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.

DPM-MVS		98.83 24	98.46 36	99.97 1	99.33 111	99.92 1	99.96 56	98.44 148	97.96 23	99.55 71	99.94 5	97.18 23	100.00 1	93.81 285	99.94 59	99.98 57

MSC_two_6792asdad				99.93 2	99.91 45	99.80 2		98.41 174					100.00 1	99.96 13	100.00 1	100.00 1

No_MVS				99.93 2	99.91 45	99.80 2		98.41 174					100.00 1	99.96 13	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 51	99.80 2	99.96 56				99.80 59	97.44 15	100.00 1	100.00 1	99.98 32	100.00 1

TestfortrainingZip				99.90 5	99.97 3	99.70 5	99.97 42	98.89 52	96.02 99	99.99 1	99.96 3	97.97 5	100.00 1		99.65 97	100.00 1

MCST-MVS		99.32 3	99.14 4	99.86 6	99.97 3	99.59 6	99.97 42	98.64 91	98.47 3	99.13 107	99.92 16	96.38 37	100.00 1	99.74 44	100.00 1	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 7	99.98 2	99.51 7	99.98 24	98.69 82	98.20 9	99.93 3	99.98 2	96.82 26	100.00 1	99.75 42	100.00 1	99.99 26

test_0728_SECOND				99.82 8	99.94 18	99.47 8	99.95 75	98.43 156					100.00 1	99.99 5	100.00 1	100.00 1

test-260524					99.95 17	99.33 9		98.42 168		99.04 115		96.44 36	100.00 1	99.98 9	99.98 32

MM		98.83 24	98.53 33	99.76 11	99.59 93	99.33 9	99.99 8	99.76 6	98.39 4	99.39 91	99.80 59	90.49 196	99.96 77	99.89 22	99.43 130	99.98 57

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 19	98.98 139	99.32 11	97.49 437	99.52 14	95.69 109	98.32 159	97.41 318	93.32 122	99.77 151	98.08 151	95.75 275	99.81 109

DVP-MVS++		99.26 6	99.09 10	99.77 9	99.91 45	99.31 12	99.95 75	98.43 156	96.48 80	99.80 28	99.93 12	97.44 15	100.00 1	99.92 17	99.98 32	100.00 1

IU-MVS					99.93 29	99.31 12		98.41 174	97.71 31	99.84 23				100.00 1	100.00 1	100.00 1

test_one_0601					99.94 18	99.30 14		98.41 174	96.63 75	99.75 42	99.93 12	97.49 11

SED-MVS		99.28 5	99.11 8	99.77 9	99.93 29	99.30 14	99.96 56	98.43 156	97.27 47	99.80 28	99.94 5	96.71 29	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 29	99.30 14		98.43 156	97.26 49	99.80 28	99.88 29	96.71 29	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 13	99.93 29	99.29 17	99.95 75	98.32 198	97.28 45	99.83 24	99.91 19	97.22 21	100.00 1	99.99 5	100.00 1	99.89 97
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
test0726					99.93 29	99.29 17	99.96 56	98.42 168	97.28 45	99.86 16	99.94 5	97.22 21

WTY-MVS		98.10 76	97.60 98	99.60 24	98.92 147	99.28 19	99.89 127	99.52 14	95.58 112	98.24 165	99.39 149	93.33 121	99.74 157	97.98 158	95.58 284	99.78 115

test_part2					99.89 51	99.25 20				99.49 79

DPE-MVS		99.26 6	99.10 9	99.74 12	99.89 51	99.24 21	99.87 133	98.44 148	97.48 39	99.64 58	99.94 5	96.68 31	99.99 40	99.99 5	100.00 1	99.99 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MVS		96.60 170	95.56 205	99.72 14	96.85 329	99.22 22	98.31 411	98.94 44	91.57 298	90.90 331	99.61 124	86.66 255	99.96 77	97.36 184	99.88 77	99.99 26

MGCNet		99.06 13	98.84 19	99.72 14	99.76 74	99.21 23	99.99 8	99.34 25	98.70 2	99.44 82	99.75 81	93.24 127	99.99 40	99.94 15	99.41 132	99.95 83

NCCC		99.37 2	99.25 2	99.71 16	99.96 9	99.15 24	99.97 42	98.62 98	98.02 22	99.90 7	99.95 4	97.33 19	100.00 1	99.54 59	100.00 1	100.00 1

CANet		98.27 63	97.82 87	99.63 19	99.72 83	99.10 25	99.98 24	98.51 131	97.00 59	98.52 146	99.71 98	87.80 231	99.95 86	99.75 42	99.38 134	99.83 105

TestfortrainingZip a		99.01 16	98.78 21	99.69 17	99.96 9	99.09 26	99.97 42	98.74 76	96.91 62	99.86 16	99.92 16	96.29 38	99.99 40	98.32 135	99.09 150	100.00 1

MG-MVS		98.91 22	98.65 27	99.68 18	99.94 18	99.07 27	99.64 240	99.44 19	97.33 44	99.00 118	99.72 95	94.03 103	99.98 52	98.73 109	100.00 1	100.00 1

HPM-MVS++		99.07 11	98.88 18	99.63 19	99.90 48	99.02 28	99.95 75	98.56 113	97.56 37	99.44 82	99.85 38	95.38 57	100.00 1	99.31 72	99.99 21	99.87 100

PAPM		98.60 37	98.42 38	99.14 73	96.05 354	98.96 29	99.90 117	99.35 24	96.68 73	98.35 158	99.66 116	96.45 35	98.51 283	99.45 66	99.89 74	99.96 75

sasdasda		97.09 138	96.32 162	99.39 46	98.93 144	98.95 30	99.72 214	97.35 322	94.45 148	97.88 181	99.42 142	86.71 252	99.52 177	98.48 124	93.97 309	99.72 122

canonicalmvs		97.09 138	96.32 162	99.39 46	98.93 144	98.95 30	99.72 214	97.35 322	94.45 148	97.88 181	99.42 142	86.71 252	99.52 177	98.48 124	93.97 309	99.72 122

TEST9					99.92 37	98.92 32	99.96 56	98.43 156	93.90 185	99.71 49	99.86 34	95.88 46	99.85 131

train_agg		98.88 23	98.65 27	99.59 27	99.92 37	98.92 32	99.96 56	98.43 156	94.35 157	99.71 49	99.86 34	95.94 43	99.85 131	99.69 51	99.98 32	99.99 26

PS-MVSNAJ		98.44 49	98.20 54	99.16 69	98.80 159	98.92 32	99.54 267	98.17 223	97.34 42	99.85 20	99.85 38	91.20 178	99.89 119	99.41 69	99.67 95	98.69 285

test_8					99.92 37	98.88 35	99.96 56	98.43 156	94.35 157	99.69 51	99.85 38	95.94 43	99.85 131

SMA-MVS		98.76 29	98.48 35	99.62 22	99.87 57	98.87 36	99.86 144	98.38 185	93.19 215	99.77 40	99.94 5	95.54 51	100.00 1	99.74 44	99.99 21	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
CHOSEN 280x420		99.01 16	99.03 11	98.95 95	99.38 108	98.87 36	98.46 401	99.42 21	97.03 57	99.02 117	99.09 190	99.35 2	98.21 318	99.73 46	99.78 88	99.77 116

DeepC-MVS_fast	96.59 1	98.81 26	98.54 32	99.62 22	99.90 48	98.85 38	99.24 319	98.47 140	98.14 16	99.08 110	99.91 19	93.09 131	100.00 1	99.04 86	99.99 21	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
thres200		96.96 145	96.21 168	99.22 59	98.97 140	98.84 39	99.85 147	99.71 7	93.17 217	96.26 247	98.88 227	89.87 204	99.51 179	94.26 273	94.91 295	99.31 219

tfpn200view9		96.79 154	95.99 177	99.19 62	98.94 142	98.82 40	99.78 181	99.71 7	92.86 233	96.02 257	98.87 234	89.33 211	99.50 181	93.84 282	94.57 299	99.27 229

thres400		96.78 156	95.99 177	99.16 69	98.94 142	98.82 40	99.78 181	99.71 7	92.86 233	96.02 257	98.87 234	89.33 211	99.50 181	93.84 282	94.57 299	99.16 242

MGCFI-Net		97.00 143	96.22 167	99.34 51	98.86 155	98.80 42	99.67 234	97.30 334	94.31 160	97.77 187	99.41 146	86.36 260	99.50 181	98.38 130	93.90 311	99.72 122

MED-MVS test				99.60 24	99.96 9	98.79 43	99.97 42	98.88 55	96.36 90	99.07 112	99.93 12		100.00 1	99.98 9	99.96 48	99.99 26

MED-MVS		99.24 8	99.12 5	99.60 24	99.96 9	98.79 43	99.97 42	98.88 55	96.91 62	99.07 112	99.92 16	97.36 18	100.00 1	99.98 9	99.98 32	100.00 1

ME-MVS		99.07 11	98.89 17	99.59 27	99.93 29	98.79 43	99.95 75	98.80 71	95.89 103	99.28 99	99.93 12	96.28 39	99.98 52	99.98 9	99.96 48	99.99 26

save fliter					99.82 66	98.79 43	99.96 56	98.40 178	97.66 33

thres600view7		96.69 165	95.87 193	99.14 73	98.90 152	98.78 47	99.74 203	99.71 7	92.59 253	95.84 260	98.86 236	89.25 213	99.50 181	93.44 295	94.50 302	99.16 242

thres100view900		96.74 162	95.92 189	99.18 63	98.90 152	98.77 48	99.74 203	99.71 7	92.59 253	95.84 260	98.86 236	89.25 213	99.50 181	93.84 282	94.57 299	99.27 229

agg_prior					99.93 29	98.77 48		98.43 156		99.63 59			99.85 131

PAPR		98.52 43	98.16 58	99.58 29	99.97 3	98.77 48	99.95 75	98.43 156	95.35 118	98.03 172	99.75 81	94.03 103	99.98 52	98.11 148	99.83 81	99.99 26

APDe-MVS		99.06 13	98.91 15	99.51 34	99.94 18	98.76 51	99.91 111	98.39 181	97.20 51	99.46 80	99.85 38	95.53 53	99.79 146	99.86 28	100.00 1	99.99 26
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SD-MVS		98.92 21	98.70 23	99.56 30	99.70 86	98.73 52	99.94 93	98.34 195	96.38 86	99.81 26	99.76 73	94.59 78	99.98 52	99.84 30	99.96 48	99.97 67
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
CDPH-MVS		98.65 35	98.36 45	99.49 37	99.94 18	98.73 52	99.87 133	98.33 196	93.97 179	99.76 41	99.87 32	94.99 69	99.75 155	98.55 119	100.00 1	99.98 57

DP-MVS Recon		98.41 53	98.02 68	99.56 30	99.97 3	98.70 54	99.92 103	98.44 148	92.06 282	98.40 156	99.84 49	95.68 49	100.00 1	98.19 143	99.71 92	99.97 67

SF-MVS		98.67 33	98.40 39	99.50 35	99.77 73	98.67 55	99.90 117	98.21 218	93.53 197	99.81 26	99.89 27	94.70 77	99.86 130	99.84 30	99.93 65	99.96 75

TSAR-MVS + MP.		98.93 20	98.77 22	99.41 44	99.74 78	98.67 55	99.77 187	98.38 185	96.73 71	99.88 13	99.74 88	94.89 71	99.59 175	99.80 33	99.98 32	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v2_base		98.23 71	97.97 72	99.02 88	98.69 165	98.66 57	99.52 269	98.08 237	97.05 56	99.86 16	99.86 34	90.65 191	99.71 161	99.39 71	98.63 167	98.69 285

alignmvs		97.81 96	97.33 113	99.25 56	98.77 161	98.66 57	99.99 8	98.44 148	94.40 156	98.41 154	99.47 138	93.65 114	99.42 191	98.57 118	94.26 305	99.67 133

DELS-MVS		98.54 41	98.22 52	99.50 35	99.15 124	98.65 59	100.00 1	98.58 105	97.70 32	98.21 167	99.24 174	92.58 149	99.94 95	98.63 117	99.94 59	99.92 93
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
3Dnovator+	91.53 11	96.31 190	95.24 223	99.52 33	96.88 328	98.64 60	99.72 214	98.24 211	95.27 121	88.42 391	98.98 210	82.76 324	99.94 95	97.10 195	99.83 81	99.96 75

ACMMP_NAP		98.49 45	98.14 59	99.54 32	99.66 90	98.62 61	99.85 147	98.37 188	94.68 139	99.53 74	99.83 51	92.87 137	100.00 1	98.66 114	99.84 80	99.99 26

ZD-MVS					99.92 37	98.57 62		98.52 128	92.34 270	99.31 95	99.83 51	95.06 64	99.80 144	99.70 50	99.97 44

test12				99.43 41	99.74 78	98.56 63		98.40 178		99.65 55		94.76 74	99.75 155		99.98 32	99.99 26

1314		96.84 152	95.96 183	99.48 40	96.74 337	98.52 64	98.31 411	98.86 59	95.82 104	89.91 346	98.98 210	87.49 239	99.96 77	97.80 168	99.73 91	99.96 75

APD-MVS		98.62 36	98.35 46	99.41 44	99.90 48	98.51 65	99.87 133	98.36 189	94.08 172	99.74 45	99.73 92	94.08 101	99.74 157	99.42 68	99.99 21	99.99 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_l_conf0.5_n_9		98.55 40	98.23 51	99.49 37	99.10 126	98.50 66	99.99 8	98.70 80	98.14 16	99.94 2	99.68 112	89.02 218	99.98 52	99.89 22	99.61 105	99.99 26

test_prior				99.43 41	99.94 18	98.49 67		98.65 88					99.80 144			99.99 26

MSLP-MVS++		99.13 9	99.01 12	99.49 37	99.94 18	98.46 68	99.98 24	98.86 59	97.10 53	99.80 28	99.94 5	95.92 45	100.00 1	99.51 60	100.00 1	100.00 1

BridgeMVS		98.27 63	97.99 70	99.11 78	98.64 171	98.43 69	99.47 279	97.79 267	94.56 142	99.74 45	98.35 285	94.33 92	99.25 197	99.12 80	99.96 48	99.64 139

MP-MVS-pluss		98.07 78	97.64 96	99.38 49	99.74 78	98.41 70	99.74 203	98.18 222	93.35 208	96.45 237	99.85 38	92.64 146	99.97 65	98.91 97	99.89 74	99.77 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_3		98.41 53	98.08 64	99.39 46	99.12 125	98.29 71	99.98 24	98.64 91	98.14 16	99.86 16	99.76 73	87.99 230	99.97 65	99.72 47	99.54 112	99.91 95

新几何1				99.42 43	99.75 77	98.27 72		98.63 97	92.69 246	99.55 71	99.82 54	94.40 85	100.00 1	91.21 328	99.94 59	99.99 26

fmvsm_s_conf0.5_n_2		97.59 112	97.28 115	98.53 130	99.01 132	98.15 73	99.98 24	98.59 103	98.17 13	99.75 42	99.63 122	81.83 333	99.94 95	99.78 36	98.79 163	97.51 326

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 90	98.60 173	98.15 73	99.58 254	97.74 276	90.34 346	99.26 101	98.32 288	94.29 94	99.23 198	99.03 89	99.89 74	99.58 160

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 124	97.74 239	98.14 75	99.31 306	97.86 261	96.43 83	99.62 62	99.69 105	85.56 276	99.68 166	99.05 83	98.31 177	97.83 311

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 124	97.74 239	98.14 75	99.31 306	97.86 261	96.43 83	99.62 62	99.69 105	85.56 276	99.68 166	99.05 83	98.31 177	97.83 311

