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.

fmvsm_l_conf0.5_n_9		98.90 15	98.79 13	99.24 46	99.34 72	97.83 80	98.70 197	99.26 16	98.85 6	99.92 1	99.51 28	93.91 107	99.95 9	99.86 1	99.79 35	99.92 2

fmvsm_s_conf0.5_n_9		98.63 29	98.66 21	98.54 110	99.40 68	95.83 204	98.79 173	99.17 37	98.94 2	99.92 1	99.61 5	92.49 125	99.93 34	99.86 1	99.76 48	99.86 13

fmvsm_l_conf0.5_n_3		98.90 15	98.74 18	99.37 28	99.36 69	98.25 57	98.89 125	99.24 20	98.77 10	99.89 3	99.59 13	93.39 113	99.96 4	99.78 10	99.76 48	99.89 8

fmvsm_s_conf0.5_n_11		98.58 36	98.57 26	98.62 100	99.42 65	97.16 119	98.97 99	98.86 91	98.91 4	99.87 4	99.66 3	91.82 153	99.95 9	99.82 6	99.82 14	98.75 264

fmvsm_s_conf0.5_n_5		98.53 46	98.35 48	99.08 64	99.07 128	97.46 95	98.68 203	99.20 33	97.50 52	99.87 4	99.50 31	91.96 150	99.96 4	99.76 11	99.65 81	99.82 23

fmvsm_s_conf0.5_n_3		98.53 46	98.45 39	98.79 86	99.23 105	97.32 100	98.80 165	99.26 16	98.82 7	99.87 4	99.60 10	90.95 197	99.93 34	99.76 11	99.73 62	99.12 208

test_vis1_n_1920		96.71 194	96.84 167	96.31 344	99.11 124	89.74 433	99.05 77	98.58 177	98.08 24	99.87 4	99.37 56	78.48 431	99.93 34	99.29 27	99.69 72	99.27 175

fmvsm_s_conf0.5_n_6		98.65 26	98.55 29	98.95 78	98.50 188	97.30 103	98.79 173	99.16 39	98.14 23	99.86 8	99.41 48	93.71 110	99.91 57	99.71 15	99.64 86	99.65 83

fmvsm_l_conf0.5_n		99.07 5	99.05 2	99.14 58	99.41 67	97.54 89	98.89 125	99.31 13	98.49 17	99.86 8	99.42 46	96.45 29	99.96 4	99.86 1	99.74 58	99.90 5

test_fmvsm_n_1920		98.87 18	99.01 3	98.45 124	99.42 65	96.43 157	98.96 105	99.36 10	98.63 13	99.86 8	99.51 28	95.91 47	99.97 1	99.72 14	99.75 54	98.94 239

fmvsm_l_conf0.5_n_a		99.09 2	99.08 1	99.11 62	99.43 64	97.48 91	98.88 132	99.30 14	98.47 18	99.85 11	99.43 45	96.71 18	99.96 4	99.86 1	99.80 25	99.89 8

fmvsm_s_conf0.5_n_10		98.66 25	98.54 31	99.02 69	99.36 69	97.21 116	98.86 143	99.23 27	98.90 5	99.83 12	99.59 13	91.57 162	99.94 14	99.79 9	99.74 58	99.89 8

fmvsm_s_conf0.5_n_4		98.35 68	98.50 34	97.90 195	99.16 116	95.08 256	98.75 178	99.24 20	98.39 19	99.81 13	99.52 25	92.35 129	99.90 65	99.74 13	99.51 115	98.71 270

test_fmvsmconf_n		98.92 13	98.87 7	99.04 68	98.88 148	97.25 113	98.82 156	99.34 11	98.75 11	99.80 14	99.61 5	95.16 78	99.95 9	99.70 17	99.80 25	99.93 1

fmvsm_s_conf0.5_n_8		98.73 23	98.62 22	99.05 67	99.35 71	97.27 107	98.80 165	99.23 27	98.93 3	99.79 15	99.59 13	92.34 130	99.95 9	99.82 6	99.71 69	99.92 2

fmvsm_s_conf0.5_n_2		98.30 75	98.21 69	98.57 105	99.25 97	97.11 122	98.66 210	99.20 33	98.82 7	99.79 15	99.60 10	89.38 246	99.92 43	99.80 8	99.38 135	98.69 272

fmvsm_s_conf0.5_n_a		98.38 63	98.42 41	98.27 139	99.09 126	95.41 231	98.86 143	99.37 9	97.69 40	99.78 17	99.61 5	92.38 128	99.91 57	99.58 23	99.43 127	99.49 112

fmvsm_s_conf0.1_n_2		98.14 81	98.02 81	98.53 113	98.88 148	97.07 124	98.69 200	98.82 102	98.78 9	99.77 18	99.61 5	88.83 268	99.91 57	99.71 15	99.07 152	98.61 282

test_cas_vis1_n_1920		97.38 144	97.36 119	97.45 238	98.95 143	93.25 350	99.00 92	98.53 189	97.70 39	99.77 18	99.35 62	84.71 362	99.85 85	98.57 53	99.66 78	99.26 182

fmvsm_s_conf0.1_n_a		98.08 82	98.04 80	98.21 147	97.66 319	95.39 236	98.89 125	99.17 37	97.24 74	99.76 20	99.67 1	91.13 186	99.88 78	99.39 26	99.41 129	99.35 148

TestfortrainingZip a		99.05 6	98.85 9	99.65 2	99.77 2	99.13 12	99.32 22	99.01 52	97.87 31	99.74 21	99.54 20	96.71 18	99.92 43	98.35 74	99.33 141	99.90 5

fmvsm_s_conf0.5_n		98.42 60	98.51 32	98.13 164	99.30 84	95.25 245	98.85 148	99.39 7	97.94 29	99.74 21	99.62 4	92.59 124	99.91 57	99.65 18	99.52 113	99.25 184

fmvsm_s_conf0.5_n_7		98.23 76	98.35 48	97.89 197	98.86 152	94.99 262	98.58 226	99.00 53	98.29 20	99.73 23	99.60 10	91.70 156	99.92 43	99.63 21	99.73 62	98.76 263

SED-MVS		99.09 2	98.91 5	99.63 5	99.71 24	99.24 5	99.02 87	98.87 85	97.65 41	99.73 23	99.48 35	97.53 8	99.94 14	98.43 68	99.81 16	99.70 67

test_241102_ONE					99.71 24	99.24 5		98.87 85	97.62 43	99.73 23	99.39 50	97.53 8	99.74 135

SD-MVS		98.64 28	98.68 19	98.53 113	99.33 75	98.36 50	98.90 121	98.85 95	97.28 69	99.72 26	99.39 50	96.63 22	97.60 453	98.17 86	99.85 6	99.64 86
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
fmvsm_s_conf0.1_n		98.18 80	98.21 69	98.11 169	98.54 186	95.24 246	98.87 135	99.24 20	97.50 52	99.70 27	99.67 1	91.33 174	99.89 69	99.47 25	99.54 110	99.21 190

test_fmvs1_n		95.90 236	95.99 217	95.63 383	98.67 173	88.32 465	99.26 33	98.22 293	96.40 125	99.67 28	99.26 80	73.91 474	99.70 144	99.02 34	99.50 116	98.87 246

test_vis1_n		95.47 258	95.13 259	96.49 326	97.77 308	90.41 421	99.27 32	98.11 318	96.58 114	99.66 29	99.18 105	67.00 489	99.62 165	99.21 28	99.40 132	99.44 126

mvsany_test1		97.69 104	97.70 92	97.66 225	98.24 242	94.18 306	97.53 385	97.53 382	95.52 184	99.66 29	99.51 28	94.30 99	99.56 175	98.38 72	98.62 180	99.23 186

test_fmvs1		96.42 209	96.67 182	95.66 382	98.82 157	88.53 461	98.80 165	98.20 296	96.39 126	99.64 31	99.20 95	80.35 416	99.67 151	99.04 32	99.57 99	98.78 259

IU-MVS					99.71 24	99.23 7		98.64 159	95.28 202	99.63 32				98.35 74	99.81 16	99.83 19

PC_three_1452									95.08 219	99.60 33	99.16 110	97.86 2	98.47 362	97.52 143	99.72 67	99.74 50

lecture		98.95 9	98.78 14	99.45 19	99.75 6	98.63 32	99.43 10	99.38 8	97.60 46	99.58 34	99.47 37	95.36 65	99.93 34	98.87 39	99.57 99	99.78 33

test0726					99.72 17	99.25 2	99.06 74	98.88 78	97.62 43	99.56 35	99.50 31	97.42 10

TSAR-MVS + MP.		98.78 20	98.62 22	99.24 46	99.69 29	98.28 55	99.14 60	98.66 154	96.84 98	99.56 35	99.31 71	96.34 33	99.70 144	98.32 76	99.73 62	99.73 55
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPE-MVS		98.92 13	98.67 20	99.65 2	99.58 38	99.20 9	98.42 269	98.91 72	97.58 47	99.54 37	99.46 42	97.10 13	99.94 14	97.64 126	99.84 11	99.83 19
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		99.08 4	98.89 6	99.64 4	99.17 112	99.23 7	99.69 1	98.88 78	97.32 65	99.53 38	99.47 37	97.81 3	99.94 14	98.47 64	99.72 67	99.74 50

test_241102_TWO								98.87 85	97.65 41	99.53 38	99.48 35	97.34 12	99.94 14	98.43 68	99.80 25	99.83 19

DVP-MVS		99.03 7	98.83 11	99.63 5	99.72 17	99.25 2	98.97 99	98.58 177	97.62 43	99.45 40	99.46 42	97.42 10	99.94 14	98.47 64	99.81 16	99.69 70
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
test_0728_THIRD									97.32 65	99.45 40	99.46 42	97.88 1	99.94 14	98.47 64	99.86 2	99.85 16

test_fmvsmconf0.1_n		98.58 36	98.44 40	98.99 71	97.73 313	97.15 120	98.84 152	98.97 57	98.75 11	99.43 42	99.54 20	93.29 115	99.93 34	99.64 20	99.79 35	99.89 8

TestfortrainingZip				99.43 21	99.13 120	99.06 16	99.32 22	98.57 179	96.88 97	99.42 43	99.05 145	96.54 24	99.73 137		98.59 182	99.51 104

MSP-MVS		98.74 22	98.55 29	99.29 39	99.75 6	98.23 58	99.26 33	98.88 78	97.52 50	99.41 44	98.78 194	96.00 43	99.79 122	97.79 113	99.59 95	99.85 16
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
APDe-MVS		99.02 8	98.84 10	99.55 11	99.57 40	98.96 19	99.39 11	98.93 65	97.38 62	99.41 44	99.54 20	96.66 20	99.84 89	98.86 40	99.85 6	99.87 12
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
FOURS1					99.82 1	98.66 30	99.69 1	98.95 61	97.46 57	99.39 46

SMA-MVS		98.58 36	98.25 63	99.56 9	99.51 47	99.04 18	98.95 106	98.80 115	93.67 311	99.37 47	99.52 25	96.52 26	99.89 69	98.06 92	99.81 16	99.76 47
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
reproduce_model		98.94 10	98.81 12	99.34 32	99.52 46	98.26 56	98.94 109	98.84 96	98.06 25	99.35 48	99.61 5	96.39 32	99.94 14	98.77 43	99.82 14	99.83 19

BridgeMVS		98.45 56	98.35 48	98.74 90	98.65 177	97.55 87	99.19 50	98.60 165	96.72 108	99.35 48	98.77 197	95.06 83	99.55 182	98.95 35	99.87 1	99.12 208

SteuartSystems-ACMMP		98.90 15	98.75 17	99.36 30	99.22 107	98.43 40	99.10 69	98.87 85	97.38 62	99.35 48	99.40 49	97.78 5	99.87 80	97.77 114	99.85 6	99.78 33
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		98.59 34	98.32 59	99.41 23	99.54 42	98.71 28	99.04 81	98.81 108	95.12 214	99.32 51	99.39 50	96.22 34	99.84 89	97.72 117	99.73 62	99.67 79

aaatest				99.52 14	99.77 2	98.86 24	99.32 22	99.24 20	96.41 124	99.30 52	99.35 62		99.92 43	98.30 77	99.80 25	99.79 29

MED-MVS		99.12 1	98.97 4	99.56 9	99.77 2	98.86 24	99.32 22	99.24 20	97.87 31	99.30 52	99.54 20	97.61 6	99.92 43	98.30 77	99.80 25	99.90 5

reproduce-ours		98.93 11	98.78 14	99.38 24	99.49 53	98.38 42	98.86 143	98.83 98	98.06 25	99.29 54	99.58 16	96.40 30	99.94 14	98.68 46	99.81 16	99.81 25

our_new_method		98.93 11	98.78 14	99.38 24	99.49 53	98.38 42	98.86 143	98.83 98	98.06 25	99.29 54	99.58 16	96.40 30	99.94 14	98.68 46	99.81 16	99.81 25

dcpmvs_2		98.08 82	98.59 25	96.56 317	99.57 40	90.34 424	99.15 57	98.38 249	96.82 100	99.29 54	99.49 34	95.78 51	99.57 172	98.94 36	99.86 2	99.77 40

test_part2					99.63 35	99.18 10				99.27 57

DeepPCF-MVS	96.37 2	97.93 90	98.48 38	96.30 345	99.00 136	89.54 440	97.43 394	98.87 85	98.16 22	99.26 58	99.38 55	96.12 39	99.64 158	98.30 77	99.77 42	99.72 59

APD-MVS		98.35 68	98.00 83	99.42 22	99.51 47	98.72 27	98.80 165	98.82 102	94.52 257	99.23 59	99.25 86	95.54 58	99.80 110	96.52 204	99.77 42	99.74 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_one_0601					99.66 31	99.25 2		98.86 91	97.55 49	99.20 60	99.47 37	97.57 7

APD-MVS_3200maxsize		98.53 46	98.33 58	99.15 57	99.50 49	97.92 75	99.15 57	98.81 108	96.24 134	99.20 60	99.37 56	95.30 69	99.80 110	97.73 116	99.67 75	99.72 59

patch_mono-2		98.36 66	98.87 7	96.82 287	99.53 43	90.68 412	98.64 213	99.29 15	97.88 30	99.19 62	99.52 25	96.80 16	99.97 1	99.11 30	99.86 2	99.82 23

aaEdge-Enhanced		98.83 19	98.60 24	99.52 14	99.58 38	98.86 24	98.69 200	98.93 65	97.00 91	99.17 63	99.35 62	96.62 23	99.90 65	98.30 77	99.80 25	99.79 29

MM		98.51 49	98.24 65	99.33 36	99.12 122	98.14 67	98.93 115	97.02 434	98.96 1	99.17 63	99.47 37	91.97 149	99.94 14	99.85 5	99.69 72	99.91 4

SR-MVS-dyc-post		98.54 45	98.35 48	99.13 59	99.49 53	97.86 76	99.11 66	98.80 115	96.49 119	99.17 63	99.35 62	95.34 67	99.82 98	97.72 117	99.65 81	99.71 63

RE-MVS-def			98.34 54		99.49 53	97.86 76	99.11 66	98.80 115	96.49 119	99.17 63	99.35 62	95.29 70		97.72 117	99.65 81	99.71 63

9.14			98.06 78		99.47 57		98.71 193	98.82 102	94.36 267	99.16 67	99.29 75	96.05 41	99.81 103	97.00 173	99.71 69

ACMMP_NAP		98.61 31	98.30 60	99.55 11	99.62 36	98.95 20	98.82 156	98.81 108	95.80 160	99.16 67	99.47 37	95.37 64	99.92 43	97.89 105	99.75 54	99.79 29

test-260524					99.64 33	99.18 10		98.83 98		99.13 69		96.51 27	99.92 43	99.03 33	99.80 25

SR-MVS		98.57 41	98.35 48	99.24 46	99.53 43	98.18 62	99.09 70	98.82 102	96.58 114	99.10 70	99.32 69	95.39 62	99.82 98	97.70 122	99.63 88	99.72 59

PGM-MVS		98.49 51	98.23 67	99.27 44	99.72 17	98.08 69	98.99 95	99.49 5	95.43 189	99.03 71	99.32 69	95.56 56	99.94 14	96.80 195	99.77 42	99.78 33

KinetiMVS		97.48 130	97.05 153	98.78 87	98.37 211	97.30 103	98.99 95	98.70 141	97.18 79	99.02 72	99.01 152	87.50 305	99.67 151	95.33 249	99.33 141	99.37 143

