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 28	99.56 56	99.02 15	99.88 5	99.85 29	99.18 8	99.96 19	99.22 37	99.92 11	99.90 1

test_0728_SECOND				99.91 2	99.84 32	99.89 3	99.57 91	99.51 101					99.96 19	98.93 66	99.86 51	99.88 5

DPE-MVS		99.46 24	99.32 31	99.91 2	99.78 44	99.88 7	99.36 194	99.51 101	98.73 53	99.88 5	99.84 38	98.72 60	99.96 19	98.16 166	99.87 40	99.88 5
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 21	99.89 4	99.90 3	99.86 10	99.36 194	99.47 158	98.79 49	99.68 53	99.81 62	98.43 81	99.97 11	98.88 72	99.90 23	99.83 29

MTAPA		99.52 13	99.39 17	99.89 4	99.90 3	99.86 10	99.66 48	99.47 158	98.79 49	99.68 53	99.81 62	98.43 81	99.97 11	98.88 72	99.90 23	99.83 29

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 32	99.90 1	99.60 73	99.48 140	99.08 11	99.91 1	99.81 62	99.20 5	99.96 19	98.91 69	99.85 58	99.79 53

DVP-MVS		99.57 7	99.47 9	99.88 6	99.85 25	99.89 3	99.57 91	99.37 226	99.10 8	99.81 22	99.80 76	98.94 31	99.96 19	98.93 66	99.86 51	99.81 41
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 48	99.20 60	99.88 6	99.90 3	99.87 9	99.30 209	99.52 88	97.18 209	99.60 83	99.79 88	98.79 47	99.95 43	98.83 86	99.91 16	99.83 29
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 38	99.27 51	99.88 6	99.89 8	99.80 26	99.67 44	99.50 120	98.70 55	99.77 33	99.49 224	98.21 95	99.95 43	98.46 141	99.77 92	99.88 5
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 26	99.88 6	99.87 15	99.86 10	99.47 147	99.48 140	98.05 122	99.76 37	99.86 23	98.82 44	99.93 69	98.82 90	99.91 16	99.84 18

ZNCC-MVS		99.47 22	99.33 28	99.87 11	99.87 15	99.81 24	99.64 58	99.67 22	98.08 116	99.55 95	99.64 166	98.91 36	99.96 19	98.72 101	99.90 23	99.82 36

region2R		99.48 19	99.35 24	99.87 11	99.88 11	99.80 26	99.65 55	99.66 27	98.13 105	99.66 64	99.68 146	98.96 25	99.96 19	98.62 115	99.87 40	99.84 18

HPM-MVS++		99.39 46	99.23 58	99.87 11	99.75 62	99.84 13	99.43 161	99.51 101	98.68 57	99.27 157	99.53 211	98.64 68	99.96 19	98.44 143	99.80 84	99.79 53

XVS		99.53 11	99.42 13	99.87 11	99.85 25	99.83 14	99.69 37	99.68 19	98.98 27	99.37 135	99.74 117	98.81 45	99.94 54	98.79 92	99.86 51	99.84 18

X-MVStestdata		96.55 290	95.45 305	99.87 11	99.85 25	99.83 14	99.69 37	99.68 19	98.98 27	99.37 135	64.01 366	98.81 45	99.94 54	98.79 92	99.86 51	99.84 18

MP-MVS		99.33 53	99.15 64	99.87 11	99.88 11	99.82 20	99.66 48	99.46 168	98.09 112	99.48 107	99.74 117	98.29 92	99.96 19	97.93 184	99.87 40	99.82 36
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 79	99.51 101	98.62 59	99.79 26	99.83 42	99.28 3	99.97 11	98.48 137	99.90 23	99.84 18
Skip Steuart: Steuart Systems R&D Blog.
testtj		99.12 85	98.87 104	99.86 18	99.72 80	99.79 30	99.44 155	99.51 101	97.29 199	99.59 86	99.74 117	98.15 100	99.96 19	96.74 272	99.69 110	99.81 41

SR-MVS		99.43 33	99.29 45	99.86 18	99.75 62	99.83 14	99.59 79	99.62 33	98.21 98	99.73 43	99.79 88	98.68 63	99.96 19	98.44 143	99.77 92	99.79 53

HFP-MVS		99.49 15	99.37 19	99.86 18	99.87 15	99.80 26	99.66 48	99.67 22	98.15 103	99.68 53	99.69 140	99.06 13	99.96 19	98.69 106	99.87 40	99.84 18

#test#		99.43 33	99.29 45	99.86 18	99.87 15	99.80 26	99.55 107	99.67 22	97.83 140	99.68 53	99.69 140	99.06 13	99.96 19	98.39 145	99.87 40	99.84 18

ACMMPR		99.49 15	99.36 21	99.86 18	99.87 15	99.79 30	99.66 48	99.67 22	98.15 103	99.67 59	99.69 140	98.95 28	99.96 19	98.69 106	99.87 40	99.84 18

PGM-MVS		99.45 26	99.31 38	99.86 18	99.87 15	99.78 37	99.58 86	99.65 32	97.84 139	99.71 46	99.80 76	99.12 11	99.97 11	98.33 153	99.87 40	99.83 29

mPP-MVS		99.44 30	99.30 41	99.86 18	99.88 11	99.79 30	99.69 37	99.48 140	98.12 107	99.50 103	99.75 111	98.78 48	99.97 11	98.57 126	99.89 33	99.83 29

test1172		99.43 33	99.29 45	99.85 25	99.75 62	99.82 20	99.60 73	99.56 56	98.28 89	99.74 41	99.79 88	98.53 72	99.95 43	98.55 132	99.78 89	99.79 53

SR-MVS-dyc-post		99.45 26	99.31 38	99.85 25	99.76 52	99.82 20	99.63 60	99.52 88	98.38 77	99.76 37	99.82 49	98.53 72	99.95 43	98.61 118	99.81 80	99.77 63

GST-MVS		99.40 45	99.24 56	99.85 25	99.86 21	99.79 30	99.60 73	99.67 22	97.97 128	99.63 73	99.68 146	98.52 74	99.95 43	98.38 147	99.86 51	99.81 41

SMA-MVS		99.44 30	99.30 41	99.85 25	99.73 75	99.83 14	99.56 98	99.47 158	97.45 183	99.78 31	99.82 49	99.18 8	99.91 91	98.79 92	99.89 33	99.81 41
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 24	99.85 25	99.76 52	99.83 14	99.63 60	99.54 71	98.36 81	99.79 26	99.82 49	98.86 40	99.95 43	98.62 115	99.81 80	99.78 61

HPM-MVS_fast		99.51 14	99.40 16	99.85 25	99.91 1	99.79 30	99.76 27	99.56 56	97.72 154	99.76 37	99.75 111	99.13 10	99.92 80	99.07 53	99.92 11	99.85 14

CP-MVS		99.45 26	99.32 31	99.85 25	99.83 36	99.75 38	99.69 37	99.52 88	98.07 117	99.53 98	99.63 172	98.93 35	99.97 11	98.74 97	99.91 16	99.83 29

APD-MVS		99.27 61	99.08 72	99.84 32	99.75 62	99.79 30	99.50 126	99.50 120	97.16 211	99.77 33	99.82 49	98.78 48	99.94 54	97.56 220	99.86 51	99.80 49
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 33	99.85 25	99.75 38	99.66 48	99.59 43	98.13 105	99.82 20	99.81 62	98.60 69	99.96 19	98.46 141	99.88 36	99.79 53

HPM-MVS		99.42 38	99.28 49	99.83 33	99.90 3	99.72 42	99.81 12	99.54 71	97.59 166	99.68 53	99.63 172	98.91 36	99.94 54	98.58 124	99.91 16	99.84 18
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MCST-MVS		99.43 33	99.30 41	99.82 35	99.79 42	99.74 41	99.29 213	99.40 207	98.79 49	99.52 100	99.62 178	98.91 36	99.90 106	98.64 113	99.75 97	99.82 36

ACMMP		99.45 26	99.32 31	99.82 35	99.89 8	99.67 52	99.62 66	99.69 18	98.12 107	99.63 73	99.84 38	98.73 59	99.96 19	98.55 132	99.83 72	99.81 41
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 71	98.97 91	99.82 35	99.17 244	99.68 49	99.81 12	99.51 101	99.20 4	98.72 252	99.89 10	95.68 177	99.97 11	98.86 79	99.86 51	99.81 41

TSAR-MVS + MP.		99.58 4	99.50 8	99.81 38	99.91 1	99.66 54	99.63 60	99.39 211	98.91 38	99.78 31	99.85 29	99.36 2	99.94 54	98.84 83	99.88 36	99.82 36
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 66	99.05 74	99.81 38	99.12 251	99.66 54	99.84 6	99.74 10	99.09 10	98.92 226	99.90 7	95.94 166	99.98 6	98.95 63	99.92 11	99.79 53

UA-Net		99.42 38	99.29 45	99.80 40	99.62 126	99.55 75	99.50 126	99.70 15	98.79 49	99.77 33	99.96 1	97.45 116	99.96 19	98.92 68	99.90 23	99.89 2

CDPH-MVS		99.13 79	98.91 99	99.80 40	99.75 62	99.71 44	99.15 249	99.41 201	96.60 257	99.60 83	99.55 202	98.83 43	99.90 106	97.48 227	99.83 72	99.78 61

QAPM		98.67 144	98.30 161	99.80 40	99.20 233	99.67 52	99.77 24	99.72 11	94.74 319	98.73 251	99.90 7	95.78 173	99.98 6	96.96 261	99.88 36	99.76 68

SF-MVS		99.38 47	99.24 56	99.79 43	99.79 42	99.68 49	99.57 91	99.54 71	97.82 145	99.71 46	99.80 76	98.95 28	99.93 69	98.19 161	99.84 65	99.74 74

NCCC		99.34 52	99.19 61	99.79 43	99.61 130	99.65 57	99.30 209	99.48 140	98.86 40	99.21 173	99.63 172	98.72 60	99.90 106	98.25 157	99.63 123	99.80 49

CNVR-MVS		99.42 38	99.30 41	99.78 45	99.62 126	99.71 44	99.26 229	99.52 88	98.82 44	99.39 130	99.71 129	98.96 25	99.85 131	98.59 123	99.80 84	99.77 63

DP-MVS		99.16 75	98.95 95	99.78 45	99.77 49	99.53 80	99.41 170	99.50 120	97.03 226	99.04 207	99.88 15	97.39 117	99.92 80	98.66 111	99.90 23	99.87 10

ETH3D-3000-0.1		99.21 67	99.02 82	99.77 47	99.73 75	99.69 47	99.38 187	99.51 101	97.45 183	99.61 79	99.75 111	98.51 75	99.91 91	97.45 232	99.83 72	99.71 94

train_agg		99.02 104	98.77 118	99.77 47	99.67 101	99.65 57	99.05 268	99.41 201	96.28 278	98.95 221	99.49 224	98.76 53	99.91 91	97.63 211	99.72 104	99.75 69

