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.

reproduce_model		99.76 18	99.69 22	99.98 23	99.96 96	99.93 47	100.00 1	99.42 141	98.81 59	100.00 1	100.00 1	98.98 102	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 18	99.69 22	99.98 23	99.96 96	99.94 41	100.00 1	99.42 141	98.82 55	100.00 1	100.00 1	98.99 99	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 18	99.69 22	99.98 23	99.96 96	99.94 41	100.00 1	99.42 141	98.82 55	100.00 1	100.00 1	98.99 99	100.00 1	100.00 1	100.00 1	100.00 1

MM		99.63 54	99.52 64	99.94 66	99.99 49	99.82 89	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 209	100.00 1	100.00 1	99.97 116	100.00 1

test_fmvsmvis_n_1920		99.46 79	99.37 80	99.73 123	98.88 311	99.18 171	100.00 1	99.26 279	98.85 49	99.79 190	100.00 1	97.70 166	100.00 1	99.98 76	99.86 138	100.00 1

test_fmvsm_n_1920		99.55 68	99.49 69	99.73 123	99.85 120	99.19 169	100.00 1	99.41 190	98.87 47	100.00 1	100.00 1	97.34 186	100.00 1	99.98 76	99.90 131	100.00 1

test_vis1_n_1920		97.77 227	97.24 248	99.34 181	99.79 146	98.04 253	100.00 1	99.25 281	98.88 44	100.00 1	100.00 1	77.52 390	100.00 1	99.88 106	99.85 141	100.00 1

mvsany_test1		99.57 66	99.48 72	99.85 90	99.86 119	99.54 123	100.00 1	99.36 218	98.94 37	100.00 1	100.00 1	97.97 150	100.00 1	99.88 106	99.28 167	100.00 1

patch_mono-2		99.04 132	99.79 6	96.81 335	99.92 108	90.47 385	100.00 1	99.41 190	98.95 35	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 131

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 141	98.79 63	100.00 1	100.00 1	99.54 28	100.00 1	100.00 1	100.00 1	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 141					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 60	100.00 1	100.00 1	99.54 28	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 141					100.00 1	100.00 1	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 141	99.03 21	100.00 1	100.00 1	99.50 39	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 28	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 141	99.03 21	100.00 1	100.00 1	99.56 25	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 141	99.03 21	100.00 1	100.00 1	99.50 39	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 34	99.99 49	99.90 63	100.00 1	99.79 45	97.97 118	99.97 122	100.00 1	98.97 104	100.00 1	99.94 96	100.00 1	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 141	99.04 16	100.00 1	100.00 1	99.53 31	100.00 1	100.00 1	100.00 1	100.00 1
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									98.79 63	100.00 1	100.00 1	99.61 18	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 141					100.00 1	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 54	99.51 66	100.00 1	99.90 112	100.00 1	100.00 1	99.43 124	99.00 27	100.00 1	100.00 1	99.58 24	100.00 1	97.64 277	100.00 1	100.00 1

GST-MVS		99.64 51	99.53 62	99.95 54	100.00 1	99.86 82	100.00 1	99.79 45	97.72 138	99.95 153	100.00 1	98.39 141	100.00 1	99.96 88	99.99 103	100.00 1

test_yl		99.51 70	99.37 80	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 171	100.00 1	100.00 1	99.80 5	100.00 1	99.98 76	97.75 241	99.94 136

Anonymous20240529		96.93 267	96.22 284	99.05 205	99.79 146	97.30 290	99.16 380	99.47 79	88.51 390	98.69 266	100.00 1	83.50 370	100.00 1	99.83 116	97.02 256	99.83 199

Anonymous202405211		97.87 222	97.53 233	98.90 216	99.81 131	96.70 308	99.35 356	99.46 94	92.98 360	98.83 260	99.99 187	90.63 296	100.00 1	99.70 148	97.03 255	100.00 1

DCV-MVSNet		99.51 70	99.37 80	99.95 54	99.82 125	99.90 63	100.00 1	99.47 79	97.48 171	100.00 1	100.00 1	99.80 5	100.00 1	99.98 76	97.75 241	99.94 136

SMA-MVS		99.69 39	99.59 47	99.98 23	99.99 49	99.93 47	100.00 1	99.43 124	97.50 169	100.00 1	100.00 1	99.43 52	100.00 1	100.00 1	100.00 1	100.00 1
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
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 141	98.91 41	100.00 1	100.00 1	99.22 78	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
thres100view900		99.25 111	99.01 123	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 197	99.83 178	99.96 216	97.01 193	100.00 1	99.59 175	97.85 232	99.98 112

tfpn200view9		99.26 107	99.03 121	99.96 45	99.81 131	99.89 70	100.00 1	99.94 22	97.23 192	99.83 178	99.96 216	97.04 189	100.00 1	99.59 175	97.85 232	99.98 112

CANet_DTU		99.02 139	98.90 143	99.41 170	99.88 116	98.71 205	100.00 1	99.29 256	98.84 51	100.00 1	100.00 1	94.02 250	100.00 1	98.08 260	99.96 119	99.52 256

MVS_0304		99.72 29	99.65 34	99.93 70	99.99 49	99.79 93	100.00 1	99.91 35	99.17 6	100.00 1	100.00 1	97.84 159	100.00 1	100.00 1	99.95 121	100.00 1

MP-MVS-pluss		99.61 61	99.50 67	99.97 34	99.98 86	99.92 53	100.00 1	99.42 141	97.53 164	99.77 193	100.00 1	98.77 125	100.00 1	99.99 64	100.00 1	99.99 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xiu_mvs_v1_base_debu		99.35 91	99.21 102	99.79 110	99.67 173	99.71 102	99.78 297	99.36 218	98.13 104	100.00 1	100.00 1	97.00 196	100.00 1	99.83 116	99.07 172	99.66 250

ACMMP_NAP		99.67 46	99.57 52	99.97 34	99.98 86	99.92 53	100.00 1	99.42 141	97.83 129	100.00 1	100.00 1	98.89 116	100.00 1	99.98 76	100.00 1	100.00 1

xiu_mvs_v2_base		99.51 70	99.41 74	99.82 97	99.70 160	99.73 100	99.92 271	99.40 194	98.15 102	100.00 1	100.00 1	98.50 138	100.00 1	99.85 112	99.13 170	99.74 241

xiu_mvs_v1_base		99.35 91	99.21 102	99.79 110	99.67 173	99.71 102	99.78 297	99.36 218	98.13 104	100.00 1	100.00 1	97.00 196	100.00 1	99.83 116	99.07 172	99.66 250

xiu_mvs_v1_base_debi		99.35 91	99.21 102	99.79 110	99.67 173	99.71 102	99.78 297	99.36 218	98.13 104	100.00 1	100.00 1	97.00 196	100.00 1	99.83 116	99.07 172	99.66 250

MTAPA		99.68 43	99.59 47	99.97 34	99.99 49	99.91 56	100.00 1	99.42 141	98.32 92	99.94 155	100.00 1	98.65 130	100.00 1	99.96 88	100.00 1	100.00 1

HFP-MVS		99.74 25	99.67 30	99.96 45	100.00 1	99.89 70	100.00 1	99.76 49	97.95 122	100.00 1	100.00 1	99.31 66	100.00 1	99.99 64	100.00 1	100.00 1

region2R		99.72 29	99.64 37	99.97 34	100.00 1	99.90 63	100.00 1	99.74 55	97.86 128	100.00 1	100.00 1	99.19 81	100.00 1	99.99 64	100.00 1	100.00 1

PS-MVSNAJ		99.64 51	99.57 52	99.85 90	99.78 149	99.81 90	99.95 261	99.42 141	98.38 84	100.00 1	100.00 1	98.75 126	100.00 1	99.88 106	99.99 103	99.74 241

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 44	99.96 126	100.00 1	99.21 79	100.00 1	100.00 1	100.00 1	99.99 110

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 70	100.00 1	100.00 1	99.16 83	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 261	96.06 290	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 70	100.00 1	66.97 422	99.16 83	100.00 1	100.00 1	100.00 1	100.00 1

旧先验2							100.00 1		98.11 108	100.00 1			100.00 1	99.67 159

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 125	100.00 1	100.00 1	99.20 80	100.00 1	97.91 269	100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 116				100.00 1	99.33 196		100.00 1

原ACMM1				99.93 70	100.00 1	99.80 92		99.66 63	98.18 99	100.00 1	100.00 1	99.43 52	100.00 1	99.50 187	100.00 1	100.00 1

testdata2													100.00 1	97.36 290

testdata				99.66 135	99.99 49	98.97 193		99.73 56	97.96 121	100.00 1	100.00 1	99.42 55	100.00 1	99.28 200	100.00 1	100.00 1

1314		99.38 87	99.19 106	99.96 45	98.88 311	99.89 70	99.24 366	99.93 30	98.88 44	98.79 263	100.00 1	97.02 192	100.00 1	100.00 1	100.00 1	100.00 1

LFMVS		97.42 244	96.62 265	99.81 102	99.80 142	99.50 131	99.16 380	99.56 70	94.48 322	100.00 1	100.00 1	79.35 385	100.00 1	99.89 104	97.37 250	99.94 136

VDD-MVS		96.58 282	95.99 293	98.34 250	99.52 229	95.33 327	99.18 374	99.38 209	96.64 241	99.77 193	100.00 1	72.51 402	100.00 1	100.00 1	96.94 258	99.70 246

VDDNet		96.39 294	95.55 316	98.90 216	99.27 273	97.45 281	99.15 382	99.92 34	91.28 373	99.98 117	100.00 1	73.55 398	100.00 1	99.85 112	96.98 257	99.24 260

MVS		99.22 115	98.96 131	99.98 23	99.00 298	99.95 32	99.24 366	99.94 22	98.14 103	98.88 253	100.00 1	95.63 225	100.00 1	99.85 112	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 141	99.01 26	100.00 1	100.00 1	99.33 61	100.00 1	100.00 1	100.00 1	100.00 1
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
MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 8	100.00 1	100.00 1	99.60 19	100.00 1	100.00 1	100.00 1	100.00 1

VNet		99.04 132	98.75 154	99.90 77	99.81 131	99.75 97	99.50 341	99.47 79	98.36 88	100.00 1	99.99 187	94.66 242	100.00 1	99.90 102	97.09 254	99.96 125

ACMMPR		99.74 25	99.67 30	99.96 45	100.00 1	99.89 70	100.00 1	99.76 49	97.95 122	100.00 1	100.00 1	99.29 72	100.00 1	99.99 64	100.00 1	100.00 1

thres600view7		99.24 114	99.00 125	99.95 54	99.81 131	99.87 79	100.00 1	99.94 22	97.13 197	99.83 178	99.96 216	97.01 193	100.00 1	99.54 183	97.77 240	99.97 119

MP-MVS		99.61 61	99.49 69	99.98 23	99.99 49	99.94 41	100.00 1	99.42 141	97.82 130	99.99 111	100.00 1	98.20 144	100.00 1	99.99 64	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres400		99.26 107	99.03 121	99.95 54	99.81 131	99.89 70	100.00 1	99.94 22	97.23 192	99.83 178	99.96 216	97.04 189	100.00 1	99.59 175	97.85 232	99.97 119

thres200		99.27 105	99.04 120	99.96 45	99.81 131	99.90 63	100.00 1	99.94 22	97.31 187	99.83 178	99.96 216	97.04 189	100.00 1	99.62 169	97.88 230	99.98 112

PGM-MVS		99.69 39	99.61 45	99.95 54	99.99 49	99.85 85	100.00 1	99.58 67	97.69 142	100.00 1	100.00 1	99.44 48	100.00 1	99.79 123	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 66	100.00 1	100.00 1	100.00 1	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 34	100.00 1	99.91 56	100.00 1	99.42 141	97.91 124	100.00 1	100.00 1	99.04 96	100.00 1	100.00 1	100.00 1	100.00 1

