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 8	99.61 7	99.77 74	99.38 272	99.37 123	99.58 130	99.62 51	99.41 21	99.87 49	99.92 18	98.81 49	100.00 1	99.97 2	99.93 33	99.94 17

test_fmvsm_n_1920		99.69 5	99.66 4	99.78 71	99.84 38	99.44 116	99.58 130	99.69 22	99.43 17	99.98 13	99.91 26	98.62 76	100.00 1	99.97 2	99.95 23	99.90 25

test_vis1_n_1920		98.63 213	98.40 221	99.31 193	99.86 25	97.94 295	99.67 75	99.62 51	99.43 17	99.99 2	99.91 26	87.29 427	100.00 1	99.92 24	99.92 39	99.98 2

fmvsm_s_conf0.5_n_11		99.32 79	99.16 92	99.80 64	99.83 47	99.70 60	99.57 138	99.56 90	99.45 11	99.99 2	99.93 10	94.18 299	99.99 4	99.96 13	99.98 4	99.73 122

fmvsm_s_conf0.5_n_10		99.41 59	99.24 78	99.92 1	99.83 47	99.84 20	99.53 175	99.56 90	99.45 11	99.99 2	99.92 18	94.92 252	99.99 4	99.97 2	99.97 9	99.95 11

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 28	99.47 232	99.63 46	99.45 11	99.98 13	99.89 41	97.02 147	99.99 4	99.98 1	99.96 17	99.95 11

NormalMVS		99.27 89	99.19 88	99.52 139	99.89 8	98.83 219	99.65 88	99.52 130	99.10 48	99.84 56	99.76 183	95.80 213	99.99 4	99.30 89	99.84 102	99.74 113

SymmetryMVS		99.15 114	99.02 124	99.52 139	99.72 111	98.83 219	99.65 88	99.34 308	99.10 48	99.84 56	99.76 183	95.80 213	99.99 4	99.30 89	98.72 256	99.73 122

fmvsm_s_conf0.5_n_5		99.37 68	99.21 84	99.86 34	99.80 63	99.68 64	99.42 259	99.61 60	99.37 24	99.97 25	99.86 73	94.96 247	99.99 4	99.97 2	99.93 33	99.92 23

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 28	99.54 166	99.66 32	99.46 7	99.98 13	99.89 41	97.27 133	99.99 4	99.97 2	99.95 23	99.95 11

fmvsm_l_conf0.5_n_a		99.71 2	99.67 1	99.85 43	99.86 25	99.61 85	99.56 146	99.63 46	99.48 3	99.98 13	99.83 100	98.75 60	99.99 4	99.97 2	99.96 17	99.94 17

fmvsm_l_conf0.5_n		99.71 2	99.67 1	99.85 43	99.84 38	99.63 82	99.56 146	99.63 46	99.47 4	99.98 13	99.82 113	98.75 60	99.99 4	99.97 2	99.97 9	99.94 17

test_fmvsmconf_n		99.70 4	99.64 5	99.87 21	99.80 63	99.66 71	99.48 222	99.64 42	99.45 11	99.92 30	99.92 18	98.62 76	99.99 4	99.96 13	99.99 1	99.96 7

patch_mono-2		99.26 92	99.62 6	98.16 355	99.81 57	94.59 429	99.52 177	99.64 42	99.33 28	99.73 97	99.90 33	99.00 24	99.99 4	99.69 35	99.98 4	99.89 29

h-mvs33		97.70 327	97.28 350	98.97 241	99.70 122	97.27 323	99.36 289	99.45 242	98.94 78	99.66 124	99.64 248	94.93 250	99.99 4	99.48 64	84.36 465	99.65 169

xiu_mvs_v1_base_debu		99.29 85	99.27 73	99.34 185	99.63 164	98.97 183	99.12 367	99.51 151	98.86 84	99.84 56	99.47 315	98.18 104	99.99 4	99.50 57	99.31 191	99.08 294

xiu_mvs_v1_base		99.29 85	99.27 73	99.34 185	99.63 164	98.97 183	99.12 367	99.51 151	98.86 84	99.84 56	99.47 315	98.18 104	99.99 4	99.50 57	99.31 191	99.08 294

xiu_mvs_v1_base_debi		99.29 85	99.27 73	99.34 185	99.63 164	98.97 183	99.12 367	99.51 151	98.86 84	99.84 56	99.47 315	98.18 104	99.99 4	99.50 57	99.31 191	99.08 294

EPNet		98.86 178	98.71 185	99.30 198	97.20 454	98.18 275	99.62 106	98.91 399	99.28 31	98.63 358	99.81 128	95.96 201	99.99 4	99.24 99	99.72 148	99.73 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 11	99.83 47	99.74 54	99.51 186	99.62 51	99.46 7	99.99 2	99.90 33	96.60 171	99.98 20	99.95 16	99.95 23	99.96 7

MM		99.40 64	99.28 69	99.74 80	99.67 134	99.31 135	99.52 177	98.87 406	99.55 1	99.74 95	99.80 146	96.47 179	99.98 20	99.97 2	99.97 9	99.94 17

test_cas_vis1_n_1920		99.16 110	99.01 129	99.61 109	99.81 57	98.86 213	99.65 88	99.64 42	99.39 22	99.97 25	99.94 6	93.20 328	99.98 20	99.55 50	99.91 46	99.99 1

test_vis1_n		97.92 285	97.44 326	99.34 185	99.53 214	98.08 282	99.74 47	99.49 186	99.15 38	100.00 1	99.94 6	79.51 464	99.98 20	99.88 26	99.76 140	99.97 4

xiu_mvs_v2_base		99.26 92	99.25 77	99.29 201	99.53 214	98.91 203	99.02 391	99.45 242	98.80 94	99.71 107	99.26 374	98.94 34	99.98 20	99.34 81	99.23 200	98.98 308

PS-MVSNAJ		99.32 79	99.32 54	99.30 198	99.57 198	98.94 197	98.97 405	99.46 231	98.92 81	99.71 107	99.24 376	99.01 20	99.98 20	99.35 76	99.66 159	98.97 309

QAPM		98.67 208	98.30 228	99.80 64	99.20 321	99.67 68	99.77 34	99.72 14	94.74 424	98.73 338	99.90 33	95.78 215	99.98 20	96.96 355	99.88 76	99.76 107

3Dnovator	97.25 9	99.24 97	99.05 111	99.81 60	99.12 343	99.66 71	99.84 12	99.74 13	99.09 55	98.92 311	99.90 33	95.94 204	99.98 20	98.95 140	99.92 39	99.79 92

OpenMVS	96.50 16	98.47 219	98.12 240	99.52 139	99.04 362	99.53 101	99.82 16	99.72 14	94.56 427	98.08 393	99.88 52	94.73 267	99.98 20	97.47 322	99.76 140	99.06 300

fmvsm_s_conf0.5_n_3		99.37 68	99.20 86	99.87 21	99.75 92	99.70 60	99.48 222	99.66 32	99.45 11	99.99 2	99.93 10	94.64 276	99.97 29	99.94 21	99.97 9	99.95 11

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 70	99.88 10	99.56 146	99.55 100	99.15 38	99.90 34	99.90 33	99.00 24	99.97 29	99.11 117	99.91 46	99.86 42

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 254	99.65 75	99.50 197	99.61 60	99.45 11	99.87 49	99.92 18	97.31 130	99.97 29	99.95 16	99.99 1	99.97 4

test_fmvs1_n		98.41 225	98.14 237	99.21 214	99.82 53	97.71 308	99.74 47	99.49 186	99.32 29	99.99 2	99.95 3	85.32 442	99.97 29	99.82 29	99.84 102	99.96 7

CANet_DTU		98.97 166	98.87 162	99.25 208	99.33 285	98.42 267	99.08 376	99.30 335	99.16 37	99.43 191	99.75 188	95.27 235	99.97 29	98.56 210	99.95 23	99.36 266

MGCNet		99.15 114	98.96 140	99.73 83	98.92 380	99.37 123	99.37 283	96.92 463	99.51 2	99.66 124	99.78 170	96.69 166	99.97 29	99.84 28	99.97 9	99.84 53

MTAPA		99.52 28	99.39 40	99.89 11	99.90 4	99.86 18	99.66 82	99.47 220	98.79 95	99.68 113	99.81 128	98.43 89	99.97 29	98.88 150	99.90 57	99.83 63

PGM-MVS		99.45 46	99.31 60	99.86 34	99.87 20	99.78 47	99.58 130	99.65 39	97.84 234	99.71 107	99.80 146	99.12 15	99.97 29	98.33 235	99.87 79	99.83 63

mPP-MVS		99.44 50	99.30 62	99.86 34	99.88 13	99.79 41	99.69 62	99.48 198	98.12 186	99.50 175	99.75 188	98.78 53	99.97 29	98.57 207	99.89 68	99.83 63

CP-MVS		99.45 46	99.32 54	99.85 43	99.83 47	99.75 51	99.69 62	99.52 130	98.07 197	99.53 170	99.63 254	98.93 38	99.97 29	98.74 178	99.91 46	99.83 63

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 120	99.51 151	98.62 112	99.79 76	99.83 100	99.28 6	99.97 29	98.48 217	99.90 57	99.84 53
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3Dnovator+	97.12 13	99.18 104	98.97 136	99.82 57	99.17 335	99.68 64	99.81 20	99.51 151	99.20 33	98.72 339	99.89 41	95.68 219	99.97 29	98.86 158	99.86 87	99.81 79

fmvsm_s_conf0.5_n_9		99.41 59	99.28 69	99.81 60	99.84 38	99.52 105	99.48 222	99.62 51	99.46 7	99.99 2	99.92 18	95.24 239	99.96 41	99.97 2	99.97 9	99.96 7

lecture		99.60 14	99.50 19	99.89 11	99.89 8	99.90 3	99.75 42	99.59 73	99.06 61	99.88 43	99.85 80	98.41 93	99.96 41	99.28 92	99.84 102	99.83 63

KinetiMVS		99.12 129	98.92 149	99.70 87	99.67 134	99.40 121	99.67 75	99.63 46	98.73 102	99.94 28	99.81 128	94.54 282	99.96 41	98.40 226	99.93 33	99.74 113

fmvsm_s_conf0.5_n_7		99.34 75	99.29 66	99.48 155	99.70 122	98.63 240	99.42 259	99.63 46	99.46 7	99.98 13	99.88 52	95.59 222	99.96 41	99.97 2	99.98 4	99.85 46

fmvsm_s_conf0.5_n_2		99.32 79	99.13 95	99.89 11	99.80 63	99.77 48	99.44 246	99.58 78	99.47 4	99.99 2	99.93 10	94.04 304	99.96 41	99.96 13	99.93 33	99.93 22

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 177	99.54 109	99.13 41	99.89 40	99.89 41	98.96 27	99.96 41	99.04 126	99.90 57	99.85 46

our_new_method		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 177	99.54 109	99.13 41	99.89 40	99.89 41	98.96 27	99.96 41	99.04 126	99.90 57	99.85 46

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 81	99.52 177	99.65 39	99.10 48	99.98 13	99.92 18	97.35 129	99.96 41	99.94 21	99.92 39	99.95 11

fmvsm_s_conf0.5_n		99.51 29	99.40 38	99.85 43	99.84 38	99.65 75	99.51 186	99.67 27	99.13 41	99.98 13	99.92 18	96.60 171	99.96 41	99.95 16	99.96 17	99.95 11

mvsany_test1		99.50 31	99.46 28	99.62 108	99.61 183	99.09 165	98.94 411	99.48 198	99.10 48	99.96 27	99.91 26	98.85 44	99.96 41	99.72 32	99.58 169	99.82 72

test_fmvs1		98.88 172	98.79 176	99.16 219	99.69 127	97.61 312	99.55 161	99.49 186	99.32 29	99.98 13	99.91 26	91.41 376	99.96 41	99.82 29	99.92 39	99.90 25

DVP-MVS++		99.59 15	99.50 19	99.88 15	99.51 223	99.88 10	99.87 8	99.51 151	98.99 69	99.88 43	99.81 128	99.27 7	99.96 41	98.85 160	99.80 125	99.81 79

MSC_two_6792asdad				99.87 21	99.51 223	99.76 49		99.33 316					99.96 41	98.87 153	99.84 102	99.89 29

No_MVS				99.87 21	99.51 223	99.76 49		99.33 316					99.96 41	98.87 153	99.84 102	99.89 29

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 341	99.56 161	99.54 288	98.58 78	99.96 41	96.93 358	99.75 142

SED-MVS		99.61 10	99.52 14	99.88 15	99.84 38	99.90 3	99.60 113	99.48 198	99.08 56	99.91 31	99.81 128	99.20 9	99.96 41	98.91 147	99.85 94	99.79 92

test_241102_TWO								99.48 198	99.08 56	99.88 43	99.81 128	98.94 34	99.96 41	98.91 147	99.84 102	99.88 35

ZNCC-MVS		99.47 40	99.33 52	99.87 21	99.87 20	99.81 33	99.64 95	99.67 27	98.08 196	99.55 167	99.64 248	98.91 39	99.96 41	98.72 181	99.90 57	99.82 72

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 138	99.37 294	99.10 48	99.81 69	99.80 146	98.94 34	99.96 41	98.93 144	99.86 87	99.81 79
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 69	99.81 69	99.80 146	99.09 16	99.96 41	98.85 160	99.90 57	99.88 35

test_0728_SECOND				99.91 6	99.84 38	99.89 6	99.57 138	99.51 151					99.96 41	98.93 144	99.86 87	99.88 35

SR-MVS		99.43 53	99.29 66	99.86 34	99.75 92	99.83 22	99.59 120	99.62 51	98.21 168	99.73 97	99.79 163	98.68 70	99.96 41	98.44 223	99.77 137	99.79 92

DPE-MVS		99.46 42	99.32 54	99.91 6	99.78 70	99.88 10	99.36 289	99.51 151	98.73 102	99.88 43	99.84 95	98.72 67	99.96 41	98.16 250	99.87 79	99.88 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 55	99.29 66	99.80 64	99.62 172	99.55 96	99.50 197	99.70 18	98.79 95	99.77 85	99.96 1	97.45 124	99.96 41	98.92 146	99.90 57	99.89 29

