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.

CNVR-MVS		96.30 1	96.54 1	95.55 15	99.31 5	87.69 24	99.06 21	97.12 31	94.66 9	96.79 25	98.78 9	86.42 30	99.95 3	97.59 34	99.18 7	99.00 31

DPM-MVS		96.21 2	95.53 14	98.26 1	96.26 107	95.09 1	99.15 10	96.98 42	93.39 21	96.45 33	98.79 8	90.17 9	99.99 1	89.33 160	99.25 6	99.70 3

MCST-MVS		96.17 3	96.12 6	96.32 7	99.42 2	89.36 10	98.94 29	97.10 33	95.17 4	92.11 100	98.46 33	87.33 25	99.97 2	97.21 40	99.31 4	99.63 7

DVP-MVS++		96.05 4	96.41 3	94.96 24	99.05 9	85.34 61	98.13 62	96.77 67	88.38 90	97.70 12	98.77 10	92.06 3	99.84 13	97.47 35	99.37 1	99.70 3

SED-MVS		95.88 5	96.22 4	94.87 25	99.03 15	85.03 74	99.12 14	96.78 61	88.72 82	97.79 9	98.91 2	88.48 17	99.82 19	98.15 20	98.97 17	99.74 1

MM		95.85 6	95.74 10	96.15 8	96.34 104	89.50 9	99.18 8	98.10 8	95.68 1	96.64 29	97.92 73	80.72 72	99.80 26	99.16 2	97.96 58	99.15 27

NCCC		95.63 7	95.94 8	94.69 32	99.21 6	85.15 71	99.16 9	96.96 46	94.11 14	95.59 44	98.64 19	85.07 36	99.91 4	95.61 57	99.10 9	99.00 31

MSP-MVS		95.62 8	96.54 1	92.86 103	98.31 48	80.10 207	97.42 120	96.78 61	92.20 34	97.11 21	98.29 46	93.46 1	99.10 115	96.01 50	99.30 5	99.38 14
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
DVP-MVS		95.58 9	95.91 9	94.57 34	99.05 9	85.18 66	99.06 21	96.46 113	88.75 80	96.69 26	98.76 12	87.69 23	99.76 38	97.90 28	98.85 21	98.77 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
MVS_0304		95.58 9	95.44 16	96.01 10	97.63 72	89.26 12	99.27 5	96.59 96	94.71 8	97.08 22	97.99 67	78.69 105	99.86 10	99.15 3	97.85 62	98.91 35

DPE-MVS		95.32 11	95.55 13	94.64 33	98.79 23	84.87 79	97.77 88	96.74 72	86.11 149	96.54 32	98.89 6	88.39 19	99.74 46	97.67 33	99.05 12	99.31 20
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		95.32 11	95.48 15	94.85 26	98.62 34	86.04 40	97.81 85	96.93 49	92.45 28	95.69 42	98.50 28	85.38 34	99.85 11	94.75 70	99.18 7	98.65 51

patch_mono-2		95.14 13	96.08 7	92.33 136	98.44 43	77.84 283	98.43 49	97.21 24	92.58 27	97.68 14	97.65 91	86.88 27	99.83 17	98.25 16	97.60 70	99.33 18

DELS-MVS		94.98 14	94.49 30	96.44 6	96.42 103	90.59 7	99.21 7	97.02 39	94.40 13	91.46 109	97.08 122	83.32 56	99.69 58	92.83 101	98.70 31	99.04 29
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
fmvsm_l_conf0.5_n_9		94.91 15	95.60 11	92.84 106	95.20 147	80.55 189	99.45 1	96.36 129	95.17 4	98.48 3	98.55 22	80.53 76	99.78 33	98.87 7	97.79 65	98.19 79

fmvsm_l_conf0.5_n_a		94.91 15	95.30 17	93.72 62	94.50 176	84.30 87	99.14 12	96.00 159	91.94 40	97.91 7	98.60 20	84.78 38	99.77 36	98.84 8	96.03 120	97.08 177

fmvsm_l_conf0.5_n		94.89 17	95.24 18	93.86 53	94.42 179	84.61 82	99.13 13	96.15 147	92.06 37	97.92 5	98.52 27	84.52 41	99.74 46	98.76 9	95.67 127	97.22 165

CANet		94.89 17	94.64 27	95.63 13	97.55 78	88.12 18	99.06 21	96.39 123	94.07 16	95.34 46	97.80 82	76.83 143	99.87 8	97.08 42	97.64 69	98.89 36

SD-MVS		94.84 19	95.02 22	94.29 40	97.87 64	84.61 82	97.76 90	96.19 145	89.59 72	96.66 28	98.17 54	84.33 43	99.60 69	96.09 49	98.50 38	98.66 50
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
test_fmvsm_n_1920		94.81 20	95.60 11	92.45 127	95.29 143	80.96 176	99.29 4	97.21 24	94.50 12	97.29 20	98.44 34	82.15 64	99.78 33	98.56 10	97.68 68	96.61 201

TSAR-MVS + MP.		94.79 21	95.17 20	93.64 68	97.66 71	84.10 90	95.85 242	96.42 118	91.26 46	97.49 18	96.80 135	86.50 29	98.49 148	95.54 59	99.03 13	98.33 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SMA-MVS		94.70 22	94.68 26	94.76 29	98.02 59	85.94 44	97.47 113	96.77 67	85.32 168	97.92 5	98.70 17	83.09 59	99.84 13	95.79 54	99.08 10	98.49 58
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
fmvsm_l_conf0.5_n_3		94.61 23	94.92 23	93.68 66	94.52 171	82.80 118	99.33 2	96.37 127	95.08 6	97.59 17	98.48 31	77.40 128	99.79 30	98.28 14	97.21 84	98.44 62

DeepPCF-MVS	89.82 1	94.61 23	96.17 5	89.91 244	97.09 96	70.21 384	98.99 27	96.69 80	95.57 2	95.08 52	99.23 1	86.40 31	99.87 8	97.84 31	98.66 32	99.65 6

balanced_conf03		94.60 25	94.30 36	95.48 16	96.45 102	88.82 14	96.33 210	95.58 191	91.12 48	95.84 41	93.87 234	83.47 55	98.37 158	97.26 38	98.81 24	99.24 23

APDe-MVS		94.56 26	94.75 24	93.96 51	98.84 22	83.40 105	98.04 70	96.41 119	85.79 158	95.00 54	98.28 47	84.32 46	99.18 108	97.35 37	98.77 28	99.28 21
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_9		94.52 27	95.22 19	92.41 132	95.79 127	78.61 254	98.73 36	96.00 159	94.91 7	97.73 11	98.73 15	79.09 97	99.79 30	99.14 4	96.86 100	98.83 38

fmvsm_s_conf0.5_n_8		94.52 27	95.04 21	92.96 98	95.15 151	81.14 168	99.09 18	96.66 85	95.53 3	97.84 8	98.71 16	76.33 154	99.81 22	99.24 1	96.85 102	97.92 102

DeepC-MVS_fast	89.06 2	94.48 29	94.30 36	95.02 22	98.86 21	85.68 51	98.06 68	96.64 89	93.64 19	91.74 107	98.54 24	80.17 82	99.90 5	92.28 108	98.75 29	99.49 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		94.35 30	94.50 29	93.89 52	97.38 90	83.04 113	98.10 64	95.29 214	91.57 42	93.81 71	97.45 100	86.64 28	99.43 86	96.28 48	94.01 147	99.20 25

train_agg		94.28 31	94.45 31	93.74 59	98.64 31	83.71 97	97.82 83	96.65 86	84.50 196	95.16 48	98.09 60	84.33 43	99.36 91	95.91 53	98.96 19	98.16 82

MSLP-MVS++		94.28 31	94.39 33	93.97 50	98.30 49	84.06 91	98.64 42	96.93 49	90.71 55	93.08 82	98.70 17	79.98 86	99.21 101	94.12 79	99.07 11	98.63 52

MG-MVS		94.25 33	93.72 44	95.85 12	99.38 3	89.35 11	97.98 72	98.09 9	89.99 66	92.34 94	96.97 127	81.30 70	98.99 121	88.54 168	98.88 20	99.20 25

fmvsm_s_conf0.5_n_6		94.17 34	94.70 25	92.58 123	93.50 213	81.20 166	99.08 19	96.48 112	92.24 33	98.62 2	98.39 39	78.58 107	99.72 51	98.08 24	97.36 79	96.81 191

SF-MVS		94.17 34	94.05 41	94.55 35	97.56 77	85.95 42	97.73 92	96.43 117	84.02 212	95.07 53	98.74 14	82.93 60	99.38 88	95.42 61	98.51 36	98.32 68

PS-MVSNAJ		94.17 34	93.52 51	96.10 9	95.65 131	92.35 2	98.21 57	95.79 180	92.42 29	96.24 35	98.18 51	71.04 240	99.17 109	96.77 45	97.39 78	96.79 192

SteuartSystems-ACMMP		94.13 37	94.44 32	93.20 87	95.41 138	81.35 164	99.02 25	96.59 96	89.50 74	94.18 67	98.36 43	83.68 54	99.45 85	94.77 69	98.45 41	98.81 40
Skip Steuart: Steuart Systems R&D Blog.
EPNet		94.06 38	94.15 39	93.76 57	97.27 93	84.35 85	98.29 54	97.64 14	94.57 10	95.36 45	96.88 130	79.96 87	99.12 114	91.30 121	96.11 117	97.82 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		93.99 39	94.36 34	92.86 103	92.82 241	81.12 169	99.26 6	96.37 127	93.47 20	95.16 48	98.21 49	79.00 98	99.64 64	98.21 18	96.73 106	97.83 111

fmvsm_s_conf0.5_n_3		93.95 40	94.53 28	92.20 146	94.41 180	80.04 208	98.90 31	95.96 164	94.53 11	97.63 16	98.58 21	75.95 161	99.79 30	98.25 16	96.60 108	96.77 194

xiu_mvs_v2_base		93.92 41	93.26 57	95.91 11	95.07 154	92.02 6	98.19 58	95.68 186	92.06 37	96.01 40	98.14 56	70.83 244	98.96 123	96.74 47	96.57 109	96.76 196

lupinMVS		93.87 42	93.58 49	94.75 30	93.00 228	88.08 19	99.15 10	95.50 198	91.03 51	94.90 55	97.66 87	78.84 101	97.56 201	94.64 73	97.46 73	98.62 53

fmvsm_s_conf0.5_n		93.69 43	94.13 40	92.34 134	94.56 168	82.01 141	99.07 20	97.13 29	92.09 35	96.25 34	98.53 26	76.47 149	99.80 26	98.39 12	94.71 137	95.22 247

APD-MVS		93.61 44	93.59 48	93.69 65	98.76 24	83.26 108	97.21 132	96.09 151	82.41 258	94.65 61	98.21 49	81.96 67	98.81 133	94.65 72	98.36 47	99.01 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_s_conf0.5_n_4		93.59 45	94.32 35	91.41 189	93.89 198	79.24 229	98.89 32	96.53 104	92.82 25	97.37 19	98.47 32	77.21 135	99.78 33	98.11 23	95.59 129	95.21 248

PHI-MVS		93.59 45	93.63 47	93.48 79	98.05 58	81.76 153	98.64 42	97.13 29	82.60 254	94.09 68	98.49 29	80.35 77	99.85 11	94.74 71	98.62 33	98.83 38

fmvsm_s_conf0.5_n_5		93.57 47	93.75 43	93.01 95	92.87 240	82.73 119	98.93 30	95.90 172	90.96 53	95.61 43	98.39 39	76.57 147	99.63 66	98.32 13	96.24 113	96.68 200

BP-MVS1		93.55 48	93.50 52	93.71 63	92.64 249	85.39 60	97.78 87	96.84 57	89.52 73	92.00 101	97.06 124	88.21 20	98.03 173	91.45 120	96.00 122	97.70 124

ACMMP_NAP		93.46 49	93.23 58	94.17 46	97.16 94	84.28 88	96.82 174	96.65 86	86.24 147	94.27 65	97.99 67	77.94 117	99.83 17	93.39 87	98.57 34	98.39 65

MVS_111021_HR		93.41 50	93.39 55	93.47 81	97.34 91	82.83 117	97.56 105	98.27 6	89.16 78	89.71 135	97.14 117	79.77 88	99.56 76	93.65 85	97.94 59	98.02 91

fmvsm_s_conf0.5_n_a		93.34 51	93.71 45	92.22 144	93.38 216	81.71 156	98.86 33	96.98 42	91.64 41	96.85 24	98.55 22	75.58 169	99.77 36	97.88 30	93.68 156	95.18 249

lecture		93.17 52	93.57 50	91.96 158	97.80 65	78.79 249	98.50 48	96.98 42	86.61 143	94.75 60	98.16 55	78.36 111	99.35 93	93.89 81	97.12 89	97.75 118

PVSNet_Blended		93.13 53	92.98 63	93.57 73	97.47 79	83.86 93	99.32 3	96.73 74	91.02 52	89.53 140	96.21 148	76.42 151	99.57 74	94.29 76	95.81 126	97.29 163

CDPH-MVS		93.12 54	92.91 65	93.74 59	98.65 30	83.88 92	97.67 96	96.26 137	83.00 244	93.22 79	98.24 48	81.31 69	99.21 101	89.12 161	98.74 30	98.14 84

dcpmvs_2		93.10 55	93.46 54	92.02 156	97.77 67	79.73 218	94.82 289	93.86 306	86.91 134	91.33 113	96.76 136	85.20 35	98.06 171	96.90 44	97.60 70	98.27 74

test_fmvsmconf0.1_n		93.08 56	93.22 59	92.65 116	88.45 358	80.81 181	99.00 26	95.11 220	93.21 22	94.00 69	97.91 75	76.84 141	99.59 70	97.91 27	96.55 110	97.54 136