VNet		97.79 98	97.40 115	98.96 76	98.88 148	97.55 87	98.63 216	98.93 65	96.74 105	99.02 72	98.84 181	90.33 218	99.83 91	98.53 56	96.66 286	99.50 107

xiu_mvs_v1_base_debu		97.60 113	97.56 99	97.72 214	98.35 214	95.98 180	97.86 357	98.51 195	97.13 84	99.01 74	98.40 240	91.56 163	99.80 110	98.53 56	98.68 175	97.37 337

xiu_mvs_v1_base		97.60 113	97.56 99	97.72 214	98.35 214	95.98 180	97.86 357	98.51 195	97.13 84	99.01 74	98.40 240	91.56 163	99.80 110	98.53 56	98.68 175	97.37 337

xiu_mvs_v1_base_debi		97.60 113	97.56 99	97.72 214	98.35 214	95.98 180	97.86 357	98.51 195	97.13 84	99.01 74	98.40 240	91.56 163	99.80 110	98.53 56	98.68 175	97.37 337

MVSMamba_PlusPlus		98.31 73	98.19 73	98.67 96	98.96 142	97.36 98	99.24 36	98.57 179	94.81 238	98.99 77	98.90 173	95.22 76	99.59 168	99.15 29	99.84 11	99.07 224

TSAR-MVS + GP.		98.38 63	98.24 65	98.81 85	99.22 107	97.25 113	98.11 322	98.29 280	97.19 78	98.99 77	99.02 148	96.22 34	99.67 151	98.52 62	98.56 186	99.51 104

CS-MVS		98.44 57	98.49 36	98.31 137	99.08 127	96.73 139	99.67 3	98.47 207	97.17 80	98.94 79	99.10 127	95.73 52	99.13 270	98.71 45	99.49 118	99.09 216

HFP-MVS		98.63 29	98.40 42	99.32 38	99.72 17	98.29 54	99.23 38	98.96 60	96.10 144	98.94 79	99.17 107	96.06 40	99.92 43	97.62 127	99.78 40	99.75 48

region2R		98.61 31	98.38 44	99.29 39	99.74 12	98.16 64	99.23 38	98.93 65	96.15 138	98.94 79	99.17 107	95.91 47	99.94 14	97.55 139	99.79 35	99.78 33

HPM-MVS_fast		98.38 63	98.13 74	99.12 61	99.75 6	97.86 76	99.44 9	98.82 102	94.46 263	98.94 79	99.20 95	95.16 78	99.74 135	97.58 134	99.85 6	99.77 40

test_fmvsmconf0.01_n		97.86 92	97.54 102	98.83 84	95.48 448	96.83 134	98.95 106	98.60 165	98.58 14	98.93 83	99.55 18	88.57 273	99.91 57	99.54 24	99.61 91	99.77 40

ACMMPR		98.59 34	98.36 46	99.29 39	99.74 12	98.15 65	99.23 38	98.95 61	96.10 144	98.93 83	99.19 102	95.70 53	99.94 14	97.62 127	99.79 35	99.78 33

DeepC-MVS_fast	96.70 1	98.55 44	98.34 54	99.18 53	99.25 97	98.04 70	98.50 250	98.78 122	97.72 36	98.92 85	99.28 76	95.27 71	99.82 98	97.55 139	99.77 42	99.69 70
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		98.49 51	98.50 34	98.46 123	99.20 110	97.05 125	99.64 4	98.50 200	97.45 58	98.88 86	99.14 115	95.25 73	99.15 265	98.83 41	99.56 107	99.20 191

MGCNet		98.23 76	97.91 86	99.21 50	98.06 275	97.96 74	98.58 226	95.51 477	98.58 14	98.87 87	99.26 80	92.99 119	99.95 9	99.62 22	99.67 75	99.73 55

EC-MVSNet		98.21 79	98.11 76	98.49 120	98.34 219	97.26 112	99.61 5	98.43 227	96.78 101	98.87 87	98.84 181	93.72 109	99.01 300	98.91 38	99.50 116	99.19 195

diffmvs_AUTHOR		97.59 116	97.44 111	98.01 183	98.26 237	95.47 227	98.12 318	98.36 255	96.38 127	98.84 89	99.10 127	91.13 186	99.26 231	98.24 85	98.56 186	99.30 164

EI-MVSNet-Vis-set		98.47 54	98.39 43	98.69 94	99.46 59	96.49 154	98.30 284	98.69 143	97.21 76	98.84 89	99.36 60	95.41 61	99.78 125	98.62 50	99.65 81	99.80 28

MSLP-MVS++		98.56 43	98.57 26	98.55 108	99.26 96	96.80 135	98.71 193	99.05 49	97.28 69	98.84 89	99.28 76	96.47 28	99.40 209	98.52 62	99.70 71	99.47 116

PHI-MVS		98.34 70	98.06 78	99.18 53	99.15 119	98.12 68	99.04 81	99.09 44	93.32 330	98.83 92	99.10 127	96.54 24	99.83 91	97.70 122	99.76 48	99.59 94

GDP-MVS		97.64 108	97.28 126	98.71 93	98.30 228	97.33 99	99.05 77	98.52 192	96.34 130	98.80 93	99.05 145	89.74 233	99.51 188	96.86 191	98.86 167	99.28 174

MVSFormer		97.57 118	97.49 105	97.84 201	98.07 271	95.76 212	99.47 7	98.40 236	94.98 227	98.79 94	98.83 185	92.34 130	98.41 375	96.91 179	99.59 95	99.34 150

lupinMVS		97.44 138	97.22 135	98.12 167	98.07 271	95.76 212	97.68 374	97.76 358	94.50 261	98.79 94	98.61 217	92.34 130	99.30 223	97.58 134	99.59 95	99.31 159

CDPH-MVS		97.94 89	97.49 105	99.28 42	99.47 57	98.44 38	97.91 347	98.67 151	92.57 364	98.77 96	98.85 180	95.93 46	99.72 138	95.56 242	99.69 72	99.68 75

CNVR-MVS		98.78 20	98.56 28	99.45 19	99.32 78	98.87 22	98.47 256	98.81 108	97.72 36	98.76 97	99.16 110	97.05 14	99.78 125	98.06 92	99.66 78	99.69 70

EI-MVSNet-UG-set		98.41 61	98.34 54	98.61 102	99.45 62	96.32 164	98.28 287	98.68 146	97.17 80	98.74 98	99.37 56	95.25 73	99.79 122	98.57 53	99.54 110	99.73 55

diffmvs		97.58 117	97.40 115	98.13 164	98.32 226	95.81 208	98.06 328	98.37 251	96.20 136	98.74 98	98.89 175	91.31 176	99.25 235	98.16 87	98.52 190	99.34 150
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
guyue		97.57 118	97.37 118	98.20 149	98.50 188	95.86 201	98.89 125	97.03 431	97.29 67	98.73 100	98.90 173	89.41 245	99.32 218	98.68 46	98.86 167	99.42 133

GST-MVS		98.43 59	98.12 75	99.34 32	99.72 17	98.38 42	99.09 70	98.82 102	95.71 166	98.73 100	99.06 143	95.27 71	99.93 34	97.07 171	99.63 88	99.72 59

UA-Net		97.96 87	97.62 94	98.98 73	98.86 152	97.47 93	98.89 125	99.08 45	96.67 111	98.72 102	99.54 20	93.15 117	99.81 103	94.87 264	98.83 170	99.65 83

NormalMVS		98.07 84	97.90 87	98.59 104	99.75 6	96.60 145	98.94 109	98.60 165	97.86 33	98.71 103	99.08 138	91.22 181	99.80 110	97.40 158	99.57 99	99.37 143

SymmetryMVS		97.84 95	97.58 96	98.62 100	99.01 134	96.60 145	98.94 109	98.44 216	97.86 33	98.71 103	99.08 138	91.22 181	99.80 110	97.40 158	97.53 262	99.47 116

LuminaMVS		97.49 129	97.18 138	98.42 130	97.50 334	97.15 120	98.45 258	97.68 361	96.56 118	98.68 105	98.78 194	89.84 230	99.32 218	98.60 51	98.57 185	98.79 255

h-mvs33		96.17 222	95.62 236	97.81 205	99.03 131	94.45 289	98.64 213	98.75 128	97.48 54	98.67 106	98.72 208	89.76 231	99.86 84	97.95 98	81.59 474	99.11 211

hse-mvs2		95.71 246	95.30 253	96.93 278	98.50 188	93.53 330	98.36 272	98.10 321	97.48 54	98.67 106	97.99 281	89.76 231	99.02 298	97.95 98	80.91 480	98.22 306

ZD-MVS					99.46 59	98.70 29		98.79 120	93.21 335	98.67 106	98.97 156	95.70 53	99.83 91	96.07 216	99.58 98

旧先验2							97.57 384		91.30 407	98.67 106			99.80 110	95.70 237

PS-MVSNAJ		97.73 100	97.77 89	97.62 229	98.68 172	95.58 219	97.34 403	98.51 195	97.29 67	98.66 110	97.88 293	94.51 92	99.90 65	97.87 107	99.17 150	97.39 335

onestephybrid01		97.54 125	97.36 119	98.06 176	98.25 239	95.63 217	98.26 290	98.33 262	96.13 139	98.65 111	99.13 118	91.02 193	99.25 235	98.07 91	98.42 208	99.31 159

xiu_mvs_v2_base		97.66 107	97.70 92	97.56 233	98.61 181	95.46 228	97.44 391	98.46 208	97.15 82	98.65 111	98.15 268	94.33 98	99.80 110	97.84 110	98.66 179	97.41 333

LFMVS		95.86 238	94.98 269	98.47 122	98.87 151	96.32 164	98.84 152	96.02 468	93.40 327	98.62 113	99.20 95	74.99 466	99.63 161	97.72 117	97.20 267	99.46 121

HPM-MVS		98.36 66	98.10 77	99.13 59	99.74 12	97.82 81	99.53 6	98.80 115	94.63 250	98.61 114	98.97 156	95.13 80	99.77 130	97.65 125	99.83 13	99.79 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
AstraMVS		97.34 152	97.24 132	97.65 226	98.13 265	94.15 307	98.94 109	96.25 467	97.47 56	98.60 115	99.28 76	89.67 235	99.41 208	98.73 44	98.07 235	99.38 142

testdata				98.26 142	99.20 110	95.36 238		98.68 146	91.89 387	98.60 115	99.10 127	94.44 97	99.82 98	94.27 295	99.44 126	99.58 98

CP-MVS		98.57 41	98.36 46	99.19 51	99.66 31	97.86 76	99.34 17	98.87 85	95.96 151	98.60 115	99.13 118	96.05 41	99.94 14	97.77 114	99.86 2	99.77 40

jason		97.32 154	97.08 149	98.06 176	97.45 340	95.59 218	97.87 355	97.91 345	94.79 240	98.55 118	98.83 185	91.12 188	99.23 247	97.58 134	99.60 93	99.34 150
jason: jason.
MCST-MVS		98.65 26	98.37 45	99.48 17	99.60 37	98.87 22	98.41 270	98.68 146	97.04 88	98.52 119	98.80 188	96.78 17	99.83 91	97.93 100	99.61 91	99.74 50

BP-MVS1		97.82 96	97.51 104	98.76 89	98.25 239	97.39 97	99.15 57	97.68 361	96.69 109	98.47 120	99.10 127	90.29 219	99.51 188	98.60 51	99.35 138	99.37 143

XVS		98.70 24	98.49 36	99.34 32	99.70 27	98.35 51	99.29 28	98.88 78	97.40 59	98.46 121	99.20 95	95.90 49	99.89 69	97.85 108	99.74 58	99.78 33

X-MVStestdata		94.06 369	92.30 395	99.34 32	99.70 27	98.35 51	99.29 28	98.88 78	97.40 59	98.46 121	43.50 553	95.90 49	99.89 69	97.85 108	99.74 58	99.78 33

MG-MVS		97.81 97	97.60 95	98.44 126	99.12 122	95.97 185	97.75 369	98.78 122	96.89 96	98.46 121	99.22 90	93.90 108	99.68 150	94.81 268	99.52 113	99.67 79

viewmamba		97.55 121	97.45 110	97.87 199	98.22 246	95.13 253	98.35 273	98.35 256	96.57 116	98.45 124	99.15 114	91.60 160	99.18 256	97.99 96	98.36 215	99.29 167

test_fmvsmvis_n_1920		98.44 57	98.51 32	98.23 146	98.33 223	96.15 172	98.97 99	99.15 41	98.55 16	98.45 124	99.55 18	94.26 101	99.97 1	99.65 18	99.66 78	98.57 289

NCCC		98.61 31	98.35 48	99.38 24	99.28 93	98.61 33	98.45 258	98.76 126	97.82 35	98.45 124	98.93 166	96.65 21	99.83 91	97.38 161	99.41 129	99.71 63

MVS_Test		97.28 156	97.00 156	98.13 164	98.33 223	95.97 185	98.74 182	98.07 328	94.27 270	98.44 127	98.07 273	92.48 126	99.26 231	96.43 207	98.19 230	99.16 201

MVS_111021_LR		98.34 70	98.23 67	98.67 96	99.27 94	96.90 131	97.95 340	99.58 3	97.14 83	98.44 127	99.01 152	95.03 84	99.62 165	97.91 103	99.75 54	99.50 107

balanced_ft_v1		97.54 125	97.38 117	98.02 181	98.34 219	95.58 219	99.32 22	98.40 236	95.88 155	98.43 129	98.65 215	88.95 265	99.59 168	98.94 36	99.48 121	98.90 244

hybridnocas07		97.41 141	97.21 136	97.99 185	98.24 242	95.42 230	98.21 295	98.32 266	95.97 150	98.38 130	98.93 166	90.48 210	99.21 252	97.92 102	98.46 197	99.34 150

ETV-MVS		97.96 87	97.81 88	98.40 132	98.42 201	97.27 107	98.73 188	98.55 185	96.84 98	98.38 130	97.44 335	95.39 62	99.35 214	97.62 127	98.89 163	98.58 288

test2506		94.44 340	93.91 338	96.04 354	99.02 132	88.99 451	99.06 74	79.47 528	96.96 93	98.36 132	99.26 80	77.21 446	99.52 187	96.78 196	99.04 154	99.59 94

VDDNet		95.36 270	94.53 291	97.86 200	98.10 268	95.13 253	98.85 148	97.75 359	90.46 424	98.36 132	99.39 50	73.27 476	99.64 158	97.98 97	96.58 289	98.81 253

hybrid		97.34 152	97.16 140	97.88 198	98.25 239	95.18 249	98.18 308	98.33 262	95.36 197	98.35 134	99.06 143	90.61 206	99.18 256	97.88 106	98.40 211	99.27 175

mPP-MVS		98.51 49	98.26 62	99.25 45	99.75 6	98.04 70	99.28 30	98.81 108	96.24 134	98.35 134	99.23 87	95.46 59	99.94 14	97.42 156	99.81 16	99.77 40

DELS-MVS		98.40 62	98.20 71	98.99 71	99.00 136	97.66 82	97.75 369	98.89 75	97.71 38	98.33 136	98.97 156	94.97 85	99.88 78	98.42 70	99.76 48	99.42 133
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
MVS_111021_HR		98.47 54	98.34 54	98.88 83	99.22 107	97.32 100	97.91 347	99.58 3	97.20 77	98.33 136	99.00 154	95.99 44	99.64 158	98.05 94	99.76 48	99.69 70

ZNCC-MVS		98.49 51	98.20 71	99.35 31	99.73 16	98.39 41	99.19 50	98.86 91	95.77 162	98.31 138	99.10 127	95.46 59	99.93 34	97.57 138	99.81 16	99.74 50

HPM-MVS++		98.58 36	98.25 63	99.55 11	99.50 49	99.08 13	98.72 192	98.66 154	97.51 51	98.15 139	98.83 185	95.70 53	99.92 43	97.53 142	99.67 75	99.66 82

mvsmamba		97.25 159	96.99 158	98.02 181	98.34 219	95.54 224	99.18 54	97.47 388	95.04 220	98.15 139	98.57 225	89.46 242	99.31 222	97.68 124	99.01 157	99.22 188

新几何1				99.16 56	99.34 72	98.01 72		98.69 143	90.06 432	98.13 141	98.95 163	94.60 90	99.89 69	91.97 376	99.47 122	99.59 94

API-MVS		97.41 141	97.25 128	97.91 194	98.70 167	96.80 135	98.82 156	98.69 143	94.53 255	98.11 142	98.28 255	94.50 95	99.57 172	94.12 302	99.49 118	97.37 337

ECVR-MVS		95.95 230	95.71 230	96.65 302	99.02 132	90.86 407	99.03 84	91.80 511	96.96 93	98.10 143	99.26 80	81.31 402	99.51 188	96.90 182	99.04 154	99.59 94

Casviewmamba		97.62 111	97.43 113	98.19 153	98.48 193	95.83 204	99.07 72	98.42 231	96.27 133	98.09 144	99.26 80	91.00 194	99.30 223	97.81 112	98.48 195	99.44 126

