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 60	99.38 226	99.37 107	99.58 117	99.62 41	99.41 11	99.87 31	99.92 16	98.81 47	100.00 1	99.97 1	99.93 25	99.94 11

test_fmvsm_n_1920		99.69 4	99.66 3	99.78 57	99.84 32	99.44 101	99.58 117	99.69 18	99.43 9	99.98 7	99.91 22	98.62 73	100.00 1	99.97 1	99.95 17	99.90 17

test_vis1_n_1920		98.63 170	98.40 177	99.31 156	99.86 20	97.94 256	99.67 69	99.62 41	99.43 9	99.99 2	99.91 22	87.29 380	100.00 1	99.92 13	99.92 28	99.98 2

fmvsm_l_conf0.5_n_a		99.71 1	99.67 1	99.85 32	99.86 20	99.61 72	99.56 130	99.63 39	99.48 3	99.98 7	99.83 74	98.75 58	99.99 4	99.97 1	99.96 12	99.94 11

fmvsm_l_conf0.5_n		99.71 1	99.67 1	99.85 32	99.84 32	99.63 69	99.56 130	99.63 39	99.47 4	99.98 7	99.82 83	98.75 58	99.99 4	99.97 1	99.97 7	99.94 11

test_fmvsmconf_n		99.70 3	99.64 4	99.87 15	99.80 52	99.66 58	99.48 188	99.64 36	99.45 7	99.92 18	99.92 16	98.62 73	99.99 4	99.96 7	99.99 1	99.96 7

patch_mono-2		99.26 76	99.62 5	98.16 308	99.81 46	94.59 376	99.52 157	99.64 36	99.33 15	99.73 72	99.90 29	99.00 22	99.99 4	99.69 23	99.98 4	99.89 20

h-mvs33		97.70 282	97.28 303	98.97 203	99.70 106	97.27 283	99.36 244	99.45 204	98.94 60	99.66 94	99.64 200	94.93 214	99.99 4	99.48 48	84.36 409	99.65 135

xiu_mvs_v1_base_debu		99.29 70	99.27 63	99.34 149	99.63 137	98.97 163	99.12 311	99.51 121	98.86 66	99.84 37	99.47 266	98.18 100	99.99 4	99.50 43	99.31 168	99.08 245

xiu_mvs_v1_base		99.29 70	99.27 63	99.34 149	99.63 137	98.97 163	99.12 311	99.51 121	98.86 66	99.84 37	99.47 266	98.18 100	99.99 4	99.50 43	99.31 168	99.08 245

xiu_mvs_v1_base_debi		99.29 70	99.27 63	99.34 149	99.63 137	98.97 163	99.12 311	99.51 121	98.86 66	99.84 37	99.47 266	98.18 100	99.99 4	99.50 43	99.31 168	99.08 245

EPNet		98.86 141	98.71 145	99.30 161	97.20 401	98.18 238	99.62 95	98.91 349	99.28 18	98.63 309	99.81 97	95.96 175	99.99 4	99.24 74	99.72 127	99.73 101
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		99.40 54	99.28 60	99.74 66	99.67 116	99.31 117	99.52 157	98.87 356	99.55 1	99.74 70	99.80 110	96.47 158	99.98 13	99.97 1	99.97 7	99.94 11

test_cas_vis1_n_1920		99.16 90	99.01 102	99.61 93	99.81 46	98.86 183	99.65 81	99.64 36	99.39 12	99.97 15	99.94 6	93.20 283	99.98 13	99.55 36	99.91 35	99.99 1

test_vis1_n		97.92 241	97.44 280	99.34 149	99.53 170	98.08 244	99.74 46	99.49 151	99.15 23	100.00 1	99.94 6	79.51 410	99.98 13	99.88 15	99.76 119	99.97 4

xiu_mvs_v2_base		99.26 76	99.25 67	99.29 164	99.53 170	98.91 177	99.02 334	99.45 204	98.80 75	99.71 79	99.26 323	98.94 32	99.98 13	99.34 62	99.23 173	98.98 259

PS-MVSNAJ		99.32 65	99.32 46	99.30 161	99.57 158	98.94 173	98.97 348	99.46 193	98.92 63	99.71 79	99.24 325	99.01 18	99.98 13	99.35 57	99.66 137	98.97 260

QAPM		98.67 166	98.30 184	99.80 51	99.20 273	99.67 56	99.77 34	99.72 11	94.74 373	98.73 289	99.90 29	95.78 185	99.98 13	96.96 306	99.88 58	99.76 91

3Dnovator	97.25 9	99.24 81	99.05 90	99.81 48	99.12 295	99.66 58	99.84 12	99.74 10	99.09 38	98.92 263	99.90 29	95.94 178	99.98 13	98.95 104	99.92 28	99.79 78

OpenMVS	96.50 16	98.47 175	98.12 196	99.52 120	99.04 313	99.53 88	99.82 16	99.72 11	94.56 376	98.08 343	99.88 41	94.73 230	99.98 13	97.47 274	99.76 119	99.06 251

reproduce_model		99.63 7	99.54 11	99.90 4	99.78 57	99.88 8	99.56 130	99.55 80	99.15 23	99.90 21	99.90 29	99.00 22	99.97 21	99.11 85	99.91 35	99.86 33

test_fmvsmconf0.1_n		99.55 17	99.45 24	99.86 25	99.44 208	99.65 62	99.50 173	99.61 48	99.45 7	99.87 31	99.92 16	97.31 126	99.97 21	99.95 9	99.99 1	99.97 4

test_fmvs1_n		98.41 181	98.14 193	99.21 176	99.82 42	97.71 269	99.74 46	99.49 151	99.32 16	99.99 2	99.95 3	85.32 391	99.97 21	99.82 18	99.84 84	99.96 7

CANet_DTU		98.97 131	98.87 126	99.25 171	99.33 238	98.42 230	99.08 320	99.30 286	99.16 22	99.43 154	99.75 144	95.27 202	99.97 21	98.56 171	99.95 17	99.36 219

MVS_0304		99.15 92	98.96 112	99.73 69	98.92 330	99.37 107	99.37 239	96.92 407	99.51 2	99.66 94	99.78 129	96.69 149	99.97 21	99.84 17	99.97 7	99.84 43

MTAPA		99.52 20	99.39 32	99.89 7	99.90 4	99.86 16	99.66 75	99.47 184	98.79 76	99.68 85	99.81 97	98.43 86	99.97 21	98.88 114	99.90 44	99.83 53

PGM-MVS		99.45 38	99.31 52	99.86 25	99.87 15	99.78 39	99.58 117	99.65 33	97.84 187	99.71 79	99.80 110	99.12 13	99.97 21	98.33 194	99.87 61	99.83 53

mPP-MVS		99.44 42	99.30 54	99.86 25	99.88 11	99.79 33	99.69 60	99.48 163	98.12 150	99.50 139	99.75 144	98.78 51	99.97 21	98.57 168	99.89 55	99.83 53

CP-MVS		99.45 38	99.32 46	99.85 32	99.83 39	99.75 43	99.69 60	99.52 107	98.07 160	99.53 134	99.63 206	98.93 36	99.97 21	98.74 139	99.91 35	99.83 53

SteuartSystems-ACMMP		99.54 18	99.42 25	99.87 15	99.82 42	99.81 28	99.59 109	99.51 121	98.62 91	99.79 51	99.83 74	99.28 4	99.97 21	98.48 178	99.90 44	99.84 43
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3Dnovator+	97.12 13	99.18 86	98.97 108	99.82 45	99.17 287	99.68 53	99.81 20	99.51 121	99.20 20	98.72 290	99.89 34	95.68 189	99.97 21	98.86 122	99.86 69	99.81 65

fmvsm_s_conf0.5_n_2		99.32 65	99.13 79	99.89 7	99.80 52	99.77 40	99.44 205	99.58 63	99.47 4	99.99 2	99.93 10	94.04 261	99.96 32	99.96 7	99.93 25	99.93 16

reproduce-ours		99.61 8	99.52 12	99.90 4	99.76 68	99.88 8	99.52 157	99.54 89	99.13 26	99.89 23	99.89 34	98.96 25	99.96 32	99.04 93	99.90 44	99.85 37

our_new_method		99.61 8	99.52 12	99.90 4	99.76 68	99.88 8	99.52 157	99.54 89	99.13 26	99.89 23	99.89 34	98.96 25	99.96 32	99.04 93	99.90 44	99.85 37

fmvsm_s_conf0.5_n_a		99.56 16	99.47 20	99.85 32	99.83 39	99.64 68	99.52 157	99.65 33	99.10 33	99.98 7	99.92 16	97.35 125	99.96 32	99.94 11	99.92 28	99.95 9

fmvsm_s_conf0.5_n		99.51 21	99.40 30	99.85 32	99.84 32	99.65 62	99.51 166	99.67 23	99.13 26	99.98 7	99.92 16	96.60 152	99.96 32	99.95 9	99.96 12	99.95 9

mvsany_test1		99.50 23	99.46 23	99.62 92	99.61 147	99.09 146	98.94 354	99.48 163	99.10 33	99.96 16	99.91 22	98.85 42	99.96 32	99.72 21	99.58 147	99.82 58

test_fmvs1		98.88 137	98.79 138	99.16 181	99.69 110	97.61 273	99.55 144	99.49 151	99.32 16	99.98 7	99.91 22	91.41 331	99.96 32	99.82 18	99.92 28	99.90 17

DVP-MVS++		99.59 11	99.50 16	99.88 9	99.51 178	99.88 8	99.87 8	99.51 121	98.99 51	99.88 26	99.81 97	99.27 5	99.96 32	98.85 124	99.80 104	99.81 65

MSC_two_6792asdad				99.87 15	99.51 178	99.76 41		99.33 268					99.96 32	98.87 117	99.84 84	99.89 20

No_MVS				99.87 15	99.51 178	99.76 41		99.33 268					99.96 32	98.87 117	99.84 84	99.89 20

ZD-MVS					99.71 101	99.79 33		99.61 48	96.84 291	99.56 127	99.54 240	98.58 75	99.96 32	96.93 309	99.75 121

SED-MVS		99.61 8	99.52 12	99.88 9	99.84 32	99.90 2	99.60 102	99.48 163	99.08 39	99.91 19	99.81 97	99.20 7	99.96 32	98.91 111	99.85 76	99.79 78

test_241102_TWO								99.48 163	99.08 39	99.88 26	99.81 97	98.94 32	99.96 32	98.91 111	99.84 84	99.88 26

ZNCC-MVS		99.47 32	99.33 44	99.87 15	99.87 15	99.81 28	99.64 84	99.67 23	98.08 159	99.55 131	99.64 200	98.91 37	99.96 32	98.72 142	99.90 44	99.82 58

DVP-MVS		99.57 15	99.47 20	99.88 9	99.85 26	99.89 4	99.57 124	99.37 248	99.10 33	99.81 45	99.80 110	98.94 32	99.96 32	98.93 108	99.86 69	99.81 65
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 51	99.81 45	99.80 110	99.09 14	99.96 32	98.85 124	99.90 44	99.88 26

test_0728_SECOND				99.91 2	99.84 32	99.89 4	99.57 124	99.51 121					99.96 32	98.93 108	99.86 69	99.88 26

SR-MVS		99.43 45	99.29 58	99.86 25	99.75 78	99.83 19	99.59 109	99.62 41	98.21 137	99.73 72	99.79 122	98.68 67	99.96 32	98.44 184	99.77 116	99.79 78

DPE-MVS		99.46 34	99.32 46	99.91 2	99.78 57	99.88 8	99.36 244	99.51 121	98.73 83	99.88 26	99.84 69	98.72 64	99.96 32	98.16 208	99.87 61	99.88 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 47	99.29 58	99.80 51	99.62 143	99.55 83	99.50 173	99.70 15	98.79 76	99.77 60	99.96 1	97.45 120	99.96 32	98.92 110	99.90 44	99.89 20

HFP-MVS		99.49 25	99.37 36	99.86 25	99.87 15	99.80 30	99.66 75	99.67 23	98.15 144	99.68 85	99.69 174	99.06 16	99.96 32	98.69 147	99.87 61	99.84 43

region2R		99.48 29	99.35 40	99.87 15	99.88 11	99.80 30	99.65 81	99.66 28	98.13 149	99.66 94	99.68 181	98.96 25	99.96 32	98.62 156	99.87 61	99.84 43

HPM-MVS++		99.39 56	99.23 70	99.87 15	99.75 78	99.84 18	99.43 210	99.51 121	98.68 88	99.27 196	99.53 244	98.64 72	99.96 32	98.44 184	99.80 104	99.79 78

APDe-MVS		99.66 5	99.57 8	99.92 1	99.77 64	99.89 4	99.75 42	99.56 72	99.02 44	99.88 26	99.85 59	99.18 10	99.96 32	99.22 75	99.92 28	99.90 17
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 25	99.36 38	99.86 25	99.87 15	99.79 33	99.66 75	99.67 23	98.15 144	99.67 89	99.69 174	98.95 30	99.96 32	98.69 147	99.87 61	99.84 43

