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.

DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 56	86.00 52	98.29 1	97.49 8	90.75 26	97.62 6	98.06 20	92.59 2	99.61 4	95.64 29	99.02 12	98.86 12

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 54	97.13 15	97.44 17	90.31 38	97.71 1	98.07 18	92.31 4	99.58 10	95.66 27	99.13 3	98.84 15

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 57	97.09 17	96.73 91	90.27 42	97.04 17	98.05 22	91.47 8	99.55 16	95.62 31	99.08 7	98.45 37
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
DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 27	87.28 18	95.56 111	97.51 7	89.13 84	97.14 13	97.91 29	91.64 7	99.62 2	94.61 44	99.17 2	98.86 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 30	86.29 46	97.46 7	97.40 22	89.03 89	96.20 29	98.10 12	89.39 16	99.34 38	95.88 26	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 26	97.47 13	91.73 13	96.10 30	96.69 80	89.90 12	99.30 44	94.70 42	98.04 74	99.13 2
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
CNVR-MVS		95.40 7	95.37 9	95.50 8	98.11 38	88.51 7	95.29 122	96.96 62	92.09 9	95.32 42	97.08 63	89.49 15	99.33 41	95.10 38	98.85 20	98.66 22

SMA-MVS		95.20 8	95.07 15	95.59 6	98.14 37	88.48 8	96.26 49	97.28 35	85.90 183	97.67 3	98.10 12	88.41 20	99.56 12	94.66 43	99.19 1	98.71 21
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
SteuartSystems-ACMMP		95.20 8	95.32 11	94.85 25	96.99 77	86.33 42	97.33 8	97.30 32	91.38 17	95.39 41	97.46 43	88.98 19	99.40 30	94.12 48	98.89 18	98.82 17
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		95.14 10	94.91 21	95.83 4	98.25 31	89.65 4	95.92 81	96.96 62	91.75 12	94.02 64	96.83 75	88.12 24	99.55 16	93.41 59	98.94 16	98.28 57

lecture		95.10 11	95.46 8	94.01 61	98.40 23	84.36 102	97.70 3	97.78 1	91.19 18	96.22 28	98.08 17	86.64 40	99.37 33	94.91 40	98.26 59	98.29 56

MM		95.10 11	94.91 21	95.68 5	96.09 111	88.34 9	96.68 34	94.37 271	95.08 1	94.68 50	97.72 36	82.94 95	99.64 1	97.85 4	98.76 29	99.06 7

fmvsm_s_conf0.5_n_9		94.99 13	95.50 7	93.44 81	96.51 95	82.25 177	95.76 94	96.92 67	93.37 3	97.63 5	98.43 1	84.82 71	99.16 54	98.15 1	97.92 79	98.90 11

SF-MVS		94.97 14	94.90 23	95.20 12	97.84 52	87.76 10	96.65 35	97.48 12	87.76 137	95.71 37	97.70 37	88.28 23	99.35 37	93.89 52	98.78 26	98.48 31

SD-MVS		94.96 15	95.33 10	93.88 66	97.25 74	86.69 28	96.19 52	97.11 52	90.42 34	96.95 19	97.27 51	89.53 14	96.91 288	94.38 46	98.85 20	98.03 84
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
TSAR-MVS + MP.		94.85 16	94.94 19	94.58 42	98.25 31	86.33 42	96.11 62	96.62 100	88.14 121	96.10 30	96.96 69	89.09 18	98.94 86	94.48 45	98.68 37	98.48 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
reproduce-ours		94.82 17	94.97 17	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 54	95.23 45	98.10 12	87.09 37	99.37 33	95.30 35	98.25 62	98.30 51

our_new_method		94.82 17	94.97 17	94.38 50	97.91 49	85.46 70	95.86 84	97.15 46	89.82 54	95.23 45	98.10 12	87.09 37	99.37 33	95.30 35	98.25 62	98.30 51

NCCC		94.81 19	94.69 26	95.17 14	97.83 53	87.46 17	95.66 102	96.93 66	92.34 7	93.94 65	96.58 90	87.74 27	99.44 29	92.83 68	98.40 54	98.62 23

fmvsm_l_conf0.5_n_3		94.80 20	95.01 16	94.15 59	95.64 135	85.08 77	96.09 63	97.36 24	90.98 21	97.09 15	98.12 9	84.98 68	98.94 86	97.07 14	97.80 85	98.43 39

reproduce_model		94.76 21	94.92 20	94.29 56	97.92 45	85.18 76	95.95 79	97.19 39	89.67 64	95.27 44	98.16 5	86.53 44	99.36 36	95.42 34	98.15 67	98.33 46

ACMMP_NAP		94.74 22	94.56 27	95.28 10	98.02 43	87.70 11	95.68 99	97.34 26	88.28 115	95.30 43	97.67 38	85.90 51	99.54 20	93.91 51	98.95 15	98.60 24

test_fmvsm_n_1920		94.71 23	95.11 14	93.50 80	95.79 126	84.62 87	96.15 57	97.64 3	89.85 53	97.19 12	97.89 30	86.28 47	98.71 115	97.11 13	98.08 73	97.17 138

test_fmvsmconf_n		94.60 24	94.81 24	93.98 62	94.62 196	84.96 80	96.15 57	97.35 25	89.37 73	96.03 33	98.11 10	86.36 45	99.01 69	97.45 9	97.83 83	97.96 87

fmvsm_s_conf0.5_n_8		94.56 25	95.12 13	92.87 112	95.96 122	81.32 200	95.76 94	97.57 5	93.48 2	97.53 8	98.32 2	81.78 120	99.13 56	97.91 2	97.81 84	98.16 70

HFP-MVS		94.52 26	94.40 32	94.86 24	98.61 10	86.81 25	96.94 21	97.34 26	88.63 103	93.65 70	97.21 55	86.10 49	99.49 26	92.35 82	98.77 28	98.30 51

fmvsm_s_conf0.5_n_3		94.49 27	95.13 12	92.56 133	95.49 143	81.10 210	95.93 80	97.16 45	92.96 4	97.39 10	98.13 6	83.63 83	98.80 104	97.89 3	97.61 92	97.78 102

ZNCC-MVS		94.47 28	94.28 39	95.03 16	98.52 15	86.96 20	96.85 29	97.32 30	88.24 116	93.15 80	97.04 66	86.17 48	99.62 2	92.40 79	98.81 23	98.52 27

XVS		94.45 29	94.32 35	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	97.16 61	85.02 64	99.49 26	91.99 97	98.56 50	98.47 34

MCST-MVS		94.45 29	94.20 45	95.19 13	98.46 19	87.50 16	95.00 145	97.12 50	87.13 151	92.51 105	96.30 97	89.24 17	99.34 38	93.46 56	98.62 46	98.73 19

region2R		94.43 31	94.27 41	94.92 20	98.65 8	86.67 30	96.92 25	97.23 38	88.60 106	93.58 72	97.27 51	85.22 59	99.54 20	92.21 86	98.74 31	98.56 26

ACMMPR		94.43 31	94.28 39	94.91 21	98.63 9	86.69 28	96.94 21	97.32 30	88.63 103	93.53 75	97.26 53	85.04 63	99.54 20	92.35 82	98.78 26	98.50 28

MTAPA		94.42 33	94.22 42	95.00 18	98.42 21	86.95 21	94.36 195	96.97 59	91.07 19	93.14 81	97.56 40	84.30 76	99.56 12	93.43 57	98.75 30	98.47 34

CP-MVS		94.34 34	94.21 44	94.74 37	98.39 25	86.64 32	97.60 5	97.24 36	88.53 108	92.73 96	97.23 54	85.20 60	99.32 42	92.15 89	98.83 22	98.25 64

fmvsm_l_conf0.5_n		94.29 35	94.46 30	93.79 72	95.28 150	85.43 72	95.68 99	96.43 113	86.56 167	96.84 21	97.81 34	87.56 32	98.77 108	97.14 12	96.82 111	97.16 142

MP-MVS		94.25 36	94.07 50	94.77 35	98.47 18	86.31 44	96.71 32	96.98 58	89.04 87	91.98 116	97.19 58	85.43 57	99.56 12	92.06 95	98.79 24	98.44 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 37	94.07 50	94.75 36	98.06 41	86.90 23	95.88 83	96.94 65	85.68 190	95.05 48	97.18 59	87.31 35	99.07 59	91.90 103	98.61 48	98.28 57
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		94.23 38	94.17 48	94.43 47	98.21 34	85.78 65	96.40 39	96.90 70	88.20 119	94.33 54	97.40 46	84.75 72	99.03 64	93.35 60	97.99 76	98.48 31

GST-MVS		94.21 39	93.97 54	94.90 23	98.41 22	86.82 24	96.54 37	97.19 39	88.24 116	93.26 77	96.83 75	85.48 56	99.59 8	91.43 112	98.40 54	98.30 51

MP-MVS-pluss		94.21 39	94.00 53	94.85 25	98.17 35	86.65 31	94.82 158	97.17 44	86.26 175	92.83 90	97.87 31	85.57 55	99.56 12	94.37 47	98.92 17	98.34 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_l_conf0.5_n_a		94.20 41	94.40 32	93.60 78	95.29 149	84.98 79	95.61 107	96.28 127	86.31 173	96.75 23	97.86 32	87.40 33	98.74 112	97.07 14	97.02 104	97.07 147

test_fmvsmconf0.1_n		94.20 41	94.31 37	93.88 66	92.46 300	84.80 83	96.18 54	96.82 79	89.29 78	95.68 38	98.11 10	85.10 61	98.99 76	97.38 10	97.75 89	97.86 96

DeepPCF-MVS	89.96 1	94.20 41	94.77 25	92.49 138	96.52 93	80.00 247	94.00 222	97.08 53	90.05 46	95.65 39	97.29 50	89.66 13	98.97 81	93.95 50	98.71 32	98.50 28

MVS_0304		94.18 44	93.80 58	95.34 9	94.91 175	87.62 14	95.97 76	93.01 315	92.58 6	94.22 55	97.20 57	80.56 128	99.59 8	97.04 17	98.68 37	98.81 18

CS-MVS		94.12 45	94.44 31	93.17 93	96.55 90	83.08 147	97.63 4	96.95 64	91.71 14	93.50 76	96.21 100	85.61 53	98.24 161	93.64 54	98.17 65	98.19 67

fmvsm_s_conf0.5_n_6		94.11 46	94.56 27	92.76 119	94.98 168	81.96 184	95.79 90	97.29 34	89.31 76	97.52 9	97.61 39	83.25 89	98.88 92	97.05 16	98.22 64	97.43 122

DeepC-MVS_fast	89.43 2	94.04 47	93.79 59	94.80 33	97.48 66	86.78 26	95.65 104	96.89 71	89.40 72	92.81 91	96.97 68	85.37 58	99.24 47	90.87 121	98.69 35	98.38 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		94.02 48	94.29 38	93.24 88	96.69 83	83.24 136	97.49 6	96.92 67	92.14 8	92.90 86	95.77 129	85.02 64	98.33 156	93.03 65	98.62 46	98.13 72

HPM-MVS		94.02 48	93.88 55	94.43 47	98.39 25	85.78 65	97.25 11	97.07 54	86.90 159	92.62 102	96.80 79	84.85 70	99.17 51	92.43 77	98.65 44	98.33 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		93.99 50	93.78 60	94.63 40	98.50 16	85.90 62	96.87 27	96.91 69	88.70 101	91.83 125	97.17 60	83.96 80	99.55 16	91.44 111	98.64 45	98.43 39

balanced_conf03		93.98 51	94.22 42	93.26 87	96.13 105	83.29 135	96.27 48	96.52 108	89.82 54	95.56 40	95.51 138	84.50 74	98.79 106	94.83 41	98.86 19	97.72 106

fmvsm_s_conf0.5_n_5		93.96 52	94.18 47	93.30 84	94.79 182	83.81 117	95.77 92	96.74 90	88.02 124	96.23 27	97.84 33	83.36 88	98.83 102	97.49 7	97.34 98	97.25 132

PGM-MVS		93.96 52	93.72 64	94.68 38	98.43 20	86.22 47	95.30 120	97.78 1	87.45 144	93.26 77	97.33 49	84.62 73	99.51 24	90.75 123	98.57 49	98.32 50

PHI-MVS		93.89 54	93.65 68	94.62 41	96.84 80	86.43 39	96.69 33	97.49 8	85.15 213	93.56 74	96.28 98	85.60 54	99.31 43	92.45 76	98.79 24	98.12 75

fmvsm_s_conf0.5_n_4		93.86 55	94.37 34	92.33 150	95.13 161	80.95 216	95.64 105	96.97 59	89.60 66	96.85 20	97.77 35	83.08 93	98.92 89	97.49 7	96.78 112	97.13 143

SR-MVS-dyc-post		93.82 56	93.82 57	93.82 69	97.92 45	84.57 89	96.28 46	96.76 86	87.46 142	93.75 68	97.43 44	84.24 77	99.01 69	92.73 69	97.80 85	97.88 94

APD-MVS_3200maxsize		93.78 57	93.77 61	93.80 71	97.92 45	84.19 106	96.30 42	96.87 73	86.96 155	93.92 66	97.47 42	83.88 81	98.96 83	92.71 72	97.87 81	98.26 63

fmvsm_s_conf0.5_n		93.76 58	94.06 52	92.86 113	95.62 137	83.17 139	96.14 59	96.12 146	88.13 122	95.82 36	98.04 25	83.43 84	98.48 134	96.97 18	96.23 125	96.92 162

patch_mono-2		93.74 59	94.32 35	92.01 160	97.54 62	78.37 289	93.40 251	97.19 39	88.02 124	94.99 49	97.21 55	88.35 21	98.44 144	94.07 49	98.09 71	99.23 1

MSLP-MVS++		93.72 60	94.08 49	92.65 128	97.31 70	83.43 129	95.79 90	97.33 28	90.03 47	93.58 72	96.96 69	84.87 69	97.76 206	92.19 88	98.66 41	96.76 172