CPTT-MVS		97.72 101	97.32 123	98.92 79	99.64 33	97.10 123	99.12 64	98.81 108	92.34 372	98.09 144	99.08 138	93.01 118	99.92 43	96.06 219	99.77 42	99.75 48

viewcassd2359sk11		97.53 127	97.32 123	98.16 155	98.45 197	95.83 204	98.57 235	98.42 231	95.52 184	98.07 146	99.12 122	91.81 154	99.25 235	97.46 152	98.48 195	99.41 136

test12				99.18 53	99.16 116	98.19 61		98.53 189		98.07 146		95.13 80	99.72 138		99.56 107	99.63 88

E3new		97.55 121	97.35 121	98.16 155	98.48 193	95.85 202	98.55 239	98.41 233	95.42 191	98.06 148	99.12 122	92.23 137	99.24 243	97.43 154	98.45 198	99.39 138

RRT-MVS		97.03 174	96.78 174	97.77 210	97.90 299	94.34 296	99.12 64	98.35 256	95.87 157	98.06 148	98.70 209	86.45 325	99.63 161	98.04 95	98.54 188	99.35 148

test222					99.23 105	97.17 118	97.40 395	98.66 154	88.68 453	98.05 150	98.96 161	94.14 103			99.53 112	99.61 90

DP-MVS Recon		97.86 92	97.46 108	99.06 66	99.53 43	98.35 51	98.33 276	98.89 75	92.62 361	98.05 150	98.94 164	95.34 67	99.65 155	96.04 220	99.42 128	99.19 195

Vis-MVSNet		97.42 140	97.11 147	98.34 135	98.66 174	96.23 168	99.22 42	99.00 53	96.63 113	98.04 152	99.21 93	88.05 291	99.35 214	96.01 222	99.21 147	99.45 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		95.94 233	95.78 224	96.41 336	98.99 139	90.12 426	99.04 81	92.45 510	96.99 92	98.03 153	99.27 79	81.40 401	99.48 198	96.87 188	99.04 154	99.63 88

baseline		97.64 108	97.44 111	98.25 143	98.35 214	96.20 169	99.00 92	98.32 266	96.33 132	98.03 153	99.17 107	91.35 173	99.16 261	98.10 89	98.29 222	99.39 138

test_yl		97.22 161	96.78 174	98.54 110	98.73 162	96.60 145	98.45 258	98.31 271	94.70 244	98.02 155	98.42 238	90.80 199	99.70 144	96.81 192	96.79 281	99.34 150

DCV-MVSNet		97.22 161	96.78 174	98.54 110	98.73 162	96.60 145	98.45 258	98.31 271	94.70 244	98.02 155	98.42 238	90.80 199	99.70 144	96.81 192	96.79 281	99.34 150

MTAPA		98.58 36	98.29 61	99.46 18	99.76 5	98.64 31	98.90 121	98.74 130	97.27 73	98.02 155	99.39 50	94.81 88	99.96 4	97.91 103	99.79 35	99.77 40

sss		97.39 143	96.98 160	98.61 102	98.60 182	96.61 144	98.22 294	98.93 65	93.97 285	98.01 158	98.48 233	91.98 147	99.85 85	96.45 206	98.15 231	99.39 138

E2		97.48 130	97.25 128	98.16 155	98.40 205	95.79 209	98.58 226	98.44 216	95.58 173	98.00 159	99.14 115	91.21 185	99.24 243	97.50 147	98.43 202	99.45 123

E3		97.48 130	97.25 128	98.16 155	98.38 208	95.79 209	98.58 226	98.44 216	95.58 173	98.00 159	99.14 115	91.25 179	99.24 243	97.50 147	98.44 199	99.45 123

alignmvs		97.56 120	97.07 150	99.01 70	98.66 174	98.37 49	98.83 154	98.06 333	96.74 105	98.00 159	97.65 316	90.80 199	99.48 198	98.37 73	96.56 290	99.19 195

viewmanbaseed2359cas		97.47 133	97.25 128	98.14 159	98.41 203	95.84 203	98.57 235	98.43 227	95.55 180	97.97 162	99.12 122	91.26 178	99.15 265	97.42 156	98.53 189	99.43 130

OMC-MVS		97.55 121	97.34 122	98.20 149	99.33 75	95.92 192	98.28 287	98.59 172	95.52 184	97.97 162	99.10 127	93.28 116	99.49 192	95.09 259	98.88 164	99.19 195

VDD-MVS		95.82 241	95.23 255	97.61 230	98.84 156	93.98 311	98.68 203	97.40 397	95.02 224	97.95 164	99.34 68	74.37 472	99.78 125	98.64 49	96.80 280	99.08 220

casdiffmvs		97.63 110	97.41 114	98.28 138	98.33 223	96.14 173	98.82 156	98.32 266	96.38 127	97.95 164	99.21 93	91.23 180	99.23 247	98.12 88	98.37 213	99.48 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PRO-TEST		96.74 190	97.06 152	95.76 378	98.37 211	88.85 454	99.06 74	98.02 338	96.35 129	97.94 166	98.76 202	87.22 310	99.49 192	98.42 70	99.40 132	98.94 239

PVSNet_BlendedMVS		96.73 193	96.60 186	97.12 261	99.25 97	95.35 240	98.26 290	99.26 16	94.28 269	97.94 166	97.46 332	92.74 122	99.81 103	96.88 185	93.32 358	96.20 439

PVSNet_Blended		97.38 144	97.12 146	98.14 159	99.25 97	95.35 240	97.28 409	99.26 16	93.13 340	97.94 166	98.21 263	92.74 122	99.81 103	96.88 185	99.40 132	99.27 175

DPM-MVS		97.55 121	96.99 158	99.23 49	99.04 130	98.55 34	97.17 423	98.35 256	94.85 237	97.93 169	98.58 222	95.07 82	99.71 143	92.60 355	99.34 139	99.43 130

MP-MVS		98.33 72	98.01 82	99.28 42	99.75 6	98.18 62	99.22 42	98.79 120	96.13 139	97.92 170	99.23 87	94.54 91	99.94 14	96.74 198	99.78 40	99.73 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Elysia		96.64 197	96.02 214	98.51 115	98.04 279	97.30 103	98.74 182	98.60 165	95.04 220	97.91 171	98.84 181	83.59 386	99.48 198	94.20 298	99.25 145	98.75 264

StellarMVS		96.64 197	96.02 214	98.51 115	98.04 279	97.30 103	98.74 182	98.60 165	95.04 220	97.91 171	98.84 181	83.59 386	99.48 198	94.20 298	99.25 145	98.75 264

MDTV_nov1_ep13_2view						84.26 487	96.89 446		90.97 416	97.90 173		89.89 229		93.91 309		99.18 200

test_prior2							97.80 364		96.12 142	97.89 174	98.69 210	95.96 45		96.89 183	99.60 93

viewmacassd2359aftdt		97.32 154	97.07 150	98.08 172	98.30 228	95.69 215	98.62 219	98.44 216	95.56 175	97.86 175	99.22 90	89.91 228	99.14 268	97.29 164	98.43 202	99.42 133

E5new		97.37 146	97.16 140	97.98 187	98.30 228	95.41 231	98.87 135	98.45 212	95.56 175	97.84 176	99.19 102	90.39 214	99.25 235	97.61 130	98.22 226	99.29 167

E6new		97.37 146	97.16 140	97.98 187	98.28 234	95.40 234	98.87 135	98.45 212	95.55 180	97.84 176	99.20 95	90.44 212	99.25 235	97.61 130	98.22 226	99.29 167

E6		97.37 146	97.16 140	97.98 187	98.28 234	95.40 234	98.87 135	98.45 212	95.55 180	97.84 176	99.20 95	90.44 212	99.25 235	97.61 130	98.22 226	99.29 167

E5		97.37 146	97.16 140	97.98 187	98.30 228	95.41 231	98.87 135	98.45 212	95.56 175	97.84 176	99.19 102	90.39 214	99.25 235	97.61 130	98.22 226	99.29 167

原ACMM1				98.65 98	99.32 78	96.62 142		98.67 151	93.27 334	97.81 180	98.97 156	95.18 77	99.83 91	93.84 311	99.46 125	99.50 107

E4		97.37 146	97.13 145	98.12 167	98.27 236	95.70 214	98.59 222	98.44 216	95.56 175	97.80 181	99.18 105	90.57 208	99.26 231	97.45 153	98.28 224	99.40 137

viewmambaseed2359dif		97.01 176	96.84 167	97.51 235	98.19 252	94.21 304	98.16 311	98.23 292	93.61 317	97.78 182	99.13 118	90.79 202	99.18 256	97.24 165	98.40 211	99.15 202

114514_t		96.93 181	96.27 202	98.92 79	99.50 49	97.63 84	98.85 148	98.90 73	84.80 483	97.77 183	99.11 125	92.84 120	99.66 154	94.85 265	99.77 42	99.47 116

casdiffmvs_mvg		97.72 101	97.48 107	98.44 126	98.42 201	96.59 149	98.92 118	98.44 216	96.20 136	97.76 184	99.20 95	91.66 159	99.23 247	98.27 84	98.41 210	99.49 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PMMVS		96.60 200	96.33 200	97.41 242	97.90 299	93.93 313	97.35 402	98.41 233	92.84 353	97.76 184	97.45 334	91.10 190	99.20 253	96.26 212	97.91 240	99.11 211

PVSNet	91.96 18	96.35 213	96.15 206	96.96 276	99.17 112	92.05 385	96.08 468	98.68 146	93.69 307	97.75 186	97.80 303	88.86 267	99.69 149	94.26 296	99.01 157	99.15 202

TEST9					99.31 80	98.50 36	97.92 345	98.73 133	92.63 360	97.74 187	98.68 211	96.20 36	99.80 110

train_agg		97.97 86	97.52 103	99.33 36	99.31 80	98.50 36	97.92 345	98.73 133	92.98 346	97.74 187	98.68 211	96.20 36	99.80 110	96.59 199	99.57 99	99.68 75

FE-MVS		95.62 252	94.90 273	97.78 207	98.37 211	94.92 267	97.17 423	97.38 399	90.95 417	97.73 189	97.70 309	85.32 350	99.63 161	91.18 391	98.33 218	98.79 255

mmtdpeth		93.12 391	92.61 387	94.63 424	97.60 323	89.68 437	99.21 45	97.32 403	94.02 279	97.72 190	94.42 466	77.01 451	99.44 205	99.05 31	77.18 492	94.78 475

CANet		98.05 85	97.76 90	98.90 82	98.73 162	97.27 107	98.35 273	98.78 122	97.37 64	97.72 190	98.96 161	91.53 167	99.92 43	98.79 42	99.65 81	99.51 104

test_8					99.29 89	98.44 38	97.89 353	98.72 135	92.98 346	97.70 192	98.66 214	96.20 36	99.80 110

hybridcas		97.52 128	97.29 125	98.20 149	98.44 198	96.00 178	99.02 87	98.39 242	96.12 142	97.69 193	99.23 87	90.77 204	99.17 259	97.55 139	98.42 208	99.44 126

MP-MVS-pluss		98.31 73	97.92 85	99.49 16	99.72 17	98.88 21	98.43 266	98.78 122	94.10 275	97.69 193	99.42 46	95.25 73	99.92 43	98.09 90	99.80 25	99.67 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
sasdasda		97.67 105	97.23 133	98.98 73	98.70 167	98.38 42	99.34 17	98.39 242	96.76 103	97.67 195	97.40 339	92.26 134	99.49 192	98.28 81	96.28 305	99.08 220

canonicalmvs		97.67 105	97.23 133	98.98 73	98.70 167	98.38 42	99.34 17	98.39 242	96.76 103	97.67 195	97.40 339	92.26 134	99.49 192	98.28 81	96.28 305	99.08 220

PVSNet_Blended_VisFu		97.70 103	97.46 108	98.44 126	99.27 94	95.91 193	98.63 216	99.16 39	94.48 262	97.67 195	98.88 176	92.80 121	99.91 57	97.11 169	99.12 151	99.50 107

WTY-MVS		97.37 146	96.92 163	98.72 92	98.86 152	96.89 133	98.31 281	98.71 138	95.26 203	97.67 195	98.56 226	92.21 139	99.78 125	95.89 224	96.85 279	99.48 114

dtuplus		97.00 177	96.83 169	97.51 235	98.18 258	94.21 304	98.21 295	98.20 296	94.42 266	97.66 199	99.22 90	90.18 223	99.17 259	97.01 172	98.36 215	99.13 207

viewdifsd2359ckpt07		97.20 164	97.05 153	97.65 226	98.40 205	94.33 298	98.39 271	98.43 227	95.67 168	97.66 199	99.08 138	90.04 225	99.32 218	97.47 151	98.29 222	99.31 159

Effi-MVS+		97.12 171	96.69 180	98.39 133	98.19 252	96.72 140	97.37 399	98.43 227	93.71 304	97.65 201	98.02 277	92.20 140	99.25 235	96.87 188	97.79 245	99.19 195

thisisatest0530		96.01 227	95.36 246	97.97 191	98.38 208	95.52 225	98.88 132	94.19 498	94.04 277	97.64 202	98.31 253	83.82 384	99.46 203	95.29 253	97.70 251	98.93 241

tttt0517		96.07 225	95.51 239	97.78 207	98.41 203	94.84 270	99.28 30	94.33 494	94.26 271	97.64 202	98.64 216	84.05 377	99.47 202	95.34 248	97.60 254	99.03 228

viewdifsd2359ckpt11		96.30 215	96.13 207	96.81 288	98.10 268	92.10 381	98.49 253	98.40 236	96.02 146	97.61 204	99.31 71	86.37 327	99.29 226	97.52 143	93.36 357	99.04 226

viewmsd2359difaftdt		96.30 215	96.13 207	96.81 288	98.10 268	92.10 381	98.49 253	98.40 236	96.02 146	97.61 204	99.31 71	86.37 327	99.30 223	97.52 143	93.37 356	99.04 226

HyFIR lowres test		96.90 183	96.49 193	98.14 159	99.33 75	95.56 221	97.38 397	99.65 2	92.34 372	97.61 204	98.20 264	89.29 249	99.10 279	96.97 175	97.60 254	99.77 40

viewdifsd2359ckpt13		97.24 160	96.97 161	98.06 176	98.43 199	95.77 211	98.59 222	98.34 260	94.81 238	97.60 207	98.94 164	90.78 203	99.09 280	96.93 178	98.33 218	99.32 158

ACMMP		98.23 76	97.95 84	99.09 63	99.74 12	97.62 85	99.03 84	99.41 6	95.98 149	97.60 207	99.36 60	94.45 96	99.93 34	97.14 168	98.85 169	99.70 67
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
MGCFI-Net		97.62 111	97.19 137	98.92 79	98.66 174	98.20 60	99.32 22	98.38 249	96.69 109	97.58 209	97.42 338	92.10 143	99.50 191	98.28 81	96.25 308	99.08 220

agg_prior					99.30 84	98.38 42		98.72 135		97.57 210			99.81 103

tpmrst		95.63 251	95.69 233	95.44 391	97.54 330	88.54 460	96.97 435	97.56 375	93.50 321	97.52 211	96.93 390	89.49 238	99.16 261	95.25 255	96.42 296	98.64 280

MDTV_nov1_ep13			95.40 241		97.48 335	88.34 464	96.85 451	97.29 407	93.74 300	97.48 212	97.26 349	89.18 252	99.05 288	91.92 377	97.43 264

FA-MVS(test-final)		96.41 212	95.94 218	97.82 204	98.21 248	95.20 248	97.80 364	97.58 372	93.21 335	97.36 213	97.70 309	89.47 240	99.56 175	94.12 302	97.99 237	98.71 270

viewdifsd2359ckpt09		97.13 170	96.79 172	98.14 159	98.43 199	95.90 194	98.52 242	98.37 251	94.32 268	97.33 214	98.86 179	90.23 222	99.16 261	96.81 192	98.25 225	99.36 147

icg_test_0407_2		96.56 204	96.50 192	96.73 293	97.99 286	92.82 364	97.18 420	98.27 281	95.16 208	97.30 215	98.79 190	91.53 167	98.10 409	94.74 270	97.54 258	99.27 175

IMVS_0407		96.74 190	96.64 184	97.05 267	97.99 286	92.82 364	98.45 258	98.27 281	95.16 208	97.30 215	98.79 190	91.53 167	99.06 287	94.74 270	97.54 258	99.27 175

EPMVS		94.99 295	94.48 294	96.52 323	97.22 355	91.75 390	97.23 411	91.66 512	94.11 274	97.28 217	96.81 399	85.70 340	98.84 326	93.04 336	97.28 266	98.97 234

EIA-MVS		97.75 99	97.58 96	98.27 139	98.38 208	96.44 156	99.01 90	98.60 165	95.88 155	97.26 218	97.53 329	94.97 85	99.33 217	97.38 161	99.20 148	99.05 225

SSM_0404		97.26 158	97.00 156	98.03 179	98.46 195	95.99 179	98.62 219	98.44 216	94.77 241	97.24 219	98.93 166	91.22 181	99.28 228	96.54 201	98.74 174	98.84 250