SPE-MVS-test		92.98 57	93.67 46	90.90 210	96.52 101	76.87 306	98.68 39	94.73 241	90.36 63	94.84 57	97.89 77	77.94 117	97.15 241	94.28 78	97.80 64	98.70 49

fmvsm_s_conf0.5_n_2		92.97 58	93.38 56	91.73 172	94.10 192	80.64 186	98.96 28	95.89 173	94.09 15	97.05 23	98.40 38	68.92 255	99.80 26	98.53 11	94.50 141	94.74 260

alignmvs		92.97 58	92.26 84	95.12 21	95.54 135	87.77 22	98.67 40	96.38 124	88.04 101	93.01 83	97.45 100	79.20 95	98.60 139	93.25 93	88.76 215	98.99 33

fmvsm_s_conf0.1_n		92.93 60	93.16 60	92.24 141	90.52 318	81.92 145	98.42 50	96.24 139	91.17 47	96.02 39	98.35 44	75.34 180	99.74 46	97.84 31	94.58 139	95.05 252

HFP-MVS		92.89 61	92.86 68	92.98 97	98.71 25	81.12 169	97.58 103	96.70 78	85.20 173	91.75 106	97.97 72	78.47 108	99.71 54	90.95 126	98.41 43	98.12 87

NormalMVS		92.88 62	92.97 64	92.59 122	97.80 65	82.02 139	97.94 75	94.70 242	92.34 30	92.15 98	96.53 143	77.03 136	98.57 141	91.13 124	97.12 89	97.19 170

fmvsm_s_conf0.5_n_7		92.88 62	93.82 42	90.08 235	92.79 244	76.45 314	98.54 46	96.74 72	92.28 32	95.22 47	98.49 29	74.91 187	98.15 169	98.28 14	97.13 88	95.63 232

PAPM		92.87 64	92.40 78	94.30 39	92.25 268	87.85 21	96.40 205	96.38 124	91.07 50	88.72 157	96.90 128	82.11 65	97.37 224	90.05 148	97.70 67	97.67 126

GDP-MVS		92.85 65	92.55 75	93.75 58	92.82 241	85.76 47	97.63 97	95.05 224	88.34 92	93.15 80	97.10 121	86.92 26	98.01 175	87.95 176	94.00 148	97.47 145

ZNCC-MVS		92.75 66	92.60 73	93.23 86	98.24 51	81.82 151	97.63 97	96.50 108	85.00 183	91.05 118	97.74 84	78.38 109	99.80 26	90.48 138	98.34 48	98.07 89

PAPR		92.74 67	92.17 88	94.45 36	98.89 20	84.87 79	97.20 134	96.20 143	87.73 110	88.40 161	98.12 57	78.71 104	99.76 38	87.99 175	96.28 112	98.74 43

CS-MVS		92.73 68	93.48 53	90.48 223	96.27 106	75.93 327	98.55 45	94.93 228	89.32 75	94.54 63	97.67 86	78.91 100	97.02 246	93.80 82	97.32 81	98.49 58

jason		92.73 68	92.23 85	94.21 44	90.50 319	87.30 30	98.65 41	95.09 221	90.61 57	92.76 88	97.13 118	75.28 181	97.30 227	93.32 91	96.75 105	98.02 91
jason: jason.
myMVS_eth3d28		92.72 70	92.23 85	94.21 44	96.16 110	87.46 29	97.37 124	96.99 41	88.13 99	88.18 166	95.47 171	84.12 48	98.04 172	92.46 107	91.17 191	97.14 173

ETV-MVS		92.72 70	92.87 66	92.28 140	94.54 170	81.89 147	97.98 72	95.21 218	89.77 70	93.11 81	96.83 132	77.23 134	97.50 209	95.74 55	95.38 131	97.44 151

region2R		92.72 70	92.70 70	92.79 108	98.68 26	80.53 194	97.53 108	96.51 106	85.22 171	91.94 104	97.98 70	77.26 130	99.67 62	90.83 132	98.37 46	98.18 80

reproduce-ours		92.70 73	93.02 61	91.75 169	97.45 81	77.77 287	96.16 222	95.94 168	84.12 208	92.45 89	98.43 35	80.06 84	99.24 97	95.35 62	97.18 85	98.24 76

our_new_method		92.70 73	93.02 61	91.75 169	97.45 81	77.77 287	96.16 222	95.94 168	84.12 208	92.45 89	98.43 35	80.06 84	99.24 97	95.35 62	97.18 85	98.24 76

XVS		92.69 75	92.71 69	92.63 119	98.52 37	80.29 197	97.37 124	96.44 115	87.04 132	91.38 110	97.83 81	77.24 132	99.59 70	90.46 140	98.07 54	98.02 91

ACMMPR		92.69 75	92.67 71	92.75 110	98.66 28	80.57 188	97.58 103	96.69 80	85.20 173	91.57 108	97.92 73	77.01 138	99.67 62	90.95 126	98.41 43	98.00 96

UBG		92.68 77	92.35 79	93.70 64	95.61 132	85.65 54	97.25 130	97.06 36	87.92 104	89.28 144	95.03 192	86.06 33	98.07 170	92.24 109	90.69 196	97.37 157

WTY-MVS		92.65 78	91.68 97	95.56 14	96.00 115	88.90 13	98.23 56	97.65 13	88.57 85	89.82 134	97.22 115	79.29 92	99.06 118	89.57 156	88.73 216	98.73 47

MP-MVS		92.61 79	92.67 71	92.42 131	98.13 56	79.73 218	97.33 127	96.20 143	85.63 160	90.53 125	97.66 87	78.14 115	99.70 57	92.12 111	98.30 50	97.85 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		92.58 80	92.35 79	93.29 83	97.30 92	82.53 123	96.44 201	96.04 157	84.68 191	89.12 147	98.37 42	77.48 127	99.74 46	93.31 92	98.38 45	97.59 134
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		92.54 81	92.60 73	92.34 134	98.50 40	79.90 211	98.40 51	96.40 121	84.75 187	90.48 127	98.09 60	77.40 128	99.21 101	91.15 123	98.23 52	97.92 102

reproduce_model		92.53 82	92.87 66	91.50 185	97.41 85	77.14 304	96.02 229	95.91 171	83.65 230	92.45 89	98.39 39	79.75 89	99.21 101	95.27 65	96.98 94	98.14 84

testing11		92.48 83	92.04 92	93.78 56	95.94 119	86.00 41	97.56 105	97.08 34	87.52 115	89.32 143	95.40 173	84.60 39	98.02 174	91.93 117	89.04 211	97.32 159

SymmetryMVS		92.45 84	92.33 81	92.82 107	95.19 148	82.02 139	97.94 75	97.43 17	92.34 30	92.15 98	96.53 143	77.03 136	98.57 141	91.13 124	91.19 189	97.87 106

MTAPA		92.45 84	92.31 82	92.86 103	97.90 61	80.85 180	92.88 344	96.33 131	87.92 104	90.20 131	98.18 51	76.71 146	99.76 38	92.57 105	98.09 53	97.96 101

GST-MVS		92.43 86	92.22 87	93.04 94	98.17 54	81.64 159	97.40 122	96.38 124	84.71 190	90.90 121	97.40 105	77.55 126	99.76 38	89.75 153	97.74 66	97.72 121

fmvsm_s_conf0.1_n_a		92.38 87	92.49 76	92.06 153	88.08 363	81.62 160	97.97 74	96.01 158	90.62 56	96.58 30	98.33 45	74.09 200	99.71 54	97.23 39	93.46 161	94.86 256

MVSMamba_PlusPlus		92.37 88	91.55 100	94.83 27	95.37 140	87.69 24	95.60 254	95.42 207	74.65 371	93.95 70	92.81 253	83.11 58	97.70 192	94.49 74	98.53 35	99.11 28

sasdasda		92.27 89	91.22 106	95.41 17	95.80 125	88.31 15	97.09 150	94.64 252	88.49 87	92.99 84	97.31 107	72.68 216	98.57 141	93.38 89	88.58 222	99.36 16

canonicalmvs		92.27 89	91.22 106	95.41 17	95.80 125	88.31 15	97.09 150	94.64 252	88.49 87	92.99 84	97.31 107	72.68 216	98.57 141	93.38 89	88.58 222	99.36 16

fmvsm_s_conf0.1_n_2		92.26 91	92.48 77	91.60 180	92.29 264	80.55 189	98.73 36	94.33 277	93.80 18	96.18 36	98.11 58	66.93 271	99.75 43	98.19 19	93.74 155	94.50 267

SR-MVS		92.16 92	92.27 83	91.83 167	98.37 45	78.41 260	96.67 187	95.76 181	82.19 262	91.97 102	98.07 64	76.44 150	98.64 137	93.71 84	97.27 82	98.45 61

test_fmvsmvis_n_1920		92.12 93	92.10 90	92.17 148	90.87 310	81.04 172	98.34 53	93.90 303	92.71 26	87.24 178	97.90 76	74.83 188	99.72 51	96.96 43	96.20 114	95.76 230

VNet		92.11 94	91.22 106	94.79 28	96.91 97	86.98 31	97.91 78	97.96 10	86.38 146	93.65 73	95.74 158	70.16 249	98.95 125	93.39 87	88.87 214	98.43 63

CSCG		92.02 95	91.65 98	93.12 90	98.53 36	80.59 187	97.47 113	97.18 27	77.06 351	84.64 212	97.98 70	83.98 50	99.52 79	90.72 134	97.33 80	99.23 24

MGCFI-Net		91.95 96	91.03 112	94.72 31	95.68 130	86.38 36	96.93 166	94.48 261	88.25 95	92.78 87	97.24 113	72.34 221	98.46 151	93.13 98	88.43 229	99.32 19

PGM-MVS		91.93 97	91.80 95	92.32 138	98.27 50	79.74 217	95.28 265	97.27 22	83.83 222	90.89 122	97.78 83	76.12 158	99.56 76	88.82 164	97.93 61	97.66 127

testing99		91.91 98	91.35 103	93.60 71	95.98 117	85.70 49	97.31 128	96.92 51	86.82 137	88.91 151	95.25 176	84.26 47	97.89 185	88.80 165	87.94 235	97.21 167

testing91		91.90 99	91.31 105	93.66 67	95.99 116	85.68 51	97.39 123	96.89 52	86.75 141	88.85 153	95.23 179	83.93 51	97.90 184	88.91 162	87.89 236	97.41 153

mPP-MVS		91.88 100	91.82 94	92.07 152	98.38 44	78.63 253	97.29 129	96.09 151	85.12 179	88.45 160	97.66 87	75.53 170	99.68 60	89.83 149	98.02 57	97.88 104

EI-MVSNet-Vis-set		91.84 101	91.77 96	92.04 155	97.60 74	81.17 167	96.61 188	96.87 54	88.20 97	89.19 145	97.55 99	78.69 105	99.14 111	90.29 145	90.94 193	95.80 224

EIA-MVS		91.73 102	92.05 91	90.78 215	94.52 171	76.40 316	98.06 68	95.34 212	89.19 77	88.90 152	97.28 112	77.56 125	97.73 191	90.77 133	96.86 100	98.20 78

EC-MVSNet		91.73 102	92.11 89	90.58 219	93.54 207	77.77 287	98.07 67	94.40 272	87.44 117	92.99 84	97.11 120	74.59 194	96.87 260	93.75 83	97.08 91	97.11 174

DP-MVS Recon		91.72 104	90.85 114	94.34 38	99.50 1	85.00 76	98.51 47	95.96 164	80.57 286	88.08 169	97.63 93	76.84 141	99.89 7	85.67 195	94.88 134	98.13 86

CHOSEN 280x420		91.71 105	91.85 93	91.29 194	94.94 158	82.69 120	87.89 398	96.17 146	85.94 155	87.27 177	94.31 216	90.27 8	95.65 318	94.04 80	95.86 124	95.53 237

HY-MVS	84.06 6	91.63 106	90.37 128	95.39 19	96.12 112	88.25 17	90.22 375	97.58 15	88.33 93	90.50 126	91.96 271	79.26 93	99.06 118	90.29 145	89.07 210	98.88 37

HPM-MVS		91.62 107	91.53 101	91.89 162	97.88 63	79.22 231	96.99 156	95.73 184	82.07 264	89.50 142	97.19 116	75.59 168	98.93 128	90.91 128	97.94 59	97.54 136
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_LR		91.60 108	91.64 99	91.47 187	95.74 128	78.79 249	96.15 224	96.77 67	88.49 87	88.64 158	97.07 123	72.33 222	99.19 107	93.13 98	96.48 111	96.43 206

DeepC-MVS	86.58 3	91.53 109	91.06 111	92.94 100	94.52 171	81.89 147	95.95 233	95.98 162	90.76 54	83.76 227	96.76 136	73.24 211	99.71 54	91.67 119	96.96 95	97.22 165
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_yl		91.46 110	90.53 121	94.24 42	97.41 85	85.18 66	98.08 65	97.72 11	80.94 277	89.85 132	96.14 149	75.61 166	98.81 133	90.42 143	88.56 224	98.74 43

DCV-MVSNet		91.46 110	90.53 121	94.24 42	97.41 85	85.18 66	98.08 65	97.72 11	80.94 277	89.85 132	96.14 149	75.61 166	98.81 133	90.42 143	88.56 224	98.74 43

PAPM_NR		91.46 110	90.82 115	93.37 82	98.50 40	81.81 152	95.03 284	96.13 148	84.65 192	86.10 193	97.65 91	79.24 94	99.75 43	83.20 223	96.88 98	98.56 55

testing3-2		91.37 113	91.01 113	92.44 129	95.93 120	83.77 96	98.83 34	97.45 16	86.88 135	86.63 185	94.69 206	84.57 40	97.75 190	89.65 154	84.44 269	95.80 224

MVSFormer		91.36 114	90.57 120	93.73 61	93.00 228	88.08 19	94.80 291	94.48 261	80.74 282	94.90 55	97.13 118	78.84 101	95.10 347	83.77 212	97.46 73	98.02 91

EI-MVSNet-UG-set		91.35 115	91.22 106	91.73 172	97.39 88	80.68 184	96.47 198	96.83 58	87.92 104	88.30 164	97.36 106	77.84 120	99.13 113	89.43 159	89.45 206	95.37 241

SR-MVS-dyc-post		91.29 116	91.45 102	90.80 213	97.76 69	76.03 322	96.20 219	95.44 203	80.56 287	90.72 123	97.84 79	75.76 165	98.61 138	91.99 114	96.79 103	97.75 118

