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 67	99.38 244	99.37 116	99.58 125	99.62 46	99.41 18	99.87 43	99.92 17	98.81 47	100.00 1	99.97 1	99.93 30	99.94 15

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 64	99.84 34	99.44 109	99.58 125	99.69 18	99.43 14	99.98 11	99.91 24	98.62 73	100.00 1	99.97 1	99.95 20	99.90 23

test_vis1_n_1920		98.63 188	98.40 195	99.31 172	99.86 22	97.94 274	99.67 70	99.62 46	99.43 14	99.99 2	99.91 24	87.29 400	100.00 1	99.92 21	99.92 36	99.98 2

NormalMVS		99.27 83	99.19 83	99.52 132	99.89 8	98.83 201	99.65 83	99.52 118	99.10 41	99.84 50	99.76 157	95.80 191	99.99 4	99.30 81	99.84 95	99.74 104

SymmetryMVS		99.15 104	99.02 111	99.52 132	99.72 104	98.83 201	99.65 83	99.34 278	99.10 41	99.84 50	99.76 157	95.80 191	99.99 4	99.30 81	98.72 230	99.73 113

fmvsm_s_conf0.5_n_5		99.37 63	99.21 79	99.86 29	99.80 57	99.68 57	99.42 235	99.61 55	99.37 21	99.97 22	99.86 64	94.96 225	99.99 4	99.97 1	99.93 30	99.92 21

fmvsm_l_conf0.5_n_3		99.61 8	99.51 16	99.92 1	99.84 34	99.82 26	99.54 158	99.66 28	99.46 7	99.98 11	99.89 37	97.27 130	99.99 4	99.97 1	99.95 20	99.95 11

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 37	99.86 22	99.61 78	99.56 139	99.63 42	99.48 3	99.98 11	99.83 88	98.75 58	99.99 4	99.97 1	99.96 15	99.94 15

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 37	99.84 34	99.63 75	99.56 139	99.63 42	99.47 4	99.98 11	99.82 97	98.75 58	99.99 4	99.97 1	99.97 8	99.94 15

test_fmvsmconf_n		99.70 3	99.64 4	99.87 18	99.80 57	99.66 64	99.48 202	99.64 38	99.45 11	99.92 27	99.92 17	98.62 73	99.99 4	99.96 11	99.99 1	99.96 7

patch_mono-2		99.26 86	99.62 5	98.16 328	99.81 51	94.59 400	99.52 168	99.64 38	99.33 23	99.73 89	99.90 31	99.00 22	99.99 4	99.69 32	99.98 4	99.89 26

h-mvs33		97.70 301	97.28 323	98.97 220	99.70 115	97.27 302	99.36 264	99.45 219	98.94 71	99.66 111	99.64 221	94.93 228	99.99 4	99.48 59	84.36 434	99.65 149

xiu_mvs_v1_base_debu		99.29 79	99.27 69	99.34 165	99.63 150	98.97 175	99.12 336	99.51 136	98.86 77	99.84 50	99.47 288	98.18 101	99.99 4	99.50 54	99.31 184	99.08 266

xiu_mvs_v1_base		99.29 79	99.27 69	99.34 165	99.63 150	98.97 175	99.12 336	99.51 136	98.86 77	99.84 50	99.47 288	98.18 101	99.99 4	99.50 54	99.31 184	99.08 266

xiu_mvs_v1_base_debi		99.29 79	99.27 69	99.34 165	99.63 150	98.97 175	99.12 336	99.51 136	98.86 77	99.84 50	99.47 288	98.18 101	99.99 4	99.50 54	99.31 184	99.08 266

EPNet		98.86 158	98.71 160	99.30 177	97.20 426	98.18 254	99.62 101	98.91 369	99.28 26	98.63 331	99.81 111	95.96 180	99.99 4	99.24 90	99.72 141	99.73 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_8		99.54 20	99.42 28	99.89 8	99.83 43	99.74 48	99.51 177	99.62 46	99.46 7	99.99 2	99.90 31	96.60 155	99.98 17	99.95 13	99.95 20	99.96 7

MM		99.40 59	99.28 65	99.74 73	99.67 127	99.31 128	99.52 168	98.87 376	99.55 1	99.74 87	99.80 125	96.47 162	99.98 17	99.97 1	99.97 8	99.94 15

test_cas_vis1_n_1920		99.16 101	99.01 116	99.61 102	99.81 51	98.86 196	99.65 83	99.64 38	99.39 19	99.97 22	99.94 6	93.20 301	99.98 17	99.55 47	99.91 43	99.99 1

test_vis1_n		97.92 259	97.44 299	99.34 165	99.53 186	98.08 261	99.74 47	99.49 166	99.15 31	100.00 1	99.94 6	79.51 435	99.98 17	99.88 23	99.76 133	99.97 4

xiu_mvs_v2_base		99.26 86	99.25 73	99.29 180	99.53 186	98.91 190	99.02 359	99.45 219	98.80 87	99.71 96	99.26 346	98.94 32	99.98 17	99.34 74	99.23 189	98.98 280

PS-MVSNAJ		99.32 74	99.32 50	99.30 177	99.57 174	98.94 185	98.97 373	99.46 208	98.92 74	99.71 96	99.24 348	99.01 18	99.98 17	99.35 69	99.66 152	98.97 281

QAPM		98.67 183	98.30 202	99.80 58	99.20 293	99.67 61	99.77 34	99.72 11	94.74 395	98.73 311	99.90 31	95.78 193	99.98 17	96.96 328	99.88 69	99.76 99

3Dnovator	97.25 9	99.24 91	99.05 101	99.81 54	99.12 315	99.66 64	99.84 12	99.74 10	99.09 48	98.92 284	99.90 31	95.94 183	99.98 17	98.95 120	99.92 36	99.79 86

OpenMVS	96.50 16	98.47 193	98.12 214	99.52 132	99.04 334	99.53 94	99.82 16	99.72 11	94.56 398	98.08 365	99.88 46	94.73 244	99.98 17	97.47 295	99.76 133	99.06 272

fmvsm_s_conf0.5_n_3		99.37 63	99.20 81	99.87 18	99.75 85	99.70 54	99.48 202	99.66 28	99.45 11	99.99 2	99.93 10	94.64 252	99.97 26	99.94 18	99.97 8	99.95 11

reproduce_model		99.63 7	99.54 11	99.90 5	99.78 63	99.88 9	99.56 139	99.55 91	99.15 31	99.90 31	99.90 31	99.00 22	99.97 26	99.11 101	99.91 43	99.86 39

test_fmvsmconf0.1_n		99.55 19	99.45 26	99.86 29	99.44 226	99.65 68	99.50 185	99.61 55	99.45 11	99.87 43	99.92 17	97.31 127	99.97 26	99.95 13	99.99 1	99.97 4

test_fmvs1_n		98.41 199	98.14 211	99.21 193	99.82 47	97.71 287	99.74 47	99.49 166	99.32 24	99.99 2	99.95 3	85.32 413	99.97 26	99.82 26	99.84 95	99.96 7

CANet_DTU		98.97 146	98.87 141	99.25 187	99.33 257	98.42 246	99.08 345	99.30 305	99.16 30	99.43 171	99.75 162	95.27 213	99.97 26	98.56 187	99.95 20	99.36 238

MVS_0304		99.15 104	98.96 126	99.73 76	98.92 352	99.37 116	99.37 259	96.92 432	99.51 2	99.66 111	99.78 144	96.69 152	99.97 26	99.84 25	99.97 8	99.84 50

MTAPA		99.52 24	99.39 36	99.89 8	99.90 4	99.86 17	99.66 77	99.47 199	98.79 88	99.68 102	99.81 111	98.43 86	99.97 26	98.88 130	99.90 54	99.83 60

PGM-MVS		99.45 42	99.31 56	99.86 29	99.87 17	99.78 41	99.58 125	99.65 35	97.84 208	99.71 96	99.80 125	99.12 13	99.97 26	98.33 212	99.87 72	99.83 60

mPP-MVS		99.44 46	99.30 58	99.86 29	99.88 13	99.79 35	99.69 61	99.48 178	98.12 169	99.50 156	99.75 162	98.78 51	99.97 26	98.57 184	99.89 65	99.83 60

CP-MVS		99.45 42	99.32 50	99.85 37	99.83 43	99.75 45	99.69 61	99.52 118	98.07 179	99.53 151	99.63 227	98.93 36	99.97 26	98.74 155	99.91 43	99.83 60

SteuartSystems-ACMMP		99.54 20	99.42 28	99.87 18	99.82 47	99.81 30	99.59 115	99.51 136	98.62 105	99.79 68	99.83 88	99.28 4	99.97 26	98.48 194	99.90 54	99.84 50
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3Dnovator+	97.12 13	99.18 97	98.97 122	99.82 51	99.17 307	99.68 57	99.81 20	99.51 136	99.20 28	98.72 312	99.89 37	95.68 197	99.97 26	98.86 138	99.86 80	99.81 73

fmvsm_s_conf0.5_n_9		99.41 55	99.28 65	99.81 54	99.84 34	99.52 98	99.48 202	99.62 46	99.46 7	99.99 2	99.92 17	95.24 217	99.96 38	99.97 1	99.97 8	99.96 7

lecture		99.60 12	99.50 17	99.89 8	99.89 8	99.90 2	99.75 42	99.59 68	99.06 54	99.88 37	99.85 71	98.41 90	99.96 38	99.28 84	99.84 95	99.83 60

KinetiMVS		99.12 116	98.92 131	99.70 80	99.67 127	99.40 114	99.67 70	99.63 42	98.73 95	99.94 25	99.81 111	94.54 258	99.96 38	98.40 203	99.93 30	99.74 104

fmvsm_s_conf0.5_n_7		99.34 70	99.29 62	99.48 143	99.70 115	98.63 220	99.42 235	99.63 42	99.46 7	99.98 11	99.88 46	95.59 200	99.96 38	99.97 1	99.98 4	99.85 43

fmvsm_s_conf0.5_n_2		99.32 74	99.13 89	99.89 8	99.80 57	99.77 42	99.44 223	99.58 73	99.47 4	99.99 2	99.93 10	94.04 277	99.96 38	99.96 11	99.93 30	99.93 20

reproduce-ours		99.61 8	99.52 12	99.90 5	99.76 75	99.88 9	99.52 168	99.54 100	99.13 34	99.89 34	99.89 37	98.96 25	99.96 38	99.04 109	99.90 54	99.85 43

our_new_method		99.61 8	99.52 12	99.90 5	99.76 75	99.88 9	99.52 168	99.54 100	99.13 34	99.89 34	99.89 37	98.96 25	99.96 38	99.04 109	99.90 54	99.85 43

fmvsm_s_conf0.5_n_a		99.56 18	99.47 22	99.85 37	99.83 43	99.64 74	99.52 168	99.65 35	99.10 41	99.98 11	99.92 17	97.35 126	99.96 38	99.94 18	99.92 36	99.95 11

fmvsm_s_conf0.5_n		99.51 25	99.40 34	99.85 37	99.84 34	99.65 68	99.51 177	99.67 23	99.13 34	99.98 11	99.92 17	96.60 155	99.96 38	99.95 13	99.96 15	99.95 11

mvsany_test1		99.50 27	99.46 25	99.62 101	99.61 161	99.09 158	98.94 379	99.48 178	99.10 41	99.96 24	99.91 24	98.85 42	99.96 38	99.72 29	99.58 162	99.82 66

test_fmvs1		98.88 152	98.79 153	99.16 198	99.69 120	97.61 291	99.55 153	99.49 166	99.32 24	99.98 11	99.91 24	91.41 349	99.96 38	99.82 26	99.92 36	99.90 23

DVP-MVS++		99.59 13	99.50 17	99.88 12	99.51 195	99.88 9	99.87 8	99.51 136	98.99 62	99.88 37	99.81 111	99.27 5	99.96 38	98.85 140	99.80 118	99.81 73

MSC_two_6792asdad				99.87 18	99.51 195	99.76 43		99.33 286					99.96 38	98.87 133	99.84 95	99.89 26

No_MVS				99.87 18	99.51 195	99.76 43		99.33 286					99.96 38	98.87 133	99.84 95	99.89 26

ZD-MVS					99.71 110	99.79 35		99.61 55	96.84 313	99.56 144	99.54 261	98.58 75	99.96 38	96.93 331	99.75 135

SED-MVS		99.61 8	99.52 12	99.88 12	99.84 34	99.90 2	99.60 108	99.48 178	99.08 49	99.91 28	99.81 111	99.20 7	99.96 38	98.91 127	99.85 87	99.79 86

test_241102_TWO								99.48 178	99.08 49	99.88 37	99.81 111	98.94 32	99.96 38	98.91 127	99.84 95	99.88 32

ZNCC-MVS		99.47 36	99.33 48	99.87 18	99.87 17	99.81 30	99.64 90	99.67 23	98.08 178	99.55 148	99.64 221	98.91 37	99.96 38	98.72 158	99.90 54	99.82 66

DVP-MVS		99.57 17	99.47 22	99.88 12	99.85 28	99.89 5	99.57 132	99.37 265	99.10 41	99.81 62	99.80 125	98.94 32	99.96 38	98.93 124	99.86 80	99.81 73
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 62	99.81 62	99.80 125	99.09 14	99.96 38	98.85 140	99.90 54	99.88 32

test_0728_SECOND				99.91 3	99.84 34	99.89 5	99.57 132	99.51 136					99.96 38	98.93 124	99.86 80	99.88 32