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 124	97.74 239	98.14 75	99.31 306	97.86 261	96.43 83	99.62 62	99.69 105	85.56 276	99.68 166	99.05 83	98.31 177	97.83 311

fmvsm_s_conf0.5_n_8		98.38 57	98.05 66	99.35 50	99.20 119	98.12 78	99.98 24	98.81 67	98.22 7	99.80 28	99.71 98	87.37 242	99.97 65	99.91 20	99.48 122	99.97 67

fmvsm_s_conf0.5_n_6		98.27 63	97.96 75	99.23 58	97.66 252	98.11 79	99.98 24	98.64 91	97.85 27	99.87 14	99.72 95	88.86 221	99.93 105	99.64 55	99.36 136	99.63 146

fmvsm_s_conf0.1_n_2		97.25 128	96.85 134	98.43 140	98.08 217	98.08 80	99.92 103	97.76 275	98.05 20	99.65 55	99.58 128	80.88 347	99.93 105	99.59 57	98.17 182	97.29 327

fmvsm_s_conf0.5_n_3		97.95 81	97.66 94	98.81 101	98.99 137	98.07 81	99.98 24	98.81 67	98.18 12	99.89 11	99.70 101	84.15 306	99.97 65	99.76 41	99.50 120	98.39 295

baseline1		95.78 219	94.86 238	98.54 128	98.47 188	98.07 81	99.06 339	97.99 245	92.68 247	94.13 296	98.62 262	93.28 125	98.69 262	93.79 287	85.76 373	98.84 276

test_prior4						98.05 83	99.94 93

sss		97.57 113	97.03 127	99.18 63	98.37 194	98.04 84	99.73 210	99.38 22	93.46 202	98.76 133	99.06 195	91.21 177	99.89 119	96.33 227	97.01 236	99.62 147

GG-mvs-BLEND				98.54 128	98.21 207	98.01 85	93.87 482	98.52 128		97.92 176	97.92 305	99.02 3	97.94 336	98.17 144	99.58 110	99.67 133

ET-MVSNet_ETH3D		94.37 272	93.28 293	97.64 201	98.30 199	97.99 86	99.99 8	97.61 291	94.35 157	71.57 492	99.45 141	96.23 40	95.34 456	96.91 206	85.14 380	99.59 154

BP-MVS1		98.33 59	98.18 56	98.81 101	97.44 272	97.98 87	99.96 56	98.17 223	94.88 130	98.77 130	99.59 125	97.59 8	99.08 211	98.24 141	98.93 156	99.36 205

test_yl		97.83 92	97.37 111	99.21 60	99.18 120	97.98 87	99.64 240	99.27 27	91.43 305	97.88 181	98.99 208	95.84 47	99.84 139	98.82 102	95.32 290	99.79 112

DCV-MVSNet		97.83 92	97.37 111	99.21 60	99.18 120	97.98 87	99.64 240	99.27 27	91.43 305	97.88 181	98.99 208	95.84 47	99.84 139	98.82 102	95.32 290	99.79 112

gg-mvs-nofinetune		93.51 302	91.86 329	98.47 135	97.72 244	97.96 90	92.62 493	98.51 131	74.70 486	97.33 200	69.59 520	98.91 4	97.79 340	97.77 173	99.56 111	99.67 133

MTAPA		98.29 62	97.96 75	99.30 52	99.85 62	97.93 91	99.39 292	98.28 205	95.76 106	97.18 206	99.88 29	92.74 141	100.00 1	98.67 112	99.88 77	99.99 26

fmvsm_l_conf0.5_n_a		99.00 18	98.91 15	99.28 53	99.21 118	97.91 92	99.98 24	98.85 62	98.25 5	99.92 5	99.75 81	94.72 75	99.97 65	99.87 26	99.64 98	99.95 83

fmvsm_s_conf0.5_n_10		98.24 69	97.90 80	99.26 55	99.24 117	97.88 93	99.99 8	98.76 73	98.20 9	99.92 5	99.74 88	85.97 267	99.94 95	99.72 47	99.53 114	99.96 75

lecture		98.67 33	98.46 36	99.28 53	99.86 59	97.88 93	99.97 42	99.25 30	96.07 97	99.79 37	99.70 101	92.53 151	99.98 52	99.51 60	99.48 122	99.97 67

114514_t		97.41 122	96.83 135	99.14 73	99.51 102	97.83 95	99.89 127	98.27 207	88.48 384	99.06 114	99.66 116	90.30 199	99.64 174	96.32 228	99.97 44	99.96 75

VNet		97.21 131	96.57 149	99.13 77	98.97 140	97.82 96	99.03 346	99.21 32	94.31 160	99.18 105	98.88 227	86.26 262	99.89 119	98.93 93	94.32 303	99.69 130

GDP-MVS		97.88 86	97.59 100	98.75 106	97.59 259	97.81 97	99.95 75	97.37 320	94.44 151	99.08 110	99.58 128	97.13 25	99.08 211	94.99 251	98.17 182	99.37 203

fmvsm_l_conf0.5_n		98.94 19	98.84 19	99.25 56	99.17 122	97.81 97	99.98 24	98.86 59	98.25 5	99.90 7	99.76 73	94.21 98	99.97 65	99.87 26	99.52 115	99.98 57

fmvsm_s_conf0.5_n_11		98.03 79	97.89 82	98.46 137	99.35 110	97.76 99	99.99 8	98.04 241	98.20 9	99.90 7	99.78 67	86.21 263	99.95 86	99.89 22	99.68 94	97.65 317

MVSTER		95.53 230	95.22 224	96.45 276	98.56 175	97.72 100	99.91 111	97.67 281	92.38 269	91.39 325	97.14 325	97.24 20	97.30 361	94.80 259	87.85 356	94.34 362

SteuartSystems-ACMMP		99.02 15	98.97 14	99.18 63	98.72 164	97.71 101	99.98 24	98.44 148	96.85 64	99.80 28	99.91 19	97.57 9	99.85 131	99.44 67	99.99 21	99.99 26
Skip Steuart: Steuart Systems R&D Blog.
QAPM		95.40 233	94.17 258	99.10 79	96.92 323	97.71 101	99.40 288	98.68 84	89.31 361	88.94 375	98.89 226	82.48 326	99.96 77	93.12 303	99.83 81	99.62 147

MVSFormer		96.94 146	96.60 147	97.95 170	97.28 293	97.70 103	99.55 265	97.27 344	91.17 313	99.43 84	99.54 134	90.92 186	96.89 391	94.67 264	99.62 100	99.25 233

lupinMVS		97.85 90	97.60 98	98.62 116	97.28 293	97.70 103	99.99 8	97.55 298	95.50 116	99.43 84	99.67 114	90.92 186	98.71 257	98.40 129	99.62 100	99.45 190

fmvsm_s_conf0.5_n_5		98.08 77	97.71 92	99.17 66	98.67 167	97.69 105	99.99 8	98.57 107	97.40 40	99.89 11	99.69 105	85.99 266	99.96 77	99.80 33	99.40 133	99.85 103

FOURS1					99.92 37	97.66 106	99.95 75	98.36 189	95.58 112	99.52 76

ZNCC-MVS		98.31 60	98.03 67	99.17 66	99.88 55	97.59 107	99.94 93	98.44 148	94.31 160	98.50 149	99.82 54	93.06 132	99.99 40	98.30 137	99.99 21	99.93 88

GST-MVS		98.27 63	97.97 72	99.17 66	99.92 37	97.57 108	99.93 100	98.39 181	94.04 177	98.80 127	99.74 88	92.98 134	100.00 1	98.16 145	99.76 89	99.93 88

CANet_DTU		96.76 157	96.15 171	98.60 118	98.78 160	97.53 109	99.84 152	97.63 285	97.25 50	99.20 102	99.64 119	81.36 339	99.98 52	92.77 307	98.89 157	98.28 299

thisisatest0515		97.41 122	97.02 128	98.59 121	97.71 246	97.52 110	99.97 42	98.54 123	91.83 289	97.45 195	99.04 197	97.50 10	99.10 210	94.75 261	96.37 255	99.16 242

旧先验1					99.76 74	97.52 110		98.64 91			99.85 38	95.63 50			99.94 59	99.99 26

XVS		98.70 32	98.55 31	99.15 71	99.94 18	97.50 112	99.94 93	98.42 168	96.22 93	99.41 87	99.78 67	94.34 90	99.96 77	98.92 95	99.95 54	99.99 26

X-MVStestdata		93.83 289	92.06 324	99.15 71	99.94 18	97.50 112	99.94 93	98.42 168	96.22 93	99.41 87	41.37 547	94.34 90	99.96 77	98.92 95	99.95 54	99.99 26

OpenMVS	90.15 15	94.77 254	93.59 277	98.33 146	96.07 353	97.48 114	99.56 262	98.57 107	90.46 342	86.51 419	98.95 219	78.57 374	99.94 95	93.86 281	99.74 90	97.57 323

3Dnovator	91.47 12	96.28 193	95.34 219	99.08 82	96.82 331	97.47 115	99.45 284	98.81 67	95.52 115	89.39 362	99.00 205	81.97 330	99.95 86	97.27 186	99.83 81	99.84 104

HFP-MVS		98.56 39	98.37 43	99.14 73	99.96 9	97.43 116	99.95 75	98.61 99	94.77 134	99.31 95	99.85 38	94.22 96	100.00 1	98.70 110	99.98 32	99.98 57

FMVSNet3		92.69 324	91.58 334	95.99 289	98.29 200	97.42 117	99.26 318	97.62 288	89.80 357	89.68 352	95.32 402	81.62 337	96.27 431	87.01 402	85.65 374	94.29 364

test222					99.55 98	97.41 118	99.34 300	98.55 119	91.86 288	99.27 100	99.83 51	93.84 110			99.95 54	99.99 26

jason		97.24 129	96.86 133	98.38 145	95.73 368	97.32 119	99.97 42	97.40 316	95.34 119	98.60 145	99.54 134	87.70 233	98.56 278	97.94 159	99.47 125	99.25 233
jason: jason.
reproduce-ours		98.78 27	98.67 24	99.09 80	99.70 86	97.30 120	99.74 203	98.25 209	97.10 53	99.10 108	99.90 23	94.59 78	99.99 40	99.77 38	99.91 71	99.99 26

our_new_method		98.78 27	98.67 24	99.09 80	99.70 86	97.30 120	99.74 203	98.25 209	97.10 53	99.10 108	99.90 23	94.59 78	99.99 40	99.77 38	99.91 71	99.99 26

myMVS_eth3d28		97.86 88	97.59 100	98.68 110	98.50 185	97.26 122	99.92 103	98.55 119	93.79 188	98.26 163	98.75 246	95.20 59	99.48 187	98.93 93	96.40 253	99.29 224

MSP-MVS		99.09 10	99.12 5	98.98 92	99.93 29	97.24 123	99.95 75	98.42 168	97.50 38	99.52 76	99.88 29	97.43 17	99.71 161	99.50 62	99.98 32	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
MVS_Test		96.46 179	95.74 197	98.61 117	98.18 210	97.23 124	99.31 306	97.15 366	91.07 319	98.84 124	97.05 331	88.17 228	98.97 218	94.39 268	97.50 201	99.61 151

nrg030		93.51 302	92.53 316	96.45 276	94.36 401	97.20 125	99.81 169	97.16 363	91.60 297	89.86 348	97.46 316	86.37 259	97.68 344	95.88 236	80.31 423	94.46 349

region2R		98.54 41	98.37 43	99.05 83	99.96 9	97.18 126	99.96 56	98.55 119	94.87 131	99.45 81	99.85 38	94.07 102	100.00 1	98.67 112	100.00 1	99.98 57

ACMMPR		98.50 44	98.32 47	99.05 83	99.96 9	97.18 126	99.95 75	98.60 101	94.77 134	99.31 95	99.84 49	93.73 112	100.00 1	98.70 110	99.98 32	99.98 57

MVS_111021_HR		98.72 31	98.62 29	99.01 89	99.36 109	97.18 126	99.93 100	99.90 1	96.81 69	98.67 137	99.77 71	93.92 105	99.89 119	99.27 75	99.94 59	99.96 75

MP-MVS		98.23 71	97.97 72	99.03 85	99.94 18	97.17 129	99.95 75	98.39 181	94.70 138	98.26 163	99.81 58	91.84 172	100.00 1	98.85 101	99.97 44	99.93 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
KinetiMVS		96.10 199	95.29 222	98.53 130	97.08 304	97.12 130	99.56 262	98.12 234	94.78 133	98.44 151	98.94 221	80.30 358	99.39 192	91.56 325	98.79 163	99.06 254

ETVMVS		97.03 142	96.64 145	98.20 153	98.67 167	97.12 130	99.89 127	98.57 107	91.10 318	98.17 168	98.59 265	93.86 109	98.19 319	95.64 241	95.24 292	99.28 226

testing3-2		97.72 106	97.43 109	98.60 118	98.55 178	97.11 132	100.00 1	99.23 31	93.78 189	97.90 177	98.73 248	95.50 54	99.69 165	98.53 122	94.63 297	98.99 264

reproduce_model		98.75 30	98.66 26	99.03 85	99.71 84	97.10 133	99.73 210	98.23 213	97.02 58	99.18 105	99.90 23	94.54 82	99.99 40	99.77 38	99.90 73	99.99 26

PHI-MVS		98.41 53	98.21 53	99.03 85	99.86 59	97.10 133	99.98 24	98.80 71	90.78 332	99.62 62	99.78 67	95.30 58	100.00 1	99.80 33	99.93 65	99.99 26

SR-MVS		98.46 47	98.30 50	98.93 96	99.88 55	97.04 135	99.84 152	98.35 191	94.92 128	99.32 94	99.80 59	93.35 120	99.78 148	99.30 73	99.95 54	99.96 75

PGM-MVS		98.34 58	98.13 60	98.99 90	99.92 37	97.00 136	99.75 199	99.50 17	93.90 185	99.37 92	99.76 73	93.24 127	100.00 1	97.75 175	99.96 48	99.98 57

原ACMM1				98.96 94	99.73 81	96.99 137		98.51 131	94.06 175	99.62 62	99.85 38	94.97 70	99.96 77	95.11 248	99.95 54	99.92 93

PVSNet_BlendedMVS		96.05 202	95.82 194	96.72 267	99.59 93	96.99 137	99.95 75	99.10 34	94.06 175	98.27 161	95.80 374	89.00 219	99.95 86	99.12 80	87.53 363	93.24 436

PVSNet_Blended		97.94 82	97.64 96	98.83 100	99.59 93	96.99 137	100.00 1	99.10 34	95.38 117	98.27 161	99.08 191	89.00 219	99.95 86	99.12 80	99.25 141	99.57 162

mPP-MVS		98.39 56	98.20 54	98.97 93	99.97 3	96.92 140	99.95 75	98.38 185	95.04 124	98.61 142	99.80 59	93.39 118	100.00 1	98.64 115	100.00 1	99.98 57

test2506		97.53 114	97.19 120	98.58 122	98.66 169	96.90 141	98.81 376	99.77 5	94.93 126	97.95 175	98.96 214	92.51 152	99.20 203	94.93 253	98.15 184	99.64 139

CNLPA		97.76 101	97.38 110	98.92 97	99.53 99	96.84 142	99.87 133	98.14 232	93.78 189	96.55 233	99.69 105	92.28 159	99.98 52	97.13 193	99.44 129	99.93 88

usedtu_dtu_shiyan1		92.78 319	91.73 330	95.92 294	93.03 428	96.82 143	99.83 160	97.79 267	90.58 335	90.09 339	95.04 415	84.75 292	96.72 404	88.19 382	86.23 370	94.23 369

FE-MVSNET3		92.78 319	91.73 330	95.92 294	93.03 428	96.82 143	99.83 160	97.79 267	90.58 335	90.09 339	95.04 415	84.75 292	96.72 404	88.20 381	86.23 370	94.23 369

LuminaMVS		96.63 168	96.21 168	97.87 179	95.58 379	96.82 143	99.12 328	97.67 281	94.47 146	97.88 181	98.31 290	87.50 238	98.71 257	98.07 152	97.29 212	98.10 305

testing222		97.08 141	96.75 140	98.06 164	98.56 175	96.82 143	99.85 147	98.61 99	92.53 261	98.84 124	98.84 240	93.36 119	98.30 309	95.84 237	94.30 304	99.05 256

FIs		94.10 281	93.43 283	96.11 286	94.70 394	96.82 143	99.58 254	98.93 48	92.54 260	89.34 364	97.31 321	87.62 235	97.10 374	94.22 275	86.58 367	94.40 355

EPNet		98.49 45	98.40 39	98.77 105	99.62 92	96.80 148	99.90 117	99.51 16	97.60 34	99.20 102	99.36 152	93.71 113	99.91 112	97.99 156	98.71 166	99.61 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Elysia		94.50 266	93.38 288	97.85 180	96.49 344	96.70 149	98.98 351	97.78 271	90.81 326	96.19 250	98.55 272	73.63 423	98.98 216	89.41 358	98.56 169	97.88 309

StellarMVS		94.50 266	93.38 288	97.85 180	96.49 344	96.70 149	98.98 351	97.78 271	90.81 326	96.19 250	98.55 272	73.63 423	98.98 216	89.41 358	98.56 169	97.88 309

thisisatest0530		97.10 136	96.72 142	98.22 152	97.60 258	96.70 149	99.92 103	98.54 123	91.11 317	97.07 210	98.97 212	97.47 13	99.03 213	93.73 290	96.09 261	98.92 270