DeepC-MVS_fast	98.69 1	99.49 15	99.39 17	99.77 47	99.63 120	99.59 68	99.36 194	99.46 168	99.07 13	99.79 26	99.82 49	98.85 41	99.92 80	98.68 108	99.87 40	99.82 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		99.01 107	98.76 120	99.76 50	99.67 101	99.62 61	98.99 284	99.40 207	96.26 281	98.87 234	99.49 224	98.77 51	99.91 91	97.69 208	99.72 104	99.75 69

xxxxxxxxxxxxxcwj		99.43 33	99.32 31	99.75 51	99.76 52	99.59 68	99.14 251	99.53 82	99.00 22	99.71 46	99.80 76	98.95 28	99.93 69	98.19 161	99.84 65	99.74 74

Regformer-2		99.54 9	99.47 9	99.75 51	99.71 86	99.52 83	99.49 136	99.49 128	98.94 33	99.83 17	99.76 106	99.01 16	99.94 54	99.15 46	99.87 40	99.80 49

新几何1				99.75 51	99.75 62	99.59 68		99.54 71	96.76 243	99.29 152	99.64 166	98.43 81	99.94 54	96.92 266	99.66 118	99.72 87

1121		99.09 94	98.87 104	99.75 51	99.74 70	99.60 65	99.27 220	99.48 140	96.82 242	99.25 164	99.65 159	98.38 86	99.93 69	97.53 223	99.67 117	99.73 81

test12				99.75 51	99.64 117	99.61 63		99.29 263		99.21 173		98.38 86	99.89 114		99.74 100	99.74 74

CPTT-MVS		99.11 90	98.90 100	99.74 56	99.80 41	99.46 91	99.59 79	99.49 128	97.03 226	99.63 73	99.69 140	97.27 124	99.96 19	97.82 193	99.84 65	99.81 41

LS3D		99.27 61	99.12 67	99.74 56	99.18 238	99.75 38	99.56 98	99.57 50	98.45 71	99.49 106	99.85 29	97.77 110	99.94 54	98.33 153	99.84 65	99.52 148

ETH3 D test6400		98.70 140	98.35 156	99.73 58	99.69 96	99.60 65	99.16 245	99.45 180	95.42 307	99.27 157	99.60 185	97.39 117	99.91 91	95.36 305	99.83 72	99.70 96

Regformer-4		99.59 3	99.54 4	99.73 58	99.76 52	99.41 96	99.58 86	99.49 128	99.02 15	99.88 5	99.80 76	99.00 22	99.94 54	99.45 18	99.92 11	99.84 18

VNet		99.11 90	98.90 100	99.73 58	99.52 149	99.56 73	99.41 170	99.39 211	99.01 18	99.74 41	99.78 95	95.56 180	99.92 80	99.52 7	98.18 208	99.72 87

ETH3D cwj APD-0.16		99.06 98	98.84 110	99.72 61	99.51 151	99.60 65	99.23 234	99.44 189	97.04 224	99.39 130	99.67 152	98.30 91	99.92 80	97.27 239	99.69 110	99.64 120

Regformer-1		99.53 11	99.47 9	99.72 61	99.71 86	99.44 93	99.49 136	99.46 168	98.95 32	99.83 17	99.76 106	99.01 16	99.93 69	99.17 43	99.87 40	99.80 49

114514_t		98.93 113	98.67 128	99.72 61	99.85 25	99.53 80	99.62 66	99.59 43	92.65 338	99.71 46	99.78 95	98.06 103	99.90 106	98.84 83	99.91 16	99.74 74

PHI-MVS		99.30 56	99.17 63	99.70 64	99.56 144	99.52 83	99.58 86	99.80 8	97.12 215	99.62 77	99.73 124	98.58 70	99.90 106	98.61 118	99.91 16	99.68 103

Regformer-3		99.57 7	99.53 5	99.68 65	99.76 52	99.29 107	99.58 86	99.44 189	99.01 18	99.87 10	99.80 76	98.97 24	99.91 91	99.44 20	99.92 11	99.83 29

test_prior3		99.21 67	99.05 74	99.68 65	99.67 101	99.48 88	98.96 292	99.56 56	98.34 83	99.01 210	99.52 214	98.68 63	99.83 145	97.96 181	99.74 100	99.74 74

test_prior				99.68 65	99.67 101	99.48 88		99.56 56					99.83 145			99.74 74

DPM-MVS		98.95 112	98.71 124	99.66 68	99.63 120	99.55 75	98.64 326	99.10 287	97.93 131	99.42 119	99.55 202	98.67 66	99.80 161	95.80 294	99.68 115	99.61 128

PAPM_NR		99.04 101	98.84 110	99.66 68	99.74 70	99.44 93	99.39 182	99.38 217	97.70 156	99.28 154	99.28 282	98.34 89	99.85 131	96.96 261	99.45 132	99.69 99

MVS_111021_HR		99.41 42	99.32 31	99.66 68	99.72 80	99.47 90	98.95 296	99.85 6	98.82 44	99.54 96	99.73 124	98.51 75	99.74 178	98.91 69	99.88 36	99.77 63

AdaColmap		99.01 107	98.80 115	99.66 68	99.56 144	99.54 77	99.18 243	99.70 15	98.18 102	99.35 141	99.63 172	96.32 154	99.90 106	97.48 227	99.77 92	99.55 141

原ACMM1				99.65 72	99.73 75	99.33 101		99.47 158	97.46 180	99.12 189	99.66 158	98.67 66	99.91 91	97.70 207	99.69 110	99.71 94

DELS-MVS		99.48 19	99.42 13	99.65 72	99.72 80	99.40 98	99.05 268	99.66 27	99.14 6	99.57 90	99.80 76	98.46 79	99.94 54	99.57 3	99.84 65	99.60 130
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 85	98.95 95	99.65 72	99.74 70	99.70 46	99.27 220	99.57 50	96.40 274	99.42 119	99.68 146	98.75 56	99.80 161	97.98 180	99.72 104	99.44 169

MVS_111021_LR		99.41 42	99.33 28	99.65 72	99.77 49	99.51 85	98.94 298	99.85 6	98.82 44	99.65 69	99.74 117	98.51 75	99.80 161	98.83 86	99.89 33	99.64 120

HyFIR lowres test		99.11 90	98.92 97	99.65 72	99.90 3	99.37 99	99.02 277	99.91 3	97.67 161	99.59 86	99.75 111	95.90 169	99.73 185	99.53 6	99.02 166	99.86 11

OPU-MVS				99.64 77	99.56 144	99.72 42	99.60 73				99.70 133	99.27 4	99.42 246	98.24 158	99.80 84	99.79 53

EI-MVSNet-UG-set		99.58 4	99.57 1	99.64 77	99.78 44	99.14 126	99.60 73	99.45 180	99.01 18	99.90 3	99.83 42	98.98 23	99.93 69	99.59 1	99.95 6	99.86 11

EI-MVSNet-Vis-set		99.58 4	99.56 3	99.64 77	99.78 44	99.15 125	99.61 72	99.45 180	99.01 18	99.89 4	99.82 49	99.01 16	99.92 80	99.56 4	99.95 6	99.85 14

F-COLMAP		99.19 69	99.04 77	99.64 77	99.78 44	99.27 110	99.42 168	99.54 71	97.29 199	99.41 123	99.59 188	98.42 84	99.93 69	98.19 161	99.69 110	99.73 81

DeepC-MVS	98.35 2	99.30 56	99.19 61	99.64 77	99.82 37	99.23 114	99.62 66	99.55 64	98.94 33	99.63 73	99.95 2	95.82 172	99.94 54	99.37 21	99.97 3	99.73 81
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 49	99.61 82	99.86 21	99.07 135	99.47 147	99.93 2	97.66 162	99.71 46	99.86 23	97.73 111	99.96 19	99.47 16	99.82 78	99.79 53

WTY-MVS		99.06 98	98.88 103	99.61 82	99.62 126	99.16 121	99.37 190	99.56 56	98.04 123	99.53 98	99.62 178	96.84 136	99.94 54	98.85 81	98.49 195	99.72 87

CANet		99.25 65	99.14 65	99.59 84	99.41 180	99.16 121	99.35 200	99.57 50	98.82 44	99.51 102	99.61 182	96.46 149	99.95 43	99.59 1	99.98 2	99.65 113

1112_ss		98.98 109	98.77 118	99.59 84	99.68 100	99.02 139	99.25 231	99.48 140	97.23 206	99.13 187	99.58 191	96.93 135	99.90 106	98.87 76	98.78 182	99.84 18

CNLPA		99.14 77	98.99 87	99.59 84	99.58 138	99.41 96	99.16 245	99.44 189	98.45 71	99.19 179	99.49 224	98.08 102	99.89 114	97.73 202	99.75 97	99.48 159

alignmvs		98.81 131	98.56 146	99.58 87	99.43 176	99.42 95	99.51 120	98.96 302	98.61 60	99.35 141	98.92 320	94.78 208	99.77 170	99.35 22	98.11 214	99.54 143

Test_1112_low_res		98.89 115	98.66 131	99.57 88	99.69 96	98.95 152	99.03 274	99.47 158	96.98 228	99.15 185	99.23 289	96.77 140	99.89 114	98.83 86	98.78 182	99.86 11

IS-MVSNet		99.05 100	98.87 104	99.57 88	99.73 75	99.32 102	99.75 28	99.20 276	98.02 126	99.56 91	99.86 23	96.54 147	99.67 206	98.09 170	99.13 154	99.73 81

casdiffmvs		99.13 79	98.98 90	99.56 90	99.65 115	99.16 121	99.56 98	99.50 120	98.33 86	99.41 123	99.86 23	95.92 167	99.83 145	99.45 18	99.16 150	99.70 96

Vis-MVSNet		99.12 85	98.97 91	99.56 90	99.78 44	99.10 131	99.68 42	99.66 27	98.49 67	99.86 11	99.87 20	94.77 211	99.84 136	99.19 40	99.41 135	99.74 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_yl		98.86 119	98.63 133	99.54 92	99.49 160	99.18 118	99.50 126	99.07 292	98.22 96	99.61 79	99.51 218	95.37 186	99.84 136	98.60 121	98.33 198	99.59 134

DCV-MVSNet		98.86 119	98.63 133	99.54 92	99.49 160	99.18 118	99.50 126	99.07 292	98.22 96	99.61 79	99.51 218	95.37 186	99.84 136	98.60 121	98.33 198	99.59 134

testdata				99.54 92	99.75 62	98.95 152		99.51 101	97.07 221	99.43 116	99.70 133	98.87 39	99.94 54	97.76 198	99.64 121	99.72 87

LFMVS		97.90 215	97.35 260	99.54 92	99.52 149	99.01 141	99.39 182	98.24 339	97.10 219	99.65 69	99.79 88	84.79 349	99.91 91	99.28 32	98.38 197	99.69 99

ab-mvs		98.86 119	98.63 133	99.54 92	99.64 117	99.19 116	99.44 155	99.54 71	97.77 148	99.30 149	99.81 62	94.20 233	99.93 69	99.17 43	98.82 179	99.49 158