MP-MVS		99.33 63	99.15 77	99.87 15	99.88 11	99.82 25	99.66 75	99.46 193	98.09 155	99.48 143	99.74 149	98.29 95	99.96 32	97.93 226	99.87 61	99.82 58
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 108	98.90 120	99.74 66	99.80 52	99.46 99	99.59 109	99.49 151	97.03 278	99.63 109	99.69 174	97.27 129	99.96 32	97.82 237	99.84 84	99.81 65

PVSNet_Blended_VisFu		99.36 60	99.28 60	99.61 93	99.86 20	99.07 151	99.47 194	99.93 2	97.66 210	99.71 79	99.86 54	97.73 115	99.96 32	99.47 50	99.82 97	99.79 78

UGNet		98.87 138	98.69 147	99.40 141	99.22 270	98.72 197	99.44 205	99.68 20	99.24 19	99.18 220	99.42 277	92.74 293	99.96 32	99.34 62	99.94 23	99.53 176
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 65	99.32 46	99.32 155	99.85 26	98.29 233	99.71 55	99.66 28	98.11 152	99.41 161	99.80 110	98.37 92	99.96 32	98.99 99	99.96 12	99.72 108

ACMMP		99.45 38	99.32 46	99.82 45	99.89 8	99.67 56	99.62 95	99.69 18	98.12 150	99.63 109	99.84 69	98.73 63	99.96 32	98.55 174	99.83 93	99.81 65
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_conf0.1_n_2		99.37 58	99.22 71	99.81 48	99.77 64	99.75 43	99.46 197	99.60 54	99.47 4	99.98 7	99.94 6	94.98 211	99.95 63	99.97 1	99.79 111	99.73 101

test_fmvsmconf0.01_n		99.22 83	99.03 94	99.79 54	98.42 381	99.48 96	99.55 144	99.51 121	99.39 12	99.78 56	99.93 10	94.80 222	99.95 63	99.93 12	99.95 17	99.94 11

SR-MVS-dyc-post		99.45 38	99.31 52	99.85 32	99.76 68	99.82 25	99.63 90	99.52 107	98.38 113	99.76 66	99.82 83	98.53 79	99.95 63	98.61 159	99.81 100	99.77 86

GST-MVS		99.40 54	99.24 68	99.85 32	99.86 20	99.79 33	99.60 102	99.67 23	97.97 172	99.63 109	99.68 181	98.52 80	99.95 63	98.38 187	99.86 69	99.81 65

CANet		99.25 80	99.14 78	99.59 96	99.41 216	99.16 136	99.35 249	99.57 67	98.82 71	99.51 138	99.61 215	96.46 159	99.95 63	99.59 31	99.98 4	99.65 135

MP-MVS-pluss		99.37 58	99.20 73	99.88 9	99.90 4	99.87 15	99.30 260	99.52 107	97.18 260	99.60 119	99.79 122	98.79 50	99.95 63	98.83 130	99.91 35	99.83 53
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 47	99.27 63	99.88 9	99.89 8	99.80 30	99.67 69	99.50 141	98.70 85	99.77 60	99.49 257	98.21 98	99.95 63	98.46 182	99.77 116	99.88 26
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
testdata2													99.95 63	96.67 321

APD-MVS_3200maxsize		99.48 29	99.35 40	99.85 32	99.76 68	99.83 19	99.63 90	99.54 89	98.36 117	99.79 51	99.82 83	98.86 41	99.95 63	98.62 156	99.81 100	99.78 84

RPMNet		96.72 330	95.90 343	99.19 178	99.18 279	98.49 222	99.22 294	99.52 107	88.72 409	99.56 127	97.38 403	94.08 260	99.95 63	86.87 411	98.58 219	99.14 237

sss		99.17 88	99.05 90	99.53 114	99.62 143	98.97 163	99.36 244	99.62 41	97.83 188	99.67 89	99.65 194	97.37 124	99.95 63	99.19 77	99.19 176	99.68 125

MVSMamba_PlusPlus		99.46 34	99.41 29	99.64 85	99.68 114	99.50 93	99.75 42	99.50 141	98.27 127	99.87 31	99.92 16	98.09 104	99.94 74	99.65 27	99.95 17	99.47 196

fmvsm_s_conf0.1_n_a		99.26 76	99.06 89	99.85 32	99.52 175	99.62 70	99.54 148	99.62 41	98.69 86	99.99 2	99.96 1	94.47 246	99.94 74	99.88 15	99.92 28	99.98 2

fmvsm_s_conf0.1_n		99.29 70	99.10 83	99.86 25	99.70 106	99.65 62	99.53 156	99.62 41	98.74 82	99.99 2	99.95 3	94.53 244	99.94 74	99.89 14	99.96 12	99.97 4

TSAR-MVS + MP.		99.58 12	99.50 16	99.81 48	99.91 1	99.66 58	99.63 90	99.39 232	98.91 64	99.78 56	99.85 59	99.36 2	99.94 74	98.84 127	99.88 58	99.82 58
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 135	98.75 141	99.39 145	99.46 201	98.61 208	99.76 37	99.50 141	98.06 164	99.81 45	99.88 41	93.91 268	99.94 74	99.11 85	99.27 171	99.61 151

mamv4		99.33 63	99.42 25	99.07 189	99.67 116	97.73 264	99.42 217	99.60 54	98.15 144	99.94 17	99.91 22	98.42 88	99.94 74	99.72 21	99.96 12	99.54 170

XVS		99.53 19	99.42 25	99.87 15	99.85 26	99.83 19	99.69 60	99.68 20	98.98 54	99.37 172	99.74 149	98.81 47	99.94 74	98.79 135	99.86 69	99.84 43

X-MVStestdata		96.55 333	95.45 352	99.87 15	99.85 26	99.83 19	99.69 60	99.68 20	98.98 54	99.37 172	64.01 426	98.81 47	99.94 74	98.79 135	99.86 69	99.84 43

旧先验2							98.96 349		96.70 298	99.47 144			99.94 74	98.19 204

新几何1				99.75 63	99.75 78	99.59 75		99.54 89	96.76 294	99.29 190	99.64 200	98.43 86	99.94 74	96.92 311	99.66 137	99.72 108

testdata				99.54 106	99.75 78	98.95 170		99.51 121	97.07 272	99.43 154	99.70 164	98.87 40	99.94 74	97.76 244	99.64 140	99.72 108

HPM-MVS		99.42 47	99.28 60	99.83 44	99.90 4	99.72 47	99.81 20	99.54 89	97.59 215	99.68 85	99.63 206	98.91 37	99.94 74	98.58 165	99.91 35	99.84 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 84	99.10 83	99.45 134	99.89 8	98.52 218	99.39 232	99.94 1	98.73 83	99.11 229	99.89 34	95.50 194	99.94 74	99.50 43	99.97 7	99.89 20

APD-MVS		99.27 74	99.08 87	99.84 43	99.75 78	99.79 33	99.50 173	99.50 141	97.16 262	99.77 60	99.82 83	98.78 51	99.94 74	97.56 265	99.86 69	99.80 74
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 29	99.42 25	99.65 79	99.72 96	99.40 106	99.05 326	99.66 28	99.14 25	99.57 126	99.80 110	98.46 84	99.94 74	99.57 34	99.84 84	99.60 154
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		99.06 117	98.88 125	99.61 93	99.62 143	99.16 136	99.37 239	99.56 72	98.04 167	99.53 134	99.62 211	96.84 143	99.94 74	98.85 124	98.49 227	99.72 108

DeepC-MVS	98.35 2	99.30 68	99.19 74	99.64 85	99.82 42	99.23 129	99.62 95	99.55 80	98.94 60	99.63 109	99.95 3	95.82 184	99.94 74	99.37 56	99.97 7	99.73 101
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 74	99.12 81	99.74 66	99.18 279	99.75 43	99.56 130	99.57 67	98.45 106	99.49 142	99.85 59	97.77 114	99.94 74	98.33 194	99.84 84	99.52 177

GDP-MVS		99.08 114	98.89 123	99.64 85	99.53 170	99.34 111	99.64 84	99.48 163	98.32 122	99.77 60	99.66 192	95.14 208	99.93 92	98.97 103	99.50 153	99.64 142

SDMVSNet		99.11 108	98.90 120	99.75 63	99.81 46	99.59 75	99.81 20	99.65 33	98.78 79	99.64 106	99.88 41	94.56 240	99.93 92	99.67 25	98.26 239	99.72 108

FE-MVS		98.48 174	98.17 189	99.40 141	99.54 169	98.96 167	99.68 66	98.81 363	95.54 357	99.62 113	99.70 164	93.82 271	99.93 92	97.35 283	99.46 155	99.32 225

SF-MVS		99.38 57	99.24 68	99.79 54	99.79 55	99.68 53	99.57 124	99.54 89	97.82 192	99.71 79	99.80 110	98.95 30	99.93 92	98.19 204	99.84 84	99.74 96

dcpmvs_2		99.23 82	99.58 7	98.16 308	99.83 39	94.68 374	99.76 37	99.52 107	99.07 41	99.98 7	99.88 41	98.56 77	99.93 92	99.67 25	99.98 4	99.87 31

Anonymous20240529		98.09 211	97.68 248	99.34 149	99.66 126	98.44 227	99.40 228	99.43 218	93.67 383	99.22 207	99.89 34	90.23 347	99.93 92	99.26 73	98.33 233	99.66 131

ACMMP_NAP		99.47 32	99.34 42	99.88 9	99.87 15	99.86 16	99.47 194	99.48 163	98.05 166	99.76 66	99.86 54	98.82 46	99.93 92	98.82 134	99.91 35	99.84 43

EI-MVSNet-UG-set		99.58 12	99.57 8	99.64 85	99.78 57	99.14 141	99.60 102	99.45 204	99.01 46	99.90 21	99.83 74	98.98 24	99.93 92	99.59 31	99.95 17	99.86 33

无先验							98.99 342	99.51 121	96.89 288				99.93 92	97.53 268		99.72 108

VDDNet		97.55 296	97.02 315	99.16 181	99.49 191	98.12 243	99.38 237	99.30 286	95.35 359	99.68 85	99.90 29	82.62 403	99.93 92	99.31 65	98.13 250	99.42 208

ab-mvs		98.86 141	98.63 154	99.54 106	99.64 134	99.19 131	99.44 205	99.54 89	97.77 196	99.30 187	99.81 97	94.20 254	99.93 92	99.17 81	98.82 208	99.49 189

F-COLMAP		99.19 84	99.04 92	99.64 85	99.78 57	99.27 124	99.42 217	99.54 89	97.29 251	99.41 161	99.59 220	98.42 88	99.93 92	98.19 204	99.69 132	99.73 101

BP-MVS1		99.12 103	98.94 116	99.65 79	99.51 178	99.30 119	99.67 69	98.92 344	98.48 103	99.84 37	99.69 174	94.96 212	99.92 104	99.62 30	99.79 111	99.71 117

Anonymous202405211		98.30 192	97.98 213	99.26 170	99.57 158	98.16 239	99.41 220	98.55 385	96.03 351	99.19 216	99.74 149	91.87 318	99.92 104	99.16 82	98.29 238	99.70 119

EI-MVSNet-Vis-set		99.58 12	99.56 10	99.64 85	99.78 57	99.15 140	99.61 101	99.45 204	99.01 46	99.89 23	99.82 83	99.01 18	99.92 104	99.56 35	99.95 17	99.85 37

VDD-MVS		97.73 276	97.35 292	98.88 223	99.47 199	97.12 291	99.34 252	98.85 358	98.19 139	99.67 89	99.85 59	82.98 401	99.92 104	99.49 47	98.32 237	99.60 154

VNet		99.11 108	98.90 120	99.73 69	99.52 175	99.56 81	99.41 220	99.39 232	99.01 46	99.74 70	99.78 129	95.56 192	99.92 104	99.52 41	98.18 246	99.72 108

XVG-OURS-SEG-HR		98.69 164	98.62 159	98.89 221	99.71 101	97.74 263	99.12 311	99.54 89	98.44 109	99.42 157	99.71 160	94.20 254	99.92 104	98.54 175	98.90 202	99.00 256

mvsmamba		99.06 117	98.96 112	99.36 147	99.47 199	98.64 204	99.70 56	99.05 328	97.61 214	99.65 101	99.83 74	96.54 155	99.92 104	99.19 77	99.62 143	99.51 184

HPM-MVS_fast		99.51 21	99.40 30	99.85 32	99.91 1	99.79 33	99.76 37	99.56 72	97.72 201	99.76 66	99.75 144	99.13 12	99.92 104	99.07 91	99.92 28	99.85 37