TSAR-MVS + GP.		93.66 61	93.41 72	94.41 49	96.59 87	86.78 26	94.40 187	93.93 289	89.77 61	94.21 56	95.59 136	87.35 34	98.61 126	92.72 71	96.15 128	97.83 99

fmvsm_s_conf0.5_n_a		93.57 62	93.76 62	93.00 104	95.02 163	83.67 121	96.19 52	96.10 148	87.27 147	95.98 34	98.05 22	83.07 94	98.45 142	96.68 20	95.51 139	96.88 165

CANet		93.54 63	93.20 77	94.55 43	95.65 134	85.73 67	94.94 148	96.69 96	91.89 11	90.69 145	95.88 121	81.99 116	99.54 20	93.14 63	97.95 78	98.39 41

dcpmvs_2		93.49 64	94.19 46	91.38 196	97.69 59	76.78 328	94.25 199	96.29 124	88.33 112	94.46 52	96.88 72	88.07 25	98.64 121	93.62 55	98.09 71	98.73 19

fmvsm_s_conf0.5_n_2		93.47 65	93.83 56	92.39 145	95.36 146	81.19 206	95.20 134	96.56 105	90.37 36	97.13 14	98.03 26	77.47 172	98.96 83	97.79 5	96.58 117	97.03 151

NormalMVS		93.46 66	93.16 78	94.37 52	98.40 23	86.20 48	96.30 42	96.27 128	91.65 15	92.68 98	96.13 107	77.97 163	98.84 99	90.75 123	98.26 59	98.07 77

fmvsm_s_conf0.1_n		93.46 66	93.66 67	92.85 114	93.75 251	83.13 141	96.02 72	95.74 180	87.68 139	95.89 35	98.17 4	82.78 98	98.46 138	96.71 19	96.17 127	96.98 156

MVS_111021_HR		93.45 68	93.31 73	93.84 68	96.99 77	84.84 81	93.24 264	97.24 36	88.76 98	91.60 130	95.85 124	86.07 50	98.66 117	91.91 101	98.16 66	98.03 84

MVSMamba_PlusPlus		93.44 69	93.54 70	93.14 95	96.58 89	83.05 148	96.06 68	96.50 110	84.42 233	94.09 60	95.56 137	85.01 67	98.69 116	94.96 39	98.66 41	97.67 109

test_fmvsmvis_n_1920		93.44 69	93.55 69	93.10 97	93.67 259	84.26 104	95.83 88	96.14 142	89.00 91	92.43 107	97.50 41	83.37 87	98.72 113	96.61 21	97.44 94	96.32 189

train_agg		93.44 69	93.08 79	94.52 44	97.53 63	86.49 37	94.07 214	96.78 83	81.86 297	92.77 93	96.20 101	87.63 29	99.12 57	92.14 90	98.69 35	97.94 88

EC-MVSNet		93.44 69	93.71 65	92.63 129	95.21 155	82.43 171	97.27 10	96.71 94	90.57 33	92.88 87	95.80 127	83.16 90	98.16 167	93.68 53	98.14 68	97.31 124

DELS-MVS		93.43 73	93.25 75	93.97 63	95.42 145	85.04 78	93.06 273	97.13 49	90.74 28	91.84 123	95.09 161	86.32 46	99.21 49	91.22 113	98.45 52	97.65 110
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
HPM-MVS_fast		93.40 74	93.22 76	93.94 65	98.36 27	84.83 82	97.15 14	96.80 82	85.77 187	92.47 106	97.13 62	82.38 102	99.07 59	90.51 128	98.40 54	97.92 91

DeepC-MVS	88.79 3	93.31 75	92.99 82	94.26 57	96.07 113	85.83 63	94.89 151	96.99 57	89.02 90	89.56 164	97.37 48	82.51 101	99.38 31	92.20 87	98.30 57	97.57 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
sasdasda		93.27 76	92.75 86	94.85 25	95.70 131	87.66 12	96.33 40	96.41 115	90.00 48	94.09 60	94.60 186	82.33 104	98.62 124	92.40 79	92.86 208	98.27 59

canonicalmvs		93.27 76	92.75 86	94.85 25	95.70 131	87.66 12	96.33 40	96.41 115	90.00 48	94.09 60	94.60 186	82.33 104	98.62 124	92.40 79	92.86 208	98.27 59

ACMMP		93.24 78	92.88 84	94.30 55	98.09 40	85.33 74	96.86 28	97.45 16	88.33 112	90.15 158	97.03 67	81.44 121	99.51 24	90.85 122	95.74 135	98.04 83
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
CSCG		93.23 79	93.05 80	93.76 73	98.04 42	84.07 108	96.22 51	97.37 23	84.15 236	90.05 159	95.66 133	87.77 26	99.15 55	89.91 133	98.27 58	98.07 77

fmvsm_s_conf0.1_n_a		93.19 80	93.26 74	92.97 106	92.49 298	83.62 124	96.02 72	95.72 183	86.78 161	96.04 32	98.19 3	82.30 106	98.43 146	96.38 22	95.42 145	96.86 166

test_fmvsmconf0.01_n		93.19 80	93.02 81	93.71 76	89.25 397	84.42 100	96.06 68	96.29 124	89.06 85	94.68 50	98.13 6	79.22 147	98.98 80	97.22 11	97.24 99	97.74 104

fmvsm_s_conf0.1_n_2		93.16 82	93.42 71	92.37 146	94.62 196	81.13 208	95.23 127	95.89 169	90.30 40	96.74 24	98.02 27	76.14 184	98.95 85	97.64 6	96.21 126	97.03 151

fmvsm_s_conf0.5_n_7		93.15 83	93.76 62	91.31 199	94.42 214	79.48 259	94.52 177	97.14 48	89.33 75	94.17 58	98.09 16	81.83 118	97.49 230	96.33 23	98.02 75	96.95 158

alignmvs		93.08 84	92.50 92	94.81 32	95.62 137	87.61 15	95.99 74	96.07 151	89.77 61	94.12 59	94.87 170	80.56 128	98.66 117	92.42 78	93.10 204	98.15 71

MGCFI-Net		93.03 85	92.63 89	94.23 58	95.62 137	85.92 59	96.08 64	96.33 122	89.86 52	93.89 67	94.66 183	82.11 111	98.50 132	92.33 84	92.82 211	98.27 59

EI-MVSNet-Vis-set		93.01 86	92.92 83	93.29 85	95.01 164	83.51 128	94.48 179	95.77 177	90.87 22	92.52 104	96.67 82	84.50 74	99.00 74	91.99 97	94.44 171	97.36 123

casdiffmvs_mvg		92.96 87	92.83 85	93.35 83	94.59 198	83.40 131	95.00 145	96.34 121	90.30 40	92.05 114	96.05 111	83.43 84	98.15 168	92.07 92	95.67 136	98.49 30
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		92.83 88	92.54 91	93.68 77	96.10 110	84.71 85	95.66 102	96.39 117	91.92 10	93.22 79	96.49 93	83.16 90	98.87 93	84.47 212	95.47 142	97.45 121

CDPH-MVS		92.83 88	92.30 95	94.44 45	97.79 54	86.11 51	94.06 216	96.66 97	80.09 328	92.77 93	96.63 87	86.62 41	99.04 63	87.40 165	98.66 41	98.17 69

SymmetryMVS		92.81 90	92.31 94	94.32 54	96.15 103	86.20 48	96.30 42	94.43 267	91.65 15	92.68 98	96.13 107	77.97 163	98.84 99	90.75 123	94.72 159	97.92 91

ETV-MVS		92.74 91	92.66 88	92.97 106	95.20 156	84.04 112	95.07 141	96.51 109	90.73 29	92.96 85	91.19 314	84.06 78	98.34 154	91.72 106	96.54 118	96.54 184

EI-MVSNet-UG-set		92.74 91	92.62 90	93.12 96	94.86 178	83.20 138	94.40 187	95.74 180	90.71 30	92.05 114	96.60 89	84.00 79	98.99 76	91.55 109	93.63 184	97.17 138

DPM-MVS		92.58 93	91.74 103	95.08 15	96.19 102	89.31 5	92.66 287	96.56 105	83.44 255	91.68 129	95.04 162	86.60 43	98.99 76	85.60 192	97.92 79	96.93 161

casdiffmvs		92.51 94	92.43 93	92.74 122	94.41 215	81.98 182	94.54 176	96.23 136	89.57 67	91.96 118	96.17 105	82.58 100	98.01 186	90.95 119	95.45 144	98.23 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BP-MVS1		92.48 95	92.07 98	93.72 75	94.50 207	84.39 101	95.90 82	94.30 274	90.39 35	92.67 100	95.94 117	74.46 214	98.65 119	93.14 63	97.35 97	98.13 72

MVS_111021_LR		92.47 96	92.29 96	92.98 105	95.99 119	84.43 98	93.08 270	96.09 149	88.20 119	91.12 140	95.72 132	81.33 123	97.76 206	91.74 105	97.37 96	96.75 173

3Dnovator+	87.14 4	92.42 97	91.37 108	95.55 7	95.63 136	88.73 6	97.07 19	96.77 85	90.84 23	84.02 307	96.62 88	75.95 193	99.34 38	87.77 159	97.68 90	98.59 25

baseline		92.39 98	92.29 96	92.69 126	94.46 210	81.77 187	94.14 205	96.27 128	89.22 80	91.88 121	96.00 113	82.35 103	97.99 188	91.05 115	95.27 150	98.30 51

VNet		92.24 99	91.91 100	93.24 88	96.59 87	83.43 129	94.84 157	96.44 112	89.19 82	94.08 63	95.90 119	77.85 169	98.17 166	88.90 145	93.38 193	98.13 72

GDP-MVS		92.04 100	91.46 107	93.75 74	94.55 204	84.69 86	95.60 110	96.56 105	87.83 134	93.07 84	95.89 120	73.44 235	98.65 119	90.22 131	96.03 130	97.91 93

CPTT-MVS		91.99 101	91.80 101	92.55 134	98.24 33	81.98 182	96.76 31	96.49 111	81.89 296	90.24 152	96.44 95	78.59 155	98.61 126	89.68 135	97.85 82	97.06 148

EIA-MVS		91.95 102	91.94 99	91.98 164	95.16 158	80.01 246	95.36 115	96.73 91	88.44 109	89.34 169	92.16 277	83.82 82	98.45 142	89.35 138	97.06 102	97.48 119

DP-MVS Recon		91.95 102	91.28 111	93.96 64	98.33 29	85.92 59	94.66 170	96.66 97	82.69 275	90.03 160	95.82 126	82.30 106	99.03 64	84.57 210	96.48 121	96.91 163

KinetiMVS		91.82 104	91.30 109	93.39 82	94.72 189	83.36 133	95.45 113	96.37 119	90.33 37	92.17 111	96.03 112	72.32 252	98.75 109	87.94 157	96.34 123	98.07 77

EPNet		91.79 105	91.02 117	94.10 60	90.10 384	85.25 75	96.03 71	92.05 342	92.83 5	87.39 213	95.78 128	79.39 145	99.01 69	88.13 154	97.48 93	98.05 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmanbaseed2359cas		91.78 106	91.58 105	92.37 146	94.32 221	81.07 211	93.76 236	95.96 161	87.26 148	91.50 132	95.88 121	80.92 127	97.97 192	89.70 134	94.92 155	98.07 77

MG-MVS		91.77 107	91.70 104	92.00 163	97.08 76	80.03 245	93.60 244	95.18 225	87.85 133	90.89 143	96.47 94	82.06 114	98.36 151	85.07 198	97.04 103	97.62 111

Vis-MVSNet		91.75 108	91.23 112	93.29 85	95.32 148	83.78 118	96.14 59	95.98 158	89.89 50	90.45 149	96.58 90	75.09 205	98.31 159	84.75 204	96.90 107	97.78 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator	86.66 5	91.73 109	90.82 122	94.44 45	94.59 198	86.37 41	97.18 13	97.02 56	89.20 81	84.31 302	96.66 83	73.74 231	99.17 51	86.74 175	97.96 77	97.79 101

EPP-MVSNet		91.70 110	91.56 106	92.13 159	95.88 123	80.50 230	97.33 8	95.25 221	86.15 178	89.76 163	95.60 135	83.42 86	98.32 158	87.37 167	93.25 197	97.56 116

MVSFormer		91.68 111	91.30 109	92.80 116	93.86 244	83.88 115	95.96 77	95.90 167	84.66 229	91.76 126	94.91 167	77.92 166	97.30 255	89.64 136	97.11 100	97.24 133

Effi-MVS+		91.59 112	91.11 114	93.01 103	94.35 220	83.39 132	94.60 172	95.10 229	87.10 152	90.57 148	93.10 248	81.43 122	98.07 182	89.29 140	94.48 169	97.59 114

IS-MVSNet		91.43 113	91.09 116	92.46 139	95.87 125	81.38 199	96.95 20	93.69 301	89.72 63	89.50 167	95.98 115	78.57 156	97.77 205	83.02 232	96.50 120	98.22 66

PVSNet_Blended_VisFu		91.38 114	90.91 119	92.80 116	96.39 97	83.17 139	94.87 153	96.66 97	83.29 260	89.27 171	94.46 195	80.29 131	99.17 51	87.57 162	95.37 146	96.05 208

diffmvs		91.37 115	91.23 112	91.77 182	93.09 278	80.27 234	92.36 296	95.52 199	87.03 154	91.40 136	94.93 166	80.08 133	97.44 238	92.13 91	94.56 166	97.61 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		91.31 116	91.11 114	91.93 169	94.37 216	80.14 238	93.46 249	95.80 175	86.46 170	91.35 137	93.77 226	82.21 109	98.09 179	87.57 162	94.95 154	97.55 117

OMC-MVS		91.23 117	90.62 125	93.08 99	96.27 100	84.07 108	93.52 246	95.93 163	86.95 156	89.51 165	96.13 107	78.50 157	98.35 153	85.84 190	92.90 207	96.83 171