HFP-MVS		99.49 33	99.37 44	99.86 34	99.87 20	99.80 38	99.66 82	99.67 27	98.15 175	99.68 113	99.69 221	99.06 18	99.96 41	98.69 186	99.87 79	99.84 53

region2R		99.48 37	99.35 48	99.87 21	99.88 13	99.80 38	99.65 88	99.66 32	98.13 183	99.66 124	99.68 229	98.96 27	99.96 41	98.62 195	99.87 79	99.84 53

HPM-MVS++		99.39 66	99.23 82	99.87 21	99.75 92	99.84 20	99.43 252	99.51 151	98.68 109	99.27 240	99.53 292	98.64 75	99.96 41	98.44 223	99.80 125	99.79 92

APDe-MVS		99.66 6	99.57 10	99.92 1	99.77 78	99.89 6	99.75 42	99.56 90	99.02 62	99.88 43	99.85 80	99.18 12	99.96 41	99.22 100	99.92 39	99.90 25
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 33	99.36 46	99.86 34	99.87 20	99.79 41	99.66 82	99.67 27	98.15 175	99.67 119	99.69 221	98.95 32	99.96 41	98.69 186	99.87 79	99.84 53

MP-MVS		99.33 77	99.15 93	99.87 21	99.88 13	99.82 28	99.66 82	99.46 231	98.09 192	99.48 179	99.74 193	98.29 99	99.96 41	97.93 272	99.87 79	99.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 135	98.90 154	99.74 80	99.80 63	99.46 114	99.59 120	99.49 186	97.03 328	99.63 141	99.69 221	97.27 133	99.96 41	97.82 283	99.84 102	99.81 79

PVSNet_Blended_VisFu		99.36 72	99.28 69	99.61 109	99.86 25	99.07 170	99.47 232	99.93 2	97.66 259	99.71 107	99.86 73	97.73 119	99.96 41	99.47 66	99.82 117	99.79 92

UGNet		98.87 175	98.69 187	99.40 175	99.22 318	98.72 232	99.44 246	99.68 24	99.24 32	99.18 265	99.42 326	92.74 338	99.96 41	99.34 81	99.94 31	99.53 219
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 79	99.32 54	99.32 191	99.85 31	98.29 270	99.71 57	99.66 32	98.11 188	99.41 199	99.80 146	98.37 96	99.96 41	98.99 132	99.96 17	99.72 132

ACMMP		99.45 46	99.32 54	99.82 57	99.89 8	99.67 68	99.62 106	99.69 22	98.12 186	99.63 141	99.84 95	98.73 66	99.96 41	98.55 213	99.83 113	99.81 79
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
MED-MVS test				99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.34 26	99.90 34	99.83 100		99.95 76	98.83 166	99.89 68	99.83 63

MED-MVS		99.66 6	99.60 8	99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.18 34	99.90 34	99.83 100	99.30 4	99.95 76	98.83 166	99.89 68	99.83 63

TestfortrainingZip a		99.73 1	99.67 1	99.92 1	99.88 13	99.91 2	99.69 62	99.87 6	99.34 26	99.90 34	99.83 100	99.30 4	99.95 76	99.32 84	99.89 68	99.90 25

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 223	99.67 68	99.50 197	99.64 42	99.43 17	99.98 13	99.78 170	97.26 135	99.95 76	99.95 16	99.93 33	99.92 23

fmvsm_s_conf0.5_n_4		99.36 72	99.24 78	99.73 83	99.78 70	99.53 101	99.49 214	99.60 67	99.42 20	99.99 2	99.86 73	95.15 242	99.95 76	99.95 16	99.89 68	99.73 122

fmvsm_s_conf0.1_n_2		99.37 68	99.22 83	99.81 60	99.77 78	99.75 51	99.46 236	99.60 67	99.47 4	99.98 13	99.94 6	94.98 246	99.95 76	99.97 2	99.79 132	99.73 122

test_fmvsmconf0.01_n		99.22 100	99.03 116	99.79 68	98.42 434	99.48 111	99.55 161	99.51 151	99.39 22	99.78 81	99.93 10	94.80 259	99.95 76	99.93 23	99.95 23	99.94 17

SR-MVS-dyc-post		99.45 46	99.31 60	99.85 43	99.76 82	99.82 28	99.63 101	99.52 130	98.38 137	99.76 91	99.82 113	98.53 82	99.95 76	98.61 198	99.81 120	99.77 100

GST-MVS		99.40 64	99.24 78	99.85 43	99.86 25	99.79 41	99.60 113	99.67 27	97.97 218	99.63 141	99.68 229	98.52 83	99.95 76	98.38 228	99.86 87	99.81 79

CANet		99.25 96	99.14 94	99.59 113	99.41 262	99.16 155	99.35 294	99.57 85	98.82 89	99.51 174	99.61 263	96.46 180	99.95 76	99.59 45	99.98 4	99.65 169

MP-MVS-pluss		99.37 68	99.20 86	99.88 15	99.90 4	99.87 17	99.30 309	99.52 130	97.18 310	99.60 153	99.79 163	98.79 52	99.95 76	98.83 166	99.91 46	99.83 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 55	99.27 73	99.88 15	99.89 8	99.80 38	99.67 75	99.50 174	98.70 106	99.77 85	99.49 306	98.21 102	99.95 76	98.46 221	99.77 137	99.88 35
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 76	96.67 370

APD-MVS_3200maxsize		99.48 37	99.35 48	99.85 43	99.76 82	99.83 22	99.63 101	99.54 109	98.36 141	99.79 76	99.82 113	98.86 43	99.95 76	98.62 195	99.81 120	99.78 98

RPMNet		96.72 379	95.90 392	99.19 216	99.18 327	98.49 259	99.22 347	99.52 130	88.72 462	99.56 161	97.38 459	94.08 303	99.95 76	86.87 467	98.58 263	99.14 286

sss		99.17 108	99.05 111	99.53 133	99.62 172	98.97 183	99.36 289	99.62 51	97.83 235	99.67 119	99.65 242	97.37 128	99.95 76	99.19 103	99.19 203	99.68 155

MVSMamba_PlusPlus		99.46 42	99.41 37	99.64 101	99.68 132	99.50 108	99.75 42	99.50 174	98.27 152	99.87 49	99.92 18	98.09 108	99.94 92	99.65 41	99.95 23	99.47 243

fmvsm_s_conf0.1_n_a		99.26 92	99.06 109	99.85 43	99.52 220	99.62 83	99.54 166	99.62 51	98.69 107	99.99 2	99.96 1	94.47 286	99.94 92	99.88 26	99.92 39	99.98 2

fmvsm_s_conf0.1_n		99.29 85	99.10 99	99.86 34	99.70 122	99.65 75	99.53 175	99.62 51	98.74 101	99.99 2	99.95 3	94.53 284	99.94 92	99.89 25	99.96 17	99.97 4

TSAR-MVS + MP.		99.58 16	99.50 19	99.81 60	99.91 1	99.66 71	99.63 101	99.39 277	98.91 82	99.78 81	99.85 80	99.36 2	99.94 92	98.84 163	99.88 76	99.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 170	98.75 179	99.39 180	99.46 247	98.61 244	99.76 37	99.50 174	98.06 201	99.81 69	99.88 52	93.91 311	99.94 92	99.11 117	99.27 194	99.61 186

mamv4		99.33 77	99.42 32	99.07 227	99.67 134	97.73 303	99.42 259	99.60 67	98.15 175	99.94 28	99.91 26	98.42 91	99.94 92	99.72 32	99.96 17	99.54 213

XVS		99.53 27	99.42 32	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 210	99.74 193	98.81 49	99.94 92	98.79 174	99.86 87	99.84 53

X-MVStestdata		96.55 382	95.45 401	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 210	64.01 482	98.81 49	99.94 92	98.79 174	99.86 87	99.84 53

旧先验2							98.96 406		96.70 348	99.47 180			99.94 92	98.19 246

新几何1				99.75 77	99.75 92	99.59 88		99.54 109	96.76 344	99.29 233	99.64 248	98.43 89	99.94 92	96.92 360	99.66 159	99.72 132

testdata				99.54 125	99.75 92	98.95 193		99.51 151	97.07 322	99.43 191	99.70 210	98.87 42	99.94 92	97.76 292	99.64 162	99.72 132

HPM-MVS		99.42 55	99.28 69	99.83 56	99.90 4	99.72 56	99.81 20	99.54 109	97.59 265	99.68 113	99.63 254	98.91 39	99.94 92	98.58 204	99.91 46	99.84 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 101	99.10 99	99.45 163	99.89 8	98.52 254	99.39 276	99.94 1	98.73 102	99.11 274	99.89 41	95.50 225	99.94 92	99.50 57	99.97 9	99.89 29

APD-MVS		99.27 89	99.08 105	99.84 55	99.75 92	99.79 41	99.50 197	99.50 174	97.16 312	99.77 85	99.82 113	98.78 53	99.94 92	97.56 313	99.86 87	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 37	99.42 32	99.65 95	99.72 111	99.40 121	99.05 383	99.66 32	99.14 40	99.57 160	99.80 146	98.46 87	99.94 92	99.57 48	99.84 102	99.60 189
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		99.06 148	98.88 161	99.61 109	99.62 172	99.16 155	99.37 283	99.56 90	98.04 210	99.53 170	99.62 259	96.84 158	99.94 92	98.85 160	98.49 271	99.72 132

DeepC-MVS	98.35 2	99.30 83	99.19 88	99.64 101	99.82 53	99.23 148	99.62 106	99.55 100	98.94 78	99.63 141	99.95 3	95.82 211	99.94 92	99.37 75	99.97 9	99.73 122
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 89	99.12 97	99.74 80	99.18 327	99.75 51	99.56 146	99.57 85	98.45 130	99.49 178	99.85 80	97.77 118	99.94 92	98.33 235	99.84 102	99.52 220

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 283	99.70 18	99.18 34	99.83 64	99.83 100	98.74 65	99.93 110	98.83 166	99.89 68	99.83 63

GDP-MVS		99.08 143	98.89 158	99.64 101	99.53 214	99.34 127	99.64 95	99.48 198	98.32 147	99.77 85	99.66 240	95.14 243	99.93 110	98.97 138	99.50 176	99.64 176

SDMVSNet		99.11 135	98.90 154	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 138	99.88 52	94.56 279	99.93 110	99.67 37	98.26 286	99.72 132

FE-MVS		98.48 218	98.17 233	99.40 175	99.54 213	98.96 187	99.68 72	98.81 413	95.54 407	99.62 145	99.70 210	93.82 314	99.93 110	97.35 331	99.46 178	99.32 272

SF-MVS		99.38 67	99.24 78	99.79 68	99.79 68	99.68 64	99.57 138	99.54 109	97.82 240	99.71 107	99.80 146	98.95 32	99.93 110	98.19 246	99.84 102	99.74 113

dcpmvs_2		99.23 98	99.58 9	98.16 355	99.83 47	94.68 426	99.76 37	99.52 130	99.07 58	99.98 13	99.88 52	98.56 80	99.93 110	99.67 37	99.98 4	99.87 40

Anonymous20240529		98.09 255	97.68 293	99.34 185	99.66 147	98.44 264	99.40 272	99.43 262	93.67 434	99.22 252	99.89 41	90.23 393	99.93 110	99.26 98	98.33 278	99.66 163

ACMMP_NAP		99.47 40	99.34 50	99.88 15	99.87 20	99.86 18	99.47 232	99.48 198	98.05 203	99.76 91	99.86 73	98.82 48	99.93 110	98.82 173	99.91 46	99.84 53

EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 101	99.78 70	99.14 160	99.60 113	99.45 242	99.01 64	99.90 34	99.83 100	98.98 26	99.93 110	99.59 45	99.95 23	99.86 42

无先验							98.99 399	99.51 151	96.89 338				99.93 110	97.53 316		99.72 132

VDDNet		97.55 342	97.02 364	99.16 219	99.49 237	98.12 281	99.38 281	99.30 335	95.35 409	99.68 113	99.90 33	82.62 455	99.93 110	99.31 86	98.13 298	99.42 255

ab-mvs		98.86 178	98.63 197	99.54 125	99.64 160	99.19 150	99.44 246	99.54 109	97.77 244	99.30 230	99.81 128	94.20 296	99.93 110	99.17 109	98.82 250	99.49 234

F-COLMAP		99.19 101	99.04 113	99.64 101	99.78 70	99.27 143	99.42 259	99.54 109	97.29 301	99.41 199	99.59 268	98.42 91	99.93 110	98.19 246	99.69 153	99.73 122

BP-MVS1		99.12 129	98.94 146	99.65 95	99.51 223	99.30 138	99.67 75	98.92 394	98.48 126	99.84 56	99.69 221	94.96 247	99.92 123	99.62 44	99.79 132	99.71 143

Anonymous202405211		98.30 236	97.98 257	99.26 207	99.57 198	98.16 276	99.41 264	98.55 438	96.03 401	99.19 261	99.74 193	91.87 363	99.92 123	99.16 112	98.29 285	99.70 146

EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 101	99.78 70	99.15 159	99.61 112	99.45 242	99.01 64	99.89 40	99.82 113	99.01 20	99.92 123	99.56 49	99.95 23	99.85 46

VDD-MVS		97.73 321	97.35 338	98.88 263	99.47 245	97.12 331	99.34 297	98.85 408	98.19 170	99.67 119	99.85 80	82.98 453	99.92 123	99.49 61	98.32 282	99.60 189

VNet		99.11 135	98.90 154	99.73 83	99.52 220	99.56 94	99.41 264	99.39 277	99.01 64	99.74 95	99.78 170	95.56 223	99.92 123	99.52 55	98.18 294	99.72 132

XVG-OURS-SEG-HR		98.69 206	98.62 202	98.89 260	99.71 117	97.74 302	99.12 367	99.54 109	98.44 133	99.42 194	99.71 206	94.20 296	99.92 123	98.54 214	98.90 244	99.00 305

mvsmamba		99.06 148	98.96 140	99.36 182	99.47 245	98.64 239	99.70 58	99.05 378	97.61 264	99.65 133	99.83 100	96.54 176	99.92 123	99.19 103	99.62 165	99.51 229

HPM-MVS_fast		99.51 29	99.40 38	99.85 43	99.91 1	99.79 41	99.76 37	99.56 90	97.72 250	99.76 91	99.75 188	99.13 14	99.92 123	99.07 123	99.92 39	99.85 46

