.36 366	99.45 189	98.94 329	93.45 375	98.90 257	99.44 262	91.35 325	99.59 259	97.31 272	98.07 245	99.29 220

baseline2		97.87 240	97.55 251	98.82 229	99.18 269	98.02 241	99.41 209	96.58 400	96.97 269	96.51 368	99.17 320	93.43 270	99.57 260	97.71 239	99.03 186	98.86 258

MS-PatchMatch		97.24 307	97.32 287	96.99 348	98.45 367	93.51 377	98.82 353	99.32 270	97.41 229	98.13 331	99.30 302	88.99 349	99.56 261	95.68 332	99.80 102	97.90 379

TinyColmap		97.12 310	96.89 309	97.83 322	99.07 297	95.52 344	98.57 374	98.74 358	97.58 206	97.81 345	99.79 118	88.16 361	99.56 261	95.10 344	97.21 292	98.39 351

USDC		97.34 301	97.20 296	97.75 327	99.07 297	95.20 351	98.51 378	99.04 319	97.99 162	98.31 319	99.86 49	89.02 348	99.55 263	95.67 333	97.36 288	98.49 338

MSLP-MVS++		99.46 32	99.47 17	99.44 133	99.60 148	99.16 131	99.41 209	99.71 13	98.98 48	99.45 140	99.78 124	99.19 9	99.54 264	99.28 66	99.84 82	99.63 140

TAMVS		99.12 100	99.08 84	99.24 167	99.46 195	98.55 206	99.51 156	99.46 188	98.09 147	99.45 140	99.82 79	98.34 90	99.51 265	98.70 135	98.93 192	99.67 122

EPNet_dtu		98.03 216	97.96 208	98.23 295	98.27 370	95.54 343	99.23 278	98.75 355	99.02 38	97.82 344	99.71 155	96.11 168	99.48 266	93.04 369	99.65 135	99.69 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		95.97 332	95.69 334	96.81 355	97.78 377	92.79 381	99.16 289	98.93 330	96.16 328	94.08 385	99.22 315	82.72 388	99.47 267	95.67 333	97.50 275	98.17 362

MVP-Stereo		97.81 253	97.75 234	97.99 312	97.53 381	96.60 317	98.96 336	98.85 346	97.22 246	97.23 357	99.36 285	95.28 199	99.46 268	95.51 335	99.78 109	97.92 378
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 166	98.67 143	98.30 289	99.35 226	95.59 340	99.50 163	99.55 79	98.60 86	99.39 160	99.83 70	94.48 240	99.45 269	98.75 129	98.56 216	99.85 35

test-LLR		98.06 208	97.90 215	98.55 259	98.79 334	97.10 283	98.67 366	97.75 386	97.34 234	98.61 301	98.85 352	94.45 242	99.45 269	97.25 275	99.38 155	99.10 232

TESTMET0.1,1		97.55 285	97.27 295	98.40 280	98.93 318	96.53 318	98.67 366	97.61 389	96.96 270	98.64 297	99.28 306	88.63 356	99.45 269	97.30 273	99.38 155	99.21 227

test-mter		97.49 295	97.13 300	98.55 259	98.79 334	97.10 283	98.67 366	97.75 386	96.65 290	98.61 301	98.85 352	88.23 360	99.45 269	97.25 275	99.38 155	99.10 232

mvs_anonymous		99.03 117	98.99 101	99.16 175	99.38 219	98.52 212	99.51 156	99.38 234	97.79 182	99.38 162	99.81 93	97.30 126	99.45 269	99.35 55	98.99 189	99.51 177

tfpnnormal		97.84 246	97.47 261	98.98 195	99.20 263	99.22 125	99.64 81	99.61 48	96.32 315	98.27 323	99.70 159	93.35 272	99.44 274	95.69 331	95.40 332	98.27 357

v7n		97.87 240	97.52 255	98.92 205	98.76 343	98.58 204	99.84 11	99.46 188	96.20 324	98.91 255	99.70 159	94.89 213	99.44 274	96.03 322	93.89 360	98.75 270

jajsoiax		98.43 172	98.28 178	98.88 216	98.60 360	98.43 222	99.82 15	99.53 98	98.19 131	98.63 298	99.80 106	93.22 275	99.44 274	99.22 72	97.50 275	98.77 266

mvs_tets		98.40 178	98.23 180	98.91 209	98.67 353	98.51 214	99.66 72	99.53 98	98.19 131	98.65 296	99.81 93	92.75 284	99.44 274	99.31 62	97.48 279	98.77 266

Vis-MVSNet (Re-imp)		98.87 132	98.72 137	99.31 150	99.71 96	98.88 174	99.80 24	99.44 207	97.91 168	99.36 167	99.78 124	95.49 193	99.43 278	97.91 215	99.11 177	99.62 142

OPU-MVS				99.64 80	99.56 158	99.72 42	99.60 97				99.70 159	99.27 5	99.42 279	98.24 189	99.80 102	99.79 74

Anonymous20231211		97.88 238	97.54 254	98.90 211	99.71 96	98.53 208	99.48 179	99.57 66	94.16 366	98.81 270	99.68 175	93.23 273	99.42 279	98.84 118	94.42 351	98.76 268