PAPM_NR		91.22 118	90.78 123	92.52 136	97.60 61	81.46 196	94.37 193	96.24 135	86.39 172	87.41 210	94.80 175	82.06 114	98.48 134	82.80 238	95.37 146	97.61 112

PS-MVSNAJ		91.18 119	90.92 118	91.96 166	95.26 153	82.60 170	92.09 309	95.70 184	86.27 174	91.84 123	92.46 267	79.70 139	98.99 76	89.08 142	95.86 132	94.29 282

xiu_mvs_v2_base		91.13 120	90.89 120	91.86 175	94.97 169	82.42 172	92.24 302	95.64 191	86.11 182	91.74 128	93.14 246	79.67 142	98.89 91	89.06 143	95.46 143	94.28 283

guyue		91.12 121	90.84 121	91.96 166	94.59 198	80.57 228	94.87 153	93.71 300	88.96 92	91.14 139	95.22 152	73.22 239	97.76 206	92.01 96	93.81 182	97.54 118

nrg030		91.08 122	90.39 126	93.17 93	93.07 279	86.91 22	96.41 38	96.26 132	88.30 114	88.37 189	94.85 173	82.19 110	97.64 217	91.09 114	82.95 340	94.96 249

mamv4		90.92 123	91.78 102	88.33 320	95.67 133	70.75 404	92.92 280	96.02 157	81.90 293	88.11 192	95.34 147	85.88 52	96.97 283	95.22 37	95.01 153	97.26 131

lupinMVS		90.92 123	90.21 130	93.03 102	93.86 244	83.88 115	92.81 284	93.86 293	79.84 331	91.76 126	94.29 200	77.92 166	98.04 184	90.48 129	97.11 100	97.17 138

RRT-MVS		90.85 125	90.70 124	91.30 200	94.25 223	76.83 327	94.85 156	96.13 145	89.04 87	90.23 153	94.88 169	70.15 280	98.72 113	91.86 104	94.88 156	98.34 44

h-mvs33		90.80 126	90.15 133	92.75 121	96.01 115	82.66 164	95.43 114	95.53 198	89.80 57	93.08 82	95.64 134	75.77 194	99.00 74	92.07 92	78.05 397	96.60 179

jason		90.80 126	90.10 134	92.90 110	93.04 282	83.53 127	93.08 270	94.15 282	80.22 325	91.41 135	94.91 167	76.87 176	97.93 197	90.28 130	96.90 107	97.24 133
jason: jason.
VDD-MVS		90.74 128	89.92 142	93.20 90	96.27 100	83.02 150	95.73 96	93.86 293	88.42 111	92.53 103	96.84 74	62.09 358	98.64 121	90.95 119	92.62 218	97.93 90

mamba_0404		90.73 129	90.08 135	92.69 126	95.00 167	83.13 141	94.32 196	95.00 237	85.41 201	89.84 161	95.35 145	76.13 185	97.98 190	85.46 195	94.18 175	96.95 158

PVSNet_Blended		90.73 129	90.32 128	91.98 164	96.12 106	81.25 202	92.55 291	96.83 77	82.04 288	89.10 173	92.56 265	81.04 125	98.85 97	86.72 177	95.91 131	95.84 216

AstraMVS		90.69 131	90.30 129	91.84 178	93.81 247	79.85 252	94.76 163	92.39 330	88.96 92	91.01 142	95.87 123	70.69 269	97.94 196	92.49 75	92.70 212	97.73 105

test_yl		90.69 131	90.02 140	92.71 123	95.72 129	82.41 174	94.11 208	95.12 227	85.63 191	91.49 133	94.70 177	74.75 209	98.42 147	86.13 185	92.53 220	97.31 124

DCV-MVSNet		90.69 131	90.02 140	92.71 123	95.72 129	82.41 174	94.11 208	95.12 227	85.63 191	91.49 133	94.70 177	74.75 209	98.42 147	86.13 185	92.53 220	97.31 124

API-MVS		90.66 134	90.07 136	92.45 141	96.36 98	84.57 89	96.06 68	95.22 224	82.39 278	89.13 172	94.27 203	80.32 130	98.46 138	80.16 289	96.71 114	94.33 281

xiu_mvs_v1_base_debu		90.64 135	90.05 137	92.40 142	93.97 240	84.46 95	93.32 255	95.46 202	85.17 208	92.25 108	94.03 208	70.59 271	98.57 129	90.97 116	94.67 161	94.18 284

xiu_mvs_v1_base		90.64 135	90.05 137	92.40 142	93.97 240	84.46 95	93.32 255	95.46 202	85.17 208	92.25 108	94.03 208	70.59 271	98.57 129	90.97 116	94.67 161	94.18 284

xiu_mvs_v1_base_debi		90.64 135	90.05 137	92.40 142	93.97 240	84.46 95	93.32 255	95.46 202	85.17 208	92.25 108	94.03 208	70.59 271	98.57 129	90.97 116	94.67 161	94.18 284

HQP_MVS		90.60 138	90.19 131	91.82 179	94.70 192	82.73 160	95.85 86	96.22 137	90.81 24	86.91 219	94.86 171	74.23 218	98.12 169	88.15 152	89.99 253	94.63 262

LuminaMVS		90.55 139	89.81 144	92.77 118	92.78 293	84.21 105	94.09 212	94.17 281	85.82 184	91.54 131	94.14 207	69.93 281	97.92 198	91.62 108	94.21 174	96.18 197

FIs		90.51 140	90.35 127	90.99 217	93.99 239	80.98 214	95.73 96	97.54 6	89.15 83	86.72 226	94.68 179	81.83 118	97.24 263	85.18 197	88.31 286	94.76 260

mamba_test_0407		90.47 141	89.80 145	92.46 139	94.76 183	82.66 164	93.98 224	95.00 237	85.41 201	88.96 177	95.35 145	76.13 185	97.88 201	85.46 195	93.15 201	96.85 167

mvsmamba		90.33 142	89.69 148	92.25 157	95.17 157	81.64 189	95.27 125	93.36 306	84.88 220	89.51 165	94.27 203	69.29 296	97.42 240	89.34 139	96.12 129	97.68 108

MAR-MVS		90.30 143	89.37 158	93.07 101	96.61 86	84.48 94	95.68 99	95.67 186	82.36 280	87.85 200	92.85 253	76.63 182	98.80 104	80.01 290	96.68 115	95.91 211
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
FC-MVSNet-test		90.27 144	90.18 132	90.53 232	93.71 256	79.85 252	95.77 92	97.59 4	89.31 76	86.27 237	94.67 182	81.93 117	97.01 281	84.26 214	88.09 289	94.71 261

CANet_DTU		90.26 145	89.41 157	92.81 115	93.46 266	83.01 151	93.48 247	94.47 266	89.43 71	87.76 205	94.23 205	70.54 275	99.03 64	84.97 199	96.39 122	96.38 187

SDMVSNet		90.19 146	89.61 151	91.93 169	96.00 116	83.09 146	92.89 281	95.98 158	88.73 99	86.85 223	95.20 156	72.09 254	97.08 274	88.90 145	89.85 259	95.63 226

Elysia		90.12 147	89.10 165	93.18 91	93.16 273	84.05 110	95.22 129	96.27 128	85.16 211	90.59 146	94.68 179	64.64 341	98.37 149	86.38 181	95.77 133	97.12 144

StellarMVS		90.12 147	89.10 165	93.18 91	93.16 273	84.05 110	95.22 129	96.27 128	85.16 211	90.59 146	94.68 179	64.64 341	98.37 149	86.38 181	95.77 133	97.12 144

OPM-MVS		90.12 147	89.56 152	91.82 179	93.14 275	83.90 114	94.16 204	95.74 180	88.96 92	87.86 199	95.43 143	72.48 249	97.91 199	88.10 156	90.18 251	93.65 319
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LFMVS		90.08 150	89.13 164	92.95 108	96.71 82	82.32 176	96.08 64	89.91 398	86.79 160	92.15 113	96.81 77	62.60 356	98.34 154	87.18 169	93.90 179	98.19 67

GeoE		90.05 151	89.43 156	91.90 174	95.16 158	80.37 233	95.80 89	94.65 261	83.90 241	87.55 209	94.75 176	78.18 162	97.62 219	81.28 269	93.63 184	97.71 107

viewmambaseed2359dif		90.04 152	89.78 146	90.83 223	92.85 290	77.92 300	92.23 303	95.01 233	81.90 293	90.20 154	95.45 140	79.64 144	97.34 253	87.52 164	93.17 199	97.23 136

PAPR		90.02 153	89.27 163	92.29 154	95.78 127	80.95 216	92.68 286	96.22 137	81.91 292	86.66 227	93.75 228	82.23 108	98.44 144	79.40 301	94.79 158	97.48 119

PVSNet_BlendedMVS		89.98 154	89.70 147	90.82 225	96.12 106	81.25 202	93.92 228	96.83 77	83.49 254	89.10 173	92.26 275	81.04 125	98.85 97	86.72 177	87.86 293	92.35 368

icg_test_0403		89.97 155	89.64 149	90.96 220	93.72 252	77.75 311	93.00 275	95.34 216	85.53 196	88.77 182	94.49 191	78.49 158	97.84 202	84.75 204	92.65 213	97.28 127

PS-MVSNAJss		89.97 155	89.62 150	91.02 214	91.90 318	80.85 220	95.26 126	95.98 158	86.26 175	86.21 239	94.29 200	79.70 139	97.65 215	88.87 147	88.10 287	94.57 267

XVG-OURS-SEG-HR		89.95 157	89.45 154	91.47 193	94.00 238	81.21 205	91.87 314	96.06 153	85.78 186	88.55 185	95.73 131	74.67 213	97.27 259	88.71 148	89.64 264	95.91 211

UGNet		89.95 157	88.95 171	92.95 108	94.51 206	83.31 134	95.70 98	95.23 222	89.37 73	87.58 207	93.94 216	64.00 346	98.78 107	83.92 219	96.31 124	96.74 174
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
UniMVSNet_NR-MVSNet		89.92 159	89.29 161	91.81 181	93.39 268	83.72 119	94.43 185	97.12 50	89.80 57	86.46 230	93.32 237	83.16 90	97.23 264	84.92 200	81.02 370	94.49 275

AdaColmap		89.89 160	89.07 167	92.37 146	97.41 67	83.03 149	94.42 186	95.92 164	82.81 272	86.34 236	94.65 184	73.89 227	99.02 67	80.69 280	95.51 139	95.05 244

hse-mvs2		89.88 161	89.34 159	91.51 190	94.83 180	81.12 209	93.94 226	93.91 292	89.80 57	93.08 82	93.60 231	75.77 194	97.66 214	92.07 92	77.07 404	95.74 221

icg_test_0407		89.85 162	89.51 153	90.88 222	93.72 252	77.75 311	93.07 272	95.34 216	85.53 196	88.34 190	94.49 191	77.69 170	97.60 220	84.75 204	92.65 213	97.28 127

UniMVSNet (Re)		89.80 163	89.07 167	92.01 160	93.60 262	84.52 92	94.78 161	97.47 13	89.26 79	86.44 233	92.32 272	82.10 112	97.39 251	84.81 203	80.84 374	94.12 288

HQP-MVS		89.80 163	89.28 162	91.34 198	94.17 227	81.56 190	94.39 189	96.04 154	88.81 95	85.43 265	93.97 215	73.83 229	97.96 193	87.11 172	89.77 262	94.50 273

FA-MVS(test-final)		89.66 165	88.91 173	91.93 169	94.57 202	80.27 234	91.36 326	94.74 257	84.87 221	89.82 162	92.61 264	74.72 212	98.47 137	83.97 218	93.53 187	97.04 150

VPA-MVSNet		89.62 166	88.96 170	91.60 187	93.86 244	82.89 155	95.46 112	97.33 28	87.91 128	88.43 188	93.31 238	74.17 221	97.40 248	87.32 168	82.86 345	94.52 270

WTY-MVS		89.60 167	88.92 172	91.67 185	95.47 144	81.15 207	92.38 295	94.78 255	83.11 264	89.06 175	94.32 198	78.67 154	96.61 304	81.57 265	90.89 240	97.24 133

Vis-MVSNet (Re-imp)		89.59 168	89.44 155	90.03 259	95.74 128	75.85 342	95.61 107	90.80 380	87.66 141	87.83 202	95.40 144	76.79 178	96.46 318	78.37 307	96.73 113	97.80 100

VDDNet		89.56 169	88.49 186	92.76 119	95.07 162	82.09 179	96.30 42	93.19 310	81.05 319	91.88 121	96.86 73	61.16 374	98.33 156	88.43 151	92.49 222	97.84 98

114514_t		89.51 170	88.50 184	92.54 135	98.11 38	81.99 181	95.16 137	96.36 120	70.19 425	85.81 247	95.25 151	76.70 180	98.63 123	82.07 253	96.86 110	97.00 155

QAPM		89.51 170	88.15 195	93.59 79	94.92 173	84.58 88	96.82 30	96.70 95	78.43 355	83.41 323	96.19 104	73.18 240	99.30 44	77.11 323	96.54 118	96.89 164

CLD-MVS		89.47 172	88.90 174	91.18 205	94.22 225	82.07 180	92.13 307	96.09 149	87.90 129	85.37 271	92.45 268	74.38 216	97.56 224	87.15 170	90.43 246	93.93 297
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 173	88.90 174	91.12 206	94.47 208	81.49 194	95.30 120	96.14 142	86.73 163	85.45 262	95.16 158	69.89 283	98.10 171	87.70 160	89.23 271	93.77 312

CDS-MVSNet		89.45 173	88.51 183	92.29 154	93.62 261	83.61 126	93.01 274	94.68 260	81.95 290	87.82 203	93.24 242	78.69 153	96.99 282	80.34 286	93.23 198	96.28 192
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		89.41 175	88.64 179	91.71 184	94.74 186	80.81 221	93.54 245	95.10 229	83.11 264	86.82 225	90.67 337	79.74 138	97.75 210	80.51 284	93.55 186	96.57 182

ab-mvs		89.41 175	88.35 188	92.60 130	95.15 160	82.65 168	92.20 305	95.60 193	83.97 240	88.55 185	93.70 230	74.16 222	98.21 165	82.46 243	89.37 267	96.94 160