WBMVS		94.52 265	94.03 263	95.98 290	98.38 192	96.68 152	99.92 103	97.63 285	90.75 333	89.64 356	95.25 408	96.77 27	96.90 390	94.35 271	83.57 393	94.35 360

PVSNet_Blended_VisFu		97.27 127	96.81 137	98.66 113	98.81 158	96.67 153	99.92 103	98.64 91	94.51 144	96.38 245	98.49 276	89.05 217	99.88 125	97.10 195	98.34 175	99.43 194

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 99	99.73 81	96.63 154	99.97 42	97.92 255	98.07 19	98.76 133	99.55 132	95.00 68	99.94 95	99.91 20	97.68 198	99.99 26

CP-MVS		98.45 48	98.32 47	98.87 98	99.96 9	96.62 155	99.97 42	98.39 181	94.43 152	98.90 122	99.87 32	94.30 93	100.00 1	99.04 86	99.99 21	99.99 26

VortexMVS		94.11 280	93.50 281	95.94 292	97.70 247	96.61 156	99.35 299	97.18 359	93.52 200	89.57 359	95.74 376	87.55 237	96.97 385	95.76 240	85.13 381	94.23 369

reproduce_monomvs		95.38 234	95.07 231	96.32 282	99.32 113	96.60 157	99.76 193	98.85 62	96.65 74	87.83 401	96.05 371	99.52 1	98.11 323	96.58 220	81.07 415	94.25 367

APD-MVS_3200maxsize		98.25 68	98.08 64	98.78 103	99.81 68	96.60 157	99.82 167	98.30 203	93.95 181	99.37 92	99.77 71	92.84 138	99.76 154	98.95 91	99.92 68	99.97 67

UBG		97.84 91	97.69 93	98.29 149	98.38 192	96.59 159	99.90 117	98.53 126	93.91 184	98.52 146	98.42 283	96.77 27	99.17 206	98.54 120	96.20 258	99.11 249

EI-MVSNet-Vis-set		98.27 63	98.11 62	98.75 106	99.83 65	96.59 159	99.40 288	98.51 131	95.29 120	98.51 148	99.76 73	93.60 116	99.71 161	98.53 122	99.52 115	99.95 83

ETV-MVS		97.92 84	97.80 88	98.25 151	98.14 214	96.48 161	99.98 24	97.63 285	95.61 111	99.29 98	99.46 140	92.55 150	98.82 233	99.02 90	98.54 171	99.46 185

TESTMET0.1,1		96.74 162	96.26 164	98.16 155	97.36 284	96.48 161	99.96 56	98.29 204	91.93 285	95.77 263	98.07 298	95.54 51	98.29 310	90.55 344	98.89 157	99.70 125

HPM-MVS_fast		97.80 97	97.50 103	98.68 110	99.79 70	96.42 163	99.88 130	98.16 228	91.75 294	98.94 120	99.54 134	91.82 173	99.65 173	97.62 179	99.99 21	99.99 26

test_fmvsmconf_n		98.43 51	98.32 47	98.78 103	98.12 216	96.41 164	99.99 8	98.83 66	98.22 7	99.67 53	99.64 119	91.11 182	99.94 95	99.67 53	99.62 100	99.98 57

Test_1112_low_res		95.72 221	94.83 239	98.42 142	97.79 235	96.41 164	99.65 236	96.65 431	92.70 245	92.86 312	96.13 367	92.15 165	99.30 195	91.88 321	93.64 313	99.55 164

1112_ss		96.01 204	95.20 225	98.42 142	97.80 234	96.41 164	99.65 236	96.66 430	92.71 244	92.88 311	99.40 147	92.16 164	99.30 195	91.92 320	93.66 312	99.55 164

HPM-MVS		97.96 80	97.72 90	98.68 110	99.84 64	96.39 167	99.90 117	98.17 223	92.61 251	98.62 141	99.57 131	91.87 171	99.67 169	98.87 100	99.99 21	99.99 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SR-MVS-dyc-post		98.31 60	98.17 57	98.71 108	99.79 70	96.37 168	99.76 193	98.31 200	94.43 152	99.40 89	99.75 81	93.28 125	99.78 148	98.90 98	99.92 68	99.97 67

RE-MVS-def			98.13 60		99.79 70	96.37 168	99.76 193	98.31 200	94.43 152	99.40 89	99.75 81	92.95 135		98.90 98	99.92 68	99.97 67

EI-MVSNet-UG-set		98.14 74	97.99 70	98.60 118	99.80 69	96.27 170	99.36 298	98.50 137	95.21 122	98.30 160	99.75 81	93.29 124	99.73 160	98.37 132	99.30 139	99.81 109

Effi-MVS+		96.30 191	95.69 199	98.16 155	97.85 231	96.26 171	97.41 440	97.21 356	90.37 344	98.65 140	98.58 268	86.61 256	98.70 260	97.11 194	97.37 207	99.52 173

cascas		94.64 260	93.61 274	97.74 193	97.82 233	96.26 171	99.96 56	97.78 271	85.76 423	94.00 297	97.54 315	76.95 391	99.21 200	97.23 190	95.43 287	97.76 315

ab-mvs		94.69 257	93.42 284	98.51 133	98.07 218	96.26 171	96.49 461	98.68 84	90.31 347	94.54 284	97.00 334	76.30 400	99.71 161	95.98 234	93.38 317	99.56 163

MDTV_nov1_ep13_2view						96.26 171	96.11 469		91.89 286	98.06 171		94.40 85		94.30 272		99.67 133

guyue		97.15 134	96.82 136	98.15 158	97.56 261	96.25 175	99.71 219	97.84 264	95.75 107	98.13 170	98.65 257	87.58 236	98.82 233	98.29 138	97.91 194	99.36 205

UniMVSNet (Re)		93.07 313	92.13 321	95.88 296	94.84 391	96.24 176	99.88 130	98.98 41	92.49 264	89.25 366	95.40 396	87.09 246	97.14 370	93.13 302	78.16 435	94.26 365

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 115	95.76 365	96.20 177	99.94 93	98.05 240	98.17 13	98.89 123	99.42 142	87.65 234	99.90 114	99.50 62	99.60 108	99.82 107

FC-MVSNet-test		93.81 292	93.15 297	95.80 301	94.30 403	96.20 177	99.42 286	98.89 52	92.33 271	89.03 374	97.27 323	87.39 241	96.83 397	93.20 298	86.48 368	94.36 357

VPA-MVSNet		92.70 323	91.55 336	96.16 285	95.09 387	96.20 177	98.88 367	99.00 39	91.02 321	91.82 322	95.29 406	76.05 404	97.96 333	95.62 242	81.19 410	94.30 363

diffmvs		97.00 143	96.64 145	98.09 162	97.64 254	96.17 180	99.81 169	97.19 357	94.67 140	98.95 119	99.28 161	86.43 257	98.76 249	98.37 132	97.42 204	99.33 212
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPM_NR		98.12 75	97.93 78	98.70 109	99.94 18	96.13 181	99.82 167	98.43 156	94.56 142	97.52 191	99.70 101	94.40 85	99.98 52	97.00 198	99.98 32	99.99 26

ACMMP		97.74 103	97.44 107	98.66 113	99.92 37	96.13 181	99.18 324	99.45 18	94.84 132	96.41 244	99.71 98	91.40 175	99.99 40	97.99 156	98.03 191	99.87 100
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
EPMVS		96.53 176	96.01 176	98.09 162	98.43 190	96.12 183	96.36 463	99.43 20	93.53 197	97.64 189	95.04 415	94.41 84	98.38 300	91.13 330	98.11 187	99.75 118

testing11		97.48 116	97.27 116	98.10 161	98.36 195	96.02 184	99.92 103	98.45 143	93.45 204	98.15 169	98.70 252	95.48 55	99.22 199	97.85 165	95.05 294	99.07 253

PCF-MVS	94.20 5	95.18 239	94.10 259	98.43 140	98.55 178	95.99 185	97.91 430	97.31 333	90.35 345	89.48 361	99.22 175	85.19 284	99.89 119	90.40 349	98.47 173	99.41 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline2		96.71 164	96.49 152	97.37 235	95.63 377	95.96 186	99.74 203	98.88 55	92.94 229	91.61 323	98.97 212	97.72 7	98.62 273	94.83 258	98.08 190	97.53 325

DeepC-MVS	94.51 4	96.92 149	96.40 160	98.45 138	99.16 123	95.90 187	99.66 235	98.06 238	96.37 89	94.37 291	99.49 137	83.29 320	99.90 114	97.63 178	99.61 105	99.55 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tttt0517		96.85 151	96.49 152	97.92 174	97.48 269	95.89 188	99.85 147	98.54 123	90.72 334	96.63 227	98.93 224	97.47 13	99.02 214	93.03 304	95.76 274	98.85 275

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 124	99.28 114	95.84 189	99.99 8	98.57 107	98.17 13	99.93 3	99.74 88	87.04 247	99.97 65	99.86 28	99.59 109	99.83 105

PVSNet	91.05 13	97.13 135	96.69 144	98.45 138	99.52 100	95.81 190	99.95 75	99.65 12	94.73 136	99.04 115	99.21 178	84.48 302	99.95 86	94.92 254	98.74 165	99.58 160

MVS_111021_LR		98.42 52	98.38 41	98.53 130	99.39 107	95.79 191	99.87 133	99.86 2	96.70 72	98.78 128	99.79 63	92.03 168	99.90 114	99.17 79	99.86 79	99.88 98

CPTT-MVS		97.64 110	97.32 114	98.58 122	99.97 3	95.77 192	99.96 56	98.35 191	89.90 355	98.36 157	99.79 63	91.18 181	99.99 40	98.37 132	99.99 21	99.99 26

NR-MVSNet		91.56 349	90.22 359	95.60 304	94.05 407	95.76 193	98.25 414	98.70 80	91.16 315	80.78 461	96.64 350	83.23 321	96.57 410	91.41 326	77.73 439	94.46 349

mvs_anonymous		95.65 227	95.03 233	97.53 214	98.19 209	95.74 194	99.33 301	97.49 307	90.87 323	90.47 337	97.10 327	88.23 227	97.16 368	95.92 235	97.66 199	99.68 131

FMVSNet2		91.02 358	89.56 372	95.41 313	97.53 264	95.74 194	98.98 351	97.41 315	87.05 405	88.43 389	95.00 420	71.34 432	96.24 433	85.12 417	85.21 379	94.25 367

UA-Net		96.54 175	95.96 183	98.27 150	98.23 205	95.71 196	98.00 427	98.45 143	93.72 193	98.41 154	99.27 165	88.71 224	99.66 172	91.19 329	97.69 196	99.44 193

testing99		97.17 132	96.91 130	97.95 170	98.35 197	95.70 197	99.91 111	98.43 156	92.94 229	97.36 198	98.72 249	94.83 72	99.21 200	97.00 198	94.64 296	98.95 266

LFMVS		94.75 256	93.56 279	98.30 148	99.03 131	95.70 197	98.74 382	97.98 247	87.81 397	98.47 150	99.39 149	67.43 450	99.53 176	98.01 154	95.20 293	99.67 133

IB-MVS	92.85 6	94.99 246	93.94 267	98.16 155	97.72 244	95.69 199	99.99 8	98.81 67	94.28 163	92.70 313	96.90 338	95.08 63	99.17 206	96.07 232	73.88 458	99.60 153
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
testing91		97.16 133	96.90 131	97.97 168	98.35 197	95.67 200	99.91 111	98.42 168	92.91 231	97.33 200	98.72 249	94.81 73	99.21 200	96.98 200	94.63 297	99.03 261

EC-MVSNet		97.38 124	97.24 117	97.80 183	97.41 274	95.64 201	99.99 8	97.06 393	94.59 141	99.63 59	99.32 154	89.20 216	98.14 321	98.76 107	99.23 143	99.62 147

FA-MVS(test-final)		95.86 210	95.09 230	98.15 158	97.74 239	95.62 202	96.31 465	98.17 223	91.42 307	96.26 247	96.13 367	90.56 194	99.47 189	92.18 312	97.07 227	99.35 209

AdaColmap		97.23 130	96.80 138	98.51 133	99.99 1	95.60 203	99.09 332	98.84 65	93.32 210	96.74 225	99.72 95	86.04 265	100.00 1	98.01 154	99.43 130	99.94 87

test_fmvsmconf0.01_n		96.39 184	95.74 197	98.32 147	91.47 459	95.56 204	99.84 152	97.30 334	97.74 30	97.89 179	99.35 153	79.62 362	99.85 131	99.25 76	99.24 142	99.55 164

VPNet		91.81 341	90.46 352	95.85 298	94.74 393	95.54 205	98.98 351	98.59 103	92.14 278	90.77 335	97.44 317	68.73 443	97.54 350	94.89 257	77.89 437	94.46 349

casdiffmvs_mvg		96.43 181	95.94 187	97.89 178	97.44 272	95.47 206	99.86 144	97.29 342	93.35 208	96.03 255	99.19 181	85.39 280	98.72 256	97.89 164	97.04 231	99.49 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		96.75 159	96.41 159	97.79 185	97.20 298	95.46 207	99.69 229	97.15 366	94.46 147	98.78 128	99.21 178	85.64 273	98.77 247	98.27 139	97.31 211	99.13 246

test-LLR		96.47 178	96.04 175	97.78 187	97.02 311	95.44 208	99.96 56	98.21 218	94.07 173	95.55 269	96.38 356	93.90 107	98.27 314	90.42 347	98.83 161	99.64 139

test-mter		96.39 184	95.93 188	97.78 187	97.02 311	95.44 208	99.96 56	98.21 218	91.81 291	95.55 269	96.38 356	95.17 60	98.27 314	90.42 347	98.83 161	99.64 139

SDMVSNet		94.80 251	93.96 266	97.33 240	98.92 147	95.42 210	99.59 252	98.99 40	92.41 266	92.55 315	97.85 309	75.81 405	98.93 222	97.90 163	91.62 324	97.64 318

API-MVS		97.86 88	97.66 94	98.47 135	99.52 100	95.41 211	99.47 279	98.87 58	91.68 296	98.84 124	99.85 38	92.34 158	99.99 40	98.44 127	99.96 48	100.00 1

XXY-MVS		91.82 340	90.46 352	95.88 296	93.91 410	95.40 212	98.87 370	97.69 280	88.63 381	87.87 400	97.08 328	74.38 418	97.89 337	91.66 323	84.07 390	94.35 360

SSM_0404		95.75 220	95.16 227	97.50 219	97.53 264	95.39 213	99.11 330	97.25 348	90.81 326	95.27 276	98.83 241	84.74 294	98.67 265	95.24 246	97.69 196	98.45 292

NormalMVS		97.90 85	97.85 85	98.04 166	99.86 59	95.39 213	99.61 247	97.78 271	96.52 78	98.61 142	99.31 157	92.73 142	99.67 169	96.77 214	99.48 122	99.06 254

SymmetryMVS		97.64 110	97.46 104	98.17 154	98.74 163	95.39 213	99.61 247	99.26 29	96.52 78	98.61 142	99.31 157	92.73 142	99.67 169	96.77 214	95.63 282	99.45 190

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 176	97.02 311	95.34 216	99.95 75	98.45 143	97.87 26	97.02 211	99.59 125	89.64 206	99.98 52	99.41 69	99.34 138	98.42 294

testdata				98.42 142	99.47 104	95.33 217		98.56 113	93.78 189	99.79 37	99.85 38	93.64 115	99.94 95	94.97 252	99.94 59	100.00 1

hybridnocas07		96.57 173	96.16 170	97.81 182	97.36 284	95.32 218	99.81 169	97.12 372	94.17 167	98.02 173	98.90 225	85.05 286	98.80 242	97.85 165	97.18 217	99.32 214

mamba_0408		94.98 247	94.09 260	97.64 201	97.14 299	95.31 219	93.48 488	97.08 384	90.48 340	94.40 288	98.62 262	84.49 300	98.67 265	93.99 277	97.18 217	98.93 267

SSM_04072		94.77 254	94.09 260	96.82 262	97.14 299	95.31 219	93.48 488	97.08 384	90.48 340	94.40 288	98.62 262	84.49 300	96.21 434	93.99 277	97.18 217	98.93 267

SSM_0407		95.62 228	94.95 236	97.61 206	97.14 299	95.31 219	99.00 349	97.25 348	90.81 326	94.40 288	98.83 241	84.74 294	98.58 275	95.24 246	97.18 217	98.93 267