HY-MVS	97.30 7	98.85 148	98.64 153	99.47 131	99.42 211	99.08 149	99.62 95	99.36 249	97.39 243	99.28 191	99.68 181	96.44 161	99.92 104	98.37 189	98.22 241	99.40 213

DP-MVS		99.16 90	98.95 114	99.78 57	99.77 64	99.53 88	99.41 220	99.50 141	97.03 278	99.04 245	99.88 41	97.39 121	99.92 104	98.66 151	99.90 44	99.87 31

IB-MVS	95.67 18	96.22 339	95.44 353	98.57 261	99.21 271	96.70 319	98.65 383	97.74 402	96.71 297	97.27 367	98.54 379	86.03 385	99.92 104	98.47 181	86.30 407	99.10 240
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 25	99.39 32	99.77 60	99.63 137	99.59 75	99.36 244	99.46 193	99.07 41	99.79 51	99.82 83	98.85 42	99.92 104	98.68 149	99.87 61	99.82 58
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		99.46 34	99.39 32	99.67 74	99.55 166	99.58 80	99.74 46	99.51 121	98.42 110	99.87 31	99.84 69	98.05 107	99.91 116	99.58 33	99.94 23	99.52 177

9.14			99.10 83		99.72 96		99.40 228	99.51 121	97.53 225	99.64 106	99.78 129	98.84 44	99.91 116	97.63 256	99.82 97

SMA-MVS		99.44 42	99.30 54	99.85 32	99.73 92	99.83 19	99.56 130	99.47 184	97.45 234	99.78 56	99.82 83	99.18 10	99.91 116	98.79 135	99.89 55	99.81 65
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					99.67 116	99.65 62	99.05 326	99.41 223	96.22 336	98.95 259	99.49 257	98.77 54	99.91 116

train_agg		99.02 123	98.77 139	99.77 60	99.67 116	99.65 62	99.05 326	99.41 223	96.28 330	98.95 259	99.49 257	98.76 55	99.91 116	97.63 256	99.72 127	99.75 92

test_8					99.67 116	99.61 72	99.03 331	99.41 223	96.28 330	98.93 262	99.48 263	98.76 55	99.91 116

agg_prior					99.67 116	99.62 70		99.40 229		98.87 272			99.91 116

原ACMM1				99.65 79	99.73 92	99.33 112		99.47 184	97.46 231	99.12 227	99.66 192	98.67 69	99.91 116	97.70 253	99.69 132	99.71 117

LFMVS		97.90 244	97.35 292	99.54 106	99.52 175	99.01 158	99.39 232	98.24 392	97.10 270	99.65 101	99.79 122	84.79 394	99.91 116	99.28 69	98.38 230	99.69 121

XVG-OURS		98.73 162	98.68 148	98.88 223	99.70 106	97.73 264	98.92 356	99.55 80	98.52 100	99.45 147	99.84 69	95.27 202	99.91 116	98.08 215	98.84 206	99.00 256

PLC	97.94 4	99.02 123	98.85 130	99.53 114	99.66 126	99.01 158	99.24 288	99.52 107	96.85 290	99.27 196	99.48 263	98.25 97	99.91 116	97.76 244	99.62 143	99.65 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 289	97.06 314	99.47 131	99.61 147	99.09 146	98.04 409	99.25 298	91.24 400	98.51 319	99.70 164	94.55 242	99.91 116	92.76 388	99.85 76	99.42 208
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mmtdpeth		96.95 325	96.71 324	97.67 344	99.33 238	94.90 371	99.89 2	99.28 292	98.15 144	99.72 77	98.57 378	86.56 383	99.90 128	99.82 18	89.02 402	98.20 372

UWE-MVS		97.58 295	97.29 302	98.48 272	99.09 303	96.25 339	99.01 339	96.61 413	97.86 182	99.19 216	99.01 350	88.72 362	99.90 128	97.38 281	98.69 214	99.28 228

test_vis1_rt		95.81 349	95.65 348	96.32 375	99.67 116	91.35 402	99.49 184	96.74 411	98.25 130	95.24 390	98.10 396	74.96 411	99.90 128	99.53 39	98.85 205	97.70 396

FA-MVS(test-final)		98.75 159	98.53 170	99.41 140	99.55 166	99.05 154	99.80 25	99.01 333	96.59 312	99.58 123	99.59 220	95.39 197	99.90 128	97.78 240	99.49 154	99.28 228

MCST-MVS		99.43 45	99.30 54	99.82 45	99.79 55	99.74 46	99.29 265	99.40 229	98.79 76	99.52 136	99.62 211	98.91 37	99.90 128	98.64 153	99.75 121	99.82 58

CDPH-MVS		99.13 97	98.91 119	99.80 51	99.75 78	99.71 49	99.15 305	99.41 223	96.60 310	99.60 119	99.55 235	98.83 45	99.90 128	97.48 272	99.83 93	99.78 84

NCCC		99.34 62	99.19 74	99.79 54	99.61 147	99.65 62	99.30 260	99.48 163	98.86 66	99.21 210	99.63 206	98.72 64	99.90 128	98.25 200	99.63 142	99.80 74

114514_t		98.93 133	98.67 149	99.72 71	99.85 26	99.53 88	99.62 95	99.59 59	92.65 395	99.71 79	99.78 129	98.06 106	99.90 128	98.84 127	99.91 35	99.74 96

1112_ss		98.98 129	98.77 139	99.59 96	99.68 114	99.02 156	99.25 286	99.48 163	97.23 257	99.13 225	99.58 224	96.93 142	99.90 128	98.87 117	98.78 211	99.84 43

PHI-MVS		99.30 68	99.17 76	99.70 72	99.56 162	99.52 91	99.58 117	99.80 8	97.12 266	99.62 113	99.73 155	98.58 75	99.90 128	98.61 159	99.91 35	99.68 125

AdaColmap		99.01 127	98.80 135	99.66 75	99.56 162	99.54 85	99.18 300	99.70 15	98.18 142	99.35 178	99.63 206	96.32 164	99.90 128	97.48 272	99.77 116	99.55 168

COLMAP_ROB	97.56 6	98.86 141	98.75 141	99.17 180	99.88 11	98.53 214	99.34 252	99.59 59	97.55 221	98.70 297	99.89 34	95.83 183	99.90 128	98.10 210	99.90 44	99.08 245
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 188	98.03 208	99.31 156	99.63 137	98.56 211	99.54 148	96.75 410	97.53 225	99.73 72	99.65 194	91.25 335	99.89 140	98.62 156	99.56 148	99.48 190

tttt0517		98.42 179	98.14 193	99.28 168	99.66 126	98.38 231	99.74 46	96.85 408	97.68 207	99.79 51	99.74 149	91.39 332	99.89 140	98.83 130	99.56 148	99.57 165

test12				99.75 63	99.64 134	99.61 72		99.29 290		99.21 210		98.38 91	99.89 140		99.74 124	99.74 96

Test_1112_low_res		98.89 136	98.66 152	99.57 101	99.69 110	98.95 170	99.03 331	99.47 184	96.98 280	99.15 223	99.23 326	96.77 146	99.89 140	98.83 130	98.78 211	99.86 33

CNLPA		99.14 95	98.99 104	99.59 96	99.58 156	99.41 105	99.16 302	99.44 212	98.45 106	99.19 216	99.49 257	98.08 105	99.89 140	97.73 248	99.75 121	99.48 190

sd_testset		98.75 159	98.57 166	99.29 164	99.81 46	98.26 235	99.56 130	99.62 41	98.78 79	99.64 106	99.88 41	92.02 315	99.88 145	99.54 37	98.26 239	99.72 108

APD_test1		95.87 347	96.49 329	94.00 382	99.53 170	84.01 411	99.54 148	99.32 278	95.91 353	97.99 348	99.85 59	85.49 389	99.88 145	91.96 391	98.84 206	98.12 376

diffmvs		99.14 95	99.02 98	99.51 122	99.61 147	98.96 167	99.28 270	99.49 151	98.46 105	99.72 77	99.71 160	96.50 157	99.88 145	99.31 65	99.11 183	99.67 128
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		98.86 141	98.80 135	99.03 195	99.76 68	98.79 192	99.28 270	99.91 3	97.42 240	99.67 89	99.37 294	97.53 118	99.88 145	98.98 100	97.29 297	98.42 357

PVSNet_Blended		99.08 114	98.97 108	99.42 139	99.76 68	98.79 192	98.78 370	99.91 3	96.74 295	99.67 89	99.49 257	97.53 118	99.88 145	98.98 100	99.85 76	99.60 154

MVS		97.28 314	96.55 327	99.48 128	98.78 348	98.95 170	99.27 275	99.39 232	83.53 413	98.08 343	99.54 240	96.97 140	99.87 150	94.23 370	99.16 177	99.63 147

MG-MVS		99.13 97	99.02 98	99.45 134	99.57 158	98.63 205	99.07 321	99.34 261	98.99 51	99.61 116	99.82 83	97.98 109	99.87 150	97.00 302	99.80 104	99.85 37

MSDG		98.98 129	98.80 135	99.53 114	99.76 68	99.19 131	98.75 373	99.55 80	97.25 254	99.47 144	99.77 137	97.82 112	99.87 150	96.93 309	99.90 44	99.54 170

ETV-MVS		99.26 76	99.21 72	99.40 141	99.46 201	99.30 119	99.56 130	99.52 107	98.52 100	99.44 152	99.27 321	98.41 90	99.86 153	99.10 88	99.59 146	99.04 252

thisisatest0515		98.14 206	97.79 232	99.19 178	99.50 189	98.50 221	98.61 385	96.82 409	96.95 284	99.54 132	99.43 275	91.66 327	99.86 153	98.08 215	99.51 152	99.22 234

thres600view7		97.86 250	97.51 266	98.92 212	99.72 96	97.95 254	99.59 109	98.74 371	97.94 174	99.27 196	98.62 375	91.75 321	99.86 153	93.73 375	98.19 245	98.96 262

lupinMVS		99.13 97	99.01 102	99.46 133	99.51 178	98.94 173	99.05 326	99.16 313	97.86 182	99.80 49	99.56 232	97.39 121	99.86 153	98.94 105	99.85 76	99.58 162

PVSNet	96.02 17	98.85 148	98.84 132	98.89 221	99.73 92	97.28 282	98.32 401	99.60 54	97.86 182	99.50 139	99.57 229	96.75 147	99.86 153	98.56 171	99.70 131	99.54 170

MAR-MVS		98.86 141	98.63 154	99.54 106	99.37 229	99.66 58	99.45 199	99.54 89	96.61 307	99.01 248	99.40 285	97.09 133	99.86 153	97.68 255	99.53 151	99.10 240
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		97.44 308	97.02 315	98.71 249	99.18 279	96.89 313	99.19 298	99.04 329	97.78 195	98.31 330	98.29 388	85.41 390	99.85 159	98.01 221	97.95 255	99.39 214

test2506		96.81 329	96.65 325	97.29 356	99.74 85	92.21 399	99.60 102	85.06 430	99.13 26	99.77 60	99.93 10	87.82 378	99.85 159	99.38 55	99.38 160	99.80 74

AllTest		98.87 138	98.72 143	99.31 156	99.86 20	98.48 224	99.56 130	99.61 48	97.85 185	99.36 175	99.85 59	95.95 176	99.85 159	96.66 322	99.83 93	99.59 158

TestCases				99.31 156	99.86 20	98.48 224		99.61 48	97.85 185	99.36 175	99.85 59	95.95 176	99.85 159	96.66 322	99.83 93	99.59 158

jason		99.13 97	99.03 94	99.45 134	99.46 201	98.87 180	99.12 311	99.26 296	98.03 169	99.79 51	99.65 194	97.02 138	99.85 159	99.02 97	99.90 44	99.65 135
jason: jason.
CNVR-MVS		99.42 47	99.30 54	99.78 57	99.62 143	99.71 49	99.26 284	99.52 107	98.82 71	99.39 168	99.71 160	98.96 25	99.85 159	98.59 164	99.80 104	99.77 86

PAPM_NR		99.04 120	98.84 132	99.66 75	99.74 85	99.44 101	99.39 232	99.38 240	97.70 205	99.28 191	99.28 318	98.34 93	99.85 159	96.96 306	99.45 156	99.69 121

testing99		97.36 311	96.94 318	98.63 254	99.18 279	96.70 319	99.30 260	98.93 341	97.71 202	98.23 335	98.26 389	84.92 393	99.84 166	98.04 220	97.85 262	99.35 220

testing222		97.16 319	96.50 328	99.16 181	99.16 289	98.47 226	99.27 275	98.66 381	97.71 202	98.23 335	98.15 392	82.28 406	99.84 166	97.36 282	97.66 268	99.18 236

test1111		98.04 221	98.11 197	97.83 335	99.74 85	93.82 384	99.58 117	95.40 417	99.12 31	99.65 101	99.93 10	90.73 340	99.84 166	99.43 53	99.38 160	99.82 58