XVG-OURS		89.40 177	88.70 178	91.52 189	94.06 232	81.46 196	91.27 330	96.07 151	86.14 179	88.89 180	95.77 129	68.73 305	97.26 261	87.39 166	89.96 255	95.83 217

test_vis1_n_1920		89.39 178	89.84 143	88.04 329	92.97 286	72.64 381	94.71 167	96.03 156	86.18 177	91.94 120	96.56 92	61.63 362	95.74 355	93.42 58	95.11 152	95.74 221

mvs_anonymous		89.37 179	89.32 160	89.51 288	93.47 265	74.22 360	91.65 321	94.83 251	82.91 270	85.45 262	93.79 224	81.23 124	96.36 325	86.47 179	94.09 176	97.94 88

DU-MVS		89.34 180	88.50 184	91.85 177	93.04 282	83.72 119	94.47 182	96.59 102	89.50 68	86.46 230	93.29 240	77.25 174	97.23 264	84.92 200	81.02 370	94.59 265

TAMVS		89.21 181	88.29 192	91.96 166	93.71 256	82.62 169	93.30 259	94.19 279	82.22 283	87.78 204	93.94 216	78.83 150	96.95 285	77.70 316	92.98 206	96.32 189

icg_test_0407_2		89.15 182	88.97 169	89.68 281	93.72 252	77.75 311	88.26 392	95.34 216	85.53 196	88.34 190	94.49 191	77.69 170	93.99 391	84.75 204	92.65 213	97.28 127

ACMM	84.12 9	89.14 183	88.48 187	91.12 206	94.65 195	81.22 204	95.31 118	96.12 146	85.31 205	85.92 245	94.34 196	70.19 279	98.06 183	85.65 191	88.86 276	94.08 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		89.10 184	88.64 179	90.48 238	95.53 142	74.97 351	96.08 64	84.89 431	88.13 122	90.16 157	96.65 84	63.29 351	98.10 171	86.14 183	96.90 107	98.39 41

EI-MVSNet		89.10 184	88.86 176	89.80 273	91.84 320	78.30 291	93.70 241	95.01 233	85.73 188	87.15 214	95.28 149	79.87 136	97.21 266	83.81 221	87.36 301	93.88 301

ECVR-MVS		89.09 186	88.53 182	90.77 227	95.62 137	75.89 341	96.16 55	84.22 433	87.89 131	90.20 154	96.65 84	63.19 353	98.10 171	85.90 188	96.94 105	98.33 46

CNLPA		89.07 187	87.98 199	92.34 149	96.87 79	84.78 84	94.08 213	93.24 307	81.41 310	84.46 292	95.13 160	75.57 201	96.62 301	77.21 321	93.84 181	95.61 228

mamba_0408		89.06 188	87.92 202	92.50 137	94.76 183	82.66 164	79.84 444	94.64 262	85.18 206	88.96 177	95.00 163	76.00 190	97.98 190	83.74 223	93.15 201	96.85 167

PLC	84.53 7	89.06 188	88.03 197	92.15 158	97.27 73	82.69 163	94.29 197	95.44 207	79.71 333	84.01 308	94.18 206	76.68 181	98.75 109	77.28 320	93.41 192	95.02 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_djsdf		89.03 190	88.64 179	90.21 249	90.74 369	79.28 269	95.96 77	95.90 167	84.66 229	85.33 273	92.94 252	74.02 224	97.30 255	89.64 136	88.53 279	94.05 294

HY-MVS	83.01 12	89.03 190	87.94 201	92.29 154	94.86 178	82.77 156	92.08 310	94.49 265	81.52 309	86.93 217	92.79 259	78.32 161	98.23 162	79.93 291	90.55 244	95.88 214

ACMP	84.23 8	89.01 192	88.35 188	90.99 217	94.73 187	81.27 201	95.07 141	95.89 169	86.48 168	83.67 316	94.30 199	69.33 292	97.99 188	87.10 174	88.55 278	93.72 317
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss		88.93 193	88.26 194	90.94 221	94.05 233	80.78 222	91.71 318	95.38 211	81.55 308	88.63 184	93.91 220	75.04 206	95.47 367	82.47 242	91.61 228	96.57 182

TranMVSNet+NR-MVSNet		88.84 194	87.95 200	91.49 191	92.68 296	83.01 151	94.92 150	96.31 123	89.88 51	85.53 256	93.85 223	76.63 182	96.96 284	81.91 257	79.87 387	94.50 273

CHOSEN 1792x2688		88.84 194	87.69 207	92.30 153	96.14 104	81.42 198	90.01 362	95.86 172	74.52 394	87.41 210	93.94 216	75.46 202	98.36 151	80.36 285	95.53 138	97.12 144

MVSTER		88.84 194	88.29 192	90.51 235	92.95 287	80.44 231	93.73 238	95.01 233	84.66 229	87.15 214	93.12 247	72.79 244	97.21 266	87.86 158	87.36 301	93.87 302

test_cas_vis1_n_1920		88.83 197	88.85 177	88.78 304	91.15 348	76.72 329	93.85 232	94.93 243	83.23 263	92.81 91	96.00 113	61.17 373	94.45 381	91.67 107	94.84 157	95.17 240

OpenMVS	83.78 11	88.74 198	87.29 217	93.08 99	92.70 295	85.39 73	96.57 36	96.43 113	78.74 350	80.85 355	96.07 110	69.64 287	99.01 69	78.01 314	96.65 116	94.83 257

thisisatest0530		88.67 199	87.61 209	91.86 175	94.87 177	80.07 241	94.63 171	89.90 399	84.00 239	88.46 187	93.78 225	66.88 320	98.46 138	83.30 228	92.65 213	97.06 148

Effi-MVS+-dtu		88.65 200	88.35 188	89.54 285	93.33 269	76.39 335	94.47 182	94.36 272	87.70 138	85.43 265	89.56 367	73.45 234	97.26 261	85.57 193	91.28 232	94.97 246

tttt0517		88.61 201	87.78 206	91.11 209	94.96 170	77.81 306	95.35 116	89.69 402	85.09 215	88.05 197	94.59 188	66.93 318	98.48 134	83.27 229	92.13 225	97.03 151

BH-untuned		88.60 202	88.13 196	90.01 262	95.24 154	78.50 285	93.29 260	94.15 282	84.75 226	84.46 292	93.40 234	75.76 196	97.40 248	77.59 317	94.52 168	94.12 288

sd_testset		88.59 203	87.85 205	90.83 223	96.00 116	80.42 232	92.35 297	94.71 258	88.73 99	86.85 223	95.20 156	67.31 312	96.43 320	79.64 295	89.85 259	95.63 226

NR-MVSNet		88.58 204	87.47 213	91.93 169	93.04 282	84.16 107	94.77 162	96.25 134	89.05 86	80.04 369	93.29 240	79.02 149	97.05 279	81.71 264	80.05 384	94.59 265

mamba_test_0407_2		88.57 205	87.92 202	90.51 235	94.76 183	82.66 164	79.84 444	94.64 262	85.18 206	88.96 177	95.00 163	76.00 190	92.03 415	83.74 223	93.15 201	96.85 167

VortexMVS		88.42 206	88.01 198	89.63 282	93.89 243	78.82 275	93.82 233	95.47 201	86.67 165	84.53 290	91.99 289	72.62 247	96.65 299	89.02 144	84.09 326	93.41 329

1112_ss		88.42 206	87.33 216	91.72 183	94.92 173	80.98 214	92.97 278	94.54 264	78.16 361	83.82 311	93.88 221	78.78 152	97.91 199	79.45 297	89.41 266	96.26 193

WR-MVS		88.38 208	87.67 208	90.52 234	93.30 270	80.18 236	93.26 262	95.96 161	88.57 107	85.47 261	92.81 257	76.12 187	96.91 288	81.24 270	82.29 350	94.47 278

BH-RMVSNet		88.37 209	87.48 212	91.02 214	95.28 150	79.45 261	92.89 281	93.07 313	85.45 200	86.91 219	94.84 174	70.35 276	97.76 206	73.97 354	94.59 165	95.85 215

IterMVS-LS		88.36 210	87.91 204	89.70 277	93.80 248	78.29 292	93.73 238	95.08 231	85.73 188	84.75 283	91.90 293	79.88 135	96.92 287	83.83 220	82.51 346	93.89 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
X-MVStestdata		88.31 211	86.13 260	94.85 25	98.54 13	86.60 34	96.93 23	97.19 39	90.66 31	92.85 88	23.41 458	85.02 64	99.49 26	91.99 97	98.56 50	98.47 34

LCM-MVSNet-Re		88.30 212	88.32 191	88.27 322	94.71 191	72.41 386	93.15 265	90.98 373	87.77 136	79.25 379	91.96 290	78.35 160	95.75 354	83.04 231	95.62 137	96.65 178

jajsoiax		88.24 213	87.50 211	90.48 238	90.89 362	80.14 238	95.31 118	95.65 190	84.97 218	84.24 303	94.02 211	65.31 337	97.42 240	88.56 149	88.52 280	93.89 298

VPNet		88.20 214	87.47 213	90.39 243	93.56 263	79.46 260	94.04 217	95.54 197	88.67 102	86.96 216	94.58 189	69.33 292	97.15 268	84.05 217	80.53 379	94.56 268

TAPA-MVS	84.62 6	88.16 215	87.01 225	91.62 186	96.64 85	80.65 224	94.39 189	96.21 140	76.38 374	86.19 240	95.44 141	79.75 137	98.08 181	62.75 421	95.29 148	96.13 200
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		88.10 216	87.28 218	90.57 230	94.96 170	80.07 241	94.27 198	91.29 366	86.74 162	87.41 210	94.00 213	76.77 179	96.20 331	80.77 278	79.31 393	95.44 230

Anonymous20240529		88.09 217	86.59 242	92.58 132	96.53 92	81.92 185	95.99 74	95.84 173	74.11 398	89.06 175	95.21 155	61.44 366	98.81 103	83.67 226	87.47 298	97.01 154

HyFIR lowres test		88.09 217	86.81 230	91.93 169	96.00 116	80.63 225	90.01 362	95.79 176	73.42 405	87.68 206	92.10 283	73.86 228	97.96 193	80.75 279	91.70 227	97.19 137

mvs_tets		88.06 219	87.28 218	90.38 245	90.94 358	79.88 250	95.22 129	95.66 188	85.10 214	84.21 304	93.94 216	63.53 349	97.40 248	88.50 150	88.40 284	93.87 302

F-COLMAP		87.95 220	86.80 231	91.40 195	96.35 99	80.88 219	94.73 165	95.45 205	79.65 334	82.04 342	94.61 185	71.13 261	98.50 132	76.24 333	91.05 238	94.80 259

LS3D		87.89 221	86.32 253	92.59 131	96.07 113	82.92 154	95.23 127	94.92 244	75.66 381	82.89 330	95.98 115	72.48 249	99.21 49	68.43 391	95.23 151	95.64 225

anonymousdsp		87.84 222	87.09 221	90.12 254	89.13 398	80.54 229	94.67 169	95.55 195	82.05 286	83.82 311	92.12 280	71.47 259	97.15 268	87.15 170	87.80 296	92.67 356

v2v482		87.84 222	87.06 222	90.17 250	90.99 354	79.23 272	94.00 222	95.13 226	84.87 221	85.53 256	92.07 286	74.45 215	97.45 235	84.71 209	81.75 358	93.85 305

WR-MVS_H		87.80 224	87.37 215	89.10 297	93.23 271	78.12 295	95.61 107	97.30 32	87.90 129	83.72 314	92.01 288	79.65 143	96.01 340	76.36 330	80.54 378	93.16 340

AUN-MVS		87.78 225	86.54 245	91.48 192	94.82 181	81.05 212	93.91 230	93.93 289	83.00 267	86.93 217	93.53 232	69.50 290	97.67 212	86.14 183	77.12 403	95.73 223

PCF-MVS	84.11 10	87.74 226	86.08 264	92.70 125	94.02 234	84.43 98	89.27 375	95.87 171	73.62 403	84.43 294	94.33 197	78.48 159	98.86 95	70.27 377	94.45 170	94.81 258
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous202405211		87.68 227	86.13 260	92.31 152	96.66 84	80.74 223	94.87 153	91.49 361	80.47 324	89.46 168	95.44 141	54.72 410	98.23 162	82.19 249	89.89 257	97.97 86

V42		87.68 227	86.86 227	90.15 252	90.58 374	80.14 238	94.24 201	95.28 220	83.66 248	85.67 251	91.33 309	74.73 211	97.41 246	84.43 213	81.83 356	92.89 350

thres600view7		87.65 229	86.67 237	90.59 229	96.08 112	78.72 276	94.88 152	91.58 357	87.06 153	88.08 195	92.30 273	68.91 302	98.10 171	70.05 384	91.10 233	94.96 249

XXY-MVS		87.65 229	86.85 228	90.03 259	92.14 308	80.60 227	93.76 236	95.23 222	82.94 269	84.60 286	94.02 211	74.27 217	95.49 366	81.04 272	83.68 332	94.01 296

Test_1112_low_res		87.65 229	86.51 246	91.08 210	94.94 172	79.28 269	91.77 316	94.30 274	76.04 379	83.51 321	92.37 270	77.86 168	97.73 211	78.69 306	89.13 273	96.22 194

thres100view900		87.63 232	86.71 234	90.38 245	96.12 106	78.55 282	95.03 144	91.58 357	87.15 150	88.06 196	92.29 274	68.91 302	98.10 171	70.13 381	91.10 233	94.48 276