HY-MVS	97.30 7	98.85 187	98.64 196	99.47 160	99.42 257	99.08 168	99.62 106	99.36 296	97.39 293	99.28 234	99.68 229	96.44 182	99.92 123	98.37 230	98.22 289	99.40 260

DP-MVS		99.16 110	98.95 144	99.78 71	99.77 78	99.53 101	99.41 264	99.50 174	97.03 328	99.04 291	99.88 52	97.39 125	99.92 123	98.66 190	99.90 57	99.87 40

IB-MVS	95.67 18	96.22 388	95.44 402	98.57 305	99.21 319	96.70 365	98.65 440	97.74 457	96.71 347	97.27 419	98.54 431	86.03 436	99.92 123	98.47 220	86.30 463	99.10 289
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 33	99.39 40	99.77 74	99.63 164	99.59 88	99.36 289	99.46 231	99.07 58	99.79 76	99.82 113	98.85 44	99.92 123	98.68 188	99.87 79	99.82 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LuminaMVS		99.23 98	99.10 99	99.61 109	99.35 279	99.31 135	99.46 236	99.13 366	98.61 113	99.86 53	99.89 41	96.41 185	99.91 135	99.67 37	99.51 174	99.63 181

balanced_conf03		99.46 42	99.39 40	99.67 90	99.55 206	99.58 93	99.74 47	99.51 151	98.42 134	99.87 49	99.84 95	98.05 111	99.91 135	99.58 47	99.94 31	99.52 220

9.14			99.10 99		99.72 111		99.40 272	99.51 151	97.53 275	99.64 138	99.78 170	98.84 46	99.91 135	97.63 304	99.82 117

SMA-MVS		99.44 50	99.30 62	99.85 43	99.73 107	99.83 22	99.56 146	99.47 220	97.45 284	99.78 81	99.82 113	99.18 12	99.91 135	98.79 174	99.89 68	99.81 79
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					99.67 134	99.65 75	99.05 383	99.41 267	96.22 386	98.95 307	99.49 306	98.77 56	99.91 135

train_agg		99.02 156	98.77 177	99.77 74	99.67 134	99.65 75	99.05 383	99.41 267	96.28 380	98.95 307	99.49 306	98.76 57	99.91 135	97.63 304	99.72 148	99.75 109

test_8					99.67 134	99.61 85	99.03 388	99.41 267	96.28 380	98.93 310	99.48 312	98.76 57	99.91 135

agg_prior					99.67 134	99.62 83		99.40 274		98.87 320			99.91 135

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 220	97.46 281	99.12 272	99.66 240	98.67 72	99.91 135	97.70 301	99.69 153	99.71 143

LFMVS		97.90 288	97.35 338	99.54 125	99.52 220	99.01 177	99.39 276	98.24 446	97.10 320	99.65 133	99.79 163	84.79 445	99.91 135	99.28 92	98.38 275	99.69 149

XVG-OURS		98.73 204	98.68 188	98.88 263	99.70 122	97.73 303	98.92 413	99.55 100	98.52 122	99.45 183	99.84 95	95.27 235	99.91 135	98.08 261	98.84 248	99.00 305

PLC	97.94 4	99.02 156	98.85 168	99.53 133	99.66 147	99.01 177	99.24 340	99.52 130	96.85 340	99.27 240	99.48 312	98.25 101	99.91 135	97.76 292	99.62 165	99.65 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 335	97.06 363	99.47 160	99.61 183	99.09 165	98.04 466	99.25 347	91.24 453	98.51 369	99.70 210	94.55 281	99.91 135	92.76 441	99.85 94	99.42 255
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Elysia		98.88 172	98.65 194	99.58 116	99.58 193	99.34 127	99.65 88	99.52 130	98.26 155	99.83 64	99.87 65	93.37 322	99.90 148	97.81 285	99.91 46	99.49 234

StellarMVS		98.88 172	98.65 194	99.58 116	99.58 193	99.34 127	99.65 88	99.52 130	98.26 155	99.83 64	99.87 65	93.37 322	99.90 148	97.81 285	99.91 46	99.49 234

AstraMVS		99.09 141	99.03 116	99.25 208	99.66 147	98.13 279	99.57 138	98.24 446	98.82 89	99.91 31	99.88 52	95.81 212	99.90 148	99.72 32	99.67 158	99.74 113

mmtdpeth		96.95 374	96.71 373	97.67 394	99.33 285	94.90 421	99.89 2	99.28 341	98.15 175	99.72 102	98.57 430	86.56 432	99.90 148	99.82 29	89.02 458	98.20 425

UWE-MVS		97.58 341	97.29 349	98.48 318	99.09 351	96.25 385	99.01 396	96.61 469	97.86 228	99.19 261	99.01 401	88.72 408	99.90 148	97.38 329	98.69 257	99.28 275

test_vis1_rt		95.81 398	95.65 397	96.32 430	99.67 134	91.35 458	99.49 214	96.74 467	98.25 160	95.24 443	98.10 449	74.96 465	99.90 148	99.53 53	98.85 247	97.70 449

FA-MVS(test-final)		98.75 201	98.53 213	99.41 174	99.55 206	99.05 173	99.80 25	99.01 383	96.59 362	99.58 157	99.59 268	95.39 229	99.90 148	97.78 288	99.49 177	99.28 275

MCST-MVS		99.43 53	99.30 62	99.82 57	99.79 68	99.74 54	99.29 314	99.40 274	98.79 95	99.52 172	99.62 259	98.91 39	99.90 148	98.64 192	99.75 142	99.82 72

CDPH-MVS		99.13 122	98.91 152	99.80 64	99.75 92	99.71 58	99.15 361	99.41 267	96.60 360	99.60 153	99.55 283	98.83 47	99.90 148	97.48 320	99.83 113	99.78 98

NCCC		99.34 75	99.19 88	99.79 68	99.61 183	99.65 75	99.30 309	99.48 198	98.86 84	99.21 255	99.63 254	98.72 67	99.90 148	98.25 242	99.63 164	99.80 88

114514_t		98.93 168	98.67 189	99.72 86	99.85 31	99.53 101	99.62 106	99.59 73	92.65 448	99.71 107	99.78 170	98.06 110	99.90 148	98.84 163	99.91 46	99.74 113

1112_ss		98.98 164	98.77 177	99.59 113	99.68 132	99.02 175	99.25 335	99.48 198	97.23 307	99.13 270	99.58 272	96.93 152	99.90 148	98.87 153	98.78 253	99.84 53

PHI-MVS		99.30 83	99.17 91	99.70 87	99.56 202	99.52 105	99.58 130	99.80 11	97.12 316	99.62 145	99.73 199	98.58 78	99.90 148	98.61 198	99.91 46	99.68 155

AdaColmap		99.01 160	98.80 173	99.66 91	99.56 202	99.54 98	99.18 356	99.70 18	98.18 173	99.35 219	99.63 254	96.32 187	99.90 148	97.48 320	99.77 137	99.55 211

COLMAP_ROB	97.56 6	98.86 178	98.75 179	99.17 218	99.88 13	98.53 250	99.34 297	99.59 73	97.55 271	98.70 346	99.89 41	95.83 210	99.90 148	98.10 256	99.90 57	99.08 294
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 232	98.03 252	99.31 193	99.63 164	98.56 247	99.54 166	96.75 466	97.53 275	99.73 97	99.65 242	91.25 381	99.89 163	98.62 195	99.56 170	99.48 237

tttt0517		98.42 223	98.14 237	99.28 205	99.66 147	98.38 268	99.74 47	96.85 464	97.68 256	99.79 76	99.74 193	91.39 377	99.89 163	98.83 166	99.56 170	99.57 207

test12				99.75 77	99.64 160	99.61 85		99.29 339		99.21 255		98.38 95	99.89 163		99.74 145	99.74 113

Test_1112_low_res		98.89 171	98.66 192	99.57 120	99.69 127	98.95 193	99.03 388	99.47 220	96.98 330	99.15 268	99.23 377	96.77 163	99.89 163	98.83 166	98.78 253	99.86 42

CNLPA		99.14 120	98.99 132	99.59 113	99.58 193	99.41 120	99.16 358	99.44 251	98.45 130	99.19 261	99.49 306	98.08 109	99.89 163	97.73 296	99.75 142	99.48 237

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 155	99.64 160	98.85 214	99.32 303	99.48 198	98.50 124	99.81 69	99.81 128	96.82 159	99.88 168	99.40 71	99.12 215	99.71 143

guyue		99.16 110	99.04 113	99.52 139	99.69 127	98.92 202	99.59 120	98.81 413	98.73 102	99.90 34	99.87 65	95.34 232	99.88 168	99.66 40	99.81 120	99.74 113

sd_testset		98.75 201	98.57 209	99.29 201	99.81 57	98.26 272	99.56 146	99.62 51	98.78 98	99.64 138	99.88 52	92.02 360	99.88 168	99.54 51	98.26 286	99.72 132

APD_test1		95.87 396	96.49 378	94.00 438	99.53 214	84.01 467	99.54 166	99.32 326	95.91 403	97.99 398	99.85 80	85.49 440	99.88 168	91.96 445	98.84 248	98.12 429

diffmvs		99.14 120	99.02 124	99.51 144	99.61 183	98.96 187	99.28 319	99.49 186	98.46 128	99.72 102	99.71 206	96.50 178	99.88 168	99.31 86	99.11 217	99.67 159
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		98.86 178	98.80 173	99.03 233	99.76 82	98.79 225	99.28 319	99.91 3	97.42 290	99.67 119	99.37 344	97.53 122	99.88 168	98.98 133	97.29 346	98.42 410

PVSNet_Blended		99.08 143	98.97 136	99.42 173	99.76 82	98.79 225	98.78 427	99.91 3	96.74 345	99.67 119	99.49 306	97.53 122	99.88 168	98.98 133	99.85 94	99.60 189

viewdifsd2359ckpt07		99.11 135	99.00 131	99.43 171	99.63 164	98.73 230	99.45 239	99.54 109	98.33 145	99.62 145	99.81 128	96.17 192	99.87 175	99.27 95	99.14 208	99.69 149

viewdifsd2359ckpt11		98.78 196	98.74 181	98.89 260	99.67 134	97.04 341	99.50 197	99.58 78	98.26 155	99.56 161	99.90 33	94.36 289	99.87 175	99.49 61	98.32 282	99.77 100

viewmsd2359difaftdt		98.78 196	98.74 181	98.90 256	99.67 134	97.04 341	99.50 197	99.58 78	98.26 155	99.56 161	99.90 33	94.36 289	99.87 175	99.49 61	98.32 282	99.77 100

MVS		97.28 363	96.55 376	99.48 155	98.78 401	98.95 193	99.27 324	99.39 277	83.53 469	98.08 393	99.54 288	96.97 150	99.87 175	94.23 421	99.16 204	99.63 181

MG-MVS		99.13 122	99.02 124	99.45 163	99.57 198	98.63 240	99.07 377	99.34 308	98.99 69	99.61 150	99.82 113	97.98 113	99.87 175	97.00 351	99.80 125	99.85 46

MSDG		98.98 164	98.80 173	99.53 133	99.76 82	99.19 150	98.75 430	99.55 100	97.25 304	99.47 180	99.77 179	97.82 116	99.87 175	96.93 358	99.90 57	99.54 213

ETV-MVS		99.26 92	99.21 84	99.40 175	99.46 247	99.30 138	99.56 146	99.52 130	98.52 122	99.44 188	99.27 372	98.41 93	99.86 181	99.10 120	99.59 168	99.04 301

thisisatest0515		98.14 250	97.79 276	99.19 216	99.50 235	98.50 258	98.61 442	96.82 465	96.95 334	99.54 168	99.43 324	91.66 372	99.86 181	98.08 261	99.51 174	99.22 283

thres600view7		97.86 294	97.51 312	98.92 250	99.72 111	97.95 293	99.59 120	98.74 423	97.94 220	99.27 240	98.62 427	91.75 366	99.86 181	93.73 427	98.19 293	98.96 311

lupinMVS		99.13 122	99.01 129	99.46 162	99.51 223	98.94 197	99.05 383	99.16 362	97.86 228	99.80 74	99.56 280	97.39 125	99.86 181	98.94 141	99.85 94	99.58 204

PVSNet	96.02 17	98.85 187	98.84 170	98.89 260	99.73 107	97.28 322	98.32 458	99.60 67	97.86 228	99.50 175	99.57 277	96.75 164	99.86 181	98.56 210	99.70 152	99.54 213

MAR-MVS		98.86 178	98.63 197	99.54 125	99.37 275	99.66 71	99.45 239	99.54 109	96.61 357	99.01 294	99.40 334	97.09 142	99.86 181	97.68 303	99.53 173	99.10 289
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
mamba_0408		99.08 143	98.96 140	99.44 168	99.62 172	98.88 206	99.25 335	99.47 220	98.05 203	99.37 210	99.81 128	96.85 154	99.85 187	98.98 133	99.25 197	99.60 189

SSM_0404		99.16 110	99.06 109	99.44 168	99.65 155	98.96 187	99.49 214	99.50 174	98.14 180	99.62 145	99.85 80	96.85 154	99.85 187	99.19 103	99.26 196	99.52 220

testing91		97.44 355	97.02 364	98.71 292	99.18 327	96.89 359	99.19 354	99.04 379	97.78 243	98.31 380	98.29 441	85.41 441	99.85 187	98.01 267	97.95 303	99.39 261

test2506		96.81 378	96.65 374	97.29 409	99.74 100	92.21 455	99.60 113	85.06 486	99.13 41	99.77 85	99.93 10	87.82 425	99.85 187	99.38 74	99.38 183	99.80 88

AllTest		98.87 175	98.72 183	99.31 193	99.86 25	98.48 261	99.56 146	99.61 60	97.85 231	99.36 216	99.85 80	95.95 202	99.85 187	96.66 371	99.83 113	99.59 200

TestCases				99.31 193	99.86 25	98.48 261		99.61 60	97.85 231	99.36 216	99.85 80	95.95 202	99.85 187	96.66 371	99.83 113	99.59 200

jason		99.13 122	99.03 116	99.45 163	99.46 247	98.87 210	99.12 367	99.26 345	98.03 212	99.79 76	99.65 242	97.02 147	99.85 187	99.02 130	99.90 57	99.65 169
jason: jason.
CNVR-MVS		99.42 55	99.30 62	99.78 71	99.62 172	99.71 58	99.26 333	99.52 130	98.82 89	99.39 206	99.71 206	98.96 27	99.85 187	98.59 203	99.80 125	99.77 100