ECVR-MVS		98.04 221	98.05 206	98.00 321	99.74 85	94.37 379	99.59 109	94.98 418	99.13 26	99.66 94	99.93 10	90.67 341	99.84 166	99.40 54	99.38 160	99.80 74

test_yl		98.86 141	98.63 154	99.54 106	99.49 191	99.18 133	99.50 173	99.07 325	98.22 135	99.61 116	99.51 251	95.37 198	99.84 166	98.60 162	98.33 233	99.59 158

DCV-MVSNet		98.86 141	98.63 154	99.54 106	99.49 191	99.18 133	99.50 173	99.07 325	98.22 135	99.61 116	99.51 251	95.37 198	99.84 166	98.60 162	98.33 233	99.59 158

Fast-Effi-MVS+		98.70 163	98.43 174	99.51 122	99.51 178	99.28 122	99.52 157	99.47 184	96.11 346	99.01 248	99.34 304	96.20 168	99.84 166	97.88 229	98.82 208	99.39 214

TSAR-MVS + GP.		99.36 60	99.36 38	99.36 147	99.67 116	98.61 208	99.07 321	99.33 268	99.00 49	99.82 44	99.81 97	99.06 16	99.84 166	99.09 89	99.42 158	99.65 135

tpmrst		98.33 189	98.48 172	97.90 329	99.16 289	94.78 372	99.31 258	99.11 318	97.27 252	99.45 147	99.59 220	95.33 200	99.84 166	98.48 178	98.61 216	99.09 244

Vis-MVSNet		99.12 103	98.97 108	99.56 103	99.78 57	99.10 145	99.68 66	99.66 28	98.49 102	99.86 35	99.87 50	94.77 227	99.84 166	99.19 77	99.41 159	99.74 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 170	98.34 180	99.51 122	99.40 221	99.03 155	98.80 368	99.36 249	96.33 327	99.00 252	99.12 340	98.46 84	99.84 166	95.23 356	99.37 167	99.66 131

PatchMatch-RL		98.84 151	98.62 159	99.52 120	99.71 101	99.28 122	99.06 324	99.77 9	97.74 200	99.50 139	99.53 244	95.41 196	99.84 166	97.17 296	99.64 140	99.44 206

EPP-MVSNet		99.13 97	98.99 104	99.53 114	99.65 132	99.06 152	99.81 20	99.33 268	97.43 238	99.60 119	99.88 41	97.14 131	99.84 166	99.13 83	98.94 197	99.69 121

testing11		97.50 301	97.10 312	98.71 249	99.20 273	96.91 311	99.29 265	98.82 361	97.89 179	98.21 338	98.40 383	85.63 388	99.83 179	98.45 183	98.04 253	99.37 218

thres100view900		97.76 268	97.45 275	98.69 251	99.72 96	97.86 260	99.59 109	98.74 371	97.93 175	99.26 200	98.62 375	91.75 321	99.83 179	93.22 380	98.18 246	98.37 363

tfpn200view9		97.72 278	97.38 288	98.72 247	99.69 110	97.96 252	99.50 173	98.73 377	97.83 188	99.17 221	98.45 381	91.67 325	99.83 179	93.22 380	98.18 246	98.37 363

test_prior				99.68 73	99.67 116	99.48 96		99.56 72					99.83 179			99.74 96

1314		98.68 165	98.54 169	99.11 187	98.89 333	98.65 202	99.27 275	99.49 151	96.89 288	97.99 348	99.56 232	97.72 116	99.83 179	97.74 247	99.27 171	98.84 268

thres400		97.77 267	97.38 288	98.92 212	99.69 110	97.96 252	99.50 173	98.73 377	97.83 188	99.17 221	98.45 381	91.67 325	99.83 179	93.22 380	98.18 246	98.96 262

casdiffmvs		99.13 97	98.98 107	99.56 103	99.65 132	99.16 136	99.56 130	99.50 141	98.33 121	99.41 161	99.86 54	95.92 179	99.83 179	99.45 52	99.16 177	99.70 119
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 25	99.48 18	99.54 106	99.78 57	99.30 119	99.89 2	99.58 63	98.56 96	99.73 72	99.69 174	98.55 78	99.82 186	99.69 23	99.85 76	99.48 190

MVS_Test		99.10 112	98.97 108	99.48 128	99.49 191	99.14 141	99.67 69	99.34 261	97.31 249	99.58 123	99.76 141	97.65 117	99.82 186	98.87 117	99.07 189	99.46 201

dp		97.75 272	97.80 231	97.59 348	99.10 300	93.71 387	99.32 255	98.88 354	96.48 319	99.08 236	99.55 235	92.67 299	99.82 186	96.52 326	98.58 219	99.24 233

RPSCF		98.22 196	98.62 159	96.99 362	99.82 42	91.58 401	99.72 52	99.44 212	96.61 307	99.66 94	99.89 34	95.92 179	99.82 186	97.46 275	99.10 186	99.57 165

PMMVS		98.80 155	98.62 159	99.34 149	99.27 256	98.70 198	98.76 372	99.31 282	97.34 246	99.21 210	99.07 342	97.20 130	99.82 186	98.56 171	98.87 203	99.52 177

UBG		97.85 251	97.48 269	98.95 206	99.25 262	97.64 271	99.24 288	98.74 371	97.90 178	98.64 307	98.20 391	88.65 366	99.81 191	98.27 199	98.40 229	99.42 208

EIA-MVS		99.18 86	99.09 86	99.45 134	99.49 191	99.18 133	99.67 69	99.53 102	97.66 210	99.40 166	99.44 273	98.10 103	99.81 191	98.94 105	99.62 143	99.35 220

Effi-MVS+		98.81 152	98.59 165	99.48 128	99.46 201	99.12 144	98.08 408	99.50 141	97.50 229	99.38 170	99.41 281	96.37 163	99.81 191	99.11 85	98.54 224	99.51 184

thres200		97.61 293	97.28 303	98.62 255	99.64 134	98.03 246	99.26 284	98.74 371	97.68 207	99.09 235	98.32 387	91.66 327	99.81 191	92.88 385	98.22 241	98.03 382

tpmvs		97.98 232	98.02 210	97.84 334	99.04 313	94.73 373	99.31 258	99.20 308	96.10 350	98.76 287	99.42 277	94.94 213	99.81 191	96.97 305	98.45 228	98.97 260

casdiffmvs_mvg		99.15 92	99.02 98	99.55 105	99.66 126	99.09 146	99.64 84	99.56 72	98.26 129	99.45 147	99.87 50	96.03 173	99.81 191	99.54 37	99.15 180	99.73 101
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	98.81 152	99.37 36	97.12 360	99.60 152	91.75 400	98.61 385	99.44 212	99.35 14	99.83 43	99.85 59	98.70 66	99.81 191	99.02 97	99.91 35	99.81 65

DPM-MVS		98.95 132	98.71 145	99.66 75	99.63 137	99.55 83	98.64 384	99.10 319	97.93 175	99.42 157	99.55 235	98.67 69	99.80 198	95.80 341	99.68 135	99.61 151

DP-MVS Recon		99.12 103	98.95 114	99.65 79	99.74 85	99.70 51	99.27 275	99.57 67	96.40 326	99.42 157	99.68 181	98.75 58	99.80 198	97.98 223	99.72 127	99.44 206

MVS_111021_LR		99.41 51	99.33 44	99.65 79	99.77 64	99.51 92	98.94 354	99.85 6	98.82 71	99.65 101	99.74 149	98.51 81	99.80 198	98.83 130	99.89 55	99.64 142

CS-MVS		99.50 23	99.48 18	99.54 106	99.76 68	99.42 103	99.90 1	99.55 80	98.56 96	99.78 56	99.70 164	98.65 71	99.79 201	99.65 27	99.78 113	99.41 211

Fast-Effi-MVS+-dtu		98.77 158	98.83 134	98.60 256	99.41 216	96.99 305	99.52 157	99.49 151	98.11 152	99.24 202	99.34 304	96.96 141	99.79 201	97.95 225	99.45 156	99.02 255

baseline1		98.31 190	97.95 217	99.38 146	99.50 189	98.74 195	99.59 109	98.93 341	98.41 111	99.14 224	99.60 218	94.59 238	99.79 201	98.48 178	93.29 378	99.61 151

baseline		99.15 92	99.02 98	99.53 114	99.66 126	99.14 141	99.72 52	99.48 163	98.35 118	99.42 157	99.84 69	96.07 171	99.79 201	99.51 42	99.14 181	99.67 128

PVSNet_0	94.43 19	96.09 344	95.47 351	97.94 326	99.31 246	94.34 381	97.81 410	99.70 15	97.12 266	97.46 361	98.75 372	89.71 352	99.79 201	97.69 254	81.69 413	99.68 125

API-MVS		99.04 120	99.03 94	99.06 191	99.40 221	99.31 117	99.55 144	99.56 72	98.54 98	99.33 182	99.39 289	98.76 55	99.78 206	96.98 304	99.78 113	98.07 379

OMC-MVS		99.08 114	99.04 92	99.20 177	99.67 116	98.22 237	99.28 270	99.52 107	98.07 160	99.66 94	99.81 97	97.79 113	99.78 206	97.79 239	99.81 100	99.60 154

GeoE		98.85 148	98.62 159	99.53 114	99.61 147	99.08 149	99.80 25	99.51 121	97.10 270	99.31 184	99.78 129	95.23 206	99.77 208	98.21 202	99.03 192	99.75 92

alignmvs		98.81 152	98.56 168	99.58 99	99.43 209	99.42 103	99.51 166	98.96 339	98.61 92	99.35 178	98.92 362	94.78 224	99.77 208	99.35 57	98.11 251	99.54 170

tpm cat1		97.39 310	97.36 290	97.50 351	99.17 287	93.73 386	99.43 210	99.31 282	91.27 399	98.71 291	99.08 341	94.31 252	99.77 208	96.41 330	98.50 226	99.00 256

CostFormer		97.72 278	97.73 244	97.71 342	99.15 293	94.02 383	99.54 148	99.02 332	94.67 374	99.04 245	99.35 300	92.35 311	99.77 208	98.50 177	97.94 256	99.34 223

MGCFI-Net		99.01 127	98.85 130	99.50 127	99.42 211	99.26 125	99.82 16	99.48 163	98.60 93	99.28 191	98.81 367	97.04 137	99.76 212	99.29 68	97.87 260	99.47 196

test_241102_ONE					99.84 32	99.90 2		99.48 163	99.07 41	99.91 19	99.74 149	99.20 7	99.76 212

MDTV_nov1_ep13			98.32 182		99.11 297	94.44 378	99.27 275	98.74 371	97.51 228	99.40 166	99.62 211	94.78 224	99.76 212	97.59 259	98.81 210

sasdasda		99.02 123	98.86 128	99.51 122	99.42 211	99.32 113	99.80 25	99.48 163	98.63 89	99.31 184	98.81 367	97.09 133	99.75 215	99.27 71	97.90 257	99.47 196

canonicalmvs		99.02 123	98.86 128	99.51 122	99.42 211	99.32 113	99.80 25	99.48 163	98.63 89	99.31 184	98.81 367	97.09 133	99.75 215	99.27 71	97.90 257	99.47 196

Effi-MVS+-dtu		98.78 156	98.89 123	98.47 277	99.33 238	96.91 311	99.57 124	99.30 286	98.47 104	99.41 161	98.99 352	96.78 145	99.74 217	98.73 141	99.38 160	98.74 282

patchmatchnet-post											98.70 373	94.79 223	99.74 217

SCA		98.19 200	98.16 190	98.27 303	99.30 247	95.55 353	99.07 321	98.97 337	97.57 218	99.43 154	99.57 229	92.72 294	99.74 217	97.58 260	99.20 175	99.52 177

BH-untuned		98.42 179	98.36 178	98.59 257	99.49 191	96.70 319	99.27 275	99.13 317	97.24 256	98.80 282	99.38 291	95.75 186	99.74 217	97.07 300	99.16 177	99.33 224

BH-RMVSNet		98.41 181	98.08 202	99.40 141	99.41 216	98.83 188	99.30 260	98.77 367	97.70 205	98.94 261	99.65 194	92.91 289	99.74 217	96.52 326	99.55 150	99.64 142

MVS_111021_HR		99.41 51	99.32 46	99.66 75	99.72 96	99.47 98	98.95 352	99.85 6	98.82 71	99.54 132	99.73 155	98.51 81	99.74 217	98.91 111	99.88 58	99.77 86

test_post											65.99 424	94.65 237	99.73 223

XVG-ACMP-BASELINE		97.83 257	97.71 246	98.20 305	99.11 297	96.33 335	99.41 220	99.52 107	98.06 164	99.05 244	99.50 254	89.64 354	99.73 223	97.73 248	97.38 295	98.53 345