MAR-MVS		98.86 119	98.63 133	99.54 92	99.37 191	99.66 54	99.45 151	99.54 71	96.61 255	99.01 210	99.40 251	97.09 128	99.86 125	97.68 210	99.53 130	99.10 192
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		98.85 127	98.62 138	99.53 98	99.61 130	99.08 133	99.80 17	99.51 101	97.10 219	99.31 147	99.78 95	95.23 194	99.77 170	98.21 159	99.03 164	99.75 69

baseline		99.15 76	99.02 82	99.53 98	99.66 110	99.14 126	99.72 32	99.48 140	98.35 82	99.42 119	99.84 38	96.07 160	99.79 164	99.51 8	99.14 153	99.67 106

sss		99.17 73	99.05 74	99.53 98	99.62 126	98.97 146	99.36 194	99.62 33	97.83 140	99.67 59	99.65 159	97.37 121	99.95 43	99.19 40	99.19 149	99.68 103

EPP-MVSNet		99.13 79	98.99 87	99.53 98	99.65 115	99.06 136	99.81 12	99.33 243	97.43 187	99.60 83	99.88 15	97.14 126	99.84 136	99.13 47	98.94 170	99.69 99

PLC	97.94 4	99.02 104	98.85 109	99.53 98	99.66 110	99.01 141	99.24 233	99.52 88	96.85 238	99.27 157	99.48 230	98.25 94	99.91 91	97.76 198	99.62 124	99.65 113
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSDG		98.98 109	98.80 115	99.53 98	99.76 52	99.19 116	98.75 316	99.55 64	97.25 203	99.47 108	99.77 102	97.82 108	99.87 122	96.93 264	99.90 23	99.54 143

PatchMatch-RL		98.84 130	98.62 138	99.52 104	99.71 86	99.28 108	99.06 266	99.77 9	97.74 153	99.50 103	99.53 211	95.41 184	99.84 136	97.17 250	99.64 121	99.44 169

OpenMVS	96.50 16	98.47 152	98.12 170	99.52 104	99.04 267	99.53 80	99.82 10	99.72 11	94.56 322	98.08 302	99.88 15	94.73 214	99.98 6	97.47 229	99.76 96	99.06 203

CS-MVS		99.37 48	99.33 28	99.51 106	99.47 168	99.51 85	99.81 12	99.57 50	98.37 80	99.65 69	99.56 198	98.21 95	99.77 170	99.54 5	99.77 92	99.27 184

Fast-Effi-MVS+		98.70 140	98.43 151	99.51 106	99.51 151	99.28 108	99.52 116	99.47 158	96.11 296	99.01 210	99.34 268	96.20 158	99.84 136	97.88 187	98.82 179	99.39 175

canonicalmvs		99.02 104	98.86 108	99.51 106	99.42 177	99.32 102	99.80 17	99.48 140	98.63 58	99.31 147	98.81 323	97.09 128	99.75 177	99.27 34	97.90 218	99.47 164

diffmvs		99.14 77	99.02 82	99.51 106	99.61 130	98.96 150	99.28 215	99.49 128	98.46 70	99.72 45	99.71 129	96.50 148	99.88 119	99.31 29	99.11 155	99.67 106

PAPR		98.63 148	98.34 157	99.51 106	99.40 185	99.03 138	98.80 311	99.36 227	96.33 275	99.00 215	99.12 303	98.46 79	99.84 136	95.23 307	99.37 140	99.66 109

Effi-MVS+		98.81 131	98.59 144	99.48 111	99.46 170	99.12 130	98.08 349	99.50 120	97.50 179	99.38 133	99.41 248	96.37 153	99.81 156	99.11 49	98.54 192	99.51 154

MVS		97.28 278	96.55 286	99.48 111	98.78 300	98.95 152	99.27 220	99.39 211	83.53 353	98.08 302	99.54 207	96.97 133	99.87 122	94.23 319	99.16 150	99.63 124

MVS_Test		99.10 93	98.97 91	99.48 111	99.49 160	99.14 126	99.67 44	99.34 236	97.31 197	99.58 88	99.76 106	97.65 113	99.82 152	98.87 76	99.07 161	99.46 166

HY-MVS	97.30 7	98.85 127	98.64 132	99.47 114	99.42 177	99.08 133	99.62 66	99.36 227	97.39 192	99.28 154	99.68 146	96.44 151	99.92 80	98.37 149	98.22 204	99.40 174

PCF-MVS	97.08 14	97.66 258	97.06 279	99.47 114	99.61 130	99.09 132	98.04 350	99.25 268	91.24 343	98.51 279	99.70 133	94.55 223	99.91 91	92.76 336	99.85 58	99.42 171
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
lupinMVS		99.13 79	99.01 86	99.46 116	99.51 151	98.94 155	99.05 268	99.16 281	97.86 135	99.80 24	99.56 198	97.39 117	99.86 125	98.94 64	99.85 58	99.58 138

EIA-MVS		99.18 71	99.09 71	99.45 117	99.49 160	99.18 118	99.67 44	99.53 82	97.66 162	99.40 128	99.44 239	98.10 101	99.81 156	98.94 64	99.62 124	99.35 177

jason		99.13 79	99.03 79	99.45 117	99.46 170	98.87 162	99.12 253	99.26 266	98.03 125	99.79 26	99.65 159	97.02 131	99.85 131	99.02 57	99.90 23	99.65 113
jason: jason.
CHOSEN 1792x2688		99.19 69	99.10 69	99.45 117	99.89 8	98.52 196	99.39 182	99.94 1	98.73 53	99.11 191	99.89 10	95.50 182	99.94 54	99.50 9	99.97 3	99.89 2

MG-MVS		99.13 79	99.02 82	99.45 117	99.57 140	98.63 184	99.07 263	99.34 236	98.99 25	99.61 79	99.82 49	97.98 105	99.87 122	97.00 257	99.80 84	99.85 14

MSLP-MVS++		99.46 24	99.47 9	99.44 121	99.60 134	99.16 121	99.41 170	99.71 13	98.98 27	99.45 111	99.78 95	99.19 7	99.54 230	99.28 32	99.84 65	99.63 124

PVSNet_Blended		99.08 96	98.97 91	99.42 122	99.76 52	98.79 173	98.78 313	99.91 3	96.74 244	99.67 59	99.49 224	97.53 114	99.88 119	98.98 60	99.85 58	99.60 130

ETV-MVS		99.26 63	99.21 59	99.40 123	99.46 170	99.30 106	99.56 98	99.52 88	98.52 65	99.44 115	99.27 285	98.41 85	99.86 125	99.10 50	99.59 126	99.04 204

BH-RMVSNet		98.41 158	98.08 175	99.40 123	99.41 180	98.83 169	99.30 209	98.77 319	97.70 156	98.94 223	99.65 159	92.91 261	99.74 178	96.52 280	99.55 129	99.64 120

UGNet		98.87 116	98.69 126	99.40 123	99.22 229	98.72 177	99.44 155	99.68 19	99.24 3	99.18 182	99.42 244	92.74 265	99.96 19	99.34 26	99.94 9	99.53 147
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		98.31 165	97.95 190	99.38 126	99.50 158	98.74 175	99.59 79	98.93 304	98.41 75	99.14 186	99.60 185	94.59 220	99.79 164	98.48 137	93.29 325	99.61 128

TSAR-MVS + GP.		99.36 50	99.36 21	99.36 127	99.67 101	98.61 187	99.07 263	99.33 243	99.00 22	99.82 20	99.81 62	99.06 13	99.84 136	99.09 51	99.42 134	99.65 113

Anonymous20240529		98.09 187	97.68 219	99.34 128	99.66 110	98.44 204	99.40 178	99.43 197	93.67 329	99.22 170	99.89 10	90.23 314	99.93 69	99.26 35	98.33 198	99.66 109

xiu_mvs_v1_base_debu		99.29 58	99.27 51	99.34 128	99.63 120	98.97 146	99.12 253	99.51 101	98.86 40	99.84 13	99.47 233	98.18 97	99.99 1	99.50 9	99.31 141	99.08 197

xiu_mvs_v1_base		99.29 58	99.27 51	99.34 128	99.63 120	98.97 146	99.12 253	99.51 101	98.86 40	99.84 13	99.47 233	98.18 97	99.99 1	99.50 9	99.31 141	99.08 197

xiu_mvs_v1_base_debi		99.29 58	99.27 51	99.34 128	99.63 120	98.97 146	99.12 253	99.51 101	98.86 40	99.84 13	99.47 233	98.18 97	99.99 1	99.50 9	99.31 141	99.08 197

PMMVS		98.80 134	98.62 138	99.34 128	99.27 217	98.70 178	98.76 315	99.31 254	97.34 194	99.21 173	99.07 305	97.20 125	99.82 152	98.56 129	98.87 176	99.52 148

CSCG		99.32 54	99.32 31	99.32 133	99.85 25	98.29 210	99.71 34	99.66 27	98.11 109	99.41 123	99.80 76	98.37 88	99.96 19	98.99 59	99.96 5	99.72 87

thisisatest0530		98.35 163	98.03 180	99.31 134	99.63 120	98.56 189	99.54 110	96.75 356	97.53 176	99.73 43	99.65 159	91.25 304	99.89 114	98.62 115	99.56 127	99.48 159

AllTest		98.87 116	98.72 122	99.31 134	99.86 21	98.48 202	99.56 98	99.61 35	97.85 137	99.36 138	99.85 29	95.95 164	99.85 131	96.66 278	99.83 72	99.59 134

TestCases				99.31 134	99.86 21	98.48 202		99.61 35	97.85 137	99.36 138	99.85 29	95.95 164	99.85 131	96.66 278	99.83 72	99.59 134

Vis-MVSNet (Re-imp)		98.87 116	98.72 122	99.31 134	99.71 86	98.88 161	99.80 17	99.44 189	97.91 133	99.36 138	99.78 95	95.49 183	99.43 245	97.91 185	99.11 155	99.62 126

PS-MVSNAJ		99.32 54	99.32 31	99.30 138	99.57 140	98.94 155	98.97 291	99.46 168	98.92 37	99.71 46	99.24 288	99.01 16	99.98 6	99.35 22	99.66 118	98.97 212

VPA-MVSNet		98.29 168	97.95 190	99.30 138	99.16 246	99.54 77	99.50 126	99.58 49	98.27 91	99.35 141	99.37 259	92.53 275	99.65 213	99.35 22	94.46 309	98.72 239

EPNet		98.86 119	98.71 124	99.30 138	97.20 346	98.18 215	99.62 66	98.91 309	99.28 2	98.63 270	99.81 62	95.96 163	99.99 1	99.24 36	99.72 104	99.73 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part1		97.75 240	97.24 273	99.29 141	99.59 136	99.63 60	99.65 55	99.49 128	96.17 289	98.44 284	99.69 140	89.80 318	99.47 233	98.68 108	93.66 321	98.78 225

xiu_mvs_v2_base		99.26 63	99.25 55	99.29 141	99.53 147	98.91 159	99.02 277	99.45 180	98.80 48	99.71 46	99.26 286	98.94 31	99.98 6	99.34 26	99.23 146	98.98 211