IS-MVSNet		97.22 161	96.88 164	98.25 143	98.85 155	96.36 162	99.19 50	97.97 339	95.39 193	97.23 220	98.99 155	91.11 189	98.93 313	94.60 282	98.59 182	99.47 116

testing3-2		95.45 261	95.34 247	95.77 377	98.69 170	88.75 456	98.87 135	97.21 416	96.13 139	97.22 221	97.68 314	77.95 439	99.65 155	97.58 134	96.77 283	98.91 243

EPP-MVSNet		97.46 134	97.28 126	97.99 185	98.64 178	95.38 237	99.33 21	98.31 271	93.61 317	97.19 222	99.07 142	94.05 104	99.23 247	96.89 183	98.43 202	99.37 143

thisisatest0515		95.61 255	94.89 274	97.76 211	98.15 264	95.15 252	96.77 454	94.41 492	92.95 348	97.18 223	97.43 336	84.78 359	99.45 204	94.63 278	97.73 250	98.68 274

IMVS_0403		96.74 190	96.61 185	97.12 261	97.99 286	92.82 364	98.47 256	98.27 281	95.16 208	97.13 224	98.79 190	91.44 170	99.26 231	94.74 270	97.54 258	99.27 175

CANet_DTU		96.96 180	96.55 188	98.21 147	98.17 262	96.07 177	97.98 338	98.21 294	97.24 74	97.13 224	98.93 166	86.88 317	99.91 57	95.00 262	99.37 137	98.66 278

CHOSEN 1792x2688		97.12 171	96.80 170	98.08 172	99.30 84	94.56 287	98.05 329	99.71 1	93.57 319	97.09 226	98.91 172	88.17 285	99.89 69	96.87 188	99.56 107	99.81 25

PatchT		93.06 392	91.97 399	96.35 341	96.69 391	92.67 369	94.48 500	97.08 425	86.62 469	97.08 227	92.23 495	87.94 293	97.90 434	78.89 496	96.69 284	98.49 293

PatchmatchNet		95.71 246	95.52 237	96.29 346	97.58 325	90.72 411	96.84 452	97.52 383	94.06 276	97.08 227	96.96 385	89.24 251	98.90 319	92.03 373	98.37 213	99.26 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MAR-MVS		96.91 182	96.40 196	98.45 124	98.69 170	96.90 131	98.66 210	98.68 146	92.40 371	97.07 229	97.96 284	91.54 166	99.75 133	93.68 315	98.92 161	98.69 272
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
PAPM_NR		97.46 134	97.11 147	98.50 118	99.50 49	96.41 159	98.63 216	98.60 165	95.18 207	97.06 230	98.06 274	94.26 101	99.57 172	93.80 313	98.87 166	99.52 101

TAMVS		97.02 175	96.79 172	97.70 217	98.06 275	95.31 243	98.52 242	98.31 271	93.95 286	97.05 231	98.61 217	93.49 112	98.52 357	95.33 249	97.81 244	99.29 167

CSCG		97.85 94	97.74 91	98.20 149	99.67 30	95.16 250	99.22 42	99.32 12	93.04 344	97.02 232	98.92 171	95.36 65	99.91 57	97.43 154	99.64 86	99.52 101

CDS-MVSNet		96.99 178	96.69 180	97.90 195	98.05 277	95.98 180	98.20 299	98.33 262	93.67 311	96.95 233	98.49 232	93.54 111	98.42 368	95.24 256	97.74 249	99.31 159
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
XVG-OURS-SEG-HR		96.51 206	96.34 199	97.02 269	98.77 160	93.76 318	97.79 366	98.50 200	95.45 188	96.94 234	99.09 135	87.87 296	99.55 182	96.76 197	95.83 319	97.74 323

CR-MVSNet		94.76 314	94.15 318	96.59 313	97.00 369	93.43 333	94.96 489	97.56 375	92.46 365	96.93 235	96.24 423	88.15 286	97.88 439	87.38 450	96.65 287	98.46 295

RPMNet		92.81 394	91.34 405	97.24 250	97.00 369	93.43 333	94.96 489	98.80 115	82.27 490	96.93 235	92.12 496	86.98 315	99.82 98	76.32 504	96.65 287	98.46 295

SCA		95.46 259	95.13 259	96.46 332	97.67 317	91.29 399	97.33 404	97.60 371	94.68 247	96.92 237	97.10 360	83.97 379	98.89 320	92.59 357	98.32 221	99.20 191

PatchMatch-RL		96.59 201	96.03 213	98.27 139	99.31 80	96.51 153	97.91 347	99.06 47	93.72 303	96.92 237	98.06 274	88.50 278	99.65 155	91.77 381	99.00 159	98.66 278

DeepC-MVS	95.98 3	97.88 91	97.58 96	98.77 88	99.25 97	96.93 129	98.83 154	98.75 128	96.96 93	96.89 239	99.50 31	90.46 211	99.87 80	97.84 110	99.76 48	99.52 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffseed414692147		96.97 179	96.55 188	98.25 143	98.26 237	96.28 167	98.93 115	98.33 262	94.99 225	96.87 240	99.09 135	88.97 263	99.07 284	95.70 237	97.77 247	99.39 138

XVG-OURS		96.55 205	96.41 195	96.99 270	98.75 161	93.76 318	97.50 388	98.52 192	95.67 168	96.83 241	99.30 74	88.95 265	99.53 184	95.88 225	96.26 307	97.69 326

AdaColmap		97.15 169	96.70 179	98.48 121	99.16 116	96.69 141	98.01 334	98.89 75	94.44 264	96.83 241	98.68 211	90.69 205	99.76 131	94.36 290	99.29 144	98.98 233

CostFormer		94.95 303	94.73 280	95.60 385	97.28 351	89.06 448	97.53 385	96.89 444	89.66 439	96.82 243	96.72 403	86.05 334	98.95 312	95.53 244	96.13 313	98.79 255

UGNet		96.78 189	96.30 201	98.19 153	98.24 242	95.89 199	98.88 132	98.93 65	97.39 61	96.81 244	97.84 297	82.60 391	99.90 65	96.53 203	99.49 118	98.79 255
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
CNLPA		97.45 137	97.03 155	98.73 91	99.05 129	97.44 96	98.07 327	98.53 189	95.32 200	96.80 245	98.53 227	93.32 114	99.72 138	94.31 294	99.31 143	99.02 229

CHOSEN 280x420		97.18 166	97.18 138	97.20 252	98.81 158	93.27 347	95.78 475	99.15 41	95.25 204	96.79 246	98.11 271	92.29 133	99.07 284	98.56 55	99.85 6	99.25 184

mamba_0408		96.81 188	96.38 197	98.09 171	98.19 252	95.90 194	95.69 476	98.32 266	94.51 258	96.75 247	98.73 205	90.99 195	99.27 230	95.83 227	98.43 202	99.10 213

SSM_04072		96.71 194	96.38 197	97.68 220	98.19 252	95.90 194	95.69 476	98.32 266	94.51 258	96.75 247	98.73 205	90.99 195	98.02 424	95.83 227	98.43 202	99.10 213

SSM_0407		97.17 167	96.87 165	98.08 172	98.19 252	95.90 194	98.52 242	98.44 216	94.77 241	96.75 247	98.93 166	91.22 181	99.22 251	96.54 201	98.43 202	99.10 213

HY-MVS	93.96 8	96.82 187	96.23 205	98.57 105	98.46 195	97.00 126	98.14 315	98.21 294	93.95 286	96.72 250	97.99 281	91.58 161	99.76 131	94.51 286	96.54 291	98.95 238

PAPR		96.84 186	96.24 204	98.65 98	98.72 166	96.92 130	97.36 401	98.57 179	93.33 329	96.67 251	97.57 325	94.30 99	99.56 175	91.05 399	98.59 182	99.47 116

Anonymous20240529		95.10 287	94.22 312	97.75 212	99.01 134	94.26 301	98.87 135	98.83 98	85.79 477	96.64 252	98.97 156	78.73 428	99.85 85	96.27 211	94.89 325	99.12 208

UWE-MVS		94.30 347	93.89 341	95.53 386	97.83 303	88.95 452	97.52 387	93.25 503	94.44 264	96.63 253	97.07 367	78.70 429	99.28 228	91.99 374	97.56 257	98.36 300

thres600view7		95.49 257	94.77 277	97.67 222	98.98 140	95.02 258	98.85 148	96.90 442	95.38 194	96.63 253	96.90 392	84.29 369	99.59 168	88.65 437	96.33 298	98.40 297

thres100view900		95.38 267	94.70 282	97.41 242	98.98 140	94.92 267	98.87 135	96.90 442	95.38 194	96.61 255	96.88 393	84.29 369	99.56 175	88.11 441	96.29 302	97.76 321

Vis-MVSNet (Re-imp)		96.87 184	96.55 188	97.83 202	98.73 162	95.46 228	99.20 48	98.30 278	94.96 229	96.60 256	98.87 177	90.05 224	98.59 352	93.67 317	98.60 181	99.46 121

CVMVSNet		95.43 263	96.04 212	93.57 445	97.93 297	83.62 490	98.12 318	98.59 172	95.68 167	96.56 257	99.02 148	87.51 303	97.51 458	93.56 321	97.44 263	99.60 92

RPSCF		94.87 308	95.40 241	93.26 451	98.89 147	82.06 497	98.33 276	98.06 333	90.30 429	96.56 257	99.26 80	87.09 312	99.49 192	93.82 312	96.32 299	98.24 304

tfpn200view9		95.32 274	94.62 286	97.43 240	98.94 144	94.98 263	98.68 203	96.93 440	95.33 198	96.55 259	96.53 412	84.23 373	99.56 175	88.11 441	96.29 302	97.76 321

thres400		95.38 267	94.62 286	97.65 226	98.94 144	94.98 263	98.68 203	96.93 440	95.33 198	96.55 259	96.53 412	84.23 373	99.56 175	88.11 441	96.29 302	98.40 297

thres200		95.25 277	94.57 289	97.28 248	98.81 158	94.92 267	98.20 299	97.11 423	95.24 206	96.54 261	96.22 427	84.58 366	99.53 184	87.93 447	96.50 293	97.39 335

ab-mvs		96.42 209	95.71 230	98.55 108	98.63 179	96.75 138	97.88 354	98.74 130	93.84 293	96.54 261	98.18 266	85.34 348	99.75 133	95.93 223	96.35 297	99.15 202

Anonymous202405211		95.28 276	94.49 293	97.67 222	99.00 136	93.75 320	98.70 197	97.04 430	90.66 420	96.49 263	98.80 188	78.13 435	99.83 91	96.21 215	95.36 324	99.44 126

ADS-MVSNet2		94.58 326	94.40 303	95.11 401	98.00 284	88.74 457	96.04 469	97.30 406	90.15 430	96.47 264	96.64 409	87.89 294	97.56 456	90.08 411	97.06 271	99.02 229

ADS-MVSNet		95.00 293	94.45 299	96.63 307	98.00 284	91.91 387	96.04 469	97.74 360	90.15 430	96.47 264	96.64 409	87.89 294	98.96 307	90.08 411	97.06 271	99.02 229

Effi-MVS+-dtu		96.29 217	96.56 187	95.51 387	97.89 301	90.22 425	98.80 165	98.10 321	96.57 116	96.45 266	96.66 406	90.81 198	98.91 316	95.72 234	97.99 237	97.40 334

ETVMVS		94.50 334	93.44 368	97.68 220	98.18 258	95.35 240	98.19 302	97.11 423	93.73 301	96.40 267	95.39 455	74.53 469	98.84 326	91.10 393	96.31 300	98.84 250

PLC	95.07 4	97.20 164	96.78 174	98.44 126	99.29 89	96.31 166	98.14 315	98.76 126	92.41 370	96.39 268	98.31 253	94.92 87	99.78 125	94.06 305	98.77 173	99.23 186
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm		94.13 361	93.80 347	95.12 400	96.50 401	87.91 471	97.44 391	95.89 474	92.62 361	96.37 269	96.30 422	84.13 376	98.30 391	93.24 328	91.66 382	99.14 205

TAPA-MVS	93.98 7	95.35 271	94.56 290	97.74 213	99.13 120	94.83 272	98.33 276	98.64 159	86.62 469	96.29 270	98.61 217	94.00 106	99.29 226	80.00 490	99.41 129	99.09 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FBQ-MVS		94.89 307	94.10 322	97.26 249	98.07 271	93.75 320	98.48 255	97.26 411	94.51 258	96.28 271	95.64 452	76.88 453	99.07 284	93.29 327	96.47 295	98.96 237

dtuonly		95.08 290	95.10 263	95.02 405	96.53 398	87.27 476	96.33 467	97.21 416	93.41 326	96.28 271	98.51 231	87.71 298	98.99 302	91.88 378	98.01 236	98.80 254

myMVS_eth3d28		95.12 285	94.62 286	96.64 306	98.17 262	92.17 377	98.02 333	97.32 403	95.41 192	96.22 273	96.05 433	78.01 437	99.13 270	95.22 257	97.16 268	98.60 283

baseline1		95.84 239	95.12 261	98.01 183	98.49 192	95.98 180	98.73 188	97.03 431	95.37 196	96.22 273	98.19 265	89.96 227	99.16 261	94.60 282	87.48 437	98.90 244

tpm2		94.19 356	93.76 352	95.46 390	97.23 354	89.04 449	97.31 407	96.85 448	87.08 462	96.21 275	96.79 400	83.75 385	98.74 337	92.43 365	96.23 310	98.59 286

UBG		95.32 274	94.72 281	97.13 259	98.05 277	93.26 348	97.87 355	97.20 419	94.96 229	96.18 276	95.66 451	80.97 408	99.35 214	94.47 288	97.08 270	98.78 259

F-COLMAP		97.09 173	96.80 170	97.97 191	99.45 62	94.95 266	98.55 239	98.62 164	93.02 345	96.17 277	98.58 222	94.01 105	99.81 103	93.95 307	98.90 162	99.14 205

GeoE		96.58 203	96.07 210	98.10 170	98.35 214	95.89 199	99.34 17	98.12 315	93.12 341	96.09 278	98.87 177	89.71 234	98.97 303	92.95 339	98.08 234	99.43 130

JIA-IIPM		93.35 381	92.49 391	95.92 365	96.48 403	90.65 413	95.01 487	96.96 438	85.93 475	96.08 279	87.33 515	87.70 301	98.78 335	91.35 389	95.58 322	98.34 301

BH-RMVSNet		95.92 235	95.32 251	97.69 218	98.32 226	94.64 279	98.19 302	97.45 393	94.56 253	96.03 280	98.61 217	85.02 353	99.12 273	90.68 404	99.06 153	99.30 164

dp		94.15 360	93.90 339	94.90 410	97.31 350	86.82 478	96.97 435	97.19 420	91.22 412	96.02 281	96.61 411	85.51 344	99.02 298	90.00 415	94.30 327	98.85 248

EPNet		97.28 156	96.87 165	98.51 115	94.98 457	96.14 173	98.90 121	97.02 434	98.28 21	95.99 282	99.11 125	91.36 172	99.89 69	96.98 174	99.19 149	99.50 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		97.16 168	96.66 183	98.68 95	98.53 187	97.19 117	98.93 115	98.90 73	92.83 354	95.99 282	99.37 56	92.12 142	99.87 80	93.67 317	99.57 99	98.97 234

SDMVSNet		96.85 185	96.42 194	98.14 159	99.30 84	96.38 160	99.21 45	99.23 27	95.92 152	95.96 284	98.76 202	85.88 337	99.44 205	97.93 100	95.59 320	98.60 283

sd_testset		96.17 222	95.76 225	97.42 241	99.30 84	94.34 296	98.82 156	99.08 45	95.92 152	95.96 284	98.76 202	82.83 390	99.32 218	95.56 242	95.59 320	98.60 283

AUN-MVS		94.53 331	93.73 354	96.92 281	98.50 188	93.52 331	98.34 275	98.10 321	93.83 295	95.94 286	97.98 283	85.59 343	99.03 294	94.35 291	80.94 479	98.22 306

testing222		94.12 363	93.03 378	97.37 247	98.02 282	94.66 277	97.94 343	96.65 458	94.63 250	95.78 287	95.76 442	71.49 479	98.92 314	91.17 392	95.88 317	98.52 291

TR-MVS		94.94 305	94.20 313	97.17 256	97.75 309	94.14 308	97.59 382	97.02 434	92.28 376	95.75 288	97.64 319	83.88 381	98.96 307	89.77 417	96.15 312	98.40 297

WB-MVSnew		94.19 356	94.04 325	94.66 422	96.82 383	92.14 378	97.86 357	95.96 471	93.50 321	95.64 289	96.77 401	88.06 290	97.99 428	84.87 469	96.86 277	93.85 492