EPNet		99.62 59	99.69 22	99.42 169	99.99 49	98.37 226	100.00 1	99.89 37	98.83 53	100.00 1	100.00 1	98.97 104	100.00 1	99.90 102	99.61 161	99.89 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 11	100.00 1	100.00 1	99.39 59	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 61	99.07 11	100.00 1	100.00 1	99.59 22	100.00 1	100.00 1	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 54	100.00 1	99.84 87	100.00 1	99.42 141	97.77 135	100.00 1	100.00 1	99.07 90	100.00 1	100.00 1	100.00 1	100.00 1

DELS-MVS		99.62 59	99.56 57	99.82 97	99.92 108	99.45 140	100.00 1	99.78 47	98.92 39	99.73 198	100.00 1	97.70 166	100.00 1	99.93 98	100.00 1	100.00 1
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
CPTT-MVS		99.49 75	99.38 77	99.85 90	100.00 1	99.54 123	100.00 1	99.42 141	97.58 159	99.98 117	100.00 1	97.43 183	100.00 1	99.99 64	100.00 1	100.00 1

UGNet		98.41 200	98.11 208	99.31 188	99.54 219	98.55 215	99.18 374	100.00 1	98.64 73	99.79 190	99.04 343	87.61 336	100.00 1	99.30 199	99.89 132	99.40 259
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
sss		99.45 80	99.34 87	99.80 107	99.76 152	99.50 131	100.00 1	99.91 35	97.72 138	99.98 117	99.94 229	98.45 139	100.00 1	99.53 185	98.75 182	99.89 168

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 114	99.99 111	100.00 1	99.72 12	100.00 1	99.96 88	100.00 1	100.00 1

QAPM		98.99 145	98.66 163	99.96 45	99.01 294	99.87 79	99.88 281	99.93 30	97.99 114	98.68 267	100.00 1	93.17 262	100.00 1	99.32 197	100.00 1	100.00 1

PAPM_NR		99.74 25	99.66 33	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 127	100.00 1	100.00 1	99.60 19	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 28	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 100	100.00 1	100.00 1	99.51 35	100.00 1	100.00 1	100.00 1	100.00 1

RPSCF		97.37 246	98.24 200	94.76 363	99.80 142	84.57 400	99.99 217	99.05 357	94.95 307	99.82 186	100.00 1	94.03 249	100.00 1	98.15 259	98.38 198	99.70 246

CSCG		99.28 104	99.35 85	99.05 205	99.99 49	97.15 296	100.00 1	99.47 79	97.44 175	99.42 216	100.00 1	97.83 161	100.00 1	99.99 64	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 110	99.97 90	99.37 150	99.96 255	99.94 22	98.48 79	100.00 1	100.00 1	98.92 113	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 49	99.57 52	99.89 79	99.99 49	99.66 110	99.75 306	99.73 56	98.16 100	99.75 196	100.00 1	98.90 115	100.00 1	99.96 88	99.88 134	100.00 1
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
CNLPA		99.72 29	99.65 34	99.91 74	99.97 90	99.72 101	100.00 1	99.47 79	98.43 82	99.88 171	100.00 1	99.14 86	100.00 1	99.97 86	100.00 1	100.00 1

PHI-MVS		99.50 73	99.39 76	99.82 97	100.00 1	99.45 140	100.00 1	99.94 22	96.38 258	100.00 1	100.00 1	98.18 145	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 102	99.25 95	99.44 164	100.00 1	98.32 232	100.00 1	99.86 38	98.04 111	100.00 1	100.00 1	96.10 218	100.00 1	99.55 180	99.73 150	100.00 1

PVSNet_0	93.57 19	96.41 290	95.74 307	98.41 245	99.84 121	95.22 329	100.00 1	100.00 1	98.08 109	97.55 332	99.78 265	84.40 362	100.00 1	100.00 1	81.99 395	100.00 1

DeepPCF-MVS	98.03 4	98.54 188	99.72 19	94.98 360	99.99 49	84.94 399	100.00 1	99.42 141	99.98 1	100.00 1	100.00 1	98.11 147	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 23	99.67 30	99.99 12	99.99 49	99.96 24	99.73 312	99.52 72	99.06 13	100.00 1	100.00 1	98.80 124	100.00 1	99.95 94	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.75 23	99.68 28	99.97 34	100.00 1	99.91 56	99.98 243	99.47 79	99.09 10	100.00 1	100.00 1	98.59 134	100.00 1	99.95 94	100.00 1	100.00 1

AdaColmap		99.44 81	99.26 94	99.95 54	100.00 1	99.86 82	99.70 317	99.99 13	98.53 76	99.90 166	100.00 1	95.34 227	100.00 1	99.92 99	100.00 1	100.00 1

PLC	98.56 2	99.70 36	99.74 16	99.58 149	100.00 1	98.79 199	100.00 1	99.54 71	98.58 75	99.96 126	100.00 1	99.59 22	100.00 1	100.00 1	100.00 1	99.94 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		99.49 75	99.36 83	99.89 79	99.97 90	99.66 110	99.74 307	99.95 19	97.89 125	100.00 1	100.00 1	96.71 208	100.00 1	100.00 1	100.00 1	100.00 1
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
3Dnovator+	95.58 15	99.03 134	98.71 160	99.96 45	98.99 301	99.89 70	100.00 1	99.51 76	98.96 32	98.32 291	100.00 1	92.78 268	100.00 1	99.87 109	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 129	98.76 153	99.96 45	98.86 315	99.90 63	99.98 243	99.93 30	98.95 35	98.49 282	100.00 1	92.91 266	100.00 1	99.71 145	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 165	98.41 186	99.78 114	98.89 310	99.81 90	99.99 217	99.76 49	98.02 112	98.02 309	100.00 1	91.44 284	100.00 1	99.63 168	99.97 116	99.55 254

UBG		99.36 90	99.27 90	99.63 138	99.63 190	99.01 186	100.00 1	99.43 124	96.99 207	100.00 1	99.92 234	99.69 15	99.99 98	99.74 136	98.06 220	99.88 181

fmvsm_l_conf0.5_n_a		99.63 54	99.55 59	99.86 87	99.83 124	99.58 118	100.00 1	99.36 218	98.98 30	100.00 1	100.00 1	97.85 157	99.99 98	100.00 1	99.94 124	100.00 1

fmvsm_l_conf0.5_n		99.63 54	99.56 57	99.86 87	99.81 131	99.59 116	100.00 1	99.36 218	98.98 30	100.00 1	100.00 1	97.92 153	99.99 98	100.00 1	99.95 121	100.00 1

fmvsm_s_conf0.5_n_a		99.32 98	99.15 111	99.81 102	99.80 142	99.47 139	100.00 1	99.35 229	98.22 95	100.00 1	100.00 1	95.21 232	99.99 98	99.96 88	99.86 138	99.98 112

fmvsm_s_conf0.5_n		99.21 116	99.01 123	99.83 95	99.84 121	99.53 125	100.00 1	99.38 209	98.29 94	100.00 1	100.00 1	93.62 255	99.99 98	99.99 64	99.93 127	99.98 112

test_fmvsmconf_n		99.56 67	99.46 73	99.86 87	99.68 165	99.58 118	100.00 1	99.31 246	98.92 39	99.88 171	100.00 1	97.35 185	99.99 98	99.98 76	99.99 103	100.00 1

test_cas_vis1_n_1920		98.63 178	98.25 197	99.77 117	99.69 161	99.32 153	100.00 1	99.31 246	98.84 51	99.96 126	100.00 1	87.42 338	99.99 98	99.14 207	99.86 138	100.00 1

test_vis1_n		96.69 277	95.81 301	99.32 186	99.14 279	97.98 256	99.97 249	98.98 369	98.45 81	100.00 1	100.00 1	66.44 408	99.99 98	99.78 129	99.57 163	100.00 1

h-mvs33		97.03 262	96.53 268	98.51 237	99.79 146	95.90 319	99.45 345	99.45 102	98.21 96	100.00 1	99.78 265	97.49 177	99.99 98	99.72 141	74.92 405	99.65 253

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 187	100.00 1	100.00 1	99.32 64	99.99 98	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 54	99.52 64	99.97 34	99.99 49	99.91 56	100.00 1	99.42 141	97.62 150	100.00 1	100.00 1	98.65 130	99.99 98	99.99 64	100.00 1	100.00 1

SF-MVS		99.66 48	99.57 52	99.95 54	99.99 49	99.85 85	100.00 1	99.42 141	97.67 143	100.00 1	100.00 1	99.05 93	99.99 98	100.00 1	100.00 1	100.00 1

9.14			99.57 52		99.99 49		100.00 1	99.42 141	97.54 162	100.00 1	100.00 1	99.15 85	99.99 98	100.00 1	100.00 1

SR-MVS		99.68 43	99.58 49	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 158	100.00 1	100.00 1	98.99 99	99.99 98	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 124	99.05 15	100.00 1	100.00 1	99.45 47	99.99 98	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 116	99.06 13	100.00 1	100.00 1	99.56 25	99.99 98	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		99.65 49	99.55 59	99.97 34	99.99 49	99.91 56	100.00 1	99.48 78	97.54 162	100.00 1	100.00 1	98.97 104	99.99 98	99.98 76	100.00 1	100.00 1

CDPH-MVS		99.73 28	99.64 37	99.99 12	100.00 1	99.97 21	100.00 1	99.42 141	98.02 112	100.00 1	100.00 1	99.32 64	99.99 98	100.00 1	100.00 1	100.00 1

TSAR-MVS + GP.		99.61 61	99.69 22	99.35 180	99.99 49	98.06 251	100.00 1	99.36 218	99.83 2	100.00 1	100.00 1	98.95 108	99.99 98	100.00 1	99.11 171	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 152	99.95 32	100.00 1	99.42 141	98.69 68	100.00 1	100.00 1	99.52 34	99.99 98	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_BlendedMVS		98.71 169	98.62 168	98.98 212	99.98 86	99.60 114	100.00 1	100.00 1	97.23 192	100.00 1	99.03 346	96.57 211	99.99 98	100.00 1	94.75 298	97.35 367

PVSNet_Blended		99.48 77	99.36 83	99.83 95	99.98 86	99.60 114	100.00 1	100.00 1	97.79 133	100.00 1	100.00 1	96.57 211	99.99 98	100.00 1	99.88 134	99.90 162

HY-MVS	96.53 9	99.50 73	99.35 85	99.96 45	99.81 131	99.93 47	99.64 324	100.00 1	97.97 118	99.84 175	99.85 250	98.94 110	99.99 98	99.86 110	98.23 211	99.95 131

PatchMatch-RL		99.02 139	98.78 151	99.74 120	99.99 49	99.29 156	100.00 1	100.00 1	98.38 84	99.89 169	99.81 259	93.14 264	99.99 98	97.85 271	99.98 113	99.95 131

F-COLMAP		99.64 51	99.64 37	99.67 132	99.99 49	99.07 177	100.00 1	99.44 116	98.30 93	99.90 166	100.00 1	99.18 82	99.99 98	99.91 101	100.00 1	99.94 136

testing91		99.18 118	99.10 115	99.41 170	99.60 201	98.43 218	100.00 1	99.43 124	96.76 225	99.82 186	99.92 234	99.05 93	99.98 123	99.62 169	97.67 245	99.81 213

testing99		99.18 118	99.10 115	99.41 170	99.60 201	98.43 218	100.00 1	99.43 124	96.76 225	99.84 175	99.92 234	99.06 91	99.98 123	99.62 169	97.67 245	99.81 213

test_8					100.00 1	99.91 56	100.00 1	99.42 141	97.70 140	100.00 1	100.00 1	99.51 35	99.98 123

MVS_Test		98.93 154	98.65 164	99.77 117	99.62 197	99.50 131	99.99 217	99.19 303	95.52 293	99.96 126	99.86 245	96.54 213	99.98 123	98.65 234	98.48 189	99.82 204

RPMNet		95.26 329	93.82 338	99.56 152	99.31 269	98.86 196	99.13 385	99.42 141	79.82 408	99.96 126	95.13 401	95.69 224	99.98 123	77.54 411	98.40 194	99.84 195