CP-MVSNet		87.63 232	87.26 220	88.74 308	93.12 276	76.59 332	95.29 122	96.58 103	88.43 110	83.49 322	92.98 251	75.28 203	95.83 349	78.97 303	81.15 366	93.79 307

thres400		87.62 234	86.64 238	90.57 230	95.99 119	78.64 279	94.58 173	91.98 346	86.94 157	88.09 193	91.77 295	69.18 298	98.10 171	70.13 381	91.10 233	94.96 249

v1144		87.61 235	86.79 232	90.06 258	91.01 353	79.34 265	93.95 225	95.42 210	83.36 259	85.66 252	91.31 312	74.98 207	97.42 240	83.37 227	82.06 352	93.42 328

ICG_test_0404		87.60 236	86.84 229	89.89 266	93.72 252	77.75 311	88.56 387	95.34 216	85.53 196	79.98 370	94.49 191	66.54 328	94.64 380	84.75 204	92.65 213	97.28 127

tfpn200view9		87.58 237	86.64 238	90.41 242	95.99 119	78.64 279	94.58 173	91.98 346	86.94 157	88.09 193	91.77 295	69.18 298	98.10 171	70.13 381	91.10 233	94.48 276

BH-w/o		87.57 238	87.05 223	89.12 296	94.90 176	77.90 302	92.41 293	93.51 303	82.89 271	83.70 315	91.34 308	75.75 197	97.07 276	75.49 338	93.49 189	92.39 366

UniMVSNet_ETH3D		87.53 239	86.37 250	91.00 216	92.44 301	78.96 274	94.74 164	95.61 192	84.07 238	85.36 272	94.52 190	59.78 382	97.34 253	82.93 233	87.88 292	96.71 175

ET-MVSNet_ETH3D		87.51 240	85.91 272	92.32 151	93.70 258	83.93 113	92.33 299	90.94 376	84.16 235	72.09 424	92.52 266	69.90 282	95.85 348	89.20 141	88.36 285	97.17 138

1314		87.51 240	86.57 243	90.34 247	92.42 302	79.74 255	92.63 288	95.35 215	78.35 356	80.14 366	91.62 303	74.05 223	97.15 268	81.05 271	93.53 187	94.12 288

v8		87.50 242	86.71 234	89.89 266	91.37 338	79.40 262	94.50 178	95.38 211	84.81 224	83.60 319	91.33 309	76.05 188	97.42 240	82.84 236	80.51 381	92.84 352

Fast-Effi-MVS+-dtu		87.44 243	86.72 233	89.63 282	92.04 312	77.68 316	94.03 218	93.94 288	85.81 185	82.42 335	91.32 311	70.33 277	97.06 277	80.33 287	90.23 250	94.14 287

MVS		87.44 243	86.10 263	91.44 194	92.61 297	83.62 124	92.63 288	95.66 188	67.26 431	81.47 347	92.15 278	77.95 165	98.22 164	79.71 293	95.48 141	92.47 362

FE-MVS		87.40 245	86.02 266	91.57 188	94.56 203	79.69 256	90.27 349	93.72 299	80.57 322	88.80 181	91.62 303	65.32 336	98.59 128	74.97 346	94.33 173	96.44 185

FMVSNet3		87.40 245	86.11 262	91.30 200	93.79 250	83.64 123	94.20 203	94.81 253	83.89 242	84.37 295	91.87 294	68.45 308	96.56 309	78.23 311	85.36 315	93.70 318

test_fmvs1		87.34 247	87.56 210	86.68 368	90.59 373	71.80 390	94.01 220	94.04 287	78.30 357	91.97 117	95.22 152	56.28 401	93.71 397	92.89 67	94.71 160	94.52 270

thisisatest0515		87.33 248	85.99 267	91.37 197	93.49 264	79.55 257	90.63 344	89.56 407	80.17 326	87.56 208	90.86 327	67.07 317	98.28 160	81.50 266	93.02 205	96.29 191

PS-CasMVS		87.32 249	86.88 226	88.63 311	92.99 285	76.33 337	95.33 117	96.61 101	88.22 118	83.30 327	93.07 249	73.03 242	95.79 353	78.36 308	81.00 372	93.75 314

GBi-Net		87.26 250	85.98 268	91.08 210	94.01 235	83.10 143	95.14 138	94.94 239	83.57 250	84.37 295	91.64 299	66.59 325	96.34 326	78.23 311	85.36 315	93.79 307

test1		87.26 250	85.98 268	91.08 210	94.01 235	83.10 143	95.14 138	94.94 239	83.57 250	84.37 295	91.64 299	66.59 325	96.34 326	78.23 311	85.36 315	93.79 307

v1192		87.25 252	86.33 252	90.00 263	90.76 368	79.04 273	93.80 234	95.48 200	82.57 276	85.48 260	91.18 316	73.38 238	97.42 240	82.30 246	82.06 352	93.53 322

v10		87.25 252	86.38 249	89.85 268	91.19 344	79.50 258	94.48 179	95.45 205	83.79 246	83.62 318	91.19 314	75.13 204	97.42 240	81.94 256	80.60 376	92.63 358

DP-MVS		87.25 252	85.36 289	92.90 110	97.65 60	83.24 136	94.81 159	92.00 344	74.99 389	81.92 344	95.00 163	72.66 245	99.05 61	66.92 403	92.33 223	96.40 186

miper_ehance_all_eth		87.22 255	86.62 241	89.02 300	92.13 309	77.40 320	90.91 339	94.81 253	81.28 313	84.32 300	90.08 353	79.26 146	96.62 301	83.81 221	82.94 341	93.04 345

test2506		87.21 256	86.28 255	90.02 261	95.62 137	73.64 367	96.25 50	71.38 456	87.89 131	90.45 149	96.65 84	55.29 407	98.09 179	86.03 187	96.94 105	98.33 46

thres200		87.21 256	86.24 257	90.12 254	95.36 146	78.53 283	93.26 262	92.10 340	86.42 171	88.00 198	91.11 320	69.24 297	98.00 187	69.58 385	91.04 239	93.83 306

v144192		87.19 258	86.35 251	89.74 274	90.64 372	78.24 293	93.92 228	95.43 208	81.93 291	85.51 258	91.05 323	74.21 220	97.45 235	82.86 235	81.56 360	93.53 322

FMVSNet2		87.19 258	85.82 275	91.30 200	94.01 235	83.67 121	94.79 160	94.94 239	83.57 250	83.88 310	92.05 287	66.59 325	96.51 313	77.56 318	85.01 318	93.73 316

c3_l		87.14 260	86.50 247	89.04 299	92.20 306	77.26 321	91.22 333	94.70 259	82.01 289	84.34 299	90.43 342	78.81 151	96.61 304	83.70 225	81.09 367	93.25 334

testing91		87.11 261	86.18 258	89.92 265	94.43 213	75.38 350	91.53 323	92.27 336	86.48 168	86.50 228	90.24 345	61.19 372	97.53 226	82.10 251	90.88 241	96.84 170

Baseline_NR-MVSNet		87.07 262	86.63 240	88.40 315	91.44 333	77.87 304	94.23 202	92.57 327	84.12 237	85.74 250	92.08 284	77.25 174	96.04 336	82.29 247	79.94 385	91.30 391

v148		87.04 263	86.32 253	89.21 293	90.94 358	77.26 321	93.71 240	94.43 267	84.84 223	84.36 298	90.80 331	76.04 189	97.05 279	82.12 250	79.60 390	93.31 331

test_fmvs1_n		87.03 264	87.04 224	86.97 359	89.74 392	71.86 388	94.55 175	94.43 267	78.47 353	91.95 119	95.50 139	51.16 421	93.81 395	93.02 66	94.56 166	95.26 237

v1921920		86.97 265	86.06 265	89.69 278	90.53 377	78.11 296	93.80 234	95.43 208	81.90 293	85.33 273	91.05 323	72.66 245	97.41 246	82.05 254	81.80 357	93.53 322

tt0805		86.92 266	85.74 281	90.48 238	92.22 305	79.98 248	95.63 106	94.88 247	83.83 244	84.74 284	92.80 258	57.61 396	97.67 212	85.48 194	84.42 322	93.79 307

miper_enhance_ethall		86.90 267	86.18 258	89.06 298	91.66 329	77.58 318	90.22 355	94.82 252	79.16 340	84.48 291	89.10 372	79.19 148	96.66 298	84.06 216	82.94 341	92.94 348

MonoMVSNet		86.89 268	86.55 244	87.92 333	89.46 396	73.75 364	94.12 206	93.10 311	87.82 135	85.10 276	90.76 333	69.59 288	94.94 378	86.47 179	82.50 347	95.07 243

v7n		86.81 269	85.76 279	89.95 264	90.72 370	79.25 271	95.07 141	95.92 164	84.45 232	82.29 336	90.86 327	72.60 248	97.53 226	79.42 300	80.52 380	93.08 344

PEN-MVS		86.80 270	86.27 256	88.40 315	92.32 304	75.71 345	95.18 135	96.38 118	87.97 126	82.82 331	93.15 245	73.39 237	95.92 344	76.15 334	79.03 395	93.59 320

cl22		86.78 271	85.98 268	89.18 295	92.34 303	77.62 317	90.84 340	94.13 284	81.33 312	83.97 309	90.15 350	73.96 225	96.60 306	84.19 215	82.94 341	93.33 330

v1240		86.78 271	85.85 274	89.56 284	90.45 379	77.79 308	93.61 243	95.37 213	81.65 303	85.43 265	91.15 318	71.50 258	97.43 239	81.47 267	82.05 354	93.47 326

TR-MVS		86.78 271	85.76 279	89.82 270	94.37 216	78.41 287	92.47 292	92.83 319	81.11 318	86.36 234	92.40 269	68.73 305	97.48 231	73.75 358	89.85 259	93.57 321

PatchMatch-RL		86.77 274	85.54 283	90.47 241	95.88 123	82.71 162	90.54 346	92.31 334	79.82 332	84.32 300	91.57 307	68.77 304	96.39 322	73.16 360	93.48 191	92.32 369

testing3-2		86.72 275	86.71 234	86.74 367	96.11 109	65.92 426	93.39 252	89.65 405	89.46 69	87.84 201	92.79 259	59.17 388	97.60 220	81.31 268	90.72 242	96.70 176

testing99		86.72 275	85.73 282	89.69 278	94.23 224	74.91 353	91.35 327	90.97 374	86.14 179	86.36 234	90.22 346	59.41 385	97.48 231	82.24 248	90.66 243	96.69 177

PAPM		86.68 277	85.39 287	90.53 232	93.05 281	79.33 268	89.79 365	94.77 256	78.82 347	81.95 343	93.24 242	76.81 177	97.30 255	66.94 401	93.16 200	94.95 253

pm-mvs1		86.61 278	85.54 283	89.82 270	91.44 333	80.18 236	95.28 124	94.85 249	83.84 243	81.66 345	92.62 263	72.45 251	96.48 315	79.67 294	78.06 396	92.82 353

GA-MVS		86.61 278	85.27 292	90.66 228	91.33 341	78.71 278	90.40 348	93.81 296	85.34 204	85.12 275	89.57 366	61.25 369	97.11 273	80.99 275	89.59 265	96.15 198

Anonymous20231211		86.59 280	85.13 295	90.98 219	96.52 93	81.50 192	96.14 59	96.16 141	73.78 401	83.65 317	92.15 278	63.26 352	97.37 252	82.82 237	81.74 359	94.06 293

test_vis1_n		86.56 281	86.49 248	86.78 366	88.51 403	72.69 378	94.68 168	93.78 298	79.55 335	90.70 144	95.31 148	48.75 427	93.28 403	93.15 62	93.99 177	94.38 280

DIV-MVS_self_test		86.53 282	85.78 276	88.75 306	92.02 314	76.45 334	90.74 341	94.30 274	81.83 299	83.34 325	90.82 330	75.75 197	96.57 307	81.73 263	81.52 362	93.24 335

cl____		86.52 283	85.78 276	88.75 306	92.03 313	76.46 333	90.74 341	94.30 274	81.83 299	83.34 325	90.78 332	75.74 199	96.57 307	81.74 262	81.54 361	93.22 336

eth_miper_zixun_eth		86.50 284	85.77 278	88.68 309	91.94 315	75.81 343	90.47 347	94.89 245	82.05 286	84.05 306	90.46 341	75.96 192	96.77 292	82.76 239	79.36 392	93.46 327

baseline2		86.50 284	85.39 287	89.84 269	91.12 349	76.70 330	91.88 313	88.58 411	82.35 281	79.95 371	90.95 325	73.42 236	97.63 218	80.27 288	89.95 256	95.19 239

EPNet_dtu		86.49 286	85.94 271	88.14 327	90.24 382	72.82 376	94.11 208	92.20 338	86.66 166	79.42 378	92.36 271	73.52 232	95.81 351	71.26 369	93.66 183	95.80 219
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing11		86.44 287	85.35 290	89.69 278	94.29 222	75.40 349	91.30 328	90.53 384	84.76 225	85.06 277	90.13 351	58.95 391	97.45 235	82.08 252	91.09 237	96.21 196

cascas		86.43 288	84.98 298	90.80 226	92.10 311	80.92 218	90.24 353	95.91 166	73.10 408	83.57 320	88.39 385	65.15 338	97.46 234	84.90 202	91.43 230	94.03 295

reproduce_monomvs		86.37 289	85.87 273	87.87 334	93.66 260	73.71 365	93.44 250	95.02 232	88.61 105	82.64 334	91.94 291	57.88 395	96.68 297	89.96 132	79.71 389	93.22 336

SCA		86.32 290	85.18 294	89.73 276	92.15 307	76.60 331	91.12 334	91.69 353	83.53 253	85.50 259	88.81 378	66.79 321	96.48 315	76.65 326	90.35 248	96.12 201

LTVRE_ROB	82.13 13	86.26 291	84.90 301	90.34 247	94.44 212	81.50 192	92.31 301	94.89 245	83.03 266	79.63 376	92.67 261	69.69 286	97.79 204	71.20 370	86.26 310	91.72 379
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
DTE-MVSNet		86.11 292	85.48 285	87.98 330	91.65 330	74.92 352	94.93 149	95.75 179	87.36 146	82.26 337	93.04 250	72.85 243	95.82 350	74.04 353	77.46 401	93.20 338

