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.

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 49	99.89 3	99.75 29	99.56 57	99.02 15	99.88 5	99.85 29	99.18 9	99.96 19	99.22 39	99.92 11	99.90 1

test_0728_SECOND				99.91 2	99.84 32	99.89 3	99.57 93	99.51 103					99.96 19	98.93 68	99.86 51	99.88 6

DPE-MVS		99.46 24	99.32 31	99.91 2	99.78 44	99.88 7	99.36 196	99.51 103	98.73 53	99.88 5	99.84 38	98.72 61	99.96 19	98.16 170	99.87 40	99.88 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
zzz-MVS		99.49 15	99.36 22	99.89 4	99.90 3	99.86 10	99.36 196	99.47 160	98.79 49	99.68 55	99.81 62	98.43 82	99.97 11	98.88 74	99.90 23	99.83 30

MTAPA		99.52 13	99.39 17	99.89 4	99.90 3	99.86 10	99.66 50	99.47 160	98.79 49	99.68 55	99.81 62	98.43 82	99.97 11	98.88 74	99.90 23	99.83 30

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 32	99.90 1	99.60 75	99.48 142	99.08 11	99.91 1	99.81 62	99.20 6	99.96 19	98.91 71	99.85 58	99.79 55

DVP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 93	99.37 228	99.10 8	99.81 23	99.80 76	98.94 32	99.96 19	98.93 68	99.86 51	99.81 42
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
MP-MVS-pluss		99.37 49	99.20 62	99.88 6	99.90 3	99.87 9	99.30 211	99.52 90	97.18 212	99.60 85	99.79 88	98.79 48	99.95 44	98.83 89	99.91 16	99.83 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 39	99.27 52	99.88 6	99.89 8	99.80 26	99.67 46	99.50 122	98.70 55	99.77 34	99.49 225	98.21 97	99.95 44	98.46 144	99.77 95	99.88 6
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
ACMMP_NAP		99.47 22	99.34 27	99.88 6	99.87 15	99.86 10	99.47 149	99.48 142	98.05 125	99.76 38	99.86 23	98.82 45	99.93 70	98.82 93	99.91 16	99.84 19

No_MVS				99.87 11	99.51 152	99.76 38		99.33 245					99.96 19	98.87 78	99.84 65	99.89 2

ZNCC-MVS		99.47 22	99.33 29	99.87 11	99.87 15	99.81 24	99.64 60	99.67 22	98.08 119	99.55 97	99.64 168	98.91 37	99.96 19	98.72 104	99.90 23	99.82 37

region2R		99.48 19	99.35 25	99.87 11	99.88 11	99.80 26	99.65 57	99.66 27	98.13 108	99.66 66	99.68 148	98.96 26	99.96 19	98.62 118	99.87 40	99.84 19

HPM-MVS++		99.39 47	99.23 60	99.87 11	99.75 62	99.84 13	99.43 163	99.51 103	98.68 57	99.27 160	99.53 212	98.64 69	99.96 19	98.44 146	99.80 86	99.79 55

XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 38	99.68 19	98.98 27	99.37 138	99.74 117	98.81 46	99.94 55	98.79 95	99.86 51	99.84 19

X-MVStestdata		96.55 292	95.45 307	99.87 11	99.85 25	99.83 14	99.69 38	99.68 19	98.98 27	99.37 138	64.01 369	98.81 46	99.94 55	98.79 95	99.86 51	99.84 19

MP-MVS		99.33 54	99.15 66	99.87 11	99.88 11	99.82 20	99.66 50	99.46 170	98.09 115	99.48 110	99.74 117	98.29 94	99.96 19	97.93 188	99.87 40	99.82 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		99.54 9	99.42 13	99.87 11	99.82 37	99.81 24	99.59 81	99.51 103	98.62 59	99.79 27	99.83 42	99.28 4	99.97 11	98.48 140	99.90 23	99.84 19
Skip Steuart: Steuart Systems R&D Blog.
testtj		99.12 87	98.87 106	99.86 19	99.72 80	99.79 30	99.44 157	99.51 103	97.29 202	99.59 88	99.74 117	98.15 103	99.96 19	96.74 276	99.69 113	99.81 42

SR-MVS		99.43 34	99.29 46	99.86 19	99.75 62	99.83 14	99.59 81	99.62 33	98.21 100	99.73 44	99.79 88	98.68 64	99.96 19	98.44 146	99.77 95	99.79 55

HFP-MVS		99.49 15	99.37 20	99.86 19	99.87 15	99.80 26	99.66 50	99.67 22	98.15 106	99.68 55	99.69 141	99.06 14	99.96 19	98.69 109	99.87 40	99.84 19

#test#		99.43 34	99.29 46	99.86 19	99.87 15	99.80 26	99.55 109	99.67 22	97.83 143	99.68 55	99.69 141	99.06 14	99.96 19	98.39 148	99.87 40	99.84 19

ACMMPR		99.49 15	99.36 22	99.86 19	99.87 15	99.79 30	99.66 50	99.67 22	98.15 106	99.67 61	99.69 141	98.95 29	99.96 19	98.69 109	99.87 40	99.84 19

PGM-MVS		99.45 26	99.31 38	99.86 19	99.87 15	99.78 37	99.58 88	99.65 32	97.84 142	99.71 48	99.80 76	99.12 12	99.97 11	98.33 156	99.87 40	99.83 30

mPP-MVS		99.44 30	99.30 42	99.86 19	99.88 11	99.79 30	99.69 38	99.48 142	98.12 110	99.50 106	99.75 111	98.78 49	99.97 11	98.57 129	99.89 33	99.83 30

test1172		99.43 34	99.29 46	99.85 26	99.75 62	99.82 20	99.60 75	99.56 57	98.28 90	99.74 42	99.79 88	98.53 73	99.95 44	98.55 135	99.78 92	99.79 55

SR-MVS-dyc-post		99.45 26	99.31 38	99.85 26	99.76 52	99.82 20	99.63 62	99.52 90	98.38 78	99.76 38	99.82 49	98.53 73	99.95 44	98.61 121	99.81 82	99.77 65

GST-MVS		99.40 46	99.24 58	99.85 26	99.86 21	99.79 30	99.60 75	99.67 22	97.97 131	99.63 74	99.68 148	98.52 75	99.95 44	98.38 150	99.86 51	99.81 42

SMA-MVS		99.44 30	99.30 42	99.85 26	99.73 75	99.83 14	99.56 100	99.47 160	97.45 186	99.78 32	99.82 49	99.18 9	99.91 92	98.79 95	99.89 33	99.81 42
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
APD-MVS_3200maxsize		99.48 19	99.35 25	99.85 26	99.76 52	99.83 14	99.63 62	99.54 73	98.36 82	99.79 27	99.82 49	98.86 41	99.95 44	98.62 118	99.81 82	99.78 63

HPM-MVS_fast		99.51 14	99.40 16	99.85 26	99.91 1	99.79 30	99.76 28	99.56 57	97.72 157	99.76 38	99.75 111	99.13 11	99.92 81	99.07 55	99.92 11	99.85 15

CP-MVS		99.45 26	99.32 31	99.85 26	99.83 36	99.75 39	99.69 38	99.52 90	98.07 120	99.53 100	99.63 174	98.93 36	99.97 11	98.74 100	99.91 16	99.83 30

APD-MVS		99.27 63	99.08 74	99.84 33	99.75 62	99.79 30	99.50 128	99.50 122	97.16 214	99.77 34	99.82 49	98.78 49	99.94 55	97.56 224	99.86 51	99.80 51
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
abl_6		99.44 30	99.31 38	99.83 34	99.85 25	99.75 39	99.66 50	99.59 43	98.13 108	99.82 21	99.81 62	98.60 70	99.96 19	98.46 144	99.88 36	99.79 55

HPM-MVS		99.42 39	99.28 50	99.83 34	99.90 3	99.72 43	99.81 13	99.54 73	97.59 169	99.68 55	99.63 174	98.91 37	99.94 55	98.58 127	99.91 16	99.84 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MCST-MVS		99.43 34	99.30 42	99.82 36	99.79 42	99.74 42	99.29 215	99.40 209	98.79 49	99.52 103	99.62 180	98.91 37	99.90 107	98.64 116	99.75 100	99.82 37

ACMMP		99.45 26	99.32 31	99.82 36	99.89 8	99.67 53	99.62 68	99.69 18	98.12 110	99.63 74	99.84 38	98.73 60	99.96 19	98.55 135	99.83 73	99.81 42
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
3Dnovator+	97.12 13	99.18 73	98.97 93	99.82 36	99.17 247	99.68 50	99.81 13	99.51 103	99.20 4	98.72 255	99.89 10	95.68 180	99.97 11	98.86 82	99.86 51	99.81 42

TSAR-MVS + MP.		99.58 4	99.50 8	99.81 39	99.91 1	99.66 55	99.63 62	99.39 213	98.91 38	99.78 32	99.85 29	99.36 2	99.94 55	98.84 86	99.88 36	99.82 37
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator	97.25 9	99.24 68	99.05 76	99.81 39	99.12 254	99.66 55	99.84 7	99.74 10	99.09 10	98.92 229	99.90 7	95.94 169	99.98 6	98.95 65	99.92 11	99.79 55

UA-Net		99.42 39	99.29 46	99.80 41	99.62 126	99.55 76	99.50 128	99.70 15	98.79 49	99.77 34	99.96 1	97.45 119	99.96 19	98.92 70	99.90 23	99.89 2

CDPH-MVS		99.13 81	98.91 101	99.80 41	99.75 62	99.71 45	99.15 251	99.41 203	96.60 260	99.60 85	99.55 203	98.83 44	99.90 107	97.48 231	99.83 73	99.78 63

QAPM		98.67 146	98.30 163	99.80 41	99.20 236	99.67 53	99.77 25	99.72 11	94.74 322	98.73 254	99.90 7	95.78 176	99.98 6	96.96 265	99.88 36	99.76 70

SF-MVS		99.38 48	99.24 58	99.79 44	99.79 42	99.68 50	99.57 93	99.54 73	97.82 148	99.71 48	99.80 76	98.95 29	99.93 70	98.19 165	99.84 65	99.74 76

NCCC		99.34 52	99.19 63	99.79 44	99.61 130	99.65 58	99.30 211	99.48 142	98.86 40	99.21 176	99.63 174	98.72 61	99.90 107	98.25 161	99.63 126	99.80 51

CNVR-MVS		99.42 39	99.30 42	99.78 46	99.62 126	99.71 45	99.26 231	99.52 90	98.82 44	99.39 133	99.71 129	98.96 26	99.85 132	98.59 126	99.80 86	99.77 65

DP-MVS		99.16 77	98.95 97	99.78 46	99.77 49	99.53 81	99.41 172	99.50 122	97.03 229	99.04 210	99.88 15	97.39 120	99.92 81	98.66 114	99.90 23	99.87 11

ETH3D-3000-0.1		99.21 69	99.02 84	99.77 48	99.73 75	99.69 48	99.38 189	99.51 103	97.45 186	99.61 81	99.75 111	98.51 76	99.91 92	97.45 236	99.83 73	99.71 96

train_agg		99.02 106	98.77 120	99.77 48	99.67 101	99.65 58	99.05 270	99.41 203	96.28 281	98.95 224	99.49 225	98.76 54	99.91 92	97.63 215	99.72 107	99.75 71