VPNet		97.84 246	97.44 269	99.01 191	99.21 261	98.94 168	99.48 179	99.57 66	98.38 105	99.28 183	99.73 150	88.89 350	99.39 281	99.19 74	93.27 367	98.71 277

nrg030		98.64 163	98.42 168	99.28 162	99.05 302	99.69 47	99.81 19	99.46 188	98.04 158	99.01 240	99.82 79	96.69 148	99.38 282	99.34 59	94.59 348	98.78 263

GA-MVS		97.85 243	97.47 261	99.00 193	99.38 219	97.99 243	98.57 374	99.15 304	97.04 265	98.90 257	99.30 302	89.83 342	99.38 282	96.70 307	98.33 226	99.62 142

UniMVSNet (Re)		98.29 186	98.00 204	99.13 180	99.00 307	99.36 107	99.49 174	99.51 117	97.95 164	98.97 248	99.13 325	96.30 164	99.38 282	98.36 181	93.34 365	98.66 305

FIs		98.78 150	98.63 148	99.23 169	99.18 269	99.54 81	99.83 14	99.59 58	98.28 116	98.79 274	99.81 93	96.75 146	99.37 285	99.08 85	96.38 307	98.78 263

PS-MVSNAJss		98.92 129	98.92 113	98.90 211	98.78 337	98.53 208	99.78 31	99.54 87	98.07 152	99.00 244	99.76 136	99.01 18	99.37 285	99.13 80	97.23 291	98.81 261

CDS-MVSNet		99.09 109	99.03 91	99.25 165	99.42 204	98.73 191	99.45 189	99.46 188	98.11 144	99.46 139	99.77 132	98.01 106	99.37 285	98.70 135	98.92 194	99.66 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 336	95.16 341	97.51 336	99.30 239	93.69 374	98.88 347	95.78 401	85.09 398	98.78 275	92.65 401	91.29 326	99.37 285	94.85 348	99.85 74	99.46 195

v1192		97.81 253	97.44 269	98.91 209	98.88 323	98.68 194	99.51 156	99.34 253	96.18 326	99.20 205	99.34 292	94.03 256	99.36 289	95.32 341	95.18 336	98.69 285

EI-MVSNet		98.67 160	98.67 143	98.68 245	99.35 226	97.97 244	99.50 163	99.38 234	96.93 275	99.20 205	99.83 70	97.87 109	99.36 289	98.38 177	97.56 269	98.71 277

MVSTER		98.49 167	98.32 175	99.00 193	99.35 226	99.02 151	99.54 140	99.38 234	97.41 229	99.20 205	99.73 150	93.86 263	99.36 289	98.87 108	97.56 269	98.62 318

gg-mvs-nofinetune		96.17 329	95.32 340	98.73 239	98.79 334	98.14 235	99.38 226	94.09 407	91.07 388	98.07 335	91.04 405	89.62 346	99.35 292	96.75 304	99.09 181	98.68 290

pm-mvs1		97.68 274	97.28 292	98.88 216	99.06 299	98.62 201	99.50 163	99.45 199	96.32 315	97.87 342	99.79 118	92.47 298	99.35 292	97.54 255	93.54 364	98.67 297

OurMVSNet-221017-0		97.88 238	97.77 229	98.19 297	98.71 349	96.53 318	99.88 3	99.00 323	97.79 182	98.78 275	99.94 6	91.68 316	99.35 292	97.21 277	96.99 298	98.69 285

EGC-MVSNET		82.80 370	77.86 376	97.62 332	97.91 374	96.12 331	99.33 243	99.28 284	8.40 413	25.05 414	99.27 309	84.11 383	99.33 295	89.20 387	98.22 234	97.42 387

pmmvs6		96.53 321	96.09 326	97.82 324	98.69 351	95.47 345	99.37 228	99.47 179	93.46 374	97.41 351	99.78 124	87.06 369	99.33 295	96.92 299	92.70 374	98.65 307

V42		98.06 208	97.79 224	98.86 223	98.98 313	98.84 180	99.69 58	99.34 253	96.53 301	99.30 179	99.37 282	94.67 230	99.32 297	97.57 252	94.66 346	98.42 347

lessismore_v0				97.79 326	98.69 351	95.44 347		94.75 405		95.71 376	99.87 45	88.69 353	99.32 297	95.89 325	94.93 343	98.62 318

OpenMVS_ROB	92.34 20	94.38 350	93.70 356	96.41 360	97.38 383	93.17 379	99.06 311	98.75 355	86.58 396	94.84 383	98.26 376	81.53 393	99.32 297	89.01 388	97.87 253	96.76 390

v8		97.95 230	97.63 247	98.93 203	98.95 317	98.81 186	99.80 24	99.41 217	96.03 338	99.10 224	99.42 266	94.92 211	99.30 300	96.94 296	94.08 357	98.66 305

v1921920		97.80 255	97.45 264	98.84 227	98.80 333	98.53 208	99.52 149	99.34 253	96.15 330	99.24 194	99.47 256	93.98 258	99.29 301	95.40 339	95.13 338	98.69 285

anonymousdsp		98.44 171	98.28 178	98.94 201	98.50 365	98.96 162	99.77 33	99.50 137	97.07 260	98.87 263	99.77 132	94.76 223	99.28 302	98.66 142	97.60 265	98.57 333