WTY-MVS		99.54 69	99.40 75	99.95 54	99.81 131	99.93 47	100.00 1	100.00 1	97.98 116	99.84 175	100.00 1	98.94 110	99.98 123	99.86 110	98.21 212	99.94 136

ab-mvs		98.42 198	98.02 217	99.61 141	99.71 158	99.00 189	99.10 388	99.64 64	96.70 234	99.04 246	99.81 259	90.64 295	99.98 123	99.64 165	97.93 227	99.84 195

testing11		99.26 107	99.19 106	99.46 161	99.64 188	98.61 211	100.00 1	99.43 124	96.94 210	99.92 161	99.94 229	99.43 52	99.97 130	99.67 159	97.79 239	99.82 204

sasdasda		99.03 134	98.73 156	99.94 66	99.75 154	99.95 32	100.00 1	99.30 250	97.64 147	100.00 1	100.00 1	95.22 230	99.97 130	99.76 132	96.90 259	99.91 151

FE-MVS		99.16 121	98.99 127	99.66 135	99.65 182	99.18 171	99.58 332	99.43 124	95.24 302	99.91 164	99.59 302	99.37 60	99.97 130	98.31 251	99.81 146	99.83 199

TEST9					100.00 1	99.95 32	100.00 1	99.42 141	97.65 145	100.00 1	100.00 1	99.53 31	99.97 130

train_agg		99.71 33	99.63 41	99.97 34	100.00 1	99.95 32	100.00 1	99.42 141	97.70 140	100.00 1	100.00 1	99.51 35	99.97 130	100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 77		99.42 141		100.00 1			99.97 130

canonicalmvs		99.03 134	98.73 156	99.94 66	99.75 154	99.95 32	100.00 1	99.30 250	97.64 147	100.00 1	100.00 1	95.22 230	99.97 130	99.76 132	96.90 259	99.91 151

EI-MVSNet-UG-set		99.69 39	99.63 41	99.87 84	99.99 49	99.64 112	99.95 261	99.44 116	98.35 90	100.00 1	100.00 1	98.98 102	99.97 130	99.98 76	100.00 1	100.00 1

test_prior				99.90 77	100.00 1	99.75 97		99.73 56					99.97 130			100.00 1

test12				99.95 54	99.99 49	99.89 70		99.42 141		100.00 1		99.24 77	99.97 130		100.00 1	100.00 1

APD-MVS		99.68 43	99.58 49	99.97 34	99.99 49	99.96 24	100.00 1	99.42 141	97.53 164	100.00 1	100.00 1	99.27 75	99.97 130	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DP-MVS		98.86 159	98.54 175	99.81 102	99.97 90	99.45 140	99.52 339	99.40 194	94.35 326	98.36 287	100.00 1	96.13 217	99.97 130	99.12 210	100.00 1	100.00 1

LS3D		99.31 100	99.13 112	99.87 84	99.99 49	99.71 102	99.55 335	99.46 94	97.32 185	99.82 186	100.00 1	96.85 203	99.97 130	99.14 207	100.00 1	99.92 149

MGCFI-Net		99.01 141	98.70 162	99.93 70	99.74 156	99.94 41	100.00 1	99.29 256	97.60 157	100.00 1	100.00 1	95.10 234	99.96 143	99.74 136	96.85 261	99.91 151

fmvsm_s_conf0.1_n_a		98.71 169	98.36 193	99.78 114	99.09 284	99.42 144	100.00 1	99.26 279	97.42 177	100.00 1	100.00 1	89.78 308	99.96 143	99.82 121	99.85 141	99.97 119

fmvsm_s_conf0.1_n		98.77 164	98.42 185	99.82 97	99.47 247	99.52 128	100.00 1	99.27 272	97.53 164	100.00 1	100.00 1	89.73 310	99.96 143	99.84 115	99.93 127	99.97 119

test_fmvs1_n		97.43 243	96.86 256	99.15 201	99.68 165	97.48 280	99.99 217	98.98 369	98.82 55	100.00 1	100.00 1	74.85 397	99.96 143	99.67 159	99.70 152	100.00 1

test_fmvs1		98.37 203	98.04 215	99.34 181	99.84 121	98.07 249	100.00 1	99.00 366	98.85 49	100.00 1	100.00 1	85.11 359	99.96 143	99.69 155	99.88 134	100.00 1

FA-MVS(test-final)		99.00 142	98.75 154	99.73 123	99.63 190	99.43 143	99.83 287	99.43 124	95.84 283	99.52 208	99.37 323	97.84 159	99.96 143	97.63 278	99.68 153	99.79 233

CANet		99.40 84	99.24 98	99.89 79	99.99 49	99.76 96	100.00 1	99.73 56	98.40 83	99.78 192	100.00 1	95.28 228	99.96 143	100.00 1	99.99 103	99.96 125

MSP-MVS		99.81 11	99.77 9	99.94 66	100.00 1	99.86 82	100.00 1	99.42 141	98.87 47	100.00 1	100.00 1	99.65 17	99.96 143	100.00 1	100.00 1	100.00 1
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
RRT-MVS		98.75 167	98.52 178	99.44 164	99.65 182	98.57 214	99.90 275	99.08 342	96.51 249	99.96 126	99.95 223	92.59 274	99.96 143	99.60 173	99.45 166	99.81 213

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 84	100.00 1	99.64 112	99.98 243	99.44 116	98.35 90	99.99 111	100.00 1	99.04 96	99.96 143	99.98 76	100.00 1	100.00 1

HPM-MVS_fast		99.60 64	99.49 69	99.91 74	99.99 49	99.78 94	100.00 1	99.42 141	97.09 199	100.00 1	100.00 1	98.95 108	99.96 143	99.98 76	100.00 1	100.00 1

HPM-MVS		99.59 65	99.50 67	99.89 79	100.00 1	99.70 106	100.00 1	99.42 141	97.46 173	100.00 1	100.00 1	98.60 133	99.96 143	99.99 64	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
BH-w/o		98.82 162	98.81 149	98.88 218	99.62 197	96.71 307	100.00 1	99.28 262	97.09 199	98.81 261	100.00 1	94.91 238	99.96 143	99.54 183	100.00 1	99.96 125

test_vis1_rt		93.10 349	92.93 350	93.58 373	99.63 190	85.07 398	99.99 217	93.71 419	97.49 170	90.96 391	97.10 393	60.40 410	99.95 156	99.24 203	97.90 229	95.72 395

AllTest		98.55 185	98.40 187	98.99 210	99.93 105	97.35 286	100.00 1	99.40 194	97.08 201	99.09 240	99.98 192	93.37 258	99.95 156	96.94 299	99.84 143	99.68 248

TestCases				98.99 210	99.93 105	97.35 286		99.40 194	97.08 201	99.09 240	99.98 192	93.37 258	99.95 156	96.94 299	99.84 143	99.68 248

CHOSEN 1792x2688		99.00 142	98.91 140	99.25 196	99.90 112	97.79 271	100.00 1	99.99 13	98.79 63	98.28 294	100.00 1	93.63 254	99.95 156	99.66 163	99.95 121	100.00 1

114514_t		99.39 85	99.25 95	99.81 102	99.97 90	99.48 138	100.00 1	99.42 141	95.53 291	100.00 1	100.00 1	98.37 142	99.95 156	99.97 86	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 96	99.18 109	99.78 114	99.82 125	99.49 134	100.00 1	99.95 19	97.36 180	99.63 204	100.00 1	96.45 215	99.95 156	99.79 123	99.65 157	99.89 168

MVS_111021_LR		99.70 36	99.65 34	99.88 83	99.96 96	99.70 106	100.00 1	99.97 17	98.96 32	100.00 1	100.00 1	97.93 152	99.95 156	99.99 64	100.00 1	100.00 1

MVS_111021_HR		99.71 33	99.63 41	99.93 70	99.95 100	99.83 88	100.00 1	100.00 1	98.89 43	100.00 1	100.00 1	97.85 157	99.95 156	100.00 1	100.00 1	100.00 1

DeepC-MVS	97.84 5	99.00 142	98.80 150	99.60 143	99.93 105	99.03 182	100.00 1	99.40 194	98.61 74	99.33 226	100.00 1	92.23 278	99.95 156	99.74 136	99.96 119	99.83 199
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	98.23 3	98.69 172	98.37 191	99.62 140	99.78 149	99.02 184	99.23 371	99.06 355	96.43 252	98.08 303	100.00 1	94.72 241	99.95 156	98.16 258	99.91 130	99.90 162
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		98.90 157	98.63 167	99.70 128	99.92 108	99.25 162	100.00 1	99.37 212	95.71 285	99.40 222	100.00 1	96.58 210	99.95 156	96.80 306	99.94 124	99.91 151

COLMAP_ROB	97.10 7	98.29 207	98.17 205	98.65 229	99.94 103	97.39 283	99.30 362	99.40 194	95.64 286	97.75 323	100.00 1	92.69 273	99.95 156	98.89 220	99.92 129	98.62 270
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UWE-MVS		99.18 118	99.06 118	99.51 154	99.67 173	98.80 198	100.00 1	99.43 124	96.80 222	99.93 160	99.86 245	99.79 7	99.94 168	97.78 273	98.33 204	99.80 230

test_fmvsmconf0.1_n		99.25 111	99.05 119	99.82 97	98.92 307	99.55 121	100.00 1	99.23 290	98.91 41	99.75 196	99.97 201	94.79 240	99.94 168	99.94 96	99.99 103	99.97 119

UA-Net		99.06 129	98.83 146	99.74 120	99.52 229	99.40 146	99.08 391	99.45 102	97.64 147	99.83 178	100.00 1	95.80 221	99.94 168	98.35 249	99.80 148	99.88 181

XVG-OURS-SEG-HR		98.27 210	98.31 195	98.14 266	99.59 205	95.92 317	100.00 1	99.36 218	98.48 79	99.21 231	100.00 1	89.27 317	99.94 168	99.76 132	99.17 168	98.56 271

balanced_conf03		99.43 82	99.28 89	99.85 90	99.68 165	99.68 108	99.97 249	99.28 262	97.03 204	99.96 126	99.97 201	97.90 154	99.93 172	99.77 130	100.00 1	99.94 136

alignmvs		99.38 87	99.21 102	99.91 74	99.73 157	99.92 53	100.00 1	99.51 76	97.61 154	100.00 1	100.00 1	99.06 91	99.93 172	99.83 116	97.12 253	99.90 162

cascas		98.43 196	98.07 213	99.50 157	99.65 182	99.02 184	100.00 1	99.22 293	94.21 329	99.72 199	99.98 192	92.03 281	99.93 172	99.68 156	98.12 217	99.54 255

MVSMamba_PlusPlus		99.39 85	99.25 95	99.80 107	99.68 165	99.59 116	99.99 217	99.30 250	96.66 239	99.96 126	99.97 201	97.89 155	99.92 175	99.76 132	100.00 1	99.90 162

XVG-OURS		98.30 205	98.36 193	98.13 269	99.58 210	95.91 318	100.00 1	99.36 218	98.69 68	99.23 230	100.00 1	91.20 287	99.92 175	99.34 195	97.82 235	98.56 271

test_fmvsmconf0.01_n		98.60 180	98.24 200	99.67 132	96.90 383	99.21 167	99.99 217	99.04 360	98.80 60	99.57 206	99.96 216	90.12 302	99.91 177	99.89 104	99.89 132	99.90 162

APD_test1		93.07 350	94.14 336	89.85 381	99.18 277	72.49 409	99.76 304	98.90 376	92.86 364	96.35 357	99.94 229	75.56 395	99.91 177	86.73 393	97.98 222	97.15 372

diffmvs		98.96 149	98.73 156	99.63 138	99.54 219	99.16 173	100.00 1	99.18 310	97.33 184	99.96 126	100.00 1	94.60 243	99.91 177	99.66 163	98.33 204	99.82 204
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmvs		98.59 181	98.38 189	99.23 197	99.69 161	97.90 263	99.31 361	99.47 79	94.52 320	99.68 202	99.28 328	97.64 169	99.89 180	97.71 275	98.17 216	99.89 168

mvsmamba		99.05 131	98.98 128	99.27 194	99.57 214	98.10 247	100.00 1	99.28 262	95.92 277	99.96 126	99.97 201	96.73 207	99.89 180	99.72 141	99.65 157	99.81 213