MonoMVSNet		95.51 256	95.45 240	95.68 380	95.54 444	90.87 406	98.92 118	97.37 400	95.79 161	95.53 290	97.38 341	89.58 237	97.68 449	96.40 208	92.59 368	98.49 293

VPA-MVSNet		95.75 244	95.11 262	97.69 218	97.24 353	97.27 107	98.94 109	99.23 27	95.13 213	95.51 291	97.32 346	85.73 339	98.91 316	97.33 163	89.55 411	96.89 361

testing91		94.98 297	94.25 311	97.20 252	97.94 295	93.41 335	98.00 336	97.58 372	94.99 225	95.45 292	96.04 435	77.20 447	99.42 207	94.97 263	96.02 315	98.78 259

testing99		94.83 309	94.08 323	97.07 266	97.94 295	93.13 354	98.10 324	97.17 421	94.86 235	95.34 293	96.00 439	76.31 456	99.40 209	95.08 260	95.90 316	98.68 274

HQP_MVS		96.14 224	95.90 220	96.85 285	97.42 342	94.60 285	98.80 165	98.56 183	97.28 69	95.34 293	98.28 255	87.09 312	99.03 294	96.07 216	94.27 328	96.92 353

plane_prior3						94.61 283			97.02 89	95.34 293

testing11		95.00 293	94.28 307	97.16 257	97.96 294	93.36 341	98.09 325	97.06 429	94.94 233	95.33 296	96.15 429	76.89 452	99.40 209	95.77 233	96.30 301	98.72 267

Fast-Effi-MVS+		96.28 219	95.70 232	98.03 179	98.29 232	95.97 185	98.58 226	98.25 290	91.74 390	95.29 297	97.23 353	91.03 192	99.15 265	92.90 341	97.96 239	98.97 234

test_fmvs2		93.43 379	93.58 361	92.95 458	96.97 372	83.91 489	99.19 50	97.24 413	95.74 163	95.20 298	98.27 258	69.65 481	98.72 339	96.26 212	93.73 345	96.24 437

EI-MVSNet		95.96 229	95.83 222	96.36 340	97.93 297	93.70 325	98.12 318	98.27 281	93.70 306	95.07 299	99.02 148	92.23 137	98.54 355	94.68 275	93.46 351	96.84 368

MVSTER		96.06 226	95.72 227	97.08 265	98.23 245	95.93 191	98.73 188	98.27 281	94.86 235	95.07 299	98.09 272	88.21 284	98.54 355	96.59 199	93.46 351	96.79 372

OPM-MVS		95.69 249	95.33 250	96.76 292	96.16 418	94.63 280	98.43 266	98.39 242	96.64 112	95.02 301	98.78 194	85.15 352	99.05 288	95.21 258	94.20 331	96.60 398
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Fast-Effi-MVS+-dtu		95.87 237	95.85 221	95.91 366	97.74 312	91.74 391	98.69 200	98.15 311	95.56 175	94.92 302	97.68 314	88.98 262	98.79 334	93.19 330	97.78 246	97.20 341

TESTMET0.1,1		94.18 359	93.69 357	95.63 383	96.92 375	89.12 447	96.91 441	94.78 489	93.17 337	94.88 303	96.45 416	78.52 430	98.92 314	93.09 333	98.50 192	98.85 248

VPNet		94.99 295	94.19 314	97.40 244	97.16 362	96.57 150	98.71 193	98.97 57	95.67 168	94.84 304	98.24 262	80.36 415	98.67 344	96.46 205	87.32 441	96.96 348

1112_ss		96.63 199	96.00 216	98.50 118	98.56 183	96.37 161	98.18 308	98.10 321	92.92 349	94.84 304	98.43 236	92.14 141	99.58 171	94.35 291	96.51 292	99.56 100

test-LLR		95.10 287	94.87 275	95.80 374	96.77 385	89.70 435	96.91 441	95.21 481	95.11 215	94.83 306	95.72 447	87.71 298	98.97 303	93.06 334	98.50 192	98.72 267

test-mter		94.08 367	93.51 365	95.80 374	96.77 385	89.70 435	96.91 441	95.21 481	92.89 351	94.83 306	95.72 447	77.69 441	98.97 303	93.06 334	98.50 192	98.72 267

Test_1112_low_res		96.34 214	95.66 235	98.36 134	98.56 183	95.94 188	97.71 372	98.07 328	92.10 382	94.79 308	97.29 348	91.75 155	99.56 175	94.17 300	96.50 293	99.58 98

UWE-MVS-28		92.79 395	92.51 390	93.62 444	96.46 404	86.28 480	97.93 344	92.71 508	94.17 272	94.78 309	97.16 357	81.05 407	96.43 481	81.45 484	96.86 277	98.14 311

GA-MVS		94.81 310	94.03 327	97.14 258	97.15 363	93.86 315	96.76 455	97.58 372	94.00 283	94.76 310	97.04 375	80.91 409	98.48 359	91.79 380	96.25 308	99.09 216

nomal-1		94.97 299	94.34 305	96.86 284	97.79 306	92.62 370	98.19 302	96.71 454	93.89 289	94.74 311	96.05 433	79.44 423	99.09 280	95.58 241	96.68 285	98.86 247

BH-untuned		95.95 230	95.72 227	96.65 302	98.55 185	92.26 376	98.23 293	97.79 357	93.73 301	94.62 312	98.01 279	88.97 263	99.00 301	93.04 336	98.51 191	98.68 274

test_djsdf		96.00 228	95.69 233	96.93 278	95.72 438	95.49 226	99.47 7	98.40 236	94.98 227	94.58 313	97.86 294	89.16 253	98.41 375	96.91 179	94.12 336	96.88 362

cascas		94.63 322	93.86 343	96.93 278	96.91 377	94.27 300	96.00 472	98.51 195	85.55 480	94.54 314	96.23 425	84.20 375	98.87 323	95.80 231	96.98 276	97.66 327

DP-MVS		96.59 201	95.93 219	98.57 105	99.34 72	96.19 171	98.70 197	98.39 242	89.45 443	94.52 315	99.35 62	91.85 151	99.85 85	92.89 343	98.88 164	99.68 75

gg-mvs-nofinetune		92.21 403	90.58 412	97.13 259	96.75 388	95.09 255	95.85 473	89.40 518	85.43 481	94.50 316	81.98 522	80.80 412	98.40 381	92.16 367	98.33 218	97.88 318

mvs_anonymous		96.70 196	96.53 191	97.18 255	98.19 252	93.78 317	98.31 281	98.19 299	94.01 282	94.47 317	98.27 258	92.08 145	98.46 363	97.39 160	97.91 240	99.31 159

HQP-NCC					97.20 357		98.05 329		96.43 121	94.45 318

ACMP_Plane					97.20 357		98.05 329		96.43 121	94.45 318

HQP4-MVS										94.45 318			98.96 307			96.87 365

HQP-MVS		95.72 245	95.40 241	96.69 299	97.20 357	94.25 302	98.05 329	98.46 208	96.43 121	94.45 318	97.73 306	86.75 318	98.96 307	95.30 251	94.18 332	96.86 367

MSDG		95.93 234	95.30 253	97.83 202	98.90 146	95.36 238	96.83 453	98.37 251	91.32 406	94.43 322	98.73 205	90.27 220	99.60 167	90.05 413	98.82 171	98.52 291

dmvs_re		94.48 337	94.18 316	95.37 393	97.68 316	90.11 427	98.54 241	97.08 425	94.56 253	94.42 323	97.24 352	84.25 371	97.76 446	91.02 400	92.83 365	98.24 304

nrg030		96.28 219	95.72 227	97.96 193	96.90 378	98.15 65	99.39 11	98.31 271	95.47 187	94.42 323	98.35 246	92.09 144	98.69 340	97.50 147	89.05 420	97.04 344

CLD-MVS		95.62 252	95.34 247	96.46 332	97.52 333	93.75 320	97.27 410	98.46 208	95.53 183	94.42 323	98.00 280	86.21 331	98.97 303	96.25 214	94.37 326	96.66 390
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		95.62 252	95.34 247	96.47 329	97.46 337	93.54 328	98.99 95	98.54 187	94.67 248	94.36 326	98.77 197	85.39 345	99.11 275	95.71 235	94.15 334	96.76 375

LGP-MVS_train				96.47 329	97.46 337	93.54 328		98.54 187	94.67 248	94.36 326	98.77 197	85.39 345	99.11 275	95.71 235	94.15 334	96.76 375

v144192		94.39 343	93.70 356	96.48 328	96.06 422	94.35 295	98.58 226	98.16 310	91.45 399	94.33 328	97.02 378	87.50 305	98.45 364	91.08 396	89.11 419	96.63 392

IMVS_0404		95.82 241	95.52 237	96.73 293	97.99 286	92.82 364	97.23 411	98.27 281	95.16 208	94.31 329	98.79 190	85.63 341	98.10 409	94.74 270	97.54 258	99.27 175

V42		94.78 312	94.14 319	96.70 298	96.33 410	95.22 247	98.97 99	98.09 325	92.32 374	94.31 329	97.06 371	88.39 279	98.55 354	92.90 341	88.87 424	96.34 432

VortexMVS		95.95 230	95.79 223	96.42 335	98.29 232	93.96 312	98.68 203	98.31 271	96.02 146	94.29 331	97.57 325	89.47 240	98.37 382	97.51 146	91.93 376	96.94 351

ACMM	93.85 9	95.69 249	95.38 245	96.61 310	97.61 322	93.84 316	98.91 120	98.44 216	95.25 204	94.28 332	98.47 234	86.04 336	99.12 273	95.50 245	93.95 341	96.87 365
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		95.46 259	95.21 256	96.22 348	98.12 266	93.72 324	98.32 280	98.13 314	93.71 304	94.26 333	97.31 347	92.24 136	98.10 409	94.63 278	90.12 402	96.84 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		94.20 355	93.47 367	96.40 338	95.98 426	94.08 309	98.52 242	98.15 311	91.33 405	94.25 334	97.20 356	86.41 326	98.42 368	90.04 414	89.39 416	96.69 389

BH-w/o		95.38 267	95.08 264	96.26 347	98.34 219	91.79 388	97.70 373	97.43 395	92.87 352	94.24 335	97.22 354	88.66 271	98.84 326	91.55 387	97.70 251	98.16 310

XVG-ACMP-BASELINE		94.54 329	94.14 319	95.75 379	96.55 397	91.65 393	98.11 322	98.44 216	94.96 229	94.22 336	97.90 290	79.18 426	99.11 275	94.05 306	93.85 343	96.48 426

v1144		94.59 325	93.92 336	96.60 312	96.21 412	94.78 276	98.59 222	98.14 313	91.86 389	94.21 337	97.02 378	87.97 292	98.41 375	91.72 382	89.57 409	96.61 396

v1192		94.32 346	93.58 361	96.53 322	96.10 420	94.45 289	98.50 250	98.17 308	91.54 397	94.19 338	97.06 371	86.95 316	98.43 367	90.14 409	89.57 409	96.70 384

PAPM		94.95 303	94.00 331	97.78 207	97.04 368	95.65 216	96.03 471	98.25 290	91.23 411	94.19 338	97.80 303	91.27 177	98.86 325	82.61 480	97.61 253	98.84 250

Patchmatch-test		94.42 341	93.68 358	96.63 307	97.60 323	91.76 389	94.83 493	97.49 387	89.45 443	94.14 340	97.10 360	88.99 259	98.83 329	85.37 466	98.13 232	99.29 167

v1240		94.06 369	93.29 373	96.34 342	96.03 424	93.90 314	98.44 264	98.17 308	91.18 414	94.13 341	97.01 380	86.05 334	98.42 368	89.13 431	89.50 413	96.70 384

GBi-Net		94.49 335	93.80 347	96.56 317	98.21 248	95.00 259	98.82 156	98.18 302	92.46 365	94.09 342	97.07 367	81.16 404	97.95 430	92.08 369	92.14 373	96.72 380

test1		94.49 335	93.80 347	96.56 317	98.21 248	95.00 259	98.82 156	98.18 302	92.46 365	94.09 342	97.07 367	81.16 404	97.95 430	92.08 369	92.14 373	96.72 380

FMVSNet3		94.97 299	94.26 310	97.11 263	98.18 258	96.62 142	98.56 238	98.26 289	93.67 311	94.09 342	97.10 360	84.25 371	98.01 425	92.08 369	92.14 373	96.70 384

MIMVSNet		93.26 385	92.21 396	96.41 336	97.73 313	93.13 354	95.65 478	97.03 431	91.27 410	94.04 345	96.06 432	75.33 462	97.19 463	86.56 456	96.23 310	98.92 242

FIs		96.51 206	96.12 209	97.67 222	97.13 364	97.54 89	99.36 14	99.22 32	95.89 154	94.03 346	98.35 246	91.98 147	98.44 366	96.40 208	92.76 366	97.01 345

v2v482		94.69 315	94.03 327	96.65 302	96.17 416	94.79 275	98.67 208	98.08 326	92.72 356	94.00 347	97.16 357	87.69 302	98.45 364	92.91 340	88.87 424	96.72 380

testing3		93.19 388	92.48 392	95.30 396	98.07 271	92.27 374	98.64 213	97.17 421	93.94 288	93.98 348	97.04 375	67.97 486	96.01 487	88.40 439	97.14 269	97.63 328

FC-MVSNet-test		96.42 209	96.05 211	97.53 234	96.95 373	97.27 107	99.36 14	99.23 27	95.83 159	93.93 349	98.37 244	92.00 146	98.32 387	96.02 221	92.72 367	97.00 346

UniMVSNet (Re)		95.78 243	95.19 257	97.58 231	96.99 371	97.47 93	98.79 173	99.18 36	95.60 171	93.92 350	97.04 375	91.68 157	98.48 359	95.80 231	87.66 436	96.79 372

miper_enhance_ethall		95.10 287	94.75 279	96.12 352	97.53 332	93.73 323	96.61 460	98.08 326	92.20 380	93.89 351	96.65 408	92.44 127	98.30 391	94.21 297	91.16 388	96.34 432

UniMVSNet_NR-MVSNet		95.71 246	95.15 258	97.40 244	96.84 381	96.97 127	98.74 182	99.24 20	95.16 208	93.88 352	97.72 308	91.68 157	98.31 389	95.81 229	87.25 442	96.92 353

DU-MVS		95.42 264	94.76 278	97.40 244	96.53 398	96.97 127	98.66 210	98.99 56	95.43 189	93.88 352	97.69 311	88.57 273	98.31 389	95.81 229	87.25 442	96.92 353

usedtu_dtu_shiyan1		94.96 301	94.28 307	96.98 273	95.93 429	96.11 175	97.08 429	98.39 242	93.62 315	93.86 354	96.40 418	88.28 281	98.21 398	92.61 352	92.36 371	96.63 392

FE-MVSNET3		94.96 301	94.28 307	96.98 273	95.93 429	96.11 175	97.08 429	98.39 242	93.62 315	93.86 354	96.40 418	88.28 281	98.21 398	92.61 352	92.36 371	96.63 392

Baseline_NR-MVSNet		94.35 344	93.81 346	95.96 364	96.20 413	94.05 310	98.61 221	96.67 456	91.44 400	93.85 356	97.60 322	88.57 273	98.14 405	94.39 289	86.93 445	95.68 454

PS-MVSNAJss		96.43 208	96.26 203	96.92 281	95.84 435	95.08 256	99.16 56	98.50 200	95.87 157	93.84 357	98.34 250	94.51 92	98.61 348	96.88 185	93.45 353	97.06 343

UniMVSNet_ETH3D		94.24 353	93.33 371	96.97 275	97.19 360	93.38 339	98.74 182	98.57 179	91.21 413	93.81 358	98.58 222	72.85 478	98.77 336	95.05 261	93.93 342	98.77 262

tt0805		94.54 329	93.85 344	96.63 307	97.98 292	93.06 359	98.77 177	97.84 348	93.67 311	93.80 359	98.04 276	76.88 453	98.96 307	94.79 269	92.86 364	97.86 320

tpmvs		94.60 323	94.36 304	95.33 395	97.46 337	88.60 459	96.88 449	97.68 361	91.29 408	93.80 359	96.42 417	88.58 272	99.24 243	91.06 397	96.04 314	98.17 309

WBMVS		94.56 327	94.04 325	96.10 353	98.03 281	93.08 358	97.82 363	98.18 302	94.02 279	93.77 361	96.82 398	81.28 403	98.34 384	95.47 247	91.00 391	96.88 362

3Dnovator	94.51 5	97.46 134	96.93 162	99.07 65	97.78 307	97.64 83	99.35 16	99.06 47	97.02 89	93.75 362	99.16 110	89.25 250	99.92 43	97.22 167	99.75 54	99.64 86

eth_miper_zixun_eth		94.68 317	94.41 302	95.47 389	97.64 320	91.71 392	96.73 457	98.07 328	92.71 357	93.64 363	97.21 355	90.54 209	98.17 402	93.38 323	89.76 406	96.54 412