PVSNet_Blended_VisFu		91.24 117	90.77 116	92.66 115	95.09 152	82.40 131	97.77 88	95.87 177	88.26 94	86.39 188	93.94 232	76.77 144	99.27 95	88.80 165	94.00 148	96.31 212

APD-MVS_3200maxsize		91.23 118	91.35 103	90.89 211	97.89 62	76.35 317	96.30 213	95.52 196	79.82 309	91.03 119	97.88 78	74.70 190	98.54 145	92.11 112	96.89 97	97.77 116

diffmvs		91.17 119	90.74 117	92.44 129	93.11 227	82.50 128	96.25 216	93.62 327	87.79 108	90.40 129	95.93 153	73.44 209	97.42 215	93.62 86	92.55 171	97.41 153
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvg		91.13 120	90.45 124	93.17 89	92.99 231	83.58 101	97.46 115	94.56 258	87.69 111	87.19 179	94.98 197	74.50 195	97.60 197	91.88 118	92.79 168	98.34 66
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing222		91.09 121	90.49 123	92.87 102	95.82 123	85.04 73	96.51 196	97.28 21	86.05 152	89.13 146	95.34 175	80.16 83	96.62 273	85.82 193	88.31 231	96.96 181

test_fmvsmconf0.01_n		91.08 122	90.68 118	92.29 139	82.43 420	80.12 206	97.94 75	93.93 299	92.07 36	91.97 102	97.60 94	67.56 263	99.53 78	97.09 41	95.56 130	97.21 167

CHOSEN 1792x2688		91.07 123	90.21 132	93.64 68	95.18 149	83.53 102	96.26 215	96.13 148	88.92 79	84.90 205	93.10 250	72.86 213	99.62 68	88.86 163	95.67 127	97.79 115

ETVMVS		90.99 124	90.26 129	93.19 88	95.81 124	85.64 55	96.97 161	97.18 27	85.43 165	88.77 156	94.86 199	82.00 66	96.37 280	82.70 228	88.60 221	97.57 135

CANet_DTU		90.98 125	90.04 138	93.83 54	94.76 164	86.23 38	96.32 211	93.12 351	93.11 23	93.71 72	96.82 134	63.08 302	99.48 83	84.29 205	95.12 133	95.77 229

test2506		90.96 126	90.39 126	92.65 116	93.54 207	82.46 129	96.37 206	97.35 19	86.78 139	87.55 173	95.25 176	77.83 121	97.50 209	84.07 207	94.80 135	97.98 98

thisisatest0515		90.95 127	90.26 129	93.01 95	94.03 197	84.27 89	97.91 78	96.67 82	83.18 237	86.87 183	95.51 170	88.66 15	97.85 186	80.46 248	89.01 212	96.92 185

casdiffmvs		90.95 127	90.39 126	92.63 119	92.82 241	82.53 123	96.83 172	94.47 264	87.69 111	88.47 159	95.56 169	74.04 201	97.54 205	90.90 129	92.74 169	97.83 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sss		90.87 129	89.96 141	93.60 71	94.15 188	83.84 95	97.14 143	98.13 7	85.93 156	89.68 136	96.09 151	71.67 232	99.30 94	87.69 180	89.16 209	97.66 127

diffmvs_AUTHOR		90.86 130	90.41 125	92.24 141	92.01 284	82.22 136	96.18 221	93.64 326	87.28 122	90.46 128	95.64 164	72.82 214	97.39 220	93.17 95	92.46 174	97.11 174

baseline		90.76 131	90.10 135	92.74 111	92.90 239	82.56 122	94.60 294	94.56 258	87.69 111	89.06 149	95.67 162	73.76 204	97.51 208	90.43 142	92.23 181	98.16 82

viewmanbaseed2359cas		90.74 132	90.07 137	92.76 109	92.98 232	82.93 116	96.53 193	94.28 280	87.08 131	88.96 150	95.64 164	72.03 229	97.58 199	90.85 130	92.26 179	97.76 117

Effi-MVS+		90.70 133	89.90 144	93.09 92	93.61 204	83.48 103	95.20 272	92.79 357	83.22 236	91.82 105	95.70 160	71.82 231	97.48 211	91.25 122	93.67 157	98.32 68

MAR-MVS		90.63 134	90.22 131	91.86 164	98.47 42	78.20 271	97.18 136	96.61 92	83.87 219	88.18 166	98.18 51	68.71 256	99.75 43	83.66 217	97.15 87	97.63 130
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
MVS		90.60 135	88.64 164	96.50 5	94.25 184	90.53 8	93.33 332	97.21 24	77.59 342	78.88 286	97.31 107	71.52 235	99.69 58	89.60 155	98.03 56	99.27 22

xiu_mvs_v1_base_debu		90.54 136	89.54 147	93.55 74	92.31 256	87.58 26	96.99 156	94.87 232	87.23 125	93.27 76	97.56 96	57.43 348	98.32 160	92.72 102	93.46 161	94.74 260

xiu_mvs_v1_base		90.54 136	89.54 147	93.55 74	92.31 256	87.58 26	96.99 156	94.87 232	87.23 125	93.27 76	97.56 96	57.43 348	98.32 160	92.72 102	93.46 161	94.74 260

xiu_mvs_v1_base_debi		90.54 136	89.54 147	93.55 74	92.31 256	87.58 26	96.99 156	94.87 232	87.23 125	93.27 76	97.56 96	57.43 348	98.32 160	92.72 102	93.46 161	94.74 260

mvsmamba		90.53 139	90.08 136	91.88 163	94.81 162	80.93 177	93.94 315	94.45 266	88.24 96	87.02 182	92.35 260	68.04 258	95.80 306	94.86 68	97.03 93	98.92 34

baseline2		90.39 140	90.21 132	90.93 207	90.86 311	80.99 174	95.20 272	97.41 18	86.03 154	80.07 276	94.61 207	90.58 6	97.47 212	87.29 184	89.86 203	94.35 268

ACMMP		90.39 140	89.97 140	91.64 177	97.58 76	78.21 270	96.78 178	96.72 76	84.73 189	84.72 209	97.23 114	71.22 237	99.63 66	88.37 173	92.41 177	97.08 177
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
HPM-MVS_fast		90.38 142	90.17 134	91.03 204	97.61 73	77.35 298	97.15 142	95.48 199	79.51 315	88.79 154	96.90 128	71.64 234	98.81 133	87.01 188	97.44 75	96.94 182

MVS_Test		90.29 143	89.18 152	93.62 70	95.23 144	84.93 77	94.41 297	94.66 249	84.31 201	90.37 130	91.02 285	75.13 183	97.82 187	83.11 225	94.42 142	98.12 87

API-MVS		90.18 144	88.97 157	93.80 55	98.66 28	82.95 115	97.50 112	95.63 190	75.16 366	86.31 189	97.69 85	72.49 219	99.90 5	81.26 244	96.07 118	98.56 55

PVSNet_BlendedMVS		90.05 145	89.96 141	90.33 228	97.47 79	83.86 93	98.02 71	96.73 74	87.98 102	89.53 140	89.61 306	76.42 151	99.57 74	94.29 76	79.59 304	87.57 376

ET-MVSNet_ETH3D		90.01 146	89.03 153	92.95 99	94.38 181	86.77 33	98.14 59	96.31 134	89.30 76	63.33 410	96.72 139	90.09 10	93.63 385	90.70 136	82.29 291	98.46 60

test_vis1_n_1920		89.95 147	90.59 119	88.03 287	92.36 255	68.98 393	99.12 14	94.34 275	93.86 17	93.64 74	97.01 126	51.54 380	99.59 70	96.76 46	96.71 107	95.53 237

test_cas_vis1_n_1920		89.90 148	90.02 139	89.54 253	90.14 329	74.63 338	98.71 38	94.43 269	93.04 24	92.40 92	96.35 146	53.41 376	99.08 117	95.59 58	96.16 115	94.90 254

viewmacassd2359aftdt		89.89 149	89.01 156	92.52 125	91.56 292	82.46 129	96.32 211	94.06 295	86.41 145	88.11 168	95.01 194	69.68 251	97.47 212	88.73 167	91.19 189	97.63 130

guyue		89.85 150	89.33 151	91.40 190	92.53 253	80.15 205	96.82 174	95.68 186	89.66 71	86.43 187	94.23 219	67.00 269	97.16 237	91.96 116	89.65 204	96.89 186

TESTMET0.1,1		89.83 151	89.34 150	91.31 192	92.54 252	80.19 203	97.11 146	96.57 99	86.15 148	86.85 184	91.83 276	79.32 91	96.95 251	81.30 242	92.35 178	96.77 194

EPP-MVSNet		89.76 152	89.72 146	89.87 245	93.78 200	76.02 324	97.22 131	96.51 106	79.35 317	85.11 201	95.01 194	84.82 37	97.10 244	87.46 183	88.21 233	96.50 204

CPTT-MVS		89.72 153	89.87 145	89.29 256	98.33 47	73.30 349	97.70 94	95.35 211	75.68 362	87.40 174	97.44 103	70.43 246	98.25 163	89.56 157	96.90 96	96.33 211

RRT-MVS		89.67 154	88.67 163	92.67 114	94.44 178	81.08 171	94.34 301	94.45 266	86.05 152	85.79 195	92.39 259	63.39 300	98.16 168	93.22 94	93.95 151	98.76 42

thisisatest0530		89.65 155	89.02 154	91.53 182	93.46 214	80.78 182	96.52 194	96.67 82	81.69 270	83.79 226	94.90 198	88.85 14	97.68 193	77.80 276	87.49 243	96.14 215

3Dnovator+	82.88 8	89.63 156	87.85 179	94.99 23	94.49 177	86.76 34	97.84 82	95.74 183	86.10 150	75.47 332	96.02 152	65.00 287	99.51 81	82.91 227	97.07 92	98.72 48

viewmambaseed2359dif		89.52 157	89.02 154	91.03 204	92.24 269	78.83 241	95.89 238	93.77 320	83.04 241	88.28 165	95.80 157	72.08 227	97.40 218	89.76 152	90.32 198	96.87 189

CDS-MVSNet		89.50 158	88.96 158	91.14 201	91.94 288	80.93 177	97.09 150	95.81 179	84.26 206	84.72 209	94.20 222	80.31 78	95.64 319	83.37 222	88.96 213	96.85 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		89.46 159	89.92 143	88.06 285	94.64 165	69.57 390	96.22 217	94.95 227	87.27 124	91.37 112	96.54 142	65.88 279	97.39 220	88.54 168	93.89 152	97.23 164

HyFIR lowres test		89.36 160	88.60 165	91.63 179	94.91 160	80.76 183	95.60 254	95.53 194	82.56 255	84.03 220	91.24 282	78.03 116	96.81 264	87.07 187	88.41 230	97.32 159

3Dnovator	82.32 10	89.33 161	87.64 184	94.42 37	93.73 203	85.70 49	97.73 92	96.75 71	86.73 142	76.21 321	95.93 153	62.17 306	99.68 60	81.67 237	97.81 63	97.88 104

h-mvs33		89.30 162	88.95 159	90.36 227	95.07 154	76.04 321	96.96 163	97.11 32	90.39 61	92.22 96	95.10 189	74.70 190	98.86 130	93.14 96	65.89 400	96.16 214

LFMVS		89.27 163	87.64 184	94.16 48	97.16 94	85.52 58	97.18 136	94.66 249	79.17 323	89.63 138	96.57 141	55.35 365	98.22 164	89.52 158	89.54 205	98.74 43

MVSTER		89.25 164	88.92 160	90.24 231	95.98 117	84.66 81	96.79 177	95.36 209	87.19 128	80.33 271	90.61 292	90.02 11	95.97 295	85.38 198	78.64 313	90.09 316

KinetiMVS		89.13 165	87.95 177	92.65 116	92.16 274	82.39 132	97.04 154	96.05 155	86.59 144	88.08 169	94.85 200	61.54 317	98.38 157	81.28 243	93.99 150	97.19 170

CostFormer		89.08 166	88.39 169	91.15 200	93.13 225	79.15 234	88.61 390	96.11 150	83.14 238	89.58 139	86.93 347	83.83 53	96.87 260	88.22 174	85.92 258	97.42 152

PVSNet	82.34 9	89.02 167	87.79 181	92.71 113	95.49 136	81.50 162	97.70 94	97.29 20	87.76 109	85.47 199	95.12 188	56.90 354	98.90 129	80.33 249	94.02 146	97.71 123

AstraMVS		88.99 168	88.35 170	90.92 208	90.81 314	78.29 263	96.73 181	94.24 282	89.96 67	86.13 192	95.04 191	62.12 310	97.41 216	92.54 106	87.57 242	97.06 179

test-mter		88.95 169	88.60 165	89.98 240	92.26 266	77.23 300	97.11 146	95.96 164	85.32 168	86.30 190	91.38 279	76.37 153	96.78 267	80.82 245	91.92 183	95.94 220

1314		88.94 170	87.20 198	94.17 46	93.21 220	85.73 48	93.33 332	96.64 89	82.89 246	75.98 324	96.36 145	66.83 273	99.39 87	83.52 221	96.02 121	97.39 156

UA-Net		88.92 171	88.48 168	90.24 231	94.06 194	77.18 302	93.04 340	94.66 249	87.39 119	91.09 117	93.89 233	74.92 186	98.18 167	75.83 303	91.43 187	95.35 242

thres200		88.92 171	87.65 183	92.73 112	96.30 105	85.62 56	97.85 81	98.86 1	84.38 200	84.82 206	93.99 231	75.12 184	98.01 175	70.86 344	86.67 247	94.56 266

Vis-MVSNet (Re-imp)		88.88 173	88.87 162	88.91 264	93.89 198	74.43 341	96.93 166	94.19 287	84.39 199	83.22 237	95.67 162	78.24 112	94.70 361	78.88 269	94.40 143	97.61 133

baseline1		88.85 174	87.49 191	92.93 101	95.21 146	86.85 32	95.47 259	94.61 255	87.29 121	83.11 239	94.99 196	80.70 73	96.89 257	82.28 233	73.72 338	95.05 252