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 48	99.63 120	99.59 69	99.36 196	99.46 170	99.07 13	99.79 27	99.82 49	98.85 42	99.92 81	98.68 111	99.87 40	99.82 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		99.01 109	98.76 122	99.76 51	99.67 101	99.62 62	98.99 286	99.40 209	96.26 284	98.87 237	99.49 225	98.77 52	99.91 92	97.69 212	99.72 107	99.75 71

xxxxxxxxxxxxxcwj		99.43 34	99.32 31	99.75 52	99.76 52	99.59 69	99.14 253	99.53 84	99.00 22	99.71 48	99.80 76	98.95 29	99.93 70	98.19 165	99.84 65	99.74 76

Regformer-2		99.54 9	99.47 9	99.75 52	99.71 86	99.52 84	99.49 138	99.49 130	98.94 33	99.83 18	99.76 106	99.01 17	99.94 55	99.15 48	99.87 40	99.80 51

新几何1				99.75 52	99.75 62	99.59 69		99.54 73	96.76 246	99.29 155	99.64 168	98.43 82	99.94 55	96.92 270	99.66 121	99.72 89

1121		99.09 96	98.87 106	99.75 52	99.74 70	99.60 66	99.27 222	99.48 142	96.82 245	99.25 167	99.65 161	98.38 87	99.93 70	97.53 227	99.67 120	99.73 83

test12				99.75 52	99.64 117	99.61 64		99.29 266		99.21 176		98.38 87	99.89 115		99.74 103	99.74 76

CPTT-MVS		99.11 92	98.90 102	99.74 57	99.80 41	99.46 93	99.59 81	99.49 130	97.03 229	99.63 74	99.69 141	97.27 127	99.96 19	97.82 197	99.84 65	99.81 42

LS3D		99.27 63	99.12 69	99.74 57	99.18 241	99.75 39	99.56 100	99.57 51	98.45 71	99.49 109	99.85 29	97.77 113	99.94 55	98.33 156	99.84 65	99.52 151

ETH3 D test6400		98.70 142	98.35 158	99.73 59	99.69 96	99.60 66	99.16 247	99.45 182	95.42 310	99.27 160	99.60 187	97.39 120	99.91 92	95.36 309	99.83 73	99.70 98

Regformer-4		99.59 3	99.54 4	99.73 59	99.76 52	99.41 98	99.58 88	99.49 130	99.02 15	99.88 5	99.80 76	99.00 23	99.94 55	99.45 19	99.92 11	99.84 19

VNet		99.11 92	98.90 102	99.73 59	99.52 150	99.56 74	99.41 172	99.39 213	99.01 18	99.74 42	99.78 95	95.56 183	99.92 81	99.52 7	98.18 212	99.72 89

ETH3D cwj APD-0.16		99.06 100	98.84 112	99.72 62	99.51 152	99.60 66	99.23 236	99.44 191	97.04 227	99.39 133	99.67 154	98.30 93	99.92 81	97.27 243	99.69 113	99.64 122

Regformer-1		99.53 11	99.47 9	99.72 62	99.71 86	99.44 95	99.49 138	99.46 170	98.95 32	99.83 18	99.76 106	99.01 17	99.93 70	99.17 45	99.87 40	99.80 51

114514_t		98.93 115	98.67 130	99.72 62	99.85 25	99.53 81	99.62 68	99.59 43	92.65 341	99.71 48	99.78 95	98.06 106	99.90 107	98.84 86	99.91 16	99.74 76

PHI-MVS		99.30 57	99.17 65	99.70 65	99.56 144	99.52 84	99.58 88	99.80 8	97.12 218	99.62 78	99.73 124	98.58 71	99.90 107	98.61 121	99.91 16	99.68 105

Regformer-3		99.57 7	99.53 5	99.68 66	99.76 52	99.29 109	99.58 88	99.44 191	99.01 18	99.87 10	99.80 76	98.97 25	99.91 92	99.44 21	99.92 11	99.83 30

test_prior3		99.21 69	99.05 76	99.68 66	99.67 101	99.48 89	98.96 294	99.56 57	98.34 84	99.01 213	99.52 215	98.68 64	99.83 146	97.96 185	99.74 103	99.74 76

test_prior				99.68 66	99.67 101	99.48 89		99.56 57					99.83 146			99.74 76

DPM-MVS		98.95 114	98.71 126	99.66 69	99.63 120	99.55 76	98.64 328	99.10 290	97.93 134	99.42 122	99.55 203	98.67 67	99.80 162	95.80 298	99.68 118	99.61 130

PAPM_NR		99.04 103	98.84 112	99.66 69	99.74 70	99.44 95	99.39 184	99.38 219	97.70 159	99.28 157	99.28 285	98.34 91	99.85 132	96.96 265	99.45 136	99.69 101

MVS_111021_HR		99.41 43	99.32 31	99.66 69	99.72 80	99.47 91	98.95 298	99.85 6	98.82 44	99.54 98	99.73 124	98.51 76	99.74 180	98.91 71	99.88 36	99.77 65

AdaColmap		99.01 109	98.80 117	99.66 69	99.56 144	99.54 78	99.18 245	99.70 15	98.18 104	99.35 144	99.63 174	96.32 157	99.90 107	97.48 231	99.77 95	99.55 143

原ACMM1				99.65 73	99.73 75	99.33 103		99.47 160	97.46 183	99.12 192	99.66 160	98.67 67	99.91 92	97.70 211	99.69 113	99.71 96

DELS-MVS		99.48 19	99.42 13	99.65 73	99.72 80	99.40 100	99.05 270	99.66 27	99.14 6	99.57 92	99.80 76	98.46 80	99.94 55	99.57 4	99.84 65	99.60 132
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
DP-MVS Recon		99.12 87	98.95 97	99.65 73	99.74 70	99.70 47	99.27 222	99.57 51	96.40 277	99.42 122	99.68 148	98.75 57	99.80 162	97.98 184	99.72 107	99.44 172

MVS_111021_LR		99.41 43	99.33 29	99.65 73	99.77 49	99.51 86	98.94 300	99.85 6	98.82 44	99.65 71	99.74 117	98.51 76	99.80 162	98.83 89	99.89 33	99.64 122

HyFIR lowres test		99.11 92	98.92 99	99.65 73	99.90 3	99.37 101	99.02 279	99.91 3	97.67 164	99.59 88	99.75 111	95.90 172	99.73 187	99.53 6	99.02 170	99.86 12

OPU-MVS				99.64 78	99.56 144	99.72 43	99.60 75				99.70 133	99.27 5	99.42 249	98.24 162	99.80 86	99.79 55

EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 78	99.78 44	99.14 129	99.60 75	99.45 182	99.01 18	99.90 3	99.83 42	98.98 24	99.93 70	99.59 2	99.95 6	99.86 12

EI-MVSNet-Vis-set		99.58 4	99.56 3	99.64 78	99.78 44	99.15 128	99.61 74	99.45 182	99.01 18	99.89 4	99.82 49	99.01 17	99.92 81	99.56 5	99.95 6	99.85 15

F-COLMAP		99.19 71	99.04 79	99.64 78	99.78 44	99.27 112	99.42 170	99.54 73	97.29 202	99.41 126	99.59 190	98.42 85	99.93 70	98.19 165	99.69 113	99.73 83

DeepC-MVS	98.35 2	99.30 57	99.19 63	99.64 78	99.82 37	99.23 116	99.62 68	99.55 66	98.94 33	99.63 74	99.95 2	95.82 175	99.94 55	99.37 23	99.97 3	99.73 83
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_Blended_VisFu		99.36 50	99.28 50	99.61 83	99.86 21	99.07 138	99.47 149	99.93 2	97.66 165	99.71 48	99.86 23	97.73 114	99.96 19	99.47 17	99.82 79	99.79 55

WTY-MVS		99.06 100	98.88 105	99.61 83	99.62 126	99.16 124	99.37 192	99.56 57	98.04 126	99.53 100	99.62 180	96.84 139	99.94 55	98.85 84	98.49 199	99.72 89

CANet		99.25 67	99.14 67	99.59 85	99.41 183	99.16 124	99.35 202	99.57 51	98.82 44	99.51 105	99.61 184	96.46 152	99.95 44	99.59 2	99.98 2	99.65 115

1112_ss		98.98 111	98.77 120	99.59 85	99.68 100	99.02 142	99.25 233	99.48 142	97.23 209	99.13 190	99.58 193	96.93 138	99.90 107	98.87 78	98.78 186	99.84 19

CNLPA		99.14 79	98.99 89	99.59 85	99.58 138	99.41 98	99.16 247	99.44 191	98.45 71	99.19 182	99.49 225	98.08 105	99.89 115	97.73 206	99.75 100	99.48 162

alignmvs		98.81 133	98.56 148	99.58 88	99.43 179	99.42 97	99.51 122	98.96 305	98.61 60	99.35 144	98.92 323	94.78 211	99.77 172	99.35 24	98.11 218	99.54 145

DROMVSNet		99.44 30	99.39 17	99.58 88	99.56 144	99.49 87	99.88 1	99.58 49	98.38 78	99.73 44	99.69 141	98.20 98	99.70 203	99.64 1	99.82 79	99.54 145

Test_1112_low_res		98.89 117	98.66 133	99.57 90	99.69 96	98.95 155	99.03 276	99.47 160	96.98 231	99.15 188	99.23 292	96.77 143	99.89 115	98.83 89	98.78 186	99.86 12

IS-MVSNet		99.05 102	98.87 106	99.57 90	99.73 75	99.32 104	99.75 29	99.20 279	98.02 129	99.56 93	99.86 23	96.54 150	99.67 209	98.09 174	99.13 158	99.73 83

casdiffmvs		99.13 81	98.98 92	99.56 92	99.65 115	99.16 124	99.56 100	99.50 122	98.33 87	99.41 126	99.86 23	95.92 170	99.83 146	99.45 19	99.16 154	99.70 98

Vis-MVSNet		99.12 87	98.97 93	99.56 92	99.78 44	99.10 134	99.68 43	99.66 27	98.49 67	99.86 11	99.87 20	94.77 214	99.84 137	99.19 42	99.41 139	99.74 76
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_yl		98.86 121	98.63 135	99.54 94	99.49 162	99.18 121	99.50 128	99.07 295	98.22 98	99.61 81	99.51 219	95.37 189	99.84 137	98.60 124	98.33 202	99.59 136

DCV-MVSNet		98.86 121	98.63 135	99.54 94	99.49 162	99.18 121	99.50 128	99.07 295	98.22 98	99.61 81	99.51 219	95.37 189	99.84 137	98.60 124	98.33 202	99.59 136

testdata				99.54 94	99.75 62	98.95 155		99.51 103	97.07 224	99.43 119	99.70 133	98.87 40	99.94 55	97.76 202	99.64 124	99.72 89