Test_1112_low_res		98.83 161	98.60 171	99.51 154	99.69 161	98.75 201	99.99 217	99.14 323	96.81 221	98.84 258	99.06 340	97.45 180	99.89 180	98.66 232	97.75 241	99.89 168

1112_ss		98.91 155	98.71 160	99.51 154	99.69 161	98.75 201	99.99 217	99.15 318	96.82 220	98.84 258	100.00 1	97.45 180	99.89 180	98.66 232	97.75 241	99.89 168

IB-MVS	96.24 12	97.54 238	96.95 253	99.33 184	99.67 173	98.10 247	100.00 1	99.47 79	97.42 177	99.26 229	99.69 277	98.83 121	99.89 180	99.43 189	78.77 403	100.00 1
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
SDMVSNet		98.49 193	98.08 211	99.73 123	99.82 125	99.53 125	99.99 217	99.45 102	97.62 150	99.38 223	99.86 245	90.06 305	99.88 185	99.92 99	96.61 264	99.79 233

mamv4		98.95 152	99.11 114	98.46 240	99.68 165	95.67 324	99.14 384	99.27 272	96.43 252	99.94 155	99.97 201	97.79 162	99.88 185	99.77 130	100.00 1	99.84 195

testing222		99.14 123	98.94 136	99.73 123	99.67 173	99.51 129	100.00 1	99.43 124	96.90 216	99.99 111	99.90 239	98.55 136	99.86 187	98.85 222	97.18 252	99.81 213

dcpmvs_2		98.87 158	99.53 62	96.90 329	99.87 118	90.88 384	99.94 266	99.07 347	98.20 98	100.00 1	100.00 1	98.69 129	99.86 187	100.00 1	100.00 1	99.95 131

tpm cat1		98.05 217	97.76 225	98.92 215	99.50 238	97.10 299	99.77 302	99.30 250	90.20 384	99.72 199	98.71 365	97.71 165	99.86 187	96.75 310	98.20 213	99.81 213

tpmrst		98.98 148	98.93 138	99.14 202	99.61 199	97.74 272	99.52 339	99.36 218	96.05 274	99.98 117	99.64 290	99.04 96	99.86 187	98.94 217	98.19 214	99.82 204

MDTV_nov1_ep13			98.94 136		99.53 222	98.36 228	99.39 352	99.46 94	96.54 246	99.99 111	99.63 294	98.92 113	99.86 187	98.30 254	98.71 183

OMC-MVS		99.27 105	99.38 77	98.96 213	99.95 100	97.06 300	100.00 1	99.40 194	98.83 53	99.88 171	100.00 1	97.01 193	99.86 187	99.47 188	99.84 143	99.97 119

mmtdpeth		94.58 333	94.18 335	95.81 352	98.82 320	91.09 383	99.99 217	98.61 385	96.38 258	100.00 1	97.23 392	76.52 393	99.85 193	99.82 121	80.22 399	96.48 384

thisisatest0530		99.37 89	99.27 90	99.69 129	99.59 205	99.41 145	100.00 1	99.46 94	96.46 251	99.90 166	100.00 1	99.44 48	99.85 193	98.97 216	99.58 162	99.80 230

thisisatest0515		99.42 83	99.31 88	99.74 120	99.59 205	99.55 121	100.00 1	99.46 94	96.65 240	99.92 161	100.00 1	99.44 48	99.85 193	99.09 212	99.63 160	99.81 213

tttt0517		99.34 94	99.23 101	99.67 132	99.57 214	99.38 147	100.00 1	99.46 94	96.33 263	99.89 169	100.00 1	99.44 48	99.84 196	98.93 218	99.46 165	99.78 236

casdiffmvs		98.65 174	98.38 189	99.46 161	99.52 229	98.74 204	100.00 1	99.15 318	96.91 214	99.05 245	100.00 1	92.75 269	99.83 197	99.70 148	98.38 198	99.81 213
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		98.64 175	98.65 164	98.60 233	99.59 205	96.17 314	100.00 1	99.28 262	96.67 238	98.41 285	100.00 1	94.52 244	99.83 197	99.41 191	100.00 1	99.81 213

DeepMVS_CX				89.98 380	98.90 309	71.46 411		99.18 310	97.61 154	96.92 344	99.83 253	86.07 351	99.83 197	96.02 318	97.65 247	98.65 269

dp		98.72 168	98.61 169	99.03 208	99.53 222	97.39 283	99.45 345	99.39 207	95.62 288	99.94 155	99.52 313	98.83 121	99.82 200	96.77 309	98.42 193	99.89 168

BH-RMVSNet		98.46 194	98.08 211	99.59 145	99.61 199	99.19 169	100.00 1	99.28 262	97.06 203	98.95 249	100.00 1	88.99 320	99.82 200	98.83 225	100.00 1	99.77 237

PMMVS		99.12 124	98.97 130	99.58 149	99.57 214	98.98 191	100.00 1	99.30 250	97.14 196	99.96 126	100.00 1	96.53 214	99.82 200	99.70 148	98.49 188	99.94 136

kuosan		98.55 185	98.53 177	98.62 231	99.66 180	96.16 315	100.00 1	99.44 116	93.93 336	99.81 189	99.98 192	97.58 170	99.81 203	98.08 260	98.28 207	99.89 168

test2506		99.48 77	99.38 77	99.75 119	99.89 114	99.51 129	99.45 345	100.00 1	98.38 84	99.83 178	100.00 1	98.86 117	99.81 203	99.25 201	98.78 179	99.94 136

ECVR-MVS		98.43 196	98.14 206	99.32 186	99.89 114	98.21 240	99.46 343	100.00 1	98.38 84	99.47 214	100.00 1	87.91 331	99.80 205	99.35 194	98.78 179	99.94 136

test1111		98.42 198	98.12 207	99.29 189	99.88 116	98.15 242	99.46 343	100.00 1	98.36 88	99.42 216	100.00 1	87.91 331	99.79 206	99.31 198	98.78 179	99.94 136

EIA-MVS		99.26 107	99.19 106	99.45 163	99.63 190	98.75 201	100.00 1	99.27 272	96.93 211	99.95 153	100.00 1	97.47 179	99.79 206	99.74 136	99.72 151	99.82 204

ETV-MVS		99.34 94	99.24 98	99.64 137	99.58 210	99.33 152	100.00 1	99.25 281	97.57 160	99.96 126	100.00 1	97.44 182	99.79 206	99.70 148	99.65 157	99.81 213

TR-MVS		98.14 214	97.74 226	99.33 184	99.59 205	98.28 235	99.27 363	99.21 299	96.42 255	99.15 236	99.94 229	88.87 323	99.79 206	98.88 221	98.29 206	99.93 147

casdiffmvs_mvg		98.64 175	98.39 188	99.40 174	99.50 238	98.60 212	100.00 1	99.22 293	96.85 218	99.10 239	100.00 1	92.75 269	99.78 210	99.71 145	98.35 200	99.81 213
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sd_testset		97.81 225	97.48 234	98.79 224	99.82 125	96.80 305	99.32 358	99.45 102	97.62 150	99.38 223	99.86 245	85.56 357	99.77 211	99.72 141	96.61 264	99.79 233

Effi-MVS+		98.58 182	98.24 200	99.61 141	99.60 201	99.26 160	97.85 407	99.10 336	96.22 269	99.97 122	99.89 240	93.75 252	99.77 211	99.43 189	98.34 201	99.81 213

baseline1		98.91 155	98.61 169	99.81 102	99.71 158	99.77 95	99.78 297	99.44 116	97.51 168	98.81 261	99.99 187	98.25 143	99.76 213	98.60 240	95.41 274	99.89 168

lupinMVS		99.29 103	99.16 110	99.69 129	99.45 251	99.49 134	100.00 1	99.15 318	97.45 174	99.97 122	100.00 1	96.76 204	99.76 213	99.67 159	100.00 1	99.81 213

EPP-MVSNet		99.10 126	99.00 125	99.40 174	99.51 234	98.68 207	99.92 271	99.43 124	95.47 297	99.65 203	100.00 1	99.51 35	99.76 213	99.53 185	98.00 221	99.75 240

baseline		98.69 172	98.45 184	99.41 170	99.52 229	98.67 208	100.00 1	99.17 315	97.03 204	99.13 237	100.00 1	93.17 262	99.74 216	99.70 148	98.34 201	99.81 213

CostFormer		98.84 160	98.77 152	99.04 207	99.41 257	97.58 277	99.67 322	99.35 229	94.66 315	99.96 126	99.36 324	99.28 74	99.74 216	99.41 191	97.81 236	99.81 213

Fast-Effi-MVS+		98.40 201	98.02 217	99.55 153	99.63 190	99.06 179	100.00 1	99.15 318	95.07 304	99.42 216	99.95 223	93.26 261	99.73 218	97.44 285	98.24 210	99.87 191

jason		99.11 125	98.96 131	99.59 145	99.17 278	99.31 155	100.00 1	99.13 327	97.38 179	99.83 178	100.00 1	95.54 226	99.72 219	99.57 179	99.97 116	99.74 241
jason: jason.
EPMVS		99.25 111	99.13 112	99.60 143	99.60 201	99.20 168	99.60 330	100.00 1	96.93 211	99.92 161	99.36 324	99.05 93	99.71 220	98.77 227	98.94 176	99.90 162

SPE-MVS-test		99.31 100	99.27 90	99.43 167	99.99 49	98.77 200	100.00 1	99.19 303	97.24 191	99.96 126	100.00 1	97.56 174	99.70 221	99.68 156	99.81 146	99.82 204

Vis-MVSNet		98.52 190	98.25 197	99.34 181	99.68 165	98.55 215	99.68 321	99.41 190	97.34 183	99.94 155	100.00 1	90.38 301	99.70 221	99.03 214	98.84 177	99.76 239
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	96.40 10	97.64 231	97.37 240	98.45 242	99.94 103	95.70 323	100.00 1	99.40 194	97.65 145	99.53 207	100.00 1	99.31 66	99.66 223	80.48 406	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETVMVS		99.16 121	98.98 128	99.69 129	99.67 173	99.56 120	100.00 1	99.45 102	96.36 260	99.98 117	99.95 223	98.65 130	99.64 224	99.11 211	97.63 248	99.88 181

CS-MVS		99.33 96	99.27 90	99.50 157	99.99 49	99.00 189	100.00 1	99.13 327	97.26 190	99.96 126	100.00 1	97.79 162	99.64 224	99.64 165	99.67 155	99.87 191

Fast-Effi-MVS+-dtu		98.38 202	98.56 174	97.82 294	99.58 210	94.44 352	100.00 1	99.16 316	96.75 227	99.51 209	99.63 294	95.03 236	99.60 226	97.71 275	99.67 155	99.42 258

PAPM		99.78 16	99.76 12	99.85 90	99.01 294	99.95 32	100.00 1	99.75 52	99.37 3	99.99 111	100.00 1	99.76 11	99.60 226	100.00 1	100.00 1	100.00 1

tt0805		96.52 283	96.23 283	97.40 304	99.30 272	93.55 360	99.32 358	99.45 102	96.75 227	97.88 316	99.99 187	79.99 383	99.59 228	97.39 289	95.98 267	99.06 265

GeoE		98.06 216	97.65 231	99.29 189	99.47 247	98.41 220	100.00 1	99.19 303	94.85 309	98.88 253	100.00 1	91.21 286	99.59 228	97.02 297	98.19 214	99.88 181

test_post1							99.32 358				88.24 414	99.33 61	99.59 228	98.31 251

test_post											89.05 412	99.49 41	99.59 228

ADS-MVSNet		98.70 171	98.51 180	99.28 192	99.51 234	98.39 223	99.24 366	99.44 116	95.52 293	99.96 126	99.70 274	97.57 172	99.58 232	97.11 295	98.54 185	99.88 181

Patchmatch-test		97.83 224	97.42 236	99.06 203	99.08 285	97.66 275	98.66 401	99.21 299	93.65 342	98.25 298	99.58 304	99.47 45	99.57 233	90.25 379	98.59 184	99.95 131

