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 69	99.38 264	99.37 118	99.58 126	99.62 47	99.41 20	99.87 45	99.92 17	98.81 47	100.00 1	99.97 2	99.93 32	99.94 17

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 66	99.84 35	99.44 111	99.58 126	99.69 18	99.43 16	99.98 12	99.91 25	98.62 73	100.00 1	99.97 2	99.95 22	99.90 25

test_vis1_n_1920		98.63 206	98.40 214	99.31 185	99.86 22	97.94 287	99.67 71	99.62 47	99.43 16	99.99 2	99.91 25	87.29 420	100.00 1	99.92 23	99.92 38	99.98 2

fmvsm_s_conf0.5_n_10		99.41 56	99.24 75	99.92 1	99.83 44	99.84 19	99.53 170	99.56 86	99.45 11	99.99 2	99.92 17	94.92 246	99.99 4	99.97 2	99.97 8	99.95 11

fmvsm_l_conf0.5_n_9		99.58 14	99.47 22	99.92 1	99.85 28	99.82 27	99.47 224	99.63 42	99.45 11	99.98 12	99.89 40	97.02 144	99.99 4	99.98 1	99.96 16	99.95 11

NormalMVS		99.27 85	99.19 85	99.52 134	99.89 8	98.83 212	99.65 84	99.52 125	99.10 43	99.84 52	99.76 176	95.80 207	99.99 4	99.30 87	99.84 97	99.74 109

SymmetryMVS		99.15 110	99.02 118	99.52 134	99.72 106	98.83 212	99.65 84	99.34 298	99.10 43	99.84 52	99.76 176	95.80 207	99.99 4	99.30 87	98.72 248	99.73 118

fmvsm_s_conf0.5_n_5		99.37 65	99.21 81	99.86 31	99.80 59	99.68 59	99.42 250	99.61 56	99.37 23	99.97 24	99.86 70	94.96 241	99.99 4	99.97 2	99.93 32	99.92 23

fmvsm_l_conf0.5_n_3		99.61 8	99.51 16	99.92 1	99.84 35	99.82 27	99.54 161	99.66 28	99.46 7	99.98 12	99.89 40	97.27 130	99.99 4	99.97 2	99.95 22	99.95 11

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 39	99.86 22	99.61 80	99.56 141	99.63 42	99.48 3	99.98 12	99.83 97	98.75 58	99.99 4	99.97 2	99.96 16	99.94 17

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 39	99.84 35	99.63 77	99.56 141	99.63 42	99.47 4	99.98 12	99.82 106	98.75 58	99.99 4	99.97 2	99.97 8	99.94 17

test_fmvsmconf_n		99.70 3	99.64 4	99.87 20	99.80 59	99.66 66	99.48 214	99.64 38	99.45 11	99.92 29	99.92 17	98.62 73	99.99 4	99.96 13	99.99 1	99.96 7

patch_mono-2		99.26 88	99.62 5	98.16 347	99.81 53	94.59 420	99.52 172	99.64 38	99.33 25	99.73 92	99.90 32	99.00 22	99.99 4	99.69 34	99.98 4	99.89 28

h-mvs33		97.70 320	97.28 343	98.97 233	99.70 117	97.27 315	99.36 279	99.45 234	98.94 73	99.66 117	99.64 240	94.93 244	99.99 4	99.48 63	84.36 455	99.65 161

xiu_mvs_v1_base_debu		99.29 81	99.27 70	99.34 177	99.63 158	98.97 178	99.12 356	99.51 144	98.86 79	99.84 52	99.47 307	98.18 101	99.99 4	99.50 56	99.31 186	99.08 286

xiu_mvs_v1_base		99.29 81	99.27 70	99.34 177	99.63 158	98.97 178	99.12 356	99.51 144	98.86 79	99.84 52	99.47 307	98.18 101	99.99 4	99.50 56	99.31 186	99.08 286

xiu_mvs_v1_base_debi		99.29 81	99.27 70	99.34 177	99.63 158	98.97 178	99.12 356	99.51 144	98.86 79	99.84 52	99.47 307	98.18 101	99.99 4	99.50 56	99.31 186	99.08 286

EPNet		98.86 171	98.71 178	99.30 190	97.20 446	98.18 267	99.62 102	98.91 389	99.28 28	98.63 350	99.81 121	95.96 195	99.99 4	99.24 97	99.72 143	99.73 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_8		99.54 21	99.42 29	99.89 10	99.83 44	99.74 50	99.51 181	99.62 47	99.46 7	99.99 2	99.90 32	96.60 167	99.98 19	99.95 15	99.95 22	99.96 7

MM		99.40 61	99.28 66	99.74 75	99.67 129	99.31 130	99.52 172	98.87 396	99.55 1	99.74 90	99.80 139	96.47 174	99.98 19	99.97 2	99.97 8	99.94 17

test_cas_vis1_n_1920		99.16 106	99.01 123	99.61 104	99.81 53	98.86 206	99.65 84	99.64 38	99.39 21	99.97 24	99.94 6	93.20 321	99.98 19	99.55 49	99.91 45	99.99 1

test_vis1_n		97.92 278	97.44 319	99.34 177	99.53 206	98.08 274	99.74 47	99.49 178	99.15 33	100.00 1	99.94 6	79.51 456	99.98 19	99.88 25	99.76 135	99.97 4

xiu_mvs_v2_base		99.26 88	99.25 74	99.29 193	99.53 206	98.91 197	99.02 380	99.45 234	98.80 89	99.71 100	99.26 366	98.94 32	99.98 19	99.34 80	99.23 195	98.98 300

PS-MVSNAJ		99.32 76	99.32 51	99.30 190	99.57 190	98.94 192	98.97 394	99.46 223	98.92 76	99.71 100	99.24 368	99.01 18	99.98 19	99.35 75	99.66 154	98.97 301

QAPM		98.67 201	98.30 221	99.80 60	99.20 313	99.67 63	99.77 34	99.72 11	94.74 415	98.73 330	99.90 32	95.78 209	99.98 19	96.96 347	99.88 71	99.76 103

3Dnovator	97.25 9	99.24 93	99.05 107	99.81 56	99.12 335	99.66 66	99.84 12	99.74 10	99.09 50	98.92 303	99.90 32	95.94 198	99.98 19	98.95 135	99.92 38	99.79 88

OpenMVS	96.50 16	98.47 212	98.12 233	99.52 134	99.04 354	99.53 96	99.82 16	99.72 11	94.56 418	98.08 385	99.88 51	94.73 261	99.98 19	97.47 314	99.76 135	99.06 292

fmvsm_s_conf0.5_n_3		99.37 65	99.20 83	99.87 20	99.75 87	99.70 56	99.48 214	99.66 28	99.45 11	99.99 2	99.93 10	94.64 270	99.97 28	99.94 20	99.97 8	99.95 11

reproduce_model		99.63 7	99.54 11	99.90 7	99.78 65	99.88 9	99.56 141	99.55 95	99.15 33	99.90 33	99.90 32	99.00 22	99.97 28	99.11 113	99.91 45	99.86 41

test_fmvsmconf0.1_n		99.55 20	99.45 27	99.86 31	99.44 246	99.65 70	99.50 191	99.61 56	99.45 11	99.87 45	99.92 17	97.31 127	99.97 28	99.95 15	99.99 1	99.97 4

test_fmvs1_n		98.41 218	98.14 230	99.21 206	99.82 49	97.71 300	99.74 47	99.49 178	99.32 26	99.99 2	99.95 3	85.32 434	99.97 28	99.82 28	99.84 97	99.96 7

CANet_DTU		98.97 159	98.87 156	99.25 200	99.33 277	98.42 259	99.08 365	99.30 325	99.16 32	99.43 184	99.75 181	95.27 229	99.97 28	98.56 202	99.95 22	99.36 258

MVS_0304		99.15 110	98.96 134	99.73 78	98.92 372	99.37 118	99.37 274	96.92 453	99.51 2	99.66 117	99.78 163	96.69 163	99.97 28	99.84 27	99.97 8	99.84 52

MTAPA		99.52 25	99.39 37	99.89 10	99.90 4	99.86 17	99.66 78	99.47 212	98.79 90	99.68 106	99.81 121	98.43 86	99.97 28	98.88 145	99.90 56	99.83 62

PGM-MVS		99.45 43	99.31 57	99.86 31	99.87 17	99.78 43	99.58 126	99.65 35	97.84 227	99.71 100	99.80 139	99.12 13	99.97 28	98.33 227	99.87 74	99.83 62

mPP-MVS		99.44 47	99.30 59	99.86 31	99.88 13	99.79 37	99.69 62	99.48 190	98.12 179	99.50 168	99.75 181	98.78 51	99.97 28	98.57 199	99.89 67	99.83 62

CP-MVS		99.45 43	99.32 51	99.85 39	99.83 44	99.75 47	99.69 62	99.52 125	98.07 190	99.53 163	99.63 246	98.93 36	99.97 28	98.74 170	99.91 45	99.83 62

SteuartSystems-ACMMP		99.54 21	99.42 29	99.87 20	99.82 49	99.81 32	99.59 116	99.51 144	98.62 107	99.79 71	99.83 97	99.28 4	99.97 28	98.48 209	99.90 56	99.84 52
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3Dnovator+	97.12 13	99.18 100	98.97 130	99.82 53	99.17 327	99.68 59	99.81 20	99.51 144	99.20 30	98.72 331	99.89 40	95.68 213	99.97 28	98.86 153	99.86 82	99.81 75

fmvsm_s_conf0.5_n_9		99.41 56	99.28 66	99.81 56	99.84 35	99.52 100	99.48 214	99.62 47	99.46 7	99.99 2	99.92 17	95.24 233	99.96 40	99.97 2	99.97 8	99.96 7

lecture		99.60 12	99.50 17	99.89 10	99.89 8	99.90 2	99.75 42	99.59 69	99.06 56	99.88 39	99.85 77	98.41 90	99.96 40	99.28 90	99.84 97	99.83 62

KinetiMVS		99.12 123	98.92 143	99.70 82	99.67 129	99.40 116	99.67 71	99.63 42	98.73 97	99.94 27	99.81 121	94.54 276	99.96 40	98.40 218	99.93 32	99.74 109

fmvsm_s_conf0.5_n_7		99.34 72	99.29 63	99.48 148	99.70 117	98.63 232	99.42 250	99.63 42	99.46 7	99.98 12	99.88 51	95.59 216	99.96 40	99.97 2	99.98 4	99.85 45

fmvsm_s_conf0.5_n_2		99.32 76	99.13 91	99.89 10	99.80 59	99.77 44	99.44 238	99.58 74	99.47 4	99.99 2	99.93 10	94.04 297	99.96 40	99.96 13	99.93 32	99.93 22

reproduce-ours		99.61 8	99.52 12	99.90 7	99.76 77	99.88 9	99.52 172	99.54 104	99.13 36	99.89 36	99.89 40	98.96 25	99.96 40	99.04 122	99.90 56	99.85 45

our_new_method		99.61 8	99.52 12	99.90 7	99.76 77	99.88 9	99.52 172	99.54 104	99.13 36	99.89 36	99.89 40	98.96 25	99.96 40	99.04 122	99.90 56	99.85 45

fmvsm_s_conf0.5_n_a		99.56 19	99.47 22	99.85 39	99.83 44	99.64 76	99.52 172	99.65 35	99.10 43	99.98 12	99.92 17	97.35 126	99.96 40	99.94 20	99.92 38	99.95 11

fmvsm_s_conf0.5_n		99.51 26	99.40 35	99.85 39	99.84 35	99.65 70	99.51 181	99.67 23	99.13 36	99.98 12	99.92 17	96.60 167	99.96 40	99.95 15	99.96 16	99.95 11

mvsany_test1		99.50 28	99.46 26	99.62 103	99.61 175	99.09 160	98.94 400	99.48 190	99.10 43	99.96 26	99.91 25	98.85 42	99.96 40	99.72 31	99.58 164	99.82 68

test_fmvs1		98.88 165	98.79 169	99.16 211	99.69 122	97.61 304	99.55 156	99.49 178	99.32 26	99.98 12	99.91 25	91.41 369	99.96 40	99.82 28	99.92 38	99.90 25

DVP-MVS++		99.59 13	99.50 17	99.88 14	99.51 215	99.88 9	99.87 8	99.51 144	98.99 64	99.88 39	99.81 121	99.27 5	99.96 40	98.85 155	99.80 120	99.81 75

MSC_two_6792asdad				99.87 20	99.51 215	99.76 45		99.33 306					99.96 40	98.87 148	99.84 97	99.89 28

No_MVS				99.87 20	99.51 215	99.76 45		99.33 306					99.96 40	98.87 148	99.84 97	99.89 28

ZD-MVS					99.71 112	99.79 37		99.61 56	96.84 333	99.56 154	99.54 280	98.58 75	99.96 40	96.93 350	99.75 137

SED-MVS		99.61 8	99.52 12	99.88 14	99.84 35	99.90 2	99.60 109	99.48 190	99.08 51	99.91 30	99.81 121	99.20 7	99.96 40	98.91 142	99.85 89	99.79 88

test_241102_TWO								99.48 190	99.08 51	99.88 39	99.81 121	98.94 32	99.96 40	98.91 142	99.84 97	99.88 34

ZNCC-MVS		99.47 37	99.33 49	99.87 20	99.87 17	99.81 32	99.64 91	99.67 23	98.08 189	99.55 160	99.64 240	98.91 37	99.96 40	98.72 173	99.90 56	99.82 68

DVP-MVS		99.57 18	99.47 22	99.88 14	99.85 28	99.89 5	99.57 134	99.37 285	99.10 43	99.81 64	99.80 139	98.94 32	99.96 40	98.93 139	99.86 82	99.81 75
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 64	99.81 64	99.80 139	99.09 14	99.96 40	98.85 155	99.90 56	99.88 34

test_0728_SECOND				99.91 5	99.84 35	99.89 5	99.57 134	99.51 144					99.96 40	98.93 139	99.86 82	99.88 34

SR-MVS		99.43 50	99.29 63	99.86 31	99.75 87	99.83 21	99.59 116	99.62 47	98.21 163	99.73 92	99.79 156	98.68 67	99.96 40	98.44 215	99.77 132	99.79 88