XVG-ACMP-BASELINE		86.00 293	84.84 303	89.45 289	91.20 343	78.00 298	91.70 319	95.55 195	85.05 216	82.97 329	92.25 276	54.49 411	97.48 231	82.93 233	87.45 300	92.89 350

MVP-Stereo		85.97 294	84.86 302	89.32 291	90.92 360	82.19 178	92.11 308	94.19 279	78.76 349	78.77 385	91.63 302	68.38 309	96.56 309	75.01 345	93.95 178	89.20 419
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		85.90 295	85.09 296	88.35 317	90.79 365	77.42 319	91.83 315	95.70 184	80.77 321	80.08 368	90.02 355	66.74 323	96.37 323	81.88 258	87.97 291	91.26 392

test-LLR		85.87 296	85.41 286	87.25 351	90.95 356	71.67 393	89.55 369	89.88 400	83.41 256	84.54 288	87.95 392	67.25 314	95.11 374	81.82 259	93.37 194	94.97 246

FMVSNet1		85.85 297	84.11 317	91.08 210	92.81 291	83.10 143	95.14 138	94.94 239	81.64 304	82.68 332	91.64 299	59.01 390	96.34 326	75.37 340	83.78 329	93.79 307

PatchmatchNet		85.85 297	84.70 305	89.29 292	91.76 324	75.54 346	88.49 388	91.30 365	81.63 305	85.05 278	88.70 382	71.71 255	96.24 330	74.61 350	89.05 274	96.08 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
myMVS_eth3d28		85.80 299	85.26 293	87.42 346	94.73 187	69.92 411	90.60 345	90.95 375	87.21 149	86.06 243	90.04 354	59.47 383	96.02 338	74.89 347	93.35 196	96.33 188

CostFormer		85.77 300	84.94 300	88.26 323	91.16 347	72.58 384	89.47 373	91.04 372	76.26 377	86.45 232	89.97 357	70.74 268	96.86 291	82.35 245	87.07 306	95.34 236

PMMVS		85.71 301	84.96 299	87.95 331	88.90 401	77.09 323	88.68 385	90.06 394	72.32 415	86.47 229	90.76 333	72.15 253	94.40 383	81.78 261	93.49 189	92.36 367

PVSNet	78.82 18	85.55 302	84.65 306	88.23 325	94.72 189	71.93 387	87.12 409	92.75 323	78.80 348	84.95 280	90.53 339	64.43 344	96.71 296	74.74 348	93.86 180	96.06 207

UBG		85.51 303	84.57 310	88.35 317	94.21 226	71.78 391	90.07 360	89.66 404	82.28 282	85.91 246	89.01 374	61.30 367	97.06 277	76.58 329	92.06 226	96.22 194

IterMVS-SCA-FT		85.45 304	84.53 311	88.18 326	91.71 326	76.87 326	90.19 357	92.65 326	85.40 203	81.44 348	90.54 338	66.79 321	95.00 377	81.04 272	81.05 368	92.66 357

pmmvs4		85.43 305	83.86 322	90.16 251	90.02 387	82.97 153	90.27 349	92.67 325	75.93 380	80.73 357	91.74 297	71.05 262	95.73 356	78.85 305	83.46 336	91.78 378

mvsany_test1		85.42 306	85.30 291	85.77 380	87.95 415	75.41 348	87.61 406	80.97 441	76.82 371	88.68 183	95.83 125	77.44 173	90.82 427	85.90 188	86.51 308	91.08 399

ACMH	80.38 17	85.36 307	83.68 324	90.39 243	94.45 211	80.63 225	94.73 165	94.85 249	82.09 285	77.24 394	92.65 262	60.01 380	97.58 222	72.25 365	84.87 319	92.96 347
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		85.35 308	84.64 308	87.49 343	90.77 367	72.59 383	94.01 220	94.40 270	84.72 227	79.62 377	93.17 244	61.91 360	96.72 294	81.99 255	81.16 364	93.16 340

CR-MVSNet		85.35 308	83.76 323	90.12 254	90.58 374	79.34 265	85.24 422	91.96 348	78.27 358	85.55 254	87.87 395	71.03 263	95.61 359	73.96 355	89.36 268	95.40 232

tpmrst		85.35 308	84.99 297	86.43 371	90.88 363	67.88 419	88.71 384	91.43 363	80.13 327	86.08 242	88.80 380	73.05 241	96.02 338	82.48 241	83.40 338	95.40 232

miper_lstm_enhance		85.27 311	84.59 309	87.31 348	91.28 342	74.63 355	87.69 403	94.09 286	81.20 317	81.36 350	89.85 361	74.97 208	94.30 386	81.03 274	79.84 388	93.01 346

IB-MVS	80.51 15	85.24 312	83.26 330	91.19 204	92.13 309	79.86 251	91.75 317	91.29 366	83.28 261	80.66 359	88.49 384	61.28 368	98.46 138	80.99 275	79.46 391	95.25 238
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
CHOSEN 280x420		85.15 313	83.99 320	88.65 310	92.47 299	78.40 288	79.68 446	92.76 322	74.90 391	81.41 349	89.59 365	69.85 285	95.51 363	79.92 292	95.29 148	92.03 374

RPSCF		85.07 314	84.27 312	87.48 344	92.91 289	70.62 406	91.69 320	92.46 328	76.20 378	82.67 333	95.22 152	63.94 347	97.29 258	77.51 319	85.80 312	94.53 269

MS-PatchMatch		85.05 315	84.16 315	87.73 336	91.42 336	78.51 284	91.25 331	93.53 302	77.50 364	80.15 365	91.58 305	61.99 359	95.51 363	75.69 337	94.35 172	89.16 420

ACMH+	81.04 14	85.05 315	83.46 327	89.82 270	94.66 194	79.37 263	94.44 184	94.12 285	82.19 284	78.04 388	92.82 256	58.23 393	97.54 225	73.77 357	82.90 344	92.54 359

mmtdpeth		85.04 317	84.15 316	87.72 337	93.11 277	75.74 344	94.37 193	92.83 319	84.98 217	89.31 170	86.41 411	61.61 364	97.14 271	92.63 74	62.11 439	90.29 407

WBMVS		84.97 318	84.18 314	87.34 347	94.14 231	71.62 395	90.20 356	92.35 331	81.61 306	84.06 305	90.76 333	61.82 361	96.52 312	78.93 304	83.81 328	93.89 298

IterMVS		84.88 319	83.98 321	87.60 339	91.44 333	76.03 339	90.18 358	92.41 329	83.24 262	81.06 354	90.42 343	66.60 324	94.28 387	79.46 296	80.98 373	92.48 361
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 320	83.09 333	90.14 253	93.80 248	80.05 243	89.18 378	93.09 312	78.89 344	78.19 386	91.91 292	65.86 335	97.27 259	68.47 390	88.45 282	93.11 342

testing222		84.84 321	83.32 328	89.43 290	94.15 230	75.94 340	91.09 335	89.41 409	84.90 219	85.78 248	89.44 368	52.70 418	96.28 329	70.80 376	91.57 229	96.07 205

tpm		84.73 322	84.02 319	86.87 364	90.33 380	68.90 414	89.06 380	89.94 397	80.85 320	85.75 249	89.86 360	68.54 307	95.97 341	77.76 315	84.05 327	95.75 220

tfpnnormal		84.72 323	83.23 331	89.20 294	92.79 292	80.05 243	94.48 179	95.81 174	82.38 279	81.08 353	91.21 313	69.01 301	96.95 285	61.69 423	80.59 377	90.58 406

SD_0403		84.71 324	84.65 306	84.92 390	92.95 287	65.95 425	92.07 311	93.23 308	83.82 245	79.03 380	93.73 229	73.90 226	92.91 409	63.02 420	90.05 252	95.89 213

CVMVSNet		84.69 325	84.79 304	84.37 394	91.84 320	64.92 432	93.70 241	91.47 362	66.19 434	86.16 241	95.28 149	67.18 316	93.33 402	80.89 277	90.42 247	94.88 255

SSC-MVS3.2		84.60 326	84.19 313	85.85 379	92.74 294	68.07 416	88.15 394	93.81 296	87.42 145	83.76 313	91.07 322	62.91 354	95.73 356	74.56 351	83.24 339	93.75 314

test-mter		84.54 327	83.64 325	87.25 351	90.95 356	71.67 393	89.55 369	89.88 400	79.17 339	84.54 288	87.95 392	55.56 403	95.11 374	81.82 259	93.37 194	94.97 246

ETVMVS		84.43 328	82.92 337	88.97 302	94.37 216	74.67 354	91.23 332	88.35 413	83.37 258	86.06 243	89.04 373	55.38 405	95.67 358	67.12 399	91.34 231	96.58 181

TransMVSNet (Re)		84.43 328	83.06 335	88.54 312	91.72 325	78.44 286	95.18 135	92.82 321	82.73 274	79.67 375	92.12 280	73.49 233	95.96 342	71.10 374	68.73 428	91.21 393

pmmvs5		84.21 330	82.84 340	88.34 319	88.95 400	76.94 325	92.41 293	91.91 350	75.63 382	80.28 363	91.18 316	64.59 343	95.57 360	77.09 324	83.47 335	92.53 360

dmvs_re		84.20 331	83.22 332	87.14 357	91.83 322	77.81 306	90.04 361	90.19 390	84.70 228	81.49 346	89.17 371	64.37 345	91.13 425	71.58 368	85.65 314	92.46 363

tpm2		84.08 332	82.94 336	87.48 344	91.39 337	71.27 396	89.23 377	90.37 386	71.95 417	84.64 285	89.33 369	67.30 313	96.55 311	75.17 342	87.09 305	94.63 262

test_fmvs2		83.98 333	84.03 318	83.83 399	87.16 418	67.53 423	93.93 227	92.89 317	77.62 363	86.89 222	93.53 232	47.18 431	92.02 417	90.54 126	86.51 308	91.93 376

COLMAP_ROB	80.39 16	83.96 334	82.04 343	89.74 274	95.28 150	79.75 254	94.25 199	92.28 335	75.17 387	78.02 389	93.77 226	58.60 392	97.84 202	65.06 412	85.92 311	91.63 381
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		83.95 335	81.53 346	91.21 203	90.58 374	79.34 265	85.24 422	96.76 86	71.44 419	85.55 254	82.97 431	70.87 266	98.91 90	61.01 425	89.36 268	95.40 232

SixPastTwentyTwo		83.91 336	82.90 338	86.92 361	90.99 354	70.67 405	93.48 247	91.99 345	85.54 194	77.62 393	92.11 282	60.59 376	96.87 290	76.05 335	77.75 398	93.20 338

EPMVS		83.90 337	82.70 341	87.51 341	90.23 383	72.67 379	88.62 386	81.96 439	81.37 311	85.01 279	88.34 386	66.31 329	94.45 381	75.30 341	87.12 304	95.43 231

WB-MVSnew		83.77 338	83.28 329	85.26 387	91.48 332	71.03 400	91.89 312	87.98 414	78.91 342	84.78 282	90.22 346	69.11 300	94.02 390	64.70 413	90.44 245	90.71 401

TESTMET0.1,1		83.74 339	82.85 339	86.42 372	89.96 388	71.21 398	89.55 369	87.88 415	77.41 365	83.37 324	87.31 400	56.71 399	93.65 399	80.62 282	92.85 210	94.40 279

UWE-MVS		83.69 340	83.09 333	85.48 382	93.06 280	65.27 431	90.92 338	86.14 423	79.90 330	86.26 238	90.72 336	57.17 398	95.81 351	71.03 375	92.62 218	95.35 235

pmmvs6		83.42 341	81.60 345	88.87 303	88.01 413	77.87 304	94.96 147	94.24 278	74.67 393	78.80 384	91.09 321	60.17 379	96.49 314	77.06 325	75.40 410	92.23 371

AllTest		83.42 341	81.39 347	89.52 286	95.01 164	77.79 308	93.12 266	90.89 378	77.41 365	76.12 403	93.34 235	54.08 413	97.51 228	68.31 392	84.27 324	93.26 332

tpmvs		83.35 343	82.07 342	87.20 355	91.07 351	71.00 402	88.31 391	91.70 352	78.91 342	80.49 362	87.18 404	69.30 295	97.08 274	68.12 395	83.56 334	93.51 325

USDC		82.76 344	81.26 349	87.26 350	91.17 345	74.55 356	89.27 375	93.39 305	78.26 359	75.30 410	92.08 284	54.43 412	96.63 300	71.64 367	85.79 313	90.61 403

Patchmtry		82.71 345	80.93 351	88.06 328	90.05 386	76.37 336	84.74 427	91.96 348	72.28 416	81.32 351	87.87 395	71.03 263	95.50 365	68.97 387	80.15 383	92.32 369

PatchT		82.68 346	81.27 348	86.89 363	90.09 385	70.94 403	84.06 429	90.15 391	74.91 390	85.63 253	83.57 426	69.37 291	94.87 379	65.19 409	88.50 281	94.84 256

MIMVSNet		82.59 347	80.53 352	88.76 305	91.51 331	78.32 290	86.57 413	90.13 392	79.32 336	80.70 358	88.69 383	52.98 417	93.07 407	66.03 407	88.86 276	94.90 254

test0.0.03 1		82.41 348	81.69 344	84.59 392	88.23 409	72.89 375	90.24 353	87.83 416	83.41 256	79.86 373	89.78 362	67.25 314	88.99 437	65.18 410	83.42 337	91.90 377