HyFIR lowres test		99.11 108	98.92 117	99.65 79	99.90 4	99.37 107	99.02 334	99.91 3	97.67 209	99.59 122	99.75 144	95.90 181	99.73 223	99.53 39	99.02 194	99.86 33

DeepMVS_CX				93.34 385	99.29 251	82.27 414		99.22 304	85.15 411	96.33 382	99.05 345	90.97 338	99.73 223	93.57 377	97.77 265	98.01 383

Patchmatch-test		97.93 238	97.65 251	98.77 244	99.18 279	97.07 296	99.03 331	99.14 316	96.16 341	98.74 288	99.57 229	94.56 240	99.72 227	93.36 379	99.11 183	99.52 177

LPG-MVS_test		98.22 196	98.13 195	98.49 270	99.33 238	97.05 298	99.58 117	99.55 80	97.46 231	99.24 202	99.83 74	92.58 301	99.72 227	98.09 211	97.51 281	98.68 300

LGP-MVS_train				98.49 270	99.33 238	97.05 298		99.55 80	97.46 231	99.24 202	99.83 74	92.58 301	99.72 227	98.09 211	97.51 281	98.68 300

BH-w/o		98.00 230	97.89 226	98.32 295	99.35 233	96.20 341	99.01 339	98.90 351	96.42 324	98.38 326	99.00 351	95.26 204	99.72 227	96.06 334	98.61 216	99.03 253

ACMP	97.20 11	98.06 215	97.94 219	98.45 280	99.37 229	97.01 303	99.44 205	99.49 151	97.54 224	98.45 323	99.79 122	91.95 317	99.72 227	97.91 227	97.49 286	98.62 328
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 225	97.90 222	98.40 288	99.23 266	96.80 317	99.70 56	99.60 54	97.12 266	98.18 340	99.70 164	91.73 323	99.72 227	98.39 186	97.45 288	98.68 300
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							99.23 290				65.14 425	94.18 257	99.71 233	97.58 260

ADS-MVSNet		98.20 199	98.08 202	98.56 264	99.33 238	96.48 330	99.23 290	99.15 314	96.24 334	99.10 232	99.67 187	94.11 258	99.71 233	96.81 314	99.05 190	99.48 190

JIA-IIPM		97.50 301	97.02 315	98.93 210	98.73 357	97.80 262	99.30 260	98.97 337	91.73 398	98.91 264	94.86 413	95.10 209	99.71 233	97.58 260	97.98 254	99.28 228

EPMVS		97.82 260	97.65 251	98.35 292	98.88 334	95.98 345	99.49 184	94.71 420	97.57 218	99.26 200	99.48 263	92.46 308	99.71 233	97.87 231	99.08 188	99.35 220

TDRefinement		95.42 353	94.57 360	97.97 323	89.83 423	96.11 344	99.48 188	98.75 368	96.74 295	96.68 379	99.88 41	88.65 366	99.71 233	98.37 189	82.74 412	98.09 378

ACMM	97.58 5	98.37 187	98.34 180	98.48 272	99.41 216	97.10 292	99.56 130	99.45 204	98.53 99	99.04 245	99.85 59	93.00 285	99.71 233	98.74 139	97.45 288	98.64 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 235	97.77 237	98.57 261	99.59 154	96.61 326	99.45 199	99.08 322	98.21 137	98.88 269	99.80 110	88.66 365	99.70 239	98.58 165	97.72 266	99.39 214

CHOSEN 280x420		99.12 103	99.13 79	99.08 188	99.66 126	97.89 257	98.43 395	99.71 13	98.88 65	99.62 113	99.76 141	96.63 151	99.70 239	99.46 51	99.99 1	99.66 131

EC-MVSNet		99.44 42	99.39 32	99.58 99	99.56 162	99.49 94	99.88 4	99.58 63	98.38 113	99.73 72	99.69 174	98.20 99	99.70 239	99.64 29	99.82 97	99.54 170

PatchmatchNet		98.31 190	98.36 178	98.19 306	99.16 289	95.32 362	99.27 275	98.92 344	97.37 244	99.37 172	99.58 224	94.90 217	99.70 239	97.43 278	99.21 174	99.54 170
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 210	97.99 212	98.44 283	99.41 216	96.96 309	99.60 102	99.56 72	98.09 155	98.15 341	99.91 22	90.87 339	99.70 239	98.88 114	97.45 288	98.67 307
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 301	96.90 319	99.29 164	99.23 266	98.78 194	99.32 255	98.90 351	97.52 227	98.56 316	98.09 397	84.72 395	99.69 244	97.86 232	97.88 259	99.39 214

HQP_MVS		98.27 195	98.22 188	98.44 283	99.29 251	96.97 307	99.39 232	99.47 184	98.97 57	99.11 229	99.61 215	92.71 296	99.69 244	97.78 240	97.63 269	98.67 307

plane_prior5								99.47 184					99.69 244	97.78 240	97.63 269	98.67 307

D2MVS		98.41 181	98.50 171	98.15 311	99.26 258	96.62 325	99.40 228	99.61 48	97.71 202	98.98 254	99.36 297	96.04 172	99.67 247	98.70 144	97.41 293	98.15 375

IS-MVSNet		99.05 119	98.87 126	99.57 101	99.73 92	99.32 113	99.75 42	99.20 308	98.02 170	99.56 127	99.86 54	96.54 155	99.67 247	98.09 211	99.13 182	99.73 101

CLD-MVS		98.16 204	98.10 198	98.33 293	99.29 251	96.82 316	98.75 373	99.44 212	97.83 188	99.13 225	99.55 235	92.92 287	99.67 247	98.32 196	97.69 267	98.48 349
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 316	97.30 300	97.09 361	99.43 209	93.31 392	99.73 50	98.87 356	98.83 70	99.28 191	99.80 110	84.45 396	99.66 250	97.88 229	97.45 288	98.30 365

AUN-MVS		96.88 327	96.31 333	98.59 257	99.48 198	97.04 301	99.27 275	99.22 304	97.44 237	98.51 319	99.41 281	91.97 316	99.66 250	97.71 251	83.83 410	99.07 250

UniMVSNet_ETH3D		97.32 313	96.81 321	98.87 227	99.40 221	97.46 276	99.51 166	99.53 102	95.86 354	98.54 318	99.77 137	82.44 404	99.66 250	98.68 149	97.52 280	99.50 188

OPM-MVS		98.19 200	98.10 198	98.45 280	98.88 334	97.07 296	99.28 270	99.38 240	98.57 95	99.22 207	99.81 97	92.12 313	99.66 250	98.08 215	97.54 278	98.61 337
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 241	97.78 235	98.32 295	99.46 201	96.68 323	99.56 130	99.54 89	98.41 111	97.79 357	99.87 50	90.18 348	99.66 250	98.05 219	97.18 302	98.62 328

hse-mvs2		97.50 301	97.14 309	98.59 257	99.49 191	97.05 298	99.28 270	99.22 304	98.94 60	99.66 94	99.42 277	94.93 214	99.65 255	99.48 48	83.80 411	99.08 245

VPA-MVSNet		98.29 193	97.95 217	99.30 161	99.16 289	99.54 85	99.50 173	99.58 63	98.27 127	99.35 178	99.37 294	92.53 303	99.65 255	99.35 57	94.46 360	98.72 284

TR-MVS		97.76 268	97.41 286	98.82 236	99.06 309	97.87 258	98.87 362	98.56 384	96.63 306	98.68 299	99.22 327	92.49 304	99.65 255	95.40 352	97.79 264	98.95 264

reproduce_monomvs		97.89 245	97.87 227	97.96 325	99.51 178	95.45 358	99.60 102	99.25 298	99.17 21	98.85 277	99.49 257	89.29 357	99.64 258	99.35 57	96.31 318	98.78 271

gm-plane-assit					98.54 377	92.96 394			94.65 375		99.15 335		99.64 258	97.56 265

HQP4-MVS										98.66 300			99.64 258			98.64 319

HQP-MVS		98.02 225	97.90 222	98.37 291	99.19 276	96.83 314	98.98 345	99.39 232	98.24 131	98.66 300	99.40 285	92.47 305	99.64 258	97.19 293	97.58 274	98.64 319

PAPM		97.59 294	97.09 313	99.07 189	99.06 309	98.26 235	98.30 402	99.10 319	94.88 369	98.08 343	99.34 304	96.27 166	99.64 258	89.87 399	98.92 200	99.31 226

TAPA-MVS	97.07 15	97.74 274	97.34 295	98.94 208	99.70 106	97.53 274	99.25 286	99.51 121	91.90 397	99.30 187	99.63 206	98.78 51	99.64 258	88.09 406	99.87 61	99.65 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 185	98.09 201	99.24 173	99.26 258	99.32 113	99.56 130	99.55 80	97.45 234	98.71 291	99.83 74	93.23 280	99.63 264	98.88 114	96.32 317	98.76 277

ITE_SJBPF				98.08 314	99.29 251	96.37 333		98.92 344	98.34 119	98.83 278	99.75 144	91.09 336	99.62 265	95.82 339	97.40 294	98.25 369

LF4IMVS		97.52 298	97.46 274	97.70 343	98.98 323	95.55 353	99.29 265	98.82 361	98.07 160	98.66 300	99.64 200	89.97 349	99.61 266	97.01 301	96.68 307	97.94 390

tpm		97.67 288	97.55 260	98.03 316	99.02 315	95.01 368	99.43 210	98.54 386	96.44 322	99.12 227	99.34 304	91.83 320	99.60 267	97.75 246	96.46 313	99.48 190

tpm2		97.44 308	97.34 295	97.74 341	99.15 293	94.36 380	99.45 199	98.94 340	93.45 388	98.90 266	99.44 273	91.35 333	99.59 268	97.31 284	98.07 252	99.29 227

baseline2		97.87 248	97.55 260	98.82 236	99.18 279	98.02 247	99.41 220	96.58 414	96.97 281	96.51 380	99.17 332	93.43 277	99.57 269	97.71 251	99.03 192	98.86 266

MS-PatchMatch		97.24 318	97.32 298	96.99 362	98.45 380	93.51 391	98.82 366	99.32 278	97.41 241	98.13 342	99.30 314	88.99 359	99.56 270	95.68 345	99.80 104	97.90 393

TinyColmap		97.12 321	96.89 320	97.83 335	99.07 307	95.52 356	98.57 388	98.74 371	97.58 217	97.81 356	99.79 122	88.16 373	99.56 270	95.10 357	97.21 300	98.39 361

USDC		97.34 312	97.20 307	97.75 340	99.07 307	95.20 364	98.51 392	99.04 329	97.99 171	98.31 330	99.86 54	89.02 358	99.55 272	95.67 346	97.36 296	98.49 348

MSLP-MVS++		99.46 34	99.47 20	99.44 138	99.60 152	99.16 136	99.41 220	99.71 13	98.98 54	99.45 147	99.78 129	99.19 9	99.54 273	99.28 69	99.84 84	99.63 147

TAMVS		99.12 103	99.08 87	99.24 173	99.46 201	98.55 212	99.51 166	99.46 193	98.09 155	99.45 147	99.82 83	98.34 93	99.51 274	98.70 144	98.93 198	99.67 128

EPNet_dtu		98.03 223	97.96 215	98.23 304	98.27 383	95.54 355	99.23 290	98.75 368	99.02 44	97.82 355	99.71 160	96.11 170	99.48 275	93.04 383	99.65 139	99.69 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 331	96.22 335	97.97 323	97.00 405	96.28 337	98.66 382	99.03 331	96.61 307	96.93 377	99.79 122	87.20 381	99.47 276	96.65 324	94.13 367	98.16 374

EG-PatchMatch MVS		95.97 346	95.69 347	96.81 369	97.78 390	92.79 395	99.16 302	98.93 341	96.16 341	94.08 398	99.22 327	82.72 402	99.47 276	95.67 346	97.50 283	98.17 373

MVP-Stereo		97.81 262	97.75 242	97.99 322	97.53 394	96.60 327	98.96 349	98.85 358	97.22 258	97.23 368	99.36 297	95.28 201	99.46 278	95.51 348	99.78 113	97.92 392
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 172	98.67 149	98.30 297	99.35 233	95.59 352	99.50 173	99.55 80	98.60 93	99.39 168	99.83 74	94.48 245	99.45 279	98.75 138	98.56 222	99.85 37

test-LLR		98.06 215	97.90 222	98.55 266	98.79 345	97.10 292	98.67 379	97.75 400	97.34 246	98.61 312	98.85 364	94.45 247	99.45 279	97.25 287	99.38 160	99.10 240

