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.

AdaColmap		97.23 130	96.80 139	98.51 132	99.99 1	95.60 199	99.09 305	98.84 65	93.32 203	96.74 214	99.72 94	86.04 259	100.00 1	98.01 152	99.43 129	99.94 86

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 21	98.69 81	98.20 9	99.93 2	99.98 2	96.82 26	100.00 1	99.75 41	100.00 1	99.99 24

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 39	98.64 90	98.47 3	99.13 104	99.92 17	96.38 36	100.00 1	99.74 43	100.00 1	100.00 1

mPP-MVS		98.39 56	98.20 54	98.97 92	99.97 3	96.92 138	99.95 72	98.38 183	95.04 123	98.61 138	99.80 58	93.39 117	100.00 1	98.64 114	100.00 1	99.98 56

CPTT-MVS		97.64 110	97.32 114	98.58 121	99.97 3	95.77 188	99.96 53	98.35 189	89.90 336	98.36 153	99.79 62	91.18 176	99.99 39	98.37 130	99.99 21	99.99 24

DP-MVS Recon		98.41 53	98.02 68	99.56 29	99.97 3	98.70 52	99.92 100	98.44 147	92.06 269	98.40 152	99.84 48	95.68 47	100.00 1	98.19 141	99.71 92	99.97 66

PAPR		98.52 43	98.16 58	99.58 28	99.97 3	98.77 46	99.95 72	98.43 155	95.35 117	98.03 167	99.75 80	94.03 102	99.98 50	98.11 146	99.83 81	99.99 24

MED-MVS test				99.60 23	99.96 8	98.79 41	99.97 39	98.88 54	96.36 88	99.07 109	99.93 11		100.00 1	99.98 9	99.96 46	99.99 24

MED-MVS		99.15 8	99.00 12	99.60 23	99.96 8	98.79 41	99.97 39	98.88 54	95.89 101	99.07 109	99.93 11	97.36 17	100.00 1	99.98 9	99.96 46	99.99 24

TestfortrainingZip a		99.09 10	98.87 19	99.76 10	99.96 8	99.27 18	99.97 39	98.88 54	96.36 88	99.07 109	99.93 11	97.36 17	100.00 1	98.32 133	99.96 46	100.00 1

HFP-MVS		98.56 39	98.37 43	99.14 72	99.96 8	97.43 114	99.95 72	98.61 98	94.77 133	99.31 92	99.85 37	94.22 95	100.00 1	98.70 109	99.98 32	99.98 56

region2R		98.54 41	98.37 43	99.05 82	99.96 8	97.18 124	99.96 53	98.55 118	94.87 130	99.45 79	99.85 37	94.07 101	100.00 1	98.67 111	100.00 1	99.98 56

ACMMPR		98.50 44	98.32 47	99.05 82	99.96 8	97.18 124	99.95 72	98.60 100	94.77 133	99.31 92	99.84 48	93.73 111	100.00 1	98.70 109	99.98 32	99.98 56

NCCC		99.37 2	99.25 2	99.71 16	99.96 8	99.15 23	99.97 39	98.62 97	98.02 22	99.90 6	99.95 3	97.33 19	100.00 1	99.54 58	100.00 1	100.00 1

CP-MVS		98.45 48	98.32 47	98.87 97	99.96 8	96.62 151	99.97 39	98.39 179	94.43 150	98.90 119	99.87 31	94.30 92	100.00 1	99.04 84	99.99 21	99.99 24

test_one_0601					99.94 16	99.30 12		98.41 172	96.63 73	99.75 40	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 16	99.47 7	99.95 72	98.43 155					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 32	98.55 31	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	99.78 66	94.34 89	99.96 75	98.92 94	99.95 54	99.99 24

X-MVStestdata		93.83 272	92.06 307	99.15 70	99.94 16	97.50 110	99.94 90	98.42 167	96.22 92	99.41 84	41.37 483	94.34 89	99.96 75	98.92 94	99.95 54	99.99 24

test_prior				99.43 40	99.94 16	98.49 65		98.65 87					99.80 142			99.99 24

MSLP-MVS++		99.13 9	99.01 11	99.49 36	99.94 16	98.46 66	99.98 21	98.86 59	97.10 53	99.80 26	99.94 4	95.92 43	100.00 1	99.51 59	100.00 1	100.00 1

APDe-MVS		99.06 14	98.91 15	99.51 33	99.94 16	98.76 49	99.91 108	98.39 179	97.20 51	99.46 78	99.85 37	95.53 51	99.79 144	99.86 27	100.00 1	99.99 24
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS		98.23 71	97.97 72	99.03 84	99.94 16	97.17 127	99.95 72	98.39 179	94.70 137	98.26 159	99.81 57	91.84 167	100.00 1	98.85 100	99.97 42	99.93 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 35	98.36 45	99.49 36	99.94 16	98.73 50	99.87 130	98.33 194	93.97 175	99.76 39	99.87 31	94.99 67	99.75 153	98.55 118	100.00 1	99.98 56

PAPM_NR		98.12 75	97.93 78	98.70 108	99.94 16	96.13 177	99.82 159	98.43 155	94.56 141	97.52 184	99.70 100	94.40 84	99.98 50	97.00 192	99.98 32	99.99 24

MG-MVS		98.91 22	98.65 27	99.68 17	99.94 16	99.07 25	99.64 222	99.44 19	97.33 44	99.00 115	99.72 94	94.03 102	99.98 50	98.73 108	100.00 1	100.00 1

ME-MVS		99.07 12	98.89 17	99.59 26	99.93 27	98.79 41	99.95 72	98.80 71	95.89 101	99.28 96	99.93 11	96.28 37	99.98 50	99.98 9	99.96 46	99.99 24

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 27	99.30 12	99.96 53	98.43 155	97.27 47	99.80 26	99.94 4	96.71 29	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 27	99.31 10		98.41 172	97.71 31	99.84 21				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 12		98.43 155	97.26 49	99.80 26	99.88 28	96.71 29	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 13	99.93 27	99.29 15	99.95 72	98.32 196	97.28 45	99.83 22	99.91 18	97.22 21	100.00 1	99.99 5	100.00 1	99.89 96
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 27	99.29 15	99.96 53	98.42 167	97.28 45	99.86 15	99.94 4	97.22 21

MSP-MVS		99.09 10	99.12 5	98.98 91	99.93 27	97.24 121	99.95 72	98.42 167	97.50 38	99.52 74	99.88 28	97.43 16	99.71 159	99.50 61	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
agg_prior					99.93 27	98.77 46		98.43 155		99.63 57			99.85 129

FOURS1					99.92 35	97.66 104	99.95 72	98.36 187	95.58 111	99.52 74

ZD-MVS					99.92 35	98.57 60		98.52 127	92.34 257	99.31 92	99.83 50	95.06 62	99.80 142	99.70 49	99.97 42

GST-MVS		98.27 63	97.97 72	99.17 65	99.92 35	97.57 106	99.93 97	98.39 179	94.04 173	98.80 124	99.74 87	92.98 133	100.00 1	98.16 143	99.76 89	99.93 87

TEST9					99.92 35	98.92 30	99.96 53	98.43 155	93.90 181	99.71 47	99.86 33	95.88 44	99.85 129

train_agg		98.88 23	98.65 27	99.59 26	99.92 35	98.92 30	99.96 53	98.43 155	94.35 155	99.71 47	99.86 33	95.94 41	99.85 129	99.69 50	99.98 32	99.99 24

test_8					99.92 35	98.88 33	99.96 53	98.43 155	94.35 155	99.69 49	99.85 37	95.94 41	99.85 129

PGM-MVS		98.34 58	98.13 60	98.99 89	99.92 35	97.00 134	99.75 184	99.50 17	93.90 181	99.37 89	99.76 72	93.24 126	100.00 1	97.75 171	99.96 46	99.98 56

ACMMP		97.74 103	97.44 107	98.66 112	99.92 35	96.13 177	99.18 298	99.45 18	94.84 131	96.41 228	99.71 97	91.40 170	99.99 39	97.99 154	98.03 189	99.87 99
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
DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 43	99.31 10	99.95 72	98.43 155	96.48 78	99.80 26	99.93 11	97.44 14	100.00 1	99.92 16	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 43	99.80 2		98.41 172					100.00 1	99.96 12	100.00 1	100.00 1

No_MVS				99.93 2	99.91 43	99.80 2		98.41 172					100.00 1	99.96 12	100.00 1	100.00 1

HPM-MVS++		99.07 12	98.88 18	99.63 18	99.90 46	99.02 26	99.95 72	98.56 112	97.56 37	99.44 80	99.85 37	95.38 55	100.00 1	99.31 71	99.99 21	99.87 99

APD-MVS		98.62 36	98.35 46	99.41 43	99.90 46	98.51 63	99.87 130	98.36 187	94.08 168	99.74 43	99.73 91	94.08 100	99.74 155	99.42 67	99.99 21	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 26	98.54 32	99.62 21	99.90 46	98.85 36	99.24 293	98.47 139	98.14 16	99.08 107	99.91 18	93.09 130	100.00 1	99.04 84	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
OPU-MVS				99.93 2	99.89 49	99.80 2	99.96 53				99.80 58	97.44 14	100.00 1	100.00 1	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 12	99.89 49	99.24 20	99.87 130	98.44 147	97.48 39	99.64 56	99.94 4	96.68 31	99.99 39	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 49	99.25 19				99.49 77

CSCG		97.10 136	97.04 126	97.27 230	99.89 49	91.92 319	99.90 114	99.07 37	88.67 360	95.26 260	99.82 53	93.17 129	99.98 50	98.15 144	99.47 124	99.90 95

ZNCC-MVS		98.31 60	98.03 67	99.17 65	99.88 53	97.59 105	99.94 90	98.44 147	94.31 158	98.50 145	99.82 53	93.06 131	99.99 39	98.30 135	99.99 21	99.93 87

SR-MVS		98.46 47	98.30 50	98.93 95	99.88 53	97.04 133	99.84 149	98.35 189	94.92 127	99.32 91	99.80 58	93.35 119	99.78 146	99.30 72	99.95 54	99.96 74

9.14			98.38 41		99.87 55		99.91 108	98.33 194	93.22 206	99.78 37	99.89 26	94.57 80	99.85 129	99.84 29	99.97 42

SMA-MVS		98.76 29	98.48 35	99.62 21	99.87 55	98.87 34	99.86 141	98.38 183	93.19 208	99.77 38	99.94 4	95.54 49	100.00 1	99.74 43	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
NormalMVS		97.90 85	97.85 85	98.04 165	99.86 57	95.39 209	99.61 229	97.78 264	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 202	99.48 121	99.06 242

lecture		98.67 33	98.46 36	99.28 52	99.86 57	97.88 91	99.97 39	99.25 30	96.07 96	99.79 35	99.70 100	92.53 149	99.98 50	99.51 59	99.48 121	99.97 66

PHI-MVS		98.41 53	98.21 53	99.03 84	99.86 57	97.10 131	99.98 21	98.80 71	90.78 316	99.62 60	99.78 66	95.30 56	100.00 1	99.80 32	99.93 65	99.99 24

MTAPA		98.29 62	97.96 75	99.30 51	99.85 60	97.93 89	99.39 271	98.28 203	95.76 105	97.18 199	99.88 28	92.74 140	100.00 1	98.67 111	99.88 77	99.99 24

LS3D		95.84 204	95.11 217	98.02 166	99.85 60	95.10 227	98.74 355	98.50 136	87.22 382	93.66 281	99.86 33	87.45 235	99.95 84	90.94 319	99.81 87	99.02 246

HPM-MVS		97.96 80	97.72 90	98.68 109	99.84 62	96.39 163	99.90 114	98.17 218	92.61 242	98.62 137	99.57 130	91.87 166	99.67 167	98.87 99	99.99 21	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set		98.27 63	98.11 62	98.75 105	99.83 63	96.59 155	99.40 267	98.51 130	95.29 119	98.51 144	99.76 72	93.60 115	99.71 159	98.53 121	99.52 114	99.95 82

save fliter					99.82 64	98.79 41	99.96 53	98.40 176	97.66 33

PLC	95.54 3	97.93 83	97.89 82	98.05 164	99.82 64	94.77 238	99.92 100	98.46 141	93.93 178	97.20 197	99.27 162	95.44 54	99.97 63	97.41 177	99.51 117	99.41 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 68	98.08 64	98.78 102	99.81 66	96.60 153	99.82 159	98.30 201	93.95 177	99.37 89	99.77 70	92.84 137	99.76 152	98.95 90	99.92 68	99.97 66

EI-MVSNet-UG-set		98.14 74	97.99 70	98.60 117	99.80 67	96.27 166	99.36 277	98.50 136	95.21 121	98.30 156	99.75 80	93.29 123	99.73 158	98.37 130	99.30 138	99.81 108

SR-MVS-dyc-post		98.31 60	98.17 57	98.71 107	99.79 68	96.37 164	99.76 180	98.31 198	94.43 150	99.40 86	99.75 80	93.28 124	99.78 146	98.90 97	99.92 68	99.97 66

RE-MVS-def			98.13 60		99.79 68	96.37 164	99.76 180	98.31 198	94.43 150	99.40 86	99.75 80	92.95 134		98.90 97	99.92 68	99.97 66

HPM-MVS_fast		97.80 97	97.50 103	98.68 109	99.79 68	96.42 159	99.88 127	98.16 223	91.75 280	98.94 117	99.54 133	91.82 168	99.65 171	97.62 174	99.99 21	99.99 24

SF-MVS		98.67 33	98.40 39	99.50 34	99.77 71	98.67 53	99.90 114	98.21 213	93.53 193	99.81 24	99.89 26	94.70 76	99.86 128	99.84 29	99.93 65	99.96 74

MGCNet		99.06 14	98.84 20	99.72 14	99.76 72	99.21 22	99.99 5	99.34 25	98.70 2	99.44 80	99.75 80	93.24 126	99.99 39	99.94 14	99.41 131	99.95 82

旧先验1					99.76 72	97.52 108		98.64 90			99.85 37	95.63 48			99.94 59	99.99 24

OMC-MVS		97.28 126	97.23 118	97.41 220	99.76 72	93.36 286	99.65 218	97.95 245	96.03 97	97.41 190	99.70 100	89.61 202	99.51 177	96.73 204	98.25 179	99.38 195