DPE-MVS		99.46 39	99.32 51	99.91 5	99.78 65	99.88 9	99.36 279	99.51 144	98.73 97	99.88 39	99.84 92	98.72 64	99.96 40	98.16 242	99.87 74	99.88 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 52	99.29 63	99.80 60	99.62 166	99.55 91	99.50 191	99.70 15	98.79 90	99.77 80	99.96 1	97.45 121	99.96 40	98.92 141	99.90 56	99.89 28

HFP-MVS		99.49 30	99.37 41	99.86 31	99.87 17	99.80 34	99.66 78	99.67 23	98.15 170	99.68 106	99.69 214	99.06 16	99.96 40	98.69 178	99.87 74	99.84 52

region2R		99.48 34	99.35 45	99.87 20	99.88 13	99.80 34	99.65 84	99.66 28	98.13 177	99.66 117	99.68 221	98.96 25	99.96 40	98.62 187	99.87 74	99.84 52

HPM-MVS++		99.39 63	99.23 79	99.87 20	99.75 87	99.84 19	99.43 243	99.51 144	98.68 104	99.27 232	99.53 284	98.64 72	99.96 40	98.44 215	99.80 120	99.79 88

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 73	99.89 5	99.75 42	99.56 86	99.02 57	99.88 39	99.85 77	99.18 10	99.96 40	99.22 98	99.92 38	99.90 25
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 30	99.36 43	99.86 31	99.87 17	99.79 37	99.66 78	99.67 23	98.15 170	99.67 112	99.69 214	98.95 30	99.96 40	98.69 178	99.87 74	99.84 52

MP-MVS		99.33 74	99.15 89	99.87 20	99.88 13	99.82 27	99.66 78	99.46 223	98.09 185	99.48 172	99.74 186	98.29 96	99.96 40	97.93 264	99.87 74	99.82 68
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 129	98.90 148	99.74 75	99.80 59	99.46 109	99.59 116	99.49 178	97.03 320	99.63 134	99.69 214	97.27 130	99.96 40	97.82 275	99.84 97	99.81 75

PVSNet_Blended_VisFu		99.36 69	99.28 66	99.61 104	99.86 22	99.07 165	99.47 224	99.93 2	97.66 251	99.71 100	99.86 70	97.73 116	99.96 40	99.47 65	99.82 112	99.79 88

UGNet		98.87 168	98.69 180	99.40 168	99.22 310	98.72 224	99.44 238	99.68 20	99.24 29	99.18 257	99.42 318	92.74 331	99.96 40	99.34 80	99.94 30	99.53 211
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 76	99.32 51	99.32 183	99.85 28	98.29 262	99.71 57	99.66 28	98.11 181	99.41 192	99.80 139	98.37 93	99.96 40	98.99 128	99.96 16	99.72 127

ACMMP		99.45 43	99.32 51	99.82 53	99.89 8	99.67 63	99.62 102	99.69 18	98.12 179	99.63 134	99.84 92	98.73 63	99.96 40	98.55 205	99.83 108	99.81 75
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
fmvsm_s_conf0.5_n_6		99.54 21	99.44 28	99.85 39	99.51 215	99.67 63	99.50 191	99.64 38	99.43 16	99.98 12	99.78 163	97.26 132	99.95 75	99.95 15	99.93 32	99.92 23

fmvsm_s_conf0.5_n_4		99.36 69	99.24 75	99.73 78	99.78 65	99.53 96	99.49 208	99.60 63	99.42 19	99.99 2	99.86 70	95.15 236	99.95 75	99.95 15	99.89 67	99.73 118

fmvsm_s_conf0.1_n_2		99.37 65	99.22 80	99.81 56	99.77 73	99.75 47	99.46 228	99.60 63	99.47 4	99.98 12	99.94 6	94.98 240	99.95 75	99.97 2	99.79 127	99.73 118

test_fmvsmconf0.01_n		99.22 96	99.03 112	99.79 63	98.42 426	99.48 106	99.55 156	99.51 144	99.39 21	99.78 76	99.93 10	94.80 253	99.95 75	99.93 22	99.95 22	99.94 17

SR-MVS-dyc-post		99.45 43	99.31 57	99.85 39	99.76 77	99.82 27	99.63 97	99.52 125	98.38 132	99.76 86	99.82 106	98.53 79	99.95 75	98.61 190	99.81 115	99.77 96

GST-MVS		99.40 61	99.24 75	99.85 39	99.86 22	99.79 37	99.60 109	99.67 23	97.97 211	99.63 134	99.68 221	98.52 80	99.95 75	98.38 220	99.86 82	99.81 75

CANet		99.25 92	99.14 90	99.59 108	99.41 254	99.16 150	99.35 284	99.57 81	98.82 84	99.51 167	99.61 255	96.46 175	99.95 75	99.59 44	99.98 4	99.65 161

MP-MVS-pluss		99.37 65	99.20 83	99.88 14	99.90 4	99.87 16	99.30 298	99.52 125	97.18 302	99.60 146	99.79 156	98.79 50	99.95 75	98.83 161	99.91 45	99.83 62
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 52	99.27 70	99.88 14	99.89 8	99.80 34	99.67 71	99.50 166	98.70 101	99.77 80	99.49 298	98.21 99	99.95 75	98.46 213	99.77 132	99.88 34
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 75	96.67 362

APD-MVS_3200maxsize		99.48 34	99.35 45	99.85 39	99.76 77	99.83 21	99.63 97	99.54 104	98.36 136	99.79 71	99.82 106	98.86 41	99.95 75	98.62 187	99.81 115	99.78 94

RPMNet		96.72 372	95.90 385	99.19 208	99.18 319	98.49 251	99.22 336	99.52 125	88.72 452	99.56 154	97.38 449	94.08 296	99.95 75	86.87 457	98.58 255	99.14 278

sss		99.17 104	99.05 107	99.53 128	99.62 166	98.97 178	99.36 279	99.62 47	97.83 228	99.67 112	99.65 234	97.37 125	99.95 75	99.19 101	99.19 198	99.68 148

MVSMamba_PlusPlus		99.46 39	99.41 34	99.64 96	99.68 127	99.50 103	99.75 42	99.50 166	98.27 147	99.87 45	99.92 17	98.09 105	99.94 88	99.65 40	99.95 22	99.47 235

fmvsm_s_conf0.1_n_a		99.26 88	99.06 105	99.85 39	99.52 212	99.62 78	99.54 161	99.62 47	98.69 102	99.99 2	99.96 1	94.47 280	99.94 88	99.88 25	99.92 38	99.98 2

fmvsm_s_conf0.1_n		99.29 81	99.10 95	99.86 31	99.70 117	99.65 70	99.53 170	99.62 47	98.74 96	99.99 2	99.95 3	94.53 278	99.94 88	99.89 24	99.96 16	99.97 4

TSAR-MVS + MP.		99.58 14	99.50 17	99.81 56	99.91 1	99.66 66	99.63 97	99.39 269	98.91 77	99.78 76	99.85 77	99.36 2	99.94 88	98.84 158	99.88 71	99.82 68
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 163	98.75 172	99.39 172	99.46 239	98.61 236	99.76 37	99.50 166	98.06 194	99.81 64	99.88 51	93.91 304	99.94 88	99.11 113	99.27 189	99.61 178

mamv4		99.33 74	99.42 29	99.07 219	99.67 129	97.73 295	99.42 250	99.60 63	98.15 170	99.94 27	99.91 25	98.42 88	99.94 88	99.72 31	99.96 16	99.54 205

XVS		99.53 24	99.42 29	99.87 20	99.85 28	99.83 21	99.69 62	99.68 20	98.98 67	99.37 203	99.74 186	98.81 47	99.94 88	98.79 166	99.86 82	99.84 52

X-MVStestdata		96.55 375	95.45 394	99.87 20	99.85 28	99.83 21	99.69 62	99.68 20	98.98 67	99.37 203	64.01 472	98.81 47	99.94 88	98.79 166	99.86 82	99.84 52

旧先验2							98.96 395		96.70 340	99.47 173			99.94 88	98.19 238

新几何1				99.75 72	99.75 87	99.59 83		99.54 104	96.76 336	99.29 225	99.64 240	98.43 86	99.94 88	96.92 352	99.66 154	99.72 127

testdata				99.54 120	99.75 87	98.95 188		99.51 144	97.07 314	99.43 184	99.70 203	98.87 40	99.94 88	97.76 284	99.64 157	99.72 127

HPM-MVS		99.42 52	99.28 66	99.83 52	99.90 4	99.72 52	99.81 20	99.54 104	97.59 257	99.68 106	99.63 246	98.91 37	99.94 88	98.58 196	99.91 45	99.84 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 97	99.10 95	99.45 156	99.89 8	98.52 246	99.39 267	99.94 1	98.73 97	99.11 266	99.89 40	95.50 219	99.94 88	99.50 56	99.97 8	99.89 28

APD-MVS		99.27 85	99.08 101	99.84 51	99.75 87	99.79 37	99.50 191	99.50 166	97.16 304	99.77 80	99.82 106	98.78 51	99.94 88	97.56 305	99.86 82	99.80 84
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 34	99.42 29	99.65 90	99.72 106	99.40 116	99.05 372	99.66 28	99.14 35	99.57 153	99.80 139	98.46 84	99.94 88	99.57 47	99.84 97	99.60 181
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		99.06 142	98.88 155	99.61 104	99.62 166	99.16 150	99.37 274	99.56 86	98.04 203	99.53 163	99.62 251	96.84 155	99.94 88	98.85 155	98.49 263	99.72 127

DeepC-MVS	98.35 2	99.30 79	99.19 85	99.64 96	99.82 49	99.23 143	99.62 102	99.55 95	98.94 73	99.63 134	99.95 3	95.82 205	99.94 88	99.37 74	99.97 8	99.73 118
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 85	99.12 93	99.74 75	99.18 319	99.75 47	99.56 141	99.57 81	98.45 125	99.49 171	99.85 77	97.77 115	99.94 88	98.33 227	99.84 97	99.52 212

GDP-MVS		99.08 137	98.89 152	99.64 96	99.53 206	99.34 122	99.64 91	99.48 190	98.32 142	99.77 80	99.66 232	95.14 237	99.93 106	98.97 134	99.50 171	99.64 168

SDMVSNet		99.11 129	98.90 148	99.75 72	99.81 53	99.59 83	99.81 20	99.65 35	98.78 93	99.64 131	99.88 51	94.56 273	99.93 106	99.67 36	98.26 278	99.72 127

FE-MVS		98.48 211	98.17 226	99.40 168	99.54 205	98.96 182	99.68 68	98.81 403	95.54 399	99.62 138	99.70 203	93.82 307	99.93 106	97.35 323	99.46 173	99.32 264

SF-MVS		99.38 64	99.24 75	99.79 63	99.79 63	99.68 59	99.57 134	99.54 104	97.82 233	99.71 100	99.80 139	98.95 30	99.93 106	98.19 238	99.84 97	99.74 109

dcpmvs_2		99.23 94	99.58 7	98.16 347	99.83 44	94.68 417	99.76 37	99.52 125	99.07 53	99.98 12	99.88 51	98.56 77	99.93 106	99.67 36	99.98 4	99.87 39

Anonymous20240529		98.09 248	97.68 286	99.34 177	99.66 142	98.44 256	99.40 263	99.43 254	93.67 425	99.22 244	99.89 40	90.23 386	99.93 106	99.26 96	98.33 270	99.66 156

ACMMP_NAP		99.47 37	99.34 47	99.88 14	99.87 17	99.86 17	99.47 224	99.48 190	98.05 196	99.76 86	99.86 70	98.82 46	99.93 106	98.82 165	99.91 45	99.84 52

EI-MVSNet-UG-set		99.58 14	99.57 8	99.64 96	99.78 65	99.14 155	99.60 109	99.45 234	99.01 59	99.90 33	99.83 97	98.98 24	99.93 106	99.59 44	99.95 22	99.86 41

无先验							98.99 388	99.51 144	96.89 330				99.93 106	97.53 308		99.72 127

VDDNet		97.55 335	97.02 357	99.16 211	99.49 229	98.12 273	99.38 272	99.30 325	95.35 401	99.68 106	99.90 32	82.62 447	99.93 106	99.31 84	98.13 290	99.42 247

ab-mvs		98.86 171	98.63 190	99.54 120	99.64 154	99.19 145	99.44 238	99.54 104	97.77 237	99.30 222	99.81 121	94.20 290	99.93 106	99.17 107	98.82 242	99.49 226

F-COLMAP		99.19 97	99.04 109	99.64 96	99.78 65	99.27 138	99.42 250	99.54 104	97.29 293	99.41 192	99.59 260	98.42 88	99.93 106	98.19 238	99.69 148	99.73 118

BP-MVS1		99.12 123	98.94 140	99.65 90	99.51 215	99.30 133	99.67 71	98.92 384	98.48 121	99.84 52	99.69 214	94.96 241	99.92 118	99.62 43	99.79 127	99.71 136

Anonymous202405211		98.30 229	97.98 250	99.26 199	99.57 190	98.16 268	99.41 255	98.55 428	96.03 393	99.19 253	99.74 186	91.87 356	99.92 118	99.16 108	98.29 277	99.70 139

EI-MVSNet-Vis-set		99.58 14	99.56 10	99.64 96	99.78 65	99.15 154	99.61 108	99.45 234	99.01 59	99.89 36	99.82 106	99.01 18	99.92 118	99.56 48	99.95 22	99.85 45

VDD-MVS		97.73 314	97.35 331	98.88 255	99.47 237	97.12 323	99.34 287	98.85 398	98.19 165	99.67 112	99.85 77	82.98 445	99.92 118	99.49 60	98.32 274	99.60 181

VNet		99.11 129	98.90 148	99.73 78	99.52 212	99.56 89	99.41 255	99.39 269	99.01 59	99.74 90	99.78 163	95.56 217	99.92 118	99.52 54	98.18 286	99.72 127

XVG-OURS-SEG-HR		98.69 199	98.62 195	98.89 252	99.71 112	97.74 294	99.12 356	99.54 104	98.44 128	99.42 187	99.71 199	94.20 290	99.92 118	98.54 206	98.90 236	99.00 297

mvsmamba		99.06 142	98.96 134	99.36 174	99.47 237	98.64 231	99.70 58	99.05 368	97.61 256	99.65 126	99.83 97	96.54 171	99.92 118	99.19 101	99.62 160	99.51 221