AdaColmap		88.81 175	87.61 187	92.39 133	99.33 4	79.95 209	96.70 186	95.58 191	77.51 343	83.05 240	96.69 140	61.90 315	99.72 51	84.29 205	93.47 160	97.50 142

OMC-MVS		88.80 176	88.16 174	90.72 216	95.30 142	77.92 280	94.81 290	94.51 260	86.80 138	84.97 204	96.85 131	67.53 264	98.60 139	85.08 199	87.62 239	95.63 232

114514_t		88.79 177	87.57 189	92.45 127	98.21 53	81.74 154	96.99 156	95.45 202	75.16 366	82.48 243	95.69 161	68.59 257	98.50 147	80.33 249	95.18 132	97.10 176

mvs_anonymous		88.68 178	87.62 186	91.86 164	94.80 163	81.69 157	93.53 327	94.92 229	82.03 265	78.87 287	90.43 295	75.77 164	95.34 332	85.04 200	93.16 165	98.55 57

Vis-MVSNet		88.67 179	87.82 180	91.24 196	92.68 245	78.82 242	96.95 164	93.85 307	87.55 114	87.07 181	95.13 187	63.43 299	97.21 234	77.58 283	96.15 116	97.70 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet		88.67 179	88.16 174	90.20 233	93.61 204	76.86 307	96.77 180	93.07 352	84.02 212	83.62 230	95.60 167	74.69 193	96.24 287	78.43 273	93.66 158	97.49 143

IB-MVS	85.34 4	88.67 179	87.14 201	93.26 84	93.12 226	84.32 86	98.76 35	97.27 22	87.19 128	79.36 282	90.45 294	83.92 52	98.53 146	84.41 204	69.79 367	96.93 183
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
1112_ss		88.60 182	87.47 193	92.00 157	93.21 220	80.97 175	96.47 198	92.46 360	83.64 231	80.86 264	97.30 110	80.24 80	97.62 196	77.60 282	85.49 263	97.40 155

tttt0517		88.57 183	88.19 173	89.71 251	93.00 228	75.99 325	95.67 249	96.67 82	80.78 281	81.82 256	94.40 215	88.97 13	97.58 199	76.05 301	86.31 251	95.57 235

UWE-MVS		88.56 184	88.91 161	87.50 301	94.17 187	72.19 361	95.82 244	97.05 37	84.96 184	84.78 207	93.51 244	81.33 68	94.75 359	79.43 260	89.17 208	95.57 235

tfpn200view9		88.48 185	87.15 199	92.47 126	96.21 108	85.30 64	97.44 116	98.85 2	83.37 234	83.99 221	93.82 236	75.36 177	97.93 178	69.04 352	86.24 254	94.17 270

test-LLR		88.48 185	87.98 176	89.98 240	92.26 266	77.23 300	97.11 146	95.96 164	83.76 225	86.30 190	91.38 279	72.30 223	96.78 267	80.82 245	91.92 183	95.94 220

TAMVS		88.48 185	87.79 181	90.56 220	91.09 305	79.18 232	96.45 200	95.88 175	83.64 231	83.12 238	93.33 245	75.94 162	95.74 314	82.40 230	88.27 232	96.75 197

thres400		88.42 188	87.15 199	92.23 143	96.21 108	85.30 64	97.44 116	98.85 2	83.37 234	83.99 221	93.82 236	75.36 177	97.93 178	69.04 352	86.24 254	93.45 286

tpmrst		88.36 189	87.38 195	91.31 192	94.36 182	79.92 210	87.32 402	95.26 216	85.32 168	88.34 162	86.13 364	80.60 75	96.70 269	83.78 211	85.34 266	97.30 162

ECVR-MVS		88.35 190	87.25 197	91.65 176	93.54 207	79.40 225	96.56 192	90.78 395	86.78 139	85.57 197	95.25 176	57.25 352	97.56 201	84.73 203	94.80 135	97.98 98

thres100view900		88.30 191	86.95 206	92.33 136	96.10 113	84.90 78	97.14 143	98.85 2	82.69 252	83.41 234	93.66 240	75.43 174	97.93 178	69.04 352	86.24 254	94.17 270

VDD-MVS		88.28 192	87.02 204	92.06 153	95.09 152	80.18 204	97.55 107	94.45 266	83.09 239	89.10 148	95.92 155	47.97 397	98.49 148	93.08 100	86.91 246	97.52 141

BH-w/o		88.24 193	87.47 193	90.54 222	95.03 157	78.54 255	97.41 121	93.82 312	84.08 210	78.23 293	94.51 210	69.34 254	97.21 234	80.21 253	94.58 139	95.87 223

hse-mvs2		88.22 194	88.21 172	88.25 281	93.54 207	73.41 346	95.41 262	95.89 173	90.39 61	92.22 96	94.22 220	74.70 190	96.66 272	93.14 96	64.37 405	94.69 265

test1111		88.11 195	87.04 203	91.35 191	93.15 223	78.79 249	96.57 190	90.78 395	86.88 135	85.04 202	95.20 182	57.23 353	97.39 220	83.88 209	94.59 138	97.87 106

IMVS_0403		88.07 196	87.02 204	91.24 196	92.30 259	78.81 244	93.62 323	93.84 308	85.14 175	84.36 214	94.49 211	69.49 252	97.46 214	81.33 238	88.61 217	97.46 146

thres600view7		88.06 197	86.70 214	92.15 150	96.10 113	85.17 70	97.14 143	98.85 2	82.70 251	83.41 234	93.66 240	75.43 174	97.82 187	67.13 361	85.88 259	93.45 286

Test_1112_low_res		88.03 198	86.73 211	91.94 161	93.15 223	80.88 179	96.44 201	92.41 363	83.59 233	80.74 266	91.16 283	80.18 81	97.59 198	77.48 285	85.40 264	97.36 158

LuminaMVS		88.02 199	86.89 208	91.43 188	88.65 356	83.16 110	94.84 288	94.41 271	83.67 229	86.56 186	91.95 273	62.04 311	96.88 259	89.78 151	90.06 200	94.24 269

PLC	83.97 7	88.00 200	87.38 195	89.83 247	98.02 59	76.46 313	97.16 140	94.43 269	79.26 322	81.98 253	96.28 147	69.36 253	99.27 95	77.71 280	92.25 180	93.77 280
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CLD-MVS		87.97 201	87.48 192	89.44 254	92.16 274	80.54 193	98.14 59	94.92 229	91.41 44	79.43 281	95.40 173	62.34 305	97.27 230	90.60 137	82.90 283	90.50 306
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Fast-Effi-MVS+		87.93 202	86.94 207	90.92 208	94.04 195	79.16 233	98.26 55	93.72 322	81.29 273	83.94 224	92.90 252	69.83 250	96.68 270	76.70 293	91.74 185	96.93 183

HQP-MVS		87.91 203	87.55 190	88.98 263	92.08 279	78.48 256	97.63 97	94.80 237	90.52 58	82.30 246	94.56 208	65.40 283	97.32 225	87.67 181	83.01 280	91.13 298

IMVS_0407		87.82 204	86.72 212	91.14 201	92.30 259	78.81 244	93.34 331	93.84 308	85.14 175	83.68 228	94.49 211	67.75 259	97.14 242	81.33 238	88.61 217	97.46 146

reproduce_monomvs		87.80 205	87.60 188	88.40 275	96.56 100	80.26 200	95.80 245	96.32 133	91.56 43	73.60 344	88.36 322	88.53 16	96.25 286	90.47 139	67.23 393	88.67 351

test_fmvs1		87.79 206	88.52 167	85.62 337	92.98 232	64.31 413	97.88 80	92.42 362	87.95 103	92.24 95	95.82 156	47.94 398	98.44 155	95.31 64	94.09 144	94.09 274

WBMVS		87.73 207	86.79 209	90.56 220	95.61 132	85.68 51	97.63 97	95.52 196	83.77 224	78.30 292	88.44 321	86.14 32	95.78 308	82.54 229	73.15 345	90.21 311

UGNet		87.73 207	86.55 216	91.27 195	95.16 150	79.11 235	96.35 208	96.23 140	88.14 98	87.83 172	90.48 293	50.65 385	99.09 116	80.13 254	94.03 145	95.60 234
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
FA-MVS(test-final)		87.71 209	86.23 220	92.17 148	94.19 186	80.55 189	87.16 404	96.07 154	82.12 263	85.98 194	88.35 323	72.04 228	98.49 148	80.26 251	89.87 202	97.48 144

SSM_0404		87.69 210	86.26 218	91.95 159	92.94 234	83.02 114	94.69 293	92.33 365	80.11 302	84.65 211	94.18 223	64.68 292	96.90 255	82.34 231	90.44 197	95.94 220

EPNet_dtu		87.65 211	87.89 178	86.93 314	94.57 167	71.37 376	96.72 182	96.50 108	88.56 86	87.12 180	95.02 193	75.91 163	94.01 377	66.62 365	90.00 201	95.42 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test1		87.58 212	88.22 171	85.67 335	89.78 333	67.18 400	95.25 269	87.93 416	83.96 215	88.79 154	97.06 124	72.52 218	94.53 367	92.21 110	86.45 250	95.30 244

icg_test_0407_2		87.55 213	86.59 215	90.43 224	92.30 259	78.81 244	92.17 353	93.84 308	85.14 175	83.68 228	94.49 211	67.75 259	95.02 351	81.33 238	88.61 217	97.46 146

HQP_MVS		87.50 214	87.09 202	88.74 268	91.86 289	77.96 277	97.18 136	94.69 245	89.89 68	81.33 259	94.15 225	64.77 290	97.30 227	87.08 185	82.82 284	90.96 300

EPMVS		87.47 215	85.90 223	92.18 147	95.41 138	82.26 135	87.00 405	96.28 135	85.88 157	84.23 216	85.57 371	75.07 185	96.26 284	71.14 342	92.50 172	98.03 90

tpm2		87.35 216	86.26 218	90.62 218	92.93 238	78.67 252	88.06 397	95.99 161	79.33 318	87.40 174	86.43 358	80.28 79	96.40 278	80.23 252	85.73 262	96.79 192

SSM_0407		87.33 217	85.87 224	91.71 175	92.94 234	82.53 123	94.30 304	92.33 365	80.11 302	83.50 231	94.18 223	64.68 292	96.80 266	82.34 231	88.51 226	95.79 226

ab-mvs		87.08 218	84.94 241	93.48 79	93.34 217	83.67 99	88.82 387	95.70 185	81.18 274	84.55 213	90.14 301	62.72 303	98.94 127	85.49 197	82.54 288	97.85 109

SDMVSNet		87.02 219	85.61 226	91.24 196	94.14 189	83.30 107	93.88 317	95.98 162	84.30 203	79.63 279	92.01 267	58.23 338	97.68 193	90.28 147	82.02 292	92.75 289

CNLPA		86.96 220	85.37 231	91.72 174	97.59 75	79.34 228	97.21 132	91.05 390	74.22 373	78.90 285	96.75 138	67.21 268	98.95 125	74.68 313	90.77 194	96.88 188

BH-untuned		86.95 221	85.94 222	89.99 239	94.52 171	77.46 295	96.78 178	93.37 340	81.80 267	76.62 311	93.81 238	66.64 274	97.02 246	76.06 300	93.88 153	95.48 239

QAPM		86.88 222	84.51 245	93.98 49	94.04 195	85.89 45	97.19 135	96.05 155	73.62 378	75.12 335	95.62 166	62.02 312	99.74 46	70.88 343	96.06 119	96.30 213

BH-RMVSNet		86.84 223	85.28 233	91.49 186	95.35 141	80.26 200	96.95 164	92.21 367	82.86 248	81.77 258	95.46 172	59.34 330	97.64 195	69.79 350	93.81 154	96.57 203

PatchmatchNet		86.83 224	85.12 238	91.95 159	94.12 191	82.27 134	86.55 409	95.64 189	84.59 194	82.98 241	84.99 383	77.26 130	95.96 298	68.61 355	91.34 188	97.64 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
nrg030		86.79 225	85.43 229	90.87 212	88.76 349	85.34 61	97.06 153	94.33 277	84.31 201	80.45 269	91.98 270	72.36 220	96.36 281	88.48 171	71.13 354	90.93 302

PCF-MVS	84.09 5	86.77 226	85.00 240	92.08 151	92.06 282	83.07 112	92.14 354	94.47 264	79.63 313	76.90 307	94.78 202	71.15 238	99.20 106	72.87 328	91.05 192	93.98 276
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FIs		86.73 227	86.10 221	88.61 271	90.05 330	80.21 202	96.14 225	96.95 47	85.56 163	78.37 291	92.30 261	76.73 145	95.28 336	79.51 258	79.27 307	90.35 308

cascas		86.50 228	84.48 247	92.55 124	92.64 249	85.95 42	97.04 154	95.07 223	75.32 364	80.50 267	91.02 285	54.33 373	97.98 177	86.79 190	87.62 239	93.71 281

VDDNet		86.44 229	84.51 245	92.22 144	91.56 292	81.83 150	97.10 149	94.64 252	69.50 405	87.84 171	95.19 183	48.01 396	97.92 183	89.82 150	86.92 245	96.89 186

viewmsd2359difaftdt		86.38 230	85.29 232	89.67 252	90.42 321	75.65 331	95.27 268	92.45 361	85.54 164	84.28 215	94.73 203	62.16 307	97.39 220	87.78 178	74.97 333	95.96 218

GeoE		86.36 231	85.20 234	89.83 247	93.17 222	76.13 319	97.53 108	92.11 368	79.58 314	80.99 262	94.01 228	66.60 275	96.17 290	73.48 325	89.30 207	97.20 169

test_fmvs1_n		86.34 232	86.72 212	85.17 345	87.54 370	63.64 418	96.91 168	92.37 364	87.49 116	91.33 113	95.58 168	40.81 425	98.46 151	95.00 67	93.49 159	93.41 288

TR-MVS		86.30 233	84.93 242	90.42 225	94.63 166	77.58 293	96.57 190	93.82 312	80.30 297	82.42 245	95.16 185	58.74 334	97.55 203	74.88 311	87.82 237	96.13 216