新几何1				99.42 42	99.75 75	98.27 70		98.63 96	92.69 237	99.55 69	99.82 53	94.40 84	100.00 1	91.21 311	99.94 59	99.99 24

MP-MVS-pluss		98.07 78	97.64 96	99.38 48	99.74 76	98.41 68	99.74 187	98.18 217	93.35 201	96.45 225	99.85 37	92.64 144	99.97 63	98.91 96	99.89 74	99.77 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 20	98.77 22	99.41 43	99.74 76	98.67 53	99.77 174	98.38 183	96.73 69	99.88 12	99.74 87	94.89 69	99.59 173	99.80 32	99.98 32	99.97 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				99.43 40	99.74 76	98.56 61		98.40 176		99.65 53		94.76 72	99.75 153		99.98 32	99.99 24

原ACMM1				98.96 93	99.73 79	96.99 135		98.51 130	94.06 171	99.62 60	99.85 37	94.97 68	99.96 75	95.11 232	99.95 54	99.92 92

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 98	99.73 79	96.63 150	99.97 39	97.92 250	98.07 19	98.76 130	99.55 131	95.00 66	99.94 93	99.91 19	97.68 196	99.99 24

CANet		98.27 63	97.82 87	99.63 18	99.72 81	99.10 24	99.98 21	98.51 130	97.00 59	98.52 142	99.71 97	87.80 226	99.95 84	99.75 41	99.38 133	99.83 104

reproduce_model		98.75 30	98.66 26	99.03 84	99.71 82	97.10 131	99.73 194	98.23 211	97.02 58	99.18 102	99.90 22	94.54 81	99.99 39	99.77 37	99.90 73	99.99 24

F-COLMAP		96.93 148	96.95 129	96.87 243	99.71 82	91.74 324	99.85 144	97.95 245	93.11 215	95.72 249	99.16 180	92.35 155	99.94 93	95.32 228	99.35 136	98.92 254

reproduce-ours		98.78 27	98.67 24	99.09 79	99.70 84	97.30 118	99.74 187	98.25 207	97.10 53	99.10 105	99.90 22	94.59 77	99.99 39	99.77 37	99.91 71	99.99 24

our_new_method		98.78 27	98.67 24	99.09 79	99.70 84	97.30 118	99.74 187	98.25 207	97.10 53	99.10 105	99.90 22	94.59 77	99.99 39	99.77 37	99.91 71	99.99 24

SD-MVS		98.92 21	98.70 23	99.56 29	99.70 84	98.73 50	99.94 90	98.34 193	96.38 84	99.81 24	99.76 72	94.59 77	99.98 50	99.84 29	99.96 46	99.97 66
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
patch_mono-2		98.24 69	99.12 5	95.59 284	99.67 87	86.91 408	99.95 72	98.89 52	97.60 34	99.90 6	99.76 72	96.54 34	99.98 50	99.94 14	99.82 85	99.88 97

ACMMP_NAP		98.49 45	98.14 59	99.54 31	99.66 88	98.62 59	99.85 144	98.37 186	94.68 138	99.53 72	99.83 50	92.87 136	100.00 1	98.66 113	99.84 80	99.99 24

DeepPCF-MVS	95.94 2	97.71 107	98.98 13	93.92 350	99.63 89	81.76 444	99.96 53	98.56 112	99.47 1	99.19 101	99.99 1	94.16 99	100.00 1	99.92 16	99.93 65	100.00 1

EPNet		98.49 45	98.40 39	98.77 104	99.62 90	96.80 144	99.90 114	99.51 16	97.60 34	99.20 99	99.36 151	93.71 112	99.91 110	97.99 154	98.71 164	99.61 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 24	98.53 33	99.76 10	99.59 91	99.33 8	99.99 5	99.76 6	98.39 4	99.39 88	99.80 58	90.49 191	99.96 75	99.89 21	99.43 129	99.98 56

PVSNet_BlendedMVS		96.05 195	95.82 189	96.72 249	99.59 91	96.99 135	99.95 72	99.10 34	94.06 171	98.27 157	95.80 355	89.00 214	99.95 84	99.12 78	87.53 347	93.24 409

PVSNet_Blended		97.94 82	97.64 96	98.83 99	99.59 91	96.99 135	100.00 1	99.10 34	95.38 116	98.27 157	99.08 184	89.00 214	99.95 84	99.12 78	99.25 140	99.57 157

PatchMatch-RL		96.04 196	95.40 204	97.95 168	99.59 91	95.22 222	99.52 249	99.07 37	93.96 176	96.49 224	98.35 270	82.28 307	99.82 141	90.15 335	99.22 143	98.81 261

dcpmvs_2		97.42 121	98.09 63	95.42 291	99.58 95	87.24 404	99.23 294	96.95 382	94.28 161	98.93 118	99.73 91	94.39 87	99.16 205	99.89 21	99.82 85	99.86 101

test222					99.55 96	97.41 116	99.34 279	98.55 118	91.86 275	99.27 97	99.83 50	93.84 109			99.95 54	99.99 24

CNLPA		97.76 101	97.38 110	98.92 96	99.53 97	96.84 140	99.87 130	98.14 227	93.78 185	96.55 222	99.69 104	92.28 157	99.98 50	97.13 187	99.44 128	99.93 87

API-MVS		97.86 88	97.66 94	98.47 134	99.52 98	95.41 207	99.47 259	98.87 58	91.68 281	98.84 121	99.85 37	92.34 156	99.99 39	98.44 126	99.96 46	100.00 1

PVSNet	91.05 13	97.13 135	96.69 145	98.45 137	99.52 98	95.81 186	99.95 72	99.65 12	94.73 135	99.04 113	99.21 173	84.48 290	99.95 84	94.92 238	98.74 163	99.58 155

114514_t		97.41 122	96.83 136	99.14 72	99.51 100	97.83 93	99.89 124	98.27 205	88.48 364	99.06 112	99.66 115	90.30 194	99.64 172	96.32 213	99.97 42	99.96 74

cl22		93.77 277	93.25 281	95.33 295	99.49 101	94.43 248	99.61 229	98.09 230	90.38 324	89.16 351	95.61 363	90.56 189	97.34 337	91.93 302	84.45 368	94.21 354

testdata				98.42 141	99.47 102	95.33 213		98.56 112	93.78 185	99.79 35	99.85 37	93.64 114	99.94 93	94.97 236	99.94 59	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 157	99.47 102	94.79 237	99.05 316	98.76 73	92.65 240	98.66 135	99.82 53	88.52 220	99.98 50	98.12 145	99.63 99	99.67 129
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
DP-MVS		94.54 249	93.42 271	97.91 174	99.46 104	94.04 263	98.93 334	97.48 301	81.15 437	90.04 322	99.55 131	87.02 243	99.95 84	88.97 347	98.11 185	99.73 119

MVS_111021_LR		98.42 52	98.38 41	98.53 129	99.39 105	95.79 187	99.87 130	99.86 2	96.70 70	98.78 125	99.79 62	92.03 163	99.90 112	99.17 77	99.86 79	99.88 97

CHOSEN 280x420		99.01 17	99.03 10	98.95 94	99.38 106	98.87 34	98.46 374	99.42 21	97.03 57	99.02 114	99.09 183	99.35 2	98.21 299	99.73 45	99.78 88	99.77 115

MVS_111021_HR		98.72 31	98.62 29	99.01 88	99.36 107	97.18 124	99.93 97	99.90 1	96.81 67	98.67 134	99.77 70	93.92 104	99.89 117	99.27 73	99.94 59	99.96 74

fmvsm_s_conf0.5_n_11		98.03 79	97.89 82	98.46 136	99.35 108	97.76 97	99.99 5	98.04 236	98.20 9	99.90 6	99.78 66	86.21 257	99.95 84	99.89 21	99.68 94	97.65 299

DPM-MVS		98.83 24	98.46 36	99.97 1	99.33 109	99.92 1	99.96 53	98.44 147	97.96 23	99.55 69	99.94 4	97.18 23	100.00 1	93.81 269	99.94 59	99.98 56

TAPA-MVS	92.12 8	94.42 257	93.60 263	96.90 242	99.33 109	91.78 323	99.78 169	98.00 239	89.89 337	94.52 266	99.47 137	91.97 164	99.18 202	69.90 455	99.52 114	99.73 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 221	95.07 219	96.32 264	99.32 111	96.60 153	99.76 180	98.85 62	96.65 72	87.83 373	96.05 352	99.52 1	98.11 304	96.58 208	81.07 397	94.25 349

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 123	99.28 112	95.84 185	99.99 5	98.57 106	98.17 13	99.93 2	99.74 87	87.04 242	99.97 63	99.86 27	99.59 108	99.83 104

SPE-MVS-test		97.88 86	97.94 77	97.70 192	99.28 112	95.20 223	99.98 21	97.15 349	95.53 113	99.62 60	99.79 62	92.08 162	98.38 282	98.75 107	99.28 139	99.52 169

test_fmvsm_n_1920		98.44 49	98.61 30	97.92 172	99.27 114	95.18 224	100.00 1	98.90 50	98.05 20	99.80 26	99.73 91	92.64 144	99.99 39	99.58 57	99.51 117	98.59 271

fmvsm_s_conf0.5_n_10		98.24 69	97.90 80	99.26 54	99.24 115	97.88 91	99.99 5	98.76 73	98.20 9	99.92 4	99.74 87	85.97 261	99.94 93	99.72 46	99.53 113	99.96 74

fmvsm_l_conf0.5_n_a		99.00 18	98.91 15	99.28 52	99.21 116	97.91 90	99.98 21	98.85 62	98.25 5	99.92 4	99.75 80	94.72 74	99.97 63	99.87 25	99.64 97	99.95 82

fmvsm_s_conf0.5_n_8		98.38 57	98.05 66	99.35 49	99.20 117	98.12 76	99.98 21	98.81 67	98.22 7	99.80 26	99.71 97	87.37 237	99.97 63	99.91 19	99.48 121	99.97 66

test_yl		97.83 92	97.37 111	99.21 59	99.18 118	97.98 85	99.64 222	99.27 27	91.43 290	97.88 174	98.99 196	95.84 45	99.84 137	98.82 101	95.32 274	99.79 111

DCV-MVSNet		97.83 92	97.37 111	99.21 59	99.18 118	97.98 85	99.64 222	99.27 27	91.43 290	97.88 174	98.99 196	95.84 45	99.84 137	98.82 101	95.32 274	99.79 111

fmvsm_l_conf0.5_n		98.94 19	98.84 20	99.25 55	99.17 120	97.81 95	99.98 21	98.86 59	98.25 5	99.90 6	99.76 72	94.21 97	99.97 63	99.87 25	99.52 114	99.98 56

DeepC-MVS	94.51 4	96.92 149	96.40 159	98.45 137	99.16 121	95.90 183	99.66 217	98.06 233	96.37 87	94.37 272	99.49 136	83.29 300	99.90 112	97.63 173	99.61 104	99.55 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.54 41	98.22 52	99.50 34	99.15 122	98.65 57	100.00 1	98.58 104	97.70 32	98.21 162	99.24 169	92.58 147	99.94 93	98.63 116	99.94 59	99.92 92
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
fmvsm_l_conf0.5_n_3		98.41 53	98.08 64	99.39 45	99.12 123	98.29 69	99.98 21	98.64 90	98.14 16	99.86 15	99.76 72	87.99 225	99.97 63	99.72 46	99.54 111	99.91 94

fmvsm_l_conf0.5_n_9		98.55 40	98.23 51	99.49 36	99.10 124	98.50 64	99.99 5	98.70 79	98.14 16	99.94 1	99.68 111	89.02 213	99.98 50	99.89 21	99.61 104	99.99 24

CS-MVS		97.79 99	97.91 79	97.43 218	99.10 124	94.42 249	99.99 5	97.10 361	95.07 122	99.68 50	99.75 80	92.95 134	98.34 286	98.38 128	99.14 145	99.54 163

Anonymous202405211		93.10 295	91.99 308	96.40 260	99.10 124	89.65 373	98.88 340	97.93 247	83.71 421	94.00 278	98.75 232	68.79 413	99.88 123	95.08 233	91.71 307	99.68 127

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 193	99.06 127	94.41 250	99.98 21	98.97 43	97.34 42	99.63 57	99.69 104	87.27 238	99.97 63	99.62 55	99.06 150	98.62 270

HyFIR lowres test		96.66 165	96.43 156	97.36 225	99.05 128	93.91 268	99.70 208	99.80 3	90.54 320	96.26 231	98.08 283	92.15 160	98.23 298	96.84 201	95.46 269	99.93 87

LFMVS		94.75 243	93.56 266	98.30 147	99.03 129	95.70 193	98.74 355	97.98 242	87.81 375	98.47 146	99.39 148	67.43 422	99.53 174	98.01 152	95.20 277	99.67 129

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 219	99.01 130	94.69 240	99.97 39	98.76 73	97.91 25	99.87 13	99.76 72	86.70 249	99.93 103	99.67 52	99.12 148	97.64 300

fmvsm_s_conf0.5_n_2		97.59 112	97.28 115	98.53 129	99.01 130	98.15 71	99.98 21	98.59 102	98.17 13	99.75 40	99.63 121	81.83 313	99.94 93	99.78 35	98.79 161	97.51 308

AllTest		92.48 310	91.64 313	95.00 304	99.01 130	88.43 391	98.94 332	96.82 396	86.50 391	88.71 356	98.47 265	74.73 388	99.88 123	85.39 387	96.18 244	96.71 314

TestCases				95.00 304	99.01 130	88.43 391		96.82 396	86.50 391	88.71 356	98.47 265	74.73 388	99.88 123	85.39 387	96.18 244	96.71 314

COLMAP_ROB	90.47 14	92.18 317	91.49 319	94.25 338	99.00 134	88.04 397	98.42 380	96.70 403	82.30 432	88.43 365	99.01 193	76.97 363	99.85 129	86.11 383	96.50 236	94.86 325
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_3		97.95 81	97.66 94	98.81 100	98.99 135	98.07 79	99.98 21	98.81 67	98.18 12	99.89 10	99.70 100	84.15 293	99.97 63	99.76 40	99.50 119	98.39 278

test_fmvs1		95.35 222	95.68 196	94.36 334	98.99 135	84.98 419	99.96 53	96.65 405	97.60 34	99.73 45	98.96 202	71.58 403	99.93 103	98.31 134	99.37 134	98.17 283