PAPM_NR		99.04 153	98.84 170	99.66 91	99.74 100	99.44 116	99.39 276	99.38 285	97.70 254	99.28 234	99.28 369	98.34 97	99.85 187	96.96 355	99.45 179	99.69 149

E2		99.15 114	99.03 116	99.49 152	99.65 155	98.93 201	99.49 214	99.52 130	98.14 180	99.72 102	99.88 52	96.57 175	99.84 196	99.17 109	99.13 211	99.72 132

E3		99.15 114	99.03 116	99.49 152	99.62 172	98.91 203	99.49 214	99.52 130	98.13 183	99.72 102	99.88 52	96.61 170	99.84 196	99.17 109	99.13 211	99.72 132

viewcassd2359sk11		99.18 104	99.08 105	99.49 152	99.65 155	98.95 193	99.48 222	99.51 151	98.10 191	99.72 102	99.87 65	97.13 138	99.84 196	99.13 114	99.14 208	99.69 149

testing99		97.36 358	96.94 367	98.63 298	99.18 327	96.70 365	99.30 309	98.93 391	97.71 251	98.23 385	98.26 442	84.92 444	99.84 196	98.04 266	97.85 310	99.35 267

testing222		97.16 368	96.50 377	99.16 219	99.16 337	98.47 263	99.27 324	98.66 434	97.71 251	98.23 385	98.15 445	82.28 458	99.84 196	97.36 330	97.66 316	99.18 285

test1111		98.04 265	98.11 241	97.83 384	99.74 100	93.82 439	99.58 130	95.40 473	99.12 46	99.65 133	99.93 10	90.73 386	99.84 196	99.43 69	99.38 183	99.82 72

ECVR-MVS		98.04 265	98.05 250	98.00 368	99.74 100	94.37 433	99.59 120	94.98 474	99.13 41	99.66 124	99.93 10	90.67 387	99.84 196	99.40 71	99.38 183	99.80 88

test_yl		98.86 178	98.63 197	99.54 125	99.49 237	99.18 152	99.50 197	99.07 375	98.22 166	99.61 150	99.51 300	95.37 230	99.84 196	98.60 201	98.33 278	99.59 200

DCV-MVSNet		98.86 178	98.63 197	99.54 125	99.49 237	99.18 152	99.50 197	99.07 375	98.22 166	99.61 150	99.51 300	95.37 230	99.84 196	98.60 201	98.33 278	99.59 200

Fast-Effi-MVS+		98.70 205	98.43 218	99.51 144	99.51 223	99.28 141	99.52 177	99.47 220	96.11 396	99.01 294	99.34 354	96.20 191	99.84 196	97.88 275	98.82 250	99.39 261

TSAR-MVS + GP.		99.36 72	99.36 46	99.36 182	99.67 134	98.61 244	99.07 377	99.33 316	99.00 67	99.82 68	99.81 128	99.06 18	99.84 196	99.09 121	99.42 181	99.65 169

tpmrst		98.33 233	98.48 216	97.90 377	99.16 337	94.78 422	99.31 307	99.11 368	97.27 302	99.45 183	99.59 268	95.33 233	99.84 196	98.48 217	98.61 260	99.09 293

Vis-MVSNet		99.12 129	98.97 136	99.56 122	99.78 70	99.10 164	99.68 72	99.66 32	98.49 125	99.86 53	99.87 65	94.77 264	99.84 196	99.19 103	99.41 182	99.74 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 213	98.34 224	99.51 144	99.40 267	99.03 174	98.80 425	99.36 296	96.33 377	99.00 298	99.12 391	98.46 87	99.84 196	95.23 407	99.37 190	99.66 163

PatchMatch-RL		98.84 190	98.62 202	99.52 139	99.71 117	99.28 141	99.06 381	99.77 12	97.74 249	99.50 175	99.53 292	95.41 228	99.84 196	97.17 344	99.64 162	99.44 253

EPP-MVSNet		99.13 122	98.99 132	99.53 133	99.65 155	99.06 171	99.81 20	99.33 316	97.43 288	99.60 153	99.88 52	97.14 137	99.84 196	99.13 114	98.94 235	99.69 149

SSM_0407		99.13 122	99.03 116	99.43 171	99.62 172	98.88 206	99.51 186	99.50 174	98.14 180	99.37 210	99.85 80	96.85 154	99.83 212	99.19 103	99.25 197	99.60 189

testing3-2		97.84 299	97.70 291	98.24 350	99.53 214	95.37 410	99.55 161	98.67 433	98.46 128	99.27 240	99.34 354	86.58 431	99.83 212	99.32 84	98.63 259	99.52 220

testing11		97.50 348	97.10 361	98.71 292	99.20 321	96.91 357	99.29 314	98.82 411	97.89 225	98.21 388	98.40 436	85.63 439	99.83 212	98.45 222	98.04 301	99.37 265

thres100view900		97.76 313	97.45 321	98.69 294	99.72 111	97.86 299	99.59 120	98.74 423	97.93 221	99.26 245	98.62 427	91.75 366	99.83 212	93.22 433	98.18 294	98.37 416

tfpn200view9		97.72 323	97.38 334	98.72 289	99.69 127	97.96 290	99.50 197	98.73 429	97.83 235	99.17 266	98.45 434	91.67 370	99.83 212	93.22 433	98.18 294	98.37 416

test_prior				99.68 89	99.67 134	99.48 111		99.56 90					99.83 212			99.74 113

1314		98.68 207	98.54 212	99.11 225	98.89 384	98.65 237	99.27 324	99.49 186	96.89 338	97.99 398	99.56 280	97.72 120	99.83 212	97.74 295	99.27 194	98.84 317

thres400		97.77 312	97.38 334	98.92 250	99.69 127	97.96 290	99.50 197	98.73 429	97.83 235	99.17 266	98.45 434	91.67 370	99.83 212	93.22 433	98.18 294	98.96 311

casdiffmvs		99.13 122	98.98 135	99.56 122	99.65 155	99.16 155	99.56 146	99.50 174	98.33 145	99.41 199	99.86 73	95.92 205	99.83 212	99.45 68	99.16 204	99.70 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		99.49 33	99.48 22	99.54 125	99.78 70	99.30 138	99.89 2	99.58 78	98.56 118	99.73 97	99.69 221	98.55 81	99.82 221	99.69 35	99.85 94	99.48 237

MVS_Test		99.10 140	98.97 136	99.48 155	99.49 237	99.14 160	99.67 75	99.34 308	97.31 299	99.58 157	99.76 183	97.65 121	99.82 221	98.87 153	99.07 226	99.46 248

dp		97.75 317	97.80 275	97.59 400	99.10 348	93.71 442	99.32 303	98.88 404	96.48 369	99.08 282	99.55 283	92.67 344	99.82 221	96.52 375	98.58 263	99.24 281

RPSCF		98.22 240	98.62 202	96.99 416	99.82 53	91.58 457	99.72 53	99.44 251	96.61 357	99.66 124	99.89 41	95.92 205	99.82 221	97.46 323	99.10 223	99.57 207

PMMVS		98.80 194	98.62 202	99.34 185	99.27 303	98.70 233	98.76 429	99.31 330	97.34 296	99.21 255	99.07 393	97.20 136	99.82 221	98.56 210	98.87 245	99.52 220

UBG		97.85 295	97.48 315	98.95 244	99.25 310	97.64 310	99.24 340	98.74 423	97.90 224	98.64 356	98.20 444	88.65 412	99.81 226	98.27 240	98.40 273	99.42 255

EIA-MVS		99.18 104	99.09 104	99.45 163	99.49 237	99.18 152	99.67 75	99.53 125	97.66 259	99.40 204	99.44 322	98.10 107	99.81 226	98.94 141	99.62 165	99.35 267

Effi-MVS+		98.81 191	98.59 208	99.48 155	99.46 247	99.12 163	98.08 465	99.50 174	97.50 279	99.38 208	99.41 330	96.37 186	99.81 226	99.11 117	98.54 268	99.51 229

thres200		97.61 339	97.28 350	98.62 299	99.64 160	98.03 284	99.26 333	98.74 423	97.68 256	99.09 280	98.32 440	91.66 372	99.81 226	92.88 438	98.22 289	98.03 435

tpmvs		97.98 276	98.02 254	97.84 383	99.04 362	94.73 423	99.31 307	99.20 357	96.10 400	98.76 336	99.42 326	94.94 249	99.81 226	96.97 354	98.45 272	98.97 309

casdiffmvs_mvg		99.15 114	99.02 124	99.55 124	99.66 147	99.09 165	99.64 95	99.56 90	98.26 155	99.45 183	99.87 65	96.03 198	99.81 226	99.54 51	99.15 207	99.73 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepPCF-MVS	98.18 3	98.81 191	99.37 44	97.12 413	99.60 189	91.75 456	98.61 442	99.44 251	99.35 25	99.83 64	99.85 80	98.70 69	99.81 226	99.02 130	99.91 46	99.81 79

viewmacassd2359aftdt		99.08 143	98.94 146	99.50 149	99.66 147	98.96 187	99.51 186	99.54 109	98.27 152	99.42 194	99.89 41	95.88 209	99.80 233	99.20 102	99.11 217	99.76 107

viewmanbaseed2359cas		99.18 104	99.07 108	99.50 149	99.62 172	99.01 177	99.50 197	99.52 130	98.25 160	99.68 113	99.82 113	96.93 152	99.80 233	99.15 113	99.11 217	99.70 146

IMVS_0403		98.86 178	98.89 158	98.78 284	99.55 206	96.93 352	99.58 130	99.44 251	98.05 203	99.68 113	99.80 146	96.81 160	99.80 233	98.15 252	98.92 238	99.60 189

DPM-MVS		98.95 167	98.71 185	99.66 91	99.63 164	99.55 96	98.64 441	99.10 369	97.93 221	99.42 194	99.55 283	98.67 72	99.80 233	95.80 392	99.68 156	99.61 186

DP-MVS Recon		99.12 129	98.95 144	99.65 95	99.74 100	99.70 60	99.27 324	99.57 85	96.40 376	99.42 194	99.68 229	98.75 60	99.80 233	97.98 269	99.72 148	99.44 253

MVS_111021_LR		99.41 59	99.33 52	99.65 95	99.77 78	99.51 107	98.94 411	99.85 9	98.82 89	99.65 133	99.74 193	98.51 84	99.80 233	98.83 166	99.89 68	99.64 176

viewmambaseed2359dif		99.01 160	98.90 154	99.32 191	99.58 193	98.51 256	99.33 300	99.54 109	97.85 231	99.44 188	99.85 80	96.01 199	99.79 239	99.41 70	99.13 211	99.67 159

CS-MVS		99.50 31	99.48 22	99.54 125	99.76 82	99.42 118	99.90 1	99.55 100	98.56 118	99.78 81	99.70 210	98.65 74	99.79 239	99.65 41	99.78 134	99.41 258

Fast-Effi-MVS+-dtu		98.77 200	98.83 172	98.60 300	99.41 262	96.99 347	99.52 177	99.49 186	98.11 188	99.24 247	99.34 354	96.96 151	99.79 239	97.95 271	99.45 179	99.02 304

baseline1		98.31 234	97.95 261	99.38 181	99.50 235	98.74 229	99.59 120	98.93 391	98.41 135	99.14 269	99.60 266	94.59 277	99.79 239	98.48 217	93.29 429	99.61 186

baseline		99.15 114	99.02 124	99.53 133	99.66 147	99.14 160	99.72 53	99.48 198	98.35 142	99.42 194	99.84 95	96.07 195	99.79 239	99.51 56	99.14 208	99.67 159

PVSNet_0	94.43 19	96.09 393	95.47 400	97.94 373	99.31 293	94.34 435	97.81 467	99.70 18	97.12 316	97.46 413	98.75 424	89.71 398	99.79 239	97.69 302	81.69 469	99.68 155

API-MVS		99.04 153	99.03 116	99.06 229	99.40 267	99.31 135	99.55 161	99.56 90	98.54 120	99.33 224	99.39 338	98.76 57	99.78 245	96.98 353	99.78 134	98.07 432

OMC-MVS		99.08 143	99.04 113	99.20 215	99.67 134	98.22 274	99.28 319	99.52 130	98.07 197	99.66 124	99.81 128	97.79 117	99.78 245	97.79 287	99.81 120	99.60 189

GeoE		98.85 187	98.62 202	99.53 133	99.61 183	99.08 168	99.80 25	99.51 151	97.10 320	99.31 226	99.78 170	95.23 240	99.77 247	98.21 244	99.03 229	99.75 109

alignmvs		98.81 191	98.56 211	99.58 116	99.43 255	99.42 118	99.51 186	98.96 389	98.61 113	99.35 219	98.92 414	94.78 261	99.77 247	99.35 76	98.11 299	99.54 213

tpm cat1		97.39 357	97.36 336	97.50 403	99.17 335	93.73 441	99.43 252	99.31 330	91.27 452	98.71 340	99.08 392	94.31 294	99.77 247	96.41 380	98.50 270	99.00 305

CostFormer		97.72 323	97.73 288	97.71 392	99.15 341	94.02 438	99.54 166	99.02 382	94.67 425	99.04 291	99.35 350	92.35 356	99.77 247	98.50 216	97.94 304	99.34 270

MGCFI-Net		99.01 160	98.85 168	99.50 149	99.42 257	99.26 144	99.82 16	99.48 198	98.60 115	99.28 234	98.81 419	97.04 146	99.76 251	99.29 91	97.87 308	99.47 243

test_241102_ONE					99.84 38	99.90 3		99.48 198	99.07 58	99.91 31	99.74 193	99.20 9	99.76 251

MDTV_nov1_ep13			98.32 226		99.11 345	94.44 431	99.27 324	98.74 423	97.51 278	99.40 204	99.62 259	94.78 261	99.76 251	97.59 307	98.81 252

viewdifsd2359ckpt09		99.01 160	98.87 162	99.40 175	99.62 172	98.79 225	99.44 246	99.51 151	97.76 245	99.35 219	99.69 221	96.42 184	99.75 254	98.97 138	99.11 217	99.66 163

sasdasda		99.02 156	98.86 165	99.51 144	99.42 257	99.32 131	99.80 25	99.48 198	98.63 110	99.31 226	98.81 419	97.09 142	99.75 254	99.27 95	97.90 305	99.47 243

canonicalmvs		99.02 156	98.86 165	99.51 144	99.42 257	99.32 131	99.80 25	99.48 198	98.63 110	99.31 226	98.81 419	97.09 142	99.75 254	99.27 95	97.90 305	99.47 243