TESTMET0.1,1		97.55 296	97.27 306	98.40 288	98.93 328	96.53 328	98.67 379	97.61 403	96.96 282	98.64 307	99.28 318	88.63 368	99.45 279	97.30 285	99.38 160	99.21 235

test-mter		97.49 306	97.13 311	98.55 266	98.79 345	97.10 292	98.67 379	97.75 400	96.65 302	98.61 312	98.85 364	88.23 372	99.45 279	97.25 287	99.38 160	99.10 240

mvs_anonymous		99.03 122	98.99 104	99.16 181	99.38 226	98.52 218	99.51 166	99.38 240	97.79 193	99.38 170	99.81 97	97.30 127	99.45 279	99.35 57	98.99 195	99.51 184

tfpnnormal		97.84 255	97.47 272	98.98 201	99.20 273	99.22 130	99.64 84	99.61 48	96.32 328	98.27 334	99.70 164	93.35 279	99.44 284	95.69 344	95.40 343	98.27 367

v7n		97.87 248	97.52 264	98.92 212	98.76 355	98.58 210	99.84 12	99.46 193	96.20 337	98.91 264	99.70 164	94.89 218	99.44 284	96.03 335	93.89 372	98.75 279

jajsoiax		98.43 178	98.28 185	98.88 223	98.60 372	98.43 228	99.82 16	99.53 102	98.19 139	98.63 309	99.80 110	93.22 282	99.44 284	99.22 75	97.50 283	98.77 275

mvs_tets		98.40 184	98.23 187	98.91 216	98.67 365	98.51 220	99.66 75	99.53 102	98.19 139	98.65 306	99.81 97	92.75 291	99.44 284	99.31 65	97.48 287	98.77 275

Vis-MVSNet (Re-imp)		98.87 138	98.72 143	99.31 156	99.71 101	98.88 179	99.80 25	99.44 212	97.91 177	99.36 175	99.78 129	95.49 195	99.43 288	97.91 227	99.11 183	99.62 149

OPU-MVS				99.64 85	99.56 162	99.72 47	99.60 102				99.70 164	99.27 5	99.42 289	98.24 201	99.80 104	99.79 78

Anonymous20231211		97.88 246	97.54 263	98.90 218	99.71 101	98.53 214	99.48 188	99.57 67	94.16 379	98.81 280	99.68 181	93.23 280	99.42 289	98.84 127	94.42 362	98.76 277

ttmdpeth		97.80 264	97.63 255	98.29 298	98.77 353	97.38 279	99.64 84	99.36 249	98.78 79	96.30 383	99.58 224	92.34 312	99.39 291	98.36 191	95.58 338	98.10 377

VPNet		97.84 255	97.44 280	99.01 197	99.21 271	98.94 173	99.48 188	99.57 67	98.38 113	99.28 191	99.73 155	88.89 360	99.39 291	99.19 77	93.27 379	98.71 286

nrg030		98.64 169	98.42 175	99.28 168	99.05 312	99.69 52	99.81 20	99.46 193	98.04 167	99.01 248	99.82 83	96.69 149	99.38 293	99.34 62	94.59 359	98.78 271

GA-MVS		97.85 251	97.47 272	99.00 199	99.38 226	97.99 249	98.57 388	99.15 314	97.04 277	98.90 266	99.30 314	89.83 351	99.38 293	96.70 319	98.33 233	99.62 149

UniMVSNet (Re)		98.29 193	98.00 211	99.13 186	99.00 317	99.36 110	99.49 184	99.51 121	97.95 173	98.97 256	99.13 337	96.30 165	99.38 293	98.36 191	93.34 377	98.66 315

FIs		98.78 156	98.63 154	99.23 175	99.18 279	99.54 85	99.83 15	99.59 59	98.28 125	98.79 284	99.81 97	96.75 147	99.37 296	99.08 90	96.38 315	98.78 271

PS-MVSNAJss		98.92 134	98.92 117	98.90 218	98.78 348	98.53 214	99.78 32	99.54 89	98.07 160	99.00 252	99.76 141	99.01 18	99.37 296	99.13 83	97.23 299	98.81 269

CDS-MVSNet		99.09 113	99.03 94	99.25 171	99.42 211	98.73 196	99.45 199	99.46 193	98.11 152	99.46 146	99.77 137	98.01 108	99.37 296	98.70 144	98.92 200	99.66 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 350	95.16 355	97.51 350	99.30 247	93.69 388	98.88 360	95.78 415	85.09 412	98.78 285	92.65 415	91.29 334	99.37 296	94.85 362	99.85 76	99.46 201

v1192		97.81 262	97.44 280	98.91 216	98.88 334	98.68 199	99.51 166	99.34 261	96.18 339	99.20 213	99.34 304	94.03 262	99.36 300	95.32 354	95.18 347	98.69 295

EI-MVSNet		98.67 166	98.67 149	98.68 252	99.35 233	97.97 250	99.50 173	99.38 240	96.93 287	99.20 213	99.83 74	97.87 110	99.36 300	98.38 187	97.56 276	98.71 286

MVSTER		98.49 173	98.32 182	99.00 199	99.35 233	99.02 156	99.54 148	99.38 240	97.41 241	99.20 213	99.73 155	93.86 270	99.36 300	98.87 117	97.56 276	98.62 328

gg-mvs-nofinetune		96.17 342	95.32 354	98.73 246	98.79 345	98.14 241	99.38 237	94.09 421	91.07 402	98.07 346	91.04 419	89.62 355	99.35 303	96.75 316	99.09 187	98.68 300

pm-mvs1		97.68 285	97.28 303	98.88 223	99.06 309	98.62 206	99.50 173	99.45 204	96.32 328	97.87 353	99.79 122	92.47 305	99.35 303	97.54 267	93.54 376	98.67 307

OurMVSNet-221017-0		97.88 246	97.77 237	98.19 306	98.71 361	96.53 328	99.88 4	99.00 334	97.79 193	98.78 285	99.94 6	91.68 324	99.35 303	97.21 289	96.99 306	98.69 295

EGC-MVSNET		82.80 384	77.86 390	97.62 346	97.91 387	96.12 343	99.33 254	99.28 292	8.40 427	25.05 428	99.27 321	84.11 397	99.33 306	89.20 401	98.22 241	97.42 401

pmmvs6		96.53 334	96.09 339	97.82 337	98.69 363	95.47 357	99.37 239	99.47 184	93.46 387	97.41 362	99.78 129	87.06 382	99.33 306	96.92 311	92.70 386	98.65 317

V42		98.06 215	97.79 232	98.86 230	98.98 323	98.84 185	99.69 60	99.34 261	96.53 314	99.30 187	99.37 294	94.67 235	99.32 308	97.57 264	94.66 357	98.42 357

lessismore_v0				97.79 339	98.69 363	95.44 360		94.75 419		95.71 389	99.87 50	88.69 364	99.32 308	95.89 338	94.93 354	98.62 328

OpenMVS_ROB	92.34 20	94.38 364	93.70 370	96.41 374	97.38 396	93.17 393	99.06 324	98.75 368	86.58 410	94.84 396	98.26 389	81.53 407	99.32 308	89.01 402	97.87 260	96.76 404

v8		97.95 237	97.63 255	98.93 210	98.95 327	98.81 191	99.80 25	99.41 223	96.03 351	99.10 232	99.42 277	94.92 216	99.30 311	96.94 308	94.08 369	98.66 315

v1921920		97.80 264	97.45 275	98.84 234	98.80 344	98.53 214	99.52 157	99.34 261	96.15 343	99.24 202	99.47 266	93.98 264	99.29 312	95.40 352	95.13 349	98.69 295

anonymousdsp		98.44 177	98.28 185	98.94 208	98.50 378	98.96 167	99.77 34	99.50 141	97.07 272	98.87 272	99.77 137	94.76 228	99.28 313	98.66 151	97.60 272	98.57 343

MVSFormer		99.17 88	99.12 81	99.29 164	99.51 178	98.94 173	99.88 4	99.46 193	97.55 221	99.80 49	99.65 194	97.39 121	99.28 313	99.03 95	99.85 76	99.65 135

test_djsdf		98.67 166	98.57 166	98.98 201	98.70 362	98.91 177	99.88 4	99.46 193	97.55 221	99.22 207	99.88 41	95.73 187	99.28 313	99.03 95	97.62 271	98.75 279

cascas		97.69 283	97.43 284	98.48 272	98.60 372	97.30 281	98.18 406	99.39 232	92.96 391	98.41 324	98.78 371	93.77 273	99.27 316	98.16 208	98.61 216	98.86 266

v144192		97.92 241	97.60 258	98.87 227	98.83 343	98.65 202	99.55 144	99.34 261	96.20 337	99.32 183	99.40 285	94.36 249	99.26 317	96.37 331	95.03 351	98.70 291

dmvs_re		98.08 213	98.16 190	97.85 332	99.55 166	94.67 375	99.70 56	98.92 344	98.15 144	99.06 242	99.35 300	93.67 276	99.25 318	97.77 243	97.25 298	99.64 142

v2v482		98.06 215	97.77 237	98.92 212	98.90 332	98.82 189	99.57 124	99.36 249	96.65 302	99.19 216	99.35 300	94.20 254	99.25 318	97.72 250	94.97 352	98.69 295

v1240		97.69 283	97.32 298	98.79 242	98.85 341	98.43 228	99.48 188	99.36 249	96.11 346	99.27 196	99.36 297	93.76 274	99.24 320	94.46 366	95.23 346	98.70 291

WBMVS		97.74 274	97.50 267	98.46 278	99.24 264	97.43 277	99.21 296	99.42 220	97.45 234	98.96 258	99.41 281	88.83 361	99.23 321	98.94 105	96.02 323	98.71 286

v1144		97.98 232	97.69 247	98.85 233	98.87 337	98.66 201	99.54 148	99.35 256	96.27 332	99.23 206	99.35 300	94.67 235	99.23 321	96.73 317	95.16 348	98.68 300

v10		97.85 251	97.52 264	98.86 230	98.99 320	98.67 200	99.75 42	99.41 223	95.70 355	98.98 254	99.41 281	94.75 229	99.23 321	96.01 337	94.63 358	98.67 307

WR-MVS_H		98.13 207	97.87 227	98.90 218	99.02 315	98.84 185	99.70 56	99.59 59	97.27 252	98.40 325	99.19 331	95.53 193	99.23 321	98.34 193	93.78 374	98.61 337

miper_enhance_ethall		98.16 204	98.08 202	98.41 286	98.96 326	97.72 266	98.45 394	99.32 278	96.95 284	98.97 256	99.17 332	97.06 136	99.22 325	97.86 232	95.99 326	98.29 366

GG-mvs-BLEND				98.45 280	98.55 376	98.16 239	99.43 210	93.68 422		97.23 368	98.46 380	89.30 356	99.22 325	95.43 351	98.22 241	97.98 388

FC-MVSNet-test		98.75 159	98.62 159	99.15 185	99.08 306	99.45 100	99.86 11	99.60 54	98.23 134	98.70 297	99.82 83	96.80 144	99.22 325	99.07 91	96.38 315	98.79 270

UniMVSNet_NR-MVSNet		98.22 196	97.97 214	98.96 204	98.92 330	98.98 160	99.48 188	99.53 102	97.76 197	98.71 291	99.46 270	96.43 162	99.22 325	98.57 168	92.87 384	98.69 295

DU-MVS		98.08 213	97.79 232	98.96 204	98.87 337	98.98 160	99.41 220	99.45 204	97.87 181	98.71 291	99.50 254	94.82 220	99.22 325	98.57 168	92.87 384	98.68 300

cl____		98.01 228	97.84 230	98.55 266	99.25 262	97.97 250	98.71 377	99.34 261	96.47 321	98.59 315	99.54 240	95.65 190	99.21 330	97.21 289	95.77 332	98.46 354

WR-MVS		98.06 215	97.73 244	99.06 191	98.86 340	99.25 127	99.19 298	99.35 256	97.30 250	98.66 300	99.43 275	93.94 265	99.21 330	98.58 165	94.28 364	98.71 286

test_0402		96.64 332	96.24 334	97.85 332	98.85 341	96.43 332	99.44 205	99.26 296	93.52 385	96.98 375	99.52 247	88.52 369	99.20 332	92.58 390	97.50 283	97.93 391

SixPastTwentyTwo		97.50 301	97.33 297	98.03 316	98.65 366	96.23 340	99.77 34	98.68 380	97.14 263	97.90 351	99.93 10	90.45 342	99.18 333	97.00 302	96.43 314	98.67 307

cl22		97.85 251	97.64 254	98.48 272	99.09 303	97.87 258	98.60 387	99.33 268	97.11 269	98.87 272	99.22 327	92.38 310	99.17 334	98.21 202	95.99 326	98.42 357

WB-MVSnew		97.65 290	97.65 251	97.63 345	98.78 348	97.62 272	99.13 308	98.33 389	97.36 245	99.07 237	98.94 358	95.64 191	99.15 335	92.95 384	98.68 215	96.12 411