MVSFormer		99.17 73	99.12 67	99.29 141	99.51 151	98.94 155	99.88 1	99.46 168	97.55 171	99.80 24	99.65 159	97.39 117	99.28 271	99.03 55	99.85 58	99.65 113

tttt0517		98.42 156	98.14 168	99.28 144	99.66 110	98.38 208	99.74 31	96.85 354	97.68 158	99.79 26	99.74 117	91.39 301	99.89 114	98.83 86	99.56 127	99.57 139

nrg030		98.64 147	98.42 152	99.28 144	99.05 266	99.69 47	99.81 12	99.46 168	98.04 123	99.01 210	99.82 49	96.69 143	99.38 250	99.34 26	94.59 308	98.78 225

Anonymous202405211		98.30 167	97.98 185	99.26 146	99.57 140	98.16 216	99.41 170	98.55 335	96.03 301	99.19 179	99.74 117	91.87 288	99.92 80	99.16 45	98.29 203	99.70 96

CANet_DTU		98.97 111	98.87 104	99.25 147	99.33 199	98.42 207	99.08 262	99.30 258	99.16 5	99.43 116	99.75 111	95.27 190	99.97 11	98.56 129	99.95 6	99.36 176

CDS-MVSNet		99.09 94	99.03 79	99.25 147	99.42 177	98.73 176	99.45 151	99.46 168	98.11 109	99.46 110	99.77 102	98.01 104	99.37 253	98.70 103	98.92 173	99.66 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XXY-MVS		98.38 161	98.09 174	99.24 149	99.26 219	99.32 102	99.56 98	99.55 64	97.45 183	98.71 253	99.83 42	93.23 253	99.63 221	98.88 72	96.32 270	98.76 231

TAMVS		99.12 85	99.08 72	99.24 149	99.46 170	98.55 190	99.51 120	99.46 168	98.09 112	99.45 111	99.82 49	98.34 89	99.51 231	98.70 103	98.93 171	99.67 106

FIs		98.78 135	98.63 133	99.23 151	99.18 238	99.54 77	99.83 9	99.59 43	98.28 89	98.79 246	99.81 62	96.75 141	99.37 253	99.08 52	96.38 268	98.78 225

OMC-MVS		99.08 96	99.04 77	99.20 152	99.67 101	98.22 214	99.28 215	99.52 88	98.07 117	99.66 64	99.81 62	97.79 109	99.78 168	97.79 195	99.81 80	99.60 130

thisisatest0515		98.14 182	97.79 204	99.19 153	99.50 158	98.50 199	98.61 327	96.82 355	96.95 232	99.54 96	99.43 241	91.66 297	99.86 125	98.08 174	99.51 131	99.22 186

RPMNet		96.72 288	95.90 298	99.19 153	99.18 238	98.49 200	99.22 239	99.52 88	88.72 349	99.56 91	97.38 346	94.08 239	99.95 43	86.87 355	98.58 188	99.14 189

COLMAP_ROB	97.56 6	98.86 119	98.75 121	99.17 155	99.88 11	98.53 192	99.34 203	99.59 43	97.55 171	98.70 259	99.89 10	95.83 171	99.90 106	98.10 169	99.90 23	99.08 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDDNet		97.55 263	97.02 280	99.16 156	99.49 160	98.12 220	99.38 187	99.30 258	95.35 308	99.68 53	99.90 7	82.62 353	99.93 69	99.31 29	98.13 213	99.42 171

mvs_anonymous		99.03 103	98.99 87	99.16 156	99.38 189	98.52 196	99.51 120	99.38 217	97.79 146	99.38 133	99.81 62	97.30 122	99.45 236	99.35 22	98.99 168	99.51 154

FC-MVSNet-test		98.75 138	98.62 138	99.15 158	99.08 260	99.45 92	99.86 5	99.60 40	98.23 95	98.70 259	99.82 49	96.80 137	99.22 281	99.07 53	96.38 268	98.79 224

UniMVSNet (Re)		98.29 168	98.00 183	99.13 159	99.00 272	99.36 100	99.49 136	99.51 101	97.95 129	98.97 219	99.13 300	96.30 155	99.38 250	98.36 151	93.34 324	98.66 269

1314		98.68 143	98.54 147	99.11 160	98.89 284	98.65 182	99.27 220	99.49 128	96.89 236	97.99 307	99.56 198	97.72 112	99.83 145	97.74 201	99.27 144	98.84 221

CHOSEN 280x420		99.12 85	99.13 66	99.08 161	99.66 110	97.89 231	98.43 337	99.71 13	98.88 39	99.62 77	99.76 106	96.63 144	99.70 201	99.46 17	99.99 1	99.66 109

PAPM		97.59 262	97.09 278	99.07 162	99.06 263	98.26 213	98.30 344	99.10 287	94.88 316	98.08 302	99.34 268	96.27 156	99.64 216	89.87 345	98.92 173	99.31 181

WR-MVS		98.06 190	97.73 215	99.06 163	98.86 292	99.25 112	99.19 242	99.35 232	97.30 198	98.66 262	99.43 241	93.94 242	99.21 286	98.58 124	94.28 313	98.71 241

API-MVS		99.04 101	99.03 79	99.06 163	99.40 185	99.31 105	99.55 107	99.56 56	98.54 63	99.33 145	99.39 255	98.76 53	99.78 168	96.98 259	99.78 89	98.07 330

ET-MVSNet_ETH3D		96.49 292	95.64 303	99.05 165	99.53 147	98.82 170	98.84 307	97.51 351	97.63 164	84.77 354	99.21 293	92.09 285	98.91 326	98.98 60	92.21 335	99.41 173

RRT_MVS		98.60 149	98.44 150	99.05 165	98.88 285	99.14 126	99.49 136	99.38 217	97.76 149	99.29 152	99.86 23	95.38 185	99.36 257	98.81 91	97.16 254	98.64 273

SD-MVS		99.41 42	99.52 6	99.05 165	99.74 70	99.68 49	99.46 150	99.52 88	99.11 7	99.88 5	99.91 5	99.43 1	97.70 347	98.72 101	99.93 10	99.77 63
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		98.86 119	98.80 115	99.03 168	99.76 52	98.79 173	99.28 215	99.91 3	97.42 189	99.67 59	99.37 259	97.53 114	99.88 119	98.98 60	97.29 249	98.42 313

NR-MVSNet		97.97 208	97.61 226	99.02 169	98.87 289	99.26 111	99.47 147	99.42 199	97.63 164	97.08 328	99.50 221	95.07 197	99.13 295	97.86 189	93.59 322	98.68 254

VPNet		97.84 224	97.44 248	99.01 170	99.21 231	98.94 155	99.48 142	99.57 50	98.38 77	99.28 154	99.73 124	88.89 327	99.39 248	99.19 40	93.27 326	98.71 241

CP-MVSNet		98.09 187	97.78 207	99.01 170	98.97 278	99.24 113	99.67 44	99.46 168	97.25 203	98.48 282	99.64 166	93.79 246	99.06 304	98.63 114	94.10 316	98.74 237

GA-MVS		97.85 221	97.47 240	99.00 172	99.38 189	97.99 224	98.57 330	99.15 282	97.04 224	98.90 229	99.30 278	89.83 317	99.38 250	96.70 275	98.33 198	99.62 126

MVSTER		98.49 151	98.32 159	99.00 172	99.35 194	99.02 139	99.54 110	99.38 217	97.41 190	99.20 176	99.73 124	93.86 245	99.36 257	98.87 76	97.56 230	98.62 283

tfpnnormal		97.84 224	97.47 240	98.98 174	99.20 233	99.22 115	99.64 58	99.61 35	96.32 276	98.27 296	99.70 133	93.35 252	99.44 241	95.69 296	95.40 293	98.27 322

test_djsdf		98.67 144	98.57 145	98.98 174	98.70 311	98.91 159	99.88 1	99.46 168	97.55 171	99.22 170	99.88 15	95.73 175	99.28 271	99.03 55	97.62 225	98.75 233

hse-mvs3		97.70 251	97.28 269	98.97 176	99.70 93	97.27 251	99.36 194	99.45 180	98.94 33	99.66 64	99.64 166	94.93 199	99.99 1	99.48 14	84.36 348	99.65 113

UniMVSNet_NR-MVSNet		98.22 171	97.97 186	98.96 177	98.92 282	98.98 143	99.48 142	99.53 82	97.76 149	98.71 253	99.46 237	96.43 152	99.22 281	98.57 126	92.87 331	98.69 249

DU-MVS		98.08 189	97.79 204	98.96 177	98.87 289	98.98 143	99.41 170	99.45 180	97.87 134	98.71 253	99.50 221	94.82 205	99.22 281	98.57 126	92.87 331	98.68 254

PS-CasMVS		97.93 210	97.59 229	98.95 179	98.99 273	99.06 136	99.68 42	99.52 88	97.13 213	98.31 293	99.68 146	92.44 281	99.05 305	98.51 135	94.08 317	98.75 233

anonymousdsp		98.44 154	98.28 162	98.94 180	98.50 326	98.96 150	99.77 24	99.50 120	97.07 221	98.87 234	99.77 102	94.76 212	99.28 271	98.66 111	97.60 226	98.57 298

TAPA-MVS	97.07 15	97.74 243	97.34 263	98.94 180	99.70 93	97.53 244	99.25 231	99.51 101	91.90 340	99.30 149	99.63 172	98.78 48	99.64 216	88.09 351	99.87 40	99.65 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v8		97.95 209	97.63 225	98.93 182	98.95 280	98.81 172	99.80 17	99.41 201	96.03 301	99.10 194	99.42 244	94.92 201	99.30 269	96.94 263	94.08 317	98.66 269

JIA-IIPM		97.50 269	97.02 280	98.93 182	98.73 306	97.80 236	99.30 209	98.97 300	91.73 341	98.91 227	94.86 354	95.10 196	99.71 195	97.58 215	97.98 216	99.28 183

v7n		97.87 218	97.52 234	98.92 184	98.76 304	98.58 188	99.84 6	99.46 168	96.20 286	98.91 227	99.70 133	94.89 203	99.44 241	96.03 289	93.89 319	98.75 233

v2v482		98.06 190	97.77 209	98.92 184	98.90 283	98.82 170	99.57 91	99.36 227	96.65 251	99.19 179	99.35 265	94.20 233	99.25 276	97.72 204	94.97 302	98.69 249

thres600view7		97.86 220	97.51 236	98.92 184	99.72 80	97.95 229	99.59 79	98.74 323	97.94 130	99.27 157	98.62 330	91.75 291	99.86 125	93.73 324	98.19 207	98.96 214

thres400		97.77 235	97.38 256	98.92 184	99.69 96	97.96 227	99.50 126	98.73 328	97.83 140	99.17 183	98.45 335	91.67 295	99.83 145	93.22 329	98.18 208	98.96 214

v1192		97.81 231	97.44 248	98.91 188	98.88 285	98.68 179	99.51 120	99.34 236	96.18 288	99.20 176	99.34 268	94.03 240	99.36 257	95.32 306	95.18 297	98.69 249

mvs_tets		98.40 160	98.23 164	98.91 188	98.67 314	98.51 198	99.66 48	99.53 82	98.19 99	98.65 268	99.81 62	92.75 263	99.44 241	99.31 29	97.48 240	98.77 229