SR-MVS		99.43 49	99.29 62	99.86 29	99.75 85	99.83 20	99.59 115	99.62 46	98.21 155	99.73 89	99.79 137	98.68 67	99.96 38	98.44 200	99.77 130	99.79 86

DPE-MVS		99.46 38	99.32 50	99.91 3	99.78 63	99.88 9	99.36 264	99.51 136	98.73 95	99.88 37	99.84 83	98.72 64	99.96 38	98.16 227	99.87 72	99.88 32
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 51	99.29 62	99.80 58	99.62 156	99.55 89	99.50 185	99.70 15	98.79 88	99.77 77	99.96 1	97.45 121	99.96 38	98.92 126	99.90 54	99.89 26

HFP-MVS		99.49 29	99.37 40	99.86 29	99.87 17	99.80 32	99.66 77	99.67 23	98.15 162	99.68 102	99.69 195	99.06 16	99.96 38	98.69 163	99.87 72	99.84 50

region2R		99.48 33	99.35 44	99.87 18	99.88 13	99.80 32	99.65 83	99.66 28	98.13 167	99.66 111	99.68 202	98.96 25	99.96 38	98.62 172	99.87 72	99.84 50

HPM-MVS++		99.39 61	99.23 77	99.87 18	99.75 85	99.84 19	99.43 228	99.51 136	98.68 102	99.27 214	99.53 265	98.64 72	99.96 38	98.44 200	99.80 118	99.79 86

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 71	99.89 5	99.75 42	99.56 83	99.02 55	99.88 37	99.85 71	99.18 10	99.96 38	99.22 91	99.92 36	99.90 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 29	99.36 42	99.86 29	99.87 17	99.79 35	99.66 77	99.67 23	98.15 162	99.67 106	99.69 195	98.95 30	99.96 38	98.69 163	99.87 72	99.84 50

MP-MVS		99.33 72	99.15 87	99.87 18	99.88 13	99.82 26	99.66 77	99.46 208	98.09 174	99.48 160	99.74 167	98.29 96	99.96 38	97.93 245	99.87 72	99.82 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 122	98.90 135	99.74 73	99.80 57	99.46 107	99.59 115	99.49 166	97.03 300	99.63 126	99.69 195	97.27 130	99.96 38	97.82 256	99.84 95	99.81 73

PVSNet_Blended_VisFu		99.36 67	99.28 65	99.61 102	99.86 22	99.07 163	99.47 211	99.93 2	97.66 231	99.71 96	99.86 64	97.73 116	99.96 38	99.47 61	99.82 110	99.79 86

UGNet		98.87 155	98.69 162	99.40 157	99.22 290	98.72 212	99.44 223	99.68 20	99.24 27	99.18 239	99.42 299	92.74 311	99.96 38	99.34 74	99.94 28	99.53 192
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 74	99.32 50	99.32 171	99.85 28	98.29 249	99.71 56	99.66 28	98.11 171	99.41 178	99.80 125	98.37 93	99.96 38	98.99 115	99.96 15	99.72 122

ACMMP		99.45 42	99.32 50	99.82 51	99.89 8	99.67 61	99.62 101	99.69 18	98.12 169	99.63 126	99.84 83	98.73 63	99.96 38	98.55 190	99.83 106	99.81 73
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 20	99.44 27	99.85 37	99.51 195	99.67 61	99.50 185	99.64 38	99.43 14	99.98 11	99.78 144	97.26 132	99.95 73	99.95 13	99.93 30	99.92 21

fmvsm_s_conf0.5_n_4		99.36 67	99.24 74	99.73 76	99.78 63	99.53 94	99.49 197	99.60 62	99.42 17	99.99 2	99.86 64	95.15 220	99.95 73	99.95 13	99.89 65	99.73 113

fmvsm_s_conf0.1_n_2		99.37 63	99.22 78	99.81 54	99.77 71	99.75 45	99.46 214	99.60 62	99.47 4	99.98 11	99.94 6	94.98 224	99.95 73	99.97 1	99.79 125	99.73 113

test_fmvsmconf0.01_n		99.22 94	99.03 106	99.79 61	98.42 406	99.48 104	99.55 153	99.51 136	99.39 19	99.78 73	99.93 10	94.80 236	99.95 73	99.93 20	99.95 20	99.94 15

SR-MVS-dyc-post		99.45 42	99.31 56	99.85 37	99.76 75	99.82 26	99.63 96	99.52 118	98.38 129	99.76 83	99.82 97	98.53 79	99.95 73	98.61 175	99.81 113	99.77 94

GST-MVS		99.40 59	99.24 74	99.85 37	99.86 22	99.79 35	99.60 108	99.67 23	97.97 193	99.63 126	99.68 202	98.52 80	99.95 73	98.38 205	99.86 80	99.81 73

CANet		99.25 90	99.14 88	99.59 106	99.41 234	99.16 148	99.35 269	99.57 78	98.82 82	99.51 155	99.61 236	96.46 163	99.95 73	99.59 42	99.98 4	99.65 149

MP-MVS-pluss		99.37 63	99.20 81	99.88 12	99.90 4	99.87 16	99.30 281	99.52 118	97.18 282	99.60 136	99.79 137	98.79 50	99.95 73	98.83 146	99.91 43	99.83 60
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 51	99.27 69	99.88 12	99.89 8	99.80 32	99.67 70	99.50 156	98.70 99	99.77 77	99.49 279	98.21 99	99.95 73	98.46 198	99.77 130	99.88 32
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 73	96.67 343

APD-MVS_3200maxsize		99.48 33	99.35 44	99.85 37	99.76 75	99.83 20	99.63 96	99.54 100	98.36 133	99.79 68	99.82 97	98.86 41	99.95 73	98.62 172	99.81 113	99.78 92

RPMNet		96.72 352	95.90 365	99.19 195	99.18 299	98.49 238	99.22 317	99.52 118	88.72 431	99.56 144	97.38 428	94.08 276	99.95 73	86.87 436	98.58 237	99.14 258

sss		99.17 99	99.05 101	99.53 126	99.62 156	98.97 175	99.36 264	99.62 46	97.83 209	99.67 106	99.65 215	97.37 125	99.95 73	99.19 93	99.19 192	99.68 139

MVSMamba_PlusPlus		99.46 38	99.41 33	99.64 94	99.68 125	99.50 101	99.75 42	99.50 156	98.27 143	99.87 43	99.92 17	98.09 105	99.94 86	99.65 38	99.95 20	99.47 215

fmvsm_s_conf0.1_n_a		99.26 86	99.06 100	99.85 37	99.52 192	99.62 76	99.54 158	99.62 46	98.69 100	99.99 2	99.96 1	94.47 262	99.94 86	99.88 23	99.92 36	99.98 2

fmvsm_s_conf0.1_n		99.29 79	99.10 93	99.86 29	99.70 115	99.65 68	99.53 167	99.62 46	98.74 94	99.99 2	99.95 3	94.53 260	99.94 86	99.89 22	99.96 15	99.97 4

TSAR-MVS + MP.		99.58 14	99.50 17	99.81 54	99.91 1	99.66 64	99.63 96	99.39 249	98.91 75	99.78 73	99.85 71	99.36 2	99.94 86	98.84 143	99.88 69	99.82 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 150	98.75 156	99.39 161	99.46 219	98.61 224	99.76 37	99.50 156	98.06 183	99.81 62	99.88 46	93.91 284	99.94 86	99.11 101	99.27 187	99.61 166

mamv4		99.33 72	99.42 28	99.07 206	99.67 127	97.73 282	99.42 235	99.60 62	98.15 162	99.94 25	99.91 24	98.42 88	99.94 86	99.72 29	99.96 15	99.54 186

XVS		99.53 23	99.42 28	99.87 18	99.85 28	99.83 20	99.69 61	99.68 20	98.98 65	99.37 189	99.74 167	98.81 47	99.94 86	98.79 151	99.86 80	99.84 50

X-MVStestdata		96.55 355	95.45 374	99.87 18	99.85 28	99.83 20	99.69 61	99.68 20	98.98 65	99.37 189	64.01 451	98.81 47	99.94 86	98.79 151	99.86 80	99.84 50

旧先验2							98.96 374		96.70 320	99.47 161			99.94 86	98.19 223

新几何1				99.75 70	99.75 85	99.59 81		99.54 100	96.76 316	99.29 208	99.64 221	98.43 86	99.94 86	96.92 333	99.66 152	99.72 122

testdata				99.54 118	99.75 85	98.95 182		99.51 136	97.07 294	99.43 171	99.70 184	98.87 40	99.94 86	97.76 265	99.64 155	99.72 122

HPM-MVS		99.42 51	99.28 65	99.83 50	99.90 4	99.72 50	99.81 20	99.54 100	97.59 237	99.68 102	99.63 227	98.91 37	99.94 86	98.58 181	99.91 43	99.84 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 95	99.10 93	99.45 150	99.89 8	98.52 234	99.39 252	99.94 1	98.73 95	99.11 248	99.89 37	95.50 203	99.94 86	99.50 54	99.97 8	99.89 26

APD-MVS		99.27 83	99.08 98	99.84 49	99.75 85	99.79 35	99.50 185	99.50 156	97.16 284	99.77 77	99.82 97	98.78 51	99.94 86	97.56 286	99.86 80	99.80 82
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 33	99.42 28	99.65 88	99.72 104	99.40 114	99.05 351	99.66 28	99.14 33	99.57 143	99.80 125	98.46 84	99.94 86	99.57 45	99.84 95	99.60 169
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		99.06 132	98.88 140	99.61 102	99.62 156	99.16 148	99.37 259	99.56 83	98.04 186	99.53 151	99.62 232	96.84 146	99.94 86	98.85 140	98.49 245	99.72 122

DeepC-MVS	98.35 2	99.30 77	99.19 83	99.64 94	99.82 47	99.23 141	99.62 101	99.55 91	98.94 71	99.63 126	99.95 3	95.82 189	99.94 86	99.37 68	99.97 8	99.73 113
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 83	99.12 91	99.74 73	99.18 299	99.75 45	99.56 139	99.57 78	98.45 122	99.49 159	99.85 71	97.77 115	99.94 86	98.33 212	99.84 95	99.52 193

GDP-MVS		99.08 129	98.89 138	99.64 94	99.53 186	99.34 120	99.64 90	99.48 178	98.32 138	99.77 77	99.66 213	95.14 221	99.93 104	98.97 119	99.50 169	99.64 156

SDMVSNet		99.11 122	98.90 135	99.75 70	99.81 51	99.59 81	99.81 20	99.65 35	98.78 91	99.64 123	99.88 46	94.56 255	99.93 104	99.67 34	98.26 258	99.72 122

FE-MVS		98.48 192	98.17 207	99.40 157	99.54 185	98.96 179	99.68 67	98.81 383	95.54 379	99.62 130	99.70 184	93.82 287	99.93 104	97.35 304	99.46 171	99.32 244

SF-MVS		99.38 62	99.24 74	99.79 61	99.79 61	99.68 57	99.57 132	99.54 100	97.82 213	99.71 96	99.80 125	98.95 30	99.93 104	98.19 223	99.84 95	99.74 104

dcpmvs_2		99.23 92	99.58 7	98.16 328	99.83 43	94.68 397	99.76 37	99.52 118	99.07 51	99.98 11	99.88 46	98.56 77	99.93 104	99.67 34	99.98 4	99.87 37

Anonymous20240529		98.09 229	97.68 267	99.34 165	99.66 138	98.44 243	99.40 248	99.43 234	93.67 405	99.22 226	99.89 37	90.23 366	99.93 104	99.26 89	98.33 252	99.66 145

ACMMP_NAP		99.47 36	99.34 46	99.88 12	99.87 17	99.86 17	99.47 211	99.48 178	98.05 185	99.76 83	99.86 64	98.82 46	99.93 104	98.82 150	99.91 43	99.84 50

EI-MVSNet-UG-set		99.58 14	99.57 8	99.64 94	99.78 63	99.14 153	99.60 108	99.45 219	99.01 57	99.90 31	99.83 88	98.98 24	99.93 104	99.59 42	99.95 20	99.86 39

无先验							98.99 367	99.51 136	96.89 310				99.93 104	97.53 289		99.72 122

VDDNet		97.55 316	97.02 337	99.16 198	99.49 209	98.12 260	99.38 257	99.30 305	95.35 381	99.68 102	99.90 31	82.62 426	99.93 104	99.31 78	98.13 270	99.42 227

ab-mvs		98.86 158	98.63 172	99.54 118	99.64 147	99.19 143	99.44 223	99.54 100	97.77 217	99.30 205	99.81 111	94.20 270	99.93 104	99.17 97	98.82 224	99.49 206

F-COLMAP		99.19 95	99.04 103	99.64 94	99.78 63	99.27 136	99.42 235	99.54 100	97.29 273	99.41 178	99.59 241	98.42 88	99.93 104	98.19 223	99.69 146	99.73 113

BP-MVS1		99.12 116	98.94 130	99.65 88	99.51 195	99.30 131	99.67 70	98.92 364	98.48 118	99.84 50	99.69 195	94.96 225	99.92 116	99.62 41	99.79 125	99.71 131

Anonymous202405211		98.30 210	97.98 231	99.26 186	99.57 174	98.16 255	99.41 240	98.55 408	96.03 373	99.19 235	99.74 167	91.87 336	99.92 116	99.16 98	98.29 257	99.70 133

EI-MVSNet-Vis-set		99.58 14	99.56 10	99.64 94	99.78 63	99.15 152	99.61 107	99.45 219	99.01 57	99.89 34	99.82 97	99.01 18	99.92 116	99.56 46	99.95 20	99.85 43