Effi-MVS+-dtu		98.78 196	98.89 158	98.47 323	99.33 285	96.91 357	99.57 138	99.30 335	98.47 127	99.41 199	98.99 404	96.78 162	99.74 257	98.73 180	99.38 183	98.74 333

patchmatchnet-post											98.70 425	94.79 260	99.74 257

SCA		98.19 244	98.16 234	98.27 349	99.30 294	95.55 401	99.07 377	98.97 387	97.57 268	99.43 191	99.57 277	92.72 339	99.74 257	97.58 308	99.20 202	99.52 220

BH-untuned		98.42 223	98.36 222	98.59 301	99.49 237	96.70 365	99.27 324	99.13 366	97.24 306	98.80 331	99.38 341	95.75 216	99.74 257	97.07 349	99.16 204	99.33 271

BH-RMVSNet		98.41 225	98.08 246	99.40 175	99.41 262	98.83 219	99.30 309	98.77 419	97.70 254	98.94 309	99.65 242	92.91 334	99.74 257	96.52 375	99.55 172	99.64 176

MVS_111021_HR		99.41 59	99.32 54	99.66 91	99.72 111	99.47 113	98.95 409	99.85 9	98.82 89	99.54 168	99.73 199	98.51 84	99.74 257	98.91 147	99.88 76	99.77 100

test_post											65.99 480	94.65 275	99.73 263

XVG-ACMP-BASELINE		97.83 302	97.71 290	98.20 352	99.11 345	96.33 381	99.41 264	99.52 130	98.06 201	99.05 290	99.50 303	89.64 400	99.73 263	97.73 296	97.38 343	98.53 398

HyFIR lowres test		99.11 135	98.92 149	99.65 95	99.90 4	99.37 123	99.02 391	99.91 3	97.67 258	99.59 156	99.75 188	95.90 207	99.73 263	99.53 53	99.02 231	99.86 42

DeepMVS_CX				93.34 441	99.29 298	82.27 470		99.22 353	85.15 467	96.33 435	99.05 396	90.97 384	99.73 263	93.57 429	97.77 313	98.01 436

Patchmatch-test		97.93 282	97.65 296	98.77 285	99.18 327	97.07 336	99.03 388	99.14 365	96.16 391	98.74 337	99.57 277	94.56 279	99.72 267	93.36 431	99.11 217	99.52 220

LPG-MVS_test		98.22 240	98.13 239	98.49 316	99.33 285	97.05 338	99.58 130	99.55 100	97.46 281	99.24 247	99.83 100	92.58 346	99.72 267	98.09 257	97.51 329	98.68 351

LGP-MVS_train				98.49 316	99.33 285	97.05 338		99.55 100	97.46 281	99.24 247	99.83 100	92.58 346	99.72 267	98.09 257	97.51 329	98.68 351

BH-w/o		98.00 274	97.89 270	98.32 341	99.35 279	96.20 387	99.01 396	98.90 401	96.42 374	98.38 376	99.00 402	95.26 237	99.72 267	96.06 385	98.61 260	99.03 302

ACMP	97.20 11	98.06 259	97.94 263	98.45 326	99.37 275	97.01 345	99.44 246	99.49 186	97.54 274	98.45 373	99.79 163	91.95 362	99.72 267	97.91 273	97.49 334	98.62 381
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 269	97.90 266	98.40 334	99.23 314	96.80 363	99.70 58	99.60 67	97.12 316	98.18 390	99.70 210	91.73 368	99.72 267	98.39 227	97.45 336	98.68 351
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
viewdifsd2359ckpt13		99.06 148	98.93 148	99.45 163	99.63 164	98.96 187	99.50 197	99.51 151	97.83 235	99.28 234	99.80 146	96.68 168	99.71 273	99.05 125	99.12 215	99.68 155

test_post1							99.23 343				65.14 481	94.18 299	99.71 273	97.58 308

ADS-MVSNet		98.20 243	98.08 246	98.56 309	99.33 285	96.48 376	99.23 343	99.15 363	96.24 384	99.10 277	99.67 235	94.11 301	99.71 273	96.81 363	99.05 227	99.48 237

JIA-IIPM		97.50 348	97.02 364	98.93 248	98.73 410	97.80 301	99.30 309	98.97 387	91.73 451	98.91 312	94.86 469	95.10 244	99.71 273	97.58 308	97.98 302	99.28 275

EPMVS		97.82 305	97.65 296	98.35 338	98.88 385	95.98 391	99.49 214	94.71 476	97.57 268	99.26 245	99.48 312	92.46 353	99.71 273	97.87 277	99.08 225	99.35 267

TDRefinement		95.42 404	94.57 412	97.97 370	89.83 479	96.11 390	99.48 222	98.75 420	96.74 345	96.68 432	99.88 52	88.65 412	99.71 273	98.37 230	82.74 468	98.09 431

ACMM	97.58 5	98.37 231	98.34 224	98.48 318	99.41 262	97.10 332	99.56 146	99.45 242	98.53 121	99.04 291	99.85 80	93.00 330	99.71 273	98.74 178	97.45 336	98.64 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 279	97.77 281	98.57 305	99.59 191	96.61 372	99.45 239	99.08 372	98.21 168	98.88 317	99.80 146	88.66 411	99.70 280	98.58 204	97.72 314	99.39 261

CHOSEN 280x420		99.12 129	99.13 95	99.08 226	99.66 147	97.89 296	98.43 452	99.71 16	98.88 83	99.62 145	99.76 183	96.63 169	99.70 280	99.46 67	99.99 1	99.66 163

EC-MVSNet		99.44 50	99.39 40	99.58 116	99.56 202	99.49 109	99.88 4	99.58 78	98.38 137	99.73 97	99.69 221	98.20 103	99.70 280	99.64 43	99.82 117	99.54 213

PatchmatchNet		98.31 234	98.36 222	98.19 353	99.16 337	95.32 411	99.27 324	98.92 394	97.37 294	99.37 210	99.58 272	94.90 254	99.70 280	97.43 326	99.21 201	99.54 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 254	97.99 256	98.44 329	99.41 262	96.96 351	99.60 113	99.56 90	98.09 192	98.15 391	99.91 26	90.87 385	99.70 280	98.88 150	97.45 336	98.67 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 348	96.90 368	99.29 201	99.23 314	98.78 228	99.32 303	98.90 401	97.52 277	98.56 366	98.09 450	84.72 446	99.69 285	97.86 278	97.88 307	99.39 261

HQP_MVS		98.27 239	98.22 232	98.44 329	99.29 298	96.97 349	99.39 276	99.47 220	98.97 75	99.11 274	99.61 263	92.71 341	99.69 285	97.78 288	97.63 317	98.67 359

plane_prior5								99.47 220					99.69 285	97.78 288	97.63 317	98.67 359

D2MVS		98.41 225	98.50 215	98.15 358	99.26 306	96.62 371	99.40 272	99.61 60	97.71 251	98.98 301	99.36 347	96.04 197	99.67 288	98.70 183	97.41 341	98.15 428

IS-MVSNet		99.05 152	98.87 162	99.57 120	99.73 107	99.32 131	99.75 42	99.20 357	98.02 215	99.56 161	99.86 73	96.54 176	99.67 288	98.09 257	99.13 211	99.73 122

CLD-MVS		98.16 248	98.10 242	98.33 339	99.29 298	96.82 362	98.75 430	99.44 251	97.83 235	99.13 270	99.55 283	92.92 332	99.67 288	98.32 237	97.69 315	98.48 402
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 365	97.30 347	97.09 414	99.43 255	93.31 448	99.73 51	98.87 406	98.83 88	99.28 234	99.80 146	84.45 447	99.66 291	97.88 275	97.45 336	98.30 418

AUN-MVS		96.88 376	96.31 382	98.59 301	99.48 244	97.04 341	99.27 324	99.22 353	97.44 287	98.51 369	99.41 330	91.97 361	99.66 291	97.71 299	83.83 466	99.07 299

UniMVSNet_ETH3D		97.32 362	96.81 370	98.87 267	99.40 267	97.46 316	99.51 186	99.53 125	95.86 404	98.54 368	99.77 179	82.44 456	99.66 291	98.68 188	97.52 328	99.50 233

OPM-MVS		98.19 244	98.10 242	98.45 326	98.88 385	97.07 336	99.28 319	99.38 285	98.57 117	99.22 252	99.81 128	92.12 358	99.66 291	98.08 261	97.54 326	98.61 390
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 285	97.78 279	98.32 341	99.46 247	96.68 369	99.56 146	99.54 109	98.41 135	97.79 409	99.87 65	90.18 394	99.66 291	98.05 265	97.18 351	98.62 381

IMVS_0407		98.86 178	98.91 152	98.72 289	99.55 206	96.93 352	99.50 197	99.44 251	98.05 203	99.66 124	99.80 146	97.13 138	99.65 296	98.15 252	98.92 238	99.60 189

hse-mvs2		97.50 348	97.14 358	98.59 301	99.49 237	97.05 338	99.28 319	99.22 353	98.94 78	99.66 124	99.42 326	94.93 250	99.65 296	99.48 64	83.80 467	99.08 294

VPA-MVSNet		98.29 237	97.95 261	99.30 198	99.16 337	99.54 98	99.50 197	99.58 78	98.27 152	99.35 219	99.37 344	92.53 348	99.65 296	99.35 76	94.46 410	98.72 335

TR-MVS		97.76 313	97.41 332	98.82 276	99.06 357	97.87 297	98.87 419	98.56 437	96.63 356	98.68 348	99.22 378	92.49 349	99.65 296	95.40 403	97.79 312	98.95 313

reproduce_monomvs		97.89 289	97.87 271	97.96 372	99.51 223	95.45 406	99.60 113	99.25 347	99.17 36	98.85 325	99.49 306	89.29 403	99.64 300	99.35 76	96.31 367	98.78 321

gm-plane-assit					98.54 430	92.96 450			94.65 426		99.15 386		99.64 300	97.56 313

HQP4-MVS										98.66 349			99.64 300			98.64 372

HQP-MVS		98.02 269	97.90 266	98.37 337	99.19 324	96.83 360	98.98 402	99.39 277	98.24 162	98.66 349	99.40 334	92.47 350	99.64 300	97.19 341	97.58 322	98.64 372

PAPM		97.59 340	97.09 362	99.07 227	99.06 357	98.26 272	98.30 459	99.10 369	94.88 420	98.08 393	99.34 354	96.27 189	99.64 300	89.87 453	98.92 238	99.31 273

TAPA-MVS	97.07 15	97.74 319	97.34 341	98.94 246	99.70 122	97.53 313	99.25 335	99.51 151	91.90 450	99.30 230	99.63 254	98.78 53	99.64 300	88.09 460	99.87 79	99.65 169
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 229	98.09 245	99.24 211	99.26 306	99.32 131	99.56 146	99.55 100	97.45 284	98.71 340	99.83 100	93.23 325	99.63 306	98.88 150	96.32 366	98.76 327

ITE_SJBPF				98.08 361	99.29 298	96.37 379		98.92 394	98.34 143	98.83 326	99.75 188	91.09 382	99.62 307	95.82 390	97.40 342	98.25 422

LF4IMVS		97.52 345	97.46 320	97.70 393	98.98 373	95.55 401	99.29 314	98.82 411	98.07 197	98.66 349	99.64 248	89.97 395	99.61 308	97.01 350	96.68 356	97.94 443

tpm		97.67 334	97.55 305	98.03 363	99.02 364	95.01 418	99.43 252	98.54 439	96.44 372	99.12 272	99.34 354	91.83 365	99.60 309	97.75 294	96.46 362	99.48 237

tpm2		97.44 355	97.34 341	97.74 391	99.15 341	94.36 434	99.45 239	98.94 390	93.45 439	98.90 314	99.44 322	91.35 378	99.59 310	97.31 332	98.07 300	99.29 274

SSM_04072		99.06 148	98.96 140	99.35 184	99.62 172	98.88 206	99.25 335	99.47 220	98.05 203	99.37 210	99.81 128	96.85 154	99.58 311	98.98 133	99.25 197	99.60 189

SD_0403		97.55 342	97.53 309	97.62 396	99.61 183	93.64 445	99.72 53	99.44 251	98.03 212	98.62 361	99.39 338	96.06 196	99.57 312	87.88 462	99.01 232	99.66 163

baseline2		97.87 292	97.55 305	98.82 276	99.18 327	98.02 285	99.41 264	96.58 470	96.97 331	96.51 433	99.17 383	93.43 320	99.57 312	97.71 299	99.03 229	98.86 315

MS-PatchMatch		97.24 367	97.32 345	96.99 416	98.45 433	93.51 447	98.82 423	99.32 326	97.41 291	98.13 392	99.30 365	88.99 405	99.56 314	95.68 396	99.80 125	97.90 446

TinyColmap		97.12 370	96.89 369	97.83 384	99.07 355	95.52 404	98.57 445	98.74 423	97.58 267	97.81 408	99.79 163	88.16 419	99.56 314	95.10 408	97.21 349	98.39 414

USDC		97.34 360	97.20 355	97.75 389	99.07 355	95.20 413	98.51 449	99.04 379	97.99 216	98.31 380	99.86 73	89.02 404	99.55 316	95.67 397	97.36 344	98.49 401

MSLP-MVS++		99.46 42	99.47 24	99.44 168	99.60 189	99.16 155	99.41 264	99.71 16	98.98 72	99.45 183	99.78 170	99.19 11	99.54 317	99.28 92	99.84 102	99.63 181

UWE-MVS-28		97.36 358	97.24 354	97.75 389	98.84 394	94.44 431	99.24 340	97.58 459	97.98 217	99.00 298	99.00 402	91.35 378	99.53 318	93.75 426	98.39 274	99.27 279

TAMVS		99.12 129	99.08 105	99.24 211	99.46 247	98.55 248	99.51 186	99.46 231	98.09 192	99.45 183	99.82 113	98.34 97	99.51 319	98.70 183	98.93 236	99.67 159