Anonymous20231211		97.88 216	97.54 233	98.90 190	99.71 86	98.53 192	99.48 142	99.57 50	94.16 325	98.81 242	99.68 146	93.23 253	99.42 246	98.84 83	94.42 311	98.76 231

PS-MVSNAJss		98.92 114	98.92 97	98.90 190	98.78 300	98.53 192	99.78 22	99.54 71	98.07 117	99.00 215	99.76 106	99.01 16	99.37 253	99.13 47	97.23 250	98.81 222

WR-MVS_H		98.13 183	97.87 200	98.90 190	99.02 270	98.84 166	99.70 35	99.59 43	97.27 201	98.40 287	99.19 294	95.53 181	99.23 278	98.34 152	93.78 320	98.61 292

mvs-test1		98.86 119	98.84 110	98.89 193	99.33 199	97.77 237	99.44 155	99.30 258	98.47 68	99.10 194	99.43 241	96.78 138	99.95 43	98.73 99	99.02 166	98.96 214

XVG-OURS-SEG-HR		98.69 142	98.62 138	98.89 193	99.71 86	97.74 238	99.12 253	99.54 71	98.44 74	99.42 119	99.71 129	94.20 233	99.92 80	98.54 134	98.90 175	99.00 208

PVSNet	96.02 17	98.85 127	98.84 110	98.89 193	99.73 75	97.28 250	98.32 343	99.60 40	97.86 135	99.50 103	99.57 195	96.75 141	99.86 125	98.56 129	99.70 109	99.54 143

jajsoiax		98.43 155	98.28 162	98.88 196	98.60 321	98.43 205	99.82 10	99.53 82	98.19 99	98.63 270	99.80 76	93.22 255	99.44 241	99.22 37	97.50 236	98.77 229

pm-mvs1		97.68 254	97.28 269	98.88 196	99.06 263	98.62 185	99.50 126	99.45 180	96.32 276	97.87 310	99.79 88	92.47 277	99.35 261	97.54 222	93.54 323	98.67 261

VDD-MVS		97.73 244	97.35 260	98.88 196	99.47 168	97.12 257	99.34 203	98.85 315	98.19 99	99.67 59	99.85 29	82.98 351	99.92 80	99.49 13	98.32 202	99.60 130

XVG-OURS		98.73 139	98.68 127	98.88 196	99.70 93	97.73 239	98.92 299	99.55 64	98.52 65	99.45 111	99.84 38	95.27 190	99.91 91	98.08 174	98.84 178	99.00 208

UniMVSNet_ETH3D		97.32 277	96.81 283	98.87 200	99.40 185	97.46 246	99.51 120	99.53 82	95.86 303	98.54 278	99.77 102	82.44 354	99.66 209	98.68 108	97.52 233	99.50 157

v144192		97.92 213	97.60 227	98.87 200	98.83 295	98.65 182	99.55 107	99.34 236	96.20 286	99.32 146	99.40 251	94.36 228	99.26 275	96.37 285	95.03 301	98.70 245

CR-MVSNet		98.17 178	97.93 193	98.87 200	99.18 238	98.49 200	99.22 239	99.33 243	96.96 230	99.56 91	99.38 256	94.33 229	99.00 313	94.83 313	98.58 188	99.14 189

v10		97.85 221	97.52 234	98.86 203	98.99 273	98.67 180	99.75 28	99.41 201	95.70 304	98.98 217	99.41 248	94.75 213	99.23 278	96.01 290	94.63 307	98.67 261

V42		98.06 190	97.79 204	98.86 203	98.98 276	98.84 166	99.69 37	99.34 236	96.53 261	99.30 149	99.37 259	94.67 217	99.32 266	97.57 219	94.66 306	98.42 313

TransMVSNet (Re)		97.15 281	96.58 285	98.86 203	99.12 251	98.85 165	99.49 136	98.91 309	95.48 306	97.16 326	99.80 76	93.38 251	99.11 300	94.16 321	91.73 336	98.62 283

v1144		97.98 205	97.69 218	98.85 206	98.87 289	98.66 181	99.54 110	99.35 232	96.27 280	99.23 169	99.35 265	94.67 217	99.23 278	96.73 273	95.16 298	98.68 254

v1921920		97.80 233	97.45 243	98.84 207	98.80 296	98.53 192	99.52 116	99.34 236	96.15 293	99.24 165	99.47 233	93.98 241	99.29 270	95.40 303	95.13 299	98.69 249

FMVSNet3		98.03 196	97.76 212	98.84 207	99.39 188	98.98 143	99.40 178	99.38 217	96.67 249	99.07 201	99.28 282	92.93 258	98.98 315	97.10 252	96.65 259	98.56 299

baseline2		97.87 218	97.55 230	98.82 209	99.18 238	98.02 222	99.41 170	96.58 358	96.97 229	96.51 333	99.17 295	93.43 250	99.57 226	97.71 205	99.03 164	98.86 219

TR-MVS		97.76 236	97.41 253	98.82 209	99.06 263	97.87 232	98.87 305	98.56 334	96.63 254	98.68 261	99.22 290	92.49 276	99.65 213	95.40 303	97.79 220	98.95 217

pmmvs4		98.13 183	97.90 195	98.81 211	98.61 320	98.87 162	98.99 284	99.21 275	96.44 270	99.06 205	99.58 191	95.90 169	99.11 300	97.18 249	96.11 274	98.46 310

Patchmtry		97.75 240	97.40 254	98.81 211	99.10 256	98.87 162	99.11 259	99.33 243	94.83 317	98.81 242	99.38 256	94.33 229	99.02 310	96.10 287	95.57 289	98.53 300

FMVSNet2		97.72 246	97.36 258	98.80 213	99.51 151	98.84 166	99.45 151	99.42 199	96.49 263	98.86 239	99.29 280	90.26 311	98.98 315	96.44 282	96.56 262	98.58 297

v1240		97.69 252	97.32 266	98.79 214	98.85 293	98.43 205	99.48 142	99.36 227	96.11 296	99.27 157	99.36 262	93.76 248	99.24 277	94.46 316	95.23 296	98.70 245

PatchT		97.03 284	96.44 288	98.79 214	98.99 273	98.34 209	99.16 245	99.07 292	92.13 339	99.52 100	97.31 349	94.54 224	98.98 315	88.54 349	98.73 184	99.03 205

Patchmatch-test		97.93 210	97.65 222	98.77 216	99.18 238	97.07 262	99.03 274	99.14 284	96.16 291	98.74 250	99.57 195	94.56 222	99.72 189	93.36 328	99.11 155	99.52 148

TranMVSNet+NR-MVSNet		97.93 210	97.66 221	98.76 217	98.78 300	98.62 185	99.65 55	99.49 128	97.76 149	98.49 281	99.60 185	94.23 232	98.97 322	98.00 179	92.90 329	98.70 245

gg-mvs-nofinetune		96.17 299	95.32 307	98.73 218	98.79 297	98.14 218	99.38 187	94.09 363	91.07 345	98.07 305	91.04 359	89.62 322	99.35 261	96.75 271	99.09 159	98.68 254

bset_n11_16_dypcd		98.16 179	97.97 186	98.73 218	98.26 331	98.28 212	97.99 351	98.01 344	97.68 158	99.10 194	99.63 172	95.68 177	99.15 291	98.78 95	96.55 263	98.75 233

tfpn200view9		97.72 246	97.38 256	98.72 220	99.69 96	97.96 227	99.50 126	98.73 328	97.83 140	99.17 183	98.45 335	91.67 295	99.83 145	93.22 329	98.18 208	98.37 319

PEN-MVS		97.76 236	97.44 248	98.72 220	98.77 303	98.54 191	99.78 22	99.51 101	97.06 223	98.29 295	99.64 166	92.63 272	98.89 328	98.09 170	93.16 327	98.72 239

thres100view900		97.76 236	97.45 243	98.69 222	99.72 80	97.86 234	99.59 79	98.74 323	97.93 131	99.26 162	98.62 330	91.75 291	99.83 145	93.22 329	98.18 208	98.37 319

EI-MVSNet		98.67 144	98.67 128	98.68 223	99.35 194	97.97 225	99.50 126	99.38 217	96.93 235	99.20 176	99.83 42	97.87 106	99.36 257	98.38 147	97.56 230	98.71 241

Baseline_NR-MVSNet		97.76 236	97.45 243	98.68 223	99.09 258	98.29 210	99.41 170	98.85 315	95.65 305	98.63 270	99.67 152	94.82 205	99.10 302	98.07 177	92.89 330	98.64 273

thres200		97.61 261	97.28 269	98.62 225	99.64 117	98.03 221	99.26 229	98.74 323	97.68 158	99.09 199	98.32 339	91.66 297	99.81 156	92.88 333	98.22 204	98.03 333

Fast-Effi-MVS+-dtu		98.77 137	98.83 114	98.60 226	99.41 180	96.99 271	99.52 116	99.49 128	98.11 109	99.24 165	99.34 268	96.96 134	99.79 164	97.95 183	99.45 132	99.02 207

hse-mvs2		97.50 269	97.14 276	98.59 227	99.49 160	97.05 264	99.28 215	99.22 272	98.94 33	99.66 64	99.42 244	94.93 199	99.65 213	99.48 14	83.80 350	99.08 197

AUN-MVS		96.88 285	96.31 290	98.59 227	99.48 167	97.04 267	99.27 220	99.22 272	97.44 186	98.51 279	99.41 248	91.97 286	99.66 209	97.71 205	83.83 349	99.07 202

BH-untuned		98.42 156	98.36 154	98.59 227	99.49 160	96.70 283	99.27 220	99.13 285	97.24 205	98.80 244	99.38 256	95.75 174	99.74 178	97.07 255	99.16 150	99.33 180

IterMVS-LS		98.46 153	98.42 152	98.58 230	99.59 136	98.00 223	99.37 190	99.43 197	96.94 234	99.07 201	99.59 188	97.87 106	99.03 308	98.32 155	95.62 288	98.71 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		97.73 244	97.45 243	98.57 231	99.45 175	97.50 245	99.02 277	98.98 299	96.11 296	99.41 123	99.14 299	90.28 310	98.74 330	95.74 295	98.93 171	99.47 164