LFMVS		97.90 217	97.35 262	99.54 94	99.52 150	99.01 144	99.39 184	98.24 342	97.10 222	99.65 71	99.79 88	84.79 352	99.91 92	99.28 34	98.38 201	99.69 101

ab-mvs		98.86 121	98.63 135	99.54 94	99.64 117	99.19 119	99.44 157	99.54 73	97.77 151	99.30 152	99.81 62	94.20 236	99.93 70	99.17 45	98.82 183	99.49 161

MAR-MVS		98.86 121	98.63 135	99.54 94	99.37 194	99.66 55	99.45 153	99.54 73	96.61 258	99.01 213	99.40 254	97.09 131	99.86 126	97.68 214	99.53 134	99.10 195
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
GeoE		98.85 129	98.62 140	99.53 100	99.61 130	99.08 136	99.80 17	99.51 103	97.10 222	99.31 150	99.78 95	95.23 197	99.77 172	98.21 163	99.03 168	99.75 71

baseline		99.15 78	99.02 84	99.53 100	99.66 110	99.14 129	99.72 33	99.48 142	98.35 83	99.42 122	99.84 38	96.07 163	99.79 165	99.51 9	99.14 157	99.67 108

sss		99.17 75	99.05 76	99.53 100	99.62 126	98.97 149	99.36 196	99.62 33	97.83 143	99.67 61	99.65 161	97.37 124	99.95 44	99.19 42	99.19 153	99.68 105

EPP-MVSNet		99.13 81	98.99 89	99.53 100	99.65 115	99.06 139	99.81 13	99.33 245	97.43 190	99.60 85	99.88 15	97.14 129	99.84 137	99.13 49	98.94 174	99.69 101

PLC	97.94 4	99.02 106	98.85 111	99.53 100	99.66 110	99.01 144	99.24 235	99.52 90	96.85 241	99.27 160	99.48 231	98.25 96	99.91 92	97.76 202	99.62 127	99.65 115
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		98.98 111	98.80 117	99.53 100	99.76 52	99.19 119	98.75 318	99.55 66	97.25 206	99.47 111	99.77 102	97.82 111	99.87 123	96.93 268	99.90 23	99.54 145

PatchMatch-RL		98.84 132	98.62 140	99.52 106	99.71 86	99.28 110	99.06 268	99.77 9	97.74 156	99.50 106	99.53 212	95.41 187	99.84 137	97.17 254	99.64 124	99.44 172

OpenMVS	96.50 16	98.47 154	98.12 172	99.52 106	99.04 270	99.53 81	99.82 11	99.72 11	94.56 325	98.08 305	99.88 15	94.73 217	99.98 6	97.47 233	99.76 98	99.06 206

Fast-Effi-MVS+		98.70 142	98.43 153	99.51 108	99.51 152	99.28 110	99.52 118	99.47 160	96.11 299	99.01 213	99.34 271	96.20 161	99.84 137	97.88 191	98.82 183	99.39 178

canonicalmvs		99.02 106	98.86 110	99.51 108	99.42 180	99.32 104	99.80 17	99.48 142	98.63 58	99.31 150	98.81 326	97.09 131	99.75 179	99.27 36	97.90 222	99.47 167

diffmvs		99.14 79	99.02 84	99.51 108	99.61 130	98.96 153	99.28 217	99.49 130	98.46 70	99.72 47	99.71 129	96.50 151	99.88 120	99.31 31	99.11 159	99.67 108

PAPR		98.63 150	98.34 159	99.51 108	99.40 188	99.03 141	98.80 313	99.36 229	96.33 278	99.00 218	99.12 306	98.46 80	99.84 137	95.23 311	99.37 144	99.66 111

Effi-MVS+		98.81 133	98.59 146	99.48 112	99.46 171	99.12 133	98.08 351	99.50 122	97.50 182	99.38 136	99.41 250	96.37 156	99.81 157	99.11 51	98.54 196	99.51 157

MVS		97.28 280	96.55 288	99.48 112	98.78 303	98.95 155	99.27 222	99.39 213	83.53 356	98.08 305	99.54 208	96.97 136	99.87 123	94.23 323	99.16 154	99.63 126

MVS_Test		99.10 95	98.97 93	99.48 112	99.49 162	99.14 129	99.67 46	99.34 238	97.31 200	99.58 90	99.76 106	97.65 116	99.82 153	98.87 78	99.07 165	99.46 169

HY-MVS	97.30 7	98.85 129	98.64 134	99.47 115	99.42 180	99.08 136	99.62 68	99.36 229	97.39 195	99.28 157	99.68 148	96.44 154	99.92 81	98.37 152	98.22 208	99.40 177

PCF-MVS	97.08 14	97.66 260	97.06 281	99.47 115	99.61 130	99.09 135	98.04 352	99.25 271	91.24 346	98.51 282	99.70 133	94.55 226	99.91 92	92.76 340	99.85 58	99.42 174
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
lupinMVS		99.13 81	99.01 88	99.46 117	99.51 152	98.94 158	99.05 270	99.16 284	97.86 138	99.80 25	99.56 200	97.39 120	99.86 126	98.94 66	99.85 58	99.58 140

EIA-MVS		99.18 73	99.09 73	99.45 118	99.49 162	99.18 121	99.67 46	99.53 84	97.66 165	99.40 131	99.44 240	98.10 104	99.81 157	98.94 66	99.62 127	99.35 180

CS-MVS-test		99.30 57	99.25 56	99.45 118	99.46 171	99.23 116	99.80 17	99.57 51	98.28 90	99.53 100	99.44 240	98.16 102	99.79 165	99.38 22	99.61 129	99.34 182

jason		99.13 81	99.03 81	99.45 118	99.46 171	98.87 165	99.12 255	99.26 269	98.03 128	99.79 27	99.65 161	97.02 134	99.85 132	99.02 59	99.90 23	99.65 115
jason: jason.
CHOSEN 1792x2688		99.19 71	99.10 71	99.45 118	99.89 8	98.52 199	99.39 184	99.94 1	98.73 53	99.11 194	99.89 10	95.50 185	99.94 55	99.50 10	99.97 3	99.89 2

MG-MVS		99.13 81	99.02 84	99.45 118	99.57 140	98.63 187	99.07 265	99.34 238	98.99 25	99.61 81	99.82 49	97.98 108	99.87 123	97.00 261	99.80 86	99.85 15

MSLP-MVS++		99.46 24	99.47 9	99.44 123	99.60 134	99.16 124	99.41 172	99.71 13	98.98 27	99.45 114	99.78 95	99.19 8	99.54 233	99.28 34	99.84 65	99.63 126

CS-MVS		99.34 52	99.31 38	99.43 124	99.44 178	99.47 91	99.68 43	99.56 57	98.41 75	99.62 78	99.41 250	98.35 90	99.76 176	99.52 7	99.76 98	99.05 207

PVSNet_Blended		99.08 98	98.97 93	99.42 125	99.76 52	98.79 176	98.78 315	99.91 3	96.74 247	99.67 61	99.49 225	97.53 117	99.88 120	98.98 62	99.85 58	99.60 132

ETV-MVS		99.26 65	99.21 61	99.40 126	99.46 171	99.30 108	99.56 100	99.52 90	98.52 65	99.44 118	99.27 288	98.41 86	99.86 126	99.10 52	99.59 130	99.04 208

BH-RMVSNet		98.41 160	98.08 177	99.40 126	99.41 183	98.83 172	99.30 211	98.77 322	97.70 159	98.94 226	99.65 161	92.91 264	99.74 180	96.52 284	99.55 133	99.64 122

UGNet		98.87 118	98.69 128	99.40 126	99.22 232	98.72 180	99.44 157	99.68 19	99.24 3	99.18 185	99.42 246	92.74 268	99.96 19	99.34 28	99.94 9	99.53 150
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
baseline1		98.31 167	97.95 192	99.38 129	99.50 160	98.74 178	99.59 81	98.93 307	98.41 75	99.14 189	99.60 187	94.59 223	99.79 165	98.48 140	93.29 329	99.61 130

TSAR-MVS + GP.		99.36 50	99.36 22	99.36 130	99.67 101	98.61 190	99.07 265	99.33 245	99.00 22	99.82 21	99.81 62	99.06 14	99.84 137	99.09 53	99.42 138	99.65 115

Anonymous20240529		98.09 189	97.68 221	99.34 131	99.66 110	98.44 207	99.40 180	99.43 199	93.67 332	99.22 173	99.89 10	90.23 317	99.93 70	99.26 37	98.33 202	99.66 111

xiu_mvs_v1_base_debu		99.29 60	99.27 52	99.34 131	99.63 120	98.97 149	99.12 255	99.51 103	98.86 40	99.84 13	99.47 234	98.18 99	99.99 1	99.50 10	99.31 145	99.08 200

xiu_mvs_v1_base		99.29 60	99.27 52	99.34 131	99.63 120	98.97 149	99.12 255	99.51 103	98.86 40	99.84 13	99.47 234	98.18 99	99.99 1	99.50 10	99.31 145	99.08 200

xiu_mvs_v1_base_debi		99.29 60	99.27 52	99.34 131	99.63 120	98.97 149	99.12 255	99.51 103	98.86 40	99.84 13	99.47 234	98.18 99	99.99 1	99.50 10	99.31 145	99.08 200

PMMVS		98.80 136	98.62 140	99.34 131	99.27 220	98.70 181	98.76 317	99.31 257	97.34 197	99.21 176	99.07 308	97.20 128	99.82 153	98.56 132	98.87 180	99.52 151

CSCG		99.32 55	99.32 31	99.32 136	99.85 25	98.29 213	99.71 35	99.66 27	98.11 112	99.41 126	99.80 76	98.37 89	99.96 19	98.99 61	99.96 5	99.72 89

thisisatest0530		98.35 165	98.03 182	99.31 137	99.63 120	98.56 192	99.54 112	96.75 359	97.53 179	99.73 44	99.65 161	91.25 307	99.89 115	98.62 118	99.56 131	99.48 162

AllTest		98.87 118	98.72 124	99.31 137	99.86 21	98.48 205	99.56 100	99.61 35	97.85 140	99.36 141	99.85 29	95.95 167	99.85 132	96.66 282	99.83 73	99.59 136

TestCases				99.31 137	99.86 21	98.48 205		99.61 35	97.85 140	99.36 141	99.85 29	95.95 167	99.85 132	96.66 282	99.83 73	99.59 136

Vis-MVSNet (Re-imp)		98.87 118	98.72 124	99.31 137	99.71 86	98.88 164	99.80 17	99.44 191	97.91 136	99.36 141	99.78 95	95.49 186	99.43 248	97.91 189	99.11 159	99.62 128

PS-MVSNAJ		99.32 55	99.32 31	99.30 141	99.57 140	98.94 158	98.97 293	99.46 170	98.92 37	99.71 48	99.24 291	99.01 17	99.98 6	99.35 24	99.66 121	98.97 216

VPA-MVSNet		98.29 170	97.95 192	99.30 141	99.16 249	99.54 78	99.50 128	99.58 49	98.27 93	99.35 144	99.37 262	92.53 278	99.65 216	99.35 24	94.46 313	98.72 243