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 18	98.98 137	99.32 9	97.49 409	99.52 14	95.69 108	98.32 155	97.41 303	93.32 121	99.77 149	98.08 149	95.75 259	99.81 108

VNet		97.21 131	96.57 150	99.13 76	98.97 138	97.82 94	99.03 319	99.21 32	94.31 158	99.18 102	98.88 214	86.26 256	99.89 117	98.93 92	94.32 287	99.69 126

thres200		96.96 145	96.21 166	99.22 58	98.97 138	98.84 37	99.85 144	99.71 7	93.17 210	96.26 231	98.88 214	89.87 199	99.51 177	94.26 257	94.91 279	99.31 212

tfpn200view9		96.79 153	95.99 173	99.19 61	98.94 140	98.82 38	99.78 169	99.71 7	92.86 225	96.02 239	98.87 221	89.33 206	99.50 179	93.84 266	94.57 283	99.27 221

thres400		96.78 155	95.99 173	99.16 68	98.94 140	98.82 38	99.78 169	99.71 7	92.86 225	96.02 239	98.87 221	89.33 206	99.50 179	93.84 266	94.57 283	99.16 230

sasdasda		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 198	97.35 314	94.45 146	97.88 174	99.42 141	86.71 247	99.52 175	98.48 123	93.97 293	99.72 121

Anonymous20231211		89.86 367	88.44 375	94.13 341	98.93 142	90.68 351	98.54 371	98.26 206	76.28 450	86.73 387	95.54 367	70.60 409	97.56 330	90.82 322	80.27 406	94.15 362

canonicalmvs		97.09 138	96.32 160	99.39 45	98.93 142	98.95 28	99.72 198	97.35 314	94.45 146	97.88 174	99.42 141	86.71 247	99.52 175	98.48 123	93.97 293	99.72 121

SDMVSNet		94.80 238	93.96 253	97.33 228	98.92 145	95.42 206	99.59 234	98.99 40	92.41 253	92.55 296	97.85 294	75.81 378	98.93 220	97.90 160	91.62 308	97.64 300

sd_testset		93.55 284	92.83 288	95.74 282	98.92 145	90.89 347	98.24 388	98.85 62	92.41 253	92.55 296	97.85 294	71.07 408	98.68 251	93.93 263	91.62 308	97.64 300

EPNet_dtu		95.71 210	95.39 205	96.66 251	98.92 145	93.41 282	99.57 239	98.90 50	96.19 94	97.52 184	98.56 255	92.65 143	97.36 335	77.89 435	98.33 174	99.20 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 76	97.60 98	99.60 23	98.92 145	99.28 17	99.89 124	99.52 14	95.58 111	98.24 161	99.39 148	93.33 120	99.74 155	97.98 156	95.58 268	99.78 114

CHOSEN 1792x2688		96.81 152	96.53 151	97.64 196	98.91 149	93.07 288	99.65 218	99.80 3	95.64 109	95.39 256	98.86 223	84.35 292	99.90 112	96.98 194	99.16 144	99.95 82

thres100view900		96.74 160	95.92 185	99.18 62	98.90 150	98.77 46	99.74 187	99.71 7	92.59 244	95.84 243	98.86 223	89.25 208	99.50 179	93.84 266	94.57 283	99.27 221

thres600view7		96.69 163	95.87 188	99.14 72	98.90 150	98.78 45	99.74 187	99.71 7	92.59 244	95.84 243	98.86 223	89.25 208	99.50 179	93.44 279	94.50 286	99.16 230

MSDG		94.37 259	93.36 278	97.40 221	98.88 152	93.95 267	99.37 275	97.38 310	85.75 402	90.80 315	99.17 177	84.11 295	99.88 123	86.35 379	98.43 172	98.36 280

MGCFI-Net		97.00 143	96.22 165	99.34 50	98.86 153	98.80 40	99.67 216	97.30 326	94.31 158	97.77 180	99.41 145	86.36 254	99.50 179	98.38 128	93.90 295	99.72 121

h-mvs33		94.92 235	94.36 239	96.59 253	98.85 154	91.29 339	98.93 334	98.94 44	95.90 99	98.77 127	98.42 268	90.89 184	99.77 149	97.80 164	70.76 445	98.72 267

Anonymous20240529		92.10 318	90.65 330	96.47 255	98.82 155	90.61 353	98.72 357	98.67 86	75.54 454	93.90 280	98.58 253	66.23 426	99.90 112	94.70 247	90.67 311	98.90 257

PVSNet_Blended_VisFu		97.27 127	96.81 138	98.66 112	98.81 156	96.67 149	99.92 100	98.64 90	94.51 143	96.38 229	98.49 261	89.05 212	99.88 123	97.10 189	98.34 173	99.43 190

PS-MVSNAJ		98.44 49	98.20 54	99.16 68	98.80 157	98.92 30	99.54 247	98.17 218	97.34 42	99.85 18	99.85 37	91.20 173	99.89 117	99.41 68	99.67 95	98.69 268

CANet_DTU		96.76 156	96.15 168	98.60 117	98.78 158	97.53 107	99.84 149	97.63 279	97.25 50	99.20 99	99.64 118	81.36 319	99.98 50	92.77 290	98.89 155	98.28 282

mvsany_test1		97.82 95	97.90 80	97.55 207	98.77 159	93.04 291	99.80 165	97.93 247	96.95 61	99.61 67	99.68 111	90.92 181	99.83 139	99.18 76	98.29 178	99.80 110

alignmvs		97.81 96	97.33 113	99.25 55	98.77 159	98.66 55	99.99 5	98.44 147	94.40 154	98.41 150	99.47 137	93.65 113	99.42 189	98.57 117	94.26 289	99.67 129

SymmetryMVS		97.64 110	97.46 104	98.17 153	98.74 161	95.39 209	99.61 229	99.26 29	96.52 76	98.61 138	99.31 156	92.73 141	99.67 167	96.77 202	95.63 266	99.45 186

SteuartSystems-ACMMP		99.02 16	98.97 14	99.18 62	98.72 162	97.71 99	99.98 21	98.44 147	96.85 62	99.80 26	99.91 18	97.57 8	99.85 129	99.44 66	99.99 21	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 71	97.97 72	99.02 87	98.69 163	98.66 55	99.52 249	98.08 232	97.05 56	99.86 15	99.86 33	90.65 186	99.71 159	99.39 70	98.63 165	98.69 268

miper_enhance_ethall		94.36 261	93.98 252	95.49 285	98.68 164	95.24 220	99.73 194	97.29 329	93.28 205	89.86 327	95.97 353	94.37 88	97.05 358	92.20 294	84.45 368	94.19 355

fmvsm_s_conf0.5_n_5		98.08 77	97.71 92	99.17 65	98.67 165	97.69 103	99.99 5	98.57 106	97.40 40	99.89 10	99.69 104	85.99 260	99.96 75	99.80 32	99.40 132	99.85 102

ETVMVS		97.03 142	96.64 146	98.20 152	98.67 165	97.12 128	99.89 124	98.57 106	91.10 302	98.17 163	98.59 250	93.86 108	98.19 300	95.64 225	95.24 276	99.28 219

test2506		97.53 114	97.19 120	98.58 121	98.66 167	96.90 139	98.81 349	99.77 5	94.93 125	97.95 169	98.96 202	92.51 150	99.20 200	94.93 237	98.15 182	99.64 135

ECVR-MVS		95.66 213	95.05 220	97.51 212	98.66 167	93.71 272	98.85 346	98.45 142	94.93 125	96.86 210	98.96 202	75.22 384	99.20 200	95.34 227	98.15 182	99.64 135

mamv4		95.24 225	96.90 131	90.25 412	98.65 169	72.11 462	98.28 385	97.64 278	89.99 335	95.93 241	98.25 278	94.74 73	99.11 206	99.01 89	99.64 97	99.53 167

balanced_conf03		98.27 63	97.99 70	99.11 77	98.64 170	98.43 67	99.47 259	97.79 262	94.56 141	99.74 43	98.35 270	94.33 91	99.25 194	99.12 78	99.96 46	99.64 135

fmvsm_s_conf0.5_n_a		97.73 105	97.72 90	97.77 186	98.63 171	94.26 257	99.96 53	98.92 49	97.18 52	99.75 40	99.69 104	87.00 244	99.97 63	99.46 64	98.89 155	99.08 240

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 89	98.60 172	98.15 71	99.58 236	97.74 269	90.34 327	99.26 98	98.32 273	94.29 93	99.23 195	99.03 87	99.89 74	99.58 155

testing222		97.08 141	96.75 141	98.06 163	98.56 173	96.82 141	99.85 144	98.61 98	92.53 248	98.84 121	98.84 227	93.36 118	98.30 290	95.84 221	94.30 288	99.05 244

test1111		95.57 216	94.98 223	97.37 223	98.56 173	93.37 285	98.86 344	98.45 142	94.95 124	96.63 216	98.95 207	75.21 385	99.11 206	95.02 234	98.14 184	99.64 135

MVSTER		95.53 217	95.22 212	96.45 258	98.56 173	97.72 98	99.91 108	97.67 274	92.38 256	91.39 306	97.14 310	97.24 20	97.30 342	94.80 243	87.85 340	94.34 344

testing3-2		97.72 106	97.43 109	98.60 117	98.55 176	97.11 130	100.00 1	99.23 31	93.78 185	97.90 171	98.73 234	95.50 52	99.69 163	98.53 121	94.63 281	98.99 248

VDD-MVS		93.77 277	92.94 286	96.27 265	98.55 176	90.22 362	98.77 354	97.79 262	90.85 308	96.82 212	99.42 141	61.18 446	99.77 149	98.95 90	94.13 290	98.82 260

tpmvs		94.28 263	93.57 265	96.40 260	98.55 176	91.50 337	95.70 447	98.55 118	87.47 377	92.15 299	94.26 418	91.42 169	98.95 219	88.15 358	95.85 255	98.76 263

UGNet		95.33 223	94.57 235	97.62 200	98.55 176	94.85 232	98.67 363	99.32 26	95.75 106	96.80 213	96.27 342	72.18 400	99.96 75	94.58 250	99.05 151	98.04 288
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
PCF-MVS	94.20 5	95.18 227	94.10 246	98.43 139	98.55 176	95.99 181	97.91 402	97.31 325	90.35 326	89.48 340	99.22 170	85.19 276	99.89 117	90.40 332	98.47 171	99.41 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 198	96.49 152	94.34 335	98.51 181	89.99 367	99.39 271	98.57 106	93.14 212	97.33 193	98.31 275	93.44 116	94.68 435	93.69 276	95.98 249	98.34 281

UWE-MVS		96.79 153	96.72 143	97.00 237	98.51 181	93.70 273	99.71 201	98.60 100	92.96 220	97.09 200	98.34 272	96.67 33	98.85 226	92.11 300	96.50 236	98.44 276

myMVS_eth3d28		97.86 88	97.59 100	98.68 109	98.50 183	97.26 120	99.92 100	98.55 118	93.79 184	98.26 159	98.75 232	95.20 57	99.48 185	98.93 92	96.40 239	99.29 217

test_vis1_n_1920		95.44 219	95.31 208	95.82 279	98.50 183	88.74 385	99.98 21	97.30 326	97.84 28	99.85 18	99.19 175	66.82 424	99.97 63	98.82 101	99.46 126	98.76 263

BH-w/o		95.71 210	95.38 206	96.68 250	98.49 185	92.28 310	99.84 149	97.50 299	92.12 266	92.06 302	98.79 230	84.69 286	98.67 253	95.29 229	99.66 96	99.09 238

baseline1		95.78 206	94.86 226	98.54 127	98.47 186	98.07 79	99.06 312	97.99 240	92.68 238	94.13 277	98.62 247	93.28 124	98.69 250	93.79 271	85.76 355	98.84 259

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 205	98.44 187	95.16 226	99.97 39	98.65 87	97.95 24	99.62 60	99.78 66	86.09 258	99.94 93	99.69 50	99.50 119	97.66 298

EPMVS		96.53 172	96.01 172	98.09 161	98.43 188	96.12 179	96.36 434	99.43 20	93.53 193	97.64 182	95.04 395	94.41 83	98.38 282	91.13 313	98.11 185	99.75 117

kuosan		93.17 292	92.60 294	94.86 311	98.40 189	89.54 375	98.44 376	98.53 125	84.46 416	88.49 361	97.92 291	90.57 188	97.05 358	83.10 404	93.49 298	97.99 289

WBMVS		94.52 252	94.03 250	95.98 271	98.38 190	96.68 148	99.92 100	97.63 279	90.75 317	89.64 335	95.25 388	96.77 27	96.90 370	94.35 255	83.57 375	94.35 342

UBG		97.84 91	97.69 93	98.29 148	98.38 190	96.59 155	99.90 114	98.53 125	93.91 180	98.52 142	98.42 268	96.77 27	99.17 203	98.54 119	96.20 243	99.11 237

sss		97.57 113	97.03 127	99.18 62	98.37 192	98.04 82	99.73 194	99.38 22	93.46 197	98.76 130	99.06 188	91.21 172	99.89 117	96.33 212	97.01 227	99.62 142

testing11		97.48 116	97.27 116	98.10 160	98.36 193	96.02 180	99.92 100	98.45 142	93.45 199	98.15 164	98.70 237	95.48 53	99.22 196	97.85 162	95.05 278	99.07 241

BH-untuned		95.18 227	94.83 227	96.22 266	98.36 193	91.22 340	99.80 165	97.32 324	90.91 306	91.08 309	98.67 239	83.51 297	98.54 264	94.23 258	99.61 104	98.92 254

testing91		97.16 133	96.90 131	97.97 167	98.35 195	95.67 196	99.91 108	98.42 167	92.91 223	97.33 193	98.72 235	94.81 71	99.21 197	96.98 194	94.63 281	99.03 245

testing99		97.17 132	96.91 130	97.95 168	98.35 195	95.70 193	99.91 108	98.43 155	92.94 221	97.36 191	98.72 235	94.83 70	99.21 197	97.00 192	94.64 280	98.95 250