MVSFormer		99.17 85	99.12 78	99.29 158	99.51 174	98.94 168	99.88 3	99.46 188	97.55 210	99.80 44	99.65 187	97.39 120	99.28 302	99.03 88	99.85 74	99.65 129

test_djsdf		98.67 160	98.57 160	98.98 195	98.70 350	98.91 172	99.88 3	99.46 188	97.55 210	99.22 199	99.88 37	95.73 185	99.28 302	99.03 88	97.62 264	98.75 270

cascas		97.69 272	97.43 273	98.48 265	98.60 360	97.30 272	98.18 392	99.39 226	92.96 378	98.41 313	98.78 359	93.77 266	99.27 305	98.16 196	98.61 210	98.86 258

v144192		97.92 234	97.60 249	98.87 220	98.83 332	98.65 197	99.55 136	99.34 253	96.20 324	99.32 175	99.40 273	94.36 244	99.26 306	96.37 318	95.03 340	98.70 281

dmvs_re		98.08 206	98.16 183	97.85 319	99.55 162	94.67 361	99.70 54	98.92 333	98.15 136	99.06 234	99.35 288	93.67 269	99.25 307	97.77 231	97.25 290	99.64 136

v2v482		98.06 208	97.77 229	98.92 205	98.90 321	98.82 184	99.57 118	99.36 243	96.65 290	99.19 208	99.35 288	94.20 249	99.25 307	97.72 238	94.97 341	98.69 285

v1240		97.69 272	97.32 287	98.79 235	98.85 330	98.43 222	99.48 179	99.36 243	96.11 333	99.27 188	99.36 285	93.76 267	99.24 309	94.46 352	95.23 335	98.70 281

v1144		97.98 225	97.69 239	98.85 226	98.87 326	98.66 196	99.54 140	99.35 249	96.27 319	99.23 198	99.35 288	94.67 230	99.23 310	96.73 305	95.16 337	98.68 290

v10		97.85 243	97.52 255	98.86 223	98.99 310	98.67 195	99.75 40	99.41 217	95.70 342	98.98 246	99.41 270	94.75 224	99.23 310	96.01 324	94.63 347	98.67 297

WR-MVS_H		98.13 200	97.87 220	98.90 211	99.02 305	98.84 180	99.70 54	99.59 58	97.27 240	98.40 314	99.19 319	95.53 191	99.23 310	98.34 182	93.78 362	98.61 327

miper_enhance_ethall		98.16 197	98.08 195	98.41 278	98.96 316	97.72 260	98.45 380	99.32 270	96.95 272	98.97 248	99.17 320	97.06 135	99.22 313	97.86 220	95.99 316	98.29 356

GG-mvs-BLEND				98.45 272	98.55 363	98.16 233	99.43 199	93.68 408		97.23 357	98.46 367	89.30 347	99.22 313	95.43 338	98.22 234	97.98 374

FC-MVSNet-test		98.75 153	98.62 153	99.15 179	99.08 296	99.45 97	99.86 10	99.60 54	98.23 126	98.70 287	99.82 79	96.80 143	99.22 313	99.07 86	96.38 307	98.79 262

UniMVSNet_NR-MVSNet		98.22 189	97.97 207	98.96 198	98.92 319	98.98 155	99.48 179	99.53 98	97.76 186	98.71 281	99.46 260	96.43 161	99.22 313	98.57 158	92.87 372	98.69 285

DU-MVS		98.08 206	97.79 224	98.96 198	98.87 326	98.98 155	99.41 209	99.45 199	97.87 170	98.71 281	99.50 245	94.82 215	99.22 313	98.57 158	92.87 372	98.68 290

cl____		98.01 221	97.84 222	98.55 259	99.25 254	97.97 244	98.71 364	99.34 253	96.47 308	98.59 304	99.54 232	95.65 188	99.21 318	97.21 277	95.77 322	98.46 344

WR-MVS		98.06 208	97.73 236	99.06 185	98.86 329	99.25 122	99.19 285	99.35 249	97.30 238	98.66 290	99.43 264	93.94 259	99.21 318	98.58 155	94.28 353	98.71 277

test_0402		96.64 319	96.24 322	97.85 319	98.85 330	96.43 322	99.44 195	99.26 287	93.52 372	96.98 364	99.52 239	88.52 357	99.20 320	92.58 376	97.50 275	97.93 377

SixPastTwentyTwo		97.50 290	97.33 286	98.03 306	98.65 354	96.23 329	99.77 33	98.68 366	97.14 251	97.90 340	99.93 9	90.45 334	99.18 321	97.00 290	96.43 306	98.67 297

cl22		97.85 243	97.64 246	98.48 265	99.09 293	97.87 252	98.60 373	99.33 260	97.11 257	98.87 263	99.22 315	92.38 303	99.17 322	98.21 190	95.99 316	98.42 347