HPM-MVS_fast		99.51 26	99.40 35	99.85 39	99.91 1	99.79 37	99.76 37	99.56 86	97.72 242	99.76 86	99.75 181	99.13 12	99.92 118	99.07 119	99.92 38	99.85 45

HY-MVS	97.30 7	98.85 180	98.64 189	99.47 153	99.42 249	99.08 163	99.62 102	99.36 286	97.39 285	99.28 226	99.68 221	96.44 177	99.92 118	98.37 222	98.22 281	99.40 252

DP-MVS		99.16 106	98.95 138	99.78 66	99.77 73	99.53 96	99.41 255	99.50 166	97.03 320	99.04 283	99.88 51	97.39 122	99.92 118	98.66 182	99.90 56	99.87 39

IB-MVS	95.67 18	96.22 381	95.44 395	98.57 297	99.21 311	96.70 357	98.65 429	97.74 447	96.71 339	97.27 411	98.54 423	86.03 428	99.92 118	98.47 212	86.30 453	99.10 281
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 30	99.39 37	99.77 69	99.63 158	99.59 83	99.36 279	99.46 223	99.07 53	99.79 71	99.82 106	98.85 42	99.92 118	98.68 180	99.87 74	99.82 68
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 94	99.10 95	99.61 104	99.35 271	99.31 130	99.46 228	99.13 356	98.61 108	99.86 49	99.89 40	96.41 179	99.91 130	99.67 36	99.51 169	99.63 173

balanced_conf03		99.46 39	99.39 37	99.67 85	99.55 198	99.58 88	99.74 47	99.51 144	98.42 129	99.87 45	99.84 92	98.05 108	99.91 130	99.58 46	99.94 30	99.52 212

9.14			99.10 95		99.72 106		99.40 263	99.51 144	97.53 267	99.64 131	99.78 163	98.84 44	99.91 130	97.63 296	99.82 112

SMA-MVS		99.44 47	99.30 59	99.85 39	99.73 102	99.83 21	99.56 141	99.47 212	97.45 276	99.78 76	99.82 106	99.18 10	99.91 130	98.79 166	99.89 67	99.81 75
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					99.67 129	99.65 70	99.05 372	99.41 259	96.22 378	98.95 299	99.49 298	98.77 54	99.91 130

train_agg		99.02 150	98.77 170	99.77 69	99.67 129	99.65 70	99.05 372	99.41 259	96.28 372	98.95 299	99.49 298	98.76 55	99.91 130	97.63 296	99.72 143	99.75 105

test_8					99.67 129	99.61 80	99.03 377	99.41 259	96.28 372	98.93 302	99.48 304	98.76 55	99.91 130

agg_prior					99.67 129	99.62 78		99.40 266		98.87 312			99.91 130

原ACMM1				99.65 90	99.73 102	99.33 125		99.47 212	97.46 273	99.12 264	99.66 232	98.67 69	99.91 130	97.70 293	99.69 148	99.71 136

LFMVS		97.90 281	97.35 331	99.54 120	99.52 212	99.01 172	99.39 267	98.24 436	97.10 312	99.65 126	99.79 156	84.79 437	99.91 130	99.28 90	98.38 267	99.69 142

XVG-OURS		98.73 197	98.68 181	98.88 255	99.70 117	97.73 295	98.92 402	99.55 95	98.52 117	99.45 176	99.84 92	95.27 229	99.91 130	98.08 253	98.84 240	99.00 297

PLC	97.94 4	99.02 150	98.85 161	99.53 128	99.66 142	99.01 172	99.24 329	99.52 125	96.85 332	99.27 232	99.48 304	98.25 98	99.91 130	97.76 284	99.62 160	99.65 161
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 328	97.06 356	99.47 153	99.61 175	99.09 160	98.04 455	99.25 337	91.24 443	98.51 361	99.70 203	94.55 275	99.91 130	92.76 432	99.85 89	99.42 247
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Elysia		98.88 165	98.65 187	99.58 111	99.58 185	99.34 122	99.65 84	99.52 125	98.26 150	99.83 60	99.87 62	93.37 315	99.90 143	97.81 277	99.91 45	99.49 226

StellarMVS		98.88 165	98.65 187	99.58 111	99.58 185	99.34 122	99.65 84	99.52 125	98.26 150	99.83 60	99.87 62	93.37 315	99.90 143	97.81 277	99.91 45	99.49 226

AstraMVS		99.09 135	99.03 112	99.25 200	99.66 142	98.13 271	99.57 134	98.24 436	98.82 84	99.91 30	99.88 51	95.81 206	99.90 143	99.72 31	99.67 153	99.74 109

mmtdpeth		96.95 367	96.71 366	97.67 386	99.33 277	94.90 412	99.89 2	99.28 331	98.15 170	99.72 97	98.57 422	86.56 425	99.90 143	99.82 28	89.02 448	98.20 417

UWE-MVS		97.58 334	97.29 342	98.48 310	99.09 343	96.25 377	99.01 385	96.61 459	97.86 221	99.19 253	99.01 393	88.72 401	99.90 143	97.38 321	98.69 249	99.28 267

test_vis1_rt		95.81 391	95.65 390	96.32 420	99.67 129	91.35 448	99.49 208	96.74 457	98.25 155	95.24 435	98.10 441	74.96 457	99.90 143	99.53 52	98.85 239	97.70 441

FA-MVS(test-final)		98.75 194	98.53 206	99.41 167	99.55 198	99.05 168	99.80 25	99.01 373	96.59 354	99.58 150	99.59 260	95.39 223	99.90 143	97.78 280	99.49 172	99.28 267

MCST-MVS		99.43 50	99.30 59	99.82 53	99.79 63	99.74 50	99.29 303	99.40 266	98.79 90	99.52 165	99.62 251	98.91 37	99.90 143	98.64 184	99.75 137	99.82 68

CDPH-MVS		99.13 116	98.91 146	99.80 60	99.75 87	99.71 54	99.15 350	99.41 259	96.60 352	99.60 146	99.55 275	98.83 45	99.90 143	97.48 312	99.83 108	99.78 94

NCCC		99.34 72	99.19 85	99.79 63	99.61 175	99.65 70	99.30 298	99.48 190	98.86 79	99.21 247	99.63 246	98.72 64	99.90 143	98.25 234	99.63 159	99.80 84

114514_t		98.93 161	98.67 182	99.72 81	99.85 28	99.53 96	99.62 102	99.59 69	92.65 438	99.71 100	99.78 163	98.06 107	99.90 143	98.84 158	99.91 45	99.74 109

1112_ss		98.98 157	98.77 170	99.59 108	99.68 127	99.02 170	99.25 324	99.48 190	97.23 299	99.13 262	99.58 264	96.93 149	99.90 143	98.87 148	98.78 245	99.84 52

PHI-MVS		99.30 79	99.17 88	99.70 82	99.56 194	99.52 100	99.58 126	99.80 8	97.12 308	99.62 138	99.73 192	98.58 75	99.90 143	98.61 190	99.91 45	99.68 148

AdaColmap		99.01 154	98.80 166	99.66 86	99.56 194	99.54 93	99.18 345	99.70 15	98.18 168	99.35 212	99.63 246	96.32 181	99.90 143	97.48 312	99.77 132	99.55 203

COLMAP_ROB	97.56 6	98.86 171	98.75 172	99.17 210	99.88 13	98.53 242	99.34 287	99.59 69	97.55 263	98.70 338	99.89 40	95.83 204	99.90 143	98.10 248	99.90 56	99.08 286
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 225	98.03 245	99.31 185	99.63 158	98.56 239	99.54 161	96.75 456	97.53 267	99.73 92	99.65 234	91.25 374	99.89 158	98.62 187	99.56 165	99.48 229

tttt0517		98.42 216	98.14 230	99.28 197	99.66 142	98.38 260	99.74 47	96.85 454	97.68 248	99.79 71	99.74 186	91.39 370	99.89 158	98.83 161	99.56 165	99.57 199

test12				99.75 72	99.64 154	99.61 80		99.29 329		99.21 247		98.38 92	99.89 158		99.74 140	99.74 109

Test_1112_low_res		98.89 164	98.66 185	99.57 115	99.69 122	98.95 188	99.03 377	99.47 212	96.98 322	99.15 260	99.23 369	96.77 160	99.89 158	98.83 161	98.78 245	99.86 41

CNLPA		99.14 114	98.99 126	99.59 108	99.58 185	99.41 115	99.16 347	99.44 243	98.45 125	99.19 253	99.49 298	98.08 106	99.89 158	97.73 288	99.75 137	99.48 229

diffmvs_AUTHOR		99.19 97	99.10 95	99.48 148	99.64 154	98.85 207	99.32 292	99.48 190	98.50 119	99.81 64	99.81 121	96.82 156	99.88 163	99.40 70	99.12 208	99.71 136

guyue		99.16 106	99.04 109	99.52 134	99.69 122	98.92 196	99.59 116	98.81 403	98.73 97	99.90 33	99.87 62	95.34 226	99.88 163	99.66 39	99.81 115	99.74 109

sd_testset		98.75 194	98.57 202	99.29 193	99.81 53	98.26 264	99.56 141	99.62 47	98.78 93	99.64 131	99.88 51	92.02 353	99.88 163	99.54 50	98.26 278	99.72 127

APD_test1		95.87 389	96.49 371	94.00 428	99.53 206	84.01 457	99.54 161	99.32 316	95.91 395	97.99 390	99.85 77	85.49 432	99.88 163	91.96 435	98.84 240	98.12 421

diffmvs		99.14 114	99.02 118	99.51 139	99.61 175	98.96 182	99.28 308	99.49 178	98.46 123	99.72 97	99.71 199	96.50 173	99.88 163	99.31 84	99.11 210	99.67 152
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		98.86 171	98.80 166	99.03 225	99.76 77	98.79 218	99.28 308	99.91 3	97.42 282	99.67 112	99.37 336	97.53 119	99.88 163	98.98 129	97.29 338	98.42 402

PVSNet_Blended		99.08 137	98.97 130	99.42 166	99.76 77	98.79 218	98.78 416	99.91 3	96.74 337	99.67 112	99.49 298	97.53 119	99.88 163	98.98 129	99.85 89	99.60 181

viewdifsd2359ckpt07		99.11 129	99.00 125	99.43 164	99.63 158	98.73 222	99.45 231	99.54 104	98.33 140	99.62 138	99.81 121	96.17 186	99.87 170	99.27 93	99.14 203	99.69 142

viewdifsd2359ckpt11		98.78 189	98.74 174	98.89 252	99.67 129	97.04 333	99.50 191	99.58 74	98.26 150	99.56 154	99.90 32	94.36 283	99.87 170	99.49 60	98.32 274	99.77 96

viewmsd2359difaftdt		98.78 189	98.74 174	98.90 248	99.67 129	97.04 333	99.50 191	99.58 74	98.26 150	99.56 154	99.90 32	94.36 283	99.87 170	99.49 60	98.32 274	99.77 96

MVS		97.28 356	96.55 369	99.48 148	98.78 393	98.95 188	99.27 313	99.39 269	83.53 459	98.08 385	99.54 280	96.97 147	99.87 170	94.23 413	99.16 199	99.63 173

MG-MVS		99.13 116	99.02 118	99.45 156	99.57 190	98.63 232	99.07 366	99.34 298	98.99 64	99.61 143	99.82 106	97.98 110	99.87 170	97.00 343	99.80 120	99.85 45

MSDG		98.98 157	98.80 166	99.53 128	99.76 77	99.19 145	98.75 419	99.55 95	97.25 296	99.47 173	99.77 172	97.82 113	99.87 170	96.93 350	99.90 56	99.54 205

ETV-MVS		99.26 88	99.21 81	99.40 168	99.46 239	99.30 133	99.56 141	99.52 125	98.52 117	99.44 181	99.27 364	98.41 90	99.86 176	99.10 116	99.59 163	99.04 293

thisisatest0515		98.14 243	97.79 269	99.19 208	99.50 227	98.50 250	98.61 431	96.82 455	96.95 326	99.54 161	99.43 316	91.66 365	99.86 176	98.08 253	99.51 169	99.22 275

thres600view7		97.86 287	97.51 305	98.92 242	99.72 106	97.95 285	99.59 116	98.74 413	97.94 213	99.27 232	98.62 419	91.75 359	99.86 176	93.73 419	98.19 285	98.96 303

lupinMVS		99.13 116	99.01 123	99.46 155	99.51 215	98.94 192	99.05 372	99.16 352	97.86 221	99.80 69	99.56 272	97.39 122	99.86 176	98.94 136	99.85 89	99.58 196

PVSNet	96.02 17	98.85 180	98.84 163	98.89 252	99.73 102	97.28 314	98.32 447	99.60 63	97.86 221	99.50 168	99.57 269	96.75 161	99.86 176	98.56 202	99.70 147	99.54 205

MAR-MVS		98.86 171	98.63 190	99.54 120	99.37 267	99.66 66	99.45 231	99.54 104	96.61 349	99.01 286	99.40 326	97.09 139	99.86 176	97.68 295	99.53 168	99.10 281
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		99.08 137	98.96 134	99.44 161	99.62 166	98.88 199	99.25 324	99.47 212	98.05 196	99.37 203	99.81 121	96.85 151	99.85 182	98.98 129	99.25 192	99.60 181

SSM_0404		99.16 106	99.06 105	99.44 161	99.65 150	98.96 182	99.49 208	99.50 166	98.14 175	99.62 138	99.85 77	96.85 151	99.85 182	99.19 101	99.26 191	99.52 212

testing91		97.44 348	97.02 357	98.71 284	99.18 319	96.89 351	99.19 343	99.04 369	97.78 236	98.31 372	98.29 433	85.41 433	99.85 182	98.01 259	97.95 295	99.39 253

test2506		96.81 371	96.65 367	97.29 401	99.74 95	92.21 445	99.60 109	85.06 476	99.13 36	99.77 80	99.93 10	87.82 418	99.85 182	99.38 73	99.38 178	99.80 84