X-MVStestdata		86.26 234	84.14 256	92.63 119	98.52 37	80.29 197	97.37 124	96.44 115	87.04 132	91.38 110	20.73 464	77.24 132	99.59 70	90.46 140	98.07 54	98.02 91

AUN-MVS		86.25 235	85.57 227	88.26 280	93.57 206	73.38 347	95.45 260	95.88 175	83.94 216	85.47 199	94.21 221	73.70 207	96.67 271	83.54 219	64.41 404	94.73 264

OpenMVS	79.58 14	86.09 236	83.62 266	93.50 77	90.95 307	86.71 35	97.44 116	95.83 178	75.35 363	72.64 358	95.72 159	57.42 351	99.64 64	71.41 337	95.85 125	94.13 273

FE-MVS		86.06 237	84.15 255	91.78 168	94.33 183	79.81 212	84.58 422	96.61 92	76.69 356	85.00 203	87.38 338	70.71 245	98.37 158	70.39 347	91.70 186	97.17 172

FC-MVSNet-test		85.96 238	85.39 230	87.66 294	89.38 346	78.02 274	95.65 251	96.87 54	85.12 179	77.34 300	91.94 274	76.28 156	94.74 360	77.09 288	78.82 311	90.21 311

miper_enhance_ethall		85.95 239	85.20 234	88.19 284	94.85 161	79.76 214	96.00 230	94.06 295	82.98 245	77.74 298	88.76 314	79.42 90	95.46 328	80.58 247	72.42 347	89.36 330

OPM-MVS		85.84 240	85.10 239	88.06 285	88.34 360	77.83 284	95.72 247	94.20 286	87.89 107	80.45 269	94.05 227	58.57 335	97.26 231	83.88 209	82.76 286	89.09 337
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		85.80 241	85.20 234	87.59 297	91.55 294	77.41 296	95.13 278	95.36 209	80.43 292	80.33 271	94.71 204	73.72 205	95.97 295	76.96 291	78.64 313	89.39 324

GA-MVS		85.79 242	84.04 257	91.02 206	89.47 344	80.27 199	96.90 169	94.84 235	85.57 161	80.88 263	89.08 309	56.56 358	96.47 277	77.72 279	85.35 265	96.34 209

XVG-OURS-SEG-HR		85.74 243	85.16 237	87.49 303	90.22 324	71.45 374	91.29 365	94.09 293	81.37 272	83.90 225	95.22 180	60.30 323	97.53 207	85.58 196	84.42 271	93.50 284

MonoMVSNet		85.68 244	84.22 253	90.03 237	88.43 359	77.83 284	92.95 343	91.46 380	87.28 122	78.11 294	85.96 366	66.31 278	94.81 357	90.71 135	76.81 324	97.46 146

SCA		85.63 245	83.64 265	91.60 180	92.30 259	81.86 149	92.88 344	95.56 193	84.85 185	82.52 242	85.12 381	58.04 341	95.39 329	73.89 321	87.58 241	97.54 136

Elysia		85.62 246	83.66 262	91.51 183	88.76 349	82.21 137	95.15 276	94.70 242	76.96 353	84.13 217	92.20 263	50.81 383	97.26 231	77.81 274	92.42 175	95.06 250

StellarMVS		85.62 246	83.66 262	91.51 183	88.76 349	82.21 137	95.15 276	94.70 242	76.96 353	84.13 217	92.20 263	50.81 383	97.26 231	77.81 274	92.42 175	95.06 250

test_vis1_n		85.60 248	85.70 225	85.33 342	84.79 401	64.98 411	96.83 172	91.61 379	87.36 120	91.00 120	94.84 201	36.14 432	97.18 236	95.66 56	93.03 166	93.82 279

tpm		85.55 249	84.47 248	88.80 267	90.19 326	75.39 333	88.79 388	94.69 245	84.83 186	83.96 223	85.21 377	78.22 113	94.68 363	76.32 299	78.02 321	96.34 209

mamv4		85.50 250	86.76 210	81.72 388	93.23 219	54.93 445	89.95 378	92.94 354	69.96 402	79.00 284	92.20 263	80.69 74	94.22 373	92.06 113	90.77 194	96.01 217

UniMVSNet_NR-MVSNet		85.49 251	84.59 244	88.21 283	89.44 345	79.36 226	96.71 184	96.41 119	85.22 171	78.11 294	90.98 287	76.97 140	95.14 344	79.14 266	68.30 381	90.12 314

gg-mvs-nofinetune		85.48 252	82.90 279	93.24 85	94.51 175	85.82 46	79.22 435	96.97 45	61.19 431	87.33 176	53.01 453	90.58 6	96.07 291	86.07 192	97.23 83	97.81 114

VortexMVS		85.45 253	84.40 249	88.63 270	93.25 218	81.66 158	95.39 264	94.34 275	87.15 130	75.10 336	87.65 334	66.58 276	95.19 340	86.89 189	73.21 344	89.03 341

UWE-MVS-28		85.41 254	86.36 217	82.59 380	91.12 304	66.81 405	93.88 317	97.03 38	83.86 221	78.55 288	93.84 235	77.76 123	88.55 427	73.47 326	87.69 238	92.41 293

IMVS_0404		85.34 255	83.69 259	90.29 229	92.30 259	78.81 244	90.62 372	93.84 308	85.14 175	72.51 361	94.49 211	54.36 372	94.61 364	81.33 238	88.61 217	97.46 146

VPA-MVSNet		85.32 256	83.83 258	89.77 250	90.25 323	82.63 121	96.36 207	97.07 35	83.03 243	81.21 261	89.02 311	61.58 316	96.31 283	85.02 201	70.95 356	90.36 307

UniMVSNet (Re)		85.31 257	84.23 252	88.55 272	89.75 335	80.55 189	96.72 182	96.89 52	85.42 166	78.40 290	88.93 312	75.38 176	95.52 326	78.58 271	68.02 384	89.57 323

mamba_0408		85.26 258	83.10 275	91.74 171	92.94 234	82.53 123	72.52 450	91.77 374	80.36 294	83.50 231	94.01 228	64.97 288	96.90 255	79.37 261	88.51 226	95.79 226

XVG-OURS		85.18 259	84.38 250	87.59 297	90.42 321	71.73 371	91.06 369	94.07 294	82.00 266	83.29 236	95.08 190	56.42 359	97.55 203	83.70 216	83.42 276	93.49 285

cl22		85.11 260	84.17 254	87.92 288	95.06 156	78.82 242	95.51 257	94.22 285	79.74 311	76.77 308	87.92 330	75.96 160	95.68 315	79.93 256	72.42 347	89.27 332

TAPA-MVS	81.61 12	85.02 261	83.67 261	89.06 260	96.79 98	73.27 352	95.92 235	94.79 239	74.81 369	80.47 268	96.83 132	71.07 239	98.19 166	49.82 433	92.57 170	95.71 231
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		85.00 262	83.66 262	89.02 262	95.86 122	74.55 340	92.49 348	93.60 328	79.30 320	79.29 283	91.47 277	58.53 336	98.45 153	70.22 348	92.17 182	94.07 275

PS-MVSNAJss		84.91 263	84.30 251	86.74 315	85.89 389	74.40 342	94.95 285	94.16 289	83.93 217	76.45 314	90.11 302	71.04 240	95.77 309	83.16 224	79.02 310	90.06 318

CVMVSNet		84.83 264	85.57 227	82.63 379	91.55 294	60.38 431	95.13 278	95.03 225	80.60 285	82.10 252	94.71 204	66.40 277	90.19 420	74.30 318	90.32 198	97.31 161

FMVSNet3		84.71 265	82.71 283	90.70 217	94.55 169	87.71 23	95.92 235	94.67 248	81.73 269	75.82 327	88.08 328	66.99 270	94.47 368	71.23 339	75.38 330	89.91 320

VPNet		84.69 266	82.92 278	90.01 238	89.01 348	83.45 104	96.71 184	95.46 201	85.71 159	79.65 278	92.18 266	56.66 357	96.01 294	83.05 226	67.84 387	90.56 305

SSM_04072		84.64 267	83.10 275	89.25 257	92.94 234	82.53 123	72.52 450	91.77 374	80.36 294	83.50 231	94.01 228	64.97 288	89.41 423	79.37 261	88.51 226	95.79 226

sd_testset		84.62 268	83.11 274	89.17 258	94.14 189	77.78 286	91.54 364	94.38 273	84.30 203	79.63 279	92.01 267	52.28 378	96.98 249	77.67 281	82.02 292	92.75 289

Effi-MVS+-dtu		84.61 269	84.90 243	83.72 367	91.96 286	63.14 421	94.95 285	93.34 341	85.57 161	79.79 277	87.12 344	61.99 313	95.61 322	83.55 218	85.83 260	92.41 293

miper_ehance_all_eth		84.57 270	83.60 267	87.50 301	92.64 249	78.25 266	95.40 263	93.47 332	79.28 321	76.41 315	87.64 335	76.53 148	95.24 338	78.58 271	72.42 347	89.01 343

DU-MVS		84.57 270	83.33 272	88.28 279	88.76 349	79.36 226	96.43 203	95.41 208	85.42 166	78.11 294	90.82 288	67.61 261	95.14 344	79.14 266	68.30 381	90.33 309

F-COLMAP		84.50 272	83.44 271	87.67 293	95.22 145	72.22 359	95.95 233	93.78 317	75.74 361	76.30 318	95.18 184	59.50 328	98.45 153	72.67 330	86.59 249	92.35 295

Anonymous202405211		84.41 273	81.93 294	91.85 166	96.78 99	78.41 260	97.44 116	91.34 384	70.29 400	84.06 219	94.26 218	41.09 422	98.96 123	79.46 259	82.65 287	98.17 81

WR-MVS		84.32 274	82.96 277	88.41 274	89.38 346	80.32 196	96.59 189	96.25 138	83.97 214	76.63 310	90.36 296	67.53 264	94.86 355	75.82 304	70.09 365	90.06 318

dp		84.30 275	82.31 288	90.28 230	94.24 185	77.97 276	86.57 408	95.53 194	79.94 308	80.75 265	85.16 379	71.49 236	96.39 279	63.73 381	83.36 277	96.48 205

LPG-MVS_test		84.20 276	83.49 270	86.33 321	90.88 308	73.06 353	95.28 265	94.13 290	82.20 260	76.31 316	93.20 246	54.83 370	96.95 251	83.72 214	80.83 297	88.98 344

dmvs_re		84.10 277	82.90 279	87.70 292	91.41 298	73.28 350	90.59 373	93.19 345	85.02 181	77.96 297	93.68 239	57.92 346	96.18 289	75.50 306	80.87 296	93.63 282

WB-MVSnew		84.08 278	83.51 269	85.80 330	91.34 299	76.69 311	95.62 253	96.27 136	81.77 268	81.81 257	92.81 253	58.23 338	94.70 361	66.66 364	87.06 244	85.99 400

ACMP	81.66 11	84.00 279	83.22 273	86.33 321	91.53 296	72.95 357	95.91 237	93.79 316	83.70 228	73.79 343	92.22 262	54.31 374	96.89 257	83.98 208	79.74 302	89.16 335
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IterMVS-LS		83.93 280	82.80 282	87.31 307	91.46 297	77.39 297	95.66 250	93.43 335	80.44 290	75.51 331	87.26 341	73.72 205	95.16 343	76.99 289	70.72 358	89.39 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XXY-MVS		83.84 281	82.00 293	89.35 255	87.13 372	81.38 163	95.72 247	94.26 281	80.15 301	75.92 326	90.63 291	61.96 314	96.52 275	78.98 268	73.28 343	90.14 313

c3_l		83.80 282	82.65 284	87.25 309	92.10 278	77.74 291	95.25 269	93.04 353	78.58 332	76.01 323	87.21 343	75.25 182	95.11 346	77.54 284	68.89 375	88.91 349

LCM-MVSNet-Re		83.75 283	83.54 268	84.39 360	93.54 207	64.14 415	92.51 347	84.03 437	83.90 218	66.14 398	86.59 352	67.36 266	92.68 392	84.89 202	92.87 167	96.35 208

ACMM	80.70 13	83.72 284	82.85 281	86.31 324	91.19 301	72.12 363	95.88 239	94.29 279	80.44 290	77.02 305	91.96 271	55.24 366	97.14 242	79.30 264	80.38 299	89.67 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		83.63 285	81.38 302	90.39 226	93.53 212	78.19 272	85.56 416	95.09 221	70.78 398	78.51 289	83.28 398	74.80 189	97.03 245	66.77 363	84.05 272	95.95 219

CR-MVSNet		83.53 286	81.36 303	90.06 236	90.16 327	79.75 215	79.02 437	91.12 387	84.24 207	82.27 250	80.35 414	75.45 172	93.67 384	63.37 384	86.25 252	96.75 197

v2v482		83.46 287	81.86 295	88.25 281	86.19 383	79.65 220	96.34 209	94.02 297	81.56 271	77.32 301	88.23 325	65.62 280	96.03 292	77.77 277	69.72 369	89.09 337

NR-MVSNet		83.35 288	81.52 301	88.84 265	88.76 349	81.31 165	94.45 296	95.16 219	84.65 192	67.81 387	90.82 288	70.36 247	94.87 354	74.75 312	66.89 397	90.33 309

Fast-Effi-MVS+-dtu		83.33 289	82.60 285	85.50 339	89.55 342	69.38 391	96.09 228	91.38 381	82.30 259	75.96 325	91.41 278	56.71 355	95.58 324	75.13 310	84.90 268	91.54 296

cl____		83.27 290	82.12 290	86.74 315	92.20 270	75.95 326	95.11 280	93.27 343	78.44 335	74.82 338	87.02 346	74.19 198	95.19 340	74.67 314	69.32 371	89.09 337

DIV-MVS_self_test		83.27 290	82.12 290	86.74 315	92.19 271	75.92 328	95.11 280	93.26 344	78.44 335	74.81 339	87.08 345	74.19 198	95.19 340	74.66 315	69.30 372	89.11 336