WB-MVSnew		97.65 279	97.65 243	97.63 331	98.78 337	97.62 265	99.13 295	98.33 375	97.36 233	99.07 229	98.94 346	95.64 189	99.15 323	92.95 370	98.68 209	96.12 397

IterMVS-SCA-FT		97.82 251	97.75 234	98.06 305	99.57 154	96.36 324	99.02 321	99.49 147	97.18 248	98.71 281	99.72 154	92.72 287	99.14 324	97.44 265	95.86 321	98.67 297

pmmvs5		97.52 287	97.30 289	98.16 299	98.57 362	96.73 309	99.27 264	98.90 339	96.14 331	98.37 316	99.53 236	91.54 322	99.14 324	97.51 257	95.87 320	98.63 316

v148		97.79 256	97.55 251	98.50 262	98.74 344	97.72 260	99.54 140	99.33 260	96.26 320	98.90 257	99.51 242	94.68 229	99.14 324	97.83 224	93.15 369	98.63 316

miper_ehance_all_eth		98.18 195	98.10 191	98.41 278	99.23 256	97.72 260	98.72 363	99.31 274	96.60 297	98.88 260	99.29 304	97.29 127	99.13 327	97.60 246	95.99 316	98.38 352

NR-MVSNet		97.97 228	97.61 248	99.02 190	98.87 326	99.26 120	99.47 185	99.42 215	97.63 201	97.08 362	99.50 245	95.07 207	99.13 327	97.86 220	93.59 363	98.68 290

IterMVS		97.83 248	97.77 229	98.02 308	99.58 152	96.27 327	99.02 321	99.48 159	97.22 246	98.71 281	99.70 159	92.75 284	99.13 327	97.46 263	96.00 315	98.67 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 351	94.90 343	91.84 376	97.24 387	80.01 406	98.52 377	99.48 159	89.01 393	91.99 394	99.67 181	85.67 373	99.13 327	95.44 337	97.03 297	96.39 394
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 213	97.96 208	98.33 285	99.26 250	97.38 271	98.56 376	99.31 274	96.65 290	98.88 260	99.52 239	96.58 152	99.12 331	97.39 268	95.53 330	98.47 341

pmmvs4		98.13 200	97.90 215	98.81 232	98.61 359	98.87 175	98.99 329	99.21 297	96.44 309	99.06 234	99.58 217	95.90 179	99.11 332	97.18 283	96.11 313	98.46 344

TransMVSNet (Re)		97.15 309	96.58 314	98.86 223	99.12 285	98.85 179	99.49 174	98.91 337	95.48 345	97.16 360	99.80 106	93.38 271	99.11 332	94.16 358	91.73 377	98.62 318

ambc				93.06 374	92.68 405	82.36 399	98.47 379	98.73 363		95.09 381	97.41 388	55.55 406	99.10 334	96.42 316	91.32 378	97.71 380

Baseline_NR-MVSNet		97.76 258	97.45 264	98.68 245	99.09 293	98.29 227	99.41 209	98.85 346	95.65 343	98.63 298	99.67 181	94.82 215	99.10 334	98.07 206	92.89 371	98.64 309

test_vis3_rt		87.04 366	85.81 369	90.73 380	93.99 404	81.96 401	99.76 36	90.23 415	92.81 380	81.35 403	91.56 403	40.06 412	99.07 336	94.27 355	88.23 390	91.15 403

CP-MVSNet		98.09 204	97.78 227	99.01 191	98.97 315	99.24 123	99.67 67	99.46 188	97.25 242	98.48 311	99.64 193	93.79 265	99.06 337	98.63 145	94.10 356	98.74 273

PS-CasMVS		97.93 231	97.59 250	98.95 200	98.99 310	99.06 147	99.68 64	99.52 103	97.13 252	98.31 319	99.68 175	92.44 302	99.05 338	98.51 166	94.08 357	98.75 270

K. test v3		97.10 311	96.79 311	98.01 309	98.72 347	96.33 325	99.87 7	97.05 392	97.59 204	96.16 372	99.80 106	88.71 352	99.04 339	96.69 308	96.55 304	98.65 307

new_pmnet		96.38 325	96.03 327	97.41 338	98.13 373	95.16 354	99.05 313	99.20 298	93.94 367	97.39 354	98.79 358	91.61 321	99.04 339	90.43 383	95.77 322	98.05 368

DIV-MVS_self_test		98.01 221	97.85 221	98.48 265	99.24 255	97.95 248	98.71 364	99.35 249	96.50 302	98.60 303	99.54 232	95.72 186	99.03 341	97.21 277	95.77 322	98.46 344

IterMVS-LS		98.46 170	98.42 168	98.58 253	99.59 150	98.00 242	99.37 228	99.43 213	96.94 274	99.07 229	99.59 213	97.87 109	99.03 341	98.32 185	95.62 327	98.71 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 279	97.68 240	97.55 335	98.62 357	94.97 356	98.84 351	99.30 278	96.83 281	98.19 328	99.34 292	97.01 138	99.02 343	95.00 347	96.01 314	98.64 309