VDD-MVS		97.73 295	97.35 311	98.88 240	99.47 217	97.12 310	99.34 272	98.85 378	98.19 157	99.67 106	99.85 71	82.98 424	99.92 116	99.49 58	98.32 256	99.60 169

VNet		99.11 122	98.90 135	99.73 76	99.52 192	99.56 87	99.41 240	99.39 249	99.01 57	99.74 87	99.78 144	95.56 201	99.92 116	99.52 52	98.18 266	99.72 122

XVG-OURS-SEG-HR		98.69 181	98.62 177	98.89 238	99.71 110	97.74 281	99.12 336	99.54 100	98.44 125	99.42 174	99.71 180	94.20 270	99.92 116	98.54 191	98.90 218	99.00 277

mvsmamba		99.06 132	98.96 126	99.36 163	99.47 217	98.64 219	99.70 57	99.05 348	97.61 236	99.65 118	99.83 88	96.54 159	99.92 116	99.19 93	99.62 158	99.51 201

HPM-MVS_fast		99.51 25	99.40 34	99.85 37	99.91 1	99.79 35	99.76 37	99.56 83	97.72 222	99.76 83	99.75 162	99.13 12	99.92 116	99.07 107	99.92 36	99.85 43

HY-MVS	97.30 7	98.85 165	98.64 171	99.47 147	99.42 229	99.08 161	99.62 101	99.36 266	97.39 265	99.28 209	99.68 202	96.44 165	99.92 116	98.37 207	98.22 261	99.40 232

DP-MVS		99.16 101	98.95 128	99.78 64	99.77 71	99.53 94	99.41 240	99.50 156	97.03 300	99.04 265	99.88 46	97.39 122	99.92 116	98.66 167	99.90 54	99.87 37

IB-MVS	95.67 18	96.22 361	95.44 375	98.57 280	99.21 291	96.70 338	98.65 408	97.74 426	96.71 319	97.27 391	98.54 403	86.03 407	99.92 116	98.47 197	86.30 432	99.10 261
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 29	99.39 36	99.77 67	99.63 150	99.59 81	99.36 264	99.46 208	99.07 51	99.79 68	99.82 97	98.85 42	99.92 116	98.68 165	99.87 72	99.82 66
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 92	99.10 93	99.61 102	99.35 251	99.31 128	99.46 214	99.13 336	98.61 106	99.86 47	99.89 37	96.41 167	99.91 128	99.67 34	99.51 167	99.63 161

balanced_conf03		99.46 38	99.39 36	99.67 83	99.55 182	99.58 86	99.74 47	99.51 136	98.42 126	99.87 43	99.84 83	98.05 108	99.91 128	99.58 44	99.94 28	99.52 193

9.14			99.10 93		99.72 104		99.40 248	99.51 136	97.53 247	99.64 123	99.78 144	98.84 44	99.91 128	97.63 277	99.82 110

SMA-MVS		99.44 46	99.30 58	99.85 37	99.73 100	99.83 20	99.56 139	99.47 199	97.45 256	99.78 73	99.82 97	99.18 10	99.91 128	98.79 151	99.89 65	99.81 73
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					99.67 127	99.65 68	99.05 351	99.41 239	96.22 358	98.95 280	99.49 279	98.77 54	99.91 128

train_agg		99.02 138	98.77 154	99.77 67	99.67 127	99.65 68	99.05 351	99.41 239	96.28 352	98.95 280	99.49 279	98.76 55	99.91 128	97.63 277	99.72 141	99.75 100

test_8					99.67 127	99.61 78	99.03 356	99.41 239	96.28 352	98.93 283	99.48 285	98.76 55	99.91 128

agg_prior					99.67 127	99.62 76		99.40 246		98.87 293			99.91 128

原ACMM1				99.65 88	99.73 100	99.33 123		99.47 199	97.46 253	99.12 246	99.66 213	98.67 69	99.91 128	97.70 274	99.69 146	99.71 131

LFMVS		97.90 262	97.35 311	99.54 118	99.52 192	99.01 170	99.39 252	98.24 415	97.10 292	99.65 118	99.79 137	84.79 416	99.91 128	99.28 84	98.38 249	99.69 135

XVG-OURS		98.73 179	98.68 163	98.88 240	99.70 115	97.73 282	98.92 381	99.55 91	98.52 115	99.45 164	99.84 83	95.27 213	99.91 128	98.08 234	98.84 222	99.00 277

PLC	97.94 4	99.02 138	98.85 145	99.53 126	99.66 138	99.01 170	99.24 310	99.52 118	96.85 312	99.27 214	99.48 285	98.25 98	99.91 128	97.76 265	99.62 158	99.65 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 309	97.06 336	99.47 147	99.61 161	99.09 158	98.04 434	99.25 317	91.24 422	98.51 341	99.70 184	94.55 257	99.91 128	92.76 413	99.85 87	99.42 227
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Elysia		98.88 152	98.65 169	99.58 109	99.58 170	99.34 120	99.65 83	99.52 118	98.26 145	99.83 58	99.87 57	93.37 295	99.90 141	97.81 258	99.91 43	99.49 206

StellarMVS		98.88 152	98.65 169	99.58 109	99.58 170	99.34 120	99.65 83	99.52 118	98.26 145	99.83 58	99.87 57	93.37 295	99.90 141	97.81 258	99.91 43	99.49 206

AstraMVS		99.09 127	99.03 106	99.25 187	99.66 138	98.13 258	99.57 132	98.24 415	98.82 82	99.91 28	99.88 46	95.81 190	99.90 141	99.72 29	99.67 151	99.74 104

mmtdpeth		96.95 347	96.71 346	97.67 367	99.33 257	94.90 393	99.89 2	99.28 311	98.15 162	99.72 94	98.57 402	86.56 405	99.90 141	99.82 26	89.02 427	98.20 397

UWE-MVS		97.58 315	97.29 322	98.48 291	99.09 323	96.25 358	99.01 364	96.61 438	97.86 203	99.19 235	99.01 373	88.72 381	99.90 141	97.38 302	98.69 231	99.28 247

test_vis1_rt		95.81 371	95.65 370	96.32 400	99.67 127	91.35 427	99.49 197	96.74 436	98.25 148	95.24 415	98.10 421	74.96 436	99.90 141	99.53 50	98.85 221	97.70 421

FA-MVS(test-final)		98.75 176	98.53 188	99.41 156	99.55 182	99.05 166	99.80 25	99.01 353	96.59 334	99.58 140	99.59 241	95.39 207	99.90 141	97.78 261	99.49 170	99.28 247

MCST-MVS		99.43 49	99.30 58	99.82 51	99.79 61	99.74 48	99.29 286	99.40 246	98.79 88	99.52 153	99.62 232	98.91 37	99.90 141	98.64 169	99.75 135	99.82 66

CDPH-MVS		99.13 110	98.91 134	99.80 58	99.75 85	99.71 52	99.15 330	99.41 239	96.60 332	99.60 136	99.55 256	98.83 45	99.90 141	97.48 293	99.83 106	99.78 92

NCCC		99.34 70	99.19 83	99.79 61	99.61 161	99.65 68	99.30 281	99.48 178	98.86 77	99.21 229	99.63 227	98.72 64	99.90 141	98.25 219	99.63 157	99.80 82

114514_t		98.93 148	98.67 164	99.72 79	99.85 28	99.53 94	99.62 101	99.59 68	92.65 417	99.71 96	99.78 144	98.06 107	99.90 141	98.84 143	99.91 43	99.74 104

1112_ss		98.98 144	98.77 154	99.59 106	99.68 125	99.02 168	99.25 307	99.48 178	97.23 279	99.13 244	99.58 245	96.93 145	99.90 141	98.87 133	98.78 227	99.84 50

PHI-MVS		99.30 77	99.17 86	99.70 80	99.56 178	99.52 98	99.58 125	99.80 8	97.12 288	99.62 130	99.73 173	98.58 75	99.90 141	98.61 175	99.91 43	99.68 139

AdaColmap		99.01 142	98.80 150	99.66 84	99.56 178	99.54 91	99.18 325	99.70 15	98.18 160	99.35 195	99.63 227	96.32 169	99.90 141	97.48 293	99.77 130	99.55 184

COLMAP_ROB	97.56 6	98.86 158	98.75 156	99.17 197	99.88 13	98.53 230	99.34 272	99.59 68	97.55 243	98.70 319	99.89 37	95.83 188	99.90 141	98.10 229	99.90 54	99.08 266
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 206	98.03 226	99.31 172	99.63 150	98.56 227	99.54 158	96.75 435	97.53 247	99.73 89	99.65 215	91.25 354	99.89 156	98.62 172	99.56 163	99.48 209

tttt0517		98.42 197	98.14 211	99.28 184	99.66 138	98.38 247	99.74 47	96.85 433	97.68 228	99.79 68	99.74 167	91.39 350	99.89 156	98.83 146	99.56 163	99.57 180

test12				99.75 70	99.64 147	99.61 78		99.29 309		99.21 229		98.38 92	99.89 156		99.74 138	99.74 104

Test_1112_low_res		98.89 151	98.66 167	99.57 113	99.69 120	98.95 182	99.03 356	99.47 199	96.98 302	99.15 242	99.23 349	96.77 149	99.89 156	98.83 146	98.78 227	99.86 39

CNLPA		99.14 108	98.99 118	99.59 106	99.58 170	99.41 113	99.16 327	99.44 228	98.45 122	99.19 235	99.49 279	98.08 106	99.89 156	97.73 269	99.75 135	99.48 209

guyue		99.16 101	99.04 103	99.52 132	99.69 120	98.92 189	99.59 115	98.81 383	98.73 95	99.90 31	99.87 57	95.34 210	99.88 161	99.66 37	99.81 113	99.74 104

sd_testset		98.75 176	98.57 184	99.29 180	99.81 51	98.26 251	99.56 139	99.62 46	98.78 91	99.64 123	99.88 46	92.02 333	99.88 161	99.54 48	98.26 258	99.72 122

APD_test1		95.87 369	96.49 351	94.00 407	99.53 186	84.01 436	99.54 158	99.32 296	95.91 375	97.99 370	99.85 71	85.49 411	99.88 161	91.96 416	98.84 222	98.12 401

diffmvs		99.14 108	99.02 111	99.51 137	99.61 161	98.96 179	99.28 291	99.49 166	98.46 120	99.72 94	99.71 180	96.50 161	99.88 161	99.31 78	99.11 199	99.67 142
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		98.86 158	98.80 150	99.03 212	99.76 75	98.79 207	99.28 291	99.91 3	97.42 262	99.67 106	99.37 316	97.53 119	99.88 161	98.98 116	97.29 318	98.42 382

PVSNet_Blended		99.08 129	98.97 122	99.42 155	99.76 75	98.79 207	98.78 395	99.91 3	96.74 317	99.67 106	99.49 279	97.53 119	99.88 161	98.98 116	99.85 87	99.60 169

MVS		97.28 336	96.55 349	99.48 143	98.78 373	98.95 182	99.27 296	99.39 249	83.53 438	98.08 365	99.54 261	96.97 143	99.87 167	94.23 394	99.16 193	99.63 161

MG-MVS		99.13 110	99.02 111	99.45 150	99.57 174	98.63 220	99.07 346	99.34 278	98.99 62	99.61 133	99.82 97	97.98 110	99.87 167	97.00 324	99.80 118	99.85 43

MSDG		98.98 144	98.80 150	99.53 126	99.76 75	99.19 143	98.75 398	99.55 91	97.25 276	99.47 161	99.77 153	97.82 113	99.87 167	96.93 331	99.90 54	99.54 186

ETV-MVS		99.26 86	99.21 79	99.40 157	99.46 219	99.30 131	99.56 139	99.52 118	98.52 115	99.44 169	99.27 344	98.41 90	99.86 170	99.10 104	99.59 161	99.04 273

thisisatest0515		98.14 224	97.79 250	99.19 195	99.50 207	98.50 237	98.61 410	96.82 434	96.95 306	99.54 149	99.43 297	91.66 345	99.86 170	98.08 234	99.51 167	99.22 255

thres600view7		97.86 268	97.51 285	98.92 229	99.72 104	97.95 272	99.59 115	98.74 393	97.94 195	99.27 214	98.62 399	91.75 339	99.86 170	93.73 400	98.19 265	98.96 283

lupinMVS		99.13 110	99.01 116	99.46 149	99.51 195	98.94 185	99.05 351	99.16 332	97.86 203	99.80 66	99.56 253	97.39 122	99.86 170	98.94 121	99.85 87	99.58 177

PVSNet	96.02 17	98.85 165	98.84 147	98.89 238	99.73 100	97.28 301	98.32 426	99.60 62	97.86 203	99.50 156	99.57 250	96.75 150	99.86 170	98.56 187	99.70 145	99.54 186

MAR-MVS		98.86 158	98.63 172	99.54 118	99.37 247	99.66 64	99.45 217	99.54 100	96.61 329	99.01 268	99.40 307	97.09 136	99.86 170	97.68 276	99.53 166	99.10 261
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
testing91		97.44 328	97.02 337	98.71 267	99.18 299	96.89 332	99.19 323	99.04 349	97.78 216	98.31 352	98.29 413	85.41 412	99.85 176	98.01 240	97.95 275	99.39 233

test2506		96.81 351	96.65 347	97.29 381	99.74 93	92.21 424	99.60 108	85.06 455	99.13 34	99.77 77	99.93 10	87.82 398	99.85 176	99.38 67	99.38 176	99.80 82

AllTest		98.87 155	98.72 158	99.31 172	99.86 22	98.48 240	99.56 139	99.61 55	97.85 206	99.36 192	99.85 71	95.95 181	99.85 176	96.66 344	99.83 106	99.59 173