EPNet		98.86 121	98.71 126	99.30 141	97.20 349	98.18 218	99.62 68	98.91 312	99.28 2	98.63 273	99.81 62	95.96 166	99.99 1	99.24 38	99.72 107	99.73 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part1		97.75 242	97.24 275	99.29 144	99.59 136	99.63 61	99.65 57	99.49 130	96.17 292	98.44 287	99.69 141	89.80 321	99.47 236	98.68 111	93.66 325	98.78 229

xiu_mvs_v2_base		99.26 65	99.25 56	99.29 144	99.53 148	98.91 162	99.02 279	99.45 182	98.80 48	99.71 48	99.26 289	98.94 32	99.98 6	99.34 28	99.23 150	98.98 215

MVSFormer		99.17 75	99.12 69	99.29 144	99.51 152	98.94 158	99.88 1	99.46 170	97.55 174	99.80 25	99.65 161	97.39 120	99.28 274	99.03 57	99.85 58	99.65 115

tttt0517		98.42 158	98.14 170	99.28 147	99.66 110	98.38 211	99.74 32	96.85 357	97.68 161	99.79 27	99.74 117	91.39 304	99.89 115	98.83 89	99.56 131	99.57 141

nrg030		98.64 149	98.42 154	99.28 147	99.05 269	99.69 48	99.81 13	99.46 170	98.04 126	99.01 213	99.82 49	96.69 146	99.38 253	99.34 28	94.59 312	98.78 229

Anonymous202405211		98.30 169	97.98 187	99.26 149	99.57 140	98.16 219	99.41 172	98.55 338	96.03 304	99.19 182	99.74 117	91.87 291	99.92 81	99.16 47	98.29 207	99.70 98

CANet_DTU		98.97 113	98.87 106	99.25 150	99.33 202	98.42 210	99.08 264	99.30 261	99.16 5	99.43 119	99.75 111	95.27 193	99.97 11	98.56 132	99.95 6	99.36 179

CDS-MVSNet		99.09 96	99.03 81	99.25 150	99.42 180	98.73 179	99.45 153	99.46 170	98.11 112	99.46 113	99.77 102	98.01 107	99.37 256	98.70 106	98.92 177	99.66 111
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		98.38 163	98.09 176	99.24 152	99.26 222	99.32 104	99.56 100	99.55 66	97.45 186	98.71 256	99.83 42	93.23 256	99.63 224	98.88 74	96.32 274	98.76 235

TAMVS		99.12 87	99.08 74	99.24 152	99.46 171	98.55 193	99.51 122	99.46 170	98.09 115	99.45 114	99.82 49	98.34 91	99.51 234	98.70 106	98.93 175	99.67 108

FIs		98.78 137	98.63 135	99.23 154	99.18 241	99.54 78	99.83 10	99.59 43	98.28 90	98.79 249	99.81 62	96.75 144	99.37 256	99.08 54	96.38 272	98.78 229

OMC-MVS		99.08 98	99.04 79	99.20 155	99.67 101	98.22 217	99.28 217	99.52 90	98.07 120	99.66 66	99.81 62	97.79 112	99.78 170	97.79 199	99.81 82	99.60 132

thisisatest0515		98.14 184	97.79 206	99.19 156	99.50 160	98.50 202	98.61 329	96.82 358	96.95 235	99.54 98	99.43 243	91.66 300	99.86 126	98.08 178	99.51 135	99.22 189

RPMNet		96.72 290	95.90 300	99.19 156	99.18 241	98.49 203	99.22 241	99.52 90	88.72 352	99.56 93	97.38 349	94.08 242	99.95 44	86.87 359	98.58 192	99.14 192

COLMAP_ROB	97.56 6	98.86 121	98.75 123	99.17 158	99.88 11	98.53 195	99.34 205	99.59 43	97.55 174	98.70 262	99.89 10	95.83 174	99.90 107	98.10 173	99.90 23	99.08 200
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDDNet		97.55 265	97.02 282	99.16 159	99.49 162	98.12 223	99.38 189	99.30 261	95.35 311	99.68 55	99.90 7	82.62 356	99.93 70	99.31 31	98.13 217	99.42 174

mvs_anonymous		99.03 105	98.99 89	99.16 159	99.38 192	98.52 199	99.51 122	99.38 219	97.79 149	99.38 136	99.81 62	97.30 125	99.45 239	99.35 24	98.99 172	99.51 157

FC-MVSNet-test		98.75 140	98.62 140	99.15 161	99.08 263	99.45 94	99.86 6	99.60 40	98.23 97	98.70 262	99.82 49	96.80 140	99.22 284	99.07 55	96.38 272	98.79 228

UniMVSNet (Re)		98.29 170	98.00 185	99.13 162	99.00 275	99.36 102	99.49 138	99.51 103	97.95 132	98.97 222	99.13 303	96.30 158	99.38 253	98.36 154	93.34 328	98.66 273

1314		98.68 145	98.54 149	99.11 163	98.89 287	98.65 185	99.27 222	99.49 130	96.89 239	97.99 310	99.56 200	97.72 115	99.83 146	97.74 205	99.27 148	98.84 225

CHOSEN 280x420		99.12 87	99.13 68	99.08 164	99.66 110	97.89 234	98.43 339	99.71 13	98.88 39	99.62 78	99.76 106	96.63 147	99.70 203	99.46 18	99.99 1	99.66 111

PAPM		97.59 264	97.09 280	99.07 165	99.06 266	98.26 216	98.30 346	99.10 290	94.88 319	98.08 305	99.34 271	96.27 159	99.64 219	89.87 349	98.92 177	99.31 185

WR-MVS		98.06 192	97.73 217	99.06 166	98.86 295	99.25 114	99.19 244	99.35 234	97.30 201	98.66 265	99.43 243	93.94 245	99.21 289	98.58 127	94.28 317	98.71 245

API-MVS		99.04 103	99.03 81	99.06 166	99.40 188	99.31 107	99.55 109	99.56 57	98.54 63	99.33 148	99.39 258	98.76 54	99.78 170	96.98 263	99.78 92	98.07 334

ET-MVSNet_ETH3D		96.49 294	95.64 305	99.05 168	99.53 148	98.82 173	98.84 309	97.51 354	97.63 167	84.77 357	99.21 296	92.09 288	98.91 329	98.98 62	92.21 339	99.41 176

RRT_MVS		98.60 151	98.44 152	99.05 168	98.88 288	99.14 129	99.49 138	99.38 219	97.76 152	99.29 155	99.86 23	95.38 188	99.36 260	98.81 94	97.16 258	98.64 277

SD-MVS		99.41 43	99.52 6	99.05 168	99.74 70	99.68 50	99.46 152	99.52 90	99.11 7	99.88 5	99.91 5	99.43 1	97.70 351	98.72 104	99.93 10	99.77 65
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
PVSNet_BlendedMVS		98.86 121	98.80 117	99.03 171	99.76 52	98.79 176	99.28 217	99.91 3	97.42 192	99.67 61	99.37 262	97.53 117	99.88 120	98.98 62	97.29 253	98.42 317

NR-MVSNet		97.97 210	97.61 228	99.02 172	98.87 292	99.26 113	99.47 149	99.42 201	97.63 167	97.08 331	99.50 222	95.07 200	99.13 298	97.86 193	93.59 326	98.68 258

VPNet		97.84 226	97.44 250	99.01 173	99.21 234	98.94 158	99.48 144	99.57 51	98.38 78	99.28 157	99.73 124	88.89 330	99.39 251	99.19 42	93.27 330	98.71 245

CP-MVSNet		98.09 189	97.78 209	99.01 173	98.97 281	99.24 115	99.67 46	99.46 170	97.25 206	98.48 285	99.64 168	93.79 249	99.06 307	98.63 117	94.10 320	98.74 241

GA-MVS		97.85 223	97.47 242	99.00 175	99.38 192	97.99 227	98.57 332	99.15 285	97.04 227	98.90 232	99.30 281	89.83 320	99.38 253	96.70 279	98.33 202	99.62 128

MVSTER		98.49 153	98.32 161	99.00 175	99.35 197	99.02 142	99.54 112	99.38 219	97.41 193	99.20 179	99.73 124	93.86 248	99.36 260	98.87 78	97.56 234	98.62 287

tfpnnormal		97.84 226	97.47 242	98.98 177	99.20 236	99.22 118	99.64 60	99.61 35	96.32 279	98.27 299	99.70 133	93.35 255	99.44 244	95.69 300	95.40 297	98.27 326

test_djsdf		98.67 146	98.57 147	98.98 177	98.70 314	98.91 162	99.88 1	99.46 170	97.55 174	99.22 173	99.88 15	95.73 178	99.28 274	99.03 57	97.62 229	98.75 237

hse-mvs3		97.70 253	97.28 271	98.97 179	99.70 93	97.27 254	99.36 196	99.45 182	98.94 33	99.66 66	99.64 168	94.93 202	99.99 1	99.48 15	84.36 352	99.65 115

UniMVSNet_NR-MVSNet		98.22 173	97.97 188	98.96 180	98.92 285	98.98 146	99.48 144	99.53 84	97.76 152	98.71 256	99.46 238	96.43 155	99.22 284	98.57 129	92.87 335	98.69 253

DU-MVS		98.08 191	97.79 206	98.96 180	98.87 292	98.98 146	99.41 172	99.45 182	97.87 137	98.71 256	99.50 222	94.82 208	99.22 284	98.57 129	92.87 335	98.68 258

PS-CasMVS		97.93 212	97.59 231	98.95 182	98.99 276	99.06 139	99.68 43	99.52 90	97.13 216	98.31 296	99.68 148	92.44 284	99.05 308	98.51 138	94.08 321	98.75 237

anonymousdsp		98.44 156	98.28 164	98.94 183	98.50 329	98.96 153	99.77 25	99.50 122	97.07 224	98.87 237	99.77 102	94.76 215	99.28 274	98.66 114	97.60 230	98.57 302

TAPA-MVS	97.07 15	97.74 245	97.34 265	98.94 183	99.70 93	97.53 247	99.25 233	99.51 103	91.90 343	99.30 152	99.63 174	98.78 49	99.64 219	88.09 355	99.87 40	99.65 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v8		97.95 211	97.63 227	98.93 185	98.95 283	98.81 175	99.80 17	99.41 203	96.03 304	99.10 197	99.42 246	94.92 204	99.30 272	96.94 267	94.08 321	98.66 273

JIA-IIPM		97.50 271	97.02 282	98.93 185	98.73 309	97.80 239	99.30 211	98.97 303	91.73 344	98.91 230	94.86 357	95.10 199	99.71 197	97.58 219	97.98 220	99.28 187

v7n		97.87 220	97.52 236	98.92 187	98.76 307	98.58 191	99.84 7	99.46 170	96.20 289	98.91 230	99.70 133	94.89 206	99.44 244	96.03 293	93.89 323	98.75 237

v2v482		98.06 192	97.77 211	98.92 187	98.90 286	98.82 173	99.57 93	99.36 229	96.65 254	99.19 182	99.35 268	94.20 236	99.25 279	97.72 208	94.97 306	98.69 253

thres600view7		97.86 222	97.51 238	98.92 187	99.72 80	97.95 232	99.59 81	98.74 326	97.94 133	99.27 160	98.62 333	91.75 294	99.86 126	93.73 328	98.19 211	98.96 218

thres400		97.77 237	97.38 258	98.92 187	99.69 96	97.96 230	99.50 128	98.73 331	97.83 143	99.17 186	98.45 338	91.67 298	99.83 146	93.22 333	98.18 212	98.96 218

v1192		97.81 233	97.44 250	98.91 191	98.88 288	98.68 182	99.51 122	99.34 238	96.18 291	99.20 179	99.34 271	94.03 243	99.36 260	95.32 310	95.18 301	98.69 253

mvs_tets		98.40 162	98.23 166	98.91 191	98.67 317	98.51 201	99.66 50	99.53 84	98.19 101	98.65 271	99.81 62	92.75 266	99.44 244	99.31 31	97.48 244	98.77 233