Patchmtry		97.75 262	97.40 276	98.81 232	99.10 290	98.87 175	99.11 304	99.33 260	94.83 358	98.81 270	99.38 279	94.33 245	99.02 343	96.10 320	95.57 328	98.53 335

N_pmnet		94.95 345	95.83 332	92.31 375	98.47 366	79.33 407	99.12 298	92.81 413	93.87 368	97.68 347	99.13 325	93.87 262	99.01 345	91.38 380	96.19 311	98.59 331

CR-MVSNet		98.17 196	97.93 213	98.87 220	99.18 269	98.49 216	99.22 282	99.33 260	96.96 270	99.56 120	99.38 279	94.33 245	99.00 346	94.83 349	98.58 213	99.14 229

c3_l		98.12 202	98.04 200	98.38 282	99.30 239	97.69 264	98.81 354	99.33 260	96.67 288	98.83 268	99.34 292	97.11 131	98.99 347	97.58 248	95.34 333	98.48 339

test0.0.03 1		97.71 270	97.42 274	98.56 257	98.41 369	97.82 255	98.78 357	98.63 368	97.34 234	98.05 336	98.98 342	94.45 242	98.98 348	95.04 346	97.15 295	98.89 257

PatchT		97.03 313	96.44 318	98.79 235	98.99 310	98.34 226	99.16 289	99.07 315	92.13 382	99.52 129	97.31 392	94.54 238	98.98 348	88.54 390	98.73 207	99.03 245

GBi-Net		97.68 274	97.48 259	98.29 290	99.51 174	97.26 276	99.43 199	99.48 159	96.49 303	99.07 229	99.32 299	90.26 336	98.98 348	97.10 285	96.65 300	98.62 318

test1		97.68 274	97.48 259	98.29 290	99.51 174	97.26 276	99.43 199	99.48 159	96.49 303	99.07 229	99.32 299	90.26 336	98.98 348	97.10 285	96.65 300	98.62 318

FMVSNet3		98.03 216	97.76 233	98.84 227	99.39 217	98.98 155	99.40 217	99.38 234	96.67 288	99.07 229	99.28 306	92.93 279	98.98 348	97.10 285	96.65 300	98.56 334

FMVSNet2		97.72 267	97.36 279	98.80 234	99.51 174	98.84 180	99.45 189	99.42 215	96.49 303	98.86 267	99.29 304	90.26 336	98.98 348	96.44 315	96.56 303	98.58 332

FMVSNet1		96.84 316	96.36 320	98.29 290	99.32 237	97.26 276	99.43 199	99.48 159	95.11 350	98.55 306	99.32 299	83.95 384	98.98 348	95.81 327	96.26 310	98.62 318

ppachtmachnet_test		97.49 295	97.45 264	97.61 333	98.62 357	95.24 350	98.80 355	99.46 188	96.11 333	98.22 326	99.62 204	96.45 159	98.97 355	93.77 360	95.97 319	98.61 327

TranMVSNet+NR-MVSNet		97.93 231	97.66 242	98.76 238	98.78 337	98.62 201	99.65 78	99.49 147	97.76 186	98.49 310	99.60 211	94.23 248	98.97 355	98.00 210	92.90 370	98.70 281

test_method		91.10 361	91.36 363	90.31 381	95.85 394	73.72 414	94.89 402	99.25 289	68.39 405	95.82 375	99.02 337	80.50 395	98.95 357	93.64 362	94.89 345	98.25 359

ADS-MVSNet2		98.02 218	98.07 198	97.87 318	99.33 231	95.19 352	99.23 278	99.08 312	96.24 321	99.10 224	99.67 181	94.11 253	98.93 358	96.81 302	99.05 184	99.48 183

ET-MVSNet_ETH3D		96.49 322	95.64 336	99.05 187	99.53 166	98.82 184	98.84 351	97.51 390	97.63 201	84.77 399	99.21 318	92.09 306	98.91 359	98.98 93	92.21 376	99.41 204

miper_lstm_enhance		98.00 223	97.91 214	98.28 293	99.34 230	97.43 270	98.88 347	99.36 243	96.48 306	98.80 272	99.55 227	95.98 172	98.91 359	97.27 274	95.50 331	98.51 337

PEN-MVS		97.76 258	97.44 269	98.72 240	98.77 342	98.54 207	99.78 31	99.51 117	97.06 262	98.29 322	99.64 193	92.63 293	98.89 361	98.09 199	93.16 368	98.72 275

testing3		97.28 303	96.76 312	98.82 229	99.37 222	98.07 239	99.45 189	99.36 243	97.56 209	97.89 341	98.95 345	83.70 385	98.82 362	96.03 322	98.56 216	99.58 154

testgi		97.65 279	97.50 258	98.13 303	99.36 225	96.45 321	99.42 206	99.48 159	97.76 186	97.87 342	99.45 261	91.09 328	98.81 363	94.53 351	98.52 219	99.13 231

testf1		90.42 364	90.68 365	89.65 384	97.78 377	73.97 412	99.13 295	98.81 351	89.62 390	91.80 395	98.93 347	62.23 404	98.80 364	86.61 398	91.17 379	96.19 395