HQP4-MVS										99.17 232			99.57 233			97.77 273

HQP-MVS		97.73 228	97.85 222	97.39 305	99.07 286	94.82 336	100.00 1	99.40 194	99.04 16	99.17 232	99.97 201	88.61 326	99.57 233	99.79 123	95.58 268	97.77 273

CLD-MVS		97.64 231	97.74 226	97.36 307	99.01 294	94.76 344	100.00 1	99.34 235	99.30 4	99.00 247	99.97 201	87.49 337	99.57 233	99.96 88	95.58 268	97.75 286
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		97.21 251	97.18 251	97.32 310	98.08 350	94.66 345	100.00 1	99.28 262	98.65 72	98.92 250	99.98 192	86.03 353	99.56 237	98.28 255	95.41 274	97.72 321
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LPG-MVS_test		97.31 248	97.32 242	97.28 313	98.85 316	94.60 348	100.00 1	99.37 212	97.35 181	98.85 256	99.98 192	86.66 345	99.56 237	99.55 180	95.26 280	97.70 329

LGP-MVS_train				97.28 313	98.85 316	94.60 348		99.37 212	97.35 181	98.85 256	99.98 192	86.66 345	99.56 237	99.55 180	95.26 280	97.70 329

ITE_SJBPF				96.84 333	98.96 304	93.49 361		98.12 392	98.12 107	98.35 288	99.97 201	84.45 361	99.56 237	95.63 325	95.25 282	97.49 360

ACMP	97.00 8	97.19 252	97.16 252	97.27 315	98.97 303	94.58 351	100.00 1	99.32 240	97.97 118	97.45 334	99.98 192	85.79 355	99.56 237	99.70 148	95.24 283	97.67 339
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		97.71 230	97.82 224	97.37 306	99.00 298	94.80 339	100.00 1	99.40 194	99.00 27	99.08 242	99.97 201	88.58 328	99.55 242	99.79 123	95.57 272	97.76 275

plane_prior5								99.40 194					99.55 242	99.79 123	95.57 272	97.76 275

ACMM	97.17 6	97.37 246	97.40 238	97.29 312	99.01 294	94.64 347	100.00 1	99.25 281	98.07 110	98.44 284	99.98 192	87.38 339	99.55 242	99.25 201	95.19 286	97.69 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
patchmatchnet-post											97.79 387	99.41 57	99.54 245

SCA		98.30 205	97.98 219	99.23 197	99.41 257	98.25 237	99.99 217	99.45 102	96.91 214	99.76 195	99.58 304	89.65 312	99.54 245	98.31 251	98.79 178	99.91 151

XVG-ACMP-BASELINE		96.60 281	96.52 270	96.84 333	98.41 332	93.29 365	99.99 217	99.32 240	97.76 137	98.51 280	99.29 327	81.95 376	99.54 245	98.40 246	95.03 293	97.68 335

ACMH+	96.20 13	96.49 288	96.33 280	97.00 323	99.06 290	93.80 358	99.81 291	99.31 246	97.32 185	95.89 366	99.97 201	82.62 374	99.54 245	98.34 250	94.63 300	97.65 344

ACMH	96.25 11	96.77 271	96.62 265	97.21 316	98.96 304	94.43 353	99.64 324	99.33 237	97.43 176	96.55 355	99.97 201	83.52 369	99.54 245	99.07 213	95.13 290	97.66 340
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TDRefinement		91.93 355	90.48 363	96.27 346	81.60 419	92.65 372	99.10 388	97.61 407	93.96 335	93.77 381	99.85 250	80.03 381	99.53 250	97.82 272	70.59 409	96.63 383

JIA-IIPM		97.09 257	96.34 279	99.36 179	98.88 311	98.59 213	99.81 291	99.43 124	84.81 401	99.96 126	90.34 411	98.55 136	99.52 251	97.00 298	98.28 207	99.98 112

IS-MVSNet		99.08 127	98.91 140	99.59 145	99.65 182	99.38 147	99.78 297	99.24 286	96.70 234	99.51 209	100.00 1	98.44 140	99.52 251	98.47 245	98.39 196	99.88 181

dongtai		98.29 207	98.25 197	98.42 244	99.58 210	95.86 320	100.00 1	99.44 116	93.46 349	99.69 201	99.97 201	97.53 175	99.51 253	96.28 316	98.27 209	99.89 168

Effi-MVS+-dtu		98.51 192	98.86 145	97.47 303	99.77 151	94.21 355	100.00 1	98.94 371	97.61 154	99.91 164	98.75 364	95.89 219	99.51 253	99.36 193	99.48 164	98.68 268

PatchmatchNet		99.03 134	98.96 131	99.26 195	99.49 242	98.33 230	99.38 353	99.45 102	96.64 241	99.96 126	99.58 304	99.49 41	99.50 255	97.63 278	99.00 175	99.93 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		95.50 325	95.12 330	96.64 337	98.69 322	93.00 367	99.40 351	97.75 404	96.40 257	96.14 362	99.87 243	79.47 384	99.50 255	93.62 352	94.72 299	97.40 365

EC-MVSNet		99.19 117	99.09 117	99.48 160	99.42 255	99.07 177	100.00 1	99.21 299	96.95 209	99.96 126	100.00 1	96.88 202	99.48 257	99.64 165	99.79 149	99.88 181

LTVRE_ROB	95.29 16	96.32 298	96.10 288	96.99 324	98.55 327	93.88 357	99.45 345	99.28 262	94.50 321	96.46 356	99.52 313	84.86 360	99.48 257	97.26 293	95.03 293	97.59 354
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
AUN-MVS		96.26 301	95.67 313	98.06 276	99.68 165	95.60 325	99.82 290	99.42 141	96.78 224	99.88 171	99.80 262	94.84 239	99.47 259	97.48 284	73.29 407	99.12 263

D2MVS		97.63 234	97.83 223	97.05 320	98.83 318	94.60 348	100.00 1	99.82 40	96.89 217	98.28 294	99.03 346	94.05 248	99.47 259	98.58 242	94.97 296	97.09 373

tpm2		98.64 175	98.58 173	98.81 223	99.42 255	97.12 297	99.69 319	99.37 212	93.63 343	99.94 155	99.67 282	98.96 107	99.47 259	98.62 239	97.95 226	99.83 199

HyFIR lowres test		99.32 98	99.24 98	99.58 149	99.95 100	99.26 160	100.00 1	99.99 13	96.72 232	99.29 228	99.91 237	99.49 41	99.47 259	99.74 136	98.08 219	100.00 1

hse-mvs2		96.79 270	96.38 276	98.04 282	99.68 165	95.54 326	99.81 291	99.42 141	98.21 96	100.00 1	99.80 262	97.49 177	99.46 263	99.72 141	73.27 408	99.12 263

reproduce_monomvs		98.61 179	98.54 175	98.82 220	99.97 90	99.28 157	100.00 1	99.33 237	98.51 78	97.87 317	99.24 330	99.98 3	99.45 264	99.02 215	92.93 315	97.74 308

USDC		95.90 319	95.70 309	96.50 341	98.60 326	92.56 373	100.00 1	98.30 388	97.77 135	96.92 344	99.94 229	81.25 380	99.45 264	93.54 353	94.96 297	97.49 360

gm-plane-assit					99.52 229	97.26 292			95.86 280		100.00 1		99.43 266	98.76 228

MS-PatchMatch		95.66 323	95.87 299	95.05 356	97.80 359	89.25 389	98.88 397	99.30 250	96.35 261	96.86 347	99.01 348	81.35 379	99.43 266	93.30 355	99.98 113	96.46 385

CHOSEN 280x420		99.85 3	99.87 1	99.80 107	99.99 49	99.97 21	99.97 249	99.98 16	98.96 32	100.00 1	100.00 1	99.96 4	99.42 268	100.00 1	100.00 1	100.00 1

VPA-MVSNet		97.03 262	96.43 274	98.82 220	98.64 324	99.32 153	99.38 353	99.47 79	96.73 231	98.91 252	98.94 355	87.00 343	99.40 269	99.23 204	89.59 357	97.76 275

LF4IMVS		96.19 304	96.18 285	96.23 347	98.26 341	92.09 375	100.00 1	97.89 401	97.82 130	97.94 312	99.87 243	82.71 373	99.38 270	97.41 287	93.71 305	97.20 370

UniMVSNet_ETH3D		95.28 328	94.41 334	97.89 292	98.91 308	95.14 330	99.13 385	99.35 229	92.11 368	97.17 341	99.66 284	70.28 405	99.36 271	97.88 270	95.18 287	99.16 261

GA-MVS		97.72 229	97.27 246	99.06 203	99.24 276	97.93 262	100.00 1	99.24 286	95.80 284	98.99 248	99.64 290	89.77 309	99.36 271	95.12 334	97.62 249	99.89 168

XXY-MVS		97.14 256	96.63 264	98.67 228	98.65 323	98.92 194	99.54 337	99.29 256	95.57 290	97.63 326	99.83 253	87.79 335	99.35 273	98.39 247	92.95 314	97.75 286

test_fmvs2		95.17 331	95.23 327	95.01 357	98.95 306	88.99 391	99.99 217	97.77 403	97.79 133	98.58 273	99.70 274	73.36 399	99.34 274	95.88 319	95.03 293	96.70 381

GG-mvs-BLEND				99.59 145	99.54 219	99.49 134	99.17 379	99.52 72		99.96 126	99.68 281	100.00 1	99.33 275	99.71 145	99.99 103	99.96 125

dmvs_re		97.54 238	97.88 221	96.54 340	99.55 218	90.35 386	99.86 283	99.46 94	97.00 206	99.41 221	100.00 1	90.78 294	99.30 276	99.60 173	95.24 283	99.96 125

EPNet_dtu		98.53 189	98.23 203	99.43 167	99.92 108	99.01 186	99.96 255	99.47 79	98.80 60	99.96 126	99.96 216	98.56 135	99.30 276	87.78 391	99.68 153	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		98.24 211	98.22 204	98.32 252	99.13 280	95.79 321	99.53 338	99.12 333	95.20 303	99.96 126	99.36 324	97.58 170	99.28 278	97.41 287	96.67 262	99.88 181

baseline2		98.99 145	98.93 138	99.18 200	99.26 275	99.15 174	100.00 1	99.46 94	96.71 233	96.79 350	100.00 1	99.42 55	99.25 279	98.75 229	99.94 124	99.15 262

MonoMVSNet		98.55 185	98.64 166	98.26 256	98.21 344	95.76 322	99.94 266	99.16 316	96.23 266	99.47 214	99.24 330	96.75 206	99.22 280	99.61 172	99.17 168	99.81 213

gg-mvs-nofinetune		96.95 266	96.10 288	99.50 157	99.41 257	99.36 151	99.07 393	99.52 72	83.69 403	99.96 126	83.60 419	100.00 1	99.20 281	99.68 156	99.99 103	99.96 125

TAMVS		98.76 165	98.73 156	98.86 219	99.44 253	97.69 273	99.57 333	99.34 235	96.57 244	99.12 238	99.81 259	98.83 121	99.16 282	97.97 268	97.91 228	99.73 245

MVP-Stereo		96.51 285	96.48 272	96.60 339	95.65 394	94.25 354	98.84 398	98.16 390	95.85 282	95.23 369	99.04 343	92.54 276	99.13 283	92.98 357	99.98 113	96.43 386
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Vis-MVSNet (Re-imp)		98.99 145	98.89 144	99.29 189	99.64 188	98.89 195	99.98 243	99.31 246	96.74 229	99.48 211	100.00 1	98.11 147	99.10 284	98.39 247	98.34 201	99.89 168

EG-PatchMatch MVS		92.94 351	92.49 355	94.29 368	95.87 391	87.07 396	99.07 393	98.11 393	93.19 357	88.98 397	98.66 368	70.89 403	99.08 285	92.43 362	95.21 285	96.72 380

MVS-HIRNet		94.12 340	92.73 354	98.29 253	99.33 268	95.95 316	99.38 353	99.19 303	74.54 411	98.26 297	86.34 415	86.07 351	99.06 286	91.60 367	99.87 137	99.85 194

mvs_anonymous		98.80 163	98.60 171	99.38 178	99.57 214	99.24 164	100.00 1	99.21 299	95.87 278	98.92 250	99.82 256	96.39 216	99.03 287	99.13 209	98.50 187	99.88 181