ET-MVSNet_ETH3D		94.37 259	93.28 280	97.64 196	98.30 197	97.99 84	99.99 5	97.61 285	94.35 155	71.57 460	99.45 140	96.23 38	95.34 425	96.91 199	85.14 362	99.59 149

AUN-MVS		93.28 289	92.60 294	95.34 294	98.29 198	90.09 365	99.31 283	98.56 112	91.80 279	96.35 230	98.00 286	89.38 205	98.28 293	92.46 291	69.22 451	97.64 300

FMVSNet3		92.69 305	91.58 315	95.99 270	98.29 198	97.42 115	99.26 292	97.62 282	89.80 338	89.68 331	95.32 382	81.62 317	96.27 401	87.01 375	85.65 356	94.29 346

PMMVS		96.76 156	96.76 140	96.76 247	98.28 200	92.10 314	99.91 108	97.98 242	94.12 166	99.53 72	99.39 148	86.93 245	98.73 243	96.95 197	97.73 193	99.45 186

hse-mvs2		94.38 258	94.08 249	95.31 296	98.27 201	90.02 366	99.29 288	98.56 112	95.90 99	98.77 127	98.00 286	90.89 184	98.26 297	97.80 164	69.20 452	97.64 300

PVSNet_0	88.03 19	91.80 325	90.27 339	96.38 262	98.27 201	90.46 357	99.94 90	99.61 13	93.99 174	86.26 397	97.39 305	71.13 407	99.89 117	98.77 105	67.05 459	98.79 262

UA-Net		96.54 171	95.96 179	98.27 149	98.23 203	95.71 192	98.00 400	98.45 142	93.72 189	98.41 150	99.27 162	88.71 219	99.66 170	91.19 312	97.69 194	99.44 189

test_cas_vis1_n_1920		96.59 168	96.23 163	97.65 195	98.22 204	94.23 258	99.99 5	97.25 334	97.77 29	99.58 68	99.08 184	77.10 358	99.97 63	97.64 172	99.45 127	98.74 265

FE-MVS		95.70 212	95.01 222	97.79 182	98.21 205	94.57 242	95.03 448	98.69 81	88.90 354	97.50 186	96.19 344	92.60 146	99.49 184	89.99 337	97.94 191	99.31 212

GG-mvs-BLEND				98.54 127	98.21 205	98.01 83	93.87 453	98.52 127		97.92 170	97.92 291	99.02 3	97.94 317	98.17 142	99.58 109	99.67 129

mvs_anonymous		95.65 214	95.03 221	97.53 209	98.19 207	95.74 190	99.33 280	97.49 300	90.87 307	90.47 318	97.10 312	88.23 222	97.16 349	95.92 219	97.66 197	99.68 127

MVS_Test		96.46 174	95.74 192	98.61 116	98.18 208	97.23 122	99.31 283	97.15 349	91.07 303	98.84 121	97.05 316	88.17 223	98.97 216	94.39 252	97.50 199	99.61 146

BH-RMVSNet		95.18 227	94.31 242	97.80 180	98.17 209	95.23 221	99.76 180	97.53 295	92.52 249	94.27 275	99.25 168	76.84 365	98.80 233	90.89 321	99.54 111	99.35 203

dongtai		91.55 331	91.13 324	92.82 380	98.16 210	86.35 409	99.47 259	98.51 130	83.24 424	85.07 407	97.56 299	90.33 193	94.94 431	76.09 444	91.73 306	97.18 311

RPSCF		91.80 325	92.79 290	88.83 424	98.15 211	69.87 464	98.11 396	96.60 407	83.93 419	94.33 273	99.27 162	79.60 341	99.46 188	91.99 301	93.16 303	97.18 311

ETV-MVS		97.92 84	97.80 88	98.25 150	98.14 212	96.48 157	99.98 21	97.63 279	95.61 110	99.29 95	99.46 139	92.55 148	98.82 229	99.02 88	98.54 169	99.46 181

IS-MVSNet		96.29 187	95.90 186	97.45 215	98.13 213	94.80 236	99.08 307	97.61 285	92.02 271	95.54 254	98.96 202	90.64 187	98.08 306	93.73 274	97.41 203	99.47 179

test_fmvsmconf_n		98.43 51	98.32 47	98.78 102	98.12 214	96.41 160	99.99 5	98.83 66	98.22 7	99.67 51	99.64 118	91.11 177	99.94 93	99.67 52	99.62 100	99.98 56

fmvsm_s_conf0.1_n_2		97.25 128	96.85 135	98.43 139	98.08 215	98.08 78	99.92 100	97.76 268	98.05 20	99.65 53	99.58 127	80.88 326	99.93 103	99.59 56	98.17 180	97.29 309

ab-mvs		94.69 244	93.42 271	98.51 132	98.07 216	96.26 167	96.49 432	98.68 83	90.31 328	94.54 265	97.00 318	76.30 373	99.71 159	95.98 218	93.38 301	99.56 158

XVG-OURS-SEG-HR		94.79 239	94.70 234	95.08 301	98.05 217	89.19 377	99.08 307	97.54 293	93.66 190	94.87 263	99.58 127	78.78 349	99.79 144	97.31 180	93.40 300	96.25 318

EIA-MVS		97.53 114	97.46 104	97.76 188	98.04 218	94.84 233	99.98 21	97.61 285	94.41 153	97.90 171	99.59 124	92.40 154	98.87 224	98.04 151	99.13 146	99.59 149

XVG-OURS		94.82 236	94.74 233	95.06 302	98.00 219	89.19 377	99.08 307	97.55 291	94.10 167	94.71 264	99.62 122	80.51 332	99.74 155	96.04 217	93.06 305	96.25 318

mvsmamba		96.94 146	96.73 142	97.55 207	97.99 220	94.37 254	99.62 225	97.70 271	93.13 213	98.42 149	97.92 291	88.02 224	98.75 241	98.78 104	99.01 152	99.52 169

dp		95.05 230	94.43 237	96.91 240	97.99 220	92.73 299	96.29 437	97.98 242	89.70 339	95.93 241	94.67 408	93.83 110	98.45 270	86.91 378	96.53 235	99.54 163

tpmrst		96.27 189	95.98 175	97.13 232	97.96 222	93.15 287	96.34 435	98.17 218	92.07 267	98.71 133	95.12 392	93.91 105	98.73 243	94.91 240	96.62 233	99.50 175

TR-MVS		94.54 249	93.56 266	97.49 214	97.96 222	94.34 255	98.71 358	97.51 298	90.30 329	94.51 267	98.69 238	75.56 379	98.77 237	92.82 289	95.99 248	99.35 203

Vis-MVSNet (Re-imp)		96.32 184	95.98 175	97.35 227	97.93 224	94.82 235	99.47 259	98.15 226	91.83 276	95.09 261	99.11 182	91.37 171	97.47 333	93.47 278	97.43 200	99.74 118

MDTV_nov1_ep13			95.69 194		97.90 225	94.15 260	95.98 443	98.44 147	93.12 214	97.98 168	95.74 357	95.10 60	98.58 260	90.02 336	96.92 229

Fast-Effi-MVS+		95.02 232	94.19 244	97.52 211	97.88 226	94.55 243	99.97 39	97.08 365	88.85 356	94.47 268	97.96 290	84.59 287	98.41 274	89.84 339	97.10 220	99.59 149

ADS-MVSNet2		93.80 276	93.88 256	93.55 363	97.87 227	85.94 413	94.24 449	96.84 393	90.07 332	96.43 226	94.48 413	90.29 195	95.37 424	87.44 365	97.23 211	99.36 199

ADS-MVSNet		94.79 239	94.02 251	97.11 234	97.87 227	93.79 269	94.24 449	98.16 223	90.07 332	96.43 226	94.48 413	90.29 195	98.19 300	87.44 365	97.23 211	99.36 199

Effi-MVS+		96.30 186	95.69 194	98.16 154	97.85 229	96.26 167	97.41 411	97.21 341	90.37 325	98.65 136	98.58 253	86.61 251	98.70 249	97.11 188	97.37 205	99.52 169

PatchmatchNet		95.94 199	95.45 201	97.39 222	97.83 230	94.41 250	96.05 441	98.40 176	92.86 225	97.09 200	95.28 387	94.21 97	98.07 308	89.26 345	98.11 185	99.70 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 247	93.61 261	97.74 190	97.82 231	96.26 167	99.96 53	97.78 264	85.76 400	94.00 278	97.54 300	76.95 364	99.21 197	97.23 185	95.43 271	97.76 297

1112_ss		96.01 197	95.20 213	98.42 141	97.80 232	96.41 160	99.65 218	96.66 404	92.71 235	92.88 292	99.40 146	92.16 159	99.30 192	91.92 303	93.66 296	99.55 159

E3new		96.75 158	96.43 156	97.71 191	97.79 233	94.83 234	99.80 165	97.33 318	93.52 195	97.49 187	99.31 156	87.73 227	98.83 227	97.52 175	97.40 204	99.48 178

Test_1112_low_res		95.72 208	94.83 227	98.42 141	97.79 233	96.41 160	99.65 218	96.65 405	92.70 236	92.86 293	96.13 348	92.15 160	99.30 192	91.88 304	93.64 297	99.55 159

Effi-MVS+-dtu		94.53 251	95.30 209	92.22 388	97.77 235	82.54 437	99.59 234	97.06 369	94.92 127	95.29 258	95.37 380	85.81 262	97.89 318	94.80 243	97.07 221	96.23 320

tpm cat1		93.51 285	92.52 300	96.47 255	97.77 235	91.47 338	96.13 439	98.06 233	80.98 438	92.91 291	93.78 422	89.66 200	98.87 224	87.03 374	96.39 240	99.09 238

FA-MVS(test-final)		95.86 202	95.09 218	98.15 157	97.74 237	95.62 198	96.31 436	98.17 218	91.42 292	96.26 231	96.13 348	90.56 189	99.47 187	92.18 295	97.07 221	99.35 203

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 283	97.86 256	96.43 81	99.62 60	99.69 104	85.56 269	99.68 164	99.05 81	98.31 175	97.83 293

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 283	97.86 256	96.43 81	99.62 60	99.69 104	85.56 269	99.68 164	99.05 81	98.31 175	97.83 293

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 123	97.74 237	98.14 73	99.31 283	97.86 256	96.43 81	99.62 60	99.69 104	85.56 269	99.68 164	99.05 81	98.31 175	97.83 293

EPP-MVSNet		96.69 163	96.60 148	96.96 239	97.74 237	93.05 290	99.37 275	98.56 112	88.75 358	95.83 245	99.01 193	96.01 39	98.56 262	96.92 198	97.20 213	99.25 223

gg-mvs-nofinetune		93.51 285	91.86 312	98.47 134	97.72 242	97.96 88	92.62 459	98.51 130	74.70 457	97.33 193	69.59 474	98.91 4	97.79 321	97.77 169	99.56 110	99.67 129

IB-MVS	92.85 6	94.99 233	93.94 254	98.16 154	97.72 242	95.69 195	99.99 5	98.81 67	94.28 161	92.70 294	96.90 320	95.08 61	99.17 203	96.07 216	73.88 438	99.60 148
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
thisisatest0515		97.41 122	97.02 128	98.59 120	97.71 244	97.52 108	99.97 39	98.54 122	91.83 276	97.45 188	99.04 190	97.50 9	99.10 208	94.75 245	96.37 241	99.16 230

VortexMVS		94.11 265	93.50 268	95.94 273	97.70 245	96.61 152	99.35 278	97.18 344	93.52 195	89.57 338	95.74 357	87.55 232	96.97 366	95.76 224	85.13 363	94.23 351

viewdifsd2359ckpt09		96.21 191	95.77 190	97.53 209	97.69 246	94.50 246	99.78 169	97.23 339	92.88 224	96.58 219	99.26 166	84.85 281	98.66 256	96.61 206	97.02 226	99.43 190

Syy-MVS		90.00 365	90.63 331	88.11 432	97.68 247	74.66 460	99.71 201	98.35 189	90.79 314	92.10 300	98.67 239	79.10 347	93.09 451	63.35 467	95.95 252	96.59 316

myMVS_eth3d		94.46 256	94.76 232	93.55 363	97.68 247	90.97 342	99.71 201	98.35 189	90.79 314	92.10 300	98.67 239	92.46 153	93.09 451	87.13 371	95.95 252	96.59 316

test_fmvs1_n		94.25 264	94.36 239	93.92 350	97.68 247	83.70 426	99.90 114	96.57 408	97.40 40	99.67 51	98.88 214	61.82 443	99.92 109	98.23 140	99.13 146	98.14 286

fmvsm_s_conf0.5_n_6		98.27 63	97.96 75	99.23 57	97.66 250	98.11 77	99.98 21	98.64 90	97.85 27	99.87 13	99.72 94	88.86 216	99.93 103	99.64 54	99.36 135	99.63 141

RRT-MVS		96.24 190	95.68 196	97.94 171	97.65 251	94.92 231	99.27 291	97.10 361	92.79 231	97.43 189	97.99 288	81.85 312	99.37 191	98.46 125	98.57 166	99.53 167

diffmvs		97.00 143	96.64 146	98.09 161	97.64 252	96.17 176	99.81 161	97.19 342	94.67 139	98.95 116	99.28 159	86.43 252	98.76 239	98.37 130	97.42 202	99.33 206
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		96.59 168	96.23 163	97.66 194	97.63 253	94.70 239	99.77 174	97.33 318	93.41 200	97.34 192	99.17 177	86.72 246	98.83 227	97.40 178	97.32 208	99.46 181

viewdifsd2359ckpt13		96.19 192	95.77 190	97.45 215	97.62 254	94.40 252	99.70 208	97.23 339	92.76 233	96.63 216	99.05 189	84.96 280	98.64 257	96.65 205	97.35 206	99.31 212

Vis-MVSNet		95.72 208	95.15 216	97.45 215	97.62 254	94.28 256	99.28 289	98.24 209	94.27 163	96.84 211	98.94 209	79.39 342	98.76 239	93.25 280	98.49 170	99.30 215
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 136	96.72 143	98.22 151	97.60 256	96.70 145	99.92 100	98.54 122	91.11 301	97.07 202	98.97 200	97.47 12	99.03 211	93.73 274	96.09 246	98.92 254