APD_test2		90.42 364	90.68 365	89.65 384	97.78 377	73.97 412	99.13 295	98.81 351	89.62 390	91.80 395	98.93 347	62.23 404	98.80 364	86.61 398	91.17 379	96.19 395

MIMVSNet		97.73 265	97.45 264	98.57 254	99.45 200	97.50 268	99.02 321	98.98 325	96.11 333	99.41 153	99.14 324	90.28 335	98.74 366	95.74 329	98.93 192	99.47 189

LCM-MVSNet-Re		97.83 248	98.15 185	96.87 354	99.30 239	92.25 384	99.59 103	98.26 376	97.43 226	96.20 371	99.13 325	96.27 165	98.73 367	98.17 195	98.99 189	99.64 136

Syy-MVS		97.09 312	97.14 298	96.95 351	99.00 307	92.73 382	99.29 254	99.39 226	97.06 262	97.41 351	98.15 378	93.92 261	98.68 368	91.71 378	98.34 224	99.45 198

myMVS_eth3d		96.89 314	96.37 319	98.43 277	99.00 307	97.16 280	99.29 254	99.39 226	97.06 262	97.41 351	98.15 378	83.46 386	98.68 368	95.27 342	98.34 224	99.45 198

DTE-MVSNet		97.51 289	97.19 297	98.46 271	98.63 356	98.13 236	99.84 11	99.48 159	96.68 287	97.97 339	99.67 181	92.92 280	98.56 370	96.88 301	92.60 375	98.70 281

PC_three_1452									98.18 134	99.84 34	99.70 159	99.31 3	98.52 371	98.30 187	99.80 102	99.81 61

mvsany_test3		93.77 353	93.45 357	94.74 366	95.78 395	88.01 392	99.64 81	98.25 377	98.28 116	94.31 384	97.97 385	68.89 400	98.51 372	97.50 258	90.37 384	97.71 380

UnsupCasMVSNet_bld		93.53 354	92.51 360	96.58 359	97.38 383	93.82 370	98.24 389	99.48 159	91.10 387	93.10 389	96.66 394	74.89 398	98.37 373	94.03 359	87.71 391	97.56 385

Anonymous20240521		96.20 328	95.89 331	97.13 345	97.72 380	94.96 357	99.79 30	99.29 282	93.01 377	97.20 359	99.03 335	89.69 344	98.36 374	91.16 381	96.13 312	98.07 366

test_f		91.90 360	91.26 364	93.84 369	95.52 399	85.92 394	99.69 58	98.53 373	95.31 347	93.87 386	96.37 396	55.33 407	98.27 375	95.70 330	90.98 382	97.32 388

MDA-MVSNet_test_wron		95.45 338	94.60 345	98.01 309	98.16 372	97.21 279	99.11 304	99.24 291	93.49 373	80.73 405	98.98 342	93.02 277	98.18 376	94.22 357	94.45 350	98.64 309

UnsupCasMVSNet_eth		96.44 323	96.12 324	97.40 339	98.65 354	95.65 338	99.36 233	99.51 117	97.13 252	96.04 374	98.99 340	88.40 358	98.17 377	96.71 306	90.27 385	98.40 350

KD-MVS_2432*1600		94.62 346	93.72 354	97.31 340	97.19 389	95.82 336	98.34 384	99.20 298	95.00 354	97.57 348	98.35 372	87.95 363	98.10 378	92.87 372	77.00 403	98.01 370

miper_refine_blended		94.62 346	93.72 354	97.31 340	97.19 389	95.82 336	98.34 384	99.20 298	95.00 354	97.57 348	98.35 372	87.95 363	98.10 378	92.87 372	77.00 403	98.01 370

YYNet1		95.36 340	94.51 347	97.92 315	97.89 375	97.10 283	99.10 306	99.23 292	93.26 376	80.77 404	99.04 334	92.81 283	98.02 380	94.30 353	94.18 355	98.64 309

EU-MVSNet		97.98 225	98.03 201	97.81 325	98.72 347	96.65 314	99.66 72	99.66 28	98.09 147	98.35 317	99.82 79	95.25 203	98.01 381	97.41 267	95.30 334	98.78 263

Gipuma		90.99 362	90.15 367	93.51 370	98.73 345	90.12 390	93.98 403	99.45 199	79.32 401	92.28 392	94.91 398	69.61 399	97.98 382	87.42 394	95.67 326	92.45 401
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 341	94.73 344	97.15 343	95.53 398	95.94 334	99.35 238	99.10 309	95.13 348	93.55 387	97.54 387	88.15 362	97.91 383	94.58 350	89.69 388	97.61 383

PM-MVS		92.96 357	92.23 361	95.14 365	95.61 396	89.98 391	99.37 228	98.21 379	94.80 359	95.04 382	97.69 386	65.06 401	97.90 384	94.30 353	89.98 387	97.54 386

MDA-MVSNet-bldmvs		94.96 344	93.98 351	97.92 315	98.24 371	97.27 274	99.15 292	99.33 260	93.80 369	80.09 406	99.03 335	88.31 359	97.86 385	93.49 364	94.36 352	98.62 318