IterMVS-SCA-FT		97.82 260	97.75 242	98.06 315	99.57 158	96.36 334	99.02 334	99.49 151	97.18 260	98.71 291	99.72 159	92.72 294	99.14 336	97.44 277	95.86 331	98.67 307

pmmvs5		97.52 298	97.30 300	98.16 308	98.57 375	96.73 318	99.27 275	98.90 351	96.14 344	98.37 327	99.53 244	91.54 330	99.14 336	97.51 269	95.87 330	98.63 326

v148		97.79 266	97.55 260	98.50 269	98.74 356	97.72 266	99.54 148	99.33 268	96.26 333	98.90 266	99.51 251	94.68 234	99.14 336	97.83 236	93.15 381	98.63 326

miper_ehance_all_eth		98.18 202	98.10 198	98.41 286	99.23 266	97.72 266	98.72 376	99.31 282	96.60 310	98.88 269	99.29 316	97.29 128	99.13 339	97.60 258	95.99 326	98.38 362

NR-MVSNet		97.97 235	97.61 257	99.02 196	98.87 337	99.26 125	99.47 194	99.42 220	97.63 212	97.08 373	99.50 254	95.07 210	99.13 339	97.86 232	93.59 375	98.68 300

IterMVS		97.83 257	97.77 237	98.02 318	99.58 156	96.27 338	99.02 334	99.48 163	97.22 258	98.71 291	99.70 164	92.75 291	99.13 339	97.46 275	96.00 325	98.67 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 365	94.90 357	91.84 390	97.24 400	80.01 420	98.52 391	99.48 163	89.01 407	91.99 407	99.67 187	85.67 387	99.13 339	95.44 350	97.03 305	96.39 408
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 220	97.96 215	98.33 293	99.26 258	97.38 279	98.56 390	99.31 282	96.65 302	98.88 269	99.52 247	96.58 153	99.12 343	97.39 280	95.53 341	98.47 351

pmmvs4		98.13 207	97.90 222	98.81 239	98.61 371	98.87 180	98.99 342	99.21 307	96.44 322	99.06 242	99.58 224	95.90 181	99.11 344	97.18 295	96.11 322	98.46 354

TransMVSNet (Re)		97.15 320	96.58 326	98.86 230	99.12 295	98.85 184	99.49 184	98.91 349	95.48 358	97.16 371	99.80 110	93.38 278	99.11 344	94.16 372	91.73 390	98.62 328

ambc				93.06 388	92.68 419	82.36 413	98.47 393	98.73 377		95.09 394	97.41 402	55.55 420	99.10 346	96.42 329	91.32 391	97.71 394

Baseline_NR-MVSNet		97.76 268	97.45 275	98.68 252	99.09 303	98.29 233	99.41 220	98.85 358	95.65 356	98.63 309	99.67 187	94.82 220	99.10 346	98.07 218	92.89 383	98.64 319

test_vis3_rt		87.04 380	85.81 383	90.73 394	93.99 418	81.96 415	99.76 37	90.23 429	92.81 393	81.35 417	91.56 417	40.06 426	99.07 348	94.27 369	88.23 404	91.15 417

CP-MVSNet		98.09 211	97.78 235	99.01 197	98.97 325	99.24 128	99.67 69	99.46 193	97.25 254	98.48 322	99.64 200	93.79 272	99.06 349	98.63 155	94.10 368	98.74 282

PS-CasMVS		97.93 238	97.59 259	98.95 206	98.99 320	99.06 152	99.68 66	99.52 107	97.13 264	98.31 330	99.68 181	92.44 309	99.05 350	98.51 176	94.08 369	98.75 279

K. test v3		97.10 322	96.79 322	98.01 319	98.72 359	96.33 335	99.87 8	97.05 406	97.59 215	96.16 385	99.80 110	88.71 363	99.04 351	96.69 320	96.55 312	98.65 317

new_pmnet		96.38 338	96.03 340	97.41 352	98.13 386	95.16 367	99.05 326	99.20 308	93.94 380	97.39 365	98.79 370	91.61 329	99.04 351	90.43 397	95.77 332	98.05 381

DIV-MVS_self_test		98.01 228	97.85 229	98.48 272	99.24 264	97.95 254	98.71 377	99.35 256	96.50 315	98.60 314	99.54 240	95.72 188	99.03 353	97.21 289	95.77 332	98.46 354

IterMVS-LS		98.46 176	98.42 175	98.58 260	99.59 154	98.00 248	99.37 239	99.43 218	96.94 286	99.07 237	99.59 220	97.87 110	99.03 353	98.32 196	95.62 337	98.71 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 290	97.68 248	97.55 349	98.62 369	94.97 369	98.84 364	99.30 286	96.83 293	98.19 339	99.34 304	97.01 139	99.02 355	95.00 360	96.01 324	98.64 319

Patchmtry		97.75 272	97.40 287	98.81 239	99.10 300	98.87 180	99.11 317	99.33 268	94.83 371	98.81 280	99.38 291	94.33 250	99.02 355	96.10 333	95.57 339	98.53 345

N_pmnet		94.95 359	95.83 345	92.31 389	98.47 379	79.33 421	99.12 311	92.81 427	93.87 381	97.68 358	99.13 337	93.87 269	99.01 357	91.38 394	96.19 320	98.59 341

CR-MVSNet		98.17 203	97.93 220	98.87 227	99.18 279	98.49 222	99.22 294	99.33 268	96.96 282	99.56 127	99.38 291	94.33 250	99.00 358	94.83 363	98.58 219	99.14 237

c3_l		98.12 209	98.04 207	98.38 290	99.30 247	97.69 270	98.81 367	99.33 268	96.67 300	98.83 278	99.34 304	97.11 132	98.99 359	97.58 260	95.34 344	98.48 349

test0.0.03 1		97.71 281	97.42 285	98.56 264	98.41 382	97.82 261	98.78 370	98.63 382	97.34 246	98.05 347	98.98 354	94.45 247	98.98 360	95.04 359	97.15 303	98.89 265

PatchT		97.03 324	96.44 330	98.79 242	98.99 320	98.34 232	99.16 302	99.07 325	92.13 396	99.52 136	97.31 406	94.54 243	98.98 360	88.54 404	98.73 213	99.03 253

GBi-Net		97.68 285	97.48 269	98.29 298	99.51 178	97.26 285	99.43 210	99.48 163	96.49 316	99.07 237	99.32 311	90.26 344	98.98 360	97.10 297	96.65 308	98.62 328

test1		97.68 285	97.48 269	98.29 298	99.51 178	97.26 285	99.43 210	99.48 163	96.49 316	99.07 237	99.32 311	90.26 344	98.98 360	97.10 297	96.65 308	98.62 328

FMVSNet3		98.03 223	97.76 241	98.84 234	99.39 224	98.98 160	99.40 228	99.38 240	96.67 300	99.07 237	99.28 318	92.93 286	98.98 360	97.10 297	96.65 308	98.56 344

FMVSNet2		97.72 278	97.36 290	98.80 241	99.51 178	98.84 185	99.45 199	99.42 220	96.49 316	98.86 276	99.29 316	90.26 344	98.98 360	96.44 328	96.56 311	98.58 342

FMVSNet1		96.84 328	96.36 332	98.29 298	99.32 245	97.26 285	99.43 210	99.48 163	95.11 363	98.55 317	99.32 311	83.95 398	98.98 360	95.81 340	96.26 319	98.62 328

ppachtmachnet_test		97.49 306	97.45 275	97.61 347	98.62 369	95.24 363	98.80 368	99.46 193	96.11 346	98.22 337	99.62 211	96.45 160	98.97 367	93.77 374	95.97 329	98.61 337

TranMVSNet+NR-MVSNet		97.93 238	97.66 250	98.76 245	98.78 348	98.62 206	99.65 81	99.49 151	97.76 197	98.49 321	99.60 218	94.23 253	98.97 367	98.00 222	92.90 382	98.70 291

MVStest1		96.08 345	95.48 350	97.89 330	98.93 328	96.70 319	99.56 130	99.35 256	92.69 394	91.81 408	99.46 270	89.90 350	98.96 369	95.00 360	92.61 387	98.00 386

test_method		91.10 375	91.36 377	90.31 395	95.85 408	73.72 428	94.89 416	99.25 298	68.39 419	95.82 388	99.02 349	80.50 409	98.95 370	93.64 376	94.89 356	98.25 369

ADS-MVSNet2		98.02 225	98.07 205	97.87 331	99.33 238	95.19 365	99.23 290	99.08 322	96.24 334	99.10 232	99.67 187	94.11 258	98.93 371	96.81 314	99.05 190	99.48 190

ET-MVSNet_ETH3D		96.49 335	95.64 349	99.05 193	99.53 170	98.82 189	98.84 364	97.51 404	97.63 212	84.77 413	99.21 330	92.09 314	98.91 372	98.98 100	92.21 389	99.41 211

miper_lstm_enhance		98.00 230	97.91 221	98.28 302	99.34 237	97.43 277	98.88 360	99.36 249	96.48 319	98.80 282	99.55 235	95.98 174	98.91 372	97.27 286	95.50 342	98.51 347

MonoMVSNet		98.38 185	98.47 173	98.12 313	98.59 374	96.19 342	99.72 52	98.79 366	97.89 179	99.44 152	99.52 247	96.13 169	98.90 374	98.64 153	97.54 278	99.28 228

PEN-MVS		97.76 268	97.44 280	98.72 247	98.77 353	98.54 213	99.78 32	99.51 121	97.06 274	98.29 333	99.64 200	92.63 300	98.89 375	98.09 211	93.16 380	98.72 284

testing3		97.28 314	96.76 323	98.82 236	99.37 229	98.07 245	99.45 199	99.36 249	97.56 220	97.89 352	98.95 357	83.70 399	98.82 376	96.03 335	98.56 222	99.58 162

testgi		97.65 290	97.50 267	98.13 312	99.36 232	96.45 331	99.42 217	99.48 163	97.76 197	97.87 353	99.45 272	91.09 336	98.81 377	94.53 365	98.52 225	99.13 239

testf1		90.42 378	90.68 379	89.65 398	97.78 390	73.97 426	99.13 308	98.81 363	89.62 404	91.80 409	98.93 359	62.23 418	98.80 378	86.61 412	91.17 392	96.19 409

APD_test2		90.42 378	90.68 379	89.65 398	97.78 390	73.97 426	99.13 308	98.81 363	89.62 404	91.80 409	98.93 359	62.23 418	98.80 378	86.61 412	91.17 392	96.19 409

MIMVSNet		97.73 276	97.45 275	98.57 261	99.45 207	97.50 275	99.02 334	98.98 336	96.11 346	99.41 161	99.14 336	90.28 343	98.74 380	95.74 342	98.93 198	99.47 196

LCM-MVSNet-Re		97.83 257	98.15 192	96.87 368	99.30 247	92.25 398	99.59 109	98.26 390	97.43 238	96.20 384	99.13 337	96.27 166	98.73 381	98.17 207	98.99 195	99.64 142

Syy-MVS		97.09 323	97.14 309	96.95 365	99.00 317	92.73 396	99.29 265	99.39 232	97.06 274	97.41 362	98.15 392	93.92 267	98.68 382	91.71 392	98.34 231	99.45 204

myMVS_eth3d		96.89 326	96.37 331	98.43 285	99.00 317	97.16 289	99.29 265	99.39 232	97.06 274	97.41 362	98.15 392	83.46 400	98.68 382	95.27 355	98.34 231	99.45 204

DTE-MVSNet		97.51 300	97.19 308	98.46 278	98.63 368	98.13 242	99.84 12	99.48 163	96.68 299	97.97 350	99.67 187	92.92 287	98.56 384	96.88 313	92.60 388	98.70 291

PC_three_1452									98.18 142	99.84 37	99.70 164	99.31 3	98.52 385	98.30 198	99.80 104	99.81 65

mvsany_test3		93.77 367	93.45 371	94.74 380	95.78 409	88.01 406	99.64 84	98.25 391	98.28 125	94.31 397	97.97 399	68.89 414	98.51 386	97.50 270	90.37 397	97.71 394

UnsupCasMVSNet_bld		93.53 368	92.51 374	96.58 373	97.38 396	93.82 384	98.24 403	99.48 163	91.10 401	93.10 402	96.66 408	74.89 412	98.37 387	94.03 373	87.71 405	97.56 399

Anonymous20240521		96.20 341	95.89 344	97.13 359	97.72 393	94.96 370	99.79 31	99.29 290	93.01 390	97.20 370	99.03 347	89.69 353	98.36 388	91.16 395	96.13 321	98.07 379

test_f		91.90 374	91.26 378	93.84 383	95.52 413	85.92 408	99.69 60	98.53 387	95.31 360	93.87 399	96.37 410	55.33 421	98.27 389	95.70 343	90.98 395	97.32 402

MDA-MVSNet_test_wron		95.45 352	94.60 359	98.01 319	98.16 385	97.21 288	99.11 317	99.24 301	93.49 386	80.73 419	98.98 354	93.02 284	98.18 390	94.22 371	94.45 361	98.64 319