Anonymous20231211		97.88 218	97.54 235	98.90 193	99.71 86	98.53 195	99.48 144	99.57 51	94.16 328	98.81 245	99.68 148	93.23 256	99.42 249	98.84 86	94.42 315	98.76 235

PS-MVSNAJss		98.92 116	98.92 99	98.90 193	98.78 303	98.53 195	99.78 23	99.54 73	98.07 120	99.00 218	99.76 106	99.01 17	99.37 256	99.13 49	97.23 254	98.81 226

WR-MVS_H		98.13 185	97.87 202	98.90 193	99.02 273	98.84 169	99.70 36	99.59 43	97.27 204	98.40 290	99.19 297	95.53 184	99.23 281	98.34 155	93.78 324	98.61 296

mvs-test1		98.86 121	98.84 112	98.89 196	99.33 202	97.77 240	99.44 157	99.30 261	98.47 68	99.10 197	99.43 243	96.78 141	99.95 44	98.73 102	99.02 170	98.96 218

XVG-OURS-SEG-HR		98.69 144	98.62 140	98.89 196	99.71 86	97.74 241	99.12 255	99.54 73	98.44 74	99.42 122	99.71 129	94.20 236	99.92 81	98.54 137	98.90 179	99.00 212

PVSNet	96.02 17	98.85 129	98.84 112	98.89 196	99.73 75	97.28 253	98.32 345	99.60 40	97.86 138	99.50 106	99.57 197	96.75 144	99.86 126	98.56 132	99.70 112	99.54 145

jajsoiax		98.43 157	98.28 164	98.88 199	98.60 324	98.43 208	99.82 11	99.53 84	98.19 101	98.63 273	99.80 76	93.22 258	99.44 244	99.22 39	97.50 240	98.77 233

pm-mvs1		97.68 256	97.28 271	98.88 199	99.06 266	98.62 188	99.50 128	99.45 182	96.32 279	97.87 313	99.79 88	92.47 280	99.35 264	97.54 226	93.54 327	98.67 265

VDD-MVS		97.73 246	97.35 262	98.88 199	99.47 170	97.12 260	99.34 205	98.85 318	98.19 101	99.67 61	99.85 29	82.98 354	99.92 81	99.49 14	98.32 206	99.60 132

XVG-OURS		98.73 141	98.68 129	98.88 199	99.70 93	97.73 242	98.92 301	99.55 66	98.52 65	99.45 114	99.84 38	95.27 193	99.91 92	98.08 178	98.84 182	99.00 212

UniMVSNet_ETH3D		97.32 279	96.81 285	98.87 203	99.40 188	97.46 249	99.51 122	99.53 84	95.86 306	98.54 281	99.77 102	82.44 357	99.66 212	98.68 111	97.52 237	99.50 160

v144192		97.92 215	97.60 229	98.87 203	98.83 298	98.65 185	99.55 109	99.34 238	96.20 289	99.32 149	99.40 254	94.36 231	99.26 278	96.37 289	95.03 305	98.70 249

CR-MVSNet		98.17 180	97.93 195	98.87 203	99.18 241	98.49 203	99.22 241	99.33 245	96.96 233	99.56 93	99.38 259	94.33 232	99.00 316	94.83 317	98.58 192	99.14 192

v10		97.85 223	97.52 236	98.86 206	98.99 276	98.67 183	99.75 29	99.41 203	95.70 307	98.98 220	99.41 250	94.75 216	99.23 281	96.01 294	94.63 311	98.67 265

V42		98.06 192	97.79 206	98.86 206	98.98 279	98.84 169	99.69 38	99.34 238	96.53 264	99.30 152	99.37 262	94.67 220	99.32 269	97.57 223	94.66 310	98.42 317

TransMVSNet (Re)		97.15 283	96.58 287	98.86 206	99.12 254	98.85 168	99.49 138	98.91 312	95.48 309	97.16 329	99.80 76	93.38 254	99.11 303	94.16 325	91.73 340	98.62 287

v1144		97.98 207	97.69 220	98.85 209	98.87 292	98.66 184	99.54 112	99.35 234	96.27 283	99.23 172	99.35 268	94.67 220	99.23 281	96.73 277	95.16 302	98.68 258

v1921920		97.80 235	97.45 245	98.84 210	98.80 299	98.53 195	99.52 118	99.34 238	96.15 296	99.24 168	99.47 234	93.98 244	99.29 273	95.40 307	95.13 303	98.69 253

FMVSNet3		98.03 198	97.76 214	98.84 210	99.39 191	98.98 146	99.40 180	99.38 219	96.67 252	99.07 204	99.28 285	92.93 261	98.98 318	97.10 256	96.65 263	98.56 303

baseline2		97.87 220	97.55 232	98.82 212	99.18 241	98.02 225	99.41 172	96.58 361	96.97 232	96.51 336	99.17 298	93.43 253	99.57 229	97.71 209	99.03 168	98.86 223

TR-MVS		97.76 238	97.41 255	98.82 212	99.06 266	97.87 235	98.87 307	98.56 337	96.63 257	98.68 264	99.22 293	92.49 279	99.65 216	95.40 307	97.79 224	98.95 221

pmmvs4		98.13 185	97.90 197	98.81 214	98.61 323	98.87 165	98.99 286	99.21 278	96.44 273	99.06 208	99.58 193	95.90 172	99.11 303	97.18 253	96.11 278	98.46 314

Patchmtry		97.75 242	97.40 256	98.81 214	99.10 259	98.87 165	99.11 261	99.33 245	94.83 320	98.81 245	99.38 259	94.33 232	99.02 313	96.10 291	95.57 293	98.53 304

FMVSNet2		97.72 248	97.36 260	98.80 216	99.51 152	98.84 169	99.45 153	99.42 201	96.49 266	98.86 242	99.29 283	90.26 314	98.98 318	96.44 286	96.56 266	98.58 301

v1240		97.69 254	97.32 268	98.79 217	98.85 296	98.43 208	99.48 144	99.36 229	96.11 299	99.27 160	99.36 265	93.76 251	99.24 280	94.46 320	95.23 300	98.70 249

PatchT		97.03 286	96.44 290	98.79 217	98.99 276	98.34 212	99.16 247	99.07 295	92.13 342	99.52 103	97.31 352	94.54 227	98.98 318	88.54 353	98.73 188	99.03 209

Patchmatch-test		97.93 212	97.65 224	98.77 219	99.18 241	97.07 265	99.03 276	99.14 287	96.16 294	98.74 253	99.57 197	94.56 225	99.72 191	93.36 332	99.11 159	99.52 151

TranMVSNet+NR-MVSNet		97.93 212	97.66 223	98.76 220	98.78 303	98.62 188	99.65 57	99.49 130	97.76 152	98.49 284	99.60 187	94.23 235	98.97 325	98.00 183	92.90 333	98.70 249

gg-mvs-nofinetune		96.17 301	95.32 309	98.73 221	98.79 300	98.14 221	99.38 189	94.09 366	91.07 348	98.07 308	91.04 362	89.62 325	99.35 264	96.75 275	99.09 163	98.68 258

bset_n11_16_dypcd		98.16 181	97.97 188	98.73 221	98.26 334	98.28 215	97.99 353	98.01 347	97.68 161	99.10 197	99.63 174	95.68 180	99.15 294	98.78 98	96.55 267	98.75 237

tfpn200view9		97.72 248	97.38 258	98.72 223	99.69 96	97.96 230	99.50 128	98.73 331	97.83 143	99.17 186	98.45 338	91.67 298	99.83 146	93.22 333	98.18 212	98.37 323

PEN-MVS		97.76 238	97.44 250	98.72 223	98.77 306	98.54 194	99.78 23	99.51 103	97.06 226	98.29 298	99.64 168	92.63 275	98.89 331	98.09 174	93.16 331	98.72 243

thres100view900		97.76 238	97.45 245	98.69 225	99.72 80	97.86 237	99.59 81	98.74 326	97.93 134	99.26 165	98.62 333	91.75 294	99.83 146	93.22 333	98.18 212	98.37 323

EI-MVSNet		98.67 146	98.67 130	98.68 226	99.35 197	97.97 228	99.50 128	99.38 219	96.93 238	99.20 179	99.83 42	97.87 109	99.36 260	98.38 150	97.56 234	98.71 245

Baseline_NR-MVSNet		97.76 238	97.45 245	98.68 226	99.09 261	98.29 213	99.41 172	98.85 318	95.65 308	98.63 273	99.67 154	94.82 208	99.10 305	98.07 181	92.89 334	98.64 277

thres200		97.61 263	97.28 271	98.62 228	99.64 117	98.03 224	99.26 231	98.74 326	97.68 161	99.09 202	98.32 342	91.66 300	99.81 157	92.88 337	98.22 208	98.03 337

Fast-Effi-MVS+-dtu		98.77 139	98.83 116	98.60 229	99.41 183	96.99 274	99.52 118	99.49 130	98.11 112	99.24 168	99.34 271	96.96 137	99.79 165	97.95 187	99.45 136	99.02 211

hse-mvs2		97.50 271	97.14 278	98.59 230	99.49 162	97.05 267	99.28 217	99.22 275	98.94 33	99.66 66	99.42 246	94.93 202	99.65 216	99.48 15	83.80 354	99.08 200

AUN-MVS		96.88 287	96.31 292	98.59 230	99.48 169	97.04 270	99.27 222	99.22 275	97.44 189	98.51 282	99.41 250	91.97 289	99.66 212	97.71 209	83.83 353	99.07 205

BH-untuned		98.42 158	98.36 156	98.59 230	99.49 162	96.70 286	99.27 222	99.13 288	97.24 208	98.80 247	99.38 259	95.75 177	99.74 180	97.07 259	99.16 154	99.33 184

IterMVS-LS		98.46 155	98.42 154	98.58 233	99.59 136	98.00 226	99.37 192	99.43 199	96.94 237	99.07 204	99.59 190	97.87 109	99.03 311	98.32 158	95.62 292	98.71 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		97.73 246	97.45 245	98.57 234	99.45 177	97.50 248	99.02 279	98.98 302	96.11 299	99.41 126	99.14 302	90.28 313	98.74 333	95.74 299	98.93 175	99.47 167