EPNet_dtu		98.03 267	97.96 259	98.23 351	98.27 436	95.54 403	99.23 343	98.75 420	99.02 62	97.82 407	99.71 206	96.11 194	99.48 320	93.04 436	99.65 161	99.69 149
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 380	96.22 384	97.97 370	97.00 458	96.28 383	98.66 439	99.03 381	96.61 357	96.93 430	99.79 163	87.20 428	99.47 321	96.65 373	94.13 417	98.16 427

EG-PatchMatch MVS		95.97 395	95.69 396	96.81 423	97.78 443	92.79 451	99.16 358	98.93 391	96.16 391	94.08 452	99.22 378	82.72 454	99.47 321	95.67 397	97.50 331	98.17 426

myMVS_eth3d28		97.69 328	97.34 341	98.73 287	99.27 303	97.52 314	99.33 300	98.78 418	98.03 212	98.82 328	98.49 432	86.64 430	99.46 323	98.44 223	98.24 288	99.23 282

MVP-Stereo		97.81 307	97.75 286	97.99 369	97.53 447	96.60 373	98.96 406	98.85 408	97.22 308	97.23 420	99.36 347	95.28 234	99.46 323	95.51 399	99.78 134	97.92 445
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 215	98.67 189	98.30 343	99.35 279	95.59 400	99.50 197	99.55 100	98.60 115	99.39 206	99.83 100	94.48 285	99.45 325	98.75 177	98.56 266	99.85 46

test-LLR		98.06 259	97.90 266	98.55 311	98.79 398	97.10 332	98.67 436	97.75 455	97.34 296	98.61 362	98.85 416	94.45 287	99.45 325	97.25 335	99.38 183	99.10 289

TESTMET0.1,1		97.55 342	97.27 353	98.40 334	98.93 378	96.53 374	98.67 436	97.61 458	96.96 332	98.64 356	99.28 369	88.63 414	99.45 325	97.30 333	99.38 183	99.21 284

test-mter		97.49 353	97.13 360	98.55 311	98.79 398	97.10 332	98.67 436	97.75 455	96.65 352	98.61 362	98.85 416	88.23 418	99.45 325	97.25 335	99.38 183	99.10 289

mvs_anonymous		99.03 155	98.99 132	99.16 219	99.38 272	98.52 254	99.51 186	99.38 285	97.79 241	99.38 208	99.81 128	97.30 131	99.45 325	99.35 76	98.99 233	99.51 229

tfpnnormal		97.84 299	97.47 318	98.98 239	99.20 321	99.22 149	99.64 95	99.61 60	96.32 378	98.27 384	99.70 210	93.35 324	99.44 330	95.69 395	95.40 393	98.27 420

v7n		97.87 292	97.52 310	98.92 250	98.76 408	98.58 246	99.84 12	99.46 231	96.20 387	98.91 312	99.70 210	94.89 255	99.44 330	96.03 386	93.89 422	98.75 329

jajsoiax		98.43 222	98.28 229	98.88 263	98.60 425	98.43 265	99.82 16	99.53 125	98.19 170	98.63 358	99.80 146	93.22 327	99.44 330	99.22 100	97.50 331	98.77 325

mvs_tets		98.40 228	98.23 231	98.91 254	98.67 418	98.51 256	99.66 82	99.53 125	98.19 170	98.65 355	99.81 128	92.75 336	99.44 330	99.31 86	97.48 335	98.77 325

sc_t1		95.75 399	95.05 406	97.87 379	98.83 395	94.61 428	99.21 349	99.45 242	87.45 463	97.97 400	99.85 80	81.19 461	99.43 334	98.27 240	93.20 431	99.57 207

Vis-MVSNet (Re-imp)		98.87 175	98.72 183	99.31 193	99.71 117	98.88 206	99.80 25	99.44 251	97.91 223	99.36 216	99.78 170	95.49 226	99.43 334	97.91 273	99.11 217	99.62 184

OPU-MVS				99.64 101	99.56 202	99.72 56	99.60 113				99.70 210	99.27 7	99.42 336	98.24 243	99.80 125	99.79 92

Anonymous20231211		97.88 290	97.54 308	98.90 256	99.71 117	98.53 250	99.48 222	99.57 85	94.16 430	98.81 329	99.68 229	93.23 325	99.42 336	98.84 163	94.42 412	98.76 327

ttmdpeth		97.80 309	97.63 300	98.29 344	98.77 406	97.38 319	99.64 95	99.36 296	98.78 98	96.30 436	99.58 272	92.34 357	99.39 338	98.36 232	95.58 388	98.10 430

VPNet		97.84 299	97.44 326	99.01 235	99.21 319	98.94 197	99.48 222	99.57 85	98.38 137	99.28 234	99.73 199	88.89 406	99.39 338	99.19 103	93.27 430	98.71 337

nrg030		98.64 212	98.42 219	99.28 205	99.05 360	99.69 63	99.81 20	99.46 231	98.04 210	99.01 294	99.82 113	96.69 166	99.38 340	99.34 81	94.59 409	98.78 321

GA-MVS		97.85 295	97.47 318	99.00 237	99.38 272	97.99 287	98.57 445	99.15 363	97.04 327	98.90 314	99.30 365	89.83 397	99.38 340	96.70 368	98.33 278	99.62 184

UniMVSNet (Re)		98.29 237	98.00 255	99.13 224	99.00 367	99.36 126	99.49 214	99.51 151	97.95 219	98.97 303	99.13 388	96.30 188	99.38 340	98.36 232	93.34 428	98.66 368

FIs		98.78 196	98.63 197	99.23 213	99.18 327	99.54 98	99.83 15	99.59 73	98.28 150	98.79 333	99.81 128	96.75 164	99.37 343	99.08 122	96.38 364	98.78 321

PS-MVSNAJss		98.92 169	98.92 149	98.90 256	98.78 401	98.53 250	99.78 32	99.54 109	98.07 197	99.00 298	99.76 183	99.01 20	99.37 343	99.13 114	97.23 348	98.81 318

CDS-MVSNet		99.09 141	99.03 116	99.25 208	99.42 257	98.73 230	99.45 239	99.46 231	98.11 188	99.46 182	99.77 179	98.01 112	99.37 343	98.70 183	98.92 238	99.66 163
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 399	95.16 404	97.51 402	99.30 294	93.69 443	98.88 417	95.78 471	85.09 468	98.78 334	92.65 471	91.29 380	99.37 343	94.85 413	99.85 94	99.46 248

v1192		97.81 307	97.44 326	98.91 254	98.88 385	98.68 234	99.51 186	99.34 308	96.18 389	99.20 258	99.34 354	94.03 305	99.36 347	95.32 405	95.18 397	98.69 346

EI-MVSNet		98.67 208	98.67 189	98.68 295	99.35 279	97.97 288	99.50 197	99.38 285	96.93 337	99.20 258	99.83 100	97.87 114	99.36 347	98.38 228	97.56 324	98.71 337

MVSTER		98.49 217	98.32 226	99.00 237	99.35 279	99.02 175	99.54 166	99.38 285	97.41 291	99.20 258	99.73 199	93.86 313	99.36 347	98.87 153	97.56 324	98.62 381

gg-mvs-nofinetune		96.17 391	95.32 403	98.73 287	98.79 398	98.14 278	99.38 281	94.09 477	91.07 455	98.07 396	91.04 475	89.62 401	99.35 350	96.75 365	99.09 224	98.68 351

pm-mvs1		97.68 331	97.28 350	98.88 263	99.06 357	98.62 242	99.50 197	99.45 242	96.32 378	97.87 405	99.79 163	92.47 350	99.35 350	97.54 315	93.54 426	98.67 359

OurMVSNet-221017-0		97.88 290	97.77 281	98.19 353	98.71 414	96.53 374	99.88 4	99.00 384	97.79 241	98.78 334	99.94 6	91.68 369	99.35 350	97.21 337	96.99 355	98.69 346

EGC-MVSNET		82.80 439	77.86 445	97.62 396	97.91 440	96.12 389	99.33 300	99.28 341	8.40 483	25.05 484	99.27 372	84.11 448	99.33 353	89.20 455	98.22 289	97.42 457

pmmvs6		96.53 383	96.09 388	97.82 386	98.69 416	95.47 405	99.37 283	99.47 220	93.46 438	97.41 414	99.78 170	87.06 429	99.33 353	96.92 360	92.70 438	98.65 370

V42		98.06 259	97.79 276	98.86 270	98.98 373	98.84 216	99.69 62	99.34 308	96.53 364	99.30 230	99.37 344	94.67 272	99.32 355	97.57 312	94.66 407	98.42 410

lessismore_v0				97.79 388	98.69 416	95.44 408		94.75 475		95.71 442	99.87 65	88.69 410	99.32 355	95.89 389	94.93 404	98.62 381

OpenMVS_ROB	92.34 20	94.38 417	93.70 423	96.41 429	97.38 449	93.17 449	99.06 381	98.75 420	86.58 466	94.84 449	98.26 442	81.53 459	99.32 355	89.01 456	97.87 308	96.76 460

v8		97.95 281	97.63 300	98.93 248	98.95 377	98.81 224	99.80 25	99.41 267	96.03 401	99.10 277	99.42 326	94.92 252	99.30 358	96.94 357	94.08 419	98.66 368

v1921920		97.80 309	97.45 321	98.84 274	98.80 397	98.53 250	99.52 177	99.34 308	96.15 393	99.24 247	99.47 315	93.98 307	99.29 359	95.40 403	95.13 399	98.69 346

anonymousdsp		98.44 221	98.28 229	98.94 246	98.50 431	98.96 187	99.77 34	99.50 174	97.07 322	98.87 320	99.77 179	94.76 265	99.28 360	98.66 190	97.60 320	98.57 396

MVSFormer		99.17 108	99.12 97	99.29 201	99.51 223	98.94 197	99.88 4	99.46 231	97.55 271	99.80 74	99.65 242	97.39 125	99.28 360	99.03 128	99.85 94	99.65 169

test_djsdf		98.67 208	98.57 209	98.98 239	98.70 415	98.91 203	99.88 4	99.46 231	97.55 271	99.22 252	99.88 52	95.73 217	99.28 360	99.03 128	97.62 319	98.75 329

VortexMVS		98.67 208	98.66 192	98.68 295	99.62 172	97.96 290	99.59 120	99.41 267	98.13 183	99.31 226	99.70 210	95.48 227	99.27 363	99.40 71	97.32 345	98.79 319

SSC-MVS3.2		97.34 360	97.15 357	97.93 374	99.02 364	95.76 397	99.48 222	99.58 78	97.62 263	99.09 280	99.53 292	87.95 421	99.27 363	96.42 378	95.66 386	98.75 329

cascas		97.69 328	97.43 330	98.48 318	98.60 425	97.30 321	98.18 463	99.39 277	92.96 444	98.41 374	98.78 423	93.77 316	99.27 363	98.16 250	98.61 260	98.86 315

v144192		97.92 285	97.60 303	98.87 267	98.83 395	98.65 237	99.55 161	99.34 308	96.20 387	99.32 225	99.40 334	94.36 289	99.26 366	96.37 382	95.03 401	98.70 342

dmvs_re		98.08 257	98.16 234	97.85 381	99.55 206	94.67 427	99.70 58	98.92 394	98.15 175	99.06 288	99.35 350	93.67 319	99.25 367	97.77 291	97.25 347	99.64 176

v2v482		98.06 259	97.77 281	98.92 250	98.90 383	98.82 222	99.57 138	99.36 296	96.65 352	99.19 261	99.35 350	94.20 296	99.25 367	97.72 298	94.97 402	98.69 346

v1240		97.69 328	97.32 345	98.79 282	98.85 392	98.43 265	99.48 222	99.36 296	96.11 396	99.27 240	99.36 347	93.76 317	99.24 369	94.46 417	95.23 396	98.70 342

WBMVS		97.74 319	97.50 313	98.46 324	99.24 312	97.43 317	99.21 349	99.42 264	97.45 284	98.96 305	99.41 330	88.83 407	99.23 370	98.94 141	96.02 372	98.71 337

v1144		97.98 276	97.69 292	98.85 273	98.87 388	98.66 236	99.54 166	99.35 303	96.27 382	99.23 251	99.35 350	94.67 272	99.23 370	96.73 366	95.16 398	98.68 351

v10		97.85 295	97.52 310	98.86 270	98.99 370	98.67 235	99.75 42	99.41 267	95.70 405	98.98 301	99.41 330	94.75 266	99.23 370	96.01 388	94.63 408	98.67 359

WR-MVS_H		98.13 251	97.87 271	98.90 256	99.02 364	98.84 216	99.70 58	99.59 73	97.27 302	98.40 375	99.19 382	95.53 224	99.23 370	98.34 234	93.78 424	98.61 390

miper_enhance_ethall		98.16 248	98.08 246	98.41 332	98.96 376	97.72 305	98.45 451	99.32 326	96.95 334	98.97 303	99.17 383	97.06 145	99.22 374	97.86 278	95.99 375	98.29 419

GG-mvs-BLEND				98.45 326	98.55 429	98.16 276	99.43 252	93.68 478		97.23 420	98.46 433	89.30 402	99.22 374	95.43 402	98.22 289	97.98 441

FC-MVSNet-test		98.75 201	98.62 202	99.15 223	99.08 354	99.45 115	99.86 11	99.60 67	98.23 165	98.70 346	99.82 113	96.80 161	99.22 374	99.07 123	96.38 364	98.79 319

UniMVSNet_NR-MVSNet		98.22 240	97.97 258	98.96 242	98.92 380	98.98 180	99.48 222	99.53 125	97.76 245	98.71 340	99.46 319	96.43 183	99.22 374	98.57 207	92.87 436	98.69 346

DU-MVS		98.08 257	97.79 276	98.96 242	98.87 388	98.98 180	99.41 264	99.45 242	97.87 227	98.71 340	99.50 303	94.82 257	99.22 374	98.57 207	92.87 436	98.68 351

cl____		98.01 272	97.84 274	98.55 311	99.25 310	97.97 288	98.71 434	99.34 308	96.47 371	98.59 365	99.54 288	95.65 220	99.21 379	97.21 337	95.77 381	98.46 407