SD_0403		94.28 351	94.46 296	93.73 442	98.02 282	85.32 485	98.31 281	98.40 236	94.75 243	93.59 364	98.16 267	89.01 258	96.54 478	82.32 481	97.58 256	99.34 150

ITE_SJBPF				95.44 391	97.42 342	91.32 398		97.50 385	95.09 218	93.59 364	98.35 246	81.70 399	98.88 322	89.71 419	93.39 355	96.12 442

TranMVSNet+NR-MVSNet		95.14 284	94.48 294	97.11 263	96.45 405	96.36 162	99.03 84	99.03 50	95.04 220	93.58 366	97.93 287	88.27 283	98.03 423	94.13 301	86.90 447	96.95 350

COLMAP_ROB	93.27 12	95.33 273	94.87 275	96.71 296	99.29 89	93.24 351	98.58 226	98.11 318	89.92 434	93.57 367	99.10 127	86.37 327	99.79 122	90.78 402	98.10 233	97.09 342
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm cat1		93.36 380	92.80 382	95.07 404	97.58 325	87.97 470	96.76 455	97.86 347	82.17 491	93.53 368	96.04 435	86.13 332	99.13 270	89.24 429	95.87 318	98.10 312

AllTest		95.24 278	94.65 285	96.99 270	99.25 97	93.21 352	98.59 222	98.18 302	91.36 402	93.52 369	98.77 197	84.67 363	99.72 138	89.70 420	97.87 242	98.02 315

TestCases				96.99 270	99.25 97	93.21 352		98.18 302	91.36 402	93.52 369	98.77 197	84.67 363	99.72 138	89.70 420	97.87 242	98.02 315

miper_ehance_all_eth		95.01 292	94.69 283	95.97 363	97.70 315	93.31 344	97.02 433	98.07 328	92.23 377	93.51 371	96.96 385	91.85 151	98.15 404	93.68 315	91.16 388	96.44 429

SSC-MVS3.2		93.59 378	93.13 376	94.97 407	96.81 384	89.71 434	97.95 340	98.49 205	94.59 252	93.50 372	96.91 391	77.74 440	98.37 382	91.69 383	90.47 397	96.83 370

FMVSNet2		94.47 338	93.61 360	97.04 268	98.21 248	96.43 157	98.79 173	98.27 281	92.46 365	93.50 372	97.09 364	81.16 404	98.00 427	91.09 394	91.93 376	96.70 384

v148		94.29 349	93.76 352	95.91 366	96.10 420	92.93 362	98.58 226	97.97 339	92.59 363	93.47 374	96.95 387	88.53 277	98.32 387	92.56 359	87.06 444	96.49 424

c3_l		94.79 311	94.43 301	95.89 368	97.75 309	93.12 356	97.16 425	98.03 335	92.23 377	93.46 375	97.05 374	91.39 171	98.01 425	93.58 320	89.21 418	96.53 414

Syy-MVS		92.55 399	92.61 387	92.38 461	97.39 346	83.41 491	97.91 347	97.46 389	93.16 338	93.42 376	95.37 456	84.75 360	96.12 485	77.00 502	96.99 273	97.60 329

myMVS_eth3d		92.73 396	92.01 398	94.89 411	97.39 346	90.94 404	97.91 347	97.46 389	93.16 338	93.42 376	95.37 456	68.09 485	96.12 485	88.34 440	96.99 273	97.60 329

pmmvs4		94.69 315	93.99 333	96.81 288	95.74 437	95.94 188	97.40 395	97.67 364	90.42 426	93.37 378	97.59 323	89.08 256	98.20 400	92.97 338	91.67 381	96.30 435

PCF-MVS	93.45 11	94.68 317	93.43 369	98.42 130	98.62 180	96.77 137	95.48 482	98.20 296	84.63 484	93.34 379	98.32 252	88.55 276	99.81 103	84.80 472	98.96 160	98.68 274
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl22		94.68 317	94.19 314	96.13 351	98.11 267	93.60 326	96.94 437	98.31 271	92.43 369	93.32 380	96.87 395	86.51 321	98.28 395	94.10 304	91.16 388	96.51 421

XXY-MVS		95.20 281	94.45 299	97.46 237	96.75 388	96.56 151	98.86 143	98.65 158	93.30 332	93.27 381	98.27 258	84.85 357	98.87 323	94.82 267	91.26 387	96.96 348

jajsoiax		95.45 261	95.03 266	96.73 293	95.42 452	94.63 280	99.14 60	98.52 192	95.74 163	93.22 382	98.36 245	83.87 382	98.65 345	96.95 177	94.04 337	96.91 358

reproduce_monomvs		94.77 313	94.67 284	95.08 403	98.40 205	89.48 441	98.80 165	98.64 159	97.57 48	93.21 383	97.65 316	80.57 414	98.83 329	97.72 117	89.47 414	96.93 352

mvs_tets		95.41 266	95.00 267	96.65 302	95.58 443	94.42 291	99.00 92	98.55 185	95.73 165	93.21 383	98.38 243	83.45 388	98.63 346	97.09 170	94.00 339	96.91 358

anonymousdsp		95.42 264	94.91 272	96.94 277	95.10 456	95.90 194	99.14 60	98.41 233	93.75 298	93.16 385	97.46 332	87.50 305	98.41 375	95.63 240	94.03 338	96.50 423

v8		94.47 338	93.77 350	96.57 316	96.36 408	94.83 272	99.05 77	98.19 299	91.92 386	93.16 385	96.97 383	88.82 270	98.48 359	91.69 383	87.79 433	96.39 430

WR-MVS		95.15 283	94.46 296	97.22 251	96.67 393	96.45 155	98.21 295	98.81 108	94.15 273	93.16 385	97.69 311	87.51 303	98.30 391	95.29 253	88.62 426	96.90 360

EPNet_dtu		95.21 280	94.95 271	95.99 359	96.17 416	90.45 419	98.16 311	97.27 410	96.77 102	93.14 388	98.33 251	90.34 217	98.42 368	85.57 463	98.81 172	99.09 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		96.29 217	95.40 241	98.96 76	97.85 302	97.60 86	99.23 38	98.93 65	89.76 437	93.11 389	99.02 148	89.11 255	99.93 34	91.99 374	99.62 90	99.34 150

GG-mvs-BLEND				96.59 313	96.34 409	94.98 263	96.51 464	88.58 520		93.10 390	94.34 472	80.34 417	98.05 421	89.53 423	96.99 273	96.74 377

v10		94.29 349	93.55 363	96.51 324	96.39 407	94.80 274	98.99 95	98.19 299	91.35 404	93.02 391	96.99 381	88.09 288	98.41 375	90.50 406	88.41 428	96.33 434

3Dnovator+	94.38 6	97.43 139	96.78 174	99.38 24	97.83 303	98.52 35	99.37 13	98.71 138	97.09 87	92.99 392	99.13 118	89.36 247	99.89 69	96.97 175	99.57 99	99.71 63

D2MVS		95.18 282	95.08 264	95.48 388	97.10 366	92.07 384	98.30 284	99.13 43	94.02 279	92.90 393	96.73 402	89.48 239	98.73 338	94.48 287	93.60 350	95.65 455

Patchmtry		93.22 386	92.35 394	95.84 373	96.77 385	93.09 357	94.66 496	97.56 375	87.37 461	92.90 393	96.24 423	88.15 286	97.90 434	87.37 451	90.10 403	96.53 414

DIV-MVS_self_test		94.52 332	94.03 327	95.99 359	97.57 329	93.38 339	97.05 431	97.94 342	91.74 390	92.81 395	97.10 360	89.12 254	98.07 417	92.60 355	90.30 399	96.53 414

Anonymous20231211		94.10 365	93.26 374	96.61 310	99.11 124	94.28 299	99.01 90	98.88 78	86.43 471	92.81 395	97.57 325	81.66 400	98.68 343	94.83 266	89.02 422	96.88 362

cl____		94.51 333	94.01 330	96.02 355	97.58 325	93.40 338	97.05 431	97.96 341	91.73 392	92.76 397	97.08 366	89.06 257	98.13 406	92.61 352	90.29 400	96.52 417

miper_lstm_enhance		94.33 345	94.07 324	95.11 401	97.75 309	90.97 403	97.22 413	98.03 335	91.67 394	92.76 397	96.97 383	90.03 226	97.78 444	92.51 362	89.64 408	96.56 409

v7n		94.19 356	93.43 369	96.47 329	95.90 432	94.38 294	99.26 33	98.34 260	91.99 384	92.76 397	97.13 359	88.31 280	98.52 357	89.48 425	87.70 434	96.52 417

MVS		94.67 320	93.54 364	98.08 172	96.88 379	96.56 151	98.19 302	98.50 200	78.05 502	92.69 400	98.02 277	91.07 191	99.63 161	90.09 410	98.36 215	98.04 314

DSMNet-mixed		92.52 401	92.58 389	92.33 462	94.15 468	82.65 495	98.30 284	94.26 496	89.08 449	92.65 401	95.73 445	85.01 354	95.76 489	86.24 458	97.76 248	98.59 286

EU-MVSNet		93.66 374	94.14 319	92.25 465	95.96 428	83.38 492	98.52 242	98.12 315	94.69 246	92.61 402	98.13 270	87.36 309	96.39 483	91.82 379	90.00 404	96.98 347

IterMVS-SCA-FT		94.11 364	93.87 342	94.85 414	97.98 292	90.56 418	97.18 420	98.11 318	93.75 298	92.58 403	97.48 331	83.97 379	97.41 460	92.48 364	91.30 385	96.58 405

pmmvs5		93.65 376	92.97 380	95.68 380	95.49 447	92.37 373	98.20 299	97.28 409	89.66 439	92.58 403	97.26 349	82.14 394	98.09 413	93.18 331	90.95 392	96.58 405

blended_shiyan8		91.42 408	89.89 421	96.01 356	91.50 495	93.30 345	97.48 389	97.83 349	86.93 464	92.57 405	92.37 493	82.46 392	98.13 406	92.86 346	74.99 500	96.61 396

WR-MVS_H		95.05 291	94.46 296	96.81 288	96.86 380	95.82 207	99.24 36	99.24 20	93.87 292	92.53 406	96.84 397	90.37 216	98.24 397	93.24 328	87.93 432	96.38 431

ACMP	93.49 10	95.34 272	94.98 269	96.43 334	97.67 317	93.48 332	98.73 188	98.44 216	94.94 233	92.53 406	98.53 227	84.50 368	99.14 268	95.48 246	94.00 339	96.66 390
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0.0.03 1		94.08 367	93.51 365	95.80 374	95.53 446	92.89 363	97.38 397	95.97 470	95.11 215	92.51 408	96.66 406	87.71 298	96.94 468	87.03 453	93.67 346	97.57 331

IB-MVS	91.98 17	93.27 384	91.97 399	97.19 254	97.47 336	93.41 335	97.09 428	95.99 469	93.32 330	92.47 409	95.73 445	78.06 436	99.53 184	94.59 284	82.98 467	98.62 281
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
IterMVS		94.09 366	93.85 344	94.80 418	97.99 286	90.35 423	97.18 420	98.12 315	93.68 309	92.46 410	97.34 343	84.05 377	97.41 460	92.51 362	91.33 384	96.62 395
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan6		91.37 409	89.84 422	95.98 362	91.49 496	93.28 346	97.48 389	97.83 349	86.93 464	92.43 411	92.36 494	82.44 393	98.06 418	92.74 351	74.82 503	96.59 401

CP-MVSNet		94.94 305	94.30 306	96.83 286	96.72 390	95.56 221	99.11 66	98.95 61	93.89 289	92.42 412	97.90 290	87.19 311	98.12 408	94.32 293	88.21 429	96.82 371

wanda-best-256-512		91.17 416	89.60 426	95.88 369	91.33 499	92.99 360	96.89 446	97.82 352	86.89 467	92.36 413	91.75 500	81.83 396	98.06 418	92.75 348	74.82 503	96.59 401

FE-blended-shiyan7		91.17 416	89.60 426	95.88 369	91.33 499	92.99 360	96.89 446	97.82 352	86.89 467	92.36 413	91.75 500	81.83 396	98.06 418	92.75 348	74.82 503	96.59 401

usedtu_blend_shiyan5		90.87 426	89.15 433	96.01 356	91.33 499	93.35 342	98.12 318	97.36 401	81.93 493	92.36 413	91.75 500	81.83 396	98.09 413	92.88 344	74.82 503	96.59 401

PS-CasMVS		94.67 320	93.99 333	96.71 296	96.68 392	95.26 244	99.13 63	99.03 50	93.68 309	92.33 416	97.95 285	85.35 347	98.10 409	93.59 319	88.16 431	96.79 372

FMVSNet1		93.19 388	92.07 397	96.56 317	97.54 330	95.00 259	98.82 156	98.18 302	90.38 427	92.27 417	97.07 367	73.68 475	97.95 430	89.36 427	91.30 385	96.72 380

blend_shiyan4		90.76 427	89.01 436	95.99 359	91.69 494	93.35 342	97.44 391	97.83 349	86.93 464	92.23 418	91.98 497	75.19 464	98.09 413	92.88 344	74.96 501	96.52 417

PEN-MVS		94.42 341	93.73 354	96.49 326	96.28 411	94.84 270	99.17 55	99.00 53	93.51 320	92.23 418	97.83 300	86.10 333	97.90 434	92.55 360	86.92 446	96.74 377

sc_t1		91.01 421	89.39 428	95.85 372	95.99 425	90.39 422	98.43 266	97.64 367	78.79 499	92.20 420	97.94 286	66.00 492	98.60 351	91.59 386	85.94 455	98.57 289

gbinet_0.2-2-1-0.02		91.03 420	89.37 432	96.01 356	91.39 497	93.41 335	97.19 418	97.82 352	87.00 463	92.18 421	91.87 499	78.97 427	98.04 422	93.13 332	74.75 507	96.60 398

OurMVSNet-221017-0		94.21 354	94.00 331	94.85 414	95.60 442	89.22 446	98.89 125	97.43 395	95.29 201	92.18 421	98.52 230	82.86 389	98.59 352	93.46 322	91.76 379	96.74 377

MS-PatchMatch		93.84 373	93.63 359	94.46 432	96.18 415	89.45 442	97.76 368	98.27 281	92.23 377	92.13 423	97.49 330	79.50 422	98.69 340	89.75 418	99.38 135	95.25 462

ppachtmachnet_test		93.22 386	92.63 386	94.97 407	95.45 450	90.84 408	96.88 449	97.88 346	90.60 421	92.08 424	97.26 349	88.08 289	97.86 440	85.12 468	90.33 398	96.22 438

1314		96.25 221	95.73 226	97.79 206	97.13 364	95.55 223	98.19 302	98.59 172	93.47 323	92.03 425	97.82 301	91.33 174	99.49 192	94.62 280	98.44 199	98.32 303

baseline2		95.11 286	94.52 292	96.87 283	96.65 394	93.56 327	98.27 289	94.10 500	93.45 324	92.02 426	97.43 336	87.45 308	99.19 254	93.88 310	97.41 265	97.87 319

DTE-MVSNet		93.98 371	93.26 374	96.14 350	96.06 422	94.39 293	99.20 48	98.86 91	93.06 343	91.78 427	97.81 302	85.87 338	97.58 455	90.53 405	86.17 451	96.46 428

LF4IMVS		93.14 390	92.79 383	94.20 437	95.88 433	88.67 458	97.66 376	97.07 427	93.81 296	91.71 428	97.65 316	77.96 438	98.81 332	91.47 388	91.92 378	95.12 465

mvs5depth		91.23 414	90.17 417	94.41 434	92.09 490	89.79 431	95.26 485	96.50 461	90.73 419	91.69 429	97.06 371	76.12 458	98.62 347	88.02 445	84.11 463	94.82 472

our_test_3		93.65 376	93.30 372	94.69 420	95.45 450	89.68 437	96.91 441	97.65 365	91.97 385	91.66 430	96.88 393	89.67 235	97.93 433	88.02 445	91.49 383	96.48 426

testgi		93.06 392	92.45 393	94.88 412	96.43 406	89.90 429	98.75 178	97.54 381	95.60 171	91.63 431	97.91 289	74.46 471	97.02 466	86.10 459	93.67 346	97.72 325

ArgMatch-SfM		90.55 430	89.69 423	93.14 454	95.91 431	86.12 482	97.20 415	96.81 450	92.91 350	91.39 432	96.95 387	65.65 494	97.72 448	88.03 444	82.36 468	95.57 456

tfpnnormal		93.66 374	92.70 385	96.55 321	96.94 374	95.94 188	98.97 99	99.19 35	91.04 415	91.38 433	97.34 343	84.94 355	98.61 348	85.45 465	89.02 422	95.11 466