AllTest		98.87 168	98.72 176	99.31 185	99.86 22	98.48 253	99.56 141	99.61 56	97.85 224	99.36 209	99.85 77	95.95 196	99.85 182	96.66 363	99.83 108	99.59 192

TestCases				99.31 185	99.86 22	98.48 253		99.61 56	97.85 224	99.36 209	99.85 77	95.95 196	99.85 182	96.66 363	99.83 108	99.59 192

jason		99.13 116	99.03 112	99.45 156	99.46 239	98.87 203	99.12 356	99.26 335	98.03 205	99.79 71	99.65 234	97.02 144	99.85 182	99.02 126	99.90 56	99.65 161
jason: jason.
CNVR-MVS		99.42 52	99.30 59	99.78 66	99.62 166	99.71 54	99.26 322	99.52 125	98.82 84	99.39 199	99.71 199	98.96 25	99.85 182	98.59 195	99.80 120	99.77 96

PAPM_NR		99.04 147	98.84 163	99.66 86	99.74 95	99.44 111	99.39 267	99.38 277	97.70 246	99.28 226	99.28 361	98.34 94	99.85 182	96.96 347	99.45 174	99.69 142

viewcassd2359sk11		99.18 100	99.08 101	99.49 147	99.65 150	98.95 188	99.48 214	99.51 144	98.10 184	99.72 97	99.87 62	97.13 135	99.84 191	99.13 110	99.14 203	99.69 142

testing99		97.36 351	96.94 360	98.63 290	99.18 319	96.70 357	99.30 298	98.93 381	97.71 243	98.23 377	98.26 434	84.92 436	99.84 191	98.04 258	97.85 302	99.35 259

testing222		97.16 361	96.50 370	99.16 211	99.16 329	98.47 255	99.27 313	98.66 424	97.71 243	98.23 377	98.15 437	82.28 450	99.84 191	97.36 322	97.66 308	99.18 277

test1111		98.04 258	98.11 234	97.83 376	99.74 95	93.82 429	99.58 126	95.40 463	99.12 41	99.65 126	99.93 10	90.73 379	99.84 191	99.43 68	99.38 178	99.82 68

ECVR-MVS		98.04 258	98.05 243	98.00 360	99.74 95	94.37 424	99.59 116	94.98 464	99.13 36	99.66 117	99.93 10	90.67 380	99.84 191	99.40 70	99.38 178	99.80 84

test_yl		98.86 171	98.63 190	99.54 120	99.49 229	99.18 147	99.50 191	99.07 365	98.22 161	99.61 143	99.51 292	95.37 224	99.84 191	98.60 193	98.33 270	99.59 192

DCV-MVSNet		98.86 171	98.63 190	99.54 120	99.49 229	99.18 147	99.50 191	99.07 365	98.22 161	99.61 143	99.51 292	95.37 224	99.84 191	98.60 193	98.33 270	99.59 192

Fast-Effi-MVS+		98.70 198	98.43 211	99.51 139	99.51 215	99.28 136	99.52 172	99.47 212	96.11 388	99.01 286	99.34 346	96.20 185	99.84 191	97.88 267	98.82 242	99.39 253

TSAR-MVS + GP.		99.36 69	99.36 43	99.36 174	99.67 129	98.61 236	99.07 366	99.33 306	99.00 62	99.82 63	99.81 121	99.06 16	99.84 191	99.09 117	99.42 176	99.65 161

tpmrst		98.33 226	98.48 209	97.90 369	99.16 329	94.78 413	99.31 296	99.11 358	97.27 294	99.45 176	99.59 260	95.33 227	99.84 191	98.48 209	98.61 252	99.09 285

Vis-MVSNet		99.12 123	98.97 130	99.56 117	99.78 65	99.10 159	99.68 68	99.66 28	98.49 120	99.86 49	99.87 62	94.77 258	99.84 191	99.19 101	99.41 177	99.74 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 206	98.34 217	99.51 139	99.40 259	99.03 169	98.80 414	99.36 286	96.33 369	99.00 290	99.12 383	98.46 84	99.84 191	95.23 399	99.37 185	99.66 156

PatchMatch-RL		98.84 183	98.62 195	99.52 134	99.71 112	99.28 136	99.06 370	99.77 9	97.74 241	99.50 168	99.53 284	95.41 222	99.84 191	97.17 336	99.64 157	99.44 245

EPP-MVSNet		99.13 116	98.99 126	99.53 128	99.65 150	99.06 166	99.81 20	99.33 306	97.43 280	99.60 146	99.88 51	97.14 134	99.84 191	99.13 110	98.94 227	99.69 142

SSM_0407		99.13 116	99.03 112	99.43 164	99.62 166	98.88 199	99.51 181	99.50 166	98.14 175	99.37 203	99.85 77	96.85 151	99.83 205	99.19 101	99.25 192	99.60 181

testing3-2		97.84 292	97.70 284	98.24 342	99.53 206	95.37 401	99.55 156	98.67 423	98.46 123	99.27 232	99.34 346	86.58 424	99.83 205	99.32 83	98.63 251	99.52 212

testing11		97.50 341	97.10 354	98.71 284	99.20 313	96.91 349	99.29 303	98.82 401	97.89 218	98.21 380	98.40 428	85.63 431	99.83 205	98.45 214	98.04 293	99.37 257

thres100view900		97.76 306	97.45 314	98.69 286	99.72 106	97.86 291	99.59 116	98.74 413	97.93 214	99.26 237	98.62 419	91.75 359	99.83 205	93.22 424	98.18 286	98.37 408

tfpn200view9		97.72 316	97.38 327	98.72 281	99.69 122	97.96 282	99.50 191	98.73 419	97.83 228	99.17 258	98.45 426	91.67 363	99.83 205	93.22 424	98.18 286	98.37 408

test_prior				99.68 84	99.67 129	99.48 106		99.56 86					99.83 205			99.74 109

1314		98.68 200	98.54 205	99.11 217	98.89 376	98.65 229	99.27 313	99.49 178	96.89 330	97.99 390	99.56 272	97.72 117	99.83 205	97.74 287	99.27 189	98.84 309

thres400		97.77 305	97.38 327	98.92 242	99.69 122	97.96 282	99.50 191	98.73 419	97.83 228	99.17 258	98.45 426	91.67 363	99.83 205	93.22 424	98.18 286	98.96 303

casdiffmvs		99.13 116	98.98 129	99.56 117	99.65 150	99.16 150	99.56 141	99.50 166	98.33 140	99.41 192	99.86 70	95.92 199	99.83 205	99.45 67	99.16 199	99.70 139
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 30	99.48 20	99.54 120	99.78 65	99.30 133	99.89 2	99.58 74	98.56 113	99.73 92	99.69 214	98.55 78	99.82 214	99.69 34	99.85 89	99.48 229

MVS_Test		99.10 134	98.97 130	99.48 148	99.49 229	99.14 155	99.67 71	99.34 298	97.31 291	99.58 150	99.76 176	97.65 118	99.82 214	98.87 148	99.07 218	99.46 240

dp		97.75 310	97.80 268	97.59 392	99.10 340	93.71 432	99.32 292	98.88 394	96.48 361	99.08 274	99.55 275	92.67 337	99.82 214	96.52 367	98.58 255	99.24 273

RPSCF		98.22 233	98.62 195	96.99 407	99.82 49	91.58 447	99.72 53	99.44 243	96.61 349	99.66 117	99.89 40	95.92 199	99.82 214	97.46 315	99.10 215	99.57 199

PMMVS		98.80 187	98.62 195	99.34 177	99.27 295	98.70 225	98.76 418	99.31 320	97.34 288	99.21 247	99.07 385	97.20 133	99.82 214	98.56 202	98.87 237	99.52 212

UBG		97.85 288	97.48 308	98.95 236	99.25 302	97.64 302	99.24 329	98.74 413	97.90 217	98.64 348	98.20 436	88.65 405	99.81 219	98.27 232	98.40 265	99.42 247

EIA-MVS		99.18 100	99.09 100	99.45 156	99.49 229	99.18 147	99.67 71	99.53 120	97.66 251	99.40 197	99.44 314	98.10 104	99.81 219	98.94 136	99.62 160	99.35 259

Effi-MVS+		98.81 184	98.59 201	99.48 148	99.46 239	99.12 158	98.08 454	99.50 166	97.50 271	99.38 201	99.41 322	96.37 180	99.81 219	99.11 113	98.54 260	99.51 221

thres200		97.61 332	97.28 343	98.62 291	99.64 154	98.03 276	99.26 322	98.74 413	97.68 248	99.09 272	98.32 432	91.66 365	99.81 219	92.88 429	98.22 281	98.03 427

tpmvs		97.98 269	98.02 247	97.84 375	99.04 354	94.73 414	99.31 296	99.20 347	96.10 392	98.76 328	99.42 318	94.94 243	99.81 219	96.97 346	98.45 264	98.97 301

casdiffmvs_mvg		99.15 110	99.02 118	99.55 119	99.66 142	99.09 160	99.64 91	99.56 86	98.26 150	99.45 176	99.87 62	96.03 192	99.81 219	99.54 50	99.15 202	99.73 118
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	98.81 184	99.37 41	97.12 405	99.60 181	91.75 446	98.61 431	99.44 243	99.35 24	99.83 60	99.85 77	98.70 66	99.81 219	99.02 126	99.91 45	99.81 75

viewmacassd2359aftdt		99.08 137	98.94 140	99.50 144	99.66 142	98.96 182	99.51 181	99.54 104	98.27 147	99.42 187	99.89 40	95.88 203	99.80 226	99.20 100	99.11 210	99.76 103

viewmanbaseed2359cas		99.18 100	99.07 104	99.50 144	99.62 166	99.01 172	99.50 191	99.52 125	98.25 155	99.68 106	99.82 106	96.93 149	99.80 226	99.15 109	99.11 210	99.70 139

IMVS_0403		98.86 171	98.89 152	98.78 276	99.55 198	96.93 344	99.58 126	99.44 243	98.05 196	99.68 106	99.80 139	96.81 157	99.80 226	98.15 244	98.92 230	99.60 181

DPM-MVS		98.95 160	98.71 178	99.66 86	99.63 158	99.55 91	98.64 430	99.10 359	97.93 214	99.42 187	99.55 275	98.67 69	99.80 226	95.80 384	99.68 151	99.61 178

DP-MVS Recon		99.12 123	98.95 138	99.65 90	99.74 95	99.70 56	99.27 313	99.57 81	96.40 368	99.42 187	99.68 221	98.75 58	99.80 226	97.98 261	99.72 143	99.44 245

MVS_111021_LR		99.41 56	99.33 49	99.65 90	99.77 73	99.51 102	98.94 400	99.85 6	98.82 84	99.65 126	99.74 186	98.51 81	99.80 226	98.83 161	99.89 67	99.64 168

viewmambaseed2359dif		99.01 154	98.90 148	99.32 183	99.58 185	98.51 248	99.33 289	99.54 104	97.85 224	99.44 181	99.85 77	96.01 193	99.79 232	99.41 69	99.13 206	99.67 152

CS-MVS		99.50 28	99.48 20	99.54 120	99.76 77	99.42 113	99.90 1	99.55 95	98.56 113	99.78 76	99.70 203	98.65 71	99.79 232	99.65 40	99.78 129	99.41 250

Fast-Effi-MVS+-dtu		98.77 193	98.83 165	98.60 292	99.41 254	96.99 339	99.52 172	99.49 178	98.11 181	99.24 239	99.34 346	96.96 148	99.79 232	97.95 263	99.45 174	99.02 296

baseline1		98.31 227	97.95 254	99.38 173	99.50 227	98.74 221	99.59 116	98.93 381	98.41 130	99.14 261	99.60 258	94.59 271	99.79 232	98.48 209	93.29 421	99.61 178

baseline		99.15 110	99.02 118	99.53 128	99.66 142	99.14 155	99.72 53	99.48 190	98.35 137	99.42 187	99.84 92	96.07 189	99.79 232	99.51 55	99.14 203	99.67 152

PVSNet_0	94.43 19	96.09 386	95.47 393	97.94 365	99.31 285	94.34 426	97.81 456	99.70 15	97.12 308	97.46 405	98.75 416	89.71 391	99.79 232	97.69 294	81.69 459	99.68 148

API-MVS		99.04 147	99.03 112	99.06 221	99.40 259	99.31 130	99.55 156	99.56 86	98.54 115	99.33 216	99.39 330	98.76 55	99.78 238	96.98 345	99.78 129	98.07 424

OMC-MVS		99.08 137	99.04 109	99.20 207	99.67 129	98.22 266	99.28 308	99.52 125	98.07 190	99.66 117	99.81 121	97.79 114	99.78 238	97.79 279	99.81 115	99.60 181

GeoE		98.85 180	98.62 195	99.53 128	99.61 175	99.08 163	99.80 25	99.51 144	97.10 312	99.31 218	99.78 163	95.23 234	99.77 240	98.21 236	99.03 221	99.75 105

alignmvs		98.81 184	98.56 204	99.58 111	99.43 247	99.42 113	99.51 181	98.96 379	98.61 108	99.35 212	98.92 406	94.78 255	99.77 240	99.35 75	98.11 291	99.54 205

tpm cat1		97.39 350	97.36 329	97.50 395	99.17 327	93.73 431	99.43 243	99.31 320	91.27 442	98.71 332	99.08 384	94.31 288	99.77 240	96.41 372	98.50 262	99.00 297

CostFormer		97.72 316	97.73 281	97.71 384	99.15 333	94.02 428	99.54 161	99.02 372	94.67 416	99.04 283	99.35 342	92.35 349	99.77 240	98.50 208	97.94 296	99.34 262

MGCFI-Net		99.01 154	98.85 161	99.50 144	99.42 249	99.26 139	99.82 16	99.48 190	98.60 110	99.28 226	98.81 411	97.04 143	99.76 244	99.29 89	97.87 300	99.47 235

test_241102_ONE					99.84 35	99.90 2		99.48 190	99.07 53	99.91 30	99.74 186	99.20 7	99.76 244