IB-MVS	95.67 18	96.22 298	95.44 308	98.57 234	99.21 234	96.70 286	98.65 327	97.74 352	96.71 249	97.27 325	98.54 336	86.03 348	99.92 81	98.47 143	86.30 350	99.10 195
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
ADS-MVSNet		98.20 176	98.08 177	98.56 236	99.33 202	96.48 294	99.23 236	99.15 285	96.24 286	99.10 197	99.67 154	94.11 240	99.71 197	96.81 273	99.05 166	99.48 162

test0.0.03 1		97.71 252	97.42 254	98.56 236	98.41 332	97.82 238	98.78 315	98.63 335	97.34 197	98.05 309	98.98 320	94.45 229	98.98 318	95.04 314	97.15 259	98.89 222

cl-mvsnet____		98.01 203	97.84 204	98.55 238	99.25 226	97.97 228	98.71 322	99.34 238	96.47 272	98.59 279	99.54 208	95.65 182	99.21 289	97.21 247	95.77 287	98.46 314

test-LLR		98.06 192	97.90 197	98.55 238	98.79 300	97.10 261	98.67 324	97.75 350	97.34 197	98.61 276	98.85 324	94.45 229	99.45 239	97.25 245	99.38 140	99.10 195

test-mter		97.49 274	97.13 279	98.55 238	98.79 300	97.10 261	98.67 324	97.75 350	96.65 254	98.61 276	98.85 324	88.23 338	99.45 239	97.25 245	99.38 140	99.10 195

v148		97.79 236	97.55 232	98.50 241	98.74 308	97.72 243	99.54 112	99.33 245	96.26 284	98.90 232	99.51 219	94.68 219	99.14 295	97.83 196	93.15 332	98.63 285

LPG-MVS_test		98.22 173	98.13 171	98.49 242	99.33 202	97.05 267	99.58 88	99.55 66	97.46 183	99.24 168	99.83 42	92.58 276	99.72 191	98.09 174	97.51 238	98.68 258

LGP-MVS_train				98.49 242	99.33 202	97.05 267		99.55 66	97.46 183	99.24 168	99.83 42	92.58 276	99.72 191	98.09 174	97.51 238	98.68 258

cl-mvsnet2		97.85 223	97.64 226	98.48 244	99.09 261	97.87 235	98.60 331	99.33 245	97.11 221	98.87 237	99.22 293	92.38 285	99.17 293	98.21 163	95.99 281	98.42 317

cl-mvsnet1		98.01 203	97.85 203	98.48 244	99.24 227	97.95 232	98.71 322	99.35 234	96.50 265	98.60 278	99.54 208	95.72 179	99.03 311	97.21 247	95.77 287	98.46 314

cascas		97.69 254	97.43 253	98.48 244	98.60 324	97.30 252	98.18 350	99.39 213	92.96 340	98.41 289	98.78 329	93.77 250	99.27 277	98.16 170	98.61 189	98.86 223

ACMM	97.58 5	98.37 164	98.34 159	98.48 244	99.41 183	97.10 261	99.56 100	99.45 182	98.53 64	99.04 210	99.85 29	93.00 260	99.71 197	98.74 100	97.45 245	98.64 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		98.78 137	98.89 104	98.47 248	99.33 202	96.91 280	99.57 93	99.30 261	98.47 68	99.41 126	98.99 317	96.78 141	99.74 180	98.73 102	99.38 140	98.74 241

DTE-MVSNet		97.51 270	97.19 277	98.46 249	98.63 320	98.13 222	99.84 7	99.48 142	96.68 251	97.97 311	99.67 154	92.92 262	98.56 335	96.88 272	92.60 338	98.70 249

OPM-MVS		98.19 177	98.10 173	98.45 250	98.88 288	97.07 265	99.28 217	99.38 219	98.57 62	99.22 173	99.81 62	92.12 287	99.66 212	98.08 178	97.54 236	98.61 296
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
GG-mvs-BLEND				98.45 250	98.55 327	98.16 219	99.43 163	93.68 367		97.23 326	98.46 337	89.30 327	99.22 284	95.43 306	98.22 208	97.98 342

ACMP	97.20 11	98.06 192	97.94 194	98.45 250	99.37 194	97.01 272	99.44 157	99.49 130	97.54 177	98.45 286	99.79 88	91.95 290	99.72 191	97.91 189	97.49 243	98.62 287
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		98.27 172	98.22 167	98.44 253	99.29 215	96.97 276	99.39 184	99.47 160	98.97 30	99.11 194	99.61 184	92.71 271	99.69 207	97.78 200	97.63 227	98.67 265

ACMH	97.28 8	98.10 188	97.99 186	98.44 253	99.41 183	96.96 278	99.60 75	99.56 57	98.09 115	98.15 303	99.91 5	90.87 311	99.70 203	98.88 74	97.45 245	98.67 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_ehance_all_eth		98.18 179	98.10 173	98.41 255	99.23 228	97.72 243	98.72 321	99.31 257	96.60 260	98.88 235	99.29 283	97.29 126	99.13 298	97.60 217	95.99 281	98.38 322

miper_enhance_ethall		98.16 181	98.08 177	98.41 255	98.96 282	97.72 243	98.45 338	99.32 254	96.95 235	98.97 222	99.17 298	97.06 133	99.22 284	97.86 193	95.99 281	98.29 325

TESTMET0.1,1		97.55 265	97.27 274	98.40 257	98.93 284	96.53 292	98.67 324	97.61 353	96.96 233	98.64 272	99.28 285	88.63 334	99.45 239	97.30 242	99.38 140	99.21 190

LTVRE_ROB	97.16 12	98.02 200	97.90 197	98.40 257	99.23 228	96.80 284	99.70 36	99.60 40	97.12 218	98.18 302	99.70 133	91.73 296	99.72 191	98.39 148	97.45 245	98.68 258
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
cl_fuxian		98.12 187	98.04 181	98.38 259	99.30 211	97.69 246	98.81 312	99.33 245	96.67 252	98.83 243	99.34 271	97.11 130	98.99 317	97.58 219	95.34 298	98.48 308

HQP-MVS		98.02 200	97.90 197	98.37 260	99.19 238	96.83 281	98.98 290	99.39 213	98.24 94	98.66 265	99.40 254	92.47 280	99.64 219	97.19 251	97.58 232	98.64 277

EPMVS		97.82 231	97.65 224	98.35 261	98.88 288	95.98 306	99.49 138	94.71 365	97.57 172	99.26 165	99.48 231	92.46 283	99.71 197	97.87 192	99.08 164	99.35 180

eth_miper_zixun_eth		98.05 197	97.96 190	98.33 262	99.26 222	97.38 251	98.56 334	99.31 257	96.65 254	98.88 235	99.52 215	96.58 148	99.12 302	97.39 240	95.53 295	98.47 310

CLD-MVS		98.16 181	98.10 173	98.33 262	99.29 215	96.82 283	98.75 318	99.44 191	97.83 143	99.13 190	99.55 203	92.92 262	99.67 209	98.32 158	97.69 226	98.48 308
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-w/o		98.00 205	97.89 201	98.32 264	99.35 197	96.20 303	99.01 284	98.90 314	96.42 275	98.38 291	99.00 316	95.26 195	99.72 191	96.06 292	98.61 189	99.03 209

ACMH+	97.24 10	97.92 215	97.78 209	98.32 264	99.46 171	96.68 288	99.56 100	99.54 73	98.41 75	97.79 317	99.87 20	90.18 318	99.66 212	98.05 182	97.18 257	98.62 287

CVMVSNet		98.57 152	98.67 130	98.30 266	99.35 197	95.59 313	99.50 128	99.55 66	98.60 61	99.39 133	99.83 42	94.48 228	99.45 239	98.75 99	98.56 195	99.85 15

GBi-Net		97.68 256	97.48 240	98.29 267	99.51 152	97.26 256	99.43 163	99.48 142	96.49 266	99.07 204	99.32 278	90.26 314	98.98 318	97.10 256	96.65 263	98.62 287

test1		97.68 256	97.48 240	98.29 267	99.51 152	97.26 256	99.43 163	99.48 142	96.49 266	99.07 204	99.32 278	90.26 314	98.98 318	97.10 256	96.65 263	98.62 287

FMVSNet1		96.84 288	96.36 291	98.29 267	99.32 209	97.26 256	99.43 163	99.48 142	95.11 314	98.55 280	99.32 278	83.95 353	98.98 318	95.81 297	96.26 275	98.62 287

miper_lstm_enhance		98.00 205	97.91 196	98.28 270	99.34 201	97.43 250	98.88 305	99.36 229	96.48 270	98.80 247	99.55 203	95.98 165	98.91 329	97.27 243	95.50 296	98.51 306

SCA		98.19 177	98.16 168	98.27 271	99.30 211	95.55 314	99.07 265	98.97 303	97.57 172	99.43 119	99.57 197	92.72 269	99.74 180	97.58 219	99.20 152	99.52 151

EPNet_dtu		98.03 198	97.96 190	98.23 272	98.27 333	95.54 316	99.23 236	98.75 323	99.02 15	97.82 315	99.71 129	96.11 162	99.48 235	93.04 336	99.65 123	99.69 101
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-ACMP-BASELINE		97.83 228	97.71 219	98.20 273	99.11 256	96.33 299	99.41 172	99.52 90	98.06 124	99.05 209	99.50 222	89.64 324	99.73 187	97.73 206	97.38 251	98.53 304

OurMVSNet-221017-0		97.88 218	97.77 211	98.19 274	98.71 313	96.53 292	99.88 1	99.00 300	97.79 149	98.78 250	99.94 3	91.68 297	99.35 264	97.21 247	96.99 261	98.69 253

PatchmatchNet		98.31 167	98.36 156	98.19 274	99.16 249	95.32 322	99.27 222	98.92 309	97.37 196	99.37 138	99.58 193	94.90 205	99.70 203	97.43 238	99.21 151	99.54 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs5		97.52 268	97.30 270	98.16 276	98.57 326	96.73 285	99.27 222	98.90 314	96.14 297	98.37 292	99.53 212	91.54 303	99.14 295	97.51 229	95.87 285	98.63 285

D2MVS		98.41 160	98.50 150	98.15 277	99.26 222	96.62 290	99.40 180	99.61 35	97.71 158	98.98 220	99.36 265	96.04 164	99.67 209	98.70 106	97.41 249	98.15 332

testgi		97.65 261	97.50 239	98.13 278	99.36 196	96.45 295	99.42 170	99.48 142	97.76 152	97.87 313	99.45 239	91.09 308	98.81 332	94.53 319	98.52 197	99.13 194

RRT_test8_iter05		97.72 248	97.60 229	98.08 279	99.23 228	96.08 305	99.63 62	99.49 130	97.54 177	98.94 226	99.81 62	87.99 341	99.35 264	99.21 41	96.51 269	98.81 226

ITE_SJBPF				98.08 279	99.29 215	96.37 297		98.92 309	98.34 84	98.83 243	99.75 111	91.09 308	99.62 225	95.82 296	97.40 250	98.25 328

IterMVS-SCA-FT		97.82 231	97.75 215	98.06 281	99.57 140	96.36 298	99.02 279	99.49 130	97.18 212	98.71 256	99.72 128	92.72 269	99.14 295	97.44 237	95.86 286	98.67 265

SixPastTwentyTwo		97.50 271	97.33 267	98.03 282	98.65 318	96.23 302	99.77 25	98.68 334	97.14 215	97.90 312	99.93 4	90.45 312	99.18 292	97.00 261	96.43 271	98.67 265

tpm		97.67 259	97.55 232	98.03 282	99.02 273	95.01 328	99.43 163	98.54 339	96.44 273	99.12 192	99.34 271	91.83 293	99.60 227	97.75 204	96.46 270	99.48 162