LTVRE_ROB	92.95 15	94.60 323	93.90 339	96.68 300	97.41 345	94.42 291	98.52 242	98.59 172	91.69 393	91.21 434	98.35 246	84.87 356	99.04 291	91.06 397	93.44 354	96.60 398
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
OpenMVS	93.04 13	95.83 240	95.00 267	98.32 136	97.18 361	97.32 100	99.21 45	98.97 57	89.96 433	91.14 435	99.05 145	86.64 320	99.92 43	93.38 323	99.47 122	97.73 324

pm-mvs1		93.94 372	93.06 377	96.59 313	96.49 402	95.16 250	98.95 106	98.03 335	92.32 374	91.08 436	97.84 297	84.54 367	98.41 375	92.16 367	86.13 454	96.19 440

ArgMatch-Sym		90.92 423	90.22 416	93.02 455	95.81 436	86.50 479	97.32 405	97.01 437	92.67 358	91.02 437	97.35 342	66.90 490	97.17 464	88.53 438	85.40 457	95.39 459

MVS-HIRNet		89.46 446	88.40 443	92.64 459	97.58 325	82.15 496	94.16 505	93.05 507	75.73 509	90.90 438	82.52 520	79.42 424	98.33 386	83.53 477	98.68 175	97.43 332

FMVSNet5		91.81 404	90.92 408	94.49 429	97.21 356	92.09 383	98.00 336	97.55 380	89.31 446	90.86 439	95.61 453	74.48 470	95.32 493	85.57 463	89.70 407	96.07 444

USDC		93.33 383	92.71 384	95.21 397	96.83 382	90.83 409	96.91 441	97.50 385	93.84 293	90.72 440	98.14 269	77.69 441	98.82 331	89.51 424	93.21 361	95.97 446

MVP-Stereo		94.28 351	93.92 336	95.35 394	94.95 458	92.60 371	97.97 339	97.65 365	91.61 395	90.68 441	97.09 364	86.32 330	98.42 368	89.70 420	99.34 139	95.02 470
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	92.99 14	94.30 347	93.77 350	95.88 369	97.81 305	92.04 386	98.71 193	98.37 251	93.99 284	90.60 442	98.47 234	80.86 411	99.05 288	92.75 348	92.40 370	96.55 411

CL-MVSNet_self_test		90.11 437	89.14 434	93.02 455	91.86 492	88.23 467	96.51 464	98.07 328	90.49 422	90.49 443	94.41 467	84.75 360	95.34 492	80.79 486	74.95 502	95.50 457

0.4-1-1-0.1		90.89 424	88.97 438	96.67 301	94.15 468	92.76 368	95.28 484	95.03 486	89.11 448	90.43 444	89.57 510	75.41 461	99.04 291	94.70 274	77.06 493	98.20 308

KD-MVS_self_test		90.38 432	89.38 430	93.40 448	92.85 485	88.94 453	97.95 340	97.94 342	90.35 428	90.25 445	93.96 475	79.82 418	95.94 488	84.62 474	76.69 497	95.33 460

ttmdpeth		92.61 398	91.96 401	94.55 426	94.10 470	90.60 417	98.52 242	97.29 407	92.67 358	90.18 446	97.92 288	79.75 420	97.79 442	91.09 394	86.15 453	95.26 461

Anonymous20231206		91.66 406	91.10 407	93.33 449	94.02 474	87.35 474	98.58 226	97.26 411	90.48 423	90.16 447	96.31 421	83.83 383	96.53 479	79.36 493	89.90 405	96.12 442

SixPastTwentyTwo		93.34 382	92.86 381	94.75 419	95.67 439	89.41 444	98.75 178	96.67 456	93.89 289	90.15 448	98.25 261	80.87 410	98.27 396	90.90 401	90.64 394	96.57 407

0.4-1-1-0.2		90.43 431	88.45 442	96.38 339	93.34 480	92.12 379	93.88 506	95.04 485	88.62 454	90.00 449	88.31 513	75.31 463	99.03 294	94.61 281	76.91 495	98.01 317

PVSNet_0	88.72 19	91.28 413	90.03 419	95.00 406	97.99 286	87.29 475	94.84 492	98.50 200	92.06 383	89.86 450	95.19 458	79.81 419	99.39 212	92.27 366	69.79 518	98.33 302

ACMH	92.88 16	94.55 328	93.95 335	96.34 342	97.63 321	93.26 348	98.81 164	98.49 205	93.43 325	89.74 451	98.53 227	81.91 395	99.08 283	93.69 314	93.30 359	96.70 384
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0320		90.26 436	88.73 441	94.86 413	96.12 419	90.62 415	98.17 310	97.63 368	77.46 503	89.68 452	96.04 435	69.19 483	97.79 442	88.98 432	85.29 458	96.16 441

pmmvs6		91.77 405	90.63 411	95.17 399	94.69 464	91.24 400	98.67 208	97.92 344	86.14 473	89.62 453	97.56 328	75.79 460	98.34 384	90.75 403	84.56 460	95.94 447

TinyColmap		92.31 402	91.53 403	94.65 423	96.92 375	89.75 432	96.92 439	96.68 455	90.45 425	89.62 453	97.85 296	76.06 459	98.81 332	86.74 454	92.51 369	95.41 458

Anonymous20240521		91.18 415	90.44 413	93.42 446	93.70 475	88.47 462	98.94 109	97.56 375	88.46 455	89.56 455	95.08 461	77.15 449	96.97 467	83.92 475	89.55 411	94.82 472

TransMVSNet (Re)		92.67 397	91.51 404	96.15 349	96.58 396	94.65 278	98.90 121	96.73 451	90.86 418	89.46 456	97.86 294	85.62 342	98.09 413	86.45 457	81.12 477	95.71 453

NR-MVSNet		94.98 297	94.16 317	97.44 239	96.53 398	97.22 115	98.74 182	98.95 61	94.96 229	89.25 457	97.69 311	89.32 248	98.18 401	94.59 284	87.40 439	96.92 353

0.3-1-1-0.015		90.29 434	88.21 446	96.51 324	93.56 477	92.44 372	94.41 501	95.03 486	88.71 452	89.20 458	88.50 512	73.12 477	99.04 291	94.67 277	76.70 496	98.05 313

LCM-MVSNet-Re		95.22 279	95.32 251	94.91 409	98.18 258	87.85 472	98.75 178	95.66 475	95.11 215	88.96 459	96.85 396	90.26 221	97.65 450	95.65 239	98.44 199	99.22 188

KD-MVS_2432*1600		89.61 443	87.96 451	94.54 427	94.06 472	91.59 394	95.59 479	97.63 368	89.87 435	88.95 460	94.38 469	78.28 433	96.82 470	84.83 470	68.05 519	95.21 463

miper_refine_blended		89.61 443	87.96 451	94.54 427	94.06 472	91.59 394	95.59 479	97.63 368	89.87 435	88.95 460	94.38 469	78.28 433	96.82 470	84.83 470	68.05 519	95.21 463

test_fmvs3		87.17 454	87.06 457	87.50 480	91.21 502	75.66 507	99.05 77	96.61 459	92.79 355	88.85 462	92.78 489	43.72 511	93.49 505	93.95 307	84.56 460	93.34 496

TDRefinement		91.06 419	89.68 424	95.21 397	85.35 527	91.49 396	98.51 249	97.07 427	91.47 398	88.83 463	97.84 297	77.31 445	99.09 280	92.79 347	77.98 490	95.04 469

MASt3R-SfM		85.54 459	85.89 459	84.50 489	90.13 513	66.13 525	92.89 508	95.33 480	85.73 478	88.77 464	96.36 420	52.50 505	94.89 499	86.66 455	84.65 459	92.50 502

tt0320-xc		89.79 440	88.11 447	94.84 416	96.19 414	90.61 416	98.16 311	97.22 414	77.35 504	88.75 465	96.70 405	65.94 493	97.63 452	89.31 428	83.39 465	96.28 436

N_pmnet		87.12 456	87.77 453	85.17 486	95.46 449	61.92 531	97.37 399	70.66 543	85.83 476	88.73 466	96.04 435	85.33 349	97.76 446	80.02 488	90.48 396	95.84 450

dtuonlycased		91.29 411	91.26 406	91.36 469	95.63 441	84.25 488	96.93 438	97.21 416	92.16 381	88.34 467	96.47 414	79.56 421	95.18 496	87.37 451	87.70 434	94.64 476

test_0402		91.32 410	90.27 415	94.48 430	96.60 395	91.12 401	98.50 250	97.22 414	86.10 474	88.30 468	96.98 382	77.65 443	97.99 428	78.13 498	92.94 363	94.34 478

test20.03		90.89 424	90.38 414	92.43 460	93.48 478	88.14 468	98.33 276	97.56 375	93.40 327	87.96 469	96.71 404	80.69 413	94.13 503	79.15 494	86.17 451	95.01 471

MIMVSNet1		89.67 442	88.28 445	93.82 441	92.81 486	91.08 402	98.01 334	97.45 393	87.95 458	87.90 470	95.87 441	67.63 488	94.56 501	78.73 497	88.18 430	95.83 451

mvsany_test3		88.80 448	88.04 448	91.09 470	89.78 515	81.57 498	97.83 362	95.49 478	93.81 296	87.53 471	93.95 476	56.14 502	97.43 459	94.68 275	83.13 466	94.26 479

Patchmatch-RL test		91.49 407	90.85 409	93.41 447	91.37 498	84.40 486	92.81 509	95.93 473	91.87 388	87.25 472	94.87 462	88.99 259	96.53 479	92.54 361	82.00 471	99.30 164

pmmvs3		86.67 457	84.86 463	92.11 466	88.16 519	87.19 477	96.63 459	94.75 490	79.88 496	87.22 473	92.75 491	66.56 491	95.20 495	81.24 485	76.56 498	93.96 489

dongtai		82.47 466	81.88 468	84.22 490	95.19 455	76.03 505	94.59 499	74.14 533	82.63 488	87.19 474	96.09 431	64.10 496	87.85 521	58.91 523	84.11 463	88.78 515

test_vis1_rt		91.29 411	90.65 410	93.19 453	97.45 340	86.25 481	98.57 235	90.90 516	93.30 332	86.94 475	93.59 478	62.07 499	99.11 275	97.48 150	95.58 322	94.22 482

K. test v3		92.55 399	91.91 402	94.48 430	95.64 440	89.24 445	99.07 72	94.88 488	94.04 277	86.78 476	97.59 323	77.64 444	97.64 451	92.08 369	89.43 415	96.57 407

lessismore_v0				94.45 433	94.93 459	88.44 463		91.03 515		86.77 477	97.64 319	76.23 457	98.42 368	90.31 408	85.64 456	96.51 421

APD_test1		88.22 451	88.01 449	88.86 477	95.98 426	74.66 514	97.21 414	96.44 463	83.96 486	86.66 478	97.90 290	60.95 500	97.84 441	82.73 478	90.23 401	94.09 485

ambc				89.49 474	86.66 522	75.78 506	92.66 510	96.72 452		86.55 479	92.50 492	46.01 509	97.90 434	90.32 407	82.09 470	94.80 474

PM-MVS		87.77 452	86.55 458	91.40 468	91.03 506	83.36 493	96.92 439	95.18 483	91.28 409	86.48 480	93.42 480	53.27 504	96.74 472	89.43 426	81.97 472	94.11 484

OpenMVS_ROB	86.42 20	89.00 447	87.43 455	93.69 443	93.08 484	89.42 443	97.91 347	96.89 444	78.58 500	85.86 481	94.69 463	69.48 482	98.29 394	77.13 501	93.29 360	93.36 495

UnsupCasMVSNet_eth		90.99 422	89.92 420	94.19 438	94.08 471	89.83 430	97.13 427	98.67 151	93.69 307	85.83 482	96.19 428	75.15 465	96.74 472	89.14 430	79.41 484	96.00 445

new_pmnet		90.06 438	89.00 437	93.22 452	94.18 466	88.32 465	96.42 466	96.89 444	86.19 472	85.67 483	93.62 477	77.18 448	97.10 465	81.61 483	89.29 417	94.23 481

dmvs_testset		87.64 453	88.93 440	83.79 491	95.25 453	63.36 527	97.20 415	91.17 513	93.07 342	85.64 484	95.98 440	85.30 351	91.52 513	69.42 515	87.33 440	96.49 424

test_f		86.07 458	85.39 460	88.10 478	89.28 517	75.57 508	97.73 371	96.33 465	89.41 445	85.35 485	91.56 503	43.31 513	95.53 490	91.32 390	84.23 462	93.21 497

EG-PatchMatch MVS		91.13 418	90.12 418	94.17 439	94.73 463	89.00 450	98.13 317	97.81 356	89.22 447	85.32 486	96.46 415	67.71 487	98.42 368	87.89 449	93.82 344	95.08 467

pmmvs-eth3d		90.36 433	89.05 435	94.32 436	91.10 504	92.12 379	97.63 381	96.95 439	88.86 451	84.91 487	93.13 484	78.32 432	96.74 472	88.70 435	81.81 473	94.09 485

FE-MVSNET2		90.29 434	88.94 439	94.36 435	90.48 510	92.27 374	98.45 258	97.82 352	91.59 396	84.90 488	93.10 485	73.92 473	96.42 482	87.92 448	82.26 469	94.39 477

FE-MVSNET		88.56 449	87.09 456	92.99 457	89.93 514	89.99 428	98.15 314	95.59 476	88.42 456	84.87 489	92.90 487	74.82 467	94.99 498	77.88 499	81.21 476	93.99 488

DeepMVS_CX				86.78 481	97.09 367	72.30 515		95.17 484	75.92 508	84.34 490	95.19 458	70.58 480	95.35 491	79.98 491	89.04 421	92.68 499

usedtu_dtu_shiyan2		84.80 461	82.31 466	92.27 464	86.38 524	85.55 484	97.77 367	96.56 460	78.34 501	83.90 491	93.50 479	54.16 503	95.32 493	77.55 500	72.62 511	95.92 448

new-patchmatchnet		88.50 450	87.45 454	91.67 467	90.31 512	85.89 483	97.16 425	97.33 402	89.47 442	83.63 492	92.77 490	76.38 455	95.06 497	82.70 479	77.29 491	94.06 487

UnsupCasMVSNet_bld		87.17 454	85.12 462	93.31 450	91.94 491	88.77 455	94.92 491	98.30 278	84.30 485	82.30 493	90.04 508	63.96 497	97.25 462	85.85 462	74.47 510	93.93 490

WB-MVS		84.86 460	85.33 461	83.46 492	89.48 516	69.56 519	98.19 302	96.42 464	89.55 441	81.79 494	94.67 464	84.80 358	90.12 516	52.44 525	80.64 481	90.69 508

CMPMVS	66.06 21	89.70 441	89.67 425	89.78 473	93.19 483	76.56 504	97.00 434	98.35 256	80.97 494	81.57 495	97.75 305	74.75 468	98.61 348	89.85 416	93.63 348	94.17 483
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SSC-MVS		84.27 463	84.71 464	82.96 497	89.19 518	68.83 520	98.08 326	96.30 466	89.04 450	81.37 496	94.47 465	84.60 365	89.89 517	49.80 528	79.52 483	90.15 509

MVStest1		89.53 445	87.99 450	94.14 440	94.39 465	90.42 420	98.25 292	96.84 449	82.81 487	81.18 497	97.33 345	77.09 450	96.94 468	85.27 467	78.79 485	95.06 468

test_method		79.03 472	78.17 471	81.63 498	86.06 525	54.40 542	82.75 529	96.89 444	39.54 534	80.98 498	95.57 454	58.37 501	94.73 500	84.74 473	78.61 486	95.75 452

kuosan		78.45 476	77.69 475	80.72 499	92.73 487	75.32 509	94.63 498	74.51 532	75.96 506	80.87 499	93.19 483	63.23 498	79.99 531	42.56 535	81.56 475	86.85 522

RoMa-SfM		83.81 464	82.08 467	89.00 476	93.33 481	79.94 501	95.51 481	92.48 509	79.75 497	79.89 500	95.69 450	46.23 508	93.20 508	78.90 495	76.93 494	93.87 491

DenseAffine		84.37 462	82.38 465	90.31 472	94.17 467	82.89 494	94.98 488	94.23 497	82.16 492	79.68 501	94.33 473	46.28 507	94.25 502	80.01 489	75.62 499	93.78 493

ET-MVSNet_ETH3D		94.13 361	92.98 379	97.58 231	98.22 246	96.20 169	97.31 407	95.37 479	94.53 255	79.56 502	97.63 321	86.51 321	97.53 457	96.91 179	90.74 393	99.02 229

RoMa-HiRes		79.77 469	77.89 472	85.41 485	90.81 507	74.77 513	94.26 503	86.78 522	75.97 505	77.00 503	94.37 471	39.39 518	90.60 514	74.98 507	67.46 521	90.84 507