TranMVSNet+NR-MVSNet		83.24 292	81.71 297	87.83 289	87.71 367	78.81 244	96.13 227	94.82 236	84.52 195	76.18 322	90.78 290	64.07 295	94.60 365	74.60 316	66.59 399	90.09 316

Anonymous20240529		83.15 293	80.60 314	90.80 213	95.74 128	78.27 265	96.81 176	94.92 229	60.10 436	81.89 255	92.54 257	45.82 406	98.82 132	79.25 265	78.32 319	95.31 243

eth_miper_zixun_eth		83.12 294	82.01 292	86.47 320	91.85 291	74.80 336	94.33 302	93.18 347	79.11 324	75.74 330	87.25 342	72.71 215	95.32 334	76.78 292	67.13 394	89.27 332

MS-PatchMatch		83.05 295	81.82 296	86.72 319	89.64 339	79.10 236	94.88 287	94.59 257	79.70 312	70.67 374	89.65 305	50.43 387	96.82 263	70.82 346	95.99 123	84.25 415

V42		83.04 296	81.53 300	87.57 299	86.27 382	79.09 237	95.87 240	94.11 292	80.35 296	77.22 303	86.79 350	65.32 285	96.02 293	77.74 278	70.14 361	87.61 375

tpmvs		83.04 296	80.77 310	89.84 246	95.43 137	77.96 277	85.59 415	95.32 213	75.31 365	76.27 319	83.70 394	73.89 202	97.41 216	59.53 398	81.93 294	94.14 272

test_djsdf		83.00 298	82.45 287	84.64 353	84.07 410	69.78 387	94.80 291	94.48 261	80.74 282	75.41 333	87.70 333	61.32 320	95.10 347	83.77 212	79.76 300	89.04 340

v1144		82.90 299	81.27 304	87.78 291	86.29 381	79.07 238	96.14 225	93.93 299	80.05 305	77.38 299	86.80 349	65.50 281	95.93 300	75.21 309	70.13 362	88.33 362

test0.0.03 1		82.79 300	82.48 286	83.74 366	86.81 375	72.22 359	96.52 194	95.03 225	83.76 225	73.00 354	93.20 246	72.30 223	88.88 425	64.15 379	77.52 322	90.12 314

FMVSNet2		82.79 300	80.44 316	89.83 247	92.66 246	85.43 59	95.42 261	94.35 274	79.06 326	74.46 340	87.28 339	56.38 360	94.31 371	69.72 351	74.68 335	89.76 321

D2MVS		82.67 302	81.55 299	86.04 328	87.77 366	76.47 312	95.21 271	96.58 98	82.66 253	70.26 377	85.46 374	60.39 322	95.80 306	76.40 297	79.18 308	85.83 403

MVP-Stereo		82.65 303	81.67 298	85.59 338	86.10 386	78.29 263	93.33 332	92.82 356	77.75 340	69.17 384	87.98 329	59.28 331	95.76 310	71.77 334	96.88 98	82.73 423
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs4		82.54 304	80.79 309	87.79 290	86.11 385	80.49 195	93.55 326	93.18 347	77.29 346	73.35 350	89.40 308	65.26 286	95.05 350	75.32 308	73.61 339	87.83 370

v144192		82.43 305	80.73 311	87.54 300	85.81 390	78.22 267	95.98 231	93.78 317	79.09 325	77.11 304	86.49 354	64.66 294	95.91 301	74.20 319	69.42 370	88.49 356

GBi-Net		82.42 306	80.43 317	88.39 276	92.66 246	81.95 142	94.30 304	93.38 337	79.06 326	75.82 327	85.66 367	56.38 360	93.84 380	71.23 339	75.38 330	89.38 326

test1		82.42 306	80.43 317	88.39 276	92.66 246	81.95 142	94.30 304	93.38 337	79.06 326	75.82 327	85.66 367	56.38 360	93.84 380	71.23 339	75.38 330	89.38 326

v148		82.41 308	80.89 308	86.99 313	86.18 384	76.81 308	96.27 214	93.82 312	80.49 289	75.28 334	86.11 365	67.32 267	95.75 311	75.48 307	67.03 396	88.42 360

v1192		82.31 309	80.55 315	87.60 296	85.94 387	78.47 259	95.85 242	93.80 315	79.33 318	76.97 306	86.51 353	63.33 301	95.87 302	73.11 327	70.13 362	88.46 358

LS3D		82.22 310	79.94 325	89.06 260	97.43 84	74.06 345	93.20 338	92.05 369	61.90 426	73.33 351	95.21 181	59.35 329	99.21 101	54.54 420	92.48 173	93.90 278

jajsoiax		82.12 311	81.15 306	85.03 347	84.19 408	70.70 379	94.22 309	93.95 298	83.07 240	73.48 346	89.75 304	49.66 391	95.37 331	82.24 234	79.76 300	89.02 342

v1921920		82.02 312	80.23 319	87.41 304	85.62 391	77.92 280	95.79 246	93.69 323	78.86 329	76.67 309	86.44 356	62.50 304	95.83 304	72.69 329	69.77 368	88.47 357

myMVS_eth3d		81.93 313	82.18 289	81.18 391	92.13 276	67.18 400	93.97 313	94.23 283	82.43 256	73.39 347	93.57 242	76.98 139	87.86 431	50.53 431	82.34 289	88.51 354

v8		81.88 314	80.06 323	87.32 306	86.63 376	79.04 239	94.41 297	93.65 325	78.77 330	73.19 353	85.57 371	66.87 272	95.81 305	73.84 323	67.61 389	87.11 384

mvs_tets		81.74 315	80.71 312	84.84 348	84.22 407	70.29 383	93.91 316	93.78 317	82.77 250	73.37 349	89.46 307	47.36 402	95.31 335	81.99 235	79.55 306	88.92 348

v1240		81.70 316	79.83 327	87.30 308	85.50 392	77.70 292	95.48 258	93.44 333	78.46 334	76.53 313	86.44 356	60.85 321	95.84 303	71.59 336	70.17 360	88.35 361

PVSNet_0	77.72 15	81.70 316	78.95 335	89.94 243	90.77 315	76.72 310	95.96 232	96.95 47	85.01 182	70.24 378	88.53 319	52.32 377	98.20 165	86.68 191	44.08 449	94.89 255

miper_lstm_enhance		81.66 318	80.66 313	84.67 352	91.19 301	71.97 366	91.94 356	93.19 345	77.86 339	72.27 362	85.26 375	73.46 208	93.42 388	73.71 324	67.05 395	88.61 352

DP-MVS		81.47 319	78.28 338	91.04 203	98.14 55	78.48 256	95.09 283	86.97 420	61.14 432	71.12 371	92.78 256	59.59 326	99.38 88	53.11 424	86.61 248	95.27 246

v10		81.43 320	79.53 329	87.11 311	86.38 378	78.87 240	94.31 303	93.43 335	77.88 338	73.24 352	85.26 375	65.44 282	95.75 311	72.14 333	67.71 388	86.72 388

pmmvs5		81.34 321	79.54 328	86.73 318	85.02 399	76.91 305	96.22 217	91.65 377	77.65 341	73.55 345	88.61 316	55.70 363	94.43 369	74.12 320	73.35 342	88.86 350

SD_0403		81.29 322	81.13 307	81.78 387	90.20 325	60.43 430	89.97 377	91.31 386	83.87 219	71.78 365	93.08 251	63.86 296	89.61 422	60.00 397	86.07 257	95.30 244

ADS-MVSNet		81.26 323	78.36 337	89.96 242	93.78 200	79.78 213	79.48 433	93.60 328	73.09 384	80.14 273	79.99 417	62.15 308	95.24 338	59.49 399	83.52 274	94.85 257

Baseline_NR-MVSNet		81.22 324	80.07 322	84.68 351	85.32 397	75.12 335	96.48 197	88.80 411	76.24 360	77.28 302	86.40 359	67.61 261	94.39 370	75.73 305	66.73 398	84.54 412

tt0805		81.20 325	79.06 334	87.61 295	86.50 377	72.97 356	93.66 321	95.48 199	74.11 374	76.23 320	91.99 269	41.36 421	97.40 218	77.44 286	74.78 334	92.45 292

SSC-MVS3.2		81.06 326	79.49 330	85.75 333	89.78 333	73.00 355	94.40 300	95.23 217	83.76 225	76.61 312	87.82 332	49.48 392	94.88 353	66.80 362	71.56 352	89.38 326

WR-MVS_H		81.02 327	80.09 320	83.79 364	88.08 363	71.26 377	94.46 295	96.54 102	80.08 304	72.81 357	86.82 348	70.36 247	92.65 393	64.18 378	67.50 390	87.46 381

CP-MVSNet		81.01 328	80.08 321	83.79 364	87.91 365	70.51 380	94.29 308	95.65 188	80.83 279	72.54 360	88.84 313	63.71 297	92.32 398	68.58 356	68.36 380	88.55 353

anonymousdsp		80.98 329	79.97 324	84.01 361	81.73 422	70.44 382	92.49 348	93.58 330	77.10 350	72.98 355	86.31 360	57.58 347	94.90 352	79.32 263	78.63 315	86.69 389

UniMVSNet_ETH3D		80.86 330	78.75 336	87.22 310	86.31 380	72.02 364	91.95 355	93.76 321	73.51 379	75.06 337	90.16 300	43.04 415	95.66 316	76.37 298	78.55 316	93.98 276

testing3		80.74 331	81.17 305	79.44 401	91.15 303	63.48 419	97.16 140	95.76 181	80.83 279	71.36 368	93.15 249	78.22 113	87.30 436	43.19 444	79.67 303	87.55 379

IterMVS		80.67 332	79.16 332	85.20 344	89.79 332	76.08 320	92.97 342	91.86 371	80.28 298	71.20 370	85.14 380	57.93 345	91.34 410	72.52 331	70.74 357	88.18 365
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		80.62 333	77.77 343	89.14 259	93.43 215	77.24 299	91.89 357	90.18 399	69.86 404	68.02 386	91.94 274	52.21 379	98.84 131	59.32 401	83.12 278	91.35 297

IterMVS-SCA-FT		80.51 334	79.10 333	84.73 350	89.63 340	74.66 337	92.98 341	91.81 373	80.05 305	71.06 372	85.18 378	58.04 341	91.40 409	72.48 332	70.70 359	88.12 366

PS-CasMVS		80.27 335	79.18 331	83.52 370	87.56 369	69.88 386	94.08 311	95.29 214	80.27 299	72.08 363	88.51 320	59.22 332	92.23 400	67.49 358	68.15 383	88.45 359

pm-mvs1		80.05 336	78.02 341	86.15 326	85.42 393	75.81 329	95.11 280	92.69 359	77.13 348	70.36 376	87.43 337	58.44 337	95.27 337	71.36 338	64.25 406	87.36 382

RPMNet		79.85 337	75.92 357	91.64 177	90.16 327	79.75 215	79.02 437	95.44 203	58.43 441	82.27 250	72.55 442	73.03 212	98.41 156	46.10 440	86.25 252	96.75 197

PatchT		79.75 338	76.85 350	88.42 273	89.55 342	75.49 332	77.37 441	94.61 255	63.07 421	82.46 244	73.32 439	75.52 171	93.41 389	51.36 427	84.43 270	96.36 207

Anonymous20231211		79.72 339	77.19 347	87.33 305	95.59 134	77.16 303	95.18 275	94.18 288	59.31 439	72.57 359	86.20 363	47.89 399	95.66 316	74.53 317	69.24 373	89.18 334

test_fmvs2		79.59 340	79.90 326	78.67 405	82.86 419	55.82 442	95.20 272	89.55 403	81.09 275	80.12 275	89.80 303	34.31 437	93.51 387	87.82 177	78.36 318	86.69 389

ADS-MVSNet2		79.57 341	77.53 344	85.71 334	93.78 200	72.13 362	79.48 433	86.11 427	73.09 384	80.14 273	79.99 417	62.15 308	90.14 421	59.49 399	83.52 274	94.85 257

FMVSNet1		79.50 342	76.54 353	88.39 276	88.47 357	81.95 142	94.30 304	93.38 337	73.14 383	72.04 364	85.66 367	43.86 409	93.84 380	65.48 372	72.53 346	89.38 326

PEN-MVS		79.47 343	78.26 339	83.08 373	86.36 379	68.58 394	93.85 319	94.77 240	79.76 310	71.37 367	88.55 317	59.79 324	92.46 394	64.50 376	65.40 401	88.19 364

XVG-ACMP-BASELINE		79.38 344	77.90 342	83.81 363	84.98 400	67.14 404	89.03 386	93.18 347	80.26 300	72.87 356	88.15 327	38.55 427	96.26 284	76.05 301	78.05 320	88.02 367

v7n		79.32 345	77.34 345	85.28 343	84.05 411	72.89 358	93.38 329	93.87 305	75.02 368	70.68 373	84.37 387	59.58 327	95.62 321	67.60 357	67.50 390	87.32 383

MIMVSNet		79.18 346	75.99 356	88.72 269	87.37 371	80.66 185	79.96 431	91.82 372	77.38 345	74.33 341	81.87 405	41.78 418	90.74 416	66.36 370	83.10 279	94.76 259

JIA-IIPM		79.00 347	77.20 346	84.40 359	89.74 337	64.06 416	75.30 445	95.44 203	62.15 425	81.90 254	59.08 451	78.92 99	95.59 323	66.51 368	85.78 261	93.54 283

USDC		78.65 348	76.25 354	85.85 329	87.58 368	74.60 339	89.58 381	90.58 398	84.05 211	63.13 411	88.23 325	40.69 426	96.86 262	66.57 367	75.81 328	86.09 398

DTE-MVSNet		78.37 349	77.06 348	82.32 383	85.22 398	67.17 403	93.40 328	93.66 324	78.71 331	70.53 375	88.29 324	59.06 333	92.23 400	61.38 391	63.28 410	87.56 377

Patchmatch-test		78.25 350	74.72 365	88.83 266	91.20 300	74.10 344	73.91 448	88.70 414	59.89 437	66.82 393	85.12 381	78.38 109	94.54 366	48.84 436	79.58 305	97.86 108

tfpnnormal		78.14 351	75.42 359	86.31 324	88.33 361	79.24 229	94.41 297	96.22 141	73.51 379	69.81 380	85.52 373	55.43 364	95.75 311	47.65 438	67.86 386	83.95 418

mmtdpeth		78.04 352	76.76 351	81.86 386	89.60 341	66.12 408	92.34 352	87.18 419	76.83 355	85.55 198	76.49 430	46.77 403	97.02 246	90.85 130	45.24 446	82.43 427