MDTV_nov1_ep13			98.32 219		99.11 337	94.44 422	99.27 313	98.74 413	97.51 270	99.40 197	99.62 251	94.78 255	99.76 244	97.59 299	98.81 244

sasdasda		99.02 150	98.86 158	99.51 139	99.42 249	99.32 126	99.80 25	99.48 190	98.63 105	99.31 218	98.81 411	97.09 139	99.75 247	99.27 93	97.90 297	99.47 235

canonicalmvs		99.02 150	98.86 158	99.51 139	99.42 249	99.32 126	99.80 25	99.48 190	98.63 105	99.31 218	98.81 411	97.09 139	99.75 247	99.27 93	97.90 297	99.47 235

Effi-MVS+-dtu		98.78 189	98.89 152	98.47 315	99.33 277	96.91 349	99.57 134	99.30 325	98.47 122	99.41 192	98.99 396	96.78 159	99.74 249	98.73 172	99.38 178	98.74 325

patchmatchnet-post											98.70 417	94.79 254	99.74 249

SCA		98.19 237	98.16 227	98.27 341	99.30 286	95.55 392	99.07 366	98.97 377	97.57 260	99.43 184	99.57 269	92.72 332	99.74 249	97.58 300	99.20 197	99.52 212

BH-untuned		98.42 216	98.36 215	98.59 293	99.49 229	96.70 357	99.27 313	99.13 356	97.24 298	98.80 323	99.38 333	95.75 210	99.74 249	97.07 341	99.16 199	99.33 263

BH-RMVSNet		98.41 218	98.08 239	99.40 168	99.41 254	98.83 212	99.30 298	98.77 409	97.70 246	98.94 301	99.65 234	92.91 327	99.74 249	96.52 367	99.55 167	99.64 168

MVS_111021_HR		99.41 56	99.32 51	99.66 86	99.72 106	99.47 108	98.95 398	99.85 6	98.82 84	99.54 161	99.73 192	98.51 81	99.74 249	98.91 142	99.88 71	99.77 96

test_post											65.99 470	94.65 269	99.73 255

XVG-ACMP-BASELINE		97.83 295	97.71 283	98.20 344	99.11 337	96.33 373	99.41 255	99.52 125	98.06 194	99.05 282	99.50 295	89.64 393	99.73 255	97.73 288	97.38 335	98.53 390

HyFIR lowres test		99.11 129	98.92 143	99.65 90	99.90 4	99.37 118	99.02 380	99.91 3	97.67 250	99.59 149	99.75 181	95.90 201	99.73 255	99.53 52	99.02 223	99.86 41

DeepMVS_CX				93.34 431	99.29 290	82.27 460		99.22 343	85.15 457	96.33 427	99.05 388	90.97 377	99.73 255	93.57 421	97.77 305	98.01 428

Patchmatch-test		97.93 275	97.65 289	98.77 277	99.18 319	97.07 328	99.03 377	99.14 355	96.16 383	98.74 329	99.57 269	94.56 273	99.72 259	93.36 423	99.11 210	99.52 212

LPG-MVS_test		98.22 233	98.13 232	98.49 308	99.33 277	97.05 330	99.58 126	99.55 95	97.46 273	99.24 239	99.83 97	92.58 339	99.72 259	98.09 249	97.51 321	98.68 343

LGP-MVS_train				98.49 308	99.33 277	97.05 330		99.55 95	97.46 273	99.24 239	99.83 97	92.58 339	99.72 259	98.09 249	97.51 321	98.68 343

BH-w/o		98.00 267	97.89 263	98.32 333	99.35 271	96.20 379	99.01 385	98.90 391	96.42 366	98.38 368	99.00 394	95.26 231	99.72 259	96.06 377	98.61 252	99.03 294

ACMP	97.20 11	98.06 252	97.94 256	98.45 318	99.37 267	97.01 337	99.44 238	99.49 178	97.54 266	98.45 365	99.79 156	91.95 355	99.72 259	97.91 265	97.49 326	98.62 373
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 262	97.90 259	98.40 326	99.23 306	96.80 355	99.70 58	99.60 63	97.12 308	98.18 382	99.70 203	91.73 361	99.72 259	98.39 219	97.45 328	98.68 343
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		99.06 142	98.93 142	99.45 156	99.63 158	98.96 182	99.50 191	99.51 144	97.83 228	99.28 226	99.80 139	96.68 165	99.71 265	99.05 121	99.12 208	99.68 148

test_post1							99.23 332				65.14 471	94.18 293	99.71 265	97.58 300

ADS-MVSNet		98.20 236	98.08 239	98.56 301	99.33 277	96.48 368	99.23 332	99.15 353	96.24 376	99.10 269	99.67 227	94.11 294	99.71 265	96.81 355	99.05 219	99.48 229

JIA-IIPM		97.50 341	97.02 357	98.93 240	98.73 402	97.80 293	99.30 298	98.97 377	91.73 441	98.91 304	94.86 459	95.10 238	99.71 265	97.58 300	97.98 294	99.28 267

EPMVS		97.82 298	97.65 289	98.35 330	98.88 377	95.98 383	99.49 208	94.71 466	97.57 260	99.26 237	99.48 304	92.46 346	99.71 265	97.87 269	99.08 217	99.35 259

TDRefinement		95.42 397	94.57 405	97.97 362	89.83 469	96.11 382	99.48 214	98.75 410	96.74 337	96.68 424	99.88 51	88.65 405	99.71 265	98.37 222	82.74 458	98.09 423

ACMM	97.58 5	98.37 224	98.34 217	98.48 310	99.41 254	97.10 324	99.56 141	99.45 234	98.53 116	99.04 283	99.85 77	93.00 323	99.71 265	98.74 170	97.45 328	98.64 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 272	97.77 274	98.57 297	99.59 183	96.61 364	99.45 231	99.08 362	98.21 163	98.88 309	99.80 139	88.66 404	99.70 272	98.58 196	97.72 306	99.39 253

CHOSEN 280x420		99.12 123	99.13 91	99.08 218	99.66 142	97.89 288	98.43 441	99.71 13	98.88 78	99.62 138	99.76 176	96.63 166	99.70 272	99.46 66	99.99 1	99.66 156

EC-MVSNet		99.44 47	99.39 37	99.58 111	99.56 194	99.49 104	99.88 4	99.58 74	98.38 132	99.73 92	99.69 214	98.20 100	99.70 272	99.64 42	99.82 112	99.54 205

PatchmatchNet		98.31 227	98.36 215	98.19 345	99.16 329	95.32 402	99.27 313	98.92 384	97.37 286	99.37 203	99.58 264	94.90 248	99.70 272	97.43 318	99.21 196	99.54 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 247	97.99 249	98.44 321	99.41 254	96.96 343	99.60 109	99.56 86	98.09 185	98.15 383	99.91 25	90.87 378	99.70 272	98.88 145	97.45 328	98.67 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 341	96.90 361	99.29 193	99.23 306	98.78 220	99.32 292	98.90 391	97.52 269	98.56 358	98.09 442	84.72 438	99.69 277	97.86 270	97.88 299	99.39 253

HQP_MVS		98.27 232	98.22 225	98.44 321	99.29 290	96.97 341	99.39 267	99.47 212	98.97 70	99.11 266	99.61 255	92.71 334	99.69 277	97.78 280	97.63 309	98.67 351

plane_prior5								99.47 212					99.69 277	97.78 280	97.63 309	98.67 351

D2MVS		98.41 218	98.50 208	98.15 350	99.26 298	96.62 363	99.40 263	99.61 56	97.71 243	98.98 293	99.36 339	96.04 191	99.67 280	98.70 175	97.41 333	98.15 420

IS-MVSNet		99.05 146	98.87 156	99.57 115	99.73 102	99.32 126	99.75 42	99.20 347	98.02 208	99.56 154	99.86 70	96.54 171	99.67 280	98.09 249	99.13 206	99.73 118

CLD-MVS		98.16 241	98.10 235	98.33 331	99.29 290	96.82 354	98.75 419	99.44 243	97.83 228	99.13 262	99.55 275	92.92 325	99.67 280	98.32 229	97.69 307	98.48 394
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 358	97.30 340	97.09 406	99.43 247	93.31 438	99.73 51	98.87 396	98.83 83	99.28 226	99.80 139	84.45 439	99.66 283	97.88 267	97.45 328	98.30 410

AUN-MVS		96.88 369	96.31 375	98.59 293	99.48 236	97.04 333	99.27 313	99.22 343	97.44 279	98.51 361	99.41 322	91.97 354	99.66 283	97.71 291	83.83 456	99.07 291

UniMVSNet_ETH3D		97.32 355	96.81 363	98.87 259	99.40 259	97.46 308	99.51 181	99.53 120	95.86 396	98.54 360	99.77 172	82.44 448	99.66 283	98.68 180	97.52 320	99.50 225

OPM-MVS		98.19 237	98.10 235	98.45 318	98.88 377	97.07 328	99.28 308	99.38 277	98.57 112	99.22 244	99.81 121	92.12 351	99.66 283	98.08 253	97.54 318	98.61 382
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 278	97.78 272	98.32 333	99.46 239	96.68 361	99.56 141	99.54 104	98.41 130	97.79 401	99.87 62	90.18 387	99.66 283	98.05 257	97.18 343	98.62 373

IMVS_0407		98.86 171	98.91 146	98.72 281	99.55 198	96.93 344	99.50 191	99.44 243	98.05 196	99.66 117	99.80 139	97.13 135	99.65 288	98.15 244	98.92 230	99.60 181

hse-mvs2		97.50 341	97.14 351	98.59 293	99.49 229	97.05 330	99.28 308	99.22 343	98.94 73	99.66 117	99.42 318	94.93 244	99.65 288	99.48 63	83.80 457	99.08 286

VPA-MVSNet		98.29 230	97.95 254	99.30 190	99.16 329	99.54 93	99.50 191	99.58 74	98.27 147	99.35 212	99.37 336	92.53 341	99.65 288	99.35 75	94.46 402	98.72 327

TR-MVS		97.76 306	97.41 325	98.82 268	99.06 349	97.87 289	98.87 408	98.56 427	96.63 348	98.68 340	99.22 370	92.49 342	99.65 288	95.40 395	97.79 304	98.95 305

reproduce_monomvs		97.89 282	97.87 264	97.96 364	99.51 215	95.45 397	99.60 109	99.25 337	99.17 31	98.85 317	99.49 298	89.29 396	99.64 292	99.35 75	96.31 359	98.78 313

gm-plane-assit					98.54 422	92.96 440			94.65 417		99.15 378		99.64 292	97.56 305

HQP4-MVS										98.66 341			99.64 292			98.64 364

HQP-MVS		98.02 262	97.90 259	98.37 329	99.19 316	96.83 352	98.98 391	99.39 269	98.24 157	98.66 341	99.40 326	92.47 343	99.64 292	97.19 333	97.58 314	98.64 364

PAPM		97.59 333	97.09 355	99.07 219	99.06 349	98.26 264	98.30 448	99.10 359	94.88 411	98.08 385	99.34 346	96.27 183	99.64 292	89.87 443	98.92 230	99.31 265

TAPA-MVS	97.07 15	97.74 312	97.34 334	98.94 238	99.70 117	97.53 305	99.25 324	99.51 144	91.90 440	99.30 222	99.63 246	98.78 51	99.64 292	88.09 450	99.87 74	99.65 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 222	98.09 238	99.24 203	99.26 298	99.32 126	99.56 141	99.55 95	97.45 276	98.71 332	99.83 97	93.23 318	99.63 298	98.88 145	96.32 358	98.76 319

ITE_SJBPF				98.08 353	99.29 290	96.37 371		98.92 384	98.34 138	98.83 318	99.75 181	91.09 375	99.62 299	95.82 382	97.40 334	98.25 414

LF4IMVS		97.52 338	97.46 313	97.70 385	98.98 365	95.55 392	99.29 303	98.82 401	98.07 190	98.66 341	99.64 240	89.97 388	99.61 300	97.01 342	96.68 348	97.94 435

tpm		97.67 327	97.55 298	98.03 355	99.02 356	95.01 409	99.43 243	98.54 429	96.44 364	99.12 264	99.34 346	91.83 358	99.60 301	97.75 286	96.46 354	99.48 229

tpm2		97.44 348	97.34 334	97.74 383	99.15 333	94.36 425	99.45 231	98.94 380	93.45 430	98.90 306	99.44 314	91.35 371	99.59 302	97.31 324	98.07 292	99.29 266

SSM_04072		99.06 142	98.96 134	99.35 176	99.62 166	98.88 199	99.25 324	99.47 212	98.05 196	99.37 203	99.81 121	96.85 151	99.58 303	98.98 129	99.25 192	99.60 181

SD_0403		97.55 335	97.53 302	97.62 388	99.61 175	93.64 435	99.72 53	99.44 243	98.03 205	98.62 353	99.39 330	96.06 190	99.57 304	87.88 452	99.01 224	99.66 156

baseline2		97.87 285	97.55 298	98.82 268	99.18 319	98.02 277	99.41 255	96.58 460	96.97 323	96.51 425	99.17 375	93.43 313	99.57 304	97.71 291	99.03 221	98.86 307

MS-PatchMatch		97.24 360	97.32 338	96.99 407	98.45 425	93.51 437	98.82 412	99.32 316	97.41 283	98.13 384	99.30 357	88.99 398	99.56 306	95.68 388	99.80 120	97.90 438

TinyColmap		97.12 363	96.89 362	97.83 376	99.07 347	95.52 395	98.57 434	98.74 413	97.58 259	97.81 400	99.79 156	88.16 412	99.56 306	95.10 400	97.21 341	98.39 406

USDC		97.34 353	97.20 348	97.75 381	99.07 347	95.20 404	98.51 438	99.04 369	97.99 209	98.31 372	99.86 70	89.02 397	99.55 308	95.67 389	97.36 336	98.49 393

MSLP-MVS++		99.46 39	99.47 22	99.44 161	99.60 181	99.16 150	99.41 255	99.71 13	98.98 67	99.45 176	99.78 163	99.19 9	99.54 309	99.28 90	99.84 97	99.63 173

UWE-MVS-28		97.36 351	97.24 347	97.75 381	98.84 386	94.44 422	99.24 329	97.58 449	97.98 210	99.00 290	99.00 394	91.35 371	99.53 310	93.75 418	98.39 266	99.27 271