IB-MVS	95.67 18	96.22 296	95.44 306	98.57 231	99.21 231	96.70 283	98.65 325	97.74 349	96.71 246	97.27 322	98.54 333	86.03 345	99.92 80	98.47 140	86.30 346	99.10 192
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		98.20 174	98.08 175	98.56 233	99.33 199	96.48 291	99.23 234	99.15 282	96.24 283	99.10 194	99.67 152	94.11 237	99.71 195	96.81 269	99.05 162	99.48 159

test0.0.03 1		97.71 250	97.42 252	98.56 233	98.41 329	97.82 235	98.78 313	98.63 332	97.34 194	98.05 306	98.98 317	94.45 226	98.98 315	95.04 310	97.15 255	98.89 218

cl-mvsnet____		98.01 201	97.84 202	98.55 235	99.25 223	97.97 225	98.71 320	99.34 236	96.47 269	98.59 276	99.54 207	95.65 179	99.21 286	97.21 243	95.77 283	98.46 310

test-LLR		98.06 190	97.90 195	98.55 235	98.79 297	97.10 258	98.67 322	97.75 347	97.34 194	98.61 273	98.85 321	94.45 226	99.45 236	97.25 241	99.38 136	99.10 192

test-mter		97.49 272	97.13 277	98.55 235	98.79 297	97.10 258	98.67 322	97.75 347	96.65 251	98.61 273	98.85 321	88.23 335	99.45 236	97.25 241	99.38 136	99.10 192

v148		97.79 234	97.55 230	98.50 238	98.74 305	97.72 240	99.54 110	99.33 243	96.26 281	98.90 229	99.51 218	94.68 216	99.14 292	97.83 192	93.15 328	98.63 281

LPG-MVS_test		98.22 171	98.13 169	98.49 239	99.33 199	97.05 264	99.58 86	99.55 64	97.46 180	99.24 165	99.83 42	92.58 273	99.72 189	98.09 170	97.51 234	98.68 254

LGP-MVS_train				98.49 239	99.33 199	97.05 264		99.55 64	97.46 180	99.24 165	99.83 42	92.58 273	99.72 189	98.09 170	97.51 234	98.68 254

cl-mvsnet2		97.85 221	97.64 224	98.48 241	99.09 258	97.87 232	98.60 329	99.33 243	97.11 218	98.87 234	99.22 290	92.38 282	99.17 290	98.21 159	95.99 277	98.42 313

cl-mvsnet1		98.01 201	97.85 201	98.48 241	99.24 224	97.95 229	98.71 320	99.35 232	96.50 262	98.60 275	99.54 207	95.72 176	99.03 308	97.21 243	95.77 283	98.46 310

cascas		97.69 252	97.43 251	98.48 241	98.60 321	97.30 249	98.18 348	99.39 211	92.96 337	98.41 286	98.78 326	93.77 247	99.27 274	98.16 166	98.61 185	98.86 219

ACMM	97.58 5	98.37 162	98.34 157	98.48 241	99.41 180	97.10 258	99.56 98	99.45 180	98.53 64	99.04 207	99.85 29	93.00 257	99.71 195	98.74 97	97.45 241	98.64 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		98.78 135	98.89 102	98.47 245	99.33 199	96.91 277	99.57 91	99.30 258	98.47 68	99.41 123	98.99 314	96.78 138	99.74 178	98.73 99	99.38 136	98.74 237

DTE-MVSNet		97.51 268	97.19 275	98.46 246	98.63 317	98.13 219	99.84 6	99.48 140	96.68 248	97.97 308	99.67 152	92.92 259	98.56 332	96.88 268	92.60 334	98.70 245

OPM-MVS		98.19 175	98.10 171	98.45 247	98.88 285	97.07 262	99.28 215	99.38 217	98.57 62	99.22 170	99.81 62	92.12 284	99.66 209	98.08 174	97.54 232	98.61 292

GG-mvs-BLEND				98.45 247	98.55 324	98.16 216	99.43 161	93.68 364		97.23 323	98.46 334	89.30 324	99.22 281	95.43 302	98.22 204	97.98 338

ACMP	97.20 11	98.06 190	97.94 192	98.45 247	99.37 191	97.01 269	99.44 155	99.49 128	97.54 174	98.45 283	99.79 88	91.95 287	99.72 189	97.91 185	97.49 239	98.62 283
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		98.27 170	98.22 165	98.44 250	99.29 212	96.97 273	99.39 182	99.47 158	98.97 30	99.11 191	99.61 182	92.71 268	99.69 204	97.78 196	97.63 223	98.67 261

ACMH	97.28 8	98.10 186	97.99 184	98.44 250	99.41 180	96.96 275	99.60 73	99.56 56	98.09 112	98.15 300	99.91 5	90.87 308	99.70 201	98.88 72	97.45 241	98.67 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_ehance_all_eth		98.18 177	98.10 171	98.41 252	99.23 225	97.72 240	98.72 319	99.31 254	96.60 257	98.88 232	99.29 280	97.29 123	99.13 295	97.60 213	95.99 277	98.38 318

miper_enhance_ethall		98.16 179	98.08 175	98.41 252	98.96 279	97.72 240	98.45 336	99.32 251	96.95 232	98.97 219	99.17 295	97.06 130	99.22 281	97.86 189	95.99 277	98.29 321

TESTMET0.1,1		97.55 263	97.27 272	98.40 254	98.93 281	96.53 289	98.67 322	97.61 350	96.96 230	98.64 269	99.28 282	88.63 331	99.45 236	97.30 238	99.38 136	99.21 187

LTVRE_ROB	97.16 12	98.02 198	97.90 195	98.40 254	99.23 225	96.80 281	99.70 35	99.60 40	97.12 215	98.18 299	99.70 133	91.73 293	99.72 189	98.39 145	97.45 241	98.68 254
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		98.12 185	98.04 179	98.38 256	99.30 208	97.69 243	98.81 310	99.33 243	96.67 249	98.83 240	99.34 268	97.11 127	98.99 314	97.58 215	95.34 294	98.48 304

HQP-MVS		98.02 198	97.90 195	98.37 257	99.19 235	96.83 278	98.98 288	99.39 211	98.24 92	98.66 262	99.40 251	92.47 277	99.64 216	97.19 247	97.58 228	98.64 273

EPMVS		97.82 229	97.65 222	98.35 258	98.88 285	95.98 303	99.49 136	94.71 362	97.57 169	99.26 162	99.48 230	92.46 280	99.71 195	97.87 188	99.08 160	99.35 177

eth_miper_zixun_eth		98.05 195	97.96 188	98.33 259	99.26 219	97.38 248	98.56 332	99.31 254	96.65 251	98.88 232	99.52 214	96.58 145	99.12 299	97.39 236	95.53 291	98.47 306

CLD-MVS		98.16 179	98.10 171	98.33 259	99.29 212	96.82 280	98.75 316	99.44 189	97.83 140	99.13 187	99.55 202	92.92 259	99.67 206	98.32 155	97.69 222	98.48 304
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		98.00 203	97.89 199	98.32 261	99.35 194	96.20 300	99.01 282	98.90 311	96.42 272	98.38 288	99.00 313	95.26 192	99.72 189	96.06 288	98.61 185	99.03 205

ACMH+	97.24 10	97.92 213	97.78 207	98.32 261	99.46 170	96.68 285	99.56 98	99.54 71	98.41 75	97.79 314	99.87 20	90.18 315	99.66 209	98.05 178	97.18 253	98.62 283

CVMVSNet		98.57 150	98.67 128	98.30 263	99.35 194	95.59 310	99.50 126	99.55 64	98.60 61	99.39 130	99.83 42	94.48 225	99.45 236	98.75 96	98.56 191	99.85 14

GBi-Net		97.68 254	97.48 238	98.29 264	99.51 151	97.26 253	99.43 161	99.48 140	96.49 263	99.07 201	99.32 275	90.26 311	98.98 315	97.10 252	96.65 259	98.62 283

test1		97.68 254	97.48 238	98.29 264	99.51 151	97.26 253	99.43 161	99.48 140	96.49 263	99.07 201	99.32 275	90.26 311	98.98 315	97.10 252	96.65 259	98.62 283

FMVSNet1		96.84 286	96.36 289	98.29 264	99.32 206	97.26 253	99.43 161	99.48 140	95.11 311	98.55 277	99.32 275	83.95 350	98.98 315	95.81 293	96.26 271	98.62 283

miper_lstm_enhance		98.00 203	97.91 194	98.28 267	99.34 198	97.43 247	98.88 303	99.36 227	96.48 267	98.80 244	99.55 202	95.98 162	98.91 326	97.27 239	95.50 292	98.51 302

SCA		98.19 175	98.16 166	98.27 268	99.30 208	95.55 311	99.07 263	98.97 300	97.57 169	99.43 116	99.57 195	92.72 266	99.74 178	97.58 215	99.20 148	99.52 148

EPNet_dtu		98.03 196	97.96 188	98.23 269	98.27 330	95.54 313	99.23 234	98.75 320	99.02 15	97.82 312	99.71 129	96.11 159	99.48 232	93.04 332	99.65 120	99.69 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVG-ACMP-BASELINE		97.83 226	97.71 217	98.20 270	99.11 253	96.33 296	99.41 170	99.52 88	98.06 121	99.05 206	99.50 221	89.64 321	99.73 185	97.73 202	97.38 247	98.53 300

OurMVSNet-221017-0		97.88 216	97.77 209	98.19 271	98.71 310	96.53 289	99.88 1	99.00 297	97.79 146	98.78 247	99.94 3	91.68 294	99.35 261	97.21 243	96.99 257	98.69 249

PatchmatchNet		98.31 165	98.36 154	98.19 271	99.16 246	95.32 319	99.27 220	98.92 306	97.37 193	99.37 135	99.58 191	94.90 202	99.70 201	97.43 234	99.21 147	99.54 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs5		97.52 266	97.30 268	98.16 273	98.57 323	96.73 282	99.27 220	98.90 311	96.14 294	98.37 289	99.53 211	91.54 300	99.14 292	97.51 225	95.87 281	98.63 281

D2MVS		98.41 158	98.50 148	98.15 274	99.26 219	96.62 287	99.40 178	99.61 35	97.71 155	98.98 217	99.36 262	96.04 161	99.67 206	98.70 103	97.41 245	98.15 328

testgi		97.65 259	97.50 237	98.13 275	99.36 193	96.45 292	99.42 168	99.48 140	97.76 149	97.87 310	99.45 238	91.09 305	98.81 329	94.53 315	98.52 193	99.13 191

RRT_test8_iter05		97.72 246	97.60 227	98.08 276	99.23 225	96.08 302	99.63 60	99.49 128	97.54 174	98.94 223	99.81 62	87.99 338	99.35 261	99.21 39	96.51 265	98.81 222

ITE_SJBPF				98.08 276	99.29 212	96.37 294		98.92 306	98.34 83	98.83 240	99.75 111	91.09 305	99.62 222	95.82 292	97.40 246	98.25 324

IterMVS-SCA-FT		97.82 229	97.75 213	98.06 278	99.57 140	96.36 295	99.02 277	99.49 128	97.18 209	98.71 253	99.72 128	92.72 266	99.14 292	97.44 233	95.86 282	98.67 261