UnsupCasMVSNet_eth		96.44 336	96.12 337	97.40 353	98.65 366	95.65 350	99.36 244	99.51 121	97.13 264	96.04 387	98.99 352	88.40 370	98.17 391	96.71 318	90.27 398	98.40 360

KD-MVS_2432*1600		94.62 360	93.72 368	97.31 354	97.19 402	95.82 348	98.34 398	99.20 308	95.00 367	97.57 359	98.35 385	87.95 375	98.10 392	92.87 386	77.00 417	98.01 383

miper_refine_blended		94.62 360	93.72 368	97.31 354	97.19 402	95.82 348	98.34 398	99.20 308	95.00 367	97.57 359	98.35 385	87.95 375	98.10 392	92.87 386	77.00 417	98.01 383

YYNet1		95.36 354	94.51 361	97.92 327	97.89 388	97.10 292	99.10 319	99.23 302	93.26 389	80.77 418	99.04 346	92.81 290	98.02 394	94.30 367	94.18 366	98.64 319

EU-MVSNet		97.98 232	98.03 208	97.81 338	98.72 359	96.65 324	99.66 75	99.66 28	98.09 155	98.35 328	99.82 83	95.25 205	98.01 395	97.41 279	95.30 345	98.78 271

Gipuma		90.99 376	90.15 381	93.51 384	98.73 357	90.12 404	93.98 417	99.45 204	79.32 415	92.28 405	94.91 412	69.61 413	97.98 396	87.42 408	95.67 336	92.45 415
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 355	94.73 358	97.15 357	95.53 412	95.94 346	99.35 249	99.10 319	95.13 361	93.55 400	97.54 401	88.15 374	97.91 397	94.58 364	89.69 401	97.61 397

PM-MVS		92.96 371	92.23 375	95.14 379	95.61 410	89.98 405	99.37 239	98.21 393	94.80 372	95.04 395	97.69 400	65.06 415	97.90 398	94.30 367	89.98 400	97.54 400

MDA-MVSNet-bldmvs		94.96 358	93.98 365	97.92 327	98.24 384	97.27 283	99.15 305	99.33 268	93.80 382	80.09 420	99.03 347	88.31 371	97.86 399	93.49 378	94.36 363	98.62 328

Patchmatch-RL test		95.84 348	95.81 346	95.95 377	95.61 410	90.57 403	98.24 403	98.39 388	95.10 365	95.20 392	98.67 374	94.78 224	97.77 400	96.28 332	90.02 399	99.51 184

Anonymous20231206		96.22 339	96.03 340	96.79 370	97.31 399	94.14 382	99.63 90	99.08 322	96.17 340	97.04 374	99.06 344	93.94 265	97.76 401	86.96 410	95.06 350	98.47 351

SD-MVS		99.41 51	99.52 12	99.05 193	99.74 85	99.68 53	99.46 197	99.52 107	99.11 32	99.88 26	99.91 22	99.43 1	97.70 402	98.72 142	99.93 25	99.77 86
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		97.25 316	97.35 292	96.95 365	97.84 389	93.61 390	99.57 124	96.63 412	96.13 345	98.87 272	98.61 377	94.59 238	97.70 402	95.08 358	98.86 204	99.55 168

dongtai		93.26 369	92.93 373	94.25 381	99.39 224	85.68 409	97.68 412	93.27 423	92.87 392	96.85 378	99.39 289	82.33 405	97.48 404	76.78 417	97.80 263	99.58 162

pmmvs3		94.09 366	93.25 372	96.60 372	94.76 417	94.49 377	98.92 356	98.18 395	89.66 403	96.48 381	98.06 398	86.28 384	97.33 405	89.68 400	87.20 406	97.97 389

KD-MVS_self_test		95.00 357	94.34 362	96.96 364	97.07 404	95.39 361	99.56 130	99.44 212	95.11 363	97.13 372	97.32 405	91.86 319	97.27 406	90.35 398	81.23 414	98.23 371

FMVSNet5		96.43 337	96.19 336	97.15 357	99.11 297	95.89 347	99.32 255	99.52 107	94.47 378	98.34 329	99.07 342	87.54 379	97.07 407	92.61 389	95.72 335	98.47 351

new-patchmatchnet		94.48 363	94.08 364	95.67 378	95.08 415	92.41 397	99.18 300	99.28 292	94.55 377	93.49 401	97.37 404	87.86 377	97.01 408	91.57 393	88.36 403	97.61 397

LCM-MVSNet		86.80 382	85.22 386	91.53 392	87.81 424	80.96 418	98.23 405	98.99 335	71.05 417	90.13 412	96.51 409	48.45 425	96.88 409	90.51 396	85.30 408	96.76 404

CL-MVSNet_self_test		94.49 362	93.97 366	96.08 376	96.16 407	93.67 389	98.33 400	99.38 240	95.13 361	97.33 366	98.15 392	92.69 298	96.57 410	88.67 403	79.87 415	97.99 387

MIMVSNet1		95.51 351	95.04 356	96.92 367	97.38 396	95.60 351	99.52 157	99.50 141	93.65 384	96.97 376	99.17 332	85.28 392	96.56 411	88.36 405	95.55 340	98.60 340

test20.03		96.12 343	95.96 342	96.63 371	97.44 395	95.45 358	99.51 166	99.38 240	96.55 313	96.16 385	99.25 324	93.76 274	96.17 412	87.35 409	94.22 365	98.27 367

tmp_tt		82.80 384	81.52 387	86.66 400	66.61 430	68.44 429	92.79 419	97.92 397	68.96 418	80.04 421	99.85 59	85.77 386	96.15 413	97.86 232	43.89 423	95.39 413

test_fmvs3		92.10 373	91.77 376	93.08 387	96.19 406	86.25 407	99.82 16	98.62 383	96.65 302	95.19 393	96.90 407	55.05 422	95.93 414	96.63 325	90.92 396	97.06 403

kuosan		90.92 377	90.11 382	93.34 385	98.78 348	85.59 410	98.15 407	93.16 425	89.37 406	92.07 406	98.38 384	81.48 408	95.19 415	62.54 424	97.04 304	99.25 232

dmvs_testset		95.02 356	96.12 337	91.72 391	99.10 300	80.43 419	99.58 117	97.87 399	97.47 230	95.22 391	98.82 366	93.99 263	95.18 416	88.09 406	94.91 355	99.56 167

PMMVS2		86.87 381	85.37 385	91.35 393	90.21 422	83.80 412	98.89 359	97.45 405	83.13 414	91.67 411	95.03 411	48.49 424	94.70 417	85.86 414	77.62 416	95.54 412

PMVS	70.75 22	75.98 390	74.97 391	79.01 406	70.98 429	55.18 431	93.37 418	98.21 393	65.08 423	61.78 424	93.83 414	21.74 431	92.53 418	78.59 416	91.12 394	89.34 419
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 383	85.65 384	82.75 404	86.77 425	63.39 430	98.35 397	98.92 344	74.11 416	83.39 415	98.98 354	50.85 423	92.40 419	84.54 415	94.97 352	92.46 414

WB-MVS		93.10 370	94.10 363	90.12 396	95.51 414	81.88 416	99.73 50	99.27 295	95.05 366	93.09 403	98.91 363	94.70 233	91.89 420	76.62 418	94.02 371	96.58 406

SSC-MVS		92.73 372	93.73 367	89.72 397	95.02 416	81.38 417	99.76 37	99.23 302	94.87 370	92.80 404	98.93 359	94.71 232	91.37 421	74.49 420	93.80 373	96.42 407

MVE	76.82 21	76.91 389	74.31 393	84.70 401	85.38 427	76.05 425	96.88 415	93.17 424	67.39 420	71.28 422	89.01 421	21.66 432	87.69 422	71.74 421	72.29 419	90.35 418
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 386	79.88 388	82.81 403	90.75 421	76.38 424	97.69 411	95.76 416	66.44 421	83.52 414	92.25 416	62.54 417	87.16 423	68.53 422	61.40 420	84.89 421

EMVS		80.02 387	79.22 389	82.43 405	91.19 420	76.40 423	97.55 414	92.49 428	66.36 422	83.01 416	91.27 418	64.63 416	85.79 424	65.82 423	60.65 421	85.08 420

ANet_high		77.30 388	74.86 392	84.62 402	75.88 428	77.61 422	97.63 413	93.15 426	88.81 408	64.27 423	89.29 420	36.51 427	83.93 425	75.89 419	52.31 422	92.33 416

wuyk23d		40.18 391	41.29 396	36.84 407	86.18 426	49.12 432	79.73 420	22.81 432	27.64 424	25.46 427	28.45 427	21.98 430	48.89 426	55.80 425	23.56 426	12.51 424

test123		39.01 393	42.50 395	28.53 408	39.17 431	20.91 433	98.75 373	19.17 433	19.83 426	38.57 425	66.67 423	33.16 428	15.42 427	37.50 427	29.66 425	49.26 422

testmvs		39.17 392	43.78 394	25.37 409	36.04 432	16.84 434	98.36 396	26.56 431	20.06 425	38.51 426	67.32 422	29.64 429	15.30 428	37.59 426	39.90 424	43.98 423

mmdepth		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

monomultidepth		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

test_blank		0.13 397	0.17 400	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	1.57 428	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

uanet_test		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

DCPMVS		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

cdsmvs_eth3d_5k		24.64 394	32.85 397	0.00 410	0.00 433	0.00 435	0.00 421	99.51 121	0.00 428	0.00 429	99.56 232	96.58 153	0.00 429	0.00 428	0.00 427	0.00 425

pcd_1.5k_mvsjas		8.27 396	11.03 399	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	99.01 18	0.00 429	0.00 428	0.00 427	0.00 425

sosnet-low-res		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

sosnet		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

uncertanet		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

Regformer		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

ab-mvs-re		8.30 395	11.06 398	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	99.58 224	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

uanet		0.02 398	0.03 401	0.00 410	0.00 433	0.00 435	0.00 421	0.00 434	0.00 428	0.00 429	0.27 429	0.00 433	0.00 429	0.00 428	0.00 427	0.00 425

WAC-MVS						97.16 289								95.47 349

FOURS1					99.91 1	99.93 1	99.87 8	99.56 72	99.10 33	99.81 45

test_one_0601					99.81 46	99.88 8		99.49 151	98.97 57	99.65 101	99.81 97	99.09 14

eth-test2					0.00 433

eth-test					0.00 433

RE-MVS-def			99.34 42		99.76 68	99.82 25	99.63 90	99.52 107	98.38 113	99.76 66	99.82 83	98.75 58		98.61 159	99.81 100	99.77 86

IU-MVS					99.84 32	99.88 8		99.32 278	98.30 124	99.84 37				98.86 122	99.85 76	99.89 20

save fliter					99.76 68	99.59 75	99.14 307	99.40 229	99.00 49

test0726					99.85 26	99.89 4	99.62 95	99.50 141	99.10 33	99.86 35	99.82 83	98.94 32

GSMVS																99.52 177

test_part2					99.81 46	99.83 19				99.77 60

sam_mvs1												94.86 219				99.52 177

sam_mvs												94.72 231

MTGPA								99.47 184

MTMP							99.54 148	98.88 354

test9_res														97.49 271	99.72 127	99.75 92

agg_prior2														97.21 289	99.73 126	99.75 92

test_prior4						99.56 81	98.99 342

test_prior2							98.96 349		98.34 119	99.01 248	99.52 247	98.68 67		97.96 224	99.74 124

新几何2							99.01 339

旧先验1					99.74 85	99.59 75		99.54 89			99.69 174	98.47 83			99.68 135	99.73 101

原ACMM2							98.95 352

test222					99.75 78	99.49 94	98.91 358	99.49 151	96.42 324	99.34 181	99.65 194	98.28 96			99.69 132	99.72 108

segment_acmp												98.96 25

testdata1							98.85 363		98.32 122

plane_prior7					99.29 251	97.03 302

plane_prior6					99.27 256	96.98 306						92.71 296

plane_prior4											99.61 215

plane_prior3						97.00 304			98.69 86	99.11 229

plane_prior2							99.39 232		98.97 57

plane_prior1					99.26 258

plane_prior						96.97 307	99.21 296		98.45 106						97.60 272

n2								0.00 434

nn								0.00 434

door-mid								98.05 396

test11								99.35 256

door								97.92 397

HQP5-MVS						96.83 314

HQP-NCC					99.19 276		98.98 345		98.24 131	98.66 300

ACMP_Plane					99.19 276		98.98 345		98.24 131	98.66 300

BP-MVS														97.19 293

HQP3-MVS								99.39 232							97.58 274

HQP2-MVS												92.47 305

NP-MVS					99.23 266	96.92 310					99.40 285

MDTV_nov1_ep13_2view						95.18 366	99.35 249		96.84 291	99.58 123		95.19 207		97.82 237		99.46 201

ACMMP++_ref															97.19 301

ACMMP++															97.43 292

Test By Simon												98.75 58

High-resolution multi-view benchmark