TAMVS		99.12 123	99.08 101	99.24 203	99.46 239	98.55 240	99.51 181	99.46 223	98.09 185	99.45 176	99.82 106	98.34 94	99.51 311	98.70 175	98.93 228	99.67 152

EPNet_dtu		98.03 260	97.96 252	98.23 343	98.27 428	95.54 394	99.23 332	98.75 410	99.02 57	97.82 399	99.71 199	96.11 188	99.48 312	93.04 427	99.65 156	99.69 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 373	96.22 377	97.97 362	97.00 450	96.28 375	98.66 428	99.03 371	96.61 349	96.93 422	99.79 156	87.20 421	99.47 313	96.65 365	94.13 409	98.16 419

EG-PatchMatch MVS		95.97 388	95.69 389	96.81 414	97.78 435	92.79 441	99.16 347	98.93 381	96.16 383	94.08 443	99.22 370	82.72 446	99.47 313	95.67 389	97.50 323	98.17 418

myMVS_eth3d28		97.69 321	97.34 334	98.73 279	99.27 295	97.52 306	99.33 289	98.78 408	98.03 205	98.82 320	98.49 424	86.64 423	99.46 315	98.44 215	98.24 280	99.23 274

MVP-Stereo		97.81 300	97.75 279	97.99 361	97.53 439	96.60 365	98.96 395	98.85 398	97.22 300	97.23 412	99.36 339	95.28 228	99.46 315	95.51 391	99.78 129	97.92 437
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 208	98.67 182	98.30 335	99.35 271	95.59 391	99.50 191	99.55 95	98.60 110	99.39 199	99.83 97	94.48 279	99.45 317	98.75 169	98.56 258	99.85 45

test-LLR		98.06 252	97.90 259	98.55 303	98.79 390	97.10 324	98.67 425	97.75 445	97.34 288	98.61 354	98.85 408	94.45 281	99.45 317	97.25 327	99.38 178	99.10 281

TESTMET0.1,1		97.55 335	97.27 346	98.40 326	98.93 370	96.53 366	98.67 425	97.61 448	96.96 324	98.64 348	99.28 361	88.63 407	99.45 317	97.30 325	99.38 178	99.21 276

test-mter		97.49 346	97.13 353	98.55 303	98.79 390	97.10 324	98.67 425	97.75 445	96.65 344	98.61 354	98.85 408	88.23 411	99.45 317	97.25 327	99.38 178	99.10 281

mvs_anonymous		99.03 149	98.99 126	99.16 211	99.38 264	98.52 246	99.51 181	99.38 277	97.79 234	99.38 201	99.81 121	97.30 128	99.45 317	99.35 75	98.99 225	99.51 221

tfpnnormal		97.84 292	97.47 311	98.98 231	99.20 313	99.22 144	99.64 91	99.61 56	96.32 370	98.27 376	99.70 203	93.35 317	99.44 322	95.69 387	95.40 385	98.27 412

v7n		97.87 285	97.52 303	98.92 242	98.76 400	98.58 238	99.84 12	99.46 223	96.20 379	98.91 304	99.70 203	94.89 249	99.44 322	96.03 378	93.89 414	98.75 321

jajsoiax		98.43 215	98.28 222	98.88 255	98.60 417	98.43 257	99.82 16	99.53 120	98.19 165	98.63 350	99.80 139	93.22 320	99.44 322	99.22 98	97.50 323	98.77 317

mvs_tets		98.40 221	98.23 224	98.91 246	98.67 410	98.51 248	99.66 78	99.53 120	98.19 165	98.65 347	99.81 121	92.75 329	99.44 322	99.31 84	97.48 327	98.77 317

sc_t1		95.75 392	95.05 399	97.87 371	98.83 387	94.61 419	99.21 338	99.45 234	87.45 453	97.97 392	99.85 77	81.19 453	99.43 326	98.27 232	93.20 423	99.57 199

Vis-MVSNet (Re-imp)		98.87 168	98.72 176	99.31 185	99.71 112	98.88 199	99.80 25	99.44 243	97.91 216	99.36 209	99.78 163	95.49 220	99.43 326	97.91 265	99.11 210	99.62 176

OPU-MVS				99.64 96	99.56 194	99.72 52	99.60 109				99.70 203	99.27 5	99.42 328	98.24 235	99.80 120	99.79 88

Anonymous20231211		97.88 283	97.54 301	98.90 248	99.71 112	98.53 242	99.48 214	99.57 81	94.16 421	98.81 321	99.68 221	93.23 318	99.42 328	98.84 158	94.42 404	98.76 319

ttmdpeth		97.80 302	97.63 293	98.29 336	98.77 398	97.38 311	99.64 91	99.36 286	98.78 93	96.30 428	99.58 264	92.34 350	99.39 330	98.36 224	95.58 380	98.10 422

VPNet		97.84 292	97.44 319	99.01 227	99.21 311	98.94 192	99.48 214	99.57 81	98.38 132	99.28 226	99.73 192	88.89 399	99.39 330	99.19 101	93.27 422	98.71 329

nrg030		98.64 205	98.42 212	99.28 197	99.05 352	99.69 58	99.81 20	99.46 223	98.04 203	99.01 286	99.82 106	96.69 163	99.38 332	99.34 80	94.59 401	98.78 313

GA-MVS		97.85 288	97.47 311	99.00 229	99.38 264	97.99 279	98.57 434	99.15 353	97.04 319	98.90 306	99.30 357	89.83 390	99.38 332	96.70 360	98.33 270	99.62 176

UniMVSNet (Re)		98.29 230	98.00 248	99.13 216	99.00 359	99.36 121	99.49 208	99.51 144	97.95 212	98.97 295	99.13 380	96.30 182	99.38 332	98.36 224	93.34 420	98.66 360

FIs		98.78 189	98.63 190	99.23 205	99.18 319	99.54 93	99.83 15	99.59 69	98.28 145	98.79 325	99.81 121	96.75 161	99.37 335	99.08 118	96.38 356	98.78 313

PS-MVSNAJss		98.92 162	98.92 143	98.90 248	98.78 393	98.53 242	99.78 32	99.54 104	98.07 190	99.00 290	99.76 176	99.01 18	99.37 335	99.13 110	97.23 340	98.81 310

CDS-MVSNet		99.09 135	99.03 112	99.25 200	99.42 249	98.73 222	99.45 231	99.46 223	98.11 181	99.46 175	99.77 172	98.01 109	99.37 335	98.70 175	98.92 230	99.66 156
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 392	95.16 397	97.51 394	99.30 286	93.69 433	98.88 406	95.78 461	85.09 458	98.78 326	92.65 461	91.29 373	99.37 335	94.85 405	99.85 89	99.46 240

v1192		97.81 300	97.44 319	98.91 246	98.88 377	98.68 226	99.51 181	99.34 298	96.18 381	99.20 250	99.34 346	94.03 298	99.36 339	95.32 397	95.18 389	98.69 338

EI-MVSNet		98.67 201	98.67 182	98.68 287	99.35 271	97.97 280	99.50 191	99.38 277	96.93 329	99.20 250	99.83 97	97.87 111	99.36 339	98.38 220	97.56 316	98.71 329

MVSTER		98.49 210	98.32 219	99.00 229	99.35 271	99.02 170	99.54 161	99.38 277	97.41 283	99.20 250	99.73 192	93.86 306	99.36 339	98.87 148	97.56 316	98.62 373

gg-mvs-nofinetune		96.17 384	95.32 396	98.73 279	98.79 390	98.14 270	99.38 272	94.09 467	91.07 445	98.07 388	91.04 465	89.62 394	99.35 342	96.75 357	99.09 216	98.68 343

pm-mvs1		97.68 324	97.28 343	98.88 255	99.06 349	98.62 234	99.50 191	99.45 234	96.32 370	97.87 397	99.79 156	92.47 343	99.35 342	97.54 307	93.54 418	98.67 351

OurMVSNet-221017-0		97.88 283	97.77 274	98.19 345	98.71 406	96.53 366	99.88 4	99.00 374	97.79 234	98.78 326	99.94 6	91.68 362	99.35 342	97.21 329	96.99 347	98.69 338

EGC-MVSNET		82.80 430	77.86 436	97.62 388	97.91 432	96.12 381	99.33 289	99.28 331	8.40 473	25.05 474	99.27 364	84.11 440	99.33 345	89.20 445	98.22 281	97.42 447

pmmvs6		96.53 376	96.09 381	97.82 378	98.69 408	95.47 396	99.37 274	99.47 212	93.46 429	97.41 406	99.78 163	87.06 422	99.33 345	96.92 352	92.70 430	98.65 362

V42		98.06 252	97.79 269	98.86 262	98.98 365	98.84 209	99.69 62	99.34 298	96.53 356	99.30 222	99.37 336	94.67 266	99.32 347	97.57 304	94.66 399	98.42 402

lessismore_v0				97.79 380	98.69 408	95.44 399		94.75 465		95.71 434	99.87 62	88.69 403	99.32 347	95.89 381	94.93 396	98.62 373

OpenMVS_ROB	92.34 20	94.38 409	93.70 415	96.41 419	97.38 441	93.17 439	99.06 370	98.75 410	86.58 456	94.84 441	98.26 434	81.53 451	99.32 347	89.01 446	97.87 300	96.76 450

v8		97.95 274	97.63 293	98.93 240	98.95 369	98.81 217	99.80 25	99.41 259	96.03 393	99.10 269	99.42 318	94.92 246	99.30 350	96.94 349	94.08 411	98.66 360

v1921920		97.80 302	97.45 314	98.84 266	98.80 389	98.53 242	99.52 172	99.34 298	96.15 385	99.24 239	99.47 307	93.98 300	99.29 351	95.40 395	95.13 391	98.69 338

anonymousdsp		98.44 214	98.28 222	98.94 238	98.50 423	98.96 182	99.77 34	99.50 166	97.07 314	98.87 312	99.77 172	94.76 259	99.28 352	98.66 182	97.60 312	98.57 388

MVSFormer		99.17 104	99.12 93	99.29 193	99.51 215	98.94 192	99.88 4	99.46 223	97.55 263	99.80 69	99.65 234	97.39 122	99.28 352	99.03 124	99.85 89	99.65 161

test_djsdf		98.67 201	98.57 202	98.98 231	98.70 407	98.91 197	99.88 4	99.46 223	97.55 263	99.22 244	99.88 51	95.73 211	99.28 352	99.03 124	97.62 311	98.75 321

VortexMVS		98.67 201	98.66 185	98.68 287	99.62 166	97.96 282	99.59 116	99.41 259	98.13 177	99.31 218	99.70 203	95.48 221	99.27 355	99.40 70	97.32 337	98.79 311

SSC-MVS3.2		97.34 353	97.15 350	97.93 366	99.02 356	95.76 388	99.48 214	99.58 74	97.62 255	99.09 272	99.53 284	87.95 414	99.27 355	96.42 370	95.66 378	98.75 321

cascas		97.69 321	97.43 323	98.48 310	98.60 417	97.30 313	98.18 452	99.39 269	92.96 434	98.41 366	98.78 415	93.77 309	99.27 355	98.16 242	98.61 252	98.86 307

v144192		97.92 278	97.60 296	98.87 259	98.83 387	98.65 229	99.55 156	99.34 298	96.20 379	99.32 217	99.40 326	94.36 283	99.26 358	96.37 374	95.03 393	98.70 334

dmvs_re		98.08 250	98.16 227	97.85 373	99.55 198	94.67 418	99.70 58	98.92 384	98.15 170	99.06 280	99.35 342	93.67 312	99.25 359	97.77 283	97.25 339	99.64 168

v2v482		98.06 252	97.77 274	98.92 242	98.90 375	98.82 215	99.57 134	99.36 286	96.65 344	99.19 253	99.35 342	94.20 290	99.25 359	97.72 290	94.97 394	98.69 338

v1240		97.69 321	97.32 338	98.79 274	98.85 384	98.43 257	99.48 214	99.36 286	96.11 388	99.27 232	99.36 339	93.76 310	99.24 361	94.46 409	95.23 388	98.70 334

WBMVS		97.74 312	97.50 306	98.46 316	99.24 304	97.43 309	99.21 338	99.42 256	97.45 276	98.96 297	99.41 322	88.83 400	99.23 362	98.94 136	96.02 364	98.71 329

v1144		97.98 269	97.69 285	98.85 265	98.87 380	98.66 228	99.54 161	99.35 293	96.27 374	99.23 243	99.35 342	94.67 266	99.23 362	96.73 358	95.16 390	98.68 343

v10		97.85 288	97.52 303	98.86 262	98.99 362	98.67 227	99.75 42	99.41 259	95.70 397	98.98 293	99.41 322	94.75 260	99.23 362	96.01 380	94.63 400	98.67 351

WR-MVS_H		98.13 244	97.87 264	98.90 248	99.02 356	98.84 209	99.70 58	99.59 69	97.27 294	98.40 367	99.19 374	95.53 218	99.23 362	98.34 226	93.78 416	98.61 382

miper_enhance_ethall		98.16 241	98.08 239	98.41 324	98.96 368	97.72 297	98.45 440	99.32 316	96.95 326	98.97 295	99.17 375	97.06 142	99.22 366	97.86 270	95.99 367	98.29 411

GG-mvs-BLEND				98.45 318	98.55 421	98.16 268	99.43 243	93.68 468		97.23 412	98.46 425	89.30 395	99.22 366	95.43 394	98.22 281	97.98 433

FC-MVSNet-test		98.75 194	98.62 195	99.15 215	99.08 346	99.45 110	99.86 11	99.60 63	98.23 160	98.70 338	99.82 106	96.80 158	99.22 366	99.07 119	96.38 356	98.79 311

UniMVSNet_NR-MVSNet		98.22 233	97.97 251	98.96 234	98.92 372	98.98 175	99.48 214	99.53 120	97.76 238	98.71 332	99.46 311	96.43 178	99.22 366	98.57 199	92.87 428	98.69 338

DU-MVS		98.08 250	97.79 269	98.96 234	98.87 380	98.98 175	99.41 255	99.45 234	97.87 220	98.71 332	99.50 295	94.82 251	99.22 366	98.57 199	92.87 428	98.68 343

cl____		98.01 265	97.84 267	98.55 303	99.25 302	97.97 280	98.71 423	99.34 298	96.47 363	98.59 357	99.54 280	95.65 214	99.21 371	97.21 329	95.77 373	98.46 399