GDP-MVS		97.88 86	97.59 100	98.75 105	97.59 257	97.81 95	99.95 72	97.37 313	94.44 149	99.08 107	99.58 127	97.13 25	99.08 209	94.99 235	98.17 180	99.37 197

miper_ehance_all_eth		93.16 293	92.60 294	94.82 312	97.57 258	93.56 277	99.50 253	97.07 368	88.75 358	88.85 355	95.52 369	90.97 180	96.74 380	90.77 323	84.45 368	94.17 356

guyue		97.15 134	96.82 137	98.15 157	97.56 259	96.25 171	99.71 201	97.84 259	95.75 106	98.13 165	98.65 242	87.58 231	98.82 229	98.29 136	97.91 192	99.36 199

viewmanbaseed2359cas		96.45 175	96.07 169	97.59 205	97.55 260	94.59 241	99.70 208	97.33 318	93.62 192	97.00 206	99.32 153	85.57 268	98.71 246	97.26 184	97.33 207	99.47 179

testing3		93.92 270	94.23 243	92.99 377	97.54 261	90.23 361	99.99 5	99.16 33	90.57 319	91.33 308	98.63 246	92.99 132	92.52 456	82.46 408	95.39 272	96.22 321

SSM_0404		95.75 207	95.16 215	97.50 213	97.53 262	95.39 209	99.11 303	97.25 334	90.81 310	95.27 259	98.83 228	84.74 283	98.67 253	95.24 230	97.69 194	98.45 275

LCM-MVSNet-Re		92.31 314	92.60 294	91.43 397	97.53 262	79.27 454	99.02 321	91.83 469	92.07 267	80.31 431	94.38 416	83.50 298	95.48 421	97.22 186	97.58 198	99.54 163

GBi-Net		90.88 342	89.82 348	94.08 342	97.53 262	91.97 315	98.43 377	96.95 382	87.05 383	89.68 331	94.72 404	71.34 404	96.11 407	87.01 375	85.65 356	94.17 356

test1		90.88 342	89.82 348	94.08 342	97.53 262	91.97 315	98.43 377	96.95 382	87.05 383	89.68 331	94.72 404	71.34 404	96.11 407	87.01 375	85.65 356	94.17 356

FMVSNet2		91.02 339	89.56 353	95.41 292	97.53 262	95.74 190	98.98 324	97.41 308	87.05 383	88.43 365	95.00 398	71.34 404	96.24 403	85.12 390	85.21 361	94.25 349

tttt0517		96.85 150	96.49 152	97.92 172	97.48 267	95.89 184	99.85 144	98.54 122	90.72 318	96.63 216	98.93 212	97.47 12	99.02 212	93.03 287	95.76 258	98.85 258

BP-MVS1		98.33 59	98.18 56	98.81 100	97.44 268	97.98 85	99.96 53	98.17 218	94.88 129	98.77 127	99.59 124	97.59 7	99.08 209	98.24 139	98.93 154	99.36 199

casdiffmvs_mvg		96.43 176	95.94 183	97.89 176	97.44 268	95.47 202	99.86 141	97.29 329	93.35 201	96.03 238	99.19 175	85.39 273	98.72 245	97.89 161	97.04 223	99.49 177
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E2		96.36 181	95.95 181	97.60 202	97.41 270	94.52 244	99.71 201	97.33 318	93.20 207	97.02 203	99.07 186	85.37 274	98.82 229	97.27 181	97.14 217	99.46 181

EC-MVSNet		97.38 124	97.24 117	97.80 180	97.41 270	95.64 197	99.99 5	97.06 369	94.59 140	99.63 57	99.32 153	89.20 211	98.14 302	98.76 106	99.23 142	99.62 142

viewdifsd2359ckpt07		95.83 205	95.42 203	97.07 235	97.40 272	93.04 291	99.60 232	97.24 337	92.39 255	96.09 237	99.14 181	83.07 303	98.93 220	97.02 191	96.87 230	99.23 226

c3_l		92.53 309	91.87 311	94.52 324	97.40 272	92.99 293	99.40 267	96.93 387	87.86 373	88.69 358	95.44 374	89.95 198	96.44 393	90.45 329	80.69 402	94.14 365

viewmambaseed2359dif		95.92 201	95.55 200	97.04 236	97.38 274	93.41 282	99.78 169	96.97 380	91.14 300	96.58 219	99.27 162	84.85 281	98.75 241	96.87 200	97.12 219	98.97 249

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 202	97.38 274	94.40 252	99.90 114	98.64 90	96.47 80	99.51 76	99.65 117	84.99 279	99.93 103	99.22 75	99.09 149	98.46 274

E3		96.36 181	95.95 181	97.60 202	97.37 276	94.52 244	99.71 201	97.33 318	93.18 209	97.02 203	99.07 186	85.45 272	98.82 229	97.27 181	97.14 217	99.46 181

CDS-MVSNet		96.34 183	96.07 169	97.13 232	97.37 276	94.96 229	99.53 248	97.91 251	91.55 284	95.37 257	98.32 273	95.05 63	97.13 352	93.80 270	95.75 259	99.30 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 160	96.26 162	98.16 154	97.36 278	96.48 157	99.96 53	98.29 202	91.93 272	95.77 246	98.07 284	95.54 49	98.29 291	90.55 327	98.89 155	99.70 124

miper_lstm_enhance		91.81 322	91.39 321	93.06 376	97.34 279	89.18 379	99.38 273	96.79 398	86.70 390	87.47 379	95.22 389	90.00 197	95.86 416	88.26 356	81.37 391	94.15 362

baseline		96.43 176	95.98 175	97.76 188	97.34 279	95.17 225	99.51 251	97.17 346	93.92 179	96.90 209	99.28 159	85.37 274	98.64 257	97.50 176	96.86 232	99.46 181

cl____		92.31 314	91.58 315	94.52 324	97.33 281	92.77 295	99.57 239	96.78 399	86.97 387	87.56 377	95.51 370	89.43 204	96.62 385	88.60 350	82.44 383	94.16 361

SD_0403		92.63 308	93.38 275	90.40 411	97.32 282	77.91 456	97.75 407	98.03 238	91.89 273	90.83 314	98.29 277	82.00 309	93.79 444	88.51 354	95.75 259	99.52 169

DIV-MVS_self_test		92.32 313	91.60 314	94.47 328	97.31 283	92.74 297	99.58 236	96.75 400	86.99 386	87.64 375	95.54 367	89.55 203	96.50 390	88.58 351	82.44 383	94.17 356

casdiffmvs		96.42 178	95.97 178	97.77 186	97.30 284	94.98 228	99.84 149	97.09 364	93.75 188	96.58 219	99.26 166	85.07 277	98.78 236	97.77 169	97.04 223	99.54 163
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		94.36 261	93.48 269	96.99 238	97.29 285	93.54 278	99.96 53	96.72 402	88.35 367	93.43 282	98.94 209	82.05 308	98.05 309	88.12 360	96.48 238	99.37 197

eth_miper_zixun_eth		92.41 312	91.93 309	93.84 354	97.28 286	90.68 351	98.83 347	96.97 380	88.57 363	89.19 350	95.73 360	89.24 210	96.69 383	89.97 338	81.55 389	94.15 362

MVSFormer		96.94 146	96.60 148	97.95 168	97.28 286	97.70 101	99.55 245	97.27 331	91.17 297	99.43 82	99.54 133	90.92 181	96.89 371	94.67 248	99.62 100	99.25 223

lupinMVS		97.85 90	97.60 98	98.62 115	97.28 286	97.70 101	99.99 5	97.55 291	95.50 115	99.43 82	99.67 113	90.92 181	98.71 246	98.40 127	99.62 100	99.45 186

diffmvs_AUTHOR		96.75 158	96.41 158	97.79 182	97.20 289	95.46 203	99.69 211	97.15 349	94.46 145	98.78 125	99.21 173	85.64 266	98.77 237	98.27 137	97.31 209	99.13 234

mamba_0408		94.98 234	94.09 247	97.64 196	97.14 290	95.31 214	93.48 456	97.08 365	90.48 321	94.40 269	98.62 247	84.49 288	98.67 253	93.99 261	97.18 214	98.93 251

SSM_04072		94.77 241	94.09 247	96.82 244	97.14 290	95.31 214	93.48 456	97.08 365	90.48 321	94.40 269	98.62 247	84.49 288	96.21 404	93.99 261	97.18 214	98.93 251

SSM_0407		95.62 215	94.95 224	97.61 201	97.14 290	95.31 214	99.00 322	97.25 334	90.81 310	94.40 269	98.83 228	84.74 283	98.58 260	95.24 230	97.18 214	98.93 251

SCA		94.69 244	93.81 258	97.33 228	97.10 293	94.44 247	98.86 344	98.32 196	93.30 204	96.17 236	95.59 365	76.48 371	97.95 315	91.06 315	97.43 200	99.59 149

viewmacassd2359aftdt		95.93 200	95.45 201	97.36 225	97.09 294	94.12 262	99.57 239	97.26 333	93.05 218	96.50 223	99.17 177	82.76 304	98.68 251	96.61 206	97.04 223	99.28 219

KinetiMVS		96.10 193	95.29 210	98.53 129	97.08 295	97.12 128	99.56 242	98.12 229	94.78 132	98.44 147	98.94 209	80.30 336	99.39 190	91.56 308	98.79 161	99.06 242

TAMVS		95.85 203	95.58 198	96.65 252	97.07 296	93.50 279	99.17 299	97.82 261	91.39 294	95.02 262	98.01 285	92.20 158	97.30 342	93.75 273	95.83 256	99.14 233

Fast-Effi-MVS+-dtu		93.72 280	93.86 257	93.29 368	97.06 297	86.16 410	99.80 165	96.83 394	92.66 239	92.58 295	97.83 296	81.39 318	97.67 326	89.75 340	96.87 230	96.05 323

CostFormer		96.10 193	95.88 187	96.78 246	97.03 298	92.55 305	97.08 421	97.83 260	90.04 334	98.72 132	94.89 402	95.01 65	98.29 291	96.54 209	95.77 257	99.50 175

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 174	97.02 299	95.34 212	99.95 72	98.45 142	97.87 26	97.02 203	99.59 124	89.64 201	99.98 50	99.41 68	99.34 137	98.42 277

test-LLR		96.47 173	96.04 171	97.78 184	97.02 299	95.44 204	99.96 53	98.21 213	94.07 169	95.55 252	96.38 337	93.90 106	98.27 295	90.42 330	98.83 159	99.64 135

test-mter		96.39 179	95.93 184	97.78 184	97.02 299	95.44 204	99.96 53	98.21 213	91.81 278	95.55 252	96.38 337	95.17 58	98.27 295	90.42 330	98.83 159	99.64 135

icg_test_0407_2		95.04 231	94.78 231	95.84 278	96.97 302	91.64 330	98.63 366	97.12 354	92.33 258	95.60 250	98.88 214	85.65 264	96.56 388	92.12 296	95.70 262	99.32 208

IMVS_0407		95.21 226	94.80 230	96.46 257	96.97 302	91.64 330	98.81 349	97.12 354	92.33 258	95.60 250	98.88 214	85.65 264	98.42 272	92.12 296	95.70 262	99.32 208

IMVS_0404		93.83 272	93.17 282	95.80 280	96.97 302	91.64 330	97.78 406	97.12 354	92.33 258	90.87 313	98.88 214	76.78 366	96.43 394	92.12 296	95.70 262	99.32 208

IMVS_0403		95.25 224	94.81 229	96.58 254	96.97 302	91.64 330	98.97 329	97.12 354	92.33 258	95.43 255	98.88 214	85.78 263	98.79 234	92.12 296	95.70 262	99.32 208

gm-plane-assit					96.97 302	93.76 271			91.47 288		98.96 202		98.79 234	94.92 238

WB-MVSnew		92.90 299	92.77 291	93.26 370	96.95 307	93.63 275	99.71 201	98.16 223	91.49 285	94.28 274	98.14 281	81.33 320	96.48 391	79.47 425	95.46 269	89.68 452

QAPM		95.40 220	94.17 245	99.10 78	96.92 308	97.71 99	99.40 267	98.68 83	89.31 342	88.94 354	98.89 213	82.48 306	99.96 75	93.12 286	99.83 81	99.62 142

KD-MVS_2432*1600		88.00 387	86.10 391	93.70 359	96.91 309	94.04 263	97.17 418	97.12 354	84.93 411	81.96 421	92.41 435	92.48 151	94.51 437	79.23 426	52.68 473	92.56 421

miper_refine_blended		88.00 387	86.10 391	93.70 359	96.91 309	94.04 263	97.17 418	97.12 354	84.93 411	81.96 421	92.41 435	92.48 151	94.51 437	79.23 426	52.68 473	92.56 421

tpm2		95.47 218	95.18 214	96.35 263	96.91 309	91.70 328	96.96 424	97.93 247	88.04 371	98.44 147	95.40 376	93.32 121	97.97 312	94.00 260	95.61 267	99.38 195

FMVSNet5		88.32 383	87.47 385	90.88 400	96.90 312	88.39 393	97.28 414	95.68 431	82.60 431	84.67 409	92.40 437	79.83 339	91.16 461	76.39 443	81.51 390	93.09 412

3Dnovator+	91.53 11	96.31 185	95.24 211	99.52 32	96.88 313	98.64 58	99.72 198	98.24 209	95.27 120	88.42 367	98.98 198	82.76 304	99.94 93	97.10 189	99.83 81	99.96 74

Patchmatch-test		92.65 307	91.50 318	96.10 269	96.85 314	90.49 356	91.50 464	97.19 342	82.76 430	90.23 319	95.59 365	95.02 64	98.00 311	77.41 437	96.98 228	99.82 106

MVS		96.60 167	95.56 199	99.72 14	96.85 314	99.22 21	98.31 383	98.94 44	91.57 283	90.90 312	99.61 123	86.66 250	99.96 75	97.36 179	99.88 77	99.99 24

3Dnovator	91.47 12	96.28 188	95.34 207	99.08 81	96.82 316	97.47 113	99.45 264	98.81 67	95.52 114	89.39 341	99.00 195	81.97 310	99.95 84	97.27 181	99.83 81	99.84 103