TestCases				99.31 172	99.86 22	98.48 240		99.61 55	97.85 206	99.36 192	99.85 71	95.95 181	99.85 176	96.66 344	99.83 106	99.59 173

jason		99.13 110	99.03 106	99.45 150	99.46 219	98.87 193	99.12 336	99.26 315	98.03 188	99.79 68	99.65 215	97.02 141	99.85 176	99.02 113	99.90 54	99.65 149
jason: jason.
CNVR-MVS		99.42 51	99.30 58	99.78 64	99.62 156	99.71 52	99.26 305	99.52 118	98.82 82	99.39 185	99.71 180	98.96 25	99.85 176	98.59 180	99.80 118	99.77 94

PAPM_NR		99.04 135	98.84 147	99.66 84	99.74 93	99.44 109	99.39 252	99.38 257	97.70 226	99.28 209	99.28 341	98.34 94	99.85 176	96.96 328	99.45 172	99.69 135

testing99		97.36 331	96.94 340	98.63 273	99.18 299	96.70 338	99.30 281	98.93 361	97.71 223	98.23 357	98.26 414	84.92 415	99.84 183	98.04 239	97.85 282	99.35 239

testing222		97.16 341	96.50 350	99.16 198	99.16 309	98.47 242	99.27 296	98.66 404	97.71 223	98.23 357	98.15 417	82.28 429	99.84 183	97.36 303	97.66 288	99.18 257

test1111		98.04 239	98.11 215	97.83 357	99.74 93	93.82 409	99.58 125	95.40 442	99.12 39	99.65 118	99.93 10	90.73 359	99.84 183	99.43 64	99.38 176	99.82 66

ECVR-MVS		98.04 239	98.05 224	98.00 341	99.74 93	94.37 404	99.59 115	94.98 443	99.13 34	99.66 111	99.93 10	90.67 360	99.84 183	99.40 65	99.38 176	99.80 82

test_yl		98.86 158	98.63 172	99.54 118	99.49 209	99.18 145	99.50 185	99.07 345	98.22 153	99.61 133	99.51 273	95.37 208	99.84 183	98.60 178	98.33 252	99.59 173

DCV-MVSNet		98.86 158	98.63 172	99.54 118	99.49 209	99.18 145	99.50 185	99.07 345	98.22 153	99.61 133	99.51 273	95.37 208	99.84 183	98.60 178	98.33 252	99.59 173

Fast-Effi-MVS+		98.70 180	98.43 192	99.51 137	99.51 195	99.28 134	99.52 168	99.47 199	96.11 368	99.01 268	99.34 326	96.20 173	99.84 183	97.88 248	98.82 224	99.39 233

TSAR-MVS + GP.		99.36 67	99.36 42	99.36 163	99.67 127	98.61 224	99.07 346	99.33 286	99.00 60	99.82 61	99.81 111	99.06 16	99.84 183	99.09 105	99.42 174	99.65 149

tpmrst		98.33 207	98.48 190	97.90 350	99.16 309	94.78 394	99.31 279	99.11 338	97.27 274	99.45 164	99.59 241	95.33 211	99.84 183	98.48 194	98.61 234	99.09 265

Vis-MVSNet		99.12 116	98.97 122	99.56 115	99.78 63	99.10 157	99.68 67	99.66 28	98.49 117	99.86 47	99.87 57	94.77 241	99.84 183	99.19 93	99.41 175	99.74 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 188	98.34 198	99.51 137	99.40 239	99.03 167	98.80 393	99.36 266	96.33 349	99.00 272	99.12 363	98.46 84	99.84 183	95.23 380	99.37 183	99.66 145

PatchMatch-RL		98.84 168	98.62 177	99.52 132	99.71 110	99.28 134	99.06 349	99.77 9	97.74 221	99.50 156	99.53 265	95.41 206	99.84 183	97.17 317	99.64 155	99.44 225

EPP-MVSNet		99.13 110	98.99 118	99.53 126	99.65 145	99.06 164	99.81 20	99.33 286	97.43 260	99.60 136	99.88 46	97.14 134	99.84 183	99.13 99	98.94 213	99.69 135

testing3-2		97.84 273	97.70 265	98.24 323	99.53 186	95.37 382	99.55 153	98.67 403	98.46 120	99.27 214	99.34 326	86.58 404	99.83 196	99.32 77	98.63 233	99.52 193

testing11		97.50 321	97.10 334	98.71 267	99.20 293	96.91 330	99.29 286	98.82 381	97.89 200	98.21 360	98.40 408	85.63 410	99.83 196	98.45 199	98.04 273	99.37 237

thres100view900		97.76 287	97.45 294	98.69 269	99.72 104	97.86 278	99.59 115	98.74 393	97.93 196	99.26 219	98.62 399	91.75 339	99.83 196	93.22 405	98.18 266	98.37 388

tfpn200view9		97.72 297	97.38 307	98.72 265	99.69 120	97.96 269	99.50 185	98.73 399	97.83 209	99.17 240	98.45 406	91.67 343	99.83 196	93.22 405	98.18 266	98.37 388

test_prior				99.68 82	99.67 127	99.48 104		99.56 83					99.83 196			99.74 104

1314		98.68 182	98.54 187	99.11 204	98.89 356	98.65 217	99.27 296	99.49 166	96.89 310	97.99 370	99.56 253	97.72 117	99.83 196	97.74 268	99.27 187	98.84 289

thres400		97.77 286	97.38 307	98.92 229	99.69 120	97.96 269	99.50 185	98.73 399	97.83 209	99.17 240	98.45 406	91.67 343	99.83 196	93.22 405	98.18 266	98.96 283

casdiffmvs		99.13 110	98.98 121	99.56 115	99.65 145	99.16 148	99.56 139	99.50 156	98.33 137	99.41 178	99.86 64	95.92 184	99.83 196	99.45 63	99.16 193	99.70 133
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 29	99.48 20	99.54 118	99.78 63	99.30 131	99.89 2	99.58 73	98.56 111	99.73 89	99.69 195	98.55 78	99.82 204	99.69 32	99.85 87	99.48 209

MVS_Test		99.10 126	98.97 122	99.48 143	99.49 209	99.14 153	99.67 70	99.34 278	97.31 271	99.58 140	99.76 157	97.65 118	99.82 204	98.87 133	99.07 205	99.46 220

dp		97.75 291	97.80 249	97.59 372	99.10 320	93.71 412	99.32 276	98.88 374	96.48 341	99.08 256	99.55 256	92.67 317	99.82 204	96.52 348	98.58 237	99.24 253

RPSCF		98.22 214	98.62 177	96.99 387	99.82 47	91.58 426	99.72 53	99.44 228	96.61 329	99.66 111	99.89 37	95.92 184	99.82 204	97.46 296	99.10 202	99.57 180

PMMVS		98.80 172	98.62 177	99.34 165	99.27 275	98.70 213	98.76 397	99.31 300	97.34 268	99.21 229	99.07 365	97.20 133	99.82 204	98.56 187	98.87 219	99.52 193

UBG		97.85 269	97.48 288	98.95 223	99.25 282	97.64 289	99.24 310	98.74 393	97.90 199	98.64 329	98.20 416	88.65 385	99.81 209	98.27 217	98.40 247	99.42 227

EIA-MVS		99.18 97	99.09 97	99.45 150	99.49 209	99.18 145	99.67 70	99.53 113	97.66 231	99.40 183	99.44 295	98.10 104	99.81 209	98.94 121	99.62 158	99.35 239

Effi-MVS+		98.81 169	98.59 183	99.48 143	99.46 219	99.12 156	98.08 433	99.50 156	97.50 251	99.38 187	99.41 303	96.37 168	99.81 209	99.11 101	98.54 242	99.51 201

thres200		97.61 313	97.28 323	98.62 274	99.64 147	98.03 263	99.26 305	98.74 393	97.68 228	99.09 254	98.32 412	91.66 345	99.81 209	92.88 410	98.22 261	98.03 407

tpmvs		97.98 250	98.02 228	97.84 356	99.04 334	94.73 395	99.31 279	99.20 327	96.10 372	98.76 309	99.42 299	94.94 227	99.81 209	96.97 327	98.45 246	98.97 281

casdiffmvs_mvg		99.15 104	99.02 111	99.55 117	99.66 138	99.09 158	99.64 90	99.56 83	98.26 145	99.45 164	99.87 57	96.03 178	99.81 209	99.54 48	99.15 196	99.73 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepPCF-MVS	98.18 3	98.81 169	99.37 40	97.12 385	99.60 166	91.75 425	98.61 410	99.44 228	99.35 22	99.83 58	99.85 71	98.70 66	99.81 209	99.02 113	99.91 43	99.81 73

DPM-MVS		98.95 147	98.71 160	99.66 84	99.63 150	99.55 89	98.64 409	99.10 339	97.93 196	99.42 174	99.55 256	98.67 69	99.80 216	95.80 365	99.68 149	99.61 166

DP-MVS Recon		99.12 116	98.95 128	99.65 88	99.74 93	99.70 54	99.27 296	99.57 78	96.40 348	99.42 174	99.68 202	98.75 58	99.80 216	97.98 242	99.72 141	99.44 225

MVS_111021_LR		99.41 55	99.33 48	99.65 88	99.77 71	99.51 100	98.94 379	99.85 6	98.82 82	99.65 118	99.74 167	98.51 81	99.80 216	98.83 146	99.89 65	99.64 156

CS-MVS		99.50 27	99.48 20	99.54 118	99.76 75	99.42 111	99.90 1	99.55 91	98.56 111	99.78 73	99.70 184	98.65 71	99.79 219	99.65 38	99.78 127	99.41 230

Fast-Effi-MVS+-dtu		98.77 175	98.83 149	98.60 275	99.41 234	96.99 324	99.52 168	99.49 166	98.11 171	99.24 221	99.34 326	96.96 144	99.79 219	97.95 244	99.45 172	99.02 276

baseline1		98.31 208	97.95 235	99.38 162	99.50 207	98.74 210	99.59 115	98.93 361	98.41 127	99.14 243	99.60 239	94.59 253	99.79 219	98.48 194	93.29 401	99.61 166

baseline		99.15 104	99.02 111	99.53 126	99.66 138	99.14 153	99.72 53	99.48 178	98.35 134	99.42 174	99.84 83	96.07 176	99.79 219	99.51 53	99.14 197	99.67 142

PVSNet_0	94.43 19	96.09 366	95.47 373	97.94 346	99.31 265	94.34 406	97.81 435	99.70 15	97.12 288	97.46 385	98.75 396	89.71 371	99.79 219	97.69 275	81.69 438	99.68 139

API-MVS		99.04 135	99.03 106	99.06 208	99.40 239	99.31 128	99.55 153	99.56 83	98.54 113	99.33 199	99.39 311	98.76 55	99.78 224	96.98 326	99.78 127	98.07 404

OMC-MVS		99.08 129	99.04 103	99.20 194	99.67 127	98.22 253	99.28 291	99.52 118	98.07 179	99.66 111	99.81 111	97.79 114	99.78 224	97.79 260	99.81 113	99.60 169

GeoE		98.85 165	98.62 177	99.53 126	99.61 161	99.08 161	99.80 25	99.51 136	97.10 292	99.31 201	99.78 144	95.23 218	99.77 226	98.21 221	99.03 208	99.75 100

alignmvs		98.81 169	98.56 186	99.58 109	99.43 227	99.42 111	99.51 177	98.96 359	98.61 106	99.35 195	98.92 386	94.78 238	99.77 226	99.35 69	98.11 271	99.54 186

tpm cat1		97.39 330	97.36 309	97.50 375	99.17 307	93.73 411	99.43 228	99.31 300	91.27 421	98.71 313	99.08 364	94.31 268	99.77 226	96.41 353	98.50 244	99.00 277

CostFormer		97.72 297	97.73 262	97.71 365	99.15 313	94.02 408	99.54 158	99.02 352	94.67 396	99.04 265	99.35 322	92.35 329	99.77 226	98.50 193	97.94 276	99.34 242

MGCFI-Net		99.01 142	98.85 145	99.50 142	99.42 229	99.26 137	99.82 16	99.48 178	98.60 108	99.28 209	98.81 391	97.04 140	99.76 230	99.29 83	97.87 280	99.47 215

test_241102_ONE					99.84 34	99.90 2		99.48 178	99.07 51	99.91 28	99.74 167	99.20 7	99.76 230

MDTV_nov1_ep13			98.32 200		99.11 317	94.44 402	99.27 296	98.74 393	97.51 250	99.40 183	99.62 232	94.78 238	99.76 230	97.59 280	98.81 226

sasdasda		99.02 138	98.86 143	99.51 137	99.42 229	99.32 124	99.80 25	99.48 178	98.63 103	99.31 201	98.81 391	97.09 136	99.75 233	99.27 87	97.90 277	99.47 215

canonicalmvs		99.02 138	98.86 143	99.51 137	99.42 229	99.32 124	99.80 25	99.48 178	98.63 103	99.31 201	98.81 391	97.09 136	99.75 233	99.27 87	97.90 277	99.47 215

Effi-MVS+-dtu		98.78 173	98.89 138	98.47 296	99.33 257	96.91 330	99.57 132	99.30 305	98.47 119	99.41 178	98.99 376	96.78 148	99.74 235	98.73 157	99.38 176	98.74 305

patchmatchnet-post											98.70 397	94.79 237	99.74 235

SCA		98.19 218	98.16 208	98.27 322	99.30 266	95.55 373	99.07 346	98.97 357	97.57 240	99.43 171	99.57 250	92.72 312	99.74 235	97.58 281	99.20 191	99.52 193