WR-MVS		98.06 252	97.73 281	99.06 221	98.86 383	99.25 141	99.19 343	99.35 293	97.30 292	98.66 341	99.43 316	93.94 301	99.21 371	98.58 196	94.28 406	98.71 329

test_0402		96.64 374	96.24 376	97.85 373	98.85 384	96.43 370	99.44 238	99.26 335	93.52 427	96.98 420	99.52 288	88.52 408	99.20 373	92.58 434	97.50 323	97.93 436

icg_test_0407_2		98.79 188	98.86 158	98.57 297	99.55 198	96.93 344	99.07 366	99.44 243	98.05 196	99.66 117	99.80 139	97.13 135	99.18 374	98.15 244	98.92 230	99.60 181

SixPastTwentyTwo		97.50 341	97.33 337	98.03 355	98.65 411	96.23 378	99.77 34	98.68 422	97.14 305	97.90 395	99.93 10	90.45 381	99.18 374	97.00 343	96.43 355	98.67 351

cl22		97.85 288	97.64 292	98.48 310	99.09 343	97.87 289	98.60 433	99.33 306	97.11 311	98.87 312	99.22 370	92.38 348	99.17 376	98.21 236	95.99 367	98.42 402

tt0320		95.71 394	95.07 398	97.62 388	99.05 352	95.02 408	99.25 324	99.52 125	86.81 454	97.97 392	99.72 196	83.58 443	99.15 377	96.38 373	93.35 419	98.68 343

WB-MVSnew		97.65 329	97.65 289	97.63 387	98.78 393	97.62 303	99.13 353	98.33 433	97.36 287	99.07 275	98.94 402	95.64 215	99.15 377	92.95 428	98.68 250	96.12 457

IterMVS-SCA-FT		97.82 298	97.75 279	98.06 354	99.57 190	96.36 372	99.02 380	99.49 178	97.18 302	98.71 332	99.72 196	92.72 332	99.14 379	97.44 317	95.86 372	98.67 351

pmmvs5		97.52 338	97.30 340	98.16 347	98.57 420	96.73 356	99.27 313	98.90 391	96.14 386	98.37 369	99.53 284	91.54 368	99.14 379	97.51 309	95.87 371	98.63 371

v148		97.79 304	97.55 298	98.50 307	98.74 401	97.72 297	99.54 161	99.33 306	96.26 375	98.90 306	99.51 292	94.68 265	99.14 379	97.83 274	93.15 425	98.63 371

IMVS_0404		98.53 209	98.52 207	98.55 303	99.55 198	96.93 344	99.20 341	99.44 243	98.05 196	98.96 297	99.80 139	94.66 268	99.13 382	98.15 244	98.92 230	99.60 181

miper_ehance_all_eth		98.18 239	98.10 235	98.41 324	99.23 306	97.72 297	98.72 422	99.31 320	96.60 352	98.88 309	99.29 359	97.29 129	99.13 382	97.60 298	95.99 367	98.38 407

NR-MVSNet		97.97 272	97.61 295	99.02 226	98.87 380	99.26 139	99.47 224	99.42 256	97.63 253	97.08 418	99.50 295	95.07 239	99.13 382	97.86 270	93.59 417	98.68 343

IterMVS		97.83 295	97.77 274	98.02 357	99.58 185	96.27 376	99.02 380	99.48 190	97.22 300	98.71 332	99.70 203	92.75 329	99.13 382	97.46 315	96.00 366	98.67 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 410	94.90 401	91.84 436	97.24 445	80.01 466	98.52 437	99.48 190	89.01 450	91.99 453	99.67 227	85.67 430	99.13 382	95.44 393	97.03 346	96.39 454
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 257	97.96 252	98.33 331	99.26 298	97.38 311	98.56 436	99.31 320	96.65 344	98.88 309	99.52 288	96.58 169	99.12 387	97.39 320	95.53 383	98.47 396

pmmvs4		98.13 244	97.90 259	98.81 271	98.61 416	98.87 203	98.99 388	99.21 346	96.44 364	99.06 280	99.58 264	95.90 201	99.11 388	97.18 335	96.11 363	98.46 399

TransMVSNet (Re)		97.15 362	96.58 368	98.86 262	99.12 335	98.85 207	99.49 208	98.91 389	95.48 400	97.16 416	99.80 139	93.38 314	99.11 388	94.16 415	91.73 435	98.62 373

ambc				93.06 434	92.68 465	82.36 459	98.47 439	98.73 419		95.09 439	97.41 448	55.55 466	99.10 390	96.42 370	91.32 436	97.71 439

Baseline_NR-MVSNet		97.76 306	97.45 314	98.68 287	99.09 343	98.29 262	99.41 255	98.85 398	95.65 398	98.63 350	99.67 227	94.82 251	99.10 390	98.07 256	92.89 427	98.64 364

test_vis3_rt		87.04 426	85.81 429	90.73 440	93.99 464	81.96 461	99.76 37	90.23 475	92.81 436	81.35 463	91.56 463	40.06 472	99.07 392	94.27 412	88.23 450	91.15 463

CP-MVSNet		98.09 248	97.78 272	99.01 227	98.97 367	99.24 142	99.67 71	99.46 223	97.25 296	98.48 364	99.64 240	93.79 308	99.06 393	98.63 186	94.10 410	98.74 325

PS-CasMVS		97.93 275	97.59 297	98.95 236	98.99 362	99.06 166	99.68 68	99.52 125	97.13 306	98.31 372	99.68 221	92.44 347	99.05 394	98.51 207	94.08 411	98.75 321

K. test v3		97.10 364	96.79 364	98.01 358	98.72 404	96.33 373	99.87 8	97.05 452	97.59 257	96.16 430	99.80 139	88.71 402	99.04 395	96.69 361	96.55 353	98.65 362

new_pmnet		96.38 380	96.03 382	97.41 397	98.13 431	95.16 407	99.05 372	99.20 347	93.94 422	97.39 409	98.79 414	91.61 367	99.04 395	90.43 441	95.77 373	98.05 426

DIV-MVS_self_test		98.01 265	97.85 266	98.48 310	99.24 304	97.95 285	98.71 423	99.35 293	96.50 357	98.60 356	99.54 280	95.72 212	99.03 397	97.21 329	95.77 373	98.46 399

IterMVS-LS		98.46 213	98.42 212	98.58 296	99.59 183	98.00 278	99.37 274	99.43 254	96.94 328	99.07 275	99.59 260	97.87 111	99.03 397	98.32 229	95.62 379	98.71 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 329	97.68 286	97.55 393	98.62 414	94.97 410	98.84 410	99.30 325	96.83 335	98.19 381	99.34 346	97.01 146	99.02 399	95.00 403	96.01 365	98.64 364

Patchmtry		97.75 310	97.40 326	98.81 271	99.10 340	98.87 203	99.11 362	99.33 306	94.83 413	98.81 321	99.38 333	94.33 286	99.02 399	96.10 376	95.57 381	98.53 390

N_pmnet		94.95 404	95.83 387	92.31 435	98.47 424	79.33 467	99.12 356	92.81 473	93.87 423	97.68 402	99.13 380	93.87 305	99.01 401	91.38 438	96.19 361	98.59 386

CR-MVSNet		98.17 240	97.93 257	98.87 259	99.18 319	98.49 251	99.22 336	99.33 306	96.96 324	99.56 154	99.38 333	94.33 286	99.00 402	94.83 406	98.58 255	99.14 278

c3_l		98.12 246	98.04 244	98.38 328	99.30 286	97.69 301	98.81 413	99.33 306	96.67 342	98.83 318	99.34 346	97.11 138	98.99 403	97.58 300	95.34 386	98.48 394

test0.0.03 1		97.71 319	97.42 324	98.56 301	98.41 427	97.82 292	98.78 416	98.63 425	97.34 288	98.05 389	98.98 398	94.45 281	98.98 404	95.04 402	97.15 344	98.89 306

PatchT		97.03 366	96.44 372	98.79 274	98.99 362	98.34 261	99.16 347	99.07 365	92.13 439	99.52 165	97.31 452	94.54 276	98.98 404	88.54 448	98.73 247	99.03 294

GBi-Net		97.68 324	97.48 308	98.29 336	99.51 215	97.26 317	99.43 243	99.48 190	96.49 358	99.07 275	99.32 354	90.26 383	98.98 404	97.10 337	96.65 349	98.62 373

test1		97.68 324	97.48 308	98.29 336	99.51 215	97.26 317	99.43 243	99.48 190	96.49 358	99.07 275	99.32 354	90.26 383	98.98 404	97.10 337	96.65 349	98.62 373

FMVSNet3		98.03 260	97.76 278	98.84 266	99.39 262	98.98 175	99.40 263	99.38 277	96.67 342	99.07 275	99.28 361	92.93 324	98.98 404	97.10 337	96.65 349	98.56 389

FMVSNet2		97.72 316	97.36 329	98.80 273	99.51 215	98.84 209	99.45 231	99.42 256	96.49 358	98.86 316	99.29 359	90.26 383	98.98 404	96.44 369	96.56 352	98.58 387

FMVSNet1		96.84 370	96.36 374	98.29 336	99.32 284	97.26 317	99.43 243	99.48 190	95.11 405	98.55 359	99.32 354	83.95 441	98.98 404	95.81 383	96.26 360	98.62 373

ppachtmachnet_test		97.49 346	97.45 314	97.61 391	98.62 414	95.24 403	98.80 414	99.46 223	96.11 388	98.22 379	99.62 251	96.45 176	98.97 411	93.77 417	95.97 370	98.61 382

TranMVSNet+NR-MVSNet		97.93 275	97.66 288	98.76 278	98.78 393	98.62 234	99.65 84	99.49 178	97.76 238	98.49 363	99.60 258	94.23 289	98.97 411	98.00 260	92.90 426	98.70 334

MVStest1		96.08 387	95.48 392	97.89 370	98.93 370	96.70 357	99.56 141	99.35 293	92.69 437	91.81 454	99.46 311	89.90 389	98.96 413	95.00 403	92.61 431	98.00 431

tt0320-xc		95.31 400	94.59 404	97.45 396	98.92 372	94.73 414	99.20 341	99.31 320	86.74 455	97.23 412	99.72 196	81.14 454	98.95 414	97.08 340	91.98 434	98.67 351

test_method		91.10 421	91.36 423	90.31 441	95.85 453	73.72 474	94.89 462	99.25 337	68.39 465	95.82 433	99.02 392	80.50 455	98.95 414	93.64 420	94.89 398	98.25 414

ADS-MVSNet2		98.02 262	98.07 242	97.87 371	99.33 277	95.19 405	99.23 332	99.08 362	96.24 376	99.10 269	99.67 227	94.11 294	98.93 416	96.81 355	99.05 219	99.48 229

ET-MVSNet_ETH3D		96.49 377	95.64 391	99.05 223	99.53 206	98.82 215	98.84 410	97.51 450	97.63 253	84.77 459	99.21 373	92.09 352	98.91 417	98.98 129	92.21 433	99.41 250

miper_lstm_enhance		98.00 267	97.91 258	98.28 340	99.34 276	97.43 309	98.88 406	99.36 286	96.48 361	98.80 323	99.55 275	95.98 194	98.91 417	97.27 326	95.50 384	98.51 392

MonoMVSNet		98.38 222	98.47 210	98.12 352	98.59 419	96.19 380	99.72 53	98.79 407	97.89 218	99.44 181	99.52 288	96.13 187	98.90 419	98.64 184	97.54 318	99.28 267

PEN-MVS		97.76 306	97.44 319	98.72 281	98.77 398	98.54 241	99.78 32	99.51 144	97.06 316	98.29 375	99.64 240	92.63 338	98.89 420	98.09 249	93.16 424	98.72 327

testing3		97.28 356	96.76 365	98.82 268	99.37 267	98.07 275	99.45 231	99.36 286	97.56 262	97.89 396	98.95 401	83.70 442	98.82 421	96.03 378	98.56 258	99.58 196

testgi		97.65 329	97.50 306	98.13 351	99.36 270	96.45 369	99.42 250	99.48 190	97.76 238	97.87 397	99.45 313	91.09 375	98.81 422	94.53 408	98.52 261	99.13 280

testf1		90.42 424	90.68 425	89.65 444	97.78 435	73.97 472	99.13 353	98.81 403	89.62 447	91.80 455	98.93 403	62.23 464	98.80 423	86.61 458	91.17 437	96.19 455

APD_test2		90.42 424	90.68 425	89.65 444	97.78 435	73.97 472	99.13 353	98.81 403	89.62 447	91.80 455	98.93 403	62.23 464	98.80 423	86.61 458	91.17 437	96.19 455

MIMVSNet		97.73 314	97.45 314	98.57 297	99.45 245	97.50 307	99.02 380	98.98 376	96.11 388	99.41 192	99.14 379	90.28 382	98.74 425	95.74 385	98.93 228	99.47 235

LCM-MVSNet-Re		97.83 295	98.15 229	96.87 413	99.30 286	92.25 444	99.59 116	98.26 434	97.43 280	96.20 429	99.13 380	96.27 183	98.73 426	98.17 241	98.99 225	99.64 168

Syy-MVS		97.09 365	97.14 351	96.95 410	99.00 359	92.73 442	99.29 303	99.39 269	97.06 316	97.41 406	98.15 437	93.92 303	98.68 427	91.71 436	98.34 268	99.45 243

myMVS_eth3d		96.89 368	96.37 373	98.43 323	99.00 359	97.16 321	99.29 303	99.39 269	97.06 316	97.41 406	98.15 437	83.46 444	98.68 427	95.27 398	98.34 268	99.45 243

DTE-MVSNet		97.51 340	97.19 349	98.46 316	98.63 413	98.13 271	99.84 12	99.48 190	96.68 341	97.97 392	99.67 227	92.92 325	98.56 429	96.88 354	92.60 432	98.70 334