IterMVS		97.83 228	97.77 211	98.02 284	99.58 138	96.27 301	99.02 279	99.48 142	97.22 210	98.71 256	99.70 133	92.75 266	99.13 298	97.46 234	96.00 280	98.67 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDA-MVSNet_test_wron		95.45 308	94.60 314	98.01 285	98.16 336	97.21 259	99.11 261	99.24 273	93.49 335	80.73 362	98.98 320	93.02 259	98.18 339	94.22 324	94.45 314	98.64 277

K. test v3		97.10 285	96.79 286	98.01 285	98.72 311	96.33 299	99.87 5	97.05 356	97.59 169	96.16 340	99.80 76	88.71 331	99.04 309	96.69 280	96.55 267	98.65 275

MVP-Stereo		97.81 233	97.75 215	97.99 287	97.53 342	96.60 291	98.96 294	98.85 318	97.22 210	97.23 326	99.36 265	95.28 192	99.46 238	95.51 304	99.78 92	97.92 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		95.42 309	94.57 315	97.97 288	89.83 364	96.11 304	99.48 144	98.75 323	96.74 247	96.68 335	99.88 15	88.65 333	99.71 197	98.37 152	82.74 355	98.09 333

PVSNet_0	94.43 19	96.09 303	95.47 306	97.94 289	99.31 210	94.34 338	97.81 354	99.70 15	97.12 218	97.46 321	98.75 330	89.71 322	99.79 165	97.69 212	81.69 356	99.68 105

DWT-MVSNet_test		97.53 267	97.40 256	97.93 290	99.03 272	94.86 332	99.57 93	98.63 335	96.59 262	98.36 293	98.79 327	89.32 326	99.74 180	98.14 172	98.16 216	99.20 191

MDA-MVSNet-bldmvs		94.96 313	93.98 319	97.92 291	98.24 335	97.27 254	99.15 251	99.33 245	93.80 331	80.09 363	99.03 313	88.31 337	97.86 348	93.49 331	94.36 316	98.62 287

YYNet1		95.36 310	94.51 316	97.92 291	97.89 338	97.10 261	99.10 263	99.23 274	93.26 338	80.77 361	99.04 312	92.81 265	98.02 343	94.30 321	94.18 319	98.64 277

tpmrst		98.33 166	98.48 151	97.90 293	99.16 249	94.78 333	99.31 209	99.11 289	97.27 204	99.45 114	99.59 190	95.33 191	99.84 137	98.48 140	98.61 189	99.09 199

ADS-MVSNet2		98.02 200	98.07 180	97.87 294	99.33 202	95.19 325	99.23 236	99.08 293	96.24 286	99.10 197	99.67 154	94.11 240	98.93 328	96.81 273	99.05 166	99.48 162

test_0402		96.64 291	96.24 293	97.85 295	98.85 296	96.43 296	99.44 157	99.26 269	93.52 334	96.98 333	99.52 215	88.52 335	99.20 291	92.58 342	97.50 240	97.93 345

tpmvs		97.98 207	98.02 184	97.84 296	99.04 270	94.73 334	99.31 209	99.20 279	96.10 303	98.76 252	99.42 246	94.94 201	99.81 157	96.97 264	98.45 200	98.97 216

TinyColmap		97.12 284	96.89 284	97.83 297	99.07 264	95.52 317	98.57 332	98.74 326	97.58 171	97.81 316	99.79 88	88.16 339	99.56 230	95.10 312	97.21 255	98.39 321

pmmvs6		96.53 293	96.09 296	97.82 298	98.69 315	95.47 318	99.37 192	99.47 160	93.46 336	97.41 322	99.78 95	87.06 346	99.33 268	96.92 270	92.70 337	98.65 275

EU-MVSNet		97.98 207	98.03 182	97.81 299	98.72 311	96.65 289	99.66 50	99.66 27	98.09 115	98.35 294	99.82 49	95.25 196	98.01 344	97.41 239	95.30 299	98.78 229

lessismore_v0				97.79 300	98.69 315	95.44 320		94.75 364		95.71 344	99.87 20	88.69 332	99.32 269	95.89 295	94.93 308	98.62 287

USDC		97.34 278	97.20 276	97.75 301	99.07 264	95.20 324	98.51 336	99.04 298	97.99 130	98.31 296	99.86 23	89.02 328	99.55 232	95.67 302	97.36 252	98.49 307

tpm2		97.44 276	97.34 265	97.74 302	99.15 252	94.36 337	99.45 153	98.94 306	93.45 337	98.90 232	99.44 240	91.35 305	99.59 228	97.31 241	98.07 219	99.29 186

CostFormer		97.72 248	97.73 217	97.71 303	99.15 252	94.02 340	99.54 112	99.02 299	94.67 323	99.04 210	99.35 268	92.35 286	99.77 172	98.50 139	97.94 221	99.34 182

LF4IMVS		97.52 268	97.46 244	97.70 304	98.98 279	95.55 314	99.29 215	98.82 321	98.07 120	98.66 265	99.64 168	89.97 319	99.61 226	97.01 260	96.68 262	97.94 344

ppachtmachnet_test		97.49 274	97.45 245	97.61 305	98.62 321	95.24 323	98.80 313	99.46 170	96.11 299	98.22 300	99.62 180	96.45 153	98.97 325	93.77 327	95.97 284	98.61 296

MVS_0304		96.79 289	96.52 289	97.59 306	99.22 232	94.92 331	99.04 275	99.59 43	96.49 266	98.43 288	98.99 317	80.48 360	99.39 251	97.15 255	99.27 148	98.47 310

dp		97.75 242	97.80 205	97.59 306	99.10 259	93.71 343	99.32 207	98.88 316	96.48 270	99.08 203	99.55 203	92.67 274	99.82 153	96.52 284	98.58 192	99.24 188

our_test_3		97.65 261	97.68 221	97.55 308	98.62 321	94.97 329	98.84 309	99.30 261	96.83 244	98.19 301	99.34 271	97.01 135	99.02 313	95.00 315	96.01 279	98.64 277

MVS-HIRNet		95.75 306	95.16 310	97.51 309	99.30 211	93.69 344	98.88 305	95.78 362	85.09 355	98.78 250	92.65 359	91.29 306	99.37 256	94.85 316	99.85 58	99.46 169

tpm cat1		97.39 277	97.36 260	97.50 310	99.17 247	93.73 342	99.43 163	99.31 257	91.27 345	98.71 256	99.08 307	94.31 234	99.77 172	96.41 288	98.50 198	99.00 212

new_pmnet		96.38 297	96.03 297	97.41 311	98.13 337	95.16 327	99.05 270	99.20 279	93.94 329	97.39 323	98.79 327	91.61 302	99.04 309	90.43 347	95.77 287	98.05 336

UnsupCasMVSNet_eth		96.44 295	96.12 295	97.40 312	98.65 318	95.65 311	99.36 196	99.51 103	97.13 216	96.04 342	98.99 317	88.40 336	98.17 340	96.71 278	90.27 343	98.40 320

KD-MVS_2432*1600		94.62 315	93.72 321	97.31 313	97.19 350	95.82 309	98.34 342	99.20 279	95.00 317	97.57 319	98.35 340	87.95 342	98.10 341	92.87 338	77.00 360	98.01 338

miper_refine_blended		94.62 315	93.72 321	97.31 313	97.19 350	95.82 309	98.34 342	99.20 279	95.00 317	97.57 319	98.35 340	87.95 342	98.10 341	92.87 338	77.00 360	98.01 338

pmmvs-eth3d		95.34 311	94.73 313	97.15 315	95.53 357	95.94 307	99.35 202	99.10 290	95.13 312	93.55 350	97.54 347	88.15 340	97.91 346	94.58 318	89.69 346	97.61 349

FMVSNet5		96.43 296	96.19 294	97.15 315	99.11 256	95.89 308	99.32 207	99.52 90	94.47 327	98.34 295	99.07 308	87.54 345	97.07 355	92.61 341	95.72 290	98.47 310

Anonymous20240521		96.20 300	95.89 301	97.13 317	97.72 341	94.96 330	99.79 22	99.29 266	93.01 339	97.20 328	99.03 313	89.69 323	98.36 338	91.16 345	96.13 277	98.07 334

DeepPCF-MVS	98.18 3	98.81 133	99.37 20	97.12 318	99.60 134	91.75 353	98.61 329	99.44 191	99.35 1	99.83 18	99.85 29	98.70 63	99.81 157	99.02 59	99.91 16	99.81 42

MS-PatchMatch		97.24 282	97.32 268	96.99 319	98.45 331	93.51 347	98.82 311	99.32 254	97.41 193	98.13 304	99.30 281	88.99 329	99.56 230	95.68 301	99.80 86	97.90 347

RPSCF		98.22 173	98.62 140	96.99 319	99.82 37	91.58 354	99.72 33	99.44 191	96.61 258	99.66 66	99.89 10	95.92 170	99.82 153	97.46 234	99.10 162	99.57 141

DIV-MVS_2432*1600		95.00 312	94.34 317	96.96 321	97.07 352	95.39 321	99.56 100	99.44 191	95.11 314	97.13 330	97.32 351	91.86 292	97.27 354	90.35 348	81.23 357	98.23 330

DSMNet-mixed		97.25 281	97.35 262	96.95 322	97.84 339	93.61 346	99.57 93	96.63 360	96.13 298	98.87 237	98.61 335	94.59 223	97.70 351	95.08 313	98.86 181	99.55 143

MIMVSNet1		95.51 307	95.04 311	96.92 323	97.38 344	95.60 312	99.52 118	99.50 122	93.65 333	96.97 334	99.17 298	85.28 351	96.56 359	88.36 354	95.55 294	98.60 299

LCM-MVSNet-Re		97.83 228	98.15 169	96.87 324	99.30 211	92.25 352	99.59 81	98.26 341	97.43 190	96.20 339	99.13 303	96.27 159	98.73 334	98.17 169	98.99 172	99.64 122

EG-PatchMatch MVS		95.97 304	95.69 304	96.81 325	97.78 340	92.79 350	99.16 247	98.93 307	96.16 294	94.08 349	99.22 293	82.72 355	99.47 236	95.67 302	97.50 240	98.17 331

Anonymous20231206		96.22 298	96.03 297	96.79 326	97.31 347	94.14 339	99.63 62	99.08 293	96.17 292	97.04 332	99.06 310	93.94 245	97.76 350	86.96 358	95.06 304	98.47 310

test20.03		96.12 302	95.96 299	96.63 327	97.44 343	95.45 319	99.51 122	99.38 219	96.55 263	96.16 340	99.25 290	93.76 251	96.17 360	87.35 357	94.22 318	98.27 326

pmmvs3		94.09 321	93.25 324	96.60 328	94.76 359	94.49 335	98.92 301	98.18 345	89.66 349	96.48 337	98.06 345	86.28 347	97.33 353	89.68 350	87.20 349	97.97 343