BH-untuned		98.42 197	98.36 196	98.59 276	99.49 209	96.70 338	99.27 296	99.13 336	97.24 278	98.80 304	99.38 313	95.75 194	99.74 235	97.07 322	99.16 193	99.33 243

BH-RMVSNet		98.41 199	98.08 220	99.40 157	99.41 234	98.83 201	99.30 281	98.77 389	97.70 226	98.94 282	99.65 215	92.91 307	99.74 235	96.52 348	99.55 165	99.64 156

MVS_111021_HR		99.41 55	99.32 50	99.66 84	99.72 104	99.47 106	98.95 377	99.85 6	98.82 82	99.54 149	99.73 173	98.51 81	99.74 235	98.91 127	99.88 69	99.77 94

test_post											65.99 449	94.65 251	99.73 241

XVG-ACMP-BASELINE		97.83 276	97.71 264	98.20 325	99.11 317	96.33 354	99.41 240	99.52 118	98.06 183	99.05 264	99.50 276	89.64 373	99.73 241	97.73 269	97.38 315	98.53 370

HyFIR lowres test		99.11 122	98.92 131	99.65 88	99.90 4	99.37 116	99.02 359	99.91 3	97.67 230	99.59 139	99.75 162	95.90 186	99.73 241	99.53 50	99.02 210	99.86 39

DeepMVS_CX				93.34 410	99.29 270	82.27 439		99.22 323	85.15 436	96.33 407	99.05 368	90.97 357	99.73 241	93.57 402	97.77 285	98.01 408

Patchmatch-test		97.93 256	97.65 270	98.77 261	99.18 299	97.07 315	99.03 356	99.14 335	96.16 363	98.74 310	99.57 250	94.56 255	99.72 245	93.36 404	99.11 199	99.52 193

LPG-MVS_test		98.22 214	98.13 213	98.49 289	99.33 257	97.05 317	99.58 125	99.55 91	97.46 253	99.24 221	99.83 88	92.58 319	99.72 245	98.09 230	97.51 301	98.68 323

LGP-MVS_train				98.49 289	99.33 257	97.05 317		99.55 91	97.46 253	99.24 221	99.83 88	92.58 319	99.72 245	98.09 230	97.51 301	98.68 323

BH-w/o		98.00 248	97.89 244	98.32 314	99.35 251	96.20 360	99.01 364	98.90 371	96.42 346	98.38 348	99.00 374	95.26 215	99.72 245	96.06 358	98.61 234	99.03 274

ACMP	97.20 11	98.06 233	97.94 237	98.45 299	99.37 247	97.01 322	99.44 223	99.49 166	97.54 246	98.45 345	99.79 137	91.95 335	99.72 245	97.91 246	97.49 306	98.62 353
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 243	97.90 240	98.40 307	99.23 286	96.80 336	99.70 57	99.60 62	97.12 288	98.18 362	99.70 184	91.73 341	99.72 245	98.39 204	97.45 308	98.68 323
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_post1							99.23 313				65.14 450	94.18 273	99.71 251	97.58 281

ADS-MVSNet		98.20 217	98.08 220	98.56 283	99.33 257	96.48 349	99.23 313	99.15 333	96.24 356	99.10 251	99.67 208	94.11 274	99.71 251	96.81 336	99.05 206	99.48 209

JIA-IIPM		97.50 321	97.02 337	98.93 227	98.73 382	97.80 280	99.30 281	98.97 357	91.73 420	98.91 285	94.86 438	95.10 222	99.71 251	97.58 281	97.98 274	99.28 247

EPMVS		97.82 279	97.65 270	98.35 311	98.88 357	95.98 364	99.49 197	94.71 445	97.57 240	99.26 219	99.48 285	92.46 326	99.71 251	97.87 250	99.08 204	99.35 239

TDRefinement		95.42 377	94.57 385	97.97 343	89.83 448	96.11 363	99.48 202	98.75 390	96.74 317	96.68 404	99.88 46	88.65 385	99.71 251	98.37 207	82.74 437	98.09 403

ACMM	97.58 5	98.37 205	98.34 198	98.48 291	99.41 234	97.10 311	99.56 139	99.45 219	98.53 114	99.04 265	99.85 71	93.00 303	99.71 251	98.74 155	97.45 308	98.64 344
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 253	97.77 255	98.57 280	99.59 168	96.61 345	99.45 217	99.08 342	98.21 155	98.88 290	99.80 125	88.66 384	99.70 257	98.58 181	97.72 286	99.39 233

CHOSEN 280x420		99.12 116	99.13 89	99.08 205	99.66 138	97.89 275	98.43 420	99.71 13	98.88 76	99.62 130	99.76 157	96.63 154	99.70 257	99.46 62	99.99 1	99.66 145

EC-MVSNet		99.44 46	99.39 36	99.58 109	99.56 178	99.49 102	99.88 4	99.58 73	98.38 129	99.73 89	99.69 195	98.20 100	99.70 257	99.64 40	99.82 110	99.54 186

PatchmatchNet		98.31 208	98.36 196	98.19 326	99.16 309	95.32 383	99.27 296	98.92 364	97.37 266	99.37 189	99.58 245	94.90 231	99.70 257	97.43 299	99.21 190	99.54 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 228	97.99 230	98.44 302	99.41 234	96.96 328	99.60 108	99.56 83	98.09 174	98.15 363	99.91 24	90.87 358	99.70 257	98.88 130	97.45 308	98.67 331
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 321	96.90 341	99.29 180	99.23 286	98.78 209	99.32 276	98.90 371	97.52 249	98.56 338	98.09 422	84.72 417	99.69 262	97.86 251	97.88 279	99.39 233

HQP_MVS		98.27 213	98.22 206	98.44 302	99.29 270	96.97 326	99.39 252	99.47 199	98.97 68	99.11 248	99.61 236	92.71 314	99.69 262	97.78 261	97.63 289	98.67 331

plane_prior5								99.47 199					99.69 262	97.78 261	97.63 289	98.67 331

D2MVS		98.41 199	98.50 189	98.15 331	99.26 278	96.62 344	99.40 248	99.61 55	97.71 223	98.98 275	99.36 319	96.04 177	99.67 265	98.70 160	97.41 313	98.15 400

IS-MVSNet		99.05 134	98.87 141	99.57 113	99.73 100	99.32 124	99.75 42	99.20 327	98.02 190	99.56 144	99.86 64	96.54 159	99.67 265	98.09 230	99.13 198	99.73 113

CLD-MVS		98.16 222	98.10 216	98.33 312	99.29 270	96.82 335	98.75 398	99.44 228	97.83 209	99.13 244	99.55 256	92.92 305	99.67 265	98.32 214	97.69 287	98.48 374
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 338	97.30 320	97.09 386	99.43 227	93.31 417	99.73 51	98.87 376	98.83 81	99.28 209	99.80 125	84.45 418	99.66 268	97.88 248	97.45 308	98.30 390

AUN-MVS		96.88 349	96.31 355	98.59 276	99.48 216	97.04 320	99.27 296	99.22 323	97.44 259	98.51 341	99.41 303	91.97 334	99.66 268	97.71 272	83.83 435	99.07 271

UniMVSNet_ETH3D		97.32 335	96.81 343	98.87 244	99.40 239	97.46 295	99.51 177	99.53 113	95.86 376	98.54 340	99.77 153	82.44 427	99.66 268	98.68 165	97.52 300	99.50 205

OPM-MVS		98.19 218	98.10 216	98.45 299	98.88 357	97.07 315	99.28 291	99.38 257	98.57 110	99.22 226	99.81 111	92.12 331	99.66 268	98.08 234	97.54 298	98.61 362
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 259	97.78 253	98.32 314	99.46 219	96.68 342	99.56 139	99.54 100	98.41 127	97.79 381	99.87 57	90.18 367	99.66 268	98.05 238	97.18 323	98.62 353

hse-mvs2		97.50 321	97.14 331	98.59 276	99.49 209	97.05 317	99.28 291	99.22 323	98.94 71	99.66 111	99.42 299	94.93 228	99.65 273	99.48 59	83.80 436	99.08 266

VPA-MVSNet		98.29 211	97.95 235	99.30 177	99.16 309	99.54 91	99.50 185	99.58 73	98.27 143	99.35 195	99.37 316	92.53 321	99.65 273	99.35 69	94.46 382	98.72 307

TR-MVS		97.76 287	97.41 305	98.82 253	99.06 329	97.87 276	98.87 387	98.56 407	96.63 328	98.68 321	99.22 350	92.49 322	99.65 273	95.40 376	97.79 284	98.95 285

reproduce_monomvs		97.89 263	97.87 245	97.96 345	99.51 195	95.45 378	99.60 108	99.25 317	99.17 29	98.85 298	99.49 279	89.29 376	99.64 276	99.35 69	96.31 339	98.78 293

gm-plane-assit					98.54 402	92.96 419			94.65 397		99.15 358		99.64 276	97.56 286

HQP4-MVS										98.66 322			99.64 276			98.64 344

HQP-MVS		98.02 243	97.90 240	98.37 310	99.19 296	96.83 333	98.98 370	99.39 249	98.24 149	98.66 322	99.40 307	92.47 323	99.64 276	97.19 314	97.58 294	98.64 344

PAPM		97.59 314	97.09 335	99.07 206	99.06 329	98.26 251	98.30 427	99.10 339	94.88 391	98.08 365	99.34 326	96.27 171	99.64 276	89.87 424	98.92 216	99.31 245

TAPA-MVS	97.07 15	97.74 293	97.34 314	98.94 225	99.70 115	97.53 292	99.25 307	99.51 136	91.90 419	99.30 205	99.63 227	98.78 51	99.64 276	88.09 431	99.87 72	99.65 149
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 203	98.09 219	99.24 190	99.26 278	99.32 124	99.56 139	99.55 91	97.45 256	98.71 313	99.83 88	93.23 298	99.63 282	98.88 130	96.32 338	98.76 299

ITE_SJBPF				98.08 334	99.29 270	96.37 352		98.92 364	98.34 135	98.83 299	99.75 162	91.09 355	99.62 283	95.82 363	97.40 314	98.25 394

LF4IMVS		97.52 318	97.46 293	97.70 366	98.98 345	95.55 373	99.29 286	98.82 381	98.07 179	98.66 322	99.64 221	89.97 368	99.61 284	97.01 323	96.68 328	97.94 415

tpm		97.67 308	97.55 279	98.03 336	99.02 336	95.01 390	99.43 228	98.54 409	96.44 344	99.12 246	99.34 326	91.83 338	99.60 285	97.75 267	96.46 334	99.48 209

tpm2		97.44 328	97.34 314	97.74 364	99.15 313	94.36 405	99.45 217	98.94 360	93.45 410	98.90 287	99.44 295	91.35 351	99.59 286	97.31 305	98.07 272	99.29 246

baseline2		97.87 266	97.55 279	98.82 253	99.18 299	98.02 264	99.41 240	96.58 439	96.97 303	96.51 405	99.17 355	93.43 293	99.57 287	97.71 272	99.03 208	98.86 287

MS-PatchMatch		97.24 340	97.32 318	96.99 387	98.45 405	93.51 416	98.82 391	99.32 296	97.41 263	98.13 364	99.30 337	88.99 378	99.56 288	95.68 369	99.80 118	97.90 418

TinyColmap		97.12 343	96.89 342	97.83 357	99.07 327	95.52 376	98.57 413	98.74 393	97.58 239	97.81 380	99.79 137	88.16 392	99.56 288	95.10 381	97.21 321	98.39 386

USDC		97.34 333	97.20 328	97.75 362	99.07 327	95.20 385	98.51 417	99.04 349	97.99 191	98.31 352	99.86 64	89.02 377	99.55 290	95.67 370	97.36 316	98.49 373

MSLP-MVS++		99.46 38	99.47 22	99.44 154	99.60 166	99.16 148	99.41 240	99.71 13	98.98 65	99.45 164	99.78 144	99.19 9	99.54 291	99.28 84	99.84 95	99.63 161

UWE-MVS-28		97.36 331	97.24 327	97.75 362	98.84 366	94.44 402	99.24 310	97.58 428	97.98 192	99.00 272	99.00 374	91.35 351	99.53 292	93.75 399	98.39 248	99.27 251

TAMVS		99.12 116	99.08 98	99.24 190	99.46 219	98.55 228	99.51 177	99.46 208	98.09 174	99.45 164	99.82 97	98.34 94	99.51 293	98.70 160	98.93 214	99.67 142

EPNet_dtu		98.03 241	97.96 233	98.23 324	98.27 408	95.54 375	99.23 313	98.75 390	99.02 55	97.82 379	99.71 180	96.11 175	99.48 294	93.04 408	99.65 154	99.69 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 353	96.22 357	97.97 343	97.00 430	96.28 356	98.66 407	99.03 351	96.61 329	96.93 402	99.79 137	87.20 401	99.47 295	96.65 346	94.13 389	98.16 399

EG-PatchMatch MVS		95.97 368	95.69 369	96.81 394	97.78 415	92.79 420	99.16 327	98.93 361	96.16 363	94.08 423	99.22 350	82.72 425	99.47 295	95.67 370	97.50 303	98.17 398

myMVS_eth3d28		97.69 302	97.34 314	98.73 263	99.27 275	97.52 293	99.33 274	98.78 388	98.03 188	98.82 301	98.49 404	86.64 403	99.46 297	98.44 200	98.24 260	99.23 254