WR-MVS		92.31 333	91.25 341	95.48 309	94.45 399	95.29 222	99.60 250	98.68 84	90.10 350	88.07 398	96.89 339	80.68 351	96.80 399	93.14 301	79.67 427	94.36 357

UniMVSNet_NR-MVSNet		92.95 315	92.11 322	95.49 306	94.61 396	95.28 223	99.83 160	99.08 36	91.49 300	89.21 369	96.86 341	87.14 245	96.73 402	93.20 298	77.52 440	94.46 349

DU-MVS		92.46 330	91.45 339	95.49 306	94.05 407	95.28 223	99.81 169	98.74 76	92.25 277	89.21 369	96.64 350	81.66 335	96.73 402	93.20 298	77.52 440	94.46 349

miper_enhance_ethall		94.36 274	93.98 265	95.49 306	98.68 166	95.24 225	99.73 210	97.29 342	93.28 212	89.86 348	95.97 372	94.37 89	97.05 377	92.20 311	84.45 386	94.19 375

BH-RMVSNet		95.18 239	94.31 254	97.80 183	98.17 211	95.23 226	99.76 193	97.53 302	92.52 262	94.27 294	99.25 172	76.84 392	98.80 242	90.89 338	99.54 112	99.35 209

PatchMatch-RL		96.04 203	95.40 212	97.95 170	99.59 93	95.22 227	99.52 269	99.07 37	93.96 180	96.49 235	98.35 285	82.28 327	99.82 143	90.15 352	99.22 144	98.81 278

SPE-MVS-test		97.88 86	97.94 77	97.70 196	99.28 114	95.20 228	99.98 24	97.15 366	95.53 114	99.62 62	99.79 63	92.08 167	98.38 300	98.75 108	99.28 140	99.52 173

test_fmvsm_n_1920		98.44 49	98.61 30	97.92 174	99.27 116	95.18 229	100.00 1	98.90 50	98.05 20	99.80 28	99.73 92	92.64 146	99.99 40	99.58 58	99.51 118	98.59 288

baseline		96.43 181	95.98 179	97.76 191	97.34 286	95.17 230	99.51 271	97.17 361	93.92 183	96.90 217	99.28 161	85.37 281	98.64 271	97.50 181	96.86 241	99.46 185

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 210	98.44 189	95.16 231	99.97 42	98.65 88	97.95 24	99.62 62	99.78 67	86.09 264	99.94 95	99.69 51	99.50 120	97.66 316

hybrid		96.53 176	96.15 171	97.67 197	97.39 278	95.12 232	99.80 175	97.15 366	93.38 206	98.23 166	99.16 186	85.20 283	98.70 260	97.92 160	97.15 222	99.20 239

LS3D		95.84 212	95.11 229	98.02 167	99.85 62	95.10 233	98.74 382	98.50 137	87.22 404	93.66 300	99.86 34	87.45 240	99.95 86	90.94 336	99.81 87	99.02 262

onestephybrid01		96.75 159	96.44 156	97.71 194	97.47 270	95.03 234	99.83 160	97.27 344	94.15 168	98.66 138	99.25 172	85.72 270	98.81 237	98.42 128	97.17 221	99.28 226

casdiffmvs		96.42 183	95.97 182	97.77 189	97.30 291	94.98 235	99.84 152	97.09 383	93.75 192	96.58 230	99.26 169	85.07 285	98.78 246	97.77 173	97.04 231	99.54 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
pmmvs4		92.10 337	91.07 345	95.18 320	92.82 438	94.96 236	99.48 278	96.83 420	87.45 400	88.66 381	96.56 354	83.78 311	96.83 397	89.29 363	84.77 384	93.75 421

CDS-MVSNet		96.34 188	96.07 173	97.13 249	97.37 281	94.96 236	99.53 268	97.91 256	91.55 299	95.37 274	98.32 288	95.05 65	97.13 371	93.80 286	95.75 275	99.30 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
balanced_ft_v1		96.88 150	96.52 151	97.96 169	98.60 173	94.94 238	99.41 287	97.56 297	93.53 197	99.42 86	97.89 308	83.33 319	99.31 194	99.29 74	99.62 100	99.64 139

RRT-MVS		96.24 196	95.68 201	97.94 173	97.65 253	94.92 239	99.27 316	97.10 380	92.79 239	97.43 196	97.99 302	81.85 332	99.37 193	98.46 126	98.57 168	99.53 172

UGNet		95.33 236	94.57 247	97.62 205	98.55 178	94.85 240	98.67 390	99.32 26	95.75 107	96.80 224	96.27 361	72.18 428	99.96 77	94.58 266	99.05 153	98.04 306
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
EIA-MVS		97.53 114	97.46 104	97.76 191	98.04 220	94.84 241	99.98 24	97.61 291	94.41 155	97.90 177	99.59 125	92.40 156	98.87 226	98.04 153	99.13 147	99.59 154

E3new		96.75 159	96.43 157	97.71 194	97.79 235	94.83 242	99.80 175	97.33 326	93.52 200	97.49 194	99.31 157	87.73 232	98.83 230	97.52 180	97.40 206	99.48 182

Vis-MVSNet (Re-imp)		96.32 189	95.98 179	97.35 239	97.93 226	94.82 243	99.47 279	98.15 231	91.83 289	95.09 278	99.11 189	91.37 176	97.47 352	93.47 294	97.43 202	99.74 119

IS-MVSNet		96.29 192	95.90 190	97.45 224	98.13 215	94.80 244	99.08 334	97.61 291	92.02 284	95.54 271	98.96 214	90.64 192	98.08 325	93.73 290	97.41 205	99.47 183

MAR-MVS		97.43 117	97.19 120	98.15 158	99.47 104	94.79 245	99.05 343	98.76 73	92.65 249	98.66 138	99.82 54	88.52 225	99.98 52	98.12 147	99.63 99	99.67 133
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
PLC	95.54 3	97.93 83	97.89 82	98.05 165	99.82 66	94.77 246	99.92 103	98.46 142	93.93 182	97.20 204	99.27 165	95.44 56	99.97 65	97.41 182	99.51 118	99.41 198
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
casdiffseed414692147		95.07 242	94.26 255	97.50 219	97.01 314	94.70 247	99.58 254	97.02 397	91.27 311	94.66 283	98.82 243	80.79 349	98.55 281	93.39 296	95.79 272	99.27 229

viewcassd2359sk11		96.59 171	96.23 165	97.66 199	97.63 255	94.70 247	99.77 187	97.33 326	93.41 205	97.34 199	99.17 183	86.72 251	98.83 230	97.40 183	97.32 210	99.46 185

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 229	99.01 132	94.69 249	99.97 42	98.76 73	97.91 25	99.87 14	99.76 73	86.70 254	99.93 105	99.67 53	99.12 149	97.64 318

viewmanbaseed2359cas		96.45 180	96.07 173	97.59 210	97.55 262	94.59 250	99.70 226	97.33 326	93.62 196	97.00 214	99.32 154	85.57 275	98.71 257	97.26 189	97.33 209	99.47 183

FE-MVS		95.70 225	95.01 234	97.79 185	98.21 207	94.57 251	95.03 477	98.69 82	88.90 373	97.50 193	96.19 363	92.60 148	99.49 186	89.99 354	97.94 193	99.31 219

Fast-Effi-MVS+		95.02 245	94.19 257	97.52 216	97.88 228	94.55 252	99.97 42	97.08 384	88.85 375	94.47 287	97.96 304	84.59 299	98.41 292	89.84 356	97.10 226	99.59 154

E2		96.36 186	95.95 185	97.60 207	97.41 274	94.52 253	99.71 219	97.33 326	93.20 214	97.02 211	99.07 193	85.37 281	98.82 233	97.27 186	97.14 223	99.46 185

E3		96.36 186	95.95 185	97.60 207	97.37 281	94.52 253	99.71 219	97.33 326	93.18 216	97.02 211	99.07 193	85.45 279	98.82 233	97.27 186	97.14 223	99.46 185

viewdifsd2359ckpt09		96.21 197	95.77 195	97.53 214	97.69 248	94.50 255	99.78 181	97.23 353	92.88 232	96.58 230	99.26 169	84.85 290	98.66 268	96.61 218	97.02 234	99.43 194

hybridcas		96.09 201	95.62 203	97.50 219	97.37 281	94.44 256	99.84 152	97.16 363	93.16 218	96.03 255	99.21 178	84.19 305	98.65 270	96.53 222	97.07 227	99.42 197

SCA		94.69 257	93.81 271	97.33 240	97.10 302	94.44 256	98.86 371	98.32 198	93.30 211	96.17 253	95.59 385	76.48 398	97.95 334	91.06 332	97.43 202	99.59 154

cl22		93.77 294	93.25 294	95.33 316	99.49 103	94.43 258	99.61 247	98.09 235	90.38 343	89.16 372	95.61 383	90.56 194	97.34 356	91.93 319	84.45 386	94.21 374

CS-MVS		97.79 99	97.91 79	97.43 228	99.10 126	94.42 259	99.99 8	97.10 380	95.07 123	99.68 52	99.75 81	92.95 135	98.34 304	98.38 130	99.14 146	99.54 168

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 197	99.06 129	94.41 260	99.98 24	98.97 43	97.34 42	99.63 59	99.69 105	87.27 243	99.97 65	99.62 56	99.06 152	98.62 287

PatchmatchNet		95.94 207	95.45 208	97.39 234	97.83 232	94.41 260	96.05 470	98.40 178	92.86 233	97.09 208	95.28 407	94.21 98	98.07 327	89.26 365	98.11 187	99.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
viewdifsd2359ckpt13		96.19 198	95.77 195	97.45 224	97.62 256	94.40 262	99.70 226	97.23 353	92.76 241	96.63 227	99.05 196	84.96 289	98.64 271	96.65 217	97.35 208	99.31 219

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 207	97.38 279	94.40 262	99.90 117	98.64 91	96.47 82	99.51 78	99.65 118	84.99 288	99.93 105	99.22 77	99.09 150	98.46 291

viewmamba		96.61 169	96.34 161	97.42 229	97.26 296	94.37 264	99.83 160	97.16 363	94.51 144	97.89 179	99.26 169	86.38 258	98.66 268	97.70 176	97.06 230	99.23 236

mvsmamba		96.94 146	96.73 141	97.55 212	97.99 222	94.37 264	99.62 243	97.70 278	93.13 221	98.42 153	97.92 305	88.02 229	98.75 251	98.78 105	99.01 154	99.52 173

TR-MVS		94.54 262	93.56 279	97.49 222	97.96 224	94.34 266	98.71 385	97.51 305	90.30 348	94.51 286	98.69 253	75.56 406	98.77 247	92.82 306	95.99 263	99.35 209

Vis-MVSNet		95.72 221	95.15 228	97.45 224	97.62 256	94.28 267	99.28 314	98.24 211	94.27 165	96.84 220	98.94 221	79.39 364	98.76 249	93.25 297	98.49 172	99.30 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_a		97.73 105	97.72 90	97.77 189	98.63 172	94.26 268	99.96 56	98.92 49	97.18 52	99.75 42	99.69 105	87.00 249	99.97 65	99.46 65	98.89 157	99.08 252

0.4-1-1-0.2		94.14 279	93.02 301	97.51 217	95.45 381	94.25 269	100.00 1	98.22 214	88.53 383	96.83 221	96.95 336	92.25 161	98.57 277	96.34 226	72.65 464	99.70 125

E4		96.01 204	95.53 207	97.44 227	97.05 307	94.23 270	99.57 258	97.30 334	92.72 242	96.47 236	99.03 198	83.98 309	98.83 230	96.92 204	96.77 242	99.27 229

test_cas_vis1_n_1920		96.59 171	96.23 165	97.65 200	98.22 206	94.23 270	99.99 8	97.25 348	97.77 29	99.58 70	99.08 191	77.10 385	99.97 65	97.64 177	99.45 128	98.74 282

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 204	95.65 375	94.21 272	99.83 160	98.50 137	96.27 92	99.65 55	99.64 119	84.72 296	99.93 105	99.04 86	98.84 160	98.74 282

0.3-1-1-0.015		94.22 278	93.13 299	97.49 222	95.50 380	94.17 273	100.00 1	98.22 214	88.44 386	97.14 207	97.04 333	92.73 142	98.59 274	96.45 225	72.65 464	99.70 125

MDTV_nov1_ep13			95.69 199		97.90 227	94.15 274	95.98 472	98.44 148	93.12 222	97.98 174	95.74 376	95.10 62	98.58 275	90.02 353	96.92 238

tfpnnormal		89.29 397	87.61 404	94.34 356	94.35 402	94.13 275	98.95 358	98.94 44	83.94 441	84.47 439	95.51 390	74.84 414	97.39 353	77.05 471	80.41 421	91.48 464

viewmacassd2359aftdt		95.93 208	95.45 208	97.36 237	97.09 303	94.12 276	99.57 258	97.26 347	93.05 226	96.50 234	99.17 183	82.76 324	98.68 263	96.61 218	97.04 231	99.28 226

KD-MVS_2432*1600		88.00 407	86.10 411	93.70 387	96.91 324	94.04 277	97.17 446	97.12 372	84.93 434	81.96 451	92.41 462	92.48 153	94.51 469	79.23 457	52.68 515	92.56 448

miper_refine_blended		88.00 407	86.10 411	93.70 387	96.91 324	94.04 277	97.17 446	97.12 372	84.93 434	81.96 451	92.41 462	92.48 153	94.51 469	79.23 457	52.68 515	92.56 448

DP-MVS		94.54 262	93.42 284	97.91 176	99.46 106	94.04 277	98.93 361	97.48 308	81.15 461	90.04 343	99.55 132	87.02 248	99.95 86	88.97 367	98.11 187	99.73 120

0.4-1-1-0.1		94.07 284	92.95 302	97.42 229	95.24 385	94.00 280	100.00 1	98.22 214	88.27 390	96.81 223	96.93 337	92.27 160	98.56 278	96.21 231	72.63 466	99.70 125

TranMVSNet+NR-MVSNet		91.68 348	90.61 351	94.87 329	93.69 414	93.98 281	99.69 229	98.65 88	91.03 320	88.44 386	96.83 345	80.05 360	96.18 435	90.26 351	76.89 448	94.45 354

MSDG		94.37 272	93.36 291	97.40 233	98.88 154	93.95 282	99.37 296	97.38 317	85.75 425	90.80 334	99.17 183	84.11 308	99.88 125	86.35 406	98.43 174	98.36 297

HyFIR lowres test		96.66 167	96.43 157	97.36 237	99.05 130	93.91 283	99.70 226	99.80 3	90.54 338	96.26 247	98.08 297	92.15 165	98.23 317	96.84 208	95.46 285	99.93 88

v2v482		91.30 351	90.07 365	95.01 324	93.13 422	93.79 284	99.77 187	97.02 397	88.05 392	89.25 366	95.37 400	80.73 350	97.15 369	87.28 396	80.04 426	94.09 395

ADS-MVSNet		94.79 252	94.02 264	97.11 251	97.87 229	93.79 284	94.24 478	98.16 228	90.07 351	96.43 242	94.48 435	90.29 200	98.19 319	87.44 391	97.23 213	99.36 205

gm-plane-assit					96.97 317	93.76 286			91.47 303		98.96 214		98.79 244	94.92 254

ECVR-MVS		95.66 226	95.05 232	97.51 217	98.66 169	93.71 287	98.85 373	98.45 143	94.93 126	96.86 218	98.96 214	75.22 411	99.20 203	95.34 243	98.15 184	99.64 139

UWE-MVS		96.79 154	96.72 142	97.00 254	98.51 183	93.70 288	99.71 219	98.60 101	92.96 228	97.09 208	98.34 287	96.67 33	98.85 229	92.11 317	96.50 250	98.44 293

v1144		91.09 357	89.83 366	94.87 329	93.25 421	93.69 289	99.62 243	96.98 403	86.83 411	89.64 356	94.99 421	80.94 345	97.05 377	85.08 418	81.16 411	93.87 415

Casviewmamba		96.25 195	95.89 191	97.32 242	97.45 271	93.68 290	99.80 175	97.22 355	93.38 206	96.86 218	99.28 161	84.64 298	98.87 226	97.18 192	97.19 216	99.41 198

WB-MVSnew		92.90 316	92.77 308	93.26 398	96.95 322	93.63 291	99.71 219	98.16 228	91.49 300	94.28 293	98.14 295	81.33 340	96.48 417	79.47 455	95.46 285	89.68 484