EI-MVSNet		93.73 279	93.40 274	94.74 313	96.80 317	92.69 300	99.06 312	97.67 274	88.96 351	91.39 306	99.02 191	88.75 218	97.30 342	91.07 314	87.85 340	94.22 352

CVMVSNet		94.68 246	94.94 225	93.89 353	96.80 317	86.92 407	99.06 312	98.98 41	94.45 146	94.23 276	99.02 191	85.60 267	95.31 426	90.91 320	95.39 272	99.43 190

IterMVS-LS		92.69 305	92.11 305	94.43 332	96.80 317	92.74 297	99.45 264	96.89 390	88.98 349	89.65 334	95.38 379	88.77 217	96.34 398	90.98 318	82.04 386	94.22 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 170	96.46 155	96.91 240	96.79 320	92.50 306	99.90 114	97.38 310	96.02 98	97.79 179	99.32 153	86.36 254	98.99 213	98.26 138	96.33 242	99.23 226

IterMVS		90.91 341	90.17 343	93.12 373	96.78 321	90.42 359	98.89 338	97.05 372	89.03 346	86.49 392	95.42 375	76.59 369	95.02 428	87.22 370	84.09 371	93.93 383
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 151	95.96 179	99.48 39	96.74 322	98.52 62	98.31 383	98.86 59	95.82 103	89.91 325	98.98 198	87.49 234	99.96 75	97.80 164	99.73 91	99.96 74

IterMVS-SCA-FT		90.85 344	90.16 344	92.93 378	96.72 323	89.96 368	98.89 338	96.99 376	88.95 352	86.63 389	95.67 361	76.48 371	95.00 429	87.04 373	84.04 374	93.84 390

MVS-HIRNet		86.22 394	83.19 407	95.31 296	96.71 324	90.29 360	92.12 461	97.33 318	62.85 468	86.82 386	70.37 473	69.37 412	97.49 332	75.12 446	97.99 190	98.15 284

viewdifsd2359ckpt11		94.09 267	93.63 260	95.46 289	96.68 325	88.92 382	99.62 225	97.12 354	93.07 216	95.73 247	99.22 170	77.05 359	98.88 223	96.52 210	87.69 345	98.58 272

viewmsd2359difaftdt		94.09 267	93.64 259	95.46 289	96.68 325	88.92 382	99.62 225	97.13 353	93.07 216	95.73 247	99.22 170	77.05 359	98.89 222	96.52 210	87.70 344	98.58 272

VDDNet		93.12 294	91.91 310	96.76 247	96.67 327	92.65 303	98.69 361	98.21 213	82.81 429	97.75 181	99.28 159	61.57 444	99.48 185	98.09 148	94.09 291	98.15 284

dmvs_re		93.20 291	93.15 283	93.34 366	96.54 328	83.81 425	98.71 358	98.51 130	91.39 294	92.37 298	98.56 255	78.66 351	97.83 320	93.89 264	89.74 312	98.38 279

Elysia		94.50 253	93.38 275	97.85 178	96.49 329	96.70 145	98.98 324	97.78 264	90.81 310	96.19 234	98.55 257	73.63 395	98.98 214	89.41 341	98.56 167	97.88 291

StellarMVS		94.50 253	93.38 275	97.85 178	96.49 329	96.70 145	98.98 324	97.78 264	90.81 310	96.19 234	98.55 257	73.63 395	98.98 214	89.41 341	98.56 167	97.88 291

MIMVSNet		90.30 357	88.67 371	95.17 300	96.45 331	91.64 330	92.39 460	97.15 349	85.99 397	90.50 317	93.19 430	66.95 423	94.86 433	82.01 412	93.43 299	99.01 247

CR-MVSNet		93.45 288	92.62 293	95.94 273	96.29 332	92.66 301	92.01 462	96.23 416	92.62 241	96.94 207	93.31 428	91.04 178	96.03 412	79.23 426	95.96 250	99.13 234

RPMNet		89.76 369	87.28 386	97.19 231	96.29 332	92.66 301	92.01 462	98.31 198	70.19 464	96.94 207	85.87 466	87.25 239	99.78 146	62.69 468	95.96 250	99.13 234

tt0805		91.28 334	90.18 342	94.60 319	96.26 334	87.55 400	98.39 381	98.72 77	89.00 348	89.22 347	98.47 265	62.98 439	98.96 218	90.57 326	88.00 339	97.28 310

Patchmtry		89.70 370	88.49 374	93.33 367	96.24 335	89.94 371	91.37 465	96.23 416	78.22 447	87.69 374	93.31 428	91.04 178	96.03 412	80.18 424	82.10 385	94.02 373

test_vis1_rt		86.87 392	86.05 394	89.34 420	96.12 336	78.07 455	99.87 130	83.54 481	92.03 270	78.21 442	89.51 449	45.80 465	99.91 110	96.25 214	93.11 304	90.03 448

JIA-IIPM		91.76 328	90.70 329	94.94 306	96.11 337	87.51 401	93.16 458	98.13 228	75.79 453	97.58 183	77.68 471	92.84 137	97.97 312	88.47 355	96.54 234	99.33 206

OpenMVS	90.15 15	94.77 241	93.59 264	98.33 145	96.07 338	97.48 112	99.56 242	98.57 106	90.46 323	86.51 391	98.95 207	78.57 352	99.94 93	93.86 265	99.74 90	97.57 305

PAPM		98.60 37	98.42 38	99.14 72	96.05 339	98.96 27	99.90 114	99.35 24	96.68 71	98.35 154	99.66 115	96.45 35	98.51 265	99.45 65	99.89 74	99.96 74

CLD-MVS		94.06 269	93.90 255	94.55 323	96.02 340	90.69 350	99.98 21	97.72 270	96.62 75	91.05 311	98.85 226	77.21 357	98.47 266	98.11 146	89.51 318	94.48 330
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 354	88.75 370	95.25 298	95.99 341	90.16 363	91.22 466	97.54 293	76.80 449	97.26 196	86.01 465	91.88 165	96.07 411	66.16 463	95.91 254	99.51 173

ACMH+	89.98 16	90.35 355	89.54 354	92.78 382	95.99 341	86.12 411	98.81 349	97.18 344	89.38 341	83.14 417	97.76 297	68.42 417	98.43 271	89.11 346	86.05 354	93.78 393

DeepMVS_CX				82.92 443	95.98 343	58.66 474		96.01 422	92.72 234	78.34 441	95.51 370	58.29 451	98.08 306	82.57 407	85.29 359	92.03 429

ACMP	92.05 9	92.74 303	92.42 302	93.73 355	95.91 344	88.72 386	99.81 161	97.53 295	94.13 165	87.00 385	98.23 279	74.07 392	98.47 266	96.22 215	88.86 325	93.99 378
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 283	93.03 285	95.35 293	95.86 345	86.94 406	99.87 130	96.36 414	96.85 62	99.54 71	98.79 230	52.41 459	99.83 139	98.64 114	98.97 153	99.29 217

HQP-NCC					95.78 346		99.87 130		96.82 64	93.37 283

ACMP_Plane					95.78 346		99.87 130		96.82 64	93.37 283

HQP-MVS		94.61 248	94.50 236	94.92 307	95.78 346	91.85 320	99.87 130	97.89 252	96.82 64	93.37 283	98.65 242	80.65 330	98.39 278	97.92 158	89.60 313	94.53 326

NP-MVS					95.77 349	91.79 322					98.65 242

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 114	95.76 350	96.20 173	99.94 90	98.05 235	98.17 13	98.89 120	99.42 141	87.65 229	99.90 112	99.50 61	99.60 107	99.82 106

plane_prior6					95.76 350	91.72 327						80.47 334

ACMM	91.95 10	92.88 300	92.52 300	93.98 349	95.75 352	89.08 381	99.77 174	97.52 297	93.00 219	89.95 324	97.99 288	76.17 375	98.46 269	93.63 277	88.87 324	94.39 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 272	92.84 287	96.80 245	95.73 353	93.57 276	99.88 127	97.24 337	92.57 246	92.92 290	96.66 329	78.73 350	97.67 326	87.75 363	94.06 292	99.17 229

plane_prior1					95.73 353

jason		97.24 129	96.86 134	98.38 144	95.73 353	97.32 117	99.97 39	97.40 309	95.34 118	98.60 141	99.54 133	87.70 228	98.56 262	97.94 157	99.47 124	99.25 223
jason: jason.
mmtdpeth		88.52 381	87.75 383	90.85 402	95.71 356	83.47 431	98.94 332	94.85 446	88.78 357	97.19 198	89.58 448	63.29 437	98.97 216	98.54 119	62.86 467	90.10 447

HQP_MVS		94.49 255	94.36 239	94.87 308	95.71 356	91.74 324	99.84 149	97.87 254	96.38 84	93.01 288	98.59 250	80.47 334	98.37 284	97.79 167	89.55 316	94.52 328

plane_prior7					95.71 356	91.59 336

ITE_SJBPF				92.38 385	95.69 359	85.14 417		95.71 430	92.81 228	89.33 344	98.11 282	70.23 410	98.42 272	85.91 385	88.16 337	93.59 401

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 199	95.65 360	94.21 259	99.83 156	98.50 136	96.27 91	99.65 53	99.64 118	84.72 285	99.93 103	99.04 84	98.84 158	98.74 265

ACMH	89.72 17	90.64 348	89.63 351	93.66 361	95.64 361	88.64 389	98.55 369	97.45 302	89.03 346	81.62 424	97.61 298	69.75 411	98.41 274	89.37 343	87.62 346	93.92 384
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 162	96.49 152	97.37 223	95.63 362	95.96 182	99.74 187	98.88 54	92.94 221	91.61 304	98.97 200	97.72 6	98.62 259	94.83 242	98.08 188	97.53 307

FMVSNet1		88.50 382	86.64 389	94.08 342	95.62 363	91.97 315	98.43 377	96.95 382	83.00 427	86.08 399	94.72 404	59.09 450	96.11 407	81.82 414	84.07 372	94.17 356

LuminaMVS		96.63 166	96.21 166	97.87 177	95.58 364	96.82 141	99.12 301	97.67 274	94.47 144	97.88 174	98.31 275	87.50 233	98.71 246	98.07 150	97.29 210	98.10 287

LPG-MVS_test		92.96 297	92.71 292	93.71 357	95.43 365	88.67 387	99.75 184	97.62 282	92.81 228	90.05 320	98.49 261	75.24 382	98.40 276	95.84 221	89.12 320	94.07 370

LGP-MVS_train				93.71 357	95.43 365	88.67 387		97.62 282	92.81 228	90.05 320	98.49 261	75.24 382	98.40 276	95.84 221	89.12 320	94.07 370

tpm		93.70 281	93.41 273	94.58 321	95.36 367	87.41 402	97.01 422	96.90 389	90.85 308	96.72 215	94.14 419	90.40 192	96.84 375	90.75 324	88.54 332	99.51 173

D2MVS		92.76 302	92.59 298	93.27 369	95.13 368	89.54 375	99.69 211	99.38 22	92.26 263	87.59 376	94.61 410	85.05 278	97.79 321	91.59 307	88.01 338	92.47 424

VPA-MVSNet		92.70 304	91.55 317	96.16 267	95.09 369	96.20 173	98.88 340	99.00 39	91.02 305	91.82 303	95.29 386	76.05 377	97.96 314	95.62 226	81.19 392	94.30 345

LTVRE_ROB	88.28 18	90.29 358	89.05 365	94.02 345	95.08 370	90.15 364	97.19 417	97.43 304	84.91 413	83.99 413	97.06 315	74.00 393	98.28 293	84.08 396	87.71 342	93.62 400
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
TinyColmap		87.87 389	86.51 390	91.94 391	95.05 371	85.57 415	97.65 408	94.08 456	84.40 417	81.82 423	96.85 324	62.14 442	98.33 287	80.25 423	86.37 353	91.91 431

test0.0.03 1		93.86 271	93.61 261	94.64 317	95.02 372	92.18 313	99.93 97	98.58 104	94.07 169	87.96 371	98.50 260	93.90 106	94.96 430	81.33 415	93.17 302	96.78 313

UniMVSNet (Re)		93.07 296	92.13 304	95.88 275	94.84 373	96.24 172	99.88 127	98.98 41	92.49 251	89.25 345	95.40 376	87.09 241	97.14 351	93.13 285	78.16 416	94.26 347

USDC		90.00 365	88.96 366	93.10 375	94.81 374	88.16 395	98.71 358	95.54 435	93.66 190	83.75 415	97.20 309	65.58 428	98.31 289	83.96 399	87.49 348	92.85 418

VPNet		91.81 322	90.46 333	95.85 277	94.74 375	95.54 201	98.98 324	98.59 102	92.14 265	90.77 316	97.44 302	68.73 415	97.54 331	94.89 241	77.89 418	94.46 331

FIs		94.10 266	93.43 270	96.11 268	94.70 376	96.82 141	99.58 236	98.93 48	92.54 247	89.34 343	97.31 306	87.62 230	97.10 355	94.22 259	86.58 351	94.40 337

UniMVSNet_ETH3D		90.06 364	88.58 373	94.49 327	94.67 377	88.09 396	97.81 405	97.57 290	83.91 420	88.44 363	97.41 303	57.44 452	97.62 328	91.41 309	88.59 331	97.77 296

UniMVSNet_NR-MVSNet		92.95 298	92.11 305	95.49 285	94.61 378	95.28 218	99.83 156	99.08 36	91.49 285	89.21 348	96.86 323	87.14 240	96.73 381	93.20 281	77.52 421	94.46 331

test_fmvs2		89.47 374	89.70 350	88.77 427	94.54 379	75.74 457	99.83 156	94.70 452	94.71 136	91.08 309	96.82 328	54.46 455	97.78 323	92.87 288	88.27 335	92.80 419

MonoMVSNet		94.82 236	94.43 237	95.98 271	94.54 379	90.73 349	99.03 319	97.06 369	93.16 211	93.15 287	95.47 373	88.29 221	97.57 329	97.85 162	91.33 310	99.62 142