MVP-Stereo		97.81 281	97.75 260	97.99 342	97.53 419	96.60 346	98.96 374	98.85 378	97.22 280	97.23 392	99.36 319	95.28 212	99.46 297	95.51 372	99.78 127	97.92 417
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 190	98.67 164	98.30 316	99.35 251	95.59 372	99.50 185	99.55 91	98.60 108	99.39 185	99.83 88	94.48 261	99.45 299	98.75 154	98.56 240	99.85 43

test-LLR		98.06 233	97.90 240	98.55 285	98.79 370	97.10 311	98.67 404	97.75 424	97.34 268	98.61 334	98.85 388	94.45 263	99.45 299	97.25 308	99.38 176	99.10 261

TESTMET0.1,1		97.55 316	97.27 326	98.40 307	98.93 350	96.53 347	98.67 404	97.61 427	96.96 304	98.64 329	99.28 341	88.63 387	99.45 299	97.30 306	99.38 176	99.21 256

test-mter		97.49 326	97.13 333	98.55 285	98.79 370	97.10 311	98.67 404	97.75 424	96.65 324	98.61 334	98.85 388	88.23 391	99.45 299	97.25 308	99.38 176	99.10 261

mvs_anonymous		99.03 137	98.99 118	99.16 198	99.38 244	98.52 234	99.51 177	99.38 257	97.79 214	99.38 187	99.81 111	97.30 128	99.45 299	99.35 69	98.99 211	99.51 201

tfpnnormal		97.84 273	97.47 291	98.98 218	99.20 293	99.22 142	99.64 90	99.61 55	96.32 350	98.27 356	99.70 184	93.35 297	99.44 304	95.69 368	95.40 365	98.27 392

v7n		97.87 266	97.52 283	98.92 229	98.76 380	98.58 226	99.84 12	99.46 208	96.20 359	98.91 285	99.70 184	94.89 232	99.44 304	96.03 359	93.89 394	98.75 301

jajsoiax		98.43 196	98.28 203	98.88 240	98.60 397	98.43 244	99.82 16	99.53 113	98.19 157	98.63 331	99.80 125	93.22 300	99.44 304	99.22 91	97.50 303	98.77 297

mvs_tets		98.40 202	98.23 205	98.91 233	98.67 390	98.51 236	99.66 77	99.53 113	98.19 157	98.65 328	99.81 111	92.75 309	99.44 304	99.31 78	97.48 307	98.77 297

sc_t1		95.75 372	95.05 379	97.87 352	98.83 367	94.61 399	99.21 319	99.45 219	87.45 432	97.97 372	99.85 71	81.19 432	99.43 308	98.27 217	93.20 403	99.57 180

Vis-MVSNet (Re-imp)		98.87 155	98.72 158	99.31 172	99.71 110	98.88 192	99.80 25	99.44 228	97.91 198	99.36 192	99.78 144	95.49 204	99.43 308	97.91 246	99.11 199	99.62 164

OPU-MVS				99.64 94	99.56 178	99.72 50	99.60 108				99.70 184	99.27 5	99.42 310	98.24 220	99.80 118	99.79 86

Anonymous20231211		97.88 264	97.54 282	98.90 235	99.71 110	98.53 230	99.48 202	99.57 78	94.16 401	98.81 302	99.68 202	93.23 298	99.42 310	98.84 143	94.42 384	98.76 299

ttmdpeth		97.80 283	97.63 274	98.29 317	98.77 378	97.38 298	99.64 90	99.36 266	98.78 91	96.30 408	99.58 245	92.34 330	99.39 312	98.36 209	95.58 360	98.10 402

VPNet		97.84 273	97.44 299	99.01 214	99.21 291	98.94 185	99.48 202	99.57 78	98.38 129	99.28 209	99.73 173	88.89 379	99.39 312	99.19 93	93.27 402	98.71 309

nrg030		98.64 187	98.42 193	99.28 184	99.05 332	99.69 56	99.81 20	99.46 208	98.04 186	99.01 268	99.82 97	96.69 152	99.38 314	99.34 74	94.59 381	98.78 293

GA-MVS		97.85 269	97.47 291	99.00 216	99.38 244	97.99 266	98.57 413	99.15 333	97.04 299	98.90 287	99.30 337	89.83 370	99.38 314	96.70 341	98.33 252	99.62 164

UniMVSNet (Re)		98.29 211	98.00 229	99.13 203	99.00 339	99.36 119	99.49 197	99.51 136	97.95 194	98.97 277	99.13 360	96.30 170	99.38 314	98.36 209	93.34 400	98.66 340

FIs		98.78 173	98.63 172	99.23 192	99.18 299	99.54 91	99.83 15	99.59 68	98.28 141	98.79 306	99.81 111	96.75 150	99.37 317	99.08 106	96.38 336	98.78 293

PS-MVSNAJss		98.92 149	98.92 131	98.90 235	98.78 373	98.53 230	99.78 32	99.54 100	98.07 179	99.00 272	99.76 157	99.01 18	99.37 317	99.13 99	97.23 320	98.81 290

CDS-MVSNet		99.09 127	99.03 106	99.25 187	99.42 229	98.73 211	99.45 217	99.46 208	98.11 171	99.46 163	99.77 153	98.01 109	99.37 317	98.70 160	98.92 216	99.66 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 372	95.16 377	97.51 374	99.30 266	93.69 413	98.88 385	95.78 440	85.09 437	98.78 307	92.65 440	91.29 353	99.37 317	94.85 386	99.85 87	99.46 220

v1192		97.81 281	97.44 299	98.91 233	98.88 357	98.68 214	99.51 177	99.34 278	96.18 361	99.20 232	99.34 326	94.03 278	99.36 321	95.32 378	95.18 369	98.69 318

EI-MVSNet		98.67 183	98.67 164	98.68 270	99.35 251	97.97 267	99.50 185	99.38 257	96.93 309	99.20 232	99.83 88	97.87 111	99.36 321	98.38 205	97.56 296	98.71 309

MVSTER		98.49 191	98.32 200	99.00 216	99.35 251	99.02 168	99.54 158	99.38 257	97.41 263	99.20 232	99.73 173	93.86 286	99.36 321	98.87 133	97.56 296	98.62 353

gg-mvs-nofinetune		96.17 364	95.32 376	98.73 263	98.79 370	98.14 257	99.38 257	94.09 446	91.07 424	98.07 368	91.04 444	89.62 374	99.35 324	96.75 338	99.09 203	98.68 323

pm-mvs1		97.68 305	97.28 323	98.88 240	99.06 329	98.62 222	99.50 185	99.45 219	96.32 350	97.87 377	99.79 137	92.47 323	99.35 324	97.54 288	93.54 398	98.67 331

OurMVSNet-221017-0		97.88 264	97.77 255	98.19 326	98.71 386	96.53 347	99.88 4	99.00 354	97.79 214	98.78 307	99.94 6	91.68 342	99.35 324	97.21 310	96.99 327	98.69 318

EGC-MVSNET		82.80 409	77.86 415	97.62 369	97.91 412	96.12 362	99.33 274	99.28 311	8.40 452	25.05 453	99.27 344	84.11 419	99.33 327	89.20 426	98.22 261	97.42 426

pmmvs6		96.53 356	96.09 361	97.82 359	98.69 388	95.47 377	99.37 259	99.47 199	93.46 409	97.41 386	99.78 144	87.06 402	99.33 327	96.92 333	92.70 410	98.65 342

V42		98.06 233	97.79 250	98.86 247	98.98 345	98.84 198	99.69 61	99.34 278	96.53 336	99.30 205	99.37 316	94.67 249	99.32 329	97.57 285	94.66 379	98.42 382

lessismore_v0				97.79 361	98.69 388	95.44 380		94.75 444		95.71 414	99.87 57	88.69 383	99.32 329	95.89 362	94.93 376	98.62 353

OpenMVS_ROB	92.34 20	94.38 389	93.70 395	96.41 399	97.38 421	93.17 418	99.06 349	98.75 390	86.58 435	94.84 421	98.26 414	81.53 430	99.32 329	89.01 427	97.87 280	96.76 429

v8		97.95 255	97.63 274	98.93 227	98.95 349	98.81 206	99.80 25	99.41 239	96.03 373	99.10 251	99.42 299	94.92 230	99.30 332	96.94 330	94.08 391	98.66 340

v1921920		97.80 283	97.45 294	98.84 251	98.80 369	98.53 230	99.52 168	99.34 278	96.15 365	99.24 221	99.47 288	93.98 280	99.29 333	95.40 376	95.13 371	98.69 318

anonymousdsp		98.44 195	98.28 203	98.94 225	98.50 403	98.96 179	99.77 34	99.50 156	97.07 294	98.87 293	99.77 153	94.76 242	99.28 334	98.66 167	97.60 292	98.57 368

MVSFormer		99.17 99	99.12 91	99.29 180	99.51 195	98.94 185	99.88 4	99.46 208	97.55 243	99.80 66	99.65 215	97.39 122	99.28 334	99.03 111	99.85 87	99.65 149

test_djsdf		98.67 183	98.57 184	98.98 218	98.70 387	98.91 190	99.88 4	99.46 208	97.55 243	99.22 226	99.88 46	95.73 195	99.28 334	99.03 111	97.62 291	98.75 301

VortexMVS		98.67 183	98.66 167	98.68 270	99.62 156	97.96 269	99.59 115	99.41 239	98.13 167	99.31 201	99.70 184	95.48 205	99.27 337	99.40 65	97.32 317	98.79 291

SSC-MVS3.2		97.34 333	97.15 330	97.93 347	99.02 336	95.76 369	99.48 202	99.58 73	97.62 235	99.09 254	99.53 265	87.95 394	99.27 337	96.42 351	95.66 358	98.75 301

cascas		97.69 302	97.43 303	98.48 291	98.60 397	97.30 300	98.18 431	99.39 249	92.96 413	98.41 346	98.78 395	93.77 289	99.27 337	98.16 227	98.61 234	98.86 287

v144192		97.92 259	97.60 277	98.87 244	98.83 367	98.65 217	99.55 153	99.34 278	96.20 359	99.32 200	99.40 307	94.36 265	99.26 340	96.37 355	95.03 373	98.70 314

dmvs_re		98.08 231	98.16 208	97.85 354	99.55 182	94.67 398	99.70 57	98.92 364	98.15 162	99.06 262	99.35 322	93.67 292	99.25 341	97.77 264	97.25 319	99.64 156

v2v482		98.06 233	97.77 255	98.92 229	98.90 355	98.82 204	99.57 132	99.36 266	96.65 324	99.19 235	99.35 322	94.20 270	99.25 341	97.72 271	94.97 374	98.69 318

v1240		97.69 302	97.32 318	98.79 259	98.85 364	98.43 244	99.48 202	99.36 266	96.11 368	99.27 214	99.36 319	93.76 290	99.24 343	94.46 390	95.23 368	98.70 314

WBMVS		97.74 293	97.50 286	98.46 297	99.24 284	97.43 296	99.21 319	99.42 236	97.45 256	98.96 279	99.41 303	88.83 380	99.23 344	98.94 121	96.02 344	98.71 309

v1144		97.98 250	97.69 266	98.85 250	98.87 360	98.66 216	99.54 158	99.35 273	96.27 354	99.23 225	99.35 322	94.67 249	99.23 344	96.73 339	95.16 370	98.68 323

v10		97.85 269	97.52 283	98.86 247	98.99 342	98.67 215	99.75 42	99.41 239	95.70 377	98.98 275	99.41 303	94.75 243	99.23 344	96.01 361	94.63 380	98.67 331

WR-MVS_H		98.13 225	97.87 245	98.90 235	99.02 336	98.84 198	99.70 57	99.59 68	97.27 274	98.40 347	99.19 354	95.53 202	99.23 344	98.34 211	93.78 396	98.61 362

miper_enhance_ethall		98.16 222	98.08 220	98.41 305	98.96 348	97.72 284	98.45 419	99.32 296	96.95 306	98.97 277	99.17 355	97.06 139	99.22 348	97.86 251	95.99 347	98.29 391

GG-mvs-BLEND				98.45 299	98.55 401	98.16 255	99.43 228	93.68 447		97.23 392	98.46 405	89.30 375	99.22 348	95.43 375	98.22 261	97.98 413

FC-MVSNet-test		98.75 176	98.62 177	99.15 202	99.08 326	99.45 108	99.86 11	99.60 62	98.23 152	98.70 319	99.82 97	96.80 147	99.22 348	99.07 107	96.38 336	98.79 291

UniMVSNet_NR-MVSNet		98.22 214	97.97 232	98.96 221	98.92 352	98.98 172	99.48 202	99.53 113	97.76 218	98.71 313	99.46 292	96.43 166	99.22 348	98.57 184	92.87 408	98.69 318

DU-MVS		98.08 231	97.79 250	98.96 221	98.87 360	98.98 172	99.41 240	99.45 219	97.87 202	98.71 313	99.50 276	94.82 234	99.22 348	98.57 184	92.87 408	98.68 323

cl____		98.01 246	97.84 248	98.55 285	99.25 282	97.97 267	98.71 402	99.34 278	96.47 343	98.59 337	99.54 261	95.65 198	99.21 353	97.21 310	95.77 353	98.46 379

WR-MVS		98.06 233	97.73 262	99.06 208	98.86 363	99.25 139	99.19 323	99.35 273	97.30 272	98.66 322	99.43 297	93.94 281	99.21 353	98.58 181	94.28 386	98.71 309

test_0402		96.64 354	96.24 356	97.85 354	98.85 364	96.43 351	99.44 223	99.26 315	93.52 407	96.98 400	99.52 269	88.52 388	99.20 355	92.58 415	97.50 303	97.93 416