Patchmatch-RL test		95.84 334	95.81 333	95.95 363	95.61 396	90.57 389	98.24 389	98.39 374	95.10 352	95.20 379	98.67 362	94.78 219	97.77 386	96.28 319	90.02 386	99.51 177

Anonymous20231206		96.22 326	96.03 327	96.79 356	97.31 386	94.14 368	99.63 85	99.08 312	96.17 327	97.04 363	99.06 332	93.94 259	97.76 387	86.96 396	95.06 339	98.47 341

SD-MVS		99.41 49	99.52 11	99.05 187	99.74 80	99.68 48	99.46 188	99.52 103	99.11 26	99.88 22	99.91 21	99.43 1	97.70 388	98.72 133	99.93 27	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		97.25 305	97.35 281	96.95 351	97.84 376	93.61 376	99.57 118	96.63 398	96.13 332	98.87 263	98.61 365	94.59 233	97.70 388	95.08 345	98.86 198	99.55 161

dongtai		93.26 355	92.93 359	94.25 367	99.39 217	85.68 395	97.68 398	93.27 409	92.87 379	96.85 366	99.39 277	82.33 391	97.48 390	76.78 403	97.80 256	99.58 154

pmmvs3		94.09 352	93.25 358	96.60 358	94.76 403	94.49 363	98.92 343	98.18 381	89.66 389	96.48 369	98.06 384	86.28 370	97.33 391	89.68 386	87.20 392	97.97 375

KD-MVS_self_test		95.00 343	94.34 348	96.96 350	97.07 391	95.39 348	99.56 124	99.44 207	95.11 350	97.13 361	97.32 391	91.86 311	97.27 392	90.35 384	81.23 400	98.23 361

FMVSNet5		96.43 324	96.19 323	97.15 343	99.11 287	95.89 335	99.32 244	99.52 103	94.47 365	98.34 318	99.07 330	87.54 367	97.07 393	92.61 375	95.72 325	98.47 341

new-patchmatchnet		94.48 349	94.08 350	95.67 364	95.08 401	92.41 383	99.18 287	99.28 284	94.55 364	93.49 388	97.37 390	87.86 365	97.01 394	91.57 379	88.36 389	97.61 383

LCM-MVSNet		86.80 368	85.22 372	91.53 378	87.81 410	80.96 404	98.23 391	98.99 324	71.05 403	90.13 398	96.51 395	48.45 411	96.88 395	90.51 382	85.30 394	96.76 390

CL-MVSNet_self_test		94.49 348	93.97 352	96.08 362	96.16 393	93.67 375	98.33 386	99.38 234	95.13 348	97.33 355	98.15 378	92.69 291	96.57 396	88.67 389	79.87 401	97.99 373

MIMVSNet1		95.51 337	95.04 342	96.92 353	97.38 383	95.60 339	99.52 149	99.50 137	93.65 371	96.97 365	99.17 320	85.28 378	96.56 397	88.36 391	95.55 329	98.60 330

test20.03		96.12 330	95.96 329	96.63 357	97.44 382	95.45 346	99.51 156	99.38 234	96.55 300	96.16 372	99.25 312	93.76 267	96.17 398	87.35 395	94.22 354	98.27 357

tmp_tt		82.80 370	81.52 373	86.66 386	66.61 416	68.44 415	92.79 405	97.92 383	68.96 404	80.04 407	99.85 54	85.77 372	96.15 399	97.86 220	43.89 409	95.39 399

test_fmvs3		92.10 359	91.77 362	93.08 373	96.19 392	86.25 393	99.82 15	98.62 369	96.65 290	95.19 380	96.90 393	55.05 408	95.93 400	96.63 312	90.92 383	97.06 389

kuosan		90.92 363	90.11 368	93.34 371	98.78 337	85.59 396	98.15 393	93.16 411	89.37 392	92.07 393	98.38 371	81.48 394	95.19 401	62.54 410	97.04 296	99.25 224

dmvs_testset		95.02 342	96.12 324	91.72 377	99.10 290	80.43 405	99.58 111	97.87 385	97.47 219	95.22 378	98.82 354	93.99 257	95.18 402	88.09 392	94.91 344	99.56 160

PMMVS2		86.87 367	85.37 371	91.35 379	90.21 408	83.80 398	98.89 346	97.45 391	83.13 400	91.67 397	95.03 397	48.49 410	94.70 403	85.86 400	77.62 402	95.54 398

PMVS	70.75 22	75.98 376	74.97 377	79.01 392	70.98 415	55.18 417	93.37 404	98.21 379	65.08 409	61.78 410	93.83 400	21.74 417	92.53 404	78.59 402	91.12 381	89.34 405
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 369	85.65 370	82.75 390	86.77 411	63.39 416	98.35 383	98.92 333	74.11 402	83.39 401	98.98 342	50.85 409	92.40 405	84.54 401	94.97 341	92.46 400

WB-MVS		93.10 356	94.10 349	90.12 382	95.51 400	81.88 402	99.73 49	99.27 286	95.05 353	93.09 390	98.91 351	94.70 228	91.89 406	76.62 404	94.02 359	96.58 392