WR-MVS		92.31 314	91.25 322	95.48 288	94.45 381	95.29 217	99.60 232	98.68 83	90.10 331	88.07 370	96.89 321	80.68 329	96.80 379	93.14 284	79.67 409	94.36 339

nrg030		93.51 285	92.53 299	96.45 258	94.36 382	97.20 123	99.81 161	97.16 348	91.60 282	89.86 327	97.46 301	86.37 253	97.68 325	95.88 220	80.31 405	94.46 331

tfpnnormal		89.29 377	87.61 384	94.34 335	94.35 383	94.13 261	98.95 331	98.94 44	83.94 418	84.47 410	95.51 370	74.84 387	97.39 334	77.05 440	80.41 403	91.48 434

FC-MVSNet-test		93.81 275	93.15 283	95.80 280	94.30 384	96.20 173	99.42 266	98.89 52	92.33 258	89.03 353	97.27 308	87.39 236	96.83 377	93.20 281	86.48 352	94.36 339

SSC-MVS3.2		89.59 372	88.66 372	92.38 385	94.29 385	86.12 411	99.49 255	97.66 277	90.28 330	88.63 360	95.18 390	64.46 433	96.88 373	85.30 389	82.66 380	94.14 365

MS-PatchMatch		90.65 347	90.30 338	91.71 396	94.22 386	85.50 416	98.24 388	97.70 271	88.67 360	86.42 394	96.37 339	67.82 420	98.03 310	83.62 401	99.62 100	91.60 432

WR-MVS_H		91.30 332	90.35 336	94.15 339	94.17 387	92.62 304	99.17 299	98.94 44	88.87 355	86.48 393	94.46 415	84.36 291	96.61 386	88.19 357	78.51 414	93.21 410

DU-MVS		92.46 311	91.45 320	95.49 285	94.05 388	95.28 218	99.81 161	98.74 76	92.25 264	89.21 348	96.64 331	81.66 315	96.73 381	93.20 281	77.52 421	94.46 331

NR-MVSNet		91.56 330	90.22 340	95.60 283	94.05 388	95.76 189	98.25 387	98.70 79	91.16 299	80.78 430	96.64 331	83.23 301	96.57 387	91.41 309	77.73 420	94.46 331

CP-MVSNet		91.23 336	90.22 340	94.26 337	93.96 390	92.39 309	99.09 305	98.57 106	88.95 352	86.42 394	96.57 334	79.19 345	96.37 396	90.29 333	78.95 411	94.02 373

XXY-MVS		91.82 321	90.46 333	95.88 275	93.91 391	95.40 208	98.87 343	97.69 273	88.63 362	87.87 372	97.08 313	74.38 391	97.89 318	91.66 306	84.07 372	94.35 342

PS-CasMVS		90.63 349	89.51 356	93.99 348	93.83 392	91.70 328	98.98 324	98.52 127	88.48 364	86.15 398	96.53 336	75.46 380	96.31 400	88.83 348	78.86 413	93.95 381

test_0402		85.58 396	83.94 401	90.50 408	93.81 393	85.04 418	98.55 369	95.20 443	76.01 451	79.72 436	95.13 391	64.15 435	96.26 402	66.04 464	86.88 350	90.21 445

XVG-ACMP-BASELINE		91.22 337	90.75 328	92.63 384	93.73 394	85.61 414	98.52 373	97.44 303	92.77 232	89.90 326	96.85 324	66.64 425	98.39 278	92.29 293	88.61 329	93.89 386

TranMVSNet+NR-MVSNet		91.68 329	90.61 332	94.87 308	93.69 395	93.98 266	99.69 211	98.65 87	91.03 304	88.44 363	96.83 327	80.05 338	96.18 405	90.26 334	76.89 429	94.45 336

TransMVSNet (Re)		87.25 390	85.28 397	93.16 372	93.56 396	91.03 341	98.54 371	94.05 458	83.69 422	81.09 428	96.16 345	75.32 381	96.40 395	76.69 441	68.41 455	92.06 428

v10		90.25 359	88.82 368	94.57 322	93.53 397	93.43 281	99.08 307	96.87 392	85.00 410	87.34 383	94.51 411	80.93 325	97.02 365	82.85 406	79.23 410	93.26 408

testgi		89.01 379	88.04 380	91.90 392	93.49 398	84.89 420	99.73 194	95.66 432	93.89 183	85.14 405	98.17 280	59.68 448	94.66 436	77.73 436	88.88 323	96.16 322

v8		90.54 351	89.17 361	94.66 316	93.43 399	93.40 284	99.20 296	96.94 386	85.76 400	87.56 377	94.51 411	81.96 311	97.19 348	84.94 392	78.25 415	93.38 406

V42		91.28 334	90.12 345	94.74 313	93.42 400	93.46 280	99.68 214	97.02 373	87.36 379	89.85 329	95.05 394	81.31 321	97.34 337	87.34 368	80.07 407	93.40 404

pm-mvs1		89.36 376	87.81 382	94.01 346	93.40 401	91.93 318	98.62 367	96.48 412	86.25 395	83.86 414	96.14 347	73.68 394	97.04 361	86.16 382	75.73 434	93.04 414

v1144		91.09 338	89.83 347	94.87 308	93.25 402	93.69 274	99.62 225	96.98 378	86.83 389	89.64 335	94.99 399	80.94 324	97.05 358	85.08 391	81.16 393	93.87 388

v1192		90.62 350	89.25 360	94.72 315	93.13 403	93.07 288	99.50 253	97.02 373	86.33 394	89.56 339	95.01 396	79.22 344	97.09 357	82.34 410	81.16 393	94.01 375

v2v482		91.30 332	90.07 346	95.01 303	93.13 403	93.79 269	99.77 174	97.02 373	88.05 370	89.25 345	95.37 380	80.73 328	97.15 350	87.28 369	80.04 408	94.09 369

OPM-MVS		93.21 290	92.80 289	94.44 330	93.12 405	90.85 348	99.77 174	97.61 285	96.19 94	91.56 305	98.65 242	75.16 386	98.47 266	93.78 272	89.39 319	93.99 378
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 345	89.52 355	94.59 320	93.11 406	92.77 295	99.56 242	96.99 376	86.38 393	89.82 330	94.95 401	80.50 333	97.10 355	83.98 398	80.41 403	93.90 385

PEN-MVS		90.19 361	89.06 364	93.57 362	93.06 407	90.90 346	99.06 312	98.47 139	88.11 369	85.91 400	96.30 341	76.67 367	95.94 415	87.07 372	76.91 428	93.89 386

v1240		90.20 360	88.79 369	94.44 330	93.05 408	92.27 311	99.38 273	96.92 388	85.89 398	89.36 342	94.87 403	77.89 356	97.03 363	80.66 419	81.08 396	94.01 375

v148		90.70 346	89.63 351	93.92 350	92.97 409	90.97 342	99.75 184	96.89 390	87.51 376	88.27 368	95.01 396	81.67 314	97.04 361	87.40 367	77.17 426	93.75 394

v1921920		90.46 352	89.12 362	94.50 326	92.96 410	92.46 307	99.49 255	96.98 378	86.10 396	89.61 337	95.30 383	78.55 353	97.03 363	82.17 411	80.89 401	94.01 375

MVStest1		85.03 402	82.76 411	91.83 393	92.95 411	89.16 380	98.57 368	94.82 447	71.68 462	68.54 465	95.11 393	83.17 302	95.66 419	74.69 447	65.32 462	90.65 441

tt0320-xc		82.94 417	80.35 424	90.72 406	92.90 412	83.54 429	96.85 427	94.73 450	63.12 467	79.85 435	93.77 423	49.43 463	95.46 422	80.98 418	71.54 443	93.16 411

Baseline_NR-MVSNet		90.33 356	89.51 356	92.81 381	92.84 413	89.95 369	99.77 174	93.94 459	84.69 415	89.04 352	95.66 362	81.66 315	96.52 389	90.99 317	76.98 427	91.97 430

test_method		80.79 423	79.70 426	84.08 440	92.83 414	67.06 466	99.51 251	95.42 437	54.34 472	81.07 429	93.53 425	44.48 467	92.22 458	78.90 431	77.23 425	92.94 416

pmmvs4		92.10 318	91.07 326	95.18 299	92.82 415	94.96 229	99.48 258	96.83 394	87.45 378	88.66 359	96.56 335	83.78 296	96.83 377	89.29 344	84.77 366	93.75 394

LF4IMVS		89.25 378	88.85 367	90.45 410	92.81 416	81.19 447	98.12 395	94.79 448	91.44 289	86.29 396	97.11 311	65.30 431	98.11 304	88.53 353	85.25 360	92.07 427

tt0320		83.56 416	81.15 419	90.77 404	92.77 417	83.58 428	96.83 428	95.52 436	63.26 466	81.36 426	92.54 433	53.26 457	95.77 417	80.45 420	74.38 437	92.96 415

DTE-MVSNet		89.40 375	88.24 378	92.88 379	92.66 418	89.95 369	99.10 304	98.22 212	87.29 380	85.12 406	96.22 343	76.27 374	95.30 427	83.56 402	75.74 433	93.41 403

EU-MVSNet		90.14 363	90.34 337	89.54 419	92.55 419	81.06 448	98.69 361	98.04 236	91.41 293	86.59 390	96.84 326	80.83 327	93.31 449	86.20 381	81.91 387	94.26 347

APD_test1		81.15 421	80.92 421	81.86 444	92.45 420	59.76 473	96.04 442	93.61 462	73.29 460	77.06 445	96.64 331	44.28 468	96.16 406	72.35 451	82.52 381	89.67 454

sc_t1		85.01 403	82.46 413	92.67 383	92.44 421	83.09 433	97.39 412	95.72 429	65.06 465	85.64 403	96.16 345	49.50 462	97.34 337	84.86 393	75.39 435	97.57 305

our_test_3		90.39 353	89.48 358	93.12 373	92.40 422	89.57 374	99.33 280	96.35 415	87.84 374	85.30 404	94.99 399	84.14 294	96.09 410	80.38 421	84.56 367	93.71 399

ppachtmachnet_test		89.58 373	88.35 376	93.25 371	92.40 422	90.44 358	99.33 280	96.73 401	85.49 405	85.90 401	95.77 356	81.09 323	96.00 414	76.00 445	82.49 382	93.30 407

v7n		89.65 371	88.29 377	93.72 356	92.22 424	90.56 355	99.07 311	97.10 361	85.42 407	86.73 387	94.72 404	80.06 337	97.13 352	81.14 416	78.12 417	93.49 402

dmvs_testset		83.79 412	86.07 393	76.94 448	92.14 425	48.60 483	96.75 429	90.27 473	89.48 340	78.65 439	98.55 257	79.25 343	86.65 471	66.85 461	82.69 379	95.57 324

PS-MVSNAJss		93.64 282	93.31 279	94.61 318	92.11 426	92.19 312	99.12 301	97.38 310	92.51 250	88.45 362	96.99 319	91.20 173	97.29 345	94.36 253	87.71 342	94.36 339

pmmvs5		90.17 362	89.09 363	93.40 365	92.10 427	89.77 372	99.74 187	95.58 434	85.88 399	87.24 384	95.74 357	73.41 397	96.48 391	88.54 352	83.56 376	93.95 381

N_pmnet		80.06 427	80.78 422	77.89 447	91.94 428	45.28 485	98.80 352	56.82 487	78.10 448	80.08 433	93.33 426	77.03 361	95.76 418	68.14 459	82.81 378	92.64 420

test_djsdf		92.83 301	92.29 303	94.47 328	91.90 429	92.46 307	99.55 245	97.27 331	91.17 297	89.96 323	96.07 351	81.10 322	96.89 371	94.67 248	88.91 322	94.05 372

SixPastTwentyTwo		88.73 380	88.01 381	90.88 400	91.85 430	82.24 439	98.22 392	95.18 444	88.97 350	82.26 420	96.89 321	71.75 402	96.67 384	84.00 397	82.98 377	93.72 398

K. test v3		88.05 386	87.24 387	90.47 409	91.82 431	82.23 440	98.96 330	97.42 306	89.05 345	76.93 447	95.60 364	68.49 416	95.42 423	85.87 386	81.01 399	93.75 394

OurMVSNet-221017-0		89.81 368	89.48 358	90.83 403	91.64 432	81.21 446	98.17 394	95.38 439	91.48 287	85.65 402	97.31 306	72.66 398	97.29 345	88.15 358	84.83 365	93.97 380

mvs_tets		91.81 322	91.08 325	94.00 347	91.63 433	90.58 354	98.67 363	97.43 304	92.43 252	87.37 382	97.05 316	71.76 401	97.32 340	94.75 245	88.68 328	94.11 368

Gipuma		66.95 440	65.00 440	72.79 453	91.52 434	67.96 465	66.16 477	95.15 445	47.89 474	58.54 471	67.99 476	29.74 472	87.54 470	50.20 475	77.83 419	62.87 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 179	95.74 192	98.32 146	91.47 435	95.56 200	99.84 149	97.30 326	97.74 30	97.89 173	99.35 152	79.62 340	99.85 129	99.25 74	99.24 141	99.55 159

jajsoiax		91.92 320	91.18 323	94.15 339	91.35 436	90.95 345	99.00 322	97.42 306	92.61 242	87.38 381	97.08 313	72.46 399	97.36 335	94.53 251	88.77 326	94.13 367

MDA-MVSNet-bldmvs		84.09 410	81.52 417	91.81 394	91.32 437	88.00 398	98.67 363	95.92 424	80.22 441	55.60 474	93.32 427	68.29 418	93.60 447	73.76 448	76.61 430	93.82 392

MVP-Stereo		90.93 340	90.45 335	92.37 387	91.25 438	88.76 384	98.05 399	96.17 418	87.27 381	84.04 411	95.30 383	78.46 354	97.27 347	83.78 400	99.70 93	91.09 435
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 398	83.32 406	92.10 389	90.96 439	88.58 390	99.20 296	96.52 410	79.70 443	57.12 473	92.69 432	79.11 346	93.86 443	77.10 439	77.46 423	93.86 389

YYNet1		85.50 399	83.33 405	92.00 390	90.89 440	88.38 394	99.22 295	96.55 409	79.60 444	57.26 472	92.72 431	79.09 348	93.78 445	77.25 438	77.37 424	93.84 390

anonymousdsp		91.79 327	90.92 327	94.41 333	90.76 441	92.93 294	98.93 334	97.17 346	89.08 344	87.46 380	95.30 383	78.43 355	96.92 369	92.38 292	88.73 327	93.39 405

lessismore_v0				90.53 407	90.58 442	80.90 449		95.80 426		77.01 446	95.84 354	66.15 427	96.95 367	83.03 405	75.05 436	93.74 397