SixPastTwentyTwo		97.50 269	97.33 265	98.03 279	98.65 315	96.23 299	99.77 24	98.68 331	97.14 212	97.90 309	99.93 4	90.45 309	99.18 289	97.00 257	96.43 267	98.67 261

tpm		97.67 257	97.55 230	98.03 279	99.02 270	95.01 325	99.43 161	98.54 336	96.44 270	99.12 189	99.34 268	91.83 290	99.60 224	97.75 200	96.46 266	99.48 159

IterMVS		97.83 226	97.77 209	98.02 281	99.58 138	96.27 298	99.02 277	99.48 140	97.22 207	98.71 253	99.70 133	92.75 263	99.13 295	97.46 230	96.00 276	98.67 261
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDA-MVSNet_test_wron		95.45 306	94.60 312	98.01 282	98.16 333	97.21 256	99.11 259	99.24 270	93.49 332	80.73 359	98.98 317	93.02 256	98.18 335	94.22 320	94.45 310	98.64 273

K. test v3		97.10 283	96.79 284	98.01 282	98.72 308	96.33 296	99.87 4	97.05 353	97.59 166	96.16 337	99.80 76	88.71 328	99.04 306	96.69 276	96.55 263	98.65 271

MVP-Stereo		97.81 231	97.75 213	97.99 284	97.53 339	96.60 288	98.96 292	98.85 315	97.22 207	97.23 323	99.36 262	95.28 189	99.46 235	95.51 300	99.78 89	97.92 342
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement		95.42 307	94.57 313	97.97 285	89.83 361	96.11 301	99.48 142	98.75 320	96.74 244	96.68 332	99.88 15	88.65 330	99.71 195	98.37 149	82.74 351	98.09 329

PVSNet_0	94.43 19	96.09 301	95.47 304	97.94 286	99.31 207	94.34 335	97.81 352	99.70 15	97.12 215	97.46 318	98.75 327	89.71 319	99.79 164	97.69 208	81.69 352	99.68 103

DWT-MVSNet_test		97.53 265	97.40 254	97.93 287	99.03 269	94.86 329	99.57 91	98.63 332	96.59 259	98.36 290	98.79 324	89.32 323	99.74 178	98.14 168	98.16 212	99.20 188

MDA-MVSNet-bldmvs		94.96 311	93.98 317	97.92 288	98.24 332	97.27 251	99.15 249	99.33 243	93.80 328	80.09 360	99.03 310	88.31 334	97.86 344	93.49 327	94.36 312	98.62 283

YYNet1		95.36 308	94.51 314	97.92 288	97.89 335	97.10 258	99.10 261	99.23 271	93.26 335	80.77 358	99.04 309	92.81 262	98.02 339	94.30 317	94.18 315	98.64 273

tpmrst		98.33 164	98.48 149	97.90 290	99.16 246	94.78 330	99.31 207	99.11 286	97.27 201	99.45 111	99.59 188	95.33 188	99.84 136	98.48 137	98.61 185	99.09 196

ADS-MVSNet2		98.02 198	98.07 178	97.87 291	99.33 199	95.19 322	99.23 234	99.08 290	96.24 283	99.10 194	99.67 152	94.11 237	98.93 325	96.81 269	99.05 162	99.48 159

test_0402		96.64 289	96.24 291	97.85 292	98.85 293	96.43 293	99.44 155	99.26 266	93.52 331	96.98 330	99.52 214	88.52 332	99.20 288	92.58 338	97.50 236	97.93 341

tpmvs		97.98 205	98.02 182	97.84 293	99.04 267	94.73 331	99.31 207	99.20 276	96.10 300	98.76 249	99.42 244	94.94 198	99.81 156	96.97 260	98.45 196	98.97 212

TinyColmap		97.12 282	96.89 282	97.83 294	99.07 261	95.52 314	98.57 330	98.74 323	97.58 168	97.81 313	99.79 88	88.16 336	99.56 227	95.10 308	97.21 251	98.39 317

pmmvs6		96.53 291	96.09 294	97.82 295	98.69 312	95.47 315	99.37 190	99.47 158	93.46 333	97.41 319	99.78 95	87.06 343	99.33 265	96.92 266	92.70 333	98.65 271

EU-MVSNet		97.98 205	98.03 180	97.81 296	98.72 308	96.65 286	99.66 48	99.66 27	98.09 112	98.35 291	99.82 49	95.25 193	98.01 340	97.41 235	95.30 295	98.78 225

lessismore_v0				97.79 297	98.69 312	95.44 317		94.75 361		95.71 341	99.87 20	88.69 329	99.32 266	95.89 291	94.93 304	98.62 283

USDC		97.34 276	97.20 274	97.75 298	99.07 261	95.20 321	98.51 334	99.04 295	97.99 127	98.31 293	99.86 23	89.02 325	99.55 229	95.67 298	97.36 248	98.49 303

tpm2		97.44 274	97.34 263	97.74 299	99.15 249	94.36 334	99.45 151	98.94 303	93.45 334	98.90 229	99.44 239	91.35 302	99.59 225	97.31 237	98.07 215	99.29 182

CostFormer		97.72 246	97.73 215	97.71 300	99.15 249	94.02 337	99.54 110	99.02 296	94.67 320	99.04 207	99.35 265	92.35 283	99.77 170	98.50 136	97.94 217	99.34 179

LF4IMVS		97.52 266	97.46 242	97.70 301	98.98 276	95.55 311	99.29 213	98.82 318	98.07 117	98.66 262	99.64 166	89.97 316	99.61 223	97.01 256	96.68 258	97.94 340

ppachtmachnet_test		97.49 272	97.45 243	97.61 302	98.62 318	95.24 320	98.80 311	99.46 168	96.11 296	98.22 297	99.62 178	96.45 150	98.97 322	93.77 323	95.97 280	98.61 292

MVS_0304		96.79 287	96.52 287	97.59 303	99.22 229	94.92 328	99.04 273	99.59 43	96.49 263	98.43 285	98.99 314	80.48 357	99.39 248	97.15 251	99.27 144	98.47 306

dp		97.75 240	97.80 203	97.59 303	99.10 256	93.71 340	99.32 205	98.88 313	96.48 267	99.08 200	99.55 202	92.67 271	99.82 152	96.52 280	98.58 188	99.24 185

our_test_3		97.65 259	97.68 219	97.55 305	98.62 318	94.97 326	98.84 307	99.30 258	96.83 241	98.19 298	99.34 268	97.01 132	99.02 310	95.00 311	96.01 275	98.64 273

MVS-HIRNet		95.75 304	95.16 308	97.51 306	99.30 208	93.69 341	98.88 303	95.78 359	85.09 352	98.78 247	92.65 356	91.29 303	99.37 253	94.85 312	99.85 58	99.46 166

tpm cat1		97.39 275	97.36 258	97.50 307	99.17 244	93.73 339	99.43 161	99.31 254	91.27 342	98.71 253	99.08 304	94.31 231	99.77 170	96.41 284	98.50 194	99.00 208

new_pmnet		96.38 295	96.03 295	97.41 308	98.13 334	95.16 324	99.05 268	99.20 276	93.94 326	97.39 320	98.79 324	91.61 299	99.04 306	90.43 343	95.77 283	98.05 332

UnsupCasMVSNet_eth		96.44 293	96.12 293	97.40 309	98.65 315	95.65 308	99.36 194	99.51 101	97.13 213	96.04 339	98.99 314	88.40 333	98.17 336	96.71 274	90.27 339	98.40 316

KD-MVS_2432*1600		94.62 313	93.72 319	97.31 310	97.19 347	95.82 306	98.34 340	99.20 276	95.00 314	97.57 316	98.35 337	87.95 339	98.10 337	92.87 334	77.00 356	98.01 334

miper_refine_blended		94.62 313	93.72 319	97.31 310	97.19 347	95.82 306	98.34 340	99.20 276	95.00 314	97.57 316	98.35 337	87.95 339	98.10 337	92.87 334	77.00 356	98.01 334

pmmvs-eth3d		95.34 309	94.73 311	97.15 312	95.53 354	95.94 304	99.35 200	99.10 287	95.13 309	93.55 347	97.54 344	88.15 337	97.91 342	94.58 314	89.69 342	97.61 345

FMVSNet5		96.43 294	96.19 292	97.15 312	99.11 253	95.89 305	99.32 205	99.52 88	94.47 324	98.34 292	99.07 305	87.54 342	97.07 351	92.61 337	95.72 286	98.47 306

Anonymous20240521		96.20 298	95.89 299	97.13 314	97.72 338	94.96 327	99.79 21	99.29 263	93.01 336	97.20 325	99.03 310	89.69 320	98.36 334	91.16 341	96.13 273	98.07 330

DeepPCF-MVS	98.18 3	98.81 131	99.37 19	97.12 315	99.60 134	91.75 350	98.61 327	99.44 189	99.35 1	99.83 17	99.85 29	98.70 62	99.81 156	99.02 57	99.91 16	99.81 41

MS-PatchMatch		97.24 280	97.32 266	96.99 316	98.45 328	93.51 344	98.82 309	99.32 251	97.41 190	98.13 301	99.30 278	88.99 326	99.56 227	95.68 297	99.80 84	97.90 343

RPSCF		98.22 171	98.62 138	96.99 316	99.82 37	91.58 351	99.72 32	99.44 189	96.61 255	99.66 64	99.89 10	95.92 167	99.82 152	97.46 230	99.10 158	99.57 139

DIV-MVS_2432*1600		95.00 310	94.34 315	96.96 318	97.07 349	95.39 318	99.56 98	99.44 189	95.11 311	97.13 327	97.32 348	91.86 289	97.27 350	90.35 344	81.23 353	98.23 326

DSMNet-mixed		97.25 279	97.35 260	96.95 319	97.84 336	93.61 343	99.57 91	96.63 357	96.13 295	98.87 234	98.61 332	94.59 220	97.70 347	95.08 309	98.86 177	99.55 141

MIMVSNet1		95.51 305	95.04 309	96.92 320	97.38 341	95.60 309	99.52 116	99.50 120	93.65 330	96.97 331	99.17 295	85.28 348	96.56 355	88.36 350	95.55 290	98.60 295

LCM-MVSNet-Re		97.83 226	98.15 167	96.87 321	99.30 208	92.25 349	99.59 79	98.26 338	97.43 187	96.20 336	99.13 300	96.27 156	98.73 331	98.17 165	98.99 168	99.64 120

EG-PatchMatch MVS		95.97 302	95.69 302	96.81 322	97.78 337	92.79 347	99.16 245	98.93 304	96.16 291	94.08 346	99.22 290	82.72 352	99.47 233	95.67 298	97.50 236	98.17 327

Anonymous20231206		96.22 296	96.03 295	96.79 323	97.31 344	94.14 336	99.63 60	99.08 290	96.17 289	97.04 329	99.06 307	93.94 242	97.76 346	86.96 354	95.06 300	98.47 306

test20.03		96.12 300	95.96 297	96.63 324	97.44 340	95.45 316	99.51 120	99.38 217	96.55 260	96.16 337	99.25 287	93.76 248	96.17 356	87.35 353	94.22 314	98.27 322

pmmvs3		94.09 319	93.25 322	96.60 325	94.76 356	94.49 332	98.92 299	98.18 342	89.66 346	96.48 334	98.06 342	86.28 344	97.33 349	89.68 346	87.20 345	97.97 339