WR-MVS		98.06 259	97.73 288	99.06 229	98.86 391	99.25 146	99.19 354	99.35 303	97.30 300	98.66 349	99.43 324	93.94 308	99.21 379	98.58 204	94.28 414	98.71 337

test_0402		96.64 381	96.24 383	97.85 381	98.85 392	96.43 378	99.44 246	99.26 345	93.52 436	96.98 428	99.52 296	88.52 415	99.20 381	92.58 443	97.50 331	97.93 444

icg_test_0407_2		98.79 195	98.86 165	98.57 305	99.55 206	96.93 352	99.07 377	99.44 251	98.05 203	99.66 124	99.80 146	97.13 138	99.18 382	98.15 252	98.92 238	99.60 189

SixPastTwentyTwo		97.50 348	97.33 344	98.03 363	98.65 419	96.23 386	99.77 34	98.68 432	97.14 313	97.90 403	99.93 10	90.45 388	99.18 382	97.00 351	96.43 363	98.67 359

cl22		97.85 295	97.64 299	98.48 318	99.09 351	97.87 297	98.60 444	99.33 316	97.11 319	98.87 320	99.22 378	92.38 355	99.17 384	98.21 244	95.99 375	98.42 410

tt0320		95.71 401	95.07 405	97.62 396	99.05 360	95.02 417	99.25 335	99.52 130	86.81 464	97.97 400	99.72 203	83.58 451	99.15 385	96.38 381	93.35 427	98.68 351

WB-MVSnew		97.65 336	97.65 296	97.63 395	98.78 401	97.62 311	99.13 364	98.33 443	97.36 295	99.07 283	98.94 410	95.64 221	99.15 385	92.95 437	98.68 258	96.12 467

IterMVS-SCA-FT		97.82 305	97.75 286	98.06 362	99.57 198	96.36 380	99.02 391	99.49 186	97.18 310	98.71 340	99.72 203	92.72 339	99.14 387	97.44 325	95.86 380	98.67 359

pmmvs5		97.52 345	97.30 347	98.16 355	98.57 428	96.73 364	99.27 324	98.90 401	96.14 394	98.37 377	99.53 292	91.54 375	99.14 387	97.51 317	95.87 379	98.63 379

v148		97.79 311	97.55 305	98.50 315	98.74 409	97.72 305	99.54 166	99.33 316	96.26 383	98.90 314	99.51 300	94.68 271	99.14 387	97.83 282	93.15 433	98.63 379

IMVS_0404		98.53 216	98.52 214	98.55 311	99.55 206	96.93 352	99.20 352	99.44 251	98.05 203	98.96 305	99.80 146	94.66 274	99.13 390	98.15 252	98.92 238	99.60 189

miper_ehance_all_eth		98.18 246	98.10 242	98.41 332	99.23 314	97.72 305	98.72 433	99.31 330	96.60 360	98.88 317	99.29 367	97.29 132	99.13 390	97.60 306	95.99 375	98.38 415

NR-MVSNet		97.97 279	97.61 302	99.02 234	98.87 388	99.26 144	99.47 232	99.42 264	97.63 261	97.08 426	99.50 303	95.07 245	99.13 390	97.86 278	93.59 425	98.68 351

IterMVS		97.83 302	97.77 281	98.02 365	99.58 193	96.27 384	99.02 391	99.48 198	97.22 308	98.71 340	99.70 210	92.75 336	99.13 390	97.46 323	96.00 374	98.67 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 418	94.90 408	91.84 446	97.24 453	80.01 476	98.52 448	99.48 198	89.01 460	91.99 463	99.67 235	85.67 438	99.13 390	95.44 401	97.03 354	96.39 464
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 264	97.96 259	98.33 339	99.26 306	97.38 319	98.56 447	99.31 330	96.65 352	98.88 317	99.52 296	96.58 173	99.12 395	97.39 328	95.53 391	98.47 404

pmmvs4		98.13 251	97.90 266	98.81 279	98.61 424	98.87 210	98.99 399	99.21 356	96.44 372	99.06 288	99.58 272	95.90 207	99.11 396	97.18 343	96.11 371	98.46 407

TransMVSNet (Re)		97.15 369	96.58 375	98.86 270	99.12 343	98.85 214	99.49 214	98.91 399	95.48 408	97.16 424	99.80 146	93.38 321	99.11 396	94.16 423	91.73 443	98.62 381

ambc				93.06 444	92.68 475	82.36 469	98.47 450	98.73 429		95.09 447	97.41 458	55.55 475	99.10 398	96.42 378	91.32 444	97.71 447

Baseline_NR-MVSNet		97.76 313	97.45 321	98.68 295	99.09 351	98.29 270	99.41 264	98.85 408	95.65 406	98.63 358	99.67 235	94.82 257	99.10 398	98.07 264	92.89 435	98.64 372

test_vis3_rt		87.04 435	85.81 438	90.73 450	93.99 474	81.96 471	99.76 37	90.23 485	92.81 446	81.35 473	91.56 473	40.06 481	99.07 400	94.27 420	88.23 460	91.15 473

CP-MVSNet		98.09 255	97.78 279	99.01 235	98.97 375	99.24 147	99.67 75	99.46 231	97.25 304	98.48 372	99.64 248	93.79 315	99.06 401	98.63 194	94.10 418	98.74 333

PS-CasMVS		97.93 282	97.59 304	98.95 244	98.99 370	99.06 171	99.68 72	99.52 130	97.13 314	98.31 380	99.68 229	92.44 354	99.05 402	98.51 215	94.08 419	98.75 329

K. test v3		97.10 371	96.79 371	98.01 366	98.72 412	96.33 381	99.87 8	97.05 462	97.59 265	96.16 438	99.80 146	88.71 409	99.04 403	96.69 369	96.55 361	98.65 370

new_pmnet		96.38 387	96.03 389	97.41 405	98.13 439	95.16 416	99.05 383	99.20 357	93.94 431	97.39 417	98.79 422	91.61 374	99.04 403	90.43 451	95.77 381	98.05 434

DIV-MVS_self_test		98.01 272	97.85 273	98.48 318	99.24 312	97.95 293	98.71 434	99.35 303	96.50 365	98.60 364	99.54 288	95.72 218	99.03 405	97.21 337	95.77 381	98.46 407

IterMVS-LS		98.46 220	98.42 219	98.58 304	99.59 191	98.00 286	99.37 283	99.43 262	96.94 336	99.07 283	99.59 268	97.87 114	99.03 405	98.32 237	95.62 387	98.71 337
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 336	97.68 293	97.55 401	98.62 422	94.97 419	98.84 421	99.30 335	96.83 343	98.19 389	99.34 354	97.01 149	99.02 407	95.00 411	96.01 373	98.64 372

Patchmtry		97.75 317	97.40 333	98.81 279	99.10 348	98.87 210	99.11 373	99.33 316	94.83 422	98.81 329	99.38 341	94.33 292	99.02 407	96.10 384	95.57 389	98.53 398

N_pmnet		94.95 412	95.83 394	92.31 445	98.47 432	79.33 477	99.12 367	92.81 483	93.87 432	97.68 410	99.13 388	93.87 312	99.01 409	91.38 448	96.19 369	98.59 394

CR-MVSNet		98.17 247	97.93 264	98.87 267	99.18 327	98.49 259	99.22 347	99.33 316	96.96 332	99.56 161	99.38 341	94.33 292	99.00 410	94.83 414	98.58 263	99.14 286

c3_l		98.12 253	98.04 251	98.38 336	99.30 294	97.69 309	98.81 424	99.33 316	96.67 350	98.83 326	99.34 354	97.11 141	98.99 411	97.58 308	95.34 394	98.48 402

test0.0.03 1		97.71 326	97.42 331	98.56 309	98.41 435	97.82 300	98.78 427	98.63 435	97.34 296	98.05 397	98.98 406	94.45 287	98.98 412	95.04 410	97.15 352	98.89 314

PatchT		97.03 373	96.44 379	98.79 282	98.99 370	98.34 269	99.16 358	99.07 375	92.13 449	99.52 172	97.31 462	94.54 282	98.98 412	88.54 458	98.73 255	99.03 302

GBi-Net		97.68 331	97.48 315	98.29 344	99.51 223	97.26 325	99.43 252	99.48 198	96.49 366	99.07 283	99.32 362	90.26 390	98.98 412	97.10 345	96.65 357	98.62 381

test1		97.68 331	97.48 315	98.29 344	99.51 223	97.26 325	99.43 252	99.48 198	96.49 366	99.07 283	99.32 362	90.26 390	98.98 412	97.10 345	96.65 357	98.62 381

FMVSNet3		98.03 267	97.76 285	98.84 274	99.39 270	98.98 180	99.40 272	99.38 285	96.67 350	99.07 283	99.28 369	92.93 331	98.98 412	97.10 345	96.65 357	98.56 397

FMVSNet2		97.72 323	97.36 336	98.80 281	99.51 223	98.84 216	99.45 239	99.42 264	96.49 366	98.86 324	99.29 367	90.26 390	98.98 412	96.44 377	96.56 360	98.58 395

FMVSNet1		96.84 377	96.36 381	98.29 344	99.32 292	97.26 325	99.43 252	99.48 198	95.11 413	98.55 367	99.32 362	83.95 449	98.98 412	95.81 391	96.26 368	98.62 381

ppachtmachnet_test		97.49 353	97.45 321	97.61 399	98.62 422	95.24 412	98.80 425	99.46 231	96.11 396	98.22 387	99.62 259	96.45 181	98.97 419	93.77 425	95.97 378	98.61 390

TranMVSNet+NR-MVSNet		97.93 282	97.66 295	98.76 286	98.78 401	98.62 242	99.65 88	99.49 186	97.76 245	98.49 371	99.60 266	94.23 295	98.97 419	98.00 268	92.90 434	98.70 342

MVStest1		96.08 394	95.48 399	97.89 378	98.93 378	96.70 365	99.56 146	99.35 303	92.69 447	91.81 464	99.46 319	89.90 396	98.96 421	95.00 411	92.61 439	98.00 439

tt0320-xc		95.31 407	94.59 411	97.45 404	98.92 380	94.73 423	99.20 352	99.31 330	86.74 465	97.23 420	99.72 203	81.14 462	98.95 422	97.08 348	91.98 442	98.67 359

test_method		91.10 430	91.36 432	90.31 451	95.85 461	73.72 484	94.89 473	99.25 347	68.39 475	95.82 441	99.02 400	80.50 463	98.95 422	93.64 428	94.89 406	98.25 422

ADS-MVSNet2		98.02 269	98.07 249	97.87 379	99.33 285	95.19 414	99.23 343	99.08 372	96.24 384	99.10 277	99.67 235	94.11 301	98.93 424	96.81 363	99.05 227	99.48 237

ET-MVSNet_ETH3D		96.49 384	95.64 398	99.05 231	99.53 214	98.82 222	98.84 421	97.51 460	97.63 261	84.77 469	99.21 381	92.09 359	98.91 425	98.98 133	92.21 441	99.41 258

miper_lstm_enhance		98.00 274	97.91 265	98.28 348	99.34 284	97.43 317	98.88 417	99.36 296	96.48 369	98.80 331	99.55 283	95.98 200	98.91 425	97.27 334	95.50 392	98.51 400

MonoMVSNet		98.38 229	98.47 217	98.12 360	98.59 427	96.19 388	99.72 53	98.79 417	97.89 225	99.44 188	99.52 296	96.13 193	98.90 427	98.64 192	97.54 326	99.28 275

PEN-MVS		97.76 313	97.44 326	98.72 289	98.77 406	98.54 249	99.78 32	99.51 151	97.06 324	98.29 383	99.64 248	92.63 345	98.89 428	98.09 257	93.16 432	98.72 335

testing3		97.28 363	96.76 372	98.82 276	99.37 275	98.07 283	99.45 239	99.36 296	97.56 270	97.89 404	98.95 409	83.70 450	98.82 429	96.03 386	98.56 266	99.58 204

testgi		97.65 336	97.50 313	98.13 359	99.36 278	96.45 377	99.42 259	99.48 198	97.76 245	97.87 405	99.45 321	91.09 382	98.81 430	94.53 416	98.52 269	99.13 288

testf1		90.42 433	90.68 434	89.65 454	97.78 443	73.97 482	99.13 364	98.81 413	89.62 457	91.80 465	98.93 411	62.23 473	98.80 431	86.61 468	91.17 445	96.19 465

APD_test2		90.42 433	90.68 434	89.65 454	97.78 443	73.97 482	99.13 364	98.81 413	89.62 457	91.80 465	98.93 411	62.23 473	98.80 431	86.61 468	91.17 445	96.19 465

MIMVSNet		97.73 321	97.45 321	98.57 305	99.45 253	97.50 315	99.02 391	98.98 386	96.11 396	99.41 199	99.14 387	90.28 389	98.74 433	95.74 393	98.93 236	99.47 243

LCM-MVSNet-Re		97.83 302	98.15 236	96.87 422	99.30 294	92.25 454	99.59 120	98.26 444	97.43 288	96.20 437	99.13 388	96.27 189	98.73 434	98.17 249	98.99 233	99.64 176

Syy-MVS		97.09 372	97.14 358	96.95 419	99.00 367	92.73 452	99.29 314	99.39 277	97.06 324	97.41 414	98.15 445	93.92 310	98.68 435	91.71 446	98.34 276	99.45 251

myMVS_eth3d		96.89 375	96.37 380	98.43 331	99.00 367	97.16 329	99.29 314	99.39 277	97.06 324	97.41 414	98.15 445	83.46 452	98.68 435	95.27 406	98.34 276	99.45 251

DTE-MVSNet		97.51 347	97.19 356	98.46 324	98.63 421	98.13 279	99.84 12	99.48 198	96.68 349	97.97 400	99.67 235	92.92 332	98.56 437	96.88 362	92.60 440	98.70 342

PC_three_1452									98.18 173	99.84 56	99.70 210	99.31 3	98.52 438	98.30 239	99.80 125	99.81 79

mvsany_test3		93.77 421	93.45 424	94.74 436	95.78 462	88.01 462	99.64 95	98.25 445	98.28 150	94.31 450	97.97 452	68.89 469	98.51 439	97.50 318	90.37 450	97.71 447

UnsupCasMVSNet_bld		93.53 423	92.51 429	96.58 427	97.38 449	93.82 439	98.24 460	99.48 198	91.10 454	93.10 458	96.66 464	74.89 466	98.37 440	94.03 424	87.71 461	97.56 454