E5new		95.83 213	95.39 213	97.15 245	97.03 308	93.59 292	99.32 304	97.30 334	92.58 255	96.45 237	99.00 205	83.37 316	98.81 237	96.81 210	96.65 245	99.04 257

E5		95.83 213	95.39 213	97.15 245	97.03 308	93.59 292	99.32 304	97.30 334	92.58 255	96.45 237	99.00 205	83.37 316	98.81 237	96.81 210	96.65 245	99.04 257

E6new		95.83 213	95.39 213	97.14 247	97.00 315	93.58 294	99.31 306	97.30 334	92.57 257	96.45 237	99.01 201	83.44 314	98.81 237	96.80 212	96.66 243	99.04 257

E6		95.83 213	95.39 213	97.14 247	97.00 315	93.58 294	99.31 306	97.30 334	92.57 257	96.45 237	99.01 201	83.44 314	98.81 237	96.80 212	96.66 243	99.04 257

GA-MVS		93.83 289	92.84 304	96.80 263	95.73 368	93.57 296	99.88 130	97.24 351	92.57 257	92.92 309	96.66 348	78.73 372	97.67 345	87.75 389	94.06 308	99.17 241

miper_ehance_all_eth		93.16 310	92.60 311	94.82 333	97.57 260	93.56 297	99.50 273	97.07 392	88.75 377	88.85 376	95.52 389	90.97 185	96.74 401	90.77 340	84.45 386	94.17 377

GeoE		94.36 274	93.48 282	96.99 255	97.29 292	93.54 298	99.96 56	96.72 428	88.35 388	93.43 301	98.94 221	82.05 328	98.05 328	88.12 386	96.48 252	99.37 203

TAMVS		95.85 211	95.58 204	96.65 270	97.07 305	93.50 299	99.17 325	97.82 266	91.39 309	95.02 279	98.01 299	92.20 163	97.30 361	93.75 289	95.83 271	99.14 245

V42		91.28 353	90.12 364	94.74 334	93.42 419	93.46 300	99.68 232	97.02 397	87.36 401	89.85 350	95.05 414	81.31 341	97.34 356	87.34 394	80.07 425	93.40 431

v10		90.25 378	88.82 387	94.57 343	93.53 416	93.43 301	99.08 334	96.87 418	85.00 433	87.34 411	94.51 433	80.93 346	97.02 384	82.85 433	79.23 428	93.26 435

viewmambaseed2359dif		95.92 209	95.55 206	97.04 253	97.38 279	93.41 302	99.78 181	96.97 405	91.14 316	96.58 230	99.27 165	84.85 290	98.75 251	96.87 207	97.12 225	98.97 265

EPNet_dtu		95.71 223	95.39 213	96.66 269	98.92 147	93.41 302	99.57 258	98.90 50	96.19 95	97.52 191	98.56 270	92.65 145	97.36 354	77.89 466	98.33 176	99.20 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v8		90.54 370	89.17 380	94.66 337	93.43 418	93.40 304	99.20 322	96.94 411	85.76 423	87.56 405	94.51 433	81.96 331	97.19 367	84.94 419	78.25 434	93.38 433

test1111		95.57 229	94.98 235	97.37 235	98.56 175	93.37 305	98.86 371	98.45 143	94.95 125	96.63 227	98.95 219	75.21 412	99.11 209	95.02 250	98.14 186	99.64 139

OMC-MVS		97.28 126	97.23 118	97.41 232	99.76 74	93.36 306	99.65 236	97.95 250	96.03 98	97.41 197	99.70 101	89.61 207	99.51 179	96.73 216	98.25 181	99.38 201

dtuplus		95.79 218	95.42 210	96.93 257	97.24 297	93.16 307	99.78 181	96.93 412	91.69 295	96.18 252	99.29 160	83.80 310	98.73 253	96.83 209	97.02 234	98.89 274

tpmrst		96.27 194	95.98 179	97.13 249	97.96 224	93.15 308	96.34 464	98.17 223	92.07 280	98.71 136	95.12 412	93.91 106	98.73 253	94.91 256	96.62 247	99.50 179

v1192		90.62 369	89.25 379	94.72 336	93.13 422	93.07 309	99.50 273	97.02 397	86.33 417	89.56 360	95.01 418	79.22 366	97.09 376	82.34 438	81.16 411	94.01 402

CHOSEN 1792x2688		96.81 153	96.53 150	97.64 201	98.91 151	93.07 309	99.65 236	99.80 3	95.64 110	95.39 273	98.86 236	84.35 304	99.90 114	96.98 200	99.16 145	99.95 83

EPP-MVSNet		96.69 165	96.60 147	96.96 256	97.74 239	93.05 311	99.37 296	98.56 113	88.75 377	95.83 262	99.01 201	96.01 41	98.56 278	96.92 204	97.20 215	99.25 233

viewdifsd2359ckpt07		95.83 213	95.42 210	97.07 252	97.40 276	93.04 312	99.60 250	97.24 351	92.39 268	96.09 254	99.14 188	83.07 323	98.93 222	97.02 197	96.87 239	99.23 236

mvsany_test1		97.82 95	97.90 80	97.55 212	98.77 161	93.04 312	99.80 175	97.93 252	96.95 61	99.61 69	99.68 112	90.92 186	99.83 141	99.18 78	98.29 180	99.80 111

c3_l		92.53 328	91.87 328	94.52 345	97.40 276	92.99 314	99.40 288	96.93 412	87.86 395	88.69 379	95.44 394	89.95 203	96.44 419	90.45 346	80.69 420	94.14 387

anonymousdsp		91.79 346	90.92 346	94.41 354	90.76 466	92.93 315	98.93 361	97.17 361	89.08 363	87.46 408	95.30 403	78.43 377	96.92 388	92.38 309	88.73 343	93.39 432

cl____		92.31 333	91.58 334	94.52 345	97.33 288	92.77 316	99.57 258	96.78 425	86.97 409	87.56 405	95.51 390	89.43 209	96.62 408	88.60 370	82.44 401	94.16 382

v144192		90.79 364	89.52 374	94.59 341	93.11 425	92.77 316	99.56 262	96.99 401	86.38 416	89.82 351	94.95 423	80.50 355	97.10 374	83.98 425	80.41 421	93.90 412

DIV-MVS_self_test		92.32 332	91.60 333	94.47 349	97.31 290	92.74 318	99.58 254	96.75 426	86.99 408	87.64 403	95.54 387	89.55 208	96.50 414	88.58 371	82.44 401	94.17 377

IterMVS-LS		92.69 324	92.11 322	94.43 353	96.80 332	92.74 318	99.45 284	96.89 416	88.98 368	89.65 355	95.38 399	88.77 222	96.34 427	90.98 335	82.04 404	94.22 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dp		95.05 243	94.43 249	96.91 258	97.99 222	92.73 320	96.29 466	97.98 247	89.70 358	95.93 259	94.67 430	93.83 111	98.45 288	86.91 405	96.53 249	99.54 168

EI-MVSNet		93.73 296	93.40 287	94.74 334	96.80 332	92.69 321	99.06 339	97.67 281	88.96 370	91.39 325	99.02 199	88.75 223	97.30 361	91.07 331	87.85 356	94.22 372

CR-MVSNet		93.45 305	92.62 310	95.94 292	96.29 347	92.66 322	92.01 496	96.23 442	92.62 250	96.94 215	93.31 452	91.04 183	96.03 442	79.23 457	95.96 265	99.13 246

RPMNet		89.76 389	87.28 406	97.19 244	96.29 347	92.66 322	92.01 496	98.31 200	70.19 494	96.94 215	85.87 503	87.25 244	99.78 148	62.69 503	95.96 265	99.13 246

VDDNet		93.12 311	91.91 327	96.76 265	96.67 342	92.65 324	98.69 388	98.21 218	82.81 453	97.75 188	99.28 161	61.57 473	99.48 187	98.09 150	94.09 307	98.15 302

WR-MVS_H		91.30 351	90.35 355	94.15 363	94.17 406	92.62 325	99.17 325	98.94 44	88.87 374	86.48 421	94.46 437	84.36 303	96.61 409	88.19 382	78.51 432	93.21 437

CostFormer		96.10 199	95.88 192	96.78 264	97.03 308	92.55 326	97.08 449	97.83 265	90.04 353	98.72 135	94.89 424	95.01 67	98.29 310	96.54 221	95.77 273	99.50 179

AstraMVS		96.57 173	96.46 155	96.91 258	96.79 335	92.50 327	99.90 117	97.38 317	96.02 99	97.79 186	99.32 154	86.36 260	98.99 215	98.26 140	96.33 256	99.23 236

v1921920		90.46 371	89.12 381	94.50 347	92.96 432	92.46 328	99.49 275	96.98 403	86.10 419	89.61 358	95.30 403	78.55 375	97.03 382	82.17 439	80.89 419	94.01 402

test_djsdf		92.83 318	92.29 320	94.47 349	91.90 452	92.46 328	99.55 265	97.27 344	91.17 313	89.96 344	96.07 370	81.10 342	96.89 391	94.67 264	88.91 338	94.05 399

CP-MVSNet		91.23 355	90.22 359	94.26 358	93.96 409	92.39 330	99.09 332	98.57 107	88.95 371	86.42 422	96.57 353	79.19 367	96.37 425	90.29 350	78.95 429	94.02 400

BH-w/o		95.71 223	95.38 218	96.68 268	98.49 187	92.28 331	99.84 152	97.50 306	92.12 279	92.06 321	98.79 244	84.69 297	98.67 265	95.29 245	99.66 96	99.09 250

v1240		90.20 379	88.79 388	94.44 351	93.05 427	92.27 332	99.38 294	96.92 414	85.89 421	89.36 363	94.87 425	77.89 381	97.03 382	80.66 448	81.08 414	94.01 402

PS-MVSNAJss		93.64 299	93.31 292	94.61 339	92.11 449	92.19 333	99.12 328	97.38 317	92.51 263	88.45 385	96.99 335	91.20 178	97.29 364	94.36 269	87.71 358	94.36 357

test0.0.03 1		93.86 288	93.61 274	94.64 338	95.02 390	92.18 334	99.93 100	98.58 105	94.07 173	87.96 399	98.50 275	93.90 107	94.96 461	81.33 443	93.17 318	96.78 331

PMMVS		96.76 157	96.76 139	96.76 265	98.28 202	92.10 335	99.91 111	97.98 247	94.12 170	99.53 74	99.39 149	86.93 250	98.73 253	96.95 203	97.73 195	99.45 190

GBi-Net		90.88 361	89.82 367	94.08 369	97.53 264	91.97 336	98.43 404	96.95 407	87.05 405	89.68 352	94.72 426	71.34 432	96.11 437	87.01 402	85.65 374	94.17 377

test1		90.88 361	89.82 367	94.08 369	97.53 264	91.97 336	98.43 404	96.95 407	87.05 405	89.68 352	94.72 426	71.34 432	96.11 437	87.01 402	85.65 374	94.17 377

FMVSNet1		88.50 402	86.64 409	94.08 369	95.62 378	91.97 336	98.43 404	96.95 407	83.00 451	86.08 427	94.72 426	59.09 479	96.11 437	81.82 442	84.07 390	94.17 377

pm-mvs1		89.36 396	87.81 402	94.01 373	93.40 420	91.93 339	98.62 394	96.48 438	86.25 418	83.86 444	96.14 366	73.68 422	97.04 380	86.16 409	75.73 453	93.04 441

CSCG		97.10 136	97.04 126	97.27 243	99.89 51	91.92 340	99.90 117	99.07 37	88.67 379	95.26 277	99.82 54	93.17 130	99.98 52	98.15 146	99.47 125	99.90 96

wanda-best-256-512		87.82 410	85.71 417	94.15 363	86.66 489	91.88 341	99.76 193	97.08 384	79.46 470	88.37 392	92.36 465	78.01 378	96.43 420	88.39 377	61.26 498	94.14 387

FE-blended-shiyan7		87.82 410	85.71 417	94.15 363	86.66 489	91.88 341	99.76 193	97.08 384	79.46 470	88.37 392	92.36 465	78.01 378	96.43 420	88.39 377	61.26 498	94.14 387

HQP5-MVS						91.85 343

HQP-MVS		94.61 261	94.50 248	94.92 328	95.78 361	91.85 343	99.87 133	97.89 257	96.82 66	93.37 302	98.65 257	80.65 352	98.39 296	97.92 160	89.60 329	94.53 344

usedtu_blend_shiyan5		86.75 418	84.29 426	94.16 361	86.66 489	91.83 345	97.42 438	95.23 467	69.94 495	88.37 392	92.36 465	78.01 378	96.50 414	89.35 361	61.26 498	94.14 387

blend_shiyan4		90.13 383	88.79 388	94.17 360	87.12 485	91.83 345	99.75 199	97.08 384	79.27 474	88.69 379	92.53 460	92.25 161	96.50 414	89.35 361	73.04 462	94.18 376

blended_shiyan8		87.82 410	85.71 417	94.16 361	86.54 494	91.79 347	99.72 214	97.08 384	79.32 472	88.44 386	92.35 468	77.88 382	96.56 411	88.53 373	61.51 497	94.15 383

NP-MVS					95.77 364	91.79 347					98.65 257

TAPA-MVS	92.12 8	94.42 270	93.60 276	96.90 260	99.33 111	91.78 349	99.78 181	98.00 244	89.89 356	94.52 285	99.47 138	91.97 169	99.18 205	69.90 485	99.52 115	99.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HQP_MVS		94.49 268	94.36 251	94.87 329	95.71 371	91.74 350	99.84 152	97.87 259	96.38 86	93.01 307	98.59 265	80.47 356	98.37 302	97.79 171	89.55 332	94.52 346

plane_prior						91.74 350	99.86 144		96.76 70						89.59 331

F-COLMAP		96.93 148	96.95 129	96.87 261	99.71 84	91.74 350	99.85 147	97.95 250	93.11 223	95.72 266	99.16 186	92.35 157	99.94 95	95.32 244	99.35 137	98.92 270

blended_shiyan6		87.74 413	85.62 420	94.09 368	86.53 495	91.73 353	99.72 214	97.08 384	79.32 472	88.22 396	92.31 470	77.82 383	96.43 420	88.31 379	61.26 498	94.13 392

plane_prior6					95.76 365	91.72 354						80.47 356

PS-CasMVS		90.63 368	89.51 375	93.99 375	93.83 411	91.70 355	98.98 351	98.52 128	88.48 384	86.15 426	96.53 355	75.46 407	96.31 430	88.83 368	78.86 431	93.95 408

tpm2		95.47 231	95.18 226	96.35 281	96.91 324	91.70 355	96.96 452	97.93 252	88.04 393	98.44 151	95.40 396	93.32 122	97.97 331	94.00 276	95.61 283	99.38 201

dtuonly		93.89 287	93.16 296	96.08 288	94.37 400	91.67 357	99.15 327	95.04 472	91.79 293	94.74 281	98.72 249	81.01 344	98.31 307	87.29 395	96.33 256	98.27 300

icg_test_0407_2		95.04 244	94.78 243	95.84 299	96.97 317	91.64 358	98.63 393	97.12 372	92.33 271	95.60 267	98.88 227	85.65 271	96.56 411	92.12 313	95.70 278	99.32 214

IMVS_0407		95.21 238	94.80 242	96.46 275	96.97 317	91.64 358	98.81 376	97.12 372	92.33 271	95.60 267	98.88 227	85.65 271	98.42 290	92.12 313	95.70 278	99.32 214

IMVS_0404		93.83 289	93.17 295	95.80 301	96.97 317	91.64 358	97.78 434	97.12 372	92.33 271	90.87 332	98.88 227	76.78 393	96.43 420	92.12 313	95.70 278	99.32 214