TESTMET0.1,1		99.08 127	98.96 131	99.44 164	99.63 190	99.38 147	100.00 1	99.45 102	95.53 291	99.48 211	100.00 1	99.71 13	99.02 288	96.84 303	99.99 103	99.91 151

ttmdpeth		96.24 302	95.88 298	97.32 310	97.80 359	96.61 311	99.95 261	98.77 382	97.80 132	93.42 383	99.28 328	86.42 348	99.01 289	97.63 278	91.84 333	96.33 388

test-LLR		99.03 134	98.91 140	99.40 174	99.40 262	99.28 157	100.00 1	99.45 102	96.70 234	99.42 216	99.12 336	99.31 66	99.01 289	96.82 304	99.99 103	99.91 151

test-mter		98.96 149	98.82 147	99.40 174	99.40 262	99.28 157	100.00 1	99.45 102	95.44 301	99.42 216	99.12 336	99.70 14	99.01 289	96.82 304	99.99 103	99.91 151

tfpnnormal		96.36 295	95.69 312	98.37 248	98.55 327	98.71 205	99.69 319	99.45 102	93.16 358	96.69 354	99.71 271	88.44 330	98.99 292	94.17 344	91.38 342	97.41 364

Anonymous20231211		96.29 299	95.70 309	98.07 272	99.80 142	97.49 279	99.15 382	99.40 194	89.11 387	97.75 323	99.45 318	88.93 322	98.98 293	98.26 256	89.47 359	97.73 315

nrg030		97.64 231	97.27 246	98.75 226	98.34 334	99.53 125	100.00 1	99.22 293	96.21 270	98.27 296	99.95 223	94.40 245	98.98 293	99.23 204	89.78 356	97.75 286

mvs5depth		93.81 342	93.00 349	96.23 347	94.25 402	93.33 364	97.43 409	98.07 395	93.47 348	94.15 380	99.58 304	77.52 390	98.97 295	93.64 351	88.92 365	96.39 387

cl22		98.23 212	98.11 208	98.58 235	99.82 125	99.01 186	100.00 1	99.28 262	96.92 213	98.33 290	99.21 333	98.09 149	98.97 295	98.72 230	92.61 318	97.76 275

CVMVSNet		98.56 184	98.47 183	98.82 220	99.11 281	97.67 274	99.74 307	99.47 79	97.57 160	99.06 244	100.00 1	95.72 223	98.97 295	98.21 257	97.33 251	99.83 199

VPNet		96.41 290	95.76 306	98.33 251	98.61 325	98.30 234	99.48 342	99.45 102	96.98 208	98.87 255	99.88 242	81.57 377	98.93 298	99.22 206	87.82 374	97.76 275

v7n		96.06 315	95.42 325	97.99 286	97.58 369	97.35 286	99.86 283	99.11 334	92.81 365	97.91 315	99.49 315	90.99 292	98.92 299	92.51 360	88.49 370	97.70 329

jajsoiax		97.07 259	96.79 260	97.89 292	97.28 380	97.12 297	99.95 261	99.19 303	96.55 245	97.31 337	99.69 277	87.35 341	98.91 300	98.70 231	95.12 291	97.66 340

mvs_tets		97.00 265	96.69 262	97.94 288	97.41 379	97.27 291	99.60 330	99.18 310	96.51 249	97.35 336	99.69 277	86.53 347	98.91 300	98.84 223	95.09 292	97.65 344

CDS-MVSNet		98.96 149	98.95 135	99.01 209	99.48 244	98.36 228	99.93 269	99.37 212	96.79 223	99.31 227	99.83 253	99.77 10	98.91 300	98.07 262	97.98 222	99.77 237
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FIs		97.95 221	97.73 228	98.62 231	98.53 329	99.24 164	100.00 1	99.43 124	96.74 229	97.87 317	99.82 256	95.27 229	98.89 303	98.78 226	93.07 312	97.74 308

v1192		96.18 305	95.49 319	98.26 256	98.01 352	98.15 242	99.99 217	99.08 342	93.36 352	98.54 276	98.97 353	89.47 315	98.89 303	91.15 370	90.82 347	97.75 286

UniMVSNet (Re)		97.29 250	96.85 257	98.59 234	98.49 330	99.13 175	100.00 1	99.42 141	96.52 248	98.24 300	98.90 358	94.93 237	98.89 303	97.54 282	87.61 375	97.75 286

EI-MVSNet		97.98 220	97.93 220	98.16 265	99.11 281	97.84 268	99.74 307	99.29 256	94.39 325	98.65 268	100.00 1	97.21 187	98.88 306	97.62 281	95.31 278	97.75 286

MVSTER		98.58 182	98.52 178	98.77 225	99.65 182	99.68 108	100.00 1	99.29 256	95.63 287	98.65 268	99.80 262	99.78 8	98.88 306	98.59 241	95.31 278	97.73 315

pm-mvs1		95.76 321	95.01 331	98.00 284	98.23 343	97.45 281	99.24 366	99.04 360	93.13 359	95.93 365	99.72 269	86.28 349	98.84 308	95.62 326	87.92 373	97.72 321

V42		96.65 278	96.16 287	98.11 271	98.17 348	98.23 238	99.99 217	99.09 341	93.97 334	98.74 265	99.05 342	91.09 289	98.82 309	95.46 328	89.90 354	97.27 369

CMPMVS	66.12 22	90.65 363	92.04 356	86.46 388	96.18 388	66.87 418	98.03 406	99.38 209	83.38 404	85.49 405	99.55 310	77.59 389	98.80 310	94.44 341	94.31 303	93.72 403
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJss		98.03 218	98.06 214	97.94 288	97.63 364	97.33 289	99.89 279	99.23 290	96.27 265	98.03 307	99.59 302	98.75 126	98.78 311	98.52 243	94.61 301	97.70 329

OurMVSNet-221017-0		96.14 311	95.98 294	96.62 338	97.49 374	93.44 362	99.92 271	98.16 390	95.86 280	97.65 325	99.95 223	85.71 356	98.78 311	94.93 336	94.18 304	97.64 347

OpenMVS_ROB	88.34 20	91.89 356	91.12 358	94.19 370	95.55 396	87.63 394	99.26 364	98.03 396	86.61 398	90.65 395	96.82 395	70.14 406	98.78 311	86.54 394	96.50 266	96.15 389

pmmvs6		93.64 343	92.87 351	95.94 351	97.47 376	91.41 380	98.92 395	99.02 363	87.84 394	95.01 371	99.61 300	77.24 392	98.77 314	94.33 342	86.41 384	97.63 348

v8		96.35 296	95.73 308	98.21 261	98.11 349	98.23 238	99.94 266	99.07 347	92.66 366	98.29 293	99.00 349	91.46 283	98.77 314	94.17 344	88.83 368	97.62 350

miper_enhance_ethall		98.33 204	98.27 196	98.51 237	99.66 180	99.04 181	100.00 1	99.22 293	97.53 164	98.51 280	99.38 322	99.49 41	98.75 316	98.02 264	92.61 318	97.76 275

EGC-MVSNET		79.46 378	74.04 386	95.72 353	96.00 390	92.73 370	99.09 390	99.04 360	5.08 423	16.72 423	98.71 365	73.03 400	98.74 317	82.05 403	96.64 263	95.69 396

v1921920		96.16 309	95.50 317	98.14 266	97.88 358	97.96 259	99.99 217	99.07 347	93.33 353	98.60 272	99.24 330	89.37 316	98.71 318	91.28 368	90.74 349	97.75 286

lessismore_v0				96.05 349	97.55 370	91.80 377		99.22 293		91.87 389	99.91 237	83.50 370	98.68 319	92.48 361	90.42 353	97.68 335

v2v482		96.70 276	96.18 285	98.27 254	98.04 351	98.39 223	100.00 1	99.13 327	94.19 331	98.58 273	99.08 339	90.48 299	98.67 320	95.69 323	90.44 352	97.75 286

v144192		96.40 293	95.81 301	98.17 264	97.89 357	98.11 245	99.99 217	99.06 355	93.39 351	98.75 264	99.09 338	90.43 300	98.66 321	93.10 356	90.55 351	97.75 286

cl____		97.54 238	97.32 242	98.18 262	99.47 247	98.14 244	100.00 1	99.10 336	94.16 332	97.60 330	99.63 294	97.52 176	98.65 322	96.47 311	91.97 331	97.76 275

v1144		96.51 285	95.97 295	98.13 269	97.98 354	98.04 253	99.99 217	99.08 342	93.51 347	98.62 271	98.98 350	90.98 293	98.62 323	93.79 350	90.79 348	97.74 308

WBMVS		98.19 213	98.10 210	98.47 239	99.63 190	99.03 182	100.00 1	99.32 240	95.46 298	98.39 286	99.40 321	99.69 15	98.61 324	98.64 235	92.39 323	97.76 275

v1240		95.96 317	95.25 326	98.07 272	97.91 356	97.87 267	99.96 255	99.07 347	93.24 356	98.64 270	98.96 354	88.98 321	98.61 324	89.58 384	90.92 346	97.75 286

v10		96.14 311	95.50 317	98.07 272	98.19 346	97.96 259	99.83 287	99.07 347	92.10 369	98.07 304	98.94 355	91.07 290	98.61 324	92.41 363	89.82 355	97.63 348

anonymousdsp		97.16 254	96.88 255	98.00 284	97.08 382	98.06 251	99.81 291	99.15 318	94.58 317	97.84 319	99.62 298	90.49 298	98.60 327	97.98 265	95.32 277	97.33 368

v148		96.29 299	95.84 300	97.63 297	97.74 361	96.53 312	100.00 1	99.07 347	93.52 346	98.01 310	99.42 320	91.22 285	98.60 327	96.37 315	87.22 378	97.75 286

MVSFormer		98.94 153	98.82 147	99.28 192	99.45 251	99.49 134	100.00 1	99.13 327	95.46 298	99.97 122	100.00 1	96.76 204	98.59 329	98.63 237	100.00 1	99.74 241

test_djsdf		97.55 237	97.38 239	98.07 272	97.50 372	97.99 255	100.00 1	99.13 327	95.46 298	98.47 283	99.85 250	92.01 282	98.59 329	98.63 237	95.36 276	97.62 350

test_0402		94.35 335	93.70 340	96.32 345	97.92 355	93.60 359	99.61 329	98.85 378	88.19 393	94.68 374	99.48 316	80.01 382	98.58 331	89.39 385	95.15 289	96.77 379

miper_ehance_all_eth		97.81 225	97.66 230	98.23 258	99.49 242	98.37 226	99.99 217	99.11 334	94.78 310	98.25 298	99.21 333	98.18 145	98.57 332	97.35 291	92.61 318	97.76 275

FC-MVSNet-test		97.84 223	97.63 232	98.45 242	98.30 339	99.05 180	100.00 1	99.43 124	96.63 243	97.61 329	99.82 256	95.19 233	98.57 332	98.64 235	93.05 313	97.73 315

WR-MVS		97.09 257	96.64 263	98.46 240	98.43 331	99.09 176	99.97 249	99.33 237	95.62 288	97.76 320	99.67 282	91.17 288	98.56 334	98.49 244	89.28 362	97.74 308

WR-MVS_H		96.73 273	96.32 281	97.95 287	98.26 341	97.88 265	99.72 314	99.43 124	95.06 305	96.99 343	98.68 367	93.02 265	98.53 335	97.43 286	88.33 371	97.43 363

ambc				88.45 383	86.84 415	70.76 412	97.79 408	98.02 398		90.91 392	95.14 400	38.69 418	98.51 336	94.97 335	84.23 388	96.09 392

eth_miper_zixun_eth		97.47 242	97.28 244	98.06 276	99.41 257	97.94 261	99.62 328	99.08 342	94.46 323	98.19 301	99.56 309	96.91 201	98.50 337	96.78 307	91.49 339	97.74 308