UnsupCasMVSNet_bld		93.53 322	92.51 325	96.58 329	97.38 344	93.82 341	98.24 347	99.48 142	91.10 347	93.10 352	96.66 353	74.89 361	98.37 337	94.03 326	87.71 348	97.56 351

OpenMVS_ROB	92.34 20	94.38 319	93.70 323	96.41 330	97.38 344	93.17 348	99.06 268	98.75 323	86.58 353	94.84 348	98.26 343	81.53 358	99.32 269	89.01 351	97.87 223	96.76 353

CL-MVSNet_2432*1600		94.49 317	93.97 320	96.08 331	96.16 353	93.67 345	98.33 344	99.38 219	95.13 312	97.33 324	98.15 344	92.69 273	96.57 358	88.67 352	79.87 358	97.99 341

Patchmatch-RL test		95.84 305	95.81 303	95.95 332	95.61 355	90.57 355	98.24 347	98.39 340	95.10 316	95.20 345	98.67 332	94.78 211	97.77 349	96.28 290	90.02 344	99.51 157

new-patchmatchnet		94.48 318	94.08 318	95.67 333	95.08 358	92.41 351	99.18 245	99.28 268	94.55 326	93.49 351	97.37 350	87.86 344	97.01 356	91.57 343	88.36 347	97.61 349

PM-MVS		92.96 323	92.23 326	95.14 334	95.61 355	89.98 357	99.37 192	98.21 343	94.80 321	95.04 347	97.69 346	65.06 363	97.90 347	94.30 321	89.98 345	97.54 352

Gipuma		90.99 325	90.15 328	93.51 335	98.73 309	90.12 356	93.98 360	99.45 182	79.32 358	92.28 353	94.91 356	69.61 362	97.98 345	87.42 356	95.67 291	92.45 359
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				93.34 336	99.29 215	82.27 360		99.22 275	85.15 354	96.33 338	99.05 311	90.97 310	99.73 187	93.57 330	97.77 225	98.01 338

ambc				93.06 337	92.68 360	82.36 359	98.47 337	98.73 331		95.09 346	97.41 348	55.55 366	99.10 305	96.42 287	91.32 341	97.71 348

N_pmnet		94.95 314	95.83 302	92.31 338	98.47 330	79.33 363	99.12 255	92.81 370	93.87 330	97.68 318	99.13 303	93.87 247	99.01 315	91.38 344	96.19 276	98.59 300

CMPMVS	69.68 23	94.13 320	94.90 312	91.84 339	97.24 348	80.01 362	98.52 335	99.48 142	89.01 350	91.99 354	99.67 154	85.67 350	99.13 298	95.44 305	97.03 260	96.39 355
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet		86.80 327	85.22 331	91.53 340	87.81 365	80.96 361	98.23 349	98.99 301	71.05 360	90.13 356	96.51 354	48.45 369	96.88 357	90.51 346	85.30 351	96.76 353

PMMVS2		86.87 326	85.37 330	91.35 341	90.21 363	83.80 358	98.89 304	97.45 355	83.13 357	91.67 355	95.03 355	48.49 368	94.70 362	85.86 360	77.62 359	95.54 356

test_method		91.10 324	91.36 327	90.31 342	95.85 354	73.72 368	94.89 359	99.25 271	68.39 362	95.82 343	99.02 315	80.50 359	98.95 327	93.64 329	94.89 309	98.25 328

tmp_tt		82.80 329	81.52 332	86.66 343	66.61 371	68.44 369	92.79 362	97.92 348	68.96 361	80.04 364	99.85 29	85.77 349	96.15 361	97.86 193	43.89 366	95.39 357

MVE	76.82 21	76.91 333	74.31 337	84.70 344	85.38 368	76.05 367	96.88 358	93.17 368	67.39 363	71.28 365	89.01 364	21.66 375	87.69 365	71.74 364	72.29 362	90.35 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 332	74.86 336	84.62 345	75.88 369	77.61 364	97.63 356	93.15 369	88.81 351	64.27 366	89.29 363	36.51 370	83.93 368	75.89 363	52.31 365	92.33 360

E-PMN		80.61 330	79.88 333	82.81 346	90.75 362	76.38 366	97.69 355	95.76 363	66.44 364	83.52 358	92.25 360	62.54 365	87.16 366	68.53 365	61.40 363	84.89 364

FPMVS		84.93 328	85.65 329	82.75 347	86.77 366	63.39 370	98.35 341	98.92 309	74.11 359	83.39 359	98.98 320	50.85 367	92.40 364	84.54 361	94.97 306	92.46 358

EMVS		80.02 331	79.22 334	82.43 348	91.19 361	76.40 365	97.55 357	92.49 371	66.36 365	83.01 360	91.27 361	64.63 364	85.79 367	65.82 366	60.65 364	85.08 363

PMVS	70.75 22	75.98 334	74.97 335	79.01 349	70.98 370	55.18 371	93.37 361	98.21 343	65.08 366	61.78 367	93.83 358	21.74 374	92.53 363	78.59 362	91.12 342	89.34 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 335	41.29 340	36.84 350	86.18 367	49.12 372	79.73 363	22.81 373	27.64 367	25.46 370	28.45 370	21.98 373	48.89 369	55.80 367	23.56 369	12.51 367

test123		39.01 337	42.50 339	28.53 351	39.17 372	20.91 373	98.75 318	19.17 374	19.83 369	38.57 368	66.67 366	33.16 371	15.42 370	37.50 369	29.66 368	49.26 365

testmvs		39.17 336	43.78 338	25.37 352	36.04 373	16.84 374	98.36 340	26.56 372	20.06 368	38.51 369	67.32 365	29.64 372	15.30 371	37.59 368	39.90 367	43.98 366

uanet_test		0.02 341	0.03 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

cdsmvs_eth3d_5k		24.64 338	32.85 341	0.00 353	0.00 374	0.00 375	0.00 364	99.51 103	0.00 370	0.00 371	99.56 200	96.58 148	0.00 372	0.00 370	0.00 370	0.00 368

pcd_1.5k_mvsjas		8.27 340	11.03 343	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	99.01 17	0.00 372	0.00 370	0.00 370	0.00 368

sosnet-low-res		0.02 341	0.03 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

sosnet		0.02 341	0.03 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uncertanet		0.02 341	0.03 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

Regformer		0.02 341	0.03 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

ab-mvs-re		8.30 339	11.06 342	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	99.58 193	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uanet		0.02 341	0.03 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.27 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

PC_three_1452									98.18 104	99.84 13	99.70 133	99.31 3	98.52 336	98.30 160	99.80 86	99.81 42

eth-test2					0.00 374

eth-test					0.00 374

ZD-MVS					99.71 86	99.79 30		99.61 35	96.84 242	99.56 93	99.54 208	98.58 71	99.96 19	96.93 268	99.75 100

RE-MVS-def			99.34 27		99.76 52	99.82 20	99.63 62	99.52 90	98.38 78	99.76 38	99.82 49	98.75 57		98.61 121	99.81 82	99.77 65

IU-MVS					99.84 32	99.88 7		99.32 254	98.30 89	99.84 13				98.86 82	99.85 58	99.89 2

test_241102_TWO								99.48 142	99.08 11	99.88 5	99.81 62	98.94 32	99.96 19	98.91 71	99.84 65	99.88 6

test_241102_ONE					99.84 32	99.90 1		99.48 142	99.07 13	99.91 1	99.74 117	99.20 6	99.76 176

9.14			99.10 71		99.72 80		99.40 180	99.51 103	97.53 179	99.64 73	99.78 95	98.84 43	99.91 92	97.63 215	99.82 79

save fliter					99.76 52	99.59 69	99.14 253	99.40 209	99.00 22

test_0728_THIRD									98.99 25	99.81 23	99.80 76	99.09 13	99.96 19	98.85 84	99.90 23	99.88 6

test0726					99.85 25	99.89 3	99.62 68	99.50 122	99.10 8	99.86 11	99.82 49	98.94 32

GSMVS																99.52 151

test_part2					99.81 40	99.83 14				99.77 34

sam_mvs1												94.86 207				99.52 151

sam_mvs												94.72 218

MTGPA								99.47 160

test_post1							99.23 236				65.14 368	94.18 239	99.71 197	97.58 219

test_post											65.99 367	94.65 222	99.73 187

patchmatchnet-post											98.70 331	94.79 210	99.74 180

MTMP							99.54 112	98.88 316

gm-plane-assit					98.54 328	92.96 349			94.65 324		99.15 301		99.64 219	97.56 224

test9_res														97.49 230	99.72 107	99.75 71

TEST9					99.67 101	99.65 58	99.05 270	99.41 203	96.22 288	98.95 224	99.49 225	98.77 52	99.91 92

test_8					99.67 101	99.61 64	99.03 276	99.41 203	96.28 281	98.93 228	99.48 231	98.76 54	99.91 92

agg_prior2														97.21 247	99.73 106	99.75 71

agg_prior					99.67 101	99.62 62		99.40 209		98.87 237			99.91 92

test_prior4						99.56 74	98.99 286

test_prior2							98.96 294		98.34 84	99.01 213	99.52 215	98.68 64		97.96 185	99.74 103

旧先验2							98.96 294		96.70 250	99.47 111			99.94 55	98.19 165

新几何2							99.01 284

旧先验1					99.74 70	99.59 69		99.54 73			99.69 141	98.47 79			99.68 118	99.73 83

无先验							98.99 286	99.51 103	96.89 239				99.93 70	97.53 227		99.72 89

原ACMM2							98.95 298

test222					99.75 62	99.49 87	98.91 303	99.49 130	96.42 275	99.34 147	99.65 161	98.28 95			99.69 113	99.72 89

testdata2													99.95 44	96.67 281

segment_acmp												98.96 26

testdata1							98.85 308		98.32 88

plane_prior7					99.29 215	97.03 271

plane_prior6					99.27 220	96.98 275						92.71 271

plane_prior5								99.47 160					99.69 207	97.78 200	97.63 227	98.67 265

plane_prior4											99.61 184

plane_prior3						97.00 273			98.69 56	99.11 194

plane_prior2							99.39 184		98.97 30

plane_prior1					99.26 222

plane_prior						96.97 276	99.21 243		98.45 71						97.60 230

n2								0.00 375

nn								0.00 375

door-mid								98.05 346

test11								99.35 234

door								97.92 348

HQP5-MVS						96.83 281

HQP-NCC					99.19 238		98.98 290		98.24 94	98.66 265

ACMP_Plane					99.19 238		98.98 290		98.24 94	98.66 265

BP-MVS														97.19 251

HQP4-MVS										98.66 265			99.64 219			98.64 277

HQP3-MVS								99.39 213							97.58 232

HQP2-MVS												92.47 280

NP-MVS					99.23 228	96.92 279					99.40 254

MDTV_nov1_ep13_2view						95.18 326	99.35 202		96.84 242	99.58 90		95.19 198		97.82 197		99.46 169

MDTV_nov1_ep13			98.32 161		99.11 256	94.44 336	99.27 222	98.74 326	97.51 181	99.40 131	99.62 180	94.78 211	99.76 176	97.59 218	98.81 185

ACMMP++_ref															97.19 256

ACMMP++															97.43 248

Test By Simon												98.75 57

High-resolution multi-view benchmark