IMVS_0403		95.25 237	94.81 241	96.58 272	96.97 317	91.64 358	98.97 356	97.12 372	92.33 271	95.43 272	98.88 227	85.78 269	98.79 244	92.12 313	95.70 278	99.32 214

plane_prior3						91.64 358			96.63 75	93.01 307

MIMVSNet		90.30 376	88.67 391	95.17 321	96.45 346	91.64 358	92.39 494	97.15 366	85.99 420	90.50 336	93.19 455	66.95 451	94.86 465	82.01 440	93.43 315	99.01 263

plane_prior7					95.71 371	91.59 364

gbinet_0.2-2-1-0.02		87.63 414	85.51 421	93.99 375	87.22 484	91.56 365	99.81 169	97.36 321	79.54 469	88.60 383	93.29 454	73.76 421	96.34 427	89.27 364	60.78 503	94.06 398

tpmvs		94.28 276	93.57 278	96.40 278	98.55 178	91.50 366	95.70 476	98.55 119	87.47 399	92.15 318	94.26 441	91.42 174	98.95 221	88.15 384	95.85 270	98.76 280

tpm cat1		93.51 302	92.52 317	96.47 273	97.77 237	91.47 367	96.13 468	98.06 238	80.98 462	92.91 310	93.78 446	89.66 205	98.87 226	87.03 401	96.39 254	99.09 250

h-mvs33		94.92 248	94.36 251	96.59 271	98.85 156	91.29 368	98.93 361	98.94 44	95.90 101	98.77 130	98.42 283	90.89 189	99.77 151	97.80 168	70.76 470	98.72 284

BH-untuned		95.18 239	94.83 239	96.22 284	98.36 195	91.22 369	99.80 175	97.32 332	90.91 322	91.08 328	98.67 254	83.51 312	98.54 282	94.23 274	99.61 105	98.92 270

TransMVSNet (Re)		87.25 415	85.28 423	93.16 400	93.56 415	91.03 370	98.54 398	94.05 488	83.69 445	81.09 458	96.16 364	75.32 408	96.40 424	76.69 472	68.41 480	92.06 458

WAC-MVS						90.97 371								86.10 411

myMVS_eth3d		94.46 269	94.76 244	93.55 391	97.68 249	90.97 371	99.71 219	98.35 191	90.79 330	92.10 319	98.67 254	92.46 155	93.09 483	87.13 398	95.95 267	96.59 334

v148		90.70 365	89.63 370	93.92 378	92.97 431	90.97 371	99.75 199	96.89 416	87.51 398	88.27 395	95.01 418	81.67 334	97.04 380	87.40 393	77.17 445	93.75 421

jajsoiax		91.92 339	91.18 342	94.15 363	91.35 460	90.95 374	99.00 349	97.42 313	92.61 251	87.38 409	97.08 328	72.46 427	97.36 354	94.53 267	88.77 342	94.13 392

PEN-MVS		90.19 380	89.06 383	93.57 390	93.06 426	90.90 375	99.06 339	98.47 140	88.11 391	85.91 428	96.30 360	76.67 394	95.94 445	87.07 399	76.91 447	93.89 413

sd_testset		93.55 301	92.83 305	95.74 303	98.92 147	90.89 376	98.24 415	98.85 62	92.41 266	92.55 315	97.85 309	71.07 436	98.68 263	93.93 279	91.62 324	97.64 318

OPM-MVS		93.21 307	92.80 306	94.44 351	93.12 424	90.85 377	99.77 187	97.61 291	96.19 95	91.56 324	98.65 257	75.16 413	98.47 284	93.78 288	89.39 335	93.99 405
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MonoMVSNet		94.82 249	94.43 249	95.98 290	94.54 397	90.73 378	99.03 346	97.06 393	93.16 218	93.15 306	95.47 393	88.29 226	97.57 348	97.85 165	91.33 326	99.62 147

CLD-MVS		94.06 285	93.90 268	94.55 344	96.02 355	90.69 379	99.98 24	97.72 277	96.62 77	91.05 330	98.85 239	77.21 384	98.47 284	98.11 148	89.51 334	94.48 348
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
eth_miper_zixun_eth		92.41 331	91.93 326	93.84 382	97.28 293	90.68 380	98.83 374	96.97 405	88.57 382	89.19 371	95.73 379	89.24 215	96.69 406	89.97 355	81.55 407	94.15 383

Anonymous20231211		89.86 387	88.44 395	94.13 367	98.93 144	90.68 380	98.54 398	98.26 208	76.28 479	86.73 415	95.54 387	70.60 437	97.56 349	90.82 339	80.27 424	94.15 383

Anonymous20240529		92.10 337	90.65 349	96.47 273	98.82 157	90.61 382	98.72 384	98.67 87	75.54 483	93.90 299	98.58 268	66.23 455	99.90 114	94.70 263	90.67 327	98.90 273

mvs_tets		91.81 341	91.08 344	94.00 374	91.63 457	90.58 383	98.67 390	97.43 311	92.43 265	87.37 410	97.05 331	71.76 429	97.32 359	94.75 261	88.68 344	94.11 394

v7n		89.65 391	88.29 397	93.72 384	92.22 447	90.56 384	99.07 338	97.10 380	85.42 430	86.73 415	94.72 426	80.06 359	97.13 371	81.14 444	78.12 436	93.49 429

Patchmatch-test		92.65 326	91.50 337	96.10 287	96.85 329	90.49 385	91.50 499	97.19 357	82.76 454	90.23 338	95.59 385	95.02 66	98.00 330	77.41 468	96.98 237	99.82 107

PVSNet_0	88.03 19	91.80 344	90.27 358	96.38 280	98.27 203	90.46 386	99.94 93	99.61 13	93.99 178	86.26 425	97.39 320	71.13 435	99.89 119	98.77 106	67.05 484	98.79 279

ppachtmachnet_test		89.58 393	88.35 396	93.25 399	92.40 445	90.44 387	99.33 301	96.73 427	85.49 428	85.90 429	95.77 375	81.09 343	96.00 444	76.00 475	82.49 400	93.30 434

IterMVS		90.91 360	90.17 362	93.12 401	96.78 336	90.42 388	98.89 365	97.05 396	89.03 365	86.49 420	95.42 395	76.59 396	95.02 459	87.22 397	84.09 389	93.93 410
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS-HIRNet		86.22 420	83.19 436	95.31 317	96.71 339	90.29 389	92.12 495	97.33 326	62.85 503	86.82 414	70.37 518	69.37 440	97.49 351	75.12 476	97.99 192	98.15 302

testing3		93.92 286	94.23 256	92.99 405	97.54 263	90.23 390	99.99 8	99.16 33	90.57 337	91.33 327	98.63 261	92.99 133	92.52 487	82.46 436	95.39 288	96.22 339

VDD-MVS		93.77 294	92.94 303	96.27 283	98.55 178	90.22 391	98.77 381	97.79 267	90.85 324	96.82 222	99.42 142	61.18 475	99.77 151	98.95 91	94.13 306	98.82 277

PatchT		90.38 373	88.75 390	95.25 319	95.99 356	90.16 392	91.22 501	97.54 300	76.80 478	97.26 203	86.01 502	91.88 170	96.07 441	66.16 495	95.91 269	99.51 177

LTVRE_ROB	88.28 18	90.29 377	89.05 384	94.02 372	95.08 388	90.15 393	97.19 445	97.43 311	84.91 436	83.99 443	97.06 330	74.00 420	98.28 312	84.08 423	87.71 358	93.62 427
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		93.28 306	92.60 311	95.34 315	98.29 200	90.09 394	99.31 306	98.56 113	91.80 292	96.35 246	98.00 300	89.38 210	98.28 312	92.46 308	69.22 477	97.64 318

hse-mvs2		94.38 271	94.08 262	95.31 317	98.27 203	90.02 395	99.29 313	98.56 113	95.90 101	98.77 130	98.00 300	90.89 189	98.26 316	97.80 168	69.20 478	97.64 318

UWE-MVS-28		95.95 206	96.49 152	94.34 356	98.51 183	89.99 396	99.39 292	98.57 107	93.14 220	97.33 200	98.31 290	93.44 117	94.68 467	93.69 292	95.98 264	98.34 298

IterMVS-SCA-FT		90.85 363	90.16 363	92.93 406	96.72 338	89.96 397	98.89 365	96.99 401	88.95 371	86.63 417	95.67 380	76.48 398	95.00 460	87.04 400	84.04 392	93.84 417

DTE-MVSNet		89.40 395	88.24 398	92.88 407	92.66 441	89.95 398	99.10 331	98.22 214	87.29 402	85.12 434	96.22 362	76.27 401	95.30 458	83.56 429	75.74 452	93.41 430

Baseline_NR-MVSNet		90.33 375	89.51 375	92.81 409	92.84 435	89.95 398	99.77 187	93.94 489	84.69 438	89.04 373	95.66 381	81.66 335	96.52 413	90.99 334	76.98 446	91.97 460

Patchmtry		89.70 390	88.49 394	93.33 395	96.24 350	89.94 400	91.37 500	96.23 442	78.22 476	87.69 402	93.31 452	91.04 183	96.03 442	80.18 454	82.10 403	94.02 400

pmmvs5		90.17 381	89.09 382	93.40 393	92.10 450	89.77 401	99.74 203	95.58 459	85.88 422	87.24 412	95.74 376	73.41 425	96.48 417	88.54 372	83.56 394	93.95 408

Anonymous202405211		93.10 312	91.99 325	96.40 278	99.10 126	89.65 402	98.88 367	97.93 252	83.71 444	94.00 297	98.75 246	68.79 441	99.88 125	95.08 249	91.71 323	99.68 131

our_test_3		90.39 372	89.48 377	93.12 401	92.40 445	89.57 403	99.33 301	96.35 441	87.84 396	85.30 432	94.99 421	84.14 307	96.09 440	80.38 451	84.56 385	93.71 426

kuosan		93.17 309	92.60 311	94.86 332	98.40 191	89.54 404	98.44 403	98.53 126	84.46 439	88.49 384	97.92 305	90.57 193	97.05 377	83.10 431	93.49 314	97.99 307

D2MVS		92.76 321	92.59 315	93.27 397	95.13 386	89.54 404	99.69 229	99.38 22	92.26 276	87.59 404	94.61 432	85.05 286	97.79 340	91.59 324	88.01 354	92.47 452

XVG-OURS-SEG-HR		94.79 252	94.70 246	95.08 322	98.05 219	89.19 406	99.08 334	97.54 300	93.66 194	94.87 280	99.58 128	78.78 371	99.79 146	97.31 185	93.40 316	96.25 336

XVG-OURS		94.82 249	94.74 245	95.06 323	98.00 221	89.19 406	99.08 334	97.55 298	94.10 171	94.71 282	99.62 123	80.51 354	99.74 157	96.04 233	93.06 321	96.25 336

miper_lstm_enhance		91.81 341	91.39 340	93.06 404	97.34 286	89.18 408	99.38 294	96.79 424	86.70 413	87.47 407	95.22 409	90.00 202	95.86 446	88.26 380	81.37 409	94.15 383

MVStest1		85.03 431	82.76 440	91.83 421	92.95 433	89.16 409	98.57 395	94.82 475	71.68 491	68.54 497	95.11 413	83.17 322	95.66 450	74.69 477	65.32 487	90.65 471

ACMM	91.95 10	92.88 317	92.52 317	93.98 377	95.75 367	89.08 410	99.77 187	97.52 304	93.00 227	89.95 345	97.99 302	76.17 402	98.46 287	93.63 293	88.87 340	94.39 356
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
viewdifsd2359ckpt11		94.09 282	93.63 273	95.46 310	96.68 340	88.92 411	99.62 243	97.12 372	93.07 224	95.73 264	99.22 175	77.05 386	98.88 225	96.52 223	87.69 361	98.58 289

viewmsd2359difaftdt		94.09 282	93.64 272	95.46 310	96.68 340	88.92 411	99.62 243	97.13 371	93.07 224	95.73 264	99.22 175	77.05 386	98.89 224	96.52 223	87.70 360	98.58 289

MVP-Stereo		90.93 359	90.45 354	92.37 415	91.25 462	88.76 413	98.05 426	96.17 444	87.27 403	84.04 441	95.30 403	78.46 376	97.27 366	83.78 427	99.70 93	91.09 465
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_vis1_n_1920		95.44 232	95.31 220	95.82 300	98.50 185	88.74 414	99.98 24	97.30 334	97.84 28	99.85 20	99.19 181	66.82 453	99.97 65	98.82 102	99.46 127	98.76 280

ACMP	92.05 9	92.74 322	92.42 319	93.73 383	95.91 359	88.72 415	99.81 169	97.53 302	94.13 169	87.00 413	98.23 293	74.07 419	98.47 284	96.22 230	88.86 341	93.99 405
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		92.96 314	92.71 309	93.71 385	95.43 382	88.67 416	99.75 199	97.62 288	92.81 236	90.05 341	98.49 276	75.24 409	98.40 294	95.84 237	89.12 336	94.07 396

LGP-MVS_train				93.71 385	95.43 382	88.67 416		97.62 288	92.81 236	90.05 341	98.49 276	75.24 409	98.40 294	95.84 237	89.12 336	94.07 396

ACMH	89.72 17	90.64 367	89.63 370	93.66 389	95.64 376	88.64 418	98.55 396	97.45 309	89.03 365	81.62 454	97.61 313	69.75 439	98.41 292	89.37 360	87.62 362	93.92 411
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MDA-MVSNet_test_wron		85.51 426	83.32 435	92.10 417	90.96 463	88.58 419	99.20 322	96.52 436	79.70 467	57.12 510	92.69 458	79.11 368	93.86 475	77.10 470	77.46 442	93.86 416

AllTest		92.48 329	91.64 332	95.00 325	99.01 132	88.43 420	98.94 359	96.82 422	86.50 414	88.71 377	98.47 280	74.73 415	99.88 125	85.39 414	96.18 259	96.71 332

TestCases				95.00 325	99.01 132	88.43 420		96.82 422	86.50 414	88.71 377	98.47 280	74.73 415	99.88 125	85.39 414	96.18 259	96.71 332

FMVSNet5		88.32 403	87.47 405	90.88 428	96.90 327	88.39 422	97.28 443	95.68 456	82.60 455	84.67 438	92.40 464	79.83 361	91.16 493	76.39 473	81.51 408	93.09 439

YYNet1		85.50 427	83.33 434	92.00 418	90.89 464	88.38 423	99.22 321	96.55 435	79.60 468	57.26 509	92.72 457	79.09 370	93.78 477	77.25 469	77.37 443	93.84 417

USDC		90.00 385	88.96 385	93.10 403	94.81 392	88.16 424	98.71 385	95.54 460	93.66 194	83.75 445	97.20 324	65.58 457	98.31 307	83.96 426	87.49 364	92.85 445

UniMVSNet_ETH3D		90.06 384	88.58 393	94.49 348	94.67 395	88.09 425	97.81 433	97.57 296	83.91 443	88.44 386	97.41 318	57.44 481	97.62 347	91.41 326	88.59 347	97.77 314

COLMAP_ROB	90.47 14	92.18 336	91.49 338	94.25 359	99.00 136	88.04 426	98.42 407	96.70 429	82.30 456	88.43 389	99.01 201	76.97 390	99.85 131	86.11 410	96.50 250	94.86 343
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MDA-MVSNet-bldmvs		84.09 439	81.52 446	91.81 422	91.32 461	88.00 427	98.67 390	95.92 450	80.22 465	55.60 511	93.32 451	68.29 446	93.60 479	73.76 478	76.61 449	93.82 419

FE-MVSNET2		83.57 444	81.36 447	90.20 440	82.83 511	87.59 428	98.28 413	96.04 447	85.33 431	74.13 488	87.45 495	59.16 478	93.26 482	79.12 461	69.91 472	89.77 483

tt0805		91.28 353	90.18 361	94.60 340	96.26 349	87.55 429	98.39 409	98.72 78	89.00 367	89.22 368	98.47 280	62.98 468	98.96 220	90.57 343	88.00 355	97.28 328

JIA-IIPM		91.76 347	90.70 348	94.94 327	96.11 352	87.51 430	93.16 491	98.13 233	75.79 482	97.58 190	77.68 513	92.84 138	97.97 331	88.47 376	96.54 248	99.33 212

tpm		93.70 298	93.41 286	94.58 342	95.36 384	87.41 431	97.01 450	96.90 415	90.85 324	96.72 226	94.14 443	90.40 197	96.84 395	90.75 341	88.54 348	99.51 177

ttmdpeth		88.23 405	87.06 408	91.75 423	89.91 474	87.35 432	98.92 364	95.73 453	87.92 394	84.02 442	96.31 359	68.23 447	96.84 395	86.33 407	76.12 450	91.06 466

dcpmvs_2		97.42 121	98.09 63	95.42 312	99.58 97	87.24 433	99.23 320	96.95 407	94.28 163	98.93 121	99.73 92	94.39 88	99.16 208	99.89 22	99.82 85	99.86 102

pmmvs-eth3d		84.03 440	81.97 444	90.20 440	84.15 505	87.09 434	98.10 424	94.73 478	83.05 450	74.10 489	87.77 493	65.56 458	94.01 472	81.08 445	69.24 476	89.49 487

test_vis1_n		93.61 300	93.03 300	95.35 314	95.86 360	86.94 435	99.87 133	96.36 440	96.85 64	99.54 73	98.79 244	52.41 488	99.83 141	98.64 115	98.97 155	99.29 224