SixPastTwentyTwo		97.50 321	97.33 317	98.03 336	98.65 391	96.23 359	99.77 34	98.68 402	97.14 285	97.90 375	99.93 10	90.45 361	99.18 356	97.00 324	96.43 335	98.67 331

cl22		97.85 269	97.64 273	98.48 291	99.09 323	97.87 276	98.60 412	99.33 286	97.11 291	98.87 293	99.22 350	92.38 328	99.17 357	98.21 221	95.99 347	98.42 382

tt0320		95.71 374	95.07 378	97.62 369	99.05 332	95.02 389	99.25 307	99.52 118	86.81 433	97.97 372	99.72 177	83.58 422	99.15 358	96.38 354	93.35 399	98.68 323

WB-MVSnew		97.65 310	97.65 270	97.63 368	98.78 373	97.62 290	99.13 333	98.33 412	97.36 267	99.07 257	98.94 382	95.64 199	99.15 358	92.95 409	98.68 232	96.12 436

IterMVS-SCA-FT		97.82 279	97.75 260	98.06 335	99.57 174	96.36 353	99.02 359	99.49 166	97.18 282	98.71 313	99.72 177	92.72 312	99.14 360	97.44 298	95.86 352	98.67 331

pmmvs5		97.52 318	97.30 320	98.16 328	98.57 400	96.73 337	99.27 296	98.90 371	96.14 366	98.37 349	99.53 265	91.54 348	99.14 360	97.51 290	95.87 351	98.63 351

v148		97.79 285	97.55 279	98.50 288	98.74 381	97.72 284	99.54 158	99.33 286	96.26 355	98.90 287	99.51 273	94.68 248	99.14 360	97.83 255	93.15 405	98.63 351

miper_ehance_all_eth		98.18 220	98.10 216	98.41 305	99.23 286	97.72 284	98.72 401	99.31 300	96.60 332	98.88 290	99.29 339	97.29 129	99.13 363	97.60 279	95.99 347	98.38 387

NR-MVSNet		97.97 253	97.61 276	99.02 213	98.87 360	99.26 137	99.47 211	99.42 236	97.63 233	97.08 398	99.50 276	95.07 223	99.13 363	97.86 251	93.59 397	98.68 323

IterMVS		97.83 276	97.77 255	98.02 338	99.58 170	96.27 357	99.02 359	99.48 178	97.22 280	98.71 313	99.70 184	92.75 309	99.13 363	97.46 296	96.00 346	98.67 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 390	94.90 381	91.84 415	97.24 425	80.01 445	98.52 416	99.48 178	89.01 429	91.99 432	99.67 208	85.67 409	99.13 363	95.44 374	97.03 326	96.39 433
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 238	97.96 233	98.33 312	99.26 278	97.38 298	98.56 415	99.31 300	96.65 324	98.88 290	99.52 269	96.58 157	99.12 367	97.39 301	95.53 363	98.47 376

pmmvs4		98.13 225	97.90 240	98.81 256	98.61 396	98.87 193	98.99 367	99.21 326	96.44 344	99.06 262	99.58 245	95.90 186	99.11 368	97.18 316	96.11 343	98.46 379

TransMVSNet (Re)		97.15 342	96.58 348	98.86 247	99.12 315	98.85 197	99.49 197	98.91 369	95.48 380	97.16 396	99.80 125	93.38 294	99.11 368	94.16 396	91.73 415	98.62 353

ambc				93.06 413	92.68 444	82.36 438	98.47 418	98.73 399		95.09 419	97.41 427	55.55 445	99.10 370	96.42 351	91.32 416	97.71 419

Baseline_NR-MVSNet		97.76 287	97.45 294	98.68 270	99.09 323	98.29 249	99.41 240	98.85 378	95.65 378	98.63 331	99.67 208	94.82 234	99.10 370	98.07 237	92.89 407	98.64 344

test_vis3_rt		87.04 405	85.81 408	90.73 419	93.99 443	81.96 440	99.76 37	90.23 454	92.81 415	81.35 442	91.56 442	40.06 451	99.07 372	94.27 393	88.23 429	91.15 442

CP-MVSNet		98.09 229	97.78 253	99.01 214	98.97 347	99.24 140	99.67 70	99.46 208	97.25 276	98.48 344	99.64 221	93.79 288	99.06 373	98.63 171	94.10 390	98.74 305

PS-CasMVS		97.93 256	97.59 278	98.95 223	98.99 342	99.06 164	99.68 67	99.52 118	97.13 286	98.31 352	99.68 202	92.44 327	99.05 374	98.51 192	94.08 391	98.75 301

K. test v3		97.10 344	96.79 344	98.01 339	98.72 384	96.33 354	99.87 8	97.05 431	97.59 237	96.16 410	99.80 125	88.71 382	99.04 375	96.69 342	96.55 333	98.65 342

new_pmnet		96.38 360	96.03 362	97.41 377	98.13 411	95.16 388	99.05 351	99.20 327	93.94 402	97.39 389	98.79 394	91.61 347	99.04 375	90.43 422	95.77 353	98.05 406

DIV-MVS_self_test		98.01 246	97.85 247	98.48 291	99.24 284	97.95 272	98.71 402	99.35 273	96.50 337	98.60 336	99.54 261	95.72 196	99.03 377	97.21 310	95.77 353	98.46 379

IterMVS-LS		98.46 194	98.42 193	98.58 279	99.59 168	98.00 265	99.37 259	99.43 234	96.94 308	99.07 257	99.59 241	97.87 111	99.03 377	98.32 214	95.62 359	98.71 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 310	97.68 267	97.55 373	98.62 394	94.97 391	98.84 389	99.30 305	96.83 315	98.19 361	99.34 326	97.01 142	99.02 379	95.00 384	96.01 345	98.64 344

Patchmtry		97.75 291	97.40 306	98.81 256	99.10 320	98.87 193	99.11 342	99.33 286	94.83 393	98.81 302	99.38 313	94.33 266	99.02 379	96.10 357	95.57 361	98.53 370

N_pmnet		94.95 384	95.83 367	92.31 414	98.47 404	79.33 446	99.12 336	92.81 452	93.87 403	97.68 382	99.13 360	93.87 285	99.01 381	91.38 419	96.19 341	98.59 366

CR-MVSNet		98.17 221	97.93 238	98.87 244	99.18 299	98.49 238	99.22 317	99.33 286	96.96 304	99.56 144	99.38 313	94.33 266	99.00 382	94.83 387	98.58 237	99.14 258

c3_l		98.12 227	98.04 225	98.38 309	99.30 266	97.69 288	98.81 392	99.33 286	96.67 322	98.83 299	99.34 326	97.11 135	98.99 383	97.58 281	95.34 366	98.48 374

test0.0.03 1		97.71 300	97.42 304	98.56 283	98.41 407	97.82 279	98.78 395	98.63 405	97.34 268	98.05 369	98.98 378	94.45 263	98.98 384	95.04 383	97.15 324	98.89 286

PatchT		97.03 346	96.44 352	98.79 259	98.99 342	98.34 248	99.16 327	99.07 345	92.13 418	99.52 153	97.31 431	94.54 258	98.98 384	88.54 429	98.73 229	99.03 274

GBi-Net		97.68 305	97.48 288	98.29 317	99.51 195	97.26 304	99.43 228	99.48 178	96.49 338	99.07 257	99.32 334	90.26 363	98.98 384	97.10 318	96.65 329	98.62 353

test1		97.68 305	97.48 288	98.29 317	99.51 195	97.26 304	99.43 228	99.48 178	96.49 338	99.07 257	99.32 334	90.26 363	98.98 384	97.10 318	96.65 329	98.62 353

FMVSNet3		98.03 241	97.76 259	98.84 251	99.39 242	98.98 172	99.40 248	99.38 257	96.67 322	99.07 257	99.28 341	92.93 304	98.98 384	97.10 318	96.65 329	98.56 369

FMVSNet2		97.72 297	97.36 309	98.80 258	99.51 195	98.84 198	99.45 217	99.42 236	96.49 338	98.86 297	99.29 339	90.26 363	98.98 384	96.44 350	96.56 332	98.58 367

FMVSNet1		96.84 350	96.36 354	98.29 317	99.32 264	97.26 304	99.43 228	99.48 178	95.11 385	98.55 339	99.32 334	83.95 420	98.98 384	95.81 364	96.26 340	98.62 353

ppachtmachnet_test		97.49 326	97.45 294	97.61 371	98.62 394	95.24 384	98.80 393	99.46 208	96.11 368	98.22 359	99.62 232	96.45 164	98.97 391	93.77 398	95.97 350	98.61 362

TranMVSNet+NR-MVSNet		97.93 256	97.66 269	98.76 262	98.78 373	98.62 222	99.65 83	99.49 166	97.76 218	98.49 343	99.60 239	94.23 269	98.97 391	98.00 241	92.90 406	98.70 314

MVStest1		96.08 367	95.48 372	97.89 351	98.93 350	96.70 338	99.56 139	99.35 273	92.69 416	91.81 433	99.46 292	89.90 369	98.96 393	95.00 384	92.61 411	98.00 411

tt0320-xc		95.31 380	94.59 384	97.45 376	98.92 352	94.73 395	99.20 322	99.31 300	86.74 434	97.23 392	99.72 177	81.14 433	98.95 394	97.08 321	91.98 414	98.67 331

test_method		91.10 400	91.36 402	90.31 420	95.85 433	73.72 453	94.89 441	99.25 317	68.39 444	95.82 413	99.02 372	80.50 434	98.95 394	93.64 401	94.89 378	98.25 394

ADS-MVSNet2		98.02 243	98.07 223	97.87 352	99.33 257	95.19 386	99.23 313	99.08 342	96.24 356	99.10 251	99.67 208	94.11 274	98.93 396	96.81 336	99.05 206	99.48 209

ET-MVSNet_ETH3D		96.49 357	95.64 371	99.05 210	99.53 186	98.82 204	98.84 389	97.51 429	97.63 233	84.77 438	99.21 353	92.09 332	98.91 397	98.98 116	92.21 413	99.41 230

miper_lstm_enhance		98.00 248	97.91 239	98.28 321	99.34 256	97.43 296	98.88 385	99.36 266	96.48 341	98.80 304	99.55 256	95.98 179	98.91 397	97.27 307	95.50 364	98.51 372

MonoMVSNet		98.38 203	98.47 191	98.12 333	98.59 399	96.19 361	99.72 53	98.79 387	97.89 200	99.44 169	99.52 269	96.13 174	98.90 399	98.64 169	97.54 298	99.28 247

PEN-MVS		97.76 287	97.44 299	98.72 265	98.77 378	98.54 229	99.78 32	99.51 136	97.06 296	98.29 355	99.64 221	92.63 318	98.89 400	98.09 230	93.16 404	98.72 307

testing3		97.28 336	96.76 345	98.82 253	99.37 247	98.07 262	99.45 217	99.36 266	97.56 242	97.89 376	98.95 381	83.70 421	98.82 401	96.03 359	98.56 240	99.58 177

testgi		97.65 310	97.50 286	98.13 332	99.36 250	96.45 350	99.42 235	99.48 178	97.76 218	97.87 377	99.45 294	91.09 355	98.81 402	94.53 389	98.52 243	99.13 260

testf1		90.42 403	90.68 404	89.65 423	97.78 415	73.97 451	99.13 333	98.81 383	89.62 426	91.80 434	98.93 383	62.23 443	98.80 403	86.61 437	91.17 417	96.19 434

APD_test2		90.42 403	90.68 404	89.65 423	97.78 415	73.97 451	99.13 333	98.81 383	89.62 426	91.80 434	98.93 383	62.23 443	98.80 403	86.61 437	91.17 417	96.19 434

MIMVSNet		97.73 295	97.45 294	98.57 280	99.45 225	97.50 294	99.02 359	98.98 356	96.11 368	99.41 178	99.14 359	90.28 362	98.74 405	95.74 366	98.93 214	99.47 215

LCM-MVSNet-Re		97.83 276	98.15 210	96.87 393	99.30 266	92.25 423	99.59 115	98.26 413	97.43 260	96.20 409	99.13 360	96.27 171	98.73 406	98.17 226	98.99 211	99.64 156

Syy-MVS		97.09 345	97.14 331	96.95 390	99.00 339	92.73 421	99.29 286	99.39 249	97.06 296	97.41 386	98.15 417	93.92 283	98.68 407	91.71 417	98.34 250	99.45 223

myMVS_eth3d		96.89 348	96.37 353	98.43 304	99.00 339	97.16 308	99.29 286	99.39 249	97.06 296	97.41 386	98.15 417	83.46 423	98.68 407	95.27 379	98.34 250	99.45 223

DTE-MVSNet		97.51 320	97.19 329	98.46 297	98.63 393	98.13 258	99.84 12	99.48 178	96.68 321	97.97 372	99.67 208	92.92 305	98.56 409	96.88 335	92.60 412	98.70 314

PC_three_1452									98.18 160	99.84 50	99.70 184	99.31 3	98.52 410	98.30 216	99.80 118	99.81 73

mvsany_test3		93.77 392	93.45 396	94.74 405	95.78 434	88.01 431	99.64 90	98.25 414	98.28 141	94.31 422	97.97 424	68.89 439	98.51 411	97.50 291	90.37 422	97.71 419

UnsupCasMVSNet_bld		93.53 393	92.51 399	96.58 398	97.38 421	93.82 409	98.24 428	99.48 178	91.10 423	93.10 427	96.66 433	74.89 437	98.37 412	94.03 397	87.71 430	97.56 424