SSC-MVS		92.73 358	93.73 353	89.72 383	95.02 402	81.38 403	99.76 36	99.23 292	94.87 357	92.80 391	98.93 347	94.71 227	91.37 407	74.49 406	93.80 361	96.42 393

MVE	76.82 21	76.91 375	74.31 379	84.70 387	85.38 413	76.05 411	96.88 401	93.17 410	67.39 406	71.28 408	89.01 407	21.66 418	87.69 408	71.74 407	72.29 405	90.35 404
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 372	79.88 374	82.81 389	90.75 407	76.38 410	97.69 397	95.76 402	66.44 407	83.52 400	92.25 402	62.54 403	87.16 409	68.53 408	61.40 406	84.89 407

EMVS		80.02 373	79.22 375	82.43 391	91.19 406	76.40 409	97.55 400	92.49 414	66.36 408	83.01 402	91.27 404	64.63 402	85.79 410	65.82 409	60.65 407	85.08 406

ANet_high		77.30 374	74.86 378	84.62 388	75.88 414	77.61 408	97.63 399	93.15 412	88.81 394	64.27 409	89.29 406	36.51 413	83.93 411	75.89 405	52.31 408	92.33 402

wuyk23d		40.18 377	41.29 382	36.84 393	86.18 412	49.12 418	79.73 406	22.81 418	27.64 410	25.46 413	28.45 413	21.98 416	48.89 412	55.80 411	23.56 412	12.51 410

test123		39.01 379	42.50 381	28.53 394	39.17 417	20.91 419	98.75 360	19.17 419	19.83 412	38.57 411	66.67 409	33.16 414	15.42 413	37.50 413	29.66 411	49.26 408

testmvs		39.17 378	43.78 380	25.37 395	36.04 418	16.84 420	98.36 382	26.56 417	20.06 411	38.51 412	67.32 408	29.64 415	15.30 414	37.59 412	39.90 410	43.98 409

test_blank		0.13 383	0.17 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	1.57 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		24.64 380	32.85 383	0.00 396	0.00 419	0.00 421	0.00 407	99.51 117	0.00 414	0.00 415	99.56 224	96.58 152	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		8.27 382	11.03 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	99.01 18	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.30 381	11.06 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	99.58 217	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.02 384	0.03 387	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.27 415	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						97.16 280								95.47 336

FOURS1					99.91 1	99.93 1	99.87 7	99.56 71	99.10 27	99.81 41

test_one_0601					99.81 46	99.88 8		99.49 147	98.97 51	99.65 94	99.81 93	99.09 14

eth-test2					0.00 419

eth-test					0.00 419

RE-MVS-def			99.34 40		99.76 65	99.82 22	99.63 85	99.52 103	98.38 105	99.76 60	99.82 79	98.75 55		98.61 149	99.81 98	99.77 82

IU-MVS					99.84 32	99.88 8		99.32 270	98.30 115	99.84 34				98.86 113	99.85 74	99.89 19

save fliter					99.76 65	99.59 71	99.14 294	99.40 223	99.00 43

test0726					99.85 26	99.89 4	99.62 90	99.50 137	99.10 27	99.86 32	99.82 79	98.94 29

GSMVS																99.52 170

test_part2					99.81 46	99.83 16				99.77 55

sam_mvs1												94.86 214				99.52 170

sam_mvs												94.72 226

MTGPA								99.47 179

MTMP							99.54 140	98.88 342

test9_res														97.49 259	99.72 123	99.75 88

agg_prior2														97.21 277	99.73 122	99.75 88

test_prior4						99.56 77	98.99 329

test_prior2							98.96 336		98.34 111	99.01 240	99.52 239	98.68 64		97.96 212	99.74 120

新几何2							99.01 326

旧先验1					99.74 80	99.59 71		99.54 87			99.69 169	98.47 80			99.68 131	99.73 97

原ACMM2							98.95 339

test222					99.75 73	99.49 91	98.91 345	99.49 147	96.42 311	99.34 173	99.65 187	98.28 93			99.69 128	99.72 103

segment_acmp												98.96 24

testdata1							98.85 350		98.32 114

plane_prior7					99.29 243	97.03 293

plane_prior6					99.27 248	96.98 297						92.71 289

plane_prior4											99.61 208

plane_prior3						97.00 295			98.69 79	99.11 221

plane_prior2							99.39 221		98.97 51

plane_prior1					99.26 250

plane_prior						96.97 298	99.21 284		98.45 98						97.60 265

n2								0.00 420

nn								0.00 420

door-mid								98.05 382

test11								99.35 249

door								97.92 383

HQP5-MVS						96.83 305

HQP-NCC					99.19 266		98.98 332		98.24 123	98.66 290

ACMP_Plane					99.19 266		98.98 332		98.24 123	98.66 290

BP-MVS														97.19 281

HQP3-MVS								99.39 226							97.58 267

HQP2-MVS												92.47 298

NP-MVS					99.23 256	96.92 301					99.40 273

MDTV_nov1_ep13_2view						95.18 353	99.35 238		96.84 279	99.58 116		95.19 205		97.82 225		99.46 195

ACMMP++_ref															97.19 293

ACMMP++															97.43 284

Test By Simon												98.75 55

High-resolution multi-view benchmark