CVMVSNet		94.68 259	94.94 237	93.89 381	96.80 332	86.92 436	99.06 339	98.98 41	94.45 148	94.23 295	99.02 199	85.60 274	95.31 457	90.91 337	95.39 288	99.43 194

patch_mono-2		98.24 69	99.12 5	95.59 305	99.67 89	86.91 437	99.95 75	98.89 52	97.60 34	99.90 7	99.76 73	96.54 34	99.98 52	99.94 15	99.82 85	99.88 98

dongtai		91.55 350	91.13 343	92.82 408	98.16 212	86.35 438	99.47 279	98.51 131	83.24 447	85.07 436	97.56 314	90.33 198	94.94 462	76.09 474	91.73 322	97.18 329

Fast-Effi-MVS+-dtu		93.72 297	93.86 270	93.29 396	97.06 306	86.16 439	99.80 175	96.83 420	92.66 248	92.58 314	97.83 311	81.39 338	97.67 345	89.75 357	96.87 239	96.05 341

SSC-MVS3.2		89.59 392	88.66 392	92.38 413	94.29 404	86.12 440	99.49 275	97.66 284	90.28 349	88.63 382	95.18 410	64.46 462	96.88 393	85.30 416	82.66 398	94.14 387

ACMH+	89.98 16	90.35 374	89.54 373	92.78 410	95.99 356	86.12 440	98.81 376	97.18 359	89.38 360	83.14 447	97.76 312	68.42 445	98.43 289	89.11 366	86.05 372	93.78 420

ADS-MVSNet2		93.80 293	93.88 269	93.55 391	97.87 229	85.94 442	94.24 478	96.84 419	90.07 351	96.43 242	94.48 435	90.29 200	95.37 455	87.44 391	97.23 213	99.36 205

XVG-ACMP-BASELINE		91.22 356	90.75 347	92.63 412	93.73 413	85.61 443	98.52 400	97.44 310	92.77 240	89.90 347	96.85 342	66.64 454	98.39 296	92.29 310	88.61 345	93.89 413

TinyColmap		87.87 409	86.51 410	91.94 419	95.05 389	85.57 444	97.65 436	94.08 486	84.40 440	81.82 453	96.85 342	62.14 471	98.33 305	80.25 453	86.37 369	91.91 461

MS-PatchMatch		90.65 366	90.30 357	91.71 424	94.22 405	85.50 445	98.24 415	97.70 278	88.67 379	86.42 422	96.37 358	67.82 448	98.03 329	83.62 428	99.62 100	91.60 462

ITE_SJBPF				92.38 413	95.69 374	85.14 446		95.71 455	92.81 236	89.33 365	98.11 296	70.23 438	98.42 290	85.91 412	88.16 353	93.59 428

test_0402		85.58 424	83.94 430	90.50 436	93.81 412	85.04 447	98.55 396	95.20 469	76.01 480	79.72 467	95.13 411	64.15 464	96.26 432	66.04 497	86.88 366	90.21 476

test_fmvs1		95.35 235	95.68 201	94.36 355	98.99 137	84.98 448	99.96 56	96.65 431	97.60 34	99.73 47	98.96 214	71.58 431	99.93 105	98.31 136	99.37 135	98.17 301

testgi		89.01 399	88.04 400	91.90 420	93.49 417	84.89 449	99.73 210	95.66 457	93.89 187	85.14 433	98.17 294	59.68 477	94.66 468	77.73 467	88.88 339	96.16 340

mvs5depth		84.87 433	82.90 439	90.77 432	85.59 499	84.84 450	91.10 502	93.29 495	83.14 449	85.07 436	94.33 440	62.17 470	97.32 359	78.83 463	72.59 467	90.14 478

TDRefinement		84.76 434	82.56 441	91.38 426	74.58 524	84.80 451	97.36 442	94.56 482	84.73 437	80.21 463	96.12 369	63.56 465	98.39 296	87.92 387	63.97 491	90.95 469

pmmvs6		85.69 423	83.84 431	91.26 427	90.00 473	84.41 452	97.82 432	96.15 445	75.86 481	81.29 457	95.39 398	61.21 474	96.87 394	83.52 430	73.29 460	92.50 451

MIMVSNet1		82.58 447	80.51 452	88.78 452	86.68 488	84.20 453	96.65 458	95.41 463	78.75 475	78.59 472	92.44 461	51.88 489	89.76 499	65.26 498	78.95 429	92.38 455

dmvs_re		93.20 308	93.15 297	93.34 394	96.54 343	83.81 454	98.71 385	98.51 131	91.39 309	92.37 317	98.56 270	78.66 373	97.83 339	93.89 280	89.74 328	98.38 296

FE-MVSNET		81.05 451	78.81 459	87.79 461	81.98 512	83.70 455	98.23 417	91.78 502	81.27 460	74.29 487	87.44 496	60.92 476	90.67 498	64.92 499	68.43 479	89.01 492

test_fmvs1_n		94.25 277	94.36 251	93.92 378	97.68 249	83.70 455	99.90 117	96.57 434	97.40 40	99.67 53	98.88 227	61.82 472	99.92 111	98.23 142	99.13 147	98.14 304

tt0320		83.56 445	81.15 448	90.77 432	92.77 440	83.58 457	96.83 456	95.52 461	63.26 501	81.36 456	92.54 459	53.26 486	95.77 448	80.45 449	74.38 457	92.96 442

tt0320-xc		82.94 446	80.35 453	90.72 434	92.90 434	83.54 458	96.85 455	94.73 478	63.12 502	79.85 466	93.77 447	49.43 494	95.46 453	80.98 447	71.54 468	93.16 438

UnsupCasMVSNet_eth		85.52 425	83.99 428	90.10 442	89.36 477	83.51 459	96.65 458	97.99 245	89.14 362	75.89 483	93.83 445	63.25 467	93.92 473	81.92 441	67.90 483	92.88 444

mmtdpeth		88.52 401	87.75 403	90.85 430	95.71 371	83.47 460	98.94 359	94.85 474	88.78 376	97.19 205	89.58 483	63.29 466	98.97 218	98.54 120	62.86 493	90.10 479

sc_t1		85.01 432	82.46 442	92.67 411	92.44 444	83.09 461	97.39 441	95.72 454	65.06 499	85.64 431	96.16 364	49.50 493	97.34 356	84.86 420	75.39 454	97.57 323

OpenMVS_ROB	79.82 20	83.77 442	81.68 445	90.03 443	88.30 481	82.82 462	98.46 401	95.22 468	73.92 488	76.00 482	91.29 473	55.00 483	96.94 387	68.40 488	88.51 349	90.34 473

Anonymous20240521		85.15 430	83.81 432	89.16 449	88.32 480	82.69 463	98.80 379	95.74 452	79.72 466	81.53 455	90.99 474	65.38 459	94.16 471	72.69 480	81.11 413	90.63 472

new_pmnet		84.49 438	82.92 438	89.21 448	90.03 472	82.60 464	96.89 454	95.62 458	80.59 463	75.77 484	89.17 485	65.04 461	94.79 466	72.12 482	81.02 416	90.23 475

Effi-MVS+-dtu		94.53 264	95.30 221	92.22 416	97.77 237	82.54 465	99.59 252	97.06 393	94.92 128	95.29 275	95.37 400	85.81 268	97.89 337	94.80 259	97.07 227	96.23 338

pmmvs3		80.27 454	77.77 460	87.76 462	80.32 517	82.43 466	98.23 417	91.97 500	72.74 490	78.75 470	87.97 492	57.30 482	90.99 495	70.31 484	62.37 495	89.87 481

SixPastTwentyTwo		88.73 400	88.01 401	90.88 428	91.85 453	82.24 467	98.22 419	95.18 470	88.97 369	82.26 450	96.89 339	71.75 430	96.67 407	84.00 424	82.98 395	93.72 425

K. test v3		88.05 406	87.24 407	90.47 437	91.82 455	82.23 468	98.96 357	97.42 313	89.05 364	76.93 479	95.60 384	68.49 444	95.42 454	85.87 413	81.01 417	93.75 421

UnsupCasMVSNet_bld		79.97 457	77.03 463	88.78 452	85.62 498	81.98 469	93.66 484	97.35 322	75.51 484	70.79 493	83.05 506	48.70 495	94.91 463	78.31 465	60.29 505	89.46 488

EG-PatchMatch MVS		85.35 428	83.81 432	89.99 444	90.39 468	81.89 470	98.21 420	96.09 446	81.78 458	74.73 485	93.72 448	51.56 490	97.12 373	79.16 460	88.61 345	90.96 468

CL-MVSNet_self_test		84.50 437	83.15 437	88.53 455	86.00 496	81.79 471	98.82 375	97.35 322	85.12 432	83.62 446	90.91 476	76.66 395	91.40 492	69.53 486	60.36 504	92.40 453

DeepPCF-MVS	95.94 2	97.71 107	98.98 13	93.92 378	99.63 91	81.76 472	99.96 56	98.56 113	99.47 1	99.19 104	99.99 1	94.16 100	100.00 1	99.92 17	99.93 65	100.00 1

EGC-MVSNET		69.38 468	63.76 480	86.26 467	90.32 469	81.66 473	96.24 467	93.85 490	0.99 551	3.22 553	92.33 469	52.44 487	92.92 485	59.53 510	84.90 382	84.21 505

OurMVSNet-221017-0		89.81 388	89.48 377	90.83 431	91.64 456	81.21 474	98.17 421	95.38 464	91.48 302	85.65 430	97.31 321	72.66 426	97.29 364	88.15 384	84.83 383	93.97 407

LF4IMVS		89.25 398	88.85 386	90.45 438	92.81 439	81.19 475	98.12 422	94.79 476	91.44 304	86.29 424	97.11 326	65.30 460	98.11 323	88.53 373	85.25 378	92.07 457

EU-MVSNet		90.14 382	90.34 356	89.54 446	92.55 442	81.06 476	98.69 388	98.04 241	91.41 308	86.59 418	96.84 344	80.83 348	93.31 481	86.20 408	81.91 405	94.26 365

lessismore_v0				90.53 435	90.58 467	80.90 477		95.80 451		77.01 478	95.84 373	66.15 456	96.95 386	83.03 432	75.05 455	93.74 424

KD-MVS_self_test		83.59 443	82.06 443	88.20 459	86.93 486	80.70 478	97.21 444	96.38 439	82.87 452	82.49 449	88.97 486	67.63 449	92.32 488	73.75 479	62.30 496	91.58 463

test20.03		84.72 436	83.99 428	86.91 464	88.19 482	80.62 479	98.88 367	95.94 449	88.36 387	78.87 469	94.62 431	68.75 442	89.11 502	66.52 494	75.82 451	91.00 467

Anonymous20231206		86.32 419	85.42 422	89.02 450	89.11 478	80.53 480	99.05 343	95.28 465	85.43 429	82.82 448	93.92 444	74.40 417	93.44 480	66.99 492	81.83 406	93.08 440

new-patchmatchnet		81.19 449	79.34 457	86.76 465	82.86 510	80.36 481	97.92 428	95.27 466	82.09 457	72.02 491	86.87 499	62.81 469	90.74 497	71.10 483	63.08 492	89.19 490

dtuonlycased		86.10 421	85.82 416	86.95 463	91.84 454	79.57 482	99.27 316	94.89 473	86.79 412	79.46 468	94.46 437	66.85 452	90.93 496	80.41 450	78.44 433	90.34 473

LCM-MVSNet-Re		92.31 333	92.60 311	91.43 425	97.53 264	79.27 483	99.02 348	91.83 501	92.07 280	80.31 462	94.38 439	83.50 313	95.48 452	97.22 191	97.58 200	99.54 168

test_vis1_rt		86.87 417	86.05 414	89.34 447	96.12 351	78.07 484	99.87 133	83.54 518	92.03 283	78.21 474	89.51 484	45.80 496	99.91 112	96.25 229	93.11 320	90.03 480

SD_0403		92.63 327	93.38 288	90.40 439	97.32 289	77.91 485	97.75 435	98.03 243	91.89 286	90.83 333	98.29 292	82.00 329	93.79 476	88.51 375	95.75 275	99.52 173

ArgMatch-Sym		85.85 422	85.07 425	88.21 458	92.84 435	77.63 486	98.42 407	94.70 480	89.91 354	84.33 440	96.72 347	51.42 491	94.89 464	82.48 435	74.80 456	92.10 456

ArgMatch-SfM		85.25 429	84.17 427	88.48 456	92.99 430	77.23 487	97.92 428	94.24 484	90.50 339	85.08 435	95.65 382	49.84 492	95.83 447	81.06 446	70.22 471	92.39 454

test_fmvs2		89.47 394	89.70 369	88.77 454	94.54 397	75.74 488	99.83 160	94.70 480	94.71 137	91.08 328	96.82 346	54.46 484	97.78 342	92.87 305	88.27 351	92.80 446

Patchmatch-RL test		86.90 416	85.98 415	89.67 445	84.45 503	75.59 489	89.71 507	92.43 497	86.89 410	77.83 476	90.94 475	94.22 96	93.63 478	87.75 389	69.61 474	99.79 112

usedtu_dtu_shiyan2		75.87 462	72.37 467	86.39 466	76.18 522	75.49 490	96.53 460	93.82 491	64.74 500	72.53 490	88.48 488	37.67 500	91.12 494	64.13 500	57.22 508	92.56 448

DSMNet-mixed		88.28 404	88.24 398	88.42 457	89.64 475	75.38 491	98.06 425	89.86 506	85.59 427	88.20 397	92.14 471	76.15 403	91.95 491	78.46 464	96.05 262	97.92 308

Syy-MVS		90.00 385	90.63 350	88.11 460	97.68 249	74.66 492	99.71 219	98.35 191	90.79 330	92.10 319	98.67 254	79.10 369	93.09 483	63.35 501	95.95 267	96.59 334

PM-MVS		80.47 453	78.88 458	85.26 468	83.79 508	72.22 493	95.89 474	91.08 503	85.71 426	76.56 481	88.30 489	36.64 502	93.90 474	82.39 437	69.57 475	89.66 486

DenseAffine		75.91 461	73.39 465	83.47 473	89.52 476	71.86 494	93.39 490	89.29 511	71.44 492	66.83 498	90.32 480	30.65 504	89.67 500	68.20 489	60.88 502	88.88 493

mvsany_test3		82.12 448	81.14 449	85.06 469	81.87 513	70.41 495	97.09 448	92.14 499	91.27 311	77.84 475	88.73 487	39.31 499	95.49 451	90.75 341	71.24 469	89.29 489

RPSCF		91.80 344	92.79 307	88.83 451	98.15 213	69.87 496	98.11 423	96.60 433	83.93 442	94.33 292	99.27 165	79.60 363	99.46 190	91.99 318	93.16 319	97.18 329

Gipuma		66.95 477	65.00 477	72.79 492	91.52 458	67.96 497	66.16 529	95.15 471	47.89 516	58.54 508	67.99 525	29.74 507	87.54 507	50.20 517	77.83 438	62.87 524
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LoFTR		74.41 465	70.88 468	84.99 470	86.56 493	67.85 498	93.74 483	89.63 508	69.46 496	54.95 512	87.39 497	30.76 503	96.92 388	61.37 505	64.06 490	90.19 477

RoMa-SfM		74.91 464	72.77 466	81.35 478	88.00 483	67.35 499	93.55 487	86.23 516	68.27 497	66.79 499	92.92 456	30.40 505	87.68 504	66.14 496	62.62 494	89.02 491

test_method		80.79 452	79.70 455	84.08 471	92.83 437	67.06 500	99.51 271	95.42 462	54.34 513	81.07 459	93.53 449	44.48 497	92.22 490	78.90 462	77.23 444	92.94 443

DKM		72.18 466	69.80 469	79.34 481	86.79 487	65.15 501	92.70 492	84.00 517	67.67 498	61.97 503	89.63 482	23.69 521	85.17 510	67.39 491	54.35 513	87.70 497