EG-PatchMatch MVS		85.35 400	83.81 403	89.99 417	90.39 443	81.89 442	98.21 393	96.09 420	81.78 434	74.73 453	93.72 424	51.56 461	97.12 354	79.16 429	88.61 329	90.96 438

EGC-MVSNET		69.38 433	63.76 443	86.26 437	90.32 444	81.66 445	96.24 438	93.85 460	0.99 484	3.22 485	92.33 438	52.44 458	92.92 454	59.53 471	84.90 364	84.21 465

CMPMVS	61.59 21	84.75 406	85.14 398	83.57 441	90.32 444	62.54 469	96.98 423	97.59 289	74.33 458	69.95 462	96.66 329	64.17 434	98.32 288	87.88 362	88.41 334	89.84 450
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 409	82.92 409	89.21 421	90.03 446	82.60 436	96.89 426	95.62 433	80.59 439	75.77 452	89.17 450	65.04 432	94.79 434	72.12 452	81.02 398	90.23 444

pmmvs6		85.69 395	83.84 402	91.26 399	90.00 447	84.41 423	97.82 404	96.15 419	75.86 452	81.29 427	95.39 378	61.21 445	96.87 374	83.52 403	73.29 439	92.50 423

ttmdpeth		88.23 385	87.06 388	91.75 395	89.91 448	87.35 403	98.92 337	95.73 428	87.92 372	84.02 412	96.31 340	68.23 419	96.84 375	86.33 380	76.12 431	91.06 436

DSMNet-mixed		88.28 384	88.24 378	88.42 430	89.64 449	75.38 459	98.06 398	89.86 474	85.59 404	88.20 369	92.14 439	76.15 376	91.95 459	78.46 433	96.05 247	97.92 290

UnsupCasMVSNet_eth		85.52 397	83.99 399	90.10 415	89.36 450	83.51 430	96.65 430	97.99 240	89.14 343	75.89 451	93.83 421	63.25 438	93.92 441	81.92 413	67.90 458	92.88 417

Anonymous20231206		86.32 393	85.42 396	89.02 423	89.11 451	80.53 452	99.05 316	95.28 440	85.43 406	82.82 418	93.92 420	74.40 390	93.44 448	66.99 460	81.83 388	93.08 413

Anonymous20240521		85.15 401	83.81 403	89.16 422	88.32 452	82.69 435	98.80 352	95.74 427	79.72 442	81.53 425	90.99 442	65.38 430	94.16 439	72.69 450	81.11 395	90.63 442

OpenMVS_ROB	79.82 20	83.77 413	81.68 416	90.03 416	88.30 453	82.82 434	98.46 374	95.22 442	73.92 459	76.00 450	91.29 441	55.00 454	96.94 368	68.40 458	88.51 333	90.34 443

test20.03		84.72 407	83.99 399	86.91 435	88.19 454	80.62 451	98.88 340	95.94 423	88.36 366	78.87 437	94.62 409	68.75 414	89.11 466	66.52 462	75.82 432	91.00 437

KD-MVS_self_test		83.59 414	82.06 414	88.20 431	86.93 455	80.70 450	97.21 416	96.38 413	82.87 428	82.49 419	88.97 451	67.63 421	92.32 457	73.75 449	62.30 469	91.58 433

MIMVSNet1		82.58 418	80.51 423	88.78 425	86.68 456	84.20 424	96.65 430	95.41 438	78.75 446	78.59 440	92.44 434	51.88 460	89.76 465	65.26 465	78.95 411	92.38 426

CL-MVSNet_self_test		84.50 408	83.15 408	88.53 429	86.00 457	81.79 443	98.82 348	97.35 314	85.12 409	83.62 416	90.91 444	76.66 368	91.40 460	69.53 456	60.36 470	92.40 425

UnsupCasMVSNet_bld		79.97 429	77.03 434	88.78 425	85.62 458	81.98 441	93.66 454	97.35 314	75.51 455	70.79 461	83.05 468	48.70 464	94.91 432	78.31 434	60.29 471	89.46 457

mvs5depth		84.87 404	82.90 410	90.77 404	85.59 459	84.84 421	91.10 467	93.29 464	83.14 425	85.07 407	94.33 417	62.17 441	97.32 340	78.83 432	72.59 442	90.14 446

Patchmatch-RL test		86.90 391	85.98 395	89.67 418	84.45 460	75.59 458	89.71 470	92.43 466	86.89 388	77.83 444	90.94 443	94.22 95	93.63 446	87.75 363	69.61 448	99.79 111

pmmvs-eth3d		84.03 411	81.97 415	90.20 413	84.15 461	87.09 405	98.10 397	94.73 450	83.05 426	74.10 457	87.77 457	65.56 429	94.01 440	81.08 417	69.24 450	89.49 456

test_fmvs3		79.99 428	80.17 425	79.45 446	84.02 462	62.83 467	99.05 316	93.49 463	88.29 368	80.06 434	86.65 463	28.09 474	88.00 467	88.63 349	73.27 440	87.54 463

PM-MVS		80.47 425	78.88 428	85.26 438	83.79 463	72.22 461	95.89 445	91.08 471	85.71 403	76.56 449	88.30 453	36.64 470	93.90 442	82.39 409	69.57 449	89.66 455

new-patchmatchnet		81.19 420	79.34 427	86.76 436	82.86 464	80.36 453	97.92 401	95.27 441	82.09 433	72.02 458	86.87 462	62.81 440	90.74 463	71.10 453	63.08 466	89.19 459

FE-MVSNET2		83.57 415	81.36 418	90.20 413	82.83 465	87.59 399	98.28 385	96.04 421	85.33 408	74.13 456	87.45 458	59.16 449	93.26 450	79.12 430	69.91 446	89.77 451

FE-MVSNET		81.05 422	78.81 429	87.79 433	81.98 466	83.70 426	98.23 390	91.78 470	81.27 436	74.29 455	87.44 459	60.92 447	90.67 464	64.92 466	68.43 454	89.01 460

mvsany_test3		82.12 419	81.14 420	85.06 439	81.87 467	70.41 463	97.09 420	92.14 467	91.27 296	77.84 443	88.73 452	39.31 469	95.49 420	90.75 324	71.24 444	89.29 458

WB-MVS		76.28 431	77.28 433	73.29 452	81.18 468	54.68 477	97.87 403	94.19 455	81.30 435	69.43 463	90.70 445	77.02 362	82.06 475	35.71 480	68.11 457	83.13 466

test_f		78.40 430	77.59 432	80.81 445	80.82 469	62.48 470	96.96 424	93.08 465	83.44 423	74.57 454	84.57 467	27.95 475	92.63 455	84.15 395	72.79 441	87.32 464

SSC-MVS		75.42 432	76.40 435	72.49 456	80.68 470	53.62 478	97.42 410	94.06 457	80.42 440	68.75 464	90.14 447	76.54 370	81.66 476	33.25 481	66.34 461	82.19 467

FE-MVSNET1		80.74 424	78.10 430	88.66 428	80.60 471	83.26 432	97.26 415	95.88 425	78.83 445	71.95 459	87.05 461	45.50 466	93.05 453	76.67 442	69.12 453	89.68 452

pmmvs3		80.27 426	77.77 431	87.76 434	80.32 472	82.43 438	98.23 390	91.97 468	72.74 461	78.75 438	87.97 456	57.30 453	90.99 462	70.31 454	62.37 468	89.87 449

testf1		68.38 436	66.92 437	72.78 454	78.80 473	50.36 480	90.95 468	87.35 479	55.47 470	58.95 469	88.14 454	20.64 479	87.60 468	57.28 472	64.69 463	80.39 469

APD_test2		68.38 436	66.92 437	72.78 454	78.80 473	50.36 480	90.95 468	87.35 479	55.47 470	58.95 469	88.14 454	20.64 479	87.60 468	57.28 472	64.69 463	80.39 469

ambc				83.23 442	77.17 475	62.61 468	87.38 472	94.55 454		76.72 448	86.65 463	30.16 471	96.36 397	84.85 394	69.86 447	90.73 440

test_vis3_rt		68.82 434	66.69 439	75.21 451	76.24 476	60.41 472	96.44 433	68.71 486	75.13 456	50.54 477	69.52 475	16.42 484	96.32 399	80.27 422	66.92 460	68.89 473

TDRefinement		84.76 405	82.56 412	91.38 398	74.58 477	84.80 422	97.36 413	94.56 453	84.73 414	80.21 432	96.12 350	63.56 436	98.39 278	87.92 361	63.97 465	90.95 439

E-PMN		52.30 444	52.18 446	52.67 462	71.51 478	45.40 484	93.62 455	76.60 484	36.01 478	43.50 479	64.13 478	27.11 476	67.31 481	31.06 482	26.06 477	45.30 480

EMVS		51.44 446	51.22 448	52.11 463	70.71 479	44.97 486	94.04 451	75.66 485	35.34 480	42.40 480	61.56 481	28.93 473	65.87 482	27.64 483	24.73 478	45.49 479

PMMVS2		67.15 439	64.15 442	76.14 450	70.56 480	62.07 471	93.89 452	87.52 478	58.09 469	60.02 468	78.32 470	22.38 478	84.54 473	59.56 470	47.03 475	81.80 468

FPMVS		68.72 435	68.72 436	68.71 458	65.95 481	44.27 487	95.97 444	94.74 449	51.13 473	53.26 475	90.50 446	25.11 477	83.00 474	60.80 469	80.97 400	78.87 471

wuyk23d		20.37 450	20.84 453	18.99 466	65.34 482	27.73 489	50.43 478	7.67 490	9.50 483	8.01 484	6.34 484	6.13 487	26.24 483	23.40 484	10.69 482	2.99 481

LCM-MVSNet		67.77 438	64.73 441	76.87 449	62.95 483	56.25 476	89.37 471	93.74 461	44.53 475	61.99 467	80.74 469	20.42 481	86.53 472	69.37 457	59.50 472	87.84 461

MVE	53.74 22	51.54 445	47.86 449	62.60 460	59.56 484	50.93 479	79.41 475	77.69 483	35.69 479	36.27 481	61.76 480	5.79 488	69.63 479	37.97 479	36.61 476	67.24 474
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 442	52.24 445	67.66 459	49.27 485	56.82 475	83.94 473	82.02 482	70.47 463	33.28 482	64.54 477	17.23 483	69.16 480	45.59 477	23.85 479	77.02 472

tmp_tt		65.23 441	62.94 444	72.13 457	44.90 486	50.03 482	81.05 474	89.42 477	38.45 476	48.51 478	99.90 22	54.09 456	78.70 478	91.84 305	18.26 480	87.64 462

PMVS	49.05 23	53.75 443	51.34 447	60.97 461	40.80 487	34.68 488	74.82 476	89.62 476	37.55 477	28.67 483	72.12 472	7.09 486	81.63 477	43.17 478	68.21 456	66.59 475
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 448	39.14 451	33.31 464	19.94 488	24.83 490	98.36 382	9.75 489	15.53 482	51.31 476	87.14 460	19.62 482	17.74 484	47.10 476	3.47 483	57.36 477

testmvs		40.60 447	44.45 450	29.05 465	19.49 489	14.11 491	99.68 214	18.47 488	20.74 481	64.59 466	98.48 264	10.95 485	17.09 485	56.66 474	11.01 481	55.94 478

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.02 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

eth-test2					0.00 490

eth-test					0.00 490

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

cdsmvs_eth3d_5k		23.43 449	31.24 452	0.00 467	0.00 490	0.00 492	0.00 479	98.09 230	0.00 485	0.00 486	99.67 113	83.37 299	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		7.60 452	10.13 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	91.20 173	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

ab-mvs-re		8.28 451	11.04 454	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	99.40 146	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 486	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

TestfortrainingZip							99.97 39

WAC-MVS						90.97 342								86.10 384

PC_three_1452									96.96 60	99.80 26	99.79 62	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 155	97.27 47	99.80 26	99.94 4	97.18 23	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 78	99.83 22	99.91 18	97.87 5	100.00 1	99.92 16	100.00 1	100.00 1

GSMVS																99.59 149

sam_mvs1												94.72 74				99.59 149

sam_mvs												94.25 94

MTGPA								98.28 203

test_post1							95.78 446				59.23 482	93.20 128	97.74 324	91.06 315

test_post											63.35 479	94.43 82	98.13 303

patchmatchnet-post											91.70 440	95.12 59	97.95 315

MTMP							99.87 130	96.49 411

test9_res														99.71 48	99.99 21	100.00 1

agg_prior2														99.48 63	100.00 1	100.00 1

test_prior4						98.05 81	99.94 90

test_prior2							99.95 72		95.78 104	99.73 45	99.76 72	96.00 40		99.78 35	100.00 1

旧先验2							99.46 263		94.21 164	99.85 18			99.95 84	96.96 196

新几何2							99.40 267

无先验							99.49 255	98.71 78	93.46 197				100.00 1	94.36 253		99.99 24

原ACMM2							99.90 114

testdata2													99.99 39	90.54 328

segment_acmp												96.68 31

testdata1							99.28 289		96.35 90

plane_prior5								97.87 254					98.37 284	97.79 167	89.55 316	94.52 328

plane_prior4											98.59 250

plane_prior3						91.64 330			96.63 73	93.01 288

plane_prior2							99.84 149		96.38 84

plane_prior						91.74 324	99.86 141		96.76 68						89.59 315

n2								0.00 491

nn								0.00 491

door-mid								89.69 475

test11								98.44 147

door								90.31 472

HQP5-MVS						91.85 320

BP-MVS														97.92 158

HQP4-MVS										93.37 283			98.39 278			94.53 326

HQP3-MVS								97.89 252							89.60 313

HQP2-MVS												80.65 330

MDTV_nov1_ep13_2view						96.26 167	96.11 440		91.89 273	98.06 166		94.40 84		94.30 256		99.67 129

ACMMP++_ref															87.04 349

ACMMP++															88.23 336

Test By Simon												92.82 139

High-resolution multi-view benchmark