ACMH	75.40 17	77.99 353	74.96 361	87.10 312	90.67 316	76.41 315	93.19 339	91.64 378	72.47 390	63.44 409	87.61 336	43.34 412	97.16 237	58.34 404	73.94 337	87.72 371
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	73.68 18	77.99 353	75.74 358	84.74 349	90.45 320	72.02 364	86.41 410	91.12 387	72.57 389	66.63 395	87.27 340	54.95 369	96.98 249	56.29 414	75.98 325	85.21 407
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
Syy-MVS		77.97 355	78.05 340	77.74 409	92.13 276	56.85 438	93.97 313	94.23 283	82.43 256	73.39 347	93.57 242	57.95 344	87.86 431	32.40 452	82.34 289	88.51 354

our_test_3		77.90 356	75.37 360	85.48 340	85.39 394	76.74 309	93.63 322	91.67 376	73.39 382	65.72 400	84.65 386	58.20 340	93.13 391	57.82 406	67.87 385	86.57 391

RPSCF		77.73 357	76.63 352	81.06 392	88.66 355	55.76 443	87.77 399	87.88 417	64.82 419	74.14 342	92.79 255	49.22 393	96.81 264	67.47 359	76.88 323	90.62 304

KD-MVS_2432*1600		77.63 358	74.92 363	85.77 331	90.86 311	79.44 223	88.08 395	93.92 301	76.26 358	67.05 391	82.78 400	72.15 225	91.92 403	61.53 388	41.62 452	85.94 401

miper_refine_blended		77.63 358	74.92 363	85.77 331	90.86 311	79.44 223	88.08 395	93.92 301	76.26 358	67.05 391	82.78 400	72.15 225	91.92 403	61.53 388	41.62 452	85.94 401

ACMH+	76.62 16	77.47 360	74.94 362	85.05 346	91.07 306	71.58 373	93.26 336	90.01 400	71.80 393	64.76 404	88.55 317	41.62 419	96.48 276	62.35 387	71.00 355	87.09 385

Patchmtry		77.36 361	74.59 366	85.67 335	89.75 335	75.75 330	77.85 440	91.12 387	60.28 434	71.23 369	80.35 414	75.45 172	93.56 386	57.94 405	67.34 392	87.68 373

ppachtmachnet_test		77.19 362	74.22 370	86.13 327	85.39 394	78.22 267	93.98 312	91.36 383	71.74 394	67.11 390	84.87 384	56.67 356	93.37 390	52.21 425	64.59 403	86.80 387

OurMVSNet-221017-0		77.18 363	76.06 355	80.55 395	83.78 414	60.00 433	90.35 374	91.05 390	77.01 352	66.62 396	87.92 330	47.73 400	94.03 376	71.63 335	68.44 379	87.62 374

TransMVSNet (Re)		76.94 364	74.38 368	84.62 354	85.92 388	75.25 334	95.28 265	89.18 408	73.88 377	67.22 388	86.46 355	59.64 325	94.10 375	59.24 402	52.57 433	84.50 413

EU-MVSNet		76.92 365	76.95 349	76.83 414	84.10 409	54.73 446	91.77 359	92.71 358	72.74 387	69.57 381	88.69 315	58.03 343	87.43 435	64.91 375	70.00 366	88.33 362

Patchmatch-RL test		76.65 366	74.01 373	84.55 355	77.37 437	64.23 414	78.49 439	82.84 442	78.48 333	64.63 405	73.40 438	76.05 159	91.70 408	76.99 289	57.84 419	97.72 121

FMVSNet5		76.46 367	74.16 371	83.35 372	90.05 330	76.17 318	89.58 381	89.85 401	71.39 396	65.29 403	80.42 413	50.61 386	87.70 434	61.05 393	69.24 373	86.18 396

SixPastTwentyTwo		76.04 368	74.32 369	81.22 390	84.54 403	61.43 428	91.16 367	89.30 407	77.89 337	64.04 406	86.31 360	48.23 394	94.29 372	63.54 383	63.84 408	87.93 369

AllTest		75.92 369	73.06 377	84.47 356	92.18 272	67.29 398	91.07 368	84.43 433	67.63 410	63.48 407	90.18 298	38.20 428	97.16 237	57.04 410	73.37 340	88.97 346

CL-MVSNet_self_test		75.81 370	74.14 372	80.83 394	78.33 433	67.79 397	94.22 309	93.52 331	77.28 347	69.82 379	81.54 408	61.47 319	89.22 424	57.59 408	53.51 429	85.48 405

COLMAP_ROB	73.24 19	75.74 371	73.00 378	83.94 362	92.38 254	69.08 392	91.85 358	86.93 421	61.48 429	65.32 402	90.27 297	42.27 417	96.93 254	50.91 429	75.63 329	85.80 404
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CMPMVS	54.94 21	75.71 372	74.56 367	79.17 403	79.69 428	55.98 440	89.59 380	93.30 342	60.28 434	53.85 441	89.07 310	47.68 401	96.33 282	76.55 294	81.02 295	85.22 406
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20231206		75.29 373	73.64 374	80.22 397	80.75 423	63.38 420	93.36 330	90.71 397	73.09 384	67.12 389	83.70 394	50.33 388	90.85 415	53.63 423	70.10 364	86.44 392

EG-PatchMatch MVS		74.92 374	72.02 382	83.62 368	83.76 416	73.28 350	93.62 323	92.04 370	68.57 408	58.88 430	83.80 393	31.87 442	95.57 325	56.97 412	78.67 312	82.00 431

testgi		74.88 375	73.40 375	79.32 402	80.13 427	61.75 425	93.21 337	86.64 425	79.49 316	66.56 397	91.06 284	35.51 435	88.67 426	56.79 413	71.25 353	87.56 377

pmmvs6		74.65 376	71.67 383	83.60 369	79.13 430	69.94 385	93.31 335	90.88 394	61.05 433	65.83 399	84.15 390	43.43 411	94.83 356	66.62 365	60.63 415	86.02 399

test_vis1_rt		73.96 377	72.40 380	78.64 406	83.91 412	61.16 429	95.63 252	68.18 459	76.32 357	60.09 427	74.77 433	29.01 448	97.54 205	87.74 179	75.94 326	77.22 442

K. test v3		73.62 378	71.59 384	79.69 399	82.98 418	59.85 434	90.85 371	88.83 410	77.13 348	58.90 429	82.11 402	43.62 410	91.72 407	65.83 371	54.10 428	87.50 380

pmmvs-eth3d		73.59 379	70.66 387	82.38 381	76.40 441	73.38 347	89.39 385	89.43 405	72.69 388	60.34 426	77.79 423	46.43 405	91.26 412	66.42 369	57.06 420	82.51 424

kuosan		73.55 380	72.39 381	77.01 412	89.68 338	66.72 406	85.24 419	93.44 333	67.76 409	60.04 428	83.40 397	71.90 230	84.25 443	45.34 441	54.75 424	80.06 438

MDA-MVSNet_test_wron		73.54 381	70.43 389	82.86 375	84.55 402	71.85 368	91.74 360	91.32 385	67.63 410	46.73 446	81.09 411	55.11 367	90.42 419	55.91 416	59.76 416	86.31 394

YYNet1		73.53 382	70.43 389	82.85 376	84.52 404	71.73 371	91.69 361	91.37 382	67.63 410	46.79 445	81.21 410	55.04 368	90.43 418	55.93 415	59.70 417	86.38 393

UnsupCasMVSNet_eth		73.25 383	70.57 388	81.30 389	77.53 435	66.33 407	87.24 403	93.89 304	80.38 293	57.90 434	81.59 406	42.91 416	90.56 417	65.18 374	48.51 440	87.01 386

DSMNet-mixed		73.13 384	72.45 379	75.19 420	77.51 436	46.82 451	85.09 420	82.01 444	67.61 414	69.27 383	81.33 409	50.89 382	86.28 438	54.54 420	83.80 273	92.46 291

OpenMVS_ROB	68.52 20	73.02 385	69.57 392	83.37 371	80.54 426	71.82 369	93.60 325	88.22 415	62.37 424	61.98 418	83.15 399	35.31 436	95.47 327	45.08 442	75.88 327	82.82 421

test_0402		72.68 386	69.54 393	82.09 384	88.67 354	71.81 370	92.72 346	86.77 424	61.52 428	62.21 417	83.91 392	43.22 413	93.76 383	34.60 450	72.23 350	80.72 437

TinyColmap		72.41 387	68.99 396	82.68 377	88.11 362	69.59 389	88.41 391	85.20 429	65.55 416	57.91 433	84.82 385	30.80 444	95.94 299	51.38 426	68.70 376	82.49 426

sc_t1		72.37 388	68.03 399	85.39 341	83.78 414	70.51 380	91.27 366	83.70 439	52.46 446	68.29 385	82.02 403	30.58 445	94.81 357	64.50 376	55.69 422	90.85 303

test20.03		72.36 389	71.15 385	75.98 418	77.79 434	59.16 435	92.40 350	89.35 406	74.09 375	61.50 421	84.32 388	48.09 395	85.54 441	50.63 430	62.15 413	83.24 419

LF4IMVS		72.36 389	70.82 386	76.95 413	79.18 429	56.33 439	86.12 412	86.11 427	69.30 406	63.06 412	86.66 351	33.03 440	92.25 399	65.33 373	68.64 377	82.28 428

Anonymous20240521		72.06 391	69.91 391	78.50 407	77.11 438	61.67 427	91.62 363	90.97 392	65.52 417	62.37 416	79.05 420	36.32 431	90.96 414	57.75 407	68.52 378	82.87 420

dmvs_testset		72.00 392	73.36 376	67.91 426	83.83 413	31.90 466	85.30 418	77.12 451	82.80 249	63.05 413	92.46 258	61.54 317	82.55 448	42.22 447	71.89 351	89.29 331

MDA-MVSNet-bldmvs		71.45 393	67.94 400	81.98 385	85.33 396	68.50 395	92.35 351	88.76 412	70.40 399	42.99 449	81.96 404	46.57 404	91.31 411	48.75 437	54.39 427	86.11 397

mvs5depth		71.40 394	68.36 398	80.54 396	75.31 445	65.56 410	79.94 432	85.14 430	69.11 407	71.75 366	81.59 406	41.02 423	93.94 378	60.90 394	50.46 436	82.10 429

MVS-HIRNet		71.36 395	67.00 401	84.46 358	90.58 317	69.74 388	79.15 436	87.74 418	46.09 450	61.96 419	50.50 454	45.14 407	95.64 319	53.74 422	88.11 234	88.00 368

KD-MVS_self_test		70.97 396	69.31 394	75.95 419	76.24 443	55.39 444	87.45 400	90.94 393	70.20 401	62.96 414	77.48 425	44.01 408	88.09 429	61.25 392	53.26 430	84.37 414

tt0320		70.21 397	66.07 405	82.64 378	83.42 417	70.82 378	89.63 379	84.10 436	49.75 449	62.71 415	77.28 426	33.35 438	92.45 396	58.78 403	55.62 423	84.64 411

tt0320-xc		69.70 398	65.27 409	82.99 374	84.33 405	71.92 367	89.56 383	82.08 443	50.11 447	61.87 420	77.50 424	30.48 446	92.34 397	60.30 395	51.20 435	84.71 410

ttmdpeth		69.58 399	66.92 403	77.54 411	75.95 444	62.40 423	88.09 394	84.32 435	62.87 423	65.70 401	86.25 362	36.53 430	88.53 428	55.65 418	46.96 445	81.70 434

test_fmvs3		69.56 400	69.19 395	70.67 424	69.01 450	47.05 450	90.87 370	86.81 422	71.31 397	66.79 394	77.15 427	16.40 455	83.17 446	81.84 236	62.51 412	81.79 433

dongtai		69.47 401	68.98 397	70.93 423	86.87 374	58.45 436	88.19 393	93.18 347	63.98 420	56.04 437	80.17 416	70.97 243	79.24 450	33.46 451	47.94 442	75.09 444

MIMVSNet1		69.44 402	66.65 404	77.84 408	76.48 440	62.84 422	87.42 401	88.97 409	66.96 415	57.75 435	79.72 419	32.77 441	85.83 440	46.32 439	63.42 409	84.85 409

PM-MVS		69.32 403	66.93 402	76.49 415	73.60 447	55.84 441	85.91 413	79.32 449	74.72 370	61.09 423	78.18 422	21.76 451	91.10 413	70.86 344	56.90 421	82.51 424

TDRefinement		69.20 404	65.78 407	79.48 400	66.04 455	62.21 424	88.21 392	86.12 426	62.92 422	61.03 424	85.61 370	33.23 439	94.16 374	55.82 417	53.02 431	82.08 430

new-patchmatchnet		68.85 405	65.93 406	77.61 410	73.57 448	63.94 417	90.11 376	88.73 413	71.62 395	55.08 439	73.60 437	40.84 424	87.22 437	51.35 428	48.49 441	81.67 435

UnsupCasMVSNet_bld		68.60 406	64.50 410	80.92 393	74.63 446	67.80 396	83.97 424	92.94 354	65.12 418	54.63 440	68.23 447	35.97 433	92.17 402	60.13 396	44.83 447	82.78 422

mvsany_test3		67.19 407	65.34 408	72.72 422	63.08 456	48.57 449	83.12 427	78.09 450	72.07 391	61.21 422	77.11 428	22.94 450	87.78 433	78.59 270	51.88 434	81.80 432