LCM-MVSNet		78.70 475	76.24 481	86.08 482	77.26 543	71.99 516	94.34 502	96.72 452	61.62 520	76.53 504	89.33 511	33.91 532	92.78 510	81.85 482	74.60 508	93.46 494

DKM		81.60 467	79.57 470	87.68 479	92.65 488	78.36 502	94.65 497	91.17 513	79.69 498	76.11 505	93.98 474	37.88 523	91.54 512	79.64 492	70.38 515	93.15 498

PMMVS2		77.95 478	75.44 482	85.46 484	82.54 531	74.95 511	94.23 504	93.08 506	72.80 511	74.68 506	87.38 514	36.36 526	91.56 511	73.95 509	63.94 523	89.87 510

testf1		79.02 473	77.70 473	82.99 495	88.10 520	66.90 523	94.67 494	93.11 504	71.08 515	74.02 507	93.41 481	34.15 529	93.25 506	72.25 511	78.50 487	88.82 513

APD_test2		79.02 473	77.70 473	82.99 495	88.10 520	66.90 523	94.67 494	93.11 504	71.08 515	74.02 507	93.41 481	34.15 529	93.25 506	72.25 511	78.50 487	88.82 513

Gipuma		78.40 477	76.75 480	83.38 493	95.54 444	80.43 499	79.42 530	97.40 397	64.67 519	73.46 509	80.82 524	45.65 510	93.14 509	66.32 518	87.43 438	76.56 528
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DKM-HiRes		79.25 470	77.01 479	85.98 483	91.20 503	75.07 510	93.65 507	87.84 521	75.94 507	73.36 510	92.80 488	34.20 528	90.26 515	76.66 503	67.44 522	92.62 500

YYNet1		90.70 429	89.39 428	94.62 425	94.79 462	90.65 413	97.20 415	97.46 389	87.54 460	72.54 511	95.74 443	86.51 321	96.66 476	86.00 460	86.76 449	96.54 412

MDA-MVSNet_test_wron		90.71 428	89.38 430	94.68 421	94.83 460	90.78 410	97.19 418	97.46 389	87.60 459	72.41 512	95.72 447	86.51 321	96.71 475	85.92 461	86.80 448	96.56 409

MVS_clip		51.49 503	54.55 506	42.29 532	67.55 556	32.35 559	60.25 549	21.09 562	22.72 553	71.30 513	91.13 504	33.91 532	28.07 557	61.97 522	61.05 526	66.44 532

MDA-MVSNet-bldmvs		89.97 439	88.35 444	94.83 417	95.21 454	91.34 397	97.64 378	97.51 384	88.36 457	71.17 514	96.13 430	79.22 425	96.63 477	83.65 476	86.27 450	96.52 417

LoFTR		83.16 465	80.62 469	90.80 471	92.28 489	80.01 500	95.35 483	94.33 494	80.44 495	70.79 515	92.93 486	46.38 506	98.17 402	75.01 506	78.03 489	94.24 480

FPMVS		77.62 479	77.14 478	79.05 503	79.25 538	60.97 533	95.79 474	95.94 472	65.96 518	67.93 516	94.40 468	37.73 524	88.88 520	68.83 516	88.46 427	87.29 519

test_vis3_rt		79.22 471	77.40 476	84.67 487	86.44 523	74.85 512	97.66 376	81.43 526	84.98 482	67.12 517	81.91 523	28.09 537	97.60 453	88.96 433	80.04 482	81.55 525

MatchFormer		80.21 468	77.20 477	89.24 475	91.79 493	77.21 503	95.16 486	93.59 502	72.46 513	67.08 518	89.93 509	43.14 514	97.90 434	67.07 517	74.55 509	92.61 501

SP-DiffGlue		70.13 484	69.16 487	73.04 512	77.73 541	57.48 537	88.44 523	74.91 531	50.96 526	66.64 519	85.99 516	41.44 515	73.46 537	64.21 519	72.15 512	88.19 518

PDCNetPlus		71.79 483	69.26 486	79.39 502	85.67 526	69.92 518	90.34 518	62.32 545	72.62 512	65.36 520	90.26 505	39.20 520	86.38 523	75.32 505	42.24 540	81.88 524

PMatch-SfM		73.49 482	70.32 484	83.00 494	85.01 528	68.63 521	90.17 520	79.05 529	71.64 514	63.27 521	91.93 498	17.27 548	89.10 519	74.59 508	59.95 528	91.26 503

ELoFTR		75.37 480	72.33 483	84.51 488	84.48 529	68.41 522	91.57 513	88.78 519	73.84 510	62.84 522	90.14 506	27.38 538	94.11 504	71.45 514	60.46 527	91.00 505

tmp_tt		68.90 487	66.97 488	74.68 505	50.78 559	59.95 534	87.13 526	83.47 525	38.80 535	62.21 523	96.23 425	64.70 495	76.91 533	88.91 434	30.49 548	87.19 520

SP-SuperGlue		68.14 489	66.58 489	72.81 513	90.65 509	55.53 539	91.37 514	73.04 535	49.07 529	61.03 524	80.24 527	38.13 522	74.06 536	45.46 531	70.26 516	88.84 512

VLMVS_CLIP		53.81 502	55.23 504	49.55 520	44.37 560	26.59 563	64.46 547	73.52 534	28.42 549	60.82 525	83.22 518	22.09 541	59.35 546	62.16 521	58.00 530	62.70 533

SP-LightGlue		68.17 488	66.54 490	73.06 511	91.08 505	55.79 538	91.09 515	72.78 536	48.55 530	60.77 526	79.95 528	38.55 521	74.10 535	45.47 530	70.64 514	89.28 511

E-PMN		64.94 496	64.25 497	67.02 516	82.28 532	59.36 535	91.83 512	85.63 523	52.69 523	60.22 527	77.28 532	41.06 516	80.12 530	46.15 529	41.14 541	61.57 536

PMatch-Up-SfM		70.03 485	66.48 491	80.70 500	82.00 533	63.20 528	88.10 524	71.07 539	67.59 517	60.07 528	90.10 507	14.49 553	87.80 522	71.95 513	52.95 533	91.09 504

ALIKED-NN		66.93 492	64.81 495	73.32 509	93.41 479	62.03 530	87.55 525	71.25 538	50.21 527	59.98 529	82.57 519	39.72 517	84.03 527	34.94 539	63.64 524	73.90 530

ALIKED-LG		67.40 490	65.16 494	74.11 507	93.21 482	62.30 529	88.98 521	71.99 537	55.04 521	59.47 530	82.33 521	39.27 519	85.49 525	32.61 542	63.58 525	74.55 529

SP-NN		67.39 491	65.69 492	72.49 515	90.68 508	55.34 540	90.33 519	71.01 541	46.77 532	59.09 531	79.83 529	37.26 525	73.38 538	44.68 532	71.51 513	88.74 516

EMVS		64.07 497	63.26 499	66.53 517	81.73 534	58.81 536	91.85 511	84.75 524	51.93 525	59.09 531	75.13 535	43.32 512	79.09 532	42.03 536	39.47 542	61.69 535

ALIKED-MNN		65.35 495	62.68 500	73.35 508	93.70 475	61.07 532	88.63 522	70.76 542	47.76 531	57.06 533	80.59 525	34.03 531	85.39 526	32.73 541	58.87 529	73.59 531

SP-MNN		66.66 493	64.70 496	72.53 514	90.32 511	55.08 541	91.01 516	71.05 540	44.81 533	56.48 534	79.62 530	35.87 527	74.11 534	43.13 534	69.98 517	88.39 517

XFeat-NN		56.16 500	56.10 503	56.36 519	72.10 550	42.54 554	76.45 533	61.18 546	38.16 536	53.08 535	76.48 533	32.95 534	65.67 540	44.15 533	50.31 537	60.87 537

VLMVS		37.31 516	39.19 517	31.67 536	40.61 561	24.46 564	44.56 551	28.63 560	5.66 557	51.94 536	71.15 537	25.03 539	27.90 558	33.30 540	51.87 534	42.64 538

MVE	62.14 22	63.28 498	59.38 501	74.99 504	74.33 548	65.47 526	85.55 527	80.50 527	52.02 524	51.10 537	75.00 536	10.91 560	80.50 529	51.60 527	53.40 532	78.99 526
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
XFeat-MNN		55.84 501	55.19 505	57.82 518	69.33 554	43.25 549	78.25 532	62.64 544	37.53 537	50.90 538	76.32 534	32.43 535	68.13 539	42.00 537	47.26 539	62.07 534

ANet_high		69.08 486	65.37 493	80.22 501	65.99 557	71.96 517	90.91 517	90.09 517	82.62 489	49.93 539	78.39 531	29.36 536	81.75 528	62.49 520	38.52 544	86.95 521

GLUNet-SfM		61.12 499	56.63 502	74.58 506	69.78 553	53.99 543	78.71 531	76.81 530	49.09 528	49.42 540	80.47 526	24.43 540	85.82 524	51.80 526	29.17 549	83.92 523

PMVS	61.03 23	65.95 494	63.57 498	73.09 510	57.90 558	51.22 544	85.05 528	93.93 501	54.45 522	44.32 541	83.57 517	13.22 555	89.15 518	58.68 524	81.00 478	78.91 527
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SIFT-NN		49.27 504	49.25 507	49.32 521	83.88 530	45.20 545	74.57 534	53.44 547	32.44 538	42.88 542	64.93 539	20.60 542	61.35 541	16.59 545	53.96 531	41.40 539

SIFT-MNN		47.78 505	47.47 508	48.69 522	81.04 535	44.17 546	73.46 535	53.36 548	31.82 539	38.54 543	63.76 540	18.11 546	61.27 542	15.96 547	51.17 535	40.64 542

SIFT-NN-NCMNet		47.55 506	47.18 509	48.67 523	79.60 537	44.09 547	73.43 536	52.90 549	31.82 539	38.38 544	63.56 543	18.47 543	61.19 543	15.91 548	50.50 536	40.74 541

SIFT-NN-CMatch		45.31 507	44.49 510	47.75 524	76.46 544	42.98 552	70.17 540	49.20 552	31.63 542	37.94 545	63.68 542	18.19 545	59.32 547	15.91 548	37.27 545	40.95 540

MVS_baseline		19.65 523	22.57 526	10.89 540	26.60 562	2.25 567	14.08 552	3.93 566	1.15 559	37.00 546	69.35 538	4.91 563	0.00 561	17.88 543	28.24 550	30.42 552

SIFT-NN-PointCN		43.09 511	42.61 513	44.51 530	72.48 549	37.95 558	70.10 541	46.55 554	30.16 548	34.48 547	61.93 547	18.02 547	55.90 552	15.40 551	34.41 546	39.69 544

SIFT-ConvMatch		43.26 510	42.18 514	46.50 527	78.34 540	43.05 550	68.67 542	47.17 553	31.06 543	30.28 548	62.56 545	15.43 550	58.95 549	14.92 552	31.22 547	37.51 547

SIFT-NN-UMatch		44.69 509	43.84 512	47.24 526	74.56 547	42.59 553	71.89 538	49.78 550	31.80 541	29.27 549	63.70 541	18.26 544	59.43 545	15.86 550	39.43 543	39.71 543

SIFT-CM-Cal		41.25 513	40.03 516	44.88 529	77.37 542	41.08 556	65.71 546	41.18 557	30.42 547	28.83 550	61.42 549	14.88 552	56.40 550	14.13 555	26.37 553	37.16 548

SIFT-UMatch		42.35 512	41.04 515	46.29 528	76.09 545	41.80 555	70.21 539	45.21 555	30.75 545	27.33 551	62.62 544	15.13 551	59.11 548	14.72 553	27.30 551	37.95 546

SIFT-NCM-Cal		44.98 508	44.20 511	47.33 525	79.81 536	43.05 550	72.12 537	49.31 551	30.81 544	25.90 552	61.87 548	15.80 549	60.28 544	14.09 556	48.07 538	38.66 545

SIFT-PCN-Cal		36.85 517	36.40 520	38.19 534	71.43 552	30.42 561	64.34 548	37.72 559	27.48 551	22.98 553	57.03 550	12.99 556	51.22 553	12.51 557	21.13 555	32.92 551

SIFT-UM-Cal		39.93 514	38.61 518	43.88 531	76.08 546	39.30 557	68.10 543	37.89 558	30.49 546	22.74 554	62.27 546	13.89 554	56.16 551	14.17 554	21.90 554	36.17 549

SIFT-PointCN		37.89 515	37.50 519	39.07 533	71.45 551	31.31 560	66.27 545	41.69 556	27.82 550	22.63 555	56.73 551	12.00 558	50.56 554	12.18 558	26.71 552	35.34 550

SIFT-NCMNet		32.45 518	31.84 522	34.30 535	68.74 555	28.10 562	57.85 550	24.54 561	27.25 552	19.31 556	52.59 552	9.75 561	45.69 555	10.92 559	15.56 557	29.13 553

testmvs		21.48 521	24.95 524	11.09 539	14.89 563	6.47 566	96.56 461	9.87 564	7.55 555	17.93 557	39.02 554	9.43 562	5.90 560	16.56 546	12.72 558	20.91 555

test123		20.95 522	23.72 525	12.64 538	13.54 564	8.19 565	96.55 463	6.13 565	7.48 556	16.74 558	37.98 555	12.97 557	6.05 559	16.69 544	5.43 559	23.68 554

wuyk23d		30.17 519	30.18 523	30.16 537	78.61 539	43.29 548	66.79 544	14.21 563	17.31 554	14.82 559	11.93 558	11.55 559	41.43 556	37.08 538	19.30 556	5.76 556

EGC-MVSNET		75.22 481	69.54 485	92.28 463	94.81 461	89.58 439	97.64 378	96.50 461	1.82 558	5.57 560	95.74 443	68.21 484	96.26 484	73.80 510	91.71 380	90.99 506

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		23.98 520	31.98 521	0.00 541	0.00 565	0.00 568	0.00 553	98.59 172	0.00 560	0.00 561	98.61 217	90.60 207	0.00 561	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		7.88 525	10.50 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	94.51 92	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		8.20 524	10.94 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	98.43 236	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	88.11 469	96.56 461	97.31 405	85.66 479
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														80.13 487	90.51 395	95.88 449
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													97.78 444
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						90.94 404								88.66 436

MSC_two_6792asdad				99.62 7	99.17 112	99.08 13		98.63 162					99.94 14	98.53 56	99.80 25	99.86 13

No_MVS				99.62 7	99.17 112	99.08 13		98.63 162					99.94 14	98.53 56	99.80 25	99.86 13

eth-test2					0.00 565

eth-test					0.00 565

OPU-MVS				99.37 28	99.24 104	99.05 17	99.02 87				99.16 110	97.81 3	99.37 213	97.24 165	99.73 62	99.70 67

save fliter					99.46 59	98.38 42	98.21 295	98.71 138	97.95 28

test_0728_SECOND				99.71 1	99.72 17	99.35 1	98.97 99	98.88 78					99.94 14	98.47 64	99.81 16	99.84 18

GSMVS																99.20 191

sam_mvs1												89.45 243				99.20 191

sam_mvs												88.99 259

MTGPA								98.74 130

test_post1							96.68 458				30.43 557	87.85 297	98.69 340	92.59 357

test_post											31.83 556	88.83 268	98.91 316

patchmatchnet-post											95.10 460	89.42 244	98.89 320

MTMP							98.89 125	94.14 499

gm-plane-assit					95.88 433	87.47 473			89.74 438		96.94 389		99.19 254	93.32 326

test9_res														96.39 210	99.57 99	99.69 70

agg_prior2														95.87 226	99.57 99	99.68 75

test_prior4						98.01 72	97.86 357

test_prior				99.19 51	99.31 80	98.22 59		98.84 96					99.70 144			99.65 83

新几何2							97.64 378

旧先验1					99.29 89	97.48 91		98.70 141			99.09 135	95.56 56			99.47 122	99.61 90

无先验							97.58 383	98.72 135	91.38 401				99.87 80	93.36 325		99.60 92

原ACMM2							97.67 375

testdata2													99.89 69	91.65 385

segment_acmp												96.85 15

testdata1							97.32 405		96.34 130

plane_prior7					97.42 342	94.63 280

plane_prior6					97.35 349	94.61 283						87.09 312

plane_prior5								98.56 183					99.03 294	96.07 216	94.27 328	96.92 353

plane_prior4											98.28 255

plane_prior2							98.80 165		97.28 69

plane_prior1					97.37 348

plane_prior						94.60 285	98.44 264		96.74 105						94.22 330

n2								0.00 567

nn								0.00 567

door-mid								94.37 493

test11								98.66 154

door								94.64 491

HQP5-MVS						94.25 302

BP-MVS														95.30 251

HQP3-MVS								98.46 208							94.18 332

HQP2-MVS												86.75 318

NP-MVS					97.28 351	94.51 288					97.73 306

ACMMP++_ref															92.97 362

ACMMP++															93.61 349

Test By Simon												94.64 89

High-resolution multi-view benchmark