Anonymous20240521		96.20 363	95.89 366	97.13 384	97.72 418	94.96 392	99.79 31	99.29 309	93.01 412	97.20 395	99.03 370	89.69 372	98.36 413	91.16 420	96.13 342	98.07 404

test_f		91.90 399	91.26 403	93.84 408	95.52 438	85.92 433	99.69 61	98.53 410	95.31 382	93.87 424	96.37 435	55.33 446	98.27 414	95.70 367	90.98 420	97.32 427

MDA-MVSNet_test_wron		95.45 376	94.60 383	98.01 339	98.16 410	97.21 307	99.11 342	99.24 320	93.49 408	80.73 444	98.98 378	93.02 302	98.18 415	94.22 395	94.45 383	98.64 344

UnsupCasMVSNet_eth		96.44 358	96.12 359	97.40 378	98.65 391	95.65 370	99.36 264	99.51 136	97.13 286	96.04 412	98.99 376	88.40 389	98.17 416	96.71 340	90.27 423	98.40 385

KD-MVS_2432*1600		94.62 385	93.72 393	97.31 379	97.19 427	95.82 367	98.34 423	99.20 327	95.00 389	97.57 383	98.35 410	87.95 394	98.10 417	92.87 411	77.00 442	98.01 408

miper_refine_blended		94.62 385	93.72 393	97.31 379	97.19 427	95.82 367	98.34 423	99.20 327	95.00 389	97.57 383	98.35 410	87.95 394	98.10 417	92.87 411	77.00 442	98.01 408

YYNet1		95.36 378	94.51 386	97.92 348	97.89 413	97.10 311	99.10 344	99.23 321	93.26 411	80.77 443	99.04 369	92.81 308	98.02 419	94.30 391	94.18 388	98.64 344

EU-MVSNet		97.98 250	98.03 226	97.81 360	98.72 384	96.65 343	99.66 77	99.66 28	98.09 174	98.35 350	99.82 97	95.25 216	98.01 420	97.41 300	95.30 367	98.78 293

Gipuma		90.99 401	90.15 406	93.51 409	98.73 382	90.12 429	93.98 442	99.45 219	79.32 440	92.28 430	94.91 437	69.61 438	97.98 421	87.42 433	95.67 357	92.45 440
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 379	94.73 382	97.15 382	95.53 437	95.94 365	99.35 269	99.10 339	95.13 383	93.55 425	97.54 426	88.15 393	97.91 422	94.58 388	89.69 426	97.61 422

PM-MVS		92.96 396	92.23 400	95.14 404	95.61 435	89.98 430	99.37 259	98.21 417	94.80 394	95.04 420	97.69 425	65.06 440	97.90 423	94.30 391	89.98 425	97.54 425

MDA-MVSNet-bldmvs		94.96 383	93.98 390	97.92 348	98.24 409	97.27 302	99.15 330	99.33 286	93.80 404	80.09 445	99.03 370	88.31 390	97.86 424	93.49 403	94.36 385	98.62 353

Patchmatch-RL test		95.84 370	95.81 368	95.95 402	95.61 435	90.57 428	98.24 428	98.39 411	95.10 387	95.20 417	98.67 398	94.78 238	97.77 425	96.28 356	90.02 424	99.51 201

Anonymous20231206		96.22 361	96.03 362	96.79 395	97.31 424	94.14 407	99.63 96	99.08 342	96.17 362	97.04 399	99.06 367	93.94 281	97.76 426	86.96 435	95.06 372	98.47 376

SD-MVS		99.41 55	99.52 12	99.05 210	99.74 93	99.68 57	99.46 214	99.52 118	99.11 40	99.88 37	99.91 24	99.43 1	97.70 427	98.72 158	99.93 30	99.77 94
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		97.25 338	97.35 311	96.95 390	97.84 414	93.61 415	99.57 132	96.63 437	96.13 367	98.87 293	98.61 401	94.59 253	97.70 427	95.08 382	98.86 220	99.55 184

dongtai		93.26 394	92.93 398	94.25 406	99.39 242	85.68 434	97.68 437	93.27 448	92.87 414	96.85 403	99.39 311	82.33 428	97.48 429	76.78 442	97.80 283	99.58 177

pmmvs3		94.09 391	93.25 397	96.60 397	94.76 442	94.49 401	98.92 381	98.18 419	89.66 425	96.48 406	98.06 423	86.28 406	97.33 430	89.68 425	87.20 431	97.97 414

KD-MVS_self_test		95.00 382	94.34 387	96.96 389	97.07 429	95.39 381	99.56 139	99.44 228	95.11 385	97.13 397	97.32 430	91.86 337	97.27 431	90.35 423	81.23 439	98.23 396

FMVSNet5		96.43 359	96.19 358	97.15 382	99.11 317	95.89 366	99.32 276	99.52 118	94.47 400	98.34 351	99.07 365	87.54 399	97.07 432	92.61 414	95.72 356	98.47 376

new-patchmatchnet		94.48 388	94.08 389	95.67 403	95.08 440	92.41 422	99.18 325	99.28 311	94.55 399	93.49 426	97.37 429	87.86 397	97.01 433	91.57 418	88.36 428	97.61 422

LCM-MVSNet		86.80 407	85.22 411	91.53 417	87.81 449	80.96 443	98.23 430	98.99 355	71.05 442	90.13 437	96.51 434	48.45 450	96.88 434	90.51 421	85.30 433	96.76 429

CL-MVSNet_self_test		94.49 387	93.97 391	96.08 401	96.16 432	93.67 414	98.33 425	99.38 257	95.13 383	97.33 390	98.15 417	92.69 316	96.57 435	88.67 428	79.87 440	97.99 412

MIMVSNet1		95.51 375	95.04 380	96.92 392	97.38 421	95.60 371	99.52 168	99.50 156	93.65 406	96.97 401	99.17 355	85.28 414	96.56 436	88.36 430	95.55 362	98.60 365

test20.03		96.12 365	95.96 364	96.63 396	97.44 420	95.45 378	99.51 177	99.38 257	96.55 335	96.16 410	99.25 347	93.76 290	96.17 437	87.35 434	94.22 387	98.27 392

tmp_tt		82.80 409	81.52 412	86.66 425	66.61 455	68.44 454	92.79 444	97.92 421	68.96 443	80.04 446	99.85 71	85.77 408	96.15 438	97.86 251	43.89 448	95.39 438

test_fmvs3		92.10 398	91.77 401	93.08 412	96.19 431	86.25 432	99.82 16	98.62 406	96.65 324	95.19 418	96.90 432	55.05 447	95.93 439	96.63 347	90.92 421	97.06 428

kuosan		90.92 402	90.11 407	93.34 410	98.78 373	85.59 435	98.15 432	93.16 450	89.37 428	92.07 431	98.38 409	81.48 431	95.19 440	62.54 449	97.04 325	99.25 252

dmvs_testset		95.02 381	96.12 359	91.72 416	99.10 320	80.43 444	99.58 125	97.87 423	97.47 252	95.22 416	98.82 390	93.99 279	95.18 441	88.09 431	94.91 377	99.56 183

PMMVS2		86.87 406	85.37 410	91.35 418	90.21 447	83.80 437	98.89 384	97.45 430	83.13 439	91.67 436	95.03 436	48.49 449	94.70 442	85.86 439	77.62 441	95.54 437

PMVS	70.75 22	75.98 415	74.97 416	79.01 431	70.98 454	55.18 456	93.37 443	98.21 417	65.08 448	61.78 449	93.83 439	21.74 456	92.53 443	78.59 441	91.12 419	89.34 444
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 408	85.65 409	82.75 429	86.77 450	63.39 455	98.35 422	98.92 364	74.11 441	83.39 440	98.98 378	50.85 448	92.40 444	84.54 440	94.97 374	92.46 439

WB-MVS		93.10 395	94.10 388	90.12 421	95.51 439	81.88 441	99.73 51	99.27 314	95.05 388	93.09 428	98.91 387	94.70 247	91.89 445	76.62 443	94.02 393	96.58 431

SSC-MVS		92.73 397	93.73 392	89.72 422	95.02 441	81.38 442	99.76 37	99.23 321	94.87 392	92.80 429	98.93 383	94.71 246	91.37 446	74.49 445	93.80 395	96.42 432

MVE	76.82 21	76.91 414	74.31 418	84.70 426	85.38 452	76.05 450	96.88 440	93.17 449	67.39 445	71.28 447	89.01 446	21.66 457	87.69 447	71.74 446	72.29 444	90.35 443
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 411	79.88 413	82.81 428	90.75 446	76.38 449	97.69 436	95.76 441	66.44 446	83.52 439	92.25 441	62.54 442	87.16 448	68.53 447	61.40 445	84.89 446

EMVS		80.02 412	79.22 414	82.43 430	91.19 445	76.40 448	97.55 439	92.49 453	66.36 447	83.01 441	91.27 443	64.63 441	85.79 449	65.82 448	60.65 446	85.08 445

ANet_high		77.30 413	74.86 417	84.62 427	75.88 453	77.61 447	97.63 438	93.15 451	88.81 430	64.27 448	89.29 445	36.51 452	83.93 450	75.89 444	52.31 447	92.33 441

wuyk23d		40.18 416	41.29 421	36.84 432	86.18 451	49.12 457	79.73 445	22.81 457	27.64 449	25.46 452	28.45 452	21.98 455	48.89 451	55.80 450	23.56 451	12.51 449

test123		39.01 418	42.50 420	28.53 433	39.17 456	20.91 458	98.75 398	19.17 458	19.83 451	38.57 450	66.67 448	33.16 453	15.42 452	37.50 452	29.66 450	49.26 447

testmvs		39.17 417	43.78 419	25.37 434	36.04 457	16.84 459	98.36 421	26.56 456	20.06 450	38.51 451	67.32 447	29.64 454	15.30 453	37.59 451	39.90 449	43.98 448

mmdepth		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

monomultidepth		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

test_blank		0.13 422	0.17 425	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	1.57 453	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet_test		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

DCPMVS		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

cdsmvs_eth3d_5k		24.64 419	32.85 422	0.00 435	0.00 458	0.00 460	0.00 446	99.51 136	0.00 453	0.00 454	99.56 253	96.58 157	0.00 454	0.00 453	0.00 452	0.00 450

pcd_1.5k_mvsjas		8.27 421	11.03 424	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	99.01 18	0.00 454	0.00 453	0.00 452	0.00 450

sosnet-low-res		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

sosnet		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uncertanet		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

Regformer		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

ab-mvs-re		8.30 420	11.06 423	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	99.58 245	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

uanet		0.02 423	0.03 426	0.00 435	0.00 458	0.00 460	0.00 446	0.00 459	0.00 453	0.00 454	0.27 454	0.00 458	0.00 454	0.00 453	0.00 452	0.00 450

WAC-MVS						97.16 308								95.47 373

FOURS1					99.91 1	99.93 1	99.87 8	99.56 83	99.10 41	99.81 62

test_one_0601					99.81 51	99.88 9		99.49 166	98.97 68	99.65 118	99.81 111	99.09 14

eth-test2					0.00 458

eth-test					0.00 458

RE-MVS-def			99.34 46		99.76 75	99.82 26	99.63 96	99.52 118	98.38 129	99.76 83	99.82 97	98.75 58		98.61 175	99.81 113	99.77 94

IU-MVS					99.84 34	99.88 9		99.32 296	98.30 140	99.84 50				98.86 138	99.85 87	99.89 26

save fliter					99.76 75	99.59 81	99.14 332	99.40 246	99.00 60

test0726					99.85 28	99.89 5	99.62 101	99.50 156	99.10 41	99.86 47	99.82 97	98.94 32

GSMVS																99.52 193

test_part2					99.81 51	99.83 20				99.77 77

sam_mvs1												94.86 233				99.52 193

sam_mvs												94.72 245

MTGPA								99.47 199

MTMP							99.54 158	98.88 374

test9_res														97.49 292	99.72 141	99.75 100

agg_prior2														97.21 310	99.73 140	99.75 100

test_prior4						99.56 87	98.99 367

test_prior2							98.96 374		98.34 135	99.01 268	99.52 269	98.68 67		97.96 243	99.74 138

新几何2							99.01 364

旧先验1					99.74 93	99.59 81		99.54 100			99.69 195	98.47 83			99.68 149	99.73 113

原ACMM2							98.95 377

test222					99.75 85	99.49 102	98.91 383	99.49 166	96.42 346	99.34 198	99.65 215	98.28 97			99.69 146	99.72 122

segment_acmp												98.96 25

testdata1							98.85 388		98.32 138

plane_prior7					99.29 270	97.03 321

plane_prior6					99.27 275	96.98 325						92.71 314

plane_prior4											99.61 236

plane_prior3						97.00 323			98.69 100	99.11 248

plane_prior2							99.39 252		98.97 68

plane_prior1					99.26 278

plane_prior						96.97 326	99.21 319		98.45 122						97.60 292

n2								0.00 459

nn								0.00 459

door-mid								98.05 420

test11								99.35 273

door								97.92 421

HQP5-MVS						96.83 333

HQP-NCC					99.19 296		98.98 370		98.24 149	98.66 322

ACMP_Plane					99.19 296		98.98 370		98.24 149	98.66 322

BP-MVS														97.19 314

HQP3-MVS								99.39 249							97.58 294

HQP2-MVS												92.47 323

NP-MVS					99.23 286	96.92 329					99.40 307

MDTV_nov1_ep13_2view						95.18 387	99.35 269		96.84 313	99.58 140		95.19 219		97.82 256		99.46 220

ACMMP++_ref															97.19 322

ACMMP++															97.43 312

Test By Simon												98.75 58

High-resolution multi-view benchmark