UniMVSNet_NR-MVSNet		97.16 254	96.80 258	98.22 259	98.38 333	98.41 220	100.00 1	99.45 102	96.14 272	97.76 320	99.64 290	95.05 235	98.50 337	97.98 265	86.84 379	97.75 286

DU-MVS		96.93 267	96.49 271	98.22 259	98.31 337	98.41 220	100.00 1	99.37 212	96.41 256	97.76 320	99.65 286	92.14 279	98.50 337	97.98 265	86.84 379	97.75 286

pmmvs4		97.17 253	96.80 258	98.27 254	97.68 363	98.64 210	100.00 1	99.18 310	94.22 328	98.55 275	99.71 271	93.67 253	98.47 340	95.66 324	92.57 321	97.71 328

pmmvs5		95.94 318	95.61 314	96.95 326	97.42 377	94.66 345	100.00 1	98.08 394	93.60 344	97.05 342	99.43 319	87.02 342	98.46 341	95.76 320	92.12 327	97.72 321

SixPastTwentyTwo		95.71 322	95.49 319	96.38 343	97.42 377	93.01 366	99.84 286	98.23 389	94.75 311	95.98 364	99.97 201	85.35 358	98.43 342	94.71 338	93.17 311	97.69 333

WB-MVSnew		97.02 264	97.24 248	96.37 344	99.44 253	97.36 285	100.00 1	99.43 124	96.12 273	99.35 225	99.89 240	93.60 256	98.42 343	88.91 390	98.39 196	93.33 405

NR-MVSNet		96.63 279	96.04 291	98.38 247	98.31 337	98.98 191	99.22 373	99.35 229	95.87 278	94.43 378	99.65 286	92.73 271	98.40 344	96.78 307	88.05 372	97.75 286

TransMVSNet (Re)		94.78 332	93.72 339	97.93 290	98.34 334	97.88 265	99.23 371	97.98 399	91.60 371	94.55 375	99.71 271	87.89 333	98.36 345	89.30 386	84.92 386	97.56 356

Baseline_NR-MVSNet		96.16 309	95.70 309	97.56 302	98.28 340	96.79 306	100.00 1	97.86 402	91.93 370	97.63 326	99.47 317	92.14 279	98.35 346	97.13 294	86.83 381	97.54 357

IterMVS-SCA-FT		96.72 275	96.42 275	97.62 299	99.40 262	96.83 304	99.99 217	99.14 323	94.65 316	97.55 332	99.72 269	89.65 312	98.31 347	95.62 326	92.05 328	97.73 315

DIV-MVS_self_test		97.52 241	97.35 241	98.05 280	99.46 250	98.11 245	100.00 1	99.10 336	94.21 329	97.62 328	99.63 294	97.65 168	98.29 348	96.47 311	91.98 330	97.76 275

IterMVS		96.76 272	96.46 273	97.63 297	99.41 257	96.89 302	99.99 217	99.13 327	94.74 313	97.59 331	99.66 284	89.63 314	98.28 349	95.71 322	92.31 325	97.72 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_vis3_rt		79.61 377	78.19 382	83.86 391	88.68 414	69.56 413	99.81 291	82.19 427	86.78 397	68.57 415	84.51 418	25.06 424	98.26 350	89.18 388	78.94 402	83.75 415

MVStest1		94.27 336	93.30 345	97.19 317	98.83 318	97.18 295	99.93 269	98.79 381	86.80 396	84.88 408	99.04 343	94.32 247	98.25 351	90.55 375	86.57 383	96.12 391

c3_l		97.58 235	97.42 236	98.06 276	99.48 244	98.16 241	99.96 255	99.10 336	94.54 319	98.13 302	99.20 335	97.87 156	98.25 351	97.28 292	91.20 344	97.75 286

test0.0.03 1		98.12 215	98.03 216	98.39 246	99.11 281	98.07 249	100.00 1	99.93 30	96.70 234	96.91 346	99.95 223	99.31 66	98.19 353	91.93 364	98.44 191	98.91 266

CP-MVSNet		96.73 273	96.25 282	98.18 262	98.21 344	98.67 208	99.77 302	99.32 240	95.06 305	97.20 340	99.65 286	90.10 303	98.19 353	98.06 263	88.90 366	97.66 340

PS-CasMVS		96.34 297	95.78 305	98.03 283	98.18 347	98.27 236	99.71 315	99.32 240	94.75 311	96.82 349	99.65 286	86.98 344	98.15 355	97.74 274	88.85 367	97.66 340

our_test_3		96.51 285	96.35 278	96.98 325	97.61 366	95.05 331	99.98 243	99.01 365	94.68 314	96.77 352	99.06 340	95.87 220	98.14 356	91.81 365	92.37 324	97.75 286

new_pmnet		94.11 341	93.47 343	96.04 350	96.60 386	92.82 369	99.97 249	98.91 374	90.21 383	95.26 368	98.05 386	85.89 354	98.14 356	84.28 398	92.01 329	97.16 371

K. test v3		95.46 326	95.14 329	96.40 342	97.53 371	93.40 363	99.99 217	99.23 290	95.49 296	92.70 388	99.73 268	84.26 363	98.12 358	93.94 349	93.38 310	97.68 335

Patchmtry		96.81 269	96.37 277	98.14 266	99.31 269	98.55 215	98.91 396	99.00 366	90.45 380	97.92 314	98.98 350	96.94 199	98.12 358	94.27 343	91.53 338	97.75 286

FMVSNet3		97.30 249	96.95 253	98.37 248	99.65 182	99.25 162	99.71 315	99.28 262	94.23 327	98.53 277	98.91 357	93.30 260	98.11 360	95.31 330	93.60 306	97.73 315

N_pmnet		91.88 357	93.37 344	87.40 386	97.24 381	66.33 419	99.90 275	91.05 422	89.77 386	95.65 367	98.58 371	90.05 306	98.11 360	85.39 395	92.72 317	97.75 286

IterMVS-LS		97.56 236	97.44 235	97.92 291	99.38 266	97.90 263	99.89 279	99.10 336	94.41 324	98.32 291	99.54 312	97.21 187	98.11 360	97.50 283	91.62 336	97.75 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		98.02 219	97.71 229	98.93 214	99.31 269	98.86 196	99.13 385	99.00 366	96.53 247	99.96 126	98.98 350	96.94 199	98.10 363	91.18 369	98.40 194	99.84 195

FMVSNet2		96.22 303	95.60 315	98.06 276	99.53 222	98.33 230	99.45 345	99.27 272	93.71 338	98.03 307	98.84 360	84.23 364	98.10 363	93.97 348	93.40 309	97.73 315

ppachtmachnet_test		96.17 307	95.89 297	97.02 322	97.61 366	95.24 328	99.99 217	99.24 286	93.31 354	96.71 353	99.62 298	94.34 246	98.07 365	89.87 380	92.30 326	97.75 286

GBi-Net		96.07 313	95.80 303	96.89 330	99.53 222	94.87 333	99.18 374	99.27 272	93.71 338	98.53 277	98.81 361	84.23 364	98.07 365	95.31 330	93.60 306	97.72 321

test1		96.07 313	95.80 303	96.89 330	99.53 222	94.87 333	99.18 374	99.27 272	93.71 338	98.53 277	98.81 361	84.23 364	98.07 365	95.31 330	93.60 306	97.72 321

FMVSNet1		94.45 334	93.63 341	96.89 330	98.87 314	94.87 333	99.18 374	99.27 272	90.95 377	97.31 337	98.81 361	72.89 401	98.07 365	92.61 358	92.81 316	97.72 321

PatchT		95.90 319	94.95 333	98.75 226	99.03 292	98.39 223	99.08 391	99.32 240	85.52 399	99.96 126	94.99 403	97.94 151	98.05 369	80.20 407	98.47 190	99.81 213

TranMVSNet+NR-MVSNet		96.45 289	96.01 292	97.79 295	98.00 353	97.62 276	100.00 1	99.35 229	95.98 275	97.31 337	99.64 290	90.09 304	98.00 370	96.89 302	86.80 382	97.75 286

test_method		91.04 362	91.10 359	90.85 378	98.34 334	77.63 405	100.00 1	98.93 373	76.69 409	96.25 360	98.52 373	70.44 404	97.98 371	89.02 389	91.74 334	96.92 377

miper_lstm_enhance		97.40 245	97.28 244	97.75 296	99.48 244	97.52 278	100.00 1	99.07 347	94.08 333	98.01 310	99.61 300	97.38 184	97.98 371	96.44 314	91.47 341	97.76 275

ET-MVSNet_ETH3D		96.41 290	95.48 321	99.20 199	99.81 131	99.75 97	100.00 1	99.02 363	97.30 189	78.33 411	100.00 1	97.73 164	97.94 373	99.70 148	87.41 376	99.92 149

ADS-MVSNet2		98.28 209	98.51 180	97.62 299	99.51 234	95.03 332	99.24 366	99.41 190	95.52 293	99.96 126	99.70 274	97.57 172	97.94 373	97.11 295	98.54 185	99.88 181

PEN-MVS		96.01 316	95.48 321	97.58 301	97.74 361	97.26 292	99.90 275	99.29 256	94.55 318	96.79 350	99.55 310	87.38 339	97.84 375	96.92 301	87.24 377	97.65 344

Syy-MVS		96.17 307	96.57 267	95.00 358	99.50 238	87.37 395	100.00 1	99.57 68	96.23 266	98.07 304	100.00 1	92.41 277	97.81 376	85.34 396	97.96 224	99.82 204

myMVS_eth3d		98.52 190	98.51 180	98.53 236	99.50 238	97.98 256	100.00 1	99.57 68	96.23 266	98.07 304	100.00 1	99.09 89	97.81 376	96.17 317	97.96 224	99.82 204

testing3		98.44 195	98.37 191	98.65 229	99.51 234	98.32 232	100.00 1	99.62 66	96.43 252	97.93 313	99.99 187	99.11 87	97.81 376	94.88 337	97.80 237	99.82 204

testgi		96.18 305	95.93 296	96.93 328	98.98 302	94.20 356	100.00 1	99.07 347	97.16 195	96.06 363	99.86 245	84.08 367	97.79 379	90.38 378	97.80 237	98.81 267

testf1		84.40 374	84.79 376	83.23 392	95.71 392	58.71 425	98.79 399	97.75 404	81.58 405	84.94 406	98.07 384	45.33 416	97.73 380	77.09 413	83.85 389	93.24 406

APD_test2		84.40 374	84.79 376	83.23 392	95.71 392	58.71 425	98.79 399	97.75 404	81.58 405	84.94 406	98.07 384	45.33 416	97.73 380	77.09 413	83.85 389	93.24 406

MIMVSNet		97.06 260	96.73 261	98.05 280	99.38 266	96.64 310	98.47 403	99.35 229	93.41 350	99.48 211	98.53 372	89.66 311	97.70 382	94.16 346	98.11 218	99.80 230

LCM-MVSNet-Re		96.52 283	97.21 250	94.44 364	99.27 273	85.80 397	99.85 285	96.61 414	95.98 275	92.75 387	98.48 374	93.97 251	97.55 383	99.58 178	98.43 192	99.98 112

DTE-MVSNet		95.52 324	94.99 332	97.08 319	97.49 374	96.45 313	100.00 1	99.25 281	93.82 337	96.17 361	99.57 308	87.81 334	97.18 384	94.57 339	86.26 385	97.62 350

mvsany_test3		89.36 367	88.96 371	90.56 379	91.95 405	78.97 404	99.74 307	96.59 415	96.84 219	89.25 396	96.07 397	52.59 412	97.11 385	95.17 333	82.44 394	95.58 398

UnsupCasMVSNet_bld		89.50 366	88.00 372	93.99 371	95.30 397	88.86 392	98.52 402	99.28 262	85.50 400	87.80 401	94.11 405	61.63 409	96.96 386	90.63 373	79.26 400	96.15 389

Anonymous20240521		93.29 346	92.76 353	94.90 362	95.64 395	91.27 381	99.97 249	98.82 379	87.04 395	94.71 373	98.19 381	83.86 368	96.80 387	84.04 399	92.56 322	96.64 382