MVStest1		66.93 408	63.01 412	78.69 404	78.56 431	71.43 375	85.51 417	86.81 422	49.79 448	48.57 444	84.15 390	53.46 375	83.31 444	43.14 445	37.15 455	81.34 436

new_pmnet		66.18 409	63.18 411	75.18 421	76.27 442	61.74 426	83.79 425	84.66 432	56.64 443	51.57 442	71.85 445	31.29 443	87.93 430	49.98 432	62.55 411	75.86 443

pmmvs3		65.75 410	62.18 413	76.45 416	67.12 454	64.54 412	88.68 389	85.05 431	54.77 445	57.54 436	73.79 436	29.40 447	86.21 439	55.49 419	47.77 443	78.62 440

test_f		64.01 411	62.13 414	69.65 425	63.00 457	45.30 456	83.66 426	80.68 446	61.30 430	55.70 438	72.62 441	14.23 457	84.64 442	69.84 349	58.11 418	79.00 439

N_pmnet		61.30 412	60.20 415	64.60 431	84.32 406	17.00 472	91.67 362	10.98 470	61.77 427	58.45 432	78.55 421	49.89 390	91.83 406	42.27 446	63.94 407	84.97 408

WB-MVS		57.26 413	56.22 416	60.39 437	69.29 449	35.91 464	86.39 411	70.06 457	59.84 438	46.46 447	72.71 440	51.18 381	78.11 451	15.19 461	34.89 456	67.14 450

test_method		56.77 414	54.53 418	63.49 433	76.49 439	40.70 459	75.68 444	74.24 453	19.47 461	48.73 443	71.89 444	19.31 452	65.80 461	57.46 409	47.51 444	83.97 417

APD_test1		56.56 415	53.58 419	65.50 428	67.93 453	46.51 453	77.24 443	72.95 454	38.09 452	42.75 450	75.17 432	13.38 458	82.78 447	40.19 448	54.53 426	67.23 449

SSC-MVS		56.01 416	54.96 417	59.17 438	68.42 451	34.13 465	84.98 421	69.23 458	58.08 442	45.36 448	71.67 446	50.30 389	77.46 452	14.28 462	32.33 457	65.91 451

FPMVS		55.09 417	52.93 420	61.57 435	55.98 459	40.51 460	83.11 428	83.41 441	37.61 453	34.95 454	71.95 443	14.40 456	76.95 453	29.81 453	65.16 402	67.25 448

test_vis3_rt		54.10 418	51.04 421	63.27 434	58.16 458	46.08 455	84.17 423	49.32 469	56.48 444	36.56 453	49.48 456	8.03 465	91.91 405	67.29 360	49.87 437	51.82 455

LCM-MVSNet		52.52 419	48.24 422	65.35 429	47.63 466	41.45 458	72.55 449	83.62 440	31.75 454	37.66 452	57.92 452	9.19 464	76.76 454	49.26 434	44.60 448	77.84 441

EGC-MVSNET		52.46 420	47.56 423	67.15 427	81.98 421	60.11 432	82.54 429	72.44 455	0.11 467	0.70 468	74.59 434	25.11 449	83.26 445	29.04 454	61.51 414	58.09 452

PMMVS2		50.90 421	46.31 424	64.67 430	55.53 460	46.67 452	77.30 442	71.02 456	40.89 451	34.16 455	59.32 450	9.83 463	76.14 456	40.09 449	28.63 458	71.21 445

ANet_high		46.22 422	41.28 429	61.04 436	39.91 468	46.25 454	70.59 452	76.18 452	58.87 440	23.09 460	48.00 457	12.58 460	66.54 460	28.65 455	13.62 461	70.35 446

testf1		45.70 423	42.41 425	55.58 439	53.29 463	40.02 461	68.96 453	62.67 463	27.45 456	29.85 456	61.58 448	5.98 466	73.83 458	28.49 456	43.46 450	52.90 453

APD_test2		45.70 423	42.41 425	55.58 439	53.29 463	40.02 461	68.96 453	62.67 463	27.45 456	29.85 456	61.58 448	5.98 466	73.83 458	28.49 456	43.46 450	52.90 453

Gipuma		45.11 425	42.05 427	54.30 441	80.69 424	51.30 448	35.80 459	83.81 438	28.13 455	27.94 459	34.53 459	11.41 462	76.70 455	21.45 458	54.65 425	34.90 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		41.54 426	41.93 428	40.38 444	20.10 470	26.84 468	61.93 456	59.09 465	14.81 463	28.51 458	80.58 412	35.53 434	48.33 465	63.70 382	13.11 462	45.96 458

PMVS	34.80 23	39.19 427	35.53 430	50.18 442	29.72 469	30.30 467	59.60 457	66.20 462	26.06 458	17.91 462	49.53 455	3.12 468	74.09 457	18.19 460	49.40 438	46.14 456
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	35.65 22	33.85 428	29.49 433	46.92 443	41.86 467	36.28 463	50.45 458	56.52 466	18.75 462	18.28 461	37.84 458	2.41 469	58.41 462	18.71 459	20.62 459	46.06 457
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 429	32.39 431	33.65 445	53.35 462	25.70 469	74.07 447	53.33 467	21.08 459	17.17 463	33.63 461	11.85 461	54.84 463	12.98 463	14.04 460	20.42 460

EMVS		31.70 430	31.45 432	32.48 446	50.72 465	23.95 470	74.78 446	52.30 468	20.36 460	16.08 464	31.48 462	12.80 459	53.60 464	11.39 464	13.10 463	19.88 461

cdsmvs_eth3d_5k		21.43 431	28.57 434	0.00 450	0.00 473	0.00 475	0.00 461	95.93 170	0.00 468	0.00 469	97.66 87	63.57 298	0.00 469	0.00 468	0.00 467	0.00 465

wuyk23d		14.10 432	13.89 435	14.72 447	55.23 461	22.91 471	33.83 460	3.56 471	4.94 464	4.11 465	2.28 467	2.06 470	19.66 466	10.23 465	8.74 464	1.59 464

testmvs		9.92 433	12.94 436	0.84 449	0.65 471	0.29 474	93.78 320	0.39 472	0.42 465	2.85 466	15.84 465	0.17 472	0.30 468	2.18 466	0.21 465	1.91 463

test123		9.07 434	11.73 437	1.11 448	0.50 472	0.77 473	89.44 384	0.20 473	0.34 466	2.15 467	10.72 466	0.34 471	0.32 467	1.79 467	0.08 466	2.23 462

ab-mvs-re		8.11 435	10.81 438	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	97.30 110	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

pcd_1.5k_mvsjas		5.92 436	7.89 439	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	71.04 240	0.00 469	0.00 468	0.00 467	0.00 465

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

WAC-MVS						67.18 400								49.00 435

FOURS1					98.51 39	78.01 275	98.13 62	96.21 142	83.04 241	94.39 64

MSC_two_6792asdad				97.14 3	99.05 9	92.19 4		96.83 58					99.81 22	98.08 24	98.81 24	99.43 11

PC_three_1452									91.12 48	98.33 4	98.42 37	92.51 2	99.81 22	98.96 6	99.37 1	99.70 3

No_MVS				97.14 3	99.05 9	92.19 4		96.83 58					99.81 22	98.08 24	98.81 24	99.43 11

test_one_0601					98.91 18	84.56 84		96.70 78	88.06 100	96.57 31	98.77 10	88.04 21

eth-test2					0.00 473

eth-test					0.00 473

ZD-MVS					99.09 8	83.22 109		96.60 95	82.88 247	93.61 75	98.06 65	82.93 60	99.14 111	95.51 60	98.49 39

RE-MVS-def			91.18 110		97.76 69	76.03 322	96.20 219	95.44 203	80.56 287	90.72 123	97.84 79	73.36 210		91.99 114	96.79 103	97.75 118

IU-MVS					99.03 15	85.34 61		96.86 56	92.05 39	98.74 1				98.15 20	98.97 17	99.42 13

OPU-MVS				97.30 2	99.19 7	92.31 3	99.12 14				98.54 24	92.06 3	99.84 13	99.11 5	99.37 1	99.74 1

test_241102_TWO								96.78 61	88.72 82	97.70 12	98.91 2	87.86 22	99.82 19	98.15 20	99.00 15	99.47 9

test_241102_ONE					99.03 15	85.03 74		96.78 61	88.72 82	97.79 9	98.90 5	88.48 17	99.82 19

9.14			94.26 38		98.10 57		98.14 59	96.52 105	84.74 188	94.83 58	98.80 7	82.80 62	99.37 90	95.95 52	98.42 42

save fliter					98.24 51	83.34 106	98.61 44	96.57 99	91.32 45

test_0728_THIRD									88.38 90	96.69 26	98.76 12	89.64 12	99.76 38	97.47 35	98.84 23	99.38 14

test_0728_SECOND				95.14 20	99.04 14	86.14 39	99.06 21	96.77 67					99.84 13	97.90 28	98.85 21	99.45 10

test0726					99.05 9	85.18 66	99.11 17	96.78 61	88.75 80	97.65 15	98.91 2	87.69 23

GSMVS																97.54 136

test_part2					98.90 19	85.14 72				96.07 38

sam_mvs1												77.59 124				97.54 136

sam_mvs												75.35 179

ambc				76.02 417	68.11 452	51.43 447	64.97 455	89.59 402		60.49 425	74.49 435	17.17 454	92.46 394	61.50 390	52.85 432	84.17 416

MTGPA								96.33 131

test_post1							85.88 414				30.24 463	73.77 203	95.07 349	73.89 321

test_post											33.80 460	76.17 157	95.97 295

patchmatchnet-post											77.09 429	77.78 122	95.39 329

GG-mvs-BLEND				93.49 78	94.94 158	86.26 37	81.62 430	97.00 40		88.32 163	94.30 217	91.23 5	96.21 288	88.49 170	97.43 76	98.00 96

MTMP							97.53 108	68.16 460

gm-plane-assit					92.27 265	79.64 221			84.47 198		95.15 186		97.93 178	85.81 194

test9_res														96.00 51	99.03 13	98.31 70

TEST9					98.64 31	83.71 97	97.82 83	96.65 86	84.29 205	95.16 48	98.09 60	84.39 42	99.36 91

test_8					98.63 33	83.64 100	97.81 85	96.63 91	84.50 196	95.10 51	98.11 58	84.33 43	99.23 99

agg_prior2														94.30 75	99.00 15	98.57 54

agg_prior					98.59 35	83.13 111		96.56 101		94.19 66			99.16 110

TestCases				84.47 356	92.18 272	67.29 398		84.43 433	67.63 410	63.48 407	90.18 298	38.20 428	97.16 237	57.04 410	73.37 340	88.97 346

test_prior4						82.34 133	97.75 91

test_prior2							98.37 52		86.08 151	94.57 62	98.02 66	83.14 57		95.05 66	98.79 27

test_prior				93.09 92	98.68 26	81.91 146		96.40 121					99.06 118			98.29 72

旧先验2							96.97 161		74.06 376	96.10 37			97.76 189	88.38 172

新几何2							96.42 204

新几何1				93.12 90	97.44 83	81.60 161		96.71 77	74.54 372	91.22 116	97.57 95	79.13 96	99.51 81	77.40 287	98.46 40	98.26 75

旧先验1					97.39 88	79.58 222		96.54 102			98.08 63	84.00 49			97.42 77	97.62 132

无先验							96.87 170	96.78 61	77.39 344				99.52 79	79.95 255		98.43 63

原ACMM2							96.84 171

原ACMM1				91.22 199	97.77 67	78.10 273		96.61 92	81.05 276	91.28 115	97.42 104	77.92 119	98.98 122	79.85 257	98.51 36	96.59 202

test222					96.15 111	78.41 260	95.87 240	96.46 113	71.97 392	89.66 137	97.45 100	76.33 154			98.24 51	98.30 71

testdata2													99.48 83	76.45 296

segment_acmp												82.69 63

testdata				90.13 234	95.92 121	74.17 343		96.49 111	73.49 381	94.82 59	97.99 67	78.80 103	97.93 178	83.53 220	97.52 72	98.29 72

testdata1							95.57 256		87.44 117

test12				94.25 41	98.34 46	85.55 57		96.35 130		92.36 93		80.84 71	99.22 100		98.31 49	97.98 98

plane_prior7					91.86 289	77.55 294

plane_prior6					91.98 285	77.92 280						64.77 290

plane_prior5								94.69 245					97.30 227	87.08 185	82.82 284	90.96 300

plane_prior4											94.15 225

plane_prior3						77.75 290			90.17 65	81.33 259

plane_prior2							97.18 136		89.89 68

plane_prior1					91.95 287

plane_prior						77.96 277	97.52 111		90.36 63						82.96 282

n2								0.00 474

nn								0.00 474

door-mid								79.75 448

lessismore_v0				79.98 398	80.59 425	58.34 437		80.87 445		58.49 431	83.46 396	43.10 414	93.89 379	63.11 385	48.68 439	87.72 371

LGP-MVS_train				86.33 321	90.88 308	73.06 353		94.13 290	82.20 260	76.31 316	93.20 246	54.83 370	96.95 251	83.72 214	80.83 297	88.98 344

test11								96.50 108

door								80.13 447

HQP5-MVS						78.48 256

HQP-NCC					92.08 279		97.63 97		90.52 58	82.30 246

ACMP_Plane					92.08 279		97.63 97		90.52 58	82.30 246

BP-MVS														87.67 181

HQP4-MVS										82.30 246			97.32 225			91.13 298

HQP3-MVS								94.80 237							83.01 280

HQP2-MVS												65.40 283

NP-MVS					92.04 283	78.22 267					94.56 208

MDTV_nov1_ep13_2view						81.74 154	86.80 406		80.65 284	85.65 196		74.26 197		76.52 295		96.98 180

MDTV_nov1_ep13			83.69 259		94.09 193	81.01 173	86.78 407	96.09 151	83.81 223	84.75 208	84.32 388	74.44 196	96.54 274	63.88 380	85.07 267

ACMMP++_ref															78.45 317

ACMMP++															79.05 309

Test By Simon												71.65 233

ITE_SJBPF				82.38 381	87.00 373	65.59 409		89.55 403	79.99 307	69.37 382	91.30 281	41.60 420	95.33 333	62.86 386	74.63 336	86.24 395

DeepMVS_CX				64.06 432	78.53 432	43.26 457		68.11 461	69.94 403	38.55 451	76.14 431	18.53 453	79.34 449	43.72 443	41.62 452	69.57 447

High-resolution multi-view benchmark