PC_three_1452									98.18 168	99.84 52	99.70 203	99.31 3	98.52 430	98.30 231	99.80 120	99.81 75

mvsany_test3		93.77 413	93.45 416	94.74 426	95.78 454	88.01 452	99.64 91	98.25 435	98.28 145	94.31 442	97.97 444	68.89 460	98.51 431	97.50 310	90.37 442	97.71 439

UnsupCasMVSNet_bld		93.53 414	92.51 420	96.58 418	97.38 441	93.82 429	98.24 449	99.48 190	91.10 444	93.10 448	96.66 454	74.89 458	98.37 432	94.03 416	87.71 451	97.56 444

Anonymous20240521		96.20 383	95.89 386	97.13 404	97.72 438	94.96 411	99.79 31	99.29 329	93.01 433	97.20 415	99.03 390	89.69 392	98.36 433	91.16 439	96.13 362	98.07 424

test_f		91.90 420	91.26 424	93.84 429	95.52 458	85.92 454	99.69 62	98.53 430	95.31 402	93.87 444	96.37 456	55.33 467	98.27 434	95.70 386	90.98 440	97.32 448

MDA-MVSNet_test_wron		95.45 396	94.60 403	98.01 358	98.16 430	97.21 320	99.11 362	99.24 340	93.49 428	80.73 465	98.98 398	93.02 322	98.18 435	94.22 414	94.45 403	98.64 364

UnsupCasMVSNet_eth		96.44 378	96.12 379	97.40 398	98.65 411	95.65 389	99.36 279	99.51 144	97.13 306	96.04 432	98.99 396	88.40 409	98.17 436	96.71 359	90.27 443	98.40 405

KD-MVS_2432*1600		94.62 405	93.72 413	97.31 399	97.19 447	95.82 386	98.34 444	99.20 347	95.00 409	97.57 403	98.35 430	87.95 414	98.10 437	92.87 430	77.00 463	98.01 428

miper_refine_blended		94.62 405	93.72 413	97.31 399	97.19 447	95.82 386	98.34 444	99.20 347	95.00 409	97.57 403	98.35 430	87.95 414	98.10 437	92.87 430	77.00 463	98.01 428

YYNet1		95.36 398	94.51 406	97.92 367	97.89 433	97.10 324	99.10 364	99.23 341	93.26 431	80.77 464	99.04 389	92.81 328	98.02 439	94.30 410	94.18 408	98.64 364

EU-MVSNet		97.98 269	98.03 245	97.81 379	98.72 404	96.65 362	99.66 78	99.66 28	98.09 185	98.35 370	99.82 106	95.25 232	98.01 440	97.41 319	95.30 387	98.78 313

Gipuma		90.99 422	90.15 427	93.51 430	98.73 402	90.12 450	93.98 463	99.45 234	79.32 461	92.28 451	94.91 458	69.61 459	97.98 441	87.42 454	95.67 377	92.45 461
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 399	94.73 402	97.15 402	95.53 457	95.94 384	99.35 284	99.10 359	95.13 403	93.55 446	97.54 447	88.15 413	97.91 442	94.58 407	89.69 447	97.61 442

PM-MVS		92.96 417	92.23 421	95.14 425	95.61 455	89.98 451	99.37 274	98.21 438	94.80 414	95.04 440	97.69 445	65.06 461	97.90 443	94.30 410	89.98 445	97.54 445

MDA-MVSNet-bldmvs		94.96 403	93.98 410	97.92 367	98.24 429	97.27 315	99.15 350	99.33 306	93.80 424	80.09 466	99.03 390	88.31 410	97.86 444	93.49 422	94.36 405	98.62 373

Patchmatch-RL test		95.84 390	95.81 388	95.95 423	95.61 455	90.57 449	98.24 449	98.39 431	95.10 407	95.20 437	98.67 418	94.78 255	97.77 445	96.28 375	90.02 444	99.51 221

Anonymous20231206		96.22 381	96.03 382	96.79 415	97.31 444	94.14 427	99.63 97	99.08 362	96.17 382	97.04 419	99.06 387	93.94 301	97.76 446	86.96 456	95.06 392	98.47 396

SD-MVS		99.41 56	99.52 12	99.05 223	99.74 95	99.68 59	99.46 228	99.52 125	99.11 42	99.88 39	99.91 25	99.43 1	97.70 447	98.72 173	99.93 32	99.77 96
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		97.25 358	97.35 331	96.95 410	97.84 434	93.61 436	99.57 134	96.63 458	96.13 387	98.87 312	98.61 421	94.59 271	97.70 447	95.08 401	98.86 238	99.55 203

dongtai		93.26 415	92.93 419	94.25 427	99.39 262	85.68 455	97.68 458	93.27 469	92.87 435	96.85 423	99.39 330	82.33 449	97.48 449	76.78 463	97.80 303	99.58 196

pmmvs3		94.09 411	93.25 418	96.60 417	94.76 462	94.49 421	98.92 402	98.18 440	89.66 446	96.48 426	98.06 443	86.28 427	97.33 450	89.68 444	87.20 452	97.97 434

KD-MVS_self_test		95.00 402	94.34 407	96.96 409	97.07 449	95.39 400	99.56 141	99.44 243	95.11 405	97.13 417	97.32 451	91.86 357	97.27 451	90.35 442	81.23 460	98.23 416

FMVSNet5		96.43 379	96.19 378	97.15 402	99.11 337	95.89 385	99.32 292	99.52 125	94.47 420	98.34 371	99.07 385	87.54 419	97.07 452	92.61 433	95.72 376	98.47 396

new-patchmatchnet		94.48 408	94.08 409	95.67 424	95.08 460	92.41 443	99.18 345	99.28 331	94.55 419	93.49 447	97.37 450	87.86 417	97.01 453	91.57 437	88.36 449	97.61 442

LCM-MVSNet		86.80 428	85.22 432	91.53 438	87.81 470	80.96 464	98.23 451	98.99 375	71.05 463	90.13 458	96.51 455	48.45 471	96.88 454	90.51 440	85.30 454	96.76 450

CL-MVSNet_self_test		94.49 407	93.97 411	96.08 422	96.16 452	93.67 434	98.33 446	99.38 277	95.13 403	97.33 410	98.15 437	92.69 336	96.57 455	88.67 447	79.87 461	97.99 432

MIMVSNet1		95.51 395	95.04 400	96.92 412	97.38 441	95.60 390	99.52 172	99.50 166	93.65 426	96.97 421	99.17 375	85.28 435	96.56 456	88.36 449	95.55 382	98.60 385

FE-MVSNET		94.07 412	93.36 417	96.22 421	94.05 463	94.71 416	99.56 141	98.36 432	93.15 432	93.76 445	97.55 446	86.47 426	96.49 457	87.48 453	89.83 446	97.48 446

test20.03		96.12 385	95.96 384	96.63 416	97.44 440	95.45 397	99.51 181	99.38 277	96.55 355	96.16 430	99.25 367	93.76 310	96.17 458	87.35 455	94.22 407	98.27 412

tmp_tt		82.80 430	81.52 433	86.66 446	66.61 476	68.44 475	92.79 465	97.92 442	68.96 464	80.04 467	99.85 77	85.77 429	96.15 459	97.86 270	43.89 469	95.39 459

test_fmvs3		92.10 419	91.77 422	93.08 433	96.19 451	86.25 453	99.82 16	98.62 426	96.65 344	95.19 438	96.90 453	55.05 468	95.93 460	96.63 366	90.92 441	97.06 449

kuosan		90.92 423	90.11 428	93.34 431	98.78 393	85.59 456	98.15 453	93.16 471	89.37 449	92.07 452	98.38 429	81.48 452	95.19 461	62.54 470	97.04 345	99.25 272

dmvs_testset		95.02 401	96.12 379	91.72 437	99.10 340	80.43 465	99.58 126	97.87 444	97.47 272	95.22 436	98.82 410	93.99 299	95.18 462	88.09 450	94.91 397	99.56 202

PMMVS2		86.87 427	85.37 431	91.35 439	90.21 468	83.80 458	98.89 405	97.45 451	83.13 460	91.67 457	95.03 457	48.49 470	94.70 463	85.86 460	77.62 462	95.54 458

PMVS	70.75 22	75.98 436	74.97 437	79.01 452	70.98 475	55.18 477	93.37 464	98.21 438	65.08 469	61.78 470	93.83 460	21.74 477	92.53 464	78.59 462	91.12 439	89.34 465
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 429	85.65 430	82.75 450	86.77 471	63.39 476	98.35 443	98.92 384	74.11 462	83.39 461	98.98 398	50.85 469	92.40 465	84.54 461	94.97 394	92.46 460

WB-MVS		93.10 416	94.10 408	90.12 442	95.51 459	81.88 462	99.73 51	99.27 334	95.05 408	93.09 449	98.91 407	94.70 264	91.89 466	76.62 464	94.02 413	96.58 452

SSC-MVS		92.73 418	93.73 412	89.72 443	95.02 461	81.38 463	99.76 37	99.23 341	94.87 412	92.80 450	98.93 403	94.71 263	91.37 467	74.49 466	93.80 415	96.42 453

MVE	76.82 21	76.91 435	74.31 439	84.70 447	85.38 473	76.05 471	96.88 461	93.17 470	67.39 466	71.28 468	89.01 467	21.66 478	87.69 468	71.74 467	72.29 465	90.35 464
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 432	79.88 434	82.81 449	90.75 467	76.38 470	97.69 457	95.76 462	66.44 467	83.52 460	92.25 462	62.54 463	87.16 469	68.53 468	61.40 466	84.89 467

EMVS		80.02 433	79.22 435	82.43 451	91.19 466	76.40 469	97.55 460	92.49 474	66.36 468	83.01 462	91.27 464	64.63 462	85.79 470	65.82 469	60.65 467	85.08 466

ANet_high		77.30 434	74.86 438	84.62 448	75.88 474	77.61 468	97.63 459	93.15 472	88.81 451	64.27 469	89.29 466	36.51 473	83.93 471	75.89 465	52.31 468	92.33 462

wuyk23d		40.18 437	41.29 442	36.84 453	86.18 472	49.12 478	79.73 466	22.81 478	27.64 470	25.46 473	28.45 473	21.98 476	48.89 472	55.80 471	23.56 472	12.51 470

test123		39.01 439	42.50 441	28.53 454	39.17 477	20.91 479	98.75 419	19.17 479	19.83 472	38.57 471	66.67 469	33.16 474	15.42 473	37.50 473	29.66 471	49.26 468

testmvs		39.17 438	43.78 440	25.37 455	36.04 478	16.84 480	98.36 442	26.56 477	20.06 471	38.51 472	67.32 468	29.64 475	15.30 474	37.59 472	39.90 470	43.98 469

mmdepth		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

monomultidepth		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

test_blank		0.13 443	0.17 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	1.57 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uanet_test		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

DCPMVS		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

cdsmvs_eth3d_5k		24.64 440	32.85 443	0.00 456	0.00 479	0.00 481	0.00 467	99.51 144	0.00 474	0.00 475	99.56 272	96.58 169	0.00 475	0.00 474	0.00 473	0.00 471

pcd_1.5k_mvsjas		8.27 442	11.03 445	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	99.01 18	0.00 475	0.00 474	0.00 473	0.00 471

sosnet-low-res		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

sosnet		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uncertanet		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

Regformer		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

ab-mvs-re		8.30 441	11.06 444	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	99.58 264	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uanet		0.02 444	0.03 447	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.27 475	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

WAC-MVS						97.16 321								95.47 392

FOURS1					99.91 1	99.93 1	99.87 8	99.56 86	99.10 43	99.81 64

test_one_0601					99.81 53	99.88 9		99.49 178	98.97 70	99.65 126	99.81 121	99.09 14

eth-test2					0.00 479

eth-test					0.00 479

RE-MVS-def			99.34 47		99.76 77	99.82 27	99.63 97	99.52 125	98.38 132	99.76 86	99.82 106	98.75 58		98.61 190	99.81 115	99.77 96

IU-MVS					99.84 35	99.88 9		99.32 316	98.30 144	99.84 52				98.86 153	99.85 89	99.89 28

save fliter					99.76 77	99.59 83	99.14 352	99.40 266	99.00 62

test0726					99.85 28	99.89 5	99.62 102	99.50 166	99.10 43	99.86 49	99.82 106	98.94 32

GSMVS																99.52 212

test_part2					99.81 53	99.83 21				99.77 80

sam_mvs1												94.86 250				99.52 212

sam_mvs												94.72 262

MTGPA								99.47 212

MTMP							99.54 161	98.88 394

test9_res														97.49 311	99.72 143	99.75 105

agg_prior2														97.21 329	99.73 142	99.75 105

test_prior4						99.56 89	98.99 388

test_prior2							98.96 395		98.34 138	99.01 286	99.52 288	98.68 67		97.96 262	99.74 140

新几何2							99.01 385

旧先验1					99.74 95	99.59 83		99.54 104			99.69 214	98.47 83			99.68 151	99.73 118

原ACMM2							98.95 398

test222					99.75 87	99.49 104	98.91 404	99.49 178	96.42 366	99.34 215	99.65 234	98.28 97			99.69 148	99.72 127

segment_acmp												98.96 25

testdata1							98.85 409		98.32 142

plane_prior7					99.29 290	97.03 336

plane_prior6					99.27 295	96.98 340						92.71 334

plane_prior4											99.61 255

plane_prior3						97.00 338			98.69 102	99.11 266

plane_prior2							99.39 267		98.97 70

plane_prior1					99.26 298

plane_prior						96.97 341	99.21 338		98.45 125						97.60 312

n2								0.00 480

nn								0.00 480

door-mid								98.05 441

test11								99.35 293

door								97.92 442

HQP5-MVS						96.83 352

HQP-NCC					99.19 316		98.98 391		98.24 157	98.66 341

ACMP_Plane					99.19 316		98.98 391		98.24 157	98.66 341

BP-MVS														97.19 333

HQP3-MVS								99.39 269							97.58 314

HQP2-MVS												92.47 343

NP-MVS					99.23 306	96.92 348					99.40 326

MDTV_nov1_ep13_2view						95.18 406	99.35 284		96.84 333	99.58 150		95.19 235		97.82 275		99.46 240

ACMMP++_ref															97.19 342

ACMMP++															97.43 332

Test By Simon												98.75 58

High-resolution multi-view benchmark