EG-PatchMatch MVS		82.37 349	80.34 355	88.46 314	90.27 381	79.35 264	92.80 285	94.33 273	77.14 369	73.26 421	90.18 349	47.47 430	96.72 294	70.25 378	87.32 303	89.30 416

tpm cat1		81.96 350	80.27 356	87.01 358	91.09 350	71.02 401	87.38 407	91.53 360	66.25 433	80.17 364	86.35 413	68.22 310	96.15 334	69.16 386	82.29 350	93.86 304

our_test_3		81.93 351	80.46 354	86.33 373	88.46 406	73.48 369	88.46 389	91.11 368	76.46 372	76.69 399	88.25 388	66.89 319	94.36 384	68.75 388	79.08 394	91.14 395

ppachtmachnet_test		81.84 352	80.07 360	87.15 356	88.46 406	74.43 359	89.04 381	92.16 339	75.33 385	77.75 391	88.99 375	66.20 331	95.37 369	65.12 411	77.60 399	91.65 380

gg-mvs-nofinetune		81.77 353	79.37 368	88.99 301	90.85 364	77.73 315	86.29 414	79.63 444	74.88 392	83.19 328	69.05 447	60.34 377	96.11 335	75.46 339	94.64 164	93.11 342

CL-MVSNet_self_test		81.74 354	80.53 352	85.36 384	85.96 424	72.45 385	90.25 351	93.07 313	81.24 315	79.85 374	87.29 401	70.93 265	92.52 411	66.95 400	69.23 424	91.11 397

Patchmatch-RL test		81.67 355	79.96 361	86.81 365	85.42 429	71.23 397	82.17 437	87.50 419	78.47 353	77.19 395	82.50 433	70.81 267	93.48 400	82.66 240	72.89 414	95.71 224

ADS-MVSNet2		81.66 356	79.71 365	87.50 342	91.35 339	74.19 361	83.33 432	88.48 412	72.90 410	82.24 338	85.77 417	64.98 339	93.20 405	64.57 414	83.74 330	95.12 241

K. test v3		81.59 357	80.15 359	85.91 378	89.89 390	69.42 413	92.57 290	87.71 417	85.56 193	73.44 420	89.71 364	55.58 402	95.52 362	77.17 322	69.76 422	92.78 354

ADS-MVSNet		81.56 358	79.78 362	86.90 362	91.35 339	71.82 389	83.33 432	89.16 410	72.90 410	82.24 338	85.77 417	64.98 339	93.76 396	64.57 414	83.74 330	95.12 241

sc_t1		81.53 359	78.67 380	90.12 254	90.78 366	78.64 279	93.91 230	90.20 389	68.42 428	80.82 356	89.88 359	46.48 433	96.76 293	76.03 336	71.47 418	94.96 249

FMVSNet5		81.52 360	79.60 366	87.27 349	91.17 345	77.95 299	91.49 324	92.26 337	76.87 370	76.16 402	87.91 394	51.67 419	92.34 413	67.74 396	81.16 364	91.52 384

dp		81.47 361	80.23 357	85.17 388	89.92 389	65.49 429	86.74 411	90.10 393	76.30 376	81.10 352	87.12 405	62.81 355	95.92 344	68.13 394	79.88 386	94.09 291

Patchmatch-test		81.37 362	79.30 369	87.58 340	90.92 360	74.16 362	80.99 439	87.68 418	70.52 423	76.63 400	88.81 378	71.21 260	92.76 410	60.01 429	86.93 307	95.83 217

EU-MVSNet		81.32 363	80.95 350	82.42 407	88.50 405	63.67 436	93.32 255	91.33 364	64.02 438	80.57 361	92.83 255	61.21 371	92.27 414	76.34 331	80.38 382	91.32 390

test_0402		81.30 364	79.17 373	87.67 338	93.19 272	78.17 294	92.98 277	91.71 351	75.25 386	76.02 406	90.31 344	59.23 386	96.37 323	50.22 442	83.63 333	88.47 427

JIA-IIPM		81.04 365	78.98 377	87.25 351	88.64 402	73.48 369	81.75 438	89.61 406	73.19 407	82.05 341	73.71 443	66.07 334	95.87 347	71.18 372	84.60 321	92.41 365

Anonymous20231206		81.03 366	79.77 364	84.82 391	87.85 416	70.26 408	91.42 325	92.08 341	73.67 402	77.75 391	89.25 370	62.43 357	93.08 406	61.50 424	82.00 355	91.12 396

mvs5depth		80.98 367	79.15 374	86.45 370	84.57 432	73.29 371	87.79 399	91.67 354	80.52 323	82.20 340	89.72 363	55.14 408	95.93 343	73.93 356	66.83 431	90.12 409

pmmvs-eth3d		80.97 368	78.72 379	87.74 335	84.99 431	79.97 249	90.11 359	91.65 355	75.36 384	73.51 419	86.03 414	59.45 384	93.96 394	75.17 342	72.21 415	89.29 418

testgi		80.94 369	80.20 358	83.18 400	87.96 414	66.29 424	91.28 329	90.70 383	83.70 247	78.12 387	92.84 254	51.37 420	90.82 427	63.34 417	82.46 348	92.43 364

CMPMVS	59.16 21	80.52 370	79.20 372	84.48 393	83.98 433	67.63 422	89.95 364	93.84 295	64.79 437	66.81 435	91.14 319	57.93 394	95.17 372	76.25 332	88.10 287	90.65 402
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing3		80.46 371	79.59 367	83.06 402	93.44 267	64.64 433	93.33 254	85.47 428	84.34 234	79.93 372	90.84 329	44.35 439	92.39 412	57.06 436	87.56 297	92.16 373

Anonymous20240521		80.44 372	79.21 371	84.11 397	85.75 427	67.89 418	92.86 283	93.23 308	75.61 383	75.59 409	87.47 399	50.03 422	94.33 385	71.14 373	81.21 363	90.12 409

LF4IMVS		80.37 373	79.07 376	84.27 396	86.64 420	69.87 412	89.39 374	91.05 371	76.38 374	74.97 412	90.00 356	47.85 429	94.25 388	74.55 352	80.82 375	88.69 425

KD-MVS_self_test		80.20 374	79.24 370	83.07 401	85.64 428	65.29 430	91.01 337	93.93 289	78.71 351	76.32 401	86.40 412	59.20 387	92.93 408	72.59 363	69.35 423	91.00 400

tt0320		80.13 375	77.41 384	88.29 321	90.50 378	78.02 297	93.10 269	90.71 382	66.06 435	76.75 398	86.97 407	49.56 425	95.40 368	71.65 366	71.41 419	91.46 388

Syy-MVS		80.07 376	79.78 362	80.94 411	91.92 316	59.93 443	89.75 367	87.40 420	81.72 301	78.82 382	87.20 402	66.29 330	91.29 423	47.06 444	87.84 294	91.60 382

UnsupCasMVSNet_eth		80.07 376	78.27 382	85.46 383	85.24 430	72.63 382	88.45 390	94.87 248	82.99 268	71.64 427	88.07 391	56.34 400	91.75 420	73.48 359	63.36 437	92.01 375

test20.03		79.95 378	79.08 375	82.55 404	85.79 426	67.74 421	91.09 335	91.08 369	81.23 316	74.48 416	89.96 358	61.63 362	90.15 429	60.08 427	76.38 406	89.76 411

TDRefinement		79.81 379	77.34 385	87.22 354	79.24 446	75.48 347	93.12 266	92.03 343	76.45 373	75.01 411	91.58 305	49.19 426	96.44 319	70.22 380	69.18 425	89.75 412

TinyColmap		79.76 380	77.69 383	85.97 375	91.71 326	73.12 372	89.55 369	90.36 387	75.03 388	72.03 425	90.19 348	46.22 436	96.19 333	63.11 418	81.03 369	88.59 426

myMVS_eth3d		79.67 381	78.79 378	82.32 408	91.92 316	64.08 434	89.75 367	87.40 420	81.72 301	78.82 382	87.20 402	45.33 437	91.29 423	59.09 431	87.84 294	91.60 382

tt0320-xc		79.63 382	76.66 391	88.52 313	91.03 352	78.72 276	93.00 275	89.53 408	66.37 432	76.11 405	87.11 406	46.36 435	95.32 371	72.78 362	67.67 429	91.51 385

OpenMVS_ROB	74.94 19	79.51 383	77.03 390	86.93 360	87.00 419	76.23 338	92.33 299	90.74 381	68.93 427	74.52 415	88.23 389	49.58 424	96.62 301	57.64 434	84.29 323	87.94 430

MIMVSNet1		79.38 384	77.28 386	85.69 381	86.35 421	73.67 366	91.61 322	92.75 323	78.11 362	72.64 423	88.12 390	48.16 428	91.97 419	60.32 426	77.49 400	91.43 389

YYNet1		79.22 385	77.20 387	85.28 386	88.20 411	72.66 380	85.87 416	90.05 396	74.33 396	62.70 438	87.61 397	66.09 333	92.03 415	66.94 401	72.97 413	91.15 394

MDA-MVSNet_test_wron		79.21 386	77.19 388	85.29 385	88.22 410	72.77 377	85.87 416	90.06 394	74.34 395	62.62 440	87.56 398	66.14 332	91.99 418	66.90 404	73.01 412	91.10 398

UWE-MVS-28		78.98 387	78.38 381	80.80 412	88.18 412	60.66 442	90.65 343	78.51 446	78.84 346	77.93 390	90.93 326	59.08 389	89.02 436	50.96 441	90.33 249	92.72 355

MDA-MVSNet-bldmvs		78.85 388	76.31 393	86.46 369	89.76 391	73.88 363	88.79 383	90.42 385	79.16 340	59.18 443	88.33 387	60.20 378	94.04 389	62.00 422	68.96 426	91.48 387

KD-MVS_2432*1600		78.50 389	76.02 396	85.93 376	86.22 422	74.47 357	84.80 425	92.33 332	79.29 337	76.98 396	85.92 415	53.81 415	93.97 392	67.39 397	57.42 444	89.36 414

miper_refine_blended		78.50 389	76.02 396	85.93 376	86.22 422	74.47 357	84.80 425	92.33 332	79.29 337	76.98 396	85.92 415	53.81 415	93.97 392	67.39 397	57.42 444	89.36 414

PM-MVS		78.11 391	76.12 395	84.09 398	83.54 435	70.08 409	88.97 382	85.27 430	79.93 329	74.73 414	86.43 410	34.70 447	93.48 400	79.43 299	72.06 416	88.72 424

test_vis1_rt		77.96 392	76.46 392	82.48 406	85.89 425	71.74 392	90.25 351	78.89 445	71.03 422	71.30 428	81.35 435	42.49 441	91.05 426	84.55 211	82.37 349	84.65 433

test_fmvs3		77.67 393	77.16 389	79.22 415	79.52 445	61.14 440	92.34 298	91.64 356	73.98 399	78.86 381	86.59 408	27.38 451	87.03 439	88.12 155	75.97 408	89.50 413

PVSNet_0	73.20 20	77.22 394	74.83 400	84.37 394	90.70 371	71.10 399	83.09 434	89.67 403	72.81 412	73.93 418	83.13 428	60.79 375	93.70 398	68.54 389	50.84 449	88.30 428

DSMNet-mixed		76.94 395	76.29 394	78.89 416	83.10 437	56.11 452	87.78 400	79.77 443	60.65 442	75.64 408	88.71 381	61.56 365	88.34 438	60.07 428	89.29 270	92.21 372

ttmdpeth		76.55 396	74.64 401	82.29 409	82.25 440	67.81 420	89.76 366	85.69 426	70.35 424	75.76 407	91.69 298	46.88 432	89.77 431	66.16 406	63.23 438	89.30 416

new-patchmatchnet		76.41 397	75.17 399	80.13 413	82.65 439	59.61 444	87.66 404	91.08 369	78.23 360	69.85 431	83.22 427	54.76 409	91.63 422	64.14 416	64.89 435	89.16 420

UnsupCasMVSNet_bld		76.23 398	73.27 402	85.09 389	83.79 434	72.92 374	85.65 419	93.47 304	71.52 418	68.84 433	79.08 438	49.77 423	93.21 404	66.81 405	60.52 441	89.13 422

mvsany_test3		74.95 399	73.26 403	80.02 414	74.61 450	63.16 438	85.53 420	78.42 447	74.16 397	74.89 413	86.46 409	36.02 446	89.09 435	82.39 244	66.91 430	87.82 431

dmvs_testset		74.57 400	75.81 398	70.86 426	87.72 417	40.47 461	87.05 410	77.90 451	82.75 273	71.15 429	85.47 419	67.98 311	84.12 448	45.26 445	76.98 405	88.00 429

MVS-HIRNet		73.70 401	72.20 404	78.18 419	91.81 323	56.42 451	82.94 435	82.58 437	55.24 445	68.88 432	66.48 448	55.32 406	95.13 373	58.12 433	88.42 283	83.01 436

MVStest1		72.91 402	69.70 407	82.54 405	78.14 447	73.05 373	88.21 393	86.21 422	60.69 441	64.70 436	90.53 339	46.44 434	85.70 444	58.78 432	53.62 446	88.87 423

new_pmnet		72.15 403	70.13 406	78.20 418	82.95 438	65.68 427	83.91 430	82.40 438	62.94 440	64.47 437	79.82 437	42.85 440	86.26 443	57.41 435	74.44 411	82.65 438

test_f		71.95 404	70.87 405	75.21 422	74.21 452	59.37 445	85.07 424	85.82 425	65.25 436	70.42 430	83.13 428	23.62 452	82.93 450	78.32 309	71.94 417	83.33 435

pmmvs3		71.81 405	68.71 408	81.11 410	75.86 449	70.42 407	86.74 411	83.66 434	58.95 444	68.64 434	80.89 436	36.93 445	89.52 433	63.10 419	63.59 436	83.39 434

APD_test1		69.04 406	66.26 412	77.36 421	80.51 443	62.79 439	85.46 421	83.51 435	54.11 447	59.14 444	84.79 422	23.40 454	89.61 432	55.22 437	70.24 421	79.68 442

N_pmnet		68.89 407	68.44 409	70.23 427	89.07 399	28.79 466	88.06 395	19.50 466	69.47 426	71.86 426	84.93 420	61.24 370	91.75 420	54.70 438	77.15 402	90.15 408