UnsupCasMVSNet_bld		93.53 320	92.51 323	96.58 326	97.38 341	93.82 338	98.24 345	99.48 140	91.10 344	93.10 349	96.66 350	74.89 358	98.37 333	94.03 322	87.71 344	97.56 347

OpenMVS_ROB	92.34 20	94.38 317	93.70 321	96.41 327	97.38 341	93.17 345	99.06 266	98.75 320	86.58 350	94.84 345	98.26 340	81.53 355	99.32 266	89.01 347	97.87 219	96.76 349

CL-MVSNet_2432*1600		94.49 315	93.97 318	96.08 328	96.16 350	93.67 342	98.33 342	99.38 217	95.13 309	97.33 321	98.15 341	92.69 270	96.57 354	88.67 348	79.87 354	97.99 337

Patchmatch-RL test		95.84 303	95.81 301	95.95 329	95.61 352	90.57 352	98.24 345	98.39 337	95.10 313	95.20 342	98.67 329	94.78 208	97.77 345	96.28 286	90.02 340	99.51 154

new-patchmatchnet		94.48 316	94.08 316	95.67 330	95.08 355	92.41 348	99.18 243	99.28 265	94.55 323	93.49 348	97.37 347	87.86 341	97.01 352	91.57 339	88.36 343	97.61 345

PM-MVS		92.96 321	92.23 324	95.14 331	95.61 352	89.98 354	99.37 190	98.21 340	94.80 318	95.04 344	97.69 343	65.06 360	97.90 343	94.30 317	89.98 341	97.54 348

Gipuma		90.99 323	90.15 326	93.51 332	98.73 306	90.12 353	93.98 358	99.45 180	79.32 355	92.28 350	94.91 353	69.61 359	97.98 341	87.42 352	95.67 287	92.45 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX				93.34 333	99.29 212	82.27 357		99.22 272	85.15 351	96.33 335	99.05 308	90.97 307	99.73 185	93.57 326	97.77 221	98.01 334

ambc				93.06 334	92.68 357	82.36 356	98.47 335	98.73 328		95.09 343	97.41 345	55.55 363	99.10 302	96.42 283	91.32 337	97.71 344

N_pmnet		94.95 312	95.83 300	92.31 335	98.47 327	79.33 360	99.12 253	92.81 367	93.87 327	97.68 315	99.13 300	93.87 244	99.01 312	91.38 340	96.19 272	98.59 296

CMPMVS	69.68 23	94.13 318	94.90 310	91.84 336	97.24 345	80.01 359	98.52 333	99.48 140	89.01 347	91.99 351	99.67 152	85.67 347	99.13 295	95.44 301	97.03 256	96.39 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet		86.80 325	85.22 329	91.53 337	87.81 362	80.96 358	98.23 347	98.99 298	71.05 357	90.13 353	96.51 351	48.45 366	96.88 353	90.51 342	85.30 347	96.76 349

PMMVS2		86.87 324	85.37 328	91.35 338	90.21 360	83.80 355	98.89 302	97.45 352	83.13 354	91.67 352	95.03 352	48.49 365	94.70 358	85.86 356	77.62 355	95.54 352

test_method		91.10 322	91.36 325	90.31 339	95.85 351	73.72 365	94.89 357	99.25 268	68.39 359	95.82 340	99.02 312	80.50 356	98.95 324	93.64 325	94.89 305	98.25 324

tmp_tt		82.80 327	81.52 330	86.66 340	66.61 368	68.44 366	92.79 360	97.92 345	68.96 358	80.04 361	99.85 29	85.77 346	96.15 357	97.86 189	43.89 362	95.39 353

MVE	76.82 21	76.91 331	74.31 335	84.70 341	85.38 365	76.05 364	96.88 356	93.17 365	67.39 360	71.28 362	89.01 361	21.66 372	87.69 361	71.74 360	72.29 358	90.35 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		77.30 330	74.86 334	84.62 342	75.88 366	77.61 361	97.63 354	93.15 366	88.81 348	64.27 363	89.29 360	36.51 367	83.93 364	75.89 359	52.31 361	92.33 356

E-PMN		80.61 328	79.88 331	82.81 343	90.75 359	76.38 363	97.69 353	95.76 360	66.44 361	83.52 355	92.25 357	62.54 362	87.16 362	68.53 361	61.40 359	84.89 360

FPMVS		84.93 326	85.65 327	82.75 344	86.77 363	63.39 367	98.35 339	98.92 306	74.11 356	83.39 356	98.98 317	50.85 364	92.40 360	84.54 357	94.97 302	92.46 354

EMVS		80.02 329	79.22 332	82.43 345	91.19 358	76.40 362	97.55 355	92.49 368	66.36 362	83.01 357	91.27 358	64.63 361	85.79 363	65.82 362	60.65 360	85.08 359

PMVS	70.75 22	75.98 332	74.97 333	79.01 346	70.98 367	55.18 368	93.37 359	98.21 340	65.08 363	61.78 364	93.83 355	21.74 371	92.53 359	78.59 358	91.12 338	89.34 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 333	41.29 338	36.84 347	86.18 364	49.12 369	79.73 361	22.81 370	27.64 364	25.46 367	28.45 367	21.98 370	48.89 365	55.80 363	23.56 365	12.51 363

test123		39.01 335	42.50 337	28.53 348	39.17 369	20.91 370	98.75 316	19.17 371	19.83 366	38.57 365	66.67 363	33.16 368	15.42 366	37.50 365	29.66 364	49.26 361

testmvs		39.17 334	43.78 336	25.37 349	36.04 370	16.84 371	98.36 338	26.56 369	20.06 365	38.51 366	67.32 362	29.64 369	15.30 367	37.59 364	39.90 363	43.98 362

uanet_test		0.02 339	0.03 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		24.64 336	32.85 339	0.00 350	0.00 371	0.00 372	0.00 362	99.51 101	0.00 367	0.00 368	99.56 198	96.58 145	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		8.27 338	11.03 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	99.01 16	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.02 339	0.03 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.02 339	0.03 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.02 339	0.03 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.02 339	0.03 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		8.30 337	11.06 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	99.58 191	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.02 339	0.03 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.27 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ZD-MVS					99.71 86	99.79 30		99.61 35	96.84 239	99.56 91	99.54 207	98.58 70	99.96 19	96.93 264	99.75 97

RE-MVS-def			99.34 26		99.76 52	99.82 20	99.63 60	99.52 88	98.38 77	99.76 37	99.82 49	98.75 56		98.61 118	99.81 80	99.77 63

IU-MVS					99.84 32	99.88 7		99.32 251	98.30 88	99.84 13				98.86 79	99.85 58	99.89 2

test_241102_TWO								99.48 140	99.08 11	99.88 5	99.81 62	98.94 31	99.96 19	98.91 69	99.84 65	99.88 5

test_241102_ONE					99.84 32	99.90 1		99.48 140	99.07 13	99.91 1	99.74 117	99.20 5	99.76 175

9.14			99.10 69		99.72 80		99.40 178	99.51 101	97.53 176	99.64 72	99.78 95	98.84 42	99.91 91	97.63 211	99.82 78

save fliter					99.76 52	99.59 68	99.14 251	99.40 207	99.00 22

test_0728_THIRD									98.99 25	99.81 22	99.80 76	99.09 12	99.96 19	98.85 81	99.90 23	99.88 5

test0726					99.85 25	99.89 3	99.62 66	99.50 120	99.10 8	99.86 11	99.82 49	98.94 31

GSMVS																99.52 148

test_part2					99.81 40	99.83 14				99.77 33

sam_mvs1												94.86 204				99.52 148

sam_mvs												94.72 215

MTGPA								99.47 158

test_post1							99.23 234				65.14 365	94.18 236	99.71 195	97.58 215

test_post											65.99 364	94.65 219	99.73 185

patchmatchnet-post											98.70 328	94.79 207	99.74 178

MTMP							99.54 110	98.88 313

gm-plane-assit					98.54 325	92.96 346			94.65 321		99.15 298		99.64 216	97.56 220

test9_res														97.49 226	99.72 104	99.75 69

TEST9					99.67 101	99.65 57	99.05 268	99.41 201	96.22 285	98.95 221	99.49 224	98.77 51	99.91 91

test_8					99.67 101	99.61 63	99.03 274	99.41 201	96.28 278	98.93 225	99.48 230	98.76 53	99.91 91

agg_prior2														97.21 243	99.73 103	99.75 69

agg_prior					99.67 101	99.62 61		99.40 207		98.87 234			99.91 91

test_prior4						99.56 73	98.99 284

test_prior2							98.96 292		98.34 83	99.01 210	99.52 214	98.68 63		97.96 181	99.74 100

旧先验2							98.96 292		96.70 247	99.47 108			99.94 54	98.19 161

新几何2							99.01 282

旧先验1					99.74 70	99.59 68		99.54 71			99.69 140	98.47 78			99.68 115	99.73 81

无先验							98.99 284	99.51 101	96.89 236				99.93 69	97.53 223		99.72 87

原ACMM2							98.95 296

test222					99.75 62	99.49 87	98.91 301	99.49 128	96.42 272	99.34 144	99.65 159	98.28 93			99.69 110	99.72 87

testdata2													99.95 43	96.67 277

segment_acmp												98.96 25

testdata1							98.85 306		98.32 87

plane_prior7					99.29 212	97.03 268

plane_prior6					99.27 217	96.98 272						92.71 268

plane_prior5								99.47 158					99.69 204	97.78 196	97.63 223	98.67 261

plane_prior4											99.61 182

plane_prior3						97.00 270			98.69 56	99.11 191

plane_prior2							99.39 182		98.97 30

plane_prior1					99.26 219

plane_prior						96.97 273	99.21 241		98.45 71						97.60 226

n2								0.00 372

nn								0.00 372

door-mid								98.05 343

test11								99.35 232

door								97.92 345

HQP5-MVS						96.83 278

HQP-NCC					99.19 235		98.98 288		98.24 92	98.66 262

ACMP_Plane					99.19 235		98.98 288		98.24 92	98.66 262

BP-MVS														97.19 247

HQP4-MVS										98.66 262			99.64 216			98.64 273

HQP3-MVS								99.39 211							97.58 228

HQP2-MVS												92.47 277

NP-MVS					99.23 225	96.92 276					99.40 251

MDTV_nov1_ep13_2view						95.18 323	99.35 200		96.84 239	99.58 88		95.19 195		97.82 193		99.46 166

MDTV_nov1_ep13			98.32 159		99.11 253	94.44 333	99.27 220	98.74 323	97.51 178	99.40 128	99.62 178	94.78 208	99.76 175	97.59 214	98.81 181

ACMMP++_ref															97.19 252

ACMMP++															97.43 244

Test By Simon												98.75 56

High-resolution multi-view benchmark