MatchFormer		70.84 467	66.72 474	83.19 475	85.99 497	64.61 502	93.58 486	88.62 512	59.32 508	50.64 515	82.31 510	28.00 510	96.79 400	52.52 516	59.50 506	88.18 494

test_fmvs3		79.99 456	80.17 454	79.45 480	84.02 507	62.83 503	99.05 343	93.49 494	88.29 389	80.06 465	86.65 500	28.09 509	88.00 503	88.63 369	73.27 461	87.54 499

ambc				83.23 474	77.17 520	62.61 504	87.38 509	94.55 483		76.72 480	86.65 500	30.16 506	96.36 426	84.85 421	69.86 473	90.73 470

CMPMVS	61.59 21	84.75 435	85.14 424	83.57 472	90.32 469	62.54 505	96.98 451	97.59 295	74.33 487	69.95 494	96.66 348	64.17 463	98.32 306	87.88 388	88.41 350	89.84 482
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_f		78.40 459	77.59 461	80.81 479	80.82 515	62.48 506	96.96 452	93.08 496	83.44 446	74.57 486	84.57 505	27.95 511	92.63 486	84.15 422	72.79 463	87.32 500

PMMVS2		67.15 476	64.15 479	76.14 487	70.56 530	62.07 507	93.89 481	87.52 513	58.09 509	60.02 504	78.32 512	22.38 523	84.54 511	59.56 509	47.03 520	81.80 510

DKM-HiRes		68.91 470	66.34 476	76.62 486	84.17 504	60.69 508	90.78 506	78.55 521	62.17 505	58.82 507	87.54 494	20.94 525	82.56 514	63.05 502	51.00 517	86.61 501

test_vis3_rt		68.82 471	66.69 475	75.21 490	76.24 521	60.41 509	96.44 462	68.71 526	75.13 485	50.54 516	69.52 521	16.42 534	96.32 429	80.27 452	66.92 485	68.89 521

RoMa-HiRes		69.18 469	67.02 471	75.65 488	83.52 509	60.31 510	90.80 505	76.82 523	62.46 504	62.85 501	90.44 479	24.75 518	83.07 512	60.58 507	50.97 518	83.58 506

APD_test1		81.15 450	80.92 450	81.86 477	92.45 443	59.76 511	96.04 471	93.61 493	73.29 489	77.06 477	96.64 350	44.28 498	96.16 436	72.35 481	82.52 399	89.67 485

DeepMVS_CX				82.92 476	95.98 358	58.66 512		96.01 448	92.72 242	78.34 473	95.51 390	58.29 480	98.08 325	82.57 434	85.29 377	92.03 459

ANet_high		56.10 484	52.24 492	67.66 500	49.27 553	56.82 513	83.94 517	82.02 519	70.47 493	33.28 536	64.54 529	17.23 533	69.16 527	45.59 520	23.85 537	77.02 518

PDCNetPlus		59.83 481	57.26 484	67.55 501	76.18 522	56.71 514	87.01 510	45.27 541	59.54 507	48.80 518	83.01 507	26.63 513	76.54 522	62.12 504	26.78 533	69.40 520

LCM-MVSNet		67.77 475	64.73 478	76.87 485	62.95 540	56.25 515	89.37 508	93.74 492	44.53 517	61.99 502	80.74 511	20.42 530	86.53 509	69.37 487	59.50 506	87.84 496

WB-MVS		76.28 460	77.28 462	73.29 491	81.18 514	54.68 516	97.87 431	94.19 485	81.30 459	69.43 495	90.70 477	77.02 389	82.06 515	35.71 524	68.11 482	83.13 507

SSC-MVS		75.42 463	76.40 464	72.49 496	80.68 516	53.62 517	97.42 438	94.06 487	80.42 464	68.75 496	90.14 481	76.54 397	81.66 516	33.25 525	66.34 486	82.19 508

ELoFTR		64.32 479	60.56 482	75.60 489	73.46 527	53.20 518	86.50 514	80.09 520	60.74 506	45.95 521	82.48 509	16.05 535	89.20 501	56.48 515	43.34 522	84.38 504

PMatch-SfM		62.12 480	58.57 483	72.76 495	74.34 525	52.97 519	84.95 516	65.57 527	56.89 510	46.61 520	85.70 504	9.51 544	80.54 518	60.53 508	43.03 523	84.77 502

MVE	53.74 22	51.54 494	47.86 498	62.60 503	59.56 547	50.93 520	79.41 524	77.69 522	35.69 523	36.27 533	61.76 533	5.79 555	69.63 526	37.97 523	36.61 526	67.24 522
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MASt3R-SfM		78.94 458	79.57 456	77.07 483	84.15 505	50.74 521	91.56 498	92.34 498	83.22 448	80.84 460	94.16 442	36.67 501	92.30 489	79.45 456	73.71 459	88.16 495

testf1		68.38 473	66.92 472	72.78 493	78.80 518	50.36 522	90.95 503	87.35 514	55.47 511	58.95 505	88.14 490	20.64 528	87.60 505	57.28 511	64.69 488	80.39 515

APD_test2		68.38 473	66.92 472	72.78 493	78.80 518	50.36 522	90.95 503	87.35 514	55.47 511	58.95 505	88.14 490	20.64 528	87.60 505	57.28 511	64.69 488	80.39 515

tmp_tt		65.23 478	62.94 481	72.13 497	44.90 554	50.03 524	81.05 523	89.42 510	38.45 519	48.51 519	99.90 23	54.09 485	78.70 520	91.84 322	18.26 541	87.64 498

dmvs_testset		83.79 441	86.07 413	76.94 484	92.14 448	48.60 525	96.75 457	90.27 505	89.48 359	78.65 471	98.55 272	79.25 365	86.65 508	66.85 493	82.69 397	95.57 342

PMatch-Up-SfM		57.92 482	53.93 486	69.90 498	69.97 531	46.69 526	81.36 521	55.29 537	51.90 514	43.17 527	82.54 508	7.86 549	78.44 521	57.13 513	36.17 527	84.58 503

ALIKED-LG		54.29 489	52.28 491	60.32 506	88.90 479	45.51 527	81.66 519	56.33 533	38.60 518	42.62 528	70.81 517	25.00 517	75.20 524	19.87 537	46.76 521	60.24 525

ALIKED-NN		54.48 488	52.67 490	59.89 510	90.79 465	45.45 528	81.25 522	55.75 536	34.99 525	44.87 522	71.98 516	25.50 515	74.36 525	21.88 535	47.04 519	59.85 526

E-PMN		52.30 493	52.18 493	52.67 513	71.51 528	45.40 529	93.62 485	76.60 524	36.01 522	43.50 526	64.13 530	27.11 512	67.31 528	31.06 526	26.06 534	45.30 533

N_pmnet		80.06 455	80.78 451	77.89 482	91.94 451	45.28 530	98.80 379	56.82 532	78.10 477	80.08 464	93.33 450	77.03 388	95.76 449	68.14 490	82.81 396	92.64 447

EMVS		51.44 495	51.22 496	52.11 514	70.71 529	44.97 531	94.04 480	75.66 525	35.34 524	42.40 529	61.56 534	28.93 508	65.87 529	27.64 532	24.73 535	45.49 531

ALIKED-MNN		52.51 492	50.15 497	59.60 512	90.05 471	44.33 532	81.60 520	54.93 538	32.36 528	40.96 530	68.77 522	20.90 526	75.30 523	20.00 536	41.78 524	59.18 527

FPMVS		68.72 472	68.72 470	68.71 499	65.95 535	44.27 533	95.97 473	94.74 477	51.13 515	53.26 513	90.50 478	25.11 516	83.00 513	60.80 506	80.97 418	78.87 517

SP-DiffGlue		56.84 483	55.72 485	60.19 508	65.70 536	40.86 534	81.89 518	60.28 529	34.62 526	50.39 517	76.88 514	26.61 514	58.81 534	48.21 518	56.94 509	80.90 514

GLUNet-SfM		51.10 496	46.61 499	64.56 502	61.54 544	39.88 535	79.38 525	65.13 528	36.09 521	33.36 535	69.94 519	14.50 536	78.76 519	42.46 522	17.10 542	75.02 519

SP-LightGlue		55.29 485	53.65 488	60.20 507	85.58 500	39.12 536	86.36 515	57.52 531	32.34 529	44.34 524	67.75 526	24.36 519	59.32 533	29.62 528	54.98 511	82.17 509

SP-NN		55.28 487	53.59 489	60.34 505	86.63 492	39.01 537	86.70 512	56.31 534	31.08 530	43.77 525	68.45 523	23.39 522	60.24 530	29.19 530	56.76 510	81.77 511

SP-SuperGlue		55.29 485	53.71 487	60.00 509	85.11 501	38.86 538	86.96 511	57.95 530	32.77 527	44.54 523	68.00 524	23.90 520	59.51 532	29.61 529	54.59 512	81.63 512

SP-MNN		53.97 490	52.04 494	59.73 511	84.72 502	38.63 539	86.51 513	55.94 535	29.25 531	40.20 531	67.48 527	22.18 524	59.59 531	27.79 531	54.33 514	80.98 513

PMVS	49.05 23	53.75 491	51.34 495	60.97 504	40.80 555	34.68 540	74.82 526	89.62 509	37.55 520	28.67 537	72.12 515	7.09 551	81.63 517	43.17 521	68.21 481	66.59 523
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SIFT-NN		35.94 501	36.54 504	34.16 517	73.93 526	29.52 541	62.74 530	37.28 542	19.65 535	27.91 538	49.19 536	11.66 537	46.35 537	9.19 538	37.30 525	26.61 534

SIFT-MNN		34.10 502	34.41 505	33.17 519	68.99 532	28.51 542	60.22 532	36.81 543	19.08 538	24.04 540	47.28 539	10.06 541	45.04 538	8.72 539	34.47 528	25.97 537

SIFT-NN-NCMNet		33.88 503	34.14 506	33.10 520	66.88 534	28.42 543	60.42 531	36.72 544	19.15 536	24.06 539	47.14 540	10.24 539	44.77 539	8.72 539	33.94 530	26.10 536

XFeat-MNN		41.51 498	41.24 502	42.32 515	55.40 551	28.19 544	69.39 528	46.53 539	23.57 533	34.47 534	63.21 532	20.04 531	52.41 535	27.43 533	31.08 532	46.37 530

XFeat-NN		42.54 497	42.87 501	41.54 516	59.73 546	27.86 545	69.53 527	45.34 540	24.36 532	37.16 532	64.79 528	20.84 527	51.40 536	30.01 527	34.12 529	45.36 532

wuyk23d		20.37 516	20.84 519	18.99 533	65.34 537	27.73 546	50.43 542	7.67 558	9.50 550	8.01 552	6.34 551	6.13 554	26.24 551	23.40 534	10.69 549	2.99 548

SIFT-ConvMatch		30.09 507	29.76 511	31.09 524	65.16 538	27.56 547	54.13 538	31.17 548	18.55 540	17.88 543	45.89 542	8.40 546	42.26 544	8.11 544	18.51 540	23.46 542

SIFT-NCM-Cal		31.73 504	31.67 507	31.91 522	67.18 533	27.55 548	58.36 534	33.09 547	18.38 541	14.93 547	45.16 545	8.60 545	43.82 540	7.62 548	31.68 531	24.36 540

SIFT-NN-CMatch		31.71 505	31.56 508	32.16 521	62.58 541	27.53 549	56.45 535	33.28 546	19.00 539	23.65 541	47.34 537	10.05 542	42.72 542	8.71 541	22.96 538	26.24 535

SIFT-NN-UMatch		31.23 506	31.05 510	31.79 523	60.08 545	27.23 550	58.49 533	33.65 545	19.14 537	17.30 544	47.31 538	10.12 540	42.88 541	8.67 542	24.67 536	25.27 538

SIFT-UMatch		29.40 509	28.87 513	30.98 525	62.08 543	26.57 551	56.09 536	29.45 550	18.31 542	15.86 546	46.00 541	8.23 547	42.54 543	7.99 545	15.81 543	23.85 541

SIFT-CM-Cal		28.34 510	27.90 514	29.63 526	63.75 539	25.98 552	50.66 541	26.18 552	18.12 544	16.88 545	44.64 546	8.08 548	39.70 545	7.65 547	15.19 545	23.22 543

test123		37.68 500	39.14 503	33.31 518	19.94 556	24.83 553	98.36 410	9.75 557	15.53 549	51.31 514	87.14 498	19.62 532	17.74 552	47.10 519	3.47 551	57.36 528

SIFT-UM-Cal		27.47 511	27.02 515	28.83 529	62.12 542	24.58 554	53.60 539	23.46 553	18.14 543	12.85 549	45.56 543	7.49 550	39.45 546	7.68 546	12.30 546	22.45 544

SIFT-NN-PointCN		29.63 508	29.72 512	29.36 527	57.55 548	23.55 555	56.07 537	30.57 549	17.99 545	20.99 542	45.21 544	9.94 543	39.33 547	8.40 543	20.81 539	25.20 539

SIFT-PointCN		25.49 512	25.71 516	24.84 530	56.17 549	18.65 556	51.37 540	26.53 551	16.31 546	12.78 550	39.87 549	6.41 553	34.09 549	6.51 550	15.42 544	21.77 545

SIFT-PCN-Cal		24.67 513	24.81 517	24.24 531	56.13 550	18.04 557	49.05 543	23.39 554	16.07 547	12.99 548	40.17 548	6.97 552	34.68 548	6.71 549	11.81 547	19.99 546

SIFT-NCMNet		21.21 515	21.22 518	21.17 532	52.99 552	16.41 558	42.12 544	14.05 556	15.89 548	10.70 551	35.85 550	5.14 556	29.82 550	5.80 551	8.44 550	17.28 547

testmvs		40.60 499	44.45 500	29.05 528	19.49 557	14.11 559	99.68 232	18.47 555	20.74 534	64.59 500	98.48 279	10.95 538	17.09 553	56.66 514	11.01 548	55.94 529

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.02 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

cdsmvs_eth3d_5k		23.43 514	31.24 509	0.00 534	0.00 558	0.00 560	0.00 545	98.09 235	0.00 552	0.00 554	99.67 114	83.37 316	0.00 554	0.00 552	0.00 552	0.00 549

pcd_1.5k_mvsjas		7.60 518	10.13 521	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	91.20 178	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		8.28 517	11.04 520	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	99.40 147	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 553	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

PC_three_1452									96.96 60	99.80 28	99.79 63	97.49 11	100.00 1	99.99 5	99.98 32	100.00 1

eth-test2					0.00 558

eth-test					0.00 558

test_241102_TWO								98.43 156	97.27 47	99.80 28	99.94 5	97.18 23	100.00 1	100.00 1	100.00 1	100.00 1

9.14			98.38 41		99.87 57		99.91 111	98.33 196	93.22 213	99.78 39	99.89 27	94.57 81	99.85 131	99.84 30	99.97 44

test_0728_THIRD									96.48 80	99.83 24	99.91 19	97.87 6	100.00 1	99.92 17	100.00 1	100.00 1

GSMVS																99.59 154

sam_mvs1												94.72 75				99.59 154

sam_mvs												94.25 95

MTGPA								98.28 205

test_post1							95.78 475				59.23 535	93.20 129	97.74 343	91.06 332

test_post											63.35 531	94.43 83	98.13 322

patchmatchnet-post											91.70 472	95.12 61	97.95 334

MTMP							99.87 133	96.49 437

test9_res														99.71 49	99.99 21	100.00 1

agg_prior2														99.48 64	100.00 1	100.00 1

test_prior2							99.95 75		95.78 105	99.73 47	99.76 73	96.00 42		99.78 36	100.00 1

旧先验2							99.46 283		94.21 166	99.85 20			99.95 86	96.96 202

新几何2							99.40 288

无先验							99.49 275	98.71 79	93.46 202				100.00 1	94.36 269		99.99 26

原ACMM2							99.90 117

testdata2													99.99 40	90.54 345

segment_acmp												96.68 31

testdata1							99.28 314		96.35 91

plane_prior5								97.87 259					98.37 302	97.79 171	89.55 332	94.52 346

plane_prior4											98.59 265

plane_prior2							99.84 152		96.38 86

plane_prior1					95.73 368

n2								0.00 559

nn								0.00 559

door-mid								89.69 507

test11								98.44 148

door								90.31 504

HQP-NCC					95.78 361		99.87 133		96.82 66	93.37 302

ACMP_Plane					95.78 361		99.87 133		96.82 66	93.37 302

BP-MVS														97.92 160

HQP4-MVS										93.37 302			98.39 296			94.53 344

HQP3-MVS								97.89 257							89.60 329

HQP2-MVS												80.65 352

ACMMP++_ref															87.04 365

ACMMP++															88.23 352

Test By Simon												92.82 140

High-resolution multi-view benchmark