WB-MVS		67.92 408	67.49 410	69.21 430	81.09 441	41.17 460	88.03 396	78.00 450	73.50 404	62.63 439	83.11 430	63.94 347	86.52 441	25.66 456	51.45 448	79.94 441

SSC-MVS		67.06 409	66.56 411	68.56 432	80.54 442	40.06 462	87.77 401	77.37 453	72.38 414	61.75 441	82.66 432	63.37 350	86.45 442	24.48 457	48.69 451	79.16 443

LCM-MVSNet		66.00 410	62.16 415	77.51 420	64.51 460	58.29 446	83.87 431	90.90 377	48.17 449	54.69 446	73.31 444	16.83 460	86.75 440	65.47 408	61.67 440	87.48 432

test_vis3_rt		65.12 411	62.60 413	72.69 424	71.44 453	60.71 441	87.17 408	65.55 457	63.80 439	53.22 447	65.65 450	14.54 461	89.44 434	76.65 326	65.38 433	67.91 448

FPMVS		64.63 412	62.55 414	70.88 425	70.80 454	56.71 447	84.42 428	84.42 432	51.78 448	49.57 448	81.61 434	23.49 453	81.48 451	40.61 451	76.25 407	74.46 444

EGC-MVSNET		61.97 413	56.37 418	78.77 417	89.63 394	73.50 368	89.12 379	82.79 436	0.21 463	1.24 464	84.80 421	39.48 442	90.04 430	44.13 446	75.94 409	72.79 445

PMMVS2		59.60 414	56.40 417	69.21 430	68.83 457	46.58 456	73.02 451	77.48 452	55.07 446	49.21 449	72.95 445	17.43 459	80.04 452	49.32 443	44.33 452	80.99 440

testf1		59.54 415	56.11 419	69.85 428	69.28 455	56.61 449	80.37 441	76.55 454	42.58 452	45.68 451	75.61 439	11.26 462	84.18 446	43.20 448	60.44 442	68.75 446

APD_test2		59.54 415	56.11 419	69.85 428	69.28 455	56.61 449	80.37 441	76.55 454	42.58 452	45.68 451	75.61 439	11.26 462	84.18 446	43.20 448	60.44 442	68.75 446

ANet_high		58.88 417	54.22 422	72.86 423	56.50 463	56.67 448	80.75 440	86.00 424	73.09 409	37.39 455	64.63 451	22.17 455	79.49 453	43.51 447	23.96 457	82.43 439

dongtai		58.82 418	58.24 416	60.56 435	83.13 436	45.09 459	82.32 436	48.22 465	67.61 430	61.70 442	69.15 446	38.75 443	76.05 454	32.01 453	41.31 453	60.55 450

Gipuma		57.99 419	54.91 421	67.24 433	88.51 403	65.59 428	52.21 454	90.33 388	43.58 451	42.84 454	51.18 455	20.29 457	85.07 445	34.77 452	70.45 420	51.05 454
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		53.51 420	53.30 423	54.13 439	76.06 448	45.36 458	80.11 443	48.36 464	59.63 443	54.84 445	63.43 452	37.41 444	62.07 459	20.73 459	39.10 454	54.96 453

PMVS	47.18 22	52.22 421	48.46 425	63.48 434	45.72 465	46.20 457	73.41 450	78.31 448	41.03 454	30.06 457	65.68 449	6.05 464	83.43 449	30.04 454	65.86 432	60.80 449
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		50.52 422	48.47 424	56.66 437	52.26 464	18.98 468	41.51 456	81.40 440	10.10 458	44.59 453	75.01 442	28.51 449	68.16 455	53.54 439	49.31 450	82.83 437

MVE	39.65 23	43.39 423	38.59 429	57.77 436	56.52 462	48.77 455	55.38 453	58.64 461	29.33 457	28.96 458	52.65 454	4.68 465	64.62 458	28.11 455	33.07 455	59.93 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		43.23 424	42.29 426	46.03 440	65.58 459	37.41 463	73.51 449	64.62 458	33.99 455	28.47 459	47.87 456	19.90 458	67.91 456	22.23 458	24.45 456	32.77 455

EMVS		42.07 425	41.12 427	44.92 441	63.45 461	35.56 465	73.65 448	63.48 459	33.05 456	26.88 460	45.45 457	21.27 456	67.14 457	19.80 460	23.02 458	32.06 456

tmp_tt		35.64 426	39.24 428	24.84 442	14.87 466	23.90 467	62.71 452	51.51 463	6.58 460	36.66 456	62.08 453	44.37 438	30.34 462	52.40 440	22.00 459	20.27 457

cdsmvs_eth3d_5k		22.14 427	29.52 430	0.00 446	0.00 469	0.00 471	0.00 457	95.76 178	0.00 464	0.00 465	94.29 200	75.66 200	0.00 465	0.00 464	0.00 463	0.00 461

wuyk23d		21.27 428	20.48 431	23.63 443	68.59 458	36.41 464	49.57 455	6.85 467	9.37 459	7.89 461	4.46 463	4.03 466	31.37 461	17.47 461	16.07 460	3.12 458

testmvs		8.92 429	11.52 432	1.12 445	1.06 467	0.46 470	86.02 415	0.65 468	0.62 461	2.74 462	9.52 461	0.31 468	0.45 464	2.38 462	0.39 461	2.46 460

test123		8.76 430	11.22 433	1.39 444	0.85 468	0.97 469	85.76 418	0.35 469	0.54 462	2.45 463	8.14 462	0.60 467	0.48 463	2.16 463	0.17 462	2.71 459

ab-mvs-re		7.82 431	10.43 434	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	93.88 221	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

pcd_1.5k_mvsjas		6.64 432	8.86 435	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	79.70 139	0.00 465	0.00 464	0.00 463	0.00 461

mmdepth		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

monomultidepth		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

test_blank		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uanet_test		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

DCPMVS		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

sosnet-low-res		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

sosnet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uncertanet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

Regformer		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

uanet		0.00 433	0.00 436	0.00 446	0.00 469	0.00 471	0.00 457	0.00 470	0.00 464	0.00 465	0.00 464	0.00 469	0.00 465	0.00 464	0.00 463	0.00 461

WAC-MVS						64.08 434								59.14 430

FOURS1					98.86 1	85.54 69	98.29 1	97.49 8	89.79 60	96.29 26

MSC_two_6792asdad				96.52 1	97.78 56	90.86 1		96.85 74					99.61 4	96.03 24	99.06 9	99.07 5

PC_three_1452									82.47 277	97.09 15	97.07 65	92.72 1	98.04 184	92.70 73	99.02 12	98.86 12

No_MVS				96.52 1	97.78 56	90.86 1		96.85 74					99.61 4	96.03 24	99.06 9	99.07 5

test_one_0601					98.58 11	85.83 63		97.44 17	91.05 20	96.78 22	98.06 20	91.45 11

eth-test2					0.00 469

eth-test					0.00 469

ZD-MVS					98.15 36	86.62 33		97.07 54	83.63 249	94.19 57	96.91 71	87.57 31	99.26 46	91.99 97	98.44 53

RE-MVS-def			93.68 66		97.92 45	84.57 89	96.28 46	96.76 86	87.46 142	93.75 68	97.43 44	82.94 95		92.73 69	97.80 85	97.88 94

IU-MVS					98.77 5	86.00 52		96.84 76	81.26 314	97.26 11				95.50 33	99.13 3	99.03 8

OPU-MVS				96.21 3	98.00 44	90.85 3	97.13 15				97.08 63	92.59 2	98.94 86	92.25 85	98.99 14	98.84 15

test_241102_TWO								97.44 17	90.31 38	97.62 6	98.07 18	91.46 10	99.58 10	95.66 27	99.12 6	98.98 10

test_241102_ONE					98.77 5	85.99 54		97.44 17	90.26 44	97.71 1	97.96 28	92.31 4	99.38 31

9.14			94.47 29		97.79 54		96.08 64	97.44 17	86.13 181	95.10 47	97.40 46	88.34 22	99.22 48	93.25 61	98.70 34

save fliter					97.85 51	85.63 68	95.21 132	96.82 79	89.44 70

test_0728_THIRD									90.75 26	97.04 17	98.05 22	92.09 6	99.55 16	95.64 29	99.13 3	99.13 2

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 17	97.49 8					99.61 4	95.62 31	99.08 7	98.99 9

test0726					98.78 3	85.93 57	97.19 12	97.47 13	90.27 42	97.64 4	98.13 6	91.47 8

GSMVS																96.12 201

test_part2					98.55 12	87.22 19				96.40 25

sam_mvs1												71.70 256				96.12 201

sam_mvs												70.60 270

ambc				83.06 402	79.99 444	63.51 437	77.47 447	92.86 318		74.34 417	84.45 423	28.74 448	95.06 376	73.06 361	68.89 427	90.61 403

MTGPA								96.97 59

test_post1							88.00 397				9.81 460	69.31 294	95.53 361	76.65 326

test_post											10.29 459	70.57 274	95.91 346

patchmatchnet-post											83.76 425	71.53 257	96.48 315

GG-mvs-BLEND				87.94 332	89.73 393	77.91 301	87.80 398	78.23 449		80.58 360	83.86 424	59.88 381	95.33 370	71.20 370	92.22 224	90.60 405

MTMP							96.16 55	60.64 460

gm-plane-assit					89.60 395	68.00 417			77.28 368		88.99 375		97.57 223	79.44 298

test9_res														91.91 101	98.71 32	98.07 77

TEST9					97.53 63	86.49 37	94.07 214	96.78 83	81.61 306	92.77 93	96.20 101	87.71 28	99.12 57

test_8					97.49 65	86.30 45	94.02 219	96.76 86	81.86 297	92.70 97	96.20 101	87.63 29	99.02 67

agg_prior2														90.54 126	98.68 37	98.27 59

agg_prior					97.38 68	85.92 59		96.72 93		92.16 112			98.97 81

TestCases				89.52 286	95.01 164	77.79 308		90.89 378	77.41 365	76.12 403	93.34 235	54.08 413	97.51 228	68.31 392	84.27 324	93.26 332

test_prior4						85.96 56	94.11 208

test_prior2							94.12 206		87.67 140	92.63 101	96.39 96	86.62 41		91.50 110	98.67 40

test_prior				93.82 69	97.29 72	84.49 93		96.88 72					98.87 93			98.11 76

旧先验2							93.36 253		71.25 420	94.37 53			97.13 272	86.74 175

新几何2							93.11 268

新几何1				93.10 97	97.30 71	84.35 103		95.56 194	71.09 421	91.26 138	96.24 99	82.87 97	98.86 95	79.19 302	98.10 70	96.07 205

旧先验1					96.79 81	81.81 186		95.67 186			96.81 77	86.69 39			97.66 91	96.97 157

无先验							93.28 261	96.26 132	73.95 400				99.05 61	80.56 283		96.59 180

原ACMM2							92.94 279

原ACMM1				92.01 160	97.34 69	81.05 212		96.81 81	78.89 344	90.45 149	95.92 118	82.65 99	98.84 99	80.68 281	98.26 59	96.14 199

test222					96.55 90	81.70 188	92.22 304	95.01 233	68.36 429	90.20 154	96.14 106	80.26 132			97.80 85	96.05 208

testdata2													98.75 109	78.30 310

segment_acmp												87.16 36

testdata				90.49 237	96.40 96	77.89 303		95.37 213	72.51 413	93.63 71	96.69 80	82.08 113	97.65 215	83.08 230	97.39 95	95.94 210

testdata1							92.15 306		87.94 127

test12				94.34 53	97.13 75	86.15 50		96.29 124		91.04 141		85.08 62	99.01 69		98.13 69	97.86 96

plane_prior7					94.70 192	82.74 159

plane_prior6					94.52 205	82.75 157						74.23 218

plane_prior5								96.22 137					98.12 169	88.15 152	89.99 253	94.63 262

plane_prior4											94.86 171

plane_prior3						82.75 157			90.26 44	86.91 219

plane_prior2							95.85 86		90.81 24

plane_prior1					94.59 198

plane_prior						82.73 160	95.21 132		89.66 65						89.88 258

n2								0.00 470

nn								0.00 470

door-mid								85.49 427

lessismore_v0				86.04 374	88.46 406	68.78 415		80.59 442		73.01 422	90.11 352	55.39 404	96.43 320	75.06 344	65.06 434	92.90 349

LGP-MVS_train				91.12 206	94.47 208	81.49 194		96.14 142	86.73 163	85.45 262	95.16 158	69.89 283	98.10 171	87.70 160	89.23 271	93.77 312

test11								96.57 104

door								85.33 429

HQP5-MVS						81.56 190

HQP-NCC					94.17 227		94.39 189		88.81 95	85.43 265

ACMP_Plane					94.17 227		94.39 189		88.81 95	85.43 265

BP-MVS														87.11 172

HQP4-MVS										85.43 265			97.96 193			94.51 272

HQP3-MVS								96.04 154							89.77 262

HQP2-MVS												73.83 229

NP-MVS					94.37 216	82.42 172					93.98 214

MDTV_nov1_ep13_2view						55.91 453	87.62 405		73.32 406	84.59 287		70.33 277		74.65 349		95.50 229

MDTV_nov1_ep13			83.56 326		91.69 328	69.93 410	87.75 402	91.54 359	78.60 352	84.86 281	88.90 377	69.54 289	96.03 337	70.25 378	88.93 275

ACMMP++_ref															87.47 298

ACMMP++															88.01 290

Test By Simon												80.02 134

ITE_SJBPF				88.24 324	91.88 319	77.05 324		92.92 316	85.54 194	80.13 367	93.30 239	57.29 397	96.20 331	72.46 364	84.71 320	91.49 386

DeepMVS_CX				56.31 438	74.23 451	51.81 454		56.67 462	44.85 450	48.54 450	75.16 441	27.87 450	58.74 460	40.92 450	52.22 447	58.39 452

High-resolution multi-view benchmark