KD-MVS_2432*1600		94.15 338	93.08 347	97.35 308	99.53 222	97.83 269	99.63 326	99.19 303	92.88 362	96.29 358	97.68 388	98.84 119	96.70 388	89.73 381	63.92 412	97.53 358

miper_refine_blended		94.15 338	93.08 347	97.35 308	99.53 222	97.83 269	99.63 326	99.19 303	92.88 362	96.29 358	97.68 388	98.84 119	96.70 388	89.73 381	63.92 412	97.53 358

test_f		86.87 372	86.06 375	89.28 382	91.45 410	76.37 407	99.87 282	97.11 409	91.10 375	88.46 398	93.05 408	38.31 419	96.66 390	91.77 366	83.46 392	94.82 399

MDA-MVSNet_test_wron		92.61 352	91.09 360	97.19 317	96.71 385	97.26 292	100.00 1	99.14 323	88.61 389	67.90 417	98.32 380	89.03 319	96.57 391	90.47 377	89.59 357	97.74 308

UnsupCasMVSNet_eth		94.25 337	93.89 337	95.34 354	97.63 364	92.13 374	99.73 312	99.36 218	94.88 308	92.78 385	98.63 369	82.72 372	96.53 392	94.57 339	84.73 387	97.36 366

tmp_tt		75.80 383	74.26 385	80.43 396	52.91 428	53.67 427	87.42 415	97.98 399	61.80 415	67.04 418	100.00 1	76.43 394	96.40 393	96.47 311	28.26 420	91.23 410

Gipuma		84.73 373	83.50 378	88.40 384	97.50 372	82.21 402	88.87 413	99.05 357	65.81 413	85.71 404	90.49 410	53.70 411	96.31 394	78.64 409	91.74 334	86.67 412
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		92.44 353	90.92 361	97.03 321	96.20 387	97.06 300	99.99 217	99.14 323	88.21 392	67.93 416	98.43 377	88.63 325	96.28 395	90.64 372	89.08 364	97.74 308

EU-MVSNet		96.63 279	96.53 268	96.94 327	97.59 368	96.87 303	99.76 304	99.47 79	96.35 261	96.85 348	99.78 265	92.57 275	96.27 396	95.33 329	91.08 345	97.68 335

pmmvs-eth3d		91.73 358	90.67 362	94.92 361	91.63 408	92.71 371	99.90 275	98.54 386	91.19 374	88.08 399	95.50 399	79.31 386	96.13 397	90.55 375	81.32 398	95.91 394

PM-MVS		88.39 368	87.41 373	91.31 377	91.73 407	82.02 403	99.79 296	96.62 413	91.06 376	90.71 394	95.73 398	48.60 414	95.96 398	90.56 374	81.91 397	95.97 393

MDA-MVSNet-bldmvs		91.65 359	89.94 367	96.79 336	96.72 384	96.70 308	99.42 350	98.94 371	88.89 388	66.97 419	98.37 378	81.43 378	95.91 399	89.24 387	89.46 360	97.75 286

Patchmatch-RL test		93.49 344	93.63 341	93.05 375	91.78 406	83.41 401	98.21 405	96.95 411	91.58 372	91.05 390	97.64 390	99.40 58	95.83 400	94.11 347	81.95 396	99.91 151

Anonymous20231206		93.45 345	93.17 346	94.30 367	95.00 399	89.69 388	99.98 243	98.43 387	93.30 355	94.50 377	98.59 370	90.52 297	95.73 401	77.46 412	90.73 350	97.48 362

DSMNet-mixed		95.18 330	95.21 328	95.08 355	96.03 389	90.21 387	99.65 323	93.64 420	92.91 361	98.34 289	97.40 391	90.05 306	95.51 402	91.02 371	97.86 231	99.51 257

pmmvs3		90.62 364	89.36 370	94.40 365	90.53 413	91.49 379	100.00 1	96.73 412	84.21 402	93.65 382	96.65 396	82.56 375	94.83 403	82.28 402	77.62 404	96.89 378

KD-MVS_self_test		91.16 360	90.09 365	94.35 366	94.44 401	91.27 381	99.74 307	99.08 342	90.82 378	94.53 376	94.91 404	86.11 350	94.78 404	82.67 401	68.52 410	96.99 375

FMVSNet5		95.32 327	95.43 324	94.99 359	99.39 265	92.99 368	99.25 365	99.24 286	90.45 380	97.44 335	98.45 375	95.78 222	94.39 405	87.02 392	91.88 332	97.59 354

new-patchmatchnet		90.30 365	89.46 369	92.84 376	90.77 411	88.55 393	99.83 287	98.80 380	90.07 385	87.86 400	95.00 402	78.77 387	94.30 406	84.86 397	79.15 401	95.68 397

LCM-MVSNet		79.01 381	76.93 384	85.27 389	78.28 421	68.01 417	96.57 410	98.03 396	55.10 417	82.03 410	93.27 407	31.99 423	93.95 407	82.72 400	74.37 406	93.84 402

CL-MVSNet_self_test		91.07 361	90.35 364	93.24 374	93.27 403	89.16 390	99.55 335	99.25 281	92.34 367	95.23 369	97.05 394	88.86 324	93.59 408	80.67 405	66.95 411	96.96 376

MIMVSNet1		91.96 354	91.20 357	94.23 369	94.94 400	91.69 378	99.34 357	99.22 293	88.23 391	94.18 379	98.45 375	75.52 396	93.41 409	79.37 408	91.49 339	97.60 353

test20.03		93.11 348	92.85 352	93.88 372	95.19 398	91.83 376	100.00 1	98.87 377	93.68 341	92.76 386	98.88 359	89.20 318	92.71 410	77.88 410	89.19 363	97.09 373

test_fmvs3		87.19 371	87.02 374	87.71 385	92.69 404	76.64 406	99.96 255	97.27 408	93.55 345	90.82 393	94.03 406	38.00 420	92.19 411	93.49 354	83.35 393	94.32 400

dmvs_testset		93.27 347	95.48 321	86.65 387	98.74 321	68.42 416	99.92 271	98.91 374	96.19 271	93.28 384	100.00 1	91.06 291	91.67 412	89.64 383	91.54 337	99.86 193

PMMVS2		79.15 380	77.28 383	84.76 390	82.34 418	72.66 408	99.70 317	95.11 418	71.68 412	84.78 409	90.87 409	32.05 422	89.99 413	75.53 415	63.45 414	91.64 409

FPMVS		77.92 382	79.45 380	73.34 401	76.87 422	46.81 428	98.24 404	99.05 357	59.89 416	73.55 412	98.34 379	36.81 421	86.55 414	80.96 404	91.35 343	86.65 413

PMVS	60.66 23	65.98 388	65.05 389	68.75 404	55.06 427	38.40 429	88.19 414	96.98 410	48.30 421	44.82 422	88.52 413	12.22 427	86.49 415	67.58 416	83.79 391	81.35 417
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		88.24 369	90.09 365	82.68 394	91.56 409	69.51 414	100.00 1	98.73 383	90.72 379	87.29 402	98.12 382	92.87 267	85.01 416	62.19 417	89.34 361	93.54 404

SSC-MVS		87.61 370	89.47 368	82.04 395	90.63 412	68.77 415	99.99 217	98.66 384	90.34 382	86.70 403	98.08 383	92.72 272	84.12 417	59.41 420	88.71 369	93.22 408

MVE	68.59 21	67.22 386	64.68 390	74.84 398	74.67 424	62.32 423	95.84 411	90.87 423	50.98 418	58.72 420	81.05 420	12.20 428	78.95 418	61.06 419	56.75 415	83.24 416
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		70.72 384	70.06 387	72.69 402	83.92 417	65.48 421	99.95 261	92.72 421	49.88 419	72.30 413	86.26 416	47.17 415	77.43 419	53.83 421	44.49 417	75.17 419

EMVS		69.88 385	69.09 388	72.24 403	84.70 416	65.82 420	99.96 255	87.08 426	49.82 420	71.51 414	84.74 417	49.30 413	75.32 420	50.97 422	43.71 418	75.59 418

test123		79.44 379	79.23 381	80.05 397	80.03 420	71.72 410	100.00 1	77.93 428	62.52 414	94.81 372	99.69 277	78.21 388	74.53 421	92.57 359	27.33 421	93.90 401

testmvs		80.17 376	81.95 379	74.80 399	58.54 426	59.58 424	100.00 1	87.14 425	76.09 410	99.61 205	100.00 1	67.06 407	74.19 422	98.84 223	50.30 416	90.64 411

ANet_high		66.05 387	63.44 391	73.88 400	61.14 425	63.45 422	95.68 412	87.18 424	79.93 407	47.35 421	80.68 421	22.35 425	72.33 423	61.24 418	35.42 419	85.88 414

wuyk23d		28.28 389	29.73 393	23.92 405	75.89 423	32.61 430	66.50 416	12.88 429	16.09 422	14.59 424	16.59 423	12.35 426	32.36 424	39.36 423	13.36 422	6.79 420

mmdepth		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

monomultidepth		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

test_blank		0.07 393	0.09 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.79 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet_test		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

DCPMVS		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

cdsmvs_eth3d_5k		24.41 390	32.55 392	0.00 406	0.00 429	0.00 431	0.00 417	99.39 207	0.00 424	0.00 425	100.00 1	93.55 257	0.00 425	0.00 424	0.00 423	0.00 421

pcd_1.5k_mvsjas		8.24 392	10.99 395	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	98.75 126	0.00 425	0.00 424	0.00 423	0.00 421

sosnet-low-res		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

sosnet		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uncertanet		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

Regformer		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

ab-mvs-re		8.33 391	11.11 394	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	100.00 1	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet		0.01 394	0.02 397	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.14 425	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

WAC-MVS						97.98 256								95.74 321

FOURS1					100.00 1	99.97 21	100.00 1	99.42 141	98.52 77	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 141	98.72 67	100.00 1	100.00 1	99.60 19

eth-test2					0.00 429

eth-test					0.00 429

RE-MVS-def			99.55 59		99.99 49	99.91 56	100.00 1	99.42 141	97.62 150	100.00 1	100.00 1	98.94 110		99.99 64	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 141	99.12 7	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 49	99.93 47	100.00 1	99.42 141	98.93 38

test0726					100.00 1	99.99 5	100.00 1	99.42 141	99.04 16	100.00 1	100.00 1	99.53 31

GSMVS																99.91 151

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 72				99.91 151

sam_mvs												99.33 61

MTGPA								99.42 141

MTMP							100.00 1	99.18 310

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

test_prior4						99.93 47	100.00 1

test_prior2							100.00 1		98.82 55	100.00 1	100.00 1	99.47 45		100.00 1	100.00 1

新几何2							100.00 1

旧先验1					99.99 49	99.88 77		99.82 40			100.00 1	99.27 75			100.00 1	100.00 1

原ACMM2							100.00 1

test222					99.99 49	99.90 63	100.00 1	99.69 62	97.66 144	100.00 1	100.00 1	99.30 71			100.00 1	100.00 1

segment_acmp												99.55 27

testdata1							100.00 1		98.77 66

plane_prior7					99.00 298	94.78 343

plane_prior6					99.06 290	94.80 339						88.58 328

plane_prior4											99.97 201

plane_prior3						94.79 342			99.03 21	99.08 242

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 293

plane_prior						94.80 339	100.00 1		99.03 21						95.58 268

n2								0.00 430

nn								0.00 430

door-mid								96.32 416

test11								99.42 141

door								96.13 417

HQP5-MVS						94.82 336

HQP-NCC					99.07 286		100.00 1		99.04 16	99.17 232

ACMP_Plane					99.07 286		100.00 1		99.04 16	99.17 232

BP-MVS														99.79 123

HQP3-MVS								99.40 194							95.58 268

HQP2-MVS												88.61 326

NP-MVS					99.07 286	94.81 338					99.97 201

MDTV_nov1_ep13_2view						99.24 164	99.56 334		96.31 264	99.96 126		98.86 117		98.92 219		99.89 168

ACMMP++_ref															94.58 302

ACMMP++															95.17 288

Test By Simon												99.10 88

High-resolution multi-view benchmark