Anonymous20240521		96.20 390	95.89 393	97.13 412	97.72 446	94.96 420	99.79 31	99.29 339	93.01 443	97.20 423	99.03 398	89.69 399	98.36 441	91.16 449	96.13 370	98.07 432

test_f		91.90 429	91.26 433	93.84 439	95.52 466	85.92 464	99.69 62	98.53 440	95.31 410	93.87 453	96.37 466	55.33 476	98.27 442	95.70 394	90.98 448	97.32 458

MDA-MVSNet_test_wron		95.45 403	94.60 410	98.01 366	98.16 438	97.21 328	99.11 373	99.24 350	93.49 437	80.73 475	98.98 406	93.02 329	98.18 443	94.22 422	94.45 411	98.64 372

UnsupCasMVSNet_eth		96.44 385	96.12 386	97.40 406	98.65 419	95.65 398	99.36 289	99.51 151	97.13 314	96.04 440	98.99 404	88.40 416	98.17 444	96.71 367	90.27 451	98.40 413

KD-MVS_2432*1600		94.62 413	93.72 421	97.31 407	97.19 455	95.82 395	98.34 455	99.20 357	95.00 418	97.57 411	98.35 438	87.95 421	98.10 445	92.87 439	77.00 473	98.01 436

miper_refine_blended		94.62 413	93.72 421	97.31 407	97.19 455	95.82 395	98.34 455	99.20 357	95.00 418	97.57 411	98.35 438	87.95 421	98.10 445	92.87 439	77.00 473	98.01 436

YYNet1		95.36 405	94.51 413	97.92 375	97.89 441	97.10 332	99.10 375	99.23 351	93.26 441	80.77 474	99.04 397	92.81 335	98.02 447	94.30 418	94.18 416	98.64 372

EU-MVSNet		97.98 276	98.03 252	97.81 387	98.72 412	96.65 370	99.66 82	99.66 32	98.09 192	98.35 378	99.82 113	95.25 238	98.01 448	97.41 327	95.30 395	98.78 321

Gipuma		90.99 431	90.15 436	93.51 440	98.73 410	90.12 460	93.98 474	99.45 242	79.32 471	92.28 461	94.91 468	69.61 468	97.98 449	87.42 464	95.67 385	92.45 471
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 406	94.73 409	97.15 410	95.53 465	95.94 393	99.35 294	99.10 369	95.13 411	93.55 455	97.54 457	88.15 420	97.91 450	94.58 415	89.69 456	97.61 450

PM-MVS		92.96 426	92.23 430	95.14 435	95.61 463	89.98 461	99.37 283	98.21 448	94.80 423	95.04 448	97.69 453	65.06 470	97.90 451	94.30 418	89.98 453	97.54 455

MDA-MVSNet-bldmvs		94.96 411	93.98 418	97.92 375	98.24 437	97.27 323	99.15 361	99.33 316	93.80 433	80.09 476	99.03 398	88.31 417	97.86 452	93.49 430	94.36 413	98.62 381

Patchmatch-RL test		95.84 397	95.81 395	95.95 433	95.61 463	90.57 459	98.24 460	98.39 441	95.10 415	95.20 445	98.67 426	94.78 261	97.77 453	96.28 383	90.02 452	99.51 229

Anonymous20231206		96.22 388	96.03 389	96.79 424	97.31 452	94.14 437	99.63 101	99.08 372	96.17 390	97.04 427	99.06 395	93.94 308	97.76 454	86.96 466	95.06 400	98.47 404

SD-MVS		99.41 59	99.52 14	99.05 231	99.74 100	99.68 64	99.46 236	99.52 130	99.11 47	99.88 43	99.91 26	99.43 1	97.70 455	98.72 181	99.93 33	99.77 100
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		97.25 365	97.35 338	96.95 419	97.84 442	93.61 446	99.57 138	96.63 468	96.13 395	98.87 320	98.61 429	94.59 277	97.70 455	95.08 409	98.86 246	99.55 211

FE-MVSNET2		95.10 408	94.44 414	97.08 415	95.08 468	95.97 392	99.51 186	99.37 294	95.02 417	94.10 451	97.57 455	86.18 435	97.66 457	93.28 432	89.86 454	97.61 450

FE-MVSNET1		93.64 422	92.69 428	96.48 428	94.12 472	94.21 436	99.34 297	99.38 285	93.42 440	93.33 457	97.58 454	74.82 467	97.65 458	92.56 444	89.64 457	97.58 453

dongtai		93.26 424	92.93 427	94.25 437	99.39 270	85.68 465	97.68 469	93.27 479	92.87 445	96.85 431	99.39 338	82.33 457	97.48 459	76.78 473	97.80 311	99.58 204

pmmvs3		94.09 419	93.25 426	96.60 426	94.76 471	94.49 430	98.92 413	98.18 450	89.66 456	96.48 434	98.06 451	86.28 434	97.33 460	89.68 454	87.20 462	97.97 442

KD-MVS_self_test		95.00 410	94.34 415	96.96 418	97.07 457	95.39 409	99.56 146	99.44 251	95.11 413	97.13 425	97.32 461	91.86 364	97.27 461	90.35 452	81.23 470	98.23 424

FMVSNet5		96.43 386	96.19 385	97.15 410	99.11 345	95.89 394	99.32 303	99.52 130	94.47 429	98.34 379	99.07 393	87.54 426	97.07 462	92.61 442	95.72 384	98.47 404

new-patchmatchnet		94.48 416	94.08 417	95.67 434	95.08 468	92.41 453	99.18 356	99.28 341	94.55 428	93.49 456	97.37 460	87.86 424	97.01 463	91.57 447	88.36 459	97.61 450

LCM-MVSNet		86.80 437	85.22 441	91.53 448	87.81 480	80.96 474	98.23 462	98.99 385	71.05 473	90.13 468	96.51 465	48.45 480	96.88 464	90.51 450	85.30 464	96.76 460

CL-MVSNet_self_test		94.49 415	93.97 419	96.08 432	96.16 460	93.67 444	98.33 457	99.38 285	95.13 411	97.33 418	98.15 445	92.69 343	96.57 465	88.67 457	79.87 471	97.99 440

MIMVSNet1		95.51 402	95.04 407	96.92 421	97.38 449	95.60 399	99.52 177	99.50 174	93.65 435	96.97 429	99.17 383	85.28 443	96.56 466	88.36 459	95.55 390	98.60 393

FE-MVSNET		94.07 420	93.36 425	96.22 431	94.05 473	94.71 425	99.56 146	98.36 442	93.15 442	93.76 454	97.55 456	86.47 433	96.49 467	87.48 463	89.83 455	97.48 456

test20.03		96.12 392	95.96 391	96.63 425	97.44 448	95.45 406	99.51 186	99.38 285	96.55 363	96.16 438	99.25 375	93.76 317	96.17 468	87.35 465	94.22 415	98.27 420

tmp_tt		82.80 439	81.52 442	86.66 456	66.61 486	68.44 485	92.79 476	97.92 452	68.96 474	80.04 477	99.85 80	85.77 437	96.15 469	97.86 278	43.89 479	95.39 469

test_fmvs3		92.10 428	91.77 431	93.08 443	96.19 459	86.25 463	99.82 16	98.62 436	96.65 352	95.19 446	96.90 463	55.05 477	95.93 470	96.63 374	90.92 449	97.06 459

kuosan		90.92 432	90.11 437	93.34 441	98.78 401	85.59 466	98.15 464	93.16 481	89.37 459	92.07 462	98.38 437	81.48 460	95.19 471	62.54 480	97.04 353	99.25 280

dmvs_testset		95.02 409	96.12 386	91.72 447	99.10 348	80.43 475	99.58 130	97.87 454	97.47 280	95.22 444	98.82 418	93.99 306	95.18 472	88.09 460	94.91 405	99.56 210

PMMVS2		86.87 436	85.37 440	91.35 449	90.21 478	83.80 468	98.89 416	97.45 461	83.13 470	91.67 467	95.03 467	48.49 479	94.70 473	85.86 470	77.62 472	95.54 468

PMVS	70.75 22	75.98 445	74.97 446	79.01 462	70.98 485	55.18 487	93.37 475	98.21 448	65.08 479	61.78 480	93.83 470	21.74 486	92.53 474	78.59 472	91.12 447	89.34 475
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 438	85.65 439	82.75 460	86.77 481	63.39 486	98.35 454	98.92 394	74.11 472	83.39 471	98.98 406	50.85 478	92.40 475	84.54 471	94.97 402	92.46 470

WB-MVS		93.10 425	94.10 416	90.12 452	95.51 467	81.88 472	99.73 51	99.27 344	95.05 416	93.09 459	98.91 415	94.70 270	91.89 476	76.62 474	94.02 421	96.58 462

SSC-MVS		92.73 427	93.73 420	89.72 453	95.02 470	81.38 473	99.76 37	99.23 351	94.87 421	92.80 460	98.93 411	94.71 269	91.37 477	74.49 476	93.80 423	96.42 463

MVE	76.82 21	76.91 444	74.31 448	84.70 457	85.38 483	76.05 481	96.88 472	93.17 480	67.39 476	71.28 478	89.01 477	21.66 487	87.69 478	71.74 477	72.29 475	90.35 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 441	79.88 443	82.81 459	90.75 477	76.38 480	97.69 468	95.76 472	66.44 477	83.52 470	92.25 472	62.54 472	87.16 479	68.53 478	61.40 476	84.89 477

EMVS		80.02 442	79.22 444	82.43 461	91.19 476	76.40 479	97.55 471	92.49 484	66.36 478	83.01 472	91.27 474	64.63 471	85.79 480	65.82 479	60.65 477	85.08 476

ANet_high		77.30 443	74.86 447	84.62 458	75.88 484	77.61 478	97.63 470	93.15 482	88.81 461	64.27 479	89.29 476	36.51 482	83.93 481	75.89 475	52.31 478	92.33 472

wuyk23d		40.18 446	41.29 451	36.84 463	86.18 482	49.12 488	79.73 477	22.81 488	27.64 480	25.46 483	28.45 483	21.98 485	48.89 482	55.80 481	23.56 482	12.51 480

test123		39.01 448	42.50 450	28.53 464	39.17 487	20.91 489	98.75 430	19.17 489	19.83 482	38.57 481	66.67 479	33.16 483	15.42 483	37.50 483	29.66 481	49.26 478

testmvs		39.17 447	43.78 449	25.37 465	36.04 488	16.84 490	98.36 453	26.56 487	20.06 481	38.51 482	67.32 478	29.64 484	15.30 484	37.59 482	39.90 480	43.98 479

mmdepth		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

monomultidepth		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

test_blank		0.13 452	0.17 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	1.57 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uanet_test		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

DCPMVS		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

cdsmvs_eth3d_5k		24.64 449	32.85 452	0.00 466	0.00 489	0.00 491	0.00 478	99.51 151	0.00 484	0.00 485	99.56 280	96.58 173	0.00 485	0.00 484	0.00 483	0.00 481

pcd_1.5k_mvsjas		8.27 451	11.03 454	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	99.01 20	0.00 485	0.00 484	0.00 483	0.00 481

sosnet-low-res		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

sosnet		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uncertanet		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

Regformer		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

ab-mvs-re		8.30 450	11.06 453	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	99.58 272	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uanet		0.02 453	0.03 456	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.27 485	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

TestfortrainingZip							99.69 62

WAC-MVS						97.16 329								95.47 400

FOURS1					99.91 1	99.93 1	99.87 8	99.56 90	99.10 48	99.81 69

test_one_0601					99.81 57	99.88 10		99.49 186	98.97 75	99.65 133	99.81 128	99.09 16

eth-test2					0.00 489

eth-test					0.00 489

RE-MVS-def			99.34 50		99.76 82	99.82 28	99.63 101	99.52 130	98.38 137	99.76 91	99.82 113	98.75 60		98.61 198	99.81 120	99.77 100

IU-MVS					99.84 38	99.88 10		99.32 326	98.30 149	99.84 56				98.86 158	99.85 94	99.89 29

save fliter					99.76 82	99.59 88	99.14 363	99.40 274	99.00 67

test0726					99.85 31	99.89 6	99.62 106	99.50 174	99.10 48	99.86 53	99.82 113	98.94 34

GSMVS																99.52 220

test_part2					99.81 57	99.83 22				99.77 85

sam_mvs1												94.86 256				99.52 220

sam_mvs												94.72 268

MTGPA								99.47 220

MTMP							99.54 166	98.88 404

test9_res														97.49 319	99.72 148	99.75 109

agg_prior2														97.21 337	99.73 147	99.75 109

test_prior4						99.56 94	98.99 399

test_prior2							98.96 406		98.34 143	99.01 294	99.52 296	98.68 70		97.96 270	99.74 145

新几何2							99.01 396

旧先验1					99.74 100	99.59 88		99.54 109			99.69 221	98.47 86			99.68 156	99.73 122

原ACMM2							98.95 409

test222					99.75 92	99.49 109	98.91 415	99.49 186	96.42 374	99.34 223	99.65 242	98.28 100			99.69 153	99.72 132

segment_acmp												98.96 27

testdata1							98.85 420		98.32 147

plane_prior7					99.29 298	97.03 344

plane_prior6					99.27 303	96.98 348						92.71 341

plane_prior4											99.61 263

plane_prior3						97.00 346			98.69 107	99.11 274

plane_prior2							99.39 276		98.97 75

plane_prior1					99.26 306

plane_prior						96.97 349	99.21 349		98.45 130						97.60 320

n2								0.00 490

nn								0.00 490

door-mid								98.05 451

test11								99.35 303

door								97.92 452

HQP5-MVS						96.83 360

HQP-NCC					99.19 324		98.98 402		98.24 162	98.66 349

ACMP_Plane					99.19 324		98.98 402		98.24 162	98.66 349

BP-MVS														97.19 341

HQP3-MVS								99.39 277							97.58 322

HQP2-MVS												92.47 350

NP-MVS					99.23 314	96.92 356					99.40 334

MDTV_nov1_ep13_2view						95.18 415	99.35 294		96.84 341	99.58 157		95.19 241		97.82 283		99.46 248

ACMMP++_ref															97.19 350

ACMMP++															97.43 340

Test By Simon												98.75 60

High-resolution multi-view benchmark